diff --git a/README.md b/README.md
index 8cb547e1b..c61e22798 100644
--- a/README.md
+++ b/README.md
@@ -11,29 +11,34 @@ NeuroData's MR Graphs package, **m2g**, is a turn-key pipeline which uses struct
 # Contents
 
 - [Overview](#overview)
+- [Documentation](#documentation)
 - [System Requirements](#system-requirements)
 - [Installation Guide](#installation-guide)
-- [Docker](#docker)
-- [Tutorial](#tutorial)
-- [Outputs](#outputs)
 - [Usage](#usage)
-- [Working with S3 Datasets](#Working-with-S3-Datasets)
-- [Example Datasets](#example-datasets)
-- [Documentation](#documentation)
 - [License](#license)
-- [Manuscript Reproduction](#manuscript-reproduction)
 - [Issues](#issues)
+- [Citing `m2g`](#citing-m2g)
 
 # Overview
 
 The **m2g** pipeline has been developed as a beginner-friendly solution for human connectome estimation by providing robust and reliable estimates of connectivity across a wide range of datasets. The pipelines are explained and derivatives analyzed in our pre-print, available on [BiorXiv](https://www.biorxiv.org/content/10.1101/2021.11.01.466686v1.full).
 
+# Documentation
+
+Check out some [resources](http://m2g.io) on our website, or our [function reference](https://ndmg.neurodata.io/) for more information about **m2g**.
+
 # System Requirements
 
+## Hardware Requirements
+
+**m2g** pipelines requires only a standard computer with enough RAM (< 16 GB).
+
+## Software Requirements
+
 The **m2g** pipeline:
 
 - was developed and tested primarily on Mac OS (10,11), Ubuntu (16, 18, 20), and CentOS (5, 6);
-- made to work on Python 3.7;
+- made to work on Python 3.8;
 - is wrapped in a [Docker container](https://hub.docker.com/r/neurodata/m2g);
 - has install instructions via a Dockerfile;
 - requires no non-standard hardware to run;
@@ -42,386 +47,24 @@ The **m2g** pipeline:
   - For binaries required to install AFNI, FSL, INDI, ICA_AROMA, see the [Dockerfile](Dockerfile)
 - takes approximately 1-core, < 16-GB of RAM, and 1-2 hours to run for most datasets (varies based on data).
 
-# Demo
-
-To install and run a tutorial of the latest Docker image of m2g, pull the docker image from DockerHub using the following command. Then enter it using `docker run`:
-
-```
-docker pull neurodata/m2g:latest
-docker run -ti --entrypoint /bin/bash neurodata/m2g:latest
-```
-
-Once inside of the Docker container, download a tutorial dataset of fMRI and diffusion MRI data from the `open-neurodata` AWS S3 bucket to the `/input` directory in your container (make sure you are connected to the internet):
-
-```
-aws s3 sync --no-sign-request s3://open-neurodata/m2g/TUTORIAL /input
-```
-
-Now you can run the `m2g` pipeline for both the functional and diffusion MRI data using the command below. The number of `seeds` is intentionally set lower than recommended, along with a larger than recommended `voxelsize` for a faster run time (approximately 25 minutes). For more information as to what these input arguments represent, see the Tutorial section below.
-
-```
-m2g --participant_label 0025864 --session_label 1 --parcellation AAL_ --pipeline both --seeds 1 --voxelsize 4mm /input /output
-```
-
-Once the pipeline is done running, the resulting outputs can be found in `/output/sub-0025864/ses-1/`, see Outputs section below for a description of each file.
-
-# Installation Guide
-
-## Docker
-
-While you can install **m2g** from `pip` using the command `pip install m2g`, as there are several dependencies needed for both **m2g** and **CPAC**, it is highly recommended to use **m2g** through a docker container:
-
-**m2g** is available through Dockerhub, and the most recent docker image can be pulled using:
-
-    docker pull neurodata/m2g:latest
-
-The image can then be used to create a container and run directly with the following command (and any additional options you may require for Docker, such as volume mounting):
+# Installation
 
-    docker run -ti --entrypoint /bin/bash neurodata/m2g:latest
-
-**m2g** docker containers can also be made from m2g's Dockerfile.
-
-    git clone https://github.com/neurodata/m2g.git
-    cd m2g
-    docker build -t <imagename:uniquelabel> .
-
-Where "uniquelabel" can be whatever you wish to call this Docker image (for example, m2g:latest). Additional information about building Docker images can be found [here](https://docs.docker.com/engine/reference/commandline/image_build/).
-Creating the Docker image should take several minutes if this is the first time you have used this docker file.
-In order to create a docker container from the docker image and access it, use the following command to both create and enter the container:
-
-    docker run -it --entrypoint /bin/bash m2g:uniquelabel
-
-## Local Installation [COMING SOON]
-
-We highly recommend the use of the Docker container provided above.
-
-Due to the versioning required for CPAC, along with `m2g-d`, we are currently working on streamlining the installation of `m2g`. Stay tuned for updates.
-
-- Requires numerous system level dependencies with specified versions. As such, CPAC on its own runs on a docker container, and we recommond the usage
+Instructions can be found within our documentation: https://docs.neurodata.io/m2g/tutorials/install.html
 
 # Usage
 
-The **m2g** pipeline can be used to generate connectomes as a command-line utility on [BIDS datasets](http://bids.neuroimaging.io) with the following:
-
-    m2g --pipeline <pipe> /input/bids/dataset /output/directory
-
-Note that more options are available which can be helpful if running on the Amazon cloud, which can be found and documented by running `m2g -h`.
-
-## Docker Container Usage
-
-If running with the Docker container shown above, the `entrypoint` is already set to `m2g`, so the pipeline can be run directly from the host-system command line as follows:
-
-    docker run -ti -v /path/to/local/data:/data neurodata/m2g /data/ /data/outputs
-
-This will run **m2g** on the local data and save the output files to the directory /path/to/local/data/outputs. Note that if you have created the docker image from github, replace `neurodata/m2g` with `imagename:uniquelabel`.
-
-Also note that currently, running `m2g` on a single bids-formatted dataset directory only runs a single scan. To run the entire dataset, we recommend parallelizing on a high-performance cluster or using `m2g`'s s3 integration.
-
-# Tutorial
-
-## Structural Connectome Pipeline (`m2g-d`)
-
-Once you have the pipeline up and running, you can run the structural connectome pipeline with:
-
-    m2g --pipeline dwi <input_directory> <output_directory>
-
-We recommend specifying an atlas and lowering the default seed density on test runs (although, for real runs, we recommend using the default seeding -- lowering seeding simply decreases runtime):
-
-    m2g --pipeline dwi --seeds 1 --parcellation Desikan <input_directory> <output_directory>
+Instructions can be found within our documentation: https://docs.neurodata.io/m2g/tutorials/usage.html
 
-You can set a particular scan and session as well (recommended for batch scripts):
+# License
 
-    m2g --pipeline dwi --seeds 1 --parcellation Desikan --participant_label <label> --session_label <label> <input_directory> <output_directory>
+This project is covered under the [Polyform License](https://github.com/neurodata/m2g/blob/deploy/LICENSE).
 
-## Functional Connectome Pipeline (`m2g-f`)
+# Issues
 
-Once you have the pipeline up and running, you can run the functional connectome pipeline with:
-
-    m2g --pipeline func <input_directory> <output_directory>
-
-We recommend specifying an atlas and lowering the default seed density on test runs (although, for real runs, we recommend using the default seeding -- lowering seeding simply decreases runtime):
-
-    m2g --pipeline func --seeds 1 --parcellation Desikan <input_directory> <output_directory>
-
-You can set a particular scan and session as well (recommended for batch scripts):
-
-    m2g --pipeline func --seeds 1 --parcellation Desikan --participant_label <label> --session_label <label> <input_directory> <output_directory>
-
-## Running both `m2g-d` and `m2g-f`
-
-Both pipelines can be run by setting the `pipeline` parameter to `both`:
-
-    m2g --pipeline both <input_directory> <output_directory>
-
-# Outputs
-
-## Diffusion Pipeline
-
-The organization of the output files generated by the m2g-d pipeline are shown below. If you only care about the connectome edgelists (**m2g**'s fundamental output), you can find them in `/output/connectomes_d`.
-
-```
-File labels that may appear on output files, these denote additional actions m2g may have done:
-RAS = File was originally in RAS orientation, so no reorientation was necessary
-reor_RAS = File has been reoriented into RAS+ orientation
-nores = File originally had the desired voxel size specified by the user (default 2mmx2mmx2mm), resulting in no reslicing
-res = The file has been resliced to the desired voxel size specified by the user
-
-/output
-    /anat_d
-
-        /preproc
-            t1w_aligned_mni.nii.gz = preprocessed t1w_brain anatomical image in mni space
-            t1w_brain.nii.gz = t1w anatomical image with only the brain
-            t1w_seg_mixeltype.nii.gz = mixeltype image of t1w image (denotes where there are more than one tissue type in each voxel)
-            t1w_seg_pve_0.nii.gz = probability map of Cerebrospinal fluid for original t1w image
-            t1w_seg_pve_1.nii.gz = probability map of grey matter for original t1w image
-            t1w_seg_pve_2.nii.gz = probability map of white matter for original t1w image
-            t1w_seg_pveseg.nii.gz = t1w image mapping wm, gm, ventricle, and csf areas
-            t1w_wm_thr.nii.gz = binary white matter mask for resliced t1w image
-
-        /registered
-            t1w_corpuscallosum.nii.gz = atlas corpus callosum mask in t1w space
-            t1w_corpuscallosum_dwi.nii.gz = atlas corpus callosum in dwi space
-            t1w_csf_mask_dwi.nii.gz = t1w csf mask in dwi space
-            t1w_gm_in_dwi.nii.gz = t1w grey matter probability map in dwi space
-            t1w_in_dwi.nii.gz = t1w in dwi space
-            t1w_wm_gm_int_in_dwi.nii.gz = t1w white matter-grey matter interfact in dwi space
-            t1w_wm_gm_int_in_dwi_bin.nii.gz = binary mask of t12_2m_gm_int_in_dwi.nii.gz
-            t1w_wm_in_dwi.nii.gz = atlas white matter probability map in dwi space
-
-    /dwi
-        /fiber
-            Streamline track file(s)
-
-        /preproc    (files created during the preprocessing of the dwi data)
-            #_B0.nii.gz = B0 image (there can be multiple B0 images per dwi file, # is the numerical location of each B0 image)
-            bval.bval = original b-values for dwi image
-            bvec.bvec = original b-vectors for dwi image
-            bvecs_reor.bvecs = bvec_scaled.bvec data reoriented to RAS+ orientation
-            bvec_scaled.bvec = b-vectors normalized to be of unit length, only non-zero b-values are changed
-            eddy_corrected_data.nii.gz = eddy corrected dwi image
-            eddy_corrected_data.ecclog = eddy correction log output
-            eddy_corrected_data_reor_RAS.nii.gz = eddy corrected dwi image reoriented to RAS orientation
-            eddy_corrected_data_reor_RAS_res.nii.gz = eddy corrected image reoriented to RAS orientation and resliced to desired voxel resolution
-            nodif_B0.nii.gz = mean of all B0 images
-            nodif_B0_bet.nii.gz = nodif_B0 image with all non-brain matter removed
-            nodif_B0_bet_mask.nii.gz = mask of nodif_B0_bet.nii.gz brain
-            tensor_fa.nii.gz = tensor image fractional anisotropy map
-
-        /tensor
-            Contains the rgb tensor file(s) for the dwi data if tractography is being done in MNI space
-
-    /connectomes_d
-            Location of connectome(s) created by the pipeline, with a directory given to each atlas you use for your analysis
-
-    /qa_d
-        /graphs_plotting
-            Png file of an adjacency matrix made from the connectome
-        /reg
-            <atlas>_atlas_2_nodif_B0_bet.png = overlay of registered atlas on top of anatomical image
-            qa_fast.png = overlay of white/grey matter and csf regions on top of anatomical image
-            t1w_aligned_mni_2_MNI152_T1_<vox>_brain.png = overlay of registered anatomical image on top of MNI152 anatomical reference image
-            t1w_corpuscallosum_dwi_2_nodif_B0_bet.png = corpus callosum region highlighted on registered anatomical image
-            t1w_csf_mask_dwi_2_nodif_B0_bet.png = overlay of csf mask on top of registered anatomical image
-            t1w_gm_in_dwi_2_nodif_B0_bet.png = overlay of grey matter mask on top of registered anatomical image
-            t1w_in_dwi_2_nodif_B0_bet.png = overlay of dwi image on top of anatomical image registered to dwi space
-            t1w_vent_mask_dwi_2_nodif_B0_bet.png = display of ventrical masks
-            t1w_wm_in_dwi_2_nodif_B0_bet.png = overlay of white matter mask on top of registered anatomical image
-        /skull_strip
-            qa_skullstrip__<sub>_<ses>_T1w_reor_RAS_res.png = overlay of skullstripped anatomical image on top of original anatomical image
-
-    /tmp_d
-        /reg_a (Intermediate files created during the processing of the anatomical data)
-            mni2t1w_warp.nii.gz = nonlinear warp coefficients/fields for mni to t1w space
-            t1w_csf_mask_dwi_bin.nii.gz = binary mask of t1w_csf_mask_dwi.nii.gz
-            t1w_gm_in_dwi_bin.nii.gz = binary mask of t12_gm_in_dwi.nii.gz
-            t1w_vent_csf_in_dwi.nii.gz = t1w ventricle+csf mask in dwi space
-            t1w_vent_mask_dwi.nii.gz = atlas ventricle mask in dwi space
-            t1w_wm_edge.nii.gz = mask of the outer border of the resliced t1w white matter
-            t1w_wm_in_dwi_bin.nii.gz = binary mask of t12_wm_in_dwi.nii.gz
-            vent_mask_mni.nii.gz = altas ventricle mask in mni space using roi_2_mni_mat
-            vent_mask_t1w.nii.gz = atlas ventricle mask in t1w space
-            warp_t12mni.nii.gz = nonlinear warp coefficients/fields for t1w to mni space
-
-        /reg_m (Intermediate files created during the processing of the diffusion data)
-            dwi2t1w_bbr_xfm.mat = affine transform matrix of t1w_wm_edge.nii.gz to t1w space
-            dwi2t1w_xfm.mat = inverse transform matrix of t1w2dwi_xfm.mat
-            roi_2_mni.mat = affine transform matrix of selected atlas to mni space
-            t1w2dwi_bbr_xfm.mat = inverse transform matrix of dwi2t1w_bbr_xfm.mat
-            t1w2dwi_xfm.mat = affine transform matrix of t1w_brain.nii.gz to nodif_B0.nii.gz space
-            t1wtissue2dwi_xfm.mat = affine transform matrix of t1w_brain.nii.gz to nodif_B0.nii.gz, using t1w2dwi_bbr_xfm.mat or t1w2dwi_xfm.mat as a starting point
-            xfm_mni2t1w_init.mat = inverse transform matrix of xfm_t1w2mni_init.mat
-            xfm_t1w2mni_init.mat = affine transform matrix of preprocessed t1w_brain to mni space
-```
-
-## Functional Pipeline
-
-The organization of the output files generated by the m2g-f pipeline are shown below. If you only care about the connectome edgelists (**m2g**'s fundamental output), you can find them in `/output/connectomes_f`.
-
-```
-File labels that may appear on output files, these denote additional actions m2g may have done:
-RAS = File was originally in RAS orientation, so no reorientation was necessary
-reor_RAS = File has been reoriented into RAS+ orientation
-nores = File originally had the desired voxel size specified by the user (default 2mmx2mmx2mm), resulting in no reslicing
-res = The file has been resliced to the desired voxel size specified by the user
-
-/output
-    /anat_f
-        /anatomical_brain
-            <subject>_<session>_T1w_resample_calc.nii.gz = resampled and skullstripped brain from anatomical image
-        /anatomical_brain_mask
-            <subject>_<session>_T1w_resample_skullstrip_calc.nii.gz = mask of resampled and skullstripped brain from anatomical image
-        /anatomical_csf_mask
-            segment_seg_0_maths_maths.nii.gz = mask of csf area of anatomical image
-        /anatomical_gm_mask
-            segment_seg_1_maths_maths.nii.gz = mask of grey matter area of anatomical image
-        /anatomical_reorient
-            <subject>_<session>_T1w_resample.nii.gz = reorientated and resampled anatomical image
-        /anatomical_to_mni_nonlinear_xfm
-            <subject>_<session>_T1w_resample_fieldwarp.nii.gz = fieldwarp for registering the anatomical image to MNI space
-        /anatomical_to_standard
-            <subject>_<session>_T1w_resample_calc_warp.nii.gz = anatomical image registered to MNI space
-        /anatomical_wm_mask
-            segment_seg_2_maths_maths.nii.gz = mask of white matter area of anatomical image
-        /seg_mixeltype
-            segment_mixeltype.nii.gz = mixeltype representation of anatomical image
-        /seg_partial_volume_files
-            segment_pve_0.nii.gz = mask of grey matter regions of anatomical image
-            segment_pve_1.nii.gz = mask of grey matter/white matter boundary of anatomical image
-            segment_pve_2.nii.gz = mask of white matter regions of anatomical image
-        /seg_partial_volume_map
-            segment_pveseg.nii.gz = partial volume map of anatomical image
-        /seg_probability_maps
-            segment_prob_0.nii.gz = probability map of anatomical image for grey matter
-            segment_prob_1.nii.gz = probability map of grey/white matter boundary
-            segment_prob_2.nii.gz = probability map of anatomical image for white matter
-    /connectomes_f
-        /<atlas>
-            <sub>_<ses>_func_<atlas>_abs_edgelist.csv = edgelist file for estimated connectome where the absolute value of the correlation is given
-            <sub>_<ses>_func_<atlas>_edgelist.csv = edgelist file for estimated connectome
-    /func
-        /preproc
-            /coordinate_transformation
-                <subject>_<session>_task-rest_bold_calc_tshift_resample.aff12.1D =
-            /frame_wise_displacement_jenkinson
-                FD_J.1D = vector containing Jenkinson measurement of framewise displacement for each frame of the functional image file
-            /frame_wise_displacement_power
-                FD.1D = vector containing power of framewise displacement for each frame of the functional image file
-            /functional_brain_mask
-                <subject>_<session>_task-rest_bold_calc_tshift_resample_volreg_mask.nii.gz = brain mask for the functional image
-            /functional_brain_mask_to_standard
-                <subject>_<session>_task-rest_bold_calc_tshift_rasample_volreg_mask_warp.nii.gz = functional brain mask registered to MNI152 space
-            /functional_freq_filtered
-                bandpassed_demeaned_filtered.nii.gz = frequency filtered functional file
-            /functional_nuisance_regressors
-                nuisance_regressors.1D
-            /functional_nuisance_residuals
-                residuals.nii.gz
-            /functional_preprocessed
-                <subject>_<session>_task-rest_bold_calc_tshift_resample_volreg_calc_maths.nii.gz = skullstripped brain from motion corrected functional image file resampled to voxel dimensions specified by user
-            /functional_preprocessed_mask
-                <subject>_<session>_task-rest_bold_calc_tshift_resample_volreg_calc_maths_maths.nii.gz = mask for image contained in /functional_preprocessed
-            /motion_correct
-                <subject>_<session>_task-rest_bold_calc_tshift_resample_volreg.nii.gz = motion corrected functional image file resampled to voxel dimensions specified by user
-            /motion_correct_to_standard_smooth
-                /_fwhm_4
-                    <subject>_<session>_task-rest_bold_calc_tshift_resample_volreg_warp_maths.nii.gz
-            /motion_params
-                motion_parameters.txt = statistical measurements of motion correction performed on functional image
-            /raw_functional
-                <subject>_<session>_task-rest_bold.nii.gz = unaltered input functional image
-            /slice_time_corrected
-                <subject>_<session>_task-rest_bold_calc_tshift.nii.gz = slice time corrected functional image
-        /register
-            /functional_to_anat_linear_xfm
-                <subject>_<session>_task-rest_bold_calc_tshift_resample_volreg_calc_tstat_flirt.mat =
-            /functional_to_standard
-                bandpassed_demeaned_filtered_warp.nii.gz = bandpassed and demeaned filtered warp map for registering the functional image to MNI space
-            /max_displacement
-                max_displacement.1D
-            /mean_functional
-                <subject>_<session>_task-rest_bold_calc_tshift_resample_volreg_calc_tstat.nii.gz = mean functional image from all volumes
-            /mean_functional_in_anat
-                <subject>_<session>_task-rest_bold_calc_tshift_resample_volreg_calc_tstat_flirt.nii.gz = mean functional image registered to the anatomical image
-            /mean_functional_to_standard
-                <subject>_<session>_task-rest_bold_calc_tshift_resample_volreg_calc_tstat_warp.nii.gz = mean functional image registered to MNI space
-            /movement_parameters
-                <subject>_<session>_task-rest_bold_calc_tshift_resample.1D = movement parameters applied to each volumen of functional image
-            /power_params
-                pow_params.txt = different measurements on the power of functional images
-            /roi_timeseries
-                /<atlas>
-                    roi_stats.csv = mean voxel intensity for each region of interest at each time point, used to calculate functional connectome
-                    roi_stats.npz = pickeled version of roi_stats.csv
-
-    /log_f
-        callback.log = nipype log for modules made for pipeline
-        cpac_data_config_<date>.yml = file containing functional and anatomical image directory location
-        cpac_individual_timing_m2g.csv = record of the elapsed time from the run of m2g-f
-        cpac_pipeline_config_<date>.yml = copy of CPAC configuration file
-        functional_pipeline_settings.yaml = record of CPACP pipeline parameter settings
-        pypeline.lock = intermediate file created for pipeline running
-        pypeline.log = nipype log with record of everything printed to terminal
-        subject_info_<subject>_<session>.pkl = pickle file of functional and anatomical file information
-
-    /qa_f
-        /carpet
-            carpet_seg.png
-        /csf_gm_wm_a
-            montage_csf_gm_wm_a.png = axial view of mask of csf/grey matter/white matter regions overlaid on top of anatomical image
-        /csf_gm_wm_s
-            montage_csf_gm_wm_s.png = sagittal view of mask of csf/grey matter/white matter regions overlaid on top of anatomical image
-        /mean_func_with_mni_edge_a
-            MNI_edge_on_mean_func_mni_a.png = axial view of outline of MNI reference anatimical image overlaid on top of mean functional image
-        /mean_func_with_mni_edge_s
-            MNI_edge_on_mean_func_mni_s.png = sagittal view of outline of MNI reference anatimical image overlaid on top of mean functional image
-        /mean_func_with_t1_edge_a
-            t1_edge_on_mean_func_in_t1_a.png = axial view of outline of anatomical image overlaid on top of mean functional image registered to the anatomical image
-        /mean_func_with_t1_edge_s
-            t1_edge_on_mean_func_in_t1_s.png = sagittal view of outline of anatomical image overlaid on top of mean functional image registered to the anatomical image
-        /mni_normalized_anatomical_a
-            mni_anat_a.png = axial view of anatomical image registered to MNI image
-        /mni_normalized_anatomical_s
-            mni_anat_s.png = sagittal view of anatomical image registered to MNI image
-        /movement_rot_plot
-            motion_rot_plot.png = movement rotation plot for rotation correction of functional image
-        /movement_trans_plot
-            motion_trans_plot.png = movement translational plot for translation correction of functional image
-        /skullstrip_vis_a
-            skull_vis_a.png = axial view of original anatomical image overlaid on top of skullstripped anatomical image
-        /skullstrip_vis_s
-            skull_vis_s.png = sagittal view of original anatomical image overlaid on top of skullstripped anatomical imag
-        /snr_a
-            snr_a.png = axial view of signal to noise ratio for functional image
-        /snr_hist
-            snr_hist_plot.png = signal to noise ratio intensity plot
-        /snr_s
-            snr_s.png = sagittal view of signal to noise ratio for functional image
-        /snr_val
-            average_snr_file.txt = single value of average signal to noise ratio for functional image
-
-```
-
-## Working with S3 Datasets
-
-**m2g** has the ability to work on datasets stored on [Amazon's Simple Storage Service](https://aws.amazon.com/s3/), assuming they are in BIDS format. Doing so requires you to set your AWS credentials and read the related s3 bucket documentation. You can find a guide [here](https://github.com/neurodata/m2g/blob/deploy/tutorials/Batch.ipynb).
-
-## Example Datasets
-
-Derivatives have been produced on a variety of datasets, all of which are made available on [our website](http://m2g.io). Each of these datsets is available for access and download from their respective sources. Alternatively, example datasets on the [BIDS website](http://bids.neuroimaging.io) which contain diffusion data can be used and have been tested; `ds114`, for example.
-
-## Documentation
-
-Check out some [resources](http://m2g.io) on our website, or our [function reference](https://ndmg.neurodata.io/) for more information about **m2g**.
-
-## License
-
-This project is covered under the [Apache 2.0 License](https://github.com/neurodata/m2g/blob/m2g/LICENSE.txt).
-
-## Manuscript Reproduction
+If you're having trouble, notice a bug, or want to contribute (such as a fix to the bug you may have just found) feel free to open a git issue or pull request. Enjoy!
 
-The figures produced in our manuscript linked in the [Overview](#overview) are all generated from code contained within Jupyter notebooks and made available at our [paper's Github repository](https://github.com/neurodata/ndmg-paper).
+# Citing `m2g`
 
-## Issues
+If you find `m2g` useful in your work, please cite the package via the [m2g paper](https://www.biorxiv.org/content/10.1101/2021.11.01.466686)
 
-If you're having trouble, notice a bug, or want to contribute (such as a fix to the bug you may have just found) feel free to open a git issue or pull request. Enjoy!
+> Chung, J., Lawrence, R., Loftus, A., Kiar, G., Bridgeford, E. W., Roncal, W. G., Chandrashekhar, V., ... & Consortium for Reliability and Reproducibility (CoRR). (2024). A low-resource reliable pipeline to democratize multi-modal connectome estimation and analysis. bioRxiv, 2024-04.
diff --git a/docs/Makefile b/docs/Makefile
deleted file mode 100644
index 298ea9e21..000000000
--- a/docs/Makefile
+++ /dev/null
@@ -1,19 +0,0 @@
-# Minimal makefile for Sphinx documentation
-#
-
-# You can set these variables from the command line.
-SPHINXOPTS    =
-SPHINXBUILD   = sphinx-build
-SOURCEDIR     = .
-BUILDDIR      = _build
-
-# Put it first so that "make" without argument is like "make help".
-help:
-	@$(SPHINXBUILD) -M help "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O)
-
-.PHONY: help Makefile
-
-# Catch-all target: route all unknown targets to Sphinx using the new
-# "make mode" option.  $(O) is meant as a shortcut for $(SPHINXOPTS).
-%: Makefile
-	@$(SPHINXBUILD) -M $@ "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O)
\ No newline at end of file
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diff --git a/docs/conf.py b/docs/conf.py
index fc3232520..cb25d9851 100644
--- a/docs/conf.py
+++ b/docs/conf.py
@@ -53,6 +53,9 @@
     # "toctree_filter",
 ]
 
+# nbxphinx
+nbsphinx_execute = "never"
+
 # -- numpydoc
 # Below is needed to prevent errors
 numpydoc_show_class_members = False
diff --git a/docs/diffusion.rst b/docs/diffusion.rst
index ba95a7d63..e7829ea83 100644
--- a/docs/diffusion.rst
+++ b/docs/diffusion.rst
@@ -4,14 +4,13 @@ Diffusion Pipeline
 
 .. contents:: Table of Contents
 
-Here we take a deep-dive into the modules of the `NDMG-d` pipeline. We will explain algorithm and parameter choices that were implemented at each step and the justification for why they were used over alternatives.
-
+Here we take a deep-dive into the modules of the ``m2g-d`` pipeline. We will explain algorithm and parameter choices that were implemented at each step and the justification for why they were used over alternatives.
 
 Output Summary Table
 ====================
 
-.. list-table:: `NDMG-d` **IO Breakdown**. Below, we look at the inputs, outputs, and QA figures produced by `NDMG-d`.
-    :widths: 10 5 10 50
+.. list-table:: ``m2g-d`` **IO Breakdown**. Below, we look at the inputs, outputs, and QA figures produced by ``m2g-d``. See `Pipeline Outputs`_ for more detailed file structure.
+    :widths: 10 15 10 5
     :header-rows: 1
     :stub-columns: 0
 
@@ -19,7 +18,7 @@ Output Summary Table
       - Inputs
       - Outputs
       - QA figures
-    * - Registration
+    * - Preprocessing & Registration
       - raw dMRI, raw T1w, template
       - aligned dMRI
       - Figure S2
@@ -37,59 +36,165 @@ Output Summary Table
       - Figure S4
 
 
-Output subject-level pipeline summary
+Pipeline Summary
 =====================================
 
-.. figure:: ./_static/ndmg-d-detailed-pipeline.jpg
+.. figure:: ./_static/m2g_pipeline.png
     :align: left
     :figwidth: 700px
 
-    Figure S1: `NDMG-d` **detailed pipeline**. The `NDMG-d` pipeline consists of 4 main steps: Registration (**D1**), Tensor Estimation (**D2**), Tractography (**D3**), and Graph Generation (**D4**). Each of these sections leverages publicely available tools and data to robustly produce the desired derivative of each step. Alongside derivative production, `NDMG` produces QA figures at each stage, as can be seen in **D1-4**, that enable qualitative evaluation of the pipeline's performance.
+    Figure 1: The ``m2g-d`` pipeline transforms Nifti-formatted dMRI data into sparse structural connectomes. The ``m2g-d`` pipeline consists of 4 main steps: 1) preprocessing & registration, 2) tensor estimation,  3) tractography, and 4) graph generation. Each of these sections leverages publicely available tools and data to robustly produce the desired derivative of each step. Alongside derivative production, ``m2g-d`` produces QA figures at each stage that enable qualitative evaluation of the pipeline's performance.
 
 
-Registration
-============
+----------------------------
+Preprocessing & Registration
+----------------------------
 
-Registration in `NDMG` leverages FSL and the Nilearn Python package. `NDMG` uses linear registrations because non-linear methods had higher variability across studies and increased the time requirements of the pipeline dramatically.
+Registration in ``m2g`` leverages FSL and the Nilearn Python package. ``m2g`` uses linear registrations because non-linear methods had higher variability across studies and increased the time requirements of the pipeline dramatically.
 
-The first step in the registration module is eddy-current correction and dMRI self-alignment to the volume-stack's BO volume (Figure S1). `NDMG` uses FSL's ``eddy_correct``, rather than the newer ``eddy`` function, because ``eddy`` requires substantially longer to run or relies on GPY acceleration, which would reduce the accessibility of `NDMG`. Once the dMRI data is self-aligned, it is aligned to the same-individual T1w image through FSL's ``epi_reg`` mini-pipeline. This tool performs a linear alignment between each image in the dMRI volume-stack and the T1w volume. The T1w volume is then aligned to the MNI152 template using linear registration computed by FSL's ``flirt``. This alignment is computed using the 1 millimeter (mm) MNI152 atlas, to enable higher freedom in terms of the parcellations that may be used, such as near-voxelwise parcellations that have been generated at 1 mm. FSL's non-linear registration, ``fnirt``, is not used in `NDMG` as the performance was found to vary significantly based on the collection protocol of the T1w images, often resulting in either slightly improved or significantly deteriorated performance.
+The first step in the registration module is eddy-current correction and dMRI self-alignment to the volume-stack's BO volume (Figure S1). ``m2g`` uses FSL's ``eddy_correct``, rather than the newer ``eddy`` function, because ``eddy`` requires substantially longer to run or relies on GPU acceleration, which would reduce the accessibility of ``m2g``. Once the dMRI data is self-aligned, it is aligned to the same-individual T1w image through FSL's ``epi_reg`` mini-pipeline. This tool performs a linear alignment between each image in the dMRI volume-stack and the T1w volume. The T1w volume is then aligned to the MNI152 template using linear registration computed by FSL's ``flirt``. This alignment is computed using the 1 millimeter (mm) MNI152 atlas, to enable higher freedom in terms of the parcellations that may be used, such as near-voxelwise parcellations that have been generated at 1 mm. FSL's non-linear registration, ``fnirt``, is not used in ``m2g`` as the performance was found to vary significantly based on the collection protocol of the T1w images, often resulting in either slightly improved or significantly deteriorated performance.
 
-The transform mapping the T1w volume to the template is then applied to the dMRI image stack, resulting in the dMRI image being aligned to the MNI152 template in stereotaxic-coordinate space. However, while ``flirt`` aligns the images in stereotaxic space, it does not guarantee an overlap of the data in voxelspace. Uisng Nilearn's ``resample``, `NDMG` ensures that images are aligned in both voxel- and stereotaxic-coordinates so that all analyses can be performed equivalently either with or without considering the image affine-transforms mapping the data matrix to the real-world coordinates.
+The transform mapping the T1w volume to the template is then applied to the dMRI image stack, resulting in the dMRI image being aligned to the MNI152 template in stereotaxic-coordinate space. However, while ``flirt`` aligns the images in stereotaxic space, it does not guarantee an overlap of the data in voxelspace. Uisng Nilearn's ``resample``, ``m2g`` ensures that images are aligned in both voxel- and stereotaxic-coordinates so that all analyses can be performed equivalently either with or without considering the image affine-transforms mapping the data matrix to the real-world coordinates.
 
-Finally, `NDMG` produces a QA plot showing three slices of the first BO volume of the aligned dMRI image overlaid on the MNI152 template in the three principle coordinate planes (Figure S2).
+Finally, ``m2g`` produces a QA plot showing three slices of the first BO volume of the aligned dMRI image overlaid on the MNI152 template in the three principle coordinate planes (Figure S2).
 
 .. figure:: ./_static/registration-qa.png
     :align: left
     :figwidth: 700px
 
-    Figure S2: `NDMG-d` **Registration QA**. `NDMG-d` produces registration QA showing the zeroth slice of the dMRI sequence in green overlaid on the template brain in purple.
-
+    Figure S2: ``m2g-d`` **Registration QA**. ``m2g-d`` produces registration QA showing the zeroth slice of the dMRI sequence in green overlaid on the template brain in purple.
 
+-----------------
 Tensor Estimation
-=================
+-----------------
 
 .. _DiPy: http://nipy.org/dipy/examples_built/reconst_dti.html
 
-Once the dMRI volumes have been aligned to the template, `NDMG` begins diffusion-specific processing on the data. All diffusion processing in `NDMG` is performed using the DiPy Python package. The diffusion processing in `NDMG` is performed after alignment to ease cross-connectome comparisons.
+Once the dMRI volumes have been aligned to the template, ``m2g`` begins diffusion-specific processing on the data. All diffusion processing in ``m2g`` is performed using the DiPy Python package. The diffusion processing in ``m2g`` is performed after alignment to ease cross-connectome comparisons.
 
-While high-dimensional diffusion models, such as orientation distribution functions (ODFs) or q-ball, enable reconstruction of crossing fibers and complex fiber trajectories, these methods are designed for images with a large number of diffusion volumes/directions for a given image. Because `NDMG` is designed to be robust across a wide range of dMRI studies, including diffusion tensor imaging, `NDMG` uses a lower-dimensional tensor model. The model, described in detail on DiPy_'s website, computes a 6-component tensor for each voxel in the image. This reduces the dMRI image stack to a single 6-dimensional image that can be used for tractography. `NDMG` generates a QA plot showing slices of the FA map derived from the tensors in nine panels, as below (Figure S3)
+While high-dimensional diffusion models, such as orientation distribution functions (ODFs) or q-ball, enable reconstruction of crossing fibers and complex fiber trajectories, these methods are designed for images with a large number of diffusion volumes/directions for a given image. Because ``m2g`` is designed to be robust across a wide range of dMRI studies, including diffusion tensor imaging, ``m2g`` uses a lower-dimensional tensor model. The model, described in detail on DiPy_'s website, computes a 6-component tensor for each voxel in the image. This reduces the dMRI image stack to a single 6-dimensional image that can be used for tractography. ``m2g`` generates a QA plot showing slices of the FA map derived from the tensors in nine panels, as below (Figure S3)
 
 .. figure:: ./_static/tensor-qa.png
     :align: left
     :figwidth: 700px
 
-    Figure S3: `NDMG-d` **Tensor Estimation QA**. `NDMG-d` produces tensor QA showing the voxelwise deterministic tensor model fit to the aligned dMRI sequence.
-
+    Figure S3: ``m2g-d`` **Tensor Estimation QA**. ``m2g-d`` produces tensor QA showing the voxelwise deterministic tensor model fit to the aligned dMRI sequence.
 
+-----------------
 Tractography
-=============
-
-`NDMG` uses DiPy's deterministic tractography algorithm, ``EuDX``. Integration of tensor estimation and tractography methods is minimally complex with this tractography method, as it has been designed to operate on the tensors produced by DiPy in the previous step. Probabilistic tractography would be significantly more computationally expensive, and it remains unclear how well it would perform on data with a small number of diffusion directions. The QA figure for tractography shows a subset of the resolved streamlines in an axial projection of the brain mask with the fibers contained therein (Figure S4). This QA figure allows the user to verify, for example, that streamlines are following expected patterns within the brain and do not leave the boundary of the mask.
+-----------------
 
+``m2g`` uses DiPy's deterministic tractography algorithm, ``EuDX``. Integration of tensor estimation and tractography methods is minimally complex with this tractography method, as it has been designed to operate on the tensors produced by DiPy in the previous step. Probabilistic tractography would be significantly more computationally expensive, and it remains unclear how well it would perform on data with a small number of diffusion directions. The QA figure for tractography shows a subset of the resolved streamlines in an axial projection of the brain mask with the fibers contained therein (Figure S4). This QA figure allows the user to verify, for example, that streamlines are following expected patterns within the brain and do not leave the boundary of the mask.
 
+-----------------
 Graph Estimation
-================
+-----------------
+
+``m2g`` uses the fiber streamlines to generate connectomes across multiple parcellations. Graphs are formed by contracting voxels into graph vertices depending on spatial, anatomical, or functional similarity. Given a parcellation with vertices :math:`V` and a corresponding mapping :math:`P(v_i)` indicating the voxels within a region :math:`i`, we contract our fiber streamlines as follows. To form a graph :math:`G(V, E, w)`, we know that :math:`V = \left\{v_i\right\}_{i=1}^N` is the set of all unique vertices in our parcellation. :math:`E = V \times V` is a collection of possible edges between different vertex regions. :math:`w : V \times V \to \mathbb{Z}_+` is a weighting function for each edge in the graph. Here, :math:`w(v_i,v_j) = \sum_{w \in P(v_j)}{\sum_{w \in P(v_i)}\mathbb{i}\left\{F_{u,w}\right\}}` where :math:`F_{u,w}` is true if a fiber tract exists between voxels :math:`u` and :math:`w`, and false if there is no fiber tract between voxels :math:`u` and :math:`w`.
+
+The connectomes generated are graph objects, with nodes in the graph representing regions of interest (ROIs) and edges representing connectivity via fibers. An undirected edge is added to the graph for each pair of ROIs a given streamline passes through. Edges are undirected because dMRI data lacks direction information. Edge weight is the number of streamlines which pass through a given pair of regions. ``m2g`` uses 24 parcellations, including all standard public dMRI parcellations known by the authors. Users may run ``m2g`` using any additional parcellation defined in MNI152 space simply by providing access to it on the command-line. To package an additional parcellation with ``m2g``, please contact the maintainers. The QA for graph generation depicts a number of graph statistics for each of the parcellation schemes. We typically generate this figure at the population level, as depicted in Figure S4.
 
-`NDMG` uses the fiber streamlines to generate connectomes across multiple parcellations. Graphs are formed by contracting voxels into graph vertices depending on spatial, anatomical, or functional similarity. Given a parcellation with vertices :math:`V` and a corresponding mapping :math:`P(v_i)` indicating the voxels within a region :math:`i`, we contract our fiber streamlines as follows. To form a graph :math:`G(V, E, w)`, we know that :math:`V = \left\{v_i\right\}_{i=1}^N` is the set of all unique vertices in our parcellation. :math:`E = V \times V` is a collection of possible edges between different vertex regions. :math:`w : V \times V \to \mathbb{Z}_+` is a weighting function for each edge in the graph. Here, :math:`w(v_i,v_j) = \sum_{w \in P(v_j)}{\sum_{w \in P(v_i)}\mathbb{i}\left\{F_{u,w}\right\}}` where :math:`F_{u,w}` is true if a fiber tract exists between voxels :math:`u` and :math:`w`, and false if there is no fiber tract between voxels :math:`u` and :math:`w`.
+.. figure:: ./_static/qa-d/connectome.png
+    :align: left
+    :figwidth: 700px
+
+    Figure S4: ``m2g-d`` **Connectome QA**. ``m2g-d`` produces connectome QA showing normalized connectome such that edge weights are between 0 and 1.
+
+
+Pipeline Outputs
+================
 
-The connectomes generated are graph objects, with nodes in the graph representing regions of interest (ROIs) and edges representing connectivity via fibers. An undirected edge is added to the graph for each pair of ROIs a given streamline passes through. Edges are undirected because dMRI data lacks direction information. Edge weight is the number of streamlines which pass through a given pair of regions. `NDMG` uses 24 parcellations, including all standard public dMRI parcellations known by the authors. Users may run `NDMG` using any additional parcellation defined in MNI152 space simply by providing access to it on the command-line. To package an additional parcellation with `NDMG`, please contact the maintainers. The QA for graph generation depicts a number of graph statistics for each of the parcellation schemes. We typically generate this figure at the population level, as depicted in Figure S4.
+The organization of the output files generated by the m2g-d pipeline are shown below. If you only care about the connectome edgelists (**m2g**'s fundamental output), you can find them in `/output/connectomes_d`. File labels that may appear on output files, these denote additional actions m2g may have done:
+
+#. ``RAS`` = File was originally in RAS orientation, so no reorientation was necessary
+#. ``reor_RAS`` = File has been reoriented into RAS+ orientation
+#. ``nores`` = File originally had the desired voxel size specified by the user (default 2mmx2mmx2mm), resulting in no reslicing
+#. ``res`` = The file has been resliced to the desired voxel size specified by the user
+
+.. code-block::
+
+    /output
+        /anat_d
+            /preproc
+                t1w_aligned_mni.nii.gz = preprocessed t1w_brain anatomical image in mni space
+                t1w_brain.nii.gz = t1w anatomical image with only the brain
+                t1w_seg_mixeltype.nii.gz = mixeltype image of t1w image (denotes where there are more than one tissue type in each voxel)
+                t1w_seg_pve_0.nii.gz = probability map of Cerebrospinal fluid for original t1w image
+                t1w_seg_pve_1.nii.gz = probability map of grey matter for original t1w image
+                t1w_seg_pve_2.nii.gz = probability map of white matter for original t1w image
+                t1w_seg_pveseg.nii.gz = t1w image mapping wm, gm, ventricle, and csf areas
+                t1w_wm_thr.nii.gz = binary white matter mask for resliced t1w image
+
+            /registered
+                t1w_corpuscallosum.nii.gz = atlas corpus callosum mask in t1w space
+                t1w_corpuscallosum_dwi.nii.gz = atlas corpus callosum in dwi space
+                t1w_csf_mask_dwi.nii.gz = t1w csf mask in dwi space
+                t1w_gm_in_dwi.nii.gz = t1w grey matter probability map in dwi space
+                t1w_in_dwi.nii.gz = t1w in dwi space
+                t1w_wm_gm_int_in_dwi.nii.gz = t1w white matter-grey matter interfact in dwi space
+                t1w_wm_gm_int_in_dwi_bin.nii.gz = binary mask of t12_2m_gm_int_in_dwi.nii.gz
+                t1w_wm_in_dwi.nii.gz = atlas white matter probability map in dwi space
+
+        /dwi
+            /fiber
+                Streamline track file(s)
+
+            /preproc    (files created during the preprocessing of the dwi data)
+                #_B0.nii.gz = B0 image (there can be multiple B0 images per dwi file, # is the numerical location of each B0 image)
+                bval.bval = original b-values for dwi image
+                bvec.bvec = original b-vectors for dwi image
+                bvecs_reor.bvecs = bvec_scaled.bvec data reoriented to RAS+ orientation
+                bvec_scaled.bvec = b-vectors normalized to be of unit length, only non-zero b-values are changed
+                eddy_corrected_data.nii.gz = eddy corrected dwi image
+                eddy_corrected_data.ecclog = eddy correction log output
+                eddy_corrected_data_reor_RAS.nii.gz = eddy corrected dwi image reoriented to RAS orientation
+                eddy_corrected_data_reor_RAS_res.nii.gz = eddy corrected image reoriented to RAS orientation and resliced to desired voxel resolution
+                nodif_B0.nii.gz = mean of all B0 images
+                nodif_B0_bet.nii.gz = nodif_B0 image with all non-brain matter removed
+                nodif_B0_bet_mask.nii.gz = mask of nodif_B0_bet.nii.gz brain
+                tensor_fa.nii.gz = tensor image fractional anisotropy map
+
+            /tensor
+                Contains the rgb tensor file(s) for the dwi data if tractography is being done in MNI space
+
+        /connectomes_d
+                Location of connectome(s) created by the pipeline, with a directory given to each atlas you use for your analysis
+
+        /qa_d
+            /graphs_plotting
+                Png file of an adjacency matrix made from the connectome
+            /reg
+                <atlas>_atlas_2_nodif_B0_bet.png = overlay of registered atlas on top of anatomical image
+                qa_fast.png = overlay of white/grey matter and csf regions on top of anatomical image
+                t1w_aligned_mni_2_MNI152_T1_<vox>_brain.png = overlay of registered anatomical image on top of MNI152 anatomical reference image
+                t1w_corpuscallosum_dwi_2_nodif_B0_bet.png = corpus callosum region highlighted on registered anatomical image
+                t1w_csf_mask_dwi_2_nodif_B0_bet.png = overlay of csf mask on top of registered anatomical image
+                t1w_gm_in_dwi_2_nodif_B0_bet.png = overlay of grey matter mask on top of registered anatomical image
+                t1w_in_dwi_2_nodif_B0_bet.png = overlay of dwi image on top of anatomical image registered to dwi space
+                t1w_vent_mask_dwi_2_nodif_B0_bet.png = display of ventrical masks
+                t1w_wm_in_dwi_2_nodif_B0_bet.png = overlay of white matter mask on top of registered anatomical image
+            /skull_strip
+                qa_skullstrip__<sub>_<ses>_T1w_reor_RAS_res.png = overlay of skullstripped anatomical image on top of original anatomical image
+
+        /tmp_d
+            /reg_a (Intermediate files created during the processing of the anatomical data)
+                mni2t1w_warp.nii.gz = nonlinear warp coefficients/fields for mni to t1w space
+                t1w_csf_mask_dwi_bin.nii.gz = binary mask of t1w_csf_mask_dwi.nii.gz
+                t1w_gm_in_dwi_bin.nii.gz = binary mask of t12_gm_in_dwi.nii.gz
+                t1w_vent_csf_in_dwi.nii.gz = t1w ventricle+csf mask in dwi space
+                t1w_vent_mask_dwi.nii.gz = atlas ventricle mask in dwi space
+                t1w_wm_edge.nii.gz = mask of the outer border of the resliced t1w white matter
+                t1w_wm_in_dwi_bin.nii.gz = binary mask of t12_wm_in_dwi.nii.gz
+                vent_mask_mni.nii.gz = altas ventricle mask in mni space using roi_2_mni_mat
+                vent_mask_t1w.nii.gz = atlas ventricle mask in t1w space
+                warp_t12mni.nii.gz = nonlinear warp coefficients/fields for t1w to mni space
+
+            /reg_m (Intermediate files created during the processing of the diffusion data)
+                dwi2t1w_bbr_xfm.mat = affine transform matrix of t1w_wm_edge.nii.gz to t1w space
+                dwi2t1w_xfm.mat = inverse transform matrix of t1w2dwi_xfm.mat
+                roi_2_mni.mat = affine transform matrix of selected atlas to mni space
+                t1w2dwi_bbr_xfm.mat = inverse transform matrix of dwi2t1w_bbr_xfm.mat
+                t1w2dwi_xfm.mat = affine transform matrix of t1w_brain.nii.gz to nodif_B0.nii.gz space
+                t1wtissue2dwi_xfm.mat = affine transform matrix of t1w_brain.nii.gz to nodif_B0.nii.gz, using t1w2dwi_bbr_xfm.mat or t1w2dwi_xfm.mat as a starting point
+                xfm_mni2t1w_init.mat = inverse transform matrix of xfm_t1w2mni_init.mat
+                xfm_t1w2mni_init.mat = affine transform matrix of preprocessed t1w_brain to mni space
diff --git a/docs/docker.rst b/docs/docker.rst
new file mode 100644
index 000000000..35c0de59c
--- /dev/null
+++ b/docs/docker.rst
@@ -0,0 +1,39 @@
+.. include:: links.rst
+
+
+Docker
+------
+
+You can learn more about Docker and how to install it in the `Docker installation`_ documentation.
+Please make sure you follow the Docker installation instructions. You can check your Docker Runtime installation running their ``hello-world`` image:
+
+.. code-block:: bash
+
+    $ docker run --rm hello-world
+
+If you have a functional installation, then you should obtain the following output:
+
+.. code-block::
+
+    Hello from Docker!
+    This message shows that your installation appears to be working correctly.
+
+    To generate this message, Docker took the following steps:
+    1. The Docker client contacted the Docker daemon.
+    2. The Docker daemon pulled the "hello-world" image from the Docker Hub.
+        (amd64)
+    3. The Docker daemon created a new container from that image which runs the
+        executable that produces the output you are currently reading.
+    4. The Docker daemon streamed that output to the Docker client, which sent it
+        to your terminal.
+
+    To try something more ambitious, you can run an Ubuntu container with:
+    $ docker run -it ubuntu bash
+
+    Share images, automate workflows, and more with a free Docker ID:
+    https://hub.docker.com/
+
+    For more examples and ideas, visit:
+    https://docs.docker.com/get-started/
+
+After checking your Docker Engine is capable of running Docker images, you are ready to pull your ``m2g`` container image.
\ No newline at end of file
diff --git a/docs/functional.rst b/docs/functional.rst
index 2600c2ca9..18b61ebde 100644
--- a/docs/functional.rst
+++ b/docs/functional.rst
@@ -8,4 +8,229 @@ Here we discuss the CPAC implementation of the m2g-f pipeline.
 
 Output Summary Table
 ====================
-Example
\ No newline at end of file
+
+.. list-table:: ``m2g-f`` **IO Breakdown**. Below, we look at the inputs, outputs, and QA figures produced by ``m2g-f``. See `Pipeline Outputs`_ for more detailed file structure.
+    :widths: 10 15 10 5
+    :header-rows: 1
+    :stub-columns: 0
+
+    * - Step
+      - Inputs
+      - Outputs
+      - QA figures
+    * - Preprocessing
+      - raw fMRI, raw T1w
+      - preprocessed fMRI
+      - Figure S2
+    * - Registration
+      - preprocessed fMRI, preprocessed T1w, template
+      - aligned fMRI
+      -
+    * - Nuisance Correction
+      - aligned fMRI
+      - corrected fMRI
+      - Figure S3
+    * - Graph Generation
+      - corrected fMRI, parcellations
+      - connectome
+      -
+
+
+Pipeline Summary
+==============================
+
+.. figure:: ./_static/m2g_pipeline.png
+    :align: left
+    :figwidth: 700px
+
+    Figure 1: The ``m2g-f`` organizes the data for processing by CPAC’s functional pipeline that we developed here. The ``m2g-f`` pipeline consists of 4 main steps: 1) preprocessing, 2) registration,  3) nuisance correction, and 4) graph generation. Each of these sections leverages publicely available tools and data to robustly produce the desired derivative of each step. Alongside derivative production, ``m2g-f`` produces QA figures at each stage that enable qualitative evaluation of the pipeline's performance.
+
+
+-------------
+Preprocessing
+-------------
+
+The ``m2g-f`` pipeline in CPAC uses AFNI's SkullStripping function with a variable shrink factor between 0.4 and 0.6 over 250 iterations with nearest neighbor interpolation to eliminate all non-neuronal structures from the anatomical image. The resulting anatomical file is resampled to the desired resolution voxel size using FSL's FNIRT. Slice timing correction is then performed using AFNI's 3dTshift, utilizing the TR value and scan acquisition method provided by the user. The resulting image is then motion corrected using AFNI's 3dvolreg, aligning all images to the first image in the functional MRI's timeseries.
+
+Finally, ``m2g`` produces a QA plot showing the stripped image overlayed with the removed skull (Figure S2).
+
+.. figure:: ./_static/qa-f/func_skullstrip.png
+    :align: left
+    :figwidth: 700px
+
+    Figure S2: ``m2g-f`` **Preprocessing QA**. ``m2g-f`` produces QA plot showing the stripped image overlayed with the removed skull.
+
+-----------------
+Registration
+-----------------
+
+Nonlinear boundary based registration of the preprocessed fMRI and anatomical images are performed in order to transform them into MNI152 space. The MNI152 6th generation anatomical reference image  is used in this registration process, as it is FSL's preferred image. The functional image is then registered to the anatomical scan using FSL ``bet`` with BBR. FSL's standard white matter, grey matter, and cerebral spinal fluid masks are then registered onto the functional image using FSL's ``FAST`` thresholding, resulting in segmentation tissue masks. The specified atlases that are going to be used in the connectome generation are also registered to the functional space using FSL's ``FLIRT ``function.
+
+
+-------------------
+Nuisance Correction
+-------------------
+
+Nuisance correction of the functional image is performed using the component based noise correction (``CompCor``) method. Using ''noise ROIs'', or areas of white matter and cerebral spinal fluid whose intensity values are unlikely to be modulated by neural activity, physiological noise can be isolated.Based on this assumption, physiological noise in gray matter regions can be corrected for by regressing out principal components from noise ROIs. CompCor uses the previously registered white matter and cerebral spinal fluid masks (csf) in order to determine noise ROIs. A principal component analysis is applied using the top five components of the white matter and csf to characterize the times series data from the noise ROIs. Significant principal components are then introduced as covariates in a general linear model as an estimate for the physiological noise single space. Polynomial Detrending is also performed to remove linear or quadratic trends in the timeseries, likely from changes in scanner heat or subject movement. After nuisance regression, frequency filtering occurs on the functional data using a bandpass filter from 0.01 Hz to 0.1 Hz to account for low-frequency drift and high-frequency noise.
+
+Finally, ``m2g`` produces a QA plot showing the plots for translation and rotation correction of the functional images (Figure S3).
+
+.. figure:: ./_static/qa-f/func_motion_plot.png
+    :align: left
+    :figwidth: 700px
+
+    Figure S3: ``m2g-f`` **Nuisance Correction QA**. ``m2g-f`` produces QA showing movement translational plot for translation correction of functional image.
+
+-----------------
+Graph Estimation
+-----------------
+
+With the anatomical files and atlases registered to the functional image, the estimation of the functional connectomes for each atlas can occur. For each ROI, the timeseries of the average of all voxels within the ROI at each collection time point is calculated. This timeseries is then used to calculate the Pearson's correlation coefficient  between each pair of ROIs. The functional connectome is then rank-transformed by replacing the magnitude of the correlation with its relative rank, from smallest to largest. The resulting adjacency matrix of correlations is then saved, along with an edgelist file containing the same information.
+
+
+Pipeline Outputs
+================
+
+The organization of the output files generated by the m2g-f pipeline are shown below. If you only care about the connectome edgelists (``m2g``'s fundamental output), you can find them in `/output/connectomes_f`. File labels that may appear on output files, these denote additional actions m2g may have done:
+
+#. ``RAS`` = File was originally in RAS orientation, so no reorientation was necessary
+#. ``reor_RAS`` = File has been reoriented into RAS+ orientation
+#. ``nores`` = File originally had the desired voxel size specified by the user (default 2mmx2mmx2mm), resulting in no reslicing
+#. ``res`` = The file has been resliced to the desired voxel size specified by the user
+
+.. code-block::
+
+    /output
+        /anat_f
+            /anatomical_brain
+                <subject>_<session>_T1w_resample_calc.nii.gz = resampled and skullstripped brain from anatomical image
+            /anatomical_brain_mask
+                <subject>_<session>_T1w_resample_skullstrip_calc.nii.gz = mask of resampled and skullstripped brain from anatomical image
+            /anatomical_csf_mask
+                segment_seg_0_maths_maths.nii.gz = mask of csf area of anatomical image
+            /anatomical_gm_mask
+                segment_seg_1_maths_maths.nii.gz = mask of grey matter area of anatomical image
+            /anatomical_reorient
+                <subject>_<session>_T1w_resample.nii.gz = reorientated and resampled anatomical image
+            /anatomical_to_mni_nonlinear_xfm
+                <subject>_<session>_T1w_resample_fieldwarp.nii.gz = fieldwarp for registering the anatomical image to MNI space
+            /anatomical_to_standard
+                <subject>_<session>_T1w_resample_calc_warp.nii.gz = anatomical image registered to MNI space
+            /anatomical_wm_mask
+                segment_seg_2_maths_maths.nii.gz = mask of white matter area of anatomical image
+            /seg_mixeltype
+                segment_mixeltype.nii.gz = mixeltype representation of anatomical image
+            /seg_partial_volume_files
+                segment_pve_0.nii.gz = mask of grey matter regions of anatomical image
+                segment_pve_1.nii.gz = mask of grey matter/white matter boundary of anatomical image
+                segment_pve_2.nii.gz = mask of white matter regions of anatomical image
+            /seg_partial_volume_map
+                segment_pveseg.nii.gz = partial volume map of anatomical image
+            /seg_probability_maps
+                segment_prob_0.nii.gz = probability map of anatomical image for grey matter
+                segment_prob_1.nii.gz = probability map of grey/white matter boundary
+                segment_prob_2.nii.gz = probability map of anatomical image for white matter
+        /connectomes_f
+            /<atlas>
+                <sub>_<ses>_func_<atlas>_abs_edgelist.csv = edgelist file for estimated connectome where the absolute value of the correlation is given
+                <sub>_<ses>_func_<atlas>_edgelist.csv = edgelist file for estimated connectome
+        /func
+            /preproc
+                /coordinate_transformation
+                    <subject>_<session>_task-rest_bold_calc_tshift_resample.aff12.1D =
+                /frame_wise_displacement_jenkinson
+                    FD_J.1D = vector containing Jenkinson measurement of framewise displacement for each frame of the functional image file
+                /frame_wise_displacement_power
+                    FD.1D = vector containing power of framewise displacement for each frame of the functional image file
+                /functional_brain_mask
+                    <subject>_<session>_task-rest_bold_calc_tshift_resample_volreg_mask.nii.gz = brain mask for the functional image
+                /functional_brain_mask_to_standard
+                    <subject>_<session>_task-rest_bold_calc_tshift_rasample_volreg_mask_warp.nii.gz = functional brain mask registered to MNI152 space
+                /functional_freq_filtered
+                    bandpassed_demeaned_filtered.nii.gz = frequency filtered functional file
+                /functional_nuisance_regressors
+                    nuisance_regressors.1D
+                /functional_nuisance_residuals
+                    residuals.nii.gz
+                /functional_preprocessed
+                    <subject>_<session>_task-rest_bold_calc_tshift_resample_volreg_calc_maths.nii.gz = skullstripped brain from motion corrected functional image file resampled to voxel dimensions specified by user
+                /functional_preprocessed_mask
+                    <subject>_<session>_task-rest_bold_calc_tshift_resample_volreg_calc_maths_maths.nii.gz = mask for image contained in /functional_preprocessed
+                /motion_correct
+                    <subject>_<session>_task-rest_bold_calc_tshift_resample_volreg.nii.gz = motion corrected functional image file resampled to voxel dimensions specified by user
+                /motion_correct_to_standard_smooth
+                    /_fwhm_4
+                        <subject>_<session>_task-rest_bold_calc_tshift_resample_volreg_warp_maths.nii.gz
+                /motion_params
+                    motion_parameters.txt = statistical measurements of motion correction performed on functional image
+                /raw_functional
+                    <subject>_<session>_task-rest_bold.nii.gz = unaltered input functional image
+                /slice_time_corrected
+                    <subject>_<session>_task-rest_bold_calc_tshift.nii.gz = slice time corrected functional image
+            /register
+                /functional_to_anat_linear_xfm
+                    <subject>_<session>_task-rest_bold_calc_tshift_resample_volreg_calc_tstat_flirt.mat =
+                /functional_to_standard
+                    bandpassed_demeaned_filtered_warp.nii.gz = bandpassed and demeaned filtered warp map for registering the functional image to MNI space
+                /max_displacement
+                    max_displacement.1D
+                /mean_functional
+                    <subject>_<session>_task-rest_bold_calc_tshift_resample_volreg_calc_tstat.nii.gz = mean functional image from all volumes
+                /mean_functional_in_anat
+                    <subject>_<session>_task-rest_bold_calc_tshift_resample_volreg_calc_tstat_flirt.nii.gz = mean functional image registered to the anatomical image
+                /mean_functional_to_standard
+                    <subject>_<session>_task-rest_bold_calc_tshift_resample_volreg_calc_tstat_warp.nii.gz = mean functional image registered to MNI space
+                /movement_parameters
+                    <subject>_<session>_task-rest_bold_calc_tshift_resample.1D = movement parameters applied to each volumen of functional image
+                /power_params
+                    pow_params.txt = different measurements on the power of functional images
+                /roi_timeseries
+                    /<atlas>
+                        roi_stats.csv = mean voxel intensity for each region of interest at each time point, used to calculate functional connectome
+                        roi_stats.npz = pickeled version of roi_stats.csv
+
+        /log_f
+            callback.log = nipype log for modules made for pipeline
+            cpac_data_config_<date>.yml = file containing functional and anatomical image directory location
+            cpac_individual_timing_m2g.csv = record of the elapsed time from the run of m2g-f
+            cpac_pipeline_config_<date>.yml = copy of CPAC configuration file
+            functional_pipeline_settings.yaml = record of CPACP pipeline parameter settings
+            pypeline.lock = intermediate file created for pipeline running
+            pypeline.log = nipype log with record of everything printed to terminal
+            subject_info_<subject>_<session>.pkl = pickle file of functional and anatomical file information
+
+        /qa_f
+            /carpet
+                carpet_seg.png
+            /csf_gm_wm_a
+                montage_csf_gm_wm_a.png = axial view of mask of csf/grey matter/white matter regions overlaid on top of anatomical image
+            /csf_gm_wm_s
+                montage_csf_gm_wm_s.png = sagittal view of mask of csf/grey matter/white matter regions overlaid on top of anatomical image
+            /mean_func_with_mni_edge_a
+                MNI_edge_on_mean_func_mni_a.png = axial view of outline of MNI reference anatimical image overlaid on top of mean functional image
+            /mean_func_with_mni_edge_s
+                MNI_edge_on_mean_func_mni_s.png = sagittal view of outline of MNI reference anatimical image overlaid on top of mean functional image
+            /mean_func_with_t1_edge_a
+                t1_edge_on_mean_func_in_t1_a.png = axial view of outline of anatomical image overlaid on top of mean functional image registered to the anatomical image
+            /mean_func_with_t1_edge_s
+                t1_edge_on_mean_func_in_t1_s.png = sagittal view of outline of anatomical image overlaid on top of mean functional image registered to the anatomical image
+            /mni_normalized_anatomical_a
+                mni_anat_a.png = axial view of anatomical image registered to MNI image
+            /mni_normalized_anatomical_s
+                mni_anat_s.png = sagittal view of anatomical image registered to MNI image
+            /movement_rot_plot
+                motion_rot_plot.png = movement rotation plot for rotation correction of functional image
+            /movement_trans_plot
+                motion_trans_plot.png = movement translational plot for translation correction of functional image
+            /skullstrip_vis_a
+                skull_vis_a.png = axial view of original anatomical image overlaid on top of skullstripped anatomical image
+            /skullstrip_vis_s
+                skull_vis_s.png = sagittal view of original anatomical image overlaid on top of skullstripped anatomical imag
+            /snr_a
+                snr_a.png = axial view of signal to noise ratio for functional image
+            /snr_hist
+                snr_hist_plot.png = signal to noise ratio intensity plot
+            /snr_s
+                snr_s.png = sagittal view of signal to noise ratio for functional image
+            /snr_val
+                average_snr_file.txt = single value of average signal to noise ratio for functional image
diff --git a/docs/index.rst b/docs/index.rst
index 4f56527a7..c0dcbf4e7 100644
--- a/docs/index.rst
+++ b/docs/index.rst
@@ -20,11 +20,13 @@ Join us on `GitHub <https://github.com/neurodata/m2g>`_.
 
 .. toctree::
    :caption: Documentation
-   :maxdepth: 3
+   :maxdepth: 1
 
    install
+   usage
    diffusion
    functional
+   paper/index
 
 .. toctree::
    :maxdepth: 1
@@ -32,5 +34,4 @@ Join us on `GitHub <https://github.com/neurodata/m2g>`_.
 
    m2g @ GitHub <http://www.github.com/neurodata/m2g/>
    m2g @ PyPI <https://pypi.org/project/m2g/>
-   Issue Tracker <https://github.com/neurodata/m2g/issues>
-   NeuroData Lab <http://neurodata.io/>
\ No newline at end of file
+   m2g lecture <https://neurodata.io/talks/ndmg.pdf>
\ No newline at end of file
diff --git a/docs/install.rst b/docs/install.rst
index 26a688a69..81ba671ec 100644
--- a/docs/install.rst
+++ b/docs/install.rst
@@ -1,177 +1,109 @@
-******************
-Install
-******************
+.. include:: links.rst
+
+------------
+Installation
+------------
 
 .. contents:: Table of Contents
 
+There are two ways to install **m2g**:
 
-Summary
-===================
+* using container technologies - `Docker Container`_ - (RECOMMENDED) or
+* within a Manually Prepared Environment (For ``m2g-d`` pipeline).
 
-Pull docker container::
+However, the manually prepared environment is not recommended, as it is more complex and error-prone.
 
-    docker pull neurodata/m2g
 
-Run dmri participant pipeline::
+Docker Container
+================================================
+**m2g** is distributed as a Docker image, which is the recommended way to run the pipeline. See :doc:`Docker <./docker>`.
 
-    docker run -ti -v /path/to/local/data:/data neurodata/m2g /data/ /data/outputs
+The Docker image contains all the necessary software dependencies, and the pipeline is executed in a containerized environment.
+This ensures that the pipeline runs in a consistent environment, regardless of the host system.
 
-System Requirements
-====================
-.. TODO: update package versions
+The most recent docker image can be pulled using::
+
+    $ docker pull neurodata/m2g:latest
+
+The image can then be used to create a container and run directly with the following command (and any additional options you may require for Docker, such as volume mounting)::
+
+    $ docker run -ti --entrypoint /bin/bash neurodata/m2g:latest
+
+**m2g** docker containers can also be made from m2g's Dockerfile::
+
+    $ git clone https://github.com/neurodata/m2g.git
+    $ cd m2g
+    $ docker build -t <imagename:uniquelabel> .
+
+Where "uniquelabel" can be whatever you wish to call this Docker image (for example, `m2g:latest`).
+Additional information about building Docker images can be found `here <https://docs.docker.com/engine/reference/commandline/image_build/>`_.
+Creating the Docker image should take several minutes if this is the first time you have used this docker file.
+In order to create a docker container from the docker image and access it, use the following command to both create and enter the container::
 
-The m2g pipeline was developed and tested primarily on Mac OSX, Ubuntu (16, 18, 20, 22), and CentOS (5, 6);
+    $ docker run -it --entrypoint /bin/bash m2g:uniquelabel
 
-Made to work on Python 3.8;
+Manually Prepared Environment (For ``m2g-d`` pipeline)
+=======================================================
 
-Is wrapped in a Docker container;
+.. warning::
 
-Has install instructions via a Dockerfile;
+   This method is not recommended! Please consider using containers.
 
-Requires no non-standard hardware to run;
+.. warning::
 
-Has key features built upon FSL, Dipy, Nibabel, Nilearn, Networkx, Numpy, Scipy, Scikit-Learn, and others;
+   Without Docker, you can only run ``m2g-d`` portion of the pipeline. ``m2g-f`` requires CPAC, which also runs
+   on a Docker container.
 
-Takes approximately 1-core, 8-GB of RAM, and 1 hour to run for most datasets.
-
-While m2g is quite robust to Python package versions (with only few exceptions, mentioned in the installation guide), an example of possible versions (taken from the m2g Docker Image with version v0.3.0) is shown below. Note: this list excludes many libraries which are standard with a Python distribution, and a complete list with all packages and versions can be produced by running pip freeze within the Docker container mentioned above. ::
-
-    awscli==1.16.210 , boto3==1.9.200 , botocore==1.12.200 , colorama==0.3.9 , configparser>=3.7.4 ,
-    Cython==0.29.13 , dipy==0.16.0 , duecredit==0.7.0 , fury==0.3.0 , graspy==0.0.3 , ipython==7.7.0 ,
-    matplotlib==3.1.1 , networkx==2.3 , nibabel==2.5.0 , nilearn==0.5.2 , numpy==1.17.0 , pandas==0.25.0,
-    Pillow==6.1.0 , plotly==1.12.9, pybids==0.6.4 , python-dateutil==2.8.0 , PyVTK==0.5.18 ,
-    requests==2.22.0 , s3transfer==0.2.1 , setuptools>=40.0 scikit-image==0.13.0 , scikit-learn==0.21.3 ,
-    scipy==1.3.0 , sklearn==8.0 , vtk==8.1.2
-
-Installation Guide
-==================
-.. TODO: add links to external packages
-
-Currently, the Docker image is recommended.
-
-``pip`` and Github installations are also available.
-
-Docker
---------------
-.. _Dockerhub : https://hub.docker.com/r/neurodata/m2g/
-.. _documentation : https://docs.docker.com/
-
-The neurodata/m3r-release Docker container enables users to run end-to-end connectome estimation on structural MRI or functional MRI right from container launch. The pipeline requires that data be organized in accordance with the BIDS spec. If the data you wish to process is available on S3 you simply need to provide your s3 credentials at build time and the pipeline will auto-retrieve your data for processing.
-
-If you have never used Docker before, it is useful to run through the Docker documentation_.
-
-**Getting Docker container**::
-
-    $ docker pull neurodata/m2g
 
-*(A) I do not wish to use S3:*
+Make sure all of **m2g**'s `External Dependencies`_ are installed.
+These tools must be installed and their binaries available in the
+system's ``$PATH``.
+A relatively interpretable description of how your environment can be set-up
+is found in the `Dockerfile <https://github.com/neurodata/m2g/blob/deploy/Dockerfile>`_.
 
-You are good to go!
+On a functional Python 3.8 (or above) environment with ``pip`` installed,
+**m2g** can be installed using the habitual command ::
 
-*(B) I wish to use S3:*
+    $ python -m pip install m2g
 
-Add your secret key/access id to a file called credentials.csv in this directory on your local machine. A dummy file has been provided to make the format we expect clear. (This is how AWS provides credentials)
+Check your installation with the ``--version`` argument ::
+
+    $ m2g --version
+
+
+External Dependencies
+---------------------
+
+**m2g** requires other neuroimaging software that are not handled by the Python's packaging system (Pypi):
+
+- FSL_ (version 6.0.6.5)
+- ANTs_ (version 2.4.3)
+- AFNI_ (version 23.3.09)
+- `C3D <https://sourceforge.net/projects/c3d/>`_ (version 1.3.0)
+
+
+Requirements
+====================
 
-**Processing Data**
+Hardware Requirements
+---------------------
 
-Below is the help output generated by running **m2g** with the ``-h`` command. All parameters are explained in this output. ::
+The pipeline only requires 1-core and 16-GB of RAM, and takes approximately 1 hour to run for most datasets.
 
-    $ docker run -ti neurodata/m2g -h
 
-    usage: m2g_bids [-h]
-                 [--participant_label PARTICIPANT_LABEL [PARTICIPANT_LABEL ...]]
-                 [--session_label SESSION_LABEL [SESSION_LABEL ...]]
-                 [--run_label RUN_LABEL [RUN_LABEL ...]] [--bucket BUCKET]
-                 [--remote_path REMOTE_PATH] [--push_data] [--dataset DATASET]
-                 [--atlas ATLAS] [--debug] [--sked] [--skreg] [--vox VOX] [-c]
-                 [--mod MOD] [--tt TT] [--mf MF] [--sp SP] [--seeds SEEDS]
-                 [--modif MODIF]
-                 bids_dir output_dir
+Python Requirements
+-------------------
 
-    This is an end-to-end connectome estimation pipeline from M3r Images.
+The m2g pipeline was developed and tested for Python <=3.8 and <=3.10 on linux systems, such as Ubuntu, CentOS and macOS.
+With `Docker execution`_, **m2g** can run on almost all systems that support Docker.
 
-    positional arguments:
-    bids_dir              The directory with the input dataset formatted
-                          according to the BIDS standard.
-    output_dir            The directory where the output files should be stored.
-                          If you are running group level analysis this folder
-                          should be prepopulated with the results of the
-                          participant level analysis.
+While m2g is quite robust to Python package versions (with only few exceptions, mentioned in the installation guide), an example of possible versions (taken from the m2g Docker Image with version v0.3.0) is shown below. ::
 
-    optional arguments:
-    -h, --help            show this help message and exit
-    --participant_label PARTICIPANT_LABEL [PARTICIPANT_LABEL ...]
-                          The label(s) of the participant(s) that should be
-                          analyzed. The label corresponds to
-                          sub-<participant_label> from the BIDS spec (so it does
-                          not include "sub-"). If this parameter is not provided
-                          all subjects should be analyzed. Multiple participants
-                          can be specified with a space separated list.
-    --session_label SESSION_LABEL [SESSION_LABEL ...]
-                          The label(s) of the session that should be analyzed.
-                          The label corresponds to ses-<participant_label> from
-                          the BIDS spec (so it does not include "ses-"). If this
-                          parameter is not provided all sessions should be
-                          analyzed. Multiple sessions can be specified with a
-                          space separated list.
-    --run_label RUN_LABEL [RUN_LABEL ...]
-                          The label(s) of the run that should be analyzed. The
-                          label corresponds to run-<run_label> from the BIDS
-                          spec (so it does not include "task-"). If this
-                          parameter is not provided all runs should be analyzed.
-                          Multiple runs can be specified with a space separated
-                          list.
-    --bucket BUCKET       The name of an S3 bucket which holds BIDS organized
-                          data. You must have built your bucket with credentials
-                          to the S3 bucket you wish to access.
-    --remote_path REMOTE_PATH
-                          The path to the data on your S3 bucket. The data will
-                          be downloaded to the provided bids_dir on your
-                          machine.
-    --push_data           flag to push derivatives back up to S3.
-    --dataset DATASET     The name of the dataset you are perfoming QC on.
-    --atlas ATLAS         The atlas being analyzed in QC (if you only want one).
-    --debug               If False, remove any old files in the output
-                          directory.
-    --sked                Whether to skip eddy correction if it has already been
-                          run.
-    --skreg               whether or not to skip registration
-    --vox VOX             Voxel size to use for template registrations (e.g.
-                          default is '2mm')
-    -c, --clean           Whether or not to delete intemediates
-    --mod MOD             Determinstic (det) or probabilistic (prob) tracking.
-                          Default is det.
-    --tt TT               Tracking approach: local or particle. Default is
-                        local.
-    --mf MF               Diffusion model: csd or csa. Default is csd.
-    --sp SP               Space for tractography. Default is native.
-    --seeds SEEDS         Seeding density for tractography. Default is 20.
-    --modif MODIF         Name of folder on s3 to push to. If empty, push to a
-                          folder with m2g's version number.
-
-In order to share data between our container and the rest of our machine in Docker, we need to mount a volume. Docker does this with the -v flag. Docker expects its input formatted as: ``-v path/to/local/data:/path/in/container``. We'll do this when we launch our container, as well as give it a helpful name so we can locate it later on.
-
-**To run m2g on data** ::
-
-    docker run -ti -v /path/to/local/data:/data neurodata/m2g /data/ /data/outputs
-
-
-Pip
--------------
-
-m2g relies on FSL, Dipy, networkx, and nibabel, numpy scipy, scikit-learn, scikit-image, nilearn. You should install FSL through the instructions on their website, then follow install other Python dependencies with the following::
-
-    pip install m2g
-
-The only known packages which require a specific version are plotly and networkx, due to backwards-compatability breaking changes.
-
-Installation shouldn't take more than a few minutes, but depends on your internet connection.
-
-Github
------------
-
-To install directly from Github, run::
-
-    git clone https://github.com/neurodata/m2g
-    cd m2g
-    python setup.py install
+    boto3==1.28.4
+    configparser>=3.7.4
+    dipy==0.16.0
+    graspologic>=3.3.0
+    networkx==2.3
+    nibabel==2.5.0
+    nilearn==0.5.2
+    numpy==1.17.0
\ No newline at end of file
diff --git a/docs/links.rst b/docs/links.rst
new file mode 100644
index 000000000..b6d5fd73b
--- /dev/null
+++ b/docs/links.rst
@@ -0,0 +1,23 @@
+.. _Nipype: https://nipype.readthedocs.io/en/latest/
+.. _BIDS: https://bids.neuroimaging.io/
+.. _`BIDS Derivatives`: https://bids-specification.readthedocs.io/en/stable/05-derivatives/01-introduction.html
+.. _Installation: installation.html
+.. _workflows: workflows.html
+.. _FSL: https://fsl.fmrib.ox.ac.uk/fsl/fslwiki/
+.. _ANTs: https://stnava.github.io/ANTs/
+.. _FreeSurfer: https://surfer.nmr.mgh.harvard.edu/
+.. _`submillimeter reconstruction`: https://surfer.nmr.mgh.harvard.edu/fswiki/SubmillimeterRecon
+.. _`mri_robust_template`: https://surfer.nmr.mgh.harvard.edu/fswiki/mri_robust_template
+.. _AFNI: https://afni.nimh.nih.gov/
+.. _GIFTI: https://www.nitrc.org/projects/gifti/
+.. _`Connectome Workbench`: https://www.humanconnectome.org/software/connectome-workbench.html
+.. _`HCP Pipelines`: https://humanconnectome.org/software/hcp-mr-pipelines/
+.. _`Docker Engine`: https://www.docker.com/products/container-runtime
+.. _`Docker installation`: https://docs.docker.com/install/
+.. _`Docker Hub`: https://hub.docker.com/r/nipreps/fmriprep/tags
+.. _Singularity: https://github.com/singularityware/singularity
+..  _SPM: https://www.fil.ion.ucl.ac.uk/spm/software/spm12/
+.. _TACC: https://www.tacc.utexas.edu/
+.. _tedana: https://github.com/me-ica/tedana
+.. _`T2* workflow`: https://tedana.readthedocs.io/en/latest/generated/tedana.workflows.t2smap_workflow.html#tedana.workflows.t2smap_workflow  # noqa
+.. _`citation boilerplate`: https://www.nipreps.org/intro/transparency/#citation-boilerplates
\ No newline at end of file
diff --git a/docs/make.bat b/docs/make.bat
deleted file mode 100644
index 27f573b87..000000000
--- a/docs/make.bat
+++ /dev/null
@@ -1,35 +0,0 @@
-@ECHO OFF
-
-pushd %~dp0
-
-REM Command file for Sphinx documentation
-
-if "%SPHINXBUILD%" == "" (
-	set SPHINXBUILD=sphinx-build
-)
-set SOURCEDIR=.
-set BUILDDIR=_build
-
-if "%1" == "" goto help
-
-%SPHINXBUILD% >NUL 2>NUL
-if errorlevel 9009 (
-	echo.
-	echo.The 'sphinx-build' command was not found. Make sure you have Sphinx
-	echo.installed, then set the SPHINXBUILD environment variable to point
-	echo.to the full path of the 'sphinx-build' executable. Alternatively you
-	echo.may add the Sphinx directory to PATH.
-	echo.
-	echo.If you don't have Sphinx installed, grab it from
-	echo.http://sphinx-doc.org/
-	exit /b 1
-)
-
-%SPHINXBUILD% -M %1 %SOURCEDIR% %BUILDDIR% %SPHINXOPTS%
-goto end
-
-:help
-%SPHINXBUILD% -M help %SOURCEDIR% %BUILDDIR% %SPHINXOPTS%
-
-:end
-popd
diff --git a/docs/paper/data/diffusion_disc.csv b/docs/paper/data/diffusion_disc.csv
new file mode 100644
index 000000000..13f466c33
--- /dev/null
+++ b/docs/paper/data/diffusion_disc.csv
@@ -0,0 +1,37 @@
+,Mean,MRN1 [10/2],IPCAS8 [13/2],NKI24 [20/2],NKI1 [20/2],BNU1 [57/2],XHCUMS [24/5],HNU1 [30/10],SWU4 [235/2]
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+Schaefer1000,0.954432711328701,0.925,0.91025641025641,0.986111111111111,0.985526315789473,0.962562656641604,0.948125291103866,0.995614303959131,0.925470044991712
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+AICHA [384],0.947746069660946,0.891666666666667,0.884615384615385,1.0,1.0,0.953790726817043,0.955390411870384,0.993893997445722,0.911771252663983
+CAPRSC [333],0.945211049606759,0.858333333333333,0.902243589743589,0.984477124183006,0.984210526315789,0.953399122807017,0.927180887783618,0.990630906768838,0.917762254321572
+Destrieux [75],0.939254161853878,0.9,0.951923076923077,0.962418300653595,0.967763157894737,0.950501253132832,0.941147635404884,0.983984674329502,0.9004641250296
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+Brodmann [40],0.93082426298797,0.991666666666667,0.939102564102564,0.996732026143791,0.996710526315789,0.94000626566416,0.941882499390068,0.954375478927203,0.895017759886337
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+Desikan [70],0.926628325790949,0.883333333333333,0.822115384615385,0.994281045751634,0.996052631578947,0.928728070175439,0.942155409264311,0.968117496807152,0.893697608335307
+HOC [48],0.926642461800074,0.833333333333333,0.9375,0.984477124183006,0.982894736842105,0.948934837092732,0.935805186361923,0.953597701149425,0.893460809850817
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+AAL [116],0.921419620189735,0.825,0.842948717948718,0.999183006535948,0.999342105263158,0.946193609022556,0.947710814647238,0.957561941251596,0.881715605020128
+Hammersmith [83],0.920061712300581,0.766666666666667,0.857371794871795,0.98202614379085,0.986842105263158,0.934602130325814,0.902217518353405,0.956896551724138,0.897912621359223
+Slab1068,0.913979012372542,0.925,0.831730769230769,0.994281045751634,0.993421052631579,0.90906954887218,0.922655608878391,0.959618135376756,0.875653563817191
+DS00195,0.90558808443815,0.85,0.911858974358974,1.0,1.0,0.961309523809524,0.949094424390622,0.913390804597701,0.860516220696188
+DS00096,0.905536562919994,0.816666666666667,0.884615384615385,0.995915032679739,0.995394736842105,0.953320802005012,0.918276921562756,0.887425287356322,0.894295524508643
+Yeo-17-lib,0.902908137582342,0.891666666666667,0.916666666666667,0.941993464052288,0.950657894736842,0.9390664160401,0.900791109410695,0.930789272030651,0.866400663035757
+DS00071,0.902919803682931,0.8,0.866987179487179,0.995098039215686,0.995394736842105,0.951754385964912,0.939178005855346,0.889690932311622,0.883112716078617
+MICCAI [136],0.898906135835056,0.75,0.782051282051282,0.996732026143791,0.998026315789473,0.919251253132832,0.897388659702353,0.898416347381865,0.89700686715605
+Yeo-17,0.894582229477393,0.933333333333333,0.846153846153846,0.93218954248366,0.9375,0.915256892230576,0.902486789429326,0.913312899106002,0.870153919014918
+DS00108,0.886363936009161,0.775,0.884615384615385,1.0,0.998684210526316,0.972979323308271,0.940486587042828,0.841077905491698,0.868218091404215
+DS00140,0.872131174061706,0.783333333333333,0.921474358974359,1.0,1.0,0.954573934837093,0.941678899960077,0.834418901660281,0.837315889178309
+HOS [48],0.865542677261997,0.625,0.778846153846154,0.937091503267974,0.930263157894737,0.878916040100251,0.844218711879256,0.866941251596424,0.875659483779304
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+Tissue [3],0.539207776573186,0.5,0.560897435897436,0.506535947712418,0.506578947368421,0.550595238095238,0.532980335240757,0.523258620689655,0.553856855316126
+dsizes,60.0,49.0,56.0,68.0,70.0,124.0,127.0,290.0,400.0
diff --git a/docs/paper/data/discrim_results.ods b/docs/paper/data/discrim_results.ods
new file mode 100644
index 000000000..2e4d14449
Binary files /dev/null and b/docs/paper/data/discrim_results.ods differ
diff --git a/docs/paper/data/dwi_ipsi_aal.csv b/docs/paper/data/dwi_ipsi_aal.csv
new file mode 100644
index 000000000..6c8b33830
--- /dev/null
+++ b/docs/paper/data/dwi_ipsi_aal.csv
@@ -0,0 +1,3979 @@
+Dataset Atlas Type Value
+SWU4 AAL Ipsi 0.8940082356672104
+SWU4 AAL Ipsi 0.8627047247198005
+SWU4 AAL Ipsi 0.8806769493412013
+SWU4 AAL Ipsi 0.873106579446481
+SWU4 AAL Ipsi 0.9112307899501698
+SWU4 AAL Ipsi 0.8092559640666649
+SWU4 AAL Ipsi 0.7728479410883659
+SWU4 AAL Ipsi 0.8015816450559098
+SWU4 AAL Ipsi 0.8424891784237306
+SWU4 AAL Ipsi 0.7818362817745038
+SWU4 AAL Ipsi 0.9417644776595685
+SWU4 AAL Ipsi 0.8677553087031543
+SWU4 AAL Ipsi 0.7946610190355112
+SWU4 AAL Ipsi 0.8642312654536976
+SWU4 AAL Ipsi 0.9008764240822295
+SWU4 AAL Ipsi 0.832776181850186
+SWU4 AAL Ipsi 0.819414199356209
+SWU4 AAL Ipsi 0.8124595239754239
+SWU4 AAL Ipsi 0.8371000141010442
+SWU4 AAL Ipsi 0.7935336796800333
+SWU4 AAL Ipsi 0.8456019301784462
+SWU4 AAL Ipsi 0.8090332429421828
+SWU4 AAL Ipsi 0.8498394423266202
+SWU4 AAL Ipsi 0.887171011806387
+SWU4 AAL Ipsi 0.8135350579124427
+SWU4 AAL Ipsi 0.8818730426454322
+SWU4 AAL Ipsi 0.818290251879585
+SWU4 AAL Ipsi 0.8060340421659826
+SWU4 AAL Ipsi 0.8435517073183171
+SWU4 AAL Ipsi 0.862265571448373
+SWU4 AAL Ipsi 0.7951389319941581
+SWU4 AAL Ipsi 0.8642446648842814
+SWU4 AAL Ipsi 0.8858911647432731
+SWU4 AAL Ipsi 0.8421588283529376
+SWU4 AAL Ipsi 0.8233344200042768
+SWU4 AAL Ipsi 0.8068990045525029
+SWU4 AAL Ipsi 0.8493726921096173
+SWU4 AAL Ipsi 0.8329918055164709
+SWU4 AAL Ipsi 0.89725991234772
+SWU4 AAL Ipsi 0.8651519771669818
+SWU4 AAL Ipsi 0.8215103617528628
+SWU4 AAL Ipsi 0.8170339701076125
+SWU4 AAL Ipsi 0.8970857100642989
+SWU4 AAL Ipsi 0.9119644377388727
+SWU4 AAL Ipsi 0.842535335435093
+SWU4 AAL Ipsi 0.9254281555176275
+SWU4 AAL Ipsi 0.799641834170224
+SWU4 AAL Ipsi 0.8537011225475741
+SWU4 AAL Ipsi 0.8912391184339038
+SWU4 AAL Ipsi 0.8441665709546868
+SWU4 AAL Ipsi 0.8066842186925752
+SWU4 AAL Ipsi 0.7999726270437597
+SWU4 AAL Ipsi 0.8342856627299547
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+SWU4 AAL Ipsi 0.8708976065521761
+SWU4 AAL Ipsi 0.8148011176277309
+SWU4 AAL Ipsi 0.8633855024386391
+SWU4 AAL Ipsi 0.8582286224346122
+SWU4 AAL Ipsi 0.851619302140463
+SWU4 AAL Ipsi 0.8155370214436934
+SWU4 AAL Ipsi 0.7612380295706415
+SWU4 AAL Ipsi 0.8051723590915605
+SWU4 AAL Ipsi 0.9073473946844858
+SWU4 AAL Ipsi 0.8739761454949784
+SWU4 AAL Ipsi 0.8407990436534278
+SWU4 AAL Ipsi 0.8227989534047658
+SWU4 AAL Ipsi 0.8978193833632937
+SWU4 AAL Ipsi 0.8448235942597716
+SWU4 AAL Ipsi 0.8074985443223325
+SWU4 AAL Ipsi 0.8105204644633687
+SWU4 AAL Ipsi 0.8128766158881103
+SWU4 AAL Ipsi 0.8823693664231785
+SWU4 AAL Ipsi 0.891273514490958
+SWU4 AAL Ipsi 0.833124723891661
+SWU4 AAL Ipsi 0.7760059424166298
+SWU4 AAL Ipsi 0.7806517020055896
+SWU4 AAL Ipsi 0.8464834478648074
+SWU4 AAL Ipsi 0.8666907264029132
+SWU4 AAL Ipsi 0.865974170872795
+SWU4 AAL Ipsi 0.7974218560200135
+SWU4 AAL Ipsi 0.751751514514058
+SWU4 AAL Ipsi 0.841441141795512
+SWU4 AAL Ipsi 0.7957722464545816
+SWU4 AAL Ipsi 0.8440829643382675
+SWU4 AAL Ipsi 0.8161258906872203
+SWU4 AAL Ipsi 0.7440337551699959
+SWU4 AAL Ipsi 0.8143355989920767
+SWU4 AAL Ipsi 0.8686055252772036
+SWU4 AAL Ipsi 0.8622105200813976
+SWU4 AAL Ipsi 0.84772380081027
+SWU4 AAL Ipsi 0.8231805291460467
+SWU4 AAL Ipsi 0.8623399459467939
+SWU4 AAL Ipsi 0.8180341762037914
+SWU4 AAL Ipsi 0.7713016004038156
+SWU4 AAL Ipsi 0.8101472225995536
+SWU4 AAL Ipsi 0.8289061433489975
+SWU4 AAL Ipsi 0.821542061922904
+SWU4 AAL Ipsi 0.8069016273221756
+SWU4 AAL Ipsi 0.85964911705873
+SWU4 AAL Ipsi 0.8569482198755856
+SWU4 AAL Ipsi 0.8819212799823212
+SWU4 AAL Ipsi 0.8387623088277838
+SWU4 AAL Ipsi 0.8575925044332148
+SWU4 AAL Ipsi 0.9133478438954591
+SWU4 AAL Ipsi 0.8170972329932976
+SWU4 AAL Ipsi 0.8983499471934914
+SWU4 AAL Ipsi 0.8492167344230696
+SWU4 AAL Ipsi 0.7963696443796278
+SWU4 AAL Ipsi 0.8241040347651708
+SWU4 AAL Ipsi 0.8133439648411982
+SWU4 AAL Ipsi 0.8529331580153685
+SWU4 AAL Ipsi 0.8728637168367254
+SWU4 AAL Ipsi 0.8795039515194631
+SWU4 AAL Ipsi 0.8300779816303935
+SWU4 AAL Ipsi 0.9118484765199708
+SWU4 AAL Ipsi 0.8034242728406534
+SWU4 AAL Ipsi 0.8709348233460822
+SWU4 AAL Ipsi 0.826399914831287
+SWU4 AAL Ipsi 0.8750131675786206
+SWU4 AAL Ipsi 0.9055187582709455
+SWU4 AAL Ipsi 0.8897910812348679
+SWU4 AAL Ipsi 0.823639578082584
+SWU4 AAL Ipsi 0.8355631906521248
+SWU4 AAL Ipsi 0.9288074679470594
+SWU4 AAL Ipsi 0.7569239473703439
+SWU4 AAL Ipsi 0.8513616457013601
+SWU4 AAL Ipsi 0.8348279929101847
+SWU4 AAL Ipsi 0.8915979476481969
+SWU4 AAL Ipsi 0.8961396051021814
+SWU4 AAL Ipsi 0.8167163688135085
+SWU4 AAL Ipsi 0.8932330334060147
+SWU4 AAL Ipsi 0.8113504711629466
+SWU4 AAL Ipsi 0.8195522836734387
+SWU4 AAL Ipsi 0.863460311532619
+SWU4 AAL Ipsi 0.9059938242036687
+SWU4 AAL Ipsi 0.8404012545990078
+SWU4 AAL Ipsi 0.8715507820663162
+SWU4 AAL Ipsi 0.8275944649107507
+SWU4 AAL Ipsi 0.8133651168576576
+SWU4 AAL Ipsi 0.8732786252150107
+SWU4 AAL Ipsi 0.8381162411052832
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+SWU4 AAL Contra 0.17793562840083701
+SWU4 AAL Contra 0.16006424273789105
+SWU4 AAL Contra 0.15291451099620565
+SWU4 AAL Contra 0.11881636701146708
+SWU4 AAL Contra 0.10318263726374576
+SWU4 AAL Contra 0.18144007961327685
+SWU4 AAL Contra 0.12323279233522393
+SWU4 AAL Contra 0.17236842390291876
+SWU4 AAL Contra 0.10603872361838329
+SWU4 AAL Contra 0.13290826158706723
+SWU4 AAL Contra 0.11678564873659929
+SWU4 AAL Contra 0.1395700457846734
+SWU4 AAL Contra 0.19074672852653665
+SWU4 AAL Contra 0.1864198133081154
+SWU4 AAL Contra 0.20708449129342535
+SWU4 AAL Contra 0.17827379647072863
+SWU4 AAL Contra 0.17058592215649282
+SWU4 AAL Contra 0.13909226749208617
+SWU4 AAL Contra 0.17123631014984297
+SWU4 AAL Contra 0.09652142685955649
+SWU4 AAL Contra 0.08398813452719538
+SWU4 AAL Contra 0.20251856520858216
+SWU4 AAL Contra 0.13821769108464027
+SWU4 AAL Contra 0.16979354584737577
+SWU4 AAL Contra 0.2306603340556541
+SWU4 AAL Contra 0.1476366509824418
+SWU4 AAL Contra 0.11088789417972052
+SWU4 AAL Contra 0.22901460599551726
+SWU4 AAL Contra 0.19284002325363048
+SWU4 AAL Contra 0.13843587367005009
+SWU4 AAL Contra 0.10691384149896578
+SWU4 AAL Contra 0.09672594756994343
+SWU4 AAL Contra 0.22372651328859144
+SWU4 AAL Contra 0.1359461502285687
+SWU4 AAL Contra 0.17218648758033658
+SWU4 AAL Contra 0.16944916085002626
+SWU4 AAL Contra 0.15800985331208267
+SWU4 AAL Contra 0.18259009581267782
+SWU4 AAL Contra 0.15744769165931363
+SWU4 AAL Contra 0.19086638984998244
+SWU4 AAL Contra 0.15451093274705516
+SWU4 AAL Contra 0.11367703568348328
+SWU4 AAL Contra 0.04028341677677592
+SWU4 AAL Contra 0.16097131060551131
+SWU4 AAL Contra 0.14117250967359918
+SWU4 AAL Contra 0.18668568436191427
+SWU4 AAL Contra 0.13092539014287607
+SWU4 AAL Contra 0.21364857391435663
+SWU4 AAL Contra 0.19223796774054272
+SWU4 AAL Contra 0.16488626368912562
+SWU4 AAL Contra 0.14352068091662212
+SWU4 AAL Contra 0.19397155700312113
+SWU4 AAL Contra 0.2034393153561757
+SWU4 AAL Contra 0.19214620978450606
+SWU4 AAL Contra 0.11550492684073085
+SWU4 AAL Contra 0.1622037616060055
+SWU4 AAL Contra 0.18404366133475555
+SWU4 AAL Contra 0.16035009607060993
+SWU4 AAL Contra 0.20223345872221052
+SWU4 AAL Contra 0.1609114184247474
+SWU4 AAL Contra 0.1605990003605886
+SWU4 AAL Contra 0.08659658396060212
+SWU4 AAL Contra 0.14784954379639592
+SWU4 AAL Contra 0.16907660128036722
+SWU4 AAL Contra 0.1716503770891788
+SWU4 AAL Contra 0.11479502045225198
+SWU4 AAL Contra 0.18779956115617072
+SWU4 AAL Contra 0.12372415820290575
+SWU4 AAL Contra 0.1313945146515093
+SWU4 AAL Contra 0.11869094288221908
+SWU4 AAL Contra 0.16583996263394957
+SWU4 AAL Contra 0.10183201309866471
+SWU4 AAL Contra 0.12354276639527602
+SWU4 AAL Contra 0.14087608947676938
+SWU4 AAL Contra 0.17411377117324559
+SWU4 AAL Contra 0.1600459201904645
+SWU4 AAL Contra 0.1436570719334948
+SWU4 AAL Contra 0.10718186415812633
+SWU4 AAL Contra 0.26180963850438727
+SWU4 AAL Contra 0.11723340198067131
+SWU4 AAL Contra 0.14646281819801496
+SWU4 AAL Contra 0.17114157457358245
+SWU4 AAL Contra 0.08292099567379203
+SWU4 AAL Contra 0.153067121644923
+SWU4 AAL Contra 0.15367143911154404
+SWU4 AAL Contra 0.2032714937758529
+SWU4 AAL Contra 0.1393840866368818
+SWU4 AAL Contra 0.177808544823108
+SWU4 AAL Contra 0.17282062319987415
+SWU4 AAL Contra 0.1842198928970418
+SWU4 AAL Contra 0.150957077225813
+SWU4 AAL Contra 0.08424024513498675
+SWU4 AAL Contra 0.17019728803932244
+SWU4 AAL Contra 0.14085348360009145
+SWU4 AAL Contra 0.13342995473261207
+SWU4 AAL Contra 0.08720372258174619
+SWU4 AAL Contra 0.14359964555311322
+SWU4 AAL Contra 0.1783112918300649
+SWU4 AAL Contra 0.18670947487833717
+SWU4 AAL Contra 0.18159108987485365
+SWU4 AAL Contra 0.1531611661033048
+SWU4 AAL Contra 0.12368662963075075
+SWU4 AAL Contra 0.14389999365547917
+SWU4 AAL Contra 0.17559794410836624
+SWU4 AAL Contra 0.19414856295190003
+SWU4 AAL Contra 0.14294636996887639
+SWU4 AAL Contra 0.16075853988533234
+SWU4 AAL Contra 0.08685109533737505
+SWU4 AAL Contra 0.17472799988048718
+SWU4 AAL Contra 0.1381591312358189
+SWU4 AAL Contra 0.1562650462804695
+SWU4 AAL Contra 0.12280963146293711
+SWU4 AAL Contra 0.10485410736387248
+SWU4 AAL Contra 0.1985041367075112
+SWU4 AAL Contra 0.16184537328184606
+SWU4 AAL Contra 0.13968448118613377
+SWU4 AAL Contra 0.1989666650381578
+SWU4 AAL Contra 0.12138273242705334
+SWU4 AAL Contra 0.15756433533766417
+SWU4 AAL Contra 0.13620101274265742
+SWU4 AAL Contra 0.1444595344725117
+SWU4 AAL Contra 0.08525366697820881
+SWU4 AAL Contra 0.11147448071686682
+SWU4 AAL Contra 0.06585655439219332
+SWU4 AAL Contra 0.22890628663640986
+SWU4 AAL Contra 0.13184748348943784
+SWU4 AAL Contra 0.12219564776940084
+SWU4 AAL Contra 0.11230130254886657
+SWU4 AAL Contra 0.16998583970726702
+SWU4 AAL Contra 0.11616460534897018
+SWU4 AAL Contra 0.13172214549569036
+SWU4 AAL Contra 0.2090429269088903
+SWU4 AAL Contra 0.08538238416727767
+SWU4 AAL Contra 0.22684474953264544
+SWU4 AAL Contra 0.2553087486577358
+SWU4 AAL Contra 0.12137422039727216
+SWU4 AAL Contra 0.23304508915904307
+SWU4 AAL Contra 0.08389182409221839
+SWU4 AAL Contra 0.10699657047194971
+SWU4 AAL Contra 0.18674407827158887
+SWU4 AAL Contra 0.08687792337021014
+SWU4 AAL Contra 0.11176683148991058
+SWU4 AAL Contra 0.19311794946839012
+SWU4 AAL Contra 0.13879176804521118
+SWU4 AAL Contra 0.17086143101843287
+SWU4 AAL Contra 0.1642544798883485
+SWU4 AAL Contra 0.18883911121177654
+SWU4 AAL Contra 0.16607216822582296
+SWU4 AAL Contra 0.1804876835676728
+SWU4 AAL Contra 0.1747673951212331
+SWU4 AAL Contra 0.16575296477956317
+SWU4 AAL Contra 0.15593593090432534
+SWU4 AAL Contra 0.09881310566329911
+SWU4 AAL Contra 0.12912140296027486
+SWU4 AAL Contra 0.09985028962973252
+SWU4 AAL Contra 0.1320064939019212
+SWU4 AAL Contra 0.11927034189921638
+SWU4 AAL Contra 0.1436662225054038
+SWU4 AAL Contra 0.14737566411818065
+SWU4 AAL Contra 0.09566095233170487
+SWU4 AAL Contra 0.21482921938620964
+SWU4 AAL Contra 0.15860659186936865
+SWU4 AAL Contra 0.1935586181978175
+SWU4 AAL Contra 0.18284114611922275
+SWU4 AAL Contra 0.1526344845792273
+SWU4 AAL Contra 0.16141132685412696
+SWU4 AAL Contra 0.15983748021876046
+SWU4 AAL Contra 0.16083975872238634
+SWU4 AAL Contra 0.1394676595055847
+SWU4 AAL Contra 0.10584114146805086
+SWU4 AAL Contra 0.16115298849636675
+SWU4 AAL Contra 0.18852475674502295
+SWU4 AAL Contra 0.14729950117641658
+SWU4 AAL Contra 0.17134478389789431
+SWU4 AAL Contra 0.13283474249130017
+SWU4 AAL Contra 0.1309090771981397
+SWU4 AAL Contra 0.16590934363265034
+SWU4 AAL Contra 0.15419808508399677
+SWU4 AAL Contra 0.09374440016439824
+SWU4 AAL Contra 0.17919213402800074
+SWU4 AAL Contra 0.23979622718696256
+SWU4 AAL Contra 0.11608080359676648
+SWU4 AAL Contra 0.14333995756123727
+SWU4 AAL Contra 0.11867772701688394
+SWU4 AAL Contra 0.15592793963364415
+SWU4 AAL Contra 0.12395485920912148
+SWU4 AAL Contra 0.2292371318657399
+SWU4 AAL Contra 0.17532498517190592
+SWU4 AAL Contra 0.17717232513388148
+SWU4 AAL Contra 0.1520389317778337
+SWU4 AAL Contra 0.13187988663772357
+SWU4 AAL Contra 0.14438342760809483
+SWU4 AAL Contra 0.16333557161168397
+SWU4 AAL Contra 0.07090725157382098
+SWU4 AAL Contra 0.1186762762610002
+SWU4 AAL Contra 0.13659689734797278
+SWU4 AAL Contra 0.14149132866351552
+SWU4 AAL Contra 0.22159561104443593
+SWU4 AAL Contra 0.19139827083750766
+SWU4 AAL Contra 0.16389792983466533
+SWU4 AAL Contra 0.22740157328593244
+SWU4 AAL Contra 0.1188095564439293
+SWU4 AAL Contra 0.15486672708081367
+SWU4 AAL Contra 0.14586167365851832
+SWU4 AAL Contra 0.17129207803322272
+SWU4 AAL Contra 0.045096393217515254
+SWU4 AAL Contra 0.1382802279989182
+SWU4 AAL Contra 0.13942407586604033
+SWU4 AAL Contra 0.16553362157242008
+SWU4 AAL Contra 0.07088553958934289
+SWU4 AAL Contra 0.22082923112382982
+SWU4 AAL Contra 0.13563642939085738
+SWU4 AAL Contra 0.11600190956694845
+SWU4 AAL Contra 0.14521998893474986
+SWU4 AAL Contra 0.1611748082851808
+SWU4 AAL Contra 0.1346628604708968
+SWU4 AAL Contra 0.2318615527748836
+SWU4 AAL Contra 0.1961547093471277
+SWU4 AAL Contra 0.17336868582197432
+SWU4 AAL Contra 0.1327517157046312
+SWU4 AAL Contra 0.20139821350713244
+SWU4 AAL Contra 0.1257474985380282
+SWU4 AAL Contra 0.16384069288607947
+SWU4 AAL Contra 0.2124979598323197
+SWU4 AAL Contra 0.10266441045094796
+SWU4 AAL Contra 0.19548258397007803
+SWU4 AAL Contra 0.10297699481865286
+SWU4 AAL Contra 0.1956406558939678
+SWU4 AAL Contra 0.15089204262591346
+SWU4 AAL Contra 0.2222031657386474
+SWU4 AAL Contra 0.1895080038338092
+SWU4 AAL Contra 0.10299194476409666
+SWU4 AAL Contra 0.1253980159567134
+SWU4 AAL Contra 0.2444993614677468
+SWU4 AAL Contra 0.13239110131027848
+SWU4 AAL Contra 0.16454612663040608
+SWU4 AAL Contra 0.12442695986130119
+SWU4 AAL Contra 0.16885200419532811
+SWU4 AAL Contra 0.1707023154811798
+SWU4 AAL Contra 0.15818791009913344
+SWU4 AAL Contra 0.14550381536425536
+SWU4 AAL Contra 0.11399685585206473
+SWU4 AAL Contra 0.17240714391876744
+SWU4 AAL Contra 0.13951048288784
+SWU4 AAL Contra 0.17617435132009426
+SWU4 AAL Contra 0.14185204908499793
+SWU4 AAL Contra 0.15930204987266308
+SWU4 AAL Contra 0.15841733486810702
+SWU4 AAL Contra 0.09880246826482553
+SWU4 AAL Contra 0.11733699578397372
+SWU4 AAL Contra 0.11707602929437083
+SWU4 AAL Contra 0.12115067719335776
+SWU4 AAL Contra 0.20293502527753082
+SWU4 AAL Contra 0.19485325252772265
+SWU4 AAL Contra 0.09328077178642265
+SWU4 AAL Contra 0.15191514169632894
+SWU4 AAL Contra 0.13042097178909673
+Dataset Atlas Type Value
+HNU1 AAL Ipsi 0.9586392315690112
+HNU1 AAL Ipsi 0.9642230607259381
+HNU1 AAL Ipsi 0.9957560006521285
+HNU1 AAL Ipsi 0.954839617425516
+HNU1 AAL Ipsi 0.9566548425558918
+HNU1 AAL Ipsi 0.9721664353687549
+HNU1 AAL Ipsi 0.9105371480302424
+HNU1 AAL Ipsi 0.9698551488158436
+HNU1 AAL Ipsi 0.9703000964869962
+HNU1 AAL Ipsi 0.8741618442374726
+HNU1 AAL Ipsi 0.9831049179112775
+HNU1 AAL Ipsi 0.9917066470997723
+HNU1 AAL Ipsi 0.9916873443099069
+HNU1 AAL Ipsi 0.9437907146293733
+HNU1 AAL Ipsi 0.981625295730219
+HNU1 AAL Ipsi 0.9664445412713936
+HNU1 AAL Ipsi 0.9683100505517204
+HNU1 AAL Ipsi 0.9464864964139514
+HNU1 AAL Ipsi 0.9482978096442533
+HNU1 AAL Ipsi 0.9914785134503912
+HNU1 AAL Ipsi 0.9500348905816762
+HNU1 AAL Ipsi 0.9412837569690259
+HNU1 AAL Ipsi 0.9811227198173844
+HNU1 AAL Ipsi 0.9868565541869878
+HNU1 AAL Ipsi 0.9629326493299971
+HNU1 AAL Ipsi 0.9866950525201761
+HNU1 AAL Ipsi 0.9726798803623369
+HNU1 AAL Ipsi 0.9838579768891978
+HNU1 AAL Ipsi 0.9326300936326711
+HNU1 AAL Ipsi 0.9379790216623127
+HNU1 AAL Ipsi 0.9543073957664057
+HNU1 AAL Ipsi 0.9778220202450891
+HNU1 AAL Ipsi 0.9608495169714918
+HNU1 AAL Ipsi 0.9338036828597523
+HNU1 AAL Ipsi 0.9526853765729532
+HNU1 AAL Ipsi 0.9513563699718792
+HNU1 AAL Ipsi 0.9750438458924325
+HNU1 AAL Ipsi 0.9817058402659272
+HNU1 AAL Ipsi 0.976315868345154
+HNU1 AAL Ipsi 0.9792114077430795
+HNU1 AAL Ipsi 0.9841411058711967
+HNU1 AAL Ipsi 0.9695849528276772
+HNU1 AAL Ipsi 0.954499084196641
+HNU1 AAL Ipsi 0.9590645870519073
+HNU1 AAL Ipsi 0.9465484431052655
+HNU1 AAL Ipsi 0.9843193698974927
+HNU1 AAL Ipsi 0.9857646530491564
+HNU1 AAL Ipsi 0.9534203446449895
+HNU1 AAL Ipsi 0.9487225353163669
+HNU1 AAL Ipsi 0.9943892520718588
+HNU1 AAL Ipsi 0.970955298573511
+HNU1 AAL Ipsi 0.952294564589576
+HNU1 AAL Ipsi 0.9721575063954982
+HNU1 AAL Ipsi 0.9809910015745491
+HNU1 AAL Ipsi 0.9821902566123817
+HNU1 AAL Ipsi 0.9719867357049778
+HNU1 AAL Ipsi 0.9601350243475862
+HNU1 AAL Ipsi 0.9741806317856379
+HNU1 AAL Ipsi 0.9864177283209816
+HNU1 AAL Ipsi 0.9501348991279629
+HNU1 AAL Ipsi 0.9335155202374312
+HNU1 AAL Ipsi 0.9710031246463023
+HNU1 AAL Ipsi 0.9700566323366491
+HNU1 AAL Ipsi 0.9653624566321922
+HNU1 AAL Ipsi 0.9463573796847211
+HNU1 AAL Ipsi 0.9645977569034425
+HNU1 AAL Ipsi 0.9281794000178614
+HNU1 AAL Ipsi 0.9663689735657824
+HNU1 AAL Ipsi 0.9636811015880423
+HNU1 AAL Ipsi 0.9817194855050754
+HNU1 AAL Ipsi 0.9839902481546972
+HNU1 AAL Ipsi 0.9863416898518416
+HNU1 AAL Ipsi 0.983558569200964
+HNU1 AAL Ipsi 0.9463800004213667
+HNU1 AAL Ipsi 0.9200382802038697
+HNU1 AAL Ipsi 0.9612317998936907
+HNU1 AAL Ipsi 0.9749062019781916
+HNU1 AAL Ipsi 0.9625342670417298
+HNU1 AAL Ipsi 0.9825657199278224
+HNU1 AAL Ipsi 0.980338645038774
+HNU1 AAL Ipsi 0.985673844165231
+HNU1 AAL Ipsi 0.9810486656858641
+HNU1 AAL Ipsi 0.9406833910111619
+HNU1 AAL Ipsi 0.9538043027346342
+HNU1 AAL Ipsi 0.9820535979937943
+HNU1 AAL Ipsi 0.9926759020749244
+HNU1 AAL Ipsi 0.9582414067582843
+HNU1 AAL Ipsi 0.9763675800079061
+HNU1 AAL Ipsi 0.9301711186150329
+HNU1 AAL Ipsi 0.9821403092236307
+HNU1 AAL Ipsi 0.9800214324657989
+HNU1 AAL Ipsi 0.9712706667271362
+HNU1 AAL Ipsi 0.9510736892214544
+HNU1 AAL Ipsi 0.9651246278318474
+HNU1 AAL Ipsi 0.9739977125135407
+HNU1 AAL Ipsi 0.9628143941263734
+HNU1 AAL Ipsi 0.9664110741670625
+HNU1 AAL Ipsi 0.9545123655319336
+HNU1 AAL Ipsi 0.9421737457951399
+HNU1 AAL Ipsi 0.9118331766984696
+HNU1 AAL Ipsi 0.9694306864107197
+HNU1 AAL Ipsi 0.9382478974211288
+HNU1 AAL Ipsi 0.9755662140469339
+HNU1 AAL Ipsi 0.9252450845078198
+HNU1 AAL Ipsi 0.9639656823892593
+HNU1 AAL Ipsi 0.9473333754508296
+HNU1 AAL Ipsi 0.9860838615473556
+HNU1 AAL Ipsi 0.9842722901948087
+HNU1 AAL Ipsi 0.970135653041221
+HNU1 AAL Ipsi 0.959592941717358
+HNU1 AAL Ipsi 0.9360039462780502
+HNU1 AAL Ipsi 0.9797474420046791
+HNU1 AAL Ipsi 0.9332462629822788
+HNU1 AAL Ipsi 0.9448927081997287
+HNU1 AAL Ipsi 0.9827610249520556
+HNU1 AAL Ipsi 0.9890833670522711
+HNU1 AAL Ipsi 0.9875669147202949
+HNU1 AAL Ipsi 0.9920869767596502
+HNU1 AAL Ipsi 0.9818530093759451
+HNU1 AAL Ipsi 0.9492173048584143
+HNU1 AAL Ipsi 0.9912935078170378
+HNU1 AAL Ipsi 0.9870210793563117
+HNU1 AAL Ipsi 0.9965525911169991
+HNU1 AAL Ipsi 0.9555052588517313
+HNU1 AAL Ipsi 0.9279065300676707
+HNU1 AAL Ipsi 0.9885533055261395
+HNU1 AAL Ipsi 0.9384143427848018
+HNU1 AAL Ipsi 0.9785823188153059
+HNU1 AAL Ipsi 0.9501612787006151
+HNU1 AAL Ipsi 0.9755961930384573
+HNU1 AAL Ipsi 0.9804079940414663
+HNU1 AAL Ipsi 0.9758700103973124
+HNU1 AAL Ipsi 0.9887558464873831
+HNU1 AAL Ipsi 0.9693233234646437
+HNU1 AAL Ipsi 0.9383515653891447
+HNU1 AAL Ipsi 0.9582132182879712
+HNU1 AAL Ipsi 0.9744089107766662
+HNU1 AAL Ipsi 0.978547945992912
+HNU1 AAL Ipsi 0.9633173375058385
+HNU1 AAL Ipsi 0.981168205566829
+HNU1 AAL Ipsi 0.9239624890143159
+HNU1 AAL Ipsi 0.9775783369200586
+HNU1 AAL Ipsi 0.968690594873675
+HNU1 AAL Ipsi 0.9914176935929739
+HNU1 AAL Ipsi 0.989545762071841
+HNU1 AAL Ipsi 0.9620882381353242
+HNU1 AAL Ipsi 0.9822913773174076
+HNU1 AAL Ipsi 0.9633904529749552
+HNU1 AAL Ipsi 0.9243471380887626
+HNU1 AAL Ipsi 0.9684708762177173
+HNU1 AAL Ipsi 0.9261730814920409
+HNU1 AAL Ipsi 0.9835134762175731
+HNU1 AAL Ipsi 0.9455171542325547
+HNU1 AAL Ipsi 0.9506561897273339
+HNU1 AAL Ipsi 0.9425709499688295
+HNU1 AAL Ipsi 0.9463352303232041
+HNU1 AAL Ipsi 0.9582159907693937
+HNU1 AAL Ipsi 0.9108142122739781
+HNU1 AAL Ipsi 0.9863446275680511
+HNU1 AAL Ipsi 0.9949557852310438
+HNU1 AAL Ipsi 0.970214136076342
+HNU1 AAL Ipsi 0.9528252911025114
+HNU1 AAL Ipsi 0.9900343827856385
+HNU1 AAL Ipsi 0.9960942424356357
+HNU1 AAL Ipsi 0.9899946854863222
+HNU1 AAL Ipsi 0.9697907518412145
+HNU1 AAL Ipsi 0.9792302512157669
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+HNU1 AAL Contra 0.02500396510705789
+HNU1 AAL Contra 0.08581339188395912
+HNU1 AAL Contra 0.028296244728346912
+HNU1 AAL Contra 0.028125822332353844
+HNU1 AAL Contra 0.12075517543875322
+HNU1 AAL Contra 0.015540896119779121
+HNU1 AAL Contra 0.007451725586850044
+HNU1 AAL Contra 0.0076389778032050215
+HNU1 AAL Contra 0.05252205637103701
+HNU1 AAL Contra 0.016624732434563822
+HNU1 AAL Contra 0.0314001147011641
+HNU1 AAL Contra 0.029992208874635357
+HNU1 AAL Contra 0.049760921824687915
+HNU1 AAL Contra 0.048877607951045315
+HNU1 AAL Contra 0.007487051337264147
+HNU1 AAL Contra 0.04582539663826579
+HNU1 AAL Contra 0.0551417168607219
+HNU1 AAL Contra 0.01708075574638406
+HNU1 AAL Contra 0.012705297645990269
+HNU1 AAL Contra 0.03464159453036912
+HNU1 AAL Contra 0.012455460274034868
+HNU1 AAL Contra 0.025883084966115282
+HNU1 AAL Contra 0.015487899884228724
+HNU1 AAL Contra 0.06445838685825733
+HNU1 AAL Contra 0.0600171329811956
+HNU1 AAL Contra 0.0430189684014332
+HNU1 AAL Contra 0.02155695854101506
+HNU1 AAL Contra 0.036924997624363895
+HNU1 AAL Contra 0.06390537224027878
+HNU1 AAL Contra 0.04457275647112023
+HNU1 AAL Contra 0.04486489790927986
+HNU1 AAL Contra 0.023834177424872816
+HNU1 AAL Contra 0.017760883283560864
+HNU1 AAL Contra 0.021784421299195927
+HNU1 AAL Contra 0.01980716185597478
+HNU1 AAL Contra 0.015245444645191291
+HNU1 AAL Contra 0.028020633723493837
+HNU1 AAL Contra 0.04402747701270477
+HNU1 AAL Contra 0.03970941471835653
+HNU1 AAL Contra 0.052097679906559946
+HNU1 AAL Contra 0.014806682872785552
+HNU1 AAL Contra 0.014019173856165397
+HNU1 AAL Contra 0.04500777124282802
+HNU1 AAL Contra 0.04764011573046255
+HNU1 AAL Contra 0.005277924876478224
+HNU1 AAL Contra 0.02701673781757601
+HNU1 AAL Contra 0.045484425808386866
+HNU1 AAL Contra 0.02609913207351624
+HNU1 AAL Contra 0.01848473318581433
+HNU1 AAL Contra 0.017180407986729644
+HNU1 AAL Contra 0.025660955285588226
+HNU1 AAL Contra 0.03679387445442408
+HNU1 AAL Contra 0.023669766931781983
+HNU1 AAL Contra 0.012870832680294651
+HNU1 AAL Contra 0.04631173295325017
+HNU1 AAL Contra 0.06188064288833441
+HNU1 AAL Contra 0.026476436202650964
+HNU1 AAL Contra 0.028551449881403596
+HNU1 AAL Contra 0.03222041857269638
+HNU1 AAL Contra 0.051490796418306777
+HNU1 AAL Contra 0.033253101428481174
+HNU1 AAL Contra 0.06776110595494199
+HNU1 AAL Contra 0.03088024231007321
+HNU1 AAL Contra 0.03517141086783443
+HNU1 AAL Contra 0.01724171700457811
+HNU1 AAL Contra 0.015171112048182809
+HNU1 AAL Contra 0.012912491610465409
+HNU1 AAL Contra 0.01617130526547906
+HNU1 AAL Contra 0.05049756390594014
+HNU1 AAL Contra 0.07696858419316488
+HNU1 AAL Contra 0.03619147921304613
+HNU1 AAL Contra 0.022698065794104886
+HNU1 AAL Contra 0.03572105147005691
+HNU1 AAL Contra 0.016444229300228538
+HNU1 AAL Contra 0.017757905634133795
+HNU1 AAL Contra 0.013514934691200568
+HNU1 AAL Contra 0.017743099313550448
+HNU1 AAL Contra 0.056308666453583664
+HNU1 AAL Contra 0.04398718555024558
+HNU1 AAL Contra 0.017109066179283376
+HNU1 AAL Contra 0.006961036853741152
+HNU1 AAL Contra 0.03870850885705736
+HNU1 AAL Contra 0.022481171262348113
+HNU1 AAL Contra 0.06687551577874508
+HNU1 AAL Contra 0.01700430333365675
+HNU1 AAL Contra 0.018583420546339872
+HNU1 AAL Contra 0.02841942684948626
+HNU1 AAL Contra 0.04631330749834502
+HNU1 AAL Contra 0.032489411841584
+HNU1 AAL Contra 0.025241624921871708
+HNU1 AAL Contra 0.035521134542456576
+HNU1 AAL Contra 0.031148540139515235
+HNU1 AAL Contra 0.04282520109215431
+HNU1 AAL Contra 0.05476692751654699
+HNU1 AAL Contra 0.08410263190224987
+HNU1 AAL Contra 0.028670441202998952
+HNU1 AAL Contra 0.061395815274708325
+HNU1 AAL Contra 0.02331680305625777
+HNU1 AAL Contra 0.07069633181642519
+HNU1 AAL Contra 0.033865193406688754
+HNU1 AAL Contra 0.049261816333798676
+HNU1 AAL Contra 0.013146612209212845
+HNU1 AAL Contra 0.0151492059942909
+HNU1 AAL Contra 0.0290787898568867
+HNU1 AAL Contra 0.03684993803159088
+HNU1 AAL Contra 0.06137949979739275
+HNU1 AAL Contra 0.01902396041035772
+HNU1 AAL Contra 0.06275446459188755
+HNU1 AAL Contra 0.05259973544665078
+HNU1 AAL Contra 0.016157134524582447
+HNU1 AAL Contra 0.010166052160936476
+HNU1 AAL Contra 0.012072432498170527
+HNU1 AAL Contra 0.007410676234029627
+HNU1 AAL Contra 0.01771899556216676
+HNU1 AAL Contra 0.0484668517136828
+HNU1 AAL Contra 0.0077207371021530405
+HNU1 AAL Contra 0.011758167500687628
+HNU1 AAL Contra 0.00315891604052438
+HNU1 AAL Contra 0.04221991978865189
+HNU1 AAL Contra 0.06826119813371108
+HNU1 AAL Contra 0.010375829353158566
+HNU1 AAL Contra 0.05857272833117246
+HNU1 AAL Contra 0.021044353224122913
+HNU1 AAL Contra 0.048357754729217085
+HNU1 AAL Contra 0.023018187631969216
+HNU1 AAL Contra 0.017763229421395875
+HNU1 AAL Contra 0.022504311123577574
+HNU1 AAL Contra 0.010391130662103213
+HNU1 AAL Contra 0.02887041566145827
+HNU1 AAL Contra 0.05769810851296978
+HNU1 AAL Contra 0.038710179791513415
+HNU1 AAL Contra 0.024670832383012733
+HNU1 AAL Contra 0.020025491310182594
+HNU1 AAL Contra 0.0343723581326815
+HNU1 AAL Contra 0.018113290223080728
+HNU1 AAL Contra 0.07079637267395769
+HNU1 AAL Contra 0.02112278700284345
+HNU1 AAL Contra 0.02981085784455287
+HNU1 AAL Contra 0.00810150225012522
+HNU1 AAL Contra 0.009721667164907091
+HNU1 AAL Contra 0.035222113354401556
+HNU1 AAL Contra 0.016652180536016702
+HNU1 AAL Contra 0.034029632771622555
+HNU1 AAL Contra 0.07251774014093212
+HNU1 AAL Contra 0.030495858098740638
+HNU1 AAL Contra 0.06998916013304823
+HNU1 AAL Contra 0.014531336489128632
+HNU1 AAL Contra 0.05311179011255563
+HNU1 AAL Contra 0.04702854654504297
+HNU1 AAL Contra 0.05517481399785094
+HNU1 AAL Contra 0.05271555889616665
+HNU1 AAL Contra 0.03952499812210851
+HNU1 AAL Contra 0.08468312488001195
+HNU1 AAL Contra 0.01302243310301037
+HNU1 AAL Contra 0.004864878419833993
+HNU1 AAL Contra 0.027852751521408006
+HNU1 AAL Contra 0.044612920632492975
+HNU1 AAL Contra 0.00923378184860793
+HNU1 AAL Contra 0.0038445688369497344
+HNU1 AAL Contra 0.00947449507380602
+HNU1 AAL Contra 0.029282935085659614
+HNU1 AAL Contra 0.01959441038510741
+HNU1 AAL Contra 0.039474823080578536
+HNU1 AAL Contra 0.016528327677681462
+HNU1 AAL Contra 0.0271205972736585
+HNU1 AAL Contra 0.04624630696817533
+HNU1 AAL Contra 0.0172967857585199
+HNU1 AAL Contra 0.04032190635451505
+HNU1 AAL Contra 0.045067112744267376
+HNU1 AAL Contra 0.025784253974519434
+HNU1 AAL Contra 0.01566769746643589
+HNU1 AAL Contra 0.034793772262149
+HNU1 AAL Contra 0.023301007836614127
+HNU1 AAL Contra 0.03138519683193844
+HNU1 AAL Contra 0.03110067751984756
+HNU1 AAL Contra 0.053977817519907316
+HNU1 AAL Contra 0.06150116313773268
+HNU1 AAL Contra 0.03951455204348947
+HNU1 AAL Contra 0.02772927526626817
+HNU1 AAL Contra 0.02311721766063808
+HNU1 AAL Contra 0.038982085276077015
+HNU1 AAL Contra 0.019994054671490204
+HNU1 AAL Contra 0.02863908543625067
+HNU1 AAL Contra 0.028413702383188535
+HNU1 AAL Contra 0.05233466814365538
+HNU1 AAL Contra 0.05894031941276151
+HNU1 AAL Contra 0.019803132072384867
+HNU1 AAL Contra 0.037324208395585326
+HNU1 AAL Contra 0.04891741685400596
+HNU1 AAL Contra 0.06169354645507817
+HNU1 AAL Contra 0.08304782663096998
+HNU1 AAL Contra 0.04158780669952066
+HNU1 AAL Contra 0.04960920753223626
+HNU1 AAL Contra 0.028425918517344162
+HNU1 AAL Contra 0.009167140182887692
+HNU1 AAL Contra 0.022085823366776218
+HNU1 AAL Contra 0.057504656327775606
+HNU1 AAL Contra 0.05112120953557951
+HNU1 AAL Contra 0.061924251361252555
+HNU1 AAL Contra 0.029300783577049787
+HNU1 AAL Contra 0.0752209969527468
+HNU1 AAL Contra 0.02463288485974833
+HNU1 AAL Contra 0.02608666805934233
+HNU1 AAL Contra 0.009848366324321662
+HNU1 AAL Contra 0.02744842535310558
+HNU1 AAL Contra 0.006929065131298128
+HNU1 AAL Contra 0.009222672144038789
+HNU1 AAL Contra 0.017353732307015885
+HNU1 AAL Contra 0.013651799952432753
+HNU1 AAL Contra 0.0526001017256317
+HNU1 AAL Contra 0.013313407006257463
+HNU1 AAL Contra 0.031143174550428476
+HNU1 AAL Contra 0.05546026096484625
+HNU1 AAL Contra 0.054198968447196315
+HNU1 AAL Contra 0.015550313851403265
+HNU1 AAL Contra 0.055013198459344166
+HNU1 AAL Contra 0.022605235803614288
+HNU1 AAL Contra 0.005250396078478249
+HNU1 AAL Contra 0.04909306737646781
+HNU1 AAL Contra 0.017584329038287726
+HNU1 AAL Contra 0.07083749665761291
+HNU1 AAL Contra 0.053092531367923794
+HNU1 AAL Contra 0.019625739409562155
+HNU1 AAL Contra 0.04178307873119344
+HNU1 AAL Contra 0.03503039877447461
+HNU1 AAL Contra 0.045388637408922565
+HNU1 AAL Contra 0.013812097661315996
+HNU1 AAL Contra 0.022404135840984045
+HNU1 AAL Contra 0.02767313040205313
+HNU1 AAL Contra 0.06455083982759784
+HNU1 AAL Contra 0.044637220183436736
+HNU1 AAL Contra 0.03129252026026883
+HNU1 AAL Contra 0.029196525001184772
+HNU1 AAL Contra 0.03220888427077074
+HNU1 AAL Contra 0.02378167979204504
+HNU1 AAL Contra 0.030404511444159444
+HNU1 AAL Contra 0.025184060555650395
+HNU1 AAL Contra 0.046976801717030746
+HNU1 AAL Contra 0.014926833198105663
+HNU1 AAL Contra 0.006262361814144692
+HNU1 AAL Contra 0.014556095968002959
+HNU1 AAL Contra 0.08678413062466389
+HNU1 AAL Contra 0.014211231452334798
+HNU1 AAL Contra 0.027360532813084294
+HNU1 AAL Contra 0.03962846389303914
+HNU1 AAL Contra 0.022750703599466744
+HNU1 AAL Contra 0.027755871938254226
+HNU1 AAL Contra 0.0073169952749658354
+HNU1 AAL Contra 0.05699806047382857
+HNU1 AAL Contra 0.04152716556387349
+HNU1 AAL Contra 0.010438580327482867
+HNU1 AAL Contra 0.02458925144701042
+HNU1 AAL Contra 0.05098337444428402
+HNU1 AAL Contra 0.03368232020106718
+HNU1 AAL Contra 0.39476029172828825
+HNU1 AAL Contra 0.030251854640932937
+HNU1 AAL Contra 0.030926124010810078
+HNU1 AAL Contra 0.017147945923856423
+HNU1 AAL Contra 0.01588028652871101
+HNU1 AAL Contra 0.0036091209183198515
+HNU1 AAL Contra 0.0565002823396546
+HNU1 AAL Contra 0.0346047979081747
+HNU1 AAL Contra 0.09576159322807692
+HNU1 AAL Contra 0.02676057653249415
+HNU1 AAL Contra 0.03563273644994592
+HNU1 AAL Contra 0.018321580307476677
+HNU1 AAL Contra 0.024971326206121424
+HNU1 AAL Contra 0.03690292913888844
+HNU1 AAL Contra 0.016159652769473835
+HNU1 AAL Contra 0.009604926654304492
+HNU1 AAL Contra 0.05335730371927013
+HNU1 AAL Contra 0.014569204433139742
+HNU1 AAL Contra 0.033248132236371776
+HNU1 AAL Contra 0.047185194466846575
+HNU1 AAL Contra 0.05936251730658283
+HNU1 AAL Contra 0.017377326597756135
+HNU1 AAL Contra 0.017417456284357737
+HNU1 AAL Contra 0.07663855890723684
+HNU1 AAL Contra 0.01689945131092135
+HNU1 AAL Contra 0.038638645066666505
+HNU1 AAL Contra 0.03394642482596059
+HNU1 AAL Contra 0.040838065802725816
+HNU1 AAL Contra 0.03499940295980786
+HNU1 AAL Contra 0.039566728399968984
+HNU1 AAL Contra 0.010832965014654155
+HNU1 AAL Contra 0.030037953840208288
+HNU1 AAL Contra 0.0255934116035949
+HNU1 AAL Contra 0.026414961273905926
+HNU1 AAL Contra 0.025118473723560013
+HNU1 AAL Contra 0.010470266551509278
+HNU1 AAL Contra 0.013105297755863728
+HNU1 AAL Contra 0.015011142760406226
+HNU1 AAL Contra 0.029997501720184287
+HNU1 AAL Contra 0.06367535525292449
+HNU1 AAL Contra 0.04258190646193796
+Dataset Atlas Type Value
+NKIENH AAL Ipsi 0.9040013231560975
+NKIENH AAL Ipsi 0.9298178756251247
+NKIENH AAL Ipsi 0.8848540470785076
+NKIENH AAL Ipsi 0.9054564496667923
+NKIENH AAL Ipsi 0.8740590552579157
+NKIENH AAL Ipsi 0.8967312831389184
+NKIENH AAL Ipsi 0.8815018112263205
+NKIENH AAL Ipsi 0.9221862562270025
+NKIENH AAL Ipsi 0.7925377824667648
+NKIENH AAL Ipsi 0.8878905156037534
+NKIENH AAL Ipsi 0.9138763998620419
+NKIENH AAL Ipsi 0.8684765182781059
+NKIENH AAL Ipsi 0.906150305997552
+NKIENH AAL Ipsi 0.9099956894837574
+NKIENH AAL Ipsi 0.9096236828967119
+NKIENH AAL Ipsi 0.8956787648533557
+NKIENH AAL Ipsi 0.8625830505496437
+NKIENH AAL Ipsi 0.873554909046437
+NKIENH AAL Ipsi 0.8974137298996546
+NKIENH AAL Ipsi 0.9374042732919405
+NKIENH AAL Ipsi 0.9382117261259355
+NKIENH AAL Ipsi 0.8688052322787797
+NKIENH AAL Ipsi 0.8421937292721617
+NKIENH AAL Ipsi 0.9573476323050056
+NKIENH AAL Ipsi 0.881602963294809
+NKIENH AAL Ipsi 0.9233384316943968
+NKIENH AAL Ipsi 0.8967987189885
+NKIENH AAL Ipsi 0.8036088399479339
+NKIENH AAL Ipsi 0.782141575254569
+NKIENH AAL Ipsi 0.8884862303705195
+NKIENH AAL Ipsi 0.8461734931570668
+NKIENH AAL Ipsi 0.8315819957311782
+NKIENH AAL Ipsi 0.9324293980213498
+NKIENH AAL Ipsi 0.8961199342185479
+NKIENH AAL Ipsi 0.8538425524792358
+NKIENH AAL Ipsi 0.8302438752453861
+NKIENH AAL Ipsi 0.9102482372852222
+NKIENH AAL Ipsi 0.8693642089245668
+NKIENH AAL Ipsi 0.8642223504979532
+NKIENH AAL Ipsi 0.7752753636419226
+NKIENH AAL Ipsi 0.9270836809820722
+NKIENH AAL Ipsi 0.853350329912722
+NKIENH AAL Ipsi 0.8704817283776197
+NKIENH AAL Ipsi 0.8447590866642652
+NKIENH AAL Ipsi 0.8434067819752531
+NKIENH AAL Ipsi 0.9183925423955683
+NKIENH AAL Ipsi 0.9220342522860213
+NKIENH AAL Ipsi 0.8495900644797424
+NKIENH AAL Ipsi 0.863196688908293
+NKIENH AAL Ipsi 0.8535804077022126
+NKIENH AAL Ipsi 0.9015288967085284
+NKIENH AAL Ipsi 0.853445459608079
+NKIENH AAL Ipsi 0.9138542730677562
+NKIENH AAL Ipsi 0.9238216600323335
+NKIENH AAL Ipsi 0.8522914881496787
+NKIENH AAL Ipsi 0.8557707825197639
+NKIENH AAL Ipsi 0.8851763821434425
+NKIENH AAL Ipsi 0.9172500251474385
+NKIENH AAL Ipsi 0.8586839915214008
+NKIENH AAL Ipsi 0.8647739691677381
+NKIENH AAL Ipsi 0.8879589017686765
+NKIENH AAL Ipsi 0.8878301716066188
+NKIENH AAL Ipsi 0.9181012227513344
+NKIENH AAL Ipsi 0.835019119817774
+NKIENH AAL Ipsi 0.8358164739358518
+NKIENH AAL Ipsi 0.8980359178478219
+NKIENH AAL Ipsi 0.9165956882912993
+NKIENH AAL Ipsi 0.8739058889328686
+NKIENH AAL Ipsi 0.884446180647521
+NKIENH AAL Ipsi 0.8936335789164378
+NKIENH AAL Ipsi 0.8981154885834522
+NKIENH AAL Ipsi 0.8852975635908962
+NKIENH AAL Ipsi 0.9503083604742729
+NKIENH AAL Ipsi 0.8850536514022228
+NKIENH AAL Ipsi 0.8216268782720393
+NKIENH AAL Ipsi 0.8835802668502007
+NKIENH AAL Ipsi 0.8481196897810013
+NKIENH AAL Ipsi 0.8119805789011914
+NKIENH AAL Ipsi 0.8685354221306155
+NKIENH AAL Ipsi 0.8340779787172314
+NKIENH AAL Ipsi 0.8906827399435381
+NKIENH AAL Ipsi 0.7924546282374367
+NKIENH AAL Ipsi 0.8429785100758723
+NKIENH AAL Ipsi 0.8332427181731984
+NKIENH AAL Ipsi 0.8922789890798696
+NKIENH AAL Ipsi 0.8911055974325436
+NKIENH AAL Ipsi 0.8693399351791872
+NKIENH AAL Ipsi 0.8640761742975998
+NKIENH AAL Ipsi 0.9292139273856102
+NKIENH AAL Ipsi 0.8476100916599764
+NKIENH AAL Ipsi 0.9350331294400778
+NKIENH AAL Ipsi 0.9404743696474049
+NKIENH AAL Ipsi 0.947478704811398
+NKIENH AAL Ipsi 0.8092573221220424
+NKIENH AAL Ipsi 0.8540668358277044
+NKIENH AAL Ipsi 0.8406911340721454
+NKIENH AAL Ipsi 0.8860058496796832
+NKIENH AAL Ipsi 0.9264975658523146
+NKIENH AAL Ipsi 0.9053443999423634
+NKIENH AAL Ipsi 0.8792728936004893
+NKIENH AAL Ipsi 0.8242746179740718
+NKIENH AAL Ipsi 0.8758538939017626
+NKIENH AAL Ipsi 0.8196692127285886
+NKIENH AAL Ipsi 0.8946620081595325
+NKIENH AAL Ipsi 0.8720233120389816
+NKIENH AAL Ipsi 0.8777247486884215
+NKIENH AAL Ipsi 0.8930460973784136
+NKIENH AAL Ipsi 0.889211653536206
+NKIENH AAL Ipsi 0.9237783162244382
+NKIENH AAL Ipsi 0.8847712603299425
+NKIENH AAL Ipsi 0.8887153567944394
+NKIENH AAL Ipsi 0.8059608669620405
+NKIENH AAL Ipsi 0.8646272973443482
+NKIENH AAL Ipsi 0.8669808954322663
+NKIENH AAL Ipsi 0.9078434410396006
+NKIENH AAL Ipsi 0.8940982600126016
+NKIENH AAL Ipsi 0.9160087357991777
+NKIENH AAL Ipsi 0.905072372981867
+NKIENH AAL Ipsi 0.8651860602480803
+NKIENH AAL Ipsi 0.9007674449376301
+NKIENH AAL Ipsi 0.8200082500465076
+NKIENH AAL Ipsi 0.8646416322357403
+NKIENH AAL Ipsi 0.8707933183234151
+NKIENH AAL Ipsi 0.9246517816731495
+NKIENH AAL Ipsi 0.8369839206981217
+NKIENH AAL Ipsi 0.8478016328291795
+NKIENH AAL Ipsi 0.9074449714601115
+NKIENH AAL Ipsi 0.936075477494807
+NKIENH AAL Ipsi 0.8643969102240158
+NKIENH AAL Hom 0.006126762056110536
+NKIENH AAL Hom 0.004505761129396348
+NKIENH AAL Hom 0.006156920464684165
+NKIENH AAL Hom 0.006370882949131086
+NKIENH AAL Hom 0.0071312139238171824
+NKIENH AAL Hom 0.006562399636418724
+NKIENH AAL Hom 0.007927468541530975
+NKIENH AAL Hom 0.005245003875914645
+NKIENH AAL Hom 0.012896851744633836
+NKIENH AAL Hom 0.00720236267573895
+NKIENH AAL Hom 0.005666387972468678
+NKIENH AAL Hom 0.005861183956432116
+NKIENH AAL Hom 0.006168584251325989
+NKIENH AAL Hom 0.006290245388593268
+NKIENH AAL Hom 0.0061526993489379195
+NKIENH AAL Hom 0.004646387310865581
+NKIENH AAL Hom 0.009556441428085087
+NKIENH AAL Hom 0.010403596514024923
+NKIENH AAL Hom 0.007537689127014259
+NKIENH AAL Hom 0.0040074084908164696
+NKIENH AAL Hom 0.003339316246195523
+NKIENH AAL Hom 0.007959288292912191
+NKIENH AAL Hom 0.008825640676031874
+NKIENH AAL Hom 0.002852713849529999
+NKIENH AAL Hom 0.008920977952747046
+NKIENH AAL Hom 0.00417220025367455
+NKIENH AAL Hom 0.007377253727618691
+NKIENH AAL Hom 0.008507669183392004
+NKIENH AAL Hom 0.011741722103945102
+NKIENH AAL Hom 0.0063103828743261104
+NKIENH AAL Hom 0.00884164332706681
+NKIENH AAL Hom 0.010012886241271465
+NKIENH AAL Hom 0.003623303826327977
+NKIENH AAL Hom 0.007428908373466634
+NKIENH AAL Hom 0.0077640754879754635
+NKIENH AAL Hom 0.008906240252577965
+NKIENH AAL Hom 0.005284267039576186
+NKIENH AAL Hom 0.008030262699779567
+NKIENH AAL Hom 0.0075194954084388905
+NKIENH AAL Hom 0.015669782387578685
+NKIENH AAL Hom 0.005192130539441755
+NKIENH AAL Hom 0.009513688361091632
+NKIENH AAL Hom 0.009019626797140192
+NKIENH AAL Hom 0.012618056189676432
+NKIENH AAL Hom 0.00867914223401468
+NKIENH AAL Hom 0.0067488950244419445
+NKIENH AAL Hom 0.004933029348430063
+NKIENH AAL Hom 0.007863276018777848
+NKIENH AAL Hom 0.00983988014068799
+NKIENH AAL Hom 0.009633937471530962
+NKIENH AAL Hom 0.006619799549099594
+NKIENH AAL Hom 0.011099406606042722
+NKIENH AAL Hom 0.004648663602315288
+NKIENH AAL Hom 0.003899055850572655
+NKIENH AAL Hom 0.010493122615144746
+NKIENH AAL Hom 0.00789263324594275
+NKIENH AAL Hom 0.006111316496874496
+NKIENH AAL Hom 0.006091268436797967
+NKIENH AAL Hom 0.007466910334083146
+NKIENH AAL Hom 0.008604290444677866
+NKIENH AAL Hom 0.008056354608717117
+NKIENH AAL Hom 0.008888100718492636
+NKIENH AAL Hom 0.005051065061611567
+NKIENH AAL Hom 0.010152612019645584
+NKIENH AAL Hom 0.006991035247379137
+NKIENH AAL Hom 0.0067782146121239464
+NKIENH AAL Hom 0.004869942664309038
+NKIENH AAL Hom 0.008723247161763464
+NKIENH AAL Hom 0.006174710155683633
+NKIENH AAL Hom 0.005898958944104462
+NKIENH AAL Hom 0.004246349339307307
+NKIENH AAL Hom 0.00611211193572394
+NKIENH AAL Hom 0.003274483150787553
+NKIENH AAL Hom 0.0061563480953333504
+NKIENH AAL Hom 0.00918203892962376
+NKIENH AAL Hom 0.008511841406718772
+NKIENH AAL Hom 0.010711635697094888
+NKIENH AAL Hom 0.013942538637744103
+NKIENH AAL Hom 0.008341116086854755
+NKIENH AAL Hom 0.01041066308032056
+NKIENH AAL Hom 0.007101613160169542
+NKIENH AAL Hom 0.014620574493300611
+NKIENH AAL Hom 0.010043285151436078
+NKIENH AAL Hom 0.012124084421541939
+NKIENH AAL Hom 0.00675065088450738
+NKIENH AAL Hom 0.00775419676992502
+NKIENH AAL Hom 0.007577398734652082
+NKIENH AAL Hom 0.007227218270046723
+NKIENH AAL Hom 0.004185960975790122
+NKIENH AAL Hom 0.01027973511684861
+NKIENH AAL Hom 0.004021428075561349
+NKIENH AAL Hom 0.004038280517811445
+NKIENH AAL Hom 0.0029276751488774705
+NKIENH AAL Hom 0.009647000370584425
+NKIENH AAL Hom 0.007780920152546627
+NKIENH AAL Hom 0.009188905447130719
+NKIENH AAL Hom 0.006733869330798089
+NKIENH AAL Hom 0.004711985310978658
+NKIENH AAL Hom 0.006173556250988007
+NKIENH AAL Hom 0.008555410744144114
+NKIENH AAL Hom 0.00879770460830934
+NKIENH AAL Hom 0.007189660971304155
+NKIENH AAL Hom 0.009904878820646525
+NKIENH AAL Hom 0.0062381125901156765
+NKIENH AAL Hom 0.008786371365898346
+NKIENH AAL Hom 0.006930328931156036
+NKIENH AAL Hom 0.007830373816478367
+NKIENH AAL Hom 0.008296053473294998
+NKIENH AAL Hom 0.0038380907527945478
+NKIENH AAL Hom 0.006136776059384547
+NKIENH AAL Hom 0.005955264129925239
+NKIENH AAL Hom 0.010706645910669381
+NKIENH AAL Hom 0.00951926748307759
+NKIENH AAL Hom 0.007735317571140586
+NKIENH AAL Hom 0.006191111547428926
+NKIENH AAL Hom 0.0065314290202912175
+NKIENH AAL Hom 0.0073180572301562935
+NKIENH AAL Hom 0.005706036789251102
+NKIENH AAL Hom 0.009660982844680423
+NKIENH AAL Hom 0.005138247082829226
+NKIENH AAL Hom 0.01055237566290202
+NKIENH AAL Hom 0.009319346471555964
+NKIENH AAL Hom 0.008928044767099566
+NKIENH AAL Hom 0.004659132287232051
+NKIENH AAL Hom 0.009739018437192453
+NKIENH AAL Hom 0.012208517339769366
+NKIENH AAL Hom 0.006754336370267109
+NKIENH AAL Hom 0.004870099691503278
+NKIENH AAL Hom 0.0074138235629124605
+NKIENH AAL Contra 0.08987191478779197
+NKIENH AAL Contra 0.06567636324547889
+NKIENH AAL Contra 0.10898903245680824
+NKIENH AAL Contra 0.08817266738407667
+NKIENH AAL Contra 0.11880973081826711
+NKIENH AAL Contra 0.09670631722466293
+NKIENH AAL Contra 0.11057072023214848
+NKIENH AAL Contra 0.07256873989708294
+NKIENH AAL Contra 0.19456536578860137
+NKIENH AAL Contra 0.10490712172050765
+NKIENH AAL Contra 0.08045721216548939
+NKIENH AAL Contra 0.12566229776546198
+NKIENH AAL Contra 0.08768110975112199
+NKIENH AAL Contra 0.08371406512764934
+NKIENH AAL Contra 0.0842236177543502
+NKIENH AAL Contra 0.09967484783577873
+NKIENH AAL Contra 0.12786050802227128
+NKIENH AAL Contra 0.11604149443953814
+NKIENH AAL Contra 0.09504858097333109
+NKIENH AAL Contra 0.05858831821724295
+NKIENH AAL Contra 0.058448957627868954
+NKIENH AAL Contra 0.12323547942830816
+NKIENH AAL Contra 0.14898063005180634
+NKIENH AAL Contra 0.039799653845464356
+NKIENH AAL Contra 0.10947605875244389
+NKIENH AAL Contra 0.07248936805192867
+NKIENH AAL Contra 0.0958240272838813
+NKIENH AAL Contra 0.18788349086867417
+NKIENH AAL Contra 0.20611670264148593
+NKIENH AAL Contra 0.10520338675515432
+NKIENH AAL Contra 0.14498486351586637
+NKIENH AAL Contra 0.15840511802755033
+NKIENH AAL Contra 0.06394729815232224
+NKIENH AAL Contra 0.09645115740798554
+NKIENH AAL Contra 0.13839337203278876
+NKIENH AAL Contra 0.16084988450203588
+NKIENH AAL Contra 0.08446749567520166
+NKIENH AAL Contra 0.12260552837565364
+NKIENH AAL Contra 0.128258154093608
+NKIENH AAL Contra 0.2090548539704987
+NKIENH AAL Contra 0.06772418847848606
+NKIENH AAL Contra 0.13713598172618646
+NKIENH AAL Contra 0.12049864482524009
+NKIENH AAL Contra 0.14262285714605832
+NKIENH AAL Contra 0.14791407579073226
+NKIENH AAL Contra 0.0748585625799897
+NKIENH AAL Contra 0.07303271836554857
+NKIENH AAL Contra 0.14254665950147974
+NKIENH AAL Contra 0.12696343095101895
+NKIENH AAL Contra 0.13678565482625646
+NKIENH AAL Contra 0.09185130374237192
+NKIENH AAL Contra 0.13545513378587828
+NKIENH AAL Contra 0.0814970633299285
+NKIENH AAL Contra 0.07227928411709385
+NKIENH AAL Contra 0.13721538923517654
+NKIENH AAL Contra 0.13633658423429335
+NKIENH AAL Contra 0.10871230135968296
+NKIENH AAL Contra 0.07665870641576353
+NKIENH AAL Contra 0.13384909814451604
+NKIENH AAL Contra 0.12662174038758411
+NKIENH AAL Contra 0.1039847436226064
+NKIENH AAL Contra 0.1032817276748886
+NKIENH AAL Contra 0.07684771218705404
+NKIENH AAL Contra 0.1548282681625804
+NKIENH AAL Contra 0.15719249081676911
+NKIENH AAL Contra 0.09518586754005412
+NKIENH AAL Contra 0.07853436904439166
+NKIENH AAL Contra 0.11737086390536797
+NKIENH AAL Contra 0.1093791091967954
+NKIENH AAL Contra 0.10046746213945774
+NKIENH AAL Contra 0.09763816207724041
+NKIENH AAL Contra 0.10859032447337984
+NKIENH AAL Contra 0.046417156374939504
+NKIENH AAL Contra 0.10879000050244382
+NKIENH AAL Contra 0.16919108279833694
+NKIENH AAL Contra 0.10790789174308045
+NKIENH AAL Contra 0.14116867452190382
+NKIENH AAL Contra 0.1740768824610645
+NKIENH AAL Contra 0.1231234617825297
+NKIENH AAL Contra 0.155511358202448
+NKIENH AAL Contra 0.10221564689629237
+NKIENH AAL Contra 0.19292479726926265
+NKIENH AAL Contra 0.14697820477269163
+NKIENH AAL Contra 0.1546331974052597
+NKIENH AAL Contra 0.10097036003562306
+NKIENH AAL Contra 0.10114020579753139
+NKIENH AAL Contra 0.12308266608616075
+NKIENH AAL Contra 0.12869660743235356
+NKIENH AAL Contra 0.0666001116385997
+NKIENH AAL Contra 0.14211017322317504
+NKIENH AAL Contra 0.0609454424843609
+NKIENH AAL Contra 0.055487349834783616
+NKIENH AAL Contra 0.04959362003972457
+NKIENH AAL Contra 0.18109567750737318
+NKIENH AAL Contra 0.13815224401974896
+NKIENH AAL Contra 0.15011996048072393
+NKIENH AAL Contra 0.10726028098951874
+NKIENH AAL Contra 0.06879044883670672
+NKIENH AAL Contra 0.08848204380664863
+NKIENH AAL Contra 0.11217169565536653
+NKIENH AAL Contra 0.1669276774176189
+NKIENH AAL Contra 0.11695644512693328
+NKIENH AAL Contra 0.17042590845076483
+NKIENH AAL Contra 0.0990998792503519
+NKIENH AAL Contra 0.11919031659512001
+NKIENH AAL Contra 0.11534492238042242
+NKIENH AAL Contra 0.09912352880510804
+NKIENH AAL Contra 0.10249229299049897
+NKIENH AAL Contra 0.07238359302276733
+NKIENH AAL Contra 0.10909196361067294
+NKIENH AAL Contra 0.10532937907563542
+NKIENH AAL Contra 0.18333248712729014
+NKIENH AAL Contra 0.12585343517257422
+NKIENH AAL Contra 0.12528378699659315
+NKIENH AAL Contra 0.08596544741297053
+NKIENH AAL Contra 0.0993703109671072
+NKIENH AAL Contra 0.07667320697066599
+NKIENH AAL Contra 0.08922159022888182
+NKIENH AAL Contra 0.12515295690723924
+NKIENH AAL Contra 0.09409430797954071
+NKIENH AAL Contra 0.16943937429059036
+NKIENH AAL Contra 0.12603902129270378
+NKIENH AAL Contra 0.1202786369094853
+NKIENH AAL Contra 0.07068908603961845
+NKIENH AAL Contra 0.15327706086468587
+NKIENH AAL Contra 0.1399898498310511
+NKIENH AAL Contra 0.08580069216962136
+NKIENH AAL Contra 0.0590544228136897
+NKIENH AAL Contra 0.1281892662130718
+Dataset Atlas Type Value
+XHCUMS AAL Ipsi 0.8974688495881656
+XHCUMS AAL Ipsi 0.8666497597152
+XHCUMS AAL Ipsi 0.8129428599954971
+XHCUMS AAL Ipsi 0.8665451742262662
+XHCUMS AAL Ipsi 0.9106959314304961
+XHCUMS AAL Ipsi 0.8936776409034382
+XHCUMS AAL Ipsi 0.8838506511481729
+XHCUMS AAL Ipsi 0.8996050501156515
+XHCUMS AAL Ipsi 0.8713934040437188
+XHCUMS AAL Ipsi 0.8880268486233316
+XHCUMS AAL Ipsi 0.8744545487138742
+XHCUMS AAL Ipsi 0.8208322856220777
+XHCUMS AAL Ipsi 0.8977734138721472
+XHCUMS AAL Ipsi 0.904910213379671
+XHCUMS AAL Ipsi 0.8941129798388515
+XHCUMS AAL Ipsi 0.8931812885500854
+XHCUMS AAL Ipsi 0.9791832508469758
+XHCUMS AAL Ipsi 0.8965073286210065
+XHCUMS AAL Ipsi 0.8906024568724288
+XHCUMS AAL Ipsi 0.8181761104784608
+XHCUMS AAL Ipsi 0.9019738572427892
+XHCUMS AAL Ipsi 0.8642007267200228
+XHCUMS AAL Ipsi 0.9131614517583952
+XHCUMS AAL Ipsi 0.9177310841258828
+XHCUMS AAL Ipsi 0.9279264545752047
+XHCUMS AAL Ipsi 0.8867892295888709
+XHCUMS AAL Ipsi 0.9396597692537496
+XHCUMS AAL Ipsi 0.9663628153136526
+XHCUMS AAL Ipsi 0.9240559944699881
+XHCUMS AAL Ipsi 0.8606537352078041
+XHCUMS AAL Ipsi 0.9221846610085094
+XHCUMS AAL Ipsi 0.9257363340848995
+XHCUMS AAL Ipsi 0.8323024429907381
+XHCUMS AAL Ipsi 0.8764138755857532
+XHCUMS AAL Ipsi 0.9102527399131963
+XHCUMS AAL Ipsi 0.8843170085968086
+XHCUMS AAL Ipsi 0.9548970585821225
+XHCUMS AAL Ipsi 0.9256527564882706
+XHCUMS AAL Ipsi 0.943386285818466
+XHCUMS AAL Ipsi 0.929285776334613
+XHCUMS AAL Ipsi 0.9367867998992359
+XHCUMS AAL Ipsi 0.8932111644644732
+XHCUMS AAL Ipsi 0.9132994677557971
+XHCUMS AAL Ipsi 0.8932962951117445
+XHCUMS AAL Ipsi 0.959471540395061
+XHCUMS AAL Ipsi 0.9337393334380178
+XHCUMS AAL Ipsi 0.8799315316106913
+XHCUMS AAL Ipsi 0.8238019102635123
+XHCUMS AAL Ipsi 0.9245210826623951
+XHCUMS AAL Ipsi 0.8442475190040372
+XHCUMS AAL Ipsi 0.8964754578216043
+XHCUMS AAL Ipsi 0.9499778310076376
+XHCUMS AAL Ipsi 0.9217319614595485
+XHCUMS AAL Ipsi 0.901476941314039
+XHCUMS AAL Ipsi 0.961027971839517
+XHCUMS AAL Ipsi 0.898628413834618
+XHCUMS AAL Ipsi 0.907430839556952
+XHCUMS AAL Ipsi 0.8492220812920981
+XHCUMS AAL Ipsi 0.8771683111076642
+XHCUMS AAL Ipsi 0.9373535511383129
+XHCUMS AAL Ipsi 0.8861235179158667
+XHCUMS AAL Ipsi 0.919175239991161
+XHCUMS AAL Ipsi 0.8917983801379391
+XHCUMS AAL Ipsi 0.8683621580474736
+XHCUMS AAL Ipsi 0.9103546147476592
+XHCUMS AAL Ipsi 0.8996538382681778
+XHCUMS AAL Ipsi 0.893349218888647
+XHCUMS AAL Ipsi 0.8788199606801356
+XHCUMS AAL Ipsi 0.8913532884400293
+XHCUMS AAL Ipsi 0.8980088663824534
+XHCUMS AAL Ipsi 0.894981232761126
+XHCUMS AAL Ipsi 0.9103545502783483
+XHCUMS AAL Ipsi 0.8826751313812722
+XHCUMS AAL Ipsi 0.9184753744175816
+XHCUMS AAL Ipsi 0.8960171187208306
+XHCUMS AAL Ipsi 0.855793065770172
+XHCUMS AAL Ipsi 0.893229567327375
+XHCUMS AAL Ipsi 0.9344605650617732
+XHCUMS AAL Ipsi 0.8302869377900128
+XHCUMS AAL Ipsi 0.9453393692611137
+XHCUMS AAL Ipsi 0.8577051210081477
+XHCUMS AAL Ipsi 0.9169507405667956
+XHCUMS AAL Ipsi 0.8868008021674817
+XHCUMS AAL Ipsi 0.9200565164918855
+XHCUMS AAL Ipsi 0.9184153071534092
+XHCUMS AAL Ipsi 0.8961781865061713
+XHCUMS AAL Ipsi 0.9334517887924609
+XHCUMS AAL Ipsi 0.8669679441504126
+XHCUMS AAL Ipsi 0.8914535705741266
+XHCUMS AAL Ipsi 0.9279182081305439
+XHCUMS AAL Ipsi 0.9602543655223975
+XHCUMS AAL Ipsi 0.8996566838749964
+XHCUMS AAL Ipsi 0.8643140908479601
+XHCUMS AAL Ipsi 0.9363294332790464
+XHCUMS AAL Ipsi 0.9120812696896293
+XHCUMS AAL Ipsi 0.936635124785197
+XHCUMS AAL Ipsi 0.8571447686924726
+XHCUMS AAL Ipsi 0.952669652828589
+XHCUMS AAL Ipsi 0.8918767168048913
+XHCUMS AAL Ipsi 0.896463120153276
+XHCUMS AAL Ipsi 0.8864194136384577
+XHCUMS AAL Ipsi 0.8921984859314449
+XHCUMS AAL Ipsi 0.9405191206607917
+XHCUMS AAL Ipsi 0.9419272906963154
+XHCUMS AAL Ipsi 0.9140246097771932
+XHCUMS AAL Ipsi 0.9411879026879392
+XHCUMS AAL Ipsi 0.9282527344305951
+XHCUMS AAL Ipsi 0.9467354985398541
+XHCUMS AAL Ipsi 0.9041014745417044
+XHCUMS AAL Ipsi 0.8498044022107583
+XHCUMS AAL Ipsi 0.8844249100384463
+XHCUMS AAL Ipsi 0.9306209944688114
+XHCUMS AAL Ipsi 0.930493175295226
+XHCUMS AAL Ipsi 0.8968231638357977
+XHCUMS AAL Ipsi 0.8801884219968316
+XHCUMS AAL Ipsi 0.9165615552987308
+XHCUMS AAL Ipsi 0.8621303599852944
+XHCUMS AAL Hom 0.007314447858707371
+XHCUMS AAL Hom 0.009331454104687232
+XHCUMS AAL Hom 0.007376839824986012
+XHCUMS AAL Hom 0.007180621772785579
+XHCUMS AAL Hom 0.006770033664954746
+XHCUMS AAL Hom 0.007441848512969443
+XHCUMS AAL Hom 0.008781602592520902
+XHCUMS AAL Hom 0.006969352351580571
+XHCUMS AAL Hom 0.008471999349697277
+XHCUMS AAL Hom 0.008134902684668668
+XHCUMS AAL Hom 0.007521242674790175
+XHCUMS AAL Hom 0.011496174871453253
+XHCUMS AAL Hom 0.006452520104774317
+XHCUMS AAL Hom 0.0063072547482158695
+XHCUMS AAL Hom 0.007037929699869815
+XHCUMS AAL Hom 0.007871877691756398
+XHCUMS AAL Hom 0.0012347651064005805
+XHCUMS AAL Hom 0.007623121821639383
+XHCUMS AAL Hom 0.007214206662508163
+XHCUMS AAL Hom 0.009953010655689494
+XHCUMS AAL Hom 0.0062418662984919365
+XHCUMS AAL Hom 0.009019877930035386
+XHCUMS AAL Hom 0.005125360086255815
+XHCUMS AAL Hom 0.007194975547301084
+XHCUMS AAL Hom 0.005087382898713508
+XHCUMS AAL Hom 0.007958266337022348
+XHCUMS AAL Hom 0.004265339351412804
+XHCUMS AAL Hom 0.00220613043136073
+XHCUMS AAL Hom 0.004618675834937471
+XHCUMS AAL Hom 0.00975416344181194
+XHCUMS AAL Hom 0.004064989242756447
+XHCUMS AAL Hom 0.005696548945991718
+XHCUMS AAL Hom 0.009232219831630976
+XHCUMS AAL Hom 0.00955639985359391
+XHCUMS AAL Hom 0.005342573043814276
+XHCUMS AAL Hom 0.007763296431455529
+XHCUMS AAL Hom 0.002553673278039605
+XHCUMS AAL Hom 0.005392988185027383
+XHCUMS AAL Hom 0.003887814217942294
+XHCUMS AAL Hom 0.004632397976294807
+XHCUMS AAL Hom 0.00507632882693761
+XHCUMS AAL Hom 0.007071789136331817
+XHCUMS AAL Hom 0.0045460720567461225
+XHCUMS AAL Hom 0.005796409017319187
+XHCUMS AAL Hom 0.002120162825005981
+XHCUMS AAL Hom 0.005731585322064012
+XHCUMS AAL Hom 0.006907630850349536
+XHCUMS AAL Hom 0.011492431546835977
+XHCUMS AAL Hom 0.006064757908849971
+XHCUMS AAL Hom 0.009689600309915447
+XHCUMS AAL Hom 0.006165325034075903
+XHCUMS AAL Hom 0.0029809328457065447
+XHCUMS AAL Hom 0.006035403872495612
+XHCUMS AAL Hom 0.007138894281188583
+XHCUMS AAL Hom 0.001994551157012915
+XHCUMS AAL Hom 0.0062666029810120785
+XHCUMS AAL Hom 0.006470389915488227
+XHCUMS AAL Hom 0.009796640033182948
+XHCUMS AAL Hom 0.007147754917279005
+XHCUMS AAL Hom 0.00425473610901456
+XHCUMS AAL Hom 0.008034009649211327
+XHCUMS AAL Hom 0.007856300586851475
+XHCUMS AAL Hom 0.006909803748551936
+XHCUMS AAL Hom 0.008114562080791848
+XHCUMS AAL Hom 0.005873075861483259
+XHCUMS AAL Hom 0.006463330737644782
+XHCUMS AAL Hom 0.008203049878184305
+XHCUMS AAL Hom 0.008522920235768247
+XHCUMS AAL Hom 0.00631451945125841
+XHCUMS AAL Hom 0.009034584259710746
+XHCUMS AAL Hom 0.006951099925679512
+XHCUMS AAL Hom 0.004593921231489135
+XHCUMS AAL Hom 0.008150585676552236
+XHCUMS AAL Hom 0.005509074972142273
+XHCUMS AAL Hom 0.006467510710043831
+XHCUMS AAL Hom 0.010715136516349132
+XHCUMS AAL Hom 0.006292839211549259
+XHCUMS AAL Hom 0.0055587460898460575
+XHCUMS AAL Hom 0.009417338617830623
+XHCUMS AAL Hom 0.003337682059240464
+XHCUMS AAL Hom 0.009173250270376819
+XHCUMS AAL Hom 0.0060737235157267075
+XHCUMS AAL Hom 0.007189258648213538
+XHCUMS AAL Hom 0.004912084955885057
+XHCUMS AAL Hom 0.006269911749518008
+XHCUMS AAL Hom 0.0059126577050997095
+XHCUMS AAL Hom 0.004211671407467295
+XHCUMS AAL Hom 0.009831783216999679
+XHCUMS AAL Hom 0.007237796151530777
+XHCUMS AAL Hom 0.005593130062611062
+XHCUMS AAL Hom 0.0032686187570054297
+XHCUMS AAL Hom 0.007396929485056084
+XHCUMS AAL Hom 0.010954969731180954
+XHCUMS AAL Hom 0.004540173925521357
+XHCUMS AAL Hom 0.006615769400169162
+XHCUMS AAL Hom 0.004511993994677711
+XHCUMS AAL Hom 0.008605516629619125
+XHCUMS AAL Hom 0.0037659829539718926
+XHCUMS AAL Hom 0.006770650829919595
+XHCUMS AAL Hom 0.007235610566188315
+XHCUMS AAL Hom 0.00704221228842507
+XHCUMS AAL Hom 0.007276605679924809
+XHCUMS AAL Hom 0.003382646925983874
+XHCUMS AAL Hom 0.003916078187285889
+XHCUMS AAL Hom 0.005328542088284811
+XHCUMS AAL Hom 0.0037647769814031498
+XHCUMS AAL Hom 0.004754598160147828
+XHCUMS AAL Hom 0.003859236513574395
+XHCUMS AAL Hom 0.006385484546087582
+XHCUMS AAL Hom 0.009773235519756304
+XHCUMS AAL Hom 0.009172428449950532
+XHCUMS AAL Hom 0.004520201240463459
+XHCUMS AAL Hom 0.005635663112165533
+XHCUMS AAL Hom 0.008062501536090139
+XHCUMS AAL Hom 0.00784670907650902
+XHCUMS AAL Hom 0.005301699180516824
+XHCUMS AAL Hom 0.009508568543324197
+XHCUMS AAL Contra 0.095216702553127
+XHCUMS AAL Contra 0.12401878618011285
+XHCUMS AAL Contra 0.17968030017951686
+XHCUMS AAL Contra 0.12627420400094824
+XHCUMS AAL Contra 0.08253403490454914
+XHCUMS AAL Contra 0.09888051058359236
+XHCUMS AAL Contra 0.1073677462593062
+XHCUMS AAL Contra 0.09342559753276793
+XHCUMS AAL Contra 0.12013459660658392
+XHCUMS AAL Contra 0.10383824869199972
+XHCUMS AAL Contra 0.11802420861133567
+XHCUMS AAL Contra 0.16767153950646904
+XHCUMS AAL Contra 0.09577406602307852
+XHCUMS AAL Contra 0.08878253187211306
+XHCUMS AAL Contra 0.09884909046127863
+XHCUMS AAL Contra 0.09894683375815827
+XHCUMS AAL Contra 0.019581984046623604
+XHCUMS AAL Contra 0.09586954955735422
+XHCUMS AAL Contra 0.10218333646506308
+XHCUMS AAL Contra 0.1718708788658497
+XHCUMS AAL Contra 0.09178427645871887
+XHCUMS AAL Contra 0.12677939534994181
+XHCUMS AAL Contra 0.08171318815534895
+XHCUMS AAL Contra 0.07507394032681615
+XHCUMS AAL Contra 0.06698616252608179
+XHCUMS AAL Contra 0.10525250407410679
+XHCUMS AAL Contra 0.056074891394837616
+XHCUMS AAL Contra 0.03143105425498662
+XHCUMS AAL Contra 0.07132532969507441
+XHCUMS AAL Contra 0.12959210135038393
+XHCUMS AAL Contra 0.07375034974873419
+XHCUMS AAL Contra 0.06856711696910872
+XHCUMS AAL Contra 0.15846533717763095
+XHCUMS AAL Contra 0.11402972456065284
+XHCUMS AAL Contra 0.08440468704298935
+XHCUMS AAL Contra 0.10791969497173581
+XHCUMS AAL Contra 0.042549268139837955
+XHCUMS AAL Contra 0.06895425532670202
+XHCUMS AAL Contra 0.052725899963591706
+XHCUMS AAL Contra 0.0660818256890923
+XHCUMS AAL Contra 0.05813687127382652
+XHCUMS AAL Contra 0.09971704639919496
+XHCUMS AAL Contra 0.08215446018745681
+XHCUMS AAL Contra 0.10090729587093632
+XHCUMS AAL Contra 0.038408296779933
+XHCUMS AAL Contra 0.06052908123991817
+XHCUMS AAL Contra 0.1131608375389592
+XHCUMS AAL Contra 0.16470565818965166
+XHCUMS AAL Contra 0.06941415942875497
+XHCUMS AAL Contra 0.14606288068604736
+XHCUMS AAL Contra 0.09735921714431975
+XHCUMS AAL Contra 0.04704123614665585
+XHCUMS AAL Contra 0.07223263466795586
+XHCUMS AAL Contra 0.09138416440477243
+XHCUMS AAL Contra 0.03697747700347001
+XHCUMS AAL Contra 0.09510498318436997
+XHCUMS AAL Contra 0.08609877052755972
+XHCUMS AAL Contra 0.14098127867471896
+XHCUMS AAL Contra 0.1156839339750567
+XHCUMS AAL Contra 0.058391712752672534
+XHCUMS AAL Contra 0.10584247243492188
+XHCUMS AAL Contra 0.07296845942198747
+XHCUMS AAL Contra 0.101291816113509
+XHCUMS AAL Contra 0.12352327987173449
+XHCUMS AAL Contra 0.08377230939085756
+XHCUMS AAL Contra 0.09388283099417742
+XHCUMS AAL Contra 0.09844773123316862
+XHCUMS AAL Contra 0.1126571190840962
+XHCUMS AAL Contra 0.10233219210871237
+XHCUMS AAL Contra 0.09295654935783593
+XHCUMS AAL Contra 0.0980676673131945
+XHCUMS AAL Contra 0.08505152849016251
+XHCUMS AAL Contra 0.10917428294217561
+XHCUMS AAL Contra 0.07601555061027607
+XHCUMS AAL Contra 0.09751537056912556
+XHCUMS AAL Contra 0.13349179771347888
+XHCUMS AAL Contra 0.10047759346107572
+XHCUMS AAL Contra 0.059980688848380696
+XHCUMS AAL Contra 0.16029572359215655
+XHCUMS AAL Contra 0.051322948679645784
+XHCUMS AAL Contra 0.13312162872147554
+XHCUMS AAL Contra 0.07697553591747773
+XHCUMS AAL Contra 0.10600993918430472
+XHCUMS AAL Contra 0.0750313985522295
+XHCUMS AAL Contra 0.07531478109707275
+XHCUMS AAL Contra 0.097909155788729
+XHCUMS AAL Contra 0.06233653980007179
+XHCUMS AAL Contra 0.12320027263258779
+XHCUMS AAL Contra 0.10130863327434271
+XHCUMS AAL Contra 0.06648866180684498
+XHCUMS AAL Contra 0.036477015720597035
+XHCUMS AAL Contra 0.09294638663994749
+XHCUMS AAL Contra 0.12473093942085894
+XHCUMS AAL Contra 0.05913039279543222
+XHCUMS AAL Contra 0.08130296091020153
+XHCUMS AAL Contra 0.058852881220125365
+XHCUMS AAL Contra 0.13424971467790833
+XHCUMS AAL Contra 0.04356436421743916
+XHCUMS AAL Contra 0.10135263236518906
+XHCUMS AAL Contra 0.09630126928053565
+XHCUMS AAL Contra 0.10653837407311732
+XHCUMS AAL Contra 0.10052490838863029
+XHCUMS AAL Contra 0.056098232413224494
+XHCUMS AAL Contra 0.05415663111639873
+XHCUMS AAL Contra 0.08064684813452191
+XHCUMS AAL Contra 0.05504732033065765
+XHCUMS AAL Contra 0.06699266740925713
+XHCUMS AAL Contra 0.04940526494657159
+XHCUMS AAL Contra 0.08951304091220795
+XHCUMS AAL Contra 0.1404223622694854
+XHCUMS AAL Contra 0.10640266151160326
+XHCUMS AAL Contra 0.06485880429072512
+XHCUMS AAL Contra 0.06387116159260847
+XHCUMS AAL Contra 0.09511433462811211
+XHCUMS AAL Contra 0.1119648689266594
+XHCUMS AAL Contra 0.0781367455207523
+XHCUMS AAL Contra 0.12836107147138137
+Dataset Atlas Type Value
+BNU1 AAL Ipsi 0.9273671312759618
+BNU1 AAL Ipsi 0.921422183170414
+BNU1 AAL Ipsi 0.8893506612036286
+BNU1 AAL Ipsi 0.934685315079593
+BNU1 AAL Ipsi 0.9355550096177971
+BNU1 AAL Ipsi 0.937805391677196
+BNU1 AAL Ipsi 0.9364518322125855
+BNU1 AAL Ipsi 0.8613870860267528
+BNU1 AAL Ipsi 0.87968133524108
+BNU1 AAL Ipsi 0.9834283210225027
+BNU1 AAL Ipsi 0.9396052943228905
+BNU1 AAL Ipsi 0.8571494730042322
+BNU1 AAL Ipsi 0.9215009270828873
+BNU1 AAL Ipsi 0.954172374329167
+BNU1 AAL Ipsi 0.9566371444147533
+BNU1 AAL Ipsi 0.9444583955524384
+BNU1 AAL Ipsi 0.9445284597993984
+BNU1 AAL Ipsi 0.9344692237155509
+BNU1 AAL Ipsi 0.9255430876887243
+BNU1 AAL Ipsi 0.9403763463544863
+BNU1 AAL Ipsi 0.9725809872270351
+BNU1 AAL Ipsi 0.9356031841185009
+BNU1 AAL Ipsi 0.9247872678758675
+BNU1 AAL Ipsi 0.8917483805637114
+BNU1 AAL Ipsi 0.8978053158921602
+BNU1 AAL Ipsi 0.8661593108518084
+BNU1 AAL Ipsi 0.9368709634718149
+BNU1 AAL Ipsi 0.9332229406838888
+BNU1 AAL Ipsi 0.9431820113194078
+BNU1 AAL Ipsi 0.5730489560446881
+BNU1 AAL Ipsi 0.9090406709996687
+BNU1 AAL Ipsi 0.9064208692300898
+BNU1 AAL Ipsi 0.9235147106277909
+BNU1 AAL Ipsi 0.9339167173805077
+BNU1 AAL Ipsi 0.9263719373151722
+BNU1 AAL Ipsi 0.9257041148656691
+BNU1 AAL Ipsi 0.9465313697777279
+BNU1 AAL Ipsi 0.947933309318808
+BNU1 AAL Ipsi 0.9147391247374779
+BNU1 AAL Ipsi 0.9076658761547757
+BNU1 AAL Ipsi 0.7093873789765217
+BNU1 AAL Ipsi 0.946798244197548
+BNU1 AAL Ipsi 0.9012805375952566
+BNU1 AAL Ipsi 0.9317934154828208
+BNU1 AAL Ipsi 0.9357565251315825
+BNU1 AAL Ipsi 0.9344721702321366
+BNU1 AAL Ipsi 0.8607218042455577
+BNU1 AAL Ipsi 0.9209692678875997
+BNU1 AAL Ipsi 0.9424057669786842
+BNU1 AAL Ipsi 0.9379896075722881
+BNU1 AAL Ipsi 0.9319393538871614
+BNU1 AAL Ipsi 0.8898597874630017
+BNU1 AAL Ipsi 0.9107579722817162
+BNU1 AAL Ipsi 0.8796852465110294
+BNU1 AAL Ipsi 0.9069366982794755
+BNU1 AAL Ipsi 0.967480808096402
+BNU1 AAL Ipsi 0.9477337587736925
+BNU1 AAL Ipsi 0.9521828956947022
+BNU1 AAL Ipsi 0.9427225314271314
+BNU1 AAL Ipsi 0.9088590228778525
+BNU1 AAL Ipsi 0.9321023846169622
+BNU1 AAL Ipsi 0.9454018941661092
+BNU1 AAL Ipsi 0.925537891438409
+BNU1 AAL Ipsi 0.91473448741106
+BNU1 AAL Ipsi 0.9254362191846247
+BNU1 AAL Ipsi 0.9046909877572281
+BNU1 AAL Ipsi 0.9222767797415441
+BNU1 AAL Ipsi 0.9007674921088036
+BNU1 AAL Ipsi 0.9083926619597649
+BNU1 AAL Ipsi 0.9558267061062269
+BNU1 AAL Ipsi 0.9417849744107816
+BNU1 AAL Ipsi 0.9387220355382383
+BNU1 AAL Ipsi 0.9390101949632261
+BNU1 AAL Ipsi 0.9026494846738942
+BNU1 AAL Ipsi 0.9365954284883798
+BNU1 AAL Ipsi 0.910593229578299
+BNU1 AAL Ipsi 0.87545552009056
+BNU1 AAL Ipsi 0.9752199833961033
+BNU1 AAL Ipsi 0.956097754542253
+BNU1 AAL Ipsi 0.900409783976717
+BNU1 AAL Ipsi 0.9085337484064245
+BNU1 AAL Ipsi 0.9335458366019779
+BNU1 AAL Ipsi 0.9202321317159806
+BNU1 AAL Ipsi 0.9258266065749499
+BNU1 AAL Ipsi 0.8907099937638877
+BNU1 AAL Ipsi 0.9814932850104473
+BNU1 AAL Ipsi 0.9322584813364451
+BNU1 AAL Ipsi 0.890394549719462
+BNU1 AAL Ipsi 0.8440780870139392
+BNU1 AAL Ipsi 0.9243629146859667
+BNU1 AAL Ipsi 0.9477585087075702
+BNU1 AAL Ipsi 0.9132624569394632
+BNU1 AAL Ipsi 0.9425213156211675
+BNU1 AAL Ipsi 0.9090582108221199
+BNU1 AAL Ipsi 0.9394221238501456
+BNU1 AAL Ipsi 0.9590448945108843
+BNU1 AAL Ipsi 0.9278892050240534
+BNU1 AAL Ipsi 0.9431435642321806
+BNU1 AAL Ipsi 0.8599822352265009
+BNU1 AAL Ipsi 0.9279516142993162
+BNU1 AAL Ipsi 0.8937924790969994
+BNU1 AAL Ipsi 0.9359825896973634
+BNU1 AAL Ipsi 0.8740862954944864
+BNU1 AAL Ipsi 0.9531987670413122
+BNU1 AAL Ipsi 0.9636263758178762
+BNU1 AAL Ipsi 0.9419980821562133
+BNU1 AAL Ipsi 0.8917028728062169
+BNU1 AAL Ipsi 0.9110373314257849
+BNU1 AAL Ipsi 0.8868730743679467
+BNU1 AAL Ipsi 0.9413546843572028
+BNU1 AAL Ipsi 0.9254018351802383
+BNU1 AAL Ipsi 0.9409131414668173
+BNU1 AAL Ipsi 0.8920697712229763
+BNU1 AAL Ipsi 0.7990434930189112
+BNU1 AAL Hom 0.005782754059710532
+BNU1 AAL Hom 0.005640719878242041
+BNU1 AAL Hom 0.006862045249340752
+BNU1 AAL Hom 0.004245549160696573
+BNU1 AAL Hom 0.003711215187756126
+BNU1 AAL Hom 0.0027183223922757085
+BNU1 AAL Hom 0.003748425308261605
+BNU1 AAL Hom 0.009175966593624291
+BNU1 AAL Hom 0.006916850743392169
+BNU1 AAL Hom 0.0014029468612848908
+BNU1 AAL Hom 0.0032156109686308568
+BNU1 AAL Hom 0.010983432526017299
+BNU1 AAL Hom 0.005959540519815181
+BNU1 AAL Hom 0.0027809985550327184
+BNU1 AAL Hom 0.0030437913664699404
+BNU1 AAL Hom 0.002892753216865048
+BNU1 AAL Hom 0.0033556428753780034
+BNU1 AAL Hom 0.0021505702518562334
+BNU1 AAL Hom 0.004434081218356373
+BNU1 AAL Hom 0.004018925678163741
+BNU1 AAL Hom 0.0011316914599176272
+BNU1 AAL Hom 0.005485083656018619
+BNU1 AAL Hom 0.0030364995769399813
+BNU1 AAL Hom 0.00770663396414596
+BNU1 AAL Hom 0.007687966348673011
+BNU1 AAL Hom 0.013225088734674213
+BNU1 AAL Hom 0.0059589084043390715
+BNU1 AAL Hom 0.004070816394917461
+BNU1 AAL Hom 0.004915593079184866
+BNU1 AAL Hom 0.011481002354442956
+BNU1 AAL Hom 0.006480447254934726
+BNU1 AAL Hom 0.005196164232511956
+BNU1 AAL Hom 0.004593972731812345
+BNU1 AAL Hom 0.005599507551116054
+BNU1 AAL Hom 0.0038407234279606173
+BNU1 AAL Hom 0.006722257687947304
+BNU1 AAL Hom 0.0025511272937573212
+BNU1 AAL Hom 0.0023532967539521793
+BNU1 AAL Hom 0.005816150103275288
+BNU1 AAL Hom 0.006829340679901673
+BNU1 AAL Hom 0.02154452323076101
+BNU1 AAL Hom 0.004838364469687557
+BNU1 AAL Hom 0.00834714020093032
+BNU1 AAL Hom 0.005420870993079966
+BNU1 AAL Hom 0.00378578967473907
+BNU1 AAL Hom 0.004417971837969765
+BNU1 AAL Hom 0.009191292127536475
+BNU1 AAL Hom 0.005719099566457116
+BNU1 AAL Hom 0.0032032597769977135
+BNU1 AAL Hom 0.004997473740810442
+BNU1 AAL Hom 0.005123855023507302
+BNU1 AAL Hom 0.008732627000070527
+BNU1 AAL Hom 0.005362077317498957
+BNU1 AAL Hom 0.008175334063683431
+BNU1 AAL Hom 0.008246268251051847
+BNU1 AAL Hom 0.0022153497996014277
+BNU1 AAL Hom 0.002995538559592097
+BNU1 AAL Hom 0.003488121725400129
+BNU1 AAL Hom 0.0046135840384771455
+BNU1 AAL Hom 0.005842993298763814
+BNU1 AAL Hom 0.004216684694102373
+BNU1 AAL Hom 0.003952047318945182
+BNU1 AAL Hom 0.0037418534845848955
+BNU1 AAL Hom 0.00644982662465226
+BNU1 AAL Hom 0.00428767402166901
+BNU1 AAL Hom 0.0037478107174880432
+BNU1 AAL Hom 0.0052578185162438255
+BNU1 AAL Hom 0.0070572138968040005
+BNU1 AAL Hom 0.009400138301162538
+BNU1 AAL Hom 0.002222013501916438
+BNU1 AAL Hom 0.00494054567327429
+BNU1 AAL Hom 0.005624272695179195
+BNU1 AAL Hom 0.003354148273768698
+BNU1 AAL Hom 0.00654796119792711
+BNU1 AAL Hom 0.005518647440102922
+BNU1 AAL Hom 0.0060999781594687635
+BNU1 AAL Hom 0.008543179209335701
+BNU1 AAL Hom 0.001460705250743004
+BNU1 AAL Hom 0.0032917327455711538
+BNU1 AAL Hom 0.007346838019473801
+BNU1 AAL Hom 0.006824816199647994
+BNU1 AAL Hom 0.004065255936244606
+BNU1 AAL Hom 0.006106186509062883
+BNU1 AAL Hom 0.005860719087016518
+BNU1 AAL Hom 0.007859302500973688
+BNU1 AAL Hom 0.0017125016734358001
+BNU1 AAL Hom 0.004610577113247103
+BNU1 AAL Hom 0.008426690266383003
+BNU1 AAL Hom 0.009243069341599958
+BNU1 AAL Hom 0.0038265993880919704
+BNU1 AAL Hom 0.0021415968846642017
+BNU1 AAL Hom 0.004293430380786654
+BNU1 AAL Hom 0.00521703597804074
+BNU1 AAL Hom 0.006822018924967608
+BNU1 AAL Hom 0.0038913407157241337
+BNU1 AAL Hom 0.00294130216106403
+BNU1 AAL Hom 0.0052162302129544705
+BNU1 AAL Hom 0.0045319692581083995
+BNU1 AAL Hom 0.012804744239510946
+BNU1 AAL Hom 0.005651300414109801
+BNU1 AAL Hom 0.005947928003603009
+BNU1 AAL Hom 0.003706623262558361
+BNU1 AAL Hom 0.008534266682503778
+BNU1 AAL Hom 0.0034305951647963205
+BNU1 AAL Hom 0.002724290116350394
+BNU1 AAL Hom 0.0028109142746404986
+BNU1 AAL Hom 0.00848501529223761
+BNU1 AAL Hom 0.006636207059628447
+BNU1 AAL Hom 0.007693482502018456
+BNU1 AAL Hom 0.0033272194310434286
+BNU1 AAL Hom 0.0051578134589038566
+BNU1 AAL Hom 0.0034967955607758745
+BNU1 AAL Hom 0.010874382427910587
+BNU1 AAL Hom 0.017055634944181088
+BNU1 AAL Contra 0.06685011466432766
+BNU1 AAL Contra 0.07293709695134389
+BNU1 AAL Contra 0.1037872935470307
+BNU1 AAL Contra 0.06106913575971046
+BNU1 AAL Contra 0.06073377519444677
+BNU1 AAL Contra 0.05947628593052824
+BNU1 AAL Contra 0.059799742479152894
+BNU1 AAL Contra 0.12943694737962289
+BNU1 AAL Contra 0.11340181401552775
+BNU1 AAL Contra 0.015168732116212447
+BNU1 AAL Contra 0.05717909470847861
+BNU1 AAL Contra 0.1318670944697506
+BNU1 AAL Contra 0.07253953239729752
+BNU1 AAL Contra 0.04304662711580023
+BNU1 AAL Contra 0.04031906421877668
+BNU1 AAL Contra 0.052648851230696604
+BNU1 AAL Contra 0.05211589732522364
+BNU1 AAL Contra 0.06338020603259287
+BNU1 AAL Contra 0.07002283109291937
+BNU1 AAL Contra 0.05560472796734994
+BNU1 AAL Contra 0.026287321313047243
+BNU1 AAL Contra 0.05891173222548058
+BNU1 AAL Contra 0.07217623254719249
+BNU1 AAL Contra 0.10054498547214265
+BNU1 AAL Contra 0.09450671775916683
+BNU1 AAL Contra 0.12061560041351736
+BNU1 AAL Contra 0.05717012812384609
+BNU1 AAL Contra 0.06270624292119378
+BNU1 AAL Contra 0.051902395601407275
+BNU1 AAL Contra 0.4154700416008689
+BNU1 AAL Contra 0.08447888174539656
+BNU1 AAL Contra 0.0883829665373982
+BNU1 AAL Contra 0.07189131664039679
+BNU1 AAL Contra 0.060483775068376246
+BNU1 AAL Contra 0.06978733925686713
+BNU1 AAL Contra 0.06757362744638364
+BNU1 AAL Contra 0.050917502928514734
+BNU1 AAL Contra 0.04971339392723979
+BNU1 AAL Contra 0.07944472515924687
+BNU1 AAL Contra 0.08550478316532267
+BNU1 AAL Contra 0.2690680977927173
+BNU1 AAL Contra 0.04836339133276441
+BNU1 AAL Contra 0.09037232220381307
+BNU1 AAL Contra 0.06278571352409927
+BNU1 AAL Contra 0.060457685193678466
+BNU1 AAL Contra 0.06110985792989366
+BNU1 AAL Contra 0.13008690362690584
+BNU1 AAL Contra 0.07331163254594321
+BNU1 AAL Contra 0.054390973244318096
+BNU1 AAL Contra 0.05701291868690146
+BNU1 AAL Contra 0.06293679108933123
+BNU1 AAL Contra 0.10140758553692779
+BNU1 AAL Contra 0.08387995040078482
+BNU1 AAL Contra 0.11213941942528716
+BNU1 AAL Contra 0.08481703346947256
+BNU1 AAL Contra 0.030303842103996576
+BNU1 AAL Contra 0.049270702666715384
+BNU1 AAL Contra 0.044328982579897674
+BNU1 AAL Contra 0.05266388453439151
+BNU1 AAL Contra 0.08529798382338372
+BNU1 AAL Contra 0.0636809306889355
+BNU1 AAL Contra 0.05064605851494561
+BNU1 AAL Contra 0.07072025507700602
+BNU1 AAL Contra 0.07881568596428776
+BNU1 AAL Contra 0.07027610679370631
+BNU1 AAL Contra 0.09156120152528381
+BNU1 AAL Contra 0.07246540174221204
+BNU1 AAL Contra 0.09217529399439241
+BNU1 AAL Contra 0.08220719973907262
+BNU1 AAL Contra 0.04195128039185665
+BNU1 AAL Contra 0.05327447991594409
+BNU1 AAL Contra 0.05565369176658255
+BNU1 AAL Contra 0.05763565676300516
+BNU1 AAL Contra 0.09080255412817874
+BNU1 AAL Contra 0.05788592407151725
+BNU1 AAL Contra 0.08330679226223221
+BNU1 AAL Contra 0.11600130070010428
+BNU1 AAL Contra 0.02331931135315376
+BNU1 AAL Contra 0.04061051271217585
+BNU1 AAL Contra 0.09224337800380923
+BNU1 AAL Contra 0.08464143539392742
+BNU1 AAL Contra 0.06238890746177755
+BNU1 AAL Contra 0.07366168177495655
+BNU1 AAL Contra 0.06831267433803367
+BNU1 AAL Contra 0.10143070373513859
+BNU1 AAL Contra 0.016794213316116934
+BNU1 AAL Contra 0.06313094155030788
+BNU1 AAL Contra 0.101178760014155
+BNU1 AAL Contra 0.14667884364446082
+BNU1 AAL Contra 0.07181048592594139
+BNU1 AAL Contra 0.05009989440776566
+BNU1 AAL Contra 0.08244411267975019
+BNU1 AAL Contra 0.05226164840079182
+BNU1 AAL Contra 0.0841197702529125
+BNU1 AAL Contra 0.056686535434130325
+BNU1 AAL Contra 0.03801380332805176
+BNU1 AAL Contra 0.06689456476299221
+BNU1 AAL Contra 0.052324466509711
+BNU1 AAL Contra 0.1272130205339882
+BNU1 AAL Contra 0.06639708528657401
+BNU1 AAL Contra 0.1002595928993975
+BNU1 AAL Contra 0.060310787040078244
+BNU1 AAL Contra 0.11737943782300987
+BNU1 AAL Contra 0.0433706377938915
+BNU1 AAL Contra 0.033649334065773355
+BNU1 AAL Contra 0.05519100356914617
+BNU1 AAL Contra 0.09981211190154551
+BNU1 AAL Contra 0.08232646151458668
+BNU1 AAL Contra 0.10543344313003483
+BNU1 AAL Contra 0.05531809621175375
+BNU1 AAL Contra 0.06944035136085787
+BNU1 AAL Contra 0.055590062972406866
+BNU1 AAL Contra 0.09705584634911311
+BNU1 AAL Contra 0.18390087203690772
+Dataset Atlas Type Value
+BNU3 AAL Ipsi 0.9598538658171265
+BNU3 AAL Ipsi 0.9322954202661395
+BNU3 AAL Ipsi 0.970076113165306
+BNU3 AAL Ipsi 0.9337994788306841
+BNU3 AAL Ipsi 0.9348342104536617
+BNU3 AAL Ipsi 0.9648467392908605
+BNU3 AAL Ipsi 0.9155086270068616
+BNU3 AAL Ipsi 0.9655144490156357
+BNU3 AAL Ipsi 0.9338026208514785
+BNU3 AAL Ipsi 0.9458298945076988
+BNU3 AAL Ipsi 0.9425443557136379
+BNU3 AAL Ipsi 0.9065838248822988
+BNU3 AAL Ipsi 0.9220409846556408
+BNU3 AAL Ipsi 0.9660106492218454
+BNU3 AAL Ipsi 0.9427608982878267
+BNU3 AAL Ipsi 0.9569103218018641
+BNU3 AAL Ipsi 0.9654032143365098
+BNU3 AAL Ipsi 0.94407846388984
+BNU3 AAL Ipsi 0.9121271409085079
+BNU3 AAL Ipsi 0.9273268431416162
+BNU3 AAL Ipsi 0.9143346047114197
+BNU3 AAL Ipsi 0.9345957086921866
+BNU3 AAL Ipsi 0.9322835988603074
+BNU3 AAL Ipsi 0.9603738914838759
+BNU3 AAL Ipsi 0.9330956698116106
+BNU3 AAL Ipsi 0.9569401111889557
+BNU3 AAL Ipsi 0.9580461628170186
+BNU3 AAL Ipsi 0.9243804212014981
+BNU3 AAL Ipsi 0.9571157753301879
+BNU3 AAL Ipsi 0.9445629597122585
+BNU3 AAL Ipsi 0.9458254939665969
+BNU3 AAL Ipsi 0.9408647240056784
+BNU3 AAL Ipsi 0.9069728086793063
+BNU3 AAL Ipsi 0.9528987405260063
+BNU3 AAL Ipsi 0.9251795428637881
+BNU3 AAL Ipsi 0.915911535445137
+BNU3 AAL Ipsi 0.9061196110482532
+BNU3 AAL Ipsi 0.9498187622143403
+BNU3 AAL Ipsi 0.9130333623497199
+BNU3 AAL Ipsi 0.9401064370013351
+BNU3 AAL Ipsi 0.9124634151120194
+BNU3 AAL Ipsi 0.9014940517034551
+BNU3 AAL Ipsi 0.9019356618404161
+BNU3 AAL Ipsi 0.9281743990315914
+BNU3 AAL Ipsi 0.9403471325556156
+BNU3 AAL Ipsi 0.9552287302909561
+BNU3 AAL Hom 0.0030355702070343697
+BNU3 AAL Hom 0.004925837636133196
+BNU3 AAL Hom 0.0014197664330267094
+BNU3 AAL Hom 0.003420852175708945
+BNU3 AAL Hom 0.004787788591870192
+BNU3 AAL Hom 0.0022010778456396053
+BNU3 AAL Hom 0.0032862567450536273
+BNU3 AAL Hom 0.0019805814350122284
+BNU3 AAL Hom 0.004640065666350135
+BNU3 AAL Hom 0.0022308327167809444
+BNU3 AAL Hom 0.0038666582317547237
+BNU3 AAL Hom 0.005493059689377131
+BNU3 AAL Hom 0.0040075211258519105
+BNU3 AAL Hom 0.002430386848261111
+BNU3 AAL Hom 0.0021032618428909245
+BNU3 AAL Hom 0.003054669365725933
+BNU3 AAL Hom 0.002026738728488124
+BNU3 AAL Hom 0.0017138169883887783
+BNU3 AAL Hom 0.005656550864942826
+BNU3 AAL Hom 0.004643089138204788
+BNU3 AAL Hom 0.00534733245499398
+BNU3 AAL Hom 0.003111560483084598
+BNU3 AAL Hom 0.0033180003210401703
+BNU3 AAL Hom 0.0021818436119216433
+BNU3 AAL Hom 0.003575196943155393
+BNU3 AAL Hom 0.002407371938649503
+BNU3 AAL Hom 0.003755775926293049
+BNU3 AAL Hom 0.0030634808394278405
+BNU3 AAL Hom 0.003860074540089649
+BNU3 AAL Hom 0.0027360096112496045
+BNU3 AAL Hom 0.003978970053461808
+BNU3 AAL Hom 0.0025779261279525586
+BNU3 AAL Hom 0.004504956782337112
+BNU3 AAL Hom 0.0036801258963574035
+BNU3 AAL Hom 0.004959723711557679
+BNU3 AAL Hom 0.00587761280646616
+BNU3 AAL Hom 0.004941290172685667
+BNU3 AAL Hom 0.003690121589244902
+BNU3 AAL Hom 0.00604673605766448
+BNU3 AAL Hom 0.0041897717134409965
+BNU3 AAL Hom 0.005593277741615759
+BNU3 AAL Hom 0.005928833139589762
+BNU3 AAL Hom 0.006560759258863123
+BNU3 AAL Hom 0.0040722524315722165
+BNU3 AAL Hom 0.002485647096695548
+BNU3 AAL Hom 0.001767666629866415
+BNU3 AAL Contra 0.0371105639758391
+BNU3 AAL Contra 0.06277874209772724
+BNU3 AAL Contra 0.02850412040166732
+BNU3 AAL Contra 0.06277966899360696
+BNU3 AAL Contra 0.060378000954468186
+BNU3 AAL Contra 0.0329521828634999
+BNU3 AAL Contra 0.08120511624808474
+BNU3 AAL Contra 0.03250496954935213
+BNU3 AAL Contra 0.06155731348217141
+BNU3 AAL Contra 0.05193927277552021
+BNU3 AAL Contra 0.053588986054607386
+BNU3 AAL Contra 0.08792311542832404
+BNU3 AAL Contra 0.07395149421850737
+BNU3 AAL Contra 0.031558963929893456
+BNU3 AAL Contra 0.05513583986928232
+BNU3 AAL Contra 0.04003500883240994
+BNU3 AAL Contra 0.03257004693500214
+BNU3 AAL Contra 0.054207719121771275
+BNU3 AAL Contra 0.08221630822654932
+BNU3 AAL Contra 0.06803006772017903
+BNU3 AAL Contra 0.08031806283358632
+BNU3 AAL Contra 0.06229273082472888
+BNU3 AAL Contra 0.06439840081865243
+BNU3 AAL Contra 0.037444264904202454
+BNU3 AAL Contra 0.06332913324523393
+BNU3 AAL Contra 0.040652516872394795
+BNU3 AAL Contra 0.03819806125668839
+BNU3 AAL Contra 0.07255609795907404
+BNU3 AAL Contra 0.03902415012972245
+BNU3 AAL Contra 0.05270103067649192
+BNU3 AAL Contra 0.05019553597994129
+BNU3 AAL Contra 0.05655734986636897
+BNU3 AAL Contra 0.08852223453835663
+BNU3 AAL Contra 0.04342113357763631
+BNU3 AAL Contra 0.0698607334246542
+BNU3 AAL Contra 0.0782108517483968
+BNU3 AAL Contra 0.08893909877906106
+BNU3 AAL Contra 0.04649111619641487
+BNU3 AAL Contra 0.08091990159261567
+BNU3 AAL Contra 0.05570379128522393
+BNU3 AAL Contra 0.08194330714636475
+BNU3 AAL Contra 0.09257711515695519
+BNU3 AAL Contra 0.09150357890072082
+BNU3 AAL Contra 0.06775334853683644
+BNU3 AAL Contra 0.0571672203476888
+BNU3 AAL Contra 0.043003603079177416
+Dataset Atlas Type Value
+IPCAS_8 AAL Ipsi 0.933429859966343
+IPCAS_8 AAL Ipsi 0.918886617716124
+IPCAS_8 AAL Ipsi 0.9194364855535452
+IPCAS_8 AAL Ipsi 0.9689799915591472
+IPCAS_8 AAL Ipsi 0.9505086926574787
+IPCAS_8 AAL Ipsi 0.9442594866670114
+IPCAS_8 AAL Ipsi 0.9669978621102037
+IPCAS_8 AAL Ipsi 0.9574722750150877
+IPCAS_8 AAL Ipsi 0.9310311005177456
+IPCAS_8 AAL Ipsi 0.9387231962606981
+IPCAS_8 AAL Ipsi 0.9072955920107916
+IPCAS_8 AAL Ipsi 0.9558788390644208
+IPCAS_8 AAL Ipsi 0.9631325946020906
+IPCAS_8 AAL Ipsi 0.9553431634317433
+IPCAS_8 AAL Ipsi 0.8969096349224623
+IPCAS_8 AAL Ipsi 0.9614150226164262
+IPCAS_8 AAL Ipsi 0.9701915245283639
+IPCAS_8 AAL Ipsi 0.9670352165589242
+IPCAS_8 AAL Ipsi 0.9514556201830331
+IPCAS_8 AAL Ipsi 0.9222537527184003
+IPCAS_8 AAL Ipsi 0.9244834173398697
+IPCAS_8 AAL Ipsi 0.9572984916104845
+IPCAS_8 AAL Ipsi 0.955826706334872
+IPCAS_8 AAL Ipsi 0.9363243647662073
+IPCAS_8 AAL Ipsi 0.9178933130954463
+IPCAS_8 AAL Ipsi 0.9647162075646891
+IPCAS_8 AAL Hom 0.006162932626829693
+IPCAS_8 AAL Hom 0.005037682111507138
+IPCAS_8 AAL Hom 0.004449400050546131
+IPCAS_8 AAL Hom 0.0022984859503035362
+IPCAS_8 AAL Hom 0.0030253700597580343
+IPCAS_8 AAL Hom 0.0023089683459332107
+IPCAS_8 AAL Hom 0.0019555289606682596
+IPCAS_8 AAL Hom 0.002718757121863179
+IPCAS_8 AAL Hom 0.0054822350410925575
+IPCAS_8 AAL Hom 0.002335884929082612
+IPCAS_8 AAL Hom 0.006127496627648927
+IPCAS_8 AAL Hom 0.003544263197404097
+IPCAS_8 AAL Hom 0.0023802197992684915
+IPCAS_8 AAL Hom 0.0036161555249601108
+IPCAS_8 AAL Hom 0.0056210213489048255
+IPCAS_8 AAL Hom 0.0019365466662421983
+IPCAS_8 AAL Hom 0.0015030732059996238
+IPCAS_8 AAL Hom 0.0017842064003461408
+IPCAS_8 AAL Hom 0.0038374497458412904
+IPCAS_8 AAL Hom 0.004495534415998917
+IPCAS_8 AAL Hom 0.0037271283573845197
+IPCAS_8 AAL Hom 0.0026833788769197765
+IPCAS_8 AAL Hom 0.002906500781444283
+IPCAS_8 AAL Hom 0.0036944110863307983
+IPCAS_8 AAL Hom 0.004945610678699211
+IPCAS_8 AAL Hom 0.002321604978276973
+IPCAS_8 AAL Contra 0.060407207406827285
+IPCAS_8 AAL Contra 0.07607570017236882
+IPCAS_8 AAL Contra 0.07611411439590868
+IPCAS_8 AAL Contra 0.028721522490549288
+IPCAS_8 AAL Contra 0.046465937282763284
+IPCAS_8 AAL Contra 0.05343154498705533
+IPCAS_8 AAL Contra 0.031046608929128028
+IPCAS_8 AAL Contra 0.03980896786304914
+IPCAS_8 AAL Contra 0.06348666444116184
+IPCAS_8 AAL Contra 0.05894091881021927
+IPCAS_8 AAL Contra 0.08657691136155955
+IPCAS_8 AAL Contra 0.04057689773817501
+IPCAS_8 AAL Contra 0.03448718559864099
+IPCAS_8 AAL Contra 0.04104068104329659
+IPCAS_8 AAL Contra 0.09746934372863283
+IPCAS_8 AAL Contra 0.036648430717331634
+IPCAS_8 AAL Contra 0.028305402265636517
+IPCAS_8 AAL Contra 0.031180577040729688
+IPCAS_8 AAL Contra 0.04470693007112556
+IPCAS_8 AAL Contra 0.07325071286560082
+IPCAS_8 AAL Contra 0.07178945430274575
+IPCAS_8 AAL Contra 0.0400181295125957
+IPCAS_8 AAL Contra 0.0412667928836837
+IPCAS_8 AAL Contra 0.059981224147461924
+IPCAS_8 AAL Contra 0.07716107622585446
+IPCAS_8 AAL Contra 0.032962187457033956
+Dataset Atlas Type Value
+NKI1 AAL Ipsi 0.8827704515450102
+NKI1 AAL Ipsi 0.88146956863586
+NKI1 AAL Ipsi 0.9658544485170577
+NKI1 AAL Ipsi 0.878380140670597
+NKI1 AAL Ipsi 0.9361535952436272
+NKI1 AAL Ipsi 0.8549703882207863
+NKI1 AAL Ipsi 0.8558196791917315
+NKI1 AAL Ipsi 0.9094167424480066
+NKI1 AAL Ipsi 0.932215893613333
+NKI1 AAL Ipsi 0.8684173977106524
+NKI1 AAL Ipsi 0.8988254453672382
+NKI1 AAL Ipsi 0.9357807537936716
+NKI1 AAL Ipsi 0.901411459879539
+NKI1 AAL Ipsi 0.8816352153245716
+NKI1 AAL Ipsi 0.8827209879010626
+NKI1 AAL Ipsi 0.9348133984293564
+NKI1 AAL Ipsi 0.9139144877118168
+NKI1 AAL Ipsi 0.8852860180208383
+NKI1 AAL Ipsi 0.9619236987155824
+NKI1 AAL Ipsi 0.8194743795516831
+NKI1 AAL Ipsi 0.8968571282464252
+NKI1 AAL Ipsi 0.8032349851174083
+NKI1 AAL Ipsi 0.8807222122315159
+NKI1 AAL Ipsi 0.8662116742663291
+NKI1 AAL Ipsi 0.871752603821811
+NKI1 AAL Ipsi 0.8867981092089687
+NKI1 AAL Ipsi 0.8995356774024795
+NKI1 AAL Ipsi 0.9141325014622733
+NKI1 AAL Ipsi 0.8631951976189876
+NKI1 AAL Ipsi 0.9504261465268218
+NKI1 AAL Ipsi 0.880348088497217
+NKI1 AAL Ipsi 0.9080735402306391
+NKI1 AAL Ipsi 0.9485371871695453
+NKI1 AAL Ipsi 0.8318487941814714
+NKI1 AAL Ipsi 0.8909383163258314
+NKI1 AAL Ipsi 0.9068682324750774
+NKI1 AAL Ipsi 0.8521080811198191
+NKI1 AAL Ipsi 0.9568060892479444
+NKI1 AAL Ipsi 0.8639043654730942
+NKI1 AAL Ipsi 0.8532186475693049
+NKI1 AAL Hom 0.007707280665940256
+NKI1 AAL Hom 0.00546992052299225
+NKI1 AAL Hom 0.002337756327323079
+NKI1 AAL Hom 0.008457103172754228
+NKI1 AAL Hom 0.004381653111014072
+NKI1 AAL Hom 0.010040858990354496
+NKI1 AAL Hom 0.007989567352617594
+NKI1 AAL Hom 0.005466818334965141
+NKI1 AAL Hom 0.003948639211510363
+NKI1 AAL Hom 0.0084998378881412
+NKI1 AAL Hom 0.006950190086193354
+NKI1 AAL Hom 0.002404616097140379
+NKI1 AAL Hom 0.006474625756548844
+NKI1 AAL Hom 0.006212111504470621
+NKI1 AAL Hom 0.008021301487270129
+NKI1 AAL Hom 0.004498281865115115
+NKI1 AAL Hom 0.0065433733587700596
+NKI1 AAL Hom 0.006582407990403575
+NKI1 AAL Hom 0.0025797828328474864
+NKI1 AAL Hom 0.014724003108817548
+NKI1 AAL Hom 0.0055593425999800414
+NKI1 AAL Hom 0.012819360901799179
+NKI1 AAL Hom 0.008647188763220178
+NKI1 AAL Hom 0.007671332168264716
+NKI1 AAL Hom 0.009573255060226587
+NKI1 AAL Hom 0.00994961540485476
+NKI1 AAL Hom 0.005371877777121904
+NKI1 AAL Hom 0.003225736010918308
+NKI1 AAL Hom 0.009780689585592858
+NKI1 AAL Hom 0.0025214495619574036
+NKI1 AAL Hom 0.00870752397831902
+NKI1 AAL Hom 0.007032727761194962
+NKI1 AAL Hom 0.003240984847675654
+NKI1 AAL Hom 0.009870841171244657
+NKI1 AAL Hom 0.008040747272043257
+NKI1 AAL Hom 0.0052480954856794085
+NKI1 AAL Hom 0.008554585839641762
+NKI1 AAL Hom 0.002733085471846073
+NKI1 AAL Hom 0.010692052831089637
+NKI1 AAL Hom 0.008293870822510051
+NKI1 AAL Contra 0.10952226778904957
+NKI1 AAL Contra 0.1130605108411477
+NKI1 AAL Contra 0.03180779515561925
+NKI1 AAL Contra 0.11316275615664877
+NKI1 AAL Contra 0.0594647516453588
+NKI1 AAL Contra 0.13498875278885916
+NKI1 AAL Contra 0.1361907534556509
+NKI1 AAL Contra 0.08511643921702826
+NKI1 AAL Contra 0.06383546717515663
+NKI1 AAL Contra 0.1230827644012064
+NKI1 AAL Contra 0.0942243645465685
+NKI1 AAL Contra 0.06181463010918794
+NKI1 AAL Contra 0.09211391436391217
+NKI1 AAL Contra 0.1121526731709578
+NKI1 AAL Contra 0.10925771061166728
+NKI1 AAL Contra 0.06068831970552848
+NKI1 AAL Contra 0.07954213892941309
+NKI1 AAL Contra 0.10813157398875818
+NKI1 AAL Contra 0.035496518451570196
+NKI1 AAL Contra 0.16580161733949936
+NKI1 AAL Contra 0.09758352915359475
+NKI1 AAL Contra 0.1839456539807925
+NKI1 AAL Contra 0.11063059900526385
+NKI1 AAL Contra 0.12611699356540618
+NKI1 AAL Contra 0.11867414111796248
+NKI1 AAL Contra 0.10325227538617647
+NKI1 AAL Contra 0.09509244482039857
+NKI1 AAL Contra 0.08264176252680834
+NKI1 AAL Contra 0.12702411279541959
+NKI1 AAL Contra 0.04705240391122084
+NKI1 AAL Contra 0.11094438752446395
+NKI1 AAL Contra 0.08489373200816593
+NKI1 AAL Contra 0.048221827982779054
+NKI1 AAL Contra 0.15828036464728396
+NKI1 AAL Contra 0.10102093640212534
+NKI1 AAL Contra 0.08788367203924317
+NKI1 AAL Contra 0.13933733304053914
+NKI1 AAL Contra 0.04046082528020953
+NKI1 AAL Contra 0.12540358169581614
+NKI1 AAL Contra 0.13848748160818503
+Dataset Atlas Type Value
+NKI24_new AAL Ipsi 0.883146332534669
+NKI24_new AAL Ipsi 0.8820018273262025
+NKI24_new AAL Ipsi 0.965944606266993
+NKI24_new AAL Ipsi 0.8779062532055938
+NKI24_new AAL Ipsi 0.9362691017470753
+NKI24_new AAL Ipsi 0.8551191339012875
+NKI24_new AAL Ipsi 0.8554875179095401
+NKI24_new AAL Ipsi 0.9091125540613032
+NKI24_new AAL Ipsi 0.9322951330895861
+NKI24_new AAL Ipsi 0.8685392939279056
+NKI24_new AAL Ipsi 0.9000121329551765
+NKI24_new AAL Ipsi 0.9354205016846522
+NKI24_new AAL Ipsi 0.9014697794129426
+NKI24_new AAL Ipsi 0.8816683679271429
+NKI24_new AAL Ipsi 0.8832603660766669
+NKI24_new AAL Ipsi 0.934251525602455
+NKI24_new AAL Ipsi 0.9136020033063931
+NKI24_new AAL Ipsi 0.8850045588020219
+NKI24_new AAL Ipsi 0.8192365407816254
+NKI24_new AAL Ipsi 0.8961589044662318
+NKI24_new AAL Ipsi 0.8042526168700703
+NKI24_new AAL Ipsi 0.8800227296312889
+NKI24_new AAL Ipsi 0.8670002619419912
+NKI24_new AAL Ipsi 0.8719323959125594
+NKI24_new AAL Ipsi 0.8874408692397494
+NKI24_new AAL Ipsi 0.8984771203376397
+NKI24_new AAL Ipsi 0.8631832293293704
+NKI24_new AAL Ipsi 0.9503892778005307
+NKI24_new AAL Ipsi 0.8797621260340956
+NKI24_new AAL Ipsi 0.908417850175949
+NKI24_new AAL Ipsi 0.9487080732129447
+NKI24_new AAL Ipsi 0.8319170223686176
+NKI24_new AAL Ipsi 0.8913966429487711
+NKI24_new AAL Ipsi 0.9068061989180132
+NKI24_new AAL Ipsi 0.8531561829826669
+NKI24_new AAL Ipsi 0.9573409946150508
+NKI24_new AAL Ipsi 0.8636105101055177
+NKI24_new AAL Ipsi 0.8522514274540043
+NKI24_new AAL Hom 0.007664661620218641
+NKI24_new AAL Hom 0.0054786772051098
+NKI24_new AAL Hom 0.0023421389794965877
+NKI24_new AAL Hom 0.008502601705519116
+NKI24_new AAL Hom 0.0043605709455671856
+NKI24_new AAL Hom 0.010040703861924167
+NKI24_new AAL Hom 0.008048527785776237
+NKI24_new AAL Hom 0.00550538028191988
+NKI24_new AAL Hom 0.0040084285241190586
+NKI24_new AAL Hom 0.00853464303471012
+NKI24_new AAL Hom 0.006296296199240419
+NKI24_new AAL Hom 0.002365310494078916
+NKI24_new AAL Hom 0.006497733255728269
+NKI24_new AAL Hom 0.006137135440535669
+NKI24_new AAL Hom 0.00797813893811204
+NKI24_new AAL Hom 0.004516025462881742
+NKI24_new AAL Hom 0.006580785782149444
+NKI24_new AAL Hom 0.006579952471022928
+NKI24_new AAL Hom 0.014735327379946773
+NKI24_new AAL Hom 0.005641231769962552
+NKI24_new AAL Hom 0.012689883689507839
+NKI24_new AAL Hom 0.008597774116062863
+NKI24_new AAL Hom 0.007641137379331416
+NKI24_new AAL Hom 0.009620951481674604
+NKI24_new AAL Hom 0.009880569989625507
+NKI24_new AAL Hom 0.0054160879151276065
+NKI24_new AAL Hom 0.00981237982927222
+NKI24_new AAL Hom 0.002461065744215517
+NKI24_new AAL Hom 0.008735447158076068
+NKI24_new AAL Hom 0.007024284356345591
+NKI24_new AAL Hom 0.003227802612537991
+NKI24_new AAL Hom 0.009891055648485836
+NKI24_new AAL Hom 0.008063405555646577
+NKI24_new AAL Hom 0.005218442294296904
+NKI24_new AAL Hom 0.008551453512902457
+NKI24_new AAL Hom 0.002718853961411421
+NKI24_new AAL Hom 0.01066990217141081
+NKI24_new AAL Hom 0.008293621649969057
+NKI24_new AAL Contra 0.10918900584511233
+NKI24_new AAL Contra 0.11251949546868771
+NKI24_new AAL Contra 0.03171325475351043
+NKI24_new AAL Contra 0.11359114508888705
+NKI24_new AAL Contra 0.05937032730735752
+NKI24_new AAL Contra 0.13484016223678835
+NKI24_new AAL Contra 0.13646395430468358
+NKI24_new AAL Contra 0.08538206565677692
+NKI24_new AAL Contra 0.06369643838629481
+NKI24_new AAL Contra 0.12292606303738426
+NKI24_new AAL Contra 0.09369157084558312
+NKI24_new AAL Contra 0.06221418782126887
+NKI24_new AAL Contra 0.09203248733132913
+NKI24_new AAL Contra 0.11219449663232141
+NKI24_new AAL Contra 0.10876149498522109
+NKI24_new AAL Contra 0.06123244893466326
+NKI24_new AAL Contra 0.07981721091145753
+NKI24_new AAL Contra 0.10841548872695521
+NKI24_new AAL Contra 0.16602813183842777
+NKI24_new AAL Contra 0.09819986376380559
+NKI24_new AAL Contra 0.18305749944042188
+NKI24_new AAL Contra 0.1113794962526482
+NKI24_new AAL Contra 0.1253586006786774
+NKI24_new AAL Contra 0.11844665260576595
+NKI24_new AAL Contra 0.10267856077062504
+NKI24_new AAL Contra 0.09610679174723266
+NKI24_new AAL Contra 0.12700439084135737
+NKI24_new AAL Contra 0.0471496564552537
+NKI24_new AAL Contra 0.1115024268078283
+NKI24_new AAL Contra 0.08455786546770544
+NKI24_new AAL Contra 0.04806412417451728
+NKI24_new AAL Contra 0.1581919219828965
+NKI24_new AAL Contra 0.1005399514955824
+NKI24_new AAL Contra 0.08797535878768997
+NKI24_new AAL Contra 0.13829236350443055
+NKI24_new AAL Contra 0.039940151423537754
+NKI24_new AAL Contra 0.12571958772307146
+NKI24_new AAL Contra 0.1394549508960267
+Dataset Atlas Type Value
+ABIDEII-TCD_1 AAL Ipsi 0.8874905199122184
+ABIDEII-TCD_1 AAL Ipsi 0.966638756595719
+ABIDEII-TCD_1 AAL Ipsi 0.9724647149544722
+ABIDEII-TCD_1 AAL Ipsi 0.9490866729258511
+ABIDEII-TCD_1 AAL Ipsi 0.8966910747831977
+ABIDEII-TCD_1 AAL Ipsi 0.913267205502892
+ABIDEII-TCD_1 AAL Ipsi 0.9513083903407363
+ABIDEII-TCD_1 AAL Ipsi 0.9125647224544086
+ABIDEII-TCD_1 AAL Ipsi 0.968302742330826
+ABIDEII-TCD_1 AAL Ipsi 0.9575727563378407
+ABIDEII-TCD_1 AAL Ipsi 0.8846823900855642
+ABIDEII-TCD_1 AAL Ipsi 0.9035165945462389
+ABIDEII-TCD_1 AAL Ipsi 0.9290113200859529
+ABIDEII-TCD_1 AAL Ipsi 0.9673406809918073
+ABIDEII-TCD_1 AAL Ipsi 0.8675156383561345
+ABIDEII-TCD_1 AAL Ipsi 0.9241745917169255
+ABIDEII-TCD_1 AAL Ipsi 0.9243473422879369
+ABIDEII-TCD_1 AAL Ipsi 0.9691480999380758
+ABIDEII-TCD_1 AAL Ipsi 0.9202951138940519
+ABIDEII-TCD_1 AAL Hom 0.01006452510811334
+ABIDEII-TCD_1 AAL Hom 0.0020097799947238495
+ABIDEII-TCD_1 AAL Hom 0.002410268493492129
+ABIDEII-TCD_1 AAL Hom 0.0040193312620196795
+ABIDEII-TCD_1 AAL Hom 0.007240238911033875
+ABIDEII-TCD_1 AAL Hom 0.0058860144505561126
+ABIDEII-TCD_1 AAL Hom 0.003526773906809092
+ABIDEII-TCD_1 AAL Hom 0.004927185206639145
+ABIDEII-TCD_1 AAL Hom 0.0023901760109214533
+ABIDEII-TCD_1 AAL Hom 0.0027341464999891675
+ABIDEII-TCD_1 AAL Hom 0.008364700721152462
+ABIDEII-TCD_1 AAL Hom 0.00798911937129165
+ABIDEII-TCD_1 AAL Hom 0.005292507718240422
+ABIDEII-TCD_1 AAL Hom 0.0023645465128967866
+ABIDEII-TCD_1 AAL Hom 0.012598338522336723
+ABIDEII-TCD_1 AAL Hom 0.005025663006658664
+ABIDEII-TCD_1 AAL Hom 0.0034923154051135904
+ABIDEII-TCD_1 AAL Hom 0.0033307636896712962
+ABIDEII-TCD_1 AAL Hom 0.007758862942211332
+ABIDEII-TCD_1 AAL Contra 0.10244495497966824
+ABIDEII-TCD_1 AAL Contra 0.03135146340955714
+ABIDEII-TCD_1 AAL Contra 0.025125016552035705
+ABIDEII-TCD_1 AAL Contra 0.046893995812129184
+ABIDEII-TCD_1 AAL Contra 0.09606868630576848
+ABIDEII-TCD_1 AAL Contra 0.0808467800465519
+ABIDEII-TCD_1 AAL Contra 0.04516483575245453
+ABIDEII-TCD_1 AAL Contra 0.08250809233895233
+ABIDEII-TCD_1 AAL Contra 0.02930708165825248
+ABIDEII-TCD_1 AAL Contra 0.03969309716217011
+ABIDEII-TCD_1 AAL Contra 0.10695290919328337
+ABIDEII-TCD_1 AAL Contra 0.08849428608246941
+ABIDEII-TCD_1 AAL Contra 0.06569617219580667
+ABIDEII-TCD_1 AAL Contra 0.030294772495295927
+ABIDEII-TCD_1 AAL Contra 0.11988602312152868
+ABIDEII-TCD_1 AAL Contra 0.07079974527641585
+ABIDEII-TCD_1 AAL Contra 0.07216034230694951
+ABIDEII-TCD_1 AAL Contra 0.027521136372252904
+ABIDEII-TCD_1 AAL Contra 0.0719460231637367
+Dataset Atlas Type Value
+ABIDEII-BNI_1 AAL Ipsi 0.9586430230265847
+ABIDEII-BNI_1 AAL Ipsi 0.8697229827274962
+ABIDEII-BNI_1 AAL Ipsi 0.9253061621651677
+ABIDEII-BNI_1 AAL Ipsi 0.9801217224509726
+ABIDEII-BNI_1 AAL Ipsi 0.9042494700234557
+ABIDEII-BNI_1 AAL Ipsi 0.9275846309772279
+ABIDEII-BNI_1 AAL Ipsi 0.9201469622648797
+ABIDEII-BNI_1 AAL Ipsi 0.9450751794404382
+ABIDEII-BNI_1 AAL Ipsi 0.9350290209350872
+ABIDEII-BNI_1 AAL Ipsi 0.9556952260638418
+ABIDEII-BNI_1 AAL Ipsi 0.9295254271706295
+ABIDEII-BNI_1 AAL Ipsi 0.93262131483548
+ABIDEII-BNI_1 AAL Ipsi 0.8445661868718908
+ABIDEII-BNI_1 AAL Ipsi 0.92798040118286
+ABIDEII-BNI_1 AAL Ipsi 0.958882576802162
+ABIDEII-BNI_1 AAL Ipsi 0.8649178230989796
+ABIDEII-BNI_1 AAL Ipsi 0.7784640984703947
+ABIDEII-BNI_1 AAL Ipsi 0.9277437903509155
+ABIDEII-BNI_1 AAL Ipsi 0.9473064073732239
+ABIDEII-BNI_1 AAL Ipsi 0.9498146499656024
+ABIDEII-BNI_1 AAL Ipsi 0.9215966871644907
+ABIDEII-BNI_1 AAL Ipsi 0.95298495204989
+ABIDEII-BNI_1 AAL Ipsi 0.9077843421018171
+ABIDEII-BNI_1 AAL Ipsi 0.9559914275676588
+ABIDEII-BNI_1 AAL Ipsi 0.893673264534112
+ABIDEII-BNI_1 AAL Ipsi 0.922928377078239
+ABIDEII-BNI_1 AAL Ipsi 0.9559687609946426
+ABIDEII-BNI_1 AAL Ipsi 0.9705191002275412
+ABIDEII-BNI_1 AAL Ipsi 0.9520428785450429
+ABIDEII-BNI_1 AAL Hom 0.003158041514205898
+ABIDEII-BNI_1 AAL Hom 0.005716812709021624
+ABIDEII-BNI_1 AAL Hom 0.006244179096178707
+ABIDEII-BNI_1 AAL Hom 0.0023842807445507673
+ABIDEII-BNI_1 AAL Hom 0.00499903901411454
+ABIDEII-BNI_1 AAL Hom 0.006477510845076553
+ABIDEII-BNI_1 AAL Hom 0.005516878451971728
+ABIDEII-BNI_1 AAL Hom 0.0045353357672785755
+ABIDEII-BNI_1 AAL Hom 0.004056944990457578
+ABIDEII-BNI_1 AAL Hom 0.003968221556087374
+ABIDEII-BNI_1 AAL Hom 0.004608472996798198
+ABIDEII-BNI_1 AAL Hom 0.004148098172140033
+ABIDEII-BNI_1 AAL Hom 0.008803707063228
+ABIDEII-BNI_1 AAL Hom 0.003820913407590888
+ABIDEII-BNI_1 AAL Hom 0.004357679714802874
+ABIDEII-BNI_1 AAL Hom 0.011633178906316823
+ABIDEII-BNI_1 AAL Hom 0.0061336428407103676
+ABIDEII-BNI_1 AAL Hom 0.004374399934043674
+ABIDEII-BNI_1 AAL Hom 0.004330859126184565
+ABIDEII-BNI_1 AAL Hom 0.002738269139444979
+ABIDEII-BNI_1 AAL Hom 0.0070593054244916156
+ABIDEII-BNI_1 AAL Hom 0.004005057136119338
+ABIDEII-BNI_1 AAL Hom 0.008867328843910175
+ABIDEII-BNI_1 AAL Hom 0.005372980137032466
+ABIDEII-BNI_1 AAL Hom 0.005753086010087603
+ABIDEII-BNI_1 AAL Hom 0.006279858965063805
+ABIDEII-BNI_1 AAL Hom 0.0032981294097075187
+ABIDEII-BNI_1 AAL Hom 0.0028226140298953464
+ABIDEII-BNI_1 AAL Hom 0.004646257411891116
+ABIDEII-BNI_1 AAL Contra 0.03819893545920943
+ABIDEII-BNI_1 AAL Contra 0.12456020456348217
+ABIDEII-BNI_1 AAL Contra 0.06844965873865358
+ABIDEII-BNI_1 AAL Contra 0.01749399680447662
+ABIDEII-BNI_1 AAL Contra 0.09075149096242975
+ABIDEII-BNI_1 AAL Contra 0.06593785817769551
+ABIDEII-BNI_1 AAL Contra 0.07433615928314859
+ABIDEII-BNI_1 AAL Contra 0.050389484792283265
+ABIDEII-BNI_1 AAL Contra 0.06091403407445516
+ABIDEII-BNI_1 AAL Contra 0.04033655238007077
+ABIDEII-BNI_1 AAL Contra 0.06586609983257238
+ABIDEII-BNI_1 AAL Contra 0.06323058699237993
+ABIDEII-BNI_1 AAL Contra 0.1466301060648812
+ABIDEII-BNI_1 AAL Contra 0.0681986854095491
+ABIDEII-BNI_1 AAL Contra 0.03675974348303508
+ABIDEII-BNI_1 AAL Contra 0.1234489979947036
+ABIDEII-BNI_1 AAL Contra 0.21540225868889493
+ABIDEII-BNI_1 AAL Contra 0.06788180971504088
+ABIDEII-BNI_1 AAL Contra 0.04836273350059147
+ABIDEII-BNI_1 AAL Contra 0.04744708089495266
+ABIDEII-BNI_1 AAL Contra 0.07134400741101767
+ABIDEII-BNI_1 AAL Contra 0.043009990813990644
+ABIDEII-BNI_1 AAL Contra 0.08334832905427268
+ABIDEII-BNI_1 AAL Contra 0.038635592295308725
+ABIDEII-BNI_1 AAL Contra 0.10057364945580037
+ABIDEII-BNI_1 AAL Contra 0.07079176395669709
+ABIDEII-BNI_1 AAL Contra 0.04073310959564996
+ABIDEII-BNI_1 AAL Contra 0.026658285742563514
+ABIDEII-BNI_1 AAL Contra 0.04331086404306604
+Dataset Atlas Type Value
+ABIDEII-SDSU_1 AAL Ipsi 0.8004469397142101
+ABIDEII-SDSU_1 AAL Ipsi 0.8826366684135337
+ABIDEII-SDSU_1 AAL Ipsi 0.9100397551779528
+ABIDEII-SDSU_1 AAL Ipsi 0.9139180978290306
+ABIDEII-SDSU_1 AAL Ipsi 0.8812246296307881
+ABIDEII-SDSU_1 AAL Ipsi 0.9354504136916154
+ABIDEII-SDSU_1 AAL Ipsi 0.9110890821342511
+ABIDEII-SDSU_1 AAL Ipsi 0.891556649504399
+ABIDEII-SDSU_1 AAL Ipsi 0.903383901824976
+ABIDEII-SDSU_1 AAL Ipsi 0.890940857239446
+ABIDEII-SDSU_1 AAL Ipsi 0.8843943802912559
+ABIDEII-SDSU_1 AAL Ipsi 0.8466875087698843
+ABIDEII-SDSU_1 AAL Ipsi 0.8775677099017659
+ABIDEII-SDSU_1 AAL Ipsi 0.9386277303675881
+ABIDEII-SDSU_1 AAL Ipsi 0.9175104718779682
+ABIDEII-SDSU_1 AAL Ipsi 0.881704607215385
+ABIDEII-SDSU_1 AAL Ipsi 0.8585352969356853
+ABIDEII-SDSU_1 AAL Ipsi 0.9149864549038713
+ABIDEII-SDSU_1 AAL Ipsi 0.888130343415746
+ABIDEII-SDSU_1 AAL Ipsi 0.8875476933091304
+ABIDEII-SDSU_1 AAL Ipsi 0.8993995189093711
+ABIDEII-SDSU_1 AAL Ipsi 0.9140593037634285
+ABIDEII-SDSU_1 AAL Ipsi 0.9036653763701108
+ABIDEII-SDSU_1 AAL Hom 0.01324412414084123
+ABIDEII-SDSU_1 AAL Hom 0.006964974120045658
+ABIDEII-SDSU_1 AAL Hom 0.004558863170432549
+ABIDEII-SDSU_1 AAL Hom 0.005482361196253145
+ABIDEII-SDSU_1 AAL Hom 0.011454683750562559
+ABIDEII-SDSU_1 AAL Hom 0.004039821255588569
+ABIDEII-SDSU_1 AAL Hom 0.006780233716851551
+ABIDEII-SDSU_1 AAL Hom 0.008140655606818654
+ABIDEII-SDSU_1 AAL Hom 0.007693365937535704
+ABIDEII-SDSU_1 AAL Hom 0.006689722640228086
+ABIDEII-SDSU_1 AAL Hom 0.007903726990423413
+ABIDEII-SDSU_1 AAL Hom 0.008385873555045048
+ABIDEII-SDSU_1 AAL Hom 0.009146741775407358
+ABIDEII-SDSU_1 AAL Hom 0.0036152110769056235
+ABIDEII-SDSU_1 AAL Hom 0.0040677466158580335
+ABIDEII-SDSU_1 AAL Hom 0.007537612037178844
+ABIDEII-SDSU_1 AAL Hom 0.011267686964258217
+ABIDEII-SDSU_1 AAL Hom 0.006285462607357883
+ABIDEII-SDSU_1 AAL Hom 0.006412601391109642
+ABIDEII-SDSU_1 AAL Hom 0.008032940210468917
+ABIDEII-SDSU_1 AAL Hom 0.006775653755898457
+ABIDEII-SDSU_1 AAL Hom 0.0046089013105575505
+ABIDEII-SDSU_1 AAL Hom 0.007763806199686528
+ABIDEII-SDSU_1 AAL Contra 0.18630893614494867
+ABIDEII-SDSU_1 AAL Contra 0.11039835746642067
+ABIDEII-SDSU_1 AAL Contra 0.0854013816516147
+ABIDEII-SDSU_1 AAL Contra 0.08059954097471635
+ABIDEII-SDSU_1 AAL Contra 0.10732068661864934
+ABIDEII-SDSU_1 AAL Contra 0.06050976505279605
+ABIDEII-SDSU_1 AAL Contra 0.08213068414889729
+ABIDEII-SDSU_1 AAL Contra 0.10030269488878239
+ABIDEII-SDSU_1 AAL Contra 0.08892273223748837
+ABIDEII-SDSU_1 AAL Contra 0.10236942012032585
+ABIDEII-SDSU_1 AAL Contra 0.1077018927183206
+ABIDEII-SDSU_1 AAL Contra 0.14492661767507062
+ABIDEII-SDSU_1 AAL Contra 0.11328554832282677
+ABIDEII-SDSU_1 AAL Contra 0.0577570585555063
+ABIDEII-SDSU_1 AAL Contra 0.0784217815061738
+ABIDEII-SDSU_1 AAL Contra 0.11075778074743609
+ABIDEII-SDSU_1 AAL Contra 0.13019701610005646
+ABIDEII-SDSU_1 AAL Contra 0.07872808248877078
+ABIDEII-SDSU_1 AAL Contra 0.10545705519314434
+ABIDEII-SDSU_1 AAL Contra 0.10441936648040064
+ABIDEII-SDSU_1 AAL Contra 0.09382482733473047
+ABIDEII-SDSU_1 AAL Contra 0.08133179492601395
+ABIDEII-SDSU_1 AAL Contra 0.0885708174302027
+Dataset Atlas Type Value
+MRN_1 AAL Ipsi 0.7485068138568336
+MRN_1 AAL Ipsi 0.781530293753696
+MRN_1 AAL Ipsi 0.8222713569719241
+MRN_1 AAL Ipsi 0.8213259844093902
+MRN_1 AAL Ipsi 0.8193412704953322
+MRN_1 AAL Ipsi 0.7981232422858818
+MRN_1 AAL Ipsi 0.8253039626545281
+MRN_1 AAL Ipsi 0.7989669262973469
+MRN_1 AAL Ipsi 0.8192164818294945
+MRN_1 AAL Ipsi 0.8190500278173755
+MRN_1 AAL Ipsi 0.7416781775122276
+MRN_1 AAL Ipsi 0.8011345838063163
+MRN_1 AAL Ipsi 0.7915305388756212
+MRN_1 AAL Ipsi 0.8252263748731966
+MRN_1 AAL Ipsi 0.7359126029940714
+MRN_1 AAL Ipsi 0.7679918611582257
+MRN_1 AAL Ipsi 0.8312300218626497
+MRN_1 AAL Ipsi 0.7500075879804655
+MRN_1 AAL Ipsi 0.8173580741599143
+MRN_1 AAL Hom 0.012203135329154163
+MRN_1 AAL Hom 0.011741872623796279
+MRN_1 AAL Hom 0.011043627149999834
+MRN_1 AAL Hom 0.010466421963615538
+MRN_1 AAL Hom 0.009577700161978122
+MRN_1 AAL Hom 0.00995477208157257
+MRN_1 AAL Hom 0.00916811125155663
+MRN_1 AAL Hom 0.010225433911514294
+MRN_1 AAL Hom 0.00986057827135425
+MRN_1 AAL Hom 0.009607271904372437
+MRN_1 AAL Hom 0.01220034328838915
+MRN_1 AAL Hom 0.010451909227953086
+MRN_1 AAL Hom 0.011742711944580991
+MRN_1 AAL Hom 0.008838182961186004
+MRN_1 AAL Hom 0.013767883215275561
+MRN_1 AAL Hom 0.011954051362340137
+MRN_1 AAL Hom 0.0081764099389985
+MRN_1 AAL Hom 0.01597746846760518
+MRN_1 AAL Hom 0.010240237239083655
+MRN_1 AAL Contra 0.23929005081401228
+MRN_1 AAL Contra 0.2067278336225077
+MRN_1 AAL Contra 0.16668501587807602
+MRN_1 AAL Contra 0.16820759362699425
+MRN_1 AAL Contra 0.1710810293426896
+MRN_1 AAL Contra 0.1919219856325456
+MRN_1 AAL Contra 0.1655279260939153
+MRN_1 AAL Contra 0.1908076397911388
+MRN_1 AAL Contra 0.17092293989915128
+MRN_1 AAL Contra 0.17134270027825205
+MRN_1 AAL Contra 0.2461214791993832
+MRN_1 AAL Contra 0.18841350696573061
+MRN_1 AAL Contra 0.1967267491797978
+MRN_1 AAL Contra 0.16593544216561734
+MRN_1 AAL Contra 0.2503195137906531
+MRN_1 AAL Contra 0.22005408747943414
+MRN_1 AAL Contra 0.1605935681983518
+MRN_1 AAL Contra 0.2340149435519293
+MRN_1 AAL Contra 0.172401688601002
diff --git a/docs/paper/data/dwi_ipsi_dkt.csv b/docs/paper/data/dwi_ipsi_dkt.csv
new file mode 100644
index 000000000..e05be9fad
--- /dev/null
+++ b/docs/paper/data/dwi_ipsi_dkt.csv
@@ -0,0 +1,3982 @@
+Dataset Atlas Type Value
+SWU4 DKT Ipsi 0.8718599412223755
+SWU4 DKT Ipsi 0.8620211814345433
+SWU4 DKT Ipsi 0.8263799352325578
+SWU4 DKT Ipsi 0.8645002809107104
+SWU4 DKT Ipsi 0.8688796461693615
+SWU4 DKT Ipsi 0.8316268727024045
+SWU4 DKT Ipsi 0.7621615952485924
+SWU4 DKT Ipsi 0.812629071124475
+SWU4 DKT Ipsi 0.8680849355834758
+SWU4 DKT Ipsi 0.7474758812418889
+SWU4 DKT Ipsi 0.875991708271893
+SWU4 DKT Ipsi 0.8249756710955837
+SWU4 DKT Ipsi 0.8157606833956343
+SWU4 DKT Ipsi 0.8231860784707318
+SWU4 DKT Ipsi 0.8449611780273135
+SWU4 DKT Ipsi 0.856846610267786
+SWU4 DKT Ipsi 0.8640673674759857
+SWU4 DKT Ipsi 0.8520403621797912
+SWU4 DKT Ipsi 0.8190174288135119
+SWU4 DKT Ipsi 0.8058581716900728
+SWU4 DKT Ipsi 0.8202963981414512
+SWU4 DKT Ipsi 0.8128368642564501
+SWU4 DKT Ipsi 0.8432007342004494
+SWU4 DKT Ipsi 0.8978756265133275
+SWU4 DKT Ipsi 0.823481890768302
+SWU4 DKT Ipsi 0.8581302876747318
+SWU4 DKT Ipsi 0.7888679940817475
+SWU4 DKT Ipsi 0.8233001678597824
+SWU4 DKT Ipsi 0.8348902714673345
+SWU4 DKT Ipsi 0.8478146353210319
+SWU4 DKT Ipsi 0.8075500321859541
+SWU4 DKT Ipsi 0.8818001868374316
+SWU4 DKT Ipsi 0.8709728349986063
+SWU4 DKT Ipsi 0.8127130552225494
+SWU4 DKT Ipsi 0.792560421600839
+SWU4 DKT Ipsi 0.7427818075108213
+SWU4 DKT Ipsi 0.901101298803496
+SWU4 DKT Ipsi 0.8205702562313539
+SWU4 DKT Ipsi 0.881372865848621
+SWU4 DKT Ipsi 0.877337421091541
+SWU4 DKT Ipsi 0.8493828945516249
+SWU4 DKT Ipsi 0.8689127660964014
+SWU4 DKT Ipsi 0.861139834012768
+SWU4 DKT Ipsi 0.9195413960253186
+SWU4 DKT Ipsi 0.8254833821032737
+SWU4 DKT Ipsi 0.9117937144735795
+SWU4 DKT Ipsi 0.7827174036388395
+SWU4 DKT Ipsi 0.8419497087577595
+SWU4 DKT Ipsi 0.8237199319953318
+SWU4 DKT Ipsi 0.8782599273498785
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+SWU4 DKT Ipsi 0.7918397595583367
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+SWU4 DKT Ipsi 0.8665810289017997
+SWU4 DKT Ipsi 0.8062108380273826
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+SWU4 DKT Ipsi 0.8005261256041234
+SWU4 DKT Ipsi 0.8739179594505958
+SWU4 DKT Ipsi 0.8439783789284941
+SWU4 DKT Ipsi 0.8302527843902244
+SWU4 DKT Ipsi 0.8032677973333437
+SWU4 DKT Ipsi 0.8511407323937917
+SWU4 DKT Ipsi 0.9039518057165361
+SWU4 DKT Ipsi 0.8365226764443081
+SWU4 DKT Ipsi 0.7788767468168006
+SWU4 DKT Ipsi 0.863825872087247
+SWU4 DKT Ipsi 0.8505841657776485
+SWU4 DKT Ipsi 0.8803989247094307
+SWU4 DKT Ipsi 0.8982067782444217
+SWU4 DKT Ipsi 0.7671243586336454
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+SWU4 DKT Ipsi 0.8920281701879401
+SWU4 DKT Ipsi 0.8803025686995251
+SWU4 DKT Ipsi 0.8493385268198818
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+SWU4 DKT Ipsi 0.791523178807947
+SWU4 DKT Ipsi 0.8244031054343323
+SWU4 DKT Ipsi 0.8146670938635753
+SWU4 DKT Ipsi 0.820097936902227
+SWU4 DKT Ipsi 0.8264621093375998
+SWU4 DKT Ipsi 0.7832200570891397
+SWU4 DKT Ipsi 0.7924783862921013
+SWU4 DKT Ipsi 0.8398384112279411
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+SWU4 DKT Ipsi 0.8523921836752135
+SWU4 DKT Ipsi 0.8786189534256429
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+Dataset Atlas Type Value
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+NKIENH DKT Contra 0.16189358478951155
+NKIENH DKT Contra 0.11281122498935008
+NKIENH DKT Contra 0.1078806720148105
+NKIENH DKT Contra 0.14194148618817493
+NKIENH DKT Contra 0.12818724538386037
+NKIENH DKT Contra 0.08631910666575324
+NKIENH DKT Contra 0.13661010587863465
+NKIENH DKT Contra 0.09983259722965533
+NKIENH DKT Contra 0.10755892508632338
+NKIENH DKT Contra 0.11065724272486238
+NKIENH DKT Contra 0.12511229473735297
+NKIENH DKT Contra 0.09530840887049269
+NKIENH DKT Contra 0.15073434967244909
+NKIENH DKT Contra 0.10287883240085012
+NKIENH DKT Contra 0.09427007740235951
+NKIENH DKT Contra 0.10240725870767642
+NKIENH DKT Contra 0.10802394817690583
+NKIENH DKT Contra 0.13952714369954336
+NKIENH DKT Contra 0.18019828438290478
+NKIENH DKT Contra 0.09912052659706781
+NKIENH DKT Contra 0.08135696762955028
+NKIENH DKT Contra 0.1306352288488211
+NKIENH DKT Contra 0.10600651306744008
+NKIENH DKT Contra 0.1293981518076025
+NKIENH DKT Contra 0.14265523967640759
+NKIENH DKT Contra 0.1717847652918851
+NKIENH DKT Contra 0.09938057489764711
+NKIENH DKT Contra 0.085764337630814
+NKIENH DKT Contra 0.08425926390149255
+NKIENH DKT Contra 0.09741761245044293
+NKIENH DKT Contra 0.10863202799606135
+NKIENH DKT Contra 0.1127998235884271
+NKIENH DKT Contra 0.15787713272239065
+NKIENH DKT Contra 0.15574919377200747
+NKIENH DKT Contra 0.17408777888292865
+NKIENH DKT Contra 0.11460198114333453
+NKIENH DKT Contra 0.1304889962379789
+NKIENH DKT Contra 0.1622638843483513
+NKIENH DKT Contra 0.09051214486845528
+NKIENH DKT Contra 0.0784300510137715
+NKIENH DKT Contra 0.09716471662185819
+NKIENH DKT Contra 0.07599451632823871
+NKIENH DKT Contra 0.1051933111418313
+NKIENH DKT Contra 0.15020142658720276
+NKIENH DKT Contra 0.10099644109054198
+NKIENH DKT Contra 0.10604373183709769
+NKIENH DKT Contra 0.11442846115055817
+NKIENH DKT Contra 0.10684177689518874
+NKIENH DKT Contra 0.15845735988053858
+NKIENH DKT Contra 0.08042683059919857
+NKIENH DKT Contra 0.07217892411972278
+NKIENH DKT Contra 0.11537701977929384
+Dataset Atlas Type Value
+XHCUMS DKT Ipsi 0.881523897296387
+XHCUMS DKT Ipsi 0.8692482280885135
+XHCUMS DKT Ipsi 0.8154715271281572
+XHCUMS DKT Ipsi 0.8534969903344115
+XHCUMS DKT Ipsi 0.9114334032268988
+XHCUMS DKT Ipsi 0.8925801338421055
+XHCUMS DKT Ipsi 0.8781975199072
+XHCUMS DKT Ipsi 0.8805443922005359
+XHCUMS DKT Ipsi 0.8398012801130641
+XHCUMS DKT Ipsi 0.882257255594487
+XHCUMS DKT Ipsi 0.8985863365436678
+XHCUMS DKT Ipsi 0.8462742949449532
+XHCUMS DKT Ipsi 0.8816659128065666
+XHCUMS DKT Ipsi 0.863129354707478
+XHCUMS DKT Ipsi 0.8923828175290164
+XHCUMS DKT Ipsi 0.8826298707385138
+XHCUMS DKT Ipsi 0.9095988747329242
+XHCUMS DKT Ipsi 0.8667024413905745
+XHCUMS DKT Ipsi 0.8662596841334559
+XHCUMS DKT Ipsi 0.8117077323051131
+XHCUMS DKT Ipsi 0.8158743425347149
+XHCUMS DKT Ipsi 0.9018155980066145
+XHCUMS DKT Ipsi 0.9002618884227321
+XHCUMS DKT Ipsi 0.9086855605778217
+XHCUMS DKT Ipsi 0.8900990220187011
+XHCUMS DKT Ipsi 0.8895280032497412
+XHCUMS DKT Ipsi 0.8802889789568356
+XHCUMS DKT Ipsi 0.9360203323806744
+XHCUMS DKT Ipsi 0.8777529490598011
+XHCUMS DKT Ipsi 0.8719469269840412
+XHCUMS DKT Ipsi 0.894468912846207
+XHCUMS DKT Ipsi 0.9180363043235982
+XHCUMS DKT Ipsi 0.8076615577785861
+XHCUMS DKT Ipsi 0.8562071689852117
+XHCUMS DKT Ipsi 0.8614017587795713
+XHCUMS DKT Ipsi 0.900600767731007
+XHCUMS DKT Ipsi 0.926777454787447
+XHCUMS DKT Ipsi 0.9036357334232664
+XHCUMS DKT Ipsi 0.9201615023882644
+XHCUMS DKT Ipsi 0.9221297023574404
+XHCUMS DKT Ipsi 0.9224709056584409
+XHCUMS DKT Ipsi 0.8909609903248594
+XHCUMS DKT Ipsi 0.9003072759256354
+XHCUMS DKT Ipsi 0.8474912958859157
+XHCUMS DKT Ipsi 0.9135645425140145
+XHCUMS DKT Ipsi 0.9154910690941801
+XHCUMS DKT Ipsi 0.8653542112833968
+XHCUMS DKT Ipsi 0.801522518726958
+XHCUMS DKT Ipsi 0.8826183997242966
+XHCUMS DKT Ipsi 0.9032669686101625
+XHCUMS DKT Ipsi 0.8739882253195966
+XHCUMS DKT Ipsi 0.9395055979113353
+XHCUMS DKT Ipsi 0.8738464495431
+XHCUMS DKT Ipsi 0.8712772593915601
+XHCUMS DKT Ipsi 0.9139547820664025
+XHCUMS DKT Ipsi 0.9021515697623594
+XHCUMS DKT Ipsi 0.9027960566597361
+XHCUMS DKT Ipsi 0.8504212995382785
+XHCUMS DKT Ipsi 0.8631554195576658
+XHCUMS DKT Ipsi 0.9035894017046533
+XHCUMS DKT Ipsi 0.8631546349286181
+XHCUMS DKT Ipsi 0.9074214095332259
+XHCUMS DKT Ipsi 0.8980229962333155
+XHCUMS DKT Ipsi 0.8480602817025845
+XHCUMS DKT Ipsi 0.8695685962177035
+XHCUMS DKT Ipsi 0.880852496639631
+XHCUMS DKT Ipsi 0.888365395293291
+XHCUMS DKT Ipsi 0.8679860851551373
+XHCUMS DKT Ipsi 0.863917394115645
+XHCUMS DKT Ipsi 0.8850357465201784
+XHCUMS DKT Ipsi 0.8881556714003236
+XHCUMS DKT Ipsi 0.9135228348064288
+XHCUMS DKT Ipsi 0.8648606891794055
+XHCUMS DKT Ipsi 0.9178059423294648
+XHCUMS DKT Ipsi 0.8915589498750476
+XHCUMS DKT Ipsi 0.8254230090004706
+XHCUMS DKT Ipsi 0.923439818569242
+XHCUMS DKT Ipsi 0.8932897080996303
+XHCUMS DKT Ipsi 0.8790078787888339
+XHCUMS DKT Ipsi 0.900685745187261
+XHCUMS DKT Ipsi 0.8218782850088342
+XHCUMS DKT Ipsi 0.9451166907452142
+XHCUMS DKT Ipsi 0.8756153554228782
+XHCUMS DKT Ipsi 0.8932979643694184
+XHCUMS DKT Ipsi 0.9086929287401788
+XHCUMS DKT Ipsi 0.8952978654051065
+XHCUMS DKT Ipsi 0.9052527761195776
+XHCUMS DKT Ipsi 0.8676637298235921
+XHCUMS DKT Ipsi 0.8681091289040888
+XHCUMS DKT Ipsi 0.9189455624330549
+XHCUMS DKT Ipsi 0.9172531089366381
+XHCUMS DKT Ipsi 0.8950995700839407
+XHCUMS DKT Ipsi 0.8747621134864314
+XHCUMS DKT Ipsi 0.9118700684220453
+XHCUMS DKT Ipsi 0.905286924973692
+XHCUMS DKT Ipsi 0.9282103112426324
+XHCUMS DKT Ipsi 0.8529052937182691
+XHCUMS DKT Ipsi 0.9429010660046214
+XHCUMS DKT Ipsi 0.8764968290351427
+XHCUMS DKT Ipsi 0.8798394398180578
+XHCUMS DKT Ipsi 0.8685519461846429
+XHCUMS DKT Ipsi 0.8870032310094844
+XHCUMS DKT Ipsi 0.9009925224407979
+XHCUMS DKT Ipsi 0.9180823029761176
+XHCUMS DKT Ipsi 0.897477984772965
+XHCUMS DKT Ipsi 0.9141223047016779
+XHCUMS DKT Ipsi 0.908580366795049
+XHCUMS DKT Ipsi 0.9207552818323088
+XHCUMS DKT Ipsi 0.8960059760450033
+XHCUMS DKT Ipsi 0.8416139186563178
+XHCUMS DKT Ipsi 0.8770764786401181
+XHCUMS DKT Ipsi 0.8872547374832879
+XHCUMS DKT Ipsi 0.9167692533824588
+XHCUMS DKT Ipsi 0.8823133397961213
+XHCUMS DKT Ipsi 0.8935723229732923
+XHCUMS DKT Ipsi 0.8710218166686298
+XHCUMS DKT Ipsi 0.8678927609895832
+XHCUMS DKT Hom 0.01353601401476929
+XHCUMS DKT Hom 0.01440454961961128
+XHCUMS DKT Hom 0.02298968808465856
+XHCUMS DKT Hom 0.013839651052631291
+XHCUMS DKT Hom 0.0060181262534883435
+XHCUMS DKT Hom 0.011880100893616453
+XHCUMS DKT Hom 0.014849982058571862
+XHCUMS DKT Hom 0.01225715625909895
+XHCUMS DKT Hom 0.01841642487547378
+XHCUMS DKT Hom 0.00823540181821135
+XHCUMS DKT Hom 0.008217013975719173
+XHCUMS DKT Hom 0.020139519979297426
+XHCUMS DKT Hom 0.012235364498251784
+XHCUMS DKT Hom 0.011230398055138016
+XHCUMS DKT Hom 0.010123729421975036
+XHCUMS DKT Hom 0.013310654564102308
+XHCUMS DKT Hom 0.0050198267486897385
+XHCUMS DKT Hom 0.015369461898942654
+XHCUMS DKT Hom 0.01108020996933729
+XHCUMS DKT Hom 0.02375944308328248
+XHCUMS DKT Hom 0.01919605775105783
+XHCUMS DKT Hom 0.006035147166501113
+XHCUMS DKT Hom 0.010795848057824558
+XHCUMS DKT Hom 0.009645161198252874
+XHCUMS DKT Hom 0.009457600726048496
+XHCUMS DKT Hom 0.012812982063361632
+XHCUMS DKT Hom 0.00784586446976071
+XHCUMS DKT Hom 0.003626076075931443
+XHCUMS DKT Hom 0.012712579659141812
+XHCUMS DKT Hom 0.013360194336654904
+XHCUMS DKT Hom 0.009542018639855177
+XHCUMS DKT Hom 0.0027868982797751457
+XHCUMS DKT Hom 0.024863232131459977
+XHCUMS DKT Hom 0.014260356883068067
+XHCUMS DKT Hom 0.016585962138088193
+XHCUMS DKT Hom 0.010207121623137382
+XHCUMS DKT Hom 0.006974318260323981
+XHCUMS DKT Hom 0.008514136361081446
+XHCUMS DKT Hom 0.006340633511215738
+XHCUMS DKT Hom 0.003255725612515304
+XHCUMS DKT Hom 0.006627757176694278
+XHCUMS DKT Hom 0.008583298467828071
+XHCUMS DKT Hom 0.0064157079292562645
+XHCUMS DKT Hom 0.017700953163491746
+XHCUMS DKT Hom 0.006115655600119628
+XHCUMS DKT Hom 0.009175515661606263
+XHCUMS DKT Hom 0.013289086399427191
+XHCUMS DKT Hom 0.028046127042699587
+XHCUMS DKT Hom 0.0081819555591311
+XHCUMS DKT Hom 0.008752567977626854
+XHCUMS DKT Hom 0.013438305447288194
+XHCUMS DKT Hom 0.004445804560231693
+XHCUMS DKT Hom 0.012862408091063206
+XHCUMS DKT Hom 0.011242139736283753
+XHCUMS DKT Hom 0.007928039912775652
+XHCUMS DKT Hom 0.010042139570563066
+XHCUMS DKT Hom 0.008194238381828048
+XHCUMS DKT Hom 0.016143742847871016
+XHCUMS DKT Hom 0.013989863111311014
+XHCUMS DKT Hom 0.009186855990283816
+XHCUMS DKT Hom 0.013454018608683678
+XHCUMS DKT Hom 0.008455135634285288
+XHCUMS DKT Hom 0.010360649060909263
+XHCUMS DKT Hom 0.018992404766683537
+XHCUMS DKT Hom 0.012920336264621132
+XHCUMS DKT Hom 0.010829411780551074
+XHCUMS DKT Hom 0.012982681626285042
+XHCUMS DKT Hom 0.01654667881609816
+XHCUMS DKT Hom 0.01770799172006941
+XHCUMS DKT Hom 0.013613862492479867
+XHCUMS DKT Hom 0.008343819282161904
+XHCUMS DKT Hom 0.008255826988678886
+XHCUMS DKT Hom 0.013350268489291868
+XHCUMS DKT Hom 0.008043554201891843
+XHCUMS DKT Hom 0.009509725973192214
+XHCUMS DKT Hom 0.021143598300210426
+XHCUMS DKT Hom 0.00709140162275692
+XHCUMS DKT Hom 0.009050995048027588
+XHCUMS DKT Hom 0.011928231004680838
+XHCUMS DKT Hom 0.010723911445022553
+XHCUMS DKT Hom 0.01709445725860393
+XHCUMS DKT Hom 0.003233265164806252
+XHCUMS DKT Hom 0.013440805614500206
+XHCUMS DKT Hom 0.010876823644448684
+XHCUMS DKT Hom 0.0066486203453060356
+XHCUMS DKT Hom 0.006949821560253861
+XHCUMS DKT Hom 0.006606683889203063
+XHCUMS DKT Hom 0.013249832274359682
+XHCUMS DKT Hom 0.015870229866437744
+XHCUMS DKT Hom 0.007440021991146668
+XHCUMS DKT Hom 0.006497724607476499
+XHCUMS DKT Hom 0.013898837772457283
+XHCUMS DKT Hom 0.011047181518737782
+XHCUMS DKT Hom 0.009196318563267965
+XHCUMS DKT Hom 0.008137429560375367
+XHCUMS DKT Hom 0.005478327476523323
+XHCUMS DKT Hom 0.01902080519904323
+XHCUMS DKT Hom 0.004959701544075313
+XHCUMS DKT Hom 0.016202299906007418
+XHCUMS DKT Hom 0.011007479630647472
+XHCUMS DKT Hom 0.01896024906541381
+XHCUMS DKT Hom 0.009470766185907684
+XHCUMS DKT Hom 0.00819886641780137
+XHCUMS DKT Hom 0.006906448617911143
+XHCUMS DKT Hom 0.009520016973369098
+XHCUMS DKT Hom 0.006893505377447092
+XHCUMS DKT Hom 0.006909835056522474
+XHCUMS DKT Hom 0.0053431344550559255
+XHCUMS DKT Hom 0.014700935196838523
+XHCUMS DKT Hom 0.014205363952915507
+XHCUMS DKT Hom 0.010608913074796271
+XHCUMS DKT Hom 0.010503830250721399
+XHCUMS DKT Hom 0.008902019988510288
+XHCUMS DKT Hom 0.0131783050935462
+XHCUMS DKT Hom 0.011407720258407037
+XHCUMS DKT Hom 0.013541991361095218
+XHCUMS DKT Hom 0.01648479716605618
+XHCUMS DKT Contra 0.10494008868884366
+XHCUMS DKT Contra 0.11634722229187518
+XHCUMS DKT Contra 0.16153878478718428
+XHCUMS DKT Contra 0.13266335861295717
+XHCUMS DKT Contra 0.08254847051961281
+XHCUMS DKT Contra 0.09553976526427806
+XHCUMS DKT Contra 0.10695249803422814
+XHCUMS DKT Contra 0.10719845154036509
+XHCUMS DKT Contra 0.1417822950114621
+XHCUMS DKT Contra 0.10950734258730166
+XHCUMS DKT Contra 0.09319664948061296
+XHCUMS DKT Contra 0.13358618507574938
+XHCUMS DKT Contra 0.10609872269518165
+XHCUMS DKT Contra 0.125640247237384
+XHCUMS DKT Contra 0.09749345304900861
+XHCUMS DKT Contra 0.10405947469738382
+XHCUMS DKT Contra 0.08538129851838606
+XHCUMS DKT Contra 0.11792809671048289
+XHCUMS DKT Contra 0.12266010589720684
+XHCUMS DKT Contra 0.16453282461160446
+XHCUMS DKT Contra 0.16492959971422724
+XHCUMS DKT Contra 0.09214925482688435
+XHCUMS DKT Contra 0.0889422635194433
+XHCUMS DKT Contra 0.08166927822392542
+XHCUMS DKT Contra 0.10044337725525049
+XHCUMS DKT Contra 0.0976590146868971
+XHCUMS DKT Contra 0.11186515657340376
+XHCUMS DKT Contra 0.06035359154339414
+XHCUMS DKT Contra 0.1095344712810571
+XHCUMS DKT Contra 0.11469287867930396
+XHCUMS DKT Contra 0.09598906851393778
+XHCUMS DKT Contra 0.07917679739662663
+XHCUMS DKT Contra 0.16747521008995395
+XHCUMS DKT Contra 0.12953247413172028
+XHCUMS DKT Contra 0.12201227908234054
+XHCUMS DKT Contra 0.08919211064585568
+XHCUMS DKT Contra 0.066248226952229
+XHCUMS DKT Contra 0.0878501302156521
+XHCUMS DKT Contra 0.07349786410051987
+XHCUMS DKT Contra 0.07461457203004423
+XHCUMS DKT Contra 0.07090133716486478
+XHCUMS DKT Contra 0.10045571120731252
+XHCUMS DKT Contra 0.09327701614510835
+XHCUMS DKT Contra 0.1348077509505926
+XHCUMS DKT Contra 0.08031980188586586
+XHCUMS DKT Contra 0.07533341524421365
+XHCUMS DKT Contra 0.12135670231717591
+XHCUMS DKT Contra 0.17043135423034242
+XHCUMS DKT Contra 0.10919964471657231
+XHCUMS DKT Contra 0.08798046341221062
+XHCUMS DKT Contra 0.11257346923311522
+XHCUMS DKT Contra 0.056048597528433074
+XHCUMS DKT Contra 0.11329114236583679
+XHCUMS DKT Contra 0.11748060087215616
+XHCUMS DKT Contra 0.07811717802082184
+XHCUMS DKT Contra 0.08780629066707746
+XHCUMS DKT Contra 0.08900970495843587
+XHCUMS DKT Contra 0.13343495761385052
+XHCUMS DKT Contra 0.12285471733102313
+XHCUMS DKT Contra 0.08722374230506288
+XHCUMS DKT Contra 0.12339134646269827
+XHCUMS DKT Contra 0.0841234548324888
+XHCUMS DKT Contra 0.09161635470577528
+XHCUMS DKT Contra 0.13294731353073194
+XHCUMS DKT Contra 0.11751106751767532
+XHCUMS DKT Contra 0.10831809157981785
+XHCUMS DKT Contra 0.09865192308042403
+XHCUMS DKT Contra 0.1154672360287645
+XHCUMS DKT Contra 0.11837461416428557
+XHCUMS DKT Contra 0.10135039098734175
+XHCUMS DKT Contra 0.10350050931751453
+XHCUMS DKT Contra 0.07822133820489231
+XHCUMS DKT Contra 0.12178904233130267
+XHCUMS DKT Contra 0.07415050346864334
+XHCUMS DKT Contra 0.09893132415176016
+XHCUMS DKT Contra 0.15343339269931897
+XHCUMS DKT Contra 0.06946877980800116
+XHCUMS DKT Contra 0.09765929685234212
+XHCUMS DKT Contra 0.10906389020648531
+XHCUMS DKT Contra 0.08859034336771651
+XHCUMS DKT Contra 0.16102725773256188
+XHCUMS DKT Contra 0.05165004408997952
+XHCUMS DKT Contra 0.11094383896262167
+XHCUMS DKT Contra 0.09582521198613282
+XHCUMS DKT Contra 0.08465845091451515
+XHCUMS DKT Contra 0.09775231303463966
+XHCUMS DKT Contra 0.08814053999121936
+XHCUMS DKT Contra 0.11908643790204823
+XHCUMS DKT Contra 0.1160206412294735
+XHCUMS DKT Contra 0.07361441557579837
+XHCUMS DKT Contra 0.07624916645588539
+XHCUMS DKT Contra 0.09100159214360198
+XHCUMS DKT Contra 0.11419070499483076
+XHCUMS DKT Contra 0.07893361301468667
+XHCUMS DKT Contra 0.08657564546593258
+XHCUMS DKT Contra 0.06631136128084422
+XHCUMS DKT Contra 0.12807390108268765
+XHCUMS DKT Contra 0.052139232451303245
+XHCUMS DKT Contra 0.10730087105884986
+XHCUMS DKT Contra 0.10915308055129476
+XHCUMS DKT Contra 0.11248780474994322
+XHCUMS DKT Contra 0.10352600280460787
+XHCUMS DKT Contra 0.0908086111414008
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+XHCUMS DKT Contra 0.0930019982536659
+XHCUMS DKT Contra 0.07898418992087503
+XHCUMS DKT Contra 0.0845097981484285
+XHCUMS DKT Contra 0.07390158371263524
+XHCUMS DKT Contra 0.0892930887581582
+XHCUMS DKT Contra 0.14418071739076666
+XHCUMS DKT Contra 0.11231460828508566
+XHCUMS DKT Contra 0.10224143226599078
+XHCUMS DKT Contra 0.07432872662903087
+XHCUMS DKT Contra 0.10450835511033253
+XHCUMS DKT Contra 0.09501995676830076
+XHCUMS DKT Contra 0.11543619197027506
+XHCUMS DKT Contra 0.11562244184436055
+Dataset Atlas Type Value
+BNU1 DKT Ipsi 0.9274267735650175
+BNU1 DKT Ipsi 0.8883458771002781
+BNU1 DKT Ipsi 0.862026527739508
+BNU1 DKT Ipsi 0.8963103991114819
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+BNU3 DKT Contra 0.11181620560977401
+BNU3 DKT Contra 0.06716189728985124
+BNU3 DKT Contra 0.11407125895609271
+BNU3 DKT Contra 0.07959143458492457
+BNU3 DKT Contra 0.06024580598826569
+BNU3 DKT Contra 0.09079320904835958
+BNU3 DKT Contra 0.08442878907888263
+BNU3 DKT Contra 0.08578255640627684
+BNU3 DKT Contra 0.08646656741217211
+BNU3 DKT Contra 0.0896614538565323
+BNU3 DKT Contra 0.1006771149727775
+BNU3 DKT Contra 0.07366009102395979
+BNU3 DKT Contra 0.06101520527384873
+BNU3 DKT Contra 0.09652711263611284
+BNU3 DKT Contra 0.05519601691434141
+BNU3 DKT Contra 0.06267942516825589
+BNU3 DKT Contra 0.11552752173843965
+BNU3 DKT Contra 0.11772254374088671
+BNU3 DKT Contra 0.0809249095336419
+BNU3 DKT Contra 0.09413432899875541
+BNU3 DKT Contra 0.092772209141253
+BNU3 DKT Contra 0.08953615439728406
+BNU3 DKT Contra 0.060048497134057126
+BNU3 DKT Contra 0.07945190232010342
+BNU3 DKT Contra 0.08600966249414516
+BNU3 DKT Contra 0.07111014367192664
+BNU3 DKT Contra 0.10788226784350059
+BNU3 DKT Contra nan
+BNU3 DKT Contra 0.07348978232146307
+BNU3 DKT Contra 0.08118274631399952
+BNU3 DKT Contra 0.0726810992356467
+BNU3 DKT Contra 0.09415597363117387
+BNU3 DKT Contra 0.12977492615623487
+BNU3 DKT Contra 0.052505694396201924
+BNU3 DKT Contra 0.11390422021087589
+BNU3 DKT Contra 0.12165205143552647
+BNU3 DKT Contra 0.12782665458106007
+BNU3 DKT Contra 0.07001252480204971
+BNU3 DKT Contra 0.0872711959516707
+BNU3 DKT Contra 0.07675308762347227
+BNU3 DKT Contra 0.10442365668269588
+BNU3 DKT Contra 0.09539166412218673
+BNU3 DKT Contra 0.10381495337544547
+BNU3 DKT Contra 0.11152546257326405
+BNU3 DKT Contra 0.08877011265629883
+BNU3 DKT Contra 0.09059674258565951
+Dataset Atlas Type Value
+IPCAS_8 DKT Ipsi 0.9332666915257614
+IPCAS_8 DKT Ipsi 0.895175157594792
+IPCAS_8 DKT Ipsi 0.9049131227530737
+IPCAS_8 DKT Ipsi 0.9490451926833134
+IPCAS_8 DKT Ipsi 0.9082600502425411
+IPCAS_8 DKT Ipsi 0.9169706199160569
+IPCAS_8 DKT Ipsi 0.9332875001244407
+IPCAS_8 DKT Ipsi 0.9347595351262015
+IPCAS_8 DKT Ipsi 0.9050845128768832
+IPCAS_8 DKT Ipsi 0.904604412858628
+IPCAS_8 DKT Ipsi 0.9029802501026373
+IPCAS_8 DKT Ipsi 0.9314828551267387
+IPCAS_8 DKT Ipsi 0.941282442466553
+IPCAS_8 DKT Ipsi 0.9354346442655184
+IPCAS_8 DKT Ipsi 0.8657362705879268
+IPCAS_8 DKT Ipsi 0.9165977542526363
+IPCAS_8 DKT Ipsi 0.9342938850422009
+IPCAS_8 DKT Ipsi 0.9044362584735256
+IPCAS_8 DKT Ipsi 0.9083880468797938
+IPCAS_8 DKT Ipsi 0.8737532325029931
+IPCAS_8 DKT Ipsi 0.9040322969003323
+IPCAS_8 DKT Ipsi 0.9171120078525467
+IPCAS_8 DKT Ipsi 0.9190214888538025
+IPCAS_8 DKT Ipsi 0.9133653121404863
+IPCAS_8 DKT Ipsi 0.9132520625004519
+IPCAS_8 DKT Ipsi 0.945555210632426
+IPCAS_8 DKT Hom 0.006555877698112612
+IPCAS_8 DKT Hom 0.011170300942208522
+IPCAS_8 DKT Hom 0.009800934627625418
+IPCAS_8 DKT Hom 0.0016343114392931197
+IPCAS_8 DKT Hom 0.008362472212733568
+IPCAS_8 DKT Hom 0.008418621455801563
+IPCAS_8 DKT Hom 0.005501156273479028
+IPCAS_8 DKT Hom 0.007242161979290876
+IPCAS_8 DKT Hom 0.009149067507762223
+IPCAS_8 DKT Hom 0.01136175078534977
+IPCAS_8 DKT Hom 0.011273484279991742
+IPCAS_8 DKT Hom 0.006043693832017709
+IPCAS_8 DKT Hom 0.006080491148782306
+IPCAS_8 DKT Hom 0.007382108386981079
+IPCAS_8 DKT Hom 0.018503421973469145
+IPCAS_8 DKT Hom 0.008195166790822916
+IPCAS_8 DKT Hom 0.007628196072891651
+IPCAS_8 DKT Hom 0.007346087315031414
+IPCAS_8 DKT Hom 0.010450016663718933
+IPCAS_8 DKT Hom 0.012831606298333305
+IPCAS_8 DKT Hom 0.015152566888605543
+IPCAS_8 DKT Hom 0.0060202857454466135
+IPCAS_8 DKT Hom 0.009038076576068569
+IPCAS_8 DKT Hom 0.010637306110574824
+IPCAS_8 DKT Hom 0.01011330195295836
+IPCAS_8 DKT Hom 0.0042760992150535065
+IPCAS_8 DKT Contra 0.060177430776126035
+IPCAS_8 DKT Contra 0.09365454146299938
+IPCAS_8 DKT Contra 0.08528594261930095
+IPCAS_8 DKT Contra 0.049320495877393476
+IPCAS_8 DKT Contra 0.08337747754472533
+IPCAS_8 DKT Contra 0.07461075862814154
+IPCAS_8 DKT Contra 0.0612113436020803
+IPCAS_8 DKT Contra 0.0579983028945077
+IPCAS_8 DKT Contra 0.08576641961535464
+IPCAS_8 DKT Contra 0.08403383635602221
+IPCAS_8 DKT Contra 0.08574626561737096
+IPCAS_8 DKT Contra 0.062473451041243605
+IPCAS_8 DKT Contra 0.05263706638466465
+IPCAS_8 DKT Contra 0.05718324734750057
+IPCAS_8 DKT Contra 0.11576030743860406
+IPCAS_8 DKT Contra 0.07520707895654076
+IPCAS_8 DKT Contra 0.05807791888490741
+IPCAS_8 DKT Contra 0.08821765421144304
+IPCAS_8 DKT Contra 0.08116193645648724
+IPCAS_8 DKT Contra 0.11341516119867365
+IPCAS_8 DKT Contra 0.08081513621106218
+IPCAS_8 DKT Contra 0.07686770640200676
+IPCAS_8 DKT Contra 0.07194043457012894
+IPCAS_8 DKT Contra 0.07599738174893882
+IPCAS_8 DKT Contra 0.07663463554658974
+IPCAS_8 DKT Contra 0.050168690152520456
+Dataset Atlas Type Value
+NKI1 DKT Ipsi 0.8227442464565952
+NKI1 DKT Ipsi 0.8570541119959355
+NKI1 DKT Ipsi 0.9065761590946138
+NKI1 DKT Ipsi 0.809136211376242
+NKI1 DKT Ipsi 0.9008979933383469
+NKI1 DKT Ipsi 0.8109543685473344
+NKI1 DKT Ipsi 0.836122311240032
+NKI1 DKT Ipsi 0.8743886365059321
+NKI1 DKT Ipsi 0.91947269943868
+NKI1 DKT Ipsi 0.8559421470892793
+NKI1 DKT Ipsi 0.8408867083720504
+NKI1 DKT Ipsi 0.8957988513750549
+NKI1 DKT Ipsi 0.8664690746389868
+NKI1 DKT Ipsi 0.9056880477029416
+NKI1 DKT Ipsi 0.8671369986023547
+NKI1 DKT Ipsi 0.9119825854186417
+NKI1 DKT Ipsi 0.8719192948388663
+NKI1 DKT Ipsi 0.8659339610021769
+NKI1 DKT Ipsi 0.8819657636385991
+NKI1 DKT Ipsi 0.8350155756777197
+NKI1 DKT Ipsi 0.8716901361090148
+NKI1 DKT Ipsi 0.7484911661011998
+NKI1 DKT Ipsi 0.8377941320103134
+NKI1 DKT Ipsi 0.8793961890100461
+NKI1 DKT Ipsi 0.854112704398332
+NKI1 DKT Ipsi 0.8632454932387418
+NKI1 DKT Ipsi 0.8612585190298496
+NKI1 DKT Ipsi 0.8706371041943517
+NKI1 DKT Ipsi 0.7951472952643959
+NKI1 DKT Ipsi 0.8976638986332388
+NKI1 DKT Ipsi 0.847754428228816
+NKI1 DKT Ipsi 0.8718581495868997
+NKI1 DKT Ipsi 0.8789397657756911
+NKI1 DKT Ipsi 0.8049901727475919
+NKI1 DKT Ipsi 0.8519843816455807
+NKI1 DKT Ipsi 0.879344200249823
+NKI1 DKT Ipsi 0.8388419384756896
+NKI1 DKT Ipsi 0.9159520552125981
+NKI1 DKT Ipsi 0.831718281637827
+NKI1 DKT Ipsi 0.8585458067544829
+NKI1 DKT Hom 0.021503285156253816
+NKI1 DKT Hom 0.01687559898850327
+NKI1 DKT Hom 0.005321006916144657
+NKI1 DKT Hom 0.02495699626898878
+NKI1 DKT Hom 0.008785900874662776
+NKI1 DKT Hom 0.024075481841288517
+NKI1 DKT Hom 0.021348887231877847
+NKI1 DKT Hom 0.01376853605326902
+NKI1 DKT Hom 0.00770369110392924
+NKI1 DKT Hom 0.020922815810596358
+NKI1 DKT Hom 0.014485777199021058
+NKI1 DKT Hom 0.011643772548822647
+NKI1 DKT Hom 0.01771107560231164
+NKI1 DKT Hom 0.008860526161914893
+NKI1 DKT Hom 0.016765373406182987
+NKI1 DKT Hom 0.006473407125402805
+NKI1 DKT Hom 0.02099832910906515
+NKI1 DKT Hom 0.014330515003017335
+NKI1 DKT Hom 0.00556871805011615
+NKI1 DKT Hom 0.02048832365681707
+NKI1 DKT Hom 0.016926701917340373
+NKI1 DKT Hom 0.03073971038356583
+NKI1 DKT Hom 0.021290897382043466
+NKI1 DKT Hom 0.011314150388165042
+NKI1 DKT Hom 0.015788857337274743
+NKI1 DKT Hom 0.02026814626679598
+NKI1 DKT Hom 0.012206196939092638
+NKI1 DKT Hom 0.016287994108478008
+NKI1 DKT Hom 0.026625650339160582
+NKI1 DKT Hom 0.006682105259549201
+NKI1 DKT Hom 0.015439112525945603
+NKI1 DKT Hom 0.0162448662383704
+NKI1 DKT Hom 0.010405989939076212
+NKI1 DKT Hom 0.025312452694665092
+NKI1 DKT Hom 0.01600825580941594
+NKI1 DKT Hom 0.014801220909148472
+NKI1 DKT Hom 0.024457846236590357
+NKI1 DKT Hom 0.006572054175388483
+NKI1 DKT Hom 0.02654148284520985
+NKI1 DKT Hom 0.01549584825840893
+NKI1 DKT Contra 0.155752468387151
+NKI1 DKT Contra 0.1260702890155612
+NKI1 DKT Contra 0.08810283398924156
+NKI1 DKT Contra 0.1659067923547692
+NKI1 DKT Contra 0.09031610578699041
+NKI1 DKT Contra 0.16497014961137715
+NKI1 DKT Contra 0.1425288015280901
+NKI1 DKT Contra 0.11184282744079888
+NKI1 DKT Contra 0.0728236094573907
+NKI1 DKT Contra 0.12313503710012438
+NKI1 DKT Contra 0.14462751442892852
+NKI1 DKT Contra 0.09255737607612247
+NKI1 DKT Contra 0.11581984975870156
+NKI1 DKT Contra 0.08545142613514348
+NKI1 DKT Contra 0.11609762799146234
+NKI1 DKT Contra 0.08154400745595551
+NKI1 DKT Contra 0.10708237605206858
+NKI1 DKT Contra 0.11973552399480579
+NKI1 DKT Contra 0.1124655183112847
+NKI1 DKT Contra 0.14449610066546323
+NKI1 DKT Contra 0.11138316197364484
+NKI1 DKT Contra 0.22076912351523442
+NKI1 DKT Contra 0.14091497060764321
+NKI1 DKT Contra 0.10928966060178878
+NKI1 DKT Contra 0.1300984382643933
+NKI1 DKT Contra 0.11648636049446222
+NKI1 DKT Contra 0.12653528403105774
+NKI1 DKT Contra 0.11307490169717027
+NKI1 DKT Contra 0.1782270543964436
+NKI1 DKT Contra 0.09565399610721194
+NKI1 DKT Contra 0.13680645924523838
+NKI1 DKT Contra 0.1118969841747299
+NKI1 DKT Contra 0.1106542442852327
+NKI1 DKT Contra 0.169697374557743
+NKI1 DKT Contra 0.13200736254500345
+NKI1 DKT Contra 0.10585457884102853
+NKI1 DKT Contra 0.13670021528772006
+NKI1 DKT Contra 0.07747589061201336
+NKI1 DKT Contra 0.1417402355169631
+NKI1 DKT Contra 0.12595834498710815
+Dataset Atlas Type Value
+NKI24_new DKT Ipsi 0.823186598747077
+NKI24_new DKT Ipsi 0.8574867103060178
+NKI24_new DKT Ipsi 0.9058369115966657
+NKI24_new DKT Ipsi 0.8094899002949593
+NKI24_new DKT Ipsi 0.9011604787926423
+NKI24_new DKT Ipsi 0.8109468844883757
+NKI24_new DKT Ipsi 0.8358238554338799
+NKI24_new DKT Ipsi 0.8741925772843584
+NKI24_new DKT Ipsi 0.9194751513741487
+NKI24_new DKT Ipsi 0.8557505620518678
+NKI24_new DKT Ipsi 0.8409890246204018
+NKI24_new DKT Ipsi 0.895449712535985
+NKI24_new DKT Ipsi 0.8662187098453008
+NKI24_new DKT Ipsi 0.9053773884653759
+NKI24_new DKT Ipsi 0.8680607175697679
+NKI24_new DKT Ipsi 0.9112475377052537
+NKI24_new DKT Ipsi 0.8715162976484059
+NKI24_new DKT Ipsi 0.866236122714911
+NKI24_new DKT Ipsi 0.8350704269706757
+NKI24_new DKT Ipsi 0.8706860205986869
+NKI24_new DKT Ipsi 0.7495323712273508
+NKI24_new DKT Ipsi 0.8370960172513827
+NKI24_new DKT Ipsi 0.8794524484232019
+NKI24_new DKT Ipsi 0.8536337261189434
+NKI24_new DKT Ipsi 0.8635919296165125
+NKI24_new DKT Ipsi 0.8610982295240229
+NKI24_new DKT Ipsi 0.7951013569532499
+NKI24_new DKT Ipsi 0.8971291804526161
+NKI24_new DKT Ipsi 0.8477132879791514
+NKI24_new DKT Ipsi 0.8720560048618577
+NKI24_new DKT Ipsi 0.8796202609008618
+NKI24_new DKT Ipsi 0.8049617725552972
+NKI24_new DKT Ipsi 0.8524241231092299
+NKI24_new DKT Ipsi 0.8791531404172768
+NKI24_new DKT Ipsi 0.8401832638349216
+NKI24_new DKT Ipsi 0.91641724574102
+NKI24_new DKT Ipsi 0.8315232957224823
+NKI24_new DKT Ipsi 0.8576329546766296
+NKI24_new DKT Hom 0.02135662637808984
+NKI24_new DKT Hom 0.016862266807746464
+NKI24_new DKT Hom 0.005416101643810622
+NKI24_new DKT Hom 0.02481769961946247
+NKI24_new DKT Hom 0.008827855840294756
+NKI24_new DKT Hom 0.024085237472573746
+NKI24_new DKT Hom 0.02148563655467813
+NKI24_new DKT Hom 0.013865011207706671
+NKI24_new DKT Hom 0.00764618824142765
+NKI24_new DKT Hom 0.021032718797736077
+NKI24_new DKT Hom 0.014505154035639177
+NKI24_new DKT Hom 0.011718628743272085
+NKI24_new DKT Hom 0.006704919445454981
+NKI24_new DKT Hom 0.008834705766945344
+NKI24_new DKT Hom 0.016634468627066912
+NKI24_new DKT Hom 0.006500208468046966
+NKI24_new DKT Hom 0.0209685164610174
+NKI24_new DKT Hom 0.014237002884618691
+NKI24_new DKT Hom 0.02037916735077245
+NKI24_new DKT Hom 0.017188764528775786
+NKI24_new DKT Hom 0.030499380932728023
+NKI24_new DKT Hom 0.02120541858167179
+NKI24_new DKT Hom 0.011408103240034481
+NKI24_new DKT Hom 0.015748162489095545
+NKI24_new DKT Hom 0.02011897957475753
+NKI24_new DKT Hom 0.012365748961332253
+NKI24_new DKT Hom 0.026665551612814854
+NKI24_new DKT Hom 0.00662539665400973
+NKI24_new DKT Hom 0.015432848402396117
+NKI24_new DKT Hom 0.015994460287036605
+NKI24_new DKT Hom 0.010299313907242794
+NKI24_new DKT Hom 0.025252946852312427
+NKI24_new DKT Hom 0.015991731469632838
+NKI24_new DKT Hom 0.014721250071348236
+NKI24_new DKT Hom 0.02430478869736196
+NKI24_new DKT Hom 0.00645211646224022
+NKI24_new DKT Hom 0.026603293652337125
+NKI24_new DKT Hom 0.01555485386809703
+NKI24_new DKT Contra 0.15545677487483311
+NKI24_new DKT Contra 0.12565102288623575
+NKI24_new DKT Contra 0.0887469867595237
+NKI24_new DKT Contra 0.16569240008557828
+NKI24_new DKT Contra 0.09001166536706297
+NKI24_new DKT Contra 0.16496787803905055
+NKI24_new DKT Contra 0.14269050801144195
+NKI24_new DKT Contra 0.1119424115079349
+NKI24_new DKT Contra 0.07287866038442369
+NKI24_new DKT Contra 0.12321671915039607
+NKI24_new DKT Contra 0.14450582134395895
+NKI24_new DKT Contra 0.09283165872074287
+NKI24_new DKT Contra 0.1270763707092442
+NKI24_new DKT Contra 0.08578790576767878
+NKI24_new DKT Contra 0.11530481380316521
+NKI24_new DKT Contra 0.08225225382669939
+NKI24_new DKT Contra 0.10751518589057672
+NKI24_new DKT Contra 0.11952687440047034
+NKI24_new DKT Contra 0.1445504056785519
+NKI24_new DKT Contra 0.11212521487253722
+NKI24_new DKT Contra 0.2199682478399212
+NKI24_new DKT Contra 0.14169856416694554
+NKI24_new DKT Contra 0.1091394483367636
+NKI24_new DKT Contra 0.13061811139196108
+NKI24_new DKT Contra 0.11628909080872991
+NKI24_new DKT Contra 0.1265360215146448
+NKI24_new DKT Contra 0.17823309143393523
+NKI24_new DKT Contra 0.0962454228933741
+NKI24_new DKT Contra 0.1368538636184525
+NKI24_new DKT Contra 0.1119495348511056
+NKI24_new DKT Contra 0.11008042519189545
+NKI24_new DKT Contra 0.16978528059239037
+NKI24_new DKT Contra 0.13158414542113725
+NKI24_new DKT Contra 0.10612560951137498
+NKI24_new DKT Contra 0.1355119474677164
+NKI24_new DKT Contra 0.07713063779673973
+NKI24_new DKT Contra 0.1418734106251806
+NKI24_new DKT Contra 0.12681219145527334
+Dataset Atlas Type Value
+ABIDEII-TCD_1 DKT Ipsi 0.8810364878564941
+ABIDEII-TCD_1 DKT Ipsi 0.9371974383755817
+ABIDEII-TCD_1 DKT Ipsi 0.9467763472432917
+ABIDEII-TCD_1 DKT Ipsi 0.9176830431520177
+ABIDEII-TCD_1 DKT Ipsi 0.8639712502716632
+ABIDEII-TCD_1 DKT Ipsi 0.9023398173367688
+ABIDEII-TCD_1 DKT Ipsi 0.9191909915387285
+ABIDEII-TCD_1 DKT Ipsi 0.8964331555136155
+ABIDEII-TCD_1 DKT Ipsi 0.9319945483063068
+ABIDEII-TCD_1 DKT Ipsi 0.9349310411349671
+ABIDEII-TCD_1 DKT Ipsi 0.8891623715689442
+ABIDEII-TCD_1 DKT Ipsi 0.9035010502563486
+ABIDEII-TCD_1 DKT Ipsi 0.9163693344747696
+ABIDEII-TCD_1 DKT Ipsi 0.9467921702344627
+ABIDEII-TCD_1 DKT Ipsi 0.8509953550837264
+ABIDEII-TCD_1 DKT Ipsi 0.9139185893376927
+ABIDEII-TCD_1 DKT Ipsi 0.897691735677435
+ABIDEII-TCD_1 DKT Ipsi 0.9428657593555269
+ABIDEII-TCD_1 DKT Ipsi 0.8922588103306851
+ABIDEII-TCD_1 DKT Hom 0.018185885940183357
+ABIDEII-TCD_1 DKT Hom 0.010637389320584605
+ABIDEII-TCD_1 DKT Hom 0.006889499200141361
+ABIDEII-TCD_1 DKT Hom 0.0051952841826806906
+ABIDEII-TCD_1 DKT Hom 0.023676094673331472
+ABIDEII-TCD_1 DKT Hom 0.015336113213212293
+ABIDEII-TCD_1 DKT Hom 0.006368767753586755
+ABIDEII-TCD_1 DKT Hom 0.006544000170887869
+ABIDEII-TCD_1 DKT Hom 0.00610627640466493
+ABIDEII-TCD_1 DKT Hom 0.002988586446048899
+ABIDEII-TCD_1 DKT Hom 0.012396720779272959
+ABIDEII-TCD_1 DKT Hom 0.01356426139800572
+ABIDEII-TCD_1 DKT Hom 0.012651085661604037
+ABIDEII-TCD_1 DKT Hom 0.006582962213494157
+ABIDEII-TCD_1 DKT Hom 0.02772808228772688
+ABIDEII-TCD_1 DKT Hom 0.011095434945383544
+ABIDEII-TCD_1 DKT Hom 0.01139614952893773
+ABIDEII-TCD_1 DKT Hom 0.00579873967849409
+ABIDEII-TCD_1 DKT Hom 0.013901494627339493
+ABIDEII-TCD_1 DKT Contra 0.10077762620332255
+ABIDEII-TCD_1 DKT Contra 0.05216517230383377
+ABIDEII-TCD_1 DKT Contra 0.04633415355656689
+ABIDEII-TCD_1 DKT Contra 0.07712167266530161
+ABIDEII-TCD_1 DKT Contra 0.11235265505500533
+ABIDEII-TCD_1 DKT Contra 0.08232406945001884
+ABIDEII-TCD_1 DKT Contra 0.07444024070768478
+ABIDEII-TCD_1 DKT Contra 0.09702284431549671
+ABIDEII-TCD_1 DKT Contra 0.06189917528902831
+ABIDEII-TCD_1 DKT Contra 0.06208037241898401
+ABIDEII-TCD_1 DKT Contra 0.09844090765178289
+ABIDEII-TCD_1 DKT Contra 0.0829346883456456
+ABIDEII-TCD_1 DKT Contra 0.0709795798636263
+ABIDEII-TCD_1 DKT Contra 0.046624867552043085
+ABIDEII-TCD_1 DKT Contra 0.12127656262854677
+ABIDEII-TCD_1 DKT Contra 0.07498597571692375
+ABIDEII-TCD_1 DKT Contra 0.09091211479362732
+ABIDEII-TCD_1 DKT Contra 0.05133550096597905
+ABIDEII-TCD_1 DKT Contra 0.0938396950419754
+Dataset Atlas Type Value
+ABIDEII-BNI_1 DKT Ipsi 0.9338449271919845
+ABIDEII-BNI_1 DKT Ipsi 0.8764707513492529
+ABIDEII-BNI_1 DKT Ipsi 0.9162884251011001
+ABIDEII-BNI_1 DKT Ipsi 0.9427721490191961
+ABIDEII-BNI_1 DKT Ipsi 0.8885831921980791
+ABIDEII-BNI_1 DKT Ipsi 0.9293833388204702
+ABIDEII-BNI_1 DKT Ipsi 0.9215447961107253
+ABIDEII-BNI_1 DKT Ipsi 0.9301685035418557
+ABIDEII-BNI_1 DKT Ipsi 0.9104615023749487
+ABIDEII-BNI_1 DKT Ipsi 0.9319217524720885
+ABIDEII-BNI_1 DKT Ipsi 0.945287072460458
+ABIDEII-BNI_1 DKT Ipsi 0.9234019279309302
+ABIDEII-BNI_1 DKT Ipsi 0.8722673298724147
+ABIDEII-BNI_1 DKT Ipsi 0.8940392363595285
+ABIDEII-BNI_1 DKT Ipsi 0.9457926679242188
+ABIDEII-BNI_1 DKT Ipsi 0.8775019389927451
+ABIDEII-BNI_1 DKT Ipsi 0.8818689861665171
+ABIDEII-BNI_1 DKT Ipsi 0.9340728275425682
+ABIDEII-BNI_1 DKT Ipsi 0.9011760500347772
+ABIDEII-BNI_1 DKT Ipsi 0.9380423189856084
+ABIDEII-BNI_1 DKT Ipsi 0.9028912970758831
+ABIDEII-BNI_1 DKT Ipsi 0.9166184762756976
+ABIDEII-BNI_1 DKT Ipsi 0.9192961842138873
+ABIDEII-BNI_1 DKT Ipsi 0.9307665801813974
+ABIDEII-BNI_1 DKT Ipsi 0.9108267596159726
+ABIDEII-BNI_1 DKT Ipsi 0.8750814073066652
+ABIDEII-BNI_1 DKT Ipsi 0.9182066663982571
+ABIDEII-BNI_1 DKT Ipsi 0.9382960692114906
+ABIDEII-BNI_1 DKT Ipsi 0.9217538653886962
+ABIDEII-BNI_1 DKT Hom 0.002336412897777074
+ABIDEII-BNI_1 DKT Hom 0.009414122131834195
+ABIDEII-BNI_1 DKT Hom 0.009013726280365375
+ABIDEII-BNI_1 DKT Hom 0.003991110644478275
+ABIDEII-BNI_1 DKT Hom 0.004877356868809353
+ABIDEII-BNI_1 DKT Hom 0.0037084010589509817
+ABIDEII-BNI_1 DKT Hom 0.0010417700168667526
+ABIDEII-BNI_1 DKT Hom 0.0075038442484253055
+ABIDEII-BNI_1 DKT Hom 0.008383108125089009
+ABIDEII-BNI_1 DKT Hom 0.00721065537198592
+ABIDEII-BNI_1 DKT Hom 0.005676140581282476
+ABIDEII-BNI_1 DKT Hom 0.010373274335190912
+ABIDEII-BNI_1 DKT Hom 0.016246208826087977
+ABIDEII-BNI_1 DKT Hom 0.004717126455274589
+ABIDEII-BNI_1 DKT Hom 0.0012349982230241395
+ABIDEII-BNI_1 DKT Hom 0.011001988464988014
+ABIDEII-BNI_1 DKT Hom 0.01036360244022412
+ABIDEII-BNI_1 DKT Hom 0.006073962148878894
+ABIDEII-BNI_1 DKT Hom 0.011953783783034841
+ABIDEII-BNI_1 DKT Hom 0.0021001487879705586
+ABIDEII-BNI_1 DKT Hom 0.004751856456907549
+ABIDEII-BNI_1 DKT Hom 0.009748911659698345
+ABIDEII-BNI_1 DKT Hom 0.013268470929552547
+ABIDEII-BNI_1 DKT Hom 0.0008435298954159395
+ABIDEII-BNI_1 DKT Hom 0.013751663448966696
+ABIDEII-BNI_1 DKT Hom 0.01531861515974554
+ABIDEII-BNI_1 DKT Hom 0.009302179932808146
+ABIDEII-BNI_1 DKT Hom 0.006438132558874158
+ABIDEII-BNI_1 DKT Hom 0.004915474745656608
+ABIDEII-BNI_1 DKT Contra 0.06381865991023841
+ABIDEII-BNI_1 DKT Contra 0.1141151265189129
+ABIDEII-BNI_1 DKT Contra 0.07469784861853454
+ABIDEII-BNI_1 DKT Contra 0.05323674033632562
+ABIDEII-BNI_1 DKT Contra 0.10653945093311153
+ABIDEII-BNI_1 DKT Contra 0.06690826012057884
+ABIDEII-BNI_1 DKT Contra 0.07741343387240798
+ABIDEII-BNI_1 DKT Contra 0.06232765220971903
+ABIDEII-BNI_1 DKT Contra 0.0811553894999623
+ABIDEII-BNI_1 DKT Contra 0.060867592155925576
+ABIDEII-BNI_1 DKT Contra 0.04903678695825958
+ABIDEII-BNI_1 DKT Contra 0.06622479773387888
+ABIDEII-BNI_1 DKT Contra 0.11148646130149732
+ABIDEII-BNI_1 DKT Contra 0.10124363718519686
+ABIDEII-BNI_1 DKT Contra 0.05297233385275705
+ABIDEII-BNI_1 DKT Contra 0.11149607254226684
+ABIDEII-BNI_1 DKT Contra 0.10776741139325877
+ABIDEII-BNI_1 DKT Contra 0.059853210308552916
+ABIDEII-BNI_1 DKT Contra 0.086870166182188
+ABIDEII-BNI_1 DKT Contra 0.059857532226421054
+ABIDEII-BNI_1 DKT Contra 0.09235684646720932
+ABIDEII-BNI_1 DKT Contra 0.07363261206460403
+ABIDEII-BNI_1 DKT Contra 0.06743534485656016
+ABIDEII-BNI_1 DKT Contra 0.06838988992318676
+ABIDEII-BNI_1 DKT Contra 0.0754215769350607
+ABIDEII-BNI_1 DKT Contra 0.10959997753358929
+ABIDEII-BNI_1 DKT Contra 0.07249115366893473
+ABIDEII-BNI_1 DKT Contra 0.05526579822963531
+ABIDEII-BNI_1 DKT Contra 0.07333065986564721
+Dataset Atlas Type Value
+ABIDEII-SDSU_1 DKT Ipsi 0.8194487917421199
+ABIDEII-SDSU_1 DKT Ipsi 0.8738341279198538
+ABIDEII-SDSU_1 DKT Ipsi 0.9085480749643512
+ABIDEII-SDSU_1 DKT Ipsi 0.9338434765504411
+ABIDEII-SDSU_1 DKT Ipsi 0.8790084806035453
+ABIDEII-SDSU_1 DKT Ipsi 0.9164301459017162
+ABIDEII-SDSU_1 DKT Ipsi 0.8845555858128625
+ABIDEII-SDSU_1 DKT Ipsi 0.8751772126386084
+ABIDEII-SDSU_1 DKT Ipsi 0.8963493423713147
+ABIDEII-SDSU_1 DKT Ipsi 0.878501299068559
+ABIDEII-SDSU_1 DKT Ipsi 0.8847012569849986
+ABIDEII-SDSU_1 DKT Ipsi 0.8643471509434941
+ABIDEII-SDSU_1 DKT Ipsi 0.8611345570228452
+ABIDEII-SDSU_1 DKT Ipsi 0.9371625655157914
+ABIDEII-SDSU_1 DKT Ipsi 0.8896324234843559
+ABIDEII-SDSU_1 DKT Ipsi 0.9085467638653528
+ABIDEII-SDSU_1 DKT Ipsi 0.8616429400986313
+ABIDEII-SDSU_1 DKT Ipsi 0.9342003798913189
+ABIDEII-SDSU_1 DKT Ipsi 0.9037446691376395
+ABIDEII-SDSU_1 DKT Ipsi 0.8901038835590116
+ABIDEII-SDSU_1 DKT Ipsi 0.8910526617158664
+ABIDEII-SDSU_1 DKT Ipsi 0.9286665203452705
+ABIDEII-SDSU_1 DKT Ipsi 0.8858929993141992
+ABIDEII-SDSU_1 DKT Hom 0.032281673019728445
+ABIDEII-SDSU_1 DKT Hom 0.020096513737658927
+ABIDEII-SDSU_1 DKT Hom 0.014437213813138247
+ABIDEII-SDSU_1 DKT Hom 0.006896495201357225
+ABIDEII-SDSU_1 DKT Hom 0.018514183854443315
+ABIDEII-SDSU_1 DKT Hom 0.011542288669661812
+ABIDEII-SDSU_1 DKT Hom 0.014807811865774019
+ABIDEII-SDSU_1 DKT Hom 0.019536069730568272
+ABIDEII-SDSU_1 DKT Hom 0.016410868686090624
+ABIDEII-SDSU_1 DKT Hom 0.01569575284116015
+ABIDEII-SDSU_1 DKT Hom 0.017762894033053183
+ABIDEII-SDSU_1 DKT Hom 0.010314321164691692
+ABIDEII-SDSU_1 DKT Hom 0.01904772334341287
+ABIDEII-SDSU_1 DKT Hom 0.0085827563938907
+ABIDEII-SDSU_1 DKT Hom 0.015760386330170656
+ABIDEII-SDSU_1 DKT Hom 0.019135821731856992
+ABIDEII-SDSU_1 DKT Hom 0.015981622300733765
+ABIDEII-SDSU_1 DKT Hom 0.01050903815931622
+ABIDEII-SDSU_1 DKT Hom 0.01526231548310534
+ABIDEII-SDSU_1 DKT Hom 0.017581526349136005
+ABIDEII-SDSU_1 DKT Hom 0.020449580239083134
+ABIDEII-SDSU_1 DKT Hom 0.010563025542678474
+ABIDEII-SDSU_1 DKT Hom 0.01795519259790616
+ABIDEII-SDSU_1 DKT Contra 0.14826953523815162
+ABIDEII-SDSU_1 DKT Contra 0.1060693583424873
+ABIDEII-SDSU_1 DKT Contra 0.07701471122251057
+ABIDEII-SDSU_1 DKT Contra 0.0592600282482017
+ABIDEII-SDSU_1 DKT Contra 0.1024773355420114
+ABIDEII-SDSU_1 DKT Contra 0.07202756542862196
+ABIDEII-SDSU_1 DKT Contra 0.10063660232136343
+ABIDEII-SDSU_1 DKT Contra 0.10528671763082333
+ABIDEII-SDSU_1 DKT Contra 0.0872397889425947
+ABIDEII-SDSU_1 DKT Contra 0.10580294809028085
+ABIDEII-SDSU_1 DKT Contra 0.09753584898194827
+ABIDEII-SDSU_1 DKT Contra 0.12533852789181413
+ABIDEII-SDSU_1 DKT Contra 0.11981771963374185
+ABIDEII-SDSU_1 DKT Contra 0.05425467809031793
+ABIDEII-SDSU_1 DKT Contra 0.09460719018547341
+ABIDEII-SDSU_1 DKT Contra 0.07231741440279023
+ABIDEII-SDSU_1 DKT Contra 0.12237543760063495
+ABIDEII-SDSU_1 DKT Contra 0.05529058194936486
+ABIDEII-SDSU_1 DKT Contra 0.08099301537925518
+ABIDEII-SDSU_1 DKT Contra 0.09231459009185239
+ABIDEII-SDSU_1 DKT Contra 0.08849775804505051
+ABIDEII-SDSU_1 DKT Contra 0.06077045411205101
+ABIDEII-SDSU_1 DKT Contra 0.09615180808789461
+Dataset Atlas Type Value
+MRN_1 DKT Ipsi 0.7858649502593887
+MRN_1 DKT Ipsi 0.791115014147899
+MRN_1 DKT Ipsi 0.8531095017803655
+MRN_1 DKT Ipsi 0.8479249256746859
+MRN_1 DKT Ipsi 0.8571630764092729
+MRN_1 DKT Ipsi 0.81535089144919
+MRN_1 DKT Ipsi 0.835818760879196
+MRN_1 DKT Ipsi 0.8108855995453341
+MRN_1 DKT Ipsi 0.8316444546036296
+MRN_1 DKT Ipsi 0.8377937240883145
+MRN_1 DKT Ipsi 0.7609690721713236
+MRN_1 DKT Ipsi 0.7968105776797406
+MRN_1 DKT Ipsi 0.8069660117909612
+MRN_1 DKT Ipsi 0.8204556692417387
+MRN_1 DKT Ipsi 0.7465856515077032
+MRN_1 DKT Ipsi 0.8042850458732034
+MRN_1 DKT Ipsi 0.861441673481211
+MRN_1 DKT Ipsi 0.7746411184997657
+MRN_1 DKT Ipsi 0.8429948990355123
+MRN_1 DKT Hom 0.032668895984713245
+MRN_1 DKT Hom 0.020640707425661556
+MRN_1 DKT Hom 0.007331862219199974
+MRN_1 DKT Hom 0.015303782552737115
+MRN_1 DKT Hom 0.015900790267367428
+MRN_1 DKT Hom 0.01973083118183178
+MRN_1 DKT Hom 0.015447930499459403
+MRN_1 DKT Hom 0.02232313919895424
+MRN_1 DKT Hom 0.017666857322996363
+MRN_1 DKT Hom 0.01703102823726356
+MRN_1 DKT Hom 0.03309389287698654
+MRN_1 DKT Hom 0.012313445873477411
+MRN_1 DKT Hom 0.02596929232487606
+MRN_1 DKT Hom 0.017961292025753715
+MRN_1 DKT Hom 0.027751917217327948
+MRN_1 DKT Hom 0.02459746317034365
+MRN_1 DKT Hom 0.0165548568689326
+MRN_1 DKT Hom 0.029798173771236797
+MRN_1 DKT Hom 0.012468538219319113
+MRN_1 DKT Contra 0.18146615375589806
+MRN_1 DKT Contra 0.18824427842643948
+MRN_1 DKT Contra 0.1395586360004345
+MRN_1 DKT Contra 0.13677129177257696
+MRN_1 DKT Contra 0.12693613332335968
+MRN_1 DKT Contra 0.16491827736897813
+MRN_1 DKT Contra 0.1487333086213446
+MRN_1 DKT Contra 0.1667912612557116
+MRN_1 DKT Contra 0.15068868807337402
+MRN_1 DKT Contra 0.14517524767442186
+MRN_1 DKT Contra 0.20593703495168986
+MRN_1 DKT Contra 0.19087597644678195
+MRN_1 DKT Contra 0.16706469588416273
+MRN_1 DKT Contra 0.1615830387325075
+MRN_1 DKT Contra 0.22566243127496882
+MRN_1 DKT Contra 0.17111749095645298
+MRN_1 DKT Contra 0.12200346964985631
+MRN_1 DKT Contra 0.19556070772899747
+MRN_1 DKT Contra 0.14453656274516857
diff --git a/docs/paper/data/dwi_ipsi_hammer.csv b/docs/paper/data/dwi_ipsi_hammer.csv
new file mode 100644
index 000000000..b1787ca79
--- /dev/null
+++ b/docs/paper/data/dwi_ipsi_hammer.csv
@@ -0,0 +1,3979 @@
+Dataset Atlas Type Value
+SWU4 Hammer Ipsi 0.924214727261108
+SWU4 Hammer Ipsi 0.8804587180863847
+SWU4 Hammer Ipsi 0.8647205404651201
+SWU4 Hammer Ipsi 0.9025538853346377
+SWU4 Hammer Ipsi 0.9112009242734461
+SWU4 Hammer Ipsi 0.8380729730821556
+SWU4 Hammer Ipsi 0.743742787101359
+SWU4 Hammer Ipsi 0.8341811787537297
+SWU4 Hammer Ipsi 0.9103392624970807
+SWU4 Hammer Ipsi 0.7359624988560662
+SWU4 Hammer Ipsi 0.9581570309424353
+SWU4 Hammer Ipsi 0.8886537960457214
+SWU4 Hammer Ipsi 0.7853635686787885
+SWU4 Hammer Ipsi 0.8724451886534202
+SWU4 Hammer Ipsi 0.9214301117923237
+SWU4 Hammer Ipsi 0.8757523488269929
+SWU4 Hammer Ipsi 0.8971051473096211
+SWU4 Hammer Ipsi 0.8784309372149232
+SWU4 Hammer Ipsi 0.8690575518669686
+SWU4 Hammer Ipsi 0.8471073059217696
+SWU4 Hammer Ipsi 0.8550134399581849
+SWU4 Hammer Ipsi 0.851854470377487
+SWU4 Hammer Ipsi 0.888001593686214
+SWU4 Hammer Ipsi 0.9318895877620007
+SWU4 Hammer Ipsi 0.8296319430760392
+SWU4 Hammer Ipsi 0.894021345306458
+SWU4 Hammer Ipsi 0.8339474408327388
+SWU4 Hammer Ipsi 0.8753156106009126
+SWU4 Hammer Ipsi 0.8556110366790753
+SWU4 Hammer Ipsi 0.869164985862074
+SWU4 Hammer Ipsi 0.8251332445069072
+SWU4 Hammer Ipsi 0.912708256768345
+SWU4 Hammer Ipsi 0.9228145469487636
+SWU4 Hammer Ipsi 0.8577507555659176
+SWU4 Hammer Ipsi 0.827544080259426
+SWU4 Hammer Ipsi 0.7465078006357132
+SWU4 Hammer Ipsi 0.925131271856944
+SWU4 Hammer Ipsi 0.8568820222172118
+SWU4 Hammer Ipsi 0.9254388124558571
+SWU4 Hammer Ipsi 0.8926893346132471
+SWU4 Hammer Ipsi 0.8578381501798003
+SWU4 Hammer Ipsi 0.897053491151727
+SWU4 Hammer Ipsi 0.9232598263519509
+SWU4 Hammer Ipsi 0.9570069477814956
+SWU4 Hammer Ipsi 0.8591383076290121
+SWU4 Hammer Ipsi 0.9352843656905238
+SWU4 Hammer Ipsi 0.8160281969443556
+SWU4 Hammer Ipsi 0.8653994135932016
+SWU4 Hammer Ipsi 0.8930703232673805
+SWU4 Hammer Ipsi 0.9173961965436159
+SWU4 Hammer Ipsi 0.852226854130872
+SWU4 Hammer Ipsi 0.8396232332618558
+SWU4 Hammer Ipsi 0.8544823186307309
+SWU4 Hammer Ipsi 0.8924049336968083
+SWU4 Hammer Ipsi 0.9179054150891798
+SWU4 Hammer Ipsi 0.818414623001151
+SWU4 Hammer Ipsi 0.9404458787905398
+SWU4 Hammer Ipsi 0.876699758925929
+SWU4 Hammer Ipsi 0.903162899151548
+SWU4 Hammer Ipsi 0.8257471273285438
+SWU4 Hammer Ipsi 0.7572507859892458
+SWU4 Hammer Ipsi 0.8421060907514348
+SWU4 Hammer Ipsi 0.928878322839711
+SWU4 Hammer Ipsi 0.8892065560836759
+SWU4 Hammer Ipsi 0.8624332629110526
+SWU4 Hammer Ipsi 0.855400451212634
+SWU4 Hammer Ipsi 0.9139288910548471
+SWU4 Hammer Ipsi 0.9216284722625436
+SWU4 Hammer Ipsi 0.8528724740839436
+SWU4 Hammer Ipsi 0.8118496711847769
+SWU4 Hammer Ipsi 0.8878490572125581
+SWU4 Hammer Ipsi 0.8613560262346178
+SWU4 Hammer Ipsi 0.9337860544535642
+SWU4 Hammer Ipsi 0.9064352135843339
+SWU4 Hammer Ipsi 0.7916247589419181
+SWU4 Hammer Ipsi 0.8186123106287979
+SWU4 Hammer Ipsi 0.9178904682109114
+SWU4 Hammer Ipsi 0.9068644307864858
+SWU4 Hammer Ipsi 0.9059167999869067
+SWU4 Hammer Ipsi 0.8045958129430836
+SWU4 Hammer Ipsi 0.823043500143607
+SWU4 Hammer Ipsi 0.8871025530826386
+SWU4 Hammer Ipsi 0.8444807993410465
+SWU4 Hammer Ipsi 0.8871193772875262
+SWU4 Hammer Ipsi 0.8444031987512243
+SWU4 Hammer Ipsi 0.8061443966458387
+SWU4 Hammer Ipsi 0.8015433953704761
+SWU4 Hammer Ipsi 0.9020498285920491
+SWU4 Hammer Ipsi 0.9024530810315586
+SWU4 Hammer Ipsi 0.873732790846879
+SWU4 Hammer Ipsi 0.9105490868414544
+SWU4 Hammer Ipsi 0.8833073001048454
+SWU4 Hammer Ipsi 0.7881621670757787
+SWU4 Hammer Ipsi 0.8358978525558153
+SWU4 Hammer Ipsi 0.8195493972819946
+SWU4 Hammer Ipsi 0.8667002802670144
+SWU4 Hammer Ipsi 0.8494272666501369
+SWU4 Hammer Ipsi 0.8410870355770387
+SWU4 Hammer Ipsi 0.9180714089891466
+SWU4 Hammer Ipsi 0.8913206467369243
+SWU4 Hammer Ipsi 0.8995935286483052
+SWU4 Hammer Ipsi 0.8578420652125258
+SWU4 Hammer Ipsi 0.871156825628687
+SWU4 Hammer Ipsi 0.9399975973030346
+SWU4 Hammer Ipsi 0.7665170846418166
+SWU4 Hammer Ipsi 0.9196159582348182
+SWU4 Hammer Ipsi 0.8634828741498063
+SWU4 Hammer Ipsi 0.8193490657602904
+SWU4 Hammer Ipsi 0.8489231879296288
+SWU4 Hammer Ipsi 0.794139692164616
+SWU4 Hammer Ipsi 0.9162540510359767
+SWU4 Hammer Ipsi 0.8856971404224575
+SWU4 Hammer Ipsi 0.9154647326548351
+SWU4 Hammer Ipsi 0.9042344321256813
+SWU4 Hammer Ipsi 0.9435189688215656
+SWU4 Hammer Ipsi 0.8447079714489629
+SWU4 Hammer Ipsi 0.8989782853309435
+SWU4 Hammer Ipsi 0.8484496280975607
+SWU4 Hammer Ipsi 0.8721201471059609
+SWU4 Hammer Ipsi 0.9342510686165967
+SWU4 Hammer Ipsi 0.9451639277379388
+SWU4 Hammer Ipsi 0.8632699933410645
+SWU4 Hammer Ipsi 0.8961122558556297
+SWU4 Hammer Ipsi 0.9528943549536778
+SWU4 Hammer Ipsi 0.7960140464070767
+SWU4 Hammer Ipsi 0.8553724907560298
+SWU4 Hammer Ipsi 0.9103635233484068
+SWU4 Hammer Ipsi 0.8549055363060248
+SWU4 Hammer Ipsi 0.921429269137124
+SWU4 Hammer Ipsi 0.8359506436298412
+SWU4 Hammer Ipsi 0.9050373996067513
+SWU4 Hammer Ipsi 0.8641298238216212
+SWU4 Hammer Ipsi 0.8828399700554549
+SWU4 Hammer Ipsi 0.9173199222233593
+SWU4 Hammer Ipsi 0.9286529484940824
+SWU4 Hammer Ipsi 0.8576297882007816
+SWU4 Hammer Ipsi 0.8892820445723371
+SWU4 Hammer Ipsi 0.8913682048430365
+SWU4 Hammer Ipsi 0.9032831613096577
+SWU4 Hammer Ipsi 0.8742463779253381
+SWU4 Hammer Ipsi 0.9256687347659083
+SWU4 Hammer Ipsi 0.9281216601708551
+SWU4 Hammer Ipsi 0.7760339051751887
+SWU4 Hammer Ipsi 0.7960694865779044
+SWU4 Hammer Ipsi 0.8827380241761434
+SWU4 Hammer Ipsi 0.838388258616587
+SWU4 Hammer Ipsi 0.8779468771735272
+SWU4 Hammer Ipsi 0.8195154978251519
+SWU4 Hammer Ipsi 0.8942119798058589
+SWU4 Hammer Ipsi 0.7600603911174275
+SWU4 Hammer Ipsi 0.8998242052902541
+SWU4 Hammer Ipsi 0.760974947326837
+SWU4 Hammer Ipsi 0.9084961079473947
+SWU4 Hammer Ipsi 0.9242613409254166
+SWU4 Hammer Ipsi 0.8981085754733041
+SWU4 Hammer Ipsi 0.8431281674548273
+SWU4 Hammer Ipsi 0.845642885905902
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+NKIENH Hammer Contra 0.04647780027536219
+NKIENH Hammer Contra 0.045852155085366574
+NKIENH Hammer Contra 0.09415648396326078
+NKIENH Hammer Contra 0.06066011843979366
+NKIENH Hammer Contra 0.07770588695259421
+NKIENH Hammer Contra 0.07437178270438782
+NKIENH Hammer Contra 0.06857879602231713
+NKIENH Hammer Contra 0.04416398211861063
+NKIENH Hammer Contra 0.108018055890562
+NKIENH Hammer Contra 0.049647295134831855
+NKIENH Hammer Contra 0.04589669264271262
+NKIENH Hammer Contra 0.09715285205079749
+NKIENH Hammer Contra 0.027726394200061382
+NKIENH Hammer Contra 0.06828694715226169
+NKIENH Hammer Contra 0.040374908801733105
+NKIENH Hammer Contra 0.08510615634395771
+NKIENH Hammer Contra 0.08764311894534889
+NKIENH Hammer Contra 0.05566793836921286
+NKIENH Hammer Contra 0.06651122669034631
+NKIENH Hammer Contra 0.03253084527682656
+NKIENH Hammer Contra 0.04417465807857948
+NKIENH Hammer Contra 0.036973430204660734
+NKIENH Hammer Contra 0.0943238806686373
+NKIENH Hammer Contra 0.04209236306265533
+NKIENH Hammer Contra 0.05777072768204068
+NKIENH Hammer Contra 0.04290360396863357
+NKIENH Hammer Contra 0.05101018084163399
+NKIENH Hammer Contra 0.11034017129726342
+NKIENH Hammer Contra 0.12781411054056335
+NKIENH Hammer Contra 0.10610374034311663
+NKIENH Hammer Contra 0.11287307007567954
+NKIENH Hammer Contra 0.08117308669208133
+NKIENH Hammer Contra 0.03129720031386755
+NKIENH Hammer Contra 0.07293618015945937
+NKIENH Hammer Contra 0.05637002989005069
+NKIENH Hammer Contra 0.12893864619255652
+NKIENH Hammer Contra 0.04282819257218091
+NKIENH Hammer Contra 0.09555974346893839
+NKIENH Hammer Contra 0.07139893359404419
+NKIENH Hammer Contra 0.14870085566246666
+NKIENH Hammer Contra 0.023518097670592274
+NKIENH Hammer Contra 0.08793919342791114
+NKIENH Hammer Contra 0.09741755311189523
+NKIENH Hammer Contra 0.09377408553848562
+NKIENH Hammer Contra 0.08858115616173969
+NKIENH Hammer Contra 0.04744921533764985
+NKIENH Hammer Contra 0.029366130837093187
+NKIENH Hammer Contra 0.0684732925027571
+NKIENH Hammer Contra 0.052729134857652696
+NKIENH Hammer Contra 0.08060682152912672
+NKIENH Hammer Contra 0.043892742442883693
+NKIENH Hammer Contra 0.07758408356201632
+NKIENH Hammer Contra 0.07281220087461915
+NKIENH Hammer Contra 0.053243425651852146
+NKIENH Hammer Contra 0.1118712146140717
+NKIENH Hammer Contra 0.10363860169705702
+NKIENH Hammer Contra 0.09436973667057268
+NKIENH Hammer Contra 0.06307301138595056
+NKIENH Hammer Contra 0.05319979415303059
+NKIENH Hammer Contra 0.06361508366152696
+NKIENH Hammer Contra 0.07570513130216254
+NKIENH Hammer Contra 0.09369767345513515
+NKIENH Hammer Contra 0.040451931338591894
+NKIENH Hammer Contra 0.0811870805653031
+NKIENH Hammer Contra 0.09795234398225679
+NKIENH Hammer Contra 0.06724687020991219
+NKIENH Hammer Contra 0.05536557814319568
+NKIENH Hammer Contra 0.04397435667796202
+NKIENH Hammer Contra 0.09469479027610384
+NKIENH Hammer Contra 0.0770979873685349
+NKIENH Hammer Contra 0.0936914615571701
+NKIENH Hammer Contra 0.09406128373560618
+NKIENH Hammer Contra 0.034676634208877656
+NKIENH Hammer Contra 0.09425398940097068
+NKIENH Hammer Contra 0.12154885485121232
+NKIENH Hammer Contra 0.04297477971732538
+NKIENH Hammer Contra 0.08710000387813834
+NKIENH Hammer Contra 0.11294606785164955
+NKIENH Hammer Contra 0.08577839437919244
+NKIENH Hammer Contra 0.09562624664571584
+NKIENH Hammer Contra 0.06325550549581957
+NKIENH Hammer Contra 0.11128675458826019
+NKIENH Hammer Contra 0.06283242837788364
+NKIENH Hammer Contra 0.07380500210583157
+NKIENH Hammer Contra 0.05339023857643174
+NKIENH Hammer Contra 0.052247383502384635
+NKIENH Hammer Contra 0.07032736357029798
+NKIENH Hammer Contra 0.08585614245163846
+NKIENH Hammer Contra 0.03221032794495536
+NKIENH Hammer Contra 0.09775397068501449
+NKIENH Hammer Contra 0.02886710489919212
+NKIENH Hammer Contra 0.04646161426216613
+NKIENH Hammer Contra 0.022365133842656513
+NKIENH Hammer Contra 0.10373917605120869
+NKIENH Hammer Contra 0.07803894850394595
+NKIENH Hammer Contra 0.10795687174281782
+NKIENH Hammer Contra 0.0509641246059556
+NKIENH Hammer Contra 0.0588836357944129
+NKIENH Hammer Contra 0.0352193958125977
+NKIENH Hammer Contra 0.09499431890482564
+NKIENH Hammer Contra 0.09276035278640891
+NKIENH Hammer Contra 0.09557651479221105
+NKIENH Hammer Contra 0.11757426070362162
+NKIENH Hammer Contra 0.04556839584323683
+NKIENH Hammer Contra 0.05080227444023096
+NKIENH Hammer Contra 0.06223135061314258
+NKIENH Hammer Contra 0.03332251435885101
+NKIENH Hammer Contra 0.10358687404494027
+NKIENH Hammer Contra 0.08522868551157674
+NKIENH Hammer Contra 0.09430863744020282
+NKIENH Hammer Contra 0.11902837798905215
+NKIENH Hammer Contra 0.1258207688842452
+NKIENH Hammer Contra 0.06949246588093254
+NKIENH Hammer Contra 0.07948391352272023
+NKIENH Hammer Contra 0.0860373967697512
+NKIENH Hammer Contra 0.055913896278514175
+NKIENH Hammer Contra 0.04452349187725529
+NKIENH Hammer Contra 0.0525663277956856
+NKIENH Hammer Contra 0.04450000596613256
+NKIENH Hammer Contra 0.0522255850888496
+NKIENH Hammer Contra 0.10008481564037644
+NKIENH Hammer Contra 0.032037500881557365
+NKIENH Hammer Contra 0.0754326242638523
+NKIENH Hammer Contra 0.059995035986474377
+NKIENH Hammer Contra 0.06988203039960451
+NKIENH Hammer Contra 0.09807227763926644
+NKIENH Hammer Contra 0.03649577175637109
+NKIENH Hammer Contra 0.05163231744402815
+NKIENH Hammer Contra 0.07507275210983662
+Dataset Atlas Type Value
+XHCUMS Hammer Ipsi 0.9213290553367746
+XHCUMS Hammer Ipsi 0.9208244984876051
+XHCUMS Hammer Ipsi 0.8320716225913222
+XHCUMS Hammer Ipsi 0.871788544474279
+XHCUMS Hammer Ipsi 0.9483352748973963
+XHCUMS Hammer Ipsi 0.9265479501359273
+XHCUMS Hammer Ipsi 0.924967799891374
+XHCUMS Hammer Ipsi 0.9218293590476188
+XHCUMS Hammer Ipsi 0.8925023600092525
+XHCUMS Hammer Ipsi 0.9477324050842654
+XHCUMS Hammer Ipsi 0.925933491751077
+XHCUMS Hammer Ipsi 0.8861284246058986
+XHCUMS Hammer Ipsi 0.9139255336383987
+XHCUMS Hammer Ipsi 0.917237242598494
+XHCUMS Hammer Ipsi 0.9500195511705771
+XHCUMS Hammer Ipsi 0.9231336054511885
+XHCUMS Hammer Ipsi 0.9852176302761577
+XHCUMS Hammer Ipsi 0.9241378914248136
+XHCUMS Hammer Ipsi 0.915376522209809
+XHCUMS Hammer Ipsi 0.8480325085855575
+XHCUMS Hammer Ipsi 0.916611347425276
+XHCUMS Hammer Ipsi 0.9382374926730961
+XHCUMS Hammer Ipsi 0.9261154019649172
+XHCUMS Hammer Ipsi 0.9525034098469252
+XHCUMS Hammer Ipsi 0.9222442244058467
+XHCUMS Hammer Ipsi 0.918041769986485
+XHCUMS Hammer Ipsi 0.9502418595855144
+XHCUMS Hammer Ipsi 0.9741967026149686
+XHCUMS Hammer Ipsi 0.9299437532706997
+XHCUMS Hammer Ipsi 0.906890686943892
+XHCUMS Hammer Ipsi 0.9325860182615472
+XHCUMS Hammer Ipsi 0.947047630063281
+XHCUMS Hammer Ipsi 0.8789528485814809
+XHCUMS Hammer Ipsi 0.8919791386685924
+XHCUMS Hammer Ipsi 0.8961892999131538
+XHCUMS Hammer Ipsi 0.912117555206837
+XHCUMS Hammer Ipsi 0.9579952328398129
+XHCUMS Hammer Ipsi 0.9586292859840332
+XHCUMS Hammer Ipsi 0.96146125877081
+XHCUMS Hammer Ipsi 0.9746886342429876
+XHCUMS Hammer Ipsi 0.9678930872665632
+XHCUMS Hammer Ipsi 0.9276133018270593
+XHCUMS Hammer Ipsi 0.9411678582656656
+XHCUMS Hammer Ipsi 0.9084887392396312
+XHCUMS Hammer Ipsi 0.9674962914824453
+XHCUMS Hammer Ipsi 0.9486590404695697
+XHCUMS Hammer Ipsi 0.9078695398608713
+XHCUMS Hammer Ipsi 0.8352323883581576
+XHCUMS Hammer Ipsi 0.9434395100657812
+XHCUMS Hammer Ipsi 0.916544792861559
+XHCUMS Hammer Ipsi 0.9324767139987409
+XHCUMS Hammer Ipsi 0.9675802115171035
+XHCUMS Hammer Ipsi 0.9477823001822713
+XHCUMS Hammer Ipsi 0.9230146385184707
+XHCUMS Hammer Ipsi 0.9625454209516955
+XHCUMS Hammer Ipsi 0.9513222091747938
+XHCUMS Hammer Ipsi 0.9469134231364419
+XHCUMS Hammer Ipsi 0.8664531718519338
+XHCUMS Hammer Ipsi 0.8840205050936695
+XHCUMS Hammer Ipsi 0.9428571297638281
+XHCUMS Hammer Ipsi 0.9047199832927028
+XHCUMS Hammer Ipsi 0.9222139334838344
+XHCUMS Hammer Ipsi 0.9326515587145033
+XHCUMS Hammer Ipsi 0.8658150255041884
+XHCUMS Hammer Ipsi 0.9070476549272419
+XHCUMS Hammer Ipsi 0.9266569180558676
+XHCUMS Hammer Ipsi 0.9534300837244628
+XHCUMS Hammer Ipsi 0.9093643526509853
+XHCUMS Hammer Ipsi 0.9020478328251501
+XHCUMS Hammer Ipsi 0.9491106071664067
+XHCUMS Hammer Ipsi 0.9506481598440898
+XHCUMS Hammer Ipsi 0.9602189144061963
+XHCUMS Hammer Ipsi 0.8820868141537491
+XHCUMS Hammer Ipsi 0.9569422755829495
+XHCUMS Hammer Ipsi 0.9532424397557725
+XHCUMS Hammer Ipsi 0.8639105652140316
+XHCUMS Hammer Ipsi 0.942269088076877
+XHCUMS Hammer Ipsi 0.9201134620574628
+XHCUMS Hammer Ipsi 0.8844164785330865
+XHCUMS Hammer Ipsi 0.9441913178587302
+XHCUMS Hammer Ipsi 0.8706620570981064
+XHCUMS Hammer Ipsi 0.9614098054377316
+XHCUMS Hammer Ipsi 0.9225335477802403
+XHCUMS Hammer Ipsi 0.9124257425742575
+XHCUMS Hammer Ipsi 0.9636629691874402
+XHCUMS Hammer Ipsi 0.9473807672913972
+XHCUMS Hammer Ipsi 0.9664392419090843
+XHCUMS Hammer Ipsi 0.9082764787427237
+XHCUMS Hammer Ipsi 0.9119818562866049
+XHCUMS Hammer Ipsi 0.9675075101032955
+XHCUMS Hammer Ipsi 0.9697177061225749
+XHCUMS Hammer Ipsi 0.9597774584274277
+XHCUMS Hammer Ipsi 0.9084635479033643
+XHCUMS Hammer Ipsi 0.9458787129408871
+XHCUMS Hammer Ipsi 0.9435560934246291
+XHCUMS Hammer Ipsi 0.9714622369960839
+XHCUMS Hammer Ipsi 0.8714783613762281
+XHCUMS Hammer Ipsi 0.9764257003690657
+XHCUMS Hammer Ipsi 0.8852927105750098
+XHCUMS Hammer Ipsi 0.9044963467230124
+XHCUMS Hammer Ipsi 0.8787488990661464
+XHCUMS Hammer Ipsi 0.9286660353885183
+XHCUMS Hammer Ipsi 0.935440041964209
+XHCUMS Hammer Ipsi 0.9643184764437983
+XHCUMS Hammer Ipsi 0.9312685217377763
+XHCUMS Hammer Ipsi 0.9573584190131821
+XHCUMS Hammer Ipsi 0.9387421356741098
+XHCUMS Hammer Ipsi 0.9663351654874798
+XHCUMS Hammer Ipsi 0.9119206451609014
+XHCUMS Hammer Ipsi 0.8786672694267257
+XHCUMS Hammer Ipsi 0.9330618551656471
+XHCUMS Hammer Ipsi 0.9428925833085787
+XHCUMS Hammer Ipsi 0.9603172602470734
+XHCUMS Hammer Ipsi 0.9228426331330821
+XHCUMS Hammer Ipsi 0.9450065699172888
+XHCUMS Hammer Ipsi 0.9316080437012171
+XHCUMS Hammer Ipsi 0.9081111809593132
+XHCUMS Hammer Hom 0.01051314129872008
+XHCUMS Hammer Hom 0.010185652513879663
+XHCUMS Hammer Hom 0.02237509124250139
+XHCUMS Hammer Hom 0.01232312661377985
+XHCUMS Hammer Hom 0.007149068794449888
+XHCUMS Hammer Hom 0.0110640004567135
+XHCUMS Hammer Hom 0.010832819287706177
+XHCUMS Hammer Hom 0.011023341764015017
+XHCUMS Hammer Hom 0.015063343145265163
+XHCUMS Hammer Hom 0.008985216175183948
+XHCUMS Hammer Hom 0.009293482723582868
+XHCUMS Hammer Hom 0.018144360138614226
+XHCUMS Hammer Hom 0.009136829922203934
+XHCUMS Hammer Hom 0.006483626773014657
+XHCUMS Hammer Hom 0.0081553003586208
+XHCUMS Hammer Hom 0.010955832542009176
+XHCUMS Hammer Hom 0.0021747490226010757
+XHCUMS Hammer Hom 0.01143492199024196
+XHCUMS Hammer Hom 0.010660318612512643
+XHCUMS Hammer Hom 0.019597838348651687
+XHCUMS Hammer Hom 0.009102396992256593
+XHCUMS Hammer Hom 0.0076773330289146525
+XHCUMS Hammer Hom 0.007932131416925997
+XHCUMS Hammer Hom 0.008356483321050833
+XHCUMS Hammer Hom 0.007898661003981764
+XHCUMS Hammer Hom 0.011305273556874294
+XHCUMS Hammer Hom 0.004755069767945799
+XHCUMS Hammer Hom 0.0035029741886389203
+XHCUMS Hammer Hom 0.010434415911660775
+XHCUMS Hammer Hom 0.011973812361376318
+XHCUMS Hammer Hom 0.007108131818342621
+XHCUMS Hammer Hom 0.005432745302699441
+XHCUMS Hammer Hom 0.020315359738010855
+XHCUMS Hammer Hom 0.011145527378473515
+XHCUMS Hammer Hom 0.011053846918605678
+XHCUMS Hammer Hom 0.014462304513975757
+XHCUMS Hammer Hom 0.005698489160621699
+XHCUMS Hammer Hom 0.004973942818827456
+XHCUMS Hammer Hom 0.005459157172161301
+XHCUMS Hammer Hom 0.003559078597204921
+XHCUMS Hammer Hom 0.005776074850099671
+XHCUMS Hammer Hom 0.009501654732142274
+XHCUMS Hammer Hom 0.00999884144336801
+XHCUMS Hammer Hom 0.012081218561733388
+XHCUMS Hammer Hom 0.005673834315172631
+XHCUMS Hammer Hom 0.006327454997502728
+XHCUMS Hammer Hom 0.012330710011214115
+XHCUMS Hammer Hom 0.019510396478875123
+XHCUMS Hammer Hom 0.007906849811059727
+XHCUMS Hammer Hom 0.008158244270831318
+XHCUMS Hammer Hom 0.007726036051868775
+XHCUMS Hammer Hom 0.00535824286707964
+XHCUMS Hammer Hom 0.006513054523961098
+XHCUMS Hammer Hom 0.009077538712873008
+XHCUMS Hammer Hom 0.0058952255641904795
+XHCUMS Hammer Hom 0.008136387979438537
+XHCUMS Hammer Hom 0.006706507902947443
+XHCUMS Hammer Hom 0.013888699825846389
+XHCUMS Hammer Hom 0.010248905780438468
+XHCUMS Hammer Hom 0.0053835564375988324
+XHCUMS Hammer Hom 0.010057649235167861
+XHCUMS Hammer Hom 0.006669807427260293
+XHCUMS Hammer Hom 0.007540768645682421
+XHCUMS Hammer Hom 0.013710146398410238
+XHCUMS Hammer Hom 0.00869947518778619
+XHCUMS Hammer Hom 0.009254932372589616
+XHCUMS Hammer Hom 0.010406346069870299
+XHCUMS Hammer Hom 0.013160304780641322
+XHCUMS Hammer Hom 0.011904813194956492
+XHCUMS Hammer Hom 0.011990644629629992
+XHCUMS Hammer Hom 0.005657159871157507
+XHCUMS Hammer Hom 0.005554675278810044
+XHCUMS Hammer Hom 0.01014946439941759
+XHCUMS Hammer Hom 0.006442661307983455
+XHCUMS Hammer Hom 0.00816257247816302
+XHCUMS Hammer Hom 0.01737281037711533
+XHCUMS Hammer Hom 0.006495932616553946
+XHCUMS Hammer Hom 0.007727904224161734
+XHCUMS Hammer Hom 0.014013922748328555
+XHCUMS Hammer Hom 0.009189491468537925
+XHCUMS Hammer Hom 0.01097245929452676
+XHCUMS Hammer Hom 0.0057249042385343245
+XHCUMS Hammer Hom 0.010539657202912812
+XHCUMS Hammer Hom 0.00926455553164012
+XHCUMS Hammer Hom 0.004670454106766263
+XHCUMS Hammer Hom 0.0060815021166963955
+XHCUMS Hammer Hom 0.004046449021047803
+XHCUMS Hammer Hom 0.011929177143046896
+XHCUMS Hammer Hom 0.013495178463644173
+XHCUMS Hammer Hom 0.005628476791190228
+XHCUMS Hammer Hom 0.004464079101349431
+XHCUMS Hammer Hom 0.010749948675836583
+XHCUMS Hammer Hom 0.010272825695114944
+XHCUMS Hammer Hom 0.006061956780332584
+XHCUMS Hammer Hom 0.007311517570009778
+XHCUMS Hammer Hom 0.004519164731643716
+XHCUMS Hammer Hom 0.012764506858084999
+XHCUMS Hammer Hom 0.0032411494791338216
+XHCUMS Hammer Hom 0.013443977211728856
+XHCUMS Hammer Hom 0.010010394735350038
+XHCUMS Hammer Hom 0.012791746232244066
+XHCUMS Hammer Hom 0.011180860140658375
+XHCUMS Hammer Hom 0.005811155970248452
+XHCUMS Hammer Hom 0.006236843209282612
+XHCUMS Hammer Hom 0.0079582935208264
+XHCUMS Hammer Hom 0.006034482159861477
+XHCUMS Hammer Hom 0.006110684973804375
+XHCUMS Hammer Hom 0.005448819392419811
+XHCUMS Hammer Hom 0.009959537808009622
+XHCUMS Hammer Hom 0.010460644418064364
+XHCUMS Hammer Hom 0.00920707040888738
+XHCUMS Hammer Hom 0.007025933616536406
+XHCUMS Hammer Hom 0.006497519579993711
+XHCUMS Hammer Hom 0.01227344465940332
+XHCUMS Hammer Hom 0.00870822518846457
+XHCUMS Hammer Hom 0.009022383022945061
+XHCUMS Hammer Hom 0.012220761759813755
+XHCUMS Hammer Contra 0.06815780336450536
+XHCUMS Hammer Contra 0.06898984899851525
+XHCUMS Hammer Contra 0.14555328616617644
+XHCUMS Hammer Contra 0.1158883289119411
+XHCUMS Hammer Contra 0.044515656308153774
+XHCUMS Hammer Contra 0.062388049407359175
+XHCUMS Hammer Contra 0.06419938082091982
+XHCUMS Hammer Contra 0.06714729918836625
+XHCUMS Hammer Contra 0.09243429684548235
+XHCUMS Hammer Contra 0.04328237874055063
+XHCUMS Hammer Contra 0.0647730255253402
+XHCUMS Hammer Contra 0.09572721525548723
+XHCUMS Hammer Contra 0.07693763643939742
+XHCUMS Hammer Contra 0.0762791306284913
+XHCUMS Hammer Contra 0.04182514847080207
+XHCUMS Hammer Contra 0.06591056200680231
+XHCUMS Hammer Contra 0.012607620701241244
+XHCUMS Hammer Contra 0.06442718658494444
+XHCUMS Hammer Contra 0.0739631591776783
+XHCUMS Hammer Contra 0.13236965306579088
+XHCUMS Hammer Contra 0.07428625558246744
+XHCUMS Hammer Contra 0.0540851742979893
+XHCUMS Hammer Contra 0.06595246661815682
+XHCUMS Hammer Contra 0.03914010683202399
+XHCUMS Hammer Contra 0.06985711459017148
+XHCUMS Hammer Contra 0.07065295645664074
+XHCUMS Hammer Contra 0.04500307064653977
+XHCUMS Hammer Contra 0.022300323196392514
+XHCUMS Hammer Contra 0.05962183081763956
+XHCUMS Hammer Contra 0.08113550069473169
+XHCUMS Hammer Contra 0.06030584992011019
+XHCUMS Hammer Contra 0.04751962463401956
+XHCUMS Hammer Contra 0.1007317916805082
+XHCUMS Hammer Contra 0.09687533395293409
+XHCUMS Hammer Contra 0.09275685316824056
+XHCUMS Hammer Contra 0.07342014027918725
+XHCUMS Hammer Contra 0.03630627799956537
+XHCUMS Hammer Contra 0.03639677119713934
+XHCUMS Hammer Contra 0.03307958405702866
+XHCUMS Hammer Contra 0.021752287159807534
+XHCUMS Hammer Contra 0.026330837883337153
+XHCUMS Hammer Contra 0.06288504344079845
+XHCUMS Hammer Contra 0.048833300290966386
+XHCUMS Hammer Contra 0.07943004219863531
+XHCUMS Hammer Contra 0.026829874202382045
+XHCUMS Hammer Contra 0.04501350453292753
+XHCUMS Hammer Contra 0.07979975012791464
+XHCUMS Hammer Contra 0.14525721516296725
+XHCUMS Hammer Contra 0.048653640123159064
+XHCUMS Hammer Contra 0.07529696286760966
+XHCUMS Hammer Contra 0.05979724994939035
+XHCUMS Hammer Contra 0.02706154561581684
+XHCUMS Hammer Contra 0.04570464529376763
+XHCUMS Hammer Contra 0.06790782276865628
+XHCUMS Hammer Contra 0.03155935348411403
+XHCUMS Hammer Contra 0.04054140284576766
+XHCUMS Hammer Contra 0.046380068960610685
+XHCUMS Hammer Contra 0.11965812832221981
+XHCUMS Hammer Contra 0.1057305891258921
+XHCUMS Hammer Contra 0.051759313798573146
+XHCUMS Hammer Contra 0.08522236747212938
+XHCUMS Hammer Contra 0.07111625908890527
+XHCUMS Hammer Contra 0.059807672639814256
+XHCUMS Hammer Contra 0.12047482809740138
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+BNU3 Hammer Hom 0.0105559840858007
+BNU3 Hammer Hom 0.008229706255054937
+BNU3 Hammer Hom 0.004541683683207633
+BNU3 Hammer Hom 0.006707785282509837
+BNU3 Hammer Hom 0.005196236459900559
+BNU3 Hammer Hom 0.003417066275614731
+BNU3 Hammer Hom 0.007867205442526795
+BNU3 Hammer Hom 0.010645078281919748
+BNU3 Hammer Hom 0.008498655981071121
+BNU3 Hammer Hom 0.009173590443432668
+BNU3 Hammer Hom 0.009422148226527122
+BNU3 Hammer Hom 0.008122375034375984
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+BNU3 Hammer Hom 0.01134177220342329
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+BNU3 Hammer Hom 0.010727557506025234
+BNU3 Hammer Hom 0.007700219726015112
+BNU3 Hammer Hom 0.007579636373298875
+BNU3 Hammer Hom 0.00527646270504103
+BNU3 Hammer Hom 0.008436291623548621
+BNU3 Hammer Hom 0.009793328788009457
+BNU3 Hammer Hom 0.011190824328493578
+BNU3 Hammer Hom 0.008284355684057975
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+BNU3 Hammer Hom 0.006647550051319326
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+BNU3 Hammer Contra 0.06448500043141597
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+BNU3 Hammer Contra 0.025357667253492967
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+BNU3 Hammer Contra 0.056777020911024476
+BNU3 Hammer Contra 0.08822857371226812
+BNU3 Hammer Contra 0.07136105466152168
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+Dataset Atlas Type Value
+IPCAS_8 Hammer Ipsi 0.9710553171717684
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+IPCAS_8 Hammer Ipsi 0.9733265195405097
+IPCAS_8 Hammer Ipsi 0.9721215821567807
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+IPCAS_8 Hammer Ipsi 0.9491597668511778
+IPCAS_8 Hammer Ipsi 0.9349995162385266
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+IPCAS_8 Hammer Ipsi 0.9667723889200507
+IPCAS_8 Hammer Ipsi 0.9111210414269618
+IPCAS_8 Hammer Ipsi 0.96782140913383
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+IPCAS_8 Hammer Ipsi 0.9592334682950702
+IPCAS_8 Hammer Ipsi 0.9373678767284009
+IPCAS_8 Hammer Ipsi 0.9833914645902747
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+IPCAS_8 Hammer Hom 0.010331527679067351
+IPCAS_8 Hammer Hom 0.008380177167966366
+IPCAS_8 Hammer Hom 0.0029238330628378645
+IPCAS_8 Hammer Hom 0.005642993937998797
+IPCAS_8 Hammer Hom 0.005734355023299692
+IPCAS_8 Hammer Hom 0.004586018362961789
+IPCAS_8 Hammer Hom 0.0042017115350265945
+IPCAS_8 Hammer Hom 0.007819055537800006
+IPCAS_8 Hammer Hom 0.007811709537629584
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+Dataset Atlas Type Value
+NKI1 Hammer Ipsi 0.8887737834465952
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+NKI1 Hammer Ipsi 0.9758753651092431
+NKI1 Hammer Ipsi 0.8895216590622591
+NKI1 Hammer Ipsi 0.9417404546513363
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+NKI1 Hammer Ipsi 0.8797487991478928
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+NKI1 Hammer Ipsi 0.9577688604259034
+NKI1 Hammer Ipsi 0.8998491190649389
+NKI1 Hammer Ipsi 0.9249422192065477
+NKI1 Hammer Ipsi 0.9482473975393303
+NKI1 Hammer Ipsi 0.9116660570926416
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+NKI1 Hammer Ipsi 0.9768722988186297
+NKI1 Hammer Ipsi 0.9098018063673112
+NKI1 Hammer Ipsi 0.9265731886063364
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+NKI1 Hammer Ipsi 0.9136637062460785
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+NKI1 Hammer Ipsi 0.9412629444602898
+NKI1 Hammer Ipsi 0.8757288532821286
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+NKI1 Hammer Ipsi 0.8891411829141669
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+NKI1 Hammer Hom 0.010355377210754678
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+NKI1 Hammer Hom 0.01893091750608059
+NKI1 Hammer Hom 0.004746430977837507
+NKI1 Hammer Hom 0.01918092495042819
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+NKI1 Hammer Contra 0.09429009867365572
+NKI1 Hammer Contra 0.08665781935957598
+NKI1 Hammer Contra 0.021226726047859683
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+Dataset Atlas Type Value
+NKI24_new Hammer Ipsi 0.8891886772523232
+NKI24_new Hammer Ipsi 0.9019706061091199
+NKI24_new Hammer Ipsi 0.975434279176679
+NKI24_new Hammer Ipsi 0.8899979343110928
+NKI24_new Hammer Ipsi 0.9415812053281848
+NKI24_new Hammer Ipsi 0.8655573462417891
+NKI24_new Hammer Ipsi 0.8792567723635486
+NKI24_new Hammer Ipsi 0.9332483952657052
+NKI24_new Hammer Ipsi 0.9578902590248803
+NKI24_new Hammer Ipsi 0.8996522563818857
+NKI24_new Hammer Ipsi 0.9248915335825242
+NKI24_new Hammer Ipsi 0.9481429545202847
+NKI24_new Hammer Ipsi 0.9115738633469337
+NKI24_new Hammer Ipsi 0.913273952484748
+NKI24_new Hammer Ipsi 0.8939449286830116
+NKI24_new Hammer Ipsi 0.976573588843706
+NKI24_new Hammer Ipsi 0.9096661329427246
+NKI24_new Hammer Ipsi 0.9265052652773254
+NKI24_new Hammer Ipsi 0.8826095078223194
+NKI24_new Hammer Ipsi 0.9137205022935866
+NKI24_new Hammer Ipsi 0.8307108549824943
+NKI24_new Hammer Ipsi 0.909631138543199
+NKI24_new Hammer Ipsi 0.8955200337606951
+NKI24_new Hammer Ipsi 0.917470993813511
+NKI24_new Hammer Ipsi 0.9145567594350584
+NKI24_new Hammer Ipsi 0.9111481491673089
+NKI24_new Hammer Ipsi 0.8619440438316215
+NKI24_new Hammer Ipsi 0.9674983120541719
+NKI24_new Hammer Ipsi 0.8998158327259954
+NKI24_new Hammer Ipsi 0.9152322228144398
+NKI24_new Hammer Ipsi 0.960013834875697
+NKI24_new Hammer Ipsi 0.8461078838378752
+NKI24_new Hammer Ipsi 0.9225743092725095
+NKI24_new Hammer Ipsi 0.9410508900435631
+NKI24_new Hammer Ipsi 0.8770852337931171
+NKI24_new Hammer Ipsi 0.9603138007715902
+NKI24_new Hammer Ipsi 0.8887545040026282
+NKI24_new Hammer Ipsi 0.8916254106096281
+NKI24_new Hammer Hom 0.01685225777032836
+NKI24_new Hammer Hom 0.011155849862938508
+NKI24_new Hammer Hom 0.002919443056945612
+NKI24_new Hammer Hom 0.011012049851958961
+NKI24_new Hammer Hom 0.005434088610959214
+NKI24_new Hammer Hom 0.013417568543617985
+NKI24_new Hammer Hom 0.01434580284856358
+NKI24_new Hammer Hom 0.008044019174579486
+NKI24_new Hammer Hom 0.007347728537547612
+NKI24_new Hammer Hom 0.014119470365651258
+NKI24_new Hammer Hom 0.00925337975348919
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+NKI24_new Hammer Hom 0.009961576494760106
+NKI24_new Hammer Hom 0.0055811385933980644
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+NKI24_new Hammer Hom 0.009061873733370184
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+NKI24_new Hammer Hom 0.01918724936783224
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+NKI24_new Hammer Contra 0.09395906497734846
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+NKI24_new Hammer Contra 0.05298470606085601
+NKI24_new Hammer Contra 0.12102508521459283
+NKI24_new Hammer Contra 0.10639742478788779
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+NKI24_new Hammer Contra 0.034762012437572085
+NKI24_new Hammer Contra 0.08622827325246302
+NKI24_new Hammer Contra 0.0658550866639866
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+NKI24_new Hammer Contra 0.018495485754853536
+NKI24_new Hammer Contra 0.07918358297538954
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+NKI24_new Hammer Contra 0.07611772364821386
+NKI24_new Hammer Contra 0.1533556812498161
+NKI24_new Hammer Contra 0.07626893694088874
+NKI24_new Hammer Contra 0.09257003132724903
+NKI24_new Hammer Contra 0.06895473950108645
+NKI24_new Hammer Contra 0.07259656492398897
+NKI24_new Hammer Contra 0.07967928266850408
+NKI24_new Hammer Contra 0.12102504687505355
+NKI24_new Hammer Contra 0.027181524470289466
+NKI24_new Hammer Contra 0.08740211224367492
+NKI24_new Hammer Contra 0.07480620069080007
+NKI24_new Hammer Contra 0.034405026530904916
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+NKI24_new Hammer Contra 0.10412376814492677
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+NKI24_new Hammer Contra 0.09205824662953957
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+Dataset Atlas Type Value
+ABIDEII-TCD_1 Hammer Ipsi 0.8661529888995567
+ABIDEII-TCD_1 Hammer Ipsi 0.9707966661057394
+ABIDEII-TCD_1 Hammer Ipsi 0.9641013283205884
+ABIDEII-TCD_1 Hammer Ipsi 0.95182532439046
+ABIDEII-TCD_1 Hammer Ipsi 0.9044060092806859
+ABIDEII-TCD_1 Hammer Ipsi 0.9104334430946345
+ABIDEII-TCD_1 Hammer Ipsi 0.9504754915508282
+ABIDEII-TCD_1 Hammer Ipsi 0.912982780057027
+ABIDEII-TCD_1 Hammer Ipsi 0.9787789350337314
+ABIDEII-TCD_1 Hammer Ipsi 0.95849372429594
+ABIDEII-TCD_1 Hammer Ipsi 0.9046596953721519
+ABIDEII-TCD_1 Hammer Ipsi 0.9205622729586017
+ABIDEII-TCD_1 Hammer Ipsi 0.9441527878723447
+ABIDEII-TCD_1 Hammer Ipsi 0.9639249933126256
+ABIDEII-TCD_1 Hammer Ipsi 0.8423546485514728
+ABIDEII-TCD_1 Hammer Ipsi 0.9366521988750268
+ABIDEII-TCD_1 Hammer Ipsi 0.9073276194279475
+ABIDEII-TCD_1 Hammer Ipsi 0.9779966793830652
+ABIDEII-TCD_1 Hammer Ipsi 0.9176467275258932
+ABIDEII-TCD_1 Hammer Hom 0.016716864778632823
+ABIDEII-TCD_1 Hammer Hom 0.005678108276605935
+ABIDEII-TCD_1 Hammer Hom 0.006139114520094501
+ABIDEII-TCD_1 Hammer Hom 0.008823535767771512
+ABIDEII-TCD_1 Hammer Hom 0.015377689289659326
+ABIDEII-TCD_1 Hammer Hom 0.01405222813216084
+ABIDEII-TCD_1 Hammer Hom 0.008219486750473686
+ABIDEII-TCD_1 Hammer Hom 0.013567560587948703
+ABIDEII-TCD_1 Hammer Hom 0.00449976234838629
+ABIDEII-TCD_1 Hammer Hom 0.006417565567588337
+ABIDEII-TCD_1 Hammer Hom 0.012375232164461488
+ABIDEII-TCD_1 Hammer Hom 0.012444257254112825
+ABIDEII-TCD_1 Hammer Hom 0.010156708586065209
+ABIDEII-TCD_1 Hammer Hom 0.003933703544288826
+ABIDEII-TCD_1 Hammer Hom 0.01961242724698751
+ABIDEII-TCD_1 Hammer Hom 0.009599080355742707
+ABIDEII-TCD_1 Hammer Hom 0.01012557853919369
+ABIDEII-TCD_1 Hammer Hom 0.004204797718950659
+ABIDEII-TCD_1 Hammer Hom 0.010307811096147252
+ABIDEII-TCD_1 Hammer Contra 0.11713014632181051
+ABIDEII-TCD_1 Hammer Contra 0.023525225617654577
+ABIDEII-TCD_1 Hammer Contra 0.02975955715931711
+ABIDEII-TCD_1 Hammer Contra 0.03935113984176848
+ABIDEII-TCD_1 Hammer Contra 0.08021630142965479
+ABIDEII-TCD_1 Hammer Contra 0.07551432877320467
+ABIDEII-TCD_1 Hammer Contra 0.041305021698698076
+ABIDEII-TCD_1 Hammer Contra 0.07344965935502422
+ABIDEII-TCD_1 Hammer Contra 0.016721302617882378
+ABIDEII-TCD_1 Hammer Contra 0.03508871013647166
+ABIDEII-TCD_1 Hammer Contra 0.08296507246338666
+ABIDEII-TCD_1 Hammer Contra 0.06699346978728549
+ABIDEII-TCD_1 Hammer Contra 0.04569050354159005
+ABIDEII-TCD_1 Hammer Contra 0.032141303143085526
+ABIDEII-TCD_1 Hammer Contra 0.1380329242015397
+ABIDEII-TCD_1 Hammer Contra 0.05374872076923052
+ABIDEII-TCD_1 Hammer Contra 0.08254680203285884
+ABIDEII-TCD_1 Hammer Contra 0.017798522897984163
+ABIDEII-TCD_1 Hammer Contra 0.07204546137795954
+Dataset Atlas Type Value
+ABIDEII-BNI_1 Hammer Ipsi 0.9589958670960737
+ABIDEII-BNI_1 Hammer Ipsi 0.92334816805558
+ABIDEII-BNI_1 Hammer Ipsi 0.9330643374577876
+ABIDEII-BNI_1 Hammer Ipsi 0.9860718956052359
+ABIDEII-BNI_1 Hammer Ipsi 0.9371862118292605
+ABIDEII-BNI_1 Hammer Ipsi 0.9601064130950916
+ABIDEII-BNI_1 Hammer Ipsi 0.9589238968596628
+ABIDEII-BNI_1 Hammer Ipsi 0.9638683459512085
+ABIDEII-BNI_1 Hammer Ipsi 0.9584583460869344
+ABIDEII-BNI_1 Hammer Ipsi 0.9702970098374601
+ABIDEII-BNI_1 Hammer Ipsi 0.9657874951224696
+ABIDEII-BNI_1 Hammer Ipsi 0.9422421451429199
+ABIDEII-BNI_1 Hammer Ipsi 0.8681740336300752
+ABIDEII-BNI_1 Hammer Ipsi 0.9411763261266505
+ABIDEII-BNI_1 Hammer Ipsi 0.9817706097293378
+ABIDEII-BNI_1 Hammer Ipsi 0.8868277592855217
+ABIDEII-BNI_1 Hammer Ipsi 0.9652756302340793
+ABIDEII-BNI_1 Hammer Ipsi 0.9455956075435665
+ABIDEII-BNI_1 Hammer Ipsi 0.967477355744847
+ABIDEII-BNI_1 Hammer Ipsi 0.9647607567237475
+ABIDEII-BNI_1 Hammer Ipsi 0.9329961919885102
+ABIDEII-BNI_1 Hammer Ipsi 0.9622890804641255
+ABIDEII-BNI_1 Hammer Ipsi 0.9180121809597839
+ABIDEII-BNI_1 Hammer Ipsi 0.9757725482669334
+ABIDEII-BNI_1 Hammer Ipsi 0.9363072309786685
+ABIDEII-BNI_1 Hammer Ipsi 0.9253683981884492
+ABIDEII-BNI_1 Hammer Ipsi 0.953059498174209
+ABIDEII-BNI_1 Hammer Ipsi 0.9777433061033268
+ABIDEII-BNI_1 Hammer Ipsi 0.9506648808838413
+ABIDEII-BNI_1 Hammer Hom 0.005965031284859126
+ABIDEII-BNI_1 Hammer Hom 0.01516141306546118
+ABIDEII-BNI_1 Hammer Hom 0.009660719746050967
+ABIDEII-BNI_1 Hammer Hom 0.0028399278791102307
+ABIDEII-BNI_1 Hammer Hom 0.009728387572012021
+ABIDEII-BNI_1 Hammer Hom 0.006705549923036216
+ABIDEII-BNI_1 Hammer Hom 0.007097142523493329
+ABIDEII-BNI_1 Hammer Hom 0.007107929287873375
+ABIDEII-BNI_1 Hammer Hom 0.006452532504689137
+ABIDEII-BNI_1 Hammer Hom 0.003823561081672522
+ABIDEII-BNI_1 Hammer Hom 0.006591407298371619
+ABIDEII-BNI_1 Hammer Hom 0.005961272320014417
+ABIDEII-BNI_1 Hammer Hom 0.014603981389037268
+ABIDEII-BNI_1 Hammer Hom 0.007853389212037416
+ABIDEII-BNI_1 Hammer Hom 0.0019874774947902397
+ABIDEII-BNI_1 Hammer Hom 0.013141181537956658
+ABIDEII-BNI_1 Hammer Hom 0.006153615200677682
+ABIDEII-BNI_1 Hammer Hom 0.007584546828996578
+ABIDEII-BNI_1 Hammer Hom 0.006251992370398896
+ABIDEII-BNI_1 Hammer Hom 0.005095290196178214
+ABIDEII-BNI_1 Hammer Hom 0.010124354585038004
+ABIDEII-BNI_1 Hammer Hom 0.005189151727744231
+ABIDEII-BNI_1 Hammer Hom 0.012023964598418983
+ABIDEII-BNI_1 Hammer Hom 0.004569390017247466
+ABIDEII-BNI_1 Hammer Hom 0.008025497626844255
+ABIDEII-BNI_1 Hammer Hom 0.008164700635474
+ABIDEII-BNI_1 Hammer Hom 0.007969351955278803
+ABIDEII-BNI_1 Hammer Hom 0.0029875601551468934
+ABIDEII-BNI_1 Hammer Hom 0.00636530571430303
+ABIDEII-BNI_1 Hammer Contra 0.03503910161906719
+ABIDEII-BNI_1 Hammer Contra 0.061490418878958904
+ABIDEII-BNI_1 Hammer Contra 0.057274942796161465
+ABIDEII-BNI_1 Hammer Contra 0.011088176515653919
+ABIDEII-BNI_1 Hammer Contra 0.05308540059872743
+ABIDEII-BNI_1 Hammer Contra 0.03318803698187218
+ABIDEII-BNI_1 Hammer Contra 0.033978960616843835
+ABIDEII-BNI_1 Hammer Contra 0.02902372476091816
+ABIDEII-BNI_1 Hammer Contra 0.03508912140837655
+ABIDEII-BNI_1 Hammer Contra 0.02587942908086728
+ABIDEII-BNI_1 Hammer Contra 0.02762109757915875
+ABIDEII-BNI_1 Hammer Contra 0.05179658253706569
+ABIDEII-BNI_1 Hammer Contra 0.11722198498088761
+ABIDEII-BNI_1 Hammer Contra 0.0509702846613121
+ABIDEII-BNI_1 Hammer Contra 0.01624191277587196
+ABIDEII-BNI_1 Hammer Contra 0.10003105917652161
+ABIDEII-BNI_1 Hammer Contra 0.028570754565243033
+ABIDEII-BNI_1 Hammer Contra 0.046819845627436935
+ABIDEII-BNI_1 Hammer Contra 0.026270651884754137
+ABIDEII-BNI_1 Hammer Contra 0.0301439530800743
+ABIDEII-BNI_1 Hammer Contra 0.05687945342645177
+ABIDEII-BNI_1 Hammer Contra 0.03252176780813022
+ABIDEII-BNI_1 Hammer Contra 0.06996385444179712
+ABIDEII-BNI_1 Hammer Contra 0.01965806171581916
+ABIDEII-BNI_1 Hammer Contra 0.05566727139448726
+ABIDEII-BNI_1 Hammer Contra 0.06646690117607686
+ABIDEII-BNI_1 Hammer Contra 0.03897114987051218
+ABIDEII-BNI_1 Hammer Contra 0.019269133741526313
+ABIDEII-BNI_1 Hammer Contra 0.042969813401855676
+Dataset Atlas Type Value
+ABIDEII-SDSU_1 Hammer Ipsi 0.8664379968072288
+ABIDEII-SDSU_1 Hammer Ipsi 0.9059426921777587
+ABIDEII-SDSU_1 Hammer Ipsi 0.9338363260311919
+ABIDEII-SDSU_1 Hammer Ipsi 0.9563274689598836
+ABIDEII-SDSU_1 Hammer Ipsi 0.93009491363436
+ABIDEII-SDSU_1 Hammer Ipsi 0.9310433329406527
+ABIDEII-SDSU_1 Hammer Ipsi 0.9330855927722798
+ABIDEII-SDSU_1 Hammer Ipsi 0.9059799917706406
+ABIDEII-SDSU_1 Hammer Ipsi 0.908050872475161
+ABIDEII-SDSU_1 Hammer Ipsi 0.9083151350529878
+ABIDEII-SDSU_1 Hammer Ipsi 0.9037178294895603
+ABIDEII-SDSU_1 Hammer Ipsi 0.8903399990701208
+ABIDEII-SDSU_1 Hammer Ipsi 0.918325261400849
+ABIDEII-SDSU_1 Hammer Ipsi 0.935725434863968
+ABIDEII-SDSU_1 Hammer Ipsi 0.9489788688716766
+ABIDEII-SDSU_1 Hammer Ipsi 0.920955981763504
+ABIDEII-SDSU_1 Hammer Ipsi 0.9216486745804036
+ABIDEII-SDSU_1 Hammer Ipsi 0.9477495850469427
+ABIDEII-SDSU_1 Hammer Ipsi 0.9374469987854158
+ABIDEII-SDSU_1 Hammer Ipsi 0.8997265682195059
+ABIDEII-SDSU_1 Hammer Ipsi 0.8832179870505502
+ABIDEII-SDSU_1 Hammer Ipsi 0.9395383631433374
+ABIDEII-SDSU_1 Hammer Ipsi 0.9412616799958389
+ABIDEII-SDSU_1 Hammer Hom 0.023569678577071
+ABIDEII-SDSU_1 Hammer Hom 0.015138765189439308
+ABIDEII-SDSU_1 Hammer Hom 0.008774991770339684
+ABIDEII-SDSU_1 Hammer Hom 0.007750835922745766
+ABIDEII-SDSU_1 Hammer Hom 0.019898632458210948
+ABIDEII-SDSU_1 Hammer Hom 0.00958072524701609
+ABIDEII-SDSU_1 Hammer Hom 0.01255103680705816
+ABIDEII-SDSU_1 Hammer Hom 0.013339007586634595
+ABIDEII-SDSU_1 Hammer Hom 0.013019885843239586
+ABIDEII-SDSU_1 Hammer Hom 0.012135643643911597
+ABIDEII-SDSU_1 Hammer Hom 0.01492770065372973
+ABIDEII-SDSU_1 Hammer Hom 0.017108172194370382
+ABIDEII-SDSU_1 Hammer Hom 0.014651182096832563
+ABIDEII-SDSU_1 Hammer Hom 0.007349125838043161
+ABIDEII-SDSU_1 Hammer Hom 0.009443912382199743
+ABIDEII-SDSU_1 Hammer Hom 0.015212520074018167
+ABIDEII-SDSU_1 Hammer Hom 0.011788331487000737
+ABIDEII-SDSU_1 Hammer Hom 0.010965407286018903
+ABIDEII-SDSU_1 Hammer Hom 0.009759785008903414
+ABIDEII-SDSU_1 Hammer Hom 0.012492138344776634
+ABIDEII-SDSU_1 Hammer Hom 0.01626211378388837
+ABIDEII-SDSU_1 Hammer Hom 0.007918752914835167
+ABIDEII-SDSU_1 Hammer Hom 0.013346183250682325
+ABIDEII-SDSU_1 Hammer Contra 0.10999232461570023
+ABIDEII-SDSU_1 Hammer Contra 0.07891854263280203
+ABIDEII-SDSU_1 Hammer Contra 0.05738868219846835
+ABIDEII-SDSU_1 Hammer Contra 0.035921695117370644
+ABIDEII-SDSU_1 Hammer Contra 0.050006453907429
+ABIDEII-SDSU_1 Hammer Contra 0.059375941812331204
+ABIDEII-SDSU_1 Hammer Contra 0.054363370420662076
+ABIDEII-SDSU_1 Hammer Contra 0.08068100064272482
+ABIDEII-SDSU_1 Hammer Contra 0.07892924168159943
+ABIDEII-SDSU_1 Hammer Contra 0.07954922130310058
+ABIDEII-SDSU_1 Hammer Contra 0.08135446985670998
+ABIDEII-SDSU_1 Hammer Contra 0.09255182873550871
+ABIDEII-SDSU_1 Hammer Contra 0.06702355650231841
+ABIDEII-SDSU_1 Hammer Contra 0.056925439297988906
+ABIDEII-SDSU_1 Hammer Contra 0.04157721874612361
+ABIDEII-SDSU_1 Hammer Contra 0.06383149816247786
+ABIDEII-SDSU_1 Hammer Contra 0.06656299393259572
+ABIDEII-SDSU_1 Hammer Contra 0.041285007667038354
+ABIDEII-SDSU_1 Hammer Contra 0.05279321620568071
+ABIDEII-SDSU_1 Hammer Contra 0.08778129343571739
+ABIDEII-SDSU_1 Hammer Contra 0.10051989916556138
+ABIDEII-SDSU_1 Hammer Contra 0.052542883941827455
+ABIDEII-SDSU_1 Hammer Contra 0.04539213675347885
+Dataset Atlas Type Value
+MRN_1 Hammer Ipsi 0.8101602996782018
+MRN_1 Hammer Ipsi 0.7899808853330661
+MRN_1 Hammer Ipsi 0.858464584730775
+MRN_1 Hammer Ipsi 0.8687114881156314
+MRN_1 Hammer Ipsi 0.8611423917669473
+MRN_1 Hammer Ipsi 0.8385354547569387
+MRN_1 Hammer Ipsi 0.8791502149376965
+MRN_1 Hammer Ipsi 0.8470104463081366
+MRN_1 Hammer Ipsi 0.8561224264279904
+MRN_1 Hammer Ipsi 0.8704022214892536
+MRN_1 Hammer Ipsi 0.7843992266365275
+MRN_1 Hammer Ipsi 0.8224414810968278
+MRN_1 Hammer Ipsi 0.8388176943213173
+MRN_1 Hammer Ipsi 0.8503704254400437
+MRN_1 Hammer Ipsi 0.7765376136887002
+MRN_1 Hammer Ipsi 0.830618453337071
+MRN_1 Hammer Ipsi 0.8737119758186815
+MRN_1 Hammer Ipsi 0.7856105117999349
+MRN_1 Hammer Ipsi 0.8440285909479613
+MRN_1 Hammer Hom 0.021155702983730255
+MRN_1 Hammer Hom 0.018695818289382507
+MRN_1 Hammer Hom 0.01384778586251611
+MRN_1 Hammer Hom 0.013959495111389006
+MRN_1 Hammer Hom 0.013661147068988664
+MRN_1 Hammer Hom 0.01738051559236081
+MRN_1 Hammer Hom 0.012956821510181923
+MRN_1 Hammer Hom 0.017376856642122747
+MRN_1 Hammer Hom 0.01402896062316578
+MRN_1 Hammer Hom 0.012912540003744083
+MRN_1 Hammer Hom 0.024330710614333177
+MRN_1 Hammer Hom 0.017372692657607925
+MRN_1 Hammer Hom 0.019691311630886648
+MRN_1 Hammer Hom 0.012403213988803645
+MRN_1 Hammer Hom 0.026129911614986
+MRN_1 Hammer Hom 0.019229798271398275
+MRN_1 Hammer Hom 0.013118520778012144
+MRN_1 Hammer Hom 0.021294227290021773
+MRN_1 Hammer Hom 0.014446395779205439
+MRN_1 Hammer Contra 0.168683997338068
+MRN_1 Hammer Contra 0.1913232963775514
+MRN_1 Hammer Contra 0.12768762940670883
+MRN_1 Hammer Contra 0.11732901677297966
+MRN_1 Hammer Contra 0.12519646116406408
+MRN_1 Hammer Contra 0.14408402965070052
+MRN_1 Hammer Contra 0.1078929635521216
+MRN_1 Hammer Contra 0.1356126970497407
+MRN_1 Hammer Contra 0.12984861294884384
+MRN_1 Hammer Contra 0.11668523850700233
+MRN_1 Hammer Contra 0.19127006274913927
+MRN_1 Hammer Contra 0.16018582624556427
+MRN_1 Hammer Contra 0.14149099404779614
+MRN_1 Hammer Contra 0.13722636057115264
+MRN_1 Hammer Contra 0.19733247469631374
+MRN_1 Hammer Contra 0.15015174839153075
+MRN_1 Hammer Contra 0.11316950340330627
+MRN_1 Hammer Contra 0.1930952609100433
+MRN_1 Hammer Contra 0.14152501327283326
diff --git a/docs/paper/data/func_ipsi_aal.csv b/docs/paper/data/func_ipsi_aal.csv
new file mode 100644
index 000000000..4fbd59f66
--- /dev/null
+++ b/docs/paper/data/func_ipsi_aal.csv
@@ -0,0 +1,10519 @@
+Dataset Atlas Type Value
+SWU4 AAL Ipsi 0.3809864125831821
+SWU4 AAL Ipsi 0.24719758832425892
+SWU4 AAL Ipsi 0.18557390502117366
+SWU4 AAL Ipsi 0.22453663067150637
+SWU4 AAL Ipsi 0.2700859513006655
+SWU4 AAL Ipsi 0.29444635299455535
+SWU4 AAL Ipsi 0.2435037274652148
+SWU4 AAL Ipsi 0.2866381170598911
+SWU4 AAL Ipsi 0.23421222625529342
+SWU4 AAL Ipsi 0.2723878932244404
+SWU4 AAL Ipsi 0.25417404022988505
+SWU4 AAL Ipsi 0.29329745977011495
+SWU4 AAL Ipsi 0.24633672323049002
+SWU4 AAL Ipsi 0.28171957199032066
+SWU4 AAL Ipsi 0.19327461191772535
+SWU4 AAL Ipsi 0.40739747186932845
+SWU4 AAL Ipsi 0.2849568272837266
+SWU4 AAL Ipsi 0.2585197743496672
+SWU4 AAL Ipsi 0.22647609800362978
+SWU4 AAL Ipsi 0.355922128554144
+SWU4 AAL Ipsi 0.2792920756200847
+SWU4 AAL Ipsi 0.2532367770719903
+SWU4 AAL Ipsi 0.27306482819116756
+SWU4 AAL Ipsi 0.3575868587416818
+SWU4 AAL Ipsi 0.28222788354506956
+SWU4 AAL Ipsi 0.3506331161524501
+SWU4 AAL Ipsi 0.21181741651542646
+SWU4 AAL Ipsi 0.32926064761040524
+SWU4 AAL Ipsi 0.309888796430732
+SWU4 AAL Ipsi 0.257309887477314
+SWU4 AAL Ipsi 0.25283446763460377
+SWU4 AAL Ipsi 0.41622664035087725
+SWU4 AAL Ipsi 0.3884538759830611
+SWU4 AAL Ipsi 0.31078447912885665
+SWU4 AAL Ipsi 0.2888184564428312
+SWU4 AAL Ipsi 0.22255288415003027
+SWU4 AAL Ipsi 0.4308437265577737
+SWU4 AAL Ipsi 0.29079337174833636
+SWU4 AAL Ipsi 0.23473439655172415
+SWU4 AAL Ipsi 0.28119153871748337
+SWU4 AAL Ipsi 0.37841423260738055
+SWU4 AAL Ipsi 0.3509875369026013
+SWU4 AAL Ipsi 0.2196060217785844
+SWU4 AAL Ipsi 0.24007083908045976
+SWU4 AAL Ipsi 0.2315955686630369
+SWU4 AAL Ipsi 0.28170130671506355
+SWU4 AAL Ipsi 0.2537701954022989
+SWU4 AAL Ipsi 0.2882141863278887
+SWU4 AAL Ipsi 0.27685334361766484
+SWU4 AAL Ipsi 0.32763626618269814
+SWU4 AAL Ipsi 0.28040033182093166
+SWU4 AAL Ipsi 0.2560061887477314
+SWU4 AAL Ipsi 0.3962963469449486
+SWU4 AAL Ipsi 0.21516677737447065
+SWU4 AAL Ipsi 0.28332396309739866
+SWU4 AAL Ipsi 0.33595646430732
+SWU4 AAL Ipsi 0.32599313430127047
+SWU4 AAL Ipsi 0.36594927404718697
+SWU4 AAL Ipsi 0.377443162431942
+SWU4 AAL Ipsi 0.3184303009679371
+SWU4 AAL Ipsi 0.4554699150030248
+SWU4 AAL Ipsi 0.3193763611615245
+SWU4 AAL Ipsi 0.23601138021778587
+SWU4 AAL Ipsi 0.30463699485783424
+SWU4 AAL Ipsi 0.24756982970356925
+SWU4 AAL Ipsi 0.25352677495462794
+SWU4 AAL Ipsi 0.31432290108892924
+SWU4 AAL Ipsi 0.24965272413793102
+SWU4 AAL Ipsi 0.24329528735632183
+SWU4 AAL Ipsi 0.3612197825166364
+SWU4 AAL Ipsi 0.38869124107683006
+SWU4 AAL Ipsi 0.37276769540229887
+SWU4 AAL Ipsi 0.2354433729582577
+SWU4 AAL Ipsi 0.23961230036297643
+SWU4 AAL Ipsi 0.3837781935874168
+SWU4 AAL Ipsi 0.4047585396249244
+SWU4 AAL Ipsi 0.23841472171808833
+SWU4 AAL Ipsi 0.3349948520871144
+SWU4 AAL Ipsi 0.26527641379310346
+SWU4 AAL Ipsi 0.3943213542044767
+SWU4 AAL Ipsi 0.23394600453720507
+SWU4 AAL Ipsi 0.2223070054446461
+SWU4 AAL Ipsi 0.3173313548094374
+SWU4 AAL Ipsi 0.2863903711433757
+SWU4 AAL Ipsi 0.31817937568058074
+SWU4 AAL Ipsi 0.2937667447065941
+SWU4 AAL Ipsi 0.24162530369026014
+SWU4 AAL Ipsi 0.34865481397459164
+SWU4 AAL Ipsi 0.3518972943133697
+SWU4 AAL Ipsi 0.30449511615245006
+SWU4 AAL Ipsi 0.25590656442831217
+SWU4 AAL Ipsi 0.31004552419842707
+SWU4 AAL Ipsi 0.24618387840290382
+SWU4 AAL Ipsi 0.3055861881427707
+SWU4 AAL Ipsi 0.2668939016938899
+SWU4 AAL Ipsi 0.2212205263157895
+SWU4 AAL Ipsi 0.2651297238354507
+SWU4 AAL Ipsi 0.2313762885662432
+SWU4 AAL Ipsi 0.3331981467029643
+SWU4 AAL Ipsi 0.24015962764670296
+SWU4 AAL Ipsi 0.28304483968542044
+SWU4 AAL Ipsi 0.35527159467634606
+SWU4 AAL Ipsi 0.30779364186327884
+SWU4 AAL Ipsi 0.30254789624924383
+SWU4 AAL Ipsi 0.25903223169993955
+SWU4 AAL Ipsi 0.26943016152450094
+SWU4 AAL Ipsi 0.286371184815487
+SWU4 AAL Ipsi 0.25456496218995767
+SWU4 AAL Ipsi 0.36043301663641863
+SWU4 AAL Ipsi 0.28009733605565634
+SWU4 AAL Ipsi 0.2684379758015729
+SWU4 AAL Ipsi 0.25591610042347246
+SWU4 AAL Ipsi 0.27003331518451307
+SWU4 AAL Ipsi 0.220082961584997
+SWU4 AAL Ipsi 0.4285838623714458
+SWU4 AAL Ipsi 0.22256914458560192
+SWU4 AAL Ipsi 0.27569834906231094
+SWU4 AAL Ipsi 0.3210174872958258
+SWU4 AAL Ipsi 0.3022029591651543
+SWU4 AAL Ipsi 0.25801181367211135
+SWU4 AAL Ipsi 0.2986460462794919
+SWU4 AAL Ipsi 0.3700827504537205
+SWU4 AAL Ipsi 0.27131786237144584
+SWU4 AAL Ipsi 0.3599906745311555
+SWU4 AAL Ipsi 0.257981463399879
+SWU4 AAL Ipsi 0.3237052168784029
+SWU4 AAL Ipsi 0.3211068263762855
+SWU4 AAL Ipsi 0.19963943103448276
+SWU4 AAL Ipsi 0.28983381911675743
+SWU4 AAL Ipsi 0.35202930127041737
+SWU4 AAL Ipsi 0.43974270326678766
+SWU4 AAL Ipsi 0.22917281125226863
+SWU4 AAL Ipsi 0.3825919888082275
+SWU4 AAL Ipsi 0.3063633469449486
+SWU4 AAL Ipsi 0.26021562310949786
+SWU4 AAL Ipsi 0.3177194694494858
+SWU4 AAL Ipsi 0.2641821191772535
+SWU4 AAL Ipsi 0.327636242891712
+SWU4 AAL Ipsi 0.2506823868723533
+SWU4 AAL Ipsi 0.24055638475499092
+SWU4 AAL Ipsi 0.316966018753781
+SWU4 AAL Ipsi 0.3661223663036903
+SWU4 AAL Ipsi 0.3139711860254084
+SWU4 AAL Ipsi 0.23418561252268602
+SWU4 AAL Ipsi 0.23905015880217786
+SWU4 AAL Ipsi 0.29804476043557165
+SWU4 AAL Ipsi 0.24987002934059285
+SWU4 AAL Ipsi 0.4104456781609196
+SWU4 AAL Ipsi 0.3236566104053236
+SWU4 AAL Ipsi 0.24134364670296432
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+SWU4 AAL Contra 0.30579080586634405
+SWU4 AAL Contra 0.3283988726942848
+SWU4 AAL Contra 0.2130488908376172
+SWU4 AAL Contra 0.24526539673420014
+SWU4 AAL Contra 0.24503302600544302
+SWU4 AAL Contra 0.49595016117326884
+SWU4 AAL Contra 0.27402611732688237
+SWU4 AAL Contra 0.31393364197157547
+SWU4 AAL Contra 0.24156502902933172
+SWU4 AAL Contra 0.31407410281221654
+SWU4 AAL Contra 0.23959704293921982
+SWU4 AAL Contra 0.25128619715754463
+SWU4 AAL Contra 0.3091107998185667
+SWU4 AAL Contra 0.2897219730873904
+SWU4 AAL Contra 0.27337752918052616
+SWU4 AAL Contra 0.3893472899909283
+SWU4 AAL Contra 0.2675651212579377
+SWU4 AAL Contra 0.33043881947384335
+SWU4 AAL Contra 0.2046576056849108
+SWU4 AAL Contra 0.33403124886604174
+SWU4 AAL Contra 0.23614184426973087
+SWU4 AAL Contra 0.3158855687934684
+SWU4 AAL Contra 0.3157448482007862
+SWU4 AAL Contra 0.2679614995464167
+SWU4 AAL Contra 0.23091618475960085
+SWU4 AAL Contra 0.2557818905352283
+SWU4 AAL Contra 0.23172456697913518
+SWU4 AAL Contra 0.26353660840641063
+SWU4 AAL Contra 0.30956099546416693
+SWU4 AAL Contra 0.2235166510432416
+SWU4 AAL Contra 0.3016266516480193
+SWU4 AAL Contra 0.2749458521318416
+SWU4 AAL Contra 0.2358017526459026
+SWU4 AAL Contra 0.29823596915633505
+SWU4 AAL Contra 0.2661690244934986
+SWU4 AAL Contra 0.30533677351073485
+SWU4 AAL Contra 0.18812756788630175
+SWU4 AAL Contra 0.25367983338373146
+SWU4 AAL Contra 0.6102539664348352
+SWU4 AAL Contra 0.19341702207438766
+SWU4 AAL Contra 0.2627027668581796
+SWU4 AAL Contra 0.2369953051103719
+SWU4 AAL Contra 0.38728140217719986
+SWU4 AAL Contra 0.25095654248563654
+SWU4 AAL Contra 0.3213331130934382
+SWU4 AAL Contra 0.47362740489869976
+SWU4 AAL Contra 0.23148448896280618
+SWU4 AAL Contra 0.21075660175385547
+SWU4 AAL Contra 0.2658882567281524
+SWU4 AAL Contra 0.3041875397641367
+SWU4 AAL Contra 0.2838034635621409
+SWU4 AAL Contra 0.22989778076806774
+SWU4 AAL Contra 0.33111716631387966
+SWU4 AAL Contra 0.39830184668884183
+SWU4 AAL Contra 0.28902464953129725
+SWU4 AAL Contra 0.3536658430601754
+SWU4 AAL Contra 0.2862317834895676
+SWU4 AAL Contra 0.27442142455397645
+SWU4 AAL Contra 0.23520297883277894
+SWU4 AAL Contra 0.21944364680979742
+SWU4 AAL Contra 0.20723514061082554
+SWU4 AAL Contra 0.2962315306924705
+SWU4 AAL Contra 0.30734910130027215
+SWU4 AAL Contra 0.30521427608104024
+SWU4 AAL Contra 0.286494288478984
+SWU4 AAL Contra 0.3294507695796795
+SWU4 AAL Contra 0.38342712276988206
+SWU4 AAL Contra 0.24566746265497433
+SWU4 AAL Contra 0.21000323737526458
+SWU4 AAL Contra 0.2320287487148473
+SWU4 AAL Contra 0.2400893779860901
+SWU4 AAL Contra 0.29130043513758697
+SWU4 AAL Contra 0.1974870187481101
+SWU4 AAL Contra 0.423386777744179
+SWU4 AAL Contra 0.25856437254309045
+SWU4 AAL Contra 0.3734835899606894
+SWU4 AAL Contra 0.24055356607196857
+SWU4 AAL Contra 0.24963862201390985
+SWU4 AAL Contra 0.2668038091926217
+SWU4 AAL Contra 0.263990933776837
+SWU4 AAL Contra 0.23343141548231028
+SWU4 AAL Contra 0.28065546567886307
+SWU4 AAL Contra 0.23119575113395827
+SWU4 AAL Contra 0.2645767859086786
+SWU4 AAL Contra 0.28013124553976415
+SWU4 AAL Contra 0.3627094496522528
+SWU4 AAL Contra 0.25239872210462655
+SWU4 AAL Contra 0.44451372452373755
+SWU4 AAL Contra 0.2963073102509828
+SWU4 AAL Contra 0.36668446779558517
+SWU4 AAL Contra 0.3023143804052011
+SWU4 AAL Contra 0.32414257030541277
+SWU4 AAL Contra 0.2452647142425159
+SWU4 AAL Contra 0.32756330299364983
+SWU4 AAL Contra 0.3030469331720593
+SWU4 AAL Contra 0.2960878067735107
+SWU4 AAL Contra 0.2843061212579377
+SWU4 AAL Contra 0.24644505352283036
+SWU4 AAL Contra 0.23829315603265797
+SWU4 AAL Contra 0.2874373997580889
+SWU4 AAL Contra 0.45473687420622927
+SWU4 AAL Contra 0.31751575536740245
+SWU4 AAL Contra 0.2421965890535228
+SWU4 AAL Contra 0.3103162143937103
+SWU4 AAL Contra 0.2901838040520109
+SWU4 AAL Contra 0.2640048736014515
+SWU4 AAL Contra 0.18704187087995164
+SWU4 AAL Contra 0.2580754481403084
+SWU4 AAL Contra 0.238068118234049
+SWU4 AAL Contra 0.3223258751133958
+SWU4 AAL Contra 0.3122748729966737
+SWU4 AAL Contra 0.24684124009676445
+SWU4 AAL Contra 0.2186585155730269
+SWU4 AAL Contra 0.3112841590565467
+SWU4 AAL Contra 0.2529790792258845
+SWU4 AAL Contra 0.2615379189597823
+SWU4 AAL Contra 0.1495003728454793
+SWU4 AAL Contra 0.2612878197762322
+SWU4 AAL Contra 0.30823137042636833
+SWU4 AAL Contra 0.27139834593286966
+SWU4 AAL Contra 0.30705902902933174
+SWU4 AAL Contra 0.25229124614454185
+SWU4 AAL Contra 0.22393786846084063
+SWU4 AAL Contra 0.20365736468097972
+SWU4 AAL Contra 0.28267474387662533
+SWU4 AAL Contra 0.2975346909585727
+SWU4 AAL Contra 0.30015515119443603
+SWU4 AAL Contra 0.26525410009071665
+SWU4 AAL Contra 0.2630008457816752
+SWU4 AAL Contra 0.31039558361052316
+SWU4 AAL Contra 0.2676751006954944
+SWU4 AAL Contra 0.34021969942546115
+SWU4 AAL Contra 0.297460326277593
+SWU4 AAL Contra 0.26049574962201394
+SWU4 AAL Contra 0.3298427181735712
+SWU4 AAL Contra 0.24739013940127003
+SWU4 AAL Contra 0.2893641363773813
+SWU4 AAL Contra 0.3845980892047173
+SWU4 AAL Contra 0.27363836014514664
+SWU4 AAL Contra 0.2972525978228001
+SWU4 AAL Contra 0.26691612216510435
+SWU4 AAL Contra 0.25770043604475357
+SWU4 AAL Contra 0.32888279256123376
+SWU4 AAL Contra 0.28730717508315695
+SWU4 AAL Contra 0.23219154036891443
+SWU4 AAL Contra 0.36362998004233443
+SWU4 AAL Contra 0.3254495158754158
+SWU4 AAL Contra 0.3705095566979135
+SWU4 AAL Contra 0.40695729059570607
+SWU4 AAL Contra 0.27780298881161175
+SWU4 AAL Contra 0.242848328696704
+SWU4 AAL Contra 0.2498672848503175
+SWU4 AAL Contra 0.23131745449047475
+SWU4 AAL Contra 0.24666812397943752
+SWU4 AAL Contra 0.23419964197157545
+SWU4 AAL Contra 0.2841594962201391
+SWU4 AAL Contra 0.2593437130329604
+SWU4 AAL Contra 0.33663711551254916
+SWU4 AAL Contra 0.3186173392803145
+SWU4 AAL Contra 0.32345136286664644
+SWU4 AAL Contra 0.19844525703054128
+SWU4 AAL Contra 0.21537537889325675
+SWU4 AAL Contra 0.2830408929543393
+SWU4 AAL Contra 0.3937270015119444
+SWU4 AAL Contra 0.31501890384033865
+SWU4 AAL Contra 0.21277223677048682
+SWU4 AAL Contra 0.2575148218929543
+SWU4 AAL Contra 0.3909977426670698
+SWU4 AAL Contra 0.25313534290898093
+SWU4 AAL Contra 0.2738466700937406
+SWU4 AAL Contra 0.2823509815542788
+SWU4 AAL Contra 0.3412211487753251
+SWU4 AAL Contra 0.2508321297248261
+SWU4 AAL Contra 0.35844830178409437
+SWU4 AAL Contra 0.3425952706380405
+SWU4 AAL Contra 0.25290475052918054
+SWU4 AAL Contra 0.2712200907166616
+SWU4 AAL Contra 0.424707331115815
+SWU4 AAL Contra 0.23584997671605684
+SWU4 AAL Contra 0.26198594436044753
+SWU4 AAL Contra 0.3484342534018748
+SWU4 AAL Contra 0.3371171799213789
+SWU4 AAL Contra 0.3086103710311461
+SWU4 AAL Contra 0.25966613305110375
+SWU4 AAL Contra 0.25450097853039005
+SWU4 AAL Contra 0.2964601390988812
+SWU4 AAL Contra 0.2562324956153613
+SWU4 AAL Contra 0.3046345285757484
+SWU4 AAL Contra 0.2812402503779861
+SWU4 AAL Contra 0.268524995161778
+SWU4 AAL Contra 0.2929704962201391
+SWU4 AAL Contra 0.22848802328394316
+SWU4 AAL Contra 0.2452915500453583
+SWU4 AAL Contra 0.3219061775022679
+SWU4 AAL Contra 0.26487765164801935
+SWU4 AAL Contra 0.24992445993347445
+SWU4 AAL Contra 0.22716580949501056
+SWU4 AAL Contra 0.28329101028122167
+SWU4 AAL Contra 0.2592747614151799
+SWU4 AAL Contra 0.15035175385545813
+SWU4 AAL Contra 0.23345858058663443
+SWU4 AAL Contra 0.3274046437859086
+SWU4 AAL Contra 0.3228767075899607
+SWU4 AAL Contra 0.23151170003023885
+SWU4 AAL Contra 0.21531657332930149
+SWU4 AAL Contra 0.2634579585727245
+SWU4 AAL Contra 0.21610937012397943
+SWU4 AAL Contra 0.30066397550650137
+SWU4 AAL Contra 0.2753234768672512
+SWU4 AAL Contra 0.2662405954036891
+SWU4 AAL Contra 0.25576828485031755
+SWU4 AAL Contra 0.23607555276685818
+SWU4 AAL Contra 0.4456433631690353
+SWU4 AAL Contra 0.35003603658905347
+SWU4 AAL Contra 0.36395582340489874
+SWU4 AAL Contra 0.23107714938010282
+SWU4 AAL Contra 0.35391069337768366
+SWU4 AAL Contra 0.2724172552162081
+SWU4 AAL Contra 0.2277439035379498
+SWU4 AAL Contra 0.2949737456909586
+SWU4 AAL Contra 0.273982470517085
+SWU4 AAL Contra 0.2644822788025401
+SWU4 AAL Contra 0.33946820199576655
+SWU4 AAL Contra 0.3110219449652252
+SWU4 AAL Contra 0.2622712452373752
+SWU4 AAL Contra 0.24350457605080134
+SWU4 AAL Contra 0.30197249228908374
+Dataset Atlas Type Value
+HNU1 AAL Ipsi 0.3031935780399274
+HNU1 AAL Ipsi 0.3632487592256503
+HNU1 AAL Ipsi 0.2840428681185723
+HNU1 AAL Ipsi 0.277476666969147
+HNU1 AAL Ipsi 0.3525320719903206
+HNU1 AAL Ipsi 0.31614056896551723
+HNU1 AAL Ipsi 0.40050455595886275
+HNU1 AAL Ipsi 0.2537925698729583
+HNU1 AAL Ipsi 0.2423049525105868
+HNU1 AAL Ipsi 0.5476454237749546
+HNU1 AAL Ipsi 0.27768367876588024
+HNU1 AAL Ipsi 0.38406348669086504
+HNU1 AAL Ipsi 0.33828740048396855
+HNU1 AAL Ipsi 0.2511396503327284
+HNU1 AAL Ipsi 0.26911329794313366
+HNU1 AAL Ipsi 0.34260394676346034
+HNU1 AAL Ipsi 0.22400488475499097
+HNU1 AAL Ipsi 0.23328121506352087
+HNU1 AAL Ipsi 0.2530999207501512
+HNU1 AAL Ipsi 0.24567635601935875
+HNU1 AAL Ipsi 0.22074135904416214
+HNU1 AAL Ipsi 0.3581876327888687
+HNU1 AAL Ipsi 0.32620555535390205
+HNU1 AAL Ipsi 0.28625323835450694
+HNU1 AAL Ipsi 0.47726448850574715
+HNU1 AAL Ipsi 0.2286748729582577
+HNU1 AAL Ipsi 0.38240820387174834
+HNU1 AAL Ipsi 0.3379166376285541
+HNU1 AAL Ipsi 0.26469605747126435
+HNU1 AAL Ipsi 0.27634907017543864
+HNU1 AAL Ipsi 0.27941849122807016
+HNU1 AAL Ipsi 0.26656104718693285
+HNU1 AAL Ipsi 0.2090727425892317
+HNU1 AAL Ipsi 0.28634225166364186
+HNU1 AAL Ipsi 0.2905934582577132
+HNU1 AAL Ipsi 0.3863362316999395
+HNU1 AAL Ipsi 0.31965495220810647
+HNU1 AAL Ipsi 0.21574911101028435
+HNU1 AAL Ipsi 0.3452028735632184
+HNU1 AAL Ipsi 0.24231806382335147
+HNU1 AAL Ipsi 0.20760128644888082
+HNU1 AAL Ipsi 0.287470718693285
+HNU1 AAL Ipsi 0.23831827313974596
+HNU1 AAL Ipsi 0.2907696854204477
+HNU1 AAL Ipsi 0.32805186297640654
+HNU1 AAL Ipsi 0.24958118058076226
+HNU1 AAL Ipsi 0.20872893466424683
+HNU1 AAL Ipsi 0.4955176745311554
+HNU1 AAL Ipsi 0.5148101385359952
+HNU1 AAL Ipsi 0.42384601663641863
+HNU1 AAL Ipsi 0.22803465093768907
+HNU1 AAL Ipsi 0.3329138762855415
+HNU1 AAL Ipsi 0.3386903663036903
+HNU1 AAL Ipsi 0.45217536297640654
+HNU1 AAL Ipsi 0.26466558650937694
+HNU1 AAL Ipsi 0.34220478493647916
+HNU1 AAL Ipsi 0.29842583726557775
+HNU1 AAL Ipsi 0.31716252782819115
+HNU1 AAL Ipsi 0.2812412362371446
+HNU1 AAL Ipsi 0.2927631978221416
+HNU1 AAL Ipsi 0.27214866878402905
+HNU1 AAL Ipsi 0.32246904083484573
+HNU1 AAL Ipsi 0.29391163853599517
+HNU1 AAL Ipsi 0.23896197943133696
+HNU1 AAL Ipsi 0.27195472807017546
+HNU1 AAL Ipsi 0.2836632673926195
+HNU1 AAL Ipsi 0.19284915396249244
+HNU1 AAL Ipsi 0.30797029280096794
+HNU1 AAL Ipsi 0.21796784966727165
+HNU1 AAL Ipsi 0.3691456349062311
+HNU1 AAL Ipsi 0.29384726769509983
+HNU1 AAL Ipsi 0.24359427162734423
+HNU1 AAL Ipsi 0.42972990592861465
+HNU1 AAL Ipsi 0.3047708439201452
+HNU1 AAL Ipsi 0.30756672746521474
+HNU1 AAL Ipsi 0.32172636388384757
+HNU1 AAL Ipsi 0.23374335753176043
+HNU1 AAL Ipsi 0.22286146007259527
+HNU1 AAL Ipsi 0.22883278433151846
+HNU1 AAL Ipsi 0.2391489032062916
+HNU1 AAL Ipsi 0.36276486267392616
+HNU1 AAL Ipsi 0.23747491228070175
+HNU1 AAL Ipsi 0.3253983738656988
+HNU1 AAL Ipsi 0.3531553387779795
+HNU1 AAL Ipsi 0.24387194585601937
+HNU1 AAL Ipsi 0.3205819742891712
+HNU1 AAL Ipsi 0.26701697973381733
+HNU1 AAL Ipsi 0.19982205747126436
+HNU1 AAL Ipsi 0.28156663611615246
+HNU1 AAL Ipsi 0.2857450958862674
+HNU1 AAL Ipsi 0.2807526739261948
+HNU1 AAL Ipsi 0.3476600771324864
+HNU1 AAL Ipsi 0.293484166061706
+HNU1 AAL Ipsi 0.1933686787658802
+HNU1 AAL Ipsi 0.2882983744706594
+HNU1 AAL Ipsi 0.267474092861464
+HNU1 AAL Ipsi 0.2619760789473684
+HNU1 AAL Ipsi 0.3637700762250454
+HNU1 AAL Ipsi 0.2697230402298851
+HNU1 AAL Ipsi 0.29692953508771924
+HNU1 AAL Ipsi 0.3008339927404719
+HNU1 AAL Ipsi 0.2647121718088324
+HNU1 AAL Ipsi 0.3498924298245614
+HNU1 AAL Ipsi 0.2244989452510587
+HNU1 AAL Ipsi 0.2648346409558378
+HNU1 AAL Ipsi 0.34988734664246823
+HNU1 AAL Ipsi 0.32091239503932245
+HNU1 AAL Ipsi 0.444175630369026
+HNU1 AAL Ipsi 0.26879365245009074
+HNU1 AAL Ipsi 0.2887454004839686
+HNU1 AAL Ipsi 0.38106283151845133
+HNU1 AAL Ipsi 0.317304314277072
+HNU1 AAL Ipsi 0.26911213036902604
+HNU1 AAL Ipsi 0.2809097676950998
+HNU1 AAL Ipsi 0.2489021261343013
+HNU1 AAL Ipsi 0.22203656503327285
+HNU1 AAL Ipsi 0.2633176061705989
+HNU1 AAL Ipsi 0.22531682304900183
+HNU1 AAL Ipsi 0.3449779159104658
+HNU1 AAL Ipsi 0.4290117731397459
+HNU1 AAL Ipsi 0.24642512734422262
+HNU1 AAL Ipsi 0.37340667967332114
+HNU1 AAL Ipsi 0.26784175620084694
+HNU1 AAL Ipsi 0.20461567392619476
+HNU1 AAL Ipsi 0.23980308923169996
+HNU1 AAL Ipsi 0.20410397307924985
+HNU1 AAL Ipsi 0.30651061524500905
+HNU1 AAL Ipsi 0.3985013647912886
+HNU1 AAL Ipsi 0.3851749637023593
+HNU1 AAL Ipsi 0.2957179156079855
+HNU1 AAL Ipsi 0.2909456536600121
+HNU1 AAL Ipsi 0.3142182513611615
+HNU1 AAL Ipsi 0.48780069267997583
+HNU1 AAL Ipsi 0.30860360254083485
+HNU1 AAL Ipsi 0.23957548336358137
+HNU1 AAL Ipsi 0.21280025771324865
+HNU1 AAL Ipsi 0.36829632456140354
+HNU1 AAL Ipsi 0.29910542135511187
+HNU1 AAL Ipsi 0.23589999667271627
+HNU1 AAL Ipsi 0.3243518179068361
+HNU1 AAL Ipsi 0.29829960980036296
+HNU1 AAL Ipsi 0.23513206503327283
+HNU1 AAL Ipsi 0.28533294041137325
+HNU1 AAL Ipsi 0.2397635852994555
+HNU1 AAL Ipsi 0.26731794041137324
+HNU1 AAL Ipsi 0.31656854869933454
+HNU1 AAL Ipsi 0.2672108877797943
+HNU1 AAL Ipsi 0.1905559452510587
+HNU1 AAL Ipsi 0.28410407289776163
+HNU1 AAL Ipsi 0.2567560423472474
+HNU1 AAL Ipsi 0.25437492165759223
+HNU1 AAL Ipsi 0.22204911736237146
+HNU1 AAL Ipsi 0.24264170024198425
+HNU1 AAL Ipsi 0.25226734633998793
+HNU1 AAL Ipsi 0.20372931246218995
+HNU1 AAL Ipsi 0.5490155145190563
+HNU1 AAL Ipsi 0.25169835450695704
+HNU1 AAL Ipsi 0.3030106624319419
+HNU1 AAL Ipsi 0.2328192416817907
+HNU1 AAL Ipsi 0.33496567090139134
+HNU1 AAL Ipsi 0.24895690713853597
+HNU1 AAL Ipsi 0.2855714431336963
+HNU1 AAL Ipsi 0.2798288115547489
+HNU1 AAL Ipsi 0.29380547852389594
+HNU1 AAL Ipsi 0.297083115245009
+HNU1 AAL Ipsi 0.2725211905626134
+HNU1 AAL Ipsi 0.2610402153660012
+HNU1 AAL Ipsi 0.25919102571082886
+HNU1 AAL Ipsi 0.3456827531760436
+HNU1 AAL Ipsi 0.5098446929824562
+HNU1 AAL Ipsi 0.29214825983061105
+HNU1 AAL Ipsi 0.301525667876588
+HNU1 AAL Ipsi 0.20527633424077435
+HNU1 AAL Ipsi 0.3354373082274652
+HNU1 AAL Ipsi 0.34082642740471863
+HNU1 AAL Ipsi 0.2999692994555354
+HNU1 AAL Ipsi 0.34184978826376283
+HNU1 AAL Ipsi 0.3153387150635209
+HNU1 AAL Ipsi 0.38532865607985484
+HNU1 AAL Ipsi 0.410922948275862
+HNU1 AAL Ipsi 0.29786999334543257
+HNU1 AAL Ipsi 0.2338188369630974
+HNU1 AAL Ipsi 0.47318044676346044
+HNU1 AAL Ipsi 0.21871435692679975
+HNU1 AAL Ipsi 0.3480462861464005
+HNU1 AAL Ipsi 0.22094939624924384
+HNU1 AAL Ipsi 0.28038142558983664
+HNU1 AAL Ipsi 0.2506080747126437
+HNU1 AAL Ipsi 0.278070034785239
+HNU1 AAL Ipsi 0.18703720477918934
+HNU1 AAL Ipsi 0.2721312498487598
+HNU1 AAL Ipsi 0.3080138333333333
+HNU1 AAL Ipsi 0.3214140801572897
+HNU1 AAL Ipsi 0.3897773166969146
+HNU1 AAL Ipsi 0.5058181370235935
+HNU1 AAL Ipsi 0.44584084966727167
+HNU1 AAL Ipsi 0.2625492894736842
+HNU1 AAL Ipsi 0.2401009274047187
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+HNU1 AAL Contra 0.49344489749017234
+HNU1 AAL Contra 0.5098901530087693
+HNU1 AAL Contra 0.4172403758693681
+HNU1 AAL Contra 0.22368101360749923
+HNU1 AAL Contra 0.32231586906561843
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+HNU1 AAL Contra 0.3315793311158149
+HNU1 AAL Contra 0.296076123072271
+HNU1 AAL Contra 0.3119112346537647
+HNU1 AAL Contra 0.274551092228606
+HNU1 AAL Contra 0.27960627275476263
+HNU1 AAL Contra 0.269264354702147
+HNU1 AAL Contra 0.3174411034169943
+HNU1 AAL Contra 0.2853408884185062
+HNU1 AAL Contra 0.22893829301481705
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+HNU1 AAL Contra 0.28223907015421834
+HNU1 AAL Contra 0.19194688902328394
+HNU1 AAL Contra 0.2961565179921378
+HNU1 AAL Contra 0.21437447354097372
+HNU1 AAL Contra 0.35744627577865135
+HNU1 AAL Contra 0.28812919352887817
+HNU1 AAL Contra 0.23868584306017537
+HNU1 AAL Contra 0.42499850498941644
+HNU1 AAL Contra 0.2980012062292108
+HNU1 AAL Contra 0.3041612189295434
+HNU1 AAL Contra 0.31626671696401576
+HNU1 AAL Contra 0.22789293438161476
+HNU1 AAL Contra 0.20945333504687028
+HNU1 AAL Contra 0.21623829543392803
+HNU1 AAL Contra 0.23405957786513457
+HNU1 AAL Contra 0.36033179498034473
+HNU1 AAL Contra 0.2305911817357121
+HNU1 AAL Contra 0.3149139712730571
+HNU1 AAL Contra 0.3504917741155126
+HNU1 AAL Contra 0.24453299969761114
+HNU1 AAL Contra 0.3085250084668884
+HNU1 AAL Contra 0.26458516328999093
+HNU1 AAL Contra 0.20175202540066528
+HNU1 AAL Contra 0.2735021076504385
+HNU1 AAL Contra 0.2863923879649229
+HNU1 AAL Contra 0.2754871644995464
+HNU1 AAL Contra 0.34410409464771696
+HNU1 AAL Contra 0.2823910689446628
+HNU1 AAL Contra 0.19129734774720292
+HNU1 AAL Contra 0.2783707837919564
+HNU1 AAL Contra 0.25994331236770485
+HNU1 AAL Contra 0.2545959274266707
+HNU1 AAL Contra 0.3588951118838827
+HNU1 AAL Contra 0.2475717248261264
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+HNU1 AAL Contra 0.2923182588448745
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+HNU1 AAL Contra 0.24973690202600546
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+HNU1 AAL Contra 0.30801899123072274
+HNU1 AAL Contra 0.43873222921076505
+HNU1 AAL Contra 0.25610490504989414
+HNU1 AAL Contra 0.2743153646809798
+HNU1 AAL Contra 0.38061227577865137
+HNU1 AAL Contra 0.31060011218627154
+HNU1 AAL Contra 0.26397955730269124
+HNU1 AAL Contra 0.269671456304808
+HNU1 AAL Contra 0.2460844118536438
+HNU1 AAL Contra 0.22204451375869366
+HNU1 AAL Contra 0.25848807922588446
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+HNU1 AAL Contra 0.3347204590263079
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+HNU1 AAL Contra 0.24080356183852436
+HNU1 AAL Contra 0.355617231629876
+HNU1 AAL Contra 0.2578155385545812
+HNU1 AAL Contra 0.20186303144844267
+HNU1 AAL Contra 0.24164994859389172
+HNU1 AAL Contra 0.20659259086785606
+HNU1 AAL Contra 0.28717695282733596
+HNU1 AAL Contra 0.39632755851224677
+HNU1 AAL Contra 0.37970238554581187
+HNU1 AAL Contra 0.2793019501058361
+HNU1 AAL Contra 0.27322246507408526
+HNU1 AAL Contra 0.3061337345025703
+HNU1 AAL Contra 0.4860398506198972
+HNU1 AAL Contra 0.3092324324160871
+HNU1 AAL Contra 0.24002396492289085
+HNU1 AAL Contra 0.2048060855760508
+HNU1 AAL Contra 0.3644967819776232
+HNU1 AAL Contra 0.2974820535228303
+HNU1 AAL Contra 0.21944107408527366
+HNU1 AAL Contra 0.3239892022981554
+HNU1 AAL Contra 0.2852001390988812
+HNU1 AAL Contra 0.22976607831871787
+HNU1 AAL Contra 0.2773372038100998
+HNU1 AAL Contra 0.24134987118234047
+HNU1 AAL Contra 0.2625067729059571
+HNU1 AAL Contra 0.3170186537647414
+HNU1 AAL Contra 0.25954314272754764
+HNU1 AAL Contra 0.18619296462050197
+HNU1 AAL Contra 0.28214739431508923
+HNU1 AAL Contra 0.25540181886906566
+HNU1 AAL Contra 0.24385093045055942
+HNU1 AAL Contra 0.22194165920774117
+HNU1 AAL Contra 0.23771930148170548
+HNU1 AAL Contra 0.2400539534321137
+HNU1 AAL Contra 0.19944909797399457
+HNU1 AAL Contra 0.5498412449349864
+HNU1 AAL Contra 0.24516293982461448
+HNU1 AAL Contra 0.30007349228908375
+HNU1 AAL Contra 0.2345214659812519
+HNU1 AAL Contra 0.3296937027517387
+HNU1 AAL Contra 0.24860073540973693
+HNU1 AAL Contra 0.2721469002116722
+HNU1 AAL Contra 0.26744036528575743
+HNU1 AAL Contra 0.2893252730571515
+HNU1 AAL Contra 0.2890969404293922
+HNU1 AAL Contra 0.27020056365285755
+HNU1 AAL Contra 0.25944809918355005
+HNU1 AAL Contra 0.2597452410039311
+HNU1 AAL Contra 0.33991144058058664
+HNU1 AAL Contra 0.507065635923798
+HNU1 AAL Contra 0.28344919050498946
+HNU1 AAL Contra 0.29870927547626247
+HNU1 AAL Contra 0.19476069277290592
+HNU1 AAL Contra 0.3296083456304808
+HNU1 AAL Contra 0.32913603326277596
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+HNU1 AAL Contra 0.4054962134865437
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+HNU1 AAL Contra 0.22550079860901118
+HNU1 AAL Contra 0.4732788149380103
+HNU1 AAL Contra 0.20631820441487753
+HNU1 AAL Contra 0.342885801330511
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+HNU1 AAL Contra 0.27422209071666165
+HNU1 AAL Contra 0.24233454309041425
+HNU1 AAL Contra 0.2738649528273359
+HNU1 AAL Contra 0.18693430450559417
+HNU1 AAL Contra 0.2696870063501663
+HNU1 AAL Contra 0.30121724523737525
+HNU1 AAL Contra 0.3137726035681887
+HNU1 AAL Contra 0.3878314218324766
+HNU1 AAL Contra 0.5023570317508316
+HNU1 AAL Contra 0.4382660459631086
+HNU1 AAL Contra 0.25665440580586635
+HNU1 AAL Contra 0.23531889053522834
+HNU1 AAL Contra 0.29737485122467494
+HNU1 AAL Contra 0.25732457544602355
+HNU1 AAL Contra 0.2524096691865739
+HNU1 AAL Contra 0.2795370955548836
+HNU1 AAL Contra 0.2524026062896885
+HNU1 AAL Contra 0.19441779437556694
+HNU1 AAL Contra 0.3330592558209858
+HNU1 AAL Contra 0.21285274024795886
+HNU1 AAL Contra 0.22013131539159358
+HNU1 AAL Contra 0.31653724523737525
+HNU1 AAL Contra 0.4196352022981555
+HNU1 AAL Contra 0.17175201088599937
+HNU1 AAL Contra 0.25680575264590266
+HNU1 AAL Contra 0.3022392788025401
+HNU1 AAL Contra 0.27072490081644995
+HNU1 AAL Contra 0.34972112912004844
+HNU1 AAL Contra 0.2610194838221953
+HNU1 AAL Contra 0.2630863426065921
+HNU1 AAL Contra 0.28401174629573633
+HNU1 AAL Contra 0.31305964862413066
+HNU1 AAL Contra 0.2266249809495011
+HNU1 AAL Contra 0.2850071130934381
+HNU1 AAL Contra 0.2235755615361355
+HNU1 AAL Contra 0.39275082733595407
+HNU1 AAL Contra 0.2765214182038101
+HNU1 AAL Contra 0.21120439159358936
+HNU1 AAL Contra 0.35332299516177806
+HNU1 AAL Contra 0.32045592016933777
+HNU1 AAL Contra 0.34281199879044455
+HNU1 AAL Contra 0.30227077895373455
+HNU1 AAL Contra 0.3661564980344723
+HNU1 AAL Contra 0.3499620565467191
+HNU1 AAL Contra 0.19031070426368307
+HNU1 AAL Contra 0.3305236483217418
+HNU1 AAL Contra 0.3033079640157242
+HNU1 AAL Contra 0.2442699718778349
+HNU1 AAL Contra 0.2696248856970063
+HNU1 AAL Contra 0.2222856867251285
+HNU1 AAL Contra 0.3087779785303901
+HNU1 AAL Contra 0.37924947112186264
+HNU1 AAL Contra 0.3428556855155731
+HNU1 AAL Contra 0.35898510583610527
+HNU1 AAL Contra 0.23153182642878742
+HNU1 AAL Contra 0.26914437133353497
+HNU1 AAL Contra 0.3086139513153916
+HNU1 AAL Contra 0.27243308920471726
+HNU1 AAL Contra 0.33010610795282735
+HNU1 AAL Contra 0.3011330792258845
+HNU1 AAL Contra 0.27845956728152405
+HNU1 AAL Contra 0.29792263380707595
+HNU1 AAL Contra 0.21566805594194136
+HNU1 AAL Contra 0.22888691533111583
+HNU1 AAL Contra 0.21502224765648625
+HNU1 AAL Contra 0.3040630843664953
+HNU1 AAL Contra 0.253981104021772
+HNU1 AAL Contra 0.25283619201693375
+HNU1 AAL Contra 0.28669262564257636
+HNU1 AAL Contra 0.2536096401572422
+HNU1 AAL Contra 0.39665100090716665
+HNU1 AAL Contra 0.32231510644088296
+HNU1 AAL Contra 0.25606179679467794
+HNU1 AAL Contra 0.34483105805866343
+HNU1 AAL Contra 0.24899032627759296
+HNU1 AAL Contra 0.3928372766858179
+HNU1 AAL Contra 0.27559791502872694
+HNU1 AAL Contra 0.23973448654369517
+HNU1 AAL Contra 0.29938662987602055
+HNU1 AAL Contra 0.2852549688539462
+HNU1 AAL Contra 0.2866732679165407
+HNU1 AAL Contra 0.19858468793468403
+HNU1 AAL Contra 0.2634595830057454
+HNU1 AAL Contra 0.21444008829755065
+HNU1 AAL Contra 0.24698696703961293
+HNU1 AAL Contra 0.2968709165406713
+HNU1 AAL Contra 0.32422857967946783
+HNU1 AAL Contra 0.26310798155427884
+HNU1 AAL Contra 0.21186635258542486
+HNU1 AAL Contra 0.2154108082854551
+HNU1 AAL Contra 0.286089742364681
+HNU1 AAL Contra 0.2678411146053825
+HNU1 AAL Contra 0.2971751076504385
+HNU1 AAL Contra 0.23407075869368008
+HNU1 AAL Contra 0.35252994254611425
+HNU1 AAL Contra 0.2852555796794678
+HNU1 AAL Contra 0.32897829936498335
+HNU1 AAL Contra 0.2907670556395525
+HNU1 AAL Contra 0.2634416286664651
+HNU1 AAL Contra 0.2741705385545812
+HNU1 AAL Contra 0.40428866797701846
+HNU1 AAL Contra 0.2910898049591775
+HNU1 AAL Contra 0.2667594708194738
+HNU1 AAL Contra 0.4716874859389174
+HNU1 AAL Contra 0.2126949138191715
+HNU1 AAL Contra 0.3124728007257333
+HNU1 AAL Contra 0.3658381756879347
+HNU1 AAL Contra 0.4204884012700333
+HNU1 AAL Contra 0.2113218938615059
+HNU1 AAL Contra 0.23372310704566074
+HNU1 AAL Contra 0.29148168491079524
+HNU1 AAL Contra 0.2956946138494103
+HNU1 AAL Contra 0.285675673722407
+HNU1 AAL Contra 0.2598591941336559
+Dataset Atlas Type Value
+NYU_2 AAL Ipsi 0.2554958826376285
+NYU_2 AAL Ipsi 0.22397069782214155
+NYU_2 AAL Ipsi 0.2719235108892922
+NYU_2 AAL Ipsi 0.26931157803992745
+NYU_2 AAL Ipsi 0.3226783532970357
+NYU_2 AAL Ipsi 0.32401076739261947
+NYU_2 AAL Ipsi 0.27537538808227463
+NYU_2 AAL Ipsi 0.21937886418632788
+NYU_2 AAL Ipsi 0.34699782274652147
+NYU_2 AAL Ipsi 0.20148691621294618
+NYU_2 AAL Ipsi 0.26334680429522084
+NYU_2 AAL Ipsi 0.4928425148215367
+NYU_2 AAL Ipsi 0.3281362295825771
+NYU_2 AAL Ipsi 0.2445469479733817
+NYU_2 AAL Ipsi 0.22554104930429525
+NYU_2 AAL Ipsi 0.28802993496672713
+NYU_2 AAL Ipsi 0.3228649891107078
+NYU_2 AAL Ipsi 0.2982014869933454
+NYU_2 AAL Ipsi 0.3025444143980641
+NYU_2 AAL Ipsi 0.21792880187537814
+NYU_2 AAL Ipsi 0.24103128826376286
+NYU_2 AAL Ipsi 0.531129488505747
+NYU_2 AAL Ipsi 0.2607729806412583
+NYU_2 AAL Ipsi 0.2552479960677556
+NYU_2 AAL Ipsi 0.3542766860254083
+NYU_2 AAL Ipsi 0.26043904083484576
+NYU_2 AAL Ipsi 0.24782766182698127
+NYU_2 AAL Ipsi 0.33293743284936483
+NYU_2 AAL Ipsi 0.2834922002419843
+NYU_2 AAL Ipsi 0.22422981488203267
+NYU_2 AAL Ipsi 0.2907212628554144
+NYU_2 AAL Ipsi 0.23985898699334546
+NYU_2 AAL Ipsi 0.3729911225045372
+NYU_2 AAL Ipsi 0.2981260686630369
+NYU_2 AAL Ipsi 0.33158406291591047
+NYU_2 AAL Ipsi 0.2589638336358137
+NYU_2 AAL Ipsi 0.3130439134906231
+NYU_2 AAL Ipsi 0.2927648539019964
+NYU_2 AAL Ipsi 0.20468126467029646
+NYU_2 AAL Ipsi 0.2567649860859044
+NYU_2 AAL Ipsi 0.24688558378705383
+NYU_2 AAL Ipsi 0.25471234089534184
+NYU_2 AAL Ipsi 0.2796232377495463
+NYU_2 AAL Ipsi 0.35239016424682396
+NYU_2 AAL Ipsi 0.33622351603145795
+NYU_2 AAL Ipsi 0.3151108490623109
+NYU_2 AAL Ipsi 0.29139515396249244
+NYU_2 AAL Ipsi 0.2840051373260738
+NYU_2 AAL Ipsi 0.19056966787658802
+NYU_2 AAL Ipsi 0.3096021911675741
+NYU_2 AAL Ipsi 0.28171770326678763
+NYU_2 AAL Ipsi 0.29615502147610406
+NYU_2 AAL Ipsi 0.5893252102238354
+NYU_2 AAL Ipsi 0.23536810707803993
+NYU_2 AAL Ipsi 0.22874168542044768
+NYU_2 AAL Ipsi 0.4997243215366002
+NYU_2 AAL Ipsi 0.23202566515426495
+NYU_2 AAL Ipsi 0.35654790653357527
+NYU_2 AAL Ipsi 0.2630911929824561
+NYU_2 AAL Ipsi 0.22236829915305503
+NYU_2 AAL Ipsi 0.24300415335753175
+NYU_2 AAL Ipsi 0.2757482546884452
+NYU_2 AAL Ipsi 0.3202705435571688
+NYU_2 AAL Ipsi 0.3226075241984271
+NYU_2 AAL Ipsi 0.24069311282516634
+NYU_2 AAL Ipsi 0.2749119975801573
+NYU_2 AAL Ipsi 0.3226279534180278
+NYU_2 AAL Ipsi 0.2590364213551119
+NYU_2 AAL Ipsi 0.258708112522686
+NYU_2 AAL Ipsi 0.3258965659407139
+NYU_2 AAL Ipsi 0.2342804697519661
+NYU_2 AAL Ipsi 0.4166937912885662
+NYU_2 AAL Ipsi 0.24662023684210527
+NYU_2 AAL Ipsi 0.24430627767695096
+NYU_2 AAL Ipsi 0.2725449721718088
+NYU_2 AAL Ipsi 0.25258357592256503
+NYU_2 AAL Ipsi 0.25701166999395036
+NYU_2 AAL Ipsi 0.3014014612825166
+NYU_2 AAL Ipsi 0.3833435139140956
+NYU_2 AAL Ipsi 0.35302810102843313
+NYU_2 AAL Ipsi 0.25016957199032064
+NYU_2 AAL Ipsi 0.2637080036297641
+NYU_2 AAL Ipsi 0.29295761282516636
+NYU_2 AAL Ipsi 0.2627913121597096
+NYU_2 AAL Ipsi 0.27827275771324866
+NYU_2 AAL Ipsi 0.23216173774954627
+NYU_2 AAL Ipsi 0.3594235341802783
+NYU_2 AAL Ipsi 0.2788859724742892
+NYU_2 AAL Ipsi 0.3181214364791288
+NYU_2 AAL Ipsi 0.34161253236539624
+NYU_2 AAL Ipsi 0.2800406639443436
+NYU_2 AAL Ipsi 0.20827208439201453
+NYU_2 AAL Ipsi 0.3328430468844525
+NYU_2 AAL Ipsi 0.20482458560193587
+NYU_2 AAL Ipsi 0.4838388805202662
+NYU_2 AAL Ipsi 0.2251083632788869
+NYU_2 AAL Ipsi 0.2512201684815487
+NYU_2 AAL Ipsi 0.23759726164549302
+NYU_2 AAL Ipsi 0.2742147138535995
+NYU_2 AAL Ipsi 0.24447242679975803
+NYU_2 AAL Ipsi 0.29902352661826986
+NYU_2 AAL Ipsi 0.23613239110707804
+NYU_2 AAL Ipsi 0.3112832189957653
+NYU_2 AAL Ipsi 0.2993814038112523
+NYU_2 AAL Ipsi 0.2541103042952208
+NYU_2 AAL Ipsi 0.37628393920145187
+NYU_2 AAL Ipsi 0.2653143082274652
+NYU_2 AAL Ipsi 0.19280301119177254
+NYU_2 AAL Ipsi 0.2861939431336963
+NYU_2 AAL Ipsi 0.22214863399879006
+NYU_2 AAL Ipsi 0.23341799909255898
+NYU_2 AAL Ipsi 0.2868856842105263
+NYU_2 AAL Ipsi 0.46565872655777374
+NYU_2 AAL Ipsi 0.21591055263157896
+NYU_2 AAL Ipsi 0.3790717371445856
+NYU_2 AAL Ipsi 0.22339257622504535
+NYU_2 AAL Ipsi 0.25944237719298247
+NYU_2 AAL Ipsi 0.24263545039322443
+NYU_2 AAL Ipsi 0.27421720024198426
+NYU_2 AAL Ipsi 0.42190054627949186
+NYU_2 AAL Ipsi 0.4089693729582577
+NYU_2 AAL Ipsi 0.2563916935874168
+NYU_2 AAL Ipsi 0.23156521657592258
+NYU_2 AAL Ipsi 0.3955896176648518
+NYU_2 AAL Ipsi 0.21009624410163336
+NYU_2 AAL Ipsi 0.2540274921355112
+NYU_2 AAL Ipsi 0.3117538590441621
+NYU_2 AAL Ipsi 0.3423148865698729
+NYU_2 AAL Ipsi 0.2955379727767695
+NYU_2 AAL Ipsi 0.20871799092558985
+NYU_2 AAL Ipsi 0.2494012713248639
+NYU_2 AAL Ipsi 0.1897375181488203
+NYU_2 AAL Ipsi 0.4717444186327889
+NYU_2 AAL Ipsi 0.23565571324863882
+NYU_2 AAL Ipsi 0.24772516515426496
+NYU_2 AAL Ipsi 0.298466573200242
+NYU_2 AAL Ipsi 0.24325609407138538
+NYU_2 AAL Ipsi 0.2831957341197822
+NYU_2 AAL Ipsi 0.28619072897761644
+NYU_2 AAL Ipsi 0.20466839413188143
+NYU_2 AAL Ipsi 0.43408913127646703
+NYU_2 AAL Ipsi 0.2682161019358742
+NYU_2 AAL Ipsi 0.5243047752571083
+NYU_2 AAL Ipsi 0.27575745462794915
+NYU_2 AAL Ipsi 0.3076201400483969
+NYU_2 AAL Ipsi 0.3063426346037508
+NYU_2 AAL Ipsi 0.3026683333333333
+NYU_2 AAL Ipsi 0.29283432456140346
+NYU_2 AAL Ipsi 0.2466288548094374
+NYU_2 AAL Ipsi 0.22589975347852392
+NYU_2 AAL Ipsi 0.5032675683605565
+NYU_2 AAL Ipsi 0.28002702994555356
+NYU_2 AAL Ipsi 0.2474554204476709
+NYU_2 AAL Ipsi 0.3386595780399274
+NYU_2 AAL Ipsi 0.3381154334543255
+NYU_2 AAL Ipsi 0.3703761624319419
+NYU_2 AAL Ipsi 0.2796591300665457
+NYU_2 AAL Ipsi 0.3259725843920145
+NYU_2 AAL Ipsi 0.21471347610405325
+NYU_2 AAL Ipsi 0.35080293405928614
+NYU_2 AAL Ipsi 0.45258608771929826
+NYU_2 AAL Ipsi 0.31846300393224436
+NYU_2 AAL Ipsi 0.30274165093768907
+NYU_2 AAL Ipsi 0.2612240949788263
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+NYU_2 AAL Contra 0.25871437254309043
+NYU_2 AAL Contra 0.2653287208950711
+NYU_2 AAL Contra 0.23991367100090716
+NYU_2 AAL Contra 0.27283643604475355
+NYU_2 AAL Contra 0.22268428091926218
+NYU_2 AAL Contra 0.18874993196250378
+NYU_2 AAL Contra 0.29942524644693075
+NYU_2 AAL Contra 0.23278955548835803
+NYU_2 AAL Contra 0.48992595947989115
+NYU_2 AAL Contra 0.2720598064711219
+NYU_2 AAL Contra 0.265070361657091
+NYU_2 AAL Contra 0.22732647989114
+NYU_2 AAL Contra 0.4762187393407923
+NYU_2 AAL Contra 0.4343361212579377
+NYU_2 AAL Contra 0.40141008255216204
+NYU_2 AAL Contra 0.339420895978228
+NYU_2 AAL Contra 0.2666070302388872
+NYU_2 AAL Contra 0.2607235594194134
+NYU_2 AAL Contra 0.1956571517992138
+NYU_2 AAL Contra 0.2685515312972483
+NYU_2 AAL Contra 0.32973995131539163
+NYU_2 AAL Contra 0.23971626700937404
+NYU_2 AAL Contra 0.2363519038403387
+NYU_2 AAL Contra 0.38252476443906863
+NYU_2 AAL Contra 0.33101489839733894
+NYU_2 AAL Contra 0.1906401342606592
+NYU_2 AAL Contra 0.319991609918355
+NYU_2 AAL Contra 0.22977352676141521
+NYU_2 AAL Contra 0.4768835261566374
+NYU_2 AAL Contra 0.2662826108255216
+NYU_2 AAL Contra 0.2534712588448745
+NYU_2 AAL Contra 0.37976020048382214
+NYU_2 AAL Contra 0.2825630241911098
+NYU_2 AAL Contra 0.45654824070154215
+NYU_2 AAL Contra 0.23488953643785906
+NYU_2 AAL Contra 0.2807028451768975
+NYU_2 AAL Contra 0.26180462261868764
+NYU_2 AAL Contra 0.23777320290293316
+NYU_2 AAL Contra 0.2058865503477472
+NYU_2 AAL Contra 0.2886636555790747
+NYU_2 AAL Contra 0.26449589144239494
+NYU_2 AAL Contra 0.2000083435137587
+NYU_2 AAL Contra 0.19988753220441485
+NYU_2 AAL Contra 0.370141508315694
+NYU_2 AAL Contra 0.2760824281826429
+NYU_2 AAL Contra 0.3092704789839734
+NYU_2 AAL Contra 0.3766434076201996
+NYU_2 AAL Contra 0.3249742918052615
+NYU_2 AAL Contra 0.4484856301784094
+NYU_2 AAL Contra 0.28440763169035377
+NYU_2 AAL Contra 0.22439195615361357
+NYU_2 AAL Contra 0.3520682131841548
+NYU_2 AAL Contra 0.2324796316903538
+NYU_2 AAL Contra 0.23441426398548534
+NYU_2 AAL Contra 0.3465026087087995
+NYU_2 AAL Contra 0.35656976595101303
+NYU_2 AAL Contra 0.4659076440882976
+NYU_2 AAL Contra 0.25131498518294526
+NYU_2 AAL Contra 0.24511140550347746
+NYU_2 AAL Contra 0.41308457605080134
+NYU_2 AAL Contra 0.2575811242818264
+NYU_2 AAL Contra 0.19610186543695193
+NYU_2 AAL Contra 0.31925169186573926
+NYU_2 AAL Contra 0.20851562594496523
+NYU_2 AAL Contra 0.28278413426065924
+NYU_2 AAL Contra 0.3760026413667977
+NYU_2 AAL Contra 0.2837135052918052
+NYU_2 AAL Contra 0.25544206440882977
+NYU_2 AAL Contra 0.33609328575748415
+NYU_2 AAL Contra 0.3673111690353795
+NYU_2 AAL Contra 0.27007352676141516
+NYU_2 AAL Contra 0.2933245473238585
+Dataset Atlas Type Value
+XHCUMS AAL Ipsi 0.3365637683000605
+XHCUMS AAL Ipsi 0.223146798245614
+XHCUMS AAL Ipsi 0.3002197238354507
+XHCUMS AAL Ipsi 0.3718208593466425
+XHCUMS AAL Ipsi 0.35449562189957656
+XHCUMS AAL Ipsi 0.45698238384754997
+XHCUMS AAL Ipsi 0.2490695973986691
+XHCUMS AAL Ipsi 0.24939203992740472
+XHCUMS AAL Ipsi 0.3329790977011495
+XHCUMS AAL Ipsi 0.22954613218390807
+XHCUMS AAL Ipsi 0.3690841309739867
+XHCUMS AAL Ipsi 0.2489159398064126
+XHCUMS AAL Ipsi 0.2681579482758621
+XHCUMS AAL Ipsi 0.33371410405323654
+XHCUMS AAL Ipsi 0.3608200819721718
+XHCUMS AAL Ipsi 0.26136946733212346
+XHCUMS AAL Ipsi 0.25487905021173624
+XHCUMS AAL Ipsi 0.3268145656382336
+XHCUMS AAL Ipsi 0.4454884180278282
+XHCUMS AAL Ipsi 0.3728642256503328
+XHCUMS AAL Ipsi 0.3469330725952813
+XHCUMS AAL Ipsi 0.4175440217785844
+XHCUMS AAL Ipsi 0.3498755284331518
+XHCUMS AAL Ipsi 0.2381397934059286
+XHCUMS AAL Ipsi 0.2931556128251664
+XHCUMS AAL Ipsi 0.30686048699334545
+XHCUMS AAL Ipsi 0.3903780120992135
+XHCUMS AAL Ipsi 0.25893812220205686
+XHCUMS AAL Ipsi 0.30062366636418636
+XHCUMS AAL Ipsi 0.39591476346037513
+XHCUMS AAL Ipsi 0.2591783269812462
+XHCUMS AAL Ipsi 0.2714349543254688
+XHCUMS AAL Ipsi 0.29725837053841503
+XHCUMS AAL Ipsi 0.25079948094373866
+XHCUMS AAL Ipsi 0.35539347156684814
+XHCUMS AAL Ipsi 0.33916176013309135
+XHCUMS AAL Ipsi 0.41371254990925593
+XHCUMS AAL Ipsi 0.33505284754990927
+XHCUMS AAL Ipsi 0.26946328796128255
+XHCUMS AAL Ipsi 0.2703554509981851
+XHCUMS AAL Ipsi 0.35042245735027217
+XHCUMS AAL Ipsi 0.4750315843920145
+XHCUMS AAL Ipsi 0.19800417997580155
+XHCUMS AAL Ipsi 0.36686133726557774
+XHCUMS AAL Ipsi 0.20152349516031456
+XHCUMS AAL Ipsi 0.4007158832425893
+XHCUMS AAL Ipsi 0.2814577873563218
+XHCUMS AAL Ipsi 0.4148607229280097
+XHCUMS AAL Ipsi 0.22898955837870538
+XHCUMS AAL Ipsi 0.34911672655777376
+XHCUMS AAL Ipsi 0.30411219903206294
+XHCUMS AAL Ipsi 0.3229485465819722
+XHCUMS AAL Ipsi 0.42284470175438593
+XHCUMS AAL Ipsi 0.2214311176648518
+XHCUMS AAL Ipsi 0.23975436902601333
+XHCUMS AAL Ipsi 0.29724187991530554
+XHCUMS AAL Ipsi 0.4621541814882032
+XHCUMS AAL Ipsi 0.34680091288566245
+XHCUMS AAL Ipsi 0.31796078009679374
+XHCUMS AAL Ipsi 0.20786100907441016
+XHCUMS AAL Ipsi 0.29266845372050815
+XHCUMS AAL Ipsi 0.2501166878402904
+XHCUMS AAL Ipsi 0.4077645015124017
+XHCUMS AAL Ipsi 0.33419827586206896
+XHCUMS AAL Ipsi 0.27156329945553537
+XHCUMS AAL Ipsi 0.32811629461584996
+XHCUMS AAL Ipsi 0.29157618390804596
+XHCUMS AAL Ipsi 0.27855062068965514
+XHCUMS AAL Ipsi 0.2505012447065941
+XHCUMS AAL Ipsi 0.36401913490623106
+XHCUMS AAL Ipsi 0.3211008765880218
+XHCUMS AAL Ipsi 0.34247602147610406
+XHCUMS AAL Ipsi 0.30278714730792494
+XHCUMS AAL Ipsi 0.24699126013309136
+XHCUMS AAL Ipsi 0.3404346742286752
+XHCUMS AAL Ipsi 0.3617427752571083
+XHCUMS AAL Ipsi 0.42329775075620085
+XHCUMS AAL Ipsi 0.32736200967937085
+XHCUMS AAL Ipsi 0.3365372673926194
+XHCUMS AAL Ipsi 0.2540786430732002
+XHCUMS AAL Ipsi 0.2941005662431942
+XHCUMS AAL Ipsi 0.3069688650937689
+XHCUMS AAL Ipsi 0.30849866031457956
+XHCUMS AAL Ipsi 0.33884057380520266
+XHCUMS AAL Ipsi 0.5142359395039322
+XHCUMS AAL Ipsi 0.3326155148215366
+XHCUMS AAL Ipsi 0.30807455989110705
+XHCUMS AAL Ipsi 0.2620011358136721
+XHCUMS AAL Ipsi 0.4729652265577738
+XHCUMS AAL Ipsi 0.25532335601935874
+XHCUMS AAL Ipsi 0.4050951612220206
+XHCUMS AAL Ipsi 0.31299339866908654
+XHCUMS AAL Ipsi 0.35882600998185116
+XHCUMS AAL Ipsi 0.31873086418632796
+XHCUMS AAL Ipsi 0.32447777525710825
+XHCUMS AAL Ipsi 0.236470647307925
+XHCUMS AAL Ipsi 0.2938004951603146
+XHCUMS AAL Ipsi 0.40311490986085907
+XHCUMS AAL Ipsi 0.22800298941318814
+XHCUMS AAL Ipsi 0.21815135601935873
+XHCUMS AAL Ipsi 0.3520774891107078
+XHCUMS AAL Ipsi 0.27145538959467636
+XHCUMS AAL Ipsi 0.41654192135511187
+XHCUMS AAL Ipsi 0.2611476333938294
+XHCUMS AAL Ipsi 0.20611726406533576
+XHCUMS AAL Ipsi 0.21977419116757413
+XHCUMS AAL Ipsi 0.253635001814882
+XHCUMS AAL Ipsi 0.24520087447065944
+XHCUMS AAL Ipsi 0.36883645523290987
+XHCUMS AAL Ipsi 0.24063781306715065
+XHCUMS AAL Ipsi 0.6033094513006654
+XHCUMS AAL Ipsi 0.3963400175438597
+XHCUMS AAL Ipsi 0.33685041833030854
+XHCUMS AAL Ipsi 0.2312603082274652
+XHCUMS AAL Ipsi 0.3176182543859649
+XHCUMS AAL Ipsi 0.2573331255293406
+XHCUMS AAL Ipsi 0.27594145281306715
+XHCUMS AAL Ipsi 0.2153398502722323
+XHCUMS AAL Ipsi 0.3659985710828797
+XHCUMS AAL Ipsi 0.24162151451905628
+XHCUMS AAL Ipsi 0.41716392135511193
+XHCUMS AAL Ipsi 0.3374548514821537
+XHCUMS AAL Ipsi 0.27960750816696917
+XHCUMS AAL Ipsi 0.30164990108892925
+XHCUMS AAL Ipsi 0.25463309709618875
+XHCUMS AAL Ipsi 0.2946454742891712
+XHCUMS AAL Ipsi 0.3064881022383545
+XHCUMS AAL Ipsi 0.5060485045372051
+XHCUMS AAL Ipsi 0.2241272380520266
+XHCUMS AAL Ipsi 0.25542370538415
+XHCUMS AAL Ipsi 0.20921338384754992
+XHCUMS AAL Ipsi 0.3171282967332123
+XHCUMS AAL Ipsi 0.23585125529340592
+XHCUMS AAL Ipsi 0.25172762431941925
+XHCUMS AAL Ipsi 0.29424832335148216
+XHCUMS AAL Ipsi 0.30456249334543256
+XHCUMS AAL Ipsi 0.472940424077435
+XHCUMS AAL Ipsi 0.2933336851179673
+XHCUMS AAL Ipsi 0.4060306944948579
+XHCUMS AAL Ipsi 0.28345402177858436
+XHCUMS AAL Ipsi 0.3012709924379915
+XHCUMS AAL Ipsi 0.26981554869933455
+XHCUMS AAL Ipsi 0.4041807613430127
+XHCUMS AAL Ipsi 0.4300331246218996
+XHCUMS AAL Ipsi 0.33483386055656383
+XHCUMS AAL Ipsi 0.4014575689655172
+XHCUMS AAL Ipsi 0.4558503396854205
+XHCUMS AAL Ipsi 0.28830889897156686
+XHCUMS AAL Ipsi 0.357708611615245
+XHCUMS AAL Ipsi 0.202519760738052
+XHCUMS AAL Ipsi 0.1999757156684816
+XHCUMS AAL Ipsi 0.349279983061101
+XHCUMS AAL Ipsi 0.4276320980036298
+XHCUMS AAL Ipsi 0.36764259891107076
+XHCUMS AAL Ipsi 0.35093525105868123
+XHCUMS AAL Ipsi 0.25333061887477315
+XHCUMS AAL Ipsi 0.2875068932244404
+XHCUMS AAL Ipsi 0.3941095825771325
+XHCUMS AAL Ipsi 0.3962396690865093
+XHCUMS AAL Ipsi 0.22233503811252267
+XHCUMS AAL Ipsi 0.21900227132486386
+XHCUMS AAL Ipsi 0.39016691954022986
+XHCUMS AAL Ipsi 0.21815901633393828
+XHCUMS AAL Ipsi 0.4465587631578947
+XHCUMS AAL Ipsi 0.3508539618874773
+XHCUMS AAL Ipsi 0.30641667967332126
+XHCUMS AAL Ipsi 0.3327015420447671
+XHCUMS AAL Ipsi 0.21791957834240772
+XHCUMS AAL Ipsi 0.220088925892317
+XHCUMS AAL Ipsi 0.4315005843920145
+XHCUMS AAL Ipsi 0.24263933514821534
+XHCUMS AAL Ipsi 0.2154063472474289
+XHCUMS AAL Ipsi 0.33207742196007256
+XHCUMS AAL Ipsi 0.259689556261343
+XHCUMS AAL Ipsi 0.42767108197217174
+XHCUMS AAL Ipsi 0.2773148136721113
+XHCUMS AAL Ipsi 0.326733887477314
+XHCUMS AAL Ipsi 0.3421938844525106
+XHCUMS AAL Ipsi 0.24705204234724742
+XHCUMS AAL Ipsi 0.22969520477918934
+XHCUMS AAL Ipsi 0.2814144724742892
+XHCUMS AAL Ipsi 0.35753764519056264
+XHCUMS AAL Ipsi 0.37171678433151845
+XHCUMS AAL Ipsi 0.2432210453720508
+XHCUMS AAL Ipsi 0.36328229461585
+XHCUMS AAL Ipsi 0.3692415066545675
+XHCUMS AAL Ipsi 0.24877067211131276
+XHCUMS AAL Ipsi 0.2930047138535995
+XHCUMS AAL Ipsi 0.3045551206896552
+XHCUMS AAL Ipsi 0.23927571294615851
+XHCUMS AAL Ipsi 0.3893486001209921
+XHCUMS AAL Ipsi 0.35451126950998185
+XHCUMS AAL Ipsi 0.29420047338173017
+XHCUMS AAL Ipsi 0.5035783236539625
+XHCUMS AAL Ipsi 0.2881526799758016
+XHCUMS AAL Ipsi 0.3890643596491228
+XHCUMS AAL Ipsi 0.23097133030852995
+XHCUMS AAL Ipsi 0.24450780308529946
+XHCUMS AAL Ipsi 0.24530097701149425
+XHCUMS AAL Ipsi 0.2868669842710224
+XHCUMS AAL Ipsi 0.2251696391409558
+XHCUMS AAL Ipsi 0.44019817695099817
+XHCUMS AAL Ipsi 0.2771766769509982
+XHCUMS AAL Ipsi 0.2606109549304295
+XHCUMS AAL Ipsi 0.2398301031457955
+XHCUMS AAL Ipsi 0.3933966796733212
+XHCUMS AAL Ipsi 0.29132268753781004
+XHCUMS AAL Ipsi 0.2540976430732002
+XHCUMS AAL Ipsi 0.2568693838475499
+XHCUMS AAL Ipsi 0.30742344101633395
+XHCUMS AAL Ipsi 0.24157031790683603
+XHCUMS AAL Ipsi 0.4273002192982456
+XHCUMS AAL Ipsi 0.3501083254688446
+XHCUMS AAL Ipsi 0.37541559044162126
+XHCUMS AAL Ipsi 0.22711215517241382
+XHCUMS AAL Ipsi 0.2405780977011494
+XHCUMS AAL Ipsi 0.2034451300665457
+XHCUMS AAL Ipsi 0.2244006624319419
+XHCUMS AAL Ipsi 0.2551973239564428
+XHCUMS AAL Ipsi 0.3718086228070175
+XHCUMS AAL Ipsi 0.337796591954023
+XHCUMS AAL Ipsi 0.295008351784634
+XHCUMS AAL Ipsi 0.271975961584997
+XHCUMS AAL Ipsi 0.39585694978826375
+XHCUMS AAL Ipsi 0.40990145553539026
+XHCUMS AAL Ipsi 0.36727844646098
+XHCUMS AAL Ipsi 0.2618508599516031
+XHCUMS AAL Ipsi 0.26216012129461586
+XHCUMS AAL Ipsi 0.327253908953418
+XHCUMS AAL Ipsi 0.4019546391409558
+XHCUMS AAL Ipsi 0.2312192825166364
+XHCUMS AAL Ipsi 0.35642279854809433
+XHCUMS AAL Ipsi 0.2549479077434967
+XHCUMS AAL Ipsi 0.4501098451300665
+XHCUMS AAL Ipsi 0.2050859413188143
+XHCUMS AAL Ipsi 0.25309747156684814
+XHCUMS AAL Ipsi 0.26325474621899575
+XHCUMS AAL Ipsi 0.24544363702359345
+XHCUMS AAL Ipsi 0.254149796430732
+XHCUMS AAL Ipsi 0.21798554113732604
+XHCUMS AAL Ipsi 0.367461778584392
+XHCUMS AAL Ipsi 0.22983689957652756
+XHCUMS AAL Ipsi 0.37011307138536
+XHCUMS AAL Ipsi 0.2178583269812462
+XHCUMS AAL Ipsi 0.4972929909255898
+XHCUMS AAL Ipsi 0.2673838877797943
+XHCUMS AAL Ipsi 0.3018580341802783
+XHCUMS AAL Hom 0.45563596491228064
+XHCUMS AAL Hom 0.3895571403508772
+XHCUMS AAL Hom 0.33384798245614034
+XHCUMS AAL Hom 0.518070350877193
+XHCUMS AAL Hom 0.46044791228070175
+XHCUMS AAL Hom 0.516490701754386
+XHCUMS AAL Hom 0.3681920350877193
+XHCUMS AAL Hom 0.4623300877192982
+XHCUMS AAL Hom 0.4797480350877193
+XHCUMS AAL Hom 0.3707294736842105
+XHCUMS AAL Hom 0.5059735438596491
+XHCUMS AAL Hom 0.3846477894736842
+XHCUMS AAL Hom 0.41780399999999995
+XHCUMS AAL Hom 0.4358254736842106
+XHCUMS AAL Hom 0.5171155087719298
+XHCUMS AAL Hom 0.43334633333333333
+XHCUMS AAL Hom 0.37714170175438594
+XHCUMS AAL Hom 0.4569200526315789
+XHCUMS AAL Hom 0.5497907017543859
+XHCUMS AAL Hom 0.5046292105263158
+XHCUMS AAL Hom 0.48215296491228066
+XHCUMS AAL Hom 0.534268245614035
+XHCUMS AAL Hom 0.4886553333333334
+XHCUMS AAL Hom 0.43173584210526317
+XHCUMS AAL Hom 0.4265286315789473
+XHCUMS AAL Hom 0.43883522807017544
+XHCUMS AAL Hom 0.48976554385964904
+XHCUMS AAL Hom 0.44105385964912286
+XHCUMS AAL Hom 0.44820154385964917
+XHCUMS AAL Hom 0.5992030526315789
+XHCUMS AAL Hom 0.46236908771929824
+XHCUMS AAL Hom 0.4528418245614035
+XHCUMS AAL Hom 0.44791724561403506
+XHCUMS AAL Hom 0.402321596491228
+XHCUMS AAL Hom 0.4973815087719298
+XHCUMS AAL Hom 0.41903738596491225
+XHCUMS AAL Hom 0.5557352280701755
+XHCUMS AAL Hom 0.4543275614035088
+XHCUMS AAL Hom 0.40699199999999996
+XHCUMS AAL Hom 0.4519506315789473
+XHCUMS AAL Hom 0.4251735087719298
+XHCUMS AAL Hom 0.5975952631578948
+XHCUMS AAL Hom 0.4004011052631579
+XHCUMS AAL Hom 0.4745228947368421
+XHCUMS AAL Hom 0.379249
+XHCUMS AAL Hom 0.5650209298245614
+XHCUMS AAL Hom 0.40216784210526313
+XHCUMS AAL Hom 0.5217212105263157
+XHCUMS AAL Hom 0.3720416666666667
+XHCUMS AAL Hom 0.48637514035087714
+XHCUMS AAL Hom 0.43839991228070174
+XHCUMS AAL Hom 0.4818551578947369
+XHCUMS AAL Hom 0.5436946315789474
+XHCUMS AAL Hom 0.3906338245614035
+XHCUMS AAL Hom 0.4042460877192982
+XHCUMS AAL Hom 0.42182056140350876
+XHCUMS AAL Hom 0.521035245614035
+XHCUMS AAL Hom 0.45631677192982456
+XHCUMS AAL Hom 0.4317298947368421
+XHCUMS AAL Hom 0.276394
+XHCUMS AAL Hom 0.4300145438596491
+XHCUMS AAL Hom 0.4260124035087719
+XHCUMS AAL Hom 0.5558262105263158
+XHCUMS AAL Hom 0.4410062456140351
+XHCUMS AAL Hom 0.4129066140350877
+XHCUMS AAL Hom 0.44079908771929827
+XHCUMS AAL Hom 0.3985519824561403
+XHCUMS AAL Hom 0.43473342105263163
+XHCUMS AAL Hom 0.2940458771929824
+XHCUMS AAL Hom 0.4970128771929824
+XHCUMS AAL Hom 0.45084594736842104
+XHCUMS AAL Hom 0.4693332280701754
+XHCUMS AAL Hom 0.4335989649122807
+XHCUMS AAL Hom 0.3549874736842105
+XHCUMS AAL Hom 0.513329403508772
+XHCUMS AAL Hom 0.48328361403508774
+XHCUMS AAL Hom 0.552599649122807
+XHCUMS AAL Hom 0.4713418596491228
+XHCUMS AAL Hom 0.4487130175438596
+XHCUMS AAL Hom 0.37008198245614027
+XHCUMS AAL Hom 0.40886292982456146
+XHCUMS AAL Hom 0.4245318245614035
+XHCUMS AAL Hom 0.39688438596491227
+XHCUMS AAL Hom 0.48041338596491234
+XHCUMS AAL Hom 0.6620651228070176
+XHCUMS AAL Hom 0.498259
+XHCUMS AAL Hom 0.36957836842105257
+XHCUMS AAL Hom 0.3844613684210526
+XHCUMS AAL Hom 0.5608548596491228
+XHCUMS AAL Hom 0.4020441754385965
+XHCUMS AAL Hom 0.5346574561403509
+XHCUMS AAL Hom 0.4669364210526316
+XHCUMS AAL Hom 0.49997596491228075
+XHCUMS AAL Hom 0.4839456842105263
+XHCUMS AAL Hom 0.5131755614035088
+XHCUMS AAL Hom 0.42405319298245614
+XHCUMS AAL Hom 0.4722010350877193
+XHCUMS AAL Hom 0.512683052631579
+XHCUMS AAL Hom 0.3738104736842105
+XHCUMS AAL Hom 0.39085954385964916
+XHCUMS AAL Hom 0.5185188596491228
+XHCUMS AAL Hom 0.39272345614035087
+XHCUMS AAL Hom 0.5818089298245614
+XHCUMS AAL Hom 0.3953965438596491
+XHCUMS AAL Hom 0.3021830175438597
+XHCUMS AAL Hom 0.4206199824561404
+XHCUMS AAL Hom 0.43304338596491226
+XHCUMS AAL Hom 0.381932649122807
+XHCUMS AAL Hom 0.4892598245614035
+XHCUMS AAL Hom 0.416976947368421
+XHCUMS AAL Hom 0.7120196491228071
+XHCUMS AAL Hom 0.5012502982456141
+XHCUMS AAL Hom 0.47300375438596487
+XHCUMS AAL Hom 0.36429887719298243
+XHCUMS AAL Hom 0.45259761403508775
+XHCUMS AAL Hom 0.3967464210526315
+XHCUMS AAL Hom 0.4308417894736841
+XHCUMS AAL Hom 0.3095155438596491
+XHCUMS AAL Hom 0.49133373684210524
+XHCUMS AAL Hom 0.30379094736842105
+XHCUMS AAL Hom 0.5835217543859649
+XHCUMS AAL Hom 0.5031947719298245
+XHCUMS AAL Hom 0.46980270175438593
+XHCUMS AAL Hom 0.4739819649122806
+XHCUMS AAL Hom 0.39252226315789474
+XHCUMS AAL Hom 0.4073110877192983
+XHCUMS AAL Hom 0.44176324561403507
+XHCUMS AAL Hom 0.6168462631578948
+XHCUMS AAL Hom 0.3014802456140351
+XHCUMS AAL Hom 0.4353090877192982
+XHCUMS AAL Hom 0.36816070175438603
+XHCUMS AAL Hom 0.507716298245614
+XHCUMS AAL Hom 0.36828778947368424
+XHCUMS AAL Hom 0.36938092982456144
+XHCUMS AAL Hom 0.39202822807017546
+XHCUMS AAL Hom 0.4223807894736842
+XHCUMS AAL Hom 0.5894434385964912
+XHCUMS AAL Hom 0.4154331052631579
+XHCUMS AAL Hom 0.46093454385964916
+XHCUMS AAL Hom 0.38605524561403515
+XHCUMS AAL Hom 0.4490041403508772
+XHCUMS AAL Hom 0.4588080526315789
+XHCUMS AAL Hom 0.5524525789473684
+XHCUMS AAL Hom 0.5208606842105262
+XHCUMS AAL Hom 0.47214250877192987
+XHCUMS AAL Hom 0.5082355614035088
+XHCUMS AAL Hom 0.5503671929824562
+XHCUMS AAL Hom 0.37904152631578947
+XHCUMS AAL Hom 0.42682907017543853
+XHCUMS AAL Hom 0.3892920175438596
+XHCUMS AAL Hom 0.3764279298245614
+XHCUMS AAL Hom 0.5208583859649123
+XHCUMS AAL Hom 0.5222312631578948
+XHCUMS AAL Hom 0.47650512280701757
+XHCUMS AAL Hom 0.47302077192982456
+XHCUMS AAL Hom 0.4590658596491229
+XHCUMS AAL Hom 0.39748185964912286
+XHCUMS AAL Hom 0.5533600350877194
+XHCUMS AAL Hom 0.552620649122807
+XHCUMS AAL Hom 0.3506366315789473
+XHCUMS AAL Hom 0.42240210526315786
+XHCUMS AAL Hom 0.5388075789473684
+XHCUMS AAL Hom 0.4091624912280702
+XHCUMS AAL Hom 0.5541828245614037
+XHCUMS AAL Hom 0.49237324561403506
+XHCUMS AAL Hom 0.41513175438596495
+XHCUMS AAL Hom 0.5072021403508772
+XHCUMS AAL Hom 0.4163624210526316
+XHCUMS AAL Hom 0.36025205263157895
+XHCUMS AAL Hom 0.5338083157894737
+XHCUMS AAL Hom 0.4217911578947368
+XHCUMS AAL Hom 0.3792350350877193
+XHCUMS AAL Hom 0.42903547368421047
+XHCUMS AAL Hom 0.35891377192982454
+XHCUMS AAL Hom 0.5149866842105263
+XHCUMS AAL Hom 0.4283028596491228
+XHCUMS AAL Hom 0.49959859649122806
+XHCUMS AAL Hom 0.4618737192982456
+XHCUMS AAL Hom 0.389783
+XHCUMS AAL Hom 0.43026015789473687
+XHCUMS AAL Hom 0.4132272105263157
+XHCUMS AAL Hom 0.4616696140350877
+XHCUMS AAL Hom 0.44919261403508776
+XHCUMS AAL Hom 0.40466370175438604
+XHCUMS AAL Hom 0.4664121403508771
+XHCUMS AAL Hom 0.4576703157894737
+XHCUMS AAL Hom 0.3219108245614035
+XHCUMS AAL Hom 0.42817277192982456
+XHCUMS AAL Hom 0.48234063157894735
+XHCUMS AAL Hom 0.3997641578947368
+XHCUMS AAL Hom 0.523559947368421
+XHCUMS AAL Hom 0.47420573684210526
+XHCUMS AAL Hom 0.4549655087719298
+XHCUMS AAL Hom 0.5955496315789474
+XHCUMS AAL Hom 0.428665052631579
+XHCUMS AAL Hom 0.5244657368421053
+XHCUMS AAL Hom 0.35625177192982455
+XHCUMS AAL Hom 0.4674435087719298
+XHCUMS AAL Hom 0.4477697894736842
+XHCUMS AAL Hom 0.4027956315789473
+XHCUMS AAL Hom 0.41354701754385964
+XHCUMS AAL Hom 0.5619884736842105
+XHCUMS AAL Hom 0.4295872631578947
+XHCUMS AAL Hom 0.390795947368421
+XHCUMS AAL Hom 0.31997031578947366
+XHCUMS AAL Hom 0.5206057719298246
+XHCUMS AAL Hom 0.40304177192982454
+XHCUMS AAL Hom 0.3157131578947368
+XHCUMS AAL Hom 0.3725216315789473
+XHCUMS AAL Hom 0.4397131578947368
+XHCUMS AAL Hom 0.43990666666666667
+XHCUMS AAL Hom 0.5551846491228071
+XHCUMS AAL Hom 0.5366375789473684
+XHCUMS AAL Hom 0.5003003859649123
+XHCUMS AAL Hom 0.3704236140350877
+XHCUMS AAL Hom 0.3456082105263157
+XHCUMS AAL Hom 0.27613394736842106
+XHCUMS AAL Hom 0.4386180877192983
+XHCUMS AAL Hom 0.42935424561403507
+XHCUMS AAL Hom 0.4707366842105263
+XHCUMS AAL Hom 0.47006033333333325
+XHCUMS AAL Hom 0.3889243508771929
+XHCUMS AAL Hom 0.43845691228070177
+XHCUMS AAL Hom 0.5239834210526316
+XHCUMS AAL Hom 0.5440151228070176
+XHCUMS AAL Hom 0.47761168421052624
+XHCUMS AAL Hom 0.44358124561403506
+XHCUMS AAL Hom 0.40076412280701756
+XHCUMS AAL Hom 0.4393545614035088
+XHCUMS AAL Hom 0.5190730350877193
+XHCUMS AAL Hom 0.43513184210526307
+XHCUMS AAL Hom 0.495831350877193
+XHCUMS AAL Hom 0.3322105964912281
+XHCUMS AAL Hom 0.5714368596491228
+XHCUMS AAL Hom 0.4000537894736842
+XHCUMS AAL Hom 0.4558437368421053
+XHCUMS AAL Hom 0.40420477192982457
+XHCUMS AAL Hom 0.3992211052631579
+XHCUMS AAL Hom 0.456039298245614
+XHCUMS AAL Hom 0.43470138596491226
+XHCUMS AAL Hom 0.4900392456140351
+XHCUMS AAL Hom 0.29599452631578943
+XHCUMS AAL Hom 0.4638722280701754
+XHCUMS AAL Hom 0.34667463157894735
+XHCUMS AAL Hom 0.6246738596491227
+XHCUMS AAL Hom 0.393903947368421
+XHCUMS AAL Hom 0.40254301754385957
+XHCUMS AAL Contra 0.3352810302388872
+XHCUMS AAL Contra 0.22303770456607197
+XHCUMS AAL Contra 0.28379934290898096
+XHCUMS AAL Contra 0.3672492349561536
+XHCUMS AAL Contra 0.3515507426670698
+XHCUMS AAL Contra 0.4549200003023889
+XHCUMS AAL Contra 0.24055975476262473
+XHCUMS AAL Contra 0.24649836105231326
+XHCUMS AAL Contra 0.3302557877230118
+XHCUMS AAL Contra 0.22313172422134867
+XHCUMS AAL Contra 0.3653892546114303
+XHCUMS AAL Contra 0.24493125642576352
+XHCUMS AAL Contra 0.26596348896280614
+XHCUMS AAL Contra 0.32134314847293616
+XHCUMS AAL Contra 0.3594528760205625
+XHCUMS AAL Contra 0.2578063429089809
+XHCUMS AAL Contra 0.24586709918355004
+XHCUMS AAL Contra 0.3139543108557605
+XHCUMS AAL Contra 0.44575649984880555
+XHCUMS AAL Contra 0.37164972240701544
+XHCUMS AAL Contra 0.3433486716056849
+XHCUMS AAL Contra 0.4166041167221046
+XHCUMS AAL Contra 0.34695366465074085
+XHCUMS AAL Contra 0.23832140822497733
+XHCUMS AAL Contra 0.2835496068944663
+XHCUMS AAL Contra 0.3024001551254914
+XHCUMS AAL Contra 0.37988935107348054
+XHCUMS AAL Contra 0.25790988146356214
+XHCUMS AAL Contra 0.2966373934079226
+XHCUMS AAL Contra 0.3861182667069852
+XHCUMS AAL Contra 0.26189472936195946
+XHCUMS AAL Contra 0.2709736344118537
+XHCUMS AAL Contra 0.29061495585122465
+XHCUMS AAL Contra 0.2457853032960387
+XHCUMS AAL Contra 0.3533268224977321
+XHCUMS AAL Contra 0.3298719531297248
+XHCUMS AAL Contra 0.41146594526761415
+XHCUMS AAL Contra 0.3321593737526458
+XHCUMS AAL Contra 0.26414197550650137
+XHCUMS AAL Contra 0.2651564030843665
+XHCUMS AAL Contra 0.3451488224977321
+XHCUMS AAL Contra 0.4688296537647415
+XHCUMS AAL Contra 0.20076023888720892
+XHCUMS AAL Contra 0.3625439872996673
+XHCUMS AAL Contra 0.198166484729362
+XHCUMS AAL Contra 0.39643016540671305
+XHCUMS AAL Contra 0.26790292893861506
+XHCUMS AAL Contra 0.40865000574538857
+XHCUMS AAL Contra 0.22901882038100996
+XHCUMS AAL Contra 0.34350738584820073
+XHCUMS AAL Contra 0.3023356168732991
+XHCUMS AAL Contra 0.31858309192621714
+XHCUMS AAL Contra 0.4215946250377986
+XHCUMS AAL Contra 0.21734707136377385
+XHCUMS AAL Contra 0.2342139165406713
+XHCUMS AAL Contra 0.2839063834290898
+XHCUMS AAL Contra 0.4559614275778652
+XHCUMS AAL Contra 0.3448650586634412
+XHCUMS AAL Contra 0.3071745663743574
+XHCUMS AAL Contra 0.20183981221651046
+XHCUMS AAL Contra 0.28578312397943756
+XHCUMS AAL Contra 0.24118863108557606
+XHCUMS AAL Contra 0.40836833232537045
+XHCUMS AAL Contra 0.3223322146960992
+XHCUMS AAL Contra 0.2674819467795585
+XHCUMS AAL Contra 0.32683858361052315
+XHCUMS AAL Contra 0.2860655455095253
+XHCUMS AAL Contra 0.2779171279104929
+XHCUMS AAL Contra 0.24016433746598123
+XHCUMS AAL Contra 0.3570322473540974
+XHCUMS AAL Contra 0.3001977620199577
+XHCUMS AAL Contra 0.3379937925612337
+XHCUMS AAL Contra 0.29840379014212276
+XHCUMS AAL Contra 0.24534237647414572
+XHCUMS AAL Contra 0.33087690565467187
+XHCUMS AAL Contra 0.3527816271545207
+XHCUMS AAL Contra 0.42007166706985183
+XHCUMS AAL Contra 0.32564720290293314
+XHCUMS AAL Contra 0.33178647989114
+XHCUMS AAL Contra 0.24565470698518296
+XHCUMS AAL Contra 0.28987234895675834
+XHCUMS AAL Contra 0.2949960323556093
+XHCUMS AAL Contra 0.287214496522528
+XHCUMS AAL Contra 0.32921547051708494
+XHCUMS AAL Contra 0.5085211902026006
+XHCUMS AAL Contra 0.3264462633807076
+XHCUMS AAL Contra 0.30172679891140003
+XHCUMS AAL Contra 0.2570818058663441
+XHCUMS AAL Contra 0.46855456939824613
+XHCUMS AAL Contra 0.25421404717266405
+XHCUMS AAL Contra 0.3997017931660115
+XHCUMS AAL Contra 0.3086621215603266
+XHCUMS AAL Contra 0.3521671478681584
+XHCUMS AAL Contra 0.3161937952827336
+XHCUMS AAL Contra 0.32395643332325375
+XHCUMS AAL Contra 0.23819014454188084
+XHCUMS AAL Contra 0.28811747958875117
+XHCUMS AAL Contra 0.39660995343211375
+XHCUMS AAL Contra 0.2268011811309344
+XHCUMS AAL Contra 0.21296803296038705
+XHCUMS AAL Contra 0.3493760625944965
+XHCUMS AAL Contra 0.272272125188993
+XHCUMS AAL Contra 0.4144881236770486
+XHCUMS AAL Contra 0.2573001808285455
+XHCUMS AAL Contra 0.20015467342001816
+XHCUMS AAL Contra 0.2223323159963713
+XHCUMS AAL Contra 0.24855745690958572
+XHCUMS AAL Contra 0.24573788206833988
+XHCUMS AAL Contra 0.3673487121257938
+XHCUMS AAL Contra 0.24029396008466886
+XHCUMS AAL Contra 0.6010421185364379
+XHCUMS AAL Contra 0.3911692872694285
+XHCUMS AAL Contra 0.3320588327789537
+XHCUMS AAL Contra 0.22892015996371332
+XHCUMS AAL Contra 0.3160132975506501
+XHCUMS AAL Contra 0.2487889395222256
+XHCUMS AAL Contra 0.2720559537345026
+XHCUMS AAL Contra 0.21155902812216512
+XHCUMS AAL Contra 0.36099396855155724
+XHCUMS AAL Contra 0.23490441578469914
+XHCUMS AAL Contra 0.4094743293014817
+XHCUMS AAL Contra 0.3340050783187179
+XHCUMS AAL Contra 0.27881388327789536
+XHCUMS AAL Contra 0.30013611400060475
+XHCUMS AAL Contra 0.24840703810099787
+XHCUMS AAL Contra 0.2864989552464469
+XHCUMS AAL Contra 0.30364150922286065
+XHCUMS AAL Contra 0.5013393386755367
+XHCUMS AAL Contra 0.21676147928636225
+XHCUMS AAL Contra 0.25271675385545816
+XHCUMS AAL Contra 0.20795277290595707
+XHCUMS AAL Contra 0.3164886840036287
+XHCUMS AAL Contra 0.23291723949198673
+XHCUMS AAL Contra 0.24772605594194133
+XHCUMS AAL Contra 0.28611317236165706
+XHCUMS AAL Contra 0.30116256637435745
+XHCUMS AAL Contra 0.47203782310250986
+XHCUMS AAL Contra 0.28997555609313574
+XHCUMS AAL Contra 0.4011419555488358
+XHCUMS AAL Contra 0.27788871666162684
+XHCUMS AAL Contra 0.29796407045660717
+XHCUMS AAL Contra 0.2637863903840339
+XHCUMS AAL Contra 0.40234717387360147
+XHCUMS AAL Contra 0.4300830686422739
+XHCUMS AAL Contra 0.3276332089507106
+XHCUMS AAL Contra 0.3950291203507711
+XHCUMS AAL Contra 0.4532629461747808
+XHCUMS AAL Contra 0.28404496673722407
+XHCUMS AAL Contra 0.35534851375869364
+XHCUMS AAL Contra 0.20207412972482613
+XHCUMS AAL Contra 0.19698855972180226
+XHCUMS AAL Contra 0.3458301188388267
+XHCUMS AAL Contra 0.42316204475355307
+XHCUMS AAL Contra 0.36270400695494404
+XHCUMS AAL Contra 0.34609384638645296
+XHCUMS AAL Contra 0.24999378439673423
+XHCUMS AAL Contra 0.2736004408829755
+XHCUMS AAL Contra 0.37956722255820985
+XHCUMS AAL Contra 0.3921529241003931
+XHCUMS AAL Contra 0.21711756183852432
+XHCUMS AAL Contra 0.22070896068944665
+XHCUMS AAL Contra 0.3863379987904445
+XHCUMS AAL Contra 0.21512290202600542
+XHCUMS AAL Contra 0.44064554671908074
+XHCUMS AAL Contra 0.32879504293921985
+XHCUMS AAL Contra 0.3037625654671908
+XHCUMS AAL Contra 0.32878837586936804
+XHCUMS AAL Contra 0.21900736800725734
+XHCUMS AAL Contra 0.2157318406410644
+XHCUMS AAL Contra 0.4322276050801331
+XHCUMS AAL Contra 0.24162584608406412
+XHCUMS AAL Contra 0.20874530541276082
+XHCUMS AAL Contra 0.3051641778046568
+XHCUMS AAL Contra 0.25932215784699125
+XHCUMS AAL Contra 0.40921429029331724
+XHCUMS AAL Contra 0.26975021560326584
+XHCUMS AAL Contra 0.32553386362261866
+XHCUMS AAL Contra 0.33459838433625644
+XHCUMS AAL Contra 0.2446215025703054
+XHCUMS AAL Contra 0.22847685334139703
+XHCUMS AAL Contra 0.26747458542485636
+XHCUMS AAL Contra 0.34672839612942247
+XHCUMS AAL Contra 0.3614670178409434
+XHCUMS AAL Contra 0.23909261626852132
+XHCUMS AAL Contra 0.3600347674629574
+XHCUMS AAL Contra 0.3674232633807076
+XHCUMS AAL Contra 0.2328836507408527
+XHCUMS AAL Contra 0.29525908829755065
+XHCUMS AAL Contra 0.3000077024493499
+XHCUMS AAL Contra 0.2322495149682492
+XHCUMS AAL Contra 0.38666920229815543
+XHCUMS AAL Contra 0.3503687892349561
+XHCUMS AAL Contra 0.2889132794073178
+XHCUMS AAL Contra 0.5032040665255518
+XHCUMS AAL Contra 0.28754061324463265
+XHCUMS AAL Contra 0.3907697218022377
+XHCUMS AAL Contra 0.21742805080133049
+XHCUMS AAL Contra 0.23637292530994858
+XHCUMS AAL Contra 0.24194390293317206
+XHCUMS AAL Contra 0.2711021439371031
+XHCUMS AAL Contra 0.23039778016329002
+XHCUMS AAL Contra 0.4376541524039916
+XHCUMS AAL Contra 0.271693063804052
+XHCUMS AAL Contra 0.24954435167825822
+XHCUMS AAL Contra 0.23572612549138192
+XHCUMS AAL Contra 0.3878625712125794
+XHCUMS AAL Contra 0.2763921765951013
+XHCUMS AAL Contra 0.24796217780465676
+XHCUMS AAL Contra 0.25076751496824917
+XHCUMS AAL Contra 0.30465389023283945
+XHCUMS AAL Contra 0.23730543120653158
+XHCUMS AAL Contra 0.42409729724826123
+XHCUMS AAL Contra 0.34817143997580885
+XHCUMS AAL Contra 0.35702592016933776
+XHCUMS AAL Contra 0.22546264499546417
+XHCUMS AAL Contra 0.2328838711823405
+XHCUMS AAL Contra 0.19656213093438163
+XHCUMS AAL Contra 0.22508804384638648
+XHCUMS AAL Contra 0.25140227819776234
+XHCUMS AAL Contra 0.36295898427577866
+XHCUMS AAL Contra 0.3320646477169639
+XHCUMS AAL Contra 0.29230267402479587
+XHCUMS AAL Contra 0.2656954708194738
+XHCUMS AAL Contra 0.3806795987299667
+XHCUMS AAL Contra 0.4035937952827336
+XHCUMS AAL Contra 0.36584176171756877
+XHCUMS AAL Contra 0.2515264003628666
+XHCUMS AAL Contra 0.2597664121560327
+XHCUMS AAL Contra 0.32656704324160873
+XHCUMS AAL Contra 0.3989509863925007
+XHCUMS AAL Contra 0.2301261094647717
+XHCUMS AAL Contra 0.35652487843967345
+XHCUMS AAL Contra 0.24524665315996375
+XHCUMS AAL Contra 0.44548563078318715
+XHCUMS AAL Contra 0.20087592742667074
+XHCUMS AAL Contra 0.24991732295131538
+XHCUMS AAL Contra 0.2556833988509223
+XHCUMS AAL Contra 0.23266079407317808
+XHCUMS AAL Contra 0.25208250982763836
+XHCUMS AAL Contra 0.21856486694889626
+XHCUMS AAL Contra 0.36013395161778045
+XHCUMS AAL Contra 0.22044153462352584
+XHCUMS AAL Contra 0.36139871152101605
+XHCUMS AAL Contra 0.21593845902630782
+XHCUMS AAL Contra 0.4953349540368915
+XHCUMS AAL Contra 0.2652352319322649
+XHCUMS AAL Contra 0.287623349863925
+Dataset Atlas Type Value
+UPSM_1 AAL Ipsi 0.3921364277071991
+UPSM_1 AAL Ipsi 0.29008010647307925
+UPSM_1 AAL Ipsi 0.22805179219600724
+UPSM_1 AAL Ipsi 0.22634530006049605
+UPSM_1 AAL Ipsi 0.3467186975196612
+UPSM_1 AAL Ipsi 0.31734083424077436
+UPSM_1 AAL Ipsi 0.2710974361766485
+UPSM_1 AAL Ipsi 0.2580850607985481
+UPSM_1 AAL Ipsi 0.26286586872353296
+UPSM_1 AAL Ipsi 0.27455194767090135
+UPSM_1 AAL Ipsi 0.2515787800967937
+UPSM_1 AAL Ipsi 0.26379657441016335
+UPSM_1 AAL Ipsi 0.23354906624319421
+UPSM_1 AAL Ipsi 0.3921364277071991
+UPSM_1 AAL Ipsi 0.3962199963702359
+UPSM_1 AAL Ipsi 0.384184497277677
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+UPSM_1 AAL Hom 0.3764278245614035
+UPSM_1 AAL Hom 0.4581154912280702
+UPSM_1 AAL Hom 0.44198984210526315
+UPSM_1 AAL Hom 0.5443777719298246
+UPSM_1 AAL Hom 0.3659252807017544
+UPSM_1 AAL Hom 0.36694463157894736
+UPSM_1 AAL Hom 0.4832169649122807
+UPSM_1 AAL Hom 0.4863046666666666
+UPSM_1 AAL Hom 0.5533884210526316
+UPSM_1 AAL Hom 0.458123
+UPSM_1 AAL Hom 0.4651523157894737
+UPSM_1 AAL Hom 0.5185855789473685
+UPSM_1 AAL Hom 0.43139959649122805
+UPSM_1 AAL Hom 0.3964031052631579
+UPSM_1 AAL Hom 0.4071203859649123
+UPSM_1 AAL Hom 0.35302314035087723
+UPSM_1 AAL Hom 0.5287560350877193
+UPSM_1 AAL Hom 0.33761617543859646
+UPSM_1 AAL Hom 0.41726687719298244
+UPSM_1 AAL Hom 0.6002476842105263
+UPSM_1 AAL Hom 0.5009553684210526
+UPSM_1 AAL Hom 0.4035028421052631
+UPSM_1 AAL Hom 0.4713263333333333
+UPSM_1 AAL Hom 0.38841298245614037
+UPSM_1 AAL Hom 0.43236835087719294
+UPSM_1 AAL Hom 0.39006300000000005
+UPSM_1 AAL Hom 0.40008150877192983
+UPSM_1 AAL Hom 0.5458532280701754
+UPSM_1 AAL Hom 0.39429322807017547
+UPSM_1 AAL Hom 0.371823350877193
+UPSM_1 AAL Hom 0.3772472280701754
+UPSM_1 AAL Hom 0.43917401754385965
+UPSM_1 AAL Hom 0.5288784210526315
+UPSM_1 AAL Hom 0.39298542105263157
+UPSM_1 AAL Hom 0.4568099122807017
+UPSM_1 AAL Hom 0.37662859649122804
+UPSM_1 AAL Hom 0.3859085614035088
+UPSM_1 AAL Hom 0.3706638245614035
+UPSM_1 AAL Hom 0.4731819122807017
+UPSM_1 AAL Hom 0.43687924561403507
+UPSM_1 AAL Hom 0.564895350877193
+UPSM_1 AAL Hom 0.40302252631578944
+UPSM_1 AAL Hom 0.44322596491228067
+UPSM_1 AAL Hom 0.4234427894736842
+UPSM_1 AAL Hom 0.439492052631579
+UPSM_1 AAL Hom 0.45672329824561403
+UPSM_1 AAL Hom 0.46423100000000006
+UPSM_1 AAL Contra 0.3873602588448745
+UPSM_1 AAL Contra 0.28703431448442696
+UPSM_1 AAL Contra 0.22347611974599335
+UPSM_1 AAL Contra 0.22156434502570307
+UPSM_1 AAL Contra 0.3437315591170245
+UPSM_1 AAL Contra 0.3065570798306622
+UPSM_1 AAL Contra 0.2701196945872392
+UPSM_1 AAL Contra 0.2573640668279407
+UPSM_1 AAL Contra 0.2575603453280919
+UPSM_1 AAL Contra 0.2551735192016934
+UPSM_1 AAL Contra 0.2482282403991533
+UPSM_1 AAL Contra 0.25762362594496524
+UPSM_1 AAL Contra 0.22329507559721803
+UPSM_1 AAL Contra 0.3873602588448745
+UPSM_1 AAL Contra 0.3815648775325068
+UPSM_1 AAL Contra 0.3600752289083762
+UPSM_1 AAL Contra 0.3476713979437556
+UPSM_1 AAL Contra 0.20935285666767464
+UPSM_1 AAL Contra 0.5121772113698215
+UPSM_1 AAL Contra 0.236756286059873
+UPSM_1 AAL Contra 0.32695758633202293
+UPSM_1 AAL Contra 0.2180305037798609
+UPSM_1 AAL Contra 0.232310933776837
+UPSM_1 AAL Contra 0.31501488932567284
+UPSM_1 AAL Contra 0.2355640151194436
+UPSM_1 AAL Contra 0.33871633293014813
+UPSM_1 AAL Contra 0.337648586332023
+UPSM_1 AAL Contra 0.3993835585122468
+UPSM_1 AAL Contra 0.3209442328394315
+UPSM_1 AAL Contra 0.3531746177804657
+UPSM_1 AAL Contra 0.28072320350771096
+UPSM_1 AAL Contra 0.24126676988206835
+UPSM_1 AAL Contra 0.2800955724221349
+UPSM_1 AAL Contra 0.3131172612639855
+UPSM_1 AAL Contra 0.2833365210160266
+UPSM_1 AAL Contra 0.30039461263985484
+UPSM_1 AAL Contra 0.3562508802540067
+UPSM_1 AAL Contra 0.29185606229210764
+UPSM_1 AAL Contra 0.3682915799818567
+UPSM_1 AAL Contra 0.20621785697006345
+UPSM_1 AAL Contra 0.25422649349863924
+UPSM_1 AAL Contra 0.22559543695192014
+UPSM_1 AAL Contra 0.39563382310250983
+UPSM_1 AAL Contra 0.35163159872996674
+UPSM_1 AAL Contra 0.2915813677048685
+UPSM_1 AAL Contra 0.32986294587239184
+UPSM_1 AAL Contra 0.27785764711218625
+UPSM_1 AAL Contra 0.3113730341699425
+UPSM_1 AAL Contra 0.29128151043241607
+UPSM_1 AAL Contra 0.21441803326277592
+UPSM_1 AAL Contra 0.23364916873299063
+UPSM_1 AAL Contra 0.40442839491986693
+UPSM_1 AAL Contra 0.5244721941336559
+UPSM_1 AAL Contra 0.38933660719685514
+UPSM_1 AAL Contra 0.285318245842153
+UPSM_1 AAL Contra 0.41120867160568486
+UPSM_1 AAL Contra 0.3624268088902329
+UPSM_1 AAL Contra 0.3298003791956456
+UPSM_1 AAL Contra 0.465071042636831
+UPSM_1 AAL Contra 0.34698566344118537
+UPSM_1 AAL Contra 0.31787655790746894
+UPSM_1 AAL Contra 0.23777677592984578
+UPSM_1 AAL Contra 0.3427999440580587
+UPSM_1 AAL Contra 0.26479515452071367
+UPSM_1 AAL Contra 0.36911392500755974
+UPSM_1 AAL Contra 0.35066957484124583
+UPSM_1 AAL Contra 0.34215074629573633
+UPSM_1 AAL Contra 0.23604767311762925
+UPSM_1 AAL Contra 0.31435881493801027
+UPSM_1 AAL Contra 0.3617306601149078
+UPSM_1 AAL Contra 0.2558743311158149
+UPSM_1 AAL Contra 0.450161886604173
+UPSM_1 AAL Contra 0.25653207771394015
+UPSM_1 AAL Contra 0.37450274357423646
+UPSM_1 AAL Contra 0.19804402237677654
+UPSM_1 AAL Contra 0.30907860991835506
+UPSM_1 AAL Contra 0.2613070462654974
+UPSM_1 AAL Contra 0.259828286059873
+UPSM_1 AAL Contra 0.205109286059873
+UPSM_1 AAL Contra 0.2136099546416692
+UPSM_1 AAL Contra 0.302630936195948
+UPSM_1 AAL Contra 0.48134361536135467
+UPSM_1 AAL Contra 0.37048957453885695
+UPSM_1 AAL Contra 0.20308162413063202
+UPSM_1 AAL Contra 0.3079682434230421
+UPSM_1 AAL Contra 0.32597232143937105
+UPSM_1 AAL Contra 0.3072277311762927
+UPSM_1 AAL Contra 0.3733610039310553
+UPSM_1 AAL Contra 0.2862996824916843
+UPSM_1 AAL Contra 0.19693271121862715
+UPSM_1 AAL Contra 0.34958765315996376
+UPSM_1 AAL Contra 0.23189064832174178
+UPSM_1 AAL Contra 0.23217177895373453
+UPSM_1 AAL Contra 0.3277367441790142
+UPSM_1 AAL Contra 0.2630396652555186
+UPSM_1 AAL Contra 0.2561337520411249
+UPSM_1 AAL Contra 0.2682017311762927
+UPSM_1 AAL Contra 0.32898187329906253
+UPSM_1 AAL Contra 0.26346494889628064
+UPSM_1 AAL Contra 0.21690674145751437
+UPSM_1 AAL Contra 0.331276609918355
+UPSM_1 AAL Contra 0.25336535560931356
+UPSM_1 AAL Contra 0.3503709150287269
+UPSM_1 AAL Contra 0.4415559540368914
+UPSM_1 AAL Contra 0.2119944070154218
+UPSM_1 AAL Contra 0.2470110196552767
+UPSM_1 AAL Contra 0.24731640459631085
+UPSM_1 AAL Contra 0.22108696159661326
+UPSM_1 AAL Contra 0.25797214182038103
+UPSM_1 AAL Contra 0.3495385285757484
+UPSM_1 AAL Contra 0.25432956607196855
+UPSM_1 AAL Contra 0.23096354460235866
+UPSM_1 AAL Contra 0.2273945947989114
+UPSM_1 AAL Contra 0.41891113909888117
+UPSM_1 AAL Contra 0.33786422255820986
+UPSM_1 AAL Contra 0.2617098058663441
+UPSM_1 AAL Contra 0.20014604596310856
+UPSM_1 AAL Contra 0.3333810523132749
+UPSM_1 AAL Contra 0.36467306682794076
+UPSM_1 AAL Contra 0.3289399038403387
+UPSM_1 AAL Contra 0.2560614411853644
+UPSM_1 AAL Contra 0.39787062715452076
+UPSM_1 AAL Contra 0.31986132506803744
+UPSM_1 AAL Contra 0.3863370353794981
+UPSM_1 AAL Contra 0.23748327456909588
+UPSM_1 AAL Contra 0.296924413667977
+UPSM_1 AAL Contra 0.23285596552766857
+UPSM_1 AAL Contra 0.5325155833081342
+UPSM_1 AAL Contra 0.22056783822195342
+UPSM_1 AAL Contra 0.23290200574538855
+UPSM_1 AAL Contra 0.26002780405201087
+UPSM_1 AAL Contra 0.26061116994254613
+UPSM_1 AAL Contra 0.2745746876322951
+UPSM_1 AAL Contra 0.20656430934381614
+UPSM_1 AAL Contra 0.2324280096764439
+UPSM_1 AAL Contra 0.2456259827638343
+UPSM_1 AAL Contra 0.25147441397036585
+UPSM_1 AAL Contra 0.3708758896280617
+UPSM_1 AAL Contra 0.2990080039310553
+UPSM_1 AAL Contra 0.23800291986694888
+UPSM_1 AAL Contra 0.3052338974901723
+UPSM_1 AAL Contra 0.3036163341397037
+UPSM_1 AAL Contra 0.20022630148170548
+UPSM_1 AAL Contra 0.23949047293619596
+UPSM_1 AAL Contra 0.28736365104324163
+UPSM_1 AAL Contra 0.402898763531902
+UPSM_1 AAL Contra 0.256082054429997
+UPSM_1 AAL Contra 0.26001475869368007
+UPSM_1 AAL Contra 0.283781054429997
+UPSM_1 AAL Contra 0.3726392555185969
+UPSM_1 AAL Contra 0.2794576534623526
+UPSM_1 AAL Contra 0.24161597913516783
+UPSM_1 AAL Contra 0.24070881130934382
+UPSM_1 AAL Contra 0.330454193831267
+UPSM_1 AAL Contra 0.2602413344420925
+UPSM_1 AAL Contra 0.3367954015724221
+UPSM_1 AAL Contra 0.25739619262171154
+UPSM_1 AAL Contra 0.30775245690958575
+UPSM_1 AAL Contra 0.27428870486846085
+UPSM_1 AAL Contra 0.36329837980042334
+UPSM_1 AAL Contra 0.300055543695192
+UPSM_1 AAL Contra 0.22813530964620504
+UPSM_1 AAL Contra 0.20864352948291504
+UPSM_1 AAL Contra 0.2568480997883278
+UPSM_1 AAL Contra 0.23533402419110971
+UPSM_1 AAL Contra 0.2273982981554279
+UPSM_1 AAL Contra 0.22432357635319025
+UPSM_1 AAL Contra 0.31586686846084067
+UPSM_1 AAL Contra 0.26107770819473847
+UPSM_1 AAL Contra 0.23022383277895372
+UPSM_1 AAL Contra 0.2216577051708497
+UPSM_1 AAL Contra 0.35753951133958267
+UPSM_1 AAL Contra 0.4128079467795585
+UPSM_1 AAL Contra 0.2619492083459329
+UPSM_1 AAL Contra 0.24260994103416994
+UPSM_1 AAL Contra 0.44273870970668283
+UPSM_1 AAL Contra 0.22210264076201996
+UPSM_1 AAL Contra 0.2607575935893559
+UPSM_1 AAL Contra 0.28842802691260966
+UPSM_1 AAL Contra 0.4773133903840339
+UPSM_1 AAL Contra 0.37329807287571815
+UPSM_1 AAL Contra 0.26190073510734807
+UPSM_1 AAL Contra 0.31040695403689145
+UPSM_1 AAL Contra 0.22056623435137584
+UPSM_1 AAL Contra 0.2993073755669791
+UPSM_1 AAL Contra 0.25157964045963105
+UPSM_1 AAL Contra 0.36436127033565163
+UPSM_1 AAL Contra 0.22786307106138493
+UPSM_1 AAL Contra 0.23032859117024493
+UPSM_1 AAL Contra 0.3123925446023586
+UPSM_1 AAL Contra 0.31534433444209253
+UPSM_1 AAL Contra 0.38828799455700025
+UPSM_1 AAL Contra 0.2988143643785909
+UPSM_1 AAL Contra 0.25886080858784394
+UPSM_1 AAL Contra 0.3618108530390082
+UPSM_1 AAL Contra 0.3351127317810704
+UPSM_1 AAL Contra 0.26376064560024187
+UPSM_1 AAL Contra 0.26585665134563047
+UPSM_1 AAL Contra 0.20908080858784395
+UPSM_1 AAL Contra 0.3804584185061989
+UPSM_1 AAL Contra 0.21267783156939826
+UPSM_1 AAL Contra 0.27045811732688235
+UPSM_1 AAL Contra 0.42056064076201993
+UPSM_1 AAL Contra 0.3528362207438766
+UPSM_1 AAL Contra 0.2606708884185062
+UPSM_1 AAL Contra 0.3300071781070457
+UPSM_1 AAL Contra 0.23022994194133656
+UPSM_1 AAL Contra 0.27728761354702147
+UPSM_1 AAL Contra 0.2646302131841548
+UPSM_1 AAL Contra 0.2872943661929241
+UPSM_1 AAL Contra 0.4179354547928636
+UPSM_1 AAL Contra 0.22102826338070755
+UPSM_1 AAL Contra 0.23064715331115815
+UPSM_1 AAL Contra 0.2339709401270033
+UPSM_1 AAL Contra 0.3344979165406713
+UPSM_1 AAL Contra 0.37511151315391594
+UPSM_1 AAL Contra 0.21231762836407622
+UPSM_1 AAL Contra 0.31870988055639554
+UPSM_1 AAL Contra 0.21759617599032358
+UPSM_1 AAL Contra 0.2610076422739643
+UPSM_1 AAL Contra 0.22399381796189902
+UPSM_1 AAL Contra 0.28238837103114606
+UPSM_1 AAL Contra 0.25639744814030846
+UPSM_1 AAL Contra 0.41846613395827037
+UPSM_1 AAL Contra 0.2514509706682794
+UPSM_1 AAL Contra 0.30078701511944356
+UPSM_1 AAL Contra 0.23799782854550955
+UPSM_1 AAL Contra 0.30700502993649836
+UPSM_1 AAL Contra 0.24029807045660714
+UPSM_1 AAL Contra 0.25507667644390686
+Dataset Atlas Type Value
+BNU3 AAL Ipsi 0.25479024894131885
+BNU3 AAL Ipsi 0.31080658106473086
+BNU3 AAL Ipsi 0.3337203145795523
+BNU3 AAL Ipsi 0.2999714540229885
+BNU3 AAL Ipsi 0.38276431760435575
+BNU3 AAL Ipsi 0.3241456467029643
+BNU3 AAL Ipsi 0.3283354065335753
+BNU3 AAL Ipsi 0.2297626941923775
+BNU3 AAL Ipsi 0.29199031699939504
+BNU3 AAL Ipsi 0.290782242891712
+BNU3 AAL Ipsi 0.275445076830006
+BNU3 AAL Ipsi 0.4176151500302481
+BNU3 AAL Ipsi 0.25404064700544465
+BNU3 AAL Ipsi 0.25923479431336965
+BNU3 AAL Ipsi 0.3215815006049607
+BNU3 AAL Ipsi 0.34581030369026017
+BNU3 AAL Ipsi 0.19991297398669086
+BNU3 AAL Ipsi 0.519135570477919
+BNU3 AAL Ipsi 0.3094233027828191
+BNU3 AAL Ipsi 0.37395852752571085
+BNU3 AAL Ipsi 0.2860296954022989
+BNU3 AAL Ipsi 0.3113479186327889
+BNU3 AAL Ipsi 0.27262361131276464
+BNU3 AAL Ipsi 0.32293653206291595
+BNU3 AAL Ipsi 0.3113855390199637
+BNU3 AAL Ipsi 0.36639115063520866
+BNU3 AAL Ipsi 0.20036807471264367
+BNU3 AAL Ipsi 0.27417899969751963
+BNU3 AAL Ipsi 0.3693444461584997
+BNU3 AAL Ipsi 0.3472671521476104
+BNU3 AAL Ipsi 0.3146762692075015
+BNU3 AAL Ipsi 0.33982235753176043
+BNU3 AAL Ipsi 0.2632910949788264
+BNU3 AAL Ipsi 0.2901910021173624
+BNU3 AAL Ipsi 0.22694003024803389
+BNU3 AAL Ipsi 0.28034203569268
+BNU3 AAL Ipsi 0.24461650060496065
+BNU3 AAL Ipsi 0.23681896944948577
+BNU3 AAL Ipsi 0.387030612522686
+BNU3 AAL Ipsi 0.3532291367211131
+BNU3 AAL Ipsi 0.3139146833030853
+BNU3 AAL Ipsi 0.2624939379915306
+BNU3 AAL Ipsi 0.3038405499092559
+BNU3 AAL Ipsi 0.21312575892316998
+BNU3 AAL Ipsi 0.3448865831820932
+BNU3 AAL Ipsi 0.37275612825166365
+BNU3 AAL Ipsi 0.33986544252873563
+BNU3 AAL Ipsi 0.36869816091954016
+BNU3 AAL Ipsi 0.279044241984271
+BNU3 AAL Ipsi 0.27211706745311554
+BNU3 AAL Ipsi 0.21427189261947974
+BNU3 AAL Ipsi 0.3631833315184513
+BNU3 AAL Ipsi 0.2903440142165759
+BNU3 AAL Ipsi 0.24889731941923773
+BNU3 AAL Ipsi 0.34992239443436174
+BNU3 AAL Ipsi 0.30203487991530553
+BNU3 AAL Ipsi 0.3928431826981246
+BNU3 AAL Ipsi 0.2815920529340593
+BNU3 AAL Ipsi 0.4131422141560799
+BNU3 AAL Ipsi 0.2692758774954628
+BNU3 AAL Ipsi 0.47260330006049606
+BNU3 AAL Ipsi 0.40305608257713244
+BNU3 AAL Ipsi 0.29821866031457955
+BNU3 AAL Ipsi 0.24206248124621899
+BNU3 AAL Ipsi 0.24108238475499094
+BNU3 AAL Ipsi 0.3324612256503327
+BNU3 AAL Ipsi 0.2672216297640653
+BNU3 AAL Ipsi 0.2914386249243799
+BNU3 AAL Ipsi 0.2717741566848155
+BNU3 AAL Ipsi 0.22441191500302482
+BNU3 AAL Ipsi 0.29522180792498487
+BNU3 AAL Ipsi 0.3809472059891107
+BNU3 AAL Ipsi 0.33206330096793707
+BNU3 AAL Ipsi 0.29445991923774956
+BNU3 AAL Ipsi 0.3223981820931639
+BNU3 AAL Ipsi 0.2869521957047792
+BNU3 AAL Ipsi 0.22815893768905018
+BNU3 AAL Ipsi 0.3185218557168784
+BNU3 AAL Ipsi 0.32372415486993344
+BNU3 AAL Ipsi 0.35037998820326677
+BNU3 AAL Ipsi 0.2990131699939504
+BNU3 AAL Ipsi 0.2724758351482154
+BNU3 AAL Ipsi 0.29775471203871745
+BNU3 AAL Ipsi 0.33951244404113734
+BNU3 AAL Ipsi 0.31433416878402903
+BNU3 AAL Ipsi 0.3703889948578342
+BNU3 AAL Ipsi 0.4290533877797943
+BNU3 AAL Ipsi 0.4039253197217181
+BNU3 AAL Ipsi 0.34220764428312156
+BNU3 AAL Ipsi 0.3034577316999395
+BNU3 AAL Ipsi 0.30846217574107687
+BNU3 AAL Ipsi 0.2809034280096794
+BNU3 AAL Ipsi 0.2890961727162734
+BNU3 AAL Ipsi 0.2015004993950393
+BNU3 AAL Ipsi 0.26382329522081066
+BNU3 AAL Ipsi 0.3281507262552934
+BNU3 AAL Ipsi 0.27409504718693284
+BNU3 AAL Ipsi 0.34805446884452507
+BNU3 AAL Ipsi 0.23242092075015122
+BNU3 AAL Ipsi 0.23574961615245008
+BNU3 AAL Ipsi 0.37843978765880226
+BNU3 AAL Ipsi 0.2782155232909861
+BNU3 AAL Ipsi 0.3177851733212341
+BNU3 AAL Ipsi 0.24573876255293403
+BNU3 AAL Ipsi 0.43629838445251057
+BNU3 AAL Ipsi 0.4560892456140352
+BNU3 AAL Ipsi 0.3334111645493043
+BNU3 AAL Ipsi 0.474114925892317
+BNU3 AAL Ipsi 0.2293932253478524
+BNU3 AAL Ipsi 0.4755975063520871
+BNU3 AAL Ipsi 0.5158685865093768
+BNU3 AAL Ipsi 0.4140150541439806
+BNU3 AAL Ipsi 0.24282638838475498
+BNU3 AAL Ipsi 0.37330532274652145
+BNU3 AAL Ipsi 0.2767048581367211
+BNU3 AAL Ipsi 0.26079104325468844
+BNU3 AAL Ipsi 0.3329276385359952
+BNU3 AAL Ipsi 0.31823386388384756
+BNU3 AAL Ipsi 0.3340373027828191
+BNU3 AAL Ipsi 0.22802033212341197
+BNU3 AAL Ipsi 0.3173480193587417
+BNU3 AAL Ipsi 0.3211628445251059
+BNU3 AAL Ipsi 0.33320844706594066
+BNU3 AAL Ipsi 0.3596756718088324
+BNU3 AAL Ipsi 0.21258904022988506
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+IPCAS7 AAL Contra 0.35286682552162085
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+IPCAS7 AAL Contra 0.18812934563048078
+IPCAS7 AAL Contra 0.32684123979437557
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+IPCAS7 AAL Contra 0.23796094677955848
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+IPCAS7 AAL Contra 0.2148481644995464
+IPCAS7 AAL Contra 0.29383970728757186
+IPCAS7 AAL Contra 0.25144573178107044
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+IPCAS7 AAL Contra 0.4111426195947989
+IPCAS7 AAL Contra 0.24187830057453885
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+IPCAS7 AAL Contra 0.31150657091019057
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+IPCAS7 AAL Contra 0.4329994865436952
+IPCAS7 AAL Contra 0.2876185560931358
+IPCAS7 AAL Contra 0.20685081644995465
+IPCAS7 AAL Contra 0.23851943029936498
+Dataset Atlas Type Value
+SWU1 AAL Ipsi 0.4063735931639444
+SWU1 AAL Ipsi 0.27165829975801575
+SWU1 AAL Ipsi 0.2614290859044162
+SWU1 AAL Ipsi 0.24976409830611013
+SWU1 AAL Ipsi 0.34216070175438595
+SWU1 AAL Ipsi 0.2814615786448881
+SWU1 AAL Ipsi 0.3006260172413793
+SWU1 AAL Ipsi 0.36010532425892317
+SWU1 AAL Ipsi 0.3912328614640049
+SWU1 AAL Ipsi 0.24519608499697518
+SWU1 AAL Ipsi 0.38888272655777373
+SWU1 AAL Ipsi 0.2755050486993345
+SWU1 AAL Ipsi 0.35519543768905015
+SWU1 AAL Ipsi 0.2511948058076225
+SWU1 AAL Ipsi 0.30023118027828194
+SWU1 AAL Ipsi 0.2744974857834241
+SWU1 AAL Ipsi 0.25464135843920144
+SWU1 AAL Ipsi 0.24547733061101026
+SWU1 AAL Ipsi 0.3266579978826376
+SWU1 AAL Ipsi 0.2966465456745312
+SWU1 AAL Ipsi 0.27239904416212946
+SWU1 AAL Ipsi 0.35836433212341195
+SWU1 AAL Ipsi 0.43662210707803994
+SWU1 AAL Ipsi 0.35682142710223835
+SWU1 AAL Ipsi 0.24838734664246825
+SWU1 AAL Ipsi 0.35301340986085905
+SWU1 AAL Ipsi 0.24528567271627344
+SWU1 AAL Ipsi 0.29080351905626134
+SWU1 AAL Ipsi 0.2626775856019359
+SWU1 AAL Ipsi 0.3495820108892922
+SWU1 AAL Ipsi 0.3684778635813672
+SWU1 AAL Ipsi 0.23750157592256502
+SWU1 AAL Ipsi 0.3164387398669086
+SWU1 AAL Ipsi 0.356069868723533
+SWU1 AAL Ipsi 0.38167785722928016
+SWU1 AAL Ipsi 0.24381010344827586
+SWU1 AAL Ipsi 0.28204965819721717
+SWU1 AAL Ipsi 0.2667065499092559
+SWU1 AAL Ipsi 0.36123751935874165
+SWU1 AAL Ipsi 0.3730263566243194
+SWU1 AAL Ipsi 0.3246833805202662
+SWU1 AAL Ipsi 0.3133273109497882
+SWU1 AAL Ipsi 0.3959226709013914
+SWU1 AAL Ipsi 0.22611934724742888
+SWU1 AAL Ipsi 0.2597896911675741
+SWU1 AAL Ipsi 0.29978429431336967
+SWU1 AAL Ipsi 0.3201332752571083
+SWU1 AAL Ipsi 0.2247687425892317
+SWU1 AAL Ipsi 0.24496755021173622
+SWU1 AAL Ipsi 0.33120834513006653
+SWU1 AAL Ipsi 0.3586109201451905
+SWU1 AAL Ipsi 0.2570458835450696
+SWU1 AAL Ipsi 0.30891875771324867
+SWU1 AAL Ipsi 0.22440919358741682
+SWU1 AAL Ipsi 0.29622284845735025
+SWU1 AAL Ipsi 0.3181813384754991
+SWU1 AAL Ipsi 0.24655775710828798
+SWU1 AAL Ipsi 0.2237280880217786
+SWU1 AAL Ipsi 0.3941872676950998
+SWU1 AAL Ipsi 0.2206715483968542
+SWU1 AAL Ipsi 0.2488086920750151
+SWU1 AAL Ipsi 0.19803055414398066
+SWU1 AAL Ipsi 0.3681279274047187
+SWU1 AAL Ipsi 0.24684090562613428
+SWU1 AAL Ipsi 0.3731161621294615
+SWU1 AAL Ipsi 0.3511961128251664
+SWU1 AAL Ipsi 0.25259974591651546
+SWU1 AAL Ipsi 0.3306333886872354
+SWU1 AAL Ipsi 0.3406228466424682
+SWU1 AAL Ipsi 0.4827007773744707
+SWU1 AAL Ipsi 0.2499296270417423
+SWU1 AAL Ipsi 0.21935786176648517
+SWU1 AAL Ipsi 0.2986515514216576
+SWU1 AAL Ipsi 0.3582072834240774
+SWU1 AAL Ipsi 0.22070967241379313
+SWU1 AAL Ipsi 0.34836217059891106
+SWU1 AAL Ipsi 0.2970180626134301
+SWU1 AAL Ipsi 0.3533960272232305
+SWU1 AAL Ipsi 0.27641627888687237
+SWU1 AAL Ipsi 0.2826829065335753
+SWU1 AAL Ipsi 0.3472618106473079
+SWU1 AAL Ipsi 0.29148878886872354
+SWU1 AAL Ipsi 0.2235534476709014
+SWU1 AAL Ipsi 0.4108626442831216
+SWU1 AAL Ipsi 0.26821303327283724
+SWU1 AAL Ipsi 0.3777721182698125
+SWU1 AAL Ipsi 0.29844226830006054
+SWU1 AAL Ipsi 0.25359316666666665
+SWU1 AAL Ipsi 0.19519381851179676
+SWU1 AAL Ipsi 0.26329852056866304
+SWU1 AAL Ipsi 0.2741787123411978
+SWU1 AAL Ipsi 0.30236772141560797
+SWU1 AAL Ipsi 0.2794795033272837
+SWU1 AAL Ipsi 0.26670284694494856
+SWU1 AAL Ipsi 0.38673927162734417
+SWU1 AAL Ipsi 0.39563984301270416
+SWU1 AAL Ipsi 0.3742763605565638
+SWU1 AAL Ipsi 0.27750842710223833
+SWU1 AAL Ipsi 0.3617901176648518
+SWU1 AAL Ipsi 0.2151148590441621
+SWU1 AAL Ipsi 0.47433583303085297
+SWU1 AAL Ipsi 0.24034755202661828
+SWU1 AAL Ipsi 0.2961339358741682
+SWU1 AAL Ipsi 0.2917702595281307
+SWU1 AAL Ipsi 0.2989877129461585
+SWU1 AAL Ipsi 0.3974728064125832
+SWU1 AAL Ipsi 0.19923457894736843
+SWU1 AAL Ipsi 0.2257495977011494
+SWU1 AAL Ipsi 0.25441190350877196
+SWU1 AAL Ipsi 0.2923665568663037
+SWU1 AAL Ipsi 0.2858789140955838
+SWU1 AAL Ipsi 0.35311947580157294
+SWU1 AAL Ipsi 0.33224170629159105
+SWU1 AAL Ipsi 0.3310224824561403
+SWU1 AAL Ipsi 0.31933260859044166
+SWU1 AAL Ipsi 0.3514147404718694
+SWU1 AAL Ipsi 0.3451379150030248
+SWU1 AAL Ipsi 0.366510631276467
+SWU1 AAL Ipsi 0.2392436439806412
+SWU1 AAL Hom 0.5281054385964912
+SWU1 AAL Hom 0.4417268771929825
+SWU1 AAL Hom 0.44939226315789477
+SWU1 AAL Hom 0.44168071929824554
+SWU1 AAL Hom 0.4335777192982456
+SWU1 AAL Hom 0.38668205263157895
+SWU1 AAL Hom 0.4437018245614035
+SWU1 AAL Hom 0.45047243859649133
+SWU1 AAL Hom 0.5313598245614035
+SWU1 AAL Hom 0.41223038596491224
+SWU1 AAL Hom 0.49842657894736836
+SWU1 AAL Hom 0.4444198771929824
+SWU1 AAL Hom 0.44358312280701756
+SWU1 AAL Hom 0.36284975438596495
+SWU1 AAL Hom 0.4110786666666667
+SWU1 AAL Hom 0.35466000000000003
+SWU1 AAL Hom 0.36893264912280704
+SWU1 AAL Hom 0.36692424561403514
+SWU1 AAL Hom 0.4288298245614035
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+Dataset Atlas Type Value
+BNU1 AAL Ipsi 0.42165641984271024
+BNU1 AAL Ipsi 0.39404803206291583
+BNU1 AAL Ipsi 0.31901526073805203
+BNU1 AAL Ipsi 0.3588710677555959
+BNU1 AAL Ipsi 0.29181831639443434
+BNU1 AAL Ipsi 0.3192795263157895
+BNU1 AAL Ipsi 0.388442018753781
+BNU1 AAL Ipsi 0.2801892014519056
+BNU1 AAL Ipsi 0.2757322450090744
+BNU1 AAL Ipsi 0.35711892468239564
+BNU1 AAL Ipsi 0.33486852752571084
+BNU1 AAL Ipsi 0.2802127846339988
+BNU1 AAL Ipsi 0.27755128917120386
+BNU1 AAL Ipsi 0.34314294888082275
+BNU1 AAL Ipsi 0.3190563045977011
+BNU1 AAL Ipsi 0.26689953024803387
+BNU1 AAL Ipsi 0.38238422202056865
+BNU1 AAL Ipsi 0.46037232456140353
+BNU1 AAL Ipsi 0.2947063623714459
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+BNU1 AAL Ipsi 0.3757371288566243
+BNU1 AAL Ipsi 0.32032424319419234
+BNU1 AAL Ipsi 0.29020430611010284
+BNU1 AAL Ipsi 0.2754010202661827
+BNU1 AAL Ipsi 0.35844693194192373
+BNU1 AAL Ipsi 0.22931812008469446
+BNU1 AAL Ipsi 0.32178553266787663
+BNU1 AAL Ipsi 0.2817040117967332
+BNU1 AAL Ipsi 0.2712891394434362
+BNU1 AAL Ipsi 0.34419917816091955
+BNU1 AAL Ipsi 0.3055193829401089
+BNU1 AAL Ipsi 0.2653766591046582
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+BNU1 AAL Ipsi 0.403973162431942
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+BNU1 AAL Ipsi 0.3664668835450696
+BNU1 AAL Ipsi 0.2440007353297036
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+BNU1 AAL Ipsi 0.38362903266787657
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+BNU1 AAL Ipsi 0.37181203418027825
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+BNU1 AAL Ipsi 0.319728816091954
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+BNU1 AAL Hom 0.5716130175438596
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+BNU1 AAL Hom 0.4759741929824562
+BNU1 AAL Hom 0.4378927368421053
+BNU1 AAL Hom 0.43680508771929827
+BNU1 AAL Hom 0.5314250877192982
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+BNU1 AAL Hom 0.4110356666666667
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+BNU1 AAL Hom 0.38229492982456137
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+BNU1 AAL Hom 0.41596624561403506
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+BNU1 AAL Hom 0.41071498245614035
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+BNU1 AAL Contra 0.25665757605080136
+BNU1 AAL Contra 0.2680453816147566
+BNU1 AAL Contra 0.3568198234048987
+BNU1 AAL Contra 0.25957280314484427
+BNU1 AAL Contra 0.2735374336256426
+BNU1 AAL Contra 0.35021534835198065
+BNU1 AAL Contra 0.25016268309646206
+BNU1 AAL Contra 0.30497947293619593
+BNU1 AAL Contra 0.28762232537042637
+BNU1 AAL Contra 0.5036586014514666
+BNU1 AAL Contra 0.2647643649833686
+BNU1 AAL Contra 0.3327049485938918
+BNU1 AAL Contra 0.2833557508315694
+BNU1 AAL Contra 0.34965216843060176
+Dataset Atlas Type Value
+BMB_1 AAL Ipsi 0.26930703085299457
+BMB_1 AAL Ipsi 0.2479984210526316
+BMB_1 AAL Ipsi 0.3159511397459165
+BMB_1 AAL Ipsi 0.3333789676346038
+BMB_1 AAL Ipsi 0.3037293106473079
+BMB_1 AAL Ipsi 0.26671550846944947
+BMB_1 AAL Ipsi 0.2856901140350877
+BMB_1 AAL Ipsi 0.28908386176648515
+BMB_1 AAL Ipsi 0.3554481430732002
+BMB_1 AAL Ipsi 0.37889745977011496
+BMB_1 AAL Ipsi 0.27488368330308527
+BMB_1 AAL Ipsi 0.26397538142770716
+BMB_1 AAL Ipsi 0.3493559649122807
+BMB_1 AAL Ipsi 0.2636959767090139
+BMB_1 AAL Ipsi 0.2549847020568663
+BMB_1 AAL Ipsi 0.3917260251058681
+BMB_1 AAL Ipsi 0.2439913823351482
+BMB_1 AAL Ipsi 0.30256563732607383
+BMB_1 AAL Ipsi 0.3982736890502117
+BMB_1 AAL Ipsi 0.2921947259528131
+BMB_1 AAL Ipsi 0.2640815813672111
+BMB_1 AAL Ipsi 0.40383043647912886
+BMB_1 AAL Ipsi 0.26636431639443436
+BMB_1 AAL Ipsi 0.456167664246824
+BMB_1 AAL Ipsi 0.2659776675741077
+BMB_1 AAL Ipsi 0.29311073442226254
+BMB_1 AAL Ipsi 0.32552520477918934
+BMB_1 AAL Ipsi 0.36036668965517243
+BMB_1 AAL Ipsi 0.243133778584392
+BMB_1 AAL Ipsi 0.3987383203266788
+BMB_1 AAL Ipsi 0.25040280369026013
+BMB_1 AAL Ipsi 0.3102877658802178
+BMB_1 AAL Ipsi 0.35481635904416214
+BMB_1 AAL Ipsi 0.38710282940108887
+BMB_1 AAL Ipsi 0.2703396358136721
+BMB_1 AAL Ipsi 0.4774065980036297
+BMB_1 AAL Ipsi 0.35728853145795525
+BMB_1 AAL Ipsi 0.3245014189352692
+BMB_1 AAL Ipsi 0.34546555535390194
+BMB_1 AAL Ipsi 0.2698599534180278
+BMB_1 AAL Ipsi 0.3229844352692075
+BMB_1 AAL Ipsi 0.25456182244404113
+BMB_1 AAL Ipsi 0.3627221960072595
+BMB_1 AAL Ipsi 0.24027733424077433
+BMB_1 AAL Ipsi 0.3178291881427707
+BMB_1 AAL Ipsi 0.25187668935269203
+BMB_1 AAL Ipsi 0.3499531588021778
+BMB_1 AAL Ipsi 0.3628081164549304
+BMB_1 AAL Ipsi 0.2801619413188143
+BMB_1 AAL Ipsi 0.2954619110707804
+BMB_1 AAL Ipsi 0.24479939503932246
+BMB_1 AAL Ipsi 0.2815966470054446
+BMB_1 AAL Ipsi 0.3213936730187538
+BMB_1 AAL Ipsi 0.4019116049606775
+BMB_1 AAL Ipsi 0.4755375308529946
+BMB_1 AAL Ipsi 0.2812490130066546
+BMB_1 AAL Ipsi 0.2943679428312159
+BMB_1 AAL Ipsi 0.2306598136721113
+BMB_1 AAL Ipsi 0.31428196975196615
+BMB_1 AAL Ipsi 0.41665590018148824
+BMB_1 AAL Ipsi 0.2514480444646098
+BMB_1 AAL Ipsi 0.3342457897761645
+BMB_1 AAL Ipsi 0.4616367743496672
+BMB_1 AAL Ipsi 0.3126385462794919
+BMB_1 AAL Ipsi 0.2891289192377495
+BMB_1 AAL Ipsi 0.28489816182698124
+BMB_1 AAL Ipsi 0.2722222655777375
+BMB_1 AAL Ipsi 0.28925133030852995
+BMB_1 AAL Ipsi 0.36598879189352695
+BMB_1 AAL Ipsi 0.2838866929824561
+BMB_1 AAL Ipsi 0.30229830490018145
+BMB_1 AAL Ipsi 0.25707242256503327
+BMB_1 AAL Ipsi 0.33346003539019964
+BMB_1 AAL Ipsi 0.3565629543254688
+BMB_1 AAL Ipsi 0.2688447558983666
+BMB_1 AAL Ipsi 0.3696471061705989
+BMB_1 AAL Ipsi 0.3311474909255898
+BMB_1 AAL Ipsi 0.27440132788868726
+BMB_1 AAL Ipsi 0.295777184815487
+BMB_1 AAL Ipsi 0.27138327888687236
+BMB_1 AAL Ipsi 0.3610348300060497
+BMB_1 AAL Ipsi 0.4018324140955838
+BMB_1 AAL Ipsi 0.3682110039322444
+BMB_1 AAL Ipsi 0.3331999280096794
+BMB_1 AAL Ipsi 0.2954390559588627
+BMB_1 AAL Ipsi 0.3003571808832426
+BMB_1 AAL Ipsi 0.2656962834240774
+BMB_1 AAL Ipsi 0.2798730057471264
+BMB_1 AAL Ipsi 0.38269086993345436
+BMB_1 AAL Ipsi 0.2911825096793708
+BMB_1 AAL Ipsi 0.3472098557168784
+BMB_1 AAL Ipsi 0.2621478118572293
+BMB_1 AAL Ipsi 0.24857679764065335
+BMB_1 AAL Ipsi 0.3107962105263158
+BMB_1 AAL Ipsi 0.2632073399879008
+BMB_1 AAL Ipsi 0.3488387395644283
+BMB_1 AAL Ipsi 0.3237510556563824
+BMB_1 AAL Ipsi 0.31617947126436785
+BMB_1 AAL Ipsi 0.26605564791288566
+BMB_1 AAL Ipsi 0.22867205414398062
+BMB_1 AAL Ipsi 0.2582979195402299
+BMB_1 AAL Ipsi 0.2874234497882638
+BMB_1 AAL Ipsi 0.26204720114942526
+BMB_1 AAL Ipsi 0.23075124621899576
+BMB_1 AAL Ipsi 0.2887355468844525
+BMB_1 AAL Ipsi 0.2718780241984271
+BMB_1 AAL Ipsi 0.2943806696914701
+BMB_1 AAL Ipsi 0.24014961131276463
+BMB_1 AAL Ipsi 0.2657228726557774
+BMB_1 AAL Ipsi 0.27037381851179676
+BMB_1 AAL Ipsi 0.2865020015124017
+BMB_1 AAL Ipsi 0.3549445133091349
+BMB_1 AAL Ipsi 0.29747217755595884
+BMB_1 AAL Ipsi 0.3113954627949183
+BMB_1 AAL Ipsi 0.2529058287961283
+BMB_1 AAL Ipsi 0.29411183575317607
+BMB_1 AAL Ipsi 0.38260218693284936
+BMB_1 AAL Ipsi 0.30384467180883246
+BMB_1 AAL Ipsi 0.3120510970961887
+BMB_1 AAL Ipsi 0.3354974364791289
+BMB_1 AAL Hom 0.41551256140350884
+BMB_1 AAL Hom 0.3617846666666667
+BMB_1 AAL Hom 0.43678070175438605
+BMB_1 AAL Hom 0.489889701754386
+BMB_1 AAL Hom 0.423550403508772
+BMB_1 AAL Hom 0.3958746666666667
+BMB_1 AAL Hom 0.43816724561403514
+BMB_1 AAL Hom 0.3973869649122807
+BMB_1 AAL Hom 0.4759269473684211
+BMB_1 AAL Hom 0.47305638596491223
+BMB_1 AAL Hom 0.3940721403508772
+BMB_1 AAL Hom 0.3894467719298246
+BMB_1 AAL Hom 0.4569042280701754
+BMB_1 AAL Hom 0.37151989473684216
+BMB_1 AAL Hom 0.3899970877192982
+BMB_1 AAL Hom 0.49152242105263155
+BMB_1 AAL Hom 0.3812869298245614
+BMB_1 AAL Hom 0.44427240350877195
+BMB_1 AAL Hom 0.5139436842105263
+BMB_1 AAL Hom 0.43317308771929824
+BMB_1 AAL Hom 0.3986121578947369
+BMB_1 AAL Hom 0.5104497192982457
+BMB_1 AAL Hom 0.41410822807017544
+BMB_1 AAL Hom 0.5424520701754385
+BMB_1 AAL Hom 0.4147916140350877
+BMB_1 AAL Hom 0.43204966666666667
+BMB_1 AAL Hom 0.4423169122807018
+BMB_1 AAL Hom 0.5415512105263158
+BMB_1 AAL Hom 0.471584
+BMB_1 AAL Hom 0.4948359298245613
+BMB_1 AAL Hom 0.3908273157894737
+BMB_1 AAL Hom 0.4505170350877193
+BMB_1 AAL Hom 0.45739070175438595
+BMB_1 AAL Hom 0.5037914561403508
+BMB_1 AAL Hom 0.3973695438596491
+BMB_1 AAL Hom 0.5937145438596492
+BMB_1 AAL Hom 0.4925560175438597
+BMB_1 AAL Hom 0.4385632807017543
+BMB_1 AAL Hom 0.4388376666666666
+BMB_1 AAL Hom 0.4186779649122807
+BMB_1 AAL Hom 0.43134601754385965
+BMB_1 AAL Hom 0.38856363157894735
+BMB_1 AAL Hom 0.45284722807017547
+BMB_1 AAL Hom 0.3794376666666667
+BMB_1 AAL Hom 0.4565661578947368
+BMB_1 AAL Hom 0.36916263157894735
+BMB_1 AAL Hom 0.46383587719298247
+BMB_1 AAL Hom 0.4883025263157894
+BMB_1 AAL Hom 0.4463505263157895
+BMB_1 AAL Hom 0.48002570175438597
+BMB_1 AAL Hom 0.4192363333333334
+BMB_1 AAL Hom 0.37642636842105265
+BMB_1 AAL Hom 0.5518460350877192
+BMB_1 AAL Hom 0.5195802456140352
+BMB_1 AAL Hom 0.5650855614035087
+BMB_1 AAL Hom 0.4081529649122807
+BMB_1 AAL Hom 0.4148354385964912
+BMB_1 AAL Hom 0.4051972456140351
+BMB_1 AAL Hom 0.4547139122807018
+BMB_1 AAL Hom 0.5120740350877193
+BMB_1 AAL Hom 0.3920221929824561
+BMB_1 AAL Hom 0.4806748947368421
+BMB_1 AAL Hom 0.5510735789473684
+BMB_1 AAL Hom 0.4471790701754386
+BMB_1 AAL Hom 0.4277735964912281
+BMB_1 AAL Hom 0.4312809473684211
+BMB_1 AAL Hom 0.39795029824561406
+BMB_1 AAL Hom 0.38680705263157894
+BMB_1 AAL Hom 0.4897382280701755
+BMB_1 AAL Hom 0.4805670350877193
+BMB_1 AAL Hom 0.4090045438596491
+BMB_1 AAL Hom 0.37694196491228077
+BMB_1 AAL Hom 0.4241434385964913
+BMB_1 AAL Hom 0.4977698245614035
+BMB_1 AAL Hom 0.46206129824561404
+BMB_1 AAL Hom 0.5511803684210527
+BMB_1 AAL Hom 0.44658636842105265
+BMB_1 AAL Hom 0.39862678947368424
+BMB_1 AAL Hom 0.45545182456140354
+BMB_1 AAL Hom 0.4205412982456141
+BMB_1 AAL Hom 0.4852482631578947
+BMB_1 AAL Hom 0.5018201929824562
+BMB_1 AAL Hom 0.5283455087719299
+BMB_1 AAL Hom 0.4477569824561404
+BMB_1 AAL Hom 0.42148031578947365
+BMB_1 AAL Hom 0.45284938596491225
+BMB_1 AAL Hom 0.47014940350877193
+BMB_1 AAL Hom 0.4405051578947369
+BMB_1 AAL Hom 0.5045254561403509
+BMB_1 AAL Hom 0.44052392982456146
+BMB_1 AAL Hom 0.4451741929824562
+BMB_1 AAL Hom 0.4056659298245614
+BMB_1 AAL Hom 0.41570061403508773
+BMB_1 AAL Hom 0.4514762105263158
+BMB_1 AAL Hom 0.4299855614035088
+BMB_1 AAL Hom 0.4577025263157895
+BMB_1 AAL Hom 0.44860515789473676
+BMB_1 AAL Hom 0.44037687719298246
+BMB_1 AAL Hom 0.43073522807017545
+BMB_1 AAL Hom 0.39252391228070177
+BMB_1 AAL Hom 0.39507128070175435
+BMB_1 AAL Hom 0.42859140350877195
+BMB_1 AAL Hom 0.3753424385964912
+BMB_1 AAL Hom 0.3877845964912281
+BMB_1 AAL Hom 0.455460596491228
+BMB_1 AAL Hom 0.45226287719298247
+BMB_1 AAL Hom 0.42259150877192986
+BMB_1 AAL Hom 0.3700300701754386
+BMB_1 AAL Hom 0.35882364912280706
+BMB_1 AAL Hom 0.38289950877192985
+BMB_1 AAL Hom 0.4302691403508772
+BMB_1 AAL Hom 0.4856688070175439
+BMB_1 AAL Hom 0.43409392982456135
+BMB_1 AAL Hom 0.448104298245614
+BMB_1 AAL Hom 0.41315228070175436
+BMB_1 AAL Hom 0.4140864385964912
+BMB_1 AAL Hom 0.4613634035087719
+BMB_1 AAL Hom 0.426457350877193
+BMB_1 AAL Hom 0.44668964912280695
+BMB_1 AAL Hom 0.5194998070175438
+BMB_1 AAL Contra 0.2663964399758089
+BMB_1 AAL Contra 0.24482954944058058
+BMB_1 AAL Contra 0.31413993468400364
+BMB_1 AAL Contra 0.3264517118234049
+BMB_1 AAL Contra 0.30136755941941334
+BMB_1 AAL Contra 0.25088974931962504
+BMB_1 AAL Contra 0.2774226386452978
+BMB_1 AAL Contra 0.2913568805563955
+BMB_1 AAL Contra 0.35313352494708194
+BMB_1 AAL Contra 0.3559611784094346
+BMB_1 AAL Contra 0.26512135893559113
+BMB_1 AAL Contra 0.2513425657695797
+BMB_1 AAL Contra 0.33408849410341696
+BMB_1 AAL Contra 0.253173676141518
+BMB_1 AAL Contra 0.2541278820683399
+BMB_1 AAL Contra 0.3812313810099788
+BMB_1 AAL Contra 0.2329465827033565
+BMB_1 AAL Contra 0.28918736316903537
+BMB_1 AAL Contra 0.39484757393407915
+BMB_1 AAL Contra 0.29008115754460234
+BMB_1 AAL Contra 0.2511463271847596
+BMB_1 AAL Contra 0.397956371031146
+BMB_1 AAL Contra 0.2609969603870578
+BMB_1 AAL Contra 0.43857362957363166
+BMB_1 AAL Contra 0.2634546413667977
+BMB_1 AAL Contra 0.28914089265195037
+BMB_1 AAL Contra 0.3163468382219534
+BMB_1 AAL Contra 0.3600037692772906
+BMB_1 AAL Contra 0.24266209434532807
+BMB_1 AAL Contra 0.3873931539159359
+BMB_1 AAL Contra 0.2432893377683701
+BMB_1 AAL Contra 0.301339011793166
+BMB_1 AAL Contra 0.33365377683701236
+BMB_1 AAL Contra 0.3798101711521016
+BMB_1 AAL Contra 0.26137486967039614
+BMB_1 AAL Contra 0.47585112156032655
+BMB_1 AAL Contra 0.3538226788630179
+BMB_1 AAL Contra 0.3181027311762927
+BMB_1 AAL Contra 0.32807680949501056
+BMB_1 AAL Contra 0.2623961242818264
+BMB_1 AAL Contra 0.3169673565164802
+BMB_1 AAL Contra 0.252512404293922
+BMB_1 AAL Contra 0.3477478339885092
+BMB_1 AAL Contra 0.23847936951920168
+BMB_1 AAL Contra 0.31786385515573023
+BMB_1 AAL Contra 0.24879855367402479
+BMB_1 AAL Contra 0.3454204408829755
+BMB_1 AAL Contra 0.35112521318415485
+BMB_1 AAL Contra 0.2745750958572724
+BMB_1 AAL Contra 0.28924105684910795
+BMB_1 AAL Contra 0.2456119978832779
+BMB_1 AAL Contra 0.26959319231932266
+BMB_1 AAL Contra 0.3178506589053523
+BMB_1 AAL Contra 0.3948980647112187
+BMB_1 AAL Contra 0.4679881705473239
+BMB_1 AAL Contra 0.2684834299969761
+BMB_1 AAL Contra 0.2845415104324161
+BMB_1 AAL Contra 0.22894865799818565
+BMB_1 AAL Contra 0.3093546776534623
+BMB_1 AAL Contra 0.40600545842153
+BMB_1 AAL Contra 0.23828028273359544
+BMB_1 AAL Contra 0.310322335651648
+BMB_1 AAL Contra 0.4551054339280314
+BMB_1 AAL Contra 0.30357186180828544
+BMB_1 AAL Contra 0.28641148866041727
+BMB_1 AAL Contra 0.2800830390081645
+BMB_1 AAL Contra 0.2630736576957968
+BMB_1 AAL Contra 0.28127068581796194
+BMB_1 AAL Contra 0.36390069549440585
+BMB_1 AAL Contra 0.27717465951013004
+BMB_1 AAL Contra 0.29723065013607497
+BMB_1 AAL Contra 0.24678676746295736
+BMB_1 AAL Contra 0.3239201034169943
+BMB_1 AAL Contra 0.34527868732990624
+BMB_1 AAL Contra 0.2688704085273662
+BMB_1 AAL Contra 0.36977711279104925
+BMB_1 AAL Contra 0.31662647837919566
+BMB_1 AAL Contra 0.2662176368309646
+BMB_1 AAL Contra 0.27062430934381615
+BMB_1 AAL Contra 0.26600167039612943
+BMB_1 AAL Contra 0.3564549265195041
+BMB_1 AAL Contra 0.39043567553674025
+BMB_1 AAL Contra 0.369667510432416
+BMB_1 AAL Contra 0.3081643335349259
+BMB_1 AAL Contra 0.2786450326579982
+BMB_1 AAL Contra 0.29631784003628664
+BMB_1 AAL Contra 0.26111676353190205
+BMB_1 AAL Contra 0.2758720290293317
+BMB_1 AAL Contra 0.38174615845176896
+BMB_1 AAL Contra 0.2803380453583308
+BMB_1 AAL Contra 0.31991147837919565
+BMB_1 AAL Contra 0.25792849138191715
+BMB_1 AAL Contra 0.23817444118536438
+BMB_1 AAL Contra 0.3085332990625945
+BMB_1 AAL Contra 0.2530019089809495
+BMB_1 AAL Contra 0.3474904182038101
+BMB_1 AAL Contra 0.31310950952524946
+BMB_1 AAL Contra 0.30624497883277896
+BMB_1 AAL Contra 0.2598642201390989
+BMB_1 AAL Contra 0.22337775022679163
+BMB_1 AAL Contra 0.2559134650740853
+BMB_1 AAL Contra 0.2833712252797097
+BMB_1 AAL Contra 0.25732057211974596
+BMB_1 AAL Contra 0.2210356413667977
+BMB_1 AAL Contra 0.2794447054732386
+BMB_1 AAL Contra 0.26083332325370423
+BMB_1 AAL Contra 0.2872269537345026
+BMB_1 AAL Contra 0.23227790565467193
+BMB_1 AAL Contra 0.25324527487148474
+BMB_1 AAL Contra 0.2506404179014212
+BMB_1 AAL Contra 0.27229443967342004
+BMB_1 AAL Contra 0.34658023828243123
+BMB_1 AAL Contra 0.2914889392198367
+BMB_1 AAL Contra 0.2869245252494708
+BMB_1 AAL Contra 0.2473125207136377
+BMB_1 AAL Contra 0.28852851043241606
+BMB_1 AAL Contra 0.36455554732385853
+BMB_1 AAL Contra 0.2946375642576353
+BMB_1 AAL Contra 0.30809935107348047
+BMB_1 AAL Contra 0.3312868252192319
+Dataset Atlas Type Value
+IBATRT AAL Ipsi 0.30865469933454326
+IBATRT AAL Ipsi 0.31274111675741073
+IBATRT AAL Ipsi 0.2974223502722323
+IBATRT AAL Ipsi 0.4042172332123412
+IBATRT AAL Ipsi 0.42677537840290386
+IBATRT AAL Ipsi 0.35406898699334544
+IBATRT AAL Ipsi 0.3252289621899576
+IBATRT AAL Ipsi 0.3816450459770115
+IBATRT AAL Ipsi 0.39219132032667875
+IBATRT AAL Ipsi 0.34919966848154865
+IBATRT AAL Ipsi 0.2541919585601936
+IBATRT AAL Ipsi 0.30749699909255895
+IBATRT AAL Ipsi 0.357142078644888
+IBATRT AAL Ipsi 0.45207006957047796
+IBATRT AAL Ipsi 0.3211047943133697
+IBATRT AAL Ipsi 0.24046135632183907
+IBATRT AAL Ipsi 0.31181410344827587
+IBATRT AAL Ipsi 0.27657201058681186
+IBATRT AAL Ipsi 0.2285731309739867
+IBATRT AAL Ipsi 0.3675672961282516
+IBATRT AAL Ipsi 0.3129454957652752
+IBATRT AAL Ipsi 0.3502153230490018
+IBATRT AAL Ipsi 0.32160081427707204
+IBATRT AAL Ipsi 0.32758107168784023
+IBATRT AAL Ipsi 0.4388173411978221
+IBATRT AAL Ipsi 0.25719530369026017
+IBATRT AAL Ipsi 0.335648983061101
+IBATRT AAL Ipsi 0.27430747549909257
+IBATRT AAL Ipsi 0.3242882341197822
+IBATRT AAL Ipsi 0.31135386025408346
+IBATRT AAL Ipsi 0.2992700490018149
+IBATRT AAL Ipsi 0.3123278218390805
+IBATRT AAL Ipsi 0.2792397876588022
+IBATRT AAL Ipsi 0.28726203085299457
+IBATRT AAL Ipsi 0.30753560738052027
+IBATRT AAL Ipsi 0.4174670961887478
+IBATRT AAL Ipsi 0.21912000967937084
+IBATRT AAL Ipsi 0.3008781560798548
+IBATRT AAL Ipsi 0.328622832123412
+IBATRT AAL Ipsi 0.30531317059891105
+IBATRT AAL Ipsi 0.3767094600725953
+IBATRT AAL Ipsi 0.3899368049001815
+IBATRT AAL Ipsi 0.34427622383545065
+IBATRT AAL Ipsi 0.4087334809437386
+IBATRT AAL Ipsi 0.5130776261343013
+IBATRT AAL Ipsi 0.43544963399879005
+IBATRT AAL Ipsi 0.35976929370840893
+IBATRT AAL Ipsi 0.3158509754990925
+IBATRT AAL Ipsi 0.3282234500907441
+IBATRT AAL Ipsi 0.21278870840895342
+IBATRT AAL Ipsi 0.3074700054446461
+IBATRT AAL Ipsi 0.3044145320629159
+IBATRT AAL Ipsi 0.36611957168784026
+IBATRT AAL Ipsi 0.34464379340592866
+IBATRT AAL Ipsi 0.5183011285541439
+IBATRT AAL Ipsi 0.30353021960072596
+IBATRT AAL Ipsi 0.4223999609800363
+IBATRT AAL Ipsi 0.25013442347247433
+IBATRT AAL Ipsi 0.3091116488203267
+IBATRT AAL Ipsi 0.4256037858439201
+IBATRT AAL Ipsi 0.3101567670901391
+IBATRT AAL Ipsi 0.3048747731397459
+IBATRT AAL Ipsi 0.4026369758015729
+IBATRT AAL Ipsi 0.3744317268602541
+IBATRT AAL Ipsi 0.43123419691470055
+IBATRT AAL Ipsi 0.31095419298245613
+IBATRT AAL Ipsi 0.5335858738656988
+IBATRT AAL Ipsi 0.3968359195402299
+IBATRT AAL Ipsi 0.22855854960677557
+IBATRT AAL Ipsi 0.4690180241984271
+IBATRT AAL Ipsi 0.32790800030248035
+IBATRT AAL Ipsi 0.4447246152450091
+IBATRT AAL Ipsi 0.28405427737447064
+IBATRT AAL Ipsi 0.4993845281306716
+IBATRT AAL Ipsi 0.33080095341802784
+IBATRT AAL Ipsi 0.4540932395644283
+IBATRT AAL Ipsi 0.27100403932244405
+IBATRT AAL Ipsi 0.4338484836660617
+IBATRT AAL Ipsi 0.3700865671506352
+IBATRT AAL Ipsi 0.395337352692075
+IBATRT AAL Ipsi 0.4058528744706594
+IBATRT AAL Ipsi 0.27368818905021175
+IBATRT AAL Ipsi 0.3734134794313369
+IBATRT AAL Ipsi 0.2642679697519661
+IBATRT AAL Ipsi 0.2554853675136116
+IBATRT AAL Ipsi 0.2558953690260133
+IBATRT AAL Ipsi 0.45651075468844526
+IBATRT AAL Ipsi 0.3463242673926195
+IBATRT AAL Ipsi 0.2494021040532365
+IBATRT AAL Ipsi 0.37250503055051426
+IBATRT AAL Ipsi 0.44928433061101025
+IBATRT AAL Ipsi 0.36830665970961884
+IBATRT AAL Ipsi 0.2953664110707804
+IBATRT AAL Ipsi 0.2977354007864489
+IBATRT AAL Ipsi 0.2024722362371446
+IBATRT AAL Ipsi 0.2497924664246824
+IBATRT AAL Ipsi 0.33769481004234725
+IBATRT AAL Ipsi 0.3364326624319419
+IBATRT AAL Ipsi 0.28839076406533576
+IBATRT AAL Ipsi 0.18826550907441017
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+IBATRT AAL Hom 0.5576600350877193
+IBATRT AAL Hom 0.4492551403508772
+IBATRT AAL Hom 0.5012886140350877
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+IBATRT AAL Hom 0.5845453157894737
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+IBATRT AAL Hom 0.49657
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+IBATRT AAL Hom 0.36065645614035086
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+IBATRT AAL Hom 0.4688556666666667
+IBATRT AAL Hom 0.5728593333333334
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+IBATRT AAL Hom 0.44047622807017545
+IBATRT AAL Hom 0.5457932105263158
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+IBATRT AAL Hom 0.48455528070175435
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+IBATRT AAL Hom 0.6771750877192982
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+IBATRT AAL Hom 0.4705775263157895
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+IBATRT AAL Hom 0.6220773684210527
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+IBATRT AAL Hom 0.3974355789473684
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+IBATRT AAL Hom 0.5511465614035088
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+IBATRT AAL Hom 0.49742624561403515
+IBATRT AAL Hom 0.46579715789473686
+IBATRT AAL Hom 0.40650805263157896
+IBATRT AAL Hom 0.39572692982456137
+IBATRT AAL Contra 0.3062226262473541
+IBATRT AAL Contra 0.3114268893256728
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+IBATRT AAL Contra 0.32676486483217415
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+IBATRT AAL Contra 0.3360427308739038
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+IBATRT AAL Contra 0.31753131871787116
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+IBATRT AAL Contra 0.42104669640157244
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+IBATRT AAL Contra 0.4155136697913516
+IBATRT AAL Contra 0.3071006220139099
+IBATRT AAL Contra 0.5189131273057151
+IBATRT AAL Contra 0.39453465043846386
+IBATRT AAL Contra 0.22508772210462658
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+IBATRT AAL Contra 0.4300851902026005
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+IBATRT AAL Contra 0.3315723719383127
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+IBATRT AAL Contra 0.33338898488055646
+IBATRT AAL Contra 0.3316055597218022
+IBATRT AAL Contra 0.275891505896583
+IBATRT AAL Contra 0.18535413909888115
+Dataset Atlas Type Value
+IPCAS_6 AAL Ipsi 0.22031659770114942
+IPCAS_6 AAL Ipsi 0.27713929461584996
+IPCAS_6 AAL Ipsi 0.23370289564428312
+IPCAS_6 AAL Ipsi 0.230178109800363
+IPCAS_6 AAL Ipsi 0.2639391709013914
+IPCAS_6 AAL Ipsi 0.22297737356321842
+IPCAS_6 AAL Ipsi 0.27756099485783425
+IPCAS_6 AAL Ipsi 0.18493639201451903
+IPCAS_6 AAL Ipsi 0.29450733393829404
+IPCAS_6 AAL Ipsi 0.23780622202056867
+IPCAS_6 AAL Ipsi 0.22626036721113127
+IPCAS_6 AAL Ipsi 0.2113387537810042
+IPCAS_6 AAL Ipsi 0.24211315093768904
+IPCAS_6 AAL Ipsi 0.23827275408348456
+IPCAS_6 AAL Ipsi 0.26463337477313975
+IPCAS_6 AAL Ipsi 0.3225148853599516
+IPCAS_6 AAL Ipsi 0.2461411185722928
+IPCAS_6 AAL Ipsi 0.18451272111312764
+IPCAS_6 AAL Ipsi 0.219752908953418
+IPCAS_6 AAL Ipsi 0.23295747126436783
+IPCAS_6 AAL Ipsi 0.31515000120992137
+IPCAS_6 AAL Ipsi 0.20988818542044768
+IPCAS_6 AAL Ipsi 0.2202111300665457
+IPCAS_6 AAL Ipsi 0.20551270235934666
+IPCAS_6 AAL Ipsi 0.24738151421657592
+IPCAS_6 AAL Ipsi 0.3081457997580157
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+IPCAS_6 AAL Ipsi 0.25201041076830005
+IPCAS_6 AAL Ipsi 0.28789836932849366
+IPCAS_6 AAL Ipsi 0.266542467937084
+IPCAS_6 AAL Ipsi 0.32169660314579557
+IPCAS_6 AAL Ipsi 0.324930700846945
+IPCAS_6 AAL Ipsi 0.2191984001814882
+IPCAS_6 AAL Ipsi 0.21740385238959467
+IPCAS_6 AAL Ipsi 0.2385917934059286
+IPCAS_6 AAL Ipsi 0.22867559286146402
+IPCAS_6 AAL Ipsi 0.28923870447670896
+IPCAS_6 AAL Ipsi 0.3110016578947369
+IPCAS_6 AAL Ipsi 0.26750870387174835
+IPCAS_6 AAL Ipsi 0.3101222837265578
+IPCAS_6 AAL Ipsi 0.24937660738052028
+IPCAS_6 AAL Ipsi 0.20893844918330307
+IPCAS_6 AAL Ipsi 0.3433037546884452
+IPCAS_6 AAL Ipsi 0.2061026512401694
+IPCAS_6 AAL Ipsi 0.298451018753781
+IPCAS_6 AAL Ipsi 0.22037731094978824
+IPCAS_6 AAL Ipsi 0.22157673502722322
+IPCAS_6 AAL Ipsi 0.20416321536600124
+IPCAS_6 AAL Ipsi 0.27933656110102845
+IPCAS_6 AAL Ipsi 0.24685766394434364
+IPCAS_6 AAL Ipsi 0.2490382084089534
+IPCAS_6 AAL Ipsi 0.21611404960677558
+IPCAS_6 AAL Ipsi 0.273592762552934
+IPCAS_6 AAL Ipsi 0.25009608348457346
+IPCAS_6 AAL Ipsi 0.26220463611615247
+IPCAS_6 AAL Ipsi 0.2436790114942529
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+IPCAS_6 AAL Ipsi 0.19852342679975804
+IPCAS_6 AAL Ipsi 0.29326775983061104
+IPCAS_6 AAL Ipsi 0.2014769531155475
+IPCAS_6 AAL Ipsi 0.2247880629159105
+IPCAS_6 AAL Ipsi 0.2058676748336358
+IPCAS_6 AAL Ipsi 0.32569395372050824
+IPCAS_6 AAL Ipsi 0.25927000635208713
+IPCAS_6 AAL Ipsi 0.2895006506352087
+IPCAS_6 AAL Ipsi 0.29221402752571085
+IPCAS_6 AAL Ipsi 0.2676797434966727
+IPCAS_6 AAL Ipsi 0.23634370296430735
+IPCAS_6 AAL Ipsi 0.2647386288566243
+IPCAS_6 AAL Ipsi 0.29352504627949183
+IPCAS_6 AAL Ipsi 0.23046046460980035
+IPCAS_6 AAL Ipsi 0.2770668641863279
+IPCAS_6 AAL Ipsi 0.2519471252268603
+IPCAS_6 AAL Ipsi 0.3066645381125227
+IPCAS_6 AAL Ipsi 0.22610665668481547
+IPCAS_6 AAL Ipsi 0.2735197301875378
+IPCAS_6 AAL Ipsi 0.26916050211736237
+IPCAS_6 AAL Ipsi 0.20111517120387173
+IPCAS_6 AAL Ipsi 0.19000099969751968
+IPCAS_6 AAL Ipsi 0.26299435359951606
+IPCAS_6 AAL Ipsi 0.21903221324863884
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+IPCAS_6 AAL Ipsi 0.3204727816091954
+IPCAS_6 AAL Ipsi 0.23331595341802785
+IPCAS_6 AAL Ipsi 0.21403747791893526
+IPCAS_6 AAL Ipsi 0.2804153548094374
+IPCAS_6 AAL Ipsi 0.2740471799758016
+IPCAS_6 AAL Ipsi 0.25830120598911066
+IPCAS_6 AAL Ipsi 0.25044561282516636
+IPCAS_6 AAL Ipsi 0.23238099062310952
+IPCAS_6 AAL Hom 0.32702050877192984
+IPCAS_6 AAL Hom 0.3987795263157895
+IPCAS_6 AAL Hom 0.34764587719298246
+IPCAS_6 AAL Hom 0.3473731403508772
+IPCAS_6 AAL Hom 0.41763770175438597
+IPCAS_6 AAL Hom 0.3186097368421053
+IPCAS_6 AAL Hom 0.401497
+IPCAS_6 AAL Hom 0.38429366666666664
+IPCAS_6 AAL Hom 0.43326673684210537
+IPCAS_6 AAL Hom 0.3413058070175439
+IPCAS_6 AAL Hom 0.3668141929824561
+IPCAS_6 AAL Hom 0.31251861403508774
+IPCAS_6 AAL Hom 0.37576999999999994
+IPCAS_6 AAL Hom 0.39986514035087717
+IPCAS_6 AAL Hom 0.3979844210526316
+IPCAS_6 AAL Hom 0.44524975438596487
+IPCAS_6 AAL Hom 0.4031583157894737
+IPCAS_6 AAL Hom 0.25265915789473686
+IPCAS_6 AAL Hom 0.33971003508771924
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+IPCAS_6 AAL Hom 0.43381757894736844
+IPCAS_6 AAL Hom 0.34305657894736846
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+IPCAS_6 AAL Hom 0.3916214736842106
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+IPCAS_6 AAL Hom 0.4463621403508772
+IPCAS_6 AAL Hom 0.3149331403508771
+IPCAS_6 AAL Hom 0.42172522807017543
+IPCAS_6 AAL Hom 0.38452384210526325
+IPCAS_6 AAL Hom 0.42398363157894736
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+IPCAS_6 AAL Hom 0.40033329824561403
+IPCAS_6 AAL Hom 0.38709122807017543
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+IPCAS_6 AAL Hom 0.30002052631578946
+IPCAS_6 AAL Hom 0.4213557017543859
+IPCAS_6 AAL Hom 0.29703649122807013
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+IPCAS_6 AAL Hom 0.30158301754385963
+IPCAS_6 AAL Hom 0.43188098245614037
+IPCAS_6 AAL Hom 0.4187557543859649
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+IPCAS_6 AAL Hom 0.3973005614035087
+IPCAS_6 AAL Hom 0.41354157894736837
+IPCAS_6 AAL Hom 0.41853742105263153
+IPCAS_6 AAL Hom 0.3624194385964913
+IPCAS_6 AAL Hom 0.37009735087719303
+IPCAS_6 AAL Hom 0.339710649122807
+IPCAS_6 AAL Contra 0.2156513035984276
+IPCAS_6 AAL Contra 0.27215201451466586
+IPCAS_6 AAL Contra 0.22785855125491383
+IPCAS_6 AAL Contra 0.22162017417599034
+IPCAS_6 AAL Contra 0.26333452918052613
+IPCAS_6 AAL Contra 0.21148040368914423
+IPCAS_6 AAL Contra 0.26434755246446934
+IPCAS_6 AAL Contra 0.18508173299062594
+IPCAS_6 AAL Contra 0.2860359752041125
+IPCAS_6 AAL Contra 0.22612790625944965
+IPCAS_6 AAL Contra 0.22236099062594497
+IPCAS_6 AAL Contra 0.2035295929845782
+IPCAS_6 AAL Contra 0.24194788811611734
+IPCAS_6 AAL Contra 0.23812316480193532
+IPCAS_6 AAL Contra 0.25654996583005746
+IPCAS_6 AAL Contra 0.31479969368007255
+IPCAS_6 AAL Contra 0.24127849742969457
+IPCAS_6 AAL Contra 0.17470729210765049
+IPCAS_6 AAL Contra 0.21501178742062294
+IPCAS_6 AAL Contra 0.22825181100695496
+IPCAS_6 AAL Contra 0.31317699092833384
+IPCAS_6 AAL Contra 0.19662770154218323
+IPCAS_6 AAL Contra 0.21153460991835502
+IPCAS_6 AAL Contra 0.19422974992440278
+IPCAS_6 AAL Contra 0.24640720078621106
+IPCAS_6 AAL Contra 0.29142248926519504
+IPCAS_6 AAL Contra 0.26857601602661024
+IPCAS_6 AAL Contra 0.24510152766858181
+IPCAS_6 AAL Contra 0.27766081584517693
+IPCAS_6 AAL Contra 0.2639331929241004
+IPCAS_6 AAL Contra 0.3166301297248261
+IPCAS_6 AAL Contra 0.3077752890837617
+IPCAS_6 AAL Contra 0.21238427335954038
+IPCAS_6 AAL Contra 0.21601039310553372
+IPCAS_6 AAL Contra 0.22674913456304807
+IPCAS_6 AAL Contra 0.21768401995766554
+IPCAS_6 AAL Contra 0.2788376274569096
+IPCAS_6 AAL Contra 0.307764690353795
+IPCAS_6 AAL Contra 0.2598843032960387
+IPCAS_6 AAL Contra 0.30655012125793774
+IPCAS_6 AAL Contra 0.2511753692168128
+IPCAS_6 AAL Contra 0.2000498863017841
+IPCAS_6 AAL Contra 0.3291255636528576
+IPCAS_6 AAL Contra 0.20018035833081343
+IPCAS_6 AAL Contra 0.2966414094345328
+IPCAS_6 AAL Contra 0.2076313108557605
+IPCAS_6 AAL Contra 0.2206995766555791
+IPCAS_6 AAL Contra 0.18987438312670094
+IPCAS_6 AAL Contra 0.2680029026307832
+IPCAS_6 AAL Contra 0.2341940311460538
+IPCAS_6 AAL Contra 0.24278462866646508
+IPCAS_6 AAL Contra 0.21249180979739946
+IPCAS_6 AAL Contra 0.27102537345025707
+IPCAS_6 AAL Contra 0.2528175742364681
+IPCAS_6 AAL Contra 0.2571677874206229
+IPCAS_6 AAL Contra 0.244808638342909
+IPCAS_6 AAL Contra 0.2757542594496522
+IPCAS_6 AAL Contra 0.19004572996673721
+IPCAS_6 AAL Contra 0.28573221620804357
+IPCAS_6 AAL Contra 0.19789794496522525
+IPCAS_6 AAL Contra 0.2242970553371636
+IPCAS_6 AAL Contra 0.19834779377078923
+IPCAS_6 AAL Contra 0.31575642243725427
+IPCAS_6 AAL Contra 0.24877376504384638
+IPCAS_6 AAL Contra 0.28538135742364684
+IPCAS_6 AAL Contra 0.28957854671908073
+IPCAS_6 AAL Contra 0.26085483368612034
+IPCAS_6 AAL Contra 0.2306447756274569
+IPCAS_6 AAL Contra 0.263966817961899
+IPCAS_6 AAL Contra 0.2935278850922286
+IPCAS_6 AAL Contra 0.23116665709101908
+IPCAS_6 AAL Contra 0.2541641708497127
+IPCAS_6 AAL Contra 0.25005468642273965
+IPCAS_6 AAL Contra 0.3017181599637134
+IPCAS_6 AAL Contra 0.2206985929845782
+IPCAS_6 AAL Contra 0.26949079437556694
+IPCAS_6 AAL Contra 0.264543425461143
+IPCAS_6 AAL Contra 0.19432685666767463
+IPCAS_6 AAL Contra 0.19118616328999094
+IPCAS_6 AAL Contra 0.25997293649833686
+IPCAS_6 AAL Contra 0.21478008950710617
+IPCAS_6 AAL Contra 0.30728184941034165
+IPCAS_6 AAL Contra 0.3155143571212579
+IPCAS_6 AAL Contra 0.21966107952827335
+IPCAS_6 AAL Contra 0.21170059419413367
+IPCAS_6 AAL Contra 0.2568611254913819
+IPCAS_6 AAL Contra 0.2622214000604778
+IPCAS_6 AAL Contra 0.25249332567281524
+IPCAS_6 AAL Contra 0.2501690813426066
+IPCAS_6 AAL Contra 0.2229567160568491
+Dataset Atlas Type Value
+MRN_1 AAL Ipsi 0.311504888384755
+MRN_1 AAL Ipsi 0.2981936932849365
+MRN_1 AAL Ipsi 0.27135608892921964
+MRN_1 AAL Ipsi 0.29214380913490623
+MRN_1 AAL Ipsi 0.2669851793708409
+MRN_1 AAL Ipsi 0.2329864343617665
+MRN_1 AAL Ipsi 0.36906606745311554
+MRN_1 AAL Ipsi 0.2948158735632184
+MRN_1 AAL Ipsi 0.2484630907441016
+MRN_1 AAL Ipsi 0.37900636781609187
+MRN_1 AAL Ipsi 0.2322946185722928
+MRN_1 AAL Ipsi 0.3297299627949184
+MRN_1 AAL Ipsi 0.42450093708408954
+MRN_1 AAL Ipsi 0.4116632928009679
+MRN_1 AAL Ipsi 0.2859129143980641
+MRN_1 AAL Ipsi 0.4060864443436176
+MRN_1 AAL Ipsi 0.23678462159709618
+MRN_1 AAL Ipsi 0.2576867622504537
+MRN_1 AAL Ipsi 0.3926897876588022
+MRN_1 AAL Ipsi 0.24942441530550516
+MRN_1 AAL Ipsi 0.25190412946158497
+MRN_1 AAL Ipsi 0.24850344010889291
+MRN_1 AAL Ipsi 0.3016382822141561
+MRN_1 AAL Ipsi 0.23846419237749547
+MRN_1 AAL Ipsi 0.24442400725952812
+MRN_1 AAL Ipsi 0.35054425922565036
+MRN_1 AAL Ipsi 0.24210491076830004
+MRN_1 AAL Ipsi 0.28643903327283726
+MRN_1 AAL Ipsi 0.2551507447065941
+MRN_1 AAL Ipsi 0.3124121727162734
+MRN_1 AAL Ipsi 0.22655637235329704
+MRN_1 AAL Ipsi 0.22147456261343013
+MRN_1 AAL Ipsi 0.30533061010284335
+MRN_1 AAL Ipsi 0.27234074440411377
+MRN_1 AAL Ipsi 0.24294490199637025
+MRN_1 AAL Ipsi 0.3600919177253479
+MRN_1 AAL Ipsi 0.29901757289776165
+MRN_1 AAL Ipsi 0.2915095308529945
+MRN_1 AAL Ipsi 0.2675502988505747
+MRN_1 AAL Ipsi 0.23714520840895342
+MRN_1 AAL Ipsi 0.2500662522686025
+MRN_1 AAL Ipsi 0.30440712129461583
+MRN_1 AAL Ipsi 0.24116570024198428
+MRN_1 AAL Ipsi 0.29352531427707196
+MRN_1 AAL Ipsi 0.21220532456140354
+MRN_1 AAL Ipsi 0.36720577404718696
+MRN_1 AAL Ipsi 0.5748263950393224
+MRN_1 AAL Ipsi 0.22881135359951604
+MRN_1 AAL Ipsi 0.2940865922565033
+MRN_1 AAL Ipsi 0.2518183590441621
+MRN_1 AAL Ipsi 0.39641369147005445
+MRN_1 AAL Ipsi 0.308936592861464
+MRN_1 AAL Ipsi 0.3481261963097399
+MRN_1 AAL Ipsi 0.23499151663641862
+MRN_1 AAL Ipsi 0.37416675589836657
+MRN_1 AAL Ipsi 0.20930875680580763
+MRN_1 AAL Ipsi 0.40604468602540833
+MRN_1 AAL Ipsi 0.4299268418027828
+MRN_1 AAL Ipsi 0.27536609104658194
+MRN_1 AAL Ipsi 0.3047951043557169
+MRN_1 AAL Ipsi 0.3223615045372051
+MRN_1 AAL Ipsi 0.3140436805807622
+MRN_1 AAL Ipsi 0.36498379733817304
+MRN_1 AAL Ipsi 0.2986357552934059
+MRN_1 AAL Ipsi 0.38205001996370236
+MRN_1 AAL Ipsi 0.29027520992135514
+MRN_1 AAL Ipsi 0.3411613777979431
+MRN_1 AAL Ipsi 0.2855527247428917
+MRN_1 AAL Ipsi 0.33075631397459165
+MRN_1 AAL Ipsi 0.31805154900181487
+MRN_1 AAL Ipsi 0.20964577404718693
+MRN_1 AAL Ipsi 0.27249361010284334
+MRN_1 AAL Ipsi 0.3609025949788264
+MRN_1 AAL Ipsi 0.23805744827586206
+MRN_1 AAL Ipsi 0.36126810012099214
+MRN_1 AAL Ipsi 0.3402436082879613
+MRN_1 AAL Ipsi 0.24086536751361162
+MRN_1 AAL Ipsi 0.36314042286751363
+MRN_1 AAL Ipsi 0.4070194612825166
+MRN_1 AAL Ipsi 0.24766239534180276
+MRN_1 AAL Ipsi 0.3805840248033878
+MRN_1 AAL Ipsi 0.2680656234119782
+MRN_1 AAL Ipsi 0.22951095674531152
+MRN_1 AAL Ipsi 0.27909207471264363
+MRN_1 AAL Ipsi 0.3005995958862674
+MRN_1 AAL Ipsi 0.3233754875983061
+MRN_1 AAL Ipsi 0.3465871427707199
+MRN_1 AAL Ipsi 0.28961311040532367
+MRN_1 AAL Ipsi 0.2992253418027828
+MRN_1 AAL Ipsi 0.26834442135511194
+MRN_1 AAL Ipsi 0.2297318853599516
+MRN_1 AAL Hom 0.4074844561403509
+MRN_1 AAL Hom 0.41860529824561404
+MRN_1 AAL Hom 0.4062932105263158
+MRN_1 AAL Hom 0.4244884385964912
+MRN_1 AAL Hom 0.43924228070175436
+MRN_1 AAL Hom 0.40940450877192985
+MRN_1 AAL Hom 0.5266029824561403
+MRN_1 AAL Hom 0.4565171052631579
+MRN_1 AAL Hom 0.3889262456140351
+MRN_1 AAL Hom 0.5397609122807017
+MRN_1 AAL Hom 0.38249791228070185
+MRN_1 AAL Hom 0.4082105087719298
+MRN_1 AAL Hom 0.5702632807017544
+MRN_1 AAL Hom 0.49752414035087716
+MRN_1 AAL Hom 0.4493662807017544
+MRN_1 AAL Hom 0.5019061403508772
+MRN_1 AAL Hom 0.38540794736842104
+MRN_1 AAL Hom 0.43342900000000006
+MRN_1 AAL Hom 0.5196201578947369
+MRN_1 AAL Hom 0.40505122807017546
+MRN_1 AAL Hom 0.46646771929824554
+MRN_1 AAL Hom 0.3738422631578947
+MRN_1 AAL Hom 0.44681729824561406
+MRN_1 AAL Hom 0.4640573684210526
+MRN_1 AAL Hom 0.41292438596491227
+MRN_1 AAL Hom 0.45893340350877193
+MRN_1 AAL Hom 0.47304803508771937
+MRN_1 AAL Hom 0.4220871578947368
+MRN_1 AAL Hom 0.38312768421052634
+MRN_1 AAL Hom 0.4391527368421052
+MRN_1 AAL Hom 0.3705770877192982
+MRN_1 AAL Hom 0.3234249649122807
+MRN_1 AAL Hom 0.4220444561403509
+MRN_1 AAL Hom 0.40635375438596494
+MRN_1 AAL Hom 0.420825
+MRN_1 AAL Hom 0.5036590175438597
+MRN_1 AAL Hom 0.4123960877192983
+MRN_1 AAL Hom 0.45384363157894736
+MRN_1 AAL Hom 0.41377229824561407
+MRN_1 AAL Hom 0.4236494210526316
+MRN_1 AAL Hom 0.3626744561403509
+MRN_1 AAL Hom 0.38711610526315793
+MRN_1 AAL Hom 0.345177649122807
+MRN_1 AAL Hom 0.4492155438596492
+MRN_1 AAL Hom 0.33877303508771933
+MRN_1 AAL Hom 0.4961782280701754
+MRN_1 AAL Hom 0.717797456140351
+MRN_1 AAL Hom 0.3671356140350877
+MRN_1 AAL Hom 0.5124843333333333
+MRN_1 AAL Hom 0.3514661929824561
+MRN_1 AAL Hom 0.582076403508772
+MRN_1 AAL Hom 0.4428852280701754
+MRN_1 AAL Hom 0.4705021052631579
+MRN_1 AAL Hom 0.37322791228070173
+MRN_1 AAL Hom 0.5613556140350877
+MRN_1 AAL Hom 0.3820362280701754
+MRN_1 AAL Hom 0.5563564385964912
+MRN_1 AAL Hom 0.5522679122807017
+MRN_1 AAL Hom 0.43901929824561403
+MRN_1 AAL Hom 0.4269746315789474
+MRN_1 AAL Hom 0.45991026315789474
+MRN_1 AAL Hom 0.4218631228070175
+MRN_1 AAL Hom 0.4600835087719298
+MRN_1 AAL Hom 0.42065712280701756
+MRN_1 AAL Hom 0.5139135964912281
+MRN_1 AAL Hom 0.3918244561403509
+MRN_1 AAL Hom 0.47669080701754385
+MRN_1 AAL Hom 0.46393470175438606
+MRN_1 AAL Hom 0.4631722631578947
+MRN_1 AAL Hom 0.44138519298245615
+MRN_1 AAL Hom 0.3554822631578947
+MRN_1 AAL Hom 0.40292438596491226
+MRN_1 AAL Hom 0.4951116315789474
+MRN_1 AAL Hom 0.39261045614035084
+MRN_1 AAL Hom 0.512196052631579
+MRN_1 AAL Hom 0.48143822807017544
+MRN_1 AAL Hom 0.3909182456140351
+MRN_1 AAL Hom 0.5277202456140351
+MRN_1 AAL Hom 0.5111634385964913
+MRN_1 AAL Hom 0.3433864912280702
+MRN_1 AAL Hom 0.5208697368421052
+MRN_1 AAL Hom 0.38806349122807016
+MRN_1 AAL Hom 0.34681912280701754
+MRN_1 AAL Hom 0.385984701754386
+MRN_1 AAL Hom 0.41413742105263157
+MRN_1 AAL Hom 0.4574076666666667
+MRN_1 AAL Hom 0.45943510526315784
+MRN_1 AAL Hom 0.48780191228070174
+MRN_1 AAL Hom 0.41892628070175436
+MRN_1 AAL Hom 0.38524663157894734
+MRN_1 AAL Hom 0.39629528070175435
+MRN_1 AAL Contra 0.3029213513758694
+MRN_1 AAL Contra 0.2959528993045056
+MRN_1 AAL Contra 0.25904678288478983
+MRN_1 AAL Contra 0.2950108046567886
+MRN_1 AAL Contra 0.2615232896885395
+MRN_1 AAL Contra 0.2283185684910795
+MRN_1 AAL Contra 0.3640421212579377
+MRN_1 AAL Contra 0.28745667493196253
+MRN_1 AAL Contra 0.23930050196552766
+MRN_1 AAL Contra 0.3715139301481706
+MRN_1 AAL Contra 0.2306624378590868
+MRN_1 AAL Contra 0.32260994042939223
+MRN_1 AAL Contra 0.4244632524947082
+MRN_1 AAL Contra 0.40268089083761716
+MRN_1 AAL Contra 0.28122568732990627
+MRN_1 AAL Contra 0.4041970919262171
+MRN_1 AAL Contra 0.23466230208648323
+MRN_1 AAL Contra 0.2561499277290596
+MRN_1 AAL Contra 0.39220916752343515
+MRN_1 AAL Contra 0.24846061143029935
+MRN_1 AAL Contra 0.2409933834290898
+MRN_1 AAL Contra 0.24231614786815844
+MRN_1 AAL Contra 0.2981102657998186
+MRN_1 AAL Contra 0.23678962201390988
+MRN_1 AAL Contra 0.2376732515875416
+MRN_1 AAL Contra 0.3432932794073178
+MRN_1 AAL Contra 0.2421377822800121
+MRN_1 AAL Contra 0.28527082189295433
+MRN_1 AAL Contra 0.2494038457816752
+MRN_1 AAL Contra 0.2819589419413366
+MRN_1 AAL Contra 0.22198881614756577
+MRN_1 AAL Contra 0.21095816691865737
+MRN_1 AAL Contra 0.3009201557302691
+MRN_1 AAL Contra 0.26703816087087995
+MRN_1 AAL Contra 0.23478247323858484
+MRN_1 AAL Contra 0.33481003084366495
+MRN_1 AAL Contra 0.278713791956456
+MRN_1 AAL Contra 0.28934989204717265
+MRN_1 AAL Contra 0.26545775718173575
+MRN_1 AAL Contra 0.2371999346840036
+MRN_1 AAL Contra 0.24894353371635924
+MRN_1 AAL Contra 0.3007151279104929
+MRN_1 AAL Contra 0.23647995917750225
+MRN_1 AAL Contra 0.28569057242213486
+MRN_1 AAL Contra 0.20410611248866042
+MRN_1 AAL Contra 0.36559266132446333
+MRN_1 AAL Contra 0.5679009428485031
+MRN_1 AAL Contra 0.22860126368309647
+MRN_1 AAL Contra 0.29177567311762925
+MRN_1 AAL Contra 0.24651562927124285
+MRN_1 AAL Contra 0.3900044711218627
+MRN_1 AAL Contra 0.3082932751738736
+MRN_1 AAL Contra 0.34460838433625646
+MRN_1 AAL Contra 0.2318227768370124
+MRN_1 AAL Contra 0.36923778016329
+MRN_1 AAL Contra 0.20562153825219232
+MRN_1 AAL Contra 0.4026701312367705
+MRN_1 AAL Contra 0.4207073420018143
+MRN_1 AAL Contra 0.2766050749924403
+MRN_1 AAL Contra 0.2969306229210765
+MRN_1 AAL Contra 0.3177281590565467
+MRN_1 AAL Contra 0.3128065446023586
+MRN_1 AAL Contra 0.354597234048987
+MRN_1 AAL Contra 0.2944473816147566
+MRN_1 AAL Contra 0.3618611260961596
+MRN_1 AAL Contra 0.28999239612942246
+MRN_1 AAL Contra 0.33940711944360447
+MRN_1 AAL Contra 0.2833650320532204
+MRN_1 AAL Contra 0.32689283489567583
+MRN_1 AAL Contra 0.3046130441487753
+MRN_1 AAL Contra 0.2064479600846689
+MRN_1 AAL Contra 0.26169444239491985
+MRN_1 AAL Contra 0.35641733141820375
+MRN_1 AAL Contra 0.2361057224070154
+MRN_1 AAL Contra 0.34907856697913514
+MRN_1 AAL Contra 0.32978356788630175
+MRN_1 AAL Contra 0.2367191288176595
+MRN_1 AAL Contra 0.36138783126700935
+MRN_1 AAL Contra 0.39917778530390075
+MRN_1 AAL Contra 0.24259553099485942
+MRN_1 AAL Contra 0.37631541003931057
+MRN_1 AAL Contra 0.2689118884185062
+MRN_1 AAL Contra 0.22995811309343814
+MRN_1 AAL Contra 0.2626314070154218
+MRN_1 AAL Contra 0.2970835751436347
+MRN_1 AAL Contra 0.3206153728454793
+MRN_1 AAL Contra 0.33469587964922887
+MRN_1 AAL Contra 0.28714888358028423
+MRN_1 AAL Contra 0.29226610220743876
+MRN_1 AAL Contra 0.2630801354702147
+MRN_1 AAL Contra 0.23101793952222557
+Dataset Atlas Type Value
+IPCAS3 AAL Ipsi 0.32181684150030243
+IPCAS3 AAL Ipsi 0.28970255565638237
+IPCAS3 AAL Ipsi 0.4117538738656987
+IPCAS3 AAL Ipsi 0.33642289866908653
+IPCAS3 AAL Ipsi 0.3212144137931034
+IPCAS3 AAL Ipsi 0.3061475193587417
+IPCAS3 AAL Ipsi 0.2827393257713249
+IPCAS3 AAL Ipsi 0.25492983061101027
+IPCAS3 AAL Ipsi 0.47807363762855415
+IPCAS3 AAL Ipsi 0.22585928826376284
+IPCAS3 AAL Ipsi 0.2645616170598911
+IPCAS3 AAL Ipsi 0.25247034059286144
+IPCAS3 AAL Ipsi 0.23491713702359346
+IPCAS3 AAL Ipsi 0.2424678009679371
+IPCAS3 AAL Ipsi 0.2622953575317604
+IPCAS3 AAL Ipsi 0.369251241984271
+IPCAS3 AAL Ipsi 0.34982748850574713
+IPCAS3 AAL Ipsi 0.2293810598911071
+IPCAS3 AAL Ipsi 0.23952251240169387
+IPCAS3 AAL Ipsi 0.3266928061101029
+IPCAS3 AAL Ipsi 0.24513230852994555
+IPCAS3 AAL Ipsi 0.3483785916515427
+IPCAS3 AAL Ipsi 0.24109546884452512
+IPCAS3 AAL Ipsi 0.38991548669086507
+IPCAS3 AAL Ipsi 0.2733515701754386
+IPCAS3 AAL Ipsi 0.22434009346642467
+IPCAS3 AAL Ipsi 0.3878759788263763
+IPCAS3 AAL Ipsi 0.30464182849364796
+IPCAS3 AAL Ipsi 0.31737927011494255
+IPCAS3 AAL Ipsi 0.34950920780399275
+IPCAS3 AAL Ipsi 0.2973310214761041
+IPCAS3 AAL Ipsi 0.2584936333938294
+IPCAS3 AAL Ipsi 0.24972136932849365
+IPCAS3 AAL Ipsi 0.27125022655777375
+IPCAS3 AAL Ipsi 0.2997690139140956
+IPCAS3 AAL Ipsi 0.4730293130671507
+IPCAS3 AAL Ipsi 0.4347505614035087
+IPCAS3 AAL Ipsi 0.22067138233514821
+IPCAS3 AAL Ipsi 0.2697947676950998
+IPCAS3 AAL Ipsi 0.32571820719903205
+IPCAS3 AAL Ipsi 0.2896089885057471
+IPCAS3 AAL Ipsi 0.3188807244404113
+IPCAS3 AAL Ipsi 0.27392484422262553
+IPCAS3 AAL Ipsi 0.32563047005444645
+IPCAS3 AAL Ipsi 0.2968331630369026
+IPCAS3 AAL Ipsi 0.22677895735027223
+IPCAS3 AAL Ipsi 0.2680466479128856
+IPCAS3 AAL Ipsi 0.25428490199637027
+IPCAS3 AAL Ipsi 0.23422999183303084
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+IPCAS3 AAL Hom 0.4091275087719298
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+IPCAS3 AAL Contra 0.27762338433625644
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+IPCAS3 AAL Contra 0.45658135621409135
+IPCAS3 AAL Contra 0.2148633271847596
+IPCAS3 AAL Contra 0.26437974387662533
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+IPCAS3 AAL Contra 0.21980223707287572
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+IPCAS3 AAL Contra 0.36406247958875115
+IPCAS3 AAL Contra 0.34202957605080136
+IPCAS3 AAL Contra 0.22921878681584518
+IPCAS3 AAL Contra 0.23664562654974297
+IPCAS3 AAL Contra 0.3265594711218627
+IPCAS3 AAL Contra 0.23659879407317808
+IPCAS3 AAL Contra 0.3449163507710916
+IPCAS3 AAL Contra 0.23263341124886602
+IPCAS3 AAL Contra 0.37761546416691866
+IPCAS3 AAL Contra 0.2619412567281524
+IPCAS3 AAL Contra 0.2147066316903538
+IPCAS3 AAL Contra 0.38836289053522827
+IPCAS3 AAL Contra 0.2970178291502873
+IPCAS3 AAL Contra 0.3096182346537647
+IPCAS3 AAL Contra 0.34429372180223766
+IPCAS3 AAL Contra 0.29177256576957966
+IPCAS3 AAL Contra 0.25915619050498945
+IPCAS3 AAL Contra 0.24451405019655276
+IPCAS3 AAL Contra 0.26669361233746597
+IPCAS3 AAL Contra 0.30122219836710007
+IPCAS3 AAL Contra 0.47011282461445414
+IPCAS3 AAL Contra 0.40016027245237373
+IPCAS3 AAL Contra 0.21662623132748712
+IPCAS3 AAL Contra 0.2619251814333232
+IPCAS3 AAL Contra 0.32270675960084666
+IPCAS3 AAL Contra 0.2923768784396734
+IPCAS3 AAL Contra 0.31713924342304206
+IPCAS3 AAL Contra 0.2732299355911702
+IPCAS3 AAL Contra 0.31934398004233444
+IPCAS3 AAL Contra 0.29086714454188084
+IPCAS3 AAL Contra 0.21986245267614155
+IPCAS3 AAL Contra 0.26330194163894766
+IPCAS3 AAL Contra 0.2546164995464167
+IPCAS3 AAL Contra 0.22939856274569098
+IPCAS3 AAL Contra 0.24110508708799516
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+IPCAS3 AAL Contra 0.5246717544602358
+IPCAS3 AAL Contra 0.36703103054127617
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+IPCAS3 AAL Contra 0.22003762654974296
+IPCAS3 AAL Contra 0.18510801451466585
+IPCAS3 AAL Contra 0.2917121188388267
+IPCAS3 AAL Contra 0.355385969156335
+IPCAS3 AAL Contra 0.2960409135167826
+IPCAS3 AAL Contra 0.3426390619897188
+IPCAS3 AAL Contra 0.2504032857574841
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+IPCAS3 AAL Contra 0.25654220078621104
+IPCAS3 AAL Contra 0.27911442818264287
+IPCAS3 AAL Contra 0.30469212125793765
+IPCAS3 AAL Contra 0.2922342664045963
+IPCAS3 AAL Contra 0.2572100405201088
+IPCAS3 AAL Contra 0.26219692168128217
+IPCAS3 AAL Contra 0.2270212570305413
+IPCAS3 AAL Contra 0.38720950831569395
+IPCAS3 AAL Contra 0.31136097822800124
+IPCAS3 AAL Contra 0.20276179467795583
+IPCAS3 AAL Contra 0.2962612022981554
+IPCAS3 AAL Contra 0.35527081463562143
+IPCAS3 AAL Contra 0.3170478947686725
+IPCAS3 AAL Contra 0.1892870184457212
+IPCAS3 AAL Contra 0.21603816026610223
+IPCAS3 AAL Contra 0.27595942273964313
+IPCAS3 AAL Contra 0.2273627462957363
+IPCAS3 AAL Contra 0.24427520108859993
+Dataset Atlas Type Value
+Utah1 AAL Ipsi 0.3025999918330309
+Utah1 AAL Ipsi 0.35655816696914705
+Utah1 AAL Ipsi 0.25928108529945554
+Utah1 AAL Ipsi 0.27861495493042954
+Utah1 AAL Ipsi 0.2399980973986691
+Utah1 AAL Ipsi 0.41418430096793707
+Utah1 AAL Ipsi 0.3859148100423473
+Utah1 AAL Ipsi 0.3548873841500303
+Utah1 AAL Ipsi 0.2676296346037507
+Utah1 AAL Ipsi 0.3146914207501512
+Utah1 AAL Ipsi 0.2397833097398669
+Utah1 AAL Ipsi 0.2606255399274047
+Utah1 AAL Ipsi 0.42521522504537207
+Utah1 AAL Ipsi 0.26178572171808834
+Utah1 AAL Ipsi 0.28694291742286754
+Utah1 AAL Ipsi 0.2920841161524501
+Utah1 AAL Ipsi 0.22982473532970357
+Utah1 AAL Ipsi 0.24938281488203268
+Utah1 AAL Ipsi 0.250308055353902
+Utah1 AAL Ipsi 0.3446702843315185
+Utah1 AAL Ipsi 0.2562140789473684
+Utah1 AAL Ipsi 0.21134229280096792
+Utah1 AAL Ipsi 0.23029297005444646
+Utah1 AAL Ipsi 0.28440155021173624
+Utah1 AAL Ipsi 0.2638073049001815
+Utah1 AAL Ipsi 0.24814189322444044
+Utah1 AAL Ipsi 0.25706457229280094
+Utah1 AAL Ipsi 0.23093420568663037
+Utah1 AAL Ipsi 0.4925232855414398
+Utah1 AAL Ipsi 0.48569164670296433
+Utah1 AAL Ipsi 0.27480664640048397
+Utah1 AAL Ipsi 0.4823944349667272
+Utah1 AAL Ipsi 0.38740101421657597
+Utah1 AAL Ipsi 0.37081105263157893
+Utah1 AAL Ipsi 0.20616958711433758
+Utah1 AAL Ipsi 0.2736046358136721
+Utah1 AAL Ipsi 0.32933456261343014
+Utah1 AAL Ipsi 0.2745875807622505
+Utah1 AAL Ipsi 0.2593935045372051
+Utah1 AAL Ipsi 0.2322962050816697
+Utah1 AAL Ipsi 0.23176511222020568
+Utah1 AAL Ipsi 0.25218547368421057
+Utah1 AAL Ipsi 0.2737613127646703
+Utah1 AAL Ipsi 0.33806768632788864
+Utah1 AAL Ipsi 0.2398188641863279
+Utah1 AAL Ipsi 0.40352010889292195
+Utah1 AAL Ipsi 0.280955593768905
+Utah1 AAL Ipsi 0.3427011521476104
+Utah1 AAL Ipsi 0.2988986225045372
+Utah1 AAL Ipsi 0.2316447320024198
+Utah1 AAL Ipsi 0.33696630127041743
+Utah1 AAL Ipsi 0.266584240169389
+Utah1 AAL Ipsi 0.29036920054446463
+Utah1 AAL Ipsi 0.528477868723533
+Utah1 AAL Ipsi 0.3094522413793104
+Utah1 AAL Ipsi 0.30600461312764676
+Utah1 AAL Ipsi 0.4646103944343618
+Utah1 AAL Ipsi 0.34060438112522684
+Utah1 AAL Ipsi 0.2791893009679371
+Utah1 AAL Ipsi 0.20466331881427707
+Utah1 AAL Ipsi 0.27906565305505143
+Utah1 AAL Ipsi 0.41464065789473686
+Utah1 AAL Ipsi 0.22611269842710224
+Utah1 AAL Ipsi 0.2162551566848155
+Utah1 AAL Ipsi 0.4124143197217181
+Utah1 AAL Ipsi 0.20464897277676952
+Utah1 AAL Ipsi 0.3881226445856019
+Utah1 AAL Ipsi 0.44908652480338773
+Utah1 AAL Ipsi 0.2868888157894737
+Utah1 AAL Ipsi 0.2855601176648518
+Utah1 AAL Ipsi 0.2875958049001815
+Utah1 AAL Ipsi 0.39684447307924986
+Utah1 AAL Ipsi 0.3037609872958258
+Utah1 AAL Ipsi 0.35766045372050814
+Utah1 AAL Ipsi 0.3368474313369631
+Utah1 AAL Ipsi 0.37498909981851175
+Utah1 AAL Ipsi 0.28744013702359344
+Utah1 AAL Ipsi 0.2649299969751966
+Utah1 AAL Hom 0.5126585964912281
+Utah1 AAL Hom 0.47887582456140354
+Utah1 AAL Hom 0.38670207017543856
+Utah1 AAL Hom 0.41727426315789473
+Utah1 AAL Hom 0.33076708771929825
+Utah1 AAL Hom 0.5596134210526316
+Utah1 AAL Hom 0.4928472982456141
+Utah1 AAL Hom 0.5231617017543859
+Utah1 AAL Hom 0.41293173684210527
+Utah1 AAL Hom 0.5103278421052633
+Utah1 AAL Hom 0.31868112280701755
+Utah1 AAL Hom 0.37920815789473683
+Utah1 AAL Hom 0.5791492631578946
+Utah1 AAL Hom 0.4482375964912281
+Utah1 AAL Hom 0.4267414210526315
+Utah1 AAL Hom 0.4002894912280702
+Utah1 AAL Hom 0.3621823157894737
+Utah1 AAL Hom 0.34281284210526314
+Utah1 AAL Hom 0.40443577192982455
+Utah1 AAL Hom 0.5001432456140351
+Utah1 AAL Hom 0.3956878596491228
+Utah1 AAL Hom 0.3546564035087719
+Utah1 AAL Hom 0.34694738596491226
+Utah1 AAL Hom 0.41214126315789473
+Utah1 AAL Hom 0.3971421403508771
+Utah1 AAL Hom 0.4119572456140351
+Utah1 AAL Hom 0.3721278245614035
+Utah1 AAL Hom 0.40851063157894735
+Utah1 AAL Hom 0.5811498070175439
+Utah1 AAL Hom 0.6241999122807017
+Utah1 AAL Hom 0.38437964912280703
+Utah1 AAL Hom 0.5610705614035087
+Utah1 AAL Hom 0.5394996140350877
+Utah1 AAL Hom 0.4700284385964913
+Utah1 AAL Hom 0.32373121052631576
+Utah1 AAL Hom 0.40722250877192984
+Utah1 AAL Hom 0.419879947368421
+Utah1 AAL Hom 0.4173125087719299
+Utah1 AAL Hom 0.3863987719298246
+Utah1 AAL Hom 0.35184970175438596
+Utah1 AAL Hom 0.3934657894736842
+Utah1 AAL Hom 0.4109296140350877
+Utah1 AAL Hom 0.3905802456140351
+Utah1 AAL Hom 0.4858085789473684
+Utah1 AAL Hom 0.4149615087719298
+Utah1 AAL Hom 0.4927142280701754
+Utah1 AAL Hom 0.3689455087719299
+Utah1 AAL Hom 0.44478028070175446
+Utah1 AAL Hom 0.44190977192982456
+Utah1 AAL Hom 0.3636009298245614
+Utah1 AAL Hom 0.48557889473684207
+Utah1 AAL Hom 0.4197406842105263
+Utah1 AAL Hom 0.3785562982456141
+Utah1 AAL Hom 0.6437198596491228
+Utah1 AAL Hom 0.47257901754385967
+Utah1 AAL Hom 0.4866124385964912
+Utah1 AAL Hom 0.6175076666666666
+Utah1 AAL Hom 0.4543309824561404
+Utah1 AAL Hom 0.419873649122807
+Utah1 AAL Hom 0.3394935087719298
+Utah1 AAL Hom 0.41272605263157897
+Utah1 AAL Hom 0.5480957543859649
+Utah1 AAL Hom 0.3783077192982457
+Utah1 AAL Hom 0.3534025263157895
+Utah1 AAL Hom 0.5350151052631579
+Utah1 AAL Hom 0.3594704736842106
+Utah1 AAL Hom 0.5212850877192983
+Utah1 AAL Hom 0.6247824385964913
+Utah1 AAL Hom 0.4150445263157895
+Utah1 AAL Hom 0.43814540350877196
+Utah1 AAL Hom 0.41219389473684204
+Utah1 AAL Hom 0.5005556140350876
+Utah1 AAL Hom 0.46741771929824555
+Utah1 AAL Hom 0.45858514035087716
+Utah1 AAL Hom 0.45943226315789476
+Utah1 AAL Hom 0.483044403508772
+Utah1 AAL Hom 0.4435206140350877
+Utah1 AAL Hom 0.40165954385964914
+Utah1 AAL Contra 0.29708797157544603
+Utah1 AAL Contra 0.3416906059872997
+Utah1 AAL Contra 0.2471251768974902
+Utah1 AAL Contra 0.273830127608104
+Utah1 AAL Contra 0.23380993045055945
+Utah1 AAL Contra 0.3813816214091321
+Utah1 AAL Contra 0.3662798769277291
+Utah1 AAL Contra 0.35034474811006955
+Utah1 AAL Contra 0.2609557342001814
+Utah1 AAL Contra 0.3084478043543997
+Utah1 AAL Contra 0.22437618264287876
+Utah1 AAL Contra 0.25393795796794677
+Utah1 AAL Contra 0.42664444390686423
+Utah1 AAL Contra 0.2524889047475053
+Utah1 AAL Contra 0.28852503961294224
+Utah1 AAL Contra 0.2933786948896281
+Utah1 AAL Contra 0.22366692289083762
+Utah1 AAL Contra 0.2389511203507711
+Utah1 AAL Contra 0.24309311097671607
+Utah1 AAL Contra 0.3338413967342002
+Utah1 AAL Contra 0.24923685817961902
+Utah1 AAL Contra 0.20644880193528878
+Utah1 AAL Contra 0.2215451865739341
+Utah1 AAL Contra 0.277869416087088
+Utah1 AAL Contra 0.2586396637435743
+Utah1 AAL Contra 0.24004126005442997
+Utah1 AAL Contra 0.2524815161778046
+Utah1 AAL Contra 0.22259133655881463
+Utah1 AAL Contra 0.48297133565164796
+Utah1 AAL Contra 0.47912383459328695
+Utah1 AAL Contra 0.2740855155730269
+Utah1 AAL Contra 0.4630355216208043
+Utah1 AAL Contra 0.37640164469307535
+Utah1 AAL Contra 0.36474178439673416
+Utah1 AAL Contra 0.18973897671605683
+Utah1 AAL Contra 0.27065866827940727
+Utah1 AAL Contra 0.3243992766858179
+Utah1 AAL Contra 0.27551174720290295
+Utah1 AAL Contra 0.25320183671000907
+Utah1 AAL Contra 0.23034116419715756
+Utah1 AAL Contra 0.223910444511642
+Utah1 AAL Contra 0.23694892319322647
+Utah1 AAL Contra 0.26179483610523135
+Utah1 AAL Contra 0.33221512397943753
+Utah1 AAL Contra 0.232563933776837
+Utah1 AAL Contra 0.3899548373147868
+Utah1 AAL Contra 0.25913140338675533
+Utah1 AAL Contra 0.3346916870275174
+Utah1 AAL Contra 0.28962222074387667
+Utah1 AAL Contra 0.2293582264892652
+Utah1 AAL Contra 0.33480408406410644
+Utah1 AAL Contra 0.26271065376474145
+Utah1 AAL Contra 0.2857832252797097
+Utah1 AAL Contra 0.518232536740248
+Utah1 AAL Contra 0.3043312830359843
+Utah1 AAL Contra 0.2976226023586332
+Utah1 AAL Contra 0.4634145563955247
+Utah1 AAL Contra 0.32977013849410336
+Utah1 AAL Contra 0.27004726428787423
+Utah1 AAL Contra 0.20159530329603872
+Utah1 AAL Contra 0.2716371832476565
+Utah1 AAL Contra 0.40160762382824305
+Utah1 AAL Contra 0.20835870275173873
+Utah1 AAL Contra 0.21280945751436348
+Utah1 AAL Contra 0.4123980305412761
+Utah1 AAL Contra 0.20055037314786817
+Utah1 AAL Contra 0.3823119374055035
+Utah1 AAL Contra 0.45064457574841243
+Utah1 AAL Contra 0.2781959032355609
+Utah1 AAL Contra 0.27866211067432717
+Utah1 AAL Contra 0.2785910916238282
+Utah1 AAL Contra 0.3928690148170547
+Utah1 AAL Contra 0.3014350692470517
+Utah1 AAL Contra 0.3453465273661929
+Utah1 AAL Contra 0.3287318122165104
+Utah1 AAL Contra 0.3723124526761415
+Utah1 AAL Contra 0.2846559380102812
+Utah1 AAL Contra 0.25135078379195647
+Dataset Atlas Type Value
+NYU_1 AAL Ipsi 0.2546482583182093
+NYU_1 AAL Ipsi 0.35375620538415004
+NYU_1 AAL Ipsi 0.28825790139140955
+NYU_1 AAL Ipsi 0.33398406533575314
+NYU_1 AAL Ipsi 0.29627984452510586
+NYU_1 AAL Ipsi 0.31851675408348457
+NYU_1 AAL Ipsi 0.353677503629764
+NYU_1 AAL Ipsi 0.2528972229280097
+NYU_1 AAL Ipsi 0.2954289900181488
+NYU_1 AAL Ipsi 0.2092141512401694
+NYU_1 AAL Ipsi 0.3020020883242589
+NYU_1 AAL Ipsi 0.2087789594676346
+NYU_1 AAL Ipsi 0.2340348290986086
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+NYU_1 AAL Ipsi 0.28437638415003025
+NYU_1 AAL Ipsi 0.24451781215970964
+NYU_1 AAL Ipsi 0.2764706887477314
+NYU_1 AAL Ipsi 0.2855555157289776
+NYU_1 AAL Ipsi 0.26269921990320627
+NYU_1 AAL Ipsi 0.2682078608590442
+NYU_1 AAL Ipsi 0.34980319147005445
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+NYU_1 AAL Hom 0.4664755263157895
+NYU_1 AAL Hom 0.39532884210526315
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+NYU_1 AAL Contra 0.24520906380405202
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+NYU_1 AAL Contra 0.1992122676141518
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+NYU_1 AAL Contra 0.20268929180526157
+NYU_1 AAL Contra 0.22311081644995462
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+NYU_1 AAL Contra 0.4140446274569096
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+NYU_1 AAL Contra 0.3525383828243121
+NYU_1 AAL Contra 0.2615952803144844
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+NYU_1 AAL Contra 0.23578332537042637
+NYU_1 AAL Contra 0.28290006894466285
+NYU_1 AAL Contra 0.23869523677048685
+NYU_1 AAL Contra 0.3238300762019958
+NYU_1 AAL Contra 0.21183729452676137
+NYU_1 AAL Contra 0.26209986362261867
+NYU_1 AAL Contra 0.24129164711218626
+NYU_1 AAL Contra 0.24029094768672515
+NYU_1 AAL Contra 0.24653578742062293
+NYU_1 AAL Contra 0.30328788660417294
+NYU_1 AAL Contra 0.27625535470214696
+NYU_1 AAL Contra 0.29158114575143634
+NYU_1 AAL Contra 0.23429569186573937
+NYU_1 AAL Contra 0.30026753492591474
+NYU_1 AAL Contra 0.2910451980647112
+NYU_1 AAL Contra 0.32570946779558513
+NYU_1 AAL Contra 0.2450394726338071
+NYU_1 AAL Contra 0.4282538751133958
+NYU_1 AAL Contra 0.29834154671908075
+NYU_1 AAL Contra 0.2159830447535531
+NYU_1 AAL Contra 0.4276849038403386
+NYU_1 AAL Contra 0.18767780193528877
+NYU_1 AAL Contra 0.4331016815845177
+NYU_1 AAL Contra 0.23090771121862716
+NYU_1 AAL Contra 0.20869648714847291
+NYU_1 AAL Contra 0.2082190610825522
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+NYU_1 AAL Contra 0.24578052857574842
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+NYU_1 AAL Contra 0.4250485684910795
+NYU_1 AAL Contra 0.23095932657998183
+NYU_1 AAL Contra 0.27020950075597217
+NYU_1 AAL Contra 0.28090375143634716
+Dataset Atlas Type Value
+UWM AAL Ipsi 0.27820256624319417
+UWM AAL Ipsi 0.34589177676950994
+UWM AAL Ipsi 0.32186886600120995
+UWM AAL Ipsi 0.27308693284936475
+UWM AAL Ipsi 0.21611664035087716
+UWM AAL Ipsi 0.4391809903206292
+UWM AAL Ipsi 0.3840635350877193
+UWM AAL Ipsi 0.31650731941923776
+UWM AAL Ipsi 0.24843186963097397
+UWM AAL Ipsi 0.3254708012704174
+UWM AAL Ipsi 0.28168939413188143
+UWM AAL Ipsi 0.40986242135511186
+UWM AAL Ipsi 0.5041515350877194
+UWM AAL Ipsi 0.2823162286751361
+UWM AAL Ipsi 0.3815622761645493
+UWM AAL Ipsi 0.3448795825771324
+UWM AAL Ipsi 0.26283927253478523
+UWM AAL Ipsi 0.2638300338777979
+UWM AAL Ipsi 0.29716174107682997
+UWM AAL Ipsi 0.2642508015728978
+UWM AAL Ipsi 0.3077202628554144
+UWM AAL Ipsi 0.22975698245614035
+UWM AAL Ipsi 0.30603087749546276
+UWM AAL Ipsi 0.24686416122202054
+UWM AAL Ipsi 0.33937884815486996
+UWM AAL Ipsi 0.24877151361161526
+UWM AAL Ipsi 0.26969625710828793
+UWM AAL Ipsi 0.42108727767695103
+UWM AAL Ipsi 0.38633819993950397
+UWM AAL Ipsi 0.48901218421052634
+UWM AAL Ipsi 0.2958540517241379
+UWM AAL Ipsi 0.30420451209921356
+UWM AAL Ipsi 0.2387323986690865
+UWM AAL Ipsi 0.26549218753781006
+UWM AAL Ipsi 0.36325573926194793
+UWM AAL Ipsi 0.2649023393829401
+UWM AAL Ipsi 0.32022996218995764
+UWM AAL Ipsi 0.292305274954628
+UWM AAL Ipsi 0.3220896300665457
+UWM AAL Ipsi 0.3787354032062916
+UWM AAL Ipsi 0.42599320659407136
+UWM AAL Ipsi 0.3605372289776165
+UWM AAL Ipsi 0.3862718457350272
+UWM AAL Ipsi 0.3183715190562614
+UWM AAL Ipsi 0.2757185943738657
+UWM AAL Ipsi 0.3470583427102239
+UWM AAL Ipsi 0.24502820356926802
+UWM AAL Ipsi 0.3180049050211736
+UWM AAL Ipsi 0.3486607253478524
+UWM AAL Ipsi 0.4153492217180883
+UWM AAL Ipsi 0.2432938784029038
+UWM AAL Ipsi 0.2445909742891712
+UWM AAL Ipsi 0.33383552873563216
+UWM AAL Ipsi 0.29674601905626136
+UWM AAL Ipsi 0.5295044664246823
+UWM AAL Ipsi 0.27410449697519657
+UWM AAL Ipsi 0.25894371476104056
+UWM AAL Ipsi 0.2309500819721718
+UWM AAL Ipsi 0.4177965450695705
+UWM AAL Ipsi 0.319351516938899
+UWM AAL Ipsi 0.28528408862673926
+UWM AAL Ipsi 0.45212728886872355
+UWM AAL Ipsi 0.400659407138536
+UWM AAL Ipsi 0.34668571264367815
+UWM AAL Ipsi 0.23909383303085296
+UWM AAL Ipsi 0.33932909346642465
+UWM AAL Ipsi 0.3064557144585602
+UWM AAL Ipsi 0.3397201606170599
+UWM AAL Ipsi 0.3467945716878402
+UWM AAL Ipsi 0.2957494637023593
+UWM AAL Ipsi 0.40295263702359346
+UWM AAL Ipsi 0.35958865335753176
+UWM AAL Ipsi 0.2987415248033878
+UWM AAL Ipsi 0.3932220087719298
+UWM AAL Ipsi 0.23320268542044767
+UWM AAL Hom 0.38582700000000003
+UWM AAL Hom 0.48827970175438595
+UWM AAL Hom 0.4393841403508772
+UWM AAL Hom 0.40690773684210524
+UWM AAL Hom 0.4266054736842106
+UWM AAL Hom 0.5528314561403509
+UWM AAL Hom 0.4830468947368421
+UWM AAL Hom 0.4366073859649122
+UWM AAL Hom 0.4082331228070175
+UWM AAL Hom 0.45863649122807015
+UWM AAL Hom 0.42324917543859647
+UWM AAL Hom 0.5618555438596491
+UWM AAL Hom 0.597272456140351
+UWM AAL Hom 0.39948189473684215
+UWM AAL Hom 0.47243824561403513
+UWM AAL Hom 0.46228498245614036
+UWM AAL Hom 0.39614147368421054
+UWM AAL Hom 0.3858799298245614
+UWM AAL Hom 0.45017429824561406
+UWM AAL Hom 0.37398577192982463
+UWM AAL Hom 0.4411088771929824
+UWM AAL Hom 0.39942207017543857
+UWM AAL Hom 0.4306922631578947
+UWM AAL Hom 0.3565351578947369
+UWM AAL Hom 0.4619627543859649
+UWM AAL Hom 0.39644559649122807
+UWM AAL Hom 0.4461255438596491
+UWM AAL Hom 0.4820777894736843
+UWM AAL Hom 0.530626245614035
+UWM AAL Hom 0.5828020701754386
+UWM AAL Hom 0.4361551228070175
+UWM AAL Hom 0.4380286140350877
+UWM AAL Hom 0.3745903333333333
+UWM AAL Hom 0.46058215789473683
+UWM AAL Hom 0.5090247192982456
+UWM AAL Hom 0.34430510526315783
+UWM AAL Hom 0.44219038596491234
+UWM AAL Hom 0.46167971929824564
+UWM AAL Hom 0.421622649122807
+UWM AAL Hom 0.5024653859649123
+UWM AAL Hom 0.5554858596491229
+UWM AAL Hom 0.4862228771929824
+UWM AAL Hom 0.4729729298245614
+UWM AAL Hom 0.42095452631578945
+UWM AAL Hom 0.42024466666666666
+UWM AAL Hom 0.46435735087719293
+UWM AAL Hom 0.37969464912280704
+UWM AAL Hom 0.42548717543859643
+UWM AAL Hom 0.4530296666666667
+UWM AAL Hom 0.5053532105263158
+UWM AAL Hom 0.39093291228070176
+UWM AAL Hom 0.39702373684210523
+UWM AAL Hom 0.4615341403508772
+UWM AAL Hom 0.40507049122807015
+UWM AAL Hom 0.6187866315789473
+UWM AAL Hom 0.41268761403508775
+UWM AAL Hom 0.4677379298245614
+UWM AAL Hom 0.38761099999999993
+UWM AAL Hom 0.549102350877193
+UWM AAL Hom 0.46395943859649125
+UWM AAL Hom 0.3811121052631579
+UWM AAL Hom 0.5236023157894737
+UWM AAL Hom 0.49653729824561404
+UWM AAL Hom 0.4526662105263157
+UWM AAL Hom 0.3610068596491228
+UWM AAL Hom 0.44679449122807013
+UWM AAL Hom 0.4327766140350877
+UWM AAL Hom 0.5158374210526315
+UWM AAL Hom 0.5019249649122808
+UWM AAL Hom 0.41475261403508773
+UWM AAL Hom 0.4962591403508772
+UWM AAL Hom 0.5046580877192982
+UWM AAL Hom 0.38393036842105266
+UWM AAL Hom 0.5043923684210526
+UWM AAL Hom 0.3968235614035088
+UWM AAL Contra 0.2665115352283036
+UWM AAL Contra 0.33357192137889324
+UWM AAL Contra 0.3102527626247354
+UWM AAL Contra 0.252419730571515
+UWM AAL Contra 0.21334781403084366
+UWM AAL Contra 0.42814574569095853
+UWM AAL Contra 0.374238777744179
+UWM AAL Contra 0.3084896177804657
+UWM AAL Contra 0.24114299667372244
+UWM AAL Contra 0.31192072089507106
+UWM AAL Contra 0.26402930662231633
+UWM AAL Contra 0.3980656120350771
+UWM AAL Contra 0.49378468491079525
+UWM AAL Contra 0.26977478590867854
+UWM AAL Contra 0.35965486029634114
+UWM AAL Contra 0.332438967039613
+UWM AAL Contra 0.2512097121257938
+UWM AAL Contra 0.2612872745690959
+UWM AAL Contra 0.28625110099788326
+UWM AAL Contra 0.24813289839733899
+UWM AAL Contra 0.28659609495010585
+UWM AAL Contra 0.22465006894466283
+UWM AAL Contra 0.29960305805866344
+UWM AAL Contra 0.23880405745388572
+UWM AAL Contra 0.3255648732990626
+UWM AAL Contra 0.243137867553674
+UWM AAL Contra 0.25841124221348655
+UWM AAL Contra 0.40275193105533713
+UWM AAL Contra 0.3770495935893559
+UWM AAL Contra 0.4790798633202299
+UWM AAL Contra 0.27755039159358935
+UWM AAL Contra 0.29148270033262774
+UWM AAL Contra 0.2223450193528878
+UWM AAL Contra 0.24486042667069854
+UWM AAL Contra 0.356858725733293
+UWM AAL Contra 0.2430595687934684
+UWM AAL Contra 0.3068265969156335
+UWM AAL Contra 0.2852668938615059
+UWM AAL Contra 0.3095016552766858
+UWM AAL Contra 0.3664931260961597
+UWM AAL Contra 0.4092514233444209
+UWM AAL Contra 0.34813157393407923
+UWM AAL Contra 0.37859062685213185
+UWM AAL Contra 0.3087249555488358
+UWM AAL Contra 0.26250546719080736
+UWM AAL Contra 0.3416418790444512
+UWM AAL Contra 0.23540635409736924
+UWM AAL Contra 0.3035573042032053
+UWM AAL Contra 0.33146137586936797
+UWM AAL Contra 0.4081168726942848
+UWM AAL Contra 0.2291061530087693
+UWM AAL Contra 0.2328004974296946
+UWM AAL Contra 0.3154627973994557
+UWM AAL Contra 0.2839998267311763
+UWM AAL Contra 0.5182868993045056
+UWM AAL Contra 0.2694928391291201
+UWM AAL Contra 0.2499109876020562
+UWM AAL Contra 0.2254097701844572
+UWM AAL Contra 0.408315945570003
+UWM AAL Contra 0.30046721923193226
+UWM AAL Contra 0.26838801028122167
+UWM AAL Contra 0.4368186295736317
+UWM AAL Contra 0.38779371242818267
+UWM AAL Contra 0.3309273271847596
+UWM AAL Contra 0.22643057514363474
+UWM AAL Contra 0.32770364076201997
+UWM AAL Contra 0.2994215282733595
+UWM AAL Contra 0.33373864771696393
+UWM AAL Contra 0.33679751224674936
+UWM AAL Contra 0.28408531236770485
+UWM AAL Contra 0.3848798167523435
+UWM AAL Contra 0.3502235724221349
+UWM AAL Contra 0.2870185748412458
+UWM AAL Contra 0.38249873389779254
+UWM AAL Contra 0.22116229966737225
+Dataset Atlas Type Value
+IPCAS_2 AAL Ipsi 0.23695532123411978
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+JHNU_bids AAL Ipsi 0.3911838669086509
+JHNU_bids AAL Ipsi 0.231790813369631
+JHNU_bids AAL Ipsi 0.43076052419842714
+JHNU_bids AAL Ipsi 0.24339451935874168
+JHNU_bids AAL Ipsi 0.2659196261343013
+JHNU_bids AAL Ipsi 0.24220127071990324
+JHNU_bids AAL Ipsi 0.25975294252873565
+JHNU_bids AAL Ipsi 0.3636848233514822
+JHNU_bids AAL Ipsi 0.2969318617664852
+JHNU_bids AAL Ipsi 0.2845695069570478
+JHNU_bids AAL Ipsi 0.3192586796733212
+JHNU_bids AAL Ipsi 0.2608226361161525
+JHNU_bids AAL Ipsi 0.31910348699334545
+JHNU_bids AAL Ipsi 0.272023721415608
+JHNU_bids AAL Ipsi 0.27102024137931036
+JHNU_bids AAL Ipsi 0.26545306049606776
+JHNU_bids AAL Ipsi 0.24481366848154873
+JHNU_bids AAL Ipsi 0.3589656521476104
+JHNU_bids AAL Ipsi 0.25792086085904414
+JHNU_bids AAL Ipsi 0.24840802843315185
+JHNU_bids AAL Ipsi 0.2257828445251059
+JHNU_bids AAL Ipsi 0.24002205958862674
+JHNU_bids AAL Ipsi 0.42417962250453717
+JHNU_bids AAL Ipsi 0.2448355577737447
+JHNU_bids AAL Ipsi 0.25829896521476103
+JHNU_bids AAL Ipsi 0.264853833938294
+JHNU_bids AAL Ipsi 0.24488036206896552
+JHNU_bids AAL Ipsi 0.3090741318814277
+JHNU_bids AAL Ipsi 0.38285848003629763
+JHNU_bids AAL Ipsi 0.2539545601935874
+JHNU_bids AAL Ipsi 0.3023937997580157
+JHNU_bids AAL Ipsi 0.27961571506352084
+JHNU_bids AAL Ipsi 0.2808062731397459
+JHNU_bids AAL Ipsi 0.3145221715063521
+JHNU_bids AAL Ipsi 0.2583812752571083
+JHNU_bids AAL Ipsi 0.2987046548699335
+JHNU_bids AAL Ipsi 0.23429828523895946
+JHNU_bids AAL Ipsi 0.2673595617059891
+JHNU_bids AAL Ipsi 0.2820878971566848
+JHNU_bids AAL Ipsi 0.2707936258318209
+JHNU_bids AAL Ipsi 0.2654107555958863
+JHNU_bids AAL Ipsi 0.25172607683000603
+JHNU_bids AAL Ipsi 0.26247814065335756
+JHNU_bids AAL Ipsi 0.21012138959467633
+JHNU_bids AAL Ipsi 0.3015958617664851
+JHNU_bids AAL Ipsi 0.37792071778584396
+JHNU_bids AAL Ipsi 0.32237777646702964
+JHNU_bids AAL Ipsi 0.3335172495462795
+JHNU_bids AAL Ipsi 0.2901869016938899
+JHNU_bids AAL Ipsi 0.2879257652752571
+JHNU_bids AAL Ipsi 0.25870436630369026
+JHNU_bids AAL Ipsi 0.35091324742891716
+JHNU_bids AAL Ipsi 0.2937453387779794
+JHNU_bids AAL Ipsi 0.25005682274652147
+JHNU_bids AAL Hom 0.4834215087719298
+JHNU_bids AAL Hom 0.5127148245614035
+JHNU_bids AAL Hom 0.5445855614035087
+JHNU_bids AAL Hom 0.4723923333333333
+JHNU_bids AAL Hom 0.38366375438596484
+JHNU_bids AAL Hom 0.3759912280701754
+JHNU_bids AAL Hom 0.5058320877192982
+JHNU_bids AAL Hom 0.3621395789473684
+JHNU_bids AAL Hom 0.6218281578947368
+JHNU_bids AAL Hom 0.39443138596491223
+JHNU_bids AAL Hom 0.4102244561403508
+JHNU_bids AAL Hom 0.3555410701754386
+JHNU_bids AAL Hom 0.3687618421052632
+JHNU_bids AAL Hom 0.44036987719298243
+JHNU_bids AAL Hom 0.4588055087719298
+JHNU_bids AAL Hom 0.41281387719298246
+JHNU_bids AAL Hom 0.43248284210526317
+JHNU_bids AAL Hom 0.38340036842105263
+JHNU_bids AAL Hom 0.44338847368421047
+JHNU_bids AAL Hom 0.41415815789473687
+JHNU_bids AAL Hom 0.4863814385964912
+JHNU_bids AAL Hom 0.39138884210526315
+JHNU_bids AAL Hom 0.4050545087719298
+JHNU_bids AAL Hom 0.48421771929824564
+JHNU_bids AAL Hom 0.4100639649122807
+JHNU_bids AAL Hom 0.3509409473684211
+JHNU_bids AAL Hom 0.3921791578947368
+JHNU_bids AAL Hom 0.40122682456140357
+JHNU_bids AAL Hom 0.5872289298245614
+JHNU_bids AAL Hom 0.37816799999999995
+JHNU_bids AAL Hom 0.3967357368421053
+JHNU_bids AAL Hom 0.3741931929824561
+JHNU_bids AAL Hom 0.36777489473684216
+JHNU_bids AAL Hom 0.4623954385964913
+JHNU_bids AAL Hom 0.4794172456140351
+JHNU_bids AAL Hom 0.4217679824561404
+JHNU_bids AAL Hom 0.5207294736842105
+JHNU_bids AAL Hom 0.4556887894736842
+JHNU_bids AAL Hom 0.43071447368421045
+JHNU_bids AAL Hom 0.45246319298245613
+JHNU_bids AAL Hom 0.41822422807017545
+JHNU_bids AAL Hom 0.37756429824561405
+JHNU_bids AAL Hom 0.3854828421052631
+JHNU_bids AAL Hom 0.40516307017543857
+JHNU_bids AAL Hom 0.4430065964912281
+JHNU_bids AAL Hom 0.3764222631578947
+JHNU_bids AAL Hom 0.4396571403508773
+JHNU_bids AAL Hom 0.3854321929824561
+JHNU_bids AAL Hom 0.39885873684210527
+JHNU_bids AAL Hom 0.3442722456140351
+JHNU_bids AAL Hom 0.4491523684210526
+JHNU_bids AAL Hom 0.5226012982456141
+JHNU_bids AAL Hom 0.4993168421052632
+JHNU_bids AAL Hom 0.4982121052631579
+JHNU_bids AAL Hom 0.4019403157894737
+JHNU_bids AAL Hom 0.4317579649122807
+JHNU_bids AAL Hom 0.42070045614035084
+JHNU_bids AAL Hom 0.4439802807017544
+JHNU_bids AAL Hom 0.46270370175438597
+JHNU_bids AAL Hom 0.4216409122807018
+JHNU_bids AAL Contra 0.3110217886301785
+JHNU_bids AAL Contra 0.3840755019655277
+JHNU_bids AAL Contra 0.36530488509222864
+JHNU_bids AAL Contra 0.3010698512246749
+JHNU_bids AAL Contra 0.20958143634714244
+JHNU_bids AAL Contra 0.246043612337466
+JHNU_bids AAL Contra 0.38441177351073486
+JHNU_bids AAL Contra 0.22035884457211977
+JHNU_bids AAL Contra 0.4313390964620502
+JHNU_bids AAL Contra 0.2416403329301482
+JHNU_bids AAL Contra 0.2642046090111884
+JHNU_bids AAL Contra 0.23361582733595404
+JHNU_bids AAL Contra 0.24633308406410645
+JHNU_bids AAL Contra 0.3479825361354702
+JHNU_bids AAL Contra 0.29224686997278504
+JHNU_bids AAL Contra 0.2765603159963713
+JHNU_bids AAL Contra 0.30268602086483215
+JHNU_bids AAL Contra 0.2599152071363774
+JHNU_bids AAL Contra 0.307646837012398
+JHNU_bids AAL Contra 0.26726763955246446
+JHNU_bids AAL Contra 0.26122428968853945
+JHNU_bids AAL Contra 0.2618011146053825
+JHNU_bids AAL Contra 0.24153953069247053
+JHNU_bids AAL Contra 0.3561299368007257
+JHNU_bids AAL Contra 0.24887454762624736
+JHNU_bids AAL Contra 0.2436867641366798
+JHNU_bids AAL Contra 0.2231884299969761
+JHNU_bids AAL Contra 0.24082784759600848
+JHNU_bids AAL Contra 0.42179785576050793
+JHNU_bids AAL Contra 0.2405260931357726
+JHNU_bids AAL Contra 0.2481938104021772
+JHNU_bids AAL Contra 0.2542411638947687
+JHNU_bids AAL Contra 0.23929303840338673
+JHNU_bids AAL Contra 0.3076922434230421
+JHNU_bids AAL Contra 0.3735411354702147
+JHNU_bids AAL Contra 0.24060002237677652
+JHNU_bids AAL Contra 0.3003946347142425
+JHNU_bids AAL Contra 0.2759702319322649
+JHNU_bids AAL Contra 0.27815924886604176
+JHNU_bids AAL Contra 0.30789427033565164
+JHNU_bids AAL Contra 0.2550960356818869
+JHNU_bids AAL Contra 0.2929870852736619
+JHNU_bids AAL Contra 0.22752301360749924
+JHNU_bids AAL Contra 0.26158587813728457
+JHNU_bids AAL Contra 0.2780681920169338
+JHNU_bids AAL Contra 0.26317117084971275
+JHNU_bids AAL Contra 0.2598223229513154
+JHNU_bids AAL Contra 0.2418719558512247
+JHNU_bids AAL Contra 0.26177558179618987
+JHNU_bids AAL Contra 0.2087651233746598
+JHNU_bids AAL Contra 0.2977462667069852
+JHNU_bids AAL Contra 0.37408771091623827
+JHNU_bids AAL Contra 0.3189170882975506
+JHNU_bids AAL Contra 0.32491634381614753
+JHNU_bids AAL Contra 0.28489753825219233
+JHNU_bids AAL Contra 0.2834990039310553
+JHNU_bids AAL Contra 0.24981436468097973
+JHNU_bids AAL Contra 0.33907878983973383
+JHNU_bids AAL Contra 0.2872589392198367
+JHNU_bids AAL Contra 0.2462911983671001
+Dataset Atlas Type Value
+IACAS_1 AAL Ipsi 0.20629127313974593
+IACAS_1 AAL Ipsi 0.2133773917120387
+IACAS_1 AAL Ipsi 0.27449467906836056
+IACAS_1 AAL Ipsi 0.24917892135511194
+IACAS_1 AAL Ipsi 0.21304655868118572
+IACAS_1 AAL Ipsi 0.23644341379310344
+IACAS_1 AAL Ipsi 0.22836066061705992
+IACAS_1 AAL Ipsi 0.29998057350272234
+IACAS_1 AAL Ipsi 0.17888226527525708
+IACAS_1 AAL Ipsi 0.19366807743496672
+IACAS_1 AAL Ipsi 0.34728052722323044
+IACAS_1 AAL Ipsi 0.23961299032062913
+IACAS_1 AAL Ipsi 0.35389836085904414
+IACAS_1 AAL Ipsi 0.23145933968542043
+IACAS_1 AAL Ipsi 0.40587452661826984
+IACAS_1 AAL Ipsi 0.26770880278281917
+IACAS_1 AAL Ipsi 0.26212037658802173
+IACAS_1 AAL Ipsi 0.19259438112522687
+IACAS_1 AAL Ipsi 0.23772141863278884
+IACAS_1 AAL Ipsi 0.21520680308529944
+IACAS_1 AAL Ipsi 0.2320260378100423
+IACAS_1 AAL Ipsi 0.26191752692075015
+IACAS_1 AAL Ipsi 0.19625641379310346
+IACAS_1 AAL Ipsi 0.2400609313369631
+IACAS_1 AAL Ipsi 0.2944541379310345
+IACAS_1 AAL Ipsi 0.20711133424077435
+IACAS_1 AAL Ipsi 0.2422412020568663
+IACAS_1 AAL Ipsi 0.20605093496672716
+IACAS_1 AAL Ipsi 0.31489423351482154
+IACAS_1 AAL Ipsi 0.2349499237749546
+IACAS_1 AAL Ipsi 0.22647445977011493
+IACAS_1 AAL Ipsi 0.18333946551724137
+IACAS_1 AAL Ipsi 0.2574680490018149
+IACAS_1 AAL Ipsi 0.23708278130671506
+IACAS_1 AAL Ipsi 0.23040060284331518
+IACAS_1 AAL Ipsi 0.30509662280701755
+IACAS_1 AAL Ipsi 0.23991572111312764
+IACAS_1 AAL Ipsi 0.26466901058681186
+IACAS_1 AAL Ipsi 0.20209386781609195
+IACAS_1 AAL Ipsi 0.24222523411978222
+IACAS_1 AAL Ipsi 0.19143457168784025
+IACAS_1 AAL Ipsi 0.22343794978826378
+IACAS_1 AAL Ipsi 0.21723875801572898
+IACAS_1 AAL Ipsi 0.2520136288566243
+IACAS_1 AAL Ipsi 0.2386461379310345
+IACAS_1 AAL Ipsi 0.2390621013309135
+IACAS_1 AAL Ipsi 0.23999449727767694
+IACAS_1 AAL Ipsi 0.2839719912280702
+IACAS_1 AAL Ipsi 0.22023931790683604
+IACAS_1 AAL Ipsi 0.23157146914700547
+IACAS_1 AAL Ipsi 0.2923904095583787
+IACAS_1 AAL Ipsi 0.21593853236539629
+IACAS_1 AAL Ipsi 0.22434868239564426
+IACAS_1 AAL Ipsi 0.22986088717483363
+IACAS_1 AAL Ipsi 0.30786821657592256
+IACAS_1 AAL Hom 0.32070459649122807
+IACAS_1 AAL Hom 0.36638584210526315
+IACAS_1 AAL Hom 0.4397465438596491
+IACAS_1 AAL Hom 0.43207407017543864
+IACAS_1 AAL Hom 0.37403538596491226
+IACAS_1 AAL Hom 0.3582109122807018
+IACAS_1 AAL Hom 0.3633639824561403
+IACAS_1 AAL Hom 0.43240824561403507
+IACAS_1 AAL Hom 0.36668050877192976
+IACAS_1 AAL Hom 0.36830594736842104
+IACAS_1 AAL Hom 0.49967122807017544
+IACAS_1 AAL Hom 0.386487
+IACAS_1 AAL Hom 0.5440546666666666
+IACAS_1 AAL Hom 0.37211096491228074
+IACAS_1 AAL Hom 0.5203585263157895
+IACAS_1 AAL Hom 0.4035027719298246
+IACAS_1 AAL Hom 0.42476580701754385
+IACAS_1 AAL Hom 0.4058295263157895
+IACAS_1 AAL Hom 0.3695004035087719
+IACAS_1 AAL Hom 0.307096403508772
+IACAS_1 AAL Hom 0.36622233333333337
+IACAS_1 AAL Hom 0.3925064561403509
+IACAS_1 AAL Hom 0.3318245964912281
+IACAS_1 AAL Hom 0.3622254035087719
+IACAS_1 AAL Hom 0.48391766666666663
+IACAS_1 AAL Hom 0.3124595964912281
+IACAS_1 AAL Hom 0.4343939298245614
+IACAS_1 AAL Hom 0.36194928070175436
+IACAS_1 AAL Hom 0.4330418421052632
+IACAS_1 AAL Hom 0.3739554912280702
+IACAS_1 AAL Hom 0.40685508771929835
+IACAS_1 AAL Hom 0.3045088771929824
+IACAS_1 AAL Hom 0.42176108771929827
+IACAS_1 AAL Hom 0.42915845614035086
+IACAS_1 AAL Hom 0.3525337368421053
+IACAS_1 AAL Hom 0.459366403508772
+IACAS_1 AAL Hom 0.3392245263157895
+IACAS_1 AAL Hom 0.4141506315789473
+IACAS_1 AAL Hom 0.3137178596491228
+IACAS_1 AAL Hom 0.45047312280701757
+IACAS_1 AAL Hom 0.28242668421052636
+IACAS_1 AAL Hom 0.38925156140350875
+IACAS_1 AAL Hom 0.31962914035087725
+IACAS_1 AAL Hom 0.3564993859649123
+IACAS_1 AAL Hom 0.4232295614035088
+IACAS_1 AAL Hom 0.38694149122807014
+IACAS_1 AAL Hom 0.4312875263157895
+IACAS_1 AAL Hom 0.4581670701754387
+IACAS_1 AAL Hom 0.41655450877192984
+IACAS_1 AAL Hom 0.3765222807017544
+IACAS_1 AAL Hom 0.4812194035087719
+IACAS_1 AAL Hom 0.35484301754385966
+IACAS_1 AAL Hom 0.3278857192982456
+IACAS_1 AAL Hom 0.387717649122807
+IACAS_1 AAL Hom 0.48695163157894733
+IACAS_1 AAL Contra 0.19771025642576354
+IACAS_1 AAL Contra 0.21140089688539462
+IACAS_1 AAL Contra 0.26442882491684305
+IACAS_1 AAL Contra 0.23962010976716058
+IACAS_1 AAL Contra 0.20441516480193528
+IACAS_1 AAL Contra 0.2323057571817357
+IACAS_1 AAL Contra 0.22341635923798003
+IACAS_1 AAL Contra 0.27133928757181736
+IACAS_1 AAL Contra 0.16816120199576656
+IACAS_1 AAL Contra 0.18745874296945872
+IACAS_1 AAL Contra 0.3387799930450559
+IACAS_1 AAL Contra 0.23607525279709707
+IACAS_1 AAL Contra 0.3479005300876928
+IACAS_1 AAL Contra 0.22680701451466587
+IACAS_1 AAL Contra 0.3956368503175083
+IACAS_1 AAL Contra 0.23675744783791955
+IACAS_1 AAL Contra 0.2602004451164197
+IACAS_1 AAL Contra 0.19013408285455097
+IACAS_1 AAL Contra 0.2219476555790747
+IACAS_1 AAL Contra 0.20967538312670095
+IACAS_1 AAL Contra 0.22227897792561233
+IACAS_1 AAL Contra 0.2605774076201996
+IACAS_1 AAL Contra 0.19096999727850014
+IACAS_1 AAL Contra 0.22860523283943152
+IACAS_1 AAL Contra 0.28741105261566374
+IACAS_1 AAL Contra 0.20069892984578167
+IACAS_1 AAL Contra 0.23036670365890538
+IACAS_1 AAL Contra 0.20067710130027216
+IACAS_1 AAL Contra 0.3093680054429997
+IACAS_1 AAL Contra 0.2327485926821893
+IACAS_1 AAL Contra 0.21703302146960993
+IACAS_1 AAL Contra 0.1732610607801633
+IACAS_1 AAL Contra 0.2517296437859087
+IACAS_1 AAL Contra 0.23882430511037198
+IACAS_1 AAL Contra 0.22234494254611428
+IACAS_1 AAL Contra 0.2963356945872392
+IACAS_1 AAL Contra 0.23029202449349861
+IACAS_1 AAL Contra 0.25494993226489265
+IACAS_1 AAL Contra 0.19334936286664653
+IACAS_1 AAL Contra 0.23290855216208042
+IACAS_1 AAL Contra 0.18148660840641062
+IACAS_1 AAL Contra 0.21759833202298157
+IACAS_1 AAL Contra 0.21172055972180223
+IACAS_1 AAL Contra 0.24269350771091625
+IACAS_1 AAL Contra 0.23733426368309646
+IACAS_1 AAL Contra 0.2340132839431509
+IACAS_1 AAL Contra 0.23508910009071668
+IACAS_1 AAL Contra 0.27188507892349556
+IACAS_1 AAL Contra 0.21818222800120957
+IACAS_1 AAL Contra 0.23007771061384943
+IACAS_1 AAL Contra 0.2841497456909585
+IACAS_1 AAL Contra 0.2167565923798004
+IACAS_1 AAL Contra 0.22463531781070453
+IACAS_1 AAL Contra 0.2208907332930148
+IACAS_1 AAL Contra 0.30127521197459933
+Dataset Atlas Type Value
+SWU2 AAL Ipsi 0.2299567846339988
+SWU2 AAL Ipsi 0.26060484906231096
+SWU2 AAL Ipsi 0.3180099095583787
+SWU2 AAL Ipsi 0.20784778735632184
+SWU2 AAL Ipsi 0.38732222776769515
+SWU2 AAL Ipsi 0.21108438717483366
+SWU2 AAL Ipsi 0.3456553856624319
+SWU2 AAL Ipsi 0.3550140871143376
+SWU2 AAL Ipsi 0.2898823799153055
+SWU2 AAL Ipsi 0.37684859739866916
+SWU2 AAL Ipsi 0.343375742891712
+SWU2 AAL Ipsi 0.2804435015124017
+SWU2 AAL Ipsi 0.20798159104658195
+SWU2 AAL Ipsi 0.2600326155474894
+SWU2 AAL Ipsi 0.25339361826981244
+SWU2 AAL Ipsi 0.2345057915910466
+SWU2 AAL Ipsi 0.2119390846944949
+SWU2 AAL Ipsi 0.28762862068965517
+SWU2 AAL Ipsi 0.18720075378100426
+SWU2 AAL Ipsi 0.3224182111312765
+SWU2 AAL Ipsi 0.36045254718693287
+SWU2 AAL Ipsi 0.33632956896551724
+SWU2 AAL Ipsi 0.2883357159709619
+SWU2 AAL Ipsi 0.27439440804597703
+SWU2 AAL Ipsi 0.44946323018753787
+SWU2 AAL Ipsi 0.3279829452510587
+SWU2 AAL Ipsi 0.2882578962492438
+SWU2 AAL Ipsi 0.286731149122807
+SWU2 AAL Ipsi 0.3084381960072595
+SWU2 AAL Ipsi 0.2154483720508167
+SWU2 AAL Ipsi 0.2634141282516636
+SWU2 AAL Ipsi 0.23962186993345436
+SWU2 AAL Ipsi 0.24604526769509982
+SWU2 AAL Ipsi 0.2526543106473079
+SWU2 AAL Ipsi 0.24489811070780398
+SWU2 AAL Ipsi 0.2154022574107683
+SWU2 AAL Ipsi 0.2594359751966122
+SWU2 AAL Ipsi 0.19734883938294012
+SWU2 AAL Ipsi 0.2728633787053842
+SWU2 AAL Ipsi 0.22766346551724137
+SWU2 AAL Ipsi 0.27177574591651543
+SWU2 AAL Ipsi 0.1914764522081065
+SWU2 AAL Ipsi 0.28607613218390804
+SWU2 AAL Ipsi 0.2598477655777374
+SWU2 AAL Ipsi 0.3074543055051422
+SWU2 AAL Ipsi 0.25895042770719906
+SWU2 AAL Ipsi 0.34432286448880817
+SWU2 AAL Ipsi 0.23240131760435576
+SWU2 AAL Ipsi 0.2195970756200847
+SWU2 AAL Ipsi 0.23680043678160917
+SWU2 AAL Ipsi 0.3032020462794918
+SWU2 AAL Ipsi 0.4355227123411978
+SWU2 AAL Ipsi 0.24351931064730795
+SWU2 AAL Ipsi 0.3436813502722323
+SWU2 AAL Hom 0.3648171578947368
+SWU2 AAL Hom 0.4325045263157895
+SWU2 AAL Hom 0.4244868245614035
+SWU2 AAL Hom 0.38963894736842103
+SWU2 AAL Hom 0.500528596491228
+SWU2 AAL Hom 0.37262399999999996
+SWU2 AAL Hom 0.49693557894736845
+SWU2 AAL Hom 0.475265052631579
+SWU2 AAL Hom 0.4718619473684211
+SWU2 AAL Hom 0.5628890350877193
+SWU2 AAL Hom 0.5054998947368421
+SWU2 AAL Hom 0.43344278947368414
+SWU2 AAL Hom 0.41581364912280705
+SWU2 AAL Hom 0.4217015263157895
+SWU2 AAL Hom 0.4217408245614035
+SWU2 AAL Hom 0.3882938421052632
+SWU2 AAL Hom 0.38293000000000005
+SWU2 AAL Hom 0.44444773684210526
+SWU2 AAL Hom 0.34912956140350876
+SWU2 AAL Hom 0.4867962280701754
+SWU2 AAL Hom 0.4878496666666667
+SWU2 AAL Hom 0.5365114035087719
+SWU2 AAL Hom 0.4559248771929824
+SWU2 AAL Hom 0.42246157894736835
+SWU2 AAL Hom 0.5516265964912281
+SWU2 AAL Hom 0.5298689298245615
+SWU2 AAL Hom 0.45261640350877197
+SWU2 AAL Hom 0.40784845614035087
+SWU2 AAL Hom 0.4651347894736842
+SWU2 AAL Hom 0.36808921052631577
+SWU2 AAL Hom 0.3791524561403508
+SWU2 AAL Hom 0.438312403508772
+SWU2 AAL Hom 0.38030331578947363
+SWU2 AAL Hom 0.4391806666666667
+SWU2 AAL Hom 0.38734780701754384
+SWU2 AAL Hom 0.44156173684210526
+SWU2 AAL Hom 0.3979195087719298
+SWU2 AAL Hom 0.3593399298245614
+SWU2 AAL Hom 0.3955895614035088
+SWU2 AAL Hom 0.366898701754386
+SWU2 AAL Hom 0.4138234561403509
+SWU2 AAL Hom 0.3549001578947368
+SWU2 AAL Hom 0.40137317543859646
+SWU2 AAL Hom 0.3837281228070175
+SWU2 AAL Hom 0.46250792982456146
+SWU2 AAL Hom 0.40952649122807017
+SWU2 AAL Hom 0.5135753859649124
+SWU2 AAL Hom 0.3879450877192982
+SWU2 AAL Hom 0.33669601754385964
+SWU2 AAL Hom 0.43548347368421053
+SWU2 AAL Hom 0.45581822807017547
+SWU2 AAL Hom 0.5766348596491228
+SWU2 AAL Hom 0.3886007368421052
+SWU2 AAL Hom 0.4602184035087719
+SWU2 AAL Contra 0.22642685061989723
+SWU2 AAL Contra 0.24995990867856063
+SWU2 AAL Contra 0.3152169543392804
+SWU2 AAL Contra 0.1950372654974297
+SWU2 AAL Contra 0.3782333752645902
+SWU2 AAL Contra 0.21292564529785304
+SWU2 AAL Contra 0.344450993045056
+SWU2 AAL Contra 0.35034603265799813
+SWU2 AAL Contra 0.28547987602056246
+SWU2 AAL Contra 0.3761285016631389
+SWU2 AAL Contra 0.341507931357726
+SWU2 AAL Contra 0.2801101569398246
+SWU2 AAL Contra 0.20832823132748715
+SWU2 AAL Contra 0.2512828125188993
+SWU2 AAL Contra 0.23771480465678865
+SWU2 AAL Contra 0.23651120048382218
+SWU2 AAL Contra 0.20397913909888118
+SWU2 AAL Contra 0.26999571091623825
+SWU2 AAL Contra 0.18262553915935892
+SWU2 AAL Contra 0.3134896567886302
+SWU2 AAL Contra 0.35473560024191114
+SWU2 AAL Contra 0.3345174756576958
+SWU2 AAL Contra 0.27966213365588144
+SWU2 AAL Contra 0.270558985787723
+SWU2 AAL Contra 0.4459825984275779
+SWU2 AAL Contra 0.3134301115814938
+SWU2 AAL Contra 0.28634940066525555
+SWU2 AAL Contra 0.2819789180526157
+SWU2 AAL Contra 0.30373568430601755
+SWU2 AAL Contra 0.21609937314786815
+SWU2 AAL Contra 0.2565941481705473
+SWU2 AAL Contra 0.24038701693377684
+SWU2 AAL Contra 0.2437318246144542
+SWU2 AAL Contra 0.24901472210462652
+SWU2 AAL Contra 0.2371658929543393
+SWU2 AAL Contra 0.21460268491079526
+SWU2 AAL Contra 0.2495570595706078
+SWU2 AAL Contra 0.19761182884789838
+SWU2 AAL Contra 0.2676496274569096
+SWU2 AAL Contra 0.2193758351980647
+SWU2 AAL Contra 0.26538226096159667
+SWU2 AAL Contra 0.19059339340792256
+SWU2 AAL Contra 0.27080820320532206
+SWU2 AAL Contra 0.250318843967342
+SWU2 AAL Contra 0.3011951067432719
+SWU2 AAL Contra 0.25376790353794976
+SWU2 AAL Contra 0.3393050033262776
+SWU2 AAL Contra 0.2301489325672815
+SWU2 AAL Contra 0.21450381554278802
+SWU2 AAL Contra 0.2341798191714545
+SWU2 AAL Contra 0.29441924221348653
+SWU2 AAL Contra 0.42119576867251285
+SWU2 AAL Contra 0.24022007741155124
+SWU2 AAL Contra 0.32529311339582706
+Dataset Atlas Type Value
+SWU3 AAL Ipsi 0.30260167241379315
+SWU3 AAL Ipsi 0.38087197580157295
+SWU3 AAL Ipsi 0.2938011397459165
+SWU3 AAL Ipsi 0.38900956987295826
+SWU3 AAL Ipsi 0.2413850257108288
+SWU3 AAL Ipsi 0.43471202752571086
+SWU3 AAL Ipsi 0.3167259219600726
+SWU3 AAL Ipsi 0.2724615671506352
+SWU3 AAL Ipsi 0.23978030641258316
+SWU3 AAL Ipsi 0.24296828251663644
+SWU3 AAL Ipsi 0.3018999506957048
+SWU3 AAL Ipsi 0.2526237289776164
+SWU3 AAL Ipsi 0.27333480308529945
+SWU3 AAL Ipsi 0.3811238067150635
+SWU3 AAL Ipsi 0.216236722323049
+SWU3 AAL Ipsi 0.22416677797943135
+SWU3 AAL Ipsi 0.2396594673321234
+SWU3 AAL Ipsi 0.27740988082274654
+SWU3 AAL Ipsi 0.2610339869933454
+SWU3 AAL Ipsi 0.32750672050816704
+SWU3 AAL Ipsi 0.3095552020568663
+SWU3 AAL Ipsi 0.3130724531155475
+SWU3 AAL Ipsi 0.31581677253478524
+SWU3 AAL Ipsi 0.32938147852389593
+SWU3 AAL Ipsi 0.26052771476104053
+SWU3 AAL Ipsi 0.275134129461585
+SWU3 AAL Ipsi 0.2632834748941319
+SWU3 AAL Ipsi 0.26076144646098004
+SWU3 AAL Ipsi 0.24928692861464002
+SWU3 AAL Ipsi 0.28060117604355717
+SWU3 AAL Ipsi 0.2950584797338173
+SWU3 AAL Ipsi 0.24950616152450092
+SWU3 AAL Ipsi 0.2593597516636419
+SWU3 AAL Ipsi 0.26180786539624923
+SWU3 AAL Ipsi 0.3067377186932849
+SWU3 AAL Ipsi 0.2772601566848155
+SWU3 AAL Ipsi 0.26841653387779796
+SWU3 AAL Ipsi 0.2677314700544465
+SWU3 AAL Ipsi 0.30985596158499695
+SWU3 AAL Ipsi 0.23008926436781607
+SWU3 AAL Ipsi 0.25439914942528735
+SWU3 AAL Ipsi 0.28396418905021176
+SWU3 AAL Ipsi 0.25923848336358135
+SWU3 AAL Ipsi 0.19810654990925589
+SWU3 AAL Ipsi 0.23125961343012708
+SWU3 AAL Ipsi 0.2748014316394435
+SWU3 AAL Ipsi 0.36571254174228673
+SWU3 AAL Ipsi 0.2802026597096189
+SWU3 AAL Hom 0.40762242105263163
+SWU3 AAL Hom 0.5059596491228071
+SWU3 AAL Hom 0.4267517017543859
+SWU3 AAL Hom 0.4712160350877193
+SWU3 AAL Hom 0.4028490701754386
+SWU3 AAL Hom 0.5360809298245615
+SWU3 AAL Hom 0.4577219649122807
+SWU3 AAL Hom 0.41579166666666667
+SWU3 AAL Hom 0.4457234736842105
+SWU3 AAL Hom 0.3620000701754386
+SWU3 AAL Hom 0.46934145614035094
+SWU3 AAL Hom 0.4002938771929825
+SWU3 AAL Hom 0.40303919298245616
+SWU3 AAL Hom 0.5569069649122808
+SWU3 AAL Hom 0.37062317543859646
+SWU3 AAL Hom 0.3564273157894737
+SWU3 AAL Hom 0.4064796666666666
+SWU3 AAL Hom 0.36952847368421055
+SWU3 AAL Hom 0.40031491228070176
+SWU3 AAL Hom 0.4525073859649122
+SWU3 AAL Hom 0.438751701754386
+SWU3 AAL Hom 0.4436460526315789
+SWU3 AAL Hom 0.44844277192982457
+SWU3 AAL Hom 0.4524644912280702
+SWU3 AAL Hom 0.44326463157894735
+SWU3 AAL Hom 0.4271546842105263
+SWU3 AAL Hom 0.3922119824561403
+SWU3 AAL Hom 0.3456120526315789
+SWU3 AAL Hom 0.41665114035087725
+SWU3 AAL Hom 0.49081889473684215
+SWU3 AAL Hom 0.4711995087719298
+SWU3 AAL Hom 0.42088449122807026
+SWU3 AAL Hom 0.3988765789473685
+SWU3 AAL Hom 0.4144814385964912
+SWU3 AAL Hom 0.4442248771929824
+SWU3 AAL Hom 0.43025649122807014
+SWU3 AAL Hom 0.4112554210526315
+SWU3 AAL Hom 0.42651931578947366
+SWU3 AAL Hom 0.45603250877192986
+SWU3 AAL Hom 0.4287189824561404
+SWU3 AAL Hom 0.42819347368421046
+SWU3 AAL Hom 0.46325063157894736
+SWU3 AAL Hom 0.39578678947368423
+SWU3 AAL Hom 0.34351112280701757
+SWU3 AAL Hom 0.3574530350877192
+SWU3 AAL Hom 0.4335681578947368
+SWU3 AAL Hom 0.5428390701754386
+SWU3 AAL Hom 0.41078128070175435
+SWU3 AAL Contra 0.29399682219534323
+SWU3 AAL Contra 0.3747123017840944
+SWU3 AAL Contra 0.285797947989114
+SWU3 AAL Contra 0.38420542697308735
+SWU3 AAL Contra 0.24200615512549137
+SWU3 AAL Contra 0.427700751738736
+SWU3 AAL Contra 0.3091219259147263
+SWU3 AAL Contra 0.2600044399758089
+SWU3 AAL Contra 0.23649330722709402
+SWU3 AAL Contra 0.23560410250982766
+SWU3 AAL Contra 0.2975737529482915
+SWU3 AAL Contra 0.25399672966434833
+SWU3 AAL Contra 0.26504020501965525
+SWU3 AAL Contra 0.3750499089809495
+SWU3 AAL Contra 0.2139710054429997
+SWU3 AAL Contra 0.2214163480495918
+SWU3 AAL Contra 0.23474514575143635
+SWU3 AAL Contra 0.26407258542485634
+SWU3 AAL Contra 0.2606904910795283
+SWU3 AAL Contra 0.31890108043543997
+SWU3 AAL Contra 0.2993837184759601
+SWU3 AAL Contra 0.30676797278500156
+SWU3 AAL Contra 0.309528338070759
+SWU3 AAL Contra 0.3189773937103114
+SWU3 AAL Contra 0.2571597889325673
+SWU3 AAL Contra 0.27584033293014815
+SWU3 AAL Contra 0.2530110889023284
+SWU3 AAL Contra 0.24643616117326883
+SWU3 AAL Contra 0.24636708799516177
+SWU3 AAL Contra 0.27598083912912
+SWU3 AAL Contra 0.29227693166011487
+SWU3 AAL Contra 0.2493634487450862
+SWU3 AAL Contra 0.25729046084064106
+SWU3 AAL Contra 0.2582321294224373
+SWU3 AAL Contra 0.29809836982159055
+SWU3 AAL Contra 0.2721233743574237
+SWU3 AAL Contra 0.2609151756879347
+SWU3 AAL Contra 0.26645211339582703
+SWU3 AAL Contra 0.30402826277592987
+SWU3 AAL Contra 0.2284337453885697
+SWU3 AAL Contra 0.2523809298457817
+SWU3 AAL Contra 0.28050690051406113
+SWU3 AAL Contra 0.2509220226791654
+SWU3 AAL Contra 0.19545449138191717
+SWU3 AAL Contra 0.2199336634411854
+SWU3 AAL Contra 0.27197279225884485
+SWU3 AAL Contra 0.3628437248261264
+SWU3 AAL Contra 0.2714562760810402
+Dataset Atlas Type Value
+IPCAS4 AAL Ipsi 0.28057264458560194
+IPCAS4 AAL Ipsi 0.37391377253478525
+IPCAS4 AAL Ipsi 0.31981231246218994
+IPCAS4 AAL Ipsi 0.27918602298850576
+IPCAS4 AAL Ipsi 0.30972852722323047
+IPCAS4 AAL Ipsi 0.5062852879612826
+IPCAS4 AAL Ipsi 0.4100582549909256
+IPCAS4 AAL Ipsi 0.4926068838475499
+IPCAS4 AAL Ipsi 0.29673527011494255
+IPCAS4 AAL Ipsi 0.3950804703569268
+IPCAS4 AAL Ipsi 0.43937437840290383
+IPCAS4 AAL Ipsi 0.2625775508166969
+IPCAS4 AAL Ipsi 0.289610907138536
+IPCAS4 AAL Ipsi 0.25694317937084094
+IPCAS4 AAL Ipsi 0.23457296249243798
+IPCAS4 AAL Ipsi 0.24070021264367816
+IPCAS4 AAL Ipsi 0.3233900980036298
+IPCAS4 AAL Ipsi 0.30579926467029644
+IPCAS4 AAL Ipsi 0.30735091500302475
+IPCAS4 AAL Ipsi 0.2928847988505747
+IPCAS4 AAL Ipsi 0.23678133121597092
+IPCAS4 AAL Ipsi 0.25929731518451304
+IPCAS4 AAL Ipsi 0.2540922843315184
+IPCAS4 AAL Ipsi 0.31495048729582575
+IPCAS4 AAL Ipsi 0.34158676830006046
+IPCAS4 AAL Ipsi 0.3515957976406534
+IPCAS4 AAL Ipsi 0.261926368723533
+IPCAS4 AAL Ipsi 0.44625466182698126
+IPCAS4 AAL Ipsi 0.3583126358136721
+IPCAS4 AAL Ipsi 0.3421736681790683
+IPCAS4 AAL Ipsi 0.350638958862674
+IPCAS4 AAL Ipsi 0.2758750819721718
+IPCAS4 AAL Ipsi 0.34208739715668485
+IPCAS4 AAL Ipsi 0.301278038415003
+IPCAS4 AAL Ipsi 0.32320574016938897
+IPCAS4 AAL Ipsi 0.3263748660012099
+IPCAS4 AAL Ipsi 0.30304243345432547
+IPCAS4 AAL Ipsi 0.33208694676346034
+IPCAS4 AAL Ipsi 0.22805796642468237
+IPCAS4 AAL Ipsi 0.24031836085904418
+IPCAS4 AAL Hom 0.4115833859649123
+IPCAS4 AAL Hom 0.496673701754386
+IPCAS4 AAL Hom 0.4582917192982456
+IPCAS4 AAL Hom 0.42478945614035085
+IPCAS4 AAL Hom 0.4542889824561403
+IPCAS4 AAL Hom 0.6339656666666666
+IPCAS4 AAL Hom 0.5432134385964912
+IPCAS4 AAL Hom 0.5988340701754387
+IPCAS4 AAL Hom 0.42253284210526315
+IPCAS4 AAL Hom 0.5210468421052632
+IPCAS4 AAL Hom 0.5631103684210526
+IPCAS4 AAL Hom 0.44207168421052634
+IPCAS4 AAL Hom 0.4602391929824562
+IPCAS4 AAL Hom 0.4152216666666666
+IPCAS4 AAL Hom 0.38506210526315787
+IPCAS4 AAL Hom 0.40380492982456145
+IPCAS4 AAL Hom 0.4611718596491228
+IPCAS4 AAL Hom 0.4285111929824561
+IPCAS4 AAL Hom 0.4725234736842105
+IPCAS4 AAL Hom 0.3952886666666667
+IPCAS4 AAL Hom 0.3883350526315789
+IPCAS4 AAL Hom 0.44980954385964916
+IPCAS4 AAL Hom 0.40793503508771933
+IPCAS4 AAL Hom 0.4557945263157895
+IPCAS4 AAL Hom 0.44085245614035085
+IPCAS4 AAL Hom 0.45090859649122805
+IPCAS4 AAL Hom 0.4174584210526316
+IPCAS4 AAL Hom 0.545526701754386
+IPCAS4 AAL Hom 0.47877791228070177
+IPCAS4 AAL Hom 0.4674377543859649
+IPCAS4 AAL Hom 0.469072701754386
+IPCAS4 AAL Hom 0.4163561929824562
+IPCAS4 AAL Hom 0.4789221754385965
+IPCAS4 AAL Hom 0.41706515789473686
+IPCAS4 AAL Hom 0.48777391228070177
+IPCAS4 AAL Hom 0.5049609122807017
+IPCAS4 AAL Hom 0.4432935087719299
+IPCAS4 AAL Hom 0.4839407192982456
+IPCAS4 AAL Hom 0.4173635789473685
+IPCAS4 AAL Hom 0.4509732631578947
+IPCAS4 AAL Contra 0.2824028364076202
+IPCAS4 AAL Contra 0.36412189355911706
+IPCAS4 AAL Contra 0.31685015875415784
+IPCAS4 AAL Contra 0.26858672452373755
+IPCAS4 AAL Contra 0.30929517387360145
+IPCAS4 AAL Contra 0.5059848690656183
+IPCAS4 AAL Contra 0.3978903804052011
+IPCAS4 AAL Contra 0.4824411514968249
+IPCAS4 AAL Contra 0.2900402255820986
+IPCAS4 AAL Contra 0.38151652645902634
+IPCAS4 AAL Contra 0.43911686029634106
+IPCAS4 AAL Contra 0.2595650057453886
+IPCAS4 AAL Contra 0.2873545182945268
+IPCAS4 AAL Contra 0.25210755518596917
+IPCAS4 AAL Contra 0.23527888599939523
+IPCAS4 AAL Contra 0.24258834472331417
+IPCAS4 AAL Contra 0.31852039431508916
+IPCAS4 AAL Contra 0.3011663362564258
+IPCAS4 AAL Contra 0.3074439818566677
+IPCAS4 AAL Contra 0.2847290625944965
+IPCAS4 AAL Contra 0.23703243543997582
+IPCAS4 AAL Contra 0.25641153008769274
+IPCAS4 AAL Contra 0.2513061085576051
+IPCAS4 AAL Contra 0.31304498971877837
+IPCAS4 AAL Contra 0.33091516722104636
+IPCAS4 AAL Contra 0.3185762149984881
+IPCAS4 AAL Contra 0.25141788478983973
+IPCAS4 AAL Contra 0.43494595282733595
+IPCAS4 AAL Contra 0.3536527532506804
+IPCAS4 AAL Contra 0.33704882280012094
+IPCAS4 AAL Contra 0.34537210250982764
+IPCAS4 AAL Contra 0.27528953553069246
+IPCAS4 AAL Contra 0.33791732960387055
+IPCAS4 AAL Contra 0.28392736226186877
+IPCAS4 AAL Contra 0.31748800967644386
+IPCAS4 AAL Contra 0.3153633299062594
+IPCAS4 AAL Contra 0.29336566586029633
+IPCAS4 AAL Contra 0.32910021136982154
+IPCAS4 AAL Contra 0.23005170244934986
+IPCAS4 AAL Contra 0.23553446779558515
+Dataset Atlas Type Value
+IPCAS_8 AAL Ipsi 0.3393352441016334
+IPCAS_8 AAL Ipsi 0.4812255127041742
+IPCAS_8 AAL Ipsi 0.23889382819116758
+IPCAS_8 AAL Ipsi 0.3978653309134906
+IPCAS_8 AAL Ipsi 0.5316925142165759
+IPCAS_8 AAL Ipsi 0.3218928006654568
+IPCAS_8 AAL Ipsi 0.37413197701149425
+IPCAS_8 AAL Ipsi 0.45274121808832424
+IPCAS_8 AAL Ipsi 0.4123790111917725
+IPCAS_8 AAL Ipsi 0.4434138212341198
+IPCAS_8 AAL Ipsi 0.6374535798548094
+IPCAS_8 AAL Ipsi 0.36424605958862677
+IPCAS_8 AAL Ipsi 0.36320631760435573
+IPCAS_8 AAL Ipsi 0.3685343998790079
+IPCAS_8 AAL Ipsi 0.4710473024803388
+IPCAS_8 AAL Ipsi 0.28379605353902
+IPCAS_8 AAL Ipsi 0.4188106146400484
+IPCAS_8 AAL Ipsi 0.2775093675136116
+IPCAS_8 AAL Ipsi 0.3593142359346642
+IPCAS_8 AAL Ipsi 0.2793096863278887
+IPCAS_8 AAL Ipsi 0.25664070447670906
+IPCAS_8 AAL Ipsi 0.38635403418027836
+IPCAS_8 AAL Ipsi 0.38070598124621896
+IPCAS_8 AAL Ipsi 0.3480732707199032
+IPCAS_8 AAL Ipsi 0.4113906367211131
+IPCAS_8 AAL Ipsi 0.30325547549909254
+IPCAS_8 AAL Hom 0.40963814035087714
+IPCAS_8 AAL Hom 0.5782088771929825
+IPCAS_8 AAL Hom 0.449825350877193
+IPCAS_8 AAL Hom 0.5325195087719299
+IPCAS_8 AAL Hom 0.6052608245614034
+IPCAS_8 AAL Hom 0.46362047368421055
+IPCAS_8 AAL Hom 0.5334743333333334
+IPCAS_8 AAL Hom 0.5551194035087719
+IPCAS_8 AAL Hom 0.5598702631578947
+IPCAS_8 AAL Hom 0.5433630701754385
+IPCAS_8 AAL Hom 0.7439767192982456
+IPCAS_8 AAL Hom 0.468422
+IPCAS_8 AAL Hom 0.511594947368421
+IPCAS_8 AAL Hom 0.5270242631578947
+IPCAS_8 AAL Hom 0.6152491052631579
+IPCAS_8 AAL Hom 0.40672089473684214
+IPCAS_8 AAL Hom 0.5271958771929826
+IPCAS_8 AAL Hom 0.42453324561403505
+IPCAS_8 AAL Hom 0.5440660175438596
+IPCAS_8 AAL Hom 0.35572831578947367
+IPCAS_8 AAL Hom 0.37595394736842097
+IPCAS_8 AAL Hom 0.5540540877192982
+IPCAS_8 AAL Hom 0.5909604210526315
+IPCAS_8 AAL Hom 0.4584445438596491
+IPCAS_8 AAL Hom 0.5734234210526314
+IPCAS_8 AAL Hom 0.4155404912280702
+IPCAS_8 AAL Contra 0.333876553069247
+IPCAS_8 AAL Contra 0.47599984971273057
+IPCAS_8 AAL Contra 0.2385120813426066
+IPCAS_8 AAL Contra 0.39603680737828845
+IPCAS_8 AAL Contra 0.5334515787723012
+IPCAS_8 AAL Contra 0.31684826065920774
+IPCAS_8 AAL Contra 0.3772016610220744
+IPCAS_8 AAL Contra 0.4473168996068945
+IPCAS_8 AAL Contra 0.4094090281221651
+IPCAS_8 AAL Contra 0.4353637048684609
+IPCAS_8 AAL Contra 0.6350164154823101
+IPCAS_8 AAL Contra 0.35687655276685815
+IPCAS_8 AAL Contra 0.35813352343513755
+IPCAS_8 AAL Contra 0.36537538494103416
+IPCAS_8 AAL Contra 0.46611890625944963
+IPCAS_8 AAL Contra 0.2814048853946175
+IPCAS_8 AAL Contra 0.40952837284547927
+IPCAS_8 AAL Contra 0.2785560347747203
+IPCAS_8 AAL Contra 0.35972301663138795
+IPCAS_8 AAL Contra 0.2753111348654369
+IPCAS_8 AAL Contra 0.24231190172361655
+IPCAS_8 AAL Contra 0.38490431539159364
+IPCAS_8 AAL Contra 0.3814342727547626
+IPCAS_8 AAL Contra 0.3472319023283943
+IPCAS_8 AAL Contra 0.40570533323253705
+IPCAS_8 AAL Contra 0.3011039346840036
+Dataset Atlas Type Value
+ABIDEII-SDSU_1 AAL Ipsi 0.2988147676950998
+ABIDEII-SDSU_1 AAL Ipsi 0.24892771718088327
+ABIDEII-SDSU_1 AAL Ipsi 0.2630522192982456
+ABIDEII-SDSU_1 AAL Ipsi 0.32522802359346636
+ABIDEII-SDSU_1 AAL Ipsi 0.20730603539019962
+ABIDEII-SDSU_1 AAL Ipsi 0.3632682002419842
+ABIDEII-SDSU_1 AAL Ipsi 0.4653895490018149
+ABIDEII-SDSU_1 AAL Ipsi 0.2128531836055656
+ABIDEII-SDSU_1 AAL Ipsi 0.24458851240169388
+ABIDEII-SDSU_1 AAL Ipsi 0.24464978493647913
+ABIDEII-SDSU_1 AAL Ipsi 0.2216843611615245
+ABIDEII-SDSU_1 AAL Ipsi 0.19663659800362973
+ABIDEII-SDSU_1 AAL Ipsi 0.492916610707804
+ABIDEII-SDSU_1 AAL Ipsi 0.32883623200241985
+ABIDEII-SDSU_1 AAL Ipsi 0.35633502722323046
+ABIDEII-SDSU_1 AAL Ipsi 0.5247390490018149
+ABIDEII-SDSU_1 AAL Ipsi 0.1913207985480944
+ABIDEII-SDSU_1 AAL Ipsi 0.2885882788868724
+ABIDEII-SDSU_1 AAL Ipsi 0.22409949183303085
+ABIDEII-SDSU_1 AAL Ipsi 0.25674861343012706
+ABIDEII-SDSU_1 AAL Ipsi 0.2971504782214156
+ABIDEII-SDSU_1 AAL Ipsi 0.25118071476104054
+ABIDEII-SDSU_1 AAL Ipsi 0.19946140865093767
+ABIDEII-SDSU_1 AAL Ipsi 0.4239924295220811
+ABIDEII-SDSU_1 AAL Hom 0.4616231929824562
+ABIDEII-SDSU_1 AAL Hom 0.3889539649122807
+ABIDEII-SDSU_1 AAL Hom 0.3991064210526316
+ABIDEII-SDSU_1 AAL Hom 0.45668705263157894
+ABIDEII-SDSU_1 AAL Hom 0.3300754912280702
+ABIDEII-SDSU_1 AAL Hom 0.4898139649122807
+ABIDEII-SDSU_1 AAL Hom 0.5920026315789475
+ABIDEII-SDSU_1 AAL Hom 0.2989598596491228
+ABIDEII-SDSU_1 AAL Hom 0.34834598245614035
+ABIDEII-SDSU_1 AAL Hom 0.42358657894736845
+ABIDEII-SDSU_1 AAL Hom 0.36442157894736843
+ABIDEII-SDSU_1 AAL Hom 0.34356663157894735
+ABIDEII-SDSU_1 AAL Hom 0.6154986842105263
+ABIDEII-SDSU_1 AAL Hom 0.4779528070175439
+ABIDEII-SDSU_1 AAL Hom 0.5150889999999999
+ABIDEII-SDSU_1 AAL Hom 0.6476836140350877
+ABIDEII-SDSU_1 AAL Hom 0.3197938421052632
+ABIDEII-SDSU_1 AAL Hom 0.40831198245614037
+ABIDEII-SDSU_1 AAL Hom 0.37492607017543866
+ABIDEII-SDSU_1 AAL Hom 0.4075557192982456
+ABIDEII-SDSU_1 AAL Hom 0.45298047368421046
+ABIDEII-SDSU_1 AAL Hom 0.44154026315789474
+ABIDEII-SDSU_1 AAL Hom 0.27197684210526313
+ABIDEII-SDSU_1 AAL Hom 0.5274668771929825
+ABIDEII-SDSU_1 AAL Contra 0.2857859123072271
+ABIDEII-SDSU_1 AAL Contra 0.23754082189295433
+ABIDEII-SDSU_1 AAL Contra 0.250262144239492
+ABIDEII-SDSU_1 AAL Contra 0.31969910069549434
+ABIDEII-SDSU_1 AAL Contra 0.19580698548533415
+ABIDEII-SDSU_1 AAL Contra 0.34716777290595713
+ABIDEII-SDSU_1 AAL Contra 0.45734675446023587
+ABIDEII-SDSU_1 AAL Contra 0.20375775869368007
+ABIDEII-SDSU_1 AAL Contra 0.22690221832476565
+ABIDEII-SDSU_1 AAL Contra 0.23040971635923801
+ABIDEII-SDSU_1 AAL Contra 0.21928829603870575
+ABIDEII-SDSU_1 AAL Contra 0.1904894711218627
+ABIDEII-SDSU_1 AAL Contra 0.47566982280012093
+ABIDEII-SDSU_1 AAL Contra 0.3186050356818869
+ABIDEII-SDSU_1 AAL Contra 0.3421749785303901
+ABIDEII-SDSU_1 AAL Contra 0.5207184662836407
+ABIDEII-SDSU_1 AAL Contra 0.18559128424553975
+ABIDEII-SDSU_1 AAL Contra 0.2736619401270033
+ABIDEII-SDSU_1 AAL Contra 0.21916723283943154
+ABIDEII-SDSU_1 AAL Contra 0.2421910184457212
+ABIDEII-SDSU_1 AAL Contra 0.2955324526761415
+ABIDEII-SDSU_1 AAL Contra 0.24803234109464772
+ABIDEII-SDSU_1 AAL Contra 0.1888579618990021
+ABIDEII-SDSU_1 AAL Contra 0.3987675618385243
+Dataset Atlas Type Value
+ABIDEII-TCD_1 AAL Ipsi 0.21824332577132485
+ABIDEII-TCD_1 AAL Ipsi 0.22533591258318209
+ABIDEII-TCD_1 AAL Ipsi 0.5493456282516637
+ABIDEII-TCD_1 AAL Ipsi 0.2745607894736842
+ABIDEII-TCD_1 AAL Ipsi 0.3326939948578343
+ABIDEII-TCD_1 AAL Ipsi 0.28927503811252264
+ABIDEII-TCD_1 AAL Ipsi 0.2445674056261343
+ABIDEII-TCD_1 AAL Ipsi 0.21479261857229281
+ABIDEII-TCD_1 AAL Ipsi 0.3211142377495463
+ABIDEII-TCD_1 AAL Ipsi 0.33108790229885054
+ABIDEII-TCD_1 AAL Ipsi 0.32896212431941924
+ABIDEII-TCD_1 AAL Ipsi 0.27184172958257713
+ABIDEII-TCD_1 AAL Ipsi 0.24922382123411976
+ABIDEII-TCD_1 AAL Ipsi 0.2188007764670296
+ABIDEII-TCD_1 AAL Ipsi 0.22712471990320626
+ABIDEII-TCD_1 AAL Ipsi 0.5030147562008469
+ABIDEII-TCD_1 AAL Ipsi 0.25027180671506355
+ABIDEII-TCD_1 AAL Ipsi 0.22219959739866907
+ABIDEII-TCD_1 AAL Ipsi 0.3581492540834846
+ABIDEII-TCD_1 AAL Ipsi 0.29402595220810646
+ABIDEII-TCD_1 AAL Ipsi 0.27390912885662433
+ABIDEII-TCD_1 AAL Hom 0.3808311929824561
+ABIDEII-TCD_1 AAL Hom 0.37644829824561404
+ABIDEII-TCD_1 AAL Hom 0.6599616140350876
+ABIDEII-TCD_1 AAL Hom 0.36662068421052635
+ABIDEII-TCD_1 AAL Hom 0.4696298596491228
+ABIDEII-TCD_1 AAL Hom 0.4477282807017544
+ABIDEII-TCD_1 AAL Hom 0.42437610526315783
+ABIDEII-TCD_1 AAL Hom 0.3371572280701754
+ABIDEII-TCD_1 AAL Hom 0.4247804561403509
+ABIDEII-TCD_1 AAL Hom 0.4136992807017544
+ABIDEII-TCD_1 AAL Hom 0.5090051754385965
+ABIDEII-TCD_1 AAL Hom 0.3816183333333333
+ABIDEII-TCD_1 AAL Hom 0.3983633157894737
+ABIDEII-TCD_1 AAL Hom 0.32485222807017544
+ABIDEII-TCD_1 AAL Hom 0.36025580701754384
+ABIDEII-TCD_1 AAL Hom 0.6211481052631579
+ABIDEII-TCD_1 AAL Hom 0.34512398245614034
+ABIDEII-TCD_1 AAL Hom 0.361585
+ABIDEII-TCD_1 AAL Hom 0.5167584561403509
+ABIDEII-TCD_1 AAL Hom 0.4622942982456141
+ABIDEII-TCD_1 AAL Hom 0.4282986315789474
+ABIDEII-TCD_1 AAL Contra 0.21569632355609317
+ABIDEII-TCD_1 AAL Contra 0.2208764463259752
+ABIDEII-TCD_1 AAL Contra 0.5443963794980345
+ABIDEII-TCD_1 AAL Contra 0.2611441200483822
+ABIDEII-TCD_1 AAL Contra 0.3255722546114303
+ABIDEII-TCD_1 AAL Contra 0.27648770365890535
+ABIDEII-TCD_1 AAL Contra 0.23886441699425456
+ABIDEII-TCD_1 AAL Contra 0.2118760350771092
+ABIDEII-TCD_1 AAL Contra 0.3117838373147868
+ABIDEII-TCD_1 AAL Contra 0.3139566093135773
+ABIDEII-TCD_1 AAL Contra 0.3221642515875416
+ABIDEII-TCD_1 AAL Contra 0.26341234502570304
+ABIDEII-TCD_1 AAL Contra 0.2463864980344723
+ABIDEII-TCD_1 AAL Contra 0.21401992621711521
+ABIDEII-TCD_1 AAL Contra 0.2187831850619897
+ABIDEII-TCD_1 AAL Contra 0.49921372422134863
+ABIDEII-TCD_1 AAL Contra 0.24163405261566373
+ABIDEII-TCD_1 AAL Contra 0.21688011097671606
+ABIDEII-TCD_1 AAL Contra 0.3561402485636529
+ABIDEII-TCD_1 AAL Contra 0.2904216504384639
+ABIDEII-TCD_1 AAL Contra 0.26810872301179317
diff --git a/docs/paper/data/func_ipsi_dkt.csv b/docs/paper/data/func_ipsi_dkt.csv
new file mode 100644
index 000000000..6ae412e78
--- /dev/null
+++ b/docs/paper/data/func_ipsi_dkt.csv
@@ -0,0 +1,10519 @@
+Dataset Atlas Type Value
+SWU4 DKT Ipsi 0.443298787804878
+SWU4 DKT Ipsi 0.2922447719512195
+SWU4 DKT Ipsi 0.1994534585365854
+SWU4 DKT Ipsi 0.2488013542682927
+SWU4 DKT Ipsi 0.28799896890243903
+SWU4 DKT Ipsi 0.30758377317073177
+SWU4 DKT Ipsi 0.2539547652439024
+SWU4 DKT Ipsi 0.3019043176829268
+SWU4 DKT Ipsi 0.23373883780487803
+SWU4 DKT Ipsi 0.2871412841463415
+SWU4 DKT Ipsi 0.28648306463414636
+SWU4 DKT Ipsi 0.356762293902439
+SWU4 DKT Ipsi 0.24862822743902438
+SWU4 DKT Ipsi 0.3020969323170732
+SWU4 DKT Ipsi 0.22204714329268296
+SWU4 DKT Ipsi 0.43457717317073175
+SWU4 DKT Ipsi 0.30953659817073176
+SWU4 DKT Ipsi 0.2874761829268293
+SWU4 DKT Ipsi 0.2349501006097561
+SWU4 DKT Ipsi 0.3840465054878049
+SWU4 DKT Ipsi 0.30284435
+SWU4 DKT Ipsi 0.2479606951219512
+SWU4 DKT Ipsi 0.31142737926829267
+SWU4 DKT Ipsi 0.42272334451219507
+SWU4 DKT Ipsi 0.3118367634146341
+SWU4 DKT Ipsi 0.3900783134146341
+SWU4 DKT Ipsi 0.21467803292682927
+SWU4 DKT Ipsi 0.35563449939024394
+SWU4 DKT Ipsi 0.31786567439024394
+SWU4 DKT Ipsi 0.2676005817073171
+SWU4 DKT Ipsi 0.27213716097560975
+SWU4 DKT Ipsi 0.4310471396341463
+SWU4 DKT Ipsi 0.3619899231707317
+SWU4 DKT Ipsi 0.32007616097560976
+SWU4 DKT Ipsi 0.283979543902439
+SWU4 DKT Ipsi 0.22064899451219513
+SWU4 DKT Ipsi 0.4704378195121951
+SWU4 DKT Ipsi 0.3351610243902439
+SWU4 DKT Ipsi 0.26673423536585367
+SWU4 DKT Ipsi 0.2988201713414634
+SWU4 DKT Ipsi 0.3959714256097561
+SWU4 DKT Ipsi 0.3664582518292683
+SWU4 DKT Ipsi 0.24100432682926828
+SWU4 DKT Ipsi 0.24338885670731705
+SWU4 DKT Ipsi 0.25121259024390247
+SWU4 DKT Ipsi 0.299789731097561
+SWU4 DKT Ipsi 0.296802968902439
+SWU4 DKT Ipsi 0.312689712804878
+SWU4 DKT Ipsi 0.2720604993902439
+SWU4 DKT Ipsi 0.3606258243902439
+SWU4 DKT Ipsi 0.3033203347560976
+SWU4 DKT Ipsi 0.2697870804878049
+SWU4 DKT Ipsi 0.4044508371951219
+SWU4 DKT Ipsi 0.2605105640243902
+SWU4 DKT Ipsi 0.3014274042682927
+SWU4 DKT Ipsi 0.36352311097560985
+SWU4 DKT Ipsi 0.31347180182926826
+SWU4 DKT Ipsi 0.39725721829268285
+SWU4 DKT Ipsi 0.3871581731707317
+SWU4 DKT Ipsi 0.35198902621951217
+SWU4 DKT Ipsi 0.5013712701219512
+SWU4 DKT Ipsi 0.3571640384146342
+SWU4 DKT Ipsi 0.26130246036585364
+SWU4 DKT Ipsi 0.3074047298780488
+SWU4 DKT Ipsi 0.2596395292682927
+SWU4 DKT Ipsi 0.2644848969512195
+SWU4 DKT Ipsi 0.32264627500000004
+SWU4 DKT Ipsi 0.26996001341463416
+SWU4 DKT Ipsi 0.28518550670731707
+SWU4 DKT Ipsi 0.3836036310975609
+SWU4 DKT Ipsi 0.4265231573170732
+SWU4 DKT Ipsi 0.371978068902439
+SWU4 DKT Ipsi 0.24728157378048782
+SWU4 DKT Ipsi 0.24315316951219515
+SWU4 DKT Ipsi 0.4243102304878049
+SWU4 DKT Ipsi 0.4837301695121951
+SWU4 DKT Ipsi 0.24169934451219513
+SWU4 DKT Ipsi 0.3539884286585366
+SWU4 DKT Ipsi 0.28625586402439024
+SWU4 DKT Ipsi 0.43665622012195127
+SWU4 DKT Ipsi 0.2406213225609756
+SWU4 DKT Ipsi 0.25274958658536584
+SWU4 DKT Ipsi 0.33619910853658536
+SWU4 DKT Ipsi 0.3156367487804878
+SWU4 DKT Ipsi 0.3387759579268293
+SWU4 DKT Ipsi 0.3064014115853658
+SWU4 DKT Ipsi 0.236804293902439
+SWU4 DKT Ipsi 0.36064569634146343
+SWU4 DKT Ipsi 0.4125058396341464
+SWU4 DKT Ipsi 0.311030581097561
+SWU4 DKT Ipsi 0.275579525
+SWU4 DKT Ipsi 0.3387337554878049
+SWU4 DKT Ipsi 0.27394228658536585
+SWU4 DKT Ipsi 0.3274489603658537
+SWU4 DKT Ipsi 0.2569124182926829
+SWU4 DKT Ipsi 0.22582880548780487
+SWU4 DKT Ipsi 0.28653514268292685
+SWU4 DKT Ipsi 0.2677726128048781
+SWU4 DKT Ipsi 0.3679145396341463
+SWU4 DKT Ipsi 0.25623299512195125
+SWU4 DKT Ipsi 0.3261388024390244
+SWU4 DKT Ipsi 0.38585114634146345
+SWU4 DKT Ipsi 0.34028171158536585
+SWU4 DKT Ipsi 0.3294177731707317
+SWU4 DKT Ipsi 0.2731192225609756
+SWU4 DKT Ipsi 0.27952511890243903
+SWU4 DKT Ipsi 0.32132235731707315
+SWU4 DKT Ipsi 0.2767476908536585
+SWU4 DKT Ipsi 0.36907945000000003
+SWU4 DKT Ipsi 0.29482987987804876
+SWU4 DKT Ipsi 0.2777600548780488
+SWU4 DKT Ipsi 0.2487944817073171
+SWU4 DKT Ipsi 0.25969746463414634
+SWU4 DKT Ipsi 0.24351843902439022
+SWU4 DKT Ipsi 0.46580889756097565
+SWU4 DKT Ipsi 0.23366178170731708
+SWU4 DKT Ipsi 0.29054620731707315
+SWU4 DKT Ipsi 0.3467948798780488
+SWU4 DKT Ipsi 0.356464756097561
+SWU4 DKT Ipsi 0.2668137963414634
+SWU4 DKT Ipsi 0.30845187682926833
+SWU4 DKT Ipsi 0.38230723963414626
+SWU4 DKT Ipsi 0.3135175701219512
+SWU4 DKT Ipsi 0.40051899146341463
+SWU4 DKT Ipsi 0.267879181097561
+SWU4 DKT Ipsi 0.33855292012195126
+SWU4 DKT Ipsi 0.33637747621951225
+SWU4 DKT Ipsi 0.2235803408536585
+SWU4 DKT Ipsi 0.28302182926829267
+SWU4 DKT Ipsi 0.42568980426829267
+SWU4 DKT Ipsi 0.5186591847560975
+SWU4 DKT Ipsi 0.2451270707317073
+SWU4 DKT Ipsi 0.3540149347560975
+SWU4 DKT Ipsi 0.3093582804878049
+SWU4 DKT Ipsi 0.2573439780487805
+SWU4 DKT Ipsi 0.3213216115853659
+SWU4 DKT Ipsi 0.27647481219512193
+SWU4 DKT Ipsi 0.36162960609756095
+SWU4 DKT Ipsi 0.2752902506097561
+SWU4 DKT Ipsi 0.24877060243902438
+SWU4 DKT Ipsi 0.3545389536585366
+SWU4 DKT Ipsi 0.388078875
+SWU4 DKT Ipsi 0.3238845487804878
+SWU4 DKT Ipsi 0.2743933304878049
+SWU4 DKT Ipsi 0.24550850914634142
+SWU4 DKT Ipsi 0.3117477835365854
+SWU4 DKT Ipsi 0.2526509670731707
+SWU4 DKT Ipsi 0.4536560426829268
+SWU4 DKT Ipsi 0.35331056097560976
+SWU4 DKT Ipsi 0.24861051219512192
+SWU4 DKT Ipsi 0.22978885975609759
+SWU4 DKT Ipsi 0.38527177256097556
+SWU4 DKT Ipsi 0.29606241585365856
+SWU4 DKT Ipsi 0.2705034152439024
+SWU4 DKT Ipsi 0.38807716585365853
+SWU4 DKT Ipsi 0.25290387743902437
+SWU4 DKT Ipsi 0.31452648719512194
+SWU4 DKT Ipsi 0.30245797987804873
+SWU4 DKT Ipsi 0.3590882609756097
+SWU4 DKT Ipsi 0.2847795475609756
+SWU4 DKT Ipsi 0.2971672621951219
+SWU4 DKT Ipsi 0.42666660060975614
+SWU4 DKT Ipsi 0.35571116524390245
+SWU4 DKT Ipsi 0.4588142195121951
+SWU4 DKT Ipsi 0.22850003902439023
+SWU4 DKT Ipsi 0.30012505182926835
+SWU4 DKT Ipsi 0.40238985670731703
+SWU4 DKT Ipsi 0.2545207286585366
+SWU4 DKT Ipsi 0.29428174390243905
+SWU4 DKT Ipsi 0.32917827560975615
+SWU4 DKT Ipsi 0.3130885896341463
+SWU4 DKT Ipsi 0.3326403890243903
+SWU4 DKT Ipsi 0.27772361097560977
+SWU4 DKT Ipsi 0.3013346170731708
+SWU4 DKT Ipsi 0.3530477670731707
+SWU4 DKT Ipsi 0.3410775414634146
+SWU4 DKT Ipsi 0.24312874756097558
+SWU4 DKT Ipsi 0.29349231707317075
+SWU4 DKT Ipsi 0.3142324152439025
+SWU4 DKT Ipsi 0.5200707926829268
+SWU4 DKT Ipsi 0.2812380646341463
+SWU4 DKT Ipsi 0.2828753067073171
+SWU4 DKT Ipsi 0.3606366176829268
+SWU4 DKT Ipsi 0.24445317560975613
+SWU4 DKT Ipsi 0.30917108597560977
+SWU4 DKT Ipsi 0.2734876085365854
+SWU4 DKT Ipsi 0.2108256475609756
+SWU4 DKT Ipsi 0.2865199323170732
+SWU4 DKT Ipsi 0.34795705792682924
+SWU4 DKT Ipsi 0.3355187554878049
+SWU4 DKT Ipsi 0.27815638658536584
+SWU4 DKT Ipsi 0.4781765682926829
+SWU4 DKT Ipsi 0.2822662585365853
+SWU4 DKT Ipsi 0.4242237341463414
+SWU4 DKT Ipsi 0.30466270609756096
+SWU4 DKT Ipsi 0.3413165006097561
+SWU4 DKT Ipsi 0.2226219518292683
+SWU4 DKT Ipsi 0.26201023719512195
+SWU4 DKT Ipsi 0.25145864390243905
+SWU4 DKT Ipsi 0.531347493902439
+SWU4 DKT Ipsi 0.2942818585365854
+SWU4 DKT Ipsi 0.3314000335365854
+SWU4 DKT Ipsi 0.2568945414634146
+SWU4 DKT Ipsi 0.3399376597560975
+SWU4 DKT Ipsi 0.2841699932926829
+SWU4 DKT Ipsi 0.26315231341463413
+SWU4 DKT Ipsi 0.35375915304878053
+SWU4 DKT Ipsi 0.30104666036585365
+SWU4 DKT Ipsi 0.30659900975609755
+SWU4 DKT Ipsi 0.4231997097560976
+SWU4 DKT Ipsi 0.27129366402439026
+SWU4 DKT Ipsi 0.32788310548780486
+SWU4 DKT Ipsi 0.209994787804878
+SWU4 DKT Ipsi 0.34195474451219515
+SWU4 DKT Ipsi 0.26447763048780487
+SWU4 DKT Ipsi 0.3547728445121951
+SWU4 DKT Ipsi 0.35180016585365853
+SWU4 DKT Ipsi 0.28858170792682925
+SWU4 DKT Ipsi 0.2626913609756097
+SWU4 DKT Ipsi 0.2586665012195122
+SWU4 DKT Ipsi 0.24760975365853657
+SWU4 DKT Ipsi 0.2810515359756098
+SWU4 DKT Ipsi 0.35478418231707315
+SWU4 DKT Ipsi 0.2552459853658537
+SWU4 DKT Ipsi 0.3416399274390243
+SWU4 DKT Ipsi 0.3577530231707317
+SWU4 DKT Ipsi 0.25232832987804876
+SWU4 DKT Ipsi 0.31355881036585365
+SWU4 DKT Ipsi 0.2965502993902439
+SWU4 DKT Ipsi 0.32270016158536585
+SWU4 DKT Ipsi 0.213230831097561
+SWU4 DKT Ipsi 0.2775261853658536
+SWU4 DKT Ipsi 0.6567964475609755
+SWU4 DKT Ipsi 0.20603495853658538
+SWU4 DKT Ipsi 0.3002138817073171
+SWU4 DKT Ipsi 0.27740420426829265
+SWU4 DKT Ipsi 0.4019858725609756
+SWU4 DKT Ipsi 0.2684188768292683
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+XHCUMS DKT Contra 0.25173935975609757
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+XHCUMS DKT Contra 0.282031068902439
+XHCUMS DKT Contra 0.43143074146341465
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+XHCUMS DKT Contra 0.3606996335365854
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+XHCUMS DKT Contra 0.27907525121951215
+XHCUMS DKT Contra 0.30915748231707313
+Dataset Atlas Type Value
+UPSM_1 DKT Ipsi 0.47496035304878054
+UPSM_1 DKT Ipsi 0.29132091036585367
+UPSM_1 DKT Ipsi 0.23419984207317074
+UPSM_1 DKT Ipsi 0.23920247926829266
+UPSM_1 DKT Ipsi 0.3536992914634146
+UPSM_1 DKT Ipsi 0.35362796646341466
+UPSM_1 DKT Ipsi 0.2982833201219512
+UPSM_1 DKT Ipsi 0.29883898780487805
+UPSM_1 DKT Ipsi 0.2636661664634146
+UPSM_1 DKT Ipsi 0.333775243902439
+UPSM_1 DKT Ipsi 0.29774087865853655
+UPSM_1 DKT Ipsi 0.29081869634146335
+UPSM_1 DKT Ipsi 0.25104957195121946
+UPSM_1 DKT Ipsi 0.47496035304878054
+UPSM_1 DKT Ipsi 0.42310768109756103
+UPSM_1 DKT Ipsi 0.4123091878048781
+UPSM_1 DKT Ipsi 0.3803103847560976
+UPSM_1 DKT Ipsi 0.22996193780487803
+UPSM_1 DKT Ipsi 0.5872787445121951
+UPSM_1 DKT Ipsi 0.2474373243902439
+UPSM_1 DKT Ipsi 0.35932197195121945
+UPSM_1 DKT Ipsi 0.23693289024390243
+UPSM_1 DKT Ipsi 0.2412467707317073
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+UPSM_1 DKT Ipsi 0.2906980615853659
+UPSM_1 DKT Ipsi 0.378011712195122
+UPSM_1 DKT Ipsi 0.35762398048780486
+UPSM_1 DKT Ipsi 0.42301150914634145
+UPSM_1 DKT Ipsi 0.3550018115853658
+UPSM_1 DKT Ipsi 0.41517708658536584
+UPSM_1 DKT Ipsi 0.3077789262195122
+UPSM_1 DKT Ipsi 0.24260883963414637
+UPSM_1 DKT Ipsi 0.3636929451219512
+UPSM_1 DKT Ipsi 0.38332652865853656
+UPSM_1 DKT Ipsi 0.2941112487804878
+UPSM_1 DKT Ipsi 0.286178625
+UPSM_1 DKT Ipsi 0.42154218780487807
+UPSM_1 DKT Ipsi 0.2835289371951219
+UPSM_1 DKT Ipsi 0.40860944146341466
+UPSM_1 DKT Ipsi 0.20652841036585365
+UPSM_1 DKT Ipsi 0.28243309024390245
+UPSM_1 DKT Ipsi 0.2322233835365854
+UPSM_1 DKT Ipsi 0.5102223957317074
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+UPSM_1 DKT Ipsi 0.2723355262195122
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+UPSM_1 DKT Ipsi 0.4362689408536585
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+UPSM_1 DKT Ipsi 0.5030662012195122
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+UPSM_1 DKT Ipsi 0.33939722926829274
+UPSM_1 DKT Ipsi 0.254337331097561
+UPSM_1 DKT Ipsi 0.40183163963414636
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+UPSM_1 DKT Ipsi 0.40215180914634147
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+UPSM_1 DKT Ipsi 0.37169942987804877
+UPSM_1 DKT Ipsi 0.2713394835365854
+UPSM_1 DKT Ipsi 0.3099987158536585
+UPSM_1 DKT Ipsi 0.4214458384146341
+UPSM_1 DKT Ipsi 0.2743583341463415
+UPSM_1 DKT Ipsi 0.5365742024390243
+UPSM_1 DKT Ipsi 0.2891945024390244
+UPSM_1 DKT Ipsi 0.44370791707317075
+UPSM_1 DKT Ipsi 0.22219341158536585
+UPSM_1 DKT Ipsi 0.3214218030487805
+UPSM_1 DKT Ipsi 0.3178439359756097
+UPSM_1 DKT Ipsi 0.27226103597560974
+UPSM_1 DKT Ipsi 0.2556126493902439
+UPSM_1 DKT Ipsi 0.229974675
+UPSM_1 DKT Ipsi 0.34532572073170736
+UPSM_1 DKT Ipsi 0.567958850609756
+UPSM_1 DKT Ipsi 0.41381783353658536
+UPSM_1 DKT Ipsi 0.2038761
+UPSM_1 DKT Ipsi 0.38007753353658535
+UPSM_1 DKT Ipsi 0.3860565530487805
+UPSM_1 DKT Ipsi 0.34509434085365853
+UPSM_1 DKT Ipsi 0.4311094414634147
+UPSM_1 DKT Ipsi 0.3136765707317073
+UPSM_1 DKT Ipsi 0.2132334493902439
+UPSM_1 DKT Ipsi 0.4595884506097561
+UPSM_1 DKT Ipsi 0.24830345853658536
+UPSM_1 DKT Ipsi 0.2678977835365854
+UPSM_1 DKT Ipsi 0.390411293902439
+UPSM_1 DKT Ipsi 0.2624917676829268
+UPSM_1 DKT Ipsi 0.28876692256097564
+UPSM_1 DKT Ipsi 0.28109098170731706
+UPSM_1 DKT Ipsi 0.37311232195121946
+UPSM_1 DKT Ipsi 0.27706576951219514
+UPSM_1 DKT Ipsi 0.23827443414634145
+UPSM_1 DKT Ipsi 0.3459132786585366
+UPSM_1 DKT Ipsi 0.27227326219512193
+UPSM_1 DKT Ipsi 0.4255972981707318
+UPSM_1 DKT Ipsi 0.4741059237804878
+UPSM_1 DKT Ipsi 0.23887152012195123
+UPSM_1 DKT Ipsi 0.2661357024390244
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+UPSM_1 DKT Ipsi 0.4186849408536585
+UPSM_1 DKT Ipsi 0.3302351804878049
+UPSM_1 DKT Ipsi 0.2503706859756098
+UPSM_1 DKT Ipsi 0.23963943719512198
+UPSM_1 DKT Ipsi 0.4103385304878049
+UPSM_1 DKT Ipsi 0.37939380426829267
+UPSM_1 DKT Ipsi 0.2727442402439025
+UPSM_1 DKT Ipsi 0.2230258993902439
+UPSM_1 DKT Ipsi 0.3764519713414634
+UPSM_1 DKT Ipsi 0.4200548945121951
+UPSM_1 DKT Ipsi 0.4638822378048781
+UPSM_1 DKT Ipsi 0.26714981585365855
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+Dataset Atlas Type Value
+SWU1 DKT Ipsi 0.4602719591463415
+SWU1 DKT Ipsi 0.2706051713414634
+SWU1 DKT Ipsi 0.2991530408536585
+SWU1 DKT Ipsi 0.2717023524390244
+SWU1 DKT Ipsi 0.36988681097560977
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+SWU1 DKT Ipsi 0.3227541158536586
+SWU1 DKT Ipsi 0.35218641951219515
+SWU1 DKT Ipsi 0.40210056341463407
+SWU1 DKT Ipsi 0.27893414329268296
+SWU1 DKT Ipsi 0.3887705890243902
+SWU1 DKT Ipsi 0.2755574225609756
+SWU1 DKT Ipsi 0.36422692256097555
+SWU1 DKT Ipsi 0.2550899640243902
+SWU1 DKT Ipsi 0.3431850006097561
+SWU1 DKT Ipsi 0.35671144878048777
+SWU1 DKT Ipsi 0.26466099146341465
+SWU1 DKT Ipsi 0.2362433182926829
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+SWU1 DKT Ipsi 0.2915275463414634
+SWU1 DKT Ipsi 0.39616046280487804
+SWU1 DKT Ipsi 0.47073161524390245
+SWU1 DKT Ipsi 0.3873103926829269
+SWU1 DKT Ipsi 0.25666080670731706
+SWU1 DKT Ipsi 0.3295772402439025
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+SWU1 DKT Ipsi 0.3172063817073171
+SWU1 DKT Ipsi 0.2802605774390244
+SWU1 DKT Ipsi 0.34334661890243906
+SWU1 DKT Ipsi 0.3895964853658536
+SWU1 DKT Ipsi 0.24534470609756098
+SWU1 DKT Ipsi 0.3423166951219513
+SWU1 DKT Ipsi 0.422994625
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+SWU1 DKT Ipsi 0.26805265792682925
+SWU1 DKT Ipsi 0.2790270036585366
+SWU1 DKT Ipsi 0.3273197597560976
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+SWU1 DKT Ipsi 0.35438934695121954
+SWU1 DKT Ipsi 0.24094013902439024
+SWU1 DKT Ipsi 0.26018413170731713
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+SWU1 DKT Ipsi 0.28515434024390246
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+SWU1 DKT Ipsi 0.23077667500000001
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+SWU1 DKT Ipsi 0.38797012804878045
+SWU1 DKT Ipsi 0.24929972926829266
+SWU1 DKT Ipsi 0.41026024207317074
+SWU1 DKT Ipsi 0.3839799451219512
+SWU1 DKT Ipsi 0.29250579329268295
+SWU1 DKT Ipsi 0.3733237359756098
+SWU1 DKT Ipsi 0.46391025487804877
+SWU1 DKT Ipsi 0.4901102347560976
+SWU1 DKT Ipsi 0.26031497073170734
+SWU1 DKT Ipsi 0.2533893829268293
+SWU1 DKT Ipsi 0.31107213414634144
+SWU1 DKT Ipsi 0.39170329146341465
+SWU1 DKT Ipsi 0.26522795060975607
+SWU1 DKT Ipsi 0.41178448780487803
+SWU1 DKT Ipsi 0.29344705792682924
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+SWU1 DKT Ipsi 0.346746875
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+SWU1 DKT Ipsi 0.38932282804878054
+SWU1 DKT Ipsi 0.3879378743902439
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+SWU1 DKT Ipsi 0.27958011463414634
+SWU1 DKT Hom 0.7455984146341463
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+SWU1 DKT Hom 0.6620925121951219
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+BMB_1 DKT Hom 0.6162958536585366
+BMB_1 DKT Hom 0.6934977804878049
+BMB_1 DKT Hom 0.7211990487804878
+BMB_1 DKT Hom 0.7177568780487804
+BMB_1 DKT Hom 0.676881243902439
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+BMB_1 DKT Hom 0.6763502682926829
+BMB_1 DKT Hom 0.733282487804878
+BMB_1 DKT Hom 0.681799536585366
+BMB_1 DKT Hom 0.6360077073170731
+BMB_1 DKT Hom 0.6545732682926829
+BMB_1 DKT Hom 0.7096848780487804
+BMB_1 DKT Hom 0.6430746097560975
+BMB_1 DKT Hom 0.7034735853658536
+BMB_1 DKT Hom 0.7380733658536586
+BMB_1 DKT Hom 0.6449580487804878
+BMB_1 DKT Hom 0.6856576097560976
+BMB_1 DKT Hom 0.7612512195121952
+BMB_1 DKT Hom 0.6821789268292684
+BMB_1 DKT Hom 0.6663415853658536
+BMB_1 DKT Hom 0.7277354878048781
+BMB_1 DKT Hom 0.6658513414634146
+BMB_1 DKT Hom 0.6837603658536585
+BMB_1 DKT Hom 0.7280722439024391
+BMB_1 DKT Hom 0.728197243902439
+BMB_1 DKT Hom 0.695797268292683
+BMB_1 DKT Hom 0.7744303658536584
+BMB_1 DKT Hom 0.7822556097560975
+BMB_1 DKT Hom 0.7222136829268292
+BMB_1 DKT Hom 0.6931321219512195
+BMB_1 DKT Hom 0.6915973902439024
+BMB_1 DKT Hom 0.6959851951219512
+BMB_1 DKT Hom 0.7092039024390244
+BMB_1 DKT Hom 0.6683405609756097
+BMB_1 DKT Hom 0.7827876585365853
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+BMB_1 DKT Hom 0.6449363658536585
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+BMB_1 DKT Hom 0.7150093902439023
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+BMB_1 DKT Hom 0.6832000731707316
+BMB_1 DKT Hom 0.6689276585365853
+BMB_1 DKT Hom 0.6891392926829268
+BMB_1 DKT Hom 0.7279777073170731
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+BMB_1 DKT Hom 0.7145788292682926
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+BMB_1 DKT Hom 0.604501975609756
+BMB_1 DKT Hom 0.655926243902439
+BMB_1 DKT Hom 0.6404438536585366
+BMB_1 DKT Hom 0.7143890731707317
+BMB_1 DKT Hom 0.7529135121951219
+BMB_1 DKT Hom 0.7390005609756097
+BMB_1 DKT Hom 0.6701129268292684
+BMB_1 DKT Hom 0.6628983170731707
+BMB_1 DKT Hom 0.6352189512195122
+BMB_1 DKT Hom 0.7258829512195122
+BMB_1 DKT Hom 0.7156960487804879
+BMB_1 DKT Hom 0.6483600975609757
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+BMB_1 DKT Hom 0.6795654634146341
+BMB_1 DKT Hom 0.6618309268292683
+BMB_1 DKT Hom 0.723006024390244
+BMB_1 DKT Hom 0.7780934146341464
+BMB_1 DKT Hom 0.710999
+BMB_1 DKT Hom 0.7245046585365854
+BMB_1 DKT Hom 0.7098942195121951
+BMB_1 DKT Hom 0.6490216829268292
+BMB_1 DKT Hom 0.683661243902439
+BMB_1 DKT Hom 0.6545727073170732
+BMB_1 DKT Hom 0.7243613658536585
+BMB_1 DKT Hom 0.6713014878048782
+BMB_1 DKT Hom 0.7273041219512195
+BMB_1 DKT Hom 0.6660846585365854
+BMB_1 DKT Hom 0.6949098536585365
+BMB_1 DKT Hom 0.6983637317073171
+BMB_1 DKT Hom 0.6698672926829269
+BMB_1 DKT Hom 0.6658308780487805
+BMB_1 DKT Hom 0.6765894634146341
+BMB_1 DKT Hom 0.6682533170731708
+BMB_1 DKT Hom 0.6451046097560975
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+BMB_1 DKT Hom 0.6865488048780488
+BMB_1 DKT Hom 0.6450732439024389
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+BMB_1 DKT Hom 0.6178463902439024
+BMB_1 DKT Hom 0.6579385853658537
+BMB_1 DKT Hom 0.7110844390243902
+BMB_1 DKT Hom 0.7073545365853658
+BMB_1 DKT Hom 0.6395057073170732
+BMB_1 DKT Hom 0.6831390487804878
+BMB_1 DKT Hom 0.6507140975609756
+BMB_1 DKT Hom 0.6876204390243902
+BMB_1 DKT Hom 0.67684
+BMB_1 DKT Hom 0.7176348292682927
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+BMB_1 DKT Contra 0.3692315371951219
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+BMB_1 DKT Contra 0.21522045304878046
+BMB_1 DKT Contra 0.2670697780487805
+BMB_1 DKT Contra 0.3994336268292683
+BMB_1 DKT Contra 0.4114905939024391
+BMB_1 DKT Contra 0.46767954207317075
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+BMB_1 DKT Contra 0.31697665975609757
+BMB_1 DKT Contra 0.3852813957317073
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+BMB_1 DKT Contra 0.3568384640243903
+BMB_1 DKT Contra 0.2459796981707317
+BMB_1 DKT Contra 0.27450918902439025
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+BMB_1 DKT Contra 0.36923631158536585
+BMB_1 DKT Contra 0.297237181097561
+BMB_1 DKT Contra 0.29432040000000004
+BMB_1 DKT Contra 0.2780695774390244
+BMB_1 DKT Contra 0.26325040182926823
+BMB_1 DKT Contra 0.26199019268292684
+BMB_1 DKT Contra 0.36460285853658536
+BMB_1 DKT Contra 0.30178925975609755
+BMB_1 DKT Contra 0.3168308091463415
+BMB_1 DKT Contra 0.26773075487804876
+BMB_1 DKT Contra 0.22170079512195123
+BMB_1 DKT Contra 0.29935472134146346
+BMB_1 DKT Contra 0.2536113542682927
+BMB_1 DKT Contra 0.343638087195122
+BMB_1 DKT Contra 0.32170027987804883
+BMB_1 DKT Contra 0.3238798359756097
+BMB_1 DKT Contra 0.23761735
+BMB_1 DKT Contra 0.22225869573170734
+BMB_1 DKT Contra 0.25383139207317074
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+BMB_1 DKT Contra 0.25876004390243906
+BMB_1 DKT Contra 0.21531152987804877
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+BMB_1 DKT Contra 0.24672817439024386
+BMB_1 DKT Contra 0.20464326768292682
+BMB_1 DKT Contra 0.2294058524390244
+BMB_1 DKT Contra 0.22675154268292683
+BMB_1 DKT Contra 0.24380765914634145
+BMB_1 DKT Contra 0.37314468353658536
+BMB_1 DKT Contra 0.2850120676829268
+BMB_1 DKT Contra 0.2897654280487805
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+BMB_1 DKT Contra 0.28452757682926827
+BMB_1 DKT Contra 0.3169783658536586
+BMB_1 DKT Contra 0.3161916780487804
+Dataset Atlas Type Value
+IBATRT DKT Ipsi 0.33536575853658535
+IBATRT DKT Ipsi 0.3542546640243902
+IBATRT DKT Ipsi 0.30910885365853663
+IBATRT DKT Ipsi 0.4360935420731708
+IBATRT DKT Ipsi 0.47296651768292686
+IBATRT DKT Ipsi 0.3608698993902439
+IBATRT DKT Ipsi 0.3699834402439024
+IBATRT DKT Ipsi 0.3756985865853658
+IBATRT DKT Ipsi 0.4556247323170732
+IBATRT DKT Ipsi 0.34453644085365853
+IBATRT DKT Ipsi 0.2706389945121951
+IBATRT DKT Ipsi 0.30365447926829264
+IBATRT DKT Ipsi 0.3709099140243903
+IBATRT DKT Ipsi 0.5177684969512195
+IBATRT DKT Ipsi 0.37331819268292676
+IBATRT DKT Ipsi 0.256730175
+IBATRT DKT Ipsi 0.3428609902439025
+IBATRT DKT Ipsi 0.27225431158536584
+IBATRT DKT Ipsi 0.2525651317073171
+IBATRT DKT Ipsi 0.40908569634146347
+IBATRT DKT Ipsi 0.32778869146341466
+IBATRT DKT Ipsi 0.3528703567073171
+IBATRT DKT Ipsi 0.3546029371951219
+IBATRT DKT Ipsi 0.311250875
+IBATRT DKT Ipsi 0.45613594695121956
+IBATRT DKT Ipsi 0.29030230670731705
+IBATRT DKT Ipsi 0.4198855878048781
+IBATRT DKT Ipsi 0.3063518256097561
+IBATRT DKT Ipsi 0.37673050670731706
+IBATRT DKT Ipsi 0.342168443902439
+IBATRT DKT Ipsi 0.3591004987804878
+IBATRT DKT Ipsi 0.3208408756097561
+IBATRT DKT Ipsi 0.3176489695121951
+IBATRT DKT Ipsi 0.2983154835365854
+IBATRT DKT Ipsi 0.3039517932926829
+IBATRT DKT Ipsi 0.48297497317073174
+IBATRT DKT Ipsi 0.2222062176829268
+IBATRT DKT Ipsi 0.2661866914634146
+IBATRT DKT Ipsi 0.3633220445121952
+IBATRT DKT Ipsi 0.3305330298780488
+IBATRT DKT Ipsi 0.451865193292683
+IBATRT DKT Ipsi 0.49695290914634155
+IBATRT DKT Ipsi 0.4167472042682927
+IBATRT DKT Ipsi 0.4123148134146342
+IBATRT DKT Ipsi 0.5513290347560975
+IBATRT DKT Ipsi 0.47951219146341456
+IBATRT DKT Ipsi 0.3540891256097561
+IBATRT DKT Ipsi 0.37201359451219507
+IBATRT DKT Ipsi 0.3377659859756097
+IBATRT DKT Ipsi 0.2067750323170732
+IBATRT DKT Ipsi 0.3115133256097561
+IBATRT DKT Ipsi 0.3219643384146342
+IBATRT DKT Ipsi 0.37840381280487806
+IBATRT DKT Ipsi 0.3411226231707317
+IBATRT DKT Ipsi 0.5702318414634147
+IBATRT DKT Ipsi 0.33244114207317077
+IBATRT DKT Ipsi 0.5057413902439024
+IBATRT DKT Ipsi 0.2742617713414634
+IBATRT DKT Ipsi 0.34578716219512196
+IBATRT DKT Ipsi 0.4522633548780488
+IBATRT DKT Ipsi 0.2995283256097561
+IBATRT DKT Ipsi 0.3120620237804878
+IBATRT DKT Ipsi 0.4471308676829268
+IBATRT DKT Ipsi 0.38378476829268293
+IBATRT DKT Ipsi 0.5506179317073171
+IBATRT DKT Ipsi 0.31895538109756094
+IBATRT DKT Ipsi 0.5419821426829269
+IBATRT DKT Ipsi 0.47315105670731705
+IBATRT DKT Ipsi 0.22379395121951218
+IBATRT DKT Ipsi 0.44729515060975616
+IBATRT DKT Ipsi 0.3371983048780488
+IBATRT DKT Ipsi 0.5258532548780488
+IBATRT DKT Ipsi 0.2915898835365854
+IBATRT DKT Ipsi 0.555831356097561
+IBATRT DKT Ipsi 0.33234654817073167
+IBATRT DKT Ipsi 0.49082941402439023
+IBATRT DKT Ipsi 0.3601752628048781
+IBATRT DKT Ipsi 0.4884868475609756
+IBATRT DKT Ipsi 0.3852564445121951
+IBATRT DKT Ipsi 0.48218081036585364
+IBATRT DKT Ipsi 0.43724103109756096
+IBATRT DKT Ipsi 0.26589516036585364
+IBATRT DKT Ipsi 0.43122558658536586
+IBATRT DKT Ipsi 0.3295176207317073
+IBATRT DKT Ipsi 0.27243881219512195
+IBATRT DKT Ipsi 0.2639816890243903
+IBATRT DKT Ipsi 0.4632434725609756
+IBATRT DKT Ipsi 0.3377987219512195
+IBATRT DKT Ipsi 0.26228487987804877
+IBATRT DKT Ipsi 0.4124357219512195
+IBATRT DKT Ipsi 0.49954145426829266
+IBATRT DKT Ipsi 0.423386468902439
+IBATRT DKT Ipsi 0.29318666036585367
+IBATRT DKT Ipsi 0.33245922439024395
+IBATRT DKT Ipsi 0.2009175542682927
+IBATRT DKT Ipsi 0.30457903414634147
+IBATRT DKT Ipsi 0.3701326603658537
+IBATRT DKT Ipsi 0.3403056176829268
+IBATRT DKT Ipsi 0.3061519695121951
+IBATRT DKT Ipsi 0.196572218902439
+IBATRT DKT Hom 0.766457
+IBATRT DKT Hom 0.7951269512195123
+IBATRT DKT Hom 0.7331307560975611
+IBATRT DKT Hom 0.7736199268292683
+IBATRT DKT Hom 0.792366756097561
+IBATRT DKT Hom 0.6445399756097562
+IBATRT DKT Hom 0.791405268292683
+IBATRT DKT Hom 0.7601890975609756
+IBATRT DKT Hom 0.7965628536585365
+IBATRT DKT Hom 0.748036243902439
+IBATRT DKT Hom 0.7003023170731707
+IBATRT DKT Hom 0.7798581219512196
+IBATRT DKT Hom 0.7085851951219512
+IBATRT DKT Hom 0.7543777804878049
+IBATRT DKT Hom 0.772041487804878
+IBATRT DKT Hom 0.704515268292683
+IBATRT DKT Hom 0.6993420975609757
+IBATRT DKT Hom 0.6810704146341463
+IBATRT DKT Hom 0.7577712682926829
+IBATRT DKT Hom 0.7770777804878048
+IBATRT DKT Hom 0.6857401463414635
+IBATRT DKT Hom 0.7523743658536586
+IBATRT DKT Hom 0.7808729512195122
+IBATRT DKT Hom 0.7738905853658536
+IBATRT DKT Hom 0.7659895853658537
+IBATRT DKT Hom 0.6550349268292682
+IBATRT DKT Hom 0.768119243902439
+IBATRT DKT Hom 0.7306393902439025
+IBATRT DKT Hom 0.7739634634146341
+IBATRT DKT Hom 0.7536820243902439
+IBATRT DKT Hom 0.7506235365853658
+IBATRT DKT Hom 0.7479631219512195
+IBATRT DKT Hom 0.7542539268292682
+IBATRT DKT Hom 0.7840901219512196
+IBATRT DKT Hom 0.7351866097560975
+IBATRT DKT Hom 0.8152231707317072
+IBATRT DKT Hom 0.7486333902439025
+IBATRT DKT Hom 0.7274838780487805
+IBATRT DKT Hom 0.7823289756097561
+IBATRT DKT Hom 0.7580517073170732
+IBATRT DKT Hom 0.7683703902439024
+IBATRT DKT Hom 0.8248818048780486
+IBATRT DKT Hom 0.7500256341463414
+IBATRT DKT Hom 0.8238582195121951
+IBATRT DKT Hom 0.8347706829268292
+IBATRT DKT Hom 0.7954391219512196
+IBATRT DKT Hom 0.755557
+IBATRT DKT Hom 0.7476698292682927
+IBATRT DKT Hom 0.7278460000000001
+IBATRT DKT Hom 0.735323487804878
+IBATRT DKT Hom 0.6859654634146342
+IBATRT DKT Hom 0.747285512195122
+IBATRT DKT Hom 0.7574026097560975
+IBATRT DKT Hom 0.7691381707317074
+IBATRT DKT Hom 0.836217
+IBATRT DKT Hom 0.7191039024390244
+IBATRT DKT Hom 0.8276458780487803
+IBATRT DKT Hom 0.7387176585365853
+IBATRT DKT Hom 0.7195504146341464
+IBATRT DKT Hom 0.811161536585366
+IBATRT DKT Hom 0.6884499024390244
+IBATRT DKT Hom 0.7543623658536585
+IBATRT DKT Hom 0.7727576097560975
+IBATRT DKT Hom 0.782269
+IBATRT DKT Hom 0.7787726829268293
+IBATRT DKT Hom 0.7287151951219513
+IBATRT DKT Hom 0.8013125853658537
+IBATRT DKT Hom 0.8095743414634146
+IBATRT DKT Hom 0.7524689024390244
+IBATRT DKT Hom 0.7880580487804879
+IBATRT DKT Hom 0.758482
+IBATRT DKT Hom 0.7777362682926829
+IBATRT DKT Hom 0.7611921951219512
+IBATRT DKT Hom 0.792793268292683
+IBATRT DKT Hom 0.7585843658536585
+IBATRT DKT Hom 0.8359334146341464
+IBATRT DKT Hom 0.7764483414634146
+IBATRT DKT Hom 0.8122798780487807
+IBATRT DKT Hom 0.7000430975609757
+IBATRT DKT Hom 0.7934496829268292
+IBATRT DKT Hom 0.7741600731707318
+IBATRT DKT Hom 0.7342369024390244
+IBATRT DKT Hom 0.7742665609756099
+IBATRT DKT Hom 0.7937103170731709
+IBATRT DKT Hom 0.7422281707317073
+IBATRT DKT Hom 0.7111027073170733
+IBATRT DKT Hom 0.7921827560975611
+IBATRT DKT Hom 0.6735745609756097
+IBATRT DKT Hom 0.7424897073170732
+IBATRT DKT Hom 0.722868975609756
+IBATRT DKT Hom 0.791803756097561
+IBATRT DKT Hom 0.8036775853658537
+IBATRT DKT Hom 0.6996556097560975
+IBATRT DKT Hom 0.7454374878048781
+IBATRT DKT Hom 0.7240889024390244
+IBATRT DKT Hom 0.759971
+IBATRT DKT Hom 0.7793961951219512
+IBATRT DKT Hom 0.7647443902439024
+IBATRT DKT Hom 0.6762629024390244
+IBATRT DKT Hom 0.7113458780487806
+IBATRT DKT Contra 0.319678468902439
+IBATRT DKT Contra 0.3417687640243902
+IBATRT DKT Contra 0.29577634817073173
+IBATRT DKT Contra 0.41867112012195123
+IBATRT DKT Contra 0.4579813414634147
+IBATRT DKT Contra 0.332764975
+IBATRT DKT Contra 0.3523338317073171
+IBATRT DKT Contra 0.350118225
+IBATRT DKT Contra 0.4385344847560976
+IBATRT DKT Contra 0.3179023512195122
+IBATRT DKT Contra 0.23790545365853658
+IBATRT DKT Contra 0.29850569390243903
+IBATRT DKT Contra 0.32683289024390244
+IBATRT DKT Contra 0.5018321542682926
+IBATRT DKT Contra 0.35974413048780485
+IBATRT DKT Contra 0.231725781097561
+IBATRT DKT Contra 0.29565272256097563
+IBATRT DKT Contra 0.2366880792682927
+IBATRT DKT Contra 0.24059678414634147
+IBATRT DKT Contra 0.3756264176829268
+IBATRT DKT Contra 0.28704254878048785
+IBATRT DKT Contra 0.3354183469512195
+IBATRT DKT Contra 0.3450060335365854
+IBATRT DKT Contra 0.29012943414634146
+IBATRT DKT Contra 0.4220079359756097
+IBATRT DKT Contra 0.24190611585365854
+IBATRT DKT Contra 0.392986775
+IBATRT DKT Contra 0.28526898902439024
+IBATRT DKT Contra 0.36222869085365855
+IBATRT DKT Contra 0.31627627073170733
+IBATRT DKT Contra 0.3416221018292683
+IBATRT DKT Contra 0.31030108414634144
+IBATRT DKT Contra 0.30288269207317076
+IBATRT DKT Contra 0.28705083170731704
+IBATRT DKT Contra 0.28245008841463415
+IBATRT DKT Contra 0.47105943048780485
+IBATRT DKT Contra 0.21168122621951221
+IBATRT DKT Contra 0.24364334817073174
+IBATRT DKT Contra 0.3418730945121951
+IBATRT DKT Contra 0.32060491768292687
+IBATRT DKT Contra 0.4231557865853659
+IBATRT DKT Contra 0.4876734079268293
+IBATRT DKT Contra 0.39530718536585363
+IBATRT DKT Contra 0.3995707457317073
+IBATRT DKT Contra 0.5426910725609756
+IBATRT DKT Contra 0.45545527987804874
+IBATRT DKT Contra 0.34614885914634147
+IBATRT DKT Contra 0.35547780243902444
+IBATRT DKT Contra 0.30118343414634147
+IBATRT DKT Contra 0.19369907378048778
+IBATRT DKT Contra 0.2910675262195122
+IBATRT DKT Contra 0.30188069878048784
+IBATRT DKT Contra 0.3673821987804878
+IBATRT DKT Contra 0.32909060548780483
+IBATRT DKT Contra 0.5567007292682927
+IBATRT DKT Contra 0.3193581414634146
+IBATRT DKT Contra 0.49926640243902437
+IBATRT DKT Contra 0.2511738768292683
+IBATRT DKT Contra 0.32683813780487814
+IBATRT DKT Contra 0.44178448658536584
+IBATRT DKT Contra 0.2599399804878049
+IBATRT DKT Contra 0.28818271768292686
+IBATRT DKT Contra 0.43040398597560975
+IBATRT DKT Contra 0.3593837567073171
+IBATRT DKT Contra 0.517612412195122
+IBATRT DKT Contra 0.3037086737804878
+IBATRT DKT Contra 0.5084561109756097
+IBATRT DKT Contra 0.4607459353658537
+IBATRT DKT Contra 0.21263073963414636
+IBATRT DKT Contra 0.4380391975609756
+IBATRT DKT Contra 0.32182493841463417
+IBATRT DKT Contra 0.4962093073170732
+IBATRT DKT Contra 0.27573713536585365
+IBATRT DKT Contra 0.5469619164634146
+IBATRT DKT Contra 0.3142728890243902
+IBATRT DKT Contra 0.48178500670731705
+IBATRT DKT Contra 0.346240312804878
+IBATRT DKT Contra 0.4787534664634146
+IBATRT DKT Contra 0.33597745365853665
+IBATRT DKT Contra 0.47081486402439027
+IBATRT DKT Contra 0.4274576164634146
+IBATRT DKT Contra 0.2519697682926829
+IBATRT DKT Contra 0.4223553262195122
+IBATRT DKT Contra 0.31543865975609753
+IBATRT DKT Contra 0.2545865323170732
+IBATRT DKT Contra 0.2504487774390244
+IBATRT DKT Contra 0.4411434298780488
+IBATRT DKT Contra 0.29769652378048783
+IBATRT DKT Contra 0.24738134024390243
+IBATRT DKT Contra 0.38394312012195125
+IBATRT DKT Contra 0.489103906707317
+IBATRT DKT Contra 0.4052765737804878
+IBATRT DKT Contra 0.2660973786585366
+IBATRT DKT Contra 0.31148066219512194
+IBATRT DKT Contra 0.18886226707317072
+IBATRT DKT Contra 0.28850142012195124
+IBATRT DKT Contra 0.35813295243902443
+IBATRT DKT Contra 0.32529848292682934
+IBATRT DKT Contra 0.2680177707317073
+IBATRT DKT Contra 0.1832799201219512
+Dataset Atlas Type Value
+IPCAS_6 DKT Ipsi 0.2479907329268293
+IPCAS_6 DKT Ipsi 0.2600645518292683
+IPCAS_6 DKT Ipsi 0.23191913841463416
+IPCAS_6 DKT Ipsi 0.2524622006097561
+IPCAS_6 DKT Ipsi 0.24512818597560976
+IPCAS_6 DKT Ipsi 0.22979690365853658
+IPCAS_6 DKT Ipsi 0.30927486707317076
+IPCAS_6 DKT Ipsi 0.19280610792682928
+IPCAS_6 DKT Ipsi 0.3513490140243903
+IPCAS_6 DKT Ipsi 0.2565043737804878
+IPCAS_6 DKT Ipsi 0.22283319390243903
+IPCAS_6 DKT Ipsi 0.23256685914634145
+IPCAS_6 DKT Ipsi 0.24728142743902443
+IPCAS_6 DKT Ipsi 0.24252458292682927
+IPCAS_6 DKT Ipsi 0.3066386286585366
+IPCAS_6 DKT Ipsi 0.38036597560975605
+IPCAS_6 DKT Ipsi 0.24353676158536586
+IPCAS_6 DKT Ipsi 0.19830161341463415
+IPCAS_6 DKT Ipsi 0.2339608737804878
+IPCAS_6 DKT Ipsi 0.2558429195121951
+IPCAS_6 DKT Ipsi 0.3069387256097561
+IPCAS_6 DKT Ipsi 0.25016840609756097
+IPCAS_6 DKT Ipsi 0.26407026585365856
+IPCAS_6 DKT Ipsi 0.23929646463414633
+IPCAS_6 DKT Ipsi 0.23568531646341462
+IPCAS_6 DKT Ipsi 0.35648082012195126
+IPCAS_6 DKT Ipsi 0.25774301646341463
+IPCAS_6 DKT Ipsi 0.2658165975609756
+IPCAS_6 DKT Ipsi 0.32250449878048787
+IPCAS_6 DKT Ipsi 0.2868496743902439
+IPCAS_6 DKT Ipsi 0.38017793048780485
+IPCAS_6 DKT Ipsi 0.3488367993902439
+IPCAS_6 DKT Ipsi 0.2391655756097561
+IPCAS_6 DKT Ipsi 0.23719993414634144
+IPCAS_6 DKT Ipsi 0.2589632048780488
+IPCAS_6 DKT Ipsi 0.2510459048780488
+IPCAS_6 DKT Ipsi 0.32386138963414635
+IPCAS_6 DKT Ipsi 0.3621932115853659
+IPCAS_6 DKT Ipsi 0.28009243597560973
+IPCAS_6 DKT Ipsi 0.31544003841463414
+IPCAS_6 DKT Ipsi 0.2659712585365854
+IPCAS_6 DKT Ipsi 0.22938456097560977
+IPCAS_6 DKT Ipsi 0.4101816219512195
+IPCAS_6 DKT Ipsi 0.2179984945121951
+IPCAS_6 DKT Ipsi 0.2855062329268293
+IPCAS_6 DKT Ipsi 0.25904086463414633
+IPCAS_6 DKT Ipsi 0.22432059146341463
+IPCAS_6 DKT Ipsi 0.2280840054878049
+IPCAS_6 DKT Ipsi 0.31789186890243903
+IPCAS_6 DKT Ipsi 0.2862403115853659
+IPCAS_6 DKT Ipsi 0.25349273231707314
+IPCAS_6 DKT Ipsi 0.21889695853658536
+IPCAS_6 DKT Ipsi 0.29261913902439024
+IPCAS_6 DKT Ipsi 0.2766963359756098
+IPCAS_6 DKT Ipsi 0.26817629695121953
+IPCAS_6 DKT Ipsi 0.23890945365853655
+IPCAS_6 DKT Ipsi 0.29469215914634145
+IPCAS_6 DKT Ipsi 0.21992169085365856
+IPCAS_6 DKT Ipsi 0.3512598256097561
+IPCAS_6 DKT Ipsi 0.1985122670731707
+IPCAS_6 DKT Ipsi 0.23269391585365853
+IPCAS_6 DKT Ipsi 0.2265288512195122
+IPCAS_6 DKT Ipsi 0.37371737439024394
+IPCAS_6 DKT Ipsi 0.321262625
+IPCAS_6 DKT Ipsi 0.3173668463414634
+IPCAS_6 DKT Ipsi 0.30564948963414634
+IPCAS_6 DKT Ipsi 0.29319875975609755
+IPCAS_6 DKT Ipsi 0.23543346219512196
+IPCAS_6 DKT Ipsi 0.2635044597560976
+IPCAS_6 DKT Ipsi 0.2819845841463414
+IPCAS_6 DKT Ipsi 0.24019670426829268
+IPCAS_6 DKT Ipsi 0.2964429426829268
+IPCAS_6 DKT Ipsi 0.24940510914634142
+IPCAS_6 DKT Ipsi 0.29545868841463413
+IPCAS_6 DKT Ipsi 0.23938801707317076
+IPCAS_6 DKT Ipsi 0.28556705731707316
+IPCAS_6 DKT Ipsi 0.2927775451219512
+IPCAS_6 DKT Ipsi 0.22285303414634144
+IPCAS_6 DKT Ipsi 0.2043506048780488
+IPCAS_6 DKT Ipsi 0.27265540426829266
+IPCAS_6 DKT Ipsi 0.22043422073170735
+IPCAS_6 DKT Ipsi 0.35014460000000003
+IPCAS_6 DKT Ipsi 0.3058094963414634
+IPCAS_6 DKT Ipsi 0.2560015896341463
+IPCAS_6 DKT Ipsi 0.22999465182926832
+IPCAS_6 DKT Ipsi 0.29592695487804876
+IPCAS_6 DKT Ipsi 0.2949572506097561
+IPCAS_6 DKT Ipsi 0.29450208353658536
+IPCAS_6 DKT Ipsi 0.26794210304878047
+IPCAS_6 DKT Ipsi 0.26889970182926826
+IPCAS_6 DKT Hom 0.6049416585365854
+IPCAS_6 DKT Hom 0.6616597073170731
+IPCAS_6 DKT Hom 0.6695081707317073
+IPCAS_6 DKT Hom 0.6054928048780488
+IPCAS_6 DKT Hom 0.7006607804878049
+IPCAS_6 DKT Hom 0.5562399512195122
+IPCAS_6 DKT Hom 0.6613399999999999
+IPCAS_6 DKT Hom 0.678588512195122
+IPCAS_6 DKT Hom 0.6643620487804878
+IPCAS_6 DKT Hom 0.554993243902439
+IPCAS_6 DKT Hom 0.6636918780487804
+IPCAS_6 DKT Hom 0.5809961463414633
+IPCAS_6 DKT Hom 0.6764771951219513
+IPCAS_6 DKT Hom 0.704660487804878
+IPCAS_6 DKT Hom 0.6756288048780488
+IPCAS_6 DKT Hom 0.6899600487804878
+IPCAS_6 DKT Hom 0.6690084878048781
+IPCAS_6 DKT Hom 0.552686024390244
+IPCAS_6 DKT Hom 0.6129047073170731
+IPCAS_6 DKT Hom 0.687924487804878
+IPCAS_6 DKT Hom 0.7222680731707316
+IPCAS_6 DKT Hom 0.549829024390244
+IPCAS_6 DKT Hom 0.5943241707317074
+IPCAS_6 DKT Hom 0.5846125853658536
+IPCAS_6 DKT Hom 0.682817756097561
+IPCAS_6 DKT Hom 0.644784512195122
+IPCAS_6 DKT Hom 0.6905595609756098
+IPCAS_6 DKT Hom 0.625083024390244
+IPCAS_6 DKT Hom 0.6895979756097561
+IPCAS_6 DKT Hom 0.7163578048780488
+IPCAS_6 DKT Hom 0.6686226829268294
+IPCAS_6 DKT Hom 0.6474598536585365
+IPCAS_6 DKT Hom 0.5527484390243903
+IPCAS_6 DKT Hom 0.6942279512195122
+IPCAS_6 DKT Hom 0.6375729268292682
+IPCAS_6 DKT Hom 0.5888874390243902
+IPCAS_6 DKT Hom 0.6035115609756098
+IPCAS_6 DKT Hom 0.7166315853658536
+IPCAS_6 DKT Hom 0.6440210487804878
+IPCAS_6 DKT Hom 0.6998491463414634
+IPCAS_6 DKT Hom 0.7291486341463415
+IPCAS_6 DKT Hom 0.6158894878048782
+IPCAS_6 DKT Hom 0.686392512195122
+IPCAS_6 DKT Hom 0.6872687804878049
+IPCAS_6 DKT Hom 0.7293176341463414
+IPCAS_6 DKT Hom 0.610516
+IPCAS_6 DKT Hom 0.7030460243902439
+IPCAS_6 DKT Hom 0.5106212926829268
+IPCAS_6 DKT Hom 0.6461710731707317
+IPCAS_6 DKT Hom 0.5646443170731706
+IPCAS_6 DKT Hom 0.6760128536585366
+IPCAS_6 DKT Hom 0.6512502926829268
+IPCAS_6 DKT Hom 0.7124576585365853
+IPCAS_6 DKT Hom 0.7169641219512194
+IPCAS_6 DKT Hom 0.6982089024390243
+IPCAS_6 DKT Hom 0.7291594390243902
+IPCAS_6 DKT Hom 0.6908967317073171
+IPCAS_6 DKT Hom 0.549628512195122
+IPCAS_6 DKT Hom 0.662293024390244
+IPCAS_6 DKT Hom 0.5982797804878048
+IPCAS_6 DKT Hom 0.7247862926829269
+IPCAS_6 DKT Hom 0.5874111707317073
+IPCAS_6 DKT Hom 0.6652716097560976
+IPCAS_6 DKT Hom 0.6407441219512195
+IPCAS_6 DKT Hom 0.6913282926829267
+IPCAS_6 DKT Hom 0.6933974390243901
+IPCAS_6 DKT Hom 0.6527037560975609
+IPCAS_6 DKT Hom 0.6638280731707318
+IPCAS_6 DKT Hom 0.7311044878048781
+IPCAS_6 DKT Hom 0.7416627560975609
+IPCAS_6 DKT Hom 0.7037024146341463
+IPCAS_6 DKT Hom 0.5295772439024391
+IPCAS_6 DKT Hom 0.6883092926829268
+IPCAS_6 DKT Hom 0.7112225609756098
+IPCAS_6 DKT Hom 0.6497693170731709
+IPCAS_6 DKT Hom 0.7065765365853659
+IPCAS_6 DKT Hom 0.6944394146341464
+IPCAS_6 DKT Hom 0.5801029024390243
+IPCAS_6 DKT Hom 0.6895287317073171
+IPCAS_6 DKT Hom 0.7086243902439026
+IPCAS_6 DKT Hom 0.6241702195121951
+IPCAS_6 DKT Hom 0.6452812195121952
+IPCAS_6 DKT Hom 0.6933791951219512
+IPCAS_6 DKT Hom 0.5704537560975609
+IPCAS_6 DKT Hom 0.7158834390243903
+IPCAS_6 DKT Hom 0.6227367073170732
+IPCAS_6 DKT Hom 0.6406388292682926
+IPCAS_6 DKT Hom 0.616864756097561
+IPCAS_6 DKT Hom 0.7042056341463415
+IPCAS_6 DKT Hom 0.6140295609756097
+IPCAS_6 DKT Contra 0.23033337195121947
+IPCAS_6 DKT Contra 0.2348828042682927
+IPCAS_6 DKT Contra 0.19959853658536583
+IPCAS_6 DKT Contra 0.2122609780487805
+IPCAS_6 DKT Contra 0.22431688048780488
+IPCAS_6 DKT Contra 0.1976423457317073
+IPCAS_6 DKT Contra 0.2732708823170732
+IPCAS_6 DKT Contra 0.17898645853658535
+IPCAS_6 DKT Contra 0.32221420487804875
+IPCAS_6 DKT Contra 0.22628569268292684
+IPCAS_6 DKT Contra 0.19626942439024392
+IPCAS_6 DKT Contra 0.20142365426829267
+IPCAS_6 DKT Contra 0.22262643658536585
+IPCAS_6 DKT Contra 0.22202838292682925
+IPCAS_6 DKT Contra 0.28330526158536584
+IPCAS_6 DKT Contra 0.3581212268292683
+IPCAS_6 DKT Contra 0.2160570219512195
+IPCAS_6 DKT Contra 0.17423759817073167
+IPCAS_6 DKT Contra 0.2024769731707317
+IPCAS_6 DKT Contra 0.23085023231707316
+IPCAS_6 DKT Contra 0.28691066890243905
+IPCAS_6 DKT Contra 0.21188458414634148
+IPCAS_6 DKT Contra 0.2393951792682927
+IPCAS_6 DKT Contra 0.20892437012195123
+IPCAS_6 DKT Contra 0.21733606219512194
+IPCAS_6 DKT Contra 0.315254225
+IPCAS_6 DKT Contra 0.2357400524390244
+IPCAS_6 DKT Contra 0.23605059999999997
+IPCAS_6 DKT Contra 0.2958078762195122
+IPCAS_6 DKT Contra 0.2542626018292683
+IPCAS_6 DKT Contra 0.35252624817073175
+IPCAS_6 DKT Contra 0.30959975609756096
+IPCAS_6 DKT Contra 0.20911194817073167
+IPCAS_6 DKT Contra 0.2166293067073171
+IPCAS_6 DKT Contra 0.2208398457317073
+IPCAS_6 DKT Contra 0.20980204573170733
+IPCAS_6 DKT Contra 0.29685930060975607
+IPCAS_6 DKT Contra 0.34866445975609756
+IPCAS_6 DKT Contra 0.24845677682926828
+IPCAS_6 DKT Contra 0.2813602390243902
+IPCAS_6 DKT Contra 0.25221782926829267
+IPCAS_6 DKT Contra 0.19878041768292684
+IPCAS_6 DKT Contra 0.38046283658536584
+IPCAS_6 DKT Contra 0.20275635365853656
+IPCAS_6 DKT Contra 0.26904452317073174
+IPCAS_6 DKT Contra 0.23221382926829268
+IPCAS_6 DKT Contra 0.2045066292682927
+IPCAS_6 DKT Contra 0.18778168353658534
+IPCAS_6 DKT Contra 0.29102717317073173
+IPCAS_6 DKT Contra 0.24164097378048782
+IPCAS_6 DKT Contra 0.22525444999999997
+IPCAS_6 DKT Contra 0.1929265548780488
+IPCAS_6 DKT Contra 0.2681881243902439
+IPCAS_6 DKT Contra 0.26098526219512197
+IPCAS_6 DKT Contra 0.24534666585365855
+IPCAS_6 DKT Contra 0.22315570487804876
+IPCAS_6 DKT Contra 0.2649854554878049
+IPCAS_6 DKT Contra 0.19357316524390245
+IPCAS_6 DKT Contra 0.3131967951219512
+IPCAS_6 DKT Contra 0.17236698963414634
+IPCAS_6 DKT Contra 0.21725137804878045
+IPCAS_6 DKT Contra 0.19301476463414635
+IPCAS_6 DKT Contra 0.35193307560975606
+IPCAS_6 DKT Contra 0.29046625365853657
+IPCAS_6 DKT Contra 0.29505095853658536
+IPCAS_6 DKT Contra 0.2834514207317073
+IPCAS_6 DKT Contra 0.2692053670731707
+IPCAS_6 DKT Contra 0.20753208780487806
+IPCAS_6 DKT Contra 0.2433971
+IPCAS_6 DKT Contra 0.2659747774390244
+IPCAS_6 DKT Contra 0.21993903658536584
+IPCAS_6 DKT Contra 0.24520671402439023
+IPCAS_6 DKT Contra 0.223830306097561
+IPCAS_6 DKT Contra 0.27143070670731706
+IPCAS_6 DKT Contra 0.20989055609756097
+IPCAS_6 DKT Contra 0.2578951487804878
+IPCAS_6 DKT Contra 0.27494467682926826
+IPCAS_6 DKT Contra 0.19321978536585366
+IPCAS_6 DKT Contra 0.18812725
+IPCAS_6 DKT Contra 0.24440536829268295
+IPCAS_6 DKT Contra 0.18980788536585366
+IPCAS_6 DKT Contra 0.321449593902439
+IPCAS_6 DKT Contra 0.2809073298780488
+IPCAS_6 DKT Contra 0.22107812865853657
+IPCAS_6 DKT Contra 0.2133347536585366
+IPCAS_6 DKT Contra 0.2493526268292683
+IPCAS_6 DKT Contra 0.26071269146341464
+IPCAS_6 DKT Contra 0.27482763780487807
+IPCAS_6 DKT Contra 0.24641810060975608
+IPCAS_6 DKT Contra 0.24204129329268292
+Dataset Atlas Type Value
+MRN_1 DKT Ipsi 0.2973046457317073
+MRN_1 DKT Ipsi 0.2998015859756098
+MRN_1 DKT Ipsi 0.28643151402439027
+MRN_1 DKT Ipsi 0.27826656402439026
+MRN_1 DKT Ipsi 0.26976031768292685
+MRN_1 DKT Ipsi 0.2544714841463414
+MRN_1 DKT Ipsi 0.35570436097560976
+MRN_1 DKT Ipsi 0.31153879756097563
+MRN_1 DKT Ipsi 0.24744526890243904
+MRN_1 DKT Ipsi 0.3755374993902439
+MRN_1 DKT Ipsi 0.24836489146341467
+MRN_1 DKT Ipsi 0.33509708414634143
+MRN_1 DKT Ipsi 0.45616753536585364
+MRN_1 DKT Ipsi 0.4169146268292682
+MRN_1 DKT Ipsi 0.2828830457317073
+MRN_1 DKT Ipsi 0.40483270121951215
+MRN_1 DKT Ipsi 0.217452937195122
+MRN_1 DKT Ipsi 0.2699213335365854
+MRN_1 DKT Ipsi 0.39587390365853653
+MRN_1 DKT Ipsi 0.24396104085365858
+MRN_1 DKT Ipsi 0.265210856097561
+MRN_1 DKT Ipsi 0.2441024829268293
+MRN_1 DKT Ipsi 0.31353716402439025
+MRN_1 DKT Ipsi 0.25328395914634144
+MRN_1 DKT Ipsi 0.23936399329268296
+MRN_1 DKT Ipsi 0.3779258164634146
+MRN_1 DKT Ipsi 0.24839648048780488
+MRN_1 DKT Ipsi 0.2665423707317073
+MRN_1 DKT Ipsi 0.26014179817073174
+MRN_1 DKT Ipsi 0.30099644329268294
+MRN_1 DKT Ipsi 0.24199665975609752
+MRN_1 DKT Ipsi 0.22692316280487804
+MRN_1 DKT Ipsi 0.32473175670731713
+MRN_1 DKT Ipsi 0.2879760268292683
+MRN_1 DKT Ipsi 0.2557416981707317
+MRN_1 DKT Ipsi 0.35213136280487806
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+Dataset Atlas Type Value
+IPCAS3 DKT Ipsi 0.3494918621951219
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+IPCAS_5 DKT Ipsi 0.24228511158536584
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+Dataset Atlas Type Value
+JHNU_bids DKT Ipsi 0.32765076890243905
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+JHNU_bids DKT Ipsi 0.25597414085365855
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+JHNU_bids DKT Ipsi 0.2788863585365854
+JHNU_bids DKT Ipsi 0.380857806707317
+JHNU_bids DKT Ipsi 0.34229796036585364
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+JHNU_bids DKT Ipsi 0.25006200975609755
+JHNU_bids DKT Hom 0.7078247073170731
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+JHNU_bids DKT Hom 0.6424133658536585
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+JHNU_bids DKT Hom 0.7670689999999999
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+JHNU_bids DKT Hom 0.699802024390244
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+JHNU_bids DKT Hom 0.7267592926829268
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+JHNU_bids DKT Contra 0.30175129024390246
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+SWU3 DKT Ipsi 0.2830862359756098
+SWU3 DKT Ipsi 0.2665184725609756
+SWU3 DKT Ipsi 0.27122878902439024
+SWU3 DKT Ipsi 0.20739647439024392
+SWU3 DKT Ipsi 0.24273793353658538
+SWU3 DKT Ipsi 0.29843909939024393
+SWU3 DKT Ipsi 0.39149796646341467
+SWU3 DKT Ipsi 0.28656185
+SWU3 DKT Hom 0.6817450243902439
+SWU3 DKT Hom 0.7726364146341462
+SWU3 DKT Hom 0.688257487804878
+SWU3 DKT Hom 0.7679913658536586
+SWU3 DKT Hom 0.6974709512195122
+SWU3 DKT Hom 0.804308219512195
+SWU3 DKT Hom 0.707188512195122
+SWU3 DKT Hom 0.7226743170731706
+SWU3 DKT Hom 0.719337951219512
+SWU3 DKT Hom 0.6248753414634146
+SWU3 DKT Hom 0.7090301463414633
+SWU3 DKT Hom 0.711771756097561
+SWU3 DKT Hom 0.6695242195121951
+SWU3 DKT Hom 0.7838153414634146
+SWU3 DKT Hom 0.6382209024390244
+SWU3 DKT Hom 0.6761816097560974
+SWU3 DKT Hom 0.7187896829268292
+SWU3 DKT Hom 0.650174731707317
+SWU3 DKT Hom 0.7074691951219513
+SWU3 DKT Hom 0.7045412682926828
+SWU3 DKT Hom 0.7307593658536585
+SWU3 DKT Hom 0.7310002682926829
+SWU3 DKT Hom 0.6962486097560976
+SWU3 DKT Hom 0.6895319756097561
+SWU3 DKT Hom 0.7569249756097561
+SWU3 DKT Hom 0.7285310243902439
+SWU3 DKT Hom 0.6635782439024391
+SWU3 DKT Hom 0.6296110975609756
+SWU3 DKT Hom 0.7007944634146341
+SWU3 DKT Hom 0.7443749512195121
+SWU3 DKT Hom 0.7247785121951219
+SWU3 DKT Hom 0.739202243902439
+SWU3 DKT Hom 0.6866941707317072
+SWU3 DKT Hom 0.7267211951219512
+SWU3 DKT Hom 0.6959428780487804
+SWU3 DKT Hom 0.7168115365853658
+SWU3 DKT Hom 0.6490960731707317
+SWU3 DKT Hom 0.693543
+SWU3 DKT Hom 0.732521268292683
+SWU3 DKT Hom 0.6805298536585366
+SWU3 DKT Hom 0.7445192195121951
+SWU3 DKT Hom 0.7050200487804877
+SWU3 DKT Hom 0.6810194390243902
+SWU3 DKT Hom 0.6422629268292683
+SWU3 DKT Hom 0.5676126341463416
+SWU3 DKT Hom 0.7147906585365854
+SWU3 DKT Hom 0.7764228048780487
+SWU3 DKT Hom 0.6802229756097561
+SWU3 DKT Contra 0.26153381707317075
+SWU3 DKT Contra 0.3853616274390244
+SWU3 DKT Contra 0.29164695609756097
+SWU3 DKT Contra 0.40532932012195116
+SWU3 DKT Contra 0.24031694817073168
+SWU3 DKT Contra 0.5080696121951219
+SWU3 DKT Contra 0.32077255304878044
+SWU3 DKT Contra 0.2603781024390244
+SWU3 DKT Contra 0.2601972969512195
+SWU3 DKT Contra 0.22924796036585363
+SWU3 DKT Contra 0.32342506280487804
+SWU3 DKT Contra 0.26054122317073175
+SWU3 DKT Contra 0.27735106036585366
+SWU3 DKT Contra 0.3959420298780488
+SWU3 DKT Contra 0.22119780609756098
+SWU3 DKT Contra 0.20122931097560975
+SWU3 DKT Contra 0.23870811585365853
+SWU3 DKT Contra 0.26514954268292684
+SWU3 DKT Contra 0.2502107286585366
+SWU3 DKT Contra 0.3218768689024391
+SWU3 DKT Contra 0.28436123231707316
+SWU3 DKT Contra 0.2750866493902439
+SWU3 DKT Contra 0.2908105414634146
+SWU3 DKT Contra 0.3468399262195122
+SWU3 DKT Contra 0.2675699945121951
+SWU3 DKT Contra 0.275706575
+SWU3 DKT Contra 0.25765561524390246
+SWU3 DKT Contra 0.24835289390243903
+SWU3 DKT Contra 0.24753640975609753
+SWU3 DKT Contra 0.2974549603658536
+SWU3 DKT Contra 0.30453864939024394
+SWU3 DKT Contra 0.24287225914634145
+SWU3 DKT Contra 0.23613833048780483
+SWU3 DKT Contra 0.27689755975609753
+SWU3 DKT Contra 0.3077862158536585
+SWU3 DKT Contra 0.26230186890243906
+SWU3 DKT Contra 0.23751558780487803
+SWU3 DKT Contra 0.25693041280487805
+SWU3 DKT Contra 0.29109652195121954
+SWU3 DKT Contra 0.2135845018292683
+SWU3 DKT Contra 0.2659997225609756
+SWU3 DKT Contra 0.24911466341463415
+SWU3 DKT Contra 0.2424533737804878
+SWU3 DKT Contra 0.19096556463414635
+SWU3 DKT Contra 0.20870096585365855
+SWU3 DKT Contra 0.27284167987804875
+SWU3 DKT Contra 0.37430870792682924
+SWU3 DKT Contra 0.2550516591463415
+Dataset Atlas Type Value
+IPCAS4 DKT Ipsi 0.25464307012195125
+IPCAS4 DKT Ipsi 0.37621904207317075
+IPCAS4 DKT Ipsi 0.30514184207317074
+IPCAS4 DKT Ipsi 0.27474108109756096
+IPCAS4 DKT Ipsi 0.3143195414634147
+IPCAS4 DKT Ipsi 0.5614538823170732
+IPCAS4 DKT Ipsi 0.4329224134146342
+IPCAS4 DKT Ipsi 0.5706138176829268
+IPCAS4 DKT Ipsi 0.3305647243902439
+IPCAS4 DKT Ipsi 0.42834059573170735
+IPCAS4 DKT Ipsi 0.46833917073170733
+IPCAS4 DKT Ipsi 0.29043038780487807
+IPCAS4 DKT Ipsi 0.281723993902439
+IPCAS4 DKT Ipsi 0.27161306280487807
+IPCAS4 DKT Ipsi 0.22781028231707315
+IPCAS4 DKT Ipsi 0.27299748475609753
+IPCAS4 DKT Ipsi 0.32242903841463416
+IPCAS4 DKT Ipsi 0.32456581036585364
+IPCAS4 DKT Ipsi 0.29906318536585363
+IPCAS4 DKT Ipsi 0.27940178536585364
+IPCAS4 DKT Ipsi 0.25748491280487806
+IPCAS4 DKT Ipsi 0.2892155359756098
+IPCAS4 DKT Ipsi 0.26907106402439024
+IPCAS4 DKT Ipsi 0.34395134207317074
+IPCAS4 DKT Ipsi 0.3430877756097562
+IPCAS4 DKT Ipsi 0.37867084268292683
+IPCAS4 DKT Ipsi 0.27452506097560975
+IPCAS4 DKT Ipsi 0.46940718475609755
+IPCAS4 DKT Ipsi 0.37264125000000003
+IPCAS4 DKT Ipsi 0.35583861768292685
+IPCAS4 DKT Ipsi 0.38118933963414636
+IPCAS4 DKT Ipsi 0.269638937195122
+IPCAS4 DKT Ipsi 0.3776675640243902
+IPCAS4 DKT Ipsi 0.33603615365853656
+IPCAS4 DKT Ipsi 0.39276586951219505
+IPCAS4 DKT Ipsi 0.3291826396341464
+IPCAS4 DKT Ipsi 0.3238383268292683
+IPCAS4 DKT Ipsi 0.3440677268292683
+IPCAS4 DKT Ipsi 0.24071947012195122
+IPCAS4 DKT Ipsi 0.2690816451219512
+IPCAS4 DKT Hom 0.6544775853658537
+IPCAS4 DKT Hom 0.6952058292682927
+IPCAS4 DKT Hom 0.6899944634146341
+IPCAS4 DKT Hom 0.6540557073170732
+IPCAS4 DKT Hom 0.6939066585365854
+IPCAS4 DKT Hom 0.817466804878049
+IPCAS4 DKT Hom 0.6976503170731707
+IPCAS4 DKT Hom 0.781052707317073
+IPCAS4 DKT Hom 0.7504050487804879
+IPCAS4 DKT Hom 0.7125355365853658
+IPCAS4 DKT Hom 0.782577243902439
+IPCAS4 DKT Hom 0.6957445121951219
+IPCAS4 DKT Hom 0.7262373658536586
+IPCAS4 DKT Hom 0.6633807317073172
+IPCAS4 DKT Hom 0.6592244634146341
+IPCAS4 DKT Hom 0.6825791219512195
+IPCAS4 DKT Hom 0.7011778536585367
+IPCAS4 DKT Hom 0.6890338536585365
+IPCAS4 DKT Hom 0.6978027560975609
+IPCAS4 DKT Hom 0.6270732195121952
+IPCAS4 DKT Hom 0.6711380731707318
+IPCAS4 DKT Hom 0.717373024390244
+IPCAS4 DKT Hom 0.659276243902439
+IPCAS4 DKT Hom 0.7051391951219512
+IPCAS4 DKT Hom 0.6209276341463414
+IPCAS4 DKT Hom 0.6085851463414634
+IPCAS4 DKT Hom 0.6640997804878048
+IPCAS4 DKT Hom 0.7036166341463415
+IPCAS4 DKT Hom 0.6989340487804877
+IPCAS4 DKT Hom 0.682206
+IPCAS4 DKT Hom 0.7345096341463415
+IPCAS4 DKT Hom 0.6802716585365853
+IPCAS4 DKT Hom 0.7311363902439025
+IPCAS4 DKT Hom 0.6298565609756098
+IPCAS4 DKT Hom 0.703745487804878
+IPCAS4 DKT Hom 0.6873825853658536
+IPCAS4 DKT Hom 0.6798810487804877
+IPCAS4 DKT Hom 0.7466393658536585
+IPCAS4 DKT Hom 0.6646696585365853
+IPCAS4 DKT Hom 0.7117145121951219
+IPCAS4 DKT Contra 0.23803017134146343
+IPCAS4 DKT Contra 0.36119441158536586
+IPCAS4 DKT Contra 0.2810124182926829
+IPCAS4 DKT Contra 0.24730402682926833
+IPCAS4 DKT Contra 0.29845009390243904
+IPCAS4 DKT Contra 0.5533373920731708
+IPCAS4 DKT Contra 0.4139933981707318
+IPCAS4 DKT Contra 0.5540961182926829
+IPCAS4 DKT Contra 0.3136691414634146
+IPCAS4 DKT Contra 0.4085341957317073
+IPCAS4 DKT Contra 0.4583629274390244
+IPCAS4 DKT Contra 0.26955618597560976
+IPCAS4 DKT Contra 0.2729885365853658
+IPCAS4 DKT Contra 0.2518355170731707
+IPCAS4 DKT Contra 0.2120507018292683
+IPCAS4 DKT Contra 0.2621265975609756
+IPCAS4 DKT Contra 0.30438303170731706
+IPCAS4 DKT Contra 0.29918248902439026
+IPCAS4 DKT Contra 0.2887980182926829
+IPCAS4 DKT Contra 0.2509758420731707
+IPCAS4 DKT Contra 0.23715191585365852
+IPCAS4 DKT Contra 0.2677856487804878
+IPCAS4 DKT Contra 0.2425419981707317
+IPCAS4 DKT Contra 0.33425675121951226
+IPCAS4 DKT Contra 0.3154289945121951
+IPCAS4 DKT Contra 0.33695596219512197
+IPCAS4 DKT Contra 0.24070868353658537
+IPCAS4 DKT Contra 0.4519376323170732
+IPCAS4 DKT Contra 0.35260448536585365
+IPCAS4 DKT Contra 0.3342674798780488
+IPCAS4 DKT Contra 0.3616202817073171
+IPCAS4 DKT Contra 0.24267653353658536
+IPCAS4 DKT Contra 0.35941488902439034
+IPCAS4 DKT Contra 0.29586050487804877
+IPCAS4 DKT Contra 0.37847819939024396
+IPCAS4 DKT Contra 0.30408721646341463
+IPCAS4 DKT Contra 0.3030220268292683
+IPCAS4 DKT Contra 0.32594872621951215
+IPCAS4 DKT Contra 0.23181922378048783
+IPCAS4 DKT Contra 0.2493027585365854
+Dataset Atlas Type Value
+IPCAS_8 DKT Ipsi 0.3753990695121951
+IPCAS_8 DKT Ipsi 0.5322266292682927
+IPCAS_8 DKT Ipsi 0.2502846725609756
+IPCAS_8 DKT Ipsi 0.4333388207317074
+IPCAS_8 DKT Ipsi 0.5349876439024391
+IPCAS_8 DKT Ipsi 0.333856075
+IPCAS_8 DKT Ipsi 0.3783741567073171
+IPCAS_8 DKT Ipsi 0.494065281097561
+IPCAS_8 DKT Ipsi 0.46852310365853655
+IPCAS_8 DKT Ipsi 0.4762824164634146
+IPCAS_8 DKT Ipsi 0.6573818963414635
+IPCAS_8 DKT Ipsi 0.3884935469512195
+IPCAS_8 DKT Ipsi 0.3990731884146342
+IPCAS_8 DKT Ipsi 0.4178430871951219
+IPCAS_8 DKT Ipsi 0.5299304079268292
+IPCAS_8 DKT Ipsi 0.27841022987804875
+IPCAS_8 DKT Ipsi 0.47040075731707315
+IPCAS_8 DKT Ipsi 0.2930197682926829
+IPCAS_8 DKT Ipsi 0.45766577926829266
+IPCAS_8 DKT Ipsi 0.27701845548780485
+IPCAS_8 DKT Ipsi 0.2796897524390244
+IPCAS_8 DKT Ipsi 0.45756719329268297
+IPCAS_8 DKT Ipsi 0.43519806402439026
+IPCAS_8 DKT Ipsi 0.3602260512195122
+IPCAS_8 DKT Ipsi 0.466250125
+IPCAS_8 DKT Ipsi 0.3043779280487805
+IPCAS_8 DKT Hom 0.6413231219512195
+IPCAS_8 DKT Hom 0.770292
+IPCAS_8 DKT Hom 0.7617388536585366
+IPCAS_8 DKT Hom 0.7737221219512195
+IPCAS_8 DKT Hom 0.7691280731707316
+IPCAS_8 DKT Hom 0.6922883902439025
+IPCAS_8 DKT Hom 0.7889102682926828
+IPCAS_8 DKT Hom 0.7694283170731706
+IPCAS_8 DKT Hom 0.7602063902439024
+IPCAS_8 DKT Hom 0.7219174146341465
+IPCAS_8 DKT Hom 0.8607845853658536
+IPCAS_8 DKT Hom 0.6840063658536585
+IPCAS_8 DKT Hom 0.7503960487804877
+IPCAS_8 DKT Hom 0.748592975609756
+IPCAS_8 DKT Hom 0.785530975609756
+IPCAS_8 DKT Hom 0.6397626585365854
+IPCAS_8 DKT Hom 0.7673079512195122
+IPCAS_8 DKT Hom 0.7325299268292683
+IPCAS_8 DKT Hom 0.7589841463414634
+IPCAS_8 DKT Hom 0.6707491707317074
+IPCAS_8 DKT Hom 0.5944256585365854
+IPCAS_8 DKT Hom 0.7926691219512195
+IPCAS_8 DKT Hom 0.7983609024390244
+IPCAS_8 DKT Hom 0.7242806097560975
+IPCAS_8 DKT Hom 0.7581711707317071
+IPCAS_8 DKT Hom 0.6640685853658537
+IPCAS_8 DKT Contra 0.3510281469512195
+IPCAS_8 DKT Contra 0.5101257018292683
+IPCAS_8 DKT Contra 0.23796054939024391
+IPCAS_8 DKT Contra 0.42589522012195125
+IPCAS_8 DKT Contra 0.5314363195121952
+IPCAS_8 DKT Contra 0.318195968902439
+IPCAS_8 DKT Contra 0.3728171195121952
+IPCAS_8 DKT Contra 0.4743950445121951
+IPCAS_8 DKT Contra 0.45487499878048787
+IPCAS_8 DKT Contra 0.4456817829268292
+IPCAS_8 DKT Contra 0.6491390402439023
+IPCAS_8 DKT Contra 0.3645189432926829
+IPCAS_8 DKT Contra 0.3857743884146342
+IPCAS_8 DKT Contra 0.3932953786585366
+IPCAS_8 DKT Contra 0.5148810432926829
+IPCAS_8 DKT Contra 0.26411084756097564
+IPCAS_8 DKT Contra 0.4480441689024391
+IPCAS_8 DKT Contra 0.2829601463414634
+IPCAS_8 DKT Contra 0.45008235975609756
+IPCAS_8 DKT Contra 0.24957365731707315
+IPCAS_8 DKT Contra 0.24501053658536584
+IPCAS_8 DKT Contra 0.4400024359756098
+IPCAS_8 DKT Contra 0.4311115603658536
+IPCAS_8 DKT Contra 0.35008423719512194
+IPCAS_8 DKT Contra 0.45439308170731707
+IPCAS_8 DKT Contra 0.28425298292682927
+Dataset Atlas Type Value
+ABIDEII-SDSU_1 DKT Ipsi 0.3583541439024391
+ABIDEII-SDSU_1 DKT Ipsi 0.27674320487804877
+ABIDEII-SDSU_1 DKT Ipsi 0.2472534920731707
+ABIDEII-SDSU_1 DKT Ipsi 0.35960803109756095
+ABIDEII-SDSU_1 DKT Ipsi 0.2220807737804878
+ABIDEII-SDSU_1 DKT Ipsi 0.37534046890243905
+ABIDEII-SDSU_1 DKT Ipsi 0.53006135
+ABIDEII-SDSU_1 DKT Ipsi 0.20988823841463417
+ABIDEII-SDSU_1 DKT Ipsi 0.2600158036585366
+ABIDEII-SDSU_1 DKT Ipsi 0.24239063780487805
+ABIDEII-SDSU_1 DKT Ipsi 0.2220587786585366
+ABIDEII-SDSU_1 DKT Ipsi 0.2162991469512195
+ABIDEII-SDSU_1 DKT Ipsi 0.5810286725609756
+ABIDEII-SDSU_1 DKT Ipsi 0.3499041451219512
+ABIDEII-SDSU_1 DKT Ipsi 0.4110806170731707
+ABIDEII-SDSU_1 DKT Ipsi 0.5653784896341463
+ABIDEII-SDSU_1 DKT Ipsi 0.19460182743902443
+ABIDEII-SDSU_1 DKT Ipsi 0.32360307743902433
+ABIDEII-SDSU_1 DKT Ipsi 0.23393941158536583
+ABIDEII-SDSU_1 DKT Ipsi 0.2646183975609756
+ABIDEII-SDSU_1 DKT Ipsi 0.3204303969512195
+ABIDEII-SDSU_1 DKT Ipsi 0.24961693109756097
+ABIDEII-SDSU_1 DKT Ipsi 0.21446248170731705
+ABIDEII-SDSU_1 DKT Ipsi 0.45311359999999995
+ABIDEII-SDSU_1 DKT Hom 0.7009802682926829
+ABIDEII-SDSU_1 DKT Hom 0.7175916097560976
+ABIDEII-SDSU_1 DKT Hom 0.5859207317073172
+ABIDEII-SDSU_1 DKT Hom 0.7559302926829268
+ABIDEII-SDSU_1 DKT Hom 0.5780518292682927
+ABIDEII-SDSU_1 DKT Hom 0.6901251219512196
+ABIDEII-SDSU_1 DKT Hom 0.8025360243902439
+ABIDEII-SDSU_1 DKT Hom 0.5648884390243902
+ABIDEII-SDSU_1 DKT Hom 0.6327061707317073
+ABIDEII-SDSU_1 DKT Hom 0.6730752926829269
+ABIDEII-SDSU_1 DKT Hom 0.6177959024390245
+ABIDEII-SDSU_1 DKT Hom 0.6302479999999999
+ABIDEII-SDSU_1 DKT Hom 0.8043356341463413
+ABIDEII-SDSU_1 DKT Hom 0.727713
+ABIDEII-SDSU_1 DKT Hom 0.6861500243902439
+ABIDEII-SDSU_1 DKT Hom 0.8116192926829268
+ABIDEII-SDSU_1 DKT Hom 0.5652003902439025
+ABIDEII-SDSU_1 DKT Hom 0.6286872439024391
+ABIDEII-SDSU_1 DKT Hom 0.6332199756097562
+ABIDEII-SDSU_1 DKT Hom 0.6433414878048781
+ABIDEII-SDSU_1 DKT Hom 0.7662415853658537
+ABIDEII-SDSU_1 DKT Hom 0.6878029268292681
+ABIDEII-SDSU_1 DKT Hom 0.4444851707317073
+ABIDEII-SDSU_1 DKT Hom 0.7291500975609756
+ABIDEII-SDSU_1 DKT Contra 0.3150887274390244
+ABIDEII-SDSU_1 DKT Contra 0.2566948256097561
+ABIDEII-SDSU_1 DKT Contra 0.218916425
+ABIDEII-SDSU_1 DKT Contra 0.3319857847560976
+ABIDEII-SDSU_1 DKT Contra 0.17453743841463415
+ABIDEII-SDSU_1 DKT Contra 0.33868993963414634
+ABIDEII-SDSU_1 DKT Contra 0.5103735823170731
+ABIDEII-SDSU_1 DKT Contra 0.17531498658536585
+ABIDEII-SDSU_1 DKT Contra 0.2176161475609756
+ABIDEII-SDSU_1 DKT Contra 0.21582357134146343
+ABIDEII-SDSU_1 DKT Contra 0.1926404908536585
+ABIDEII-SDSU_1 DKT Contra 0.18445961219512197
+ABIDEII-SDSU_1 DKT Contra 0.5664480036585366
+ABIDEII-SDSU_1 DKT Contra 0.32232784999999997
+ABIDEII-SDSU_1 DKT Contra 0.380544962804878
+ABIDEII-SDSU_1 DKT Contra 0.5542898128048781
+ABIDEII-SDSU_1 DKT Contra 0.17299808353658536
+ABIDEII-SDSU_1 DKT Contra 0.26739996463414634
+ABIDEII-SDSU_1 DKT Contra 0.19947837256097561
+ABIDEII-SDSU_1 DKT Contra 0.22267614878048778
+ABIDEII-SDSU_1 DKT Contra 0.30063645426829266
+ABIDEII-SDSU_1 DKT Contra 0.23486169451219513
+ABIDEII-SDSU_1 DKT Contra 0.17951007073170733
+ABIDEII-SDSU_1 DKT Contra 0.41105879695121944
+Dataset Atlas Type Value
+ABIDEII-TCD_1 DKT Ipsi 0.23866561524390245
+ABIDEII-TCD_1 DKT Ipsi 0.24786158170731704
+ABIDEII-TCD_1 DKT Ipsi 0.522409356707317
+ABIDEII-TCD_1 DKT Ipsi 0.2617036798780488
+ABIDEII-TCD_1 DKT Ipsi 0.34028120426829267
+ABIDEII-TCD_1 DKT Ipsi 0.31124964939024385
+ABIDEII-TCD_1 DKT Ipsi 0.26457251768292683
+ABIDEII-TCD_1 DKT Ipsi 0.21492935609756098
+ABIDEII-TCD_1 DKT Ipsi 0.325249981097561
+ABIDEII-TCD_1 DKT Ipsi 0.36100856585365854
+ABIDEII-TCD_1 DKT Ipsi 0.35088320304878046
+ABIDEII-TCD_1 DKT Ipsi 0.26538746158536586
+ABIDEII-TCD_1 DKT Ipsi 0.2645091780487805
+ABIDEII-TCD_1 DKT Ipsi 0.2247224475609756
+ABIDEII-TCD_1 DKT Ipsi 0.22733376646341463
+ABIDEII-TCD_1 DKT Ipsi 0.5554940439024391
+ABIDEII-TCD_1 DKT Ipsi 0.2468896762195122
+ABIDEII-TCD_1 DKT Ipsi 0.22814659939024393
+ABIDEII-TCD_1 DKT Ipsi 0.3526999091463415
+ABIDEII-TCD_1 DKT Ipsi 0.29605249329268296
+ABIDEII-TCD_1 DKT Ipsi 0.25423843109756095
+ABIDEII-TCD_1 DKT Hom 0.6833645609756097
+ABIDEII-TCD_1 DKT Hom 0.6756319024390245
+ABIDEII-TCD_1 DKT Hom 0.7872576341463415
+ABIDEII-TCD_1 DKT Hom 0.5838922195121952
+ABIDEII-TCD_1 DKT Hom 0.7340197317073172
+ABIDEII-TCD_1 DKT Hom 0.6520855853658537
+ABIDEII-TCD_1 DKT Hom 0.6994728536585366
+ABIDEII-TCD_1 DKT Hom 0.6581684146341463
+ABIDEII-TCD_1 DKT Hom 0.6568736341463415
+ABIDEII-TCD_1 DKT Hom 0.7093455853658536
+ABIDEII-TCD_1 DKT Hom 0.7187683414634146
+ABIDEII-TCD_1 DKT Hom 0.6125851463414634
+ABIDEII-TCD_1 DKT Hom 0.7067136097560975
+ABIDEII-TCD_1 DKT Hom 0.5994456097560975
+ABIDEII-TCD_1 DKT Hom 0.6416856097560976
+ABIDEII-TCD_1 DKT Hom 0.8363259024390243
+ABIDEII-TCD_1 DKT Hom 0.6142985853658537
+ABIDEII-TCD_1 DKT Hom 0.6622259512195122
+ABIDEII-TCD_1 DKT Hom 0.7671414146341464
+ABIDEII-TCD_1 DKT Hom 0.7250769268292683
+ABIDEII-TCD_1 DKT Hom 0.7132848780487805
+ABIDEII-TCD_1 DKT Contra 0.2155069408536585
+ABIDEII-TCD_1 DKT Contra 0.22595840609756096
+ABIDEII-TCD_1 DKT Contra 0.5098734640243903
+ABIDEII-TCD_1 DKT Contra 0.22150281036585368
+ABIDEII-TCD_1 DKT Contra 0.31438829451219513
+ABIDEII-TCD_1 DKT Contra 0.27591624146341465
+ABIDEII-TCD_1 DKT Contra 0.2416619945121951
+ABIDEII-TCD_1 DKT Contra 0.19593298658536587
+ABIDEII-TCD_1 DKT Contra 0.30694839573170735
+ABIDEII-TCD_1 DKT Contra 0.30383962317073165
+ABIDEII-TCD_1 DKT Contra 0.33285132987804883
+ABIDEII-TCD_1 DKT Contra 0.24982025487804882
+ABIDEII-TCD_1 DKT Contra 0.2459372725609756
+ABIDEII-TCD_1 DKT Contra 0.2036403408536585
+ABIDEII-TCD_1 DKT Contra 0.2030817451219512
+ABIDEII-TCD_1 DKT Contra 0.5440111609756098
+ABIDEII-TCD_1 DKT Contra 0.20760037134146342
+ABIDEII-TCD_1 DKT Contra 0.21186519268292686
+ABIDEII-TCD_1 DKT Contra 0.34376248048780483
+ABIDEII-TCD_1 DKT Contra 0.2741869359756097
+ABIDEII-TCD_1 DKT Contra 0.23249925975609756
diff --git a/docs/paper/data/func_ipsi_hammer.csv b/docs/paper/data/func_ipsi_hammer.csv
new file mode 100644
index 000000000..dd02d64f3
--- /dev/null
+++ b/docs/paper/data/func_ipsi_hammer.csv
@@ -0,0 +1,10519 @@
+Dataset Atlas Type Value
+SWU4 Hammer Ipsi 0.38433010312500004
+SWU4 Hammer Ipsi 0.2686711475
+SWU4 Hammer Ipsi 0.19220348999999998
+SWU4 Hammer Ipsi 0.222542578125
+SWU4 Hammer Ipsi 0.289016960625
+SWU4 Hammer Ipsi 0.30893159875000004
+SWU4 Hammer Ipsi 0.255831671875
+SWU4 Hammer Ipsi 0.28802709375
+SWU4 Hammer Ipsi 0.23210610750000002
+SWU4 Hammer Ipsi 0.308639574375
+SWU4 Hammer Ipsi 0.25574720125
+SWU4 Hammer Ipsi 0.329460358125
+SWU4 Hammer Ipsi 0.2489690975
+SWU4 Hammer Ipsi 0.274437540625
+SWU4 Hammer Ipsi 0.20290716875
+SWU4 Hammer Ipsi 0.4208434443750001
+SWU4 Hammer Ipsi 0.284986531875
+SWU4 Hammer Ipsi 0.26811106375
+SWU4 Hammer Ipsi 0.250078221875
+SWU4 Hammer Ipsi 0.3692864349999999
+SWU4 Hammer Ipsi 0.25322191437499997
+SWU4 Hammer Ipsi 0.25877910937500004
+SWU4 Hammer Ipsi 0.27660881187499997
+SWU4 Hammer Ipsi 0.36127327062500003
+SWU4 Hammer Ipsi 0.27885836875000003
+SWU4 Hammer Ipsi 0.3530625243749999
+SWU4 Hammer Ipsi 0.202916329375
+SWU4 Hammer Ipsi 0.317033990625
+SWU4 Hammer Ipsi 0.31137583125
+SWU4 Hammer Ipsi 0.255108523125
+SWU4 Hammer Ipsi 0.262109489375
+SWU4 Hammer Ipsi 0.41939298999999997
+SWU4 Hammer Ipsi 0.34102007375
+SWU4 Hammer Ipsi 0.29192799125
+SWU4 Hammer Ipsi 0.265976664375
+SWU4 Hammer Ipsi 0.22150066
+SWU4 Hammer Ipsi 0.42297073312500005
+SWU4 Hammer Ipsi 0.26703251875
+SWU4 Hammer Ipsi 0.23987775875
+SWU4 Hammer Ipsi 0.29575489875
+SWU4 Hammer Ipsi 0.37472241312500004
+SWU4 Hammer Ipsi 0.34914179500000003
+SWU4 Hammer Ipsi 0.22190395625
+SWU4 Hammer Ipsi 0.26768691375
+SWU4 Hammer Ipsi 0.23918139125
+SWU4 Hammer Ipsi 0.26331012625
+SWU4 Hammer Ipsi 0.27472598375
+SWU4 Hammer Ipsi 0.267602998125
+SWU4 Hammer Ipsi 0.26637077937500003
+SWU4 Hammer Ipsi 0.34010012625
+SWU4 Hammer Ipsi 0.288018338125
+SWU4 Hammer Ipsi 0.27366864125
+SWU4 Hammer Ipsi 0.38725984124999996
+SWU4 Hammer Ipsi 0.232248933125
+SWU4 Hammer Ipsi 0.27611009625
+SWU4 Hammer Ipsi 0.35299704249999997
+SWU4 Hammer Ipsi 0.28455753875
+SWU4 Hammer Ipsi 0.34344440375000007
+SWU4 Hammer Ipsi 0.37278250812499997
+SWU4 Hammer Ipsi 0.304189594375
+SWU4 Hammer Ipsi 0.4512236606250001
+SWU4 Hammer Ipsi 0.316095070625
+SWU4 Hammer Ipsi 0.241637349375
+SWU4 Hammer Ipsi 0.28949362
+SWU4 Hammer Ipsi 0.23343902375
+SWU4 Hammer Ipsi 0.250354881875
+SWU4 Hammer Ipsi 0.285656895
+SWU4 Hammer Ipsi 0.23916108062500002
+SWU4 Hammer Ipsi 0.29162891375
+SWU4 Hammer Ipsi 0.34803917312500005
+SWU4 Hammer Ipsi 0.39925600812499995
+SWU4 Hammer Ipsi 0.345521856875
+SWU4 Hammer Ipsi 0.23060602375000003
+SWU4 Hammer Ipsi 0.22334457937500002
+SWU4 Hammer Ipsi 0.4112930225
+SWU4 Hammer Ipsi 0.415771998125
+SWU4 Hammer Ipsi 0.24534823375000003
+SWU4 Hammer Ipsi 0.373836685
+SWU4 Hammer Ipsi 0.25183397
+SWU4 Hammer Ipsi 0.435232390625
+SWU4 Hammer Ipsi 0.25326042625
+SWU4 Hammer Ipsi 0.23760110124999997
+SWU4 Hammer Ipsi 0.3446647525
+SWU4 Hammer Ipsi 0.29623893
+SWU4 Hammer Ipsi 0.28846973625
+SWU4 Hammer Ipsi 0.290580716875
+SWU4 Hammer Ipsi 0.22344426750000002
+SWU4 Hammer Ipsi 0.33028369999999996
+SWU4 Hammer Ipsi 0.38193575124999996
+SWU4 Hammer Ipsi 0.318153255
+SWU4 Hammer Ipsi 0.24731025250000002
+SWU4 Hammer Ipsi 0.2958755425
+SWU4 Hammer Ipsi 0.25791548062499997
+SWU4 Hammer Ipsi 0.314691535
+SWU4 Hammer Ipsi 0.26503423
+SWU4 Hammer Ipsi 0.21633003875
+SWU4 Hammer Ipsi 0.27571676437500003
+SWU4 Hammer Ipsi 0.24170285249999998
+SWU4 Hammer Ipsi 0.36823689687499994
+SWU4 Hammer Ipsi 0.23859472562500003
+SWU4 Hammer Ipsi 0.30338107125
+SWU4 Hammer Ipsi 0.388569295
+SWU4 Hammer Ipsi 0.27624429875
+SWU4 Hammer Ipsi 0.34290672625
+SWU4 Hammer Ipsi 0.258153109375
+SWU4 Hammer Ipsi 0.264013533125
+SWU4 Hammer Ipsi 0.279927825
+SWU4 Hammer Ipsi 0.2640041325
+SWU4 Hammer Ipsi 0.34428571375000006
+SWU4 Hammer Ipsi 0.280386115
+SWU4 Hammer Ipsi 0.269965580625
+SWU4 Hammer Ipsi 0.23397643187500003
+SWU4 Hammer Ipsi 0.253223695
+SWU4 Hammer Ipsi 0.2273464825
+SWU4 Hammer Ipsi 0.403810875
+SWU4 Hammer Ipsi 0.213543934375
+SWU4 Hammer Ipsi 0.26815619749999997
+SWU4 Hammer Ipsi 0.31749619875
+SWU4 Hammer Ipsi 0.322914018125
+SWU4 Hammer Ipsi 0.27757099125
+SWU4 Hammer Ipsi 0.300907508125
+SWU4 Hammer Ipsi 0.358198043125
+SWU4 Hammer Ipsi 0.32061847125
+SWU4 Hammer Ipsi 0.37752288562499997
+SWU4 Hammer Ipsi 0.249123714375
+SWU4 Hammer Ipsi 0.311283918125
+SWU4 Hammer Ipsi 0.31486229625
+SWU4 Hammer Ipsi 0.19930590375
+SWU4 Hammer Ipsi 0.282797671875
+SWU4 Hammer Ipsi 0.39395483562500005
+SWU4 Hammer Ipsi 0.424123204375
+SWU4 Hammer Ipsi 0.2341486475
+SWU4 Hammer Ipsi 0.32447736687499995
+SWU4 Hammer Ipsi 0.2745412575
+SWU4 Hammer Ipsi 0.2725290425
+SWU4 Hammer Ipsi 0.324654668125
+SWU4 Hammer Ipsi 0.25655196
+SWU4 Hammer Ipsi 0.31828797000000003
+SWU4 Hammer Ipsi 0.27576169562499997
+SWU4 Hammer Ipsi 0.228181918125
+SWU4 Hammer Ipsi 0.308281198125
+SWU4 Hammer Ipsi 0.40419429749999997
+SWU4 Hammer Ipsi 0.279675874375
+SWU4 Hammer Ipsi 0.25299744687500003
+SWU4 Hammer Ipsi 0.21364611374999998
+SWU4 Hammer Ipsi 0.29668610687499997
+SWU4 Hammer Ipsi 0.22854765375
+SWU4 Hammer Ipsi 0.44793055249999997
+SWU4 Hammer Ipsi 0.30468453375000004
+SWU4 Hammer Ipsi 0.26150264999999995
+SWU4 Hammer Ipsi 0.229206054375
+SWU4 Hammer Ipsi 0.324775296875
+SWU4 Hammer Ipsi 0.275403895625
+SWU4 Hammer Ipsi 0.26234449125000003
+SWU4 Hammer Ipsi 0.38035306625
+SWU4 Hammer Ipsi 0.26136827
+SWU4 Hammer Ipsi 0.29252662749999997
+SWU4 Hammer Ipsi 0.27647177
+SWU4 Hammer Ipsi 0.32378448125
+SWU4 Hammer Ipsi 0.24321480937499998
+SWU4 Hammer Ipsi 0.27995594625000003
+SWU4 Hammer Ipsi 0.37449309999999997
+SWU4 Hammer Ipsi 0.33572237937500005
+SWU4 Hammer Ipsi 0.418832116875
+SWU4 Hammer Ipsi 0.23187421874999997
+SWU4 Hammer Ipsi 0.272298573125
+SWU4 Hammer Ipsi 0.38178071
+SWU4 Hammer Ipsi 0.24964021312499998
+SWU4 Hammer Ipsi 0.282215263125
+SWU4 Hammer Ipsi 0.2716464825
+SWU4 Hammer Ipsi 0.2878229975
+SWU4 Hammer Ipsi 0.31604040375
+SWU4 Hammer Ipsi 0.265608508125
+SWU4 Hammer Ipsi 0.2961888375
+SWU4 Hammer Ipsi 0.3679240274999999
+SWU4 Hammer Ipsi 0.288688409375
+SWU4 Hammer Ipsi 0.238523225
+SWU4 Hammer Ipsi 0.276569461875
+SWU4 Hammer Ipsi 0.30448322625
+SWU4 Hammer Ipsi 0.479543686875
+SWU4 Hammer Ipsi 0.30729867625000007
+SWU4 Hammer Ipsi 0.2622064125
+SWU4 Hammer Ipsi 0.3653364225
+SWU4 Hammer Ipsi 0.236863474375
+SWU4 Hammer Ipsi 0.2998802125
+SWU4 Hammer Ipsi 0.262600655625
+SWU4 Hammer Ipsi 0.205144458125
+SWU4 Hammer Ipsi 0.30400288375
+SWU4 Hammer Ipsi 0.342064315
+SWU4 Hammer Ipsi 0.30166241625
+SWU4 Hammer Ipsi 0.28212331
+SWU4 Hammer Ipsi 0.44435050875000004
+SWU4 Hammer Ipsi 0.264778605625
+SWU4 Hammer Ipsi 0.34591591312500003
+SWU4 Hammer Ipsi 0.290060154375
+SWU4 Hammer Ipsi 0.35982824812499997
+SWU4 Hammer Ipsi 0.2198503025
+SWU4 Hammer Ipsi 0.2630780575
+SWU4 Hammer Ipsi 0.245419855
+SWU4 Hammer Ipsi 0.45535056749999997
+SWU4 Hammer Ipsi 0.26594240625
+SWU4 Hammer Ipsi 0.321769554375
+SWU4 Hammer Ipsi 0.25344387062499996
+SWU4 Hammer Ipsi 0.31607733062500004
+SWU4 Hammer Ipsi 0.260126466875
+SWU4 Hammer Ipsi 0.266570123125
+SWU4 Hammer Ipsi 0.3482561699999999
+SWU4 Hammer Ipsi 0.27368841
+SWU4 Hammer Ipsi 0.29022896
+SWU4 Hammer Ipsi 0.38463720875
+SWU4 Hammer Ipsi 0.264612355625
+SWU4 Hammer Ipsi 0.283378623125
+SWU4 Hammer Ipsi 0.205311264375
+SWU4 Hammer Ipsi 0.34922138562499994
+SWU4 Hammer Ipsi 0.2622199275
+SWU4 Hammer Ipsi 0.29421338625
+SWU4 Hammer Ipsi 0.327056659375
+SWU4 Hammer Ipsi 0.282001513125
+SWU4 Hammer Ipsi 0.28084581375
+SWU4 Hammer Ipsi 0.262529329375
+SWU4 Hammer Ipsi 0.23241280812499998
+SWU4 Hammer Ipsi 0.2496983525
+SWU4 Hammer Ipsi 0.340287825625
+SWU4 Hammer Ipsi 0.225453876875
+SWU4 Hammer Ipsi 0.33244094187499995
+SWU4 Hammer Ipsi 0.36295376812499996
+SWU4 Hammer Ipsi 0.24476495937499998
+SWU4 Hammer Ipsi 0.31608051249999997
+SWU4 Hammer Ipsi 0.29526050312499996
+SWU4 Hammer Ipsi 0.333369669375
+SWU4 Hammer Ipsi 0.19200962687499998
+SWU4 Hammer Ipsi 0.237857951875
+SWU4 Hammer Ipsi 0.600941465
+SWU4 Hammer Ipsi 0.19330330750000002
+SWU4 Hammer Ipsi 0.30090689
+SWU4 Hammer Ipsi 0.23385527500000003
+SWU4 Hammer Ipsi 0.38562696875
+SWU4 Hammer Ipsi 0.254428566875
+SWU4 Hammer Ipsi 0.30129256437499996
+SWU4 Hammer Ipsi 0.47868922999999997
+SWU4 Hammer Ipsi 0.23446040562499998
+SWU4 Hammer Ipsi 0.225658121875
+SWU4 Hammer Ipsi 0.25537048
+SWU4 Hammer Ipsi 0.33228789500000006
+SWU4 Hammer Ipsi 0.351747934375
+SWU4 Hammer Ipsi 0.257783695
+SWU4 Hammer Ipsi 0.356969004375
+SWU4 Hammer Ipsi 0.403060408125
+SWU4 Hammer Ipsi 0.263411590625
+SWU4 Hammer Ipsi 0.316945184375
+SWU4 Hammer Ipsi 0.289981811875
+SWU4 Hammer Ipsi 0.296659140625
+SWU4 Hammer Ipsi 0.237876066875
+SWU4 Hammer Ipsi 0.24218230249999997
+SWU4 Hammer Ipsi 0.2329589575
+SWU4 Hammer Ipsi 0.288893488125
+SWU4 Hammer Ipsi 0.3323342375
+SWU4 Hammer Ipsi 0.29469848000000004
+SWU4 Hammer Ipsi 0.2968069025
+SWU4 Hammer Ipsi 0.33821121
+SWU4 Hammer Ipsi 0.3805808668749999
+SWU4 Hammer Ipsi 0.2525798775
+SWU4 Hammer Ipsi 0.21879408250000001
+SWU4 Hammer Ipsi 0.27519862125
+SWU4 Hammer Ipsi 0.26380152625000003
+SWU4 Hammer Ipsi 0.2967591475
+SWU4 Hammer Ipsi 0.23166519624999998
+SWU4 Hammer Ipsi 0.41920668875000006
+SWU4 Hammer Ipsi 0.257080171875
+SWU4 Hammer Ipsi 0.3965823156249999
+SWU4 Hammer Ipsi 0.273260846875
+SWU4 Hammer Ipsi 0.27393487499999997
+SWU4 Hammer Ipsi 0.275758785
+SWU4 Hammer Ipsi 0.2823981025
+SWU4 Hammer Ipsi 0.241470595625
+SWU4 Hammer Ipsi 0.290953898125
+SWU4 Hammer Ipsi 0.23382824312499997
+SWU4 Hammer Ipsi 0.295718536875
+SWU4 Hammer Ipsi 0.266470888125
+SWU4 Hammer Ipsi 0.356877423125
+SWU4 Hammer Ipsi 0.262060124375
+SWU4 Hammer Ipsi 0.48425098687499996
+SWU4 Hammer Ipsi 0.3308465125
+SWU4 Hammer Ipsi 0.36109930125
+SWU4 Hammer Ipsi 0.305843295625
+SWU4 Hammer Ipsi 0.30669767187499997
+SWU4 Hammer Ipsi 0.258906385625
+SWU4 Hammer Ipsi 0.32659660062499996
+SWU4 Hammer Ipsi 0.306258976875
+SWU4 Hammer Ipsi 0.3367779375
+SWU4 Hammer Ipsi 0.281403195625
+SWU4 Hammer Ipsi 0.258405358125
+SWU4 Hammer Ipsi 0.247457616875
+SWU4 Hammer Ipsi 0.29508247625
+SWU4 Hammer Ipsi 0.43924308125000006
+SWU4 Hammer Ipsi 0.30843651375000003
+SWU4 Hammer Ipsi 0.25177327125000004
+SWU4 Hammer Ipsi 0.331826961875
+SWU4 Hammer Ipsi 0.306819640625
+SWU4 Hammer Ipsi 0.27919836812500004
+SWU4 Hammer Ipsi 0.209197971875
+SWU4 Hammer Ipsi 0.25809698812500004
+SWU4 Hammer Ipsi 0.26240132374999997
+SWU4 Hammer Ipsi 0.33552740999999997
+SWU4 Hammer Ipsi 0.35333322749999996
+SWU4 Hammer Ipsi 0.23418518249999998
+SWU4 Hammer Ipsi 0.21886781374999997
+SWU4 Hammer Ipsi 0.3189028775
+SWU4 Hammer Ipsi 0.272826409375
+SWU4 Hammer Ipsi 0.27447939125
+SWU4 Hammer Ipsi 0.13238628875
+SWU4 Hammer Ipsi 0.26188002875000005
+SWU4 Hammer Ipsi 0.2738941875
+SWU4 Hammer Ipsi 0.29392091062500003
+SWU4 Hammer Ipsi 0.296435035
+SWU4 Hammer Ipsi 0.24433485375
+SWU4 Hammer Ipsi 0.2409312125
+SWU4 Hammer Ipsi 0.22727288999999998
+SWU4 Hammer Ipsi 0.28099068187500004
+SWU4 Hammer Ipsi 0.32087041875
+SWU4 Hammer Ipsi 0.26883128
+SWU4 Hammer Ipsi 0.28833150125
+SWU4 Hammer Ipsi 0.273902594375
+SWU4 Hammer Ipsi 0.313260844375
+SWU4 Hammer Ipsi 0.27063047249999994
+SWU4 Hammer Ipsi 0.343775351875
+SWU4 Hammer Ipsi 0.27505068312500003
+SWU4 Hammer Ipsi 0.2524861225
+SWU4 Hammer Ipsi 0.3659431725
+SWU4 Hammer Ipsi 0.24837429937499997
+SWU4 Hammer Ipsi 0.319901428125
+SWU4 Hammer Ipsi 0.360057718125
+SWU4 Hammer Ipsi 0.252063845
+SWU4 Hammer Ipsi 0.30619057375000003
+SWU4 Hammer Ipsi 0.251133546875
+SWU4 Hammer Ipsi 0.2646064525
+SWU4 Hammer Ipsi 0.3661736575
+SWU4 Hammer Ipsi 0.30472841250000005
+SWU4 Hammer Ipsi 0.228333936875
+SWU4 Hammer Ipsi 0.34164372
+SWU4 Hammer Ipsi 0.30802534375
+SWU4 Hammer Ipsi 0.369665524375
+SWU4 Hammer Ipsi 0.439102935
+SWU4 Hammer Ipsi 0.250876145625
+SWU4 Hammer Ipsi 0.24131824999999998
+SWU4 Hammer Ipsi 0.239216583125
+SWU4 Hammer Ipsi 0.24748581749999998
+SWU4 Hammer Ipsi 0.236766108125
+SWU4 Hammer Ipsi 0.240079810625
+SWU4 Hammer Ipsi 0.29296079187499996
+SWU4 Hammer Ipsi 0.279257264375
+SWU4 Hammer Ipsi 0.30484844125000005
+SWU4 Hammer Ipsi 0.36233534937499995
+SWU4 Hammer Ipsi 0.31356231187499994
+SWU4 Hammer Ipsi 0.19359185937499998
+SWU4 Hammer Ipsi 0.22183657625000003
+SWU4 Hammer Ipsi 0.2954454875
+SWU4 Hammer Ipsi 0.40666396500000007
+SWU4 Hammer Ipsi 0.31906698749999995
+SWU4 Hammer Ipsi 0.24312021
+SWU4 Hammer Ipsi 0.264096924375
+SWU4 Hammer Ipsi 0.407465459375
+SWU4 Hammer Ipsi 0.2500983625
+SWU4 Hammer Ipsi 0.287963819375
+SWU4 Hammer Ipsi 0.28945003375
+SWU4 Hammer Ipsi 0.32210711625
+SWU4 Hammer Ipsi 0.26623067499999997
+SWU4 Hammer Ipsi 0.374318049375
+SWU4 Hammer Ipsi 0.3432277449999999
+SWU4 Hammer Ipsi 0.307718015
+SWU4 Hammer Ipsi 0.27741583124999997
+SWU4 Hammer Ipsi 0.38859974875
+SWU4 Hammer Ipsi 0.21213837124999999
+SWU4 Hammer Ipsi 0.2803956875
+SWU4 Hammer Ipsi 0.3320540775
+SWU4 Hammer Ipsi 0.32180709125
+SWU4 Hammer Ipsi 0.29650900875
+SWU4 Hammer Ipsi 0.348024100625
+SWU4 Hammer Ipsi 0.260826285625
+SWU4 Hammer Ipsi 0.2884217475
+SWU4 Hammer Ipsi 0.251711698125
+SWU4 Hammer Ipsi 0.3330205125
+SWU4 Hammer Ipsi 0.2824726275
+SWU4 Hammer Ipsi 0.26921782375000003
+SWU4 Hammer Ipsi 0.308734139375
+SWU4 Hammer Ipsi 0.2245460625
+SWU4 Hammer Ipsi 0.268524924375
+SWU4 Hammer Ipsi 0.334684349375
+SWU4 Hammer Ipsi 0.265326745
+SWU4 Hammer Ipsi 0.29060028687499995
+SWU4 Hammer Ipsi 0.2727395325
+SWU4 Hammer Ipsi 0.340334915625
+SWU4 Hammer Ipsi 0.258957235625
+SWU4 Hammer Ipsi 0.16336975250000002
+SWU4 Hammer Ipsi 0.24212774499999998
+SWU4 Hammer Ipsi 0.28036945562500004
+SWU4 Hammer Ipsi 0.3288709575
+SWU4 Hammer Ipsi 0.2572014825
+SWU4 Hammer Ipsi 0.259999861875
+SWU4 Hammer Ipsi 0.286420095
+SWU4 Hammer Ipsi 0.24106069125
+SWU4 Hammer Ipsi 0.309061918125
+SWU4 Hammer Ipsi 0.30272680937499996
+SWU4 Hammer Ipsi 0.304048509375
+SWU4 Hammer Ipsi 0.257225543125
+SWU4 Hammer Ipsi 0.23680097562500002
+SWU4 Hammer Ipsi 0.410599566875
+SWU4 Hammer Ipsi 0.347818770625
+SWU4 Hammer Ipsi 0.342585619375
+SWU4 Hammer Ipsi 0.24550197875000002
+SWU4 Hammer Ipsi 0.33612247062499995
+SWU4 Hammer Ipsi 0.25924794625
+SWU4 Hammer Ipsi 0.234975206875
+SWU4 Hammer Ipsi 0.28324509
+SWU4 Hammer Ipsi 0.29194443
+SWU4 Hammer Ipsi 0.28421919375000004
+SWU4 Hammer Ipsi 0.36991442812499997
+SWU4 Hammer Ipsi 0.31459704499999996
+SWU4 Hammer Ipsi 0.23722868562499996
+SWU4 Hammer Ipsi 0.263570146875
+SWU4 Hammer Ipsi 0.317189609375
+SWU4 Hammer Hom 0.6799760769230769
+SWU4 Hammer Hom 0.5960397179487179
+SWU4 Hammer Hom 0.5520325384615387
+SWU4 Hammer Hom 0.5885541282051283
+SWU4 Hammer Hom 0.660621282051282
+SWU4 Hammer Hom 0.6733500000000001
+SWU4 Hammer Hom 0.6313924102564102
+SWU4 Hammer Hom 0.6756201025641025
+SWU4 Hammer Hom 0.6375472564102564
+SWU4 Hammer Hom 0.6960541025641026
+SWU4 Hammer Hom 0.5434709230769231
+SWU4 Hammer Hom 0.6831352564102565
+SWU4 Hammer Hom 0.6442911282051283
+SWU4 Hammer Hom 0.6002098205128206
+SWU4 Hammer Hom 0.5573404102564101
+SWU4 Hammer Hom 0.6902487692307692
+SWU4 Hammer Hom 0.584101
+SWU4 Hammer Hom 0.6895479743589743
+SWU4 Hammer Hom 0.5747684615384616
+SWU4 Hammer Hom 0.6984693589743589
+SWU4 Hammer Hom 0.594894641025641
+SWU4 Hammer Hom 0.6391009487179488
+SWU4 Hammer Hom 0.6387019230769232
+SWU4 Hammer Hom 0.6494297435897435
+SWU4 Hammer Hom 0.5957142564102564
+SWU4 Hammer Hom 0.7183071025641026
+SWU4 Hammer Hom 0.5878930256410256
+SWU4 Hammer Hom 0.6587165641025642
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+UPSM_1 Hammer Ipsi 0.530713115625
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+UPSM_1 Hammer Ipsi 0.46914908625
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+UPSM_1 Hammer Ipsi 0.25110252250000004
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+UPSM_1 Hammer Ipsi 0.214390521875
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+UPSM_1 Hammer Ipsi 0.34338547374999995
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+UPSM_1 Hammer Ipsi 0.39024232124999997
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+UPSM_1 Hammer Ipsi 0.324202735625
+UPSM_1 Hammer Ipsi 0.26130003875
+UPSM_1 Hammer Ipsi 0.22212603250000001
+UPSM_1 Hammer Ipsi 0.31986161750000003
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+UPSM_1 Hammer Ipsi 0.20649309000000002
+UPSM_1 Hammer Ipsi 0.23251975000000003
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+UPSM_1 Hammer Ipsi 0.20562060875
+UPSM_1 Hammer Ipsi 0.22818961999999998
+UPSM_1 Hammer Ipsi 0.37847057749999996
+UPSM_1 Hammer Ipsi 0.27106904125
+UPSM_1 Hammer Ipsi 0.23008913125
+UPSM_1 Hammer Ipsi 0.22580221250000002
+UPSM_1 Hammer Ipsi 0.38139947625000004
+UPSM_1 Hammer Ipsi 0.31116949625
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+UPSM_1 Hammer Ipsi 0.39221304500000004
+UPSM_1 Hammer Ipsi 0.40121109875
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+UPSM_1 Hammer Ipsi 0.38979034
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+UPSM_1 Hammer Ipsi 0.42293374624999996
+UPSM_1 Hammer Ipsi 0.35267317
+UPSM_1 Hammer Ipsi 0.21118364999999997
+UPSM_1 Hammer Ipsi 0.2844393275
+UPSM_1 Hammer Ipsi 0.26090022812499997
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+UPSM_1 Hammer Ipsi 0.21935398625000002
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+UPSM_1 Hammer Ipsi 0.384183903125
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+Dataset Atlas Type Value
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+MRN_1 Hammer Ipsi 0.27945020875
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+MRN_1 Hammer Ipsi 0.26086720750000003
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+MRN_1 Hammer Ipsi 0.30446745625
+MRN_1 Hammer Ipsi 0.4223624825
+MRN_1 Hammer Ipsi 0.37776675
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+MRN_1 Hammer Ipsi 0.372269869375
+MRN_1 Hammer Ipsi 0.199174665
+MRN_1 Hammer Ipsi 0.261984783125
+MRN_1 Hammer Ipsi 0.35690220249999993
+MRN_1 Hammer Ipsi 0.242716775
+MRN_1 Hammer Ipsi 0.24506259874999997
+MRN_1 Hammer Ipsi 0.23884166625
+MRN_1 Hammer Ipsi 0.29082263625000004
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+MRN_1 Hammer Ipsi 0.35545463999999993
+MRN_1 Hammer Ipsi 0.27099468375
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+MRN_1 Hammer Ipsi 0.21752589999999997
+MRN_1 Hammer Ipsi 0.3079758575
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+MRN_1 Hammer Ipsi 0.35240281625000003
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+MRN_1 Hammer Ipsi 0.33925304312500004
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+MRN_1 Hammer Ipsi 0.34696011624999995
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+MRN_1 Hammer Ipsi 0.244553218125
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+MRN_1 Hammer Ipsi 0.31207924125000003
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+MRN_1 Hammer Ipsi 0.235522264375
+MRN_1 Hammer Hom 0.7089236923076923
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+MRN_1 Hammer Hom 0.6928976666666667
+MRN_1 Hammer Hom 0.6157458717948717
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+MRN_1 Hammer Hom 0.6601461794871795
+MRN_1 Hammer Hom 0.6302775897435898
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+MRN_1 Hammer Contra 0.3014682435977514
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+MRN_1 Hammer Contra 0.21271605559025608
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+Dataset Atlas Type Value
+IPCAS3 Hammer Ipsi 0.347169458125
+IPCAS3 Hammer Ipsi 0.31070459437500003
+IPCAS3 Hammer Ipsi 0.407506144375
+IPCAS3 Hammer Ipsi 0.293828013125
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+IPCAS3 Hammer Ipsi 0.24617185499999997
+IPCAS3 Hammer Ipsi 0.24336714437499998
+IPCAS3 Hammer Ipsi 0.241871625
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+IPCAS3 Hammer Ipsi 0.3191312575
+IPCAS3 Hammer Ipsi 0.21168085374999998
+IPCAS3 Hammer Ipsi 0.25782517625
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+IPCAS3 Hammer Ipsi 0.23309709312499996
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+IPCAS3 Hammer Ipsi 0.38107778125
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+IPCAS3 Hammer Hom 0.6528153846153847
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+JHNU_bids Hammer Contra 0.26041827357901315
+JHNU_bids Hammer Contra 0.2219435003123048
+JHNU_bids Hammer Contra 0.2594652504684572
+JHNU_bids Hammer Contra 0.3588213960024984
+JHNU_bids Hammer Contra 0.2835970880699563
+JHNU_bids Hammer Contra 0.3188079544034978
+JHNU_bids Hammer Contra 0.273418157401624
+JHNU_bids Hammer Contra 0.28410839600249843
+JHNU_bids Hammer Contra 0.22310537476577136
+JHNU_bids Hammer Contra 0.33273373828856967
+JHNU_bids Hammer Contra 0.25322531730168646
+JHNU_bids Hammer Contra 0.22657342223610244
+Dataset Atlas Type Value
+IACAS_1 Hammer Ipsi 0.19174622625
+IACAS_1 Hammer Ipsi 0.22653786625000003
+IACAS_1 Hammer Ipsi 0.25264435687500003
+IACAS_1 Hammer Ipsi 0.242549195625
+IACAS_1 Hammer Ipsi 0.21332869
+IACAS_1 Hammer Ipsi 0.21512386437499997
+IACAS_1 Hammer Ipsi 0.22914918874999998
+IACAS_1 Hammer Ipsi 0.303449181875
+IACAS_1 Hammer Ipsi 0.19019702875
+IACAS_1 Hammer Ipsi 0.2123889
+IACAS_1 Hammer Ipsi 0.33277890499999996
+IACAS_1 Hammer Ipsi 0.25332055875000004
+IACAS_1 Hammer Ipsi 0.36533329875
+IACAS_1 Hammer Ipsi 0.23769431687500003
+IACAS_1 Hammer Ipsi 0.42756810562500003
+IACAS_1 Hammer Ipsi 0.27104973
+IACAS_1 Hammer Ipsi 0.24185097875
+IACAS_1 Hammer Ipsi 0.18686946187500003
+IACAS_1 Hammer Ipsi 0.24922494
+IACAS_1 Hammer Ipsi 0.19662276187499997
+IACAS_1 Hammer Ipsi 0.22376755000000004
+IACAS_1 Hammer Ipsi 0.26788621
+IACAS_1 Hammer Ipsi 0.199207656875
+IACAS_1 Hammer Ipsi 0.257464121875
+IACAS_1 Hammer Ipsi 0.29100190499999995
+IACAS_1 Hammer Ipsi 0.209957081875
+IACAS_1 Hammer Ipsi 0.246857414375
+IACAS_1 Hammer Ipsi 0.221518914375
+IACAS_1 Hammer Ipsi 0.289052575625
+IACAS_1 Hammer Ipsi 0.2486371725
+IACAS_1 Hammer Ipsi 0.23376360125000006
+IACAS_1 Hammer Ipsi 0.16539257062499998
+IACAS_1 Hammer Ipsi 0.29310330375
+IACAS_1 Hammer Ipsi 0.23172743562500003
+IACAS_1 Hammer Ipsi 0.22868769249999998
+IACAS_1 Hammer Ipsi 0.28516701125
+IACAS_1 Hammer Ipsi 0.245269864375
+IACAS_1 Hammer Ipsi 0.2685237975
+IACAS_1 Hammer Ipsi 0.19120211374999996
+IACAS_1 Hammer Ipsi 0.23407841875000002
+IACAS_1 Hammer Ipsi 0.195309043125
+IACAS_1 Hammer Ipsi 0.246465253125
+IACAS_1 Hammer Ipsi 0.202333268125
+IACAS_1 Hammer Ipsi 0.24450811125000002
+IACAS_1 Hammer Ipsi 0.230153696875
+IACAS_1 Hammer Ipsi 0.242422406875
+IACAS_1 Hammer Ipsi 0.263721565625
+IACAS_1 Hammer Ipsi 0.297166336875
+IACAS_1 Hammer Ipsi 0.21072663375
+IACAS_1 Hammer Ipsi 0.22137084437500001
+IACAS_1 Hammer Ipsi 0.307289315
+IACAS_1 Hammer Ipsi 0.204606205625
+IACAS_1 Hammer Ipsi 0.21124112250000004
+IACAS_1 Hammer Ipsi 0.21302857625000002
+IACAS_1 Hammer Ipsi 0.30249829500000003
+IACAS_1 Hammer Hom 0.5072951282051281
+IACAS_1 Hammer Hom 0.6110046666666668
+IACAS_1 Hammer Hom 0.5666234615384615
+IACAS_1 Hammer Hom 0.6064661025641026
+IACAS_1 Hammer Hom 0.6070039487179486
+IACAS_1 Hammer Hom 0.5107137435897438
+IACAS_1 Hammer Hom 0.48903641025641026
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+IACAS_1 Hammer Hom 0.6118525641025641
+IACAS_1 Hammer Hom 0.5690362564102566
+IACAS_1 Hammer Hom 0.5876508461538462
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+IACAS_1 Hammer Hom 0.6511422564102565
+IACAS_1 Hammer Hom 0.6006700256410256
+IACAS_1 Hammer Hom 0.691937282051282
+IACAS_1 Hammer Hom 0.6257595641025642
+IACAS_1 Hammer Hom 0.5673476923076923
+IACAS_1 Hammer Hom 0.6289901282051283
+IACAS_1 Hammer Hom 0.5843118205128205
+IACAS_1 Hammer Hom 0.5432243333333334
+IACAS_1 Hammer Hom 0.5172593589743589
+IACAS_1 Hammer Hom 0.646207282051282
+IACAS_1 Hammer Hom 0.3005801794871796
+IACAS_1 Hammer Hom 0.5778122051282051
+IACAS_1 Hammer Hom 0.6178307692307694
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+IACAS_1 Hammer Hom 0.6110452564102565
+IACAS_1 Hammer Hom 0.602481
+IACAS_1 Hammer Hom 0.6637310769230768
+IACAS_1 Hammer Hom 0.5805555128205128
+IACAS_1 Hammer Hom 0.5540642564102564
+IACAS_1 Hammer Hom 0.4329304871794872
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+IACAS_1 Hammer Hom 0.6461048205128205
+IACAS_1 Hammer Hom 0.5757605384615385
+IACAS_1 Hammer Hom 0.591300282051282
+IACAS_1 Hammer Hom 0.34243505128205126
+IACAS_1 Hammer Hom 0.6303988717948716
+IACAS_1 Hammer Hom 0.39277164102564105
+IACAS_1 Hammer Hom 0.582913794871795
+IACAS_1 Hammer Hom 0.5440305641025641
+IACAS_1 Hammer Hom 0.5692448205128203
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+IACAS_1 Hammer Hom 0.748727435897436
+IACAS_1 Hammer Hom 0.6390282564102564
+IACAS_1 Hammer Hom 0.5872303846153846
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+IACAS_1 Hammer Hom 0.4925154358974359
+IACAS_1 Hammer Hom 0.5904876153846156
+IACAS_1 Hammer Hom 0.6926532820512821
+IACAS_1 Hammer Contra 0.17859809119300438
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+IACAS_1 Hammer Contra 0.2225151374141162
+IACAS_1 Hammer Contra 0.20460933728919425
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+IACAS_1 Hammer Contra 0.1735679188007495
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+IACAS_1 Hammer Contra 0.21461396127420362
+IACAS_1 Hammer Contra 0.23290759088069954
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+IACAS_1 Hammer Contra 0.2004329875078076
+IACAS_1 Hammer Contra 0.2844125053091818
+Dataset Atlas Type Value
+SWU2 Hammer Ipsi 0.20934130437499998
+SWU2 Hammer Ipsi 0.286614336875
+SWU2 Hammer Ipsi 0.29415634812499997
+SWU2 Hammer Ipsi 0.21372649125
+SWU2 Hammer Ipsi 0.36305276062499997
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+SWU2 Hammer Ipsi 0.278597880625
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+SWU2 Hammer Ipsi 0.308922395625
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+SWU2 Hammer Ipsi 0.21792200625
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+SWU2 Hammer Ipsi 0.24604938875000001
+SWU2 Hammer Ipsi 0.209103259375
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+SWU2 Hammer Ipsi 0.318449458125
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+SWU2 Hammer Ipsi 0.25854710000000003
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+SWU2 Hammer Ipsi 0.273380480625
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+SWU2 Hammer Ipsi 0.20836992312499997
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+SWU2 Hammer Ipsi 0.25908689375
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+SWU2 Hammer Hom 0.5630446666666665
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+SWU2 Hammer Contra 0.19414466958151153
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+Dataset Atlas Type Value
+SWU3 Hammer Ipsi 0.272421455625
+SWU3 Hammer Ipsi 0.39497994062499997
+SWU3 Hammer Ipsi 0.30183858625
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+SWU3 Hammer Contra 0.3078363666458463
+SWU3 Hammer Contra 0.29473175327920054
+SWU3 Hammer Contra 0.2842749300437226
+SWU3 Hammer Contra 0.2852607776389756
+SWU3 Hammer Contra 0.30510993753903815
+SWU3 Hammer Contra 0.27905046845721426
+SWU3 Hammer Contra 0.2616069993753904
+SWU3 Hammer Contra 0.24511648657089322
+SWU3 Hammer Contra 0.23827850093691444
+SWU3 Hammer Contra 0.26587593066833226
+SWU3 Hammer Contra 0.27526613241723924
+SWU3 Hammer Contra 0.2895212498438476
+SWU3 Hammer Contra 0.24389807745159275
+SWU3 Hammer Contra 0.23653980387257967
+SWU3 Hammer Contra 0.2625036002498438
+SWU3 Hammer Contra 0.28594735290443474
+SWU3 Hammer Contra 0.27054767395377893
+SWU3 Hammer Contra 0.2348855627732667
+SWU3 Hammer Contra 0.2296025178013741
+SWU3 Hammer Contra 0.29159714366021233
+SWU3 Hammer Contra 0.21597106683322922
+SWU3 Hammer Contra 0.23943893316677076
+SWU3 Hammer Contra 0.2498160662086196
+SWU3 Hammer Contra 0.2425835084322299
+SWU3 Hammer Contra 0.1902452273579013
+SWU3 Hammer Contra 0.21807036851967523
+SWU3 Hammer Contra 0.2626564303560275
+SWU3 Hammer Contra 0.35511936727045607
+SWU3 Hammer Contra 0.24832461961274205
+Dataset Atlas Type Value
+IPCAS4 Hammer Ipsi 0.239244850625
+IPCAS4 Hammer Ipsi 0.31151042937499995
+IPCAS4 Hammer Ipsi 0.292173876875
+IPCAS4 Hammer Ipsi 0.253474223125
+IPCAS4 Hammer Ipsi 0.273649879375
+IPCAS4 Hammer Ipsi 0.5194638943750001
+IPCAS4 Hammer Ipsi 0.40966168374999995
+IPCAS4 Hammer Ipsi 0.489862795625
+IPCAS4 Hammer Ipsi 0.30373213875
+IPCAS4 Hammer Ipsi 0.363107695625
+IPCAS4 Hammer Ipsi 0.432953205625
+IPCAS4 Hammer Ipsi 0.240883298125
+IPCAS4 Hammer Ipsi 0.24184945937499996
+IPCAS4 Hammer Ipsi 0.233777189375
+IPCAS4 Hammer Ipsi 0.20829885937500003
+IPCAS4 Hammer Ipsi 0.24845491062500002
+IPCAS4 Hammer Ipsi 0.273184268125
+IPCAS4 Hammer Ipsi 0.330935258125
+IPCAS4 Hammer Ipsi 0.257381044375
+IPCAS4 Hammer Ipsi 0.25636589375
+IPCAS4 Hammer Ipsi 0.22699737062500003
+IPCAS4 Hammer Ipsi 0.29295208500000003
+IPCAS4 Hammer Ipsi 0.2378313275
+IPCAS4 Hammer Ipsi 0.29466826124999995
+IPCAS4 Hammer Ipsi 0.29721328687500004
+IPCAS4 Hammer Ipsi 0.32284665625000003
+IPCAS4 Hammer Ipsi 0.25086592875
+IPCAS4 Hammer Ipsi 0.443723845625
+IPCAS4 Hammer Ipsi 0.307217183125
+IPCAS4 Hammer Ipsi 0.303271031875
+IPCAS4 Hammer Ipsi 0.324076154375
+IPCAS4 Hammer Ipsi 0.2653582525
+IPCAS4 Hammer Ipsi 0.33876959125
+IPCAS4 Hammer Ipsi 0.279881009375
+IPCAS4 Hammer Ipsi 0.3435301137499999
+IPCAS4 Hammer Ipsi 0.30379560625
+IPCAS4 Hammer Ipsi 0.2613049825
+IPCAS4 Hammer Ipsi 0.31803204812499997
+IPCAS4 Hammer Ipsi 0.236027765
+IPCAS4 Hammer Ipsi 0.23192804312499998
+IPCAS4 Hammer Hom 0.5484995897435898
+IPCAS4 Hammer Hom 0.5983967435897436
+IPCAS4 Hammer Hom 0.5864579487179488
+IPCAS4 Hammer Hom 0.5586600512820512
+IPCAS4 Hammer Hom 0.5693348974358974
+IPCAS4 Hammer Hom 0.7664881794871794
+IPCAS4 Hammer Hom 0.6975782564102564
+IPCAS4 Hammer Hom 0.6630605384615383
+IPCAS4 Hammer Hom 0.6936124358974358
+IPCAS4 Hammer Hom 0.6184656153846156
+IPCAS4 Hammer Hom 0.6879044102564104
+IPCAS4 Hammer Hom 0.5514656153846155
+IPCAS4 Hammer Hom 0.5884375897435896
+IPCAS4 Hammer Hom 0.549984641025641
+IPCAS4 Hammer Hom 0.537134794871795
+IPCAS4 Hammer Hom 0.5990053846153847
+IPCAS4 Hammer Hom 0.5820366923076924
+IPCAS4 Hammer Hom 0.6431127948717948
+IPCAS4 Hammer Hom 0.5802472307692309
+IPCAS4 Hammer Hom 0.5244277692307691
+IPCAS4 Hammer Hom 0.5504484871794872
+IPCAS4 Hammer Hom 0.6635178974358974
+IPCAS4 Hammer Hom 0.5358404871794872
+IPCAS4 Hammer Hom 0.6025209487179486
+IPCAS4 Hammer Hom 0.5047182820512821
+IPCAS4 Hammer Hom 0.5341489743589742
+IPCAS4 Hammer Hom 0.516281076923077
+IPCAS4 Hammer Hom 0.6362183589743591
+IPCAS4 Hammer Hom 0.5662942564102564
+IPCAS4 Hammer Hom 0.5010724102564103
+IPCAS4 Hammer Hom 0.5810385897435898
+IPCAS4 Hammer Hom 0.627791641025641
+IPCAS4 Hammer Hom 0.6166107435897435
+IPCAS4 Hammer Hom 0.4965791794871795
+IPCAS4 Hammer Hom 0.643808923076923
+IPCAS4 Hammer Hom 0.5825813846153846
+IPCAS4 Hammer Hom 0.5895844871794872
+IPCAS4 Hammer Hom 0.6599969230769231
+IPCAS4 Hammer Hom 0.5953206923076924
+IPCAS4 Hammer Hom 0.5906231025641026
+IPCAS4 Hammer Contra 0.2308770037476577
+IPCAS4 Hammer Contra 0.3033929806371018
+IPCAS4 Hammer Contra 0.273190881948782
+IPCAS4 Hammer Contra 0.23590002623360398
+IPCAS4 Hammer Contra 0.2677802804497189
+IPCAS4 Hammer Contra 0.5180664684572143
+IPCAS4 Hammer Contra 0.38990322985633985
+IPCAS4 Hammer Contra 0.4689922792004997
+IPCAS4 Hammer Contra 0.29441002435977515
+IPCAS4 Hammer Contra 0.3535675252966896
+IPCAS4 Hammer Contra 0.42684625733916304
+IPCAS4 Hammer Contra 0.22964340724547155
+IPCAS4 Hammer Contra 0.2309715727670206
+IPCAS4 Hammer Contra 0.22971842973141784
+IPCAS4 Hammer Contra 0.1983028719550281
+IPCAS4 Hammer Contra 0.2381150068707058
+IPCAS4 Hammer Contra 0.2608475215490319
+IPCAS4 Hammer Contra 0.3173872916926921
+IPCAS4 Hammer Contra 0.25078794316052466
+IPCAS4 Hammer Contra 0.24007424921923798
+IPCAS4 Hammer Contra 0.2190887108057464
+IPCAS4 Hammer Contra 0.27380844784509684
+IPCAS4 Hammer Contra 0.21490374203622734
+IPCAS4 Hammer Contra 0.2903020487195503
+IPCAS4 Hammer Contra 0.2700112729544035
+IPCAS4 Hammer Contra 0.2738014709556527
+IPCAS4 Hammer Contra 0.22513309306683324
+IPCAS4 Hammer Contra 0.43398394628357273
+IPCAS4 Hammer Contra 0.29564268644597125
+IPCAS4 Hammer Contra 0.29498382323547784
+IPCAS4 Hammer Contra 0.31293943597751406
+IPCAS4 Hammer Contra 0.24753269581511556
+IPCAS4 Hammer Contra 0.3298492254840725
+IPCAS4 Hammer Contra 0.25042373766396003
+IPCAS4 Hammer Contra 0.33525486071205496
+IPCAS4 Hammer Contra 0.2772490455965022
+IPCAS4 Hammer Contra 0.24914420299812617
+IPCAS4 Hammer Contra 0.3079458388507183
+IPCAS4 Hammer Contra 0.2292139275452842
+IPCAS4 Hammer Contra 0.21862295565271705
+Dataset Atlas Type Value
+IPCAS_8 Hammer Ipsi 0.32847143312499993
+IPCAS_8 Hammer Ipsi 0.45833532875000005
+IPCAS_8 Hammer Ipsi 0.2371187975
+IPCAS_8 Hammer Ipsi 0.380628113125
+IPCAS_8 Hammer Ipsi 0.48336225937500005
+IPCAS_8 Hammer Ipsi 0.30146460875000003
+IPCAS_8 Hammer Ipsi 0.348065819375
+IPCAS_8 Hammer Ipsi 0.45726486
+IPCAS_8 Hammer Ipsi 0.39983082875000003
+IPCAS_8 Hammer Ipsi 0.44449229625000003
+IPCAS_8 Hammer Ipsi 0.587982913125
+IPCAS_8 Hammer Ipsi 0.35363701625
+IPCAS_8 Hammer Ipsi 0.36496607374999995
+IPCAS_8 Hammer Ipsi 0.35358568375000005
+IPCAS_8 Hammer Ipsi 0.456030003125
+IPCAS_8 Hammer Ipsi 0.25821812125
+IPCAS_8 Hammer Ipsi 0.42753101250000003
+IPCAS_8 Hammer Ipsi 0.28267941874999997
+IPCAS_8 Hammer Ipsi 0.36642351687499997
+IPCAS_8 Hammer Ipsi 0.25748551625
+IPCAS_8 Hammer Ipsi 0.238105395625
+IPCAS_8 Hammer Ipsi 0.3948227825
+IPCAS_8 Hammer Ipsi 0.385462919375
+IPCAS_8 Hammer Ipsi 0.32431729875000004
+IPCAS_8 Hammer Ipsi 0.3859555043749999
+IPCAS_8 Hammer Ipsi 0.2841677625
+IPCAS_8 Hammer Hom 0.588461435897436
+IPCAS_8 Hammer Hom 0.7272898205128205
+IPCAS_8 Hammer Hom 0.6605751538461537
+IPCAS_8 Hammer Hom 0.7140628205128206
+IPCAS_8 Hammer Hom 0.7073095384615383
+IPCAS_8 Hammer Hom 0.6084198461538463
+IPCAS_8 Hammer Hom 0.7189709230769231
+IPCAS_8 Hammer Hom 0.7221030256410256
+IPCAS_8 Hammer Hom 0.7190220256410255
+IPCAS_8 Hammer Hom 0.6981988205128208
+IPCAS_8 Hammer Hom 0.8012400512820514
+IPCAS_8 Hammer Hom 0.6483898461538462
+IPCAS_8 Hammer Hom 0.6979398717948718
+IPCAS_8 Hammer Hom 0.6922527948717948
+IPCAS_8 Hammer Hom 0.7323035641025641
+IPCAS_8 Hammer Hom 0.5780074102564102
+IPCAS_8 Hammer Hom 0.718327717948718
+IPCAS_8 Hammer Hom 0.6536381282051281
+IPCAS_8 Hammer Hom 0.709393717948718
+IPCAS_8 Hammer Hom 0.6022726410256408
+IPCAS_8 Hammer Hom 0.5117520000000001
+IPCAS_8 Hammer Hom 0.6902070256410257
+IPCAS_8 Hammer Hom 0.7296434615384616
+IPCAS_8 Hammer Hom 0.6597911794871794
+IPCAS_8 Hammer Hom 0.6970043846153845
+IPCAS_8 Hammer Hom 0.5825608974358973
+IPCAS_8 Hammer Contra 0.313064472204872
+IPCAS_8 Hammer Contra 0.44385310243597753
+IPCAS_8 Hammer Contra 0.23010350530918178
+IPCAS_8 Hammer Contra 0.3775523797626483
+IPCAS_8 Hammer Contra 0.48158710181136793
+IPCAS_8 Hammer Contra 0.2909039369144285
+IPCAS_8 Hammer Contra 0.3454607239225484
+IPCAS_8 Hammer Contra 0.4365615490318551
+IPCAS_8 Hammer Contra 0.39219383010618364
+IPCAS_8 Hammer Contra 0.42764036602123673
+IPCAS_8 Hammer Contra 0.5844382386008745
+IPCAS_8 Hammer Contra 0.3330396808244847
+IPCAS_8 Hammer Contra 0.3564334478450968
+IPCAS_8 Hammer Contra 0.34326675140537166
+IPCAS_8 Hammer Contra 0.4444925046845721
+IPCAS_8 Hammer Contra 0.24516944659587755
+IPCAS_8 Hammer Contra 0.40859119612742034
+IPCAS_8 Hammer Contra 0.275760645846346
+IPCAS_8 Hammer Contra 0.3635069681449095
+IPCAS_8 Hammer Contra 0.24633592254840728
+IPCAS_8 Hammer Contra 0.2196593641474079
+IPCAS_8 Hammer Contra 0.3840009425359151
+IPCAS_8 Hammer Contra 0.3804325646470955
+IPCAS_8 Hammer Contra 0.3170298669581512
+IPCAS_8 Hammer Contra 0.37672935727670204
+IPCAS_8 Hammer Contra 0.2746677613991256
+Dataset Atlas Type Value
+ABIDEII-SDSU_1 Hammer Ipsi 0.304428068125
+ABIDEII-SDSU_1 Hammer Ipsi 0.2567526675
+ABIDEII-SDSU_1 Hammer Ipsi 0.22787458875
+ABIDEII-SDSU_1 Hammer Ipsi 0.319424205
+ABIDEII-SDSU_1 Hammer Ipsi 0.210148765
+ABIDEII-SDSU_1 Hammer Ipsi 0.3413301525
+ABIDEII-SDSU_1 Hammer Ipsi 0.42580248499999995
+ABIDEII-SDSU_1 Hammer Ipsi 0.216158596875
+ABIDEII-SDSU_1 Hammer Ipsi 0.23307923500000002
+ABIDEII-SDSU_1 Hammer Ipsi 0.23742352375000003
+ABIDEII-SDSU_1 Hammer Ipsi 0.247174741875
+ABIDEII-SDSU_1 Hammer Ipsi 0.20810095687500002
+ABIDEII-SDSU_1 Hammer Ipsi 0.4772300525
+ABIDEII-SDSU_1 Hammer Ipsi 0.332020771875
+ABIDEII-SDSU_1 Hammer Ipsi 0.337777438125
+ABIDEII-SDSU_1 Hammer Ipsi 0.52537723375
+ABIDEII-SDSU_1 Hammer Ipsi 0.178240575625
+ABIDEII-SDSU_1 Hammer Ipsi 0.27385089687499997
+ABIDEII-SDSU_1 Hammer Ipsi 0.235233743125
+ABIDEII-SDSU_1 Hammer Ipsi 0.27697723500000004
+ABIDEII-SDSU_1 Hammer Ipsi 0.30618031125
+ABIDEII-SDSU_1 Hammer Ipsi 0.26361026062500004
+ABIDEII-SDSU_1 Hammer Ipsi 0.21384653437499998
+ABIDEII-SDSU_1 Hammer Ipsi 0.37863516437499994
+ABIDEII-SDSU_1 Hammer Hom 0.6136013333333334
+ABIDEII-SDSU_1 Hammer Hom 0.6374575384615385
+ABIDEII-SDSU_1 Hammer Hom 0.5197535641025641
+ABIDEII-SDSU_1 Hammer Hom 0.651937076923077
+ABIDEII-SDSU_1 Hammer Hom 0.5279676666666666
+ABIDEII-SDSU_1 Hammer Hom 0.6347255128205127
+ABIDEII-SDSU_1 Hammer Hom 0.7165906923076922
+ABIDEII-SDSU_1 Hammer Hom 0.5115116666666667
+ABIDEII-SDSU_1 Hammer Hom 0.5544171282051281
+ABIDEII-SDSU_1 Hammer Hom 0.5996077179487179
+ABIDEII-SDSU_1 Hammer Hom 0.5551491025641025
+ABIDEII-SDSU_1 Hammer Hom 0.5533694615384616
+ABIDEII-SDSU_1 Hammer Hom 0.762336435897436
+ABIDEII-SDSU_1 Hammer Hom 0.6540079487179487
+ABIDEII-SDSU_1 Hammer Hom 0.612099846153846
+ABIDEII-SDSU_1 Hammer Hom 0.7765067692307692
+ABIDEII-SDSU_1 Hammer Hom 0.48770282051282066
+ABIDEII-SDSU_1 Hammer Hom 0.5617809230769232
+ABIDEII-SDSU_1 Hammer Hom 0.5576182051282051
+ABIDEII-SDSU_1 Hammer Hom 0.6202507948717948
+ABIDEII-SDSU_1 Hammer Hom 0.6847429487179487
+ABIDEII-SDSU_1 Hammer Hom 0.6620125897435898
+ABIDEII-SDSU_1 Hammer Hom 0.4319388974358975
+ABIDEII-SDSU_1 Hammer Hom 0.6794550512820513
+ABIDEII-SDSU_1 Hammer Contra 0.27766459400374766
+ABIDEII-SDSU_1 Hammer Contra 0.23324236789506556
+ABIDEII-SDSU_1 Hammer Contra 0.20076790568394753
+ABIDEII-SDSU_1 Hammer Contra 0.2903524797001874
+ABIDEII-SDSU_1 Hammer Contra 0.17963857151780138
+ABIDEII-SDSU_1 Hammer Contra 0.3125568432229856
+ABIDEII-SDSU_1 Hammer Contra 0.4037062017489069
+ABIDEII-SDSU_1 Hammer Contra 0.18731952904434726
+ABIDEII-SDSU_1 Hammer Contra 0.20079431168019984
+ABIDEII-SDSU_1 Hammer Contra 0.2088716595877576
+ABIDEII-SDSU_1 Hammer Contra 0.23191607807620238
+ABIDEII-SDSU_1 Hammer Contra 0.19250191567770147
+ABIDEII-SDSU_1 Hammer Contra 0.4599456233603998
+ABIDEII-SDSU_1 Hammer Contra 0.3072308975640225
+ABIDEII-SDSU_1 Hammer Contra 0.3077210237351655
+ABIDEII-SDSU_1 Hammer Contra 0.5173725309181761
+ABIDEII-SDSU_1 Hammer Contra 0.163002724547158
+ABIDEII-SDSU_1 Hammer Contra 0.2331871711430356
+ABIDEII-SDSU_1 Hammer Contra 0.20451850905683946
+ABIDEII-SDSU_1 Hammer Contra 0.2378425221736415
+ABIDEII-SDSU_1 Hammer Contra 0.29189231355402867
+ABIDEII-SDSU_1 Hammer Contra 0.25022627170518424
+ABIDEII-SDSU_1 Hammer Contra 0.18024592192379763
+ABIDEII-SDSU_1 Hammer Contra 0.33169369269206744
+Dataset Atlas Type Value
+ABIDEII-TCD_1 Hammer Ipsi 0.22865285125
+ABIDEII-TCD_1 Hammer Ipsi 0.23208803
+ABIDEII-TCD_1 Hammer Ipsi 0.507073375625
+ABIDEII-TCD_1 Hammer Ipsi 0.234522298125
+ABIDEII-TCD_1 Hammer Ipsi 0.322611994375
+ABIDEII-TCD_1 Hammer Ipsi 0.299870575
+ABIDEII-TCD_1 Hammer Ipsi 0.28677516812500004
+ABIDEII-TCD_1 Hammer Ipsi 0.20439190499999998
+ABIDEII-TCD_1 Hammer Ipsi 0.289968015625
+ABIDEII-TCD_1 Hammer Ipsi 0.351965260625
+ABIDEII-TCD_1 Hammer Ipsi 0.32030585625
+ABIDEII-TCD_1 Hammer Ipsi 0.256680696875
+ABIDEII-TCD_1 Hammer Ipsi 0.252837918125
+ABIDEII-TCD_1 Hammer Ipsi 0.23685032312499998
+ABIDEII-TCD_1 Hammer Ipsi 0.2469346475
+ABIDEII-TCD_1 Hammer Ipsi 0.47711951812500003
+ABIDEII-TCD_1 Hammer Ipsi 0.24553786875
+ABIDEII-TCD_1 Hammer Ipsi 0.226510638125
+ABIDEII-TCD_1 Hammer Ipsi 0.31069441937500003
+ABIDEII-TCD_1 Hammer Ipsi 0.29746956124999996
+ABIDEII-TCD_1 Hammer Ipsi 0.26337156812499996
+ABIDEII-TCD_1 Hammer Hom 0.6349788461538461
+ABIDEII-TCD_1 Hammer Hom 0.6268055897435898
+ABIDEII-TCD_1 Hammer Hom 0.7894640256410257
+ABIDEII-TCD_1 Hammer Hom 0.5059431538461538
+ABIDEII-TCD_1 Hammer Hom 0.6754178717948717
+ABIDEII-TCD_1 Hammer Hom 0.6277778205128205
+ABIDEII-TCD_1 Hammer Hom 0.6748037179487181
+ABIDEII-TCD_1 Hammer Hom 0.584357076923077
+ABIDEII-TCD_1 Hammer Hom 0.635164846153846
+ABIDEII-TCD_1 Hammer Hom 0.6642457435897438
+ABIDEII-TCD_1 Hammer Hom 0.6728700256410257
+ABIDEII-TCD_1 Hammer Hom 0.5716978205128205
+ABIDEII-TCD_1 Hammer Hom 0.6244213589743589
+ABIDEII-TCD_1 Hammer Hom 0.5558972564102564
+ABIDEII-TCD_1 Hammer Hom 0.6083771794871795
+ABIDEII-TCD_1 Hammer Hom 0.7987386410256411
+ABIDEII-TCD_1 Hammer Hom 0.5708362307692307
+ABIDEII-TCD_1 Hammer Hom 0.6167523846153846
+ABIDEII-TCD_1 Hammer Hom 0.7190281282051281
+ABIDEII-TCD_1 Hammer Hom 0.670332717948718
+ABIDEII-TCD_1 Hammer Hom 0.6698430769230769
+ABIDEII-TCD_1 Hammer Contra 0.21403683135540286
+ABIDEII-TCD_1 Hammer Contra 0.21304077014366019
+ABIDEII-TCD_1 Hammer Contra 0.49529881511555274
+ABIDEII-TCD_1 Hammer Contra 0.1982355402873204
+ABIDEII-TCD_1 Hammer Contra 0.3064358613366646
+ABIDEII-TCD_1 Hammer Contra 0.2734418719550281
+ABIDEII-TCD_1 Hammer Contra 0.2658730580886946
+ABIDEII-TCD_1 Hammer Contra 0.19138634665833854
+ABIDEII-TCD_1 Hammer Contra 0.2699935484072455
+ABIDEII-TCD_1 Hammer Contra 0.3139769662710805
+ABIDEII-TCD_1 Hammer Contra 0.29677374703310433
+ABIDEII-TCD_1 Hammer Contra 0.23532457401623985
+ABIDEII-TCD_1 Hammer Contra 0.240959013116802
+ABIDEII-TCD_1 Hammer Contra 0.21913223797626483
+ABIDEII-TCD_1 Hammer Contra 0.22560135852592128
+ABIDEII-TCD_1 Hammer Contra 0.46472856589631484
+ABIDEII-TCD_1 Hammer Contra 0.21729606183635228
+ABIDEII-TCD_1 Hammer Contra 0.210923729544035
+ABIDEII-TCD_1 Hammer Contra 0.30128330730793257
+ABIDEII-TCD_1 Hammer Contra 0.2847798994378513
+ABIDEII-TCD_1 Hammer Contra 0.2483324971892567
diff --git a/docs/paper/data/functional_disc.csv b/docs/paper/data/functional_disc.csv
new file mode 100644
index 000000000..f2f557d7d
--- /dev/null
+++ b/docs/paper/data/functional_disc.csv
@@ -0,0 +1,37 @@
+,Mean,BNU1 [57/2],IPCAS1 [30/3-4],SWU1 [20/6],IPCAS7 [72/2],BNU3 [48/3],UPSM1 [100/2],XHCUMS [24/12],NYU2 [187/2-4],HNU1 [30/10],SWU4 [235/2]
+Schaefer1000,0.978527590325483,0.990433673469388,0.992185924581569,0.985000749962502,1.0,0.989041568163909,0.957041557107577,0.954752138883062,0.980692834412396,0.989564495530013,0.97539994147198
+Slab907,0.976019450233936,0.996811224489796,0.990774349667272,0.991480425978701,0.997750391236307,0.991996650906226,0.944042293945171,0.941729304290289,0.970672583330068,0.992618135376756,0.979033556064966
+Schaefer400,0.967886353827299,0.981611394557823,0.984398151801292,0.97655617219139,0.994816118935837,0.976014578408196,0.933608341882958,0.942230538702514,0.969495722546002,0.985633461047254,0.965547724723211
+Slab1068,0.96576829180431,0.989583333333333,0.98860657390603,0.985436122930696,0.995109546165884,0.988204294720252,0.904918045886361,0.921415758310532,0.957482054685518,0.991383141762452,0.977021655367507
+CAPRSC [333],0.961490430123815,0.979272959183674,0.986666315964825,0.974937042621553,0.997603677621283,0.97763987391647,0.912995413107283,0.917172724281844,0.959664430023048,0.983072796934866,0.966834121835829
+Schaefer300,0.960865015094629,0.976084183673469,0.97847567444348,0.969416529173541,0.993984741784038,0.96875,0.921016119139592,0.932247476085502,0.96226132424273,0.982403575989783,0.958091498805053
+AICHA [384],0.951219110796905,0.973639455782313,0.977088210280824,0.962157681589605,0.994620500782472,0.958628841607565,0.904965081937164,0.907650194693311,0.952904267258856,0.978360153256705,0.94844047212603
+Schaefer200,0.949092012763677,0.970769557823129,0.966227412609485,0.958497206718612,0.98816510172144,0.949271079590228,0.901090449417661,0.915008034239715,0.950823952441123,0.977982120051085,0.947343071745598
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diff --git a/docs/paper/discrim.py b/docs/paper/discrim.py
new file mode 100644
index 000000000..6c592e741
--- /dev/null
+++ b/docs/paper/discrim.py
@@ -0,0 +1,967 @@
+# downloads every edgelist file on s3 into a local temp directory using `boto3`
+# %%
+"""graph_stats : functionality for computing statistics on ndmg directories.
+"""
+import warnings
+from collections import namedtuple
+from pathlib import Path
+import re
+
+# from math import sqrt
+import numpy as np
+
+# from scipy.stats import rankdata
+from matplotlib import pyplot as plt
+
+# from graspy.utils import pass_to_ranks
+from graspologic.utils import pass_to_ranks
+
+# from graspy.plot import heatmap
+from graspologic.plot import heatmap
+
+# from graphutils.graph_io import NdmgGraphs
+# from graphutils.utils import replace_doc, nearest_square
+import shutil
+import os
+from functools import reduce
+import networkx as nx
+from graspologic.utils import import_edgelist
+
+# from graspy.utils import import_edgelist
+# from graphutils.utils import is_graph
+# from graphutils.utils import filter_graph_files
+# from graphutils.s3_utils import (
+#     get_matching_s3_objects,
+#     get_credentials,
+#     s3_download_graph,
+#     parse_path,
+# )
+from sklearn.metrics import euclidean_distances
+from sklearn.utils import check_X_y
+from collections import OrderedDict
+
+
+def get_credentials():
+    """
+    Get aws credentials from either environment or aws config.
+
+    Returns
+    -------
+    tuple
+         aws credentials.
+
+    Raises
+    ------
+    AttributeError
+        Occurs if credentials don't exist.
+    """
+    try:
+        config = ConfigParser()
+        config.read(os.getenv("HOME") + "/.aws/credentials")
+        return (
+            config.get("default", "aws_access_key_id"),
+            config.get("default", "aws_secret_access_key"),
+        )
+    except:
+        ACCESS = os.getenv("AWS_ACCESS_KEY_ID")
+        SECRET = os.getenv("AWS_SECRET_ACCESS_KEY")
+    if not ACCESS and SECRET:
+        raise AttributeError("No AWS credentials found.")
+    return (ACCESS, SECRET)
+
+
+def parse_path(s3_datapath):
+    """
+    Return bucket and prefix from full s3 path.
+
+    Parameters
+    ----------
+    s3_datapath : str
+        path to a bucket.
+        Should be of the form s3://bucket/prefix/.
+
+    Returns
+    -------
+    tuple
+        bucket and prefix.
+    """
+    bucket_path = str(s3_datapath).split("//")[1]
+    parts = bucket_path.split("/")
+    bucket = parts[0]
+    prefix = "/".join(parts[1:4])
+    return bucket, prefix
+
+
+def s3_client(service="s3"):
+    """
+    create an s3 client.
+
+    Parameters
+    ----------
+    service : str
+        Type of service.
+
+    Returns
+    -------
+    boto3.client
+        client with proper credentials.
+    """
+
+    ACCESS, SECRET = get_credentials()
+    return boto3.client(service, aws_access_key_id=ACCESS, aws_secret_access_key=SECRET)
+
+
+def get_matching_s3_objects(bucket, prefix="", suffix=""):
+    """
+    Generate objects in an S3 bucket.
+
+    Parameters
+    ----------
+    bucket : str
+        Name of the s3 bucket.
+    prefix : str, optional
+        Only fetch objects whose key starts with this prefix, by default ''
+    suffix : str, optional
+        Only fetch objects whose keys end with this suffix, by default ''
+    """
+    s3 = s3_client()
+    kwargs = {"Bucket": bucket}
+
+    # If the prefix is a single string (not a tuple of strings), we can
+    # do the filtering directly in the S3 API.
+    if isinstance(prefix, str):
+        kwargs["Prefix"] = prefix
+
+    while True:
+
+        # The S3 API response is a large blob of metadata.
+        # 'Contents' contains information about the listed objects.
+        resp = s3.list_objects_v2(**kwargs)
+
+        try:
+            contents = resp["Contents"]
+        except KeyError:
+            print("No contents found.")
+            return
+
+        for obj in contents:
+            key = obj["Key"]
+            if key.startswith(prefix) and key.endswith(suffix):
+                yield key
+
+        # The S3 API is paginated, returning up to 1000 keys at a time.
+        # Pass the continuation token into the next response, until we
+        # reach the final page (when this field is missing).
+        try:
+            kwargs["ContinuationToken"] = resp["NextContinuationToken"]
+        except KeyError:
+            break
+
+
+def s3_download_graph(bucket, prefix, local):
+    """
+    Given an s3 directory, copies in that directory to local.
+
+    Parameters
+    ----------
+    bucket : str
+        s3 bucket name.
+    prefix :
+        location of s3 object.
+    local : str
+        s3 object download location.
+    """
+    parent = Path(local).parent
+    if not parent.is_dir():
+        parent.mkdir(parents=True, exist_ok=True)
+    s3 = s3_client()
+    s3.download_file(bucket, prefix, local)
+
+
+def replace_doc(value):
+    """
+    Decorator for changing docstring of a function.
+
+    Parameters
+    ----------
+    value : str
+        docstring to change to.
+
+    Returns
+    -------
+    func
+        wrapper function.
+    """
+
+    def _doc(func):
+        func.__doc__ = value
+        return func
+
+    return _doc
+
+
+KEYWORDS = ["sub", "ses"]
+
+
+def is_graph(filename, atlas="", suffix=""):
+    """
+    Check if `filename` is a ndmg graph file.
+
+    Parameters
+    ----------
+    filename : str or Path
+        location of the file.
+
+    Returns
+    -------
+    bool
+        True if the file has the ndmg naming convention, else False.
+    """
+
+    if atlas:
+        atlas = atlas.lower()
+        KEYWORDS.append(atlas)
+
+    if suffix:
+        if not suffix.startswith("."):
+            suffix = "." + suffix
+
+    correct_suffix = Path(filename).suffix == suffix
+    correct_filename = all(i in str(filename) for i in KEYWORDS)
+    return correct_suffix and correct_filename
+
+
+def filter_graph_files(file_list, return_bool=False, **kwargs):
+    """
+    Generator.
+    Check if each file in `file_list` is a ndmg edgelist,
+    yield it if it is.
+
+    Parameters
+    ----------
+    return_bool : bool
+        if True, return a boolearn that says whether graph files exist in the directory.
+    file_list : iterator
+        iterator of inputs to the `is_graph` function.
+    """
+    if return_bool:
+        has_graphs = any(is_graph(x, **kwargs) for x in file_list)
+        return has_graphs
+    for filename in file_list:
+        if is_graph(filename, **kwargs):
+            yield (filename)
+
+
+def discr_stat(
+    X, Y, dissimilarity="euclidean", remove_isolates=True, return_rdfs=False
+):
+    """
+    Computes the discriminability statistic.
+    Parameters
+    ----------
+    X : array, shape (n_samples, n_features) or (n_samples, n_samples)
+    Input data. If dissimilarity=='precomputed', the input should be the dissimilarity
+    matrix.
+    Y : 1d-array, shape (n_samples)
+    Input labels.
+    dissimilarity : str, {"euclidean" (default), "precomputed"}
+    Dissimilarity measure to use:
+    - 'euclidean':
+    Pairwise Euclidean distances between points in the dataset.
+    - 'precomputed':
+    Pre-computed dissimilarities.
+    remove_isolates : bool, optional, default=True
+    Whether to remove data that have single label.
+    return_rdfs : bool, optional, default=False
+    Whether to return rdf for all data points.
+    Returns
+    -------
+    stat : float
+    Discriminability statistic.
+    rdfs : array, shape (n_samples, max{len(id)})
+    Rdfs for each sample. Only returned if ``return_rdfs==True``.
+    """
+    check_X_y(X, Y, accept_sparse=True)
+    uniques, counts = np.unique(Y, return_counts=True)
+    if (counts != 1).sum() <= 1:
+        msg = "You have passed a vector containing only a single unique sample id."
+        raise ValueError(msg)
+    if remove_isolates:
+        idx = np.isin(Y, uniques[counts != 1])
+        labels = Y[idx]
+        if dissimilarity == "euclidean":
+            X = X[idx]
+        else:
+            X = X[np.ix_(idx, idx)]
+    else:
+        labels = Y
+
+    if dissimilarity == "euclidean":
+        dissimilarities = euclidean_distances(X)
+    else:
+        dissimilarities = X
+
+    rdfs = _discr_rdf(dissimilarities, labels)
+    stat = np.nanmean(rdfs)
+
+    if return_rdfs:
+        return stat, rdfs
+    else:
+        return stat
+
+
+class NdmgDirectory:
+    """
+    Contains methods for use on a `ndmg` output directory.
+    Top-level object of this package.
+    Parameters
+    ----------
+    directory : str
+    filepath or s3 url to the directory containing graph outputs.
+    if s3, input should be `s3://bucket-prefix/`.
+    if filepath, input should be the absolute path.
+    directory : Path
+    Path object to the directory passed to NdmgGraphs.
+    Takes either an s3 bucket or a local directory string as input.
+    atlas : str
+    atlas to get graph files of.
+    delimiter : str
+    delimiter in graph files.
+    Attributes
+    ----------
+    files : list, sorted
+    List of path objects corresponding to each edgelist.
+    name : str
+    name of dataset.
+    to_directory : func
+    Send all graph files to a directory of your choosing
+    """
+
+    def __init__(self, directory, atlas="", suffix="csv", delimiter=" "):
+        if not isinstance(directory, (str, Path)):
+            message = f"Directory must be type str or Path. Instead, it is type {type(directory)}."
+            raise TypeError(message)
+        self.s3 = str(directory).startswith("s3:")
+        self.directory = directory
+        self.delimiter = delimiter
+        self.atlas = atlas
+        self.suffix = suffix
+        self.files = self._files(directory)
+        self.name = self._get_name()
+        if not len(self.files):
+            raise ValueError(f"No graphs found in {str(self.directory)}.")
+
+    def __repr__(self):
+        return f"NdmgDirectory : {str(self.directory)}"
+
+    def _files(self, directory):
+        """
+        From a directory or s3 bucket containing edgelist files,
+        return a list of edgelist files,
+        sorted.
+        This property is ground truth for how the scans should be sorted.
+        Parameters
+        ----------
+        path : directory of edgelist files or s3 bucket
+        Returns
+        -------
+        output : list, sorted
+        Sorted list of Paths to files in `path`.
+        """
+        output = []
+        # grab files from s3 instead of locally
+        if self.s3:
+            output = self._get_s3(directory, atlas=self.atlas, suffix=self.suffix)
+        else:
+            self.directory = Path(self.directory)
+            for dirname, _, files in os.walk(directory):
+                file_ends = list(
+                    filter_graph_files(files, suffix=self.suffix, atlas=self.atlas)
+                )
+                graphnames = [
+                    Path(dirname) / Path(graphname) for graphname in file_ends
+                ]
+                if all(graphname.exists for graphname in graphnames):
+                    output.extend(graphnames)
+        return sorted(output)
+
+    def _get_s3(self, path, **kwargs):
+        output = []
+        # parse bucket and path from self.directory
+        # TODO: this breaks if the s3 directory structure changes
+        bucket, prefix = parse_path(path)
+        local_dir = Path.home() / Path(f".ndmg_s3_dir/{prefix}")
+        if self.atlas:
+            local_dir = local_dir / Path(self.atlas)
+        else:
+            local_dir = local_dir / Path("no_atlas")
+        self.directory = local_dir
+
+        # if our local_dir already has graph files in it, just use that
+        is_dir = local_dir.is_dir()
+        has_graphs = False
+        if is_dir:
+            has_graphs = filter_graph_files(
+                local_dir.iterdir(), return_bool=True, **kwargs
+            )
+
+        # If has_graphs just got toggled, return all the graphs.
+        if has_graphs:
+            print(f"Local path {local_dir} found. Using that.")
+            graphs = filter_graph_files(local_dir.iterdir(), **kwargs)
+            return list(graphs)
+
+        print(f"Downloading objects from s3 into {local_dir}...")
+
+        # get generator of object names
+        unfiltered_objs = get_matching_s3_objects(
+            bucket, prefix=prefix, suffix=self.suffix
+        )
+        objs = filter_graph_files(unfiltered_objs, **kwargs)
+
+        # download each s3 graph and append local filepath to output
+        for obj in objs:
+            name = Path(obj).name
+            local = str(local_dir / Path(name))
+            print(f"Downloading {name} ...")
+            s3_download_graph(bucket, obj, local)
+            output.append(local)
+
+        # return
+        if not output:
+            raise ValueError("No graphs found in the directory given.")
+        return output
+
+    def _get_name(self):
+        """
+        return directory beneath ".ndmg_s3_dir".
+        Returns
+        -------
+        str
+        name of dataset.
+        """
+        if not self.s3:
+            return self.directory.name
+
+        parts = Path(self.directory).parts
+        dataset_index = parts.index(".ndmg_s3_dir") + 1
+        return parts[dataset_index]
+
+    def to_directory(self, dst=None):
+        """
+        Send all `self.files`to `directory`.
+        Parameters
+        ----------
+        directory : str or Path
+        directory to send files to.
+        """
+        if dst is None:
+            dst = self.directory / "graph_outputs"
+        p = Path(dst).resolve()
+        p.mkdir(parents=True, exist_ok=True)
+        for filename in self.files:
+            shutil.copy(filename, p)
+
+
+class NdmgGraphs(NdmgDirectory):
+    """
+    NdmgDirectory which contains graph objects.
+    Parameters
+    ----------
+    delimiter : str
+    The delimiter used in edgelists
+    Attributes
+    ----------
+    delimiter : str
+    The delimiter used in edgelists
+    vertices : np.ndarray
+    sorted union of all nodes across edgelists.
+    graphs : np.ndarray, shape (n, v, v), 3D
+    Volumetric numpy array, n vxv adjacency matrices corresponding to each edgelist.
+    graphs[0, :, :] corresponds to files[0].
+    subjects : np.ndarray, shape n, 1D
+    subject IDs, sorted array of all subject IDs in `dir`.
+    subjects[0] corresponds to files[0].
+    sessions : np.ndarray, shape n, 1D
+    session IDs, sorted array of all sessions.
+    sessions[0] corresponds to files[0].
+    """
+
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+        self.nx_graphs = self._nx_graphs()
+        self.vertices = self._vertices()
+        self.sort_nx_graphs()  # get vertices, sort nx graphs
+        self.graphs = self._graphs()
+        self.subjects = self._parse()[0]
+        self.sessions = self._parse()[1]
+
+    def __repr__(self):
+        return f"NdmgGraphs : {str(self.directory)}"
+
+    def _nx_graphs(self):
+        """
+        List of networkx graph objects. Hidden property, mainly for use to calculate vertices.
+        Returns
+        -------
+        nx_graphs : List[nx.Graph]
+        List of networkX graphs corresponding to subjects.
+        """
+        files_ = [str(name) for name in self.files]
+        nx_graphs = [
+            nx.read_weighted_edgelist(f, nodetype=int, delimiter=self.delimiter)
+            for f in files_
+        ]
+        return nx_graphs
+
+    def _vertices(self):
+        """
+        Calculate the unioned number of nodes across all graph files.
+        Returns
+        -------
+        np.array
+        Sorted array of unioned nodes.
+        """
+        return np.sort(reduce(np.union1d, [G.nodes for G in self.nx_graphs]))
+
+    def _graphs(self):
+        """
+        volumetric numpy array, shape (n, v, v),
+        accounting for isolate nodes by unioning the vertices of all component edgelists,
+        sorted in the same order as `self.files`.
+        Returns
+        -------
+        graphs : np.ndarray, shape (n, v, v), 3D
+        Volumetric numpy array, n vxv adjacency matrices corresponding to each
+        edgelist.
+        graphs[0, :, :] corresponds to files[0].D
+        """
+        list_of_arrays = import_edgelist(self.files, delimiter=self.delimiter)
+        if not isinstance(list_of_arrays, list):
+            list_of_arrays = [list_of_arrays]
+        return np.atleast_3d(list_of_arrays)
+
+    def _parse(self):
+        """
+        Get subject IDs
+        Returns
+        -------
+        out : np.ndarray
+        Array of strings. Each element is a subject ID.
+        """
+        # pattern = r"(?<=sub-|ses-)(¥w*)(?=_ses|_measure-correlation)" #_measure-correlation used to be _dwi
+        pattern = r"(?<=sub-)(\d*)(_ses-)(\d*)"
+        # subjects = [re.findall(pattern, str(edgelist))[0] for edgelist in self.files]
+        # sessions = [re.findall(pattern, str(edgelist))[1] for edgelist in self.files]
+        subjects = [re.findall(pattern, str(edgelist))[0][0] for edgelist in self.files]
+        sessions = [re.findall(pattern, str(edgelist))[0][2] for edgelist in self.files]
+        return np.array(subjects), np.array(sessions)
+
+    def sort_nx_graphs(self):
+        """
+        Ensure that all networkx graphs have the same number of nodes.
+        Returns
+        -------
+        None
+        """
+        for graph in self.nx_graphs:
+            graph.add_nodes_from(self.vertices)
+
+
+class NdmgStats(NdmgGraphs):
+    """Compute statistics from a ndmg directory.
+    Parameters
+    ----------
+    X : np.ndarray, shape (n, v*v), 2D
+    numpy array, created by vectorizing each adjacency matrix and stacking.
+    Methods
+    -------
+    pass_to_ranks : returns None
+    change state of object.
+    calls pass to ranks on `self.graphs`, `self.X`, or both.
+    save_X_and_Y : returns None
+    Saves `self.X` and `self.Y` into a directory.
+    discriminability : return float
+    discriminability statistic for this dataset
+    """
+
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+        self.X = self._X()
+        self.Y = self.subjects
+
+    def __repr__(self):
+        return f"NdmgStats : {str(self.directory)}"
+
+    def __len__(self):
+        return len(self.files)
+
+    def _X(self, graphs=None):
+        """
+        this will be a single matrix,
+        created by vectorizing each array in `self.graphs`,
+        and then appending that array as a row to X.
+        Parameters
+        ----------
+        graphs : None or np.ndarray
+        if None, graphs will be `self.graphs`.
+        Returns
+        -------
+        X : np.ndarray, shape (n, v*v), 2D
+        numpy array, created by vectorizing each adjacency matrix and stacking.
+        """
+        if graphs is None:
+            graphs = self.graphs
+        if graphs.ndim == 3:
+            n, v1, v2 = np.shape(graphs)
+            return np.reshape(graphs, (n, v1 * v2))
+        elif len(self.files) == 1:
+            warnings.warn("Only one graph in directory.")
+            return graphs
+        else:
+            raise ValueError("Dimensionality of input must be 3.")
+
+    def save_X_and_Y(self, output_directory="cwd", output_name=""):
+        """
+        Save `self.X` and `self.subjects` into an output directory.
+        Parameters
+        ----------
+        output_directory : str, default current working directory
+        Directory in which to save the output.
+        Returns
+        -------
+        namedtuple with str
+        namedtuple of `name.X, name.Y`. Paths to X and Y.
+        """
+        if not output_name:
+            output_name = self.name
+
+        if output_directory == "cwd":
+            output_directory = Path.cwd()
+
+        p = Path(output_directory)
+        p.mkdir(parents=True, exist_ok=True)
+
+        X_name = f"{str(p)}/{output_name}_X.csv"
+        Y_name = f"{str(p)}/{output_name}_Y.csv"
+
+        np.savetxt(X_name, self.X, fmt="%f", delimiter=",")
+        np.savetxt(Y_name, self.subjects, fmt="%s")
+
+        name = namedtuple("name", ["X", "Y"])
+        return name(X_name, Y_name)
+
+    @replace_doc(discr_stat.__doc__)
+    def discriminability(self, PTR=True, **kwargs):
+        """
+        Attach discriminability functionality to the object.
+        See `discr_stat` for full documentation.
+        Returns
+        -------
+        stat : float
+        Discriminability statistic.
+        """
+        if PTR:
+            graphs = np.copy(self.graphs)
+            # No need to pass to ranks and normalize if it's a correlation??
+            if PTR == True:
+                graphs = np.array([pass_to_ranks(graph + 2) for graph in graphs])
+            X = self._X(graphs)
+            return discr_stat(X, self.Y, **kwargs)
+
+        return discr_stat(self.X, self.Y, **kwargs)
+
+    def visualize(self, i, savedir=""):
+        """
+        Visualize the ith graph of self.graphs, passed-to-ranks.
+        Parameters
+        ----------
+        i : int
+        Graph to visualize.
+        savedir : str, optional
+        Directory to save graph into.
+        If left empty, do not save.
+        """
+        nmax = np.max(self.graphs)
+
+        if isinstance(i, int):
+            graph = pass_to_ranks(self.graphs[i])
+            sub = self.subjects[i]
+            sesh = self.sessions[i]
+
+        elif isinstance(i, np.ndarray):
+            graph = pass_to_ranks(i)
+            sub = ""
+            sesh = ""
+        else:
+            raise TypeError("Passed value must be integer or np.ndarray.")
+
+        viz = heatmap(
+            graph, title=f"sub-{sub}_session-{sesh}", xticklabels=True, yticklabels=True
+        )
+
+        # set color of title
+        viz.set_title(viz.get_title(), color="black")
+
+        # set color of colorbar ticks
+        viz.collections[0].colorbar.ax.yaxis.set_tick_params(color="black")
+
+        # set font size and color of heatmap ticks
+        for item in viz.get_xticklabels() + viz.get_yticklabels():
+            item.set_color("black")
+            item.set_fontsize(7)
+
+        if savedir:
+            p = Path(savedir).resolve()
+            if not p.is_dir():
+                p.mkdir()
+            plt.savefig(
+                p / f"sub-{sub}_sesh-{sesh}.png",
+                facecolor="white",
+                bbox_inches="tight",
+                dpi=300,
+            )
+        return viz
+
+    def visualize(self, i, savedir=""):
+        """
+        Visualize the ith graph of self.graphs, passed-to-ranks.
+        Parameters
+        ----------
+        i : int
+        Graph to visualize.
+        savedir : str, optional
+        Directory to save graph into.
+        If left empty, do not save.
+        """
+
+        nmax = np.max(self.graphs)
+
+        if isinstance(i, int):
+            graph = pass_to_ranks(self.graphs[i])
+            sub = self.subjects[i]
+            sesh = ""  # TODO
+        elif isinstance(i, np.ndarray):
+            graph = pass_to_ranks(i)
+            sub = ""
+            sesh = ""
+        else:
+            raise TypeError("Passed value must be integer or np.ndarray.")
+
+        viz = heatmap(
+            graph,
+            title=f"sub-{sub}_session-{sesh}",
+            xticklabels=True,
+            yticklabels=True,
+            vmin=0,
+            vmax=1,
+        )
+
+        # set color of title
+        viz.set_title(viz.get_title(), color="black")
+
+        # set color of colorbar ticks
+        viz.collections[0].colorbar.ax.yaxis.set_tick_params(color="black")
+
+        # set font size and color of heatmap ticks
+        for item in viz.get_xticklabels() + viz.get_yticklabels():
+            item.set_color("black")
+            item.set_fontsize(7)
+
+        if savedir:
+            p = Path(savedir).resolve()
+            if not p.is_dir():
+                p.mkdir()
+            plt.savefig(
+                p / f"sub-{sub}_sesh-{sesh}.png",
+                facecolor="white",
+                bbox_inches="tight",
+                dpi=300,
+            )
+        else:
+            plt.show()
+
+        plt.cla()
+
+
+def url_to_ndmg_dir(urls):
+    """
+    take a list of urls or filepaths,
+    get a dict of NdmgGraphs objects
+    Parameters
+    ----------
+    urls : list
+    list of urls or filepaths.
+    Each element should be of the same form as the input to a `NdmgGraphs` object.
+    Returns
+    -------
+    dict
+    dict of {dataset:NdmgGraphs} objects.
+    Raises
+    ------
+    TypeError
+    Raises error if input is not a list.
+    """
+    # checks for type
+    if isinstance(urls, str):
+        urls = [urls]
+    if not isinstance(urls, list):
+        raise TypeError("urls must be a list of URLs.")
+
+    # appends each object
+    return_value = {}
+    for url in urls:
+        try:
+            val = NdmgStats(url)
+            key = val.name
+            return_value[key] = val
+        except ValueError:
+            warnings.warn(f"Graphs for {url} not found. Skipping ...")
+            continue
+    return return_value
+
+
+def _discr_rdf(dissimilarities, labels):
+    """
+    A function for computing the reliability density function of a dataset.
+    Parameters
+    ----------
+    dissimilarities : array, shape (n_samples, n_features) or (n_samples, n_samples)
+    Input data. If dissimilarity=='precomputed', the input should be the
+    dissimilarity matrix.
+    labels : 1d-array, shape (n_samples)
+    Input labels.
+    Returns
+    -------
+    out : array, shape (n_samples, max{len(id)})
+    Rdfs for each sample. Only returned if ``return_rdfs==True``.
+    """
+    check_X_y(dissimilarities, labels, accept_sparse=True)
+
+    rdfs = []
+    for i, label in enumerate(labels):
+        di = dissimilarities[i]
+
+        # All other samples except its own label
+        idx = labels == label
+        Dij = di[~idx]
+
+        # All samples except itself
+        idx[i] = False
+        Dii = di[idx]
+
+        rdf = [1 - ((Dij < d).sum() + 0.5 * (Dij == d).sum()) / Dij.size for d in Dii]
+        rdfs.append(rdf)
+
+    out = np.full((len(rdfs), max(map(len, rdfs))), np.nan)
+    for i, rdf in enumerate(rdfs):
+        out[i, : len(rdf)] = rdf
+
+    return out
+
+
+def discrim_runner(input_dir, atlas, ptr):
+    m = NdmgStats(f"{input_dir}/{atlas}")
+    discrim = m.discriminability(PTR=ptr)
+    print(f"{atlas} analyzed: {discrim}")
+    return discrim
+
+
+def avgconnectome(input_dir, output_dir, atlas):
+    means = OrderedDict()
+    var = OrderedDict()
+
+    # get list of all files belonging to atlas:
+    connectomes = os.listdir(f"{input_dir}/{atlas}")
+
+    # itterate through list and calculate mean and variance of edgelist
+    c = 0
+    files_ = [f"{input_dir}/{atlas}/{str(name)}" for name in connectomes]
+    # print('stop, please')
+    # nx_graphs = [
+    #    nx.read_weighted_edgelist(f, nodetype=int, delimiter=" ")
+    #    for f in files_
+    # ]
+
+    list_of_arrays, verts = import_edgelist(files_, delimiter=" ", return_vertices=True)
+    if not isinstance(list_of_arrays, list):
+        list_of_arrays = [list_of_arrays]
+
+    ptr_list_of_arrays = np.array([pass_to_ranks(array) for array in list_of_arrays])
+
+    stack = np.atleast_3d(list_of_arrays)
+    ptr_stack = np.atleast_3d(ptr_list_of_arrays)
+
+    N, _, _ = stack.shape
+
+    # Calculate mean and variance:
+    mean = np.mean(stack, axis=0)
+    var = np.var(stack, axis=0)
+    ptr_mean = np.mean(ptr_stack, axis=0)
+    ptr_var = np.var(ptr_stack, axis=0)
+
+    # Check that number of vertices and dimensions of averaged connectomes match up
+    if len(verts) != mean.shape[0]:
+        print(
+            f"WARNING: Number of nodes and dimension of connectome do not match for: {atlas}!"
+        )
+
+    np.savetxt(f"{output_dir}/{atlas}/verts.csv", verts, fmt="%d", delimiter=" ")
+
+    # Convert matrix to edgelists
+    a = sum(range(1, len(verts)))
+    arr = np.zeros((a, 3))
+    z = 0
+    for num in range(len(mean)):
+        for i in range(len(mean[num])):
+            if i > num:
+                # print(f'{num+1} {i+1} {my_data[num][i]}')
+                arr[z][0] = f"{int(verts[num])}"  # f'{num+1}'
+                arr[z][1] = f"{int(verts[i])}"
+                arr[z][2] = mean[num][i]
+                z = z + 1
+
+    np.savetxt(f"{output_dir}/{atlas}/mean.csv", arr, fmt="%d %d %f", delimiter=" ")
+
+    arr = np.zeros((a, 3))
+    z = 0
+    for num in range(len(var)):
+        for i in range(len(var[num])):
+            if i > num:
+                # print(f'{num+1} {i+1} {my_data[num][i]}')
+                arr[z][0] = f"{int(verts[num])}"  # f'{num+1}'
+                arr[z][1] = f"{int(verts[i])}"
+                arr[z][2] = var[num][i]
+                z = z + 1
+
+    np.savetxt(f"{output_dir}/{atlas}/variance.csv", arr, fmt="%d %d %f", delimiter=" ")
+
+    arr = np.zeros((a, 3))
+    z = 0
+    for num in range(len(ptr_mean)):
+        for i in range(len(ptr_mean[num])):
+            if i > num:
+                # print(f'{num+1} {i+1} {my_data[num][i]}')
+                arr[z][0] = f"{int(verts[num])}"  # f'{num+1}'
+                arr[z][1] = f"{int(verts[i])}"
+                arr[z][2] = ptr_mean[num][i]
+                z = z + 1
+
+    np.savetxt(f"{output_dir}/{atlas}/mean-ptr.csv", arr, fmt="%d %d %f", delimiter=" ")
+
+    arr = np.zeros((a, 3))
+    z = 0
+    for num in range(len(ptr_var)):
+        for i in range(len(ptr_var[num])):
+            if i > num:
+                # print(f'{num+1} {i+1} {my_data[num][i]}')
+                arr[z][0] = f"{int(verts[num])}"  # f'{num+1}'
+                arr[z][1] = f"{int(verts[i])}"
+                arr[z][2] = ptr_var[num][i]
+                z = z + 1
+
+    np.savetxt(
+        f"{output_dir}/{atlas}/variance-ptr.csv", arr, fmt="%d %d %f", delimiter=" "
+    )
+
+    print(f"Averaged connectomes and variances saved for: {atlas}!")
diff --git a/docs/paper/discrim_runner.py b/docs/paper/discrim_runner.py
new file mode 100644
index 000000000..22294e22f
--- /dev/null
+++ b/docs/paper/discrim_runner.py
@@ -0,0 +1,147 @@
+# standard library imports
+import os
+from argparse import ArgumentParser
+
+# m2g imports
+# from m2g.scripts.discrim import discrim_runner, avgconnectome
+# from .discrim import discrim_runner, avgconnectome
+import discrim
+from m2g.utils import cloud_utils
+from m2g.utils.gen_utils import as_directory
+
+
+def main():
+    """Starting point of the m2g pipeline, assuming that you are using a BIDS organized dataset"""
+    parser = ArgumentParser(
+        description="This is an end-to-end connectome estimation pipeline from M3r Images."
+    )
+    parser.add_argument(
+        "input_dir",
+        help="""The directory with the input dataset
+        formatted according to the BIDS standard.
+        To use data from s3, just pass `s3://<bucket>/<dataset>` as the input directory.""",
+    )
+    parser.add_argument(
+        "output_dir",
+        help="""The local directory where the output
+        files should be stored.""",
+    )
+    parser.add_argument(
+        "pipeline",
+        help="""Pipeline that created the data, either 'dwi' or 'func'""",
+    )
+    parser.add_argument(
+        "--ptr", action="store", help="whether to pass to ranks", default=True
+    )
+    parser.add_argument(
+        "--discrim",
+        action="store",
+        help="Whether or not to calculate discriminability",
+        default=True,
+    )
+    parser.add_argument(
+        "--push_location",
+        action="store",
+        help="Name of folder on s3 to push output data to, if the folder does not exist, it will be created."
+        "Format the location as `s3://<bucket>/<path>`",
+        default=None,
+    )
+    parser.add_argument(
+        "--atlases",
+        action="store",
+        help="which atlases to use",
+        nargs="+",
+        default=None,
+    )
+    result = parser.parse_args()
+    input_dir = result.input_dir
+    output_dir = result.output_dir
+    pipe = result.pipeline
+    ptr = result.ptr
+    disc = result.discrim
+    push_location = result.push_location
+    atlases = result.atlases
+
+    ##---------------------------------------------------Start Discrim Calc--------------------------------------------------------------##
+
+    # Inputs needed:
+
+    # input_dir = location on bucket to download from
+    # push_location = where to push the discrim values and resulting connectomes
+    # ptr = whether to do PTR for functional
+    # atlases = which atlases to analyze (if none spceified, just get them all)
+
+    # grab files from s3
+    creds = bool(cloud_utils.get_credentials())
+
+    buck, remo = cloud_utils.parse_path(input_dir)
+    home = os.path.expanduser("~")
+    input_dir = as_directory(home + "/.m2g/input", remove=True)
+    if (not creds) and push_location:
+        raise AttributeError(
+            """No AWS credentials found, but "--push_location" flag called. 
+            Pushing will most likely fail."""
+        )
+
+    # Get S3 input data if needed
+    if pipe == "func":
+        if atlases is not None:
+            for atlas in atlases:
+                info = f"{atlas}"
+                cloud_utils.s3_get_data(
+                    buck, remo, input_dir, info=info
+                )  # , pipe=pipe)
+        else:
+            info = ""
+            cloud_utils.s3_get_data(buck, remo, input_dir, info=info)  # , pipe=pipe)
+    elif pipe == "dwi":
+        if atlases is not None:
+            for atlas in atlases:
+                info = f"{atlas}"
+                cloud_utils.s3_get_data(
+                    buck, remo, input_dir, info=info
+                )  # , pipe=pipe)
+        else:
+            info = ""
+            cloud_utils.s3_get_data(buck, remo, input_dir, info=info)  # , pipe=pipe)
+
+    # now /root/.m2g/input/mask_.... has all these edgelists:
+
+    latlas = os.listdir(home + "/.m2g/input")
+
+    # Calculate discrim and average and push it
+    if isinstance(ptr, str):
+        ptr = ptr == "True"
+    if isinstance(disc, str):
+        disc = disc == "True"
+
+    for at in latlas:
+        os.makedirs(f"{output_dir}/{at}", exist_ok=True)
+        if disc:
+            print(at)
+            discr = discrim.discrim_runner(input_dir, at, ptr=False)
+            discr_ptr = discrim.discrim_runner(input_dir, at, ptr=True)
+
+            f = open(f"{output_dir}/Discrim_values.txt", "a")
+            f.write(f"{at} Discriminability is : {discr}\n")
+            f.write(f"{at} PTR Discriminability is : {discr_ptr}\n")
+            f.close()
+
+        # Create averaged/variance connectomes and save them
+        # avgconnectome(input_dir, output_dir, at)
+
+        # if push_location:
+        #    print(f"Pushing to s3 at {push_location}.")
+        #    push_buck, push_remo = cloud_utils.parse_path(push_location)
+        #    cloud_utils.s3_push_data(
+        #        push_buck,
+        #        push_remo,
+        #        output_dir,
+        #        discrim=disc,
+        #        atlas=at,
+        #        creds=creds,
+        #    )
+
+
+if __name__ == "__main__":
+    main()
diff --git a/docs/paper/figure2.ipynb b/docs/paper/figure2.ipynb
new file mode 100644
index 000000000..e9adf33f3
--- /dev/null
+++ b/docs/paper/figure2.ipynb
@@ -0,0 +1,27 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Figure 2 - Group Consensus Connectomes\n",
+    "\n",
+    "Group consensus structural connectome from `m2g-d` (blue) and group consensus functional connectomes from `m2g-f` (red), using the DKT parcellation method. Structural connectomes of the datasets appear qualitatively similar, with minor deviations particularly visible in the contralateral regions of the connectomes (nodes 0-40 and 41-80). Ipsilateral connectivity is consistently more dense than contralateral connectivity in structural connectomes. The functional connectomes appear qualitatively similar to one another. Homotopic correlation is consistently higher than ipsilateral and contralateral connectivity, which agrees with existing knowledge about functional correlation in the brain."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "language_info": {
+   "name": "python"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/docs/paper/figure3.ipynb b/docs/paper/figure3.ipynb
new file mode 100644
index 000000000..583560772
--- /dev/null
+++ b/docs/paper/figure3.ipynb
@@ -0,0 +1,176 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Figure 3 - Discriminability Results\n",
+    "\n",
+    "Discriminability values from each applicable diffusion (left) and functional (right) MRI dataset. Relative row height denotes the relative size of the dataset. Rows, each representing a different parcellation, are organized from top to bottom by highest to lowest mean discriminability value. The mean discriminability value for each parcellation is displayed in the last column of both plots. The number of subjects/sessions is displayed next to the datasets' names in brackets and the number of ROI's in a given parcellation are shown in brackets if not mentioned in the parcellation name. Discriminability values for the structural connectomes was greater than 0.7 for the 32 of the 35 parcellations, while being robust to the number of scans per subject. Functional connectomes rarely had discriminability values lower than 0.7."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import seaborn as sns\n",
+    "from matplotlib import pyplot as plt\n",
+    "import matplotlib as mpl\n",
+    "\n",
+    "\n",
+    "def make_y_yticks(dsizes):\n",
+    "    \"\"\"Helper function for making y and yticks for the plot\"\"\"\n",
+    "\n",
+    "    i = 0\n",
+    "    q = [0]\n",
+    "    qq = []\n",
+    "    for idx, st in enumerate(dsizes):\n",
+    "        q.append(st + i)\n",
+    "        if idx == 0:\n",
+    "            qq.append(0.5 * st)\n",
+    "        else:\n",
+    "            qq.append(sum(dsizes[:idx]) + (0.5 * st))\n",
+    "        i = i + st\n",
+    "    y = np.array(q)\n",
+    "    yticks = np.array(qq)\n",
+    "\n",
+    "    return y, yticks"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/var/folders/hs/y8jx5sr97_x5rzsz94_4_qx00000gn/T/ipykernel_57959/3181599882.py:59: UserWarning: FixedFormatter should only be used together with FixedLocator\n",
+      "  ax[2].set_yticklabels(['0.4', 'Random', '0.6', '0.7', '0.8', '0.9', 'Optimal'], fontsize=11)\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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Tg7Zt22LRokXZFq0C6YWZrq6uWL9+faHloYgvTYy+qFevnlzrMs87cuSIUvLZtm0bZLL/Fy+6ubkpJS4REakeO64SfYXKlSuH5s2b48yZMwCA2NhY3LlzB02aNBHmZC70XLJkCRo1aoROnTopJYeM3VYByL3lfZkyZVCxYkW8efMGAPD48WOlxre2toa6urpQyJtT/KI2e/ZsvH37FkD6L+MtW7bMd6yQkBBER0cLY3k/GwCwtbUVjR8/fix3UXBu5Pli/eXuXSIiIiIiIiIiIiIiopJMkWsemZvNqNro0aOxYMECxMfHA/h/gxx1dfVs558/fx49evQQ7fZYt25ddOvWDTY2NlBTU8PTp0+xb98+BAcHAwD2798PiUSCvXv3Zhvz48ePcHV1FcVs0aIF2rVrhypVqkBLSwufPn1CUFAQ7t69i8uXL+dYwFhY7t69i65duwqFunp6ejh48CA6duyY6zojIyO0atUKDRs2hJmZGXR0dBAREYFbt27hyJEjSExMBABcvXoVAwYMwJkzZ+TeZj4hIQG9evXCkydPoKWlhR49eqBFixYwMjLChw8fcPDgQdy8eRMAkJKSghEjRsDR0REVKlRQ+P2/evUKnTp1wqtXr4RjXbt2hbe3N3R0dBSOJ6/x48fjzz//FMYaGhro2rUrWrZsCVNTU8TGxuLKlSs4fPgwUlJSkJycjG+//RY6Ojro2bOnUnORSqUYNGgQrl69ColEgs6dO6Ndu3YwNTVFVFQUfHx8RJ1Ep0yZgtatW8tdKFpYoqKiRGNDQ0O51mWed+/evQLnIpVK4eXlJYx1dXUxYMCAAsclIqLigYWrRF+pFi1aCIWrALIUrnbu3BlVqlQRCkTj4uLQuXNntG3bFv3790e7du1Qq1Yt0bYZisj4JQUAqlSpIvfajHlljpNdfDU1NVSqVEmu2BoaGqhQoQLevXuXa/yidPXqVWzYsAEAYG5uLtedq7kp6GefOZYyClfNzMwKHIOIiIiIiIiIiIiIiKgksLOzk3tuxk6DxYE8DXK++PjxIwYNGiQUmOrr62PLli3ZFp4tWbIEkydPhqenJwBg37596NGjB4YMGZJl7oYNGxATEwMgvenNwYMH4ezsnGPOsbGx+Oeff0S7Ohams2fPok+fPvj06ROA9OtgJ06cgIODQ45r2rZtix9++AGdO3fOcafEsLAwjBo1CkePHgWQXhT8119/YcSIEXLl5e3tDalUigYNGmD//v1ZmtvMnj0bCxcuxKJFiwAA0dHRWLNmDVasWCFX/C8ePXqEzp07IyQkRDg2cOBA7Nixo0A7Sublr7/+EhWtNmvWDLt27crSmGfKlCl48uQJevbsiYCAAKSlpcHNzQ3+/v4wMTFRWj5XrlyBVCqFlZUVvL29s/z5T58+Hdu2bcOoUaMApHeBXbJkSaF2gJVH5sLiL8XSeck8Lzw8HGFhYQUqvj916pTQ3AkABgwYAH19/XzHIyKi4kW+W2+IqNSxtrYWjTPf2amlpYVNmzZluTvywoUL+P7772FnZwcDAwO0a9cOM2fOxJEjR7LcfZWbzNtcGBkZyb02491aKSkp2W7/kTG+vr6+Ql9EM8b/8oVSVZKTk+Hm5ib8o8SaNWvkvqstJ8r67AHVfz5ERERERERERERERERUtFq0aCEa37lzJ9t5s2bNEjqOamlpwcfHJ8duiWXLlsWWLVvQu3dv4dj8+fMhlUqzzD137pzwfNy4cbkWrQLpxbZjxozByJEjc52nDHv37kW3bt2Ea2hVq1bF1atXcy1aHThwIHx9fdG9e/dcCztNTU3h7e0t2pnxjz/+kDs3qVSKypUr49y5cznuyLhw4ULRn+/ff/8td3wAuH79Otq0aSMqWh03bhx2795dqEWrsbGxmDx5sjB2cHDA+fPnsxStflG7dm2cPXsW5cuXB5BeZL127Vql5iSVSlGuXDmcP38+xz//kSNHYtCgQcL4yJEjiIuLU2oeispcaPr69Wu51mXXECogIKBAuXwpZP/Czc2tQPGIiKh4YeEq0VcqcwFiZGRkljnOzs44fPgwjI2Ns40RGxsLX19frFy5Er169YK5uTl69eol2tIgJ5l/4S5TpozcuWe+yyu7X94zHlMkdub4qv5isGTJEjx58gQA0KVLF9EXl/wq7M+eiIiIiIiIiIiIiIiISq+8GuQAwIcPH7B7925hPHXqVFHBZU7++OMPoSFNUFAQTpw4kW3sL2rUqCFv2oVu7dq1GDRoEJKTkwEADRs2xNWrV3MsnvxCkWt1mpqaWLx4sTC+e/euUBwsj+XLl+fZJGfMmDHC8+DgYAQHB8sVOzw8HB07dhQ1O5ozZw42btwINbXCLU3x9PQUuvCqqalhx44deX6uVlZWmD17tjDesmUL0tLSlJrXrFmzYGNjk+ucsWPHCs+TkpJw//59peagqMxFthl3cc1NdvMyN1RSRFhYGI4cOSKM7ezs0Lx583zHIyKi4oeFq0RfKT09PdE4p18au3fvjpcvX2LBggWoXLlyrjFTUlJw5MgRdOzYEX369BG+HGTn8+fPorGWlpacmQPa2tqicXbbE2SMr0jszPHl3fqgMDx69Ai//vorgPSC0Y0bNyolbmF/9kRERERERERERERERFR6ydMg58CBA0IBp5qaGqZOnSpX7EqVKqFjx47C+PTp01nmlC1bVnh+9epVueIWttmzZ2PKlCnCLoodOnTAhQsXYG5urvRztW7dWnTN7ubNm3Kt09PTQ79+/fKc16xZM9H4S5OdvMTHxyM+Pl4Yjxw5Er/88otcawsqY2dYZ2dn2NnZybVu+PDhwvPQ0FA8ePBAqXlljJ+TJk2aiAp75f28C0vt2rVRpUoVYXzixAk8fvw41zVxcXHYsGFDluMF2b1zx44dSElJEcajRo3KdyzKpzRZ8XgQUakl/97ZRFSqZP4l8cs2CNkxMDDAwoULsWDBAvj5+cHX1xdXr17F3bt38e7du2zXHDp0CK1bt8bVq1ezFMkCWe8cTE5OlvtuwqSkJNE4cxfQzPG/fCmWV8b42cUuClKpFG5ubsIv4wsWLMjzbjx5ZffZy0uezz4/Pn78mOec8PBwub9kEhERERERERERERERFVePHz+GiYmJqtPIN3ka5Fy6dEl47uDgoFABp6OjI/777z8AgJ+fX5bXGzVqJHSl3LVrF2rUqIFp06ZBV1dX7nMoS2pqKkaPHg0vLy/h2IABA7Bjxw6Fm+vIS0NDAyYmJkInVHk7ojo4OEBTUzPPeZaWlqJxdHS0wjkCwO7du9GvXz907do1X+vlFRcXh3v37gnj7t27y73W3NwcVlZWCAoKApD+89awYUOl5GVlZYUKFSrkOa9s2bIwNDREREQEgPx/3so0ZcoUTJs2DQCQlpaGgQMH4uzZszA1Nc0yNzU1FSNGjMDbt2+zvFaQJkhbt24VnmtpaWHo0KH5jkVUmEJCQuDn54fXr18jPj4eOjo6qFKlCho3bqy0Go/CEBISgrt37+LVq1eIjY2Fmpoa9PX1YWVlhZo1a6JatWqQSCSqTpNKORauEn2lMv/Ca2RklOcaiUSCZs2aie6yCwsLw8WLF/Hvv/+K7pwEgIcPH2Lq1KnYsmVLlliZv9B+/vxZ7sLVzL/gZlcYm/FY5g6jisTPLnZR8PDwEL6I29vbY/r06UqLnd1nLy95Pvv8yO5LDhERERERERERERERUWlkYmJSoq+NyNMgJ+N253Xq1FEofsYi1+ya6IwZMwZeXl5Cd9OffvoJK1asQNeuXdGhQwe0bt0atWvXLvSCm4SEBPTq1QsnTpwQjrm7u+P333/P17mlUil8fX1x6NAh3Lt3DwEBAYiNjUV8fLzwXrMjb6GjhYWFXPMyX//L2EU1N5UrV0anTp2wbds2AOkNcfr06QNvb2+FikkV9ejRI6SlpQnj/Py8fSlczalpU37I+3kD6Z/5l8JVeT/vwjR+/Hjs2LFDKAh++PAhGjZsiLlz56J79+6oUKECoqOjcenSJSxduhS3b98GAOjr64v+ftDX18/X+a9duybqPNurV68SXexPpdPRo0exfPlyXL16Nce/o+vXr49p06ZhyJAhxaIINDExEZ6enti2bZuo4D87RkZGaNOmDVxcXDBixIiiSZC+Omp5TyGi0ujVq1eicX5/0TM1NUXfvn2xe/duvHjxAq1btxa97uXlle0v+OXKlRONo6Ki5D5nxi9fmpqaWbavzxw/Li4Oqamp+Yqf31+mCyIoKAjz5s0DkL51yubNm6Ghobz7DJT12QOq+XyIiIiIiIiIiIiIiIhIdeRpkPOlCA9Iv14okUjkfkyYMEFYm911rGbNmmHZsmWiY3Fxcdi/fz/GjRuHOnXqwNzcHIMGDcKhQ4dE240r0/79+0VFqwsWLMDatWvzVZx08eJF2Nvbo0OHDvjjjz9w6dIlvH//HnFxcbkWrQLyN6mRt4lQZnmd/ws1NTV4enrCzc1NOJaUlARXV1ccO3YsX+eWR8afNQBwcnJS6Oftxo0bwlpFrpvmpbA/78JUpkwZ/Pvvv6hatapwLDg4GBMmTICVlRW0tLRgZmaGvn37CkWrHTp0yFLcZmhomK/ze3p6isYZf6aIVC0uLg79+vWDi4sLrly5kut/s/fv38ewYcPQrl07hIaGFmGWWR05cgTVq1eHu7t7nkWrABAZGYlDhw5h/PjxhZ8cfbVYuEr0lbp27Zpo3Lhx4wLHrFKlCnx8fES/wKampuLkyZNZ5mZuif7mzRu5z/PljjcAonPlFD8tLU3uLTJSU1Px/v37POMXpqlTpwp30o0dO1bU4VYZlPXZA6r5fIiIiIiIiIiIiIiIiEh15GmQo6ztzhMSErI9/uOPP+LUqVNo2rRptq+HhYXhn3/+QZ8+fVCrVi1RgamyZC5MvHjxYo755ubQoUPo2LEjHj9+nOW1smXLokKFCrCxsYGtra3wyNj0pjgUOn4hkUiwefNmjBkzRjiWnJyMvn374siRI4VyTmX9rAE5/7x9jWxsbODn54f+/fvnWoz9pdj8yJEjWYqo89M8Ky4uDvv27RPGVlZW6Nixo8JxiApDXFwcOnbsCG9vb4XWXbhwAa1atcKHDx8KKbPczZkzB7169ZK7boaoqCivhR8RlRgxMTG4fv26MDYwMECDBg2UEltXVxcTJkzAjBkzhGNPnz7NMs/Ozk40fvnyJdq2bZtn/M+fP4sKSzPHyS2+lZVVnvEDAwNFW0nkFL8wZfyyf+TIEZw6dSrX+ZGRkaKxk5OT6Mvq3bt3RZ1RK1asCAMDA+FL3MuXL+XOLSAgQDRWxedDREREREREREREREREqiNPg5yyZcsiNjYWQPq1SGNjY6Xn0alTJ3Tq1An+/v7477//cPHiRVy9ehXh4eGiea9evUKPHj3g6emJkSNHKu38/fv3h5aWltAd8vz58+jatSuOHz8OPT09uWKEhYVhxIgRQldYDQ0NuLm5oV+/fmjUqFGOHSutrKwUak5TlCQSCf7880+oq6tj48aNANKLV/v164d9+/ahd+/eSj1f2bJlRePKlStDS0srX7HMzMyUkVKpYWJigr1792LBggU4dOgQrly5gg8fPiApKQkVK1ZEkyZNMGTIENSqVQsA8PbtW2Ft2bJlUbNmTYXP+c8//yAuLk4YjxgxAmpq7MlHxcPIkSPh5+cnOlalShVMmzYNHTp0QKVKlRASEoIrV65g9erVePLkiTDv5cuXcHV1xcWLF5W6425epk2bhjVr1oiO6ejowNXVFS4uLqhTpw4sLCygpqaGqKgoPH78GDdv3sTx48dx586dIsuTvk4sXCX6Cm3evFno6AkAHTt2VOove3Xq1BGNY2JissypX7++aHzt2jWMGjUqz9g3btxAamqqMLa3t892XnbxO3TokGf8q1evisY5xS8q+bnjJXNX1IyFuF/Uq1cPFy9eBADcvn0bKSkp0NTUzDN2xs+nSpUqKFeunML5ERERERERERERERERUckkb4McExMToXB14MCBQgFjYahTpw7q1KmD6dOnQyaT4eHDhzh8+DC2bduGwMBAAOldSd3d3dGjRw+lFSd+6S6qoaGBP//8E0B619WuXbvixIkTosYyOdm2bZvQbEZNTQ3Hjh1Dly5d8lynzC6jhUEikWD9+vVQU1PD+vXrAQApKSno378//vnnH7i6uirtXJm7eu7fvx+Ojo5Ki0/pzYzkaWj04MED4XmjRo2grq6u8Lm2bt0qPFdTU1NqsTkpRiaTQSZVbUfn4tRR+ujRo9i/f7/oWNu2bXH48GGUL19eOGZgYIDatWtjyJAhGDJkiGjNtWvX8Oeff2LixIlFkrOXl1eWotWuXbtiw4YNsLa2zjLf0NAQVatWRY8ePbBo0SIEBAQIN2cQFQbelkD0lXn37h1WrFghOvbjjz8q9RyZtwAwMjLKMsfGxka48woAzpw5I9cvHadPnxaNe/Toke28li1biooq8+paml18NTU1dOvWTa51JU3G95WYmIhLly7luSYgIACvX78Wxjl99kRERERERERERERERFQ6ydsgJ+N1wHfv3hVJbkB6wWS9evUwf/58PHv2DAMGDBBei4+Px8GDB5V+vg0bNuD7778Xjl2+fBldunQRCndzc/bsWeF5p06d5CpaDQ8Plyu2qkkkEqxbtw6TJk0SjqWkpGDAgAEKb7Odm8xdPYvy543+7/nz56KmTB07dlQ4hr+/v6gwvnPnzqhcubJS8iMqCJlMhvnz54uOValSBUePHhUVrWakra2NPXv2wMHBQXT8l19+QUJCQqHl+kVoaCimTZsmOjZ48GAcO3Ys26LV7Nja2mLZsmWFkB1ROhauEn1FIiMj4erqKtoeo0ePHtlu31EQV65cEY2rVauW7bw+ffoIz4OCgvIsLk1NTcX27duFcaVKlXLMXUtLC927dxfGly5dwtOnT3ONHxUVJfqS1Lx5c5ibm+e6pjDcu3cv/e4lOR8LFiwQrX/9+rXodQMDgyznyPjZA8CWLVvyzCvznMwxiIiIiIiIiIiIiIiIqPRSpEFOu3bthOeXL18W7ahYVLS0tLBx40ZIJBLh2OPHj5V+ni8FmlOmTBGOXbt2DZ07d852Z8qMMhb6Zd5RMifnzp3LV56q4uHhIfpsUlNTMXDgwCydC/PLzMxMtCPo+fPnlRKXFLNr1y7hubq6uly7rWaWsdsqgHzFICoMhw8fxv3790XH1q5dm2dnbXV19Sx1Fh8+fJCrPqOg3N3dERUVJYwbN24MLy8vpe7GTFRQ/Gkk+kqcPn0aDRo0wM2bN4Vj1tbWObb19vT0xLZt2xT+Evny5UthKwwg/YtaTl1Lx48fD21tbWE8c+ZMpKSk5Bh71apVoi9vkydPFn3RzCzjFyAAWe4myWz27NlITEzMcX1pUqNGDXTt2lUY79u3T3T3WmavXr3CH3/8IYzt7e3Rvn37Qs2RiIiIiIiIiIiIiIiIigdFG+T07dsXGhoaANK3tffy8iqKNLMwNDSEqampMM7tWmRBrVmzBjNmzBDGfn5+6NSpE6Kjo3Nck3FHysy7WuZk7dq1+c5RVdasWSO6Vpuamopvv/0W+/btU0r8gQMHCs937tyJyMhIpcQl+YSEhIh+Lnv16oVKlSopFCM5ORk7d+4UxqampujVq5fSciQqiMx/V1lbW8v989mwYUO0atVKdExZhfs5efHihSjnL93B1dXVC/W8RIpi4SpRKZWQkIDLly9j1apVqFevHjp37oy3b98Kr5uamuLEiRM5dhQNDAzEqFGjUL16dSxevFi0RXxOTpw4gbZt2yIuLk44NmzYMFSsWDHb+ZUrVxZtm/HgwQN89913SEpKyjJ3z549os6ilpaWmDhxYq75NG3aFK6ursLYx8cHU6dOhVQqzTJ31apV2Lx5szB2cHBA3759c41f0i1btky4m0YqlaJfv354+PBhlnlv3rxBz549Re3qly5dyjtxiIiIiIiIiIiIiIiIvgKKNsgBABsbGwwaNEgY//DDDwp3O5XJZEhOTs5yPDAwUO4YwcHBomJbebdHzq+VK1di1qxZwvjmzZvo0KGDqOtdRlZWVsJzHx8fpKWl5Rr/999/x9WrV5WTbBH77bffRIW9X4pX9+zZU+DYEydORLly5QAAsbGxGDJkiMINmuQtHCaxhIQEfPvtt4iNjQUA6OrqYvXq1QrHOXz4sOi/1aFDh0JTU1NpeRLlV0pKCk6cOCE6NmTIkFybrGU2fPhw0fjatWv4+PGjMtLLVub/P7dp0wZNmjQptPMR5ZeGqhMgovz78ccfsWTJEtGxhIQExMTEiIoMM+vUqRO2bt2KypUr53mOwMBA/PTTT/jpp59Qo0YNODo6olatWjA2Noa2tjZiYmLw7NkznDlzBi9evBCtrVWrFlauXJlr/MWLF+PixYu4desWgPQ7S65evYohQ4agatWqiIqKwokTJ3DhwgVhjba2Nvbu3YsyZcrkmf/GjRtx584d4Qvs77//jhMnTmDQoEGoVKkSQkND4e3tjbt37wprjIyM8Pfff8v1i4aTkxPevXuX5XjGzrB+fn6oVq1aljmVKlWCr69vnucoLPXr18evv/6KmTNnAkjP2cHBAf3794ejoyPU1dVx9+5d/P3336Kfp++//x49evRQVdpERERERERERERERERUiBISEnDnzh1cv34dO3bsyNL4JK8GOV+sWLECZ86cQUhICKKjo9GyZUt4eHjg22+/zbXrW3BwMA4cOID169djx44daNasmej16tWrY8CAARg1ahTatm2bY7OVyMhIDB48WGhqI5FIiqSD47Jly6ChoSFcx71z5w7at2+PM2fOwNjYWDS3S5cu+O+//wCkd8ibNGkSPDw8hG61X6SkpGDFihWYP38+AEBNTS3bZj3F3cqVK6Guro7ly5cDANLS0jBkyBBIpVJ89913+Y5rYGAADw8PoTjsxIkTcHJywqZNm1CnTp0c18lkMvj5+WHHjh24ceOGcM2agPXr16NBgwZo0aJFjtfNHzx4gDFjxsDPz084tnLlSlFBtry2bt0qGru5uSkcg6gwXLt2DTExMaJjTk5OCsXIPF8qleK///7D0KFDC5hdVqmpqVm6nH/77bdKPw+RMrBwlagE+/jxo9x3YWhoaKBTp06YMmUKOnfunOf8smXLZjn2/PlzPH/+XK7ztW/fHn///TdMTEzyPM/Ro0fRvXt33LlzB0D6l9Fff/012/n6+vr466+/0LJlS7nyMDMzw4kTJ9CzZ08EBAQI72PRokXZzjc3N4e3tzdq1KghV/zAwEAEBQXlOufz58/CuTNS9C6/wvDDDz8gMjISy5cvh0wmQ0pKCnbv3o3du3dnO3/o0KElcvsRIiIiIiIiIiIiIiIi+r+iaJBjYWEBb29vODs7IzY2FtHR0Rg6dCjmzp2Lzp07o06dOjA0NERycjKioqLw9OlT3L17Fw8ePIBMJssxbmpqqnA9y9zcHC1atECDBg1gZmYGHR0dREZG4t69ezh06JDQBRIAxo0bh5o1a8rx6RTc4sWLoaGhgYULFwIA7t27h/bt2+Ps2bOi66ejRo3CsmXLhGu+GzduxJkzZ/DNN9/A1tYWSUlJeP78OQ4ePChckxwzZgxOnjyZ5zXK4urXX3+Fmpoali1bBiC9eHXo0KGQyWQYPHhwvuMOGzYM/v7+QmOlK1euoG7dumjRogXatGmDypUrQ1dXF58+fUJoaCgePnyI69evIzQ0FACK7GejpDh69CgmTpyIihUrol27drC3t4eJiQlSUlLw/v17nDt3DteuXRMVUC9YsADjx49X+Fxv3rzB6dOnhXGLFi1Qq1YtpbwPKoA0afpD1Tmo2IMHD0RjdXV1ODo6KhTD1tYW5ubmwt83ALLdDVcZ7t+/n6WOqGPHjoVyLqKCYuEqUSkikUigpaUFfX19mJmZwcrKCnZ2dmjWrBnat28PIyMjuWPNmTMHvXv3xqFDh3D27Fn4+fkhPj4+1zWampro3Lkz3Nzc0Lt3b7nPZWFhgevXr+PXX3/Fhg0b8OHDhyxztLS00KNHD6xatQo2NjZyxwaA2rVr4/79+5g/fz68vLyy3YpDV1cX33zzDVasWAFTU1OF4pd0y5YtQ6dOnTB37lxcv3492zl169bFTz/9hG+++aaIsyMiIiIiIiIiIiIiIiJlK8wGORk1b94c169fR+/evYUGOW/fvs3SXTEnuXVmBYDQ0FAcPHgQBw8ezHXed999V+TNWRYsWAANDQ3MmzcPQHrxU7t27XD27FmYmZkBSG/ac+DAATg7OwsFwy9evMDSpUuzjeni4oI//vhD7iY8xdXSpUuhrq4uFE9LpVIMGzYMUqm0QB0IV6xYAWtra0ydOhXJyckAgKtXr+Lq1at5rs3rZ+1r9f79+xybHn2hp6eHZcuWYeLEifk6x/bt20UFsOy2SsXJ06dPReMqVapAV1dX4Ti1a9cWFa5mjqssN27cEI319PSy1Ni8efMGly5dQmBgIJKTk2FsbAwLCwu0bNkSlpaWhZIXUXZYuEpUggwfPlzY3qAo2NnZwc7ODnPmzEFqaiqeP3+OFy9eIDg4GLGxsUhLS4O+vj4MDQ1Rq1Yt1K1bFzo6Ovk6l6amJubPn485c+bg6tWrePnyJUJDQ6Gvr49KlSqhdevWChXeZqarq4vVq1fj119/xcWLFxEYGIiwsDAYGhqiSpUqaNu2bb5+uQgMDMx3TsqycOFC4W7N/Grfvj2uXbuGgIAA3Lp1C8HBwZBKpahYsSLq1auHunXrKidZIiIiIiIiIiIiIiIiKnaU2SAns9q1a+PRo0fYvn07PDw84O/vn+t8Ozs7dO3aFYMHD0aDBg2yvL57924cO3YM586dExUBZadZs2aYOXMm+vTpk+/8C2Lu3LnQ0NDArFmzAACPHj0SilctLCwAAK1bt8aNGzcwadIknD9/Pts4tra2mDFjBsaNG1dkuRe2xYsXQ01NDT///DOA9OLVESNGQCqVFuia+IQJE+Di4oJly5Zhz5492TY1+kJHRwctW7ZEnz59MHDgwHyfszRycXFBWFgY7t27JyoqzcjIyAiurq5YsGABKlWqlK/zyGQybN++XRjr6+ujf//++YpFVBiePHkiGlepUiVfcTJ3Ki+swtWbN2+KxrVr14ZEIgEAXL9+HbNmzcKFCxdyXF+nTh1Mnz4dQ4cOZUE/FTqJLLce+0RERMVEWFiYcPfpF7tur0N543IqyoiIlE1NouoMiJTjZ+8Kqk6hxJLEqn7bn5Kkxt3cL8yQ4iK+Ka/qFATb+0YU6fliohNQvcL/u2LMvbAZOvqK39ynLPFRsVjWQby13MePH0vVDhnZfcfx9NuAcvyOQ/m05aKBqlOgbLRtEKPqFCgbg2xz31mKFFdZL+/toimrFfcjVZ1CgR05VvJ/P2vgFKbqFPKkW0xbEQUEqe47Q2Yp8XHwcfv/FuPLrmxC2XLFI7+4yFjMbTtBdKy0fb9RVHBwsLBFe1RUFLS0tGBoaAhbW1vUrVtXoc/m9evXePLkCYKCghATEwOpVAp9fX1YWVmhcePGqFixYiG+E+ULCAjAlStXEBISAnV1dVSoUAG1atWCg4ODqlMrkaRSKe7cuYMnT54gPDwc8fHx0NPTg7m5OWrWrIk6depAW1tb1WkWCl9fX7Rr104Yv379GtbW1grHiYqKgp+fH16/fo2oqChoamrCwsIC1tbWaN68OTQ0iun/pDL5UrgHpHd4LcqGYSVBdv9O9WZmJ5jqqva/j7D4JFRZcVp07PHjxzAxMZFrvTL+X1u7dm1RkengwYOxc+dOhePMmzcPv/zyizDW0dEROm0r05dO51906dIFPj4+mD9/Pn799VekpaXJFcfe3h6HDx9WeEdkIkWUjP+DEBERERERERERERERERERUYlnaWmJvn37KiWWjY1NqSqqsbW1ha2trarTKDXU1NTQuHFjNG7cWNWplFiGhoZwdnZWdRpEAjs7O7nnKqOXY1xcnGhcrlz+bjrPvC4xMRFSqRRqamr5zi07kZHim+P09fUxc+ZMrFq1SqE4Dx8+hKOjI06dOpVt93MiZVDuTz8RERERERERERERERERERERERFRCZe5cFVHRydfcbJblzm2MkRHR4vGFy5cEBWtVqhQAatWrYK/vz/i4uIQGxuL+0MPaLMAAQAASURBVPfvY8mSJTAyMhKtDQsLQ79+/RAbG6v0PIkAFq4SEREREREREREREREREREREZVaNjY2kEgkwmPhwoWqTqnQeHl5id6rRCJRdUolkkwqKxYPVYuPjxeNy5Qpk684RVW4GhMTIxqHhYUJzzt37oxnz55h+vTpsLOzg66uLvT19VGvXj3MnTsXz549Q7NmzUTrAwIC8OOPPyo9TyKAhatEREREREREREREREREREREREREJZpUKs32uL29PY4cOQJ9ff0c15qYmODkyZOwsrISHd++fTs+fPig1DyJAEBD1QkQEREREREREREREREREREREZFy6OjowNbWNsfXM28JXpqUK1cu1/derly5IsyGlO3x48cwMTEpsvPp6uoiOjpaGH/+/DlfcRITE7Mc09PTy29aOdLT00NUVFSW4xs2bIC2tnae68uVK4c1a9bA1dVVOJaUlIQdO3Zg5syZSs2ViIWrRERUYj2IUocu1FWdBhEpia6G6rf7IFKG6S6hqk6hxHoQyS2bFHHN0ljVKcjtc2LJ+OeHw32Lz13j5feeLdLzSeKTRWNddRl0VPj/Zpn61/l7QZos/UFEpQe/5xRPCancjE7ZNJNTVJ1CiZScfTOkEkWmU/L/eyoJ31fqVCye/40FqDqBXKhL0h/FgVoxyYOIvi6Ojo54+fKlqtNQCVdXV1HRHZUuJiYmMDU1LbLz6enpiQpXsytAlYcqC1ft7e3RqlUruWO4uLjA0tISwcHBwrELFy6wcJWUruR/myQiIiIiIiIiIiIiIiIiIiIiIiJSoszFpbGxsfmKk3mdjo4O1NSUX7aXXTFs+/btFYqhrq6Otm3bio5dv369QHkRZYeFq0REREREREREREREREREREREREQZVKxYUTR++/ZtvuJkXpc5rrJUqlQpy7G6desqHCfzmsjISKSmpuY7L6LssHCViIiIiIiIiIiIiIiIiIiIiIiI0qXJisdDxWrVqiUav3nzJl9xMq+rXbt2vnPKTXZxDQ0NFY6T3ZrIyMh85USUExauEhEREREREREREREREREREREREWWQuRD07du3iI+PVzjO06dPRePMBbHKYmdnl+WYTKZ4AXB2ayQSSb5yIsoJC1eJiIiIiIiIiIiIiIiIiIiIiIiIMqhXr55onJqaihs3bigUIyAgAB8+fBAds7e3L3Bu2alfv36WY1FRUQrHybxGIpHAyMgo33kRZYeFq0REREREREREREREREREREREREQZNG/eHOXLlxcd8/X1VSjGhQsXRGM1NTU4OzsXNLVsNW3aFGZmZqJjDx8+VDhO5jXm5uZQV1cvUG5EmbFwlYiIiIiIiIiIiIiIiIiIiIiIiCgDTU1NdOvWTXRs586dkMlkcsf466+/ROPmzZtnKS5VFjU1Nbi4uIiOnT17VqEYqampWYptW7duXeDciDJj4SoRERERERERERERERERERERERFRJv379xeNX79+jaNHj8q19v79+7h48aLo2DfffKO03LIzaNAg0fjx48cKdYn9999/ERISIjrWsWNHZaRGJMLCVSIiIiIiIiIiIiIiIiIiIiIiIkonlRWPRzHQq1cv1KtXT3TM3d0dcXFxua5LS0uDm5ub6JiFhQVGjx4t13klEono4eTkJNe69u3bo02bNqJjEyZMQGJiYp5ro6KiMH36dNExY2NjfPfdd3Kdm0gRGqpOgIhKtvDwcNy8eROhoaH4+PEjNDQ0YG5ujooVK6JZs2bQ0dFR2rlevnyJu3fvIjQ0FDExMdDQ0ICenh4sLS1RtWpV1K5dG5qamko73xdeXl4YMWJEjq9v374dw4cPV/p58+vVq1e4c+cOQkJCEBsbCx0dHRgbG6NevXqwt7eHhob8f/U7OTllaQGfkSLt74mIiIiIiIiIiIiIiIiISqvAwEDY2NgI4/Pnz8tdaCYvX19ftGvXThi/fv0a1tbWSj2HIqytrREUFJTta1ZWVggMDJQ7VmpqKvz9/fHgwQOEh4cjISEB+vr6sLCwQKNGjVCtWrV85ZiWloaXL1/i5cuXePfuHWJiYpCcnIxy5crBzMwMDRo0QI0aNaCmJn/vv8x/DpktWLAACxcuzFe+VPxIJBIsWbIELi4uwrGgoCC4uLjg0KFDKFeuXJY1ycnJGDp0KG7duiU6PnfuXJQtW7bQc16+fDmaN28ujJ88eYLu3bvj33//hYGBQbZrQkND0b17d7x79050/IcffoCurm5hpktfKRauEpUwmYso81M0mfmXKEV/aUpJScGWLVuwa9cu+Pn5QSqVZjtPR0cH7du3x7hx49CjRw+FcvwiJiYGv//+O7Zu3Yq3b9/mOldbWxsNGjRAx44d4erqikaNGuXrnEXt/v37aNy4MVJTU4Vjbdu2VahVe1paGjZv3ox169bh8ePHOc4zMTHBsGHDMGfOHBgZGRUkbSIiIiIiIiIiIiIiIiohisM1RiL6Py8vL6Gos0GDBujdu7dK81Gl6Oho/Prrr9i+fTs+fvyY47yaNWtiwoQJmDBhglzNmmbPno2LFy/izp07+Pz5c65zK1WqhJEjR2LGjBnQ19dX+D1Q6dezZ0/07dsX3t7ewrHz58+jXr16mD59Ojp06ICKFSsiNDQUV65cwW+//ZaldqN58+YYN25ckeTbrFkzuLu7w8PDQ5RvjRo1MHnyZHTr1g1WVlaQSqV4/fo1jh49Cg8PD8TExIjidOjQATNmzCiSnOnrw8JVIlLIf//9B3d3d7x48SLPuYmJiTh+/DiOHz+O9u3bY+PGjahRo4bc5zp37hyGDBmC9+/fyzU/KSkJfn5+8PPzw61bt/Dff//JfS5F2NraisbZ3T0jry+t4TMWrSoqLCwMPXv2hJ+fX55zw8PD8dtvv2Hnzp3w9vZGq1atcp1vaWkper+fPn3K9csCERERERERERERERERERHlzsvLS9j5ctiwYaWicNXQ0FDUPKlSpUp5rvHz80OfPn0QEhKS59xnz55h8uTJ+Ouvv3D8+HFYWFjkOn/lypVIS0vLO3EA7969w88//4ytW7fiwIEDaNasWa7zdXR0stQNBAUFFei6PxV/27dvx9u3b3Hjxg3hWFBQENzd3fNca2trC29vb4V2yC2oVatW4c2bNzh06JBwLCwsDPPmzcO8efPyXO/g4IB//vkH6urqhZglfc1YuEpEctuwYQMmTZok6rCqpaWFTp06oUmTJqhQoQJSUlLw/v17nDt3Dn5+fsJW8ufOnUOLFi1w9OhRUTvynJw/fx7du3cX3fmkp6cHZ2dnNGzYEKamplBXV0dkZCSePXuGmzdv4sGDB0Wydf3Lly+VFmvNmjVZWsMrIjk5GZ07d8a9e/eEY9ra2nBxcUGzZs1gZGSEuLg4PHz4EAcOHEBkZCQA4OPHj+jatStu3LiB2rVr5xh/9+7donHmu3GJiIiIiIiIiIiIiIiIiIjc3d0V6sL84sULdOnSRdTh0cTEBP369UPdunWhq6uLiIgIXLt2DUePHkVycjIA4M6dO+jYsSNu3bqFMmXK5HkebW1tNG7cGPb29rC1tYWBgQHU1dURHh6Ou3fv4tixY/j06RMAIDg4GJ06dcLly5dRv379HGM6OjpmqRuwtrZGUFCQ3O+fSh59fX2cOXMGw4YNw8GDB+Ve17p1a+zfvx/m5uaFmF1Wmpqa2L9/P3788Uf8/vvvOe6mnJ1vv/0WW7ZsQdmyZQsxQ/rasXCViOTi4eGByZMnC2OJRIIxY8Zg6dKlOW45//z5c4wfPx7nzp0DAERERKBDhw44f/48HB0dczxXQkIChg0bJipanTRpEpYsWZJrd9P379/j33//xebNmxV9eyrx6tUrLFiwAABgamoKqVSKiIgIhWKsXbtWVLRav359HDx4EDY2Nlnmrlq1Cm5ubti3bx8AIC4uDu7u7jh9+nT+3wQREREREREREREREREREcHa2rpIGi2VFlOmTBEVrQ4aNAhbtmyBrq5ulrkBAQHo3bs3Hj16BADw9/fHb7/9hrlz5+YYf9KkSejevTvatGkDLS2tHOdFR0dj0qRJ2LVrF4D06+ijR48WddUk+kJfXx///vsvjhw5guXLl+Pq1as5zq1Xrx6mTZuGoUOHQiKRFGGW/6ehoYHffvsN3333HZYtW4YjR44IReCZ6ejooFOnTpgzZ06uNT1EysLCVSLK0+3btzFjxgxhrK6ujt27d2PAgAG5rqtRowbOnj0Ld3d3/PHHHwCAxMREDBo0CPfu3cuxCPXvv//G27dvhfHEiRPh4eGRZ54VK1bExIkTMXHiRAQEBMjz1lRqzJgxSEhIAACsXr0a8+bNU7hw1cvLS3iuo6ODo0ePonLlytnO1dfXx+7du/H06VM8ePAAAHD27Fm8f/8eFStWzN+bICIiIiIiIiIiIiIiIiIiUkBoaCj+++8/YdygQQPs3Lkzxy3JbW1tcfz4cdSqVQuJiYkAgJ07d+ZauLpmzRq5cjEwMMCOHTsQEhKCs2fPAgBu3ryJ+/fv59p1tbSTSQFZmmoLsWXyNwgtci4uLnBxccH79+9x/fp1BAYGIj4+Hjo6OqhcuTKaNGmCqlWrFugcyiyEb9SoEfbv34/4+Hhcu3YNz58/R3R0NDQ1NWFqagorKys0b95cri7GRMrCwlUiypVUKsXgwYORkpIiHPPw8MizaDUjDw8PfPjwAfv37wcAvH79GjNmzMixM+rx48eF5xKJJNdfNnNia2ur8JqitG3bNuGX3o4dO2Lw4MGYN2+eQjE+f/6Mx48fC+MePXrkWLT6hYaGBkaPHo1JkyYBSP9F58GDByxcJSIiIiIiIiIiIiIiokLz6tUr+Pv7IygoCLGxsdDS0oKRkRHs7Ozg4OAATU1NpZwnJSUFFy5cwOvXrxEWFgZDQ0M4OjqiUaNGOa6RyWS4ceMG7t69i8jISJQrVw516tRBmzZtciyiU8Tjx49x9+5dBAcHQ11dHbVq1UL79u2ho6OT45q3b9/i8uXLePv2LSQSCapUqYKOHTvC2Ng433nIZDLcuXMHjx8/xsePHyGTyWBubg4HBwfY2dnlO25GycnJuHTpEoKCghAaGoqyZcuiV69esLa2znb+kydPcPfuXYSGhiI+Ph7a2tooX748rKysYG9vX6BrmAkJCfD19cWbN28QGRkJMzMztGrVCrVq1cpxTWpqKq5cuYJHjx4hJiYGhoaGaNiwIRwdHVXWLVFRDx8+xP379xEcHIwyZcrA2toa7dq1y3VnU1W4e/euaNvy0aNH5/nfW5UqVdCtWzd4e3sDAJ49e4bPnz8rpdBOIpHA3d1duIYPADdu3PiqC1dJPhUrVoSrq6uq05Cbrq4uOnbsiI4dO6o6FSIWrhJR7g4ePIinT58K4zZt2mD8+PEKx1m3bh3Onj2LyMhIAMBff/2FRYsWoUKFClnmBgYGCs/NzMxgYWGheOLFWGhoqNDBtkyZMvjzzz/zFSdzd9Zq1arJta569eqi8Zc/EyIiIiIiIiIiIiIiIiJlSEtLw6lTp7Bv3z6cOnUK79+/z3Gunp4e3NzcMGvWLJibm+cZe+HChVi0aBEAwMrKCoGBgZBKpfj111+xdu1afPz4McuaJk2awNPTE/Xq1RMd//vvvzFv3jy8fv06y5pKlSph/fr1cHFxyTWfwMBA2NjYCOPz58/DyckJly9fxrRp03Dz5s0sawwNDbFkyRJMmDBBdPz58+eYNm0aTpw4kaXTnoaGBiZNmoSlS5cqVKj36dMnLF++HJ6enggNDc12TvXq1bFo0SIMGjQoz3jDhw/HX3/9BQBo27YtfH19kZCQgPnz52P79u2IiooSzS9fvjyGDx8ujGUyGbZs2YJVq1bhxYsXuZ7L2toavXv3xurVq7MtHM0ul8+fP2Pu3Lnw9PREbGxsljWdO3fG5s2bYWVlJcrpjz/+wC+//JLtz0/t2rWxZcsWtGzZMtd8c/pZ+MLX1xft2rXLsu6vv/4S3kdmCxYswMKFC3M9L5C+0+bMmTNx586dLK9paWlh4sSJWLx4McqWLZtnrKKgzGvdymrSVLNmTdE4LCxMKXGJiCh7aqpOgIiKt8zt83/++ed83U1mZmaGiRMnCuPk5GSsX78+27kJCQnC86SkJIXPVdxNnDhR+MI2f/78fHeHNTAwEP1ZxMfHy7Uu8zwzM7N8nZ+IiIiIiIiIiIiIiIgoO2/fvkW3bt3g5eWVa9EqAMTFxeH3339Hw4YNcevWLYXP9fnzZ3Tp0gVz587NtugQSN/2u2XLlkJ8mUyGiRMn4rvvvsu2aBUA3r17hz59+uDvv/9WOKdNmzahXbt22RatAkBUVBS+//57zJo1Szh27tw5ODg44Pjx49luD52amoo1a9agb9++SEtLkyuP69evo3r16vjll19yLFoFgBcvXuDbb79F//79RTtxyuPt27dwcHDA6tWrsxStZpacnIzevXtj7NixeRatAumFoL///rvc7zc8PBzNmzfH6tWrsy1aBYBTp06hWbNmQjOl5ORkuLq6YvLkyTn+/Dx58gQdO3bE+fPn5cqjqK1YsQKdO3fOtmgVSH+Pq1evRteuXZGYmFjE2WXPyMhINM7PtW51dfUscQoi889MQTocExFR3thxlYhyFBkZiatXrwpjW1tbtGnTJt/xRowYgcWLFwtftI4dO4YlS5ZkmWdqaornz58DAKKjo+Hn5wdHR8d8n7c4OXz4MA4cOAAAqFOnDn744Yd8x9LV1UX9+vVx7949AOlfZuWRcXuDMmXKoGnTpvnOQVnkuVstPDy8CDIhIiIiIiIiIiIiIiIqXIpc8zA1NS3ETIqGtrY2WrRogcaNG6NSpUooV64cPn36BH9/fxw5cgQhISEAgJCQEHTv3h0PHjyQq/PqF+PHj8eZM2cgkUjg7OyMdu3awdjYGCEhIdi/fz/u378PIL1AduDAgfD398fKlSuFJjsNGjSAi4sLKleujISEBJw6dQrHjx8HAEilUowdOxZt27aFpaWlXPmcO3cOS5cuRVpaGqpXr46+ffuiatWqSE5OxqVLl7B//35hi/Tly5ejU6dOqFixInr16oW4uDgYGRmhX79+qFevHrS0tPDo0SP89ddfiImJAQCcOHEC69atw+TJk3PN4/z58+jRo4eoaVDdunXRrVs32NjYQE1NDU+fPsW+ffsQHBwMANi/fz8kEgn27t0r13tNTk5Gv3798PTpU0gkErRv3x7t27eHhYUFoqKi4OfnBy0tLWH+ggULcOTIEWFsbGyMnj17wt7eHkZGRkhJSUFERAT8/f1x+fJl0U6deUlLS8PAgQNx7949aGpqolevXmjRogXKly+PwMBA/P333wgICAAAfPjwAUOHDsXFixcxYcIEHDp0CADQqlUrODs7o0KFCoiMjMSRI0dw6dIlAOkF0kOHDsXTp0+hq6srd14Z6ejoCE2NgoOD8fnzZwCAvr5+js2G8irM/PvvvzF37lwA6d1Ie/fuDVtbW8hkMty7dw+7du0Sij0vXryIhQsXYvny5fnKX5maNm0KDQ0NpKamAki/ht2nT59c18hkMtE18aZNmyrUfTgvJ06cEI1btWqltNhERJQVC1eJKEeXL18W3c3XuXPnfHVb/cLa2ho1atTAs2fPAAAPHz5ETEwMypcvL5rXrFkzXLlyRRiPGDECR48ezXdn0uIiJiZG2O5DIpFg06ZN0NTULFDMSZMmYdSoUQCAR48eYcOGDVm2FMno5s2b2Lp1qzAeM2YMypUrV6AclIFdX4mIiIiIiIiIiIiI6GthZ2cn99zsOm+WFNWqVcPMmTPRv3//LNcDv/Dw8MCSJUuwePFiAMDHjx8xc+bMHLdNz+zNmzfw8vKCmZkZDh06hObNm4tenz17NsaOHQtPT08AQEBAAGbNmoV169ZBQ0MDGzduhJubm2iNu7s7vLy8MGLECADpBa+rV6/Gb7/9JldOS5YsgUwmw8KFCzFv3jyoq6sLr33//fdwc3ND9+7dkZycDABYtGgRkpOTERcXhz59+mDr1q0wNDQUxZw+fTpatWqFt2/fAgCWLl2KiRMnimJn9PHjRwwaNEgoWtXX18eWLVswYMCAbPOdPHmy8Bnt27cPPXr0wJAhQ/J8r9euXQMAWFhYwNvbGy1atMhxbkJCAtauXSuMe/bsib///ht6eno5rrlz5w7Wrl0r1zXqK1euQCaToXr16jhy5Ahq1aolen3u3Lno06cPfHx8AACXLl3CDz/8gK1bt0JPTw979uxBjx49RGtmzJiBRYsWYeHChQDSu/Bu27YNkyZNyjOf7Dg6OuLly5cAACcnJ1y4cAEA4OrqCi8vr3zFnD9/PtTU1LBixQpMmTIly8/ErFmz4OTkhKCgIADA2rVrMWvWrCw/Y0XN2NgYgwYNws6dOwEAW7duxciRI9GoUaMc16xbtw7+/v7CuCBNojK7du0aVq5cKYydnZ1Rp04dpcUnIqKs1FSdABEVX35+fqJx48aNCxwzYwypVJrt9hgjR46Emtr//3p68uQJ7OzsMHDgQOzdu1e446+kmTlzprAVyujRo9GyZcsCxxwxYgR69eoljCdOnIgJEybg6dOnonkfPnzAihUr0K5dOyQlJQFIvwNt6dKlBc6BiIiIiIiIiIiIiIiIKCNLS0s8ffoUo0ePzrFoFQC0tLTw888/Y+bMmcKxvXv3yrVbH5Be2KupqYn//vsvS9EqAKipqcHDwwMWFhbCsd9//x2pqanw8PDIUrT6xfDhw0VFjP/8849c+XzJafr06ViwYEG2haUdO3bEuHHjhPGlS5fg5+eHFi1aYP/+/dkWFFapUgVr1qwRxh8/fsx1N8ZZs2YhNDQUQPpn7OPjk23RKgCULVsWW7ZsQe/evYVj8+fPF7rC5kVTUxMnT57MtWgVAK5fvy5sU6+hoYFt27blWrQKAI0aNcJff/2VY4FuRjKZDAYGBjh37lyWolUgvfPv1q1boa2tLRxbtWoVgP8X62bnp59+Qr169YSxIj8LRUEqlWLFihWYPn16tp+TtbU1Nm/eLIyTkpLg7e1dlCnmaPXq1ahatSqA9I62Tk5O+O2334Sf3S8eP36MsWPHiroMT5gwIc8OrblJS0tDREQEfH19MWHCBLRt21boTFu1alVRM6ivllRWPB5EVGqxcJWohBsxYgQkEolCj3bt2skVO/MvhNWqVStwvtWrV8/1HED6XaazZ88WHUtOTsbevXsxcOBAVKpUCRUrVoSLiwt++eUXXLx4USjGLK4uXryILVu2AADMzc2Vtv2CRCLBvn37MGXKFGhoaEAmk2Hjxo2oXbs2ypcvDxsbG5iamqJChQr48ccfER8fD01NTYwfPx5nz57N9zYWREREREREREREREREVHIV5jVGIL2YUZ5iwy/mz5+PsmXLAkgvrMutKDOz0aNHo2HDhjm+rqOjg759+4qO1a1bF+PHj8817tChQ4Xn79+/x7t37+TKx9jYWOggm5PsupmuXbs218/MxcVFVAScuQnRFx8+fMDu3buF8dSpU+VqqPPHH39AQyN9096goKAs26bnZPz48aLCzpx8+PBBeG5kZAQTExO54itizpw5qFSpUo6vV6hQAR07dhQd69GjB7p27ZrjGolEgsGDBwvjO3fuCNvbFwe1atXCtGnTcp3TuXNnWFtbC+OcfnaKmomJCS5dugRnZ2cAwKdPnzBjxgxYWFjAzMwM1tbWKF++POrUqYPNmzdDJpPB0NAQa9euxbp16xQ6V3h4uOjvMw0NDZiYmKBdu3bYuHEjUlJSoK6ujiFDhsDPzw8VK1YsjLdMREQZsHCViHIUGRkpGhsYGBQ4ZuY7KjOf44vFixdj8eLFwpejzEJCQnD06FHMmzcPbdu2hbm5OcaOHYsXL14UOEdl+/z5M0aPHi1s5fL7778r5bP8QktLC2vWrMGjR4/Qtm1b4XhsbCwCAwMRHh4uHKtSpQoOHTqEDRs25HkHIxEREREREREREREREVFR0NPTQ7NmzYRxdrs25iRjgWlOMm8/nl3haF5rMu94mJNvvvkGOjo6uc6pV6+eqEi1du3aee5+qampCXt7+zzzOXDgAJKTkwGkd5ydOnWqXHlXqlRJVNR5+vRpudaNGDFCrnlfCpOB9I6xAQEBcq1TRFH8LHz+/BmBgYEK51ZYhg0bBolEkue8jP99PXnypDBTUkjFihXh4+MDHx8f2NjYCMfDwsIQFBSE2NhY4VirVq1w7do1uLu7y/WeFWFsbIyNGzdi+/bthVJUTUREWWVfEUZEJYaZmRn09fUVWpOYmChsWZ+bT58+icbK6M6ZuVgy4y+aGUkkEsybNw8DBgzA8uXLsW/fviz5ZBQTE4PNmzdj+/btmDVrFhYtWqT0X1bza9GiRXj+/DkAwNnZGQMHDlRqfKlUirVr1+LXX3/Fx48fc5375s0bdO/eHZ06dcKff/4pbL2gannlDaTfBWdnZ1cE2RARERERERERERERERWex48fq7wwqjCvMeZXhQoVhOfBwcFyrdHU1MxSiJgdc3Nz0djR0THPNRYWFqJxdHS0XDnJE1tLSwuGhoZCAxp51mTOKad8Ll26JDx3cHDI8t5z4+joiP/++w+AfF059fT05Oq2CiBLV9xevXph+/btaNKkidz55cbKykqu91qUPwtFIWNBam4sLS2F58Up/5iYGMyZMwdbt27Nc5fVy5cvw97eHm5ubli5cqVC9Qvq6uqwtbUVxsnJyYiIiEBCQgIAICIiAmPGjMHy5cuxceNGdOrUKX9viIiI5MbCVaISbvny5Rg+fLhCa3x9feXayiPzl9X4+HiFzpOduLg40bhcuXK5zq9evTo8PT3xxx9/wNfXFxcvXsT169dx//59REVFZZmfkpKCxYsX4927d9i2bVuB8y2o+/fvY9WqVQDS7yLcsGGDUuN//vwZrq6u8PHxEY517NgREydOhKOjI4yNjREfH48HDx5g165d2LZtG9LS0nD69Gk0btwYZ86ckevLfGEzNTVVdQpERERERERERERERERFwsTEROXXRgrzGmNmjx49wr59+3Dr1i08efIEUVFR+PTpE6RSaY5r5C2sMzIygqamZp7zMhe4yVPgmHmNvNdKMxc55hb/S+GqvMWlGXPKKZ/79+8Lz+vUqSNX3C8y5vHu3bs859vY2EBNTb6Nfq2srNC1a1fhuqa/vz+aNm0Ke3t7dO/eHW3btkXz5s2z7OApL0U+94wK82ehKMj7vjM2mCou+QcGBqJ9+/Z4/fo1gPTi0pEjR2Lw4MGoV68edHV1ERERAT8/P6xbtw5nzpxBSkoKNm7ciGvXruHcuXMwNDSU61yGhoZ4+fKl6JhMJsOzZ8+wZ88erF69GnFxcQgICICzszO2bduGYcOGKf09ExHR/8n3GwQRfZWMjIxEY2XceRUTE5PrOXKio6ODrl27YtmyZTh//jwiIyPx8uVLbN68OdsvyNu3b8fu3bsLnG9BpKWlYdSoUUhNTQUA/PTTT6LtDZRh8uTJoqLVpUuX4vTp0+jVqxcsLCygqakJAwMDtGnTBps3b8apU6dQpkwZAEBUVBRcXV2zFBMTERERERERERERERERFVRQUBB69OgBe3t7LF68GD4+PggMDERMTEyuRatAevMWeXy57qWo/KyTyWSFFluZ+URERAjPvby8IJFI5H5MmDBBWJtdE6HM8mpSlJmnpyeqVasmOvbw4UP8+uuv6Nq1K4yMjNC0aVMsWrQIr169Uih2cfxZKAolNf+kpCS4uLgIRatlypTBqVOnsHnzZrRp0wYGBgbQ1NSEhYUFevXqhdOnT+OXX34R1t+7dw9ubm4FykEikaBWrVpYtGgRHjx4IOxWKpVKMXbsWDx+/LhA8YmIKHcsXCWiHGW+uyzzHUj58eLFi1zPoQhbW1uMHj0a586dw8WLF1GpUiXR64sXL853bGVYs2YNbt++DQCwt7fH9OnTlRr/yZMn2LJlizB2cXHB7Nmzc13Tvn170S/0QUFB2LRpk1LzIiIiIiIiIiIiIiIioq/bixcv0KJFCxw/fjzLa5qamjA3N0eVKlVga2srPDLuBlkcCutKKmVtA/9lC/XcaGgotslvxYoVcevWLUyfPj3L7p9AesHgzZs3sXDhQtSoUQNubm6IjY1V6BxUMmzfvh0PHz4UxkuXLkX79u1zXTNnzhz07NlTGP/777/C9fiCsrGxwYEDB4QOwklJSVi2bJlSYpdYadLi8SCiUouFq0SUo6ZNm4rGt27dKnDMjDHU1NTQpEmTAscEgNatW8PHx0f05ejZs2cICAhQSnxFffjwAQsWLACQ/j43b96s8Be3vOzdu1f0pX3ixIlyrRs7diy0tLSE8b///qvUvIiIiIiIiIiIiIiIiOjrNnLkSLx//14Yd+3aFfv378e7d++QlJSEDx8+ICgoCC9fvhQeffr0UWHGpUfZsmWF5wYGBqLiYEUfhaF8+fJYtWoVQkJC8O+//2LixIlo2LChUDD4RVpaGrZu3YpWrVqxeLUU+ueff4TnWlpaGDNmjFzrJk2aJBor81p3w4YN0bp1a2F8/PhxFtETERUi5VZREVGp0qpVK0gkEuGXsVOnTkEmk0EikeQrXmBgIJ4/fy6M7e3tUb58eaXkCgB169aFs7Mzjh07Jhx7+vRpoX2pys2HDx+EuxDV1dUxePDgPNcEBwcLz/38/ETbZHTq1AkbN24Uzb9//75o3LhxY7ly09XVRa1atfDgwQMAgL+/v1zriIiIiIiIiIiIiIiIiPLi5+eHy5cvC+M5c+aIdgTMibI6hX7tTExMhELPgQMHZrnGWFzo6uqiT58+QsFydHQ0zpw5g3/++QeHDh1CWloaAODhw4dYvHgxVq5cqcp0SckyXuuuVasWdHV15VqX+Zq4sq91N2jQABcuXAAAREVFITIyEsbGxko9BxERpWPHVSLKkbGxMZo1ayaMAwIChF/S8mPbtm2iO5J69OhRoPyyU6dOHdE4JiZG6edQVEpKCgICAvJ8pKamCms+f/4sei0kJCRL3Pj4eNFYT09P7pwy/uKfmJiYj3dFRERERERERERERERElNXZs2eF5+XKlcNPP/0k17pXr14VVkpflVq1agnP3717p8JMFGNgYIB+/frhwIEDuHLlCnR0dITXdu3apcLMqDBkvNad3+vcgPKvdWtra4vGXwqoiYhI+Vi4SkS5mjp1qmi8YMGCfLXD//jxI9avXy+MtbS08P333xc4v8w+f/4sGhsZGSn9HMWFoaGhaPzhwwe512YshOUdYkRERERERERERERERKQsGXcZrFWrVpZCsOx8+PCBuwQqSbt27YTnly9fFjXPKSkcHR1FW8d/+PABUVFRKsxI+TQ1NYXnUqlUhZmoRsZr3fm9zg0o/1r369evhefq6uowMTFRanwiIvo/Fq4SUa5cXV1RvXp1YXzx4sV8bScxceJEREZGCuOhQ4eiQoUKSskxoytXrojG1apVU/o55NGgQQPIZDKFHlZWVsL6tm3bil47dOhQlnNkfm+nT5+WK7cXL14gMDBQGNeoUSNf75GIiIiIiIiIiIiIiIgos4xNcDI3ncmJh4dHvprnUFZ9+/aFhoYGACA6OhpeXl6qTSifatasKRqnpKSoKJPCkbHLaHHYRbSoZbzW/erVKwQEBMi17tSpU6KxMq91f/r0SRS/YcOGUFNjWRURUWHh37BElCt1dXXs2rVLdMeXu7s79u7dK3eMyZMnY//+/cLYysoKq1atynH+jz/+iHPnzimc6969e3Hr1i1hXLNmTZUVrhYFZ2dn0Xjp0qX49OlTnut+/PFH0bhLly5KzYuIiIiIiIiIiIiIiIi+XhmbtTx69EjUUCU7N27cyPXaISnGxsYGgwYNEsY//PADHj9+rFAMmUyG5ORkpeb17t07hbZdv3//vvBcV1cXpqamSs1H1aytrYXnjx49Ul0iKpL5WvecOXPyXBMdHY2lS5eKjuV0rTsuLk7hnCZPniwqIu7Xr5/CMUoTmVRWLB5EVHqxcJWI8tS0aVMsX75cGKelpWHQoEEYP368qItqZs+fP0fHjh3h4eEhHCtTpgz27NmD8uXL57juypUr6NChA5o3b47NmzcjOjo61/xSUlKwZs0aDBkyRHR89uzZebyzkq1169Zo0qSJMA4ICEC3bt3w7t27bOcnJCTAzc0NBw8eFI6VK1dOtM0GERERERERERERERERUUFkLCSTSqUYNGgQwsPDs5175MgRdO7cGSkpKexsqEQrVqwQdr+Mjo5Gy5YtsXPnzjwLR4ODg7F27VrUrFkTd+7cUWpOnp6eqFmzJtauXZvn1vD79u3D1q1bhbGLiwskEolS81G1Zs2aCc9fvXqFNWvWIDU1VYUZFa0xY8aIus7u27cPEyZMQGJiYrbzg4KC4OzsLCqEb9myJZo3b57t/E6dOmHy5Ml48OBBnrm8ffsWffv2xfbt24VjVapUwaRJk+R8N0RElB8aqk6AiEqGqVOnQkNDA1OmTIFUKoVMJsOff/6Jbdu2oVOnTmjatCkqVKiAlJQUBAcH4/z587h+/bpoSw9DQ0McOXIkx18eM7t+/TquX7+OiRMnon79+nB0dISVlRWMjIwAABEREXj48CF8fHwQEREhWvvNN99g2LBhyvsAiqlNmzahTZs2wh1jly9fRrVq1eDi4gJHR0cYGxsjPj4eDx48gLe3d5bPae3atTA2NlZF6kRERERERERERERERFQKNWjQAF27doWPjw+A9Gt+NWrUwMCBA1G/fn1oaGjg7du3OH78uLCbYoMGDVC9enXRLo6UfxYWFvD29oazszNiY2MRHR2NoUOHYu7cuejcuTPq1KkDQ0NDJCcnIyoqCk+fPsXdu3fx4MED0fVdZQsICMCUKVMwbdo0NG7cGE2bNkXVqlVhaGiIlJQUBAYG4tSpU6JdNnV1dbFkyZJCy0lVXFxcYGJiIhR1T5s2DfPmzYOVlRW0tLSEeePGjcO4ceNUlWahMTc3x+rVq0VNljZu3IgDBw7A1dUV9vb20NXVRUREBK5du4ajR4+KugCXK1cOGzduzDF+YmIiPDw84OHhgWrVqqFJkyaoU6cOjIyMULZsWcTHxyMoKAg3btzAxYsXIZVKRbH37duHsmXLFs6bJyIiACxcJSIFTJo0CVWrVoW7uztevXoFAEhOTsbx48dx/PjxXNe2bdsWf/75J2rVqpXneTL/ApiSkoJbt26JvqDkRE1NDd9//z1Wr16d59zSoGHDhjh+/DgGDhyIkJAQAEBSUhL279+f6xf7MmXKYM2aNRg+fHgRZUpERERERERERERERERfi+3bt6Nly5YICAgAAERFReVYZFarVi0cPHgQCxcuLMIMS7/mzZvj+vXr6N27N54/fw4gvbNkxk6muVFXVy+03KRSKW7cuIEbN27kOs/IyAiHDx9G1apVCy0XVdHR0cHOnTvxzTffCE2KEhIS8OTJE9G8vLrTlmSjR49GUlISZsyYgaSkJABAWFgYNm3alOs6S0tL7N27F/b29nKd5+XLl3j58qVcc+3t7fHXX3+hYcOGcs0nIqL8Y699IlJI9+7d8eTJE/zxxx9wdHTMdcuOMmXKoGvXrjhy5Ah8fX3lKloFAB8fH/j6+mL69OlwcHCQ60tR+fLlMXLkSNy6dQseHh7Q0Ph66vLbtGmDR48eYe7cubCwsMh1btmyZTFixAjcvXu3VN6ZR0RERERERERERERERKpnbm6OmzdvYuTIkdDU1Mx2jqGhIaZOnYrbt2/D2tq6aBP8StSuXRuPHj3Cpk2bUKdOnTzn29nZYfr06bh79y6aNGmi1FyGDBmCn376CU2aNMnzWq6hoSEmTpyIp0+folWrVkrNozhxdnaGv78/5s2bh5YtW8LU1FTUbfVrMHHiRNy9exfDhw+Hjo5OrnMtLCwwd+5cPHz4EC1btsx17pIlSzB06FBUrlw5zxwkEglatmyJrVu34s6dOyxaJSIqIhJZYfZ5J6JSLywsDDdu3EBoaCjCwsKgoaEBMzMzWFpaolmzZkppnx8fH4+nT5/i5cuXCA0NRVxcHNTU1KCvrw9TU1PY29ujRo0ahXbXn5eXF0aMGCGMi/NfmzKZDE+ePMG9e/cQFhaGT58+QUdHB0ZGRrCzs0ODBg2gra2d7/iq/CzCwsJgZmYmOjbzzEboGpYrshyIqHDpahTfv1+JFFFFV9UZlFwPIiWqTqFEuXbfQNUpyO1zYsm4sezw6OLTwaL83lNFer7o+GSYfv//XRuWXt4EnXKq+wstLjIW850miI59/PgRpqamKspI+bL7jrPp+gaUM+Z3HMqfbZcMVJ0CZaNb42hVp0DZ6Gj5WdUplDp1y5rlPYmyWOD/SdUpFJjPeXNVp1BgtetFqDqFPDlUTFF1Ctk6+0hP1SkIUuLj4OM2WBivuLoJZVX4nSajT5GxmN2mdH+/yUt4eDguXryIwMBAJCcnw8zMDFZWVmjTpk2ORa1UOIKDg3H9+nWEhoYiKioKWlpaMDQ0hK2tLerWrVtkP5eJiYm4f/8+AgICEBoaioSEBJQpUwbGxsaoW7cu6tev/9UVcJZU1tbWCAoKAgAsWLCgQJ2Tk5KScO/ePTx+/BiRkZFITEwUagIaNmyIWrVqQSJR/N+QP3z4AH9/fwQGBiIyMhJJSUnQ1dVF+fLlUa1aNTRo0ADlyhX832OU+VkUtez+ner1mDYwLava/w7DEpJhs/mi6NjX9v9QotKsZFw5IqJiy9TUFN27dy/Uc+jq6sLBwQEODg6Fep7SQCKRwM7ODnZ2dqpOhYiIiIiIiIiIiIiIiAgmJiZwdXVVdRqE9C3W+/btq+o0oKOjg2bNmqFZs2aqToWKEW1tbTg6OsLR0VGpcS0sLPLcuZSyIZUBaSpuOiNl0xui0iznPb6JiIiIiIiIiIiIiIiIiIiIiIiIiIiUiIWrREQKkkgkooeXl5eqUyo0Tk5Oovc6YsQIVadERERERERERERERERERETFzKJFi0TXlq2trVWdUqHx9fXNUjcQFBSk6rSIiEoUFq4SEREREREREREREREREREREREREVGR0FB1AkRExV25cuVga2ub6+ullaWlZa7vXdWGVEuAiam6qtMgIiU5G1xW1SkQKcWNMImqUyixbt4zUnUKJYrFhShVpyC3NI0Sct/saFUn8H8p331TtOeLjgO+3y+MLXUBXd0iTUEk5rPqzq1K7SwBE1NVZ0El1YfG0apOgbLR3vIr/QutmKtpYKnqFEodWWy4qlMokX5qZK7qFAqsR5U3qk6hwNQlMlWnkCd9zTRVp5At9WL0TyAJsfHwyTDuaRWP8gbF4882Qi8es1WdBBFRIbG2toaGRvalR5UqVSribIqOjo5OrtfRjYz4b91ERHlh4SoRUR5cXV3h6uqq6jRUYvfu3apOgYiIiIiIiIiIiIiIiIiIiiFfX19Vp6ASjo6OePnyparTICIq0Vi4SkRERERERERERERERERERERERAAAmTT9oeociKj0KiF79RERERERERERERERERERERERERERUUnHwlUiIiIiIiIiIiIiIiIiIiIiIiIiIioSLFwlIiIiIiIiIiIiIiIiIiIiIiIiIqIiwcJVIiIiIiIiIiIiIiIiIiIiIiIiIiIqEixcJSIiIiIiIiIiIiIiIiIiIiIiIiKiIqGh6gSIiIiIiIiIiIiIiIiIiIiIiIiomJBKIJNKVJ4DEZVeLFwlIspFYGAgbGxscnx92LBh8PLyKrqE8hAWFoZbt24hMDAQ0dHRUFdXh6GhIWrWrIlGjRpBT09P7lgLFy7EokWLcnz9/PnzcHJyUkLWRERERERERERERERERERERET0tVBTdQJEVHR8fX0hkUiEx8KFCws0LzeBgYGiGMOHD89x7sKFC0VzfX19FT6fMnJWhfj4eNjY2Ihyl0gUv2vo5MmT6NixI8zNzdGtWzdMmDABc+bMwY8//ogxY8agbdu2MDExwaBBg+Dv718I74SIiIiIiIiIiIiIiIiIiIiIiChvLFwlIlJAxYoVYWtrKzzMzc0LFG/evHkIDAzM9/q0tDSMGTMGzs7OOHv2LGQyWY5zk5KS8M8//6Bhw4ZYt25dnrGNjIxE79XKyirfeRIREREREREREREREVHR6tGjh6hxysmTJ/MV5+TJk6I4PXv2VHKmxV9UVBROnz6NZcuWoW/fvrCyshJ9JvnZpTBzc5/8PgpyrZGIiIhIVTRUnQARUUmye/fufH3xzM6NGzfg4eFRoBiTJk3Cli1bRMecnJzQoUMHVKxYESkpKQgICMDhw4fx/PlzAEBKSgomTZqE8uXLY8iQITnGdnd3h7u7uzAODAyEjY1NgfIlIiIiIiIiIiIiIiKiouHp6Ym6desiIiICADBq1Cg8evQIBgYGcseIjo7GqFGjhLGxsXGWa1Ol2enTpzFu3Di8evVK1alkS01NDfr6+qpOg4iIiEhhLFwlIlKBlJQUuLm5QSqVQiKRoGbNmnj69KlCMW7cuIE///xTGBsYGMDb2xvt27fPMnf58uVYuXIlfvzxR+HYlClT0Lt3b36ZJSIiIiIiIiIiIiIiKoUsLCywYcMGDBgwAAAQHByMiRMnYteuXXLHcHd3R3BwsDDeuHEjLCwslJ5rcRUcHFxoRatfdj9URFxcHEJDQ4Vx586dYWxsrOzUiIiIiAqdmqoTICL6Gv366694+PAhAGDkyJFwdHRUOMZff/0FmUwmjLdu3Zpt0SoASCQSzJw5ExMmTBCORUZG4vjx4wqfl4iIiIiIiIiIiIiIiEqG/v37Y+DAgcJ49+7d8Pb2lmvt4cOHsXPnTmE8aNAgfPPNN0rPsSSxtLREr1698PPPP8PExKRAsdzd3fHy5UuFHv369RPFcHNzK1AOREQ5kUqLx4OISi8WrhIRFbEnT57gl19+AQCYmZlh5cqV+Ypz+/Zt4bmZmRn69OmT55rx48eLxvfu3cvXuYmIiIiIiIiIiIiIiKhkWL9+PSpUqCCMx40bJ+ramZ3w8HCMGTNGGFesWBHr168vtByLq2rVqmHhwoU4duwYPnz4gHfv3uHQoUOYP38+dHV1izSXz58/Y/fu3cLY1NQULi4uRZoDERERkbJoqDoBIqKviUwmw+jRo5GUlAQAWL16NQwNDfMVKyIiQnhua2sLiUSS55rq1auLxpGRkfk6NxEREREREREREREREZUMRkZG8PT0RPfu3QH8vyj18OHDOa6ZMGECPn78KIw9PT1zvKbl7++P+/fv4+PHj0hKSoK5uTnq1q0LBwcHua5f5SQxMRGXLl1CUFAQwsPDoa+vDwsLC7Ru3Rrm5ub5jquIVq1aoVWrVkVyrrx4e3sjOjpaGA8dOhSampqqS4iIiIioAFi4SkRUhDZs2IArV64AADp16oTvvvsu37GMjIyE5/Hx8XKtyTzPzMws3+cnIiIiIiIiIiIiIiKikqFbt24YPXo0tmzZAgA4cuQIvLy8MHz48Cxz//nnH+zfv18YjxkzBl27dhXNSUpKwrp16+Dh4YE3b95ke05LS0vMnj0b48aNg7q6uty5vnv3DnPnzsX+/fuRmJiY5XWJRIKWLVti6dKlaN26tdxxS7qtW7eKxm5ubirKhIiIiKjg1FSdABHR1+Lt27eYPXs2AEBHRwcbN24sULwWLVoIz/39/fPc0gUAzp49Kxq3adOmQDkQERERERERERERERFRybB69WrY2NgI48mTJ2cpOv3w4QO+//57YWxjY4PffvtNNOfFixewt7fHjBkzcixaBYDg4GBMnDgR7du3x6dPn+TK8dixY6hZsyZ27NiRbdEqkL7D4eXLl9GmTRvMmDEDMplMrtglWUBAAHx9fYVxy5YtUatWLdUlRERERFRA7LhKRFRExo8fL3wpnzdvHmxtbQsUb9y4cVi/fj1SUlKQlpaGyZMnY8+ePTluuRIREYG5c+cK4/r166NTp04FykFZwsLC8pwTHh5eBJkQEREREREREREREREVLkWueZiamirtvHp6evDy8kK7du0glUoRGxuLESNG4MyZM8L1pTFjxiAyMhIAoKamBi8vL+jp6Qkx/P394eTkJHoPVatWRc+ePVGjRg1oaWkhICAA3t7eePHiBQDg4sWLcHZ2xoULF6ChkXOJgo+PD/r06YPU1FThWP369dG7d29UrlwZUVFROH/+PE6ePIm0tDQAwG+//YbU1FT8/vvvSvuciqNt27aJCnRHjRqlwmyIiIiICo6Fq0RERWDPnj04fvw4AKBOnTr44YcfChyzZs2a+PXXXzF9+nQAwN69exEeHo4FCxagefPmwhf/uLg4HD16FHPnzsXr168BACYmJrkWuRY1MzMzVadARERERERERERERERUJOzs7OSeq+xuom3atMGUKVOwevVqAMC5c+ewbt06TJo0Cdu3b8fRo0eFuVOnThXt3peYmIj+/fsLRauampr47bffMGHCBKirq4vOs3jxYixevBg///wzAODq1atYtmwZ5s+fn21e4eHhGDFihFC0qqamhnXr1mH8+PGieTNmzMD169fh6uqKkJAQAMDatWvh7OwMZ2fngnw0xVZaWhq8vLyEsb6+Pvr376+6hIjoqyCTSSCTqbaeQNXnJ6LCpabqBIio+Fu0aBEkEolCj4zbjHztIiIiMHnyZACARCLB5s2boampqZTY06ZNw9atW2FgYAAAOHv2LNq0aQN9fX1YWVnB0tIS5cuXx7fffisUrXbs2BF+fn6oXbu2UnIgIiIiIiIiIiIiIiKikuOXX34RFc/OmjULZ86cwZQpU4RjdnZ2WLJkiWjdypUr8fjxY2G8e/duTJo0KUvRKgBoaGhg0aJFopjLly9HTExMtjmtWLECoaGhwvi3337LUrT6RbNmzXDixAloaWkJx740eimNfHx88P79e2E8aNAg6OrqqjAjIiIiooJj4SoRUSGbOnUqwsLCAKRvr9KiRQulxh85ciQCAwMxduxYoYPq58+f8ebNG7x//x5SqRQAoKuri1WrVuHkyZOoWrWqUnMgIiIiIiIiIiIiIiKikqFMmTLYsWOHsHtfQkICunTpgtjYWADpnVR37tyJMmXKCGs+f/6M9evXC+MBAwbgm2++yfNcv/zyC4yMjAAA8fHx2LlzZ5Y5nz9/xrZt24RxgwYN4O7unmvczHMeP34MX1/fPPMpibZu3Soau7m5qSgTIiIiIuVh4SoR5cnQ0BC2trYKPaysrFSddrFw8uRJ4Qu4hYUFfv31V6Wf48SJE2jVqhU2bdqU63Yx8fHxmDFjBmrXro2zZ88qPQ8iIiIiIiIiIiIiIiIqGRwcHDBv3jxh/KURCgDMmzcPjRo1Es0/c+YMPn78KIzl7XBatmxZ0bb2p0+fzjLn2rVriIiIEMbjxo2DmlrepQwTJkwQmroAwLFjx+TKqSQJDQ0VvS97e3s0adJEhRkRERERKYeGqhMgouLP3d0dCxcuVGhNYGAgbGxsCiehEiI+Ph7jxo0Txr///jsMDAyUeo5FixaJ/mxq1qyJ6dOno0OHDrC0tERKSgoCAgJw5MgRrFmzBlFRUXj+/Dk6deoET09PjBw5Uqn55FfGf+jISXh4uGjbGiIiIiIiIiIiIiIiopLo8ePHMDExUXUamDt3Lo4ePYrbt28Lxxo3bow5c+ZkmXvp0iXhuZmZGRo3biz3eRwdHfHnn38CAPz8/LK8fv36ddG4a9eucsW1sbGBnZ0d/P39s41TGvz1119ITU0Vxuy2SkRERKUFC1eJiArJ3LlzERgYCABwdnbGgAEDlBp/z549oqLVXr16Yc+ePdDR0RGOaWtro379+qhfvz6GDRuG9u3bIyAgADKZDGPHjkXDhg3RsGFDpeaVH6ampqpOgYiIiIiIiIiIiIiIqEiYmJgUi2sjGhoa+P7770WNTiZMmAANjaxlBPfv3xee29nZiTqd5sXc3Fx4HhoaitTUVNE5nj9/Ljw3MDBAlSpV5I5dr149oXA1Y5zSYtu2bcJzbW1tDB48WIXZEBERESkPC1eJSOUyf7HNbbv7nGTcviS7mEXNz88Pf/zxB4D0LVA2btyo1PipqamYOXOmMK5QoQJ2794tKlrNrEqVKti7dy+aNGkCmUyG1NRU/PTTTzh69KhScyMiIiIiIiIiIiIiIqKSIfM1tZyusUVERAjPfX19C3QtLioqSlS4GxUVJTw3MzNTKFbGotjo6Ogsr/v5+eG7777LM07fvn2xfPlyhc5d2C5duoRnz54JY1dXVxgZGakwIyL6msik6Q9V50BEpRcLV4lI5cqWLSsax8fHKxwjLi5ONNbV1S1QTgU1fvx4oZh2wYIFsLa2Vmr8y5cv4927d8J4xIgRcr1nBwcHNG/eHFevXgUAnDx5EgkJCVn+DIiIiIiIiIiIiIiIiIi+yK4oNL8SEhJE44zX+RS9ZpXx+lhKSgqSkpKgra0tHEtMTERAQECecUJDQxU6b1HYunWraDxq1CgVZUJERESkfCxcJSKVMzQ0FI0z3lUpr8xfllV9t+GrV6+E5xs3bsTmzZtznf/x40fRuFq1asLzcuXK4c6dO6LXM27HAgCNGzeWO7fGjRsLhaspKSl4/vw5GjRoIPd6IiIiIiIiIiIiIiIi+rpkLCjV1dWFhYVFvmNpaIjLFPT09ITnmYta85KxIY6mpqaoaLUki42Nxf79+4Vx1apV0b59exVmRERERKRcLFwlIpWztLQUjZ88eaJwjMxrMsdUpcDAQIXXZLzzs3z58llez9yVNuMX+rxk7syamJioYHZERERERERERERERET0NTExMRGet27dGj4+PkqLnbHJTeZmL3nJON/AwCDL605OTpDJZPnOTVX27NkjKuIdOXIkJBKJCjMiIiIiUi41VSdARNSsWTPRF60rV64oHONLB1EAkEgkcHR0VEpuxVXmLrUfPnyQe21ISIhobGxsrJSciIiIiIiIiIiIiIiIqHSqVauW8Pzdu3dKjV2jRg3heXR0NN68eSP32gcPHmQbp6Tz9PQUnqurq2PEiBEqzIaIiIhI+Vi4SkQqZ2hoiEaNGgnjy5cvK9Sl9PXr17h06ZIwdnBwyPaOyqIUHR0NmUwm92PYsGGi9Rlfi46OzhK/WrVqovHp06flyistLQ3nzp0TxmXKlEHlypUVf4NERERERERERERERET01WjXrp3w3N/fX+HOqLlp1qyZaCxvN9fAwED4+/vnGKekevDgAW7duiWMnZ2dUbFiRRVmRERfI5lUUiweRFR6sXCViIqFSZMmCc+lUikmTpwIqVSa5zqpVIpJkyaJtvhwd3cvlByLk9atW6Ns2bLC+J9//hHdUZqTdevWie5SbdOmDXR0dAolRyIiIiIiIiIiIiIiIiodunTpIjSOkclk8PDwUFrsFi1aiHYI3LRpk+jaX042btwomtejRw+l5aRKW7duFY3d3NxUlAkRERFR4WHhKhEVC99++y3q1asnjI8fP44BAwbgw4cPOa4JDQ1F//79cfz4ceFY/fr1MXDgwELNtTgoU6YMJkyYIIxTUlLQrVs3XL16Ndv5MpkMGzZswIwZM0THM4+JiIiIiIiIiIiIiIiIMtPX1xc1olm5ciV8fX0VjvP58+csx7S1tTFy5EhhfPfuXaxfvz7XOA8ePMDatWuFsZ2dHZycnBTOp7hJSkrCrl27hLGFhUWpKcglIiIiykhD1QkQEQGApqYmDhw4AEdHR0RFRQEADhw4gOPHj6Nz585wdHSEiYkJJBIJwsLC4Ofnh9OnTyMhIUGIYWhoiAMHDkBTU1NVb6NIzZs3DydOnMDjx48BAMHBwWjZsiWcnJzQvn17WFpaIiUlBQEBAThy5AiePXsmWj98+HB06tRJFakTERERERERERERERFRCfPjjz/i4MGDePToEZKTk9G1a1csW7YM48ePh7a2do7rwsPDcfjwYWzYsAE//PBDtk1oZs6ciR07diA0NBQAMGXKFGhra2P06NFZ5vr5+cHV1RVJSUnCsd9++00J71D1Dh48iMjISGE8bNgwaGiwrIOIiIhKH/6GQ/QVU1MrXk2Xq1evDj8/P/Tp0wf+/v4AgMTERBw+fBiHDx/OdW2dOnVw8OBBVKtWrShSLRbKly+PU6dOwdXVFTdu3BCO+/r65nmH66hRo/Dnn38WcoZERERERERERERERERUWujq6uLgwYNo06YNQkJC8PnzZ0ydOhW//PILunTpggYNGsDIyAhSqRTR0dF48eIF7t27h9u3byMtLS3X2CYmJti+fTtcXFyQmpqKtLQ0jBkzBhs3bkTv3r1haWmJ6Oho+Pr6wsfHRxRv8uTJcHZ2LrT37eTkhHfv3mU5HhwcLDz38/PL9jplpUqVFOpM6+npKRqPGjVK/kSJiIiIShAWrhJ9RTJvvaGnp6eiTHJWvXp13LlzBzt37oSHhwcePHiQ63x7e3u4u7tj6NCh0NLSKqIsiw9LS0tcvXoVW7ZswYYNG/Dw4cMc56qpqaFTp06YNm0aOnfuXIRZEhERERERERERERERUWlQrVo13Lp1C66urvDz8wOQ3lF19+7d2L17d57r1dXVc3yta9euOHjwIAYMGCDsunj37l3cvXs3xzXTpk3DqlWrFHwXigkMDERQUFCucz5//oyAgIAsx1NTU+U+z+vXr3Hu3Dlh3KZNG1SvXl3+RImIiIhKEBauEn1FPn36JBrr6+tnO8/JyQkymaxA57K2ts53DC0tLYwaNQqjRo1CWFgYrl+/jpCQEERFRQEADA0NUaFCBTRr1gympqYFyrO48PLygpeXV77WqqurY9y4cRg3bhzevXuHW7duITg4GDExMVBXV4eBgQFsbW3RpEkTlC9fXrmJExERERERERERERER0VelYsWKuHbtGvbv349Vq1bh1q1buV4XrFq1Krp06YJvv/0WrVq1yjV2jx498PTpU8ydOxcHDhxAYmJiljkSiQQtWrTA0qVL0aZNmwK/n+Ji+/btos/Rzc1NhdkQ0ddOKgOkUtXnQESlFwtXib4ir169Eo2rVq2qokzkZ2pqip49e6o6jRKjUqVKqFSpkqrTICIiIiIiIiIiIiIiohJg+PDhGD58uMLrJBIJ+vfvj/79+yM8PBxXrlzBhw8fEBkZCXV1dZQvXx42NjaoU6cOLC0tFYpduXJl7NixA5s2bcLFixcRGBiIiIgI6OnpoUKFCmjdujUsLCwUzjm/AgMDi+Q8P//8M37++eciORcRERGRqrFwlegrcunSJdHY3t5eRZkQERERERERERERERERUWlgYmKCXr16KT2ujo4OunTpovS4RERERKR6aqpOgIiKxuPHj3Hy5Elh3LRpU5iZmakwo5KpXbt2kEgkwiM/d6CWFAsXLhS9VxsbG1WnREREREREREREREREREREREREJRwLV4lKuZSUFBw7dgxdu3ZFamqqcHzs2LEqzIqIiIiIiIiIiIiIiIiIiIiIiIi+RhqqToCICoeTkxMCAwPx8eNHJCYmil5r3bo1RowYoaLMShZNTU3Y2trm+Lq5uXkRZlO0jIyMcn3vOjo6RZgNERERERERERERERERERERERGVBixcJSqlAgMDERQUlOV4t27dsGfPHkgkEhVkVfJYWlri5cuXqk5DJdzd3eHu7q7qNIiIiIiIiIiIiIiIiIiIiKgIyaQSyKSqrStR9fmJqHCxcJWoFJNIJChfvjwqVKiAli1bYtCgQWjfvr2q0yJSmqsfdFAupayq0yAiJXGqGKfqFIiUoqUF/yElv47oyVSdQoly6JWBqlModYyePVZ1CoLoddeK9HxJSSmisXGZVOjrpBZpDhlpqvDcqiSRqEEiUVN1GlRCJUulqk6BsqEp4e83xZGaRF3VKZQ+/DsoXyQo+d8fU0tBMYGOJn9+80u3GF1plmTKRQI1SFBcfrcuLnkQERERERUfxejrBBEpU2BgoKpTICIiIiIiIiIiIiIiIiIiIiIiIhLh7V1ERERERERERERERERERERERERERFQkWLhKRERERERERERERERERERERERERERFgoWrRERERERERERERERERERERERERERUJDRUnQAREREREREREREREREREREREREVDzJp+kPVORBR6cWOq0REREREREREREREREREREREREREVCRYuEpEREREREREREREREREREREREREREWChatERERERERERERERERERERERERERFQkWLhKRERERERERERERERERERERFi4cCEkEgkkEgmsra1znBcYGCjMk0gk8PX1zXGutbW1MG/hwoVKz5mIiIhKHhauktJl/OXUy8srx3lOTk7CvOHDhxdZfl8rLy8v0Z+NqpTELyW+vr6izy7zQ9H38enTJ/j4+ODPP//EsmXLsHHjRhw5cgSRkZGF8wYKIPPPTeZHbv+NExERERERERERERERker06NFDdF3n5MmT+Ypz8uRJUZyePXsqOdPiLyoqCqdPn8ayZcvQt29fWFlZiT4TJycnhWNmLBAtyCMwMFDp75eIiIiosGmoOoGSyMvLCyNGjMjxdTU1NWhra0NPTw9mZmawtrZGrVq10KxZM7Rr1w7GxsZFmC2VFNbW1ggKCgIAWFlZKfQF488//8T3338PqVQKAFBXV8eWLVty/Tmloufv74+FCxfi0KFDSE1NzfK6pqYmunTpguXLl8POzk6umBl/bvKrbdu2ud4BSURERERERERERERERCWPp6cn6tati4iICADAqFGj8OjRIxgYGMgdIzo6GqNGjRLGxsbG2LJli7JTLbZOnz6NcePG4dWrV6pOJVtqamrQ19dXdRpEVArJZBLIZKprivYlByIqvVi4WgikUikSExORmJiIsLAw+Pv74/jx4wAADQ0NdOrUCZMnT0aXLl1UnCmVBsuWLcOcOXOEsba2Nvbs2YM+ffqoMKvSzcrKChoa///r08jIKM81mzZtwqRJk5CSkpLjnJSUFBw7dgynT5/G77//jnHjxikl37zk9o8T5cqVg62trehYQEBAIWdEREREREREREREREREBWVhYYENGzZgwIABAIDg4GBMnDgRu3btkjuGu7s7goODhfHGjRthYWGh9FyLq+Dg4EIrWjUyMspyHS4vcXFxCA0NFcadO3dm4ywiIiIqkVi4qgRmZmZZ7mJKSEhAdHQ0EhMTRcdTU1Ph4+MDHx8fdOjQAdu2bUOVKlWKMl0qRX744QesWrVKGOvp6eHQoUPo0KGDCrMq/Xx9fWFtbS33/E2bNmUpQm3evDm6deuGihUrIjo6GhcvXsTRo0chlUqRlJSECRMmoHz58hg0aFCusa2trUVFtPJ4//696O+m3M7h6uoKV1dX0TGJhHc1ERERERERERERERERlQT9+/fHwYMH8c8//wAAdu/ejT59+qBv3755rj18+DB27twpjAcNGoRvvvmm0HItCSwtLdG4cWM4ODjAw8MD4eHh+Y7l7u4Od3d3hdZMnDgR69evF8Zubm75Pn9BWVtbQyaTqez8REREVLKxcFUJli9fjuHDh2f7WkJCAm7fvg0/Pz/s2LEDDx8+FF47e/YsGjVqhAsXLqBOnTpFlG3h4y+nhS8tLQ1jx47F1q1bhWPGxsY4ceIEmjZtqsLM8hYYGKjqFIqUv7+/6AuntrY2tm/fnqVYdNq0abh9+zZcXFzw/v17yGQyjBgxAs2bN8+1SNbX11ehfJKSkmBpaSkUrhobG7M7LxERERERERERERERUSm2fv16XLhwASEhIQCAcePGoVWrVjA3N89xTXh4OMaMGSOMK1asKCqY/FpUq1YNCxcuROPGjdG4cWPRZ7Z169YCFa4q6vPnz9i9e7cwNjU1hYuLS5Gdn4iIiEiZWLhayMqWLYvWrVujdevWmDFjBk6dOgU3Nze8ffsWABAREYFu3brh+vX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",
+      "text/plain": [
+       "<Figure size 2700x1950 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Set up the plot\n",
+    "sns.set_context(\"paper\", font_scale=1)\n",
+    "rotation = 70\n",
+    "vmin = 0.48\n",
+    "vmax = 1\n",
+    "\n",
+    "fig, ax = plt.subplots(\n",
+    "    ncols=3,\n",
+    "    figsize=(9, 6.5),\n",
+    "    dpi=300,\n",
+    "    gridspec_kw={\"width_ratios\": [1, 1, 0.05]},\n",
+    "    constrained_layout=True,\n",
+    ")\n",
+    "\n",
+    "# Diffusion side\n",
+    "df = pd.read_csv(\"./data/diffusion_disc.csv\", index_col=0)\n",
+    "\n",
+    "func_datasets = df.columns.to_list()\n",
+    "dsizes = df.loc[\"dsizes\"].to_list()\n",
+    "func_atlases = df.index.to_list()[:-1]\n",
+    "tot = int(sum(dsizes))\n",
+    "Z = df.loc[func_atlases].values\n",
+    "x = np.arange(0, 72, 2)  # 36)\n",
+    "y, yticks = make_y_yticks(dsizes)\n",
+    "X, Y = np.meshgrid(y, x)\n",
+    "\n",
+    "im = ax[0].pcolormesh(X, Y, Z[::-1], cmap=\"Spectral\", vmin=vmin, vmax=vmax)  #'jet_r')\n",
+    "\n",
+    "ax[0].axvline(x=60, color=\"k\", linestyle=\"-\", lw=0.75)\n",
+    "ax[0].set_yticks(np.arange(1, len(func_atlases) * 2 + 1, 2))\n",
+    "ax[0].set_xticks(np.array(yticks))\n",
+    "ax[0].set_yticklabels(np.array(func_atlases)[::-1], fontsize=9)\n",
+    "ax[0].set_xticklabels(np.array(func_datasets), fontsize=9)\n",
+    "ax[0].tick_params(top=False, bottom=True, labeltop=False, labelbottom=True)\n",
+    "plt.setp(ax[0].get_xticklabels(), rotation=rotation, ha=\"right\", rotation_mode=\"anchor\")\n",
+    "ax[0].invert_xaxis()\n",
+    "fig.colorbar(im, cax=ax[2])\n",
+    "\n",
+    "# Functional side\n",
+    "df = pd.read_csv(\"./data/functional_disc.csv\", index_col=0)\n",
+    "\n",
+    "func_datasets = df.columns.to_list()\n",
+    "dsizes = df.loc[\"dsizes\"].to_list()\n",
+    "func_atlases = df.index.to_list()[:-1]\n",
+    "tot = int(sum(dsizes))\n",
+    "Z = df.loc[func_atlases].values\n",
+    "y, yticks = make_y_yticks(dsizes)\n",
+    "X, Y = np.meshgrid(y, x)\n",
+    "\n",
+    "ax[1].pcolormesh(X, Y, Z[::-1], cmap=\"Spectral\", vmin=vmin, vmax=vmax)  #'jet_r')\n",
+    "ax[1].axvline(x=120, color=\"k\", linestyle=\"-\", lw=0.75)\n",
+    "ax[1].set_yticks(np.arange(1, len(func_atlases) * 2 + 1, 2))\n",
+    "ax[1].set_xticks(np.array(yticks))\n",
+    "ax[1].set_yticklabels(np.array(func_atlases)[::-1], fontsize=9)\n",
+    "ax[1].set_xticklabels(np.array(func_datasets), fontsize=9)\n",
+    "ax[1].tick_params(top=False, bottom=True, labeltop=False, labelbottom=True)\n",
+    "ax[1].yaxis.set_label_position(\"right\")\n",
+    "ax[1].yaxis.tick_right()\n",
+    "ax[1].invert_xaxis()\n",
+    "plt.setp(ax[1].get_xticklabels(), rotation=rotation, ha=\"right\", rotation_mode=\"anchor\")\n",
+    "\n",
+    "ax[0].set_title(\"Structural Discriminability\", fontsize=13)\n",
+    "ax[1].set_title(\"Functional Discriminability\", fontsize=13)\n",
+    "\n",
+    "ax[2].set_yticklabels(\n",
+    "    [\"0.4\", \"Random\", \"0.6\", \"0.7\", \"0.8\", \"0.9\", \"Optimal\"], fontsize=11\n",
+    ")\n",
+    "\n",
+    "dx = 15 / 300.0\n",
+    "dy = 0 / 300.0\n",
+    "offset = mpl.transforms.ScaledTranslation(-dx, dy, fig.dpi_scale_trans)\n",
+    "# apply offset transform to all x ticklabels.\n",
+    "for axes in [ax[0], ax[1]]:\n",
+    "    for label in axes.xaxis.get_majorticklabels():\n",
+    "        label.set_transform(label.get_transform() + offset)\n",
+    "\n",
+    "for axes in ax:\n",
+    "    length = 3\n",
+    "    axes.xaxis.set_tick_params(length=length, width=1)\n",
+    "    axes.yaxis.set_tick_params(length=length, width=1)\n",
+    "\n",
+    "fig.savefig(\"./figures/figure3.pdf\", dpi=300, bbox_inches=\"tight\")\n",
+    "fig.savefig(\"./figures/figure3.png\", dpi=300, bbox_inches=\"tight\")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "m2g",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.19"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/docs/paper/figure4.ipynb b/docs/paper/figure4.ipynb
new file mode 100644
index 000000000..e732ad902
--- /dev/null
+++ b/docs/paper/figure4.ipynb
@@ -0,0 +1,239 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Figure 4 - Connectome biological plausibility results\n",
+    "\n",
+    "Analysis of the edge weights of both structural (top) and functional (bottom) connectomes estimated using the DKT parcellation method. For the structural connectomes, the mean of the percentage of streamlines observed between ipsilateral, contralateral, and homotopic ROIs was recorded and plotted for each scan. Mean Pearson correlation coefficients between ipsilateral, contralateral, and homotopic ROIs was plotted for functional connectomes. A consistent significantly higher ratio of ipsilateral connections was observed across parcellation methods for structural connectomes, as well as a higher correlation between homotopic ROIs across parcellation methods in functional connectomes."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import seaborn as sns\n",
+    "import matplotlib.pyplot as plt\n",
+    "import matplotlib as mpl\n",
+    "import numpy as np"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "datasets = [\n",
+    "    \"ABIDEII-BNI_1\",\n",
+    "    \"ABIDEII-SDSU_1\",\n",
+    "    \"ABIDEII-TCD_1\",\n",
+    "    \"BMB_1\",\n",
+    "    \"BNU1\",\n",
+    "    \"BNU3\",\n",
+    "    \"HNU1\",\n",
+    "    \"IACAS_1\",\n",
+    "    \"IBATRT\",\n",
+    "    \"IPCAS1\",\n",
+    "    \"IPCAS3\",\n",
+    "    \"IPCAS4\",\n",
+    "    \"IPCAS7\",\n",
+    "    \"IPCAS_2\",\n",
+    "    \"IPCAS_5\",\n",
+    "    \"IPCAS_6\",\n",
+    "    \"IPCAS_8\",\n",
+    "    \"JHNU_bids\",\n",
+    "    \"MRN_1\",\n",
+    "    \"NKI1\",\n",
+    "    \"NKI24_new\",\n",
+    "    \"NKIENH\",\n",
+    "    \"NYU_1\",\n",
+    "    \"NYU_2\",\n",
+    "    \"SWU1\",\n",
+    "    \"SWU2\",\n",
+    "    \"SWU3\",\n",
+    "    \"SWU4\",\n",
+    "    \"UPSM_1\",\n",
+    "    \"UWM\",\n",
+    "    \"Utah1\",\n",
+    "    \"XHCUMS\",\n",
+    "]\n",
+    "\n",
+    "replace = {\n",
+    "    \"ABIDEII-BNI_1\": \"ABIDEII-BNI\",\n",
+    "    \"ABIDEII-SDSU_1\": \"ABIDEII-SDSU\",\n",
+    "    \"ABIDEII-TCD_1\": \"ABIDEII-TCD\",\n",
+    "    \"BMB_1\": \"BMB\",\n",
+    "    \"BNU1\": \"BNU1\",\n",
+    "    \"BNU3\": \"BNU3\",\n",
+    "    \"HNU1\": \"HNU1\",\n",
+    "    \"IACAS_1\": \"IACAS\",\n",
+    "    \"IPCAS_2\": \"IPCAS2\",\n",
+    "    \"IPCAS_5\": \"IPCAS5\",\n",
+    "    \"IPCAS_6\": \"IPCAS6\",\n",
+    "    \"IPCAS_8\": \"IPCAS8\",\n",
+    "    \"JHNU_bids\": \"JHNU\",\n",
+    "    \"MRN_1\": \"MRN\",\n",
+    "    \"NKI24_new\": \"NKI24\",\n",
+    "    \"NYU_1\": \"NYU1\",\n",
+    "    \"NYU_2\": \"NYU2\",\n",
+    "    \"UPSM_1\": \"UPSM\",\n",
+    "    \"Utah1\": \"Utah\",\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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6dCkFBQWkpKTg8/no6Ojg5MmTnDlzJrq/ZVl89atfRdM0vvSlL73q47+4usqMGePrR65pGkVFRTQ1NQFQU1NzmWdMrqeeeorKysro48cff3zCJTAvR9dl0lEIIYQQQgghxLVXuCQfS7XiFtZMW14o16ZCCCFuWin5HjpqTPSQjh6wK7UwxhSwMaoqS9AXpO5Iox1iUcDh1KjeV0fu7CxmbyrDMi0u7K7FVMxRz9cxDQPDcqCoCmjgTNVwpjqIBHXS8lNZ9vr5ZJVlkFqYwoW9tfj7/ViWHZLx9vo49KvjrHzDkmv9ZRHihuZwXJe3AoUQQohr7rr7C7h7926eeOKJaAjD6XTygQ98gPe9732XrFJSX1/Pt771Lf77v/+bcDiMZVk88cQTvP71r+e22257lUZvGxgYiHmclZU17udmZWVFwzCDg4OTOq5LGRgY4J/+6Z+ij5OSkvi///f/Tvp5du/ePenHFEIIIYQQQgghEkm93UHL6bahFeYKWdMz6HA007m75ZqfO+wL01XbS8gbJiXbQ87MLDSnds3PK4QQQlxKoDBIf1YXEX8E0uMLwozHcHZGdajsO7iPNrUJy7ToTmnH8owc0Ug2sQyTgFuLWXQ5Y1UxRcW5KKpCY7iext31hLxhOt2tWDMsjLARHdiuV3ZT1XGe4mXS4lCIK7V58+apHoIQQggxJa67MMxHPvKR6MdFRUU8/fTTLFu27LLPKy0t5Ytf/CJvectbuP/++2ltbcWyLD7ykY+wc+fOaznkOF6vN+ZxUlLSuJ/r8XjGPM61YlkW73znO6mvr49u+8QnPkFxcfGrcn4hhBBCCCGEEOJayC7NJKM4nUBfEFeyE1ey81U5b8gXpmpnrX0zDxhoG6SvZYDZm2aiqpNbgVUIIYSYCE9GEvlzcmg50wGWhaoqKIoSbZekKGCNMyFjGiaaY6TdUca0dPqa+qOfVx0KJkpMEMad6iJjWppdKWboGH1NA/S3DmAapn3umPNbdNX2kDUjg+QsD0IIIYQQQoyXOtUDGK2jo4M9e/agKAqapvH73/9+XEGY0ZYuXcrvfvc7VNV+aXv27KGjo+NaDHdMwWAw5rHL5Rr3c91ud/TjQCAwaWO6lE996lP87ne/iz6+/fbb+fCHP/yqnFsIIYQQQgghhLhWIiEdb6cPzaG+akEYgK4LPdEgzLBAX5CBloExniGEEEK8ejyZHjSHiubUUDUVRVVwuDU0hzruIMywjGlp0Y+LlxaSlp8afZyc6WHWhjKypttBlrzZOczaWIaqqYT9EQbavVzYXU/jsRYG2ryYuompmzFZmOHQjLfLdzUvWQghhBBC3ISuq8owu3btwrIsFEXh3nvvZc2aNVd0nLVr13Lffffx1FNPYVkWu3bt4rHHHpvk0Y7t4kow4XB43NVhQqFQ9OPRVWKule9973t86lOfij6eNWsWv/rVr6Jhosm2YcOGa3JcIYQQQgghhBBitMqXazjym1OYhgkEmbbIw8b33YrDde1bFb10cDd6f/x5SlNmsWTD/Gt+fiGEEOJSDN3kmZMvMdA2GN2mB3UUBSKBiB1GGSsUo4CiKKiaQlpBGve/4b6Yyi/cCb5uP4Zukl6QGvPUzupujv7qNA0VLRi6CQqYukaqJwuH24ER1gn7ItHzqKqKK9WFoiqsWrKa0tUlk/p1EEIIIYQQN7brKgzT1tYW/fiOO+64qmNt3ryZp556CoDW1tarOtZEpabGvskPBoPjDsOMrgZz8XEm269//Wv+4R/+Ifq4qKiIF154gYKCgmt2TofjuvqRE0IIIYQQQghxAxrs9HH0F6fBBHWoKG7b6S4qt9WgOTXOvXiB0GCI4mVFrH7zUlKykyf1/AVz8mmv6I7bXjg3X66LhRBCTDmHA7b+22ZO/uksbWc7SM5Opqehj9BgCD1g2n2SxgrDWKA5VVJzU9j8j+txOuMrr2UUpGOZFue2V9PfPEDhgjxyZmWz86v7CQwMnWOIgoruM1AsBSNkolhq9DyqQ0NTNDKL0ylbPSPakkkIIYQQQojxuK5mYAYHR5LoWVlZV3WszMzM6Mder/eqjjVR6enpMY97e3tjxnMpfX190Y/T0tLG3vEqPfPMM7z1rW/FNO0Lj5ycHF588UXKy8uv2TmFEEIIIYQQQohXQ8vJNiwz/i7e+e01BPtHWhs3HG5moG2QBz+zJXZV+1Waf/ds6g810d8yMs9RuqaYwoV5k3YOIYQQ4mp4MpNY+/YV0cd/+dg2QoMhUAAzwROG/kyqDhXN5eChz91DclbiyuZ9zQP88d+eIzQYBuDUX1TSClII+cPogUjC50T8evQcw4ywgbvQxZYPb5QgjBBCCCGEmLDrKgyTm5sb/bixsfGqjtXc3Bz9OCcn56qONVEzZ86MedzQ0BC3LRHDMGhpaYk+vlbBlJdffpnHHnuMSMS+8EhPT+e5555j0aJF1+R8QgghhBBCCCHEq8md6kq4PdAXiAu99DUN0FHZTcG83ITPudLz3//Ju6g/2MRgu5e8OTlMW1IwqYEbIYQQYjKlF6TQcqrtkhVhhlskeTLcnHvxAk3HW3F6nMy9o5xZG0qjuz776ZeiQRgAUzfpbxlEwS46c1nKSC7G1+Wn9XQH5etnXOErE0IIIYQQN6vrKk5dWjryhvmPf/zjVR1r9PPLysqu6lgTNX9+bP/v6urqcT2vrq4OwzDGPM5k2LdvHw899BDBoL0SLjk5maeeeorVq1dP+rmEEEIIIYQQQoipMH3lNFLzUmI3KuBKiW/lABAZY5X6lYiEdKp21nLyjxW4U10se2QhxUsLJQgjhBDiutVd20vD4RZU9TJ/qyw72OLrCXDklyfprO6h80I3e79/mPMv2XPgg50+Bjt8CZ97uSCMqqkxQRgARVVoOd0+sRckhBBCCCEE11kYZuPGjXg8dmnFEydO8L3vfe+KjvP973+fY8eOAeDxeNi0adOkjXE85s+fH9Mrdd++feN63sX7LVmyZFLHdezYMe6///5o2yiXy8Uf/vAHNm7cOKnnEUIIIYQQQgghppLm1Lj73zYyc910PJlJ5M7OZvMH1jHrtrK4fV0pTgoXTE77ouBgiGc+uZ39Tx7lzDOVvPzVvbzyrQNY41oGL8TYfD1+9nz/ML/752d47rM7aDreGv1copZgQggxEfWHmgC7BVI0iTJGLsYyLfSQjmlY6EGdkM+uAHP2uSoCfUFOP33+in4vpRWm4kp2xpxWdaioDpXkzKQJH08IIYQQQojrqk1SUlISjz/+OD/5yU8A+Md//EdCoRDvf//7x32Mb33rW3zoQx8C7JKNjz/+OG63+1oMd0zJycncfvvtbNu2DYDt27djWdZlV4G9+OKL0Y9TU1MnNaRy9uxZ7r33Xvr6+gBwOBz86le/4p577pm0cwghhBBCCCGEENeL1NwUNrxnTcy2wvl5DLQP0nLSXmGelOFmw3vW4HBPzvTIuRcuMNDqjdnWcLiZ1ooOpi0quKJjdlR1cfJPZ+lvGSR3ZhbLHl1EZnH6ZAz3NafldDunnzrHYIeP/Lm5LH90IWn5qVM9rGvO0E1e/MIr0UoL/p4AL39tLwu3zqHxSAuDHT5yZ2Wz+k1LyZvz6rYKF0LcGILeEEbEwNDNkTZJo/IsqkPBmeQkHIhgGVbM58yIiR7S6W3u5+d//wf0kMFEqQ4VPaCz8R/WsOvbB4kEdTSHiubScCU7mbN55tW9QCGEEEIIcVNSrOtseVJzczPz5s0jEAhEAyTLly/nH/7hH9i6dSslJSVxz2lqauLZZ5/lu9/9LseOHYuuuEpJSeHcuXMUFxe/2i+Db37zm/zTP/1T9PGLL77Ili1bxty/v7+fadOm4ff7AXj88cf5zW9+Myljqa2tZePGjTQ3NwOgqio//vGPectb3jIpxxdCCCGEEEIIIV5LBtoGCQ2GyZ6ZheaYvKK5L35pF20VHXHblz26kKUPLZjw8fqa+nnmUy9hRMzoNneqi4c+dw9J6a/uwp+p1lHVxQuffyWm2kBytoeHPn8PzkkKM12v6g838co3DsRsM3T75rM7xRXd5khy8PAX78WTIRUUhBCXpocNTv35LHUHmhjsGCQSMtADeuKdh9oWqU4NUzftufeL7ygodjsjy5j4rQZFU/Ck27+3bv3bleTPzeXkn87SU99HZkk6S183n6wZmRM+rhBCCCGEENfdbEFxcTG//OUvefTRRzEMA8uyOHbsGO95z3sAyMzMJD8/n+TkZPx+Px0dHdFqJ6NzPcOVT6YiCAPw1re+lY997GP09vYC8G//9m8cOHAAhyPxl/yjH/1oNAgD8IEPfGDMY5eVlVFfXw9AaWkpdXV1Y+7b0tLCli1bokEYgO985zsShBHiJhboCxLyhsiYlo5yuV7QQgghhBBC3IDSC9OgcPKPm1GUljAMkzEt7YqOV/lybUwQBiDkDVOzt56FW+de0TFfq85vq45ru+HvCdBwqJlZG0qnZEyRoE7Nnnr6WwbJKc+ibO30cYWrump6qHiuCl+Pn8L5eSy6fy6uZNeY+4cGwnHbjLCOZVpYlmXffFZAD+rUHWhkwT1zrup1CSFubGF/mD/9+wv01vcxrmWyQ9kXI2ygOBQYKzOjKFhxKZnLcyaNzJen5aeSUZTGxveuucQzhBBCCCGEGJ/rLgwD8OCDD/KnP/2Jd77znbS3t0fbC1mWRW9vL729vfab61Hv1hVFiW4rKCjgySefZOvWrVP1EsjIyODDH/4w//7v/w7A0aNHecc73sGTTz6J0+mM2ffrX/863/zmN6OPt27dOiktknp6erjnnnuoqamJbvuv//ov3v3ud1/1sYUQrz1GxGDvD45Qf7AJy7RIzUvhtnevJn9u7lQPTQghhBBCiEljmRad1d0oikJuefarGgBfsHUOdQcbCQ2OhBdyy7OYvmLaFR0vOBhKvH0g8fYb2ZhfizG2X2shX5jnP7uD/pZBe8N2uLCzji0f3oivy8eFV+qIBHRKVhRRvHQkedVV08Pzn9uJHtLRQzrNx1s5t62ah790b7QywsWmLSmwKy6MCgNZll1NITgYgqHtqkO9KX82hBATc357DX1NAxN7kgJYdsVxgwRtkKyhfcbBkeQAy0IPG2gOFYfLvkVRMD+PwgV5ExuXEEIIIYQQl3DdtUkarbu7my9+8Ys8+eSTdHd3X3b/nJwc3vnOd/LhD3+YnJyp75EciUS49957efnll6PbSktLeetb30pZWRmdnZ388Y9/5ODBg9HPFxUVcfDgwYTtoIaNtzLMZz7zGT7+8Y9HH2uaRllZ2YRewwc+8IFLVqkRQrx2HPvtaU4/dT5mmzvVxaNfuR+HS5uiUQkhhBBCCDF5+pr6efm/9+Ht8AGQXpjKHR9ab1eCeZX4uv2c317NQLuX/Dk5zNlcHrPqfSKqd9ez9/uH47bf/X83UTh/6m4Ytpxqo7Wig+QsD+XrS3Gnjl3VZLJUPFvJkV+ditv+uv/YQkpeCjW76+lt6id7Ribl62fguMatk04/dY5jvz0Tt33xg/M4+3xVTEWfxQ/OY8XjiwF45Vv7qd3XSMgbZnRJhqyyLB798n1jVpY5t62aI788ianbx03NT6GjqgslZlZPYc4dM7njg+uv/gUKIW5YL39tL1Uv24snx31nYCgMczX7ONwamlMjszgdPawT9tsVrtypLubcXkbZuuk4nA5ScpLHOSghrh91Bxo580wlvh4/RQvzWfnGJfKzLIQQQlwHrsvKMMNycnL40pe+xGc+8xn27dvH/v37qayspLe3F6/XS2pqKllZWcydO5dbb72V9evX43Jd+wmY8XI6nfzud7/jgQceYN++fQDU19fz2c9+NuH+06ZN489//vMlgzATYRhG3OPq6uoJHaOnp2dSxiKEmHr1B5vitoW8YdoqOihZXjQFIxJCCCGEEGJy7fmfw9EgDMBAm5e9PzjC1v+3mZA3xGCHj4yiNJwe5yWOcnVScpJZ+cYl0cemYRLyhq8oMDJz3XRaz7RTu68RAEVVWLh1zpQGYfb/8ChVO2qjj888U8m9/28zaXkp1/S887bMov18F03HWwFQNYXljy0iJSeZ5/5jB32N/dF9q3bWcu+/335NAzHd9X0Jt1e+VBPX2qriuSrmb5mNJzMJb5cfPaTH3YH2tntpPt7KjNWJ233P3zKL0luK6ajqIiU7GUM3efrj29BDxlCZGAWnx0n7uc5JeX1CiBtXRlEaikPF0s3L7zxsHKEZRVFQNDD1xDsbEQPVpRHoDzLY4Yv+HgwOBDn40xPs/+ExFFXB4dLwZCZRtLiAVW9cQlpB6vjHKcQUaDzWwq5v2wueTcPkwq46Wis6eOy/HhhX+0QhhBBCXDvXdRhmmNvtZvPmzWzevHmqhzJhWVlZ7Nq1iy9+8Yt84xvfoLW1NW6flJQU3vSmN/GlL32J7OzsKRilEOJmoDoTX3yNtV0IIYQQQojXEl+Pn54EAYXOqm4O//wE51+uwYyYOJIcrHh8EfO3zI7bVw8bhAZDJGd5JqW9UsXzVZx+6hyhwTCZxenc8tblE2oBoWoqG96zhsUPzKO/dZDssqyrDp0YEQNfT4CUbA+ac2IVIusONlHxbCUWoDk1NIdKoC/IqT+fZf27Vl/VuC5Hc2rc8aH19NT3MdjpJW92DsmZHs6+UBUThAHoqeujZl8DczeXxx1HD+n4+4JgWjQcbQHLonRNCWn5E7vZmlWSTsOh5rjtIV842u57mKmb9LUM4MlMonB+Hi0n2+KepzpUvF2+uO2jeTKSKF1tL6DqbezHmeTE4XZgmRaqpgDKFVchEkLcPObfPZvzL9Uw0DZ46R3HUw1mFMu0sC6Rr7FM0AM6vuEQ39Dh9ZAxah+LSFBH7/ShH2mhu7aX13/hXqloLK5r5160FyCH/WGMsP3z3Ofv5+lPbOOBT21Bc6iE/WEiAV2qxQghhBCvsuu6TdKNxjAM9uzZw4ULF2hvbycrK4vp06ezefNmUlKu7QoqIcSNKeQNcfLP52g720lKtocF986haGF+wn0rnqvkyC9jy4qn5qfw+s/fg6pJIEYIIYQQQry2hf1hfvP+pzCN2GkOy7RAIS6gcP8n7iRnZlb08ck/VVDxXBWRgE5qXgpr3rac4qWFVzyehsPN7PzG/phtDrfGw/+5FU960hUf92pU7qjh+G/PEPKGcaU4Wf7oIubdNWtcz2063sq2L+8i7A1Htzk8TpxuB5kl6bzuP+6+VsO+pD3fP0zN7vq47XPvKGft21fEbDv99HlOP3WO4GCIiF/HkeTA4dJQHSob37tmzKosiQQHQjz76Zfwdvmj27LLMlE1la7q2Cq7qkPl0S/fhycziZA3zO//5RkG273RzzvcDpweJ/d/8k6ypmfQdKKN3vo+MkvSKVkxbcxV5c/9x8t0Xog91/LHFrHkdfPH/TqEEDcnf1+A37z/KYIDoakZgDKUtbnEnQl3mgvNobHhvWuYeev0V21oQkzU05/YTmd1d8x7JABnspN171pNX1M/1bvqMXWTzOkZrH/XKnLKssY42qUNtA1y9Nen6ajsIjU/hSWvm8/0FdMm42UIIYQQNyRZLvIq0jSNTZs2sWnTpqkeihDiBmAaJi9+cRe9Q6sg+xr7aTnVzpZ/3ZhwtemCe+cQCeqce/ECYX+EwgV5rH37SgnCCCGEEEKIG4Ir2UXZuhlxwQhXqotAbwBFU9AcIyvL6w83R8MwtfsaOPGHs9HPeTt97Pz6Ph7+z60kZ3quaDzVCQIaesig/mAz87eML4AymTqruznww2PRx2FfhIM/OU7W9Azy5+Ze9vnHfnMa+9blCD2o43BpZExLn+zhjltWSeJzZ06P3d50vHXoNUDEr2OZJhF/ZKiiChz6+QlKVhSN+/ooKd3N/Z+8k6odtXTV9REaDGJETBxJGooSe4N34dY5eDLtAJQ71cUjX97KUx/bzmC7F9Whomoq8++ZTWZJBtuf2ENbRUf0uXlzctjyrxsTVkXY/IH1HP7lSeoONGLqJrnl2UxbUjCu8Qshbm6dVd0EByc5CKPY7fwsYxxrby2wLlOATQ/paA6NsC986R2FmGIlK4poPx/fptCyLE784QxhbyS6ra+xn5e/updHvnzfhFsoRYI6L3zhFfy9ASzTIjgYYufX9485FyyEEEIICcMIIcRrVuuZjmgQZphlWlQ8V5nwAkhRFJY9vJClDy3ANMwJl0QXQgghhBDienfr21eQlOqidn8jAJpTpbu+D3OoZL3q0HClOFEUJSZcULOvIe5YRsSk4XDLFQdXTDNxrwhrjO3XWt3+poTbaw80XjYMY5kWfc0DaA4V1alhRoZaWlgWmlOd0kokszfN5MIrdfS3jLT7yJqeQfltpTH7Df9MWJY16ntgYUQMVE3F3xPA3xsgNXf8lXvdqW7m3zOHpz+xjYHWkUovqlOlfN0MNE1l+soipi2JrTCUlJbEo0/cR/OJNrxdPgrm5ZFdmknNvoaYIAzYN6xrdtcz987Ylk+mYTLY7iUtPwUs+3qvu7aXZz/9MuvfvZrydTPG/TqEEDef/T86NqEWSON12SDM6NZLl9nVCJvoTp3iZVdepU2IV8Oi++ZSs6eezqpuYOhH27LbgvU3DaJoCu5UV7RKYaAvSFtFx4QrENYfamKww0skoNupW8Vuj3j+pWoJwwghhBBjkDCMEEK8Rvl7Aom39ybePkxRFTRVgjBCCCGEEOLGozk1Vr1pKavetJSm4628/NW9ON0OQmETsDB1AyOs4k5zU75+JCxwcQulyTBz7XRaTrbHbFMdKqW3lEz6ucZjuAJK3Hb18q9dURUyp2fQ19iPK9mJEVExIyaqU2Xrx+4ga3rGZA933FzJTrZ+dDMXdtXT19hPdlkmszaU4XTHTnkNv05FUUBVwBy+C6tEj5N0Be2rGg41xQRhAMyIiaaprH37Cvy9AeoONJKSm0zerBwAIiGdun2NDHZ4yZudE/36dV7oHgrrWCiagjI0ts7q7pgwTG9jPy9+6RUG2rwYYQMUcKe40Jwalmlx9FenKFtTIlVAhRAJ9Tb2x7RqmzTjCdeM/tU7jv0VVUVLUBlLiOuJw+3g/k/exR8//Bz+3gBhXwRFARS7H5hlmOhBHafHGX3Olbz17G3oI+IfqTKDZREJRBhoHRz7SUIIIcRNbkrCMO985zujHyuKwg9+8IOEn5sMFx9fCCFuFIUL8xJOHkxbLGWxhRBCCCGEaDtrV9hQNRVXipNIUMcyTJzJTu78P+tJzRupAFK+fgbNJ9pinu9wa5TeUnzF5y+/rZSBdi9nn69CDxkkZ3tY87blJGddWdulq1W+fgZnX7iAZY5cQCiqEldBZZhlWbSebqe3sZ/s0ixWvmExO/57H6Zu4nA5wAUr/2oJOaVZr9ZLGJMr2cXCe+fEbPP3Buiu7SW9KI2MojTKN5RSs9euAORMctg3kxQFzWkHRhY/OC9hK6LL8Xb54rYZuknljhrOvlCFHtJxJDnQHBqFC/NZ/66VbPvP3Qy0ebEsC1M3yShJZ/3fraa7tpdgf4ihUi84PQ4cLgfphWlEAhFq9jbQWd3N+e3V6EFj5IQWhLxhnEkOnB4ngb4gwYHQlP2sCSGubx1VXRgR4/I7XkvjrUpjWfQ29ONZPPGwohCvJneKiwc/vYVd3z5I47EWVE3B4XZgREz0YARDNxmOwiRneyhYkD/hc0SCesLt6gTbLQkhhBA3kykJw/zwhz9EURQsy4oLqwx/bjIkOr4QQtwo0vJTWfmGxRz77ZnohHZ2WSaLHpg3xSMTQgghhBBi6qXkJEc/1pxatE3ownvnUHjRDYiytdPxdvk58/R5wv4I6YWprHnbCjwZV3fzbfmji1h031yCAyFScpOntFJH1oxMNrx3Dcd+fQpvl5+UHA8r37iEnLL4MIuhm7z81T20nh5p2VOyvIj7P3kntXsb0MMGpbeUUDDv0u2VpsqJP1Zw+i/nMIfadZSvn8G6d63i1nes5MQfKwj0BUnJSSYtP5WUHA8z181g+oppV3Su/Hl5wNnoY9M0CfvCaC4NI2TftAr7IrhTFdoqOtj17YPRIEzIG8YyTDrPd/H0R7fZ81gqmAZgWYR9ESwLktLdPP2J7Qy0ewkNhqKv62KRoI6iKaTmpuBOc1/R6xFC3PgG2rzRufnrnR7SSS9MnephCHFJIW+Y7toeUvNSWPrIAjovdEc/p6ix/9ZyyrNY97er0K4gwJKU5saZ7BxpkwRoLo2cmVMfTBZCCCGuV9dtm6TXwptxIYSYaovun8eMW0poP9tJcraHooX5KOMocy6EEEIIIcSNrvy2UiqercTfG4xucyU7mXtHecL9Fz8wjwX3zCbsi5CU4Z60hTpOjzOmLP5UKltTQuktxYR9EVzJzjGvHWr3NcQEYQCajrcyc910Vr5xyasx1CvWWd3NyT+ejdlWs7eBggV5zNk8k1kbS4kEdFwpzkn5HhfOz2P27WVc2FkHgBE27BZHow9tWRhhA9Wj0lndA9g3eC3DjO5iRAws04re5IpW8LEsdn3noN3mSVEwzUvPl+khg+WPLbqim2xCiJuDM8mBy+Mk5AtP9VAuSVHAkeQgNTfl8jsLMUXOv1TNkV+estsWAqW3FJNVmkFvfT9gdy5wp7jY/MF15JZnk3QVYdWZ62dQ8VwVmlPDNEwUVUHVVGZtSFzlTwghhBBTFIbZtGnTmBMOl/qcEEKIeGl5KaTlXdnEQGdVNxd212FGTErXlFCyvGiSRyeEEEIIIcTUcKe42PrROzj99Hm663rJmJbO4gfmxbRHupjm1PBkJm6V01HVxck/naW/ZZDc8myWPbKQzOL0azX8a0ZRFNyprkvu03G+K+H29vNdlK2dfi2GNWkubncV3X68ldkby1A19bKvf6LW/e0qZm8qo/VMO6efrsTb4cUwLSy72xEQXcCNJyOJQF8QU78o1KIqWKZlt/O6KPBi6iYmduuuBJ1yRw7hUMmans6c22dO5ssTQtxgym8r5cwz56/7MIylQO6s7KkehhBjGmj3cvAnx2P+MNcfamb5YwuZtqiAltPtJGd5WLg1virhlcgqyWDjP6zh6K/sKn/J2R5WPL6YvFk5V31sIYQQ4kY1JWGYHTt2XNHnhBAT5+vxU/lSDd5OH/lzc5m9qSxaHlzc3Gr3N7L7uwejF2w1extY9uhClj60YGoHJoQQQgghxCRJyUlm7d+suOrj9DX1s+1LuzAidiWPhp5m2s918tDn7iEp3U13XS8n/lBBb0MfWTMyWfbIwoTth14rUvOSx9h+/a/OHyvocq3bBmWVZPD853bi6/aDBdbQhdZwIEZzqiiqwqq/XsLhn50k7I9En6s6VDSXRjhiYl3UAkkPGWguFVO3UDX7GJZpxSViNKeGK8VJ0cICdn37AIH+IEWLClhwz2wc7uu2MLQQYgqkF6RSuCCPC531Uz2USzOh80I3bWc7JiVIIMRkazremjCh2lbRyd3/tumaVNMrvaWEGauKCfvDuJJdUiFcCCGEuAypmSrEDWyww8vTn9jO6afOU3egiYM/Oc72J3bHrTITN6fjvzsTd8F25unzMZOyQgghhBBCvJZFQjph/9WvfK98uTYahBkW8oap2VvPYKePF77wCs0n2vD3Bmk+0caLX3wFb5fvqs87VebcXk5SRmx4JDnbw+yN138Z/pnrZsQFYlSHytw7E7fHmixnnq3E3+3n4ltSigLJOclMW1LInf98GzPXzuD+T9xph1SS7BZarlSX3V4hwf0su424Xc1HURRcKS5UTUVz2hVunB4H7lQX7jQX+fNyqD3QSN2BJtrPdXH8d2d4+at7r+nrFkK8NnVW99iVq67z++hhX4Sjvzk91cMQIiFXcuI2mGNtnyyKquBOdUsQRgghhBgHWRoixA2s4rkqQoOxE7/t57poPtVGyTJph3OjMw2TM89UUr2rDtMwKVs7nWWPLERzauhhA29n/OS8HjLwdftxJWdMwYiFEEIIIYSYHJGgzoEfH6P+YBOmYTJtcQHr3rWK5EzPFR0vOBhMvH0gRPUrdehBPfb8AZ0Lr9Sx/NFF0W2dF7o59+IFAv0hpi0pYP6WWZNWsSMwEOTM0+dpP9dFal4Ki+6fS275lbeW8GQmcd/H7qDi2Sp6m/rJnpHJwvvmYBoWR399iu66XjKL01lw7xxSc6+PajFNx1up2lGLHtaZs3kmvU39dNf2klGUxpKHFpA9IzNm/+BgiLPPV9FV00N6URoL75lDWkHqhM452O5FD+lkTs+go6o7ul1RRtYduFJdvPUHj8Y8LyUnmfV/t5qFW+dQ8VwVfS0DtFV0YAGWbtrPHT6ABXpIZ9rSQvLn5ODv9pM3J5dpi/NJSnfjcDvobewnKc3N/h8ejass03a2k47KLvLn5k7otQkhbmyu5KHQ4GtgvVx3Te9UD0GIhEpvKeH4784Q6Bv1PlGBuXfNetXGEBwMUb27Hl+3n8L5eUxfOU1CMkIIIcQoEoYR4gbW1zyQcHt/84CEYW4Cx393hjPPVEYfn3mmEl9PgI3vXYPDpZFZkk5fU+zPiCvZSVr+pSezjYjBmWcraTzSgiPJwbw7yylbO/2avAYhhBBCCCGuxKGfHad2b0P0ccupdnZ96yD3fuT2KzretCWF1B9sjttevKyQmlHnGS04GIp+3Ha2k+1f3oU5FFRoP9dJ65kO7v7wxisaD0CgP0hHZRdJGUkc+OFR+lsGAeip76PpRCtb/9/mq2rVlJqbwpq3LceyLGr2NLDvB0doPtWGZVpoDo22ik7qDjTxwKfvuuKQ0ZXSwwZtZztwuBwUzMulenc9u793EMuwUB0qbRWdzL2jnDs/dFv0Od21vZx++jwD7V5yZ2bRWtGBr8sP2O0M6vY38cAn7xxXK6iQN8wr3zpAW0UHhm6iKOD0OBkq4sLoYgsZ09LHPE5mSQbr/241kaDOr//pL4R9YSK6Gb+jBc0nWgn0BQh7wzSdbKO3oYQVjy9GTVaj3+dECx6Gt0sYRggx2uzby+gcFeK7nilS215cp5xJDu7+t00c/fUpOs7bgeQlr5tP0cJ8An1B2s514slIomB+Looy+QEVb5eP5/5jRzSMc35bNWVrS9j4D2sn/VxCCCHEa9V1F4YpL7fL1s6cOZPt27df8XEefPBBKioqUBSF6urqyRqeEK8pOTOz6DjfFbc9+zXcu16Mj2mYVL5cE7e9/mATq9+8FE96EtOWFtqrF00LzaWhuTRWvHHxmKtTQ74wnVXdVDxfRfvZzuj2jvNdhAMR5m6+tmXHhRBCCCGEGA/TMKnd3xi3vaOyi8EOL2n5E6v+AVC+fgatZzqoGzquhcXMW6eTOyuHsC/ChZ11cc8ZvQDh9NPnokGYYW0VHXRe6CZvds6Ex3NuWzVHfnkSUzcxIsZIq1MFNKeG03Jw9vkqNrxnzYSPfbGjvz5FxbNV6CGdSMA+jzPZicPlIDgQovKlmpgKONda+/lOdn59PyGvXQU1rTCF3oZ+IqPavWouB1U7a1n6+gV4MpPobezn+c/vtFsRAV3V3ehBHXe6G2UothL2hTm3vZrVb1p62TEc/uXJoSCMQdgbASzCvjCKCpYJ1lAgRnNp3Pbu1Zc9njPJQfltMzi/vRrVoca15AIwQgadld0M30ureN6u2nP/x++M7pM/L5eBNm/M8xRVkSCMECKGoZuceer8VA9j3OR3mLieZRSlcccH18dsq9xRw6GfnsAcCrjmlGdx1z9viGvjeLXOPF0ZW5UGqDvQxPwts8mbM/H3l0IIIcSN6LoLw9TV1QHgcFzd0Jqbm6mrq7smiVshXisWbp1Dw+Hm6Go3gOmrplG0MH8KRyUm20C7l6O/OkX7uU5ScpNZ/OB8SpYVEgnocftapkXYF+Hsc1VUPFOJ0+1ADxuYhsmKhxaPGWip3lPP/h8eJTQQIhLUUTS7Z72q2stzKp6plDCMEEIIIYS4LlgWY7d9uMJ2EKqmsvG9a1hw7xwOPHmErrpe6vY30VbRybp3rWLuneV2GH2oMsjcO8spWT4ShvF2JK7YMdjpm3AYxtvp4/DPT2CZ9ovRQ3r0Y0UBY+ixrztwZS92lJA3zLlt9gIj0xz54ulBHYfLnrcZbPcmfO61YBomu797KBqEAeit7yfsjzB6+scI6+hhFV+PH09mEue2XYgGYQAsw8IyLYywicOlRbeP97U0HGoC7Dazwz9UlmnhTnOjhw3SC1LJLsukfP0MKl+q4czTlUxfWcSsDWUJWxcEB0IMtA5i6mbM1xmGWi4l+Lk1Qjqdld0xgaplDy+k7WxnzM/b0tcvGFe1GyHEzaPlVBuDY/xduh71NvZjGiaqJiVixPXP3xeICcKA3err5J/Pcsubl03qubrrE7cQ667vkzCMEEIIMeS6C8MAEmARYpIkZ3p48NN3UbO3gcEOHwXzcpm+YtpUD0tMIj2k8+IXduLvtVcBhBv62fXtA9z1z7dRuCCPtlEVXADSC1Nxp7o4++IFwF4l6Eyy/xRU76ln2SML484R6A+y/3+P4O8PYg6tULQMi+BACE96Eoqq4Ou9+ol2IYQQQgghJoPmUJmxujhaxWVYTnkWaQUTrwozWtPRFnoa+qOhcH9vgBe/8Aq55dlMX1FE4YJ8ipcWxp0nf15u3I1HRVXIv8yNCl+3n5N/PktXdQ/pBaksfnA+3fW90fALEFdxBsCMGGSXZcRtb63o4Py2akLeMCXLC5l/92w0pxa33zB/byB6DaA5VIyhzk+jz58359Vbsd/b2I+/J4BpmCiKgqIqGEM3m6IjGvogEtDxdfvJLc/G3xN7vaI6VAjFvg6AgnFWH9CcGpGAjqmbI0EVxQ7rYFm4UlykFaSy53uHo+doOt5KZ3UP6/52VdzxDv70OB2V3TiTnDiTnIR84ZjwTiIWoEeMmGBQcpaHhz57Nw1HWgj0BihaUkBWSfzPgRDi5hboC8b9/rtuWdDfMkjF81Usvn/eVI9GiMtqq+iMCcIMaznVPunnyixOp7smPhCTWTx2i0YhhBDiZnNdhmEmg2UNr4qSYI24ubmSXczfMnuqhyGukcajLdEgTJQF57fXcOs7VrL9id3RSXdPZhK3/f0tMRPao3m7fESCEZxJzpjtrafbiQR1rIsv5CwI+8MoqkJmcTreLh+pubLiUAghhBBCTL21f7McPaTTfKINy7TIm5PDhr+/5aqPW3+4OfqxZVp2EMGy6K7rpa95gM4LPZSuKYl73rJHFtJ+rgtv56iKHQ8vuOT750ggwvOf2xGt8NLXNEDzqXZWPHZRS6IE9zNVh0rpLdNjtjUcbWHn1/dF9++o7KKjqjuutP9o6YWpuNNchAbDqA4V1alhRgwUhx0Gyp+bw+xNZWM+f7KFvCGCgyEsw742UZ0aikK02srom7uqpvDKNw9w+/sVChfmx9yEUh0qmlNDc45UGcgtz2LOneOrdjn79jKO/PJU7M1kC7tCDdDX1E9HZReaQ8XpGbm+qt5Vz5LXzY/5vlumRePRlpjjO5McmLqJoilYul3qaHh6y4r+n7044uwLF8ibk4M7xW69oDk1Zt4a+70XQojRUrI9Uz2ECbFMi4pnq1h031yZ6xfXlUhIZ6B1kNYz7TQea0VVVXJmZiXc15ORNKFjW6aFvy9AUpp7zODy4gfm0Xi0lbBvJBhbvKyQwgV5EzqXEEIIcSO7YcMwg4ODACQnJ0/xSIQQ4toJ+SJjbA+TVpDK679wL60VHfj7AkxfWYw72YkRMXCluKIXSqZpEvFHsCz4/T8/y7wts1j2yMLoBIM7zR1dbXpxiW4jYqJqCr7uAE9/fDv3fuR2MmXloRBCCCGEmGKuZBd3fHA9wYEQpmGSnDU5N/4c7pGbEXpYH3lzPHRvLjgQonpXHYsfnB/zvJTsZF732btpPNJMoC84rooddQeaYlodWZZF2Bem7lATqXnJeDvtdriqU8WMGDiT7dCFoiqkF6aRNys75nhnnjoXF5xpOtZKb1P/mGPRnBpr3rqcPd87hGmAK9mJqrmYtaGUkuXTKF5W+Kq1rbAsi0M/O4miKlhDRVPMiIHqVHGnurAsK1olRVGUaAil4rkqtvzrRlpOtkUrZyqKwtJHFjJ9RZFddacojYxpaYS9YZzuy0+VFS7IQ3OqQ5VgRg8SHB4nqqZimRZ62MCR5IheW1mmxWDHRYsIFOxjjVp8oGoqSWluZt9ehhExySxO5+Sfz+LvDWCNWtjgSnbSVtHB4V+c5La/W30lX1YhxE0oqzTTDtslqCx2PVI0heBAEH9PgJQcmesX14fz26s59tvT+HoC6CEdVVVQHSqtFR140t2E/aPmbBVYuHXOuI/ddKKVQz89gbfTh+bSSMn2YJoWGUVpLHnd/Gh7xPTCNB789F1U7ajF2+2ncEEeM9fNmOyXKoQQQrym3ZBhmI6ODurr61EUhbw8ScEKIW5cJcsKOfxzBSNiRMtoay6N6SuKAKg/1MThX5wk0BfkSPJJFj84j0X3z+OWNy9l7w+OYJkWYW8Yy8IOyPgjnPrzOZLS3My/264oVLQon7SCFHrr+4DYQIzm0qIrEMP+CKeeOs/G9665pq+5t7GfSDBCzsxsNIf0ixZCCCGEEGNLSndP6vHm3lHO/iePAiNVSBRVRXOMhGR8PYlbiDpc2oRuUPj7Ro5jGiYhXxhMi+YTbSSlu8mfl4uv2092WSb+ngDBAbuHUVK6m43vWYO/N0DzqTZcyS6mryjC2+VPeB5fl/+SwZyytdPJKc+m8XAzqlOjbE3JpH9dx6O3sZ+B1kHcKU4iQcW+/lEUnElOXvfZu9n3v0doPtEGil35xbIsFEUh0B/E4dK4+9820Xa2k8F2L3mzs6Mh/pScZHZ9+yDdtXabgcKFeWx47xo86WOv4PZ1BQAFbbgyjaYS8UdQtJE2tKrDDimZhhn9+dBcGtmlmTHHUhSF2bfP5OxzVTHbixbns/KNSzj229Oc23YBd6obT4abrureoWPZP3eWZVF3oJHb/m41lmUx0ObFleK85PiFEDe35EwPKTnJeC9q33e9UjUVh0vDneqa6qEIAUB3bS8Hf3Icy7LQgzpgt600TQMjbKI5VebeUU5rRQfJWUks3DqXkuVF4zq2vzfAzm/sx4yYWKaFt8uHt8OLK8WFt8NHW0UH933izuh7t5ScZJaPqhro6/bj9DhwJcu/FyGEEAJusDBMf38/p0+f5hOf+ASmafePXrp06VQPSwghrpnUvBRmrpvOmWcqowkVy7LwZHrobx1k93cPRSfpw/4IR399moxp6ZTfVkru7BxO/KGCCztr7UlcdaTU7IVX6qJhGFVTuf+Td/L0x7bT1zKAoiioQyuIXMmxLZX6mvqv2WsNDobY+fV9dFR2A3bbp03vW0v+3NxxHyMSiNBV20tKTjLpBanXaqiTyjRMTv3lHDV7G8C0mLluBktev0CCQEIIIYQQU2DO7TPRQzoVz1Vh6oYdKvfEvicuXDg5i3KKFuZz8o9nAYgEdBh6X685VCzDor9lgMe+cj+aU8MyLTqqujEiBgXzcqnd18jzn98ZvRZIyU0mqzST1lGtgsCuKpN7UQWZRNLyUlh439xJeV1XaqQCjR2AGW7v6kp2kj0jE0/mUPUf08IMG4QiJu5UF9MWF0SPUbggL651wK7vjARhANoqOjnwo2Nsfv+6hOMwdJNz2y+gh0ZXBrJbGjlGtTFQVAXLItpiSnNqzFw3nUQdPlY8vhgFqHy5BkM3Kb2lhFvesoyXv7aXrgs9ANHKN8PfUz1ooAeNoa+Nwstf20t3bQ+D7T4UVWHWbaWsf/fqMVsrCCFubiXLiriwqzb6e+R6ZkQMDN0k5A3jGEf1LiGutfpDTYA9ZxfDAhSL4ECIFW9YzNqL5k2HDbZ7aT/fRUpuMoUL8mLaf9Ufaoq2tzciRvT9nx420JwaRsSk8qUa1v7NiphjdtX0sO8HR+hrHkB1qMzeVMYtb1n2qlXwE0IIIa5XU/bu8VOf+hSf/vSnE37Osiyqq6vRtKu/YH/kkUeu+hhCCHG9Mg2T1jMdJKW7MQ0TRVVQVZVjvz3N7I2lsT3sh9Tub6RkeRHpBamU3lJM3f7G+OOa9kWXHtKp2ddAX+MAKx5fhCvFRXdtL6pD4eSfz6EQO5ObXZa4L+5kOPLLk9EgDECgL8gr3zrAI1++b1zBkJp9DRz88TH7RgIwY9U0Nrx3zXU/OXz4Fyc5v606+vjUX84RGAiy7m9XTeGohBBCCCFuXgvumcOCe+ZgGia7vnWA+sPNRIIRjIhJal5KTCDiauTPzWX+PbM598KFaAsd1alFbwSGBsO0nG5HDxsEegNEAhEC/SGajrdS+VJNzLWAr8tPelEqydke/MOVaxRY9VdLSUp79au8XCzsjxDoD5KWnxJz08YyLc4+X0X17npM08SV4iR8UavYWRtL6W3sp/l4K06Pw25LYAGWRSSo09vQx8GfHmfRfXPj2msMdvrorunlYk3HWu0WR67472XTsRb6Ggdwehz2tYVlgWXhyfCgOhRC3jBG2MA0LBxJGqZuRv93bls19YebueOD62NCOr0NfTSfaiMciBAJ6FTvrqe7toe+5gHArgRqGmZMK6XRNKdG1cs1WBbRsM3ZbRdwJju49R1y3SCEiFcwP5dz2y5M3QAUu8OgBXEt/C7m8jjBgtNPn48LAAgxFYbnEs0E865gB2IvXkA47MQfKzj5p7PRn/u82dnc9S8bou0dR7OsxMcP9AdjHhsRg5e/ujdaJdDU7cCMJzOJpQ8tGNdrEkIIIW5UUxqlHuuP+eU+dznDSdpVq1bx5je/+YqPI8R4hP1hDv/8JHUHm1A1hVkby1j5hsXX/Q12cWMI9AUJ9AVRFCWmNLu/J0AknHh1jzqqAkzR4oKEE8pla6YTCeo8/7kd9DaMVHvJmZnFPf9+Ow6Xhq8nSM3u+ujnktLdLHnd/Ml6aXEaj7bEbQv0Bemu6blsdRh/X4B9PzgSM3nccKSFiueqrumYr5Ye0rnwSl3c9po9Daz6qyVS8lQIIYQQYgqpmsrt71/HUx/bRmd1N65kJ3pQ56Wv7mXz+9cxfeW0qz7HLW9expxNZTzz6ZeJ+COoo0LgkZDOS1/ZE1MtBBVcSU4iQR1XsjPmurSnto9Hn7iPxqMthLxhipcWknYdVEs89tvTnH3hAkbYwJOZxJq/WcGMoa/d8d+f4fRT56P7WqaFO81FJKCjOVVmbShjxeOLo9cKykVlV4ywQcuZDjov9NBwuJkHPnkXnsyR9kFjheoVTYmpnDlaf+vgUJtaezGCoqo43Br583LxtnsJ+yLRgjF6yIi5yWuEDbwdPrY/sZu//s7rcbgd0RtYgf4gIa/dCiscMeiq0e0V4QzdML7ENJmV6POmxfntNRKGEULEMSIGx39fMbWDsMAaHYi51K5Df+O66+LDi0JMhfL1Mzj++zPoQwvuohRAUcasEtjX1B+t+jes80IPFc9XsezhhQCUrinh2G9OY0RMVIcG2OcYHdCdtqQg5hitZzqiQZjRavc1ShhGCCHETe+GrJGWmprK+973PrZt24bDIaUTxbW153uHqd5djxE2iAR0zr1wgcO/PDmu55qGScORZk7+6SxNx1sTVvEQ4lKSMpJISo9fyenJTGLOprKYyfJh5beVRj92uh3c8cH1pOYlo4d1IkGdnJlZzL97NtW762OCMGD3xK3d2wDA+netYvMH1zHvrlmseMNiHvyPLde09ZDD7cA0rbiwZKKVExdrPtGWcBVl47H4gM31xJ5kjw81mboZrXAjhBBCCCEmrruul6bjrYT94as6TldND72N/ThcjpGKJhaceeb8pZ84AZklGax845KY9/amYWIZJmF/JPb9sWmX0R+uimLoBiFvmOBAkHAgQsgbZua6Gcy/e/aUB2FC3jDPf3YHh35+Al+3Hz2kE+gLsutbB/D1+DF0k/Pbq2Oeo6gKelDnkS9v5Y3ffIg1b1uO5tTImZkFCkSCOhdHWPSgjmVZBPqCVO6oiflccpaHosX5cWMrXzdjzKCMHtIJ+8J2myzDxIzY7YoUVcHXE0B1qKjape/w+nsC/OVj2+hvHaT1TAf+3oD9vTStaLDFCA8FacYxTaCHRq4NrFH/iwTlmkEIEa+7tteuEjZJ05CKljg8OC5jBA9H00M6etggY1r6lZ9HiEkUDauqQ3/vhzjcDtILU8es5txa0QHYgebgYIhAfxB/X4Cjvz5N7T57vjU508PG992KO81eAOdKdeP0jAScZ6wuZtaGMlpOtbHvf49w8KfHGezwJTyfckPe/RNCCCEmZsqSIg8//DBlZWUx2yzL4p3vfCeKopCfn88XvvCFcR/P6XSSmppKWVkZCxculBCMeFX4+wI0nWiN2169q57Vf73skq1b9LDB9i/vimn7Mm1pAXd8cP2YvTy9XT70kEFm8fVz8WcaJqf+fI7q3XWYhsXMW6ez7NFFCctJi8ljGiZnnqmkZm8Dhm4SCeo43Jq9ElKB5Y8uJKMondv/6VYO//wEgx0+uzTm6xdQtCh2sjerNBOnx4mqqZiYtJ3t4M//7wUK5iWuttJd38scZqIoCtNXTGP6iqtf8XoplmVx7Ldn8Hb5CXtDKKqKM9mB5tDIn5tD1vSMyx5jrMCMM+nyQZqp5E51kzsrm67qnpjtWdMz4kq8CyGEEEKIy4sEIuz4+j7aKjoBcLg11r59JeXrZ1zR8aIthy7iG2P7lTANk2lLClj79hXU7mtED+k4khy0n028CtgyTFSnhhExCA2Go21zIgGdFz6/k9d97p7r4nrt5a/tpel4K5gWFhaRgB1ed+Cg4XAzszeWJQyAm4aFETGj19tGxCAlJ5mFW+dw5BcXLUwZKqlimRaKpuBNcLNow3vWcPAnx2g80oKiKsxcN4Nb3rJszHG3numIfn1HTmHSXdtDcGCobYGiXPYmc2dVN3/5fy+SU55NaDB02cov4zZq57FaNAghbm7u4fZ44ynLMg5XU+HdMi7/XLsbncXi++de8XmEmEz1h5pRNTXaatI0TYyISd6sbO75v5twpyZuQamHdAL9wbgFsXpIZ/f3DuHJ9OD0ODjwo6MEB0KoqkJqQQpr/2YlYV8YT2YStfsa+Ok7f0doMIzDpeFIcqBqCg63ZlekG2XWqAWRQgghxM1qyhIjy5YtY9my+MmFd77znQCkpaXx9re//dUelhATYlxU8niYqdur9LhEGKZ6d11MEAag5WQ7DYebKVs7PWZ7yBdm17cP0HraTo9nTEtj4/vWklVy+RDAtXbkl6c49+JIj+GK56oIDITY8Pe3TOGobnyHf34yZpWk5lRJK0hl2qICytfPIG9ODgAly4soXlZIyBvGleyMCVr1tw5yYWctbWc76azqJhLS7UkIBXpqewn0x0+sA2QWv7o/d1Uv13Lm6fNoDhWHx4kR0on4I5RvmcGat12+V3QkECF7RgbJ2Z64mxXztsy6VsOeNOveuYqX/2sP3i4/AMnZHm579+opHpUQQgghxGvTqb+ciwZhwG5js+9/j1C0OB9PetIlnplY3twcVIcaV4WwaGF8tZEr0Xyyjf0/PIq/J4Dm0pi/xa7KWLWjlvbznUOhi4suShUFV7ITI6yih+2KJQ6XhubU8Hb5aTzSzMx1Vxb+mSw99X10VnXHbddDBg63XWXH6XGSNycnbr+0glTS8lPw9wU48KNjNJ9oi7ZMypmZTX/rAJZhoUcMe7G2MtLyKD9B4D8pzc2m991qh1sU5ZKLWgD8vQH7xu/Ql91S7PZMgd5gdB/Lsi5/k9myMHWTxqPNWJfZV1EUu71twK4eM3rfaGcoTYm7qWzoJr1N/dfF3IEQ4vqRUZTGtMX51B9unpwDjv7VM4GAjebQMPTEc6sXy5yWLpVhxHWhv2WAjsou9JCO5rIXJqqqiupWySnLGjMIY+gmZ1+4kLAyvGmaYEHVzlp6G/oI9AWjCx59nX5O/+Uc937kdl7+2l673eVgGCwLPaRjDL0H1ZwaKbkeQoNhnEkO5t5RzoJ751zTr4UQQgjxWnDdlU+ZMWMGiqJQUlIy1UMR4rLSClLJmp5Bb2NsK5lpSwpwuC/9zyvRxB9AR1V3XBjm8M9PRIMwAP0tg7zyjf089Pl74nqiv5qMiMGFV2rjttftb+SWNy8d882/uDqRoB73dVc1lUBfkJVvWoLzop89RVGiKxWGtZ3rZPsTuzEjJiFvCCMyavJ+qG9zaDBEam5yzGrMzOJ0Zm2wVxUMdnipeLaK3sY+skuzWHT/3GtSraR2f0P0Y6fbEX19xUuL4l7XaKZhcuSXJ6naUYsRMUnJTSZ3djZ9TQOk5HhYdP88ZqycZq8SHUdZ3qmSWZzO6794L+3nu8CyJ/AvN0EvhBBCiJuPoZsEegN4sjzyXuESmk+0xW0zdZPW0x1XVB3Gk57ELW9exsGfHo/e3EgrSGX5Y4uueqyBgSA7v7E/2jbTCBuceaaStIJUytaWcOKPFegBHT2kx9xHdLjt4Et6URqDbd4Exx0JveshnbqDTfh7/BQuyCd/bi4hb4jOCz2k5CZfNkQR6Auih3XS8ifWcinktdtTOVwa4cjIKmbLsnC4NWbcUgzAre9YyfYndkdD7e40F2veuozavQ0c/71dPVJRFPSQwfnt1RQvKyQ4GMKIGFh+MCMGjiQHiqJQuCDvkt/j4fYDl+P0OLF0k9GX4mbExJnkQDU1zKEQjgWoDhULC0uPvfGlKPbnTNOKLki4FMuyUB0q7lQXBfPzaDnVjqLYXz/TsFAUBU9mEt5O31CoBxwuB5pTo/FIs4RhhBBx7vqXDWx7YjcNhyYhEGPFfuz0OHCnuvB2+sd8iuZScae4CHnD0UpbY1KushWTEJPkzLOVHPzJcSLBiP33OxDBnexCG6q4V74hvhKLr9vPQLuXSMCuCpOQCZGwTnAgSH/LYNynOyq76GsZoOl461Ao1m6riAXWUBjG1E183ZA1PZ2Vb1zK9BVFY1afF0IIIW4m110Ypq6ubqqHIG5wnRfsChgFc3PHPdl1KRvft5adX98XfaOaW57FrX+78rLPG6s/e3ph/Pb6g01x2wbavPQ29JNdmjmxAU8iI2LGlV8EsEyLcECXMMw1EglGYsMrQ4ywgR7Q48IwiZz4fQXm0DHMRCVpLVA1hWmLC8gpz6avsZ+sGRnM3jQTZ5IDf1+A5/5jR7Qse+eFHhqONPPgZ7ZcMqByZcaY8LjMPMjZ56s49+JI9Rxflx89qPOGrz1A+/kuOmu6ef5zO+io7CY528OSB+cz987ySRz35FE1ddJWFwshhBDixnPhlVqO/fYMwYEQ7jQXKx5bzJzNM6d6WNclV6or4Xb3GNvHY+6d5RQvK6TlVDvudDfFSwsnJZDUeKQlGoQZrW5/I3Nun8k9//d2jv32NE3HWjEiBu40N+kFqRTMy2XWhlKaTrRx7Den455fvKQAsNv+Pv+5ndHWQSf+cJbc2dn01vdFrzdKVhSx6X1r466dw/4Ie39wmMajLWBB1owMNr53zbhX7efNybErnfjAkeQgEtTtaxCHwuzby6JVejKL03n4S1tpq+jAMu0bPzu/eYCwP0xoIASKgjvVFb3Z01HZxQOfvJPqPfVEgjopOckoikJmcTrFSwsnJwRvWSiaaleDZeQesB60gze4NUzdvlGVmp/CQJsXy2FFQzGKAoqm4kp2xbQWiXZWuqjqi2WBotnX2UbEoON816g2VwqqppBTnkVqTjK+bj+WaQ2FbHQ7FDOO60MhxM3H6XFy30fv4KlPbKO7ppeQLzyulkXjEQnoCecLRxv+9edOc43ZcjBKfpeJ64C/L8DhX5wg4rcDvcPJ15AvTHZuBiseX0Lh/Lzo/pZlcfCnx6l6udYOsCj2XLqiKXFV3gAivgimaRIJRNAjdsUkzaniTHLiTHZihAz0kGFXpIeEFZUi/ggd5+35ztyZ2Tz4mbtwJV/5e1whhBDiRiDvIsVNw98X4KWv7KG3wa7i4k5zsel9t1K4IO8yz7y0jKI0XvfZu+lt7EfVVDKLxzf5N3dzOVU7agn0jSTCU/NTEq5U05xawvCD5py6dHdgIEjziTbSClIZbI9dbZhZkk5aXsoUjezGl5zpSViRKLs0E0/m+Eq79zWNPDdReU6wQzIZJeksui++J3PVjtpoEGZYoC9I9a46Ft0/b1xjGK/y22bQUdkVs82V7GT6iqKYbXpIJ+QNk5ztQVEUavc1xh0r0B/kZ3/3B4KDoehFozPFib8nwIEfH8Od7qJ0tVQmE0IIIcS1ZRomp586x4VX6jANi9I1Jax4bNEV3ejpqulh35NHo+9tQoNh9v/oKJkl6eTNzpnkkb/2zb97Fh3nY99bphelUbTo6oLHKTnJkx5AUsdaBa/YLU+T0lxsfv+6hLsE+oOU3lJM+7lOWk61R4+3/LFF0cDK6afOR4MwYF8XNB5uwZ02Ei6pP9jEn5sGSM1NZvqqaczaUErziTZOP32e7tre6H69Df3s+Po+Hvrc+KqXOlwaG96zht3fOWhXibHsVf8Ot8aZpyvxZCSx+IH5AGgOleKlhZiGyR/+9Tn0oeCMfSPIIhyIkDS0EMPQTTKK01n1V0svO4YrlZTmxp3qwtRNTMMaqsxjYegGptcOxKgOjUggYgfUUuzQizvNzYxVRVQ8Z7dICPvCdoslVYneJEtE0RQw7cqdAHrYwJ3iwpXiYu3bV5Cak0z+3Fwu7Krj7IsXor8LTMMk5A2TNYULaIQQ17/b//FWdn/nIE0n2rDG299oHMaaaxpm6iYzbimm5WQbzmQHEb9+iZ2hp66XrpoecsuzJ22MQkxEZ1U3eiDxz6nT44pbsFq1s5aKZyuHwr4qqqZihA0cLs0OASdQf6B55N+OYi98tEyLBVvn2EGc4Yo0l2GZFl01PRz77RnW/s3lW8wLIYQQNzIJw4jrkhExOP67M9TstdujlN82g+WPLrqiSi6mYXLqz+c49rszhH1hNKeG0+MgNBhm93cP8siX77vqVXuKopA9I3NCz/FkJnHfx+/g7AsX6GvqJ7s0iwX3zk6Y1p59exkVz1bFbMubnT1lvXLrDjax538OYUZMTMNEDxs43Q4UVSElx8Ntf3/LlIzrZrLuXat4+at78ffaq2eSszyse9eqMfevO9BI/aFmAv1B0vJTcKe5CfnCdl9bh4qpx4etTMMk0JO4fKe302dP9hoWqqagOex/m96usUvgXvz8w784ScupNjwZSSzYOpf5W2Yl3HfO7TPx9wQ4+0IVkYBOZnE6a9+xIubfyvHfn+Hs81XoIYO0/BTWvn1lwlWfIV84bkIm4ovgcGkoKFTtqJUwjBBCCCGuueO/O8OZZyqjj8+9cIFAX4BN77t1wseq3dcYvzLUsrdLGCZe6eoSNrzHpOK5Kvx9AaYtKmDFGxZfl2Xkp68q5vAvTsa0LTV0g66aXv787y+gagrlG0pZ+zcrouMPDoTY8z+HaDnVjqGbpGQnMWtTKXnl2RQvKyI5y8OFV2o592I1HVVdWBY4h9oIGboBWJi6iaqp6GGDiD9Md20P3k4frRUdHPzJcRRFscv8WxaOJAfOJCcAA61eeur7yCnLGtfrK15ayB3/Zz1/+vcXQAHLsAh7IwDsf/IYafmplN4y8t58sN0brR6gh/SR0EfERI8YOJwaZbeUjBnG6W3sx4gY5JRlXVWFmLl3zqLzQg+aU0MPh8GyUFUFzaWhD63aTk5xEQlGCPYHQVGi7RPq9jehagrhofErmoJlWdH2H5qqgKJg6iZOj32NHfZFYgdgWkSCOqqmkpTuJn9uLgANR5oT/i44v62aogVSZVIIkVhafir3ffxOjv32NMd+e4ZIIHL5J00GC4KDIfx9QTvkeBl6SOf89moJw4gpk5qXEv0ze3Fllp6GPl74wk4e/PQWUvNS6G3sZ8/3DhPxj/x7ciY7cae4cHgc6CFfTHW4YZZpjVSFUxR73tapkZTupv1c12VDZrEHs6g/1CRhGCGEEDc9CcOI69KBHx2jend99HHFs1WEfRHWvTP+Zn/YH8Hh1sacvByeaA777EkqI6zbq7JSXAT6gvTU9ZI3O4dz2y5w5plKAn1Bihblc8tblpFemHbNXiNASnYyq990+RVryx9bjGVaVO2swwgbTF9RxJopeiOrh3QO/PBotMWOqqk4kxRyZ2Wz8g1LyJ+bc11OJN9o9JBu91/u8uFOcbHo/nkJA1m9jf289JXddNf1MvoaS3Oo9sS3x4EzyUHIF465iBvuR197oJE1f7M85ntq6CadF7oJe8Mj+zs13CkuCublXnbspmGy7T93MTi0CtXb5efQT4/jcGvM3liW8DnLHlnI4gfnEfZH8GTEVr+5sKuOU38+F3082OFjx3/vZdH98+ip74tutyzLXj0xVMZ0ND1kB7oSlaEXQgghhJhMpmFS+XJN3PaGwy0E+oLjrvQ3TBnjrfdY2wXMXDeDmeviK3Jeb9wpLu7859s4+OPj9Db240p2EvJZ0ZuGpmFxYWcdafmpLH7Ars6473+P0HKq3S6xH9IJe0P0t3jJmJZG/rw8qnbWsv/Jo4Bdqt/UTfSQbgdihgIiw//VQ0M3J4fCJZGgjhHScaePtEXVgzqac+R6fKLXgudevIClm3Hvzw3dYM//HKZwQX60hVVSuhvVoRLyhu337aPe14d9YaZvKGX1W5bFnaPuUBO7v32Q4EAIzamSWZLB5g+sI2t6xoTGOqx8/Qz0kE7Fs5UEq0OoTg2Xxzl0g0pBUSA4EIxeM4OFEdIxDbv1gWlY9tAVom0Shq9TTNNCczmYt6U8GqxpO9NB2B9mdPGY4cUMo1vUDlfBHU1RoK9p4IpepxDi5qGHDaYtKaDtXCcNh5pftfPWH2jEil+blZAeMmg53X5tByTEJeSUZZE3K5v2c51x71ssw65UV7WzlqR0Nwd+dIywf2iuVbH/fkcCOqbbQo8YOD2O+LDr8LGw/34rqkJSmptIUOfkHysIDAQTtkZKZPj9gtPjvMJXK4QQQtw4rvswTHd3Ny+99BKHDh2iubmZvr4+gsFgwuRsIoqisH379ms8SjGZ2s512pPDCtFqEwA1extY/ddLo2/iuut6OfDjY3TX9OJKcbLg3jksfWhBzLEsy6JqR639YNREmRkxME179ZYrxUX1nnoO/fRE9Hktp9rZ9p+7eP0Xt05Kr/erpTlUVv/1Mlb91VIsy2Kww5cwFHCxSCCC6lCvqKLOWLrr+gj7Y9+sK4pCX2P/VbecEuMTGAjy0n/ttSeeHfbqw8M/P0Fafgoly0daB/l7Azz32R0MtnvjJheMiDlUbtOk7Nbp5M7M4sgvT2JZoLnU6L+94XKcjPoRqjvQyECbF9WpYUbs8IgZMcgoTmfG6uLLjr/1TEc0CDNa1cs1Y4ZhwG4X5smI/1mu3dcQt00PGbjTXSy6fy7nX6pBD+pkzcggOBBKuIpiePXoeMYvhBBCCHE1TMNKWBrdMu1J9ImGYWaum8G5F6tj3+MovCbCHuLy8ufk8uBnthD2h2k+2cbu7xyK26d2fyOLH5gX3cc0zZEgC2DqBoH+ICf+cIb+5kHArsYarQ5p2TdoFIeCw+1AD+voYQPTsD/vcGtDx7EfW6aFw6VFz2EadiWZnJlZEw6YNB1rJdH0juqwWwm0nmmnbO10ANypbmZvLOPkn88CQzd6FNDcDhxuB3M3l+NOia202nS8le3/uSsaejd1g+66XnZ95wAPffaeCY11tLl3lDP3jnJe/OIrtJ3tJByIYIT0uJXiDD22ACticql7vtEgkQLz75pNwfw82s938pcjzZjDoZmhz6uqQsH8XLJnZNLX1E/dgSYG2gbjjmlZkFOWecWvUwhx46s90Mi+J4/i7/Jh6iNVKV4N4w3CDPN2+uzKGVdR3UuIq/HAp+7i+c/vpOl4a8zfeyOsY5kWLafa6am/aO7csgOwmBZ6QMeZ7MThsluDJbzHNRSgQbHbXlqmdWUVmxSF5Y8unPjzhBBCiBvMdRuG6ezs5MMf/jC/+tWvCIVCV3QMy7LG1ataXD8O/uQ45168YFdxARSHijvFhTJUJthOTjuJhHS2P7Gb0KC9X9gX4cTvK/BkJDHn9pE+7ZY5MtHscDvQR79xtCyKFhWQUZTGvh8cjhuLrztAy8k2pq+cdg1fsc3b5bN7rtf0klmczuIH5yVsgeTvDbDzm/vprukFILssk9v/8VZS81Ji9hvs8LL/h0dpq+i0q21sKmPlXy2dlGBP8hiT88lZnqs+trg00zBpq+ig7mATkWAE5aKm8hdeqaNkeRH+vgANh1toOtZi95W/xCyGaZr0Nw9w1z/fRv3hpujk+LDipYVxYaqOyi4URcGV7MQ0NCzDLuudNyt7XCtBx7qAC48q/x4cDNF6pgN3ipPChflxxzV0MzoJD4l/z6uqyso3LmHZIwuJBHTqDjTSdrYzYRjGmeRg1sZS5t2VuFWTEEIIIcRkcbg0ihbm03qmI2Z7elEq6YWpEz5eTlkWt717NUd/cxp/T4DkrCRWvGGJtBG4wbiSXWiuxFM4w9d5w/Mfpp74/X9XdQ/GUKBFD+lxNzwt3cLARFGJBmFUx0hQXlEVLFNB1extFnZ4XtFUpi0p4Na/XTmh13Ru2wV8PYGElRst08IyLVwXhVtueesy6g7a4XxFAc2l4Rj6uqjO+GuRk38+G1f90YwY9Db00986SEbR1VWDXfLQAtrOdsa0bbpSw2EjUzd5/os7ed1n7ub4786gqEq0kszwOUrXlLD5/bdSs6eevT84YlcB8sdfZ2lOleWPLrq6gQkhbljnt1ez8xv74wK146Uo9v853FpMS79rxhqqoCVhGDFFHG4Hq/96Gc2n2rBGvd+yLDtwOxxMVVQFDGvkb/eof2J6IAKWhebWxmwRZgEMvRcCJhRSUzQFl8fJ8kcXMfNWCccLIYQQ12UY5siRIzz44IN0dHRMqAIMMO79xfWn80I357dX2xM9moplmFi6iR6225fkzs7Gk24HMZqPt0aDMKNV76qLCcOomkrxskKajrXidDtQsEt/qprCvLtnsfLxxYBdRSKRSOjaX8iFvGGe+48dBPqCAPTU99F0vJUHPnVXXMhl7/cPR4MwAD11fez5/mHu/ffbo9ssy+Llr+6lv8V+8x0J6px+upLAQIiN711z1QGxtIJUZtxSHFc2ddH9867quOLSBtu9bHtiN94OH+FAxL5YGiqZ6RxaCWkaJs0n29j59X0YEdNenRg2xp6TtcCMmHRVd/Pyf+/jtr+/hR1f24evx4+CQs7MLNa+fQWWZdFW0clA2yD5c3JIy7dv0iiKYk+MD/0lSctPGetMMaYtKcDh1uL+3c1YZQfPavc3svcHh6NlxTOmpbHlXzeSnOXBiBgc/sVJqnfXY+om05YUULKskLaK2JtJjiRH9HiaU0NzatTsa8Cd4iLkC0ePragKSx5ewLLXLyA5UwJdQgghhHh1rH3HSl7+rz3R9+zJ2R42vOfK36vPXDeD0jUlhHxh3CkuaVt6gypeUoAnMyl67Ths1oZSwC6FP33VNOr2N8Z8XnVqKIpCelEayVkeqnfVx6/GH7pZYxomKiqe9CRMwyTkDWOaJqqq4vQ4sMyR+ReXx0neskLu+pcNON0Tn146v60azaVh+uNLA1imBSpx1UdVTWXZI4s49NPjMdtTcjwULSqIO46v25/wDpJlWdGKN1ejcEEeix+cx6GfnZjU+ajQQJhDPztBR2U3To8TVVMxIva1naqprHrTEjSXgyO/OoVlWjGtl2BoQblDJX9uLmkFEw/ZCSFufL3N/ez8xr74vwcT+FVmWfbiImeSPS8VCeno1zAUU7Qo/7qo4C1ubif+UAEJFtpZQMgXRtVUHG5HdLHvMMWhYBkWlmXP11/q31rhgjx6GwcIeUMTyaeBAmVrSrjjQ+txJkmLJCGEEAKuwzBMR0cHDz30EO3t7dEJluTkZG699VaKior42c9+hqIopKWl8fDDD9PX10dNTQ1nzpyJVoJRVZXHHnsMj0dubL6WtJ/rjH7sSnYS9kWwTBMzYpJemsptf7c6+nnTSPxu0UzwRnTt36zA3xOgp74Ph9tBxrQ0bn//OnLKsqL7FC0poKOqyy4HqtnhAqfHQfGSwkl8hYnV7K2Pm8wM+yNUvlzDyjcuAaD9fCcd57toPtWGqqkxk+Qd57sI9AejLZO6qnuik+p6SB9amWFx7sULDLZ72fL/bYz2XL8Svm4/2TMyCA2ECAyE8GQkMfeOmdHS1eLaOPCTY3g7fFiWhR4emliwgOFSmYpC6S0lHPzxMbsFEvYqQCOkoyjKJSdmFVWh4WATHec78XX7MXWT1PxUCubn0nS8lepd9XTV9ET3n3nbDNxprphAmiczidmbZiY6fBxXsouN/7CWvd8/TMhrH6NkRRFLHlpA2B9m/5NHomEVgP6WQY795jS3/f0tHP31aSpfqol+rul4Kz31fRQtzqezqhs9ZJAxLY01f7MCd6o79nViT9wnpbqjXw9FUSian/eqBWEs06L9fBdGxKBwQd6ktjATQgghxGtHWl4Kr/vs3XRdsCt15M/NueoAi6qp0cUD4sakOTXu+pcNHPjRUTov9Njtgu+Zzdw7y6P7rPvblSgKVO2owwgbqE4VV7IT1amy9PULSC9Io7exn7aznRihoffcSmxxFsswo62PktLdzNsyi/SCVEpWTCPYH+TsCxcI9AUpWpzPgnvnXFEQBuzrXodLwzLMmKD8cMUXVVUStsKYd1c5kUCEs89XEfKGKVyYx5q3rUh4g7RgXh4DbV6M0QtdFIXiZUWkZCdf0bgvljs7B6yJrdq+HMsw7YqcqgKm/fUwDRMzbGAYFs984iXKb5tBcMCupKwOVwcaen5SRhKKolCy4tpXuhVCvDbt+OreCbcpupjD7aB0bQmqolC8vBB3iosXv/QKRvgqD5yA6lDZ+A9rJ/24QkxUb1M/qqpiWGZcoMUIG5iqHbi1K+qNqh4zunLfpd4vKNDXPICqKmgONVo5bjzvM9ypLtrPddF8ok3m6oUQQogh110Y5gtf+AKtra3Rm7cf/vCH+djHPkZKil1x4Gc/+xkA+fn5/PCHP4w+r729na997Ws88cQT6LpOVVUVf/jDH5gxQ0rBvVak5I5UlVA1FXeaC8uwKFlZxJ3/57aYAEjJ8kKcHkdcCc5Eb/KSszw88Km76K7rxYgY5JbHtnLRQzqNR5uxDAtTN0C3yxLf+s6VVxUaGS9ft3/M7aZhsutbB2g40oJlWYR9EVSHPZk5/PVQVCU68QVE32Tb/UTtIMywnro+Tj11jtVvWjquselhg7aKDjSXRsG8XNrOdbLjq3ujYQtVU1j28AJ5c32NmYYZLaNvRkwUa9RENXagIznLQ+GCPPZ+f+TnSXNoaG4HRkjHnerC0A00pxZXVck0LYLeECFfKNqHvr95gMO/OInD7cAIG/YKY4eKHtKpeKYSp8eJw6WRXphK8bJCFtw7h6T02PDJpZQsL+Kx/7qf7tpePBlJ0dWKTWc7ElZqajndDkD17rqYr0vIGybYH8TX7cfh0rj1nSuZM0YoZ+b6UrqGKitFV7OmuCheXjTucV+NwU4fLz2xm4E2LwDuNBeb37+O/Lm5r8r5hRBCCHF9URSFvDk5Uz0M8RqTNT2DrR+9g0hQR3OqcSEqV7KLTe+7lbVvX0ntvgbaz3WRlO5m7h3lZE3PAOCBT95F0/FW9v/wKD31ffadFUVB1RQsI/YGpubUWPK6+dHweFpeCnmzJ+fntnhZIdW76u2WxkOtjBRFiS70cCY5E1ZLUhSFJa+bz+IH52Ea1iWrBKx4bBEdVV0MtAxGz1G8tIDb//Hqbqg2nWxl7/cOM9jhxZXsBBWYrHu/Qy85OTOJ7LIsGo+0YETMaLsnVVOIBCNUvlyL5tZQLLtVlubS7LZVqjJUCSiVhffOmaRBCSFuNL2N/Vd3AMVezJien8Itb1kOQMgbIjnTQ8gbTti67WrkzckmJWdyQoxCXKm6A414O33RtpOjDVdI0sOGPXdu2YturYsX9SZoD3nxcVRVJSXHw2C7RcgXjqZgHEmOaFA4ctG/MafHEW1t2VrRIfP1QgghxJDrKgyj6zrf//73o5MdH//4x/nkJz85rucWFBTwuc99jocffph7772X48ePc//993P48GGSkmR13GvBjFXTyJiWFq1qoigKzhQHK9+4JG4CzJXs4vb3r2P//x7B2+VHdajMuX0m87fMGvP4oyvBjFZ3sInBNh+uFJddWca036j2Nw9O3ou7hMIF+Zx9/kLC7Q1HWqjd34geMuyQi2InzA2nGu2LPmN1Me5RfdRzZ2WTkps89HUceWetOlS7jc6J1nGFYdrOdbLz6/ujJR3TC1MxdDMahAG7Qs/hX5xk+qriSS9T2l3bS+3+BsAu+z7W9+9moGoq7lS7Esvwd3R4NYDm1DB0A2+nl2c/8zIhXzjaOklR7R6xjswk1r59BdmlWUQCEf7wr8/Grv4ZOqhlcdGSUPvnDcsiHIigOtVouVs9bE++ezt9zLtz1oRXVYb9EVor2nG6HaTkjjx3rAotwxPio6tChf2RmCURpmFx4vcVlK8vTfjzOO+ucgJ9Ac5tq0YP6mROz+DWd6y44pWsE3Xwx8fsVakRA9Mw0cM6u75zkEe/fF/CFa9XIjgYov5gE0bEYPqqYtLyUjAiBjV7Guiq7SGjKI3Zm8pwJV/7oJ8QQgghhLh2nEmXfg/rTnExf8ts5m+ZnfDzJcuLeOwr97Pty7toOt6G5lSxLIuQN2xX3B0K2Sx7+Nq1E135xiUMtA7SeaEHzWFXPnGljJT0n3NH+SWebc8ZhL0h/H0BMovTE1ZdTCtI5fVfuJfGw80EvWGKlxaSUZR2VePuru3luU/vsBfTAIGwMXItNRksQFVY9ugipq8o4pDbwbnt1fZFIGDoZnSVuOpwg2KHX1zJLgyXwfSV0yhbM53SNSU4XFKJUggRz7IsNKcWt8hwIlwe+/f1cNAQwJ3qZuVfLWXf/x6+6jFebNb60kk/phATEfZH2Pe/R3AMhU9HV31BseclLdPC5XGyYOscTv35rL241XtRMOwSQRjNrUXbGxUtLmD6agfnnq8i5IvgyUxi/btWU/lyNfUHm+MqxeghAz1koGpK9H2CEEIIIa6zMMyRI0fwer1224qiIj760Y9O+Bhr1qzhRz/6EQ8//DBnz57lYx/7GP/5n/95DUYrJpvm1Ljn32+n4tlK2iu7SMtPZdHWOWSVZCTcv2hhPg9/aSuDHV6S0t1XfHPX2+mLfqyqCgzdlB69/VoqXlZI+W0zqNnTEN02bWkB5RtKef7zO6JtZBRl6L3y0JtrzalSdut0bnnzspjjqZrKHR9cz/av7KanNgwoKENvgkPhEF3VPRz6+QlWv2npmDfgTcNkz/cOxfQ27W8dJOyPxARvAAJ9QXxdPtILr25CcbTq3fXs/cHh6MXB2RcucNvfrab8tpv3wnfBPXM4/rszaA6VyNDVjqIpmBEDLHvyoaeuF0VTMSMGZsTEneayV00+tIDyoUmDfU8euXRJzYs+Z1l273lz1IQrQ49DvjAOl4PKnbWsGmrpNR7NJ9vY9e0D0UmXtPwU7vrXjShA1c5awCIc0HG4NVTVnoRfuNVe0Vi2poTq3fWAFV21qmhq9Gc50BdksG2QlLwUggMhUnOSo59TFIUVjy9m6esXEPZHSEp34+30EfaHr3k4xDRMWk63E/KF7e8Z9pe6p66P9spOCufnX/U5Oi90s/2J3dGv69Ffn2bdO1dSuaOWrgsjba4qX6rhvo/fEddGSgghhBBCXH/qDzdR8Uwlvp4AhQvzWfHYoklbGa+oCpvfv459Tx6l4XAzlmlRvLSQ4uWFqIpKyfIiskszJ+VciSSludn60TvoruvF1+Wn/nAzjUdbcLg05myeybJHFo75XNMwOfDjY1TvqscyLdxpLta+fQWlq0vi9nW6HZN6LXnk16eiQRgYdQk1SS2SUGD+PbOYNTTm2/7+FpzJTs48U0nEH47J3EQCOumFqVimRWZxOsseWUThgrxJGogQ4kalKArl62dwfnsNRiS2Om9ShttuwXaJ32mOJAeqQyXkC1N/sImwP8LyRxaSMS2d+VtmERwMceQXJ+ygzHh/Nyr2Qj6HSyPsiw0PeDLczL87cbhTiFdLR2XXUNjEbiMZ8ocxh1qCKYARMTAiBq5kJ6efOmfP6U/wvcHw4ldFVShdU8Le/zkMKLhTXJgRk/1PHmHh1rk0HWuNVp8ZNhzOMYHa/Y0sfnD+VQeAhRBCiBvBdRWGqaioiH780EMP4XCMPbxIZOxSiw899BCrV6/m8OHDPPnkk3zhC19A02Q1zGtBUpqblRO4qa6oylWFMAL9QVzJTvuG/0XVZ/LnTqz0c8gXZrDdS3phml0meZwUReG2d9/CvLtm0V3bS0ZxOnmzc3jlG/tpPt4W3W+ocjUA5etmsPkD68YMs2RNz+CxJ+7nTx95gf6mgZhyippL49wLF0gvSGXeXYkr6fQ1DeDvCcSN0zLMuK+VI8mBZxwrBbtqejjyq1N0XegmrSCVpQ8vpGxN/ESlaZgc++3p2IsFC47+5jRlt06PKwN+s1j84DycHidVO2vx9wYIDYaG+sMrKKodWgH766c5NSzTImNaGosfmE/IF+aVb+3HkeTgws66xBdiY5XoHFqVGJegseyWTeFImJN/rCAlO/mSlZmGGbrJ3u8fjll9NNjh48APj9LXPECgLzjU+gn0oEHhwmxmbSwlEtSp3l3P0ocXEvKGaTrRCurwCsiR8umqU6V6bwOVL9WgB3VScjysedsKSka1QtKcGt6OPl74/E4G2ryoDpXZG8u45a3LrtnPl6Laq2tHB2Gw7O/Xi1/cxZ3/5zaKlxZe9jghX5ijvz5F49EWnG4Hc+8sZ+F9c1EUhcM/PxHzdbVMi33/ezR2pQr21/v89hqWvn7BZL5EIYQQQkyi4GCIE7+voPlUG0npbhbcM4eZt0qp85tN0/FWXvnGAUzDxDItavbU01Xdw+s+e/ekVeZ0epxset9aQt4whm5csyoww9rOddJV3UN6YSoly4tQNZWcsixyyrKYsbp43MepfLnGvrYZEhoMs/s7h8j7cs41fw3+notaHU9WCAbstiMpLnpq+7BMK3rNP++uWZx55nzcaY2wwWC7F1eyi47Kbnoa+iQMI4QYl1veshx/b5DWinaMiElyloekdDe99X2XfJ4yXLU6bOBw2y3sGw41036uk4c+dw9JaW7mb5nF2eerCA4EiQT1+DYxiVhgGSaerFSSs5MZaB3ENEySM5O49W9X4fSMf65ViGthdHt4RVFQUOIqbAMjLcIm8P5Ac2koit0KMTU/hRWPLWKg1UugL4ip29WlLdMi6IWzL1ShuTRcyS4iwUhchSdFBcuwqNpRw+q/XjbGGYUQQoibx3UVhunpGVm5vmjRooT7qOpQ6d5Q6JLHeuCBBzh8+DC9vb288sor3HHHHZM6VvHad+SXJzm3rXroDaWBAjiG2qXklmcx987L39gfdvLPZzn91PmhC0GNZY8sZOHWuRMaT255Nrnl2YBduaHpeGvcPpYFiqaQPTPzsm1VFFXhvo9uZsfX99FwuAVFUXEkadHeoXUHmsYMw7hTXQnDEZ7MpNj2OsCi++ZetkR3cCDE9i/vJuyPYBomXbW9vPzVvdz94Q2ULJ8Ws2+gP0igLxh3jEBfkOBg6JpPbF6vFEVh/pZZ0cCJt9PH7/75Gbs0uD+MqVvRvIoRsX+e/b1Bzj5fFe0DHRwMxQUj7IND4fw8Oqq6E5bRdCY5MCJ2+U8zwQSGHtI5/PMTlCwvJDU35ZKvo6eudyjEE6vxWGu0hLeCEi0JmpqbzJFfnIqOy53m4u5/3cjad6yg8qUaTv35XPQYlmXhyUzi5B/PojlVFEXB1x1g5zf3s+kf12JGTPLn5uJIcvDSV/dGKx+ZuknlyzWk5Caz+IF5lxz/lVIUhZyyTHxdvmgQZlhwMMSOr+/j9Z+/57Jfv1e+uZ+2ik7AnvA/+uvTmIbFwvvm0lXTG7d/2BdGc2lxIZ/ehr6rfEVCCCGEuFYsy+Klr+yhu9b+2+7r8rP7OwcBJBBzk6l4voqQNzTyHl1RMCIGLSfbmL5y2qWfPEHu1PFVStRDOgPtXlJzkydUXdGyLPZ87xC1+xqj23LLs9jy4U2oDnXoBtD4Az71B5vjtpm6SdOxVuZepr3S1ZqxqpiOyu74BQOXM3wJf6n2CA4Vh1NjsMNHR1U3BfNyAcgoSmPxg/M58YcKLMNE0VT7uk+xK2UOO/LLk1Q8V0loMMy0xQWsfvNS0vJTJ/gKhRA3g6R0N/d+5Hb6WwfRQzrOJAdPf/IlIsGxWycpCtGFSRYWekjHiNhzoZGOCL///54la3oG87fM5s7/s56DPzlOX9MAmlNlsNN32XCAZYK33Wu3/05yRNvf7X/yKKZhMm8Cc7VCTLbc8mxyyrNoP9dph7dMEwVwpjixLIgEIvbP+ATeHgyvN3UmObj9n26lZMW06Pzosd+exogYhH0RLKzocfvbBu17ZC4rpk3ZMFO3MHSDkG/sxeRCCCHEzeS6CsOEwyMtWdLSElf7SEtLo7+/PyY4k8j06SOThPX19ZMzQHHDqDvQSMVzVdHHTrcDQzeZe1c5RQvzoyvUxqPldDsnfj9S1UgPGRz55SlyZ2WTPyf3isbXcqYdsCtYmIYZfbOrOlTcKS7K1oxvEtzpcZI3K4e2is6416M6xg7TpOQkM2NVMQ2HYycYlz68kNyZWVTvrsc0LGbeOn1cq/fqDjQS9keIhHT0wMgb8W1f3sOjX94aU90nKT3JLsnaHxuY8GQmkZQmbV2CgyECfUHSi9LInZVNT10fqkPDGBViGf7O+nsDDLZ7o2WFLNMaKjur2KGWoZ8rzaHS3zpIUoYbPWSv2LEMC8M0UVBY8zcraKvooOVUO/7eQNyYTN3CiBi0nu5gzuaZlxz/6FUUo6maPbFvWRaqQ7NblgG1+xqjkx9gB0AO/+Ikd//bJpY/uojs0kyqd9Uz2OGlp7Gf7poeTH2oCpJTxZHkIOgN8fx/7LC/DKpCybLCmBZgw2r3N16zMAzAvLtn03Kmw744HsWMmAT6glTvqmPZI4mDoGC3KhsOwox2fns1S143n5QcD77u2O+P5tISBueyZmRe2YsQQgghxDXXUdkdDcKMdu6FqgmFYcL+MHrIIDnr5gyT3wi6a3tjw+qWRSRgh1ESsSyLSCCC0+OMq3w6Gap21HL016cI+yNoLo1F9829ZCuj0VrPdMQEYQA6q3t45lMv4e30oSgwc/0MVr95GU735aeqNNfI9a1lWRi6fUOKyX/ZcRY/OI+6g010VfeMOxCjDFXbvNzeo699rIuOvezhhTSfaGOw3YtpmpgRA0VVojfN9LBOxB/BMuyKMk3HW+lvGeChz99z01ZYFUJc3nAblZe/thc9qONIcqCPEYixLMCwsBRrJN9nWIR99t8qs9tPaCBER2UXG9+zhtf9x92EvGGcHgc/+7s/xFWhTsSImNH/ejJGfiee+EMFszfNnLTKaEJMVGd1NwOtg1iGHUIZbi1vz7cqE64Up7ntRaueDDcPfmYLRsSk4tlKVE2hdO10ihYVcPiXJ2FUEAbsh06PE6yRCnIXV18K+yLklGVezcsVQgghbhjXVRgmPT09+vHAwEDCfbKysujv7ycSidDa2kpRUVHC/XR95E17R0fH5A5UXDOmYdJyqp3BDi/5c3PJKcu6JudpONISt00bCprMWDX+0swA9YeaxtjeHBOGsdu81NFd10fmtDRm3z4Td0rilXSezCQAHEkapm5G+5E73A5WvWnJuPp9dtX0sPPr+/D1BAh5wyiqgjvVFZ0UnbWh7JLPv+3dq0nLT6H+UDOaS2XO7TOZf/dsFEVh2pLLt3MZLRLUsSwr7mJaD+kc//0ZNr3v1ug2zaGy4rHF7HvyyMgbfQWWP7bopp7As0yLQz8/QdWOWkzdJCnDzeL75xL2RRjs8NrlMg0rOvmruR3oId3+GirAcEUYyw6vuJKdhAMRFEXBnWoHVIywAZa9+ibsj4ABqkvh3AtVLHn9Anob+hKGYcCepEjKuHxYKS0/lekrp9F4dOTf4HD579DgcABKx+lx2JPaERNTN2Oqm7SdGwmEzFhVTPGyIn7/L8+gqkpM5RojYmLo4ZifI8WA+sPNuDzOaCWoYdfgfkGMGauKKVyQR8updvtrHT0xmBGD+kPNlwzDJArwgB2Qqj/cxKwNpZz807mYzy24dw7ddb10XRgJkKblpzDvrmu7WlYIIYQQVy44GF8lEUhYXS8RPWxw4EdHqdvfiGlY5MzMYv3frSazOP3yTxbXl7HamCZQd6CRo785ja/LT2peCivfuJjSW+Lb0l6p3oY+9v/oaPT8Rtjg5J/Okl2aOa4qNR2VXXHbwv4InRe6o9fFF3bWYRkW6/9u9WWPN2fzTFpPd2Dopv0+eWgBwLFfnyanNIucmddmLgHAlezikS9tpel4C61nOuio7qblRFtcFVXAvgYZCqxoQ6GVlJxkO/CW4HupOu1rntTcZPLnxLZudiU7ue9jm6l4roq6g430BPWY0JMeNlBUNSYQNNjho/VMx7hasgohbm5tFfb8uXqZStRA3L35YUbEtMMsChz6+QnKbyuNVh4z9PgKFpc8hWkRHAji9DjRnBqhwTDB/iApOckTOo4Qk+XQz04Q8oUxhtpXgv3vIBLQL1vB/WKKqpBVnE7p2uksun8ubRWd7PneoehxT/zpLLM3lmGZVnzu1gJTN3AmOSldU0Ldgaa4OUPLtNj/o2NkTc+MVpkTQgghblbXVRimtLQ0+nFTU+KAwYIFC6irqwNg3759PProown3O3HiRPRjl2v8pXvF1IkEImz7z10xrT7m3lXO2retmPRzOdxawu2Xa/eT8FiuxMcavV0PG7zw+Z30jOq7W7Wzjvs+fkfCQMy8O2dRvaseI2zgTnVhGibOJAf3ffxOsqZnXHZMlmmx69sH8fcG7bBDiotwIELYHyG9MJVF982lfP2MS78ut4OVb1zCyjcuuez5Lmf6ymkc+dXJuFVzmlO1y0tfZPamMjKK06jd2wgKlK+fEW0hdbOq2lHL+W3V0cfB/hBHf3OG13/xHnxdARqPNnP8dxWYhomqKbErQRPMUISHqpNoLo1IMGKX1TQt3OluknM8hHxhe7WlojDQ7uXwL05y/yfv4HcfegY9GD+B4cl0j3uCdcN717Djv/fagRjLbn2khwxUp4YZMQCLsD9iV7DRLSzDbmXmTnWhaiqpF018DLQOEuwPRVcPxbho5YQFoDBUxtdu/6SHdEzdQnUonNt2gflbZo/rdVyOZVkMtHlxehwkZ3rQHCp3f3gTr3xzHxdeGapYNtReGEVhoG3QXlUyRionuywLT2ZSTBuxSDCCZcEr3zgAQO6sbJLS3Zi6SemaEmZtKMXUTWr2NNBV20NGURqzN5VNqKS9EEIIIa6tzupuTv35HH3NA+SUZrLg3jkomkLYF8YyLVSHiubUmLYs/r1Wb0MfJ/50lt6GfrKmZ7Ds4QVU76mnZk9DdJ/u2l52fG0vr//CvROeqBdTK7ssA1+3P7o4AkXB6XHELY7orutl93dHbqB4O33s+vZB0gvTyJqeQdgf5ty2ajqruknLT2XBPbNJK5hY65z6w80JryvqDjaNKwyTmhfbDtSyLMyIgXZRQL12fyNr374ipkJKIqWrS1jzthC7v3sILPvfiaKpDLR7+fNHXmDJQ/NZ9vBCe+X0NaCoCtmlWZx5ppKe2j47hIJlV3Ox7JZPpmlXeXW4NRQUHG4HG9+7hsqXa/B3B+wWWKMWLWguDVVVyS7NZP27V6NqKv6+ACd+X0FbRQfJ2cksuHc27ee78Hb40ZwaYX8EPWKQlOq2Wz97HHHXE5dqeSKEEMOSsz0MtHpjWq9dMQv6mgfw9wVIzvTQWtFBaDDxAp9LMQ2LsC+MK9VNam5ydPGgEK82PaTTVd1DyBuOq8IC8dXcLmkoKLvs0ddhL3MAAQAASURBVEXM3liGoZsc/sWJmNb2YV+Yk388O2bFOz1k4MlIYtaGUtrOdRLxh0em3YfmGo2htvYPfOqu8Y9NCCGEuAFdV2GYRYtGVsQfPnw44T6rV6/m2WefBeDJJ59MGIbp7u7ml7/8ZfRxWVnZ5A5UXBNnX7wQE4QBqNxeQ/m6GeTNzhnjWVdmzu0zqdnTEPMm05HkYOa6SwdEEpm1sYzKl2piKlKoDpVZG0bCXXX7G2KCMACD7V6qdtQmbMuSWZzOvf9+O6f+co7+1gFyZ+Ww9KH54+713dfUj7fTFzOepDQ3KPDYV+6/7MTiZAoMBGk93U7hgjwaDrcMBWIUuxSkU4tpkTRa3qwc8mZN7vf9tSxRBSJTN2k+3sbMdTPY9Z0DQxOv1lBvWDsYojgULD3BBdlQxRjTMDFHhUiC/SGCAyEUVYn++1AAPajzwudfSRjUcCQ52PKvG8dduad2XwOtpztwuOw/Qd4uP6pqV6gxdLvcdySk40xyYugmRkiHocpCrhQXi183P+Z4nswkVIeKFdTt8qQKly5NaoEz2Yke1gkP9c9VNAU9bHDopydweZyU31Z6iQNcnn1D4qA9kaQqTF81jfV/txpnkoNFD8ynZn8jZnioBZpqr/J0uOMnrkfTHCob/2Etr3xzP8GBEKZuYuhmTKCuq7qHte9YwdzNI5VfNKfGnM0zL9vCSgghhBCTz98boPKlGgbaveTPyWH2prKY6nQDbYNs+9Iu9JAddvB1+Wk53Y5lWughw34/FjJQVJ3avY24k10sfXgBqqbi6/bz/Od3EgnYN7q9nT7aznYkDLwMdvjoqumZ9OsqcW0tum8eHee7MYdWIKuaSmZJOtOWFMTsV7OnPubaFuwFEjV76ln++GJe+MIr9Db0D32mndp9Ddz/iTsnFIgZ6xpSc47vGqBsTQlnnqlkoHVwaICAosQtLrFMC9Ow0MaRYZm2uABXshPL48AIG9F2pJGAScVzVfS3DHLXv2wY1/jAbi0GCq7k8QVodn/3IJ1DFRgdLrt1rSczidScZDovdIMBKPYNK2eSEzNiEgnouFPdKKpCUnoShm5/bzWHyi1vWUbp2hKSM+3WZoZu8uIXX2Gg1W6L5e3y01rRbgdnnPb1tDtNxYgY5M3OIbc8i3OjFlCAfa02bXH+uL8GQoib16L757HvB0fQHGrMnBCXm2MZiwX1B5pYcO8cTv3lHIqiTCwwMHIYjLDBqjctvakrRouppTk1u0WXOcbP8HBl7sv9iCv2YlyH20H1rjpmbyzD1+0n2B9bAVIPGZiGOWaQ3TIt/H1Bdn59vz2+4QrhjORnFFWlp74PI2K8qvcChBBCiOvNdRWGmTlzJtOmTaOlpYW9e/cyODhIWlrsjfI3vvGNfOYznwHgmWee4eMf/zgf+9jHcDrtyYqGhgb++q//mt5eO1ThdDrZtGnTq/tCxBVpH9X6ZLS2s50JJ2176vtoONKMcyjEkpzlGfe58mbnsOkf13Li9xX0tw6SW57Fqr9aOqFjDMuekcmmf7qVY789TX/zIFnTM1j5xsUxIY/exv6Ezx1rO0DOzCw2f2Bd9LFpmDQcbcHb7iVv7qWDIo4xVr+5kp2v6pvfxuMtbPvSLnuCfqg/usOt4XQ7UVQFVVNY8tD8yx5H2IGmRDSnRt3+RgK9QdyprqEqJ2a0548zyUFwMBRzMTbcxlZRlJggTJQV22vWwn7c1zSA5lTRXBpGxIiOKyXLw46v7QPFvhjLKcti+WOLxmxzdvb5qpjHiqLY7cAME82h2m2TsFdJuDxOdE3BCBs4PU42f2Bd3MrTpDQ3c26fydnnq/j/2Xvr8DiuNG/7PlVNYpYslmXLzBgnxtgOezCTZAd2aN+BhZnZfTeD7+7w7FDm2x3c3YFkmCcwYXIcx3bMzLLIYmapoep8f5wGtdSSJZPs+NzX5URdfarqVHd19znP+T2/JwBhQYwzzol/0A8xLtHX748WngQXnFxxBme3VV6SGOb8wXpe/NZ2AoMWhtPA6XZQs7dOucM4DY4/cyb6dZdK2DRW6bKuhh6OPXmKxlMtxGfEMfv26XQ39XLu1WqVVWvZCCEQhqDucGOUGEaj0Wg0Gs3Vxdfvo/5oEwGvxYE/Hg1nQlfvqaVy93lu/8y68GLOma0VYSFMiP6OQTUOSnDi61VZntKWDPZ4OfrXU6qE6NvmcnZbZVgIE2L4Y831TcGiXNb9000cf+YMAx0D5M7NYeHb5oxYDLRHWZixLcn5/XVDhDAKX7+fE8+fZeV7FmNbNuXbqjh/sB5XgosZG0pj2ulPvamQo0+cjHZjFFC2dnyCa4fbwe2fXcfJ587SUt5Ocm4iLWfb6KyNLpGdv2DKuB1b3UkuDKeB7bfDC0CqX8ops/5oE10NPRcsMzzY7WXXz/ZTd6QRUM6mN71/yahljQH62vtpPtOGlBJvT8Thpbe5D8tvqWSZkAukLfH3+zBMNz1Nvcy8tZTqPbVhEQyAJ9lNyU0F+Pr8+OOcON0O6o80hoUwIWxLYgfU4lhgMIBtSwxTrb4tvm8+fR0DnA+WhfYku1n1waXaFVKj0YyL6WtKcMY5OPNyBYM9Pvrb++lr61cxG9uO6RI8JgIsS/1mdNV343CZBLyBkSVfLiAgMJ0muXOzmXpT4cTOr9FcRoQhyJmdRcVr1aO2cXgcBC4wFheGwOlRcfvQHCAu1YMzzhE9jg9+UIaLnYdiWza2rcYEdigpcsh5HC6T+PQ4LYTRaDQazQ3PNSWGAbjtttt45JFH8Hq9/PGPf+QDH/hA1PNz587lrrvu4umnn0YIwVe/+lW+973vMWvWLAYHBzl27JiyokUtsL7nPe8hPf3GLq9yvZCYmQCMFMQkZo6sBXvqhXL2/jpSCuvIE6fY9K+rJ5TpWLQ0n6Kl+RfV1+EULs6jcHGeWsSPodhOK0qNuV96ceztw/H1+3jhm9tpr+oMb5uxoZSV741dQiopK4H8hVOoO9wYtX3mrdPGdb7LgW3ZvPr93fj7/ZGNwUX/vGU5xKV6mLlx2g1f/mi8TF9XQv3RJkC9hgFvAGEK+tr7sPxKSAIoNxW/FbbC9g8GMB2GcooJZSmghCJCiBE1ZcdCWhLLVhM102ViByzsgM1A1yD+wQAyaE/e3zFIS3kbW766mYT0kZ/fwZ7obAenx4Gvzxfpt5QI0wgH+R0uBw6Xg6mrCvGkuNn18H4sn0XR8gKKgsKY5e9aSEpeEgf/fJzelj4cLpUpKQyBr3fkNYYmiUOxfBbEOUcsSE2EuiONvPydHeH73vLa2AEbd6KLs9sqsHy2EugMmcsGndSZevPIwI4VsKl8vYZdP9nHYLc3PAmuP9xE0fJ8LL+lSl7ZQccllxmux63RaDQajebqU3u4ge0/2kNgMIB/wI/lt3AluMLjmtbyds4frKd4WQGgFuGHI20b2wb/oB01ZrD8AUyHi7PbKln0trkx9wVInpJId2P0AnpybiKZ0/S4+3pkPPPWqSsLOfNSxYjtJSsLaDjRHHOfnuA9svOn+6ncGSmrVb2nlnX/dBOFi6MF6IlZCWz451vY/9sjdJzvIik7gYVvm0tW2fjn4J4kN4vvnRfpQ0sfr/7g9fA8N2dWJje9b8m4j+eKd1G2biqnXzwXtbg61H1psHvwgmKYHT/eG55rAdTsqwMB6/7hplH3MQwDhBKg2baM+qwOdETKmg5dwrJ8FlllGWTPyGT9x1Zx9K+n6GnqJassg4ySNJ747Iv4+nw4PA7mb5kVU4xjmALLJ/H2+sILZZYFLefaCQwGWP9Pq+hp7mWw20t6SVpYbKPRaDTjoXhZQXiMAsrtOTAY4IWvv0pLRfu4HWKEAMNphku0Z5SkKecLESzdFjyOM96pYkCGQcbUVFrL29XzQ0pKu+KdTF2lhTCayWfOHWVU7a5V8cPhCDCcBgwy+udEBMcPQYqXq/Gd0+1g3t0zOfin4+GEt/FgB2y83V5ciW7ciS7i0+PorO3GMAUOl0OVYnrL7AlepUaj0Wg0bzyuOTHMfffdxyOPPIKUkm984xu8//3vH1E24oc//CGrVq2ioaEBIQRdXV3s2bMHUPUZQ+2nTZvGt7/97at+DZqLY/Zt06nafT5sBx7wBXDGOZFSRolMfP0+Dv7pWNS+gcEA+39/lDs+t34Seh5hNOvCqTcVcvaVSlrPtYe3peYnU7auZFzHPfl8Oe1VytYwJDg4s7WC0ltGLyG1+sMrOPjHY1TvrcV0mZStm8q8e0aWZLpS9Lb2098xELVNCCWoKF5ZSNnakgseo7WinYbjzcSnxVG8PD8qqHmjUbysgOXv9nLwT8foaezFcAgcTgeH/3IS25b4er1K6zLEytZwGAgBwmEw9ebiiH26CIlBJIZTYPsnZlMrJVhDBCO+IYIn22/j9XsJDPo583JFVLA7RP6CKVTsiATcTaeJI96J7bOQUlmt25bEN+DHdChRjCfZjZTw1//3IoYhMJ0mlbvOM3/LLBa9fS7CEMzcOI2ZG6fRXtNJ85k2knMSOPiX49QdbIjMQ0dzM5UQmo8WLbt4kdxw1xsAaSlBTAAl0owptpGqxFHm1MgiVcf5Ll7+/3bQWddNIChuCgWEpG1Te7gB22cNsWiVasEtzom318dgj5ek7ARtI6zRaDQazVXCCtjs+un+8O+2bat5jH8wEF7Uti2busONFCzKw3QY5C2YQuWu89EHMgQyRpBdCXcJHz9vfg5nX6kc0W7h2+bQeLKFc69VY/ttsmdksOqDy8Ysx6i5vsmekcnydy/i8F+O4+v3YzgN0vKTObO1YvSytGUZ9DT1UrmrJmq7tCVHnzg1QgwDkDsnm3u+vAkrYF8WkUVSVgJ3f2Ej3U2qtGhSVsKEj7H8nQtJyk5g/++PMtg1iOkyw+VY3YmuUZMvQp/F5vI2zh+sxzCNqM/I+f31+Af8OEdxXY1L9ZA3PydqXnNBBMSnKzfagkW5FCzKBaC9ppOnPv9SeK4SGAxw8I/HWPV3S6NLlQCmw8RMMhnoVHPtkOOnfzDA/j8c5eYPLiUpO3HcJZY1Go0mRE9zL+e2VzPY66VgofqOikv2YMXb9LUP4Ely4+31RUodjbHg7/A4WHzvvHDZt8Vvn0vL2TZAxYC8vT4cLhNnMM7niney9h9uYsf/7KXpTGt4rOOMc1C8LJ8ZVzG5T6OJReOpFrZ973UM08ASVvT9H3Q38vX4EOYo5epRbWxLJTROW13M7DtmhJ+ad88skqYksvPH++jvGMB0O7CHJpiOcjwJeHu8OOOcLPubhdiWTfXuWkyXwfQ1JeTNn3Kpl67RaDQazXXPNbeyfNttt1FZGQno2baNaUZbuRUVFbFt2zbe//73s2PHDoComqNSSu68804efvhhUlJSrk7HNRPG1++jvaaLpKwEEjLiSS1I4Y7/t4EDfzxK1evnVS1ZS7Ljf/fRcKyZWz60HEAtDMdYTG6raB+xbbz4vQHOba+mvaqD1MIUpq8pGXed8PFgOk1u+/RaKnfV0FbVSUpeMtNWF4/b/rnheDODPV7kEGW4w+Ok6XTrqGIYV7yTle9dPKp7zJXGk6TqoMvhb5UQODyqrvlgt5e4VE/MxfqDfzrGsSdPhx8feewEt312HQnp8bRVddBe1UlqQfKE3ICud2ZtmkbtofqIMALwBh1VhBBgy6jyRrbfxkaJkDpru3C4HWEnGMuyQKj7xOe/wOQqhBh/mWg7YFN7sCGmGGbJO+bTXtNFZ7BMmMOtHFxkMGgtpVSZ1MHPuZQSK2Bx6oXy8GfAcCgHlOPPnmH27WVRbijpRamkB92Y9v7mMM445xArXhkWDWFL5BA7Xodbuc/MuTMyGZ0oA12DKpBuGlGfVyklmaVptFV2xrQ4tfzWCEeXnT/ZR3/7QHT7Ie4+MmBjuh1hsY0wDBweB2e3VXH65QrsgE18ehyrPrCUvHk5F31NGo1Go9FoYtPfOUDV6+ex/DZFy/Lx9fui3FoMU2D7g2Myy1YLSLbk+FOnqXr9PBv++Ramriyk4WgTFSFnDgGlq4oo3141RPCqkLYa+01dNY2BrkHi0+KYtrqYc0Ps2qevK6F4eQElKwpZ+sACbL+FO9F9NV4OzSQza9M0pq8t4dQL5Rz80zHaqjppCzquJOcm0d3QE26bWpDMrM3T1Rw6xgC/p6l35MYhXG63keScixduCEMw+7Yyipbl8/JDSkgOagH1lg8tj1kawPJbvPTQazSdakVKia/Xh+EwcSU4owQxI0p5DGP1h5bTVtExZvnjSEdVXGDb917nzV+/Leo81XtqY74PTadamX17GYf+fAwrYGMYBjkzM0iakhT87rGwArZy8PQGOP1COb5eH+v+6SYtiNdoNBOitaKdF77xajjeenZrJbM2T2P5uxZhOgySchLpaerFk+zG8lnYlj2qq27BolxWvHtRVLwurSiVLV/bTMWOGrw9XjwpHmoPNdBV301maTqL3z6X1Lxk7vrCrTSebKG7oRvT5SBjahppBTq2r5l89v/uCAGvhdPjwOEy8fb7sP02hkOtXxAUr44qhAliutTvc1JOIn1t/eExUE9LH2e3VTLQNYjD7VCiG69yn3O6lcuL3xtyTRIjHK8DvgDJUxJJL0qldFXRlXkRNBqNRqO5TrnmxDCGYVBcXHzBdtOnT2f79u3s2bOHl156ibq6OqSUFBcXc/vtt7Nw4cKr0FvNxXJma0V4ECkMwbTVxSy+d66q+W0aGA4T27Kx/Bamy6RiZw1z7pxBWmEKSdmJKrBsRQ8uk/OSL6ovAW+A5776SlQd9VPPnyVndhbeHh9T5mQz89bSS66vaTpNpq+dyvS1E993oGswamEdIDDox3CMnt1pW7aqQT5JdUFd8U4Kl+RSvbs2antcqof+tgH+9Imn8fX5iE/zsPSBBZSsjFiedjf2cOyp01H79bb2c/ixE2ATFfAvXJLH2n9YecME+3qb+8J/Symx/VYwE0COWmdZBl1HDIeB6TKwfHZYUCEMEf47LLQYZd4mUPdxIJYd6NB2weP5BmKLbOJSPdzzpY00nW7F1+cjOTeJv372hfDzlt8OW446PQ4sn0V3Q3RA3g5YWH4VgDl/oI7SW4pj3gMJ6fF0N/SGXYV8A34sbwBXnJOAz8L222BA0bI8Zm6cRu2hRrZ9bxdFS/OZtrp4VLen0cidl0NnbTfuBBe+Ab96f4Rg9u1lzNo8naf+/aWYr7EwBJnTIoGi/s4B2qs7AbXYMNSCNfQ2CdNQzjlDHJMC3gB9bX3hRa/+9gG2fW8Xb3voLl0+SaPRaDSay0jzmVZeeui18ELQ4UdPsOjtc6N+5x1uB5bPxpa2KhMZSqS2Jb0tfbz00Gs88MM3ccuHljPnrhl01XWTUZKGO8mlxPDdgyMXmiR01ffwl395GtuSuBNdLLlvHvHp8aQVppCaH5kTOd0OuIGdFW9ETIfB6ZfOjRhrDnQOsP4TN9Ne1UFSTiLFy/IxnSYZU9MwnQaWP3qumT0jc9zn7Gnupau+h7SilJglUofTVtVBX2s/WWUZxKV4xn2eC5GQHs89X95E0+lW/AN+pszJHjX55NyOappOtQLKVcVwmMH5hYnDpebP+QunXDBBxp3oZvOn1/LEZ59X5T9GQZgCZ5wTwzToaeqlrbIjyrFmtLms7bco31aJM86JGbARhqCnuY/ilYWc31+PtCWWzwrPvwynQe2hBs4fqKd4eUHMY2o0Gk0sDv3l+Igxx+mXKph1WxlJWQksvncu23+0B2w1vvH2eZUDxrDYrMPtYMl982MmrsWnxjHv7ohj9dwYiUhCCHLnZJM7J/syXZlGc+nYlh0u6QjBpOzgrW8HJBhcWEEbJOC1sLwW+393hIN/OkZipnLF627sCZcQ8w8EMEyBMA2EtJGo+Gj2rEzaKjpUqcQhGKbAleCi5WxbODlQo9FoNBpNhOs+MrZixQpWrFgx2d3QTICuhh52/+JgZNBo2Zx45gynXzqH4TDw9nixbYkALCDgU44JHbVdpBWmEJfiYeamaZx8rjx8TGEIFr51zkX1p2JXTZQQxrZsWis66Kzvwel2UHe4kbrDjWz+5JpLuOrxUbX7PCefO0t/5yC5c7NZfO884lI8KmtMiKiBtTCNmEEzK2Bz4PdHKX+1Estvk79gCivftzhsTXo12fyptbz00A5q9tUhLUlqfjLz7pnJnl8dwvLbCAF97QPs+N+9pBWlhmu5N59tiynIqDvUGJVtC3D+QD0VO2uYvqbkKlzR5JM9I5OeIYKYiWBbtvpQDdW/WOqzZjhN3AkurIDKXPb3+xGGUJMvQ4TFGIbDgAuIYaRUlrgpebEt2UEFOKbMygq2lyRmJ4SFPrbfCtt92wEbfwxRjZSR8kw7f7afw4+eYPVHVpAzMyuq3dy7ZtJ4siXsruJwmYhgVmZIKJZVlsGsTdPY/t97wvdd3eFGWivbuem9S8a81uHM3zKLljOttFZ04E5wIQzB0gfmM/u2MqyAzYI3zWLfb4+oaxLqGk2XSWZpepQtvNPtwHAawSwTA2GobPAQpsukYOEUuht7oz4TSkAY/dMe8FrUHmpg2uoLC001Go1Go9GMj72/OYyv349/MIC0JIZDcOTxkxQuzuX8gQZA/c67k11kFKdRd7gBOWQMBjDY5aXxVAsFC3NJK0iJynyed/dM9v/uSHiRWwKuOCcIaDjRHC675O31cegvJ3jLN28flxBB88bGP+inv31g5PaBAMk5iRQGy/KEcCe6WXLffPb++nB4myfZzeJ7517wXFJK9vziEGdeqQCp5uRz75oR0xkS1Lx+2/d2UX+0CVDziqX3z2fW5ukTucQxEYZgyuysC7YLleoI4Upw4usLzpeESf6CKaz6wNJxnTOtIIUtX93Ms19+hb62fnUMlMBF2hKH28ThdhDwWvj8PpXp7VeloeuONgZdeCTePl/Q7VHgjHOoUrJuB75+v5ozBOcult/G2+OlaHk+5dsirsqm24HpUG2az7RpMYxGo5kQQ+OiIaQt6axVjt7FywtIyIjn3GvVWD6L0y+fw/b7Ryb7CHjmS1spWpbHyvcsJukSnL80mmsFwzRIyk4Ix2N9/X6VtBoa2IfidWMkGIYJ6mjsgE1gMEB7rw9hGiOcXmxLgm0hgOQpidz9xY3EpXjY+ZN9nHy+fIgQ1gzPCxIy9FxAo9FoNJpYXPdiGM31R+3B+qiBoX8goIJBqBsy7PgSCvwGrTeHKpuX/c1CMkvTqdlfj9PjYPq6ErKmXbhcjhUcWA61dR5uaRwYDICUUU4sjSeaaTrdMmKh/XJSs79OZVkEObe9mrbKDu750iZS85Ppqu9WpV5siWEaONyOmHXAD//lOKdeiAiFag81MPjdQe7891uvWN9HwzAMNj+4Bstv4R8I4El289zXXlGL90FhjzCUer1q93kWvkUJmiZa37zxRPMNI4ZZ+NY5NJ1qobe1X2UxOk1VJolxlDCSI/+0/JbKNDBgsHswLBoxHAauBJUJaXkthMPAFe9UlpzjwA7YzNw4vprOQghWvHsR2763CytYRiDkduPt8416YVJKXPFOBIL+jkFe/cFu3vbQnVFuSLlzs9n04BpOPn+Wgc5B3Ikuelv76W3pxZPiYdamacy8dRrPfHnriPOUb6ti/pZZE1pYcie4uOPfNtB0qpX+zgGmzM4iPjWOgc5Bnv/Gq3Q39ATFLQKH24HD7SA+PY41H4kWdTrjnEy7uZjjz5zG8qnvRwR4kj1kFKcydVUhMzdNp7uxhz2/OEhLeTueZDfuRBc9zb0MDKigeSj4blxmK3uNRqPRaG5krIBN67l2lZUZHNNaPuht6WPammJSC1Ko2V+H0+2gbH0pCKg/1gTDnB6llDg9sZ0nFr51Dr1t/Zx6/iygftMN08Db60OY0c51dsDm/IEGZm0a39hL88bFFe8ieUoi3Y3RroruJBeJmbHHtLM2T2fK7CxqDzfiindSsrIAV/yFHQWr99RyZmtF+LG0JceePE3u3JyYgpSTz50NC2FA3bd7f3OY/AVTrvpi6fD5phACd6KLBW+Zzcxbp+FJnlhpsfTCVN7xvXuo3FFD4+kWvL0+knMSGewepHpvHd4eb3ieZZuS3b84iOl20F7RES6hZpgGQihBjn8gwLK/Wchg92DM83n7/Kz7h5twehycfqkCwyEwjMh4Xy8+azSaiZJWlELDseaobcIQpA4R6maWppNZmo6v38+J586qNkSHUgKDAYSAih01NJ5sYf0/raJgmBBTo7keWfi2ubz2P3uwrciagdPjUHHMYUKW8TDUSGbU/SVgCvrbBzj6xCmQkDMzk4zSNF7/2QEQIrzGkVqYQt58XSJdo9FoNJpYvKHFMBUVFXzpS1/ikUcemeyuaIYwPOBr+SMuEwGfFZ5JhUqBACRmJZCYlUD5q5V01nWTXpJG8fKCqNI6Y+Hr97H7F4eo2VcHQMlNhax410KccU7SC1Oj2tohMcAw15Xuht4rKoY5+Xz5iG2dtd00nGhmzp1l1B1pjOpTxtQ08uaNHOSe3VY1YltrRYdy1pmkOrshFw7Lb9FwojlqxC9tqWwgh5AzM5Mpc7JoPNES3mY4DQqX5nF2ayXDib+BsmATMuLZ8rXbqNlXx0DXINkzM3juK9vo7xhASHlhQcww7ICNGbQCDwVoQ9mIhmEw0D0Itnr9vd3e8bp+YjqNEXa5Y5G/YApv+dYdHH/6DEcfP4m01T0x4nyhSIsAT6I7SuQx2O2l6UwreXOjPxfZMzLwDfip3FVD9e7acOmj/rYBGk+0MPfOmXQ3RS8YQKSEwUSzrIVQGannD9Sz99eHEUJlbXc39ACo+tpS4h/0U7SigLV/v1KVMRhGXHocwjAQhnLvMV0mrjgHt31mbXiBIr0olTv+3wYCPgvTYfCnTzwVdvGRUuIfsHF4HBQs1sEnjUaj0WguF6bDUOLbGAOjtspOFr1tLoveFnHW6O8cwOlx4OuLtjRPzIwne8bogv5lDyyg/mhjVPkVYYpwGZehxNqmuTFZ+sACtn3/9fDCijAES+6bP2b53NSClKgFz/FQe7hxlO0NMcUwtYcbRjaWUHe0iVlXWbxRtn4qZ7ZWMNAZEZskZsYza/P0cHb1RHG6Hcy4tZQZt5aGt1l+i+6ml8MLzIbDxBnvpLWiAyHA4VIlYaWUKi4SdI8yTMFA5yAFi3OjHHFDFCycAsCit8+l7kj0d0RidgKlNxdd1DVoNJobl0Vvm0vL2baoUkkzN5ZGOdiG8PZ6cbhMfMHExuFIqf7j6/Wx99eHyF84RTleazTXMVNvKiQ+LY5TL5ZTvq0K02kE49023j6fioGOEQYVhojEXYcJ24c+F/2ESrrz9fs58cwZVQJ+K2ROT+f2z63n1Avl9LerRLx598wcteyiRqPRaDQ3Om9IMUx1dTVf/vKX+eUvf0kgENBimGuM4hUFHPzz8ahgsJRBUcyQcZ8juDhsOg1Kbynk2S9vjXJxObu1kk2fXBPl8jIa2773OtX76sK2w2e3VmAHbNZ8ZAVTby7izCsV4dqfhsPAhrBAIETm9PSRB76MeHti1xgf7PFSOq+I2z69lhPPnQ0PcufeNSO8qD8UOxC7hM3FqNQvN+3VnUSbwyvsgM3Um6KFTRs+cQunXzpHw7Em4tLimL15OvFpcZw/UB8V7HPFO5mxfuoV7vm1hcNlhgOcVsBm7T/exP7fHqanuQ8roEocxZxEAQh1jxumAVK5DBkOA9uyMRPUPW84DIQQ+Pp9eBLdYKCOeQFty9DYhjAEvS0TK+cUnxpHfFoczjgnhtMgMBjADthICcIhcDhNbEv12ZPkwvKPvKeHi0oGugZ54Zuv0lXXw2C3F2nbOOOc4e+X+qNNdNZ2kV2WESW+AnC4TdIKx78ocPLFsxz+8wkGur3Epcbh6/WGAz6DPSpYZAedrkB93VXuqKazppOZG6dRtr4Ub5+PrvpunAkuTj1/Fmnbqg63bRPwBuhpCbD1v3ax+kPLo+xPHS6TlvI2Bru9ODxOAl7lcIUhsC2blx/aQUZpGnPuKJuUkmkajUaj0bzRSC9Jpb+9P2qbw23GLO8YnxrH6o+s4LX/2YuvX7neJWYmcNcXN465OOROdHH7Z9dz+NETtFW0k5ybRPaMTA7+8VhUO1eCi6JleZfnwjTXPQWLctny1c1U7qrBDtiUrCyc0Jh2vIwmGhltuycpttuKJ+nixCeXQlyKhzv/fQMnny+n83wX6cWpzL6t7KKFMKNhOk1ScpPD5UdCn3d/MC4BwRBIaJ4VXEcL+CzOH6hn+bsWMveuGZx49qya3wkoWzeV4hWqBFJ8ahx3/futnHjuLJ213WRMTWP2bdNxxcd2nNJoNJrRyCxN556vbObcq1UM9nopWJg7qqNLYmYCKWEHa2tEPDeE3xugvaoTX58fd+LV/67XaC43OTMzyZmZSU9TLw3Hm/H1+TEcAne8i9TCZLoaehjoiO3q5k50BcvSB3B6nNiWjWXZCNPA4Tbx9Y2cQ5gOA2+PT4lmhcB0SYQQtJa3M9AxwIaP33ylL1mj0Wg0mjcEbygxTG1tLV/5yld45JFH8Pv9SCm18vwaxJ3oYvOn1rD/d0dpOduKK8Gp7L6Jnjs5XMoKPD7Ng8PtHFHOqPlMK6/9zx4CgwHi0+OYtXl6TOeTvvZ+qnafD4sDpCXx9fmo2FnNTe9djDPOye2fWUfFzhraqzuJT/Vwdlsl/UMGr7M2T7virir5C6bQVd8Ttc1wGOTOyQYga3oG66ZfuBRU8YoCzm2vjtqWlJNIenHqZevrxeJJdmM6DJzxTuUGY0uEIUjKSSR5SlJUW4fLZO6dM5h754yo7Xd8dj1H/3qKtupOUvOTmX/PTBJjZKrcCFTvq2Xvrw4z0DmI4TSYvraE+Vtm8dd/e5G+1v6YAqiEjDjkML2UlBJDGiNK6UhbggECEVN4MjoC02GSPTNzwtcUEniYDhMz0URKGbQNF2FXqdTCFIqX5XP40RNR+6YWppA5LVq0duSJk3TVqc+VDKp5/AMBTKcZDkAPdHuZfVsZrZUdBAYCoUtgyf3zx2URD3D8mdPs+N+9yODL5OtVYr+4VA9CCIQQ+AcDI0RKlt9Wzk11B9n7m8NYfjvs7DIa1Xtq6WnqZctXN0cF7P0D6nfP4TIxXQZI5Ujj7fXRfKaV5jOtnN9Xx91f2qQD5BqNRqPRXCKzNk2n+Uwbli+AlGrsajpNcoc41AV8VtixZfqaEgqX5NFyto34tLhxj82TcxJHlFN0J7g4+teT9LUNkD0jg2XvXDjuMYvmxiA5JzFcgvZKUbZ+KmdeqcAeMk9wxjmYtro4ZvtZm6dTd7gxajycmJVA4ZLJEXIlpMez7IEFV/w8GSWpVO+pjdomDBHO3jZiJLggVSKJFbBZct98Zm6cRntNJ6l5ySNKICVkxLP8nQuvWP81Gs2NQ1JWAovePveC7YQhuOl9S3jlu7swHQEsv4UvRlKWACzLpruph6zEC8czNZrrgcaTLXTUdGGYBpZtqbJJUrLxX9fgcJv8+gN/IeC1IgmDQiCE+tx44t2UrZtKwGvR09RLb1sf/oFAeP3KtmywVeKjMKJjsXbAwtsrw+Li9ppOSm+JPebSaDQajUYTzTUlhgkEAlRVVdHW1obL5WLq1KmkpqZecL+Ghga+9rWv8ZOf/ASfz6dFMNcB6UWpbP7kGgBe+s5rVL1+HstvI5AI08BwGiTnJlG6qogZt5ay77dHgFDZDz+W30ZKyZmXK8KDwMpd57n9s+tIzU+mvaaT+LQ4EtLjqT3UENMlwz8QCJdEcrgdzNgQsTOefccMqnafp79jgNw52WTPmPii/kSZ/6ZZtFa003ymDVCOOCv+djFxKZ6Y7fs7BwgMBkaISJb9zQK8PT5qDykb6pT8JNZ8dOW4PxP+wQDHnjxF7eFGPIkuZm6eTtGSPHpb+zjx7Fm66kIZZ2XEpcbu22gkZSdStDSPmv31OFyO8Gd1PJPt8DFyErn575ZN6LxvRHpb+3jtv/eGBS+23+bc9mqmzMkmKTuRvtb+Efs43CZFS/Kp3qvKhUkpCXgD2JYkKTuBgS5vlNNSWlEKfa0DShBjCCUmiVG2yJ3owt8fQNo2CFViadbm6WSWTtxNqXBJHin5SWEBixCC+DQPc++aia/PT2pBMlNvLsLhUmKWUy+W4+vzU7Aol+XvWjjiPm880YwVsAGpHHD8FiCxAnZQNGKy/7dHlNhOQEp+MsXL8yhZUUhKXvK4+733V4fDQpihePuUu47pNrB6Rxe5WF4Lyzu2CCaEtCU9Tb1U7qxh1ubp4e2d9T14+3xqQWKIutAwRfiz1tvaT+WuGmZunDbua9NoNBqNRjOSkpUF1B1uoGp3ZJG7bP1U8hdOoXpfLQf/cIye5j6Sc5NYev98Chbl4k5wjZplPRHK1k+lbP1UZFBYPpy2yg7aqztJK0q5qPGYRjMeUvOT2fSvqzn86Ek667rIKElj0dvnEp8W24Uwd0426z+2imNPnaavrZ8ps7JY9Pa5Y5ZveiNQtr6Uil3n6RyS3DNldhZd9d1YPntEQkII27I5v7+OkpWFJGTER7lCajQazWSTOyebtz10J7UHGxCGwJ3o4vmvb1PuFiLiCe30OOiq7yFrmhbDaN4YnN1WCRBMMoskmtUerGfWpuksfOscjj15GtuyEYaB6TSYvqaEuXfPJD7VgzMuso+UksaTLQx0DDBlbjbOOCftVZ04PSbPfW0b/Z2DSDtYShGQlo1t2RimMeHylhqNRqPR3MhcE2KY/fv385WvfIUXX3yR/v7oBdylS5fy4IMP8o53vGPEfv39/Xzzm9/koYceor+/P0oEI6Vk+fLlfOYzn7kq16C5eOKSPbjiXWHXBiHUwm1qQTLt1Z0cfvRE2MXA2+tDWnbYStgOqNIhDreqtf36zw/Q09hLf8cA0pJklKYzbU2xWgQf5pLhTnCNaoPs9DgoW3dppXfaKjtoq+4grSCFrHE4urjilQ16y7k2BjoHyZmZiTtxpJW0r9/Pjh/vVWIXCakFyaz+8Iqw9bUr3sWGT9xMX3s/Aa9FSm7SiGOMxSvf3RlVLqbxZAsr/nYRRx47yWC3N7ytek8td39p44SzUG/58ApSnjxFzf46nG4HZRtKmb6mZELHuFFpPNnCue1VWAEbwxQxnV+qXj/P/Htm0niiOfoJoUp/WQEby2/h7fOFvy9d8cqdyT/oxzINnB4HidkJ9LX0MRgs9WM6DaRlYwRFKIHBAMIAT5IHYQjicj0ULc0nLtVDwaLcCy68dDX0cOhPx2g5105STiIL3jyb3DnZmA6D2z69juNPn6b5dCsJmQnMvWsGGSVpI44xf8ss5m+ZNeo5epp66aztji5BFozICENgukwcbjPiOiWhq66bwVmZExLCHHn8BN5eX8znVJknSWDQYoT91SVg+S162yK/l3VHGtn/2yPKWtVvR53HtpS7jjvRFRTETKx8lUaj0Wg0mpEYpsGaj65k7l0z6ahVZVbSClJoq+pg+w/3hMX43Q09bPv+69z+ufVU7znP+QMNuBOczNw47ZKzOYcLYaQtee1/91L1+vnwtpKVBdzyoeVhFwqN5nKSMzOL2z6dNe72BYtGL7vxRsUV7+TO/7eeytfP01XfTUZJGkXLC+hu6OHEM2fobu6l4WiTck4NIoJzt/aaLkpWFo5xdI1Go5k83AmuKDew6Wuncu61qqDbrYq5mA7jmnCq1mguF4Ehv9exti+5bz6eJDfndlQjLcnUVUXM2zIrKvkwhBCCxMx4Tr94jt2/PKSSAe+cwfS1U3HGOXF5LaQt8VpSJSCi1rzSS1KZepMeH2g0Go1GM16EDCkQJomHHnqIT33qU0gpGa0rQgg+8IEP8OMf/zi8bffu3bzzne+kqqoqLIIJ7b9hwwY+85nPsGnTpqtyDZpLo7Winee++gq2FXn/A94ApssML9YbpkCYgt5mtYg7tK624TDwJLnV4LDPq+psD3GCiU+Px7ZsvH0+ZFA8YDgMFr9jPkvvn3/Zr0dKyc4f76NiZ014W+GSPNb+w8rLEoTe9fB+yrdVRW1Lyk7gzV+/PWZm6ERoq+rg6S+8PGK7M96Bv3/kYH/5uxcya9P0Eds1l589vz7E4b+cQAbryRoOVXonVDooROGSPNZ/bBXPfHkrNfuUA8zQrBx3shvDMAj4Avj6/ZhOE1e8k4GuwYiAItjYk+RGCIHltzCcBrNvK6Otsp2O890Mdg3i8Cg9ZcityZ3gZMbGaSx750Kc7tG1lt4+H0985vmwuArUZ/KOz60nY+pI0cvF8sI3X6XmQH2k9FH4NXCx/h9X4Ul288I3to/YzxXv5P4fvmlc57ACNn/62JN01nfHFLoIU+BwOwgM+EGImC5VF4MwBHFpHnJmZDL3rhns/vkh2qo61G+pNdpvKZguB5s+uZriZQWXpR8ajUaj0dyoVOysoXKXGu/nzMqiq66bzrpufAN+uht7R5Q+8SS7w2MfKSV2wGbOnTNY9jcLLptQpWpPLdt/uHvE9jUfXaEX1DWaa5jdvzzI0cdPYQcsCCYiOOOcrPnoSr3YpdForhu6Gnp47quvRCULTVtdrN2dNW8oyrdXseun+6M3CnjTVzdPKLEOVEzxiU8/R+8wd+81H11B46kWzm5VLjTK2dvCMAUr/nYxZeunjhl31Wg0Go1GE82k/mr+/ve/58EHHwSU4GWooCW0DdQP/s9+9jNmz57Nv/zLv/DYY49x//33EwhEL3Bu2bKFz372s6xcufLqXYTmkpC2xNfnY+bmaTSebKGvdYCknARazrZFlTuxLUlKTiKD3coZhpAzhQCC94wVUJkHIaV0iMHuQaavnUrD8SZ8/cqus3h5AYvedmVqqNcebIgSwgCcP1BP5evnmXYZankOrzcO0NPcR1tVR5QbR8AboDZYkz1/Qc64HFz6OwZibvd2+2LaN/c09U6g55qLpa26IyyEAWWLadkCicThdkR9V0op+ePHnqS3pQ9hKvGFQIknbFti+20Mt3ovBWD7LXz9RAs5gn/7BwO4E1wIU+AfCFB3uIGy9aUkZCSE70NVlkfZdQb8NuXbqrAtyS1jBDsqd52PEsKAclA5/fI5bv7gxQVJuht7aK/pIq0whZTcJALeAI0nW0aUDFLXLUjNT44S4A1lIqIyKygqcsU7lR3wMOLT4hjs9gYFfJdTeyrpbx+g8vXzVO2pRRggLTnmKUJ9aD3XrsUwGo1Go9FcAkceP8nhR08AYNs2516rxuFx4HQ78A34sfwWnkR3eExhWzbdTb244pxYfkvNSaTk8KMnqD/SyMYH15CUlXDJ/RrhDBik4USzFsNoNNcwC948m/rDjfS0qOQfIQSZ09MpWpY/yT3TaDQ3ArKvC1wehHOkO/VESMlN4p6vbOLc9mpVdn5eDoWLbyw3MM0bn2mri2mv6uTM1gqkLXHGOVhy//woIYzfG+DU8+XUH20iLtXNrM3TyS7LHHGs+iONI4QwAKdfrmD9x1bR3dBD06lW5SCTFc/qj6wgb27OFb0+jUaj0WjeiEyaGMbn8/Gxj30MiJTFSU5OZtWqVWRkZNDa2squXbvo6ekJP//lL3+ZtWvX8sADDxAIBMKOMA888ACf+9znmDt37mRdjuYiGOga5IVvbqejplOVLHGarPzbxQhD0FbRMaK95bdwxTsI+CxMh4kg6CATrDEel+Khr60fyxq+p8CV4OTt/9/dtFa2k5AeP+HSQROh4XjsIHTj8eYoMUz13lpOvVDOYI+X/IW5LHjTrHEJVkarKT7UbrG9upOXHnotLDhwxjlY/7FVTJmdPeaxs8syVSkdX/SLmDEtjY7qrpHtZ4wcyGsuP2dfqYwIYUIbbYlhGsRnxDPQPkBcqof4tDhqDzYAKrsAW2I6TZxxToSAwa7B8DGHZiDHKrcU2m75LXx9Kqunq76HA388SnxaXFi4GBLCQOQerHr9PCvfswjHKFkK3l5v7O09sUsNjYWUkj2/OMSZrRXhbdPXlbDiPYtwehwMdA4qo5uwvkUgDEFf+wD5C6aQWphC5/noe7t09fhFa654F+klqbSWt2O6DSxv5LUUpqC/rR+Hy8EoupsJIUwRFt0MdZiRloTYb2E0EmxbcmZrBUvvX3DpHdJoNBqN5gYk4LM48eyZ8GPLa4GUBAYDON0OTKeJ5Q1g+a3wWEhKcDhNpJT4Bvxhgay0JT3Nfez7zWE2fPzmS+qXbdl4UmIvYsWnxV3SsTUazZUlLtnDXV/YSPmrlXTV95A5LZ3SW4pjllTQaDSay4VsqMB+9Q/Q2QwOF2LuLYiV90QlJ06U+NS4MctZazTXO0IIVrxnEfO2zKSvpZ/UgmSccdGu3a/8504aT7aEH5/fX8+t/3c1uXOi4/K+gZFJdQD+fj/uBBe3fXodHee78Pb6yJqeHl4D0Wg0Go1GMzEmTQzz2GOP0dLSEh5g/+M//iNf//rXiY+PD7fp6+vjU5/6FD/84Q8RQtDd3c2WLVvw+dSC6apVq/jRj37EggV6Ue965LX/2UPjyWawJaqWrMHuXxzk9s+sHeHkYFs23Y29ykmm3w9CDQpzZmeRNy+bpJwk8hdN4S///Ax9rX1R53G4TbKmZ+CKd14V9XRCRuxgc3x6ZHvV7vNs/9Ge8OPuhrO0VbRz+2fXX/D409eUcPzpM1Hb0otTSStKDT/e/fMDUc4b/oEAu352gLd8Y+xSSu5EFyv/djGvP3IgLJBIzk1i3T/exM6f7Kf5dGu4bcHiXAqX5I04RndjDy3l7STnJJJVlhH1nBWwOfX8WWoO1OOKczLj1lIKF488hiYaO6BKI0nLHvG5EBK2fHUzCRnx/OGf/hp+zuEy8fktbL+NiFOTNdPtwHSqgKphGjg8KjsZiF2+RxCuXS8MgW3Z+Pr9DHYOYjgMDIcRdiIxHAbCjGQ/j1UOqGBhLkceOzlie/7CKeG/zx+s5/CjJ+hu6CFzWgZL7psX5XwUovZQQ5QQBqB8WxX5C6YwY0MpB/94LHyNAKbLwOlxkDVdHWvDx1ax6+H9NJ5owXQalK4uZvHbxy+s9PZ68Q8GVBm2YdccKlcUCIxQ6F0UwhA4PY6YoiEplVjmQqobO2DT29JPV0PPFRUFajQajUbzRsXb68U/pARj+Pc/6NBnOlR5E4JjbmecgyVvms2xv55S4/Mh44XQuKz+aNNF98c/GGDvrw9R9fp5bEuqxAGnEZ5nuxJclK2betHH12g0Vwd3oou5d82c7G5oNJobBOkbwH72p+ALOkQHfMjDWyExDTFv9eR2TqO5DhjoGKTuaCPN5W2UrioiLtVDf8cAe355iKq9tRiGKptumAa2JTn+9OkRYpj8+VMwnIZytR5C4dJIrDytMOWqXI9Go9FoNG9kJk0M89xzz4X/ftOb3sR3v/vdEW0SEhL4/ve/T21tLU888QRCCJqamhBC8A//8A/853/+J4ahM2WuR3z9Pqr21A4JBkssn4W310d/xyBz7ijjxDNnw+0tv4XpMjGdJp5kA9uySc5N4s5/20BnbTfuRBdxSR5u/3/reeZLWxloH0AY4PA4yJ2bQ+nNRVft2kpXF3Pi2bNRYhRXvJOy9ZEg9Ilnz47Yr/lMG60V7TEX/Iey8G1zsfw25a9WYvlt8ubnsPK9i8PP+/r9tMZw1ult6aOnuZfkKWMvgE9bXUzevBzqjzfhTnSRNy8HwzTY/Mk11B9torO2i/SpaeTOyR6RLXLgD0c5/syZsGAjb34O6z+2Kqxc3/G/e6PKPNUfbeKW/7OM0stQPuqNTNGyfM5srcQ3zFHF4XHQ3zHA2VcqWPjWOVGTJ9Np4ohzEhgMIIGE9DiS3Yk0n21DWhLTZZKan8zSBxbQdKqFQ48eH+EuIm0JAoTDwOEy8fdHMhYcbgeW30IYqhSTbdl4e324410ULs0bkRUxlIypaSx82xyOPn4yXKqoZGUB09eWANBS3sa2770eXlxqOtXCi9/azpv/43biUj1Rx6o/EnvxqO5IIyv/djFWwObI4yexg98hrngXy9+1KOzClJiVwOZPrsXX78NwmDhcE8uyOPzoSXoae3ElOEd1thlLGDQRbL8SsoyGHC6EGSYqHErFjmoW3zvvsvRLo9FoNJobifi0OJKyE+hpVgJ8w2GoMZEZEaA43A7W/P0KUnKTScxOwOl2kJSdwPYf7cYbrDIqTCM8XvIkj3R0sQI2p14op/ZgPa54FzNuLSV/wZQR7fb84mCwRKvE8tuAxJXgIiEjnrTCFOZvmaWdYTQajUaj0URTfTIihBmCLD8AV0AMI6VEHn0VeWo3WAFE6ULE0tsQjtFjRxrNtcrxp09z4A/Hwo+P/fUU6/7pJnb+ZB9djb3IgI0FWH4bd5ILwzBixvM8yW5u+btl7P75QZX8i0o+nXe3FsdqNBqNRnM5mTQxzMGDB8N/f/KTnxyz7ac+9SmeeOIJQLkbrFq1KqZ4RnP9MFr2o+W3iEv1sPT+BRQtyaf+WBOm2+TA74+Gg8tCCEyHSVd9D3/+l6cZ7PKCgKIledzyoeW868dvofZgA+01naQVplC4JC+qJMyVJi7Zwx2fW8/Rv56ivbqT1IJk5m+ZRWJmQrjNQOfICafaPhhz+1BMh8Hydy1k6QPzkcEyOENxuE1cCaqUylAMh4E7aXz1f+NSPVElnUA5iRQsyqVgUex6vy3lbSMca+qPNnFmawWzbyujp6mX6r21I/Y7+uRpLYa5AAULc1n0tjkc/NOx8OTI4XbgCi6gtFV24Ip3kTMrk6ZTEfcep9tBdlkGGz5+M6/+cDet59rxJLqxAzYSKFiUS8mKAkpWFFD5eg09TX3Ylo1hGrjinQhDkFqYQldtN/7BIfeTUC4xAe8QxxOpxBhSSla+LyLOGo0Fb5rN9DUltFZ2kJyTSGp+pLbu2VcqRwhI/AMBKnfVMOfOGVHbYy0egfocGqbB8ncuZMk75lF7qAFvn4/8BVNISI8f0X48JcpiUX+0EQA7cHkEL5eNMYQwIYcfjUaj0Wg0E0cIwYq/Xcwr392F5VNiW2lLjCFj8tKbiyheXhAlHC9ams+9/3UPL3z9VVorO6LKn8y+vWzEeYaLyGsPNbDmoysoWVkY3ub3BqjaUxsWJYcs+wKDATZ8bBU5s7Iu67VrNJqrR3/nAOcP1GM6TYqW5l30fEWj0WguN3KgBwwHwj1+sa088Dxy33NqrGJbyAPN0NOO2PSeK9hTjebyM9jj5dCjJ6K2+fr97PzJfvraBjBNA38oKCclltfCiDPImZUZ83glKwvJX5RLW0U78WlxF0xi1USwbRvLsrDt8dSO12g0Gs3VxDAMTNO8ZgxNJk0MU1dXB4Db7WblypVjtl2xYgVutxuv14sQgn/5l3+5Gl3UXEEcbgdOtwNfINpJwZ3gImu6Kq2TVZZBVlkGAW+AI4+dxPJFFt6llPj7fQw4BAIBEmr215P42AklpFmWT9Gy/Kt6TUNJyknk5r9bNurzufNyOLe9Omqb6TLJnhl7YBwLwzQghomFYRrMvq2Mw8MG5tPXleBOuHIBtIbjsQVO9ceamH1bGX3tAwS8FgFvQC0YmAbOOAf97aM7XQzH1+/n2JOnqDvahCfJxezbykYV57zRWPS2uaQWpLDtu7sQpohaXEktUJaZq96/lK3f3UlXXQ8ASdkJrP7wCgI+i8aTLVi+AFKqe83pdlCxs4YV71mEEIK0ghT8/YER513w5lns/+1Ruur9Q0oiiXD5JAChPoIIITCcBr5ePwlpF76m+LQ4imJkKg899oW2T19bwsnnz0aVK3B4HGGXGVAuOcXLCy7coYvAneymp7kPMZahzDWmk0FC7tzsC7fTaDQajUYTk7x5Obz1W3dQs69OifKX5dNV30NXXTcZU9NGdXp0J7i44982cPSJk9Tsq8PhdjBjQ2mUgyRAd1NvlBAmxNEnT0eJYaRlK5Frnz8shAHlSrfvd0e4+wsbL9MVazSaq0nN/jq2/2hPuHTx/t8dYeP/XX1BF1mNRqMZN8VzwB0P3uiYnMgpxt79FLjjEGVLEQmREi2yuw1762+hsQIME1G6ALH2PoRz7MQ75QqzHSw/eAcJBUnkiZ1YSzZjJGdohxjNdUNHTdeIskYA3Y09mE4TYQgccQ4CAyoJTdqSpOwEFr5lzqjHdLodTJmt43TjwbIsent76e7upq+vDymvtaCrRqPRaEIIIUhISCA5OZnExERMc2JVGS4nkyaG6e7uRghBZmbmBZVBpmmSlZVFba0KCC5ZsuRqdFFzBcmdl0NSTiI9Tb1KHCGDjifvXjiircPtoGz9VE49Xx7eZgdsDKephDBDqN5bx9L7F1zx/l8qi++dR1tlB5213YBybbnpvYsvm1hlwZtn40lxc+61auyAzdSVhcy6bfplOfZoxKXGzgiJD2739nrxD0QC9XbAwttrU7Bw/GKWrf+5k+YzEeeTxpMtrPvHmyhaOnnCp6tJ8fJ88hZMofFEc3ibO9HF3KBbSlJOIhv/dTV7f3WYjpou0opTsQM2VbvP4++PCM8CAzbSsjGcke/euXfNpPnszijhxpTZWZQsL6RwUR5HnjjJ3l8dRkqpgrKhdmLI/6QEqcqCXQqFS/PU4tLw7UvyRmxLyIjntk+v4/BjJ+io6SStMJUFb5lNYlbCiLZXgjm3l/Fq+W5Mh4kwAyNLFU0WY3RDGIKsaRlXry8ajUaj0bwBiUvxMHPjtMjjZA9TxuHE4nCZLL533pjlCntb+2Ju7xu23RXvImtaBjX7ooUzhsOgvaoT/2AAp2fSpvwajeYisAI2u39xMCyEAfD1+dn7m8Pc+f82TGLPNBrNGwnh8mDc8XfYr/4BOhrB6Ya0KcjDr6iMJ0AefBHj7o8gslXpefuFR6A1GKuxLWT5QXB6EGvfMea5ZFcLdLdBOCFSqCBSwAe//jK2Ow4xdT5i9dsRcYlX5Ho1mstF8pTEcMn4qO25SfS1KnGZ0+3AdBrYfpsZt5Zy0/uWjHB210ycgYEBampqtBOMRqPRXCdIKent7aW3txfDMCgqKiIubnLKeE9aZCzk8uLxeMbV3u2OqMwLCwvHaKm5HjAdBpseXMPeXx2i4UQzcSkeZt9exuzbRlqEAyy9fz5xKR4qd9YgkWSWpo9wVgEVXL4eiEvxcM+XNtF4soXBHi+5c7JHLfdyscxYX8qM9aWX9ZhjUbKigCOPn6S/PVICynAazNqkFgkqdtTgjHfi748IYoRg3G44LeVtUUIYACSceObMDSOGEUJw6z/fzLkd1TSdaiUxM54Zt5aGy/4EvAGe/49X6W1WCyW9LX3UH2nEcBgI00BakcmC5bPJXzAl7DBTsCiXjf+6mlPPlzPY4yV//hTm3KVENqbTxNfvx5XgxD8QQFp2RGshiQhiDEHh4jwSMkaWIZoIU28qpLW8nTNbK1QpMJfJ4nvnkl6cGrN9enEqGz5+8yWd82IpXl7Amr+Hk8+fpb99gIGuwfDk95pEgCvBhd8bYHKGHRqNRqPRaC5E5tQ0HG4zuiQlxCx7tPJ9i6k93IDtV22FaeCMd+KKd2I6rw07Wo1GM366G3pUKehhtJa3Y/ktvZim0WguG2JKCeZ9n0T2dSFtC/m7/wgLYQDwDSL3PIW456PI9kYlhJE2WJZqZ5jIs/uRa+6Nci8eihzoRf71h2APdfqVkQQeyw/Sjaw4DH4v4q4PXbHr1WguBwkZ8ZStn8qZlyvC2wxTsOI9i6h6/TyVu86rbYZB3uIcLYS5TGghjEaj0Vzf2LZNTU3NpAlirss0scm00tFcPlJyk9j04BqkLRFG7ElTCMM0mHf3TObdPRNQFoMt5W10N/RGtZu+bmqs3a9JhCHeUKVKnHFO7vjceg4/doKW8naScxKZd89M0opSAVXixuE0MZMNLL+FEALTaeKMG9/XUH/nQOztHbG3v1ExneaoQqeqPbVhIUyIgNfC3zmIK96Jr98fFsQYToOZG6OPkTc3h7y5OTHP21bZgekwMZPU968VsPD1+TFMAxmciJTcVMjqj6y45GsUQk0i590zk+6mXtIKU65oia9LpWRFASm5Sez43720VrRPdnfGxBnnJKMkleQcnW2l0Wg0Gs21iivexfJ3LeT1Rw6Gs07jUj0svX/+iLZpBSnMuaOMs69UAsFSqsDMTdPCf2s0muuHuFQPhsOIcoYZul2j0WguNyIhBWrPIG1rxHOy+TyypwPZ3QYBP/iCMTgZzIxyepH7nkMO9oFhIGYsh4w85IEXkCd3QV8XeAdgtAVs24bBPhAG8ux+rL/8fxjz1iBmjF56XqOZbFa8ZxFZZRmcP1CPK97JjPWlZExNo2BhLjM3TqO1op2U/GRy52SPKhTTjB/LskYIYdxuN8nJycTHx2MYhn6dNRqN5hpCSolt2/T399Pd3Y3Xq5I9QoKY6dOnX3Wdx3UphtG8sbiQEGa0fTb+y2r2/OoQ9UebcCU4mbV5OrOvcCkgzdgkZMRz8wdjT1iLluXRdKoFIQQOl/rqEYagYNH4yiTlzMiKGRTMnRdbvHEjMtSVZyjuJBd2QOJJcmPbNgJBXKqH7BkXtvIPkZKbRFtFR/ix6TDxJBtMXzeV5JxEipfnk5R9eQUW8WlxxKdd+/4lvn4/L3zzVTrruscsT3RNIGVYVKjRaDQajebaZfraqUyZk03toUZcCU6KluThcMeevq987xISMxOo2n0ewzSYtqaYWZv1vEijuR7xJLkpWzeV0y+di9o+755ZeqFHo9FcOVKygvEMGXGHkRL8g9i/+bL62zcQdnoONgC/F7n9jyAMiEtEHt+BKJyFrDmhmvi9yv1lVCRYIdcYAc012Ft/g+hogqR06OtC9rQhTAdi5grElOsnCVLzxkUIQemqIkpXFY14Lmt6BlnTdWnyy0lvb2+UECYtLY2cnBw9LtJoNJprnPj4eDIyMmhqaqKjQ60t2rZNb28vKSkpV7UvWgyjuW5JzErg1n++BduyddbjdcCMDaW0V3VSsbMGaUuccQ6Wv3sRiZkJ49rfk+xmxbsXseeXB7EtNflOyU9i0VvnXsluX/PUHmqg9nADrngXnmRV/gbA4TTDQrOZG6dTvbeW/vYBDMPA4TZZ9YGlmBPILJx390xqDzbg648EMfLm5bDq/Utu+MnHiWfP0FnXjbQmoIQRTIpwxuF2sOeXh8idm3PZS7NpNBqNRqO5vCRmJoRLjo6F6TBY8ObZLHjz7KvQK41Gc6VZ/q6FpOQnUb2nDtNpMH1tCcXLCya7WxqN5g2KbG/AfvEX4OtXwhTTCU6XcnSBoABGDhPCDD+IDQO9EJ+EPLMPPMHy2YYDGFn6LSYCJcTxe5G7HlP98Kt9pWEij72Gcdv7EWVLLvJKNRrN9Uh3d3f4b7fbrYUwGo1Gcx0hhCAnJ4f+/v6wQ0x3d/dVF8MIKccayV45QvZl2dnZfOMb37hg+09+8pM0NzcjhODhhx+e0Ln+9m//9mK7qdFoLjN9bf30tfWTVpyKc5Ts1rHo7xyg/mgTnmQ3efNybmgh1P7fHeHEs2cBsHwWvgG/cs/xq/rNrgQnuXNz2PgvtyAMQd2RRgI+i/z5U3AnTrzsUE9zL2e2VtDX1s+UWdlMW1N8w9e99fb6+NUH/kzAO9JO+FrEneTCdJgse+cCZt9WNtnd0Wg0Go1Go5lU5GA/GALhuvbdCDUajUajudzYfh/yxw/C4JAy9MIAw7yAo8soOJyqnJIQSghjmuAbvHwdTs3GeP/X9EK4RnODYNs2Z86cIbSEmZWVRWZm5iT3SqPRaDQTpbW1lZaWFkAJZGbMmIFhXL213UkXw0yEUFcnup9lXR+LlJo3JvLcIWTlEXC4ELNuQkwpmewuad4A9LX18+iDzyJtiZSSwR4v2BLDYeKMc2AHbOIz4nnHd+8e94+KtALI469BzUnwJCDmrkbkll7hK7m+OfSX4+z++cHJ7sa4MRwGniQ3C982hwVv0tnjGo1Go9FobkxkXxfyld8ia8+AMBClCxBr3zEuUYx9eg/y2A7w9SOK5yKW3a7FNBqNRqO57pBWAPtP34a6szGevRQ72ytohWsYGP/wA4Rz4gleGo3m+sPv91NeXh5+XFxcTHx8/CT2SKPRaDQXQ39/P9XV1eHH06dPx+l0XrXzXxNlksajxxFChEUw420vpdRKcc2kYr/+V+ThreHH8sw+jE3vQZQunMRead4IdNR2IW31XSilhODftmWTEt9PcUoDhrDxlU/DM2POuI4pX/wFsupY5HHFEYw7/g5RNOvyX8AbhL72gcnuwoSQtgQBhUvyJrsrGo1Go9FoNJOG/eIvoLFSPZA28twhMB2IDe8c0VY2VSFbahHpU5CdLcjtf4w8d/RVaG9A3PPRq9Tzycc+tRt5bDsMBsVAK+5EuCd3UUI2VSGPvors7UTklyEWrtcCJY1GoxkD6fdi/+pL0NE4WotLOfol7HuhQ0sthNFobiBs2456fDVdBDQajUZz+Rj+/T38+/1KM6limImY0kzUwGaSDG80mjByoEcFR6M22th7n8XUYpgJI30DcP40mA4onIUwrwkt36SRVpCCMATSDor+hAApyU/vYM3Ukwihtju3/gS7/x6MRbeOeTzZVh8lhFEbbeyDL2BqMcyoFC7J5dhfT012N8aN6TJY/q5FpBVc3ZqMGo1Go9FoNFcT2dOOrC9HJKZB3vSoJBnZ3RYRwgzdp/wgcu194XmGlDby5d8gyw+oxwC2rcbdQ49XdxbZVo/IeOOLje2TryNf/UP4sTyxA9lej/nmf5q0PsnGKuy//gBs5Qgsm6qQtacx3vIxhLi2F0ykbSH3PoM8/Ap4+yEpHeOWtyLKlk521zQazRsc+9BW6Gya7G5MHOPGLtWt0YSQXS3IcuVULaYvQaTcGKWDdOK7RqPRXJ9M9vf3pK0mP/zww5N1ao3m6tDdFg7IRdHZfPX7cp0ja89gP/8I+IN1hhNSMO7+MCJtyqT2azJJyIhn9u3TOfHMWYQQODwOAoMBFhVVI4QSAzo9wUD+/heQc1aNnR3Z3TbmdtnTgdz/HLKxEpGciVi8UZdQAiyfhTAF0roOBJgCpt5UyKxN0ya7JxqNRqPRaDRXDPvQy8g9TyvHF4CsIoy7PzTEvWSc47bqE2EhTJjBPnC51WKcFQBhKLH+QO9lvIII8twh5Om9al45fTFi5opRg0iyp12V5o1LHPuY3W3Igy+G3W7E4k2ItJzx9Wd4sgdAYyWy5Twiq3Bcx7jc2PufU++LFVAiJYcTmqqR5w4rYUxjFSIlA7HwVkRWwaT0cTTs7X+Cgy+BDGbFdTRiP/czDIcLMXX+iPbStqH8ALLuLMQnQ0omDPQiMgugYMakBxg1Gs11xPnrJ6knCmEgA36E4+rZ6ms01xqy4gj2S7+MCIEPvICx6W9jjh00Go1Go9FMohjmve9972SdWqO5OqRNAacb/N7o7TnFk9Ofq4RsqlLByNxpCJfn0o9nW9iv/DYihAHo68J+7S+YW/7+ko9/vSEHeqGvE1JzWLI6jhm00t/aTW/SdOz8JaQfeR0wMV0mpiOYCRnwQlcrjBWgzilWQf1hAi6RW4o92I/807dhoAcME9nZhKw8ouzH56+7YbIPYtFa0YEnyc1A52DU9kT3AFNSuuj3uWjoTEMy+YFpV7yTtsrOye6GRqPRaDQazRVDdrcidz9FlOClpQZ58CXETVsAEMmZkFMCTVVR+4rSRVHuk7KufOQJTAf4fRHxAoDhwG5vRBx+BWkHEKULEXNWXbIriX1kG3LX45ENdWeg5TwyOQOsAGLqAkRaDrK9EfvlX0NbHQhDnX/d/eEyErKnA7nrceT50+CJh/7uyOJJWx2y+gTGvf8XkZQ+og8y4Eee2Al1ZyExVc1DYjHQd0nXKq0A8tBLyLMHQAhE2TLEolsRF7DCl1YAqo5BwAchd2ArAAwiX/plpF3reWT1cYy3fHyEg4/s71EON7VnweFEzFyuXsMLOJFKKZXD0EAv5E+fcKkou7sdjrwamX+FhCx+H/ax7ZgxFrTs5x6G8v3qGqWtbvO4BKRhIormwO3vR2jXBI1GMx7ScqDq6GT3YuIIAf1dkHzjxqE01wayuUbFWnOKEckZV+48vR3I3U8pIWxCKmLhOuTuJ6Pjt7aFvfNxjJK517wrnkaj0Wg0k8GNXWdEo7mCCJcHsfJu5GuPEg7GOtwYK++Z1H5dKeRAL/YzP4GWGrXB6cHY8DeXrkrvaIK+rpHb68uRVuCGKZckpUTuegJ5/DU14REG+L3EO5zEOyBzoAERbyDzC0fWfHa4LhgoEPHJiBV3I19/QgWSpVQB77KlyF9/UTnESAkIQvez3P8C8uTrGBveiZi++Ipc97VOSm4S0o7OLp6TV8vCourw487+BF46MRdf4HJnLkmmpHSSndxNn9dDdWsmAXv04Levz49Mvw4cbDQajUaj0WguEiVgGTnekbVnoh4bm96DvfW3UF+uBCQl8xCr3xa9U1LqyBM4XTDYP2RDsGTStt8hg4kAsr5czWFu2oI8u1+JblKyELNWXtC1Jdxf20YefGnIBglWALnvOYhLBCHU32vfoZxFultDOyLPHYS4BMQtb0PaNvZT/w1dLer5jm6VrOGKUw4qAL4B5MldiBV3j+zHsz9Riy+hPgR8ShBkmBHxiTsOcqeO77oG+6C3A1JzorL65c7HlOgm9Hjv08rx5Ja3RO8vJfR2IE0T4U6A6uNqbjSiTLZUoh9PIoQENQE/8uiriPUPRFr1dmA/+l/Q3gh2QG07dxBZPA/jno+M6jyg5r4/hpbzaoPDibHufsT0JVF9lSd3Ic8dUqKe6UuUU6fThcwuRj7139GJM1IGBTESBvqQ1ceRp/ciA36MGUuRielwendEBBO6z32D4ElAVh+HXY9D4RwoKNOiGI1GMzb9MeJs1wOmAxJSJ7sXmhsYaQWQL/xc/e6CGkcu3oSx/A71fFMV9u6nVHw8bQrG8jsRhbMuKoYtrQD2X38UGecN9CBf+IUaCww/Vm879PdAgi6LrtFoNBrNcG6MVWSNZhKQtoWIS4LZq5DefsjMx5ixDDHBQalt24hzh9Qg2x2PmH0TIjN/fH0I+JUwIjFV9eUKIvc8HRHCAPgHsbf+FiO/7NIcYuKSlPBjaPYlBAObN06AT57Zizy6LSJUGexRr0nAAbYNhoE88CKkZqnJj2EoZyIEpKVjP/lDSM8DlwdqTgIgypaqckfBCZSxcD1WZxMc36GO2dWCfOy7YFlEFhWGBJqtAFgB7J2PYUydf8MIk4ZSvKKAV3/wevhxonsgSggDkBrfx9z8Wg5Wj2+RYDRMw2JKShcBy6C5O4WV08qZmhUpuzYnv5YXjs1n0K+ygBM9A7gdAdp7E5CoRYCO812c2VrBjA26xJVGo9FoNJo3HiI+OWYRpOFzMJGYhrnl75H9yvlQeEa6eogZK5CHX1GiihCGAzwJQfGFVPMUbx8McwGUx7Yja05AT3tk24mdiHX3w9l9yO42xJRSxMINMc9NwAuDwdJLvsFo55OAT43zpQ2v/QUs/4jd5dmD2InpyEMvq/mg6QjuM+QYQ4UevZ3R+7fVq4WciiNqXhEc9w97hZR4Iy1H9dHpHnkdoeNJiXz9r8hj25V4xR2PcfNbEDOWYbfVIY9sCy7sOMPiFXlqF3LFXRGHm8ZK7Bd/qRxwLAtpmpCcpa7D7yNaBBV8Pyw/GG51vX4f8uirWB2NEPCrazbNYHnjIddmW1B/Bnl2P2L2TbGvZ9+zESEMQMCPve33GIWzwOFCntqN3P+8KpHsdCkRz5n9SKdbCaqcHvD2D80zCL1Q6r2KT8J+MiiWsS3s07uVgClWGWbLUu+NdwB54EXksdcgOVOVBtPOCRqNZjRiuZ9dD5jOGzL2pLl2kCd2RoQwoITIB55HlsyFuETsJ/9HjeMAWs5j//WHSqAy2AfZRYhVb8KYMs6YXM3JiBDGttW4JiSIHf458CSofxqNRqPRaEagR49XEdu22bFjB+fOnaOxsZG0tDQKCwtZt24dCQlXb7Di8/l49dVXqaqqoqWlhaysLEpKSli7di0ul+uq9eONhAz4sXc+Cqf3qsBaQkowINWnAqSAsPywcINq39upSs6k5446iZODfcgdj6pBtj+YgeeOQ556HeOuDyHyy8bu09kD2Dv+ooJshomYvQpxy1uumF2irDkxcqN/EBoqoHjOhfeXNlSfQDbXqJr1QWtqEZ+EKFuKPLM3qr1YsO7Gqol+7rC6t3yD0cIgK6CC0Lal3uuOZjX58XsjWav15er+qT2jJk9xCarO8v7noL8bsfYdANj7noNjr4WDrhdGhs8hW2uRrXXIhgqEw6mya0vmXeYXYdjZbUtdU8APBTNGFV1JKUe9V2RfF7L6OMLphpJ56v8ToGZfHVYg8n7kpHTHbDclZeysq5S4PuYV1JKR0INEMBhw0tabyOmGPPq8HnKSO1kz4zROhwrUewMOkj39aq1Agt8ySXQPMjuvjmO1hdxSdprc1E4ABnwudpbPoLlbLQLt/Ok+pq4qwunRQwCNRqPRaK4VpG0jz+5TQW9PoiqzM6yci2YcFM6C9Fxob4hsEwZiwbqYzUX86AkDwhOPWHc/8vgONWbOyIO86citv4k0CgnVTTPyeLBPjbn7u9Vc0BOvRPzdbcjHv6/EHnYAWXcGWXVMlSgaNicUrjjIyFeuMgFfdMd8g0oAIgz1txAR95MQ3j7l+BgIltMJ+CJik1A/hzJkbimbqlQWsrdPLboERnMWlErU0dmM/fKvxy5he3Y/8sgrQ/rXj/3K7xB+L3LHo+AbUNv9XnDHq7lLwB8U/riQfq9yIe1qicxTrAB0NABCtbcCqk8I9fqEREKhOVTouquOqdcuLlHNZazAcC2Tev+aqyGGGEZaAeTR7RGxkmEqd5wAUFeOPHdIufMM9ETOH3ZxGVDX1Ncd2TachFSoP6faht4nKdVcLxYieFxCzjJAdyty5+OIOz4Yex+NRqO5XgUlod8LjWayOH8q5mZ5/pQq39jfFS1aCf2GSwmVR5GVR7EKZ2O85WNhwW/M43W2YB97Lbgv6pih33lhKDGsGUkSFUtv00IxjUaj0WhGQf9CXgUsy+Jb3/oW3/ve96ivrx/xfGJiIg888ADf/OY3SUtLu2L9GBgY4POf/zw//elPaW9vH/F8eno6H/zgB/niF79IXFzcFevHlUT6BsF0jDn4k95+VYu8rwtRMOOCopLxYD/6n1B7OhKsCgW+EOD2qOyw6uNQcVjZHZcfBCTEJWGsv1/V9x7ezxd/gaw8FrFOtvwwEABPIvbeZzAcziE11ZcisovUflIiK48gX/yFGiQHhRLy+GuQkTdqdtsl40mIzpocun0UpG9ABWSFgXzuZ2FBjQQ48grGln9Q5abWvgNSMpHnDgXruK/EmLPqilzGtYq0AqMHQCE6y1Na0UFz24oWtwz0giteZS8c34GVPx0OvAR1Z6OzIseDrfolH/tu+P2Xhok8tRuxYD3GzW8e96FkRxPyzD7kQI+61v4eRPoUxKJbESlZ0aetL0c+9T9qscF0qM/S5vciCmZEjtfTgdzxF2TNSeWqNH8NYvGmsDBGlh/E3vob9fkAiEtC3PY+9f2RkqkWIS5Aw/EmspO7KUxrJWAbGNg4TAspBYaQmIYSyrgcfoSwkTKyUFGU0UJJZiumYZGT3I0QNh5nACEkSEFWUjdTM1t47ugCVk0/i9vpRwiJbRukxQ+5FwS4HBaGZZOZ2MNN085SmNGGlALLNohz+VhddprHDizDlgbeXh8tZ9vIm58z7vdGo9FoNBrNlUVu/S2yfH/k8endGHd/BJGr3dwmgjAMjHs+qhwy6s4gElOV+8oYc76wwNrvhYKZCHcccrAP+7mHobFCNYpPhqW3K1cS0wEDfeAMClIMR8QVxTcwzNFSgndAuTOGFkJUT5WIoeEc8vReRIy5jbH67dh/+lbsTnsHIm4vKdnQFXELVIsuA2qcPrScTsAfdHwkspgCiOK54fI+0jegrru3I3KssfANqOPWnkH2dY3qgiorDg/bYCunlld+T9hhJzRf8faDOwGmlITLSsmqY2pxKaZgX0aEL0IocYrLA4O26psdfD/MIaWdpK1eC8MASwY1NEMUMYYJw+YfIeznH44IYUD1aaAXhIF9YiecOxjDRYfo9mMx2A++MeZ9wzEc6r0WhiqPCyAl8uwBrIZz4HAhZt2kFsmGi6Y0Gs2NS+60SAm964kbyCFac40Snxx7e1wSHHg+ugRiiOHjqfMnsZ/4AWLGMqg7A/HJiHmrlXC68gjSOwA1J9R4LuAfeRxHcEzjG1TjnMx8SMpAVhxGtjUoAXfJXF0yUaPRaDSaIELKC0U3NJdCZ2cn99xzDzt27Lhg24KCAp544gkWL1582ftRXV3NXXfdxYkTMdw7hjFnzhyefvppiouLL3s/LhbZ16XqrqdPQcQYdMr2BuxX/6gy51xxiHmrEcvuGOEGYXc2If/6oyjRhpi3GuOWYfXhJ4DdVI38zZfHDhQKoQJ6OcXQWhv9nMON8e5/R7gjC++yqxX7N18ZIqoZ2t6lMs9AndPvU8GvvGmIxZuV20dLrXJlQaignzCUiKRoDsbdH77oaw33r7sVhIlIioi37FO7kdt+H90wuxjzrR8fuX9XK/arf1COJQ4XTJmqlPVCqEG8bakMzpvfjLHo1kvu7xsB67mfqfJFV4Th/twXcwgRTsQEgvdpPMY7P4dIvLDIT1afUIFlK6CCyaFMU2fwOPf+3/BxZGst9m//I2I7KqUKiBgmpGRCRh7MXwev/xU6m6K7edObMBauR/p92L/6YnRWkc8bzN4IvhbpuYjbP4AxpWTUflf++Jdktu1ESnA5ApiGHRbCQORV9fodnG7IZ19VKaZhsW7mSYoy2rClesEcpoVtC0wjsl/AMvFbJg2dqZRmtSiRDNEv83D8AQOHww6/o1IKvH4nEnj55FyaulJBwLp/vIlZm6Zf4F3RaDQajUZzNZAdjdh/+OaI7SJ/BsY9H5nYsbpakbseR9aeVoH1hesx5q6+XF19wyG7W7Gf+h9VKgfA6cbY9B5kxRHk6T1DGgYXHELzML9PjX9nLEUUzUXuejwo5u6OFqCEECLGfDEoiCmag/n2f4nZP+uZn0DFocjhho5dhVBz343vRp7dr+ZWhqn6EXJCiYXpUOPszHyMNfdGCa6sx78PlUfG6RI5BMPE+D/fGlUMY7/wC2TFIfUg4FeLO2PNPwwT8Y5PYuRPV/PM1/4ceY9i76DmvYYRnTVtGEo8L6UqMxTwRa4tVLIo9Hz4UA712rz9/44oYSWlxP7uR2KLXWKV9r3SuOLUXLrhnLqWkAvsYL/qS1BMBCAWb8RYcffV7Z9Go7lmsY5sgxd/MdndmDhpUzDf/9XJ7oXmBka21GI/9l9qPCOEEr8YDrj5zTDU7W48JKSo4ZDtB+8g4fJHUo4trA0RGl+K4PjH6Vb7CwF505Wz/HXuFuP1eqmoqAg/Li0txe2emKu3RqPRaCafyf4+v75/Da9xAoEA73jHO6KEMEVFRbz73e+mpKSElpYWHnvsMfbuVeVfamtr2bJlC3v27CEv7/JZYvf09LBly5YoIczs2bO5//77yc/Pp7a2lt///vecOqVs/k6cOMGWLVvYsWMHSUmj20ZfLew9T6la7ZayeBYzVmBsfFf4eWkFsJ/+X+gLliHxDSAPvACeBMT8tarNuUPYe56G1vNg2eByhy1B5bHXkHNuRqRNuaj+yeoLC4zClog9Ix15CHjh/EkIZuOpbb6RdtghLD/4TRXs8vZHgnm1Z5ENFeBwR4QJ2BAq4RLwIUMZfhNAegeUkMZ0IHvaVZ325mr1ZN50jI3vQcQnYcxaiQ3Io6+Ctx9RNAex4q4YL4XEfvYnyrrc71PXUH4wGKwMTiKCAWS541Hs3GkYOdeOMGuyEKbjUuUqY3AZjhzOsgwG9YOfVzqaYAwxjLQtaKzE3v5H9d4PzZj1DYSPI4/tgKnzlcDjqf8Jir2GEHK/aatX91blkaBIZqhlOshX/4jdcE6V7grZlAcCyk3HHha8bqtD/u6rWDe9CfOmLSP77h0gZ+AgAQmmYYddYECGBStSCnwBEykNpmU3caimiDvnHyI7RQndhuZohIQwBPd1GMphZmpWc1Si6ljFwZwOO6qdEBKP04ctBWnxvTR1peJwO/Ak6YmjRqPRaDSTgfR71dh6SOlS2VAZzOxUgnCcLjBM5DBR7wWPbQWwn/rvyJyjpx352l+QTo/KPNVEIZuqsZ/7mcqMN51KTOH3Ym/93Ui3xIBfzcMsl2oXtLUX6XkYM5cjS+Yhq46qEkr9MRIaYiZOBEeMsdoHMeatxg6Vo5VSjYGlVGWATBc4HMgj2zDv+yTSO4C9489w+JULXDjqGtobkJ748NhSNtcoJxzDnLgYxrawu9swRxHDiFkrlRgm5FpzofmHbalx+4b7kdv+cOH2QkbmA+44JXBxecBwKpF+wBecZw4Z7weCgiZPIsy+WSXWSBsy8xAL1o8QwihkxF1nxFNXWQgDkJqNsfwO5Kk9yKqj6j4NlbwdVv5VHt8JWgyj0WhCJKVPdg8ujgmWttZoLjudTWqsNNAJBH/7hQ+2/pYJx1dDaxlDGeoEcyGGOt5Joh3FK49gn92POWvlxPqk0Wg0V5BHHnmE97///QAUFxdTVVU1uR3SjMorr7zChg0bwo+vd18VLYa5gnznO9/hxRdfDD9+5zvfycMPP4zLFakH+dnPfpbvfve7fOITn0BKSV1dHf/n//wfnnrqqcvWj0996lMcPXo0/PjBBx/kG9/4RpRryuc//3k++clP8u1vfxuAo0eP8ulPf5of/OAHl60fsZD9PdDdhp2Yiji2HVl5RJUzmXsLxswVyJpTyIMvKdFEcPFbHtmK1V6H8dZ/RjicyJOvQ2fQFjpUOx1UJt/8tcjGSiXgCAWupK0Gh57EcG112VwzQgwjezuQZ/aD34uYOj9chmg4IiUDaZgXVmxLW002Y5W6cbiiH6fnqmuJZa0YcnuxAsFAXOhLSCrhi20pAcDwAbiU0FqH3duJkZg6dl8B2Vav3Fuaa1TG4fw1yPOnI0IYKaHqKPYjn4PMAuWwM+dmuNAgu7laCRZCr8PQL9GwiCe4zduP/OO3kLd/AFG2ZPiRbihkZsFkd2GchGzGUZPD9NzRW7bVYz/zY2hvHFv81e9H7n8OefhlZUl/oeC8lGryKCXgJ8r5xgogyw9AxRF1zqFOMLGwbdj3HDIjD1G2NPq5rhbswUFMw8btiHz+Q1+tErClwGHamIZ6fsvifaQnjC9LRAjlNnOpCAGmkNxcdpbU+H5OeZeRv3D090Wj0Wg0Gs2lIzsa1eJzfzcUzoL0Kcidj6mxtScBsXADxqJbkX4v8sDzkbHQ0HKXwkA2VIxZKkm21WPveAzqzwJCiQAMIyiWUNN9+8ROTC2GiUK5Wv5Bld4B9Zq74tTrNtgL7mFCiJDQYZjzaGgeKtxxiJkrsE7thlBJoPEEiwwHIn300pWicBZi/d8g9z+vSiGFSgANtb3vaET6fcpptKFi1GOFsQOR8qa/+w/s4rmINfdG3FOd7tHH5mPx2p/hHQ+Och0zEesfiJRFGg8tNcjXn4y0H8t5RUrVLvS6hDK1HU51PZYVLCUkok8f8MEtb8VcvEkl0Gz/E5QfRJ47jD19CWLdfVEZ1UIY6nNlTWCh6krSXo/9zE9g5T3QWgdNlZHn/F71ejic6nHAh5R2lBBPo9HcwJzeO9k9uDjG45ah0VwhZEcT9vOPjBT2XosLhAE/7Hv+wnF6jeY6oKWlhX379tHc3Exrayt+v5+0tDRycnJYunQphYWFk91FjUZzjaPFMFeI7u5uvvGNb4QfL168mJ///Oc4HCNf8o997GOcPXuW73//+wA8/fTT7Nixg1tuueWS+1FZWclPfvKT8OMtW7bwzW/GsOAWgm9961ucOnWKJ598EoAf//jH/Ou//itTp0695H4MR0qp7LuPbgdvHwT8kSGkEMj6c9hWQIkmbHukC0RjFfLwK9jN1XB6d9DRIRjwcserAFXQ5UGe2k14gGoYyhkGVADLcKuss7qz2IP9iLKliPgkZGOVsssOlmGRh15CrHoTxoL1I65FlC5EehJiq7mjGhqQU6Lsq0EJYAwDElNVgHxoUyEQs1chD7zIiGDhzOXQcj5Yxz3GYNu2QY4SnPMNIB/9T+S9/xquvx4LGfCr6w+VafINIPc9pwbSwSzIsCtNwK8CsNv/hC3tC9uw26o+vDpRDMHOUIQAy4/9+hMY0xbe2LVOJ5gZPOnYlqpNP0p2KID98q+VEGY8wWRvP8TSho1G1L007L6Kso0fy2clsr88u3+kGCYlC8sySHDF6JgAgcA0bcSQ049XCHNRDL+U4R8nYGZuAwMOA4frBv4saTQajUZzmZFNVcgDLyA7miG7EFGyAPnKbyNjnIoj6u/QovRgH/L1v2K11CrxSnebcnf0e4n6Aff2YT/+PcQDn8FIzVaOjYO9kJQBQrlfyuceAV9/9NjHRi3yGyZ4EkYR2N+4SCuA3PM0IKMFFv7BoLW8oZxMDm+N7BQqyWkMExIMc7AUBbNUAkHApwZfIav60coWOd2IhRtiPxc69czlMHM59mA/8tdfjpQKDZGUEbm3kjOhpQ7khRI1QveLQJ4/hXz6f+HuDwXnXxe50FhfjuzvjlnWGEBMW4x87dEJHFBGl0YaLmSJPEH4CdMRFMQEt9m22uaOU++BYUTcIM3geLjqOFZduSrbG3p/pY08uw8y8hAL10efbup8KD8wgeu4ggT8QfH+M9DbNUSVH3LaHAzfG6J4rhbCaDSaCJVHL9zmWmSizmUazWXE3vds7CTXa5XW85PdA43mounp6eF73/sef/7znzl48OCYrhT5+fn8zd/8De973/uYO3fuVezllefQoUM89thjAKSmpvKJT3xiUvvzRuCRRx4JO9KsX7+e9evXT2p/NFcHLYa5QvzqV7+ivT1SEueb3/xmTCFMiK985Sv87Gc/o79fDaj+67/+67KIYX70ox/h96sgrBCChx56aMz23/nOd8JiGL/fz49+9KOY4plL5txB5fgSyyI5aP8s9zyNmH3TqME4eeB56OscUtok6EjhG1SCGE+CKrHU067a+AainVRsO2IhfHY/8ux+5P7nEYs3Ik/vVsHQIZl/cu8zyJkrEMOzBCGYeekM2mmL2JmDialw/DUVcA14VYA0vwzjtvfHrN8plt6OrDgCvZ3BLDYD4pMxNvwN8tgO5M4JBBLD/RDQ3408uQuxZPPo7c+fUkKYUI1SIVRQ0QqKYfy+6PdlsB+cLuSx1yCGGEb6vcjTe6G1FtKmhOrHXKjTkb73dyv78HE42rxhqT832T2YMLKvc/TnutuU2O1isk4vK8MWQWIRWsQYhnDH4RNxJBBb4CIupfxU2K9+fO1iSXrkkHWBEKZhI8+f4eyrc5ixfvQsc41Go9FoNONDtjdi//VHakzjG4DGSuSRbZEGDpf6ofb71UK7Yapxx2AfnNihxhl+r2o33HHC71NJA7/5KlZyOnQ0q2OkZKq5R1vd6CILUPMc7wBi6oIrdv3XItK2kfufR57YAb5BRMk8xM1viYi0+7sjon+HK5J4EXQXEVPnI1beA+54dQzvoCpp29sBjUOcN7KKEDNXRJ1bTF+s5qnOIc6fIYeO4Qt4wkDc8xFEwcxxXZfhicdefCty7zNDD4Kx/M6w66ux8FbsujMw0Du+LGXfgHJOaa2DX39V3ZcX7XoikEe2IWKUFwWgu1XNg4UYX99sCZkFam6KHHsfIdTnxxksGezyqGsLiT/SclT51sFe9VjKyHXWnVX/9w9G3IGCyMojMEwMI2YsQ547eO1kgdsW9AwthywIi4GkrWIuU6YibnnrJHVQo9FckwwXVl4vjBFn0miuOC3XmbjEtpEttYis68VtXKNR/OAHP+ALX/gCra2t42pfV1fHt7/9bR566CHe9a538bWvfe0N4xZz6NAhvvjFLwKqrJAWw1w6jzzyCNu2RWI2WgxzY6DFMFeIkFoPoKSkhI0bN47ZPiUlhXvvvZdf/OIXADz77LP4fL6okkqX2o+1a9dSVlY2ZvuysjLWrl3Lq6++CsDjjz9+RcQw9rEdYyuppVTBqpypKqjlg4g9cjDQ1R8UawwPptmWcpupPo6sL1eiDV+MxWorAJYEp0c9DviVUGT7H4NCDwFujwo0h55vb4ThNuWdzeq5oTXFrWAflD2EEoCEgqxOl/onVaaaSM2O+RKIpDSMt34MeeAlZHs9IqsQsXgTDPSpALvpiC0UEiIY9JMjF/hdwWttb4h5zjC2pYLvQ183YUBcggqmDRcw2AHwBqBr5ABF+n3YT3xfBVhDON3jqIEq1Wq+w6VKWo3hZHND4LsOAxXVx5FWIKbYKxyovhYYSwjjcIFhImYsj/l0ShrQO3L7EPnZxCQxw8QtcuhLNPxAYmxfG8FIQYwALNvg/P56LYbRaDQajeYyIE/sUIvqg0HXxOE/2EPHzd6BiFOLlJGykqF2Rowxk5RqAX9wyICjuUb9f3ip1VhYAaQ7biKXdN0jD76gBCmhxxWHkT0dmG/7hNoQnwxxSUoQ43SpMWnISWfJbYglm5VT5+KNsDgyj5e2BVXHkK21qsRu6cIR41yRkolx2/uwdz6mXE3ikmDWTXDq9eA8KjgHMgyYuQKjaPaErs1YshmZkY88dwhMEzFzJWJKSeT8RbMwtvwD9qGX4PzpoChm6Fh3mOLassDqV4+Hz88vJBgfjjsOOdYiUWKackByxQfnyqMRHOQKgbH8Dpi+GPvFX4zel8wCWHIr7Hwi0sbhhJnLMYrnKlFTSpZy1YGRIpahc15vP5AAwUQm4XSP7J3LgzQd45jPThbDv4P80NUyOV3RaDTXLnFJ16dznG0hO1sQqVmT3RPNjcj1Fps2zYgAXKO5DvD7/Xz4wx/m4YcfjtqekJDA+vXrWbp0KVlZWcTFxdHY2EhNTQ3PP/982OVDSsmvfvUrMjIy+M///M+rfwEajeaaRYthrgADAwO88sor4cebNm0KZ2qNxebNm8NimJ6eHrZv335BEc1YlJeXc/bs2ajjj4fNmzeHxTBnzpzh3LlzTJs27aL7EZPucQRjpI189icwaxUcfH7I9qBbidMF3mDAebggRkoV0LQCatE9VtZWaJHYP6gGh76ByLFDNccH+4O2ysHSSymZI4+TmDYy0880YUopxh0fVIE4bz/2n78z7PwCulpGFwsAIm0KYuO7orbZ236vMjgcrqAtcvC8oSxTpyeSiej3KgccIVQ/QsH2zLEV4XZG3kgBUejYsUQZUgIipl2pLD8QLYQJXbs7XgWEx3KJCQqfxPI7Rn2Nbhiulbr0E8EKKGejGJ8bEZcIJfOVW9I1hYh8NwgBSWmIRRsR0xbFbG0aQwQrwY9BrENGxaRjKWRGEbaIYQ9krO0TpDTtPGc9N13CETQajUaj0YTp71FjYBlDCDMc2wrOUUJlWpxqjuFwRcrqTITxOFMIATsfR2YWIIaL+t+gyJOvj9zYUoNsrUNk5iNMB2LFXchtfwCkmic53RjrH0DMWDbqcYVhKgFM6cIxzy+K5yqRy2CfmvMgsDublKuMy6Pet7gEjJvffFHXJ4rnIIrnjP58fhlmvkqCsf1eqDiMPLMfqo+rax3sGzZvG+U+MoID3QuVpBBCJS8IoURCozVzeRBLNiH3PAW2O7II63CruU4o0SWY3CEWb0RkFareGaaaY1sx+hKfiDlvLTKrGHlylxK0FM9FzFweKQtUX440zWA5sjHclECVHTMSlCB+9qqRz+fPGOJOez0gobcTeeRVxKpRXHs0Gs2Nx5p74an/nuxeTBxX3MSEmhrNZUSULUVWn7h+ynUJA6ZMnexeaDTjQkrJfffdF5Xcn5aWxmc/+1n+8R//EY/HM+q+hw8f5stf/jJ//vOfr0JPNRrN9cgNvrp8ZTh16lS4NBHATTeNb9Fx1aroQMvRo0cvSQxz9Gh0/ddL6cdlF8PEpwB1F2zGQA8c3QrCDAaYgwT8KovBtmJkMgyJIkt79JrnQwNYA0MyLYcHlW1L2WsvvzNm/XMRl4iYvza6pjwqi02k5ahDehKUkGR44C09d+Iij5AFshCR2ue2pUQ/w8u5TF+sBCdD7bzTpsQO6g3t/em9sUOivR0qm9V0xHD2kbGDk231MU5gwPQF0FgFPW1Bgc0Q5x8ZPJ7hwNjyD4i8y3z/XY+kZl9/GX22NWZpK7H0duTpPZevVJJhqgWGwWB260QQBsxYrsoNDPYpu/olmxFZBWN/RoWIfOOMsoAlgv8ZKmSRIkab8XRznO3GIjuxE/fs66jGsUaj0Wg01zKFs6D84IXbOZxq/mFZalEfM1KOxeVRf2eXQO2p0ecvw5G2cpOxx2jvcAISeWbvDSOGwT/K2HLImNOYtRKZnos8e0CJksuWIrIun5W2EIaar4bOd9eHlVCjoQKS0xHz1iCS0i/b+UbDcLph5gqYuQL7+GuqVPFA75A51wXwKFEIfu/ozq7CUMIZTyJiWEmhEf1ZvBGZkYf9+l+hqRJMV1Dk4lDzWsOE+CTE7FWIkFiooymYJOEcOd90OKGrTXUjpxiRUxz7xJkFar4cyzE26lqCo23DgVj7DkTpyBJjwh0HWQXQfP46WpCVyOaqye6ERqO5hjBnLsc6vgOqjsZ4dsIet0N2FbD6Xtj1+PhiPcKIOJFd6DvVdEBmQTjWqtFcbcScW5DVx+H03snuyviQdlT5R83kIKWk4VgTZ1+toqepl8BgAIfHQVJOImVrS8idlzOuRPo3Ot/5zneihDBlZWW88MILFBePMr4fwsKFC/nTn/7Erl27eOCBB65gLzUazfWKFsNcAU6ePBn1ePr06ePar6SkBNM0sYIBnuHHuVr9GC58OXnyJG95y1suqS/DEbNXIpuqVMBrrMmOlLHth6WtgnHuYHAu1MY0we+PFs5cUkeDQTeXB2PVm0ZtZty0BZlZgKw4DA4nYtZKRF7k9RZON2L5ncidjw7ZycRYeRGZWXnToO5M+Bh4EkBKxJZ/RKRnI0/tgZ52df7Sheq1OndQWVanTVGB3hh2z9EXZI502xEi4iwTa4AmgmWlhpOZH/MUIrcUselv4dwh7Jd/rbJqh9aWQUB2oRbCBBEFM5A1J66d2vTjQRjqMz6KjajIyEVmF6nyY4N9jC/YErRQcbiDjk6DamEpNRuxcD1y33NqohfwR47ncKms3LFqS5smIjMP4+4PIaUc/yRkcCyL9xE9j/n3lSRWqSQEpJ57Bm699Sr1QqPRaDSaNy5i5nLk+VNwYsfo47SQ0yRA4SzE4o3IZ34aJZQXUxdAyTxk63k1vggv+o8xPpK2ElyEHDGljM5UdbgjZVKvKyeLS0OULkCe2h29MSEVsoui22UXIYZtu2J9croQC9bBgnVX5XyxMOauhrmrsV78BZw7pO6JgH90pxRhgunA2PxeMAzsx76vHEojDQAZTLa4CTH3FkRCygX7IYpmY6RPwf7d1yPul6YDPAmIDe9ElMxTgpMQ2cEAuMOpxv7hCzKVQ0BG7oXP6fIgbnkr8sVfEqzBHKtVWPwjZi7HmD16MpGx6s3YL/w8WB65/7oQxQidGa7RaIZhvu0TWOcOw85HlVBy6gKMje8GIbAPbVVOvr0d6ju6p4MLxmyEAZ4EjOxC5K3vQj7/yIX3KV0ELTURhzAroEStsYS+rjj1m6TRTBLCMDDv/ghWZws0VU12d8aBgNozMMGynJrLg5SS0y+e49QL5fQ0j4wfd9R0UbO3jqTsBGZtns7MTdNuWFFMeXk5n/nMZ8KPMzMz2bZtG7m5Fx7nD2XVqlXs27ePbdu2jau93+/ntdde49y5c7S0tJCUlERubi5r1qwhOzt7QucejaamJrZv38758+exLIu8vDw2bNgw4Wu73Bw4cIATJ07Q0NCAw+Fg3bp1LFmyJGbbjo4Ojhw5wpkzZ2hvb0dKSUZGBtOmTWPVqlXExV35sszl5eUcP36c6upqenp6iI+PJzMzkyVLljBnzpxJ++zYts2ePXs4ffo0TU1NOJ3O8D2Unx97XXSinDx5ksOHD1NfX49lWaxcuZK1a9fGbNvb28uRI0c4ffo0ra2tBAIB0tLSKC4u5pZbbiE5eaTZw42CFsNcASorK6MeFxWNL8Bmmia5ubnU1tYCUFFRcdn6YRjGuD98+fn5GIaBHQyYXmo/hhIIqMmMLFuObKhElh8KBo6EmvB4+8cumzMUy4rUSHe4kT1tyF2PA0Z01prhAEOq8wy3MRwu+BiOy6MW0hNSkYELZGiWzFf/Ihcb/fzsVcj0PGTlETCdiLIlyNTske0ugJy1CrvyuJoshi5j1s0YoSy4+UMCrKGgd+li9W+0vg0/x+xVqs790KxUCUxdqF7b+nPDVtiF+heXMuJ1kiULsI/tgNbayMbUHIzpSxESKF2E3dmK3P3XaJcfw4Fx01su/LrfINjxKUhnXLD0VYz7OD0v6LJzAdvvq4XTDU4Phi0RY7yHcs192M/+VC36jFBtDEEYEJ8MJfMQM1ZAU6XKxnB5ELNWYMxYjgRkag72/uehqTrimJSei3HrO7HP7IM9T48MVgsBphsjsxh7gvebZdkXnTA1aUigt0t/tjQajUajuVzc+m7sgpnInY9DT/uw8qkOcMWr31/DxFi0EZFVjHzzx1Q5n/5OyCtDlC0FK4DtSQ6Wcg0KXEYrlelwKoFNdjEiuxixcB0gkBWHVD9CwaDgOMUoWTjhcc71ilx2F7K7AxlKIEhIw7j1nUhbju2ic4MgF9yKXXNauaQ4XGp+MXxObJiIZXci5qxCBp0erfs+Bb//uhLQhGKNcUmIex/ECIlXxnuPeZKQm96H/foT0NkE8SmIJZswpi0eeZzENOw5a5DHtyuBV8AXKbtrODAWbR7fuHbaEuy0PORTP1JuMxBxJA0u4Cp3AjCK5o39eSmai9z0fuTJnci2BuhqVq/jaPEFw4wkllwuV8yJEJ+MsWjTDfMdoNFoJkDxXPUviBWKI85fq/6Ftu96AvY+E1v8FyrN7vRARh5Gbhki38Ae7EfufTqS6BgaH9m2chRzujGW3wF93djb/6R+l0wn5JUph+tQ+bzQaeavQyakTjiOqtFcdu7/DNa238PRVyOx86ELsXJoHXUJCSlBsfuwezcuWY0LLlTC8WIQAhwujP6ecFzW4dBLgVcLy2/x2v/upWbvhSsj9DT3sffXh2k608rqDy3HdJpXoYfXFg899FBUpY3vf//7Fy0WycrK4t577x2zTUdHB1/84hd5+OGH6e7uHvG8YRisW7eOb37zmyxbNnoZ3RDr168PC3A+//nP84UvfIHGxkY+9rGP8eijj4bXREMIIXjHO97Bf/3XfzFlSuwysyUlJVRXV0dtq66uHlX08d73vpdHHnkk/LiqqoqpUyNi+MrKSkpKSvjVr37Fl7/8Zc6cORO1/8c//vEoMUxlZSW/+tWvePTRRzl8+HB4rXg4LpeLd73rXXz+858fl4vPeJFS8uKLL/Lb3/6WZ599loaGhlHbFhQU8OCDD/LRj34UpzO2G9Yrr7zChg0bRmz/4he/yBe/+MWY+2zdupX169fHfK6np4evfe1r/OQnP6G1tXXE80II1qxZw3e+8x2WLl06at+Hth9+3meffZbPfvazHDwY7UT85je/OUoM09jYyG9+8xv+/Oc/s2fPnhH3WwjTNHnTm97El770JebNm3fBPr3R0L+AV4DhX6BpaWnj3jctLS0shunp6bls/UhKShr3gMfpdJKQkBA+/6X2YyivvfZa5IGjEFmSocQVCSnQ1aLEMUMDlCIosog12bIciPJahJ0B9CBtCylyYaAdZLyaZLniEHmzoa0e2dUayZYMTcBCdpyhcxpm9HNSfXmK1ELE0L5fEmkQAI6dAc5cqHFMZPpCMPPVa5eYiiAFLlv/gufIW42sPBp8bQQkpiDiSiFOIPvjoL5cbQ+9R4aBsDNivk4yfSHILOjvVtmrGXmIPfsjz0s3ZCxGNlapSYAnATF1PqK6GaqbL+t1Xa/IgV7kYCKQqF6joUFWhxOylyHsSmRfzcW5xzhcwSwcv1q0sUMWtUI5sNiWCkqE7DX93qDYasgEb+jn1HIgEvMRe/dzIeTUTcjjrwVdVoQ6rh0IfgaDwY+0bMTUBQiXB2pa1OswZaU6QLMXmofcd9nLIXs5MlhCTHgS4MQ5IA1ZvAFZXw59XcFjG2A6EJ5cqGlFnJ/Y58hmCvi6JrTPNYHpxLjM3xkajUaj0dzoyGm3w0AP0u9D+AaQ7gREez2ytwPhjocpUxFnzsOZ88E9UsGVCq1+aH1dHSNnBdSXI30d4IlT4ppYpV+lG1EwD5Ger+YW+w9Hnspehjx/Ro0ZTadyZLzRxtVJs5AlRep1i08a9rprZP5qlVzh8yKTPEpIHiohZDqhaB6GLwkOHYveb85b4fxJZH83IiFVOR2NY7w/KlNWIrP8qixRq3+MOW2a6nNXK9LvA2kpt9PMAkRVE1Q1jfuUsvhWZNx5VR4V1OJtXwf0W0oElDsVUdMKNeMYKyfOhMSZyNZaZMURNWcSRvBEtvo7KU05LliqDDDZedDWqEpCX2mEAWk5iNKFiF2vX/nzaTSaNyxSpiHTFkBLrfquc8cjpi9SzzVVq/LySemQXorYuTO4lws5/U4VfzEdKqFpMJi8aINIzVe/z6jvZvo6welGxCUi++OQDUOSM+MSEYPxlzE2q9FcImY+LPobZFercmP3Dqj7NKtAJaC11KhY+ZRSSMlCnjsI3W1BMZgJJXOVY/aR7WD7oh2uYxES1tp2UHxrDXN+DCUYBmP1pgleB+J8J6JRfW5GW9TVXF6kLXntf/ZSs+/CQpih1Oyt4zVg7d+vvKEcYtrb2/n5z38efjxz5kzuv//+K3a+w4cPc/vtt9PUNPr8wbZttm7dysqVK/n617/Ogw8+OKFzHDhwgLvvvpvGxsaYz0sp+cMf/sCBAwfYvn37qIKYy82HPvQhfvzjH4+r7YMPPsif//znC7bz+Xw8/PDDPPbYYzz66KOsW3d5nFD7+vq47bbbxtW2traWj3/84zz++OP8+c9/JjU19bL0YTT27dvHli1bRn1/Qb3Hr776KitWrOAHP/gBH/nIRyZ0jq985Sv8+7//O3Ic63zf/va3eeihhy7YzrIsHn30UZ555hl+/vOfc999902oT9c7WgxzBejt7Y167PHEKB0zCkMtpYYf51L6MZE+hPoREsFcaj/GQnjiwRO0DM/Ig/hkVT6pI5hZZQQXw4dnT4VK9iRnRDYZJsxYBl2tMNirXCSS0hFCIJMzENUnkJ3NwWMOu/WlhVr0d6h/0lZBMYcTkVU4eu3xSUIIAalZV/YcWQWQngM9neB0RVlei8JZSNOBrD8X2SEuEaaUxj6WYUJW4ejnEgLypkeVltJEI+ISIbdUBQMcrkigNTMPMaVUPV+6ELKKkLWnVdkp24oEYaVUoo+i2SoA4fcRniSZwbJYQoA7Wdl9237CEyinW1nI55SEB+PStpW4qbcDWRsUdVmBoJhMqPJYQ7KLxrw2w4BZN0HtaWRnCzgckFUYvOcEJKSoNhN9zVwjv/dERh4iI08NJLpaVGA6IQUx5LtkQpQtgcNbL27fySR79M+jRqPRaDSai0MIAfHJ0ZU/k9MnVB5ReOKhdEEkl7T8gBofhUT7tq2c8UoXIkaZD4iMfEjLVeIGl0eNxW9ARGieqRmBcMdBwUz1N0DBDGRfl1qISUxFmLFDRcLpgtKFl7XkpzBjZ++NaJeUrub3l3o+w1TuslNKwttkyKXWFYdwjK8/UcfMLACXJ5jcE0yuMQxE6SJEapYS6YcWyRxOKJyNDPiRg31QdSxSMjhU7ixyZBUviUtS821pQ2vdkLlcrM4INf/LyEOULrhhP/8ajebyIoRATJ2PLJ6rko6GfFeKpPTR93O6ITVYamLOzdDRqL4Pk9Kj9hPmsBhvfhmkT4HuduXanZqFEBOPC2k0VxqRkolIyYzemJSOyI0uTyhmrUQO9KpxQnxyROxQtkTFaQd61W94Urpq0xV0GjBMFZeduVwJxbwD4XURee4gdLWptQ6CDk2hNRTTgSiao8ZumqvK6ZfOTVgIE6Jmbx2nXzrHrE03zhrJ1q1bGRgYCD/+4Ac/eMXOdebMGTZs2EBHR0d428yZM7n33nspKSmhq6uLl19+mWeffRbbtrFtm09+8pM4nU4+8YlPjOscTU1NvOlNb6KxsZHk5GTe+ta3smTJEhISEqisrOTXv/41VVVVgCr989GPfpRHH310xHFKSkpwOBz09PTQ3KySWhwOx6juKzk5OWP26zvf+U5YCDN79mze8pa3UFJSgtfr5fjx46SkjF7uds6cOaxatYrZs2eTlpaGz+ejoqKCp556ihMnTgDKbefNb34zR44cGXellPHidru55ZZbWL58Ofn5+SQlJdHZ2cnBgwd57LHHwqYQL7/8Mh/4wAf4y1/+MuIYcXFxTJs2DYC6ujoGB5UjV1paGunpsccxsco/vf7662zevDlqzXzx4sXcddddFBUVEQgEOHz4MH/4wx/o7OzEtm3+/u//nuTkZN75zneO63r/8pe/8L3vfQ+A4uJi3va2t1FWVgaoe7i/v3/UfadOncrq1auZN28e6enp2LZNTU0Nzz//PHv37gVgcHCQd7/73UybNm1crjVvFIQcj7RIMyE++MEP8rOf/Sz82LIsjHEu5K5du5bt27cDUFpayrlz5y6wx+hs3LiRl19+GYDCwkJqamousEeEoqIizp9X6vxbb72Vl1566aL7MZRXXnllXO2kbwB5ak+k5IuUQVeXoFuEYarg77TFE1okl6HA1ECPClSFyvK4PGrS5e1HuBNgSolyk9CMiRzoVdmqLg+kZOqJ6VVA9veockguD6Rkj3n/SymhvRG6W1W5oKwChDseGfArIQgoUVUgoOpAu+MRialIy1KTJ5dnXEp0WXdWZQIFyxKJotmItLEHYG8k7J52OPn6yIztaxWXB7F4ow6MazQajUZzHSD9Pqg+huxUAXGRkqHKRjrdk9wzjUYzHOkbVGWfJMqRJYY4P+Z+vZ1KvJaYBv3dyI4msG1EZv7IBTaC4p2+LhXf8PsQfi/S5UaEypCkZiPib9x68BqNRqPRXG9Iv1cJWIKxOtnbqWK3Dhdk5CIcsUUt0tuvhDRxSQh3nIob+73K4X2Y4Ph6dIbxer1UVERcokpLS3G7r915kJSSxz/1HD3NfRd9jKScRN789dtuGHeYj3/843z3u98NP963b98VWaC3bZs1a9awM+xeBl/4whf4t3/7txFrt9u3b+etb30rbW1tgBJj7N+/n7lzYyf+Di2TZBgGtm1z55138vOf/5ysrOgElsHBQd7xjnfw5JNPhrcdOXKE+fPnxzz2I488wvvf/35ACSNCQpoLMbxMEihh60MPPcTHP/7xC65Xv+td7yI1NZW///u/H/W6AX7+85/z4Q9/GK9XrbPed999/P73vx+1/Xivp7e3l7lz5/KZz3yGBx54YFS3l66uLj70oQ/xhz/8Ibzt6aef5s477xy1D7HKWo2H7u5uFi5cGO5zcnIyjzzyCG9961tHtG1ubub+++8Pr4enpaVx/PjxUct/xfq8/+u//itf/epXcbnGFjV+6lOfCrvjrFixYtR2Tz/9NO9+97vDYrAVK1awe/fuUdsPLy11qVKSyf4+184wV4DhLiw+n2/cziyhLw2IrTy72H74fBOrS305+zGU1atXj7utXL0Gee4Q9HUj8qYj8kqRzdXI1jpE6hREXmwXknEff9MdUHdOuWLkll6U84RGowFYrQK//T0q+/lGFFnc+SZkRxOypRrZ1wenX1dZHK44JUTqblWBaYcT4lMg4FXOOrYEdzzEJ6rjxCer7M+6cpUtZTogq0jVDx7oAU8wKzQuAdLzoe40NFerdp5EJWqybRWAd7lVBkl/sGRefBLMWIqx6i06M0Sj0Wg0muuKW5WDhJTKDVCj0Wg0Go1Go9FoNNcUDceaLkkIA9DT1EvD8Wby5t0YiaYhtwoAl8s1qijkUnnssceihDCf+MQn+PznPx+z7Zo1a3jsscdYt24dtm3j9Xr53Oc+x2OPPXbB89i2zfLly3n88cdxOkc6Tno8Hn7+859TWlpKV1cXAL/5zW/4j//4j4u7sAnwxS9+kX/+538eV9uf/vSn41rTfu9734tlWWFHn0cffZTGxsZLLv0UHx/P2bNnLygESUlJ4Te/+Q3Nzc1h4ckPf/jDMcUwF8vXvva1sBDG5XLx7LPPsmrVqphts7OzefLJJ1m0aBHl5eV0dHTwne98h29961vjOtcHPvCBcbf94he/OK736q677uJPf/oTGzduBGDPnj3s37//hnGH0WKYK0BiYnSAcnBwcNximKGWYMOPcyn9CNk+jZfL2Y+hOBwTuOUciTB/mHgmb5r6d3k6A6XjK+Oi0WgugCMxIui4UcnKV/8Alm++SifdcpXOo9FoNBqNZlJJHN22WKPRaDQajUaj0Wg0k8vZV6suy3HKX628YcQwTU1N4b/z8/MvKH64WP77v/87/Hd2djZf/vKXx2y/evVq3ve+94UrgDz55JPU1tZSUFBwwXN973vfiymECZGens7b3/728LH37Nkznku4JPLz8/n0pz897vbjXc8GeP/738/XvvY1zp07h9///7N33/FR1Pn/wF+f2fSeQEgDElLovfcOokhR8EDlTtSz36E/8dSzgV3xvmcvpygqIigiIEoVQu899BKSECC992Tn8/tjyCab3ZBNssluwuv5eKAzszOfee9ms9mZec/7XYotW7ZY3BKoOoqiWPxe0Ol0ePXVVzFixAgAwObNm6HX66HTWe+G7YKCAnz55ZeG+SeffLLaRJhy7u7uePfddzFt2jQAWlWct95664bvDUB77RcsWGBxbLX5WY0ePRqjRo1CdHQ0AGDDhg03TTIMS2E0AC8v43K0lXvQ1SQrK8sw7enpabU48vLyUFZmWQuPsrIy5OdXZLDWNw4iIiIiIiIiIiIiIiKi5ig3Oc9K49SvukxTkpGRYZj29m6YG0AKCwsNF/8B4J577rGoAMBjjz1mmNbr9diwYUON23Ts2BEDBgyocb2BAwcaps+ePVvj+vU1Y8aMGpMw6koIYdRO59ChQw2ynxsZMmSIoeVOYWEhTp06ZdXxN27caHSdf86cORZtN2XKFMN1+rS0NBw7dqzGbW677Ta0aNGiboFaoLwyDGCbn5WtMBmmAVTtxZaQkGDRdnq9HlevXjXMh4fXrw1Q5Tj0ej2uXLli0XaJiYlQVdVqcRARERERERERERERERE1R2VFlt2MXpPSwlKrjNMU5ObmGqat2aGissOHDxsVCpgwYYJF2/Xt2xf+/v6GeUsquFiSCAMAwcHBhunKBRIaSk1VTOorIKCikpGl16GtSafToWXLlg0Ww65duwzTXbt2tahCUHlcvXv3NsxXbgtWneb+s7IVtklqAB07djSav3jxoqFE043ExcVBr9dXO4414ggNDa1xu4sXL95wHCIiIiIiIiIiIiIiIiICHFysc7nV0bVhKnjYI09PT0MySOVuFdZ0/vx5o/lu3bpZvG337t2xefNms+OYExgYaNG47u7uhumGet6VRURE1Gm7rKws/PLLL9i8eTNiYmKQlJSEnJwclJZWn7CVnZ1d1zDNOnnyJJYtW4b9+/fjzJkzyMrKQl5enlFBh4aO4fjx44bpTp061WrbyskniYmJNa5f159VQUEBVq9ejfXr1+P48eNITExEbm4uiouLq93G2q+TPWMyTAPo2LEjHB0dDR8Ie/bswQMPPFDjdnv27DGar82Hsjndu3c3GX/06NGNHgcRERERERERERERERFRc+QZ4IHMhPpfXPYMcK95pWbCz8/PkAzTUBVSKre3AWBU7aUmldetOo45Li4ulgfWiDw9PWu1vpQS77//PubNm4e8vNq1/yoqKqrV+tW5du0a/vGPf+DXX3+t9bbWiqFcenq6YXr58uUQQtRpHEve47X9WQHA4sWLMXfuXKSmptZqO2u/TvaMyTANwM3NDSNGjMCff/4JANi8eTOklDX+gmzatMkw7eHhgWHDhtUrjsjISERGRuLChQuG8V988cUat6scR1RUFCIjI+sVBxEREREREREREREREVFzFDU8DAkH6t92JHJ4OytE0zQEBAQgNjYWAHD16lWUlpbC0dG6lXEqV15xcHCo1fiVK7jUNinEnjg41C4V4IknnsDnn39uslwIgRYtWsDV1RVOTk6G5RkZGYZkISll/YKFlggzbNgwky4mAODs7AxfX1+4urpCURTD8vj4eEM7LGvEUJm1KqgUFBTUuE5tf1YLFizAc889Z/YxPz8/uLm5wdnZ2bAsNzcXKSkpAKz/OtkzJsM0kKlTpxqSYS5duoTNmzdj7Nix1a6fnZ2NX375xTA/YcIEozdofeL4z3/+AwDYvn07zp8/j6ioqGrXP3/+PLZv326YnzJlSr1jICIiIiIiIiIiIiIiImqOgroGwLOVO3JT6t72xjPAA0FdWlkxKvvWr18/Q6eK4uJixMTEoHfv3lbdh4eHh2G6rKysVgk3lRNpKo/TnP3xxx9GiTDh4eF48sknMXbsWERFRZl97ebNm4fXXnvNajHMmTPHKBFm0qRJeOCBBzBw4MBqW1GFhoYiISHBajFU5ubmZpj28vKqVXWhylq1su7v9vHjx/HCCy8Y5gMCAvDkk09iwoQJ6Ny5s9kcg0WLFlnUyaa5YTJMA5k1axZefvllQzbcc889h3379lWb1fXSSy8ZZYXNmTOn2rHDwsIQHx8PQPsFj4uLq3bdRx99FB9++CFKS0shpcQzzzyD1atXV7v+3LlzDdOOjo547LHHql2XiIiIiIiIiIiIiIiI6GYmhEDHcZE4sORYncfoOC6izi1YmqLhw4fjo48+MsxHR0dbPRnG19fXaD41NRXBwcEWbVu57UzVcZqryj+Prl27YteuXfDy8rrhNtZscXX16lWsWLHCMP/KK6/g1VdfrXG7hmqzBQAtWrQwTE+ePBmLFy9usH3Vxqeffgq9Xg8ACAwMxKFDh2p8bzfk62TPlJpXobrw9vbGs88+a5g/fPgwZs+ejdLSUpN1P/74Y3z66aeG+QkTJtS7RVK5iIgIoyyv3377Dc8995xJ+SMpJZ599lmsWbPGsOzBBx9EeHi4VeIgIiIiIiIiIiIiIiIiao46jI1A234hddq2bb8QdBgTYeWI7NuoUaPg6upqmP/666+tvo/IyEij+ePHj1u8beV1b9Rxo7lQVRVbt241zL/00ks1JsIAWncUa4mOjjZcv/b29saLL75Y4zYZGRnIycmxWgxVdejQwTB95Ur9W6FZy5YtWwzTTz31lEVJXtb8WTUlTIZpQHPnzsWoUaMM80uWLEFUVBReeuklLFy4EG+//TYGDBiAOXPmGH65g4KC8NVXX1k1jgULFqBz585G8127dsVrr72GhQsX4tVXX0Xnzp3x3nvvGdbp0qUL3n33XavGQURERERERERERERERNTcCCEw9OF+tU6IadsvBEMf7ndTVYUBAD8/P9x3332G+dOnT+OXX36x6j569+5t1LFjw4YNFm136NAho8owAwYMsGpclqjckkhV1QbfX3p6OkpKSgzzPXr0qHGbkpIS7Nq1y2oxXL161TDdsWNHODk51bhNdHS0xePX5TUdMWKEYXrfvn0oLi62eH8NqfJrZcnPCqjda9WcMBmmATk6OmLFihUYNGiQYVl8fDzefPNNPPTQQ3jhhRewf/9+w2PBwcFYs2YNWrdubdU4vLy88Mcff6Bjx46GZadOncK8efPw0EMPYf78+Thz5ozhsY4dO+L333+3KOOPiIiIiIiIiIiIiIiI6Ganc9Rh+OMD0G9WD3gGeNxwXc8AD/Sb1QPDHx8AnaOukSK0L3PnzjVKUHj88ceRnJxcp7HS0tJMkmlcXV0xevRow/yPP/6IvLy8Gsf64osvDNM6nQ633HJLnWKqDw+PivdPQ1Y+KVe1o0hRUVGN2yxduhQZGRkNEoMl+weATz75xOLx6/KaTpgwAZ6engCAgoICfPPNNxbvryHV9rXatm0bTpw40ZAh2S0mwzQwX19f7NixA2+++SaCgoLMruPu7o4HH3wQMTEx6NOnT4PEERYWhsOHD2Pu3LnV9rbz9fXF3LlzcfjwYYSFhTVIHERERERERERERERERETNkRACHcdGYso74zHmmaEI7R8Cv1AfeLZyh1+oD0L7h2DMM0Mx5Z3x6Dg28qarCFNZZGQk3n77bcN8amoqRowYgYSEhFqNs2fPHvTp0wc7d+40eeyRRx4xTKekpODll1+ucazKCQ+TJk1CSEjd2l/VR2hoqGE6OzsbiYmJDbq/Fi1awM3NzTD/xx9/3HD9q1ev4l//+pdVY2jTpo1h+sSJEzW+D7766iuj1k41qfyaWpoY4uPjY/QeeuGFF3D69GmL9wlYnthTG5Vfq5p+Vrm5uXj00UetHkNTwWSYRqDT6fDCCy/g8uXL2LZtG77++mu89dZb+Pzzz/H7778jOTkZCxcuhJ+fn0XjxcXFQUoJKSXi4uIsjsPV1RX/+c9/kJSUhI0bN+LLL7/EW2+9hS+//BIbN25EUlIS/vOf/xj16CMiIiIiIiIiIiIiIiIiywkhENw1AMMfH4iJr47B1AUTMPHVMRj++EAEdw24qZNgKnv66acxdepUw/zZs2fRq1cvvP/++zW2pDl27BjuuusuDB48uNrEialTp2Lw4MGG+Q8++ACvv/662TY5u3btwpQpUwyPOTs744033qjDs6q/zp07G1Uyef755xu0QoxOp8OoUaMM82+//Ta2bdtmdt2jR49i+PDhSE1NhaJYL9Vg1KhR0Om0Kkl6vR6zZs1CVlaWyXqqquLjjz/GY489BgAWx1C53dXWrVuxYsUKk4o45rz00kuIiIgAAGRlZWHo0KH46aefamy1dOzYMTz99NPo37+/RfHVxrhx4wzTixYtwk8//WR2vdjYWIwZMwZnzpyx6s+qKXGoeRWyFp1Oh+HDh2P48OE2jcPJycnol4SIiIiIiIiIiIiIiIiIqDEJIfDzzz/j4YcfxrfffgsAyMjIwNNPP42XX34Zo0ePRp8+feDv7w9nZ2ckJycjISEBGzduxKVLl2ocX1EULFq0CAMHDkRmZiYA4JVXXsHSpUsxffp0hIaGIjs7G9HR0Vi3bh30er1h23feeQddunRpkOddEycnJ9xzzz348ssvAQBLlizBzz//jLCwMKMKLpMnT8Zrr71mlX0+++yzhioj+fn5GD16NCZNmoSRI0fCx8cHqampiI6OxoYNG6CqKoKDgzF58mSjtlL1ERgYiPvuu89QmWfHjh1o37497r77bnTt2hUAcPHiRaxatQpnz54FADzwwAPYvHkz4uPjaxx/8uTJ8PPzQ0ZGBvR6PaZPnw4fHx+EhITAwaEiZWLhwoXo27evYd7b2xsrVqzAqFGjkJmZiYyMDMycORMvvPACxo8fj06dOsHLywuFhYVIS0vDiRMnsG/fPkNMlSvSWMv/+3//D1999RVKSkqg1+sxc+ZMfPXVV7jlllvg7++PzMxM7N69G2vWrEFxcTE8PDzw+OOPY8GCBVaPxd4xGYaIiIiIiIiIiIiIiIiIiBqdo6MjFi1ahL59+2L+/PlIS0sDoCVkrFmzBmvWrLnh9oqi4P7778dzzz1n9vH27dtjy5YtmDBhApKTkwEAp0+fxuuvv252fSEE3n33XTz11FN1f1JW8Pbbb2PPnj2IiYkBAJSWluL8+fNG6/Ts2dNq+xs+fDheffVVzJs3D4BWgWX16tVYvXq1ybr+/v749ddfsW7dOqvtH9Aq9xw6dAjHjh0DoLXO+uijj8yuO378eHz66afo2LGjRWO7u7vj22+/xYwZM1BYWAhAq/RStfpMXl6eybY9evTA3r17MXnyZEMiTmxsrEWJQOXVbqwpIiIC//vf//Dggw8aKtRs3rwZmzdvNlnXw8MDP/74I9LT060eR1Nwc9bDISIiIiIiIiIiIiIiIiIiu/DEE08gNjYWb775Jnr16lVjK6k2bdrgueeew+nTp7Fw4UIEBQVVu27Pnj1x+vRpzJkzB56enmbXURQFo0aNwr59+/Cvf/2rXs/FGvz8/LB//3588cUXuPXWW9G6dWu4uro26D5feeUV/PDDD2jTpo3Zx52dnTFjxgwcO3bMqO2QtXh6emLnzp149NFH4eTkZHadkJAQvPfee1i/fj1cXFxqNf6kSZMQExODf/3rX+jfvz/8/PyMqsLcSPv27XH8+HF89tlnaN++/Q3XdXR0xPDhw/Hf//4Xe/furVWMlpo9ezbWrl1bbTKQTqfDhAkTcOjQIUyaNKlBYmgKhLSkGRYRERERERERERERERERUQMrLi5GbGysYT48PBzOzs42jIhsITU1FQcOHEBKSgrS0tJQVlYGHx8fBAUFoU+fPmjdunWdxi0pKcGOHTsQGxuLtLQ0uLu7IygoCCNGjECrVq2s/CyaprKyMuzduxfHjh1DdnY2fH19ERISguHDh8PHx6dRYkhPT8e2bdtw6dIllJWVITAwEJGRkRg0aBAUxfb1PuLi4rB//36kpKQgKysLbm5uaNGiBTp06IBu3brB3d29UeKQUuLQoUM4dOgQ0tPT4eXlheDgYAwePBiBgYGNEsON2PrznMkwRERERERERERERERERGQXbH3xlIiIrMPWn+e2T5siIiIiIiIiIiIiIiIiIiIiIrISJsMQERERERERERERERERERERUbPBZBgiIiIiIiIiIiIiIiIiIiIiajaYDENEREREREREREREREREREREzQaTYYiIiIiIiIiIiIiIiIiIiIio2WAyDBERERERERERERERERERERE1G0yGISIiIiIiIiIiIiIiIiIiIqJmg8kwRERERERERERERERERERERNRsMBmGiIiIiIiIiIiIiIiIiIiIiJoNJsMQERERERERERERERERERERUbPBZBgiIiIiIiIiIiIiIiIiIiIiajaYDENEREREREREREREREREREREzQaTYYiIiIiIiIiIiIiIiIiIiIio2XCwdQBVJSQkAAAcHR0RFBRU53GSkpJQUlICAGjbtq1VYiMiIiIiIiIiIiIiIiIiIiIi+2Z3yTBhYWEQQiAyMhJnz56t8zi33norjh8/DiEEysrKrBghEREREREREREREREREREREdkru0uGAQApJaSUVhmHiIiIiIiIiIiIiIiIiIiIiG4eiq0DMEcIYesQiIiIiIiIiIiIiIiIiIiIiKgJsstkGGtQVRUAoCjN9ikSERERERERERERERERERERURXNNlMkNTUVAODh4WHjSIiIiIiIiIiIiIiIiIiIiIiosTTLZJhjx44hKSkJQgi0bt3a1uEQERERERERERERERERERERUSNxsNWOExISEBcXV+3jhYWF2L59u8XjqaqK7OxsnDhxAl988YVhed++fesTJhERERERERERERERERERERE1ITZLhlm0aBFee+01s49JKXH16lWMGjWq3vuZNWtWvccgIiIiIiIiIiIiIiIiIiIioqbBZskwgJb0IoSAlNLsY7UlhDDa9u9//zvGjBlTvyCJiIiIiIiIiIiIiIiIiIiIqMmwaTIMULeklxuN5ePjg4EDB+KBBx7A9OnTrTY2EREREREREREREREREREREdk/Ia2ZjVIL2dnZyMrKMlompUR4eDiEEAgNDUV0dLTF4zk6OsLDwwNeXl5WjpSIiIiIiIiIiIiIiIiIGkNxcTFiY2MN8+Hh4XB2drZhRESW2bp1K0aNGmWYr+4yfFxcHNq1a2eYv3TpEsLCwho6PKqj8u40ABAdHY2RI0faLpgmxtaf5zarDOPt7Q1vb2+zj0kp4eDggNDQ0EaOioiIiIiIiIiIiIiIiIiIiIiaMpu3SarqvvvuAwAEBATYOBIiIiIiIiIiIiIiIiIiImoI3377Le6//37DPKtuNJ6tW7di69atAICwsDDMnj3bpvEQNQS7S4ZZtGiRrUMgIiIiIiIiIiIiIiIiIiJqlrZu3YpXX30VADBixAgmw1CzZHfJMERERERERERERERERERERE3JyJEjIaW0dRhEdJ1i6wCIiIiIiIiIiIiIiIiIiIiIiKylSVSGUVUVcXFxyMrKQn5+fq0z6oYPH95AkRERERERERERERERERERVZBS4lx2CvanxiG9KB/F+jI46xzQwsUd/f3D0N67FYQQtg6TiKhZs9tkmKKiInz77bf48ccfcejQIRQVFdVpHCEEysrKrBwdEREREREREREREREREVEFKSV2JcdiV9JFpBfnmzx+tSAbMRlX0cLZHUMCIzAkIJxJMfUUFxeHffv2ITExETqdDqGhoRgzZgy8vLxqPVZhYSEOHTqEU6dOITMzE2VlZXB3d0dQUBAiIyPRvXt3ODo6NsCzsExycjJiYmJw4cIFZGVlQVEUtGjRAh07dkT//v1tGtu5c+dw6NAhJCcno6SkBAEBAejVqxe6d+9ulfGTk5Oxa9cuXL16Fbm5uQgNDcU999xjdt2ysjKcOHECp0+fRlJSEgoKCuDt7Y2AgAAMHjwYISEhVomJ7J9dJsPs378f99xzDy5dugQAdeqtJoRgTzYiIiIiIiIiIiIiIiIianBlqh5LLx5ETMbVGtdNL87Hb/HHcSk3DXdH9IWDomuECJuukSNHYtu2bQCAefPmYf78+Th37hz+8Y9/4M8//zS5Juzi4oIHH3wQ77zzDjw8PGocPysrC6+88gq+++475OTkVLueq6srRo8ejVdffRV9+vQxeXzr1q0YNWqUYd4a16pPnDiBxYsXY82aNTh9+nS167m7u+Phhx/Gv//9b/j7+1e7nrnkq23btlWblFX+epujqioWLVqEd999F+fPnze7TmRkJN544w3MmDGj2pjKmfs5X7hwAU8++SQ2bNgAvV5vWNfb29soGaawsBC//vorfvrpJ2zduhW5ubnV7qdv376YN28ebr/99hpjoqZNsXUAVcXExGDcuHG4dOlSvT4gmAhDRERERERERERERERERA1NldLiRJjKYjKuYunFg7yuWUtbtmxBv379sGnTJrOvXVFRET799FP06dMHSUlJNxwrLi4OPXv2xMcff3zDRBhAS7j4448/sGPHjnrFXxuzZ8/GggULbpgIAwD5+fl4//330bdvX5w4caLB40pLS8OQIUPw97//vdpEGAC4cOECZs6cib/97W9GySyWWL9+PXr37o21a9fWuO3mzZsxa9YsrFmz5oaJMABw8OBBTJo0CXPnzuXvXjNnd5VhnnjiCeTm5hqyz0JDQzF79mwMHDgQISEhFmXvERERERERERERERERERE1ht3JsbVOhCkXk3EVu5NjMSQwwspRNU/Xrl3DzJkzkZOTA1dXV0ybNg39+vWDs7MzTp8+jWXLliE5ORmA1rpn/Pjx2LdvH1xdXU3GklLiL3/5C+Lj4w3L+vTpg3HjxiEsLAwuLi7IyclBfHw8Dh06hJ07d6KsrKzRnmtlQgj07t0bAwcOREREBHx8fFBYWIgzZ85gzZo1iIuLAwAkJCRg0qRJOHbsmNlWURER2vssIyMDmZmZALRKOtW1DvLz8zNZlp6ejqFDh+Ls2bOGZa1bt8bUqVPRsWNHODs748KFC1i+fDliY2MBAIsXL4arqyv+97//WfR8Y2Nj8eGHHyI3NxdeXl6488470bNnT7i7uyMxMRGbNm2qdls/Pz8MGzYMvXv3RqtWreDk5ITk5GTs3r0b69atMyTW/Pe//0VoaCjmzJljUUzU9NhVMszFixexc+dOQyLMAw88gM8++wxOTk42joyIiIiIiIiIiIiIiIiIyJiUEruSLtZrjF3JFzE4ILzaVjVUYeHChVBVFZ06dcLq1asRFRVl9Phrr72G2bNnY+XKlQC0riRvvvkm3njjDZOxoqOjceDAAQBassn333+PWbNmVbvvrKwsfP/99wgKCrLiM7oxT09PvPDCC3jooYcQFhZmdp33338f//d//4fnn38eUkrExcXhzTffxLvvvmuy7oULFwAA8+fPx6uvvgoAGDBgALZu3WpxTA8//LAhEUYIgfnz5+P55583uab/2muv4bnnnsMHH3wAAPjyyy9xxx13YMKECTXuY/HixQCAcePG4YcffkCrVq2MHjfXumnw4MF47rnncOutt8LR0dHsuOfOncNdd92F48ePAwCef/553HvvvWjRokWNMVHTY1dtknbu3GmY7tSpE7788ksmwhARERERERERERERERGRXTqXnYL04vx6jZFWlI/z2SlWiqh5U1UV3t7eWLdunUkiDAB4eXlh2bJl6N+/v2HZe++9h7S0NJN1KyeATJ069YaJMADg4+ODOXPm4K677qr7E6ildevW4c0336w2EQYAdDodnn32Wbz00kuGZV9//TWKi4utHs/69evx66+/Gub/85//4JVXXjF7Td/JyQnvv/8+7rnnHsOyl19+2eJ9denSBb/99ptJIow5Y8eOxa5duzB58uRqE2EAoH379ti4cSP8/f0BaK2vvv32W4tjoqbFrpJhUlIqPuRnzJgBRbGr8IiIiIiIiIiIiIiIiIiIDPanxllpnPiaVyIAWjWP0NDQah93cnIyVCMBgJKSEvzwww8m6yUlJRmmzSXW2AMXFxeL133++efh4eEBQGtldOjQIavHU/l17devH55++ukat/nvf/9rSFA5ePAgjhw5YtG+FixYYPHzr83rFBAQgH/84x+G+Q0bNli8LTUtdpVtUvlN2qZNGxtGQkRERERERERERERERER0Y+lF9asKUy6tKM8q4zR3Op0ODzzwQI3rDRo0CF26dDHMr1mzxmQdNzc3w/TevXutE6ANubm5YeDAgYZ5ayfDZGZmYuPGjYb5J5980qLtAgICMG7cOMP85s2ba9zG398ft9xyS+2DtNCYMWMM0w2RNET2wa6SYSonwOTm5towEiIiIiIiIiIiIiIiIiKiGyvWl9nVOM1djx49LGqbAwDjx483TB8+fBhSSqPHe/bsaZjevn075syZg6ysLGuEaTMBAQGG6StXrlh17N27dxu9hhMmTLB428ptqw4cOGDR+jqdrnYB1kLl1ykjIwNFRUUNti+yHbtKhhkxYgScnZ0BWPZLQERERERERERERERERERkK846B7sap7mrXO2lJl27djVMZ2VlISUlxejxadOmoUWLFob5jz/+GMHBwbjzzjvx2Wef4eTJkyYJNLaSnJyMDz/8ENOmTUOHDh3g5+cHR0dHCCGM/i1ZssSwTXZ2tlVjOH78uGHa39/f6LWrSeXkk8TExBrXj4iIqF1w16mqii1btuCf//wnhg4diuDgYLi7u5u8TlXbYln7tSL7YFefqr6+vpg+fTqWLFmC1atXIyUlxeLMPiIiIiIiIiIiIiIiIiKixtTCxR1XC+p/Ib2li4cVomn+anPt2N/f32g+MzPTKCnD09MTS5cuxdSpU1FQUAAAKCwsxMqVK7Fy5UrDGOPGjcPdd9+NW2+9tUGrlZhTUlKC+fPn4//+7/9QUlJSq22tXe0kPT3dMJ2amgohRJ3GsaT6jqenZ63H3b9/Px5++GEcO3as1tuyMkzzZFeVYQDggw8+QHBwMPLy8vC3v/0NZWUsCUZERERERERERERERERE9qe/f5iVxgm1yjjNnaurq8Xrurm5Gc3n5eWZrDNu3DgcOnQIkydPhqKYXjpPTU3Fjz/+iEmTJqFbt27Yvn177YOuI71ej+nTp+Ptt982SYTR6XRo1aoV2rZti4iICMO/ykkk1q5qY63qKeWJRzfi4FC7mh5bt27FyJEjzSbCeHp6Ijg4GOHh4YbXKTTU+PfNXioAkXXZVWUYAGjRogXWrl2LSZMmYdOmTRg8eDA++eQToz5iRERERERERERERERERES21t67FVo4uyO9OL/OY7R0cUeUN7tlWKKwsNDidasmXXh4mK++07FjR6xevRqJiYn4/fffER0djR07duDatWtG650+fRpjxozBqlWrMHHixNoHX0tffPEF1qxZY5jv0aMH/vnPf2LkyJEICwszW6Xmvvvuw/fff98g8VROLnJ0dETbtm3rNE7r1q2tFRIA7T1x3333Gd4bTk5OeOSRR3DnnXeid+/e8PLyMtnm0qVLCA8Pt2ocZH/sLhnmtddeAwBMnDgRX375JQ4dOoRBgwahQ4cOGDhwIAIDA+Hi4lKrMV955ZWGCJWIiIiIiIiIiIiIiIiIbmJCCAwJjMBv8cfrPMaQgIg6t5y52aSkpFi8bmpqqtG8r6/vDddv3bo1Hn30UTz66KMAtOSX1atXY+HChbh48SIAoKysDA899BBiY2Nrfc26tj766CPD9NixY/HHH3/AycnphttY0oKorlq0aGGYDggIwIULFxpsX7WxatUqJCQkAAAURcHatWsxZsyYG27TkK8T2Q+7S4aZP3++yYe9lBJnzpzB2bNn6zQmk2GIiIiIiIiIiIiIiIiIqCEMCQjHpdw0xGRcrfW23fyCMTiAFSosdfLkSYvXPXHihGHax8cHrVrVrvpOp06d0KlTJ8ydOxezZ8/Gjz/+CAC4du0aoqOjceutt9ZqvNq4cuUKzp07Z5h/4403akyEAbSKJw2lQ4cOhunU1FSUlpbC0dGxwfZnqS1bthimx48fX2MiDNCwrxPZD9PGZ3aicl8uIQSEEJBS1vofEREREREREREREREREVFDEULg7oi+6OYXXKvtuvkF4+6IvqwKUwvHjh2zuDrMxo0bDdN9+vSp8+vs6OiITz/91Gj706dP12ksS129apxY1aNHjxq3SU1NtThZqHISi6qqFm0zYsQIw3RxcTH27t1r0XYNrfJrZcnrBADR0dENFQ7ZEburDNO2bVt+4BMRERERERERERERERFRk+Gg6DArsj92J8diV/JFpBXlV7tuSxd3DAmIwOCAcF4XrSW9Xo9vv/0Wzz777A3X27dvn1FiyO23316v/fr4+MDf39+QiFNWVlav8WpStehDUVFRjW2ZPvvsM4sTWzw8PAzTOTk5Fm0TGBiIoUOHYufOnQCATz75BMOGDbNo24ZU+bUqKiqqcf3s7GwsXry4IUMiO2F3yTBxcXG2DoGIiIiIiIiIiIiIiIiIqFaEEBgSqCW5nM9Owf7UeKQV5aFYXwZnnQNaunigv38oorxbMQmmHt5++23cfffdaNOmjdnHS0tL8dRTTxnmnZ2dMWvWLJP14uPjERoaatE+r127hvT0dMO8pdvVVdXn9scff+Dee++tdv2YmBi88847Fo9fOf7z58+jpKTEojZMzz//vCGx6Oeff8bUqVNx9913W7xfvV4PKSUcHKyXplD5tVq/fj1UVYWiVN8g54knnkB2drbV9k/2y27bJBERERERERERERERERERNTVCCLT3CcCsqP54qttoPNdzPJ7qNhqzovqjvU8AE2HqQVEUZGVl4dZbb8XFixdNHs/NzcU999xj1MLnmWeeQcuWLU3WnT17NkaPHo1ff/0VxcXF1e4zKysLf/3rX6HX6wFoyTXjx4+3wrOpXlBQELp06WKYnzt3brUtkLZs2YIxY8agqKjohkkglfXr18/wPiwoKMDLL79sUVWViRMnYtq0aYb5v/71r3j11VeRn199JSQASExMxP/93/8hIiICiYmJFsVoqXHjxhmmz549i6efftrws6osJycH999/P5YsWWLx60RNm91VhiEiIiIiIiIiIiIiIiIiIqrqoYcewsqVK3Hy5El069YN06dPR//+/eHk5IQzZ85g6dKlSEpKMqzfrVs3vPjii2bHklJi69atiI6OhpeXF4YMGYI+ffogKCgI7u7uyMrKwvHjx7Fy5UpkZmYatnvxxRfh6+vb4M/1ueeew9/+9jcAQHJyMvr06YNp06Zh0KBBcHd3x9WrV7Fx40Zs377d8Fw7duyI5cuX1zh2SEgIxo0bh40bNwIAFixYgI8++ghhYWFwdnY2rPfoo4/i0UcfNdr2m2++wYULF3Ds2DHo9XrMnz8fH374ISZMmIDevXvDz88Per0emZmZOHv2LA4dOoRjx45Z62UxMXXqVLRv3x7nzp0DAHz44YfYtGkTpk+fjrCwMBQUFCAmJgYrVqxAWloaAGD+/Pl45ZVXGiwmsg9MhiEiImomZFYqZOJZCDdPILQLhI5/5omIiIiIiIiIiIio+QgMDMSyZcswdepU5OTkYPHixVi8eLHZdaOiorBhwwa4urrWOG5OTg7WrVuHdevW3XC9xx9/HC+99FKdYq+tv/71r9i6dSu++eYbAEBxcTF+/PFH/PjjjybrhoeH49dff8Ubb7xh8fiff/45Ro0ahYSEBABAUVERzpw5Y7RO5cSicl5eXti5cyfuvfde/PbbbwCAzMxMLF26FEuXLq1xv9auyuLg4IDly5djxIgRyMrKAgCcOnUKr732msm6Qgi89NJL+Otf/8pkmJsA6/8QERE1A+rRLVB/egdy169QN30Hdfl7kPnseUlEREREREREREREzcuoUaNw4MABjBs3zmzLKRcXFzz++OM4fPgwgoKCqh3n9ddfx2OPPYbw8PAa9zl48GD88ccf+PTTTxu1zdXChQvx/vvvo0WLFmYf9/DwwCOPPIIjR44gMjKyVmOHh4fj2LFj+M9//oMxY8YgMDAQLi4uFm3r4eGB1atXY+3atRg2bFiNCS5du3bF888/j9OnT6Nt27a1itMS3bt3N7wnqtOjRw/8/vvvZpNkqHkSUkpp6yBqkpmZie3bt2Pfvn1ITExEZmYmCgoKEBISgu+//97W4REREdmUzM2AuvQtQKpGy0WnQVCG32WjqIiIiIiIiIiIiIhqr7i4GLGxsYb58PBwo7YtdPMZOXIktm3bBgCYN28e5s+fb3gsLi4Oe/fuxZUrV6AoCtq2bYuxY8fC29u7VvtISkpCTEwMLl26hMzMTJSVlcHT0xOhoaHo27cvQkJCrPmUaq2oqAg7d+7EqVOnkJeXh5YtW6JNmzYYMWIE3NzcbBoboF3P37lzJ65evYr09HQ4ODjAx8cHkZGR6NatG/z9/RstltjYWOzcuRPXrl2Do6MjgoKC0KNHD3Tu3LnRYiCNrT/P7bp/woULF/D6669j+fLlKC4uNnpMSmk2u624uBgRERHIyMiAg4MDzp49e8OMPyIioibvWqxJIgwAyKsXbBAMEREREREREREREVHjCAsLQ1hYWL3HCQwMRGBgYP0DaiAuLi4YO3Ysxo4da+tQzPL19cWkSZNsHQYALeHCkmo/1PzZbZukL774Aj179sQPP/yAoqIiSCmN/lXH2dkZs2bNQlFREfLz87FkyZJGjJqIiMgGPP3MLhaevo0cCBEREREREREREREREZHt2WUyzAcffIAnnngCBQUFhsQXT09PjBgxAn/5y19q3P7ee+81TP/2228NFicREZE9EEHhQGCVLGehQPQcY5uAiIiIiIiIiIiIiIiIiGzI7pJhDh48iLlz5xrmQ0NDsWzZMmRkZCA6OhrLli0DAAghqh2jW7duaNu2LaSU2L9/PwoLCxs8biIiIltSbnsIos8tQEAYRFg3KLc/BhESZeuwiIiIiIiIiIiIiIiIiBqdg60DqOpf//oXpJQQQmDAgAHYsGEDvLy8aj3OgAEDkJCQgNLSUpw4cQL9+vVrgGiJiIjsg3B0huh7C9D3FluHQkRERERERERERERERGRTdlUZJjk5Gdu3b4cQAu7u7lixYkWdEmEAoGvXrobpc+fOWStEIiIiIiIiIiIiIiIiIiIiIrJjdpUMs337dkgpAQDTpk1DcHBwncdq2bKlYTotLa3esRERERERERERERERERERERGR/bOrNknXrl0zTPft27deY7m7uxum8/Pz6zUWERERERERUW3IkkIg7iSkqocI6wrh4l7zRkRERERERERkYuvWrbYOgYiaILtKhiksLDRMu7m51Wus3Nxcw7Snp2e9xiIiIiIiIiKylExJgLr2S6C4QJvftRLK+NkQbTraODIiIiIiIiIiIqKbg121SWrVqpVhunKVmLqIjY01TPv7+9drLCIiIiIiIiJLqTt+MSTCAADKSqBuXw6pqrYLioiIiIiIiIiI6CZiV8kwYWFhhuno6Oh6jVV5++7du9drLCIiIiIiIiJLyJJCIC3R9IG8TCAnrfEDIiIiIiIiIiIiugnZVTLMkCFD4ObmBikloqOjcfz48TqNs2fPHhw9ehRCCAQGBqJz585WjpSIiIiIiIjIDAcnwMXddLnOAXBlC18iIiIiIiIiIqLGYFfJME5OTpg6dSoAQFVVzJw5E5mZmbUaIz8/Hw899JBh/v7777dmiERERERERETVEooOovtI0+WdBkM4uzZ+QERERERERERERDchu0qGAYA33ngDTk5OEELgzJkz6NOnDzZt2mTRtufPn8fIkSNx6tQpAICXlxfmzp3bkOESERERERERGVF6jYEy6m4gOBIIbAcx5E6IwZNtHRYREREREREREdFNw8HWAVQVFhaGDz/8EI899hiEEIiLi8OECRPQuXNnTJw4EV26dDGsW1BQgOXLlyMxMRHR0dFYv3499Ho9AEBRFHz77bfw9fW11VMhIiIiIiKim5Ro3w+69v1sHQYREREREREREdFNSUgppa2DMOfdd9/Fiy++CCklpJQQQhgeKw+58rLKy3U6HT744AM88cQTjRcwEREREREREREREVEzJMtKAZ0OQthdsXkiaoaKi4sRGxtrmA8PD4ezs7MNIyIiorqw9ee53VWGKffcc8+hf//+eOihhxAbG2uUAFM1CQaoSIRp164dFi5ciFGjRjVqvEREREREREREREREzYlMS4S681cgOQ5w9YToMQpKj5G2DouIiIiIqEZ2ncY9atQonD9/Hr/88gumTZuGgIAAQ6WYyv88PT0xceJELF68GOfOnWMiDBERERERERERERFRPcjSYqh//E9LhAGAwlzIvb9Bnjto07iIiIiIiCxht5VhygkhcOedd+LOO+8EACQnJyM9PR1ZWVlwc3NDy5YtERwcDEWx67weIiIiIiIiIiIiIqKmI+4EUJRvslg9ux+69n1tEBARERERkeXsPhmmqoCAAAQEBNg6DCIiIiIiIiIiIiKiZkuqevMP6MsaNxAiIiIiojpgORUiIiIiIiIiIiIiIjIi2nYGHBxNl0f0tOp+ZGEepJkKNERERERE9dHkKsMQEREREREREREREVHDEq4eUMbdB3Xbz0BBDqDoIDoOhOgy1Crjy4JcyOilkIlntf2FdoYYMQPC1cMq4xMRERHRzY3JMEREREREREREREREZEK07Qzl3peBrBTA1dOqiSoy+kdDIgwAyPiTwPafIW55wGr7ICIiIqKbl02SYRISEozm27ZtW+1j1lB5fCIiIiIiIiIiIiIisoxQdIBfkFXHlIW5RokwhuXxpyCLCyGcXa26PyIiIiK6+dgkGSYsLAxCCACAEAJlZWVmH7OGquMTUdMjUy9Dnj8MCEBE9YVoGWLrkIioGZClJZCxR4HsNIjAdkCbjhZ9B5FSAkmXgKJ8IDiSJ+iIiIiIiIiI6kQAkOYXExERERHVk03bJElp5ouuBY8R0c1DPbsfcutPKD8wljE7oIy6ByKqt20DI6ImTRbmQf3tE63MM7RPGBHRC2LsX2vebu1XQNplbYGDM5RRMyHCe1i+b30ZEHsMMjMJomUbIKwrhKLU9akQEREREdF18uoFyPhTgIs7RPu+EO7etg6JiKohXD0h2nSAvHzGeHloFwgn3nRCREREtrV161aMGjXKMM/chabJZskwTIQhoppIfRnkvj9gdIeIVKHuWwMloicvHhNRncmY7YZEGMOyi0cgOw2ECImqfrv9f1QkwgBAWTHUrcugtG5v0ck6WVoM9bfPDGNIAKJ1e+DWh7Sy00REREREVCfq/j8gj2w2zMujW6BMegyiZWsbRkVENyJG3Q1s/Qky4TQgBERoZ4gRM2wdFhERERE1EzZJhomOjq7TY0R0kynIAQpzTZfnZwNFeYCbV+PHRETNQ0q82cUyJeHGyTAJp00XlhYD12KB0C417lae3mucTANAJp6DuBQDRPSscXsiIiIiIjIl8zIhj1Y5p1hSCHlgHcStD9kmqCpkYR6Qlwn4BkI4ONo6HCK7IFw9IW79O2RxAQDBNsRERDehb7/9Fvfff79hPjo6GiNHjqzVGLNnz8Z3331nmGfRhYZx9OhRrFq1CgDg4+ODp556yqbxEFnCJskwI0aMqNNjRHSTcfMCXDy0xBeT5e62iYmImgfvVsCV8yaLhbf/jbdz9dAS9apy8bBsv9Ul4STHQTAZhoiIqEmSqh6IOwGZmgjhFwiE94DQ2bQrNdHNJ+0KIFWTxTI10QbBVIlBSsjdqyBP7QZUPeDsBmXwVIj2fW0dGpHdEM5utg6BiIiIanD06FG8+uqrAIDQ0FAmw1CTwLMzRGS3hM4Bov9tkNuXo6JVkoDoP5HtRIioXkT3EZAXjwDFBRULW7UFwm5c3UV0Gw65dZnxwoAwiIBQy3bsE1DN8laWbU9ERER2RerLINd9BXk9yVYCQMwOrTWLo7NNYyO6qfgGAhAwarMMaAlqdSQvxUA9tBHIToUIDIMYMAmiZYhl2yZd0qpO+gZAzc8GTuyoeLC4QGu1GhgG4dWyzvEREREREVHDGTlyJKsMNQNMhiEiu6Z0GgjpFwh5/jAAQLTvA9HKwovORDchmRQH5KRpCRrePLFaHeHdEsqd/w/yxE7t9QpsB9FlSI2JdkqH/pBCQI3ZARTla/3M+91qeFymXoY8ewCQKkREL4jgCOP9dh4MeWav1u6tnE8riKg+Vn1+RERE1EhijxkSYQxSEyDP7ofoOsw2MRHdhIR3S4iOA7Tv2uV0DhB9bqnTePJaLNRN3xmqzcjEc5Cpn0OZ+W+IG1SqlVJCblkCeUE7hyEBQNEBUgJCVFpRhbwUA9FjVJ3iIyIiIiIiopoxGYaI7J4ICIMICLN1GER2TZaVQm74BjLx7PUlAqLXGCj9b7NpXPZMeLWAGDyl9tu17wdd+34my+WFI1C3LKk4YX5qN8SQO6BUuhAm3Dyh3PEU5MldQGYS4N9GS8LhneNETY7UlwGXYiDzsyFat4doEWzrkIjIBmQ1LRCRHA8wGYaoUYnhd0GEREHGnwJc3CA6D4LwrVtlGHlqj2nbpeICyAtHILoOrX7DhNOGRBiDwjwtIcbRyXi5g2OdYiMiIiIiIiLLMBmGiIioGZAnd1VKhAEACXnkT8h23SD829gsLnsgVT0gFIjKd2Jaex9ShbrvD5MT5vLAesiOAyGun+iWRQWQp/cAqZcBH3+IyF7sjU7UBMmCHKi/fQpkp2rzAESvsUxAJLoZVdfqkC0QiRqdEAKI7AUR2av+g5UWmV9eUs3y6+S1i6YLHRyBslLjZU4uEOE96xYbEVmFVLXjd6EoNo6EiJorKSWQeBbyzD7I7DSgtBhwdDZUtEPrDg16vpJqpqoq9u7di7NnzyIlJQUuLi4ICAjA4MGD0bZtW6vsIzU1Fdu3b0diYiIKCwvRrl07jBkzBi1bVl/V/dq1a9i+fTvi4+OhKAratm2L8ePHw8fHp85xHDlyBDExMUhOToZOp0NAQAD69u2LDh061HnM+igtLcXOnTtx8eJFpKamwtPTE0FBQRg2bBhatbLO8XRcXBz27duHxMRE6HQ6hIaGYsyYMfDy8rLK+DVJTEzE3r17kZycjKysLLi5uaFt27bo0aMHIiMjGyUGssNkmJ9//hnPP/88AKBfv3746aefaj3GX/7yFxw8eBAA8P7772PKlNrf9U1ERNSkGCXCVJCXz960yTAyLwty5wrIhNPagWaXwRB9b22YE10lxUBehpnlhUBeJuDTCrKsFOpvn2gVYQDtYPjCESh3/j8ITz/rx0SNTuZmaD/fFiGAiztQlA+4etTYfouaHnn4T0MijGHZkc2Q7ftB+PjbKCoisgUR1Vdru5iVUrHQwwei82DbBUVE9RfWFYg/WWWhgAjreuPtPHxNl+kcgLadgZx0IDcdCAiDMmgKhKuH1cIlIsvJ0mLI3auut2SXWovjIVMhnFxtHRoRNRNSSsgTOyFP7NDas1d9PP0KZOwxwKslRNdhEF2HMimmkRUWFuK9997Dxx9/jLQ0058RAPTt2xdvv/02xo4dW+N4s2fPxnfffQcAuO+++/Dtt98iLS0N//znP7FixQqUlhonRjs7O+PJJ5/Em2++CQeHikv1V65cwf/7f/8PK1asgKoa33Tp5OSEZ599FvPmzTPa5kb0ej0+//xzvPvuu0hMTDS7TseOHTF//nzMmDGj2nHCwsIQH29cFTU+Pr7a9235a2BOZmYmXn31VSxatAg5OTkmjyuKghEjRmDBggXo27dvtTGVGzlyJLZt2wYAmDdvHubPn49z587hH//4B/78808tKa0SFxcXPPjgg3jnnXfg4XHj7+Nbt27FqFEVbU2rjmWOqqpYunQp3n33XcTExFS7XmhoKO69914888wz8PU1cwxBVmN3yTBffPEF4uLiIITAhx9+WKcxZs2ahV9++QVCCHz++edMhiEioubPrZpsZvfGyXK2R+r6hUD6VW2mpBDyyGZA5wDR5xbr78zJBfBqoZ3grszZzXBCXF48WpEIU64oH/LEDohB/K7SlEkpIXethDy1W6sOpC8DFAdAAHDzgjLgdoj2NR+8UdNh9q5vSMikWCbDEN1khJMLlClzIE/t0iq/+QZqJ7R5kZuoSRMd+gHJcZBnD2jf7xwcIQbcDuF347ZLIqoP5LGtxonyTq5QRvwFwtMPUqoQglUoiGxJbv8F8sKhivlzB4DSYojxs20XFNmEzEzWPt95gxJZkdSXQW5ZoiW71CQnDXL3SiApFhh9L4TO7i7ZNksJCQkYN24czp07d8P1Dh48iHHjxmHOnDn44IMPapWwdObMGYwbN67aBJTi4mIsWLAA58+fx4oVKyCEwNGjRzF+/Hikpqaa3aakpARvvPEG4uPj8f3339cYQ2ZmJiZOnIg9e/bUGOvMmTOxfPly/Pjjj3Bycrrh+vVx7Ngx3HLLLUhOTq52HVVVER0djQEDBuCdd97Bv/71r1rtY8uWLbjjjjvMJtoAQFFRET799FNs2rQJ27ZtQ2Bg3dqqmpOamoqpU6di9+7dNa4bHx+Pt956C+PGjcPIkSOtFgOZsqtP1ry8POzcuRNCCLRs2RITJ06s0zgTJ05Eq1atkJKSgm3btqGwsBCurszsJiKi5kt0Har1plf1FQs9fCHCe9guKBuSKQkViTCVl5/ZBzRAMowQAsqASVD//L5SqyQB0X+ioUVS1SoSBtnm7z64mcnsVMg9v0EmngPcvSF6jILSeZCtw6repeOQJ3dq03o9UFygTTu7AwU5ULcug+IXCNGyte1iJKsSnn6QGdfMLieim49wcYPoPc7WYRCRFQmhQIyYAdl7nJbw3jLEovamwtkVytR/Qh7dApl6GcKnFUTP0YbvCEyEIbItWVIEGXvUdHncCciifAgX98YPihqdzEjSzt9cv2FJtG4PMeZvEC5sY031I6UKufkHyEvHa7ddeeLM2L+xQkwDS0tLw/Dhw42qnLRp0wYzZsxA+/btUVBQgD179mDVqlUoLi4GAHz00UcQQuCDDz6waB95eXm48847kZiYCE9PT0yfPh29evWCq6srTp48ie+++w6ZmZkAgJUrV+LLL7/ElClTcOuttxraBU2bNg29e/c2u83ixYsxadIk3HXXXdXGUFxcjLFjx+Lw4cOGZS1btsSMGTPQtWtXlJWV4fDhw1i+fDny8vIAACtWrMBf/vIXrFq1ymS8sLAwODg4IDc3FykpWlVUBwcHhIaGmt1/QECAybJz585h1KhRhucBAB06dMD06dMRFhaG7OxsbNmyBevXr4eqqlBVFc8++ywcHR3x1FNP3fhFv+7atWuYOXMmcnJy4OrqimnTpqFfv35wdnbG6dOnsWzZMkMizrlz5zB+/Hjs27fPKjkEqampGDRoEC5erLiBzt3dHRMmTED//v3RsmVL5Ofn4+LFi9ixY4fRz4Yall0lwxw6dAhlZWUQQmD06NFQ6tjGQKfTYcyYMVi6dClKSkpw6NAhDB061MrREhERWYe8ch7yzH5AXwoR0RMiometxxD+baBMelxr05GdChHYDqLPLRCOztYPuCnQl5lfXlZqfrkViPDuUKY9DXn+EKCqEJE9IVpVHBCIwHYwW0gxsF2DxdQUSX0Z1N+/0NpLAdpdMjuWQzq5QET2sm1w1ZCVS+jrS42ndTpAqpDnDzMZphkRPcdAXj5jnIAY2A4IZr9fIiKi5kR4+gG1THYV7t4QQ+5ooIiIqF6kavwdvvLy6s4jVDdUYS6gOEA48ybcpkRKCXXTd0BWRVUCmXgO2LUSYsy9NoyMmgN5YletE2EM28YeA07uhOg6zMpRUWX/+Mc/jBJhHn74YXz44YdwcXExLHvyySdx4sQJTJkyBbGxsQCADz/8EBMnTsS4cTXfBPHrr79CSomhQ4fi559/RlBQkNHj//rXvzB06FBcunQJAPD2229j/fr1SEpKwvDhw/HTTz+ZVCt55plnMHToUMTFxQEA3nzzzRsmw8ybN88o2WLy5Mn47rvv4OPjY7Teq6++ijvvvBMHDx4EAKxevRoLFy7E3//+d6P1tm7dCgD49ttvcf/99wMAQkJCcOHChRpfD0Cr9nL//fcbJcLMnz8fL7/8slEuwNy5c7Fjxw7ccccdSE/XKrA///zzGDduHLp06VLjfhYuXAhVVdGpUyesXr0aUVFRRo+/9tprmD17NlauXAkAiImJwZtvvok33njDoudRHSkl7rvvPqNEmGnTpuGTTz6ptvLMuXPn8N5778HR0bFe+6aa2dXtCJVLUvXs2bNeY/XoUXEnfE2lroiIiGxFPXsA6u+fQ144BHnpONQ/v4e6f22dxhKB7aDc+nfoZv4bysiZEJ43ca/JgFDA3dtksQjv2aC7FS2CoQycBGXwFKNEGABAm46m+2/ZGqLz4AaNqclJOF2RCFOJeqrm8pI2U91dwkZ389TcU/ZmJ4sLoB7dAjV6KdQTOyFLi20dUrVEYBiUKf+EiOwNBEVA9LsVym0P8w4uIiIiImr25NWLUDf/AHX911DP7IM0VEe1f8LZDSKkvekDge0gzJxDMEfmZkD/26dQv58H9buXof65uNpjF6kvg3pwA/Q/vQv9L/8HtbyiKNlOxjWjRJhyMvYopORxO9WdlBLyxI76jXFiJ9+HDejw4cP46aefDPN33HEHvvjiC6NEmHJdu3bF+vXr4e5eUTHM0nY9UkpERERg3bp1JokwABAcHIz333/fMB8fH49Vq1YhKioKa9euNZs8ERISYrTNsWPHcPbsWbP7T0pKwn//+1/DfP/+/fHzzz+bJMIAWlWctWvXIiQkxLDsxRdfRElJiUXP1VKrVq0yah301FNPYd68eWaLYgwbNgyrVq0yPFZcXIwXX3zRov2oqgpvb2+sW7fOJBEGALy8vLBs2TL079/fsOy9995DWlr9KrevWrUK69atM8zffffd+Pnnn2/Ygql9+/b46quvMGTIkHrtm2pmV8kwGRkV/XRbtWpVr7H8/f3NjktERGRP5MENpsuOb4MsLrRs+4RTUDd9B3XDN5AXj1o5uqZLKDoo4+8HvFpWLAvtAjGgbi0Y60OqeshCrdykGPtXKBMfgeg7AcrYv0GZOgfCyfSA62YmS4rMP1BazXI7IDoNBMr7OuvKs/kF4HC9x65QICL72CS2pkIW5UP99QPIfb9DnjsAuetXqGs+g2zAak71JVq1hTJmFnSTn4DSe9zNW4mLmiXtws166H9eAP2K/9p3QiIRERE1Ghl7/PoNPYch409CbvsJcscKW4dVK2LkTCAgrGKBfxsooy2vCKJu/A64dv3Ob6lCXjwCuXuV2XXl1p8gD23Qki/Sr0Du/BXq4U11jp2sQFfNHfg6R97cQPWTeBbIqWcr9OxUIJE3948aNQpCiFr9++6772oc94svvjBMOzs748MPP7zh731UVBSee+45w/yxY8ewZ88ei57Du+++Cw8Pj2ofnzhxoklyyjvvvGOUfFPV7bffDm/visTN/fv3m13vm2++QWlpxfm0jz/+GM7O1Z+z8vf3x9tvv22YT0lJwYoV1v3bXvm1b9WqFV5//fUbrj906FDMnj3bMP/7778jMTHRon09//zz1bZvAgAnJyejllclJSX44YcfLBq7OpWTjwICAvD555/XufsNWZ9dtUmq/MYo78VWV5Wz1srKalfisKGoqopdu3bh4sWLSEpKgq+vL9q0aYMRI0bc8APO2mJjY3Hw4EFcu3YNeXl5cHNzQ6tWrdCrVy907NiRv6BERI1ElpUCeWYSNvWlWmWMGkrtqid3Qu78tWK8uBMQmclQ+t5i7VCbJNGqLZSZ/9bu+nFytUmlHPXUbi3hqTAX8GqpVYwJ7QLRukOjx9JUiLadIHUOJiWqRbvuNoqoZsI3EMptj0A9uF7rOe7bCijMA0qLATcvKAMmQvizRdKNyFO7TU8apV6GvHgEokN/8xsRUYORW5dBXqgoqSx3/AK1pAhKz9E2jIqIiIhsTT20QWspVIk8sw+y91gIj6ZRnVZ4+EA3dQ5kdiogJYSP5TflysxkIO2y6fLzhyGH3wUhKs6ry/xsyItHTNeN2Q7ZaywTL2xE+PgDQREVCU3lyzvyuJPqR57ZZ7VxRBueN2wIf/zxh2H61ltvRZs2bWrc5uGHH8arr74KvV5rsbd27VoMGjTohtt4eXlhypQpN1zHwcEB3bp1w44dOwzbTJ48ucZtunfvbtimusowlZ9nz549jaqgVGfGjBl48sknDW2M1q5di7vvvrvG7SxRWFiI6Ohow/w999xzw0Shco899hi++eYbAIBer8eGDRvw4IMP3nAbnU6HBx54oMaxBw0ahC5duuDkyZMAgDVr1uCpp56qcTtzkpOTsXNnReW3hx9+2ChpiWzPrpJhKldzuXzZ9EtlbSQkJBimW7ZseYM1G55er8d7772Hjz/+GFevXjV53MPDAzNnzsSCBQvg69swBw1SSnz99df44IMPDL/c5oSGhuLRRx/F008/DScnpwaJhYiINMLBEWgRAqRfMX7A2Q3w9jdaJEtLtN6x+VkQIVGAf1vIQ6Z3E8ljWyF7jGSFguuEEECLYKNlUkqgpAhwcjY6UWVtMvEc5I5fKhbkpEHd+C2UvzwH4W3b7yb2TLh6QBl9L9Tty4HiAgACIrw7RI9Rtg7thkRwBHSTnzDMS32ZlhDj5gmh6GwYWRORcc388vRqlhNRg9Eu3Bw1XX58G8BkGCKiWpEp8ZBxJwEnF4ioPha3YSGyW9lmqh5IFchJB5pIMkw5UeW8S/0GM/ynQlG+SeKQYbmqr6guSo1OGfc3yB0rIONOaBVhOvaHGHC7rcOiJk6a+3ysyzg5qVYZpykLDg6Gq+uNbxKtKiUlBbm5udU+fuXKFaPrsxMmTLBo3ICAAPTu3RsHDhwAUH01lsp69eoFB4eaP+MDAgIM07179671NllZWSaPl5WV4ciRikRMS5+nk5MTxo4di+XLlwOw7Hla6vDhw0ZFKyyNqW/fvvD390dqaqohppqSYXr06GFx55nx48cbrpcfPnwYUso6JapWToQBgKlTp9Z6DGpYdvWNq3LZog0bNuDNN9+s81gbNlS0nbAku6+hZGVl4fbbb8euXbuqXScvLw8LFy7E+vXr8dtvv6FXr15WjSEnJwdTpkzB1q1ba1w3Pj4e//73v/HDDz9g3bp1Nn3tbhayqADy7D4gKxUIDIOI7A3BgyGim4YyeArUdQuBsusVzYQCMWiKlihznczPhvrbJ9rJJQDywDqg0yCt2khVZcVAfg7gY8WTOs2IjD8Jdc9vWtlRD18o/W6FaN+3YfZ17oDpQn0p1IProPSdYN0Tb82MCO8BpW1nIPUy4O4F4dX0koeEzgHw8KnTtlLVA5diIJNiAa+WEO37QdRQKarJaxECmLn4LlqGmK5LRA2rMNf8hZvCPEhVhWAlUSIii6hHt0Du+90wL4/8CeX2xyD8ea6Nmi4RGAZ55bzxQgcn7fv8TUD4BgD+bbRj1crLI3ubXkDzDdSOCfOyjNcNieK5XxsTrp4Q42drN7EoSoPeKEU3kdL6dbswKLHSOE3YkiVLMHLkyFptM3v27Bu2Sjp/3vhvV7du3Sweu3v37oZkmKrjmBMYGGjRuJU7hlROcrF0m/z8fJPHExMTUVhYaJiv7fMsT4a5cOFCnZNDqqrva79582az45jTpUsXi8fu2rWrYTorKwspKSkW/xwqO336tGHaycmpVs+PGoddfesaPHgwXF1dUVhYiCNHjuDPP//E2LFjaz3Opk2bcPiwVtLZyckJw4YNs3aoFikrK8Ndd91llAjTtm1bzJo1C2FhYUhNTcWqVasMH6KJiYmYNGkS9u/fj+Dg4OqGrRUpJaZOnWqUCOPk5ITJkydj0KBB8PPzQ3Z2Ng4ePIhff/0VBQUFAICTJ09izJgxOHr0KNzc3KwSC5mSBblQV30I5F5vk3JmL+S5g1AmPsqTzA1IqnrtLgwXD77OZHMiOBLKzH9rrQj0ZRDtumsnVyqRR/40JMIYnN4DuPsC+ZnGy928ABu0A2oKZFYK1I3fAmWlgBBAXibU6KVQvFtCVO4XbrUdSuP50mLt3+m9UC8cgWjfF2LETH4OVUM4OAJB4bYOo9FJKSE3LIJMOFWxLGY7lKlzINy8bBhZwxKdB2sJZFkpFQsDwoCInrYKqUYy/qRW4rwgF6JtJ4heY5t/0hLdHPyCtO8TBTlGi0XrqCb7N0tmp2l/g1sEsyUBETUKtSAHcu9vQGkJoCiAzhEoKYLc9zvE7Y/ZOjyiOhMDJ0H+/sX1Kp4AICAG3m6334OlVIG4k1oCj7s3RIf+EG6e9RpTGXcf1K3LgKsXAEUHEd4DYvBUk/WEokAZebd2HqLk+oVJDz+IodPqtX+yHiYlkVVZq0q3E6t9N4Ty9j/lKncqqUnldauOY46Li4vlgdVjG1n13DOs9zxVVUVOTo5V2v005mtvaVUYc3FkZmbWKRkmPb3iuo2fnx8cHR1vsDbZgl39tXd2dsZtt92GFStWQEqJBx54ALt27apVdZL4+HhDPzAhBG655RabJXP897//xZ9//mmYv+eee7Bo0SKj9kMvvPACPvroIzz11FOQUuLKlSt46KGHjHq61cfSpUuNerFFRUXh999/R/v27U3Wfeedd3DHHXcYZTi+9957mDdvnlViIVPyxPaKRJhyVy8A8SeBdswebAjq2f2Q+9dqJ/fdfaAMnAQRad1qTPZESqn1wC3IBYIj633A39hkYS7k2QPXWwO1B0K7NMsLGMLd26QFjIw/CfXgBu3CcEkRAAlUabUi2nWFPLNXS+4AAEUHZfBUHsxXQx5YB+RnVSSp6BwBZ1fIswcaJBlGRPbWkpwArQRy+R0iOu0LsTx3EAgMh+g00Or7pibs8lmjRBgAQG4G5PFtEAMn2SamRiCcXaHc8ZT2e5GZBPi3setqefJSDNSNiyrmM5Mgky5BN3WODaMisg6h6KCMugfqpm+vfweBduFmSNO7cCOL8qGu+Uw7xlL1gKMLxPC7oHQfYevQiKgZK09uRmFexUJRAri4Q6YkVL8hURMgWra+fkPPEaCkCKJdVwhfy+6AtwUZvRTy/KGK+ePbtBsNqrQulhnXtHMr/q1rrBIiPP2gm/Q4ZGGelgxzg0QgERIFZdYrwOWzgIMDENLebo9xiKh+hHdLyPQr9R/Hi5WkG0LVKiq1uW5cuRpLXl7eDda0PWs9T0B7rtZIhqkck4ODQ62SRWr72temvVbV16auP9vK7bk8PDzqNAY1LLv75jV//nysXLkSUkokJiZiwIAB+PzzzzFlypQat121ahUef/xxJCcnA9CSYV577bWGDtmsnJwcvPvuu4b5Xr164bvvvjPb823OnDk4f/48PvnkEwDA2rVrsWvXLgwZMqTecSxevNgwrSgKVqxYYTYRBgBCQkKwevVqREVFGT6clixZwmSYhpR21eximX4FgskwVieT4yC3/gTg+oXw/CyoW5ZA8Q2AaGGdakz2RBYVQF37JZB6/WSbooMYOg1KE7nwLrNToa76GCjSvoTIEzu1O3hGzrRtYI1ApsRD3bBIa1GgL9PuuJIScHQCHF20qiYARFhXiJ6jIS8eAVQVIrx7k2wn0xhkcQHkuUPG1Vr0pUCpTrs41gBEaGftrrnDf2pJOEIAjq7anaHl4k8CTeR3khqHTLts/oG0xMYNxAaEkwtE16G2DsMi6vGtpguT4yCTLkEEtmv0eIisTbRuD+XeV4DEs1oSZ+umeeFG3bpMew7lSosgtyyBGhwBpWVr2wVGRM1b4jkgOU6bLj/+kHqguAhoFVrtZkRNhXBxbxLf22XqZaNEGABAUR7kkT9Rfm5JFuRolVvKf2e9WkAZN9uidq3C1bILXsLRGQjvbnngRNQkiY4DIGOPWWUcsr6qSQrlXTIsUTmZw96THaz1PM2NZY2YysrKUFpaanFCTG1f+8otompS9bWp6/P19Ky4Ad3ek6VuVnZX47hLly54/vnnDb3IkpKScOedd6JTp0544YUX8Ouvv2LPnj2IiYnBnj178Ouvv+KFF15Ap06dMG3aNCQlJQHQEmGee+45dO9umy+aP/zwAzIyKip+LFiwwGwiTLk33njDKAvtww8/tEoc5e2iAKBfv3419ioLCgrCbbfdZpg/f/58rT48qJaqScAQfs0vMcMeaAfAVUrHSdX0wLiZkIc2ViTCAICqh9y5ArJKyXt7JQ9vMiTCGJad3Q95E1wQlqf3mibCAFqJ7aLrX9KCIrRqP+7eULqPhNJzNBNhbiThNKCYqSqkL23Q6lBKj1FQ/jofYuDtgKundidYZc5sRUjGhF+Q+Qd8q1lOtpFfzd/S/OzGjYOoAQknF63sf2jnJpkIAwCIPWq6TKpaoioRUQORKfFaIryuykl+vdYySUrVNoHdgCzKh0xJgCyvZknUDMhU8+ePZKp2A4K8Fgt1yevApeNaO3V9GZCTDvXP7822niAiuqHWHYD6npv19gdam7+ZnerH19fXaD41NdXibSuvW3Uce2Ot56koCry8rNOuvTFf+5SUlBrXqS6Ouv5sW7RoYZjOyMhAaWlpncahhmOXZ7TeeOMNXL58GYsXL4YQAlJKnD171qjSSlXlX1DL13/ggQfw5ptvNlbIJlatWmWYDgsLw5gxY264vre3N6ZPn47vv/8eALB+/XqUlJQYtVSqi8oJOeHh4RZtExERYTSfmZlZq9JSZDnRbZjWQiOvUq+7oAggrIthVqZf1ZI1VD1EZC8I3kVUd9UdyDbTA1yZeMZ0oaoHrpwHovo0fkC1VO1Ji7QrEE34Tl6ZkaS1AGkRAuFTTdnL8pYEpSXa/4XQ3qeKDhACon1fiKHTm2XLqAajc9RePycXoKRYSzaCAHwDIFp3aNBdCwdHoOsIyJO7tRNshgcUiM6DG3Tf1iALcgG1DMLDvg/27J2UqpYo4eKhvSeq07az9l3g2sWKZW5eEGzpYVdE6/aQp/cYL1R0QHCE+Q2IyDaEzvxy1f4uRBOR/ZNFBUBKHODpd8O2MMKnlXYbjqIDIGC4KcfBSas+kXAGCO3c4PFaSt2/FvL4Vi0RwNEFYsBtULrYf9UPW5IFOVpL54Ic7XiybScen9sh0SKo6i1x2nK/YMicdKh//K/inKyq125GcnEHslO18zbV3ahARGSGEAKi6zDI3SvrPkbXofx70kAiIyON5o8fP47Bgy07L3v8+HHDdFRUlFXjsraQkBC4uroaihwcP34c99xzj0XbVn6ekZGRVnsvmnvtg4MtKwhQ29f+5MmTFsd14sQJw7SPjw9atWpl8baVde5c8b2+pKQEMTEx6N27d53GooZhd5Vhyn333Xf46KOPDEkY5b90UkqTf5Ufd3NzwxdffIGFCxfaJnBoZZi2bt1qmB87dqxFHxrjxo0zTOfm5mLHjh31jqVyJlvVElfVqbyeTqeDn59fveMgU1JVgfRrEL3Haf86DoAYMQPKxEcgFO2krYw9BnXFfyGPRUPGbIe68iOop/bUMDJVR5hNABGQQkC/5jOo6xZCxp0ws04T5eppfrmbdTJ6G1p11RGEn/32or4RKVWo0T9CXb4A6p/fQ/3pHai7fjW7rgjren2jShdqhACcXQEXNyAoAsKxfsmSN522nbT3/vWEIkBo56XdvBrl7kfh4gZl0uMQoV20ajABYVAm/B0iwH4THGVxAdT1X0NdPB/qktehX/URZG5GzRuSCRl/EuqPb0Fd8rr2eh7dUu26QlGg3PYwxLDpEJF9IPrcAuXO/wfhyWQkeyL6TTA+OX69FaGo7m8vEdmGue//ig5KJ5YeJ6LaUU/thvrDq1DXLYT68wKoGxZB6svMrxzWFfBvo11cF9COP4SiJeYDkJWTnm1MXoqBPPKnlggDaO3kdq68YUVWWVYK9egWqL9/DnXzYsjy9jI3CZmZDHX5e5D7/4A8sQPq+oWQO36xdVhkhggIg2hXpWq8kytE7zGQ5w5orZNR5Zx92fW7uR2dGyVGImpeRNehEOE96rZteA8IJqM2mJCQEISEVLTA27Bhg0XbJScnG3XgGDDAvo8lHRwcjBIxLH2epaWl2Lx5s2G+uudZub2RauFNJr179zbqnGJpTIcOHTKq3mLJa3/s2DGLq8Ns3LjRMN2nT586J/8MHWr8e1u5WAbZB7tNhgGAf/zjH4iPj8f8+fONMqvM6dq1K958800kJCTg4YcfbqQIzTtz5oxRGaSBAwdatN2gQYOM5mNiYuodS+Ux9+zZg5KSkhq3qZzI069fP7i4uNQ7DjIm87OhLn8P6tr/QW7/GfLYVojWHaB0HGAoQS6lhLp3jfHFcEjtYLuMZbbqQgS2gxg2XbvLA9CSRQLbAceigasXIBNOQd3wTbNJOFLMVRFoEQIER5out0Oi91jAybgqlQjvUevqSPJaLNRN30G/+hOoRzbb7vfn4lHIcwcrRwZ5YidkgpkKPpG9tYMfQ/UIATi6XE/kUICgcKgndkL9/Quom76DvHK+MZ5BkyYcHCEmPqJV2JGq1jLJ0QVIvwq59/dajyfzsrQTwIc3QWYmWxaDXxCUCQ9CN/sN6KbOgWjbsdb7bUxy56+Q8SdhuJM1OQ7qpu9sGlNTJHMztR70edcTiUoKIff9Dnmp+u95wsERSufBUMbcC6XvLRDu3o0TLFlMuHpCmTYXym0PQxl1N5R7XoLSybLv/ETUeJTR9wDtumvfnwDAwQmi3wQtOZWIyEIyJw1y56/XL5xfXxYXAxmz3ez6QucAMfavQHllRaFoCfHlJ9jtqOKijD1mbilk7HEzy68/unGR9n32ynnIC0eg/vbpTXVMKg9tNK74CUCe3qNVgaVakaXFWqLZzhVQT+9tkPM1YuxfoYy6B6J9P4heY6FMf0ar7FRekbdq1U4pIdp0hPDkzaFUPakvg0w4DZl4DlLV2zocsiNCCIjR99Y6IUaE99C2Y1WYBjVx4kTD9Lp163D58uUat1m4cCH0+orf88pj2KvKMR49ehT79++vcZuff/7ZqNNIdc/Tw8PDMJ2TU00L8SpcXV0xevRow/yPP/6IvLy8Grf74osvDNM6nQ633HJLjdvo9Xp8++23Na63b98+oyoyt99+e43bVKdVq1YYMaLiWtxXX31l8WtDjcMu2yRV1qJFC7zyyit45ZVXkJmZidOnTyM9PR25ubnw8PBAixYt0KVLF/j4+Ng6VIPTp08bzVctAVWdsLAw6HQ6wwdr1XHq4vHHH8dvv/0GQOt/9vrrr+P111+vdv0vv/zSKAln7ty59Y6BTMm9vwNZlS6e6kuhbvsZSpuOENfvFEJJIWDuDvziAiA/S+sfSbWmdB4M2aE/UJADKRTIpabt1OSRP4HOg8xs3bSIsK5QbnkA6rGtWuneNh0h+oxvMl+qhV8QlOlztTYUedkQrdsDkbUrLycvn4W67itDUplMigWuXoSY2PhJk/KymaQXAPLyaZOkCCEExNA7oXYeBLn+GyAnzXA3oRh4O3Boo9Y+rXyM2ONQxt0HEd696vBUidCXQeocTKojyQuHgWHTLB5HXjkPdf3XQJmWYCoPbtAqe3XoZ9V4bUnqy8yfGE+9DJmdCsG/QRaTsUe1u4KrLj9/CKJdt8YPiKxGKArQxr6T2ohudkLRQTd1DmRxAWRGMoSPP4SrR80bEhFVIhNOV7lR6fry+FNAz9Gmy0uLIf/4Ujt/A6FtW1wAuLoDni2rqVprI9W173QwX4lUpiSYHtuqesgjmyFC7LttgLVUWzUnLRFoopVsa0vqy7TzFG5eEM5udRujtBjq6o+B9KsVy07vgTL5HzduK1tLQtEB7ftCtO9rvDysm5bQ5uQCQBiO7xHWFWLMLKvtn5ofmRwHdcMioDBXW+DpB+XWhyB8A2wbl75MO4d6+Szg5gnRZShEy5CaNySrEzoHYOzfgJM7IU/s1FqvVcfbX6sm04XtkRrDI488gi+//BIAUFxcjCeffBIrVqyo9rW/ePEi3nnnHcN8z5497b4yDAA88MADmDdvnqFow5w5c7B9+3Y4OZn/fpeWloZ///vfhvlWrVrhzjvvNLtuaGjFjcrZ2dlITExE69ata4zpkUceMVRiSUlJwcsvv4z333+/2vX37NmDb775xjA/adIko8o+N/L222/j7rvvRps2bcw+Xlpaiqeeesow7+zsjFmz6ve3/+mnn8a2bdsAAElJSXjsscfwww8/8PfaTth9Mkxlvr6+Fvdws6VLly4Zzbdt29ai7XQ6HYKCgpCYqB1UxcbG1juWW265BQ899BC++uorAMAbb7yBK1eu4JlnnjGqthMbG4uPP/4YH330kWHZww8/jOnTp9c7hsrKyqopIXuT0SecgUnT2pIiKFcuQrTpAACQigNUNx8t8aUyZzcozh4QfC3rx9ULMvUyVL2Z7P3cTKglJdpFrqaudUftHyq95ZrSe8fVC+hdKeNXVbV/FtIf/tN0/ctnoFyNhWhl2WeztahO7pBmmlULZw8o1f1MvPwh73oWuHoRMj8LIjgC0Ouh7l5TZUUJHNwIXVv76Ttvj6ReD9Vcw3AIyFr8Xuh3rQJKK1VakyqwezWU0K5WPWlnS/rEC0BxkXYXq05n9JiiV/k3qBZUvTT/u6/K6n/3iahZkWWlwKXjkHlZEEGREIH22yKv2dI5aS1LgKb1XZiI7IJ0cDZ7HCGcXM0eR6jnDkFmp12vCOMOlBZrxwzeraDc9gikztFuPotkZF+oZw4YJ/s4OEJp1x2qmRhlZor5Y6qsNOjs5Dk1NNUnEDLL9MKm4tXypjhOkvEnoe5cCRTmADoHiE6DIAbcXuuLPerJPZBpV40XplyGOL2vcSoutgqF2vsWyKObAUcAzm4QXYdB6X8b9IDd/I6SfZFSQt38I1CQW7EwJwPYthy62x+1XWCA1r7vcqWbq88dgnLrI4Zjj8otSqjhCSEgug6D7DIUSDwHeWYfZE4qUFIMODlDePlDdBwAtG7Pi+WNqHfv3pgxYwZ++uknAMDKlSvx+OOP44MPPoCzs3F7vFOnTmHKlClGFUzee++9Ro23rgICAvD000/j3XffBaBVQZkxYwa+/fZbeHsbV59OTEzEtGnTjKrkvPXWW0btkCrr3LkzPDw8DK/L888/j88++wxeXl5m1y83depUDB48GLt37wYAfPDBB/Dz88OLL74Ipcp1uF27duGOO+4wtGFydnbGG2+8YdFzVxQFWVlZuPXWW7F69WpEREQYPZ6bm4sHHngAe/fuNSx75pln0LJlS4vGr87kyZNx++234/fftQr0P/74I0pKSvDJJ58gIMB8suTFixfx3nvv4W9/+1uTyH1oyvgXsAFULX/k62t5+VNfX19DMkxubm4Na1vm888/R0BAABYsWICSkhIsWrQIixYtgre3N3x9fZGdnY3MzEzD+t7e3nj55Zfx9NNPW2X/le3cudPqYzZFaoELUGh6UCVOx0LEVxxQS/coyJQYVL6KJvzaQ+zd1yhxNndSVSGLPCvu/ijn4QPl+h9latrU+HSgxEyf5x3bIYoLtTZMAaGNctAhixwhC9wBtdLvvoMjRIYewoLPRlmQA5xPBKQKmWcmi7swGwo/Y2ukqi2NT1oAEO6tb/gzkKoeyM/WeoY7uUAmZACo8r7K00Ns+RPCzbMBom48UlUhT+0GcjOhnRGEVpWo/M5QT18ox+tfue5mIkuKIQtcTRLzRKGbRb/7RNTwZHEhkJqglcv38AVahmh3EVtj7NJiyLP7gaKC60u2QrRqC9G2k1XGJyKiupP52UBeFuDsqt2ZXc1xodTrIUu8K9qqAAAERKm32e9z8sp5yLzKxwta+1+R7wJxpP4t0a1NtuoLeeWC1vrHzQsioAPEsVPm1y0uhMx3QdVsb+Fs/rVojqTaArLQHdBXHNsLv0CIM3EA4mwVVqOQJUWQJ3ZcP7a5/h7fux/iWj6Ef813hRuNFXeiyu+JRhw+DpHeWIkozpBhY4GiPMDFHaLEGbhJ3sdUN7IoH/JqLkzOCV24ArFtq1YRxBZx5WdBno41iUusW2WoRjZy5MjGD4y07xZtOhhugCbb++STT7B3717Ex8cD0FrxrF27FjNmzEBUVBQKCwuxZ88erFy5EsXFxYbtnnzySYwdO9ZWYdfaq6++ik2bNuHw4cMAgFWrViEqKgozZ85Ely5dUFZWhiNHjuDnn382uhY9ZcoUPPjgg9WO6+TkhHvuucdQYWfJkiX4+eefERYWBje3impxkydPxmuvvWaYVxQFixYtwsCBAw3Xo1955RUsXboU06dPR2hoKLKzsxEdHY1169YZtaZ655130KWLZa2OH3roIaxcuRInT55Et27dMH36dPTv3x9OTk44c+YMli5diqSkitaW3bp1w4svvmjR2DVZtGgRBg8ejPPntfahv/zyC9atW4fbbrsN/fv3R4sWLVBQUIDY2Fjs3LnT0L5q5syZVtk/VY/JMA2gaq8zFxcXi7d1dXWtdpy60ul0eP3113H//ffj4YcfxubNmwFoJayys7ON1u3fvz++/vprdO3a1Sr7JvNEQBhk3AnjZV5+JhdRhV+Q1lc6/ap2osEvCMLTfnpLN3VCUYC2nSAvxVTcBeXgCMGWB82G8PSFTC80XqjqgdjjFZVyLp8Gug5r8JL5wsUN6NAXuHYRsjAPws0LCI6oaI1WDakvhbxwtKJtWjUX54SHj3UDbkaklFoyCwAR0ROIPwWZkw4oOogWQUBIe229wjytdKnOEfANgHBwhMxMhow/CVzvXS58WmlllI1OhEP7udTws2wSrl00bdEnJaDXQ/gFAmGWHXhQBeHkCkT0hEw4o5XHd3CECAy3eQllItLIonzIM/sMn/PISILISoaM6mudZNlrlyolwlzfZ0oC0LJ1k0+gJCJqamRZqfZ9XyiQuelAaqV2N+7eWisVMxcyhU4HhHaGvHwWKC4EXD0ggsIhvKq5g9TTD7hmptqzh5+Vnol1Cd9ACN9ASClr/NsnnF2B4AgteaackwsQbFmL+OZAuHlprbVTE7WqP14tAL8gW4fVOLJSzFfrzbwG1DIZBtWdg2nkdobC0QlwtM/fzZuFVFUgKRYyIwkQQmvt06pxblyrNZ2jdsNQ1fKvOgetGpitVDneKCeLCmCHryKRTbVs2RLbt2/HuHHjcO7cOQBAQkLCDau+/POf/7xhSx975OzsjD///BO33XaboQpKamoqPv7442q3ufPOO7F06dIax3777bexZ88exMRoSd6lpaWGBJByPXv2NNmuffv22LJlCyZMmIDk5GQAwOnTp/H666+b3Y8QAu+++65RS6OaBAYGYtmyZZg6dSpycnKwePFiLF682Oy6UVFR2LBhg9F1+fpo2bIldu3ahUmTJmHfPq2gQX5+PpYvX47ly5dbZR9UN82gB4j9KSoyvkBWXR82cyqX4iosLLzBmrXz5ZdfYtiwYYZEmOrs378fvXr1wkMPPWS1yjRkSrQMgQjvDnj4aHceBIYBEb3Mr+vuDdG2E0RoZybCNADhFwjRbZj2God1geg6DMLdu+YNqWkIjtTu8jOQRndvAQDKSiHPH26UcIS7N0RkbyjdhkNE9IRwteAi2NUqyQmq3vSg28EJaM07DMyRhXmQJ3Zq5UjP7IO8cARo2xmi12iInqMgQrtAKApkSjzkyd2Qiecg409CntwFmZelJcuVXyAFILNSzJyckxABoSYtkmRBDmR2mtZPvYmQGdfMP+DiChHVG8LRTKUlqpEo7wHdfQRE95Ha3/1KpKpqSVtE1PiSLhl9zgOAzMkActKtMrzMzzT/QF41y4mIqEHI7FTI49sgL8VAXjyiJatU/v6Vnw2kJJjf9uoF7ZixKF+7kUaqWiWxagivFtrF3Mo8fGqfLNDILL3wLIIiIDoPhmjdHqJdV4guQ7UkmZuIcHa7/vy7QbQIts+L9g2huov9tUgCkPoyyNTLkCVFWvXVylw9gBbB9Qiw8ciifO2cQZXkIJmZrH3OJJyGLOC5dYsknIK8elH7jC3M0xIPr16oeTsbEI5O2s2rVZf7t9FuurQVdx/ATNoLb5wjMq9t27Y4evQo5s+ff8P2OH369MGGDRvw0UcfNcm/9b6+vtixYwc++ugjtG5d/ffQDh06YOnSpVixYoVF17P9/Pywf/9+fPHFF7j11lvRunVrixNKevbsidOnT2POnDnw9DR/bURRFIwaNQr79u3Dv/71L4vGrWzUqFE4cOAAxo0bZ/bn5uLigscffxyHDx9GUJB1E5r9/f2xe/dufP3112jfvv0N142MjMT8+fPRq5f5a8NkPULyzLvVPfHEE/jss88M84WFhRZXhxkwYIChNFKXLl1w4sSJGra4MVVVMXv2bKPMtwkTJuCJJ55A//794evri9zcXBw+fBjffPONUdZft27dEB0djRYtWtQrhsrK2G+ViBqJVFXgWixkaZFWxaO4ADL9KnB8q+nKOkfonqg+K9qW9D8vAHLSTJaLAbcDEoCzi3YCzqniC6csKYI8fxBITwJaBkNE9dXudroJ6Vd+oFXXqiwoArqJjwC4fiLuUgxk9I/aY+VfkKXUTibkZZhW4/EJgBg4GXLnCu0OOKEDWoZAGToNIjAMsrgQ6h//A5Kun2B3doMYcy+Udt0b9Llag37VR0CCmZLowVHQTZ/b+AE1czLtCtTdq4CUeMDFE6LHCCjdhts6LKKbiv63T7XfwSrEgNut8vuoblkCGXvMZLlyy4NWL5Utiwu1v/856RCB7YCwbrY9KU9EZCekqoe69G2g8Hpb89Ji7Z/iALhUlHMXwVFQbnvIeNusVKi//AdAlbZAXYZBGTTpxvtNjodMiYfw8gfadoCwZdWAZkDmpEMe3gSZHKclHPUYDREcYeuwbiqyuADqT+8CJcY3cCpjZkFYcLwr87KgrvkcKE8WlhJoEQLRIhDwC4bo0L/G6rm2JksKIbf8CJl4Vlvg6gVl5EyIkEioe9ZAntxRsbKigzL+AYjWUbYJtgmQhXlQl76p3fhVmZMrlHtftlnboRuR+jLIw39q3/EVBaJ9P4juw23+Ga/uXwd5PLpigZs3lNsfhfDSru04ONjfa1mT4uJixMZWVFoLDw83upmcyBr0ej327t2LM2fOIDU1Fc7OzggMDMTgwYMRGhpq6/Cs6vDhwzh+/DhSUlKg0+kQEBCAPn36oFMn27RxLikpwY4dOxAbG4u0tDS4u7sjKCgII0aMQKtWrSweZ+TIkdi2bRsAYN68eZg/f77hsbi4OOzduxdXrlyBoiho27Ytxo4dC2/vxrkh/sKFCzhw4ACSk5ORl5cHT09PtG3bFj179kS7du0aJQZ7YOvPc5v8BdTpKi4qCSGMEiQqP2YNVcdvDB4exneMFxUVWZwMU7kaTNVx6uKtt94ySoRZsGCBSSadn58fxo4di7Fjx2Ly5Mm49957oaoqYmJi8Pe//x0rV66sdxzlmuKXLiJqemR2KtS1X1Ukkbh6QrnlfkgnF8iYraYbODlBZ6efT8LVHcg1TYZRWrWBCDE9oSJLCqH+/hmQeb335XkA5w9CmfyPmy4hRuZmQmRcBSCBshLt5IpQgKvnoehLgeJ8qL//D8hK1u5AAgAIQBHaue6i63dxKTrAyRUov6Do4gqRnw6ZnwE4Xf/Slp0MbFoE5d6Xoe76BcqVszCcMC/MBjZ8A+Xh94ySluyRMmgSZOKZitZxACAUiEG3Q7HT35GmSpYWQ93wNZSiPO0GruJcYP/vUDy8DT29iajhKQFtIVNNk2GUwDAIK3zuyd5joF4+rf0dKhcQBiWsk1VPmMuCHKirP6qoJnd6t1b97JYHmswdbDItUavgBkBE9jatqtDQ+487AXlyJ2Rxofba9RhlUvWNiJommZ4CtSin4qZ5RdGmZZnRjfTC28/kO6+aGg9FmLmPMDm25mPIkAjtH9WbLCmCuvZ/QH6WtiAvA0iKhTJ1DoR/G5vGdlNx8IKc+BDUnSuBtMuAqydErzFQonpbtLl6YjuUgkzjAhYZVyC6DgaKCiCuXQDCutplAkQ5de8myCtnK55DUQ6wdQnE1CchTu8yfm5SDxzZCF2YbS4yNgVSXwpV6k2LmpQWQlGEVb6PW52DAzDodu2fPRk8CTKiu3ZOx9ULIrKn3Z+DIrIHOp0OQ4YMwZAhQ2wdSoPr3bs3eve27G92Y3BycsKYMWMwZsyYBttHWFgYwsLCGmz8mkRGRiIy8uZpJ2qvbPJtorwHrbmiNDd6rKnw8vIyms/MzISPj49F22ZlZRmmqysRZan09HS89dZbhvmpU6fWWFJq5syZ2Lt3Lz788EMAwKpVq3DgwAH069evXrEQETUmueMX42oqhblQt/wIZea/IXf8DBTmGa0vug5r5Agtp3QdCnVLlYt0fkFANXfAyTP7KxJhyqUlQl44DNFpYANFaaccnQAIoCjPOLlDX6aVK963BshNR5WzVYB6/TuIomiPqXqtutD1u0aVzoOhntptur+SQiD+FHDhCKreOYrSIsizByG62e97DQCUkCioE/4OufMXrVS8qyfEkKlQQrvYOrTmJ+6k9t6sQj2zDzomwxBZROrLIGO2Q8adgHBwgug8CCK8R63GED1HQ8adMGpbJCJ6QQSFWyVG0bI1lDuehIzZDuRlAcGRWts0K985Ko9vM26rCEDGn4RIPAdYuQJNQ5DnDkLduszw91oe3wZl1N2Nlhwozx+GuuWHivnUy0DqZYgJDzbK/omogbl6aknx5ccEOofrbV0qfWd3cILoNsJkU+HhW/WbvbacbaytTj2xE/LYVqAgW2tBNPgOCG+tdYGMPVaRCGOjrsa6AAEAAElEQVTYQA95YgfEqHsaPdabmQgIg27a/4MsLQYcHC36TiPLSoH4k5AXj2q/h6oKFBdW/N3f+C3g4g4pBNAiBMqkx+229Za8dNx0YVE+EHvU+LxDuarnZ8iYd0vAq6VpReTAdnbdplmmXtbew4Ht7Cp5WgSEQgQ0r0oWRERE9WWz1NobJbs05UQYACaljRISEiwqd6TX63H1akUrh/Dw+p2A/e2334wqzTzxxBMWbffEE08YkmEAYOXKlUyGIaImQ5aVQl45b/pAThqQlQJx78uQf3wFpCYAjs4QXYdDGXpH4wdqIRHVB0LVaxe5CnIg2nSEGHB79Sec0q5UTOuv312j6ID0K+bXb8aEizvQsjWQcNL4AZ0DcHpPRVljcyesAAAK4OKqJcLo9YCnH0TP0RDt+wLmkmHK6SuV9638laY432RVe6R07A907G/rMJo9qVZTuVDPlpJElpJbl0FeOKxNA5BXzkGMmAGl4wCLxxDu3lCmPwN5/hCQm6G1W2hbu7t3ZWEe5O5VkJditO8WnQdD9BlvaFEk/IIgRsyo1Zi1JVMvm1+elmj1dkzWJlU91H2/G/89lirUvWugRPSEqNqusAGoRzebxhV/EjIzGcI3oMH3T0QNS7h5QnToB3lm3/UFQkt0D9TOu2ktd0ZB+AWabtw6CvBvqx0/llN0ED1GNULkNw957gDkrl8r5hNOQ2YkQ5n5vFYlpCDX/IYFpsnl1DgsTVSQuRlQ13ymJe0WFxpXyyun6rXWZU4uQPoVyJM7IXqPs3LEVuLkAhTkmC73DdbOvVRt99OydePE1UQJIaCMugfqhq8rKvZ6+EAZfpdtA6uGLMyDun4hkHL9b4KLO5SxfzNbudmq+y0uBBJOAzod0LazXSXgEBER2TubJMMsWrSoTo81FR07djSav3jxIkaMML27pKq4uDjoK11AqzpObR0/bpyp3rdvX4u2i4qKgpeXF3JytC/2p06dqlccdPNRz+yDPL0HKCuFaNcdotcYuy5xSo1LFhdqd3Ffuwjh6QfRbThEi2Dr7aC8pU2VHtYQCuDsBsXNE5j5vPX218CkvgwisjeUDuaTE/QndgJH/tROKrVuD0AApSXaCabyi0pC0e6GvAmJ1lGQV88DZaVaYpDOSWttlJ0KuHlpJ+Sk1E6IGyXjCm396+8bODhBmfG84bNMRPWGTI4z3pmTKxDaGQhoC1w5X2U8QF6NhVT1jXJRj+yfaNsZ0sFRe29WXl7LqhZENyuZm2FoqWO0/MhmoBbJMAAgnF0hug6tcyzqxkVA0iVtRl8KeXgjIARE31vqPGZtCb9AyKsXzC63ewW55i8qFeRo1fzcG6GXd15W9cuZDEPULIhh0wGfVlplCp0DRMeBUDrUfOOXEAqUiY9AHouGvHIewsMHovsIiICwBo/5ZqKe2mO6MC8DuHxGa5vTpiPkgbWm69h5wmdTJEuKIA9vgow/qV3o7zoMIqJn3cfbv7aiet2NjoX1pQBctOlrsXXeX0MTnQZB7lltvNAvCCK0E9BxgHYjk1C0pAUHZygDJtom0CZEBIZBufcVIPGs9h4JibLb88jqtp+0zyW9XjuPVFYK9c/vocya12Axy8RzUDd+C5QWaQvcvKDc9rB1z6USERE1Yzb5VnHffffV6bGmomPHjnB0dERpqXZxY8+ePXjggQdq3G7PHuMDv27dutUrjvx84zvQ3d3dLd7Ww8PDkAxTuboMUU3U49uMDgplxjUgKxli7N9sGBXZC6mqUP/4Arh+97IEIC8e1fp8W+kgTigKRJchkEf+NF4e0RPCrWkkhMiCHMisVO0u9QuHAL0eIrQzxLDpEG4Vrfj0R/4Eti3X+lBLXK9WIlBRjuR6QgcEZOI5oM/4Rn8utiaCIiCdXLS7tyoLCNVOqO76VasUUwrtRIbOEYDUqnPoKu60ER36G53YEJ2HAHnZkCd2AmXFgG8glOF3QTi5QBnzV6g/vVNxVxMAODgB1y5Anj8MYcFJd2r+hKsHlLH3Qd3+s3bBV9FBdBxg123biBqSzEq5XlnFSfubXVMSZ342TFrSAUbtjhqDzEiqSISpvPzUbsBMMoxMugR5dj+g6rXn2bazVeIQ3UdCXjwGFFa6cz4oAmhTuyo3NuHqoSXtFla569/VE3Cx/Bi2PkRIlGnbAwdnLcGViJoFUV7NpUpFFzVmB+TxrVoVztYdIAZPNbTmAQApVSApFtA5QOk1RrsjX7FuqzuCVhXE7HKtiojwbw3R5xbIw5sMN32Itp0gugxprAhvGuqGb4BKCbZq0iUo+jKtQmodGCXrShXG5ywqq9S+uNLvoD2QJUWQJ3cByXGQnn7a58iFw0BRPkRoF6DnGKi//EdL4hECgB7w9IWY9DiETytbh98kCAdHIKyrrcO4IVlWClz/Hq8tkNqNePkCSI4DgiOtv09VhbptWUUiDAAU5EDduQK6Kf+0+v6IiIiaI/tMsW3i3NzcMGLECPz5p3YhdvPmzZBSQghxw+02bdpkmPbw8MCwYfW7GOLra9y/ODk5Ga1b11yasbS0FGlpFX06/fz86hUH3VzksWjTZRePQQ7IZE9tOyRTL0Me2QyZlaL1le09vmF/TpdPGxJhDMpKII9FQ4y+12q7Ef0mAE4u2sUm/fWLTTZMBJGFeZB710DGnQCcXSG6DIHoPtLk74KUKuSulZCn92qJFKUlgKMT4OgMGXcCsijf+GD34AYtEUbbusr/oZ1LcnQBHByBpFjIkkIIJ/vsu91g2nSECOsGGRdTscwvCKLLUKC0SPuZXLsIuHppr6WiAyC0u9CLCwHd9QSFfrcaDSuEgBgwEbLPeKC4AKLSXeuiRTDQeTBwYod2ckTnoP0DgCvnACbD0HUitDOUe18GMpMBNy8IVw9bh0RkE+qZfZDblxsubskD66Dc9siN+923CDFbCa6hS5SbqOHiXWXy/CGoW35E+d9qee4gRP+J2sXVehKeflCmPa1VZ8xOB4LaQbTv1yQu2AqdA0S/WyG3/1x5KUT/2xrtrmAxcBJkWqLRnevKsDtvvu9NRDcZ9ex+yN0rDfMy4RRkZjKUGc9B6Bwg9WWQ6782tFeVABAcqd2Rb6dVC5oq0a67djNVZQ5OQJuKqtlK31sgO/TTLjp7+0P4t2ncIGsgUy9DXj4LuHtBhPewuI2QPZFpiUaJMOXUo5shrl7Qjp/LW0L2HF3juW4AgKdfRQU4ISrlwpRPXz+HoXPUvlfpyyBP7Yaan6Mlp9n4XKLUl0Fd8zmQdvl6O6cS7Xl0HQZl4CSgrATqzwu01tzlFAftOSeeBZgM03xcijGpAAxAq8zs7NYw+8xOMV/BMOkSZGkJhKNTw+yXiIioGbHJkVvv3r0BAKGhoVi5cmUNazdNU6dONSTDXLp0CZs3b8bYsWOrXT87Oxu//PKLYX7ChAlwdq7fQVNkpHE28qZNm3D//ffXuN22bdtQUlJx8jYqqpFPKFOTJaXUSpmbPqLd6WlnyTAyPxvy6BbI1MsQPq20A/kqB6ky4xpk0iUIrxZASHvLDvSbCJmRBPW3TwwtOmRmEmTiWSh3PQtRtYqGtfaZnWZ+eU66VfcjhALRczTQc3Sdx5BFBVr5bCscWKobvtFO2AFASSHk3jUABESPkcb7PLNfu9tIyoqLa6XF2oG1k6t2sJuVCuHjrz1W/vtm9qaq6+9VB0dt2sHJqNLJzUIIAYy/D+LyGcjkeAhvfyC8B1CQDXXlhxXVWwSAVu0gBk7S2ndZ+HklHBwBB9P2DcLbH1LRAapa8bMAgKJ8qKf3QoR2aTKViqhhCUUHtAiGzEyCenQzUFIE0a6b1apFNBapqsDFo9rFIncviE6DIDyZUE01k6XFWlXB8tZ+AFBSBHXvbze821E4OkEZfhfULUsq7s5084IYNKWBI67Cvw3g4WtSkUa0M67yKaWEun8dqv7Rlkf+hOwyxCrfvYS7N0TfCfUexxaUTgMhfQO1qngARFQfiMCwRtu/8GoBZcbzQPxJyOJCrdpAY7RnIiKbkuZa8+SmA4nntPan5d9tKrt6QUtm7DSwcYK8SYieo4HMJMjY4wCk1p5nxAwIF+MLzMLTT0uusDPq/rVG1WnloY1QJj8B4WFf58FqZK5tIQCkXIbMTNamiwsg9/8BSAnRu/pz3eWUnqOhblgEQF5PeCnRWggB2nc4oQAB7QBZBmQkaec+pISMi4HMSobyl2chhA2Te+NOVCTCVK7+emIH1NTLQGC41vKxMrVMqzZ75TzAyp/Nhiwp0M6tVa7SAgBObg3XssjFQ7tpq/x4x7DcHXBgUiYREZElbPIX8+jRoxBCIC/P9KJ5eHg4AKBdu3bYvHlzY4dmNbNmzcLLL7+MzEztpOhzzz2Hffv2waGaLykvvfQSCgoKDPNz5sypduywsDDEx8cD0BKK4uLizK43btw4o/m3334bd911Fzw8qr/ruLS0FC+//LLRsvHjb762GlQ3QgiIkPaQiWeMH3D1BOysj6ksLoS66mOtBzUAmRwHeSkGyvS5hot36p7VWq9fXL9sEdhOuwOsCd7dU05mpwKqCuEbAHlihyERxiAvC/LCEYjOgxpk/yIo3HzeRlB4g+yvLmRWqlaCNOkSoHPU2uMMnlLnO/9k2pWKRJjKy0/uAqokw6C8PH9xlfZ05aVXdbrrfbSv8/YHMq6aVhlWHK5XjKlIwhAdB9y0dy8KoWjlzCslF6jHtxufyAKA1ASI4gKI4Ih67U+WFEFeOKL9zKTUWjDpHABVQsafAhJOa4kDnQZC6TvB5ne6ke3JxHNQ131lOMElz+yD6DUWSv/bbByZ5WT0Eu19Xz5/cheUyf8we1JQqipw6TjktVitfHiH/hCN1AqF7FBGElBSZLo8Ka7GTUVETyiB7SATTmvJJKFdtCTFWpJlpVqlvEvHAScXiE6DoFhYxUsoCpRxf4O6abHheyWCIiAGV0nKKSupeLyy0mLtbk+/wFrH3dyIwLBGTYAx2b/OAQjvgeaT+k5ENaquuleZdoOYNHMcB0A7vmMyjFUJB0eIcfdpN8oU5gItW9fp+FWWFmvH8Y1YGU1mp0IeqXIOOzcD8tBGiBEzGi0Oqwhop7UJLKv0u6GqphfiAciTO4EakmFkUb6W7N97rFZ1prhQ+86TkwGkX9XOaQSGQej1kOcPmrY3zkoBrl4EGrvyXyUyO1WbqFr1T1WBzCQt6aXyDTDy+n+KCyFTL0NNuwJcitGq1Tq5QOkyFCKyV2OFT1Yk2nbW2nAD2t8JKQEHR4i+DXftRLh6QLTvB3lmr/Hy7iNtmyRGRGRHtm7dausQyM7Z3VWx8sSO6pJGmgpvb288++yz+Pe//w0AOHz4MGbPno1FixbB0dH4BO3HH3+MTz/91DA/YcKEerdIArSkmalTp2LVqlUAgPPnz2PixIlYtmwZgoKCTNbPzMzE7NmzsXdvxZer/v37Y+jQofWOhW4eYugdkL//r+Jkv6MzlJEz7e4ivDx/yPSCREkh5MldWpn05HhDIoxB0iXIEzshrFDKvrHJ/Gyom76rSMpoGaK1zzGnILvB4hD+bSC6DNVOmpTzDYToPrLB9lkbUqpQ1y8Eyk926EshT+0CnF0g+k+s26AlhdpJktJi7QSSomivfZW2DgC0O0ykNE54qQgOcPE0urAsxtwLueJ97a6jcooCuLhp+3N20+6S79APovc40zFvIlJfBsSdgCzIgWjdAchKNr9eVsoNL4JJVTU5sSovxUCe2aedBIvoqVXsybgKOLtX/Nz1ZYCjK7RKWfna/49ugXr+EJQxsyAielrpmVJTpB5Ya3KCWR6Lhuw6rElUEJJpiUaJMACAkiLIw5sgxt1nuv6f32tJB+Xzx7dBmfokE8NuVp5+5u929G5p0ebC3bved+fLLUuM35PJcVBLi6F0tew4SLQKhXL3C0BaotY6wDfAdB1HZ8AnwPTvj7Mb4NWiXvETEVHdiHbdITOTjBc6OEM6ukBd9ZFW1aG0GHB01ipuluPndoMRXi3q9PrK9KtQd/yinfNwdoPoPgJKIx0Dy6Q4mCvXKpMuNcr+rUk4u0IZcRfUrcu0Y1hA+65mLqHXXDJzJTL+JNQ/v6+4CcvdB2Lc34DYY5AlhUBQuNZa6Fi09uqVV751cTdOLqkuaa2RiICw6/ktqvED5dVtXNy1z4eyEu3cT+UW1rkZkD+9rVXEuf6c1KRLEGUlUDoOaKRnQNYiPP0ght8FuWulIWlShPeA6FVzhaR67XfYNMC7JeTFo1oF6w79oTAhk4iIyGI2uTqtKIpWJlpVzT7eXNqQzJ07Fxs3bkR0dDQAYMmSJdi5cydmzZqFsLAwpKamYtWqVdi/f79hm6CgIHz11VdWi+G///0vdu3ahdRU7cLu9u3bER4ejqlTp6J///7w9fVFbm4uDh8+jBUrViA3t6Kso7u7Oz7//HOrxUI3B+HtD2Xm81pf3LJSoHUHCGdXW4dlqkope4Pc65VizPRIBqCdDDOTDCP1ZVoJ5fSrEC1DtLtK7SgBSG77ybg6SdoVrWKPGSKkfYPGogy9EzKqD+S1i1oVnrCuVn+t5LmDUE/uAooLIMK6QvQZb1lFn+SEikSYyuOdPQDUMRlGerW4nhBz/SKfXgX0+VqrnipEp0GQFw5XO5boe4vRvNK6A9R7X9baS+RmAq4eQFkZ4OQM0XGAxXe1N3cyLwvqmk+B6+24JAAEhJldt7q+8zL+FNT9fwAZ1wDfQCgDbocI7Qz15C7InSsqVrxyTjsZBmiJSc5aiWcU5gJQgZKSSifRtMQndeevUBrg94CakPRrpstUvXbRvAkkwyDDTPzQWg2aLLt60SjpAABQkAN5LBpi6J0NER3ZOeHmCdF5sFaxrmIplD63VLuNNcmcNNP3JAB5fCtgYTIMoFWIQau2N1xHGTRZa51oSPwREANur1M1GyIiqj/Ra4zWmudSDLTWPB4QAyZCbvpOq4yhc9AuxJcUaq1cdDrA3RuiU8NUUqUKsrQYKC2GcPOqed2yUqhrv6xo8VNcAHlgHVQXDygNVPW2MuHVwnwFXG//Bt93fRiqNV69AHhcr9bo5gkR2RtKSHvt2NbZHTIoHHLpmyYtlERY1+rH1pdpCTWlJRWJLXmZkL9+oB0nS1lx3tDJFXB00n7fykq0ZeXtqp1cbVoVBgAQHAkR0VO7Aab8O5xQKm5mConSKgsrOm1eSu3x8hZfJUWAIyqeE7QbH8BkmCZJ6TgAsl13IDUB8PCraGPegISiq3creCIiopuZTa66eHp6Ijs7GxkZZrLKmxFHR0esWLECEydOxJ49Wh/i+Ph4vPnmm2bXDw4Oxm+//YbWrVtbLYZ27dph/fr1uPPOOw2tlYqKirBs2TIsW7as2u38/f2xdOlS9O7d22qx0M1D6ByA0C62DuOGRFCEdvBZVZDWHkW4e5s9mQEPH5NFsrQE6u+fASkJ2jwAnNwF5fbH7OLihiwphLx81vSBwlytt3FSrGGR6Da8UVoWiYBQiIDQBhlbPb0XcvvPhnl5LBpIvwox8RFIKYGrF4D8bO2ERtWfZ9U7fazhwhHtTsLiQhjuEBICSL8CdddKiPb9IPy1z33RpgNE7/Facoshlusnjpyczd75rrQMASY9bv24mxF5cL0hEcYgOU47QVWQq73EDk4QUX3MnmiTGUlQNy6qOPGVqc0r05426gtvkJuhnRgTQrubrjz5VygVd9eVKynSlmcmaxWb6ObUMsS0nZqiA3ybSNuUFubfu8LMe1qmJZpdt7rldHMQg6dC+LeGjD2ufR53GgjRWBc+CnLNL8+3fqU80bYTlL88p1UoVMsgInqZbSVGzY9MjoeMPwm4uENE9YFwrb51MRE1HuHgCDF+tlFrHnl8W0WLGCG0RPfSEsDVHaLjQIjuI/g7/P/Ze+/4OK4rS/jc191oZICITAAzCOaco6hESVTOkuPY42zvzHjGM7bXE76d2dmd+Wa/md3xOmdblmQrB4oSKeaccwJBgAAJgMgZnd79/rhVXR2RQYBSnd9PIvp1ddWr7qpX79177jlDCA74wXtfA186LGun3AKo9c+CurMTvHYuiqgBAHzxEDAEZBhurAFXX5XinnHTQGMmi2X49UvWRsoBmj+yVYV522/BpSes16d3QT38dVBGjlzjUyUmTAD4rk+Jyov5PedNiLaENPejtRRkNVQDYCG5uBJlPe3tBBJTxbLYjHl4O+Vvl1v+NdfdSWlQdzzXb6ty7mwDbpYD6dmgAayriAi485PA5HngnS/Ld+BwyvgwajRwfKv8TWQRYVwJ8q95LpGxpiGYZ9q4dSB3EjB++nB3w4YNGzZs2LDRSwwLGWbixIk4efIkmpubceDAASxf/tGVdRs1ahR2796N//k//yf+8z//E1VV0RWyKSkpeOaZZ/Av//IvyMrKGvQ+LFy4EKdOncK//du/4Sc/+UnMPpjIysrCZz7zGXzrW99Cfn60vLcNGx8ZFM4ATV0ELjlqtY2dCjIrMybPA459EK4S4nCB5qyN2hVfPhIkwgRRUwYuOWbtb5DBnk7wpSNAWyNo3FSgYEZ8VS1ySOVNlMczQd3zaaCpFtxcKwSV2yXx2g341I7otsqL0NVXhWRi/lakQCseggr9TfMnAmnZQGs4cYKm9p0YyAG/2Jwc32pIaydIcjvgl2qnuuvg1gbw2b1hNjm07H5w+RmxWvAZHsQOB7D0AVs5pJ/gqivRjd5OuTecrmBgirPHQsW4j/jS4ej7RwekMixWEMvhkuBYZ2sI+YUseeiI/cDvBVIy+nhWtx7cXAve+7oEeZPTQfPu6LWFiI3uoZbeD/3uT8Is0mjhXbdNooWyx4KKl8k9YcKdDFoY7Z1O2WNjV85mRVt42vj4gIiAoiWgomFQNMsZL4nOrvbwPhUUD8nhKCMnSunNxuCDu9qB+htAek5MCzbWkmy7FcR1fewD8OHN1rGPfQD14FdsIpQNGyMIodY8HGnJQiSFCQUzoFY81OO+uLMVvO8NIcAlJIFmrwbNu+O2UMHmzjbwqZ3gm+WgzDzQ3PWgXtoWDlofjr4PPr/faqitgH7vp1DPfCfKrjaIqFiH2e6P3T4A6EPvBgsiTMVRdf8XQBs/B5zdI2onyRmgOWtAOYNX7DjY4OqyMCIMAKC9GXrLz0BT5oOmLgxTtuGaMiBg2P/mFECtfRIUR22Yj38g8Spz1h/wA9xpEEgU4Ouy3jPh90pBiTsZmLUaatpCIGd8v2Mg+vQu8IG3rGsjLUvW434faPJc0IqHQO7kXu+PiECT54EnzQEqLoJbG4D0bPDmnxonra31Phs22QG/KNuA5NxD92cTKWzYsGHDhg0bNm4ZhiWrtnLlSpw8eRIAcP/99+OLX/wipk6dCpfLCkS1trbi17/+9aAc71Of+tSg7Ke/cDgc+M53voO//uu/xt69e1FSUoKamhqMGjUKBQUFWL9+PVJSUnq9v7Kysj73IT09Hf/wD/+Av//7v8eFCxdw7Ngx1NbWoq2tDcnJycjOzsa8efMwZ84cOEzPUxs2PsIgImD1o+COJuDaBcDpBNJzhHgAqRBTD30VfHwbuLoUlJ4Lmn9H7KB1JBEmtH0IyDDc2gj9xv8B2pvk9akdoKLFoDuei7k9uUTxgi8eCm+fOEuCF0lpt0QN5pYhXoX3yR3hvxVr8P43wRPnBJMkpBTUxs9Bb/+dWEkphwSBltzX527wrpclABTwSwBGB6TSyecFYEjnBvyAckAfegdq8jwJsJCCuv8LUg137bxUEM9ZK5KoECUiPvi2VJUTSXWxbbEQE3yjBPrcAVFd8XRKINvhAlwuQ67ZASiyyCtndoMX3gUiCbJydRn44kFw5SX5rSIDcToglhiRY0BKBjB7DbD3VUA5hczkTJCAmFbhqj/MQpJKSBz6L2QA4IAf+u0fWhZzbY3gva+CExJBRYuHt3MfAdDYqVBP/pWM074u0MQ5t04VY5BAa58CTZwtSmTJ6aDpS0CxSF5jp4IKisEVF6y2xBTQvDtuXWdt2AgBOV1Q65+B3vobScQAQFo2aEXsauehBF+/DK407qFpi0CJvV8j2rCgT24X8knAL+TnGStAqx8DEYFZgw+/Bz67F/B2gsZNA615YsisLLi9GXz0/fBGTwf40Lug+z4/JMe0YcPGwECT5sRUf6RJc3r1ef3ez6z1gc8DPvi2rNtG+FyHfR6JMxgFQXyjBHzlBNRjfy5koVvVj8tHoxtbG0TRduzU2B8qmCHKI76usGaasmBw+1Z3PfraqCkTRZVF98hvPMJ/ZxNRqowBP+DpAG5cAd+8Bt73OjBtCdTaJ8EV5+U6BmRNfbMc+vX/DfrkP0AlRKu28PmD1trbJPvrgBSiuBKMmAgsJRUTWgPpOVCL7o5LtOnVuTXdBO97A0HCjc8jKpyuRMCVIGuutkbQpi/3ed9ESorrAPC182BzbW/GeQgAG8Q3Mw40dQFQU259F+nZoOUP9vv8bAweuLkOfGSLEABH5YMW3DVkKtY2bNiwYcOGjeHDsJBhPv/5z+NHP/oRmBlNTU34l3/5l7D3mRk3b97EZz/72UE53nCTYUw4HA6sXbsWa9dGK0vcKhARZsyYgRkzZgxbH2zYGCngD18AblyRBTkAXDgABoPWPQ0AoOR00KpHe97RqDgqSvHazeP7fUDpSXBrA2js1F4TUvjEh0EiTLDt0hHwrNWgvMKYn6FVjwLKISo2zFLps7IX59ZTX1obwFdOyD4nz+tT1Ri3NYHP7QWa64D8iWKJ0E/521BQQTH4yvHwxoQkcKjKT7ATGnz9UpiCD2WNhuPxb4LbGgGXu0/VQsHdtjeDLx+TFw6nECH8XitAAkiVmscvZInmOgnQGIQISsuSyrJY+979R/kdzddndouX+vpn+tzPjzK45Bj0tt+JAoxZ3ckwKraM4Bs0oGFUi7GoAgQCgFOBLx2B3v57aTcDg0bwzARNngeatlj86b2d0qgcUKsfA1dcACckSnDP22UpDigH4EoSck5QFroL+rV/h3rwqyK3ewvAWgM3LkvfxhX1fNyKCxYRJgT63D44bDLMoIAyckBL7x/ubvQbRARMmAXqwSaRiIB7/wQoOSa2dWlZkqi+DdSRbHx0QRNmQX3ib4GKi0JcHV90y9XY9N7XwWd2BV/z8W1QD39tyEgaH1VwbYVUggcbNPjcXklsFC0Gn9wRlsjk65fB7/4Y6ulvx1ccGAjqrsdULOCb5YN/LBs2bPQL3NkmBIyudlDhTNDoiaCVj4APvSvrBlKgqQuAyXO7309XO7ixOmaxDJ/dN+JJEnzlRLgyLiDkvTN74trhjBSQOwnq3s9C73gJaGuQopbpS0Fz1w3qcfj65fjti6IVEUOhLx6SYhlmKbiZsXxY1YIoe6zYSPu8EpsIhNj5mOvnCweg6yqBZIOY4vNY7/m6wC/+d/BT34om75rrbXcS4GHA7wfAwKjRYid18kPAr2Gpw5D8ZvPWgxbdM2B1TL52LmTfsMg3AV9wPc/XL4tC8kDmWdljZX2vA+E2SA6nxHZ0ALTwLqgVD4tlU8UF+U7GT7dVf0cAuKtDCICdUlDHLXXgyktCAOzOli10H36f3NMhcSIbNmzYsGHDxsjDsMy8FixYgH/4h3/A9773PQBCfhmKBcBQ7deGDRu3P7i9WVQ3ItsvHwWvfrxPC1MqXgY+tx9oqbMaM3K7lfrnzlboN/7TqroCQDNXQa15vOe+18ZWouGb1+KTYVxu0NonwWsel2KVQQj287Vz0O//MmgDw0feE7ufyfN6/mxrA/Rr/xFcdKL0pFSdPfw1kBqYOhUtfxBcfx1ouikNThfUuqfB5w9IcDJy++T02PtJjZbU7zU6W8ODIQmJljKI3ysVUCa0XwIiPRCBuLUB+uoZ4OIhsb0Kfe/yUfDKR0AjXF3kVkIffs+SKg6tOCMjWIWI+YHPK3ZjTheYGfrwZgQDaA6n/D5+rwTPnG7QortBRmWieu674NJTQMAnih6pmda1FuqFDkg/tA5v0wGg+ir49K5bYp3BrY3Q7/7Iukdcbrl3uyMxRMrF99Ruw0Y3IIcTNH0pMH3pcHfFho0gyJ0slbvDAG6sCSPCAAA6W8FH3gfd+Ty4q0OI0Jl5dvKkB3DZ2TjtZ0BFi6OUEgEALfVA1RWgG0Uurq0E118XW7jcgt53KDMPRpl4jHYbNmwMN7ixGvrN7weJ63x8K2jhPVBLNkJPng/e9mtRyrh8FFxTBrXheVD+xPB9NNdC73gRqL4qc31fl5DoQ9d8EYolIxIt9XHa62K3DxFo2qJo9ZW0LGB098VDNG4a1HPfAZpqRXVwCOxGKTUztt1nD6RufXQL+MiW4GuuugK0NYCWPjDIPewD8ieKZZHfYzyiQs/MuHZZC7nI55U1bOTar7VB7pkINT2aPE+srgI+Q6UNsg5vbQDfuAxs+ATwwS/DuDBQDlHFG4zfzR2prGfGAiLiYN6B3ZeUkgGafyf42PsWKQZiqwalAKVAk+fLtkmpgF1EMqLAJUetmKSJgA98bh9o9WPdf9bbCd79qliNMYMmzgatfdJWdbwFYI41CtuwYcOGjZGO4R6/hy2S9t3vfhdLlizBD37wAxw8eBB1dXXw+/2GdDEP+xdjw4aNjzgCRmVKJLSW//rAxyB3MtSj/wV8bh9QfwPIHgeaubJbpQU+8WFU1RWf2wsuXtpjgJ1G5YNrK6Lbe1G5QKSiOAD9ATND7309SIRBIAD4fdC7X4GaOLtHQguf2hW96LxZDpSdAXpBpukOlJoJ9eS3RPXC0ynV3e5kwOUGX78UTkLIGgMMhVfzqNFAcjrQ0WK1KQWkZsp5RwZdcgu6JW/qUzsNv2s/0NUmQZzEFCvAqgPWb2GAtQaf+FCSPlqDps4HLbr3Y2GnxKxDgrZm4Mv4rlwJcl04XeHjAJElVezrilZBcbkBZwLork+Bxk8Pu7/JnQyasTxscypeDj62Nfx6AwGJSUZlXGgziYR6xXngVpBh9r9hEWEAwOeB3v57qE/8Xfzro2CGfGemD7oB6qFC1sbgg2srxLorOV3UiewqtF6BayvERm/MJFDCrVFgsnH7gSsvSmW8wyUV5bnj+7cf1kDlJXBHC2hcESg1s/sPxJjXAaIeove9AT67R571SWlQqx+LSzzm1gbwxcOibjBhFqhgCOY4Ix3xiMHmczsQrdIi7f6YzcwavP3FoCofQ2w3aMPzvSKXU0YOaPqScBKOckAtGvrnvQ0bNnoGH9psKTgCQCAAPrwZetoC4MIhoKrUWke01ENv+QXU898LEhOZWWyRzLk1kTHX94SNRzRx9i06o/6DRk+OSfTAmCm3th+L7gG62sGXDsvYnFsAtf7Z3o25pHpU6B0QJs4GMnLDYznK0a0CDQf84FM7o9tP7wEvuGtQ1HH7hcoLslZNSJLv2e9DdIyM5L+UdKCjKfo95QCfP4DAzQpRt523XkijyzYB7U3gS4blFSnrOdxSD9ReAxJTpdiEWYpPHE7w+f2DYlFDk+eCD71jxWNMu6bQtW5aFpAzbsDHUks2gsdNhS45Dlw+KoQhY8yg4mW25c5IRjyL9dA4Xhzw7j+CSyxFar56Cgj4bQvMIYCKGPu11nG2tGHDhg0bIxmR43fk+D7UGNaysnvuuQf33BMuI6mUAhFhypQpuHTp0jD1zIYNG8MB1oEBq4L0FpSeDeQWyiI8tL1wRr8Si5SYAlp4d6+355rY0uhcfbVnMsz8O6Xq1bRlASQ5H88/uw9gvw98fCv46mkgIRE0YwXU9BgKN13tQjZgFvsYU/69oRP6yHtw9FTh1FQT+/iNNYDfN2DCBikVRXKhgulQGz8PPrUd3N4syalF9wyJJD45nFDrnoH+4FdSaQUAKRnAxDnA2T1SNeT3ASDA6QIVxreu47YmIcKwliCSWXEUYquE0ZOiKqj40Dvg49uCEsV89CbQ3gza8Pygn++tAPu8QGMVkJoFMmWa44BIAXkThGAVrNAy4O0CwEHJc7jc8m9SKnj7Cwi4kyQwpg07JWeCpcSTlCrJxV5cn5SSAczbABx40/rtXEaFGAXCK0WDJ9mHL2QA4IqL0Y2eDpF0Hxs72E2JyVB3fhJ650tGwoCEiDH/zqHtrI0w6IPvgE9sC77mo+9DPfTVnhPtH2OwpxP6vZ9KtTYgSkjrngZNmT+s/bIx8qCPbwMffFuerzoAPrYVtOZxqAV9G+e4sw367R8ADVXymhRo5SNQs1fH/1C8xJ1ygE+HJNA6W6G3/RYqrzBKwY5vlkO//UPpP2vwmV2g+XdCmUTPjwlo2kLwsQ/C5skgFSSt0pT5Mo6yMRcIBIAENzglMzZf/OrpMHtKAOArx0GT5gC9HEdo3VOSTC4/K2oFM1eAcvpHtLJhw0bP4OY6sZJtbZC5bfFyUGuD3H8RCh5ByzLWQpg31g388r/KOiASna1i81hQLK9vXrOIMFrL513ucILd2KkW6X4IwAE/UH8dSEoDpWX1f0cF00FTFoRbDudNAM1cMfBO9gHkcIqq7fIHAb83rpLrcIAcTqiHvgY+/gG4qlQIIPM3dBvD4dKTQFsTzLV/cG3p9wBdHT0qxA4VuK1J1qROl9Evh7VWBuQ56TTSBgXT5RrjkEKThES5Z3xdUthRXQq+ehLq4W+AcsaB7vtTBFobgPqqKGVbdLZJWySB1dMxKOdGLjfUQ18FH3oXXFUK5IyXvrYa6kepmVB3fUriBoNxvLFT4Rg7FbzyEeDqKbFCHzOl11boNoYHNH56tAoV0GPBHPs8ogwc2X7tPLizFZTUfbzKRt/gcDiCxfMA0NHRgeTkvtvZ27Bhw4aN4UVHhzXPIyI4HLcmD2zC1li2YeM2Avs8QPk5CbBMmNWt8sjtBK4qhd7/hlTFpudALd4ImrZwyI+r7vwE9NZfAXXXpWHMFNDaJ4f8uNzRCrQ3S0I5NEEOqR7tCTQqH+rxvxAlmrZGCa5NXwquKQfXlInncWFxvxb2vO234LLT1uuaMmi/B2pWRALHnSSVPK314UQDEHD8Q3DxciEcxUNuAVAZkZDXAfCFg+Aj7wGpo0CL74UaZAsNKiwGFRYP6j67O5b6xN8C185LcKlwBuDphL56Sn43h1MCTmnZYhcSAWYNlJyAPrVDgkImKSMhSV6bAdZRo6HWPxv+WbP6rKvNavR7wef2g1c8PCSS0UMJvnQEeu9rkthSDvFXX/Vot9e4WvGwWAFpLd8XawAkxW0Ol1RsMgspyemSbXweuS91QL5vv1feT0yR4y7b1C0RhrUG6ioBdxIoIxdq+hLo41sRxXIZNRqorQQ4/N6hWyWbnJQKtMawN+oh0EwTZ0MVFMs5JqcPLNhuIy444AefPyBjR1IKaNYqUN4EcNNNURULRWs9+NgHt+TZdbuCj2y2iDCAKCHteBGUPwm4fhHobAMVFIOyxw5fJ230ChzwSzVxSsag2wKytxN89H1JSgXH5gB418vg8UV9ssXho1uCRBhp0OD9b4AnzYlro0C5BaBJc6Wq1IQzIbyC2YQOgK+eBs1ZG37cw5vlORmazD30LvS0hVDZA6987g245Dj0sQ+AlnrQmEmg5Q/d8nuLktOhNn0JfPBt8M1rQGYe1OJ7QXlSmU2L7gG31APn9sr3RApggN/8T/Bjfx41F+frl2Mehysu9JpUR6RA05cAsQjmNmzYGFRwYw306/9hKXGWngJ2/dFwgdFA9ljQxs9DGaqqNCof3NECeD3h69qAociZkBRNYg+zqzPm+T5PuIWMKxH0wJdAyem9UnDtL7jyIvT23weVDGjibGD9M6Drl8Et9aAxk6NsneKBiEB3fRI8cwW4phyUmQdMmHnLCpai+pOQGF/taxhByWmgVd1bqJjQ+14Hn94l1xMbxRbuZMDhANJzRDl2mEBjp4JDbfxcCXKta23ZegX8Mq84vTukMEdb73EAcIXYwvh94FM7ggU4NHm+FDyFQjlAc9aAKy5YbToAaA025lushdTLl4/CXCfT7NV9inFRRi7o7k+HtXH9DfkNcguHpijK6QKmLZJv1dMJbmscmP22jSEFjZ0CmrtOlKuN+4AmzpE5W3dgbRHDwt+w7g8bgwalFFJSUtDWJvHNlpYWZGdnd6uubcOGDRs2RhaYGS0tlvJaSkrKx0sZJh5siyQbNqLBtRXQ7/7YkvB1JULd+1lQN972twO4vRn63Z9Y6hktddAf/g4qJX1QlE66A2XkwPH4N8FNN8XLN71nIspAwX4f9Jv/KaoqWgMw7G0SU4Axk60Ksx5A6dlh1WV6x4tB+XUGgPyJUA98Ma7kLleXgaulkgkTZ4McTqmgCyHCBLc9uROIIMOQckgyYdtvwjd2JQBgcOWlbivIaM5aqZAy5YWZhZzQ2iABmLZG8I4XwSkZoG6qMri2Anx4M7juOihrNGjxRtDoSXG3v9UgdxIQQuzihEQgMx9orJZKZKWAGStA7mToi4fE2oBZqppvXgMuHjIki73yn0HKQGIKkDseau3TsS0cdCC25KuvS6qUbiMyDLfUQe940bIb0gHw2b1iRxZhTRQKGj0R9NRfize7p10kkA+8LYHuMJs0BpRBTPL7rSC4DghpxO8DcsZC3fE8qBsZZa4ug972a6Pqz1BruutToBUPidKAsV8aNw2Yux783k8lSG9eB9njblnVJc1dB977anhb4QxQZm7Pn3U4xV/expCBP/ydjI/m65LjUPf9Kbi9CbHkgziU6GEjClx+PrrR2wV++X8Ek1Z88G3Qkvug+qDwZuPWgi8fhd73hiQmnW7Q/PWDazPTUg94Q4kwBnQA+sSHcEQkU7rta2UMdVMdECWBaYvifo7u+iRw8bCQhZPTQDNXCaGjrjJ6Y0f0Mp7rrocRYQAAAR9458vAY3/e6/73F1x5EXrbb2GOU1x5Cfz2D6Ce+c4tJ/BTbgFo05djv+d0QU2cBX3lmMw/Scnc09sJPr0LtDoiwRmHwDScCUwbNmzEB5/cHm5JGyTFG6gpA7/438GPfxOUPwG08G5RjtAhSi4OQ71DOWVMDR1zM3Jl3W4irxBIzgDqIuzuWAO1FaA+qov1BezzQH/w6zAlLL56Wmz6THVQAChaAnIngauugFKzQPPv6JYgQ2OnChmztRHobAMnpggpoaoUSMuSwoR4Y6ONILilHnxmj7xwJwnhFizzz8RRUGufHNZkLmXmSUzn6BarMX+CrInrr0u8ykxSdDRLMVRiSlBBD8oh8Z8IshS3NljHmHcHcPOa2AEDooq76jGoghnQyx8SK6PONsPCKAE4vBm6rQnscAJndlv7rKsUldsBKiwNJkGXW+qE9Bap0Bvwi4XO5aPyPWWPg7rjWZt4P0KhVjwMnrlSVL4y83tlkUoJSaDCGeDys+FvjJlij41DhPT09CAZxuPxoKamBvn5+TYhxoYNGzZuAzAzampq4PFYhQPp6bde+XFYyDB/8Rd/AQDIzc3Ft7/97bD3tm/fDgBISvpoKF7YsDFY0Lv+GO5l7euC3vky1LPfHjRpz+EAlxyziDBWK/jCoSEnw3BzHdB8E8gp6NF2ZdBQekIIIMoBJCYLwUGzEBse+GL/1FyuXw4SYYKoKQOf3QuavyFqe73rD+Dz++WzAJA1Buqhr4rkcyx0NMu2nk7w6Z3gG1dAaaNAc9eDxxVJcgeQIKFRwdyjjU1SqqjbXD4GNNeCG6uBsrNRVXd84WBcMgy3NUK/9YNg1RJfbwVXl0E98U2pYhuB4OPbRCLfVHXRAeDY+wg0VgM1ZdZ2N0okIORyy7ViWv14PRJw8nuBznbw1ZNARjYoIfyZSS63fJdRlt8qQsln5IOvngkPYJsoPQV0Q4bh9mbwOz+0ZMuvnTf2Y+zLvNa0Ub3jcIQnQUOsjSg5o3siTMAP/cEvw7ylufIicPBtqLVPgqfMF2WKtFHBynR+4EvgUzvAbU2gsdNAC++6ZVWXavZqaKcLfHYf4OsCTZwNWnRPzx8cwWBvJ9BYA6Rn39ayxNxQZRFhmA1LNC/0gbegVj5i3dIhEuXdqnDZkGetKYnu91qkt4DfIHAK+MgWcNFiu3pzBIKbbkrVu/ks8Hvk98oaK1Y1g4GMXIBiLI1JBUmOvUZKhkX2jWzvBmQon4U924qXRQfaExJBk+dH7yAjN1yRxkRtBdjniUuQHizwuf2Imnh0tYNLT3ZLXh0OcHOdQYKJeKOlLnrjKfOBg28b1g2GnURKBqh42S3oqQ0bNvoKNuf+gKXGEbaB2Pzy0S2g+78AGjsV6rE/h37lf0m8JWRNC1cCULQUKDsNeLtA44tAax4PW7MTKdC0ReCGG9Y6y+ECEhJF+WKAZBg2rV0ycqPH8cpL4ZZwgMx1utqF1G+e78ntYHeSWO/VXQdfOwf18NdBeYXRx/N5wVt+Dr5+yZiLaiApTWICxhqWT+6AevIv7TloT2iosq4/5ZDfJOAHEhKhnvseKHH4bT7U4nvBU+aDb5SAUjPBeRPBv/5euDJdwC9rZk+nZauUkAikZwuZ2ITWAOswNT1yJYDu/1PoaxeEYDNtEZRBJlXz1kO7EsA7XgQSDOtiQBSQgejY0Nl94MUbB2ypPVBwQ5WQfxuqxIZx8lzQuqeD9ycf3RIen6u/Dv3ez6Ce/c6wqSzZ6B6UkSvz6L58Zt1TwLbfyVgJAKMnQd2mluS3A1JTU6GUgjaUdxobG9HR0YH09HQkJydDKWUTY2zYsGFjBIGZobVGR0cHWlpawogwSimkpt76Qu1hIcP8+7//O4gIU6ZMiSLD/OpXvwIA5OfnY+nSwbXHsGHjdgV7OqMrjQAJirTU93nSPqJgVCz1un0QwFqDd74EvnQEogrhAC3eCDWEVVvBY7c2Wi+UQ2SXAVBSer+TFFx1JfYbVVeACDIM15QFiTBBNFSBT+0ELbhLJHsjfJpp/HSw1tBv/0DsUQBwFSS5sfxhcGiQBxDlk8IZPfabXG4gf4LYANVVSoWRckgfzEVMqNd65HlfPGzJ95oI+MDnD4BWPNTj8YcDfPVUuHy2iaungVACkZEAh0lqcScbFViGtLLTDbQ3gY9vA1eVwvHw16P3mT8RqC61fhsisbYZNXQy3UOCkGR1r9oN8MG3LSIMYCW/mcMDa64EKxkWGpxSIVMkg8ASFzVlYUSYYB+ungLWPinVQRFWCjRu2rAqe6niZcBHJJGnz+4BH3wnOIbQ7DVQvRgDOOAHblyRa2LcVFG9GW40G4lYI1ETTKpUXhCCVc54UY3QAQAkSfFb8Oy6naFmr4He/oJUiZtzC2Z5fpgBfUDGyuoyYKpNhhlp4NKTMUmRXHJ80Mgw5HLLnOnAG+FvJCSCxk7p077UvPXQN65YcxiHQ5QDxvRtP4DYXdCaJ8TCqaMFyC2EWvVIzAQazbtDFAFCCSnO6IrtIYO3K3Z75FxtBIDGTA6n7fi9gM8LLj2FwOv/B2rtE6CsMfLeoc3yr6kQobXY1drEORs2RiQofwLYLDKIJzrNHKasR9ljQasfA+/+Y/h2eYVQU+aDJ84CxkyFSoxdtEfZY8GJKWFkZQADVuPUx7aCj2+VMcqVKCp2c9ZYG8RaDwWMOWLwtUEICgSs54EOgE/vBN35yaiP8+mdktz1eWX8ZljWu+YaqqUWeudLcDz4lQGd30ceWaOBUBsiY95JY6eOCCKMCRqVDxqVLy+0Biemhtstaw2ApViHyCjaSQTNWg20NoDP7BaiTMAHkAKf3oVAXaXEHSbOBkqOi0oKa+D4NuhVj0KZNjQ1ZUJAM9c+5tyJlJCH2DguyJq/DyMZhllDb/m5RQJiDb5yAkhMDSrLibVTBFobRO2vpR5ISALNXNFr+7KhBNdWgktPyBp62uJeKcXaEFBSGmjTl6Aba4Aze8D118EH3gLmrotJNLQxMDgcDhQWFuLatWtBQozH40FtbYwCBBs2bNiwMWKhlEJhYSEcjltPEB4BUf9w/PKXvwwSZf75n/95uLtjw8bIgNMVk6QAh1OkSm9j0KR54KMfRCU5KCJxPJjgS4fBlw7LC58H8HvBO15EoKoUasPzQxqYEF/mGBg3ABWctKw47dGVWlxVGnNTriqFWuKCWv+MVLmYCcP0bNDKh4Fr54JEmCD8PqD6KtR9nwef3C4KF+OmgRbd26uKF2aG/uBXUj3tcFlyu94ukREGYlc+mwhVSupN+0iA04WYUVnTc9gMMCqHbGe2kSS9gwoxoai+Cq4uA42eGNasVj8GvfknFvlGOUBL77/lVgUDBU2eJ0SHiKpHKu6+yjymTYXDKYE1c7xxuOQ3mboQuHrKatMBqU4DgMw8UGjQ2dx/S71YQFw5LpK6Xe1CXgolVAxxFb4NgOtvgPe8huB9pQOiuJNX2O1zhBuq5f5oMwiKyelQGz8XVsk4LMgrlPu8qyP8XldOSYa43HKNwhgXlANcXzUigqkjFVS0GNTZBt7xe+M7c8oYoAMyPoYG1DOG3i7RRj8Qb04xyCQPx4oHodubhDRMJPdazjjQvGiVvW6RniPzmOY6udaSUkFLN/W7WlHNXAmesQII+ONWQ3Nnq1hgFM4Qkh+09N/hBE2aM+SqMAAk4XU94tlLCjRh9tAfu4+gcdNEyeHyUUmymdXuvi7g6knoygug578HcqcIsVU5gnNTAEDZGXDAPzJIlDZs2AgDzbsDXHZWlJ66G3e72sGsgyovauZKaEBsbTwdUlhQUw69+ceyvTsZ+p7PCsElMTlciXDSHFmTh9jDgBRodvQaorfgiovgw+9aDb4u8L7XwHkF1rxv7DQpjApVI1MEkMNaR5oEHRWuQMtxVM+44oKQHyIVZ4Dw9WrFhf6d2BCB6yoNBdssYMLMEaHAQek5oFmrwGf3WI1ON2jxxuHrVA8gpUAL7gTvDyEH+z1CrtUBYx0taxDMWA7lciMAAMe3Ag7jOdnRAlw5AU5KBU7tkOvGnIf4uqQwbewU+a2SDYl8T8Tah7WxHgopjnK6gPYWUSoaLty8Fq6GY4CvHAeCNosx1J69XeDjHwpBGkKYURv/BFQ4cwg72z30+QPgXX+AuY7mEx9C3fMZ0IRZw9anoQB3toIvHALamkDjpgKT5gyaujprDf7whWDxKteUga+egnroa6D8HgqqbPQZSUlJUYQYGzZs2LBx+8AkwgyXK9CwRG9s2TIbNvoGcjhBc9aCj7wX3j5j+W2X2I4EZY0GrX0SvP9NCbgY5zqUZBiYcvM+T7hKR+kJaL8Xjoe+OmSHpjGTjYDIXqsxfyJo1qr+73PyfLHfCQ2CuRJjBt8oPTsmGceUOKaJs6E+8bdAxUUJWBRMBykHdNnZGJ8CuLUeqqAYVFDc9443VFl9VkpUcrxdkpRwpIHmrANNWxj7uNcvg2uvSYDE4QyriqMJwxdQiAeurQSf3mX4hEeAFOB0RgdrnQnin00k55eYKt+Pp0O+L6c7GExBe1P0bscXQT31LUNBxyvyvaH+9rcJKDEFatOXoPe9LlZD6dmghfeACnu45pLToq2/nAnic24SCw0pZ5Qck99hzGRg/gaQ1kBNOZCRA5qyABRqpRLwQ3/wa7Fp8hjEK3Mc9nTI72QEm2nGikH4Bmx0B756CrEIZlx6qtvniN71skWEAYCOFujtv4fjqW8Nfif7AErJAC26F7z7DyGNJONxwG8oRiWE2/tcPATMtK+17kD5E6Vi20TAL/drCBGXCmcMPxnKRkzQlAXgI1uMyuCQ9uLBVxFVd30SvOBO8PXL/U6o6Z0vG88Di1zNe18FYowvXFsBrq0AjRrd7TOaQlWMIo936F3wye2WYtSo/KBaGU2eC1rzZJ/631/QzJVAXaUoL7IWm8GVj4BGCMmMm+tkHpUzFkQKasPz4OJlYsHlqwxPwvk84Hd+DHrwyzFVieDzyvY2GcaGjREHSk6HeuKb4CsnwIc3hytchG1o2OCFFJaomSuBmSsBAIE3vx+0CwYAdLSAX/k3cEKiEF2mLQStfUpiNU4X1INfAR95D3yjFEjPgpp/54DWXlx6Ik77ySAZhpSCuv8L4H2vg6+dlzVJUhpQf0PGKafLKgbQAWPccgFKgcYVxT6wzxv/OwtFNwqutxp6/5vgUzsAIMwGmtzDr76iVj8GHl8ELj8HJKWApi8bMc/FeFBz14FTR0kRmbdLiE+hSoqG8hB1tsoapaHKIrt0hlw7fr9hsRSQdbgZ72ANLjsLmrMGNGM5+NSuiIIfoxDI0xESIyHAkQC97/Uhjdf1iHgkilDrtOmLRdHPhDZI+KGENNbQR96HY5jIMOz3iZJu6DpaB6APvAXHR4gMwy110K//n2BciM/tBU2ZD7rrU4NzgMoL0SruRnEO3f3pwTmGjTAkJSVh6tSpaGtrQ0tLC9rb28Ecs+TUhg0bNmyMABARUlJSkJ6ejtTU1GFRhDExLNGblJSU4EPLhg0bvYNadA90croomugAaOpC0OzVw92tQYEqXgaesgBoqgHSskChCauhgBkUibJiIqDqCrixxpKJHQRwezP42FZw1RVQejZo/gbQ9GXgmqug9FygoGhAlQnkSoB6+GvgE9vBNWWgzDzQvPWxJU4nzAKyx0qAzITLDZq73tqfOxmYuiD8GGOnxCbRDIRcESnrbAbrnC7QJ/4OKs51wFdPQ7//S4jFlZIqXh0A3MmSiJk0t0/d4M5W8KmdQG0FMCofNHe9JMAGCXzzGvSb/2kFDJ0JEmQkEjKLOxm07AHw0a1CCCOSZI2pAGF6dANyzbIG/FraE1Nkf2Mmg1vqgMYaIHscyPDhpoxc0NL7B+1chguUWwDHw18HM/eaUKvmrhdrlND9jC8CbfgE+OxecGM1UHZGFF1M+fCS40DdddCmL4GWPRC1T752Xq69VlMW2aiO9HTKb+HzyL5SsoXgljUaeu/rQEo6qGgJKHkYq9g+qnD23UaLuzpEkjsSjdXg1oYB3//s9wE3SmQ8GzMFpPo2vquFdyFw47Jcn6SMJAYB/kDsCueuNuhTO0DpOUDhzD4f72OB7DEG4dKocnY4AXcK4E6Sqr2CGQMipdoYWlDaKKiNfwK97w2gsRpIyQAt3hhlNcc3SoRs7OmQe2HWqpjKHVx9FVx2Rp6/RYvFyi70eNljQdlj+9VX9nTK/W+q3Znk1cZqcEud3KcQdTze+ZKQ2SCpCCqcAbrns31SG+FrF0Q1ymoB2ptA9/6J2DAkJPbrPPoDUgq0/hnwontEHSFnHChh+En77OkEb/21WM0BQFoW1F2fBOVNAI2dCqRmAjfLoz/YfFPG4Mx8WaOEgMZPuzVqOzZs2Ogz2NMJPvaBjPMN1cZ6KpxMiYQkiQnEUdllv08sh4MNLPN9sGE/ByH+pWUFVT4oLQt0x3ODdyKOOFYwEe2Ung3a+DkAQOCVfwPqrsu6xG9Y7I7KkTHMLALyeaQYZ+7aqF1zc52owfaUVzSLCEYAuK4ySIQJoqEKfHI7aGn0em44QBNngyaOPJW07kCT54ImzxU13xf/WZSWAEM1yANAQ2/+GdSyB0AJSXLJMFuKt4DENIgMRUZtFfIAlgorA8jMs9bXygm4zbkLBW3Fg+uhOErHtwy5BUDWGCEAhYBM2ydA7Me7OsAXD4qaclKazE07W+Xecbkl9hVazNYDuLFaLLIbqkG540EL7gzOKfuF1vpo5XEAaLoJ9nbd0vnjUIKPb4sqkOIrJ8Bz1g6KsiqHqoHFaWfWQHO9kOFGAEHvowCHw4GMjAxkZGRAa41AIGArxdiwYcPGCIRSCg6HA2qExKmHhQwzevRolJSUoLa2FpWVlRg/fvxwdMOGjdsOasZyYMZyI5F3VQgNOR+N+4dcCbKwvBXHmrnS8CwOaWQYVfce6Hd/DLX0ftC0RQM+Fgf80G/93+BClxurwRUXoB7+GtQAZJMjQUlpoBUP9bydwwn14FfFC7yqVIJnc9aBskZ3/7mc8aDZa8QP2sSo0aB5d/S/z+k5oHHTwNcvhzQSaPaauEQYANDHtyL44yUkSkCSA6D7vwBVML3Xx+e2JvCp7eCTOyVA6kwArl8GXzkJ9fhfRCXHut1XRyv40DsiK52UCjV3HahIAiJ8cnt45VxCogRApi8V0lVBMXBkixBdAn55TynDzkNJ0MjvExUL5bCCSwwJRK1+AnxkC/j8AWkkBZq/AeojQIKJRF+U5ahoMZTDCX1mN9DVAZowE7ToHkleLbob/PYPpBo0MtrbdBP61f+AevQboiahtVwfna3Q7/8CaDcqREN/B2KDkJUEKl4Gte5p6N1/BL9nqXnxye0ilzuIRLtY4IAfaG2UZHGoesj1y9DHPgCaboLyJ4plVmbekPblVoCmLQIf+yBc5YtU9zZaTpfc75GESOWwAq79BFdfhd7yC6uiNj0H6v4v9LkCVC29H7qqNLxKMmecXH+h5+rtAupvgHe/CtZ+Sco89udQ6dE2eR8nMGug7Kw8X1IyQNOXQq16BHrHS5bKQ0o61KYv95v00Ou+tDcDTTflmWkT4voNGj8djqe+JRXKroQoEjGXnRGyovn7Xr8M3CyPqvzUR98PU1rkE9vkOhikOSgH/JIwZePeDRjk1aT08PGl4nyQCBP87LXzwKUjoBnd2wCGfabsdOw3Ki8NWeKN665D739DElLp2VAL7wYVLQ6+T2lZ8S08u9tvV7sksK9fFpWsuetB4+MoF/RlvwfesogwANDaAP3+r6Ce+66o/kycA5SejP6gwwUQQd35CegtP7fUxEaNvmVqOzZs2Og79OafCOmZ2bJYUc4QuxVR2qK568LmymEw54QmiVb7EVwzhKxHuOQ40I3lzUDs1Gj6UvC5feHqVMoRNt6GHauuUogwgKW6CkjMKDHZsoIlMhLy0cluLj0p7ycmG4qmIRZLmkXNVDmBxBSo1Y/367wGG3zjSp/aRwq49CT0kS0yR8wrhFrx0LDbnnJzHfjyEcDvEzJM3gQQEdTKR2Qd7PdZBIqEZKCpBvqdHwmptKvdsCKNCLJx9H2DxFTQpLlgnxf6re/L+kY5DCJNQD6jHFLcEKmKN4hFS/0BEUHd+yfgXS/LOsPpErWfkHGAHE7Q6sfAyx8EN1aBX//f1n3MhgWZUr220eHWBlE3McYjrr8OvnYe6om/FNu2/iB1lIwBvq6I9qyPlNUz192I0145KPcbjZ0au2hw7FQ5zo0S6J0vibWWcoCKl4NWPQL4vfL8MK3mIwj+NnoPpdSISbLasGHDho2RjWEhwyxatAglJSUAgMceewz/7b/9N0ydOhUulzXJ9fv9qKioGBSps8LCwgHvw4aNkQJ98RB4zyuyEAVA44pA937Wrk7sAyivEGrj56G3/EySxkoZ0qV+Qy65EfrD30E5E0CT5gzsYFdPR1d86AD49C7QnZ8c2L5DwAE/uOoK0FIvCe4xU+ISB8id1C+farXqUXDRYvCNEsM6YFa/g3vBvtz1aWDfaxJ4c7jE+mvJfd1/KNKjWSkASpIbu/8o1chjpoBmr457X3BrI/Rr/y4VMV4jABDwS4VgZyv47N5eK6owM/Q7P7Sqgwy7FVw9IxVUtRVyfTldVhCICJQ/QbzpP3xBkllaS3Le2wWA5e8g2Udb8rpEFn+DACIFPr/f6pAOgI9ugR4zpU/koI8iaMp8OKbMl+rOtsagfDFfOAhUXkLcskdvJ/T+N4FxRcDZPYbdRYoo+kSBYfwQcswJs8AN1Ubw2qiOUw6gqx189H3QXf2777m9WchWgQCQmSvBSVcCuLFGrvm8QnD5WfCBtyQYmZAEWnSPyFzXVkC/++MgsYKvngJXX4V6+q9v++okSsmAuv+L0AfeFA/3zDyoxfeCRk+M/xmnCzRzhShChbYXLR6Q9SCzhv7whXBp+ZY68J4/gh74Up/2RXkTQHd/GmwGz5LTQbPXgDLzJKDW2mBdvgE/oA2CTEMV+Lf/AP3IN6CMINzHEbz9RQnmm69P7YR65OtQz35HKsVdbtCkuSB3EvjyMejTOwG/HzRzuaiJDJKPvN7/ppBITYn4iXOg7nz+tr/vbjX4Zjn43H4Z2wpngoqXmQr9QehjHyDSzoavnAAv3hgk/nFHq5DnQuHtAh98B7Spb/doXJSdNkisgfD21Iww5cMwInAorl8G+kCGiZuwGKKKXvZ0Qr/9AysZ1lwLvf0FqMTUnu0Lu9sva+i3fwjUSyKXG6rAlZeETDhAQgzHIrq0N4kd4pjJUHPWQJ/aIWoIAIL2DGOnGsQeQD37XUmuKweQV2jbPtuwMULB1WWW+h+RqEkE/PLMcCcLwT05HWr9M6Cpse14AUPpas6aEKsT4553hKznmAFPB7jkmKyLQ8ZjLj0JffAdWQuOGg21/EFQ4Yw+nQvljoe6+9PQB9+WeELWGKhlD8YnswcC0W2sLWK1UpZNS0udPFMjE+nmuTmcYjlrql6mZYGWbxILHGcCaNqiPhWO9Bd8owTcUg8aPSnuece1gR5m0kR34Oqr0Ft/Y81basqg3/kR1NN/c0u+16j+VFyA3v2K8ZwTRTs+sgXIyAWNygNNXwb19N/IOqfyQtBqK2g73uwHQNHEilAkJAKsQWOmgFY8BEpIlAI1s9AkIVHIHsxCximaB05IAkLm8wBAC+8asu+ht6D0bNCmLwtB2+GMGxMjpwtccVHiOC53eEEDM2jZpl4dj8/tt4h5JjpawJeOgOat7985uNyghXcZVknBVqil9912cxyurQSXnxGr+GmLwooPKHsMONLGCABlDU4xBI0aDVpwpyjQmMgaA5p3B9jbJUUyQVJlAHxuL5DgNogwQrLm41tBs1Z1SzBkvw984kOxiHa5QTNWQIWoEdmwYcOGDRs2esawkGGeeeYZvPTSSwCAo0eP4v77wxOOzIzy8nJMnDhxwMciIvj9I8fL1oaNgYDbm8G7/hBWKc7XLwEnPuyZQGAjDFQwHer578ni5Pply57G5YZpfcJn9wyYDMPm4j6qffBs4rj0pART2pvktTMBGDsN6oEvDLrlFOUWDFr1NABQYjJow/PAhud7/5nRk8DlZyNbwfvfsJQeKi6Ay8+KEkeMKgF9YpsEXrS2Eso6IME+p8uSAe4Nqq5EyeTC2wVcOCAEioDf6BeHJa1ozGSw1uArx6XB55GAGMmmovzSBZBDqqwQQsQIWmcr8J4/SmCSDDUZo+qR3/q/0Hd+4mO/SNZn9oAPb5Z73OEE0rLl9w0EYH3ZEfB7hchWdgZwJ0nyq7VBSIgutzVeBCvdlFSFzVoDmjgb+uJh2cZvSrJLYo2DibY+nsPFQ+CdLxuWTj753dOyRFLa3CeRKAWZFa7eTvD+N8BZYyRIryMC5J2t4JJjoFm3v90ejZ4IxyPf6Ntnlj0IuFOEMKE1aNpCkbQeCBprLInvEHDl5T5XBrPW4IPvGMSmRKke2/UHYOpC0MwVQN4EcOVl4Mh71rhnXo9d7eBX/hf0vDtAa5647QKaAwXXVoQRYQAAXW3gYx9A3fEcaI5lC6APbwbve90iit24DK4ug8MgrXFdpRDjqq8aChj3gKbFT54BANffAB/eLGSHtiYZd3weAAxcPAh9/SLUA18GjZ0yiGd9e4LrroOrr4IycoHx02KSkLjyIvTmn1pjWPlZoKoUdGfEvCGSKBvabibQ6q9Hj4WQa2bQ0NEi9yyR9QxwuIDIoHtKZuzPp8ZpjwMqXiqEq9DzcrjC5PoHE1x6MqasPp/fNyAyDCovBYkw1k41+NSOgavDuBKik0hmOwBSDiPJ9zvDno5AhTNB654Kbkpq5FiC2LBhoxt0hKy9mWX8DVq0uICJs2Uu0AulNlp0L+BOFhK93we0Nch+AFnfeWSur7f9FkhMhXrgi6CccUJCDyU6NFZDb/m5JL5rymXNULysV3bDNGkOHJPm9G4emVcg65zQuSip2OTIpDRZ40Qeb8p8WTeZBRjGZ9Xdn+4zmae/4IAffOMK+NDbQaUbBkDz74SKYWOLwhlA9rjwZ4jDFWYDPdLA5w9EEXjh84BLjveb3NDvvtTfgH7vZzJ/gaF26mmXN+sqJQZ5/TJo+UOg/EJwzdWQPhtrEB0wzifO2hoAcsbD8fDXwo/dZcwnPJ3hVmYZuVAbPyfrofwJwNVTQsKasRw0YVbfzq+uEmi8CeQVgDJywZcOQ5/cIYoc44tAyx/sN3GqV1ZCJknN5bbIeSBgyoLeq1O2NfWtvZdQ8zeAs8dJPEo5QNOXDLs6UV+hT+0A738z+JqPfQC16UvBmCXNv1Nih13twW1o4uyB2b1HQC19ADxlgRQNpo4CJswEKQf4yomY808+tStEqcxoO7sXPGNF3GuCt78QRu7mmjIEGqqEmJWZZyvL2LBhw4YNG73AsJBhHn74YWzatAlvv/02iCim+stgKMLYsPFRA1dejB3ELz8H2GSYPoOS0uB45BvQR94TNQWf16pGUQ7wtfPQ7/wYNGdNv4M/NL4odqXSIMi+A0K20Vt/bVXUAJIYrboCPrIFtPqx2J+rKQef2SP+uYXFoFmrB6zycqtAyzZJ4qrDJBQRkD02OolSUwZUnAdCAibMLL/18Q+BgDdcxpchv3/AL1wUrWMSaaIQsrA2DiK/gZnUc7kNCy6v4VnvB3LGSZVQYpqxXSDcSsnsEENUJlIygMxckVEOfhFkVWMB4QE1gxTDO1+Czh0vHvYfQ/UorioF731VXgT8cs20NlhS6UrFHFPlwwZRqrNNKknNhLYZGPZ5AWggb6JUTna0gBtugK+dBzdWhxBhAIDl2kod1fdz8HSIGpg3JEjIWs6jpU4qOkkBni7A75F+hly3XHIs+ho10Rmn/WMAUkoqCwezutCdLL9FZHDbnRT2m/QK1y8BTTXWa79PCE7n9grJzuECzV4N1oEIOXIDOgA+v1+eNZPn9f1cbmNwXWRCXSpM+epp8NwbwSCjbropRBhz7CWS3+7sHrBRmaff/qGV+G+6Cf3hb6GSUuM+w7m9GfrN78v9agb2Az6IepSxUVcH9J5X4HjqW4N74rcZ9J5XwWf3ADDSJqMniQpIxLNKH30/apzmkqPgRXeHVYnTmMmi+sMsRNKADyCCvnoaatw0meNk5sW+RwfRvo7GTxclAZc7nAAbMY+koiVioxicywBISATNXNm3440aDXXfn0Iffjdon6qW3i8Eo6FAaFVzWHss5bTeIz55PHZ7X0AzlkuFeyhyC0AhVrOUkAjHxs+BDWIyDdAyz4YNG8OEMVOMObtXnt/meK+c8nfVFfChd4BVj/a4NiIiIdAaJFpurBEV0qorMhY6Qyxcutqg974Kx8NfFwu8yOdMV4cQ2xPkmHzpCNRdnwRNmd+r0+rNOp1IQd37WSHiNNUAINDEWeDsccDR8DGQFt0jNnGR+0jLgrrns9D7Xpd1RmIqaNHdt44Ic6NEyEXNtfIdK6fMo4nAJ7aBJ8+NKswh5YB68MvgkzuCCrY0dz0odwTbiUdatZqI94wdQvCFAzLPMudaYcsKBjgAkAN84kPQvZ8FTGVNjtq4Wy4MbpaDdSDsuqPCGeBdL4cTYQCgtQHcWAMalQ+atQqYtarv56UD4G2/A5eesDo3froo25jblJ4E11VCPfXXQxYLo8lzwce3IqgWa5y/Kl7a+52MnwaUHI3e9/iBEyCoYDroNlUT5q528KHN4Y3eTvDBt0GbvgwAoMxcqCf+UpSU25rEvmjqgkHvC2WPjSayxFMa9YmiUCS4pjwmGYZb6sGlp8IbvZ3AwbfBSalCFhxXBNr4OVCkrZgNGzZs2LBhI4hhy3y+8sor+Pu//3v88Ic/RGNj43B1w4aN2wrkTo5NrEi05fYHhElzgd2vIGzlrgNSnVN5AVx5Eerez4Imzu7zril7LGjhPYYsv7H/0ZPCKtMHAi4/GzsBEfCJpUrotlqDT+8En90L1FaK57czQZKuN66ANn5uUPo01KBR+VBP/41IhHZ1gCbMgt7x+5jbcnNdmJMCXzgIPrUDUAT4Y9xNJpGl5DgY1DtLm7HTJBDq91lVgmwEjrxdkgxLSgV8Pvm3sw1orhUlm3P7gElzgJJj4cSMoPw2rKo9d4okwbWhaOKJUeUcCodTFCJe/GcwKSAzD7T0ftDkeR8bpQguOW69CA0umqevdWyv7Eh4OuT7dLjk99QBICkdNP8OOcbNctmurRH6xhVgVJ5BfAoN7BGQPabvJ1FdZl1boTArNgN+uY/Ncwr4AXIFZc259CRo0tyYu6YJM/veHxtxQSkZEvC8ciK8ffbqvtvudIUoLzBb16hJfAkIuQPu5NiKB4ZFF187DxrBZBhmDVRcBN8sF2LDpLkDt9/LGmM9zXXA+C4ZaAtA//H/BS3bBJo8D/zqv4ffV4YqHHQAXFMOtDcaiTRDqsu0WTNJRrHO59KR2L+HSW4kw1KtsRrc0dqryvSPIvSVEyGJAac8Q6uvikXh/A3hGzfVxt5Jc62l+AKDKHvzGtBQbVVbOt3AhQNgpwu06lFJkM1cIfMgE8oB1Q/ryFDw9cvgqlJQejYweZ5Io5/cAXPeR4UzQRHWR5SYDPXIN8DHtoJrK4Iy65Se3efj0/giOAaJZN3jsSbOFlJxZKJ3cuznTK/3O26azFUi9ttf8jh3tYutQMMNUQyYux64eAjweUATZoFWPSqJsvMHgPJzgDtJLNJGTxrQediwYWN4QUmpoNWPg9//pUFst+ZN6JK5M188BAQC0QpjPe17VD4cD30VuqYc/Nq/W+s1E9VXwQF/9Jw94Jd5edj8hqEPb4ajl2SYXiM5DWrj52T+k5AYtNzh3PFiSUMKVLQkZvKb25vBnk5J1s9ZK0SE0ZMGnSTAWsvaSSkg17Kd44BfiDydrZaahja+O0OBgysvxVSpJXdyry2ORwJo0pwYFn40cIvufoDrrkt8Iha5ng07YEB+l5Z6iZ2Vnwe0z7JLCvitUFos0rFyyDaNNVLEZG6akSOEZNPaDJD7xOkCl58FDYCszJeOhBBhAICFUOJKFDtLEy31QMUF6IQk8I4XRfHX6QKKl0GtfHjA5FjKHgta95RlZex0gxZs6JPCDU1dCFw9HaaOTEWLgUJrLc8Bv8Ql6q4DOWNBUxZ89IkR9TeiiVQQUkkoKCWjXzbxA0bhDFHh6mwNb88ZDzRWR21OmXGI7B2tCKo2aW0pT4c8g0zFeHYnAwlu0KQ5NrHbhg0bNmzYiMCwkWFcLhf+6Z/+Cf/4j/+Iy5cvo66uDl6vFxs2bAARYezYsfjtb387XN2zYWNkoqAYyMiVBEAIaPaaYerQRwR1lYZHcVf0wh0AwNAnPoSjH2QYAFBLNoKLFkmiJC0LGDt10MgI5HSBY+6LRK0iBLz3VfC5fZIY1H7A65cAh8sNLj8ryZhBtEAaSlBCImi6VU1DoyeBQ4MoZnuEzCuXHJM/nAnye8eDpx18Zg8C1aUSMFx4d9xgAiUmg9Y+JUHXSOsAv9fw3U4QgkRbY7hCRHMtaOoCYNpi8MWDEiAxVUsAwKHkNwoEgMYqsVVoqZeYVKhNT3SvLI930yu+ug389g/Ak+dB3fenH/3gCCCkJxOmrDmz8b0Y350OANMWA9MWAttfEHJZwBf93Qb8EtxLGQW6+5OSuL9xRaTEw8Ci2pKQCAQMKWQikXYeNbrv5xC0zIi4z4P3vfGvw2Up13g6LGKVp0OC/jnjLfUkUlLleZvc77cTaP2zQFqWEGIcTlDxctDcfpAfxxdZUtrM1vXoCLlvW+qEDKOUdX2bYC3KPyNY8YtZg9//pah5wIih54yDevArAwreUf4EIbuUnrTsiUBBlQ4+vBncXBeHRAT5PrPywY1V8pzw+xAkwyQkiR1ZPHS2mScXW3WKIQH2hKSY9gQfB3BTrTwvzarogF/GXHcycL0ECCHDMDOQP0GskUKhHEBkZXhmHuj+L4Bf/O8AO6wEDQC+eAi88mEQKdCqx0BjpoDLhExG3UiS9wZ6+wtCgoJxDR/fBvXQV0HFy4GbZUBGHih/QszPUlpWmBXPUIBrK2QOkVfYO7W7HkDp2VDrn4be+7phGahA05dGkX36vN+0LCE0HXxb7jkOAFljQQvu7vO+2NMB/dp/WJaXV04AmflQz/1XwOUOzsH1tt+BQyqt+coJqHv/pFuiKOsA+OIR+VxKJtTCu8IUimzYsDH8oGmLwDteAjwcnihlLc8elxtcegK89ol+KWdSehbY4Yx+zielidXI5PlirQQErZQAoy+dbTIf8XuBzjYEdr8CteS+ARc3sc8D3vGSFIuwBrLGQG14XtRFASEBxkm+s88D/vAFIVl72mUO404GKwXMXg1aFVtptl/9rL8BveXnslYCpJ8bPycxkpoyK2kcGt/w+4JkGJPcc9tjygLQzQpRyNMBeTYte2BA85H+gK+eBiouxFdKBWRd6U6WIovtL0ibUmIjNm4aeOuvgeY6q2DHnWypMimHoaCUIASUGCRwyisEt9RbhR6mckxCkhBarl8GUjJkvpbWO5VVrq2QYjSfV+bdoYVGAX84GQaArioFjr5vFR/4uoCT28Fd7YNSMKamLwVPWSBxnNTM3tkrhYAcTtDGz4Grr4IbqqKsy9nvg37r/1oFOgD47F6oB78KMi2UI8A+D/jAW+DLEh+jokXAkvug3LdRoWVGTmzy1QiZl5HTBfXAF0VRrKYMSEiSosgJs8Bvfl9Ufc1txxWJak0McGqmrEl9hvV68A1RPzUL83jf68H1JR94C2rTl2/5mGLDhg0bNmyMZAx7hJyIUFRUhKKi8KqvpKQkrFu3bph6ZcPGyAQ5nFCbvgw+9K4wv1MyoObd0S/FEhsWyOUGO12WDYrPa+SXQ4Iw7U0DO0ZG7tBI1k+cAySnS79DgxiuBKg51hjKXR1WUC50sejzGqoSJMlBT4cE6cZNAyWnxz0sN1aD978FrioF0rNA8+8E5RUCSal9XtwPBmjeHVIpE2IjRMXLohJQREqWj0SxF84mAkYwprleqteb60B3fyp+B26URCSjQwN4fiA9Bxg9CSiJoYTWdBPqrk+B1zwOvnZBpIovHwNg+NvrrvBEuDsJQU9uf4TVkwnWgF8HVQ6C0AGg8pIoGwySOtFIBhUtAZ/dJ9+HwxliM2R8Z0TSXnlBiEo544Hqq/Hlnd1JQGsdUFMOyi2AjqzyASQg4fca44jDuhQSEkH9qJyn7LGggmIJVnpDKk1dbjmWSXZQSq4zp0sq2kjJNiFS1OrJvxKVhbxCUJBk0zewzyO2HmlZMeXVP+4gpwu0bBOwbNPA9pOUCrXuaehdfzAIHUZwODRpk5AoBLqkNCE5hiV8ROWER/JvdO18kAgTRN118Ln90eogfQTd+UnQxNnQH/zKCsSbgXAdAOoqDJKaK8LSDEBBMVRaFrTPAw6VsWct5LKC4rDNua0RfGgz+MZlGaPNpJc5TocOKK4EIQ/MWXvbWBMONvjk9uikiynPb4xLuqkG/OELxnhsKPM4rGQGLd4Yc45CSoGdIUH/QEDGY0+nKIAULxdCyJT5vbam6PZcqkqDRJggmmrAp3dBLb1f7A2HCdzaCP3eT+V5AACpWaJymDNuwPumoiVQk+YC9VVA2qhBS07SnLWS9Lp6Sp5hLXXgva8BG57tk7oWnz9gEWFMNNWILcns1bJNU20YEUYaNfSxD+Dohgyj3/4hUHoyOH/UZ/eAHvtzqHEDtyqwYcPGIEEpmSOF2tCZ8HkNlUcdTSSOALc3y3jS0QKMnSrrXCJg9GTQ9KViuxECmn+HvF8wHbT0ASHMG5Z9Ji8XWuYSwaT/ub3QtdegHv2zARXL8MF3LBWMgB+ovgr92n+APvE9qMTUHj77thBE/Z4wMj2SUsFn9oCnLASNntjvvoVCb/utRYQBgIYq8K6XQQ98SRQ7TLgSrHmt+b0Y6msfBRARaOXD4PkbgNZ6YFT+sKg46FM7uymuAYIWoj6P/CbBD2rwqZ1Q89aDnvuv4GvnZM1ddUWuobQsIcyGzHWpaDEoKQYZZuZKcMkJKz4DAElpUqx17VxwOz67F+qRr/dYXKJP7xJSgLfLWJN7gMRkYz3gRFSBiXIAN69FW1TpgNgotTYIWWuAIKcLyOpHYUzoPkZPiqlgx5ePhhFhoANATRn40iHQrNUx98W7/mjNg/w+sZM8thWBgulQKx+NS+QeSaDUUdGKj6SgFt87fJ2KAGWPheORb4C9nVIcZazN+bE/k363NQLji4REHw9H3hMCly/yXiW5zh1Og6BuqEebCtH73wRt+tLQnZwNGzZs2LBxm2FERmG5u8m4DRsfc1BqJmjDc8PdjY8EuKUeuHkNnDZKqqbam2WRbya/QhIqNO7WyM/3FZSQCLXpy1JtUHlRAm2j8qFWPRqeeO9slUWxSf4w7SBCknR85D2wqTqkHKA1T0AVL4s6Jnu7oN/6geyTtXyH7/wQnJAoVdZz1koSqAewpxO8/40QBYVloCX39StBSEmpUI9/Eyg9IQGLMVNiVlbQ9KVCJAMkEOrtCvsOouAwqspLT4JbG2NWI7GnU5JhYXEVY5+uJGD0RKhH/wxcctxSpglFtiSmyOUGTZkHTJmHwB/+VX7PSOiALG5JBdVGuvUWj0n4YeD6ZeDjQIbJLYDa8Dz0wXcArgM6/Yj6vc3AQfNNACRjQQipytqZsgJ6hqwtjSsKt3Xw+2R/TrcE4s2xxFRtqbzcLysJuvszUp12bl9QXYgKZwEFxeDLR4DWBtDoyVJV33QT/N5PreClt8sIipcCLfX9IuSY0Ee3iPWHzwMkpxvjzNAHpdnnFTs35QTGTf3Ikgi4rVFILVljQEqBpi2CKpwpsvvXzorlRwho4d3go+/L76EUEAgZ05VTCFiRUv0jCLHUvAAIASJ0O63BJz4UhSPtF9nvxRu7VbcipYBpi4Cze8Ol1833x0wTWfiEJAix0Ei4TJ4Ldd+fynErLxpKMCHPCacLlGwllTjgl+dhaOJd6xDLOwCOBBk7/D6pbF31GNT0JfG/mI84uKlGSEg+T8TziUBz1kKf3AHe+VK4/LcjAUiW+QVNmAXKHR9z3zRqNDBqtIzRAb+l1uZwgXf/EaguA214DvrMbkna+LpERnzJff1KQsW7hjniGh4O8O4/WkQYAGhrgN72Gzie/ptB2T+53MAgJUeDKD0BVJyX68MAlxwV24i+PLsaa3pujyTLmIhQ3wyFrimXPobGKvxe8NbfAJ/+f3rfPxs2bAwpiBRQWAw0xRgLDOskKigGxVFoY8Nmkrf9ThQitAaOfQAoB9idBKRlg+75LCgzL7iOVcXLxLbEgFpwJwIBP3D4XVlDBHwR605LsQ61FUBVKTB2SrfnxZ2tkrRPz4myj+ErhjWsSQAAgNZ68O//O/jxb3ZrwRe09wyEEFVNZVHlkLVzRrYUZF07L6SgiXNAM1d0WzwTdZzmupjWIFx5Wcj2mXlAcoasyZRDLIL9HiA1CzRtEWjhXR85ZVNKTouplnLLEKrEExmHN9VcYBSPEMm9YFp++rqAm9dA46aJJe+kuWKz1dUOdieJCmDlRSGBT1sEWvNE1OHZ2wU+tdOywwIBE2aCZq8Gb4tQivd2go9tBd35ibinw55O8KF3jf67jHuB5b5ITAHSssWmKRgPSoRa8wT00S2IGRNi7l5NeKSgtkL+1dqw7JZ7mfe/CS6cFRXDYk9nOHnOVMsMaKCmHPrdH0M9+90BK1bdCtCqx0B5E6TAIiFRFIRGIJEncp1Bo/JBq3unusXl52RtH/tdY01lKMmyT543OgCuujLAXtuwYcOGDRsfLYy4bMLf/d3fAQCysgbOvLZhw4aNeNCH3gEf/xDBRe+YqZJAuVEi6gqeDivxnZELWnLfsPW1J1DOODge/S9greNL4GfmSoVc001rnc8sgSYiIGtMeNJEB8B7XgFPmAVKCq8m46unRCnH0xle3e0VL3Q+vhU6I7fHZB9v+w244oK88HulWpw1aMXDffsCDJDTBRQtiaz1Cd9m2kJQe5NxLBbiQ1tjbIUVp9uqTgIDXW1ALGleb5dBNIoRQAn4oRZvlOT91AXgc3sliGkiIxc0Y4UcIeAXUk3FBVHnCbVIAeR3CgSs69LlBi25TypKaisRFsAxA1qmXHHQUUfJb57aO4nhoQD7vODD74okr1Kg6UtAi+4dMoIDTV0ANWU+uPIi+O0fWDYmQMRvZgT2PB3y2wcirgkjeA6ioD0HpY2Sqr79b8p37fMaVWcuoCskkZuQCBBBH3oHjv6QYVwJoMX3ArGqnOZE2OQlpYJdiRKg7Oqwks1aQ2/5BdTGP+mTR7kJvnJCKsZMdLRAb/stVM74bgPsAwVXlYqcupnUTs2Cuv9PB+QhP9LAPi94uyFPDwZSR0Hd8Rxo7BRJ1EyYCZowEzxuuhDqlAJNWwyaMBM64JfKY2WqEJFU1hrVmzSS1QriqaVFSFvz4c3gE9us1ye3A22NoLu6UesyoBbeI+oYIaQLmrYItPgecPUVCR67xbKIpswXRZnQymynS/7TgSAJkUKfMtfOS/LctENzOIEEtyRzWuuNMdewsSJjPK+8CM6f8LG1VqG8QiGLJKZYqnakgPVPgw++Jc9Bk8RljsEBL+Bpl4RLHCKMCXX3p6Df/5UQAIEgMQwA+PIR6KQ08Kntwe35zB6gqQ70wBf6fi4ZuTHptEOiBNgHsN9nza9C0XQT3HRzRF57+uIh8M6XgY5W4z5KDD5D+fqlvhE5cwuAS4djtI8P30Y5olSKYlVcB1F22ko2hCIWgdaGDRvDi7nrgVO7EDPBnVsAWv90zI9xVSn0jt/L2irgE0K7WVAS0IBOEJLJrpfgeOzPgbmx1ay5qhTUWA3WWoornAky5nS1i3KkOyncOjeW2mQI9Kkd4IPvBMcsmjwftOE5a/1ESt4LVbQDhJhw7APQ+mfC+xfwA36fzDNNFUFFQCyxnJQM6Hd+LHarAb+o+FVcAB96BzRrFWjNk72z4XO5YxdqOBMkOf/2DyTGoLWsqZJSQGufAs27o0+qOVxTLoqx7mSZ8w0n2SQCHPAD5WfBLfUyz8/rXdKe/T6JETADBcVxrW/6CiqYDm6stgjIoc83kwyjHEBiqvz+5m+nnHINp2SG78+dBG5vAr/wb0BbkzzPFQlZKzULrCBksqkLRJ350DvyWzmcVoyjrRHsiU1A4dB4VSyY5wKEE6q0tsj0mblyzu3NQN4Esd+uugK+WW7NP01k5AxYzeWWwFTL8VpEGABAVwd454ugTV8O3z7gt+Y/kWMGWIhHpSdBM1cMWZcHC0QEFC0OIyPezuDWBplXZo+1yIaJKRKLVI7w4kanUx5xLrdll23C7wWSbYskGzZs2LBhIxQjlgxjw4YNG0MFrikDH98W3lhVIkooD3xRtmlrBFdcFCJI4Yzbwg6k5yCUgiT8TZ1mAImpoMe/Cd7yi+jNA34JToydGpbs5q6O8CS7idCEQslRoBsyDLc2xEzU8PmD4OUP9kkOP+b+/b64lWNq/gbw3HWAzysBm652kYcuPSXnYAQHw7ykk9OliigGKG2UBCCuX0aUJYbTJdY7gNicPfhV8KXDkoAdNRo0fWmwKpG3/tqyDPF2CfElMcUgaHRJxZQKWeAmp4Pm3SGf3fdGeLWh2QXllM+ZFisJSYDTDZq1qvdf5iCDd70cppDDx7cBXk+vK2P6AyKSCv54lV0h9hsgAlwuICFBgrHeLlh2JwDyJ4KmLgx+VM1eA540F7hxBXrXyxJ40AGEXQesASiguTbq2mRvJ+BwDRoZiNxJUGufhN72G+seJUMuHgx9aicc/SHDxFI1MuSjB2ppE/eYrKG3/94iwgCibrDnFTge/Erf9xfwA6WnJBmcVyBj+wDHmsEAH90iJEMTbY3QH/wS6vm/DbtWaPLcqISwWng3ODMPuuS4kDk7WoLBZJo8H5jUfyWgoQZNWSBKQ6FVwompYXLerLWQCCPApafAHS09ViRTYTHUw18TVSVPJ1A4U1TIlIJ6+OuSkGi6CcqfGEUcommLwSZ50ZwDuJOBEAsVvno6PIFFSsbtpFSRZG+okvuwq92oovWAS46BKy9CPfmtEZWguVWgeXeAr54RspBBUqGpC0S95dr5+LYVWodbgcXb/6jRcDz91wj8/NuG8lf4PR5pawEAfO0cAu/8COhoAeUWSPV5ek7PJzNhppAqzIpcQKpS4yRHbxmUEkJclHIchVutjRDwpSPgHS8KqRosv3OXBpJSZIM+2jDR9CXgi4fFDs1ExLObklJByx4QMquJRGmLi5zxsYnP3SkM2rBhY1jgyCtEIDUz2uo4IxeOx/8i5mfY57UI2Oa6NuCzyPBAUC0FtRVx5yF6zyuWbUekTYs72SLKBjvrFBumOOCG6vCxChBVhzGTQLMNUnz+RODCgfAiChIiA4c8o3RnG/i9nxnKGCTHnTAbOL9PyA9+Q0XT+CzSs2UdXH9d9h06J/d5xIZ51OhePfcoOQ00aa6lSGG2Fy8DzuwWcjGRkCzM98ZM7hMRRh/7QEji5vd07AOoB78yKBaBAwV3dUC/9f1gARIDoNlroFY92v3naiugN//Umm8mpkBt/Bwof+KA+0SL7gVXlwuB2FQHAUnMIeAXRcS1T8n1Fxr70X4gMRkUYgfJV05AH31frJJMkrhJKAPAB9+UOTIAPvEh1MNfF1Uis4CHIfGX9uags1gYdCA+kd5Eek440dXhABzJQNYYqLs+aZ23qSQY/B7ukdhUXaX12ZRMqHs+0++1IjfVioKr3weaPG9I1Upo+mLw6Z3h1nCkAKcLfP0y2NMBclsqL5ScJhbekfbQymHNm0ewuuhIA3e2yfqioRrIHQ+asbzPipPMDN77qijBshal7oV3QS26FzRnjSg+uhKseJbDKYS/BXdJoYgrIcr6l6bOH6QztGHDhg0bNj4aGHFkGBs2bNgYavC1GNWygFR4z1wJAOI/O6Mb39bIfdaUga+XCDFi0tyRJ+Hb3gJ4xfs7uLB1OIUbk5QKTkkH2kL8uw1JWN7xIpgIGDValApyx0tSIsp6BwDYChZSD+QhX2QFigFToaWXMS+uKTOS9ASatghcdRV8fCvQ1Sp9Xv5gTBUMUo5goI0SU0DrngbWSYUgN9dBv/sjoKVeNk5IlHPvhhBF654G//6fgKAhPAVl/rm9CZSaKds5XSDjGgs7j9oKiwgDGMFIL9DZbvFrlAoGk+BwAbkF0H/4f8Uf2uGUYE8waGFENXRA9jVjuVjkZOaC5t85bKoa3NlmSXGHtl80SFBDeN+wGRiLJQNtJgdNexOlJHHucss94/cBeYVQ89YDUxZEEVcoJQOYthBUflYq38ggnpnXg3ntZOYFz5Hrb4i9WU0ZkJAEmr0GtPjePgV844GmLQR5OsRqxFSrMNFhJe65qhRobQBGT+pZ3SXe9d+bStD+oqlWEuaRuFHSLeEtFoL2bkZylAFQ4Uzg3j/pXTVrH8GeTgl2p2fL9dHdtqUnoxu72oEbl4HCmeHbVpWCr50TifppiyWhO3keHIZdFdeUgetvgLLHDkqQfChBTpcQVc7sFsWsjDzQnDXhUt46YCTII8CGDHgv5Pkpf2LM74IcTmDyvLiPG5q1SqpTz+6TqtJRo6HWPSX2MJDfmK9EkMQMazKaOBs0d60QPM/ulTHEmWAl07rawRcPgRbc2WP/BwL2+4Crp8AtdWKlNgKUgig5HeqJb8qz27A1REGxjFeAYfkVY46RnA6M6d5CIuw4Y6cKoTcUyhFNqDHJSkZlMjdUgSsuQD35VyAjaRP3GMoBtenL8htXXRHrijlrhl0ZhpQDNGO5WA+Etk+c3eN4NBzQZ/fIH6GWBhwQUrA7EejqgD67FzR1YVxbk1CQyy1jy4kPweVn5HdZ/iDADH3xENDSABozCTRnHWj8dJGfdydJ1XjI/rmjRRJjGXmgjByoKfMRMJXXQqEc4OuXR8T9ZcOGDQv04JfBb35fijgIQFJa0AoxFrj8nKhZmMnwkPqRIIIqKo4wS+XgPuoqLSIMYChTeIGkdNCU+UDRIvDuVy2ynmFNHKnEGrbPa+div1F+Fpi9BnrPq6JcFbqGIIcQcIDguo+9neAX/jHcJu7aOSFZzF0PnD8AQMm4m5IpamwL7gRqymRlGZkcN9ZTXHoyrkJOJGj900ByKrjkhKEQuhS06B7ot38Q+9yry3o9n+WOFrEPDYVXbHPo/vi/+60Cn9wersQLgM/sBhctBhmqo7Ggd70cTrzuaofe+TIcT31rwH2ixBSox/5MCoOOvC9KfAG/KAQ52IoxeNqlqMbvA2C0ez1g1iBS0BcOyjyuq92y22LjBjKVR0JJJS114FM7ZO0dSuAARP02ZzwwbbGQSXTAsP5hoPQk9Hs/A214HmQQqsPOJzkNNGetfNfBRgW1eGPUtvrkdvDp3UBXO6hwBujBr4DqKqWAJmscaNqCfhfEccUF6Pd+FhxL+NQO0KpHoUzy2iCDEpJAm74M/s3fy+9nKtWSEYeIcR5q/bPQ7/9C1mBev1E8Y8yBlAM0afaQ9HUwwR0tYoGZmTds81vuaod+7T+smMWV4+BLR6Ae+UZwzdgrlJ4Mf3boAPjIFvC4IqiZK6FB4LN7JHaTkAhkj4MqWgyaMh+BGyUSU1IOITQShCgzf2jXmTZs2LBhw8bthtuCDMPMOHXqFA4ePIjKyko0Njaio6MDeXl5+Od//ufh7p4NGzZuNyTHCTb1wfM6FHrva5LMg0FByHgf6qGvDVnFN3e2gU/tANeUgzJyQfPW9yx5n5QqlWiejiBJA4C0JaZAzV0H/UGZ1e7zSILIDKo1VkNv+ZnhHZwSvwaWNUAOUA8WSRiVL1YYEdLyNGFWr4MOEnR5GSbxg4+8Z1UhAYC/Evr9X0I9/k1QH+RtKSMH6um/EaUXnxcomN7tQpYDflBLPThrjFS0ARL08XsBUuDNPwU/9FVQ1pj4B22M5WkflHcRCVRngijB5IwXi4fysxKQMo8VM6XLsk3TTflux0wWS6zhgt8bm0jl9wWrz4YKVDhLiF2h1Z0ATOsTIYAZBBgzaOTtAkblg+asFRJRD6QJWrZJqi9b6gy52i7L6kE5oJY/CECuGf3uj63gn7cTfOx9kQMfpCAZTZoD3v9GtAVEYbHY8mz5Gfj65eB3QIvuhloUw4bJ/FzRkmjShtMFmrJgUPobE4lG9WzkNZOQFJ+cEwd8/kC4SgAzuPwsqPwsMGnOIHTWgj69G3zoHbnelQM0cxVo5cPxiU6OONd9RLvY+4XYBR3fBvXgV8PGt3jEj5EKcieDurvunC7QuGng65fC38jIjbJTGnQ0VMmY7k4E8gqglm0C5RVa71eXShA/MjmuHPJcdrlB89ZD198AxyJ1dTQPafe5qwP6zf8MKu8w5D5Wdzw7pMftDSghMYoYyqZ9nysR0O3hpMWUTKj7Ph9XQYs9nZJUC3lW09L7JaHR1W61Ld4o9g6hVek+rzw+Q8eUjhbwpSO9q3RPSJRk4RATm/oKWrZJrCcuHAQCfrEkWNqN6slwostQGlBKxn2fobCWnAZ0tkm1M4wK/4e+2iuyEV88aCRFWX7za+fBCYnB5y4DoGmLoDY8H3N+po99IJ837CapeCmweCOiatVJ1Ha4/oZNhrFhY4RBjZkC/uw/AWUGMXLirLBqfWYOzs2YGXz0Pet5bj6DGNY6gZSsDZQDmLEiZjKeq8vCGwiWYt/YqUD2ONBjfyYEys42UWDthggDIKimEVTQMJVlElPATTclQQrIul63GdsgOD6ZCVG+cEgSqeZ5AQA00FgDNWEmsGwToANRZHMeO80gK0aQSc3zikEKigdyuUGrHgNWWYqg3FQrZAef10rgm9tn9oFcWnc9at0DQOxvRgC46kqc9tK4ZBjubJXzikRjNbitETQY1seshRTi6wyZe3FQ1YjP7rViLO4k6/dxJsCMP/CJD6VNa4QL5UYopQbMwhQNvnHFip2Ewu8F110H3fEMMGk2+P1fWfYwXe3gy8fAiSlwRFh/mVDLHwTnFYrqrytBVDoi7Kj0mT3gA29ZXbt6CmiuBT3xl1BGgcFAoA+8FXUt8qHN4OlL+0aQ6AMoKRWcPxGouRqmemtaUkVtn5EDx5N/BV13XX6/q4ZKcmIq1OrHBufaGkLow5ul34ZaF81ZG4y13Erwuf3RxTsNVeCS430rrgwtjItop9GToGauAOLYVtHijeC3fiD3r8sNOBygFQ/1ikBuw4YNGzZsfJwwoskwDQ0N+I//+A/85Cc/QU1NdKJwypQpUWQYj8eD1atXo729HU6nE7t27UJmZuYt6rENGzZGKtjnBV88KFKgyelSpeWxEiRwOPtlHcN1lUEiTBDNteATH4JWPjzAXsc4ns8ryS2DRMLXL4NP75KEe0q6LALnro/6HDmcIqF5IFximRbeHayOV/d8Fvr0LgnMNd2MTkC3NwPVV0HjpoLdSYZ9TLBnsuBOSgPN3yCVb92AiKDu+hT0+7+0qtPyJ/baKocDfvChdxHsgM9rVauZQRxvJ8Aa+uIhOFY81Kv9BvunHEBBca/6od/+gVxXWlvKNrIXSaJ6OsAH3wZ1U4mIyABYWLDRsAwAg6+eAqVkWkGxQMR28VB5EVx/A0yioEMbnu/x3IYClJYldlP1N8LfGDt1yBfrlJ4FHjXGsmRRSoIF0xZBFS8HXz0twWQzwGfad4wrglp4d++OkTYK6qlvARUXwJ4OCV5fvyzjS9FikWQGxPM9sgoOEFuHAZJhuKMVvO81CaiwEcg0A2J5haCF90gV4rXz1vXqcIIPvweeOAcUzw5swkzQqsfAxz6Q6sTMPKhVjw1pFRYlpYGmLQRfOhLePmdN39Vcasrk34A/zPpKn9oBxyCSYbiuErzvNatBB8AnP5QKyIJiUe0JkakGIAoO+98I31FGLjDWUsHg1gbwie3h23S1gw9vBt372eCx9b43ZDxKywItvBuqJ2LibQBa8wR480+sgHlyOtSG5wZFRSkeuL0Z+s3vW5Lx7c3Qb/0A6olvgjIM+5xk49p3OADtlGeAcgD5E8JJGeOLpLI18rzGTx+y/gNShRpmQQWALx0GFy8TYuQIA81YLgmXrjYg0VDkcjiB5Q9CLdgQU6ae25rAO18CV14SMsy0RaBVj4FcCaCsMVBPf1sUaLrahRCaOx7c3gxuqrGqs5XDUMqLuJ7ah5asNNQg5QAtuQ9Yct+A98WVF+U7Tk6X77g7BYPGGvkd2xqBcdNAM1b0rOI1ZooosAT88mx2usWu0CA5BRPAbY3gw++BQqwOYvbB2wk+8A7C5kUdrdKnELUfvnwUXLwcFDLWAgDXlIfZbAAspKLyczGspwAEfHGfnTZsfNTAnk7wkfdErcSdAjVnDWjaouHuVlxQQhJQtBiA0feaMnDFBSFJd7aBCqaDVj0KNNdLcQKpaEKFwylKK6GWSTVlYE9n1PqFMnLCV2Q+T/A//cEvRWXuwa8IMaa35zB5LnjXH4COppA+uYDi5daailnIn6bVYMAvygCPfB1wuKA3/1Seh5HnxhDLG61lbh1jfk2JyaIiseNFy5bXVJ4IBIBpC6M+01voi4dFUSTgs74r01IqbwJQMKP3O8vMBSIti4GhJ0/3EpSeLSTdGO1x4UqUZ6LfePb4ffIfQdZH8zcM2M5bV5fFVuJkluuo/Kx1nZhKIwENpLvAl4+CihZbcyai+HaXgMTfzJ+n4nxM8hKYRanG5QZMO1KDmAqCfBdndgNxyDAAxJaoG1ILn9sX3dhQJWuoAc6ROeCPUgACIES75tqghfZgghtrJCbV3iz3pNcjKqIzV4JWdB+TVDnjgLs+Ce5ql89n5g2affNQgSsvSUzChA6AT24Hj5kCCrGzvSWIWGsFEesa6A4R8YEe2w1we7M8HwgADGuyqQtjxoRt2LBhw4aNjztG7AznnXfewec//3ncvHkTHNObOzbcbjeWLFmCH/7whyAivPjii/jSl740hD21YcPGSAdrLbY31VetxoREoHAG0HhTrGMW3t2nQDa3NgCBQHj1l88ri3NmUW6ZvmTQg+NcesJSUzF9u1kH/+P9b0I7nFCzVkd9Vs1bD87MBV8+CkAqYkMthGjSnGBSOPDCP8UOihCBSIHu/iz4vZ9aZAynG7jzU1DFS3ofkMkaLQmNtiZRuOmFegub8rhXjkswwZkgC79YyQkY7eVngD6SYXoLLjluXVd+b0TsjeX4Die46mr455rrwIfekYqo9CyoBXeBZq8Fn9klG4QGhsxnoN8n/vS7/yCBqYTEXno5GxLFhmoPXz4KnrFi2JKh6o7noLf8wrq+MvOh1j5l9TbgB3yeHu0p+gJuugn91v8VAgqRfL8OtwTK1jwBcieD6yqjk6GAJGb7AHI4gYmzLZ2eGDZdMQN/3bX3Ehzwi0xvU42V3HW4QJPmii+4wymkjEtHZOwIPW4gAK682O2YpWavBs9cKTYwicngrnZRRhrCgBmtfQpIy5J7zeEEFS/rn3pOZh6C1jrBG5WBykvgq6dBg0SI4asRVV3eLqlwvHwUuHYOfHyrKIeZhAoANGetbHN6lwQhnQlAwA8++A6w8G6pPK6tjCYoAuDaa/KvpxP67R9av2tLHXjH78FJqaDCPiQSRiBEreuvgaqrEogfM2XIg7R84aBFhDHh94DP7xe7FUBsA3PGicWiiYAWAqQhGw8AmLoQdO1cmEUcFS+LssAa9HOIkXABAK6+OjLJMCkZUI98Q4LZDVWgnHGgeXcIiTIO9Ae/FHl3QALhFw/JOLHmCdlnYjJodvh8iFIyoJ74SyHIebugW+qBva9G92d80WCd2m0NvedVS3UAUv2tHv5aTHUWrr8B/cb/seZk5Wflvwe+FJe8xj4vcP2SJM9Yyz2kA0DRGuDKCZF791rjNl88BF7zRPcE2oYaK3EYPJFAXMWAKDJMpL0WIMml+huWQkRwYwBp2bYqjI2PDfR7Pw1ZU9dDf/g7kNZxybd8o0QI2IkpsvaMQ6JmrcEntgkxXGtRM1h0z6BZqOqT28FHtkjRh69LCPEut6hGNdUCM1fJ/e1MCH/+h1qfhhIB2xrBJUdBkWvu8UUy5zYLJXwe+bzLUE9pqBKbl4xcIHe8zGt7Uopob5Y1t8MpY5FSQkSpq7TIIn6vQUIIGZ8aq8Wu8dp5WRvEIJUCkDGth3kBTZkPNX66kGrPH5BnqKcTcCWA974GHfBDxbAC7g7s9wkZnA0ycWKKpaq4bBNo1iqQUuDOVvChd+U8ktOg5qwTAkZkH9NzQMVLZQ5nQjmgFsdXILyVoLnrRYEktOglZ5zEpOJ9xukCzVoptj8+r6Vc5EwQ0mZLPagbUkivEGrNEglPp5C+3clyX/h9FtmlpR78zo8QOL9f7pvmOnRbnAOEPz+7i2M0VgNvfR/h5CYG2CTEeIO2sP1CvNiRtyt2ex9ADqcUNUSq3jgTgLQebIn7CT7wlhXncCcFFUJo+YMgV/fKTfrcflHgM8mByx8SW/QRjPgqKqdvPRkmtwAoMWxzAwGr2KijtU9xEpqxHHx+f/hcNSEx5lgXCj76vhQXerssG7MzuxHobIW6+1Ogfqqf27Bhw4YNGx9FjEgyzB/+8Ac8//zzCATCA1YTJkxAfn4+Dh061O3nP/3pT+OHP/whAOC1116zyTA2bHzcce1cOBEGkGRuena3vuGxwJ2t0Ft/A9wokYaUTEvyNdQmwdsF/c6PoJ77r4MWxAMAtDRYf+uAlRwNSZLyuf1ADDIMAKmMjpWcj9xu+hKxHQpFeg4wWgJlqmgRtDsRfPg9IcgsugdqYu99hfnmNehtvwFaDEJEQTFw5yd7VAfhXS9bAa6AL0KJJQ6a68DezjBZ7Kj9+n0SgC09JYvOGcuhpi/t+URqKxC014mUjgYkyBMIANlW4IN9Hui3vm9VUHW1iZ3TA18ETZoNrrgoFf2tDdH709pStWCNoBd3t4Ensqq4zD7cvDZsyVDKHgv1zLeBm+USlM0rDCbJ9LGtEuzzdgJZY6DWPAkaPXHAx9RmgEgpCRABkjBd+1RQpYMKZ4APvh394b5UJPYWBcWWbVkIaGr/Kyu5rRH61f8A6iutxoQkIWOVHAPKTst2QOwAoPYDfh+4qRZ8dItIimeNgVp4jyT9zT4qBW6uReDtPwL11+V+mbMWtOjeIVHqIIdTrE1ieL33aT+zVhkS3iH3CkkygS8eGjQyDEIDjqZaFIAgO6qjBXxkC+hOS52JiEAL74ZOyQRvf0Hu7bZGuRfqKkGbvgyMil3VSqPyAUAsrCKuJwBC3rjNyTAAhFgSkbAeUsQjwXVGtKdlCRnUlI93JcjYXnEhSHYhpcQOYMp8wOsRksctUJGg9BywOVcJbe+FxcxwgTJyQGuf7NW2urEaqC4zrCCsBB9fOgJe/Xi34xERSbISgAr4RfkkhABBxcuAIVbuuR0gKi97whs7W8FH34+pMMfHt0U9X/j6ZdCNEiAOWYRLTxiKLckWIUY5ZU5lzq9Cx22/D3x0C2jlI/E7np4lc57QhIJSAMdQPIh1P8SqwjXvcVMVwpx7GlL0fQE3VMsYkzdhcNcINmwMMfjmteg1NSCJ1BhkGH3wHfCJCHvHh74a8xnIB9+W9Y/5+sQ2oKMZdMdzA+93dZlliWKu13weSxmspU5ua/MDofe40yV/h6pdsJax6NQuaKdbiDumZRApqPu/AD67V9bkfo8kwZXDUm+5dg6cmAJcPgK+eAjq4W90m7Dmaxfk8xFjE187DzXvDtD0pcZ3F2M9eGyr2O0CQVujcIiVErrae0yAkzsJNGct2J0Mvf0F6w2fB7z7FfDoyX2yJkZTTfjcVSlLmXPOWpDDCWYN/c6PLAWcjhbo7S9AKQdoarRVK619EjRmMrj8nCjjzFghqnDNdUJMD/ikGCKOLdFQgnLGQT3yX8CndoJbG0Bjp4DmrOuxkIiWPQAkp4s9NBmKPMb1wpeOgJfcNzClzrZGxI8nhLZFzKvMeySE7N179BS/iHV887UUe6CjRRRne9qDoQYaOi+kiXOsIiQT7mSgD4pN3UEtvV9ihiExOlpw55Ap4fKNCAsuIlmD1lbEnX8BgL54SAqtzP2UHAfXV0E9+ZcxFRlHDGJY1AHoUUVlKEDFy8CXDgs53xzPlEPmtwEfaOPnuv0811ZI0Y9SoNWPy74aqkF5BWJvmpwGrrwoCofjpkXd61xdaqiChebPGCg/C73tt3A8+JXBPWEbNmzYsGHjNsaII8NcvnwZn/zkJ+H3C0s8IyMD3/72t/GZz3wGeXmSCFBKdRvgXLZsGfLz81FTU4O9e/fC5/PB5bIDTTZs3G5gn0fsYFJHgdL671nLDXGkKxuj7dd63NfuVywiDCDBA9OKJAgjIdbZKhWxPVgG9QU0ZlJISCAkOKBChvN4lS59Oc6COwFPp1R++T3A6MlQ654KWpPoM3vAIZXU/N7PoZdtAs1Y1qOiB+sA9Hs/B1pqDVsJJVLbB9/uNgnGjTXhlV6uxJAAWpyAistYKHs6hRgQb9/bX5Bksvm6pgza54HqSYFiVF58IoyJgA9q4d2iatNUA664GG2/wBp8di/UPZ8BjZ2KwPGtcToaEogN+CUBGEMtIghSFmHG0yHBWIezb/7rgwT2+6QPyWlyHRmJSBP64mHw4XethoYq6M0/EUJZPwNHXHEBev+bYlVktSIY+L15DTCqhyhrDGjpA0ICM4IJVDgzSlUg5nFulIBb6kH5E4PkhO5ALjfUvX8icuMtdVIBOX0paN76fpyl0YdD7wJtEQQqb5eMRT6vBE5N+LzBr8HqlAInpoBf+TcZ13QAqCmDLj0F9ex3g9cM+zzQm39i3XveLqlISs4AxfGxHgmglAzQwrvB+98EOGDIuxuJhwEq8oQdZ9oi8LGtQo4M3a/TSnLwzfKYn+VTO6LUifj65WDlI01bFFT2AiD38sJ75O+46lje2O0jDOzzyLkZwT9MWTCsEt1UMBN8Zk90eySxqK0ZSHADCK/q5pYGeSr5feCdL4OvHJexetToQUns9QY0b70EYkOrXHPGAxN7JsSOdHBtBXjzTy3SknIK0TFStQOQZ29LHZCQFNPehxxO0MbPiXVGQzUor9C2vDFRWxGzmU01nsj22msyH1IqnIDbdDO+ckrofEgpADLPpIAfnDs+XHkJEBWC8nNAN2QYSk4HzVwVnuhyuY15YMj1kVcYnAOEfX7aIssS0LAShFKAI1GsBzhgjdUZeT1ag5pgbyf4g1+J5RQAuJOh7nju1lcx27DRX0QSQrtp51aD1BsKb6fYO0YkBzngF+JI5D5KjoNXPNwvtUjWAVmzBwLQlRdC37H+NNVWAFEhK1oia19A7nFSMo/2+y11KZOkxyzKKzt+D1w5Dtz3p8EYJbncoPkbwFljZM7s98mz2Py8I2ROXn8DXHIMNGN53HOhpJTYtAEiUald84TM3UNtRc0xSvsBbdgfRcZQzWenO7lPSWS+eipWq6gy9IUMkzrKIi6GPrsdLug//KsoBOUVSDwoou/6zG44YpFhSAFFS0BFFjmLKy9Cv/ezoBIJn/gQtPqxmEq6Qw3KHgu649m+fYYUMGeNrGEirwTW8hwdCBlm/HSxI4oHHTAUkGOtKXqv4j44n4OsqZIzomIJUUfoagfveVWuV+UEFS8BLX9I5n1LNoLrr8sYYVh9qzs/0aOKSm9Bk+dBPZolSld+L2jKfFAvLLj7AmYWK+zrlwEVQta1NoA+thXY/BOxTIphp86xVIEaq4EbV7ol0Qw3aPpSIWGGxuAcTlBvitkGuy8JiVCP/Bfol/6HWCOZFnIAuPwsuO46KGdc1OfY2wk++C74zG7Lnk45oO76VLBIhzvboF/5XxYZUDlAqx4NV+GKJH+b8HtFAbe1oVuVTRs2bNiwYePjhBFHhvmrv/oreL1eEBGmTJmCrVu3orCwsM/7Wb58Od544w10dnbi7NmzmD9//uB31oYNG0MGvnQEeu+rEjgiJbLK657qsycyswayxsR+s48VQRzwR0tyEgFQEsxpa7Iqw81+BrohScBYIF08DOgAaMoCUE+e2+OKrISocgIgWTyFLNyDi6cg+SC9z4oNpByglQ+Dlz0A+H1hZAT2+8JVYwJ+SYjvfBF8ZLP8VmueiJvI5OslQMMNi8QRgFTXlRwHuqsIj/TjdTgA0zvamSABy6CPuRHcU0p8j7tZAHJLvSjCRLaf3AH0RIaZOFsCpHFhVJ+nZ0O//D/F4srnNXzck8ICFtzVLv9Wl8UPNsfYPTgGEcjhAsZMAdqb5HvTASEeBfxAbmG3csxDAX18m6hyeDuB9GyoVY9FJZX50uHoD3o7JbDaj8AGt9RBb/m5Vc0dDBIQQAz4uiQ4EPIZteBOcNFiqXpNzwlTRIl5DL8P/N5PJQgFg18y7w5QQbFYojgcoKKlMfdDYyaLQk5LHeBOASUOrJJJAmGOiGSw4fXujLgXg/emUenqcEqSsLlO1JqCilMQ5aJ9r0HNXQ++cVkCrl3t0aSNS4eBAZBhuKYMXFUq9+rE2UNChKDiZeDjW6MDRlOig+n9PkZKBtQDXxQ1oqorUsGZ4A5LDFNmbJUXdLTEbm+Xykda/ywwrkiSw0kp4gNvPN9o0hypeo4kx02eOwhnNbRgT6dYqxhjPJ8FcP4g1KYvDSkhhv0+sWSLRZAoLAbNXmMQYuR+ouJlwOR54duNmRy0qopsBwA+8h64JITA1FgNveXnUM99t8/zmb6CMvOgHv1zIQQ01wNjJoFmrx7y4w4FuKsDSHCDlEOsL9//lRAVyCHEBC3zELiTpELfGJ/4Zjn0hy+IVD0p0NSFMpeMcV1R/kRQ/sShO4f6G1L1OaoPicLhRhxyZyTpk3UA/OHvJGFpJsucCVaFfzffK42bJlYPkRg3DZQ9VmwmtR9BorlppdEDaOXDoLwC6KungEBACKfZYyXp0FIPjJkMmrUKaG2APrkD3FwLJKXJ86G5VtT5zHNRDmDuOlF8Um3yXNUBsSB48Mu9vqf4yBaLCAMAng7obb+F+sTfih2eDRsjHWMmCbEsgoAbM8lbF8/eMQbJLuCLtjYD5D7zdPbqng87RtNN6Hd/bKlsMhsWqU75L5jUp6C1kC4/J8+IqQvAO18CmmoloUkkifeOJpn/BlWhXMH5NJefA84fAM1cAa6+KjGBMZOBgulCUjDP2fxs5PfScKP7E5o0Fzi8WfZrft7TCVRcgL5+GUjNBK16DHzlpDFeGjDVZBKMApJICxg2lG2T06F/9T1gVD7U4vtAhbGT9tzZBr56GtzaKH2IVI5w9o1IQIkpQNFi4MR2q9+kZD/Ga26skesjsjAizlqZr5yAPrkDaG8CjSsCLXtACiMiLHn40LvgoiU9W1SNEAQVEiMV/xJTg+oo7PfJXDolo0/WKDR1gdhVxSS7QK7bgA+9V3MZZJCy1vKuRCA5FWr90z3+drztN9YzVwdkTs8A5qwVZaOaMtnvmKmgez49qBbNAEC5BUOqQBSmmOz3yb3sTrGIFUTADaMgqK1J7NSdCbIePrxZCIjNdZYiU+jaPoba6EgCZeRA3f9F6EPvilJt1hiopff3qihpMMF+nxQ8NNcZxXcx5nItdWKHZn6mqwNcehK873WxDWcjdpmYDOgA9IE3oSbOBhFJwVF9yPNBB8B7XwNPnG3d42OnRhPHTQTt82zYsGHDhg0bwAgjwzQ1NeHdd6Uq3OVy4a233uoXEQYA5syZgzfeeAMAcPHiRZsMY8PGbQRubRDFhBALIL50GMgZD5rTAzkhBPrkdvCJ7ZIwUQ5ZxJsB6/Qc0Jx1fe+ccgCBiAUFETB7NXDiQ6PyywOAgYRE8PgZkYKyVv/O7gXveSX4msvPghqroZbeH/fwRATa8Dx4xgpwTZn4JZ/bFwwIUeEM0OKNEeSDHKjVj4MK+i75T2bQMBTtzdYCmVkWfmZgJOAHXzwEpI0CLYrjz111JToIyDp2EDQU2eMQFoQxAyMOh7XwdDjlnN3J8p47GWrdM5KoObZV+sZagp2LN4KcLnBbo1wbZoDS9Ftvbez5C2ptjB8XMgOQo/KhP/ilZQmlFOD1AR1G9bTTDbgSQBNmgZmhP/wtwrW644CMAK7pC+31GH1ho02L/HQQLIlDolsqe8tXToAPvWM1tNSLLdSz3wmXeY1nd9WTDVa8414+agU+lQohQITIK1dflXs3BJSS0Ws1Jz6zO0iECbYd2SLKIA4Za/jsPqi7PgmKSKIDcj9jsCxLUjOFTJFgeLqb91jqqGjVEJdb/gsh69HyTRJED7s3Wb6uS0egS09KcDrgl/25k61gG9Dv3wkA9O5XwOf2mkeUgNaDXx0wQSgSlJoJddenoPe8It+V0wWavQYqhrT/gI6TPwGOh78GANBbfgEuC6m2dLhAC++K/bmC4nDlF0B+J8MqjJQCTV8SZUXALfXgq6dBk+dJQsbvkcR/8bJuK40HG9xQJc+DvMI+kVj4/P5osmN1KVB6Epi2aJB7CbDWYsdg2hfkFkCtezpKDUStehQ8e40EWUeNjhlgpfkbwBUXwvpPc9cH98Ulx6M70N4EVJXekopLyswFrX58yI8DGAkrXxeQUxBUkevzPmorZc42ehIoIRF8owR61x9Eyc9MvLmT5P51ueVvb6eM7wE/aNJc0MqHZV8BP/SWX1gkM9bgy0eA1AyRHTePGfBLgjE1c0jIV9xcB/3+L6RaFAByC0UFLjVz0I812KDcAtCkueEqAE43aOHd4RuWHBMCqMuwDGPDIs7hBM1eE7MiNniM/ImguevAp3ZajWOngmatBpxOcP4E67szoHqh2EZE4Mx8oKEaaK4FXzsPTJgJ2vB8kHjCLXXQr/67pfDn7QRA4c9BMl5fOgJ68CvA2b3g2krQqHzQorv7pCIURagH5J65USLkahs2RjgoIQlq3dPQO35vVeRnjw0bU4OIQ/yL+SxNSBIr3urS8Dcy80EZOX3up979RyHCmHaVpqqLO0XGKTYKBJSSNW1CInDxIPjiQdCie6Ge+x5QdhpcVQpOSQeObZPPKYfxzGHDes0gpegA+MPfIrDzJemAoY5Fyx+SYzrdQu4wSTnm8c1nTk735HtyuaEe/KoksKtMSwxtxTXamiSxuuYJYO9rQnJRTsCZIMTpqQugt/5anoem4g0gfwf8BrmUgNoK6C0/g3r0z6LGbb5+WZRd/R6xAPZ0yDPYPAdXYp/tXpkZqCkX0lEAAMhYk4eMwQ6Hta4JWb9S4Uz5/LVzYiOVnAokZ4B3vWzuHHx+vzy/vJ1hpHQAQgxqru3xux9JUCsfEcuozlZpcDih1j4J+H2i2nt8q/wuyiFrgNWP9WrNz8e39ZwwDyXBDaKiZtCqTDmjVTVDQQrIygOtfESUW3tYI3JrQzj51Gy/eEgsREPXHVUlwKF3uy/KGiRw+VnwmT1gTydo4ixZM/TDLpHrrocrJpvEvaRUYFQ+KDNf1leRxSvn9gHtzYZ1MOT+8nsBj7ZIh063FF+McNCYycH19nCAu9qh3/hPK97m6ZR/Q4l7ygEYhHD2dopaaOnJaJVobY7DCuhsgz68GWrxvWKPFAlT9cwYb9XMldBHtsQms7nc/XqG2rBhw4YNGx9VjCgyzI4dO+D3+0FEePjhhzF9ev+92vPzrUV2TU3frVBs2LAxfODyc7Erya6eAnpJhuFLRyxvcLOSxJkgicFRo0W9pI9VmORwgooWy8IytH18EWjJfdCVl4Br56xjMgNHNsdcWDNrkWCPbD+9Ezz/DgkKdteXMZOtyvMFdwG1laIUkJEbg3xQB/3+z6Ge+c7A/KRNpGbKYrmr3SAbRJBTYCQA45Fh/N7Ycp5p2d0eljJyRHni1A4rAJiYYh0fkKrrO54TdQnWQEEx4PNCv/L/SaDDsEbhk9vFd3fGcvAHvwK6OsL3AwA+D/SeV6FWPxa/Uw3VEryJqQBEEtRxumU7U6EkdKHKolCCvAnApLnQb35fqpR6CyLA0yUkGFP+WgckwFd1JZqgoAOS2O0GHPBLIC0prc+KQoAkxPnSEYBZKs0ik/sAEPCBS0+C5qy1TmXqQnBVhN+10w3qb4IoEHJ9KQeAiN9IB8AmQam/iCDCgCGBQldCkAwD1tCH3oUjBhlmMKHm3QH9wa/kuIkpco+MmQyafwf47R/KdUeGPVRaNrDheVBHC9DVDiooBo3KR+Dsvtg71wHAZwTu3clyffm9YdVPPapaxQHXlAeJMEE0VIFP7QB1QwzsL2jSHKgJM6WCKyW9x7F2wMe765PAxUOiKJCcDpq1KqjmEoUFdwHVZVIlBgAOl5A0unlW8ZUT0B/+zhpPnQmg1Y+DJs4enPG+F+CuDkn2m/dvcrpIjY+d2rsd1FXG3m9dJWgoyDCndogllYnaCuh3fyyWbBFkCMrIAboJIlJSKtTjfwGUnwW3NoLGTQWFJlaYLWsE025BOaKTMiMYzNzts4A726Df/6WVxEzNgrr7U6C83hc0sKdTlLzMa8iVCCy+F9j5siTeQp9lfo+VhHW5rfEuKQ3qns9Y290oiam2xCUnACNxq8/tE4s5TweQmAJa+gDUIBPI9LbfhpM5aq+Bd74EeuCLg3qcoQLd9Umx3qi8KGPYzFVRVotcYQTriaRK3Uhm0pQFkqjrAWrFw+DiZeDqMlB6dpilknrgi1LBf+28yPzPXd+rcUEUhH4h1iHSAi4/K5aca56QljN7DAIMrCRfFFkbMsfyeUDtzaDQa6yviDeW26owNm4j0JT5UOOLZA6cmAKMmRLzGUGZuWJlcfGQ1ehwxS2WUOueEkshc26enA61oe+2guzzyvivA+FKLoCM9a4E6fPsNZIQbq4NSxjziW0yV5s8T0jGV09D+wxFFYdTyPRsqHxqbc2/tAZ8bdb6VAdEbcOdbNkpMsuak0NsgfIngnqhUEjp2aA7PwEACPzkr6KLVVoboAqKgQe/LElyvw+YPE8sjEmBHvwK+IV/BExVWUDGvcj1eMAPfXonHCGWjswsBCOzcMVUktAB+T5GT4Ra/hAoOa3H8whDbYWQEpwuy861w2esq41xkUiO5XBZfR09CbT4XvDuP4bHZbweUcMkZRCGDGUhItl/6FjrdPUYexhpoOyxUM9+R+acPi9QOAN8cgd4yy+ADiO5bqz1+Nw+IGuMKKB1A2YGzu4OVxSKhfu/CLz/CwAsqrg+jzwvM3JlTcX9JMgQASDA2yFzP/MZ7HQHn70gZRHXutp7VywRz8La75U5WSRJ5PKxQSHD6OqrQshqqgOyRoPueBYqhCCvt/3GOmbtNeDmtSjbuN4gpsKWwwmkZMDx8NfBZWfAFw5Eb+P1WFZwgMyjtTbs1IQQo+54tt8W1bcK+sJBIXG1NYLGTQOtePjWq8Kc3hVeeOZyA552iUEZcSBavDG4Hue9rwkRJt61ySzXv1LAsQ/kHk9Ol2dUJEKUnygzD5izDjgeHVuGzwO98yXQ2idvaTGcDRs2bNiwMVIxosgw169bCbpVq7qftPeEtDRrIdbW1ku7CRs2bgPw5aPQp3cBnW1SFbFk46BLeg474kie9oW8okMDbyYCPqlA7UNyjT1SSUSuBHBLHTB1gSxUSo6Kj/WkOZJ4VA5h8yelWQsZvw98cjt09lipygpddPu8sS0x/D6xxOhDgpYczqByACBEoCBMYoQOQP/hX6HWPgUaoG0GOZygZZvA239vVAAbwbzQ36ebRB/ljAebCXVTcteVACruPgGlO1rFz9qsXGINFMwCzVolsvftzaAJs+S7Nn16O9ugX/3/DHlqwzLG7wMSU8CXj4KvX4pvTQKAj2yGnrUaalQcW5Ok1PgVTKwBZxJwarscM6j2EkIecrqkTzfLwS//D0NVCL1T2TA3MauWzSCUcsr3343UMTfVRiWzAECf2ilVZV3tYme08lHQhJk998XcdelJ6K2/CQay+NROIMZxYoFmLAda6sBn98j3lZYNtfaJfo9vNGW+BEnAEkAlM6Fq3IesgepSIeX0kajCXR1SWdZ0U/pqBnDZUBWKTAo014L9vn5VfvUWNHke1H2fB5/eA+5qB02YAcxYAf7Dv0r/zGpUr0fIFu/8EJyaBbXhOSt45Oihf8HK2mTrenO45J7rIdgaD2wm0AN+GRdZbJsiFXcGE6Qcce0/Bv1YDido5kog1Ns7Auzzgnf9AVx6Qr7f9GzQrNWgoiXdBnw54Ife+1r4GOT3gi8cgOrn79Ef8MG3LBIDAHS0QG/9DdTz3+ud0kY825h4pKEBIuwZaaKjBai8BN3RIkp0zFLhPGo0cG4vuLNNlNdmr4m6j8nhlIRT5HG8XUBXW8hYHDCs6grCntm97vfV09Ant1vS/0vui0l44sqL0Me2Ai11oNGTQEvu73NVIAf84IPvgC8eFMWVyfNAKx+NeT3y/jfCq/nbGqA/+DXUc9/pdeCVj2wJv4Z8XcD238cmmvq8kMRJl1FN7BAi7KyIeyzePMRIAnJ1GXj3H632rna5D7PHgPIm9KrfPYFbG4EYNlpceQns7RxyMt5ggIwKcxQvi79RqB0DwXom9uE6p1GjY1pIUXI6aP0z4OZa8PFt4DN7oKtLQQvuBKV3c13fLA8hwljgKydEPQGwku5ATBJ+xCfF7qQP4PobIp8PEkXCWast1QITWWPE1tKGjdsI5E6Osg2Mud26p0DjisDXzgnhcOaKuFZxlJkH9fS35XmiA0JY6Y9al8NhWX9GradkTaAW3QOaPA+BPa9Ez9kDfrG1NS0UI61/TGVFBsAGgcDhtMYQ05pWBySR39kq/UhIlO2SDALnzBUSl5i6sO/rA5c7to1JQiIod3bMQgKVkYtAzvhwciYj3GLVXJuf3o1Acx3U+mdlvdjRIglZn9cgDhprHVeS2Lz1l3wda9xVzui1dfZY4J7Pgm6WgbLkGc2NNVEFShKXCMh5hSqCMqTvTldwbkDzNwx5wp+b68DlZ4CEJNDkuYPyzCeXG5i6EARDZfj0TkspBjAKbSSewKUngZ7WA9cvxU/Oh+L9n0OUe/zynB+VD1p6P6h4GfSJbcCuP/ZoD26cAYJKtoAVD3G6RMXi7s+ACqcDni7o3/xd+P3JLPOAOWt6nF/SqHx5vjZUybVgxmLGTrVsyyK6xTdK5B4YO7Vf1oW6thL80v+0iEXtjeAX/hH60/8NKj0b+vjWqM9w+VlwY3WfLTQpMy+miHBwP+OmyThoEn7N9yfNCSfDEAXteWj1Y6Di5SBXxJg3wsClJ8XKznxdcQFcfwPq2e/2T2WntUGKUdKz+maXWlMe/lopIYRPnC3rr4JiSylUByy10G6LzVjGdyLwhf2gO54TNbDQXzu3MGre6LjjGQT8HinA8YaQN11u8IWDoDFTxJLOhg0bNmzY+JhjRJFhWlutSXx6eu89TmOhvb09+HdKykeMKGBjRIIvHTFIKq2gghmyODSDKIN1jMtHpQLcfH1uL7i2Ao7H/mxQjzPcoElzRNWlK5zIRjNW9H4nob7Qns7g4lzvex0qe2z8ynwD3FIHvfNlqSxTRlDNlBBOTAWKlwM3roBvXAH2vQ5ecr/IXZpywyELT975kixCsseKUsPUhbLANhfooUhOB9IHqUqJGehsR3Dx1FgDveXnUI98I6gq019QaibYVCIxSR4hAS2avjT+h6fMB84fMJJnRlAoawxo1grxur52XoIWBcXBIBU33QS//C9iLyFHEAnS65fAzoSgfzaf2gEuOwP10FdBKRkSJGtrRNgC0iAqweGQ/ZlBnVgRBbNaKp7NRMWF+MQV1hKoDPrDB/8nCCWs+L1ASxegjOqo3vhxB/2gFRBg6zMOQ27YDGyGwaiuixGs4vKzktQ00XgT+q3vA9MWQU1f2qNCCzNDH3grIrDJkmziCIKIwxVFQCEi0PIHwQvvlns/LWtAFSyUPRa07ikZSzwdQuAwA8dEUnHmcEKf2QPH5HngS4fBJSfArCWIUH9dgnIzVkhVj5lAbW+Gfv1/y3WlA3KvB3yWbZByRpNKMvOGlAgTPOfCmaBCi7ykLx4KymUjIVH+1gHA55PgflsD9Pu/gHr+b0FOFyhnHLjmamwilXktBfyyr2lLoRZvlOTGAKraKT3HUiMy4feG+2N/xMEH3waXhCgotdTLMyNEOYkD/uikUHOd2LuYsuLKIQG0uutgn0cC5rei/7GsPzpbReWqF+owNHMl+OJhSxEHAHLG9apSul+IM2bzpcOSODBfm5XlxvfIVVeAqlLQfZ/v3WFKjgVV6STRwPIbjZva57GNy8+K+ooxzvPFQ+Cacqgn/1LIXeZ2NWXQm38aTALxlRPgqlKop/+mT/cpH3hTVDPM15ePAp3toAe+EL1tqIWOibYGUazrpToMXzsb3Rg6j4v+hPzT1S7WDIs3RqsNjJki86kIBTBzfsIlx0J2ZyYB/dCv/C/QwnuEbD5QBR+nS57RkQk/h0OeFR8R0MwVlu2YicQUUFH3FnTc1S7KeZl53SoKBJ+7XbK+57oKcPk5qCf+Mv7nzPHSVG9QDmt+ZGL0JKDcuPaUM3ZlvDl1yR7Xq/Es2OfLR6G3/94iB5/4EOrOTwCrHpNq4q42WS+ueKhfKnw2bNwOIFLAtIW9Vg8kpfp0n8Xeh0PWmkfei37TKFjhkuOyDskeG50YdziBzJAiiHFTgfQcoKXOOIARCxhfDJSdspL5ofNY1lYswJVk2JCIAhkSkqDWPwPqpSVrzHOctSpKYZbGFYEycsCdbeCze2Udkz1OVG6MmJTa8LyosJnPxcw8izTo81jrQ6cLqCmTuMFT3zLsh5XMN0O/S2+nkIX7S4bJKwz/bgGDNOSw5mrGd4Y//CsYDM6fKGNprHWC0ynzrUgyDUGeAXkTQFmjQZPn96nYoy/grg6g+SZ0dTlw0FoX88F3oB78Ciirb6SHbo915YQ836IUzRjw+0CRRK5Y+2i82Y3KbQhCbV0cCfKMnzALRAQ1Zx30uf1AfVUPttcUYV0MOW7AJ79xwAfKGQsiBe5oiVBd6jIsvdrA+98Clj/YrSUnN90ELbkP/OELQPNNaVQOoO66WAiHkmWZgUAA+q3/K69diVB3Pg+aMMt4WwM11+Q6ypsQ95nNe16x5hFm3MfnAb//C+CJv4y2xjHR1hS/MCAOaMxksQozVamNftOCO+V9lxvqrk/KPMQgS9GEWaDF9wKejnDFLgBIzwXNXn1bqIfwuf3RjR0tMp/r47iqj30ghHzjHqJxRaB7/6RXhCB2JViK1+Z6gQiqaHHsQivzulEOxI/3hVxbfh9ozBTQxs+JenhHK6hgOmjh3WIF6u0S1UJj/HXc/WkEHA7g3AGJKSpn8Jh87RzIJsPYsGHDhg0bI4sMk5NjVXfV1saQgusDKiqsRW3ofm3YGApIwPMF6/WFA+C6SqjH/nxQA5z61K7oxtpr4OqroNGTBu04scDeLoD5lkhmUkIi1KYvSTLmxhUgLQtq4V19ClrQ1AXgmrIQ9REDna3QW34B9czfBBd7zBooPSX2TElCdOEPfml5CXs6ZMHsTJAATVuT+AonpQpr//JRSfqMngRUX7Wk1gFZ43g7xc+6tkLY+ldOAPf9KdSqR6E3/yxEctgBterR/lXBhZ570WJZGHs7EUUC6WyDPrkDjgGSYfT+N+WPhET5Xrydct7JaaA5a8OSuFH9czihNn0JuHICXFspErJTF4qd07s/tpRaXIlQ934WNG4aeO9rEeQoCcDBnQJcPmrIT0OCJQ03oHe8BHXf56Syjyg6ycEayBxrBAN98QktgFUZGANceTEO6cTcILQ90rLIbwVKTEUR3QsSjAnlkMCk3xO+nvZ55TdxJoRfi4AEoNKyYiou8KWQZLzfZwVxLx+DLjsDWng31JL74vfH0wm0NkS36wAwd71RqdIJpOdArXosbuCUEhIHzTJAFS8DG9cWV18F7/oDYHoemGNzVzv0wXfAJ7YFX0MHDNKQX5RygKBlD5/aaQXQlEOuQb9H7CMmzAKnpANHtoR0wgG1/MFBOZ8+w6z001pke01VJe2XsdHllvO9cRkonAlkj7HsvOJd0z6PbFNbCb56qtt7vVeYMDNGhZRYjN2KZ9tIQEwrscZqoK4S3NECfeBtkWHOyIVatgkYXwQoh5AHPSEWbzogr0eNjq5kHkq4EiXQ6vWE2wG5encfU1Iq1GN/JpWKDdVAXgGoeNmQEcho2kLw4c3hjYmp0b7spn2AMyEsmMh1leFWSPHQaowT5phmDj3e7hIFscGndyPqGdJUA1RclHvI3O7M3ugkUEeLKGB1p+wReiytwRcOR7dXXhArqLRR4W+4EmNXFfeFjOVOARBhW9cTL5QUYBAhWClQXSU4Z3wwOUJKQd33BehdL4vqjCsRNGslaN4dxudDxh1PpzVH8PvkecAaNMCxm5JSQRNnRxGGaNqiW0KQHGxwezP48GZw5SUgNRNq3h2ikJiRC/XQV8HHPpAK59zxoEX3dlsUoI9vAx/dIs8T5QDNWRv3WcnnDwSJMEF0toIvHgomfqKQmWeNiSYcLtDCu4MvadYq8NXToiKT4Aa6AoZygLKsGUgB44ugNn2p12s61oFocjBr6ANvQT33XajZq3u1Hxs2bjdwbQX4ZjkoMz/M7uyW9qGpFohUDAlFSOJSLX1ArJlCnps0/86wsYuUA+r+L4D3vSaWcIkpoLnroOZvgN7xopVUdhhqnw6nReYMVb3QAdDEOaANzw84nkKL7gVISdGH3yeqI8sfEsvB1/+3RS4pOwO+fERiUu5kIYjmFlqqOWlZonprqnKa52EmdptqgLpKUG6BfDZYlGLAlQCUnQFyxvX5HNhYo6p7PyvFXvU3AFKgaQuAtU+CaiuEoHD+gJCOTNSUifrguqejd+pyy39hpANj3NYBqAV3DVghtzvoo++LKqnfC3S2GXNhYy7U1QY+8Bbo/j8dtOORUuB4EyVmUV3taR95BWB3ItDpj177xbK1BoCAF2isBh95D1i2Cbz1V0IqMy2zY60hzeenjiTuQH4ib6eoWNRdB5LSwOnZ8pmO1vA+OByihpOQKMSOyNNubxbb4Joy6YenE0gwC1YMIg6zELFuXjPW8skSfzL76OuC3v57qE/8HdBaD/3ez617KjMPauPngM52USb+/9l77/g4sutK+NzXATkDzDnnnPMMJ5BDTs6jUc7JkhVsyV57v13v+tv9vOsgy5ZtSVbWaIIm5xmSw5xzziAAAgSRM9Dh3e+P+6qrqrsaieCIkvr8fiMK1RVepVfv3XvuOZ2tUsA1Y6VNMos//7Iz0Htek5iWg3QPQIpz+qlGSHd9Aji7XyzrsvPExtKhBEkjp0B95K+MnXq2/dvS+4G2ZnDFGfk7txjqjo/9XhBhALvvSFge6kxQ6ex2P/VVCfNBvnoOOLE9+dgSZp70/s+BS8eMqrNRIU7LEPVPQ6JygpRPLMHP7pPnLC3DW93LinEyS/FERjYweporDs7RCPT2F8QOT0eBouFCsCweDioaLiSdeKT1wloshRRSSCGFFP4IcEuRYUaMsAPLO3fuxLe//e1+72vbNps0MHXq1BtqVwop9AR93IOkUlshxIgbJB24EKeUEkPHzbMC464O8PbnwZeOyaR61FTQ6sf77gndR1DRMNCGL/R/++nLgcYamaQDMukIZsgkvblWJr9GBpO3Pueujji+zVYOAeT/W5KzrMUHlrUEuqxkRmM1aPlDYpvitN3xOUgYrAHygcvPiPpJZxswdoZM0geNhJq0EJRT2O9zjp37+Dmgplrw1t94/Mre0rB9AEcjbkUbpaRqCwBt/AJULyb05PMDkxa4KhSi219wXzsTiMDC9RJoi5/emqon2/c8ZFesXTgI/XwDMMFUIqalG492E4DJzIG68+NCYiuPS4A6EUwHTetGXjgzV+5xMkui7tAbK6TuEDsmuS+N5a8NSICHNVzKLNFI7FmMa5D9fz2CDHx0C3jmKlB6lliGRcJAwRA7OZSWDmTnC1nMibRMqEX3AIs3yDOfmfuhVkGTPyDkn/RsIVXFB/WGT5QqbcC2AQLkGhhCAZ/eAxgyDOquurf3+QCVDhSPBA2bIFVkIyZLtZ7PD5q0oM/SxwMFGjMDvOtl6WPin7dwV+wcdVszaNcrYlHltHyy+jp7jxDSVhS4dhlcXQquOAffhs/3v43KB+QUyXMZjdhECuXr1sLsVgVHIyIln5nXO097IKlcMjfXgzf/0n4mG6pFrSmQLkGt3CKTdIkjIhQO+XDfscmLwNuft58V1kAYvZRLN/tIz+o28DiQoDm3SxDYWAChYAho6f3gN//dvWJM1StO2aq5HuiGDMPRCHDlJDhkvjlWcNnaRfFwSZTXVgAFQ0GzVicSTOL36ZTAB2L2C/q9n4kU9/y7RPFtIMaJrJPfO49vHU1dCj70rnvhkHG2/VovoGascJHKAYglTbhLiHte30t/0E507HlV5Oez8qHu+GiMREf5JfDd92XozjagpR6UXWCTZSYtkOr5aMSQU61jGOLT6d3gxRtv+F2iNY8DwXSxyyEl9mcDRJDk6iu2BcPE+f2vzu/NsaIRqZpuMgUrbY3Q7/4Uav1nZG5QMgJ09ye9t21pkPPXUdC4OUJk2feGvYKOyhhjyFhvFToPu6Nul8OqHCZHYtqQYEtstSIKpEHd/xXgyilwUw2QV2KrbQ2bIPc+b1Dv+3ILbU3294vZVJ1HgIZr0Ic3g7JyQSMm39T7lUIKHyaYWZRQzXyaAdCIScDqJ4Aze4ToEKdSctPacuhd2yYiHkbRlCYb1aqSkVJ1f/U8KBwCxswAmmsQffEfpL+aME++0XnFoPWfBTPHvgn6zF5w9RVTQNCJWEI0O9/Y+cFNClU+seEYgMIiUkqIAHFkAH3sA7fKCmCrDc6+TeYEl47Yc+fqy0A0BPWRv4b++V/LWCEaBjrCdnLXOuag0eC6Sps0Y5F8+viNdMWWIHMVtfGLsTlJ7PkYPlHGU+/8OHEn16+ICsyEeYBT5S2QDlr7NPj1HyTYwyCQBoxJTFIPFLjirB130qbIJdxlVMkk7C52J97QZ/eLIl9XO2j0NFG86+FZocmLxVY2nrSiFDBqeo+qrgDEqmvSInl3rYIHZnPvzRjLC6FO8Nn9QshqbzZjqCTW0YDMWyIhYyFmrg8g/1r/N9QBfvtH4PxBYusSCSNB9caam5/Zk/D8AxLXQ3Wp+UPL+C7CEqvSUTmfjhagLVe+87c9JZbC8c9xV7sQr/a87n6nGq+LAmNznd22q+eByotAyYjEOIE5R977OvDEXwLXy0U9ERAi8IoH+90niBXvUmBacvXseDt1AKC0DNCGz4Gb6+SeFw7tPdm3vUVi3DkFQpL7HYDGzgRfj7co8vVZ7YmTxAC5/AzQ3Zz00lEhuVsx0IjYPNO42RInT1LYSMseAKJh0/eF3QqGCY3QUKuf8G7f4U3gU7vsBXVXod/+EejhbwFDJyTaYylf3xTWU0ghhRRSSOEPGLcUGWblypUIBoMIhUJ46623UFpaijFjxvR5P6dOncKuXTI4KCgowLx5vZNlTSGFfiO+WtHCAJNUaNRU98AXkAnhDcoJdwfe8YIkda2/y04Bm38J2vjFm3bMgQCRAq14CNELh4GOZrtSJbaCUYVpqLaJMMwSAIqEZZKenmUHiyxEHQmTuEkjpWWAnvxL6Of/ThRJfH4H45/shBggChXtjqqlxmqgO9JFH6Hm3YHorleAkEfFQV+D+gb66BapTu9ss4P7zuvjD4DyBiXfQRzYCkbkFoO72gDLD9dZVVRbAWz+pbEB0YmBivwSSTqwdks3k5Jr2lxrV/5kZMeqJ+jBr4GPbpH9J6uoKhwqvtXdBG3VrNXQ1y67SRT9wo0QY0wQiZQEw6Jhb3JOIF1IBp1tEsSI6zdo4nypVmJ2B5789kSda6+Cj2yWqhlAqqPu+JiQ10hBLbxHCEyO86EF6+wKeI/ED9dfk8qqtkbxtx4xBSg/LRXc42eDMgaGeEeZOVCrH4fe/kLs+tDwicCkBXJ8wH0b2LqucEs+Fw6VoJcFS0Xn8lHospNARg7Uhs9DLXvAfZ4dreBzB4DWRtCIicCoaQNCWOCGa3LffIGEZChl5UHd8TFb9tlLaiEaBj54Bqx8YqvGxm7HENzQ3mxIVXCrK1n2Yuf2Q4+ZAdWTJ303oFFTwW2N7qSBz5/gh32rg0tPyPNlXbORU+VZa6kXwsKCuz0JjzRxAfhEHKm2cChQU2b3K8zGdk0DFJK+t+qi/OvLcEjbB0H5vSchDAiy8yQhYhFHfX4gmAY+sf2WVPYh5QOtfBi86B4g1BkjokSLhrll95UP0HB/d5QvIajsBLc1Qb/6L/LtYZaKQeWTvhcABo+RAL5FGrx6HnzpiFRtd5MYp1HTwBYJlbWQOy359fIzYoX00NeBUVOBeIUbyDvWW5DPLypXpcfdPxQO9SS40Py7ADLkg3AnaOws0NL7e308QIgpxFqUtzpaQSOnAHPWAgfeAl84AnS2iMKbUpJkJAICATM2iNjWdBZB4yN/FQtGc+kJGXN1tICVT1SHlj8EKhkJddtT0Dt+G5OQB0j6e22IebGPQP9BwQzQmieANd5B7f5CH97kIpTw4fehNn5xwJMTrKNATTn4erlNhLF/BR/f3u3zxeVnxJbDBPv5wDug4d7zFi494Zm8o2ETEiX9zfKkuHZJnpe0TPe4vbrUlRAl5QPGzuz2LnO4C3xkC7j8tKj9zFwFGjE5+QaZuaIG1VwnpFLnd/eDZ8BpmeBgOmj5Q1DdJLFSSOH3BhVnE95RLj8LPPu/7DlJ6Qnw+YNGpeTmKc1y7dXkBA1SoJWPAMMnQe94Ub7H0QiQVwJa/Tj46nlRrLL2VVcJNNeBVj0qm1tEmLP7wFuftQmZ1jseCMrfw8bLGI3IWLwIyYCvnIbO2yHj9T5cA2YGKi+A66tAxSOS2x03XAciEVHuAOTb6A8ADdVCWHISRyzUXgW3NSXO/yIhgAD9xn+AioeLCt35gE2aByTJOqFvsVbe9pzbkvLyMVFtWffpPuyEoTf/Rvp5jgLZhUJcmr4MlFsEPWWRzLnCkqSG8gErHr5x68PumnTJoV6jHM+fpRYEALneRU/6zF55nqx9ndgOrquE774vd3tMmjgP1N4sJJyWenne/EGgcAgwYR64sw1kzee6289tT4pqReV5IKcAfPE4UHnWqF4kQSTktjft/ghyDcJd8m0MdcgrYynkOtfTGqi/JkQzL5XLrg75tnsoEnKoU4gMsd0ps8+ofItDHe6CoMoLYqeWkZ1InoJ572o9isiqrwhZyNHPcNkpYPgkeM61rTZUXYB64jtCmO/qECLxAJBiueKcHN/YU1J2fq+2oz5as+vj28F7Xo3NTWnEFNDdn/zQlQ5p5iqgrhJ84TAAFuu5VY+CMnP7tp/MHG+joh6KPtnYswOQZ9FSNA6md2tJS8F00NqPgld2gC8dk3c+GnHHEEkJmSU9M6mlmreibDX4Z38lj19WPpBXLH1C/iCoBeukD08hhRRSSCGFFG4tMkxWVhbuuusuvP7664hEInj66aexefNmBIO9l3qPRqP4/OelQpmI8OSTT96s5qaQQgw0appU1DvhD4jH9EAeZ+F6sZWpKTPHCEKtfvymBZQ4EnZP7K3lV8+D25p+Lyoaaepi8JHN7oWFQ0XCErCtkAC3TD6zkAbiiSPO6mRLKYaUJHFHTBaLpzs+Cv3Gf0iSxqrUCaa5lTla6uzACCBVWye2g7qzoekrSoa7k/YW5idW0fQEfWIHeM9rjgVSlS4e4nJeNGdtr55FZhYLrJO7JIFLRo7eSRwKBO2gHJFIyIZNdbj12+AxoAe+Bn77x4nJPyvxWHUR6vHvSAVH/TWZCI6dCUTC0Ec/QKxSWBpmb08k1R3Duk/G07jZUHd/StShSk8YuX/lXeFxs5GeJefSnqSiP9xpV/B5BANpzAzQ4o0i79zVLtcjmG6TuAJpkuy0iDCAVEe99zOox78DIgJNWgCVXyIBSK1BE+Z2m6zimgroV//ZDmhdOibPhJFy5X1vQt3zOVA3yee+gCYtgBo9XYKnmXmgkhHgaAScmWsIDMYSwQqcmkeDxtqS2jRrtRAErSq4UKckaa33uaMFetfL8N37Jfs8m2uhX/l+rFKcT2yT5O9tT/Wp/dzeAr3jReDSYQAkgY6m2tgzzAfflSp9xzWnsTNFAv3KKZNEjyNvMhsp7zTAp4CIIXZFwvK8WLLv8ZV5zl0c3gTcCBlm4TqxtbOS/coHWvHwTa8eHkjEZLljykIh4PQuCWj5A+DmWnDlBajH/jzBh5wWbxBbqPMHZfuh46HWPG6scQx0vEoP5N6EOoxln7LVLUZOuclnG4fONlua3gG+iap1AwFKy3BVPauVj4pdgvUtysyVfqCj1VSXamDYeHBrU9KAK+9/y64gtWSwmUW5ZfAY6IZqYPcr7o3am8Gn93jKvcfaOnetvCNVFyXRBZa2WUowpMSCZd2ngcqLNpFF+YSQWDSsb9dm5SPgzlYhTgKG+PhR73WVAi1YByxY16djxENNXgRMXgTAkA8uHAbyB4HWfVqu44nt4LqrQjaWA5trAfubDwixpboUGDZB+sz3f25/k3VUSOUFg0EzVoImzgNqr5p7YjIzLOuhYPANS8Zz6QlRrGlpAA0dD1p0j0vCvt/77WixK9AthDrBe98Abey/qmLCca6VQr//MyEdRwzR1hr3GbUTrikDN9eCcr3PS+962T0mYg0uP+fNMfKSdgeACXNBl4+BS0/EFtG42YDj25wAZ5LHmRjvRfKH25qE+Hv9Cih/ELj+miFPm6ek7AzUuk+JElz8tloDV04CnR3eY0EiGTf4A+CdL4LHzhgw0m8KKfyuwF7zzUhInnXn3LC5VuzNZq2+aW2hwqESL0n4wQcUDIaatkwKPJyxm6Ya6Ld/5J4LGvDZfeBFG4Cm6+ArJ4FgBvissRJ0EmEAuxDq8nHTn5GJF7CMqa9fAb/3U/D2F+QbtPyBHgkaHI2A3/kJuPy0/A3p/2jtR2NKZ7F1O1rdhTDRiCgRlJ6QuVlnqxAM4pULrpvYki8g8zDrOkQjQFebFEFUXwHNv1u+oR0tQE4R1LL7+/RN41CHWNPFL79yCtzRmjDuJ59fijXiyZBEQPUl+Y6QTwqMwp2x5D6teRIoGCrHCgShpi3tM2mnz3ASN0jZ19JeCOWw6XOCvVSmqy6CaypAJd3bcqrZa8AzV4ml+cXD4F2vCBFz98vQB96CWvfp7omjMCSvUVOAkZOh3/g34PJRu+3WvLivUH6bDEYQO/GONqDpuh3XieeNxBNSPJV3hQhOM1Z6HNMniq0xmzITU7LeQes8gvZ8hS8eAS25F7zjt45DMJBbBNYRt+qONsqJltVSPOmuplzmZPFqjmCAfCAdFaL2uNk3SLO2ofe8JgVe1pGOboHa+KUen5u+gptqRVnKccO44gxw7AOX/eSHAfL5RQFq0T1SXFA8ImFu3SuMnQXkvO0mdRnbzm6RjGzUDQmJoxGg7DS4rRE0fCJo3Gzw7lfNnNPxIvgDgM8XI1xzc53MxXKLk8fDwiF5V3wmHtXWCERCUhjQF8vaFFJIIYUUUvgjwC1FhgGAv/mbv8Gbb74JZsbu3buxcuVK/PznP8fkyd1UXxk0Njbiox/9KHbu3AkACAaD+O53v3uzm5xCCpLIq62wJTn9aVBrHhdv5oE8TnoWfA99HXztsgRbhk24qZVVMfUPz9/6MSn+HYAWrAPCIbFDiIRl8rHqMVuRoXg4ADITW0fQ2heATLbNJNzLM9lp65FXEquUosFjoB7/c/CFw+DWRuD8frfEbG6xt7S7syL9BsHNtaCh48HXSh2BGAJGToavH17ZCYpEPhPgGD0DlJElXuWjeidNyqf3SPU3YIJs7XHPmbnuBLkPlm0KkUz08gZJ5dectSCfH3zPZ6F3vwoc3mSCHmm2Yk1WngQdJs53BR10zWVJ4Frkh3j/al8AfHq3VDv3IDFMY2fCN3Ym9Ns/liCpRZBwBhOTbtzPAFPCfkj2UzRCju0VPGJjb1M0LGYRFg8153bwjJXgy8fFZsuRyKGF94D3vpa4UVONSAIb2xAaNBrUS+9rPrLJJsJY1w0s1Vs+n1hl7XoJvof+tFf76w0oLcPl5Uw+P9TyB6E3GTuaYIYE6KzKnqHjXSoHlF0A9fA3wWf2yPt1+Zi8+9b1JSXVm1rHgtR8eFOC5Q+fOwCesbLXVfzMDP36v4oMs/XMWPv0BWDZY+mdr8D36Ddd26r5d0FXXvC0v4rBUluw+jrWEsjMKXSTBu0WIUYma2vy+L33oIwcqEe+CVy9IAHE4ZNumhUfd7QAoa4BSUi79lt6wv2NsN5Bi1QEAK0N4EtHbXl+A/IHQGueAC9/EIhG7CrO8XNs1SInLBUMn0/Ulro6bGWsQBp418vgkpGxykCuvwauLhWZ/5ugJEcjp7rJktbyPqiR3AqgwaOhnvovMeKYbm0ADrxr1EfMvS0/Df713yA6eRHUXZ9IkMJ2VQvGdkyggiGgkVNAZae8dcCaar2W2rsIpsN335fFFufw+0IUcaqhsTbe9Z1Qd39SqtmbaoHBo/tFXKbMHPju/6pYP0YjfZJQv1FwW5OQB1vqYopwNHUJ1F2fkN+ba8H73wFXX7btb+KTiSb4yxZJ1YLZH188AliJlBPbkFDFSwT08juW9Dyunod+5yexffOlI+DqUiGP9ido70RdpacaHVtk+QEA6yj0ez91fGf8kqgKdRqVOaNOpDX0b/4X1JonXNaXAEQB0CIvuXcOqADATlsHH2jKEs+2kPKB7v4UuOoSuK5SlBF6IMnSjJWSsO5yJIbzSkAT5nZ/3qFO6Ff+WSpqAZlzdbbHqUUy9JEt8HmRYd7/uagedMYnw6wVWL7X1riz8iIwfk63bUohhVsJ3FwHrrwAysoHRkwU0mC2h9Wfl6In4N0nDCBo3p1CHLFUIAAZz6ZngizCpZdCSpeZu8WrHOgo+OA77vFYZ5uQH5JZCjLL75GQmcP6TF9kvjWhDvmWl52U+ezUJUlJMXz+YIwIE1t26Sho/Bxg3Gx7WTQiShcg99wyErZVXaNRINIm38xgutyfzHwTz7AIvOkyvoiE4WItRrpEweXpv5Y+MSOr74RRRpK5cfKYEy1/UEiUFw7LOkPGApXngbhj8/kD4JWPSHGGzw+adwcw746+te8GQFMWCcHK+janZQDRADBkrFg0Tlsm1mFeaPf+XnB9pdjR9KDuQkoBaRnQx7a641nhLujtL8D3+Hd6dQ76vZ8D8aoT/YlTkJLvu3W/AxnyPuSmC7GUIIq1hqzVZ/iDUkgQf1i/qKTymb32wkCaqOFm5gLn9ptiF2WrPfsCYjXk84tNVW2F3I/2ZuC3/yAEYKWknTH7NZJ3Ki3TtlVXQraLxYMTGufzJNDeCLilwY6pWQh1gve/BbrnswN7rPIzSBgrA+Cy08CHTIaxQDmFEqfo7/b+ANR9XwIfeAdcdQmUWyj9cZI4WWy7KYuNWraj6CM9K+kYljtaRaXXxFMYAM2/G+quT0B/YNRhujpEUSoaBTKCwMJ10AfeAR96L/YO0vBJoHWfFptXJyHeKi50fkO62iUumV0IBNNAhUP7c4lSSCGFFFJI4Q8OtxwZZvbs2fiLv/gL/I//8T9ARNi/fz9mzJiBdevWYcOGDZg+3R5AhkIh7N+/HxUVFdiyZQt+8YtfoLlZAnZEhH/4h3/AsGF9q4RMIYX+gNIy4XvgT6Rqt6NVkqc3kaTyYdkOUCAonqyWdUq4SwJbuUUJAYhbEdzeDD76gVgLTFwAmrYMKk4iknKLQTNXgg+971wqASKlgPzBQP4gCQxYiSenpKtlJ9LaAD65EzTndtlDVh5o9hpZfd4dUtHUXAcaNl44Rpt/mdjgAZqk8LkD0B/8RiZOwXRAB4DikcDUJVDTl/Vvp15JdOWTSqu++vNePGz/EUlCGLFILc6ApPEmV+s/4yIQUCANvlWPItpQLYof9i+gYRMR/e3/BWorgcIhUIs3guurwHvfsJVmLAUQdhzHECG4/Eyv/LYBgObeIRVoVlWTPwBkFZigbxJSTCAt5jPcI3Em6YFJgjGz1wDlZ7sPKpWMhLrrk90mNSWQNA88eDT4/AEhkY2dBRo0CtEDb3tXOvsCict6AXYGxNnhH84agJnQ15SDo5Gk/ss3Au7qEJWii0fkHEpGSbXWhNmgtmYgkAbKT7T+oswcCba3N0NXnJX3wyLyAMaWQQMwZJgaD4lls7zXlhZVl6Ry0wpKOp+X2D0PA5XnoENdUI6qNxo8BurBr0Of2AEc2ZzkGTGKCCAgPRuYvBi4cNAkQq0XJL6Ej4Q81IdvEuuoKAxdOQWkZYCmLwcNHScB9WRB4gEAh0Pgrb8RtTPWQMEQqNs/MnCywfHPZ8yWI269hGpBGxSnrkJDxoKWPQDe/zbAHabiMWBXPUZCIoUMtoO1pOR7dHgTaOXD0DtfjlkwMSCE0HWfGVBJayocIqpS+9+ypbNHTfOu2rxFwazFXjGYDkycJ1XlW35t2ybGVgQALdW3x8fFvvkxZBfEkugJywHQkHESbI/H0N69QzR4NDDndk/LGCgCXzkpNkBFw4T4eIPw6v9uNvjIJqC5xqj1SfUtH9kCPW4O1MjJMm5b+xEAiVZBAKRC1PSr5PfLLYuEHX00SdU0IITGrg7ElE5iUFDJbCh6ex4ntiPh29/WCJQeBybOv6F9I6/Em0w7kBZp18vcJE4imywaMt9rywKMNfTOl6DGznITfYJpkoCKI4MiLQN058fBB9+VxFPBUKiF63tWMBo8Rir/e2P7kFMI9eDXwce2ghurhag7a3WPVbJ84ZD7HdZGMSgSskmygG115ty28qJYfsQaEf9cwf0b0G0lcQop3GrQRzaD970p5AQAKB4JtfHzkoA+utn9XgTT4Tn/GTTqpraRCodAPfIt8MF3jVVOlyTPJ8yTIhkA1vjcvSEkmR3/Dc/KTywKUT5HYtwD1nuvo0A4jISxs0U+qakQRYprl0BrvdXXUHnR+xCVF0Uhy0KoQ74xXuQFS/HW2a7ONqPe0Qze9ZJNXnLOEeNtarrahbTTT8I6pWWINWrZKffy4ROTkuApkAa67Snwykfl3FoboZ/7397nOADWhn0Fl58Bn9olyiwTF4iCc/01oHgY1KKNoJE9F3XSqKngc/vtBVrsn3mL2NjS+Lmg1Y91P3Zva7SVCZ1ovN57RecLHvYrdivNNLCneAXJfCT2PEHGDRbJJjaPhbcNUk8wakEU9I610vKHhNhy9gDAUdD4ucC8O8Hv/Qx24Y3j3e3qgP71/4Ta8AUgMw/82/9rHiEz5+1slRhdY42cjy9gK3OGOwGfjEdo4XpQ0XDo13/g3e6MrD6rNPaIuque7zvXeihj3SAoI9vbUqifKq7cVAM+tk2KqoaMBc1cOeCFpAnHbK6T+VNXB2j0dNDIyUJU66ONKWXmQj3wJ+Ajm8C1laCioaC5d8TeMW6plxh0XSWoeDg41JVQWMQH3wU98R2op/4CfPkkePMv7PlIJAR+7+eimOu04rp6DjixHTR3LdDZKoQc6zvkD7r7bh0Ff/As2Ir5DBkrMcDfI9XdFFJIIYUUUrgZuOXIMADw3//7f0dNTQ3+/d//HUSEaDSKN998E2+++WZsHWZGeXk5lixZ4lpm4dvf/ja+8IWBk4lOIYXeoCcW+YcFbqoB73kNXHkRyCmAmnuHVA/1A7TyEansvHAIMeuQzjbol/8J6uFvgeJthG4RcFcH9MvfswNalRfA5w9CP/A1qCI36UQtewA6twi8+de2IkJXOxAISjJy9m3QV046qrAdU0EdlYlLMF2qseITY5BJoithFo2AT4217QcAILugX4lDqQK7KJP74ROkQnfnS45KOCX/pWfCN6sHyc9uQGNmxhKqMaRlSqVNX/dFKm4y7UiwW5O4QFAS7PVVrkk+jZwSS3KxURSxAkNq/WdkUlp2WiTnx84E73zJrtCqr4J+60dSMaV8UpEUMsm2mJ1Vujsw0wvper50FHrfW+IpbalrEEkVIGsgI8uWzXYit0Sel9Z64Ow+k6zr6CMphiSQmVcMHN9uB2m8lGFIQc27s9eKGJRbBIqz1KKpS1xSvACAIeNABf1LwNGQsUJWM+2LwVnZkp1/U4gwAMCbf+UOyFZfBg0aCZWZB2T2HDSkzFxRmTm10/2D1mJ7Nvs2Wa9gqCg1xG/fFwJcV3uigpEXmEEVZ1zVogBARcPgW/0Yoq2NEpDUHqQmsBBlAnnyXLI2KlBALLjsM5WuvoBUPaZlQi2732NfSZq35RlXRS5fOgp19ycHvFou4bj73xTSk4WGa9Dv/CfUk3/Ro0R9b0BjZ4ncsUWG8/nkGscFeqmPFkZq5irwlMVipdfVDt7+gti6hDrkHrS3SLWudT8MuLpUVBTi+m2+eh44teuGLAq48bpYiNRfAw0aCZpzu6hKTZwvFZG5RaDigZXnvhlgrcEH3hLLvnCXWMUtfxCUlWfbsego3Eksk2jRUVkn7puv5qyFvnbZ/d0aPlFILAAwdqZYazr7naHjQZPcakHdgYaMFdJDvaNPMcF561nmjlYhGeQVG1sb1a2PvRPcUC0JfX9QrO6SWELdKLi5To4RlwDj6jIZVzmJl9Ew+KV/hF50j1hOGUIDzblN7sXh94G2ZtMnpSN6YgfUmBnAmJmA/7dxZAwGmuugzx2Q5JMlnw/YYxB/4IbVOrizPenyG03VUU4haOpSsPPbo3xQ3Vht9RlepBF/QKpwWxrMeMPRd4Y6gIYql6IOkRJbje3Pu9s/53aokVP6ZOnG5w9B73lV7mVGDmjBOqhpS7vdhvKKQSsf7vUxACQqN1rnqLU8K9GojKuLEr/fsQQUmbGZNspFTjKpZf1JJAmgW2TumEIKPYGba6WYwPlNrC0HH94EteReqPu/Cj68GXy9DFQwCJi5GrzzRbdSwpBxoBslA/YClFMIWvMEsOYJcHMtEEh3JQFp8kJwrSGqh7vs70AkbNvxAkBQSNsJpMtAmowPosnG5SxzPw8FLy+CEF84DJ53l/d8KpnyQU4B+HqZJF2b64BBI22STWzH5lhd7UlIMoawoLWZg5r2BdIBv0qwrutrAUzC4cJdwIQ5YmtUe1UWDpuA3tjGxoggBYNF2dfa3vp9/NwEpRqORoBQ501LAPOFQ6IsGsMF0MQFUI9+u/f7qK0ACgYJadr6/oQ67OQ2a/CFg0BmtkupNAFpWfJcxhcwBdNj9sM9olubZ/MdS7B/ckD5ZBwWCQtpNRqxVeTY412IdKNY6gUiUeDoxpaT/AFg9m3gnEIhzoyfK8TbuqtyLRJiMgzUlENve87M2TyUAlsbJZ7jhDk/WrQRNHoaqHCIrL7iYfCWXyFWcKV8MpfuBYm3zygYgsRCFcTaMqAYPR3IKXJbCpHqX/yyqQb6xX+058xXz4EvH4N66E9vXsynuhT69X+PPXN8coeMRRdv7Nf+KK8YtPrxxOO0N0O/9E+x4he+dslWVHT1TwyuvAiVVwK+clzeFSfZ7XqZjDWd1yMakTiczy99NCCk80jE2I6RvY9QlxzP+h5cuwze8xrotif7db4ppJBCCimk8IeCW5IMAwA/+MEPsGTJEnz9619HU1MTmBlEFPvXgnM5AOTm5uJ73/sePvaxj/2ump5CCr9TcDgkMoyWbUVdB/T7P4cKpING9S0JBwCUngUaOk48YQF7EN/aKJ7fRv3kVgOfP2gTYcIhCQx0tIB/87fQizcmJAxo6lLwjhftqn3WMokoGAzKLwE98i3wb/5fW87VGdzSEZnMxUlE8/UrUpEWjUpCafhEOZbPD7XhC9LG6lKRbZ+6pEcJ3Nh+Lx8HXz4mZJDKi0BXm/yQmQtadE+i3zIgE6C4/rMvoIXrgKYaW6Y5Iwdq7dP9kvqnKYuksgGQCVs0nBgX9AWg5t8FpGeBT+wAd7SARk0V2fuOVvD2FyQRSSSVfiseAgXTQYs2AItENlfvfT0xCBnuknsb9MmxlU+On10ggZtoWO6xRWrpbAWHu5JWEuuz+8Hv/9xNYmFIEiQQlOCJ329PRJ2qQq0NwMF3RNkh3CWL80qkqsvLoiyQJgGoJRuFJFR5QRIqUxcLmcZSivAKdloKRjdYgUwL1wPM4DN7RDFmzAzQiof6v795d4LLzgjxgsgmDig7WBBPyGFmSYalZd6QugW3NiRUJgIAn9kLXnJfzOKoRwwZB5w/YAcQ/UHAHwBfOQVYZJh5a+VYjneTRk/vm8rX8AkmiNeD/ZY/CG6oTki28tXz0DteNAl0FlKLDjueSRNMs97J68Zug0gCLhYZ0OcHho4DjZ0JpGeDxs+RarG6SnD1FVD+INCw8Z5N48aaRGl61tAH3/O0mxhI8IXDiQtbG4BrpUCS9vYFlJYBdc/nhIxYUyZ2eP6A/R0iJUn8flQFUiANKBomd+ixP4d+5n9KP2HJfEdg+tH0WF9D+SVCfPHC1XNAP8kw3FIvRFNjPcI1ZeDSk1CPfksq4uJIWLcy+NC7YmFm/X35GLi1QWzZLGUl5YNXoBnk81QApFFToDZ8HnxiO7i9RQicjnESKR+w7tOg8rPg2goJWI+a1vv+xkAtvRd6068MmdNIY6dlAmOmQx/7QCr3LYIlTPJg/BzQqke7VcbQp/eAtz0fO1/e96YoBLY2CEFy1pobrmzlhmpJHtVdlQRcTqEoRE1dKgTrzGzvhEwkBD6yGWioBq3/DAAhWyCnQBJeaRmSdDl/CLh4FDojB7T8AXkmj31gbOwMEcEfAG99zk7oWAoeyid9+IyVNxyQp9HTJAjuXjpg9mG04iEhlF45IQnbqUtBJQNHQiPLUjFO8p9mrIipHbqgfJ5WKWraUnBuoVgWaS3j4bEz+9QWrquE3vJre3zT0QLe/jy4YDDoBhV8EpA/2Fikin0ClDLk6U77eWEFlJ8F1151qYtR4VC7p0jLkH7SepbTMoDCYdKd+Pwypp27dmDbnkIKNxF89QI8iRwVMqej7AIX+Yy1BmavAV86JsS40dOAMTP61LdyOARcPgbubBPFufySvrW5tRFccQ4UTAePmRmbN9D05dKPHHzXrfDS1S7/zVwNNW4WMGoquK7Kfv9JmW+IX8h81VeSK/55EmHi4JzHNNcK0SMONM0QHzvb7IWZuUDxCLF062yT733ZKQexM7Y1AE7eFtZu5TRfQBTAiIDRs4CyM7JONArkl4Avn5D5Vz9IfFx2SsYt1lyoYCiw5nGo7PxeFZ84oe78OPTmX8v3iZTMR5c9YB+LGbz/LVHiC3cCxcOhVj3WezXOXkK7lIXNsc8fBC9cJxYu3YCZwduec1v6FI8ARk2T+EBczIYvHAYMGYaNCo6T/EOBIGjmavChd13b0azVvZ8vF48EqjzsPi0YuzF0tHkXVVjj0aAhkUYj8Ooz+gRSRulYA8XDQPd+OWYD6wW+cFjGC0ZZUO9/y1awjSZ5D3RUrLfGJrESDwQTSUBEQNEwqLluUjzNWC7KgE018l5Z4/D8wQOudEt5xaZ/cChX+QLdkoX6fSx/AOreL8l7VXnBFFyujcU3+wI+vi0xXllfJdbTE+YNUIvjjrn/rQTyFR/9QCyr+2Enm/Q4p/ckfhOsfjZu/kVmzMxeVrkWSdHinFs26JGwFPt1ttljVH/AHCNkSOtFns86lx4HkCLDpJBCCimk8MeNW5YMAwAf//jH8dBDD+GHP/whnnnmGRw5cgTRaNSlAAPIZGD69Ol44okn8JWvfAV5eQM3mEkhhd87lB63iTAO8Kmd/SLDAJCAt5ctUnwg/FaCVbWgow5FFwij/uA74EGj3dej/IxMOoKZQLjDSLsCvPNF8MgpEtCYMFeqiJ0BJStQoTXgkMHlC4egN/9KAurRMPjoFtDsNVCmgoD8AdDUJcBUb2/ZZNB7XrOVOTrbpC1pGUYhoFkSe5bKiRO5hX0mwrDWksjNzBGiyT2fBTfWyHFLRvR/Mj9+Lqj8DPjMPlh2JQh3icIBayEHLbk3liyhoePArMEndkC/9i9AXZVRRfBLodHZfaJQUDRUtp2yWCrAwl7qKHGS9UoBKg00bAJo3h3i21tx1iQ/0sAnd4Lrr8F335e9r9HRLe6qcgs6IrLYPn/cZJTd67Q1yvsaTAeIpRp+0GigrtJ4tTtIMaZ6T+UWgeK8maP73oolp+WZtIgNacZSRaw++Ox+USHq56SffH7Q0vvAS+4FmPucwE3YX1Ye1GPfFsWO1kZg+ERQSx344lFJFk1Z4npP+ep56O0vSIApkA6atQqqvwEfr+cDMNZdtsVRT1AZWdBx9jYAXIlyKhgC9cg3xR6otREYMRHUx2APBTNAd3wM/NYPvW3LALsiKJguVmmdbRJUHTUF/PaPJUiifMbqoksSfE4CFZnfAOOPbvYbEIIPdBR01yehJsx1HVZvfyEWiGMANGKyeFrH9xHx3wxL0txL1nugkUz9xZdkeT9Ag0fD99DXJXnj94sKVvUVOe8hY3oMjPfqGEQSZLO+yT6/rUCgtZyPPw00Z21Sey6xVuof+NQuu6+x0NYIPncANLP/6mMfNri5Vr6XkbA7GVVTLgnuifONT7shSMSC/mSSYX4wM6Kv/SuoaDho9ppYv0rDJ3YbHCYiYNSU/o/JANCkhaD2FvkGdbaJBd6Kh4CWBlEoAoyalHmJw51CRPP5kUwOnMNd4D2vIvadYhbLrRPbhZh67TL40jGoB/6k34QYZoZ+9ydiH2gFdjtbwTuugY9vg7r/q6DpK6Wv9EJXB/jsPkRrykHjZ4MWrIc+ttWcY5ddeayjMvba9YqQaNOz5HyscVA0KscPphvSQof5bGpJmi3e0K/zc4JmrgKqS22VIZ8ftOS+Xquz9bh/ImDiPNDEm5M4AAB196fAu16RAHowHTRtmRA4svLBHzzjbs/UJUlVhGjEZNCInq0ikoEtdcr45ecPDigZhisvCBnMPD8IdzmskZzjN7FN4qNbQGuftpcPnwgaMRlccVbaq6PSX6RlSt8caod6/Lu3rKpmCil0B8rK87bJ8JhTcKhDqvBrhFjNABBMh+qD4hY310K/+q8yVwLAu18FLb0PqpdkXj53AHrrs6IeBgCZuVD3flmKXIhAC9cjenpvoo0bAJzYDix/UOxWtv4GsfkYR4FQO6CyoRZtADKyoV/9PtBQ3evzEpCM32JFAD4gCVGDsvKgHviaKPI1XAMVDwfNvh28/01RuXCpdBhyu/JLn6P8YvPSHcJxhRTWt7CjDXT/V4Hj20WZpKUefGYP+Ow+sRmNmwt0B46EhbziTIBfLwWe/ztofwDIK4Fa8TDIwy6VWdTckJYRKxyi3GKxKG9tlG9rnPILn9wpinEWaq9Cv/kfUE/9Vb8KeZKixSsWxhI/6WnMX3HWTYQBgNoK0LAJYK+YDZFYvu5+RQqqWEtB0LL7Y5ZBauE66NzCmCURTVrQK+VBjkbAm34pioKeMNZH6VnAuFnAiZ3eqzGEENdQLUSkZOST3oIUcP9XocC9Up3kSBh654sytgx1GjuwVnnPAmkJBCMXVEDmTmf2JRJ9Zt0G4qhtq24KqtTC9YlNJgW17lPQz/5v+930+YHGavCeV8XGaQBBKx6WZ6bslBDfpyzpVrGXoxHgyklwS73Ev/pAEKOcAtDtPas49Qgv8geEFHKzTM44TklKFmqJKw4gGcbTKjeQlkjyHzQKPGQM0N4MGjwaHEc8F0Uhn4kHkq36HAhKbFtHbZI/qZjyED3+XeDcfvCulxFTdQ+m26rYKaSQQgoppPBHjluaDAMAOTk5+MY3voFvfOMbaGtrw4kTJ1BXV4fGxkZkZmaiuLgYkydPRklJ36pEUkjhDxXc5aEKAoDjk1d9wdBxgNPH2KA3AWiuq5RJe9UlILdIbFo+DHnkIePAx7aaCYQDloXA5aPuRJRVFabD7oRJV4ckbvJKgCsnjfxsSNaxkqgkxIkYu58Zeu+bklxx2NXwofehB4+BmrK4X+fE7S1SSSEHsZNcoS4gwyTzmmqAifNtX2bresy7q2/HKj0hKhJtjZJYnbUaauE6U5F3Y/2t3vRL4PRum4zgawMe/TOorDwJdHkEKvSL/wiUnbaT54BJLvgl4Vd2UuSumcGH3wfd+QnQuFngkzvcO/IHgLQ8dzKXFGjmKgmwBNKAeCnlqovg6lLvKri2Js8EDQCZqOaXiM/0paPdFEaxXTUEx+TWSYQJpMUSMnrzr4GTO4U8VDwclD/IHchlEwg1FUuoLTcyvQp8ejf46jmoR/+sT6oqrKNii5CZIwn35lpRvvBQRugrKJAGcr4TQ8d5Bu64sw367f+0q3rCneCD70LnFEJNXtT34xYMFiJWnIczjZraN6LX2JnA3ty4YDqBpq9w7zenEHSDCVY1YS705/4PcGSLqPO0NtpqR4CQ4vJLwDteApR5jy4fE4JLR6s8/5a1hS8TGD0dlD8IfOBt6cd8DhWjYeOB+mo70EsEWnJfAhGGr553V6QBkgg8syfhGqBkhBwjYpKMVv9IPvHVHmgvdQdo8qKEaknkD3ZZegzYsRyBdho8Ghg8sMegIeNttTZA3kNmYPQ0UG6xqGwUDAbyiiVw2+oIzPkDoBkrEnfaW7Q09G15EnBTra1oMXaWp8rKzYI+tUuU4Kz+uwvy7gSCseeTMnOhNnxBFMaqS42KWESe3fwS2bZc7gFXXhC7r0e+2WuFt4GAmnM7eNYaINIVS4ToQ+/Jj1Yi3kJULLv4wiHwqke9rcEarrkr5KMR2Q9Dni9z/vroFvhu/0j/Gl1bIUQYHXXb+UXCsSp9dftTiGbnS/8Wj2hY+qjWBvCxrRLcbm+x2+uENU7KzJP+zZIQ9/kBSwULkL+D6bZFRON1sVBb95kbei7J5wfd/SmxAmypBwaNvmlWDTcLlJENWpt4r2nyQnBGDvj0brl342YJYdwo9mHE5H4RprmtSe5D/uB+Kxn2eIymWvDBd8E1ZaD8waB5d8YUdfTuV+UZS8sAokGTaDBkuPj2REJiv+IAEQHrPg2cP2jUmYzsvUUc7mwDXzyU+G1MIYXfB4yYLPMKp+0nKU+VWD6+PUaEiS07uQM8cV6vVUV435sxIoxZAt77uuyjByURDnXKPNaoQwCQopE9r4LWfdpesdUjcQkArKHf+pEoyTZelySi8ts2RINGxYgbvk/+LaIv/ZPECXQUMgcDXGqg8SCSfXV1yNx34fpuLQnFkuMx1zLdVOsmwsQKM9gQpKNAul++f8nmqoA78W/Z2ugIUHYaXFMm83Vnf84aev+b8PWBDINrl91zb2seAEPib6ox1qV/6bJO5OpS6C3PSHxD+YTcseLh2PclmUIInzuQuLCzTdRzbtAC0QkaOh5cfsa9MJAmCi89IJl6I7c2APmD5LlzHmvSQinSOrvPXvfsPrGBuusTsWVq8iKgj/NiPrlDClMiXYmFQ4D9PI+aCpw7AE/LI0CKzra9AMy+DbT6cSHYdHVnvdQz1JCxCXaaSdF4Xea7Xe2OYiKWWBkUEAjImD8epIDpy6EGj4a++1NCgGtvMUqBK6BGT4V+68f2extm0PIHPG3o+VqpUdANSaxK+WLjBz6zH7zswQEd3xCRPNMjJoGPboHe+qyodM1eAxQNFxtYM5blznYpLDMW1QyAZqyEWv5gt8fgplqxIq+rAhUNA81dC8ot6n+jh4yR4rP4c+mLYm5fUTgUqLoYd0AFFPbP6tsJHe4CrpwCrp4Ht7e4Y8mA9F0L7wEarsk4d9gEGQ/++DtCTioYIupYlqKMRVAkQ6KxCjMC6QAosaCLtcxxSkYB9VU2EdCau3W2iZrvtGU3fK4ppJBCCimk8PuOW54M40RWVhYWL+5fEjmFFP5YQKOn2Uxw1/L+W1DQxPnAhcO2tQ0AGjVNgt/dgLs6oF//gS3r23gdevOvoNKzQCP7XxHdK4yZDho7S5RcLCifnfxwEAE43AXd1W4CM5ZkPuz16q/Jf1Yw2x8wagpsEyfySuzAR6hTEsiRuIkKWJLO/STDoKnGTm451T+c91r5RCZ46Hjg0jEgEExQ1+gJ3NoI/d7P7GNFusROIr/kholMXF0KnNplElpmYUcL8OZ/gD76XxPW12f2go9sEjsoAHDWi4Q7AaQ72mmqgDpawC9/Dxg3CzT/bvDRzXJvlQJNWADMXgMc+wBceRGUWwiac7tN7HIFXR1obQQ85so0fCK4qcY7KMRaAhCLNoIjEeDapURFBfcG8o+1TjDdTkxGQhJcC3XKubQ2yDNQdRHspdoEFu/wjOxEj+rmOiHnTFrQTVsce7p4BHrXy0L20FrOy1Qz0rw7oT4kmX++fNzbW/z8wT4F/biuUoJUoS7QlCVCmLISWiWjQCsf8d6uo0UUa3REkvem4o8CaVD3fRm89w0jG1wkssEe1Y0DAZWWCSzeAJ67FrznNamYD4fEAqujVSTbddT2KY+EjD86SeA5PTNGCiQAtGAd+NIxd3Wj8kHNvUOCaKXHwe3NYvdSkOhBHvsuRAyR0G8C7xXngXhCUEY2aPFG8NZnHUQYAnRUkg5P/sVN8wqn+XcC4S6RL450iXrHqkdvWsI1GVhrqcirKReLnLGz+nzOtPQ+8OtX7aCZ8oFWPZpAtKRAGtT9XwEffh987TIor0QCmIVD+38Cw8YDF43lVCQcUxbi2gp5TrpJ5ljQp/eAt78Q+3bx3jeg7v3ijbWrl9DN9aJoEV8JHTU2eXnFwKBRAAAaNAq+e7+UuI+D78q33Im2RmMdedvNbH4CSClbzQlw/3/3mvJPdxZrOUVuZTnn9bHIfkTAqV3gWWtc9jC9hkXC0d6JOa68AH3gHWDYRODikbiKd3N8hiQQwfItKzLtcCX8yD5WZ4sk9iIhOa7yAUvvBU7uknEEs/2t9Zt38dpl8IG3QT0kCHoDKhwqQfg/MJBD3YhryqGf+Vuw1Sdl5ECt/2y3tk1cWyGJ7eorQE6xEDhrrwJgUQlY+3SsYpkmzAMf/SBxbtPHMSl3tomtiGknN14HV5yDevhPgdwiuyo+GjUJYpJn0HqWnMltHQUNSSwKIJ8fNGUx9OXj9vVwIknRQgop3OogpaA2fhF86H3w1XOg7Hyxzhs2IXHl+KSjAVde7D0ZpireZg7yfaq+AoyZ0f3GNeViI2ypQ4CAQNBYPTmQW+S2H3Ki/LSobACIKS9a8QMH2ZTbmmySpywButM3IJL5mSGc0rRlUHNuT75+st0MGmVsLxD3bSfb6igrX/ocz0+/hwUks/3d9Rt101CH7M9l6VQHjoR7X1gRPxe1ipWcY/BISIqVzNyBI2Hot39s3x8dFSWV7PwEC90EdDfWGUDQkvvA18tM3McQpcbNBsUUxbpBEiIPZeWDFm0QVaOqi0Jin7wYmLUG/IvEWAmXngB3tN4Q2ZavnHLYAHqtwABp4Myebop7zMYdLcCh90AP/SkwaQH49F7v+XuvGqahn///oB77sx4JcABE4YO1qLdZ7yFBYnjFw+T3xuui5GQp8Bq7TVw4CN3WCFq0Aerzf+96vqPP/52cg6MP4JM7wbNvc83h9JHN4L2vy1jTUnG1ircAGVfEEyUGAKyj0K/9QOxHIfE2Prdf+plQJ1AwBGr5gzJnN0SY2LYntoMnLUiqEMNtTdAv/1PsPeSaMvAVY4/bR3szCzRjFfjiMWkvSQEXTZzfraomV12CPvA2UF8FKh4BWnRPn1Rt1ML10G/8u2teQTNWxooZ+wO+dlnskcvP2CotPlN45A/aseNBo8EtdSDlg5p9G7jqolj0WS9Tdan0ayseBhqvS0Gnta1lERYJ2UoxXoQ1raGWPSBqnrHvntVQBtIzQXP7/p1JIYUUUkghhT80/F6RYVJIIYWeQTmFoFWPipeoSTbSuNk3ZF9APj+w4XOg8rPg+ipJgAyf1GMSkS8d9Qww8andnmQY7uqQ4FVOASjvxtRHiBTork9Aj5kOfv8XstCqzFA+0BSxJ9KNNeAX/17IDk7yCsOWL7Zkzi0E0hDzzwaAwqEiw7/1WbDyCckgM1eCAc6JCimgLVGKmVsbgGi0Z+n8wiGGiGMFqQK27Uk0IpO7ohGglgaXBRPXVEi1eHoWaPxcgADe/ap4TysldguLN8Ym/Hz5mKe3OF84dMNkGH3tcmJyCwDqKxMCOXr/26Lk4CKQOGXqtZ0YUX53RTtHwRVnQYNGgz7yV+DNzwDlp8Hn9gGXj4KW3g/fmifAoU6xWbpwWBJqQ8Ymypsqn6gjeYCW3geuuuiukrTgC4ilwwv/R+5VepY8Az3JVVuIRmQibD1DVrI2IXCpEVOCsX4iyHOaxAKG25t7JUPLTbVi96WjpoLR3Is0kfjnfW+AB43ql1/07wJcdhr6nf90PN/7QXNuA42bI/1CEmUSrroE7bAn4r1viES4qQijvBJXVd6HAQqmg1Y9Cqx6FKw19DP/Q54P652w7pmLOMdAVyeQYaR0J8wVD/D7viSV8lWXJFCYUygWMrlFUjEWV/3F16+I/HV7C1hH3H1dGBI0z/aWHFYzVyF6ZIuQ+yyVBiIholVedNnNDej1Uj7QsvvBizdIEjPO1upmgpml6vLMXkm2RiKSlAGAklFCBOlDe6hwCNQT35W+OtQJGj09oUKPWQNaS7IqCcGrP6DJi4DLx0UBwpK79wWAqovQr/+bBEc9CXqmXV0d4F2vuJPana3g3a+BNnxuwNrpeWytwa//azeJIYgKRE/wksCGEPaipSfk+Ro/V4KsN2gl11fQhLngg+/I2MtJbDFWDDR+jrcqDAxZbcYKCcIC8l3lTvdKzIBm6J0vwnf/V/vevqJhQhyuvuL+wRrbNFRJ+wF7DGYRVsCO++a4gS31klSKRuwK5GC69CvFI0W5yiIxEIS8uu9N0ML18ltHm/ymfC67Oy47LTYZKfQI/cFvbHIeAHS0QG99Fr5Hvum5Pne0SuLG6kOuXZTxbHqW3IemGuh3fgL11F9K3100DOr2p6D3vCaqTBk5oqTQB4skjkakij6eoBLpAp/cBbX8AVG8q70al7gjID1gj7VjJy02n8wMlJ0Cl50BMrNBkxeLWsHo6aJCEIcbKU5IIYXfNSg9C7Ts/p5XTJJg7JNdZE6ht4VRL/bB6dmicBL7bmgzTyTwtVLQkDHSnsUbwa//wJs8oaPAhcOGDOf+3fke6/d+ZuwrHQlKNkl2f5qQSiwypkVWt8g1gKiH9QO0cB342AeJsRafX/bNLIpvCedGNlcn4bTNAlK2hROMGoaT+FIwBOQPgGsrgOZ6sQLtTtmmeDgwZJwUhjgRb1nkbM/Vc95xpAuHgW7IMKy1qD1UXTBWIybkHcwQZRNrvUvHwGf2gqNhiZVNW9rt+NXzvAqHCJGirREImGNdOgp9YgdUDyqMNHE++Mhmt715IF3UHfOK4bvvyxIb8/nlWnd1iFJr4gl7xm36dB5pmeBkD4XPFDnE4jc9EI2Yga526P1vSTzqxPYbahsaqqFf/p4UZGTmgmasSEqeJ6sIqMOKtZj3PxgEghkxgjtHI0DFOSF2HHzPxNEi4MvHwNWlUE98NzYv41BnAoEEgPRNzXWAsSTiznZ7/KqUTdAOd8WeQRo76+bMC8pOx4gwAORedXXIuDotA2i4JsSy/EGem3PVpeRkmNN7Et/DjhbwmX1im9lHsFFxRkut9Ilp6aCF90B5KIzFtmmodhFZuOIsuPoK1KPfBuX0jsxCQ8dBPfItKYjqape5c0+kyu7Oo6MF+s0fAu1NjiIClr6eFDByili+d3VITOX6FRHavHDIJvTbe5N+IC1D5kjxz4hStsWR1UeRkrmONZ8eMQU0aqoQhrzstNuaRe1oIC2hUkghhRRSSOH3ECkyTAop/AFCTVkMHjsLuF4mxJIkE5++gEgBo6aCHEGEHuE1EE+yXJ/Z6ybwjJ8Duu2pG1YJUJMWgjPzxOqg9ipQOARq0T2gomFg1uCX/kGSsskqiIhEyrOlwZ4IEgkZYMRkqBUPS0XOtufsbU7vEaWKeN9lZpGXtv7saAVv/iW4wigrFI+AuuNjSUkxlJYJWrAevOdVWRBMlySkzy9BOH8AaKmDfukfoe74KGjcbOiD74APvGMf8+C7Mml3SPPyie1ANCKJdcBN/HE14MYn75Rb7B1CUcpFkuFIGHx8a8/tIWV80f1umWeT0OSyk+JlX37a/i3cBd7+AnTJCPCWZ9wBjrxBkgyxlEJIgZbenzTARzmFQoTa8WIiyScaBqzYVLhTSDDKJ0Sp9hb0GEzSUbc6jKvoL57KwnIdYspBPtCqx0QS/NrlxHb3UrWELx219+lMAkXCscAOXzr6oZBhaOxM8O5XEvuPPhC09P63EgKGfGwbaOaabiWY9a6X3MfVUeidL0GNmdGnPoq72sHHtoGvXQLlFklFbTe+3r1GbYVtKaL8hhTjOrIdpDdVabRgHWjCPAAQOeXVj4OjEeiX/xk4uy+2OZ87APXQ10G50i9x5QUJSFnXsbNNglnOZzLcCUzsRnnIH0gMgH9IIJ/fLff+IYAPviN9byRsJ39Zy/tdUyYBxZkr+7RPCqYLMSX+WMzgQ++JpV5XOzBsAtTKRwZkHACY63fP58DP/W957iwFIkCSLhXnuyc01cUnmk27r3lXkQ8oys84fOo9+l8iUREJd8YIf54YMhZwyNQDkP7h6nk7uXW9DGiuA614aGDa3ktQepZU7u97A1x5Udqlo/LM5xULwfHtH0uyxYOUTEvuAxWPAF86KopmFw562A9pUU6JRvo1RlN3fxK89TmR5I9GDGnHVD06lW0CQfle3vUpUGcr+NB7khwLxxEV/AGgaDjUHR8Dl58FXy8Fujrk/IaOB7/9I3nvNMc2QSQEPrsf9ORfitLN3jcSyaO/Z5ZGvytwW5N7HMUQwlE3alF84ZDdFwI2ydv5DLQ1AtdKRY0KAE2YBzVujox307N6/exxRyt4+wtC4At3yrPmrLQFJKELgKYtB2/+hXsH/oDI13c67TVF2YGPbJI+3GEVyMe2Qd33ZbF/rC41dh0sCc1FG26qHWAKKdwqoFmrje2Ko9Akf7BYi/YSau4dQmB3kGdp9PTevUMNpk+Kn99HuqBf+xeoez4HGj4RauJ8RCfMS7AXthuhgDHTxdLD9FM0fGIsCcwN1VLV7/NL3+X45tJ9XxFl3ZPbpR1WW/zu8W9vFPW8QMEM0OPfETJPU62xM/LZFraRMACSv0Nd0i+DbJKpF3xmLukkvgTT3XMnnx+08B7ot34opFFACgoWb4SatTppe9W6T4H3vSVqNulZMm9xHscXADlVh5PN/5MQegGAwyHoN/5N7gmzJJ19PiGer3kiRnDQJ3eCd/zW3q7yAtBwTZQZ+gC+Xibxpbh7yid3AD2RYdIyoe7/qigt1ZSDCgaB5qx1xYFi9jZ1ldDbXzAWVlEZH1nk3cFjJNZxI5iyWCzRveJh/oAoJxMlVfXzRNUlcGa2PS/tL5hlzNlUA5CSeem9XxIb2vhVr543hTtO1SOZnzqfLfL5gdHTwOcPIGE+0N4ssQ1rjmWNRdub5dqHQ7F3hCMhu7io4Zrbnj0tU54/y/46PRvc1Q59coeoNg/gXJTjSfpWO5yFB5FQUtJUtyTF1oaky7mjBbzvTekHMnKgZq3ytLp2tfX0bvDRLfKHX6zn+OA74CmLQEnmXnxmT+IzFO4En9vXs0qUA5RfAlpyb6/X7w588aj0oyGPeDdroKUeNG0Z9C//u1F18duxmEgoMZbHGmhvBRUNdT+RWssjPGKKFGGVn5H+qqPVJs0oH9Si9fL/I0lsyVjL+DlFhkkhhRRSSOGPHL9XZJhLly7h3LlzaGhoQGtrK7Kzs1FQUIBJkyZh3LjeV4alkMIfAygt46ZV2MeDWYuvdEM1aNComFwyjZkO3vMa4mXN4wNh3FIP3va8az2+eEQsS7qpEug1Bo+GeuhPpU3OiUfZGUOESbJdNAIMmwh1+1NA3VXo939hJ2HySqBWPQqkZ4l6ifuMJKjmD7qDgESuSjne+aJNhAGA2gro936WtJIWANTsNeAhYyWQFEgDxswQSyA4kjasofe9CTVolFS7xM4nKudbf02sUpwtPncAvOwBkD8AGjdbJF7jbJ68Eq99BY2ZAc7Kt+2IrKBL8Qi3TGlXu32t/UFvYtXyB0FTlwJbfyPyvrGDqFiindKyAEtC2gnW4H1vSDKXyA64NV0X8ktuEbi9BTRycrcBAo6EpSovEDAJw24ILpZvb7gLCKbJ/SDysNNyrO8PStuy8mWbmgpbYhew284sQdhQh0yaS0ZIpcy42eCKs0ClJQtOYpPSCy/z2P57Qm8lsm8QlJ4Fte7TEgxsvA4E00EzV4s3em/hVdmlzXuRhAzD4ZCxbohDR4ts10v7C45GoF91+HRXXgBfPAr14NdunBDjSiCnuZ9FlzqVeV6IQKOnJe6n9ARQW+5eZgg8VlKfD73vDqZZgVGf37YgCQRBoSSBdgA0ab6LpAdAgjMm6fmHBNZR8HFTFem8bpb1GZEE7ftIhkl6vJM73BY+hryknvjugAVeyVIb8nsQmnpSvsopBLyk+XN7UEYbAHBLnQQOfQGPftf0dRk53VgNmTUnzgcuHJKgO2DeMUroC/n0bvCCdaD0bog1NwFUNAy0/rN2O9qbobe9AFw5IX8D4CunhDRr1K1i2xIJwXDcbCEf+gLeZJiMnG4TUt22L7sAtOHz0J3t4Pd/Dlw6AsAom4U6EFOMAoD2ZqjsPNDgUeDi4dDP/50kHGRPkqSLRqQv7mgBLVwH5XjOuaVeKqmd58CQtne2globgHl3ig1fbbl8n6OiBkjjZvfr/P7oYBGzo2FD+OsEjD2BPvQ+1PIHE5Uk462CrARBfCLO2FZxV7tYG+YU9DmQz5t/JeMgQL5/kU47OWWh4gz0ie2grBxwWqadSLIUFrR2t41ZnpWGa5KgcyLUAT74LtRdnwDd9iR43p3gijNSlRtMB4e7PlRlshRSuFngstOieKejoqzhtF/taJW5S02Z2KqOnwNa+UifxiE0ehrUxi+AT+wAd7aBRk3ttdItl56E55yMAYQ6oT94Fmrj50F5JVDTl0NXnLVVwixYlqLFI0BrngCqLgFZ+W77N6ciAGvbkjktU6r0d/xWKvqtb1A0Yoo3zHiBFKgH0kR3UIVDwR/9bxJDOPSejOMtENkqoRl+aWNnu7TTZSvohBnf+Rx2UMoHrHlSvpfhLrGWunrOJsKY9Xj3q+BR00D53uq+lJYJWvkwsFIIJ/rgO+CjWyWhnFsEtfxhNzFo+ERRfbPI/tZ+JidPtvOZPTKmBuTbFEgHwKCl94GGjotZ3/DhTYnbnt4Dnn9X36xfrG9ZNGITjpQvoc3JQDmFoNWPdbsOR8LQb/6HkDGC6TJOCnfJ/R08VuJUzvXLzshcoKsdNGYGaOaqbt87DnWYuE+SJHqX9U3vA5R5vjraTcFT3zZ3N9A8p+0ttjLt4fdB6z6duOrV8/Jsp2fIWMT6bmflg7yIWkks0ripFnr7b03xSjEwYS5wdIutNmWt9+Z/gB/5lpCR8kvcioxKiRJrRo4cJ9QBVJyV8Uj5Wc/29xc0bIL3HYq77zRsIri51k3aKRoOeMUEnPuOLwAAgKHj5bm0YiTtzdBbnoEi1a2SNHsRD8Nd4NITyWONyewlHSRl1lrmAhk5Sedd3HhdlKUAUce9kUKR2LwiybtRXwX94+841AgNMdHnR2zO4xwakwLGzwIy84CSkcC1y+YZNs9/43UhdddXyj3LKwaaakCZOaJEatkPttTCE/7gH6RlawoppJBCCin0Fbc8GebAgQP413/9V7zxxhuorU3yYQdQXFyMjRs34otf/CIWLOimGjmFFFIYUHAkLCoBRvaWYVRd1j4Nyi0GrX4MvOtlGcyTAk1eKAQG5z6unPIMyHDpCeAGyDB85ST07ldlYpRTBLV4A+BI/HB1qUkMRz3mMcZ2JjNHJrhZeVBP/zVQcU4CK8MngpSSijBn0jfcJRUjVqApmIHYbMcfAJkJMkcjknyJR91VcGNN0kASANDg0bFqGK6rBHsRRZpqpCrcuq6d7W4SRVeHXTkGmOBcFEAAlJkjpIOdL0nSKT1bCBR9qOZL2nYi0GN/JtZBzXX2D9VXoA+8DbVgnayXlSdSro3XHYQPcy5GFpRqKqAWFYDXf1Yqb7c9L9ffH4iROGjmSvA5j0l3NApcPGonRH1+WwmgQchCfGI7eO9roGETQUvvjSljuNDZKvc/EvbM73oi3CUVcVYyJD4pY0H5JVE5eKzYVjTVmqosYxdhJaSDGWLNdW6/IQIFZAL+4j+C7voEfPd+CVx5UbyKh4ztkwUZjZ8riXVLHtu6XlbSV/l6rAAaSNCwCfA9/h2pRE/L7L1XvYWSkXaQ1IIvEJM49oQ/YFeExW+Xld/7Y5eeSCTjhDvBxz4ArX689/vxAOWXgIZPEi9wpWyPcOezZQVdjI89nz+YIInMjde9D9Aky/niEemXI11S6RdMs59DXwBIc9yPbkhkNGct0NEqSZRoBCgeDrXmyQGtkrtl4FSDia9ytXzjB0i1BYBc0/jj116Ffv7vJFg2bdmASHTT6Gngxmr3QuUDRkwC15QbS8URCRXclFMImrRAvOztpVDz7rzhNvXY5qHjpYtOy5DvSTzJAyRkyB5IgOTzgyctkD6hsx0YNQ04uzdxRR0F11fKGKeuEigeLkmJD7sqL9wVI8LYYOjD78MXR4YBYMuRt9SZ6nHzcbOui1KgObf3eJ16AoHBjwe/ngABAABJREFU1y7Jt9cii4JdkvJIz5L/IOMCevw74F/+N1lH+aSfi4ZFAv6tH0tfPWIS0HhdSIZz18IdbXY2QInkPhHork+Af/FfZfwGANDgbc9Bj5gElQoedwsKpIGmLpFxilPtxR8ATu4QdUWjQhbbZswM207AWjfcZSdgAaBwKLhgKHjLryV5oaNA0TCo257qtboKtzbaRBjAYb3VZT/PPj/gC4D3vw2s/4z5O+5bpCNIIAdHQvK8uSxWOWa1oA+8A5q8CHzxMHj/W/YqB96Buu9L3mPKFFL4HYNbG6RoonCo2H0lgT69x6WKyldOghqqoRZvADdeF2vRaMQec145CVq4vs+KWzRsQqzIpk+4dimRXAfIslAHUFMG/Zv/BVq8ATR9ubzLWttJV0JsPknj54pigZelRuFQiQvEj4eYwSd3yTjBH7DnTZZVUk4RkFMANWdt/87PASICSkZC3fVJ4PIxGXNkZIuN2wfPOM6708QCCCCfNxmGGVh2vxB/yk7LOoEgsOc18ODRRlmWJblPyh1LAIPLT3cbw3BCzb8bPPt2IU5l5yVYFJHPD7X+c9Dbn5fEcNBYCHVHiIpXQyX5Hz6zD9G9b8h9yimSObU/vp+Pxmz4eo2h4+S75Zwj6qjMcVrqe20Lxm1NQiooGJT4bSg/Y+9fKRkT6ShQODShgIovHoF+/+f239WlwPWybq18+fQemZ96kqOAPhNhACAtS6y0xpuinGRq0X1FVztAWcnnq1af5QsA6X4TjyPQhHneFqGjprnUkgHI6Z7dD24X+yqur5IxwfDJwOVjsoLPvNOdbeAze0GGREWz1ohinAVLETjOnpGvnARfvwIalKhu0x9Q0TDQ7NtsxRVfwFZttQhtygeatQo0ZRH42Afg5nrpX2etdl0b7uoQdcnLx6W4ZcoSuU4O20caO0v6GI9iIX18O3zdqfYmUXwiIrnWpBKKhGjUVE9CjmVXx+VnoLc9JyQ05QNNXQpa9oBrvsuXjkJv+mUsLsKH34da+3S/ie80bpYU1iWDpcQSs88zfXB6lsx7oiF7HkoKmLYcKitf4retjcbS19xDX0AU32srbFWt6stQD349cTwc6oJnUHLSwqQ2uSmkkEIKKaTwx4RbNuvQ2NiIL33pS3j22WcBQPy4DZzBV2t5TU0NfvrTn+KnP/0pnnzySXz/+99Hfn7+h9rmFFL4YwSf3pPg/8wXj0hFwOjpUJMXgcfNlslSToFb/cOAgume02xyBVj62K6Gauh3f2onglvqoDf9EiqnEDRolCzLKxFCQld74g4I8tvFI+Bl90s1CnPihCmn0MigtksSJdxlzz1YS8AtI0sSxwAwwtjJEElAw4sEES/V3x1yi9xWOs7lrQ2SCLWsUazjMoyFT8D2MB4xGWQClgBAwyfC99ifQbe3yv1tqQdXl9pVBx7g6lIJwKVngSbOF99mD1BeMdifhliCigCwBu98CXr8HJCp9lYrH4F++z+BLlOpZwXdLHueirNgrUE+P3xTl4BHTgbvfVNUc0KdgPJBH9sqfsCXjsI1KQx1upMdVkVXMA1ght78a/u8So+Da69CPfGdxGR9Vp48A50ez1B3CBu5VObkPt85BcCkheC3fyxKOlaFX3qmtHfoeGDQKKgpi4HGauiLh+0gr9X2t38Eff2KKKj0Q3WDcgqg7v4U9K6XhZiUWyzt7moHCoZALe6b5D/XVoBP7pQg6qhpQo7rx8S8v8lktWgD9Fv/4aqIovl3gUyy1RPlZ+S6trdIMCmYJoGWGSv61Eexk/zVi+V9Bd35MQlWXz6OmASvP03eH9YAGAhmOp77xAQxDR7jHfIcPAZ86ZgEWHXUJPzCQGfUllt3vBs0fk5SuzdAAty04mHw4o1AqDPp/eRQp6iEtdSLLP2HYMc10KBgOjB4jJCwrISv1Z8pk4yftiy2Pne0CHEib1CvfdBdcBI8wiFbCr++CrzzRZGBX/nIjZwSAIDm3QGuvmJ///0B0PKHobf8RoLFSoGVDzRlMWjVY66xO61+TIhppceBYIYQdIx1G7c1ga+ckus2ZkbfCW/dtbloGGjmarHgS8uSd8NZ2ZdT1GNgmhuvQ2/+lVgmKKMacXav9M3xwf6sPPCmX4mfPCCV1BcPQz38ze77nIGGJZ9uJcMsqfFIGBzqlDFYTQX0qZ1AQ7UokIU65NwsxQ8YUqrPD1r+YLdWCL1GU40jEGy+b+FO1zeR5t8t5KO6SqCzDTR4DOi+LwtZ99plGdsoZcZeDDR1As1SGcqN1cCVU7I8dh4GOioJiuY6cCAIfvVf4ipPhZTDW54BHv7GjZ/rHzho6f3guqvApWOywB+IKUfxpWMxS77Y+sXDZZt9bxr5+ABQMMRUvIdElW/FIyKdf+6AvWFdJfTb/wn15F/0jtSnI/KMRUIyBPQH7Hc1kC7fdGvMHeqQhPe42WIRacEXkHWs59OJsbOA8472hbvkWNGwtP3oZumHnUSatkbwgXdAt3+k5/ankMKHCL37FVGyMwUdNGs1lIelBDO7yWzW8uPbwHNul6RlPNlUR8Fn9w2YRUV30Me2GvK5V5WCYYb7fAAYvO9N0Pg5UgSy9Tng+hWbFBkJAcoH3v8msOoxUFqGjFHOHwS62kGjp4PzB7uLOyyEQ2KPFA+lgNxC+J76ywE9Z8DESMfNdsUqdH0l+JixzXTaCXMyqQ4GZebJuN9ZPNDVJn1dRo4pUiGjBmbIgzFFyr6RZMkfkPlust8Lh8B3/1fBoQ6xUeqJNO9FPtFaFOis4pqWOnNvya1wly4Eoj63f9gE4Gw9Ys+aUQfic/t7ZeGi978FPrLZjH0INHWJqChZ3w3Pghmfp+Wr3vuGXQBFCgikgS8fE/XmZIUfNUYRNCkZpo/IyJY4zoqHJFZRfUXOb6D239kmZKPWhoS4Ik2YCz78vpAFyJC+/GlJ1Zdo+nKxYLpsxi4+v5DeLh5xrxiNSGFIWoY9B7aI221N9v7m3SH3vqYClJ0PmrLYRU5yobEGGCAyDACoJfeCJ84XC53qK1IcETJzzrRMKVQ07wfd9lTS/fD7P7OVqzsA3vcGaOE9Qrapr5K51NBx4EvHvGMGSdR2LNCkBeCqOGtcfxD68CaJNQHA4DFQd37cjg+MnQWavkIsKa3v0+zbQCMmgTvbJOZrEZN1VGzK8opjxDnWGnr3K3HKtlHo3a9AjZnZryIRyimEuv0j0G/+MHFsGCPAQLqF2P+PmvlVUMaWpISct+AuqOnLpcj0vZ8KiSa2DbvODV3thkwTAZ/aLWpbTpSMEktZHbE/f4EAaPE9fT7HFFJIIYUUUvhDxC1JhqmqqsKqVatw6dIlMHNC5SF7VXk4fnvmmWewb98+bNu2DUOG9G1Ck0IKKfQR8ZMZA666FGPrUyBNKmeSYexMYG+i8gJNW97vZvH5A4nBA9YSmDBkGBo/RyooaiuMNL4JElmVo/4AoCPQL31PAicAUDgU6q5PxhK95A8AIyYDx7cln+RbErFZeSJ9CYCUDzRxAfj0bvc5D5/Y6yoiwFTkLlwP3vmS4zwZaGmQSlQrCQDYwfhgugnWR+RcS0YCC9dBb3sefO0yKLcINOd2oHAI+O0fxoIkDICmLIHykPPVB94GH7TtovjQe1D3fRnkVVHd2igSnvHxsmgU/Pz/EUuDSFgmekPHibXUyR22166FjGzX5JWyC8CT5gOXDhsPXQYqL4Cvl4OWPwA+tVvutXU2plosNlOMhoGicTJB72yLTbYRSANa6yWp5vQyB4zCTmbPFklO+AKybysxmQztzeBXvmfLFlvrp2VKkGnW6pgkubZskJz7Y5Zk54F3wCd2gubeDrV4Y+/a6ACNnCJqLJ3tUpWnlJCQ+hg4YGPXEnsvS08AledBaz/a5zb1FzRsPNSjfybJtXAnaOws0JCxSdfnmnLod/5T2pyWLs9lNAJa8TBUL6XaY8ceMtab9Df0xqyBOBoB739LiInRMGjsbHAkDJQes59jC6EOeY+UDzRxXuLOhk+UftEZBCwYApqxCvrtH8nfwXQhwbCW/5QCZq0BRULgrg6R5J7eu76bAmm2QlL8ebU1Qb/yz7FEPh9+HzRlMdQNquj8LqBWPgL90j8a2XRDhBk0WkhDM1aAjEWXPvSe9KPa2LRMWwpa/lCfFDho/By7L45Y5AzbdobP7AUvuLtvMvBexwlmwHf/V8DXrwhRbOg4IX44FVJ8AfDpPUJINOMBwHz/ZqwA4oLTfP4g9Ae/kUAmIIpsG794YzLW1r47WsBn9wM6Alq0ASCAzx8CrpdDqjx9QDQMvekX8D36be991FZAv/J9k/hi2+4gLVPGEBYxFpDnevgkUexyorVRqkjn3H7D55TQvpYG8N7XJIidmQuavUZs5EpGyZimrdGdoIyGobc9B1J+8KmdNpnHUixiLYH/iEl0TV8BteDubolufUL+IKM4ZkgqgaD0J4F00IQ5oIkLgJKRiL7yfQnsQtT61O1Pw/fQn0Lve1PIFE41Eqv9Ha22wgdrIZFGTPLCJJzQ2gD96vdljHH9iscFhRDTUugRpBRo9Axw1aXEHwMedmoA1KzV0Hkl4M2/kvemtQEYPQ205kkoQxbTXnL6rfVChOoFwZevnLJV0gB5X/1pxuYrbgwTzAByCkFrn5ZxffkZUYectlzsPyrOSD9hPa/+INT8u8BKSfJfR814m4w1ByQpGQ3byoNWu6ri1AtSSOF3DL5yUtSdYgs0+OgW8LCJoFFT3CtHwq4EcAzRsHxnkqlAdDfnGSAwa0m8+4Pme+dISgI2IcJ6R1mDKy9CTV4I32N/Bm5thH77xzL3NaQPvnRU5m6zVkG/9oPYN4ePbJZktmdRAwN1VUKUqClz/dLX+Aa31IN3vQIuPyMWILNWQ/XSWlMtvR96xGQhfKp0E+8Ie69svvtcejxRDdNp82SRHaMR+7tq5sy8+xXoQFAKNQYQ1IN9ZWy9GSukP3Ym5Fm77W2Umds7J2XKB7Xy4X4pVFJGFjgzx7Y/tr4tMaW55ODKC+BD7zmXgE/vFvK/pdw3copthew87vi57n11tslYxpr3WUVZJOOdpCqo1vKk1ll9QCADmL0GNGgMMHiMxArWPAEeNAp4/+ceioz9RGcb9Nv/maCMQ8EMqPu/Cj70viiv5A8CzVmbdB5BPr8oAzZck7H9oNEyT4onwwDyzGttikzMwxPuio37+fwh6B0v2P3cuDlAwWDQ4NGJdoqgASXCxPZqCpT0rpdl7OUP2nP1jh5sbCGEf5eFu7X85A6oeXeAnOOu4RPcY3irDaOmdnsMNWUxdGsj+Pg2eT4Lh8o+nGo/1aXgrc+C7vmc7JMItOIh8KzVQtovGhojQvGVk56W43zxCGDFajpavK3LWhvlt34WWdH4OaAnvgN+7V9lX5ZSdNCy6TLFSDFyjFF5iVO2xPFtUjjT2Sb/hUNIGleMRo0lMYEvHAJPX+aKt6qF66DrK+WaGPtqmr0ayqMgNYUUUkghhRT+GHHLkWHC4TDWrVuHixcvuoL/q1evxsaNGzFr1iwMHjwYWVlZaGtrw/Xr13Hs2DG8/vrr2LZtm1SqMOPChQtYt24dDhw4AH+8BGYKKdxk8NXzwsrPLhA/0j9kb/jcor4t9wAF0qDu/RJ4z2tCRMgugJp3Z2LwrS+IJql4ckzCyR+Auv8r4BPbgeoySep1trkJF1oDzbX2svoq6C2/hu+BPwEgyTFcPCKJFsuHW/ZueBZGFtMXkATfpl9Ahzqgpi0DLXtA9nFOiDs0ZkafK/b54hHg6gWpvLFUX2rK7ElhIE3OmbW0KZgmE+NAEJhlEmW5RdDP/52cJwBuuCYBt0kL7Woh63hn9oAnLQA5yE3c2gg+9L67YV3t4P1vge7+VGKj07NNJVZ8MI5N5ZqWhEW4E7jcKkmEYRMSklVOmWRuqBbp08rzMhFVPtlOKUkId7aDxs6UYEfUJPItSX5SgA7L/w+mScV/fCBJKXCoI4G/w1ufsyVLw51uuVOvgJLyCZkgM0cm88lUYQBb1UcpdyA3EpYEn4M0RSOngHe/itjE2bm+jkr1yJHN4DEzulX36Q4u/+WuNujmeuD4VqMGlAmasRJq9m1Jt9dWkt8BvnAYPPdOUKGbuMqhDmlv+RlQRq7I+o6Y3K92J5xHbhFoQc+VegCkAslqsy9gWzhoLcTcSKjX/TsNHQeasthtY1M0rHu57960cd8brgQGXzgo73gkbEgVzuokNtVUa8CH3ofuaBFVqNlrhFhHBF7xMCiQDq4tB4aMBybMFSJM6QnZPhCUdzgqzyctufemWNzwkU22ooW17Mxe8LRlCfZOPe6rvRm8780YSUDNXiMWZB8WQp3S71jqWz4/0NEMmr4sVvXG1aUuKw2wBp/cKWSp8XNkfHt4k3yvutpBo6aClj+YWBU59w5Ruzh/KHa/4Uwi6CjQ1tw3GfhuYCmp8LVS8IVD7h8jIam82/8W1KDRSdXCAIDDXdA7fuvuI9qawHteA6379A21kZtroV/+Z7dM+bg50gf7fNJvh6UCHHWV4MbrnoFzvelX8kzGLPtIto1GAZ8PtPGLoMbrcg6jp8mYxqtBTcltX/t9jjoK/ca/idoKIN/gD34D9gclWLtgHXjzL+0NLCWW8wfBpBKTNgxZZhGDA2lQS+8FZea6V+toFenu3EJQHyuqoXxA/mCg/DRgrCSRlgl1z2djKlDRd34CXD5q92GhDuj3fgL1sb+RZGw8Eca+INIHRsIim8/aVmayzp1IzjHeOs95EfqjzvRHCpowV9QinIlwUiKx7wGORsBbn5V7aJF9z+wD118Db/iCkK6SyOknEFm89q81eNcr7rEYM8BRYMUTwG73b7RofUyJiqYuAaY62r3gLrH0ioSAoCgJ0LRl0sbVj4NGTRU1ioqzxlpF2e0MJ44FKa/3c6QUUvgwwKUnvZeXnUyYj1MgCBQNB+riLDLSs8CtjdKve4DGzvJcPqCIRuVb7/PL2CfcBUASgsgplIRwnOKcZQfFoQ7oQ+8B5WdlDh9Ij1np8MXD4I5mmadaxRJEMu9MlrQMdwKjpgLZeUDFedvmpw9kWNZaCgmaaqT/aqgCb/o5ojt+K9Yvy+7vUS2Tgulg65zTMoAQmQS2aXcc4Zqy8qQwxbWQ3P9a9sJa2+pewXRYSq88dtYNKfz2B9xQDdSUgVY9KrGRxmogu8DMkZzzYg10dQKTF8pcIBIGjZ3Zb9VRGjNTYjlxyr40pmd7aT53wCTQbdI6IO+jNU+hYDrU3Z8UsnhLfcyimOKsxPniYXnOo3HfHB3tlnhB05aDz+yXd6OXdT2yYRzRTPnF+uXIFrDyya6C6ZK41zxwRBgycZG6q+Ca8sQ5YVY+UDxcCBVXToOvl4GmLAHNXAkKpIGjEYn3ZebY1lyZuULmPbsvaQyTJi+SIrpOB6nEFwCXnoCuqRBLMsd7w5eOiD3q/Ltk/tnSAICFGzFxHpDb++K3voDLTjsaDXs8UnbaJodAxmCovCjP3dCxci281LKBOOVEs+u0TKg1T0J/8KxdfDF4DGjhuh7bqBbcDZ67VsZ/kTD0r/9H4nmUn42pV8aOmVuUcH9I+b0fW5fqk7FcjVetcVix9heqZCT46f8KfXYfsOPFGIlSCqgc76JlK23FIGMEHgaul0G/9UMpEIuEeyDSsbxPREB7E/Qr34d67M9i/ReNngb14NegT+0GGq+BcgcBU5fe0DmmkEIKKaSQwh8SbjmWyPe+9z0cP35cEjLMWL58OX7wgx9gxgwPj16DO+64A9/4xjdw8uRJfOlLX8L27SJJevz4cXzve9/DN76RkrdO4cOD3v6CJFAN+PD7UPd/td8T7FsdNH25JHedk6fcogRJ9B73kz8oacKLmYGKczLhLRwi9io9BMJp/JzE4AeQKNWelhmTsOXGGrFQseSO0zKl4t3y2w2HZAJTfga6vgqqcKiRUmeb5BC7Dg6pYGeVEOSZwLRlIH8AtOpR8PIHZbUeqpG44Rq44rw8S6OnSdXLIUsBICSJBEv+3bIUclZCsRa5VCVECjXvTpF8Pn8oRoSJQUeBC4e921F10UWGQW2FJ/GDr5clLANMEHXKEuDYB46Vzb9Kuas7omETdARo3p1yvf0BUagx6hPMLOoddVft6hRtgqHpWVKpdvQD+xytRAVr2b/PL4ExH4n6i1U17owNRqMJlS4c6pBqFEACYL4sxGyPLOn8uGqZmPc6s8gIt7ckr8KKEXI8pvjDJrjuARUNA82/W2xQvPbX1Q74csDlZ/tNhgHMOX/wLLj0hHk3EFMa4j2vQSt/8mrFZP7eTTWAgwzD4S7oF/9REtVKgXEVXH4Gav1neqw26tU5hEPie62j0pd0F6z1qmINd4G3vwDe/GuR2C4eIdWEo6b1eGy1+nHw5MXyDuUVA6On97oKkRtrhAjR1igKLlOWAD4/+PTexJW72t2EQCtgmZ4JlIyU98E8J1xdCq68AN99X5bq05e/Z1eE1l6V91SJPQoiITuZ7A9KQDZJovNGwdUeSg1meV/IMMwmkVBfJQvaGqHf/zkUKVC80tMAgtuagECaJCHO7IWQIh332lJtam+W5BEYMUUO535Kj8v37Pg2keqPLT8BbqqFevTbbgsinx90+0fAS+4TcoR13hYycpJXh/b3XKMR6AsH4eo0rX5LR4Frl6Ff/Aeoh76eXJGmptzzfeOr52+8fYc3uYkwgEjmA4aA6XhXTAA/nvioj20VJbz4xDqRJNezi0GFQ0HFw+2fh4wFziS+nzQ0uRpVv1Fx1ibCONt9Yjt84+cIUWHXy5KMsJIuRMYyMAB7nBBrPWJkXpAk3eKIMPr4dvDe12IJDho7S2TYe9GncXsz9Kv/IsorwQx7H1OXuO3Qzu13fwNZi7pO5XlwxdkeKpnNdu3NkgCouyqB8EC69F+dbT0mZ2jZgz2eSwoCysqD2vB56N2vCkEqrxhqwTqR029vlu9XXZUkh2aslPvR0SLjr05jhwkA169Av/p9qEf/DDR5YVzVPMTidHDPFdVcU25blFlkcQBQfqiRk8GhO8FHtsg3bfR00Lg5yc9t8BioR74FfXwb0FQDGjcbZJQPLGsSVTAE+rn/7d7QSsA7x7XKJ6TFFFK4lZBsLBynamRBLX8Q+q0fyjc0Yvej/NYP7e2synjlA81dCxrZN1I7tzWJGmNfCmz8AUn8X79iFEwMCSQzF7TmSWmf85uRmQd9ei+o4hy47JQpiJCENbragEhAxs5EQmjpaLW39/kBXxDwtGOC7GfPa7L+5EVQKx6OKQH2GlfP2992S4UFAEId4EtHwI3VoLUfBXW0SCLay1qyeITYAlpj+2C6/JeVnziHV35g4T1Q169AX7tsnweR/Ob8vqdlyDc4XgkoYkimNzBn81IH7w5658vgE9tif9OYGaD1n5X5jud+5JwGQsGGxs4Ezbld4k46KnGK+XeDhozpvs2HN4FP7LAtVqxCHiJ55pzHGDYB6sm/EGudjGxvq03L7tlS77RQPKLbuS5lZEM99HXo138gimjdQfnkeQimO9TQDNjY6FrECdbmmUvyftgtsHbQ/bHtA8m7mZblWfzGe1+XWJtFfGiuAddXgSvOSmHUoffkt5wiqOUPANkF0K/9qyOGR1KEZanuAsDQ8aAZK8V+Jz3LKG4ouR46CjiLZ5xtKT0OmrJI3r+mmti7whcOgWvKodZ9uk92071CMnKHoyCBq0uh3/mJPTfKHwS1/jOiFu3sKwxozHR4gcbPgRoxWSxz07NBvRibxbb1+cXStL0Fns+Iz+cmtCTDmOmeRBeavMh1rFiczLmOsWK9UVAwHb6Zq8AFQ6Swo6nGQbp0nFfUiifHnWs0CtRXgz941syFPd6FePKZVaQV6hALQmdhUv4gIQNeKwVfKwXO7YOefZun7WEKKaSQQgop/LHhliPD/MM//ENs4vPggw/iN7/5DQIBj0mdB6ZPn45Nmzbh8ccfx0svvQRmxt///d+nyDApfGjg2goXEQYA0FIPPrwJtOKh302jbjIopxDqwa+LlHLjdVDJSFEZcLD4bwSsNfjdn8RIBwwAQ8ZCbfiCd8DHategUaCVD4uEfle7yO7Pv8udZInfJr8E6vHvSsIrGgWXjAD/8r/JRN9JKtFR8Ls/BT/6bXdQyB+w7QQAd/DdWS3U0iCVZ9cuAzlFYpPRQ3JSH3wXfOBt+xrkFQMtjeYisZ1EjNkcaBOMcFR9Kb/8XTgU6p7PxQIj3NYAz+xfkiAU5cQFJZNZWHRjbeFb+zQ0M/jMHgkeBDOlmii+EZqFdFF+FjRtOdTj30ncWW2FTPg8qlZi10VHEbsWkZAtG2tV9QfShaQS6vQ4b5JESVwi0NvzhmTiXluRSISx9hXMsL2AeyNHzNq2YmINTF0MWnJ/wmpq0XpElQJ2vCBBmrjDIhoRRZobAO9+VXy1ddQEvSDX3QRY+NROIAkZhgaPsT25YwuVBF6s/V86Kgo/rQ2ywKo8BEMf3QLfDZJhuK5SiBFW8CeQBnXXJ5KqztDo6YbwZhDqdAd9o5DE3ds/hnr8u72yD6EhY3oMkHq2+5V/to9degIoPSnywV5y5z4/MGKSw/OcJGjtC0hyMP65q7oIvnZZpMWdATDL4oGULetr2Zj5AmLT1ZtAVT9AeYMkmRm/PL+kbzuqOJ9ICIEhCdwEMgzXVUr1Zm0F4PNLJaFXhVc0KkFwy0Ik3CXXNV49JSgB8XhLPQAirXztsqcNIWXmSMXem/9uBwh9fqiVj9xQ4I8brkk7i4eDSEEf2yqB5dZGUdjy6hf9AaC1QZRuFiSpGMzK915uVWzXVghRdfCYPpOLOWaP53HMeCsAIrHXS8+WxEp1GZA/yEimxwUiATnfQJpc1ziSLk2YJ8orDkIPDZ8ExEnbDwi8vn+O5ZSVBwyfmGhtWTBE+ltS5rsRd36aQYvXxSrZORqRIP6VU8C5A1I1b6pO+fIx4ORO0KzV3TaVr5wSAmtbo1liEj/KB75wGFjxsKzXUu9NVtFRqWhtb/augk44oAbKT0M9/f9A//b/CjHTSzkrHgvXQ43umeSYgoA7WoHCoTHlRMCQEY9tA+94Qa65PwCUnQJfOARabuZFkXg5eBKbyPMHJLjf1QE+u1e2HzIOavXjdjV3d7h2CSCfjFXMbuVfEiWrA+/E/kbZKei3fgjfQ3+a/PyunJSq8UhI1D8bqkHL7LEYFQy21d+MTSUCQeCuT4EaqkQBKSNHxvw3wR4hhRRuBDR1iSR6nXMXfxA0eaH3+kPHgebeISRLIrvSPS1DxqBd7UISm7ESyB/UrTJcPLirA7zl1/KdAQMlI6HWPg3K6934T614CPrN/3CMfQJQqx4FjZwMffcnwTteNCpvLDbI7U3gmPVEHKJhmSeOmQGc2eceP0cjxnI4kNx6yFIvO7UTuroU6qE/7dsYJuwgCbuIB6bPrLoI/s3fgs18Sa15QqwpHSCfH2rtR6Hf+5l9jsUjQHd8DLzjt8Dl42YungG642monAIgpwBKR8VupaHaFP2kSdLZnwYqHAKafyf0theE/KgjMhawYjP9tOTgxuuiLHP1vJA+Zt8G1dOY4up5FxEGEMI4zu6XPtgrnqF8oAGcv6jFG8EzV0sSvGCIW03Vq81NNRKj8vmBsCECWIVXGVkgDyUHItU9mX3wGLkHGdm28o8vAOpGtTW274xsqNuegn72fwvRxPWj2Scp4IGvAq/+i2lrkqKdhNhGMpKLKRYLZsg9iietJ9vGgiLAWJ/HjhTqlH4snqAVDgHlZ0ShxYrJtdRBv/tTIYu5FFEYqCkHPfg1oK5KyHjDJkiOIrdYVAnjn51kajKBdPDu12RO6BzPdnUALfWiNv3It3px3r0HjZ8jMUPn/Eb5QNPFmpZZi9Jla4P0KcxATQX0thfg2/gFqDs+Jn2FdT+KR4CWJsadYsdLy3BZ4fa5vZk5oDHT5Z11Lp84v9tYb2y9QBrUPZ+D3vGikCDTs6TfiPt2qRkrwPkl4HNiv0mT5g+Y6nCsLSMmSayyowX68nFgx2/lByeJ0iv2R5A+tLHajkfGfjPzFL8ppouG5Z1xXpu4d4ePb5f4gHPZ0S3g8XP6rK6bQgoppJBCCn9ouKXIMIcPH0ZlZSWICCUlJfjJT37SayKMBb/fj5/85CfYsWMHampqUFVVhcOHD2Pu3JsQeE4hhTgkr2Qv/XAb8iGD8opFkvZmoPS4rb5h4dpl8Nm9sUldMqhpy8CTFkqgK7ugV3YmpJQkjGDmJRPnJyrM+AIS7Lh8HDRxvlSdWxPctEyZ4AaCxgqj1cjxm8A9S7W10w6Dzx+EeuBPEqxiYr831dpBewsN141ce7pMnpzwBYVY4pxs+dMkiAWASkaAjPS/Pr0HfHgz0NEsSYNgeixIQJMXSnLcGVDIHwzEJZApfxBo0gKR+rWgfFDz7/I8n9gqd3wUvOYJsVlqug5s+qXbzzhm7QKx2tj0CwnoxdubKF/yRKCOmmvPdiIfkPuVkS2Tya52QyjqsqsBCWIFw7KdWrQ+YdeUlgEaPc2evEcjJuipEhOssXPSEhCJBZa6ScQ5EQkBWQXAoBHA5RPgM/sQzc4H7vg4VHqmSRKPhm/B3Yie2SvJfytoagUB0zIT/MX7CrbUguIr9XVUrm988MkBWrheJIgdwWaad6ctT97ZBr351+4AQDQi+wykeXs99xF6+wvugEG4C3rrs1BP/hdvtamJ80HVpeDTexIr4CwwA12d4PMHe22/1FfwkU0J15YrzgIndoiXfHwfmT8IuOfzwG/+X/O8meesYIgEBKNRecadwbyWenD5Wft9iDoIZKxFAtkXkESHSZbwmb3gC4eg1n40Ifh+o6C5t4OvnHCf99DxwPBJfduRV3ID6JV3el/BOiqWUtazGo0IQdZLKj3cJUHgrnYARqmDjYy4RVZRPrHrAJJLJncjpUwlI6SStPQEOBKWStl4Ul9vz629Bfrdn9iWMrnFUiW5+xX5O2ZBA7hII2mZ9vev/lrytuYVSwA3RuAyy2etgX77x/Y4QPlAC9dD9cFmgAqHgmuvJv5QNFQIRVbyz+cHgungqksSSGysluUVZ6RPT6LSpe7/imeij3x+4J7Pg8pPg+sqpfpz1NTeJfL7ihGTpf1x5BHne6luewp60y/sezh0PGjZA+A3/k3GEZ2OZACR3DufDzRkLMjnB7MGv/Uj8NVz8tyFO4EISXWu6T/5ykmgm8QVay39sCtxaAi96VlAqBPMGkQK7BN7pgQrJF8AatQ06LwSoP1iN99Ro/BGSsYI1aVQizdKkF87vjOBoCgbWGMmfwCYeweUIeWk0D24qUYIgNcuG/uGBaDlD4H8AfD238oY2kroWkH85johJRUMSQjYxwL8bU1SzbviIfCSe8XK9PxB8J5XwaOmgqYs7j6ZmZ7lUGx0PB/DxkuyLB415eBrl0FDEpWbuKbc7usAIWQd3woePNpluUerHgMrH3BkMwASgtmWXwG3PSnfovYWoKUBXDLy5vQDKaTQT1BeCdQ9n4c+8DZQVwkqHg5auB6UWwxmDVwrlRWHjJH+OdwlY1N/wD1eDHfZY5iKc6A7P97ntvDOl9xz/5py6Hd/Ct+j3+7duZSMhHryL2XsU18Fbm2APr5dyNFlpyQJrLWd9O+JIBzuBKouJS0SiSnAxhciOKE10FQL3vsG6PanenUeAITYHkiTb1i8KkCoS775fkOq7mqH3vQrqKf/OkEJhIaNh3r6r6W/DaSBLBLBhs+DG6/LGGfQaHfyedBoV8EDAKCzDbT8LiE5AXL/neSJcBcwYV7SmIYTfO0yuKFaCpiKhonK4Bv/bhdEtDeDd78CnZYhts7J9lNxzvuHirNSGLX7VblWse+AKUwZNqHHNvYFlJnT66ITIUmzPDfpmdI+S1Vm4T1yz3vaB2sZi7XU2yR8ywraqODS5IWiTNKb9hcPh9rwOegtz8jYGLBVBDvb5Pve2gSseFhIVP6gua7ofTzDiaHjZE5pFT70ah8mSEMKKB6ZOHfvbDVk5/j9GBKBLwDAMW7QUSGJx/cB4S5QJGzPwQzUvDuh3yp1qcDQmJmgmavAJ3c6SN7mt+nL5d5onUju1lGxZm1tjMVCBgIUTIe67yvgA2+Bqy6LSt+8O23VloZqub/OsbWOApeOgqMR0NBxUB/5K9NXBD8U8i6teRLY/YrEmUiBJi8ALbmvV9tyawP4epmQXYZ/CpSelVRBnEZMHjACDJeegD76AdDeBBo+CbRwHSgjR0hTmblQUxZDXzwiaktONTHlc5Mc4SiehFHetUhKgIlHRoHB42S+cvV8glUojYwrFqv0VlXlinMpMkwKKaSQQgp/9LilyDAnTths4Mceewy5uf0L2Ofm5uLxxx/H97//fQBil5Qiw6TwYYDyB3mLRfS1kj2FGDg+SG6h6jLQAxkGgAR1CnoOyCTdfsXD4MvHbYsXv/HEhlQtqwlzQXd+Avz+z4V0o3zAuNmgBXeDQh3gjjbw9uclOBHqMLY/kGrqoAlUdbRC73kVvns+59kGrrqIhKoa5yTIGVAnU7kVMcusib8juEUjJJnM5WfA254z55Umye6udgl6DZsAWnQPaOpS8LEPwE01oCHjQLNWe6oK0OongGETJciYngWatqx3sq9EwM6XJNloVdIm2B6YBHFHK/jtH4Ef+bZbhrXHCjuTaCMfYMmwWqowU5cCRzbZCfMYAYdkIpqdD1q0Iam1EK1+XMhNV07KtVN+uf49JdpZyzqxc+0h+OMLAKOmACd32temoRp4/v+DTs8ywSoFWrAONGMFeO/rMmEOd0kAJpgOeujrN+7fbj13Pj/gIWnbnT86FQyGevw74AsHgY420Kip7vtYftY8r3HJrUjYWPIoRF/5ZyFfzVrTo5pSPDgcspPATrQ2Ak3XPfsJIgKtfAQ853Zw5UXwOz/2VvxhTiSlDSDYaTHFHPPY5m3PScI6M89WfLGeqRf+j1S+BgxhjkjO8dIRO3CtfBKIVH5RWqivMgpESZ5HS52IzPvhC4jX99ZnoUZOGRCpYQtUOBTqoW+AT24Xn/XhE0FTl/ZJNh2AkGecRDhr/wNguZWAqkvepK26SrF5O/qBnQyORt0JhGhY+uH8QdKXFAyBmn93zHaHxs5yV75GQkA4DP3q96XPmbwYavHGhGpUCmYAkxYmCH8lA4e7wPvfMpZ0QUk4z14j0tLO96e5VpZZ94NIks/hLnlfw102KdRCDwE4uu0poHgEuPQEKJAGmrZMqoQvHrElyf0QCfRR03qVbAEg1etXTrkqP2nMDGDYRHDZ6dg3PYZwyCbCAO53whmsD2ZAPfqtbp97UkqUxUZPFzLJ4c3QZ/YC0bBYrSy6p1dE3R7PMSNblIC2PR8LstLIKaC5a+11cgrge+BPwM11ABEopxAAwPd/VWwXT+0y77XfVFMbgktznajqlZ021kRk33dmm5gLeEv3O9FcJ8kCUu5vvRZ5fRotZKHo/reAQ+85yHDmeD4/MOc2sZlc9gD4t38POzkC0yeFEFPmY8SIwEjLBA2fCPXQnwohp/yMsbnwAwGWZ2zqYrGyGIB78scAZoZ++8f2GFlHRRklmAHMWGlUUuKsA8JdMiZtqoFa/1lRr6q8AIDkuTOJXWusCgCoLgW/+R92P37lJFB5AXTHx5K2jcbOAue8Kf2GNfYOBKGW3Ae9+VfeGyUZu/Hl40mWHxMSXyQcsz3AyZ2GBG+e2bZG8Mv/HHsO+ew+0KSFoNueTNr2FFL4XYCGjoPv3i+5lnFDtbzjTTXSR+YVQ93zWflOWuR157jM9OUxQmUfwazBFw8n/lBfBW64BurtnD4QBJedFlW9WHGA+eakZcq5WN9yTyVPB7S2VRQ8f49Inxfq6J4QE+oAH/sA0QuHhFQ0eXGCekEColEgpwBoqnUkVAOOuSa5ie2RLuDqOWDc7IRdkc8fK/hxLc8f5KnmyldOwlPFoPSE9O/VpTIuTMs0xGyW72nR0G5PiaMR8Ds/AZeflr8BiRuMnGITYZzrn94DOMgwzBp8bBv43H5ZkJVvP3NOZOZKInnQKBl3OLgwyM4HMrIl+T+Ac5fegrLy7Bm08gkhJhwytsOvgve+LuPvlQ97Eif1qd3gg+8IiSnUaY9lfD6AFTBpIdTCdaA+KvTQmBnwffJ/IvrWD8U6uqMVsQunGbz9eajH/gw0ejpQeQG6rQH44DlTFEPdP/+xgxAAAi29F5SWBf3mD+W5DaYnL25yIpgJ+P3eVrc5hfJfqBOIOO2CFRLeFQvW3MXVRiUxvEPvizLM2JlCzh0xCeq+LwvxpaNNCO7Tlkqx1r1fFAXdqxeA7DzQnLWgsTOBbc97jC04VnDVG+JTX0F5xaC1H/X+MS3Tuy+LRsDN9aCCQUn7ipsFSssArXkCvPpx+buXc30+tx96qzx/DIgd3sYvDrgdcMJxy06JzZR5N7h5N7i6FOrhb8aIOOTzQ238ghR6ndkjz6BV+GKRXcjE0pRP+nq/6YvSs0yRngbGzICafydo0Gjp+z541u77IISrhJhGErXVgSRdpZBCCimkkMLvK24pMkx1tR14nj69/1J78dvX1NTc0L5SSKHXGDYh0WPWnwaaszb5Nil0j9wktiO9sCMZCJA/AJq1KlGZBQBZFUUV52RSk2GqgSrPAyezQHd8TOgCIyeLXHN1KcB+W1a5s9UOxJ0/CP3Wj0B3fSIhKEM5hd5UicFjbB9x5TcSx+kysQoEZRJccVbUXax9jZ0FTJgHAK7lCKZJckJHgGnL4LOUfgqHgNY80fN1UlJ9hJ6CenHgC4fAFcaf2umRPXY2cHJHYhCuqwP63Z9APfVf3NcpLVOqluL9mgPpJgmiJSETTJfJpTI+xEc3SwCEATexg2XSWjQcavry5OedngVa92lE978NHHzHEYjryR/bcZzerBfqMNfDY93OdiArF2AN3v8m6OFvgtpbwKd3yTkWDoVa/RiosPvgZG9AkxaCT2yXP9IygVC7XCflA42YBPJQ0HFtn54JMtWEHAlLUrj2KlA4FMgw956U3LewI8geCUkVEympJLx0VKTGeymZDkACDB6e0hKE7F6+nXIKgQm54K1ZQGsTEu6Zz3/DqjvdHn/QGFG2iEZs5RZA3tdoWIgwqx8Hdr4s162xxg68pWUgZqV2YpuoOFgJAR0FQl2gVfdJ0jIQNJVryZ5Jtq1FdFTenWCGXNPaCumTBvK880tsK43+7iMzR6r1nWoUQ8aB5t/Z/YYDCQLUwvXgWatFFUD5wL/+m8T1dEQsAD2+b7RoPdBSBy49KSQaq8IwFJV7fnQzdH0VfA9+7Yaaypt/5ZKq5r2vS3A+Tr5ajt0pQVyLJKcUkJYBWrBObEScY6GCIaBpy7o9Nvn8YsdjVF90TQX49R+YILUhO0RCQHoWuOxU78kw+YOgHvmWJFRaG4TYMWEuEA5Jf9Zca6+cXQDKzne/AVq7kywmkI+SkX1KovD+t8GH37f/PrEdaG0E3f3JXu+jO9CEuVCjpopMeGZe0utDuUXg+muS6Bo0WgiGtz8FHUyTAH/8+kPGQp8/CH7nJ3YlqZXsZ7aTH6RA3XwvAQjZ1rKUCKbL/tiQWQqHglY8hOixrUKSjUmKm7uRWwSauVKqpgGoUVMRnbIIOLMXkliAbJM/yIyNzHahThnLWMSyomFCwnjpH+0xVDRixgV+kcDvxubxDxkcjYDP7geqLgA5haCpy2JKgp6oKbOJMM79nDsgwXnWxqLTkWw295UGjwblFEA98i2xKig7FVuFpi13Ve/qQ+8ljO/44hHwvLuSP+fBdKh7vwze94ao0uUVQ81dK/Yuo6aJraMT/iAwbAI43AWUngRHw6DR00AZOYmEOQuBNHCoE/rVfwHqrtokR+UzqljGPiYatUlZkAQOz1rdO9J4Cin8jsA1FWJdGrN+ZeB6O/Qr34d64rs20Vgp+/+Tin0vaUYP3wNPkCeBGUDfbDmvnBLyfSRkz/EsYl5Xh62m0huw7lb5UlRm2pP/7lo3KoSPi43gi0cR3fMq1LIHQRPneR96x29FVS8zx1bLtL5tvoCQH+ITxwNkVY1kBRSmmCdWsGQRMSxcT7Q4dYLP7osRYWLLTu2yi4TiEa+MufcN8NEt9oK6ysRt/EEZj1SclfhLZq6Z83TKdWyulfHukc1Q930Z1A/iVl/A18vAtVdBRUOlwGbkVJn7WjauWstY3moHayFyDR4NilPF4bLTUmgFyLlEQsZmNct+/6pL+0yEcUKteBjaUq8BAEtNp6sdfGYf1Lw7gAlzoWoqoAsddptesZgYSJ5X5QMGj4YyBTTq8T8H73lN7BDTMnt+l0LtQPEE0MxViUcgBbXqUSHwtTaa8QbJOzF+jliChbqkL1A+sQZbuB686xXHuQLIzAW//zMAAGsthV7z7wJNXgQaPMazSIrySkDrPu1axjXlUiznNa+ORsQKqIdCJa66JARBUkKkLRnR7fo9gbLypO+Iv0/+INBUA31qh0OhZaEUWn1IhLG+FLxwqBN6x0vu82hvBu95DbT+MzehdY5jH9uGhHtaXyUx4VFTYotI+aDm3AZtETzDXW4FZGaZT+UUCoGlvtLaUJ7ZifPhW/u0vT9SoNueBM++TY5XPFzIjHGgmavkmXGqEeUWA2MH3h46hRRSSCGFFH7fcEuRYZRD6UD3hlXeDbivMo0ppDAAICKo9Z+VyevV85JUmb68zwoGKdigSfPBx7dKNbGFjJwek2oD2oaZq8BXTkvQ31o2fQVo8GhJHJzZk7ANXzoG7miRIHpaJlB9xZAuLOURuIkNROCyU8DJHaBZa9w7GzZBkszOqnzlg1r5iEzWLx0Fg0EdbeCqS4ZwsEIq0QePBnwBcGs9aOICqAmOhH00bhKsFKCCsWrxZOBI2HhtD4DEe9Ul7+V+v3c1GiAqEdcu23ZW6VmS2Lhy0lTlmapES6VF+2XiqaNiheBU5dHaVMd4fDMiYfFir6/qkUhCaRlgKzHIRvY4aTDIQPkck9RekGeSfteMco4ylfYVZ6GW3Q89aBT40PtAawP4wLvA4g03nHihxRulcujcfmnzhPlSzVc4xHMynvRUImHo1/5FfOYtFA4FsguB1npbUUJHgFHTpD91BkhCneAT2/tElCBSoFlrwPvecC+fvMjT4iRhe58ftOxB8OZfuSuqgumgtR+5qUktmrsWXHpciEPO5yAakec0EATO7pPKOiLE1IYYQpYi2NtZ6k/aSGkH0qDm3I7oie2GGJQl6yRNRLC7DeFOCar1037nw4CavBA8erpIYWfl3jzJ56HjpNrUqQ7DDBQNh975kowJJi8E1V4F+w3xyHmdgxmeRBgAopSy7tOI/vbvXUljuR8k+6q+DL5WChoypl/N55YGIdvELz+xU8iSofjAaQAJkjPKB5o4T0gtl46Br5cBBYNlWR+qH7nqEvilf3IkQczzrKMm6N87KXoLlJ0PWrjOvTAtA+qBPxECSG2FkDFmrARqytw2Kta3LpBmV/X5/KARva+aZGZPogmXngC3NtxQ0sIJCqaLZVKydkQj4Pd/bpObfH7QkvugZqwQBZ3ysy5yEM2+DawjQkpyvffGvs4K5A4aDTXvDpsknKx9aRmiXnZ0i1zLdNMXzVwNteIhCYQfeNuj/2GgowV8chdo1DRg6DiZ6zXWGElxQ7YgJd+NQLpdeen3A53tYhVmVPIoLQPqga+BT+0CHzGkWPIDp3dDXzgEdd9Xbjjh0FdwUy34wiGAGTR+zoc+d2Bm8Ns/FvUfa9mp3VAPfi058TTpsIWB4hF2wjkSsu+p8gshzRBIyeeHb/1nwNWl4MbroJKRiWMuD8JNbHk3pDjKLwHd9QlpUWc70NkqNlwL14NrK0RBwXp2xs4E11WK0qOxU2RfAOqOj4ImzAO7lIogiaKpS4RIWmds2Ky+QkeN2lcwkUxnof4akCLDpHALgtuaoN/5Txmjt7cg9qJbVewN18BXL8iY+sgm+S0tU96PtEzp23OKAK3BXR19UqUkIiHex5PVBo/pEwGeKy84VCriOirWcVZ9PbaqmzmY4xCWLV+y+atrfVMMUVclNsDRMNSUxe5VohG3KpXy2bZUE+cDJaOAXS+595s/SJRaezp8WxPgCySoCTpBY2eBd78mxTuOYg8rBkN5Jd6KyD0VLDltjWLzcIAbrtlKtc79ORRAOBJOHEuRsc0ZOwuorxSC77w7QQWDoS8fc5w0bEJ/NAoEIIpDx7Yljg8HCMwavPkZIWaZJtDYWaA7Pgq18Yuiyld53thg6US7ntITLlUcAEJYTTySxDgCfSB5ObfubJd4VuN1UdIZM0PURNtbbIKNjoo6zMXD0ETg8jNCNrLabpFd4pFTCOSV2NZLxSOg7hRVN9Ya+sIhITVHQr18dwCEOpPOKWjEZKiP/DX40jEZz2cXQI2ZDmTkQF85Zcd9tDbzskVip3pqd4w8zUe3yDmHOqVf62gBf/Ab8KF3oTZ8sdfjQyFteRE8CMgrBq18pNvt9YkdosJp7e/kTqi1T7vsGQGAG6olDhZIA42f2+17DUAszy8eseNQ/gCQniOFcqV2nyPj4xBoxY0VptwU1FbE2Q0J2FmIcZPATstt1/LmhLtNxSNkfnXgHdtWDEBMwZIItOHzUvy4/bfgy8dsQtj5Q9BdHaA1j4Myc2Xe094sxQEeY19ubRB1orIzQqZJyzJ2t+NAc9e6bfBSSCGFFFJI4Y8UtxQZZtAgO5F2/Li3HHFv4dzeud8UUrjZIH9AKhU8qhUGGhwJywD6dyDv+mGBghlQ9/8J+NgHMukpGCJWPT1a4wxwGx74E6D8tNgFDBlnT4KtxFw8WMuEJyMHfP2KTNa6C6RFIkCQgbIzQBwZhoig7vks+NhW8XrNypNrYKpSnDYIriaUnYF+9z9jE10uPwtmjlWf0YS5kmB3HUyBHNLKXFsBPvCOVLbkFEkAoe6qEACmLZMK7QuHwOcOAGBg4vxufb0TkKziuGCIXXUej2gYev/boKNbwLnFMinsbLMJL+Y8wBYpxlR7a+2yiwKQ4LnrgmV705PlESAy+btfkXaw7v5eW/D544KxvVWT8UCnmTQTia1I1SUhbRjCA5edBNeUQT3x3RuqfiN/ALTqUfCyBwAdlcRrP8DnD7qJMABQXwWafxfQUC3JuMxcIYa1NnoHNloSpbR7gpq7FjojW5JX0ShowhzPyrKk289YAS4ZAX1sqwSHx8yEmrr0pgcXKDsfNO8O8AfP2tZXVrQlasgwzn4oZkFi3gfXY8V2kg6IVSHSsAlyX5SS5LZXZZ61TagTduSfgVFTeiTR/a5B6ZnA2OQ2XgNyDOWDWvcZ6K3PAjXlQhJKzwQuH4vdAj72AWj9Z+VaWgF5EwBP8PyOA1dfke9g4i+IJWscATpurAHvfQ1ceRHIKRSygod0fgxd7fDsg0IdMqY59oH7fEdPA0ZOlSRxVzuQXQi1/AGQpeg2cV7SaueeoA+8DUQ9ZLwZcq285NH7AcrIBi242959xVnoU7vlu9HVYRPzMnPhkuHPzAVmrJZ7Egj2rLxlKSl5nVBXB9AHMgyXnYY+shloaQANGy8+9b3cnk9sd6v8RCPgnS+BR04G5ZVAPfpt4NJRcFsTaPhE0KBRiL74997ftHAIGDRKyBJ9GJPR4o2SsLtwCCAfaMqiWGCfq0tttZZ4mMppveO38D36bUmsNNdKwNf5LWppkPvjTAqFOmTs4qjmpfRMYPgE8P435X5b3++OVuhNv4Dvie/2+pxuFHzlpKijmDEMH3oP6vanQBP69/70C1fPu4gwAKQK/MhmsYT0wqBRkuSKu2dWtTMt3gje+ZKQLKMRGWMuvV/GsHHjr2TV1tZv7ExqAvKNGzSqx9NiHQXveFGSPDoK5BRBrXkcNHYm+NolWynw2mXwa//i3jgaht72PNRH/grqns9D731Nxi75g6AWrhfZ+hMO4hyRrXxkJeKVkmXxKE4RYVK4NcE7X5IxDMXNSxzfQK44A9/qx8ElI2K2ipgwD7zrZembG6rE0vDMXqgHv943QszS+8T24vwBQGux/LMUS3uL7AJ595JNq3oqWHChp7mZIQH4/DLPbPdOlCbdN2tJmseRYQAYmzczr3QS1c8dAGqvgpbcJ0VY7S1ynZbeG+tbuaYcXH1F7LqHTwIRgZtqobf8WgpszJyfVj+WQCzg5lroLb+RMWW4S85t0EioeXfZFnajpgkhx1EshEA6aPZt3Z+yNV6w1C4tXDwMmrIEfPW8GcsSMGoqmBT0ofdk/JqW4W3xwhpq6b0JYyHKH2zfPaedreP7I3bUNwmlJ2NEmNjxLh8DXTgMmrQAtPwBadrxbfLuxMNrnu08D5/fES+xn1OaOL/XTeSOVuiX/skomAA4vRvY9Ev5xnpd66qLMva1irxi2X92K1FYiEbge+zPwM21gGYpyKg4i+jpPcD+N90Egd61GKipADdUJyUNU0Y2aPoyAHbxnN7/tozxMrLtvqyrHXz+INT05ULSBWR8TSSxFWecJhICGq5Dv/p9qDVPAGNm9BiD5eZ6W73JVQQH0Mip3W7PkTB4/1txCzX03tehxs2OqajoU7vA238L6/7z/reg7v1St0U6atmD0HWVEkMDpC9YdA94z6uJ7Ti7F7zk3gGNdXBrA/joB+C6SlFLmnVb9yqEXsjOh2fsbICKC7oDjZwCtlSdYguVS9HQ9dPMVeCaCuDcfjt8AvOv1kDZaVEeuvNjiL77M7G0tr615aeBzb8C5t0pFq+N16WfnbUKasE6yQeEOoD0bOg3/l0UYyLGHtvfAlrzRALRMoUUUkghhRT+mHFLZdBnz7YD9M8//zz+9m//Fvn5+X3eT2NjI5577jnP/aaQwh8CuK0JvP0FcNlpYXtPXiTB5T9QUgxl5oCW3Pu7bYNSwOjpiWz/QBpo+ETw1XPuHwqG2JVRlRfdiWnPA0CS2Zne1e4UFOsJLOh95ZLe/bI7KMEaevcrUONmicrF+Dmghmvgox/EbJ7Usgdi7ebWBujX/lUS38wiQ8wsMryhTvCRzRIMcQaRrp6HbqmH6mU7aepS8MldrgQusvKgpiyWZGhLfeJGkTBw7RI4GpHJn5VU1hHjo55ugoZtsq42tlSWEoMV2LAm9YE0t2SpBUvmvjfWLz6/3OPYiXVTRWhJn/p8QCzGdKNqZg7FoTP7oNua5J7GLCgAhLqgzx2Eb+bKGzwWTEDkBoIingl9gJvqoMbNlgrQEZNBhUPAFee8r87Qcf06tJqy2Dvg3EvQ4DHw3Tmm39v3+7jBDLA/IAHceKunQLoEkHe84NggSSUa4Ho2aepS+XfhPeBrpRIMTVYxSxT3m1ElaW2C3vUKaPaaD5WoeDPBlRfAdVWgomGgYeN7vR0VDYPvoT8Ft7eA66vAb/ybe4X2ZuD0btDiDRL49vkASF9DSzZ2v/O2JvnX508kYZKRATfKHBzuEvUlK9BZdxX6vZ9D3fM50MgkyiFFDnUm565HTQUtugeaCDixXZ6BCfNAyx8GpWeCpy+XZzIzB0TdEAx7Ca6+Alw5CYQ9nkOfD5h3Z59UZnp93MvHhZBgqR8pBWQXQC25FzxyKnD5qKiZ5RYBxSPBr/8ruNWQ8gaPgbr7k6IG5wHy+UEjJ8u4zYncom6VLRLaePW8yL+b6lk+Vw++dgnqsT/v1RiQrxhVIdbm+xgFtIb+9d8CxcOgZt8mCRpr/Y5WoPyc986Ugnr4G71S1nKCiEBJ+mE++G6capoD1vnVV4G72uXb7XkAr2eQPNWruPG6sa1ss9cjFmJE2SlRobnJYGboXa+4k7OsoXe/CjV21oc2tmeratuJSBh8/iC0PwiathRU4H5WiQhq3acluXq9TJShJswFLdoAwBBIh00Q4nUwQ8adfXxeAIiSy7XLrrEizb8TlJ3f83kd2yrJYgstddDv/MRYvMVVsbc3C3HHubyjBai/BhoyBr77v5p4gLhrgrQMIOwTRayiYcCIKUK4sizGIN/d+GuZQgq3AphZFAYseFkW+QMxYj2Nmx0j2eoTO2z1BwtNNeAze0Gz1/S6DeQPgFY/Bl7xkBDv+/G9p8kLwce2yvzOq2hlIEEwdlE++f74g94kgqTbkyeBhnx+0MT5hsDvUBJUhgDRcA1g7Unc1FufiynXMgAMHQ91z+eg3/2Jbc3DWqw0gukJZCP9jlnP2F8CAA2b6FKkIKWgNn5BlFqqLgI5xsqwB6VQmr5CyInxcxnLunLhPWLr3NYA3vosUHZKBHUPvAN125NA8XBRynSiYIg3KXjMdFtd1xobELnGD5Rb1G17bwQJ8aHY8vOgSQvsNkyYZ5Qj7O8ESHkqIdP4uW5Sc1qWbOcLyDhp2ERwcx309t+KGmUPpFE+uVPmfsxCcLGI29bzHK/WEo0IqSVGwKHu4x7tzYge3Qrf7NXg8rPQL/6DtNean/QLDH1sK3yrH+v9Jk1GYY7IPUeOJ/PmFkuMyWsuzFqshF79F6BoKNTD3+x23ktDx4JryhLjgKTAV066FAsT0Nrgfh4stNRLv5aWAe7qAO9+Fa44Ulc7eM+roA1fSN6u/BKoJ/4CuHIC3NUOGjUdAIN3v5y4shVLu5G4jwMx8pW5/1x1EXzpGNQj30w6h/I8h9xi0IQ5YunkgEpSJDiQoLl3yJjUUu1WPtCyBzyfBX3wXbHI7Wx3vEtx1ruGvM+RsCjzxMVwuOKcxGisZzLcCT7wDqLXSkV9PNIlJMP6aikkYQBgINQB3v0KePLCAZmfp5BCCimkkMIfAm6pzPnMmTMxcuRIVFRUoL6+Hh//+MfxwgsvIBDo/cArEongE5/4BOrqhNk+cuRIzJqV8kZM4XcLZpaKy7qrEhg11Tn9hX73p8D1K/JHRMsk1gzCUxhYsI4ClRdk4j98kueElVY9Cn77x3YAMKcQyuHvGqsoTwbrWVAKNH3FwLS7q0Mk2MOdRkVCiWVQR4tUTeeL1LRasA48azXQZiQ3fX5w2SnwhSOiBtPRauxqonYQLhwWIkm4C7hwWCZw/mCsyoqPbQXPvh3UC6leysyFevBr4KNbpDqkeDho9m1S0bPqMfC7P0mspmdtKsTN8kjITpJFwwDSpC3p2UDRcLFUSgva52F5rlv3cvR0AARcPmYCpqZaKCNbKrN7UQnDpSfc1UaAkQ1mu/osO1+SfweN1L72eweZbwgsE+K6q0YtxvHcRULAlRPAAJBh+t26cwegj30gz2Znm6mcNuo9gSBQehzaKSW9YB3U/LtEMv2cQxZ68JgP1SotGZgZfHwb+PQeIBqWpMD8u3v17PcZo6dLkq6zTUhalud58QioOz4qFfJVF8AXj8hzp6MeQUyWYGkgTfzPpy6NqWJQTgHU438OlJ6A3voc0FidSKjx+yUwRxASDEwg9HopuKEKXHpCgln9VAzqD7imQuT0O1qBkZNBU5bcUPKYdRT87k9jySBb0vxjntZwzAw+sxd84ZBYcU1eJJZAmTngS0eSttm38hFJEl85KUniCXNB6VndN27oOOnHghm2ogwAgITMuPoJu/r68nEhz7Dp/02FN5/ckZQMQ6Sg1n5ExhhW0jl/MGj5g/LclZ8xJAUSmfu6q8DwiXK9B4gExW1N0G/8m7Hxc343SZ674hFQPVUd9xP68Pv2MYnkWrc2AAWDoYJpIlU/eRGYNfQzfyu/WaguBe98GXTHR5Pun1Y8DH7rR/ZYITMX6van+xSg5BPb7fseCct/nW3gLc8Atz8Fik/uW9tFIyIrn54F1lrIopatHyB/11VCb/k1VCANZFSUxC4qyfiFtViC9YPckPT86q9J4q29NfG41rcyPQtcexVoqgUGj5ZAsPV7qFPuXUeL2C2YvpjGz/ZWr4pGbGU1aYEc1ucDnz/4oZBh0NnmsqaKob1Zrm9PlhMDBCoe4b7ioc6YvRGf2A4+vQdqw+dBcURUyh8E34NfF9sNfzBBAYIKh3jKufepbQWDoR7/c/D5Q0BnmxD0eqEKA0Csp+IR6vAmQXsl9EgBWcltAGnqUklWO+/h4NFQD/xJLImvR0wUtY3G62LxuXhDr9qeQgofNsgiCliKHWmZ9niASPrV9EzQpIWJG3sR6rpb3lNbfP5E25jebpuWCfXAV6EPbQKObDLKVHBYJw0grMRjuMtYvfRx+6hOPi5b9gDAbCtQWUUfFjyKC/jqeSHCRI0iXTQCXD4G/cGzNhHGuf6FQ4CDDMO1Fd7rnT8ILH/Q3b5guijU9iEBTfklUPd9BfqZ/2lfKzZjWgZwdDPQ3gRuqHYTmVgL8X7dp8Hv/Kf9XKZnQa3xVi8j5YPa8AVRt6i6KAq84S57fuNPS7SnHkhk5bv/1lEh/ZSdgj68CTRz1f/P3nvHx3FdWcLnvk7IAAGQBMFMgjnnJIqkSJGSqJyD7XEYj9PYHocZe+Lm3flmd3bsHdsz45xlW7JyoCiJEnPOOYMEASLn1Ond749b1VXVXQ00AkXK7vP72SKqqyt1Vb137z33HCF/ZeZA3fc58J7XhBBeMFTiX5eGHCqbB2qqEdXkSEiaiNZ/HCgZDz69B3zwLQBGhHZ6t6utjgMNVfLfcFAU/+zH6gaGkIZjf6dww5/ZDZ51G/T7z0puqk82ZW4gyWmgD2SY4ePFGih+S7ZrzNWXoY9vlRxOT2AN1FdBP//PooQ3bamrSg3NXSuWNV3tiKmYeHwyzw11iQ302CTzzJwh8v6NV2rNLbLeAQ2VrsQ7rr7c8/EDkqsom+9s9Csqte4Hc72Rk0B+57wOMN4zR94TK/bSMlE2ScEymc/sTSRCdbaCT+8FzV/X6/cdx7b6KWlOKD8u76Lpy0FjZyTuM9gp89NBIpdTIBOeB78Evn5JzmXERJBLQyNXngcf2CR/eH2WUrhdaTQjWwhubY3QW35txZZmbiymNmjYb2q2xsWLR0SBFpCYKM5iDoAoLm97LrnCYxpppJFGGmn8keGWIsMAwNe//nV8+ctfBhHhtddew+rVq/G9730vJXWXY8eO4Qtf+AJ27doFQIL5r3/96zf6kNNIo0ewjoLf+gn4qnTjMiBWCBs+0a8JOTdVW0QY+/Kz+4A4Mgy3NUrnzJASkchNo0/g5lroN35gycZmZENt+CRQVCoJoYYqoKgUNGmhWAvUXZUk27CxjqIplc0Dv+PtoUuMAK8XtOGToOFjB+fYw90SZJuJDNZAd1RsieKS+uTPlOIqAH34XfC+1+UDsxDiCziTUREz2WcUws0uIlNJJRyUwlqKhADKLQTd9kjCcjVlEXRXu9j9xGcWg3HdZOQBYHSSawY8AIhAE2aBG21BvfIIoYC1dJ2VlkmHuvKAz+wRb/ZISFRJZq/qn/qAGdwGMqVLbsQEIK8I3FIH7N8k52La2BQMc004DgjhUCIRxkR7y+Duqw/gC4elexwwFHpsnf/m36FuwOMX4hYAPrgZPGkB1JqnwNOWgWvK5V02eporMaHPx9RTR1Yq3z/8jkPCmI++B7Q2gNZ/PLZ9NNeKJ3lv/t29gPwZ0tW54wUZA4YUgOashpp7h7XOuo+BZ90ulmj7XpdkSbDTea3nrIVnlbvcPHm8wMS5QnJrrrESnDFrmHxJrkdCBhnD+NwkprXWCynkAyIqcdUFkQQ233NXTgKVF2LXv1+4cMjZFQ2IpPnlY3Jt4o9h72uGJ7yReK48B+pohpp7B2hIiWtNxCwMU1GpQ8aar56GPrkDqK8S3/dxMyRRbhBNKDMHtOJh8I7fi4pYJCJJsCX3Qk2a77BA01dPGclX4wi8fsCfIUTJHkAl46Ge+XtRNPP6gJLxICLot3/ufFd1d0Bv+RXU0383qMoVfO6AQWiIPzCILdndn+6T3UJsu5XnpaDf3QEaMz1WgHAgmfVaWxNgt0Gqr3RVLUuwcYkD5RbKXKHmiiQ0S8b3+dqxadsXDklS1Vx+dq90669+0rk+M3j/m1JIC3cLaSnYmVgQZB1TV+OTOywyTPlJd5UW2Tj4yLugO/+kT+fQE2joKHBHs4uNICziq9cPfu3fYseArDwh9AY7rUJhJCzr5xeDZqxIatvAp3YZpNS4c/T6kXgT3iAEsuQc4osD/sxBI5mlAhoxQSwfLx6R+yESMorfxlwuGgYffAt07+fcv3+Dj5UCWaCZ/SCMJyGIiVpAXMEoMzehAEhTFvVY4KGMLKiH/kLupabrQPEoKYwZ80cOB8Hv/NwqMJ3dB669Cr3maVCwAxg6pl/vtDTSuFGgGSvE/hAwmguyJWZRCigcAbV4ozvBrXiU+waLR964g3UBN9WIemlzLWjYGPC9nwXe+L47AW5AcLHocCMGmCS7ZA0QBNASdwVc8gVAq5+EHj4WvO25xBVcLBq56oKM8d02m99oBDj2nowrHo9BkonKbxpPfE/GbUiF9JACuKlG8mK5hUIGtR8nIKoZF4/IOcTHet3tIH8A9PTfAdfOyjGNntpjLEc+P2jmSmDmSnCwE3xsG/j6RVBekcT6xr3M0YgQg2orgCHDRCXPhQTQF9DUxeDj24S4E7OFEiUg3vc6UHURtPHPZN3iUaB7k6t52KEW3QWes0pI73nF0swU6ha1XxPMQLgLevNPQfPWgmaudFdTKy4FLh1NYuXpBpb4oC/2RtWXoV/7dzleIgx8fsVARyuYdcqEcpq6xPh9rTwqjZkWs9Dl1nqJKSMhq+mrt2NoqBJ76v1vAFMWQ93+mGO+QJk5UI9+Dfonf+skOZjo6b71+sS6aLtd9VVBLb3XaqrMLUpsfGEGyIPos/8DiIRBE+aCFt+dUk5L3fER6Ld+BLQauc8hJaDbEwlHXHVRrpWpUtlcC666APXo13uPa9xUnwEr35oCuKtNGpFaG4ARE6Du/Zzrfrm+Enrbc5Kj9WUAM1ZI3F15HsjOB01dkpLCYDLEk8MT9l9+XH6PSFiulS/Dqbw0fCzU6qdA2fmIPv9/JL40xwsdFVKWklgGzbXWhs34iJRNlSzZM0XgM/vA89ZaNsZppJFGGmmk8UeMW44M8/nPfx4/+9nPcOjQIRARdu/ejfnz52Pp0qW4++67MWfOHAwbNgxZWVno7OxEbW0tjh49ijfffBN79uxxbGv+/Pn4/Oc/f5POJI00DFw8EiPCmOCK06CLRwCbPGrKiCbp0ohLrug9r4o8sBGk0JTF4gmdlkhMGXr7887AzCj+wRdwFAX59B6o+/8cNMwisnB3J3j3S+BLx4UUMnycKMzES80SAblDoO76VFKf2X7h/CEjUeGU3YfXD37rx+CMbEmKlIy3Pg4HRcbThMcrBbf45GGcL3UMOirJnpwCcHenu1xxH8HNNUa+JM7ehVmSeVrLZz6/YYdkdpYp8cuevQo4ts0pM6sUaMJ8qDs/5tgXTV8O9LOIT+NmiuWJPXFCBFq4HlQ2H/rNH4i/u+PkooY0r0sy1fh+vxKPrIHOJKSXQSCQ9Bf6xHbrj0jYOj/7eWoNcFDOIZAp/60WWxIqGQcqGTcox8IVZ6B3vyLdqnlFUIvuAZXN6/t2jm9LXHb5OLitCbh+Qawvgp2AxwuauRJqgHZvNHQ0PA99GWwoG5nvcw6HwMe3givOgDJzQTNXgoeOlvdUICtmhYKRZUmJMLHj72oTNSXHQhb1kskLwUe2yLuhuxOIauvzcFB+1+aaAZ1jX8CH3kkY+/jyMcODPLlXeo/brLqYZPmFhM5KDnVZ3br25UffA89eJd10o6eJ37eJQBZobmIHrT65QxKe3R2x54HrK8AXj4gMt0GmUtOWgsdMA187C8rIETWcuGIvN9UIoSleGUp5QONm9noNyOMF4rqU4wlCAKR4X38tNSu5VGGSME0FGhuZR238TL9Uh/jKSZH8N5O21ZeB65dA93zasR6VliWq+Xh8oj5ihy/JMaRwbEQEDOA9RqOngmvKE7v/PF7wuQPgRXc7SAl8cqc1rmsN1F1LnIeY46vWgMfooIQQaaSjP8kY5fGKzdAgghbeJYW8jiTy+bmFkhDWWuYADOmMVV5ZpuU+h9cn78ghJVDz73TdFGstBIWMTHeLiv7M0fsBUkrOe9vvnMvnrxsQWbNfx7L2IzJnObULuHRMrqMtwc6N/VN4uJmgKYtE6dCOrDzQuo8KSaWmXN650YhVVCIlZPcpi1Mi4FBGVtKO5hh53o7qy+Df/iPYUFak5Q9CTVvazzNMI43BBS3YACglxcZIWNTxlt4nY8LBTdDbfifv16lLQXPXxOaiYumzRyzTTBSPBE1Z/IEdO7fUQ7/0/2JxH9deAc7uE6J9pjfOqmLAezNimN5WM2MctxwOARk5oNyeY2aatBB8ardTCSavyJ18njPE3V4FMBpYbAdsFFy58jxo5CRZVjxS7N+aqi0lVh0FMnPBdRWgoaN7PNaeoM/uA2/9nfwGdmU2c5jxBWyKCTlAKE4Vw+uXIrbXB6Qwn40HBbJAixKtnDkagX7t3xzxD5/cCfXAlwbUzECZuVAPfFGaJ07vMdQ5bWq61870+5ram5kACMnAnBsyG/GE/H589D3wuQNQD38lofhPM24D7361bzv3+iXnoqOpPU86KvYvTDLnSkZSTRlGI1kf8prk80Pd/wWg/AS4qQY0dBQwZpoVS589YDWuef0pkoPYIlOf3Q/dXAv12F86iRmXj0l+zHz+dVR+p6GjgV5seNX05eCiUvCFI4DHIyo0tviWcofIHOfMXutL4ZChQCT5Oz6xDWhvBG34ZK9nQ4UlUE/+tbzDSQFDR7uqmfPxrYm/e3MtcPV0jFyUdB8l44U8HO6Wpg6iWPNFKuD2JofNEs7uk7znxj9z3A8cCUv+zVwv3A3sfQ1sKq4A4BPboR748xtmW8kg6zk0FcQ8XtCiu0HLHohdW26oknmiW/MBKXlvmLGYPTfIWrbvzzTUYlziNaVkWXMdkCbDpJFGGmmkkcatR4bxeDzYtGkTVq5cibNnz4KIwMzYs2dPAtklHswcW3/q1KnYtGkT1E0s/KWRBtBDYe36xf4l2otKgYLhCQVHmmgVcrnyfKxTPbbs7D7QqMlA2fy+73MA4LoKcN01UNEIV6nXmwGORsBn9wOV54CsfOkajlPO4UhYyCuxBQarv65Cii5+W3dF7RXp7ll6Xyz45Xd+Bq48L5+HuiRBkZkLdBkBGRke3KRAyx5MiQjDzOBdL4LP7ANAoKmLQYvvce/06G6XQJ6Upeqio0Brg1XounQM6u4/BY2eKp+3N8cF/kkKYLGPyQrs7Ah1g1/8Fnj9J0DJpF9TAF+/BFw8agv64oJxX4Z0TPgzYlL+4gHOQF6RFHN3viRBd8VZxLyaC4aBDFICswYffV+UlXRUnqN560BVF8CdraBRk92tFeIvRUY21F2fEnuZllrAnwWafTvUlMXQJ3cCV04lfklr65TsiQZ7kBsjjCgp/KXie+/PSN79eCPse1KFwxvepjjCQALxJxoGOMMgiw2ulzu31EO/9WMr4dDaIAoXuUP69I5i1klklBlcfw38/m+tRFE0IonI4pGgAb6DORIGH3gLfPEwoLzSdVh5Pva+YgBcfgK08hHpEGypk6TjsLFQ6z7W47YBIfehu106gYNdRmGQREp50d3yXNaUw/Hcm/cva3B4oNLXqYPjfN5jaK6VsbI/SEbic1ve1e4u9d3dIcnIQCZowyeA8weBygtA7hDQ9GUJREHWUfDBt43OMdt1DYeA9iYZv+esji2m7Pwei0t86aj8Jv5M411gbDMrv3/KCoAo0HS4kOwCvVg79RE0biZ498vGH7H/k3G34oyrOk9v0IffTUjacsVpcP01kNHJzm2NwPgZQM0li4hBCrT8gQT7KioYCpSWOecIAGjasj4fW19Bs1cLWcRu/eLLsIr4HS0ONRGHvVzYdi+YtlPMtnHIKGiONQpMwS65v5VyL+J5/Slb1aQKKioFFt0NbP5p3AfGQbbUWcWKCKznJfYcshAQOQPwBcA9SPGTMtTZmmvlfRfqtogQyx6w5kYfANS0peC8IlFG0ho0aT64sxXRF79tWfDNWTOoKkxuIFLAuJlQxSOhr51LeG5oaBLlh0GCPrnTKMCH5Jzn3zlgQhBNXwF0dQh5NdQFDB8HtfJRKQg++CVw/TUhx1aeN+4zq0inZq8a2AkBiUSYcMg5bkRC4O3Pg0dOAuUN7nwnjTT6A1JKCDELNjiW86YfyfzCUOzgva8B0Yhlt+n1Qd33BZmf1l0TQlnZ/BtjHZoEfHJHIhGko8UiSXp87lYSqcLrl1gqEjHGVALQX3KNMQ6nYivs9UHd/wWJV+sqRLFh6lJXogaVzROb4Z72GyMai5oan9wZI8MQEdSGT4giYMUZWdfjAzpboV/5HtQjX+2X4jCHQ+BdL1vjiscLZGZbsa3dFisrT+6/7U41HJqzZsBqLa64dCyxEaC5FnxqV5+tW+JB+cWg1U8iWnkBaHdRxWhtEGLEQJFXbMz7uyTGNa+zSRDoagOf2B7LgcSgtdwHZi7F9SQMBSFlKNtOXijF9TqD+GZav0RCsr34uZdJOGUGuqIYsDIMQfJqvYDbGsHHt0tuZvg40IzbQBPnuu89rnEKmTmGwmaK0BGgqVpicCNWYWbofW8kkhwM9ZZUyDw0fFyP+Qm6/TFg2Fjg8nGwLyD2UXH74/KT4PbmlFRQiJRrkwNHwsDVUwAzOInSMHe09P7LTpwLbP0t0GXcI8xCrEpGgo/fx7GtCUqKXHkOVHEGsFubXjvrXM+0GWJtvXODneBDb4PWJre4TXocoW7w+QNAcy1o2DhgwuyEOTqFg0IideS4IuCzB0Dz1lk2s/GEyVhekCT+7myVvHGwC868q7Geaf+eke1UBieSvKDyfOAKbWmkkUYaaaRxq+KWI8MAQHFxMfbv348vf/nL+MlPnIEcu3TJ29nKzIxPfepT+Jd/+Rfk5Ayeh30aafQbyRKbKRTY3UBEUOs/Dv2OaVlAoLHTQcseiK3DV0+7fpevnB5wITZVMGvwe89KRySMeve4WaA7P5bQxf5Bg9/+maPLnc/shXrgC7GiGAAJGsyEArMEw6YiSjQiQUYgI9Zhxoc2gy8dhVr/CVELuGzIYrK2BSRBKRwqshIT2fmgSakpUugX/kWCUPO4978JbqmD5x6R12Wtofe+BpzcIdK1kZAEQAGvHEN3B+CxEWdYQx96Bx6z4JNbKEGUSVoIB2ElzMjKW3h8VgctonExGcVkoPW+1+HpJxmG2xqh3/h+XEKTreMZPQU0eRE4Kw84vQcoPyEdZMooCNZXgt/6ia1Tn4CikXLM42eJ4kxHC/SVk+Ilb+7h0GbgyLuxsYZJgZbe12tBhHUUfOmYUSwmsWaYMEc+rDyf3KOeFKAgSQD79c0rAlrr5VwyssVzuvaq/P4xiyoXBBKTog64dL8PFFx1Qe67umuGfPo9IjscBxo7XRIYgHUPyRYQO/l4UlBpWa8StH0+3vMHXZJSGnxmX5/IMEQKNHpK4vs2p0Ds7Fw65fjikYGTYd5/VuTDzb93v2rYmfktmVzWwMUjUE98U7pIlSd1pZSWevmvx2vZjRgFZlIe0D1/Jt30xrvdAY9PSFv9Oa9wULql+jA+0PCxYFN2OrZQAQMo0NO0peCTO52y7Vl5oKku5JPcIiCnQIiEdhSNjNlekMcrVmxTlyTfaajb6BqMv2eMMaS1vm8nYSbjvD75t44KsWPstH4X02nmSvCeV43xz3j/j54+6BaMNHysvP/siiNeH+D19r+TtK3RsM0x7mWvTwpabU3gwhHg938DPn9IPvNlCKmlcARozLSkxWl158fBu1+W977PLySn+etd1+WuNpkrFAwdsDof+fzw3Pd5kdOuuSrPm72LOv45dyRgbUUOso3t5jrBTiBjqEWa9mcA2QXy73BI/mcO+IEsGeeSqK70FxwOAvvfTLSTMEmhDin4HjYU6pb/XT6O6A+/AVp6L9TMlQmrqcUbod/+mVGUywGYQQvWQ827w2WjNxY0clKsGKmPvifPmwFuqAKa60B3PP3BHEvOENDcNeDD1vwI/kzQorv7vC2+chL66PtAZ6tYUC7ckEAwAwB9ZIsU183vHX5HxqN1HwWungZfvyhKCGXz+6QQRUSghRvA89eJykX8d4tKheAZ3/3ceB3cWJ1gB8PMQMVZcH0FqGA4MG5Gz+NW/DNpFgnt32ENvnpKrDzSSOMWA7OGPrkLOLPHIPJbJFU+vg1YaJFmyOsTsu4HqAbjgN2Cw2xkMdURvYYqR0pkmB4aQpQH8CmjiJvMAjnFXYCAET2rQ8RW9wVSekeQLyDF8esuDVlKxewGHapfHc0SWxSOAFdfBh/cLPMwk1htzh0jQSGILH8gcdu9obkmkaikPICXZN5nzud9GVCrngSNmSok0VjDylwrth5kcEOl+wd2JZ4BgkongM/FkWGUx6GIwToqpBxSQMm4Piuf0OK7wTtecM6f7GOem5qfz28V0rvanfMsj0/m/ErJvI8INGcN1NL7oE/vAZtkGCIrtp6zRrZzepft4GxzTaLkeZFUoDyAPwM0oqzH1bitUXJnZl7r6mlR23z4K66xEI2bCbar2JrW2kNHA9cvJbc7t8+zuzsk/5lTIDmFSMjK58Sj5jL01dNQLjmTvoBIgaYtBaYtBeso9A+/4XaQMsfvpyUQ118T6/guI4+kNWIqzdaRpKZy3VJv5FGzLCs0jxd8eheQAvGM4wnGseXXQXYyTPw9FrOOT/xeX8HdHdAv/2vseWLsAJ2dBNzzZ475ILc1yvMXtCtcEdDRJDazRv6eTDUuO7nMRHeHfNbaIO/hSNhqZvD5hJjJGsgfBlr1BFB9SdSqdVTWJyXE8h7sPtNII4000kjjjwm3JBkGAHJycvCjH/0IX/va1/C9730Pr732Gq5eveq6LjNjzJgxuO+++/C5z30O06f3X4kgjTQGGzR1iQRWdmZ6Vp4ELf3d5pDh8Dz2l+CWepEptnUBA5DAzQ2Zg9vB3SPKT8aIMCa4/DjowuH+2UMNErimPNHuIRIEH34XdOefxBaRUlL8O/Cmw7bC+FQSA91dtq4bj3RMvfkDWT+esGAWqJUCRk6SosDwcaAF61PqcOL6SpEedSzUwKWj4LYmUO4Q6Hd/AZzYgViUx8Y6gSwJnDzeRG9iW5GVvD6RwX7/t4gVYQkQtobtXJTHSFo1A+H44hpZCbMByOnz2f1CxmGWhJ2ZgPB4QLNuB61+SgolW35lWGqEJQmjlFHwM6Srzc6PLsOT3J8BVJwGE0n3TDgoST5TRSwcku1l5sZ+M97zKnji3MTnzH68h96RbkTzOtRfg37zh1BP/Y0kHjweIOJie6SUIa8rHUJSfMwXz+XsfHBHiyR8fAHo7c+Dqy+J+k13pyXLSySqCSZJK1lXFyiJkkn/wS31khwxizsNldBv/VgSTXEFIFqwHlx3TZKzpvUCKaODRceUkkyvblp8D2gwurLjoSPuy12kafXZfSI9HImAJs4Rb3lbkoNWPAxu+b5hdwUgIxtqzTOiDOO2D8/AOty5s1WK7yYiYatLyCQoZGQBpMBtjVBEsW5DZg1cPAquOCMdl1MWgfKKRR3BjuHjgHPG82dXirp2Fnr/JvndKs7Ib2i/Zv5MwOuDSlXmuLUefO4g0NYIrr8m5E5/JmjmbVIsTaVbzlSqsY2vNH9dSmpOSbeZnQ/14JfAR7aAG6+DikpB89aCXDoQSSmolY9Bv/1Tq7PVnwl12yN922dGtihU1F9zdlMazwiV9I0QRmXzpZARCck2DOnkgVgV0Jw18ixcvwQYMs9oug5ubRhUNQOOhK1im8mTi0bk3WgkjE0lyJThz3AWYEIGibNkHPjEDudcJdwtBNmn/7ZHqz/KyAKteQpY81Tyc4lGwNuek+2zBnKLoNY8NSgEP7XmaejX/8NKTHu8ULc/ltiVOGm+PF+ApfBCSuaJ0YjcIx6ffKY8olLxzs+AR7/utO/xZ8j/ohEhKU6aDypbECN9DRquXxYSSyBLxmy7MtyICUBTDRDtgIyZ6JkQA8j5tjWA3/45oo3XoaYvdxCfafwsqIf/QhT3IiHQhLmgMR+cIkwy8NH3E5ddOAResrHHuchgQi3eCB41BXzllJByJy90WnCxBh/fbhDJIJ/PvM3ZpHLlJPSmH8P8obilDlxzGerhryY8wzGyrH3ZpaPgTSEnEfzoe1APfLHPiX1SHsDvRlohmX/BZW4Qb0GnNXjzT2JxBAOiNLPxM+4qjYDYm5zeYxVVzblP3Hz8hqgdpJHGIIDf/SVwZp9VXGRjcNYRoLMNzPrWsWEuLZMGCbtNDLM8y90dMvZ5vO52FICMhV6/NJWYMJVWw92xcx641RKJqpvPDzXr9gFuS8DhoDRgeH3AkvuAV76TGPNoDXiUk/wX6gaqLkLXXrXiab9hVWuqYGbmWN+JU2VIGbmFiURXACgYCvX4N4Br52ROMnpK7H1IoyaLsvENBhWOcJ9OFI0YvH0sNGIWG2GLFt0tBIr2ZrBS4Ld+LEVvHZU544NfBPKKwCd3SUOFxwuatjTBttWEmnEbeOgY6ENvi4KgOb8z4dL0Qb4AaMpiUZV2a7ZhyH0T6gImzgMt2Sjfm7IYqCmXnE13u6HQmwEc3ZJIYHZYu7DM8YIdcFUY7vEiGpY6/kzQnF4alY5vj1OlhcSaNqt6bq4TG6GWBmDEeCHyHN9mEaqVx1CNg5yPz2/kXMLJbZTCQejNP4V6+u+EBEEe9+sKEuvhAZJhYucbjQiBWUdlvybxHxCLMx0Vwndf4icDetvzVrwBGISmKGJKeqTE+ieVBgmTGOLx2oX4Us5RUdFIUch0We7A6CnGfWbsz5zPxc+9+vGM88kdCcQyrjwPKj8JTJhtbbtgmDRNxV9zpRLU49WGT0C/80tjvmveL0YTZv01OIIeNhRffAHAx0DBMHie/KZ8NmI89PhZ4Nf/Q+53jw98/iB47HRn42caaaSRRhpp/JHiliXDmJg+fTq+853v4Dvf+Q6qq6tx7tw5NDU1ob29HTk5ORgyZAgmT56MkpIb4/OYRhoDBWXmQD34ZbHIaKgEFY0EzVntWljr87bz3X0/afJCCa7sxR+P7wOR8DcRswhKWH6uf/ZQgwR264gBwE01Ccto4QYJkLs64tdGLAkYn9RurhPFGFKI2fKY8AgBgMbPhpp5G7irHXxkC3T1JVBuIWjOHUkl6HXVBbgmC6IRINgJnZ0PnNrtXIdgJS49XgkGuzsN0oEEZfFFOTVlMbh4FPjiEXD5CUvePSa5ScCk+aA7PgJcvwDev0l8hbvb5ZztyfwBBFzc1mglMQFJ5PgzQdOXQ61+EtzaIMVeADGVHdZA1N41zkCw2/LK1UZCz0zCmYnQ7nYhB9iXMVuBK2tJhPRw38YTvwBIh9v1S6AZKwxCRciQCIYcj6k+BLb82EdMgLrjI7GCk/lfvnBInqnuDsSkik0VHr9NNjWm5uMCokEv8vG5A4lSyDoKPrMXtOIh5+79mfDc/wVwXYUkkkeMB7QGX78I3v2KJAeZgdxC0F1/CjUiNVJFX0ET5oAPb0H88xSfWNRH3wfveSX2N9dXiBTu6iet7+QVQT3xDaDqolyH0kli6ZBfLM9GXPcp9aQO0gv46inoo1ul287rlWRcfIcla0ngBzJBIyaCw0HwlZPg65eBitPSjUUkCb5dL4Gz80RmfdkDIJ8f3N0p2/RlOJPdSoh0fGyrKFsBcV3tRsdZ8SRg/Gz0Bq48D/3mD+WamV2IPulc5ENvy/HPXt3rdihfEuh84RDQ1Q4aMxU0bGyv3+t9u8WgVY+ntu6YaVBP/x348glAKdD4WaDeFJpcoJY/BL3pR1YXMyDvvNFTHYm1nsDRCHDxMLi2QhLbVReBputAbiHUwg0x1Yl+4foloKna6u6MRoDmWui9r8NzZ+/2Wynj0lFD6ShgKZEoDzBsDPjYVujj24DuDtDoKaAVD4F68T/Xx7fLsccnog0SIV8+nvgl1uDyk/23lDI3c/hdp01RW4OQBZ/5hwHZRjALeZAe/yvQtbPgSFgUAt0IW7NuB9qawad3yzUNBcXmUSlA+WVuEE+Ia6gC114FDRtjs+/ZH7PvcXReDhL4wiFJ1ga7pMvR67Wsi7SWca61Qd4zHi9iynvx8yzXjRuqfgffhj69BzRqKmj9x2O/ARWPAt126ySIWUfdpflZSzHiAyLDAACVloFK3buvec9r4GPvW3/XVwAdzQ4LBn1sKxLmrvWVYjFmex8x68SiFSBjRPlx53jT2gA+tjXR6qGfICLQlIWiCGbH8HGJhZ3yE4mE+ppyUZab5a7YQD4/1P1/LnL69aI+wKd2O6dqWXnA+FkDPJM00hh8cPVlUQxJIH8YsXBW7g0hwnAkLLFVXQUwZDhoyqKUCGM0bZmM61dPWcfs9cv4p6OgUVPAmbnA6d0GUYRsxVGj2NhtEC5NRMJWc8VAFC3ikZULWnq/KOINEFx5XlTOzMJvXjFo9RPgrc9Z6jWkLGKrqeRhWteY1yBikGE8hrJgxJiH2a/B6BTUH1xAGdmgWbfHWXkT1IINQuQdgLVyT2Bm8PFtMg8KB2Wevugep0rYxLmirFtra77MKxKbvUEC5Q4R0s/l46IYWDwSvPd1SxEtFDR+D2Ne01gF/bt/EmXJU6KywjByaZ2tSUlUNGwM1IZPyLr28apwBGj6ctfv8IR5wMHNcI7XJGOw12cpz1acAR98GxzIApVOhFr9JPSQEvDOFwyikzbswyB/R12eFx11qm+mAjJIajoCBDKhHvxi74X9JFa63FIvT3pTDfRL37aspa+dkQaS+78AvPAtw5bGeJ600QQQDgI+49wKS6WpzK4YY+Z4OlvBV8/IdUlm1+kLGCrfPYOjEfDBzfIeVh7Q1CWg2bcnvHd52+8kL2Pa8pjzZwhxhV/4F3DBcKj1HwcNGd7rfmPbDXZZiiWA0Xgl14PueAbEDIyYkHojyrAxhnW8U62Yxs5I6es0Z7XY9XU0W8tGT014L5EvAHXXn0K//6zcC14/MLTI8T0EspKqevaIumtyjcNBSy3b5wfXXjHiKwWMnCzWtmf2xllweYQg1dGC6I/+WvjYkxeCltwHzyNfQfStn8i7KB5KWRZ9OmJYw0MakYycGzfVgA++JST17k5LPbSlDnrzz6Ce+utbh7iaRhpppJFGGjcJtzwZxo6SkpI06SWNDyUodwjotoc/uP1l54uv9P43wXUVoMIS0IINfQp8BoxkMpzZSZZ/QKChY1z7T8jNVqO9WQJJnz8u0GUJQMyuF1PlAjASGCQBdJctmWYmoACQPwCORqBf/Z4UFwFw7VVw+UmoB78kUplxUPnF0G6dbP5MoHCE2PO4KV6YNk0er1FcNNQe/BmiULTonsRrUVQKKioFz1ghx9hSJ0UpZtDCu6BMOewx04Ex08HdneDD7ziKIvB4oZbem3g8qaKh0pl4NYJ6GjdT/q6+bCPKeNzJR4BcEzaKKDHVmDgwy/ULZFnEmvjiYK+yskm6mlhLwf6BL0IffheoviRF/0AWUHPZWIksCeNoFLz/DWiPH+QPyDkqL/i40TEdyJQCsccDaGUUN7ut+87nl3N0uxfyikBL7+/lPPqIHrqikoHifNFp/GzoMTOAd38hqiehLvCWX4JXPpZA3uFoRBJPWfn9ViSg4lGglY9K8jHUBXj9ovwRl4R1JmuNZecOgBdvBGVZRWci5SjqAcY7+J5PQ+96GaivAHIKRe2kn92N+sxe8NbfGgehjW41YwpHMLrOjOdBR6SL0OMF/+CvpPPOtEIhOLsyuzskOayjwMK7oF/6f5IkYi23tDLeWzZ59BhRLNRtESMAeXaqy8H7XgcW3SOkoGTns/sVq/vUPG7T75pIVBpcyDCso9I5Gg4Co6aAApnyvxmDl6zuDygzFzQ9dbIph4Nyrwc7QWPEaohGT4F66q+hLxwGGqpkm6MmA6OnOpUWOlrAFw5LUWfCbFC+FGs5GhGlELss/pAS0Ef/C1RWIkmir+CGSiHdOd7LYseFQSTDcFeb3Fe+gNXRSAQ01YKvnbPWu3oa3FwH9cQ3E9WNYuuckkS0Wwd3OAhurQf5M9znBH2wYUl6LhcPJy4MdgKV5wBzLOvrNivOQO/4vRBDfBnAnFVQCzYkXZ9IgVY8CF50l+y7vRl8apf8e+x0Ie055LsNRKzkvd2+50ZAH9wMPrDJtu+gvB/s865ghzGusHzm8YntZFeHEPSSdfnbwUKK5WtngKPvAaOngo+8K+pGJeNFRvwDJJokAykPaGRZIqk8K0/me7cAOBwEn9qZuPzkLvDCu6z3fzIFgY4Wx59ECjRqsiiXMcuYFIkA0MY8mxzzMq650vdjNgtK5/bLfLZsnhREvT6ZG0WjUnzXUdDoqaDbH0vcRk25+8arLwNJyDCAEGLsRUgeOQn68DtAWxNoxATQ0vuSKsukkcbNBNfZFI3i5bjIA5o0+PbLHI1Av/Zv8lwZqpd84C3Q438J1Usegbw+qPs+D/3it4V0pzyWlYdSQFGpNCmc3GEQbmEUuDNAd31K5gzdHXHnahQ97XPegZ8lMPM2dwvOvm5Ja+j3nnWO5a31MoZkZgM6Q+ZBZjOFjoI2fhbEGvrcAVEQsR0WABln/ZlOdVZA1NMmLej3saql94GLR4IvHhUV0KlLbuj8AoAoPe573fr7xA6gpQF0z6djy8jjhbr38+Bz+4Bag4A1dSkoo+/k9p5AXp80FQHQm38K1BpjGdtIJICVV2prBE5sT1QpO7IFPHNlUpUPIgVs+AToyilw7RVQwTBgwlzX2IwjYWDzj4x7xL49M3aMixt3PA/4AmCvHzR3rXzPjBPtSjCpzMtSBeuYciyGjk5N4WL4WIeqnAmTfMbH3reIMCbqKoCTOw2ymIv6sS8gywN+oLvNILqY7wqSf4aDQCALfP28xNQer1OhBJBny+d3zfklnPrW3zqarnjPK0Coy2FbyZ1tMZU+KEP9UUdlLh+w22TVQG/5JTyPfK3nfdaUg6svg/KKwaMmW9bxdmViELilDmrhXb2egx3k8UKt/Qj0Oz+3SNBDR8csg3r9fnY+1KNfE5JJa4M09k2YAyISG8tQl6gqkwKVjBPL6LYGiRn8ASGjVZwVq/ppS/s37y8YDnTvtd3vGuiOSD7UfFazC6Du+TToiW+An/9naZIzmosQDsq9YazLJ3eC6ypB42cCbk0agGH15zdIYVF5D+cVgyYtAOUXgztbxbqpo8W61yJReW4ysuUa1FbIc5FGGmmkkUYaf8T4UJFh0kgjjdRBRaWguz518/Y/ZZGoCNhZ//7MAanTcLBLOiJS7KjmjhaxuLl6GsjMAc26HWrGCmDSQmHc66gEIblFIBePWjaTX5GwreBrKMGMmS5Ss0fedX4pM8/q9szIkqCR5NwRDknXQP4wUPmJGBEmhmgYfHwraM3TiSczehowfLwUOmPWTAq0+kkpBGbmWLY/1gkIzKDMF4j5hNPtj4EmzOkx+U7Z+VCP/SVw5SR0ezNo9FQoF0IVZWSBlt0PHj9LOjV8AdDkRUmVi2KHF+oCH9smsqI5Q8SCZugocDgkijS+DKv4BUjxq8DYv92Wg0iSDHEdJrITliRoVBsJDJfOYxPBTulUicfwcb36udOkBZZSjYnsAmDERCFwNNcKKW7c/cD4WRL0xsgw5v67gOsXRTkl2CEBvdkZT0qIMKZHczQqXcSN16WDnJQkO2KkhbAkQ4Jd8lluIVBYIsWr2WsGLblH42dZRB3H8tSULGI4tx98+Zj17LQ3Qb/9E6in/95SMrp0DHrni/I7e3zyPBtSzX2Fmr4MPGmBEGtyhyR0m3I0kuR+MjrzUyAX0IgJ8DzyFdkWAJSfgN73hiS+xs102C31Bse9ZXaqRcPW+ygjSx6TaATIHypEku3PW8mQWLLGKBqbyU6jw5UvHAJ7fFa3FCn5LXRU7j+vX4hWmbnyHov5n9uSpoYyE+99XYh9T3zD9RxZayG7JX5iJGJdJNQBcGuDkD1MazdvAGrtMxZBbhDArQ0iq91aL6oAM1cM2LqCu9rAu18FXzkB+LOAiXOA8wdjRWLe/QpoxYNQM1eCcobAM/eO5NuqPA+96YcxogLvfxPqjqeB8bPl3WsnwgAyxpzdB8xbO6BzAGAQIt1IJd3gUPegkEcASNc2XjX+sN1f4e7ElVvrpdiVhGTGZw+4F/BMVJeDpi8DxyfLM3MHR6Uh2TPeh2ffDu5shd78E4uoEu4GH3gLnDc0VpAUhY1OsdS7fFzG+6mLRTHJnyFjwdDRwLWzACnwhDnSIW9Hdj5QMq5fx9jnc4qEEy2B/Jnuyig6ao2D0MD89cD1C1IwDXY5yTPue4up2PHJneAj78aKNdx4HXztLNRjf9Ujke+DAq14WCTOzXeyLwC1+sk+jRs3FKFuB2EqhnA3+OgWcFeHjBnhoKHm47OUB5QngUAKGOf84r8ATbWOeS4QkX1lZMcIMf0h9/PuVyw7S8CIUdqls9nrA616HHzbw3KfeX1AxRnos/vF8nHMVCkuJpvX9jLfTTjXiXPhSWJzkUYatxKosERGT5MUYQhtwesF8oZKMXywcfGI9V43C6/NNeDf/n/gZ/6+V/U9IgKNnQ62qxmYnxWPNFQO496l4SDQ3gQqLQMHO6VIa9iKxDBoRBgDe16FvnwC6q5P9Roz94jGKqfagYnK8/Le7GqTuXUMBMofKnFp7VXn7MjrBUIRiEUyYkoytPR+UQpLomDbF1DZfFDZ4JOokoFPbE9cVnFaFEJs1518ftCM24DUBCrc91V10SKfjJmenKzNWhR4rb0n2SLJ+Bdv8dfZajU7JfsmKYk3e4uTrp5KJEWbinqAEJTNZgjzs1A3EImAj7wLWmSLxT3euOl2Kn6WqYLlHRTuba5n7HnGbRIbmSrHMPITo6XRhlvqnV8wz+vETlFTImU1gAByKpGw0YRlkGUc7wSWU9VRIS7bnzmvD9ABy5rU5we8AQehxfWMu9qkASJ++Ykd4AUbrPvLrqYMIKYeHO0EEBfH1lf2aHGrtz0njTIwfrniUaK4dWCTs9nK6wMObhYr8SF9a1imkZOgPvKf5D3vC7g3Jfb0/YxsUFy8zJeOQu95TUgfOUOgFm8UNUsiwK4iOmEOaMKcPu0vAa52UMb9k5Etf3Y0Q297Dp4HvwS97mNgs5Ehr0iUes3fzojbUH5M4tFkSkJg6370+kXVNzPH+vTsfnmO3eLlULdY3d0C8U0aaaSRRhpp3GykyTBppJHGDQFl5kI98EVhyNdfAxWOAM1bB8od0udtcVsj9Pu/iXV40eSFoBUP91iwYNbQb3zfkh8NdYF3/B5RHZXteHyQDg6j29TnUsjb+lsrGWB2g5ECJsyF2vhn4vfqC0hnQjgIlIwHRk0Bdr1oJSgCmfLfrnYJSlmDX/5XIEkQxm1NrstJKagHvySS85ePARnZoKX3QRnBI3l9oPnrwfvfsArJRMZ5wrKb8HiBgmFQU1LsROtql+Cq4gz4wCboaUtBS+6V5ad2Ay21wPDxUmwrGQ8qSc3ahllLYduQI2YAfH6/BKsdLWJv5PVJB0c0GktE6JM7oWatlH2NnORix0VWPomNazB/HXDlhCj9KJVc4lp5gLxiqFWPCUmns1WsOOau7dVfmeatAzrbwGf3yfUvGglasAFcc0W60YxuYgakyH7nn0hnutk1rbUU5TKyrYQTs6HEwAAbChrRiHWOV08Dtz8G7H0DCHc5E7teH+jhr4js/+F3gOYaSSSXnwCfPwz15DdFknqAoBETQEvuBR98K5Ygorl3JKis9Aa2dyWaCIegX/mOkTDwSKLHJMJFw9LRX1jS7w5F8vmBolL3zzxeIUDFkwuy8oC+FuGYpcPVfg+UlkHd82cp/Qaso9JNZEJ5pFgfjUoypq3R6qIngGavsknaxycgbYlMAOCoXNdAluFHbSDYaa2jo5JYIZIkXqjLeB/atuc8YKD6EvR7z8Kz9iMJH5NSQMEw8dr2eOFIlBrKTFQ2L3Gzu1+2iDAAEAlCb/0t1KgpIK8PzHpA0r/cWg/9wreshPDVU+DLR6Ee/PKAnhW96UeW7HqoG9j3unSGxST3o+Atv0Z09ytA/lCoBesTLLti29r1krMAraPQb/1E3vM66pRNNmH/XQcAGjVFCFP2JB2RvLMG4V0S22RRKWjR3eADb8USuzRqMrilIbGDE+iZAGGOhQlS7YYaV0sdaNZKqDVPWyoNpROl6DMIKg00dQl410vOhTlDXIkAqYAvHXMlIPD5g6BJ88EXj0DveVVIUJGQzGu8PvCZvVDrPioWcTXlck+a3ZhZeTJ3qS4HwDIGrvvoB0e66GpPTNzqKBzzLutM5X9++W3U1MXAtCXyDNRfS4EMY2yPo1I8zMhOsN/BpSPA5EVCKgL1OvbfKNCQ4VBP/Q1QcUZ+89FT+62GNljgSFjGopwh0kk7pMRJ6mYtxbEDxnwg1GWNE5EwEPULMX3FQ+6duFm5Mhfy+cUugoxtkmF1GQkKUSqQBZqzus/Hzmf3Ji6/eAS8/KEYQZg8XrnL3vgB+NpZGEcvygV3fxpUNl8K6Xb7hczcm65OlkYaNwo0chJo9DRwxWnprDffs1MWQS25F9SrcqbE8Qh2iipLCvM0bqg0FGHilCXam8End4Lm39n7cc9YIWoK9me1ZLyQhw+8JefhmMsyeM+rwG2PSJxtNh/cSEQiQNUF6N//M9QDXxTiXX+QkQ1X0kEgS2IC04bHAE2YHcvH0JTF8k4zbTw8PsBvKI0aTRlqxcOu8/IPDeIt+LQGwkHol78LGjYKNOv2AavTMDN4y69i8awZ76uNn0kynzRURszrbpIX4psBTIuieDL68HGDEscDAJsWy8rjrubi9hywodIX7AIrJY1DpnKaUrbzGMxnyLhmjdXgjpZeFT0okAn10F8Al46Cm+tEEcamtkkl48H2OD/UbSgiZcl/zXyRef7m/Nsk/ychOkF5oe7+U6DxOrRdNdlUWi0tA42cLNZvSQgpMXR3ujcihLrkt1JGLDlkGJBTCLQ3OtfzuOVKyVLejANXX44RYWKovwYeO11i94ZKiwxpbIMrzvSZDAMYuZYUnjvuahc1p4ZKsfqaudKh0AsA3FAF/c4vrGvV3gS95ZdA9SVQVp40AA6mQrqOSiNSxLhPlHKPUWvKoS8fA7/9c8SUl1rrDdKMQeoMBa3jTkqEMSEKRDRzpYMIAwDobJHtuD6vWnKT/R1j0kgjjTTSSOMPCDeVDNPc3Iyf//znsb+nTZuGO+/sPbhNhs2bN+PMmTOxvz/xiU8gN3fg0vBppHGrgsNB6frxBYDSibecByjlF4NWPzng7ei3fmx1deiokE+8AdCKB5N/6foldx/eg28JwcBjk03uagOf2Quydc9zU7UQLUxf7UhY8kzZBVD3fjbWiUHz10ETiXJDuXiUU9k8SdhHwsC4meD3f+PscGcNrjwHN1BpWdJTokAmPIvuAha5y5Gq5Q+Ai0uhj2+XoGfmSuDwu9J1YSYjwkEpxqUI/daPrWJqOCgJs2hE/NhNIsfFI+ALh6Du/0LqiZmKs05f7mhUbBBCQUMJBIl+0qSAE9ugz+yB2vhZ0IZPASe2S8HCnwFcPCrJTTOQ9HqBzByopfcBSzZK0aPyAnBiW3Lp3sYqIBqBskknpwLyeEG3Pwa96G5wWzNo32vgzT+WeyDcLfeDeW1qyoGrp4UsduhtUYLx+MQ2ySTr2IkIsWtkHjMDbCQ9t/xa7uNw0JACN8gFJePkGh993xkU6yhQewV8/tCgSHMDgJp7B3jaUqCpBsgfmhicpwLTu15HEbMeC3bKvef1A91dco5sFUIBKWQNRK47HhyNyPul6iIopwBslzX2+qFWPdHnIjGf228lCE1UXZDO18kLe/0+KQ8wbKwlpW0iOw/04JfB7/wcuHQ01inE+16XriNSiCXHY0Vll2S5jsq1LyyRfZikK/M7Pr90t5nKU6b8smm5lgznD4Fve8S1gEvTV4Dff9YgDXrk+ff5hUw1aYGQy+KvY8XZxH10d0AffV8Uutqa5D0wby08y/puB8bHtiV2RtZXAuUngH528HNdhfM9Bxg2ZtoqNHQbdnrhINBUDf3OL6AyshMS8xzqThzTzE7ADMMmzvw97In3wsGxF6XMHGDaMuDcfuM5NTrTpq8YtIS8CTX/TlFuqi4H8gpBw8dB731dlDzs8Gf2mEilSfPB5cdlLNAR69bPyJb7zrBso8kL4UnhWewraOZKINgpakOhLmDERKiVj/b/evXQOMwNVdDv/tIgrxn3QchQBvMAev+bUONni5WCvTDU2QrkFkI983eSnB1S8sESQHLyRa2mzZbAt0v1xxNiTHu4ISWxIoLn0a+BG6rAZ/fJXMws1jKM94tBIrW/+5jlec/IcczPdFMt6O2fG/eNT2wSFt8z6Pd4KiCPt992WoMNfXqPFFSDnaLyuPAuqFWPQ7/5Q+d705xXm+O1HZEwsO5jUMlUl6ouGPeujdRMxu/mywACmVK0nLEClFvYtxOIRtyVbEyyp10t7+LhGBHGBFeeBy4cgpqyWOZux7cBdVflPpx1+y1hr5VGGjcKtOETomhXdUHIcNOXp0aCCXULQeDKSVmQUwi17iOg4eN63l9RqRDB46FU4pwq2TYysqEe+gtpUmiuBYaNEUUSj1cI5TqaSOruagMObTYUIZI0TrjuTBkNMNlAV6uruqE7jMaHUDf48Dugdf2znKScIUJ2vXTEuXz2Kqi5d0D7AuDTe4BISIrCNjIR5RSItfaBTeD6SiEkL1gvuYKOZnnH3YTxL1UwC7GlJxVHGjtD1EcBmR8EDQJKQwW4uRp85ZTkdgZCiKk4m9jYUVMOPr0bNHt14jERgaYtdVryZmTZ7EhJYuH8odJ0su13Vh4gkAW14qH+H2s8Rk2R+WLK960NkRBw7Qzons+A3/y+EHhv1BzSY1jMmATbHsBNNdIYQgQqmw/lkiug2askn9VcY5F7PD6JGVS2RY6JfcGm5CELbJ8Z/2ZYVuqjp4EmL5I8gLna2Bmguz6V+jNVMEwaxezNIIAQamxK2UQKas2TQg43CVa5QyR/cOmo87zHzUggk5jg65dcl1P1ZaBkHLi9yVLNNmJ3clNVHiRwsAv6pW8LWR0ArpwEnz8I9chXQYYCC0fC0Htfk7mcxyu/UzQqc9MjW8D+DPDBzaKuOEixHo2aAlZewGfeE0Ycr+J+V3+GxIAO1R6D4G0qlJvPtUmGcyOzeANyX46dCTVxLjBpgeQFutqB3EKQUqBRU0Vp0+37gSzJm6aRRhpppJFGGjeXDPPf/tt/w7e+9S0AQH5+Pg4cODCg7U2cOBFPPPEEWlulQFtTU4P/8T/+x0APM400bknwtbPQb//cCngKhkHd85l+Ka/cyuD6Soe8aWz5uf1AT2QYM9EBwFJ3Uc7ldtg7xwDpxDDhC1jFRY/HIXnL1y85fKiho+BzB0TyeOwMcEs9OJ7UAUgxatoypz3B0DGg2bcnP6cUQJMXwTN5Uezv6KWj4n9sFrd9fuDa2ZQ6arj+mquqAJ/YkZjoqL3Sp6IxtzU4F8SKV6Y0vkshLKaMAuj9b8Bz3+eFwGSQmPTmn0qyK0gib6ujQLbR+eYLgKYuAaYugZ4wW+wHQnH3gtYAaehNPwLNvA1qxcMpnQtgkCh2vyJJ185WSc75MqygNtgl8qTmedVeEZ/i1U+KnVdjNfiN70tBO6VORBbiEFjIQ6xF/ScakUROfRW4uS6xq9K4jlx+HBgkMgwAkSw3VIG4qRpcfhLwB0AT5/dqycRaA51tzoKaWUiLdTQZ1y0ScpBhMIgEQGYGb/oh+JqNqGZKg2fmiNx1f+yl4okw5v5qrqSclFHLH4R+4z+sriNSUMsfEtu1hkq5t0zoqGGdRZZNhXxJSABEFonC7ET0eEDFo8AZJyySG2BIORuEwHDQktclQ1nD45GEmJmUjD23RqIn2Cn3p/28O1rAhzbLtiKQ3zmvCHTv50B5RcnJVFl5In1sRzQqdlBsJHCDHcDeVxHNyIFnXnK7IVfEjwHm8TbXJuUg9Aq3LjG7OlU0glghJvZOZfCpXYlJeZ9f7Gs6WuRvHbUS5qQAjwKU8VuY41VuIWj64CkWqNVPgrPzJeGvPNLVmEKHdn9AuYVCkjD/XrAeaKmTxDUYyMqDuuOZnq3+JswBLdwAPvKeYXdovJc9HqBkwg3vdCYi0MK7wPPXA9FIyhaPSbc3YQ54z2sJCig0aaF0wLNOLOBFw3K+zbXgljr3+7ymHPBlgPpAlB0sECmo2x4R+yczIewLCAEv3O08H4/XKBwGgYYqRJ/73yKDPna6FPCWPwg9bib4rZ/Ic8JsqCVlGPYe8URAo/hh75C9egocUxSMgo+9L8WUpffd0OtwK4Nrr4K3PYfYuyrUBd71Iui+z0M98/eiUkcEfXqP2G8BiQp85vvt7L7kFmQBQ1re4wHsdS5SUnCZuVLIzf0ABTLd1d6KShOINVxd7r6R65eAKYtBmTmgxff06zjSSOPDCPJ4YzFUX8D73rCIMADQ3gi9+adQT/9dz8XgifOAIW9I/Bo7CCXv6oJhqR93IBM0e1XCcjVnNfTlY9bc0Q6XnIPzyy7qHaxljuHp7h+hQGtwY3Xv6/UAWvMUkF8EvnhUiJzTlkHNvA3cWg/oKGjWStD4Wa6kESoqBW34ZOJG+9Pc8AFCn90H3r9JSDsFw6CWPQgaMzVhPVr+oMx/6itFeVaWyn+i0rTCR7YMiAzD1RfdP7h+SaxrXUCL7wGUR5owDKISZq4ETu8BN1aBikeB5qyR5oxRU8DlJ0BeLzBu1qBZkwIA1V8De/0SU/VVyUV5gOuXxIrKsLyB8gJRl9wX0HeimR2RkPyvYFiPtmJ8/hD0e7+O7YcPvQ21/uOgsU7/K8rIhnrkq6Ic01gNPvS2RehVMu+AjlixM8jKt5ivL7OBB4g1CZjzHSICrXkKPGMFuPYqaMjwPt9jRCTqlW/ZFB1zi6BWPhY7Vz53AIAGTVoA9ZF/AK6dk/MYOVlyG+Gg2KYSAZMWgFY9nnx/eUVi022ej4ncQlGROrnLyi2xBkIarDz9j5N7AZ/dZxFhTLQ3gU/vAc1bC26uFfXdphorNjKt6WOefnKsevfLUBPnDo4ycl4RaNkD4D2vWO/8rDyJ+0PdEm8oBcy6XfKj8fBnSkzf1SbrsbHMJKXFxyvREDBlITwbPgmuugj9/P8RYqjySEPDbQ+DO1sTm/2UAgI5oDuegsotGPB5p5FGGmmkkcYfAm4aGaayshLf/e53wcwgIvzgBz/AhAkTBrTNiRMn4gc/+AEef1wmeN/61rfw5S9/GcOGpR40p5HGhwEcjUBv+bWzmN9cK8lpt2TGhxlJiQG9BOsjyyQg7WixujqUR3yC6yrg8AAGgBFx759hY6RjOI7IQmOng7vagLprQP5QozDncnSXj0vQnZ0v8rbxigOZOaCFd4mM8/VLoLxCQ7o1teI+aw0+fwC4ckosk6YvAxW7+Hg3VCUUo6Gj4BpRgOCKMyLxPm0JKC8usZBMPSUSSrTigHThpBoM04iJcb+g8ZepumFaI8Xbcpj2U/WVidtc/YT4w3deBWB8t7UevP050JqnY+up8bPAn/gfYr114bAVdCqKSd/yiZ3gWbcnXpMk4H1vgE/ukD8iYTkf7rKROdjp7V0wDBwOgd9/VqwvTLJWZ0eKZBhYgTaRQeQh6xp1tfa8na4kSaoBQp/YAd75Iszfk/dvgrrv8z3Lsl49JVYLvoAteQGju8c4J68XCCfeXYOlbgMAqLrgJMIAktBovC5J5v4iP8kcxNVv2h00fCzUk38j3ufRMGj8bFBuIbihyp1wEewE5t8JnNghiRZfhnSCLVgvY0dDpZG8oZjMMwWyQI9+HXxyp2FRE5X71/wNxkwDqi4ayUjjHvd4gQV3iTJLbDwynqMhw13ln/n0bknoKQ/gN573SEikjIePTX4NZq8C73zBuTCQKQQYxw5YjqevZJjh46xCrn2/vXQwu4G1FtJkyfjEccTrtxJnbLvX7YpDtt+UO1rAV06BfH7Q3DuM5wvWEGhLvCKQJSSVsnlAwXAZF0zv8kEA+fyg5Q8Ayx8YtG3qI1tEZaG7AzR6qliouKg+kNcHWv9xsRLsbpcidgoqTWrBBvDs1fK92itAcy2oeCQwfvYN63ZmZukSrjoP5BSI2lGgH0S6OFBmLtSGT0Lv+L2QWvyZYktXNk9UiAAhkbgpQBWPAmXmgN0Kef6Mm+ojT2OmQT31tzIWtjeCIxGg8pwkt03lqoJhwIL1Qn5Txj3feB1680+gHv5KbIxRpWXgj/936Fe+B1w7DXgzbcQx2OZ9lGibOGWxkDXiwKf3AP0gYXBHi6jpNVaJPPnsVaCsvD5v52aDLx6B25ybLxyGKi2LkaCprsJSVEmwuAIAMsZ4d1DJOKB4NFBfYZAljbmfzy/d8XPWDOg81KrHxSKsuVYW5BZC2eaGMSSzLchPfcxOI400IHPWeHS2StG8J1U3jxf06F+Cf/u/RBGBDDVXo/DH0UjK4zd3tUuMUTAs9v6lcTPFQvfAW30/qWRWu2Ab0aKPiIZBhlJdMnA0IrFbVp6jKccEeX2gxRuBxRutQz2zV4iMJilgz2sSk/WiGMihbnnvd7WBRk8DDXXJLwwCONgJaN0vRVGuugB+/7eIjU3NtdCbfwz1+F8lxO6UUwD16NfBu14SwkO8pWI0IooXA0FuknEj2XIApDxCiIknV658JHHdrFzQ9GUDOcKk4NYGiUOycqXxwS2ulKOAYy5ASuaPXe1iARsOCVGiJ0JYf4kwdrTWQ9dWQA1LfGY4GoHe/bJzPzoKvetlqDHTE5QPyesDJi8EAdANVeBrZ5wbVHHxGXkMK+w7gUhY5jx2wh8gFla5hdIQZygA05CSxFxjiqCScVDP/IOogXs8ogqjPNCH33U05PG1c6CWBiibgjRve07Uesw5fsVZsZ9O8r7h7nZD+SVk/b4Z2UIqDHUbuRrI9VUeUT45sb3fCqq9wpyvAZbCHzP40lHwnNViodzRYswZjec61G01/Nhtoro75NwHSTFVzVoJnjAbXHFGVCnbGq3GC1KACghZfNhYp/olAHh9QsTq7gBfOwve9bJl3WeHeQ5eH1B1EdHDW4BdLzprAOEg9CvfBXw+yQl4/cZxRAHlBd37WajRUwblnNNII4000kjjDwE3jQzzi1/8AqFQCESEZcuW4ZFHEif9/cGjjz6K5cuXY9euXeju7sYvf/lLfPWrXx2UbaeRxi2D+mtS4IwDXz19Ew7mBqN4JFAwXCRMbaCy+T1+jfyZUog8ZyhOMSQxX3dNAoRQF6RoGwCNnZmwPfJ4QXc8A37355aaTNFIcG4R+Jf/1Sok5w9NJNYAFlmENTBvHbDnFevvYLcE0L/4z8DY6aDVT/a5YMnvPyud4ObfZ/dB3fNniR0n+UOdSg/m+ucPgG2dCnxyhyTI7MHxsDGSxIlXYxg2VhKLcegtkedYt3AEaM4aSx7Y4zOsQ4zrphSgIckZh0StR653Rg70yR2g0jLLp5iUJGQycx2/B184DF7+kMOuhbLz4dn4GXBHC/Sv/7sEzo5iIAMN10WaNgXwsa2G2oatC0VHjfMxOomihhxqXjFo6hLwgTediWIz+eTzW12GcrS2/JPN8oZg+Vbb5b5TYSR1NKd0Xn0BBzvBe18F7ImyYCd4z6ugjZ9x/87l49Dv/FzuUTPpYpJg7AlnUkJ88AXkXLPyQPPvTOjyGtDxN9X0aXmqoGlLhQBiT7TmFYNsCk4pbSczBzQjTuUjt1CkcyNxdkV5xfAsuhs8/04g1O1INNPEuaImY0/u+TKAMVNB/kzQ4nugdRS87w1L8rd4FNTqp6B3vQSc2mk7KAKCHaDHvwF+9bsWySUzB2p1EgJRfEIotrznRLSaeRuina3A2f2i1DB5oYx5rfVW0dV87uMT3SmAZq0UZSmbFRFNnJuSn7kJffQ9kSjuageNLAOteBhq3UfFvsYcs0dPBc1aBZw/IMn365cskpwJQzmBLx6B3vIrITACQlxc+RhQdV4KIxVn496PBJqxwiGfzqyBS8fE5iM7HzR1yS1j6aFP7BDrFQN85SS4pU6KGUmIoZQ7RKS/+wDyBaT4M0gJ0N7AW38r3Yzm30e2QD3wxb5bu7iARk2GeuKbcj8FRFKfK8/LfXpsK0As6lnmeGLIyqul9wnhbfIi8Jk9zm3OcLe60mf3gU/uBIJdoHEzQQvWJ3QjcyQsXbVNNTIHGDfTtVCXDKyjQGujFGFYy1hqIhqRsdDjlTHi2NbEuZaOgs/sA614UI6ls0VeB9WXLMUX09aLDUsKj1fGGuUBzbodKBwhpLdoGNqFDON4xlI9r6426Be/ZSk5XTsHvngU6tGvDgox6gNFst8zjoxGM1fKvLSjRe7BeCVGfwDUS0Je3fNp8N7X5N1OJL9N2TzQhLkDV1bKHwr1+DeEFKe1FKzcispTl4gCon2eZLw73RB7BjpbhdDXE/k3jTT+mJCMZJkC+VJlZIGf+lvw6d3yPDZeB4jAO34PPr4N6v4/T2r1YUIfehu8/01Re9VRUTdY/SRU2TxQyQSwK2mvN/S0fl+3ZSCQ2aPKnj6+TYqtwU4gpwBq2QOiItLTkYS6wLtecpICutvBe14B3fNnyb/XWi+FVWPs4v1vghZsgFq4oU+n1OOxBbvA7/9GVINYg0ZNBq1+qk9zU1HDiFdPiIAvHHa9lkQEDB0N9gUSYwQi0MjJfT8R+ybK5klOw66+F8gCzbytX9tjZvCJ7YZqTBg0ca7EvTeAuEwlE3q/cwNZMncynyVA7i0z7gt2Wk1UfbFJ8gaApRvFyuf65dTIMloLCeTezyZ+1t5sxVvMlgJnS53MSXpQeKXVT4Df/pmQ9YiAkZOleSTOEpjK5oFGTgLvfkUIKlpbc9XSMqg1T4G7O6Bf/ldjHkGimltxGuqRr/WoapP02Lw+YOz02N8cjYCPbElYj49vBc+9A+Tzg5trJf8QNewgdRSgduj3fwPPY3+Z+N2rp8E7XjDUGcmwr9agDZ8CDSmRnJXHA3jirmF7S5/PJ2UMHycq2tGwc05ZfRn83rMS/wByD/oyjN+K5W+v3xlfe/1izTqIoOx8cHOt5Hhiaq9G3o+1NGCMnwWOs4OlRXfLdyMhIKcQMcUkZrn+0agRp9jmqKEuIXDaLcKYLRKNSWDKyHaoKN8qMX8aaaSRRhpp3Cq4aWSYX/3qV7F//+f//J8Hddv/6T/9J2zYIAHbL37xizQZJo0/PGQk6aC5xeVs+wMikk7oLb8C6q4CpMT6IJUu3dorUmAJdkngoKMSlBKJFKXJ3J+31lEI4nBQLG/OH5QAt7QMNG8dKCMb+vf/bNsBA83VBjHBVhRQHmDcTOhNP7IS+iMniWVP+QlD0cMDgCURtPW3fVL04aYaBxEGgHS9HHobnrjCrVqwHvqN7zu7dEZNTpTsDAfBBzeD7rL8ZIkU1IZPCFnB7MzIzpckJpFB7jBUJcZMA1ykiXuCWnofuGweuPICkJ0Prr4sAa+OSiCnbUSPSChm54JgF9BUDd7xgoSc89ZCLd4oQahpBxV3bRAJJSrkQAJEKp1kdQKZCiukgKIRKZ2HPrnDIByxRUgxETGUTkgZyjA+0J1/AgpkQV+M65hkjZjahscLcIeRuFBWh0sgW5YNGyPrmkQNosQEElHi8Zi4EYoI9ZWuHt7JLAe4oUruLbO7RWv5t/l+GzdDOnjMxOKwscbz3AmMnQmaMoiqMBAyl1sykIaNGdh2M3OgHvqylcwvHgmacZuDnNXvbfszQPPXOa3aSEEZXYbk8SaMCzR7ldjNnD8o91JOAdSqJ2PS6XzhkCR0A5lGYsewW8rJl479zFzrvlQKuHAYtORe0J/8N5FmZg2MmpI8YTtiokVSNH3HIZ2JHAm7fo+1Br/7C6fveWcrMGG2KAvFVjR+wXEzU7+I5nUJZEE99BdS1GypF5WCUVMSugiTQZ/eA97zqnUolefBb/wH1JN/I1YiNVek4GEWSsfPtL63+2W5FqRAkxeCpi4VD/Ttzzvf3V1twOWjUBslAcy1V4Vo01oPgEQCP67DlLf8WmyNzL+PbxNixpDhfb5Ggw0+tStxYXOtEIRKyz74AxoEcF2FgwgDAOhoAR9+F3T7Y4OyDyICsvKg928CH3nX8p0fWSYkzu52GUOHjgWNmiTKNAYRh257GMgdAj5/CFAKNGUxaNbKhH3oU7vA25+3zuvY+6KSZSM2cqgb+tXvxpTaGACNmgrc/amUVHv48nFRuelsFUJsqMsam7SRvDc6TwEIEVx5EgqpHO6Gfu9ZKY6xlndXJGSpi4S6nIQ5UrKdolLQontiJAtmFgJxnJUUje+56Oh6bqf3WEQYE+2N4LP7QEksE242mFkU0pprQcPHiYISIDZcx7Y630XGu8oOys6HeuSrcu7NdWI3df2SEHQ9XmDExERCZxwoMwe0+slBPjPb9omkuNLTOhnZMmYf2wo0VII9PiAzR57rKYtAmVYBntubpHBsFDl472spFY6ZWa5Nd7tclz/A+C2NNGjaMuf8FAAKR8h8PpXv+zOAMdNlbmUnYrbUgQ+/DerBzlZsjN8wlDANImRrPfiV7yI6Y7lB2st0jg83A14/6NG/TFog54ozQmox0d4M/e4voYpKQT2pVdUnFvEBgOOt4uI/3/9WwtjFh94GT1k0KIReAODtz4ldr/n3tXPgLb+C577Pp76RaBL1kWTLASGaZ+fLvCBsUz8pGC5KHwMA+QJQD/w5+Oj7YolTMAw0e7WrQmYq4EObRaXT/PvwO0BbA2jtRwd0nG6goaNAM2+TODUSRswSSClDqRcSD4aDtlyD7ZlhDcBWtHdThUtGt4mERLF54jxg/Bxg/5uJNtZuiLfOMZGVJ++K7k7DVsic+4XA184kbabjrjbwtt9JrEYElE6CuvNjlk1kR7PkKYaNAU+cC37t3yReCQUt26Bpy+G5S/J50T2vScyvjdyO0YCnT+6EZzDUNUNd7tcpHJR5ha9QGjwiBonEDGVZA5XnwTXlCcqnfHa//IPIaRHfUCnxxYgJrjZxNKr/9mK9gcrmgc/td8b/RhzAFw5Z9sHBLhtxncTSL06xh+ascbWJGyhi79R4e6NwUAh6rY1CxL58HNzVBho9FfB4EP39v0hepbsTAMt9y5DnLmK7d034M+J+y7jPTYvqSMgaLzNzgX6Qr9JII4000kjjDxk3hQzT3NyMkydPgohQWFiItWvXDur2165di8LCQjQ2NuL48eNobW1FXt6HT5Y6jTSSgfKLQWNnOH24Aelw/QMEFQyF5+G/ALc3iw92X1RUWEtwZI/DGRKwmIHChUOO7n/e/nuxIDJRdUES4abspN2+hZQQE5SSYL5wBNSie6APbgauX5T1oxHg8jFg3CwJULWhEmIoynD5SXCoK/UALa5QE4NdStQAjZwE9eCXpLu7u0OsTnyBRDsYANxwPfH7RaXwPPFN6LoK8OafSudDe7N8qDzA5EVQY6eJf3WKFk+O7RePsuydyuaB562VBEfhCCDULdZDLXXSyQUAjdVynW2dEnz4XfDEeVJkLhkvHT3RiASDmoG8wh7JH7ToLvDVU7akCUkRPGVVmG2yfdM6xo5I2ApOdUS61g++Bdz1qcRjUh75uknc8mfIfVJUKhYRoW4ALAXvmSuB7k7w7pfBV05JIbG9yUmI8fqtbi3HCasb867IK4JrwitZgtckY0RtyQOG/A5EQPlJsRNbvBHIyJZkVIOxXu1VcO0VB3lroKDhY6XoZ3/2cwpAc/tot+O27ay8BJLCYEHNWwsuHgW+eFjej5MX9UjgIY8XtPpJ8JKNQFeHSMfbnid9fLu1snmPNl4X0lp7i/w29nuXNdDRLJ1Htq61pPuftEAIJxePWoo2Hi9w9RR462/cE70XDiXI7fOZve7PtT/T3foiBdjlsvuKeLUNAPKurDgjkvylE12/p6YtBU+cI4WL3CJRPgHE8i3+2QXkdzCPd9gYqCf/WkhxgcyE7i+uq3AQYQCIWtPhd0B3PNO3E7wRSKbg41LA+bAgZleUsPzq4O6n8jz40GZrgY4CledBd34cVFwKZBe4qr2Qxytd0z10oQNwKrSYy66dBTdWxywW+PTuBMtCvnYGVH4C6K1rvb1JyJBmUj3cLfe7P1NILGaHsT3prjyyntn96DVU1E7tBnd3WAniaNhQtVLWvUQkpGWvVzpd564FzV/nVBthFlWQfW/E1OJo1GTQigd7PBdXJCvUtNT3fVsfADgShn7zBzLfhTETmr4CauUjoMISqHUfg97zipxXTiHU4ntcxxnKzHV05XNdBbi6XIq2oyf3a554M0DZ+aBl9wsh0SDtMQA++p6QCQvE/jAmjc8s951m8P43wZMXJi2CcncH9Bs/EII/AHi8ULc/1me1uDTSuNVBc9dIMfDkDlEYGzsdtOLhlEnGAKRRwm15VS+kjsvHrU59R9GQRWEwM1vGGqWkoN0PBbB+wU4UVR7Q3Z+G6kG1js8dTFyoo6KAsmB98v3kFsI1JuvBtgcAuKY8cWE0An3oHagZy90tmfsAff6QECaZDeUG43pUXQC3N4NyClLaDpXNBV+IuzakQBOTzz3IF4C693OiPnbtrBCRpiwSQn8KBN5ejykzN7VmrV7AzGB7LGYuv3gUvPT+G6L0oFY8DC5bIPOAjhanSmuo28hZKXcLJGaAeyAh9aamdHY/cP6Q3Aujp0nDVk9WS5DcoOtynx80bx14q81CCwB8fvDOF8HjZrk3Xbz3rFiHm7h2Bvpn/yDb8PqFGBLsElL2c/9kWELZLDgZwJk90KOnSJx77L24c2C5juf2D4rVLGXmSm7IzP2ZysR5Q8FeH/RL/08It2YcybByl0qBz+xNtAE2ST3xMM6DMnNBKx4S9RjzvAuGgRbe5f69QQB5vFAbPwP9o2/KuZjzePPzcbNEIcf+/vZ4Jfa+4yNAxWkgGhVlpQmzb8wx5haCa5PEeKyBYKfcc5PmyxuZNfQL35J7SWvrPupqt/1GXue4RQq09D4hhprNf86jsJE7jd9GeaCWP3jDLIHTSCONNNJI48OKmzIyHjxoBS7r16/vU0CcCpRS2LBhA5599lkwMw4ePIg1awbmM55GGrcaaO1HgIObwZeOAT4/aPoyqBn9k2L9sCDVBEls/UkLxLIi4QPEBRE2S51QtxSV48CndkkAZhZvfRmilgCAhgyHWmNZgnBrgxBhQl1OpYzzB+GamIqEJAFUmCIZZuhoSy3EfhZJCuA0dDTsnbbcXOuugGF0AbuivlKKIKEuyyLK6wMFMnqVau4LKDtfujwAKe7GJZWiL/2/RMl+ZujdLwtJavg4IQs1VBkbVECwC/rV70E98lX3ZFfMdshM8jBw/QL0mb1QSWTxHehsM4grYedP6/VL0c5unRSNiCrGXZ8yim62jkkz6I2GgSikiFcyHurRr7vbA2TlynvAgN77uqVM4fVaZJjJC4HTe2S7yguavhxqZqISwEBBuYWgKYucqgikoJIla6NhgywWl4BmLQo4Sknhfv+bwJDhBrEtAtPmgq+cBDdUDaolAa15ShKsVReBnCGiqDAICi6DBa66AL5w2FIQGS7dtTR6Sq/2E/GgzFzpGIpHd4f7+sFOYGSZJUlsQnmhj74PMo9pzLSe9+vxgjd8EvjRNxErUhiJGr54FLz8wVjnPbMGn90vUtTBLilcmEmdblM5yaaA5AvIe7mrvc9WOgOGiyoSAJeEVSLIn5mohJKd7/qeR47zvIgoqf0PxxEVYsvN96PbZ6EuoLMdyCsccHGAWxuEjGNKvMc9qzR+tiiO2BHIAkpvXJfhjQYVDHMfXwsGV4nH3lXtQMXplBO+XFMuqjWBTODIFlHxyi+Gmr/e6KZ3QVcbAON+S5L85ZorvVs4XDqWoDQCQJ4jrw+xeZmpusdsEFuU0WkbtUgx9mIEYL0rImFrXDWt9kw1u1GTQSbJ1jymt38q9pHMUpTJyAateqLPNpYAZC7iYrmUUHzoIzgo3cCD1aEf2+6pXTEiTGzZiW3QRSOgpi8HjZ8FNW6mFHP8gZRJLTR0dJ8sNG8lcGN1onpVd4eoKBpzL75+Se7HYKd1H0aCYkNw/xfct3vgLYsIA0ihedtzUKOnpRVi0vhQgKMR8LGt4CsnQYFMUTt0mfsRKdDCDcDCDWDmhJwfh4Ni5dlDcZ/yitzH1N4UN/wZRpHe9QxkfFAeONQ3Yx/fKKUYYx8eH1AyDmrR3UKWvhF7yi2UPIid4A9AzV8nZIvD74JPbAe6O0BjpglJKXcIKL8IbLdHNhUXTu2EPrNH4qI1T/WL2Kj3viaFa5MIHQ1bBNi+nt/YGaDFG0UxJRwEMrKhlt0PKuxZ1ZWGDB/URoobAh1NtBkEELMlukG2JzR8rFzT7c/JMxDsEoKE1r1/eaDQUfnfpaNyP/REhiEFLEreYEJzVouakhmXmYrGXW1i8x03J+GudnDFWdsCFrUO1k4lFCKjkc6wpInZWHPsHPidn0vzTrI5dON1RN/8ETx3D/weVCsfFfJSe4uVS1Fe8Hu/AWrKZb5rP34z1+ULuMasNHGew0JdtucBjbdiCjV9OXjMdHDlOVBGDjB6yqAQyXoCKQ9o9NSEJlB4vKAF64XEVHsVMNWVvX7jGeqE+gAaP2j26sTrFvvQ42jc0Wf2ij2wackcr+BsNEpCR4D59wqJRkdjMbSOhA37O5bmAGb5nf2ZhrJ0DjBmmtjIT5idcmNfGmmkkUYaafwx4aaQYa5etRJAkycPzJ81GezbLS8vvyH7SCONmwnyBYQoMAgdKH+ooCX3gZtqAbNzSHlsbHkrieOQejcZ+nYYMpfIMH2mjQDE642RCxxglv3Ee7omPVCS7tI7/yS188rOB82/UxRGTGRkg3pIDDi+XzAMNGOFqMWY8GdKwjIZWuqAYIftPESGk8tPAMsfTGm/gwHKKQDX2BYwS2LoyimwPZkWyJYEhRmgN14Hrp52tU/hS0eNroyorbM8At72O5GF7iXhR6OniBSt8lrJCFKGWoxxv9gRCUGf3AlMWSjFsppyK0GRkS3rmzY0IHcijAvUko3Q2XlyL3W0yD1YUgY1ZzWw+kmxPsgtAvXglz1Q0O2PSxGw/DjgzwBNWw5KoohBE+eBDxjKBmYSOpZQ0oCZW2Et5KbuduvzMCQh1dYoyjmDdfxEwJjpoDG9K5x80NAntoN3vhj7m0/tglrzVIJVxUBBY6eDj29zLvT6gJGTxTP89X83bMFgdSddOipd8xcPg5Y/BOViu+LYh9ZgU+EBMNS7SIgQwa4YSYe3/17UJ8JG166ZOFdx3Ylk/F+MeGI9c3z1DPjYe0I4HDkZtHC9w+bCDg52yfsg2CmJ9j5YCdGEOWILEtuYHAN3d0qxxy7x3wu4owV8Zp9YXLU2OAoFNC91NUUqHuleRCp0f2b03tfBx7fKO1V5gSmLoW57uF+EML52FnrTj2PvRNMmSE1bah3Hwg2i/GUmOLPyoO54JuV33q0IKi0DjZpqWe8B8i7sw++WEpIpyfkzwfXXwAc3x8iCtPAuBxGJQ93Qm34kpF1mQ1I9Q+4z075u+Dig+lLiPu32FgXD5F0d7jak9EmS0IZqRo+IT54TAd6A1V3s9Qn52PS9j0ZkLMjIlrExHLSRTV0QiUjCPyvPskwy4ctILIZUXbCS2aaKTLATfOQ90MpHej+fONDkhaJ+ZrOkoFFTgIlz+7wtwCg+73xRyKY6CgwpgVr95IBt/GKwE2Fs5A5+91eIntoFteFTolp1C5FDbzjqk6g81V+L/ZvyikT1KZ60WHkuKVmXK04nbjQakd+gn/dHGml8kOB3fhEjZLLW4MvHQKuehOrBBs1OhGEdFTtik6hfNBJq1ROgoaPA9ZViy9NULSok8++U8ciuWKI8vao20pRFQpQIdSd+qLzGODAKqDhrxVz+TBmD3L4zGPD6Yu9QtfIxsePsBTR5QaICivKAyub1/t3VTwDDRoMvHxdb1ekrQKOnQB/bKs0Y4RAQDYPP7gPXXIH62H8RVY2qi4bFcER+H483Nmbz+YOgcbPEprQP4K52S3HO47Pi5XBQrktpWZ+bntS8teCZt4kKY17Rh0YBgS8eAZ/eA46ExPp75m0OQgF5vKCRk8CVcSq+OYVJye+DAX12H/jw26KUFL5Bz0AqiPTSRODPBHmT/9ZESiwv2xplbA12wYwL9c4Xoe76055zIeZ8E3DGmmyou8QI0i5ku2jEII330Ox7eheizdWg+XeKmms/G4OptAwYOVksxk10NANn98rcF5D3TZehfGwq3CjlSlinsnmgynNil6WjQFau2CjHKfxSTsGgW1X3BlqyUdRXutqsZYs3irVmwTBwa6LqIvkDCctuyLENGwP1wBeht/0OuHrGNh8UqzFzHsiV50WxiHXPeWGGNLq2N0Kt+5gsCnaJfW1Xu9i9NVaLylckKIrYbBA884tBq5+CynLPc6SRRhpppJFGGjeJDNPU1BT79/Dhg9sp6bZd+/7SSCONDw7cXAc+sAlcVyEEjPl3xpQMPgiQzw/PfZ9D9KV/BaovSqIrYsjn+wJAZi5o0d2gEROs72RkGQkIm/pBNGJ0j5EQYCKGOkVmPtSaJxPOifKLgbxi964QN9tkHQVO7YLOK4Jacm9K56YWbgCPnir2PhnZ4oGsPNBn9wHRiBRye+gcUrc9Ah49TbopMnNAU5f0LLvr8bkHbklUJG4UaPYqkb42kxNmMd2eAOvuMKSXnZ1m3N7snprw+NztOoJdIjEbr9gQf0zL7pcieKTKkJg1OoeSJuUIfPQ94MweIRllZEvxqbsdYKOjxfxuaz24D4V0NXMlot1dwP435DiqLkC/9K9Qaz8C9QEUWUgp0LSlgK3gnXTdkvFiMVZ9GTDtqZTZoZmwduL9FwqCC4b3y9LmwwaOhB3e8bJQQ2/9LXDtnPi9T1ncJ8JFMtCCDaIoYhZxvQGo1U9I4jAjC+rJvwGunoIOdgJ7Xk2wtOFDm8HTl/WclNbRxE5DlnuAs/KB8hPg7k7Lesjrl6Q9DHUIf4ZBOPPEKa8wUDIhppoghIwfxhJT3FIHrr4E9cjXHNZQAMBN1dCv/lss0cZ7XweteAhqZmqKazT3DqC5TpTFIiFDQSEDvON58IE3oTZ+1lV9i1vqpcMuO1/kwdsboV/8fxb5i7UoAY2cBJq2rE8yzzR0tJDO7GpngSzQ/HUJ60oS/B15f7IGEASOvQddewXq0a8lqGj0Br37lTjVJwbveRU8aUFMopx8AdBdnxI1te52oGjkh6aY0RPork8K0bHyApCTLwWoQfZspymLRFUnHDLs+CDFvdFToF/5XqyQwW2N4KqLUI99HWSoCvGRd63n21RPCXVZsvisJYGeM0TGKOUBfBlCvvP5wZePQx95Vyx/ug2SrNkd2xNBxX78E+eA975uqe0BojgyZ42Q7XwBYMQE4MJhUbAhJVaAZsGot05lAuDzSWEzK9cqbiqPELzi3pXJVZTcl/d6fh4v1L2fE/u3hiop6o6Z2m+bID76nhADTTRVQ2/6EdTTf+cq+d9nZBdY/7arnCgCGqrA258H3fPpge/nw4Qh7kVHsi2nuWuF9GSHksIx11xxV67LzHW30UpC0kwjjVsJ3FQjRBizM91o/OB3fg7d2Qq16O7et3H0fVElMdFQCb3ph6B7Pwt+5TuxeSU318oc96EvA+cPgivPS0PIrNt7JQJSbiHUvZ+D3vwTSy3URKhL5o8jy+Qzc3zgqMw3o1HLYmkwYZuvcs3l1Mgwo6eKNcnBzTLe5hRCLX8AlD8UfOmoELi9fonjS8Y7v6s8Yqs7cyVYa+DyMejtz4PPHZD3fEwxAkBjFfjAW1CL7hJ75RM7wFdOxOagCBr2Gx4fdMVpePpqOdJab+3PnwGEYCiqaqB0EmjEBOi3fgzkFAo5JMU5E/kCoh76IYE+uRO84/exv7mmHGiqBq16wrEerXwE/Pr3AVOlJ5Alc7AbZDXIlefB7/9G7tFeLIp6hC+Q3OpUeYy5W2/PVS+fE4GPbQOtejz5OiMmiFqInajqCwA15eA9r8CumEyZOdLYZNok9XT+pmKOa0KvD+dQXS7Xu6EKNJCmMtNCOF7Zysx1Ko+QysNByWH6/MD4OdDHtgJ7XhX1kMUbQUTgq6fl3eDxGjajIWmk6v/RDRpoSAnUE98EXzxiNawYxDCavkzyoXZk5gLj+/iOGsjxDR0NWnyv2NBr47dnADoCzswVa9jTe42Ve3uGpSmOQdDRCLD3dfCBTZY6MkHeoS11oCmLwcWjgP2bZPyquwb+9X8HP/Z1sSdNI4000kgjjTQScFMyzqGQVXT0+QYhgecC+3bt+0sjjTQ+GHBXO/Qr37EKi6314KrzUA9/tU/d9oMBtfEz4ld+7SyQlQ/MWCEdrhnZrtKetOpJ8Ns/BeqMrtCMHCnwmJ0VgAQi+cVJLUFo3cfAz/5Py39XKQlgTLnS+GIREfjIFvDoqdLpkQJo+NgYEYfrKqBf/4+YNzDvfBFq3cdA42dBn94DPvwu0NEsXevLHwANKRHv9rG9K2BwJAzOypUEv91PWHk+8OQ9DRsLdf+fS2GorUkKqG0NziRATBLWOb7QSHfrDZq0wJGYisHrMwrwvRxTbiHU498AXzsrtlzXLxrFxCwppjg6u0iSDC11Egzb/JsBMiwibKoIuYWSzEgRHOoCjm5xWkmxht73Bjy3YMexWv4A9Js/tJKtpjS0ncjkC0jyLN4Ow+cHtTYASXzD/6DQ0WL5fgOIKSKxBs7tB58/AD6zF+rBL/WZtBAPCmTCc/8XwHUVYgE2YrzY+Jif+/zAxLlQ7U3Q259P3EB3R+8y3nVXJSFn774jIUPxs/8d3N0hn4W65TlShmpSOCjrT1ksnvLhoBQ1zAJDwXCodR+1LtOxrYkd+43XgWtngDj1H731d9JFqDzG82OQN8rmp6SmRB4vaO0z0IvuBj/3vy0CpXFN9M4X4Xngz537PLJFCAFgGVkKR4jSUXe7dU18AYA1aO4dSd9hPR7X2mdA42eBr50FsgtA05a6Ex8vHDGIEXHXq6EKfHY/KEVSECBjBuwqOSZCXfLui7dLyisCerM8+BCBPF5g7AygsBQoGjHgZ9J1H3nFoHl3gnc8L4U7pURZ5czexI7eUBf4zL6Y+htftStT2OYi0YiMe8xAxRmbJaIftPYjoHEzwBVnoTf/VL5nFgsJRvLcC3h9ol4yfVnPx5+ZC3X3p6B3vGDZLZWMF6s1u81B2Xw5yoYq6Of/j7VcKUBDkvtmgtjaurw3jIQzTV0GGj5GLIZGTABXXwYfekeKjENHyTpFpUlUlHq2XOjxHJUCxs0cFBuMBMIFIPPrynNyrw0QNPM2KYKEumzvZBUbi7niDDgSHhzizYcEQiacK8UXE74AaP6d1jqlE6XgErPX8loWqknmJmrW7dB2lQtAlIpsxPw00rhl0W40mUUjTgVUrUXRZeSkXuNYPncgcWFnK7D39cRCenc7cOmoWK4ms11NAhoxAVS2ANzRYpE5TEQiwI4XDRIoECNbg4QMOnQ0ECuwUuLcqD9gLeO1x9OnQqWauRI8bZnMrbNygeZaRH/zv4CaK0Lq8frB5w5Arf0IKEmsx+/8XFRMAZnbs4bVeWAoGe55FVxYIpYca56CPrAJvO8NZ/yho6J+0FcUDJc5SiQoc9tAJsCGOg/YoXbL5/ZBPfClWLH7Dwl8+N3EZWf3gxfe5ZibU/5QqCe/KaTqaFjUOW+gaqI+skXmFMwpEZodILLUBXuyhh0IycYOZqC9MfnHDVXA+UMSP4VsdlOmutGlY4CNDAMAtOYp4P3fgFO5t7Vhq6biGzL6AuOZO7kTPG9dnywS2Zijc9WF5HkqkzBuWrYrj8znsvKAo+/BbELi6kvg8wehnvkH6H1vWPbDhkUpH3kXPPO2/tmFDjIokAlyiS1o7AyoNU9DH34HaGsEjSgDLbv/hsReycDtzeB9rxlxv0JM5VIzsOVX0BlZct/qaA/NckCMZBWNAKf3gC8dsd7X5nPJEPUmjw98Zq9BbLQp47bWQ2/6MTxPfONGnW4aaaSRRhppfKhxU8gwRUVWwruuru6G7MO+Xfv+0kgjjQ8GfP6gQ8oSABAJg0/uBN32cOrb0VoCuUBW/2VEfX7p2u9FUjm2fu4QeB7+CripBgADXR3Qv/9nOIpGpICay+D6a9LxG7+NQBZ4zmrg1G7prAWA7i6RYA51x3XMU4zwwFdPp0yGsUPvfDEhWaW3Pw+KhMHbfhdbzNfOgl/7d6in/jalooY+uQO8f5OxbZZAWimYNkA0ZVGfj3WgoOFjQes/Lsd39H3wnlecK/gC0uXc2WJ+A7RgfVISFuUXA1OXiqxsTCo7Q4g+qRKTlBJi1Jhp4K42kWweUgIOdYN//E2b5LZROMzMdf5e5nHbFWpIQS26J3bf65M7pYg5dDTUrNvdrUvamtxVblrrb8lCFo2dAVr1BPjQZrlmpWVA6UTg/EH5u3i0EGZ2vSjPTLDTSL4TEAlCH3sfatSkG+5XfdORUyBkkK52Of9oWBJddtJT43Xwuf2gGamTFnoCxdmIxIM9fkmudbUbyhFGkja3UJL1PSGnUJ6zzBwpDACS0A8773+wFns2s7Dt8QKFI6BuewSYugT63V8aShYMjJgIddenQIFMcDgItDaA25vdj72zzSoBsAZv+TVw+Thi73iPIWUfDQtxZ/TUns/HBgp2gs1kvx3Vl8A6GrtXua1Rigz2caXxuhBy3I658Xr/yDCkgIlzkxZIYoi3nortOAo+sAnRU7tAoyaD5q8Hqs6DT+0WifdxM6VL25bcI69P5JvNjlYTXj+QO6TP5/BhArMWO5tTu+X+9WWAlj8ANXXJ4O5Ha/CpXfJsWEslye8Gu7R4Zq5113m8VvHRvGdNWzKv31BfCoO3/hY8+h+EWGx+214w8fiFmAKkbjExpETe6SZ5p+I09K/+G+jOPwENHQ0+swfc3gIaPgY0axVozhpRVQPk2FjLnEpH5d3B2iAjBJzPX8FQkYRva4R++TsiIw+A978Bmr8eatFdYtEwZrqzuzQzFzR3TWrncqORrJN0kLrEachwqAf+HPrAJuDMvliBNXYdfX7nePNHArrjI6Ax08AV54DsPFHnilMsUMsfgK6rcMy9qAdCAE2cCwVAH98GdLaBxkwVK7N+xjhppPGBYthYeTcEu5zLjTkAXznVrzgWgBCh3WBac/YHproZkVOsQRtkbK2dtj2RsBAY138c/Kv/atjOYvBsY4IdwIiJCYTs3kAeL5CdD26shn7hX6z5lTbsSjOyofe/6dr4wNcvWUQYwCjiuyh0hLuh3/k5lNcvlqnm72yO9UQACOhoEtXfXuIEx/EHMkFL7nHYvcIXAE2cD977qnPlUDf46BbQmqdT3v5AwG2NRsw56oYSTpi13E8JH2i5x+OI6qQ8wOgpN+x4HKi80AcSjJG38niE3D5yMtT8O8Vi+uQO8J7XnM1T8Yg1LaW43G29Ee4WzADAFw5DclZe51zQJHy7/MaUmQu6+9OI7nwROPwOXJVfzHjT65PPerNzSgU6KgTDvpBh3v8N+Nx+4w+DvGQ/T49XCM5XTgqRwuMTdZhgF9Bx2b4lOcW6CiEoujUxRCNAcx1QYpFhuKFKrjERqGz+DSetsY4CFw6Jand2vlhwx8WSNHkhPINsG53y8TVVQ7/0r/IOsRNETUTDQmQ0Fcp7IhbZbzvWQEdrHHHR/MxQKEqmYFZ1PnFZGmmkkUYaaaQB4CaRYYqLrSTSpUuXeliz/7Bv176/NNJI4wNCPBHGZTmbagdDhrvKvupTu8QapKsNyB8Ktfwh0JjUi5Ju4I4W8N7XpUs+Kw9q9ipQkuApRp4YAqBgmPiz2ostgCV/b24/GgFv+ZXIFgMSfI6aIonB4lHAyR3iedvRIuelPBJUm8X8PgTD9n0ivsuUGWhvBh/clPiFzlbpYu3Fb5yry8E7XrAW+DOFmKR84tc8dUmvnu03GjRtKfj8AacEdmkZ6J5Pg6ougdsbQaWTQAVDpfi851WRMM3OB81ZA2WQedTaZ8BgSRwAcm/c8Uy/kmKUmRtTzCGvD9GsfGenYzQiChB115wWEYbiBmXlSl4jkAl9/iBw4RBw7Zx0QTEDp3dD734ZdO/nocbHdZznFVm/kx1DSvpMhOGOFsCfccM7a5Rhq8TRiFVQX7zRQd5R89aJfHY0YiV8PF6g8pyoVqRgy/RhBnm8oPl3gt/9pbM7CHEJsAaXRNYNAAe7wK/8q0XsM/+XlQe1/MFeZbypYKjVce8x3n32ZJ7py24m+braAZB83lwrBfNFd0snn/KAsvJi72t9fDv4wJvGsUWMQodHtqMUoLxOUsmlY+ALh5xkkGgYiPokgZjX8xySzx2APr1HCigTZgNlC9wTutn5DtIWV11w7zRO0mVIRYkWS4MJmrJIOtCicYm8cCimTMRN1XKtOttivxXXlAON10F3POP4mlp0N/SWX8GepKO5axwqQ3+I4LP7wSd3WgvC3eBtz4FLJiRViugX2hpjpA4HwsHEAgAAshG6aNZKGQfBMQskRCPG9wzSWTzBsLsdqL7sLFZ6vLAyt9a9TONnpXQKfPS9RPuKSAi89Tdg8sg+oxHwqZ3gPa+J1dikBeDrF0G5hWKpdmIbuKVe7CEmzAG/8X0n0TSvGGTMdfjAW3LNTCUZjwd8+B3wlEWiTrT+46CLh8FVF4HcIaCpSVSU+oALLbU411KHfH8G5hePQWY/Cak0eSF472vOhdn5QD8Ickn3UTwSnrs+BU1KbFDsn01d+odPOnUBKQVMXgSanJz4TcWjoB7+CvjEDrm/Rk7udU5CE+fekmp9aaTRGyiQKbY9b//ctlBJvAv0XOgzV5+8ELzvdefCrDyxfIiPZwHQqAHE/iXjgIuHE631zLlWNGwb97xAbiHIVJTtajcUCQfRLokZqK8Ukk4/lHL52PuJhFNt2Dq11IFZJ8zBE+z+/BlAl0vRFpAx+OROcFsDED/mgIAMg5ze1ijqOX2AmrkSPHyc2B37AqBJ88GXT7iuy821fdp2f8DRiBB9zx8CwIA/E2rlI2I9fQNApEQBzLSpNJGRLXmBmwQOdjnJXibpw0AUhBa/H7nhMHzDxH6VSsZDd7YBe14Bjm6BvnoKtPYjUEvvgx4xEfza94BgN1wL9TpqWSaZFpvKI3OaULdBtOvhmcsvFPuvZDCJu0o54zFjWkzT3JULuaYcOLFdVFHI+AKz3O8EYEQZ0FQt16eztedj7A0Mia9yCpJaMrof4xWLCANI44Zpq0uG+vCY6aDVTwKbfwqOWcBxYnxn327FaaBoJFBf4fzA6wOGDLPWO3cA+r1ngWgIiGrwgU2gO/8EatKClM+hr+C3f+6Yk/LJXVAPftFhWXkzwQc3S9zRw/WNjTemFa1JzjT/Nj/z+i1itVJA1Jbvce615/0xQ4eCUP4bm8NLI4000kgjjQ8jbgoZpqzM6hZ5++23b8g+7Nu17y+NNNL4YECjp4KPbEn8YPRUsd15/1npYGYN5BZB3fG0w+uaK86A7RYcLXXQm38M9cQ3QbmF/Tom1lqshJqqZUFXG/R7v4byeF275rm9WRRuomEJVF060+Il9Pno+xYRBpDgp+IM6PbHQFl5onoBWJ1d9kAmkJWUmNMjlEdkT83j01qCMtZA9RX5r1mA8fikABQO9uoBzBcOxe3HsCqZvAjqtocHnSTBLXXga+ek+DRmmmvhhZtrgeZaYOho8Y33Z0A9+GU51sZq6VKbMFtIFWOmWuoP0Qj0a/8unuWA2Ea8/yzY6xM5aF8AdNenwC310r1RPNKhdJDS8TfVQB95VwgJJeOg5q0TwlPYsHoxA2GvD6ivhFrzFPT7v7ESUCXjoe54GlAe8Ev/KsXyZIFuOAh+9bvgB/4cZLNIIF8AtPgep+2T8kAtvT/186i7Br1VfKzh8YGmLQEte+CGF8Lir7edvEMT5ohdxundiFkRmHZSFWeAP3AyDABDIStTuorMxDezYa1lXKtiJ2GCm+ugj7wjUupDSkDLHoAaBGUOPrNXnkOTyGfep/PXpWwJQmueBgpHiJVRV7uoOZmy15GwdBXGJ4v8mfLv1gbwu7+Q91FWHjD3DkneBzuB8wcstQRSQLgTCMFKbE5d4hhD+NpZ+YfX7ySRRSOgKUsSVADs0Me3g3dZXa5cXwFqqpGCzskdImNsSk7HJdcpZ4h7GnXYWHlP2cYbGjdL7DhuIGjCHNDyh+TdEQlZ11CRdX8BQgr1Zzhknvn8IfCiexydejRpPlTuELHMiURAE+cMil3MLQ9797UJ1uDy44NLHs3KtSwH7MjOB8bNBM7uiy2iacscVjo0ZjrUhk9AH30PaG8BjZoEzF4N6mwF8oqgX/23RFUfQMiRY6ZbxUoiuRdC3bH7gcbOAM1bl9IpsGkpGI/OdtmenRjW1Qa9+adQH/vPUIbNFgFAXOc4P/RlmYc114KGjQHNWR2bq3DVRRnfddTKK/sDQibOK5IxqBfiQ1/wUvlR7KqxmjPeqzqHz0+/HYX9kJun2auBrnZRHIoEgWFjoG5/vM/zlJT2teYp8K5M4OROKQQHMsGAk7CahgM0ZDho5SM3+zDSSOMDgZq6BDq7APzq95wxpj8zpTiW5qwGOlvBZ4RIjKKRUKufEEJA5TmHNRlNW9avRhiORsD73wSf3mOoDvZQuGYNRDRAEdCw0eCOZvDuV2Qc8mf2XpzvK0Jd0C/8C2jN06DRU/oUT3Nrg2Wtaz8mrYGS0a5kdCocYa1pkhy8vkQVAyIgEpG59NGtsszjtcWiRsFdeYDh41I+Zscuho52KsoMG+NuUThsbL+23xfwie1OC8JQF/R7z0KNmDhgImwyqBUPSS7KbA7zeKFWPnZzx1avYZ/DOsGm7Hh+EV4bOQGtvgACOoo7SsqwZtZq6ItHgM0/tu6nmnLw7/439J/+I2j4OODpvwe/9ZPkChUmQYUh95M/U2KPSBhJnzWjGY2mLQf5M5KeDk1aIDlIHZX8i9m4kZUnKoM2q0PHIV06bpsjmsdA0qw0aQFo7h3gTT+SmHWgBDkCEA1LA0kfSNJcF0dWUUoasLLygNFThSAyayVIecB2BeHebN6Gj4Oatgz6zR845t40fz3IUKDkaAR6z6uibmWuEwH4rR9Dj5kG5VCqHBxwTXkCORuhLvChd0Frn3H/Uqrb7moDH9wMrjwPyi4AZq0CjZrU9/xfQ5XMy1NRNWIWFWSPF6i6IO/tSFDue1LWfUWG9W00mlz9pTe0NgDFpb2vl0YaaaSRRhp/ZLgps+5Zs2Zh2LBhqK2txdWrV7Fz506sWLFi0La/c+dOXLlyBQAwbNgwzJqVWodiGmmkMXig0jLQ7FXgY9tgTuBpwlyjG+wNR6ILbQ3Qb/0E6pm/jwWEjq4HE9EI+OKR/heUKs9bRBgb9IntCR2aXHVRAsJgpxWEeAMOmXiaOC9BIpjLjxtJtbD8V3kBD4v9kc0mgQpLoO79rARhTTWSHFp0t6iKpAiOhKW7LDsPNG+tJT0cMnzRvX6jWB6R5AaRJBr8maCxKcgzJ5HEp0DWoBNh9OF3Y5YhDABDSqDu+1zserDWIgt73vCZVx7Q/HVQCzaAvD7HtXXFtbMWEca+35M7Hb895RcDPRS/k4FbG6Cf+yfL17fiDPTZfaCNn5UVPF6nRzARaMJsqFGTgepLYsdg3EvRw+/KvdqTxDAARMPQe1+Hx1bkBAA1YwV08SjwnleB5hpRi4lXikl2HtEI9KYfWoX4aFi6nbPyQfPWprSNGwUqmeAkmpnowzPTF3CwU1SkrpyUZ2bGCqiZg2NB1C/UVxkJQ0PlpLsDMYl3ACgeDZpkFSG4vhL69//XsgurugA+uw/88Ff6ZbfjQKNNzUEpQMn7gLraU94EebyikhXslG10tVpKLvaEks/okiKykYDMJKp4Y/OWX4laVzgknwWyDXsXk4DmNYiAXuDqKbHAKxgKXD4Ori6X96PXL0nTSFi2O2E2aM2TCcdtBx95N3HZhUPAY98QMkKo27KQunAIXDbPIq+VlgHDxgC1V20XRUEt3CAkobP7xPu8tAwYPzvl6zoQqHlroWfcJnZbrQ3yvo23LWDtklRlUY+JJ1oVlsg9mVsIdLeLH70/AzRpwQ0rNNx0JBsbvX1XGesJ5AuA5qySjkT78vl3Qs1eJTaNjdVA4QhXi0AaNxOeeHKSsR7NWC7jhx3DxsgYNaREih6mnVAgE1j5KFTxKJmL5KeufkN5Q8GmHRpgTBVZ/md2RtoVboIdMjb2QKqi/KGg2x9z/zASMmzZbEnlUDc0Mwab6lnd2eIgwgBAa7gb71SdweMT+t5BS0qBlt0PXnQ3EAmB+kGoSXlf/gxQRyvY67OK3Me3Ci9x+QM3bL9ppJHGhwdq9BTwg1+EPrhZ1OGGjgYtuielsZ2UR9RlFt8DhLod36F1HwPPWSPxevHofltw8N7XwMe3GQerDBUKm/qgW3GRWew/Qt0y3zSbS/qCjOzEeZMbWurAb/4AnJUHtf7jKc/LqWS8EEl9Aaeahz8jaeMDlZaJLdOFQzIOxsZa+0oU+y+NnQ4+9I787QsY5ABrnKYlGwdtDkfDxwqB3EbgRW7hB6I8y5ePJy7UUfCVk6Dpy2/IPqmoFOqpvwHKT4gC6tgZoN6sZW8wyOMVxcCDmy0LSlJoIMZvxk2FBgEEBD1evNlwFcOaqjB19ytOQohhJcb//lWwqfKS6jxFR0XdxBdAjHAVTzZRntj2aNiYns+nYBjUnX8CvfsVyf0UjgAtujumBpwUteU2FRmynhMdld+r/CQwc6UoTXW190wwIRLVRbBYAzdVw+jukPeRxyu2XH1sgqMhw91pEayBcwcki3Z6N/iOZ0DTl4vqEes4q7i4LeQPhZq6BOQLQD32l+Cz+4FwEDR+trMRo7NVFKHiFUzDQeDYVmDR3X06l1TASRRvubHKdXnK29VaiP9GXphrK4DzB8GBLLH/XfloghVTUuQVS2ySKro7oB75KvSWXwNndssyj0/yF8azJ/bQkIaDYKdhpW6o8Lo9H/Hw+fulPJZGGmmkkUYafwy4aRT0devW4de//jUA4Bvf+AZ27NgxaNv+5je/CQAgIqxbl1p3YhpppDH4UMseAE9fDtRWiBWSoVqg3Qra3e3CkB8zTf7WSSb5qbDukyGYJDnlkrTSe14RYoO96zoSkW4Lrx80ajIwYW7itpTHKFKbxx8WQoxLgYxKxoM2fqbv5wGALx2D3vY7I0lH0nW/9iPgk7uB8uMSPHn9LudmdHT1YmMCGDLWJ3Y4g31S/VOv6elcWhvA+9+EIzivvwb9u/8NZOaARkwE5xWJ4oMJHQUfeAs8aipoeArdY8mSmX1NciYBH9gU5wPO0pFx8Yj4acdZQdBkKYaRPyPRN/7Ilt6JMPJtoPE6mBkUZ4mBcweEZAMA3R3Q7/4C1NUGNev2njd57ZyrAhKfOwAMkAzD3Z2SXOmnMglNmg8+/I7Tgs3jA824MQlLvenH1jXsbAXvfAGaGWpWD9LMNxJFpfJsA5IIyciWxMiYaeLZPWmBw9ZLH3pbCCaA9WhFQtDvPQvPR/5hgMcyEsCBJMtTA7fUWbZkZqLdF7CsVzpapBAb6141bI4c8vBsSdjbO3eDnaKcEZMlVg51E75yEjhSI6RLrYVEEw7JNQ1kAoEsqNVP9aiGxKxljEj4QIMuHRKP87hEMJ/cFSPDEBHUPZ8BH9kCvnZWlK5mr5KiBXDTLOiUPwAYpC8NtopJJjw+GdPsCGQlqBLp03vAu1+W3ycSlt/CnwEQgQ+/A7XxMx9I5+9ggbUWy5ReQNOWgS8ehWM882fGrHoGE2rhXeCCYZLsJgJNWRyzKKIhJX2SXreDZq+WouDJnUCwCzRuBmiZFNnI6wPd/afQ1ZeBq6elmzQVcq3bfuasBp/fb9igAbFrFk+QMYc35bEsKlmDj7wnBTQdFXLygvU9d9gqBbiVEc4dAAZ5XlPR3tSn5amC7ASVGwRuawRfO5O4/Mwe8LL7erXBSyONNP44QKVl8JT2X4GZfAH3+Hjo6D5b8NjBWoNP7zX+MC04DdtN0444GXk7EgIqL8i4Y5Iye0OsyAxLLS2VfEVQ4iL9+n+APvk/oVIgzdLs1TKHbbxukb6Hj4O6+09BOT3EV2OmA5eOyvwtiR2nrDdN5gB1FeDyE5YqbDQCeH2gx/4SKl4Vt/aKxLzDx/crxqNVT4DK5okias4QiWl6UP4YLJAvw12Vxndj902+gCiN3NC99A20YAOQmQM+vkNix/yhOBbugo4f77XG4dqrmGp/fuwEC9aSw2trBPo63wkHDYvOONUjWAqVNGFuSsQxGjcTauwMaQbyZ6Q2b+lsNRov2Jh3GscQiwUZOH8A9NTfgQ++Bex73eU5J2nAMFVSikcCU5cAm39mbUNHAcicvc8oLQONnAyuPGct00Y8asYorfXQm38C9fTfQW34pBAWW+pEeaupVnKbIaNJbspCqFVPxprcKH8oaPE97vvOynNvlCMCNyY2Gw4GaOgo92d0AOMDAKDitNUgGQ5a+d5wEFxxGrzph/A89pc9boIjYfDFw9J4ljIMi+FTu4BLR2RMYgAw1bcM0mY4KIQWEJAzRMatoaOA84eMfEcvmLQIyjPYNP800kgjjTTS+MPATSPDfOlLX4qRYXbv3o2/+qu/wj/90z8NeLvf/OY3sXPnTsd+0kgjjRsHbqkTGeLqy9JZMP9OR5BK+UOB+E7hZPKTtuU0aT740hHn58rjameUMkZOTpTeBxw2M4BR5Ky9mmg/AAaaa6Ee/XryffgCiWx91lJAHCRwVzv0ll/azoNFMaewFOruT0L/7B+s4NzeCRfIsq5xQxUwanKP+6HiUVBrPwK99zVJauQWQS3ZGCM1Ddr5xFslREKSSAx1A9054KZqm2yoM3XEl4+Dzx8UNQYi0KSFoMX3OApifPUU9JEtVqIgVnAHaNzgKIfx9UvuyyvOQN3zZ9BbfytkL+UR0sKS+9zXb2uMI9X0tFOWROrFww4LFu5qF+nx+NWPbAHPXJlInLnB4EgYvO05SRjoKFBUCrXmaVCRu3QrRyNA1UXpkCmdFPstKSMb6v4/Bx/YBK69CioYBlpwZ4JV2aAcc0OVRYSxLz+xHbhJZBiasUJISaZ9CREwarKQCtzeqQ2VBqkwrnOvqQYc7IzJHvfrWKYukSJ0o61ja+hoUF88w02Z6WCXk/wVyIb66H+Cfv6fLUUjn18IFR4fgKCciKl8FXs3wDpVUzHHvNfjCwzhoKU+phSQkSVkGKVAZfNEFrqXrlciBRpZBo7vBsvMBfzu15btXbwAKJAJWrIRWLKxx33dLND89fJ+rq80FijQknvA5w5apDnlgVr5qPOd21IvNoes5fcJdUPUPgyVnFC3qFrd9/kP/qT6AGYGH9ospNBgF2j0VNBtD/do1UgjJ8m4efAtoKVe7O+W3X/DlDyobD4ozoJrwNskEjJWEkIWXz4Ofu/XMTn96JAS0OzVoJJxoIJhqe+neCTUY38l5OOrZ4zuR7+QYbo7EVOJAcm4PWS4ZTW5/03wYUuZiY+8C7Q39yyb7s90X96nRHZqGJpEsWzYDVIyG1QkKwCbqlm3UgWvj+BwCLh8FNxUA+5oBYW7geHjQNN7tl1II400LHBHC7jijMwjx0wbFIsXNpoGksUGfduYlg56u4qiiWAXMHkR4BInyXeNd1zBcNc4oIedAiBg6X3A9t9LA47WvX4LOgp0NIPf/jlw95/2ujplZEE9/BVRqmhtAI2Y4LCZTvq9y0dF7Yu1Mec21AWUR44zuwC04kGoGYYC5vIHRUWxpU7m0pk5UGs/6oi5OBIGv/UTizxJCrRwA1QSG5qkx0YEjJoCGjWlT98bKGj6skTiZ1Zej+pzf6ggInAkInFduFvyXUNHwslIhsRMrKUwb1rbutIV4r7XF3CcipPHC5qzBsjKE5vxTT8Cxs8GTV7YY06DiKThau9r0LVXgYJhUAvvApmNd/GImhaabDslo4mMWd4p3Z3gt38CNWcN2J8hqsymjRiRzF+jYrNE42aCbnsE+o3vS6NFqNt4JwghhsPdfbafJCJ5T5zZK3mlnCESz8Y3v3W2AtcvgcZOh8dGWOdgp8y3vb4+v7vJ4wXKFgAn4pokfBl9mvv3BTR0NDB2JnBuv6WakluYsh1rMrC9mcVuF2fmFBqvg2uvJG3a4FAX9CvflXyq0Zho3TSipIRAtsRJ8Q1u3R3g959NPj6YBM5AlkHUCYnCUWuDMT/vRRXG4wMqTsv4kFfU87pppJFGGmmk8UeIm0aGWbx4MTZu3IjXX38dAPDP//zPCAaD+Kd/+icEAn233wiFQvjmN7+Jb3/727FJ8caNG7Fo0eD4vqeRRhqJ4KARCJhFsY4W6OrLUA98sUcJU5q6JFGCP68YGGFJcdK4maBF94jnb7gbyMoTX90+SPAn7DczB2rVk9DbnrOILqVlwJw7oPe9IcVdZtDEedIF1eXS9d/S0PNOdFQKLqZagccjXWJN1cC4GT1/N0Xw1dOuXV18+TjU/HWgsvnOQq+OStBrBrykxCLE/t22Ril4FgxzWCrQxLlQE+ZIAO/PuCFECsoZImFdNCJJyljSUluKAjoiwV1csYKvnnJYX/HxraI+supx+buhCvqtnxi/i9GtF+wEMnLkHhss9YW8QqD2SuLy7HxQXhE8930e3N0hCZ2eLKaiUSncR0KJ3Ub2bnkz0Pb4oDf/FGiug5q2VAr4na3uHYnm8p4SH6MmSyIwTh1mIGpAvP9Ny94KABqqoN/6MdSTf5OgtMBNNZI0MrvJMnOhNnwypv5DBUNB6z7a72NJGckkzgdJSag/oMwcqIe/IkSn5lqxLZm8KHkia+gYIAlJCwPseiR/BtSDX5L3TGO1SD1PWtAn33Pp+qXEJJFRiFUPfVkIXIaNHLLzpRu2qcZSjXGTeTcTWToqRMy2RieJLjNX5LvtUB5JVOYPhVqb+v1FKx4Cv/bv1vPi9UGtehwoKgXveSXhOaQPWYI9VnSpPA/uaAGNnCTv6/kbgCsnweEgaOx0UFae43tcfsJ6V5mJX0De5aZ6kd0e6hYFH9sKPvCW9ffVU+A36qEe/6seu0ypbB48N0AJpjdwRwtQf03G8QHMlXrcR6gb+r1nY0QYhIJA5Tlw9WVwIFPUaVY9nrp6SFc7KJAFHjJcksrmOyQzR8hwREAgEzRuNmj5gyBSYB0V1Zr4Y7t4GLz8flBmLrj2qhAn6ytBRaWghXcJyboqjrxGHiB/8JP443KLMK1gOE7biDZ+5cEdpR9ssa8/oCElUgiOIwnRuJk9qmXd6uC2JolZ2hoMVQgG+wJSVD6zDzR+JgASQmQ/CvJcc0XmpIEsGQ9NBYo00vgQgVvqxc74+kUgrwhq3jqHta4+ux/8/m+AUJco4GXmgB74ElQvKp3c1S5zxo5W0KgpoDFTZXlrA/Tmn1gKmoUjoNZ/Qmxr+wnyeEFjponlkTkXMQvVmTky3/NnJreQVR5gzVPAb/8/q9Dd48nFmNjA5p/2/YCZgaunwV1tKdklk8cLTJyblGrAV05CH9sKdLaCRk0W1Q9T0c/esEMG0dTrA01eaBFhAFBuIdTj3xD7j3A3MGoyKI5Qyie2O8kkrCXmGztjcEhNNxg0fhZo9ZNCrG1rlDnusvsdKpsDBTdWg88fBFgb9tqjBm3bgwnu7gDv+L0jzp3dVIe3S8Yg6hj3CfMPbAbamuU5ibrkG8x7zKMg+QpK7Tny+i0bGK/PaGowmpiGlBh24Iad9pWTQP010IqHkp9TZyv0a/9uPed1FZJ/ePBLiTbnDVVAS63xvjByLMyAMsgwJikBBFSeh648D1r9lMQ0Zk6OId/3+kDrPyGqJmf2GA0FJOuaqh6sgX1vguurQPd8uvdrYwN5vKAZK4AZKwAA0fITrnkL1hHwnlfBlRdAOfnA9BWg4lLQJFsDVUsd+Ph2mReNmAiasaLHPJVa/aQoQ5rzQ69PiHSDaCPNTTXgfa9Lo2V2gfwuXp9cZzIUHl1UT7ijReZfKeQiaNRky6bV/k60bzeSXEGLT+22xizlAcggGHr8cu8oD7DqMWDv6zLf1BGJN3x+oKMZCY2TzqOTc21vkvd22GgCSlBMSoJoRHLyz/1vaRC8CY1waaSRRhpppHEr46aRYQDg29/+Nvbs2YPGxkYwM77zne/g7bffxl//9V/j8ccfT4kUEwqF8Lvf/Q7/+I//iNOnT4ONiUVRURG+/e1v3+hTSCONP2rwxcOJlipGkaJHMszsVdKZb3Zbj5oEuu3RhMK4mr8OPOt22UdOwaB0niE7DxgxHmiuA0ZNEiunva+DT1pWbXximyElGif56fVLJ0wcuKnG8DYfCSoqFZnfuECMigZPvYJ8fvdQyEje0MpHgUCmJF88XumwtiV2aPpyh5Sy3vc6+Mh7sYQhTV4IWvVk7PcgoxgFSBcYasol2BwshZjSMqB4NHD1FJwqFobyiSk/rQ35XPPa+jMTfyMAfP4AePkDIF9AkhBmQdrrE0KNjoBmrIC6/bHBOX4AtPR+8KXjzsK+xweav95aJwVlACoYChSXAvVVQtgy7WEKSoAZy4BdL0viyWNYXZlKE/tehz6+Der+LwAFw+TatDXKuSsj+B4xsddniDxeqLv/VJRs6isloTN1mXRk9RN84VDiwrZGuY9GTHAs1tt+55RV7mqDfv9ZqMe/8cEG8sPHCSEuLrlE/bQDGSxQRlbKBC6aOFfIYfEJF4/H1e6Fy09YyfPRU0ALNvR4z5IvAJrR/+QXebxAXpFBOrTZo/gzRFFp+nLQ6iedX5q3VtQ6jr4HPr1HEpOknOOQ2U21YD1weo8kMoOd8v4eO0NkwA+/a70X7YpkfbB5AqRorJ76W3l3RULAmOmxa6ZWPQG94wXjGSVQ2VzQzJtksTUAECnp2LUv8/mBsnnJez7tpEX7vWb/wg3qIhxMuClsobkWuH45plByq0Afeht8cLNNfn0R6PbHU7J26hOqL1njTjRqEYuNsY/P7hN1wBRUovTZfVJUBWT+YXbN+gMxawa1/hMx66cYolF5fs3iitdnkUWDXeBIGPrV7wJdogjAbY3gK6eBuWuE5NHWYHXE+wJQs3uxD+wnPjZpKfbVleN8Sx3y/BlYMXxCUsWY5spWHHv5FBrKm1EwMg+zH5iGonH9sxQcDKh1H4V++2eiCgCIesptD9+04xkM8MG3pJM9VliAReq6fkGKYV4v+Oh7oFVPQE1xNtTo49vAR98DOtukaLriYZmzwXj+9r9p7evQ21D3fd61IMyV58HH3jcIhpNB89aBMvqv1JZGGoMFDgfl3WkqVHa1SfH43s+CSsvAwS7wtt8Zcy7jGepoAb/wf8Gf/EdQwF19i1vqoF/6V7FEhjQu0PTlUCsfhX7/N04r2cbr0O/9Gp4HB6buTCsfBV87K+OVOQ/mqIwLrQ09NwYEskD5xeDsfIlX7Moy1h6QUmEyFXi8sq3ONiFsDwB85aTYvBrHxs214OpyieMqTsu+YupfZCmljp6asC1SChidnMDJFYl2eubynsgwHI0ICbHxOqh4FDB2xuDPVez7a7wu9pWKQGULHEQrNWUx0B+7mlT2e+kY9Ds/j90/fPR90KrHoaYuSXkb+vQeadaKRkAT54qd6g0gpeoDmxIaPoaEg/jI5dN4dcxkNGblIltHsfb6FUzpaLXss/yZYkt7/ZJLE48HGDlOnu+oQWbILQQ6moRInXAQxveV16nkF42A973hVIwBwKd2geetA2W5PzN8/qBFhImE5b5nhn7v11APf9VBmtA7X7TZ8BrKMMqLmMWa+cyYRBFmeReaypd2REJCgnr9P5zzjXiEg+ArJ8BVF2IWuf3CxLnA3tfk+hEB3gBQNAK8701RiWUGVwWBU7vBGVnAsHFQq58AlAf6pW9b5331tDTWPfDFpM8j+fxQj/8l+Pg2ye90dwL5RUD1ZTmOAYJDXdCvfs9qSGwz5mwZ2VZsGeoGn94DWnS3fKfqIvT25yQ+82WAZt8OtfCuHvdDuYWgpfdJc6bXJ7+vzYoVOQVAybjkG7A3dHgN9VrWMiz4M0BjpoGmLweGjwMf3AxurAYVjwKHusSatScyDMG478iKt+RMeyHR2NYzFcd2vSRWsgPI46WRRhpppJHGHxpuKhlmwoQJeP7557F+/XpEIhEwM86cOYOPf/zj+PKXv4zly5dj8eLFGD9+PAoKCpCTk4P29nY0NzejvLwc+/btw65du9Dc3BwjwQCA3+/HCy+8gPHje5cMTSONNAaAZOoJ3Ul8uA0QKdDCu8AL1gNa91igJ58fGEB3mB1ccQb6zR9aSa3TDdDNdUCdS4d6Uw0wdRlgKlp4vEBGFpQReAEAswZv/Z0kKeRoJaESp6xBoyYDLkmmfmPsDOmU6Gh2LFbTl8v+vD7Q8geB5Q/KcdZfA5/eIx1nw8cDtu4Nvn7JYTUAAHzugBSzJjsLAVxxBnrLr6zfvWQCaOUjoLZGoGB4v7v4iAg0+3bp2NZRxPym7ckDAuAzigWZuUK2mrkS/Pq/J24wGpFimQ9ifeLYGeS37KHri5mle/HSUSG0TFvqmiS01tfAtXMSOJsqFNn5oOUPQo3se3JDrfsT6Hd+JskjX0DIQsPGAIfeQUy+OxoFEHHKCHc0Q7/5A9Btj0jiIBoBwEBUS0eajZjTE6h4FDyPfE0kZH2BgXfIJSOxxCkHcKhLkinxaK6VxPUgvQdSAXl9UHc8A/3OL6xEWvFo0FJ3e6tbEVQ0ApyZaxSYjXvFlyHe03Hg8hPQb/3Y+rulDlx9Berhv7ihJCQaMhzc2mBJthsdWT1ZOMVbuHBdBfTv/2+MdAKvT8hMx7fJM+DxWTZ1dVetpJ9JkjHt4/yZoAV9k3YH5F7BhDmJyycvhBo/C6irEDJnXu/3L9eUS3EhM0fsbwZgZXUzQRPmgPe9LmMFkaV2ZdpVkeo1UXlLwEx+s7YsYjxep6T2LQCuveIoxAMs85IRE0ApFnm4qUYIyu1NQGmZkGbd3v12gpxDoc5WoLhyKiXLND6w2fZ1JaTbsBQrkJUHmr8ukQgDAJePWu81QMa7QJaQmPOHQu96GWhrMjo+gdhc4tBm6dosLZNOzewCqLlrEqwyBwsepbBs+AQsGz6hx/W6mrvx1v/cilCH3G/ttR2oPlWLe//rOuQOvznqIlRUCvXENy1SrE0x8MOKmJ1lfGHbnCdyFGAPEOwAv/kDRHe/LM/Bwg1ix7nrJWtb186CX/93qCf/WiTvD252bjPYKc/kmqdkLpc/FJSdD648D/36f1jF0YYqcNUFY6y9ccXgNNJICZePJVq1shYlruHjoA+8JZ/b1RMAsQ45fzCpOgAf2JyQG+BTu6DL5gPXLyZ+oaYc3NmaoDrXF1B2Pmj5Q+DNPwXYNlbpCHD5OHoksrQ1AjteFCXbE9sNW894QowxH4hGLRXWZPBniMpAvBIiYIx9WZI7GIT3rD62FQnnVn8NlJkDXno/cPhdQ3HVUEwlknmCyzy2N1BWrutV7Ol30+Eg+Pn/I3Y8ygtWSnIOd396cJqe4sDnDgjhynznHnoHav3Hb9i4H9sva+g9r8bdNywNWJMWpHSu+vC7Mpc2v11/DWipA616YvAP+MR218VT25ow5dwRdH7ifyLjN/8LHtN+LBqx/rvyUeCN7zuVICHWWsjKE0KGLyB2mrc9BP3Cv0hs1BVnYaa1ZWcNWE1RICBYK8v8mVZTlI4KuTkJGSYW75n22yaqy8FbfgVa/3HZTWcrECN22d5rSgFl84Qkcf6AEU8az3zQsPJMRk7Y+XtXFWcLJskmLESJfpJhTGIZiGzXn4Dxs4HD71jnb8Yz4SBQXyH50JGTnNcFEJXjq6ccVmEcjYhNc025zGWmLRUSTEu9rFBdDl1dDupsgxqglTRfPOpU5javbzgUa8wDEGuc4mCn2GaZpJFwN/jgZuic/5+9/w6PI7uuRfG1T3ejkXMkMnPOOQzTcHIOmqBoydK1LNmWLNmyde+zbD//3rtX/l1fWZKTchrl0WROImeYc85gJgAmACRyaHTX2e+PXdXVEWgkEpyp9X0zRJ+ucKq6wjl7r71WTr+kMzVzJXjsLHDdKfCp3cD1S/KzZBVArf1Y36SznCLAClmZJCswhEi59FEZO5IC8suAuz8B6mgRJZpf/t/9EGFMEkxmvrxrrfmmdU1azw3miPstBowAoLUo/zhkGAcOHDhw4CCI20qGAYCVK1di/fr1+OhHP4qGhgbx9mRGS0sL3nzzTbz55pt9rm+RYKz1CgsL8ctf/hIrVtx51bcOHNxpoPIpEQkYE+VxvHgj1ydlSqjeGuiDG+1glZXgvXJWJuKREx7WoIUPABPmyjLeFNDkReHWA+cOhxBhAICBmt1i49J6Q/xdCyvAvh7gnZ+AM/NA05YP2b+VXG6ohz4P3vkyuP60yJPOXg2KY8tA+WVgbwpwei/4wlHgyCbw8idB1TNE0j0GuPYkKIQMwwE/9MZfyORfa5lMnz8EPn9Ytm2RRlY8NajkOaVmgq2KD2aZZIcGDT1ewCPBD/XR/ysYRDIKKqLJTCXjglW2VD0j4jcy91c9M25feMfLEvy0Pl84Alr1XFSVcPD7gxvt+yA5TfpfVBUkJ3Frk5y3/DKQUlIttO8tcEMtKKcQNPceUIhCCmUXwPXUV0VxyOUGMnKhf/Z39nmIlPVWym5rugx+7T8k0Z+SbgdiXB6xz6gYACmrt0dsarragPLJIvs/iMo9mrgAbAVkLGQVAEWiHsUdLXI/JqcJWcMfEZixLGxuMah8MtTHviHVbt6UoFXTHYPkNCCnWAKOHm9QoSNWskKC5xFoqpNnX+mEEesiTV8utm+hQeGM3LAgXL/bKCiHelqq1NDRDJROFHWZbS+GL2gEhFRlJfOT5LmF1EyREJ68CJSePfSDCu2bRWbrB9zVBr35t5KIUi6ACLz/XahHvhhUHriTQN4UeUftelUS0EWVoJJx8hz0eEFTlvSpHjdaQGNniVVjjxn0BoCAH7r2BFwDeZaOMPjSydhfXDyeUMUz37giVfuWysv5w2LzVVAOVM8UFYykFLGpK6wUstn1i+FEx1DiTEr/KmhsBESlIxQut1gJPvJFUHFVTHIAGwHona9KEs/XZSdSAn6o1c/L+KPuZEiCJSRQbBiAh4CWBqiPfSMhtbZbgXPbLwWJMBYCPgOnN53HvGfij1VGGkQUUw0xUbCv21Qi8MgYYiA2eiMAyswDtzWZ1g4h40ur2ly5xMLAGnt2dwTHLnzlbPQGO5olgUYUMxHOl46Df/EPkswgBZq2DNzSGJ1Ub6oH6s/0qcDgwMGtAMcpcuGeTuDdn8oYJXitM8Bm4piUEEhirdtYD754VO6DyGfAzaty30XeP8plk5iHAK49GZuA0p+iS6AXfPYg6ON/L4R8y3o4EtZzpC8iDCDnJ2Y/YKqfaqiVzwyP4kekYq8JPrFDxvS+TrFZnb1Wnsm5xWKNlwA44JftXJbYA8qnAOePhB9/eo4k4mOt7/eBf/3/yjPPgjsJfPkM6PzhhBTlBoLgeCGMcGFA73wVqmLqyCqO+rqFqBGJng6go6XfAg9mDT6yKbr99D7wwgeH1YaPG2pt+54YIDDSjpkKyn6fqeoScg+9/SOZZyZ5TWsjslU762uCyn18YjuQkQOafhf4vRfCt0Ek97xyidJI6w1T/Y9kvNdjEmd6u2WsSGZ7bnzlZaqaDj7wbnRhlMsNvnAE3NokClBXL9jkHljdkve6mnM3UFAOffW8HWvxm2owyiXHG/lOJ9U/Eca69FhHWTYNCJeOi0qyx2uTiADgUkh8L5TAbx1jZ4uQP2KAWxtDumcIgTeEtBgkCEaud2gjePryod1XkXbU1jMxkkAyRmITfPFYdNwIEFWgBBSYKD1byD1TFovNUm+3FPj1cww0bZnEF1sabYVBkxTJx7eLjRUAfWKHqBr5ukzrd7953URcH/llweOkqunAxHngl7+N4D3C5rnwpgIZOUCHSUp1e4DCSrGzM+0/7ZPAQizq7QafPwwaBOHRgQMHDhw4+CDitpNhAGDt2rU4ePAgPvvZz2L9+vUAEDYA4RjsWet7iwTDzHjwwQfxve99DyUlw2dH4sCBg/iggjLQgvvB+94OTgSpeqZMKkYZ+PQ+qZwwJCgtgSkzeJOaiaDiBiB/KwV+8X/LpKRiCmj+fVEyrFwbkoAK+GUyxBq89Q+mXU0B9CvfBRouBacmXLMX6vEvDckLHRDCBN3ft8cw+33AhaPgSyfAZ/fbE8quNugNP4N67uuQyb4Ot7IAgOSIIMvVczYRpqcjZFLKEqBITgef3CnVXYORSR0zThL3zddMYkSqyEW73BLssPqXXxZWTaXWPC+KFi1mxVBuiUi/mqDKaaA5d4u0vTakwnnevaDi2Mph3N0hgb7I9v3vAPHIMMe32x+I5L8rZ6Eb64C9b4HrzOskLRu0/Anw5t8E1XW4swV89TzUE18GRQR0rEpsNgJmMhZ2INnvk+NxJYVPqC2riN4AkJoUHnjuiqj27APcWAf96r/ZlUTnDgJ1J0F3fyLhbQS7NO8eIdbU7Jb7pHis/EZd7TA2/iIYYKHSCaAJ8yRYFrr+hHm3LWlJnqSYBCJuvi7Wah0ttorCbU70hYIb66Hf+E+5hwzz2ZRdCJq7TmzqIhEneI6udklodjRLNdgwHyOVT4a651PQhzYCHS2gMeNACx8ccHUo5Y1BqKUSnz8cneqIVYnlcgNpWVDz7x1w3/sCmx7kiSQ29Mld4K2/s6uxyQUkpwDd7eB9bw7qnhsNoLwxoAf/5HZ3Y0ig+feBT+621dAs9ZLj28Ez7hrye3zYYJEFmW1VMCJw8zXw5TPyjOojsMuH3rOJMH6fmezoBq6cE9l0lwuclCLP7ns+CXXfH4P3vgm+cETevS6Xrfrj8oCmLO23y+RyS/A3NCkGAMlpoILy+CoZbTfk97Ak+oOy+i4gx7LeipPoJEhSxNcN3vYHYOmjQ1IeGC50t0QH8gGguzWGjcBtAne1gc8cAPw9oKoZ/dpkcl0N9Ls/sZMEqZlQD/4JKDexpOtIgOasNW1MQyTtlcv+NyxhTUGyF5/cKfOEWAj0Cmks0jJFG1JxbY1dWEsCKXJsbYI7W+LbzjlwcItAFVPBO1+DrXjAcl9kFch9oFwR5BVThcCTBIqwDmR/L/RL35IknZUs9rvknrDieIUVwPi5osYZ2o+xs+JaLiUKfb0WuHjUnBdFqI0mgoBP5s4XjgypHwCiE7wWyFTBIAVuaQBVJFZM1BeobBLYmhNb0Ab43CGbwNrSAN72B9DzXwcNwJaJ3/qBjCksnNknSrQXjoLbb4BKxskcO46iKB/bJsq7oQj0Am4P+PqlhBTlBoSO5thqxa2NMsYZSfVFb0pMFV94U4G0rP7XN4zYCszakAT3MJJh0NVm2kgbsedKAb8o6wFhRJjgkr4uGWOmZtpFbkZsghif3AXXc1+H0d0ObPo1AC379iQJIcfwy9xaKcCVIuNZiwgD2ONcbypo8SNSdBAHVFAOWvQQ+P1f2cdlxUmYZd6VlQ9urLVtN+2eAnljgsVKatnjtsKQaUcKT7L0L0jGILPfbpOAE++ZwzbhpnjskIptuKMlzsFb/4voQ8i8lHKKwK0RzwoAVFRlf7h4PFq9q6NZlK6SIs59V5ucjyHECqhyKnj363aDy20q8oTMpyumAKyhd7wM7ukyY7gRI6iE7IQi9p2Wldi9CSniU098GfqF/1vGjcqcB5EQ7nHtIpgIvPX3Ejv1dZpKLjHgcovyMLMoTXtTwFt+b+6IEFS41EIGo5mPi213ezNQUAZKSoHe+IIQgLrbY5Kz9IafQz2SBerL+smBAwcOHDj4kGBUkGEAoKSkBK+//jqOHDmCf/7nf8abb76JmzdjV5gANkEmNzcXDzzwAL761a9i5szbVz3nwMFwgC+dgD70HtDZAhozHrTgfhmYj2KouevAExeIrGZWQZ8e0bcLfO0i9Pu/ssku2gAQohDjMydSwYQO21Y7RgB8dj+4qw2uJ/8yfMNW9bMRCFfr6LgJ/cZ/gpY8KuclFL4u8JHNoBVPjsCR2uC2JuhX/12CML4u6aMn2a7eNgLQr/0H0NpgW1l4U8xqPDdo6pLwDVpBSetcRSLgA1ypYo8wCDIMkYJ64HPgna8KySglDRg3R4KQ1qTO7YVa8mj4etmFUB/5mqhfEMWsrlELHwDPuEuCXzlFfVuPdDTHrvBrvwFmHTtBF6eaig9skKpoC50t4Ld/jKjAhBEAn9gJWv5EzO2Qyw0qnQC+fFoa3B75z/odukO835O8ps2ElgmxOylYKUSlE+McdIy+738nxFPebDt3CDx7bb9JsJj9X/4EePHDgOEPnn/9+n+EVxpdPgMolwSuavaKx/GEuaA5dw9ofyMNvnEF+pXv2Em+S8fBl45DPfT5ka0yHAD0jpfkWvaHJFPbbwBV02P2kcongVsbwxuVC/roZsCydEhKBt31NNSMu4a1r1Q9A65YVih9gAN+sZ5Iz479jqyYKgGtULl/q5Iwcv/DaGHHRkAk0E/tAgJ+UNV00PIn4/vZd7WLgo0/tGrPkHs4KRkcp2rPwa0BebySuEjJCJJkBQw01eOy242Nl0/hWnc7ytKysa50MgoGkFgatn5OmCfVry0NCLMGunJWnlWFlVAPfi5u0ivs3g997vvNpEdAy/jh2nnw1hfFYmDFk8CKJ8XmZe+b4MY6UE4xaN49CRMe1NLHoNd/3ybikIJa8mjfpLv0bHmn+X1mJbE5lU3JAB/eBG5vBrxpEjyPTG5oI1ity2f2g6+eg3r8L0AxrONuJcbMKMKpd6OVR8bMGB3WRPraRfBr/y7XhlLg/e+IDWScdwFrA3rzr8PfP11t0NtehOuRL9yiXkeDxoyHeuSL4GNbpQLYmwpk5oGMgJCaI9UIrfeF3wcaOxN882r4Bt1eoHwKyJsCmrTAViFkNhNhJP+6Pfa2XDEIkmTahDhwcJtB2YXAkoeBLb8LIbJlhSc3k9Ns2yAisZicvFDGXSHQ7/5UFETCGg3ZblKyEF4KyoHlTwIeL/jMPuHeTJgHWvzQkI6Dzx4Ab/pNfKJ3vxtgedcc3xY/gTkcsOa3RPIMikVWHyBo3r3gaxdF3REwlTbybUsTbQghIOADnz8iCgdGoF8SOl85G06EAYQkcfkM1EMJEp+vnJVxVOQ02wgAI6GCmJolBTWRVjBpZvsIgkhBLbwf+v1fI3TeT/PuTaiwgNweoLjatg+2FFcycoDsYR4blIyV+9rwRyuaWKppymWexxAiTKgIBRhkBOwxWXIq4IsxJzYCYH8vqL5GbGNYy34tm+ygnSoJkcmygiGy4x4uD+jJv4RKIOaoZq+BcemEWF/DJMWZBUWcXSihweuXTPsj89it/YSolNLE+VBFVeCLRyVG0tst95FSgGHa5Fi2u2yqxsQjwVlwuYG7nur3GPoClU6IbVVWOR0YOwu89y15fvt90ieLPJSaCSx7XIrQQuYANHFBWMEY37wSY+PWcQfsgjqXGyisHHLRDOUUg5Y8KoQYbUhsb+oSYOoyoPkqkFUI3rteCssAOde9PUHLt+B2hptYF6uvqZkyP4yMG2oNfXCDKAv7uuV7HeNXCo4LPWLnFzqn6e6Qe8ibYhfEgcVabuercp7K7LgejZ8t5E1PUvTzTpnKTKd2OWQYBw4cOHDgAKOIDGNh5syZ+PnPfw4AOHr0KPbs2YPr16/jxo0baG9vR0ZGBvLy8lBUVISFCxdixoyBJVAcOBit4PrTonBheRrX7AFfuwD19F+NiIfyUMCN9eBjW4DONqBsolhMjGLpRak4Y7OCw5BkYygs/2OPx5y895gMfkv+GEDtSehLx6FCPKZpylLwiZ0hkxQAIJl0drWZE+8Y/Wm5HrN9qOCmyxI4KKoSSc7OFlO+1VzA3wO43TKJ9fuAm1ck6JCcaleDV8+Cmn9vtEpJYSWQUwJ0noraLwCzyrYTPNDKu9B9pGeD1oWrIHDzdak+d3lA4+fETHwTEdCP5QalpAMp6eCeLiGctTSITPTEBeET95xisU+JtCIqqopbqU5V06TiLhRpWWIjEYne7ugKFyAoN8um/3ZkMIGWPwle/1+2DLk3FbTuk6DudrGv6u2R39Lw28lMraW9twfIKQbnFidcecyRlXsWmq8BAyTDBI/BIvHAJAFEBlUBoK4GtPbjULPXRPepu0NsVy4eF9uiactBM1fecgIKH9oYnuQDJMA7wpZCiYK1FkWHyD4aAfDOV4H7Ph21jgTPL4UHz3OKRX7ZQm83+L0XoMsmQwUVGG49+OxB6G0vSpCRFGjSAtCKp8MsvMjtEZueHS8LuU65gbLxcu0e3mQnIArKQXPWDl/f9r4JPmpbTvGFI+Dudrge/bPYK1w9J0GySGUuMyB7J1okfdBAuUXgjptRRKqmtCz854kt8JlB0KaeDpxpbcBXZq5FumdkkyxRfUxJB+bcDWz6FcAEGbRA+hzoBW5cBu9ZD1r5TOz1iyrBjXUyDgrKxFtVtLA/E4n1izZAygXu7QHSs6Hu+8zg+l0yFvTg5yRYb/hBs1aD+iHGkccram973rAbNQvR+MC7Zl91+LFYB2Ilk1weuee62oScvPSxQfV/uFA6sxiT1o5DzUabHFq9pBxVC8MtirirTcYa2pBEckbuiPeNG2pFJdFK6ihRduA9b4Anzgd5U8FtN8D73wZfOScJibGzw4mIFq6eSyjpOpKgosqYVdg8fg50zV7g5C4Z+4f0kapngmavFTWFs4cAMJCSIbYmJlGcVn4EXFAO1J4CGi6aCk1+2VagV8bZpICScaKMcOOyuXEFWvjALfktHThIBKQ12J1kqyWBgVN77Op7IiA1Q8YpRVVQyx6PUttkX5eosgQ3GpLITkoGrXoONGGufOVJkmKAOAUBAMDd7WKF2VgH5BTL2D8OiZGbLkO/98v+E9H9YeIC4MjmGIoRw4zeHpkbDbW/Jig5FeqJL8mcpKsNGDMevPk34BtXw8gMcHvAV8/DOPy+zC0LyoWMGmLbGwq2yDRR7Y0x22MiPVuONdAb/n5OywJNnJ/4dhIEeZJkvBCqNAGI2m889bnh3P/EBVBZBaJOrLUUeCRgnWpBLX9SFIbbmsx5C5mks8HHWmL205sKWvQgeMPPw+9VwPw7vC2SCCOLEQKTViApNxfoaAZnFwIH3o1St6Fxs4WQevmMJPp7u01ShSFjs1CSkjvJnOtR+P56e+ScJFiAR/mlEksKHgPJti8cBqYtB7ndon4Y6DWVFRXg9kBFjBUoKx80azU4PUdiLxYh0JsGmCRwyswXFbprF8A7XrGViSwVOrdJtFVKCoAObgTuj33PJXRseWNAs9dKbMJCQbk8I70p4LGzoetrZD5/5ZyMSfLGQK16DpSeDX7qqzKubL8pY/KIOAbllca+2lLSw0g08CRLEeAwgCYtFGXa+hpQbglowX2gjFwwa3m2N9XL+fMk24WMScky7vJ4QdNXQN0ilXIaNwscajXNLPHWuoMXa0AAAQAASURBVFNynVox5nhwuUOey6Z9lidZPoeSqdl+boO1zF3KJoIvnwkW4KBqBnBqV0QHLVJ2UrRdmAMHDhw4cPAhxejKsEdgxowZDtnFwYcGfHQLomQNWxuB2pPAAKvmRxJ87SL0a/9ms+Avnwbqa0APff72dqwvhEqrepKF6ALYE1PADPC5hNji7432cmUNXv99GNmFoHGzQHPvAWUXQD30eehXvi0Bp2DFBQEBP7i7I6YFERUOXg415uH5uqHf+bEEvgCbzNHdbiaEgGBVjWEAbrOaw5NmnwdTrUPNvzcqEMbXL4IvnQCKKiR5Gy8waASAc4ehpyyBihNMC27z7EHoI5uArjZQ2SRJBsSQoaecIlDOugGcjT722d0B/dK/2h7eNXvAp/dBPfynweQMuT2gpY+BN/7Clj1NTotSpAnr49LHwG03JEgLSJJk7cdFTre7PXxhlxsxGSkVk6Fr9oL3vSWKHrklUEsfCwYlKLsA6tm/FblxIwCUTgzKUCvlgt7wc7mWe3rNIIs7/HdquwF+5bvgj30jjDQQ95gKysFtMQKfffhac2+P9M+dBJRO6Hs/REAs+V4r0B4D+u0f2QSj3m7wrlclbjBzVV+HMuzgltiBX265Piqqu0mZsuuxpMEjK9utdZLTQoLn7UDpeOiffSN6QW2Ih/3KjwxrnxMFdzRDv/9LCfoE/ABYqmnzSkHTl4ctS9mF4KrpQF2NECDra4Cr50GrngUHAqCOG1LR1d05bFLpfHJXdOO1C+Dm60HbszBYSh1ECLPuU1KJSXOG59nnYPCgefdIkj9EMYUmzMduX0eQCGOhM9CLfY21WDUmcRWuoYL9PvCOl4ETO0xilQswtP0cNQOoYbaOEaDZa+X7thu2BYZFrgTswD0AuNzgQC9464vg84clwVM2EbTq2QErGXJ9DfitH9pqLe/8BHrFk1BT+7ZZUnPWgnNLwGcPAi6XjLVqT4RsGLJNl8d+pwRMi0yXSQg2DAmi37w2oD4PFez3ARePCSmkcmpQrWfhx2dj0t3jcPNiM7LLspBTHn4u+fIZ6Ld+GLwOec96qDUfHZQSX6LQ/h7w6/8RnqTVhq0meOMqOH+MWCq2NNhWAXWnJGAf+VxNyQiT6B9NoOJquIqrwVOXiKKFRTwuHgta+pgo3K39OHjRQ/KOzBsTHDdyZyt4y2+FCMMmCdnjDb6jwFrmFd5UqOkrgMJyoO60WCOVTgBl5t2+A3fgIAJ8el94hTog93ZRJdBQa7dl5IpKWCwiV8CPqMmO9U4qnwwVx3Y2Zn96u6Ff/o4kvwGg/jT47EGoJ/8y5juHz+w31SYCUd/FRNAizbBjAi63KLt6U6LnccMOsb+gEBWKoYKIwsj5XDoRsJSrLPh7JXFqKUU01kGv/z7Us38Tu/CkqCq2AkWopUp//ZqxUt7b1v5ZA6mZoKf/GpQ0NFuseFCz15jjhQMyrp44f0CElKGCiqoGdI7CkJkvY6+kZBnLudxA3UlJhMcoGhkS3EmiymuIGgd83fb9oE2rIuv9HUvkAgw+uQucaipTWPP8YIERgcbOBM2/D/qtH0h8zPCbdtgpsk5k3MDtAVxZpj13yHhbG+D3fgX+6HhQcgLzt54uIW8YAfs8EgHNDeD2ZrCvR4qSiOR+cHtEKacqdvyVxs0GpWWB964HfD2gqYuhpi4LX6a4GjxxAfjSceDmVXBHM3D+SNQxctPl/vvfD9SiB8ET5oGvngVl5AHlk4JkL8ougCu7AJi+XOI0fl/Y/U1uD8h8HrPfB310izznc4pAU5YAldNMdeKQAiZLbSklw37Outyg9hvAEJVHOOCHfvW7wXgF37gMNm26+c3vy/gLLM9rX5dcsy4XMHYW1Pz7pFgsjk3bSIDm3w+0N4MvHJV+ub32fWMR/+IhOd0cX1s3lBmztVSHjIBN7gdJ0aZl39nVBj69T2IiFizCqqWeaX8BMIPGOi4KDhw4cODAATDKyTAOHHyYwN0xEpeQaqjRYbwh4EMbo+Qg+fIZ8NXzcauJbjvGzhI1gEgSh1KSNAIQlPwEJPgVCJlEWGx8XzfQ1gQ+uBFouwm6++NSZbroYUnMAzK5t7yNr54LysoHZV+zCkDDIIMcCt73lk2EAWRi1dVmBx5DOQdKAbklQriIlNEEonx+9f53ZPsAgp7rHm90VZd1lXbcBL/0rzDKJ0Hd95mYXs58/jD0xp/bn2v2gBtqoZ7+6ohWavHx7TYRxsL1i6LgY8qpshEAzuyTZLQm+R0LyvtUQ6HUTLie+LJU1vt9oiLjcoOmLI6qRkNBOahiCjhEnYLGzwPSssFv/Ke93M2r0G/9AOqZvwlWP5JyATEsXWjsLKjHcqBP7ASObJLfsDdSFcQPNNVDH34frgSUMGj+vWLLFFLRRdOXi4R6DHBdDfSGn9rXVGY+1IP/LW6Sh1LSxRc6VHkEZgV2DOlqbqyPqbTDx3cAt5gMQ0VV4Kb6mO2jBTR1SfS1507q074kMngeVxo+krR5C8EXj0sAPTR4FOgF73sbiCDDsL8XvPsNezkAMPyiHqE12HwWMF4BLX4YataqoXcwXvIlHoGwZCyQmQdcu4hgVRgYKB4LtfZjA7YkczD8oMJKqCe/Aj65A+hqB1VMAcbPQdu5/TGXb/fHeK+OIHjLbyW5ZL07jQjlOyuhGYNsaoHSsqDM6lBuqAXOHpB3WY8ZcA5JUtHkRcCOVySxZPWhvga84WfxFZDiQO98NeKeYbEZmzC/32A2VU4FVYoth/HKd8K/NAJyK7lCJOEtif/QwLRyJ1xZPBzgG1egX//PIFGRXR6ouz8eTIJmlWQgqyS2lZXe8XJ437UBveNlqKrpI6K0wjeugF/5jpBcojpjyH9Z+eBzh4U4EnrdW8TrQK899gVui5LbQEEF5VDPfR1ouizvzAgSI6XnABGKFHrDz2wri4BfnvcEUYPp9Uki0Z0kxAGr0rxi8qia2zlw0B9ozt0gbYCvnAXSc0CTFooqWaxl07KAkmop6AmNGbjcUPPuGdB++fR+mwhjobtd7GUX3BdjhURVM0juUUtFzLTkACB/Xz4LTJwf+xk4nGCIvciioVlD9QmXW5K0gV7ZISmZ5xoGEDrtD/jAO18BKzeQmgGasgSUlQ8AoNxi0PS7RBnYQnouaG7ipG3KLYZ69M/Ah98HtzaBiqtBs9fEtRIdLlDFFBm7jTJwZ6vEilKzQGPGRS9Qd0rmOxHveD6zHxhmMgwlJYttkVvZRM7wvcr8z5MM3euHivK6Ajy9LYA1THG5JZ5GCvTEX4Kb6kUtZevvhegQVrAh1mlRqrnF1cD4ucCGn0X3pa0Jet96uJYnYDNUVAGc3R91HpE3BvrFfxHVW8ti1NcFZFVA3fuZsHgEtzbJvVFfI+PJjhbzXa/ADbXQvh6okPgKM0Mf2yrjaVJA2USTTBR+XhO1Fe0PlFvc77YoKTmuPRgbAbFXtxRiIQUe6okvge7/LHD2oMRhsgrAN65IrAwIixvylbNDtyc6fzi6cKenU0j/hlmkFpxTs1yrHq8oyKRnD23fgwB5kkD3fArc0SIKlWf2SYwPMK3n4xBiSJlT/j5iLaRCYjFsx6wBUNlE6H1vh69jBMx7N3Q7LLHfmStHlDzvwIEDBw4c3ElwyDAOHIwSUMVkcGNtRKMCxUh+307Elcltaxq9ZJimy+bkKYBgECionmHKoXpDKpJm3AXsXR+d2AyZpPP5w+DOR0BpWaAZK4CbV8BnDgD+Lpl4JKXKPpKSARAwYwUoNRN886qok2TkQM1eCyqfNOTDiyQU2J7HsAvziCTY8uzfgFoaoS8cFS90wK6QyS8V/3Zru52ttu2AtQ2PVyZmKRlBax+p6AmVd2bgylnwkU2gefdG9Vcf3Rp9EM3XRFWkbOjnIy5uxPA9hiR9rMk7n94n1S+kxHcXEKLR+SOAKekdDxShmkKzVkv10rFtgK8LVDkNtOxxqSaauhTcUCfXQWEF9HsvRG8w4AefPZhQ9RcVVsBVWAGjtRG4dh5Ad+wFd7wMnrKk30oqyi6E+sjXpEK0qw1UPjnMmzgUbASkMiWUXNXWBN7xMqgP+wxa9Ryw9XdSTUNmxVi8oFakbVV/7SMImrMWXHvCrhwHQFOXgvLL+ljr1kItfQxG7UnxQYdZBefyAOPmJL6R6lhyuwo05+5h7euA4Ekyk64RQdq2JnBna7DajS+dkORxS4M8+5O8dkVjQ22EtziL5UccG7aBgMbOAlsBQgtZBUBebFILWZWTbo8Z5DOfO9q4pUn6DxO4s1WugezC2Go9MUDZBVHy3xOzCnHoRjQpbmJWYtscDrCvW8gIgPl+Tpb7g2FWv3okAQZAzbirz22RxytEl8mLwEsfA2pPQHe0iE1EU728Eysmg+ffB/zs76I3cO2CjAMz8xPruxGIrVTVa5KOu9rBJ7YLKadqBmjqEiGExup7fjnYIiMA9rinPxUS1uBEqoqHCXrbH8ITQIYfesvvoMon90lo4d7u2Oeqq03UfBK8jgfU162/FwJ4PCSlylimqy022Y8UkF0k6jBuD2jyIqjJi4a9nyMBItWnCl4ouLXJJsIANqk+4Jd7z3zX0PQVQdITd7bKmDeneMjvHAcOhhs0YV64DR0ApGSIiqfbk7Atslr9UVGAaLos45uUdNC6Tw2cOB5LpRIIt+gIAU2YK2q7LndU8U44WOYtRVXm/JDtoaU5NKPMfLDLnbjKzGDg8UI99N9Aw6RQGHsfSTIO9iTZ6gGx5k++bvCp3UESKZ/YAfXIF4LzG7XsMfC4WeD600B6tihkxCh66QuUXwpa+7EhH9KdDn1sq1jXWtdoURXU/Z8NWu8BiFZKsTAShUOV08TGqqMFYarJyo2gHVBOiagrkx89nb3wun1hpM4wgqcREEUWZvDL344uRgquZBYBEIGe+DJwYqf0oXQCUDYJvP/tGMUZ5jqn9oKXPdkvyZYmLRRSXWicdcx42XfL9eD+g+hqAxXYc3o2AtBv/KfM+w0jZBxn2pL6OsG7XgOPnQXKyhcl4t/9c5gdIlquA6lZQHeb/fu5k0DzYxD6bgfOHQojwgAQu6tj26EW3CfqMaaCjD6wIbZ10jDYPXI88mFXm/wbtFkLUf3OKhBrpdsISs+W+6dzgk2GAWQeZvjD7yOXR4rbiiqA3evjF8xYhBgLOiBzotKJwKzVwOH3Q5bloAJT2PXscoPu+khM+3EHDhw4cODgwwqHDOPAwSgBzVoNvnreVvhQLtCSR0adjzwVV4Nbrke2RvmFjybwhaMSAPIkhU8sZq8Vaf0bV+xJQ2qm/AbpOUBnmxBgejpNckRIwIdNBZi0LJBygVY/D15wP/Sv/2eMSQ2LxOiOlyVxAQDtN6Cvnod66POxq4EGAm8KEKnibCXGdIgkbEo6+LffBAd6JUBBALQ5YaqcBnXvH4Vvo+myHaRhLRUJSgHpGVCrnoM+e0AqRUKrEEKktbmuBohBhon0jw6iO7ydfd3icexyAxVThy57ml8a7mFvdTmUxBBDfQQA+Nr5oL994mDQrNVQMarm+MpZ0xKpBUZuiekPHAMDDMCqu56GXv89qWyKpezh94GPbUko+EIp6aBE1DJuXI4pI851p/refnIqaN0nwX6xdqIIVaIwFFeLZHGk9/gwyoonCkrLgnr6r0SNoaNZbA5uoeR2olBPfgX64LvAyd2iBMUMfu8XME7uhFr3ybgVvcH113wUuuW6EGpYS9BuxVNQWYklu0cCVD0DHKvq1+UGmq8DaVngxjqx1NIGADblvQ2RyLYsUyIDp9oAH9kCFJQCZZMTk92O1b+lj0nFcn2NNGQXQt39ibiBWu5qk0RPZKVeW5MEgzNyYq7nYHDQ+98G7383RJVrLmj18wlZx0Vibn45alqu4/BNW+J8SWE1JmXfOjJMmK0DIGMci1hVNUOuo+Q0qBkrQOMTf3+R2wOMnQV65yfgzhZ59hIJiejw+/Er7xMtyAeE/JGZH53odHvADbViO2Ph8hmgsQ60+rnY25q9GnzhCNDZIg0uj1S7hxJMWMtYKagAoISkMQwS9YmAtWESVSPQ3S7EiL7IlB6vjE2tZIAFdxKQFl/xZ7Bgf6+Mhfq6L9KFxEHlk8C7Yjzf3B7QmPFQcX6zDwwik+0ut0207+kEwGZ1sFyLetdr4COb5XpULtCsVVALH7z1/XbgIA5o1ip57h/eZFuXFFZIAjg98TEJZRdAPfM3QqYEhSWXB9Sfoioht0QiTtyBCspBq54B73oNuHEVfb6Y3B6gs1UU+kIVHxlSkHByh2kZOIJkGG2AWxpt1aghgLvbAU9y1HyKqmfI+bAs7gDbRjDYD9O2xrJQBmTOuP9dUEh8gIqrR3XM504AtzeDd7wSPn67fhF8aGO4QlDZJLP4KHyOTRPnD3ufyO2BeuhPhdRReyKouBGq/EEz7wLljgH2vgHP1Xqwzw+CRlyZM22YsZ3Y6tfmVmUf7iSowkrAtBLXx7aCX/wX0/I7Mp5hVnv5fTK/7SdWSh4v1KNfBM4fFtW7ni7AmwKuPRWuCGWhuxPc1Wbbd9edsgtgjFCVD7MfzICvC/q1fwdVThPl0dACLOs89HRKAZk3CRg3B2rO3aDsgj77fqvAcWyU0RxtJUpTFoGPbwsfk6ZkCKF+iIhrx1Y2SRQpiWROEugVe7npy8XKMpRElgDYCMj7LTlteBULyycL+cqypXO7AUo17fgsxW4lBKMx42Ec3iTvoLgdNQk01ryLFOiBz0J5U2EUVNgEL+sdRQR40+xYdHaRQ4Rx4MCBAwcOIuCQYRw4GCUgjxeuh/8UfP2iJMFKxtqTsFEEmnePKGeEVHjQnLWgrNExmYsFSvLaE6sQhr3KLwUW3AdcOCqT46Z6kXQOTu7YtDlyyWSkt1smsUQSEIyQI6X0HCC7ILYCSXODTYSxwBp8bOuQyTA0fQV406/tBpdbgneeJAAmgURr+c3cppezVQGX5DXbuqNtFLILAZjVY6ESnx4vUDEVqnwy2J0EPrkT8AORCjtx5bMrp0YTqpRLJGRNcP1p6Hd+bHveJqdDPfi5Ialv0LRlIi0cWk1YPFYUMCzEsfVBgtXugCmNu+Nlqajv7ZZtrngaKqcISMsGblwO/71uXg2Z5IZMyEklXIEZ2n96/EtCAHvnxxEBJBJy17WLA9tmf0iOQ6roh2xhIRGSE7ncUHd/AnrDz+2KrOLqkZUV76s/Hi9oyuLbsu9EwecPSzKjs9UmAXpTgavnwDteAa39aJ/rkzcFruf+O/TNa0BXG7j5GvjoFhh73xSloCWPBuXTbxUoKUWqBa+ctRPaniQhk5jqCHxipx08ClUE83XJtZpXGl6hyBro6QIf3AB2uSQgu+ajoNDnQqL9S04FPfjfwO03AX9v//LX3lRTWSuiQtfjlWBfHHDAL9VeCQTwuOky+MgmcPtNUMk40KzVAw4afhDADZfETiu07ewBYMz4hO9lbm0En9oD9PaAqqbhoxMWYlVnC651taEsLRtFt3jMRinpUt0aapNIBJRPhuvBzw1p29zRIu8Ra5sWjm8Hxs0GzkTYRBVWDvh5oBY+AL3xF2HvKZq9JjrxyRpcsxc8/94gQZyvXxTCZUo6aPxcsXk6tlWq2jtuAqkZQjLt7RYVoLxScM1uc4NmcscwwM3Xofe9BZo4P2FVm8GAlCs2oUW5pFq4r3VJgeauA297Mbx95kp5Jg433G6bfOr2htuGSodAk5fIn4WVoDnrwHtet8cxyg0kJYOmLRv+vo0yUE6REK1DSVVJyTLOJgrapPKRTdBE4NBKXm2AD24EjxkPGklVRAcOBgBSLpA3VRRRktPkGm6ohX7rh3A99dWBbYsoYZWluKieKQnmUBXU4uo+E69q0kLo4rHgl74lROm+7D39Pkm4hyYcARk7NkcW/4wADD/4yCZg3ScHvQluuAS9+bcyn/R4RYlqwf3BMSIlpUA99HmxeblyDsjIBc29G2i+Dj62VY7V5ZJnV4SiGsdIhjsYGvjy6ZjXJNedAkLmteT2QD3wOegtvwUa64ToNG2ZKBKPACgrP0h80tv/AD66Vd5lRsC2yC6bCFfFZKiOFuhf/EP0mCYSyiVFCX197/aExV+4u0PIW2wWNcSCJ1nmT33MlcKOzeWGzi4Eb/yFTS6yYoKWWpIFtxt6+0sgIlEO9vtg26lF9Md6ZmhD4jvtN2W+ac1D7QUBDpmXXrsAZMWJOd0GUEF57DMdI+5GKRlQj/05+ND74BuXQXljZG45HHOgismgqhng0OK1vDGg5U8C6TnyrNQG4E0FzV4DteD+Ae9CH9kMPrgB6GqXmGlGrihdTZwPNUSFGSICrXrWVICulTFiVj5439tiMZiRCzV7jV1EVVAu11cs63oLodencoEa64GyiVBLHoF+8/uyvnUtJ3nlWW6pmY8SspUDBw4cOHAwmuCQYRw4GG1ITgcf2QLe9gcJVsxaBWINvl4L5BSCJswbsCTtcILSs6Ge/ivgwmFwZxuobGKUPcxoA01dKgSeUKRlAZXTJEExbjZQMRX8s2/Ykw2tTQKIWYHg6zK9WHuB9Byo1c+JlHoE1Ow1ktwJ3X/ltGifYhMcQ1FjoFCTFkIbBvjoZqC7Q3yxJy0Ui6MrZ6WqKG8MUF8jE8jQ6gHDADwkBJGI6hrKygdKx0cnvXq7wWf2QU1aCFr1LPSCB8Av/Z/wygZSoDi2DDT3bnBDrfhkA4DbA7XiqSB5hllDb/6NTYQBgJ4O6G1/gOuxPx/0eaLkNKgnvgSu2SdytYUVogwQ8tvQlMXg49vDgzvpOQnLr7IRgP7Dt4C6k3Zj83Xg1e9CJ6fZFd5RnSMh5jTVSTWWN1XIBgnaH7A2pJrr5C65Rour5bo+dzBYuCTEgWSxbRkk2AiA978j9kms5Xk0/77owAUAmrFy0PuJBSqdAPWxv5OKdW8qyLGRiQv2dYG3/T5CSljLPeVNAV88AqBvMowFlVsMbqoTZStr+5eOg29eg3rma33ae4wE1PInodf/V5giFc1abdtN+M2AUq8vvGpfayC3BGrxQ9Bv/sA+L709cm9YgaNAL/Tm34h1SV9qRX0gUUU3crmlCnvvm3ajEQCSkqF/9g2gsBxq0YMgs1KSr1+E3v6SBMVTM0Fz7oaavjzu9rmpHvqV7wTPFV+7AK49KR7w/VnIfMDAtSdjf1F7AkiADMNXzonqllltxye2g+bcjdKFD6A0LXsYezowqFXPQb/7U7s6sKB8eNQ4fF2ImYTwdYtlVMAPvnhM7qOScYPaJ42bDZWeLQQWIyCWC5XTYBx8TxbQhtyf5n2st78Edc+n5B0UYuHI+9+FevSLosjU2SL3s98H+H2g+fdCzbtXLKUun5ZxDmB+3ysWl83XwIfeh7r/j0GlEwZ8HAkf75y14O0vhbdNXABKzeh3XTVtGTg9R6pNtQEaP2dAaj8D6icp0MxVYpXiTZZnEpvPUqVEqW+mPb5TSx+FLpsE3v26VE/nl0LNXQcqrBiR/o0EdM0ecM1e89zOBU1bGnOMHwvq7k+K1WXDJQAkyYfm6/LcNUx1Ro9Xth8DfPG4Q4ZxMKrAp3ZHq+jduAJurBv2Ob+u2SNJ955OUOVU0IIHwtT5SCng3j8C1dXI/nNLgMqp/Y9hjm2V91hqhqgyxErI9/YArgBApnpTqNJpIiCS941yydggnmpan9tQUpTT3dGvYmMssN8nY1pLOdPvk/djdwcwfbmQbS6fBaVng+77Y6iIcS3PXiN2KMzg335T7PHIVOtQLlB+bJtPB4MHpWTEVr9IiY4PUH4pXE98WdRMPEm3bM6llj0Bo6VRFHVdyYDLDd7/DmAYoEUPgjdb6n2mLUsskOqbCAPIfdMTANpugM/sF/JJY61tYw4ydxHiYWZaytKMFX3GRLn5mhAeCitFdfCN79lEGIY9ronqsxYVGQB87pCs39Nh9qGPIgRt2PchKUSfF7Mww+MVVcTrl+IqXN1yVE2PJtdnF4GmLY1alAN+UYosKIOat25YizeJFHDPJ0F1p8DXa0U5Z+wsmS8vehA8fblYIOcUDWq/fOEoeOvv5dlvxQE6mmWMduUsdHsz1PwYqtYDPY7CirAxMK16NuZyavYa6MY6IZGH2qjGgjvJVOg2FdqyC0U96vwRuaa8KeHFi+jfIteBAwcOHDj4MMIhwzhwMIrAAT/0a/9uS733dIBf/0+wJ8m2njm6FeqxPxtZb+l+QJ4kYOKCvqaDowo0dhZo5TPggxuBzhaxNVnyaHiis7c7vPrV8JskApONn5IuE/PcEqgnvxJXzYLGz4VyJ4GPbwf7uiSwN2sN0NECjhEwoPLJAz4e7ukCnztoVqdPB+UUQU1dAkxdEr5g6QSwEZBqmJO7JHEVqkDAbPfH7RGVgkjklQKXTsj5IDInYi6RjDUJIiotE/z4l8D73gJfOQvKyAXNWhPXOoaSUuB65AtCiOlqExWk0H23NNoJq1Bcvwj2+4ZEBqOklD4rqiglA+rxvxDLlJtXgPwy0Iy7ErZN4cObgMunY3/Z2yP38Y0rpnpPhIx12UTQfZ+RY88qGFAing9sAB96XwI7yiUVRxm5QFG1VApagU1vKmhm4iQV9veKTLhZScg9ncDZAyHH+74QsNZ+DDjwDvj8EQlQTVkCNTU8gMLatBZLThuULQkg5AGMQkuiUYdrF8ygfqQdkBmUdA/sHtIndkY3tt8Qgl3ltMH1cZCgkrGguz8F3rtekt2TF0LNudv+vnqmBC8jAkJgDdSfgj5bCvXQn4CPbRNZ6IZLtvqXlfz0dQnhpGTsiB+PmrtOktyn94F9XUL2sirErpyFfv2/oJ75GuDyQK//vv0M72oDb/8DOC0rrooNH94cndy5cVkU0G6DxdhtRbzq0QSrSvXeaF93Pvw+ePry26riRxk5cD3xJXBLg3zOLhyeDecWyzvEkoa39lc+GSolHbjnU/I+0MaQjp+KqkBFVeFtJWOlQtrXFZ4AuXgcvPuNaOWY7nborS/GtDnkEzuBefeCvClQj/4Z+NBG8LUL8oxMTrPvecMPvfsNuJ740qCPpT+o6SvAyengU7vAAb+MTacnXuFNlVNBlVNHrH+hUHPWQqdmBIlKyC2RytmyiVDF0c9FVTEZqEhsPMuNdeAzMo6gCXNHjFDP2pCxUSiBaP79UeN3fWCDvE+s9a5fBNpugJY+mtB+KCsfrsf/AtzeLAnDw++Br11EcHxtkdAtwmYkIm3yHDi43YhnC2SEJ4/52kXoPW/IuCK3BGrB/QOyDdU1e8KUMvnEDnDTZbge/4uw5YgUUDFFij0SRND2gzXiJusBUxUFpm1QhPVgvzthSU4qlxSedDTLfCvSPq0veLwmWb0nYUXNMNSeDLeQDfiB3h551x3ZJNv2psoZyHoH6pEvhhEwKTkNSE4TJQ5t2OMcs/iHYtkdOxgayicDOcURFjQURjKNxGDtWwcL7m4Xi0p3+PuST2yHnrVKyMdEQGq6FB8E/Ii6zxK9l0gBvm7o916AKq4GMkzFFCJR//T7bHKecokS4ew1UHHUodjvA7/7M7BVmJSUApq7zlYF5eD/BMol7+eMXFFxDr1/A37g4rEQFU+TEKNcth2vdBbBjSuXfGaNKIVebcjYltIQNUcfBvCNK1LgVlRlF4kkAHK5oR74nJCAGmqFbBKjAJNbGqBf/49gARwrF9Tq50Hj5yTWP2ZwzR7w5TOiVjppQRTBS573U0EV0eNdSsuKP5ZKAHrfWybZPwJ+n4zfjmwGz14z6GKYgYLGzoK699OibFlXY6uUAfZ1qlxy/blcoOqZQWst/dYPhJREkPiOoYGiKqBHSKBq1urbYifuwIEDBw4cjHY4ZBgHDkYTLhy1iTCABGh0AAhAkudaA9cvQv/sG6Ax40Bz1g3ZYufDAjV5EdCHpDKlZUlg4ubVYEUxALNyRAerxiinqF9bF6qaHj35yC6Qioa9bwYn2VQ6YcDqGXzjihCmzIkc71kPWv4EVBw5+uAEs3yyVEFHbVAm8TRlKShGQJ5SM8FuTxRxg1MyoE/sAJ89CBBBTVoItfKZAR1L3KrhlIyQIEMIvKkSrBxhUHpOwkmQSPD5Q/ErAy1lDkCCK6Hn1LREIm9KmNVUQvs0AlLBHTq5d3kA3BRyzY2rQTlemn5XwlYWzFoUOK5dsBu7O6R/IRWZfPYAaOmjUAsfBBY+GHtbp/dC735DyE9p2VCLH044cOJgELACRUpJtZFFgjETvxRJnOsPoSpNibSPILj2FPjtH0qgngi8Zz00M9TcdbLA2FnAtOXA3vXhcVkruHRqt6gmpWYCF47Ie1UH5FgsuW5PUmwFpxECTZwPmjgfesvvxK7PgmEA/lborS+CSsdH2ylBKrjjkmFC7aBC29tu9BmCZSMAnDsEvnoeyMoHTVo4qIrl0QQaPw+8/93wyjvlAk1N0MqlKYb9oTZEASLkWuEGs6K1qPKWqu8MGwnG2h4pqDUfg377R/Y5yy4ELX/CXiZBItGA973kEbn2rHcpA1AQgvjBjfJci7QIa6qX5QN+M5lnEkBDnlGUng1a/qQoLL387egdh957IwRRdLkz3n1q0sIg6Xm4wKf3Qr//a1gPZz66BWr1s6CJC4Z1PwCEOHVkk/35yGagqw209uN2mzbClgm2n9gBnnfPgCzlKEOqdXVHC2ImBceMl3leKKnOnQSaPLzn2IGDoYLGzQGf2B7emJ4LhMzbuO0G9Bv/aROPr12AXv89KRhJVNUyktgIAA2XwNcvgYoqB9t9GcN0d0hiUSeQkGcWMsoghF1AkHva8JsWadHjtNjrmQUmbg+QWzJ4m77QOSezvX9m+7fx+4R009oIPrgRtOyx8E10tctvkSQKIEFVjsy8hH9LB4mDlBLbqv3vCKkkLUtIwC0NUjAyGhSXfd2xySy95n3ictnXifWejCxCSBTaJKEFesGHN0EtfwI0drbEVDxe21qoegbUwgeCapnxwPvfsYkwgCga73kjZNwY4/0c8IPu/yz41/9P+PjSIvm4lKhMaUO+T88VEmCQOGjaIBFk3l01XdR+O9tMm6UIUp6vC/rsfqjMvITUAfsDGwHwhp9J4Rsg85v590HNWZvwNsjlBibME3WeePvZ+Uq4ErQ2oLf+HqpyWr/xUe7pgv7V/0/UmQGx8N39GtQzfzukc8DdHUL+u3oBlJkrarHx7qG+xvlGQN4Dvi7APXjCzUBhxY2N/e8AO162rzm3R57HHi9QUC7LmQVt3FArRJiwDSmgtweu575+y/ruwIEDBw4c3IlwyDAOHIwisK8zvMGqwLJ8an2d8m93O7j+NPjKOajH/gJUIH6u3NoEPrgBfOOK+LfOuTvhxLcDQK38CPRL/xoxmacgwx4g0LT4lhT9bn/2GvCEeUIuSM8ZVKCPd70aUdHAYo8zfm6fQXvqaAZ7k2VCbhh2MQop0NLHQdPjkGkmzgcf2igkhl6fKI+4PEBrg/iMm9BXzoI6Wwc06Y7b1+RU0OTFEog1AhJAUG7QzFWDVhS5ZbACm7FIAsx2u3LZhJ/kNKgBWCJFbfbU7mivYcMPGB6QO0l84QeyPRYFF75+KZwIw2wSenrDCTvaMO13Ylet8fVLYckvdLZI9Vd2oSO/PUKg/DJQ6USxBfGm2J7vqZmg2WsGXOlJ1TPsKlsL7iSgbBDKVgG/qEidPQAQgSbMB827J2Hpb73tRXkeWUFacoH3vQOeukyeHURwLX8Cxs2rQnYxAnZgkxSgFHTNXuD8IWlzuQFt3pc6APgNOWch/vW3DKH3cW+P/S46s18C5pG+9kD8Cm4AVDJOVA4i2/sg0TJr8Ns/EmUOq+3YVqjHvzSgKsPRBkpOhXrkC+A968ENl4DsQrFyMcdP/SKvJFp5RLlEphoAd7RIld4NkzSTlg117x+NjqTGIEHFVWJNd+WsvPdLqhO2jhnSfnNLQGueA7/5Q7m+g/YTZqKPKFrRJ78UqD9t26QBQG8AKK6K3kFmfmzCrZP0G1GwNoQUC7bHQ9qA3vALUFYBVIRC0JD2ZQTAJ6MVzfjcYfDSx0ApZuLF3xu7StjwCwlsgORkACBfN9ibGiLDL9XtlFMMmrIEeu+bojyYVyqJvcEmwR04GCHQogfBrQ1Anan+kFMEtfZjYXMwrtkbnfw2AkLQXfJIYjvqapdtGIaMz9weITv2ZxfRD3jnK/IuToTcQmTbSwT8ABTClFP7XtnsO8mzJMnbNxkmOU2W7ekS4gkRkJQKuuvpxA4sFiqm2KoVoeNBUvY42QhIQhUAXzsfvY3WRvt96HLb1s5tsQnVDoYOSs0ArXgS3NsD/cZ/Ba0fGQBNWwa1/Mnb28GsAvmvtTG8PacY/Op35N5lHbyuBmwxFhMMrj0B4AnQmudl/HbpuKjaTluWsOoUXzoe3agNIHcM0HAxumiJ2VTj/ne5N9kw4zkmucOyQwPssWNrg3wfNgczVWPGjIV64LMglxvGkS3A9j/Y9kyh+zy8Cbr+NNTTfzVk+ys+scMmwpjHy3veAFdMGVZb6SjbeUCePU31/aqp6h0vCREmVJmnpQF69+twDdLelY0A9Kv/ZhNsGmvBF49DPfbnoPxSUdM+uRNouQ7OK4tSN7NhKg9lF962ua4qqoROSrbvKXO+TzPugop4p/K1CzJ21dokzCQFFZYcOHDgwIEDB33DIcM4cDCKQBVTwDtesYMXVtDJ5Q73oraqjbUBPr4NtOpZcGerVLqaASRuqgdfOg711Ffv6ATWrQQVVYkUfKM5UVJKEiuGIYmtZY+DyiYObR9pWaJKMEiI9HoEAr0yCS2dEH/FjFxJZkVWqmcXQfVpG5QOWv1RCXyEKOTg9D7Am2Zfo4BYRsxcOTx+1vPvA84dkmADIG+rRKvtbiNo8iJJPhuBGDLZIUlsTxJQOgFq8SOivBByzjjgl4TtmX0AM2j8XNCih0CeJPFpvnJWAsYlYyUxeeWc6XUfEZR2uQfsRc0XjkLvfEWsMUiFK9hY8sSRlWJ5pSDLvzjWNs/sR6wqLD57wCHDjCDo3j8CDm4EXzgCpGZCTV8RV0Gk323NXgvcvCbbAoTAtfKZQcl287YXxbbC+nxwg1i+hShOxF2XWWyNQq9BNoCednlWJFcFm9Xq56Abam1ZbFI2YSs0eBn0gLcs47zy/ZWzfT9Tzf7wkc3gU7ukqnDsLND8+/qtjosHqp4hFnhah9/PbrdJZPTbQTILfbxPaNZqCSyHEJloxl2g/D4IIHWnw4gwAIDOVug960FJKUDHTWDMeNCUJbdMRnq4QDlFcl8MAmrhA2JTFaLqQLNWBcdXvO1FmwgDCOlvw8+hnv1bUCSB6Q4CudyiLHer91s5HZyZF3btApD3kdYyLnOZ4xnlAk1bDq4/HZ4AVG6gKzqpSinpcm8c3GA3KhfUwgdG6GgcAJDkWVeb/N3Taf9Ovk7wK/8GfuIv+n42DQQWSTcSrIWkYpJhyJsC5JdFVwtn5A6eEFk6Qewyk9MQTI4RiRpkUSVcj35xcNt14OAWgP0+8Pu/FDvcgF/meksfj743e2OQyICE52rMLO/TUBJwoBfIyAFKBq96y0ZAxphKybw3EOM5EN4ReackpZhWFB5JMsYiyUWvbBYKwFa3sFQsiKKT7j2d8g5LTjULJ/yAvxu8/vvgtR8HJWg1FwpKSoa67zPQW35rK9hZlsa9liqkPQaJWSSVW2yqyvTY67tcwB1M5r1TwMe2yrwmtO34dvD4eaBYZN5bBCKCWvM89Ns/tt/bSSkyN2JtkvPN5Lt7cHOeGHuVwivI2JNm3AXMiG8dFRdurzlnCtgKTC43sPABYNOvokle1r3aUCfEOF+XPX7wJEXz4kgBLpMgYyTZMVoiYMZKIQ6a95yqmga99Xex+xnwC9no4rF+Y4Pc0wU+ux/oageVTYouaqg9GXu92pPDSoZBWjbQ1hTRSIlZF9WeCCfCAHLezuwHVj8nxVgdrUBKeuLzy4vHgkSYIAw/+OhmYPEjUuTYfsMkYG+LrZINBEn2asVTie13mMFGAFw8FlQ5LZzMlZ4TZW/OjXXg3a+bxB5TAcwIiKrULbauduDAgQMHDu5EOGQYBw5GESgzH7TsMfDO12RiZdk1hCbGSIVPOs1KA67ZE11J1dMpFVrz7rlFR/ABgMsliUcLZvJULXt80InkYUWs5BApqXTuA5SWBZq6FHx8W2gr1PwEFCKunpUErJWEtYIghh8gsyrGUg0J9NoVZUPBsS2ihBRS/c2H3xcFnFFMoFCTF0H3dIocf0ez+L9nFwGXTkjSnghwJckE9txhcEo6aNYaCUSa4O1/ELUXU+6a966XSf305cD5I7Y3fFaB+DunZ5ue82wnakkBM1cPiJjELQ3QG35mk3gCvWYVvrKTjkkp8vtaSaz0XKj+qnn6so1yMHJouiwBlZYGeRe0922N0xfI7QHd8ylwW5MkNPPLBkWEYF+XSY6KaD+1G7z44f632XzdDFpGtBsGOCMv7PgoIxf01FfBv/1fcl8o+xqmqUvAl8wKOtYmT43k2k4yq2dbm0D9kWEOvAPe97b9+cgmoP0m6J5P9X0c8TB2Fmj6cvChTXabJ9nue2qu3I/d7WLbN3UZqC/7v+RUqCe+DFw6Ae64CSoZ368SCt+4HN2oNXBsK9iy0rt4DHzhKNTDf3pHEz0GAhozHuqpr4JrdgO+blDVjGDiirUBjhWIbmuS9/VwBqJHCbi7w1S5yx4R9RtyuaHu+2PoX/8/9jvJqtQlAqYsBnW1S9B82nIhhxPJO1cbAEiSoV2tYNZRijZq4QPgokrwhaNBq5phI2I4iI2UdCGhtDeHv/+JAB0AH90CWv38sOyKPF5Q6YToKubsIlBWQViTWvGkEN2s5LfbC3XXRwatgkTTlwsJ8doFWCRomrFySLYvDhzcKvC+t0T1xZpPBHqBl74F/Zn/FyotO7gcVc0AH9sWtX6UTXA81NVIIj1SpauoKqZtb8IwArZKRZSaqEV8NlUAgoS8LqCXZI6TWzJ4y7xAr6n+osw5cgQhhsgultDJdrFBbzf0+y9AfewbgyoooZKxcD3zN9CtjeDX/kMst5ntc2vFjVwe0KzVUevz4U1mQtU8b4YfSMmAWvTQgPviYIC4ci5mM185MyAyDLc2CjE4I3fQXeHuDokntt0AFVcD42ZDPf8/gCvnwEqB138vRJXTvJdcHrFPu3IG4JDCgr5AJGRlK3bEWto8yaDCYRhP9naFk2GNgBTugMVCzeMN/z70HnW5ZawSkOXUs38DPnNA4jr+HiCrADTvHvD7v5J7y+U2rTsVMGkBXHfbNowAQJl5QhTpg1zHHc19W9e2NkK/8l075ntwA2jWaqjFD9sLJcexsh1mi1uasxa8+TfhbeNmyXH2h9Qs4Oa16Pbudhjv/Bi4el5iXEnJoNlrE1Kb5vab8jtYz3yXB3C5wO3NwIntdkGM4Q+JpYc8l0kBWfnAtOVQs1YPqsgnUXBvNwAKe7+x3wfe/pIdG6maDlr5DNB0WWzqJi0ARSgv89GtcszeFFMJxryPMnJBzjPbgQMHDhw46BcOGcaBg1EGNW05eOxsqRLJyAUyciVRePE4cOmYnQiwUD5F/m1vjr3BjjjtDmKCJi4AN0UkA1MybktldCyoefdCv/tThAYbaNICUEZ8ZY7gcsseBxWWg88fATxe0JTFiUnOWuSL4IbMSb82xJPd6ovhBi6fBcbOTPyA4oCvnI3bPprJMIDYYWH2GrARALncQjL5zf9E0O7MqohWClyzF3zhGNQTXwJlF8qk+PQ+2VBo5XR3B7D/HUmMW8HT1kbwtj+Alj8u5BkigJNlHxm5UAO1Rzp7IDwg7XJLUMHoBVymVUB2IeiRL4DazCqb4up+ratowlyxvApvBY2fO6D+OUgc3N0B/eb37WBfTyd456vgtGzQEJSpKDO/X+JdnzBtMaJg+MNViOIhySv3QKSlYHIaKCUtanGVXQB+/C/EP/7GVVD+GFFuyRsDPXYW+PzhkEQMhamuUEn/qkqxEkF84agENvtQS4oHIgItewK6ZDz4rR+aAdaQit6iKtC6T0olYWpWQkE7crmBsTMTJkJR3pjoULbfFz7uAICr58SWpnxSglu+80HZBbEDjaTk2omqiDeTa8ME1oYkKWtPACkZIht/G4g2+sRO8I6XggpLVDoBdO+nQZGqRUMEFZSBZq4Cn9yBYPISELLCoofDrCE54JckZE9nuAJeybi4pAaqnBZWRckdLaLg4agpjgjI5Rb7lXd/Fv6FxwsQyfkfzv2teBr81g+EEAqIwuOaj0YvV1gpCb+Lx4Q4VTl90AkR7u4Anz8s79lpy4HeblBxNSi3ZAhH4sDBrQPX7AlTQAMglfbbXwZCiL5UOgE0dx340HsyhiIlynMxKtOZNXDtoixXXC1zo+arwWp8UVXhaGLMIEBJyTYRLpJYEszVczRRn1kS1jcuAzoRi6Q4SE4HPfjfwEe3AOcPA52tdrLfgtbRY6qeTqChtl+rkb6gsgrAj34RvPsNmUfnl4sSjK9TFAZm3BU1ZmBflyT6PV4hKFgknrQs0BD6MhrBWgOXjoMb60A5xTI2Ho4CnqEgTvwmUVILtzVBv/tzoKlOGsaMh7r747YVYILgjhbol/9VrlcAfHIn6OwB0P2fBZVPAhrrwJHxIEDGgRm5kDEaJ0aIqZgGtfgh6EvHJbYR6LWViYY4/uJrF0X5JSlF5i7M8hzIzBeCgdbx1UGsa4GUFCO63aCsAtD8e8Gz18jzITUTRAR9+aypLmirdlOVFM0xM1B3Cnz5NNibIsWKsZSizP1RWd/zKN73dpTNEh/eBJ6yOEjupenLwOcPhT8/UzNBY2f1d8oGBBo/F9xQK8VZbjdo6tKYBLuYmLkSqK+JbjcCwLFtQUIUALF4ysrvt//cfsOMRZrw+wBvCqh0fDjxJmCpZMGMpSsADJp/P9TikSWQcHcHePNvgkUTVD0TdNfTIG8KeOuLogRt4fxhcK8PasnDQtSPZUHeJfcoXG4gNSNo1UfTxC6a25vNItlOUMUUUMWUET0+Bw4cOHDg4E6DQ4Zx4GAUglLSgZBgkiUTqve9BT6wIZggp6rpoCmL5e/S8WLVELmt0qHZ+nzYQNNXAN3t4GPbpQKkoBzqrqdHjR0EjZ0J9fDnwSd2Sv+qZoAmL0xsXSJg4gLQxAUD22fFVCFbhCIp2ZSytSb2BCR5obf+Hqpy6pCDS5SeA8aFmO13CqxzQNmFoAnzpOrD12UTXDTLBJ4ZfGyb2MQYhgQyjEC0coo2pVBDrkWurwGl50I99mfgQ++Bm6+DCitAs9eEJQoTQiwFl6RkoHSCWIhl5oHGzZaE5wCCVVRcDVrxFHjvmxLsTckALXpwRNQEHAj4wpGYFhFcs3dIZJihgtJzgLxSSTaEoqgqMWJHeg5o7ExRcwj4YVUl0py18RPeBeWg+z4T3b72Y0DFFNlW7Um550xiF81aLYHyPsBax/HmZvGcH8KzSo2dCV0xBXz5tN3ocstxutxSuZwAuO0G4E0d2LOgfBKobBI4NGBp2Q1Ebr+lQYLkdwC0oXFxTz2uHruOlKxkTFhVjYzC4amYJCJRXju0Mby9cmpCRNVEod/4L+DCUQAMKDe4Zg/UQ5+/pQkr7mgWS6iQ9xNfPgMceh+04L5h3x8tfADcVG9X6rs9UKueibqmye2BWv4k9Hsv2MmA5DSopY/1uw/ubIXe+AsheMFM8q75GCh1YMkkB/1DTVoIzQx+96fSYFbxAkiMnD0AUFY+1Ee+Bly/aCfhI61CrWWTkoGJ8wetngYIWVu/+UPblsXlgbrnkw4RxsGdBSMOGcUilYVALbgfPHUp0HxNVJfSs6OW4dYmIWe3NkpDWjbUfZ8G5ZfZKfOQ+5Lyhz43oJXPCKH45lVJigd6ZT4T8Ntjx3jo6gC8gyR2JqVAfeSvgZYGUWYM9Mq+SMk4SutwNRrlEgVaixiTHE3qTgTcdgPQhsw3M3JBEeoUweWMANjXHf7+bG+2yU8ul60E2tUWLOr4IIC1AX7z+2KnCPMKOFoB9fDnh53IOxDQjJXgswfDrVGzC4HqxAqL9MZf2EQYALhyFrzld6B7Pz2gfvCRTUEiTLCt7hSorgaomGxbDUVaMgNQ05ZB+zpF7ckkxkUR6ixUTIXryS/Lej2d0EECitgZ8and4IkLBj+ubb8p/7o94QUW3e2g3GKwrwtx739tyD1qzQWrbEVocnvAroygGiY3XBK7M5OIAAC89ffQZZOA3a/ZcTPWQbUTKQgJie1Y87p+SO3cUBurVWydLDJMURXU/Z+FPrgBaG2S2MuC+4emshW5x94e6Fe/a1vC+gCuq0mYDEMFZWD0Q5by98jvRgQ+s79PMoxubwYObIj+wggAU5cAp/YKIRFA2OBOuexrY7B2mAMAv/8rcJ2tHsrnD8kfq54Va2QLvT1yf9Xshj67X+bv5ZNBaz8uJBetgcunwUkpCNpyAfYYumwiuLEO+rX/kPMIgI9vA81YCbX00RE/TgcOHDhw4OBOwQdjduPAwYcEav594EkLpXIoMz/c7mDsbND5I5IENUHVM4Fhrgj4oIOIQAsfBM+7F/D3jqhc5mBBY8YPe9Kgz/1VzwBNWw4+vh3BoN6s1cDRLcHKcLjcMinr6ZCg6BDtBmjmSrmWre0DkgCunDqk7fYHZi2VQx4vKLswsXUuHDXVXBg0YV7MiTuteg6cVQBs/b3VYk7MWYIjVvCmux1IzZSgc+hEl5QEVAxDJstmJTW8KaLMklOModoL0Lg54IMbo+wL1Jy7QWVDI9WpqUvl2dXVJpVKH5Dg6qiF1rHbeWgVt8MBtfo56Ld+BHSY13xmPtSqZxNen9Z8FNj9OvjcIQkkTl4MmrduwP0g5QJNWghMWiiqEucPgztbJRFeWJHA+gpUPinaHic9B8gbevKT7vsMcHy7VNGlZYGmL0/YxoWvX4Te9Gt5jpjHScufiJsIDtsvKeC+zwBnDwLXzgOZ+eDWJqBmd/SyRRXiXX75jBCdqmeM2nt7+/f34eIuO2Fw+v3zuOdvVyK3IntYtk8L7gdcbgmAG37Q2NmgRQ8Oy7YBQJ/cBZw9YDeYVgt84B3Qg38ybPvpD1xXE03UBMQWZiTIMCnpYvV17YKQR0vHx66UBEDjZkMVV4MvHgXcXlD1dFACyjz8/i+DRBjAJPds/jXo/s8O23E4sKEmL4Juvi7JNwuFlUL8H2YQEVDcv8rXcEBvfdEmwgCA4Yfe9geo5yYP2nLJgYNbBQ74hbQfmWgDZA5SVBVzPUrL6pMgr7f81ibCAEBnC/R7v4TrI38tVksXj9rfpWeDZieoMtAHKCMXrqf/CtxYJ+/JtCzQ9UsyLupul3lUXDDQ22seP0W/70iZudyIduUCzb8PWingpW+FE9LDPDzJVsDRhiyXlAwqmwTKKRrQcXJ3O/SGnwOWmmreGKh1n4yygmNmUUg8ukUU7AoqoO56CsgdI0QaQPoSOkbMKx2147lB4fyRIBEmiMZasRMfgXdPoqDcYqjH/lysoE17Ipq1OqEiLG6/KTHByPZLJ8D+XpAnKcZacbZlERyi2i+DKibLtVk6Eag7GX5tV04FVU6FKiwHb/kd+NIJMx7UGYM4Q0L0sbZ98bipQht+nfG5g0Bx1eDem8XVdswkdM9jxoErpsWfH3tMC2tfl1gLFVeDTDI1X7sIvfMVUe3OzAfNXCmxLuWSY/J1yf3j6wL/15fleCyCFSmAzPs9OV1UabQBZOWDHvkiVG7fhReAFFVxW1P0FxFxKiqbCFfZRFHZ83gHXhTVD/jULpsIY+HaeeDcIWDi/H7XJ18P2JtsP38jLeQsWPZT/f3+NbtN8pVln2xa4IGAw5tFycibIgUqLo9Z9BJChElKGXblnEhwdzu47lR0+4UjwNJH7XNgEWGsz0ZArqf6GmDHy8CiB6Ff/0+g5bq9vMttHguB5qwB5ZeJ5ac//P3Gx7aCpy9PzMrKgQMHDhw4+BDgAzTDceDgwwEyrZOi2pUC3fMp8LULMnHNKxW/XweDAsWYnH+YoZY/AZ55l0iO5o0BZeTCuHAkqooIyjVkiVtA1BzUI18EH9kEbr8JKhknaicJ/CZ89byoPSR5QRPni7VLAuDGOuh3f2b7C48ZL0HFPir19JFN4J2v2tu4eAy04IEoiyJSCiqrADopOdo3WhvAmHHQRzaDd75ikl5MEhBzUMrVbDB93QNiDTN9RULHlggotxhq9fPQu14V0oo3FTTvniETYYLbd7ljPrscDD+oegZ416vhZDIAGAXWVJQ3Buq5r0sAjdSAA56UlAxa8RSw4qlgG3e1Qx9+D3ztIigrXwLJA7CPIbdnUKoAtPxJ8Prv2RXTyWlQaz46LIlPcntAs1YBs1YNaD0O+KHf+qFtb6cN8MmdIpGfoHUaudygSQuASaIixp2t0FfO2KQ9ADRpIfj8EfDh92UZAMguhHrkCwOWZx9p3KxtCSPCAIC/O4Cjr5zEyj9bMiz7IKVA8+8F5t87LNuLhHWew2D4o20dRxiUkh6zppOSh0dlJ+Y+iRK2jqC0LNC05Qlvm7vbhfwS2V57Cuzriku8cTA0qCWPgCfOB189L2O08ol3NGGEuzvsJEUo2m+K8oKThHAwisFGAPr1/xAVJWveYVn7uNxAWhbUAMcigCgJIJbtbfM1cGsTaN0nQRePgq+eAzLyxPZ3GJ+5lgIlAUJUfv9X8S1SwjquZS23W/61SG6WkktPZ4gwqhICUUYO+OIxYNdrgI4YezNMBQlzG26v7MNMftL0uwalrMZbfh9+fm9cgd7wM7ie/Er4cid2gPe/bTc01kK//l9i0dNUL2o5vd2i/pGULPYcc+8WK9H0bFBh5YD7NtrA1y/F/uL6JWBG7K9uFShvjBD9B4p4BHelguomCfchd0xMi+rQuZS651PgHS+bFrMKmLwYaokoTlBKBujeT0txARH46Bbwpl+Hk1K8KVAz7wr7HAYjAPT2iNLtxeNQC+8fuJpxRg5owf3gPW/YjZl5oPn3Ad5kcJI3NhmOFJCSIgpL6z4FZVp+c1cb9Pr/sglAbU3gbX+wlZ56e8KtiYyA/EdKiAqW0gxrKRiz7OC6O4CDG4G1/f/uNPduGaeGqO1Q9czwokgIoUm//ytRX1UuKdBa8VQwdsaNdWKv1H4TVDJW4moDUaNqNOdRhiGEC1MFSNfsgSsBMgyKKuU53Nkqz75AAJbCa9gz01LmmTCv7+0ZRsi7CgiyDo2APWdiSOypYor8VlfOSoyrsBJq2WMjX/TIjHhKOORJEoXaS8flGRy5mBEADEPuN3D4GNObIse+9FGosknBAj6OVN4F5PzcuOKMQx04cODAgQMTTqbXgYPbDG6/KQPUnGLxdR4iqLjaIcHcIrDWwJUzMrkqm5hQBfJoA3e0CGkkgb5TZr54Lluf56yVgEDoMpMWDlsilAorQHd/IqqdfV1A/WmpuimbGKZ2oA9uDAuA8OH3oe7/HGjMuD73xayh3/mprVYBiNTwjldAa2IrrrARENuyyPbD74Fnroyu6sopMiugPOHywamZQPVM8G/+p3ngSqqSrAoRNgkwLrcEUCyCQ+U00FxbEYP9vVJN1dIgtkaV00Q1ZgCgCXMlANTVJnZGQ7QHY79PlHYycmPKpzsYGVBaFtS6T0JvexHoaAHcHtD0u6AmJWapNtIgpYBhUrfigF9km83KY756TmzkqqZDTV0yolVflJErkvhXz0tQb8y422+pV19jE2FCwGcPAAmSYSJBaVlQT31VpNw7boLGjAcnp4Nf/N/hC7Y0gA++Bxplcswtda2x2y+33eKeDAG9sSy5AKTfYoJh+WSRZQ+t9AeBZiROQEkUzCy+9+cPS3Ju8mJQxeTBb6+1Ebzr9WDiVc1Za5JsYsi2E4VXqjoYdlDemAGRFkc1kpIlSR5JdnZ7ZTznwMEoBp89KEQYQJ57KemSnMsuFEvmGSui1EYSgsst90CoYpLsBNxyHdTaBFRMhbpVKrYcYlOU6PKW8ilI7nHDSuCaRTNai6qEZeUXe0OAJ1kSyKHJ8uQ0oLgaatljAz8UIyBJ1Eg0XRaiUUhMiU/vjV6u4ybQ1WqrCyiXzE2nLgNS08EbfwE2k/xUOgF076dvq53QkJETR+01QRXY0QhKywKVTwmzYAEAGj93wKo+NGul2Ld02eNiKpsEhFihUkq6WMyu/Vj87ZhzIJq9BjolQ2Iyna1AXqmQD0KIVTRlsZD1LXsiXxcAkjhJZwv0+7+GyioAMvPAtSdB7iSJbfQzz1Jz1oKrpovNU2omUDXdXqd6JmDdD6HKJJYVrHKBcorA1y6KLeiZ/dL/UEURMJCRL+PgSOKbBb9PnhvWmIBIjjEpOagaw2f3g6cu6dcSioqqoJ74MvjEdqCzDSifLAULIWCtw1VXtSHj55R00KKHwI310K98JxhD4oZL4LpTUE98OfFrJadYzlmo1RRroPakKISWTuj7OFxuu4gktFjH7QYoyVQ7cUn8afZaUHXfLDUaOxO8763w+RFzuD0WAehohiqfDKqaLovcQvs3Ss0ElU6IIt1TxRSJvd71EfDGn4saayz4fUBqhqhyRoI1VGZ+mJI15ZaAuyLntpSwvbIDBw4cOHDwYcAdQYbRWuPixYtoaWlBZ2cnmGOza+Phrrtun/SlAwd9Qe96DXxkc7ACiaYuAS1/MuhH62D4wc3XJTlcWBElH8ptTRIQ1Bo0bnafcsXcfhM6VBHA44Va+3HQCNv4DBeiqkcmLhAbjQFMDtW05ZIQPbUbHPCDxs0CTV02gr0W5RW98Re29G5mPtRDfwLKyAX3dNqVb9qQ6r+eTui3fgD17N9KQCQeGuvDiTDW/i4cBhDHfsjXHZ34AGQy390epYJCeWNA42aLvYuRJH30eECPfBHUfC0YdAzCnSSB0qwCIf9YFZqm/zNlFwDN16GPbxOZ64baYECAAVDlNODePxpwtfVwKbjomj3g7S9L8JeUVHze9fQdXf19J4Eqp0GVTxGlo5SMYfUNH1U4f8hOzFvVrQBw4Qj05dOgueugFtw/YrsnGj5iz7Ag3vhhiOMKSkoGTbVVVPj4tpjL8dXzQ9rPSCC3MntA7aMRVDJenvNh1gsKtOThIW2XtZZgf91JCZpPXdbn2IdcbqiHPg/e+yb48mkgPQdq1mpQxfCPfXj7S2HXGV84Alr17KBIfez3Qb/6b3aix9cF/e5PoR78b6DKqVFJRaqeeUcSnG8luLNVEugZeVEVyh82kMsNmr0GvPv18PaZK+/sBLKDDwciFcaIAE8SqKQaavkTg94sudyS8D662W5kDSg3+K0fSjrVmyoqnP0kUocKY+erYep2icFMYLs8MvciAny99ncer8w1O5r731SEdYUoRXRBzVw5wD6FQLkAIwa5xxWhGBLLGsYi5AS3pQDlBXmSwPvfDSMN8eUzwOFNon53h4ImzBObKCtuA4gF6dSlt69TwwBa81Fg2+9FEZcUaMJc0NLHB76d9Byop74CPrkbaGsCSsaBxs8Z0pxdhShMxtxn3hioBz4HvfdNsapUpr1QsJCHofesl3GGEZDnRVoW1EOf79fKmnKKYo5l1b2fhm6/KYpAOiABk9B95pdCb/6NWHP6ukR9BBBSRaBX4jLm9jF7Dfidn4TbQRHZhUxBBRqTTEckz4uQMQE3XOqXDAOIei8tfzL+AtfOx45jndkPLHpI4s2RarE3rwKXTgCmCk6/fZiyBLzvbUTaaUO5wKf2JPYMr6+R43eZFlOAnKvqGUDVDLHkSk5PKB5JBeWguz4iqsq+LpPImRHDngvgpsugqungzlZRymppEHvOKYtHLD7Cna3gmj3g9GwhEjVfA0Cg8smglc/IQm4PaO46Uflsvha9ER0AyibJdz0d0d9HxDZp/n3gaxfCzgFNXTosBbcOHDhw4MDBBwWjlgzT09ODn/zkJ/jlL3+J/fv3o6enL2/f+CAiBAJxGNsOHNxGcH1NhOw9g0/skInEraqQ+hCBA37wxp+LhDEAuL2g5U/IRB0A156EfvtHQalTPvAuaMkjooaSWyz2VKHb2/lqeEDF74Pe9Cuoj31j1HtssxGAfvMHQGeLNGhDfIBTMxOWaWargmfsLKhxs0esr2H7DPjF7z10ktvWJOot9/4R0HxdJvraEH9gq2qltRH6le9CPfXV+P7Z8drdfSQxUtJFKSfSxzk9B0jLjrkKrfmYVNbXnRKCwtSlUoEUawIMAN404NrFkCCqWZnocgGpmdAv/R8hAPh98p/LLd8D4EvHQbUngcpp8Y9hhMCtTeDNv7UDJqzBp3YDBeV3fODxTgIpJWSqDzC4PSQJEUoUMIP/fGQTeOaqMPIjtzUByj3q1YrY7wOf3CU+9dmF8rzoi9QHSNAsNTOsuhNAVBXhUEEZ+bHtcrJGnwxzdlkWJqyqxplNF4Jt3vQkzHxsYAQO7mqXyszm6+azbMktI0zQgvtF0aSzxUxiEWjevVBDJGLx+y8ICdjvA/y94D1vAmNnQt39CVAcy0NKzwatfm5I++23X93tUjEc2b7/XWAwClcXjkbdEwCDj28DrXoO2Po7M5lEQoQJsWFzEA196D3w3jftiu6cItDyJ6GGyVbxToSavQackQuu2SvWJxPmDNjiwYGD24K8OFXjuaLcxEYAvP8dIfO73KDJi0Az7goW7zBroO4UuLlBVD1Dkru0+CGxJ6nZG7TVCCOP+LqgN/4C6qP/14jNn/nGFSCW1WBfIISpePoNAy6PB8qa17jcktiOp9qW6D5K+lYujbuqyw2aOD/qPUllk0DpOeFt4+eAI5Vr3Em2GkYIuKcjpnoO154cMRvIWwHyeKEe/XPw8a1SAJNbDJq2HJQ6umw9BwpKTgXd/QmwNS6MZ53UD9jvkzFQT4cowlTPGPb7kVkD54+A604BqRmi9lc6Aa7SCVIgGLS2MWcXRELWsGYbbg/Q2Spxnwc+O6g+kNsD17N/K8ovzVfBtaeA2hNSCFc2UQqTLhyRcU1okRKTfA74hcAwdhZowjzozjbwlt/afQTkviquFhIPueVeC/iAgJblLOskoF9ST+IHFoe0ZD2jYxBlrPaESyWSU6MLK5jlORmqeNwH+NIJs7gr/Nmjpq8AlQ9c+VHNWAGeOF/mZZl54KObwYfei1qOckvE8vcP/8eeC5w/DD57AOqxPx/+a/3GFSHgh74fJi2CWvQAKCUD3N0B4+VvA7WnAKMXQcJU1MyagOwCqIrJUsQY+k3pBFB+aXhbUaUouZ7aBXR3SGGcqYjjwIEDBw4cOBCMyoztnj178Pzzz+PCBQlaD1QJBhASzGDWG0lorbF9+3acO3cO165dQ05ODsrLy7Fy5UqkpQ3Ar3OYcPXqVezZswdXr17FjRs3kJKSgvLyckybNg1Tpkxx1ElGGHzpRNz2kbR1GGmw3wfe9xb4/BGZLE5ZEhYwu239OrLZJsIAQMAH3vJbcOkEIC0LesfL4ZNeX5dIBKekS6XNtKVQy+zquEhJWgBiTdFQa8ruj2JcPW8TYULAZ/YDCZBh9MldIkva1SbKLEseCUqPjgRYGxJkvXJOPJYjrH+43pQOzS6QKrneboRNJpVLSDPnD8dNCFNOsQQurl0Ibw9RQohahwhq2RPQ7/zYDgIoF9Syx+PaE5FSoEkLw5J57PcBWQWgsbNMX+DgwvaxKLcpxcvSNuMuUWAImPu1rl2LDGQGw7ihTibCgwAbAVENGsS9yxePxZYiv3AUcMgwDoYRNGa83O3M4decFdgK+CXx4k0BtzVBb/gF0Fgr65ZNAq35KGgU2liwEYB+7d9tj3YAfGo31ONfiktSAEzljgc+B73ld0Ki8SSDpi0FzRhmlcTyiUBhpezDgssDmrV6ePczTFj0yTkom12CK8euIzUnBeOWVSIlO/FqQO5qlyCq9e48fxh8Zr/8HrfAGouyC6Ce+ZrYXXV3isR2YcWQtsk3roQQYUKIZBePQ7/1I7ie/LIs19MJPrYVuF4L5BTKmG4Y1MP6RHtz+Jgs2H4DzHrA1crsi52wZF83VHIqaN0n5V0Mik+adQBAFBaDCii9PUJQvtoBfvlfoatnip3GEO4JZg0Yxu23nBsEaNxs0C0iiDtwMFyg8XPBx7eHW/1kFQTnQLzpN+Cz+4Nf8c5XgN5uqUIP+EUp9eo5+Q5CvqA1HwOZyXmady8wT4gUxg//JroD3e0y1hkhm2e+fMZUMLDmUjFg2QX5esJykk1JyXi5fDzOpWcj2Qhg8Y3rWNd4GcrtkTlpIiAV355pCOERWvoYQAp8eo8k86tnxFSPoBkrgfabQq7WBpCZB5q9FrzjJXseCQDZRWK7UxNtqzQax8kDBSWnyrX4AcRQkvns64J++TtAy3X5DEm044HPDZpcE3M/m38r1j3W52PboR75U1B+GWjcHCEx+LpCxn4k940Vh/D7AG+qqBIOEVRcBSquAqYsAft7ZZ+XT4Nf/TfZZ1QegeXEBHpBs9eAJswDAKh562C0NQJHtsh6bo/cR/d9Bvz6f9jEP7dX7HTBtg12TjF4zIShPAJsFFfFLNCyCLk0ZrwohoQdEgPp2WBfd5RadkxYcafIuFDAn/i4x5siz/uo9tTE1o8B8qbI8QPA9BUyT+posRcoHgtUTQPvfSuaFN9UD5w7BEycP+j9x0KUfRMA1OwBT1sGfe4QsO0loLst5DqLkbci80VUsxd4/n9AuTzQR7cAvi5QhFV62GpZ+aBFDw3n4Thw4MCBAwcfKIw6MszRo0exbt06dHR0gJkHnUAfTUQYwzDwz//8z/jOd76DK1euRH2fnp6OZ599Ft/85jeRk5MTYwvDi1deeQX/8i//gm3btkHHkk0FkJubi/vvvx8///nPbzuJ4QOL5DgEqHjtdwj4vRfCSCe88xWZJM29Wz4H/FJZXXcaSMsETV8Oyh95eXW+dCy6URviQTxulm2zAQihwFIf0RpQAB/bBi6daJM+UjJiyy2n3AEVRnFtNOKvwsxSjXPzqlkBY6KtSawGPvLXg/OTj9yPNqT68NRu+R3KJwvBqK1JfoueDvFdD01WmUoJlJIBmrkKvOu18I1acrT9yFire/4IvOMlqYzyeIXI1U/QjComQz33damWZJZKoYzEnuN84wr0theFgONNBaYuAy15VCqUktOAcXPA7/xYfi9vil2BrVxQix6EfuO/QjoSkhg0lwEAiueR3le/2m+Ct74olVtJyXKPzr93YMnHeLYAjl2Ag2EGlYwFTV0KPrEDIBfAJhnMekZ4U8EA+OQu8MEN9nObGXzhKPjlb0M98NlheX4NK84fCSPCAJBn8LFtoEUP9rkq5Y2B6/G/APu6AHfSiFRbEymoB/8b+OgW8YpPzwHNvOuWvM8HAyJC2ewSlM0enG86n9geTSK9eRV89gBo8qKhdzABkDcVNG35oNfnq+dFcv7GFSCvBDTGlDQPRFR0sgaa6sCNdUB2EfQr37GV8OpPgc8cgHryy1HV58OK3BIgKSU6mFxcPSjZfqqYAt7xMiIDzqFkUcfOJjEEyeDaCFfqMwJCTj65CzRjxaC2rQ9uBB/ZJOTy4rFQK54E5Q7unnXgwEFiILcH6pEvCAmiqV6UniYvBnlTwF1t4HMHo9bho1vBc++RCnSTCBP87uxB0Ph5QCzr4ORUoCPaxmIk4x+UliVPfk8S4ItDhtEG0GsqN5hsGA3gx+Om4aapANfjcmNTcTmS0zKx8urF4LLdLoUNxZWoychBesCPZY2XMaP1hlgruZNiJ38BISsPIW5Abg9oxZPgZY8DrOOO9Ugp0PInwQvuF+XUzDwpHMwvAx9+H9x+A1QyTsjMScngqKQ6DfqZ7mD0g49vDxJhgm2Xz4AuHhs2tWq+eS2MCAMA8PeA978jBNqCMiAzD7je3jdBoLcH6MPKExCSM+98RWIzyiVKVgsfiH9/mPNF48AG0+aor40zuKke3NMJMp9ZrtXPgxc8IDHF5FSgchrI5QYvuN9U82Ap5EpKlvE2m3GazhbgvReAe/+oz+NJBEQK6r7PiMXT9YtSnDB5UZAwQTNXibqTRXgM+AGXG/zuz8Auj8R6Fj3Ud9xfG+ZxpJgWUOaJSs1ImAxD05eDt/3B3J4WgpNygfesB+auA40ZnFJWcPtpWVBP/KUQ/9qagKIq0IR5IOUCR1zjFrjl+vAQkkK32Vgf3agN8EvfknOXqKKYyw10NIMPvScknxtXAJdHrF2VC5i1Wt7TPV1iK9t0GcgpAnd3AOcPA9oQkvbSRx3rVwcOHDhw4MDEqCPDfOELX0B7e3twIFZZWYlPfepTWLx4MUpLS5GefmdVJbS0tOChhx7C9u3b4y7T0dGBH/zgB3jrrbfw6quvYs6cOSPSl+bmZnzmM5/BSy+91O+yN2/exAsvvICf/OQncLtH3WXygQBNWigB36CfLEwllcW3rU9DBbffDFdfsdqPbQUsMszbPwTX2xUdfPYA1MN/CiqqGtG+UVJqbEsHb4qQK0JtJYKeuhSmQsIXjwXJMDRzJXh7+L1EldMAbUCf2g3KLpKKk9GIkrFAem6UtzDFqYrg+hrorS/KpNLvB6DDSQ3akITgMFRb8Z714fZhJ3ZI0MCbKr+Fyy2WQW53kABCIX7ratGD0J0tMiEESRDSkqLtx8uYUtJBaz8+4D5TWlZYHxIBGwGppLSuOV8XcPBdYPkTUA99XpYJ+MGeZDleIjl2F6S6yJsKKqoSz2MAcCehVxvYmVeM81l5yAn4sZTcKK5OzAc62C/W0Ou/bwfFervBB96VhPqctQlvh8bNAu95Q44rtL0PpR0HDgYLteIp8ORF0Kd2yzPDAimgsBL8+/9t2qd1mEQZL+AzVZeunYf+9f+EWv1c3Gfg7UBc67R47TFAQ6i0S2j7ScmgefcA8+4Z0f2MCjQ3xGmPHVwdaXBjvbw/iqsTqujk1kYhUFoqZtcugK9fMhWVIkZHViWwv1eSoC0Rx97TAT6+fUQrD8ntgVr2uFgjWhX1SSlQSx4d3Pay8kHLnxCLS/McUPVM0PTBk4s+rKDkdBlPGxFJZYukVF8DDCJxqk/slHGDhWvnod/4L6jn/vsdqRLjwMGdBPJ4Yz8PfV2xVU16u2VcdeVc9HcA+Oo5UAwyDE1fEVW0QGUTh88uJBaqpotlaF/va2YEk7vmO/F8elaQCBOK/SXVWDl1BbDtD0BbI35cPRV1pt3ODW8KLqVl4vlrdZgRVCeIZX8BoLcLxve+Aix+BK4BziNDIWqk/ZNEyZsapsBABWWgu6PnveqRL4D3vimKOunZULPXiHWOgw8mIon3JrixbvjUqm9GF4QCAN+4Kv92tUsiv7+CVtagacv6XuT9XwpZwPp8ZBOgAyBT4ZlNC6SocUX7DYmx9IbHLsKgNXD+MPSP/7soNK5+DpSaKfahETEOmjgfKjNfSECsxa4twq6MLx6V81xQ3vdxJwDKKYLrsT8H93QKGSakcIy8KVCPfwmoOwl95Syw11QuIYmV8aH3hATZlw1pyTgp+utuFwUcLao9NHNVwn1U05ZD9/rAB98F2m7KuNHlBl8+Db56TtQ2I6x/YoF7e6B3vwGc2Sdzlhl3Qc1dJ2pkKenBIswwFJQDMeLUVDA0lc1YEFumlvDG3m4hRMVTCZM1IeQpM1bh9kicdddr0t7TKYt1NAvBrO4U6ME/Ab/8r3Zh5+m9Mj5PTgOI5PrzdYHu/fSwH6cDBw4cOHBwJ2JUsRzOnTuHbdu2BYkwn/70p/Hv//7vSEq6M+WqA4EAnn766TAiTEVFBT72sY+hqqoKjY2NePnll7F3r0iR1tfX4+GHH8aePXswZsyYYe1La2sr7r333uC+AKCgoAAPPfQQpk6diry8PHR2duLs2bPYtWsX9uzZM6rUdT6IoPRsqIf+FLzvTXBjPSi3BDT/vpENBo00rAF6jHZmBq5fCiPCAJBK0oMbQfd9ZkS7RtOXgetPhTem50j1hlIi9WwpnlhVEZFVwiFVa2r6Cmh3Evj4DsDfA6qaDvb1gH/3TQBmrVjFVNA9nxoxD/TBgpQL6v4/ht78a1FdcXtAkxaB5kRPHLm7HfrtH4co5QQkkURKJmjBBYf+vGCtwSfCvc9hBGTSaPkre1OliiQ5A8jKh5q2NCgBGzy+FU+D25uBa+fttmnLQSMlv+33AXWn5JyUT04saVN3KlqqFQCf2guYlf/k9oBm3AU+8E7YMjR9uVSwz7tHqrA7W6GVwg/Hz0RtWqacJ+XCwaQUfNHXieLU+JYqUbh2Mao6TPq1GxgIGcabCvXQ58G7XhU7p8w8qLn3OMFUByMGKiiHq6AcPOduqZ4y/EBuKfidH4UvqA2bCGOBNfTOV6DGzho1SVcqKI9dnFgwOpVXPvAoKAPOH4pqHqpV0UDBvT3Qb/8IuHJWGtxJUHc9HZRsj7veqT02ESbYqIG8UnlXBhU+SKrnUzOBokpRKouFlsbY7cMImjgfqrgKfOEY4EkS5bUhqAeoacvA4+aItVdGLqif6mIHcVA9E9j7ZjRJynp29mHj1he4Znd0Y1ebjJeqZwxqmw4cOBgisguBjDxJFIdizHiQ2wPOzIu9XhwrPTVrNbRyi9qa3weqmgFaeP8wdzoc5HJDPfIF6Nf/E7h8OoE5qyQkjVgqZFoj0FQPbH0ZAHAxLTNIhJFVpXhh66R5mGmwxD18nTK/igQD6GwFNv0a2tcNlYBV8a0ApWWBVj17u7vh4FYhpzg2SSCnePj2kReb4GARH7ij2YwvhWh0WPep22vbm6Vlg6bHt33lztaYVvR8ai/04oeBAxvAx7aJzVvpBNCKp0FZ+dKX4rHg2hMAUiTWFMuq01KOYi0xmPdeAJlFTNb+4fGCkpLNbVYFi+OMn/4d0N0p23C5Jc5IJGOpYSDDWIg3TialgMppwObf2vMBZtsm9ezBMBvvqPVdbqh7Pw393gtSIOdyg8bOBM1P/LnFl89IkVVXu3l+DaA7IOPHpGTw8W2glc/0ux399o+CyicAgC2/g26+Btfdn4jf/2nLxRo2pKiEyifLORlm0Px7hEwY8El8kMj+N+77hwCXCzAMW3lRKUAzoP1mTJRt+yTDL+Sq3a+FK5wH/AjacZmxbL54HNzZ2qfNsgMHDhw4cPBhwajK0G7bti3495QpU/C9730PSvVf5TBa8S//8i/YsGFD8PPzzz+PH//4x2Hknq9//ev49re/jS996UtgZly+fBmf/exn8cYbb8Ta5KDAzHjiiSeCRBi3242///u/x1/91V/FJRpdvXr1jj//dwKooAx0/2dvdzeGD3mlQFp2lJUAVUwBEUG3xk6ecJz24QRVToNa+zHogxuBjhaZAC96KJj4VFMWgzPzwKf3SkVF7SmEJUtdHtDkcNUeNXkRYNoj8OUz4g0cAq49AdTsAU1dOqLHNhhQbjFcj39JZDTdSWHVI6HgC0fDZfDdHpl8Bfx24oNU4l7BfYF1+L6AELnakN/C44W6/zNx1YTIkwT1yBfE+7mlEVRcnVCVyaC6fO0i9Fs/sBVQUjKgHvhcn/vj5usiK97bA7g8MvG1EOFlrxbcB52eDT69DwCL1OsUqTyijFyop/8afO4gzrQ1oVb7JLhiopc1Nl89g2fGDUDtIrLSO9juj93eByi/NCxA5MBBLDBroO4UuKFOktNV04dEIKT0bNDsNQAAfXSr/YVSgHLLPRbqQe/yyN89nRJMyhteMvKgUTkNVDZJgq0WsgtB0+48qfqgZPn5w/K+mbxIrNeUq/+VRwlo6hLwmf3Azat2W+mEW56k5wPv2kQYAAj0Qm/+DVTZJFBKH+qZcSS5qbgKWHC/kIHbmuS9npEHdffH5T4srg5Xa7MwQuTSqP5l5oNmrRq+7SWnAhVThm17H0ZYYyy963Xg5E55nnq8Mv5wewavthN3/BGn3YEDByMOIgW15nlJPlpFLxl5UCuelu+nLRPCfKgSZEYeaMLcuNtUM1YMSj1qKOCTu8Q+hJTYafYBqdsnjO1oQVqgF53u8DnyzBAyaHtwLhz8H2AE0HHjMpg8wIT5wK5X5d0aaUlowfCDj20Bz1k7agjZDj48oOnLJc4QGr/LLweGI7Zj7SOnCDRlCfhkSNFTUoqoSwKgnk6wyx37fZ/kBSBJfVr2uKmEFAf+XsRUYTL84MObgAPvBpv48hnw+u9BPfM3Uhi3+GGxB7WUT7ra46t4mPMXvnxGCDDdHaJkeOOykEQmLQQtfSw4nzXe/5V8Z0EbcqwpGcAtJNVzY31sm3fTNqk/UFEl1LN/KwQebwrItCpPaN/M0Ft+J7G+sHifSezoBdDd0f92WpuAC0fDyUqsgaNboacshiqdGLvvpjqOpXhJhRUScxjmfIfubANvfVHUaDULmX/OWhgndmCz14t9eUUwSGFWcyPuvlYLj6WAPXkRULMXoF45H9qQ9yop+dsi0Vj/Bvyi6mNZMmkzjmpds4YBBF8nHP/948CBAwcOHHzIMKrIMA0NdoXZM888c0cTMdra2vC//tf/Cn6eM2cOfvrTn8a0HPrzP/9znDlzBt/97ncBAOvXr8f27duxbFnfEpCJ4gc/+AHee+89AIBSCi+88AI+8pGP9LlOSUkJvvGNbwzL/h18eEDKDJi9+1M7YJZdBFr2uHxfXAWOIRU8UoodUf0bPxeu8fGDc1Q6IWilww2XRCK46TKQWwI1/z5QdkHcdfny6dhf1NUAo5AMY6HP5FksWJUsFlIzoRY/MizVQ+Ryg8onm1U5JjxeIY2EVucVj+3XVouIgLJJI6pEwszQm34VHgDubofe8ju4nvhS7HUunYB+58dmxYffrNpIlkp8ADQu2iZPTVkMxLFPI28KaOpS3Lh6Fqg9GvV9Y0//QYUwlIy1JXBD9zN29sC248BBAmCtwe/8OChnzQBQWAH10OdBHi/Y1yU2ew218i6ZsQKUnpPw9iktM/xt402W54kVbHVJJVrw7wFse6RBSgH3/zHowlGxs8kuEDJcpGLZHQDe8HNbmc3vAx/cIFLnI2izM9ygpJAgavN1kTSvnnHLCT2xKl5hBID600AfyUeqnA4OtRALaaeKyUDVP0iAubcbyCu1g8OVU0X5LrRquKDCsbz7kIMycuFa9wnw4ofBB94BX78EyioAzb0blFsyuG2OmyM2AqHwJAMVk4ehxw4cOBgsqLga6qN/J6oqLreowpjvPsrIhXr8L8CH3ge3XAcVVIBmrQLFsBe6leCeTiGOJqdBtzYB21+SBGOMqnyO+EBmi5uBj58/id9WTcTNpBQQGDObm7D2Wm1w8bEdrXCxhgFlriyEmImdbUDtaSEMah1HDYABJlmnt0f+c8gwDm4xKDUT6skvyxixuQEorABNWTzsysa04ilQ5VRw3SkgJUOI8ZZSRX4ZkJwu8UOrAMedJFag+aWAcoEmzgeNGd/3PrILgNySMOI6IIV5fGZ/9AptTcDVc0DpBFBOEdSzfwu+cATwdYEPbADabkQXallFFCbYMMBv/UBUngBRvT6xQ45x/r0w9r4FHNyAKGgDVD4ZlBU/vjjs8FuFUxHxWOa+LZJCQETAYJQVu9rkfMd7Hhp+cFlsIksYfF128VhowRxr8CvfhV78MNTcdbH77pGCjJEC37wmKuFdbSHHyOAjW/BOVg42F9gFN1sKy9CS5MVz9eeAVc8KWdPlAlwpAHvlPPm6AEUx9wVtyH6unrNVY0J/Ux2iJJNXGlRAcuDAgQMHDj7sGFVkmOTk5ODf5eXDJxV4O/CLX/wCN2/arOtvfvObMYkwFv7pn/4JP/rRj9DVJUnVf/3Xfx0WMkxHRwf++q//Ovj5M5/5TL9EGAcOhgIaM14CZlfOSkCnZCzIJDJQVgFo9hrwoY32Cuk5oLn33KbeRoN9XVIJkl8G9eCfJL5iPCua9DtbjpKqZ4B3vhoeCPB4QSufAY0ZJ3K1wxgsoRVPgd/+ofhGA0BWIWjmXUDtKXB3u6gMmaoPkeArZ6F3vw401gM5RVALHwD1I33KPZ0il9tUD+QWi51SohKi7TfDZUktNNaCe7qkAj0CetdrdiWLN1USj34fkJQMmrRg0BXwlXHkyCvT48iXx4FI4P4R9MYXTEl0AlXPAI0S6W4HHzBcPBbm6w4AaKiVCt4pS6Bf+a4tZ1x7EnxmP9QTXwalZye2/cppQHaRbf1FSshe05cDhzeFBTNp5l0g7+1N3kSClAsYN3t4lLduE7jtRrRFISCWeHcQGQYwresSDBaPGGK8V6S9b/sgqpgMmr0WfGSTvIOUCzRzJahiMlgb4MObhOijXKBJC4CpyyTgffMauLASlJEnCnn5Y4as3uTggwPKyElI0j6hbc1cCbQ2SsJKG0B6NtTKZ297Ut2BAwfy/otnJ0FZBaCVoye+xGf2Q4dagfT2xLWmiBQfDf1IAMq72vGVkwfQ5E1GihFARkR1fZoRwGP1Z/Fy2XixVWJGSXcn7q4/K8nM2IaX4XtjSOI81G7JgYNbCErJAM27t9/l2O8DH90CvnxG7LRm3CXk8ET2QSSqlzGeI5SaAVpwP3jPGwDMd743FWrNRwes7qvWflwKj6wYTWGFxJde+U7sFQxbYYSSkoPjfOPMQSEjJCUL2cDvk3s6RE2ZSieAWhtFHSYCfGY/9IwVwI6XYu/X5b71SoVFlWJlyVqei9qQuXD5FNBIK116U4Xg7OuK4uIAEOXQROZY+aVCSArEUgEi8P53wFMWg1Ju/fOU964XQlfo+8bww+hqxa7q6AK9o9n5eOjyeWS8/SOZx5ESJVtSYo3ECSi6uJNkGUv1NqiqDWkvKINa+7HhO0gHDhw4cODgDseoimSGEmDa29v7WHL04+WXXw7+XVVVhbVr1/a5fFZWFp566in87Gc/AwC89dZb6O3tjWtjlCh+/etfo6WlBQDgcrnwD//wD0PangMHiYDcnriTO7XoQXD1DLF+SMsCjZ01KirdmTV4x6viYa4NUTy56+l+yRQWaMI8qXbvarMb3V7Q1OFReLpdoJQMqPs+A7319xJUSEoBzVolFlEjsb/0bLie/IpIfhoBCWAoBcyw/aFZG2BmCapYbe03od/8vj1hvHkV+p2fQD32F6CCspj74t5u6Je/bQdLLh03k+1/mZhijjdVZHIjPaVDlF7C9hfw20l5QKo/UtIBrUEP/QlUP9VOfaEiPRcLC6qwp/FisC0/OQ2rSiYMeFtUVAX13N8CN68ByWmOv7CDEQNfvxj7i+sXwUnJYb7eAIDudvDx7aBFDya0fXK5oR75U/CBDeArZ0EZuaBZq0ElY8Hlk8Gn9oADvaDCStD42UM6lkTBRgA4fwTc2gAqrALKJ4U9yz5wsLzoY7Qz6yBZ1kFiUNNXQF+7EN6YXQSU9v2sZ183kJEjhIOMXFDltOCznbf9IUy6XmTiO8EAeP/b0gbIetOWOESYDzG4/aYkGq5dkAT4nLvFamsYQC43aNWz4EUPilR+dtGwy9cPBtzbI/3JyB0V/XHg4IMM9vdG2FjMGNB9x74u6C0hRBh/b/xxCBCTq0IADFZgBhgKLqVR6IttNQgA8282YHLrTZzLyEa6EcDYjlZQHPJNXPR0gXt7QEnJ/S/rwMFtADNDr/8eYI5BGQCfOwT1yBf6VexNBGrOWnDpeFFA9KaCJswdFKGBcouhnvkbKaxyuUG5ol5M42aDD70XvnBKBlAaO/6iZt4F/d4LQi5we4S8YqlBMoPKJoFWPSPxkpgdIeDgxvhWj6RGVME45i5dbqi7PwG98ee2ks2Y8VDrPjXy+3Z7QLNWgve9bcfPGHJuPV7QhHlQCcSESblA8+4D734NYQ9w5ZbYmjaAG1eAiHPL3R3gQ++Br54HZZrxgASJXImCG2pjvlM0AF+MuROD0O1yC8myp1OuseQ0IcH4rULEWMyhEPh75bgDbNqvi2UptAbGzxXb2w9ynMGBAwcOHDgYIEZVNHPlypXwer3o7e3F3r17b3d3Bo3u7m5s2rQp+Pnuu+9OaACybt26IBmmvb0dW7du7ZdE0x9+9KMfBf9etWoVSkoGJ13twMFwggorJMA1isAnd4GPbbEbutqg3/0Z1Ef/R0ITcfKmQD36Z+AD74IbakHZhZIkGIyM6CgDlU6QoEJXmwQnboGEcywCC7ffBG99UeR1vSmi4jL/HhApcM3e6MoJbYBrdscnw5zeH63s0tECPrkTFEdeNayP3hTQlMXg49vD26cvj5ksJLcHyCqI3qc3BZQfu48DwVNj52BOfhnOtzUhx5uKmbmlSBpk0pJIAXlj+l/QgYOhII40NGfkAkc2iUoXEAyUgUh8ygcASskIWvWFtZdNArypYuFz6Th473oJbN79cZA3jvrGEMG+bujX/k2CdJDQFlXPBNZ98oMbqMotATLzRRo7BFQ13SHCDAI0bjaU1tBH3gc620Dlk0ALH+wzWcjN16Bf/Xfxr7cQ8INmrgT3dIFr9kSvc/j96Hdq+03wvrdBq54dpqNxcCeBe3tErauzRT63NoIvnxbS8QArt/sCpWRIgmoUQO9ZDz66RSqQ03OgVjwJqph6u7vlwMEHEuzrhn71u0GLEwbEPnfJY8D+t8DXLgJZ+VBz1wVtjaNw5Zz97urt6ZsI0wf8hgsAw9BupHl7pJGUVN9HgZBuGJjVckPylgMBEeBNA9gArp4HKp3ni4NRiitng0SYILQBPvQe6N5PD8suqLASVFg59O0QARHxH5p3L9DRDD53WO7jzHyoNc/HJXjThHlQIOijW4CeDlEnnn+/3ONaBwunODkNSM8GOlqi1uf6mvjPjel39WnBngi4oVYIHm1NoOJqiT32U8REJWOhnv8fQGOdFLndwlilmncvOCMP+uQO4HqtPJ89XlDlVNCKJxPfztJHoLMLwO+9YFs/WURCUkBWYdjybASgX/v3YJENN9aCLx6HeuzPhiUGF0R2oewj4uf2MGNcRwvORSjb5vm6URAkWpJpa6QAwyTCuNw2aSgetB9wewEKSDGeRdhSCmrcrA9ufMGBAwcOHDgYJEYVGSYnJwdPPfUUXnjhBbzyyitoaGhAYWFh/yuOMpw6dQp+vx1AXrx4cULrLVmyJOzz0aNHh0SG6ezsxJ49doB75cqVg96WAwcfdPDZg9GNhh984Rho6pLo72KAMvPwQU0SEZHIqt4mMGvo9d+3lVV8XeAD7wCeJLFNivRzttBXEDReUn0AyXZa+hiQniOy/kqBJi4ETV8ec1ltaNSpWchvfxMEhjvJBbfXBZq7btgqAcdlFmBc5i30nnbgYAigCXPBRzeH33MpGUBHs2mVZkZ/Ar0SIPKmiMRzP+DrF8VqqbcHVDUdmDAvKhjEWkO/81Ogw7a05PoaYOerGKnnOB/fHiTCBNsuHAHVnbr1Utm3CEQEte4T0O/8RKzlAKCwErT8idvarzsZNGEuXBPmJrw8734jnAgDgPe8AZ44XyoRI9XNALtCMfK+iUyEOPjQgM8dChJhgjAC4OPbhs0qaTRB1+wVxUcLHc3Q7/wU6rmvO4p5Dj60YDNxNxK2knx8W5AIE2yrPSkEF0vppbMF+toFqEe/GDtpbhHpmPueAyalyDK9PeH7C13EZQCukKxmZEI7KQXwh6zPDDCZNhcw361kFvazaV8Rsgfr/er3Aa5UIKVvu0MHDm4nuO3GgNpHG8jtAa39OHjJo2LVk13UL1EgkfE2udxQ9/0x9ObfCMHE7QFNXgSasxZovg72psiYOhRlk+Ba89yQjodvXIF+9d+Cz0a+cQVcdwrqqb8CxVAoDuuzcgHDoOYzGNDE+XBNnA8A4K42UchJRJEZZmHcgQ3gpjpQTjFozUfB2/8QNo+hqUtBGTnhK148Fq02a/jBRzaD1nx0SMfDna0SB+zpBFVOBdfVhBcTkAJcLjxefx4/rp6CG+a7MyPQi2cu1dj8SYIoGT3338VGesdL8r4wAqL+ouMoDFnqOkrZRBhI0QmqRtj6yoEDBw4cOLgDMarIMADwrW99C++//z6uXr2KT3ziE3j99dfhdo+6bvaJkydPhn0ePz4x64uqqiq4XC4Ypm9p5HYGiv379we3BQAzZshg6ObNm/jxj3+M3/3udzh37hza29tRUFCAadOm4f7778enP/1pZGSMjoo8B3c++PIZUe3QAdC4OSPvRztIkMsVm3TvcsVqdXCrce1iuMWQCT65C5i9BlQ9QyrZI0DVM+NukooqJfAaiQFUJJFyCRln9pp+l933qyOo2dCNrOS5qMy6CqU0kqbOx6QE1nXg4IMI8nhFUevYVqChDsgpBKYsBv/u/y+JeJfblpc2/EDuWNDUpX1uky8eE+KFmbTgC0dADZdAyyMqzhprw4gwwfUvHAFGitQYxxaKr18EfUDJMABA+WVQz31dfmO3B+SoTt1SxCSwGAH5PconAhl5QHtEMiO/NCYxlDLzRqiXDkY9ulrjtLfFbr/TcfZAdJvhB184Gpf07MDBBw3MDFw6Dn3puChDtN2Q5GXFFNDKZxJOYiaExrroNiMgZJHQogFtiGVmjPkaFVfJPK4v4mZRNRDwASkZ8Ddch+7qhFsF4CI2DSlIihSoF0r3xhd7scg2pGS8ahVmeLy2rUpXuyRq3W6xsPB1Rlvs6gBQVDUsihgOHIwUqLg6ZqyMSsYNy/b55jXwwY3glmugggrQnLWgjNxh2XYoKDUTSM2M3Qd/r9iGXr8IZOaDpi0FpefEXDZsm3lj4Hriy+DudrFJt8goM1fKvFIpwO8HoIGcYqgn/3LIx8FHt9gkQQttN4ALh4GJC4a8/VsBivM7xAL3dIq9uTnm5KbLwKUToHv+CKg9CfR2gapmAGNnRa/b0Rx7mx3N0Ic3gU/vFbWfCXPFPilBZeUgIak3xEZv0gLg3CG7qMDtge7uRlpPAH967AjqMtJBLmBcZwtcoeRIZqB0IlRGDnjqEolrdrbIu8TtEQKXEbAJlZbikNsj74+VHwG13wQ3Xxe1m9IJjiqMAwcOHDhwEAOjjmWSl5eH9evX4+GHH8a7776LpUuX4rvf/S4WLlx4u7uWMC5cCJ98V1QkZgfjcrlQUlKC+vp6AMD58+eH1I/Dhw+HfS4tLcVbb72FT33qU7h+PTypXF9fj/r6erz99tv4p3/6J/zbv/0bPvKRjwxp/7EQCMRhNDv4QEKf3gve8ju74ewh0Nx7oObeffs6FQc8fh503enwxqQUqLIp0M51e9vBvh7oWBEYvx8cCAB5ZdALHwLvf0eCkcoFmrESXDYZiPP7ccU0cMl48JWzdmNBBdS4ucP+m/u7/ajZfA6aNJp96WhuEHlvd3M3qp7yweVxSFcOPqRwe4HZ9juBfV3QFpE3KVUCP9qQ5MvaT4CVO+49DQDG3rfEJzsUx3dCTV8JCpEnZqjYzxTyyDNlBKDT88KKgi2o9NwPx3smz7RS+TAc620AdzSD978NvnJOLF1mrgZVToGRngf0dEUsTVBp2SBDg5c/Bb3hZ3YwNyUTatXzoh5z+Yy9inJBTV/14bhWHUSBi8dD89tR7VQyAeoDeE1oJrBmqfAlSCKbCMT8gTxeBw5iQW/6NfjsATsRRwQkpwEXj4P0r6Hu+VSf63NPF/jiUUAbYo8YkvxkIwB0dwCpmSCloDMKosdIWgPkirKJoM72uPchr/sj6L1vAQffNYnRZNsXkQKt/TiUae3mOrwZtOlXYGYE2FzU7YF7wRrw/ndgaMS2qHB5JBFNBLg86PIkY3deCa54U1DU68MiVzIyPEmA74yMeTgAKLccS1KKVPpb6gEp6VDrPjViY08HDoYFGXnQM1eHFx9l5EHNWDnkcSG3NQnRwVJqarwMXDgO9dRfjph1bVQftAH9+n8CDZfsxhO7oR77YuKkHI+pmGWdj9wx4Af+BHxkM7j9Jqh4LGjOannODfGc6fbmmHNKar35gRyj6JO7wZ0R5GtfN6j+DNSSR+02I1rtkgurY8/5e3uBna/an/e8KecvQcsmvXt9UC0tiLOHQI98ATh7CHz9Ano4Dcd2tmNq4XkAhJLWLrjIgHZz+KuFFDB1afA9wOs+Bb35t8DNK2ZcczW4uArY9qJYcmkD8CQDuWNAWYWg3l5Q6SSgdFLc8xCKO63g3IEDBw4cOBguEHOsIdTtwz/+4z8CAK5du4bvfe97sLo3adIkLF68GMXFxUhOHpidxN/93d8Nez/7wte+9jV885vfDH5ubm5GdnZ2QuvOnDkTR48eBQAsWrQIu3btGnQ/vvGNbwTPJwB8+9vfxl/+5V8GCSkulwtFRUXo7e1FU1NT2LpEhO985zv4whe+MOj9x8KmTZuGdXsORi+YWSoWIuSHodygWSsTZtzfSvD1i+JH7vcBaZlS9ZaWfbu7NSJgrUFK3e5uJAzWWq6nCMlrKqoElU+2lwv4pRLDm9qvRCxgVjy2NEiVSUq6SOaOwHnp7fLj5DtnYn437f6JcHtH3/3gwMHtgq7Za1vqWEjLgprSv+2kPrAhpu0LTVoQFczUJ3cBneFqB1QyDlSamKLfQMG+blGzCrV1S0kHTVksktUOHAwSrLXYcPlCSS8EmjhPqujPHQqzZ6DcEtBYWzmNjQDQ2iSJvax8kHKBtQE01AJtTUJaK6r4wI6JHCQGrj8t42QzhE9Z+cC4OXfUeDJRcN0pcH0oSZ6AJC9o5qqExpcOHNzp4M5WGbMA4fMv5ZKKdxBo9mqQ2xO+nmVR5OsCnz1kqxcoBRo7C5RdCL5+CXz1nBBCkpJlLpeWJfsL3Zc3NeK9JqCKKaDCvgvO9JWzwKWTCGOzZBeGjSW59pQURURaIKXnyHwylg1vhN1RgAh78krQbSUXXR54tYFFN67Dw1rGpMyAOwnILgAa68P3l5wOmrEi6jw6cDAawV1tokCSlAxkFw54/sItjWJZQwTklYIycsB1NeAY6plUPhmUgD3uYMC+LqDuFLj1hthuZ+SCI6xsAYAKK0aleidfuxAxRhHQlEUfuLE6N18D19aI5atSQhwxQbnFoBhqMFHbqK8xx68m0jKB7hhWsaRAs1Yl9DzWhzfFtOOjcbNBOUUAgPrDV3HjQjMK0m4iL6UNAEMRgxRBuZX5LhF7PcovA1VNC+93bw/gcoFcHnB3B/jETnl/BPz2e8SdJISZifODFlHc3Q5cOQfuagOlZABjxoWRUVetWtXv8Tlw4MCBAwcfRIy67Nvf//3fR8m5MTNOnTqFmpqaQW3zVpNhOjo6wj4PhLyTkmL7L0duZ6BoaWkJ+/yVr3wFgUAAGRkZ+Md//Ed88pOfRE6ODJZqa2vxrW99C9/61reExMCML33pS5g3bx4WL+4/+eTAQRS0EU2EAUQKuLdHiAejDFRUJdLK2hiVZJ3hALc0yMS5pxOckg4qmwjKKrjd3eoXpBQwbrZIzfq6ARAopxAYE560JrcHCFF/6He7REBOkfw3gkhK9SA5w4ue9vAJc0p2skOEceAgAlQ1HXzuoMjLA0IYScBij7UBeJJkPVISMAMkcZMSbf9I4+cAtSclMKtcoMIyYMzwyH3HAnlTgCmLpeqwp1OeVQUVDhHGwdDR2hAjYchAwyXQ+LnAxPlil6S1BGgLysKWJJcbyC0Ob1MuoLha/nOQENjfK/d2SvoHMrFJZROB/DKRbvemhqltfZDArMHN102rPgMAi7KExwsoBb5+CehuF6uFvDF9zhk0a7T4uqFIISsp2ZGtd3DnIJ4FWlgtXXhdHbc2gmtPhSvJWGMcrcG1YgPOdafslXp7wOePgKYtA01ZLHZJPZ1AWhZQUA40XBLFM3O/lF0oz6F+oMaMB6dmCtnFMID8MYByg88ckLFiYYUkxCOJMIBU3ienmeotocdIiJRjuJqShm7LVpkUYPjhY8ZljwdVoUoGvd1yTpQCrF0ql5CFmur7fNeyNoSAAIh9yweQgOjgzkBfNkP9ga+eD1ccbLoCVE8H/DFihkDsWGIi+2ENNF8HOppFZTS/NGxMxqzBp/eZMSXZD1s2bZFzsp7OQfUhqk+GIXaTHi8oOW3oGyyoAFobgXbbAoiKqj54RJir58CXTcKiNuQ/l9v+nRK0b6WySUDuGLkmvClCvjwUbbEO1vJMTmAMTynp4BhkmNA4N5myZI2dubjZnYUkFUCGtxMFGWasInRIGGOfFGoReOOy9I85/L1lvWuvnQcy5oF7e8A1e4PqY+zrliKjqUtumdKSAwcOHDhwMFoxajNwzBwMFln/DkbE5nYEnHp6wgftSUmJV495vd7g393d3X0s2T86O8MH7n6/HykpKdi4cSMWLAj3Ea2oqMC//Mu/YMqUKfjc5z4HQCyNvva1r2Hz5s1D6oeDDyfI5QanpIv8cSjcSVLlNUpBRGa12wcP3NUOPnfYnjx1d0jF3tQlUZ7v0RXhlaC0rFvf6RBQejYwfYVcU25P+OTwDkDZnBJc3FWHQK9UoLi9bpTNLrnNvXLgYPSBvCmgqUvBXe0S8EnN6Hc8x9qQwGZPl2mTZABaybOidHzMxDR5vMC4/4+99w6PI7nP/D/VPQk5g8gZBEEw50zuLjfnXe1qtco5WbZlny1LlsM5/Hw6n+7OVjorWJKV8+bAXS43MC5zBANA5JwzJnX9/mhgBoMZkMiJ9XkePuTUdKgGMdNdVe/3fdfAiGfOmUbYw2CEm5VCMS24Q1SvMyTO6GxBVl8yFxWENjRJqxbkpxtZd82sOpWGOUmfmodIzZvrbk07whEOjvn7HD8tOAfMz4umBy6MuQaRpUdGxI7VmYvYRZtCCmI6nP2ca6vDZZiurJFWO2sTM3Hoi08opViEOEaMDTXNH0E5PEcXk4Cw+Oe5pGvQdCEb3s5XcS/84uSRi84jkQZ0NCBS8yG9MPCtlDyELcwUOsctuakIT3rc5j2vo8mMNotPhaxiuH7OLAoZ3rCtARLGGodJ04UAzPtmRIy5UOlxQV93wEJk//DnWYgAl4G+UDEU7U1gsQQvuI9yKQzoSV8Xsuy034HAaofCdQFV/grFdCGdA+bvoyMCEW4WEkhpQGeLKeiKjEdETPx3T3o9yIaK0a3IujJEai60NwbvFD3OeKKRR5TSjKjpavE3NlfBss3+uaPudr8QZhghTNHc6M/mJK41qE8djcjKS36XrNhkyFs1pWIIoeuwdKMpcnD2Q2Rc0HzeQkd6vX43F6GZ/zeG1/f/JGISTYHLjY7hcZvzmUIznS/D/cUxMiIm+LvXETF+wUhavj+yaAiRmB4gdorLiqG1sh0keA2dAUPHi05y3AB+VSTmtd1M5Dm8HjZ6XWzIKUYO9Jqju7Y6fwzfMF6P6UqWsXR816ZQKBQKxSJl3q34ZmVlLfiKqdFOMC6Xa9zuME6nX1k80iVmOvoB8Nd//ddBQpiRfPKTn+SZZ57hpZdeAuDtt9/mwoULrFixYkp9GWbHjh3TchzFwkDmJGO89mP/wE9oaLseQRSun9uO3aIYh59DRgSL7ESMgbYp8LNpvPRdZGeZ+cIF1NWg3fspc7JCMWk8d3uov9AEmiB9xRJ0q3KEUCimA+PKu8irTRCF3z4YYN0O9M0PzGnfFIqZRna3YvzmXFCFu1hZjLx0GGxuGF6z7L+GSFiOtnz77Hd0kSKrSzEuPwcji327LqBt34FIW3yCmMWOdA1i1L4TIiJFgByEyJFzFU5Esh2teGvAloY0+J9nX6NvRDxfB9AYF8EHCjfNWN8ViunE0NuQlRfA4wSPxy9QzixE7H7CjF8Y3vb828jwEePM/h5AmkUmw4uLuhUy48FbF3QuUZCBtjZwPCq7WzFe/gH0DLmiuKvRVrzfdKkKgZQGxq++Bp1Vfs1nexNYOsDTAaPXiuN1cGqm29XoBcbhOUkhIMKG9r4/A92K8bN/MGNehgiPsFORkW6eb8QhVtW2sWUghMhF003XmRFznqKkEG1D6Hky72+/DvZu8NXMOaGvDP2uPw65vUIxWYwTryKvHPA9S4qclbDtEeQrP4D2oQihDhAlO9C2PhTyGLKjySxO8LgROSt80bOyvRHj2ksh9nAi7n0Y3hxA1vodo8TSDYidj0x4bUDWXMG4Ujvqs+5EhA+gbdlrbnP9LEbD4RA7SxAjnqOjEtAeevKGIhPZ12W6cAz0IDKKENnLA98f6MX45b9A2AhxgqcGEVGEtv6uCV3brYbsasW49mJgo+E1XS7v/TDaqJ910P61VzH2/8QvJOyORrv346ZAEpDFeRiv/tB07AGwR6Dd9b4JRXPJzRuRpcfA2YvIXA75qxEi0LmrMKGKM3+4RH/nIOFxYax7rISsbAN5ah+ytR6RkIpYf/dNzyubMjGe/5Y5xzEqFhcBJMaj79iBcagV2RUiviktEk2txygUCoXiFmfeiWEqKyvnugtTJjIy8GF5cHBw3GKYkW4wo48z1X7ous5nPvOZm+73hS98wSeGAXjjjTemTQxjCVUho1i85CxHPvUls5rJ8CDy/PmpitnHwIsMMZ8gpBdtxGdTNlYgGsoCC8elF3H+TbTMwqD9FePHYrGQt3lmsq8VilsZo73B//0mNLCZqwbCNRDw/aZQzAdkXxey4rxZtZ63eurVnPEpGNsfRh55zlehKDKWImMSwHAHG8FUnEdftXtq55wDpNdjToJbx++6ORsYNZfQQj1f1VxCy1JVmAsOSyTGqp3I0/sD2+NT/QuCIxCdTUH3mZreDrq9LtADF0Wu9DSr8bBiwWAs34qsOm++sNlAtyI234+2bm/wtpoWOM602c34E4HvHiRWbEdkLMUYPuYwmo62dD1i1GfDOPYCWm+b/x7mHoB3fo329FdDujHJ6ssYbXXB97zWGrA5gt1fe9vRnv4Kxq/+hz+GyH80s+PSgL5ORP1VRN5q5J4nkc9+0ycYWNfVzpmkfq6HRfjOm9nfw6bOJiyh1vGl1xQXWe1DQpsYtFU7g64dQPZ2ILqagq+nvRbNPbjonCAUc4dsqkKcHbrnDf++VZ0HzyB01Af+Dl46iLZsIyIpM/AYtVcwXv6+3ynj8mHExvvQ1u1Fxi/BcIQHR3rGpaBHRsMDnzJjlDoaEUmZQcceL0ZnY8jnMdrrERcPIstPIwANGSBIAxDr9iKSs81Y0egEROF600V0DGRbPcZz3zIdcwAuH0Es346283H/No3lGKGew6svom++b1LXeKsg45IwIqJN9xWv2xQrWayQlI7ILYHSI+Y8s6ajFW1CLN3g39fwYhz8DZrH6f/ZD3bD0efQH/q8+XpJFvL9fw21V83v84yiiUecJmUERc+OpnBXPvnbc3H1ubFH2hCaMB2Mwh8Dm2Nc7tvS6wFnL0ZOCZSfCfx9Epi/y2ER6BYLMqcY49I7pmhGDglSLVa07OUh7zMKhUKhUNxKqDvhDBAdHWil2NHRQWxs7Lj27ezs9P07Kipq7A0n0Y+SkhISEm6eqbljxw40TcMYsrg9c+bMlPqhuLURUfGItXfMdTcUgMhfY9q7h2gPIGgy0ER2t85ArxQTQQ72mRbkHjcidyVinDnJCsWiZyyh5SwLMK93t3K8pQqP4WVVQjor49Nn9fyK+Y+sujjkmmdGp8hjL6Dd8wlEWv6Ujqut2InMWzOUGR+PSMpEXj2OIaWvkhLdjH0R+sJyJZMeN/LwM2a1seFFZBUjdr5nzuMbfYy1WGIbexFFMb/RNt2PjE32LbSIpRuRvR3II88Gb5wQbNMfNsaCSpg+v4RcCsWNkO++aDqZ2P2OxfLsG8iVO4MWiUXeanN716DpqjQcGZeUiYiKh/y1iGWbEUIgtj2KPLUPBvsgMh5t28Nm5MXo89dcDmqjvxva6iE5K3j7vs4gh7QbIZbkQESs+R3uiDCjUwL2H7J6cTuRz30bKYQZE5eSC+0NAOi6hY/VVXDJEU6dzUrKQB8ru9rQbxTx7vWYi5cF69B2PTF25NGwgGfoecGHxT72fUehmAQhP2sADddDb19fHiRYMY69EBAZg5TIU68hS7Yh7OGILQ8i3/o1vs+VpqNt8TvMiGmIlxTxqYT85PV1IY8+N6KzXtAsfqFe7irEursQFisiZ3yFoPLEq34hzHDbpUPm92NsstkwVqz3LMV9y64W5LsvI5sqIDoRbf1diPT5Xdwm3U7kkefM5y9nHwz0+cUfHrcZp3f0eeT5t337GPVliP5utDW3mw0dTaHj5xrKkV6PT0wpdAvcxGFmOtB0DUe0+Z0tW2pNx5quFkCYDkq3Pz2m8EoO9GI8/+0ARzIsNvOP4TGLgHQLYuhZVGq6ec8Ydjf0uiEuBfJWzeQlKhQKhUKxIFBimBkgNzcwRqS6ujqoLRRer5f6en+1WV7e1AYCo8+ZlRU8YRCKyMhIYmNjaW9vB6C1VS2AKxSLAZFeiNj8APLkPnNwZLUjNtyNSBn1/ZSSQ5DXM0x5ckIxNWRzFcaL3/VNushjL6Dd9j4VO6a4JZADvcjqSwiLDbJLgiq3RNFGMw5m5ERRTBJiVHTFTHKqtYZflZ/wfXOeba9jb/oy7soonrU+KOY30jAwDv4ucGHL7cQ49Hv0J/5iYsfqakHWXTMFIZnFCE1DhEdB3mr/NumFpj348ISoG7DYEEWbp+FqZg959PkAMa+suogc7EN/ZH7ERIiiTciLhwIXgXQrYunY0bSK+Y9YujHw/9A1iLzyrm8RHIDEdEThhqB9Ex2RFMUs4UpXU0D7thT1LK1YGEivJ/CZahjXIHS1QmKg2FdExSFW7TYdyqQBCLOKX7ci7vs0QvO7JGkrdyKXbzXFMOFRQbESPsIiTfFL4JnM9hCItAJzIdDjDXzDYoEVO2FkUUh4NGLD3aZLy2CfKTqxOYKdK3w/EMMcGvf3mH8i4yA5GxEZi7V4Cyvf/i0rG8uD45ZCoVtMMUvtVVMwNAbCFoZYtgV58WBg+/KtE3cwUChuRFhE6HZHpD9GZgRiRASgj7ahe+OwIA5A05G1VxH5a9CWbUYmZZqFPbqOKFgfUgQ3JbKWIdILkXXXAq+hZ1Sx11B8m7j7Y+ZztOFFnngFOdiHyFoOuStvGtEk24Lj3gBorYPYZPrcLq5HRBMZn0rWyOcGQCuZ+ahS6RwwnWuGv0P7ujBe+i7aw3+ESJ6/TsXyzV8hr58xX7gGzb813YzZs1iR104GPm8P73f2AHLVboSmQ3iUuY/hNb+TPS7zb0c4Usogo57ZQhpejH3/abrdmC3IyvNw7EXEjsdC73P69VFCGCu4XeZ07bCAxupADDl+yuOvmO26dUj0pZtRgP09MF+KCBQKhUKhmCOUGGYGWLZsWcDr8vJydu++uRV5ZWUlXq//oW70cSbK8uWBCme7ffzVIyNjnZzO4LxJxdjIjkbTZjQmSYkHFPMObc3tyOKtpvtLTCIiRFWKiE5ErL/TFM0MExWPULnGk0K6BpBXT0J3qyk8ylkZMCE8XozDzwZWH0kD49Af0HJXBUyIjqx2USgWA7LyAsbrPwGv2xSahEej3f9pX+Y3gLDa0R7+AvLyUXMSMj7VXCwYUc0807xaeymoGvGthqvsTCkY0yVAcYvR0z5iAnQE7Q1IZz/CHj6uwxinXkcefxmQ5u9cfCraA58Njky4csKcNDW8QxPHwqwgjA6xiDFPkVIirxwPfqOpEtnZgohNmv1OjUIkpKHd83GMd18yhRKJGWib7pv+RR7FnCJsDvM+c/WE6UyRlDEUoxDa7eX9BRt5pfYS59vrsGkWNifnsDt1fldkKxTDCN0CMUlD1esjsNgg1EI4ZtQKYZHmouPwQnJHI9RdhczAuS2hW266MCdW7kIeeyGwLXdl6IV4ML9z41OhuSrwjdhk9F1PIJdtMh0wwqIQBWsRtqFnxMQMaK0175dalDne8nrMBfNhd7XR9HaA4UF+5F/QHGGIpAxkQ/kNr8fH8AKm1w1112C0S+vIa9r2MIRHI8tOmdGKhesRq/eM7zwKxTgRBeuRp14PFJ9pOmLrQ8i3fmlGrgwTnxraTSM+1XSSGRbCgCkyefclZN5q0xUqIc3nYDEj1yE0uPeTcO0kNJRDdAKkFSCf+1bwxs5+RGI6tDVgvPAd83NvGMjSo4iVOxE7Hg/eZ+S54lORPe3BbySkcrylij9UnMEjDcjIIzcxhQ9VlOIIj0Ksvg1RsG6arnhsZPmZYDGh4UVePDRvxTByoBdZcW7oxdB3r8D82zEk2HINAjJYSDjYZ/7u2cIQYVGIwvXIy8dgsNcvUnQKeO3HcO8nZuuSAmmqCjkOlOVnYIQY5mxbLftqS2kd7CWrr4P7HOFkDY4QajrCIaMIpIGITkCs2uN3IxoWzmia+QfMn19nsxLDKBQKheKWR62WzQDLli3DarXidpsDhiNHjvCxj33spvsdORIYX7Jy5cop98Nut/vELB0dHePed+S28fELZ8J8rjEOP4s8/xYwlDKdvhRxz8dV5Y5i3iD7upBn9iMbqxCxibDmjpATEtqGe5C5q5A1l03r5rzV6vd4EsiBHoxnvgFDEVPy/NuI7BK4+2M3rTYKOI5hQFOlOZD1uAFpVns4+80Bb1Imsrka4/Az5nbh0Yh1d0646ki21SNP7kO21iESUhHr7zYniRSKOUJ6PRhv/dpcMBimvxt5+BnEA58N2FbYwxCrb5vlHpq4DS8dIaqJ3YZBh7OfMIuafFJgLhJabIELBWBO8I4z8kB2tfqEMD7aG5Cn95uLZsPbdTQhLw25ldjD/QuTYDrKLMmZwoXMMjLEQiSEXqCcI0TmMvTMqRUyKOY/wuZArNgxrm0dFiuP5KzmkZzVN99YoZiHaJvuM8XII6OD8tcir5+F5KzgMeRgn/n36DHOQO/kzr/mdgybA3npCHhcZpTJDYozpGvAXPwNizQr54EeRzg9Xjepfd1YkrMRydnInnbkuy9htNUjEtPNIpADvzQXwzUNwqPN8ZYQN+57fw/8519hfOJrSLfT3H48zjAjxUI3EcEKTUes2wvr9t78uArFJBH2MLSHPo88/opZ2BebaMYGpeaZkYFn30T2tCHS8hGrbgtZeKNtvh/jd/87sNFqN+dBWqphlgQYQrcglm2GZaYLouFxma5P/T2mwG34s7ckB2G14z32vClQGH7WdIM88wZy1Z4bRlKL9Xcj68tMB8bhtqJN9ETE8Pszr+Id/i7QNCrCI3lz79PcnzW+CKZpYXCM766Bvtnrw0TxuP33GzEk4B9+Pfy9abGa46bRQp/ETL/AERC7nkC21g05cGHuZ7Ehqy+Z46C5iIsaFvBIiVmgMNQ+4vNU3t3Cz8uO+0Z5VTY7/5mex59XXiZq2FlUCLTVexChxh2JGWZk7kh0iylWUygUCoXiFmfBiGEuXbrE0aNHuXr1Kh0dHfT29hIZGUlcXBxLly5ly5YtQU4oc0V4eDi7d+/m9ddfB2D//v2mFd9NFj5fe+01378jIyPZuXPnlPpht9u54447eOmllwA4d+7cuPYrKytjYMDvPjCeiCcFyMYKnxDG11Z3FS4d9lkWLjZkXxfy0B+QVRdNm9GSbYh1d45tdayYU6TbifHcN01XGEC21iArL6E99ieIuJSg7We6cudWQJ5/2yeE8bVVXUTUXIGs8S+aCU0DWzh0jrBI9bjB6oDIONMG98X/8DvH9HcjD/4OGR6NyB2fsFL2dppWusMxTD1tyLoytCf+25gVmArFjNPeEHIyT9aVIaUxb+43Vk0nNTyahlETc2G6laQx7PwVtx7C5jAr3U+/Hti+9g7T1nscyPoyRscYwtAz5xDGpcPId35nCia9bsBpThwPVQiKyLhJX8NsI4RA5K8x3ThGEp+KiA9+dlEoZhrZWmsuJqWYC2kKxWJF5K1GezQeeeU40j0I7Y1w5RjyyjGz8GfZFrTdT/q3zypGjo5W0nSzgn2SaMu3wfJt49tYYi4yajqGPYxnkjM4EROPBCJL3+Lx/PUst0di/OHfYKDH3KWxAirOIx77ormAaBiInBKMl74P1RdvLm4Z7EX+6n9AR1PgIi6M2HdE/LAYsQAanwrpBeO7NoVihhExSYi9HwxuT0xH3PH+m++fucx0YukbilXSLf5F/vGIxGYA2deFfP7bpqht2FHE5oDIeMSWh8yN6suDRdduJ7K6FE/hFs789iJVx2vRrToFu3KI3Z1MeW8rsbYwlj/2RSxXj0N/j3n9eau41lrrF8KM4HJn46yKYURW8ZB4fhRZ8ze+V0TFQWImtNaYDVb7kEjRL2ISq29DpOZh7PuRv7jAHo42KmZIaLopgAkRASbb6udEDCMtVvB4wDVUQKNbhyKgdIxTryGKt3KsuTJwlGe14fR6OBcVy/bOoXnF5CzIWBryHNrmB8x5Qc8Ikda6O4PdQxUKhUKhuAWZ12KYgYEBvvWtb/H//t//o6Ki4qbb5+bm8tnPfpbPfe5zhIXNni1+KB555BGfGKaiooL9+/ezd+/Y1RxdXV389re/9b2+5557JhRrNBaPPfaYTwzT2trKW2+9ddPIppH9ANizZ8+U+3ErIGuvhn6j7irMghjGaxjok4hemQrGK9834ygABnqQJ14FzYJYe8es9kMxPuT1sz4hjA+PE3nhIGLne+amU4udlpqQzbK1BjEBMQwA1jGceaQ07WRHRigNv3X56PjFMJePBR/DPWhaBW+6b2J9VSimi/DowKqwYSKi540QZpgHs1byw6tHcBtmXwXwQNYKrOMUOShuDbRN92HEJEHZadAEomgTIm/8zhEiMjaEFAafaFE6B5BHngMkWG1m1APSXARwhJuxFxM433xAbHsUnAPIqkuANGOIQizYKBSTpavBXBiPSY0acxvpHMB49T/N6AUAWxjabe9D5MxipbdCMcuIpExEUiZG6dEgUaK8fNSMP8k0xS5i3V5kc7X/M6JbETsfR4SP/bma1r7aw8wF4OpLHI5N5HjMkJhft9Dr9fCza8f5K81GxJAQxkdfF1ScQ1tzu3ld1ZfNxVh7uHkPdbsIJUL10VKLHFK4CPA7GAjNLwgYdo3RdYiIRWQuQ2y6b949yyoUU0Es22wWA40kJslcvJ9FZN015LWTyNor5vyXbjEdozxu889gL/K5b2LkrAh2sho+hrOfw989TvXJel/b89fO0hIlsIaZ8zIJ9gg+u+Z2okc4kkR0NINzwPwy0K0+QVCkZXbFsyIxA7HxPuTJV31OiiJ3FaJ4y6z2Y6Jotz+Nse+HZqyPxQpxS0wnMqEj8lb55ra0p79qFplpOuSUBLjCDCMS05FNlcHtc1DwJw0v8pX/HIovsoDhMcU8mgWGXD/l5XdxrdkVuKNuAUc4zoR00GyInBIzFmmMe4dIyUF78i+RV4+DaxCRswKRmjcLV6hQKBQKxfxn3ophjh49yoc+9CHKy8uRQ6rqGzmrSCm5fv06f/mXf8l3v/tdfvKTn7Bp06bZ6m4QH/jAB/ibv/kbX9zQl770JY4dO4bFEvpH/tWvfpX+fr+9/h//8R+PeeycnByqqswc5OzsbCorK8fc9qmnnuKrX/0qjY1mhc5XvvIV3nnnHbQxRBMtLS18/etf973OyMhQYpjxEhkbuj1ijPZp4mRLNfvqSulw9pMeHsOD2avIi06c0XMCyOYqvxBmZHvpUVBimPlJzxhRab3jj1BTTJC4FNOadRShnHhuymC/OSk7MibJYoXOpsAc75EMW6mOh9ETwzdrVyhmARERg1i6AXnl3cD2oQWL+URBTDL/bdWdnGmrwWMYrIxPIyVcxSMpgtGKNkLRxsntnLE0sGoSQNMRq/YAIKsvmW5KQjOrDR3hvoU8sWo3LN+GPPg7jPLTIDTE0o2ILQ/O6yhEYQ9D3PNxZF8XeD03tK1XKCZCb2sfb3/rGG0V5rNwQl4cuz+/hYiE4OgSefxl/yI/gGsA442foX3g7xA2x2x1WaGYG+pCF/7Iuqt+MYwtDP2hz5vzBH3dkJqPcNw4Bmi6EXveC2/8nPNiyOlBt4DdXCT1SINLfZ0E3X29XmTtFeTyrQhbmFlkMLyvbjGdJNwu02lt1NhKwpDrhfS/FiCsDnOclrvSdJ4Yjnna/qiq0FcsWsSm+8HZjyw7bQowkjLRbnt6VkVfxoWDyEO/N18M9JoFFTaHGVNqeMHrNcUqusX8rOs6Ae5NABYbLkcS1af8cTMDUZKG5V40lwS7+Yzd5uzjjfqrvjhE48wb5B97geTsIpptdvN7wx4OFis7UvNn7WcwjLZuL7JoIzRVQUziTUUgsqcDbA6Efe6Ki0XcErQnv2TONWvamH0WYZFmHNaNjrX6NmTF+YBIJZFdMjcRSQ3XobfdFMMMj81cA8CI2LyeNlb0dnFp5H5SgsdNSe0187Pldpr9Twt2FZMdTchzbyG7WxEpuYi1e2f9HqxQKBQKxXxmXophDh48yD333MPAwIAvXkhKiZQSTdOIjo4mIiKCvr4+uru7MYYrcIe2u3btGrfffjuvvvoq27dvn5NriImJ4S//8i/58pe/DMCpU6f4yEc+wg9/+EOso6r7v/GNb/Ctb33L9/qee+6ZckTSMBEREfzt3/4tn/vc5wA4fPgwH/rQh/jud79LeHjgQ1FdXR0PP/wwra3+SI+vfOUr2Gy2aenLYkfkr0Ge3BcoLNCtiJKZ+x0s62rh19dP+oZtdf1d/OeVw/zl6jsDqhNmhKEc7uB2Z+h2xZwj0grMypDRpM3BYPAGSLfTdFSJiEXE+IVdsrkaee5NZE+HmVe9+vZ5ObiTzgGoumhOnhZvNav/RwpKluRA9iRi/RLToakyIFMYTYe4JYiYJOSRZ31VPz4m4jaQuQx56XDIdoViLhE73wOxyUMTlla04q2IwnVz3a2QxNnDuS1t8nEACsXNEEJDe+AzyDP7kbXXEJExZoVgah7GmTeQx14YsoLHvEfYw82FwPhUtK0PY7z6Q2Tled/x5MWDpsBkRNTFfEVEKHGZYno5/P0TPiEMQNv1Dg5//wR3fmlX0Lay6mLwAdxOqC8D5Q6jWMRI54A5vvG4/S4nw4T4XhbJ2VM7n8cN188ie9oQqfmIEIt+YyHCohD3fxrrhQPmwuOoRXhr7JIRJ5Iw2GcullddwvjJPyCyi5HVpWYRgtXmH3dZbbByF5zaFxD50mZ18FJ6LmVRsUS7XexuqmVDRxNYbeai610f9c1TUnkBefgPGAN9oFsQUfGIoo2IpMwp/bwUivmCsFgRtz2N3PaoKQCb5ec26fUgT7wyokPC1Li4nWYh0XCsjmdI3OZ2gi0MwqLMWBkpzc98Si6D0Tkg/WIYb1o/douBEBIG3D7B+fWhSGzpdiJPvYYGfKK2nFcTU7gWHkW0p5vd6++hJG5u4sdFRAzkrbrhNrK1DuPAz814Yk1HFK5H7HwPQp+bJSMhBCRlTP04UfFo7/lz5OV3oacdEtKQHY14f/t1RHSCGbm0ZPL3K9lWjzz9OrK90XTiWX8nIiZpnDsbIV2J1vZ0UJe/iiPNFRhSYve4ubf+OkucQ8XTPW0Yr/4Q7f1/EyDElp3NGM/8m28MKOvLkJUX0B774pz9PyoUCoVCMd+Yd3fE1tZWHnroIfr7+31OMBkZGXzsYx/jgQceYMWKFQHxQU6nk4sXL/L888/zwx/+kOrqaoQQ9Pf38/DDD3P58mUSE2feJSMUf/7nf86+ffs4cOAAAD/72c84ePAgH/jAB8jJyaGlpYVnnnmGd9/1Vzunpqbyve99b1r78elPf5rXX3+d3//+975+vPnmmzz11FMUFRXhdrs5deoUv/rVr+jt7fXt98gjj/CZz3xmWvuymBFWO9rDf4Q89TqyqRIRnYhYt3dGLRiPt1QGGfa6DC+n22rZnWoKHGRbPfLIc8iGcohOQFt3J6Jw/dRPnpJrxleMUNkDiJsMtBRzh0jLRyzfjrx0yN+YkocYbw77LCDLTmG881vfIE7krUbc/n5ob8B47pu+ajzZXIWsLkV7/M9Ma9R5gqy9atq6DovCHJGI295nili6WiEl14zEmESftU33Ybz0PXPiZgix9g5EmGk9rt3xAYyDvzeFN5qOKN46MRvc7BJE8VZk6RH/8Ys2Qa76TM9HZNUl5MVDSNcAIms5YvWeRTvRIXSL6QQzD91gFIq5QNjDEJsfgBEFkbKrFXnsRfOFxWZO9Bte30S/tv4uZH8PsvJC0PHktRPI7Y/Oa3cYhWK6GegepOlya1B7Y2kLgz1OHFGjIg3sYaHdFO3zT5itUEwXsuYyxms/NivYXYOmgYI93FwIDo9GFG6Y3vMN9Jpjvs5m8zXmeETb89SEjrM5tZDysuMBbREWGyuKtyFqy5C1l83rkYbfAaavA3n+HXBEDLnAuAFhVvJb7ZC5DC4dNn8WhoFHaPygYAWdQwuSbfYwfp9ViNUwWLPlYUTxZlMIM9iH8fL3obrUXGw3vCBA2sKQlw6j3fEBRP6aafjpKRTzA2EP8zkyzSr93eD0O65jtZuvpTQdYXwdHBIhSAO8bsT9n4ZrJ5C9nYj0QsSqPcRaHUQmR9Db3AdI1kWUc13kMBSGZn6OXYPEx0eYr3s6fHNAUV4P72mq9Z9uy2MYh55B1l9DRMSa4/a5cCcJgfR6MF75AfR1mg2G13RkDY9eFFHZIiwKsfYO8zp/+7/895a2OmT1JbSHvzApQaLsaTfvVcPik45GZM1ltCf/wjc/F0BqPkTFm7Fdbqc5ThuO1JN+dxiRlMnDOau5Pa2Idmc/ia/8AEdro1+opevmPajmMoy4b8hzb/mLIYZpb4CKc1AwP4uIFAqFQqGYbebdqsnf/u3f0tnZ6aue+Iu/+Av++3//7zgcoa2H7XY769atY926dfzVX/0Vf//3f8/XvvY1hBB0dHTwd3/3dwGuK7OJ1Wrld7/7Hffffz9HjpiLi1VVVfzzP/9zyO3T0tJ47rnnyMiYugJ6JJqm8dOf/pQnnniCF180J8nr6uoC4pBG8+STT/KjH/3ohtFUimBEZBxi1xOzdj73kCtScLs50JPOAYwXvmNWPAF0Npt23o6IKbs9CN2CdueHMfb/BHo7zbaMZaY1qmLeou18HFm8GdlYiYhNgvSl8+ZzLvu6MA78IsDdRF4/Cwnp0F4fHPnT3mBOKs6TalwpDYy3fhXojjTYizy5D/3RP5ny8UVaAdoT/82cnHANInJWIjKW+t/PW42WXWIO8MOjJ2zBLYRA7HoCuXIXtNVBfBoifhJxTooZR5afwXj9v/yvmyqhpRpx98fmrlOKOUW21CCPvYhsqYG4JWgb7503k6yK2UHWXcNn8W5zmAuVXjfYw9Ee+IzpDtfT7t9mJF7v0L1XiWEUtw66RUNoAmkEfiY0XaDpwc/GYsVO5Fu/CmxMSDcLBBSKRYj0ejDe/KU5thGaKRJxOwFhivvX3TntLp3y3Fu+xUpf25V3kcu2IFJyxn2cNQkZ9LmdHKi/Srd7kNyoBB7OXo3d5kDe90lE7VWMF78DhtVcYDS8/rFmgNPtkHhFSjj9+lDERQQYXi5b7T4hzIitOZyQzrqiDQhNw6gvRz7/LejrGtVDYZ7HYsU49iJa3up5MyZXKBYsETGBBXu6xS9YDY8GtylkC8Bqx5lewIXwCARQEpdG2JA4fMenN/LWN45iHWileKCFrN5EaqL8Qgfd42JPypBzVXSCea6RYhyvB6RE/vArgASbA6O1HqOyFGPvJ3AsnYRb8HTTWOEXwoxAlp2CRSCG8VF5IejegteDPP+2WXw3QeSlI8Hik8Fe5JXjoeOcpYG466PI3/9vv0OR0EAOFy44ICENscwsZouyOYjs78ZorDRdi8D822Iztx0dPdbTFrqf3W2oO4tCoVAoFCbzSgzjdrv56U9/6hsE/uM//iN//dd/Pe797XY7//Iv/0JUVBRf/epXAfjpT3/K//2//zcommi2iIuL45133uFrX/sa3/zmN2loaAjaJiIigqeeeor/+T//J/Hx8TPSj7CwMF544QW+//3v86//+q9cvRo687m4uJi/+Zu/4amnnlKD8QXA6oR0LnTUB7QJYGW86UYjK875hTAjkJeOTEv0iUjJRXvfV6G1FuzhAZE2iulHttYi666ZVqe5qybtAiESMxCJ0yu6mw5kzeXgmB8wq9i10DnTsqdj/gzuOltCVww3V5nZvlY7sqvVHPzGp07qO1bEJN1QcCZ0C0zRjUrELYG4JTffUDFnGKdfD2qTlReQHU3m/5/ilkL2dWE8/x1wD03INVVivPRd0xZ5Bt3pFPMLERETKHOxWMFiRWQV+yImRFQ8JGWaUYQj980sCrDaVihuBWzhNrI2pFP1bm1Ae9bGDGzhwTHB2rLNGIbXdI4Y7DU/W5sfVGNmxaJAdrchT+9HttYhElLNxTyPK9AFVtOG3B4EYteTiDHGZ1PqR2PFGO3XJySGAdieks/2lHy80kAfsXAohIDMIohJhqGIE1/0kZQBLpxDO5htrbUQm2wuqLoGcYZFhDyvy2o13RUaapDP/JsZuRR0QRIYWpTvaTN/1lZ78HYKhWLcCE1H2/KQGfkjhz5fVjvaHR8whWcvfRecg2AMCd80nfK8VfzkzKs4h8RwDv0cHy3aSm5UIkn5CTz6v+6l9WwZYUdP8/GmKg67EikLjyTa42Z7bzdZUQnmuS1WxKb7ke/8FlNEZ4BzwBQvDJ1PDvTh9FgxpKDpp7+id82TrHmsZFLXKtvqzeifJTkTLoIaFwv02UZ6PciTryKvHAfDi8hfi9h8PzLUPB0ghwo7J0yQwHG4PfB4cqAXefD3/oha54AZyzX88/W4zX/vehKtYB3C6n/+lCdfBSHM8d3QLUp4XBAZB1nFgedNyYXaq373MTDdolPyJnd9CoVCoVAsQuaVGObgwYP09vYihKCoqGhCQpiRfPnLX+ZnP/sZpaWl9Pb2cvDgQW677bZp7u340XWdr3zlK3zpS1/i0KFDlJWV0dTURFxcHJmZmezZs4eIiNAD6VBUVlZOui+f+MQn+MQnPsGpU6e4dOkS9fX16LpOcnIyW7ZsobBQVREvJFYnZFDX18XBxjI80iBMt/JA9kqWhEWbG4xWqg/jHqN9EghNg+SsaTueIjTGuy8iT+8HhsZBMUloD30eER49p/2aToTVHqpeHWGzQ1KW6X4x+r20/Bnv17gJizIr8UcLeuzhSLcL+ep/DlXuY/7/7f0QIjF99vupWPiMNWnT26GETLcg8uqJ4Pu64UWWHkXseGxuOqWYfTKXQXyq6Zo2jNAQq3YHbKbd/gGM137k3y45C7Hrydnrp0Ixj9j60XVY7DqVR02BWO7WLDY8vXrM7bXl22AexYsqFNOBHOjFePYbPuGLbK1BVl5A3Pepocr1UU4KYZEzIoQBELFJyMbrIdqTJ31MfXQF/fAxV+1CHjSjxBmOsNX04OsFn5uEWLXHjByuKqWopwNdSryjFo1X9neaDjdNVaYzhCCkKZvvnDFJCCWEUSimBVG4Di0hFVl2GoRAFKxDxC1BSgOW5EJzlfkZlyBtDv4Qn4RzhABu0Ovh9xVn+PNVewHTRW7J+qUYjcvRai9zW3szt7WbDiNi9W0Bglht+VZkUgay/IxZ6NVa65sbkhKklGjCwJA6Dn2QI89dZklREqkl4/9+kx438rUfIatLGeogYutDaCU7JvcDG47v6WkPaBZLpzcCb7aQR19AXnjb//riQRjoRay9PfRc4ySdVEXGUuS1EyHaiwL7s/+nyLqhYmTD63eFGS5CsFhBaGhLNwQVO8rmGrxSx/Bo6JoBSAypYSzdgWNUtK1YscucE2iq9Is7I2IgMnZS16dQKBQKxWJkXolhrl/3D3rf8573TPo4Qggef/xx/umf/sl33LkUwwyj6zq7du1i165dc90VX7SUYuFzX1YJe9IK6XD2kxwWhXV4UgUQOSuQR58PntDJXTnLvVRMBdnR5BPC+OhqQZ56fXEtdGaXmIO1UQv9Yvl2SC8wJxTa6vztq2+bV64HwhGOKN6CvHgosH31bXD0eb8QBqCrBeO1H6M99eVprSiWA2Ysk6y7hoiMMX9GowbkioWPSCswnb9GYrFBcvbcdEgxtzhDVBzfqF2xKBGahvbAZ5GnXx9ykYtFrN7jc4XxbRebhP7EX5gVpZqGiFNxeIpbF2uYlW0f38DWj64HQGgLsxJaoZgK8sq7gQ4wYD5DVF5AFKxFXjsZ8JZYvWfG+iJW7UGWnw0U+SZlBlfBjxPZ1wUeFyImKeg9rWQHhmYxxS3OQUgrgPoyGOgZ0aGh7wRNA01Hnj0AHU0ARHq8PFl3nT+kZjM4tIhZ0tfNro4WpO2a+TMdMTcz6krBakYaalsenNS1KRSK0Ij4VMSm1MA2oaHd/2nkhYPm5zwqju5lm2mrPB20f9NADz2uQaJGuCaKvR+EI8+a308WC6J4C2LDPcHnTspEJGViDPQg2xtglI/xkM8HzX2mK3zNqfqJiWHOveUXwoAZ9XPoGWTmMkT0xJ26haah3ftJM+67qdJ0lVy2FbF274SPNddIrwd5+Whwe8U5xPZHEWtuR555w//GkhzEykmuzxSsQ1RdNGPdhxBFmyDLH30lezr8QhgwxaVCM91gRv5uZRSFdP2W0Um4amqR0oLbF+kpuHpWsG6rATWlyP4eU9ATFW8e2xZmijd13Yzoeue3iPs/PblrVCgUCoVikTGvxDCtra2+f+fk5EzpWCP3H3lchWIxEm6xEW4JtvMW0QmI3U8iDz1jTigJDbF0A6JYVTQuJMa0i24on+WezCzCYjUX8o48h6wrg8gYxJrbEXmrANAe+1OoLkX2diBS8+eVEGYYse1RiE40K5F0HVG0CW3ZZrzf/1Lwxm31GD/+KricZkTFtkenFDUmDQPjhe/4qv1lZxOyrgzt/k9PuuJF4cfl9XCytZr6/i5SwqJZn5SFQ5+bCEax+QFkS40/lkto5gSPPWxO+qOYGIaUvNNYxvHmKrzSYHVCBnvTi7CMuWByY0R2ibk4M7o9Z8VUuzpp3IaX/XWXOd9ej0O3sCU5l43JOXPWn1sFERaJ2PbI+DYOj0KefROjuRoRm2wKZ0IsFioUtwJKBKO4pRnTcbATsfu9EJuMLD8DFhuieAvass0z1hURtwTtsT9FXngHutsgNR9Rsh0xwWck6RxAHvg5suoSICE+Fe2ODyLiAwWgWvEWKN7i36+3A+PAL6HqolnBP1xUZA8HRwS01oHbiUSC9LKyrZFlHc3UhEcSIyWJwwvf/d3Q1QpupymokRKfRUxkHBSsRUTEmHMzSpSqUMwKwuZArNsL60yhR4TXg636HK5Rzr4O3ULYaOcNexhiz1Ow56kxjy+9Hr87h9drfu41zYxAczkB8EpB50A0V1rNIhZr2MSWZWT1pRCNBrL6MmLF5NxhRNwS9Ef+GDnYb4phLHMzxzFepJTmnJfNYUbADmN4TaFJ0A4GuJ1omx9ALt2IbChHRCdA+tJJF6YJTUPc+WFkczWyvcEUQY2enxyO4/LtJEyximvA3xYWibQ5ME69hli2OcD5uyNhIzbvWTTNYFhUVd+dTHVND2t+93X/vJ/QYNUe6GoB3QIjbpey9irS4573/6cKhUKhUMwG80oMEx4e7vt3T0/PDba8OSP3H3lcheJWQyvahMxbbU7cRMUhIuPmukuKCSKiE0JbekYnzHpfZhoRk4S45+Oh39N0yFnBfF6uENpQJMWoWAos1sD8eY/bFKgN6GaVYXUpsqMZ7b1fClkVciNkZzN0NCGdA4HxGACGF+PEK2hpBdPqQHOr4Ta8fKf0HepGZEAfba7g88t3B1nUzgYiJhHtvX8FVReRzn5EVrH6bl9A7Kst5Y36K77Xb9RfodPVz1P5k7OjFql5iPV3I0+/bk4CCg2xfCvkjR31MdP8ouw4Fzr830c1FadxGl52pMyjaLtbGOl2Yjz7TXPSFFNcK6+fRXvsi4vy2UKhkH1d5mI+IPLXICJi5rZDCsU8QqQXmlESo0kvQOgWxLo7Yd2ds9ef2GTEjsendAx55Flk1UV/Q3sDxms/QnvySzceE/V0wGDv0AK2A+JTEMVbIb0Q+dt/Bfcg0pD4a/QluqGR73aZLo1gCmA6mmBYZCelORbUdChYj3bnhyY83psIcqAXNF2J5BWKm2DTLWxbks+bDVcD2nekFIScf7sR0vBivPgfMLJgbTiizWJDGuBxegCB02vFpnuQFgcFO3MmdB7hiAg9NxgWOcEehzr2/F87kS01GK//BLrNomeRXYK4/WmELQxhtSMyCpG1gf+fxKXgiYqjsbeDuMgYIoo2mcVrR55FJqSbz4WTnNMRyVmI5KzQ78UkQWK6ORc/jK5D8Ra0FTsxTu4zXYrKTyPLQZ5/B+2RP/YVyFnSczlQsYncuFrsFhfNvQlUd6WwoaAqcN5PGnDmjdEmRCZWu3k/UygUCoVCMb/EMCkp/oqIgwcP8id/8ieTPtahQ/6YipHHVShuRYTVDql5c90NxWRJK4CUXBjpEKPpiDW3z12fFBNCFG8JtGT1uMwJ0ZFVjj1tUHc1wFr1RkhpIN/6tWltDuDxmAvhtqHMeeeAKcCpuIDxq/+BdscHEEmZ03RFtxanWqsDhDBg2ie/21LFrtSC0DvNMMJihfw1vjkP6fWA0DCAqt52HLqVtEW02Cel5I36Kxxuus6Ax01xXAoPZ68i2rawJvq90uBw0/Wg9jOttTyQtYJIqyPEXjdH23A3cvlWaKuH2OTAKrlZpnWwN0AIM8zbDdeUGGaeIMtO+4QwPpz9yIsHEVsfnptOKRQzhKy+jLHvhz5Rsnz3RbS7PoIY5/OWQrHoySkx45DK/HEhIrsEUbBwY7WHxW/+BgktNRgvfx+RtQxRtMmcIxm5yUAvxsvfM8Usw2KV7lZEVBxExiC9XqQkYCFaApXtSaRuKibc1QAR0VB1yR+v5Ig0v3uEhnjsT9FScmfqkpE9HRhv/sJcXBUaIm8VYtcTiAX2rKyYOtLrMeN0Ki+YjiYl2xHZJXPdrXnJvZnLiXeEc7q1BoAlYVGcaavl9brLJDoiuS+zhBXx43AkrrwQKIQBQCJKtoPHjXb5GLpuRQ54SIlqIzbmEt4HvkjUkomJWMSKnX7Hq2Ei483I8UWONLwYr/4QRszLyKqLcOxFxM73ACB2PYl85Qd+sUhUAmc33s2zp15mwOtGAzZ1tvJgZSnDEhF56RDag5+fNvcUo/So6W7mHDDncQ3D1x+RXojY9aQp5mko998rAAZ7kadfRwy5D8VlxhBRkMu5SxG+TSyah4zEUbGGYAphEtIDYuUBxPKtE3ZWUygUCoVisTKvxDDbtpnRLVJKXnjhBUpLSykunng28OXLl3nuueeCjqtQzFek2wn9PRAVj7iJaruxv5s+j5OsyHj6m/o5/rMzNF5qISzWQcl9S1m2d24WZhUzhxAC7b5PIc+/jay9ioiMRazcpYQNCwix4R4zs7f0qCmEcUT4bbdH4naN/6Blp/1CGDArPlz95uSt1+13otF16GrB2PdDtPf9tRoMT4L6/q6Q7Q39nbPbkRDI/m7kO79DVl2kPCKGX2YV0Gu1gxDkRCXw4cItRFiDY/QWGm81XOPVWn8++vn2etoG+/jTlQtLFOg1DAa9wfbNBpJ+j3vSYhjAtFUeYa08V/S6nSHbe9yDs9wTxZj0tIVu7x6jXaFYoEgpMQ79PtCdz+vBOPgHtPctQwhVLatQCKEh7vggcsVOZGstIi4VkRZavCo7m5HHXkA2XIeoBLT1d85pLOOYjBzvSAmDfebYq+oisqYUefldtIe/gBjxjCwrzplCmFHIK8fR8tdAfGpgcQrgNXRcXjvlntWsec/TyO42jMoLZkSK0Mx+DC2wirglM3Kpwxiv/xc0Vw112jAFQboVcdv7ZvS8ivmH3P9T8/cZUzIhay6j3f4BROHCFbjNFEIItiTnsiU5l4b+Lv7twgEMaQpNWgd7+WnZu/zJittIDb9xkYkc7dA7THc7sqsZhEC3CPQo8zsnjEG02F5gYhGlImMp2t0fxTjzBvS0I9IKEJvuuzVicJqqAoQww8jyMzAshomKR3/iL5DN1WAYtMck8Otz+zGGxEOG28VRq5W06Hg2drebB2iuRl49jlg+9bUj4+Ih5MHf+RvKT0NKHtp7v2zGqA8VrBhDn8+gaxklZtnzhS2cfbaUjrOXWRF3nnhHN5ahyC1sYQFiGrHpPqi9Yv48NDOyXcyiq5tCoVAoFPOdeSWGSU9PZ/369Zw6dQqn08ljjz3Gvn37yMwc/4JvbW0tjz76KC6XCyEE69evJz09fQZ7rVD4kYP9yHdfRFZegKEKDG3FzhvuY5x4FXn2TfA4ISIWbcdjISeUBj1u/uvaMcq6zUrecN1K2j4vYVfNHNL+9gGO//QstjAreduzp/3aFHOLsNpn3aJaMX0I3YLY8iBsedB0dDmxD3lqX+BGVjtkFI37mEF50cN51COzkjXdbAPo7YTGShhjclsxNmNNfqXcZFJsNjD2/QiaKvEYXn6RlELf8CS+zUFlTxsvVp/nyfz1c9rH6eBoc0VQW31/F9W97WRFzp0LykSx6RbyoxIp72kNaE+wR5DkmLq99XwgIyKWCIuNPk+guK8oZmYXgW5VpLMfefZNZGMFIjoBsWo3Ij71hvuIlDwk+4PfUC6CisVGf7fPSj+Anjbo6wIVMahQ+BBLchBLcsZ8X7oGMZ7/tvm5AnD2Y7z6Q7QHPoNIL5ydTo4TsWwT8txb5guPyxTCaDpoGr26zssWjdLjzxIWHs22Jfmm06MRolABQHrNY97zMTw//gc0aY6zvIaG22ulsiONpXGm+4q8dNh0AxguetB0sIdDSi7CPnMRJLKr1S+EGdlefhq5+0lVDHELITubfUKYkRhn9qMrMcwNOdlS7RPCDGNIyYmWah7MXnnDfUVCeuhopcRgtw4fXs+k+ilyVqDPRxHiTDOW4CdE+3B00cWGaz4hDGDOlQHno2L8YhhAXjuFtFghaznCEcFkkRfeCW5svA4eJyI2w9+/+LTQcVfxacjGCuS1UwDohetYl3kd2XxgqOAN0Ici2F0D5v0FYEkOInMZIqsYtj0y6f4rFAqFQrGYmXelUP/4j/+IlBIhBFeuXGHNmjX8+7//Oz09PTfcr7e3l2984xusXbuWq1f9+ZD/8A//MNNdVih8GK/+AFl6BAZ6oLMZeegPGOdDPAwPIctOI0++agphAPo6MV77MbK3I2jbV2sv+YQwAD2Dg1xa2Y/XEvgIfe2t4AVDhUIxfxBCQ6y9A5G3Gl+wb1gU2t4PTSzbPdQg3WqHjfdAVLw5MLaHB1qvzmA+/WJmXUImaaOEL8mOKDYl5cxNh4aQHU1mhepgH1W6Tp9uMatfR1S1XgoRV7MQcXm9Y7RPbhJxLnksdw1xIxZEIiw23pu/HiFCBX0vPCyazpN567GPWHhJsEfwUPaqOezV4kR6PRjPfQt5+nVoKEdeeRfjD/8+dnXqMJnLEAVrA9uW5CCKt85cZxWKucARYVbOjsYWZsaXKBSKcSMrzplCGCnNP2arKQCZZ4hN9yNW7ACLzeyrbvEtGv5XWi6nouMY8Lppd/bzQvV53mkoQ+Su9I+VDMNcqJYShuKitPhUnHd8joa+ZAbcdtoGYjlWuxJneAo5mzORzdXIswfAPqJa3/CCbkHb9eRc/BgUtyK9nRNrV/jwhHLuBTwy9Dg0gOySYFFgVLwZa5S9CvegB2evC1e/G8MrTTfPGYxNW4yIpEwzCmh0+7ItY+5jHS0EHHJitw6LHw3DnL+vuYxx4BcYP/vHoRiqSTLYN652kZKDyB01NnZEQHQCxu//D/L0a8hT+5C/+hry1GujHA7dYHUAAuJTEGtuR7v3k4tmLkGhUCgUipli3q2K3XPPPXz0ox/lhz/8IUIIOjo6+OIXv8hf/dVfsW3bNlavXk1ycjLh4eH09/fT3NzM2bNnOXLkCIODgz4hDcBHPvIR7rnnnjm+IsWtgmytC7LNhSFl+MrQ7jCy7FRwo+FFXj+HWLU7oPni6AVNCV6rpDtZElfvf+h1Dy68hUGF4lZDWKyIOz+M7Gk3B98J6YgJClVE8TYzdmmkGMAejrZyF1K3mkK7kcSnwlCFjGJi2HQLn1u+i+MtVdT1d5IaFsPGpGwcc21HbHhN4YvhJWykWERKc2JH0+a+j+Ok/kITZW9X4HUZZG9MJ3dbVsCEzor4tCB3mEiLndyoxNnuaki6Gno49evztFxrIyo5gpUPFZOxJrQ7R1JYFH+5+k7Ku1vxGgYFMUnBE3ULnOK4FL6y9l6udjXh0K0UxCSh3ySOxOX1cKqthuaBHjIj4lgVn45+k+jIW57KC74Meh8eJ/LcWwznzYdCCGFGYhRvRTZXI2KTzUpI9fNWLDKEbkGsvQN57IXA9jW33xqRAgrFdNLWYC7oDVXWY7GawjJn/9z2KwRCtyC2P4bc8pAZNTz0HVBrD6PGMSRIHvHsdbjpOjtTCxB7P4R85Qcw2AsIU9gyYqwVvWol7qgMTr9wme6eXhLXxbPloWXYwq0YV675jxsW5d8vJRcRnzKz1xuTCEtyoKkysL1gnXKFudVIzgSL3V9wN4RIU3HqN2N1fDqHm66HaM8IsbUfaRhQexWZtwaRWQxdLRCThFi2Caxh7H87ipzBONKizOLG7n479u2PE6sKlSaMds/HkQd/h6wuBasdsXzbDaOAVsVn8HLNJX9MscUGHhebuoZcYZwD5tyJ12M6ehkGxtu/Rnv6qxOenwMQ2SWBUeZg3ieXmMInOdiHPPMGsr4MIuMQ6++G3g7TqXDpRuSv/2fgPVVKvxBmpNhFGubc30NfmFhBnUKhUCgUtzDz8snre9/7Hk6nk5///Oe+hZDBwUEOHDjAgQMHQu4zLIIRQiCl5P3vfz/f//73Z7PbiludsSaBbjQ5NJZyO8SCkUO3AgO+15pFAyGwuAO3y9qgYsEUioWCiIo3XVwms298Ctr9n0GefBXZ0YRIykRsvBcRHm3GaXlcplOV24XIKELsfI+qFpkCNt3C9pR5FjEVn+q7X6QN9pHZ30NNeJRpOGR4QLOxNXn+x56UvVPJkR+c9L2uPdNAZ103657021Hfm1lCu7OPq13NAERbHby/YOO8EEu4+t3s+x9vMdhlTjo7e128+e9HuPNLu1hSFFqsowuNpTHJs9nNWSfMYmV1wo0nj4cZ8Lj59qW3aBrwO0G+21LJJ4q2z4v/4/mK7GmfUPtoRFqBWhxRLHq0NbcjoxOQ106ClIjC9Yj8NXPdLYViQSENw4xtMEaIrz1uQIOs5XPWr5thGILeuBVExJ5AdDYyOCwM0XQzamII30Kps9+cowmLBDQQIN/5HTI1HxFnxj0m5Max+wt+JzW34eV4Qxlt3c1k2ews7ek0x1y6FXQdMUtxbNreDyLf/CWy7hoIDZG3CqHiMm45hC0MseMx5Nu/9n9eI2LNyGYFtWcbKH21DFt/A0VpTSSk2tDzViCKt5AbnciDWSvZV1eK0+vBrlu4M72Y3LAoZNkppNeDyFqOCPM7y8mBXowXvuMTp0uhIbY+hLZyFwB15xppLuummVU4LIPYdA/dzghyjgywc/Wc/AgWNCIyFnHPx5GG1/yeu8n8VoTVxieXbef5qnNU9rYT54hgb9YKlsWkI5urYNjZzPCAgSk8EUBHkxlxNdH+bb4f2dYArTVmg82BdvvTCKvNdPR8/tv+QoaWGmTVRbSHv4BIzkL2diD7O0MfWNNGOLIBQiCylyshjEKhUCgUE2BeimE0TeOnP/0pd999N1/+8pepr68HCHB9GYkceiCQUpKWlsbXvvY13v/+989qnxUKluSYtoaj7Q+zS8bcRSzdiKy8ENhosSHyg0dF25bk8fvKM/59NUFaVAyJTi9OXCAgZ3MmJfcVTeUqFIp5yaWOBko7G4mw2NiYlEPCFHJ8FxMiNQ/xwGeD2zUNseVB5Kb7QRqTqmoBkP09yAtvQ0sdJKQiVu5CRMTcfEfFrCCEgPSlUHUBpMGHKy7xYno+pTHxOCx2tmeWsCt1/i90n3u2NKit9LUySu5bij3SDpjCik8s207LQC8DXhfpEbE3dRqZLaqO1/qEMMNIQ3Jlf/mYYhhFIEebrwcIYQDKu1u50FE/bkHNrYhIzQudN586/0VwCsVsIvJWD8VTKhSKSdF4HQZ7zGgG96C/XbcgSrbPXb9uQNnbFZz81QVcfS4c4fls3ZFHToZBuEXQr+nm/XNojnF53JCbX8V58++AZ0yJrLzgE8OMpM/t4jvnXqe5u9Ws1M9ayqrOFt5XdRU8LrCHI8ZwCZ5uRGQc4oHPIgd6QdPVIuktjFa0EZleiKy+hLCHmxE+yg2N2jMNHPi3wySHt7E28yyiQ+LpAa3yLPLEq2g7HmNH3mo2JefQ7uwj3h6BrbMZ4xf/n+nmC0jdirb3g4icFebrk/sCXRqlgTzyHDJvNSIihp5m//zwoMfBsJF3T3PvrF33YmQijleZkXF8rmQ3hpRow2tKaUsxLh0xi8dGikykBI8HwqMm16+wKPTHv4hsrDTFlWn5CKs5n0F1abCjp+E1HT33ftCMzgqa3xCANB1tvF5TtIOAzGLEbhW/p1AoFArFRJiXYphhPvjBD/L000/zzDPP8Pzzz3P06FHKysowDH+Op6ZpFBYWsmXLFh588EEeeeQRdF3ZgCpmH2Gxot3+AYz9P/G7wSRlIbY8MPY+uSsRWx9Gnn7dFNHELkHb8Zjp7DCKLUty8UqDg43l9HmcLItN4YGsFURsstNe1UF4bBgRCeEzdXkKxZzxXNU5DjaW+16/01jOp5btIHuSjiq3EmbkxeQEA9I5gPHMv0NPm9lQexlZdgrt8T9DhE1ucuBWoKWsDfeghyVFiejWmX8e0Tbdi9FSDV43EQiebG0ANLQ7PoqYJ2KRG+H1GPS1BjuoGW6DvrYBnxhmmKQRlXjzBVefa0LtimBq+zrHaO9QYpgbIJbkIJZvQw5XNYIZu7dy99g7KRQKhUIxUYafKa02Mx7J8JqxEl43xo/+GpFWiNj6kBm7Nw9or+7kyA9PMawYHeyHA/sEd/z5LnbGd/JM5VnchhchBBkRcdyXaS5sY7WFPqDVjsfl5dyzpVQfr0W3WyjcnUtlkZPm3nZTCDO0oHouNomNbY0UDPSDpmMceQFvfycX0/KoTsklMSqe9YlZMxZlKubhs7Ji9hGRsYjl2+a6G/OKiy9dBQnLkioQQqIJAwtupFsg2usx9v0QUbID+64nSA03C4C8h/7gE8KYDW6Mt3+DlrkMoVuQdVf970lp/tGAumuwdAPJhQkh+zJWu2Lm0EYXV3e1mJFiIwWeAFHxIefkJ4JIyQlqk70dIbcdbheajshdhbx2YsSBAIsDkrKgtx3iUxHr7kS7QdGtQqFQKBSK0MxrMQyArus8/vjjPP744762rq4uent7iYyMJCZGVagr5g8iswjtA38HDdfB7kAkZ990H23VbuSKHeAaRNzE7WJ7Sn7ImI6k/IU9kOpw9nOhvR6rrrMqPp1wyxiTUIoJU9vXQX1fF2kRMWREzI5F83TS4ezn0AghDJhW1K/WXuJTxTvmqFe3BvLKu34hzDB9XcjSozfMZZ7UuXo7kBUXzOrSvNUIx8IS9rUO9vJm5RUun63Cdt1DcplGeJidnZ/bTOrymV0UEOmFaPd/Gnn2TWRfpxmJtXbvghDCAOgWjYTcONoqAieH7JE2YtIWhugqY00qp359Ibh9bdoc9GZhsiQsmvPUh2xfKPS5XbQM9pAcFjWrzzHazvcgl25ENpQjohPM6t9JuoGNxLhwEHnuTejvhpQ8xJYH0SZhF65QKBSKRUBKDkQnQnerGSMkpel8YrWDx42svoRsq0N76ivzwoGi8lgt4ZYB4sK66XOF0TloPk9cPFnBgeXNhOlWrJqOEIIu1wD1/Z0UxiQjirciy87ASN81Wxgifw2Hvnec6uN1vubjPz1D44fCAS+jqYmIpsDtgoEejNpSfpJRwBV3P9SVgiOSg43lfL5kN5FWe9C+CoViZujvNGPnI61mIYZFG/XZlQay9Chy9W2ImEQziqehfPRhTHFMRyMkZiAiYpEdTeB2mt+JAEJDOgcQQHx2LEV35HNlv/84UUsiJ+XoLZ0DyOtnwdmPyC4J6Vbl27ajCVlxDnQromDtLeXu6+p3c/XAdVqvtxOTGkXR3nzCY4OdssSSbKTVZt7Thv/vdCti9Z4Z6ZdIKwjt6Jle6P/33g8hu9ugbWhcbLEiNt+PNs3zbwqFQqFQ3IrMezFMKGJiYpQIRjFvERYrZE5sYCM03YxYmkY667ope7sCV5+bzHVpZK6bn4typ1tr+NX1kxhDlVQvVV/kk8u2kzlL2dqLFUNKflV+gtNttb62NQkZPJW/IbgiYh7T0N8VcsDY0N8163255ehqHaO9ZVpPI6+fxdj/U1+muTz2Atp9n0QsyZnW88wUTQPdfOviW3R39+ON8cJa6MjUKH4dDv7Huzz29fvQLTMrTBHphQGTKAuNDU+v4o3/fQj3gOkbLTTBhqdXz4qzznQQkxbNhqdXceo3FzDcpnth9qYMCvfkznHPFg7bluRyoqWKTpc5SS0Ng5hBG9bj/fRt7p/3znev113mjboreKSBRWjckV7EHenLZu38Ykk2YsnNBdjjxbjyLvLQ783vZecAXDuBLD+Nd80dpoPhNIhtFAqFQrFwEEJDu/cTGG/+EpoqTVcYi80UwwzT1wVVFyF/zVx100dy70kK898FYY4kG3sSOVa7kproPjzSACGwCP9z5omWKlMMk1aAtveDGCf3mWOelFy0LQ/S369RfaIu6Dye8j4oFDDCvRogcbAf3G5AcDUylisRQwJvwwCvhzZnHwcby7gnU1X3KxSzReryZK41V9A2EEtqVAuakAgw3TcANB2Q0NkEMYnmPG14tCkMH4nQYMg5hlW74fo58AxF5goBQiBP7kMu24yw2tj0wTXkbsmk8XILEQnhZG9Mn/A4V3Y0Yjz3bRg045XksRcR2x9FWxFcIGZcOY5861emYxWYEVD3fgKRFlxYudjwur3s+5e36Kgx5wtrgPKDVdz397cHC2JyViKyliOrL5mOZwBJmTMW/ScS0hBr70Ce3u9vTMxErNrj38YRjvbUl6HmslnolF6IiEmakf4oFAqFQnGroWYyFYpFSGNpM/u/fgjDYw5+yg9WUXxPIRueWjXHPQvEbXh5tvKsTwgDMOh183zVOT5Xoiz+p8KF9voAIQzAmbZaSuJSF1TkRGp4zHBKblC7YmYRKTnIS4eC31gyfQv80uvBOPh7nxAGANcAxuFn0B/902k7z0zyVv01Br0evG7/JHhvokFXqkQ0OGmraCe5MHEOezj/SS5M5OH/cTdV79ZiDPaTk9iKQzuPrBmAjKWIBSDgK76rkNwtWbRWtBOVHElM6sJwtZkMPS19nPj5WerPNxEW62D5PYUs21swpWNGWh388YrbONpcQXl1Iz1vt5B4zck5zyUu/OEyu/9oCxmrU6fpCqaXa13N7Kst9b32SINXa0vJjoynIGZ24iKk1wPlZ5AdjYjEDMhdaU7gT/Z4Fw+bVf/Ofl/sA4YXLh5ERsQgNtwduL2zH3nlOHS3IVLzIHfVUEyfQqFQKBYLIjYZ/ZE/Rg72Ybz4XWitCdpGugaZ66c22VLDksEzOIX0jSFTolrJja+jJS8PnD1B+4ycjxD5a9BHCXoGKzuCB6RA0nkYKLYx6BzwtaX391Dc2Y4UgM1B3WjHy6FzjRURqVAoZobVjy6n5VobF5vziQ/rwqa70bUhLYzVbopchAYJfidEseZ25OFnAo4jijYiwofGeuWnwXAHnsgWBs4+qLkMeeYcbFJhAklTiEaSR1/wCWGGWpBHn0cWrAtw1ZUeN/LIsz4hDAAeJ8aRZ9Ef/7NJn3+hUHms1ieEGWagc5Arr5ez9j0rAtqFpsE9H0NUlyJbahCxS8wx1AyK/rVN9yML1iMbyhBR8ZCxLGjMJDQNspfP+b1UoVAoFIrFhhLDKBSLkDO/u+gTwgxzeV8Zy+8uJDwu2B5yrmjs76bf6w5qr+xtx5ByQTmYzDeudTeHbu9qXlBimDh7ONtT8jk4IirJpunck7l8Dnt1i5C3GnH1OLJ2RA52Sh5i6YbpO0dnc2AG9zDN1UivZ0G4DzQP9iCHs8FHaHoGoyU0QJPex+HKOqyazvrETFKUkCskYTEOirYmYjz7TahvRwLyzH5E0SbEnqfmunvjwhFtn7eCjenC8Bq8/q/v0NvcB0Bfaz/Hf3oWi91Cwc6cKR070mrn9pSldP/v64S3+ScFDbfBiZ+dJX1VyrwURp1vD453ArjQ0TArYhjpdmI8/21oMRclJUN22/d+cvLfoe5BU/wiR638SZDXTsIIMYzs68J45t+gt9N8ffEgImcF4u6PTe7cCoVCoZjXCEcEIm8VcrQYRtMRWcVz06kRyJrLaJrAFmHDPeDGMCSaJli5GpxFSzl6rgYDieE150s0XWNN4o3Hx3GZMYTFOhjoHAxoz8tN5ZF4waFL79CqW8nu6WJTayOGIXB5rViEINkVuA+6KVZdSFGQCsViICzGwf3/cAcNF5vpat9MFJcQpW8BEjQdlxB0rtpFYngUw2Fv2spdSEckxuWj4HEj8lcjVuwEQDZVIa+eADSfCxVgRu7YHKZLzDQhGyuCG71u8/l/pDN5Z7MpZh9Na+2CmV+ZCl313WO0h5hzwnQ9I7sEkT17Ll0iPgURnzJr51MoFAqFQmGyuJ+CFIpblM664AGANCSd9d3zSgwTZw9DEyKgEgsgzhamhDBTJNrqCNkeY5s////j5aHsVRRGJ3Ops4EIi52NSdkkTHOsmCIYoVvg3k8hakqRLbWI+FTIKZmS20AQETGmHbHhDd0+zxnwuOhw9tPjHgQ7CC/oLnMuLLJVMLAnnJ+0nPJt/05jGR8q3MzyuMUtmJgs8swb0Nse2HblXWTJdkRS5hz1SjGShovNPiHMSK4duD5lMQyYlXt9bQNB7T3NfQx2OQmLDX1vm0tsY3xXjdU+GaThhepSZG8HIq3A/D4efu/yuz4hjK+t7hri+lkoXD+p84ncVci20SIfYS7gDT2fyZZaZPlpZO0V6G6HEVWNsvKC2YcFHN+mUCgUirERq3ZDWx2y/CwgwRaGtvNxRMQ8EH2HmY4NulVDt9qRcii5JDuF6LAoHk4s4XdXTuHUvGgeQX5DBFl5N+63pmts/9RG3v7mUVz9ZjFPVHIEG9+/mki9m/vf/i2DXYND5jE6HnTqe5JwDLopDusiO66PKkeEuUCu6URbHexKnZqrnkIxU0i3E5qrITwaEbdkrrszrWi6RvqqFCAFKEJuvB159QQH+zt4XdcYFF4cp17mnszlbFuSB4AoXIdeuC7oWHL4+dtiBfeI+QzDa34PZU5jZGp0Ygg3LgHRo9xmouJAt5hRdgHt8YteCAOQmB8/ofaZQBpeM3I8PBphX3jzrwqFQqFQLFbm5Eno7bffDni9a9euMd+bDkYeX6G4FYjPjqXpcmtAm2bRiEufB5NTI4i0OtiSnMvhpusB7XvTp3HQeIuyOTmHQ43lAc47YbqVTck5EzqO1zCQSCxzIEzorO2iu7GXhNw4ihNSKI5T1ROzjWnROnOVMsIRgSjZjjwfeO8Xa/fOSweI0fy24jRdrkE0oWFgIHXw2iHjuo2Va3J5paguwFLdkJIXqy8oMcwYyOaq0O1NVQtODGN4DaQE3bK4omLcA8FubgDuQU/I9oniiLZjC7f6FpqGsUfasEXapuUc083GpGwONZXjHSHs1YVgQ1L2tBxfDvZjvPBtGBKnSMzvSG3TfeYGY31umqsQkxXDrLsT2V4Pl98dslkXYA8DIRCF6zFKjyLf/o3Zm8E+c9LfHm5Ovg+fv6VGiWEUCoVikSJ0C2Lvh5CbWk1nsKRMhNUeclvZVg9tdZCQjkhIm/m+5a9BnnwV+syoDCEwXWtKtgPQ+YtaVpbrGFEGKyMqyQhvpe9HJ4l8/DFE3uoxj5u6PJnH/s99NF5sRrfrpCxLQtM1IBIjbwPy9EHfti6vlUst+fS6Inji/cv5pNHDcbuDKo+TJEckm5NzibbNP4GvQiGvn8V469fgMsXpIqsYsfdDY36+FzoiIoaK/NW8UPqOr23Q6+aZyrOkh8eSHTW2iELELTGH+har+bzscQMSwqPR7v0EwmIdc9+Joq2/E2PfjwLij0ThOkRMYByzsIcjVu5GntkfuP+oiNPRyPYG5PFXzOf3uBTEhrsQS3Kmq/uzRsaaVNJWLaH+XJOvLTYzhqW3TV/U942QlRcw3vkt9HeDbkWs2IHY/MAN57aqe9u51NFIuMXKusRMIscobFQoFAqFQjE15kQMs2fPHt+DgBACj8cT8r3pYPTxFYpbgbXvWcHr//oOHqe/OqHkvqXzsqL6oexVpIZHc66tDqtuYXNSjhI9TAPRtjA+V7Kb/XWXqe/vIjU8hjvSi8btDOPyeni26hynW00b6ZK4VB7LWUPELEyCGF6Dg/9xnKp3awEQmmDlQ8tY/YiKRlqMiK0PQ1wKXD9jThgs24zIWXHT/cZL05UWqo7XYbHp5O/MISY1alqO6/J6uNjegCYEERYbbsOLRKILjT96/330edy4LlQH7dcy2Ivb8GJdAM43s42ITfZX2I1sX0AViV6PwalfnafsnUq8Li/pq1PY/OG1hMcujqqwtJVLsNj1gOcLgKz16dNyfN2qU3J/Ead/cyGgfcWDy+atsGhJeDQfWbqVl2su0tDfRVp4DPdmrSA5bHq+a+SZ/T4hjK/t9OvIgnWmxXbsGFFMsZP/3AiLFf2eT2AUb0Uefha6W8FiRSzbAit3IX/+T/iUfsPuXq5BCIv0HyNeif4UCoVisSOiE03HghBIaSDf/OVQjMjQ9kWbELvfO6Oid2FzoD30eeSJV5GNlYiYRFNon5TJQOcgrWXtaMDt8WeJDRty1DUG8O77MfqdH0Lkrxnz2Fa7hcx1wYIe653v4+wpga2jgkGPnarOVJxeO8lLE3AUmtFR24f+KBTzFTnYj3Hg50OijqG26lI4/Tpi0/1z2LOZ5Wxb7ZjtNxLDkFaASF+KrLtquj5Z7WB1oD32p9M+fhU5K9Ae+Azy0mFwDpgFS8u3htxW23w/MjEdWX4GdIs5v3IDgbrs78Z47lu+eCXZ14lsKEd7/M8W1DgcTOef2/5kG7VnGmgtbyc6NYqczZlYbDM/9yL7ujBe+7Hf9djrRp49AHFLEEWbQu7zet1l9tWW+l7vr7vCp4t3knYTlzU50IusOAeAyF2JmKZxp0KhUCgUi5k59ciTo3Pox/neeBBCTPkYCsVCJakggQf/6U7KD1Xh6nOTuS6VlOIxFkvmGE0INifnsjl5dpT6txLJYVG8r2DjpPZ9puosJ1r8C/nn2+txej18YtnMT+GVvVPpE8KAGfF17plS0lYuISk/4QZ7KhYiQghE8RYo3jLtx7740hVO/dq/qF66r4zb/nQbaSumeXJKCGwjHBGEEMQ7wrFrOs5REVBJjkglhBkDseYOZOVFcA/629ILIW3h2Mif+d0FLr9W5ntde7qBwR4n9371tjns1fRhC7ex87ObOfyDEzh7XABkrktjxYPT5+i24v4iolOjqDhcDQLyt2eTsWZ+CyuKYpdQNAXxyY2QDeWh2+vLzMz55duQl49Bb4f/zdhkxNINUz63ll0C2SXIgR6w2BFWG7KjETk0WQ6AxWYumoysVE0vhMyiKZ9foVAoFAuY6+cChDBgxl+K7BLIXTmjpxbRiYjb3x/Urts0NF0QZ+v0C2GAYWmOcf5t9BuIYQBc/S7cAx4iEsL9+wtB8ccf4q1vHqW9qhOAuMwYtn1i6vdihWLWqL0SIIQZRlZcgEUshhkrol3XAoX4UkooP23+PKw2RPEWxL2fgMvHoL4MIuMQJdtMkeAMINIKEOMcF4v8NTcU9o1EXj3hE8L48LqRl44gtj8ysU7OAzRdI2t9+rQVa4wXWXE+OP4boOwUhBDDdLsG2F93OaBtwOvm1dqLfLRo29jnqbuG8cp/gsdpvj7yHNrdH0VkqLGXQqFQKBQ3Ys7EMDMphJmuYygUC5nIpIh556Qx2OOk/GAV/R0DpC5PJn11yoKIQrnV8BoGZ1qDq2OudjXT5RoYt7vMZKk72xiyvf5ckxLDKMaNq9/FuWdLA9qMIdeO6RDD2HQLK+LTONdeF9BeEJ1E9NBnZG9GMS9W+8U4mhDcmzkzkVOLARGfgvb4nyEvHDQX9tMLzUq2BXSfKHu7MqittaydzrpuYtOjZ79DM0DGmlQe/9/30V7ZiSPGTlRy5M13miBZ69LIClF5Pd/xuLzUnW3A6zZIX52CPWLq0U4iMh7ZHOwyJaLizL/DItEe/VPkxYPQ3ghJGYiS7dNqZx9QbRgZZ1a+us0JWDQNwiJAt0L+GkRqPmLpBoSYn04+CoVCobg5suoixol90NWCSMkxYx4mGHEka6+Ebq+5jJhhMcxY2MJtZG/KwHUhcLyp23QzTqm/Z8x9vW4vx358moqjNRgeg9jMGHZ8cgNxWbGAOf9y/3+/g47aLpCmGEahWFCMFd1lXxwOl2OxISmbw03XR6YbownB+sTAmF55+FnkBX+8s7x6Au3OD6OVbIeSBez7NNAbun1w7O9DRQi0McY+YxRC1fV1BcTsDlM1ssBhFFJKM4ZpSAgDgMeF8c7v0J768oKaN1EoFAqFYraZEzFMRUXFpN5TKBQLl56mXl75/95ksMt8aL+8r4z8ndls+7iqlppvGEiMMQSFXsMI2T6dOKJDL+DZoxZnTrViZuhu7A2KcgHoqOlCSjmpiYJu1wBOr4ekoYXhx3PXYEiDix0NACyNSeaJvPXmeWq7CHuzjy3eeLoLBPHpsWxIyr6p5e2tjohJXJAVaMN43aG/I73uEFViCxjdqpNUqMSJI+mo7WL//zrIQKfpbGSx6+z6/BbSV00t+lGs2o2sHFVpmJgOmcX+bcKjEBvvndJ5xt0fqx2x7k7ksRf8jZoF7bb3IQrWzUofFAqFQjFzyMYKjFd/6HP8kjWXkc3VaE99BeEIv8neIwgf45k3fG7FwZs/so6zv/Qy0HyZs0mJNEZFkuZ1srmrnfCssZ3uzj1bSvnBKt/rzpou3vi/h3n0X+9B0/2LoHEZ6llfsUDJWAoxSdDVEtCsLWShxzhIj4jl6YKNvFxzkXZnPwn2CO7PWkHKiO+wnqp69OMHwOtF6BpWu47QDIwTr6DPkbhvuhBZxchzbwa3jxhrKG6OyFuNPPpCgMstMGZEUlJYJAIYPfN6w6jd/u6gzydgRtr2dUFk7IT6rFAoFArFrcSciGGys7Mn9Z5CoVi4nH/+sk8IM0z5O1Usu7OA+KFqKsX8wKrpLI9L4cLQAv8wmRGxxDsiZvz8Rbfnc/1QNYbHv6jsiLaTuyXzBnspFIFELYlEt2pB4oTYzJgJC2GcXg+/Kj/JxY56JGbU0dMFG0mPiOVDS7cw4HFhSIiwmi4QjaUt7P/6Qd/vsH4Akm+LJO3DanJ8sZO9KYPrIxZKwPxdjM+OnZsOKWaNd39yxieEAfA4vRz5z5M89vV7AxbJJopYko328B8hz72F7OkwLdLX3IYYq/pwFtDW3I5MTEeWnQZNRxRtRCzJmbP+KBQKhWL6kJcOB0TfAeDsR5afQpTsGPdxRPFm5IV3wDXgb7SHI5ZtDjyf1wNtdeCIRETPvNDWarew6gMb+eaJRhr7u0FKzgMnElL5wpo7GEvuU3Ek2KWtv32ApiutpC6fn5HUCsVEEJqOdv9nkEefR9ZehvBoxKo9iML1c921GWd1Qgar4tNxGh7smiVgvqC/Y4Bj33idzUlmhJTFcGN4DTSLjmitG+uQCwaRXohYfRvy3Fu+735RuAEKF4bIXboGkF2tMNCLsDlgSfasO1R21HThGfQQf/fH4cgz5j0tLAqx9g5E3uqQ+yQ6IlmflBUQT68LwZ3pN4gfdkSALSzwvgpgdZjvKRQKhUKhGJM5i0lSKBS3Fm2Voa0e2yo6lBhmHvJY7hoGvR7Kus2qg4yIWJ4uCF3RMN3EZ8dyx5/v4PzzpXQ39JCYn8Cax0uwR049bkJx62CPsLHiwWWc/f0lX5tm0Vj7+MRjil6uuciFjnrf65bBXn589Sh/teYuNKERZgn83Tz7zKUAMRfAtTcrKLl36YxEyijmDxufXoWzx+mLe4tJj2LnZxdW1JNi4ng9Bs1XWoPaBzoH6arr9kUoTBaRnI3Y+6EpHWO6ERlFKpteoVAoFiOuwdDtzsDFN1l3DePUa9DZjFiSg9h4LyLOjCKVvR3Q0Yy488NQehTZVodITEesuwsxonJd1lzGOPALGDDjOETuKsTt70dYrMj+buTld8E5gCjejIidvOBEej1geH0Rgidbq2kECIsErxeEoE238G53M3vGqKwf61lOaDd+xpNuF3hciDA1BlDMf0RUHOLO+fXMOVsIIXDo1qD2qweu09Juw5ugE2ZxomlDbo1eCa4BjIuHFrx7jrblQeTyrdBaB3FLEHFTc7acLdwnXuX1qnOcjIzGQLC6p4O7vOC49+OI6MQZP/9gt5M3//0wLWXtAITHOdj1+Y+QmBUBVttNRTnvyV1HflQSlzobCLfY2JKcS3pE7JjbC92CWL0HefzlwPZVuxGW4N9dhUKhUCgUfpQYRqFQzAqx6dF01naHbFfMPyKtDj5VvIMOZz9eaZDomN3Ju5TiJFKKk2b1nDOJbK1DNlWak7hpBWphfJZY9VAxSfkJVJ2oxWLTyd+RQ1zmxN1ZzrbVBrV1ugao6mknN8QkS1dd8HcdErrqe5QYZpFjC7dx+xe309fWj8fpISZN3eNuBTRd4Ii2M9gd6IAnNIEj2jFHvVIoFAqFYhJkl0DVxaBmkbPC92/ZUoPx0nd9EX6y4hyysQLtvV9CntiHvHjQdBjQdMTaveghFtelaxDj9f8KEN/IinNwKgmZuQz5h38bEuBI5LEXEFsfQtt8/4QuRXrcyEN/QF47aYphMpchdj1B48DQs7rQwOJfrPS1j8KQBpnbMyh99ioRtn7CLE46BqIJS4omeYzYSGl4kUeeR14+Ah43JGWi7X4vIiFtQtegUCjmlt6WPjyGlattWaxNvRz4ptWBPPMGLHAxDGCKR2ZBQDJdyMoLvFB9niOx/j4fikumt6eT9739W8QDn5nxPpz4xVmfEAagv2OQt799zIzPG4c7jSYE65OyWJ+UNe5zauvuxNAsyNOvm/fP1HxE8ZZJ9V+hUCgUiluJeSeG+Yd/+AcA4uPj+aM/+qNJH+e73/0ujY1mVe7f/u3fTkvfFArF5Fnx4DLqzjXiHvD42jLXpZFUMPNWyIrJE2efQC68IiTGO79DXjoEDOUBpxWg3ftJVbkxS6SWJJNaMjXrcn2MiQx9jJiShNw46s83BbRpuiA+J3ZK/VAsHCIS1HfnrYQQguX3LuXUr84HtOdtyyIsVolhFAqFQrFwEEWboKkSeeU4IEG3Ijbdh4hP9W0jLx7yCWF8DPQgDz+LvHrc32Z4kSdfRaYVINLyA7evvRrShUZePwelx8DZP6LRizzyLMaqXWhhUeO+Fnn0OeTlo/7X1ZcY+O1/4IrZgifeg27TA4oUMkJU5L9cfYED9VdxJntIfqqPJ65dIrW/D6nb0fY8OWYUojzzBvLC2/6GlhqMl7+P9r6vIPR5NxWrUNyyyN5O83uitxORXgj5axCa7ns/uSiRiiM1SARSCoSQSCmQ1jB0XYe+LgB63U4utNcjkayISyPKpsYAM4m7/AwnYuKD2s9FxfLg9VKi3C6EdWbdpWtO1Qe19bcP0FbRMWNz3bKnfUgIM+TW1lCG8cw3EHs/iOjrhMT0WXHFUSgUCoVioTHvRmB///d/jxCC/Pz8KYlhvvOd73Du3DlAiWEUivlAXEYM9//DXq4duE5f+wCpJcnkbRu/+l2hWIjI+jKfEMZHfRny0mHEqt1z0ynFhNmYnM3+uisBbSlh0WRGxIXcfs3jJbSUtQWI/0ruLyI8NmxG+6lQKOaOknuXYo+0UfZ2JV6Xl+xNGSy/p3Cuu6WYBaSUlHe30ON2kh+dRLRa/FAoFAsYoWmIPU8h190J3a2QmIFwRARuNNgXcl/ZVBm6vepCsBhmrEVKiw1CHUcacPkYrN17w/77NpcSeeVEQJur343RWYl+JBXbTiv9SR7sUXaEJkgNj2ZDUnbA9q/WXOS5qvNIs6SBmnAb3ysp4e/KLhAuDTjzB2TJypARSPLqiaA2+jqhvgwyl43rGhQKxcwiO5sxnvl3n/hOXnkXUXEecddHfNvkb8+m5/gxiozrDGvnNA2E4QRpQWQUUN7dwg+vHME1JBJ8vuo8H166haLYJbN9SbcMHl3HbWggZUC7FAKnPYwoXR9jz+nDGmbF4/QGtdvCZ67wTV48BM6he7AYuv62OuRv/xVpsQECsXIX2raHZ6wPCoVCoVAsROadGAbMQet0HUdFUSgU84eopAjWPblyrruhUMwasq4s9Bv1ZaDEMAuGvenLcHu9HGupxOX1UBiTzOO5a8d8xkjIieOhf7mL64eqcfY6yViTypKixRP7pVAoQlOwM4eCnTlz3Q3FLNLndvH9K4eo6+sEQBeCh3NWsyU5d247plAoFFNERCdA9BiV7VnFIaOUiE+FrpbgdlsIQXh6oRnJ0d0aeN7lW5GNFcHOMwATcIUBCdJ/DMNj4HGZry3SoHi/hbZsA8e6SNZtLmRdQia2UY4t++uv+IQwwwuuvRYrZ6Ni2dLdDl4P1JTC0o0T6pZCoZgfyNOvB7pQMRT71lSJWJIDgG7VWbOsA2+VDekRaIbTFMVIA6x2xLZH+EPFWZ8QBsAjDf5QeYa/XH0XmlqXmBHCijaTd+plroeFB3yvpjgHSVi6IcDdZ6ZYelsuZ/9QGtC2ZFnijEUly/5u5IV3YKDXbNB00C3gdZvCGHMr5Pm3kFnLEBlFM9IPhUKhUCgWIvNSDKMELAqFQqFYFETGTqxdMS/RhcYD2Su5N6sEQ0qs45hYCY8NY8X9Mzf54PUYNF1pQSBYsixxTIt2hUKhUMwcr9WV+oQwAF4pebbyLMtjU2fMIaa6t53r3a3E2cNZEZc2ZmSfQqFQzBRi2WbT7bL8zFCDhlh/JyJ3FUbVxUAhi9WOCCEWEZqOdv+nkYefQdZchrBIxMrdaMu34a0vh/NvBe7giETkrx1/H4WGyFuDvGY6tBhec7W0ezCSHmckGpBUoZOoWdnyYGgBY7/HHdQmgXar3d8QSugDiMJ1yBOvBjaGR0N6wbivQaFQzCyyrWGM9nqfGAaAwT50qwZWO0ibKYRDILY9Ql9kLM2DPUHHaHf20+nqJ94eEfTeVHAbXgY8LqLH+O65VRCpeTyet44f1V6iZWguJM7r5b2JmYj1985KH1Y+WAwIrh64jsfpIXN9OhuemrkCUPn2b2BwhHjL8Prvt6OccGR1qRLDKBQKhUIxgnkphpkOXC4XADbbzOZDKhQKxWLA6/ZSeayW7sYekgoSSF+VgtCUMHGqiIK1ZrVRT7u/0WJHlOwI2taQkrcarnKsuRKX18uqhHTuyVyOQ585i1XFxNCFhj4PPhYd1Z288X8O0d8xCEBEQhi3f3E7sRkxc9wzhUKhuLW42tUMgOE18Ax68HoMNE1w/Pw17lg//ZPhz1Se5XDTdd/rlLBoPl28k4ix4kYUCoViBhCajtj7IeS6u0wnmKRMxJDYX7vvUxjHX4aORkRSJmLjfYio0NGiIjoBcc/Hg9q1Oz6AIQ24chwMD8QuQbvn44gJfteJ7Y+CawBZdQmhCzoHozheuyJgm5i0QLeZbtcgbsNLgiOCBHsEjQPdQwcTICUCWN89NLaLih8z8kisuQPZ0wHXTpqLlfGpaLe9D6Ev2mlYxQKlz+3ktbrLXO1qJtrqYFdqAcvjUue6W7OCSExHttWFbA94nbkM2T4knBECLFbQdERWMWG6jTDdyoA3UDxn13QiLXamk1drL3GwoQyn4SXJEcljuWvIj751HWiTirfy50Wbqe5qQeo62VHxaGL2ROJCE6x6uJhVDxdPeF9DSk60VHGxo4Ewi5UtybnkRI3hxgZItxNZXWr+7nndfhGMlGbb6HvLhJzUFAqFQqFY/CzKUZjX66WqqgqAmBi1MKSYP1zqaOCVmku0DPaQFRnP/VkryIqMn+tuKW5xnH0u9v3LW3TWdvvaMtensfuPtiinrikirHa0h7+APL3ftNqNSUKsvQMRF5wdvb/uMq/VXfa9Ptx0nXZnHx8r2jabXVYsAA5974RPCAPQ1zbAkf88yb1/e/sc9kqhUMxHZE8HsvQI9HZAeiGiYJ1aiJtGoq0OWgZ6cPa6wDBdBwxDcvk3V1gVnUZS4diT2hOlprcjQAgD0DjQzb7aUrzSy7WuZqJsDnanFrIyPn2MoygUCsX0IeJTID4lsC29ED29cGrHFQL9zg8jb3sa3E5EWOTkjmMPQ9zzcWRfF8Lj5sr3SulzN+P1ePE4vQhhxicNdjuR4Rq/un6S0o4GJJAWHsMd6UX8ovwExnCUuxDEeTwkSIHIWYnY+iBCt9DX3k9bRQfRKVHEpkcjnQNmBX/FOTAMSC9E2/sBhFqcVMwzDCn53uVD1Pd3AdA62EtFTysfXrrllhDEiLV7kVWXYLDX35a/FpGcHbjdujuRzdXQUG426FbEjscQETHowJ60Ql6uuRSwz47UgqDotalwvLmS/XVXfK9bBnv50dWjfGXN3YRZFqcoWkpJY2kL/e0DpBQnEZEQHrSNpmnkhJhfm+/8vuI077ZU+V6fbq258edOaKYQSwiwhw+5whiAAKsVPG7QNDM6yRaGWLphdi5EoVAoFIoFwqKcCf3GN75Bf38/QgiWLl06191RKADT0vy/rh7DGAozrehp43uXD/EXq/be8vaWivHR3znA8Z+dpfZMA/YIG8v25lNyf9GUBStXXi8PEMIA1Jysp/58E+mrUsbYSzFeREQMYsdjN93u0KgFLoDLnU20D/YR75hea13FwqWvvZ+Omq6g9tbrHQx2O3FET2/1mUKhWLjIjiaMZ78BziE77WsnoeJ8yCp8xeTYlVrAtdZGnxAGILxDENUIV94on1YxTEVPW1CblJJ3Gq9hGYrv63AN8JNr7/KRW2QRS6FQLG6ExWpWvE/1OBExCOC2P9nG6d9e5Owzl9B0QX+Gxj7tOq+/WkNMUSwN/V3mQiNQ399FZ/0A4boNl+FBAlZNx2sL4+yDn2JTcg4AZ5+5xPnnLiOH7gM5WzLZllcKFWeHTg7UX0O+8TPE/Z+Z8rUoFNPJ9e4WnxBmGAm801h2SzxHiJhEtCf+AnnlXejtRKQXQG6ws5+wOdAf+jyyuQr6upEJqXDubbw//yewh7N7xU7iCjZysqUKCaxNyGR9Uta09vVUa01Qm9Pr4UJHAxuTskPssbBx9bvY/78O0nq9AzBdWNY+sYKSexf+Ok+7s4/jI4QwYH7uXqstHfNzJyxWROF683dVCNMJRgdsDhjoM8Uw0oCYJMSDn0NEqOJwhUKhUChGMmdimGeffZZnn312zPebm5v52Mc+Nu7jGYZBV1cXFy5c4Pp1/4LibbfdNqV+KhTTxdHmCp8QZhin18Op1hr2pC38h3nFzCKl5I2vH/Itgg90DnL6txfRdI3lUxwMtpQHL64AtJa3KzHMLCGlZNAbnEkPBNntKmaOPrcLh25B12bPWneiWB1WNIuG4TEC2nWbjsWuj7GXQqG4FZFn9vuFMMNtVReRDdcRqXlz1KvFxfK4VO7oz+Zg83XcYZKYBo30CzoCYbrFTCMJjuBqWK80cBtenxhmmMkuYl3qaOBA/VU6nP3kRydxT+Zy4uzB51UoFIqpIDubYaAXkrNm1a1Mt+r0tvRhC7PSkW5wbefwOMtJY1cTVk0PcFhoH+zDqutBrgsVPW1sSs6h9Xo7554pDXiv5lglHvcZLLbAghVZe9V0qFELlIppoMPZz4DHTUp4NNoUiqO63YOh212h2xcjIjwKsfaOkO/JqovIy8fMiLT8tYjCdQCm2Lyxwtyopx355i8o3PkE1RFRlHY0cqT5OkII1iVmztZlLDouvHDFJ4QBkIbk9G8ukLU+jajkybmFzRfaBvtGrQ6YtI5wKAqF2P4oCIG8dsoUviSkQ3MV6DroQ8/rrgFEVwskKpdIhUKhUChGMmdimDNnzvCjH/0opKOBlJKenh5+/OMfT/i4UkrfMSMjI/nkJz855b4qFNOBy+sN2e40PLPcE8VCpLW8PaQbxNUD16csholOiaL+XFOI9oU9wFxICCEojk3hYkdDQHusLYzU8Og56tWtQ21fB7+7fpq6/i4cuoXtS/K5K6N4XsaE2cKt5O/I5tqbFQHtBbtysNgXpeGfQqGYJLK9cYz2BiWGmUa2lhTR9svgat2MNdNbUb0sNoX08BjqRlRwa0LDpgffq3rdzgkf/2pXMz++etQ3OX+6rYaq3jb+26q9QWIbhUKhmAzS7US+9mNkzVA0rCMC7banEVnF03YOQ0qaBrqJsNhCOvD2dwwAUF8yan5GgtvwYpcGmjCF8WOJDBKGXDvrzoa6z0q8bi8Wm3ouV0w/g143vyg7QWmn+bsXZw/n6fyNZEdNLn69IDoJTQh/FNgQRTELL3ZmujEuHEQe+r3vtay6iOhsQuSs8AthRvDjuktURpv/D23OPqp7T2BIg/UJGaZ4ofYKhEcjlm9DxCROuD9rEzMp72kNaLNrOisWqYNP1ZVGGgu9eO2S2FqNiE4NaUgaLjUHiGHchhfrAntOTY+IxSI0PDKwwCk76saOksJqR+x+L3Lne0BK5KE/IFuqg7aTtVcQ+Wums8sKhUKhUCx45rz0WUoZ8Ges9vH+Gd43MTGRX//612RkZMzVpSkUAYw1QFkRlzbLPVEsRNzO0KIpjyu0yGoiFN9VgD0qsNotPjuWrA2qkmA2eSRndYDwJcpq5+mCjb7JWMXM4PJ6+MHlw77FxUGvh/31VzjSHDzBNV/Y+IE1rHq4mMjkCKKSI1j96HI2vG/VXHdLoVDMM8QYFYEiUY2PppO4zBjWvXclmsV/v87ZnEHhntxpPY8uND5VvJM705eRF5XIhsQsPl28I+QCwLLYiTv7HWosD6pSbXf2Bwl1FQqFYrLIE6/4hTAAg30Y+3+CnISALxQVPa187ew+/s/5N/jn06/ws2vv4jYCx8spy5MBcEaO+MYT+L5LR4oCcqISSLAHxtVGWx1sTsoBwB4VHE/qlRa6bCHiUdIKlCuMYsq8UnPJJ4QB0yHmv64dxWsYN9hrbKJtYTySvRp9hPArIyKWvRnLJnScur5OSjsaGfAsDldbaRjIU68Ft597CxkitrLWHkZliEi3txvKkPt/hnHgF3ivnkSeexPjd19HttZOuE+bknO4I70I+5CbVpIjko8UbQ1yrloMNPR3cXhzJ9XrPdSt8HLxHjcNxeZ3eXisKXI821bL187s46+PP8fXz73O5c7QRQDzkXCLjfuySgLbdCv3ZZaMsUcgQtNNV7WIMQrnImKn2EOFQqFQKBYfc1aqkJOTw+7du4Pa33rrLYQQOBwONm3aNO7jWa1WIiMjycnJYcuWLTz00EM4HI7p7LJCMSVWJ2RQ3dvB4ebrGFJi1TTuySghfRE8pNb0dnCk6Tp9HhfLYpewKTkHXS3gTytLlibiiLYz2B04UZi1fuqClcjECO7/+zu4/HoZ3Q29JBUmsPS2PHTrwqquWOjE2ML44so7qO5tx+X1kBuVOK64nsqeNs601aILwfrELNIWwXfKbHK5s4k+T3CUxcmWarYtmZ/OCbpFY/Wjy1n96PK57opCoZjHiLV7kdWl0N/tbytYh1iSPYe9WpyU3LuUvO1ZtF3vIGpJJDGpUTNynjCLlTszirlzRNtjOWt4pvKsr7o0JzKeO9KKJnzsPk/oxehQ90iFQqGYDLLiQnCjaxDqrkHOiikd2214+fHVY/QPfWdJ4Gx7HXH2iIBFxxX3L6WptJnIlmY60yUIsDisWCw6mqFh1y0YUlIUu4THc9ciMKPn6vq6SA2PZldqIUabm1PvXGOw24lu0/GOKFDRLBr2u96HKH/ZvAcj8aQXYt3zvildn0IBcL69Lqitx+2koqeNgpikSR1zy5JclselUt7dglXTaB7o5aXqC+RGJbImIeOGcxKDXjf/dfUYZd0tANg0ncdz17J2occDuZ0w0BPc7nFBVAJYHTAiYqrXYoEQkW+9g724Sk/gGfQgpekIbA3zYj31OuKuj4y7O17DoNs9yN60ZdyeVkS/x0VMCOerxcLLNRcxwgX0wbA+sWaFh6zOCNJWLqGmt4Oflx33ibibBnr48dWj/NnKvSSFLQyH6x0pBeRHJ3Gxo4Ew3cqahEwirBMTNollW5Dn3wmMxbWHI4q3THNvFQqFQqFY+MyZGObDH/4wH/7wh4PataGH7PT0dA4cODDb3VIoZgwhBA/lrGJPWiGtg32khkcvCgV/WVcLP7hyCO/QCKW0s5Hy7lY+UDh+MZvi5uhWnV2f38w733mXgU5z0J22cglrHh9f5cDNiEgIZ/17lbPEfCAr8sYWx06vh/LuFuy6hYb+bp6rOud771DjdZ4u2MiqBOXqM15kyLRmMOTkqusUCoViviCiE9De89+QV49DbwcifSlkKxHdTBEW7Zj2aKTxsCk5h+VxqVzvbiXGFjbpqIRlsSlU93YEtAlgmYpKUCgU04Ut2EkFAOsY7ROgrKvFJ4QZybn22gAxjC3cxj1/cxuZ5yv5ZedZnBYvQtcQwJN569icnIMhJbYRC9v3Z630/bvhUjNv/J9DGG5zrGAYBhEJ4fS3DyA0QcryJCwxMWj3foKq1lqerbtM7WAP0VePcHt60bwV2ysWBmPFFk41Jiba5iAvOpFvXHiTLpcZJfZuSxWn22r4eNG2MeOD99dd8QlhAFyGl99cP0VhTDKR0/C5niuEPQziU6F9lDteeDQiPhWx5ymMA7+AISFxTlgMNkcErlFzCDl9Bu4Bv8uzlBJXvxsaahnvT+dkSzUv1Vygx+0kwmLjroxiti7y75HKnnZ0q47FbvH9/KQOLfRTdbyOMyltQbM4Xik51VrN3ZkLZ6yTGh5DavjkHcNERAzaI3+MPPU6sq0OkZCGWLtXuZApFAqFQhGCeRliK2XohSmFYjEQbQsLmV29UNlfd9knhBnmXHsdjf1dpEzhoV4RzJKiJB77+r20VXZgj7QTvWRhVDwopo8rnU38rOw4g143UkoGvG4cutWXZ28geanmAivj08acsFIEUhS7BIduZdAbaOm84KvZFAqFAhBhkYjVt811NxQzTKTVPmUh7K6UAip72rja1QyALgQPZK0k3hFxkz0VCoVifIjl25Dv/DawMS4F0vKnfGzrGO4VoUQCQghWrMrlb7yZnGuvY8Djojg2haSwsV29yrtb6HINUPHcFZ8QZvhYHTVd2CNsIKH+XBMvl7/B7X+zix/UnvGNMbrdgzxTeZZoq4MV8SoqWzE5Nifl8ErtpYC2lLBosiLjpnzsfbWXaBzoxmN4AYFV07jS2cTVrmaKYkMLY0dG0xjSQBgGHiG42tXMugU+nta2P4rx8g98ghc0HW37Y2Y8Td4qtPRCqC8DezjhqXk80V7Hr6+fxD0UWZUSFk3e+dBx553OaMYjNa7r6+TX10/6hB99Hhd/qDxLSng0uVGJU7/IeUqSI5Ka3nY8Li8jp7UcPYJTvz4KBhi0AAEAAElEQVSP93Ohf3oeabp0eQ2Dqt52wizWKYlN5hIp5bjm9ERsMuL2p2ehRwqFQqFQLGzmnRhm2A0mLGzxiAUUisVMy2DvmO1KDDP9aLpGUn7CXHdDMQd4DC+/LD/hm1A1kHgML04IcJlqd/bj9HpwhMisVgTj0K18dOkWflNxitbBPixCY310BinXdCqra8hYm4bFdvNKu0GvmzNttXQ6ByiITqQgJnkWeq9QKBQKxfRg0y18Ytl26vo66XD2kxMVT6T1xrHDHdWdnPjleVrK2ohaEsmqh5aRvTFjlnqsUCgWGtrybRheL/L8WzDQi8gqRmx9GDENEct50Ykk2CNoc/YFtG9MGjsa0K5bAt73uLz0tvQRkRiO1W5Olw563PzgymGqetsBGFgzSKbUSb1ijg+8bi/SYyC9EmExFy5dfW72HzvHYIqb0RxvqVRiGMWk2ZO2FLf0crjxOoNeN8WxKTySs3paCmGOt1QPCWEAJO6hf9f3d40phgmz2DCkwYDbhXfIFcUqDawn9iFvfz/CduPniPmMSCtAe9+XkdfPguFF5K5CjHDfE/YwyPW7Rq1OyGBpTDJXu5qJsNjJi07k2MlTVHakkxPnj7dyeay0xW4YlxjmTFtNSB/bU601i1oMc2fGMv6z9DAY/qtPrNBx9Ar6GWB1eDIn2qqD9lsVn0F5dws/LztOj9sUMeVGJfDhpVsIXyDO7C1lbZz45Tlay9qJSo5g9WMl5G5Z2MIyhUKhUCjmA/NODLN79+657oJCoZgA2VHxnG+vD2jTEDeNelEobiV6mnppKW8nOiWSxLzxfzbq+7o4116LLjQSHZH0jbD+1hAIBB4j0Io33h6OPURetWJscqMT+YtVd9Lh7Kf1XAvH/9dpjnkqAQiLdbD3L3YSmx495v5drgG+ffEtOoYspd+ov8Lm5Bwez107G91XLAC8hsGFjnranf3kRiWQE6VEjQqFYn6SHhFLekTsTbdz9rrY97V3cPWZzyadNV28/e1j3PmXdlKKk2a4lwqFYqGirdwJK3dO/3GFxseXbeMPFWco624hTLeybUkeO3q7Md74GTgiEcu3ImJDC9avvnmd07+5gKvPjTXMwsqHiim5dylvNlz1CWEAhEVQs9ZDXJ2Go1cgvRKEQOiBYoT+nkFIGXUSaeDq60L2dCCipu7koZgY3a4BTrbW0O9xsTwuZUGKCTQhuDtjOXdnLMeQBto0CMkA2p19DHqCxVtuw2CJY2zHpG3JeVxorwuIGPYiKO/voOTEK4htj0xL/+YKER6NWLGTPreThv5ukl0DN3T6DrPYWJ3gFwXn78jm1beXUd+TxJLINgbcdmr70rn74yvHPAaAe8DN2WcuUdpTzmCuC4vdjAwaxgx2W7wsi03hs8W7+NnvDuDES1ydRmKF+bseHudgxZJ07qWf/XVXcBleHLqVezKXkxoezT+ffiVg3qyip40Xqy/wRN66ubocOpz9PFt1jsudjURa7OxIyWdP2tKg7QY6B9n/9YO+aKie5j4O/se7hMU41LO1QqFQKBRTRK2WKRSKKXFPxnIqutvoHbYOBfZmLCNmEUVBKRRT4dSvz3Px5asMl/RkrEll1x9tQbfceOLq3eZKfldxOqASyGN4fTnhQgjsugWX4bfe1RDcl7lCRSRNAiEE0ZqD1358HsPjn8wb6BzkxC/Osve/jT1pf6D+qk8IM8yx5kq2JOeOa0FRsbgZ8Lj4zqV3aBzo9rUpsZRCoRgPzj4XFUdq6O8YILUkmdTl88d1rOrdWp8QxoeEqweuqwl7hUIxJyQ6Ivlk8Q68hmGOh978JfLqcd/7svQI2gOfRSwJdItpq+jg2I9P+8Zr7gEPp351nvisWK7I5oBtrQ4Lrj4X3SkGjjIdzaJhESJo/LUyKZ1r4po/UtrtBJeTVRWlGCf2m8Kc7Y+pcdssUdfXyX+UHvS5rL7VcI296cu4K6N4jns2eaZLCAMgDYlFCtwI5IgZCKumURgz9j09zh6OXQicUiIFWAyJ3fByNiqWBysuwAIXw4A51n+tthSPNNAQbEvJ46HsVePaN7kwkW2f2MCZ312kuSmB9HQPe/foRLadRcauQzjCQ+731reO0nChmagYA5lp4Paa8xMWuwUBrEvMmq7Lm7fkxibyZP56jv3otL9RwNr3rEDTNW5LK2JzUi51/Z1khMcSZrVR1tUSIIQZ5mJHA0/MYt9HYkjJ9y8f8rmqd7sHeanmIlZNZ3tKYERgxbEanxDGh4Rrb1WoZ2uFQqFQKKaIEsMoFIopkRQWxV+s3suZtlp63U6WxaaQOQ15xQrFYqClrI2LL10NaKs908C1NytYtjd/jL3AbXh5qfpCkCWuNmqy1KZb2JNaCEKgC431iZmkKfHFpOmo7gxe2AMaL7XccL+qnrbQ7b3tSgyj4K2GsgAhDJhiqQ2J2WRHKRc1hUIRmp6WPl795zcZ6BwE4OKLVyi6I59NH1wztx0bwj0YXEEOZjWzQqFQ/P/svWeAHNd5pvtUVefp6ck554CccyAIMJMiKVmyJEtWti1bzrK93ru7Xq+vvb7XXulalhVsWVm2IiVRJCUGkCAAImcMJuecu2emc1fV/dGYnml0D+JE4Dx/gPqmwumeqe4633m/91tKFFlGHx9EmyWEASAUQD/3K6QnPhUV7jjdTbxeJB2nunHsstA7LWiRJBSjgjnRTPnqHLILreRvzKHtaCcdp3oix6WXp7J+bwXmDhc/He1mCh3F72W7a5QtE2GXGb3uOFJ2CZQvnVvBg8Qvu69FhDDTHO5rZHtm8U2dPh4EpobdHPs/xzGvUgmk62hGkE0KRoPCpvRCjDdxnZUkMEoKRtUXFZcBTOaFHfgi0Dk5xivddZFtDZ1jA60U29NYm5Z3W+co21VE6Y5CQmd+hXzxVegHvR/0s68iP/O7SCnRDZOcvRP0Xw2L8GwumbITBrrXqwSVEKl2O48W1D4wc8jK/aWk5CXRdrILCYnSXYWkl6ai6zpXm07z4mgXTiDBbONQfg2Fc+SiLUvonNw2MRIRwszm1FBHjBhGDagx+90sLhAIBAKB4PZZMWKYa9eu0dvbi9PpxOfzoevxumbG58Mf/vACjkwgEFgNJnZklS71MASCZUff1cG48f6rgzcVw4z63HjU2MUkCYkdmaU0uAYwywZ2ZJWIe28esaVYkWQJXYt+xkhIi1+xNU2GNZFejysmnmmxz+v4BCuTjjnEUu2TIw9MIlMgENw5V35eHxHCTNP4RiuVD5WQnJ+0RKOaIX9DLud/eDVmAblgU+7SDEggEAhmMz4QN6zHictKfIeNUEDFctiNt8QHkoTBrGC0GMhOSOKpd21HkWT05vNk551l84EpxuUC1Jo95K7JQnr1a6zqbaYaiSFZIsnvwWYwh9UD02PpuIokxDCLQo97PCam6Tq9btcDL4Y5/Z2LTPRPUe400LY9xESWhqRqbCgquqWTZUFCChkJyQwHfKDNLNhvmBhHqt250ENfcK6O980Zv1EMo+s6g94JDLJC+o15AO8E8uU3omO+KfTTLyE9+rHo8KQ/ajutSyG1W0Z3GPiNzz+KMo+OQCuBjIo0MipmWgzraoihX/4b37Wa0a63i3KHAvy04yKfrN5NoT2Frqno+30pc2aznZxn448TL9yUy6UXrsXkowq33J7wSiAQCAQCwdwsazHMkSNH+MIXvsBrr73G1FSsivZ2EWIYwYPEUPMIo+3jJOcnkV2Tcde2uwFPAJAw2YzzO0CB4AHCmmSJG7ck3bxKKtlsxSwr+LXoCpBUs41ni9ciSetuerxv0s/571+h+0I/JpuRqgOl1DxWIWy4b0FCmo3i7QW0v9MVFV/1ZGw/59k8lFtJ/Xh/1O+rzJFOmUNY2QrC923bZGw8zZKw+IMR3Ffo44PojachFEQqWYOUV7HUQxLMI6NtsQt3ACNt4zFiGO+ED2fPBEk5idhSFmdRLyknkW0f2sC5718m5FeRZImy3UWU7ylelOsLBALBTUnPAyRuVOxJ6fkxu5buKuLaL5ujWqVKMgw1DRMa81HVaaS/RiVgUylNSuWDG3ejSDJa3TH0Yz8BwARkMYjkVKG9Gr23GQAFnRy/H4KB8EkNppkLP+AijMUk05pI+w0Cdel6/EFGUzX6roQLeExeieo3jQTNOpIGT/23aqyGm+cDJUnio1U7+GHTKdqdAxhCQTZ5PTxavgV51a7FeAnzRp/bxYhviqLE1Ejbd6sS//XfGB/wuPh28+mIA0i5I4MPlm8lwXj9fh/ujhILTaMPdsbEMspSMdtN+KdmHGslXaK4KmdZCWG8Th+dZ3sBncLNediSF+fzTG88zUWPC806y1EnFATFyPmRLj5SuYNfdF3h2ng/VsXIjqxS9maXL8rY4lHuyMCqGPHeUOy2JiVWPJ6U62D7RzZw7j+vEPAEkQ0yVQfLKN1x/7fFEggEAoFgoVmWYhiv18unP/1pvvWtbwFEXGAkSYpyhJm9qHejU8z0vmLhT/CgoGs6R798ms7TM/a8uWuy2P8HO1EMtz9h8rp8nPj3c/ReHkCSJAo357L9Ixsx2Uy3PlggEERRvC2fKz+vxzM+U9ktG2WqH57bFQbAohh5KLeKX/Zci8Qk4NGCVbf1vfbW//cOwy1hC+6AO8C5718BoPbxm4s6BLDjY5tIyXfQda4Pg1mhYn8pxVtjk+bT6LqOw2vk01X7ODXWgdPvoTwpg22ZJeIZRADA3pwKroz1Romlcm1J1CbnLOGoBCsdvbsR7Zf/Fkms63XHkLY8gbzx4BKPTDBfOHIScfZOxMSTcqMX7i7/rJ4rLzaghTQkWaL6UBmb339z0ex8UXmglOLt+Yx1urBn2LCnC5GfQCBYHkiOdKQ1e9GvHJkJmm1Imx+L2TcpJ5F9v7edc/95mYmBKezpNkp3F3H5p/Xhnw/IJA2Ecyq21ACJj4QLHvSLb8WcS2+5CDdU9WMwQtAPqjqThZUVpJod9/oyBbfJobwavtZ4HHVW7nhLRvEDL06XFRmTzRglvDD6JZDAnHB7OcB0i53fWfsw3lAAg6xglJWFGu6CoGoa3205zdXxfgBkJA7mV3Mwr5qN6QW82dcYNY+TJYmtmcWRbV3Xo4QwAC0Tw/ys8xIfKN8SDiTNUSQTJ64YFXZ+cjPHvnyaoDfsHpKc52Dz+9fe4yudP/rrhnjz/3sn0r7n/PevsO8zO8hbm73wF+9rjR/XVHTAbjTz62WbF34ct4lJMfChiq38Z+s5JoLhvGBtcjaP5NfE3b98bwlF2wpw9U5gT0/A4pi7kG6kbYypYTeZlemLJoYXCAQCgWClsizFMB/4wAf4+c9/HiNwmS1u0XU9rgBm9r4CwYNE98X+KCEMQN+VQdqOd1Kxr+S2z3P8q2forwv3p9V1nc7TvYDE3k9vm8/hCgQPBCabiUf+yz4u/6ye4eZRHDmJrHm6mpTC5FseeyCvityEJC6N9qBIMpsziihOTLvlcWNdzogQZjaNh9uEGOY2UAwyq56oYtUTVbfct69ukNPfvMDkkBuj1UDNoxWse1YktQXRZNsc/O6q/RwdaGHM56bEkc6e7HIUeflU9glWHtrpX8RUmOoXXkOv3YlkuXlrN8HKYPVTVfRdGSDkn/k9563LJqN85llgsHGYSy/MCGd1Taf+Vy1kVqZTuGlxLNVNNhPZNcIJTSAQLD/kne9CL6xG76oHayJS5WbwudHe+k9wOyGvEmnVbiSjifz1OeSvzyHoC2EwKzS+3krIH0JSJBTDzOK+ps64x+B2AhCQJFRJxqqpgA7mGxYlJQksNkhMhYAfUrKQNz+KlDG34F4wv5QnZfD7qx/i1FAH7qCfVSk5rEu7v95/XdNpeL2FtuNd6LpOyY5Cah4pn7MN2DTVh8qjniUACjfm3rJV8I1YDXdXQKcGVXRNx2BemiWKk0PtXB0Lt0NSQxrBkMbLLVcoNaZSmpnJJ6p383L3VXrcTrKsiTyaX0teQnLk+AHvRJQQZporY73o+mYkSUJKyUIq34DecmFmB1lB3nQo7pjy1+Xw7v/zBAP1wxitRrKq0pHk5VFoo+s6p799ISKEAVCDGqe/fZFn//7RhR9nYipru65xOC0r2vdLktiYVrCw175LypMy+S8bHqXX7STBYL6lCM9oNpBeOnc75VBA5a1/eof+q+HcvSRLbHjPqtvKYQkEAoFA8KCy7MQw3/zmN/nZz34WEbaUl5fzl3/5lxw8eJDs7GxMJhOSJFFeXk5TUxNOp5O2tjZef/11vvzlL9PR0YGiKPzN3/wNf/7nf77Er0YgWDwG64fjxgfqh29bDONxeiNCmNl0n+sl5A8t2eRUIFjJJGba2fXJLXd1bHVyNtXJd1ZdE3AH48c9gbjxBxlV0+h2j2NRDGTbkm59wCy8Ez6O/NOJyCJl0Bvi8k/rsWckULaraCGGK1jBZNsc/FrpxqUehuB+YrQ/NhYKgmsILMWLPhzB/JNWnMITf/UwjW+04h33krMqi7K9xVH7dF+I83cAdJ/vWzQxjEAgECxnpPwqpPzwAqE+1IX28y/CdLuKnib0rnrkpz8dyUEazArHv3qGtne6CPlD6JqObJAx28PV+cVbCwioITR05LxKfh6Y5JwjFVWSKPa6eY9zlPRNj4Yd3KbG6LQmoCJRohgwvvtPkB5wJ5KlJMeWxLPFi+OcthRc+NFV6l5uimyPd13BM+Zlywdv/prXPFONwWKg+a121KBK0ZY81j1bu9DDJegPcebbF2k/2Y2u6eSty2b7RzdidcRvNb0QaKrGO5cb8er+cFGtDoSNcXjh+0f55HsfpSgrld+p3TvnOeZywjHKSlTRrvTQByCnHLqugSUBqXYnUubc7W+MViMFG2Nb6Sw1PpefiYFY8c/UsBvPuPeORVR3irRqJ5n1J/j1/k5eyshlwmDEChwsWkdlctYtj18qFEmm0D63wOVOaHi1OSKEgbAQ7vwPrpK/PoekXMe8XEMgEAgEgvuNZbey/fd///eR/+/cuZNXXnmFxMS5e7gmJyezceNGNm7cyB/90R/x2c9+ln/6p3/iL//yL+nu7uaf//mfF2PYAsGSk5Aef8JhnyMelzkMlabnhAKBYPmTUZ6KxWHGN+GPihdsWH6JlKWkfXKE7zafiVjVliSm8eGK7TN9vW9B99m+qGr9yHnf6RJimBXIVNCHOxggw5qILNpbCVYCqTkw2hsdU4yQlLk041kAVE3j2EArV8Z7McsGtmeVsCb1wRJ4JOUksvU31s/5c5PVGD9uix8XLB6ugJeXuq7S4Bwk0WhmT0452zNv361TIBDMP/rFwzNCmGn6W6GvBfIqgLC7bvuJbiRJwpRgIuAJooU0QgGVoj0FtG4J8JNzv0DTdeyZ2bjcFmQt7BbTYbPzrfR8/sSWyMgTH+frdW8zqgZBVkiyJvIRNciD9S0mWCzUoErjG7HtY5rfamP987UY53hegLDLeu2jFdQ+WrGQQ4zh7Hcv0XqsM7Ldc6GfY/7THPqzuYUn803dy024eyfRi/Vw4hNAl9AlwKly+afX2P1bW296jnSLndLEdNomR6LiWzKKo7YlWUGq3QG1K9tJ1mQ3YUowxhRhGa0GzIlzt/SZLyR7CvJzf8C6S2+xeqyfyfQ0Etc9hDExZcGvfSPjfg9H+1sY8E6Ql5DM3uxyEk0LL+bqvTI4Z1yIYQQCgUAgiM+yEsN0dnbS0NAAgMFg4Hvf+95NhTA3YjQa+fznP4+qqnzxi1/kS1/6Eg8//DDPPffcQg1ZIFg2lO0qov5XzXjGvJGYKcFE5UOlt30OW4qVrOoMBhuiXWYKNuRgFK4wK4agpvJS11XOjXSh6Tob0vJ5qmgNFkUsjDwIKEaF3b+9lWNfPh0RxKSXp7LxfWuWeGTLB1XTooQwAO2To7zUdYX3lm26vZPMpZcQOooVRUhT+XH7BS6M9KChk2K28b7STZQ60pd6aALBTZG3PYn2y69FtUqSNh68r1ok/bD9POdHuiPbzRPDvKdkA1szi5duUMuM0l2F1L3cGCXOlA0y5XuF6GIp0XSdf2s4zqB3EgCfGuQn7RcxXG97KRAsVzRd49RQB9fG+7EoRnZkld5Xz0S6K76brj4xinRdDDM7FyIrMpZEM5qqkb8+h7FHLZwf7oj8vM/vRjaYSJBkQAfFwBBh58mX+psZNZrgutDepQb5fus5/njtwwv2+gQPLkFvKG6hhhrUCHiCNxXD3Ihvws/5H1yh51I/ZruJqofLqT5YNp/DRQ1ptJ3oiokPXBtmasSNPX1xHJRaj3aQFZIZLVTDAhgAdIxeidQumVH3OD41yLXxfjRdpzYlB1ucdlC/UbGVn3Vc4up4HwZZYWtGEU8UrFqU17DYTLeVvvDDq1Hx2scqMJjiu+TMN1JSBtLeX0MG5sdr5c6ZCHj557q3mAyGc24tE8NcHuvlD1cfwGpY2NyrxRFfdGRNWjxXJYFAIBAIVhrLanX71KlTQFiV/vjjj1NYOLddoK7P7VPx93//9/zwhz9kaGiIv/7rvxZiGMEDgdlu4rH/up+6l5sY7RgnOc/Bqicq79iicvdvb+H4V88wcG0YpHCv2u0fFa0dVhIvdFzk7PBMYuH0cCc+NcRvVNy8okWwdIz3uOi7PIAlyULh5rx7Fp/l1Gby/D8+znDLKCabidSi5PkZ6AonFFDpONlN41A/41luFKMMs1xAro73897bPFfhpjzO/eflmKRj6U6xyLWSeLOviXOzFtvH/R6+1XSSv9zwGCZl/h+TA54goGOy3Z4DkUAwF1JBNfJ7/gS98QwE/Uil6yILefcDTr+HC7PuzWkO9zUKMcws7OkJHPzsHi786CqjHU6S8xysf34VKQV31vpPML+0TQxHhDCzeWewTYhhBMuaH7Vd4OzIzDzy8lgvH6rYxurU+8NhUsouRR+LbS8nZRdH/h9vEV5WZEwFVt7ubyaoqSCBSTaADioaAU1G1mUUCSQFfKEQ7ZOjMecZ8E4w5nOTKlolCeYZi8NMSmES412uqHhSbuId5wQPf/44o23jAPgnA5z5zkUkGaoOzK8gBi1+Xl9Xdcb8blx+L3kJyQsyJ5tGDWnYx2QqDyv0VofwJ4FtAArqFAxBCbXczN9d+BXe645SZlnhSVM1pqYAthQrpTsLMNlM2I1mPlixFU3XkJCi2iPdj6x+sgp7uo22d8LfFyU7CinZXrDEo1pcTg51RIQw04z7PZwb6WJ39jzfK7MYqB8i6A0S8AZRjDKKISxAsqfbKNx0f3xXCwQCgUCwECwrMczQ0Ey/wx07bm4b6PP55vyZzWbj+eef58tf/jKXL1+moaGB6urqeRunQLBcSUizsfVD6+/pHLZkK4f+bC9elw9JkuZUnAuWJwE1FHfx6MpYL+6gnwSj+H0uN66+1BhVVXPxx3U88l/2kZgRmySdCvr5ZXcdDc5BbAYTu7PL5lwUVIwK2TX3T7uMeyXoC/Grv32L8S4XnmSNwGNBZIOM2W6KCGLMd5BoszjM7P+DnZz+9gUm+qcw2YzUPFZB6c65hbyC5cfF0Z6YmEcN0uQamteFn4AnwImvn6f7XB+6rpO3NpsdH9skqrcE94SUko20/emlHsaC4Ar44rbodAa8caIPNhnlaTzyF/uWehiCWXhvbMMyHQ/FjwsEy4Exn5tzI9FODTrwem/D/SOG2XgQvacBJmaEKtLa/Ugp2ZHtkh0F1L3SxNSwOxIz2YycK3cScKtAuIe0Xw0iI6FpEPKHkDQI+iDdlkhxYipGWQkLZ2YhSxJm4dYqWCB2fHQThz93POIOa0owseNjt+l6ep3h1tGIEGY2jW+0zqsYRjHIFGzKo/N09FwspSyZFyfrudTRgw5YFCPPF69jffrCCC1KthdQ93ITySMGbL/SUIMqyBKWRAWDRaF1rT/qO31yysePxs6x9iUjEhJ1LzXw6F/uxz6du9HA1T+BxWG573OpxdsKKN62MgQw/qkAzl4XjqxErMnzM/8e9U3FjY/MEZ8PGg+3cvpbFwFQjDIhn4o51UTh1nzWP1eLYlwcZx6BQCAQCFYiy0oMMzExEfl/VlZW3H1sNhsej4fJydhKq9msXr068v/Lly8LMYxAcIeIBbqViarrqHGcs3SIScYJlh73mIeLP66LinnGvFx64Rq7P7UlZv9/b3yHHrcTgImgjx+1X0CSJLaIKuNb0vJ2e6RSzuaUSRiVcadphIIqBlP4cWhH5kxbiatjffyq5xrD3ikK7Ck8VbiGosRoE96c2kye+dtH8Lp8eMZ9NLzazMv/8zDpZamsfqoKW7J18V6g4K5QJDlu3CDHj98tJ79xga4zvZHt3ksDHP/qGQ5+ds+8XkewcIQ0FYMsEoyLRW5CElbFGCMqKHdkLNGIBILbp9yRiUlWCNzw7L0qJWeJRiQQ3JoxvzuuCHGuBb+ViJSQhPyez0L7JfQpJ1JeJVJW9DzKaDXy6H/dx7VXmhhpGycpJ5G0h3M4N3AKRZIIzZprq5qOyRPukqpLYBuTKDysIm+T2JZRzLHB1qhzb0jLJ8Eo3AEFC0NaSQrP/cPj9F0eQEcnb002hjt0nA2444s254rfC9s+vJ6gL0jf5UEA0stSkd6bzsXRlsg+PjXI99vOUepIx2Ga/7n12mdrcY956TzdgynBhNFmJLUwmdSiJAr2FfKP3W9F9tVUDdUfQk0Avx0sU+AZ93HlxQZ2fGwTfXWDnPjaOTxjXmRFonR3Eds+vAFZmd95peDOqHu5kUsvXEMNasiKRPUj5Wx639p7Pm9xYhoX4hTWlCSm3fO546GGNC69cC2yrRgUFLuCwWJg+0c2ohjE35lAIBAIBDdjWYlhrNaZB9tgMP6DdmJiYkQM43a7SUiIby9qs83YQPb3x9qgCgQCwf2I1WCk3JFBy0R0P/SChBSSzXdmjytYeIZbxtDj2AO39g/Q23qOyaCPqqQsdmSV0ON2RoQwszk+0CrEMLfBaIczarvybQNdG0K4KyUcCRZ2ZpWyP7cSgM7JMb7dfCqyINA5Nca/NRzjs+sOxSThJEki6A3xq//nCJOWIEafxGj7OL2XBnjqbw7ec8srwcKyNbOIn3deiYolm6xUOObPVSnoD9F9rjcm3l83hMfpFaKpZc7JoXbe6G3AFfCRn5DMu4rWxQjjBPOPUVZ4vmQ9/9l6NiLyTTSaebpwzRKPTCC4NVaDkV8v28wP2s7juy7oKndkcDBfFOgIli95CckYZZmgpkXFixdoYW+pkIwmqNzCzZqY2JKtbH7/ush228QIar+Gqke/N0hQ9aaRhHEJzQAmrwSouHoneLJ4NVaDkbMjXWi6xvq0Ah7Jr1mQ1yQQTGMwKRRuzrvr4zOr0jHZjNdbu86Qv2H+xZxmu5mH/3g3HqcXLahhz0jgn+veitpHVzUCms7lgR52F85/O1CDSWHPb29l8/vXEvAEcWTbIy2OVE3D2jcjzNZC1+9/HdwpGkpQxuiXGGkbI+AJcOQLJwn5QuF9VZ2WIx04suyseqJq3sctuD2GW0Y5/4MZF2ZN1bn2SjMZZWn3dJ8AbM4o4uJoT1RLvJrkrAVzUvO5fPgnA7HxCT/+Kb/IKQgEAoFAcAuW1QrNbDeYkZGRuPuUlJQwOBhWjV+8eJFdu3bF3a+7e6ZNiN/vj7uPQCB4MNF0jdNDnVwd78OiGNmeWUx50v3TTubXSjfyreZT9F4XTmRbHby/fPMdncOnBml2DWGUFSocmSjz7JIgCJOYOSPo1DUdZJjK0Gne5cF43aa8yTVEk2uIHVklcc/hDonvuNshOc8RtW30S5SdNLKtcgOVG0ujfnZqqD2mMtavqZwf6Y4IZmbz1tHLnHvES8Aatk1PbofSkyE6T3VTvjf+702wPNiVVYY3FOTYQCteNUhZYjrPlayf98+8OIZd138wr5cRzDP14wP8pP1iZLvH7eRrjcf583WPiLaDi8C6tHxKEtOodw5glg3UpuRguoN2dgLBUrI6NZfKpEy6psawG81k25KWekgCwU2xGkw8UbCan3dejjye2AwmnihcfdPj7heC3iC+ST/29AQkOVoqU2RPhesFDLN/IqswmaWRNGSA69oB2SCTkGZDkWQO5ddwSAhgBCsIo9nA7t/eyrGvnI64wWRUpLHh3Qv3OTB7Ed+EQtAbJBRQI0VDEnD+G5dI2w41j8y/IAbCztiz3bGHmkY4+x+XsdrcjK/XMFqMSLJEyATo0LorhKRD/iUDhYmJ9F4aiAhhZtNxqkeIYZaQ7vN9ceNd5/vuWQxjlBV+q2Y39c4BBjwT5CekUJmUGRFTzTfWZAvWZAtepy8SGypTGVyjU9/0KhVJmTxdtIZ0i31Bri8QCAQCwUpnWWUTZ7cyunjxYtx91q5dy8mTJwF46aWX5hTDvPDCC5H/Z2beP4vcAoHg3vlx+0XODHdGtq+M9fKB8i2sS8tfwlHNHylmG3+w+iEGPBPo6OTcYfK92TXEt5tP4VNDkfN9omoXGVYxqZpv0opTSCtNoffSALqqgSTRs0dCtkTbZze6BtmXU45ZVvDfYLlfkyws92+Hin0lNB9pZ2rIHYkl5zko2VUYs++NbQ1uFverIY6m9BJQ9YjiwVkCPeNBOs/2CjHMMkeSJA7l1/BwXhWqrmNcgDY4RrOBgg05dJ2LTsZl12RgSxEVXMuZ08MdMTGfGuLSWC87s0pjDxDMOw6TlW2Z4nP0Roa8kwQ1lVxb0oIl3QX3jkkx3FeCe8H9z67sMsqTMrg2PoBVMbIuLR+rwbjUw1pQdF3n/A+u0Ph6K+p1d4rtH9lIzqrMyM9Pf/MCFimEuzjcDgkp3FLTZJBRDdHK5sqHSrE4hGBWsHLJW5vNuz/3JEPNI1jsZlKLkhft2olng4SyQmFB3vW5tcEr4eiEs22XyanNJDl/YcWlHqeXN/7xGCG/Sh4GTB6V4bIQplIbfikIgfC4dAm6N6g8U5aP7Ix/LtkoisqWEoMl/rKXcY74nSJLMqtSclmVsjBuMNN0T41T7xzA8N5MQt/qwuCD0UKVjq0hTDYT6FpYlOOd4M/WHhLFjAKBQCAQxGFZiWHWrFmD1WrF5/Nx/PjxuPs89dRTfPWrX0XXdb74xS/ygQ98gNWroxXqf/d3fxclptm2bdtCDlsgEKwgxv0ezs4SwkC4MP/13ob7RgwzTbbNceudbkDVNb7fei4ihIHwe/bTjot8smb3fA5PAIT8ISYHp1BMClpQQpLA7wBd1eGGvPNE0M/7yjbz/bZz+K//fgrtKTxWULsEI195mO0mnvjvD9H0ZjvOHhepRclU7C+J28ZodWoul8di29qsTokVHjW7hlBNgCc6Ee4qlRg+Ooau62KhcgUgSzLyAv6atn90I5qq03OpH3TIrs1g16e2LNwFBfNC6IZWETPx+II5gWChmQr6+Hbz6Ygle5o5gQ9VbCM3QbiOCASC+SHL6iDLeufzyJVK05ttXHulObI9NezmrX96h+f/8QnMdhNd53ppOdJBdo6CK1tDl0HSwZxgQDEpPLpvNR7DKGpApXBzHmW7Rfva+cQz7qX5SDueMS85qzIp3JyHrIiF3oXGYFLIXZV16x3nEc+4l9Bb45QUG+itDhFIAHsv5JzUUfTwRK3nYv+Ci2E6TnQT8s8862e0K2S0K/S9W8KbYSakqGghDUmSUMwKvdYp9hVWxLh2AJTvKV7QsQrCTI246TzdgyRJFG3NJyEt3CK+bFcRdS814ldDBGw65ikJgyRTvrd4aQd8B7ze28CrPfXhDQUsHzHyUH8uvfZBzAm+qM/Dcb+HeufAgrVqEggEAoFgJbOsxDBGo5Fdu3bx+uuv09/fz9GjR9mzZ0/UPo8++igFBQX09PQwOTnJ9u3b+fCHP8y6devw+Xz84he/4PDhw5GFp02bNlFTI2xJBQJBmDG/O25XilGfO070wWPAM8FE0BcTb5kYRtM1ZEkknuaTviuDBNzBsCDjegFh4miIsWQ1qopFImzPnWZJoDwpg/aJUexGMwX2lHkfk65r0NWAPtgBSelIZRuQ7pOKULPdzJqnq2+53/q0fLqnxjg+2Iam65hkhccLVpGbkByzr1FWUAwKQULM7nkjaxJBT5CQX523yiPBysVsN/PQH+7EN+lH1/QoG27B8mVtWh6NrsGomCxJIsEoWDJ+1nE5IoQBGPW7+U7LaT679qAQXgoEAsFtoOkaHZNjXBju4lJfNy6/h8TtOsUXjJj94bluyK/SfaGP8j3F9F0OPwck98vkXpXpr9VABimg82TparbkVsD6hWnd8qDj6p/kl3/zFgF3AICWtzso2prP3k+Lgsf7Eb87AHpYfJJUp6IGZonSk8P/mGymuMfOBz41yIWRbq4qPbgLVVK6ZWR95tnKEJRAkjDMyt1AuJ2cwaTw8J/s5tS3LjDcPIrZbqL28Uoq9gl3w4Wm+3wfb//LKbRQ+O/l4k/q2PeZHeStzSYhzUbC7xRyZqiZoK5i1hQOpZeTVjz/ebSFwBXw8npvQ1TMJ6l0rQph8VuRPYGYY/xqbLsugUAgEAgEy0wMA/Dcc8/x+uuvo+s6n/vc52LEMEajkc9//vO8+93vRpIkPB4PX/nKV2LOo+t6ZF+BQPDg0ut28mpPPX0eF7m2JPblVGCSlZh2J8WJaUs0wuWF3WhGghjBkM1gEkKYhSCOFUX+FQVPQbQbwYHcKtIsCQBYFCM1KdkLMhxd19Ff+xZ6++WZ2KW3kN/1e0hm24JcczFpdg3xctdV+jwusm0OnihYTVVy/Iq3p4vWsj+nklG/myyrY06L+PKkDNJsCfS5A+izfm1ZHQaS8xxCCCOIwpIobPNXEpvTCxnwTPDOYCuqrpNgMPGu4nWkmhPi7j/u9zAZ9JFnSxb21IJ5R9N1ro73xcRHfFMMeCfuuC2mQCAQLDTuUQ/DLaMkZtpJK1n6xcd+j4tvNJ5g0DuJNxQEDQwyOIt06hMD5DQrBO0SicMy0vV5msVhRkend43KUKUOMthGJLY709iycfk7y/a5XdQ7+7EaTKxPy8dmWDgxwXxz9cWGiBBmms7TPQw/Wk5Gmcjf3G8k5zqwZyYwNeRGMRlQA+GcnWwMt7E1J5oo3r4w95w7GOBfrh1h2DeFZtfw7wrhGJCpesuAdF0QszOnlJ/RhDYrW5Zw/b4CSClI4rH/up9QQEUxzHyGLGdUXWPEN4XDaMG6gj4bptFUjdPfuRgRwgCoQY0z37lI7t8/SoNzkGP+TgxJpsgC2GGtnbWekhXx3NzjHkfTY8s5u6bG2J5ZQp/HFRU3SPKc+S2BQCAQCB50lt0KzXvf+16GhoYAkGWZQCCAyRT9QPbcc8/xhS98gT/+4z8mGAzGtCDQdR2LxcI3vvENdu7cuajjFwgEy4cxv5sv1x+NKONdAS9tkyMcyK3kVz31kSmszWDiqcI1SzfQZUSSycra1Dwu3dAiZm9O+RKN6P4md3UWFocZ34Q/EjO7JX7DuBZfiYXJoI+q5CzyExYpedzdGCWEAWB8AP3KUaTNjy7OGBaIYe8UX288Qei6YqXfM8E3mk7wB6sfIvt6IsTvDlD3UiP9dUPYUq3UPlZBcVVG3PN5Q0EanQMossxHqnbwg6lTtLhHMPglshsUcjuMbPrDtYv2+u4ETddwBXzYjWaMsrLUwxEIli2SJPF00RoO5FbhCnjJtNoxxLlngprK91vPcWWsF51wYvy9pZsWTLgoeDCRCLuRqXEqPsVnuUAgWG5cfamRiz+uQ9fCs+7ctVns/8wOFOPifF61TYww4puiKDE10vbpP1rOMuZzh4UwhIUtIQsoHnDnQXuGhqwBkkZq2iBlFFG+r4TXuxroq1XD+QNdx52m845xEPefvsLuT26heFsBAP6pAEPNI9hSrMvCeeBIXzMvdV+NbL/WU89v1eyOzH2WO2Pdrrjx8W6XEMMsIf4pP41vtDHW5SSlIImqA2VYHPcu+Jdkid2f2sJbXziBz+XHlGBC18GSaCKrOoMNv7Z6wZxh3hlsZdg3BYCsyBitRiayQzhzddKGFGoOlbNxRy3J46m80dfAiHeKPFMS6yczcLW4sFSlR9YlDKaV8UxWPz7Aj9svMBH0oUgSOzJLeapoDfIKchr0jHnxjHlj4pNDbnwTfi6O9sT8TAcuj/auCDFMhiUxbjzdYmd/biX9nomIUN6iGHlPyQbsRlF8IxAIBAJBPJadGCYtLY3/8T/+xy33+93f/V0OHDjAP/3TP3H48GF6e3vRdZ2ioiIeffRR/uiP/ojCwsJFGLFAIFiunB7qjLGI9KshQrrOn649RN14HxbFyLq0/DldHx5E3le2iQxrIpdGezDKCtsyi9mRVbrUw7ovMZgUDvzxLk5+/TxjnU4MFgNVB0pZc7AqppLI6/Lh6p8kOdcxL8mmeOhDnfF/MFd8BXFmuDMihJlG1XXODHfxdNEadE3n9f/3KGMdTgBGO8fpONmNIyeRpNxEah6pIG9teGG7xTXEN5tPRT5fEo1mPr5xF1aXRPeZXuTVMiWfKIj0ql5OXB3r42edl3EFvFgUIw/lVvJQbuVSD0sgWNYkGE0kGOdOvr/V18TlWSJSdyjAd1tO8183PC6eLwTzhiRJbMss5kh/S1S83JFBusW+RKO6/zg73MnJoXYCqsqa1Fweyq2MK4ITCARz4+xxceGHV6NifZcHaXithVVPVC3otYOaytcbT9AyMRyJ7ckuY1d2GQPeCdyBQFhdOI0EqjX8rw5IiozJZuSiq5cdEyOUZKTj32VDnlJRg2p4Rwl8SToTySqnvnmB/PU5dJzu4fS3LqAGw/ON7NoM9v/+ziVziZwK+vllT11UzB0K8Er3NT5atWNJxnSnpBYm4YwjiEktTF78wQgACHgCvPK/3mJyMCwc6T7XR9vxLp74Hwcw2+9dqJJRnsbz//gEg43DKAaFzIq0RXFY6XE7o7YNZgMGk0L++wp5snQNZns4/1KTkk1NSjb1rzZz7j+v0KAN0QCkl6fy8B/vWtA2TvPJVNDHt5tPRfIjqq5zbLCVDKt9ReX+LEkWjFYDQW903tdsN2FKMKGMxP/bWSkOnpnWRDak5XNhlqhHkSQO5VVjcA7xwcZzjA53M5GYQn7tTixpeUs4WoFAIBAIljfLTgxzJ9TU1PClL31pqYchEAiWKVNB/5zxDKud/VaxABwPg6zwSH4Nj+TXLPVQHgjSilN48n8+jG/Cj9FqiFsteeFHV7n2y2a0kIZskFnzdDVr3zX/vx8pOTOmRRYAyZnzfq3FJqDF7508He+vG4wIYQACUwG0kMZ4twvPmJf+uiH2//4O8tZn84O281FCu8mgnxc6LvK7q/aR8i7Hgr6Oe2HM7+a7LadRr1vt+tQgr3TXkWGxszo1d4lHJxCsXK6MxbauCWgqja7BiHW6QDAfPJa/CpA4NdRBSAuLNZ4pWrfUw7pvODbQys87ZxzyBnonGPJO8sGKrUs4KoFg5dF7ZTBuvO/K4IKLYU4MtkUJYQCODrRS4chC0kGV4sx2ZECXMBoUzAkzi9mdU2OUONLBKGG2m/A6ox0INAUCniC9VwY59c0LUa06Bq4Nc/UXDWx4z+p5fX23S497PPLMP5vOydElGM3dsebpanovD+CfnGmVVLwtn/TS1CUc1YNN67HOiBBmmqlhNy1vt8/bva0YZHJXLW6rlyyrg3rnQHRQkijNy44IYaaZGnZz7j+vRFyvAEZaxrj6i0Y2vndlOE5fHe+PKRQCuDjas6LEMAaTwuonq7jwo2jh35pnqlEMMlsyijg30hWV41IkiQ1pBYs70HvgfWWbKHNkcM05QILBxI6sEvJMNrT/+FvwTpIGpI0NoL/9E1SjDaVy41IPWSAQCASCZcmKFsMIBALBzahOzuL0cEfc+HzinfAhSRKWxJVjR+kJBdB1blrpLrg71JCGq3cCa5IFa7Llto+by+2l9/IAV3/RGNnWQhqXXrhGZmUa2TXzLFIpWQPp+TAyy07WkoC0Zu/8XmcJWJOayzuDbbHxlLAIxD3LXldTtUgyO5Lk0uHaK02Yqu04A7FWvJ1TYwTUECZl+T5aXRrtjZsUvzDaLcQwy5wzQx281d/MRNBHhSOTp4pWk2pOWOphCa5jUuK7RpiEm4RgnlFkmScLV/Nk4dIsrt7vHOlvioldHuvlCb+HFPPyc3sTCJYr1jnmNQvlbjmbZtdw3Hi3e4zVqTkcG2i7bgEjATPP+YofFGu0W8C069ba1DyODbYiyRK6Gj7G6JNIHAq7xEwOTUUJYabpuTSwZGKYuRzD0q0rx0nMkZ3IU//rIC1vd+AZ85Jdm0nRZuF8sJS4+ifRJZ2gBQx+kLWw84arb3KJR3Zv7M4u5fxIFxNBXyRWkJAcyRXMpv/aUJQQZpq+q4MrRgyjSPGdUeaKL2dWP1VNYrad9hPdSJJE6a5CCjaEf28ljnSez1/HSy1XmNT9pJsSeL5mI2mWlTOPliWZrZnFbM0sjsT05vPgDd9zakgj6AmiaTqj//ECkxttrHm6eolGKxAIBALB8mX5rtgIBALBPbIqJYdtmcWcGuoAwm7I2zNLqE3JmZfze5xejn/1LAPXhkCC/HU57PzEppjKkeWEO+jnB23nabhe9VKZlMn7yjZhN96+aEMwNz0X+znx9XP4XH4kWSJnTy6je4y0T42SYrbxUG4lFUl3JmDpOtc7R7yP7JpMNF3n6EAL54a7ANiYXsDenHLku0hkSIoB+enfQb92AgbaISkDafUepMSl73l/r5QkprMuNY8zw53ogFUx8nBeFZXXxXHZ1RnhDwk9LIDRDCCFwGCYeR89Y17sBjOKJMWISmwGE4Zlbrc7l8G0NOdPBMuBi6M9/LD9QmT76ngf/R4Xf7r24IqxeL7f2ZZZQtfUeFQsxWyjKmlxq1oFAsG9MRmIdZXUgcmgL0YM03q8k9ajnWiqRvG2AqoOlC5KKweBYCVQuDmPiz+pwz06IyCXFYnqg+ULfu0kk3XO+P7cSq519zJu8IMGkhp+3k/og2A6BPUQikFBkiXyE5JJMlmZDPh4tKCW8YCHy/4eAp4AZrdE2TsGZF2ieFs+ybnxnSEtiTOFJ2eHOznc18i430NJYjrPFK0h25a0IO8BhMUwG9IKuDDaHYnJSDycu7IWSW3JVtY+IxxrlwuTpRKXEoMEbDpKQCKvTiG7UVnxbj0Ok5U/WP0QJ4faGfZNUWhPZWtGUdy5ljUpfu7MlhL/s2c5sjollxeVK/jUYFR8a0bREo3o3ijanE/R5lg3To/TS9/nGikfCaIZZJSQj5Ht3fDb85MTXir0UNgtS9ch4A4XOgLIeoiLP64jId1G6Y7CJRyhQCAQCATLDyGGEQgE9y2SJPHukg3szS6nz+Mi15ZMxjxWQh3/6hkGrl2vPNOnhRDn2f+Z5duD+4ftF6LsXxtdQ3y/9Rwfr961hKO6P/BP+Xn7X06hBlQAVDReSWxH6zVgsBgY9btpmxjht2p2h+22bxOj1Rg3broef7nrKm8PtETiL3fXMRHw8Uzx2rt6HZLJirT+wF0du1zRNZ0vv32YRnUYHZBNMmaTgQ3pM/a4iVl2Nrx7FW++c4Wu9SreRAnTlETBZZn06znk7FWZJBjNbM4oiojsptmbfXcCpMVkXVo+r/bUx1gib0xf3jbBwy2jXP1FIxMDk2SUp7H22Rrs6SunmuteORHH0WjU76bRNThv4k7BvbElowi/GuJIfzOTQR+VSZk8U7RWiJUEghVGRVImja7o9i4JBhN5tuSoWN3LjZz/wdXI9nDzKJODk2z54PpFGKVAsPwxmA088hf7uPTCNQabRkjMTGD1k9VkVKQt+LV3XXd4mP28m6RYqE3Ixigr/Nm2x/jy4TfoNU6BDok9EmWnDegWmcEqlYTVNrIL0mhyDfGFureQkdicUchvVGxlsmgtbZd6GLk8gJqsUnAgl5pHK5AkcOTYmeifQtd1dFVHkmfEP9fG+/lB2/nIeFomhvlK/TH+fP0jWJT4c7354H1lGylxpFE/3o/VYGJHZilFiStbtCBYOkZ8Uxw2dRJySBDSUU06XRtCZNkdlO5emSKK2SSaLBy6jVbhuWuySM5z4OydiMQkWaLmkYqFHN68YjUY+UT1Tn7acYketxO7wcz+3ArWL/O8wJ1S91ITUyMeJCSU612uO052U3WglMzK28/JLTekolXoigHV62d2jVbvRLjwru14pxDDCAQCgUBwA0IMIxAI7nsyrIlkWBPn9Zwep3dGCDOLngv9BL3BOQUMS4k3FKR+vD8m3uQaYirox25cXEebMZ+bN/ub6HO7yLEl8VBu5YqyK72RnosDESEMwHi+ht+uIwVVDJbw162GzrHB1jsSw5TvKabx9dYo623FKFO2u4iAGuLEUHvMMaeG23m0oBbzMm7Zs5i88oOT1CfPLG6pQY1J3csbvY28r2xTJJ59MJ+etHrUIBhUnaAcom1XCMtrEnnmJNY/twqAZ4vXkWZO4NJoD4oc7kW9LbPkluPwhYIM+6ZItyRgNSx+i7IUs40PVWzj552XGfW7sRlMHMyrWtaCirEuJ6/+/dtowfDf/8TAFP3Xhnjmbx/BaHkw/r69oWDc+I2VfIKlZXd2Gbuzy5Z6GAKB4B54V/Favlp/LNIO0Sgr/Frpxihhm6Zq1L0c206p6a121j5bizlBtCAVCADsGQns+tSWRb9uji2J36ndy5t9TQy4nEiNPlLecfOz4CuU7y5m8wfX8V+eeYaXP3+EvquDmEPXWxpOQdE5A9Wl+bww1hoR02jonB7uJNOayN6cCjZurYStlTHXPfRne3n9H44y2DACEphsRsa6nBRszOVUnPmaOxTg6lgfmxfQiUGWZLZnlrD9NuYpAsGtuDzai4qO2W5CDWpoqoasyMiPpmIwKfhCQUb8U6SZ7VgNyy8fNl/IiszBP9/DlZ810H9tEFuKlVWPV5Kzap5bWC8whfZUfn/1Q/jVEEZZQZbuP3e70faxuPGR9vFlK4aZCHgJaOqcre4AJFsi8oEPov3yP4Ag6BJdrmxaxvIYqFRpqRymoe4tNqYXsiOzBOk+/N0KBAKBQHCnLMkqwttvv72o19u7d++iXk8gENz/3LStyBJPNHRNX/Y27ZMBH1+8doTJYNiOvts9Tt14H3+05gCOOaytlzuKMdoBIHjdPffG38RkwMedkJzn4KE/2snFH9cx3u0itSiZDe9ZTWKWncmAj4AawqcGCWrhhG04iaHzxbojHMitvO+qe+4U74SPhtYe2BRudQCADgFPkI6RYZi1dn1upAtdDlezAugWA2pQxfreHJ7avBNZCf+OFUlmf24l+3NjE+FzcaS/mdd66gloKgZJ5kBeFQfzFt+mvCYlm+rkLKaCfmwG07J3rmh4rSUihJnGM+al83Q35XsfjMT+qpQcBrwTUTGDJIsWPAKBQDDPpFvs/Pm6R2h0DRJQQ1QlZ8WIV9WAin8qEHOsFtTwTfiFGEYgWAYU2FP4jbIt/OwvXmVqOARIaGg0vdmG2W5i/btXUbujlNGLI5FjJFRyHSP4k8yE1BDc8Ix8eayXvTlzOz9MDrlx9U5iSZwpMLn803rSS1PxG9S4x/jV0L29UIFgKZAkFJOCQlhIJssSR/qaea03PNc1ygoH86p4KLfqlqfSdJ3OyVFUXaMkMX3Zz02nsTosbP3Q+qUeRgyqrnFisJ268T7MipEdmSVUJd98zni/FVB5nT58Ez6S8hwk5ToYbokVxCTlxBZLugJefGqQTEvigolHdF2HsX4wmpBuKJDzBgN8o/kUbRPDSJJEjs3BB8q3kGWN34ZPKl0H7y/l2P/4CVMeI76Qmc4NIQarVIwJEt6pcbqmxpkI+HisoHZBXo9AIBAIBCuJJXni2b9//6KpUiVJIhQSE0yBQDC/WJMt5KzOpP/qUFS8cHPukrkVTAxOcfrbF+ivG8LiMFP7aAWrnphJQFgNRmpTcqi7wR2mMilz0V1hzgx3RoQw07hDAU4Pdy6JQGA+yN+QizXZgtcZFrsk9YcTOQazErVf5S2SEdP0e1y82HmFtskRUkw2DvxOFU/cUDmYaLIgSRJBLZxg1QFV15GAAe8E32s9i8byb4OzkHjHfVhn5z+mfWR18F500W3qo2BDLgCqFi26kGQJg9mALd0aEcLcDR2To7zUNdNOIaRrvNpTT0FCyi2TUwuBJEkkmuL3Ol9uTN9PN+IZvzNR2UrmodxK+jyuSIs7i2Lk3SXrSVjkz22BQCB4EFBk+aaOaUarkbSSFEbbx6PitlQriZkr1+FQILjfGGoaZWrYjSbruHJ0dEknqV+m7Z1O1r97FcXbCnD2TFD3ShOS18VDFZewW/1cax6AolIwWWDWs5ZRDs/pfGqQo/0ttEwMk2yysiennPyEFLrO9sYdR9e5XlY/nkvb5EhUXJakeXNndAW8NLuGcBgtlCdl3pcOD4KlZ21aHr/quYZfDSFJEgZJjizav9Q9M9cNaiqvdF+jICGF8qS53VJGfFP8e+M7jPjcADiMFj5atYO8hOSFfil4J3woBhmT7f4SsP6w7TznR7oj29fG+/lA+RbWp+Uv4agWBzWkceob52l7pwtd07EmW1j3XA1d53oJuGccVbOqM8hdM5OD8YWC/EfrWRqcA+hAhsXOB8u3kDvPf4f6aB/a698CZziHLOVVIh38MJLFRo97nC9cfQvXdWdCg6zQ53bxraZT/Onag3OuoVlTEln3W49x+psXGFIn6FujohskJFlH0XUkSeL4YCsH86owyErccwgEAoFA8KCwpPJffXZjw3lGkqQFPb9AIBDs+tQWTnztHL2XB5AkiYKNuWz7zY1LMhY1pPHG/3uUqREPAD6Xn/M/uIrRZqRyf2lkv/eUbETnfKRdUrktnSeTbt0Xeb4ZD3jix/3x4ysBg0nh4T/ZzZnvXmKwYZhUxcouYzYXTSNo1z1Jyhzp7M0uv+W5fGqQf60/zlQoLBga9bv5Ydt5EgymqKSpO+hH07SY7zyd8HesJEkcHWh5oMUwSbmJpAetpHSpjBXMVGUqAYncOplLPdciYph1afkc6W/mxqeHdfeYPLoyFj85fnmsd0nEMCuJnNVZ9F0ZjInPTmDd75gUAx+t2sGQd5KJgJdCeyqm+6yCTyAQzDDkneTnnZdpmRgmyWRlf04FO7JKb32g4I7Q9GlHvTsXu2790Hre+MfjBNxhhxiDWWH7Rzbek3BWIBDMP+5kjab9IYKW8NO9EpBYd2mmWG79u1cx3uMic+AKdmt43lU+MoYttwAPEhiMcP0zYmtGMeN9E3yt6ySDTEUWJ6+M9fHbtXtiCiCmMZgNbM0qodft5PxIFzphYfOzxWtJMdvu+TUeH2jlxc4rkflmjs3Bp6p3C9G0YN7pmRpH1TUCWghN11EkmacKV+Oaw/n28ljfTcUwP26/EBHCAEwEffxHy1n+dN3BeR/7NFPDbo7/21mGGkeQFYnCLfls+c31qAYdm8FESNcIaeq8tzQOqCGaJ4YwSgplSRkod/Dsoes6zUfa6TjZg6xIlO0ppmR7bH5nzOfmwiwhzDRv9DY8EGKY+l820XqsM7Ltdfo4891LFGzIpfVYJ6GgSlpRMts/siFKXPJS99VI0QnAsG+Kbzaf4s/XPTJvwkJd19Fe+ya4hkFTIehHbzmHPtSJ/vin+OZAIxOz7qOQpuJHYtg3RZ/HdVOBWO6qLLb/1Q4+d+kwWigE6AS0ECFdJcFgxq+G8Kkh7EIMIxAIBIIHnCXLpC+0UEUIYQQCwUJjdVg48Ee7CHjCifClrCrpvzoYEcLMpvmt9igxTILRxEcqt+N0TXHi6+cZuTjEawyRXprC7t/eSmLm3H1p55OyxHRODXXExh0Zi3L9hSKlIIlH/mJvpH82wGN+Dx2To6SYEyhKTL2t89SN9UeEMLM5OdQeJYYJ6RqyLGOXwpNcvxZO7s6esk8FHxwHjXgoRoWtH1qP54snSEgPMZUvYXRDTpcBi1/G5Z2M7JuXkMzzJet5uasOrxrELCscyKu+Z8GKUY7/uGUSCYlbUnWglP6rgzOCGAlWPVFFeunt3Uv3E5nWRDKtsZbOgjsn6Atx5ef19Fzqx2QzUX2wjOJtD65oUHD3dJ3tpeN0D4pBpmxPMdk19/YcE9RUvlp/jInr393jfg8vdFzCZjDdszBTEManBvl5x2UujvYAsD4tn2eK12JRjLd9jvTSVJ7/h8foPt+Hpurkb8iJao0iEAiWnszKNLp36xEhDIBq0unYqUWKBgKeAL2XBlhV6ozsY9I0fqOxgVfLSukwW3FYbezNLMf5nW6+M3CSzv1BkCRMNiOKSSGkaxzpb+Zde1Zx7VfNUe09ZUWifE8xiiTzvrJNPJpfgzPgJdeWNC/C5omAlxe7ZoQwuqbTOTLCF7/zMpudmax+qorMivRbnEUguDUBNcRPOi4hSzJ2oyV8DxF2o82co42L8SZzXb8aonViJCY+5JtkxDdFumVh8lJH/vkkY51OADRV59RIBy8d70a1S8jTBUaSRKE9hV8r2UiWLf5rA+icHKN5Yogkk5V1qXlx72lfKMhL3Vc51t+Kho5JVkizJPCJ6l23/RrPff8K9b9sjmz31w3hHfdS+3h0y+bxgCemqAdgbAUXnN0JHad7YmK+CT+txzpRjAqKUWFq2MPbXzzFU/9rRnB1aTS2cGnc76F7avy283e3ZLQ3LITRNfB5iDTwdg3T8ca3cJWtijkkqKtYMKLcRJAzGfAhSXB8sA1N0pElOSL21nSdkK5SlJC26E7gAoFAIBAsR5ZEDNPe3r4UlxUIBIIFYTFFMAE1RK/bicNkJc0yY8WuBuP3IZ8rfvm7Vxm5OBzZHmkb5+iXT/PEfz8wvwOeg7VpeVwa641q2VSbnM26tLxFuf5CM7syONlsY/0dVv1Ni1pi4jf0lU8yWSm0p9A1NY5ZMRDUVHR0DLISqXapTs6+w9HffxRtyeddf/soP/vzX6H16UwVQM8mDV3WKNWSo/bdlllCUNU4NdyOTFhYpOraHVVv3cim9ELe7m8mpM9KjksSW25oeyWIRTGGHZeGW0aZGJwiszyNxKzFEe0J7l+O/POJqDaHw82jqEGNst3inhTcPpd+eo3LP62PbLed6GLnxzff099R/fhARAgzm5OD7UIMM0/8uO0Cl2Y5tp0d6SKoqXywYusdncdoNVK6S3xmCATLFb8ewp+rIHk09JAGEigmA16rxrjfQ6olAV3T0TUdT9CC1Tjz2Zvl9fKp7lbY/BSGrCIuvXCNyxf78ZdPt1vVCXgCWAwWJFlizOfGUWHnwB/u4vwPrjDW5SQ5P4kN71lFSkFS5LzJZhvJ8+AGM03LxAja9UJAXdfxT/rRNZ1hu4/eIwP01w3x2P+1n7TilHm7puDBpMftxKfOtJqZzjU0u4Z5omA1xwZaUGcVpcqSxOaMwjnPZ5BkzLKCX4vOV8lIdyROvROcvRMRIQyAK1ujc3MIVBVZM+BXg4CE3Wiia2qcf298hz9b/0jcPMCLnVc4OtAS2X69t4FP1+4lyWSNxHxqkH+ue4u2yZn7NKip4IOfdlziE9W7bjnmgCdA4xutMfGrLzdS/Uh5VN4pz5Yc9z0tTUy75XXuBxRTrPgqFFAxWaN/f+PdLkY7xiOfi0ZZxhcnbWqQ7y7/E/AEaXi9hcGGEewZNmoeqSDJdv1vOhSEKMmShKIGIRTEqCgEbsj35Sckk21L4kYmAj7+s/UsLRPDSBD5G7UoRryhAPr1axgkhedL1t/V6xAIBAKB4H5jScQwRUUiaSQQCAR3yqXRHn7cfhGfGkQC1qTm8etlmzDICjmrsjBaDQS90ZOnos2xCydqUKX7XF9MfLRtnMnBqUVZaJYlmd+s3E7bxAh9Hhc5NseKd4WZT2qTs/m5dDmSNJlmTWpuzL7vL9vCt5tP0edxYTEYCWpqJIGUjo3sMzqXL9dTtruIhLT5S76uNJJzHWz90HpeOXmB9q3X7xNJ4qrdza96rvFofi0QTmS92hNe3NQ1nW7nJTq7h/jQ5p1RySZd1xnrdKIYZJLzYxMUs8mw2vlI1Q5e7rpKn8dFljWRxwtWzXsf6vuZjPI0MsofjESeYGEZ73ZFCWGmufbLJiGGEdw2AU+AupebooM6XHqhjtJdhVH263fCnGLYOeKCO8MbCnJlLPYZ+MpYH95QEKthYRbgBALB4qFqGo2uQfyhEGajETlRRtd0kKYX8HW6psYxygqJdgs5qzJp6ihiR4ELpOvtlAwyckElclb4uaD7QvhzI3F41me7DmpIxWAyUOoIu6/krMrkyf/58KK9VofREvm/GlTDrxMwesPj1EIaDa+3susTmyP7DXsnMSsGHLMW7QWCW5FkshK+e6JJNtvIsjn4cMV2XumuY8A7EZnr5sRZxJ9GkWW2ZpZECUogXDS1WC4WIyUzCohgRECiE9Q0zIrMeMBL+8RITKunQc9EzLjH/R7e6G2MEh6cHe6i3zsRldPRdI2gptLsGrqtghuvyx/lNjWNfzJAyK9iss0cbzEYeaZoLT9qvxD5PdkNZp4sXHPTa9wvVOwtYaRlLComyxKKMVYko4Vm3tMtGUUc7ot+ps+zJd20NdFcaKrG6//vUUbbxyOxjpPdPPbfHsKRXQJd9dEHGEwU+DxkaRqDRjPoRArcUs0JvKdkQ9zrfP+6EAbC96Q7FEDVNWwGE3ajOVKE9cnqXRTaHzxHXYFAIBAI4rFkbZIEAoFgqdF1nUHvJBbFMK8VWgvBVNDH91vPRSY1OnB5rJdcWxIH8qow2Yzs/d3tvPNvZ/E6fUiyRNGWPFY/VRV7MkkCWQI11kRVNty9+8XdUOpIjyQPBTMkm228r3QTL3RcioifNqUXsiOrNGbfNEsCf7jmAIOeCWRJwm400zIxTP+JfgZ+2EEbUwDUvdzIwc/ueaAFBdWHyvmBoxGDz4MkSSgmBUmWeLu/mX3ZFRgVhaP94cRWyB8i6A2CDufcXST81SRP/Ml+bMlWxrtdHPnCCSaHwj3O08tT2fd727Elz51UrkzKpHLNATRdn7fe03dC0Buk/9oQBpNCdm1mlLBHIJhvnH4PI74pchOSsRmWroVgPLyu+K3jPOMPdks5wZ3hHvWiBmLLSKfjUwR4qesqja4hEo1m9uVUsDWz+JbnrUnOwiDJUU5iEF8MK7hzVF2LtBOZjYaOqscuNsVjoH6YSy9cw9njIrU4mfXvXkVG2YP7bCUQLCdGfFP8a/0xxgNeAELXF7gNsoKu63hDASQkvtd6BkWSeDS/lp2f3MyxL2sc65IpS+3BkQQp27YhbZtpo2G6XtVvc8nk1Cv014TPK0kSGRY7D+XGmXMvAmWOdAoSkul2O5n9EZZTP7P463WG34t+j4vvtZxh0DuJBNSk5PDrZZsWzIVDcH+RZklgbWpelLMawEO54VY9NSnZ1KRko+kachyBhzcU5BddV7gy1otRVtiaUcyjBTVYFANnR7oIqCHyE5IX1Dk1Oc9BalFyxB1Gvz4lV0wyKvGfATRdZ3JoiobXW5kadpNVmY5zbfz2Tx2To1HbAx4XErHzflXXsRlMt+U8m5iZgC3FEjNPSS1KjnwuzWZLZjFljgyuOfsxK0bWpObeF/e4JxTAIMk3bS9XvrcY/5Sfa79sxjfhJ6s6A0uiic4z0X+z9oyEqJbLh/JrCGkap4Y7CKghqpOz79pNpffyQEQI48zW6F+l4k8I0HPqbd6/+V3k6Dq0XwYkMBjBZEYCPppVxguoNDoH0dGRkAioIb5Q9xbPF69ny/U5hBpUaTzXSX2gH9kgR3I6JlnBG1IjLQCNksKOrBIq77Hdt0AgEAgE9xNCDCMQCB5Iet1OvttymhGfG3SdmuRs3l+xZdlOFOudgzELIwBXxvs4kBdOvuWuzuL5f3yc8W4XFoeZhNT4Ah/FIFO6o5CWtzui4tm1GQ+0c8hyY0N6AatScujzuEg2WW8p2Jrdz7rckMblF05HJV9CfpULP7rKI3+xb8HGvNwJaCpuKYjRGn2fBzUNZ8BLstmKVw2ia3pECAPhRNmY083FH9ex8+ObOfqlUxEhDMBIyxinv3WR/b+/45ZjWAohTH/dEG9/8SQBT9ja2p6ZwME/2S3aDQnuCG8oyIXRbiYCXiqSMuO6eem6zgsdlzg11I5O2AL9sYJa9uZULP6A5yCjLBWDxUDIF+20kbtGJAsFt48j244pwUTAHYiKJxckIZtk/vXycYZ9YTGqTw3yo/YLGGSFjekFUft3TY1xZrgTVdNYm5ZHdXI27y/fzI/bL+IJBZCAdWn57M1ePvfQSsZuNFOWmE7r5EhUvCwx/bYq0Z29E7zxf45FqrQHrg3zetsxnvm/D4lnaIHgFgx4Jnilu46uqTHSLXYO5VXP+0LdzzouRYQwEH4OkZDItjpwBrwENBWzEl7IVnWdl7vrqFqTxSN/sQ/36BaAuPdy1cNlDDaEPzcKLhlI7ZTxlxvZ+95NrE3PQ7nLdhr3iiRJfKJ6F6/3NlI32Mtkh5PsBoXk/pnx5K7KQtN1vtl0kjG/BwhPca6N9/NS11XePYfzgEBwI79etpnchGTqxvswywa2Z5WwJjW6zXU8IQzA91pO0+AcRAdkKcQbfY2ousYThatJMll5oeMija4hGl1DFNpT+HjVrgVxa9v3e9t552tnGWwYIaNLwVUuYbQa0bXQ9ZbUEsbr97PDaCHDZ+Xlvz4cmUf3XOhHarbDVj1cZDaLdEv03DovIRlZkjDKyozzjA6aXyW1QaNhvJWqh0tv6iYoKzLbfnMjb3/xJOr1Zw+TzcjWD62f85hUSwK7s8vv8J1Zngx7p/hh+3k6JkcxSDJbMot4pnDtnJ+5q56oovbxSjRVRzHIBH0hdE2n63wf6JCUl8ie39qKJM+854ok81TRGp4oXIWuc0+f55OD4Wf/yQyNpn3BsOBKh04m+dy54zzWtZH9mwqQG44zrWCUqraSUrWVj0syL7Rf5J3BtsjfhKrr/KTjItXJ2chTGq/977cZc00RfC4IEhgtRgwWA5IkYTOY+HDldiaCPortqcKFWCAQCASCGxBiGIFA8MCh6Vo4GeRxE/AE0VWN8xOdBHt8fOrggaUeXlwsc1RA3BiXFfm2eoJv+eA6dF2n/WQ3uqqTvz6H7R/ZOC9jXUpGfFN0TY2TYbFTYF/5vdFNioHi2+jx7Oyd4OJP6hhpHSMpN5HcNdm4kkP4EnUSh2UsU+HJ9Gy71gcRs2Ig2+pgwDsRFU8wmEi3JGCQFQrtKbSPjUR5QJvcEpZJGLg2hLN3AlffZMy5ey72o4Y0lEV2V7oVmqpx/F/PRBJ4AFNDbk5/9yIP//HuJRyZYCUx5nfzL3VvMxEMVyUe7mtiZ1Ypzxavi9rv/Eg3J4faI9shXeMXXVcpc2TcldX0QmC0Gtnx0Y28829nI0llR46dje9dvcQjEywWQ00jjHU6Sc53kF2TeesD4qAYFTa/fy3vfP0sQaOOwQ8Go4HN719Ls2s4IoSZzTuDrVFimEujPXyv5Uzk6+bsSBeP5ddyIK+K6uRs+twuHCYLKcvcvfBucAcDXBjtYiropzo5+7aedeaL95Zt4jvNp+l2h5+JChJSeG/Zpts6tuXt9ph2BSFfiNZjHax9V+28j1UguF/whAJ8pf4o7lBYQOieGuPfm07w6dq989bCQdd1mlwzbRBDvhBBfwg0nex3bFj22vEqwVkHhJ+TTze18MzajZhsRvRY4ygAirbks+PjIa79sgnPmJea8iw2vm8NiRkJ8zL2e8FqMPF00RqeLlrDha6rXB1ojPwsZ3UmVQfL6HGPR4Qws7k02ivEMILbRpFlHsqtjLjB3C6DngkujvYQ1DRAR5FkrAYjJ4c62JVVygsdF1Fn3XxdU+O83tvA00Xz397HnpHAI3+xD9+EH9kg8Y6zI9wiJwQGSQEpLOjJsiby62WbafxhS9Q8GkA/P0XuZgd9ysyznkGSY96XTemFnB7qoMftRJYkAqEQsh8Kz8okNfo5c/Qivgkf659fddMx56/P4bl/eJzu833IBpnCTbmYbMvLeXMh0HSdrze9Ey5gJDyvPDHYjlUx8VjB3M9ckiShGML5L6PFwL7P7MDj9BLyhXBkJ855nCzJxDHyuSMyKsLPswOVakQIA2HhlN+mc6Gvi77zRtYdepZVWxMwZOYipcyIQlsmhmPEUaqu0zIxjPdng0wOuTEikdQv48rRCPqCEbfj1al5rBZOkgKBQCAQzMmKEsM0NTXR09PD+Pg4Ho+HxMREnn322aUelkAgWGF0TY0z7vPgnwoQyXhpOtd8g3Sc6qZ4W8HNT7AE1CRnk2Sy4gp40VUNTdORDTLbM0vu6nwGs4GdH9/Mtg9vQNfBYIpv9RoPbyhIvbMfXYfalJwFqdi5G17pruOtvqbIolJNcjYfrti2ZJV6d4uqadc7Wd3euH2Tfl7930fwT15PLk94OZLZh/OgFpnLZzcoFF40kJw/d9/uB4Vnitby9aZ3rifjQEbi6aK1GOTwPfB88Xq+MvE2Y4TfTyUoUXLagISELdWG0RL/0UkxKcjy4ru+3IrxbhdeZ2z7l/6rQxEbXYHgVrze2xARwkzzzmAbOzJLolyp6sb74x5/bbx/2YhhAIq3FZBdm0n/1UFMCSZyVonWYQ8CuqZz9Mun6Dw9Y5eety6bfZ/ZcVdCRmeVRNunzDh9HhJ0E08VrSanJJPB0Z64+3tD0Yspr3TXxTTsOdzXyM6sUiwGI0WJ87NAvNwY9E7w5Wszi+KH+5p4KLeSxwtuvhg0X6SYbXxm9X6GveFFrAzr7bukBdzBuHH/VPy4QCAIc2GkO3LPT6PpOicH2+dNDCNJEolGCxNBH2pADbs8XsfdMslI0gT6KgVJktBVDb87gK7qNB9u4Vv/pz08/1Jk8tZms+Njm7A4ot2iyvcUU76neF7GulBseM9qyvcUM9QyiiPLHmmPqwTif8cZbnO+KRDMRtd1Os/00nOhD5PNRMX+ElIK5s4zvNHXOOOMQrhloScUREGmeWI4Sgij6Rp+VeVwXyMBLcRDOZWkWuZfdDZ9f++3VbIzq5TJoJ9ks5WAGsKrBkk1h695tT+2CAbgIWc+zg0GLox0E9RU1qTmknGDM4xJMfA7tXu5MNpNt2uMrh+2kdoqoagz8++G11pY80zNLZ9DrUkWKh+KbZl9N3hDAc6NdOH0eylzZFCdnLUscwJdU2MRIcxszo103lQME4+btbOeTzLK0qh4qIR6uel6RCeisNF1gmYd/2SAK28M4nTmsv8z0e5odqM5rqA+0Wimo3HG1bD0hIH2bSGceRp6SGNtdgHvEcJGgUAgEAhuyrIXw7z55pt86Utf4o033sDpdEb9rKysLEYM4/f7+dM//VNUVUVRFD73uc9hMCz7lykQCBYRRZJRgyo3ln7JGrQc7ViWYhiDrPCJyp184/ARepVJTB6JvGYjVl8A9t/9eRVjWAAQ1FQuj/Yy5ndT5sig1JEed/+OyVH+vfEEPjWcXLQoBj5SuWPO/ReLrqkx3uxriorVOwc4OdTOruyyJRrVneEO+vlJx0XqxvtRJJnN6YU8VbQGo3xzoVL7ia6IEAZgqELDla0hKxK6Gv4bH6hWSesz8PDzomq5PCmDP1v3CBdHeyJtKWZbGucmJPN/bXuS7/3r64z3uUjqkzEEJTRZZ9XjlSSk2chdm0Xf5cGo81bsK46y210uWBLN4fzLDSuuFod5WSa9BMuT7qn4rlJd7vEoMcxcLmbmZdiC0JJopmRH4VIPQ7CIdJ3vpfN0Lzajl3TbON6ghd5LOu0nuu54gbNraowftJ1DJ2xRHkDnhaErFGSmU5mUiVGWI6LLaVal5ET+H9TUuFX6AU1lPOAhx3D/ildf7amPWRR/q6+JbZnFkcWnxeBORDDT5G/IofVYZ0y8YGNOnL0FAsE0nhvueQgXAFwa7aHeOUCmNZFH8mvitmC8E/bmVPCLriuEAjOtEFO7ZMweifRrOq5KDcmkXHeH1TH4JRwNKn53EEmRsSSa6bnYz/F/PcPDf7IyHRQTs+wxrVDzEpLJtSXR53FFxbdkFsUcr+oa18b7GfJOUmhPpdyRIeYMgijOfPcSja+3Rrabj7Rz4I92kbMqvttes2sIWZLRZrX91nSNPHMSvk43uqohKTK6ruMOBdB1HU2XOTXUQd1YP3+05gCJJssdjVHTdRqdgwx6J8hPSKY8aW4nQJNiIE0xoIY02l5tp/t8H0abkaoDZWSUpTLUOBJzTHZZOu3+vsg9dbiviXMj3Xy6dm+Uo59JMbAts4RaMvhpU0fMeYLeECFfCMW+OE4v434PX6w7EilyeHughU3pBbyvbPOiXP9O0Oew6prLwetWjHe7uPzTa4x1uUjOd7DuuVpSC5PvfoBzsP03NzJ8EQ6PtxD0h8KpmOuDTuye2a/7fB/uUU9Ua7492eW0T45GnS/XlkSZI4P2dBtT11t1GwMSlUeNhIw6hz67h/wK0e5XIBAIBIJbsWxVIj09PXzsYx/jjTfeAGYeHCRJmvOBCMBsNtPV1cWLL76IJEkcOHCA5557blHGLBAIVgYF9hTSdBsDRCeDMlpkdO0uZ1aLwPiJIXJ/HCCXmSq1080XyanJjEl43QlTQR//cu0oI9crEF7rbWBbZnFcy+Qft1+ICGEAfGqIH7df4LPrDt319eeDRudg3HiTa3DFiGG+23KGlolhADRd5cRQO0jwXPH6mx43WwgD4Mq+7niiyCgWBTWoIkkSuR8uJXeVmCQDJJms7MupmPPnRlnhAx8+wNWXGrmU3EVThRdPMgwqV3l8TGfPb2/l7Pcu03mmB9mgULGvmHW3sDdeKhLSbBRtyYtyQgCoeeT+6CMuWBwyLIkMemMrIzMt0VbT27NKOD/SjTZLfWVRjGxIy1/wMS40ox3jXPjRVUbbxnHkJrLu2VpyV4vP1JXEYP0IlWkd1Ga0gRT+Gx33JtF7LeeOxTDnR7pjXF306/Gni9bw62Wb+WHbeXxqeEG2KimTg3nVkX2NskKWNfa+sihG0hZRELIUdMUR1+lAz5RzUcUwd0PhpjyqD5XT+EYruqYjKxKrnqy663ZbAsGDQm1KDq/1NkS2NV3HEwqg6hpBXaN9cpR/azjO769+iBzb3YsB9+aUY1UM/OKdc3j9AVK6ZXKvhQsL7KMyj6tlXLOO0zYwgGNEJv+iAu7wXEpXtYhrYt+VQbxOH9bkuRfgJ4emuPzT+ogLy5qnq8msvHWByHiXk5G2cZJyE29r//niI5Xb+UnHRRqdg5hkhS2ZxTySVxO1T0AN8dWGY1Gf02tSc/lg+VZkIYh54JgYmOTc968w2DBMQnoCa56qIqM8jabDbVH7aSGNiy/UzSmGAbAajHhDAbTp3L4uIf1olLbeMaTHgwQyFXTzTO7fdF1gPxXyc3q4g4dnPUPdipCm8u+NJyK5FQgLkj9UsTWu+65n3It/0s/lFxvoOjMzZ+67PMiWD60nMcvO5OCMW0fxtnychgDH+1ujimFcAS9v9DbyntLYPJo9IyHmPABpJSmYF0kIA/BmX1OM2+e5kW52ZpUtuzbjRYmppJhtjN8gHt+QfufFi+4xD6/+3ZFIy6upYTeDDcM8+dcHF6Td3WNr1zHU5ONyTxdqSAMNck7rWJxgsFwvdtMh4A5EiWFWp+byoYqtvN3XxKTfQ2VqLo/k1yJJ4efNwYaRqJx1fkUm+ZViPioQCAQCwe2wLMUw9fX17N+/n5GRkRjhy/Tk9GaCmE9+8pO8+OKLAHz/+98XYhiBQBDDx1ft5F9+8Srj2SpKUCKjVSavTqHko8vPFWaa7vN9MTFd0+m+0EftY3fWt3k2b/U1R4Qw05wa6mBrRnHUhHgi4Iu7GDrsm8Lp95A8qwJmsZmrUijReGcVREvFmN8dlayZ5uxwF+8qWnvTlkl567K58uJMctnoDSdkZIOMYlJQrrfAys5cXsmN5Y7RaqTgqWJ+eLmVkC6jACM+N99tPsPvrtrHzk9sZucnwhVUU8Nu+q4MkJyftCDJlHtl1ye3kJhpp+tsLwazgYp9JVQemB+bZcGDwYG8Shpdg1EW59XJWTFtXArtqXy4chuv9dQz7Jui0J7KE4Wr7riac7nhdfp4/f85GkmgjrSM8ebnjvPYf3uItGLx2bpSSHb4yclsjYqlWF3IpiZg17xcQ78ukVmTmkdVUhZdU2PYjRayZzkoTfNU4Rq+0XQi0h5AAh4vqI0sAN2vZFrsuALe2PhdOLXcCWpIu6t2WDey5YPrqH2sAlffBMkFSYtmvS8QrGTyEpJ5LL+W13rr8asqfjWILElRDpiqrnNqqINni9fd07W2ZBaTaFc589OLUXHFpLBrYw37zAo/+MKLqMGw+MWLzshaGK+QUAxB0jsUshrlyOd5PAKeIL/62yORVqRTQ24G6od5/L89RGpR8pzHnfzGeZrfao9s30urvjsl2WzjY1U7CWoqsiShxJlfnhhqDwthdB01GC6wuDzay+b0QWpSshd8jILlQ9Af4tX//Xbkb9zZ7eLol06z7t21cQvIXL0Tc55rY3ohRwdasBvNqJqOpmoYBkIk9UpISFQfNtK7RsW5TiEkyZgUQ9Rnw7g/9pnhZpwd7orJrdSN91M33s+a1LxILBRQeeffztJ5pgctpBFwBzDaTFH3Y9PhVp7864fpPNXN5JAbs91Ew+utnP5uJ75tIRSjgtFmjLgndU2NxR2TJEns/Pgm3vz8O5H5hMVhZttvLm5rm253fLfPbvc4BfYUei8P0HqsAzWoUbK9YElds2VJ5qOV2/nP1nP0eVzIksSGtAIeya+59cE30Hq0I/K+TxP0hmg50s6G96yeryFHMMoKH6/eSUtSKSd/eZWpN4eQJ8FgVjBawo6pCem2uG3MV7VeofbyEQj5wZ6KvMfMaEoWFBo5+Ge7aXytFe+En9w1WdQ+Gi7wanEN0et2kW1zUJGUKcSLAoFAIBDEYdll2lwuF4cOHWJ4eDjyMPnYY4/xkY98hG3btpGZmUlCQsJNbToPHTpEQkICbrebw4cPL9bQBQLBCiI7PYVPbdjLyW+fxzPixWAyUP14GWXLuA+4yRq/zYTJdm+VJB1To/Hjk6NRYhirwYhZMeBXQ1H7GWUFm2Hxqlmm0VSNkdYxZIPMuoI8Xu9piKpyMUgyO7OW34J/0Bei/ldN9F8bxpZipeaRcvSc+L9bVdfCNrA3mctmlKex5plqrv6iEV3TyW6ScVVJGEwzCaQkk5V194Ezw2JzbqSLkB7d5kJD5+xwZ+TeOPPdS5EKcUmWqHq4jC0fvLck/nyjGBU2vGf1giR6BA8G+Qkp/P7q/ZwYbMcV8FKRlMm2jOK4+9am5FCbcn+1DGk70RWTQNVUnea32kn7iBDDrBSK8rwEG6OLKiRJIjMh/sLFzVifls87g9GV0RKwIW1m0cCkGG5qy1+VnMWfrD3I+ZFugprKurQ88hPu/7+ng/nVtNePRn2/rkvNI/se3CBuxniPizPfucRgwzCWJDOrHquk9vG7F5FD2HVtdiWvQHA/4g76aZscIdFooTgx7Z7PdyCvihHfFCeH2pEliaCm4g75sRnMkYU7d9B/z9cBqDpQysTAJM1vtaOFNGwpFrb95kYsjrDDatnuYprebEOSJAZ2yQyvDk+4JEmnKyWEudBxU6Fbx+nuiEhgGi2k0fB6Czs/Hr/dSN+VgSghDEDvpfDCc+X+xZuzGmWFQc8EL3ZdoXVimBSzjYdyq9iSUUTn5ChaSMPvDsC04EGWuNrRJcQwDxjd5/pi/sYh7LInG2S0UPQcOa1k7ueXxwpq8alBzo90gayTOmEk80hYCANg9EsUnzVgyEjlQrEz5vjyO2zJ3T4Z29YIoG1iJEoMc/ln9XSe7gHChbe6phNwB7AmzYj43SMejGYDyVsz6Bj2c+WFqyR6NKySBDqogbALr9EWzufcrAVjZmU6z/+fJ+i91I+syOSuzY7K2SwGWdZEet3OuPGmN9s49c0LkVjPhX6cvROsX0IH3GxbEn+45gBOvwezYsB6l3lHryv+d4vXFfs3Pp+U52RT/tFsJp6Y4s3Pv8NEf7i40OIws/u3tsS02dabzqCff3VmfO4x/vPCqzQmJgOQpCocWLuenVs3Y7Qa0XSNbzWd5Op4/8w1HRl8rGoHhlu0WxcIBAKB4EFj2Ylh/uqv/oq+vj4kScJms/Gd73yHd73rXXd0DrPZzPbt23njjTcYHR2lubmZioq52yEIBIIHk/x1OTy/+nHcIx4sDjPGOcQmy4XKA2V0ne9jdpGaxWGmcHPe3AfdBhkWe1zL+nRL9ETeKCvszirjjb7GqPiurNJFr2Ie73Ly1j+dYGokbJmanO/gw5/ezAlvJx2TY6Rb7DycV0VuQvKijutW6LrOG/9wlOGWmYW3rrO9PPIXe8m2OhjwTkzviKbq1CRmo8i3rhRc//wqKvaVMNoxjiM7EZcjxOG+RkZ8UxQlpnIorwbzfV5pPt8EvUGGO8dQtRCyUYkS4U67Y/Rc7KfhtZZIXNd0Gl5rIWdVJvnr7y8xgECQZXXcVrV27+UBOk52IykSZbuLyapavBYAC0XAHYgf98SPC5YnprQ0lEQTQX8ILRRucWOwGFCSUm998A0UJ6bxfMl6ftV9DXcogN1g5vGCWgrsKYz7PQTUEFlx3GBuJN1iv6sK1xvxhYIEtBAO0/J3KSlJTOczq/dzcrCdyaCf6uQsNmUULsi11KDKG/9wLLKg53P5Off9K5jspjtujSUQPEicGe7khfaLEdFaoT2Fj1ftwmq4+/lyv8fF2ZGu6wt0YTGMpusE1BCW6+edLzGtJEts/Y31rHu2Fq/LhyPbjqzMzKk2f3AdRpuR5pMdjK0OICkS6DroIBtlBstVgpoacafQdZ2uqXGCmkpJYhq+ORZQfRPhBdepoJ8e9zjpFntkTj1QH+sCCjBwbWhRxDA97nEujfYiAaeG2vFeL3AZ8bn5Ydt5bAYjaZaEsPh3tvOHpjNyfBAW18BCsMTcKAKfRgtprHm6mksvXIvEjFZD3KKLaWd3o6zwa6UbebpoDSFNo/WlVi65r9GzOsRIiQYSpHXKVJtNoOtMBP0okoRZMbIuLY81aXeW75qr5WKaJTredaYn8n9ZkUEKfw6oIRXFcN1dtyaTS6M9/EfrWYKBEIHKAFI5VL5txDEgM5GtEQqqGDFilBUO5FbddGxGi2FJ3VYeyqnk2vhAVOvzyqRMSu3p/Phnp2P2r/9VM7WPVWKyLXyuNKSpXBnrY9g3RZE9lcqkzEgO5l5dqPPWZse09wLIXbM4Ij9Hlp1n/vYQQ02jqEGVrKp0FGOsWGWo5QJjtkQKfB6smsrLKZk02ZMi308uOcRLrnP0/pdOHvnzfXSaJqKEMAAtE8OcG+liW2bJorw2gUAgEAhWCstqZSwQCPC1r30tsv3d736XZ5555q7OtW7dOt544w0g3HZJiGEEAkE8ZEUmMWthbdnni5xVmez5nW1cebGBqWE3WVXpbPy11fc8Md2XU8HVsT78s9pfFNpTqEqO7T37SH4NSWYr50e60XWdjekFbJ+nSZYaVGk73sVQyyiJmQlU7C/B6ojfWuPoV05HhDAAzp4Jmr7XwPv+dM9tXWsy4COka6QscmungfrhKCEMhBNKV19q5EOf2sZ3Wk7R4xon4Ani6JfQj/fz5qp3qHq4lCs/b2C820VqUTIb3r2ajIroCs3ZVcrJwEerdizSq7r/GG4Z5fDnjjNp8RM4FAQphNluRFZkNHRWXU/U914aiHt8z8V+IYYRPJDUvdzI+R9cjWy3Hutk58c3U7a7aAlHde/kb8jh6i8aY+Prc5dgNIK7prAGKS0H0/isz25ZQVq7765Otz2zhC3pRbiCXpKMVgKaytca3qHRNQhApiWRD1ZsIWeBHE8AVE3jhY6LnBvpQtV18mxJvLds04Jecz7IsSXxXMn6Bb9Oz6X+uJXtTUfbSdqcRpLJGlfQ3et2MuZ3U2RPw7HC27wJBHfKZMDHT9ovRFq4AXRNjfN6bz1PF6296/N2zmofYpDl646jKqquIQFbMopZP89Olma7CbM91klAMchs/LXVlL2rjHMXfgkQaf0iyRJBNLyhIEaTgtPv4d8bT0SKFhxGC09XVce9Xu6aLN7ub+GV7quR929DWj7vLduELSW+WHGu+HxycqidF9ovohMW9XtDQWwGY5RzwMnBdp5Ir+V171VCs94yy6SE9YIf95iHhFThiPWgkL8um7Pfk2JaIhVsyKH28UqyazLovtCHyWaidFdh1N+Gf8rPme9eovNML4pRpnxvMRt+bU1Y9KZA+Z5iXuqro790Jv/Ut1ql39qDVTJhMxjRdB2jLPNM0dq4Lb1uxo6sEk4OteMOzYjWk01WNqVHC2+nxQiaotO7WmWkQIIQZLZr5DbK2JKtrHvvKr7YeRzturAHQJehc2OI1b80MlSm4SmRWVtRws6s0tsSQi8lWTYHf7j6IU4MteH0eylLymBLehEhfyju81LIr+J1ehdcDOMNBflK/VH6PK5IbHVKLh+q2HrTzgC3S966bCoeKqH5zRl3rrLdRRRtubfCwjtBkqQ5i0RUTeM/W89yKcEGlhIMus6Tgz1cTUqLbting98GI4qX89+/guvZ+MKvtokRIYYRCAQCgeAGlpUY5ujRo0xNTSFJEnv27LlrIQxAQcGM0rqnp+cmewoEAsHKoXhrPsVb5zdJmG1L4vdW7+f4QBvjfjeljgx2ZpXG7TMrSRLbM0vmTQAzjaZqvPGPxxlsmKmYazrcxuP//aGYpNvE4BSu3smYc/TXDRHyhzCY5/5q84WCfL/tHNfG+9GB/IRkPlC+ZaZiz+Pitd4GetxOsq0ODuZVR7WKulemhtzx4yMeMqx2fq98L9/5779A9YDZfb3v9LleOk51YzCFX9dgwwiv/8NRnvpfB0nMXBlCrpXGyW9eIOAOYnfLFJ0z0L1OxesLopvBKBv4YfsFJoI+rHGS60BU0t01NsVPT52lU3OSZLXx+Jq1VGYIm3HB/UfQH+LKiw3RQR0u/qSO0p2FMTbQK4mMsjTWv3sVl39WjxbSkGSJ8r3FlOxYuspOwZ0xGfBxfrQb7+ZD1PS1kt/XhpSYgrTuIaScu6/IV2Q5UoH8YvuFiBAGYMg3ybeaTvFn6w7dcyLfPephYmCKlMIkLInmSPy13npOD3dGtns9Lr7ReII/W//IHS8e3Y+oAS0mNlKscmnDMG9dfh2zrHAgr4qHrldyBzWV7zSfot4Z/j0qksTjBavZm1O+qOMWCJaSlonhKCHMNA3OQTZNDVL3UiNTIx6yqtJZ91ztbQskMm5wHTUrRoyygUJ7StR8DMIOAaeGOmhyDeEwWdiVVTovrdT8agiTPOP4mGSykmVNZNA7iSRLaLqOqqlkW5MiQrgXOi7NuHcCE0Efr/iaePTpKq683AiqjoRE/vocErel8+/X3oy65oXRHooT09i4o5CrLzVGLTgbrQaqDpTd8+u6GUFN5eWuusiCarhVoI5PDWGfJYbxqyEyHA7WvmWmpySIz6GTMCqR3aBgUgz33JpZsLKwZySw/SMbOPPdS4T8YdFK0dZ8qg6Fvw8zK9PJrIy/sH/0S6fprxsCwoU/9b9qQdeItBI2pZiZXG9A9mpoqoasSGhmiaCuYdH1iEhLJ9y2+GBefPHZXDhMVj6zaj9HBpoZ8EyQn5DMvpyKmBY75fuKOfu9y7RtCzFWGH5ekGSZoVyFwr05PLt5G+Oal8nusOOTbJCRFBld1fA5dDQFspsV1q9dxZqSOxvjUpJqSeDJwjVRMcUqk5zvwNkzERW3OMzYFyHndGKwLUoIA3B1vI9G1yDVyfeeO5Ekie2/uZGaQ+WMdblIyXeQnL98hOPHB9u4NNYLBhOoIUKSxM+z8rFMO/jc8JUsqTDUNEKuOX471rnckQQCgUAgeJBZVmKYtrYZy7rHH3/8ns6VlDTzUDM5GbtoKhAsNKqmoaFHrHUFy4dm1xBtEyOkmG2sT8u/5xY/mq7HFY6sJLKsDp5fhArduei9PBAlhAHwOn3Uv9rC5l+PrkA0WgxI8kyVkibp9NeqjJXojDQeYWtmMTuzSuMuPP288zJ1s2xEe9xOvtV0ij9e+zATAS9fvnYUz/UJ57jfQ+vEMH+45kBMy6i7JXOOSpDpCpH+a0MYhjUMzIxdDaioAS0ihoFwhU7rsc4l7d98v+Kb8OPsnknEZDUrWJxQfzCEzWpCVhQ8oQAvdFziN7dswfCqEkkQAhjMCuV7iwHwOL38w6sv40wOgQLDAQ9fvPgWv7NxH9VpwjlGcH/hGfMS9Ibix33BFb+Isubpasr3FTPe6cSRnYg9QyQZVwq9bidfqT8WsYQ/bFR4fPezPJRbOa/XuTzWGxMb9bvpcTvvSFjbcrSD+l8243H6yFmdiSxJdJzuQdd0ZKPM+udXserx8NjPj3THHD8e8NI+MUp5Usbdv5g4jHU6qXulCfeIm6zqDGofr8ScsLzv67x12RjMM9/TniSNtu0hjFYjMuDXVF7pvka2NYmalGyODbRGhDAAqq7zUtcVapKzybAKAbLgwSDRaI4bN/okDn/leGQONjXkZqhxhKf/70Nx2z3cSJkjgzJHOq0TI5GYWVF4d8mGmLnWt5pP0TDrXjw/0sVv1eyh0H7nbe0A6sb7eKnrKiM+N6lmG08UrGbt9dYr7ynZyNebTjDud+NXVSQJhn2TvNpTz8G8KhqcsU6QIz4371SP0pkroYRkNjry2bd2E2/1Nce9/rXxAXZklfLYf93PlRcbGGkfJyknkTVPVy+4S+2obyqqJYpBVkANoelapI0NwOrUXAwmhXXbKzC83BR1jspHSzBallX6WLAIlO8toXBzHiPt49jTbDiyE295zNSwOyKEmU3L0Q4Kny2hZXIYq2JElfWoAhJvKAC6jk5YXDaNX42dW9wOqZYEnitef9N9qg+V0x4c44SlFXQwSDJmqxFJkWlQxpFNMg7NiklWCFx3UTbbTQQ8QZRJDavJSPXj5ax+8uatkVYKWz64jjc//07kmUlWJLZ8cB2KYeHF1bOdw6Lik2PzIoaZJinXQVLuwrn3BP0hpobc2DMSMOAHgwnpNtoL1k/nKA1G0C0Q9KMBWR4P7YnRop2k8RAJTpmEQhubMwp5e6AFV8Ab+XmCwcT2LOEKIxAIBALBjSyr2czo6Gjk/1lZse057oRgcGaypyhCjCBYPIKays86LnN+pAtV16hJzub5kg3CYnuZ8IPWc5wd6Ypsv9nXxKdr95J4F7+fYe8kP+24RMvEMIlGC/tyKtgjKkfvivFuV/x4V2zcmmShYFMuXWfCi04dW1VGSlQMFgP93gl+1nkZTyjAofyamGMvjsY6hQ14J+j3uKgfH4gIYaYJaCqnhtpjKmfulumk52z3BEe2nTVPhyuJjJbYibKuA3G0VgF38Ib9dFonhnEFfKSMGZi85sScaKZkR8GyX6xaThitBoxWQ2RRX5N1hsvD9u2SHJ0IalJHOPhne7j0wjXGu1wkFzhwPJnD2VAv2eNJdL3dFRbCzEJTNV6+dpnqPUIMI1j5TI24cY96SC1Mxp5uw5xowj8ZiNrHkWNf8UKYaawOC9ZF6i0vmD9e7roatRAI8FpPPVsyirDPseh7NxgkmQBqTNwo3/4iQsepbk587Vxku/VYJyFfCLPdhCRJaEGN89+/QnZNBmnFcwts5lujPdbp5Jd/8yZqMFw5PdwyRs+lAZ78qwPIyvJ1oDEnmNj7u9s58e/n8Dp9jBfpKGYFgzk6P3BxtJualOy4i9460OAcIMMqnvEFDwaljgyyrY4oNxSArAaZ0A0tUyaH3HSf76N42+05pX2saicnBttpmRgiyWRlV1YZ2Te0FemcHIsSwgAENY3DfU18pHL7Hb+eAY+LbzefRrvudjPm9/C9ljOkWRLIS0gmx+ZgbWouh/uaMCsKJtmAJEm83ttAcWIaFsWId9Z3iK7reEIB2idHkWUJ1QRnfD0k99rn/E6xXV8MtWcksONjm+74NdwLyWYbZlnBr6nouo4sgdVgJKiGv68kYH1aAbuywg41G96zGluKldbjnaBDyc5Cag6Jz78Hgb4rA1x84RquvknSS1JY/55VZJSlkbvq9vPzoUDscxBAV7mff7zyeuQBJaRrKMgRMZZRVlB1HfkGV7vVKQvXkvSt/mYOp/WgXb+9g+gYJVAAvxpE1XXMioF9ORW81hvO30iyhCnByLqCPPw1Gi3GAJneCXITlo/LyN2SXZPJu/7+UTpO9aCFNIo25y1aS/m5is/mqyhtMWh4vYWLP67DojnZlN9IZpIbQ4IVqWY70vankeIUyuq6Tt+VQTwDbkLWEAajAkZz2CFG1zGeN7ExrY+63GQCikLugJeEN5NBChdqWA0mfm/VPo70NdPrCbtb78wupX58gK6pMTKsiWzLLMZmuD/m4gKBQCAQ3AvLSgzjcMxMgicmJm6y560ZGppRoqenx6/EFwgWgl90XuH0cEdk+5pzAG/LaX6ndu/SDUoAhBNrs4UwEK7Yfau/maeL7kzsENJUvtpwPKLAnwj6eLHrChaDkS0ZRfM25geF1MLkuPG04vjxnZ/YjDXJQvOFLsZKAxjMhqhqtaMDrTycVxWTTFEkiVCs6zeyJDEVCtvf6rpOSNcAHYOkMBHw381LmpP1715F0ZY8+uuGsKVaKdiYG6mmzKpOJzHLzuTg1MyYjTJynPYi+RtnxBSeUIB/azhOj9tJ0Bsk5A1ReM1AdrPC1V808Mh/2YdjkRIZKx3FqFDzaAWXf1rPSJFK5yaVQIKOZgxXppkVQyRpJ0kSGWVpHPzTPai6xreaTlLvvArXDemMJo14WibnrModgWAloqkaJ/79HG3vdIEeFpFt+eB6Nr53DSe/fj5SNS4bZDa9b+0tzra06KEg9DSGlYcF1bdVvSdYWXS7x2NiIV2jz+OiMim+vfjdsCWzmCP90a4AhfaUO2rt0fB6a9S2FlTRVQ0tpEU5L3Sf7yOtOIVN6YW80dcYdUyq2UZJYtpdvIK5ufbLpogQZhpnt4uei/0Ubsqb12vNN3lrs3n+Hx/H2TvBqUA3Y6MtMftMt5SaayE7YR5FUwLBckeWJD5Vs4tf9dTT5Bwk0WRhb3YFfa83Mxxnf9/E7c+VjLLC3pzym7YeG/LFd3Ye9t7a8VnTdY70N3NmuBNN11iXlk9IUyNCmMh+6Jwd7iTRaOZL147S63YS0lRChB8HLNefBerG+tiRVcrhWZ+z066wNzrDnh7u5I/XPMyrPfW4QzPCYBmJHVl3347vXrEoRlal5PL2QAuariFJEhbFyEeqtpNmSSDZZCPFPNPqSpIlqg+VUy0EMA8UY51O3vz8O2hq+F4ZqB/mjX84xjN/+wi2FOttnyc5z0FSXmJUW2ufXad/o45p1j0jSRJoEPAH0DUdm2pkdWoezdooqq5jlGUO5dVQlHh3blBz4eqfpL9uEN2h8Kp+DT2kgxYWvuqSji8UJMFkpsyREXH5PpRfQ7bNwcXRHmQknAEvl8f7Iuc8N9LJJ6t3U+pY+WsPtmQrtY9WLPp1d2eXcX6kK+qzM9vqiDh4LXeGm0c5851LSGjsLL+E1egj4AVJkVCuvI1utqFsegQIu8g3ugbxhYIEf3gaU0cLRY5E2janEFLU6wJ4YEzFcM2IZyqPatlDCJmErEIy1qdRdaCMnFXhOUySycozxWsj5/5y/dEop52TQ+18ZtU+7EZRoCsQCASCB5tlJYbJzp6ptLxw4cI9nevUqVOR/xcViYVpweKg6XqM2AKgfXKUEd/UilK134/MZb3ZNUf8ZjS6BqOsKKc5NdT+QIhh/O4A7Se68U34yFuTTSBXYdzvocieelcuO3lrs8ldk0XflZkqwIR0G9WPxE/CGc0Gtv7GeorfXcaly6/H/NynBglpGqYbqpU3ZRTxzmBbVKwgIYUsq4OqpCyO9DfjCQWu91IHidC8Vo1Pk1KYTEocAZCsyBz8092c+d4l+q4MYnGYqXmkHK/Ld73Xto6sSNQ+XhlVofVGbwM9bie6phHyh0CCro0hUnplcPq49MI19vz21nl/Hfcr656tJZgs853QRXQkTEYFn6QS0ELIkoRJCTey2pg+UwV7ZbQ3qrUCgNumogVAuaFArtg2v0k9gWCxaTrcRtvxmeedoDfEiX8/x7N//yhP/s+H6Tzdg6RIlO4oXLSKwrtBH+5Be+VfYXqBzZKA/NgnkLLu/+/xB4kMS2KMIEYCMub5ufyx/Fo0XeP0UAdBTaMmJZvnitfd0TkCnsANkfDC0Q3ruJis4YXag3nVeEIBzgx3EtJUkt0mcl7084OvvEjBpjw2//raqDYEd8vUiOeO4ssNWZFJLUxmi8/A22OtqLPeUAnYlFEIwM6sUq6O9TH77U42WVmTunCV6QLBcsRutPDukg3RwTUTDDePxuybs/reXJ1vpGiOVkhzxWfzy+5rvNU/0+Lnzb4mkk3xF/JVXedwXxMj3inUoIouARIE1BBGRUGRZMyKgUfyazDJCqeG2glqGgX2FK7Nars7TUhTsRqM/HbNHl7urqNzcpQ0i52DedUUz7NA8U4Y9bm5NNaDRTEQ1FQkpPCC/qVR+t9sQg2qFG7JZ+27ajCYhKv2g0rzkfaIEGaaoDdE2ztdd9wGaO/vbOPIF08x0R9+vpbWJWC0RufOJBWkXj+l70iEgioJfSE0Qw+7d+ZQ+Zu15DmS593N4upLjVz44VUAnNkaUwdCoOsoEqjm8LNWSNdIViw8X7KeyaEpWo91EvAEKdiYy4dqt9HvcfG5K4ejzqvqOq/3NvApx+55He+DRIrZxmdW7+fYQCvD3imKElPZlVUaESQtdzpOh9uWptucWI2+SHxCk/hVdiFXJwYwnv0Fa1JzaXUNMxbwoHvdGCoCPGuY5NB4F5ltWbyUVo5m0Mk3ODD/0oVBk8BmQseEAlQdKGPtu2IdsKe5Mt4Xk/ce93s4PtjGo/m1C/LaBQKBQCBYKSwrMczOnTsj1dYvvvgik5OTJCbeuifpjQwPD/Paa68BYDab2blz57yOUyCYGx1N1+L+RJ0jLlg85lr0uJvFkIAa3/51rvhKQ1M11KAWtzf4xMAkv/q7I/hcfjRF50euK7hLZIxWI4ok8WThGnZnl93R9SRZYv8f7KTrTA9DzaMkZiZQtqf4lu190i12Us02xvzRizEliWmYlNixP1m4mpCmcn6kG1XXqEzK5D2lGwGoSs7CqhhxB2eqGw2yzMWRbp4oXBWpGl5o7BkJPPQHsd9b1YfKcfVNkpzvwJYcndRtcg2h6zr+UIigKbywI4dgIksjo11hpCU2cS24Oe4qBVPXjBBK0mT8apCQrpFhsvBY/ioKZyXl2yej32Nd1zGYFGzjEpOWGWv1RM3E8+s3L/wLEAgWkK5zfTExXdPpudRP9cFyUgpWhlW49vb3Z4QwAD432pHvo7z3zwAY8U3xWk8D3e4xMiyJPJxXFXXfC+6dIe8kzoCXwoSUSCX+fPNIfg1fbzoR5QywNbM4qhJ+PlBkmaeL1vJk4Rp0XUe5g/ZI0+Svz8XVO+NAYDArqEEVxTBzLpPNSMnOwsg1nytZz5OFqzn38zpafhZ2PQkCbcc6GGoYJn9DDqnFKRRvzY9yl7kTsqrS4y6CZ1dn3NX5lop0i53frNzOi51XGPZNkWSy8Eh+LWWO8Osoc2Tw0aodHO5rYsznptSRzmMFtStmMUYgWEhqHq1gsGGYgWthfxhJltjwnlUk5dx5zu5mZFoT2ZNdztGBGRcnh9HCwfzqmx6nahonhtrQdJ2gFkLTdQyywqjXTdiEQkeSpMicbl1aHi92XsE/5UfSdLAAetghIhgMYTZb2JJRjCxJHMir4kBeWBCg6hr/++KvcAV8Uddfl5YPQJbNwUerdszfG3KPXBztRr3+Xhiuf5YFfUGOtTRR1nvdAeelRqaG3ez99LalHKpgCQl4gvHj7htFurcmOT+JZ/72EM6eCRSjzKDVS1PD8ah9gr4QiZNg6ZhpKawFVUbODVNUMYbtqflz7lM1jbqeHo6/dYUEQELCMimhXxf/yBJIPtBlsLoknnAWoTl8vPgPx1Cvt31qfL2Vte+qQdobf44zfIOj1eTgFJ1nekCSKN6ajz0jYd5ez/1KqjmBZ4qWt6PoXMw8X2tRCvYfFJbQnugAXcfv9nLE14wsS1iQQA0RkiVeLCqhwuVi3eQgiZ1GhtL2kFKYRP1kbMcEZ+/Nuyj0e+K3n+/33Fv3BYFAIBAI7geWlRgmOzubTZs2cfbsWcbHx/nsZz/Ll7/8/7P33/FxnOe5N/6dsh27wKL3Xtk7KVZRorpkucp2XKLE/cRpvyROcc75vTnHJyflOG+KU+3YlkvsuERykSxZlWIRey/ovXdsL1PePxZYYLkLEiBBEiTn+/nYIh7MzM4udmfnuZ/rvq5/WfRx/vRP/5RwOIwgCDzyyCNYLIatscGtQRREVmcWcWasN2G8wO4iz+aaZy+DW0VdRh5laZkJSnmrJLOncPE2oPUZeZhFiYiWKH65U2w8r8alXzRz8RfNhDxhsircbPnYOrIrZxf/zj5/idBUTDAyWKcyVaBBWIst2IgiP+s6R31G3qKdkCRZpOK+UiruK13wPqIg8EzlRp5rPhLPcneZrLynfF3K7U2ixPsrN/B0+VpUXcMqzS68RTWViKaSZrJM51XHiqVeJcxI0JeUZ3+rcWTacWSmXrhzmax0qmNEUdHFWBFXM0PArUMHuJa4SH0vIF8hfjKJEiZRoj49j2frtiVFcGVaYwUuTdfjzkQAVeVZPDlVSMfUGLk56dy/aRUdgXG+036M4aCPYkcGj5WspCTNfWuemIHBEpBKKAkgW5bV1OKq6EEvjPYl/2JiEN03Qcjq4J8vvY13WiA5GvLT6hnmN1fuve3fB3cDUU3lP1qPc3G6u94sSry7fC2bboK7Xl1GHr+xYg9HhzsJqhFWugtYn1Vy7R2vE1EQQEiON1wIq5+qZ6J7Mu6UZ0kzs+rJeoaaRvAMeMmqyGTDM6uwpSe68JklmZ43Zt2adHTC3gjByRCeIR+iJNLyVgcPfWHXdQliVjxWS9+5QSa6Z4vsDY9Uk1mWcV3P83ZSn5FPfUY+QSWKRZKTok5mfr9YomGFnhN9hP0RitcWLGtXLAOD68FkkXnoC7sZaR3DNxogtzZr3rnJYtA1nck+D5Y0czyO5amy1azKLKB5ahiXycr6rJJrCiZVXSOoRPDPcfmMaiqiIGARZUKqgo6OVTLxnvK1VLlysAdEdFVHAKQIqCZAAOuUwK/v2k6OLflzLAkiH6/ZxvfajjMa8iMAK9wFPFay8oZfi5tBQkSUroMgoIRV9CtieLtP9BGYCC4qEsfg7qFkfQEdR7rpX6kyXKOhmnTcvSK71l1f9I8gCHFxvFNPozTNTbdvjkufqpN7PvleSdN0+s4NserJq4vfFkqXd5xvtRxh0u8n8mAU25RA7X4TVr9AVjOM1U6frx5rJio7KaOtVzn9owtxIcwMF15s4oFd9yMAVxj2JYjlu0/0ceBfjqEpsXrA2Rcusefz2yheW8DNQNFU3uxv5vx4PyZRZGtuBVtyy2/KYxmkpmJ7KZdfaaF3xEEw34zVFGHcYqEjzQm6jqJJKJEoURsIuoBVEJh5I4VliT6Hg3KvF7fNQ7jASdY899fzxcjPUDhPNGvRIiJbDQwMDAwM7laWXcX6i1/8Iu95z3sA+OpXv4rFYuHLX/4ysrywU/2Lv/gLvvrVryYcz8DgVvKe8rWE1ChNk0PoQJEjg49Ub76uY+mqAgEP2F0IKVwuDBaHKAh8qn4H7wx30O4ZxW2xszO/6rriq2yymY9Ub+FHHafwRsMIxLrB9hbULv2J30I63unm5H+ej/881jHB618+yHv++jHM9lgBcqRtVkw0WThdBtBBU3Qkc6ww0DQ5RHb+rVkEqHRl88X1j9I0NYQkiNSl512zG9skSphIXAySBJE02YJPCTO3NigJAs6bEJW0lOTZnLPCrOlJtaDDZKFGxXmR1U8tTTHpXmJdVjGv9F4ipCoJ4zvyq5KEMACbc8o4ONjGQGAKZfpvIQgCwyEfPcUhPrJnDwD9/km+0XQ4HtHQ6hnhq40H+f01+3DNY+NuYLDcqH2gkt4ziREBVpeFsk13kCDUZAXZAko4cVwygdnGqdGeuBBmhuh01/l8gkuDhfP2QEtcCAMQ0VR+1HGamvRc0m/CtbAkzX1bRYeXXm6m6Y12Iv4oJesL2PDB1VidyfcWJqvMg7+3k4meKYJTIXKqs+YVn11JNDT7faWGVXR12pVy+lZtpGWMzmO9VO1YvODI4jDz+P//AfrODuIb9ZNXl3NHCmHmYltCJyLvkI9X/s9+gpMxp4gT3zvHlo+to+6BxTklGhjcCeRUZ5FTvTSxP92n+jj0bycI+yJIJpHSDYXs+PRmZItMhTObCufCF+LNkoxVMuG74rtb1XUEQSTNZEFHj8UETd/Lr9cLuKj2EzWDOm1IKmigSBqWq9RfStLc/MGahxgOerHKpqTvrbGQn9NjPaiaxpqsIgpu40LkWncRv2i9QDQSi4QRZQld08nuTHx+uqYT9oYNMcw9StmWYtShZvosQ7H7BgE89RI/jzbxeW5MxCEIAp+o28FrHZe40NODJSJS1OYkODLOlb4zoiRgTV+a2oum63yv7XisXjddHwqm63RuUqh720T5MQnHoIqvSkSKCOS2iqSNi5R8opCm19uSj6doMBzlgcI6Xu+fdfFzyOZ4BI2mahz/j7NxIQyAFtU48d2zFK3Jj7vhLyU/aD+V0JDZ03GaoBK9rqY/g+vDXZzOmqcbOPLcafY3NrCtuhXFrqProGoSiharPQp6zEFYF6Xpsl3s/52R2CchoDlpeLgae5ad3Dc7GG4ejT9GRpGLmvsrrnoeq9yFVDqzaffO7pdlcbA9r3LJn7OBgYGBgcGdxrJbXX/66ad5/PHHeemllxAEga985Sv89Kc/5fOf/zxPPvkkNTXJN3MTExO8+eab/O3f/i2HDsWsFwVB4BOf+ASbNhlRBAa3Fpts5tfrtuOJBIlqGlnW67PD1C4dRj/+Cwj5we5C3PokQq3xfr5RzJLMnoIa9hTc+MSwwZ3Pn6Q/Sn9gCpfZelMWb241rQc6k8Yi/ig9p/qp2hlbPHHlp+EfjcUSyXNqjYI0O7F33GLxiFmSWZ15Y4uwoiCwp7CGF7svJIxvySm/5c9nMbR5Rtg/0DrboTT9ZzCrIlqmyOP/fS/u0ozbd4J3KA6ThU/U7eBn3efo9k3gNtvYV1RPXUZeyu3tsplna7fyl2dfRRZFRETMkowgCJwZ6+W9FesxiRJHhzvjQpgZQqrCydEe9hbe2WI6g+WFZ8jHhZ81Mt49SWZpBqueqse1RE4FRWvy2fGZzVz4WSO+ET959Tls+OBqTLabE3NzMxBkE8LK7ehn30wcb9iKYLbijYZS7ueNpB73RIIMBDzk2pxLHr9zN3JpYjBpTNN1Lk8Osi336oXeO41LLzdz8vuzQuO2g114Br08+qd7593HXZK+6Lix0g2FdB6NLYZo00IYQRQS7s/GOyeuSwwDIEoiJRsKr2vfu51TPzgfF8IAoMPJ752jfEsJlrSrR34aGCxHIoEI7Yd7CIwHyKvPoXB13pIv4l54sYl3/v0EujYdVSKLdJ3ow5HjYNOHri8qw222Mx72J7ihCICma8iijDA9Ubo8OciugmrWrqng9P+8wNl9YUQtJoQRFdBtIj/vPs/nVuye97EEQSAvhVNc0+QQzzUfQZmOyX6jv4n3V25g801wPlsIw28NUHpUp3sDRK0gBDRKLohk9CeK+x1ZNtKLDOe7exVBEOgviWANWdFUDVESEUSBbt8E/f4pCh2J9yS+aIhu3wSZFseCHBNDAwEC/28XxdNxTCHNjyjHHmPmGiBbZCSTRP2+6iV5TkNBTzxOW5RFRFlEUzSmCjV0QcdkMbG1qJTeI4NEg1HUqIKcaaf7ZB/phS7aQmNMFmqYQpDTLmGNiKQXOnkkI5f6jHwapwZJky2szy7BLse+6wPjQQLjwaRz8Q77CXnD2FzWpN/dCFORIGevcCYHeHuwxRDD3GKceWlYnRZCZPNWdw6mHj/kiUTThbhhpKiALoFgMYEiI6gKdR4PuZqCbrVT/P6PkDY9X9/3hV10He1hrHOC9EIXFdtLMV3DhVUSRT5Zv53TY710+8bJtTrZlFO2pAJwAwMDAwODO5VlJ4YB+M///E/uv/9+Tp48iSAIdHV18YUvfIEvfOELmEyzX+AdHR04HA5CodnC04wd6q5du/inf/qnW37uBgYz3EiHvz7Qjn7gR7MDAQ/aW99HzCxAyL6Duq7vASRRvKviTTTlSsPXmfHZzpY1Tzcw1DSKFtXIa5aYLNKQzBKiFCuouS12VrpvjgXslQSVKGZJime/3yh7CmpwmiwcH+4iqqusyypedBfFaMjHS90X6fCOkmlx8GBRHStu4utxeKgdURAQENBn2r8FiJh0qjKyDSHMDVDmzOTzK+9H1bVrvsd0XSfUG8CsS4iSKS5KgtgC78z9SUBJnbsenGfcwOB6CEwGeflLbxL2xt5XE91T9J4d4Mn/tQ97xtIINyvvK6VyEbF2yxFh6xNgd6I3nwBdR6jZiLDmfgBq0/N4o785aZ/a9GRB3Cu9l3izvxlNj/X3bc+r5OnytTf57O9s5ivK2qS7r1jb9EZ70thI6zjjXZNL6q6y6SNr8Y0FGG0dR5BEBFHDbDcnLGAbC503h6Gm0aQxNaox2j5O0ZrFRy4ZGNxO/GMBXv7fb8UXdC++1EzVzjLWP7uWU6PdTEaCVLtyqE3PvW6BjHfYx8nvn4svgkNsvqmEFbqO9V63GKbclcVQyBOPK52JL73S1XFm4dpsN7Pu/as4P34KcXoeLJokZKtMp3cs5WPM3NMHp0IMXBhmatDDeOckSlhFEAXebBgimKbFFvbNEjrwfOMpxr/RiaBBxbZSqveUL5m4SFU0hptGEWWR3JoshCvij1r2d5A1LOHuEYk4wBwEIjpWt4WQJ9bZYnaY2f6pzfH5/HKnyzvOmwPNjIV8lKVlsa+ojgxDiHzDhNXYe1gSEx10w1o04eeDg6282H0h3uCxyl3IR6o3X9Wd98x/XSQSmD2OKIogwPoPrKbv3ABhbwR3STorn6wjr+76opmuxCqZEiKNZIeJaERBCkPxxkK03U56bEEiqzOJ/GAIu8+EElK49IsWBvaI9BRG4zsP1ms8Fa2Oz6PKnJmUOTOTHzPditluSniuEHPQNNuXXhzrjYaSIpsAfNEw2nT0t8GtIa8hJy64Aoiodkp+GaL7EYHItJYsY0CkIpJOdJudkDWNFdEoe1UR0/aHEVZsR3DNuq5JskjljjIqFyli7/NP4YuGqXblsMpdeE3XbAMDAwMDg3uFZSmGcTgc7N+/n9/4jd/gueeei4/ruk4kEolPGjVNIxiMTdAFQYhPSp999ln++Z//ecHRSgYGyw295WSKQQ299ZQhhjG4qZRvLU6w4gSQTImdwLk12Tz+Px6g6Y02gpMh6qw2mrM9TEQCVLmyebRkJaYrCihLTbdvnOc7ztAXmMImmdhdUM2DRUsTBbQhu5QN2de3wBtWFf7l0gE8044CfiXCc81H+HTDTqpcOSn36TzaQ9Pr7USDUUo2FrLqiTok08JfP180jCAIse/GBMcRnaloyCiCLAHXEsJ4Br28+XeH8Qz4kB4M48+JddDLZgnZItGQWYh52mp9ZWYhZ8f7ko6x0m10299LBJQIzVPDWCWZmvTcJRP0zdC6vzMuhJkh7I3Qur+TNU83LOlj3ckIghgTv0wLYOZS6cpmV341Bwdb40XmFe6CpM7uNs8Ir/fNWqXrwKGhdipd2TfsWHY3sz2vkuap4YSxDLPtusS0alRlrHMCm8uKc4ncj5aSiD+aejywtCJIm8vKY3+6l4meKXxjfo595wyB0dnu5IyS9DtewLZcSctxEPYl/z3Tso3FWYM7jws/b0pyNrh0qpNXVw3iF2LXs/0DLWzOKeMDlRuu6zEGLg6TagVXjWpI8vXfE+0tqOXSxEA85lCfngfJcxYDBeC+vFkHsvo1pThOX0ZRFBCEuCDkSodfJaxw8vvnaT/cRSQQRY3EhAOR4PQ1XgfVBL51IoICESWCCRO6phMMKnT1DmPziAw1juIb9bP+/auu+3nOMNo+zht/c4jAZBBRirlWPPC7O3Dmzn4XqpFYfKuoC1h904OSwI5Pb0YQBNSoSv6KXGTzzZ2/LxX9/kn+9fKBuPPOUNBLy9QQv7dmX3y+ZXB9rHIXcGK0O2HMZbJS6pgVfQwHvfys63zCx/fCRD+HhtrZXTC/o8t450TyoA45NZls+vD1id+uhdtiZ4W7gIsTA4SUKBFNAREsaTL7t40TDA5CUCAUDcPjGg1vmEgbEwg5dXoKgpjtZjRVQ9d1pDSJ3lz1mo8pmyVWPlHH6R8mug2vflf9DV3b5qPAlo7TZEmKdq1JzzVqQLcYm8vKlo+t49i3z6ApGoIokC3ayXhRJZgJkipgD0ns+/1N5NWlrg3Ox1S/h8u/bMU34ie3Lpv6fdXxGPu5vNB5lsNDsyL8PJuTzzbsxmEyXAoNDAwMDAyW7UzBbrfzjW98g2effZYvf/nLvPTSS3Gxi66ndi548MEH+ZM/+RP27p3fctrA4I5gvkmLMZkxuMnU7q3EM+ij+c12NEXD7ray9Vc3YHUlxgS5S9LZ9quzxc+HlujxI6rC2fE+piJBaly5KbttQmqUrzceJqDGCo9BNcorvZdxmW0p7aeVsEIkGE1wQwhMBjn5/fP0nxvEkmam/uEa6vdV3fD5nxvviwthZtCJubekEsO0vNXBkW+eiv880TPFZM8Ue37zvgU/Zn1GPu2eUTRdSxh3yGZ80TBDQc9tzam/Fzj0tRN4BnxEQwolv9ToflAgkKcRVTVy/Fbev3FdfNu1mUV05I1yZKgjVlgTRPYV16d8rxvcnZwf7+P7bSeITnctZ1sdfKp+55JG6/jHA4saN0jNU2Wr2ZZbQY9/nByrM6UT3KWJgZT7XpoYMMQwV2GFu4BfqdrEG/3NcZeBx0tXIi9STNt7ZoDDXzsRFyKUbChk52e3LKtFvZL1BbQd7EoYs7os5FRnzbPH9aNEVJrfaKftUBdqVCUt10FGcTp5NVlU76lAvoa9usH1serJOvZ/5UjC4n7pxkLSCw0nHoM7j9GO8aSx/pUqU5EgskVG1TWiqsrbAy2km2zsK65f9KKrLcMai0iRRHR1dg4jiALVe64/Ki/T6uB3Vz/A8ZEuJsJBqlzZZEsOXmw9R5/mIdvu5MGiuoR5mctsY2teOe8MdSQca19Ronj46LfP0H6wC13TY985uo6uQ9QOYysh4hKwD+jIPh3FGXs9lJCCroGoCJgDs69R46utrHqq/pqRF1dD13Ve/tKMg48OCChhhaPPnWbfH+yKb1e2uYjGV9sS9rWmW8irz7kpi/M3m4NDbXEhzAwTkSBnx/tuWxTVnY53xM9Un4cHi6sZDwdo98aao1wmKx+t2ZLgLHF5cjClE0nj5OBVxTAZxekEJpKjRjOKbm6d4kNVm/he63GOjnQiCgImUUYSBIaCHmyyCZMog6ajS7HrXO3bIn739PtLICECts8/uaDHXPVEHekFTjqOdAMClTtKKV57c5yCJVHkmcqNfKflKGEtJtbJMNt4uuzmCIwMrk7NngqK1xYwcHkYm8tKXn023Sf66D0zgCXNTM2eCjKKE9/z42E/qqaRY3OmPOZk7xS/+NJbKCEFiIlJe07189h/35vg5tXjm0gQwkBMLLh/oIXHS1cu8TM1MDAwMDC481j21bA9e/awZ88ePB4Phw8f5uzZs4yNjTE5OYndbic7O5v6+nruv/9+srOXxkrRwOB2I9RsQr/0DgkVVUFEqL6+ziuDxTFwaZhzL1zGM+gluzKTde9bibtk+YoJYiIIYUk6PwRRYPNH1rLm3Q2EpkI489JuiV2ypmqcO9TCD/znCVhUZIvML+XL7Mqv4qkrJvKXJgbjQpi5nBjpSiiA6ZrO6R9doOn1NpSwSkaxi22/toHsykxe//IhJnumAIgEohz/zhkkWaTm/gp0XefS5AAtUyOkm61syi7DaV5YtvN8UTfzReNcfKkpaaz7ZD/eId+Cu9t35lfRNjXCydFuZgqhVklGEiUEZm3ADVIzcHGYtkNd6KpGxX2lFK+bv1Dlj4aJamqCDXfIE2a0dRxd11EiCuYQVL+gE3HqWB0WbNEo0k5guo4mCAJP5K7E8WaAltZ+7FNgW+cj+lEFk3XZ35YZ3CARVeGH7afjQhiA0ZCfn3ef52M1W5fscQpW5NK6vzPluMHiyLGlkWOb/3psm+caa5OMa++1WJddwrrskuveP+yPcOCfj6KEZzt1e071c+Hnjax77/Ip+m744Go8g15GWmMLzBanmZ2f3bIoF7iFcvJ752h+c7YQ7hv2k5ZtZ8VjtUv+WAazlG4sYt8f7KLptTbC/ggl6wqoe2j+RUEDg+VMeqGL8c7JhDF/poYoSaiahl+JMFOneKnnAhORAB+s2rioxyhak096oRO9VycSjKJFVRAF6h6oZOUNXq/STFb2FtYB0HawiwPffRtbUKFGFKja6aT+V5PjDp8uW0uxPYNz4/1YJJmtueXUpM/eM0XDCp1HeoDp+ODpBr2wC9reK6BOTxWnagRMHkDTQZiOIBYECi9JSMrsXF0Jq4S94RsSwzS93kYgQeSso4QU+s4OokbV+HfMuvetwjcaoPfMAOgxx6qdn91yRwphADyRZEEFwFQkmHLcYH50XefYt87Q/FY76CBKAtueqOM9j64lpCoUp2UkuVfOV1u4Vs1hJmp7xqkIoGpnGekFqQUAS4VFksm2puE0zdZzotOiEUXTMIkgmkTUsErQFftc2ycFEEiog+majmVU59DXTpBXl03FfaVX/QyVbChMcFe+mdRl5PHF9Y/RNDWESZSoS8+7rmicXv8EISVKmTPrpjs9381Y0i1Y1rgwSTKiJFK0uZDyrcnzHX80zHdbj9PqGQEg3+biYzVbkkQxl15uiQthALw5GoPiKN6/fw3npERWpZvVT9bTEUmO7ATo9KYeNzAwMDAwuNe4Y1ZdXC4Xjz76KI8++ujtPhUDg5uOkF+OsOcZ9OO/gIAH0jIQt70LIcuI0bjZjHVO8MbfHIrnvPaeGWCkdYx3/Z+HsTot19j71uKLhnih8xwXJvqRBZEtOeU8UbpqSTJhLQ4zFsetW8g78o1TvB5qw1utQiQWeWB2mDkw2MbmnHLy7bOdtUFfCF3Xk3LW1Ss6xJpeb+PiS83xnyd7Pbz5/x5mx2c2x4UwV25fc38FP2w/lWAP/PZAK/9txZ6rLobO0JCRz4vdF5K6pRoyUgssApOpi3mByWCCGEbVNNq9owgIVLqyEnLvTaLEJxt2kN5u4/BQO7Igxl+ble5C0s22pOMbxGh+q52j3zwd/7nzaC/rP7CKVU/UMREO0OUdI9PqINfm5Iftp7gw3o8OFDsy+HDVZnJsaUhmCVEWUaNqrPA9jdknYBYFNEEnNBXG5potwL3zjZMMHR/ANZ1i3nawC1XR2PXZLbfw2RvcDrp9E4RSiPmujIu5Gr3+CV7sukCnb4xsaxr7iupZm1WcsE3ppiLKthTRdWw2kqt0cxGlmwynkqVmY3Yp+/ub492YQOw7Obf89p3UPcLAhaEEIcwMPSf7l5UYxuq08Oif7mW8e5KIP0JOddZNEcJoqkbboa6k8cFLI/hG/KTlOFLsZbBUFKzINQSHBncFq56oo/d0P9Hg7OJblsnBqKxOC/xn73dFQeDkaDd7C2vJnaerPRWiJPLQH+7m3E8uM3hpGFuGjZVP1FK0On/JnodvxM87Xz+JPn1/rms6rW93klmWQd2DiY6goiCwObecDe4SIv4otvTYfbuu6ww3j+IbDcTrA4I4OwcdXTsrhJkh6oK8IzoRtwAmgfRWyBkVYc5l35nrwJF1Y46Aw81jKcd19IRzNFll9v72dvxjASL+CBnF6Qm/v9OoSc9Ned9cm25cfxdL17HeBAGtpuqc/2kjBStzKZsnxmVNZhGv9FxKcMNVNJWJcIBvNr3D+uySpHkJQE51Fk/82YO0vNVOyBuhaE0+5Vtmt9N0jeGgD6fJgsO0tHU3p8mKpmhEQwq6qqFLApiJ10xMVhOaouOYNsVyBGRW27NpFsaJqmrskudRsL+q0j4eoP1gF90n+njgd3cs6XneCFbZlPJ1Xwi+aJhvNr9Dty8WZeWQzXykejPVxmdq0fT6J/hOyzHGwwGimopALHI71+ZkY3YpNtlEscNNSZqb5zvPxoUwAINBD99uOcb/b82DCcf0DvsB0ASd1l0KE4UxQWaXMEpOh0TFmxP0nR2k+g9SR+9dGflnYGBgYGBwr7KsxDBf//rX+dSnPgXA2rVrOXXq1DX2MDC4exHrt6LXboKQH6xpCEsgcDC4NjPxQHMJ+yJ0vNNNw8M1t+msUvOdluNxC9uIrnJwqA0EeNcdZonqHfbRdqgL76NzXncdoiEFySTR5Rsj3+5ivHuSw187wcjgJJGno4h2CZNNjseHrc9K7LZItSAU9kViGfUpiASj9Psnk3Ky/UqE1/sb+VDVpms+lxybk6fK1vBS94W4ffPqzEJ25FWm3L5gZS69pxMjNsx2E1kVs5E5ff5JvtH0TrzglGmx84m67fGOEe+In5Y328kbi3DfiiLaXR4UXWNtVhGPFK+45jnfq2iqxrkXLieNX/h5I/2rVF4fbIqX+m2SiYASiRfMev2TfKvlCL+3Zh8mq0zljlJa93ciymL8+iGZJARBwO62kl44uzgQ9kXoOdmf9Ljdx3uJfHx9yuzn60HVNSbCAVwmK2ZpWd3u3dO45nGZmtuteDX80Qj/dvlQXFAzFPTyH63HSTNZEiz/RUlk93/bxsjDY0x0T+EuTb8pkSwG4LbY+VTDTl7uuUSvf5J8u4uHixoSRJwGN4f5In9k2/K85mWWZtzU458b76PxvhC6BtmdIpk9syuv6hX3tgYGBgbzkVHk4ok/e5DmN9rxjwfIr8/FscnNvzQfxDtnAVwWpXi03XDQuygxDIAt3crWj69f0nOfS8/p/rgQBkAx6YxUqjwXOo129DxmSWZTdimPla7EJEqc/1kjl37RTCQQxVXgZN37VnDhZ02Md00CsbmiJItIJil+zx+a59ZKAEoPCFgcZrRpl5gZZIvE1mc3JDV2LJa0bDuSSYoJ8ueQW5Od0tnVkWW/YQHOcmB7XiVNk0PxRWQB2F1QQ2maETm7WHrPpI767D0zQF5dDqPt44y2T5Be6IyLPS2SzGdX7OIXPRfp8IyhozMVCdLjjwkpLk0O0uoZYUVGARWuLKzS7Nw2vcDJpg+vTXq8pskhfth+Ck80hCQIbM4p593laxAFkUsvN9P0RjsRf5SS9QVs/NBqLGmLE8vUCVm8MB5BM01fDzQdUQeTPfY5EUSB7Ewn78tZgW2VRMHKXI74urjcPYaqa2hRDVNUR47Mfmb7zg4yeHmE/IbUoqFU6LrORCSAXTJjlZdmzr8UvNh9IS6EgVj967utx/ni+keT4ku9Qz4aX2uLfTc05FCzp+KmCLyXMyNBL8dHuolqCmsyi6hwxVIKNF3j281HmYgEUTWNoBIFdEyiRJdvnDbPKA6TGUkQWZdVzPnxvqRjDwY9DAU8NE4O8c5wO2FVIWe9GVuHzkSJxmRh4v38aIVKZo+I0B/EdDFCUX5GQpyXSZTYXbC86tgGBgYGBga3i2VVKRwaGop3+7/nPe+53adjYHDbEUQJjMWUm46qaZwc7aZlapgJxygWh47Vn1icCvtSx9xc7+PtH2jhzFhvrAstp4zteZWLKoiNhnxxIcxcjo908VTp6hsurt1KvMN+0MHqFQimzxYsdTX272xrGqqi8cbfHCI4GUJGoOZtmY4tCtFssNksbMur4L55BCdXklHsQnOLqAEVU3j2dSpZXzBvDvRC86EhFlu0PquYbt8EmRY7eVf5DG/60Bome6bwjcYsriWTyLZf24Bsni0ofL/tRELn1Xg4wI86TvO5FbuZ7J3ilT/fTyQw7TRxFLaszV9WXUrLlWgwSjCFM8+UOcLJvssIc4rIw0EvFklOEJUMBb30+ycpdGSw5aPrsDjMNL/Zjm/EjyiLmKwmZIvEtl/bmFCQ1lQtoTA/O66nHL8ezo/38ZPOc3iiISyixJ7CWvYV1S/JsQ2uzXjYz3jIT5EjIylCJ9fmpD4jj8bJoYTx3QWp4zRC3jDdJ/tAj8VwnAn0JjnL6MDR4c4EMcwMOdVZSy6C8UXD+JUwOVbnksTz3Q2UpmXy6Yadt/s07jkKVuWRluvAN90xOUPt/Qu7H7iTCCgRzo/3oWgaqzKTXd/e7G/mFz0XiZQJqBGVySKN4rNQeFnCXZp+02MIDAwM7i6cuWls/FBig8Vvr9rLP186wFDQgyyI8RgNEYEiR8ZtOMurMzd+NGzXufRQlGC6jiarCJEIJlHm4FAbU5N+1nZncPa/LsW39wx4eeNvDiHJUtxFRbbIKKEooixidpiRLTJZqkZQDM2kJsXQId0rY0mLCeMlUWDTh9dgsptA1ynZULjoxfxUVO8u5/KrrYQ9YZSoioCAyS6z8y53mjSJEp9u2EmHd5TRkJ/ytMykWBGDhWGexwnYbDdz6GsnaD8421xUsDKXvb+zHckkkW1N42M1W1E0lf956hdxwYSu6wSUCK/1NnJkqAOzILF+PBtvKISUb2FDXSUr3QUJtSp/NMK3Wo7Go4tUXefIcAfZ1jSyz2qc/P75+LZtB7vwDPl49Iv3L+p5Dh4eoOEtE32rFfxZOrYpgcILEhX3l6Kus+M0WdmYXRJ3pPFEgvyy6TImUcIkSoSDYVSzTu8aherDsyKWyd6pBYth2j2j/KjjFKMhP7Igsi2vgidLVy+LudTFiWRRlF+J0OUbT5hfTvRO8cr/fivuGtZzsp+eU/3s+4Nd111/VMIKIU8YR5b9jnCsap4c4hvN76BOX/QPDbXzeMlK7i+spdc/ycR0XFvs/azP+Tfxf0uSyJmxHnSdlK/biZEu9g+2xn/2ZYSxP6QTCWtoEggqIAhxjeVUnkZGv0hoPMin79/JwcFW2r2juC12dudXk29PvymvhYGBgYGBwZ3GshLDpKfPfkEXFRk27gYGBktDNKwwcHEY2SSSvyI3qVPqWy1HuDy9MKkUKGiPKqx4Vcbmmd2ueF3qmJvr4fnOMxwbmS0s/KTrHAElwkPFDQs+xtwJ1VwUTUUnoflsUXgiIQYCU+RY08i8RXaamWUZiCaRgksSk4Ua+vTLLsoiVc5sqlw59J0bTBAupA+JrP2ZCVOFnXd/8SFsKTprKreXJuXdazkyv0jvpPN9KhF/lIxegcojMsU1uax9z0qGNF/Kc8y3LU6U5jBZaHBf2+bbmZfGu/7iEfrPDRINRilck58QxzUe8jMU9Cbt1+EdI6hEufBi06wQZpq+s4MMNY2SV5e9qHO+m0kVq2V2mHHmpeEdSvybB6okhBRGXIqucWW5cOaYkkliwzOr2fDMapSISu+ZAbSoStG6gqS4MVu6ldy6bIabEsVs+StysaTdeDTZeMjPf7QejxdowprKL3svk2dzsjrz7rq3UjWNpqkhQmqUuvS8JbfUXiyarvGj9tOcHO1GB0yiyBOlq9l+hVDvo9VbeL2/iYvjA1hlme15lWzILk063uDlYd7828PxGJiT3z+H7dPJeeMA4RTRS0uNqmn8uOM0p0Z70NDJtNh5pnIjlS7jWmNwfUz0TDHWEes4vh7RliSL7Pv9nZz43jn6Lwxhz7DR8EgNVTvLbsLZ3j66feN8rfFwXAj38+7zfLhqM2uyYtd0RVN5sz8WC2mymdB1HS2qMbBCpSHgZvfntt62czcwMLh7yLam8cn67fzrpQME5tx33F9Yi9uS6DiiqVpKd5JbSemmIk794AJhX4T+lSoRmx5bSJyeEkQ1Bbwqx8c6CP4HCFEwO0yI0468QV1hYoVGKEfAPimQ1yxhFs1s+dg68uqyyShOZyoS5B8v7mc84EfXdAQRHE0qGeNyfEJuz7RRvbsck21pnSBc+U4e+sIuzv7XJSZ6pnCXZrDufStw5V072vd6iKgKFycGiGgKDRkF87od3ioqnNlUOI170Buh5v4KWt7qSHBGNtlk7Jk2zvz4YsK2AxeHaTvQRe0Ds/OagBJJEOmHVSUeXa2qGlOBEK/Z/MgiCFNw6mwf95VW8cE5jruXJwdS1rbOjfdS9Hpy48pIyxjj3ZOLctsLekLYvEKCkAUgY0JmW2lytEy3byI+lwZiTTKKhjc3sXEls2xh5xBSonyz+Uj8tVJ0jYODbWRa7OzMT90QcSuxy6aUMb42KbE2cfGl5oT4PIhFcQ5eHrmumMizL1zi0sstKCEFR7ad/A+X48+LxfqsziyKCy6XEy/2XEh4bwC82tfI1tyKhZ1vfFeBbKuDsXCioL/alcOZ8d7EXQQYKVQQdYHpfkGk8LQoBrAEYl82+Q052GQTDxbUsc1UgjXdmiAKNTAwMDAwuNdZVt+KBQWzi82BQOA2nomBgcHdwuDlYfb/w5G4YCAtx8GDv78zXiTq8IzGhTAAskkikqbRv0Kl6oiIKIusflc92ZVLY7vrj0aSYngADgy28UBRHVKqVfgU5Ntc5FqdDIcShRKrM4uuu7vktb5GXu9rRNV1BGBLbjnvLV83b5eHJxKkeWoYh2yhLiMXcYHnfiVWp4X171vJye+fZ+UrJobqNBSXwH3r63mwbv6YHwEBe1BKKYQBqN9XTWAiSPMb7ShhlYwiF01P6wwHJpBMErZ0kWCahrCxgIfWxZxUSnCzJrOIc3MsS62SiQeL6q7ruV1JSI3S4RnDLpspc8beU5IsUrKhMOX2FsmEKAhoV0y4zaKESRSZ7POk3G+q33PDYhhV0+jwjiKLEmVpmXeU29AM3kiIn3ad48JEP2ZRZmtuBY+UNCAJIoIgsOlX1rD/H47EC4CCKFC/uZwBITFiyyRK6CT+DYrs6VjH4PLBFhxZdorXFSBKIrJZSsg/T8XOT2/m7X88wmh7zI44uzqT7Z/cuCTP+dx4X1KBBuD0aM9dJYYZD/n5t8aDjIdj94uyIPLBqo3XnZW+FBwb7kq4vkc1jZ90nqXKlU3eHEGdWZJ5rGQlj5WsnPdYuq5z9LnTcSEMgBJW8f9sEB5L3n6VO/U1ZCl5o78p4fmNhwM813yEL65/1IjiMlg0R587TfOb7fGfi9cVsPvz25Dkxd1LOHPT2Pvb25f69G4rja+10fR6G5FAhOJ1BRzbMJWwSKHqOi90nmWluwBJFPHPWYwSRAFLmiXmNKbr7PniTlxXuMgYGBgYXC+5Zie/u/JBzkz24o+GaXDnJwgSes8McPqHF5js85Be5GTDB1YvaVPJYjDbzTz0hV2c+P45LmT1I0gkCN51HVR0JBF0CQhpRAMKljQzikmn9TGBSLqOgM5kEYxWaKx62URWhZuM4lgjX7rZxu+ufoATI92Mh/1UurLJy7dy0duEd9hHTlUWa55uWHIhzAy5Ndk89Ie7b8qx5zIYmOKrjYfwRsMASMJZfqV68101t7gXcRen8+Dv7eTcTy4x2eshq8LNuvetpPNoT8rtBxuH42IYTdc4OdJNWI0S1TRMokRUj81bREFADSuoUmwhXzGDqIEeUTk+1MWO/CqKHW6ApBieGSRBJBJI7c4c8S+8CWAo4KGxzk+THMbZI1B4SUSWY/OWojWpm5eyrmgKM1ljcWQW32w9pHRTEbm1qestISXKy72XuDDej1mSKLJnpBSbnB7tWTIxjK7rHB5q59hIJ6qmszariAcK65DEa99Xb8+r5OfdFxLGyp1ZFDoSHUU8A8lNWgBT/d5Fi2E63ulOiKw+V+3hzdHTWCNWBFHgzf5mPtuwG4fJjKbrtEwN442GqEnPTXJHvFXous5AILn+FtVURkJeStMyKU1z0+2bQBZFItMaM0kQ4yKxue/3TTmleKIhTox0o+oaq9yFvLt8LX9x5pWE44fUKJquY5FMqGoUHR3VDEIQzEHI7hCp2llGwao8uk/1c/zbpwlMhJAtEiserWXte4zodAMDAwMDA1hmYpj77rsPURTRdZ0zZ87c7tMxMDC4w9FUjUNfPZHgnOEb8XPs26fZ9/u7gFgmawKCgNluJm2zg73bVpNZnoE9Y+kmWwElkiRsgNgEZ8YycyEIgsDHarbwndZjceeQalcOT5clZzAvhC7vOL/snZ2MzsRuVDqzWZ+d7ERwbLiT/+o8E38ueTYnn6rfcd0LLiseraVgRS49pwcwO8xUbCtOsI/OX5GL3W0lMJHYHVSxPdlNYQZBFNj4wTWsefcKosEoAavKgbOvztlAQJQlmiIjqLoWFyL9SvUmVowV0DI1RLrZxtbciqRux8UQVKK83HuRE8PdTEWDyIKIWZIpcbj59br7rupm4TCZWZ9VwskrBFRbc8uRRYmsCjcT3VNJ+2VVuK/7fCHWhf6t5qPxeKZcq5Nfr7+PTMutcQtaKp5rORLPvw6qUd4aaEYSBB4piRUEitcW8K7/8zCdR3rQNY2yLSXYcm2cPjfM5LTFLYBNNlGXnkerJ/ZeqXblUH/Zwc9/+lp8m4ySdB7+w10Lsj13ZNl57H88gGfIh0DMIWipmE+0dCeKma7Gz7vPx4UwEOuw+3HHGRoy8m+bMCOVxbQOXJoYTBDDLITgVAjPYLJTldYZ4omcVbw21kRYUxER2JJbzsacm++EcS5FrnlQjdI0NbSoxRBN1xAQ7rr3pMHC6b8wlCCEgdgCavvBLmrur7hNZ7U8aHytjePfORP/uflAB+0lChZn4neLTwkzHPJSYE/HabKSabEnXBMFUSDT4sBpur2d+wYGBncHSkTlxH+cpf1QF7qmU7yhkAc+vj7B0XKyd4r9X5kVmU/1edn/lSM8/v88gLv49sQ0uEszeOgLuxlpPc6ZsV60aDi+KIkOgg6uIRGzLhIlgqqoaKrGaI1GxAXo03J4QSBs1wlssyY1ydhkM7vmxl1mQu5dJtL8Sde5uBAGYqLM/5q+755PzGBwZ5DfkEN+w56EsZHWsZTbOjJnayK/6LnI/oFWJEEkrKvTLpUCIGCVTChqFM0cG9IlUEVQZZBUhT7/ZFwMsyIjH4dsxq8kCl8255QRWW+n7WBik4rVZSGn+tqNam2eEX7YdopO31gsxqwMJko1hlZqrPqeyppH6ylZn7qZoMCezkp3QXxuJ4gidpeVPXoJ7gcl8utz5m1mAvhO6zGap4ZjP0RhIOCZFjMkzlGXci70Wl8jr/Y1xn9+ta+R8bA/wYVnPnYX1CAgcHionZAaZYW7gCdSOOZkV2Yy1jGRNJ5TtfjGwY4js4Irb47GWLkKOqhRFdkiMxT0cmCwlZ35Vfzb5YPxuq0oCDxdtmbBEelLiSAI5NtcSTVkkyiSY43Vc361Zhs/6TrHxYn+uAhGFAQCShSzKCFPi5OcJgtbcytIM1l4umwNuk5cuLTCXcCZsVl3GEXTEAQBkygiimbCShRF13B26hQe1BEFnZxd+bzQdJrTZ1pIy4GsER0lrHD2+Uu48tOouG/+uqmBgYGBgcG9wrISw+Tn57N3715ef/11XnjhBf7v//2/ZGRk3O7TMjAwuEOZ7PMQGA8mjQ9cHI5bN89Mwq+k3J1DceXVu9hUXePyxCCDQQ/Fjgxq0/Ou6cqSbXUkLVgAlKVlYpUW1zGWZ3fxe2v2MRTwYBKlG4o1SrWIC3BpYiBJDOOLhnmh82yCqGco6OWV3st8oHLDdZ+DuzQD9zx2t5Is8sDv7uDwv59kvGsSySRSvaeCVU9c27HFZJExWWQCodQRSMIVoVKiILIhu4QNKURA18O3Wo7QOjWCLxpGR0ch1jHV45/gFz2XeH/l+qvu/76KdThNVs6O9cTcTHLKeKCwFoDVT9bTdzYxQqpqZxlZ5dcvhtF1ne+1nogLYQCGQ16e7zjDJ+p3XPdxbzWDgam4EGYuR4Y74mIYAGeOg9VP1Sds87kVu3m19zLt3lEyLQ4eKKyjOj2HqKaiahrBwUCCEAZgsmeKiy81s/qpenpOD6CpGiXrC64qjrkZNubrsop5pecSiq4ljG/KvrtiQ1pmCoxzCKlRun0TVKcvLLt9qbHOI8KZb/xqWBxmTDY5yYraZJPZUVzN1tJKBoJTZFoct6w7bj7nMllY2CKINxLiha6zXJwYwCRKbMkp5/GSlQvqWDS4uxi8lPz5BRi4NHTPi2GaXmtN+FnQQPLpaDYNcY5rjiQIuEyxz74oCLy7fC3faj4av/bLgsi7y9det1ugwbVRwgq6jmFBb3BPcOI/ztLyVkf85+7jfUT8UR76wq74WNuhroS4FQBN0Wg/2MXGD625Zeeair2FtVyeGECVTQSVKJquIWqQNiZQfFpCyRQQwiK6ohHyhPHZhOk8JSGmbNZ1RFnEvX3xUSB3Orqu0+4ZTRr3KxH6A1OUpi2Ng+5CGQp6eKn7Ih3eUdwWOw8W1sdjAw2WhsrtpVx8qTmhlma2m+KuMFFN5Z2h2PVAFiXSTCKKpqLpOiZRQhJFVEmJqc2AuQarCjr5tllxnFmS+WT9Dl7oPEuXbxyHbGZPQQ2bcsoIfTCfqUEvo63jAFicZnZ+dguS6epzj8lwgK83vcNkOEBCH5ogEM6Age0C+2quHs/50eotHB5q5/LkIA7ZzI78Ksqd8+8T1VQuTwwyGQnQNDmUIHSRBZGAGkkSw2xMEZN7PWi6zqHBtqTx06O9PF6yCucCIs12FVQnivpSsPKJWnrP9OMfm31fVO0qu65GrLmvjzc7uVkQoMs7RlCJJIhPNF3np13nWOkuvC1RbY+VrOS5liMJtdAHCuuwybFIKafZykdrtsRjusOqwnjYj6JpHBpqYzDgociRwQOFdaRNN8WJgpiQc/9k6WpGQz56/ZMAyIIQa/YRBCQEhICOrOoUHtax+MDjCvOPTW8jppuIFKqMF+iMFkLVT3SEkMLFXzQbYhgDAwMDAwOWmRgG4Mtf/jJbt25lYmKCT37yk/zgBz+IZ/YaGBgYLAar0xKbVFwxt7KkmeMZ5iVpbjZmlyY4b7hMVh4oqr3qsaOaylcbD9Hpne2aacjI5+O1W68adSQIAs9UbuS55iMEp61SXSYr761Yt7gnN4c8++IcB1JhnydqyC6bk8Y6vKNJC+3AbPfLTcJdmsETf/YgwckQsi0mcFkM2dY0ih0ZtEwNxzs0zKLM5ryyBcdTLZaBwBRtnlFUXUuI2YloKmZJpnFy8JrHkEWJx0tX8nhpcqRKWo6Dp760j7ZD3QTGA+SvyJ3X7nehDAW9SdnFEPv7qpp2xyxcR1JkjwMpM8mvxG2x80xVcmyRSZQwiRKdjckFYYDuU/207O8k4o91th23SOz5/DYKV9/Y32QxpJttPFsb60gaCflIky08WFRHg/vWncOtwGm2EU4hcLtdtskA9+VVcm68L+Erxy6bWXcd0U2SKWZrfPb5SwnjDY/UIJslZKSEWIJbweacMn7SdS5hLN1spTZ9YQtD3249Fv/ODKsKBwZbEQV4onT1kp/rcmG0fZzWA50oYZXSTUWUXqWL9F7ClpG6eG1bQie+O5WwP4Ku62hKrAtUlEUKL0gM3J94M7sttwKHafYerT4jnz9c9zDnxmIOTmuyim7r9XA5ous6p0Z7ODfehyyKbMkppy4jb9HHiYYUjn3rNJ3HetE1naI1+Wz7tQ3Y0g0XHoO7E1XRaD/UlTQ+eGkY/1gAR1bMLUKNJM8PAZTote+9bzYF9nR+e/UDHB5qZyIUINuWRuj1Mc5FB7j0aBQdsIzrlL8t4vKZsA2GoUGI62FAQNegLD2LgBLh3HgfEVVllbvghhpS7gQEQSDdbEtwzVSjKrqq47k4iboxY9ERhxC7Jp8c7ebCxAAWUWZbXvk1721DapR/u3ww7lIzEPDw3dZj2OQd1CzwftQgmaimMhz0kmG24TBZMNvNPPrF+7nwYhNjHROkFzhZ+UQdztxYI0dEVeNzbU3XiahK3Gn3gaI6mqeGGNa8REIBtLlvDQFkSeLiRD//2X4SqyRzX24Fm3PL+Y2Ve2JOyYIYF/JanRYe+9O9jHdNEglEyKnOuqYQBuD0WG+siSWFIzPAVLHOcOsYlTvmbxaRRDEuENF1nYlwgJAaTdnA1u+f5GuNh/EpYVRNI6BEsMmmuGuSIAg4ZDOF9nT6A1NYJZkd+VVsy10aAbiqawRSxDBp6PiVyILEMAvBkWnnyf/1EB1HuvGPBclfkbPoeKQZKraX0nsm1pBnnUlfEoSEv2+2LS1ljVHVddo9I6xboua1xdDgzue3V+3lxEgXEU1ldWZRyrnwjNjHIskU2GPirw+lXdulR9FUDvS1MDbpxaQLVGbmMKWGaPOMENVUzKKErmq4OsEyrREaWSugmHRkTY/XvgN54C0FV5fOWPsEuqYjiIZA3sDAwMDg3mbZiWHWrFnDV7/6VT75yU/y/PPP88ADD/D3f//3rFlzeztJDAwM7jzsbhtlm4vpOtabMN7wcE3Cz89UbmBNZhGtnmEyzHY25pSmFIHM5fhIV4IQBuDy5CAXxvtZe42Fz0pXNl9c/yhNU0NIgkhteu512Qtrusb+gRZOjvSgo7M+u4S9hbVXFXZEVIXGySFEQaAuIw/T9ONuyC7ljf7mhCxjSRDYmmKCPtOJfCXpt6gzY75FNH80wqt9l2mZGsZltrI7v4ZaZy6Dl4bRdZ2c+iy8kRA6saKNpoOIxpqbmHXuj8ZEEVe6z+jThZlrvc+uZKrfw4UXm5js85BV7mbVk3WkZTtY8UjNtXdeIDbZlEpDhkUy3VGxJsUON26LnYkrXJiWIts+LTt1bJZvxI+uzr5ySljlyDdP8e6/ejQuwLsV1Gbk8QcZD+GPRrDK8k0Te91O7i+o4UcdpxPGVmTkk2NberedhVLpyuZjNVt5ra+R0ZCPcmcWT5SuineKLZY1TzeQlm2n/XA3ug6VO0qpukrRdgZNjS1GLfV7bnteJQElwsHBNoJqlApnFu8tX7cggdxw0Jv0nQlwdLjrrhXD9JzqZ/9XjqBrsWtCx+FuVj1Zx/r3J9uO32tUbi/j4kvNCa5mslWm7sGq23hWywNnbhqTvR5mWpkFWaS4z8FDtRs5NdFDVFNZl1XM5hTRaOlm2zW7eu9lXuy+wNuDs84758f7+UDlhpSv5dU4/p0ztB+eFdH3nhng4L8c46E/3L1k52pgsKzQdTQ19aLyXCeY0s1FNL2e7E5QtmnxouCbQbY1jXeVzdYVf7zrFKO9AzHPTh20HIGOhwXWvKDjboWJOh1/0ewszjEqkG9x8pdnfhlvanmp+wIfqNzAxpy7u+t+b2Etz3eeBR0igQhqRCW7Q+L00dP0VHay7wu7F+2S9ULX2bi7CMCZsR4+WrPlqnO18+P9CXFNEJuzvjPUYYhhrpNTo938tPMcATWKJAjcl1vJ5pwy7E4zWz+e2sHWYTJT4sigyzeBXwnHaxv69PF+Z9UDaOj8+cmXCUdikS5IYJIkZFHi9b6m+Pzhhx2niWgqO/Kr4rWpK8ksy1jUc1KmhTqiIKQUxFj8As5cBwcH2zg+0omq6azNKuKBwrqkeU3r1DA/6jjNeDiASRTZnlfJ4yWrEuoi/9V5Bp8Se19KggBCTLjlEMT4dquzivhYzVb80TBWybSkDUYmUaLKmU2bN7Fhx222kWtzLtnjQMwhqO6BG79fL99SjH/Uz4UXm3H3hXEFTIRyhbhgwyqZ2J1fzfA8jVrXG8++FBTY03mqLHmNajIcoNs3QY4tLS6AWSz/ce4ox4e7QIu10h3XujBJsYasiKYQUnWyLkHhO7Pv63BG7L+inFjFC7nB1RWrPQYmgnHhqoGBgYGBwb3KshPDfOtb3wLgE5/4BP/8z//MgQMHWL9+PWvXrmX79u1UV1fjcrmQ5YWf+sc//vGbdboGBgbLnB2f2oQrL42u471oNgFtl4vBWp0M3wQlaTE7T0EQaHDnL8o5IdWi3sz4tcQwELODvdFF+Re7L3Bg2g5VCSl0D41y5IWz7FBK2PyRtaRlJ3apdXrH+EbTO/HiXZps4RP12ylyZOAyW/lMw05+0XORHt8EeTYnDxU3UOhInsSVOTOpcGbRccVrsKdg6QQZi0XTdf718gG6fePxrqSWiWFq98vYmmIxI956kYldGnbZHC/YCILA0eHO6+oMXghlzkzsspmAEolnBgPxrOCd+QsvJPhG/bz4/7xBNBRFFEXGOyfpPTPAU196CEva9S22pyLdbGNVZiHnx/sTxrfnVdxRcQuiIPCxmi18p+VYPJas0plNvt3FD9tPkWdzsjmnHNs8rkhXMtw8ykjbOOkFTgpW5pJZlsF412T894IkoEbUJAGCfyyId9hPesHSFqIWwlzXgLuNLbnlmCWZd4baCSpRVmUWsrfw6o5et4JVmYWsylw694/KHWVX7VqcixJWOPG9czHxjKZTtrmIzR9dh8WxNO8DQRB4qLiBB4vqUDQtZte8QJR5HJnmG78bOPPji3EhzAyXXm6h4eEarK7549PuBSxpZh754v2c/8nlWMdxkZPVT9bflOi4O4mwL8x45wSCJKAr0wtLqk5OdRZrc4tZm7s8FpTvRPzRMIeGkhfpX+trXJQYRlU0Oo72JI0PXh7BN+pPuvc2MLgbkEwSJRsK6T7RlzCeWZaBc851O78+hw3PrOLcTy6jhFVki8SapxvIb7g98ZXX4tBIO6oUm5vpGqhmCDl1ptwqdr9I5c81AitkgllgnxJYmVHALwcb43NpiDkv/KTrHKszCxd1X3SrGAp4GAx6KLSnk3PFonhUUzk12k2Xd5wcWxpbcspxmFLfn9yXV4ldNvNW82UGBsbI7JbJb47NeUbbJ2h5s50Vjy38PnwqEuToUGfCmA682tt41RpJUEl2vwAIqpEFP7bBLKMhHz9oO4U2vYAeUVVe7r3ImwPNmEWJ2vQ8alw5KLpGkSODMudstPf7Kzfwt+ffSKir2CQT3miY4yNdPFBUx+6iGvYPtGAmVq8JKhECahSBWDSMTTYhCSIHBlvZsYi6yHyomsaZsV46vWMElDASAmpcH6ADAqIClX0OurZHeKtr1oHz1b5GJiIBnqmcdYcNKBGeaz5CeHq+EtU09g+0kmlxcF9eLDJqJqY3zvTrEFSjaOhICBTYXXEh3nyfsRvl3RVr+ffGw3EHJ5tk4pmqjYjTUT3nxnrxKRFyJsyEz3sw2UxU7Sy7rQKJlY/XUf9wDZFABMEucXionQ7vKFnWNHbmV5FtTWNPQQ2d3rGERq3SNDeVrvldpDyREC/3XKR5ukluT0HNgmq0N8KrvZd5va8p/llanVnIr1Rtnlf0FPaFaXqjnYmuSdylGdQ9WIlmFTk10h0Xwqhy7F0b0VREtOnPmk6gSoJ3ZqOU7cM6gSIR2SQRFWMuZqBjHxYw2WRs6dZ7fu5pYGBgYGAAy1AM8+yzz8aV04IgxG+sz5w5w9mzZ6/rmIYYxsDg3kUySax730qKHi/lny8dwKcMQN8Ar/Y18nBxA/uK6q/ruFnz2CFnW2/NQk5UUzky3AnEFkCjwVhhaLhCpfP5fjxf9vGu//1QvLNC13V+0H4qoXjnU8L8uOM0v7VqLwBFjgw+Wb9jQY//a7X38UZ/E42TQ6SZLOzMr2KFu2AJn+HiaJkcos0zEhebRFFBg46SKCuaYgUbvxYlEtCwuiwJnTy+aCjlMZcCkyjx4apNfLf1OLquE1IVBAFKHG72FNSwJbc8aZ8+/ySarlPsyIifp2/Ez8/++6v4hmOxMIIsYrGbCU6GaDvUtaTOMAAfrNxIutnGubHZKIH7l4HQYLEUO9x8Ye3DDASmkASRH7af4sXuC+iqRjSk8PMjp9jZlMPGx1fMGy+l6zpv/M0hOo/1gq4jmSTyV+Ry/+9sp+WNdgYuD+PItNPwcDVv/u1hwr7EYqwoi0bx4SaxLqv4uiKI7iamIkGaJoewy2Ym/6uPjoOzbgUd7/QQDSrs/Z3tS/qYoiBiXqTrTIE9nWyrg9FQYmffUjg1LVcm+z1JY5qi4R32GdcEwJnjYPsnr20Xfi8xcGkETdWxplnQlFjRW5RFPEPJkXAGi2MyEkzZHT4RDsRF1AtC10FL7ZBxpfjNwOBuYuvH1xMJRBm8FIusyCzLYPd/25q03crH66i5vxLvsA9nrgOzfXkKs2ciTxLRUU2gBTVAwGSRye6ToS/2fLd/fCOvtb4S31rVNSKqgi8a5pvNR3i6fA15thuPL14KdF3nhx2nODEye1+4LbciHs2s6hpfazyU0NzyzlAHn195P6553F7XZhWj9kxw8VUP3myNvtUqckggu1NkuHWMFYs4v7GQP75oPJeRUCwvZfDyMCOt4zjz0ijZUBiPYVrhzufF7vNJe668jXWIO5kL4/3xv4Ou6wTUWFRjVFMRETgx0sXpsZ5Y5LMOaSYLDxc38HBxAwX2dDa4S3hnuANBjEU7z9QuxqedPJ4oXUW1K4eLEwNcnOhH0TTUaeGSpmsElShpJkuS28/1oOs6z7Uc4fx4P0ElOhtRPWNeJQigghiFqkcqeWO8HUVTEefEMp0a7eGJklVxwcrFiYG4EGbOI3FgsJU0k4UaVy4mSYqLX2aQRYl0SeZXqjeTbrJRmuZeUofdkBJlMhIgy5oWd9PJs7n4w7UPx6KtdY3a9FzMksxoyMe/XDqAJxoiGoqiBBWKuyUKL8lc/EUz+35/JznVWUt2botFkkVsrtg154GiOqAu4fcr3AU8W3sfBwZb8UZD1KXn8eBVariarvHVxoMMBWPXEk80xHdbjyMJ4pI2rMylxzfBq32NCWPnx/s56upk+7Roai6Dk5O8/I8HEdpDiKpA98l+2g91sfl3N6Gq059IXWeuubQe+1oCIJwtMLXXSsarIQQEynptyA9YmSKMyWoiGozi7pbI9ssIFoGVj9cuKF7MwMDAwMDgbmfZiWGu5EZuGHVdv6MiHQwMDG4eL/deYjLgRwmp6NML2q/2XGZzThnp12GxeV9uBceGOxMm7pkWOxuyb41FclhViE5PzJXw7ARdF0E1gWfAy1DTaLwTbyIcYDSUvJjS658koEQWHddjlU08XrqKx0uXR9zDhcmBuBBmBh3wu2fHMgZEulUFXdMQ5nRo3CxXmLnH/9P1j9LmGcUmmyhLy4x/N6mahqJrWCSZ8bCfbzYdYTAYW0DNsabxq7XbyLU5OfzvJwiMz+a064pGJBDFkmbGPxZzPVnK7zyzJPOusjUJVuJ3Kt5psVOPf5we/0RMlOSLgKYTtMFlcQTP3x7m4T/eTW5NcofR+Z810nawKx5ZoUZU+s4N0nWsl3XvW8k6Vsa3XfFYLad/eCFh/5r7K5bMmcPAYC7Hhjv5r84zaLoOuo7ujlBnl7EEZq8DvWcHCE6G5o2Xu1UIgsDHarbynZZjjEx/F1W7cni6fHlfY8bDfvYPtDAQiHVV319QQ4ZlYR2U7soMRrsmkJTZv4dskUgvvPUuUQZ3Brb0WZGUKM/epxjiqRsn1+bELpkIXLH4XeJwLypKUDJJlGwsSopgza7KxJl7bzsbGdzdWF0WHvrCLvxjAVRFu6qTl9luIqvcfQvPbvF4oyFMokRYVZjVyQkIuo59GARZYPfnt5FR6EKURTJLMwDI7HEwFvaj6TqBaGR6wV2gZWqYf7r4Np8puo/uV7qmXc9crHq8lozi64vLuBHOj/cnCGEAjgx3UJ+Rxwp3ARfHB5JcXicjQQ4NtfFYyUrmw5mXRs8ahYEVs/WH/pUC5crCnDZnKLCnYxKleD1jhrK0LA78yzE6j8w6cGWWZfDQH+7GbDeRbU3jPeXr+Fn3eaKaikBMpHNfigVng2szN5ZI1bXYnAKmm3higpKZ1xliLmuv9l4mz+bEcVFh+EAX6sZobJHerGOymUAgwbWjLiOPSlc2R4c7MIkiYW226VXTNTRdoy79xkUKrZ4RLk8MTovc9Fjsc8xIA0EH0xy9zfNTF4hKcZUMZknCKplin2slEhfDiFdEXc/83h8d59stx7CIEu8vXMeO3EpeG2hK2Ha1OZ/MbpGcmrSEGo2ma7R7RolqGtXpOfNGQ83Hq72XeWughaimYpfNPFm6ik3TDneSKCa5Xf+y9zKeaAhd01FCMTeR3tWxmDOCCqd/dJGH/2h5xzwuxsW7ZWokLoSZy8HBtpsmhmmcHEw53jQ5mCCGUTSV/2w7ycn+LqIbo4iroaBRIrtTgmE/E6fHcPhFfGmx66KgAtOX1tg3TQwRgYkynQKXFXOaify6HNaNZTBZKxIwKzgGQWv2wAao3F5K2eZ7u3nIwMDAwMBghmUphtFTdG0ZGBgY3AjNA4NEA7MFcDWsEFI1enwTpGcuXgzjMtv4/Mr7OTDYylDAQ5HDze6CqgVHrtwoaSYLxY4Mev2TzM2FtU0J8YVQJTxrnWmTzUgpMpOtkoxFvLGvAs+Qj3MvXGKsc4L0Qhern6q/5QXQWJp7YkYugDRnzcPqFSg9b2JslxhvEKp25bArv/qmn59ZkhMm8Kqm8fPu8xwb6SKqqVS7cghFo/ROjKOpGoIoMKx5+V7bCT5Tup2hxlFEWUSJzBYMVSUm7HKXpLP/H96h98wAJruJugerWPOuhrgr0L2Koqn8oP0UZ8d60YndW6i6BkpiV3cgQ0fXdJpea0sphrn0cktMaDBnTA2r9J0dSHLkWfVEHbYMK20HOtEUjfJtJUuSqW1gcCUBJcILnWdni9ZAKE2jd61C1Ttzvof02LViOVBgT+f31+xjKOjBLMpkzuOwtlzwREL848X9cdFrp3eMC+P9/O7qB65qca5oKj/tOs/RB6YI+KK4BgQqjsVESuvet3LZdskb3H5ya7PJqnAz1jGRMN7w0M2/T7nbMYkS7ypfkxAHYZkW/i6WrR9fjxpR6T07ADrkVGey8zNblvqUDQyWJbczUmMpKbJnkGmJCVsiUQUdEDXI6BWxuUwIooAoimRXZibs92BRHT9oP0VUU+POE+ZpR4xANMJ//Nd+8o7Hxse7Juk93c8Tf/bgLRfLNU7Nszg7NcQKdwEDwamUvx8MpB6fIX19FkNhfdZtA1Bs0FmwuJgim2zi8ZKV/LTrXHyOZZVMbIkWcPFIoiP4eNckTa+3sfqpmBvEtrwK1mYV0+ufwG2x3zJn3uXKSNBLX2CKApuLPPvinInWZhXzcu8lwqqCos3+UVU9lW8P6Ohous6x3g5s3xwhXdfJKBCZLNRQwgqCJLA6rygpkkZEQJ4WP1klE6E5zi1ZFgcPFtXxRl8TwyEvJQ43m3PKFh071h+YQtP1hPUEgViz2NwSmCaCImpIgjA9j9KJqAqyIJJrc+KJhvh59wUmwgHK0jIxixKRadFWSI2i6VpMOKNoTAXCfHPgEJtfsbHpsRyGyxWiqortTBjlrR5eyOtBcQrcv2cN63bWMhby8+9Nh+JOmXbZzMdrtl418mcu58f7EhxIAkqEH7afoiTNPa8r1YzoTVO12VKZAL5sjcweibHOiZT73an4ldQuQ/ONLwVp88wLHfLseIdnlJ91n6fNMwqaiiZBJB06tqr0rlXJ6hKpHPTyoKuSl3wtRNNiYhgpBKpluq4ngCyIqH4FzS+gaRKeAR+efi/mY2ZMFpkdn9lC+ZZiMG5LDQwMDAwMklh2YhhN0669kYGBgcEikYYVuKJOoika4rACman3uRZui/2WOmeMtI4x3DxKWo6D4vWFvL9iA19vOkzUFEVRFeSQQOWR2GXd7DAl5LPbZBNbcst5Z6gj4Zjb8yrnzbFdCCFPmFf+91uEPLHJpWfAx8CFIZ74n/uu2jG41FS5srFKMiFVYWaWLwoCWR2JgpBduVU0bGig0zuG22KnNO06//gLQNU0unzjWCSZIkdGwu9e7Wvk0FB7/OeWySF8kTBScLbjQ4mo9DKBRwshiAKCNP2bOcUcd2k6l3/ZwmRPzE0m7I1w7oXLSLLIqievLwLsbuHN/mbOjM12biu6RkiJYtUSu6/sk7H3f8g7WyBRNQ0dHVmUiPgjXKnR1dGRzalvoap2lFG1o2yJnoWBQWo6vWMoc9ywBEFAlCU8eYn30dmVbtKyl4/oRBAE8u23vkP6ejg20plk2+6Jhjg+0nXV2Lhf9FziyHAHyAJWl5mAVWWg2MznKncmLaoZ3Pmomsbbg61cnOjHKpnYlltx3Z2ngiDw4O/t4Mzzl+g/N4Q13ULDQ9WUby1Z4rO+N9mQXUppWiYXxvsxiRJrs4rnXcC4GlGLxurPrmFbeAOaot014gADg9tBRFUYCnrJtNivKjRdaiRR5JnKjXy39Ri+cAglomDxC1QejQlhBFEgrz55gXpTThlpJgs/aDvJcMiHLIjxRXs1quIXNOaWWaNBheY328mpyebizxvxjQXIr89h/ftXkZZz8+7PnKbUjoBp04uzhfPcixXYM6563IGoB7PTjBJW0FQNURSRrRL9SnI05LXYkV9FlSubCxMDWCWZdVnFtL3YnnLb4ZbRhJ9tsoma9NxFP+bdhK7rvNB5lneGZ+s76zKLWOkuxGEyU+nKicf/zEeaycIn63bwXx2naZoaitcZdICUuwoIgkBwNIhV0xEQqH3bxFSeRiBDpyw7kw/uTo5nlUSRLTllHBpqxyRKyCYRRdeodGbxq7Xb+MeL+5mIxFxwT432cGq0h/+2Yvei6lRF9vRkl1wB0GLOMPr0E9OkmMeGrItEBS1BPLO7oJqvNh6KNxsMBj2km6y4zXaGQl60aUdfkyjF6l+ajmKGSXsU54+GeOjj6/CPBjh7qIlLDysE3LHjtPlO8p52lY7IREJkbECJ8L22E/zxuocRF+BSd26sL2lMnx5/qDi1GCbb4mAqEkSUxITeMYsv9lplzLPfnUqNKzdlE2B9xsKcZa6HdVklvNbXmDBvlASB9bZCjnzzFAd8HXTXRFCsOroISDGR1gyaBOOlGj22CB/YsZWhz/UwYg2DDq4JgQsf0Ak7wWSV0GNp8OS2SkSDSlzppYRVJFnixH+cTYiWMzAwMDAwMJhl2YlhDAwMDG4GxY0y4xvD6HPmx5ndIq78W+PkcqMcfe40zW/OFocyStJ5+I9288frHuHy2ADnf9ZI5OAEoiZgy7Cy8zObkS2Jl/h3la3BbbZzcrib8FSYqlA6W/JvzCq0/VBXXAgzgxJWaX6znU0funVCoVXuQuoy8mjzjKLqGgKQa01jX20JfcN9oOuUbyth1ZP1SLKY1K00FPSwf6CFsZCfsrQs9hTU4DBdf/d+h3eU77Qci0+ISxxunq3dhnM6g/3ESFfC9qqioQGiCMLMWramo4YUHA4bZVuKaXq9LcH7RjKJTPV5Y04yVxR+Wt7quOfFMOfGE4tF8nQeuC7PvlZWr0BuS6xQULyugKim8tOuc5wc6UbTdVa487GW2AlOhpKOn1WRcVPP3+DuJ6BEeL2viVbPMOlmG3sKaqhy5dDpHePkaDeqrrEhq4TqFMX+jBTxfma7CZtvtvCXWZZhuBXcAONzitVzmQgHrrrf3Ou7IIqYrCJeq0o038hqvxv5ftsJzs75vmmeGuYDlRvYnHN9okhLmoWtH1u/VKdncAXZ1rSritmuRlRT+eEcx7kcaxq/Ur0ZB4YYxsDgejg+0sXPus4TUqNIgsD2vCqeLF21ZLGvnkgQsyRjlVLP9/N9Nt4/UcugNUD/xX6k8yFEPSaE2fih1TgyZz/bEVXh/Hg/QTVCfUY+7y5fx3dajyUcT9d00geTz32kZWzaaTL2c+fRXkbbxnnX/3kYybT4e4NO7xgd3jGyLA5WugtSCga25pRzeLCN8EwMka4jRQVcZxXGohOsKC+gypUdcymYJtNiZ2f+1eOGcm1OBFGIxeHMHb9Od5Z8e3qCSHo+Bx0jhi6Zy5ODCUKYqKby9mAbx0e7MYkSeTYnn6rfgesakeBlzky251TSMTSMpuuopph7CjrE9BmzFQizKCEKAvVkM8RsFE36kEj6EBSkz/93erJ0NSZR4vhIF6quszariCdKV7F/oCUuhJmhxz/BufE+1mcvXAxc5cphpbuAU6Pd8fgtQRCQAS2qz9rD6DHHDT2sYrXJCGYRHdhbUEubZzQuhJlhKhri6fK1VDiz+LsLbzIZCaJG1ASnWVMo9rlvP9SNElLoW6nGhTAzD/pi3wVEk5R0fZuKBBkMeCi8onkqFfOJg+SrRC09UFRHR9MYmgiyRUYJKWT0izgmRERZZN175o9Fu930Xxyi+fV2IoEIxesKqNtXfU2Rh9Ns5b0V63m+40y8caTCmcWDhXU37TxtsonPNuzml72X6PSNkWN1sstdybm/Os3w6CRdH4y5aenadIDXzFOYfosIOiAInBvpxfFnAco2FKMfnW7qMkHN2xotT+sENRUBHUGKieF0NbmZPDgZwjfiJ73AiOU1MDAwMDC4kmUlhgmFQgwPDwOxm9aSEqMLzsDAYGlYVVKC8ss2hmo1FItOer9IQZ+ZvI8tzJL0ZnB+vI+3B1rxREPUpufySHEDaSm6uEZaxxKEMACTPVNcfqWFde9dyaqcIlb9ehHep/yEvWEyyzJinR9XIAki68ln5OvNhDxhIoR4kSHWf2AVq564vslhYCpZJAAQnAimHL9ZSKLIp+p3cHqsl27fODlWJ5tzyrCtN7HpiatP8EeCXr5ycT9hNRYr1eEd49LEAL+9eu+iM5whZiv83ZbjCZ0hPf4JftZ9nl+p3hzfZi66piMqJHVg5Y1bcZjMbHt2A20HO4koGv5CGN4iEMoUsI+FKTot4vIkfp3PjVO6VzFf8bcTBAG7bOa+vApG2sfxnhgnq1VAjgqUbCyk9oEqftJ1jqPDnfF9LkwM4NoikHOBBEce2SLjG7n6griBwdXQdJ1/u3yQ/mk7+oGAh+bJYXblV/P2YEv87XZipJsnS1ezuyAxJqXQkUFtei7NU8PxMUEUeP/OrVRsS0fT9DumCKYqGr4RP3a3DZN1+UxNKlzZnBjtTjl+NVIbyydf9w3ufMZC/iThJcBb/c3XLYYxWL680nMpwXFuJOTjm83v8EfrHkFaQEe3gYHBLCNBLz9qPxX/xlR1nQODrRQ5MtiwiAXwVAwGpvjP9lP0+SeRBIFNOWW8u2xtwkLyqR9e4OKLTfGfs4tcrP7MGpSwSsHK3ARXvZGgj3+9fABPNDbv/VnXed5Vtob1WcWcnnNNaLDmYu0ZJZCuMVivEXboOIcFbJOhK5N88Y0G6DnVv2jnrx93nE6Yq+TbXHxuxS5scmITR6bVwWdX7ObV3sv0eSdRG/3kHddpnmqmmWZWPlHHJ9+3g9NjPXT5xsmxpsXmzvLVm0EK7OmszSxKEIHKgsi+oqVpwijbXMTFF5uY7Jt1mjHbTUZcYAqapobi/9Z1nZASBXQUTcUkSgwFvbzYfZEPV2+65rF8bR50VUcA5DCxBjIdMrGRV5JJp3eMqKaSZXGwr7ie9fYifvLjXiJzYsgRoGZPxbyPIYkij5eu4vHSVQnjg4HUrkIDAQ+LkQYLgsDHa7eyOrOQg4NtTEWCZEk2XCcURtrHGaxVUSyQMagzVQO6LKCEVWwWGVEUGQp6OTvei6prmEU5IaZpKhLEYbKwp6CGn3SdS3jc9H4RqzdWxNF1HX8BjJRraHJMdCPEo4kEUGOxzaIsxkUxArG4pIWwKbuMU6M9CWOyILLuikavudSk5/K5ht0cHmrDGw2TO2Uhs1vHus9MzZ4K3CXL07Gz60Qvb//j0fi1c6hxlNH2cXb/t23X3HdzThkr3QV0eEdxmWyUpN38CPccWxofqZltQrn0Sgu+ET9+twbT8eViFFQLseckxN4Sgg4SQsyhKKAx2TPFZM8UNXsr0DUdXdVpWxfBpA5hwoSuaYSCYbo3qtQ3CpimG2FmhJWyRcKekdoZzMDAwMDA4F5n+VScgW9961t87nOfA2DHjh28/fbbt/mMlhZN0zh06BBtbW0MDg7idrspKSlhz549OBzLx8LewOBuZP37VjLRPYXj6DgAslVmx6c2JXU1zaBqGkNBLy6zJaVA5Ua5ONHPt1tmu8mODnfS5R3nd1Y/kGRnO9w8euXuKcedOQ6c17BbPv3ji0lOLmefv0TVjjJsKSZN/miY02O9+JUwDRn5SbFChStzufxyS9J+BavyrnoeNwNZlNicU7boBagDg21xIcwMwyEv58f7r6sY2+ubjBdL53Jxoj/+73VZxQkxSaIkYhuFzF6J0QoNBMjqFHmwONadZ7LKZFdk0j81Qeej0WlbVR1vAbTkaqx6UccSmH3flG4qWvR5321syS2np+NMwpjbYufpsrVIFSLBjSFG2sdx5jhwl6Sj6RonR7uSjjOaHiGn0IxpPNZRJpkkRFlM+XkxMFgozVNDcSHMDBo6r/U3JonwXutr5L68iqTxj9ds5a2BFi5PDOAwWdieV8kKd8FNP/elpOOdbk587xwhTxjZKrPqiTpWP7U8XK02ZJVwdqw3QXDUkJHP6mtE4KzNLObYSGfCWI41jaJrRA8Y3HlMRgIppU+TEUMseTcyVwgzw1QkRIdnjOr0nBR7GBjc3QQmg5x9/hJDl0dwZNtZ+XgdhQucA54f7095/Tw33ndDYhhV1/hG0ztxpwlV1zk63IlDtvBoyQoAJnqnEoQwAFN9Hia6p9jwzOqkY77UcyFhbqcDL3Zf4E83PMaugmr6/VMU2NMpdmTwcvM7vJzVyUwqq79Iwu/zU9sHgp44xw/7I4t6bl3e8QQhDMRiXN4ebOWR4hVJ2xc5Mni27j6OfPMULW91MLfr4uJLTVTvKmNTfhmbFjl3/lD1JmpGcmmcHIzffxYsUQSmZJJ4+I/30PhqK/1tI4xWakRrbJxSB9iilGOT7wxX31vBTOQVxN73+pyo6Bma5whmrkbOpBlrUCDkjB1jRsBR05XGxx7dA0BQiWKR5Pjx9/3BLk587xwjrWM4cx2sfrqBgpWzbpbeIR/eUT9Z5W40C4RVhQxLspNaoT2dixMDSeNFjsW/pyRBZFNO7D199OUL/LL9Ip0ZKnJZLJ4mmgFem0BGi07ULRDKgjJHFlNKkGbPMKIgENV0QmpM5DMjiJn5jt+RX4VJlDg80Eb/yAjp3QJF52fnZ707ock8RSSqo4kgyDFxkSgKREMKma0wVq6BIGC2m5DMEqsyC1O+LqmoTs/hmcoNvNJ7malIkHybi3eVrcZ9jf3LnJmUOefU8a6tJ7ntXPhZU5KIsOtYH573enHlX7vhwy6bWem+MRfsG8E34kfXdKwTxMUv8f9NowMWSUabrkXmts4KNvvODvK+v3kcgINnfwnTvW6CKGKymwiHokyuEsk+qmEyS8jm2PtwxaO189a4DQwMDAwM7nWWlRhmbGwMXdcRBIEnnnjidp/OkqGqKn/913/NP/zDP9Df35/0+7S0ND70oQ/xV3/1V7jdN1+xfCVf/OIX+fM///OEsW984xs8++yzt/xc7hU0XWc87MchW+ad0Ie8YdSommDPa3D9WNIsPPbf9zLSOkbYFyGvLnveScKliQF+3HEabzSMrutUOLN4qmw1JVcIQYJKlJAavebkMxUHBtrQFC0WcSMKSLLEYNBDq2eE2isiMebLE5/bsbZQRlrGksY0RWOsc4LidYmLqIMBD/9y+QABJVaoe72viYeK6nmouCG+TeHqfGofrKT59VlhR+mmIiq3ly763G4X4+HUURjzjV8Lq5z6q9UmzXb8PFayEl80zLnxPhRNw2m1UuPJQDs9Rdnp2DauAierf60mvs+qJ+s4c+RwQr6waJKQnTKeVQI5x2LVgsI1eWQ9VsjxkS7K0jLJtd2YO8RI0MeLPRdo94ySabHzQGEda7KWv9hma24FQSXKgcFWfNEw1a4c3l0+2xVqy7BSumG2QKLrJGRLK5pKSFXQRI3LTwoUnJdwjoh0r1fxZyv0Z/UhjbnviNfCYPnhiSQL5nRdJ6wqSaKXkBplKhIk+woLerMk83BxAw/PuSbfSUwNeDn01RPo0zbjSkjhzI8vklHkomTD7SteziCJIp+o207L1DADAQ9FjvSUkVVX8mTZKgJKhIsTsYW+Qns6H67etGSxDwbLh2KHG6skE7pCUFvtuneFEZqu0zw1xEQ4QKUrmzyb63af0pIxXzzBWNiHbyxMWVrmdc0JDAzuRFRF49W/eBvPoA8A77CfocZR9v3BTvIbrv1dOZ/7pukGXZY6vWNJkSsAp0a742KYocbUjSaDjSMpxzs8ydsrukaPb4K6jDyKHW40XePAYBsH6kdQwiKSLmCWJARJxCNH6V6nU3xeRlKm3SBEgaLV+Yt6bh3e1Ofd4Ume30PsvvLCRD9vSZ2o6xRy26S4gwU6DLeMLWhReYbjI12cGulGBzZkl/Cxmq035d7GkmZmxdN1vHlxMCYcD0xyLjDA0ZEOfnPl/dd0r7lX2JJbzoHBNkJqFIEZlxEh4bOVZrLMt3sChSvyqP9rEz1rVTz5GuaAQP5lkQ33zdZ0rqxbZlW4eeRP9iQdS1M1Dn31BJ1HetBEna5tGp56EdEiUWhP50NVGxOisbbnVXF6rJfRkC8+VuXMZtU1xOdXY6h7jB8EzhOp1NEAtTDmdiMHY84cE/UCJSdE1vTk0XD/Kv7pUqwR1yzKKJqGqmtENBWLJLOvqD7hXmZLbjlbcssZso5y5NhJPKoPySTifqSAN+0DSIKErEtEdBVdAMUGJk3APqhTcUTGMSEyXKWi+xV2rq3hsYpV8zyL1GzKKWNjdilRTcUsyQQ9IS78vBHviJ+8uhzKtxandIm+0/CNpK7F+UYDi7puXQvviJ/u470IokD51hLs7qvHis1l8PIwQ42jOLLslG0txjQnpj6vPpvGX7Zi9gpkNMPoWtDnfO3JQswdKKKp2AMCBZcksrpnNwh5ZxsY0002Rqfje1VdIyQoaFYY3SWj7bSx4VI69qBExbaSRbuNGRgYGBgY3EssKzFMZubsQnNe3q13FbgZTE5O8uSTT3Lo0KF5t/H5fHzta1/j5Zdf5qc//Snr19+6nPiLFy/y13/917fs8QygdWqYH3WcZjwcQBZEtuSW866yNfEOi2gwyjvfOEn3iX50TSe7KpOdn96MM8/ISV4Kcqqzrvp7fzTMd1uPEdU0QkqUiKZwbryP5qlhNueU8eHqTejATzrPcXykE1XXybM5eaZy46LsN/t7RwlrsxMcURYxp5nxpXAUKV5fiLsknYmeWRcB2SLR8GhN0rbXwpmXluQMMzN+JS/3XooLYWZ4vb+JLbnlpM/Jnt76sfXU7a1krGuSjEIXWRW3XtR3I1Q4sxI6/+eOXw95NldSBjvAfXmztsFmSeZDVZsIKBGapoYIqFHONkRY3ZDPym4Xzrw0yreVJEyoy7eWUKAXMOLvRxV0BElANsdsfaseq2Tn46WIVonnhk/wk+b9IMSigvYU1PBU2ZpFPYeQGiWoRHHI5gRb8P7AFN9tPYZV3pEk2lqO3F9Yy/2FtWi6hniN4rokiqx2F3J2vA9N1wkqUXR0JEFEzjDRv1lBU1UkScRiNTGmBvhu6zFc5t2UX+d7xeDepcqVw7QDeRxBEHCaLEmd0g7ZjNt89y2wdh3rjQth5tJxpGfJxTCKptIfmCLdbEv4/roWgiBQm5FHbcbC5yVWycTHa7fijYSIaiqZ1rvb/XEk6OPMWA+CILAuqzhJtHU3Y5Fk3lO+jh+0n4yLKV0mK0+WJjsL3AsElShfbTxIr38yPvZgYR2PlCQ7FtyJbM4p45e9l+M/67qOjs6Pp13oBGDfFaJxA4O7lb4zA3EhzAy6pnP5l60LEsOsyyrml32XE9w5BWBrbvkSn+nssWdIy059T5WWbWciHKDNM0KG2UaVK4fJPg/6YISgOYwoCZisMqIcW7DMnCN++0nXOd4Z6sAfDaOioQqgI6AoUXRRZ3CNwGhllOqDMu5xmc0fXjtvw8t8ZFpSb59pTXw+ISXKydFuDg62MRj0oBWqaDkqQzUq9W+acI7G5kTO3IV/X7/ae5lX+xrjP7d7RxkP+3m05OpxxNfL6dGeJAfF0ZCfI8Od7C2sXfLH8474ifgiuEvT7xghQbrZxudW7OLV3kb6ApNYIjJhVUHVdRRNQRJFdhcsrF6UXZnJ+r11mF9qjo/l1WdT92DVos+r+c12Oo/Eonx616iMlKoQBKtsoT8wxTeaj/CHax+Kz80dJjO/tfJ+To52MxzyUurIZF1W8Q3FDx5obCJij92X6eJ07BOgmUCKAAhMNAhs2bSOUXW23jUTq6zqGhbJxO+teZBsaxpDQQ/HhjsJKlEa3Pmschdiy7BSuCaPtFwHRWvy6a9XoW8ATddR0OIXHZ2Y63TxGRkRkfwmyG+KXUPWledcVyy3IAiYJRnfqJ+Xv/QWwclYraZ1fyedR3vY+zvb73gRfm5dNr2nEx2DJLO0pLXG7hN9HPiXY2hKLMr2zI8vcv9vb1+Qw9k73zhJ6/7O+M8Xft7II39yf9w9uHRDEWVbi2k/2IUcjCJGBVRp+m8ixEQtDtmCJAnUnBexdiTGnBetmRVL7i6ood07ik7sXlvTdUQEiOpMyiF6d6bzmRXbb+zFMDAwMDAwuAdYVmKYwsLZwvfU1NRVtrwzUBSFD3zgAwlCmNLSUj760Y9SXl7OyMgIL7zwAsePHwegt7eXp556imPHjiW8FjcLXdf59Kc/TTQavfbGBktCUInyXMvReNFH0TUOD7WTbXWwMz+Wg3zie+foOjabwTzaNs5bXznCU/9r320553uNS5ODRDUNRVOJaLPFuaimcna8j7rRPKYiQY4Md8R/NxT08o2md/jj9Y8saDI72DiC5XIE6mbHNEVDD2tUu5KLh5Is8tAf7ebyKy0MN4+Slu2g4dEa3MWLt45d86563vzbw2jq7AJk+bYS0guSuyt6fONJY5qu0+ufSFpMzChOJ+M6zud2Mt41ycWXmvCNeHBuE/E4VYTpPN9NOaVU3UBn98dqtvJi9wUujPdjlWS25VWwpyCxcHdspJMWz8isSEMQOM8w2x+tnfex68oLOdHaF8sURieqhLFKJlZmFpKV7ubfLh+kcWowvn1EVXmzv5lVmYVUOLOved6arvHTrvMcG+5E0TVskglfNJzQDa0Dh4fa7wgxTPPkEIeG2gmqUVZk5LMrv3rezm6Ad5evI6hGuTDeHxfC2GQToiAiWEV0HcxzuhF14Nhw57IRw3R4Run2TZBrc1KXkZcUuWawfMiyOni0ZCUv91yMi18K7ensyq/ihx2n0aYX1gXg8dJVV33f3koigSi9ZwcQgOJ1BTdkwyxKqd+f841fL5cmBvhh+yn8SgSB2ALcM5Ubb/pr6jTf/VFqZ8d6+V7bifj79fW+Rj5as+W22pLfatZnl1DlyubS5GDs+9hdcF0LG3cD+wdaEoQwEBNRr80qJt9+5zvEPFBYS1iNcmSog7Cm4rbYE1wEdeDVvkZWuAsocmTctvM0MLgVBKeSGzgAQvOMX4lFktmQVcLR4U4CaoRcaxqPl65akAPb1ahwZuG2xEQtc9mQPetwUbg6j8zyDMY7JwHwZWlE0gXsOyT+8swv0abvzAosLnK/4SfTqTC+U0dBJxKOIOki5c4sunzjtHlHqXRmxeZOmoqq6/H7uoimxEU4FqsZwQKj7zLziTUPYXMu/h5hpbuAAruLgYAnPmYRJXbnzwoe/NEI/3jpLYaDXnzRWAOMZBYRlZgrQd9qlfo3RfJX5JBXd+25IcTqIAcGW5PGDw628UBhXTxKZim5Uggzw8A849dLNKxw8F+OxRfd7W4rOz6zhfz6O8PhrcCezsdrtwIwFvLxf8+9xlQkFEtjEQTGQwt3ut3wzGqqdpUz3DyKMzeN/Ibrew1a9ncQ9kfQNZ3R0umEGB3UqIYsiUyEA3R6x6l0zb7/rLKJHflV6JpO1/Fejv7kFLYMKzX3V1yXG3LYpsH0U49Nh6c/iWKsqUw0ibjzXWSVu3FpKlZRZiocRFM0RARMZpmtueVkW9No94zytcZDRBUFVdE40tfORkcx4leHUUKxemHv6QE8tSLBrRE0E+iyHv/smwQJQdWZKNXJGEw8T2vajbkcXfpFS1wIM0Pf2UEGL49QsGL512quxoZnVjPWMRF/foIosOnDa7A4lsYZSlM1jn3nTFwIA7H36PHvnuXp//PwVfcdbR9PEMJAzB3twotNbP7I2vj57vmNbdTvq+Jr3Ucwy0HCgoo+p+1F1TQkSWbHu9dx/l/PEQnE1mZcBU7WvLsBVdGQZJEGdz6/Xred1/oauTgxgKgImKIqghBBjwo0DvQQrI5gMxuuWQYGBgYGBldjWYlhdu3ahdlsJhqNcvTo0dt9OjfM3/zN3/Daa6/Ff/6VX/kVvvGNb2Cec4PyJ3/yJ/z93/89v/M7v4Ou6/T19fGpT32KF1988aaf37/+679y+PBhAOrr62lsbLzGHgY3yuXJgYTupxlOj/bGxTAd010Uc5nsmWKiZwp3yZ0lNriTiKgKIyEfmhabDCm6lvD7mcls89Qw/f7kIoxPCdMyNcwKd0HS765kqHGEwgsS3mwNf9b0gqcGKztduPakLoxZHGbWvffGO68KV+fz8B/vofnNdsK+CMVrC6jcXUbT5Kyl/UysTo7ViTea7CKTY106W9LbxWTvFK/8+Vso4VgHRlmHTniFmaoP1lLpzrlhcYNdNvOByg18oHLDvNukcqMBaJkanlcM0+EZQxZEIpoat5SIRhWcIRmfPcT58b4r9ojFrjRPDS9IDPP2QCuHh2Yjr6YiQcKaQppgSeguCl7hGLQcOTfWx3dbj8XLDZ3eMbp9E/GCYSocJjOfrN/Ba72Xebn3UoKbjJ7k1xEjqqkpx28VHZ5R3hnqoHFqCG80FF+ErXBm8cn6HffsouydwN7CWtZkFtHmGSHdbKMmPRdREChOc3N6tBdN11iXVUzhMllUHWoa5a2/Oxwv1JntJh743R3k1Fzf9bJ8Wwlnf3IZLZr4fVu1q/xGTzXOXLc3iF02T4/1km93sbew7uo7G1wVTdf4Wdf5uBAGYlFzP+s6T0NGwT0lxnOZbWzLrbj2hnc5rZ7U9zWtnpFlI4aJBCK0vNXBRK+HzNJ0qndXYLYvTNQnCiJPlK7m0eKVqLrGt1uOMpkijqVxctAQwxjc9RSuyiPJ4g4oXHPt6B9N1/hq4yG6phsvrJIJRdMovSKS+HoQBZFfq72PH7afpMc/iSyIbMopY19R/ew2kkjJliIumUbpXqfGYkxMEpc9LVgkGYsUuyb0TI0TqNAoPSNTfFanc3Ms9kRXdFo9I3T5xuORPWE1mhSZB7GXxyLKcQFuAIVJKYyNxYthJFHksw27ODDYRuv4MGpPiJzzOo1HLiI9VktOdRaHh9oYDfkTol9VNExpJohoRPJFNjyzirp91Qt+3Fg0dPJzi2gqASVyU8Qw811Dl/raevb5SwnuE4GJEG9/5Qjv/ZvHkc131hzq4GAbqq4nRCO90d/EhuwSchYYm5xe4EzZJLVQRlrGGG4eQ4tOz481AXQh5swy57ZwvlvEg/92PO4qo6kap390Adkik5bjYNXjddQ+ULmg89hYX82R0z2g6Qhzpupmk4zZFPt8r8uOxcl4IyEi/ihhQYk7yMhDGqttmVAOv+y9TCgcIRqIxq93R/wdrEbGhoSu6YR9EcyndWzVEr5cHV2LPUddBz0Uq9tEr3jSmeUZ1z2HmmG8ezLl+ETXZEoxjK7rXJ4cpHlqGKfJwuacMlyLcMy8laQXOHn6Lx+h+0QfEX+E4nUFi3Kzuhb+0UCSkAjAM+Al7AtjSZs/Ymy0LblpMDaeHFmXV5dDuZjH1HgfqAJBNTaPnvlI5NmcnLQN4fy9UlZMOjFN6LS+3cmL//11zA4TKx6tZfVT9dORfBn8j8M/RVOCCLJOVBTRAasaYWr/fmwPPXQ9L4WBgYGBgcE9w7ISw2RkZPDEE0/w/PPP87Of/Yz+/v5b4pByM/B4PPzlX/5l/Of169fz3HPPIcvJL/lv/dZv0dLSwle+8hUAXnrpJQ4dOsSOHTtu2vkNDAzwR3/0RwBkZWXxpS99ife///037fEMYswX0zF3wWDGmeJK7qE1hVvOseFOft59gZAaRRIENF2L5y7PYJou8jhNVgRhRgyjo+k6AgKCICTtcyW9/gle7L5AU9Yg4WemFxP9kNkjUnRBZsWOW9OBlFOdFY+LCipR/qnx7URL+6I6Hilewb6iOv69aSyhmLY+qyQulplLVFNv6aL7eNjP8eEu/EqEhox8GtyLy1y//GprXAgDIOgC1otRSruslJfeGpcP1zyuAU7T/MXRTt8YUhhM0Zjdr6CDoOv84odH2fXxjSnfg6quk25aWJHj1GiiGE8WRUKqjqJrmITZv+/KBYi+bjdv9DclyVcuTPQzHPQmvId1Xcc75MPsMGN1xooeW3IreKO/OUEUJ4sSoqZxJTeSZ36jzAh+otPFaABVlLHKJjq8Yxwb7mRH/uLtrW8lE71TjLVPkF7gvOGC4J1IltVB1hUxOnk2F48us1gRXdc58s2TcSEMxFxijjx3iqe+dH2Ft7RsB/f/5n2c+N45PANebBlW1r57xZJ2MjZODsWFMHM5N95viGFukKlIKB6hN5fxcAB/NHxPOOMYJOI22+lmImk8Y5kstEQCEX7xv97EMxCLduk4DG0Hunj0v+/FZF14WUQSRSRE0ua5X7vafZyBwd2CMy+Njc+s5tQPL8QjD3Prslnx6LUjbC5PDsaFMDME1CgHBlp5T8W6+Jg/GqFxchCzJNGQkY+8wLlmvt3Fb67aizcSwizJWK4Qa3Qd7+W/us8yvE1Fnf5VWFdBgLCqIIsSkiCi6zpT+bF7iMkiHXm6F0CxxZ5vVFOxTEexpmp4AjCJImYpFl+jo2MWZBzy/Ius18Imm3kwv47QP/Uw1e8jCnSpXtoOdmF3W2l7QEMt0JBMiXUnXdCx2M3UFuSysj7x/qfTO8ZkJEilMyvlwrjLbCXX6mQ45E0Yz7Y6FhU9mYqIqvDL3sucHe/DJEpszSlnV0E167KKOTrcQbdv9jslz+ZkS075DT3elXQf700aC/siDDWOJMSULBWaqjHZ58HmssbjVJaKdu9o0pg+Pb5QMcyN4I+G+cmJkwxsA3s7OHvA3awzvD7mUiNNi4uyrQ7K0pLnfONdk3EhjK7rhP0R0HR0Tcc/GuDot05jdpgo31pyzXOpz8lnV1E1h4faURUNswKSRcIsywhAgy0P2yt+ft79Gk0bQ/itIWRikUqCDlGrzqmfXqRqfQkD/imiASVB+KcDPquCLSShRFXQdQQdal4W6d+iM7BCQ9BBjsaOB+DuErBnWkGPiQbXv2/VDUcZuUvTGWlJFmC4SzNSbv+jjtMcH+mK/3xgoJXPrdxNnm15CJavxGSRqdpRdlOObc2wIlvluLtPfNxluabzaaqIeYC0HAfnfnKZnlP9mGwytXsrKd9awp7CGs6M9RJWlbiGVBQE3BY7QwEPQ8HYtfWEIFH5uoZpF5r3BQABAABJREFUIFanDE6FOPbt03Qd72XdoyUUBo+xZmKUM1mZBOZ8r4XROT56nqcwxDAGBgYGBgZXY1mJYSDmpvL666/j9Xr50Ic+xMsvv4zdnjrTdznzne98h/Hx2Qn+X/3VX6UUwszwpS99ia9//esEAjE717/7u7+7qWKY3/qt34pHUf3VX/0VWVn33gLU7WBFRj4O2Yz/CleFTTmztr1VO8poer0t4feZ5Rl3XATNcmQk6OOXvZfo9I2TY03jwaI6HLKZH3ecjs9tZ6yNc61p9AYmgVg3l0mUMIsS9+VV4DRZ+EnXOUJqFH1aDOMyW68aG+OLhvi3y4cIKhGCKGgWYrMgHYZrNHIGReofunaH1kjrGJ4hHznVWbjmmYQthv0DzcmW9n1NrM0spjo9l8+vvJ93hjoIKGEaMvLZOOe9CnB+vI9f9FxkNOQnx5rGE6WrFuSOcyP0+Cb4t8sHCE87chwZ7mBnXhV77ZWMdcQW1ecrAMwQGE/u5I2NB1KO3wy251ZyYqQ7wVnEZbIm2HhfSabZwXB0cloEMzsebvFi94pYZRPRSKL9qkWSWZdVvKBzurKTXxRErJIJeY6Qb01mETvylrfAAkiyR587PiOGGWkd49BXT+Ad8iFKAhX3lbL12Q24zFY+XruV5zvOMBEJYhYldubXkW628lLPRcKqgiQI7MyvZu0CX9ubwat9l9FJdLKKaCoWXUYQBNq9o8taDHP026dpfn3WiahobT57fvM+JHl5RALdKXQe7aHjnVjxuOK+kgUViRdLYCIYX0Cey2Svh+Bk6LoL+kVr8ilak08kEMFkNc0rCL5ezFLqhTOz4Zh0w6SZLNgkU7zDcQaHbMYuGzbddzpTkSBDQS/5Nte84t0r2VVQzYWJ/gQRdb7NtWjB8s2i9e3OpOvYZJ+HtoNd1O+7+ndlv3+KqUiQcmcWNjm2SLI9r5IzYz0Jz9dlsrImqyhp/27fOK/3NTES8lLiyOSh4nqyrUvX4WxgcDtY8VgtZVuLGW4eIy3LvmBR83DQe83xSxMDCc5u6WYrn67fueBF/fHuScbaJ8gociWd19kjrYzVqOhX3m7qgACKpiFJIpJJwhzU0DWdoFNDIxY1pM3ZT9N1RCGlSQ4Qqy34o+H43EwXdY6NdCY41SyW3tP9TPXHXitd14lMR9IExoNIvSKRdBWzw4xZkolMi3REQcQiyTxSPCu2DqlRvtH0Dh3eseltBJ4sXRV3LJ7L+yrW8fXmd+KiH4so8d7y9Te0mD8TteiJBJEFCYsk82LPBSKawkPFDXymYRenR3vo80+Sa3OyKacsSdh0o0jm1MeTboIrTP/5QQ7/+0mCkyEEUaBieynbnt2wZPMet8WREKE1Q6Zl8TFDEBN7nRntocc/SZ7NSY2aSftrHfiG/eTWZlH/UDVme+x+bzTk458uvs1YjgfNraE3iGRdgsJDOppVwLfBhCAIlKdl8oHKjSndAyd6Zt2X1agG0yK7ufHeLW91LHie88zKzeyqqKXbN06O1Um5M4vhoAfBr7H/zw7S5YvVZPvWh2OCPkFAnC7L6AL0Rz2oika2bmNcT3xdBR0sIzqYiJ8ngFkXqTwuYhmN0L9BRzfFts1rksgZlMnfmceOT25a0PkvhJWP1dJzsj/B4aRobT75K5Kb7AYCUwlCGIiJEF/rbeQjNVsW9bhRTaVxOl6+ISMv7pC13IlMix1FQcBkkVn5WC1nn7+UsM3qdzUgSlf/TBauyiO3Npvh5lkBmmyVCXrCdB6dPd5Q4yjRkEL5rlLMkoysRtF0HVkUkQSRkZAv4bULhsN0l6tUDZhQIirRQOw9OnhpGF16lUhGkPfqGpcy0glNz2VNmoZZU3knK4N9SjR+j2pgYGBgYGCQzLITw5SVlfHjH/+YZ555hkOHDrF+/Xr++q//mieffBJRvHMWR1544YX4v8vLy3nwwQevun16ejrvf//7+da3vgXAyy+/TCQSSYhUWip+/vOf86Mf/QiAHTt28Gu/9mvs379/yR/HIBmzJPPrddt5vvMMvf5JrJKJ3QXVbJ1jrb7hg6tRIgod7/SgKRoFK3O579c33sazvjsIKlH+5fLb8difqUiQDu8oG7NLkwpXkiBS5sziE3Xbeb2/ib7AJLk2F/uK6si2prHKXcjznWfjFS9REFB0jYsTAykL4ABnxnoJqVGiWkyoMDP/100g6RK2D+Rf1ZJWjaq89Q/v0H9uKDYgxCa/G55ZfSMvC62ekZTjbdOW9kWODN5fuT7lNn3+Sb7bcjyeqz4S8vHtlqP8zqoHyLuJdviv9F6KC2FmeKuziaEfNWP1xV7Y0s1F7PrslnknsvkNOfSfH0oev4XZynl2F59t2MUb/U0MB72UpWXyYFH9VSewewtraeztZ2651d0rYp8SSTNZeKCwjld7LxPWFDRdxySKPFu7DesCJ8Ubs0vp7z6fMJZpcfA7q/cyGPDgtjjIsd0ZizdVrmwuTAwkjJlEkdI0NzD9mfr7dwh5YtcETdVpO9iFI8vO2vesoD4jn/+PvfeOjyw9y7SvEyqXpFLOWWq1OoeZzj0zPT3RE2xjksEYm7QYWOADg/FivLALuwv7LctHxhiMsQ3YBhzG9tiTp8NM5xyUc06lUuU64f3+OFJJpZK6JbW6p8fU9QNP11HVqXjOed/nvZ/7/sSOJ/HHI3htjmTxdWdBJWPRIHkOT4oN9TvBeMxa1JNTHIEsxypFkshzrFxMPBD20xfyU+TMoiHn7rtUDd8YSxHCgJVx3nm8hw1HVmaBnQGufbuVi1+9lrw9cGmY4FiYrc+tfZFlKeweO6pDSXHUArC5VOyeOy+6zRXT15uNvhKybc40B5O9mUibO8YmKxwtb+JbfddStj9e0ZyMg8jw7uSF3qucHOnERCBLEkdKN/DkCtyqqrx5/HzzQ7w53I4/HqY+u5AjZRtQlnHGvNdMD6QvEgJMD6bHn86RMHT+sf10MtrSJiv8QM0OdhdWUenN5Wc3HuK1oVYmYiGqvfk8WdGMU0k9J45GZvibm8eTi/oTsTAdM2P8+rbHMsKxDO96PHluavetrnmt0rN0HFLl7BhdNw2+2nUhxdktkIjxjd4r/MzGWzeMCSE49bkLdBzrSW6r2FHKQ7+0Lyk6CM5ZvCwsAswKYWZ3QsLQMTDJtdkJGlGcIxDbAEJZ1Dgw+yBZlhFLONF5VQcRPYFAwiYrOGSFVwdb2HMH8SThBU0dhmYmnXmEKShuU5ioMdFVA6fXjirJuFU7D5U08GBRDbkL5gZvDrUnhTBgCXte6L3KJl8peYtcC2uzC/gvO5605lZCsDmv7I7OX5cnB/hC+xlCs+OzhNAxhInH5uDkSCePlW/EJivsKapZ83OshA1Hajn3T1dStuWUZVG84fbxwqshEUnw5p+fSo6jhSnoOtFLTomXLc+uz5j9SOkGWqdHUgSaVd5cGpaJX74VumnwNzePJ515TMNEHtJoPqGiahL9F4c4/y9XsXtslG0pof+oREiPI6sypm4iAVOboKzXTv0Vmad/5CjuYnfKbyaqa0zFw1ybGuLcRB8JVcO2W6f8ooxY0OghLzjmtPjSDkzLUerOodQ931RY4s7hyss3iIfmmxMdIYm4U6T56xa4vSiqzBNlzXSMj2Eq859r6U0Fp2aCDWSbAnEdxaEiyzK6XeAZg81flzF9Co6QhC0+d55YX9G/t8DDM793lI5jPYQmwhQ3FVK9p2JJkdpSUe8AQ5Hlx0BLMRKZ4bMtJ5NzK7us8BONe2nyFa/+DdxFTGHSF/IjSxKmEHyj9wqD4WmyZmtmB0vq2fbeZnLKsuh+ux9Jkag/WE3Fjts39kmyxNFfP0jba12MtIzjKXBTsaOM1/7PibT7Xv92K/JOL5pppAhfEoaOvuiaIYBojkAIgR6bbzbIdQfxOWbQotCbn0tYtSEB6qwQRkJCV1X88TAu1bfWjyxDhgwZMmT4vue+E8P8t//23wB47rnn+Md//Efa29t5//vfT35+Pnv27KGhoYHs7Oxbuqws5tOf/vTderlLEo1GeeONN5K3H3vssRV1TDz++ONJMUwwGOT48eO3FdGslnA4zC/+4i8CoKoqf/VXf3XH1owZVkelN5df3nKEqK5hl5W0BQPVrnDgpx9gz4d2YBrmXVsg+o/Gpcn+pBBmDkOIFOtdAIRAjxsMXxih9SWTo4/WU7I9tYBw1T+EQ1GTneVzx9Clyf5lxTBzOdsLHTuQQLYr2FU7sufW3Uftb3TPC2GsHXH9O21U7ipLRh6tBd8ylvYrsTw+P96XFMLMYQjB+Yk+3lO1Zc2v6XYML5qwm7qJFtOI5tiSYpi+s4N0bu6l8ZGlFzybjtYzcGmEsbYJTMNEi+nYXTYu//sNtr63mZKN9yayqtKby09u2Lfi+2/KK+XxiUrORwbQHeAbkilqkylszCeryMtTbKI2K59r/iGcisqDhTXopsE3ei6jmSbb8stv6WB0sKSeoBbj7dEu4qZBuTuHH6zbRbbdteqC7WSPn9ZXOonOxCjfVkLjI3X31PHjPVVb6Av5k4UaCXiueluyCDF8fSwphFlI96l+tr/fWvSTJSktwsap2KjyLl3Ev9dUe/PoDk5ikxUSswIoCQlZknApNvYXr0xU8tWuCymdYo3ZhXy0af+K7ejXwvD1saW3XxvNiGFWiKEZXP92a9r2Gy+2sempRhTb+n1/NodK09F6rn+nLWX7xsca1vV51hubrPCzzQd5ofcq7YExsu0uHiltZFdBelepbhp39Tf//chDpY0UubK4MNGPhMTugko2rFMx3JyNnshwb7nuH+L4SEfytikErw61Up9dQMMtxg9zVGfl8eGsvXfzJa6ZvJpcOk/0pm3Pr85d9jGvDbUlhTBgdUP/a/cFGnOKyLY7qcsuoC771oumb491p8W1BbU4Fyf672v3tgwZ7hYNOYVszyvn8tQg5uyid5Eri4dKLUeSoUggzUkXoD2w9NhxIUNXR+k41oOpCMZrTcL5gqGZAQrf6mTLQ40AbGmq4XRsAlkHc8FlXzZBiYOh6Jh2CRsKIwUxJn5IJrdL4JeZF9DMltDicQ1Zh2rNy0BWmIQwmVPWuFQrunRxvc0QgsFwYM1imJQ4yQXCB1lVsMUkNn/XRnSfk4KHS6j05vFAgeVKsJgz4z2EtHjSqcCpqMiSTFtgjH3O9Dm0S7XzYOH6RJYcH+lIrYsAhjAxTJMoGiYC5TYR1OvBxscb0OMGLS93EA8lKN9WwoMf2r7uToWDl0fSBOUAPWcG1k0MU52Vx8c2PcTx4Q4CWozG7EIOlzasqd57eXIwpU6mx3UMr8lYg0nxFdBmY1MlSWLg0jA3qnUotaE6VQzdROjWcR3OF+w70kxBuS9l/9/pu86JkQ5CehzNNHAqNmyygr9ZEFUSFJ8SjB6QCJeCMyQovaqTO6FQtXvpWttqWCgmCxaaJNygu+c61azzQM6IxMPvsRrCmirKePaVGs5P9KHbwTcokzMqs/djO9ATJlF/lPBUhL5zQ/Rv0RjeaCA5FKSoScVlgXdydiwrQd1diPxx5ThX1ARRskyz2nLbl+PrPZdTmgwSs+LFT+588r4RPw9HAny+7RRT8QhCCKKGljy/BbU43+i9Qrbdyda8cqofrKD6wdU7/aoOlU1Pb2DT01Y04Gjr0k2GkelYUgSmmwZCCCtuU5aRFuknFVUha3pe3AjW1URBQwjoys7mn2obMSUJU5JIKAqGLOHSDBw2d8ZxMEOGDBkyZLgN950Y5nd/93eTg/W5/wohmJiY4MUXX1zTPu+1GKalpQVNm1fx7tu3soXO/fv3p9y+evXquothfud3foe+vj4AfvVXf5WtW+/MVSLD2rmdfaHquO8Oz3c1IS190RssO3+brCSjahIRDSNh4DkHfeOD9J8f4pFf3p/SISAvPkfNTlRutWizObeUlwZuYpOVlEzxufiZbXm3ntgPXUt3MZnbXtiQT9fJXjpP9GIaJjV7K9lwpG5FRZzDJQ1cX6OlvSbSCzpAWofDelPqziEYGLOESwkDPWGAANd06vsdvDqyrBhGdag88cmH6D07yPG/PI3NqSLJEiM3xxlrn+Q9//VRcivvPJpsPBrimn8QVVLYnl+x4qiBW/H0B/aS+3k7fccGME1B6eYiDvz0vN1uk6842Zlzwz/MP7afxpz9fs+M9/BkRTNHl7HmliWJ91Rt4fGKZuKGvmbnk9HWcV753ycwZwthQ1dGGb4+xpFfObCm/a2FAqeX39z+ONf8w0T1BM2+kpQuR1lZ+viQ1XsnEA0MB0lEEuTX5N7WjncpnqnawmdbThIzdNyqA83UKXRmsdFXwiNljSuyxW6bHk2zTG6fGefMeC8HViimWQvu3KUXANx5a1sY+I9IPJwgEdHSticiGrFgHE/e+saM7vyhLbjz3HSf6kMC6g5U03jk/ndYKXZl37KT/Ox4L68M3MSfiFLuzuG56m23XdxeTCKi0XmiB39fgNyqHBoO19w2b/77hY2+Ejb61icGR8wKL06MdBLVE2zIKeb9NdvTOtQz3D2uTw0vvd0/vCIxzP1M/eFqOo514++bF1Xn1+VSe2D5eMqb0+mfhyEErYHRFS8KzyRiS2/Xlt6eIcP3IxNdU4x3TJJdkkXZlmKeqtxMf9jPUCSAjIRTUZPz0Sybc8nYoZWIR4ZvjGFKgpuP6oTzrXmIAL4YusR/iVWQ7XSx7ZFGHvzmIGeUYdQEmLNll7wOyOqBgcMg6wKhGAhAcwqmy0CNCoQCc7lIQgIpYlJxRaW4LYZ3o4Opo3amtAgl7myer97Ga0Nt9CxwXwFLR3MnTpu5lTlsfW4jV19oQbbJEAVZlVEds7EZCYk9xY1sqW9adh/XpoYYjQaTYiTdNAgLE6/quCfOlyEtjjwr4DcX1CFMBBtyiu/ZorokSWx9biNbn9toxV/fpUbB5YTj6x3HVOXNW3XkzVIMzcaFzzFX74rkmsSFSSIH7DNgmgIFsE8KwgUmil3B6XVg6AbCFDz9k/tprp4XGoxEZvjnjrO0z4whIc0e84KoriFJIHSTQJ1EuFRCdwIIQh5Be5HOxpegdPOdj0NKNxXR8WYPCZeg9RENQ7GijIQMSNZvwqxxkKizxvFaTGfj9mpyW9z4+wPYym1s+FAtZVtTx77ne7u52H8eu6wiKzKGw6Bvj4Z3QqJQ8rDjBzZT3LS+jkOrodzjY0d+BZcmB5LbnIqaEtk2dG2UoasjOLOd1B+qxpWTWrsyTJOu4Hw0kCFMdNNkwgjRF5yidpXzp7vFP3ecY2o2MlszDXTTIAYpzkTnxnvZepsa7GrIr8nF7ralzc1LNxdZjlxCEJkTeRpWlPmGnOIUZx6f08UPHtzMjY7rxENW/B1CMBHMJqHbeKu0DCGBwzCIzjaI65JM1FTY1u/DfjCzhpAhQ4YMGTLcivv2SrlwIrJwQiLE4inxrXknXE9u3ryZcruhIT1zdylqampQFAXDMJbcz51y/vx5/vRP/xSAyspKfvd3f3dd958hw/3MRl8JLw+2pG3fml/OYxUb+VbvVfqDftQpk+qrKlnjVgFGmIKr32pJEcPsyK/ge/030AydRFizFvwlkK/PEC9N4PCmu/mUunN4b/U2vtN/Hd00iZs6NknBqagcLm1ke/6tuxFcvqVFFG6fKy0mY6xtkuBYiAc+uP22n0t11pylfRtT8Qj12YU8ukJL++155Zwe60nbvpw7znrxREUz3YEJgsEowrC8afKvCaQxDTzzn/3iyftiJEkiMmVNkhcKEUzdpO31LvZ+eOl4qJVybryXr3ZdSBZyv9V3lW155VR689hdUIXHtjbXJ5vLxuGf34P2kzsxTYHDs/x+Xuy/nlJgBHhtqJUDxfW3FOTZZAXbHTgkXH2hJSmEmWPg4jCTPX7ya5bvwF5v7Iq6pAMEQHFzEZ58F+HJaMr2+kM1d/11zVllj9ywOojcuU4Of2wvRau05C5yZbE1t5wLk33YZyNLnq7cvKp9LBeV1hEYv6timNr9lVz7VktKxrnqUNjwaKZLHqxrT+fJXvovDGFz2djwSG3a78OV4ySryENwLJyyPavIs6zY6E6QJImNj9Wz8bHvn++oIzDGV7suJG8PRgL8fetb/Ob2x1fcsZ2IaHz3D14nMBic3++xHp761BFszvt2qnVfcnykk5cG5uc/rYFRPtv6Fh/f9lhSiPz9StzQeWngJtf8Qzhklf3FtSt291pPXMvEXnw/xPnYHCpP/fYjdL3dx3T/DHnVOdTsq0K9xWLkcu/bs4rPY4OviGv+ofTt73JxUYbvb0ZujnHlGzcJDAcpqMtj5wc246tYW6PA2587T8ebPcnbRRvy6XpeYioeScaKDYQDfLnzPD/XfIhch5vtixZtAR4pbbztc7lzXfirTML5s/6ls/8TkjU+8/cv8pGnHqGgLo+feN8RHp4Y58roIFJE0P9/byJHTMZ2YqlcANMg6bwiZGvBXNJhoWFJ+TWVwusSiVgC9VScpkGVfT9/iMpN1kK5Ksn8XetbKY0nuwuq7riDf8cHNlN7oIrRlnH8AwE6j/VgaNbcq2JnKc1P3LoG+fZoF3ZZQV8QPSyEwKPaaV4ngeut2Ogr4a3RLpyKnaiRmK3zShQ7s3jUUUc8nLjlHPducDdrx+XbS3D5nCnzHoDGh+5PUXnJbLSQEAJDmJgqmDrEPYJrP2bFhdnCUHMW8kah7JpCexNWJNis88XOwooUIUxIi/HXN48zEQthCpF0BpptLyOh60hY4jTdDcwKcJAs8dlEs+DCV67y+G8+tKr3okU1Lnz1Gj2nB1BsMnUHqqjcXcaZSD+mAkKZPb5nv35FkUGReHO4HXuXxom/OYMWtZrZCupyOfyxvTiz0gVjbdpEiuhJsSlIkoT6aA4bKKdk071xHr4VP1q/m02+EtoCY2TZnewtqkk2z5z54iVaX+lM3vf6d9p48r88jK983jlGliSybA6CWpy4oRM35oQfEp9vP80vbnqIQtfy0e/3golYiJFoeizmnCvL3HG+2LFvLQyFAxwbbidhGjxS2sC+j+7ixGfOYs6eiz0Fbh740W0cH+5AAHZZRRNWrJGExI/U7SaoxWifGcdnd7GroAqXamPDH1XSerKb77x5npAUxzsk81pfE/17nMRlBdU0cSUM4pI1z6y4rFBiZOacGTJkyJAhw+24766WVVVV7/rYnu7u7pTbVVXLd5stRFEUSktLGRiwJv1dXV3r9poMw+Dnfu7nkkKbP/3TP8Xjufddlrq+uozXDBnWi1JnFkeLG3l9qC0Z7bM5r5TduRUosswvNj9E36VBjn/7DADmAs/KmbFgym/XI9v4ifoH+cKJE8RsOmpcouy6gtbp5634OQ7/p6W7cfYWVLPNV8ZwZJosm5OYoZPncOOxOW57bDQ8XE3HWz0pAgN3rouyHUW88KlXUl4vwM3XOtj0TOOKYrbKXdk8W7GZkcgMha4sHJKy5OuJ6RodM2PYZZX6nEJqPHk8VdrM60MtxE0Dp6JytLyZClfOXT3WbQMaW1+202YLIdwSOUMyrjYNwzTQDQlZVUACX2U2kZnILT+DyEw07bMDiAQjd/QeNNPgha7LCMPad0zXSJg6bw114LE5eGOghZ9rPnRHhVDJJqEgLfs6TWEyGkrPgNYMk9HwNBWeuydKCYzMLPm5Tg9Nk1PxzhZIFvLwr+zjzBcuMdYxic2psuFIHRuO1t71a9XZL19m6OZospgemo7w+l+8xfv+8KlVRUl9ofVt2gOWmMXA4PX+FpyoHC5duVghS3GAkf5dZSv2VX0OE7EQ0/EI5Z7c2zqfASgOmaOfOMS1b7Uw1TNNVomXLe9pwlPoyowVsAqS7W/Ojye7TvWy/6M7kWajFcu3lWBz2dj1Y1s49henMWavDYoqs/ODW5LjvQy35u3hzrTff8IwOT/ay+HSlYnZ24514h8KpCyOTQ1O03asi6ZHM5Ffq+H0cFfa9zERnqHDP0Jd9t1fRDgx3MmJ0U4iWpxGXxHPVm21ujnvAV9ofTsljudroYvEEtqqzufrwa68ct4e6kAX89+DgkSZMwtN097183QUqDu0cG4ubnnN2eOromNy1Hrfs2893+mh3pu/4mvVTl85N7OHuOkfAazdHCiuo9qdm7neZbgvmeqb5uX/cxxz9nzcd2mQ0fZxnv1vj+HMXp1ryPDNMdqOdaVcIwf6xmgfE6iLBKMd/lFmYhHcqp0PVG2n0Obmmn8Yh6LwYGE1OwuqbnvMVO8tI9F5CUyTBWoYkCSmHVG+9w9vcfB92+g60Yce16nfVU5wLMRAVEdI4BwHzPTzXHaXIFQhEc8luWpun4H8NpN4REuKZgJjM7z5f0/y9KcfJac0ixpPHh9rOsSpsW4GJqbI8Ss0B9yE88M4vHfmwOIpdFFXWDX7bzdXX7hJIqKhaxr+oUDKIvZiYloCVUi4JZW4oSMQKJLCQ0V1CNO8606vR4ob6Q1MMBiexivZQIJtZhG2z/k5FjyOrMpsPFrPzh+8e9HL9xQJHvl/9nP+ny8z0jqBM8tB85ON1ByouC+vA1tySjjuyKIzMIYxOx4QdpguBUUDSRdoLonOwxreb0kIp4QigWwKSwyDwB8Nk9ASSffkM8M9ROIxa5xnmkn3J0sGBRgCYQrUMBgqSAuOXQnQ7DDUMoqW0FYVY3X8b0/Tf3He5e3qiy1sfLyB+gM1jMx0Y8gCw1zwHQgJJJPJcJDjnz1NIqYlz19j3ZOc++pl9n14V9rzKEJKGcPqMR0tpjF+M8GFm36ufLuFR3/tAIV1a484v1PGOyZxDsU5UlNNbqnPep26zsxIkJuvtaecp2ORGBe/fpXD/yk1+vJgQR3f6b9GXEswd361yQqReIxv91zlQ+vgTHQnKCZIhokpBFpMR0/oCMfsaVs2k+fvrTmld3TsnR3t4UsdZ5Lj5beHOniyspn3/uETDF0dQXWplG8rRVFl2m4MI5kCp6TglOYFU61TIxwqrafOm8+VyQG+3H4GVVYoi7g5LnoIPgzxkMToLkGnnIOpWPGlcUlBMSQU3brOFXSrFD6x8nGpqt53S4EZMmTIkCHDPUESq7VayXBbPvGJT/BHf/RHydt+vx+fz7eix27bto2rV68CsHfvXk6dOrUur+mP//iP+fVf/3UAnn32WV544YWUv7/xxhscOXIkeftzn/scH/nIR9bluRc/T4YM7yRRXWMmEcWt2slaFFmjx3VufK89aQM7R05ZNjV7Up1b4uEELS93YCrWnCo5V5clap6soTs0SVCL4VUd1GUXJuNxQhNhgqMhFJtCblUONufKYxTCUxHG2iaJh6z4i+KmAhS7wrVvt6bdVwhBfnUudo+d3Moc7O7ln6dzZpzu4KTVKQFUeHNpyilOWfCYiIW4OjWYLIy5VBu7Cqpwq3Z00yBqaLgUO6q8soX8vtAUPcEpEoZGrsNDk694RZbME91TDF4ewdAMhDEbT2VTkGTL0UWxK6g2xcrklizL6NJNRRTU5S25v/BkhI7jPWnbK3eVkVflW9F7WYqZRIzTY9ZCtkCQWLAw7ZjNbC9x57A1r2xV+7XivgRe261db2KGxmB4mu6ZSQxhpnTUq5LM4dIG1DtwfrkdfecG8Q+kCnHmXCXs97jLbyUYuokkS8jrnA+/GGEKwpMRuk/1YRrpw6/6g9V4C1cmVA1rcd4aTRfNOhXbihfxwRJuvT3avaCzC1RZZm9R7YqcAEwhuO4fYiRidWApkswGX9FdFVt9v5OIarS81JHihihMgWmYyY5DxaZQu68ST74bLaYRGAqCgJzyrFVdV/6jc3lygLFoMG17Q3bhslbfsWCc2EwcV44Th9dO/4Uhpvqm0+6XX5Ob4iqX4fYcH+4gZqRHf+3Ir7yjSImVMBD2J8UKc7hVOweK6+66ACSsJXhrtDNt+2rP5+vFVCxM58w4QS2OKawFBUmScKt2tueX33YMcr8yFQ8zFA5gCkGxO4ti1/ILxQD9XaN0hiaIukyEAjZFpSQrm8bsIpwrEH0Gx0KMtkygxTRcOU5c9VkYLokcuxPPPYghyZBhrQxcstwcF1O2tZjC+tUt5I7cHGO0dSJlm6nA6GYzLSJGkiQeKW284zlK/+Qk1wLDyaYEJCnp7IIANQ6+IRlH2Dq3O7MdhMbDIKwpfaAB4nMmOJKEEhfk3rQeHy2SML0SShTcEyDrpDSryDYFWZEoaswnf2M+ncEJxqJBIvE4whBIs89fOGxj454anNmrO59qpkHnzDjj0RA2WaHKm4tnRqHndH/K/WxOlY2PNSAvI7LvCU7SvkCACZbrw6GSehzKvRlHCiGYTkSJGzpZpo3O13rS6jBVu8rIvYM5+f2IaYo1zzljM3HGOydJRDS8+W4K6vOWjWC6UzpnxmkPjGEKkeY0K5sgzZY3ckcV5EonATk9lnzh+K0tMEZvcNISKZipon0J61iS4wLPAMzUzv9ByJIlVAtB4YSd7UeWj/9ajBbTuPG99mStztDN5Hkhe2MuPfmhtFqNTVaQJYliyYs4mz5HUJ0qzY81EBgJYmgG2cVZ2N02AokoZ8d6kho8Pa6DCcUdCopmbfMWeKg/tLKIxdthaAZ6XMfutt9WHGSagt4zA8yMzL+fvGofFTtKkSQJ/0CAvnODaY+ze+w0P54+Dr05PUL3jHVelyUp6SptlxUeLttwJ29rTYS1BP3hqWSzoT8eYXhmGtMQCHlW3yRAkSRsdpUKTy4bcorWPL4XQvDyYAsJI1V8IksSj1c0pzksX5kcZHSBW41pmJi6Sf6gSrE7m1itQl/Ub/1uNMNq3hQgC6ueqQsTIYFsWNfPOSWZrEPOiEyR6aZuf9WKzwWPPPLImt53hgwZMmTI8G4nIwe9C4RCoZTbTufKJ7gu17wl++L9rJXe3l4+/elPJ/f/Z3/2Z+uy3wwZ3o24VNuyrgWqQ6VsczGD10aSE2bFraLX2bkyOYjHZqfSk4tdUZMTJ3lR872hwvnJ/qTlcFTX8Mcj7C2uZaZ9OqUYONY+Sf3B6mUjkBbjyXNTu89NcCyEf2CG0dYJcqtyUPMdTNqj6HawhyXc4wKRMJOLc2PtE9Ttr8KTn97d7I9H6JqZf00C6A/58dndlLitRQJrsXsY3TRn7WwlorpG6/QoOwsqUWWFrFUULYcjAVqnR5O3p+JhLkz0cbCk/pbxTKZhMnLTcsGQJClprWvqBqpDRbEp5FfnWsXb2XmtqZsMXh3Bk+9eMjbJk2+JisbaJpMLz7mVOeRWrs0GfA6nYktmoC+sHS2cb88koox3TDLeMYkW1XDmOCluKlyyiy9maFyeHGQmYUX6eG0OtudXLClWCGsJzo73oJlGstgkS1JyUl6XXXhXhTAAxc2FhCbCaLH5AkHRhoL7UggDrMqNZa3EZuJ0n+qzOjbj1uei2JWUIsxyBeulSJhLO38sLi7eDpussKewmu7gJIFEFI/NTm1W/oojMfpD/qQQBqzs8Bb/CHkOzzsSqxHVNUJaDK/NsWzcx/1OIpxIiwU1dJOFJxNDMxi4NEzT0XpsTtuygr8Mt6bYlZ0mhpGAoiUWyYUQDFwaZqp3OnnHgpo86xrel77v20X1ZUin2J1Fb3AqZZtNVshz3n13loHQdNq2iJ5gKh4h33l33TQ1c+lOzuW2323ynB7ynB6uTg0yEplJXqcieoLLk4N3XSAUSEQxhInP7l63eKyhcIAb/qFkdOVodIa67Dj1yzgOTY3NcF0bQ8z+9CQTdEOnVM5akRCm/+IQY20TyflENBBDHQvR+FAtLk9GCJPh/mZunLoYI75617mlHDplA3IMByFSn6fYlbUucxS9M4LdIYjPHr9CEghZsiKOBOgOmKo2KW5VkA3r+FRsCoZmIAnI6YR4LuhZEqom4w5ICNNAmALvuITil3FkObAVqgSG58cQkiIhK9Y5S9cMLkz0E9RiJEzdOhUo1nvXHTBVoDN8Y5zafUvHuS7HpYl+pmfngzFD47p/mIJxG4tnEFpMJzASJHeZaKsqbx5BLcZoZAYBqLJCs69kTUKY4HiY2EwMt8+1ZL1hOSRJSrqvjbdPpglhAKYHZ77vxDBrF8LEaD827xQcGg8TGAnS+FDtqpxSVoIW0xiZmJ4Vo6R/L6YMdlUBARXbShmITkNiide8QOBc4PTQG5xEliRUWcYQJkKAS7FRIecQaJtCnTERusC0Q6jcikwCS8iQcMF4rUlET6x4jmloZvI6rJkG4WJBPNtytzHGZiiUXfjzNUwh0E0TRZaRJQmPzUGFnEO/CKa4pQDIikTLKx3JOseQNErFzlLyqnxszSunY2accCKOLQqecYnpYoN4tjWO8ARC1ApxR2MbIQTD10aZ6PEjDIHdbaNieylZxcuLxv190ylCGICp3ml8ZdlkFXuXjH0Clt1e6cllIJQumLybc28hBGE9jk1WUs5TwUSMc+O9SYeW8WgQn82FawJCBUmjHyQT1JjgQFkdLsedvc6EqS85RjeFoHNmHM00kYFSTw55Dg9V3jzGY0FLWGaYmJqJGge7XzAVCDDqM1GcijXfNwVCAWQQc2LL2bcrqRJ2WbYclYSgSsoiO8uGM8uBaQjmPhZhCgzdvGUMaIYMGTJkyPAfkYwY5i4Qi6XmwNrtKx9oORzzg81oNLour+cXf/EXCYfDAPzO7/wONTU167LfDBm+H4gF44zcGCPsj+Lw2CluKqDp0XqCoyEkm0y7w89wbNK6cxQGwwH2FtXgcNvwFngITYRT9mfW2NHN1EqALkz6pidJtE2nbDc0g5GbY9TuX1mUGljWpkPX5oUkYyPT+JtBM63ugViWIJolKOiaL4mZusnwjTEaDtek72+JjniA8VgoKYYJajHiuoYmzOQCrSxJTMbWJtgbCE+nbYsbOhPREMXu5bt0ExENI2EVYWVFtqy757x1AWeOg3jI+uwTbkEsSyAZEu6AxMxwMGVhMqzFiRk6OXYnJc1F5FXnEg3EcHjty076V4NdUaj05NIbmkqpn6gLbFGNyQR91waTrz88GaH37ACqoxpvQerC2w3/cFIIA5ZDzNWpIfYW1aQ9d09wIimIkCUJu6JgCEGRK4tqbx6+FUQ+6AmdsdYJghMR7E6Vwob8FTuWADg8dpqO1jM9OIMe18kq9uL2uW7/wAUIIdDjOopNQVbuXKwSDcSY7PajxXSyijzkVfvWZb8rpf/iEImIVRCUFRlTNzG0+SKFy+fEnbvyzyjH7sQuK2mimLVEbzlVG825Jat+HMB4LP0cIrDcpKq891ag0TY9Sl9oKmm3XeHNZaNvbe/rncSV40RW5flOYwGYIq3QHQvGSUQ17K7vTyeYhKEzFAmQMHUKHF7y7oIgocSdTVCL0ReawhQCm6ywIacYjy197B4YCs4LYQCE5VZW/WAFrhwn0cD8+N/lc96xqPI/IvVZhUR1jfFoEAE4FBtb88puKZRdLxbGAi3EWGb7UsRm4gzfGCPij8yOKQtvuTgxR/Yy5/P8O4hSvFOEEEu6JkX0BCE9TtZdcIeJGRoXJ/pnXfAsJ71teRX4HKsbPyxGzC5OLF7O6w1OUuXNS+vgBegdn0AsHg5K0D05ToHv1o4ywdEQE11TLHxCYQiMhMlE5xSVu1bnCpghw70muyQrReQxR1bJ6s9JOeXZjLVNEA/Pz48Vm8y2kgq6NX+yW77ElU2Tr3jtL3qWaCDGzFCQfEkhnGWQcAriPjDtyWZ6678yxLIFbr+EJEl4C9yEJiOzTqkSbmFDH9Pnxcl2BWEKfBU5ZBV58FXkICsSHR1D9ESn0F1gj0pkjwjsEQmK7QS1AILUxghTBsWAuFcQHo2s+H0JUzA0PMmUFkaSpZQxod+dYCm/HlNb/volSxJb88ppyC4kZuhk25woK3R4Te7fFPSc7ic4Ol8T8JVnU/VA+aoFk5Ky9P3XW+Txbma8YyrFhQggOh1jZiRITtmtr0urIR6K03GsB71QQ+QKhESaIGQOm6pQ7MkmYmoEE7G0v+ctqDvkOTxUZ+XTF5xEkWRMIZAlMBEMagG8EiiKAgpk+wWyIpgpE1gphRKyTUYolsPwSud3ziwHziwH0WAMf71Am305OhKJHJP83hgHGhuJydaxPqPFcCo2jJ4og51D6JoBQiCrMrIiI4QgHkxgaIblGqXKIMPg1RGmszWGYzOYQlDlziV6YYqJGgNtdvoiZAjlmXTOjNOYU7SarySFoe4J+icmUW1gMyQSEY2eswM0P96A6lh6iSc0Hl5ye3A8TFaxF1eOk9yKnBRnX1mVKW5a2iXTa3NQ7MpOcTuRkKjNWvr+d8p0PMI1/xBRXUs2LWzOK0WRZLqDk2lj+OlEBFUIJF3M/nStqEvNaTXild+hGMYmq8jIGKSfY3uDU0mx01AkwKbcUso9PnYVVNETnMQ/GUSZlsgal5EE6DZhXSdMgZh14Z67Vi1GlmWrKXD2ohLtCaMFrX8PXx+l+sEKEhGN0dYJ9LiOM8tB2dYSsorurqg/Q4YMGTJkeLeQEcPcBRY7wSQSiRW7w8Tj89aSC11i1spXvvIVvv3tbwOwadMmPv7xj9/xPu+EQ4cOvaPPnyHDQuLhBN/6nVeQgk68WMfodE+MJz65l/ynczk33suZ7kspj9EAUVbCoYqN7N7yAGe+cJHBq6NIskTVg+WMPawwOpXeIm4Tebim00+5TtOx4uNCTxh87Ssv4ovOd7H27tQJlJo4vQ4M3epYi8R0CiZUfAPzxX05LC/5PNpgKx2DLWnby4trOVS9DbAWu793firNHtdrc3Jo7+qP6UvXDbQlBDE1tZvZXbi8bayhGfi/G00KCoQpMBI6sk3BXZJFvxrEiGrEC7MZ2zlfs5F1iR32Og4d2o5mGvxzx1lapi2Bk0PWeG/NdnYUrK4rbyUcAi5O9HNtapDu4CQhLY4hKxgAcZOSUxr2kdSOErvHTtZEHgff92ByW0zXePHCJILUSewksGX7rjRxy6Vrb6BF0gU9u6u3sa+4Nm37YkzD5Lu//wbagIKTLACm2qPs+PVdlDQt3UG93gzfHOPsFy8THAthc6o0P9HI1uc2rnl/E91TvPKl4xi6ioxKuNUgx6/w0K8cXMdXvTyxYJzez49jx1pEEEJgxA30hI7L56JyVxkP/Mi2FbtEzVE63cQ/d55Pdt0Vu7L58aZ95NhvP34YiwYZjwYp8/iSXZlrobvdxtiiaBGAptrttzye15u26VG+0zYJRfPFxS7gQGMdm3Lf+YXH4UiAEyMdTMUj1HrzOVzacMvuuRJRybl/uWxZ9gtBIpjA5rGlCLhUu8Lhhw5jW6bw+W5mNDrD3948ScQFoNJBjIPFZTxTvXXdn+sQENUT+ONRCl3eJRfGAd7+3HkCgXQL+GK9nCd/8wm63+5jun+G3KocavdVLluQznBrHsYqekf0BCXubOR1FsIMhqfpDIyT43CxObc06UIQ7Mvl+EhHyn2disp7djyGcwWd8nNjSnnhmLI3xgOfeJDCutvHipRMN/EvC87nRa4sfnzDvhUJWO8Wr5+fQTPSO18f3LZvTcLL2/GF9tP4XfMiMg244YCPbzt4R13Ummnw4rmp9O3Axi07KHWnC9daxqNEPJNp21Ul77bj9vNfucrYRBA9lhr5pdgViivLMvPhDPcl/niEQCJCmduHul/mVOwC3aes6B1JktjybBPbnm9e07737NjD9e+2MdnlJ6vEQ/MTjUnHkjnB4XqJHnvPDjAWsIQ8Pr/AiOu0PWYQKZFQnSpiNp4CoGhABY/ETLGJpyCXartKtstJ6ZZiah6sYKxjguN/fZagP0yoTMJX4OHJHzhIYY0l9h4KT/M9p594tBA9rhMRMFMrsdsspa04yGQsC1myHDDm1kwlwBaz5qe146UrOh8YmsFrf3ySsbhO4HAOpmLtx2WzoyoKxMB3KnVRVlZkHn/usSWbEYZujNL6UgfRQJzSzUVsfs+GJR18bkfn8V762ybwseA5AlD9UB1Vu8tXta/othjfPPNymivRwz+xj4rt/3EiJ69NDXJ1agibrPBAYRU1C8QFL791DDOQPh6pzW5g06H1i6Z56+/P4R3PQ9ZMbjZrJNyzIjIZLMmDRWlOEe+r2U51Vh579QSfbTmZ4hZ6tKyJoxWp8/dDWFGM/9Z9kXb/GEbcQJgmsiIzk2Ow/QU7sjnrRlepo9VK2Jw2qy9gdh92XzGHNuxb8fvZVLWFr//TCQINcwI/ae7/sCky+3fvS2lI6T7Vx8lT58giD2GYJMIaQggUm4ysymgxPfkZxLPBKLUxXS7oyRUoNh8AQcCxI49A4aIxlCQxkOvkow+ubRzwnd5rnEkMYZZY58+8fpna0yqSkKi011B/qGbJx7mHrnLzRkfa9m0NW9l4yIpBMveb9J4ZYOj6KM5sJ40P15J9C0H3ftPk9FgPLdPDuG0O9hXVpPxe1wvdNPjDSy8TzpqPYB4AakvyebpqMxeuvY4WSa2haDENWdGJ56TWLxWbQk5DPYeqNt3x6xrqcHFi0bxBlmQ8qj1FDNhls/FDO+bHsd/8Ly8RXCBOMmcEg/s0ZJ8NLaFbDX+S5TiuzmpIFY8NXTJBUYnoCUtIZkAkF+pP2fANWdfP0DGDRETHbWQjTJCnZQKjcR7+Hw+tujEtQ4YMGTJk+H4kU6W9C3i9qQPGWCy2YjHMQjeYxftZLdPT0/zKr/xK8vZf/uVfYrO9s93Dqpr5yWW4f+g810tiRkNeaCxsQOcbvTg/7GUwNgMpzhECQwiG4jOoqkpWgZej/89hEhENSZGwOVRu+kc4GxhIPmIuY7mhsJQ+ptIk/rllvhUfF9HJGHrESHm98ZzZVh1FRrVZHWu6ZhLPlpDF/P3ylnmePaV1HBvvIr5gsUORJA6U1ifvHzUNFFXFXNSxLKvKmo7p7YUVDEYDGMLEFAJFlnHIKlsKKpbcX1hLcGmyn7ihU/OBBjr+sRVTEoRKQZLs+OpzOFk2jpDBREZ3CtQ4IKxijemQaC0N8aiq8vpAOy3BcZjt7AmaGl/sOoesKOwsqFx32/8HS2p5sKQWQ5icH++jPTBGls2J8kqA0cmhtC4WoYEZFymfg12yPmtjkRhJApx2R9pnVp6dx1A8vZuzMjt/Rd/XwPVhAv3B1ONCQPvL3VRsvvvFyNhMnBN/fgY9bv3WjajJtW+0klOURd3BtYkrbn6nA6GR8p5Grk7g7wlQ2HD7Rco7xekGm6paVs2zKA4Fp8fJD/3pM2sqQAM0F5TzqdxiOmbGcSo2arPyb/sbNoXgq10XOD9hifYk4OHSDbynavOaXsPBskZaZsZSTm1ZNgc7iqpRlXt3zW8LTSw6X1u0BifYVrhy9627wXAkwF+3nkw6NvVGpmkJjvMrW44s24W7+YkNlG0qpv/CEHaXjeBoiJZXOlOuIc1HG3F5vj+jeF4f6SAi9JTv9K2JHg6WN96VRfgsVSXrNlE8Hp8n5bo6v92Fy+Nk02PrtxDxH50Cdf06nBfy7b5rvDncPv88I+18rPkwWXYnT1ZvZkKLcHPaEvd5VDs/Wv8A3hW6knSeX3pM2XWsn9INt3c72FRQzm/nFtM5M45DUanNKli3iKC18mBJXZpAqD6rgBKvb92fyxSC1pnxtPP4tB5jPBGm3LP251RRKfH6GFnQwQzgVmyUeH1LxrI8uKGR68OLIgAkeKCu/rZjKW+uB1VRMcSicbOkUL6lNDMfznBfYZgmX+2+wMWJfgRW1Ov7a7bz0H/ax/bnNzMzHCS/Nhd3rgthChKRBHa3fVWOHVkFXvZ9aNeSf1vvo6FkQxGKZM2J427B2DYF4ZHAI2F32jFNQTyUQIlDoFwwVWmNzceYwuaW2PSSjbLmEpweJ90nBhh3xOj5cQXDAX1E6bjyOh8vfprCrGwu+AcxZQmbx4HqsmMaJkKG80xiRwFFwUQAlnsEAiuqSYbiHpVd792yovNB1/E+xtunGH5KYNitc6QAIui4JZndVbWU7zfpfrsPBNjdNvb+5E6yC7LS9jV4ZYRjf3I6GUkU6A8y0e7nqU89kjKHMEyT1sAoMUOnaQnHvEAiSmfvCJKQFsgjLCba/NTtXd2cLSvPy+O/dpizX7rMVO807lwnW9/bTM3u9W9YuV/5Xv8NXh1qTd6+6B/kg/UPJJt2SjYUMdGWHk1TurF4Xa8rgf4QspDx+mWaX5fp3aUzXSZQZRmn3Y4EPFm5icfK54Uu2arKr24/Ssv0KNOJKA3ZhRS5Un9/hjA5O9ZLy/QIbTMTaHEdyQCQMHQBLplwoUT2qMR0mcBW5EJ26mnjgjpf0areb1FdAdt/djvXrpzGNMyUX6sosJNdOP86o9MxzvzDZRKBhDXlEtbrkyUJCQUjaiDsCkN7YGIrmA6QdYFhA4ciUBa8Vn+ZQJgK0pwd1ex/Eqaxpu+rc2acExPd1ucxW1KYqoacUSjsklHttmX323y0ka7jfWjR+Zqfy+ek8VDt/GNUqN1bjdBhonOK4cujeB5y4/AuXadQgX2ldRwsa1i1q9Rq6JyeJCy0tN/B1cAwz6nbqc4pYCSe6lht6ALflMp4rsn85FlCddmpzMlbl+PlQxv3ke1yc3KkE800qFBz6AtPoWsmin3e2ThoJjAksM8+Z9WuCq6/1EawSCAZkDUuU9Jjp+8BA0MBYcqoGjQeVwgXCOKVKo0NFWx0FPH13stIsoFTVtDjGsIGPXtNdnxdQTYlIuNRS0wzFzknSQgXDJwfYdOTjXf8njNkyJAhQ4Z3O++aSsyNGzc4deoUbW1t+P1+QqEQXq+X3NxcNmzYwL59+9i06c7VvetBdnZqAdfv9+Pz+Vb02Onp6eS/s7LSJ6+r4bd+67cYGbEKuh/+8Id5+OGH72h/GTJ8vxEPpXd4J1yCV4sG+PrFfjTTQDMNXIoNQ5hEDasr5MrUIH/f+hZb88qoyyok3z3v2NGcW8L+olreHusmZmgkDAObLPM9fysNz+Vg++YC61GbzLb3rbzDzp3vxpntIDYz/7q9ExLBsnmrZEmWUB0qsiboOKiRcIFvVObgo01L7jPH7uLnNh7ixf7rDISnKXFn80T5RkoWdMmqsoxDsaGb5mxBz5rI29eY6b6nsIaXB1qYiVtWupIBmwpL8NjS3UyGwgE+c/M4kbm8aQ/s/uUKLk8NElV1FJtMODGOkrCypJEBScKwgxq3PmOb08ZQ3Prcr/uHATCFSVhPJC1Gv9Bxmo6ZcX64fvea3hNYsTA2WVnSlUORZPYU1bBnNtboQvZVJmwj6IvS8CRZouqB1E46u6Kys6CSc+OpjkObc8vwLvGZHSnbwE3/CCF9/neyPa+c6qyVRdZE/EtH9C23fb3pOz+IHjfStne91bdmMczMyNKRXjMjoXsihlEdKg0P1dL6amfK9rqDVWsWwsxhV1Q25a5cpHRpsj8phAGrPPTGcBsbfcXYOhNc+1YroYkwxU2F7PyhLWTdJh5rQ04RP9bwIK8NteKPR6jNKuA9VZtx3EMhDLBsdrznLmaXr5Rjwx1JIcwcI9EZbkwPszVv+c7Z3IqcZOe0EILs0iy6Zhc6avdX0fRo3ZKPixkaJ0Y66QyM43O4OFTScEcLye8E/eH0Yr/AcvW4G2KYldD4UA2tr3aix+YLyXa3jfolIggz3H+MRGZShDBgXbdfH2rj+Zpt2BWVjzbtZyIWIqTFqfTkrqq4PxfTmLY9aF2LZxIxzoz3MBUPU59VyI6CijQnBMcqz+d3m/dUbgYEZ8Z70UyDrbllvLdm+115LlmScCrq/HhvAS71zps5nq/eyufa3kZbYEH/TPXWZZ2gdm9voG1qhLfj/QismLrtnjIONtxe9FZ/sJobL7ZhaMa8y4EkUdJcRNPR+jt+LxkyrCcnRzu5MNGfvB0zNL7cdZ667AJySrPIKbVqUr1nB7jw5auEJiK481zs/MDmNY/L7yaefDcND1Vz/lgrbY8LDKc1T5ZViZip41RsVBUW0BDI4nh2P1JMZy7HSHMKhjcZXP76TaaHZmg71k3vjwoMO5ZTnyQRcRt8+cpZfungUQLTYUzdRFZlJFlCkRWiegJJkpAksMkKCXPuHAAKElmaQkPEx6bHqmnPnUGE7ZTdYowW1hKcGuum+0GdSK5ATYA+O7QVAmQh0TycTf6+LLa/r5loIEZelS/pThfVE7w21EZHYIwchxvniWBSCDPHROcUoy0TlDRbDqBTsTCfaTnBVNyKcVIlmQ82PMDWvHLihs6/dJ7jhn+YRI2GeJ9OYbdCUbuMM2Jd07wFa3M0K9pQwDO/dxQ9YaDY5FsK/E3DZPDyCDMjQQrq85eNc3m3ENUTaWMUAXxv4GZSDLPpqQ0MXBxmenBe2NnwcM26z2XzKnOY7rdqJ95Jmc0v24l5BQU/W4PN52DrMnUFWZIpi3ro/exNXum/jK8ih7xnyzDKVbbkVvCtvitcnhoEIGYkMO2W84U8N0USUHe4movZ40xKEYSio5kGimHimHXoq/LmcqB46fnPQgbCfi7Onte2uEuY/vYwosycexrr9SoyO+prUh731t+dS4k9XYq+xyBQA8ZsGcawARIkhIFqmqizY0dFllHCJtrC0pDELed+t6J12opKV+0KiQUNNoFSk/JRJ5U7l3dCzSr28uR/eYSrL7QQGA6SX+1j6/MbcXjna0mGbvLyHx1jomPeSa/ttS6e/vSjOLNTa05T8TD/3n2J9sAYNllhb1EN76nccldEMeoy+5wTMh8ta6JlepTAgkjx0n47xVcEwVKN2GwpQwIq7DlsWUfH2Oert/F89TY6jnVz8m/P0/9eE91hRX3b3XYUu0KB04NzQV0k97ESrvtaiWKNd51hCVuxC5cqo5kmAgOBgEIbZp1MNFvn8kg/56JdGAooOhiymBUhCnS7IJwnyJqQEIaJMKxoMQCEQItqaJH0sXWGDBkyZMjwH5H7WgwTjUb5i7/4C/76r/+a7u7u296/traWj33sY/zCL/zCukQMrZXa2tQYir6+vrRtS2EYBkNDQ8nbdXW3H+Qvx6VLl/jMZz4DQG5uLv/7f//vNe8rQ4bvV8q3l3L5azdTtnXu14n5BCoqqiSjYRA1NAzTRCCQJZmErnNmrIcrk4M4FJXHyjfyeMW8qOX9tTsodmXx1e6LeG0KsiSjC5OWsiDv+7lmjCshHF47jY/UJhc6V4Kiyuz+ka289Xfnk0Wssg4b5j4HAeYXYTYUFtP10ARaQgdJQqtV+J6ji49RseR+K725/Fzz8latVd487LJCCJHSSRPWEkT0xLKL0MtxcrSLoBZP7ktC4rp/mP6Qn0pvbsp9v9N/LW1h5HikG5fXjk2yYQoTUwFhB1t8VhCDlcns8NpRbNYkuchliRTnFlTihjGfAT/7Gs5N9HGwpH7Vi8bj0SBf6jjLUCQAQlAW87J/qoTaLWUUbVi6MLfh0Tra3+zB1E2rQ0cIJEVm4+MN1B1Id7F4f80ObLLC+fE+BILt+RU8X7VtyX0XOL382rZHOTPWm+zM2pK38kl/aXPRkkHFpZvXnm39TlNYn8fM8CK3HAkK6lcmEFoPdn9wG44sO10n+xBCULe/iq3vXZvd/J1wZXgAQzOQFTmlq/dMWwfaXwyCANM0aX+jm87jPTQ/2ci29zYn7aOn4mG+1XuNjhnL5eiRsg08WFjN9vylzy/3igcKqzg+3E58gejEJitJAdo7yVR86Zz2qVhkxfuQJImmo/XJhdTB8DTHRzvw2d1szi1NFh9NIfhsy0n6QrNikiBcnhzkY5seSju/3s8Uu7Lwx9M/n8VdpveSrGIvT3ziIa584yb+/mlyq3zseP8mPHnvXIxNhpXTHZxYcnvXou0FTu+aBFcVO0q5/O83ltw+GQvzF9ffTIpUz433cWVqkI827V/189xLFFnmueptPFu1NTkGvpvsL6nj1cHWlG3NvmLyHLcWZa6EhpwiPr7tcS5NDqCbBtvyyil239qB6INHDvF0JEzPxATl+bkUelbmWOTMdvDkbz/ClW/cZLRlHJvLintsfPj29YAMGe4116aG0raZQnDDP8z+2UVn/0CA4391Jjn/jExFOfnZc2QVe++JqHw1xENxes8OMbFTxnDNLRgL7JKKJEs8VtbERDzMTSYxIgIhFs1v80zioTjtb3QTzQd94SVeCIQk0WP4+c7vvUYgMUn88KxDrMeGjokhTOySSkTXUoTQEuCxO9hVXs1geJpvBG9YWSrAQyUNPLtEDGRfaIrPtrxFsCRCPN/EsFniAVvMmusCiIE45164gGpXyK3I4bHfPJwUwphC8JmbJxiMWMKGwUiAeEOcDd0K2eOp53P/VJALfWN0zIwxFg0R1RPJsaUuTL7adZGmnGJe7L/Odf8wpjCJKTpmHvTlGfTvNCjsktl61Uv94TsTSan2WzfcaDGdV/7oGBNd88LpugNVHPjZB9bd5fVeMRWPpDnGAkzGwximaTnpeu285/eO0n9+kNB4mKKmAooa118EtOW5jQxcHiERnq8vNW2q4KHtD9z6PfQH+NqvfwcjYWDY4cxTEaKhEaR2kKSzqJKcjIiVDQlTFhjqvBjG7ZcYzA0ypoQt1+JZHZmQZBqzc9GFSbnbRyARpfAW84Fz4718tetCspTxavAm1X0SFeMqA9t1BKA6FEpcORScMTh+7DSKXcE0TPrOD6LYZEwttYlBADanyozLJFgpIRaupMwaNQPowkCddQgs8mZT+L0o7XsSxLKsV1Mt+3h/485bfo7L4bVZbqCKXcVmghbXEUKgZtup+8VNKK5bHze5lTk89At7l/1737nBFCEMQGgiQuurnWx//3zjsRCCf2g9lXTbS5gGx0c6scsqT1aurEF5Kh7m/HgfUUNjc24p9dnLR3HXZhVQ4PQyEUttbtozG8fsc7j5ta1HuTTZz3QiSmN2IaGBca4mWtj8XRvjdSaxbEG+5uRnfvGRdRfsxIJxzn/5KrIpUXVRoWufDsKKarI7VJ6r2pY8L5lC8NWBi5Cr4jBky9U7WxAVcdySHYcig6KCDYaadeJ6DBHCUj5KEqYKsgmSKZAUiTkDQlvU2r/qVJnO0khkgWcE7LMfmSv3+9NJNkOGDBkyZFgt960Y5tSpU3z4wx+ms7MzuWB5q4mNEIKuri5+8zd/k8985jN84QtfYM+ePffq5aawcWNqLmpnZ+eKXFl6enowjPlB9+L9rIaenp7k55ZIJDhw4MAt778wngngE5/4BL//+7+fvP3mm29SXr42BXuGDPcr+TW57PjAZq58/QamIUg4BZEKCftsAUaSJFyKjZiWQBFWt5eQICGsmblmGtgVlZcHW9iSV0bpAjeV/vB0erepBJPlBs8dWPu5qe5gNbnVPnpPDyDJErX7K3EVubkyOchEPEStN59jIx1INhn7Ajvj7uAkXTMT1GUXENU1To520h2YQB03qOh1UF6cT8Ph6mVdKtyqnRkthm4aSEjYFAVVlukIjLMtf3XnhjNjPWjmfGe9QBDVNW74h9MWa3uDkym3TSHQhVVoVCWFOWNmIc9mWQuQdRCqJR4yhUAzdfzxCJ9rfZv6rEI6ZyaS+fRgdSOrsws8/SH/qsUw/9h+mtFoEGGYxEIJOs0Y02N+6v9HO5vfs4FdP5xe3PQWeHj600e4/p1W/H0BPPkutjy3kfzqpRerbbLC+2t28P6aHVbRdvZ6GJ6MIKsyrpzUCa7X5uTR8qXdgG5HVrGXnT+4hUv/dj1Z9M6r9rH5mbXtb7VU7S7n/L9cSXOHWUoktFK2vbeZ4eujRPzznV6bnmxMdrsuZiYRpTUwhke105RTvC5FE0WV2f6+TWx/3zvjYidMwanPX2Ag3E+iwQAJbC5bsmA+0zqNS1j3iwcTIAQGcPOlDoaujvDsf38MxanymZvznaIxI8RXuy5gl5V1FcNopsGx4XZapkfx2BwcKq6jIefWYqw8h4efbT7ESwM3GQxPU+rO4YmK5nfMRWQhdVkFdC86lwHUZa+tgL046qXYlcXPNx/GY3PQERibF8LMoguTN4bb+InG5Qug9xuPlW+kc2Y86eIAsCO/IuU6e6+ZK5KPtU+iJ3QKHSqu3FuL7wOJKN/tv07HzAQ+u4sjZRvuK+ePdyvj0SCXpwZRJJkd+RXkOm4vSMpf5lyQ77xzoQVAXpWPnT+0hctfu4GpW7/b0i1FjLVP8sLoNfylMVSHmrx+35weoSMwTkPO8gsA9wuSlB6DcTd4vLwZm6RweqwbzTTZml/G0xVri/BbilyHmyNlq4sz87k97Kha/W8ku9jLoZ97cNWPy5DhXrPQyc80TPSYjjAFg6eHiD9ajsProPutvjQ3EQR0vd1334lhuk/1kwgniOUyf9YSoCd0ZKfCiwM3sMkKumkQN3UkJ6gLjCBcARlnloNYMI7DkIHU+YgkScgRwWS3H5+QKLwiMbrNJK4lkGTLrSBmaIt7ChBA3NB5a7gTm6IgL5hbHBvpYFdBFWWe1DHOC71XiRkaql1FTxgYCHQ7qFGQdOsZsgZAj2pgCvz9AV76X2+iV9qYqDWRyh30hv0pzgqyTWak2UgRw0gKfM/dzdCwtbgdTMQQgFu1Jd0XYoZGf9jP1Vlnj4iWsJJaZhsohAwT9YLcw7UpbhN3g9ZXO1OEMDDrIHqomtJN9755IzIdpe/cEJIEVQ+W48pe/cJzodOLU7ERW9QEVObOSZmHKqpMzd71jY7qC03x5nA7U/EI9VkFHClr4rnff4z2N7uJTEUp3VxE1QPlGJrBeMcUdo+NvCpf2n6O/+VpjIR1vPQ/IhEtABAIISEQJISB0BLImkCKCxRFIBQJWYPcQZmqSyr9PxFJie8Gax5zY3IYh6nQH5jiwkQfv7D54SXnBIZp8q3Oq8QjCUzdRJIlDN2kf4fEjm/YyOu3M1NsYo9K5AyH6DdCxENxhCmwuWxoUR1ZlVGdKnp8vnnK7rFbjSRZCkjmwuSd5IlGzD4/iiDL5uQnNuwjt9ZO+/FuJvwRqreU0rR17fWM3QWVvD7USlhPoDpVJLtM1NCYzhF8NXiVVy518NGm/SkOz6vB3x9IuW0oAqGkb+8P+9NiJwHOjveuSAzTE5zkb1vm44NPjHTy+KLmwoXIksRPNx3gaz2WE41TsXGwpJ6HSudjf1yqLSneBDDeW0A8lKDjRA+lrRIF9Xns/6ndOGx37jQ4x0jLOGe/dBl/7zSxUBybQ6WgR8Xtl5iqMpEEfPijD1OWO1/fG4sGk3UUWZFBAdM00A0jef01DevHZQhrH3POZQiBrEmYMigmCNP6XRaN2amtzaViXzlfHrnEuNewHiOg+DxUttjIr3n3NMRkyJAhQ4YMd5P7Ugxz4sQJnnrqKaLRaHLRTwira0OWZbKzs/F4PITDYWZmZjDn7I5n79fe3s6jjz7K9773PQ4ePHjPX//GjRux2WxomjWRefvtt/mpn/qp2z7u7bffTrm9dWv6AupaCIfDdHZ23v6OCxgbG2NsbCx5e+69ZMjw/cbW5zZSf7iayW4/coGNtqET88UrIUiEE9akFhDCxHQAS6yLt02PUerOQYvrTPVMIxuLS2AWznWIDlkYnTHH7sL5ifU3eq8s+Th/PIJmGvzVjWOMRALEQ1aR4LJPYvM3B2h/vYunPnVkyVxgn8PFjBZLEycuzg9fCWF9UZSAsAQx/nC6e0KB05vsZoO5WoOV2zz3OuyKSsLQk8U4m5DY6atCdalc9Q+iSgohPc7N6RHaAqM8Xr6RlwdbCCZisxFQKnNeosXu1bkODEcCjEattj4tpifzeaeqDOpOKVx/sY3Gh2vJKrYW4cJanIRpkOtwk13sZf9HVx/LJEkSwdEQJz5zlonOKZCszvODP/vAHUfuzLHlmSaqHyxntGUcd66L0s3FKS4idxNntoNHfuUApz9/keBoyOqofrLxjqzYvYUeHvnVg1z52nX0hMnGJ+qp3LG0W8658V7+rfsixuxvPd/h4T81H8J3m8VWwzRpDYwSMzSacoqXjP16J+k82UvHmz0U5siM1RkIGbSIhqzKeOwOigdtzGAtFrDANck0TGZGQ3Qc74E9WckCzkJeGWzh7HgvI5EZKjw+nqjYlFbQXw2fbztFW2B+DHLTP8xHNuynObfklo+r8ubxMxvv/bjvdjxU2sjN6RHLPWqWA8V1a3JqGYkE0mzUR6NBXh9q49nqrUwu60Kz9Pb7lSpvHr+y5VFOjXYT1GJs9JWws2DtgishBKGJCE6vHZtrbYXQy1+7wbVvzbtW9JzqJ+KP8uQnLcF7T3CSVwdbGI+FqPbmc7Ssic+1vZ38TgKJKJ9vO8VPbzzIhtuIu/6jYhomN15so/v0ALIiUX+ohqajdSkNERcm+vlK5/lkbOPLAzf5aNN+Gm/zmTZmF1LlzU0Ri6mSzCOlqxNH3IotzzRRf8gaUxq6wcm/OYuhmYw/rqFHTUzNtBYJZ9/OSDRwX4phhBCExsI4suwrHlf0BCe5NDmAIknsLqi6ZezHcsiSxKPlTWsW895NJmIhnIptyXjKDBnezewrrqU1MIZpmEkxtC0mEXphhJdOH+M9v3c0xU1zIWKZ+e47SSKcIJol0JwmmsPqopd1a+EwYRrJmF9VVlBlBR0DU7bup2gS5a0KD35oOxe+fBXJHyOn3yRQOfs+JQm7x0b+W1ZtIB5KUPKWib9aQsgCU5UwbSZLWWxKArSE5UqhR3UcjnlBOkBXcDxl7GwKQW/IcmmQZAmn1wGJBAnJtGoRpoRzQlB0fjbiKa4TlwzaShJMNVnr9YxLGHaBU7Fhn61BqE4VM2+BY40sUfLBGq4keue3SRJCmMQNK6bGFAJFkpGRUSQZIawgj/kHzL5jSXAjNsrjrE7EaGgG0ekY7jyXtUB8G8balnZ6G2uduOdimIFLwxz7i1MYs7E1F75ylSO/eoCS5tW9Drui8kzVZv69+1Lyk7XJMs9WrU9NeDn6QlP81Y1jyXnvYMjPhYk+tudXUHIgm935G7ArKsM3xjjx12eScd2Fjfkc+ZX9KcInf9908t+BZYzQNNMAGRQbyAmJnDZB3ZsyNpeKJ9/N9bEAIg/rNyXNHkUCDGFFzxAHUzd5Y6iNDzakC07HJqeZDASTc1nTFFZNzwmaA5whCWdIITYTQ8p2osW1pNBPi+nIs64wqsOOK8eGoRuWcE0BPDJ7DzUzXTjIVDRMRKSK3mQk7JLCJl8pP9G41xIxeWHXD67Pd+ixOfiFTQ/x0sBN+sN+pmJhXIotGbnpT0T5atcF/vOWI2vaf16Vdf4xZUHPAwaTtQZCglF1iq2xcFI8bizhYHSr7Yt5sf96Wnzwa0Ot7CuqJcu+tJAs3+nhZzYexDBNS5C4YF6QiCS48d12q26V52bTU43k1+Sy9yd3sutHtqIn9DUJ1G5F3+gEf3/ydYI7DVx1EvmnwDOqIckS7oCC+6qMO89Faa4v5XFu1ZZ2dVAlGVmSrbjV2d+iEOAICBKLyimyLihuVYgUgMiSyS3OJuHWadtoMu6YZEaYSGEr0g8JRh+EzTn5GTFMhgwZMmTIMMt9J4aZmJjg+eefJxKJJAufFRUV/NRP/RTPPvssW7ZsweGYH3DH43GuX7/OCy+8wOc+9zn6+vqQJIlIJMJ73/teWlpaKCi4t/mxbrebhx9+mFdeeQWAV199NaWTfzlefvnl5L+9Xi+HDx++q68zQ4YMFm6fC/dOq7u7KVJMy2werx430DARCtb/L3iMBMmiEkC23UnX232c+ceLaFGdiM9Ef06geuaFFjZZYXfB2rtBVkpddgHji2xEJaA2K5+rU4OMRGfQNSPZuWzYBaMbDJwXQ7S/0cWWZ9NdqQ6VNPBPHWdTzmPl7hzqslZ/fi10evHHI1Zhda62KGDy6wPMfLSZ7OL57u3HKjbyj22n55tvJIlKj4+ANt/C51RtNPmKUZCQhMTe0lp25FdwbryXq/4hlAUiDkMIpuIRfm3rUf7yxpvEFnQeNfuKqV3l+1EXRAaYxnwBQDLnW4QmuqZwFDj5atcFrkwNIoByj48P1j+w5siPY395mqne6eRzDFwc5swXL69rF3JWkZesonfGVaN0UxHv/V9PEJ2OYffYU+yyhRaH8QHw+pCyV9aN2nmyl7cXxItNdk9x9OOHKKxPfXxU1/haz6VkQRAse+oX+68vWXCbYyoeTnFMUSWZH61/YNWuSXeT/guWDb47ILPxNRuDWw2iOYJy1cOP7tjP5OgIF29MJxc25oqPekzHiMPZL1ymvCj93GAIk+6ZSdyzwrgb0yN0BSf5+LajZNtXH1k5EPanCGHmXsvrQ623FcO800T1BNOJKEXOrJQuTpdq4z9veYSb/hEm42Hqsgqo9OZiCsH5iT6uTQ3hVFT2FddSm1WAoRmEJyO489xpVvGdM7eOeqnNWvqYqF2jC807SZEri+drlo6DWw3DN8Y4/Q8XCI6FUewKTY/WsetHtiavZzOJKJcmBxFCsC2/fFmXkfY30iNbx1onCAwHCWcb/M3N48lzx1Q8wrWpIRKmnnLdFMDJkY6MGGYZTv/jRTre7Enenuq5RDwUTzpq6abBC71XkkIYsDqGv9l7hZ/Ke5Dxjkmyi71LLkJJksTPbDzIiZFOOgPj+BwuDpU0rNoN7na4cpxU7Cjl2F+eTi6OefwS4XxrAcfQjWSE4zvpdLQcY+0TvPXZ8wRHQ8iqTMPhGh780PZbLlC+PdrF13ouJ2+fHOnigw0PvOPxeevBYHiaf+44x0g4gCRgR0ElP9y4O939MUOGdymbc8v4obpdfOPieRKmIHtUpuqCgmJITA/O0H9+kJq9ldz4bntahGrNvvV1qFgP1E1eLrkTGHZAAkMBUwG7LKMqlpjDFCa6aaJK1rYinwcjqFOOlyc/vo3yigLsbjtv/vkpGk/bGYgYTFcLCsp8HKxsoP8fr5HQNYRhYqqQyGb++YSYm/pbmhVmhSLCEqgoJkgGaFFLkD53bl0cBydLEnkOd3JuIckSLqcDpykoPKnh9IOjTUu63yS80PF+iOeDmHt+w5prx00dm6xYLl+yxL4dTeyqKyI6HaVkUxE3xQR0zYth7LJC1DDR55wJsK6hX+k6z+6CKl4ZbFnm05dwKKsTHF//TivXvtVKIqLhznPx4I9tp+qBW8+fvAVLu3V5C9fH6W2lCFNw5gsXk9d6sOpHZ754ief/4IlV729vUS1V3jyuTA1ikxR2FlSuyPnuTjg+3DE/7xWCiJFgRovhT0SxyQonR7r4ucaDHP+r08SDCYQQmIZgtGWc81++yoGfno9OcuW6CJOg71HJihe7RfnbUMEWhcobNiRF4ClwExoL4+uXCeUZs81SC5Ah4QQ1DnpcZySY7kwCMHpqFNUEfZFu1RaxIrWttymSx525UNAnBDanii5Z360kS9QdqEJ6Jo/XRtsImQnG3OM8XNjIG8PtiFiEiGE1eckGKHGBLunc0IegkbtCoSuLH2/cw0wiyu9f/C7KojWG/vA0IS2WjFRaCZHpKHrcoHJ3OUUbCjjrGWGizhKrSIrEpCfO59tO8WvbjgJQ7c0j1+7Cn0h1l1/pmG8oPJ22zRCC0ejMsmKYORa79QpT8Mr/e4LJpFPUJH3nB3nykw9TUJeHzalic1o125WsyayEsBbnr1uOEyy36ogJt2DmeYmGfxUoodkxvgRbPtDMidFOuoOT5Ds9HCiuI9fhZmteOVdmHbbAOrduiubROz1JLNu6TuTfhKw2QffzUkojpmMKyk4LFLeNro/Ykg0XwVCcG9Mj2BUF2aZgaNYxJCsS3ieL7/g9Z8iQIUOGDN8v3HdimE9/+tNMT08nXV5+4zd+g9/7vd/D6Vx6UORwONi1axe7du3it37rt/jd3/1d/vAP/xBJkvD7/fzX//pf+Yu/+It7/C7gfe97X1IM093dzauvvspjjz227P0DgQD/+q//mrz91FNPpYh+1vL8y3XwLMUbb7zBkSPzCvLPfe5zfOQjH1nz82e49xi6SXgijMvnSg74M6yeH6l7gH/rvsAN/wiGbnVDKBoIHQybJYqRhCXAmCuE5znc1OHjW599OTmhdk/L1L5iEnzCTshjUObJ4ZnKLeStkx3/UmimQW9wku155XTOjDOxwAngaHkTOQ4X4xOWSGZxJ18se3YBry/VBnWOHfkVIATHRzoIanGafMU8VbFpTRPKw6UNDAT9ROMJhGwVBJ1hCW+7wdVv3OTgAkHH5twyfn7TYU6NdhMzNDbnlrEzv4LXh9u5MNGHhMSugkr2e6sZvjSKYleobLAWzGcSsZTn1U2TuKFxfKSDgbCfJys2MZ2IMh2P0JhTtCahUqEri2pvHr2hKSRZxjQNTBUcYfBXmOQOyGSXZvHtvmtcXjDpHQxP8/m2U3x822NIkkQ8lKD9jS78/QFyq3xsOFK7bDd2YDg4L4RZQO+ZAQ789O4VddS9G5AkCfeiCBLRcQHz+L9BIgpISA07kB75INItHJf0uM65L11OsXfXojrn/vkKT38qtXOqJziZEssyR3tg/Jav9Vu9V1McU3Rh8q/dF9noK04Rzb2T2N02wrkmA9sMIrkC17RE43GVI081UOrOoegJL+Ptk/Sc6U8WUCCp5UMIweg/daP8kJQiFtJMI0VwBpaV+rnxvjV19/uXcJ4B8CeW3n4/IITgO/3XODnShS5MvKqD99ZsSykKKpLMlrxUN6J/777EmfGe5O1LkwMcjVUz/dVB4qEEdreNbe9rpvmJ+Yrq4sWSxdtL3Dk8XNrAm8Mdyb8VOr08uspokM6Zca7OinQeKKy+L+KmVkvC0Lk2Oshb/3oO76RAQcJIGNz4bjveQg9NR+tpD4zxD21vJ4/77w5c58cb9qR9V2B1XS+FHtc5OdqdclwAhHWr6r74HBDS4nf83gzTRJakdSnqJvepGSSi2rp3T66UeChO18m+tO0tL3Ww9bmNyIrMZCyc7i4HDExO8Y0/fRlFtz6PkuZCjvw/B9PEZE7FxmPlG3msfO1RtCslND4/Biu9qTBVYaI7RfJatCm3lPrs+8sVRovrvP4nb5EIW46gIY/OW93txF6SePjpHWn3vzY1xNWpQc6O9yFLIM8KhE0E3+m/zra88nX9jd4JWkznxnfbGL4+hjvXRfMTDbeNdzFMk7+/+RYT0zNJAfmpcCdywOCDe/bfi5edIcM94cHCavwXehltSR/vhicj1OytZP9Hd3Phq1eJBxPYPTa2v38zJRvvr3MYwLfCLZaT68I1bhl2FleiKgqnx3rSomgmpAiqV6GFAH0jp/j5vMOUbyvhB/7P0wxcHEZWJCp3lSWd5U4fDnLzu5ZLn6RbzjMsca6TkCxBjBBIgKpBxWWFwa2WQ6ORMJBdMmXuHDb60hcsHyvfyFe6LqRsO1hajxIeY3pshrg639wy+qAlQBALXoapgCqscbtAICFR6s7m0fKNeGrm55l1sVQvG7uiJh1hJEnCJsvYZZXJeJg8h4cjZU28NHCD6OznOPeUNlnmYMl8VMntGLg0zIWvXEvejkxFOf5Xp3n+fzyRdFVdiuYnGuh+u49EZP57zC71Uv3gvW1CCE2ECU9G07YHBoPEgnGcWauv55a6c+6pUHbh/EqbFYkByVrySHSGV29eJx5MYOgmiUjCGscIuP7tVip2llG1yxozb3t+I58fPku4SCBrYNohaRs0ixLHWtwXUHJTRhnR0Q2Tic4pJKDonMJ4HSTcVux1CjLoLlASUKAtPVbVZhJUdip077HG7BKAJFFxVUGaPTiKGvMJjoeJBeLIisRcerckS0iKhM1l4/Av7KViRykdkQk+12Y5uEuSxGg0yHcHbvDrWx+jc2aCf+44ixZMzB88QhCNxum5OEj97rsnFrQrKookpc09VMk6VldCIqJx8m/PMnBpGAT4KrI58DMP8Gb/FIquISsSit0S0Y1EZxgMT1Pu8SFLMj+5YT//1HGWsVgQCdiSV8Z7KlfmCFXszk6L9JWRKFxDk9jQtdEFQhgLUzO58d02HvqFfQghuPatVlpe7iAeSlC4swj7e/KJ2U0as4toyClkNDLDVf/QrACt4rbNPBcm+omROicUCkzvUCnp8FC9p4Ka/ZX8a/w6vX1TyfucHe/llzY/zA/X7cJnd3F5agBVUthTVIPr1RA5rwaJuwVxt8lUgU6kFCpfFkxvlNC84BmEkkvgK8nB+8EybojUJg0JiGuaFUM2u0GYgv7X+qFpy6o/2wwZMmTIkOH7kftjhWYWTdP44he/mCya/ff//t/57d/+7RU/3uFw8D//5/8kKyuLT33qUwB88Ytf5E/+5E+wrWM25Er40Ic+xO/8zu/g91sDs0984hOcPn0aVV36I//Upz5FJDI/EfnlX/7lZfddU1NDb6/VuVFdXU1PT8/6vfAM70p6zw5w9kuXiU7HUB0Km9+zgW3vvX1ea4Z0PDY7H96wj7ih86Wvv8FFj+VQIAmQ41aBqdidRVleHkPjfryjsHk6i9ZrnamdJYBvWKbxLS9P/fYjd/113/AP8+XO88miVH1WAUdKNxBIROkKTvDaYBtvDrdT4y2wuiKU1IKdd8K6PWePuhQ7CirZUXDnk/oHC6vpuTnMW8FudIfAOyFTc1ZBNqQlRR61WQVpji1PVDTzxGyucN/5Qb7xe9/DnO3KcmY7OPrxQzTmFfHdgRsIIYgbOvHZSoeCzHBkhm/2XuEXNj1MdVbeHb2fn2jcw791X+KqNkBMNcCEcK6g5YhOVdBDfk0uF8+dAEiJmRqPhRiMTFMkefjWH7zGsBlEjYPn9ACdx3vY99Fd5JRlpxTSYsE4vWcH0OM6ik1JiS6S5PVdGL3fEOEA5uv/DElbXYHouAj55Ug7Hl32cYGhYEqxdI6Jjqlk19ccOcsUP7Jv06XUvsjJBCxBSF/If1ciMIQQtAXG6A/7KXZlsSm3NGmRvBwlD5Xx1dIODHXWCrpE0FIkeH6n9ftXbApHfvUAE91TnPzMWUZujidrl4pdQVZlzHGNp92beCXWkXRVyrW7iSyxOB3S17bgX+3NX7K4V5d1/y24zHFuoi9FfBLS4/xz5zmqvHnLdnT64xHOLhDCgCVqfWWkla0ha4EiEdE4909XyK30UdJsvf8mXzGVHh/9C7rqVEnm4QW56c9UbWV7fgUdgXF8DjdbcktRV+Fi8MpgCy8N3EzePjbcwU81Hbgv41yWoz/k5+9b3yIQiaDt0ZB3g29AxjUjUdCt0H2qn6aj9Xy953KKAE4zTT7X+ja5Dje5DjdHyjawKbcUgKrd5fSc6k95Hm+hh7wqH6H2VPElgCrLyUWFhWz0rd3haDgS4Os9l+kOTpJlc/BQaWPKd78WhCm4+G/XaX21Ez2mk1uVw76P7KKg7vbXxs6ZcV4ZbGE8GqLKm8dTlZvW7Hg2F924mEREw9BMZEUmx+HCJisp9uqmbiJNG8j6/Dlw5OY4ba91sump9YtAWi2FjflMdlvzQEdYYuuLNsbrTSqerGRzRQVblxBcvdMMXR0hEdYQCHoeNBivtz7nbqWFwQ6NH63fnRS8fLP3CidGOjGEmRR+uRUbtlnxlz8eIWbouNR7OwdfCiEEr/3xCcbaJpPb+i8M8dhvHKa4aXnXrO7gBJPBUMrv0pQFJwPdSG0q+0rrqFnGjStDhncbxRsLlhTDFM8KXhoeqqF2fyWRqSjuPFfS4ep+YzASsFYFF02J3hrr5rHypvnrh7DuI7Dik+bGSRFD43sDN/nJDftwZjloeKgm7Tke+NFtIODK12+AAO+oxFT2/N+TI1jJGidnjctI1yMUdivYoxI5IzJDTQb2GicP1DfwcGlD8tya8jyF1WTZnJwZ7yFh6GzNK+fBwmomf8rP63/yFnGHydAmjWApxPMkKxKG1PcuSzJ5LjdPVWwiz+GhyVeU9lz5Tg9PVW7iu/03kq/dpdqQSJ9bzmhR3lO1maPlTfxr1wXOjveSMHWybE7eV7OdrXkrF6R0vZUugDUNQc+ZAbY+t7xoNavYy1O/c4Qb321jZiREYX0em57akBI7dS9w5jhRHQp6PDXyxXEHkZz3mobswqQwYWHMzUIHjnEi2IWwhDAL6l2mIXj5f73JkV85QN3BakoeLidx+hJy3EAyBaYAXZ6/v0uxISQDU7P8/dQJE1M3kRUpGQ8jzRhs+o6dvr0GU9XCOkYXHcumCg+ULR2fXL6tmMKXO3AFJCZqrPdT0CPz1A/tJavIi+pQ8ZVn03NmgJN/cwbVoWJohlUTUOSkQObqN27izXNznvTfqGaaXPMP8VBpIy/euMK4SJ0HZ4/KnBxr5XuubqbjEWKGRlzXKfXksKewGqdqp8LjW/N4GSxx9wOF1Zwe60nZ/mBR9Yobcc5/+QoDF4eTt6cHZjj+V2dQf0jFbqTf31wwNy/z5PDx7Y8xHg3hUNTb1koW8lTFJv6u9a2Uuf6hkvpl6zC3YqHwPHW7tbZy83vtXPq36wDEPILvVfajdw3gzHbw+lAbNbNNbXOv5OXBm/z0xgO3dIsOanEUm4wWI0V0qbthz4d2ULO3kuv+IXrbplIeF9ETHB/u4P21O3i2eivPVs/HZ/VtHqL91W6my016d+uzKV8Skg613xZ4Zr8md66Lp//rEc5GB1j807TLKhEzPqc1A6xYMPVsmOnnAvgq7j83ygwZMmTIkOFec1+JYU6cOEEoFEKSJJqamlYlhFnIJz/5Sb70pS9x8+ZNQqEQJ06cSHE9uRfk5OTwm7/5m3zyk58E4MKFC3zkIx/hc5/7XJow58/+7M9S3GueeuqpTERShhUTHAtx4q/PJIUYetzg8tduklOWTfWD73578ncKh6LStKWKiz3jycxhAFmWqCkqYuM5J/aXRwAYZZghzUAIkl3IhmZgJAymBwKMtU1QtOHuRVTEdI1/7jhLfMHiUGdwglJPDiORGTpmYzVMU9A+M0a23UmQGMZsN5lrWqK4TSG7xEvjkZV1cwkhGLwywnj7JN5CDzX7KrGtogB1pGID0b8fQMggmwvECOXZt3hUOoZucvrzF5NCGIDYTJyzX7rMk598mCNlG/he/42kEAbAFCYRPYHX5uD0WPcdi2Gy7S4+3LiX3w9NEZ4twM69mt78MG+PdqGbJmEtjiGsnGO7rOJQVGQk3jhxhRP7/eh2gZDAPQFV344x9buvYffY2fh4Pbt/ZBv9N4Z5+e/eRpkyMTQDLaZj99hQVOs3V3egCkmWMA2T8EQEp8+JETcYDQZ4PdxFZ2CMHLubh8saebBw6QLS/YzovbFACLNge881uIUYxp3vsoQcixZZvUWeFCEMWIWdDTlFKTE9QgjyHR7+se00Je5sDhTXplkPZ9ldxBdFk8Hy4po7wRQmn7l5kpbpEWRJRpVlqry5/EzjAUYujjLaNoG3wEPD4eqUHPcuzwyKz4YZ0xGGQFatzrdLkSHKmT8GCmrzeP5/PMGXP/YNooE4siIjq/MF0Z2FlewtaKQ/NEW23cVQJMA/dZxNe51rXfDPtjt5pmorL/ReSRZych1unqq8+wLPjsA41/yrd0O5MjmYts0UgqtTQzxU2rDkYyZj4cVpA9Zx7U139es+1ZcUw8iSxM9sPMSJkQ46Zsbx2V0cLm2gwpOaBV7hyU3bthLCWpzXBltTtunC5LsD1/mlnEdWvb/FaKbBa0OtXJ0cwq4o7C2qYW9R7W0flzB0enpGGH1ziPhUnNJNRTQdrV924eOrXRcI6wkkLFe3hAtGm0xsMRjeZKCPRPhC+xn6QlOosoJNVhBCENHjs53QMKPF+HzbKX5240Eacop48Me3E/FHGWu1rqneQg8P/eJeJFliQ04xN2cjFueQJZmj5Y28NdqVLPg2ZBeuWbyimQafbTlJcNZZJqjF+XbfNTyqnQfu4Jze+lon1789/537+wK89n9P8gP/79O3XFgaDE/z2ZaTyfd2zT9Ed3CC39j+OG51aWezW5FV5MVb5CE0llrYLmzISzoeOhUbj5Q28vKCiAZDN6i8qiAtWvkcvjH2jophtjzbxNDVEWaGrWuDLS7xaEUTD27fzkQsxHf7rxNIxGjMLmRXYdVtBY33Anl28Wu6TCSFMABIlnNVs6+EnQWVBBJR3hrpAkAz5u8XMTRcWB3LeQ43zrvgjDYzEkQIyCld+SLSaMtEihAGLBHV9e+03lIMo0iy5ZQ2i2Gzoh0kAWfHernoH+BDjXtWtfg7x1A4wFgsSIXH96503srw/UfzEw0MXBpmqmc6ua3paH2KMFKxKbd07LgfsMsKcSNdBG8ieG2oDZuQID6/WGjYLReohXRNjHPy2DkA6vZXUbo5NXpPsSns/fBOSpoLeevvzlPWmWCmSsOwWy40MBupLKvowqTcl4dxKYIkmRgSyL0aFT3g8MaRmseRf7kBlvlYm3zFNC1yjSmoy+P5P3qS/33+JYIiiqLKYGiYhsHiwaWGyePlzRwsqV9y/10ne7n2nVYiU1EOb8/H/WgBJYW5jERm+O7AjbT7N2Rbn4VDUfnxxj38eOOepV/4Cliuh2Px/Gwpckqz2P/R3be933jHJMHxMIX1eese/2tzqDQ/2cjVb6bGRm1+psn6Tt4FPFy6gdbAGIPhaRRJQmBd+xKGjiLJ2GSFqqICIqURooFFwm8JDM3k0tduUHugClmSUB0qql1Bi+kYCQMFGcWh4LCrmBIIj0w8rGGbNPG2W8edaVjj7ll9GvYpQd1pG+FyHc0hMEwxL/AS4EBm2pbeiAFQtrWEjY/X0/JKJ95J6zuoP1RNzZ7KlN9VzZ4Kciuy6T7Vjx7TmeieYvDyKDaXimJTCAwFee3/nkT7pfQxgmGadMyMU5tVwCPxKr4RuUHCbR14zhkJz7jE+Z2T2GZUQloCEKiSTOfMOC3To3htdmRJ5kBxHe+r2b6m702L6zxXtgWv6uDCZD8SsKugiqNlK3dl7Tk9kLYtOBqiWa3gojGUsr3Q6aViiVjRQtfqj6mGnCJ+ZcujnBnvIaZrbM4rZXPu2gTiRcuM4ebGdu1vzrunDG7WSTgFGAJDM5BVmatTQ3hmvw+whJHf6r3Kf96y/PrRRl8xbwy3YXfbSUQ1S8glSWwtraBmr9U4OBYNLvnYsWgQU5hcnBigZXoEj83B/uJaKneWUnW4knPFnQvuLSFsMHJQouFrVrTX4V/Yi9vnYqOjmG/3XUvZtyLLlLUoRGwGca/AOwzF5wSmaWAs0WyQIUOGDBky/EfkvhLDdHV1Jf/9gz/4g2vejyRJfOADH+D3f//3k/u912IYgF//9V/npZde4vXXXwfgS1/6EidOnOBDH/oQNTU1jI+P8/Wvf50zZ84kH1NaWsrf/u3f3vPXmuHdS+/ZwTRHEoDuU/0ZMcwKCCSinBnrYUaL0ZhdxJa8MuTZysy+xgbenO5kLDyDaQoURcbhsrEvp5ozr72Vsh/ZJqNFNLArJKIJwm4DWQJlJs5L/+sYhz+25659Hx0z4ylCmDmuTA4yk4hi6CbCFMlc8piu8VNNB+iZmcTojpIThfwfyqH+UPWy0TyLOf5XZ+g9Mz+BvvFiG0/+l0dwZq/MDrigLo/qByroOze/iGxzqbfsAltI18yElSscUIjNpDtQjLVOYBomT1du5vx4HyPRQEqHvilMDNMkscTnthZeG2plJDIbMbWofvfKQAsxQ092W1lONRr5DjeFrixekXvQ7QJDBdMGgQrBjZ+EqhMG5f0mN15sp7s8wlvBHrTHTdS4ROVFhezrJlpUR/Wp1O2v4oEPbqP33ABnv2i5ROlxHVMW3PiAQMuSsHtsxM0gX+26gFNR17R4804i2R1p4gEAyXbr35wr28mGI3W0vNyRsn3be5uXvP+HG/fy+lAbN6aHcSo2xqNBrvqtgtA1/xDnxnv55S1H8C543n1FNfx79yUEAlVSUGSZZl/JmgpEt+NzrW9zcXLencImK/QGp/jiV17H8cq8IKflpXae+tQRPPmWM0lYT6DYlLRO3vASDi6SJLH1vZu48OWrKdvLt5ckF0EacqxieIHTS1vhKOfGrfYkCThU0sCGnNSFg9VwqKSejb5iWqdH8docbMotTcbSzdEXmuKF3qv0habId3p4vLyZnXfgXPXywM2UBfZjwx389MYDK4oxWZxfPoe6YHvvuQHaXutGT+hU7Sqj+tFqbLKcFsvlnUjfl7yooO5SbTxe0czjLP0bvhPGokF0kV4wG1wi330tfKXzfEpk3ED3JWK6xsbcElyKfcnOwjeH2/le93WCwQhKoUTFsMLwV8YYujrK4594KO3+M4koI9EZAIQkYczOdsSsUNGww5XGAN6pGLow0XQDU7EhS9JslIGUFFYI4MRIJw05RTizHDz5yYcJDAfR4zp5Vb5kYX1fUS1tgTFuTlsiWQk4UraBpyo380jpBnpCk/jsbiq9qxcozXHDP5wUwizkzHjvHYlhloomigcTDF4dofqB5cctC0U+c4T1BBcn+pdddLsVkixx4Kd388b/93bSzcvlc7L3J3el3O/ximZK3NlcnhxEliTKhhz0d7Wn7c+Tt7Qr073Cle3k2f/2GH3nhwhPRihpLqSgLo/hSIC/vHGM+Ky71qXJAW5Mj/CTG/a9o68XoGxrMS6fk97SVHHnnNC7LTDKzoJKRqNBTAS6aZAwU63iY4aGKik8Xbl5Xd3qwlMRjv3FaSY6rU7bvGofD/3SPrIKbx8/GppYpnN4me1zVGfl44kqhB06QiJ5LpF1kGzWouH3+m+uajxlCJN/6TiXPA9KwOGShpQO4QwZ3gnsbjtP/84RBq+MEBoPU7ShgPyapa9ZhjDpCIyTMHU25BTjuEeRoMORAJppUDEb1bEU+4preWWwJS0yXGLW+SI+G2uENS4wBCmNL3pcR+uM0fpKB5Is03Wihwc/tJONj6Vf16ofrKB0czE3b/bSHbtIxJwX4ciSjF1W0OM6lycG2IKCFtWshX9Fwu62ISsyY20TXPjq1RUJOxbSER1nRk2gYJ2fHbKa5nojSeBR7SjLnIv7zg9y8m/PJW/7T42TaA/x8P9qoiG7kJbAKD3BeSHhoeL6O24iWUjdweq0BXlZlanZc+c1E0MzeONP32boqiVUlmSJLc82seMHVhblslJ2/MBmsou9dJ/qR5Il6g5Wr8vrv1e4VBv/efMjtE5b3/W3+6+hmwamAA0DSZLYX1RD+CcL+NanX8FIzP7GZtOPJAnCExGMhEGOw0VDdiE3hgfn74cg5yY8XNVAf32CyXiI7JiC+coYkqJjzsUyAbIiIwyTeJZJ4LADxY0lbFtodSFDApE29ljIgz++g42PNTDZO42vPBvfMs1WOWXZyd/Di7//OjaXimmYsy4xEomIRrnfxc2505sQRAwN3TS54R+mZXqUbRWl7PgTOzM+E9kEz6TEpfdqqHbbbJ1p1pFVmMn3ENU13Kqdt0a72JRbuqo5c3Q6xql/uMDglREkWaJ2fyW/8aGjK3ZFmnN3kSVp2Wjto7mNoMlcnhzERFDpyeVH6x9Y1zFdiTub56u33fF+city2PyeDVz/Tltym688m83PWKKgOdcmQzMI5s6LBRPhBKrHhkBgCDPletIfnsYUIlkTXkxddgEPlzZybLgdxSZjmoJtueV8YOO8MLDSs/R5stDl5U+vvUFvaApVkpEkiTNjPfxc8yFqf6QB25V+TMNqnktELKFNtNCaUzQ8XJM8txS7snm2agsv9l9PzsU2+UrI7p1moiVV/K0rBjbXfbX0lyFDhgwZMrxj3FdXxImJieS/a2pq7mhfCx+/cL/3EpvNxr/927/xzDPP8PbbVs5ob28vf/AHf7Dk/cvKyvjmN79JRcW7Z/KUYWm6Zia47h/CpdrZXVC1bEzCeiAv0zkjK+s3Wfl+ZTQ6w19dP0Zktnvs9FgPO/Mr+WDDA4C1wPmfdx7h1aFWumYmyHN6eKS0Ec8YaQIkCQmb04Zrl49LhSPEfTJI4Bs2qX/L5PLXbtw1McxyFvQ2ZGLBBMKY7bpRALuMalNw2Ww8Vb0ZqoFHVvd8wzfGUoQwADMjIW6+1M7OH1x5Hu3hj+2h51Q/Q9fHcOe6aHyk9raLGoZp8vn2U7TMduALU+Ddp1N7KrUr3J3rTE7wBQKHbEM3UxcRBWLZmALTMBm6NkosEKN0c3FSULAcFyb6kSQZllhEnk5EkSWr6DA3WVWRyXN66JqZQLcLTNPqOJ6boRsOGDgg8HxPB6fMuXgPpmTtW3cIuvcZbPHbcAUknv8fT5Bd7CU4HubEX5/F1E20qIYe1wnUQtwtIxmCRDhhRS5JEm+Pdr/rxDBUbwF3NkRmUjZLmw7c9qEP/Ng28qp99J4dQHUoNDxcS9nm4iXva1dUnqzcxJOVm3h7tIuv9VxO+ft0IsqpsW4eK9/IWNsEbx+7wbHKEUynwFAEGgZ5qofJWJh/6jjLo2UbKFmn7PeumXHOT6QuXGumgWxCX9RPI/Pngog/xvUX29jzoR2A1cX01mgXi2lexsFl89MbUGwK7W90ocd0qh4oZ9v7091ZZEnih+t2c6R0AyPRGcrcPvKdt1+cvB0FTi8FJUuLiUJajM+2nExGNU3EwvxL5zmybI6kSGc1hLU4rw+1pWzThcmL/df5pc2P3PbxewqrueEfTtnmUFS2zx5j7W90c+ofLiT/NtExhX9ghvc8v4VvLnDA8TgdVF1J7WaWZIn6g/fOyanQlbVkTNV6/Ian4xGuTKW66Bimyb92X8TVb0cCtuWV88P1u5Pip47AGN/uu0YilgABhk3Qu1vHMyUxcnOckZvjSdecOZyKDZusEE9oxKMJSGo8BQIJ0zHXjSzhkFVihkbC0LEr1nPaFSWl4Ls48mspRwpFlvlo034Gwv5kZNDccZBld67L+XapyCUgJTJoLQhzKZnhkpezFMJLxKPdavtKKG4q5Af++D0MXR1BVmTKthYvGcWxNa88+ZlqFTrf+uYgoYn5uFnFrtC0xMLlvUaxKdTus0R6k7Ew3+2/ztnxXsJaPCW67Lp/mL7QFFXe9VtkXAuKTeHorx1k5OXjjBIESUJ1KMlFlqxZV7QSVzaKJBGd/e3NHS0SEjZFYXdhJdvz13fM+/bfnU8KYQCmeqc5+Zmzy0aRBhMxLkz0E9YTVFdlWy9y0U99Lv5lOWRJ4ge8W/j64BUCJdYBoeigICVFiuOx+e5fzTQ4NdqddO06UFxHsTt1Ee7CeF+KIFAAx0Y6aM4tWZH4MkOGu4msyFTuvHWH/kQsxN+2nMQft865TsXGhxv3rGn8tVKCiRifbz+VjHPJtbv4iQ17l3TAe756G1E9wfGR+Q77uXOUjIxnXBDNnY1kESAb1n+jegJTCETcpOy8iT5rgqHHZS7/+3UaH6ld0u3D7rZx1jOKqqs4DYga2uzpRmCagkRUww6ILBlVspEIJVBUOeXaNnBxGD66ss/ihn+Yt0a7GI4EiBs6DlmxBjWGQNItZxrZAMkE2QQ5WyJqaHS93Ufn8V6EKajdX0nDQzW0vpo+LwhPRhm4PEz1AxV8rPkwHTPjTMRC1GTlU7rMWDA4FsLmsqVE+66E8m0lPPihHVz95k1iM3G8RR4e/LHteFcgcrwdra92JoUwYI11rn6zhcpdZcuKvNZK3cFq6lYxVo8ZGhcn+pmKR6jNymejr2TZhfd7gSxJNOeWcGN6GJdiQ5PkWYGAhCrJ9IWm8eeGCTzvxrgYIqdbIM1eTxW7iq8iOzlOeN63ib63+5kus46rvH6Z6nMqEzcG+fD/9ywAb3/uPB3aJKZTIq4niOUIpjcAKmQNyPQfEWjuCHJMQbGBDsmD2BLgSLRMj/Jw6fLuf1nF3lW5WIUnI1aD1ez8R7Er2Fw2GuR8pHInbw63J11+XaotKZ64EhrmmZ9rYvJf+ghPRlFdCuQJFLuCps8LfRaiC8sx2G1z0Do9uioxzLG/Op10qBSmoPN4L5Is3VZMF9U1vtF7mcuTA8iSzO6CKmofrqL1Ox0IIdATlqu1Ylfoe3uAH3z/Tt5Xsx3NNMi+C06368muH95Kzb5KRm6M4cl3U7GzLHmuLtpQQMsrHZiaicMvEffNPkiAEdPBISGzKDbO4bnt8fhM1Rb2FdUyEPYT0uKcHe/lt89+k3KPj6crN9OQU0iNN5+WwAgyEjZZwaXaODHSmZwnyZKER7WjY0UUf7D+AWyqgjFbw3RmyRgJgyzNxqO/to+yram1q4dKG9mRX5lsDMqJ23kh+DKSLGOapnWsKDJ2t42Rm+NkF689litDhgwZMmT4fuG+EsO43fOLjcHg0rZyK2Xh4xfu916Tm5vL8ePH+cM//EP+/M//nOHh4bT7eDwefvRHf5Q/+qM/Ii/vnS2CZrhzFneXvznUxs9sPLSuXTQLqdlXyaWv3VjQfWGxmgn5fwT6Lw7RcawHPa5TuaucDUdqeXWwNSmEmePiZD8PlTZQPmsDmmV3ptmXanYdm0slHpqdyMxOWAo35HF+T5TE9LwsI1Bq0rfLwHY2PUJlvajNKqDElZ3shJ+jZsDFVGSKiM/qZrXEFiaSCX95/RgfrH+AHbdwUhgKBzg33kvCNNiWX56cqE92TS15/4llti8mYei8NdpF58w4dpdETaWHvLwc3Lm3n2ifm+hNCmHAWij2N0Fer8A3PD9p3fzMvMPM1rxy3hrtwqnYiBs6AqvT45HSDUsu1kRnYrzyh8eZHpxJPscDP7aNjY8tHXcCVg3Sqahpi4CqrOBzuBgKB6wOxdlqiCFMJjv9OMoVFLsChs7CUsncms3AVh3TI6Epsx3JwupmlHWYrDKp63AmP7fWcz30bkoQzhfYxgwKLlsOCHPdgcIQmLPxONEl7MPvdySbHfnZjyFOfRMx2AFeH9LOo0i1t++mliSJ+kPV1B9a3XlxPJp63JrCBCTGokGGro/y2v85SctDCRLChChWfrTDEmfEDJ2xWJCb0yP86pZH10Ug8vpQmyVSWGyBLgycIauoHvMKZAPsUQl/33TyPht9JRwuqefESGfShnpfUS1bbmFLvPGx+iU7YZei0JVF4R1kn6+GS5ODSSHMHAJL0LiWxZjRZdxQhsKBFT1+U24pP1Czg1cGW5jRYpR7fLy3ehueWfega99uTXtMz6l+3v+BzdRvLeC633Ih2pFfwaRtggtfvkpwLIwn38WOH9ySElFwt/HaHDxSuoFXh+ZfsyJJPF1x5zFVQS2e+tMVgoiRQAiBU1gW05enBvE53DxTZQkr5xaNFwtQJ6tMvJMywbFQmhjGrqjsL6rllc4bSAu+VlmfvTbPihPn7itLEgnToDarkL7QZIpIAUCVZH7/wotE9ASbckt5vnrrsoXhhfFUsWCcrhO9hKeilGwqpGJHaVJkc3lygNeGWvHHo9RnF/BM1ZbbRqU055Zgk5U08cv2OxTa1O6vZKp3OmWb3WOjfLsllJtJRLk6NYQiyWzNK0v+rjf6itNEYHPb7wSbU12VeNjmVHnytx/h6gstjLdPklXiZcszTeRWrI8IcT3oDU7xmZYTaKZBSItjChO7ouJU5gWMI5GZd1wMA5Bb5ePDP/EY/+fyq0SNRDLHwqGo7JuNNMu2O3m0rIkXeufdwyQk3KodRZbJc9z59W4hiUiC4f+fvf+Ok+M6z3zx76nQYbon55yBmUGOBAgCzFEiJYrKwZYc5LXv2rLX66TfvRucdu31727yrhxXtnKwJEoiKUaBIEHknAaTc84zHSud+0f19ExjZoABCJC0Nc/nIxF9pru6qrrq1Hnf93mf5/LokvGxtgki01HSslLvx6HIDH915U1C0RhmzEI6kqpdXvKO2sk1e0ZRkM1PNmLYFs/3XkrYDAi255XzvoqNSULe7gebyHjVw7lDbRzdO4XwqehpC7/bYrWnL7ccpX12LPn61Hgvv9Z0IBlTAFydSbVTm0fL9MgaGWYN/yzwbPf5JBEG3ML+tzpO8wfbHr1jdm/P9pxPEmEApowoX2s7we9ueWRJ0VJXVD5dfxembXNsrBtYsF8Jah4aDtlMlEumymziQVAsSTwdlwEqwVEl3U8IKl+SZPSAtB1mh+c49Y3zbHqqgbQsP0bEIDQWIb0wiO7Tkve9R9UwHBtHOkgpidomls9tSDn9YQPVAGxBcMCh+rLEF3L3Xfcv39hyLc6M9/GtDlfJxZEOcdvEkQ5+zYMVt1Ek4IC6KMxzTBvPhRhvfftycmzk6hhzIyHM6PLxoJlQZxNCUJ9ZQP0Ka+t5UuLo5Ay2FxqbKrj7czuWJbGuhIaHaqm/r9pt2sjw3jb1icFLy8+1Q5dGViTDSClpmxmlLzxFgT+dpuzi235Nzxkx/vLSIUZnZ7BNV3mlwVfAr96zsuXvO4WhyIxr57xI6UlKydfbj2NJiayTxItVpttsql4BzaPhDers+ISr8OHYDhe/eomKIzalHsAReDXQfIL4nLveF0KQU5kFuDm06HqN9v02UpGAZHgbgECxwTFsVFXFVmxXszFBzvGqGj1zq8s7QcIGTbDibzk3GmJuJJyiEGUbNrpfp2xLMTVpOvuL6/h62wmuTo8ghHAJJInYcbzA5Kk/fYQz37nIwLlh0ocsZotNNI9CfAViuY3EsC0y9KWKmCthbjSUJMIsRtfRPu76uW0rKr0AfKvjVFK90pY2x0a72Lm9gvVGLVdebMWKWii6guZRaX6xjdBYmPt+fS/Xy86Nx0J0zo6T5U2jPiP/tirH3CxyKrLIqchKGTMiBgPnh5IqYflnJHPlAqkn1D8tSWl2FnOLmh4E8GjZ6pRXc30B4rbFNztOJRV3ekNT/J+WI2zNLadrbty120wQy8KmQWRRrtCRkqhlEtC9jERnCehe9hfV8/qQ26AjFIHu0/jwpl2U5izfvJTh8bExp4Tu43384G9PEZ2JIRPNE3qajuZx7+Vr18lrWMMa1rCGNfys4j1FhikqWnjAHz58mC984Qu3vK233npr2e2+G1BVlS9+8Yv83u/9Hm+99Rbt7e2MjIyQnZ1NeXk59913H4HA6pOG3d3dt30f77vvviVSsmu4ecwZMX46mFr0ijs2L/Zf5lca99+R70zL9nP/F+7m5NfPMTM4hy/Ty+anGinbUnxHvu+fGyLTUV77L4fdJLpwuzyGr4wx0T3F4K7lC52DkZmUxPW1MEIGaILRvDhmGqQPKuRqfkqeqeanM2fdwsGi+2my3OGuyaUFjrmRED2nBlBUQdWe8lsOUhQh+KWGu3mu91LSh3h/UR1TL3ZTN6bTdo/JTHFCEtUBXShI4IW+y2zOLVu28+HK1BBfaTueDOxOjHXzWFkTD5SuJ2OZrnhgxfHFkFLydy1H6J6bwIiY2HGLUw40flOn4DtBAnlpzI2EyKnMYuuHNiSTJfNonxlbsk2PXyf4cA75R92u5vp7q5J+vQCPlTUxGQ9zdXok2ZXxsdodK3oTX/hBM9MDs0ikay+FwulvXqBiZ+mKv9H2vApe7m/Gq2jEHQvcui5Zup+StEwGwtPuJZE41RJwBuNEjk9RUZpNmz2GZVnM02CEBbYfZqpAagufA4mTyJfaMZuGh+rQPCoxy+Q5fzsTTW6RVBbAdI2g6nnpFoOvyUluyP7nOT+I7ELE47/8jn1fWTAbRtyEc8QyE2QY16Ln3KthpCMJ5S6SVpdO8tZ3pIMiVOK2xcHBFjbllFKclvG2uqsmYuGl7WUANmQNKFx61CCS6HbNHFKo1FOL609WbmZfYS1D0RkK/Rkpxffu431cfqGVyFSUosZ8tn1kI8G821vQvF0wriHCJMcTJIHJnmlG2ybIKA5S3FRww+RcgT+4rBpK8TUd/dfDnsJq7iqowpLOEkunyGRkyfulI4lMRimqz01RXQnsKKV8ewnxiIHHp183sXmn8Gh5E1XpuVycHMSrauzKr6Roledi1ojy5nAHw5FZSgOZ3FNUSzCR6C1JyyRd9yatfqxEsUhTUpVYzk/0J8kw84lrRVOw7YWMspL4Z0F97rL78UTFRrpf7KYvM0QsXWJ6E9YmgJQCr7YQAmmKSqbHz69vuJejo10833sxeS1kefx0zI4n9+/C5ADjsRC/uen6RYu5sTAv/cnrRKfd1vKrr7RTc3cF+z6/i5bpEb7RfjJ5K1+eGqIvNMXvbnk4pQBxLdI0D5+p380/dZ5l1oyhINieX87+4pWJmqtBw8N1hCejtB3sxDYdMkvS2fu5HehejeapYb7adjyZ8H++9yKfW383NRl57MqvpGNmLElYUhA8WLr+XSF0pGX7uevnti37t7mREOHJCLnVOei+dyf0fan/SpLEpAqBI915zKOoye7ixQoHZszi0nNX6T8/jDfgoeGhWip2vnNqbhkeP7+28QCv9l9lIDJNkT+Th0rXk7OI1PlwWSNB3ZsszOqKSyxThWB7XsVt3R+hKCiagmM514yLZZUaXuy7QigWw4gYyWdmT3WMvPY0tj3QQLAgSPl2t3P4G+0nOTexoHZ4ZKQTw7b4aO1Cl3XDQ3U0PFRHxUgnP+w+n7x3PYrK+ytcQm7H7FgKEQZcpZjXB1v5VP2CdH76CoWvlcbXsIb3Ehzp0DazlJg2a8YSz/2s2/6dUspliZeT8QjDkRlKVvjObF8angSBVOI+6z9Wt5Po9hG6jvQSDdjMFjjYHpCIBbK5CmYAut4nKD0kybvkEnJbXuug/+wgBXuLONbXiSkc8sd09j+4hcw8f5Ig5Fd14o7lWs0gUU1XIcPyuP9TY4LZKmgpMNn0nI4iBesfrFnVuTi4KM+kCAVvotHDKx2QksxhgScsmKx050rhwF1GKUPP9S3Z1tVXO2h8tI6JrqmUcUVTKNl84/ypYzu88pdvcXn9HFP73e9rDXUQ/TE88qHdN/h0KlRNwZ95e+fAlbbnW2HckZKvth3n8qJrrSKYzecb7rnu2uxmcWiojeGpaRwzEbMjuRIa5tChC9x779u3jXk7KA1kpZDOAOK2heUoyXyRL92LscWhoqycUjKo3lOeVPLpfKuH4StjoAhUwyW3WJaFqqlU7CxNrqVr91XSfqibyZ5p+nY6SJXks1oKcHSJiAg3pWY44HMVGNO0BTvvGxHIwVX+/H73OS5PDSXXJk9Vbk6J0xzb4fg/nsWKW5AguSDd+yC7PBNPgvzqU3WK0jK5Oj2C5dhELROZ2OmjI52oJ8LMveYSsErfgrkHbGQWaLqKJZdXcHSQ7MhfvcXvykqOkuul8meNGFcTRJjFODfZz9OffD9dR/tQtdTYte+Ma/G5kjryK/3Nrh1d4nVFMJtfXL9vRcXqdxq25fDqXxxmdjiUPG9p41D3PcnUZoGToZA36+XXvvAol6aHuDg5gCZUdhdU3hQx+cRYdzJfOo+4bXF4uAOvqqELFR0V07YJ28a1burJ+Ko8EQM8UbGB8mAWFycH8Sgqu/KrbthUaxk2x79yFsdy0LwahukgkNiGg+aBrPLMJaoya1jDGtawhjX8rOI9RYa5+27X6kBKyXPPPUdzczONjatj5S7G1atX+dGPfrRku+82VFXlwIEDHDhw4N3elTXcIQxFZ5cU0wAGwtN39HuLNxTw1J8+QjxsoPu0d6V49l7FG//rOMNXxwA3sLXjFgZusJ5/VzGjzGE5NnHbSvrFxqzrq2a8/vWTnD4QJpq+cJ7rugT3V+YgLgp0v+Z2Ws3Lx0rY8bFU9Yru430c/uuTyeDs/A+ucP9v3k1R482pGliGzZlvX6TjrR6ElDyxt4IdH9+M7tM4lDuKr2uK+sM65590uxCEBCXo7ve0ESVmm8nEgpSSwYsjDDeP8nxhD7bXQSyy4XptsIW9hTWUbS0mrzYnRa7eE/DQ9Ej9Dfe3bWaU7rkJHMvBjrsFbUeFoQYb5aVJpgdm0H06A9PDjLaO8+SfPEwgZyEIz1rOckwI6hrL2HdvDaHxCL50L+GJCOe+f5mRlnGCeWk89r71PLVlM1HLpDSQuaLPPLg2UGbcwopZLqlJcS2wRprHqN67fIHn/pJ1zMWivNHRigZoccjvUtgYTWP8SYnXUYmJRYkQAdEMh8ELw3zugb18p/00J0Z7EsovAilACgmqICVqFsLtWtTAFxOk1bgEpNPjvYS9NsRJtD8KrDTJbC1UH1Ho3SNxfK6s/8bsEu4rvvFvtQZXceHkaDfnJweSRBhVKEzEwpzLC5Gb6Safbd0ttC/+reaThjHL5NBQGyfGepKKRI+V35rKRq6SRqfjJgoXf1fxZZXBexUi+sLzZ6ZU0lVscO2KI8cXSCloAvSfG+LNL51Ivu4+3s941xRP/ekjyxYa321syinhpf4rS3hBm3NKOf6Vs7T+dEH2vWB9Hg/+m33X9VIP6j7uLa7np4usklQheLR8w03tlxACXSzthi1syF+iauAJeMi+huwHELEMnu0+n0jKKezKr+KJig1L1EruNNZnFbL+JhU+Qmac/3HpdWZNlwDSMjPCuYl+vrDxAfyajqoofLRmB19rP0E8QWhShMB3TZFBUxauuR15FRwd6UT3aTiWjbRdafa8LoWmx+rJLFmepKMIwV3BcjJfdG0SJstspsocVEuw96GN/HS2PZmAFMCTlZtQFYV7imrZmltK99wk2d40vtZ2YgmZajAyc0NLm0vPXU0SYebReaSX9Q/VcsTsXHLtzpoxLk0Nsf06am3gKjz9wbZHGYnMka57Sfe8/eKRoirs+uQWtj7dhBExk4lvRzp8v/tcimpS3LF5tvs8/2bzg6hC4VP1u3kwMst4LERZIGv5Z/S7BNu0Ofw3J+k96ZJ1dL/G7s9so+bu20vUWA2GIgvka6+qJYlgjpQowrVaKwkskOJe/x9HGb6yMGeMXB3jnn+1O2m59E6g0J+RQuJYDnsLa0jXffyk7zJjsRBF/gzeV7GRfP/qrQlWA92nUbW7jM4jqTaB5dtL8AaX2nL0haZcxcxFN5oUMOMz8WX6qNrtKg9FLIMLEwNLPn92oo8PVm1ZUgC9u7CGqmAuF6cG8Coa2/LKyUwQXJdTkovbFifHerClwwOl6ykLZLO3oJqTY92YjoOduLcydN8N7/01rOG9AEW4BelrlTAFrrLcnYAQAo+iEbbiibBMJJ/LK5EUhiIzvNHbBjEHTYLQBMIL3+w4hb5VIafMx5iMAwI5v+RIzhcJkyMBozsFuVckqqoghGBIhjiU1oJMLA+HsJk6eZqdTzbySqyNqG1iOg4g0RXVVXOLmpi6k9y21ECVCvGgxGj0sm/zehpXEUODG7svhlfV0BWVB0rW4WsxGXjVXf/GLkriQUlwRuHhL27gufCrS7ZlGzbVe8qZGZij78wgkHhO/ty2VTXojLaO01YRYqps0RohKHnZ7ORBZyeq8u7GDw0P1dF9vD+FRJmW46dy9/LKc1emhlKIMOCqPJwY6+aeordHOl6MzvHRJBFmMc5f6XrXyTD3Fa/j8uQQs2YMaTsYURPHdDB8gEJCWVEhTdPx7chiU0mqTVH/+WEsXTK9TSPisQgOStJ7QPNpbP/4Js4/e4X2N7qRjqRiZyll+8s4rZ5HGBYSt5lMsXEbf4RrbaoK12JGXbQWF8DDZQ3cCF9vP5kkqTpScny0G4BnqhfI0ye/fp6+s0NJW3EBqD4Nj18nvSB1LZPjTSNsGtgsIucncjNvevvYInRwwDcDG/5JMFVsYa/z0r9OYqqplkkKgqpgTrJhYDXIKEonpzJriZpjxc7S68brtnSW76WRDpZlL68QJSEeNpYlw4xEZ1OU0MG9V94YauPRW8xx3Cwc6RAy4wQ077JzzeXnWxhuHgMpF/oUJfimoeyowBvQ2fnJjWiqytbcMrbeorXncg06tpRJolRyf1neMgsgTdV5ZJEaTabHT5E/g0yPn0JvkN7TA4y2TZBeEKB6T0WSoDWPqd5pjLD7G6qagifowYqZSClpeLiOTU81rNUH1rCGNaxhDWtI4D1FhiktLWXHjh2cOXOGeDzOhz70IV5++WXKy1efHOrv7+fpp5/GMAyEEOzYsYPS0neuk24NP9so9KejILg23Ch4h6wjvAHPjd/0M4TZkRBjbRNLuihswwa/zg5RQqsYZ9aKMR+aqELwXPdFPO0G6WGNQG4axRsK8KS559YybE6rQ0Sy3CSZSPiMd1RFiI3FKA9k08eU28VuupKu99bUk1+30L1uWw4nv34+Zb+suM3Jb1zgyT966KaO8cRXztJxuCf5uu31LoyIyYFfu4umx+vpPzeEJ2KjxwWWRyI0JRksZ3v8KRL9x/7hDO2HupFCMvYxAwyBL92DSASYpmMzEQ9RFsjmod/dT9tPOxlpHSejMMj6B2tX5e89Fgslz8H80QsgGnRJBYsTVmbUouNwD5ufWggO9xZUc3y0m9gim58M3UfJqJdn/+JFwhNRFE24HTKJTYXHI4y2TrDr01sIj0eY9AxRs7cixUfasR1C4xH8GV6EAtbipIAjMSMmtrmCzi0uQaKxK4OZb6s4mooedxM2c8wQ3JsGioBrPh7Kg2nHIaj7+IXGfRQNeXltug0jzVUxcDNOLHgmzSNBhOjZJ/mbuRO8bzDGnBlDKAJPwEPMcINfKQVTTQKtS3BPVx7rnqqnpCh3VZ1U/1LRPTfBqwNXGYnOUR7I5pGyxusqXqiKwmMVTVydGcGWDqpQ0ISbEB+pNBkqsnGUhA2ZBmoiH+J2yCuYjo3hWHgVLekz/qOeC4DksZskWgDcU1TL2eFebA2k4s49ehSKBnXadoFu6jiWS2LTPBpXo6MJhZrrJ0CuvtK+ZCw0GubNM5cZyTXczqSCyhTlgncT+f50nqnexnO9F4nZFqoQ7C2ooXTCxyuLiDAAoy3jtP60k6bHV/aVB3isfANV6blcmhzCq2rszq+k8CaUYa6HnZ/cwqv/5c0kOULRFO76+W1oHpWZwVnCExHyanPwpHn4etsJ2hKJW1vaHB7pwEEuset7L+L4aFeSCDOPyXiEU2M9SfWS9VmF/P+2PkbLjKvU9eOeC0zEU5VzduUv2JmVB7P5ZN0uftJ3mUkBQVNnR6iQ3V+oJb92eVWYeWx8soGhy6PMDM6R06+S06+y6ckGtjZsYHu8inPj/djSYXNuKYX+hd86qLuS0wCGs4IK0QrqROCSgoYGlpdvn+yeJlq0POE2ek1xcSWoQkkhTtwu6H49xaphMh5h5prCG8BwdJaoZeBPEGmL0jJWrRx0PZiOTdvMKKpQqMvMf9vWBM0vtSWJMOCuKY7+n9MUNeW/43LhxWmZyYKMIhQCmhfLsdlTUM3m3FLWZy4QzyZ7p1OIMPO48pPWd5QMs1pszClhY05J8hl5p7D757eh6ApdR/uQUlJ9Vzk7P7n8vFjgT2dienbJuH9WpBR95pUbroWdsD9YLqoqCWQue/8t7tyVUhJK2MJ5BFyaGqJlZpTf2HgfhWkZfKJmF19tP07UMlCFQsDvIe5YBLgzZII1rOF24kBxHT/pu5IytjmnNEkMu92wHBufqjGSsAMWCPyaTmNW0bIxjRm3eO6bRzBK5p+pruqLbduYto3XVunzRjFV0GKu0py9eOoSgBTuuE9i+UmqHfRts3HU1D6F/s0W+5ttNmwu4cRYN6oQaIqGlJKIaaAlmhQWb94b9IAQ7PiFLTTdhJJWbUb+EpUcv6rxQOl6PKUqR7ssOo/24gsJ0m2d3Z/ZRnZ5JlnlmUz3pSriBvLSyCzO4L7f2Mvs8BzhySh51dkoXpWDgy2cnxhAUxS25pQxa8ZpnRkhqHvZX1TH+qxChKIwUb40NjZ8ku7QxLtu+5Zbnc1Dv7OfS89fZW44RMG6PDZ/sBF9BXJ8T2hi2fGuuYnbSobJdJYnP+ijy6uHvJPI9qbxW5se4MRYDydfvoynQ6Fjr+M2YUj32nVwiNkWfk2nfWaUsmB2Mp8ks1UuPW5ipLlyuONbBDk9Ch+v3EDbwU6aX2onmuEgHEH01Q4q95RRuCeTgbEJHFwlI+GAGsPNmyigehUK/On4NQ+mY1MayOTuwlpqMvKueyxT8cgStTaAM+O9fLBqC6pQMCIG7W90o3oUzJiARG7ONmykX6NsazEnv36e8EQET2OQ7wSuJkkN89CEgkAQ80lmvSb68AIRN70NvKM202kwXZ6I3UnYSuoeHiy9MaFnMTpmx+j+oErvoI1vwKHskkZtdTG7P7112fdffbWDtoOdmDGT9EdVZjOsFIJ/Q1Yhfo+Hkk2FDJxPVY4J5PpXtBtdTqkZoG12lEe582SYi5MD/KjnIjNGlIDm4aHSBvYVpdo6dx3rQ9UVrGvCGEUR5FRloTydx2uZA7xxZZhdBZW3rGi4MaeEU+OpRG0FgXJNc4y1gqpPlsfPb29+KNnc8HzvJQ4NtiafGdqUQ+0PJZ6o+7r5xTYe/b/vw5+xMI8EctMQSiL/KSVWzErmg2aH55Zn4KxhDWtYwxrW8DOK9xQZBuCP/uiPeOKJJxBC0NLSwtatW/n3//7f87nPfY709JUJBaFQiC9/+cv84R/+IZOTCwnoP/zDP3wndnsNawBcFvfdhTUcHulIjqlC8FjZO8OQX0MqHMshmuEwsFUQD0JgUJJ30S1Yaz6NdTWl7Bqd4eBgC45MdKPHHcKxKD+5eI6StxwUTSUtx8/+X91N+bYSpq0Iw40O9nx8o0tUw1WF+MkPjvHRTx3gxakWmqeG8Xs83JVfxePXFL3nRkLEZuNL9ne6bwYzZq1axt+MWXQdWyp73HtqgHjIIL82l0e/eC9XXmwjNjzO1XVhNJ8GQqAgeKJiY1K9YrJnmvZD3QAIKfDPCKKZEjNmJYlAHkUlz+smHXWvRtPj625YYL4WVem52IaNFTOTVlISQSBh671YiQYgPpd6nnJ8Af6vpgP8dLCVkegs5cFs7k6v4uAXD7kkJ8CIWJgRI8Un14yZvPmlExiVGvGg5PTrzTz+y/sp3lBA7+kBTnz1HNHpGJpXXdbnXGiC0Hj4usc20T2FagvUa3JZpT0e/Os8xOJW8pgV2yVSDQUWisBPPLCTTedKOH2qjZfqBhY6FZeB4ir5InTBT/ouc39xPXHbwpAWUl/oRTF8ClPFOmfVMDtyAz/TRJiRyCx/03w4qXIwY0TpnB3j32556LpdWV5FQxMKGkry+pRSYmoOmiVQbIkWd9V6/JpOVU4+vQkfc8ux3WSbYyc7hBzp8ELfZdZnFlJ9g0TetVhfUMx9s+WcsPuJpUvSxxTKz6qsP1BFu+hxr/dF1TuXBHNj/+54eGkRvn+TxdnYVbRx9x46PtrNZ9btXtFe7J3G7oIqtuaWMRydJduTRrrHx/lnryz73uGrY6uaqxqyimjIuv3WmlmlGXzwzx6l/9wQZsyibEsxnoDO6//jaLIbV/OqNHy6kTbP0gTjqbEenqrcdENS07uN8Vho+fF46rhP09mS6MAr8Kfz/a5zdMyO4VV17i6s5t7i1N9qS24Zm3NKMR37pqTq/Rk+3veHD9F/dpDwZJTipgKyy93Ebo43wAOl62+4jY3ZJRwd7UoZC2geqtKXEnHCpsF3Ok9zdXqY+N0GaRWS2qNaMoEJkFmaQVN6Ed1zqUUXATRmv7u2rtciqHvdrnYn9aEW0Dy31TIAoGtunH9sPU4kQQjK9qbxS+v3vS2Fkfl7azEcy2Hg3DD191Xf8nZvBY+VN/E3zYeTVm6KcC2lnqxc2gUemVxKQAKITC0//m5jvHMS27Rd0vcdFLDSvRp7P7eDPT+/HVi6XlyMh8saaB8bIW4uPNvyulT8ESXFRjbD46cimL3EFqI2PS/FjgFcEnffmUGi0+5cknVNkag4LZO7C2s4MtJJzDaT6zBTOigJ8tzRkS4+WLWFI6OuOtT82mM0FuLbHaf51aY19dg1vPdxf8l6fKrO8dFuTMdmc24pD5Tc+Hl6qzg01MZELIymqFiJ9bTl2DxdtXXZ95/46jnCF6YguVyVOPOPLFtgGwldBxWk6sZUjppQXcSNrzQUFAW0WYeg5iGeBf2bTGbKEyqrlqteIRVAQqsyRftsCEUIFKGk2DNZHrfALxPzo2K584nXo6cQIVeD91dsZCA8xWQo7DbdSHgooyFJRtj3+V1s+VAT4YkoOVVZSeLHXT+3lZ/+v29hRt25SPWo7Pn5bQhFMNw8yqXnWgiNhSlsyKdrj8W5uYXnZ/PUMJqi4k0899tmRvnsur001BeitypYpK4RVI+KtoxK4ruBwvV5FK6/Z1XvzfUuv95YafxW8ej6jZx7oxdLW6hQB8cVNhS8N+KrgO5lYySX/tcdYkEFqS705cw7PluOzXc7zqAoCl5F5YNVW9mRX8HYZjCGZLL4LoCpakm00cOr3zvP8IcsLK+r8BgcVzCO9BHYk4PlnU+RCIQNZYckOf2Cgk9UcdE/yYwZYyZBtk/TPFSuwo7Tlss3MSXt0AQYYdMlDSDwBj2Y0QUSwZYPNnHqm+eT90yPYxLb7iwJq03Hxqfqbk5t1l5CPLBiNusP6gw32ET2+4kJmwJfOg+VrmfrTSjCDYSn+burb7n7n6cTyXEY3Obl09t2MxYP8XLLKQbDMxSnZfJIWSNzb45x+tsXk58v+pHEeEwQz3cPoCqYk1TI2f3prbw2+hazQ3MA+DK83PMru1dcZ61EfLydhMi5kRDTA7Nkl2emNNyNRef4evvJpDVR2DL4Yc8FCvzp1GcuKGwrikBRFPQ0HTNqIRI/TMWuUkIfy+TIWDckwtOOuXFCZpwDt6Cc3JRdzP0l63hjqA1bSjSh8HhlE/3haS5OuvOoIyWWtJc0zapC8L6KjUlVtdaOAV7uupS8BhVdJYrF4AaVqlPu/Ds3Gubky1cY2yHoC01R4E/ngZJ11OyroOPNnsQ17c7Jmk9j8OIIR798mvu/8N5wS1jDGtawhjWs4d3Ge44M89hjj/G5z32OL3/5ywghmJqa4rd+67f4/d//fe6++262bNlCQUEBaWlpRCIRRkdHOX/+PEePHiUWiyGlTLKdP/vZz/LYY4+9y0e0hp81PFW1meqMXK5MDeFXPewuqKQo7fZ38K7hxohlQ/MTDiaAhFCpYLYa1j+vsPMTm/GkuYGrN5FAciyHWMxK2NAkCteWTWwuzpG/O8Uz//V9vDTQ7FrXJFMCYHtAWAI5aND+ww5+/pf2YEs3sFbE0iAyLduP6lGT5I2FcR+aZ/XJI8d2UpRU5iEdiZ0IgvJqcjjwa3dxAOgNTXJuvB+A4HmT1q+f43L0JOU7SqHKS/vdJtFMSWBCofCqSs9uC8daCNgeKWvE9zZ9gHMsH9kXHYZrYD614pmV5LQILJ/Aq6c+lkoXFS3mUZiWwSfqdiZft73elXou5UJXj+bRkEjihkX344Jw6XxHsMXcsWP8YuGDHPy748Q1B59wFXoiUzFUr5ZQl5GomoK2CvuxrJLlCZv5xdnsK/LyUsclbAeELVEc1/4o3DVHaDxMMM8N8su3ltBVGMHfPZYqQb5IHUY4oJpuF79QFCzH5qW+KxjLdDi7srgS03F4se/Kz3Sh5ehoV4rdB0DENjk91se9JcsnPxzbofd7XWiaQSjbQWgKnjQdKUBTVXzpGrbpIB2JoisoqsK/brqXwcgMfeEp2mZGOT3Wm6JkBO7PeXys+6bJMAAfenofjS+10XW0D6EIat9XyfqHarnSHKLrmgL7jryKZeega1G2tZjJ7unka8sjGWqy8eoL97uD5MW+K+8ZMgy48viLrWoWW6otxkrj7yQ0r0bVXQtJz4s/vppSrLfiNqe+dxH5MWVJ8tFyHHdau/FP+a6iMpjL6fGlBM3K4MoKLnm+IJ9vvAfTsVETRaTlIIS4JQKGqilU7ro16WtwFYMm4mFaZ1yljgzdxyfrdi1rW/X97rM0T7udlZpPY67QoGOvReNP3fuobGsxhevzyHNy6AtPJZOkuqLygcrN5HhvrK72TsKn6txTVMvBRfZhAPeVrLutCiCOlHy743SSCANuR+/3u8/yK437V7WNk2M9nE1cezvyKtiRX5GicrMYuv+dD38rgjn81qYHOTnWQ9iK05RVvCL5qWBdLppXxYqnrhGLN9ycjebtxFTvNFdeaiM8EaGwIZ/GR+owoxY//W9HkmoDadk+7v31veTV3LhI9XZwPRLMPGoz8vnNbQ/y/VePMTExQ9aAQuGQzs6f25KiCgjw8dqd/GPrMUaibhGoOC2Dj9RsT3lPdDrGy//5ELPDC8S+LU83sfkDqXbOH6zaQmUwh7+/+pa7rwBI4raJIgSzRpSIZSzbrd41N8GsESPjNlifrWENdxp7C2vYW1hzy5+fikdomR4hTfPQlF10XSvICxMDRG0zZQ1vOg5nxnt55BpLDsuw6T7WR2AGAkMQToSRSWsSM6HmqLujti5QbNfeFkBJU9E0l8hi+h08jkbrAxaTVTJpUSpJ2Lgskjg9FhxGxEjEYXbCCnKBEeAoC98hJHiFxsdqdxC4SWupPF+Q9/VX8vrJS9i6IGtQYSTaztVPBWh42FUvCeYFknHlPArq83j6Lx6n7/QgjuNQsb0UX4aXsbYJXvuLwzi2u6+TMyHOl1l4MxKKuAk7N8eReBQVIdyS8qGhNhqzizhQ38ArA804pgNCoHlVyrJyqAjeHiVJy7GxpUwSce4ktuaVucSr+ELzS0DzsK/o1q/z5VCUkckvFt7Fj4+dJuq3SR9VqA1nsuO3312LpHk4UtIenWCo0cYz516vWsxt+pAKqLrqFvYT8WXcsflu1xnqMvMZ12IuqSRmJa8pqcI/th/D2OIkG300A0J5Di33GERHR1AhSVhTbMjPy+TDn9vHS8PNzIWjKJqCklA27gtPcWlqMEmqXwl5viAlaZkMLrKpBNiQXZy01QnmB8gsSWdmcM4l9iRUtnNrsgmNhZNEGCBJ2Lk2rJ6fBgqHPejhpaRl27DxmDqllzUefv9eMkvSOff9y3R+5SJDWe00PVa/qjjlrZEOlwiTgFAUwo7JW8MdHBpuS9rPzs7E6Jobp+Fw6jrdFxLUf1dS+4kaopMxSrKz8Vaq4HHPw1N/8jCjbeNYcZuixvxlm8Pm0ZhVRKE/PbluApfkvf82KChJKTnx1XO0Hux01YgUQcPDtez8hKsEeG6iP0mEWYwz470pZJiafRWc+c4lNI+Gqqs4tkNatp89v3kX//niS0s+//pgG/uL6pZY464Ex3YYvjKKFbd5aMM69hfVMhYLUejPIE3zYEuH02O9tMyMYFgW54f6XGXsBIlSCPd58VzvJY6OdPGBkk28/P3jOJsSdkqOxEpYys/lLzz7LF3yfKADOereMIORGZqnh/mNj99LMC/Aia+eRdFVNM9Cg9/A+WGMiLnEXmkNa1jDGtawhp9FvOfIMAB/+7d/Szwe5xvf+EZyMRKLxTh48CAHDx5c9jPzJBghBFJKPvWpT/F3f/d37+Rur2ENSWzKKWVTzpo917uNN4bb0DI07MiC/U68SND0H3ZQ3+B2BW/KKeHwUDtmzMKMLSh3ZHQuBFmO6WCETcY7JmmPjOHVdbdos8hb3BeG9FHBkOFKnFyvUORJ02l6rJ6LP0r12t38gaZVJfnn4Q14KGrMd/1wcedBy7DxpXsZOD9Mzd0VKYFsRTCHimAO539whQs/bEuOXznXzZUyC7scEBDJtJkpETS+osPedErrS9icW7psJ/zNYuD8MJUnVHI6BTOFDsRspsug9RlQNY2MbpvqYwLdVmh8pI6SjUs75sJmnHMT/cRskw3ZJQg19ZwpSc/kRBnCloxvlIRKRUotua00xD+cf4vWJ2NIBfSooOqkRmavihm3UmzHFE25oT1B3b3VtL3exdzoQhItsySd2n0V5BLnteZLie93iTCKJchrVzj73UuMto5jRC3KtxXjPJqJpqikqTrRRd3F8/wrKcDxCRSPAlIStU30FTrwJBCzLdI0D0PXJIN+1hA2l6oxAYSs5ccBLj13lZbXOqjzaXTvspgudbBnTO5eV8/p8V4c3A7IeeT5ggghKA1kURrIojo9lzNjqeQAgUBXtOvarCwH23JoebWdvjOD6H6drc9soGzrAlns03W7+V6XW4xXhcL2vAqerNy0qm1veHwd452TDF5w5y8jR0EL6i6xzrQRikDVVEajcymk4/cC5kZCRKZj5FZlUXlXGRefu0po0T2oeVUaHq69zhbeHfSfG1oy5psWZBoeZn1mSsZ1Q07xsn7oy2G+EDTVP0N6QQBPwEN6XoD8+rc/f98IO/IrODPRl6J6UpeRz5ZVrIf06xTC7iQc6TAZj5Cu+5Yttvg1nV9q2MdYNETEMigLZi37fDdsi8uTC7+poir4MjxEvTaF+4upWVdC9V5XgltVFD5Tfxdj0Tmm4hHKgzlJC4b3Gh4v30CeL8i58T4UobAzv+KGBYibxVh0jslrrLIAOmbHMR172WvDthyMkIEv08vLA828NtCS/Fv77BhTRoR1D9YydDnVbigtx0/59neH0JfrC/BY+Y2VIj1pHu76uW0c/fKZ5No1oyjIto9sXNX3SEcSnojgy/CirWAHcTOY7JnmxT95PUk6Hrk6Tv+5IfyZvhTbjchUjMN/dYIP/Nmjt+UZMRadw3BsitMyV0XqvBZlGdn8xoceZ24kRGg8Qm51Fqqu0v5GN+Odk2QUp1O3v5K8tCC/vfkhBsLTCMSyFkgXftScQoQBOPeDy+TX5VCwLi9lrT1tRPCoGtY1tmemY1OfWZBQjhBLijkKwlWoXMMa/oXj+GgXP+g6nyTwZ3vT+JXGe1YkhE7GI0vI7A6SS1NDS8gw840Mmkej+gWDiQ0wVyqIZ4KRCYh5IgyAQDXcuKq6zcdT27eTtS2X/3n5dWaMGAFVQ2ZIJtLcRpn5aUgm/8/dnioUHBx33kuKYsj5P6PFEwSYkGD9IR2lQOeTv/s4fu/N21tbcYurL7SRG019Ll788VXWPVBz3cYNb8BD3YGqlLHmV9qTpAUA0ydxHId42MCxJI4qkZ7FB+xi2nCf2U+s34SSpnF0uBNDWqzLLORD1Vvf9jPAdhx+3HuRk2PdWI5DbUY+H67ZdkdJwz5V5//acC+Hh9vpDU1R6E9nf3HdHbH/2rirhvqmMoYujeBJ5HNu1HTzTsB2HP6+5Qjt4VFi222kLV0lI91txNG8GgYOHkVLYYU4UtI8NUyBL51ubQJPmkJsLg6OdJWRTIn0LFxDtg7CFswVgGpLFAlKIrXmDXgIPpzPsS+f4UrlGGaJe++rXi1ZzB+OzLJlFWHNp+t387W2E0lCTF1G/hJFqb2/uIPX//vRpGqzP8vH3s/t4NiXT+M4jmuZ5EBeMww1kbThnk8qeRSVR8ubCD3Xy6iIJueHyQYY3SEwg5A+ZlLf4ievJpuf/PHrybVTeCLKG//rOPf+uqBix/VjpbBpYDsODjJp2QxwdWY4SYSZh+HY9BfEKR1YuKaklBgRk/avt6KoCkP0c/XVdh77f+4nLcuPUASF61dnbaYqCr/SeA/f6TjDpalBJJL6jIKkwsnNYjwW4vxEPwJB/qBG6yLLY+lIml9qp3hDIaWbV1bQvJYe0/TYOuIhg5afdmLFLAob8tnz2e1EpJFCKprHlBHhf1w6SMQyWJdVyGNlTSuSFedGQ7z2F4eTOT/dr3Hg1+6iepO7f81Tw7w53M6cGWN9ZiHZwwqn6MFZtDkJqAhMx2YgPM1ftxymcs5J/k0seqNvbuFeG69yMHywOGqM2xbHxnt46P56VyVXJvLCcQvHdlA0BTNqrJFh1rCGNaxhDWvgPUqGURSFr33tazz66KP8wR/8AYODbufkSgUYOW+1ISUlJSX82Z/9GZ/61Kfe0X1ewxrW8N7DRCyMUBW8QQ/ScRDC7bg3MlxbEN2nUZ2ex9aZfI5bfUl599zzkvReklHIfN1L96k4nQaWZqAJcDSBFK7XeN0R3fUwz1xdR+fWD20gqzSD7uP9KJpC3f5KSjbdvEXC3l/cwaH/eYyJriniIQMEWDGLY18+Q8fhHh7+vQOoWmpype3QguWDYzkMVZsJ2yfBvPyA4ZNESwSf27M3aStxO6B53ZOcPq6QPq5w5SFJJM8N0jxBD9FGB7Mpl4/U7UqRRJ1Hf3iKv2l+K6m08XJ/M++r3IAnTceIuGOKqqD59eRxC0UQrliaYJIeQYs2CQnRDtMvab/HZOuPPBRU5mFGTSKTUTKK09n5ic1LuokX4+x4H4eGWgl91CF7KoOKXh/VRQWsu78a3a+Tj87ejnzOB8eIZEr841B8BpQJi84jvckEWNfRPtKn5vA+6J4nLSGzHTNNhO1Ka1tesJGE4waqpuBISVxa7uW6OLZPXL+WY2M6Nvm+ILbjrLqo/i8NDVlFnJ8cwHZctRw1kUhqyFpZorzzLdcD2hMTrHtTxxESAdzzb8tJK/ZyaGiBVCaAR8tSu8QL/a6K0Vdaj2FLiaooeBUNRQg25txcQfbI352ie5Et2sD5YfZ9fhdiUxrHRrqI2SZN2cV8om4nmlBv6ncWukL955uoHqvFMwvB6gz++NQLxI0FopCiKVTnF7xniDC2aXP4r0/Se2oAAE9AZ89nt/PoF+/l8vMtjLVPkl4YZOMT68gsyVjVNkciswxEpinyZ1ASyLqDe8+KCalt7Zm8kT1IJM1G0RXqsgt4umrLqrZpxixe/k+HmOyZxoq7BE9FFXiDXgrW5XL/b+67o4kwXVH5lcZ7uDI1xHBklpJAJo1ZRe9Ze6fmqWG+332OGSOKrqjsL6rlsfINjHdOEhoLk1+XSyDXVRValV3PIgUvcLs2db/Cns9sI2OZYkq+P518/8oWsO8V7MqvZFd+5R3bfprmufbUAeBX9WWJR1deauPSj68SDxmkFaVx6snYkoj2jaF27tv+OPt+eSeXnm8hPBGhqKmAHR/ffN1u1/cKavZVUryxkMFLI3gDHko2Fa6qUNZ/fsi1CBmPoPk0mh6rZ8sH355V6+WftC5RMpzomnIV0a7Zp7nRsCtrX3br68aQGef/XDhM5+wESEm25ucXt+2nLP3WlAbSC4OkFwaxLYdX//wNRlsXyHqtr3Xw+L+7H2/QS+l15vyRllQll/n59cU/OUQgx8/2j26k7oBLsp8xYsRta8lyLNuTxu78KlRFYUtOKWcn+lO2uSmnZIk10xrW8F7B7EiIiz9sZqJ7iqyyDDa9v4Hsiqyb3k7YNPhh94UUJcupeISf9F7mU/W7l7zfkZLwCqT1uYRtymLoPo3SzUX0nxtC2hr5F2zyzktsHXofEcxUJ75XJuxevO6adqjWomhTAf3jk8xFoii4VrTmfOV78dJXLvxXM0DqElSX7O5VVWKLC9MC8CkQk8SDkuKCbPZ9ductEWEAojOxFLUKdzcknaUR/uzcy1jCYVNOKY+VNyWtk260vcXwzwr0MBgBB4Fr6Yt0VUnFos3NKzAoQuGJig08Vt6ETMQ5twMv9V/hyMhCQbx9dox/aDnGv9n84G3Z/koI6l4eu8be+k7BG/CkKEa+F3B6vNdVLhMCT8CDETaxhINmgpEOlmKjSbHs7+zXdO4rqefS5AAz0Qg4CTUl6ariLoYU7v8U6apkWLr7WjigWBaxnjCTPdPkZFmEyyUCiW06OJaKoimUBbNWdTx5viC/uekBxqIhNEUh27tUKTS/Npen/+Jxhi6PIISgeGMhqqbgz/ITnzOSjXJ6D+SfhbFtiWOXrvV68bQXX6nCjHTm38pMNQzctzBphIqgs07Q3zrC4OwUc/USb1iQOSQQUtD8cvt1yTCGbTEcmVk0Fwp8qoZH1Qhqy+cefeVpcHbh/nYsxyVGLFq3hSeiNL/Yxo6P37wq0Wg0RMvMSFLVqzs0yV9deZPf2fLwTSk5XZgY4Bsdru2RdCTGrEFFtkPWgEDzLigzD5wfpnRzEVtzy3htsGUJoXh7XkXKa6EItn90E1uebsIy7GSTmy0dMnQfs4ueH6ZjY9gWAwnS1PHRbvpDU/zGxvuXzXuc+saFJBEmnibpr4nRefwwTxTtQVcVvtJ2PPmYGInOYcdN9xEyH34kNmlLuaCKKaFrB/iHJZFC10aexD1S3LwQtzg5Ksv95NNGBH+Gj5JNhQycHyYeMpD2Aon0lT8/7K53A2vrzDWsYQ1rWMPPNt6TZJh5fOYzn+GTn/wkzz77LD/+8Y85duwY7e3tOM7CQ11RFOrr69mzZw9PPvkkH/zgB1HV936Scw1rWMOdR5E/g8tTQ1iOm8RXhIJmCXq/0kbf1Yv4MrxseqoR/2thyjJUerfbOAImNgmkCiVvSRThBmIlmwrpeKuXnBaTyX1uYCLiAJKsQZXgpBuoNT2+btX7V3VX+dtOggTzArzvPz7IsX88Q8urHSkB7ljbBH2nB5Z8hxldsGyxDAtznnMiEv8nQdUVqp6ovq1EGICybSX4s3xEp2PEApJQnjufax73cSRUhS4xjS93+cD++d5LxGwTWzoYtoUjJd/vP8/nf303nd9sY6p3Bm/Qw7aPbKRmXwWjrRME89KYbT9FizO+sCEB6AJVUbGFTCY6pAKTZQ4Pv28DZVuKMWPWDYvH3+44zeuDrcnEbp8PrjaoPF6ew8a0hYBz755GZv5kBJyF4N1KXF+LMXd1mmee2cHBWAdDkVmyNT8TM2bSy16Lux1VjioRiSStIHkIC0i0lUggahkMRWb5T+de4ufX7aH8NslX/3NCQ1YhmlCYtVwJY4FgZ34ltRnLd0FFZ2NEpqOYMRNFU1E1BUXOmx5I3lexkbJAFhcmB/AoGrsLKqlOX2p7tLugCls6vNB7ibjjykrfXVDDttzV3/tzY2G6jy+1n/np0Qu0qQvqQc3TI/SEJvlozY5Vb7tleoTvdJ5mLqGcsyG7mIeGqik8LundtvA+aTg0DWVCgpfRPjNKy8woGbqP7XkVBPR3Nrly+YXWJBEGXN/3w39zkmf+/0+w61Nbb3p7P+g6x9HRBaLg1twyPl67444ROdY/WMvgxZGUMdWjMPnmKI2qylSpwDcnKCCK/89XR2Bpf6OLyZ5ppCMTxRKJY7mKYaOtE1x67irbP7o6taAbITwRQfNqeIOpv7sqlH8W6nizRpSvth1Pdpubjs1PB1oYfrUf9ZCrACEUwZanm9j0ZAMAIy3jdBzuRtqSqj3lKR2KHlVjU3YJ5ycHUr6nLiN/WSLMzcCRkqvTw3TPTZLvC7Alt+yWLKPeS5g1ohwd7WI8GqIyPZfNOaWcHu/FSKzXNKHwUMn6JYog/eeGOP3NC8nXU7NhQiEDX4YvRVkvZptELZOafZXU7HOJPLbjMGfGsKVzW22e7hT8mT5q962ehBSZjvLGXx7DNt1r2opZXHi2mfTCIDV7K27w6ZURGg8vO76skqEAzwr2VKvFt84dp21yJFlwHjdCfOnIQf74kaffFhmz7/RAChEGXPJOy2sdbP7A9QlDwbwAMwOuHYBt2cl1tFAE8ZDB0S+fIbs8i0BFkNNjvdhSJmvnEvCqGp+s24Uds5keneHJoo3oisbZiV6kdJ83T1W9Nywy1rCGaxGbjfPSn7yeVE6YGZxj8OII7/uPD5JesAqi6CL0hCaWqLyAqwS2HBzpLKsMJYB83/Jk0j2f3c6bf3WcgY4xpjcLnFId7xhUvRxlaLdkfLPA9pC0bAEI+S1+/9QPsYV0xyWoEStF/TGJRHylmiAs6U5Vimufuty+WoqDL6CTL9J44ov3vy0FkEBuGmk5fiKTC3YsAxtthrY4+BLxzZv9bfT0jvIL6/det5EDoGRjIaMtC+deSEHZ69DziGsFJSR4I+AIsDUHVVcp9KfzSGkq+V8RYql/zNvAqbHeJWPD0Vn6w1OUBX72Yth3AmHT4Phot6vIJ1zrXz2oY1hx5umwUkpMW2I4Bt64gs/vASHI0H00ZRejKypf2PQA33zuDQZnp0kfFgw22dgecGyQi9R8FSHwj0tmFoULUgUDh7R2i6BvlgfN8/yjsZ5Jnw8VG2nAutxqGrNuroHsRoR2zaNSvi21SSU6E0OI1PxK+UEo7dQYW+cwWe5g+WAwEOFbLafwNUpqWtx5YbJxvqNOoPs0NJ+GIRx+MHOZzscX8nCBScH6gzrxuZVVagFeH2pjMh5BU9REXlMSsy3uL1lHfWYBV6aXqo3u39nE4Jl2pvtnAbdZbLmc1mTv9HW/eyUcHelcQmKfNWNcmBxYNYHelg4/6rngEmGkJB6K40jJwB5J8JsOtungDXpQVAVvuhtv5vvT+WTtLn7ce5EZI0qa5uHh0gbWLbJIWgxVV1NI8KpQeKZ6G19rP46ZqCvZ0llCHhyIzNA5N75snmjwomuJG8l0aH7IwtZde7yvtR7H69GXnJewx0KNuTk8qeI2IS7zGDDSJLkXJRldECoDPQwP1DRQ/Hgmo60TpBcE2HxXOl8fPLPks3WJ/dz3y7t46U8PMdI8BghUj4Lu15kbCdF+qIsNT6xf9jytYQ1rWMMa1vCzgvd8JlVVVZ555hmeeeaZ5NjMzAyhUIhgMEhm5u0t1K5hDWu4czg73seFyQE0oXBXQRV1KwQttwt94Sk3uEq8tqWDbcPlylkaOnRis3FOfu0cZq5C7043zBcIUGByoyQjpFM5FaD2nko2PdXIs7/3IjkzKusOCUbW2VgeSUaPoG4iSOb6dBoerqVy5+21D1gtYjPxZZNrkz3TS8gwFTtLk4oX0pEEByQztYsshASomsqGkuWPJTIdZbRlnEBeGvm1N2e9oXlUHv7d/Zz65gUuzQ5he10CjLzmaZRqaLSAnrlJHOkQMQ0ct2cC23b4VvgiX/x3j6IYEs2rYeFwZKSTrsxxspUAB3ZsoLf5GIZpAQLNo+IorjqIFhQYEcvtnlAE9XdXJhMiNyLCzBkxDg+3p3Q4gitPe3Cwhe15ZeSqAVRNYapvBm/Ag2XY4EgUXcWIGFiGje5LPQHFToDf2vSgW9AbifIfR55PBNquEpESB9MPPkXHTBCDlkVitxQJqhDMmjG+2XGK39n80HtG4eOdwisDV7GkQ1D3uiotQtA5N07UMpdYlEwPzPLyfzrErBXDFjZ6yELzaeg+nbQcf1JGeEtuWYpliCMlFycHaJkZIVP3s7ugimxvGnsLa9iWV85wZJYcb4AMz+oUpOYRnogslU0A2stCSFIlfE+P9fJQSQM5vqXKStGZGJ1HejHCBmVbi0mvzuDr7SdSulgvTw1hz0QpalEJjgsmyx0UC/K6VZSqCDwGP+w+z1uLOjZfH2rlV5sOkOe7uaLI20Hv6YElY47pMHBx+KYKyABXp4dTiDDgepI3ZBUu6TS7WYTNOFNGhEJ/RordS9nWYvb/6m4uPddCeNJVrZgdnqPVO0XfFhtblwgHpptDDFwYvqF8NsBE9zTgFmsXXzCO7QAqAxdH3jYZZrp/hrf+9hSTPdMoqqBydxl7PrcDbbmC0U1ibiTEhR81M945RWZxOpueaiC36s4UPS5ODi4pyFlxmzYxRUNCfFo6knPfu0z5tmImuqc58venkqe180gv2z+6MSWp+KHqbcltSyQNWYV8uHo74Nr6jLSMoSiCgnV5qy6GSSn5WtsJLk0NJscODbXzq00H3nEC2u3CjBHlf156PdmVeX5ygEzdhy2dhU5P4aoFXIuOt3pSXnsirvWE7bPRFj1HC3zppC+aZ4+MdPJyfzMRyyBD9/H+yk1svc12T7cT47EQCmLZeXwl9J4aSBJhFqPraO8tk2GMiEF2WQbj7ZMp40IIavZVJNeS8yjbWpxUU7pVXJ4ZWvK8m5UxWroGaKi59d9ssmd6+fHeG1tIbnhiHUOXRnBsiW2451jRlAXlRQndx/sQ6VnEHYs0TSdmW8lCfqE/A+fYLN979hhW3Ebzqmz+QCPPPP4UEm7JBmoNa3in0PFWT5IIMw8zatH2etdNrymWU2YAyPIuTxrVFJV1mYVcmhzEcBbWqqpQVrQC9Wf52P9v9/Lfz/2UCSMMAmKFMdJKFCoOwcRGiVQWy7i5U46lyIW5J2GnZNs2KCwUxRMeKN7JhN2S4hJidEegZYjkelos0juTEoy4ScZrEX7wvRe579f3klt9a2sbRVXY9aktvPmlE0kbvdF1Drpfd21QQgaO5dDOGN/+oxfZ/XAT1Xsr0H06voyllh+Nj9Qx3DzK8BVX/UoogqKYn+wf2cyUOAgbsgYVTB+U/0IdFbVFrM8quKOKf450CJtxIrbh9q4oWtJC7lo1iLcLw7Y4OdZDb2iSfH86ewqqb9nq5Z8zLk8N8o32k8wZsWTjRkDzEHcWqZw5uPeKCljgGA5CkZRrGZSf1Hj91beo3lNOuEmnPz/KdKbDdAkEhyFUAGocHC84qquoVNrvpa8kdo0njPs87KyO0qRMQlTyy1cucyE3jwmfjxozypatD70jipNzoyG86V7XJimhUG9ETfyTrsLxWJ3DQj+VJJoHI7uh+GiCSKYq+NK9SeKw7Tj0iJkUFclwjmR4vc3msuuTe5qnhxFCkKZ5kmtlVSgUpWXQlF3M3YU1SXKKAPYUVrO9vIrtf1TFaOs4ZtTCNm3e+F/Hl2w7vTDIcPMogdy0myI3hq+xgpxHxFx+fDnMxKPJWMA2XFsuACMDbD9oUbehI5DjoW5/VfJzm3NL2ZhTzJwZJ6h5b1qRqjG7iC9ufYwr08NoQuEnfZeZNqJL3hdawWLbm+4lMhWlv9HC0tznhqOBhUMkHsaveVJI90K4Ei/CcvN4jpK4BK5Z+gkEsVyoeklScN614i7dnM26B2poeKgOcOPCVnOCIyOdGJaFlJIiGWRr0M1d+tK9FDcVMDM0m9zmPKYSxKg1rGENa1jDGn6W8Z4nwyyHzMzMNRLMGtawDAbC07w53M5MPEptZj77i+puSqbyTuL53kspliLnJwf4WM0OduS/vULjShiPhegPT+NXdcJWfMHfW0A4WzLcaFN2wT03M1ULn5sXR0FRyP54GR9p3Jf823zSO2tIIWtoIcC5///eS37dzZFCbhemeqcZvDSC4yyfHFpOwnrnJzYTnogycnUMBGS3wFy1YLZiISbbnlW2bIfFlZfaOPudi0l/8aKmfO77wt3o3tVfZ5klGaR/poyBrjGEaWNLh4hl4FN1PKrG1tyyFYPaPF+QztnxJPlk/qgjlsG5iX72FFbjSMnfXnmLntBC8eb0eC8fW7+TcxMDTMTD1KTnUpKWybM9FxCaii9DRUqJX9N5ZOv2VR/LaGwOy1lafAIwTYtv/9VrpJ8w8Wd6CeQH3K4c/8KxOZaGvOa382f5yEuQjDyKhpXvoXDGx2BeNKkOIyQoCZnilX575ILBvSNd5Qxv0MN4LMR4LPTPwp7jduLqtNvFowiF+YZ207Fpnx1lU04pU30zdB7pwbEkA8PjnL0rzFy+q9+c3isp/6lFTmE29/zablrnRpk1o9RnFKQUK7/dcZqzE66Ci23avHLlEttOBVlfW8bmpxqoSr+1eSK3Mgvdry2RRjdzFK6lIEhgIh5eUkSd7J3mlf/8RtJO7NJzLWR9tJRY7lIiVb8/RDUQnFAITixcr8G8AKPRuRQiDMCcGeeV/mY+Ubfrlo7vVqCvoD5w7fjAhWHOff8yMwOz5FbnsP2jG5fM163To8tuq2V69JbJMFJKnu+9xFsjHdhS4ld1nqzcxM5F3XLXqoN9889epHvLwu8hFRjcYNMaG6OCG5NhskpdO6hriW5KohPTl/72EvzSkRz8H0cJJaShHVvSdbQPb7qXXZ9cnZXTcojOxrj6cjsXftiMY7udx7NDcwxdHuGJ//AgmcW3Z64aiswwEJ6mOC1z2eKzbdmIZabTgQvDXH2lfUmB/uKPr7Luwdrk88+v6Xyqfjcx20RKkiS7ie4pDv63I0Sn3YRvMD/AA/9m36qOq3VmNIUIA+5z562RDh65xpbtnwNMx+Zvmw8zEJ5GArqi4FN1BiIzeFWNoL5w/56Z6OOR8qbU4uk1v4FAUHVSo+fxhd/Tq6g8Xb1wPbbPjPJs9/nk61kzxrfaT1Hkz6AobXUWau8UpuIRvt5+gt7QFADV6bl8qm7XKtWFlidU3Arx1bEdTn7tPO1vdmObNrZho6gKSmId3PBwHTs+von0giDtb3Rhmw6Vu8rY9uHbYC9hyGU7ZidbJjn4o16m+qbJrshiy9NN5NyETcu162Ez5haHRlrG6DjcQ+09K5MoC9fn88gf3Evzy+0MXBgmOh1D86U+fYUikutBTVEJCCVZmPIZCme+fTH5Xituc+Y7l8itzqGocXl1ujWs4b2CyNTSIiFAeDKKbTn0nR5gZnCO3OpsSjcXLa8alUBxWiYNWYVcnU5Vxruv2FVWtS2HsbZxVF0lrzYHIQQfqt7KZDzs2k04Dh5V4zP1u6+rcnlstItJO+o2XEjXEymSI4mXaFQec+i4z04qwAAL02fKrs/PRQJpuv/UQ5BzSVJ0Aqw0CBe53fuTWwWhzTJ5z3sVFUUKYoaJkFB8xc0dRIjy5peO84E/e/SWmxIqdpTygf/0CD0n+0HApawrSMVVfp0nyAAYisPxr5zj7Pcuo2oqlbvL2Pu57SmKpJpX4+HfPcBY2wRz42EK6nPpONzDhWebye1ZmOOyPD4e2ro5mQuRUjJ4cYSxtgmCBQGqdpctUTq9VfxT11mitplU9jUdG0+CEPPN9lPUZebzcGnD21bdsxybv25+k77wdHLs+Gg3v7HhvhQy7b90WI7NdzvOMGvEMBPn3JGSqGW65KN5EphYxFrRwFKg6Iwg8/wMs8As0N07ysWP2BiKjfSALWG6CvIuCByva8ub3SHRRiWR9QZ2Fe7GpViwjkEylBFjqj6X4zKbj3a0sWtsFEUV+NK9KFMjkH3z1uI3i5zKLIYujaZc144tURSFcK47fziqyxFCcWO20Z2CmVrI6JZESyUWTqIRy03JeBUdJU3HjJjJuSdeq7Pp0Ybr7ktQW4jfVKEwL7ATSIx/sGoL9xTVMhyZpSgtI6U5Zb6BRzqSoqb8JPENXFJvx5vdtB3sAgHVe8q5+5d2roqw35BV5FpqXTuevbL99LVI9/jwqbobNyXyWQLQDIFuC6Qi8Wf6ePj3DiwhWitCIfNtzAEB3ZtUsOkJTabYsoGrUFmXsXzjZtOj9Rz7xzOEsxykANsHUgiituk+a0wDTVHxqiqKUPAIFUdazN9Orgfd0u1KBYIRDW+6a+UtFLFkPSyEIN8XxDFspOkSssbsOf73P7zA5z/yCL50LxnF6cs2FuZUrNXQ1rCGNaxhDWt4b1TJ17CGNSyB6di80t+8yIKjin2FNSsmTnoTPq3znc4dc+O0TI/wa00H3nUFiLBpcHi4fcn4KwPNd4wMMw97kY0MLOS8ZgsWKirpmWmoHgPbtF1f44SMqO8atYjSLcW0vNqRkuTLKssgrzZn9ftjOcwOzeHP9C3boXUzOP/sFS482wy4Aa4Zt9B9WvL3zq/LoWLn0gKqN+jlkd8/wImvnuPKS20QM6l6wSFcKLCKVbZvqePBLTuXfG5uJMTpb11IKUYNXxmj+cXWG0rML4YtHV7qvwJAmuZJJrzijsWOvAo+ULlyUfVAcR0tMyNLxm3pJDs6WhJWMYsRty06Zsf5xYa7U8Yt6XBwsJUZI4omFDJ0P8dHu9hfVLeqLpMif0aCkGIv+ZsZtjB7DKZyoaPRRDcilAwI/HKhm1/3a6QXBpPFZX+Wj/2/eheqpnBpcpAf915kKh5BqQEnJrBU9+T7TYW9hTWcmx3AcOwFm6T5PJVwfd5lIo8pJDim7RaaVWWJEsrthrQt6LmMDM8iytYhbiIxcqeQpnmYXEZpIKB56T09wBv/63gyEXPxvhiRfJEkx81VCoYfVLlvcz1fmTnD8PB8tw28r2IjB4rrGQxPpxBhjJDbGdVaMIvzYhtj7RM88vv30vZ6F/3nBvEGPKx/sJaCdUutla6F7tfZ9emtHP3708l99Gf5qMsL0GVMpbxXEwqlgawl2zj73UtJIsw8et/qR75fWVK4SM9II6PIw+xwaGG7XpWGh+voCKV+3zwWJ5PfCax/oCZF2h0gkOtPsa6Z6J7i9f9+JEneG20d59X/8iZP/ekjKUm1lZLeN6vgsxhnxvt4Y9FzL2qbfLfzDFXpuSsq6BjbfIms6iIIwVBubNn3X4v6e6tpe72LudGQWwCyHYSquDL/AhofrbvVwwFgvGMyOVctRvexvlsmw4QnIvzkjw4yNxpyk8O4UvyegI4Vt2l5rYPdn976dnYbKSX/1HWWk2MLqiL13lycOQtD2iiqQEs8O/O6lq6XdL9OZGrpb2BGLSKT0SWklmultt/6m5NJIgxAaCzMsS+f4dEv3nvDfe+95ll2o/HVYujKKG2vd2HFLSp2llJ7T+U7slb8YfcFukOTzGv2mY6NxC28ONJVMJqHBCbj4RQyTPXe8hR7NIDscY2PrL+PLmUaAWzMLk1RzTkzvtRizkFybqKfx9JWv3Z5J/CtjlNJIgxA19wE3+08wy827LvOp1xU7irl7HcvYsVT1yM1d9/8OvvS8y20HnQLAyJh56hoCps/0EDxhkLyatx17+YPNLL5A7eXlFUXyuRqRupzJn1UoftwR1KVZZ7Q/eQfP7xqJZqKnaXk1eUw3j6JEXbX/EIRxGcNjvzdKcyYRcNDtSt+Pr8ul/y6XMY6Jnjxj19PWQsrqqD67gr0bB9a93ks6biFjPnPDqbOCRJJKF9y8kIb72vIfUe63NewhltF8YYCrr68NI4vXJ/HS3/6OhOdC/dr6ZYi7vuNvdctpn6m/i4ODbXRPDVMmuZhX1ENDVlFjLVPcOgvjyWfl1nlmTzwW3eTlxPkd7Y8TOfsOKZjU59ZkKK0dy1sx+HCxAAx23QVQIWC6tGw4xaRbEnFaY3RPodIgodmpS1mWV6bMXD/qToSxYT8bpWC4+48pIchqwMmmmC6GnwINE0nbBrEbAstKlETap45x20cVUVRFOZGw0wPzJJddmuFSSNicOGHzXSf6EdRBflPeRjNjqcQYTwRgdbvFpgdy0FRFLqP9eHP9LLzE0vXa/n1uQSrM2ifHcV3IJf62Wo63uzBMR2yyjPZ90s7URTB4OURIhMRuk8OMLTI6vPyCy08+sX73jbpejIe5vRYL15VQyKT5AzDsdBVLxPxMBOjYTpmx/ntTQ/etCLEYlyYHFwSu8wYUY6MdPJo+XtrbXAnMRiZYTIeSZ7reeESSzoEJgXxNLCSP+sCIUYq0NEQI7PFgx5zx2Zq4xiJt7kxtDtubffzeWUHY+0TvFjVyth+G3DcPEXiPUIs3HW6qiI0Ddu2+EllFQ2tl/F4Nfe7c1PtjFaD3tAkw5FZygLZlARWd99t/dAGxjsmUxpRtjzdiKKpDIRaMH2L8i/z04bjkuSmagXeUZjzGwjhErd1oWI4Fn6PB1VTcSwHoQjW15XjSbu+0uM9RbW0zoyk8MHzfIEUu6g8XzAZX5oxi6m+afrODjHSPIY36OYcHvitfXQd7WWkdQKPX3eJ/vOQ0HW0j9zqbBofqU/5fjNmMXRpBEVXKN5QiKop7CusoXtuIknYV4Xg8fKNFPpXTzLXFZUHStbxQu9lFFUkr67SSxq+NDVxzpvumEroPB4pa6Q/PJVcf2tC4cM121ZU4Fz/cB2nv30R/6RDpIBks9o8HCSGY2E5NgHNQ9URhZiiMlrvEA8ukH5S+P3CVZeZLXIo8agotkDzqmhNQb7beYaQGachq5AduRW82teMaSYInY77/b2VMZ77059iz7pqMUIRyWsM3Odp3YHq23vi1rCGNaxhDWv4Z4g1Mswa1vAexbc6TnFxcqEb+Ec9FzBsiwdKl/f5PDTUtkTyvyc0Sdvs2Ioequ8Upo0I9jKytpPxCLbjvK1ExkrI8wUpD2TTNbdQLJ0PVCy/68nqqBLFFuxbV0+vdoWoaYCUiEQCb3dBlbv//TO88aXjTPfPYsUtEK5MsebVsA2bc9+7zMYnG26ojtJ/bohj/3CG6HQMRRXU7q9i92e23pJ3+NxIiAs/bE6+ForrSZxTmUVuVTZ5NTnU7KtYkHC/BtGZGFdebHUDfAGqRyNrTpAZzODBp5cSYaSUvHD5AmefMrA8kuwBhYozGp6YYODiyE2RYcKmwVxCdlQIgV/VsYXbWrNXliVJSHNGjBf7r9AyPUKGx8eB4nosx8GraMQXSWULSCRH3azmWCy05DtXGj9QXE9Q9/LN9lMgBKOxOZ7rvcjrQ61ke9IoC2bzQMn6FWW9A7qXvQXVKUVvcIko/nHJZL1gcuP8tScZ2QbrX7HIHXSvleo95dzzK7uZHpzFjJrkVuegagqj0Tm+1n4i6aE8bceQOui4SV3dq1GbW0B6mo+f9F12r0kpUAyJrYNUJKolkBJsDZQEB0I6ki0FZQT1O9fxJiOzOD/6XzDjdgtJQOx6HGX7w3fsO1eDfYW1fLvzdMpYSVomVcEcfvTtl5Mkk3iaJFLo6qHLRDFLAFPlDmf9IwxHFyRmJfBC72U255QyFFkYt2IL12cky93uePskr/2XNxlZRODoOTnAfb+xl7KtxTfc/9p9lRQ15NNzqp/p/lm8QS/1Io0RLUTEWiC5PFGxkTRtafJmvHNp8Tx9EDKklzlSJY33FtWw5YvFXH6hlaHLo9iGTXZ5JqGxMAV1y6tZFLzDSkNVd5VjRE2uvNBKZCpK8YZCdn5ic8qc1/5Gd5IIMw8rbtN5pJdNTy504e3Mq+DQUBuRRZLPXkXlrsQz4FZwcXKpjZNMjN9fsvxzvLSxiLNtI0liJorAk6bj9ayOvOYNenji399P68EuxtoniExFiYcMgnlpND22jrItN77OrosVuBLX6wJfDhOxMLqikOHxc+XFNqLTsRSFLMeycSzX430xieRWcay5lbeG23Aciaq55KALowOUXFSYKpNEMx2UiORAXg3Rgf4U27tAXhrVe8tpfqkthRwG4MvwEsy7fiF+bjTEzODckvHR1nGMiHlDK76V7qv8RYSq4cgMp8f7MB2bLbmlVKdfn2DXdayPw399IpmFHTg/zGT3NLs/s/W6n3u7MB2bM+O9qEJgMS8tD6ZtowqRIikO7j1YmpaVMlaxo5RtH9nI5edbMCImgVw/uz61lcKibApZIVm+0nX79g7ntmPWiNI1N7FkvHVmdFk7v2vhz/Rx3xfu5sRXzzE7NIcn4GHj+9Ytscq8EQbC0xzraEcEJL7wwllyLIfs8qwkEeZO4RP79vJ3P3yNwXIDR5Xk9Clsnchjzki1MzKjFu1vdLPl6dWtP1VN4eHfPcCVF1s5+bVzaD4dzasmSWBXXmy9LhlmHvm1uez7/C7OfvcSkckowfwAOz62KVnY/nT9br7fdY5ZM4YiBDvzKqjsVrmK+wyOZji0HrCIByVtvn4un3+Fz63bS+F7TKVoDWuYR+nmIurvc8m286jYUYIVt1KIMOA+T/rODFK5a2VLM11Reai0gYdKF9Zh0pG8+VcnUp75030znPjqOe7/wt2oQqF+FfkMw7b46+bDdM5NJC1kNUXF79MQCuQ6XjKLfTwVyOeV2Q4mMwyE7a71HZVU9QtI/nt+VTBR7VCsKymWdHOVbszgWBJNU/GiErNMhA3pvVB4QqKFIOyNM3xAYboGWgcOcZddzePlG246D/LW356i/+wQADaQ9WOT0Ac1DM3dVy0mqH5TIVRoM7FR4AQcsodtClsUuo71LUuGuTg5wLc6TicJEUVbM/i5DzyE31YJ5KRhRAx+8scHmeicwrYcjFAczacnbX5nh0Jcfbmdrc+8PXWwyVjYjRuFwK958ElJ1DbdfNGiNcJ4LMSV6SE25dxYNXElDC+K21LGo8uP/0tFhu5L/u6wqEAvwfDIayr2qXA0mMtzyOlXAUlOwShSLL5PXZXaCCYlu4t5PtDJ4FTCytVmkSTzwtdoCQKb8PogFmbc5+fv6hpQhWRzej77MnJXvX6zpcPX205waWooObY7v5JnqrctSwB3bAfpSFeZqiaHJ//4YToOdxMPGZRtLaF4QwGOlPzkXD9q2EyQuOePQaIkwn/pBVMFPQqoAn/Qi8C1FnKkRFEEqkdFEwoHiuuX7Me1WJ9VyM+tu4vXB9uYNqLUZ+TzaHnTsnNHy2sdnP2nS4TGIziWg+ZV0X06gxdH2P9rd1F3oJq6A9WpRJhF6DszROMj9YTMOGfGexkanGDmh0P4e91jDeSl8eBv30NmcTo/t+4uhiOzTMbDVASzbym/tG4ig7ZDKv3ZcQK2IKcNciddIkx+fS7rHqi56W3eLNI0D/96w310z00wZ8aozchfNpcyj/k8T0WLzkRTIgez6HISgFfVEAjK4kFyr4QAjcCUQ9t+E8V0baDkop9PQaAiCBU4DDfYNIxnUfLJav62+0gyj35+op/vdpwhbBvgwb1p1MSzS8Lk5BwZivsbSCnJLE2nqKmA7NJMqvdV3JSS9xrWsIY1rGEN/1Lxrj4Nr169yhe/+MXk66effprPfOYzt7y9r3zlKzz77LPJ13/xF39BTc2dXzytYQ23G1PxCJcmB5eMHx7uWJEMMxFb2q0NblKBd1kRscCfjl/VXenIRSgLZN0RIsw8Pl2/m292nOLS5GBSIYaE53csIOnZ4/CB/I007qnhl0PZPNd7ke65CXJ9AfYX1dMfnubkWA8TrwwSHDXRhED365hxC8uwUHWVudEwl55rYbJ3mgf/zT0r7ktsLs4b//s4tpGQn7Ulba93kVGcTtOjNw6Cr8Vo+8RSuwAhsE2bvb+w44afP/L3pzDCC4Vf23YQikZ4IoJjO0sIOoeHOzihD2L63S+drHCIZphsfFHHn3FzgW9Q95Lp8TMdjxB3LAzbQjqg2PDy375FW2Y+9/3m3fxt11vJhNSsGeMb7SfZlluGV9VwkEn5ZIBcb4DaDJcMUxlcvlCz0vjrg23JhIjrU24QMuOEzDgDkRmap4b57c0PrViI2pVfycmxHuK2lex2lwKMgGSuVKQEx44Cnffa7OqrYf0Dtcmi0rXdgWcn+txOedtxz09i3MTBkg6ODefG+/iVpv1Mx6NcmBxACIGjOzi2g2Xa+CcknohANSBUIEEV3LtuHY9V3QYbg+tAnn4lSYRJjp16CVm3HZHxztuJDYZnmDYiNGQV8kz1Vg4PdxC1TNZlFfBE+QasmM3cIrWLZB78mvtL92gMyKVFbQdJx+x4SqeZXET+S5tKXFu2w+ClERRNcb27LRshBMf/8eyqyDDgeke3vd7F7FCiKP8T2H93Cf4PFBKzTJqyiyhKW37CzyxOZ6w9lRAjEHy2cjevhdppmRkhoHnZX1zHnkK3c6h6Tznth7qIzcaZ6Jqk/Y1uau+pZONjJSnWLbqi8uAKBI87iXX31bDuvpXXeWbUXNV4usfHrzUd4JX+ZgYi0xT6M3iotGFFBZfVYKWu5et1M+8oqODgcAuG5frTK4pACMHu/KpVf6836E0h+txO5NXmkFGczuxQ6n2wWuWJsWiIb7SfYCAyg8CV2C4YdImRiqYCCyQyx5aoOhRvfHtk3onuKV57/Tx2TUJu33IwYhZSkUzmOtT+SKBqroR19fvTKPrte7j03FVCYxEK1uex9ekmdJ/Ojo9v5tBfHkt2XgtFsP2jm1D1lX9PAE+ajqKKJaQszae5ij03wMbsEsoCWfQv6l4OaB72F7sqP1emhvhK23FXyh44MtLJU5WbuKdoZRWgC89eWTK/tb3eyaYnG/Bn3TmipC0dbOngUTTiZqo9W3BA4qnQMZQFNb8nKzcvUegD2Pi+9TQ+UkdsNo4/y3dDQvGOvApOjfUC7twshEBBsDVv5WLtuwFVKMs9etz9XaVqT3FTAR/4T48QnY3hSfOsSIheDrbj8LX2E1yeGiK2MY5sdCi+olJ+YVG64B1gEOWUZvGFn3s/XUf7iM3FKX2wiPY3u5lrnVny3ujszZHlNI9KcWPBsl3YN0O8q9lbQXWCkOlJ01OKak3ZxVSl5/Kt9pO0zIxycqyHyXW5eN4QqGFJ51222xks3Gf6ZDzCdzpP8+sb77+pY1nDGt4pCCHY89ntrH+wlonuKbJKM8iryeGN/30cAMMv6dtiM1vo4IkKAv09FGwr5PR4D6PREOXBbLbnll835p/qmyE8nqreaJsuedmX4aXuQBX5tTeOIU6O9dAXnsKjqJiOjSMdLMfGUlRqcvP5pd84gOIILj9/lervORQLgQwKeu+F2UKJrV/jl5SYkJVkyClQF5FhhAAt7v7DzQ/YbgEaKDksyb28sG+9DwlC5W7HfhybN4bbcZA8Vbn5hsc1j+h0jP5zQylj3oig/rsOjb+xg2PfOEdgGKYLbDofFiAEQkhCRRZz+QpbLwkM22IiHqZ7bpKWmRE8isrFyYGUxqXh6Cyvjrfw8Vq3QebS861J4pNju8duxUxUXUk+g8c6lpI5bxalgSx0RcF05s+vQEq57LUzayw/Z090TXHmu5eY7J4ioySdLU83UbJhqUJp6QoKIcupa/5LRpY3Da+quVY11/zNTJOuYqXLUFryWUUReEPueHb6FOeyl7GtkZIsr58fdJ2ndXaRum9yaeHaJKlC4CDxqRqOlAgEMc2LKW06fOkoqHQKhbHuC3yoeuuqju3MeF8KEQbgxFgPG3NKaMgqYiQyy8sDzfSFptBGbbIOxUkbhpJNhdz189sI5KYtafgKm3FmjCgBzYPh2MTnz9u81VPC9cnyJeYNSyaVOnVUgraGrUN5eg6Pl29Y9fW2IbuEDdnXV8WZ6J7ixFfP4dgOcd1mdJcgWmCTFpaUXNW58pNWqna7a1/Nt7C2m89bCCHQ/Roj0Vm+dOVNImac2GwcebekKEel4pxGeDzCia+e5eHfPQBAUdqtW45GZ2K8/j+P4TNs6nDX+47tULGnlLr9VZRsLLzphou3g9XaWXuDHnIqs7DbJ/APS2J5CUKKmkrs8qgaYUzmtzpduhDj6JZAehWMeWsyJFKRCJ9AeSCLD2x/hP95+XXClpEUZFqcE0xuSLqkGsWQRHMFfbsMTA/k9grKLs7x6B/c97bVyNewhjWsYQ1r+JeEd5UM8/u///v86Ec/QghBY2MjzzzzzNva3jPPPMOf//mf09zsqiV4PB6+9a1v3Y5dXcMa3lFELGPZJoywFXe7CZYJRqvScxmMLE0UV69yUX8noSsqT1Zu4rudZ5LH5U2M3Ulke9P4taYDDISn+S/nX8ZwHJepr6uoukJ4k0LdDrdgVB7M5leb3KAuahn8t4sHGY3NYdsOTo1FWo6g6WUd1RbYho10ZLKoAjB4YYTp/hmyVpA87j83lCTCLEbPyf6bIsNEpqOYEXPFbvT0ghsXb+Mhg562YYZ2C2I54J+Q5F0CYjb5dbnLFpaOjXah6gpCFchEUS+aJQnnQ8PDN+6kXQxFCN5fsZF/aDmWJMIAKA60HrBJ++EEL//kFMOVSzuzRqJzqIpCmvBgWhYOEk1VeV/FxuR7KtNz2Jm/UPwCV0r23hU6b6aMheSr4dhJQot7r7lEnDPjvewrWv44z0z04VE1NEUlZMaSXsDx3NREavI7PHDueDuKqpCW4yctKzVxNB4L0TUxRiQUQ5gS04P7tF502xuORSihrvNk5SaGorOMx0IoqoJH0/hg+UaGXm5PKhmoHpV9n99JZc2dL/7JwWU6jaSDHOp8R8kwpmPz1bbjXJ0eQUqJ4dhoQqApKvWZBTxS2khQ9yE1STA/QGjMJcR4ooLMEYXZIukWq6VE0RV2FVURsuKMx5cSDzM9for8GRT60+mYHcPxSoQFngiUXpr3qhIomiAeMpC2g6OC5ZOMd0/R+VYPNfsqb3hMzS+1LRBhEhg8MsjD++soasy/7mc3P93Ewf96JEVGve5AFeWl+XyW5T974dlmIlPRFKWb1p92cE/jXTRsKaR1ZpQM3cfewmry32FlmNWgfHsJXUeXWqSU71iaTCzwp/Op+t237bt3FVRx/hp1GK+qsSVn5Xsw25vG59bdzfO9FxmIzJDrDfBYedOqk3N3GkIIHvjNuznyf04z2jKOqivU7q9iy4dWR7D7atvxJMFRAs3Tw8xt8JN92VVtUL2ujQG4tiNl24qpvafqbe1z88vtaGGZ/E6JxPIBAkJlcPnjkopXLbIHJZHpGMVNBRQ3LSXglG0t5qk/eZjOo71IW1K5u4zs8hszjb1BL1V7K+g83JMyvu6+6lURFVRF4Vca93NspIvu0AR5viB3F9Yk1cqe772UJMLM4+X+ZnbnV+FRlw/z5paxunJsSWg8fEfJMD5Vpz6zgOaxIbSYmziWAvyzgo0v6xRszibnUxXEbJOm7OLrktFUXV29PU4wh8pgDpenhnCkJKB7+HDNjpuScn8nENC9NGYXc+Waws3mnFK8K/yWK+FmScoAJ8a6uZz4bs2jYkYdhppssgcUghMKadk+ipcpKN4JeINeGh5eIHRFZ2K0H+pe8r7FtnirRU5VFt6gh3goVRGtZOPNHZtQBN7A8p3Dz/Vc5OrMaPJ1uzFByafTKX/JIZw74RbF/Fpyvd0XnmbWiJLhWaaQuIY1vMO4ODnAkZFOwqZBU3YxD5Ssw6NqZJdnpjz3MoqCSCG5+oBJLD1hfeeXvJk2yPlzLyWbYI6OdPJKfzN1GfkUpWWwK79qSYOBfo1KmhmzsGImCEH7oW463uxh5yc3Y0RMbMOmYmfpstYZvaFJkK5FhUxUEBUhqE3P4yM12/lm+ym624cJnouTbtqopgNhKDmmEXrCXbvLNIHNgiKGaoJIFLrzuhTMIMyVKngthWgOKD5AOG58LNwPqVHIalvYLyMIoYRIl3Qk0ekoQlF4y2jn/RUbV22VZs0rB14Dx5JsWldF6WezuPKTVtrLBxGqzeLlwXSpw1SJjz8++xOm4lEsx8araihCEEmojy0mbLdMLxAXhq8szGfKouK0Yy000GQUvf04wK95eKJ8Iz/quZA8TL/mcX9LKZHSnXsFsC5z6ZwdmY7yyp+/kbS2GW+f5OB/PcIT/+5+siuyUt67MaeE2ow8OmYXFDvzE2usnzXsLajm4FDrwsD8yRcscq9cpJqU+E9RWgY+1WAuzyJSojC1gvJElieNC5P987dHKhK3jZO4Y0OWsUAMTiRVYrrq2pXFDY4OdvBIWcOqVEja5p/D0rXcsqSDIgQXJwcpC2TxpeY3iVgGZtTEkhb990DjKzqnjAGOvDLCtr3r2FdYQ9YiheA03UO67mXOjCfUPyBmmwghUBWXWOKfE+gRwVyhG3M7jiQWi2NpkvGoiVAEMdNkZ34l5cHbZwHUc9KNOy3Nof1JgZm4JSNIpitM/IcXcgiVu8pcBZnRMGbcAkciNIXipnxe7m8mYhk4jkwqdw432BS0q/hCguHmMWzLuSnC9XLoOz24JD+qqApG2Lil9d07ia3PbOCF//AaRWckvfcLnPlHWOIaj1kWihQUKzkYkTm38W8R31PzaBiWDcrC51zLLcmME6dtdoyr0yNJ9SG5aNsLkElCjBqF9qdcdWiQhPJhrMLhg4aJjzUyzBrWsIY1rGEN83jXyDBnz57lRz/6EQCKovDNb36TtLTVJTVXQiAQ4Bvf+Abbt2/HcRy++93v8u/+3b+jqelnx/N1Df8yUJSWQYbuY9ZM7XhZn1W4YnfogyXraZ0ZZXyRFcy9xfXviuz2eCzED7vP0zozSrru2tscKK6jMpjDxclBNEVla24ZGZ47V3RZjEzdjy1lMpiIOhK/qiMExB2TAG4yezA8zasDLVyZHmQ6Hk1+XvoglCsZr3YobFeTQeG18qqRmdiKZJiVusdV7cad4QBW3OLI35+i99Qg0pGkFwbJrshkqneBAKVoChueuLE6w/RciMsPm8TTXJmcuUrBdB3Ufx+2fLAx+T4pJS0zI3TMjjMZd2WLvUEvZszEsl2f56yPlJK3/uYLtVtyyygJZNIzO4lt2yi22z1jeSQT+RZzLUNQoSzpQjIcmw8VbeY7V05hYSEkFA3r1JelJhI+WrOD7bnldM5NkO1NY0tO6YqFwZr0PJqnh5PHDK5ixmI55mkjuuxnFyO+uFtDkPBGWghS5zNHwpGIOYcrL7bRd2aQx//v+0kvdIt+L/Recu1awnFMXboJqEX29YtPR6bXLZpkedP47c0P0pawUliXWUhA9+D8STVDl0cxoybFGwrxBq/vR327INKzkYsSqIvH30m8MdTG1cR+zCsQxSR4TcGV+CD/aMT5wuYHEEKw7cMbOPzXJ5P3du0RjdlPpDPkj6AIweacUp6u3krv3CTts2MpuYiKYDa1GXm8PtTGSHQOn6pjCrcz1DcL/lkFRRXs+NgmTn3rAtJ2GNkOY1sEjgc8cwLfqSurIsOMdyy1OgLXAulGZJiSDYU8/v/cT+vrnRhhg7ItxVTvvb6ix3jnBFZ8KYnv5CtXqNu0kU05JTRlF19X7eTdROWuMhofnaDl1Y6E0ojClqebVtVd/HaxLrOAj9Xs4JWBZibjEcoD2TxZuYn0Gzz36jLz+cKmB7Cls8Q25t2GGbe4+mo7cyMh/Nk+6u6pYvMHG1dl9TccmVlWen4wJ0ZJjo/oZAyPX8fxqmSVZLD3l3ZQUHd9u5/VIDIZIb9bZWS9g+mR2B6Sc6pigdRg4IAg4+s2wdylhWgjYhAai5BeFCS9MMiWD958TLHn57eRluWj61gfiuJaJG54Yt2qP+9VNe4tqedeUgmdpmMva/8XS3R9F6+gEpVfl8No60IHt23a2JbDG//7OEWN+Wx+ugk1S0dEHHpPDmBbDhXbSwjmB264rysRtufxkZrtfGn0IAPSQLXAPyOoP6wjpMCZtZL2lLcTz/VcpCc0SVDzJJubL00NLvtdUcugbWaMNE2nJiN/1Yostwsfq9nBD7rPcXFyAIFga24ZT1WtXjXg7eDqoue25tWQUmLFbaaLHSozc9n7uR1vu/BxqyjfXrLEpqX+/upVq6othqqr3P1LO3nzS8eTz7dgfoCdn1j9eb46PczlqSH8qofdBZVkCR+9pweJh+IUbi7k7MRSEuagEuKTv/sgpy7+1C20L94nIfAoa/L1a3j3cXa8j292nEq+Ho7O0h+e4pca9i1577oHajh+tY1Y+gKxTKgCR5VMxMOkaR6klEQsgzkzxlgshK6oHB3p4l9vuI+AvhCXpOcHKNtaTP+5ocTck7A4ShTXLcPmzb86gS/oFvUuPdfCrk9vXWJtlu9PJ2ZbGIvsdB0pmTFj/OGZF7ClgwwA90F2jaDme26QNtRg46gCoQoCuhcJGKaJPiWJex2EA+UzAUq25XJ+xwjRiIPlcRA2qAY4AjQLVEcQHIO8Nx3UREN/uBiiuSlUAjfekA6RUIy2w92s3786AkZ6foCcyiwme6ZTxks3F6F7NQrX51G4Po/DZ17AFxMYURPHdFyFGI/CZd8UqiUwE+cnZs9b8EkM20pZzwf1hQLqYqKsqqsITUFaDmYAhhstzGyFsr0+LMdGe5sxwb6iWmoz8rkyNYRP02nIKOSvDx9k0JlzGxRUhYeLGsj3LyXMdr7VmyTCzMOxHFpf7+Kun9uWMq4KhV9av4/zkwP0hiYp8KezPa8cn7o6a9J/SSgJZKEralKlYgHL6NUtWhaNOCGmP6zhmGDLII4TX/IJRcKMEUFK0FAwsJHXeMrIa5Zayxb+hWv9HLYMZo3YqsgwmQmCacQ2UxSFT4x2k6Z5kva41rxyswrND5lIFcDiYH8Lp8Z6+I2N9ydJ6MdGurAch5AZQxMqXlVDAn5Vx7BNAn02lac1bE3Scr/ETAPbcLBUiWokTp8jiYUNnu+9yJbc0tsW72kedzuTtSSJMPOwdcns7oU5V/dpND22jqP/57SbY1JdG+7zz16l9xfceVcIkXIJhHMcfCEVb9CDor799fFKqi/vpBrMrSIyFcWb7iV3xGZsxmYuDZKz/LwNbNRC/tMYc9WCgQ02sWywdIGuuIpaTswELynXusTlx/yk7zKKENgOix4ei7efyFnaAsWAePY8EWbRPubDi2+d5aMf2H8Hz8Qa1rCGNaxhDf+88K5lXb7yla8A7gLr4x//OJs23R6FiM2bN/Pxj3+cb3zjGwD8wz/8A3/+539+W7a9hjW8E5iXP/x47U6+2nY82VWV5wvwgcotK34u3ePjtzY9wOXJQddLNrPgHZd5jVomrw+18nJfM5Z0u41mzRjP9V7Er2rsKqha0ebpTuLEWHdK57SUkqhlUpORSY7XLe5MxMJ8qflN4rbFnBFLaYhBgO2BmWKbwnZXVeZakobu165bYC3bVowvw0tsNp4yXnegalXHcO77l+k5saAyMDcSwpfhZcuHmhi+MoY/y0fjI3VJ253r4Yocx0wXCFsiE8dhZIL9QJCKnQuqBd/pPM3pcTehH7NMLGmTpnlxPALLcVVxzjhDDF46yL9q3E9Av7mugxkjioWNTHjdqiYopiQuLMSwgzGroGd4UkhHjVlFRJ4dYdM5hWimjici0OMOhztP8uQfPZSy/brMAupW4S///opNDISnmTVjKAgkrl+1IyVq4rvrMlYmGmzPLefMeN+CFdc8koSYxZBktYEWA7wQnzO49EILez+3g5ahIQ72t+AIMNV5uZxrP+16CntUlY2LpHJVodCQldpBo6jKu9JVI7Y+gBxog8UJtaJqRMnKth13AvMEJwDDtpMKRJaUOBGTTmOUlq4B1leXUnVXOemFQTrf6sGxJVV3lVG4Pp+YbSbOt7tcWpdVyC837OPN4XZmjBjrMgu4v2Q9QgiOjHQCoCmqmwhWwaqwWf9LTWzcWEValp/Jvhneam1hZNfCdWFkwNmMGZ5eRVf4VA1cqDGIByWBMUHJUUibEEnJ8hshpzKLPT+/fdXnMJAXSCHcAYxvgKH9YS53nwchyPam8a8a9yeThO817PzEFpoeX8fsUIjs8gy8wYV5qv3NbjoO9+DYDtV7yln/QO1tTcDtyK9gR37FLRFbbkdi1JGSQ0OtnBjtwZYOW3JLeaSs6ZbJS0f+5iS9pxfssS7++CqOI9n+kY3X+ZSLlY5HUxXe9x8epPNwD3OjYQrW51G5q+y2Fd2LGvMZuTpO46savetNxje4xEvFTDZwY6WBUaSQlpN6DZ//wRUuv9CCbTp40nS2fWQj6+6/+Y5hVVfZ9uGNbPvwjc/TzUBXVPJ8wRQSNLjkmfm1zXLY8fHNvPYXh90Oe8vGiJjofp3IZJQTA108e6wTma0iRizKT6tkDyic/c5F9n1+F1V3lSe3I6VkIDKNlJKr0yMcGekkYhk0ZBXygaoty+5DpsfPr2+4j6//0QtIJGnTC79z6Za3/7ySUjLVO4PqUcksTsd2HE6NJ1R5hEiWX65OjzBjRJOFEnDVEL7VcSppz1DoT+eXG/bddrUOx3bJsF3H+lBUQe09Vax/sAYhBCLi8KhWx4e3bEXV1TtqJXot0het4SQSRVPwelT2fWQTBypXT966E5i3aWl8tJ6p3mmyK7LILL45FYLwRIShK6P4M32UbCrkQ//vEwxdGkHzahRvLFz1nPN8grQ8jzcH26h7RcHT4cZrzrcl5s+D4l2YZ2XimtIUlZ0FlRwf7U7Z5o68imUtwdawhncai6/tebTOjDIUmVlCsEzL8rPhE01c7TmHY0sUTaB5NKKOmWwusBL2eOBa5emoTMTDHBnp4OGyhQaMgfA0w49r9Dd4UFviZBwBj6KjJ8gwZtRE2k6KKuu5712i9p5K9EV2Hztyy/l+1zkgtZbeG5pc4vI2VSExqzRMy2Z6PUgdEJKQGceveRBxSd55SWYLKJbEyAlz4kNR13LC59r3SBWEBqotcHTJhp9opM0oWJZFJGDR9QTEEukB2+uScOctl6SErD5Ba0/HqskwAI2P1nPqm+eJzcQRiiCvJps9n01d29ek53HeHMAb9LrnDLcxQBPKknjVljIxnlpI3b/IbrHpsXUMnB9ONg14gx6cXJUrj1iYXonmVfjJ5FVarXF+ueGet00kXWy9cvWVdsq+HiczXyMekKSPKkS1fqy/2IR2jd3kYgvo1YyrisL2vHK255Uv+/d3G1JKoraJT9XvKDlXEQK/5sFj24Tsa8+VWPaf4DYpaYqK7tUw4wbGMvGFIwQTsTDVGXmMyFn0ORMrkftJblZZVvBoWThI0tTVNfjsKajmzaF2lwiT+AIh3FzP5cWW9Im/SRVMHTRjYTxsGRwebufJys0cGmzj+b5LgKt2aDo2aZrO5+vvoT6zANu0OdZ6hq5oH9KR3HMym7xPVnL8xxfp2XrN6XMkM9EoITOeshZ9O6jeW8Gl51owgri5yvl7WrhERf+61HXT0KWRJQp3RtggEPEy43FJKaquJtVb/DPuETQ+Ur+kKfBWULGzhBffPEN/vUE8ABkjgvJzGrWLGoSilsmJ0W6Go7OUBbLYmV9502qJy8GRDkdHurg0NYhH0dhTUE1j9urjEN2nIYSg40FJJA+YVxMTIBL27/5ByWiVRe8uB6m4JBZhSVehaDRO+gzMFYHU3GcJDiiWoLIgl/aZMVQTLJF6bwhTgiqSKUbVAP8chJekod03XB0fuvYPa1jDGtawhjX8TONdI8N85zvfSf7793//92/rtv/gD/4gSYb51re+tUaGWcM/C0Qtk+93neXi1CBSShqzi/nNjfczFJ3Fo6jUZOTdUEJXV1S2vkvBvO04/FXzG/SGpogmgmjLcQjoHhShcHysm113oON3NbgwOYBf04lYZlIdRghSEh/HR7uI224nkVwUciQFPSSMV0lKegRbKuoZbRtnbsS1GlB1hT2f3Z6SjLsWulfjwX97Dye+eo6xtgl8mV42PL6Omruvr8wwj+7j/UvGYrNx8uty2fyUm0wcDM9wZKSTPG+A+syCZYPUqGVydrwXxweOkSgI4ga7OXsX5IZ75iaTRBhwi2uOLYklOmuEEAl1HcFIdI5DQ+08UbE6qwyAS5ODSZuf+fNr6+6ZH9gvEAqohoMZMZOfKfNkcX/hOp499zyKIwhMLRzfdN8MM0NzpBcFuDI1TH94igJ/BptzSpIdatKRyS6OxcmzfH+Q39n8EGcn+nm5v5mR6CyWtLEsG13R2F9Uy7rrkGrWZRXygcrNfLfzDNFrSQnzCgRRUAQE+iVlh9yxebWgye4pDv73Ixw3+4httLF8y3BoIKkME9A95HoDbM2785ZHtwJRUofy5K8hL76BDM8gytYjNt/7ju9HQFtEzlqc4HXVY7FNm9f+21t05edz76/vJbcqe4nk+nLdgSuRrOK2mTog3N84vymftHQ3ybX701t48TsdbpZk4W0Ir8LFycEVrbgA2mdGOVk6TsxtjGQu36HtMWj6juD8D66QluVPIdd1n+in+eU2YtMxijcVsvXpDTftGb3jo5sYODeUTH5bfhi6W0FoC4TAqXiEF/su84m6XTe17XcSaVlL7cguPd/C2e9eSr4eb59kdjjE7k9vve3fP08ECU9GmOicIr0oSPYKKmLLoXVmlLeGOwhbcdZnFnJvcf2KSleL8XL/FX46uCB7fmionel49JbsoCJTUXrPDC4ZbzvYybZnNtyQRJTvT6cimE1vaCplfGd+Jf4M36pUzW4GsyMhTn79HIMXRzDjFvoY1E1ohIpMrGt4WwJBmuOhoH4hk9h3ZpALP2xOvjYiJse/cpa82hxyrpHafzfxRPkGvtp2HNtyXOsARfBQacN1E8V5NTl84M8epft4P80vtTE3ModQFObyHDr3uOsgEXaQaZL2exw2vuDBPwcnv36e8u0lqLrKeCzEP7YeYyQ6h2FbGI6NR1FRhODK1DDjsTC/vfmhZYs3gdw07vvgdk5+/TwO7lxYvLGADY+/PcLFVN8Mh/7yGHMjLjmoYF0ud//qLixnebKguYiwGbctvtt5hrhh4diu7cNIdI7ney/zibqdb2Lr40gAAQAASURBVGu/rsWJr52j7eCCwslk9zliczHMiEnrwS4cy8ET8LD701tuqN51O3F3YQ1nxvuIxQy3s15K9JjAujKJ8zlnVQpQdxqZxek3TYIBaPlpBye/dj75LMsqzeCh39ufQu5aDWaMKG8OpVpBRiNxOqokjR3uekGxBYFWi8gG93wZYQPHcghMKJx8/RSP/Kud6KbgaHcHRtSkeNRLY3U6/Ow5c6zhPYiZFZQwp+PRZdXGNpWW89zYlZSmE1WK5BpxMfFiMSl2KLKgFNcXmuJLV97Akg5kg7xLYbpQo+HFRYQy20GoSkpsa0YtZofmyK1eWLvrqoZP1Qlbqc0nsKiguEjpYLoSpssEigJ2giHrIIlYcaQKw5sko40Q6IdovoOhS4xI1I3TEkthxaegOypG1GS0zqboEqgxGNkvcCp0iJggXVUI2wPCdovu3hlJ/jGH0dA4kenoknXqtTAiBq/82ZtMdE0hFIFUofrpGu5636Yla8JHy5vomptg1oy5REvcxo6+8DQqSupJwLUiCupefKpOmqZzd2EtG7yFtB7sREpJxY5SHvqd/Vx5sZXwZJSihny6d1ow3cviSKljdpyW6ZGbKirfCB0Jm8n0MYX0MXcsRpzBC8NU7CxNeW/5thIuv9B67SYo377UHvW9jkuTgzzXe5HJeIQM3cdj5U3szL+xiuitYGNOCc/3XgLVJXbZQq6anaIkkh2KLRdZKqVCR2U6HqE+M58WZxQlahLogzRTY6jWRCgKlrNIM21eCWOZffAKjZhjLv3DMsj1BdjuKeFguB2pkCAbSBzHRvoWzVkeFTtuIcUCWU2oIrnuGY2668o3hxee//Nz0VgszOuDraTrXorSMtn3+V3s+PhmjOj/x95/x8d1nmfe+Pc5ZRoGGPTeCRDsXaJESqK6ZMlVco/tFCfebLKbTeJsdn+bzVuy777J7i/rTd1Ns+M4dmwn7nJRsSRKYhF7LwDRe8dg+pz2vH+cwQBDACRAUaLj4PJHJufw1JlznvPc933d12VSkFH+HfnrTobTMSzvggsSrvpSULt9Fjb55UHu/3f3Mn3oOCMihqIoKLrikqs1hbay3OfSNpeeH++2KxkXPVjSbQYwVUHpgEZtYYiW9zbS9sjqLNKXQ481Q9/DEiMhkLbDTD04LRq1e93nOmkZ/Oml17Kk/1OT/ZyY6ONXNj2wojj4RvhGz5kcS/Ur4VE+3LyLHSW1mI6NX7sx4ap2RxVmg06sLI2wAZ1s3k+qYCsQr5BE6p0FzW0u6UVNS8qPS0ouwfA+wdQCYUJhw30lzcTNNF2RMTQnYyuLRCqgpufJY4FRKB1UKO9VOfOMSTovN+YSEvKmfvJVdtawhjWsYQ1reCdxR8gwg4ODjIyMIISgqamJzZtXXkBdCTZv3kxzczPd3d0MDQ0xNDRETU3NzTdcwxruIL7Vc4Zz0/PKH5dnRrAdm08vIUv8k4jL4ZGcxBa4k/a0beHXPBj2YquNdwq6oqIIhaDuxc4URVRFUOGft5CKLrCkUoXiJuQWQoDfr2N+uIh9W/bg2A7DF8cwEibVWyrw5d88kC2uL+TJ33kQ27RRNGVVHRXL2izpbnT13d5zHM4oUwA0Bov5xQ37cwLFlGXyZ5cOMhQPY+GAB6RH4EdD01XWV80HyIPx3IKlEIKA5sGrqMQtE1WInPPvjU6yGlycGUZXVBykq9qR6aaQikuEEbhSuGrEpumkhjcpCE7FuDx0hakWh0iBjS8iKO1R0MyM/ZAu+EL7UTrm/KGBN0YL+eWN9zNxYYITXz5LbDKB7tfY/FQbW9+zAdOx+UH/RU5O9BG3DCzHJqB5sKWTUYZRuKus4aa/1f7KddQFi/jDsz8mLTPyzJl8R4NWSMJvMJtMkKgSXP04NL+u4p9wf7sRT4IJa4pUUOIIV2p7EaQrM6wIha1FNewuq6dzdoLmglICNwnWb2Zb8XZAVDYhKpve0WNej/0VzVwNj7pqOrablBAShAUg8c6Af0oyMTvN8b8/y4F/c89bOt6moipOLyCQgdtpX583r9bkCXioaC1lNjaRJegomoLmv/l07Nh4L0IVePM9GYUpgeOF6CaVvGuuelTz/noUVaH/5BBv/K9j2W2vvdrDZNc0T//fj6xq3KnYUMZdn9zB6a9fQEqINwCas4j41xVZ3fN/p+HYDpd/tDhZfu1gD9s/sGlRl9ztwPnvXub8d69mi7ENd9dy37+664YFZttyONXTzTemzmcLS/2xGfpi00taFiyEIx2OjPUsWn5+eoj3rECF6HoYCXPJpLSRNHEciboCRZ1Pte7lmz1nuRoeRRUKu8vqebr+9qqlgPu9/fi/v058yi3qaR6VhN8m/XgQb16ctGaimNJVhhGCkl6Vbfe3EqqenxP0Hl9MQEVC/4mhnygyTEG3pO27guEKC6nCloJK7tt98yS1L9/LhkfX0ftmP7EJl9g72TTvSy8dmRU3m2qyqT2vkYqkiYxEKaov5OtdpxhLRgEwHAtbSpK245L7ENjSoSc6ybplVNXWP9hM/e4aJjqnyCsOUNxQ+Ja+Byklr/+vY1kiDMB4xxSnv3KBjY9VcnmBUhhAVaCAUt+8xUJPZJJwNIEpbber0wRdqLSrudu9VaRjBl1v9C1afuG7V3M+G3GDw399ktKWEvJXYE91O1CdV8jHynby9deOkMqH4KRCzQWNodgQHY09i+xIboSUZTKcmKXEl3fbOp5vFYlwkpP/cD479gKEhyKc//YV9v7szhtsuRijiQjOdQOhbTkkCnPXazyuEtscojMxkSXCtBzRGI9Pcuzzp1EiaTb1g1s9cTh9+Dy6qt6S8tQa1nA70Roq5+xU7vvPo6g05i+tfhry+HmmcQff6T2XjZ3bQhXMGElmjWSWAKMKBX0BGaZqgY3z6yPXcuJuIQSpaoXZKknhSCbG05SsSswcFF1ZZN8X1L0Uef0kriPD5Dy1C6YrehzSIfB7PaSklSVJSgBVkip2G0eSJW6M6r7kMt0JGb6AlBJLdTD9MNrmMLbOpuwsRGoFdtJE1RVs030/KoZETYPtAzMInc8K6o7AxefaufuTO5b8jsGdg33vd15iuseNz8OtCiP74aT3Ci+cHODh2rYc9d1SX5Df3v4YF6aHiVtpNhZWkrIt/uzSQUzHQRMKlrQBgUdRCWgefmnDfqozqsKjVyb49h/9KGsld+prF3jw397Dw78xP/c8efn1Jc91LBm5rWQYKSWJkMPwZptkSJI3rVB9Sc2qDy1EWWsJ25/ZxIXvXcXJkIRbH2qi4e6fzAaS5TCejPLlzuNZklnETPFP3acp9QWXfRbfCkIeP0/WbeK5vgsoioLt2IvJKEtM8wUiq2AnVAVXhlXmritBVRXStkXdZQ+B4x4U1UvRvnKGWyxGpwbQhIIiIL1QVXaJmEN3BJWFBZQvyOHdDPahWdT1YHldkoKlgi1NqjwFbCmu5vWRTnS/hnQkwRFJtNQl3nny9Ox11Addwt2crZIjJQnTyDbQXQ2PMRAP89ltjxDy+PEVePEVeLHSFqf/8SLp6TQVbzoMHphXKFR1lacat9xWBcDLMyP8U/o0sR0WwlaRgEfVQQjWFZQusgdtuLuW8Y7cGF7RFfbsXM96vZGj4z3EzTRthRXseqDupo2Zq8XRsW4UTcVX4NrQCwGGkFyaGWFHSS3HxnsXqV8OJ2Y5OzX4lmxVZ40kpxYQYcAdZ77RfYbv9p4j7djUB4v4YNNOKpexnNW8Gps/sYlz/SfBclAsgXNdD5XtE0s+N44OTiZ1XH1Ekj8EM62CaB04fvh8+xHWFZejINw8nuneZ+UnJEUdQKmGPmShWOArcBVqmo5ptD9k5TTU+aKu8uAa1rCGNaxhDWuYxx0hw5w+fTr798cee+xtOcZjjz3GX/7lXwJw5syZNTLMGn6iYdgWF2YWd1x3zI4TMVIUeG7uiXunMZVyiymaUBCIbHA4F8RvLb5zHTl3lzfSF5sGyAacBbqPDYXzSiitoYqsEopX1bAtIycG14WCpmqMZgg/iqpQu73qls5nOWLLjbDu/gbOfetyzrKCqnzKWkroi07nEGEAemPusoeq5zutT072Zf3aTcd2fdOROCqsC5Wxs2S+Q7bMv3TnbXWgkJ7Y1KLlJb7Fvt03gi+jKuNTdbyKhmXaJB0TZWH+RLrJQs0SBKcUHCH5jnWVxL0CO5OcG1sv2PyiTn1bJT0inEOEAVd2+/Wedsb/vMP1TMftJDz7zUvkVwQ5Uz7J0UzB2HTsbFfSQoJJV3SSpoLSm15TfbCY39n9JF+5cozO6QmEBSUXIFoSJtmo4g/5SMcNbOHQf58k7yX3z3BxGuG4RUfHtW1flEQSAgIeL35FZ3BggjNDfSi6gs/r4YPNu5aUdz461s0rwx1EjCTN+aW8t3Hbkl2dP61YX1jBJ1v3cnCkg/5ZCyNlYnkAJP5JqH8ZVNV9FvvODtEdnoBZi8SFWTSfTuPe2hWR3Obw7vqtTKXi2bEmX/fyiZa7URXFtRM5N8ro1QmaSgvoL5jFsV2pd0VT0BWFbcU3nqcYTkaxYS6vkrlHHM39SzKcIh038Bf4uPLiYpn7mf5ZRq9MULXp5tZhC7HjA5up21lN34khZv0GwwU9iOsSd4XeO1vsXC1swyYdWyyZ7lgOqUj6tpNhpnpmOPftKznL+o4PUrmxbNniZ//pYY5/6Qyn9kRJljloHg3d7yZmO2bHGYqHb2iFaEu5WK0Id3hJWuaqyTCh6nzyy/OIjsdzlldvXbm9SIHHz8+33YthWyhCZFW7bjeGL4xmiTAARh5cfdJBBiJ48714LXB0h1LDR1M6xMMPbKRmU0XOPq6X3p+DumD55ZkRLs2M4Fd17ipvyCHYvhNIRdK88ZfH0U2HhmE3pIsxyZWSa2x5emVKO9XbKpnodMcsmfkZhcj8X4Y8MJdYVTSFQLGfqJHKjnNArg1l5v9TtplV21sOvnwvdTtvz9wwPBQhMhJdtHzwzDDv//STJCyD3sw5V/jz+ZmWXHWk/isjpJUFRRgFHMdGT99eImk6oxKy1HJPQM8hK0pHMnBqiE1PvnMWRcrVFG0HFyuiDZ4ZXjEZ5uhYNz/ov4jh2CgI7i5v5P2N21dFyjXTFr1vDhAZjVK2roTanVW3rEwz3j655Hc+cmls1fuqDBSgCJFzzyuKIBDOXU8zBB8q2soP/u51pALexPy1D50bdZUariMQtr/ctUaGWcMdx1N1mxmMzzCZietVIfhA4w78N7Dxuru8kc1F1fREJwl5/NQFi4ibBscnehhNROmKTDAdi5GcTSOlxJ9QKIsKyPATJtPxRfvUvBptn2ih9JL73vXk6Zz66oUcUtvmJ9fjDS6erz1Q2crXu0/iOG42Qrges7nIPH6TBzQ8GiiKgl/qGSWxTBZDumyAnALnwiA1Mxm3pMRybDRFQRUSR4GJXeBJCWwpcSyJqqs4lo3UXNWNOYFIqQgG90uGTubGr9fj1NfPZ21L0yHof8gNDoWEpGHw/OBlKgMFbCqaz014VI3dZfOF0EOjnUSMVA6hrzm/mP2VLewsqc2xOz72pTNZIgy48+ZjXzrL+/7gcQ6Pd/PmeA8TqSgpy8KrajnvrtrbZNNtS4fJZIyCfaW8ZE3gaO55J0M2kRrJhzYVLbndtvdupPVAEzMDsxRUBgmWLk8olVJy9aVOOl7twUpb1O+uZsczm9359m3CHCFz6NwI3qCHDY+2sOkmSninJwdy3jPgzq9OTvQvIsMcGu3i8GgXSctgY1EVT9dvIbhK6+rLMyP8sP8SQoBX0VyCl3QWF/Jl7t99ipq1LtN0FUxrMfNMgLQlRtJi6KUBPCnBZJ3NEa0TbVRD9ygkbQthSxSbBc+bzNkHgKNBwjZ5fuAyT9RuXBGRxDkfQ2kGuTCdKmA4EuZTm+5lb3kTQ/EwFf58gkmNrw6c4lp6PtdV4c/nvoxy64bCSi7ODGM6VjbXOUcAj1tpjo/35ti/Hfu7M3Qf6QcBFb0avlmbyCaFksYintizg5bS3NjjrSBhGXyl8zim47gKzpoH23FoCZXzYFXrkqrRbQ83ExmJcu01V5XQX+hj76d24g/5GI5ECacTRMwUE6kYadvGf5ssbOfPeT5OXTgnmiMdjSRml9xueJnlK8VMOrGIa2U4Nmk7nc2798dm+Hz7Uf7j9seXvc+2NtWTN3MJSzqkrEzcI111F9RMXHX9gYS7PLigf6poXGPkPgfH576vHCTd0UlalRImT47j6FDQIwlmeKpeKdBCAYLFfqLjcbxBD4/uaeVAicY3r54hpVqERgR3OdXs+/TqSN9rWMMa1rCGNfy0446QYUZH57vs6uvfHqbqwv2OjKz5JK7hJxtzXUVLLV+5g+6dRVO+SxZwgy+dpO36hWuKyvbimhxSxjuNu8oaSFoGr41cI2qmaSko430N23IKcTtKargaHuXs1CCaouJXPaQdE4FbsPNk1i3zB+mLTlMVKHjL8pyrwZan27BSFh2vdmOmLKo2l7P3Z3chhKAzsnQCrXN2nIeq1zOejBI30wzH3cBRCEGe7sF0HBzp0FZYwS9u2J8jX91aUEZLQRmdkYnsMs0S1BySjFZLoiEbzaOBcDsGD1S1LDr+jXB3WQNvjvfgZBIouq6Ssk2E7SrCgOupiyqYbLJJ57mJw2iJg9fjRSCwDJt0vsR+rID7nrqbFyfblzzW1YFhCpeQge060sfJ/fPJjjmZX8uxc9RUijyBRdsuhwp/Adte8RM8NN9hf+EzAtI2mlfDm+fBNh3SfosLH3IwFRuZ8RVWDVBN0C23eKsqCg4OUoCuaOhSQVxLMlKaSU6mbaykxT91naJQ83FopIvh1CwV3nwaQiU8PzhPnuqKTvI3Vw/zH7Y//o7et3caW4qr2VJcTaQkynf/5CDDeXGYsSjsBI9fR3gF4SqHnn0WZ88fxEyahMYVWg5rnPvWJR797fspaSzCMmw6Xu1m9PI4ecUB2h5ppvA6m5ug7uVXNx9gOD5LyjZpCBZnkydH/uYk3YfnO5Cqdgum79YxsCn1BXl/43byb0J63FJUzZXwmJvIUuYL1cWD7jHySgP4gm7yMzm7WB4eIB3NXW45NmenBhmMh6kMFLCrpG7J+2OhhVT31RTts/NFRAE8XH17LW7ebuh+nZKmIqZ6chWw8kr85JffugqD7ThcDo8QMVK0FJRRkel8Hjy39Dx06NzoksXPRDjJG//rGI7lkM5zW4+ttIVQBVqmM3omnbghGUZXVNZdN4YDlHjzKF+G7HgjCCG475fv5uCfHCUZdpXUCmsK2PupGyfZbMfBdGx8Cwppb/cYtLCAAzC23sb2SESG1eHRNNDg0bat7KtYuvjccn8jXYf6cgpvqq7QdI9LPPxB/wVeW2CXcmSsm09v2LesEsrbgcFzI1mS50L0nxxaMRlm0xOtjLdPMnJpnJJehclmG93nkq7mbAqL+9wxpu3RdXiDXmRGHc6WrufcUrNUCQRvolq2HCzHpi82TUDzrJjAqS1QDLDSFlbaRkqJ7tPwpAS/svkAE8kYlrSX3OelqRGUIDgL8t1SgcKp20vYyi/PI1ieR+w6UpmvwIu0F3+T6jKkrLcLnsDSBUBP3soKg2OJCN/pPZe9Jxwkb473UBcs4q4V2jukYwYv/L8HmR2eIzddo2Z7JQ/9u303tWNbCoGipYl//mWW3wghj5/7K1t4bWSecOrzeai7kPsc1u6soqi2EK+h5IwhWSxxGUZiZbYPa1jD24lCb4DPbnuU9vAYCctgfahiRU05ebqHLQsaX/J0Dw9l5obnfnSVHx06TaJU4p2RFF91OCXO4ld02h5eR1N+CUPx8KJ9bmuqp3JzKKvWUtpUTOcbvdiGTf1dtdQvY32zr7KZ5y+eI4KBVMBWcevpcxYWC9a1dbcZQpduUZtsgXuJDMxSyhi2zFgmCTRFwbGt7GreKBg+wJHYpk3GWcmdi4j5XUoBqYbl50XhoQiXn7+WJfWFm+fORSIXEAXOTA7kkGEWojc6xVeunVikbDUQC7OpqDKHCJOMpLLkUiklji0x86G3fpbTR59j2k6iOQJVV7GwcWyZbSLZXFS1pJXsanF5ZoRv9ZwlYqYwCiwsU6Ck3PmwUBVEic75yDAP5LVmt0nHDYy4QX55EH/Ihz908/v28o86OP2P85apV1/qIjIa45HP3veWrwHc7+/l/3GY8ICbg7HSSU59/QKKrrDh0eVzJ9cTYbLLyX3XHB7t4nt957OfT032M56M8m+3PLiq83x+4DIOEjNlYaYsbC+QCTfnCNFSuqp5Qrpuv6EuScGAycQOC7tA0BQq5ZqcZCkadCpmUHpB4kzYyDwPQ1vcZ9pK2/h9XjRFJZlO0/qygpiwGNoHs03ucVQLHL9AUVyCR9q2ODjSQdI2eLbp5oX+QG0ejppEM1y7GSFBsWDKk2QgNsOpyX6GMnHwgapWPr39fq6GR+mNTlPmD7K9uIb22TEOjXYRTifQhEJSzmdppZQkTdd3/NDANR6uaUMVCt2T4/zI10XsGYl/VlB7QUVPSIg6RK6EOX78JJ7371x2HLse6Viajld7mB2OUNJURMsDTTlKrWcm+0nbVo56i6ooxK006wuXJt0IRXD3J3ew7f0bSYZTFFTlo2oKnbPj/M3VI9nxYjAepisywa9uOrAqhdmbYVNRVQ65Htx83MZCV1mqOq+QM1OLlTpvFP+uBNWBED5VJ7WgacTMEBoXYtZI0hmZoG2Z7y+geXi2aSff6DntkpxFhljpcfedJZRd/zhL6H6voOgKVB2TGDUq6UIHW3PViyQ2XkcwqEZZf1HHSllICapXRfUoVLSV8ehv3Yfu17FNG+lI3vy7M/R+e4ANjoK/sIC7P7WD+l1rDeFrWMMa1rCGNVyPO1IRC4fD2b9XVNw+NvRClJfPB2EzMzM3WHMNa7jz8KoaG4uquDyTWzBrzi9ddff2W8XwxTEu/bCdxHSSyo3lbHv/xhUlExryi7mnvIk3x3vQFJWgUCjyBvj59fdSlXfn1SgeqGrlgapWHOnkBInRiThnv3GR0asTlFQE+YWP3k2iwKY6UMiF6SFeGnKl62Wmy34wHubPL7+GT9V5pnE7O5ZQ5FgtokYKU9oUe5cvwiqqwq4Pb2XHs5uRjsxRlyn0BJBSusom0kFBoKsqQd3H31w9nFVLEZDpXHOzgrqiAio7SmpziDDgFj5/vu1ejo/30h2dRI4ZpL82xmxskkZdMrbeQW7RaG2r5f7Kddmi70pRnVfIz7bew4uDlxlNRqgNFuPNVzk51T8fL3pcCerJBoepBscNJBWBogqUgI6eKdroTQW8OdvHmcl+4mYaXVFziq2FcpnuKEHWNgvAo7qKOTLzPxCU+vLYVrK6QHL4wjzhUwBaAqw8iWXYeAI6UgPLctxztDMN+CoompucCcwq7OgtwHq4AMOxCCRUjGNhUlejdD/uJl3nOgodWxKZSfBHb740x41gUExx0teHz+NBXdBJHTXTXJ4ZuS337D83HLeHuPBeC8tUsZKSmR3Q9pqKhaRzv4XIU7GTbjfPbLXD0Fab+rMmp79+gcf+wwO8+keHGb08TyroPNrL1t/cSXV9aY7VBkD1dePdZPd0DhEGoOyUZG9BDVs/tol83beixNLusgYG42GOjffi8evYsyZ1Z1R8UfeZ2P3RrXRFJzk23sPQQxbqSZuyLiVLAFA0hcqN83Mjy7H5yyuHcpJQR0a7+ZVND+QQF67Hp9bv5Y2RTo5N9JCyLGr8IWYuTXFucoby9aWrVp65U9j7qZ28/D8OZRViNK/KPT+365bVB2Jmir+4fIjx1Lw6xWM1G3isduOyCkPe/KXJAgOnh7NFj4IxhalGN2lsmy6pThViRTLpzzTt4AvtR7MS0/m6l4+u23PLiczS5mI+8IfvYrRjnF4njKfczzlrlMPnuplOx2nOL+Xd9VuozivEkZIXBy9zZKyblG3RECzm2aadVK7yXXErqN5ageZVs6QYI88dHFVP7m87s0Q3+hzKWku471/dxdlvXiI6HqeoLsTuj20jWJbHrJHktZFObMdByVgGWtLhhcEr/Mqmd44Mo3mXDuOWW77cuo/++/uZ7J4mNplgY2mE16a7SdkmAdVDa1eA2jo/Dc/Wsu4+l8zg03R2lNRxarIfEK6M94ICoiIEftVDyLtyIukcrs2O8w+dJ4hnukKb8kv4ufX34L8BscZIGAyeGUYP6MQm4limTbgNIo0C3bL5zudf4+O/9SRl/uUV7JIBBzUNQnffx0i3AFOi316LIiEE+z69m4N/fDRLfvAX+tjydBsnvnIuZ11PQKfxHbZ2aLirhrPfvJSx4stAcFPFEulI+k4O8fLAVYwSE82r5iiIXZweXjEZpv3lzgVEGBdD50YZOj9K7Y7VKzKWry+ltKWYyc75d50RkEQf8fHNnjOsD5Wzuah6xco1T9dvoaWgzFWF0nTuKmvAKknQ/uMuUjGD2u2VbHisBVVXqd1ZxcCpXOXPhrtqGbs6sYj8crtUktawhrcKVSjLkipWC+lILn/3KsWjDsULltvC5soLnbQ9vI6Hq9fTHh5jYoEdxgOVLfgsjb84+irdzgyqrrCtuJYPfHL7DeeoAKnJJM3fg949gqnGTHCUsTRawB1h7v2l2YKSKR0TCyOkomgKjpQY0kJKkWEBkN3P9eqdiuNa3BqWha6KLLExNKpQeNlmYpPE8QkKeiXxakGy5DpejYAt9yw9xhoJgx/93itYqQUUgwWFVUUVqJqbE7jRGPbGSOciIgyAKW0uTg/zQNU8qcQT8OAJ6CRmkpgpC8MvaX9KYAUEju3Om2wBWtzG63fj2rpAIaX+IA9Wty46xmoRM1N8+drxrHWWLR1sxUHP19GFmiVFzhquAqBtORz/kqvA4VgOoep89v3iHkqbi5c9xhyuvtS5aNnwhTEiYzEKKlanersUJjqnskSYhWh/ufuGZJgdJTW8NtKx6BdbqOILLFIGBhiIzzAQm6EuuLRyzlIYjodJWAY2Enxk7zElBd6AjpRgpEwK+9zcQ36/pPiKm48ovpwh7QfG4FMC4RPIuTSVdBt+Go4rhE67aipG0iRZIDG9IBWJYxl4VQ0VgTDBNylZ9z1IFsP0RoETEsw0SVRVYBomqkdF1VVOTfTz7vqteG9CsN/x3k28Ovo6Ni7RwBEgdXfe+sWOI0RNd77ZF5vm4vQwv771YTYVVWXHwHNTg3yl80R2f1JKSnx5TCfii2ydJmMxjvR2srW6li90HWW23L2HY2WSy4864MxZtNqMOLOEv3GET1Q/QUHljZsUUtE0P/q9V7O2pj1HB+g+3M8Tv/MgpuLw9a6TnJ8eImEZqELBr+nZfKdXuXlM4Mv34sv3MpNO8IOei5yY6MV0HDwLcmr9sRm6IhO3hew2h/sr1zEQm+FiRiFdV1Te17CNokz8sLeskZMTfVlbVoC6vCJ2lLy1ubFH1Xhfwzb+sfsUpmEhbYlQWfJeWo6YBu69ENA83F3WQDid5NLMSIYUI/DiKo3rQsUgc59kcjLCkdgewdR2KK8rQnalML1mxobPHZdNy6ZQeNF01VVdyiDtdzi7I8rJyy9RFyzm3Q1bGPh+Lz1H5nNNyXCKY188Q83WyltSJF/DGtawhjWs4acZd4QMoyxIjhnGYpn62wHTnE8wKbfRh3MNa3i78MGmnXzdsemYHUcC6/JL+WjLnnf0HEavjPPK5w5nuxgjozHG2id49395dEUFwmeadrC7tJ6e6CTF3jw2F1XdVh/c24GFRJhEOMl3/+MLJKaSgGSmL8zAmRHe+18fpby0gMpAAU35pVycGWY0MUtnZCK7fco2+Vr3KRrzSyi8hYIPQMoy+Xr3KS7PjCCBmkCIj7fctaxFEbikGK6LabaV1PCVzuO53Q3SJmamuLZAFcDJEGZUoWSLobV5hct6yeqKyv7KdeyvXMd3/uYFojF3G80U1FzSEFdMnvrcBgKBWyNsbSyqzPEU/9z5H+PVNAw7Q0hxm87dBGaGMIImScdNdJ+GkpFqnU7FuTQzgpQuhSVlmziOxKtqhLx+dvqrORYfwrYc15LGp6GoCi37Gxgq0LIBuCIUAroHTSjUBYtoDJbwYHVrhjR0c9iWg6opizqXy89Jhve5bSFmwsQQNqqioCEwYF76V3UTRqWePD768YcIFPnde/Q/vMDQljRjT4Plc7vVhQ1aplblaBIcgbDnE762hFTaIC+QS2QznFzFhH8JGJye4gfXzuM4Ek1X8RZ4SWkm45sl+TEVJegmvi1jvptzptah/iyMX5ti9MpEDhFmtsKh+16DE4PH8Mx4aAtV8DOtd+FTl07OT/W6hNx0QDKy0SZRKPFPSewzfZQ1FOG/tx59BcVrRQg+0LSDR2ramE4n8E3DcHIEsRGa7q2n3xfly1feIO3YOPkOzj6HSJlDy1EdRVe452d34SuYJ2WcnRpc1I01moxwfKKX+yrXcTU8xkQyRkN+cQ7xQldUJJKZdBLpSC7ODHHBHqD5jEbpd1Sa99ez/5fuWuGvc+dQ0lTEB/7wXQyccYkndTurl5TbXyl+PNSeQ4Rxl11lZ2kdjffUcf67V3IKzIqm0PbIvO3IaCKCJV0i5kLbodrzKrGyjEJMpm/53fVbbyiBfnC4g0OjXSQsgw2FFTxZuxGfqrOuoOwtv5NnrARfSp9lxkhiddkkLBO/pqMrKp2RCf4qo0B1YqKXV4Y7stv1xab5fPth/uP2J972eYE3z8P9/3ovR79wilQkTf6Ewuw6seg5m1O0Ww6Ne+to3FuXHdsBLMPiC2+8xqzuFmEUIfBlrn/0Lcp2rxa1O6rwF/qySj1zWP9Q06r3VdpcTGlzMY3AA41tzBpJijwB1P1L/1bPNO3Ap2qulL8uMWwr+65UhGBHSS2hVZK5kymDvz1/mJRjoeiuNeKV8Ch/dfUQv7Thvhz7wjkYCYMf/ZdXiYzEkNLtvh96QDCzEddCAjgqp2m+2ME9W1yFQttxcJA57/YNNdWM9EdRTYk6N5VSBHftvP2qhhVtZTzzuacYOj+KoinUbK1A1VU0r8b5710hPpmgtKWYuz6+HW9wdVYHbxWegIfHfvt+Tv/TRYZ6JxjdBekWne/RwYGwXLY79sgXTtF9qI+ZVhtrt6vM4833ZGOHmxWrFmKqJ7zk8snu6VsiwwA88pv7ufj9doYujGGVK5zcFcGUwzAOx8Z72V1ax0fWzcdcMwOzjLVPkl+eR/WWikXzurbCitzvYmMwh2w6h32f3s0xXaX/5BDgko3u/uQOJjqnOfSXJzDibh6kcmMZO57ddEvXtoY1/CTDMm3S0SXyfVKSyqgVBnUfv7H1YS7OjBBOJ2gJlWFeiPG5V77PTJUbtwhN4bTdjyMdfqb17sX7W4DYZAJfTJA3rRCpzJCJNVcFBrHQfEUSMVLgAAmT0vMOtQkYelRBK9CxFcVtMrEFTkaJ5XoiDAIU07VvkQ5oPg0zbqKYEDxvoUckhZfnt51tlvQ/JkB1VaOEImgKFLO1rXHJa+k63E90Ipe4W9gJ43sAXbjviMw57bkB4dC8QfyXr+fGih3RcbrfK5gIW+QPwmyDwCiYuwaRvXRHAzNl4QRce+aBRJgzU4O8q27zW1IEvjQzkiXCgEvOMrExHRuPPv8uac0U5S9+/yqdr/dml88OR3n1j4/wzB++66aF4NQSlqmQUdK8DWQYM7m0XaSZvLESWHVeIR9q3sUP+i8Stwx8qs5jNRuy1zyHlLX0fpLWynPslmNjSjuj9pfBnHKRA7bpYOsSzQDvBJSelagLhUYzm2kJV+XWCixknklUU6ChgK6A6ZDyOlhe4VrICJfslLBMgl6dwJQ9RxsgWQ5GkUBqEluRSNsBxx1TPH4dvBqmY990ftG0s5Z1J8q5nBzPXpuDJGEbCAOw3ftYOhJDM3m9r4P3tG7Pbv/6SC5hSghB3DQIpFTSnvnnSrHdfR188wJjzTMYuHkn23KQEiyvRLFANTIaVAr0bjfpeXOA7e+/8fu/45XuLBFmDtN9YXrfHOB07RSXw6NZlXFLOsQtI/tc31OxspjAlg5/deUQU+l4Vj06ZbuWS3Pz5RkjeZO9rA6aovKp9XsZT0aZSSeoDxbnWPL5NJ1/s/nBLCGmNq+QnaV1K87N3Qhb86roeilAn5hFtSBZIBjfZqLmebLjXFDz0hpavsnha10nOTM1iHQkZtJEJCU+SyFdLKjID7G1uJpj471MpNwYxREZRSFNYGmQp3oYL7WJNTqw1BCdkhgJE+lIFE3BLFY496yF4zEhAUOJMCcneilKKxQ1SNJBmGp2VaVL+iwGLo3SuGNNHWYNa1jDGtawhoW4I2SY0tL55PPExMQN1rx1LNzvwuOtYQ0/qQjqXj69YT9RI4VEvuOKMABXXuhcJOc9Oxxl6NwodSuU8GzIL6Yh/+adOKuF7ThcnBkmnE6yLlRKbd7Kul0mUzF+0H+RzsgEhR4/D1atz/pnX32xk8S0S4SZg2PaHPqrEzzzh08B0BIqoyVUxl9cfiOHSAMuueTSzAj7K9dxK3iu/wKXFqgBDSVm+dK1Y3x226Or2s9AbAZVKHgUDVu6neoeVeNqeAzHsHFMB6G49ho+VWdfRTOKEFQHQuxYYUB5fQAObrdffCJBoPCt36tJy2Q0GcWr6ngUjUQqhZXpjpCK20EhbDd54Dg2acvBm++h2J/HaDICuMkJn1RJWg6GsPAO2zQM53H2/HlUr4rjSExpM7hNYu7xM6JfZpu/hkazmK7oJAqCmkAhn2i9e1U2IqNXJjj1tfNM94UJluVR0ljIYDjldhICpRfc5ND0RhtHlXjSEKsVWDqoXgVbOggBukcjlOfjE/c/kCUY9R0fZKrQZLzVdrurTLC9mc6mjJKMliTroz4HYYOjSGzDtYoQisDj0bOys7cbUkq6IpNMp+M05pfckg3L24Hp/jBf+eJLmHfZSMC0LISp4PN7yHushEeqN3D18uukHBPH53Z3qoarCABQUBkkMjZPcHBUV0nG9sgs+ah9dowXB67w3sZtS55DqLoA0ye5/LiJ6XNJXtFSyUx9CucfTnP1xU6e+J0H8eatjIjhU3WKvAFCDX4qG+ZJKn997hgxy8CZSyDrMLEJ3rdjK5s2NSxSJxlcQpIeXNLC+ctD9MfmVfX2lNbz4XW7ATfxOkdysNKWm5wEBrfZlPQpdB/up/VAE+Xrf/LnXrpPo/ne22PX2R2ZXLRMZpbfXd7II5+9jyOfP0l4MEJhXQG7P7KNksYiIkaKL117k/7oNI7toCvuOBB+2qKoW1B1VWHr93XCtQ7NTzezf/tGin3Lq1UcGu3ihwOXsp8vzowwmYrzG1sfvi3S1t/vv5hNhrrkOknKMtF0l2SZsAwuTA9xcqJ/0bazRoprkXE2vE3j0ELU7qjimc89xXTfDHq+ztcmz9Ednf+NthfXrPg8FpKTvvwPr9BVOZ2NoBwpSVoGqu6lOlB4Oy/hptA8Ko/99v2c/Op5Ri6NEyj2s/mp9TTufWvqX7qiLlK8Wmqd9zVu532NbsHgyFg3x8d7MR2bbcU1PFyzOuu0+FSCr/3Vj4nunOv0BidTuLwaHuN/nP8x/3rTA4vO6/Brlzi2cYbYAw6+iKDwomBmA1kiDLj7eH72Kmcvj9IXmyZlmXgUjU2FlTy7bhdB3cvj6zbTER1nNDqLY0sUVXBveRPrKt8eBVPdpy1SfWl5oJGWBxpxbOeWFapuBwprQzz46/v4owuvMJGMAAaRyARdkQk+3bZvkdz+zMAs3Yf6ANdSa3CbwNYlVsrCk+dBAHvLG1d8/FB1PoNnF1vLhapvXVXKE/Cw68Nb2fXhrXzl2nHM6Vzi2qnJAe6vbKU6L8TJr57jygtu8SsdkCTu9lK1r5qNpVVsLqpa1TjqCXi4/5fvxjbdKsdcYbZmWyUf/KOnmOicwhv0UlR35xU017CGtwO6V6OstYShc6PZuMjyg2oJ6nfP5xY0Rc12+6fjBl/78kFm3j1fHZSWg5myuDAzTMo2lyWgA5Q0FrrqcN55IoJquQ1yUiVLtMhGTookXgWpIoGegIbnQdQKmp/ZwGsTncQx5ov74F5HhggjbNf2SDPAHxVoRSr+UZuyIzZ65DpFAQmhLqi3JdPbJU6eIDSiUNaZJNoaI38J8sVk17SrJiGyXx+eKDT+WGB+ooRpO0GB7uOusnpOTPTxXN8FaoOFPFK9gZIFc8XdZfWcnOzHXkAyAcjTPGwrni+WtofH+GLHUQy/iYlgshgs79yl55IlpABbcztWFqrSvDBwmV2ldasmxM7herVaXVFzyDHgWmC3hdx30UJFhDmkZtOMXB6ndvuNCZS126uyZMU5+At9FDeuXFXlRqjYUIYnz5MlPs6hfvfNC9R7yhrYWVLHjJEg5PEvma/ZVFTJ8Ym+nGUBVaepYOUxWMfsuNsoRa7xpXcWivsVxrZlFHqCMLZbEq0RrPuWXOQaNtMMRtAl0MwRXQBMH3TfbZHfKGj8rmCmzX0epcoCcpnE7/PS9WmL2KyNlgAz313H8rgkLGd+VSzHpM1ffMPGgJxzE6lFNmeOlKQtC+J2VjnKsRyOv3iJ6tOCHc9sRtVVImZq0f6EEJRHfcTyXCs2HDdXBWCPp2kf78XcLlA9KrbpgLK0ukgyJLESi+1Or0d4KLLM8lnO+YdI2xZp25y3qMzYfb+nfiu7lml6ux4d4XGmMoqZmlAwMs/cHNldAOtWcV+tBuX+/GzuKGWZvDrSQXt4jAKPj/srW5bMtaaiaUYvj+MNeilfX7JqBZRrB7tJd8aozHQZ2oqD5bGJbZMIj6Dcl8+H1+3KKGovRm90yrVwkpJ0zHDzISrkdQk2fEfhwC9vpLG+jrNTg+RpHqIL76PMvWhgM5NOzudu5pD5IZPJNFbaJVLalkP3XsWNjeZWk2BKh4lKh8kK9znRMvy4kY02rxjd/AJrZJg1rGENa1jDGhbijpNhLly48LYc4+LFee/XsrJ3TrJ8DWt4q8hfgS/3HJKzKUYuj+Mr8FK1sXxR5+JqkZxdHOwBi7qO32nEzTR/ceWNeYnMAVc++d0NW2+4nZmxAZmT0R1LRvl69ym8qsaW4mqmembmM0sLcL08O7gWOkthNd2u1+Ps1MCiZWPJKMPx2UVWKzfCeCrqEkGuk41OJFIIY/76bNPGk+dhW0kN6wpWNy6Wry9l7GouedET0Cmsvz0JfI+qkqd5iFsG0pFIA8jkNzJNFAgkJT0K+RMq8RJJQ1URjzy6kz+9dNBdQUqSlomTyaElQ5LjgSlaugTFjgc1X6XjfpNwjYOuWpipOM8PXM4k3SSWdAgbCb7edYo9ZfXcW3FjWwBwC3iv/M/D2IabsI1NxIlNQElTIeGhaKaTQ1DS5VDY7iAA0w8dHwEbB5/Xg61KHCnZW97I47WbcqwcHMshUunMfQFu11HaLRK60sQQGJEMPpL77CsW5I1BotxACNAMwcbRAgI7bywtfiswbIsvtB/NKTQ/WLWep+o33/ZjrRaH/vdxlJSFRGD759RpHeJWmnA6ScfsOJZ03OSfAEcB6YHyayoI2PrejYSq5ok9kXKJ7XFvSGVBgfzCzNCyZJjKDWWk7w9g+sLMO4yDGYDJdQ76lSjtL3ex7b0bb3gtUkp+OHCJI2PdmI5NmS/IB5t3ZtUtRhKRRckUB8lARZJdS9j0LGdXk7SMHCIMwMnJfnaV1tESKidsJLIdpo49fzwjT+KooNquos5KyTC90Uk6I5NUB0K0FVYsSoL/c0GRN5Al5l2/PBFO8vqfv0l03E0yzvTNMnJxjKpN5Xyn9yw94UmMuImtSmzNYMZIEKjUGSqyMQKSjV1B9j7QxoZ7lpdUn8ObYz2Llo0mI/TGpm6qhLISdGZs9xZCIrGlgybc92TathYVXOZwI7np2w1VUyhb5xLGPlN2Hx2zY4wlo9TlFdF8Cwnd6b4wnXIagZvItzOvfynd/56se+eVHULVBTzy2fve8eNej30VzexbwTtzKYwmZvnS0TcY3BbD9LrvOVsj24QuEETNNC8OXuHjLfOqUzEzzQ/1TlIZGfpkSBK/XyClzKk7OBqMOXGmZlKYjlscTRkmJ3t76D4/xC+27KNuVzWf3f0YF6eHmUknWFdQ9rYQu1eCO0mEmcO12fFF45kEXh/tXEyGGZwnluiGYMMrGv07bRIVkkp/AY/VbljVnLPt0XV0H+nPiT2KGwup33N7EvrDyyg4DSfCeMbsLBEmXuRw5RELRzPoGenmxEw/95Q38UzTjlUfc6kijaqrS6rJrGENP2245+d28fz/c5BJmWDwACTLQBMqZc1uQ8D1876xKxNYlr1AwcWFY7kE/5tNIzwBD7s/to3JH59iYl2GUKMIfD4dRVXQhOIWfSXkNMRobsPB2DZJ8zGLl0fbSc+16y8IswQCYYFiSgQCLQ3lFwR13V7MhIHm0UhGllYEAQj1QqjXwRPQM7aGNh0Hu9n9kcVxRFlLCVfmjjvHxQFqjSAf3v04KdskZhr80YWXSdgGCgqjyQjt4TF+c+uj5Oku0X57SS2PVrfx8nB7llhS6PHzm1sfyVHre2O0kzkdQkFGlXUhs3QhJKCAX9VzSIIOkt7oFNtv0cpkc1E1fvUCyYzirRCCgObhseo2Qt4AtXmFVOcVZtcXy7wzV/Iu3fPxbURGo4QH3fedJ8/D/s/clUOCfivQPCoP/OpeDv3F8aw6ZNXmcnY8u7IYWVWUG5KTn6rfwngySm9G6TOgefj4uj2rVs5QUdBSYGcuW7FBj8F4q4ODcO2ZVbB8gniVZGqL29iTKgHfFCRLXSWXHDuxhQQyIFYuSW7UKdlUQDgQw68p2XjSzuRg/LoHLaSTyDddUo3I3QdzHyWozs3znkOxMC8MXM6Zzyw8RdtyUOeEbDIDS2BAcurlC0z3hdn763cjcOebihB4FBVNUdGEwsNVbQyMn8Tyzo9JwoGKTg3HLxjdYLr3poA5Jo+w3SPLzPPljQvW3XNzskpJYyF9xwcXLS9uLELQj2FbOdcGLtFtNQ17SXuesOVVNSzpqsPMOcM9Wbf5hpbutwufbz+SVa4dTszSHh7jF9r25ajxdR/t5+gXTmHETddCTkDlpjLu/sROKtpWFt9N9ri5jlRQ0rfHYrbSQTUEm2dDPPPIPZTcwFoVYDDubm9bTrYxSAKxYgcpFN48eoWSnWUUeHxZu+LrYTk2HkVzx8+5V9GCP7Xk/Liv+XVSJXYOJ3MOUskQ0JBISyAkCFXQIaeIm0b2PbCGNaxhDWtYwxruEBlm+3a3i1BKycsvv+x2rd+GbtU5OI7Dj3/84+znbduWLlCtYQ3/nNF1qI83v3gax8okNOpCPPpb9+EPrZxMcz2qNpe7BJGFEFC15c4ma18d7sjxigU3Ib+7rJ6qwPJkjCszo8ykE9iOa7sjhJsAOzrWzZbiaiq3VNBzdHE3UbA0N9Drj01THSjkysxozliVp3nYUrwyxZyl4CrNLC4Y3sjzeyk0BBcXbBzLITgK8YX/JMEfFvhUnTOTA9TkFa5YwWPPx7bx0n9/I9vZJBTBno9vR/dqnJjo45WhdmbSCZoLSnlPw9Yb/i5LQRUKD1at5wf9FzBiBsKWCMdNKmR9qw2ouqSRF1GgE2p3+bEuRgmkFeK6heU4WSKMkO5/UoXRrQ7FR9wO33BNJliVbvE2ZZuu/KtQMRyLZNIkZqYZiM8Qtwwerdlww/PueXMgS4RZiIKtxQQ+VUO4I0z6pUnMcbeoI4VLVKk6IpldL9DW69SXlPBY7cYcK5qRxCw/6L9Id/EEiRYna6EkhUCxXZWc4j6oPaugJCXJq5KpDTJ7DM2AeLnbiFQ4LFh3REPYUTrX9eZYs9wOHB7ryiHCABwc6WBbSfWKFZzeLkz1zVCQYkFCLQMJM+kER8d7CGgeUraJ5ThZ5lVyp5fm97XhWZdPfypKzRP1DL3Qn7XPEKpA981PobzKjUlGxXsr0IfjWGmLOaVyqcLwRptIuQPTg2zjxmSYI2PdvDZyLft5IhXjb9vf5D/teAKfppOn6SSsdM42ApGTaFQQ2TFsV0kdR0a7cxKEJTdIcnVHp2gJlVPiC+JXdTdppgikq4KNf1ag2u6+CypvLi0eM1P8+aXX6I1OI3E72KoCIX5184F3JNl2u/FgVSsds2M5MuP1wSJaCso4/uWzWSLMHC78qJ2uTWmOzvTgOBJFyxQdcIc8y7Lx5XtJ3C146pcfI7DCRFbaWVoyPW0vX5hZDUIePxOZpJ4mFKxMoSj73nIkzWoRqRKTl4au5mwb0DyLJN7fKShCsKGw8i2p0sQm4i4hEVDNjAKX6iYqn2jelDOGr2FlSFgGf3HlENPeKDLz3dpzt/pcF3ymqDNwHUnv7NQgtg5ywa0tJDiagAVWR9LrFpTmxkKZeR9IAdMFBi/97VE+UPYoRXUhdpa+NVWdnxZElpHCjxiLyfHF16ma5M0obHxFof6uGg48cc+qj51XHOCp/+th2n/cRWQ0Rtm6Ylofar5txcnqQCg7hl2/fPT4WPbz0FY7q7rnWDagcWy8h/sr193QznQNa/jnCNu06TsxRGQkSum6Ymq2Vb7lJps5lDQW8eyfPM3/e/JHWHYar66g6ipvzvQRGg7wyHWxljffw1ibje3JvGNlRrVRCNaHKnJsNJbD+gebqdpYzj9ePMFlfQpFV1AUwf7Kdbw23DFXT8/BXJyQqIShNou0o+RWlzN/92s6+8qayP9WjJGuCYxwGiEFEX+CmTaw8mzyeyE4xLIwA5AImhTZCqqjkJhZuvGo9cEmTn3tHPGpZLb6qXlUtjzdhhACv+bhf19+PavoAK7KjpSSU5P9PFA1T6J+pnknj9dtZDgeocKfT8i7WLllboxXPSpmysqqXSyChNJuhfyWAiZY/L640Tw+HTewDZtA0dLKMX5N5xc37Oc7vecYiM8Q1Lw8WN3KA1WtOFLSHh6jO9pFU34JNXmFrLuvgTP/dDFnH3klfio3LiZh2tJhPBklqHnJ9/jIKw6w6z/eRXfPCGV2gJZNtWiet27BshBVm8p55nNPMdU9jTffS6gqH1s6DMXD5GmeW7bbBnde/SubDzAcD5OwDBryS1ZNhGkNleNzVFJOhmCSuc+8YUm8SmTtqufIT7ZPMPiIQE2766VKMkp+C5+qOZslAYojUDJKMZ4t+XgupDHyDDeW9usomkLatPBpOinLJC2trDLw0g+qS7wdTM+SzFi1LoUzB9v5UuIMluKA3z13YN6GG8gbkCRL5w+QNwiF7a76ccfZAV4+OUXMSeNk8kaW4xDUBc8072JHWQMz3whzcKKbSMjCG4bqiyrBqApR9+8jmx2QEiEhOOB+V3MnIRSFxys3UFhzc9W71geb6D7cn6MQU7a+hOlmiTYgcDIXNDdcqUIhaqYxHXvF90NbqMKN66RrjRTUPViOw4bCCp5p2nlTxcjbgZ7I5CILZwm8NnItS4YxEgbHvngaM2liJuYJPOPtU7zyuUO87789kVWtHu+Y5PQ/XWS6L0xRbQE7nt1C1WY3Dg1V5SOFpP1Bk3Qwo+rjkVzJm+FSeIQH/K03PNdyX372BOfIVBIwFMnp9xtI7yRnzz5PU7Ak+7ssvJ0FUOQJ4FE1YlZ6kZW5sKH0wvy9KR2JLyJIF8jF/DAHpOLu3VFBEwKv34ODJGWba2SYNaxhDWtYwxoW4I6QYerq6li/fj0dHR2Ew2G+/OUv88lPfvK27f8f/uEfmJmZQQhBa2srdXVric01/HQhHUtz7O/miTAA4YFZzn7rEvf+/O5b3u/mp9oYa59k4toU4BIedn1oC/nlb3/wcyP0XFdon0N3ZPKGpIvj4725kpSAgsiy8zc90cql718lMjJPtFF0lT0fcwl0CcvgC+1HFqgkSPyqWzhvCpbweFEbXuXWh9E9pfUcHuvOWVaXV7isYsNyqAqEuLeiiaMLFAHyHJ26Q5KZWofRNhvLJwkNK2h5kj+++GrmciSb7DI2dOYRLM2j5f5GfAVLy80WNxTygf//E/QdH8JMW9TtrCK/PMjlmRH+qfs0AI50uBIeoT82zX/e9a4bSlgvhQPVraT7Yrw23I5tQ36XBBVi1QJPFMquCvIybBfHkYxeGmPw9DBl5Q7h+y3sfOFKVuMmTQEQkMrwMSzvfECpamo26JRSkpZWNq40HRtNUXljpJOHqtffUKliTvp+ISYbbE7X96MmNNKhNNrT0Phd8E3DdBuM7Bdu0kjCvQUVPNu6mxcHr/D3146hCMGOkjpOTPSSsExXDaZQw3ZMSIMiBfgUbFUS3aDQuVmjeTJI63Nxyi6liBfD6L2QLphXLRhvkSQKLbb+UGf0ysRtJ8Ncm13a7vDa7PgdJ8P4CnyYCRNvGGxfJrEm3eKqqiikbRNVUQloHtK26RIGFJguNvlq9BzauQvoiorSLNjz7+rY2pfHdKCHWY+RQ4y7q6ieawd7SM4mqdpcQaDIjzffm02orguVcmqqHyEERtzA1sDWASGZ9UhO+2aoGrp6Q/LVmSWUpFK2yZXwKDtL69hb3sQPBi5mPcPB7ewq8wX5YvtRroRH8aoa95Q38WTdJjyqxq9sfoAT430MxmeoDBSwt7yJFwev0LmE5U9RJlmrKyq7Sut4YfAyjiaRiksMqDvjXmtJUxG1O24sSw7w7Z5z9Mams5LcjpSMJGb5Xu95fq7t3ptu/5OGpoJS/tXG+3ljtJNZI0lrqJwDla0IIRjvmFq0/uB2m8npbmSGgJXtvs38fHPvdzuTyFopGWZLUfWi90pA1VetBrYQUSPF6ckBErbB5qIqDmZIWR5VzZB/JDhgzRrUHhe80nuQ2l1V7H6ijjPhQRwpKfXl8ZHm1Xer/iShrKWEiu9ozNS6LxjFcf/zaTr7b1EV5V86zkwOkLAMhCKQtkRLu5YMMlMA9QgNT0aB73rybso0XHWqnO5+QSipYxW5g72iKsQtA6+iYTlGTlFljshnS0n34T52f3SteWIOLaHyJWtQbYWLyWyFtSFaDjTS+VpvdpknoLPtfTcmeN4IgUI/Oz+45Za3vxEeqdlA++w4KXueOLizpI7qvELSJfPxQKJw/uqF4hYELcvm1Ilr3LepbRFx/mZIRdOc/84Vhi+M4i/0sfGJ1hVZZaxhDW83jITJi7//GjMD86pJtTuqePDX7r1thJg+Y4aU10EnNzY8NTmwiAzTGYowulWimhniouK+F5qUQj7UvGtFx3OkZMKfYm9bC+XfU+gdHqcgpVN/r0pxUx5xK/M+EAtGukxR0zcLRmFmuRSLCo8FHj9PtWwl73e8HPqrE1z+UQfJIknXewVOJoye3CooPSupenPBOSkwvQnGdwuMArc5QjdMGk9p3Lt5aaKwqik8/X8/ypG/OcF4xxSegM7GJ1rZ9K717vcanaYnmjvHtBwbUyjMGolF+wvqPtYXLt841VZYwWgyglAE3qAHM2ViO05GdQA3zpbglSrNTojEizEGHjAQmoInz1XeWR8qpy64OP6z0hZv/t0Z+o4N4NiSkuYi9v/SXTkKnHOoCxbxb7c8iGFbaIqKIgRp2+Kvrx7KUa/cV9HMe5/cihE3aH+lGytlUdZSzD0/v3uRMkx7eIx/6j5NxEyhINhRWkvKMrk4M4KRUTSsuVTIz7TcfduV4VRNySpmds6O87WuU0TMFALYUlzNR29BzWUhFirlrBa6ovIz1bv5wrE3SIQchO0qzyopsnZgORCuipKwXQUKR7/uHxf8VREKAa8HxSewTRvzUpT8AZXyEoXhLQ5p20DJ2GwJCUZGvW+RSsaCD0K6pGbHsJdtIBs6P8rzJ89ibXFjKcXMEK0XPO75Y4KWb9rEKl21Kt80BAfdVWwdOj4EKSuZVeZQEHhVjQ2FlewpawDgoQ/exT2RrTz/Xw4SHY/l5AaaOn1s04u40tGPN+zam0UaYHadq7Tz8OYNPLR/541/nAw8AQ9P/h8PcelwF8fi/cQKHYaCaSZ7TmZVnOY4Prqi4lN1Kvz5q7qnPI7K7uESDnkHsTXQvCpbKmr4RMvdeFQNw7Y4PTXAaCJCdcAlj9/umC68DBE7vGAsG2ufxErbi5rRbMvGStv0HB1g87vWEx2P8eM/PJRdb7J7hlf+52Ge/r8eprA2RNvD6zh5uZN0cJ5QI1QFTVc5Pt7HA1U3JsO0hspZV1DKldFhkC4JxfaAleG2KRaYhkVXdJJ9Fes4NdlP2EiiKQq6UFAVlV/YsI/B+Aw/GriMX9VJZayuFAtKL0qKL88rwCiKoOGUwpU6iUnutcvsM+i+gxxFAUVQ4c/PsctbwxrWsIY1rGENd4gMA/Cud72Ljo4OpJT87u/+Lu9///vJz3/rXVaxWIzf/d3fzX5++umn3/I+17CGnzSMtU+6/rPXYfjC2BJrrxyegM6Tv/MgY+0TxKeSVGwoJa/41rtVbheKvXkMxMNLLl8OPdFJrs4u/j4kkrCRdGWZNYUP/em7OfGV8wydG0bVFILlQSY6pymozOc1+nISLkIoONLh2eF1dL3Qw+HUIc6X57H353ZRtSk3iZWyTE5O9jOaiFCbV8iu0rpsQWcOT9VvwZIOpyZcD+/1oXI+uMIk3/X4QOMOthXXcG12gpDHx3pRwo9SL1PZIajscAPVyQabvgdtVNO1N7ENh1P2ANGrOoUjCldf6uTJ//zgsgl+T8BD64NNOcveHHcJOCnbzEq0Ji2TP714kN/Y+vCyPrvLoSacx4aDOraluio0UlJ2QaJoCnpAz3b1+Aq8GDE3eC0YV9jxXZ2JVknfHgmWzForAZSk3aRfICzwJAWyWEPRFITtBpICgcyU5EXmM0DSNjEdG/UGMsv1e2o4/90r2cSKrUr69tioHh3bsLE1MAuh/WOC0nOS6c3zSVXFo3AuPcLY5ddz1DleGryCg8ySiTSPRtBRyPN68Ogao6kIAVVH9bjn1V2d4EO/t5MNehnfmDhH90zfogpWrEzSud9ii//W/NtvhNAy1m636hV/O7H7I1s5+CdH8c1Aei6vKQTekPvdrg9V0BWdREqJYc8pXCjYSDeZjO2qByF4w+5lz8MP8299zXyv7zxXZkYJaB72BOuY+J8d9M+ksS0b8+/OouoKgWI/m55sZdv7NrGzxPWM7giPgSawdYniZOSSFYHm1Xh95BoHqlpvObG0rqCUoOYlbqVRhIJX1WjKL+H01AAjCff+StkWB0euoSkqj9duxKfq3L+gaxTgvsp1nJ7sJ7VASaTUF2RHRu48bqY5PtGHX/NgObZL5pCS8ftUYgEvhQ0lJB2TPPXGPu7np4dyiDvgFi8uh0dv6fpvBNu06XlzgJn+MEV1IRrvqb/tnZ8AjfklS6qD5JflEV5QZAKYWOegqTq6VDEWJrUydmhKZqAo9QWzRKSV4Im6TUym4lwNj2JLB5+q86HmXbd8Xw3Hw/zFlUM5hePNRVXMRuIk0gbbKmu5q7KB73/uIKJXotquTU3fm4M0yjp+918/RdI2KPHm3VYVyNWgKzLBhelhPIrGXWX1t6zq4C/08dB920gfP8vwZpt0nqQgovHJe/eRp9/4fl/D0pizQdB9GmnLQEjQ024iGL+C1++O1bqi8EhNW862gQ7bJaTOyehnhpN3tWyhpa6KyzMj+DWdrsgkl2ZG0BUF017wrDmQNy3wxQS26TCWiPBc/wU6IxMUevw8XN3G3eWNb/t3sBpYjo0Q4m23kyvyBnhPwza+33ch23Fcl1fIw9VtS65/z8/tonZ7FcMXx/CHvLQ80LRs1/+dRmWggN/c+jDHJ/qIZIiLW4truDA9xMG8Tvo/aBEcAG9UYATcDlzNo5KKppG2Q/8PuvlOvJddH9nKpiduXCyZg5SSl//wENN9YQCi43HGO6Y48Gv3Ur/r1hUm17CG24GOV7uzRBgpXUbI4NkRBs+NULfz1u/P+HSCrkN9pKMGxublCL2LlUdOTw7gzfNgpiwU0wZVoHpUHtu0hYIV2ElPJKN8of0oU+k4qUgavVKyrltjotDghc5L1ATLmQjGSGaV9OZ8V1zifOkZiVHkFq2dhVMnCcXeAL+y+YHsO3/k0hhIydhd80SYOUxuF5RckngyHLu+dwkiTfPqZ47uHrrvAUnxnuUJy4U1BTz1fz6CmTRRdDVHJatjdgxVKIuKo5Z0aClYvRLfw9Vt9EQn6Y/NoGgKvjwvLR0610ojGL6MdaEAIjaxo1MEkiobTZ2x9TZOyOGh/Vu4v3ppS88z37hIz5F5Rd6p7hkO/vER3vv7jy87P1yYOzk82rXIxvXIWDc7SmrZ9eGtbP/AJtcSOrD4XktaBn9/7Vi2EcZB8uZYDxIwHStr4dkfm+bPLh3k32196G1p6DBsiy9dO56dU0vgwvQwJd6rd9ReuLWhmoqXfPR7EuBAskQSrxZIbe4sF3h0ZSBVsoq8wGIVFwkBRcsSVopmdYoGTRwhmamVqGlQFNC9CqZwif85Kq6LjNLcP0XmVi/ocFDuApYIbzrf6MVaMFSoVsatSAc1CTUXVUpP2jgSgiPufwsRbgMzKHJ4QHO5oqQ1Hw9JKTmbHKbrWcFkr0VJj0JFu4JAsOPZLRQ3hhh8pT97KQV9UNDvquLnV65uHudo8EJxH1N5cRwpicXSCCBP9+DLECkUoRDQPKhC8HT96gjFh/73cRJnx9iuacRKJJ4k7H2yBE+bRtq2+N+XX2cwHnZzZUJwbLyHX970wG0lxDQXlKIIschSd+FYNqeCfr1d3twYMkd+6TrUt4gw41gOnW/0sudj2/EX+rjr53ZyqfNN19ZcVVC9rtynKRc3u10PIQT7hyqZfm2A2VqYbXZjlzlymKOBYVn4dZUZI8F/ues9HBvr4ezUIPm6jwPVrdQHi2kNlVOg+zk50UfasVEmTGIvjFPQsyCfKQC/QltbHe/buYEfDFzk0vgQqSVs/MCNFTTdx0fX7bnpdaxhDWtYwxrW8C8Nd4wM89nPfpa/+Iu/wDAMBgYGeP/738/zzz+Prq9OSWAhLMviAx/4AH19fQB4vV5+8zd/83ad8hrW8BOD5ayQAjfo9FkNKtpuvYP8diA2EWeia5qCiiAlTUUcqG7l0sxI1uMa3KT8Qu/Y63FlZjRDbMiNywVuoW48GaUqEELVVO752Z1c/lEep75+geh4gpGL43S82s21X9C4roGNeCrNmyfbKUq5AWx0PM7BPz7CB/7wXfjy3SxY0jL5s0sHszLsxyfgxEQvv7zpAQaPD9P+chdmwqR2ZxXve89W3tuwDUdKvOpbG5LXFZTldP/veHYzZ75xMfsFJJpVpGNhxIwF3siCcKVN4YhCMpzi0g872PuplXWpgGu9YTtOlggzh6F4mGPjvavyKgaypCJVU/AVeLPqCDs+uJnmfQ1MdU8Tqi7g1T8+grFgO9USVF4RVNxTwylGMpL2gnzdwy++/1ECj6lERqLcXSH5x+GzxMwUMu0g1UziN3OzSEDLeKfXB4tuqm5TVBvi3p/fzamvncdImKTKBCJfRfWopFJWVp3F9gomdgBKRurbo+LN8+BISXd0koA2n7iTgGnb+BQtaxSsKAr1oRLWFZTyw4FLi87jUmKMvDIf52aH3QVLtHRPNjtMVd3+Atp9lS2cmxrKeT6LvQG2Ft/5juf1DzUjVIXXXzjLuaYEeBU8AR2hCOqDRXyidS9f6TxGe3gsY9Wj4Nd00rZ1XT5PYtgWPxy4xK9uPsAnW/dm/+3w35xkJJZCkTL7bMm0QzpmcO7bVwiWB2m+t55Pt+3j2uw412bGeXHgsmvl5XWJMEIRpGyLhGUsSyLaXVq/KAnsU3U2FlVyeWaEv+tw2089ioYjJVX+At5dv5X/dfn1Rfs6Pt7L47VLd+2X+oL86uYDvD7SyUQqRkOwmANVLdlk1+WZEZckJhRUVcFWHOIyzWR+Gt00mRjsoD02zm9sfQRVWf5+86r6IuUugHz99rzH5mCbNi/+t9eZ7JyXXW5/uZvH/38Hcqyu3k5sfmo9QxdGcRaQWIVPQdFUfFLBMR1sxfWcV003mab5NQKqzoebd62KROJTdd7XuI3hy2HC6SSWY/O1rpN8Qtl7w3fmcnh+8HIOEUZKyZn+PrZ+Q6UoJZjRO+l7xEHrMgGBZdiYSROkpOPVbpKRFA//xv47RoR5dbidH/RfwpY2AsHLQ1cp9+fjIGkLVfBE7cZVEVk2Pbmemu1VDF8YxRP00PDw7ZfU/5eETYWVvDh4BUVT8Qa9WGkLKSX3K/XUbKykMz5Bvu7j3vImKq5TzYufmqbB0BjYMW9nU35NpRSFmg2F1GQ6pbcUVTMwPY1hGFhigSKMD4wATDTbPHRXJX919RBR07Wam04n+EbPGfya/hPxLosYSb7Vc5Yr4VE0ReWusgbeU7/1hmPsW8V9levYWlxNZ4Zkva6gbNnnWAhB3a5q6jLEDkdKDo92ZRXNdpfWc0950x0bB65HoTeQ8w68MjPK31877n4o0QkHLbS4wKup4FewDRtpO5R1qfijLoH69Ncv0LCnhrySm5MVx65OZIkwC3Hl+WtrZJg13HFMdE65pPCEmZmnSISqcO7bl/HleylrWb0F4HRfmBf/4DXMpBsfOj+WaJ9QsIK5Y8DOkqUVnIUi8AR0Fgbiyxj2LMI/dp9mKh3HsRyk7ZAOwoWnTLRM4DgkRri3ooULgwNMyVRGUkGgIBEmOF4ouQAzrZAqla71HuCPCd471oinVfDt0bOcmRok9niMYLtbCLW9gOOqUCgOrjppuSBgqswWWcTq5lQhs1eJExDoHg9XZkfZ779xzKz7F8ekIY8fXVGxpIO1wGajyl/AhluY8/k1nV/ddICe6BSzRpKxb/YxcXSM7T4PVx42iZdKsMHSJdc+AOu+Z5E/AC2T7rlt2bm8TU/3kcUKl5HRGF2H+pgZmEUIaLq3npLGpUkoXZGllUi7IhM05peg6iqqvvSxr4THFtmQWNLBls4iYr7h2Bwa7XpbCsmdkYmcOfUcLkwP3VEyzHf7zjPYZmInXCWmVNkC8ouY+wssrLo713/V1z2gnggUTgn8BTrOmSj5lxxStmS2Hgyfm3QRNlgJl9TseMkQW5bwRXKAjEqVo7lzuMJumOqdoXLD4rylYzoEpwTDG13SDrixlcdWaD6kUDyokpojU89d44JLTBcJFAec66Yshm3RFZng98++wINVrUTMFC8Ptbs5pAad4Sqb0I5Cnt24m5LGIsy0hcevYyQW/ObSzQNVblwdWe3s1EDWDs2R7jgtce9Xn6qjKgqO4/BITRu7S+tXZWsUHYsxeNZlBKmWIDTmXviVFzvZ/FQbLw9d5drsOLZ0BzZdUeiKTPL9vgu8t2HbbZmLTveFOfONi1QIm95tJppPQ9VVyn35PLZAPay0uZjy9aUMXxzDWaDQrPvcfEr9HnfOPvfuuR4Lf4tN1TWExvJIWEbOOttWMO93bIeTXzxLcdTBcSBWL3By0goSO8Nm8SgqA9EZXh/tZDqdQODa93543W50RWV3WT07S2v5p+4zHLau4TwkUO4XVB+HkmswuA8SOxSuekboHrB4tnEn7cPDaGZG+VoFEAgBfs2DALYVV2fjoDWsYQ1rWMMa1jCPO0aGqa2t5Zd+6Zf4sz/7M4QQHDx4kPvuu4+///u/Z/369ave37Vr1/jkJz/JiRMnADcp95nPfIaamjufwFzDGm438suDePJ0omMxhKqge1WEorBxhR2KP8k49+3LXHjuKjLjUV27o4oHfnUv/2bzAV4f7WQmnaCloIz7K1uWlUYFyNPc4pYQIqd1QFdUFKEwlojw8lA7pmOzuaCSa89dYqbGIR2U5I8L8mYUzPEU1OQmnmzDRkvnRv9W2mbg1HBWNeXYeE+WCDOHgXiYFw6dYeYLfdll4aEIMwOzPPwb+2/hm7o5tjzdRt2uakYujSEETA1fwTZS5LYPSNTY/Pcz3R9e1TG2FldzdZGSg0BTVDpmx29KhknOprjwvauMXZ0grzTApnetZ/NT67n0ww63+1lXSd/j58T6GU6HZ9m9vp6mkkKCpQES04ulVJ9u2Mr9xZu4PD1CvsfH7rJ6t9AZhIIKNynwnyoq+MG33uTkUB+TGyXSkxXedZMxUpKve3mmcceKvoOWBxpp3FtLZCyGmS/4XMcrrre04rjJG0Bk7kFHA9UUOLbMcJGkm3+RkpRtYUk7k/ORGEnTLcBIUFRBuelBDS2dbLCiJs8dOopVbYKPrILOQghFcDTax6PO5ttaQKvJK+SDTbt4ZbidtG2xqaiSR2rafmLsUFofaKT1gUZGErO8MdJJ2EjSUlBGVSDEX1x5nbFkFL/mcSWa57rtr283ymAkkavu0R4e47nSbmLPWmhxScUJKM4IBTmWg6Iq9Bzpp/neevd+VhQ6YxOYigQPeFU1K0Ff4s2j4AZEkIbJPGrbdbpLY0ivoLogxEfa7sKn6rwy3J5RexfZLsrxVCxrCXc9TOfGHU8V/oJlpeiV6xQJ0obpfl0OSMvBtBwG7WkuzgyzPaMmsxTur1zHc/0XcpL3qlBoDZUxmois2i5uOfQc7c8hwoCbcOs61MuGR5fuXr3dKGsp4cnfeZCrL3WSmElStakcpT7CufAQQggCPi9m0kQPS+4+GiTwaDl1W6rZUFjJrJGkNzpFXV7Rks9tzEzzg/6LWRWMfRXNXJudIGqms+unHZt/7D7Ff9rx5Kqf/esJWFbKwrZsEsUKnmGBYzpc+uG1eZu5DBEGAAGTndOc+/Zl7v7EjtV+bW8ZScvg+30XiVsuwWHuqY5H0/g1DzPpBP2xaf7dlodWVaQPVeUvKet/PSJGiqPj3UwmY9QHi7m7vPEtk15/2lCdV8hTdZt5YfAyaApezcPO0jq2lNTx0uAVRpOz1AQKSdjGom09fp2KyyolfQrJkMQbE3hSAu/23K7w8Nkp6v86SUGFQjoAAzvtbDHF9MPAATio9jFrpBbNK98c6/mJIMN86dqx7LNoOjZHxrpRhcJ7Gra+rccNefzsLqtf9XY/GrjIayOd2c/9sRkiRoon6jbdztO7bTg0On+uQhFu0dkPH2zaQTid5Owr7fg7BIVD8/eHdCSjVyao31d70/lOcja9aJkUkkR0aUuANazhnUSoKp/upIlj2lnXOWk5DF8c4/n/5yBtj6zj7k/uyNnGLQpP4tM0GoMli96hZ791KacYqUhBww8cIj9TwIQVRxMKe8oallSb2lVax0A8d+7hV3U2Flbe9FoSlkFfbG7O5771bd1VsJhTnbA1yZGxbqoKQqSSDtJysC0HQ9g4Phh8UJAuhJZvSiIbBKkCCIxK8myV44EOvq1fJVbiNlKIoCC+K+PBKnDVZVQQabeYHkypKCrIWg9SMRfFZw4Sy3Hw3GLMtK2khh8PXUUYSSzHwZEOHkXjFzbsu+m85srpXnouDFEs/LQ+0JQlofTFpjk/PYQiYbRjFD8CyytJFUjU9Hx85HhgbLfA/5KdJeoo2vJzzKUEzSzD4vBfn0DJzE2vvtTFvl/cQ/O+xe+d5VQSi26gFDwHbYmDL/ftCNz529uB5d4VdzJmNmyLkxN9OGrGpnKBJVYu5hbI6/49N6+U3W++YMiXRsgUJUWSPAWEtdR+XTUqIcXShDeZsUaSrsKLYrmfe++y8RUvHTc37K3l5av9Lkll7rnXoTFQRE3CIknKzTUuYcfk8ekUJxQiPtBUMkqpcwrCEtWBmVSCb/WczXnGhKKg+xR6/XHyat2c06Xvt7tqxELkkK7qdldTs+3m49lCTKXmrYJcdUCXwTOnoqIKhdbCcp6oXf08KxVbPEcBSMcMHOnw6nBHhgiTaRBybMDmhcHLHB3v4Vc3HXhL1mKJcJKX/tvrGAmTUiC/SyNSLbnvQ1vZ3dq8SA3xoV+/l/PfvUL7y10kZ9NoHhV/yMeuj2ylsMbNH9TtrubqS52LjrXQnlJXVH62dS9f7TpJ2EgigK3FNcs2DC1E77EBjMz3pmRedcICrhOmstIWyW+M8sebe3CCIjtWnh7rR06YbBzIR/NozO7SODXZj3QyuUENhvZBokZhpsHBq7j34LXIBH/+xo9JShPHw7w/1twflkTTVYrX7JHWsIY1rGENa1gSdzQj+wd/8Ae88cYbnD9/HoATJ06wY8cOPvaxj/GZz3yGvXv33mQPcPz4cf7qr/6Kr371q6RSbtAihGDHjh38/u///tt6/mtYw52AmbZ4/r8eJB0zUFQF23KwgH2/tJPGvUt3V/1zwVTvjGs5swCDZ0foeLWbjY+3rqpDZ1dpHa8Mt2M4NulsB45AV1Vq8gr5h66T2XUvTg5hPW3kdLiUtyuUnBOMVsucQDcU18mfXOKACwL74esK5nO40jnA9WHv0LlRZocjhKpvT+H3egQqAhiaj2NfOI3eHUe8B2xvhvphgZYGNSa5dp8rTestWF2h9N6KZk5NDHAl7HaTCAQ+TUcR4qZS1rbl8NJ/e53ZYVc/emZwloFTQ6x7oIk9H9+GoihcCE1xwR6CmNsNdjU8xkw6wean25joPJolTQFUb62gpLGIEqDWW4gRM3IUV8Dt4jj/zcuMfbUXeQBUG0jOxZAS4VXZWFLJL2zYt6rElObVKK4vBGBXaT0nJlzSk1BcX2scN2mIEFheUA23A9MT0Cn35zOaiLjdaXM7lJAUNoomUUwIXZJMneqm4TM78fjU3O42KZEvTOP4TES1qzxj+zI7WSBrLExJXDdI2gZB5fapb3yv9zyHxrqynydTMfzacnLodw5VgRAfXrcbcAkEf3D2BdK2heHYxDNqALqi4lE1NEXFtJ2cfJ0QAr82T46bTsf5u443SeZLMFyP6KEDAk9EEhwCy7DRvFr2JxhNRPj81cNYUuJTNRKWSdIyUDQvXlXj/Y3bl01cT/XO8Or/PEKR5bBT1bB0yNdMqv4gyOnJAa7NjmM6DppQ8ChqNqnsVTUKPf5FHtzbSm69uLu5qAq/qpOwDGzpYDkOCDdBOQfbtBmdDt+QDPNY7QbsTIItaRlZS7XTkwOcnhxgV2kdH27efUPS40ow1Rte1fLbgZiZwqvqOWNISWMR+3/pruznZssgcc3gWmQCoQhqSor5xD13U/VMCHCt9v7y5Gt0xV0br6Dm5We37WN9UW6n7xc7jmaL5Enb5Lk+l2CkX0e6iJpphhOz1AVXJ/te7s+nNzqV/Wxn1Lp8kfnfRVEE3qCH+HQyh0g2p5gyfH5l1leXZoZ5c6wXw7HYWlzDvoqmReSr1aAzMrmICDP397lnbzgxS1dkkpbQ7VXEixhJ/uTiQSIZ9aNz00OcmRrgVzcdeFvVPP454sHq9ewpq2cwHqbUF8R0bP7k4qvYmXupNzbN31w9wme3PpKT3F3/8Dr6Tw+jmYL8Sfd+9BV4qd+TO+6c/eYlhA1Fwwrj6yRSdYdlb74HS7hE1CNjPTjSQVdUfKqeHYvTztKdpe8kxpKRRaQ0gBMTfW87GeZWkLYtjox1L1p+aKyLh3+CiLILEbcWk63Atat7om4T6vgUI0PjOf82U+PwVfUikROnKfMFebp+C5uKqpbcT+WmMhRNwbEcHEXSv9NmstlGDUSIdhzjmabtBG+zKtoa1rBStBxo5NTX3Hxcjg2KI5GOpP3lLtbd35AlS7SHx/hK54mswkWlv4Bf3LCPggXKhkspIQWmBe8SWwjtLMKravhUHVs6JC0zZ359b0UzYSPJkbFuTMemwp/Ps007V0Qm1RXVtcRzXEI6isBRMlGexLVOUcBxbEaTESxpk+f1YqgG0nbHfanA1FaXwFM0qjLbYDO5TeBoGYsdjSz3RSoyqyyzEI4G5e0qwYSG1CRadwrn3uXf/VtukXTpU3X+9aYHeHnoKr3RaUr9QR6uXk9VILTsNinL5M9efYleJ4yoAF9U0PKn3Tz1M/sYqEnxnd5z7ooSUo+ZNB9WswQJiVuklYpAWJJUidvQkZxNoaiCqy93cXdlfkbVJxfr9jdw+flr2c9SShxLonnmvxfpSM780wUa99a6v98C3Fe5jjNTgzmE/jJfkK3FN1fX2lhUSYHuy87JwFXSVKWTo9QihEBXVFpDq7eYWgnWFZRS4s3LKnzMYe8dtGQ0HZu4ZZCWFlLNSOctwnVEmAUphsXSKvObOB73+RjfKYhVQ8u3JMFBUFNzuYr5daXjqg4tsj1yuz1cIow5f1inQCESMClc4mwLdhYTTytoaQdpuc+0x6tjqA53/8wOTv7DeZKz87HxXKhZWBfiwV+7l2BlkC8PnqQzMoGmqKQtE9OyUVNgYCJUC09AJyGtReqSlnSIppKc++ZFTr52hen1DlJXKBlQ8U8JVE2hfld1thlmpWjKL+HgSPbrQncUDGGDKXFwCHg9t6wuVNxQhL/QRzKcSwKr3lpBX3R62SYaiRvv/PXVQ/zenvfccszefbg/R7HFGxeUXRPIY1HU9YvHTU/Aw56PbWfPx7aTjqWJTSYIVeW7eZcMKjeUsf0Dm7jw3FW3QUlT2Ph4C7U7cudpTQWl/McdTzCWjBDQPCu2+R44PeLeOFIS6obRe111MC3lWuFJBQJpldBVh976JMmQy1oxUmk3Z+LAqWgv8a+65JeeSQVrve7OFRco3kw1OShSIObGQykZ1uNZxaMspEQ4Aitlotg225pWR7ZawxrWsIY1rOFfCu4oGSYQCPDcc89xzz33MDw8jBCCVCrFF7/4Rb74xS8SDAbZvXs3TU1NFBYWEgwGicVihMNhent7OXXqFNGoW0SVUmYZ17W1tTz33HP4/T+ZXuVrWMNbQd+xQVcRRrjM8rlUQ3Q8fsPt/jlgaJmC2fCFMTY+vjrVm3yPj1/edD/PD1zmaniUtG3hVTX2lNZzbTY3oW3hkAqCniLLrh9bb9P6bUHzMT/Re70YmsOGwko2mPmc4VzO9ppPo373fCKmOhDi7NTgonPyjDuLlgEkwqm3hQzTH5vmb9uPEkkkMbYbsNXtVnNUQAFFQkGPZOCAQOCAIjgRnKRo8ArrQxUkbYPm/NIcv+45RIwkr4900h+bptwfZDqdT8w0MuoaAk0o7K9ovuH5DZ0byRJhpJSk4wbScmh/qRNPnofy7WWcvS+cQzRybIfn2y/woZk27vn5XQyeHSEdTVOzrZINj7e6Fh7fuET7jzux0jb5FUHu+bldVG50C51nv3mJSz/sQEpJYBTCbRn/adw8piMks0aSFwevcH/lupzE7kownoxS6PHTFqrg8swwhmPj4OComeNkbgHbI/GN2dRcVWjcV8xIYHZxN5QicRTXe3i6DULDFv0v9vEL/34f3+s7z3BilpDHx7ZYGWNnrhH0O4hWUDSQCXD8uAktBxTLTSIVWJ7bWnDpiUzmEGHA7Vg5Nt7Lfau0yHoncWF6CMOxs4SOOQgJD5S3EJJeftR7ibhiIhXXOsun6ewpa8iue3ZyEEs6aB4V25gvmM5sEASHJNJ2kEjy7y3lTy8epDMygWFbeFQNr6oR1BUsx6Yhv5ifW3/vDcljHa90Zy3DFFvgsSGNwQ+OneHN4Ai2lNgZue+0Y6ELhaDuoym/hE+su5svnD3EjJlECFgfLOfpOtdHfKp3BiNuUr7elRdPhJMYMYNQdcGySTqfpnOgqpVv9Z7Fcuxs/l8sHN4kVNo37khShMJT9Vt4qn4L7eExPt9+JOffT08O0BaqYGfpWyN5Lje2FlbfXNljteiPTfON7jOMJiN4FZX9let4onbTkiSngObhlzbex1QqjuFYVPoLctb7+oljdETGsrnliJnkb06+wX995APZYvJgfGZRkVwIgSWd6x3+EEBwFXZAc3i8ZgOfbz+CLSWWY2NqEs2E8Rab6ksqmume844PbqHjlS6X+CIEmldF97ln4S24+XGPj/fyjZ4z2c890SlGErPLKhStBLNGErePc2mlJ8Ox8CseYtbSHZEL4dgOsYk4/pBvSauC63FotCun6AIwGA9zfnroLd/TP40I6j42ZLr+v9d7PkuEmYPp2Jya7OexBd2aVZvLuf+X7+bCc1eJTcQpX1/Kro9sxRPQcaTDtdkJEobBzEwMjXl5fSCruj/X8atkvBJNx0YRSrbourXozqvC2M7Sc0frJgpf7yTipkHMSlHmC5KwDMwlzjltW6Rt8yeSDLOhsGIRkV0RgvUZm5FN71rP6JWJLAE7EXLoftDGo7gkmolUjC9dO8avb3mIyiWK0P4CH3s/tYNjXzpL73aD8VYboSmoPo2LM8MkLINf3nT/23yVa1jD0ggUBfCFfJhJEytlzbno5jiITFyboqSxiFQyzd+eOkQ8o9aleVRG5Szf77/Ix1vmCb+FNQWLCqsId/lcbPXqcAevjVwjYRlUB0J8oHEHDfnFTKSiFHsDfHTdbmrzipZVBFkKuqJyV1mjS8gTAm+eB9NMIxw3niIjqCAha/mRsk3X6jUj8OLogArRfR5mLYeUQ9b2dkk5kYzVrs9RsRWJbTrkTQlqX5ckU64ChRfQEhIjlLsTFcGu0rocMtDNcHZqkBcHrzCVsTJ9d8NWPrjCuZItHf7nuZfp9YTdU5cuoadjv0n5dy5w9r0L7FwEqF6VgR02619zyf22T8zzfjSBo7pkB0VT0AMaPYf7sdM2B/7NPYuOvePZzRhJk+4j/TimQ0lTEVPdi4meiZkU6aiB/zr778pAiF/ddIDXRjqYSsdpDJbwYHXrit4puqLyixv28e3ec/REpwhqXg5UtVKTF+IfOk8wmoygZt79LaGym+YwbhWKUPjFDfv5Tu85OmbHyNO83Fe5ju0ltRwa7SRipGkrLM+xvX674d5716u9XA+5/D9mlG6X/gey+00XwtQWqDgJDT+SDD4sMEJu/Oidhngt88pJCxRbmr8PUw9r2AUKFjZIiaqraF6VoLZ0fJGwDHS/jubTkI5EycS0UzMRXvv7N7HSFkjQvKprvyRdi+788iDlraUkIymeTDUzUlLJjNfg3Cvt9Fe43VNSgLQlZtLCm7c4R1bqy6P/h72cuNJJ+7MucQwk49sdGk+qVHS5VuCrxYbCCrYX13B2apB0PA2mpCAiKBoU6JbDM+/ZRX3w1tRZVE1h/2fu4vU/e5OYY5AMScp9+dz9iR30WGF3iBMiq0Izh7k7ImwkGEnM3rItjxFfmpBsxJZevhDeoBdvcOnvc9v7NrL+oWbCQxFCVfmLxpQ5KELckEB4PcavTdJ9uC87J1QNaHpOMrxPkKgC/wRUnhM4m3WGNiQxvcz3pwmJowq0pETYZO8pJe2qQnuDHkzbye5bgGvxnckRpEzTJcJc90gKG/JHIX9GpeqKSu9YD1W/cOtqPWtYwxrWsIY1/LTijmt119bWcurUKT7ykY/w+uuvZ1/yUkqi0SivvfYar7322pLbzkkNikzxVUrJgQMH+OpXv0pl5RoTdg0/nYhNLE16if0UkGH8oaUDlOWW3wxVgRDvadjKYHwmqyB5fKKPtG3hW5B0sqTjEvvFXEHXHVuSDQrFZ0wqB3Te/9+fdIvDzeBMmVx54RqDVSnGt4MoUUgPnebdDVsp9QXZW97EiYm+HKukmrxCthZ4GWIk5xw9AZ3SdW9PoPJP3aeJW4bbeSXACoKbAZSunKcN01sVVOlm/xRVwZaS7/Wdz3ZG+1Sdj63bw8ai+TE1aRn8ycWDjCUjWYUSXahsL6khbCQp8+fzcPX6JQsCC5GYmU+S2oaNzBT758b2/s5xkntcX+W5dYyEQVrCuReu4rUU7vvlu2m4a74LvOPVbi79oD27n8hIlFf/+DDP/I+n8OZ5uPZaLwCqrlLUbjPbCvFq9xe3fAJVcZVNXhu5xvmpQX5968MrVjk5PTnAP3adwkFiOna2yObMqWc7oKZBZILbuqOC4hnBoDGA/ymNpG1hK0sXba086HkESg4naC4o5de3PoxhW5B2+NZnf4Rj2nhNaPoejN4tSJWANgWpovluJ+HAIzfxo18tri3j335tdvwnmgzjZAr7C4kwLgThZBLnr4ZolpLefYJkSCKQ7KytIah5+eMLr2A4Nn5VR0rpSoILMd8sl/kNhaKw+QMb+YGnm1g8jcz8L22bbmJD1dAVlaCh4zWURbK6C5FeJhl0Voxmr2chTOmQp7nEp/NfukjzGwbJkIJqCrzJaQZ+fpDO13uz9kGePJ1QVT4TXdMgIViex32fuYuylpJFx0zZJq+OdBDQPK69VzyFqbjy01rmNKv7PGx5ZOXWGu2zY0suvxoeW5Y4IKUkHTXw5OmLukcXYt19DVx7tZvwUCS7LL8imLW1uxGSlolXVVekTmLYFl+4eoREpsM07di8MtxBgcfPvhsk1UuWkTG+ODO8SE4/6Rhc7BtgZ1Mj4Hb3LgWfuriosq24ZlUFpTm0hMr5t5sf5Af9lzg/PYhHU7EVi9ENNtEyyaaXNArKg6y7r4HWA4384P94mZmB3KLyxsdubkf1ynD7omWnJvp5onbjqkmJcyj3BfGqamYsXgwJqELQcpOiQ//pYY5/6QzJcArNq9L4eCN3f2BbVuHFySQtVX2+IDOWjC65r+WWr2EeESNJ2raQUqIqCppwJeaT9uL7vXFv3SJVxJl0gr++ejhrE2d+0KbpdYWiIYWiQYX+HbhqAWJeW1xXVDxCkLJMLMfGp2rsKKn7iXiPVQVClPmCi+w3b6S89U7BkQ7f6T3PiYlebCkJefx8sGknpb7gIpu+Sn/Bqsm4pmNzaLSLK+FR8jQP+yuaaVllx74jnZuO4Q9Vt9EXm6Yr4ko/akLhfY3bsh3C1VsqePiz+7ny/DUS4RSJeyR6fu6z7EjJqcl+nq5fWq2n5YEmqrdX8n+e/gHCcpC2xEgY6D6d7ugkk6kYpb7gqq5tDWu4HdA8Kg131dJ3fBDpSGzDje0UTckWkYNleYwmZvnSS68zG0yjAIrjWici4cp1lrnbP7CJ8WtT2X2Bays7Z1l7erKfHw1cyv7bcGKWz7cfYXdpPYdGO7M5wbqMFaspbaoDoayK4I3wnvqtiITDmekBFI+HzSVVXJwcJiZz59J2Rg3Mr3kwMgqKIkNskQokMNE1FXtpPmIOzVYAmqLi0RQMw6D4ioOVmL/2ye3MW1osqGLme3w8VL1yi/jO2Qm+2nkie2xXOe0wv739sRWNr68Od9CfmCegSAGWBwhIrqWnmI2oaF4ta3ek+TVSJQ6TGyWq5VoLLrx4MyiQ9Tre6PzvMnB6mFQ0jS8/t0Ct6ir3/vxu9nx8O45po/l0vvWbPyQVySUk+wt9ePOXDoqq80J8bAHpajWoDIT415sewHacbPMOwP+x+2kmUzF6olOUePNoLii9pf2vFCW+PD69YR+OlChCMJGM8bnzL2eJ2QdHOnigsoV3v0PKb6bjviMlLCnwMo8l/mERR+b6Hcx/FhKSpe7nvDFY/1WJUQCxWhh+QCAskJ4FmztQ2CXJH1VoLWrkTWUoZ569qahqWSuYmkBhVglIZBqSLMMivydzLpm40TZsPHme7H6LGwq5/KMOznzzUrYJpWZbJaHTSZSPynkSAq4Kal2Pn0izQkq4cYZP1flg0y5Off5N+vdbOeuDZGC7TV04j7pdN1czuh4p28JBYls2FpK8iKDtoIY34V5Ln97Luo23TuCu2lRO3r9v5OTQNaSAQS3F5OhJ+qJTpB3bbT6+QYPBUrHnSlG7o4pLP+xYvHzn0t9T9+E+Lny/ncR0gooNZez+6LZl7Wt9BV4qF8R5RsKk49VuJrumKajKZ8Nj6wgUri7OPPvNS1hGLiHdPwnrvieRAjRNofHTbTynX8O+nkOWeSQcDYqvzCstFV+UzDa771RvgRfHdPBKlQ3FlVyMu+9XK21hWDZoC32RXKgGbH3ek21mGu9YSsp8DWtYwxrWsIY13HEyDEBFRQWvvPIKn/vc5/jc5z7H6Kj7sr+Zz+0cAUZKSWVlJZ/97Gf5jd/4jaw9wBrW8NOI8vWLi5MA5W1vb+C+Uhi2RdwyKPT4b/oMX4/Gu2s5/53LOSQJRVNoe/TWixEvDV4has4nWYQQ2NLJSY4rQuAIgS9PJx01mIvq/Sn332OTCSZ7pilb53732963EfOeACe7T2HjYEuL89NDjCQi/Pb2x/BrOv9m84OcmOhjLBmh1JtHwjLo3D9DVFMoOm2jpwSqrrD353ahe2//UBxOJ7JFN1VTsHNySgJHA0V1PdRFIkNCsW3S2EjdtVcRmW65r3ad5D/vfDKrEHNiop/xZDTHqseUNmenBvnt7Y+v2DO4avN8QWMu4QDzfuPeOPjTKoYHkG63BNK159DTAgfJqa+dp353TTbw6znaD7jBopmyQErSMcHZb11i7yd3Ypu2W7TUFGzTpuk5SbQBJncoxOsFfp83yx6ZMZKcmOjjgaqbqxKZts23es5gShuBIGmZgJxrFkQCip2rnmE5NqmIg9YJ5hTIIJni99KZKFuHxK75H9KjanSe6sVMWghVQdoOgXFoeFHi6KDHwQzA7Dp3j81OIXt/r+2m17IahJZRM7mZRdadxtbiar7Ve3bRcg1Bx5UBmntNNKB1UEGWaei2gv2eJN+Nn8+ua0sHw7bxa3pGHcZ9HsqHdfwhhY1PtqDdFyLWmUY6Eg2FuTS86dgoFphJi9jrI3xz/AdsfrqN7e9f2ue7dkclA6eHFy2PeS3SprUEqcctLAyPTtF9uB+BIDA7Px4f+7szSHv+HotPJZgdjuIr8CKEIDYe5+CfHuWZP3xXTuIRoDc6RTpDLhBC4PV7kbE0igl15zRCMypPfvSeRdvdCMt19hV4ll4+cGaYk189T2w8ji/kZfv7N7H+oaUJJ7pP44nfOcC113qZ7gtTVBei9cEmPIHl2UddkQm+23ue0WSEfN3Lw9Vt7L9JUfxKeJSEbbokHcfCyiTb3xjpvCEZZjmIpAPX53gl2JF5QkBDfgl5mmeRxcdd5Q00Bks4Nt6D4dhsLa5ZVbHlelTnFSKRWWKgnadgpSziZZL8R8p57Kk9qJlx+9F/fz9nv3WJofOj+At9bHy8dUkLx/h0gs7Xe0nOpqjeWsmMmVi0joMkbCRvmQzTEiqnKb+U3uhUDpFi7knwKhrPNu3MKuaMXB5nqnuaguoCardXoqgK8ekEb/yvYziWQ6TcoXePwZG8yzx3uIdHmzaR/1qKztd7sE2H6q0V7P3ZneQVB6jNK1xUHASovcWuyX8pmEzFuBwenbe3dMgWK7cUrax48P3+CzlEDCVPpfsekx3f1lCmbGpfkYzcL7C9NqgCT8YeD0DTFWryCvnZ9ffcEnns7YAQgk+17uUrme51gI2FFby3YdsdPjNXAenN8Z7s51kjyZevHePjLXfz1a6TWfsJv6rzbNPOVe//y9eO5zxHl2dG+Nn19yxrR7QQPdFJvt93gYF4mBJvHo/XbsySK6WUvDHaxfHxXixps624ll9o28doIsKskaQpv2SR9UH15gqqN7tKMd/uPUvXWGzRMZdSxFmIxEyKdMpEZrroHVOStgx8Bd5lFYDWsIZ3Ant/dieO5dB/agjbdC2G5qxuSpuLiNULvnj+VWL+BI7mFvJU07XItAybwHWF0LKWEt79e49w7WAPqWiamu1VNNw1X6g9OdG/6BzCRoIfDVwEXAUNr6LSMTvOH5x7Ea+qkad5+FDzrhs+/1JKjv/dGcKv91BcLbE9kpb1ldTdv5lv9pzhOhFDDMfGXpAnyHJVJDiOJKVay6vBLNhISkiaBmoCqu18is5Hclaf3iiyTRG2F5cZIAQ+TVtVEfn4RO+iCDFlW5ydGmJ/efNNrVdOTw4sWiYFOIpEjwqIO6TNNJ48D4qmkLAMpCYJ79QwTBsFV80G6b6bLWESL4O8BdxA6cicuP566F4NMrmPHc9u5s0vnp4PewXs/NCWG5Lc3yqWsqos9QXfcTLinKXMi4OXFykUvjHayT0VTe/IOXlVDY+ikoAbEGHIdI1lVhKASaaaMCd5kVmeI1fhbidsiZCgpiX9jwpsD/gnJY7mKqraOjiamN83AmFDvFZw7UOCJyrrqAqU8uZ4D4Zts7W4mkdrNix7qqqi8LGWPfz9teMkMjFSMKbRcMo9N1VTUHQVx7SxLQdVV/EX+qjaUs7BPzqa8xz1nx5CxBzqfwyDDwr3+ZUQ6oKy0yb1JT5af3s3QhFsKKrEp+ocx8nY4mS/BMDN5cxYCboP96+oKWMhvt17ljOTA24Tnw3JAknnPRabX3Fjs+j44jnJanBlZpTXx7sQuquVaEqb89ODBDQPfk0nZZm5aroLtq0Plizb3LESlK8vZdv7N3Lxuas4tnsPtB5oonHvYtL3wJlhDv/1vN390LlRpvvCvP+/PZFjk7QUbNPmxd9/Ladpo+tQL1vfvYGpvjCBQh+tB5oIlt34WqZ6ZpC2syhlp2iCre/ZwJanN3DWHkFtV8myKa8jjuWNSErPzn8OjkD9i5KJuxwkNptqa3i6fjOFngCi5zTnJ4cwUybeNKQKrjuwANUQOfdtsDyP3uOD9BztRwhB8/566nffebXLNaxhDWtYwxruNH4iyDAAiqLwW7/1W/zar/0aX/7yl/n+97/PG2+8wdTU1LLblJSUcP/99/Pud7+bT3ziE3g8K+veX8Ma/jmjaksFTffW0XN0PplR2lLM+offHjnXlUJKyQ8HLmX9vUt9QT7YtHNV3TW6X+fx//Qg5799mfHOKQoqgmx594asR/mtYCC+WH7Xp+o5nrY+VUeqEkUobhDhSPxhQUGfK2UphEDz5A6XxyZ7STpmjkz9UCLMpZlhtpXU4td0HqhqIWEZ/PHFV5lJZwp92ySxzV4+LDbTvK1mWVnPtwqfpqMJJSP7LBb7LwuXCIM93+Wlmq6FknDI+X5StklvbJr1oXIc6dAeHsNwFnfaW9LhS9fe5Hd2vmtFnsGhqnx2PLuZs9+65HbtmC4RZi6QFQgOqA28KgZcr2JHotjQeFJDCslMrUNfaZTi3nbuqV+XUfsR2KaNmZwvfEpHcvXFTjY93kp5SwndR/tzpEdLxzUK11dyTZ2el1HJYCp1c8Wl/ulJ/ubSYSaJZ897rmsmG6aKef9gJQVaEoLDIJGINNS+Aj1PC1xvk4XR8oJEigqhtiI6Z8d5ru88w4kIBbZOqMahcNBDKmUwuFcSbnOP45uCysOSVIkg1iTQa3VOzwxw1wKrn7eK7SW1/HioPWNH4kJXFPZXvLVuetu0c8gUcTPNwZFr9EanKPXl8UBV66rkbK9HgcfPh5p28aVrb+JkOp08qopMOvgH5hM90nZQJi20oJeLvikWPkiqUNAU6RYAAhKEStVFhXrDz7pnG9ny7jZODPeSjhlZ72fVI3B0d7y0YiZVV1WKhhRsHM5/5woljUWLvKwBmvc3MHZ1ku4jbvFAUQXah8oxGFqSCAPunXNuZDB7ry9EciaVI9EcC9ok6yT5SZv8mPv8pWbTjLVPUr2lImfb6612FFUhUOCj0Pbygfs3U7u9atXyz3vK6nlt5FoOYUFXVPaWL04URsdivP7nx7KJ9tRsmmN/d4b8iiBVm5ZWDPAEPGx+18rIIBEjxd+2H82S/aJmmu/2nafIG7hxASbz50LrLUe61kmds+PLqhk4tkN8MoG/0JeTxGtNFHI+bzpn3UBEYct984o7uqLyMy138ZXOE1lCTGOwmKfrtpKn/3/svXWcZPd55vv9HShoZmaaHuYZSQMakSVblkmOYztsJ3E42cDuZrN3s3tvlu5mN5sbThxyzDLbsiSLYUbD0DPTzMxd3cV14Hf/ONXVVV3VMz0jyXay/Xw+Y6tPHYYfvO/zPo+LI2UNm7rmzcCX9I2rupr4PusPNJJdvEYa8OS5ueen1+T6F6NB/LEIuUkEuaWxZb73X19N+MP3vTxE/kd0fMVr/aktnezSa1N9NOeVcri0flMV4clQhODn2o/z0mQPF+dGWIqG0BQFgaMG8+OtR8h1eZC25PW/PMfI+YnEtiUtRTz8OycYvTiBbdoYbknv/YZjMwgEYzG+3X+N+h4ojjoLJ65N88ofv8nj/+khjlU0c3VhPEXNoy2/jB2FP3jlysVIEEUICn5AZI9YKMaVr95k7NIkriydtgebaY+Tnr955TKBQAhFcWwphOIQDO4vrd/0eLLXt6Y0ZUZMjKiJ1CXLeRY5fknFtIuKbwoChTYj7xKQv/bdCSF4sGrbDw0RZhXlWXn85p6HmAsHcKlqQrHkB41rGSxBo7ZF2Irx7/Y9SqdvGgUSCaI7wWRwOY1QJnFUpG5HhlmJhfnb7jOJdnwhGuSLAxfJc3lozivlhYlunp/oTqz/ylQvS9EgP9Z6hFo2nnMsRoK8MtXHwPIsEdPApWop4929xbdONAy8OkxhVLBYl9QvS0leRKc86+23S93CFjYLd7aLU792L9FAjKUxH4NnRgkuhKjYXkb7w838Wf/rWNJy5oi2M4e0dFBMEFJyb3H6eC2vIpeDH81M2ls/djVsi6hlJmY/trQJWTYCUIXErWoEzRif67/Av9//2IaqnSMXJrhxcYie9xpEs53vbMju4b7JRqdIR6aPiVPOZZUNY5GUuFznGZUM6dhTaIZzL7a9pJE9HcEUAispWSnj3YxUSdnJQjTI3/Wc4Xf2PpLxetYjlkHpzjIsLn/7JkOnr5JXmcv+D++8RdJT4lJVotJMuhUS9zIUruhYHZKRg6ZjI4OCZdvotuOtrAoFw7YQsbhKjLCRKky3WyzV2FR2q+TNKpS2FJFVuLk+qvX+Rgqq8xh6cwyhQOO9dZQ0fX+tPWKWyWI0RJE7K6M19J1gKRri1ak+xoNLVGblc6qy7bYkgUyxKgmMB5Yo8eQQMKJ0Lk2hCoWdhZUp6sZvF3TUJCZYhpOJv7LOtx9XQ1VXiSvOCsIGYUmkIpzFtvPeC1OiGE6yfqnNCbVYqkOCETJ+xEQSPynJr4IwwcoWXGWaJ8v3c+8dFBc055Xye/sfY3BlHpeqsvDdCbrC/Ynf3dkuLMOipLmIhiM1KJrCc58+g68oiscnyIlpTvxPOmPD/GFJzmck4RKn4MgVBKVAEJ2PUjympnxzzUfruBjoIpK9pkAITmGUO6Rw6UsdNByt2ZTtKkDEjPFmPMYqJeB2YmKBUomfKLm4KGt7a4WRnb5U9epVgq5p23g0nWzdjZSSYnc2fiPCUiwECJrzSvilHfff8fFen+rnzMwgYTPGjsJK3vP4LlpPNbI44iO/MpfcMocIFrEMJoI+Ct1ZFLmz6X1pMG1fYV+EsStTNN6TWU12unuOm9/tYX5wkcBcCN2jJYo2V6b8nPn0xcSz6HlxgEf/3SkKazeOMxXW5uOfDSISBDAAgbfAy8GP7kX3aJQt56G5VbSIRQwbhzXpvNNqTFJ5zokYJiN/GMoXHcXbD/6PveRkZTMR9DEXDmCZFthQNCaYaZNYukiyJxMYHsnAvhhNV3VUTcWb7+H1Pz+X2PfY5UkO/uhudmwyHrKFLWxhC1vYwr9U/NCQYVbhcrn4xCc+wSc+8QkABgYGmJmZYWFhAb/fT25uLsXFxZSXl9Pc/IOXrt7CFlZxeX6MFya6WYgEaMgt5on63dRk35rEEQ1EkZI0GdlbQQjB8U8dofVUE/MDC+RV5FIdr2D+QeLN2SFenepL/D0fCfAPvW/yu/seuyMf7NzSbI79/N3J32ZCmSeX+XWEBlVR+Nn2+5gLB4hEY+wqrMJSJa9O9THkn8G+5qfoko0RsTCEIL86L21CtBANphBhwFH66PJNsydJuv7C7MgaEQZACIKayUytyfZ3iAgDDsHnSJnjmZ6QvV07hcTUS7Gc6j5wgpoAqiHAkxrty9Zc2NLm73vO0umb2rBwyBcLMxNe2TRRYfcT7TQcrWHsyhTXvt7pyG3HkVeRw6l7dnNU3U7H3DgXv3CdvD4bLQp9J018VQ7R57uznZxbGeWXd56k6Vgd41cdBY1oHvhaAEVQNgPD58cJLoXTyAFG1KIxr5i+UGowSkpJVVa+Iwu7Abln8sYMf9r1Ev5iuRbozOSbHV9kq2BngXsF/FWQFy+MXG51KqCEtRooXY08rR1XKnDFN86bC0NY0naUezSD2eMWO7+ns1SjsrTDSgR6w8Uw8KRANQSubJ05K8hTg5dREBws3byFza3gUXV+acdJXpzoYTSwSIknhweq2qi4y6TO5M0ZLn2hA9/4Cjml2ex7cie1R6r4i87XmY04ZYcjgUWuL07yqztPvaXk0T3ljSxFQ7w42Y2IVznKpSiVN1VIqh21TRvbtjE96ZwyXVH5mfZ7ydbc5OkeXA+kDqv835mGWjsx2lJiEtVW2B4twvPNFbRY6ns1enEiIxlGUR0v711PtOOfCVDUUMD/7HsJt6FiCxsrg4e3rigUleXic6uY0dS2So9X+0okQ0ct5hpWvbQsSoeg8ZyKwFGvSj6369/uxj8bIOfdsFJir/U7QvBI605a7pJ8kefy8os7TvD8RDfjQR8V3lwert6esSJy6OxoxorTwdMjG5Jh7gQdixMpqleruDg3cssEbHtBOUpceSwZuqLy6lRfRjLM8LkxLnz+GsH5EJpHY9+HdrDrcafS8cMP3Iv/qy8yXhfFdEkKJxXe37wPz7p+oyW/jN/b/xjD/kWyNJ2qd0h5pDW/NM32RBGClg0ICnPhAJ8fuMBE0PGa315YyY82HcSr6XR8sytBhFlF8XNRoh/zEMLAlDYRy8Cj6nQsTsb/TfBz7cc3RbQEsEyb0YsT+MaX2dtQxOP7dxKVFhNBH8We7BSyw0THdAoRBmC+f5H+V4cQ8QrixVo7QYRZhRmzmG8UFI+u/bA44mNp1EdhXQG/tusBrsyPMRfxU5dTxK6iqk1Zbr1TWIwG+Xz/BUYDTl/XklfKx1sO3dJaYTkWZikaojIrH3dSksiOVzrezdjz1T89y3SnY7EX9kW48NmrSNumalc5vaOTyHyJajmJRamAJ0u/7Xg6GTm6h2g0mEaMVXwWlgm6IhCWIG9epflpG/uTRQyHFylwZ7E3VsrCP43wgjFI/ZEaWk423LHK4TuJUu8PzkbHlpJu3zSzYT+1OYU055WibvA+q0LBo+kc2MDmbj1mw356l2fI1T3sLKxEU1SWYpnJyClj6g1wZWE8rR2XwPnZYZpySzg9PZC2TcfiBE/cQolqJRbmT26+kiAeCiEImzGyNBceTedd1dtpzL11MioWitFwRcN0maxUON+Qd1lwXL9zy4QtbOGdgDvHRcX2Miq2p45ZpkLLCEVBqApqzAbdaZ/dIWhfLuTUY3emPLmvuIYh/1rRm2Fb68VWEv9vx5WghRAYtnVLC82JjmnG9psJIgw453kuOIqlbTR7TT6ok7BUbLDXhy826AribsPO/QgKggU27iUFYmvjwfx+yfw+R5k1eT+GbTPon6d3eYa2/PK0fQOEVyKMXphASklbaymdrJEEpWUTC8bwdDgnuDLl57U/O8e7/68HKG5M7zf3F9cyFw4gNRyVRwnCEpSMCIQuKO0Gz5yKrxmWKi3MXEDaRKJRFF0BNX69EFfzgJUKh7y80GizrzOfYz9y6zhOLBQjFjTi1lsrhMslB39sT4piy0THNANvDGPFbOqP1tB079szb12P09MDPDfeFR9zaryrZsdd2yOuhMP84dnn8MdVXgbdc9xYnORf7X7wlgqH5d5cFjP0a2XeXLqWpvls/7mE6phX1flk+33U5bx9hCEjamIOBKFapr3jwgL3ApjZAiEctU3VpRIwokjbSewrBtiqBBUUS5A/Lii5CTHVwihQiLnB7ZPM7wLpBWmB7XYIZjLjN+UQYqQQKLqCK8vFVGg504q3ha6obCtwvquSBzz0vjbEfIGBrUrypxRy8rJ4+LdPEJgP8tfffJ6pJ6y4ri8Udps0XxZOm+dSnfhZ2CQ7zrdetfEGUuzgAPZ/eBeX/2SU6zv8Kfe09IqNCEsMzWRpfJmy1s0RWC7Oj2LYdlL8TWCrEqE6cQolW8U+lcfn+s6Trbu5t6zxjmMk6wnLq2NfIVKXVWUX8JNtR1mJhfFqLvQ7LFQAeGO6n2+PXk+5vtmIn1/ZeSrFsujC3AjfGr5GNN4/7CuuJT+c2TraWDevXMVs7zwv/o/XsS2JETawoia2aePJdWNbNlbMQkkqwFrRDN743jWe+OTJDc9/34d3MdU5hxFaOxfNrVL3aANfHrvMzaUpsjSdPJcX07AwjbU5pBqD0huCnAmbnLJsAnNBpx1WBS6v7rgcSAgthvEWefn7njdZMSKoqoIhYL5JUtGlsFRrEyrC6a9ioNqC+W2SRncujzx0kJf+6HTaeV//Tg/bHmlJqLluYQtb2MIWtvB/In7oyDDr0dzcvEV62cIPPXqXZ/niwJpc45B/gb/pOs2/2fcusjJULkX8Uc58+iITHU4wo2p3Off97CG8eZu3FinfVkL5D4k1EsCVDLK7Ecukc2nqbUu83w0eqm6nb2XOURbBmTBWLLqZeLOfwEII3/gK02o3DUdq+PBP7mdJ+vjOp1/AiWMIFE0h6o8yeXMmIY8OG9l6iLTg+1zEn2E9UqrF3ym8r343BS4vl+fHmAjKRJLWsC0nsGhL1JgjcW2rgAA9BGSlTpAacoqozi7g+uIEPcsz6IqS0chHFQqaUPBusvp3pmeO4bPjCEXQcKyWB/7vk4y9MoZ/wk9xYyHtDzeje3V0dI5Xt1KyT+HCjassVVks1tpIAYpLELIMoqFlXprs5X337+Hm0z30RxcYeRdIxQm2zEqDfHuO5bF0/1xpS8TLKzQ/UsLAynziHgkEXx26wkuTPTxWuzMt8GpbNi9+8Tz+B2VqknR95eD6GyUgmi8YeQxavuZUSy03Ow5JSgQsBWwPSdUeDpSoIEg0sXuJxJAWWrbCQqtksdxGCBCKgm1LpOIEcjVVQUsK2JyZGXhbv8lCdxYfbrpz+4P18M8Fefl/n8E2nPc0MBfkjb86T1329gQRZhUx2+L16X4+3HQg067W1rNMXpjo4ebSJB5V457yphRlnHfX7WRvcQ19y7Nko9P195fBkMRcdlJgS1DaXEx7iYs+/1zK/vN0DzXZhYmEoBExuPJCNwvXF/Dmupm8MEnrsMbQEZNYtiMRXdot2J9fQl8svW24nbxvfmUuQhG89L9PM3vPMgjHl1pxC4z49+0QYVSK3dnsrail8KNw7jNXEu+hO8dF84l6Op/pY6nWZr4xfp3SqeSba7LInneUSVaDdNNds7z6Z2cT+6j9tmT6EMhDXrLdbo6VN3HoLSoOVWTl8xOtR2+7Xoai3lsu3yzmBxeZuDbNZL4P6ZVpUvPrCUfr4VF1TlS08PToDWxpQ1xtSFdUlmORtPVXpv28+mdniQZiYEtiwRhv/t1l3DluWu9vpLAmn1/9+ScYuTBBLBQj592F9DDP06M32FVYlWJHpykqLfmlacd4O/FI9XaGVhYSNi0Kgifqdm8Y4P+nvnOJdSWOvcm3Rzr4SPNBlkZ9aetnLSvcZ9Xwqj5KKBZDSulUJCsKIBhYcZJF7QW3V1YxoiYv/PfXmB9cIzhW7Cjjwd88lvE+beSrPtMzz9Gf2s+Vp647GYEkaG6VWNDOmByz4mQtt6pxT/mdyaC/FYTMGN2+GVyKyvaCijQrgGQiDED/yhxfGbrKT7fdk7YvW9p8degqF2aGMWMWulR4vHon91Q3cf6z1xg5Pw4CGo/WcvjH9m66utU3vpwgwiSj+/kBov4Y3iUI5ce70XjFvRWz7ohgebKyha8PX0tJThRMKrh9DtnWilmJtjZrWeH9ZQfIq8il96VBp62MY+rmLL7xFQ7/2N5NH/tfKgzb4tPdp1OS13uLqjlYUsdwIFXBKltzsX0T3+kqXp7s4ZmxzsTfxe5sfmHHCepzilCFSGt7m25DOFk938zLbWwpU1TIViGBoBnbsE07OzucYkm3ai1xorKFR2t2pJDFNkLtgSqGz43T/opONMsZO3r9gl3/vuG2225hCz9IVGUXMB5YQnoFphUXhjDgyPVC3vNLmZOGc30LRIMxyttL0T2p38c9ZY3MR4K8OTOIKW3Hrk5RiVjp1p8SSdQyE4oYt/rWvHlulrPSx2tRdZM2ZPHK/STRlA1JMMmwhaMOc+0JA6k4CqBVr0L2FMweEASrQYkCWasHiSeacYovri6MZyTDTHfN8vL/PpMgtQtdsPtny7kp5rClRIlBwzkNrz+peMKW9L8+jFmh8ubMEL5YmNa8Uo6WNfBg1TZWohFeH+9bc7dRYPSwZKY1xrYv2ejDUDoE2mGViUNr98GO2egCshcEqgELDXLtUhRnP0OHjISiQ9o9smwufr6DvteGiGEx/AgEq51YS67u5mPNh2nJL6Xv1SHO/v3lxHbjV6fwjS1z4CO7b/8g7gDD/gW+ObJmfxuxTL410kF1Vj6Nd6BsDM548XPPvMrytmjivhohG58d5NzsMI/UbE9ZX9oSy7TRXCoPVrXTtzznqPnGsbuoivKsPP6u580U+72wZfDN4Wv86q4H7uKKM+P8Z65Qct3C90HhEJ1SrFyg6Lpk4gFAU1BciqPmYoMWAWzH9ksmxUFWKiTL5Y5SLkDOKAQrBOEynJ2LVbWU1UKM1ZuSfFbO1kI41i93QojeCKFcm/6f1FgMRrEtiaJKinMEvQMvoc9YTO6IN2zx81hqh7lZi8oZjeZjTY4qtxCYYQOESLRpmlulak/qmEdzazTIfJZuhFgot9CC4PY738tyqUXxsnpbK55kjPgXUcSa486qlZRiQratM/VunWuzNxLrn58d5lPbT2zauhzgSGk9p6cHEu+hJhRUoaSQXRREgix2txa2AKen09VdRgNLjAeXEs96MRrkq4NXWKUASeDKwhiHjpZBf+q2iiqo2Z+5YKXruT7Heom4FXvUIRFahpWIHSiqQswj6T9uEiixuaFO0tvxAj/RepQyb27aPivaS/nQHz7G2X+8wsLQIp5cN9sfbeXLxb3MzKxgS8mKoeJWNArGFLJ8gCHJmoXcMfAEBZ58LzUfbeLa1Ciz4QCaJSgaExROgCtLp6i+gP6VOVYMJ4YgVAVVd2y5Vyol+UNgJMLR8e9JgK/KJqvIixFOVxGLBWPEgjG8+T/cduZb2MIWtrCFLbyT+KEnw2xhC/8ccG52KG1Z2DK4tjCeUc7zzb+9xMS1taqeyY4ZzvzNRR76reNv+Vwmgj5emexlLq5Q82BV21uarGwWG8WJftAVtbU5hfz6rgc4MzPI5Pg84ZcX8CwHeb5lhfAeibpTUD6kYZ0ZQSgCd44L3aOje3SkLTGiJtFgjDN/c5GHf+cEBdV5WLZN1DJT5uwC8Go6Fd7UhE19ThHn50bSzqv+bazo2QiKUDhV1capqjYWo0E+03uOydCyU229aGBkxU/clijxRLg3qvJIyy6uGlOETYPthRW8u3YnQCKJZtqZA4q6orCjsHJT1gvdLwxw4bNXAZhvsPhibheiWKegJYvHTu2goaCc4ZCPylhe4v1tf7gZpcLFX82cxXQckbCwETaYWLw21ce7a3dy6Mf28kbfK0jVqe2RChgueFUdR/m4pGBAUP2qRF3NhUiQMZufaz9Gt2+GXt8Mb8wMJCr4l2JhvjhwkWJPdkol1sqUn0AgskaEWRcwVRDYaUyYOOKBw+mjAtMFpgeIV8KjkQg0YYFqOdZVKBIz4de9BkuRND3UiL04y4qMIFQF27KJWSY2FrpLSynrSU7m/DBh6MxoggiTgIT+3klIt4zOWEW3Hp/rv+DYLEgn6DiyvEjMMjmWVPVXlZ1PVXY+RsQkfGCW4bNjuLJc2G7bIc/tKOOx3zvFQizIp7tPJ47rUTV+tPkgwZkgl798gy7fNAM7IkRyJPoOQdV1hfwVkxzLxd5v60RyQY+CFhM0/ttaBl4bSbleoQiaT9yaULISC/Pn33uepW0R1Kjz3kjDRpEKuiqwFEmW5qKlsIz31+/Bo+q0PdBExfZSuq+MMOBdRq32klNazuGiLL4x0QGKhaYrSAFRLKSAwRM2eYVeIraJV9HpeWkwJUipGYKaN+He7dtoOfj9S/YDNByp4fq3utMUnjaSRt4MOr7ZxbWvO8nYaJYk9j4DPc+VonqxrzjDS7gO95U38dpUn5O8tR1CjEBkJGAMnRsjFifCrEJaNpe+2EHr/c491b06LScbGFyZ5696TicC4q9O9fF47S7ur2pNbLswvMTQ2TEE0HhfHUV1BXdxJzZGju7mN3Y/QO/yLP5YhJb8sg2tZKZDywkiTDKuLozzI00HEvLSyTDckpeV4eTbQcw2USyRkK2fCfk3RYYZeH04hQgDMN05y+jFiYzvSU5Z5oB0blk23jwPD/yrY7zx1BVG7Xmk5gTAVV1FddkUD6dvcyfWjpGVKJZhpVhN3Q06l6b4XP/5xDtS4PLyc+3HE0oiS9FQChFmFV1LU8QsM80a4NzsMOemBokGDJASA3hq4DLTXx4m2rf27AbeGMEybU78wpFNnWc0mLn/iQai2LYkd0Zhps2poBTSSbbWBbOpdt1apS0Z95Y3oSsqT5+7TCAcoXBcoeqGiqlJbDOVJJFfnUtehRPovv7trrR99b48yJ73t79jlpb/XHBudjiFCANwbXGC/SW1PFazg9em+wmZMepyCvlgw75NW00sRUM8m0SEAUd58fnxLj7cdID31u3mWyMdie4n3+VNjElvhd2FVTw/3pU2AttTXI2qKLTkldK3kkrKKnB5WXhliotnziOlpOneOna8uy3RDyxG05VqhBDYUm6KCANQf6SG2d4FbpwdQDHBG1PZ8+R2SluKN7X9Frbwg8J7anfypzdfwZAWKM5URPNoGB8vInddUje8EuGl/3WaxWEfALpX4/injqQoHwoheKJ+Nw9XtxMwoowFF/niwCXcOMTOZEic8YAHnQKXl20bKKgAtNzfiOuNm4RdDtlMCkflJaNq5+rOk/5fizjbCOkUJ8hNRkuFBZYunfmaBNMjGH1EQY1JYnnxfdrOv1UlmVVhLVUoRMzMqgbn/+lqirqjNCRZX1rmd//Lo/iMMNPPjtE72J+23RwhvnXjlQQxsGNhnG+NdFDg8uIdsVGkCetCEdF8yfQRQe2rDkEnv8Nivl0lmrN27zx+QfvLOrPNNgsN8WRrUre8YoQ37Ks7n+2j50VHlWvssMVingVBgTfPjd+I8rn+8/zuvkfp+GZ6X9z1fD87H9+GOzuzPdbd4GoGmz+Aq4vjd0SGmemZ4/n//hrLO+PE97gkrxRgRq00y+UbT/fQ+Uwv0UCMkpZC6j7awkeaDjLknydgRtmWX87B0jrmI4FEIjwZY0EfUcvcdL9zO0x1zpI3CuUXYG4vTnWOhLwhqH8Jhh5XkBrYmiRoxdBQUCPOu25pSd+IdL4z0yNQjNU4i8TXGo9v2DjWSjj3JxNUw/k+bMXZxrIschQX91e2Zlz/TvCN4WsEMHBluzBsi7AZYzK2Qo7tJuyNYco4wSchoSwJtaic+ui91OytpO2BJkYvTzJ5fYblCWeO4y3wcO8nDqa9l4vRIK/vX2DZtJEITLdEMR3yyoywwfSmKKDcDrm6J2PRhyIhViqYKAyjJ6V2TGnzwkQXn2w/tuljlHpz+WT7fTw71sl0aJnK7AKOlzfTvTzN4Mo8Re5sTlW1pViWRkyD2YifYncO2frmv81IBkLy6v5W0bk0nTGOtlhlsfNoDcPnx0E6/cuRn9i/oTVbaHntG1I0BUVXsQ0LKSWK6qidaW6VviMOEQYccs1M2M/n+y/wG7sfzLhfy7RxZ7vw5nsoqi+kryrA1NKagpFpW47VlBu2vyywky7ZFja9xw3OiS6MWqfTUEyYb7Cp7tL46D2H0dwaIpx6TFeWjqkpuCyB27KAZMKLBCnwSJXsoiyyS7IIzqfGynLLc+7YynoLW9jCFrawhX9p2CLDbGELbwMy+Tc7y9MrE2OhWEIRJhmTN2aIrETf0gB1OrTMn3e+mkiGTIaW6fZN85u7H3rLHsi3w4HS9OpQj6qz4w6qQ98plHlz+UDDXr7yF89wZbvN4jHbkXPF8WwdKTAxdRX13Bjtj7QAThBoNTmDAstTfp79g5d5/D8+xDU5w0I0mFKxKoFc3c3RdTYh+0tquTQ/xqB/rfK8Mbd409LxbxeK3Nn8xu4HmQot89x/fRWmBFffb2DpjgwtgObVeKB1Jw817+QBuZ2OhQn6lmd5euQ6+4prKPPkwAYVteAoMxwpbbjtuZgxi2tfuwlAsNBm4F7TsWiyYhhhm3/qO48uFBQEqqZysqKF99TtAuCSZwY7S6TM/VY5KDHLomNhgvYd5djLGiJigi0xXU7FkkSCW+BrlagxQfVraxPsyu2lKMIh84wEFtOsLCRwaW40hQzjyfOgKY5HtlynEOtSNLJUHV9s3SwWAIHpcQKnvlbWqrBUsFwkArLCBqkDVpyzlHQyKQERIamP5VFSm8PzE92AU+HiVnQsU6KtUwe4k4ptcGwyXp7sZTy4RJknl1NVbRmrZN4qMlnfABQHMgdXmm4TqJwLB+jyTWObtpOAjWfZv3XhEvvuq0wkn8O+CG/+/SUmOqYRiiCr2EvUH0MTKo331nHwo3swIgZF7ix+Z+8j9C/PEbNNarMLeWOin7+/1I3It/G12Y53tHQqdUYPWTTNC5QJE7fmwhsXgqnYUUp5eykP/sYxLn/5uuONXZ3Lvid33TKJPuxf4I86XiRSl/rya1GwDQslJmg7rVM0Izj1q60pdjmBXJvvlA47wadFOLc4wvHWZg5s387iRDdSSockFVfI1lWV/shCQskjFsz8zW+0/J1EflUexz91mItf6CDsi+DK0tn9vnaq99xdXxNaCqcE3t0hQdMbKpMnJLLAUdk5WdGyoSR/MgrdWTxasZ1v9V9zKs6ArEVY/Ew/L7X62f/hXQnLvchyNI3QAxBcDGNbdgoR57tjN1IqQwG+N9HJkbJ6vJqLgTdGOPO3FxNtR9f3+jn284ffEkEoExShbIqMspEVkCoEQgj2fGAHU52zKVVrrgeLsJXl+HpKipLZ6vilepMWUHP9a2MRKSVmxMQybS5+/hreAg8V7ankpMZ76uh8pg//zJpimzvHxbaHHNJcRXspH/6/3sW26TG+PXUTXyyMW1E5Vd+Kt3CF8ZEpkFBQncfxXziSpiqUCUbY4MynLzJ2ZQppS4obCzn+qcMJYsadwLQtnhq8nPKO+GJhvjXSwSfb7wOce58JAqcaWlfUlOTV9cVJYiEzpbORQF90gVrpTll39MI4xk/t35Q6TElTEe5cF1F/arKzem8l+QeLGMm2UI04ITQuQVdyyeaLX/kmWUVe9n5wBy0nGm57nEOl9WSVWVz4y8sodrwCP0vHCEnUuBx6TkkWJz7lkHgswyK0lJ50sk2b0FLknx0ZxjIs5voX0L36HZGzNsKQP7N60pB/gcfrdnGqqhXTtu94rjHkX8hIGV5V6TtW0cy2gnK6fTNkay52FVVtSpK/PCuPJxv3853RG0QsA1UIjle0JEiNH2zcx992n2EhTnDJ0lzsHyzgyrdvJvZx5Ss3CS1FOPIT+wBHkeZyBhXMxtzNE1mWoiHOH/Ax0iKQFrQXlNLSvqWAu4UfftTnFOFWNMc6VEp0oeLSNa4vThI0YimJ0CtfvpEgwgAYYZPTf32BJ//oPWkKiF5Nx6vplHpzmI8EeXGim9WZ0+pYYFWsocKby0+03pOmepaMvPIc7mlp5vnlvjWei1jVmVjX2iQJVAjbsa8QUpA3JPHXO0ouVlw5NQ3S+V0AuSOSQI1DUBe2QIlJFFtieQVGburhko8pJWiKgldzZbS9ifijLE+uqUhapoURNgn7wrzwuy+x/0d2kZ3twTIsFF1JKHFIKekp9hEORx0lAewEwciwLGLeGEYmYQAh8NeDO1cn6o+hxiTbviNY2KkQKpAUWR4Krjp9dNZqznfdvfGo+oak1aGza+3nUk18vBJXSFF1laAZY8g3T2gxfe5sGzahxfDbSobRbmHzdyfofKYPy7TJnpQs7EiWOXEecoN37dn2vzbElaccBY9IjuSlthkigzN489yUefP46W33JCxi83RPRnW0bM0VV018e5BTks3C4BJVpyXFNyBcCu5F8C7C4lGVcJlEi4Gm6whNYEctsgacGIad/DjEqvW2TFhpgRMjsSUOyWZ1xWSsSd6imOAKC3JnBWrU+e8PfHQfi9Eg2ZrrruOZlm0nxhWwFruVUmLjkCKkZSNVx0I8LktD28E6avY6JL6ythLK2krgoxDyhQn7IhTU5Ge0nHlm9CbRLAkBx85IqgJLcdoZt1dnwh1hYGWO5rzNqXq2F5TzzNhNh6cTb0OEdO63VaejudJjKDPhzOrUt0JzXim/vPP+lGV7iqszrvvG9ADPjt0kZltoQuFkZSuP1e7Y1HF2FFZyYV2xYJbmoj5pPLVK9jJti6hlYkmJKgRR2+LELx5l35M7CS6EKW4sTFMeS0bljjLm4/NCIQSuLB3bVGl7sImythKmu2YZODvq2K8DKAItvr/J0DLzkUCabbN/LsjTv/8ikZUo0rKZH1yir1TAOsdLicTIEmhujZgRZ0oCS22CpRobU1n7tm3NiQ3O74WyQ85cuzmvlHyXZ01hVjj7eqh5OyPf62SqzUyx81MMydGyBoQiOPLj+3j1T88m4muqrnDkx/f+wAtVt7CFLWxhC1v4QWOLDLOFLbwN2F1UTc/ybMoyAewsyizXuCHe4tj0jenBtITZYjTEtcWJFGuQdwJHSxtYjoV5Y3qAqGVS4c3jQ437EpLGP2hYhkVX1TKLdXZ8og5Ih3ygRGCmzaa2W9J0Xx3d3+snFooxu1Myt09gZkHOnE3tuQgd3+qi+0QMIQRexUXUNLCFRFUUjpQ2pKnwaIrKz20/RufSFEOzczAYoWYiB39uIJEUvVssRIK8Md3PQjRIfU4Rx8qbb3u/i7Vs5rwRfCdsPD6I5Assl2PfciC/locbHRnfz/Vf4OrCOGHTwJY2L0310phTTJ7Ly4oRSZWPjgcxFFNyfXGC7YW3TpaGFkPE4r6+M212SkAuajva1IYJWsyp4HjZ6qU5r5RtBeX0Lc+iiXSbJoGjHjDlX8Z4dgFRYIEbFI+KVNdIabYOtiZY3Capfs1Z5srWaX+4ZW0dmZmUsV6225Pnpm1PHZdlf1oypyIrj/fW7eLTnaeJyQxy/QKkJshY5hNnvqix+Pnq8YBrvKJQWE511ipyxwWD/3CTU791H8HyJi7MDWPYNvW5RewurOaFiS4ilomNpDG3mHfVbC5QARA2Df7s5qsJUs9oYIkbS1P8xq4HKPJsXt53M6g7XM3173SnxaoP7W3Fzs/jbJICV228WulWCBgRkI79TLLcRFgxOf/Zqzzw606S+LW/OMdsjxMgk5YktBCm5f4G7v2Zg7zUcYP/+MzXiUiT4jmdR2t2cPDxHdhS8ic3X2FkaR4j18Qqcqrj1IhTgbr6TfjaFUp8GoqmIG1Jzb5Kjv60YylVubOMx//TQ45N1yaS508NXiZqmaTcICHImpIUd0GZz4UeFUig4xtdieDdXDjA5/rPEzAiSCkxpJPIeGGim5/dfh8eVSdoRFPee8USSEXSsTjBR5oPUnugiunO1D4OwYaSxO80Go7WUnuwmtBSGHeOi+nOWTq+2UlRfSHVeyo2dT9XsTC0lEZKKRpXqXzWzQP/5X5yXZ40H/VbIfu1MLvOqvhKQSyY5IxJYkqY8eAU8wOLvP+/PYo7x0XDvbWOGs26NsCT604hwgCMB31pxzFsm5mwn1pvIZefup7yWkhbcvnL16k/XJ22r+8Hyry51OUUpimRHIyPQwpr83niDx6h79UhwssRqnaXM19v0zF4CQCXqmFKx85vNWi3q7Bq01ZQ+ZVrQctoIIa01qzXXvwfr/PIvznpBLTj0D0aj/7e/XQ918/C4CJ5VbnseFcrOSWpbdzuilp2ltfgi4bI0d1OUP7XnaC4GTHviMhy8YsdjF6aTPy9MLTEq39ylif+8yOb2r5/eY4Lc8OY0qbCm59R8atvZe2bzYtX0/cszySWGbaFKgR/cOUZSjzZvLduNzsKnW9aR0nct2SoMcdiUahr35htS4c4vAmousrxTx3h9b84lyDTFdblc/CjuzkdGEbL1TDCJqohQQiklIwXh2kY0ggthnnzby+RU5JFxfayW96brw9fZU4GED8pKLkqqbgqyC3L5vCP3UdWoRfbsCluKkx8H6quUtJUmKYo5Mlzk1f59pM/30lMd83x+l+cI7ISBaCkpYgHfu2+t0S2L3Jn7u+L4upQilBw3UVbk6u7MWynQldTlASRrihJdarEk8Pxisy2G7fCkbIG9hXXMBvxU+jKIltfu/4STw6/s/dhBlfmMWwb1RY8de5V7IOSkmGFnAXnPPpeG2L/h3eie3UOltTRsThBb9J8b2dhJbuLqtKOvRH+qf88E0EfQnGsH3qDc3xj+Cofazl8x9e3hS18PxGJGEQCMTBtFMDCJBKz8eS4MNfNc8avTaVtHwsZzPYtULVrY1WXd9Vs52RFC39842Umgz5nTogzLfKoOgvRIFmbiCuseEz0gJqwLnamqjKjYqcSP/W8aUHBlCB3XOCZtOl9XLJSRaqFS9LQUphQe1mgT9qMvCuu8ClAqhLLAyJCovAmBQKSJ7FCCKqy8rmnLF1lUffquLJdxIIxpC2dPlM6faN/LsiLf/gGriwX0pZEV2K4svTEfGMmsIKhSgxhYCfZH1u2vTZezMAP8gQFiuIoJZgREy2mUH1TRdUVHvytY8iTkivPd7Hk8eGWNjElSeESwanKtg2fS/LIXDXAzNAlZbndFNUXsDjiS1nuznWRdxf9wK1wsLSON2YGsJPG4QqCQyV3ZiUcXAihKAoFwwq+IclK0qMsD3o5XNmQ+Lv/teHEfw/eYxLJk47VkGEzK/x8aeBSgozg1VzcU9bI6ZlUS5lTlW0bks7vBhXtpY71JeBedv6tYqVMIoVTuKNqCoqiYOsC96LheIMlfxdyrThI2GsL17sipX1MADa4ooKm1xU8QUHYYzO52yZYAX888hqeCY1c3cuHGvay7y4KyhQhyNZcibGyTHrxg4ZjQY0CqDhFSIrA43XxQFvmmElWwa2VXfpX5hCqgjvHhd/xXgcBZhbE+dgM+xc2TYZpyC2myJ3Fyup83ZSIqMS9DDlzAtuwUJOJhlJSJrOJBmK4czITyPxzQZbGlimszU9T97odxgJLfGPgCrGwibRshKrwfKyT2pwCdhbefkz0ntpdzIX9iULKbM3Fx1sOp5CddxdV8e2RDmZjEVYbKktKxoKLTIdWqCjL29CSLRk7393GVOdsghCjqAr7P7yL3U+0A9B8vJ7aw9VcXzqDpYLmUhNxBAG4MhCwO77RSXhp7bykZSPDYpV5mYIKPRdVi+LK0TEjpqM83uZCSsMh/SfBViXRqMlzf3+aD3zyflRV4ZPb7uOpwSuMBZfQFZV7yho4VdnKF0NdbHtBY2KnRahE4l2A6hsa9f/a6WNr9lXygf/+KCMXHEv6+iM1d6RGtIUtbGELW9jCv1RskWG2sIW3AYdK6xldXuD8/AgoAo+q8UT9njQWOYAry0X13grGr6QGiqp2l+PJfWuVp8uxzLYhyxnVKd5eCCF4tGYHD1ZtI2Ia5Lp+uLxIbRWmd8s1SxtIBIFsBSyXpPZgFYU1+dz/K/fwtW+dZuqwmVgxUCrpfchE/0ofocpCrAKLWDDmyPgDCBtPuisE4FQYFU9qXPpvvRhhkxkpufKlGxz7uUMpJIw7wXwkwB/feJmgEUUg6PbNcH1xkl/deWrDirmgEePPOl9h+B4TEYtXXgMNlzRqfF4+8l+PoioKw/4Fri9OJogw4FTOjAWX2FlQyUIkQMQ2neRXkoqJGbHwjS7DbYpcs4uzEsG9QHFSsi0pKLc6ObRNm1goRufSFNsKysl1uQmYUVyKlgiSghMkVYTA/9osS6/4qGpWGD5sYhqOnHeyLbUEbK/AnaMjFMG9P3MgpVJxb3ENr02lE1wy2aR4HivF1TNMVJprE1oBU0EfL0x08+u7HuBvzrzKssdwZIRXz2OVByMyVypqYYHA8YJ3BSBrFoTlbLzcKLHczj3PG4T6lyUyW9L57V5a91bg7SrEXeRh70PbKKzKx0by7NhNbFsyE1rh3OwQp6o2DlYm4/L8aJq6TcQyeHN2kMfr3l7v9qK6Au75qQNc/vJ1YiEDza2y8z3bqNlXSQ2VHC6tZ8i/QIknm/aC8tsGAmtyCnFLjbCdWu1fMKUwcW0aaUsC88EEESYZQ2dGsR/K4+tT18HrPJ+p+hhfme+g8M0cjO1uJoK+JLkeB6vEJZBI6Xw3LScbOfIT+5C2TKuIBTZF3LCkzbh/Kc6bSH5nJKYXSodVdO/afpYnncbo+fEuXpjoxm9EsKVMiX/HpM1XBq/wc+3HeWbsJlfmx8B0ZK1N28BUTNy5ToPZ9kAj8wMLTlWnBNWlcvBHd9+VisXbBVVTyCrw8ML/eIPZ3rVnWLW7nFO/fl/GSr1M2CjRXVCeS+ldKCCNXpjAHRQUztmY0dUgmcS2bKKBGMPnxtj2UDPlrSVU76tgsmPGIR0ojv3OrvduS9tnuTeXqVBqB6MKQYknm7AvTGQ5mrZNaDFM1B/DW/D29sVG2GCiYxpFV6neXZ5Q2ViPn2g9yleHrtDjm0EVCgdK6ng8rvAFTj+w70NrdifllkHWqIuQGYsHrZ0k+Y7CSu4rb0qQNDaD1gea6Ht1GP9MIEHoEIqC6lKxLUnnc30pZBgAb56HAz+yK9PuUqAIkUYEvJvg4vDZdIUJ38QKS6M+Cm9jcXVpbpQvxYlDAFfNMWLSwqvpKZZ4ebqHoBGlZ3kWj6rxI40H+NZoBzeWJrFsG8u20eKVl/ORIP/Ud47f3P0wpd4c7q1o4srYSApRTI8JisYUhJbaZlXuLLujau2qXeV88A8fo/OZPsK+CHWHqvDme4itWGguDU1XkbbENCzMsJk6bgP6Xx/ZkAwTMKL8Q++ba0lQr8LcPZJj79vNvQ0tt2xvD/3YXl76n6cTZF1FUzj84/s23Zb8MMAybV7/yzUiDMB8/yKXvtjBsZ+/e8LFfeVNXJgdJpSkDFjkztqUYtZ6SFsSWAgRdJl8aeAShm069puWM5bzqDoPbHKMcju4VI2a7MzKOIpQaMkv48LsME8NXCbc5IwpZ1stGs9rlA6q2IZNLGSge3VUxUlK9C3PMhVeoSa7YNOJLHCIqRMZiI0dixN8RB68YzWCLWzh+4nJNyfInYCVJC6LNG2KYh7y1xWCuHPcaepfzvL0fsKWkpcnezg/6xD59xRX84lt9/K/rr9ILGohhKO26VI1DNum0zd92wKfscAShm2t5ejl2n+oisBKmnc5hBVBoNiZWxUMQKRepfmCoON9RooJRTIUA8ovCYaPSoycdQnQVYWMDDl/5zwEqgGqW0VTVHYVVWYsZlE1hV3v3cblL13HjFmJC1klqkhbYsYsXFk6qkvFk+/Bm+vGN7FCzoJCON9yCn8sOx6sAE1VsIVAjUlsnZS5qxqDqk5nTKB7dLKKvdQcrCYrx03ziQbyK3NZjAa5HAmxYtjoto4RJxfk6G4eqtrGY3Ub29g1n6jn4uc7ACjrUxnbb4IiEn1sTXYBtTmFuD6+l5f+6DRmJE6GUgSHP753w/Hm3aIyK58fbznC06M3WIgGKXZn8566nSnKmptBxY5SlsaWcXt1Gp6PsVItWdgB7hydB47uSHkHrLhKg+GWCUuWZIwEFvHHIok42mo88driBJpQOFRa/7arCwcXQ6guFSu2RmoLF0OwCgLlEsPraA4FjCguoeLRdVy6hmJbiDBYHlKUYIQt1tRVpFMoIomr36bBkQEUNpiKpPsRC2E5pDJsQBFgS8KmgUDwpcFLNOWV3LEFvBCCk5UtPBO3ZdSESkyaiU90dWateXQKc71UZufzrtrtVGTl3WKvGyPf5SVoxuJFLKsn4fxfxDLRFDVjnHgjuFSNR2t38p3R60jbJhKMgg111zXEtEnRZZulwxaKpmLFTOSyhf29ab4Sfprtj7RQsb2U0cuT6B6NlhMN9L48SPcLA4k2attDzRz58X0px4yFDHpfHmSuf4H8yly2PdxMdpFDVL44OkQ0EFuL25k2sWCMC6NDmyLDZOsufmnn/UwGfYTMGPW5xWmqfx5VZ1dRFa9N9WPaNooQuFUNRShcmBvhifrMcajlWBiXouLVnBdO9+o89nunmOmeIzAfory9NIX8I4Sgfn81x4ZaeTOp8AqgvaAi47s20z1HgggT/5/8QUm4RmB5HCI/ODHgo9tbKf2kwswZJ/bfeF8dLy32McNs/B0n8W5IDWwLls/PM7RjjObj9VRk5fOru04RNKK4VC1xnxoO1zB4ZpS8N7WENV1BTR6FNWvFlnauQsGJMsq9ebdUVNvCFrawhS1s4f8kbJFhtrCFtwjbsrn4+Q6ir42yTbXR67w8+OFD1JRuXPl03ycPcuZvLzkJWSmp3lPBfZ889JbPpTmvNE2hBqDlDoK14CS7+l4dYmFoifzqPNoeaNo0UUdXVHTX2xuseDtwc2kKdBFXiEhWVgCkpMTv4Z6fPAA4THoZyQffHOB4gEvFqWCaPigoecmP8a7UKjg9DMFvTMPuzBOz1/78LLHAWmDQipqc+fQlWu9vvKvgzleHrjAfDiQqW1ShMBH0cWNpkr0ZSBtX5sf4254zGJbltPyaRImCasJsnUnNmM3Xfuu7NN5TS+RkNpa00xRSbCkZCixwsqqV18b7CMm1pIhiOrfSezkKD9z63FVd5cCP7OLsP15GS4qVJqf4k63dbcNGt5wJ3MmKVr40eAm3qmHGz1FTVHRVpTWrBOO1GQSCsgEVd0AwvttiqdZeyw/GIx6KojB2CoTfwv5uB4H5EPue3ImiKtRkF/Khxn08M3qTkGXgUTUert5OS3568i1kG7h0FQxJZNXPCEA4XsMXRob4RPYBno8O0KsuoSJAV4hZBubqRcqUK3eqULJ0FFVghE3KRgWNsXyuHgoQiFcXCRuKbkLNa5JQGYyeMLhYOI53cZKaWZX8DoXJ0xPs+K19PLPsyP6rikLUtvju2E1qsgsyXs96ZLZ5chSn3gm0nmqk8d5a/LNBsou9uLLWIme1OYXU5mze7kFXVD5QuZvPLLyJFR/teFag9pKKK8shQmUS5gEn3vzKcE+aakegxObSpV5qmxzGl6orGBGnstRKIjutIrfTItoafcsBXFUoZJkaEbFG0Ft9Z7wL4FpnT1LSXMRc2L9mmyUUrAwKRcuxCE+P3mB3URUjnVOsZCcbWkuKe4B7nSqq4586wt4P7MA/H6S4ofBtlSq/Wwy8PpJChAGYvD7D6MWJTVsE5VfmUndPDV09o+hhgSsiUDSF3e/bflfnpMb7P2ud7ZcVs1A11bHsiuNd//Z+rn3tJkNnx9DdGq2nGtnxWHoS+NGaHXym7xy2lNhSYtgWFd48Ls6NcqioHk+eOyX5DY6PvTv37X1G011zvPonbybIAt4CDw/91vE16yfT4OL8CNOhFaqzC/jxliNIHOKOdhtrE81S+GjxPl7w9zEaWiLP5eFUZRsnKu+cMOrN8/Du//AAb/zlBUYvTaCoAs2lJVRmQkvvPEF4PaKBKFe+cpPxq1O4c90YETOjVPVmyHHfm3BsvZxK8Ri2aWPpEA5ZuLPdKPHEUnNeKf/5yrOY8b68yJ3Fz7cf56PNB/na0FUuzo+m7NeSkisLY7yrZjvthRU86mnllZk+ojmS3FmF2isq+97dwsKQj/kBp8KytLX4jsevlmHx4h++wVTnHNKWdL8wQN3BKnb97HZeneoDIRzlGcNps4rGUgO4tplBbS2OG4uTCSJMAkLQGZvlPqX1ludV2lzMB/7fxxi5OI5t2NQeqErY6f1zwfzAYkZy3NiVyQxrbx6F7ix+ZdcpXpvqZya8Ql1OIScqWu9INQscS9hz/3CZwHyIgZMmy00Cr8dFTFiYtqMG9ZNtR1PGJ0bUZGnER3Zx1tv+PCzb5tnxTlAEQlOQ8XZ7bI9FyZBCYXV+yjGFELQVlNNWsPEcbyNkUqZ3EhjKWxUG3cIW3nEsDvtovKzRd8IgVBgffy4LDi6k2/tsf6SFc5+5krKstLU4o2Xb8+NdvDjZk/j7zMwgS9EQDTnFDMh0svpmbNK8ScSSxFRWOP+tRCRKlkhVAzEkwoZgvuTGhxUUw0TYztyWDcIflgeWS22WG9OVAMBR8XQFIZZPht+dDLRH1RCKQu/y7IaqnTvf3UZ2cRYXv3AN3/gKqq6iuhQivkjSvpy2KbIcIeILIxSFqhsqvkobwytRLKcoyKVoqIqK5rGRQYO65ySzBwWxPCjRs3mosI2Ad4ZAQZCFYzoTdVGuMEZ7QQWNRc59f2Wyz1GIlZKIZSTiELaUROyNqEMO2h9uIeyL0PPSIBU9kuzSPBZ2CwzFYnthJbsm83n2D17GMm3aH2nBk+PCtmzqDlaTW/72qsKsYldRFbuKqohaJq51dpGb3sd725nqnMM3toxe6mL6pIFZomJlaXzT10lXzzyf2HYvilBoOFLD4rDPUXqV8RCAECh6XKVOiJR3XBGCYxXNHKt45+z0hCLQvXqCDDN5TLCwG6y4Ou1qgQcIotLCu6DwyIE9/GPkCsRVUaUWV48JQ8N3bZbaBcFqga07KkrCgJgEK8FBiH9/lmP3C2DGi0/kKpFMXVtNSkfNUFNUun0zHFlnS34rREyDZSPMyYpWvJqLC7PDRC2T0cBSkvqUwKvpTuyg8c7VZ2zLdhTf4u/PA1Vt/MONNzGktWYPJeNNgZDk6m52bYI0koyTlS1UZ+fzwrkO5rrnKRlWyF5ydl53VWNncSn2wWwGvj1A8aCGHhXY2Fz9WidCEWjx+emNp3uRtlwjekvoeWGAql3l1OxzCg8sw+J7//VVlsYcmaDxK1MMvDHCe37/QbKLs1gZXkmyvVq7vuBEAPZu/ppuRzxzKRpZWvpcNpahrZkJr/DFgUtMBH0oQrC3qJonG/fjUp35360UJQGeqN+NIgQX50exbJu9xdW8rz7zxWQVOgSZ5DBRyXWIlUFon46lOVZcLlXjufFOVLfgI588mCCQzz/j56oym0KESUCBhUbJxLUpLMNyFFR3llPaumYhFTRi7PzodmIhg/FrUwgERfUFnPjFuPWrtPl6fJ5nS+d9e7Jx/x0VlmxhC1vYwha28C8VW2SYLWzhLaLruT56XhwAwGUI6I1w+o/P86H/9W70DCoA4FRMPfDr9yWSYm9XUvG+8ia6fTMM+teCR8fKm2i4Az97M2ry3H9Zm/wADLw2zLt//8G3rFzzg0TAiOLWNEwjxvpZhysEVe58ho0l2mQZQgjUHB0trBLBSql2md4jyZuxaXoWZg8oRHIluXOCmg6NlcAKRtipIAXHbuf64iQ9i9P0VQUo8oGexCEwI06Ffd3BzF68GyFsGtxcnEqReLWkTcQymYsE0taPWSaf7TuPmZIgEthuJ4kfzZMYYQMzYtL1XD+50VJEe9x3fN2+dEXlibrduGdsXprqIVzgrOEKCmquq+SHN5f0bz3VSGFdPty4xnk5g6lK53pW7XqTTlWxBQeLHcnig6V1aIrC6ZlBgkaUApeXiqw8mvJKqArn8B37eaSQLNbY+MskOXMCfxmQpcRtjkRc3ttmpsYGAXPbwkRe6kb36gnJ1KNljRwoqWMpGqLA5d3Qozp3VBLxR7EVwOXcMKE4PuSxYIzLo4OYT4/jzpFoHxJo2c67EbbiCfHEDY7LsUrw6jq66lR5FBXlELpfcjq67CjxBJ11hISlHZDfB6PvEVhuG8sFkTwbX7VN4zmVqi6N1693QQa152uLE5siwzTlljjJyXW4k2roO4Xm1m5pIRYNxhi/OoWiCGr2V93SJ3p7QQW7v6qyWGigxiBnAoRmsu3HnKBiXnkOxY2FLAylWmPUHaqmi8zJw6hq0ZxX6jwxVUH36hhhZ/+rXBUlCmWXJXljMDAzwp4P7KAoSe1BSknUNnEr2qYDr8dEHU/bvc67BoBAi0K5ms3k9ih5M4KcRQXdq7H/w7voW5lLbOtWNIyk73/1tYtYBh0L4/QsTYEwyZtWWKmwUUxB6aBC0RWDyJPRRNufW56Dbdl0fLOLaCBGzd4K6o/U4DcifHOkg86lKTyqzn3lTTxU3Y6yyWtbioboWJhACEeZaX2V8UaY7V/IuHyub2HTZJgbi5O8cnie5R0gDYvGYB4f232U0sb0BM9m0Hp/I5e+2JFmvWTFLCyXTe3+teCn7tY49LG9HPrYrSOHOwor+dWdpzg9M8CF2RFUIViKhfju2E0uzY/y7g+1c+kfrq1tIGD/h3e9rRZJ0pa8+bcXE0QYgLAvwrl/vMxj//4BwqbBn958Za0PmhvhwtwIv7jj5G2JMD0vDXD1qzeJBQ1Ks3ROfWgnOx9qfUu+5lkFXo7+1H5meubSOrLKHbdv+95uvPRHZxIEkrAvghExEySdVRQ3FlJQs3HbB2DaFktxMqIRNhJe8IoJpX0K4UqLmm0lHC6r5zujNxJEGHBIjF+5cZHWN3QGi2eI1RjoHi2FgJNsB/j4yUPsv1nLwOsj2IZF/cdqaDjqfFf+mQAINiVRvh6dz/QydmUqYV9nAUNnx2g8Vsd763fy/Hg3UdvC7dIpvwgFk6nvccOR1G87shJF0RRcWbozjpBOtbxlWAghELpDFv6brjeoyi7gREXzhlXF7hwXbaea7viafljgyspMTnG9DfOMEk8OH2rcd9fbh1civPL/vZlIuC0X25gRxybQ7dZwZxhnDZ4e4fxnr2KETYQiaLqvjnt+5sDb0rZdX5zgubFOJoPLqIqCy6thBA2wJaZHopS4uPcTB9/ycVZR4smhIbeYYf8CtrQJmwaWtPGoOk+P3uTxup1vq/XFFrbwVjC0Ms9z411MhZapzi6gtS4b9xuCXc+5COU786csn0Ltj6fPB9oebAIFel4cJBaIUrOvin0fdhRDbGnT5ZtmLOCj1JPD6ZmBtO27fNM8UbebAX8qGSZHc7Oj4NZWvACHS+sZXJlPVOeDRDGcOYBnEYQLIgWCQJlExIfGpiue0LclLreGGTUxb8X1EzD4uMTaYB2pCSq6BeOHZNyeObkQR2C5YMWM4pYq2eqt2+eGIzXklmXz3f/0UlLthCMxmky2VzQFd7aLyEoUd0iw+7s6C/U2sTzQjhcwbwSdc4tJGi5pFI1LyicVVF1Bc0mO/FEL7sM7eGmihwvjnYn9dvmmWeoJ8a92P8hkyAdAzLZSxguWtDkzM8i+4poN405CERz4yG72fnAHZtTEnbMWV7rxdA9Xnrqe+Htx2JewrP1+IFP/s1l4ct08/h8fZPL6DK/5BhGu6RTbwN7lWTqXptlVVMX2R1vxzwYZeH2Y4mGVhWYbPUtPjHf3F9d+3y3Gm4/X031hmOkdgpVaCFU4ykerlkeplUoCO2jgLXTT1JfNQGPQmQ+bIF0OeWb4CQV3ACL5IHXWwm6J10Uk/hZIpCrT4k1rxUXrJIPZ/LOaCPp4duwmvctzSBxCwPvq9/Cru5yqrT+9+QpDKwvYSMeCO/4MjA3ssjMh5Atz7h+vMHFtGt3jFDXse3Inu/IqaXtFpeuIQczjFDFJ4Xy2LrfGBxv23ZVSR3NeKYFgJR1XfGm/FQc9FA7lEOxaaxOklJhRE0VTEmQYM2Ji2zZqTmpcd/zaVIIMM3JhIiUWDM5Yu+fFAQ58ZDd1K9lcyk16RwAk1C+9vaS13UVVnFlnEwakEYlsKfnH3rPMR4KJv68sjJOtuXlfw55NHUtTVN7fsJf3N+xNKK1shJ2Pb2P82nSKmpKwofoFG3nJYOljuSzmxRL7sKTka0NX2F5YgUfVce/JQ9wUCFum3EM15sRA55osRp4bZ+TCBODYX+98TxttH2zlSwOX6Y1b3ra/u4L3/vjDeGw1ZT52enqA83Mjib/9RpTP9Z/n3+17LMUydAtb2MIWtrCF/xOxRYbZwhbeIoYySN3HgjEmO6apP5yu0JGMt7uy3qVqfGr7cQZW5piLBGjILaYy69aJlfUYPjeWNvkJzIfofWmQPe+/u0r5t4oh/zwDK/Pku7zsLarekJhwK7QXlKMrKm4Z9xBXnAoxjx9ML3R5lujrOUNLXimf2HYve4trGPTPIw0rNX5lSqbaLLa9oFH8Uup5ePLcCVUAgC/0X+TKwjimbRE7IpjbCa3fkGtezELcVUB/yD+fscLUlBYNOakJXP9sgJdfvEaoKprmSwuODG32JIQwUGISTar4X51j95Eqzi+OpCTSLGmTrbmIWhaP7NuD/zPjRIMxDA/oURBS0PDBzVfSlDQV8aP1J/D3nGFoZR4JGKEYOaPO5DpUIMlaEhyWVVQWFiS221tck1H9RhZI8ipzuNiyhK967bxVTcGluRA4UtFBM4YWWw2ugKU4CjIDrw8nyDDgEH/KbmGTYpk2Q5/vo7ZaY2TfWoWKiBcBGTGL7KDzfKdbLaKmRShmogolIam6HgVjgifLdqAeyGV4ZYGzc8POsaTExgavQAutbbywFyyXcLzqV++DgOFDFtmLCkYGmXJwyDqbQXtBOfuLa7mysNbOteSV3lai/J3C5M0ZXv2TswnpbHeOi4d+6zjFjZkVYzqf6cUlVYonZCIxqmoKZS1rgdoTv3SUN/7qvOMlLaBmbyVHfmIf46NwZnQgpexHMQX7tjdR6s3h4ep2nhvtxLAtbLcgJ6hS9zkDVIkWWiN0SQlTN2YoqitASsk3hq9xemaAmGVR6s3hgw372FV0++qwR4/sZfJ/jTPQFCaaDVlLEM2DsV0moDJtWOwxi3ny0FGyCrzMLq2pA6iKgjtuLbbemMtCErJNKAZvQHLoS863IqRA92opZKPZvnle+H9fxzKc72vozChTnbOcP7TMRMhp2IJmjOcnulGFwoPV6ZY/63FjcZLP9Z/Hit/nZ8c6+em2ezZVeZ9XntnjPHeD5evhj0X4fP8FTGk7FlZuGM+J0O1ZpJS7I8PsenwbA6dHmO1JSsTEKz/L20tuSfS6FaqzCyhx5yCEQBNr/cxM2M/ydoXHfu8Ug2dHEQga76ultHnzJNjNYGUmQGA+XRFqrn8RI2pybmEojYw5HvRxdX6Mw7eo4JzrX+D8Z64m/o6FDK589jqltUWUbyvZcLvNIL8yl30f2snVr91MvPQlTYXsfM/bY8GyWcwPLiaIMKtQslVmdkl8VQZqFHZEi7n/yXtuuy9NUanKymcytJz4DgG0mKD+ioZyUfDY3l2seGwiSZY24NgO3pyfxHvVRXaFjVVqYpuWQ3YTjirZ3qLUvrVqZzlVO9O/xbdSpd376lCCCAPOo5naY/HnK2dRR3Uac0s4Vt5ES34pk/YUl4Y6iAZiaG6VHY+1UXfIIRD7ZwOc+fQlZnvnUTSn4nrnx3fwlaCFYRiJfZuqhR0KEjRj9K3McW1hnN/Y/WDGatN/7iiszaesrSRNMWvbgz94gs/YpcmUxIE7IDDdTt+cbCFY5Hba78B8kDN/ewlpS6SUSEsy8MYIRQ0Fd20xuope3wyf7TvvqEMIh2RmCZucPDe2aZMtXHzkP78bl+vOk5KTQR9nZoYImVHaCyo4VFqXILn8eMthnhq8wqV4ta6uqOiKyuvT/Xg1nYer22+z9y1s4Z1Hr2+Gv+4+DTjKFP0rc4wULLC7MYtInx9tykJK8FTm0nw883yg7VRTGrHQsE3+9OarjAaWEvOQkBkjS3OlJR6b80p5X/3uhBJJY24xH2jYu6kYwImKFs7MDDKxvIRtOYQXxYSSa44VqNcvWNgpWamMq1AkkcylChFM9CwNaZgkK3em2B4JsLwCW2SYzCGwVZg4IFFCYOfINS8WIHlHUdsiaGaeqyWjuKGQoz+xnytfuUEsZODK1rHNVDJM8/F6iuoLEmMqzRCU96u0PtBIWbiYBW8UV62X8//2DHLJxLQliqaiqALLsFkaX6aivZRzc8PY0saSEgWBqihMh1cYDSxR4c1n2LeAsU6ZQcSDDH3Ls7ctwlJ1NeW8pS3pfLY3bb3BN0bY/+QuPHlvPXlrRk26vtfP5PUZvAVu2h9poaz1rY0xk6GoCjX7Kon2DCN86XPs8aCPXUVVKKrCPT99gH1P7sS/FOSMNcaVxXEADpTU8u7ajW2m1iNqmTwzdpOrC+OoQnC4tJ5HqrffMcmisKWQsR9zMxeMYWmOfZZ9i67PXwyfDV6FdoG0BGrE+b4MRSJsAQoEyliVQYlvJR3Lo2QoEmk7MR9hcWsIRyU1T/fcVt3CljZfHLjEhbkRwvFvS1dUpJR8YeAiNdmFFHuy2VNUw2hgiWQ+h64oGxLuYpbJ5YUxpoLLVGbnc6C4lpf/9xkWh33O7yGDm9/tRdEU2h5sImdI0hrW6T2VOhavzS/c1Lx/IzQcqeH6t7rTii4ajtYQmAumLJOWQxBPcXFTQBrp7ZYniRyzMuXPeOzlSWf5jv0NdP7tMGP7TIJFEs+KoKZDZf9H3l4Fo+a8Uh6t2c6LEz3OXF0onKpqY9u6GMF4cClBhEnG5YWxTZNhVhGzTJZiIYrc2RsqkdXur6L1VCPdz/endBECEMs2M1E/SkxLGdtGbYvL1wdwdcc4WzaDO8sVb/uT5kOaQFhgC4ltQ/KnfPOZXs63LTIYXZtPdvmmEcBPb7s35fw6FifSznmzNoNb2MIWtrCFLfxLxxYZZgtbeIvYiDW+Gan7dwJCCFryyzal+pAJvomVO1r+TuMbw9dSKgJemujhF3ecuGOv4BJPDo/X7ea7I9eRKxGwJZ5lQbhAogiRILH0Lc/yndHrPFi1jetLk3QsjDu5cOmw9QVg5EBuWXaaLcXO97QlyC2jgUUuzo8SNg1AIj2OV/LEvYKmZ+OSwnUuXs8ex9fZR1NuCScrW1OknTdCju7GpTje6clWRnm6J+W5h3xhnvl/XmZZRrGfBDLM55QYRAvg+sdtFAOKukyqz1l8KKsdW5VcmBtJSEkLIRgJLPIfLn2b7QUVPPArB+n4u+uMaStM7rHQitxUbHeCEJutbnWpGr+w/QR9y7N094xh9voxrweIrETQPBqNR2s5/GOb01sVQlDz0y28OHI+UX0kVAWXR6Mtr4wcl5vR7inG9BgipehHEsy3mcwN0bk4xbaC8k0FkgKzASIrUUrnJFkdkoH3O3LYQgpsl3OA4hGFgaMGUzviB7TBFDaaEJjJlATpVJSU96nYMsbhkjpemlgLDCpCYEtHgtjIJuGv7S5ygx5b28lquEPAdL1B27CHmXYDqa61RwqCQ5ucCAsh+FjLIe4rb2IsuESZN5fWvNK3pNhwt5C25OzfX04QYQCigRjnPnOF9/z+g2nrG2GDgdOjRP0xpxLLq6PqKhLJ/OgSlbucQEpuaTbv/vcPEFwIoWgK3vy4T3vLXkZ9i0z4l5C2RLEEp8w6dt7vJOFalwt4bjKGVRCXV1dNpu6DuhdStadUXcFb4LRX/9R3LqU9mwgu8w+9b/Lbex6m4jbERd2r87GffZhr3+hk9tICw4dMJsuiCUsURVPoYomA2yQLh8hU4c1jOuy03W5Vw0auU4hKgoClWkmoSJKz4Oyz/eGWlID1ta93pSTgAa7eGGR8m4JYR+w7Nzt0WzKMLW2+OXItQYQBMKXNN0Y6+J38hzd8z24sTvLceCczZStoj1tUXxDkzTrHzynN3jA5sx5dvukUwt8qOhYn7sqeB5y+v2pnOf5phxgibYlt2yiqQvXuW1c0j/gX6fJN4dVcHCiuJdflSfl9Jpy5H54J+7m3tSlFQvnthjvHhaIKbCs1gOrK1lE1hcnQcsbtJkLLHL7FfofPjWdefn7sLZNhAHY/0U7DkRpmeubJLs6iYsedt1/LU36Gz44hpaThaC0F1Xl3tH3En25bM3DSYrlK4s1zYwHXRIBdLLKX29vAPFG/m7/reZNwErOt7rKKYjnXpXs08vT0PsyMmrjifKaCaYXaqxqTOy0sj0VeVhbvqdtFVfbmyVq+aAhd0cjWXUQDUaZuzuLK0qnYUXZLom8yIQJgfjfMHBKoqkQBBv3zLMfC/E7RwzQfq6fhSA0rMwGyi7JSlE9e+ZOz+OLkbdu0GTwzipSSxiEYPiSI5jgWl4oJ0rYd9TYc+78LsyPcX3Vr26R/rjj1a/dy6UsdjF2ewpWl0/ZgEzseu7NrjVoml+dHmY8EqM8pZldR5VtWLVk/L6rqVOk7kdr+7iyspCLL+b7Gr05hmzaxsIFtOEoUqq4wfG58U2QYKSXz/YvEwgbl7aWJqmhwrFgkzhjHo+qEzZhTQS1tXLrK+5v2ZSTC2JbN+LVp5vrmyS3LofHeuhTC6MDKHJ/uPp3o024sTdG/MsfHW5xWMM/l5VRVK92+aeeeJLVFF+dGtsgwW/iBQkrJ14av8tpUH1HLGWurlkAzBYauYB4rRHY74xBFFfhnA7z4P9/gxC8cua2F2WIkyB/feDlpHOPYkgghMGwrheRS5M6iIiuPqux8jle0YEkb9Q7aH1VR+KUdJ/ni2bP0+KbR/KAHJGMPOol6WwV7VY0iZbdx5QtwyONCpvv3SpFwKrUVUAyB7VrdNhWWi0S/cyv0Ls8yHwlQ4rk1ybTtwSaajtUx0zPH0tgyiyPLLE8697PhaG0iDpFV6KX/tWFs08Zb4GHg9WH6Xh4CQPdq4LMShFTbtIgFnfXyKpzjL0VDBIwkQr1QyNJcRKIxtG8tYTXFkNnSIRIBig1mNIatq+SKOyeuWKZNNEPxhm1JIiuRt4UM8/Ifn2G6c00xc/TiJA/99vG3XSmw3JtHV7x9T0bFuuIaT64bT66b91PE+xvvwFcmCV8evMT1xTUV05cme4lZ1h0n/68ujLNCFKGIdBWjtBIKh0C2KKIJKyMz/o5rEWfb9C8hhQmW9pOyyge4zbdS7s7lgYVqOr/VQ+2BqhTF1WR0LExwdWE8oYgqJcQsCztsoQmFZ1+5xPv2H+B4RRNzEX8i1pWne/hQ476Myhkxy+TPO19Lmeu8PNyNWbiMXS7InxIUjSoIBP2vDbP3gzsc0vh0gLZXdabbLWJZktIVN7/w4IlNK6dmQn5VHvf97CEufbGDyEoUzaOx6/Ft1O6vIuyLcPVrnYlYiVAdOZrkYj1VV9OINJpbpflkQ+LvkubMBSGry8u3lXL/yd10fLML07BQdZWd726jes/tlbvuFA9Vt3NPmfOsSj05GZ+P2OD9utO7fHp6gOfGu4hYBh5V57HaHdxXnplMfuBHdjF6YQIjYjjWt/HliqLgCkHUm0r0NkIGNz53He+KwsJRk3B73Lp+dUvp9E2KS6VyQUdRUp+RoUv6/HMoetxaUwhM2+Ly/BheTedoWWOCiJhcNJOMzdgMbmELW9jCFrbwLx1bZJgtbOEtovlEPYsjvpRlnjw3VbdJev2woiiDnzdAcUPB9/dEgMngcpo05kI0yMuTvby/4c4CBxHLIFtz8WjtDgjaBM8t0ssCo65IwiYgYhnELIsXJ3o4NzvMfk8VHkPFMKyElCsICmYVvAUe9n5wB8PnxlBUheYTDSm2HGPBpXhV9iqRBKQi8DeC6lIwq3X63ycxRgdAQp9rhs6lKX5t9wO3DfjV5RRRn1vEiH8RUzqEGFUo/EjjgZT1+l4eIuqP4UGgx8BYzx+ywRaCYFV8suWB2f2Oyst8/wJmtU2O7iRjQ2YskUQ3bZve5VnsPEndr9TSOXotfpURvjJyhUsLo/ybfe/adMJREQLr7DIrX1yT8xSKYM8T7ex+352pEQVybfRcHdOyURFoutPNWdh8vOUwX/7Sc0wcDGCpOBUctvOEormSzsMRui6+QkluLr+47/5bqsKA4xcsFId0oUtofQpGHwR/vXOPbAUGj5oYXrluNi4w5bpQkQDvChSNKpQ95kxk/UaYlVg4s3SwcIgxi8Uma7nptYCVMMHSgOEotc/AyuNZ+ESUEk8O767dQfVtPJrXoz7XeefuBtOhFV6c6GY6vEJVVgEPV7dT6l0L+Bq2hYDbWqn4ZwMEMyhTLAwtOVWS6+whXvn/3iQwF0RatvOM7RizBzXm2yTX826yp2+ZD9TvTRAO1gfyc3Q3//qeRxlanmdh0c+20gryctfWefrCZaIVEjWpOHK5RRDokuTGC3JUl0ZBVR51h6pZioa4ODe67uwlYdPg0vwYj9fdPgntyfew8z3bOFqWzZ/0vIoSTvfMHvLPU+bNRREKn9p+gpcne+hfmaPAncWJimb+7OZrGLaV5rctBKiqQmSPm4aRHJqO1TtS90lYDbonw1bBtiXquscX3Yh0k4SlaJjlWCRt+XwkQNCMkZMh4DUWWHIq+uNVhkaNxlCJxcnrRdRWldL+SAuurM0pPmz0zr3VYFHrqUb6Xh3CNm2kDkIq6Lq2IUlnaWyZL71xhpvFPhRVoHt0Xpzo4Re2H0/xU6/JLuTKQjp5pDY7c7890TEdr4L10HysHm+BJ+N6m4En103T8Xr6Xx1OWb79XS0oqkJVVj5XM5xb1W1IXoqWuZ9Q7kI+fCPklufctZLJyMVx3viL8wkS0I3v9HD8U4cTdkFRy+Tc7BAjgUWK3Tkcq2hKs/kq31aK7tUw4t9rqMBmucJ2iGZiLYT7ylRfRtWz9WjOK+Vf732Ep1+6wOj1KYpGVbx+Zy9l20oSVks7CiroTErKSCmpSJJPr+xWKetTaH2yjaPHdt62DV7FTHiFLw5cYiLoQ0HQYOaT/U9LEHHuUV5lLg//znGyizInR6v3VDoE63gwfn6XwHSDqVsYRhSXorIQDTK4Mk9LfhmqrlK4zj5qadSXIMIkY/j8OPmWwp7v6MSyYGy3wUKDo6wWWXGSPaquMBv8wRC8vx9w57i475OH4JN3t33QiPJnna8xH1d6ep0B2gvK+em2e99SAqfuYBWXvtiR+A4KJxS2vawjnihCydXZUVDJsYq1Pkdza8RCBra5KrPmEKky9UPrEfKFeel/nk4oXbqyXZz85aOJxGc4STVJV1QkGoZlkb+g8IC3kX2F6d/h/NAiFz57jbn+hcT4tuu5Ph79d6cSydoXJ3pSyJ3gJBofqtpGeZzks5H8/vrttrCF7zc6Fic4NzucKIKQNphCgi0RYZvBrgka3S7MmIURMggXSS5HJuj74+/ywR89Ts3OjWMf3x69zlIseQwviZgGWZoLRRGJ7yJP9/DxlsMpbc2dEGFWUeDO4pi/Cv3Ls8xXmQy/Vzg8FttRRZUKYJChUGS1sCBuX2FlWCfpU7X11b9FwkYm48RNskaoWQdTWixGQ7clw4BDEjz91xcSYxJXtouHf+c4xUnxm9r9VdTuryIWivHVf/VdbHPtuP7ZoBOTiNssgUPartpTQVaBl+nQMhEzVcnCkjaKEETOLuG/usSubp2JXSZT7RZCskbE9dnw+jJ89LaXkQLNpVLaUsRcf6qCXlaRl7zKW8/FN4O5/oUUIgw413zz6Z5En9C/PEfn0hReTedQaT2F7tsTkzPheEUTl+dHWTHW5ja12QXsLkq1xLZsm5tLUyzHwrTml2YsiIisRBGqyKgkvRKLcCOJCLOK83PDPF63647UYSbnl4iFYonYSDqFIJkQI9fWSYi+xBXW3I7tS0YkCcSs329BtyRSJrBXHLXmWH7yKawV+YRvrtD3kqP+0fGNLg5+dDc7HktXeuxdnl3bu1w7pi0kVsxi9MIET/3DJLUHqjj+/u08un87fiNKmSd3w/t2fnaYseASCgIhBJa0GYn5UHY6cYD5RliuVGk6p2HFHCXaQz+5lxf++k3ypyUFUzqRUkHZj9bTszzL7qKqu1K6XkXTfXXUH3GUYLIKvQlSrrfAw0O/eYyLX+hgYWiJnNJsqvdWMH5lCiNiAAI9S+PYTx1m4to0S2PLFNbmO+Sd0jVV1eo9FdTsq2T86lRiWUFtPtseWhuj7XpvO62nmlieWiGvIjdhqXy38McijAd9lHpz0trCbN1Ftr5xwUdNdgHl3lxmwqmKNodKM3iFrz/uXJCwL4y/xOabIx2J5RHL4BvD16jOKsgYA8sty6HtgSY6n+tLel0FukejsksyfHyN7G1bNtlDNt4VJ1ZVOqgwtcNKUxsTwPaKKlo7FCZJJdXZto0RsZFB2wnaeASG4hzj4twol+fH+HDTAQ6X1nOkrIHBu7QZ3MIWtrCFLWzhXzq2yDBb2MJbxLaHmgn7IvS8OIARNilqKOCenz6QUoH4zwn1h2voe2WI2Z61AXRBbT6tSVLHsZDB0NlRgvMhyttLqdpd/o4oRQwHFjZYvphx+XpIKZm6MUP34AQvFo1h6M7kQQE+/p7DFBoVTMcnPYZtJargAMKBKK8v9lPaK5htcZKuAoF3WVBzU6PsRAltDzTR9kDmagFdqEle5Q6EAJGl8si/Pc63FzqJRtcCM7ZpM2ot0BX3lL4dPrntPp4b76LLN0225uJYeTP7Cqqx4tUZ4EjMgzP5d4JVTuIaQBUCRRUYOXET43ikwNZhYRf4xlcoas6GlTmnWtZem9BJwyYSinIzMM5V11hazG8osMCrU32cqtqcFYVl2lz/ds+6eyW4+Uwv2x9rw1BsVCFuGzSwbJurC2MJuWkJuAwbj6ZT4XWSEGUVhSjGIrFEYb9z9loknpC0JfN+P18dvMIv7jx5y+PpXp386jxCi2HAuW+BBoHUwNTjAUaTDGUpMm3hqpe0+54CCveV8Obpm0yb/szxqCREXTYkH0MKFFOi2ILiaRWhCPLHYNtrOZz67Ue/7xUhS9EQf975WsKuYybsp3d5hn+1+yE0ReHrQ1e5vjTp2HMU1/CBhr141MzqSJ48N4qmYJupleSubBeaO/W6FoaWmO6aQ3OrjgqBlEwfhrntNqpLQWgK1xcnWYlF+OWd9294/kIImgpKaSooTfttRl0nRQwgoOiJKsrOSqKBGFW7ytn9vnY0l8r8ciBtH+D4Wlub8CfvfK6Pjm90YoRNdK+G9hEvmUQkit1rAaRs3cV763en/L6toIxe3wyxdYcUCLwuF7sfbtuwOr24oTAlMAaQu6CQ53ITlKnB892baMdydTduVSNqmUgpidomhm2hIHhhvJvH63elvbPn54YdIszqeSsCkaWR9WQl+++gqn7q5iyBC9NQaxFym5hSIgS4FY2jt7D12QwKa/M5+muH+VLXBWbyIyiqwv6iSvSC9IB22BfhO3/0Mp2PhByCoi2JmlEQ8Ox4J5/Ydl9i3SNlDVyaH02pTGzILWZfBgLFuc9cofelNTJp5zO9PPrv7ie/6s5UTZJx9Cf3k1uazdC5cVRNoflEPdsedCSxj5Y1cmFuJMUqqTq7gP0lt7bOa7qvnq7n+lOqFIUiaDp2+wDmOw1pSy59oSNFDUfakktf7KDuUDVSwF93vc5Y0Jf4/eLcCL+661RKIkX3aBz7ucOc/psLGGGTaJaj5KR7Utu6lVh40+eW7/LysXed4Hqsm96JQUyvSe2BKg59bK0i+Mdaj/DSZA83F6fwaDrVPjcrg6mEJdUStO+o3RQRZi7sZyq0wrdHOxIkNkva3AhOkXVKUDyiUjyisDLl5/KXb3DiF45k3M++D+1g/NoUgbkgFjaRUqfSWOCoRUUsO64WcPt2cT1UTQENzKiF7rfxjtjQ4PzmkCoElmGx/OIMdpN9V1aV/9LxxsxAggizim7fDN2+6dvaE9wK7hw3D/zGMc5/5gq+iRVc2TqnTrSx63jmdrukuQg7wzsQ9cdSxrqZcPHzHSmWr7FgjDf+8jwf+l/vQdUUthdUMOR35hiWYWGFTBQJ1c/bjAYHeL03zKlfdWTng4shXvnjN5kbWCAWiCEUgSvLhaIprEwH6H6+n31POtYWs+sSMauYiwQSZJjG3BLydE9KshTI2I5vYQvfT3QuOck3XVGJWWuEZqk685TsfgvLtDDCBlNHJHP7VxPVMf6i73V+b9v7U+znbCl58XwH18ZHGS4KItaRXyWSqGWiSQVVKJR4svnJ1iNUb0DwvRP0vTLE1a/cxC7VGH3MxtZk4loSuf2M08rUc1ScKURCBSXTqsJy7GeNZNnRZDGMxLRvncpG/HdFKNRuokjBMizOfeZqypgkFoxx8fMdPPrv0ucys30LmNF0YrptS9w5DqkJ6VgtNd3nkLV7l+fSlCQVISjz5jJzw4lbuCKCurMK+VdMZvcLYoWQt6hSdVNlMmf6jskwAFU/2kj300uo0xb50w4p/N5PHHxb+uj11jHrlz833smLE2txiFen+vi59uN3VQSS5/Ly67se4OzsELNhP/W5RRwpbUghWfhjEf6y6/WU8fLD1e28q8YpAAothTn9NxeY7pxDKILa/ZXc+8mDCaL/fCTApblRYrajdJIcgzNsy1FSYvP3zTVogOYU2aRaea1H8gudqkYLOLZHStJPjvzaLY4sESYsb1tbp6AbllvihJjkw0oIe1KZaVe/epPm4/W4c1JJGKtFLhoKMbn6/jsqT8KE3C4Lfzaci45x7alpHjq8m/2PrhVfrVei6lya4uvD1wiaUUDgUtT4PFSmKEvNN1pUdCu07Kvm0twoz0RvsvxRidvQyLF1JrQAMtqP1jdImTeXX9pxkoK7JF0BhOwYV+U0wdkYOworEqogZW0lvOf3H8QybVRNYeCNEcavTDmcJSERCPIqc2+psCcUwalfu5eJjmnm+hfIq8il/khNWnzbneN6W+zGvjfWxfcmOgGH/HiwpI4PNx3YNAFbCMFPtd3DU4OXGfIvoAmFg6V1PFqzI2U9M2Zx9Ws3GT47hlAEiiYcgiCCsXsl1k7SxpZXF8Y2bAuO/OQ+rEads0/fYLnKxtcMQpoUDys8ojQxkRslZMWoWPESO71GXsudU/AuQbiQNf6kdGKBihDsfLyN6ZuzjioiTkxbjwjyxmG50uk7DZz4rqaqCOGUbD431snBkloOlNQSNKK8MtWL34jSmFvM++s3ZzO4hS1sYQtb2MK/dGz1hlvYwluEEIL9H97Fnvdvx4yaaROyf25QNYWHf+cEIxfGWRhcoqA6j4Z7a9HjMo+hpTDP/udXEgoNN7/bS9Pxeo797KG3/Vw2qpAqcWdnXL4eb/zleYbPjdP1oIHfbSM1gfQ41Rx/1fUGH2rYR4HLy2I0mPAURkLMtFBtZ36rCIWdT8FymYXX1iic08gty2HXe29t/7GtoBxdURMSreC8K6XeHGr3VTH51BvIArAFiXm9NExmV5ZhE0lkr+biAw17+QB7iQZjnPvHK3zx0kUQgvrD1Rz5if2Ubytl8PQoQoJqOJEEWwXbI7CRKQnl5OCf6XaS3o3lxVydHyOSRBISNlhRpxJMChxVkgzz1Nem+jdFhjFsi87pCeZyI+gahAod3+GcRYWgNPh012mGIgsoQrCvuIYPNuzLOJFbjoV5auAy/StzCETi2mLSwjRsYrZFKBZjYa/AWpQoZpwYJHE8rJPjk7akf2mWmGXedtJY0V7G/MAikajB3D7nRthJc+hbeW4DiSoQqUCkEC6VrnD5yrPE5iJQBKx/Rpnut3AsvCwdVBOUKJQNKZQNrp2Ib95PzLK+72SYc7NDCSLMKoJmjItzI4wEFlOkpC/Pj2FLmbATWA9XlovWU430vDCQsnzHY61pwdKQz0kqK4qCJ9eNGTNZ3GEjFJwKu/g9HAksMhNeodx75wSBIi2LJVaQOM/Zir8qg54AJ3/zXprzHALNmzODvHK1D180hCXttEeoKgp7i26dBJvunuPSFxzinrQloaUw8ktBoh8QTpJCUdA9Gs2FpTTnpQelpJRMd84RXAzxSFNbIuGRDBEnnB0s2ZiEsPdDO5jtnScWWnumux5t4+T2Kj7Xf4HleDK/Lb+M99TuuuU1gWOTdn9lK9+LSxKvtpe6qnFmdpCobfCjzal9S3KCZjPLM+HG0z1ceeoGUkjMJ2KYhaRIFa8S6t4KXtaHWW4AD05Q9qY5yzeGr/GR5oMp6w2eGcHnMRxO4iqk086OBpZS1nWrGj/XfoxvDncwHlyiLqeI99fvSatk9E2s0PvSILZpY8ZMp7o7anL1653c/8v33PU1KarCrve2s+u96clrr6bzqztPcWFuhOnwCjXZBRwsqbttm1NYm8/JXzrK5S9fxz8bJKc0m/0f3klxQyFhM8awf5FCt/e2NmJvFdcXJ7g0N4oE9pfUsq+4hshKlOBCOkEltBQh7IvQz2IKEQYgYEZ5Y7qfJ+rXSCnLU34mr89Q3FiEN9/NsWNVfDp0Kc2ia7XN2AjzkQCvTvUxFw5Qm1PI/ZUt7HnfdvZsoKCmKyqP1uxIBIPNZpOXrkSZ6Y4TcQXsenxbSkV5JthS8pXBy1ycH8WybYJmFJeq4VF1ooaB4YblSom/zGR0H2x/UWPqxsyG+8suzuJ9/+UR+l8dpis0g6ZPY2Cl9HaGbdGSX8p8JMD52WECRpRtBeXsKapGCEFhXQEFtflp6jBNx+oprMvn/GeuYoZMCnolSy2CYKIoW5I7paCfCzF+eIq6g6nV2luA8YAv4/KJoO8tkWEAyreV8MR/foSIP4rudSzWNoKmq7izXRgRE9u0EYpAc2sompIm8Z92rtem0pZFVqIsDC5S1lbCiYoWRgNL3FiaxIyYCBPqr2h4gk5DPHZpEt/ECgXVeZz/p6ssjvgSCWhpS2IhA3euCyEE84NrBP26nCJuLKVW6ysIapIS3aqi8DPb7uWLAxeZCftREOwrqUkkQrewhR8UsuNEFk1RUS2BFbdpEBKqulTypsHWbcKFSUSYOFY8Bi+P9/B4wxoB+69feYmb0RnIBlM4SjOKIDHekTiqKB5FRxGCpWiIz/Se41/ve9ddqcEko+81xxZout1KmZclkExWSSyIwyaR5LZdG6yTBDUmQdooLhybmXhC09nV+rZKpO3meEUzXu32ioZLY8vEgunj09neeaQt06zosgvTLaVVXcEyHCKoy+tcpO7VqIpbx+a5PI59nNAwbedcNU2lwpuHtyD1OrwLUP+CRM/SE0lyveTOwstSSr4wcJGri+NwD1iGoFrk8ql9J8nO2ZwltmlbdC5NE7ZitBdUpKnzlbWVIBSR1m+Ut5eyEgvz8mRvyvKYbfHs+E0+tf3EHV3LKnJdHh65RXv+4mRPChEG4MWJbg6U1FLiyeH1vzjPbK9TlCZtyeilSRRd5cQvHOH09ADfGulwFE8tkyjSUVeKfy+t+WV3nPCutnPInVVYqF8dkybdp9X/FOuWr18vGasxlWQ1JLn+25Fggmqkbuhrl5TcEEwdTdp/fF+mJomFYiiqgupSsQybxdHlNKuro2UNvDnjzIFUw/kmJeCdherXJUvbYOboKpHH5J9iVynzVRAWJt8Zvc5kaJlidzaP1u6gJa+Ez/adTypakcRW42ICNJcGloWMjw+y9hZQ8UQtfzlw2rl8RRDUDOaskHMrJJhYTIWWeWGimw83pao6Z4JvcoUrT90gMBek/lA1u9+3nanwCn/Z9XoixvLKVC+PVLfzYHkbIxcm8I0vU9RQSPWeci59sSPle18l+hf+9gmufOUGkzdm8OS52fFoK03H1hRMhSKo2VdJzb63Nu67HV6c6Obrw1cTBluaonJhboTGvBIOJ9l6RyyD7411cWNpEreqcU9ZI8cqmhO/l3hy+MUdJwmZMTShZPwO3vy7SwyfHQPAiJiYEQPNraF7deyoRSxo4cnzpLSlG/VFYTPG3/e8ybB7kdB7wbSdeJxiw+JuQbBW5VPNThuyOOrjaWsKKSSBYoliQf1ljcFTNtFVmWoc8uXNyXFa2kp4z394kO4X+oksR/EWeuh7eYims4KBewx8cVVvYYNb14hYBrZ01NamgstU5xRyorKFE5UtWLZ9R0pRW9jCFrawhS38S8cWGWYLW3iboOrqLasU/zlB1RSa7q2j6d70pOiN7/SkWZUMvjFC2wONlDZvLF95J5gbWEjIdjbmFieqN8FJ7myGZDHVOcvwuXEkEn+ZE4oydUfqGeFUfXx37CZPNu7jmcGbBGTcGzs+GbF0p1pFjUGOdJE1aVPcWEj7J1toOFKT8ICVtiTkC+PJdac8/3yXl+MVLZydHcKynSpnl6LySM12XpnowVdoYa46VqweUwM1cucKO2c+fZHxK6uBf8nQm2PYluTYzx9m5MI4k9dnKO9VGN9tYbnXDigQSJEUaIijMOqm8aE6NJfKz7Yf46nBywTNGJaUSAG2x4ltZC3iXEMGgkZkE8nk0cAi/9BzloAZJfCogS1AizkTu8JxBStPwQgvxJ+X5NL8GKpQ0gIH06Fl/rTjFRaMUELaOwEBmqLw2mQfr430IKUT2JRK0gqApTrL7HivqCWiOLdG07E6up7vx1KNtYDp+gCrFKRLY6/dtFVFEVUoCEUQCxmEC25x0PX3W3EIMIoJriC0flvBo2vMNdtEsyThAkmwweLC5aepzyniI00HKL2NBdTbhUwWOOAkdbszeKp3LE7wpLUf9waBvMMf30teeQ5D58ZQFIXm4/W0JHlcr6KsrcQJlMUshOJUGEo96nhmr6syMu9AfWAlFmY6tMJyLEy0WcOIN4WrMT4hYMWM8Dfdp/n9A++hZ3mWrw9fc9axJaoUJFM2FKHwWM2O21YfjlwYT+wjEogiLYknDI1PwcIuQSzXJm/SYl9rLmJH6vXFQjFe+MM3WBh0iBWWB5SPSbJdLkzbjlutSQTwkaYDt5QGL6or4In/8giDb4wQ8Uep3luZCEL+7r53MRFcxqvpm5J6X8XD1e0UuLL4x9430RQVPf4PHHuJ99Xvxautscp2F1VxZWEs8beUElPauBSFqGVu+O6s3Q+D69/qAmClTGJkOdZwmODNcxrlszNDG3qEbwbLsTB9K3Npy68ujPPBhr3oSecY8UfxrKS3+1LKNKu2mGXy6e7TTMSVYeajQabDK/zSjpMpQb/FER+WaRELrFn12abFyLlx+OW7vqy18wgZDJ4ZxT/jp7SlmLpD1SiqQmw+gvbsMrkTK2iNKubjBq6C20816g5VU3uwKqF6JITg/Oww3xy5llAH2V5QwY+3HnlHCH2vTfXxndEbib+7fNNMBJcIGQYdHzDRApLKLpXCCafjcOe48OS5mZ3JrAKRrA7hG1/mmT94BTOyRir1zwZ5/8/t4euj1xJ9VrE7m3fX7tzwHBejQf7kxisJe5dB/zw3Fif5jd0P3vadX4Xm1njXvz3JTM8c/pkgZW3F5FXcvi/oWBjn4nyqxVvMMlERxLAS/ZGtOv96HjCpf+PWllzePA+7n2gnPOvhwuCCY0eZBFtKzkwP8vxEF7E4Se7i/Ci9pbP8SHwMcOpX7uH0py8y17eAogrqj9Zy8Ed3o7k1KraX8fx/e42VGT8tz1ksV9mEi8G7AIXTINywPOmHODctvBLBjFopMvH/HDAaWOT1qX6WjQiteaWcqGzZUF1tsyj35tLlm8a0Han/1crzjawjbSl5abKHN2cGCZkxdhZW8b763eS5Nk5krsrpW6bNta93MvD6MLZpU3+0hoMf2Y3u1ckpzaasrYT5gVQ1yJr9lYkx+EZw57gwo+lENneOM1BTFYWfbDvKTHiFb/zhS7hGTTQjtR0OzAXJq8hh4pozVlGSkiPStp3ksypS1LYerd3OkH8+hVD5YPW2tOrv6uwCfmvPw8xHAnhVnewMloBb2ML3G0fLGjg7O+TYlyokkrd1F1WqujUsr0DVVYJVSe113OpPqIKh4Np8fTqwzE1jNvG3YjqkfWk5yUIZ379b1VKq/5diYYZWFmjMKmJ+cAlPnpv8u7DKseKKKOG8eAFEcrOYkuDPgEwqLhtBSmwdpIjbKgFIgRCOWkw0WZlidedJ8zhdqHy0eXMFRdlFWSiqSFGGAcguyUojwgAU1hVQubOMqZtrz0Fza5RvL2RpZBlb2njzPRz72cMJq9ldhZV4g4LFWHTtEoXFgeoqSt+VxejFCYdkoCkIVYkry6wlWpOVhDeDbt/MmsWmEKgulWlCXA1Mciyn+dYbA4uRIH/V/QZLUWdCpgjBk437U5Lo2cVZ7PvQDq585WZiWU5ZNns+sJ2J0Ep67AAY9/uwrXdGPW5wZR7btDEiJtKyUTQFzaMxuDKPJyASRJhkjF6cYDkU4juj1xNvklfTCZsxopaJV3NR4c3jycb9d3w+TUfr0L9zHS3qxKIS6kk436gSw4kfbSZEta6GR1jx/a376EQYPCtg5qRvLG0bERVIV/wEpECNgBqWWDEbCwszZuHJc5Nblj5mK3Jn84s7TvLCWDc3bg7jnpMUXbTwzjpzvuH3rl2IAAxd8pWuS0yLAFY8XrMQDfKF/gucrGjBlDa6ohIxDZJfFWGDriuoeTp2XNHwsfvv4ezsUMptWLUNTg7fGLZFt29j0vgqprtnefo/vIQVc+YQUzdm6H11iJVPFaYVG7040YP/n8YJ9qxZSRY1FBBeiThEMOmoUgohWBpb5vn/8RrLE858JeyLcPpvLqJoSsIG9vsBfyzCd0auJ4gw4JDbYkKha2kq5Tv+bN/5hAUWwDdHOrCk5GRlqsJN1gbEwshKlJHza+qYVnzeYcYsNI9GyZDCTKuFbVqoLmeMqSA4uIHV0rNjnQwHFp04RFwlyPYIsjQXQlG4ujDG+xv24FF1iuoKyD1ZyusFE8SynGvN8Sm02UXctOYdRRgLFANsbC4MDfLgyXbu/RlnkjLRMc3lGwOMHDQJFUhcYef9ExqEtViiDVOE4G97zvDrux9MkAK3iDBb2MIWtrCFLaRiiwyzhS1s4Y6wMJTZomhhaOktk2Fsy+aNv7qQMlHZdrSK9id2MBCYp8CVxfGKZiqybq/isBo8FwhcIUEkV66fhiMEvPhmB341guoC07uOZSChKF4spKgK9YdraDnRkPh5omOa85+9SmA2iCtLZ8e729j9xFrF/Ieb9lPhzeP60gSuuPWGV3Px6eHT8eCKvXZCElQLuu057mfzVh8RfzQRqE/G6MUJ7vnpAzz4m8eY7pxjcdzHd/OGGDKXAImmqGhCIWjGnMPHC9V0ofKpBx9Ec6mMBRb5h96zzIRXsNYHimyIeUCLkEbqASiP3Vr2VUrJFwcuETCjmLblVO3YYOpOYnqxzkbxCjyrSTbpyDWfmRnkwao2iuLJdiNq8plnX2UxL4jtyqB0I8FYTVoIh6ywKuErRCLsglTBWg3GCrAVydeHr6WpUqxHcUMhJ3/xCM9++g2wbSyPSCLarJ2GIx9CkrpvekTJrWpx5RA7pSrxlli9PwIUCZ4sFzve28g3tV5iXonpjgechY3HEgz7F/j73jf57T2PbFp69q2gNb+MS+uSqACNecVpyVVw3ov19mLJEIqg/ZEW2h/ZWNoXHPWXoz+1n7N/fzlhq1S54GGxxWlnzKiJtCUFwktJJq+hDHhm7CavTvURs0xCpoFLVcl2udaSXvHbGbVNolGTv+t5Ey1eTWRGTMerW4KqQFU4m117Gjhe2UzxJogjq8FYM2Y6hL44vPNQ84qMF5pKenr7aTlSl9IW3/hOT4IIAyBiEjtg4ip0oyWRTFQhaMsvv+25ZBV4MyqDKEKhNufWChOZELEMcnU3HlVPs9uzpMSwTbysneeuoioerGrjtal+IpZBxDJwKRrfm+jmjZlBPrntvlueR2A+mJCNt5NHwRbENaSJ2WbGbW+HIf88z411MR5cImTGcCtaIgglpSRgRvm/Ln4bl6pxuLSe99TuonpPJZ3P9FEyqDLftJZg0nWNd62zfbo0P5ogwqySgIb8Czw/0c3jdWtKPAU1eXHyReq3FAsb+MaXKai5vcqKZVjc/G4voxcn0NwqbQ800XSsnog/yrN/8Ar+Gaeqtfv5Aap2l3PkJ/bxzP/zMrGgE5yd619k7Mok7/2/H0pIu98KQohEMsYXDfG1oaspFdVdvmlenerb0MLrbmHZNi+tqwoGeGasE6+qYxUpRDwGfSdsWl/TKZxU2POB7ai6uuF7VpO0vPPZvhQiDDjjk93T7fzuvkfp8c2QpbnYXlBxy4DlmenBBBFmFQvRIFcXxjha1ngnl0z5tlLKt91ahSYZXUkBe1VRUISCLW2nb82wfiwLSh+7vcIdQHtBRSIInvz164rKc+Odaeo5F+ZGOFnZQrk3j9zyHB77vVOElyOoupLynuWV59B6qoFrX+9C2pK8EZvcERJVuVbUYvDNUeoPV3PlqRuMXZlC2pKi+gKOf+rwW7IT+35h2L/AX3W9nhifDfsX6F2e5Zd2nHxL1qX5Li8hM5oSXG/JK2NXYfoznQj6+OrQFfqX59AVFVVR6FicYCES4Nd3P3jbY13+0nW6n+9P/N338hCR5Sinfs2xKLr3U4f4wjdfZcwdQDWg3Sjmno/fvpJ628MtXP7S9ZRlFTvK0p5ruTeP9sIKRgcmUpYrukJJc1GcTKsRCxmouppq1SgEnjw32x9tSdnfb+95hCsLYwSNKO0FFbcku94JcXQLW3inUZ6Vx8+1H+PPO1/DVpxEtmLA+D4LT1BwYvd2tj38/7P333Fyned5N/59Tpmys733xQK7WPRKAmAB2EmxiOpykWXJju3IdpLX9i/+OP1NfilO7MSJ7diRLTmyJVmWLVESJRZJ7CAK0TuwC2zvvU0/5XneP87M7Mw2LCjKoeO99KEwe+bM6ecp933d17WJ575+lGGRnmd7/2cEDEp8C+Pp/omUWknqbz01JFEoQtMapYMaE80udp6DNPUc0sFk1zTnv/QOybA3vq7dWcWRXz2EGVh76LLx7jpmh+YJTcJstWeP4iX5BShFaAiiK+R8NVJD7bW0o2LRWBJAKJQN5XaASTOOJdwFokxG7UJkdrbWyv1gcYCWI83cfKM7Z3l27GHOimNLN9O27P3lvfQdPc1QeIYSy8+jW7bTvK+e5zrOc212GM1wUMFBnnVLCegmiekETd900bfrhCsl/oig5obOVN8IW3/pbh79zcNce6mD8HiUDQfrsWI2Y+0T+Av8bH2shS2P3p7Ako3u8FLiuLd8Mkf1YSW8NHAtQ4RRSuEqxXd6L7GjpDaHSL/jmS007Ktl+OoYwaIADftq0U2dyqSX8E6PZlzbswHTxwTf+trL7P7wNlofvLMxFkDn3Dhvj3ZlVO0erGnNkNbzpUkyYpFmVriWi+tIQo4BqwyX+6PTOfEYXWiEDD9B3eQXt97/ruZgAAVV+ZRtK2MmMoFISq/wSQN08M1B/hAEJhTDR0SWUtLt3g2VRYTJXQ5QNABGEmbzF4hxKG/9aDUoM2d1pA9K27O24krKm0vJL1+ewFyTV8Sn2w4yxkaO/tEpIpEotrCJV2YVRKWsZVBwa24cIUAzdHx5pqckhGf9DR4BXGWFCYUEMwpOwEXP99qve6s2LltstFJcI7gG4vKJL57LEGHSmO6ZoW/cgkUcWitpMxiepYTcttSKWguqSKm5VrDInyHCZOPGK51/q2SY9rmxZecRjnRz1LLG4+EcIgx4FvOv3rxGy3QBNdsqbzvuteN2riVvOg6Yuj+hGY1NJwzmPhAgikOpP4+nG3dQs4Iy6fVZrxBRpf6X/pyQDkHNh6sUrlREZZLe8BSXd0dQMQPd9pQOnUadqUgcY5mQg5zPXRhqKeDmw06GrOUEQBmgTG8+ljoj/LpJxLE4OdbDBxq2sY51rGMd61jHOpZinQyzjnX8X4rIZBQ77lBcX/gjBcUXo7CmgMmsxGr28h8V/WeHcogwAEOnhjm8r56HDt53R9sqrM73pMzjNuVnJQMPQfZVa7UbAAEAAElEQVTE3dR0pCWJRWwCusApUegJkD6F0gRCQuOrCl8Y8IEv5GPT4YXqhNhsnLf+8CRuysvVitlcfO4aBZWhzCRSFxoP1LbyQG1r5nd/030OACOgY7kLSR7NhaDPR3906bVdDtKV9J0ZYujSCFbcxvDpOfc5TSoQQlCzvZKa7ZV09MSYGF9Q9ZGOi5H01G+kDqF5wUe27KE0L8SftR/n0tQQlnSWTlJVKmghoLpDZ3SnxNEWFGZ8EfC/Osf1yE22Pbm8is9UMspkSiI4newSKa9qI2igmV41XdyxsbOOQQC/e/lVfrrlbnaW1nHl+RuMa1Ev2JLDZcquwPPIAkrPSg1nJsCkApIKkYpy+A0Dv25yYXKQpxt3kn+bit35FsGNz+jYrsqNDYmFf4UtEI5CZorlFw7W0DR0BHHH9ibTBoikRw7KJcSsXMromiAsyOty+eGeAWJKLVQvpc417tooYDIRpT8ynfGWXglj8XnOTvSTdB12lNayuahy1fWXw56yOm7MjHBpeiHRtL+8kbvKmzg70Z+j+gSwpbiKgPGjVbWnsem+Jmp3VDFybRx/vo+itmK+fPUkNyZHQSkCYUHtMYvXjx3jiX/54KrVfycvd/DD6WsgwNYlaZlkw/Cha5r3DKusZ0rArZkxavKLka7Eji8ksTUJwYtJNgfzKGteWyJs0/1NdLzalZFhjtRArAZ881DY7W0T4VXTjV6fyCHDjFzPDR5pUlDRqTO9V2LoGkopLOlS5g9xbLSTe6qayTdXV3UAiNpJ5u0ElYGCd111dHy0i+8PXCMpXRKujSa8QE4ajfkly6oLfKBhO/dXbeK/XXkN3dYybV/MsXiu5wK/tkoStqAyHzNoYMcdCkcFui08JTBdyyQ+dpbeuXXKRDzCF24cz7RnMkV+gQW5fF1oSCDhOrw92oUCnt26i+1PbYbv36RwXDBbJymrKOKj9x2iviA3sD0UnctsO+ZYmQDY9weuYwiNJ1KBr9LGYoyAgRXJUugSXlI3PBFdExnmxBfP0ntqoT+e6JzGitsk5pMZIkwaw1fGOPtXlzNEmDSikzG6TwzccXKkfW5sGWsBuDEz+p6TYeKuTWyRkpkjXRzponTDs2XRBY7lErnPzyc2HKJmeyXhsQjW6Rlq8oIMB6Pe8wNUBPK5v2ohOT4/urx6zPxomPo9NRyo3LCm45xKRpdfnogtu/y9RL6Zm53JM0zvfc2qcE9bJ4L3vAe3rf6MuVJyeqKXjtkxSv0hIraVCSbrQqABUSeZ0x5IJUm6Dl+8cZwtxdVU5RUymYhQ5g9xV1b1aBpbHmul69owPdYUcl4jv0ciUkRYzdSZG5rn+//hTS8plcJ03yxv/uFJnv1Pj7+nY+cfB94cvrmEqNwXmaZrfoKWd9FfA8wl47zcf5Wg7sNRLlIpdKFR7A8uaecvTg3yV51nCNsJpFJY0iGgm/h0g6HYHAORmVWTc9KVdKasTLIxcGGY2GycvOIgzw9fpn1jJGOTdD4wxzZrnL2snqzZ/uRmdEOj4/Vu7LhN4/469nxsITGglKJzfoKR2Dylz9Qy2TVFbCalZCdg/0/syqjXtD60kWsvdgDgy/fhJl0CRQHaHtlE2yMbCZXmEmpDpo/7V0nihq0El6YHsaXLjpI6KoLrhJh1vD8glaRzfoKk9FT2TFNH6l5BgXy2hLY9bZye6MH3eDlFowkiVhIBGH4dn2lypKYVpRQXvnGVqyduwhNecjCtHqM5UNgLW95ZID8Pb3eR9oICR7EZpOeLN3Biboa83nOyn9hMnMd+60jmvbwddjzdxvxohC6tJ6UImqUGIQWJtPPHMs28pmUnGMka3C/+wcrkfanDsBZF2MpL7C9W9Uz9tswO3NEY+sCn91DcUOgRlX0GrQ82U7+nhrhj8bXOs3TMeeTV2rwiPrJhN1/tPMNcWRzKNCLYPG/epKZzjGvhEdA90vnZyX5s6fKp1gNMdk1jxqD5TG6YeEJ6aiVVbeVUtS21Y323KPYtX5BQuopKZTY65ydwpEvCdZAqpcQrHa5197OrvjFDsgYoqi1cQogs9udxb/VGjo12oaTEiloIF+ovG8RnE7zz5+fJrwwtseJZDTdmRvnzmyczT8dAdIae8GTGdqmh08fVkMpxDgpNCNTFCPlP1VPZVs54R646TONddZTlLe0rhBBU5hW8ayJMGnc1NdN5bcJ7V5VCdwEHNn1H4Z+DyZ0sxEyy4hvLuYB5tV4C3VI4y95GQWg4pdq8Q+S8F8KBSJNY2HaafOJC0ZyB7pMo5dl9lW26/TlXtVXw0d97iolbk8wMzHH1ci99YmLh8FP7MCxwfSBtFzvuxf0ACs0AQd1kPmUDnD5f3YH8GY2KWZONz2ykraiKrSXVgGe1eiw1xwOPRC6luyhEJHis/vbzmZXmEIFZsBfVr0hHEpzPZR85CSc1t03H6Tybx80Pb6Tr7b7MekopXEcSGY9m4od/GwjoBqamk3SdHHUYIQSHKptJuDZnJ/q5MTuC5TqYmhfvtKIWruViu0le+9IxqrdV8PCv37eqSntBVT5FdQUZEpDhN7AcK6OWA9AYy+fZA0/gCIlfN1a9DkHdxxyJHDt58GzWdNdhS3E1l6YHeaHvCknXIeokMTSdYN5C/zfvtzBnBFZA5Twf28gtWLg6P4JR6EPGLC8WJLxYdlFeiBk75hU0anrGLm1qkQ3bOtaxjnWsYx3rWMDfCTKMUorLly9z6tQpBgcHmZmZIRaLUVlZyW//9m//nz68dazjfQUrZnPsT05n1EIKKkMc/uWDlDX/aJPkNLY/1cbA+WHs+MLAv3pbxR0FCVbC4qRt9vI7rVKo21ODlBJpuxR3gu2H4cOejLNPM/AbBsmETVm/hi8quHlYoknQEoCAsl6d8gEorC+goqWUXc9uJa94ISnbf2YoQ4TJRs/JgVWPNe07q+s6QqYShwpMv4Fu6FSssUr06B+fYuDcMOBN3BNJh0C+PyOTXLujCn8oN4G1u6yOd8YXEg9WzEZIqLylI1xF8ajO+IU+vvPzYTrmxlMqJVlQCx/SccH8sMa/bnqELz3/OtFCSd40VHYb+GOCay/fZOsTrctKNwd1zx/es2fJClAKgekzQAg02yWpcieYQohM1de24hoGLgyTt1EQLV0g42TPJjUnq2JvcSAyJdXtyWcLjFQ+zEzJPUsUccdalQzjSsnXOs+SVA5Cy40JpSt+pCMh5npWTG5KBtXxjiVQ4KOpqJzrMyMLdSUK8EFoWBGtIktNJguLg0+6V2EVKXCZdWILv1l06a1UgFutEsAFr0Lqf3ecwJGehc7JsW4er9+6qgf6ctCExqdaD3AkMsNobI66UDG1oWIAfnLTXXyt8wx9Ea+6dFNhOR9rvn3F950gWBRg470L8ra7zoYwL5tIQxGcEwgEk3MzDF8ZW9ET+9K3r/Pq0BWclpScboDM9XWkxKfpOK7MvScK3JhLUXGAMXsOlUXG0h0oHtYYvDjC1sdbF+9uWZQ2FnPkVw9y7E/PcGNrlJm2he/807Dxuwoj4flfd5/sp3xTaaZNzisJMt07m7O9xos62+5p4pZvhpHYHKCI2AleGWrn9EQf/2T7gxT4lifESKX4bt9lTo334CpFgenno8172L6MasBqGIjM8Hzf5YXz0A1ijo0mXEzNawt/YuP+FX+flK6nbLUoUDUcmyPmWCvKJJsBgz0f3c6Zv7yE7gpajhl03+sgyryGYltJDY+9C8LF6YneRSoWCw+EUt4b51t0rGfGe3mmcSf7PrmTzQ9tZLpvlqK6whUtAWpSqmhWKuifhi4Erw93sK+8MZNY3XCwwbPMczyrE92nY/gNyjetbskFHpm2dxExFeDaSzcpaVie5DA7OLfs8ujUUhJHfC5BfDZBUV0hurGQBJJKMW/F8Yvlpyd57xFRLhshw0dFIJ+JrMChwiN0CLw+SmpgBE38ZXnUbK+k/9wQb/+v00hHUiYURgOUPV3FpuYa9pY15FhWVW4uZ6JzqaJeReudKek1F5RxbWZkyfKNhe+NPeVqOFTZnLLO8J45TWiU+vP5R9se4AvtxxiIzqTUqTwEDJO6VDu/Er7SeZrrqfNxpJvTJ7lKEXNt8nRfRq1JKkk0RZiZTkZ5Y+SmZ/Fg+NAQHBvr4le3PUBhVrt1PT7GqUcixC2BKzX0WWj7HuTFjUzwe3ZonkChP6cdmR+JMN03S9mG92bc/OPCdHJ5ItRKy2+HVwZv8OpQO3NWHCEEAd3Er3udXe8i4qpUipf6r5Iq5s4gKReSFUl3mXLX7G24Csdyl36hvATO6PQsZ8f6M1W7ylVYMYvvd15lb/nt5wQrqci5UvLnN0/SkVVlvO2Xqrl/uhY7alG/u4aCqoWx+J6PbkM3NTrf6sGxJE0P1bHvEztzkqxzVhxXSkoDq9ts9Yan+LP240QdC0dKvtN7iQ827uLRNSTF1rGOHyeUUnz55ikuTQ9hpd5dSzqETK99jOo2f3D1dWZSSWFlKIoCeVQECij153GkppWG/BI6j/Zy7aWbmEBVu2BsiwKlUEKgW1B/dSHBWHtVx/HD/C5vHNAQKuZwooErsUtIV+aoZ4xeH+eV/3yUp/7dIznjhpWgmzotP9vG35zrYRFnEKGDu0qud6l9qlpo6JbY3q4A4fEGVHoKufhnylPdsfqjXNs9fNsxdMK1mU3GKAvk0/bwJtoeziXcfaf3UoYIA95Y+IvtJ0guUjqcSkQZi4Ux9dzJ5ZXpYeKOndP2ZSO/8sdD2ttX3sjRkVuZ5wq8+fM9a7QqzTf8jMXmF5QZpCLhOrz+zbPc6rvCjme25CjnLIdnm3axsaCco+euM90+RWWXRnB+4RnrPtZ3R3GuN0duLrndXfOT9IWnaSooJX9SY+tJk9E2FysPCkcFNe06iUe9QMThXz7AiS+cZeTaOEITNOyv5eDP7sGX52NLcVWOvY4AHq5t40dFdZ9J+a2UTYwmEK6i5oSnnDu+F0YPCjTHUzGWRso2aTkIvEIehxyyz2JYBTC1M7VCRoVJLVHXNRKeCgtAUrkEEJhBA03TKN94+/kMePbv1Vsrqd5aydbHW4lcPMbl2WGU9MYgmoL6Szp9d3vjEdd2M2PPveUNtBZV8tVbp3GQ3unZXhwnVqzYOVTOYxt2L7oEgrJAPn2pcVOBGUChSLoOjvK2cX/1JnaV1d/22ENlebmFDak93K3V8ZYYziFEb3ZLCUTmc9Z0HZmynzUytkC6z6B+Tw297wzg2h7h0IvrKuYdyff/45s88hv3rUnV80fF1uJqSvx5yNT1caVEE4KnG3ZQGSzgj669xWh8HpQiKR0s6RJQng02ePEUgNHrE3Qe7aXtkdWLL+7/pQO88fsniE3H0U2dwpoCDL9BMmJRs72Su356N4apYywbfFu0repN/E3XOWx30ThWgKNcaoKFfLvnAkJoGZGxtAVU2t7WcDXqv6sYPqiI1Av8MUFTT4DHfnFPZnO94Sl+2HXdK7AxwdAEfr+JbuoU+YPEZW4hCnDbgrd1rGMd61jHOv4+431Nhpmenub3f//3+cIXvsDY2FJPzU2bNi0hwySTSe6//36i0SiGYXD06FGKi4v/lo54Hev4P4/z37iSY5sTHo/y5h+e5CO/+4H3xPe4uK6Qp//tI3S83kV0MkbV1kpaH9jwnlQQ5JUsVQAAllRdrgUzfbMIQzB6RGeizZtcmxHPKkeaDrahaI0VUXgrgkCw9TWD0WYHx1QUjwgax4Mc+o19NB9aPeAeK5LMVUvClQohwdDiq65/V3kTp8d70YRHyrGkV7Fh6joagsfXQDYY65jIEGHAq2yw4zZ2wsaX56NqSwX3/IOlSeRNhRU807iDV4baSdg2wlIIDcbavEnc6DbJdMc0kYkoVsJCIhcRMdIl4AJNgm5D9WSAoN9H/UWdxayNxHwSx3KXlbUOmX72lTVwdrIfU9M9BRqlMHUdBSQdG0stTZIo5QVsw3aSyUQEM2hSd1VnqlHmyFQL1wueGFJgm8pTWUkHMwWgBLrrERN0W+CfhlhJbuiqzB+6rYT9ZCJC2E4s+13ctRcKmwrAiAs0R2UCSIYSNBSWknCtzNXN+X05BMchWUKWokx6zaz3LRUISRaC7QOk8AJQ2RVbqdWlUhSbAZryV58kvzxwjahjYbluZn8vD1zj3qqNhG6jlLMcGvJLMlVrSikGLowwdGmEewvKePbe7RSU5VG8xirAHwXhiSiBaPrieHAslzN/eYnuE320PrCRoroC7LhDUU0B8dkEV1/sQM9SmtUccNMiIgJM3UCz7bTpGUJ57wZS0TxTQESLcSswnbkfQnk2YC35d3YdfVsLGPmVfMbnUolOqdBtSJTC1E6oOuMF4OaG5nntvx7jkX96PzXbKtn6RCtDl0Yz0sAKRUldEU/v38+56QG+0X0+Zz9zVpyT4z0rtkXvjPdwYmxBoj1sJ/nLW2f4Z3ueyElE3w5XphfZUgiNfNPP5qJKnqjfRn2oeNV+Jc/wYQhtiY1KQDfxa6sPb7c81kJZcwl9ZwbRTZ2fvruBSKFLvuG/bTJzJUwnoiQcG4lCpqTa05VahqYTdyxsJXNUrW0lUwF8QX5FiPyK1fe9v6KRd8Z76JpfqBjVhIapee1mT3gyQ4bZ98mdTHZNE59daJ/2fmIHwcLb36P4XGLZguf4bILmQw3LWvNVtJYTmVhqfZZtxyNdyakvX6Dr7T6UVASK/Bz67D4a9tZyfWaE5/suM5OMEdANdKHhZt1bpRQ1PSZv/OAERTUFbHmsZcUxw51ACMGHN+zmz2+ezJA9fJqOJoQXeM1qA02hI13Jmb+8lLFKEUpQ3A/Bb8xx4HfuWUL+3PaBzQxcGGZ+ZIFs0/LAhju2lTxU2cyV6eEMeRBgd2kdbWuwNvtRUREs4Je2HOaVoRuMxuapDxXzRMM2KvMK+Pkt9/I/rryeUdfxaQYP1LSu2ncORGYyRBilFAl3aSBXAAnXwlE6Pt3AThFmfJrhVRNLFwVEbE+ZIO7avDl8k2c37AIg6Tp8s/s8YTuBiwQN7GJo/zDs+Wu1ILq2QhPzXoyXf9zYWFjuJQmyIIDmgjuv2r80NcgrQ+0Zkp1Sirhjo5sCTWhLqvfjjsVsKnlpCh0rRVz2iH+KQjNAcyoYL5Xi7ZFOzk32I5ViT3k9D9VsxvDp1O6sYvhy7hy/uK6QwuoCzr1xkSVZbAWT0eUrpdeK81MDOUQYgOvzo+zd0sDuMo+gGpmMcuV77Ux2TVNYXcCOZ9rY/eGlkvNRO8nXOs9wa96rNG8IlfCplrtX7Ee+13eFeTuRQxR6rvcCNXmFbC+9M0LpOtbxXuLm3DjXZ0dTRRvegFWmEpA+TWfWihG1LW/+mqrmT7gOR2pa2J2V1O09NZD5vOGSj+IRl5lqh3xfkJJ2iT2cwDIlZsBE0wRbO/P5wGcfRvkEBb4AozfGuQKenWX2+58iLw6cG1pTgUzYSvD5C2+l5pK5BRMyIw+a3nbWZwU4eAl9QQ75RbNT4hgGa7OyzWw8pXoWI2XVBNL0/pttUHz+zFs81riVZ7fsWXYLrw918PpwB5Z0CeomTzVuz7FHlEpxedGYGmAmGcNwBbqmoZsL6oeukph4DH3HclJJcsHglRFa9zZSt7s6Z5ynGRo7P/ijEy6WQ9Aw+dXtD/Dm8C36o9NUBQt5sKaVUv/axuL1+cV0zi+050p5hS7hKoVz0+Xic9cobSqmblf1qtvZUVqLjM1y6cJSYreUywyIV8FIbI54akyUngMAzFoxmiilblc1ve8M0HIi9yGq21WNUoq84iCP/uZhkpEkQhM5pIRPtx7k7ZFObsyOEjL93F+18V0rwWUjNhXLxE40PPWkkXtg9B6P6ez4AeXFulR2kdFyECBM0FyBu6x0jGJyp1iwQsreWMrKOv3aKA2E9NSb84YUrgTXltTvraHp7tuTSZbDM1t2Uz1eQt/0JL3XhzFjHvm98ZzO0C6J6/PsxI/UtDCViHJ0pNNTyfNYDkjDIwQFIrD9Q7nvRdhK8Cc3jpFwbUKmL+Ns/Itb7mMkNk/U8ewT10pWuOfn9vPyv38DlaUmHSwJ8PDDe9hNG++M9xCzLbaWVLPFrOT733+d6NRCLNLw6+g+HYFAMzQSjkXfPotLo0fxfcKk5Lqk5C0HgUIzNMyAwWTnNFdfvMm+T+xY7pB+ZEhXMtk1jW7qlDWX8Etb7+eFvivcmp+g2BfkwdrN3F3RxImx7oUxrhAEdTOlGO3FB4NzgsbzC/P90faJ25JhSpuK+cjvfoDxm1NohqBiU9myRXtrwYHKDZw538HlvOTCwtT9dqTijZGbJFw7o5hoaDqOdLFdF5GUSFtSftbFNw0bXvJ+LCpNdv/WTmZ9FnkEmU5G+ZOrbxOOxjxipQDbUBC3COoBHqhp4cr0MNdmRnBsB0cpCv0BdKHhSDfT9qxjHetYxzrWsY4FvG/JMC+++CK/8Au/wPj4+Io+m8vB7/dz99138/nPfx4hBF//+tf53Oc+92M80nWs4/2FxTZDALHpOJPd01S2vjeytgVV+dz1U7tvv+IdovXBZjpe7cqRrPfn+2h5YEPmb8t1MFKJqtVgJxwmWxQT29JqG55MsXIVW173kTcn2PVADbf8PThJl8JJncJJnUCRn3t/fj+VWyqYV0meP3GW6YsTFA8KmvbUsfODWzCDJr62fK48aREuU0gzRbyw4GzeDE1j3dy7QkVTU0EpP7npLr4/cI0ZoETPoywQojpYyKGqZhrzb1/lMtPvBWqUUlhRK5OUk1Kx95M72PHUygGrIzWtHKxsZiI8z5998xUm6xcIJwoYa5OImbhHpjABfZlSNsA/J2j8oSI8Ps87f3EBf75JMpKb0CrbWLKqv/vHmvdS5A9yYXKAoG3iKomh6RhC4CqJtagwT2Tt3xAahb4gmx/aSPcLqYplhwwfR9jQ9m3B4z9zkBfMW3THplCpqiPNBiRUJAME2m0qu3RcAzoetLELvSRYyPDxk5vuui3Jq9AXRBMC9zbdlEoFPjVHIOKeistH87czXyh4ZfDGMpYg3nNlFXmSwatseeGT7lVq6bbC1cXS1VKV+6WB/Nu+P32R6UxlaHoDMcfi0tQg967Bw30xHOlyfKyb9tlRot1hAm9GKZjwgoDGq108/ltHoPnHT4apbC1jMkulwYrbuEmH+dEw4fEIN9/owfDpXrVNbQFtj2xCOpKKbp3RNhele2QYpYMyBD5NxxAaW3oL6C4P4xoLcfO8WYE/T6Nfnye7fF5p0L/X4eMbG5ce4AqwXIcvtB9jLB5eCNprAtcPmgHRWs96zfR7FbdKKq6/fJOabZVUb6ng0d+8n2+fOkNXRQQnAFWxOPzp28zs0Lz3fNHjMLYowZqNS1NL+xhHSa7PjHCoqnmZXywPc4UATYk/b01y30HD5FBlM8fGunKWH67etCbJ+YqWMipaFoKRa6svXB5j8XmuzowssZVLfzY1naTQWBw53l5Sk1ELWwsCusmvbnuA37/6BoPRGfQUESbdTmXLyhdW5fOh336cvrNDJMNJ6vfULJGHXwmljcUECv0k5pM5y2u2V7LlsRa6T/TnkGzKW0o5+Ok9xKbjjLVPZJZvONSQk4Bof7WLzrd6M38n5pK8/cenePg/P8RXuk5lKhzTcvdN+aVMJ2MUmUHy34gxdcFLsg1eGKH7RD9P/duHcxTb7gQ985O8PHCNwegsNXmFfKJ5HxHHQinFrrK6lEpGB5kklhAMRGdoHxkhNr2U9BqZjBGfTyw5nkChn6f/3aP0nxkkMhmlsq2C6i0VS35/O/h0g1/edpj22THG42Ea8kvYVHjn21kOdtKh+1gfMwNzlDQWsfG+Jkx/bt/dVFDKL2zJtatMODYzyRif2XyQ0ViYiJ1kS3EVm4tXJ+hkq/C4ylMfW4x0k+lIicClxB8kbHsS456cei4c6XJmoi9DhhmITBOxkxlCVXoXyQLFRLVNeZ/El+ejYlMpkYlcJZXSpuIVFZDeT3ikto1bc+M51/PhurZ3ZbtzYWowRUzKvraKmOORemVAcWtunNZU0i1o+CjyBZmz4vh1A4nCkZ4KVZEZ5GdaD2Ta4ZcHrvHWyK3MVn84eIN5K8FHm/dw6DP7ePMPTjLdNwtAfmWI+z93AKkk/c4sjt8bP6UV9YSC0vCdVyuHxyJceO4qY+2TdN/j4Da4S+T0u8OT7C6rx4rZ/OA/vZV5z2cH5xm+MspT/+/DmTbUdSRjN8b5bqydbjWdSTIPRGf4y84z/OMdDy45BldJ+iPTJN3ccbdSii91vMO/2PfEmhPA61jHe43BlE2vJgQB3UhZPyhcKUkqjxij8Ii+ccdCGD4MTWckNpdDhtEWqbYUj+uE+iRCtzB8BiJk4iQcXMdl0/0b2Pux7eTnL/SbVW0VFNUVMN6Rq0Zl+Lz3NTK5NuWrP795klE7AlpWb5EZ6y4m2S0aBGd3fwpwITSqPAuXDbdLnK7wvQA3fZpZu5c+cBOKNwY6eLRl2xJVwxszo3x/8Hrm77hr862eizSESjJKmwJSBOKFtsWO20jHxZHgShC6hj/fh9AEtXlFTCQjJJIW0lVoEoqGBUc/f5xTBWcpritkw6EGkpEkwcIAbY9uWrMKx7tBoS+Y6bvvFHV5xeQZPizp4roSzVHePC3rMew7PZgZiyrl9WtBw8hYiqTRdKCBy8+3Z4oH0rhdYVQ23hjuYCYZyyTsbemmkuE6E/EwZyb62HF3Dc1XG+k5kSKQC2g5soFbb3bz+u8dx/DptD7YzJ6PbV9CzDU1nYfr2ni47r0lJ/k25TNuuCjluRzJABliipHA+6CByrLFXvZZz6rXMUImtmsvet1ShRmrZSGybJiUAFyQAi5/zrsW+RNQlx9ak0IUeEUvVsxCKzD4q86z3JgdRSmFLV3casCSzNZLgrOCbd8SVB+p49FHDnFmvI8XB65656LpWDjg805BAJQYHDMH+SQLY97zUwNZBG+BJjyl4UvTQ3ysee+ajjcb9XtqeOY/PMqZr1wkOh2nqq2cg5/Zixk0GZ8eJ+E4FPgC1IWKyQsGePLfPEzHa13MDs1T2lSMbuqc/+srACQjFj2PKcJNAiEUjrRI7NSxpU7jeT3TdiupuPl6N5GJCKWNxbQ+tHGJ2vS7xVTvDG/+wcnM+KqkoYiHfu1ePtt2z5J1PeXaBRiaTr6pUZkIkvdahIIJkaMunV+2tjiSpmtUb31v5k9TI3OIjVmPeOpwdERmbp5wHXQl0OLSI0IKlyQCTSmsPIHjV5hJGN8NYwdcOkYvok/pbCupoSZYSDyRBBf0pML14allCtg6X8L+iiaawgWEfzjMrX0Org9m41G+2nGKb/de5NG6LRyubnnPLMjXsY51rGMd6/i/Ae9LMsw3vvENPvWpT+EukpxramqiqqqK06dPr/r7z3zmM3z+858H4Nvf/vY6GWYdf6/gBXeXVtmu5qH6fkFecZAn/uWDXH2hnen+OUobi9jxzBbyioP0haf5Tt8lhqKzFJoBHqrdzH2rJOWr2sqZWY6PYgiShYqicY2p3pnU/jqYHw1T0VLGjmfaCJXmcXy0i2/duoAVtWAj+CsF069FmOqdofmeRr448g7RIo+wAF7QRQY9K4rXhto5VLlhSZAFvGplv27wyY37qc0rImje+eSyOJWkcRJOhggDnsf5pW9dZ9P9TatW//t1g/riUtwmH8qJk1OCgyBvRBGtTcnSLrYWUoAEO6DQLYVm6Ix3TOLP96EZAul400FfyOTAz+xZ9TxmrBiVZgE+S2PKtUF4QT7LdQgaZkYZID3BVICBlpGXDRomLYc3EHOv44jZheNU3rGX7i9n230bufz9YfqZ8oIqSnjFfAoODlZQVplPZ0cvMi55+HolgY9XcckeZSQ2z+9ffZ0NBWU82bA9kwBajKBhsqu0jnOTXkArW4wloBkgyCTulJYqMgQ0v8aBQ238x+s/YEUmjQA3D4yYp3Sj0q9wtl/34vtjeAEjIwmukRtsEkC+6ac7PMlYbJ6qvJUT4z5NZ6m5iViVJLEavnLrtBd8ciUJIwmPwOa3TIpHNJyEw6XvXOfhX7/v9hv6EbH5oY10vNpFNBWEka7E8BtouoYVsz2vcKnQTZ254TBXvtfu2VlFBJtf0xnc5RIvheIZg6cf2E9RMI+m/FIG5gc5+t3zjGyRWCFF/oSgrsfHlV+axg67iKwiVNeEgN8kdgeCDtdnRwnbSdxs6fa0urQBflfHF8wNdMRmvHOMzcb57slzXK+d847BgiEzwlRllLpXdKyH1BIp5PpVLE6MFcgbayGgZGN/eSNvDN/EUZKk63gqWQrGYmHmrDhFvtuTHJ5p2klpIMTFqQEEgrsqGnOqZWeSMaYSEWryigmt0tbOWwlAUbiGfS6Ht4ZvrWg95kiJUgqfpiNTpDJT02ktrOAji6S114KAYfITm/bzJzfezpHHbsovXUKOMINeG3mn0E2de35+P0f/+FRGijqvJECwyM/L//4NNF2jsq2cvOIAFa3ltBxuwvAbPPZbhxm5OsbscJjy5hIqN+cScPvOLCVSubbk7WvtuP7c66cJjSJfkH+84yE63+7l5IVzOd/HZxN0vNrF3o+vrXJxqneGkatjBIoCFOwu4Qvtx4k5nu1Ob3ia4dg5/smOh6jJS/WvSlJg+nGURENknu8uewpfyMSK2tgBRdTvYI5JDAcuPneN7R/byigR8k1/xirI8OlsvK9pTce5GjShsa2khm0ly1u6vRvYCYcf/Mc3mRmYI5mnmBlysUYvUbyzlBk7Tsy2KJjRqDsjqBAhtj7eSu2OSo5eusFrshtX96TkG/NL+Pm2e1e0J8tGY35Jpq/M4gkuC4nCkg5zlke+Wlwgkd39he0E5yb6eX24g6lENKO+obJ2IpQgWg0lnZK63dXc+wt3cenb1+l6uxfXkdTuqOLQZ99bu74fFwp8AX5j5yNcnRlmzkrQWlSReX7vFLoQJFzbI7SQ1V8pScjwMZ4I88X24/xc2z1sKa5Oydhv56+6zoIQ5Bk+pJI8WLOZJxu3Z0h+jnR5J0tJLI0zE7082bCdUFkeT/+7RxjumSBsJWhpqUPXNb7Xd5kroSlEzCMSu4Y39AmEBY8Wb76jc3OSDj/8L0czyRc55GCVuvhCvoW5kVKIcYdrN24Sm4kvIbw5SZf217o4+Om9zPTP8vp/P05kLs7VT1pIHXRN4EvJ1g9EZ5hORJeow+gp9bO0mmD2kxx2EvzJ9bf5p7sfW5Eouo51/DhRFVyYE6Sr2F0lqc0rZDwewZIObpY6gZ2qOi8nj2svdZCMWNTtrmHT4aYl6nGuI/EHvL5BN3T0fO8Z3/ux7UtseYQmeOT/dz8//O23mOicRghPAdVIETSrtty+sKdzboKrMyMr8lKWIp3gF4t+k8p66xCtW2VjafEZM8v2JWfbaWnIFfZuKBzbZWhymtbqXAWTy9NLx00KuDQ9tECGEYK7UkoK4M1tnIRD4aSXLI6US5Tr2aFsraple0k1X+s8i4MC3Ttt/5DEtRXx2QRKKmYG5njo1+5d0UL2/YKdpbW82HcFoUC6Asf2+v2y3oU5Sbqdb58d5bt9V5hMRCgw/TxWtzWHxF9UU8B9v3gXZ79+mcRcEjNosOOZLWu+Bpbr8PrwTfy6iatURmnNI0cofjjUDsDLhp9f+pn72fHUZmaH5inbUMKxL5zJFGuE/S7HLrUTMWyOfPRvZzwyZoW9EI+Rim0JSFfRuL5UHGJJ5iBbfnYhICHwnklD0xGu7RFaVPb63kfPpppF21gEAXZBetPePuYa4GXZw97oFmpDK495lFJcfO4a7a92edaLD+qMtbpohqeakXBthCkwXFCOIlakmKyHwFvjDDQNcLJ0Yezi13TsVMGDqev4NQNd0zg/NcDTjTtTKjCenedySKsnvhvUbK3k2f/0eM6y53oucGq8N/P3O2M9/OLW+2guKmfPR7fnrOsP+bjyQjvzbpJwU24bJ13J5GZouiwy1ywZsbDiNn2nh+g7PUTX8X6e/NcP5VhDvhsopXj7f53OGV/NDMxx+isXeejX7l2yfm3WeNaRbmY+tqepiVmrm/lYOBMH1U0dM+TDdeSaSVLvBcIlEj2ZUk7KgiE0DKFlLOETCRvNUQjDe5e0VOxvtg2sIkHd24rRQ9474JHxFFemhpjMC+NaXkBWc0HEAaEoGBKUlvqI2klOffkCc3oCqXvxTycIKMmcFeelgWvcmB3lV7c/cEfFN+tYxzrWsY51/N+M9x0Z5tatW3z605/GcbyJTFFREf/8n/9zPvvZz1JZ6SUjNU1btVr/4MGDVFVVMTY2xvHjx7FtG9NcZ8Ou4+8HWh9s5vJ3buQsK20qpmzD7Svt3w/wAhF35yyL2hZ/1nE8VaUG83aC5/suU+QLsmMFWXHd1KlqKWNudpyMRml6kp7i2fkL/JQ2FnPkVw4CEJ9PcPn5G/R1jnLy8HyOZ3gyXzG4y8U8OU5P7xiRZ2VuhZkQSBSu7RJWSZKuS3DRZKx9dpSvdZ7NVIxUBwv5hS333nECtnpLBfV7a+g82ruwUAgMv450JKPXJ3KqmGKzca692MFk9wyFNQVsf3IzBVX5GOMulEFWlAIEVJ9RTG8VTOwkN3CXWkW3AQ1m2jSMMZ2RTQ5Sd3j4rh1UR/PQDI2GfbUrTpqVUnyn7xLHBjpJJC0c06v6VYa3PwWEraS3H33hGIQLuBIx5VBXVZDZXkZ5I7OiV30XsT1Vg+2qghv9g0xtkGh4tkjl3YL2DfPU78jj7qfvpcEsYkDN8wdX3sDOqqxrnx1jNDbPr+98mIrgwj6z8Ytb78fsOMmFqQFcpcg3/NjSRdc07KSdqUgXgGZqaIbO3ooG8GvEkxZu0vUqjVbo1pwgIFMKMWnJbomndJP9iC1SH1m8PYWnHqTpHhFpNTJMa1Elp8f7yA5M+XWdfHPtNjhpDEfnuDHrBcalqzKbHN7uUjzincBM/+wdb/fd4J0/P49juRh+A+lIpCMzVXdpL+20FLHUFOFonI376ug5NYiv22Fjt/eu+0IQHRzmnt88DEDbwxuZHw0TerMH6UjySoMc+of7+OPkaTQ8meicYYsuKLkDW6i0Ss9yhAuBoHh0afKsOuVvf+ILZ+lqmCW1Ae//FCRCYNgQGlBYrRKRSvZXBQs4VLnAJJRK0j0/ScJ1aCmq4EDlhowlRBp5usnOkrVbPCRdh77INPdWNXN6vI95K44mNPyGQW9kij9rP8FnivYxNxSmbEPxioommhDcX72J+xcRI6VSfLv3IqfHez0SndD4QMM2jtS05qwXsRN8vescN1OWGS2FFfzkpv133CaPJ8LLqhgKBAqF7Tq4KAK6icSzT6oNFb+r9wk8L/Bf3naEY6NdzFlxWgorOFzT8p7YFaZRv6eGj/3eUwxdGcX0G3Qd66Pr2IINUnQqxq4PbWXLowvXXghB7c5qancuL0dvrEDK1Ve0pPHOZ35keVuUlZYvxsVvXePKd9szfw/dDzNtVuZ9SuJgK4Mz4wvKIgHdRAjPGikbQZ+PjR9s49sdFxlrdVHCUxurO69xprebb57qwij2kn6bCsv5TOuh911FXtyxMTRPVagrpQjTv8dhaKeLawK49M8MI4SnZhZGMbZHsOuFBGO/P4HK07jwYQs74PXbQhPcnB3n5f6rfGzj7RM35YF8jtS08npfO7ZleepUsHKiEI8oawiNpHQ8oukipBMvf92dRZoSLHHZ0WzIt0yCRTpNB+oJFPg5+LN7ueundiFdtaqa3fsRuqblqDK8W+wta+D46FLSioCMkpzCI/5tKfbe7z3lDVQECzg30Y9Esaesfon0/7ydIObaaFlVsoCnLuFa+HWd7/Zd4dRED65SlFy+xkea9/DOeC+arhHwmVgJG6l5Q5+nZ5s58MHb24lmo//8cE7ypbJTZ7xV4popdRilYMZh+tu9nLcEdsJGyaUk0fQ2TvzZOWIzCfp2WNhB7wFzAceyCEgD02+uSA59rH4rX755aklfbgiNGSvO9ZmR9+R+rmMdd4ptJZ51R2/YU2TRhKDIl8f2klomErfwaQa2lAtWakCDUcSt/3IZJ+KNUa+9dJOdz27hrp/exdUXOkjMJylpLGK6b3bZ8Ymd8H53dXqYN0duMpuMs6mwnA80bOfpf/cIr/zO20z3zmbWb3tk05osBl88fz5n/rgEKbWXJeuI7HhBtqLMCol6CboFzS8q8gr89D3jWVaG7URW37Pot9LbjcqZvwl0R1AdWjrW1ReNQZQrcR1JeDCMWyPRdMHrwx1cnh5KEUA9ZZTiIY0NZwzMJMzUS2JFimpfPp/5x/fyu5deJShMEolk5npM7YDKC6DbC7GPjte6ViWCuFLSH50mT/etOqf8ceLWN29Sd8OhZ7+DE/TUe2o6dMr7vOsmNMGmw01MJ6N8+eapjLVq2E7yrd6LlPrzctTsmu9ppPHueqJTMYLFgSUqeathJhkjYieRShFMjffTimtm1rgl4iR5sf8q/2DLvRTXFzE7OJchwvTtcxjb7M1HO0UHM706H3oXxPk7xeRrw7AdpLnopRFeMY6wV3gHwOtDs8gqacWzhGtTGSjwVE0X/TY4CXVvKPqeEdgZ3ujSfcgVhs+2Lvlmz3n+yY6HVjynm290c/WFjszfwyVxnAgEivyZYgKpFK5csM6MlUPiWpIzX7lI7HMLY4B0+yXwCobSfbxUijkrxssDVzk/OYCdItmk5xDgEU6KBzQGoyPU7qzCQXJirJue8CQl/hD3V29a1VrUTjr0nhxgfswr3AtuKeB0FhEGPAL/K4Pt/NLW+5f8vuXIBvJKg7zwp0cRYmnBJH7PXlsoj/grXYnpN4kFHCI1MBefo+tEH1sfbVnxGNeCuaF5wmORJcuHLo8uS2LZX97IqfFeuucnvYIZPEL7y1cv0RJRmG4qrgQo5XLxG1eZ6prmoV+7F1dK3hnv4fR4L3HXprWokkfrttxR7GUtKPOHSMgwZtx7T1zTa9sN3VNtzTN8JBwbPS4JzAriBRK5qGYgVg1TW7IWGCLTjnTPTyJ9Cj250D25PphvUBwLjXLq/MuUECeW6hYX3heFUp7S9mB0lhszoyvGzNexjnWsYx3r+PuG913E7zd/8zexLAshBJs2beLVV1+lsXHtVgJpHDp0iOeff554PM61a9fYs2fPe3+w61jH+xA7P7gF15bceqMbO+FQt7uagz9757Kc7ydcmR7KEGGycWaib9WB/eFNbQzcmkO6kmTUBqnQbUHJoIbQRE4ST7qSV3/nbWYH55lucHEc10tuCpGZms9VSlwbsEhrm2cVfCkUgmTEotj14yM3eGVLl693ncuSToXR+Dzf67vCp1oP3PE1eeAfHWJmYI6Z/jmEJjB8esbzNlC4UJ5gx21+8B/ezMhKT3ZNM3BuiL0f20HFOcXkI97kTaWyRoV9EBoXTO4APQnKAjctlStAS3rEFQCrWuPGnoX78p3kDZ7ZvJOHaleX7r02M8KJkW6smIVM9ULSYEmMxTW9JKPu4FXe2oAQxAIOX+p6h39clkdFIB8HuXgXXvBmysaVkmsb5plJbdyMQsGgYnSzROjzDA7FODfZzyc27uO7vZdziDDgVUXHHIszE/081bh96X5S+GzbPXxaHiThOggBv3f5NaaiEeKuk7FuUoDrSurLyvhQy26ChknxvMmYvkq1UOqaaCkijBkBXwTKLium9wjm68TC7NgrxQIXgtMQLwU3/RimYlsx16LMDN3WjusD9du4MTvqBVYVGJpG0PCxv+LO++Pp5ILGjKaLTKwrmb9ww0sai+94u3d8HP2zjF6f8JKmPh1lari2i5N0PPn1dOGmodG/x2G8RSINxWxZhMrZEO4VxyOd+XQ0XWPk2jgTXVMZv+kDP7OH3R/ZRmIuQUFVPpquUXOtkJhjZbzjPQjaiqvvqIJ/W0k1Rq+GoekZ6W3wKs0LzQAP3tXG9Z4bGWnvkoYidj27hWQkyci1ceSGjDD1wkaVwpaSzW8YNGzfTLRcURksYG9ZAz7dezFnkjG+2H48Y8MR0A0+1XKADzXt4vXhDsJ2ksb8Ej7UtHvNCf+h6CxfbD9ONHVNonaSoG5ipvaJUvRPTvLXX36VgkkvMLb54Y131I+dn+zPqZhzlOSF/qtsKqzIqHUAfKP7QoYIA9A5P8HXu84tG0xcDY35pfRHZtCElkkUAYQMH7qmUeLLY8aKIV3pJZd1jXfGe3i4dnMO8Ua6krH2SeyETc22Sszgyte0Mb+Un2758cnWg2dTuPGeRqLTMd78w5NLvm9/tYtdH966ZhJOywPNjN7IJVIFCv0c3rWVszdGMkmKNPaVe6TO8k3Ln+dKy7MRHo94Ck9ZmCixkCqXoGa5DjPWgv3CwcoNnBrvyVHf8Wk6+8sb6btripl8HaKedrv0wcA9CpFSN9IciWZodM1P8srQDT7Y9O7k/99LSKV4degGrw11EHUs/JrBPVUbqe53ma2RDG9PE2EWoABXV3i5OMVUo0vRWYdwlcAOev0yeEkPSzqcHO9ZExkGYMd4MX3PK4Z2CKYblDcOWYbEmQ2fbiDwnv2k4zASn8NVEk0ITM0g3/TnVN+GdB8RJ5lRtNMdCMwLKrpTBMhqI6NCZQlJXFiUvgdTY1dJXhvq4PR4L7Z02V1Wz1MN2993pKhsbC2pJs8wibs2Sim0FJFvMWatBVLJzblxrs0M49dMDlQ2LUnm/GDwOkdHbpFwbGSKDJhWPakI5FPqD/H2SGdGzQBgxorzlVunsVzH66v9BrpPR7kSoQn2/+SuzFh3LZiz4lyJjTBZaZPfK9GUQLd1tr1iMn+vj8DuIvxjEr43hWl529UMDSti4fpcdGNhLF+zvYrYbJzpvlnmy13Gt3tJZ28cq5C6IGk7bKuoXVHZ7N6qjQyEp3lj5GZWTb3An+r/0iTudazjbxua0Pjs5kN89dZpOufGCRgmR6pb2FpS49mcCUHI8GFLF6kUBys3UP2izXBkJmc7V1/o4CP/9QO0PbIJ13IxgyZH//gd+k4P5ayXXxmipKGIjtkxvnLrVOZ9uDA1SPvQMNufN0jOJSiqK6B2VzXNBxvWVNgz0z9L3+y0V+yxEtJTkRQhRsgscsqS5kUs84U3/zfikDcBhX3Q9vENDGpDuEqhoy0/N5V4VjOLvtJc2BOpoKBgaaL27oomzkx4xG4n6WDHbYSEuRcH+d63Zwn+Yj2vTXs2dH7dAKUoM4LUvr0wnikd0CkdgMa7Skm4DhOJCJqeshhRXksvTUiUQKJKkChPEpwErXeSRDhJoMC/5Lg658b5WudZr4/FI5N/uvUgwTvo567NDHNuYgBQ7C1vYGdp3Zp/C9B/doj2V7ooRmPPkEkyBFrEpbgknxgJCipD7PnYdso3lvLGcMeSMSbA2cn+JdaOuqFRWLUyOWE5xB2Lr946TSLVhyYBn2ZgaNqy1sTd4cnMZyelwDhTKzNEGPCGLsfHumktqnxPFQEXw5YuF8unvDHgUi6Mh9VEJYQXM3IXPSa2dJlORtGjCjdLKM0/DZv/WqHbsPNPFFPP+BnbLEngrlnNSeDNJ1dD94n+nL81F1AKmSJ8pR+H7F0Gpr2CIzvpUp/Mp9Pv7UMIgS40pFLIhEvc8uJ5Ra6PV25e55o1lllPExoJ1yZo+HDiNsXXFDNn+niDfoobC+n5mMFgYuHYz0/286vbH8hR50ojGbH4/n98M4v8fwv9cDFqO0uu1WrqvVVt5RRJP4GwQ6JgYWxnmDrbK2rZ+nA+I9fHiEzEcJIOvftsJvYLT9VYCiadq/yW03RH7/dimEFzWQEgw6+jLTOu8+kGn9i4j9+99AqmZiAtF5FUWCYM71Y0vKYybTlK4VgOgxdHmOic4gX3Jucn+zMxk/7IDOcnB/h/djxI9btUUFwOH7nrbj5/+S0cx0W4HvkOfUFNVBMarf5yCr46iZNwuPpzyz/g6diq4TdISDtjH6sJAa5CmgLdUl4BggRNCjRdw1WKgYOK0ssKJYQ3Hs3aRdpGajKxlIS0jnWsYx3rWMffV7yvyDCzs7O89NJLAJimyfe+9713RYQB2LlzJ88//zwAHR0d62SYdfy9gaZr7PvEDvZ+fDtKqiVew38X4S4TvABPMnM17CmrZ96K88bwTYQmCM3oNJ7TqGkqYMczW5iqdvj29aPMWwlqknk443OYCMxM7l6k412AwphX2AmJoXQK+hThJq8iLD351xyFsKH6qMsbRZeZrVWETB/biqsZiMwwnYgg8eToTc1AE4L2ubF3dU00XeOun9zFW//zHQCklFhRb2J+6i/Os/3JNiruqeL5U2e5tW2O0LSgukPHsAV23KHn9AB5E9D2HcHEFoUdglCfoqxT4AuYhBvtlAiJQFjg+D3Wj8hSe44VL8xmhSbQDY3Xhjq4p2ojAX3lyfKN2VFPhUNlFeGlLvdiSBN88yl1lCy4rsvbw518ctN+lp1ZA3X15bw+3MH5+UF8hSZW3CGZ5xLbBUZCoKTCjnnX7Hs3LzHuRBZVCHr/WNLNVKSsBl3TiCaTxB2bX9xyH//pxEtgeESWNJmltE/j44VtmUTJvtEyXi+K4Rpq0QRW5EgKa4731YaXFaGU+niiQhKuESg998IpHeJlZIhG2eekgMZQ6W2tLGpDxfyDtnv5/sB1xuLz1IWKebphB6X+0Kq/Ww6N+aXoQuAqhdA1DJ+Bk3QoGPcOLNoouHHE4tylV9hUWMGjdVso9L07xYzVEJ9N5PwthMAMGpnKVMOn4yRdpvZrjG7x2hZhaIzICMN7LHZ0m2hq4WIqpeg50U/nW73klQZpObKBUGke/pAvlXhuZywW9iqCsqRptxRX8VOb9t/RseebAX665W7+uusck8kISilMTSeom3y4eTf7Djaz+a5GRq6Pk1cSpHZHFUITJKMWQhOUDGjEt+a2l1oSgt0ueqGPe1s355Do0vhe32XGInO4tkQIQdyn+HrXWT7bdohf2XaEQl8QU9NRShF3LPy6uWzQNxvf7r2YIcKAZ4OSkI4npy0Eri1xLBc3KzB28/Vu6vfUULdrecWRxbg+M7Li8jQZJu7YtM+OLlmnc96z6yi4g2fwwZpWrs+MYEs3Q3wyNR1NCO7Lb+LEXC+JqJVRHRKGhj/kYyoZzZBhIpNRXvuvx5gf9QJWZtDg8C8fXPM5/zhhRe1lC0KtmIV0Fbqx8j2P2knCdpLKYD7NhxpIhpNcfbGD+GyCyrZyDvzMHkoKCvh060Ge77vEdDJGnm7yUG1bhuxav6eGut3VOdYLJY1FtD64nBdiLiY6p5ccuzSWr24tyUpg1+QV8XNt9/KDrDbwqYYdlPjzeKH/iqcoIQQq1XYrDZBgWOSoBF2fGf1bJ8PYCYfu430p+f1iNhxs4Jvt5zga7smQtRzp8vZoJ3uaK5iNyYX+Yhk4fu+8HEN5JGBHZIgwQIYUHHfsjBXk7XDj+7comNSouWEwX20jbXACsIhHnIGAjF2VVIrPbT/Mcz0X6JgdQxca+ysa6RwcZcby3j/Dp6P7dArMAI1mMRPXJghMQvVNHStP0fOkxpXJ06gJRb7pJ54ibFQGCvjkpn23JYyuhu8PXPeSxym8M97DnBXn59ruedfb/HFDFxr3Vm3MkAgVHjFDR+DEHW8+YWhsLPEyzD8YvM5rQwsV18dGO/kHW+7NWLVdmhrMfB8wzBQp1EY3BQVmgJ9I9YEXp5ZagNjSpdgfzFhjCSEQhk51sPCOqnpPjffw7Z5LJDQL5yEX3xxs+q7EjLqY0uQDxW1s397Gm6+fZCC20Ibpho7uN3Adl3CjYL5SUllSRP19tZjCRDM1Zmu9saOW6srS748vBh+v37Pqcf1Ey10IITg22gXCI9lpwrMATavurGMd/yfwrZ6LdM5PgBAkXIcXB64hhODZpl282H8VB4lfN9hTVs8nN+3nWz0vLdmGkorpvlka9taiBTV63hlgsmuGRCSJwEuIFlblc+RXDiI0wfGxrkwXraQkHrcIS4leqqjuFbhJl2TYYveza1OEGrgwjG6toGCRfZypIgxhedaymgvJAiAznrlNVl54KgChQUWyRmPk2BB7N1bzdqIXR1+kHptGapyQhhkVlI5qHAo08thHly+MaSoo5Sc33cXLvVcZic8QmBM0nNfxz0M4EuVMz00oys5+Cqa0BI278nEuLyRAhSmYPKzz2xe+nyGN+vJ03JiTuQ6DDwqSJalj3gwjVoyLr34LvcCk0Srkw9v2UruhAlu6fPXWaWJZBT6d8xN8f/AaH9mwZ/XrlsLRkVu80H818/fVmRGebNhGfaiEK9PD+DSduyqaqF5FcWbg4sJYXyhBIAJg0Hh3Pfs+sSPHIjyb2JyN5VQd7wRWzKL/7DDvyEGGxAyao3BS83lLOvh0PwHdWEIaL83qy6JTMZRSTFfaWWZDoPu8ueON2dEfKxnmzeGbzNYpDAvslBIMgJ4AYQiEJjDjioRPLVG1SMMjU6eOPOskbCXRDUHzdyXxCijshvysqZfQBXmjIBtSKrlryU6ktr1arGk5VHZqDO5KzYWTEpFuA1LvpG8OSjPi2opdeTWMa3GmkjEMoVHqyyM6EcPyWDUYMSh9NcG5Z3vxFfpxNYUt3cwc/YNFW2j/8jU0FyabJYYlGDOn6ZsFn8/IECYSrsNbI7f45MalsYH2VzuXqGDGzs0g2wSamRvjrVtkcew6kp4T/Yy2T5BflseBT+8l/sIFruyJYoUUmqnRWFrGJzbvp2iHN/957tdfYiQYZexAVrxDhzEjziuDNzLqme8GobI86vfUMHghd46++aGNK5Kcx+Lz+HQD6UoStoPr9wStZ9qg7BLkTbLw2KXuY8fQMFd8Q1gp28/Ut4TtBK8NdbyrIsSV0FZRzT+5+xG+03WJibl5Qv0uBddd5vZIAk357GloZK9Rw7dtr68s7IXZbKdPIfDbGrudMs7nx4iZDjJDihb4dBNHWbiWZMOLipF7BMkyUsqXCqEJhKFhROWyKkoJ10YqSUP+7Ymk61jHOtaxjnX8fcH7igzz5ptv4jheJdiHPvQh2tpWVxdYDVVVCwz/sbF3l2xexzr+LkMIgdBvE8j5O4IdpbW80H9lSSBjzxqkxI/UtHJ/9SYs6XqT5qe85ddnRviLm6e8iiipGHPmMB5yaH1OYcxKQoOCSMYf3GPEVF4kkzxveE0xcq9gttWrhglMQ2G3ouQWTN7t8IJ9E3PcIOHafLf3MuAlfQEcwJaSkOEjdBtSwmpovKuOI796kGsv32Tk2rgXrAgYhMeivPm1M9xKQEJ3kbWKuVqYbpRs/4GJ7gpcyyUZsTDDitqJ7FCdxLItjLjADngJW83QSFgWlnIxDI0CX4DWwXzOlYx7QQRd8yyRhMCSLjPJ2KqqFyHDl5m4aq6XRMg4NWVdcgA97tkaiMV5aSEIOwnyTB81eYWMxOYWJKkVBJIajz17F/+z/5i3H0NHCI9RojSQuue9q4TASTpMS9sLwiyT4BPAjtsEoWK2xZdunqQ3PIXCq5JP+iQiRfjRpLe5cLnESS6QEnbv38TU/xhlrsJlvlQSDynmm8Ep9Kx1wKsEEXjXQepesNINeFK+IgEqlHXB0iQaF9QyFV4A/ZFppJJot/EO3lJc/Z4kZgp9AZ5s2J4Jepp5JoW+AEdC1TgfhdcqBsGZBQcmEhG65if4jZ2PrGgz8G5R0VKG4ddzrr9u6ASqAlgRC3++j6q2IjpbJzwVJEPPWH3Z+TBfrSgeWbigdtyh/dWuTICz/ZUuPvCvHqSopoDXhzv44aAXUQuZPizpUmAG2JBfRk94kv9w4ftsKCjjE837qAiurfpwR2ktW4qr6ItMMxydQwjYWlxDWcAjKOVXhGh9oDnnN/6Qj4a9NbgXh4j6HeaaAQFGFCouKkYOCsKbfVxOjLC/oHGJn/SloQGs5EKw27IdIk6S37/yJroQlAfzydNNRmLzqQRmHk82bM8oeiyG5Tr0L6oiNoWOJR0kCk2CFbPR4wqzyyIpvHsgNMHwlbE1E0NWCpBmLxeQsTHKhiewdGd9Z6EvyK/vfIQLUwP0zE8StpPYw3E4Ps98dw/iwSSyOqt5cyQy4lAdXGgnz37tcoYIA97zdeKLZ/no7z21Jv/z6f5ZOl7rIjYdp353DS0PNr9nvunFdYXkl+dlFMbSqN1RteI+XCX5dvdFzk56FipFvgAfb95H3j2ltO7bTWNeCSXBBXLd1pJqthRXEbYThAx/zvuv6RoP/pN7GLo0ymT3NEU1BTQdqM9JdKyEgsqlBL7QlIZVqHBTig5ewFFnb3kuAX9zUSWbiyqX/N6XUrcQPoGTrcyleeRYLeua3I54+F7Diln84D++xeyQVyEqHckb//sUl35KeipvgowijiUdekLzlAQDQDR3Q4uK4F3Ds10ACM0LhFJkuIGpf326jiMl/tvfFuLzXrC3cEyQP6ExWyuX7a/SMLJk6auCBRT5gvx8271YroMmBFe/007XrTByt9e+W7ZHePAFTX525z1MVc9x+uItjGrJtYZ5EqaTugYuI7E5j1xo+BhPhPlSx0n+xd4PZFRM7gRSKU6N9yxZ3j47ymwyRvF7LNH+XuLZpl1IpbgwNYBUigZfEaOzs6RU6PFNKazj43T/0hhvDt/M+a2jJC8PXOMfbX8Q8MgwaehCo8Dw4yjJ/vJGPr5xX+barkSevK9qE8fHuplLKdEUZhFo1oKoneT53su4SuJarjc+LVKMHhA0vKlS1hkb6D01QDKc9Cwks5IxvqDJ7E/l0xOcR9MN5ow4v9/+Jr+67QFajmxgaOgW4CVadMuz30RA3XQeJQW379M/0ryHqJPkxqwXozCExtONOzL9+TrW8beNmWSMK9NDS5YfHenkX+17kt1l9Zwa68GnG+yv8MaLRTWFJOZy1d4QnuUxwNsXb/Bi9yWSRxQFExp15zSCts4T/+pB8oq95GvUtjy1BqWI2kmPaKnD6EHPPqXhqI2mJ+g7O0TL4Q23PQ8zaOKfEFCzNpKD8oFyIG9IkSzWMuewkiNMBtKbZ40dFIzfregfjeK/3I3brFJzyewOcikhRk9A9YiPf/YzH8a4jRXP3vIGSnsEr//1Sdywg7QtEnhjjbilYbCIzC5g78/sInlihtHrE4TKgsTuC/JqtBPwVB5jjk1SuIQKfEhHUWUFGCgNZ7wFpSmw8xS2pdDsJNfFJENHX+MXx+4j2eYjaiWx4g7ScT0Vr4DBtemRNZFhXCl5PYtMmcYL/VdzLPWOj3Xx2c330JZSbhm/OUnPOwMIIdh4X+OKdsz+kG/J+HBPWT2vDrVnFBcyy8vfvS3dRNcUr/+341gxm+sPWViVXuGAmVK0RcD9VZuIaw4XpgZyfvtISkX3zF9eov2VTo9wGsOLSwmBbmqYAe/8smNFc1Y8Yz+02lzFlZL2uVFs16WtuIrgKmPR6zMjmAGDpCMxkgrHD0rz1JF989ByXMcNKm7ek7IRXm5T2VOBRY+861PEKwS1J5Z5qRSErtn428CqWss7q0AJTF3nnqrVCfGFVQUMXhhBSU8JsuKijgiYRO/xMxOzKRvQCI5LEgFPEab0utefxyohfETnmn0NLaXsU2AGeHJuA1e/foXpQonSIH/II4pIBAnLxjFkpojOkZLvd12noNFleJeL1Lx4l5siStmOiy408gwfQggm48srd0z1zCxZ5o8JtkZK6CiZyywL6CZP1G/LWe/NPzjB8OWFPIgv5OND//xBnoxbTBpxiisLcgg0VswmNhNj/CGNxQ2gEt7Y8EchwwDc/0t3c/4bV+k7PYhuarQcaWbns1tWXN/UdGKOhSNdVDoWqDzyVc+HBC3fUARSXCHN0HB1xTt5I8QSVnpVwHskpVKMxFZWz3k3SDg23+m7xFBslmQyyXwVTBYKikZszJthNj2Rz8WjV9A0gVSC2pMKq1AQq/bGjOXFBXxm2z00f7Ccw4kI3+29zJnJPrSUYqChaUhDR8QUhUOKqSgky7z33opaC6q/pkCzUsLlyzSLE4lIhqy+jnWsYx3rWMffd7yvyDBDQwsT8Pvuu+9H2lZBQUHmcySyLgu3jnX8XUaRL8hPt9zNd3ovEbaTGELjnqqN3FXRtKbfa0IjsEgh5+2RzpTcsOMFfoTAKpbMVykKhmDD9xUTuxWRDRpmUqPiukbJjI7weSQjNW9R/6ai7s3c/E2yEMa3g6ZJbMfCVXJJPE0BUkls6bI/WM/gxRHKmksIFt25GkbT3V5CcKp7YbLsOi7Dm21iSnie6KnASqJQMd0kqejWkVJhBlKqGOnAkPLIPkLTqL6pGDggF6yXTJPNZiUfatxFdVkxpqYzcf6HjCfCCN2bNDvSm9hH7VVsf4ADlRs4PtqFHXcQUmEkUwECDS+AmLYGckFTnrWRnU/OhTb9BhuDZYTHI/yjtiP8zpkfMqd5ybWgo/P/HHiYUHEeYsD7kXQlrp1ipGTfEAXKVQTCglBUMbNxqVTNgcoNtCyTEE2jc26cP71xnHk7nkmuZ11SL/AhPTl9JeHc1y8TKs+jdnsV9btr2PuJHZz4+gUqbrqgCbSTGqNtktFDwns0lUAzBCU3Fb1P40lsi1QVvZbeywLSKjQ5EYAsWNLFlhL/36Jq1JGaVrYUV9M+O0rI8LOztBbffQZfvvkOzOQe4EQiwvXZkTuWy74dfHkm9/z8fo5/8SzSliilcB0XYl6yXTpeNWvooTwIOAsZY8AMmASKTRixUErhJB2kK3ESDnrKNsmKWlx87iqh8hA/yL+BFVAYAQPd1AnoGmE7QfvcaIZw0hue4ks3T/JPdz16WzWVNAxN96x+RCHJ+SSFvtsnVu/5B/sRf65hv9FN4oQk2igY2wf9j3n7nNWjfLPnArfmxnOqpcJjEZhxUHmpx0mAY6qUNbwi5th0z3sy3wIy5Kq/7jpLeWB5Ky5D0wkZPqJO6jomHHAkmiFAKKykgxFVNLzikdUkLlbcC2wHi9fWPobHItQO+1GaRGSREgK6yd6soHfAMNleWsOV6eGMt72jJAVmgK75CXavgWyZDb9ucKiymUOVzYxcG+fVr7wNeG1P9QlJ99MC6ReZ17HmDBhHRGYkPnRlqUpNYj7JdM8MFa0r6/0PXBjm+su3GLwwjOtKkIquY33ceKWTD/+XJ+7oHFaC0AT3/8MDvPmHJ0mkSQw1+RxYwbpKupK/fPEtzvhHAU9RYibo8kfX3sKfqpDVhODJ+u08UNu6sB8hcmyjsqHpGg37amnYtzbPc6kkl6aGuKWNE34siP9YDF/cu/r17SZ2m8DVyRADt5fUrLly7mBlM+cnB1JtOwv9ikoFxpVCT73T994mWP9e49ZbvRkijFKKZMwiUaRQaaJbNm8yNTb4qY8d4b9cfoUYdua7DFJ9ieZAXlhDL81DupKiEZv5Ks/eSCAIGAabi6oImWsj/9TurOLma904fqi9phErkSTzFzix2d2XAoIpIptfN3i4bqFYwqcb2EmHGz+4RZWlES3RmG5MSeBHHD6+5QAnxrp5Y/gmlINbJkk4Nnn4EIiMhLojF8r1o47Fzbkxtpes7VnLhlLKsxdcvByWtft8P8GnG3xy034+0rwHqRSnv3Cejisx5qolJCTDW13ePjTN0auvITQI+fw5BMqR2EJiZjGxEiEwhE5tqDiHZHR3RRN9kemcVUOGj3urNnK4uoWu8CRKKVoKK+6IINsTnsJR0rMOVCrVhwmijeAv8JJP3/vXr5KYTaCkIhlJYgYMDJ+B1BQz+w36CsIZxTKAOSvBG8M3+dBP70a8qPN19zpWlgKELgUP7dvJ8dEuLOmyo6R2RbKrqen8XNu9DEfnmLGiNOWXkW8uVWdbxzr+thC2E8vyP+YTcS4du8VLvi5mlEdO+/7gNX5y013s/shWXv2vUxnbEYBN9zVRWF1A59w4z01fxi31vpupV0TKFLte0Og/M8SWx1qITETRL8WIlydwDXIUyoQLM1ug9IbCF/XG22vBhkMNGEPnSU2AV185NbhVOqgSHQ2FFMv/RhMiQ6QQ1kLBgTS93YQbIay8satwQRnZFR6LNwZuEKKmRf/ZITbed/t4RqgsDxlxkPYCoV86kuJejciiHGeB6ae1vAr9wzXs/rC37A+vvpH53tB08k2v/9tSUs1j9dt4Z7yH4d5OT7mVBQW9jH0UirlKydEXLnL/5kMkIxYyZc2qlKe0qsTqir1pRB0rR1UmvY2obZFv+jNXzVWKlweu0VZcRcfrXZz+8sXM+h2vd7HnY9vRTC3n+TMCBpvuX3o9ywP5/NSmu/he3xXm7YQ3lqjd/K76+TROf+UiVkph1pxTUEkqpiLQU4+rb9Dhgw/toyG/hGvTw/gNk3sqm2krriI6FaPjtS7AG+PWDPmY2mWhAhr+fK8/8Gk6d1dsIOHY/FXXWdpnR1F4VoOfarmb2kVqIAAT8QhfaD+WsTX0aTqfajnA1pLlCwqChs97B0IarvTsXrSkoOGSTs1VDTtiIx1JlQ5jd4nMM685ZNQohEwVNmXFcbLVbec3QKReEKsGJJR0CzacMxBTDiohKb2mESvxSERqpQyF8gqDNFuxWSvm0bqVC2e7jvXR+VYP0l0gqMiYwwEaefDue3nzj04ycMab/9lxGyfpIg3ofkYQaRC4eRY4goBu4NMNIk6SM8lBgi4UZHMGJRQMwsxm6d36rNd+Mj/JZFoQcJnmwFUSSzr4dZOmguXVCItqCnJUMSNlkrFWSVmB5L6qjQg89dj9FY059oyjN8ZziDDgkSdu/OAm9/6Du8g1BvOgmxqG30D47GW+9Ui7aSRcm3MT/YzHw9SHitlT3rAmArkZNDn4s3vXZH3sKsmLqUKqhVbZ+6Tb3rM3vQ0KByBWrxG0FcbeEKNueNnt6UJQG1q5WO/d4J3xHkZi8ziWV8JmB8AOKKx8xWSzxf++eIzWK0amXQ1gsuNNnXiJomhzCZ/43MOZsW15IJ+f33Ivsl3lWDiDoqpbx2kyKB9WRJpclPKUfwD88xAc9WLZGVJaVkFf0PDRPjPKocrcgql1rGMd61jHOv6+4n1FhgmHFwYuhYUrS2KuBdHoQoVjKLReZbWOdfxdx87SOrYV1zCRiFDkC6xa4bIWTM2FMxY5sCCTa6fmkZoD1Zc09Osapj8100/nkrIkjq0iSBZBcBKMGPQ/LnD9IDXJouKjTFJHA4TQKJk1Gf3Tdkbxqhn2fHQb25+6c0Usa9F5JKMWyaJsqRWRIcTECxVVW8pxHYXh9xL10pXYCRvlKq+6y6eTF5UE5iVW0MGv6ZTeUOSfmuKoeotNhzdw4NN7+ODGXfzFzXdwpCSWIv4EdJM/bT/GXRWNy8q9gjfZ+4fbDvPCzcv0jk/gG3HZ3B+i/EgNL4suHOkiHNAcL2pQeVOgJg0GdjmgCQy/TknEx9Tv3OI7yVsIDbbZEqvIh9QVeXMakxMjbPjJCppCJZ4SipQo04tR6YmUJK4AUOiaTsN5Db3LIvExQbyadAkJ9V0mP3PPynKqETvJl26+Q9jx5PyziTCpOwJ4AV7NgdJuDTvucOx/neZj//0pTkz28IOiDuZ+WhGY12k6Z2B2WVRcUJTchJkWhWtCSb/G4GFA8+6r62dpjDV1uzPB5GUCLwJPcnstNhYrwU469J4cIDwRpWJTKeGJCP1nhtB9Oq0PNLPh4PKqIJXBAiqDBTnL5u3EsuvOW8sv/1Gx4WAD1dsqGbk2Tnw2zrmvX8G1XZykJ2usmxqFHRrRPbkXrsAX4NO/9igjF8a49lIH4zenUK7EcT1Ln7SCUs87g+iGhvVxiXQUVsTCF/Kh+3Rs6WIIz1LITniBxaGIxYUrXezf1bKm47diFj/4T28xen0cJRW+kI97f/EuNq9iF+PL83HkVw6y4WA9r/3JSTrvtUjkgyfrJEhKFyeZ4OR4D/mmnyfqtxHunefoH71Dab7LyL0CJTzfcIWncuQqtcS+TiqJJV38usG5yf5lyTCaEDxQ08pLA9ewIhYyFcipuaFT3QGWIcifMUnOW2TeHdvFl2cuG9xejEvfvs7l794ABQ21kolDoKpNGgtKebpxB/lmLqHm4837AHhnrAdXSQxNRyrFX3aewdC0dx0k7z21UAEqhCBvHDZ+VzHyoCBZBIVjGqUTRo6NYbDQT3QqvmRby9lXpXH1xQ4ufOMqiUhyIRkgQCjFeMcEV757g51rtBa4HSpay/jof3uSsfYJdJ9OZWv5irLWl5+/wfXEGPjS91ASEzbSVOiahim86/zSwFW2lVRTsahdeC/wl51nuDI97P2xSaHXaxy8VExFsIAtj7UQK/csuybiEepCxTzbuPO220w4Nj7dYENBGZ9qOcAXO47jKoUmBD40dMNTOdI1jXzDz33VG9lf4anNzA7OceOHnUQmolS2lbP18RZ8ee+9asx032zms0yRo/yz4IuA6yPzHoMX2N5X3kB1cTG/tvcRvnrrFEOx2YwKn0/TPbsvJSjTg3zgn91N6YYSTrx8memb3bimjVXkkQXrQiUr9vnLYfeHt/GOPUBXTcQj2vnA1HU8jagUeUF5Ae9igrSVVVMayudQVTPlgVyCQWIugZN00RC0nDBJXFZYeYrQtKD8QB5/M3w+s66SKicBsRLESknM20DXNLYUV3NjkQVbeSBE1Y/hOf9xIJ3QiIxH8EcFFbc0rjxiE8nq2hWe+krhImuxNO6qaOLSIpUJU9OXKDoeqNxA2E7w9kgnMdemIVTCR5p340uNUZZTZloLinxBXMvBTngJinSVvmmJVLurSKSsE4UmCOT7cG2Jf0cBF/dFiPotkq6DcAU+qXnJQV2jf34aTdc48OwummIbeLHrCj1TE5QQ5O7mZr47cYNEnzcm//7ANT7evJe7KzeseJy1oaL3PDmzjnW8G9TlFVNg+gmnLRqUN+bM71N8yz7HdKNE9+v48nzYUvKN7vP8q71P8tS/eZhbb/aQCCep31ND8yGvoTgx1r1kHmwHFTMNEifpoJTi9f9xnLzRGEWHNSabFsaUukXGRjdWBaW9gvo9a7OJCRYGuH/nVr4RuZqZAq8KAW7A6xuUVIvWz6pssFLV97ZCSHAXk0ZT/8r0PnPmYMtJzQiiJQsqabdDSUPRstye2rMC6/4SepNeUUyhGeBTrXcvIQ8uNpzWhIZf17i7cgNNBaX0RabRDQ1pe21mWvlNyNS4QYCQikg8QfTEFP4ZRbxsgbaqBJR3CDh8+3MpMP2U+UNMJRfitVIpNLFULWwkNodru1x87nruRpRnofrwr9/H5e9cZ3ZwntINxez52HZCZcsXCuwuq2dHSS0zVoxCM5DpZ94N7ITDdO9s5u+KKzC7gRyrksAsOCUurwy2U5NXyC9svS+HKDo7NO89c4BrKHwxaHvTYOCAwlds0JBfyuP1WykLhHiu50LOuGIiEeEvbp3it3Y/tkTp9fm+SxkiDHhFMN/sOc+/KPrAsqTSXaW1XJgc8AjSqUCVMBVFHRInKlGuJJKaDtW9pYhWwcw2kbJ39u6X0kBoyz6iKOG9x2TNGaa2KqLlNlu/ohAK/NMqYyttQ5Zd2cIW9Ri0PqfwR6HtrhDGEyuTL6691IGd8Cy/06K5Aug/N0xkMsr+j+9kqnOa6HQ8cw+m9mlEGwAjXSfnFUoYmgYuDFizNMedNN8p83pvOKkxt1l5Y+elh+1hUXuR/tp2XepCJTxQ08py2PJYC90n+0nMJZmtkdw8YiNMjZg+z8BYmL1lDTy7YfeS36VJ8Ysxl1o+nYjSOT9BoS/A5qJKNKGhmzqtDzYz0N3OXI27SP1RcCR1jHHH5o+vv8VYfCF3c2qil89tPYzxLhQVV0Ln3ATTyRhBzfDIJulNy3QcT2AfDDJwyEkp+mokmCMPHz7dWEJMLzKDPFrnqdBM9cyQjFlUtpZj+N79MQ9GZ1OfVMoqLHWIAnSlmG2C6C3wJ3Rcy8W1HAy/Tn7U4MFHdnNjdpS+yDQVgXz2lNXj0w0+1XKAF/uvcDllF1dhFtO+YYThrYCC4DigFE6BIH8Iqt6RJIpT/Vj69U51OenxfGidbL2OdaxjHetYRwbvKzJMeXl55vPExMQqa94eAwMLiYjs7a5jHev4uwtd01b1jr4d4o6FJV2KfEGKxzSGgqnJu/ImVUJC8aSGmWcgNC/wrfv0nIoj8BQmkjGLwQeE5/sqvN8W9ChiNR4rf7VaNL9uoDmCgjMWaV8e6UjO/81VandWewGvO0Dtzip0U8O1pUeMkZA3ppjfKDKqNyDQAjocyOe4L0xwRlAwJymc0NA1HTvhBSYVMFPrcuuIN4E0EcSjFmFTUOeYSBS33uyhsDqfbR/YzG/sfJRvdJ+jfW6MgDAzQZazE/3sL29cUZKzMb+UX9n3YOZvK2bxyu+8TVsEevdDohh8SuPx1u08/EAbZtBkzorTHZ5k7NgIk9/xJPilK0nMeoFEv/RkkW2/4tXxDt64OMpAbNbjtaC8Kj4lEK7ni110S5EnTJ64fy9nbpxGKdj6NUW8DKwCT/42YChiz8QoqFq+uvfqzDB2ypNYQW7gN10hpAECSm5A5TsSla9IRiyOX+vghWQHjuNd60ix4spTdqZSMW9EoTmeN/B8s8Iq9faitNRkNx2kSD+eGqkKaE8q1fV5VTAyywgmaPjW7Oe+HOLzCb7z315nwokSnBNo33RAKnS/FzkauTpOImyx5dFNa9re5qKqJbY5IrX8vUYyYtF5tIfZ4TBlG0rILw/h2i5WdEHJyHElBUeh4bHNnJvsx1WKikA+P7FpPwG/j5KGImb659BNDSfN11EKO+lgYOBaDrrho2RQY2pDSnEg6anH6EJDB5KRJCrlO6Fcl9N/eZHGYMmq6h9pvPkH7zBydaHSy4paHP2jU1RtLidR4snZTyYiNOWX8UBNS07wo3F/HQ2/0saFiasZ+es0XBSudHl9uIOu2Qmq/zSCNW9R3g3CUUxvAydfoArBQFsiM56GTBFkXLlyC/hg7WbcCYvXOq4idY2yPo2qW16gUcRdtJAPf77Pa5OkQtM1Hvmn999WOWtmYI7Lz2fM3ikZ1ij5Fuz52GZ2HlhegjlomNxV3sTlKS9pmy05fmy0612TYbJJLkITyHyN3icVTr73fM80SKyNiqhMUoiXTN76RCtnv3Y5Zzv1e2pWbHucpMPVF9oBUG5WH6VSSQug63j/e0aGAdBNndqdt7eq6nq7F7GIF7FctbUCOubG3nMyTF94eoEIAyAErl+ReKqQ+1ruQirFN28cywQxu8OT/M/rb/GPtj+4rE1J59w4z/ddZiweptAM8Gj9Fg5VNnO4uoUzE30562pKoJQi4iR5of8qrw11EFQG2vkotRc0DEswemOCwQsjPPlvHsp5Vu4EEdtTqegNT1Pqz6PYF6Q7PEl4cxTflEvlLS1D6BAKas8Luh7NvQd+3eCxVGC4qaCUf7nvSRzpcmy0i5f6r2baCFPT+MTmuygvKOPrXWc5W9yLs8sBBVXXdcp7FYcfb6Bs19qLD/rcWYa2u/gcH0qCqUPMtfHrOknX8UjH0qvArzxmU6JFeerfHlz2eoXK8giVBZm0YsSLJKFpjcJxjaK6AgYcT61EuhIranvvSgCS0kEXGqamk3Qlpr4QDC80A1TM+xnsG6GipTRTob1WfLR5D1/qOMlwSiml2BfkUy0H7th+7f80KjeXM9k9g2M7hBsXfZmqtHaki6HpGELjA1kS/W3FVXyseQ+vDLYzE4tSruXx0ZZ9OZXLaTxSt4UHazdjuy4BY2WC0p3AvRYh0O2SrJE4AVDCy15FShXTrYryHk8NLg2haRh+jYH7FI4m0KUG0kvQJnHRXRC2S/zyHPYWB9NvUJVXyM/vXFCy/eNrb5HIUjtQwPf6r7A7leBYxzrez9A1jU9u3M9Xb50iKV1c28WYkTSdM7n2hPdcu0kX13TRTZ2E69AXmaa1oZIDn96Ts62R2ByjsXk0n0AuEhlw/dCwv47JrmnmhsLoCNreNOGIzUyDBFchMt65EIho3PMLd2EGDa58r53p/llKG4vZ/NBG/PnLE0pbVClFI4K5WpWZkIvU9ErmWMgutMnzVYol7J0UtAQ0/lAx/JCn/uau9jpnJ5BXEYcBhRMQ1G5d23xHugphCJTtbVdoAjPPxEDnpxoOEMuTxF2LulDxUmUuYF9ZA0OZxK2HoG6Sb/h5qf8auhBUFBQyas/gOtJTZxQppY70ZVYQadG4+WoXG8Ykw/cIwk2gJxUV1wX1ztr6SiEEH9qwiy/fPIWTmjdoQhDQTZRSOX1lTV4Rsel4zlwtjdh0nJKGIp74Fw+uab/gPedzVpw3h28hBOwrb6C54M5jxYZPJ1Doz6gl5kcNWr5tMbFb4JYK8icE4Wp4u2AQMezdj+aCMn5xy30ZwkBxQyEDexyGtru4pkcE0VzQDQ3X9eIylQFvfHxpaqmF2UwyxkBkNkdVRCnFrRxVCQ9hO8lwbG5ZBcQ5K4FPeO80KdKTloTJJpfKSej+kHefUQLNUQQmoeU5xdQ2mNjvTTiyHznhskBcUMspvXjvWaICRu/2lFVwvSR/vNJ7tpd7E6Uf+j4g2Pi8Iliy+pwwNhNHpufa6ePAmzcPXRyh7dEWHv6N+3jp372eUTiaq09NoNJTqtT7a7ueta1/2rOvcpJuZq6lGzrlFcW0BQxuzY5j+1hQ51ju3RegIVIhIm8uXmQGViSRhMryePrfPkLHa118N9CJmQe6X8+Mzy9MDfBY/ZYlJPHSpuJlt1faVMJbw7d4aeBq5hrX5BXyS1vuJ2T62ffJnegv6Hxr9DqTlQ7K8GJJd1Vu4HCNV7hzarwnhwgD0B+Z4dLUUKYA4E5x8/Vu2l/rxIra1O+tYe/HdmSUG5UC3QFHAxDpsCZKg1jAxdAMLOmSkDYyNf8KGX4MoWFJr9jpgZpWPtCwDV9S4+V//waTXZ4ioS9kcvhzB9Y0t10O6SIv3dRRzoIyVvaUN1asKJj04Rgu0nFpObKBhv21/NXQOQZGw+g+HaEJjo508ivbj5Bn+HjY2Ej1OYeZqQhvH5pCFmpgeduPV0DFeUXZTRg+BB0/JXD83nsH2YQYhYq56EXib12ldB3rWMc61rGO9zPeV5GZ+vqFarHjx4/zm7/5m+96W0ePHs183rr1vUsErGMd6/i7B8t1eK7nIhemvKqX+rxiZqbncZtVZsKgOYKqC4rBewXJEpfQlKDuqsa+w5ugxuTt9pvYCZuteVXcdV8bf/4XrzCbJeIidZjaqeV6Ji9COsYWtx3y5zRKe3VixRI7APmTAt0RDF4cuWMyTKDAz33/8G7e/l+nMzKcpddhrsWbMHmzSAGmYFZLIlxBrEAx+pjLthfAGJdeBTngxG0G2zRv/p8ORinFfLUiWiIJzWgopeh6u5etT7RSEczHpxsElqmw7p6fXLM/7cVvXWO8YxJfwmFzp8IJgG5JzPvn8P2GF4Ur9uexz9/It45dA1JEmHAyE+hMRi1Evs7NZyCZr3Aj00glFxKCpFcVuAEwik1++ekPcPqrlxCGhkqRnoJT3n9CgDBZkz2LqRkkF0k+eyyr1GfXUxFSmkI6Et3UaZeexYxmCC/gGMiaOQuI1QpwvM24lSB1hWGnqvUWVRgJ6d1mI616o4GOhiMUBUYAR0mKfAF+ddsDP1Li+avHjnH5ntnM3yXXFLVHFY7tpgqHBOe+fnnNZJgjNS10zk/QG55KnwpP1G9b0Vrg3SIZSfLy//8NwuNeFWL3sT5Km4oz70s2hANHnEae3reDmGPnJMdnB73kZpoo56YCE8qVmH49EwxsOq9jhRThCs+OqcgXYFdpHRdG+zNEGAB/VBAagxuvdN6WDOPaLoMXh5csl7bL6bdu8HbreMaGoyc8xbWZYX5t58PMJmPcmB0jZPgIVufBpMiN0qShIOk6XuV7BWjSYeR+QaIMghNQ/7pk8AM61Js4YnGNqYd0AD7bjmg5bIgUsPX1RW2G5skng3d9/SHvvW/YX0v5xuXlo7ORJgnZfsV8lcRMCArGBcNXx9j5wZX9yKNOctkk9e3s3laCqyTWXQH6px3McZeiTsXUDoETEqlgrEJoAtuveGe8l8frvTHq1sdb0X06t97owY7bNN5Vx84PrTx+jc8mPJs/UoHjJQQkgRn8PzPMl66ioksjUr7wnHiPnMiR2QaWqPW8FxiJzy0ci5IkXAdHuhwf66IimE9tXjFd4cmc30Qdi7dGbvHR5j2ZZUopBgcn+d9DJ3BS78y8neBbPRcp9eXxRP1W+sLTjCe8oLArvf5GCM9+IO5YxB0L09WgWRIu1Nj+iveMT/fNMnhxhMb9d2YHN35zkumxeb4duMmM9Cp/b82PY7suIdOHCAoSd7skA5KGy0bKxhDsUg0j4SJ10HwaAdNE0zSuzY5wMEs629B0HqzdTFtxFVemhzGExt7yBkr8eRwf7eLi1KCXCAAQMNbmUtmlce3FDrY81oJurI3cc3XGa8uEEKB5ikEh4aPEn0dyNMacTGasADsPO8x1TbHn8igNe5chqGmCmU/kc2VuNjMeqLtp8thje+hkHlu6OFEbXK9yM51njdkWhqbTUljBrBXHki51wSIajwu+f+oNwJONv+tTu1dV31qMIl+QX9v5MIPRGRwpacwvWVK5LV3Pwkc3lyZApnpnuPjcNSa7ZyiuK2DXh7dRs+3dKaT8KNj+VBt9Z4aYGZhdNnkMsLGwnOaCcu6uaFqi/LbZKmXgq5L5GYkMz/KWfI1LdYXs/fgOWh/IlWvXhbbmZ2ctaH+lk9Zeg8tP2zg+hZACzQbT1Jl41EfTy0HCvbmV046pGFERNDR0oaEpgYvylIv83tjK6LXpOdHP5oeWPg/Zdk8qpZ4WU5KR2PyKFgjrWMf7CW3FVfzLvU/SPjNK16s9RH84jlACM0Gm6t21ZabdKljUf9vS5au3TnNjdhTLdUjgYgY1SEjSogkHt7VSVFPA6I3cwreGCzrhSuWRVTSBklAa8/GZX3uEwooQL/3b1zNj+P4zQ3Qf7+PJf/PQsgprhZX5VL+kEy2UOCHPWqXmLcXMLkGiGGQAMtIOafEXfYE4k0GqqEEZEN4AVacVM5sF8Ttojn1JgeVbrDjjwe9qlNSvbc5/4ZtXUW5GpAGUQtou1buqPULobX5/X/UmZq0Y74z3YEtJRSCfTYXl/En7scw6ptB4aNMWpkbmmDw5xvAmh0TJgjqOYQmGNzv4L84RiELjqypLIUNR+1NrvzBbiqv5rT2Pc3FygCvTwwxEZ7CkIu4mCegmpqajC8FTDdvJCwXxF/hIhnPH5aHyvMxcYa04OdbNt3svZf4+Pd7Lx5r3cmAVBa/lIDTB9qfbOPdXHond8OnkxwzyTwlMv8H0VphqlOhZBN6e8BTnJwcy+zoe6WVgn0t6+O76PHMvLVVYdGt+guf7LvNM405MTSOxjAuVkVrXTjic/atL9L4zgPNUArdQ96yvxUIMpGAFdYiwnUDZCiFSCrc6uHkwsUswuxGimSmdQhoQL/dsj/LGQbgKZS6QVwReUl643nZQqeT8Ct37yP2CMRuEFPjnoLgbJnbkXGnSL6bSIVECo/cKJjunl9tcBtXbKolMxrx5furgrHwIN0l+cPIiFdsraP9hJ9JRHvlIuhhx7xyFm1ZE8ogXyvFUFmvadXx5OoZP4tguJQ1F7P34dhr319EZmaD3xJu4plxQYUqT4XKbGrIN1IO6SVd4kh8O3ODZDbuWPZe8kiB7P76D58/2YrhL5+8T8cgSMkxlazlNB+rpOz24sK/iALWPN/A/+9/OaepGYvO8NtzBs0270HSNpieaKb45Siw6h1SSYn9eZq4KLCHVZZbHZtnPnZNhOl7r4vRXLmb+vvVGD7OD8zz0z+4noJvEpYUmPaKY1L1/BVAwp+MU6ljSzYnBSaWIOxYh04+h6dTmFfHJTV6Fxokvn80QYQCsqM3bnz/Dx/77U+9KIeaeymbOTPQxRxxdajhKIpRnm5lu80MpO3DDpxOqLaBudzXPP3eC7vu89sxO2PhCPoblHF+9dZq7jTpu/O4lXMtlpk4St1JMTpGWFFLMbhLMbfJiiwAy1QzqKWFdaXjvXXBG8IkNu6hbxk5tHetYxzrWsY6/r3hfkWEOHz6Mz+fDsixefvllent72bBhwx1v5/r165w4cQKAkpIS9u3b9x4f6TrWsY6/S/hG93mOj3WnVAsEHbNjuBsVRnxBYUMoxcQ+DV0TKFeRKFQkNsGenYV8q/8i7m4F6EwxRX1FEg4VAAtVEY5fLDvR11OJMUdmJY8FxIKS6w9Lj6wC6Lag+bS+YpXbaojNxjnzVjud+5JEQ4LQBFRchJbvKOaavcBB8aFyBvToQlBECHyFPsyPlsGfjhIo8CMdiZ1wsEJeVZY/35eTYE3mKcxhG9dyGeuY5Pnf+gEHP7uXQs2fpUCzgFL/7avEz4328dqJS8zEIhTWSUpvedfHSKlu9J0ZYvDiCL2nBpjun6W4rihjrWInnCXC05NNLol8L6niqHRFkEqRe7z/9IBXxWzuL6CgKp/RG+MYPgNrUfJbKWi8qw7Dv3JXubOklhf6riClxEJkabDkQhkQqYOx/VB/zqVycxmjeQbOhIONi7O0WNqDsSAwA+A6LNtzKy1VESK9qkvPEkAS1Ew+3LgLqyuGdilO/60e/A9tpHAFtYnVcHN2jKv+yZR+sXdM09sgfxCKuskEZ6NTMWKzcfKKlz+p2aF5Lj9/g/BYhJLGIj79zD7G6+PMJuNsLCynxL+8vPWPgo7XuzNB9DSm+2bx5fuJz8ZJRyI1Q8cMGMRm4lQa5Uvs2EqbijMRLV+eD9d0cR1JSUMRD/3avV6FmS0xLMHW10xiRZK6g3U8/uABOo/30t83yEiFd79Cs4KNJw0Egvjsj2YLdTN/NkOESWMiEeFvus5xeXoo8/zkmz7vNV1SreotUAocJPOlMLlfZeS+7XyI1Ao2/UCR9yuVdEYnkXgqRAJPZUEgKDADPNGw9bYkuMqWMqTrkfB0Q8/Y7bQ8sAE7ajN4cQSAhr01HPq5tVmvBEuCTDS79B5wMtLOeTOC2nDuPYzPJojPJSiqK0Q3NDYXVaELkbGGSWNL8Z1XiblS8mcdJ7g1P05im4vaoghug+BEujQ5dcWUV504Oj0LWbyhjUeamNzikRvGgxqvjnVwdWYYqRR7yut5tHZLRn0rryyPYHGA+GwCX9DnEQOzYAR0Wg5vIO5YvD58k875CYp9QY7UtLyrKtg7QdOBehKvduEEYGSLi+NXFM7pyBozh3hU6s9je8narA/SCFsJwnaSqryCZaufAWrTdi1KEXOsHCWjHwzeYMsKylNj8YXk+GT3NMf+5Aw3S+YI73XQTR1fnpnp585M9PGp1gP8+q6H6ZgdI2In6Z6f5PyUp0xpZb2PLgodiJZJImWS/CnvuBe3SavBsVze+B8nGL0+zkSTw+D9Lpqp4fOb2K6LQpF0HETcs3oYa1PUXJVsf7qN+GycwcIhhCbwm7pn/Zi6DZOJ5Y+hJq8ox/YG4PrMiNdWLnpXZusUwRsWTsJGX6OKiqk0khErJ1Gh+3Q2lJZyUcyTTPEqlPCskiY2uVybHqGBpWSYC5MDdIgpAoWBDMFx8i6NF7hFR98YScfG9amFamWRqsC2IFhosre8gQdqWkm6Ll0vdXHp1IIdg2tLTn/5IrU7qsgvvzPb3frQ0gps15Gc/+srdB7twbUldbuqOfiZveSVeP1lbDbOK//laIboNn5zitd/7zhP/b8P3zFR+keBdCWnv3qRyGQUzdAITEriFbljPFNo/MKW+3LI0IMXR7jw3DXmhuZxHRfpSpy4kyGBzgzMcfSP3iExn2DnB398xSrJqIXuCFyfwszqXnVDx8Kl/Ilawn+SS4Zp2lVLj2+KhOt47VTaMkV4yXTwiFmfV2fZe2uCDzXtyrGJKguEmExEPRKA65DuV98aucmn8g+s2F6tYx3vF4THIpz6ygVGro1j+STzZS6uX6HFFE6pl4RMc9Y2F1UuUWt9e6QzY+Xi03QcJbFxCQQNZMSh4bxOb2c3VmeUw587QLAkwPxIGCfpImYVrc9puE8VUbCrhOZQGffVbMKnG1x/+eaS/nJ+NELXsT62Pr7UXmSmxKbriIujewNe6YehxwX5/eRYtaykBJOdxE4rEEztEszFIH8YAmMQqxNZihfLb8dIgJKKpqPQ99jibwVtA2srTnBtl86jvZh5BqBwbZnZ5d0/s2dN29CE4INNu3i8fhsxxyKgG/yHCy/nrGMryXBsjl+59wHmNszzP06+yljazlYH/ALLdpmvUwRGWSAL4fXfO56+M6vnIl+QfDNAf9RTCPXrRmo8LjlQsZHDNS1UBb1nbN8ndnDyS+cXlH40wb5P7FjRrnM5ONLlB4M3cpYp4PsD19lf0XjHbfS2J1rJKw7Q+XYv0lFsOFhPw75aYjNxXrO7GZruX/Kb/sg0Byo3oJTizeGbnpKjyHoUBTi4SGUQdyzeHLnJ+cl+inzBJao5tXlFmST3O186R+8pj/hQfV2nf68DyrPTBc8iqniF+XVbcRWvDbSnlEoWllsl3n+ZC5WC9EGsRlDckTuuShP+lZFSh8m5nCvcJ5FK5CtFvNyLpekSXG2ZnwhQpmB6C8yenic6HSNUuvw57f+JnYzfnGRmwCPHT2+BoSPCe/+FxX++8AP2DXvvn+H3bHgKOxXzTd68Vk8KVGrIH1ImEeFy80GH4kHJhrMGvqRJRUsZlfur+VLXO15ssUggkt656w4o01OR0hAZ+y89ZU+cviKWdAgIk6szwyuSYdJoKihbYsGpC0FDfvGy6x/+3AHqdlXR8WoXUilajmxgWESWba065xbIid/sucBYPIyuaehoRB2Lv+o8yz/d/SjgzQ8W22AC1ATfnXp3+6udKBTzVYpkgSJ/QjBxa4roYISfbT3A17vOYftsSLoUjmqU9QjyZzQan2nmdTGQUZCBNGfEm9O7UtJaVMknNy3kggYvjfx/7P13mGTXed0L//YJlbq7Ouc4M92TMzCDATDIIAgQEKNEkZJIUcFK5pV1ZfuTZd9r60q29dm+lmRlWpIVKJEiRYoEA0AQRAZmMJicY+ecu3LVCXvfP3ZVddd09wSQoCGp1/OQg646deI+O7zvetdadnwn5TB9dZbmbbdPOq8IhPjFbQ/yzPB5TswMEXMyGAiscgvhQcUlv7jmE4Zg90e2cexzZ0i0LJ6zypPzlYLTMyOcy41QcUCx8TW7SACSedVxobStmLQ1Ya143Xk7O2npccfwQSjBlpdsNj369lRv1rCGNaxhDWv4x4p3FRmmrKyMxx57jG984xt4nseP/diP8eKLLxII3Hpy2Pd9fvZnfxbQE6GPf/zj79TprmENa/gHAF9KDheJMAAKH/RqyRQY+QC9HxBgCsrCIS2jLwTSEHx16HRJklSi+PrQWTbuaGV6vF8H8AxwrRXKdYBoIFTi21zcTxASTVD4mW8r+u/xabzz5nLJwwNTvHztEpmQz/b1HUx/bZjXN07j2ToilWmEeKdgy9dN6kahuaoRt6eWkYne0h0JgWP6pDcK5jp8hIT63gBVMz5zldp7VykFhrYWCo9K/JxO0JgBm4XRON/89y/gN9m47/MxKkzMgB5W6kPl7Ky9ccX71wbP8EL/RXI1PqpWMbdJMDkJ3V9R2JnFIoiXf+8wylcopZgbjKGkxDCNoprNUjhRTT6yIza+0moA1100waBOyFa4AV75/TdxMy5SytKyHUCYgo47bmyTUmYH+dTGA3yu9xhJL4chtEKDWiGAI239XDiuD5P++wnSB1xdjVjQfAVWC6oCGArsGOSiS4JP6MBLaAZydVpBhnzCXSnFyyfO0/GlxcTslZf6eOxXH6C2a3mi7ka4uDCBMAwK+sGF25XoEFT2L56zYQqmLs/QdVf7sn2c/PI5jn3uDMqXCNNguneW0dMTPPXrj7Kh/tZUhN4OCoou1yPaUIbypZYfF1qRxLAMGresfC7Rpgo2PbyByy/od8m0TcKVIR76F/dQ3VbJXZ/cw5G/PFkkbLVEq3nkyb0c+uOjDB8fo1VKqrMSGRCUY2PkswotO27+3pu2SevOpmKVV+GOG6aBaA6Any5uKz0fz5UcGesnGLCLweKk69AWrqY3N5uXsL6ureUDmU6k1Pce8n1Ws+LhgXp++qmDeEpyanaEkdQ89aFy1lXU0RCuKPpTr4ZsPMfLv/8mvifxMi4uHnbYoqarir0/uJ1wZQgn7SLEolLMraBmZy1DMVmssERBqlox2qXbvvQlb/7FCfreGEJJRagyyN0/cQdtu5v58Lo9fHXgFG6etNgdreeR1tsL6gO8MdnLhflxlJuvghaCTD2EYvkTUgqVLxEUCsLDPuRjn1Ip/supbzOSWlhCqhOU20EMIXhh9DKxXKZY3WZaBnd+fCevf+YohmUQKLNxMx6mbRCIBNj8WDfdD3bxexdeKdq1jKYWuLgwwc9uOfg9JcQ4aYfLL/Qx0ztHtKmcTQ+vJxvPIo6N0XTRoKK9ggf+2V3MRHO8NHaFOSfNhoo6HmvbctP2UoAvJV/uP8mJmWEkiqgd4ofW72VT1fJ3p6O8hl01rRyfGSoSYYQQBPNWJRN50ovydX1mwXqnQF7wPcnLv3uYzEIWVQso8B0f1xDFNlmYF5jCYGue0BN3F7PuSys/DbE4uLhLCukbN936M7j8Qi8TF6ZwA4qhO328gD6K42kSlEDbvFm+0n2zBSpoMH1lhvf/5mNYY1f55sj5kn0qpcgcnuOL/+3r2CGbjQ+tY+sTG5cpJU33ztJ/aJhETRxVpZYpEZmOoqyj/LbshCpOe8iArwe1PPyMx9Frvbhli8eXln5XTA9mG0oJf+Pnpzjxd2d5q3WG7DqFHbKK5FVX+pyZHyVk2oSFrUlRS7oTaekxVCnFufkxHmndjGWYjJ4pTTSATmaOnZlk48Pfvcz46b8/z6XnrxX/Hjk1Tiae5X3//mEA+t4YKhJhiufqSa6+3L/MiuSdxLXXBhh8awQhBFbAYtOLivM/IHEjaMIyBh/v2YdK+Fw6NoRhCioaynjl9w4j8/O1XDy3fDajNLHryF+dorI5Ssedt6eMdKto293MhfGrBNKCbEUhe6rntQLYsqeLdb9cy+UXenFSDm17WtjyWDfuxGVeGLusT9XQ52to54iiNYpvKs7OjTGbTfFLOx4uHvM9rVv4m2tvlVglBQyTc/PjHJkaWJOpX8O7GtKXfOe/v05yKsXYFp/RHR6erfAD+XfA1+owIWVxsLmHR1uXK+6VJGqFIGIF8H2fytM+bScDZKOKiw+7nKga5vihGLvur2f+s7EiEyCUFJR9x+UjT95ZovgSG09cfyhAk+tXwqGJPnyroPSyOP4m2yA4r3CqWLRxWYoVyeKL3ykLki16/DAdULJQgX99aYb+U0h9DmZKIXJC2w3l9206sLnu1pKUvivxHR+BKN4XlSekm7epZhA0LYKmxaX5CbK+h5Ev2ilgOE9MOWdMM9fsIf0lV6YU2LDQI2g4tnTBrOdYZ56+xL4f3XVb53NmbqTkb8swsTCpD1UUiTAA3fevo7q9iv4jwxiGYN3dHbdNEE24OdLeYuGL7/i4WZeMn+HLv/Yc93xoN227b4+g3XVX+7L1brgyRONYFFYQL6nP2x5JpcjKJWP9Uo4W2trbVxJDaLXeBSdNTTBCynNwpc/mqqaikmEu5TB4dJGc0HTZxHRgukdSt7WCXfVtPNi8nDRWQGRSaUmaVTMD+UVLQSTIgEAMKvtZXEsWAh9LCGSlv19ycatAGbqAS/jkrZ9ZUclUmTDTo+d8q6GioZyP/PaTvPhbr9F3apTx+/L7zLO30sLjXOMCPVcsZFgw+JBJvFnmr0dgZ6By0MCJQqbd0/ZkAubaJa7lsuEZyMay/OlLL9FrzOPYOo6kAvpSbduioTzKbC5F1nMxDYPWSBVKKQaSs8XzLBA5VlJYvh5PtG9jKDlHakkbfrxt26oKm5l4ltNfuUBqVschj372NOZdUdjDsj6uMqD34fgevfFprsdUNsF0Jkl9uJzdkRYOyWvEyBXjC62RSnbXLY/73ArSmRwXH/FI1i/G9ZoumWSTObo7G/nVPe9lLLHAxKExpoemMIMm3T/cxbp7Oui9kCoqTQIIBGVWAIHgkz13seO6WGQgUqowVVjLBMrevkWnrySnJoZwfY8yK4iHRKL4YPcutjbVMhgeQRiC9fd2ECgLkJ7LEC5ffAAqT3ZBgXIl0oVYs2Kq1cWpMxYVLYUqCMMgyvP/KoEwNGHbD5J/X/T71nLBZMP2Nspqv/dFZmtYwxrWsIY1/EPGu4oMA/Abv/EbPPPMMyilOHz4MPfddx9/9Vd/xaZNN09KLCws8IlPfII33ngDgEAgwK/+6q++06e8hjWs4V2M/sQMnlpBr6OwUBeiuG4w8wu6gq2AUopEXq53KRJujm3VzZycHca0TU248FYmw7SXVzM/t5wMA/r4yhRIlJaID1gMOPPsuoHY8RtPn+LL6iJuSEESzp2fwGhRePlCb5UvL3IqFLEewSa3lvt/4S5GzaSuQEJhCrNYoeIGoO/AYjBmvk3SfsrE8Uw8dHArVB6k+0IIkVhAGVr+1zBFfjGpCMwptnzLZHyLpPLOCjY2NvFAc88Nk5xxJ8OhiT6ywmdpZCRbD2MHBZ3f1s/MzXhIX2EFTJy0W0xe2kEL09SytqZtYIW0hEplTJKssDFMg5CySCltU2MKgVSKoGkhBHiuR+KtaQYumbqCJFdKrDEsQbA8yNVXBui5iT1Cd2UDH+nazZ9fOYyb9sjh6Qq66wINhUScaRmMnp7ACILYz3WkhBtXuPkBqD0H81vzv8gvnoWCtpcVvR/Si2KUwvAEphBMWCmawgECGb1vL+dz9uuXePD/uPuGx7oeZXYQO2jqyvulidDskrcrnywOViwmRedzaS4tTJDsizP45UuabIZORDspF8PIcu21gRta2Xy3qOmoYvCt5VVMm9/TzZWX+1nIV44ZlsH+H9tNOLq6dcv+T+ymZWcjY2cmCVUG6b6vq1jV331fF607mxi/MEUoGqR5SwNzQwsMH9eBGsMwCJo2XtLDtT2CVoDGzfVsfqz7lq7joV+6h2d/4yUmL80Un4H0JLFvT+DeY2KHbdyMi5f1tDqQgmxWEqwIYBgCL+czk4mzezjKmXVxLXt/XeK7csakbVMtC6nSpLAyYKFbcEKO0el2EQ2E2Fffyb76zls69wJOf/UCC8Mx7KCFjAri1ZJwWvDIv7qPcKW+74HI7QelBjJz2BU2fiKHWvI6n5sYJTWbZvCtEXpfGyx+no3lePUPj/Dh//4E++o72VbdzEBilmggtKKiw83w3MgFvjl0TiuCCCAIVi6fREUR7YfYOpC2lg4PpASR3GIf+fzIxWISAgo9giLnu0WFohOzwzzZsYMyW//ddVc7NZ1VDLw1gmEIOva1oaQiXBkkEAlwcX6iSIQpQCrFK+PXvmdkGN/1ee43Xy2+Q56teG3sGs2Pt7PuyZ1sCTZQ06QTGjXAxhXIK7eCVyeucWxmsco27mb57NUj/Ls9jy9TcAL4ePc+ooEQz49c0gRJY3HcszCoGTYYLddjszAF9dEoDzTr93Dq8kxRralmyGBkByB04qRAhtldu9wKbF99J6+OXyPru1jCxFVaMSkYsHEdF+FIKqb0OWx8aP0t2X8VMH5e24CN7vBLCDViaQ7CW+yLwzFBICuITySJjyc40LSeM/OjDC+RN68eNUh/S9tg5BIOJ754DiUV259a7Iv73hjkjT89BgpC9ZLcIy5m0MJ3vOKwNbzbZ7wmwdDZF/lQ1+5bsoSZfmGMLimZuEuQrYPQNJSPKKbvAOUBNhSyktKCoG2xYGT5lSNfIeO51FoRGr+Uo2warFpQnsRJOQQrghimgSd9DAS5pIP0JGLpdGRJQifh5bgWm+a/nX6eH1q/d1V1vmDF7av2rYSrrwws+2y2b5754RjV7ZU4qZXt2XKrfH4zZDyHl8au0hufpioY5v6mnlt6PmNnJvECikxUEYoLytM2+76oKP+BRpoOtLCjrpX4hXm+8gfPapUCdF+AEJiWoZOrBXWVlaAUp7964R0jw+z4gS3MDSwwc35Cz3GFIBDRxNCdNa3a/nBnGa07S5PRj7VtocwOcmx6kAHf0wqErsIrTGkEWJZuTGPpGEPJOTrK9f3cU9fOtdgUr05c0/NUw8A2dKjl1NgQO4xGKupvT11oDWv4fmHi4jTJqRSpasnILg+JXnMASEtgSj2n8bMuT7RuLZJIlyKywlisfEV0XJCsl1x82EOZuop9SMaZKEuypckmmNDrTZGfpw6fHGfDvZ1MXp7hzNcuMnF+kmzSIRCxixYywKpj6IKTWVn0JV+YYGV1tb1OHl6/zepZe2lqYmZBUdbwRF6RU62wHz02V/VCsl1gefmxLV9TYFoG6Z23Nq4EIjaNm+uZvLSYpBYIqtorb6lPiTsZBhJzVAcjtJdX88LoJb4zeomU6+jpqmkRMAvFLBUcmx7kKwOnsIRJllJyJgqylYpUI5TpaQluGGb3CZ52LjJ01uU9m7aVqGbdCNYqsQLLWN6+atdVU7vu1ubnac/BNsySWETUDlFhB0m4OaQncdIOKDRpcijFK7//Jk/9xqNUNr99O+EC9jV0cniqj7ncYqFCdTDC/ga9ZjINg81VTUUL76WwhFEkSQTy55/zPYZS85RbQTZVNfKBrp1U5u+xdOUyq9T6fpP6fpOPvv++mxKVew8NoW7N3RjQZJXgrEJ4YKXBK7ue/LIKVPH/VtgpRaWhyjFBrAP8FS159bwwtdte0SJtKUzLYNuTm7k8N4W0XEo6BQHJZq2yNHRQEW/RJBnD19ZIQkKsRZKtRLPeImBm9bUnmiRO1GDwzBhXt/vI4KJNVIEXlMVjIhPHVgaGFEglSbo5Mp6DQOTVVTVcKYvt4kZoikT5/+16D6dmR0h7Dlurm5cpOC7Fqy+d4cSOOJmoonxG0HbWIngkRt2ucmbMxdikgeCBPFnKFAYBwyyq1yy5XYRMizNPX+Ts1y/RKnyCPZLQjkr27ulmX33nLRc3XI/0wTDJ8mTJZxNbJbkWs3hO7dEa2h+vgcf195l4lrf+6iQbZmFuRxmT4TSGMAiYJoYwKLeCbKleTjbc/J4NHP3r0yilcFJ6fWBYBsf/9gwHf27/qqrGq8HLeXzub18i2ZhBGZowqkytAnNqZoQHd2ykcWMd0peMnZtkpn8eM2BSPayIzAvS1UoTYdDrOSM/vqBgvkWSWKcwHPAL1n7FBUx+XPbADFgEAyYSSa0bxppyqR232Lu+i50f3Hpb17OGNaxhDWtYwz8FvOvIMLt27eLf/tt/y3/8j/8RIQRHjx5l+/btPP744zz55JNs27atuK3jOBw9epSRkRFeeuklPvvZzxKP6woRIQS//du/TUvLjav617CGNfzjxuD0zLIgQ2Et4QcorikMCQG7tEsUAiqsIGnfxZO+thYSJrZncOS5s/iNHinbxV9hYV8ILmU8d0ULDgBhg5eveJFIpPIIGat3y4nJJC8NXsbduESpxle45fmqvYK4ST6Q1/reDp7af4CU5/CNi2fxlMTxfcAjbNnc07ie49ODCMvQyaL8Pic3SX6+7V4m63MIYHN1EzX3lPHM2IvM9ulkrZt1WRrQCMcN1h8x2FRRx/59JWbPK2IktaCr6K8LHipD+1ArwA/D+L2C2Dofw/WpvaBoPKYPK6WisrkCN+viO4vBkru39nC+Ks25qVF8V2ILiARDCFvLvPpKkvEkyvEZ2wrjm3w2vGlRftnEy/nFh1cItuaSt5aA6q5sgKTEd30MC/yV4gH5AInv+Shf4VlguAKRjzP6IUqq5FdDw3FFsk3gRgGpG3PNeR2UNB1YjFsqfEOTh3yvtOQrtko15Y1wZ10Hr45fhQpNqFFSYSpBy4jAjmg1JdM2qGmvoimvrHJkqp+v9J9GosilHcwPS9Y/DYFCkadS+K5Paja9+oG/B+h5aD29bwwRX1JdWt9TS/cD69j40HrGL06RjeVo2lp/S4GQtl3NtO1auXowXBli/d2LntnXV7TaQQvLNgmUBXj4l++hoefWSQk5S1L98+u49jdxgqdz2PlYVuUJn7kORbZd4fk+QkDNsMFCiwRUXmlAIV1JcMag7I0c2y5bDNzhkayHwrtcNSjYf62Srn+1iUtnZvCyujH5Nkhbq5ycCc/Re+Y7/OyWg2/Lg3r8nI6ej2/2GdlVsDSS/PnFQ/z8PQ+tGhi/GSrsEL6npeOX8nuslFbVWJpEKMB3fEZOjWtCkxUoKnzcLsbTMV4YvYxRlGqiKFdseBCaVzQcgysfE2TqtD2Lb8IrzeNsjk+zIVrP6dmRFfe91GJPKkXGd4pkGNBqRTvfv7LdSGwFVTKAhdz37n0bfGtkkQgTUFx4j0u2QjE41Y8dHuF0RS0/23CwaO/0dnFmhfvjSJ+LC5PsXaEa0RCCx9q2cnR6qESpASAw6DETz2AGBYYHdf0GPTMGFfs1y8SwFhtQKCnYcNhi8A4fP6ITEw8096yofFYZCPNzW+7j2yMXGErNk3Sz+FJhGgbByjAP+Z00fzBM46Y66rtrb+v6C/1SrFli+BSls4tV5sLIK92pvO1ivr8XYEdsgqbFL2x9gHPzY0xlElRng1z4oxN5o7NFXPpOb5EMI33JyS+dKw71FdMGPa/aTOySWOsqCGQEMyKNbVsIQzCWjvFnlw/xq7vfS9i6MaEtm8xRPg8tryrmN4sSpTPDU0h7US3NMAWmbXFmbrR4tmO5OBOPw54/F9RftZje4OtAtONjhA0ChoWTdCCv/ldQ+ShiiRibUjCVSfAXlw/zqYf3aHWYJduW10Vuu1p8NaykaOebiu/MXeHqzCyqQ2Jv8Wm6aJQ8m/Y9t398qSSfufh6kRA3nJrnwvw4P7fl/psSYq61JTnT7WgLRgXN501az5iUXfBIXxvlSnuS/sPDRSIMaDKM9BVmngxrBU3cjIcbgZmdkK0ThGYU9WchoEziE8nVDv9dIxCxec+v3M8dgwu8OHmFE2qcnPToKK/m8drNnP36JeITCeq7a9lwsBPT1uOOEIKDTRs42LSBw5N9fKX/FJ7j46HVykxhYC4Zo5zr7Ak7KmoIzyz2zzJP+p3sn+Cr/+NbNG2t576fv4tQxa2rKK1hDd8PFCzmFlrz73Sx+9H/UbA+yYR80pks5eXLq83vbVzPpYUJFBStiaNGiMhcltMfcJH5YUmZoCyF4UhmNkDb2evWvkpbFX7rP71MLpHT63ilScyhqCY81nXXsG7JXHspNqoazjG2uLPClUgIzUO2RnNSVi5jWak4YTEBKYDwNOSq0IOHZNlaFgAJkQlFeALmtop8X6p7dStsYYcsjNtQdbn7J/by4u8cKq5jyuoiHPyZfTf93esT1/jG0LmiSl5DqILJTFzHNEwzb+umlSsEMJ1N8Nmrb5HzXT2vyCfuC3fEzJO8c9V63ekHoPeDArfSRwBvzPdz6fw0/2L7w5Tbpf2cVJLXxns5nic276lr5466Ds7OjZVsFzQtdq1AOL4VjKdjfKnvJMOpeQKGyd2N63iifRuGMDANgyc7tvOF3uN4jl8kX7SfMhFKID1J3xuD7PnBm8cxboaIFeDT2x7gjYk+xtIxmiOVHGxaT9gKMJycJ+u7vL9zBxPpOBOZeFHBuNqKIIUiobIEDE1SyvkeOb+QJYcrsSn+16XD/PLORzCEIFwVor6nlumrsyXn0LSl/pYU+4btwjp1BYUj0O18SRs3XBg/KJi6C+wkebswhQyIknlp6T6W72cZBARTsONUGdG9Hbx68gKxjpV/09Z5a2qyzVsb6N7ayiUGFk9FaIszkYVAtU1ik48lRd6+VyGBTDVY7pJrEaDCApHSZBwrYuPP50oI6ddfbs7zcGT++/w8U4jFO6zQ/VDIsrmrYd2K5z+bTfG1wTNciU0StUPc39zDvU03Zy6Np2M8VzGAF9btaq5DEW9w2fmMzUMzbUzthiuxSaoCYe5r6tZxLDRJa3/DOl6buFayv501raT7E5z6ynmm10tmu/R+647G6GgME2h6+2klZ2MAMWWgvMLYo8nL/elZ1lUvj5H0vznMi7/1hiblC0HTSQPvcYv0el3gFrVD/Gj3vhXjCZsf7QYER/7yhJ6vBnRfPH5+iq//2+dp2FhH09Z6Nj60vjgvvBEufaeXhbkkqgm8wOKzRin6EjMcmuxjb6SV7/zX11gY0bE3L6eLZjZ/x2Kiw2V6k9KxAQ+WNrh0vcSJiOvav/7bC4Od1EQZDw/P96kMhoikDDbWtvDgUzvWFGHWsIY1rGENa1gF7zoyDMCv//qvMz09zWc+8xldse/7PPPMMzzzzDPFbZRSDA8Pc+DAgZLPCvjX//pf83M/93Pf1/Newxq+X/CV5M3Jfs7NjxE0LA40rmNz1bvTDzQxlcRzfKrbbk/K9nuBc9+8zCt9p2CHykujFjIfSzYSYCpBMGDRXlbNeGaRJHBP4wYGErNcWiL77CiPmn7BqcYMOPlFj7U8fGAKg6c6djCSWmAsHSOtnJKClHLPJmWXJugUEFtis3A9Jq/MkKlUJdujFEKCNESRDCMAwzbZu2k9QgieH7nIRCZOyLQJGBZSSSzDYHdNG0fG+4qVMkqBMMGpE/xZ5hj+oKI2WEZTpJKaYBndB7uKZJjFWKEmQJSe1M1xeWFyiXVVKUxXWzoPvM8k2aADoL4JU3u16k3jUZ3wmhtcoH1vCw0b6/Bdn9adTbTtbib5318jPSNI1QgCScHgvixm1CYQMEl7LlJJDLR4izKhf7/Ptl4FOYpKvr7ro1KKtt239l55Cw6dL8K1/Xlyk0WpBLfMS2rbFMk7sQ26KrGwnfBuXlkVSAkCKcXGLymSWyy8CkGwz6UsH0es7IPYEpER3/UpG1Ew7eIEZLGKquY2LZIAqoIRfjaf6B1MzFFuB3lP2xYqK+Dic1fJLGRo2dHE7g9vQwhB2nN4euDMErsQhVMO43cJOr9T2lCaVrElSrpZXp/oZTS1QFMkysGm7mIl3O0gWBbgff/+IXrfGCI2Fqd2XTXrDrRjFmyKtt1YrUJXELnYYWvFatgboW59zbIOQhiCtt1Nt0WEubIwyV9efZNs1iW3x0VsF3R+S1ExooMibkhXuOkOAMpmNSNjoVVq26b8e95yXje48ilBzxcVbkiRrYXgPARSisoHy9lZ08rd7d0cHrqG6/hIWyffA5aFaZtkPZdnh8/z05vvva17ARCuDjOVTTC8uzSJOMACn/v6q1Qf9YhUh9n6xEaat966f/e6ilpqckEmKe1Xmy4bJBpTq0rJW7cpMb8SrsamALANE0f6um8TmqwQikPtBci0CZyqfFUxgADX8fjGmVN8+q6HiViBFbtPYwmzpz5UTl2o/JbPa0O0fsXQdk/l986SbGlCe7LHL1qSyDwJYSAxy9m50bctn13AaiQp+wYkm6Bp8cmeu/hC33FiTgYBbKps4vTsALJFn59vw1SPpOFKkmOfP4PyFfU9NZQ3lBEb0wmj2iGT6mGD9T/Yzb69224oZ95SVsmnNmnVLaUUA4lZkl6O7mj9igo2t4rN7+mm/8gwdkaQK1dYDihLYEUsDCH46c0HufRqL0PHR6kaMzA93W467mgtEmlMwygmmKavzXJxhQyDk158f5yUS3p+cU4y0+Uz1yGxEvDBhh0cy4wQj5e+b1nf5dz82E0Vo5SviK2HoUfzSUIo1skKla/ct/WY2OBFyAXVYlvOTx2kpRjdJWk/otj8ks3INg+vUbC5voHOuTK+5lzQiRl0EhQzf5zrmoyrfFzXJ+u7jLZleeCfH+DcM5dJzaZp2tLAno9su6Wg+K2gc19riUIVwMhDgnh2BKkkWd/Du9tndDt0HrNoGLTofqCLzv23nxi8vDC1TBnKV4pXJ67yiYq7Vv3dtdg0l5riqARFEu/odp/gsA9XZhGGYPzcFLlkDrsigFshSNRJZjrBl4r6MY+GXhM7ZOOHoP+DilxQP7RkmyDeI9jxbZO6dbeujPR2MVflcGxiTCswGgaD8Tl+68K32PRNTTbqe2OIq0cG2PJzO6gOl1EdXEwe3N24HgW8MdHLRDqOI/2i1RpAmRWgq6KU1Lazpo1vDV8o2hg4Ka1o2HBN9xkTF6Z567Mnuf8XDrCGNbyb0LS1gUCZjZUvDFjmTpL/uyxnrUiEuRab4qXxKxhCEHey+EpiGybrq2qJ7/SQZulYgQAZEHnLv0XYYYv2vc28+RcnySVzep3LYsLZCpjc/VN30rmvddV++UDdOp6/fJ5Ew/JZleFB8xuKZIdgrsAjvp7/omCF4RFD6jlDrhJkQAtGWC5IucL6TUC6SZBpLKjQCAKGqQkoysfzXCKWhVJqmTXhSqhoLOf9//k9zPTOIX1FfXfNTdcjM9kkXx88WzIHHEzOYgiDoGkRMm1MoZXUGkMVxN0sjvSLl+4ruSQ1C4YUiDxBO6ynvcxvRhdnFO6RaRBzsrw1NcDD19mNPjN0nleXJNmfHT7PvY3r+cF1e3h+9BIxJ0NbWRXv79y5jEhzK/Cl5H9dPkTM0fMWR/q8Mn6NMivIgy0bAdhb10FTuJKvPX+E2d45qq4qwgsSaQttweytHJu4GeaHFjj91YvMDy1Q2Rpl14e2UttVzSOtm5BKETAtkm6O3zv3clEFMmIF+JENd2IIg9NvXSPxzQnMBR87bDH4w5XMhrUNZkElxjaMYluZyiYYTMyyLqrXkff+szt55ffeZD5PTq/prOLun7rjls7d6AwhXLSN1/XIK0IVkvwFNWUvAp4AKvJtw8tvs6wpL/lglfdq6QbZMsHwh21+afM2Lr7ZR0xkWRa4U9B2xoJtq+1Hw89bTAZmFS3hCGMVaf0u5q8zG4VDP5zFN3WYsGCJWChoCEQCCPzi/ZdKYQCRBUE4ARmpqL4imNmx+nVdT8RW+XuwuLlgZ03LiqoqvpT8yaXXi+pCM9kUX+g9zpm5UZ7s2F5UpVsJhyb7wBIsFXbyQorZTklTew37Olp4kpVJX+/r2EbItHhrehCpJLtq2ni8fStnvnie0Z0+Y1sXaYSJBsmz/ef4mb1vvwC5tqycUEUQ6Wk1ZyOvLlgVXB7vSc9neOX3D2siDIBSqIRP1zcNuj/WTefDnbSWVWGK1fvGrrvaOPrXp4pqtNKT5FIOuUSOXMph5NQ4Y2cneeRfHrzpuQ9fnMALKq3YeR1/0jJMjkwNYJ6MFYkwAFbQQvqSyroo8vwc9YMm55/yccN5dp4QYIATNTR5agVeJkLhlgkqUlAxbjC9QRJLZYibGfpY4OJfDvMTH37kti3R17CGNaxhDWv4p4B3JRkG4I/+6I84cOAAv/RLv0QsFisuFK9fMC79HCAajfK7v/u7fPKTn/zfdeprWMM7ji/1nSxW1QBcWJjgYxvuXLE6+vuF69/NXDLHq3/4FhMXdLSksqWC+37hru8bKWby8gwn/+4c/p0KKwve9aobS7yNy4NBXVkQifKD6/cylUnQVl6FVIpDk31ErACu9HUBWM4n1qwDc0IuKr5eXxUSsQI83LqJwcQcl2OTgMCVHkopGsIVbKpq5M3JfnK+h0LLAYdMm+HUPPvpWvGaKhrKiBwXxBsLxUxKW+8IQWge3DLAANM22deyjp5oPUPHRjk+14cb8LCCJgiRT64KZhIJ/DkXZavi4t/PW+wUlGxmcyn+4sqb/Ns976XnoXVkYlkuPX8N6Ut8TxII2yXPfd3dt9YGr8WnMYSBrwoldYvPpnxI4bfYpBr8JQmy/PlsFTQeXdx25NQ42XiW9//nx7CCFgujccbPTlGFQdW4wWynj7TA8zx8sWiX5VtanUEAvq1I1CjKUroqeClrqesWE1DZRI6qMYOtnxOk6iVKQf9TukKqQFICiOR5VYk2mLxLYOalT5XIV106qwSjAOEKNqeqsMNxpCexFiRzHeAcFJSPQsMxReurCmUI4uv0ba0cN2h/XQE+vuMjA5JwdYidH1hZSeJmaC2rojZUzrm5MWZyKf7nxdfpLK/hF/7NA8XA5UQ6xncunOTy1CRxI0PQtDAtEzNgIl1JqlXghhW5KgjNCzbsa13RKiHjufzB+VeZzaUAuByb4uTMCP9i+0NUBFa3MVoNdthm86O3oQOdx+iZCY7+zWkSk0nCVSF2fnALG29inbUU0cZytj7ew4VnryKFQhlQVh5i121I1yql+MrAadyCSojS7WXsPkHrS4rRBwVOFEx/sXp1dLvPzm/YVEwbJNcJRNyn4bJBxbQmGTspFyUVVhrKM7rtW0GTTCyLIQQ/3H0HD7b08HrfFV6f78eytW99LplDupILs8NcHutl08O3d0+3PbGR089NlnwmLAM343F5YZLNIzYLI3HGz0/xyL86eMuEGCEEP1y7iy+88SYLzRI7K2i6ZFAzYtJwXy3BiqC2l1qCUGVwmeJD1nMZTM4RDYRuKD+9FAWClhCCoG+Q8yRKQHnM4InUOibKx5hocCnKQOXjXV7WYyQ+wzO//hLmD61MsCgkXyvsIB/bcOctnU8B9eFy3tO2hedHLhb7vuZIlIdabm49uhrG0zGGk/M0hCvoqqilboMOxioUsSZ93UJpy7ECRtMxdvPdzU/2N3QxmJwr+Sxqh25KBO6urOdXdz/GZCZBmRXgtYleXZ2+JN/h2YqJFgf7W1cwDIML37qCn5edl56PFbTY8YHN3PH4zhWTT0oprsanGU7O0RCuYGt1M6bQwdxCouK7RU1nFY/9yv2IV07xpjmNYQnskI2SkppsiNjTo3RGKmho7WIyOYP0JJ3729jzQysHu2u6qglXhYp2UAUsVSAJlgcoqw2Tms0wtNtjYnM+AG4I/nbiBFXBlasOVyO6FlBQAht5UGh5faHHScPJKynFBLkasNKCpisGP7R/F5+RJxYV9pfMt5ItOuFhXXPouqoIlJkEy6cJ7w+x+YLN6HZfk4ilIlkPSqgl0w1R8o8jff6u9wSPRzZS2VpBdXsl6+/ppPx7aG1z58d3kks4jJweBwXBdWXE1qdAKdKeo6v3BThRwegjBgdbd7N/3a2PN0sx76ysADV/E2Woc/NjGKaBVWaR8zyU0kngeI+g/lhBvgemtsLkfhc3oolLpgdCClLtPtkorD8fpOyjjZgV0wSyHk5GJ8PdcpjbDD/wwR1v67puBy+PX11CyNUVudkKj3ijTeWkYLbT5/i2SV48O4cVMNld28ZH199RVLJqiVRqi0hT26z5SmIIc9XK47Bl87Nb7uPZ4XNcm59GzChaz9lEpxb7jcGjo0z3zlLbVX3b5No1rOGdgh20eODTd/PyXxxh1EngBRWWb+CZ+v3RiWLBE23LM9DDyXn+7PIhfKVIujmkktpm17QZSM5RsSeAlczh5G1PCstGYRrc0dlJ7PwEXtYj2lzBgU/tIRAJEB+Pl9isFbp9J+2y7kA7wlg9q17ZXMGdL0e42JZgZpfQMQChryHdBOlGQdsLitg6kbefKIUmBizP3Pt5fkbB9tbwtbqM8FZYu+UJA2YuTxRwFE7Qx8nbKgvgS5dPMjw3z49uW52cWLJLIW6qKufnye+mbXJ5YXIZGVog8JYQ+wpWQu3l1ZycHUFKiet5JeoVFJbGrk7U1pxThPPTsWyV0NYgFvkkv09QCaazpcpfju9xeKp/2fkemRrgP9zxJPsbuvCkf8vqkDPZJAaCmtDi+Hw1PlUkwugKH/38js8MFckwoEnLd9PGkdenQSk8wMv62BH7tomnExenufpKX5Hk6uU8pq/NcvXwIM6nahmv08SwzVVNWEKU2KGmPYfP9x7jpyJ3kv6bMQpX7mY86v7WpewnaxhWMUBhG+YyIra7xMqmoqGcp37jUeZHYgigKh9vy/leiU3oSti7dT3HzoyWjJVFCG0PVHMBJu/IvwNLhy1Tk8IUAjylq5lWVIbR8bilRUArHUzZMOjM8+VTxxjc5KyovNR42SB2YQY+vuolAfDaHx0p2hS3nlXIHhg5sEj0k/k2iwJpgAyCnc2vYXytbmcqvfYtKESHFxQ9b9iAwLAMqq5K0k2CROvieRb2v2p9WH4aKoQmqfdEV17nXolNFYkwBQUngHNzYwwkZvno+ju4o35ldaykm8MKWviOX2KhFepYbg95PUxh8J62LbynrTRWJMImExuXz++vVsdxpf+2bZIONnVzanYEb0lmqjZYxo6a5bGhoeNjJYqEoJ/j6DafYXOQjtEsB5u66V6l6MPNefQfHsLNehimfoZuzltkKeUxdnaSqaszNyxa6o1P8+LuKRL28nuiJDgph+mFeYaOppEiHztWOm+llMK0DV2kJqHxkmJkl48fgmBch6y9CoEwTbJyCaOpGBLVpLBsFFJ1EjOn439e3qbp2l6HPz71CvWZWgYSc5TZAQ42dvNAS8+q17OGNaxhDWtYwz8VvGvJMAA//uM/zoc//GH+5E/+hM9//vOcOnUK3/eXWZ4opdi2bRsf+9jH+PSnP01l5fdfgWINa/h+YT6X5sQSIkwBL4xe+t9ChnEzLsc+f4b+N4cRAtbf08kdH9vB0b85XSTCAMTGErz6B2/y/v/82C1VQH23GDo2CkB0EGa3sWrFhpDguxIzYFJmB2kvr6a9XLPoT8wMA5rZbwmDlOvg2xQXIoaXrxIrVB4v2W/O93hjopf7m3v4uS338drENWJOhp7KBh5o6uH14avIjI/p6v5MmBIVUdTfoOq/cVM9O75Rz1x6kky5KgblhNTJheikQXB9hKbaajbKGp79f15i+tos8r0+bqMiq7y8lK1e6M73z9NxzKTvgFdYU6GMvCzskoKsrO9yaWGSrVVNtD7RwZYnN6IcyfCJMU5+6RzZeI5geYBdH9l6y9YPQWlqOdRiQCVfEe5BTS4AYSgIWGvujqETk/Z1oQWliI0lGHhrhO77usjEShN7Il+t55mqtAkIHZARUv/PTGlZfTNgLlaFCUEufV0l4yqobq8kXBXSdkSj2oO88ahicr8oBuPsLDQd1cyY+CZdKiQUmBmK98HKCJyIQl4XnBU+NMQCtPcFmch6zG3UFfVC6fPP1UCqWdD9JUXntxV+AMyAQcTWgTO/zMf3JK27mjj4s/sJRd+eRP+lhQleHrtK2lu0yepLzPDbZ1/g3+15nNlsmt899RKJRAaFrpTxXIewb2MHLZSvkL7PpU8aYAhCIZstPQ1MZOOkPYfOitpiMOXEzFCRCFNA3M3y5lT/sgDNO4XEdIqXf+8wMh94ySxkOfIXJ6moL6d5262rluz8oW1c6E5yNjGKMmBLTRWy+taDRnE3W7wXhm1oCx4D0g3Q+yGBLBCq0O+Wn/NBQKpG0dprc+DgXl7+0iE81yFz3StU4H/ZZTaWbRYVPQAaI1Hu79rI4eQQnpR4KReV/z44D2/93SlCFUE697WRXshw9aV+ktMpGjfXse6ezqLyzlK07W5ml7eJ0dhFlATTNjBMAyflYOUWt5dKcvSlCzy2sZrQTSxXCuje3cF9h8YZ+sZo8bO67hp6HliHFbTIJXKcf+YKmYUs1R2VbHyoNMl8amaYz107Stp3EWi1mX++7cESNYCVsK26mbpQOeNz87hZFwP9XrZ9WzKaHWHfj+7izWfP0o+3LA8fnTSYmVigdx5ClkVOesV8f5kV4Kc234shBC23SMxZCl9K6kPlHGzqBhTd0QY2VTVg3KBi7kb4ysApDk8uJjO2VDXyie13Eb27jtdqx0hXK3wrr+xhL/a4zZHFsuFc0iE9nyHaXLFi+1gN++o7SbsOr4xfJenlWF9Rxwe7dt5S8NUQRpHYlHSzWAGzaAFWgFcmMAxDq0ClXVCKQHkAo5h80FXDTtrl3DcvM35uknBViE3v6eaFwADn5hdl/jvKq/mZzQcJ3KTd3C7qu2v5ke5H2D0/ziujVxnunyR01aP6rRxnswsYlkmgzGbLYz3s+9FdN9yXaRnc9/P7efUPjpCN6+rj2nXV3Pnxxd8JQ7D3ozt4+c/fYnLjoo2gHbJACJJubtl+bcNgW/WNK0StoEmiDXJVi2X4ytDEUJERdL9pE8gJTBeideV07W9n/bUhTi2zyhLkqsG3FKansENaucrL+QydGKU8bbDpFQPfUrz1MWcVqQNRkrHI+C5fiZ2nrEaw9XmLa68McPdP7qX7/nU3vKZbRSAS4KFfuofUXBov55OulBw7+yKu9Is2Fvk7gmkZnMqOsZ+3R4bZULFyEH9D9MbKUAHDwpU+GeVRyM55ChJ5npQ0FFfv85heX0om8vOvihAwdQd8+ice5fmZKzANVsjCsA1810cgaH2iU6umvcO43iquML45EYUTVvQd0FZ9wpcoTE7OjtBSVsUDzT3M59L86aU3cGRhLiqwhMlH1+9lT137ssrjucEFznztIgvDMTZ0VfPe++/ihefeKNnGSTv4ruTZX3+Jspow9/yzfbelgLaGNbyTaNpSz0f/8/vYMzDGq+l+xpw4AWkSjCmqRYgHdmxhfevyROqhyT58pfCVLJIhlVK40idgWsT8LOFIEOG5OMXEnmBPXRvvu/cuvMd9klNJBo+Ncv6ZK4ycHKd2fQ1jZyeXHStSHb4hEQbgxBfPkhhP0jIMjccVF388X5xQ2EDA1D5BdADi6/LkFoFWUVBg+kCGRZLHKooW0tQkQHmDob641tR3pfi5QpATPodHezkQ7WBD++1b4Q0cGebyi324GZeWHY2k5zMMvjUKStFxZyvh9y9XvbQNsyQ5DmAJg101rRybHiLl5hZrQgrcRyUQvsL0YeOLJqFrfpE44VQUbA319o70kKhlVnw53yshbxTgKUnWdwma1i0RYeayKf7m2lsMxuZwMy5lY4pt58rYuLuT4MM1SE/iZFyUJxGGwApZyNDy2HHfoSHMMpOh3T7zG/Uaqn5EEW69dfLrheeucvzzZ3CzLm7WK7YTAYzepZgzZglkbaygxcWFCbKeu2w9k3JzHD1xeVlRWSAr2HG5hp/6+EG+NXyew1MDJb+rsIOsX4FsXSg6G0jM8tWB04ylY1TYQR5u2bSqvc6OulbK4iaxWp9lFA6h52YT+7W67YowBDKY33jJP8VdSU2oEUqTw/zQjZVwfQtej/UjggbCACUXCTZC6vF7JdWnpZgbWigSYUCrGpmWgTKus4G7bjfShLoBA9OB5B6JMAwiVgCpFPc0rqOlT3E1oddBww8LprsL0oZqUVbGB9sXuKYqvc78sUxXx3UEEIgYBFdZ4xb7SqWKa8PCbhRaWWlPXduKa7pNVY2cnx8nWBHUxVBSYpgGTz61/22TcJvvakaeObtcqCckyHgO9ttQ7gVNTvv5rffz8vgVZrMpuipqebhl44rrO8PSazGfxdq1/scFqVaFHXZILkxwcWGCn9h097JiiWwix3P/+WXi40mUL3GyPoZtFu1LTdsoeQfjE8lVyTBSSb7Qexw3DGrRwYzizTG0bVLcczh+h0MqfyqVw9D+nMLwFVNXZhCGINFjMLpXs75MB/yIwAsJQraNYRpkndL1apEQI/LWTCYIK694lD8PJWCoJs3ITIYyO4DnSL45fA7LMG7JZmsNa1jDGtawhn/MeFeTYQAqKir45V/+ZX75l3+ZVCrFuXPnmJ2dZWFhgUgkQl1dHZs2baK+/nsn+b6GNbybMZ9Lr1hpcLNKz3cKh//8BINvLSYorrzUh+d4DB4dXbZtfDzJwnCM6o6qd/y87LDu3kzLQEh53aJ7yaLaB2UoLGHQHIny0tgVGsMVbK5qpHVJ8tGRPn6+lFzk/X+lBZZTelxV3N7jqwOnWchleH/XzmVS6saLcaxqhZMvrFa+wpjxuOPOGxOa9vzUbjh8he+4fSgzb43k64DdXJckonLMT4xx1hmmPSOpTkHtKYP5p3TyvKC+CXCKSdoHTcpmBDONLjiSuc2CbK0O2BM08lWwBidnhvj7/pNkfY9yK8hTndvZe38XdpnN8c+fITWT5vw3rxAI2at6uBcwdGIMvjYH9ymWRiaF0kScvXt6mPzKIIG0hxNRCFMQKAuQS+So7Ct9jAUUKs3rNtQQiNhFq4eqMQPhs2LQQ+YXj0LB/IcjWK+6VMzoZImX8xACXvndw2x9YuNN/cMN0+Den9nHC//9dXJJ3SgaT0LVpEG8HYK+Se2ggRfPoYQOPBbJPlBkMpgOUMHyQIOAQEuYS/2zpO81mLhDLdor+Tq4lK2DZDtUjICtDCy1OMSbtolpm2x+T/fbJsIAXFqYzAdnSnuhqUyCywtTXI5NkkpnKciaG76+z47jYgVMRNDACBgEhEAIA4XiS30ntFS3YRAxbT7WfSebq5qWVRUWMLPK59cj5mR4cewyQ4k56sMVPNSy8ZaVPgoYeHO4SIRZit7XB1Ykw2Q8h8OT/YykFmiORLm7cT3ldpBnhs5x1pvEyPdLV1LTfPbqET697cFbOo+IFSBkWmR9D0d6+JFCcFBpNScJmNpuRNmCgBXAd3227Ojgjp/cxKt/8CaeszwIrYM+CiFAuhJpCMJVYV44fpZz7iQzYwvkXJdUg9J2IwGF4WrJ7Ia3JL7rc+WlPmq6qvnWb7xUTKr3HRpi4MgIj/yrgyuSHx/as41jZ8aKFZxeTgeRG6/oRj3fKhnY5+GVTXDk5LPc17SBx9tvooeNTt7f/8/vYuLiNLN9c1S2RGnd1VQM/G15rIcNB7t4+fcOM3lxmiN/eZKTf3eOe376Tqp31PIXV94sVt6Brsr7yytv8jNbbiyVbBkmn2y6k8+8+CyJBrAT0HBSEZ5UuCGXbCzHj/6nHyB65iSvJvvw888ilBC0njeRFrieTzAUIKCsogWeRDGVidMXn+VKbApfSartMOsWKmgeDdDaWc/6ezpWtApIuQ5/fPFVJjOJ4me+UmypfnuWitdiUxya6Csm7E3D4OLCJMdmh7hywEElbAKeJGPogG3adzClwfqKWnbWtKKU4vjfnuHyC31ITxKKBtn/id107rv1KtwHWnq4v7kbqVRRueF2samqiePTmlDs5XTgXyComdb3cKmlmPQVpqXb78ipcbr2t/H6Z46SmErqdj0I52fHGHhclDyDoeQ8b04NcH9zN+8E2p0Kqv9gHnN8MWmlBEjPx3cMLj1/jY0PraOyJbrqPnxPYtbY+J9u4tLMCCHDpq2jlVBl6fjQdVc7d9cJTg8fAvRYUlD98ZTk3sb1HJkawFOSqkCYj6zbc1N7g2B5kNz+MEK5eVW9xQRKsCJAZ0c9iYE4tZuqOfCpvVgBk0907+fMbGnlcmFszzRA+RjFBOlcu89Ut0ukpYzg6SwiI2+YrCyFAgNSNXDqAy67nw5w6u8vsP7ezu+pikdZjZ78VShFTTDCRDpe8r1laCW/jH9rhNyV0BiJ8kjLJl4Yu1z8rCkc5cHmjTf4lSaePTN8ruQzAeQqFNlyxcw6bZdV/GLpRnk4yud8eoKu8hqOTuuKecM0ivewu+7GBJCUm+NafJoyK6Dt3t4miX5DtL6kDzRMgXQ1CXGhVRZVHZcqWZ2bG+OB5h5OzAwViTBLMZyc587rbMASU0me+81XiiS7xFSK8fNT1HfXMn1tFgA36+I7PmbQ0laS81le/YM3+chvvQ8r+K4PyazhnwgM02DThjY2cetjc8rTc7/r39JCbx0ybfbWdfDmVD8Bpe1699Z18KPd+/R2SvHqHx4hNqbf1dHTEwSjQcrqykjNLJLirZDNjh/YfMNzmRta4PwzV/KLXYUTXVSikIZe8wkFTqVOSqabBK6xqNBpZgEJoQXIVYNr3jjpjsgryZhLPii5er0OWq6GoRfk0oBjb129bTLMtVcHOPy/jhf/nrw0jVLaFhZg4MgIza5H5V1BplMJlNT2I3bI5smO7RybHmIul6IxHOWDXbvYVNWIJYxlRBjIq9sIA4UEyyBQbpCN6/lHpsnAkIsxhgIxoOc60mVFIERLpHKZdV9TOHpb9ref6z3KUGKeXNJBKUW8Hs6tT5D64jmC3wnif8BFBfVFKKlw0y7rwqVrv1zSITmVYvQewWye1CmA6XU+f3v5KD+18+b2KG7O48xXL+SPs+QLpdvS/Eb9377jF/t3T/lIZRbJC17Ow816jL4+SjAhscN2yVzSDBiErQBPduwg4Tqcnx9DAdXBCD+yYbkyWQEp1+HPLh8i5+fHIzfH04NnqAyE2V6znKxsCAMjt/TBl7b5IinsVrC0E8irygEYhf2K6zdc+f2SBhiFxrgk+a8MSDYozrZn+M7oJR5tXbk/iI3FWWiWLDRLslGJbygSDfnjLZtPaKkWIaH5qKDrso0ZMmh6agun50bxXZ/9zeu4r7WbazXTnN+aoj8+w7StigUlxUsRCmUKzJhi56kI5+5M44b1vqVQyIAo2jQJD6yUx/ZVCOQbKxsJGOaiauCS81VKEXezpD2Hcnu5vNW++k6uxqY4OzeGFbIQwEMtG1lXfes5E6m00nFhvdXcVEPTYCVTyTjSVximJps1lVUSfZtEmALay6v5RM9yhSzpaxKPm/WYG5ynbl01oWgAN+uCr0g3QCovIOO7vlbMFPDy2NUSMkzWd3n1pTPMLCQIILAjNmQgGZWkWwShhKBmrrSR129YnbA9mUmw4GQwDKEJWyWVLovSQF5QsdCVjzsLmNmkiLXC5i8ZVHgWuaTDxAZV/CWmIFQRxFU+vtC2XJYw8JRc/qqI/LgiFsno10OhcHyfUH6Oe2RqYI0Ms4Y1rGENa/gnj39QkZeysjLuuuvWZETXsIZ/rGgtqyJoWsUFbgErVYe808glnaICy1IMHBnRAWdv+W/MwNuT0LxdbLi3k/PPXiHXaGC6CsNVeQKLlmAtLHqVAeFAgPbyav6u72Tx913lNfz05nu5q6GrmOgBvX42s7qKTJr698GcQFr6b5mvPBZCYAqD1yd72d/QSdN1SfjJtybYhs3kJp90lSIyL2i8YpLenqJsw/JFreN7fPbqES7HplDVCscBIwuGr61/dKJHIFHFROvkHYLqK4qyQYnhGYukioBF0LCYk1maagX2mKRutED0UYzcL8jho/xCEsDnrelByqwghhAkvRxf6D1ONGVz5I/eKiqpTLtJvv2FI3y4vmxVhRjpS479zSmqZwVtEkbu1+du+ArDFdSMGOx8tJPob22n+0wfz7hXSBq6krZ8QtB8aEnESQGGwAyYNG7S7d8OWhz4yb288T+P4Ts+hi/Y1FvGxd0ZPKkXkkJR9HA2JGAJJqoyzD5hEJjz6fyGT9jRBBzpK8594zL13bXL7FSuR/PWBqJNFTiOS7YSwo5JOC2IXtCJX8d1CTdVkI1nqTzvMbWBJbEghVCCpkOSvg8sCdIUEoUGTOYSyB35pN6SqkhlKm2x5IEb0QSw+p5aMgtZ3MziS1jXXUPLjuVVgreDMiuwTJ0NdHuPORkWculihQ1oco+wwM7Aumg1otykPzFb/D7refhK4kofISDtu3zu2jH+rz2P01VRqz2vr8P1xLKVkPVd/vD8K8w7GXwl6U3McGx6kJ/dch+bqm79HlxfPXmjz7O+y++ff6VI4jk3P8axmSF+cdtDHFtBzWsoOc9kJk5jePWEdQG2YXJ/cw/PDp9f7PsLbUOAYSxW2XlSEgzY7Gho5b0PH+Dyi73MDS7ogB2UBFPMgKE9uqVC+hIvBycyIwxO+zqIG81XzCoQ3mJQsuUVRWRQkRMO4+enOPeNS0UiTAHj56cYPz9Fy/bl9ztgWvzUpnv5m6tvMZpeIBIIUPOKomLGIBdRXDuok+SWbeJKnxfHrtAQrmBv3Y2JdqDbYvPWhlWr7c88fYHJi9PFv7MZhy+88AZTObMk8Vx4w87Njy2r2FwJiYsLtB02Fn3M8/BdWVTT+PD+fXS+XsGLh88QyEDVqIGhBJmoBBPiTgZDCGzD1P0V8OX+07q62tD3Iu5kGTLnCdTB+hcG2PzaAO/5Nw8sU1l5ZfxKSRIYdPX2nfUdtJVV3/BaVsLJmWFSnlOs+jaEQcSyOTM3wnQ2iWmbOEIu2gcCtjDISR9DGPS+NsDF564V95eN53j9M0epW19DWe3KdjsrQY+tt54YT86kuPT8NWJjCerW17Dp0fXcUd/B8Zlh7LCOHnZNl1Exkb9XS3ZtmPlabqWIjyf45q+9iJPMgRBaScw2iddJvJxaRkgaSMy+Y2SYY58/Q2quVPGikCiQnoQgTPfOrUqGufSda5z56kVO3ZUi2aiwwzauqXh2+AJKwcOtpTZaG9Y1U7VQRsorZf+2lVXxga5dPNa2lZSXoyYYuWXVodrNtQxMJnGQi/xkAS25MmYvzKKkYuzMJK/+wREe+Zf3Uh4JsT5ax0hyHtfxEC6IvMpHIKV/76ZdZjfC4AFdcuuUu3DQpEaVIXILJZaLy27c9RDghuHM+xy2Pwe5lEM4evv2fDeDIQQf23Anf375MNmMJpoawiCUVxXaVn37agFL8d72reyqbaM3Pk11MMKmqsZliibXoz5cToUdJO5k8ZXCFIKgZeG7HtkKxWynLKq0r6q6KATj6Tgf6trNydkRrsUX+9zuaD17aheJ39deG6D39UGUVKw70E5yp82X+08W590tkUp+evM9KyZ8boaHWzZxNTbFVCaBEAI7ZNF1PkAw7WPku3vDNkr6z4ISWNZfYREDJYTJAq6+3L9MbcpJOTRuriNSE2b4+Cgukql7TOKdCiUcTAmm4xI+fYon9+296XNZwxrebUgvZDj39ctkU7M4Gx2soIVZtMAtEP91YvYDXbu4r2kDY+kYzZFKGsIVxf0MvDlcJMIUkIvn2PpED9m4w+iZccKVIbY81kP3/V03PKfJS9P4rl9cmwYXdC/vLZlmGB6EpmDoCZ2YFgpkIG/Vl5/D5Kq0heKKWNLv+QF0X1IcSlb4jVhdCcM3wRy5PdKjm/M48cUz+K5fJPL5jgQUSqoiMXT06Bh1Z31yOyTJFgimFT3TEdIfckm4Wa0Sm0tzLT7NpqpGyo0AcT+jye8FKFBCYQUsXE8iDYXIUxv8sIEM5MkyZj7GELaxzcU57FL80Pq9/K/Lh/Jji8Q2TB6+DdvOhVyaoeS8jnMoVVzUJDq05bETc+j8pmL0UYNsVKuv1vWZ1F7zYd/ifgIRm0BNkJmuXPFRyvw66mJqkqSbuympNzWdKq6xTdtg6fRICbTiqyhtDQHDWhRL8XzctEv5tEFF0iSnHDKey+Q9koUOhSGhfL3LbiUJmBaf3HgXMSdD2nNoDEdvaHt0Zm5kWZwQ4Oj04IpkmNHUAtlGoQvErg/TqZWIXNdjlXkUFAsopA1SLFqWrb4fAF3cIfNkdbVE1c/wdKzHClq8MHqJAw1dK84NDlWMc/lBFy+Yfx55osryY6vFDRREr0o8x2Pb+7aSOptlZmGGeK3P6NQsx3r7GA+myPkeuYC+vyUhkSUxnWyVoOHORj6csDk6NsRCuctsQym5VllgLkBQrHyDw5bNj3Tv448vvlbyuVSKnO/RGIkSsVZup6Yw+ETPXYylYkxnE7SXVZdYit0InvT5xtA5jk4P4kvJ1uomPtS1m4pAiI9t3cdfXD5MLk8UDpoWH163+5b2ezvof3OY039/nsRUinBViGwip5VphY63ZRMOTsrBiQIIHePIqw8bllliE/rW1ABfHzpLvCyFfL+kodek85jJ+H0Gkz1K20m5PlNTPpteFpieYMt7u29YUFBmBRDoMa7YRlcaLvL9wCJRUOCWw/j9BpWvGATLA1AmEaYm/dghC2EIApisr6hnODWniwB8KJLJlsR/KOx2FbU0BSVFBCvNX9ewhjWsYQ1r+KeGf1BkmDWsYQ160fH+zp18ue9kcXJbZgV4suPGyhXvBHzPXzEpLD1J9/3rufJiaSK7YWMd0aaKZdu/E6hoLOfh//NevvHKUSZEDMM0sYMWad/RVQ6ulkG2QhYZ5XItPl2SxOlPzPLq+FU+0LGTLVVNfH3wDGPpuJbsNCWeI/GyHo0XDBrekozsV0zv1osRAUQCdjF5OpCYW0aGsYIWdtyn7UxpN2yvUh364thlLse07ZQQAiEEflAh0pR4N0tnMfjkRHVgR6jFAJ9hm5hSkE3nUJ6CbBApVd67WNDQZ5LtgolNUsvZGjrg5SmJ43tFeV8FvHbhEpYnyUUUV+93SVfptjDe9xqfbnuUuhUsnzILWVKzOonXfMWk+orH3GZwywXVCxbdZg21XTpRe+e+jexVPYymFjj6pydJnpzDkaqo0ANgBUy67++iaUsDuaRD/5vDOKkcD/3SPeQSOeywTePWev7HmRfpn9LJGD+ogxdmTgc/C5EMI2Ti10jGHoVt3yk1fx86NnpTMoxSin5jnoEflLhBhZA+jVcNOk5aPPRL91DZHCVYHuDVPzrCyTevUn8SFroFblRbzjQfllQMQ7QfYl0sG6EdlbeVMjVpaWllkzR1wKl8DCrbotz9k3cQrgxx+YVe4uNJqjsr2f7U5u+6sv3O+k6eHT5PeknUTyAIGBbdlfW40ueEMYjKE2IEWkK8540An/o3B3hu4XIJGcaTvg46S614YgiBLyX9iVl21LSwsbKBK7FFu7Wu8pplFdkr4dTsCPNOpsRj2wH+8MIr/PzW+5dJ566Gzv1tnHn64rJ+ruvAcgWn49NDy9Rs5nNpjkz1468QFAbwVyHbrIRHWzczm03xyugVlK+DvDJfESRMrarjSI/qYITH2rZwd4O21pjpmy/uIx+HWVTxNQ3ssI3v6uSxETAZ2eGg8mlj7acuikQ60CQ8p9Cd5aseR89MrHjOC8OxFckwAN8YOstkNoFlmDhIJu83KZ9TxOp1tb6wDKyQfgmkkrw6fo0tVc2Eb9EyaTUMnxxHoYg1K1I1krk2SaJeIb3SAFEhbuVLWZT6vxGC0SBmwCySYYoFjUKraxSw+64ehr8+yPzQAlL4qJDB5Ud9zICFkBKpFFnfwwAidpC06wBaIrugjV1QJhvY51P11VkGj46w/jpFrqXv2VL0xWffFhnm4sJkkQgD+plkPLekoteTS9SuAFdJRlMLfO7aUcJnEoxt8ciEJSKjiMxAzZjB4NERtj5+Y6WKt4vUXJpnf31RsWjs7CQDb43w4V97mAeae5jIJGiNVBJOGTz7/EtkYzkM00CYWirbtHSAWnvLL+k781ZKoahBKCFQS95v3/XxHZ/0eIwZa+57ZgWTmk0zdHwUwzIYPTOBYQiWhtX9ACQ6wDagdlZR2bzyXGvs3CRH//o06UpJol6C1EpwITMIhuCl3ouUv5ihpquKdQfaMW0TyzD5QOdO/rb3eHHeGTRM3t+5E9AB+9t9L3fXt3EhPkEgT4YEsJRB9OkkSi7e65neOc4/c4XsfRFmskly0kNYep4lDbAyMLdLEL2sM5hj2/W+TNtEGAIlFROZuA4kB6/rbwuPTaxUKayRq4Arj/kY4XeOyN1VUcv/vfcJvjV8gdcne4sVwNurm3mguee73n9TJEpTZPWg/vVQUtHqV5D1HQwjTxQRgmCFzeMf3cPnF06RsdJIVh7XFGAJQTQQxjQMfnrzPVxamGQsHaMlUsnmqqZiIu/M1y5y+u8vFH87PjjDeUNhRBb727F0jG8NX+QH1++5ret+a2qAb49cJO5mKLMC7Kht5cHmjVTstOntGWRuMs54eICk4ZBzc5r0Y1ocaFgHwPbqFl4Zv7psvyslE6+36Czcx9EzE1R3VLHvE7v5m6kTzEYdpEmxMtxyFa84A6R6FR/v3rdsH2tYw7sFmYUsJ754ltEzE4SiQTY+tJ5Lz18jMZWiTCiilmK+3SFUbuMITYINWRZ31ncWYxT14Qrql5BgpntnWRiJM3lpesVjJqfTPPDpA7d1nmW1EdysVxwjckuUYQqTE2lDujm/Ls4tEsVloDDv1QUzRlZbtqyevNffycANvkfP2YTHijYzAqhsv/XYyNTVGV76ncOaPKQUGJqcW5jYS6kw84nRXNKBuKLtxcLBBPEtKc6PXsMMmAihi2heGb9KT2UDzdEqrdyy9HqFjg9YQYuAMmlxgsw2Zxl4n0WyalF1zfS0wlbAsqgLlZWQnQpoLavi/9zxML9//hWmMgmkUnyu9yhj6QXedwtxLMf3yPkujvAhAMLVpJGCZTAoIlOKbV+z8Cq15YjlCtSu0rHKMA22v38zr2ffQqHjAlIPcyS9HH/ff4pP9Oy/IQm+vL6sqERrWIa238zp+YehoGpAML9Bk/oL6Kqo5c76Dg5N9jE7Hae+z6T9lIlh6md4+QGPRIfEMAVm2OZweojAcLD4/lQGwrekouOvULSiP195zI47WeywjUwrcoVtCte+zF5yNSwhxOTXKlZOP5eu4xa5MsXEZh8/vIRAstq+lZ6T6d0sFmIEDJNwcPFl85ViIh2nu7KUDDOUnON0cgwjZKLIWz8tEaIRUqu3lEJQdwHK4oKa7ipqu6v5m4HXcGr18V1TcZlZDFeUkAtWRF4t5kztHHa5YKwyq9fsVp4wsaRdZaoVo5lY0aL9etSHygkZ+iVzlhCcXOnzVMf2G5KiQNvUtpRVrvjdubkxXpu4RtLNsamqkfe0biFs2TwzfL6kGOnc/DhJz+EXtt7Phmg9v7rncc7PjwN6nhq2btIB3iYmL0/z+mfe0utdqZgbXAAgVBFEGILx81MEy2xM28BKwSjOooVU/j8KNqHTmSRf7j+p6+cMAykkU92aDD3Zk39fLQMrbJEL+Vgfqeex7m2r2iMVEA2E2VXbxqnZEUKWXRIX0ydCKUdMlGouxZslSigM02BjuJZz4bmStmEKgx/t3kfAtPj3rz1NKqPnmNLUNmOFYEAhdC2WXPsSTtli4V/+nVpp/rqGNaxhDWtYwz81vOvIMD/5kz8JQGNjI7/5m7/5tvfza7/2awwNDSGE4M/+7M++V6e3hjW8o5gfjhEbi1O7voaK+tXZ+/vqO9kQrePC/ARB02JHTQsh87tLEL4dRKrC1HfXMH1truTzpq313PkjuzBtg2uvDuA7Pm17W9j/id3f1/Nr3trApzY/zn999VkmnARO2kGFwDAEwaCJZZoI0yDruwgPhOdphQQDPEvx1cEzvDHZxyOtm/nUxrv5nXMvkvFcXXElFIGMoOkkyIyk/RXwQoL4eh0EUYaC/NqwboVKjI0PrefM0xeRvrYaQWnljqq2lResF+ZLk822NLR6i6UreUAHogorIQGEZ4UmTQA1fTC7WeAGlCYH2GBJ8EyJCdhhu0jEKZvzsbNarte2LTKeo6U+lSxRSCjsu++AVyTCACzYOf6299iKFjChaJBAmY2TcrFDNmSh4bROxG+4t4UDn9pbsr0hBO3l1ZyJKeZSTrHSD0BYgt0/uJ29P7SdhZEY3/4vr5JLOMTrJYkrks07O3lyn5YR/kTLPv7k1eeIN0qcMOTKlE5GFGSdAfL+4qk6hRtU2LnFAEMhKX8jxJwMffdJHEsh87Laozt87LgqJkUq2it4o3mC0Y/pfRsOtL6kqLkMyRaY2QGV1xTxTrE81GIAtlqsMCxWoWiLqdZjgkDSZy6xwDf/wws0b22gur2SiUvTjJ6ZYPj4GHf/xB3U99xcWWU1VAcj/PNtD/CZi6+RcHOYwiBoWryvfRt1oXIONK7jWF8ffcmZ4n2t6zPZt62bSFWYu2yttFRI9hWCXMXKPKVI+w4Bw8QUBj+56R4uLUwwmlqgKRJla3XzLVVPL+QyqHzl1FL4SvHNoXO3TIapbK7g4M/u49jnz5BZyGKHLbY/tZn2PaXBBOlLLl0cJidzgMAKmkWliKlsgt21bcvUYRrDFasGqFbDZquO1zOXi4QIZeRvs68wbYMqK8wvbH2gJPFZ2VyBaZtId5GooPJBGdM2CZYFqFlfzTHGGNnh4IV1uxI+eaJdacWftMHK5/2EKYoB9ZVQ07VycO9abLqE5ASgQgahn2yifsFgQgzniQiKjOfgSp9r8Wn+48lneX/nDu7KJyvfDqwyiyv3eyy0aPs8L1hyecvQFInelAgD0Lqzieq2KHNDC0WbNtCS9Qujcapa9TMZPDpKYjKJUpo4mlgH1FhYpkmFoaWQs76LQNy0rfu2IlWrmB9agOvIMDXBMgaTc8t+s9J4dDPEnAwJN4sQokQZSirF3Q0bSHsOFxcmi4G3YuVrXtb5zdE+vG0yX2Gqv7WyMD3u03D8nSPDXP5O7zLFovh4goE3h6jpqKbLrqCqtgLC8OT/8whXX+4nMZWiui3K/EiM8XNThKJBElNJfEcrKDlLIq65pEN1n8XCgRA5wM24eFkPyxFY347zra+9zP3//C467mj9rq5j8OgIr3/maHH8yyVz2molT/ZItMLQY0In5IRiLOdhfPYwdXYZWx/vofPORbuLvkO6HyqR1FbgOT6e75NC8fnyC4gZiH49yME7t3KwpZvdde20l9dwdm4U2zDZVdv6tpQ6Cthd185oeoHXJ3oxhUHItLkj0UA8Nrxs2wuDI5xu1yTasBXA8T08Q2JIhWfBxC4Yu8PAyuogcQCTQFBfoJN28D1J1QTMdQg9QQPwwU5B1yGDWL1kdqvCjxRKLktHX1Vlcj42fkvKVG8XlmHyVOcO3tO2heHkHJWBCPXh5YTidxrSl7z8u4exB+bgER8n4OkEZ3mAJzq20dPcwYPjeo63WiEsaKLn5rwKmyEMtlY3s/U6lRvfk1z8VinZJN4gyTkeoXDpuHI5Nnlb19Ebn+ZL/SfzfwnSvsux6UEeaO4hEAmw+dFunvvsYdLpHCqkx1IptC1nYfzsrKjhifZtPD9yEU9JDCG4t3EDO2qWv88t2xvpe2NxjJdSkUvmmOmbY34oxolrfUy919NWIkuScr6l1fxOzY7wWNtWat9G/7yGNbzTUErxwn9/nfnhGKBJFof//ASgFUMMJeh53SZdKam+u57HPrSPkGUzmJhDCEFyJs3M5VmUlCRn0gghmO6dY+K8ngf6ro/v+gQipUnVG9lUrIb6njpSDTB6QJBpyFfGm2grI5vF98/QBAhp6bXY0s9BE2hkEMwM+EvF60qnxKV/X/+d1AqZ0lqZCANg+YKRjbdWra+U4tCfHsdJORiWoef1UpE2PKbuM0jVKcoyPi0XIDCr8mv2kh0w1+yXEE4LOD49SF98WpPsr+/Yg4LqQJiPbrmDpnsq+S+nn8N3wUi54Eu8IAhDEArbVAbC/Ej3vlXXBYcn+5nLpUssfl4ev8ru2jZayqpWvXZNnDmGrxRK5K2CTQFZvYYWHhhBA9/3EQYEU4vHX8mOc9uD3Ww8MsjlzJRWu8lvbgqDc/NjXIlN3VBJ1Apa7P7INo785Umt0GMaBMo0KUb6iu5zJpPrAsyE9LNtK6viR7r3URcq52BTN2/8yVH63locM7xKg2SHVpEsqBYCHJnq533t227LKnBHTQvfHDq7jBSzq3Zl67OuiloCpoUoEwjPJed7Opy0VNHilqAXp4avC3TCMUH9aYXpg1FusM2v4WowQdLJ6XNbbd9icV8AJqCE0IUJUhZtewwhViRdDSfzRSCWoIQ1LhePZ2U0KVV4AtOHQFKrWaomi6atDRy70ItTt0LBn1K3dD8MYNyPY+b0H26QvKpv6T59SytJrkaG8ZTUZGTDQimVJ49r4u5Se6W5bIrh1DwN4QoaQhU8M3yeo9MDeFKyo6aVD3TtJLKEtHJ8eogv9C3arE1PJBlKzvHpbQ8WrS2XYiAxy3QmQX24gogVYN8tFCa9XVx5pZ9smcJ0FNOtHukIRCYVtWMewYB+N0zLxHckdlZR12cysdHHUJrYUhss473tWwGK1mIAVsjEdbWV79SGfMOwBL6pcKWHaRukO42bEmEK+Oj6vbpPnuzDQGh1cEWx6sh0wbdFnlCY/5HSFuKmrnNBGFD2cobIFo+FVq2UWRYJ8fC6zRydHiQaCFE3bpGq0j8XeW4XShd2yvwyzDJMfD9fLphvZsLThVO+55M1FTub23hv29a3/VzWsIY1rGENa/jHgncdGeYv/uIvEEKwYcOG74oM8/TTT3P69Ok1Mswa/kFA+pLXP3OUwbdGAB1Q2PrERvb+0OpVMjXBMg6+Czw/7/2Zfbzye28WA2S166q556fuxLQM7vz4Lu742E7tU/1dKlK8XQy8NkTrX2cJbDBJ1Uji9Qq3QmFXWMWViXIkfk7qShEBXhiQAsOXpCyHrw2e4YGmHjwp8ZTUVVcS2l9RyJheTCmg4bgi1S5QVt6qwFasj9azIe+b7eer5CNWgB3v30xsPM7l7/RqlRLbYGE0zuUXe9n08PLnen3VtW2Y+Bmf6BgEMwaWI5jc5GMETSQ+IgddpywC5Xph9t7aDXyrcowpR8tQG/nF1IX3eATjYOQkjf2C2mGT4JROWvo5vZEndA2MryRJN0fYsgmaFgc3beK1wGESDaWVRqZtMpScJ+5klnkIm7bJjqc2c/wLZ/V1hGzskM3ej25n2/tWl0oOlQeKicDiglJCQ57YcfJL58klHPr3e0yv12ojg3YfLx4epK2smnuaNrAnWc/sqXnSlZKTH3KXBTNyvo8wBMIGN5i31bIhkjbouYksN8Df9Z0gG9Iy1QVICwYPKL75H19EhEzGtnpMbJcoTx9eBmD0AcFCD6Ta9PrZD3CdJPGSiNT1AVi0ws2mbwlCvYsRHyftcmVgHDU1QZVrYyCIjSV48XcO8eHfemJVBaJbwYZoPf///R/iamyKmJNhQ7SeMivA4ck+5nJpHtuyneRokksXh4jGLHbtWse6e3QSsaWsik9tPMBzIxeZTMcxMPDzNVaLcWhRVAowhFgxiXYzdEfreH5ULVaU5SGAiXScvvgMvfFpKuwQu2rbbqhq0HVXO+13tJKeTROuChW935fizT8/QXxkCnkgn6x2fayAiWEZtNhR9rWsyxMGJlBo24cfeRtV4GXDirIZg1StVm6ycjrIXxU32bO7k7uru5j41jAnz00SykvK9zzQxdWX+pgfieG6PrmoIuQYPPLz99DQU0e4KsSfHHuVAW9JxFBc3waXfCEUVVf0X3ZI37eOO1sZOjpCen6xOr59bwtNW1b2Jp/OJlb8fJ4MHz5wD8dPTeJIH8f3i23BztsE/X3/KbqjDdSGyki5OfoSM1TYoVuyz1JKsbDNYL7MX5LIXT2qGbYCt/ycTMvgPb9yP6/83mFGTo1D3pJDCMEbn3kr73Me5Ohfn9LXkyfYxQM+XtbT5CkhsISJIf0i6cQ2TJzrPAcXq2AhkBFUt1ctO58Hmns4Nz9WvH+gq3I334ZNWAF2npxWZgXI+V7ROiVs2TRFKvjYhn18Y+gshyf7tNVUgeyWP09XyMXgdh5+ANKNcHl8hseSOYLlN5akfzuITyaXfSZ9yZG/OqXlttEk1Ac/fTeRqjC7PrhykPCb/+EF5gYXtELUkli29CVGWvIL2+7nWHqUI4cuEoqZNF4yCaYFCsWpvz//XZFhfE/y1mdPIfP2BLEmhScNIv0ekfIgjuMy8rDSSTvbQHqKbFByYV2CLS9lefXaHA/+okH73lICX9mswM4K3FC+6lX6uJZC+JDO5x/T5Hhm8BznY+N8etuD1IbKeLDle0Nc8j1J2wmTO06FcMpg5x3rqaqp4CWWk2Fm2xbbsG2Y2o6mMKW0CjaRCmkLTNPAUT4BLN32lMQPQrxB6SplH0KzUB4zaLpqURY3qB+RhGMugw+zKD+eh5G3Ysx43x9p8aBp0V25ssXb9wPDJ8cYPT1BWAk2f1Ex1yHxgpLmdJBNP1kJzXBfUzfPj1xkJpuCZaOshpm3TlyKzEKW5HSKqrYodtjGy3olxEElFUbMR0k9/7SCZnHCF72JZcX1OD693JrQV4qTM8O8p20L/UeGOT4/jGjU42gBokJxdHqQJ9q30R+foToQ5he3PUTKz1EfKl82py2gc38bvW8MMfjWMNLXSWhZLhjdq0jVuMQbZKn1COjh1BDFCvj5XGqNDLOGdyUmL88U1/kFKKVtea3A4pw4EjOoGTHISY8/Pvsac7m07q/nPTZ8R2CPaHKdGTBxM17RbtCwDLych5PWxRfCMqjvrqXnofW3fa5eUDH4AwaOIfV6yrqJigOLdqDXTweVBf7SogmxfJulfwupib5uaPHvYuHFMggMBcGgzZyfWWmDZUhOpUjk5zWBsE3Ol3im4upTCrdSYBgGWV8y3yrZ9VIIc8RHOqXWLFaGorXSUozG5klmciVKBgBhw6ImXM6v7H4vhhC8OdlP1vcQhkGoIli0xu2qrOPJ9m10VNTckMi91DJvKa7Gp29Ihrkam2IsHSNs2mQBV/koBXYaWl/TD8h3fKyQRbAsgJvRbW3ToxtYf+8ikXU+lybjuTRFonxyzz385snnWMhbqhjCKK4Hr8VvTIYBrUJUICUjIFgW5JF/eZBwZZBQNEiwPEjc0TbN11vUrDvQXkKg9PPFLtfbiTu+x6HJPi4tTBAybe5uXM/6aB0zfXMsjMapXVdN9XWFVJWBMD/avZ+vDJwi5mSRStJTWc/G6Mpzi7Bl8+Gu3Xyp/wRByyZgWhgJSd1hj2v3+7dBhtEJ+MoxA9OFVJ1i+D4wgpJgKMA0CTzlE7Jt0q5TajGkOR6LRUpL1gwFhxjyyj2WMIhYAQ42da84JjfmCTKeLG37hX2r/Bp3p1tPf2qWeI3EjUCqXjKxxWF3exSOJ26m/5K/4MJpLiW6CJRYVORYqsha2mHomMdwan7V3TdHKmkMVzCQmF2ynlP4SvL86EUeb9/GM0PneGX8avEQlXaIBSdTJFCdnB0m5eX46c33cmZ2lGeHzzOUnEOh55x2npg2lJynPz6zqpqtt4qy0PcSR6cG+Pq6QdKdriYr5YlVs9sFCxOSLa9qW/Ha9dV4puKF1lGStUo/W8tgW3Uzn+i5q0iYWlrM4qHww7qoQwbR80uhcJdYB01k4rd8rpZhMpNLUWYH8RyPXNYtzvMis4JINETC9MjgFdXH8MHIKWpO6yBE3YZapq7O0HHSovmcxBWSeIfDc8aFYj9t1SvKB3XRnAACMU3ULGTyDCEIChMnK/GFwsxBaEaSbhTFpll7BQ5k6whue9el/9awhjWsYQ1r+L5jbTRcwxreBeh9fbBIhAEdED7/zcu07W5axk73pM/5+XFSnsOmysb/7YHTioZynvqNR5kfiSGEKFa/z2STfHPoHNdiU1QGIjzY0nNTe5OBxCxTmQRlVhBXai/c5uvshW4XV1/ux/QETZdNwCRVLbn0sIcfllotwZc0nRTMtWo5/KXBapFTSNtHmAbfGbuEbZjFqopsOsvUdkX5EieoyDRsftZkarPEq1BEJzzapctcZIF4jc/fXDtK3NXJ4o2VDdRMJAlFSyurT33pPOvv7VxGVrincf2i7YVS+K5PcB66npYIFHbYom28jIZPrWPu2jy5b0zDvEd5SwUPfPpuqtZV8vTRISJYOBktH+0FIBNSeAEtHdrfqZBvSqKnJVWjglirJOtJVJ43JNDJPUf6fGrT3XTXtJL7kd2czB7V6ZB88te0TQxESdXXUmx9YiPR5opihfr6ezpuwYJIW7hon3C0XG7YYm5wgbbdzUz3zpKslUyv14ECP6CfpSN9RtILfG3wDI/+cA+NXzY5n53EyulqHGmqkipApcAOGJz9oAeuJnE1RaJkagUr1+xonJgZKkrGXh818oOKkx/z8YN+URofO++xLjUhJtGlq5O8kFo52LokJGO4LKrDSGg8DeH+RdFeNwRXPi7IVSsQCtNx2PSSRc2oiZNyGD09Qdf+lavDbhWGEMUgYdzJ8DvnXmQup4OJrwC7a9v48Y8+uuJvN1c10Vley59fPsR8dtHTWSkwEYSDgVXbzq2iu7KBu+q7eGnsCkspMY708aTk986/XAz+fGf0Ej+39b4Vbb0KMC2DisaVv0/Npuk7NESNMphebxBvkKAUXs6nfEIx8ncX2fJLtXxq093EnQyulG+73y6vK2PzX1iMb/aJN0oCaUH1iEFZd5TNVY2c/ePTzFxeVAMZPT3Bw//yXu74kZ28cvoCF5vn8YIQjgS42pJmQ2M5A4lZrojZ1QP8uhmBIRASon0C20FXuqUdciHBGTFJ+c80UDHi0zgRpGVz/bLk+1KsVgHXXl5DuR3iJzbdzdMDZ+iNTyPyFXGF56WAiwtaFe0r/aeKwbnO8hp+ctM9+Hllleuf57XYFH935TjDNfP4psBwwfBUybUW4pWmEFTYIX5l12O37LEOEKkOE6wILuvX3ZzPoT87RsPGOnIpp6TCs2rMYMApJbsEDLOonhQ0LRQKT2pJZSF13wFQNWrQ1lhLeEeU50cuYgjBntp2akJltJRV8ultD/Dq2FXG03G6K+t5pHVziR3gLV+XFWBnbSunZkdK5LD31XcWA40/tH4vH163m28Nn+ebQ+eRSurqUE+PNddDGbpaLlEnycbfGTJMQ3ctw8fHFo+ZtzeypSoqN81cm+Otz57kgf/j7lX3s+Wxbt74k2N4jreorGTo8U5JxbE/PEGkOkzDkMKvNHQAPI/Y2MrEr1tFfCJBNp4jVSG5/JCLG0YnTO6BfX0VRLIKURcnErRwsx6+o4O6iUZFJu0QDge4+O2rxfdx/T0d9B8awlCC7jcsrtznkY0qrWZHngS3ROHHlT6j6Rjn58fZWatJPY7vYRrGLal0rYZDf3aM3td09alpGRy7fIaqj7SS3hskcDarrQ1QTGyTTLdlSboOtjAJmpYO1BsgreuqbE1RlCv3pMTEwLfBzKnFvIoBZRPQekQRCIMUEsM0qDcjzAel/q1atPmzDAMDcduEzHcSuaS2gHwnSOYFpUcn5YArqTmvP/dJ8exvvMQT//dDNG6q5wNdu/hC73F8JUmV2Cbmt1eK+pBOSCmlOPKXJ7j0fC/SVwQrAuz70V1sfHA91R2VzA/FkL4kl3QIxhWRSYNMk4v0JYEy3Xncf5t2UavZGBQ+Hz01XqqOVDhvV5O+f//8ywzlq8sNIfiBjh1FYnvcyfLC2CX64jNUmmG2pWtp8iPM9M1hBS2tCuN7XHlS4VTqRKK7dEhYMtaa+bEgYJhvy75uDWv4fmApaa0A0zLwc96yz5u3NvDlvpPM5dJIX+JmXVQI+u6RbPqiLhZRClAKN5MnAitNLhVoWzYhBL7rsZBNE3dytESqbtmK7/T8KKrCwPe1+t9Kal/LsGyTJR/cRjcrFnPVYK1GKi/sXwtVOCkHtzdOoim56jqjgEB5QCvC5JVMQ9EQE+tcvKgkHA0hhH5WPj6jHQ6NA1KrUUiFF4LYOqioLSNjqxLVEEsJZnvnkVU+LJmKCaEVOVojVVyNTfHK+FWGk/NkPZegqcnehXEoZFqsi95cUaEqEEYqbQlqCKNo81J1E/ufAmFFCEHYChA2lSZcZ3NYKDDBDGh71aZtDex8/xbKasLFuWXWd/n8tWNcXNBKuzXBCD/SvY+DTRt4efxKfu6/+LCrApFl57AUmlit1ccKSi7Skxz93Gl+4DcW178RZXPhuascOTtBqDzIpvdsoGVbIy07mrjj4zs5+/RFnLRLjQhTHwmTNEvftZBp8/TgGZTSa9kzc6PsvhjFe3mhuM3Gh9Zz14/vKfldwQ7ls1ePIDDoT8zxX898m09tvJueFQi3d9R3FK2JI1aAnHT4bPAIrGKHuOydkpr81fy6Ym6nwg9porJvgxASWyltvS0FVt6KVCmF8ABfqxsrK08eW5F8o4qypp6SdEfreapzx4pntiFaT3M4ymVnZZKZldFE5wtls7hhWUJ2kyZ8buw4v7jxbl4dHMEPLYm/OKKoxFECoYvM5JK1nfBB+DrGtowIm/8NSr97iVh6hQ0W8eGu3fy/Z76zeP6GSci0OTTZT09lAy9fZ+c4klogaFolRJArsSkuzI/zuWtHkfnIiFSKjOdi2ItKpAk3x46aVk7OlhLTG0IVNIVv3W7z7WAkNc+X+k/ihwTS0cpdGGgCiYREM8y3SmpGTDY/2s2LkUESk/m1CyCRvDnVj4KixdCumjaeG75A2nPIem4+XglhO0Dac0s4kAJB3M1yLTZ1y6T0sdSCVuxJe1qZJj8GdJyyuPpIDg+1xMJIYXgQngGnQjBen2MqNc74+yEXUQhXUXVVMb9JQEISKLOxAhZORBF1LNo+5+EHFKFZcKsN6n9mHUcYxVCCXNLRhHKliPYpYj2iJJw4s15yamSY7Wz+njyrNaxhDWtYwxr+IWONDLOGNbwLMHZmYsXPR09PlJBhFnJp/vjia8WkswDe37mTe98FCjFLq1I86fM/L77OQn4ROpVN8MW+E3lLp+UV0r6U/NXVI1xcmCDruTjSwzYswpbN7to2PrbhjreVwAPwrgvUlc0b7PimTdWPdzATzjI8NsN8i0/5iKLcVcS6BbmqPOEAcNIeImDgm2oxGSu17U2qRSdrlq7ZwwuC5hd97KCF73mMqQm+fu0FBn8+TEouJgyuLExS0eDSNVTaDTtpl+RkkuqOqpLPd9W24UnJK+NXmZqLUXlN0XbSxgpJfE8ifcVjH9vPwtUYs1+eIYBABS1Ss2lOfekcD//rg1iGibQVQvh6EVxYnCu0rUvAZGxLjvqrQTa+rpjtkFw7qKVyI5FgSQK3LJ8Q3fZgN/deXeDo9KBWVclvs6OmpUSO9Xq07W6+KQFmKcrryhbvs46F4GYWq8OjjeUMmLq9KVEaePClBBPeSgzzf/2bJ6jov8ZA7zFwfL3GDuoADIDhKqQtkTaYhkB4MJ1L8ucXD/Grd7x31XZ4bHoIWxisVtvnFOObheCqwA+BSGvfats0cQ3/5kFXpUkw1qIAB5VTBkottvPejwhyS5TF/QBcfNTj7r8yMJQolcz+HuCV8avFPqmAU7Mj3N2wbtXA6Itjl+idn0a4qmQmYmQUlWbgllQ+boaPbrgDyzB5YfRSSXJToch6Lpatg3FxN8vzIxf5+NtQagFNhlFSS2yXzQjitQoZgLJxRfchEz/rcfSvT/O+//DwqlXlt4qmzfU0ratFHp2k1pFM7VZcu8cgUJbg8sU3Ed0OWwZt7Gw+0O5LXvh/XydbpjjzpAMCbMPGQ/Hi2GWaIlHiTqbAdltS3aZhuDpAGSwLYNomEdPm4w/v4c2X3sDN+fgRuPYDkCufwZqKYUct1rXWsn/Lrhv22c3hSupD5fQnZgCt9lAfquChvOrEhmg9v7zzEf700iEuL0wslwdX8Pf9J0uC+QOJWX777AvEnSwSRX2ojA907mJjVSNJN8ufX3mTdDqr47qmrgQ2lMD0dLDWNARS6bxBW1k1P9az/7aIMMV7dl3FrZN28R2P0dMTTFycJpd0sKI2flgQSIOdE2w/X874wyYpz0EA+xu62FXTyusTvcScLD2V9dzf1M21+DSvjV1jKDaHq3xET4j4fRX8jwsvF5O83xm9xCd7DrCluomEk+VaYpqYk2UiGyftO3yka0+xYu528IPr9hC2bE7ODAOCO+s6eKJjW8k2pjB4smMH87kMx0cH8HJeQeU8jyWB4jzvr8IP3DQB9HbR89B6Bt4aYbZfJ7WVVAhDFIkwBQyfHNcqL6uQC9bf24kC3vgTbVVk2iamZeCkXZRSDF+epP8HBKmdAJoE1HnCoumKSe0KVmGO7+ErWUIsWg2R6jDCFPTd6ZKryFsDSFAIhu9T/ML2+zl96jmkUiXzHTMDuBJXuCVWUS3bG9n3Y7s489WLMO1QmTShGnLKQ65QGV8YMKazCaYzCb7cf4q+xAwh0+aexvU81ralmMy6VYydneTSc1f1XMqA8QMGU3eAMvoJ3xnA3A7bjodItQgmO9NggnLBUR6+lHmrGbWkelgf38oTdMqtIHfUd2AgeOXqZa34FwQMnaCId4F8Q5JN5nQhsm1y8J478aLj9MdnyPhuUYq+3Arw4XW7qQ7eODH2/cD4hSmO/vUpYmMJQtEg25/axJbHbo8kcjNEG8uRnp5XXg8343Hh2as0bqovkttfGL3MQGJ2GfkkZFpFu6FzX7/EmacvFRWjvKzLG//zKA09ddz1yT28+NtvkJxO5aWkBBtfsZjYo1hokVT5Ftu9etoSZXAb04LdtW2cmClN5ghgV40mAgfLg1RfMJjeUFo5LoQg4zlFIgxoW4SvD55lW3UzFXaIz1x8jelsEjfj0p/1OO0P0v1NiExCsDyAZRpMdvs40RUS8EuHWiBo2sW1XOgWk/1rWMP3G02b67FCFl7WI1uuGNvmk6qRVLoh6o5KIjE9djZsqqPjwQ4Gzl4A8iqp+caeqwanAgIJPRYDKKntd718oYNpm9hBTQA+0x7jtTPPYQY1Gfqpju3c3XhzpRhX+niGWqzEB0rY3StA+GCmwStxXLkBiWalsRKBMMEPFH630m/Fsj/dECRUjle/cJwnf/GBlY+XR7AsQPd9XVx5abESx40KrJC2TcxlHRx8VBBibYqmgEDlFNl1Fr0PS1RIEKzwERLqwxWkPYeWSCVlz6S4qtIkKvJJ/YJVktBEve5oPf/r8iGdcFa6KMZXkjI7iPR8fEdSNa1IVN+Y0KNtXiQp18mXKQgCpkl7WXWJ5ctK2BCtL30iQqB8n6pJAzsk8F1fW117ionzUzz46VKC87PD54tEGIC5XJq/unKEn9l8kLemB8gusdWN2iH21rXf8HySM2nSc8tX/QvDMXIph2CeyPnK77/J2NlFm7+R0+M8+It3076nha3v7WHjQ+vJLGSI1ESYyMX5q6tHmM+vqRvDFYyn4zjSw/V17EYowbFolt0szh/PH+5lYKtDosKjOVLJfU0bKLdDfG3wjL7L+TmaKyVfHTjNv971nhWvyZ/KEb7i8ELkKhe9aTwhV2jGK0koLRb4eBW6CAgl8mQ0vXnOd7GUqe2Li2IzAmXmLao8tNWVkVdqWg16mkBvYgZX+sXY3FIIIbivuZvB5By5nJs/Fz3fLIjseoZCFtQeS34Mad/jxRPniFgQK9dxJdOBxl6DwL5KBtPzS8M5CGGgpMT2NIHD9KDriEnfvb5WLlypK8h3Ea6SpL4+yenhC6uqU5bZQf2uKe3zVJhzZ313SZvWBDeBQKLtlCzDLG7rK8nLY1fwlUTkVUgd5UF+W9M0sITBhmgd3ZX1pL0cl/N2xk3hKD96A+uz7xVOz45oUlrABLn4titbYPhagcnvDHDvE3to293MqcNv5K98EQo4NTtMNBDiI+v2UGYH+JktB/nc1bdIuDmMvL23ZZiYoqB0qkl5AdPCEAZDyflbJsM0hqOMJRZYKnMkJKSqtWoX5L/KvzYyBOl6SLbC9B69seELjBxgwewOfY8NX8+5XUPiSklmhyDcEqXtdahZX8G+H9tNTWcVrRO9fOXyCciPqZEJMLNLz1AUm/hs03Ly6hrWsIY1rGEN/xTxj5YMk83qWUAo9Pb97Newhu8Xrq8iLyBcWfr5t0YulCSdFfCNobPsrGmlIvDuaesXFyaKRJilODzZvyIZ5sj0AGfmRvHkoiWGKz1saXBqdoRNlY3cUd+x7He3go47Wzn1zYvMt0u8gK7Cr/BtooEQL4/36mBMVJGNQtmoYMNXFVc+JopV0tKXkJFg5RfsxuJCMJgRhCoCSFdbJ6EU4UodGPKyiwuOuXCWubhXWvkuBLF1wLHS87WCJmV1Kydg76jv4I76Dr71n15m+qpWiZEWeDkf6fq88N9e04QUQ+CkXGTe9mTgrRHGz0ywr76TQ5N9BMsDZLParNaQ2q7DClooKXHC+rqFEtQNmozu8MmV68V1KRlm8Vo+vGEPQdvm+PQgCthT185THStX6yyF43ucnRsj47tsrmq8oTJHy/ZGzjx9sSRmIYRgfjAGwM4PbOHqF3VivZh8zf+rPEU2k8O3PHLJHDUTFiKtwM6LsKi8KomTVzGwQUmQ+aCa9CST7gJ9M1N01zeteo7C0IGEEhnZQsxo0ZNlyQ/0Aj+MjSs9pH2DykWJ9rsPsBhcAsLTEO71Ka8vI50nZaRLVJ31TVAmTPb4dI1HaN25+jXcDK70OTs3ylwuzYaKOtZF6xa9ua/DcGq+hAwzOzBPNp6jYWMdlxYmkZ4sks78/GwkHBfsH6vBOPi9Cbh8eN1uTs0OM59L59sw2gsdhackttBBtMHk3E32tDqqO6qwwxb9PVnGt+qAvuFBpl5w9QGPUEJwtnKS+JWjPNqx5Ybt/FbQsr2RsbNTuFWCif06s+ZmPeywhVuhGN3m0XlMV2x6OQ/f8ZnpNooyzW7WxbQNhKn717vquxY5WNfF/pUJzVaUzsZ62sqquaOug6PHrzBwj8JyNEkwV67bu+/42CGL/sQs5+bGVvWmB/ja4BmmMglCpo2nJAJBd7RuWcL5/qYNXI1Nlrw6FXaQgGmWEGEActJjJLVAmRUg63v0xmf5nXMvsrmqiY2VDYvWW77uN5XQ/1o5KFM2j2zcSlkgyIaKOpojlW876Nd9X1dRjUT6Et/xQOQJGAIm7hJMb3XxgxBMCTYcsnjvgZ107WlnLB3DHc5w9fNXODHwJi2tUZ748DbaujRxcIcd4Jnh8wRCNgFsskry8tRVQqZdDAj7SvH1obN0ltfw2atHyOWvWyrFsemhEtLR7SBgWnyoazcf6tp9021/oHMHAxNTTBJH5PtWpfL9V/62Gh7YjuD99975jtko2iGL9/67Bxk+PkpsPEFZbYTDf3Z82XZW0CwZ21fChns7cTMeb332JE7aJZdcJLeOHoR4Z+EvhY9gYJ9H7ZTF3h9eHAtd6fPVgdOcmBnCV4r2siqe7NjO+ujKdmKgk1/Rnijxjsxi32/qtjuZjONIn7117bw1PlAiOV9/RhN1fVcu6/M3P9pNz4PrScbS/KdrzxMAlCfI+u6SMUtvG8gTh9rLq/nTy4eKSZqs7/Li2GXCls0Dt6nacfivTpAtV5gOjN4nmN2qtFIQkPYcbGHQd6eLKjMx86X1Ycsm67t4eXJr8TwVeekqgS1MPOljCoM76zoYHJ9BGQp/ydRLWZCrAmUJbEvvKNtscm16ip948ABvTg1wNTaFIz26Kup4pGXTu4KkkF7I8NLvHNLqeEA2nuPY585QXld2QxWu28W6uzs49ffnySZzy76TnmRhbFFC/s76Tu6s7+T3zr3EQHIOx9dS8JYweLh10fby7Dcvw3X9tZvx6D88xJ4f3M6H/tsTPP1vniM5k9ZjkxK0H5U0pDzsUJpYYJRnGGXnB7esmiy6HpurmniyfTsvjF0m+/+x999xsmR3eT/+PlXVuSfnPHNnbs557+YcJK20CiAJRDJgQP5iwAFs/wAb+2fztcEBg00QoAAKiEVZm7R5797dm/PcODnn6encVXXO94/T02HCDbsrRJhHr9Wdru7Kp06d8/k8n+dxbcKWjw+0bcsRdDY80MG11/to6JaMb3azBeeChxo3ciVZbOOhXIntKs70DlBZW5ojwthJPYBRBkxuhtYRhZ208Qa9uOVZ6T6yxL9sUbvHMHN99bqSKnZXt7C5vOEHrvK5hjXcCN6gh7v/6X5e+/xxLj2QwPYrDI/BfEiQ6PTyVGo9zXXV1G+uwVUSr2GSkW7Re1VITToZPQSRToFwofIKtPQolNQKJlbWImZ6nWSq08V0BSb5d+e6kmrqgjdWJ9ha0cBf9WQn18s4MCsQXFydPGw8ohh8rHAcsCLjhVw2fdlihWsIhKNgRWumFVbK2iotNCje8I/xPqluOhbZ/6mdhKoC9L09hDAEddsreDEwiC1dUsLV70cgUa4YfMSg6xWToUdMrBKJ6TFyBNeMdPmNPU9w7PNnOH9kkGqfZL4tS1HwaCJDlQjyizse1Mok2f0bWYvMlGuTTmVQcZe6qyaJM+N8+5vf48F/eTf1m1Ye01yYG+XC3GhWecTJ2YE+1rz5piTtan+Yx5q38Pxwd+5Y6n2llJ6aIRPPkxrdjIu/ZDnR+NzMyLJlC3aKqJPi01vu45XRq0wmF2gOV/Bg48abkpUDpT4sn4mTLiZU+kq8ORvUmf65HBFGKaXVj4Tg4nev0LJbv7ctr0lJrZ4XNlnl/NrORxmKzeExTBJOmj+4+BqZAqKORBGvBMejsGyB41FcfMRGxgewXItLc+Ocmhrkw+GtTM5FkK7CsLK2rYbBVCpGzE4R9hTHDU999TwXn7nK8MYMA/sUykdBk12tPedjG3qsL7DiKreOKCjAcZXCce0VtqTja64FpgueGJT0Kqb3iFVVcpUSOI620l2JDAPQVVqD3/RgegTpeAZ7cRwowPWIPOFmJc6qgtPNmvAi9BQO3yw0vSTJVIFVTo5YY7hgBQ1cWyGFwhOFpiNQMajwbxPEa3V/kRsCLeHKCRdKJgXnv3WZzrvbCK8QA6zxh6nyhZhJx5edY7k3gCNdkq6dUw8CrZ4Ts1NYwmRRIffS/ASOdPFn7c5dJXGVJjyZQvDhjl2EspaUP73pLubSCWzpUhvIswSVUgyeGGHk7Di+Ei/r7+ugtL6IRfjOoXQBmxDg8ZjIbLu3LBN/9nl8cO9e1lW3IJUks9QGq2A7p6YH+Uj7LoQQNIXK+aHOvfzf7tcXT4JMIqPJRQIsF7xBC5Et4ik835vh8ZYtfP7K20WvloZuk1RYLSkGycP1kuunQSBNkH5t5YoE6RWYLjiWQmXPUWAwVe9Q8dO1fHT9QfqjM6Ti89xd34n70hynLvfCtE1wTDGxp2BnuZiooL793RearWENa1jDGtbwDwH/IMkwqVSK/v5+bdlSXv6DPpw1rCGH8cQCcSdNa7iyaPK24cF1XD/cj7TzSXR/qY+OQ8VVKT0r+Cy7StEfm1mRZPKDQspZLqcMkHSXL59Kxni69xRJJ1OoUgroiZyFyZXIxDsmwzQ80sxXwpeIKwcUDO0VPF7VyavDV1E+VRSgjzdphYDWF2HkLnBCAsuGhm6LdIlibouDJ+BBGALDY9DYbWBaJqal72XbgSaS8ykWxmJFx2DYOiGllCpKsJaUBRGGnauQA9j2gU14gzdOvCxWGwGk45lcNYBSkIlpCw5V4PerXMnRvzjDU7/zOKYwOD41gM9rEbd10klkA1BSgNc1GdzlUDko8I8pqs8rRu4sns11ldbkEgqgg/tPte/kqfadN74ZBZhOxfij7jdytlHfHoCn2nfmqv5cR3L+W5foOTyAkoqyxhLtAZ52UFJhWloKeX5Ek2Eat9fz8eCD/OGVN5g2k4is7ICSYGSUloSdV/zB08+x2V/LxmMWfXttEpUKIwX4dHK2UDZFFMyxlVSMn5+k68GViSS7q1u4vjBF0OMlaqfyzWpRaUMsjbboIKuVhI7nHS5/UGlVmKVxWLLqHHZW0yRLiglOQnhEUX0O7KDg2q4UsWqBN7K4rxVm4E1eHvzEXVi+G7/6k06GK5FJfIbFhvLanHRuzE7zh92vM5XKt+8DNe3UBkroLyCTSKWTsIvBhEwiwyv/6y0mr2qykhE2UZ8MgNDVKqadt35qO2VRueO9TQpV+kIkMjpxrQxNhoHCWhneFUHF47fY/6O7ODp7pGi5MmC+UeFJazLZqdlhrkan+Bc7HiLseeeWMAPHRvD4LebbBUJkA6RS5WKS89UudREHYRpaeUEpZMrNsRAUAteRWKbBQiZJ9/wYQctLbNHqorDpmIKI1+bi3Bj3NWzg5dErvO5eI5O1I3M8OhBoyOL1xhKRVckwGdfRSlLZyjQrGxE6OzvCU86uosTzhvI6fqRrPy+PXmEunaCjpJr3t25jPpPMBv3yRD1buhgI0tLBlk7usvRHZ5hILGg1DlNpyW5XP+qeFNReM/mhB+9gY+uNq0BvFY3b62jcXkf/0WFcW+/IG9Tvjckul/HdCjdLCnF8iktPunx8bxXTV2dx51Mc/fOTOBmHhXrFuDXD2Gff5CO/8jCVbeVcmB0tsiSRqGylbXFAeDoV49zsSI4IU4izM8PviAxzOyjzBvi1g4/zV3/4IpOjc/j7XKLtMLfewK4QlJQEWBes4rG9O6irKPu+HotpGbQfzN/bgWPDRVW6AOvv67gl8tP6+zs48eWzSLv4us5tKqJpAlpxrOOfbaZ+fT4p9MzgBY5PDWSlyTNcnBvj8vwEmyvq+dGuA6uqjyQ6rBxZpHA3jpIELR8f69hDMGnxxsglhC0ovyipuqR/5g152P7BvCx2ZCzKpeevEZ2MUb2+Ck+TQVq6GAI8hoFdMHYwHXAyDhvD1aDIEWEKcWJq4LbIMFdHRnl7/zzJEoESOiisST76vaUU2Ehm0gkMW+Av8SEMgSWMfNJi8b2qiq3OEjKDEOD1WPz3cy8hXH0fVsp/xroMyuIWV++1SZU6XDYGuXAhxoONGxhNRIg7GQZic/QsTPFTGw79wAnnA8eGc0SYQvQcHnhPyTAev8X7/v2DPP1L3y0ifC0iFSkqOWU0Pk9TqJxIJkUkk8RnWuyvaeN9Ldtyv0lHlxNrUIpUdrk36KFxRz29hwdyXztpB6QqIsqd/9Zluu5pJ1R1ayo9B2rbKfH4kCh2VjbjMfN9ZHlzGQ//q3s4981upl6NYHWFuPOR7TS31zB2+U3Gk5r0YydtfSwKzj9zgdDOclS7WqY6aWeHEG52HlcybTAmssl9IbBMQUa4WMLAIwyq/GFSrsORiV7iToaHGze9I8WuNazh+wklFd3PX+P6a324jkQ9WIqosvEZeXscW0gmO132t+pKekuYHKxt543xHm15ZBrgSip7DUbvlEQ6QVoK6YF4vSCyR7LvfCXpS9Hce3iuWT9HhpV/7yng4tzYTckwtYESFm19cysuQoBHmTiu1CprSmEloeasnreIrFVL8ctmSVJerfBCyW6b7Ki0mEezCqEGgZXOfxuvUowmIzSFym94foZpsO0Dm9j2Af1eV0oxc13y+sT13O6E1GPz+SZJvNYkUSK12kIBIpkkQ6MzXHutD8M08CQk659WzG2ETJmgYt7iR3/qXmqCpUTt4n7fY5iYSlDztkvtFS+WnR332JLTT1/giV9/YMVjvzSnVSxMwyBo5GMZfdEZdtyAQL+IB5s2sqOqiesLU5R7A7R7Kvhi+ussfTNqK67imMtqpAmPYVIfLOWTXftuuv+i9QIeNj3SxYXvXAH0fXBtScf2+lwMID6jx0xOxtFqttkvxi9N4WTcHPmrEIYQtJVUopSi78IotuMUNaHF+pr5Jkl1v8n0OkkmpFBCkszGH+J2mq+cO4KqAWUpXBekLfGV+ghZXgJmMdFnpn+Oi89cxbVdRrdJTYSBJdVFK7T5LNnFsMEXg+aXJWYKpvZmz8UF5RU5W55lT0LBZlW2EMkTBdfPEsLzsh3jGXFIjcQJta9MWir3BXm0cRPPDl3EDRRfQLlsleJOwszxwvMWm+kqmG+Q+I9EMB6DxUangJR08PgMRFLi+mHofvB+wyE8aUGdB8eQuFJiZ4ulDEf/JyRYtsCb0qon0z2zK5JhhBC8L7yJL8+fJGVoa7nqQJiPdOxCKki5ThERBsDI9oG2chFA2ONDKoUtHZKOTUlWbcaRLk80b+FQ/bplBKmV5iRHv3Caa6/05T5ffbmXh//1PdR0vTuixeW5cd6a7CPu6HGhKYzc7fcaOm7VEqrIxbsNYdAQKGMovryYyRSGLsAg33TawpU0hcoZic9jJx3ctIuZVYuUQpKO2/hLfbSGK9hccetFY1sqGvil7Q/w9RffZno0QuWQgZAwst0ubreFxExzyTIBGALHrwt0PCk9L8opdClQaZeMJ8NFOcZvnfoutpRIV9IRquLRjR30fLsHruc+pAABAABJREFUJ6WI14Eb1ApFQmoVIdNr4PV5uKfj+zv3X8Ma1rCGNazh7wv+wZFhbNvmN37jN0in0wgh2Lx58w/6kNawBlKOzReuHeV6lswStLx8snMfG8u1hENFSxmP/Oo9nP/WZSJjUarXVbLzw1uKlUSACm+QSCa1bPt/F+TbC7GxvB5TiGWV+1srltvifGfwfLGSBvnJi5GdJZR43nkS4rmxS2RKwJQeDLRP8WE5SEJkllWqAqhyg3WijKqvRXHLTbzprDSnUNR4A8Q2mPgsi0OdO5k/McyYnESYgtY9jRz8iT1ce7WXgWPDRdsMTgmC8wJVporIFvet28i2X6+m540BXNul7UDzLal2bHhwHcNnx3SidZFIYwhMj4FrG7gZt9gKxxDEpxNEx2I82badJ7Mey2dnhvni5WOk42kcJXG94C+1GN/sMrZR0fyapPKcwDRMZrc5+BuD7Kxv5rGWrcsP6jbxzOCFHBEG8ipHO6uaCVpeTv3VOS5/ryf3fWQ8ipNydIvIWroIIahoySdS3bTLumfA32ow0yqJVSmE1BNK1wMoRX8gzrBviOYK2PaCl3g6DbZi8BFBtF0n1U2pk8xGQXQtEBEEb5As3V/TxkwqzuHx6ziuh5Rta7lfBxyvro5aSfnF9oM3KQgPSxbashPfAllh73xWLjgbETAcaHtWUZJtYtIDvU8J7LDetl1vUqA/XIS71m/AybjLAoSF6J4b44vXj+dUNCp9QX52091U+UPaoisZzVmNCCE4NtXPj3Yd4PzsKHEnTdKxcZXEMkzOzozQWVrDmb/pZvLqNI5H0b/fYa4ljZtIIAKGroCUOtgUmBdUzJhsuL9j1et8u0jMJwkdSZFu08EVYRpYAUPHIrLXwBS6GvzdoPPuNvxvn0ZkbFzb1eS37AhLAV6/BQJiTpoTUwPc/w7ICI50OT41wLktUahxCM4W38PFCkslYOgBgXdBUnFRB1XKe2D8QDbIn73/SSdDf3SG4azf9WLgCQVutqLdk03OuUrx4shlLs+P6+SGoQN3Ap3MlhIMSx+jZZg0BFcnOGSku6zPX9xHRjr4KSYD7qhswmOYXI1MUurx4zctopkUadchIx0EWtpYoNVLUgWkS6UgFcuQ8GS01QqAh1xwqWrSww9t28PG7e8NEQbgxJfOMXp+Ak/AwrAEdsLGzVrrTHW4OF6y/Zf+vYPk977zHBueMbBTDhnl0P+kQaw6X3GZeO0w//zHP7CsCk5k/6eUygY59fcV3iDBVdQsrL+lhKvHa/HhT93LX//Sd3AR+IcEDRMmQghKav089d/u+Vs5jqW45xcOcOJL5xg4PozpMem6t52dH7m1d1p0IoZyFb4SH5mEjXS1Zc8ySfessoonXPzFyaxtS8rV/SRo6fK+6Axf7jnBp7fcu+J+ZYOFaecVtBZRYvgIeXRU/8ltuwg8G2Xs4qRuDyGJMAR3/ex+vEH9m8hYlGf/48s6KQOMd08hP+wSq1xM7As8holvRmLF9DnUXTMIDMww9SvzKx5b3E7z7NBFQpaXPdWtNyT6uVLylbHTpMq0TIYysrZPRdDVlIYDoVFFcl0Gb9ibU/RaSWTNzOhETKYkS3g0tEKJ9kEUkJUqF1InbQwXVLlJ/xaHVNbKRgCzqThfvHa8iJA3HJ/n24Pn+ZF3aKP3XqGQNH0ry98NwtUh7vyZfbzye0dyFzteB+N3ClK1CUZOf48n2reScGye7j1VpBjwQ+v2LCNCljeV5tQMcxCCrnvacx+3P7mJkbNjpKOagCMdnbwtJMMoqZi6PnNLZJgLs6N8uedErk98dfQaP7v5LsoKrArrNlbzyK/mn7mZvjn6jw6xr7GZa5FJHDuv8hiICMrGBcnEPG7j8ulDeLh4QfmIoM0uZaoiqx6jFJ2hGn6ocw9HJ/s5PJEf3740coX5dJKPd+696XmtYQ1/mzj7jW7Of+ty7vNg3wJuCXhDxdnk+XSSjOvw3HA3Z6aHEUBrqIKYk8a1AtSOeCidtXnjwBzSylbGZxEtdbn6iMtup4K5nnkgS5K3DKwCS0PpSFITSdw6iWndeBzjNU1te7PkOTWkfhZzKmsuWAmt+jB+UOTG7cuwmkjMSr8rVC5bdQPZAgdD5JLqHp9VpAByqxiKz3ElMoFUKi/WYYDjBSsDMqT/tXH09cwOPj2GiZpJg9IkSNdxsdKKmnOAUGx5vJ2addpzd0NZLRPJaNF+hYLqHiNHhFnE7MD8qse6mtrKjWyVl6LaH84VMMwNRbTiCeSs/SyfhWtLXFsWkU0O1LbzwvClom01BEtpDVfyTrH7Y9soqQtz8ZkrTPfMYlgmfUcGmbo6rQkC66sQBtgJbUezCNd26X72Kjs+tHqc+shnTtB7ZBDfhyBeSW7eIITAtPPbSpUorRIs5KIYGQqYaZU0XDSYyk43lVS4GZcHWjcuI16Od+txYySQwQ6tUJWzSuO3MvDDTbuIvDHF1SsjTG8XKBNKexXRdgMp9DNnmlo111yqnpvbtC7kUUCsBV2dVUgeWLp7Bf4ZSfdz17jn5w+seGxXX+5l6umrNNS6XL1vhYIjwMgUxFiyCI3quEC6EqRT3IHYJVDW7dJ43mJsm5uz5Raujlm6QkC2f5nZblB+wWFiU5Z4V7APZYLQzsXUXzYwpP5yNYWVSy9c48yXzrFBSBbqFD6f4GP/ZD9V/jAnpgYIWB4yroMj9exdcysEActLIltsaAgDQ2hrxozr4ChJieXnsbYd3FXfCeiiyxeHLzOVitEaruCxli3UBfLkw/nRBa692ld0bE7a5ew3unn4X73zOd23B87z7NAF3Yeh57aLNmzbKxrxmRbNoQrKvH5OzQyxsayWUm+Aj63bzR9cfHXZ3DglHQ5UtRdZuAoh+OmNd/Lc0EXeunCVQEJQf8WkdMJgYoNWwb7jjnU8vGlrrgjsVtEYKucXPvAo117tYzA6wrGNc/hCAstQxYWgUugCvdw0qaCdZ/tu6YHOt0xsP/Tvd0GpHHHKkS6O6eLGbZSrUAouR0eR0QSNDSVcqJ1lYq9+BlW24MyjDDqqa3isZQuNNyFarmENa1jDGtbwjwU/MDLM5z//eT7/+c+v+v3IyAgPPvjgLW9PSkkkEuHatWskk3l7lkcfffRdHeca1vBe4Lnh7hwRBrQM/BevH+fXdz+O19SPYe36ah76l3ffcDv3N27g81ffLortrC/V9hV/l1Dq9fPD6/by9f4zOQ/mbRUN3L9C9XDPwhRew8x5IS9CZJMyHsPkjtp3lhzPuA4npweLgkteYeFTOrAdrS6e5Bo2lEc87PjxLbz92VN4Coqg3bSL/6UY4cP6fl0MnyFS42LWKapiPpp2NeALedn6xEbOfuMS8elEjnTgDXrY111CZm8lF+e0CsOddeu4r2E9Qgiq191eIKZpRz33/MJBTn7lHDPxOQzLINVqsFApCUyZWP0Fk0JD4A16dfDEYxCdinPlxetEJ+OU1ofZ/Lpitk4wtFtPtFwcLJ+FnXIYP2RQN25ReVFSdsbB8sdxG0aZ+6l6AtvfudUOQO/C9LJltpQMxmZZH6rh2mv9ueXSlaSVQ2STDvCV9SlIOniDHrY/qQNJSire/uwpiLk0dls0dkM8niLaACMPCAgUzDdNwchel9CMQBoevGMuXa9CycN1hO+owpxwOHbhGtNdEmlC2bhB+zGLup9f3coC4M66dVxfmORqZEwfdzbRFx4CO6RI1RZGdvQ/0guxWkX5dUWsQaD8xUkWMw2tLyiSnQY+y8MmUYNVJxkeGUUIQXSjQSasSQnSBNsoDggsonQMep+5TC+XqWwv56F/eTf+kuKkpS1dvtp7Kpc8Au2p/u2Bc/zkxkOcvz5Ayk7rAxQCT0Dba0UyCf75tvv53XMvkhYOHkNbtpycHiRoebDP6usxsNdhtjVbFeWCoQw8IRNPVJN76gkz9bMWfzx1lA2ZWh5p2pxL9L5TvP3ZU/jPJumYsBjf6OL4FHWTAfbdv4nB2Cxhj58769bdtBLzVrC9uokzM8NYPgsn7ZCSNgLwB7xFFZmRFSzkbgWfu/o2l+bHyTQ7yFoXM6X7sWSZAkOgXG0HkqoUZMoBtP1I59cUvjh0PKsYvVOQqtIS565SWNlKSSEEQcuDKQxSroPrakKT39RJYVu6nJ0ZJi0dDAQiKJCOVgFShn4ulZAknAwtoQq2VayuVBD26MqrwSX2Wk3BMkoLEpWLeLrvNMen8ooBL2dtL3zZd6ejXBzpsqm8nuH4fI4Mo2ReJEmJrNJTQTWWZRgkN3toyEqVj12cZOziBMHKIB2HWooUuG4Vdsrh+ut9uWtqeS3cjIubcXEsge3NBvoKg6IKIiEb8IFSTG1TxKpkURLnas0Cr333NLse2cS3B87liKZCCCzDwJEyV7krgAU7iQJKPf4i0iFoNaf3AmOJCBdmR/GaFrurmle8d6mFdJF62iKik3GkK79v9kg3gjfo5c6f2cedP3N7VcCQJx4YpoE36CEdyyBchTcG6TJYfMsIoSsTd1YvrXTWSj7OCoo9/dEZIplkUbJ+ETs2d3D07DDYEpmduZlKcG9H8Zjqvl88xPlvXWLo9CjekJeND3Wy7lBeVe/S89dyRBiAuSZJ1OfgwdSJFKUJK6mQQoX1cx2t1QnFljdnCe33FikTpV2bZDrDc1fOA/Cs9wL/fO+DNIRWJsP1LEwRczOYXhM37SLkknLnRZEXCVXdUH1OcbVREvWlco+DJQxURmqCa5Y4453P8dtwzew7dHHTht64kAorlf3bFISnBIO7C1RwfFaOkOdVJkZBMLx7bmzF8/nbRNv+Zk4/fRHpFCeSlipIvlfouKOFY38ZxE7YpPwuAx/U1lwIwWQ6yheuHtXJ1IJ1FPDs0EV2VDYVEW73fnInL/+Pw9hJB5W1bGg/2FJUSVxaF+bJ//QI19/oJzGbZPzy5DKVRYCSupuruGVcZ9lYZjIZ5atnjvOw20795loC5XmSvWu7vPi7bzB8ekxbSpiCAx9t5mpFhJmYTdm4QdN5E6EEgXloumDQ15FPcATHofqs/russYT2j3cxVJ5gY6mX3Y7FtTf7sa/GKZ+c5drBK5zYOb7smE9PD/H+1m3vSjVuDWt4L6Gk4spLPUXLSicMxm07R4pfxPqyWp7uO82ZmXwxSNRJc2fdOq0aug+ubBnmcM/rywgnSsGCk6Lpn25jy3XF3HCEjhaTb5tXdb8uJemYjZFSTHz7Ol8PjnDfL95BTefqSgSbyuqLjmUR0lGaL2CpHHElXQn9H1ii/lJ8hNmVlyTooXgdl3wxwy1B4PrATAs8PovKcJjWktsnZjzde3rZOA8Wx+UQGFFUHVcM7XJwLRdf2AtCcFddJ3Xl1RiWgXQk/hKfLpiQiprOSu762TwB9OGmTfQuTDMU1eN2yzR5rHEro/YlJMXvpMIilaU4WNvOWxO9RYQIn2Gyr6at6HcTiQVeH7/OTCpOe0kV9zZ0rUiYKa0P4y/xkRaiiEZf0VK2THXlwcYNOFLy1kQvaddhU3kdH+7Yteqx3iraDzRz8svncqRjgNh0ghNfOccDv3QnLXsaufJi/jkShsDyexg8Mcz6+zuwfBbXX+9jpn+e8qZS1t/XwfxIhN4jgwA0XTDpO+AgTTA9Bn6fF59lsr22hng6RmeVxbhvUlclFEBIMKKKzrcsptskAthX28x9jctjcaGqIEopEtXA8loFVlaFgboFP/c9soVXg1cYaB/BFYvKSgblPboYKFGf/XGQVYgw5AgvMpD92yBXzISx8u6nO2H0wvxKB8t07yxHv3BaE9Z9jrY+XyTdFMIATxKkpdWcml6S1FyB9MfL6PXEka4qCsqERjWZrv60oLbHS6JcsVAnmdimUELlCrYUYAcVsaaC1QvGpQYC/5yi/pygdlRb6DbvbqCyrXzZuaRjGU599QKZRAY3I/HPKQzL4fhfnuXx/9/9WqVJGAQsL66UOWUV0AQYsUi8k9rGUdoSy1AcEo089cCBXBx6JD7Pn15+Mze/vDA3Rl90hn+98xFmL8zw9udOMTccQbkqZ6++iPnhBd4pzs4M88ro1RwRRl8qRdDyUeLx8ZMbDxHJJPmTS4dzysSmEHykYzf7a9qo9IWYTcbISFkwj1DsKFuumB72+PjYuj24vztMJpEfw7We0edy8OHW3PW4HZyZGdbxn1aD/Xs3cm7wPGYyiomes6RcBykldTM+Zksz2ChcY3mjFgYEMiZb/PVc2hpHqTmUpZ8DMwNKKHBB2vkXkAL6RYTKYAnj+0WB6pEmywTH4KnSDbSVv7u47RrWsIY1rGEN/5DwAyPD9Pf38+qrr65Yna6UIpVK8dprr932dgsr3mtqavjpn/7pd32sa1jDO0EmYXPyr84xcGyYE48nccsM7SGcbZ8p16ZnYfq2pRh/ZtNdHB7vIe6k2VRez731Xd+vU3hX2F3dwpaKBoZis5R5g9QEVg5gl3uDTKaiBD3ebFWDnijXBUtpC1fycNOmVde9GQ6P9+AuCQ5kXAeZdll/QpE5COksj0i40HbEIBwKUN1RQe3GKiav6EpWpRROOhtAAiZabQYOuihDy8MG51xSXzxJVUcFFc1l7PzQZo782UldnaUUru1y18d207S+/oaqHLeD9gPNtOxp5Ov/5lnObI0y36BlUJWA0pQfa8wmMCeo6zUxbYP6zTWg4JnfeolMXE8A7ZRWLChLmPQdIBdsWJR5d3wQEzbetA4iKFeSmE3y2h+8zUf++/ty1+OdoMIfIhGfX77cF8S1XeykVtgQQrBQLel5ODvBE4KxO2HdK4LOprpc0C02FSc2lfdSdm0XYStCY+D4dVJcZa+7Y7vYQcnFD2iLlZDh40c3HGBbs05cuhtdjJNxRp+eBAMMV7DuzlYat9fd8Jy+0X+GgegsrlJI72JWT9tvyaK3bTZhauggiZFRVPbCzGaIFriBmRlwwpCpgo6z+lp3fLiObf9sE9/7r68xcWkaWe4ismWFblZiGLIkAKX30XAaWs/n79Vs/zxnv9HNwR/bXXT8I/F5Ek6xLYJUiguzoxw/fZX0lRh0LBJ5FHbCxjANqv1h4k4GV6llVX9HJ/vZVuNDzipm24qfRY9hYFgGv/rAY4wlInzu6ttZvWFt8zIQm+Wfb73/HT8vdsph5JxOOtX0mtT0LgZGXQ4+2sJDGzetvvINcD0yyXcHLzCSiFAXKOGJlq1sqWjgg207iGSS9EVnsHwWlYaPpGMvq8BbX1b7DvY5xfnZEW3xZAA+fb9LJ6AxGiK6zWIyGUUaYHgAG5DgBGF2j0HTW4KSccXmbxlYFR66fn0L3xy9ULQPQxjUBUp5tHkzn7v6dq6iKuXaOWsp0KpJQIHftYYCfKaVrUC78T37oXV7+OyVt5jN2q5UeANFVfFKKUbPT3ClZ5i3qvpyVhMAUTuFzLY1reCgQ+A1/hI6Sqp4efQqSTujqxFtbeUE5PzdFw/NFAYSRX90luR3J7n6cm9u38f/8jQldWEC5QE2PdxJ2/6by7dDtk+1i9u55bNIxzM4aZeyfogt7UYU+LM5X9NjEm1agdAm4OSVXu5+bCcf79zHN/rOkHBt/X5bEk+zDBPLMPnO4AX+ycZDfHPgHH3RGUKWl3sb1nOgtv2WzuVGODLRyzf7z+Z2/eLIZX5m4120LUnklDWW4A16ioKOANXrKn4gRJh3i4qWMsqbS5kfXsAwDXwlPpyMQ9sFg76DrlZuMXTF9T0NXctU7XZXtXBkopdC5ochjJwqk7nKc7OlupHtTS1cmh7LkYjqSkq5t6lYYcrjt9jzw9vZ88Pb+e7Aeb44cwHr9CX217bxQMMGopPF5IL5Jp0gsaRBwO9FKUU0k8o9M/oA4dq9Do1X4myrbOTU1KC2VSI7VkjJnEBdzEnypcNH+JePPbHieSwSTLwBD7YQWt3OVUhLYAqBrVyQUNmtaDgCCx1aDceLiTK0qpQrJH6vhZ1wUCZUn1U0HYGBRyBdKYqrfIsqcQWuF7zS4K5gG4Y5h3DmdVVvdiwnsskSsSRhElhFZelvE6GqIPd8+iDH//IMidkklt9iy+PriyzA3kt4Ah7W39/B1Zd6WdgkkJZ+Byz2xa6SJDL2smszm06QcGxMQ3BuZkTPVTbV88RvPMCVl3rJJDK07G5k/QoKcIFyP9uf1O/l8UtTvPTfDxeRf5p3NVDVfnPy/2BstlghTCrS8Qzn08Mk3hon/BWT7fs7iYxGSUVSCEto67RsQ3ZdmPnKIHc/0kX/sWKLp0w8Q/lrBptPwEK1whvVCTIBWH4TdbCEr/uvIpMKkpCKpmkZNqgfMVEorr81wEKnXGaHKtHWaWtkmDX8XUEqliEdzxT1h2Vjguoek+gulVu+ubyeTWV1fK3vzLJtHJ8a4AOt27AMkxpfGF9MYPuXFKJITey1hWTdXe35fc2W873hywxPTlMyCi1nLKyMIJlJcfiPj/PU//tYESGnEB9p37kCGUbB0q5csboazCoQgOUaOELmlc3cLOFagTI1QT23g9xaK2/MDFlUB0r40a4Dt6VIoJQikkwwnlwoJhmI/D/BeYFXmjRcBm8cptdJqtaHuXvjRvZnCSg7P7yF0399ASE0Kccb9LDpn2zlc9fe5vrcJIG0yS63jrbvZTClxA4oOoJl3PFP27n8uJOzCQKt5rPro6sr7dUGSvjZzXfxwtAlxpILNAXLeKxlS5HC8URygT+4+GrO5rM3Ok333Bi/tO2BZfMp02Oy75M7OPJnJ3NkZctnsu9HdizbtyEMHm/ZwmPNm1GoIsLru8FM/9yyMS5ogj1A64FmTk4OslAj8cYFNb0G9myG8cvTPP1L39WFYJJcW77+Wh/td+Tf6zU9BpmgyfhGnexvqCjjIx27aD+kyWCulJx78xvMkSdECQnCAWErqgZMqgZ0Q73j365sadmypxHV4mX0YAZ1q6EdAYlmg+6ZUZ65dBbXk1XhBUAxu0Grn1iL6io2YIll28j9u2g/ll2mHDCMbPxkhcdICcWlLcutOwH6jw3rQqpommAShBTZZzXLSMluRxqgfPp6lQxBeZ+2Zgu/nMT3ONgFInQ1ZxTBBQPTbyIdF2/KxDsu8DkGs/sV0pHYMjtDdhWBMcXUHlGcbVkMDdmKymGDih6Fa0n2fnw7W9+/skLt7MA88yLF/EYXKwllfYAjGT6jSdqby+sJWz5iThpDCAxhIJXMkTq8poVUknQ8g8qOp3wRQeq7o/TSz6aHdRz5rYm+ZYrecSfD4atXGf2dbjLxTI7Yk0nYKLSaFbAiiedWcWF2dMXe0VVuzh7p2aGLRRbdrlJ8o/8M2yoatIVqUuFZlMRRmiIyfGSYLR9crkgO0HGodRnRs6yx5B2dx/ND3bw0mu8Dz0wNUbngIePNIIUiY8ocDyraqNhb1U6VL8yJ6QFGExH9PjFMfMLETtqE+yXPbxglUZKPIypTx2SDk5ApcgnUWzZTikF3Huld+nwJ5mpd3vjDY5TWhalovf3zW8Ma1rCGNazhHyJ+4DZJasmg62bLb3WbW7du5S/+4i+oqnp3/pVrWMM7xZufOc7waT1RMVKKtE9X13oKAqB+6/YfwfVlte8omfr9wHQqxnQqRnOofJnPLOjEaNdNjvWBxg38Ve/JXFUDaFuMT61fWfb0dnB9YQqvaZEsTPAr8M9DZcRH6JsZ5qtdXB+UT5v4lYUddvjy738PIQTtXVWUN5UiHUnP4QEdrPMpBg7KrMSynoQkKhTDWx0Gjg0Trgpy9Auni6TrnbTLiS+f5dgXThObSVC7vpp9P7LjloL6N4JpGbj3lTLviWhZViFw/TAbzOAtM5hLK2Y3ODw62cY9P7qX009fIDGbxHVk1uZEIm2JmpN4ogI7rAkjRnYyaWQU1kJexWQxUOOkXYbPjtF5V9vqB3cTPNi4gb+8dqwoj7utopG6QCndz1/T1SvZSfvgHfq8XI/IVQsN3Q37pvIRG1+JF8MycDIOwhC56ncjo/2n7RJy5eI2LlKAsnUwJRNyeXriLBsbG3SFjcfk4X99D+PdU0RGF6jqqLihF3LKtTkxOcCJqUHctItjFlS3ZFUzFq/h0iSdH4uySYWjJKEJSNbo34sCp6NUpUD1KxyP4un5c3zuyEWsewXBNhBzSifzFHlrb8BwFEY2Nhe+onAiGcwC27XRcxPLzmNpEibtOqRdB0PAF+dP4yuTGK5WoFEie5wZxUwqvsyLXSpJwrFRKI7dmcbTYueOb/HqZJIOpkcxcnGC55NXUWYxUWwkPk/PwjRdZTdW5FkNhil0xaO9vBLN9Jgrr3QTTKdi/PmVt3KB59GFef7s7GE+wmYOHNzIL2y5l8lklLTrUB8s5cvXj3OhQFVgb3Urm99Bdc71hakiQorOnIPcFuADmw/xmctv6iCjzAf3FlUQ0tUGvgK7lm2PbKS6ohpGl++nPljKtspGHmvezIsjl7Fdt3i/N4ErJQt2CkdJPGL1a1wXKOVXdz5Cf3QWhaKjpCoXmB6MzfLFVw4z5cQxbcikFE7awRf25fqgpQE70ESCJ9t28GjTZv7o+ZfoMWZxreLnAvKP4SJhx5yXeSKMVKRiaZSrSMcy+MI+Jq9Mc9fPySKFjdUQLA9Q2VZeJBOfSWrSmD/so+MizG7KaJsYka3ay0jqL+uxgGEZ+JKCOFr2XCf2BYYLbsLBSTnsqmpma0UDY4kIX+k5wWQyhm2ncv2FIyVSKSKZJH7Lwy9suRdHuppw8R4QMVOOzTODF4r67rTr8N2hC8tsfiyfxd5P7uDtz57KJyr8ehno9nJ2doTh2By1wRL2VLW8o6q87ydcR3Lt1V6Gz4zhC/vY9uQmLj1/jZneOUzLoH1/G4d+eh9XU1McnxrAli67qppziaZCvK91Gwknw9HJfjLSyY579LO5qbx+xXEU6Lb6U5sOcWF2lMHYLNX+MHuqW3PqSEXHKyX/9ewLDMZmc8vGEhGmUzE2r69mvDuvUmhmh0aLlhOuUvnnpeAGSxPe3DpDYFLbJJhCcGd1By9ev6STfwX7H0nNY6ccTf5egnWlVVT6gsymE3gCHjwBfe4bS+u4MDeKTEuErVjoFEzOKZLVBq4PXDRJZpFC5AiJAVRdNWl400UoRcmAYmbLCu07u5LlCkzL4N6heuIvjJCKpamogZntemhgJ2w8ykNVMIQttZ2gRGEguLOuc8X78reN1j2NNO+sJz6dwF/uzyUi3iu4UtI9P8Z0KkZbuIr9P7KTQJmfqYkrCFPbFnl8WTUxxIrkrVKPn7iT5k8uHc6pFTw71M0TLVt44NMHb/lY6jfX8Ni/u48rL/eSiqRo3F7Hhgdv7T4sHcvYKQdbSFQIrt/toHAYvHCRdRe8GIZBciGlldW8EFmn+92yfsXs4Dy+Em/eusmVSFdheQSeWUllgfOTJ+jBG/DQsz6Zq3CWrkQ5kpEdippeA9MVmK4gMC6JtWZwpNTqkVlrwUXrjzWs4QeJiSvTHPvLM8wPRXAzru4/s31NqkSTGZtLyglZXu6p72JTRT2zqXieKF0AW7o4UisNVraWs+erFbwVniWTFUATCrzCxBSCTeXFTOHtlU1sKW3gy//rG8vs4GKTceZHFlZVIRlNFioVLD2uxVFgPvFeJE22EhQYttKEFyFAKp2od7NkmsK5XtE+VlCTKdilJQyebNvOA40bboucceqr57n4zFXSto394wIREkVnIACvY1A2lu+jq4ZMqoZMNnnr2H93fnyy7f0bqd9cw/DpMTxBi5qttfzx9FvMRmNkMg4zFgwbC5R1wobjHsomTKJEOPoXp7n/Fw9Rs76KoVOjeAIeuu5uo7x5dWUYgI6San5uS7GlSt/CdNaapZI3J3pIuy7SzSqJWgbjyQVOjQ5QclliJ21a9jRS3qSzwuvuaqNqXaW2vrQM2u9oIVS5upWeEMsJr+8GwfK8mp8SCuUobeucErz+f49y+Y40/fdIlKurVCY3Sjq/LimRXjKOzVSTQ7zVwG+b1F43YTLOwkQ+8S8QNJ+3aLyoaLu/lfsfKo6PmYbBJ5r28JmeI3peLnUMwXAFlX3k2t+mRzqp21i94jmYHpOFD4axE8vV2JZB6X34LQ+pZJo/PvYq9qId9ZLnSHm1U6WQWaWX3BfZyoQbhft9IFLa6jc4DpGC2j99joJoyFlRUdEwDZy0g1K6KKKiWzG7veBBVALDEChTYST1YceaFH0fMtj+ggcjI9j8bQO1v4LxmXnCw4rggoUV1lavrfua8Jf5sDwm6+5u4y05xDODF7AFLHp5jh0SKxOLssVKoVkD06Nja07GXdX67aw1waWPLDI9YDwKnd9S+JKK+eEI5c1l/Mymu/jGwFn6ozPU+MP4TItIJonXtLivoQvfPDw/fAE7oO0eW05bCCW48lJvjgxTqChTiMHr48gs2SvrAgWAk7Tx+Cw8AYudH95ygxt5Y3gME9PQRQFukXKQyCmDX4tMLlvPlpLLY2P4jiZRHQX+YELgiwlix6bhgyvvc88PbyOTyDBwbBjpKqo7K7nrZ/fd9hw56di8Pn4t91k6knQszWQqjTWtiBaI0yglSCTTnJ8Z4Vd2PMT727bxzf6zHB7vQQhBOpbBimo1p2RXwYOR/dPIQN1RxfRuQaowJKmg4pwssh4s/FKakEnb9Lw5wL41Mswa1rCGNaxhDcAPkAyza9cufuInfmLZ8s9//vMIISgpKeHDH/7wLW/P4/EQDodpb2/njjvu4MCBd59IX8Ma3ikS88kcYx+g9prJwD4HJ+PiCWh1mPpAKe3hv59kLakkT/ee5uT0oFYZFoLHmrdwf+OGm667FHtrWglYHo5M9JJybbZWNL5najfl3gAeYeA6JjauViyQ0HBeT3Z8AS81CxI341KzvorxxALn70qSDumZR290nKeClWzZ3kbvm1qudqFOFgS88pOmuXqXU189z/Evns2ROBbnVErpKtfFgMnk1Wle/J3DfPh3HiuS1b1djF6coHtsBNGqSSzSIucVjMcg4PdAmcJzqBpf2Mv11/uxkwXVsotFBwrqjyqGHtRBjMWEc/0J7VO79PfATf3ab4btlU38zKa7ODLRS8LJsKW8gbvrO0nH0px5+gLeoJd0PINtSZLV4BbEOZSARIWivCsf1Bk8OYqTdkhH00XBQE/AQ8s5Re9dksUIhDR1ElCaCukRONImk4pxdmaYbRWN+C0PQggattbSsPXGZK6kY/N7p19ibGGetCXBLAiyLoFhK121kW0XIcvHp9YfoO5XPbz5mRPMzS0gFAi3eN3AlMK2Hc59WJKqUOBopkykGTzNJgIDh3yUSbgKYS/uE/xTeoLu2m6OBOIvW159XO0Ps7WigYtzY7hKks5WVnsND6BIVCpqrxkoA6bbJY4PbOHy7cHzlHsDuSQnQMKxdWWSYWF5LOxmsBJu3tNc6GPyD7m8/d23GPu4IF0K3pCniKgSW0H6+1ZhekzW3dnK9QLLLdDV5b4yH7OpOEGPN2cFdCs4NT2UI8JkEhnctK5cfPnaRSa/M8Rjv34/teV5z+8f33AHA9FZxpMRmkMV79iOaTFAtLRV2UiOTPQhlcIwRC7IptABer/Xy/YtTTTFDVzbpfRgFdE2EyMdZ3tFI+fnNCNGKomrFP0LM/zFtaM81LiRodgcx6b6b3xgS6r1XCVpDJYtI0etBEMYrCstDsxOp2L84dnXiHmS4IF0UFdDebKEGE9AW3CtxJfeW6PJKkPxedwaAzWtj8/MaDsyJfLHaQhtw7ehrBbfiO7kpCtJRdNFClmZeAZvyEv3M1dviQwDcOdP7+Xl//kmiblULphfqAKw49sehve6qH1hSr1+6icsnL68bVzDdYvZDU6B1LFOsszuMPCG9HY8hklruJK5dEKnW4QoIJFrKx6/5aXE4yeTyDB4YhQn49Cyu5FQ1epJglvBeHJhmT87wGB0doVfQ9c97dR0VTF4YgTTa9JxsIVAuR9XSj5z+U16o/lzPzLeyy9suff7psQRGYvS++YAriNp29d0Q5LjIt78k2MMHBvJfR44NswDv3wn5U2lmB4Tf6nuS7cFG9lWubo9GIBCsa2ykfaSKq5GJrkamUAB2ysabyrXbwqDnVXN7KxaWaUo4zocneznjfHrDGSJMIsjlIx0OD7Zz8MPPsrgqVHmhyIA1F43md2uFbr0PsTKby+BlpnPnQecmxvVbU5RtIZvFtKx9IpkGEMY/PTGu/ibvtP0RqfxmRZ31HZwNTKBxzQx/F7SWfLy5B6B6ZCrvF88FwPBDm8D4q+m8McEssTESbnEWySmjQ4IFxJPsysLr4ErFecjYzTGXJSraHhLvyPnNgKWoHZA8As/+Qhf7jnB+dkRFOA3rWWKaT9IGKZxS1ZBt4uUY/PHlw8zUqDYt6e6hU98cB+V8Xr+94VXi34vhOBgbQenZ4byy4DHW7bwveFLy2w7nh++xN7q1mV2atHJGCe+dI7RCxP4S31sfWIDmx7Rc4DqdZW3bSMKUB8sY31pDdey9rSOdJCWTooBoGBmK5RPulRPalWmRCX0fVATrwHGDoFxKcmP/vMHOfoXpxm7MIl0JMIAJ+3kT3ix6UvFoV/Yxzl5ctnx2D5F30EH0xZUDAoypsS2F99JipSUbKtsfE/IimtYw7tBaiHNy//zTZyUbuOmZWp1GEOQqTS49ISDWebBSOh3SNROs76slkp/iPpAKeNFJBToLK3OKvhpfOin7qXyc8d5rW6UhTqFZZmUhYN8ZN3uFa0WDVPg8VvLlTcEufHQSui+NJi1WblZcV2h1ETxx6JfZZUGFSA9CttX8FuFntfcbNi7wrar/CHubVh/W0SY4188w+mnL+Ym5eWnBFN7BEZI5Ih4XtMi6JrUXV1+/lrdLkLf27rvbr+jhep1laQW0pz40lleOHqWyUMu0lA4BZYb811wqtmm8bJJ/RWT4dNjnP7aRUbOjOENetn40LqbEmGWwpEun7v6NlcLkt1eaZKKpbLkERCWVlZ+7emT1J3RF/HM1y5y4FO72PiQJkiWNZSw44Obb2vf7xVK6sKE7q7iWGCcWJXEs6CoO6GoG/ZwoW+QS50OvrAXJ+3gOhLHr5g5YFJ+wuTanQ6RlkVilstkl2TL9ywEWkFxuncutx+/38vuRzbnik2CljdH6t+1rp17Tg9wnFEyIUVwXrDuoo+HfmYPKKjurMyRh1bDMBGUmWU73ORVpAxIp22kR0GIAn7Z4kQrr2ohlz2mi9tfgR22ZATqeqDuuKLmFFz8Ga38J6S2WhYGeDzWiqTwzrtaOfmVc7nPbS9CrAUy2eYppBZvchHIgLaTQQmiTTDUZSP9grn14AnEqBQeSvrA8OmLYgVMtnxsEzX15bntp/r6sKSBa0ukR/9OeVfpexSUDYL/UobFkeXVl3vY9ZEtyxQzFzJJXo30FLFQ7BIY3y/oOmbl4nWNoTI+vaTwwZYuhhCYwuDU7DWC84K0A964HlsDRTHBzeX1XFzBErR62stk4aR78RYJwZbH17Pp0a4bks9uhgO17ZycHiRgeUm5No6UGELwUNPGXFy51Bsgai8n61z56mVK3kxScbdgfqNul4FxxfqTXozA6o3Y8lnc/XMH2P+pXbi2W0RoW8RSK8CVsJBJYmcVyKWzGENQOD4IX1NEWwrWFwrXEiTTNkPxORpD5XyofSc7q5rpnhrlwgvdlHQr+h9apd0Yej7U/l3F+CFBtAWsJNRdFpRdhrmVRZ8QLjgJp8gidw1rWMMa1rCGf+z4gZFhPvShD/GhD31o2fLPf/7zANTW1vLZz372b/uw1rCG9wROyima49Vd1xLvExtdfJUWmysa+EDrtr+3gc+TU4OcmB7MfXaV4pmhi6wvq31Hyd4tFQ1sqVhZyvLd4O76Lo4O9KIyLovqqN6IInjBwfEKLJ+FYRoYAYPG7fUcVtM5IgxAqkTxfPwad7XupHF7HaPnJ/AkKJgI6t8pwIop7CX3fbHwZSVk4hkGT4zQdW/HOz6/3jcH8Sb0DgTFCiSGWCRdCMYTC1x89qqWSGWFGIeAiuvgW4DIFkGw2kfwnE1wWGFbNsrRgYvFyjx/qY/mXfn7NdY9Sc8b/UhX0X5HC617bpwQXMRKKkeTAxFcWyvX+Et8CMPB9S9JvArAgESdPuG5wXne+vOTCEPgCXhwbBfpSCyfhcdvUTMM3mdsptpclIBIB9hhcAv4IBnp8ueXjxD2+llfWsPH1u0pkm1eDW+O9jAyN6erBMnK+Yp8qbxw88k8w9EBCGlBRSDIv9j9CFX+EFTDI792L29/6Qwz08MkqrP3Tin8M1BxGSY3uKQqFk8+C6XJKDmxGVcHfpWR/ZmEhsMKM3v5pCtzRJPNj648a/6Rrv28PHKFwxM9pBwbr2niMSwc08EB4mWK5osWU50S0wVPUA8j5jNJ9la3EvZE6Y/OIJXCY5jaNkcqDFMgyryU9bnMVGp5Xf+EQjhw9qeUVjMxtfRuoNQAoavOO0vfmSrMIg58ahemZdJzuB8pFe0HW6h4soH/dvYFrU5gGNxZ18n7WrbeUn+cyaqkSEfipN3cfXaEJDad4MJ3rnDgU7uK1mkrqVxmH3O76CqtwRTGMr/1lGsTySRIujaOdPOxRLQCR1UwzPu37qJ0f4AXRy7zV8PdkO26K71BnmrbQX90htMzw5hCMG8nmZ9Ncm56mIS7JPmwak5B5eRXTMPgg23L5clvFUcn+0k7+YCNoXTQU1rkiCVBy8tTbZs5PNHDbDpBqcfPo82b6SytYSoZ5c+vHNHKNGEPCaXbmpUGw29oRSiUVj2oaeOehi7mzXmAovu5CNfW1amJ+eQtn0NFazlP/c4TTFyaZHY4wom/PFsUUPNkBFuvlPDRn3uf3sdmyXku0XtkAKVg252tTAR7mczEUNmKSssR2FUGfdGZIgJRe7iKnug0PsPK2YIIBIYQPNC4nuhQlBd/5w3SMR1yPfnlc9z5s/vpKJBgv11U+kIYQhT5uwM3tDMsayjJ2Z8s4vzsSBERBjTR5uhk34rE2rnBea693oeddGg/2ELTjttTWBo+O8Zrv/92jqx66blr7P/UzlxV5EqYH1koIsKAfg+e/9YlHv/1B25r//3RGf78ylu5+xS0vPzTTXfTHK64JfLYSoiMRTn5tQsc9Y4y0pXBsYrf8IWfbCXJWJL3/fsHGTo5QnQyTu2Gah5pFLwwfInRRITGoM4UnJoeLBrfCMjZEyySrqJumpoxi6n6fD8hJNSegOS9KcLVoRWPuSYQ5ue33EPadbCEgWkYvH5UV1YukouctIuLJGnKZccBsL+jg7Eal5nYHIZh4PELMqE0wgZhZcdCShS9Lm3pIhRMdQELioa39fHWH4P6YwrTa+AvMXB/XNIbncLC0LY5SvHa2DXqA6U5wt0/RByZ6C0iwri2y9HhXhrHfNx5YAtPtm7nueHuXILlrrpOnmzbzv6aVk7PDGMIwd7qVtpLqnhuqBs34+I6EsMQmF4TacBAbJbtlflSWelKXvzdw8QmtcVlYjbJ8S+exfJZdN3bvuwYlVL0RqeZTSfoLKmm0r9yGwNNRH1x5BIX58YYi2RQyi0idwPMrFfYlS5WxGS8080RYQBtD7Y5iSsUM33zGKahCQFJO9cYs0NsTK+FJ+ChurmCqokQM2l9PoZpoDwCx1LMtGpLsvH1ettWKj9mFwq6x0d4f+u2W71da1jD9wUDJ4ZzRJjx9S6T611cj6Ax5qNyQwUeNV00sR1PLnB+bpRdVc18smsfn7v6NnNZUnyNP8xHO3YXbT9UFeTxf3kfDyZs5twkcZWhOVS+qiKcEIKND3Vy/tuXi5a37Gm8YQJ2/O0xjB2qqJBiha0XfTJckbW0XT7YXZzWKZMionJuMyYrkGmy2ymQxTRk3ja32hfi01vuu633v1KKi89cLSCiQt3bCk8C1KNlJP0uftPD5vJ6DpW1cfS7x4jF8xbC5S1lGKbgO7/5Um5Of/GZq+z9xHZe/945MpbE9imkUjjeJUEMAU4ARre6zLRLNn4Tzn/zEtKVOCmX/mNDNG6t455PH6SsoYRbwdHJ/iIiDMBcMg5Kz6kBlKOJ6cHhAg9EpdVxOg613FZRkXQl492TOGmXhq21OXU60CTeoVO6QKBlTyO+8PKikZUQs9Oc3hklnTAQKYVdAsMPCsKvGqRK9BjCdVy8Ia9Wn4ykSFVCrEoSaaWouUlLMbrV5Z6WCu74qb1cfbmHiSvThGtCbH6ki2vmHM+eOUwkk6LMG+D9rdvYlSVIf+Kj93H3mTEGz48RKvHT9cvtt0V+L/UEiGRuoQAlS2Qpeg5yzWRJm1lSMHEjwtlKz50nrsdoANXntZLfIgzL5EBd+4oFLWWNpdRvrmH0wkROyabjO4q+92vLbSFAWgKBWuR1ZLkmipEDYC4WJXkV0zsNfPV+1h21iGw1uLYhzemBV6iZCPOB1u1srqjnwuyoHi8WnVsBsaeg+KzmDDS9Kot+mphL0fvm4LJxT182nmL5LE3CzW4n0SyojVRT1lhMcLIK+hKVVUMeTszx1+mLxNoVSEW8EiINkq3PeWjZnY/X7atp5frCVM5ezkBwR6CVddV+JsUQKLWsEGX43BjbPrCyvdOtor2kih/p2s8Lw5eyauMVfKB1Gx0Fc937G9bzl9eOaaUxJTEQdJZUEz86gSGh+TVtrSot8CTBE3RZ94m2m+7bF1red3Q/f43uZ6+SjKSo31zLgR/btWp/Vu0Pa4sqO0UmnskRlnxzMLdNrNDWFY6Q1AXy9629pArnTJThUw5KKrwLAsNhmdKLYUPpoCbAVFxWBOcNvLOK8nFw0RZKIo1WIyp43kIjgFKEqm/4MlzDGtawhjWs4R8V/m5pkWfxbiyS1rCGvwsorS+hvKmU+ZF8hVTVgMGOUAOPffSeG6z59wMX58dXXN49N/aOlQ9Wg2u7TPfOESjzUVp/a8GVRdR5w2x4zmBkvUEi4BIag5pTWu0k49ogBJbXpHFHHf51IRIzy/ue+TKHhJNhzw9vY3ZwnmT/AsFpSNWbIJUOJilF9TmVL4pZoQtbSUnFySyvrr8dSFdSeUEx2aEnTaKgMm0xuKakYvbFMY6/1qPlrinOEQlD4C/1IV2FPy2oOiPovLuZ6xP92K6bl5pR+rfVXZUc/LHdOcnsa6/18fZnT+WOaeDYMLs+unVZ4vNWUVIXwvGDcBTxCsXERpUPniyZVM6mdICv/9gws80uE+sljk9RPmJSd9aEVP5GhOcMQtMCO+Xgm19UwVm8CNnricKRLtcWpvj81bf55e0P3vR4rw1lAyDkqwZV1t9auFkbCgHKELnAnpWBXUNlVN0VYiK5QGIuybH/fJR0PEPXSZhplSRrBOG4QelFibBhoangQJdch8WzNAAzofcfHlRUn4Nom2BonSA8Am1zASrqS9n82Hra9q+sLuAxTB5r2UJdsJQvXT8OShO3FtuOGVNEQjbCNPAELERBFdNcOsH/s/V+xuLz/M/zL4MQ2Ek7F8AxpGDTM4KmkMVErc34IV0xJhx9FtLUDU26Co/H4Kn2nZR489mpszPDvDF+nWgmxYbyOh5r3rLMDmEpTI/JgR/bxf5P7QS0Ys1/OfMcdlbZwpaS18auUeUP5eR4b4TtlU28PnaNTCIf9AAIX5FkEhlmeldWx3i3WF9WS8jysmCnimXQDQtHSk2EoTjeuKG8jn+y8RDObIYxzzwvDHWTyQaSBDApJWPJBZrCFZyZHcnyrxQp1yEjl2QNl/VpSxZkr8WDydZlai+3g7idXhS3yZ2PmYHSSYO6yjI2bGjlYG07lb4Qd9V3knAyeDEZPjnGiZfPcakpguNxdd/us7BsiaNczJCVq062hMH/s/U+Qtm2U7O+iqad9Vw7MoAdBCuRT3KC7mcbttZxOxBCq1Vdf70fJ+MiEzaegAchtOJMTVclkdEFyhpLMS2DXR/dyq6Pbs2t/+zpYcKGH9dxESYYQS3NvdSy6v1t2/iTS29m9ylwpEtbuJLHW7ayrbKR53/7tRwRRp+L4thfnKFlTyOW950RMEq9fu6sXcfhibzfugAebb69qtyhgsR7IYZXWD58dozv/dfXdWWbUlx6/hrb3r+JO35qzy3v79RXz+eIMIs48zcX6bynfVW7mdhUfMXl0cmVl98If917KkeEAUg4Gb7ef5Z/seOh294WQDqe4YXffo2r6+KMdbrYBvp9vaQSfvGTR5hUB8KYhkH7wWIy1D/dfHfR5/9z8TUuz08gAMswyEgXS5jE7XRWpUoQsrzsGa3k4uUJoq1gpqCqG8rSFqPnJqjpvLHqTmE1b2OonJH4PI50SUsH6dHqRllOaNG5hDweakIlbPlXd3Pu25cY7Z4kMZkgNJYiUScw7EWiq8oPdrJjCEsYoCQz2wS1J1VeKQRQrqRhay1fGzjDQjqVV86TLt6ow+nJwRuSYeJ2mhPTg0TSSbrKathcXv+eEd6djIt03HelJHgz9BUQ0wrf+W9e6Gb+mTEe+3f3sW93G+PJCFU+rWrlSknStWkJVbChvJZKXwilFGo4Tcar+x0XraTiK/EtswEauziZI8KAfv+4tuTU0+ep21TNhC/B9YUpyrwBNpc38KWe4/Qv6GT8Yp/zUNPKY02fafH+1u28v3U7f/X2Wxye78t/KcC1BLPrFJEW3afaPt2GRcGPbFPx5ssXcuQAIQTeoIdMvIAQkx0LeQIWwcoA7/Nt5YvXjucsY6QXLFsn3RC6mt3xaJKnUdAdRaKJ27pfa1jD9wOLahxjG12GdufHG0OlKaaZXbHCYyalrVUagmX82s5HGIjNYmDQGq5YtQ/0Bj3UcWsKcDs/vAXTa3L99X5c26VtfxO7PrY6cczJuITPZfB0gfSxxL5odXgXIFUJxSPp/F9OkGXzryLcKMlvg5VVLrPignVWOT+x716qb0AiXgnpaGZZ4Y0Aqs4pDuxqY/dHi6/L+/79A1x7tY+5YW3523lXaxERBsA2JV+YOknsId1rCUdbxuXVO4oVO5SAjF8xtVHQclFl+8Nsu+me5IXffo0P/vajKyaal2Kp/YlytVWiaedJQwDl3YrwVHFy2Um7zA5GqN90a0UT0YkYL/33w7nxmydgce8/u4PGbXWMX5rild87kuvrj3/xLPf/0p00bLm5LfnZmWHS0sHj96CkyimGTna61F9ZjMXo3wpDYHgMgvOCRFl2jEO+qAnArjVp2dtIzxv9OGmXnU9toaqjgqHYHH918UTu1kcySb58/Tg1/nAu9ta8q6GoWOl28GjzZv78yhFUwb2+ZSxtJrkgU8H3QNGmBVrFVrGynRB5K0/Hp1VipADl0fO02nOSe9evTHjIJDKkIikwBNNbFZF1AuFAw1sKq8SD8gmqH2rkeGwwf3iLcz4/SCdLjhMuhlKMNKR44tfu4rNX30JKSdp26E/P8Efdr/PR2h3MJGOkTbdYGGeRZYN+31sZsDKCpiOysGYKwzKwfCYj58eXkWEqfZrw6w3owNKibV256ee+f3bHsvNOLqR4/a9O8oZvmIU6idfnobw0hKOkHrskbJCKZJmCO0rY/UP5eachDH6kaz8PNW5kdG6OgS9dJ3qxj7NoJRU7VTBgFroPj4xEee0PdPzKzbi0H2hm+wc33bYV9aLqpVJqxXfGjsomavxhBmKzSKUQhsFkKopRrvBN6j7JtPPqf9Xrq25K0lFSMXRmjInLU4Srg6y7q42hU6Oc/HJeUWi8e5IXf+cNnvqvj614TqZh8KH2HXzx8rFsnyoQjqLqnGLooZVfCEYaZp8dpf2H83OkC9++jOkxcNIu1WcVkU6BMvJ9oOFA27MKMwlDjxvMt+uxpJKC8DS0f1vgiyhqzmvVw8U4pJmG5sNaJbt+8837sjWsYQ1rWMMa/rHg7xwZpq9PB6s8nu+PRPoa1vC3hbt//gCv/f5bTCVi9N7hEK+HK+Fp5npO8JH2XatWQf19QHAVW5Gg9d4G6ofPjnHkMydyybymnfXc8+mDqyaulkJKRXAKOoYFdqIgqCXA4/cQqgxw4Md20bqviUQmjfH68mp3n9eDIeHV33+bVCRNIOxj8+uKkW0uzo4gNVWlpL4wSkmfXm9pCEEYgkCpb1k1hWGKooqMd4L5oQjGlMO6r8FU1kM2XQ7KJ7AdnXwNRQyCJ9IIy8hVvrgWeDwmFnqZEALT0pM2f6mPPT+8jeGzYzqQkD0nYRm07G7g0X9zX/46SsXZr3cvO64L373Cpke6VrRJuBFmUnG+OnmSyx+3yRQqYKxAhFHA5flxnmjdSl9gget35YO2yTKXeKXBwwutlNaGcW1J2/4mTv/NRcYuTlJ9PsPEPi2Xq5Zse/E2jSYijCUiNGQr5VdDucyTNdRidSB6Qi5cnRxsfEuRrFJEN5p4lEF1n0lgg8HvX3iVofgcdtLGc69L6/MKT1JS1gehcYV3wUX5BJP3mcxvXFpxtBzCEPp+OmClFYOPCVyvTsZMbYFpr8OPrV9P2yo2G4XYWtFApS/IVCyaS4oJBU29HhyvYsYnlgUG6oO60qUhVM6WykbOT+QrTAGqewxk2qXvAcl8S16ZR3h0EspMKfwJwUc7drCtpaWICHN+doQvXj+e+3x0sp/B2Cy/vO3BW0o4Lv7m4txojghTiNPTQ7dEhmkNVbCu20d3tbbxES7UnFaUDoKDS2AFqd33AoYQdJXWcHF+LCch7DUsTMPAZ1p4DFOfV7YB+4SJEZf86Z88R8RNokxBdKuDXKKUcGF2lD3V+QRvyrVXvD6FEHa2StYkp0LkWYDm1xW48/DIOzvHdCxN7LkJnM5sohGFyiY9W/u8fPLRu7nAJF+6eIzYYJTKC4o2s5x0NE1kNApA336H1EaFL+xFCIHPsnBtiZKa6OZISUdpFQOx2SI1Mu/H67i2aZgUuhKx8U39HAKEa0Ls+aHbq9i//lo/117VG/CGPDhJBztpI4TA8ptMXZ/lO//+JR78lbtWDLZvq2zk8HgPljffhwZMD11lxUH/5lAFv7rzEc7ODOdsBhefQ6UUE1emtLKXIbSNFjrRPT8ceUf2I4t4sm07LeEKLsyN4jUsDta2016SD+w5GZdz3+hm4PgIltek6752Nj3SVfSsLh7nUtQFlpNe3/zMCewCmwTpSM5/5zI7ntpEsOLmla+u7RIZiS5bbicdYhMxKlbxUa/qqMCwjGUkmtr1t2dxOZ9OMJVNGBZiPLlANJMq6utuhqODvbw4dIm5TAL/bpeFGln8blil8lai+MylN7l3pJ6el/qxUzYtexrZ/bFty5JWn95yL1cjk/RFZ6jyh5hJxfnWwDlk7r2scJVkdIuk8Rs+3CFtQ2h6TIQl8IVvbyz4vpatfObSYRJONjNCrgtYlpOpiHlpCJbx3NBF3u4aJNPhEOxxKf8eLLRCsja/jnAUKitZL1wdUFaGQFkKJ0AxGUZB65PtfHvqrWICjgBbKBZGo5DPGxRhNhXn/3S/lpNxPzzRw97qFj7eue+2rsNSuLbLiS+do+dwP64tqd9cwx3/ZC8lNasrorxTVPnDENFWQG4BUdsfE0RGo3Q/d43dH9tGR4kmO0YySf740htMp+LY0iXjOpT7AmyhltKjGabuzgsiSKko6ZW89oVXEYZg3V1t7PmhbTqpm4V0Jel4BqQiMuLwe1/9LrM7jBzx+m8yp0inMuDmCSjPD19iW2VjUZXtSnhk9w5OHx8nmU5r6XvLwDFczKzq/mIiTHrybUIJwIBUqlia3/JaoMBxXDKNFsIr8E3D9ic34fFZbPc18YvbQpycGiQjHY5M9GJ5TVy/fnactIODu2yy0JK5PbL/Gtbw/UDrviZO/tV5JjYWW8OZXoOkm8FjmBgIbQfiahXPFn9F7neGMHJ9RCEiowvM9M9T3lxK5Srv29UgDMH2JzfdcpGF5TUJB/1UnUswfkfW+ixrrSJNyEnFLrVkMSmuFinI5Kt3xh3OwwNOdjjnBBQ97jz/7ewLbK9q4uPr9t5yTEhbyS4fk0CeyATawvf54W4uzY0RaPNy14F1bKltJzGfJDFbrHQ4vMMlWpW1YlU6kVpMICquSDGkLjTIVKpssUNBfEUqUgtp+t8eylkY3QhlS62xsmP+ljMmnpQgXQLhKYF3wIElQz1hCEpqQ8wNRzj/rcvMDsxT2VbG9ic3U9GyfO5+/Etni4jMdtLhyJ+e4MO/+wRHv3AqN191HRc7afPq7x3hw7/7BP6SGxddFM+ZRO5yuEJRMm1QMWwQ35i/oBWlYXZTwuj8lCbpek0My0C5CmEKNtY28sxvvaKVJoBz37zE7o9tpWdbeulwCIW2712pEE0pxezAPABV7RXLvl+K7ZVNGELg3qwodaUxZkEFgeEsquQu/X7l7RguuBJYSlpTeRWmwccE8cYs1zuTTfZnFN3fucq9nz64bNM9hwdIRFIM3wNzm/I7jzcLWt6SNFz1YF8bQ3wie6y5U87+1iKnEiWlS0y6fHfoAq4jiWfSyOyxOo7kiyOn9NiyUJFDKIQEYWtrJ8OF0IxB0wWDkfsUCw3giUHNWUVlv36EAqXL5wEt4Qo2ltVyJTKJN+hFBRQGgk9supNQ+fK5z2u//zavdI6RKNdsm3Qqw6hhY5kmXmHosXlWtrrjg50rEqzrgqX0/fV1ohfnc8s8fgthgrRlrpDQMA3slMPgieGcitL5b18mOhXnnp8/sGy7t4Kl8Zy5dAKvYXI1MslAbDZnGe0oTcSO3uPH/E6CTCn453T8y/SY3P0z+5ZZTi3FG390jIFjw7nP3c9dW3HukphNMnJ+YlXV651VzZQ0mvz1V99AupJ40GX0ToFcqduQUHsBTh4+z5WXemjb38yuj20jOhnHE9BkOv+8pPPriumdglQF+GYV9UfBm4ZIO8y3Z4nWUj8HkQbF2H5F42Foek0RHtKFcH5pUnPdIJAyqNlcSe36d16otIY1rGENa1jDPzT8ncvGt7XdXNJuDWv4+4CKljI+9P8+xn87/jy2G8dvGrlJq9/08FT7zlvelpNNWs6k46wrqS6SjvxB4I66Dk7NDBURR4KmJyfVequI22meG+7m0tw4YY+Pu+s72Vej+4BMwubwHx0jIW1S1YpARDBydpzz37zEnh/efpMta3h8Fk076osmO6Cly6NdgskdLvWtKVoEhAMB9ta1cXJ6UAeZsknLO1s66f7eNS6H5kjuUwQjBnX9Fq0nTComAjz5n+/nG+bzTDKV274Q+v/2fGIHjVtrqd9Uw/EvnuX6631IV+ENeTnwY7tuS752EVJJplNxUoMx7SUtwD8PLa9kE0geQWK7RaJCUSksKnrAcQTKgmSzwcguRaJJS8A2j3n5+L4D9Lw2QGR0gap1lex8ajOeoJd0PIPpNVFSJ7hMn8nElWkSc0mCFTp4ZadskvPLJXWdlENiLnnLMsmL+PzVtxlPLqAsgVysolopQIKu8B5LLjCfTnC5bA4VK461LDRIag42sm1zO66UnJsdIf7xUgJ9FlXHU8SmYwyVZvB4TWJuZlEoV1eO5671zSujHt26nZOvDpAK60o6ZQgdAHF0bdXcJkHtCUVoAgIRxexBuHaPzeWGKdwFhUThCpdMpWLgPkVoHGa2CZQJVkwHUFKVi3LEq2Q5F2EsBhIUTqWJ9CpcP1nCk5Zy/otrRyn1+lcMVhfCY5j8wpZ7+cJLrzMQn8UXFzR0m5RMGygUlQsenYTNImR5ubc+bzfyiXV7iXTP0mvZWha4x6D5rMl8Jyw0FZMtlNBBJ9OGQNDHoc7lFk5vjPcsWzaWWKBnYYqusptXu8RnEvQcHqBPTSFrXAyrOLJt3AKhBmD0wgSBlxNsSivS5eCNaAueRTTtvD3rltvB/tp2rken8RUcuscw2FrRSG9kGtJSV0xJcHG5GBuDdVmLISufmMypNiuIJpJsKKvljfHrKKVy3tfLkF0nNKzILHIospyVxsOKmosgDIN09XJP71vF6b+5iHU8QY0wmVznoiWVYItdxY9++m6ejV7m+MQAqYUUyg/D+yD6RpKSSy6+sBfDNKgYMpjqtHHTLpbfQqZczLSibFQx15LBNE0GZ2b407nDPNC0gSc7d3JuYJC/6TulpbMdcMIw+KjBlm+YtDZU88RvPHDToFoh+qMzvDByiVSXS9WAgWVrlRon42L5zBzBRdqSM09foOE3ixWo5oYjVL5hU1lqMlmRxvKYlHoDfKJzb5GcvlKKa5FJPS4orV5G3Bu/NIWdsHOJbcNj4g16MEyDYOW7I20JIdhd3cLu6pXtll77/bcYPDGCAkyPwYkvncNJu0XJrF1Vzbw53sNoIpJbVuENcKhu3bLtRceXE1mUK+l5c5DtH7h5gsz0mJTUhYlOFBNSLJ9J+AbEgkCZn90f28rJr5zPLyv3s/s2yVEBy4vHMJY9Xz7DzCkW3QpeeOkU33Yu60paIFWvbQvMFHpBQTM1s0kNSxj4LQ+mMOiZniR5fIK6Cd2Orr3Sx8JYjEd+7R5eHr3CkYleEk6GLRUNfKhtBxvLtSJSf3SG54ctbTWElmL3GCbjDRm6PGZRANsX9tJx6PZsuNaX1bKhtI6zM8MopbAMg5RydMze1eelBHjS0PJdydMlxzgSHwB0hWii2iX6kGD904rZDTD8kEAoPX5wstaBwtFJLhB4o1oFIAcBvhIv3UMjCGv5e1+ZUH1FrEr0e2n0So4Is4iT00PcWddJS/jmSajVcOZrF7n6Sm/u8/ilKV7932/x5H96+B1vczXcXd/JqekhYql8otS/IKgcNJCuZOD4MJse7colbJ4f6s4SYRySjmaQzKQSnJBD+DdJNr/oYWKDi+2DcL+kqjuDHdTt5PIL13HSDns/vh3Lb+GkHE2MyaoVZGoFE10uJLW1o5KKmJNBGGC5+tnPxDP4S31ci0zelAxT6Qvxz3bdz/dGLjEaj2THjxFcQz9IIiUxbG1RgQ3S0GRdE8HbG2bwWy5dhy0sWx+/W2Ey/kNBZkgAinJfkId25tXDmkLlueRkb3SawdhcTr3NsAw8cXKWmQClUwZPPbD73d3ANazhPUCwIsC9nz7I8YnXIWt5sjhuMZWiwhdkYjaCtHV7ru4zufzcWdr+3f2rkiCPfuE0V1/O92Ptd7Rw18/ePFn5bhD5VBmTsaSej2XtapXKErlz88p8X2/YgAX+WUWqasl8IPsOWn36dYtKGgXruybYjsv52VHKvUGebLu1uIZhGtSsr2L03ETRctNnFqnNfu7qW/RFZwCYyyT5677TSBT7Ktvwl/pILeTfV/NNEiEE0lA4HpUbRwgXlBD5cYUAn2ni93tRSlG+IFGq2E7V9Ogfp6K3Nhe4s24dJ6YHiwgl/rRJSbeLxzWo8FkIQ9BwsJnx7qkiRZuu+9oBeOG/vKYVL9DqL2MXJvnAf3p4WYxl9HzxNQNIzqcYuzjBwpgeG2YSeVW06ESMb/3bF3jff3hwVdtH0OT154a6yaSySqgAKEquSuyUzSN0Et5UT29smnJvgAM17ZTs92Mnbb42fJaTs0O5bZV4fFS+7TAfLyajnf3GJXxdxcp00pW4aW1PdbU3ROc97TkV4oWJGK/+7yM5Enh5cyn3/+IhSupWVyIyV7A/XQ0is4qaS5ZEeuOVRb7gAV2gtezZcvWzaoeg+ycEmRL9zizE9BaInF5gJUQn4ySFzVzWcVWh+wAEjO2UNAyAN23QfMZgeKdEetDKioXnQX5dUxiMJiKkU+kixSJENvZmCMyMVq/JOXkJUB6FNwL1100aL5pcfMIhlW1KbgCGHhYYz0kqx2w2PLByQc6Pb7iDw+M9XJmfIOzxcVfduhXjwHPDEfqnp0jsXXIPbUVGOaikPjYzOxfsuoEN9fDpsWXLBNry0szGT5TSZLilhVEDx4bZ+4ntBN9FcdB4YoGv9JxgNBHBQCCROSLM4r6TdobZJg/jP2kgURgZaDoueHDz5lULHBYxcWV6WWw4MZskvUq/5aadFZcvYl1HAzvsGo5VTTLfIXQ7WPZOEATmoO6IRElIxzL0HB4gOhWnvKWM6fF55jdYpCyXUL+k5TWFYRmYHotM2kYpiGdreJQQuH6Vi+tM7hU4PvDEFCpsUD1m0uWtwgk7NN5Zz84Pb7nh8a9hDWtYwxrW8I8Nf+fIMGtYwz8kjCYjzJBcFvA5NT10y2SYuJ3hjy69zkQynww6UNPGx9bduj3Ae43WcCU/teEQ3xu5xHQyRh1huvqDXBu4zrq7Wm/ZzujPrhzJWSIs2Cm+2nsKgWBvTSvjlybpXZ9ifLOWHRUuNF+wKDkxcstkGIA7fnIPifmkDhwphTANrnwconUOQjj0Xz/OM4MX+I097+OTOw5SNVzCyckh7KRN83QA36kIX2oYIXVAT2pmcZnqcOn6usK+7jB8ZowHfukQ3/i1F0gvZIkhQrDpkS72fTx/nAd/Yjc7PrSZ+GyCipay25YQBbg6P8HTfaeZzySRKZfQXofGNymKwwlbUdYtqQ5YCGysMh/jbS5Du10S5XoCZdpaHnhyu+JKzQKP/PKdRfu5/FIPyYLKMenqCZkwRVHFsDfopayxJKfKsAh/qe+GCcaVMBKfZzy5gJNyyLhOTmFFGSBcUVSVZyAIWl6Uq3jhv7zO1N4F8BZX9+IRHDdGiU8ovjt0gflMEq9hYpQaVDwR4Gc2PcTX+s/QszCNpUwc6eK3PLmkXq2/hMabqMIAVFWW8ETTFr4WvZirpFMFFcbKUCTqwRyFng9AqsbF8pqkRAaVLQ7GAOWDaBsk6sVibAW7FBw/uWBLduvkIi1LgkeGYeAPWzRethitSIIXxBLLDFcpjk0O3JQMA7pi7xFPF8e/c6ZouUDwWLIDpz1M78I0Vf4wh+o6iir8/JaHO6INVL5UXIGYrNJVcPaSwIIyQFkCo9zLrx//Fo2hcp5o3pIL+MTslQMUMSez4vJCTPfO8uLvvIGddHA8CueDNqJEFnnF761uvcEW8oiMLuhgUkb7MxdCmILWfU23tJ13gt3VLcyk47w+do2U61DlC/GB+i0Mf7kXGtKoMoVA5Ao4lVBIjyio0Mu2nYL+QqZcNpbXcbC2nbcn+nJfeAwTj2GQch3tV56GyvMS/ywkq8H1Caw0lF9VhEf1Zj0Bi+Zd70ztam4oQt9bQ0hH0n7UpP6SSbJcEpwzePynt+OWCk72DpKKpouC4aNbXTZcVDhpF2/QoHzcoLHbZGqnwrVd3KRDU7dJpF7bjUnbQWYrT1+8fonN6Wq+eeQoTrNujwowsjLmFT/ewhN33HFbCZvvDl7gtbFrpBszyBqX0a2CzS968ERkNhNT/PvZoUjR58mr03zvd95A2pImoCosqD1Qwwc+dRemkT+OtOvw38+9yHB8HqUUpmFwd10nn+jSShR2yuH1P3gbwzJyfba0XZyUYMsTG95VkPJm6HtrkJ7DA7lqYScp8IQ8XP7e9SIyjMcw+fkt93BscoDh+By1gRIO1Xbk7KsKYfksMkufdaFV124Vu39oK2/832NF7Wf7BzcX9QMrYcvjG2jcVsfw2XH8JV5a9zXjDd6eeqXXMNlc3sCp6SFMIXLvmUN1nUUEpxshOhHjjYGrUPSIKYQrdLV78WKQEPJ4scz8l07GIVojqLuWXzZxeYpnL53j1Wg+WXl+dpTpVIxPd9ytqxWVyvYJxTsyvSYP/osDnPvGpaztVwl7f2Rnrkr0VpGcTzF8fgzKNNnGFRLTL3BQGCpL9gHaj1vIqQxHx/qQAZXNp7goBPF6uPhTmvgbmMoqv5lgZKuIpZkdG6AITQikJ2+TZBgGhmHgSxsYXgMrpZMaiyIB4WlBh7G6ktLIKpZfI/H5d0WG6Tk8sGzZ/FCEmf65W6r0vh1U+8P84tb7+frRY/SNjROcFNRfNrCjGaQjmelz+dq/eJYDn9rF+vs7uL6gCeBpt5DcqpAGxGokQpp0vqXbTiqSxvQVt53eI4Ps/5Gd3PMLB3jrz06SzCoRGh6TVKMJaMUB6UqclKMl4Q2Fyt4UobQCVann1vqypkAZTwW24Kv28udjR0nHsmMQAfg0qTU8JDCSikinTryFQlphLN4iGd7h0n5Sh01mPuAnGrTxorOBCRy+eP04/3rHw8sqm/3SwnFd3R0KkEgqQkF2ftPDpJmgLBTkvg/spq759tSm1rCG7xda9jSy72oHZ6aHs4qTuk2XePx8Qm3l26++RSpsUTIlKJk2iBDl8ovX2fnU8oTbyLnxIiIMQP/bQzTtqGfdnbc27r5djMTn6fcukJNDzRJZCtU7l0Jk7fWkKTCVQJgCHIWIS+qOKEbvF7ooYRkhZgkRRhUsvhGEwpUSD9pm52ZkGFdKplIxyrx+tj6+gZm+OZy0g+tIkJpcPdM/RyaRYUolckSYQhwe7+FgbQc7P7yFo58/nVtuZUBWmySEQ6HXszK17aBhgwro8acvqwzcEq7gsbs6eOvMMTJZYpQwDDx+/X3TjlsrDKgLlvILW+7hldGrTMYXsE9HqTpmYDgKx3aRrmT7hzaz75M7me6Z4eorfTgph5Y9jXTe3ca5b13KEWEWkUnYXH+9f1kC2F/qW17AI6CkNoQnYJGOZYpiHAhBKprmwneucMdPrh5zq/aH+Wj7br50/AjC0re+5opJ7bDAG/JwKGstvbummCTsCXj44a697Iw0cy0ySZk3wN7qVr775y8s24d0JK3xMMcKPqdj2q7XejvO0ZnTjJwd54FsPOfNzxwvUkOcH17gyJ+d5LF/d9+ybS/Ca1oIJVCsUhSRhZnS6kB2TmUJcnNLWD4eXQWGLTQBdaVnJUteMzKQLgWVJZkUqvlJ3+qKN7Xrq0i/rnJEatefLwhJVsNEq039kJeWsxbpsMPURi1DqCTLVaCUfv5SdgaplnyROxkdF5CmWmbLZodhbLMkWgupsCpQUhIoQzFx0KD6eXPVIgWPYfJA4wYeaNyw4veLcNIOUqjCKJH+2y6wHlLgZly2UE9jaPUYlzfkWfasGJZBqDpIfDpRNM6x/MUXTElFOpp5x/NMqRSfv/o2M2mt4iRRLGRSxeeVLQiIYWNlCT5myGT6fQYtu/KkosRcEunKZWS2mf65Ffdt+SxcO7NkmUnjLfRnhz69nxdOP6OHriYUNmzD0f9VXlY5UtdibGHyyjSbfnIzr0SmsH3ZtrFLsNdswvfX82QSNtpoU+VI/NKTJ8IsYnonWCkdQ5zd4FJVEeDHDt510+NewxrWsIY1rOEfI9bIMGtYw99xvDZ2rYgIA3BsaoD9Ne20lbxzm4N3i43ldWwsr+PSC9c48aVzjDPDOHDx2as88EuHaNx+44lDf3QmR4QpxOHx6+ytaWXQXGBsSz4ooUwY2unQdOH2jjNYEeADv/UwV17q4czXLjLYkCRa52bVW/Rv5jNJvtJzkp/edCfvb91O8OvRbFVElDPNk6Q2Fc840hUwtwnqr8Frf/A2D/zyIfb80FaOx4bprY5hlJhU1HqI2xlCnnzpjCfoIdGTJD6ToHFH/S3bPQEknAxfuHaUTLZ6SgrFzDbwRaDqfPFvnZSDa7t4gx6cRoue7Q7K1JYjoD3LRbaa+8T0II80b86fWzLD8b84DULopKEAoRR20qZha92yqqJ9n9zBK//7LS2dipYt3vfJHbnqpFvFdO8smYSuqhKe4noKM6krHYQQuYScEILSMUgPxCltEsy26omh40crpKBJZ0cn+3PbybguQcvDXCbJsakBfm7zPQzH55hJxTk9PcSl+XEUuqr3Rzr33ZL9DsD54AyehInrutkgiNKyuLY+jlizVppwQoAQmIbI+YcXTe7N4s+yoI0uwxI+jMew2B9uZubNScyBFM4hibPCLUi5NsPxlYMAK2HdnS10P3eV+HQit8wX9rLlwS5CVcEVVRwWsf7eDq6/qtWQlFIoVxGOWxiWRGQEZpqcL73Inn/S1VYy/dEZ/vTKm/zK9oeo9ofZVFbH4SU2I2bWOuhmOP30Reykjj5YtmDDaxYD+11kqyLg8XJfQ1dOkaoQK3lXV3dWYpgGhqmr5QvRcah1md3Ie4nzVwcYvjLKZreUfVu76Opq5I0/PMbk2SnWn4SJjYpkDfjmFPFmg0RNvvJ8qaiQkbUMqZrVx/vRjt3cWbeOL147zlgikiNeeAyL8KxB5XeTXH1YMbclv72mV7NEGHTfVtVRwb5P7ritc4pOxHj1f7/FxNXpvB2XIfDYFoGoPnh/iY+5TAI77aCWXPNMmT4WVVDN2HzO4sH16xkZmWP+zSk8acHI1jSuR0fPjGzFoVSS179xCrcyv02BDuRZXouqtorbIsJMJWO8PnYN0MGzjO1iBxSj21w63jLBWG4tVtlWDmjS6+vj1zh26SrsyVB/2SAYMfDHBAsvTxJ9KEZ5U1794G/6TjMYm819dqTLa+PX2F/TRmdZDWPdk2QSNpZXV9U6aW2hFSj3c+DHdt3yOb0TnH76YpFsPuj3RyqaRrqy6Jr6TQ/3NnQt38gSrH9gHRe/e6Vou/4SH637bl2Nrm1fM+HfDNHzxgCu49J+oIWGrctVpSJjUS6/cJ3YdJzaDdVsfKiT8uYyypuLA8dKKXoWpphOx+koqVpVnWI2FedzWdUzqSR2xiU8Z9I+E2IzpXCL+cCR8xPZakMNgU7caSIhRdFv09HHZzsOVrhATUiCP1rcpzkZlzeHruOGlH5vC4Fru/SNT/Lnn/sW1U6AbR/aREVlkLl0omjd3dUtNLTWMnF5ipn+OSavzvDK/3iTfZ/cQcehW090nv1mN6ERycxivkmBSCksC8ITAm9KUNNjUjFi4ODgmgrXAGVq8gVCgSFwpcJwIV0p8EWh/nWJJwG9HxKky8nlLaNNMH6XoPm1bHvKJksP7NjApakIPfFJLCebVJDQ8qbB5l9cvZ3WBUoYjUew0w5uRivamB6TSuP21f8KoYozLzdd/m4hB5OEvxRh3Swox0WqfJ9s+UykIzn2F6dp2lVPuTfAfCaZre3Ow7JMHI/CXlT8zyoWWUvGuzJrs9K8s4GP/I/38a1/+wLRiZi2Fl0o6OeVwnUkhlA52wKUJnaXOl62FtjdrXhOSnG+u4/zX7mIGsoQrZZcf5+L6TVylcYKncBbd9qD7Yd4mw2GQAg9mPSGvLj7Pdy1ZzulbWX8zsgry/YznYoxmYrm+gFXSq7MjXNtegJL6e0r9PsnGbC5/7cepFz48QQ8tzzWXMMa/rbwwfYdTKVjjCV09i1gevhk1z4WXpqicmh5tnumb+V5xVI1DulqFZLRCxPfNzLMeCKvFuEGyCenb5Ckd7NdtVAQMExcR1HSLWl+SWE4EO3Sczl3cYife2SX9MW3+igrrY7h2i7KdXFtd9UimYtzo3y97ywLdgpLGBxsbKftYDM9rw8sivdgekymrs3w1p+dpP7HVp6XLRYVbHhgHaUNJfQdGQSgYZOH7ySuaAWcJSchTSiVXvY0dNBZWs1MKk5dsJTtFY2YhkHzxlpe+Z9HmO6d1TaJhmD7Bzfdlg1nc6iCH1t/kCsv9XDstTOAAUED0AMeX8iLaRnUbayhbmPxnC8yGiWTtFFSYZgGlk/HBxLzyWX72fzYek5+RasUSldiGILW/c2UNZSy+bH1nPpqcTBlcVurJc4LsS1Qx66ve0iWKrwJgSctdJGOq0hF04RXifcIIdhUXs+m8nzMrLQ+zNT12SU/hE3NTXwEL88NdzMfj2GlofmMRXhGN/DhM2NMXZ8hXB1ieun6aLJ9aiGNfxUSuWu7lA4p5m8i7CekJsSUXYaZHWjiSjZeISjiUxVjyTw0bym0yn4yWQJzdtvSS258C4LqKc+q1mmt+5qo+myQ3lQib4ldcPxDBxXV4wrLFqw7auGsN8l4XWJuZuVjFyClpGRQEenME0sW1WCEzD7SxvKTX1SkidZLcHRBEQY4Pj2OiTXC+Y+4DCbn6QreXOl2JYxemODtz53E7LPxRiBTmicnCSVouGRQPmKSCSlKJgUN9cCDq29v00NdHP1CnjDnOtoKNT6dAAXBSj9NuxqYHZhnprf4+QhVB4vmq7eLodhcjgizCLFIbCT7T7Ytmdk4m7R1gZMwDa225fp580+O594/1V2V3PNzB3KFet6AByfjIAyRU7oBaN7diHRc+o8Oa8uiMh+HfmrvLcV2PCUerJBF2pEoVRyrMBxNICvRXS5mVvFqptpmpkNyPn0Rt9LE0pcZwzQ4nRpni2Eg4jZKKQxTUD1oML0gSRQ2E5Vv31oFUyGF4LQ7xgPxOZpD7y1xfg1rWMMa1rCGfwj4O0eG+Y//8T++59v8zd/8zfd8m2tYw62gKVROfaCU8WSxjOeeVawFVsJAbHl1D2gySX2wlKidotIXxBC3Rz54L2Anbc58rbtomXQkJ7964aZkmLhzY6WHkXAcYYoiD2wAZ//tJxeOPXuBc8d7kJEEU1mFl6Vz8t7oNKBl6BflQZ2MQ7Jy5Vl6qtrAGrTIxDM8959fY2aHYHC3rqoy4gZv9Fzhcvcg/8+O+6loLWemf46X/8ebOWlib8jLg798JzXrb60atHtuLEeEAV2hgRDMd0HV+eUzd+Uq0rEMQ8ZctjJmcYIuEEJ7XFuGgbvEtuHwHx4jOZ/K5xyzkyyl4O6f279sP43b6/nQbz9K/9FhpCNpO9B82/ZI3c9d5exXzuF5n4tTorJy9dnzdPR9Krd9bGxuont+DKUUG8N18LyWpGg5YxGvtElU5CtiLIyi6yWyJ5N2HSzDZDRLxGoOVdAcqmBnVTMxO40tXSp8t9fGxuIR3IyLIcD1FdwLA/yTMLtFFCQrFWm5gtxq/vbk4yir5rsUhb72YdNHrQrx9uwA7lYF2xZXFssaulKK4fg83XNjbLlJEgm0+s/jv34/3c9eZaZvjrKmUrY+vuGWLL4q28q57xcP8fr/PUpsKo5hCsqGvNS7IYZEBFMqjJQOZpSNChY2GEVJIVtKTkwN8njLFh5p3sRgfJbBmA66WMLgIx27CK+gIrEUswPFgZqSaYNtzxoc+pX9dGxvKlLcAIjPJjj+xbOMnBnDE/Sw6eFOtj+5GWEIatdX03Gohd43B7WctiMRhmDjQ53v2CP7VvCXbxzmaGJQx4Y9cPL8KJ8a38vQyREAzDTUnYDFRjNyjyKeC5YUVq3qBmFlwEpCZ2+As9/opuuedhqqyvi5zXfzpevH6cn2h43+Ukqem+f6QVXkf60MGLtLUN6nCAb9PPDP76RlT8NtJ/UOf+Y4U72zOcl90IleO+FgWibVXZXUdFVR6joY6eUPRDBLxikkWHga/VQdqMN5NkM8Pc3gbkcHHbM/kR6tAmC6IPuSVC8YzLQXW3fhqtu2/RuMzeautGEamD4Lx3GZWudiN1tUU4l4Po4/y+kyPQa7P7oNR7r88aU3GE8ukAqkUB2K2RbJ1hcsAgv6oCNjC0XBxfOzI8v2r5TizYkeOstqsApUGEwrL2tdUhv+vloT2Emb2HRckykLCTFSUdVxe+SiQtzxk3twUja9RwZRriJUHeTef3bHbSu0VLVX3FBRIzK6wLP/6ZUceW70/ARDp0Z5/NfvLzr2jOvwp1eO0F9QfX1fw3re35q3T0o6Nl7D5Om+07kxoEq6YEvctCL4RpLDbxxDueqWkoL+Ei8Vxw2iWXs6JXQwfTFRsNj4DCerSgYIVyL9Uo8XgJDhoVbztVDo6k3pSuJxgSMFpsfAG/SQieuEgGtBaibNiS+c5f2/vItXggOMJCKYQrC7qoVHmzfT+/Ygb73ZjekTBDKC1EKaNz9zgqp1lZTeQJJ/cexhGgYTl6aomBNE6gWRRn0ihoQD6SZ8b0dz46ZAuZ/4TAIj7eKWLa8oll6BSGqLwnSpJh5M7tYE5hyE7gNmNguaX9NXwhf2cvDHd1PZXMbWb/iJRCXRFoGVhNrTisDYcin4QjzYuJEzQ4M46XzZckmfZPjMdTb8St2q690MHXe0cuWlYovAkrowVR3fn+D2qb++gJuR+MJenLSLndQNadFeDbRS4NjFSe7fsoHPX30bSxjYWdKMZZiYhkGgNMDD79vG3JVZwjUhBo4PMz9cPA+q31KDN6gTDKZlsOfj23j9/xwFBSVTBmXjBrFWkU3C6Hdc8yuKhQ5tmVA6KfipDx1a9v4uxGBslr+8eoyx6Tm4S1E6YVA6KpAZicfQ1i+O1NmHwJwiNGcQrdb78/isovYVCvhYt7MNqSTecYu0WzyOE2jCAGhlis9eeYvZeIyMVyGUVl8Q2eG2m3YxTAOv//tHnl3DGt4NSr0BfnnbgwzEZkm5NutKqvGaFn2NK8/dyxpXnvcFyjUrznUkmUQmZ4U23j2JnXZuqyjkVtGctShTFstUGm4IBcpSJG0HMwPzLYpwG1T0QPszivEDgukdOilv2Pr9YsYhVcOtk2CyEBJk2iWtXIzjGb70J1/n0X973zKyx0ImyRevHcfJJlcdJXl18AqtMYNgRsuLWn4PHr++jkOnx9jzEzvxmxapJX1UIeGiflMN9ZtqiE7EOPXV85SWSZLtLBO6MQ3Bb937Qfy+lfuqUGWQD/ynh5kbjrAwHqW6o/IdWUCnXYfh6VmUUIglkgcL47EV14lOxhg8MULKdDAkmClNKvKVehnf5PK/zr+MEIJ91a3cWbeOTQ93cuG7V0jHsgVmpmB2YI74TIKdT23BSTuc/uuLgML05i1NbyWxHyj1U1FXirFEKTdcEyJUmb8ekdEFhs+M4Qv7aNvfVKRMOBWL0j08TPiROqb7ZlEF05J1d7VRWhfmDsLsq2nl6f//C6T74hhLrlVkLEpZYwmGKZBL4mfacmX1B+LkV87T+h1J7CeFLt5ZBY4fak8pqs9AokmQLgU3qOMSi6EJhcg963nbJIHf0u9c6WYVUlY7HAlikWQDuViG9OrnLjQFn9y/uu2TYRo8+q/uYeIzL3LtiYILqQRmRhNrY9WK8jGByCjWP29wZVsK1UzexWnpIVmQaBCYWbshIfW5epICTwZSi12gSz7DovR5C0PgD3mRjiQTt3G8WQUZBcIB2w9f7T/Jv6l8/JZtmxfR/fw1jn7uVG681vYsDD0MqSqtelg2JGg9bWE6+e3GzDjf/DfPY5gGnXe3sfmx9Qgj//2GB9ehlOLyiz2kFlIk51IYnmyMRkBiLkVtVxVbHt/AS797mNiUJq94Q17u+tl9Rdu6Xax0/l7DIiMdAqaHdMpGuQorlS0EyEI6utjCY5gc/dypIiLm9PVZ3vjjYzzx6w9w7luXOPO1i7gZiXRchGXgC3nxhX1s+8BGSuvC7Pnh7STnU5S3lN1ycV/I46PU62fBLlDUWWxIJqwbDdHkMUmEEpgek5G2DMN3ZPt1n7ZBMgR4LQsnkQEXErUGZXMWTipL0E4qOr8J15+CZI1ug4adLQQEkJqcJT3guhn+1/lXeKx5c1HR4xrWsIY1rGENa/g7SIb5D//hP7znFVJrZJg1/CDx4xsO8tXeU/RHZzCFYFdVM+9r2XrL61f5QivK3Q7EZvjeyCUy0qXMG+DD7TtvKbn9XmJhIoaTWp7Ynx+KLKsAX4p1JTV4DLPIJ1o6kvJJwVunT5LZnsEX9mEnbVxbIrKVpY3rbq4EkdueUvzZK69wjnHUfjB2KTwL2TLVrMTmYndTkk2qzw3N59Z3bUlgWjG3JMolDYi1CY5vyOCbVTSeNZnYIPWcx5W4rkIImLQSfO3/vMIP/6tHeftzp4o8ujPxDEf+/AQf+u3HbulczCVkJ8M0sLwmprFoiL58HSEE0pGAwFAgs4GKQuyuzid8Y9NxRs6Ng9CCnKBjFcIQ1HRWUta4ckAoXB1i2/s33tJ5LIWdcjj3zUsIBOtft7h0b4ZUlZas9sbBE4fQOPzkBw+xrquBjOto/2YHvqq+jYvElxDs+K6Hy/c7RJp0dCJjufngZME5L/ph169ggXQrxIqVUOsN06+SGFJBatGKAWqPKewSQapa3z9XyWLBhMVbqrJ+9Q5Fjkj6ni0/h8V1fMLEWJDUP5+i50PpFeSGi5kwAi2D7DFMjk7237S/UErx/7H33vGRpXeZ7/c9oaJyzrlzzt0Te3KyPfYYHEjGeIE1GLiEu7C77N6FS4ZdLgtLMgYTvDjgbI8nz3T3dA7TOUmtnFOppMonvPePt1RBUnere2aw8er5fGZaOqo6dc6pN/5+z+95Ln/nOp1v9GCnHBp31LHtgxvvKAGdmEvipBx8RV5c28WOOVR+NkLTB+u5OjyCbxaqbuhM3W8Q981byWSvOeGo5KLf8PCpDfvpnZtizkrQVliZp7p0M4xdm8RK2CTmkuimhuE1EEIFpmrqyxYl0qSUvP7HRwil7WuScynOffUKmqFn2vi9P7WL1n1NjF2dIFDqp3Vf4x3bgtwJRiNhTsQH8vp4MiB5/tp5mkU2QZmLynOScIfylJ6HsAVaSuKNQu0ZQXG3xPJGOX/9Cldf6uLxX3uA0qYSfnr9/Uwno7hS4osIvhp+gUhlOtCpSRyPSjDYfhh6SGN3cz0XK0OMT1psKW9Ytu1LLBRnsitLhMkp/AJNVXI98ktK4terG+yK1XLI7M80aT0BtUeVhdv2D20kPDrL1Y4o3UVznOk9gmeNRvGIw+gaB91KEwfScE1oPK9TXlJIZDRG02mD4U0OtkdVdX6gaROV/jsj9VX6VVDWSTmkYimkAMur+nGySDCsJfD+kMljPfUUen2039dMUU0hZ6cGM2SJeVsj15CMrnVpPaEhNLGIwHEz8uvk1Sn+9x99DU0XGTKpFBLHUKpIq/a33tE93SkMr4Fu6ooIs2BOmhkI03NsgNa9yycDz0M3NB781D72/Nh2EnNJimsL31aw9Wa4/EJnhggzj6meEIPnRmnanvUnenP0Rh4RBpSK35byejQEX+45y0A0hEczmLPieHUT13EzCmrxYkmiQPWvy9+5viwyTMO2Olq/FCTaN8dUs5uRol8YuFdBUNAcScMhQdUj5VgNBtWBIna1N3Lm8Bmme2ewE3Z6fQDF3TC1QVmLpeLqhEZSUDiefcbho5P8wicfJpyK49EM/IZJf2SaT4dPEHnMQgIFI9D2usDnmvSfHGTjexZX7aYcm6/1nOPEYA9W0qJkXMfa7DJb5iIkFI1qVHRrFI1rPPaLGyj/o1I6X++m98QgVtxizHFJFciF0xuQJu4aqjISqRRiQuuWaCcCpAeq1lTQtLOOTe9Zm0lKjZ0aw2wEOwjJchgOCurflFz6zjXu+6mlCY/VgSLWf9tgqN4mFZAUjWqU92oMyVHmJqIU3qFt5Dy2f2gjyUiSvpNDSFdS1lzCfT+9611TEgn1zQCqctn0Gkq9xXYWPWd/kZf20ho+sfZe3hi+ztWZURzp4tEMNAT3VbfTUtfIuvuVQkHz7gbe+J9HmR1RicLSxmL2fXxH3jmbdzbwyK+YXH+1m2Q0xZbqambXGhw+eZlkX4zSsy6eaUnZFYnQBG33NtDUenOikStd/rHzBNOxSGYhNVvtItz0nGm5+Pw+vDpgQHtFMdVrdWp0QbggQsjMT/rPq+BpQmNvVQsHRrry/r6htJaitFXkF26cZtZKIBBojlJXsE0wk/MWEpLPXD3C6uIqHmtYd9drzxWs4N2EEIKWwvyCjaad9ZS3luYpwfhLfKx9bGnlrPb7mrn0/DVlpzu/wBOC6FSM81+9zI6P3Jma4HJQHShib1Urr8SvpI/cRklrYdGALrHT/IXe9wv6XGj+tqT+TUn9mxCthXgZhFsgvEYs3p/lnhe1n5WgyOzpBLpwJVoSKi5KKs5AnAQv/d5BfuRvn8sb3y+FRjJEGFDrSztuM1EnaEm/zE7Y6KZSrJSuxJAaH2rbwRdunCLpOiAlFf4CHq1fg+O6XJ4ZYSIeodFXwvnfO0UslKAxoDHelLalzllelvmCNyXC5KK0oZjSBep5y8VLg1c4ONJJvC6FfNai+bSepz5UtXrpoqFjr13iwqNJoqUawpWUXIf6Qy4TD5tcFEOQFrIbis4wZyVY3V9AKpLC8Oo4KRfpSmKhBNdevcH2D21ix4c3Ex6eY+jcaOYzPAFz2fGN3T+6ldf/vyNKiRFlI7n7R7dm1qrzSsrzOPuVSzz+aw9QVFPIFw8d5c1Yr1o7C6j6gSAPjtbhztk0bK2l7d7sGtHQdJrqKrnRna/UB1DRWoon4KF5T2NG+WcerXsbb2kLOnxxDMMCM6Ysw2ReliCnkeuSsd2C2VYovSgZelhk/y7TKh5pzTg9nlZdmt+3OQINnYRmqwKLvKBUTsBDpFX/cqBZijhSe0RSc1lw/OBJin/NT2X70u2jqqOCH//kE/z+qReJFKs+pLmKICJd8MSyBGtjxGbVqMbZ52xcA+yAmL+Z9KVJpf6CUnoxEuCbgNqIn4puDT0iqdhbzRvrxpgmnr0XIVRRlwAmLKqOu/Q9RNaKKb1+ly5MRCKMxmZvaV+0EHbS5sLXr2DP23sJ8IVg1ZckySLwGoKgMMkVKknFLBxLEXLtlM349UkGz4/wyC/dl0f6XvNIO2seaafrUC9HP3N60WffONxH273NPPt7jzN6ZQLHcqhdX7VIBfBO0RAsodpfmKeK7tF0VhdXEbGSTM/NUnDdoXhI0Plgdr+m6QKPprOpqI7vvJUvIy5dyciFMb756y8z3jmFpqk2JgwNgSqU2P/z+wiWB0g4Fgci3VyNjBLs9HJ/TQfrSpdn+1Zk+JacbvzTUP6GhafaS0LXiMeSjGzJvlCmg38uQpEYPWAmwD8r0E2BFQdpucTLYGK7ajSao/qDkGmitVCFj/MxyPnp9uWhq7QWltNRfHeqQytYwQpWsIIVfD/ie44MM4+8itLbYH7TuNR7VqSHV/DdRoWvgJ9Z/wARK4EhdHzGnVUyP1C7igvTQyqgkUah6eViSKliAIRTcf6p8wS/uvVxij1359F6NyisCmJ49czGfx5FtbevPvcbJh9q286Xus+Qch2clINnwCbwxhxdqQjR8y7uB1SVwTxMTWNHxfJllU9P9nPBGU9LtirrmniZQDgSqWdVM4QQvKdJ+XWXNuZvQkuvw/TadOUXat/umoJUmk2TKIOeh9w8EkNuZC0hba682Ml078yi65sdiRCZiGZkO2+F9aW1BAwPMTvrZesJmOxrbGCuYoh4OI5jZXe7mq6BlJR0Sia3qmvRU1mJZ6+ms7W8gccbsp7akYkoAiWHP09ymheU2fLc0gSu6UQUQ9Mp8viW/PvtEJmMYsVtpCsZXu9gFQk0K5tIbXxF0lpdSVuHIm549PS0pUPbfc10vt6jrlMKKns0ZmvcrDz/PHKEVDQhKDJ9y7LmWC7e07aZP594HUc6KtCShKqTkurTMJy263VtFw0yNheao64r4PGQnFPVkhVvqUqXiW0Cxwf+cUBAtApcH4uILinLwdAkExtl1g5nEbJt0UDDK9QLc0loN8PFb13jrX+5qOJBmuD6q91ExqM88sv3LfvZDJweRkqlUiSdNGEsBvbfD9JW4EFogrpN1egzEfpnpxUXRhfofh1XU33edh2MNMFiYWD+Vhi7Nskrf3AQO6nal7IPc/EVeqnfWsPRvz1DeHiWsuYStj63gbLmEiZvTGeIMLnoPNCTCYgKIajfXEP9Mjyk3wl0jY/l2WLM/zRdkOKeLa0MnB7Oe73QBEHLYP03XHrvh5lmiXBUmzMswcYzfowBG82bo3QRszj3tSvs//l9AJR502OSD4obivDOTZAoBNsr0owt1aGmNwle0wfxjKh2dXCki0+ufwD/MuY53VREj1zyk7KvE/gKvax/YlUeyejpe7Yz/iv9zDYq9ajibjWm1e+sZetzGzg61s2N3nPZe9Jchu8TkBQIW2ImBK6hxgIzAe39Qfb97E7e/IsT1HQmqLiuqqvWbG+mQHP42mdfSCddm9nw9OrbzmlNBWWsDlZybkYRlzLqVimwpI0nYJLUXdyHitiW4/0+ncjKQRs+QykhuJJkUH3TG55evcjvfG9VC9/uVxWsmWfnQsFLMZyUhuUqa6Kx7TC+XuL6BbVGIQ/vWX7/uRkmExG+3nuOy2PDiLBL7RWNXYVN7PrhLTgppSaxoLgYoQscy+XI35yiem3FXXvJ+4q8N5V4fycwO7Z0BXJ4dJYL05L+SIgqXwHXwmNLvu7qzBhHx7qZS9sRJB2LlOMgEOiuwDbTCQYJ4+02jecN4uHEkudaCMOj88Sv7qf6y5foPTDG8EaHoaYEKdwFz1vNBwXDUHxdYkxM857/+hAVzYpQ9cx/e4TJ7mle++PDhPrDCAFVpyTJEkGkXs1VZlzQccTIqzqWUtIVHufc9BC60NhW0cg/dR4nbjrIFCAlkRoY2C5pPJAkNrP0fX2t9xxHB29k1hhjDWkLuwQgYLbGxRMXrBalDJ0b4eK3rjJ0fjSzn5ttcNLJxAV5FKHuHU0psUkBk9tZbI2Rfk91cTG7/8seZqw4cd3BTFsyhJpdRvZmT5wshd4nBVunb74vlVKiTzs0Ti6eiK2YtcQ7lgfDa3D/J/ew+0eT2Ennriru7wSljcVM5kjeGz6dVNRFyyGNljQUUbtBkVBWF1exurhKtY3ZcV4cuEJ/ZJrXRq5zaOwG723axN7qVoprC3nf7zzGdN8MQhOUNZUs+fl1G6qp25AluDiWQ+TCAH2nsqoyQoAnaLLjw7dOovdHQoRTi60yIhWSsgGNqRY3s7YImh4+sGUXk1siDERCPGDW0z07ybXwGAHDw7017dxX0545x5ONGzA1g5MTvdiuy5byBp5KFzmEkrEccqNAiwKm6pOOrtqmKTSmklGOjvfQMzfF/7Xp4TuuBl/BCr4b0A2Nx371ATrf6GbixjRF1QWsebT9pnO6v9jH1uc28OZfnsB1JEIXmD4DTdfoPz18R2QYx3YZvTyukq4bq2+pKvNc61ZmwhFOTPVnbEry9lALCw3cnJ8Xkiw16H1GUNIp0SQER2BiM4TXLq/PSg0w0q/VQKRAsyVF3VBzMvu6+EyCye4Qle1Zi6GFhTBOmjyuuQLN1NNkcoljqQKk6rWV+It9bKSOjqKneGnwCicmepmIR/jjC6+hC414usjASTkUrnFoP2bgi2nUXNEYXeemFQoEHt3gg63blnWPd4tzU4O8MnRV3aupkSzUuHGPTfBbGt6ooKKjjI77Wxa9z5UurxcPEBMybbMrCK1TY+tsYwpN5LeNw2PdVIw0K0XPVHb/66Rchi+Osf1DKgb04M/to/twH6NXJgiW+Vn9UNuyYjQANeuq+MAfPkX/qSGklDTtrMdfrAISidkkZ76Yn6CPzyR460sXaXy2lUPRnrximXERZXiNzUfu2bfkZ21631qGL44Rz1lnzVt6Auz92DY8fpOeo6qAoO2eJrb94KZbXn9BRYDBUUGqQNVYIUGasBTby/FArAaEJdBS6RhFulO5riJjI8DNHRaEJOqm10O5fU1m35uBFAgpM2s84SgyjOrLAi2o41oul759PbNvXQrlLaV8NHgP/3T1hLLSMgSaobO6qIof+o3tXHm5k8vf7sSxHOyoReVZGN+p4lCKVCAzC01hoUhyAlJeSBWBiNn8wqc+iIbG0Mw03zzVq0hv2vxeWRFqtISk8XWIe100K0tYAPWMpSNxkjaBO4wRx0JxUjEr+zxzHqN3DjwBDfyCqjXlTPWECJT6CA/NghAk5pKkCiRuAHqODfLKH77J47/2QIa8NZdK8MZIJ5flIIk9NrVXtIxaKZCJS2i6Rt3Gu1dAXAghBD++eh//0nOGrpkJSLq0xYt5rnID1RsqCPXP8O0vvEYqkqJZCkZ3KMtkz7BL3ckkp8vP5OWEpCtJRJLgSiZuTOcVcYo0gSQVtzLr67+/diyjjgvQNTvBj67azaay+tte+2zfLGTqBrOTjBSCVCTFeHgKX6EXGdRw/I76m5bb9rPtzUhBMKRliDKpQuh+v1B2YSgiDJqgYBA8Uy6hNfmKToYrMuvKi6GRFTLMClawghWsYAU5+J4jwzQ1Nd0RgSUWixEKhbDt+cStQAhBY+OdV56uYAXvJgrMuyML1ASK+NTG/RwaucF0MkpbUQU9s5OZZMs8bOlyYXqI+2reuST/7eAJeNj4nrWc/fKlzDGhCbb9wMZbvCuLLeUNrC6upmduklN/cQZxXW3qbY9krloS6HUxNwSIuRYNwRKeatxAmW/5FbYXp4dhAUlOE1D2liTWLEiUQFlBkA+0b2Njmar6rllXRcPWWgbPjqCbOm7MoeObqvorUQGpIMw2pyUMNJGptjASgpQ/P7pmJAUFk4JkNIXpNxZVnGumlkf2uRW8usG/W3sPX+05y0B0hoDh4YGaDh6uX0N0c4zX/+QIwxeySXPpqurZwAy0HTEY2O6Q8kv8M7A2Wc79TatY19aaV11f1lySsdfQdKVQgIDqtZWLqvknExE+13WSoegMAiW//JH2nctKgufCEzCxEhaRgM1kq1ABkLQMq+uFyd0aZUfneP43XmP/L+wDqWyVQv1hihuKWP1QKwNvjSBdyb6OegbnrmD5FgdVNCHw6gb3VLfxVOMGCm9D3pFSMnp5gvDIHJUdZbe01lhTUs2PFW3jWwdP4ehQ3C0pSLuYlF6TTG0QoEk0S1UJSwGNb0BgyCW+UxJ3oKBLUjig3lNxXiVOdEuRZ/qegPAqkS8XDEhNYvsg3HqLysT5siPAcVwSsQSm32RL+a039VJK3vqXiyTCCSSgaQIz4GH4whhzY5GbShMvhObTGeqwmG6SaI6g9Kqk9JpKuMbDCYQQdB3oBaB8DiY2C2yvUg/wCoMDI130zE3xU2vvyxKhlonL37mO60h0Q8dbIFTllJS0P9BMz5F+XFs9mKGZUSY6p3jvbz+Wra5aADu5hLXV24CVtJWVzjLkd2tKS9XA5cp0zFBdt2cGxq9N0rizjp7D/VhxW40pfgOhaXiEYN0bLrFSl+lGFzOhUd6vUVVTREibASBc4zLVrAYxa3qS/Qs+W0qJv8hL1QlJ72Ok25lAaFm1I8t1Mt/NSDTMm4OdPNayntvBW+ClaWc93Uf6SOXYJOmmRrA8QGOOEgdAbCqGJyypmMk/z8ygIi9dmM4nBalz6WCAqRkgJXbKwbVd6swCnvmNhwmU+nn4l+7l1f/+JnOjEUxHY+jMCL3xgczYePbLl0jMJdn1Q1tue0+PJFuYOTVMuNYlWipJBZRFhkpYqLFxZkGCtq2oIvOzpmv4Cr2kUjb+Oh+Bn6+lcX3Los95onY9I7FZLkwP4UqJR+jUHXTxR1RlcDKSZGqNZGSrGk8FEPKn+Jurh/mPW5+44740D8d1+fSVN5mYnVOS3B7o3eIgjvcR/ZMo7fe3oJs6ngIvqWhKzUciS0x3bZfBsyOs3q9UFmIz6lncLTnmnUbVqnLGr03mHZNCcrBymL7OLElO8Wvlov1KxErmrc2EEBkFPFuILEHKhdF1LgKHBwLLD1AWVga5/9/vpuw713HeuEJflYv0qoDuvLKScMEbhtZvg11vcHFPkqM9L6INKiLoPTXtPN6yDn+Jj9BgmOE9ysrPNcE3CR0TQQouW8icIW+uwuXAlknGLvZhCA2PpvPmaBeOlGimBjnjY7gVGg+wJKnQch3emhzIEKilRsbGQgrV95Ewu0YQPj/HdN8MVtxiLmgzu0rDNHSMyflnKHByqmvTDxxXAKYiyiWL0kocC+ZH4ULkWpjfmXge3dTxBz081byRB2tXkdjjB1J5r3dNgdx38zlPCEHD1loGzuSPQQUVgUUE67uBt8CL9yYf7zouyUgKX6H3baslbf3gRl7748MZxSDd0Fm1v4HkXJL4TILajdVs+cD6RZ8jhGAsHqE/GsqQGy3X4au9Z+korqTCV4AQixWuboWZwTAv/8Eh1Y5yl9ZCECj13/a5ejQjfQ9anuWqcCWRcqUUZtkpTE3nPY0b+Vb/Ba7OZElu9cES/sv2p/EuMVbqQuPxhnU8voT0vE830YXAkRIrbiNQbVGPQWBSEq8TeIqz6+TR+CxdsxOsXklUrOB7HINnR+g5NoCmCdrua2b9k6tv/yagqLZwyb2u4Vv+OiQ8Mser//1NopNKDcMTMHnw5/ZRs+7marFPr97E+WNDJBxFoHRRqq1aCtBV4YLUsgnuRSzeXGgQ3qJRdkESLZVMb7z1WCuc9NwmRTraOq8UQVoRA8KrwHlTWXaSfslCUtymsjq+1X8xo5I5j4puDU9AIxWVmfG6oqOMe39yZ/aZWXGOjHWn71swm0qQcm0ChgdDUwp+Uy0OFd2CgiFoegOKezRiOzy072hiT3ULrYUVLER4ZA4n5VDaVPy2iw7fmhzIeWgCb9CD43UIvLeKeyvaaNxWtyQRvW9umnjAhfRyev4qptskurH4mpKOjVnuyyPCKEgik1mFFd3QWPVgK6sebL2r+/EVeVn9cNui45Pd05nvKRdj1ye5/toMsjqn4UmVoL8WGl30+nkUVhXw3t9+jJ6j/cSm49RurKZ2fXYOMbwGu390K5veu5bOgz1Ep2IMvjVM0876mxL7Ox5u5bWyQRzf4usEcvpHej2vSyKNynZW2jKrdoIqwCgYksw1qf0HNkgviiCSSzzLBDZyO58iBJT0CObStplSV2qkwoHRvYLpbS5rX5XMTWSLCW6GbZVNeA2TFwYuMxSdwXFdppNR/rT3EKOVMxjPOFQdlxR1Krth/6RkpkNg+8AqEMSqFRHHNclfSwpBOGjzvy4d5CfW3sNXz5zOL8ZyBVpaaafmmk7fvQ7xEgFazvnmX6pDWZ+g5A5tuoPlAbyFHuykjbWgfQkhkK5LLBRn9PI4FW1lrH9qFW/+5Unidoqep2CuUd2QJwzy2DhD50dp2FpL0rH588sHmUpGkaYk0eYSanDZ8KKJL6Le03IXKp+5cFyX/ug0QcNL1QIl1nJfkOeM9bzwuYO4MdDcWV7iANs/vImCigBuuriq5AaUdguEITA9qgGOjU/gOsrCWoh07MeVaKbOksOVlMSm47hScmiki8szI2hCwxRaZj17YKTztmSYqd4QgbeSiAfIWGCB2m8UjZDp/1JKvLaGZ9YhVSSzVn7pviVcVbxkxtVnz8d1pzaQIcJAVhGmdEyn9ISk6ryk/wnBXJ16vy9HBWqpdewKVrCCFaxgBf8n43tuZuzt7b3j91iWxenTp/nbv/1bPvvZz+I4Do899hh//ud/jmF8z93iClZwx6j2F/EDbdnqnL+LHF3ydeJOTavfAWx671rKW0roOzmEbuq03998RwFvv2GyprCa02kiTMovufyYRSqgFv/eVIJNFfV8bPXeO742r64sG9wFiezANHS8ZVC9ppKn/stDi9734M/tpe/EIKNXJpjunyHUHyY4KPHNenE+UsYZbTRjH5SKqiqjwLTAMCWxSgCJnhC0H9HRXEHD5hoKygNc/Na1vM9Z9UDrHVnONARL+bmND5FwLDyanrHKCJYHeM9vPsoXf+6bhAbC6QJpiXQkwcogvgGX0j6IuTY+oWPqEU7JM3R+s0sp5jiSlt0NbH52HTs+vJnj//gWuqmjmzreAg/3/uSuRdfyD9ePZ6pfJXAxNMwfnHuJQtNHc2EZD9etoXQZm/ozX7yIEIJ4RrBABUGErpL/iQq1oZ/qCXH0b08zMxAmFlJtZfTKBIFSH+/5fx/FV+jFsV0O/NcrxPerjahw06czJFX+An5hxyMZxYuL08Ocnx7Co+msoxL9RpJAqZ/6rbVIV/L6Hx9m9MpE9rva38reH99+0/vYsW8NI98ZYKJrmlQ0m0jzT0LLyzCyG5KlEt8E1ByVFA4CAopPgBWH3DinkIoIA+rfhgMQrU/bX+TGk0W6WsoG28MShBiReaSQVsdxofy8ZNNtVE1Ofu5cXsWZ60hS0RS+Ii/JaIrlGsj07bIZGs8G+aK1AtsnqTynriu3eqf2GBhRyfADytbL8Kkgcn8kxKnJfu6pXhxgvBWi09kAp6ZrePyqv8yNRTJEmHmkYhY33uxj/VOr8RV7SYTzyYbNO5WdWHwmQWQqSmlD8V1JAc+NRTj292cYvTyB4TNY/VAr235g4y1VRzpKK2kySuhLhpj/MoUDtZd1kpEUmiZ47v97hm/955eJTsVIzCYxfAb3/uwunh+6TG/hHK4OxaNQOWGw8Zk1HPrz44yucejflh0bp1fHODbSzc7yZox0UGn4whijlyeoxYd11mJwpwrmmOhIA5y0iJN0JalYCtdyOXTlAn47yp6Pbbtl5S7Avo9vx/QbdL7RQypmYXh1mnc1sPMjm/PkmgF6Tw4umaiITato+FL2TJoQdBRV0z03BUJgeA1Mv86H1+4jUKgIGKc/f55EOInpN5FSMjceRdM1vAXZztZ1oIdtP7Ax81xuhoIiP9WdOtWdOpMtDt177fkLybxmYdKzpbCcPVUtHB/vBcCSDrbuEi60OW4Ncvr8MB9fs49VxVX0Hh/g7JcvMTcepaaxiDXPbcRT56fNKuH5S6+QslK4jkS6ktCadCBNqqrDVDRFRMCVmVG2lDdwN7gWHiOUii8ih413uFS+HKJ5lzqvbmhohpa1wMq5f9NnkphNcvjTJzMEzkCZn6ad9bTf20x56/LXD+801j+5ioEzw8rSIQ3f45VcsKeUrZDjInQNzasq/UyRbQ/l3iBNwVKOLDinN21NF0rFlDS3JdHTU8TkWsmW3cu3zQToPT7AmS9cQAdaTul03+Nk1iOmFJBwqb6u4S3ROf+4TTIgcTUl8T6VjPLq0FXmrAS7nlzFWc84kzkcr0SFYKRJ8rF7d3Pmn88Tn0kw2wZXH3SwXDXf2zjKEkc3SDo2fmFkmTiAZgs8QYO5JVR2pJRpq8KllDzB9JtKuj1qqepNCePtDoP3zVfZKvU1/yTEK8ExyNoNpnMpUlcWalq6wFKT4ArQELioRExhr0u8ShFFnZRDTCb5dv9F1hRXU95aytBQJKu0J8DjN/EW35pAu+tHthCZjBLqVyQgf4mP+/797nfFzmsenW/0cParl0iEkwQrAuz4yKbMXHU3qFpXQe2vrOZ8Tz/+hM79zatYv315ScHLoZFFxyRwOTTKvdVtnJrs48bsJCUeP/uq2267Pjz5uXNqbJcyYwUmhFBrkLlUhvB9M9QFi2kqKKU/EsJb4CEVs5Xlky6Ilsp5cTNs1+Hz3acJGp68xO5gJMTXe89RE1DnWa4qnd8w2VHRxInxXhxLJeGlEFTf0JitlWrN7Uo0PftZc6nlqUOtYAXfLVz89jXe+lJW2aL7SD97P759WaSBmrWVFNUWZmzS5nEnhIMT//hWhggDas1+5G9O8v4/eHLJ9XMoGePTVw+rfY9UCUf/NLR/VaIn08piGlz5mMhXjblFYYFhGhheycDjds4L5YKfwQwrgoBwIVa5NLvG9YFlKFKeFlNn8Jf6KG/LX//4DQ+fWHMPX+87x2B0hiKfn8JjCcoHdBCKKCl0wWO/ej/Vq/OJQeenhnCRJB2blGtnbIJTjo2h6eimhiVgpsTGc13Nd8HrUDXl4b2PridYmD9Gx2cTHPyz44xfV4zUwuoCHvzU3rdF+Fxk+SkUib1xSx3NtbeYy4QifDgpF+lkSQBen0l5YRGTiXySRH2whApfYY6aTvo0upbjR/z2cDk0wsXpYTy6wa7KZuqDJZm/FVYtXcgVrAzQPziJrF7Q9KTEZ996/+QNeqhoK+Py9U7OfvkSIxfH2PD0mszeZW4swgu//UbGorvrQC9NO+t58FNLx9NGG1LMTearCGeuaiFRLOd3Vwc9qVQPXdUsqTsoiVULjPRW2jGXFoAhbQuV/V39o9tQcVUpJ41vhNAapYQhBdg+pdrSv81mi7G8ebnCV8BkIoKuaSRTFjdmJ0EqRUKrQNL3MLTPQmAMinohOCTpf1KQKgWhgeO7CVNOwJWZEX7/xAtMGNH812iKAG8mYGizk+ERzRO0tVRaFQYoHBCsO3/nyn+6qbP9Bzdx9O9OI9MEXAChCwxPWuU5bQ093TfDsc+eIVgRoLMpyVwOlyVVLOi632ZmOEzD1lrOTQ0ylVR9SGgCT9DE0izGVju0XfSy9tH2uyaMAXSGx/nnrlNEbNVAVhVV8mzxBua6ZymqKaCyo5zTX7wAERctp4Gc++plPEETgcCTJnskI0mchMQw9cwaTuiCqlUVTPWEkFIRYTwBZVe7CEIgheQfOo9zbmqQpKOeYUpoBNLrwqVUBudhuQ6XQyP0DY6Q8kkyWxyh4ph6RFJ6Wh13DRhcbTFbL9HjIIvSsco09KREc9W9lWMCDno67mAHF09OErCCSi1RxGzajhpcea+DHjAyMRRDaOysaF7mN7OCFaxgBStYwf8Z+L5gipimyd69e9m7dy8/8RM/wTPPPMNnPvMZJicn+cpXvvLdvrwVrOAdx87KJq7M5FeMmJrO5mVIOL4bqNtUQ92mu7cM0Q2NkoYiZgZnGVnnZIgw8xu4S6ERusLjdyzxuLeqlbOTA1g5iTstCcU3AAPu/+TuJd+n6Rqt+5po3acsmZKRJLHpOEW1hYym5jhzcSxTXeAJmNiGRu2Yge9sgmgZ2LpLYFTiMVxaHmqheXcDLZrAX+zjxpt9SFfSuq+RdU+suuNnBarydCFCg2GSc6o6WMkoC3RTwxMwefSX7+Po351hsns6s1G0EzZjl8fxFnrRdI2L37pGLBTn3p/cRfW6SgbfGsb0mzTvbsC7oKJvNBbOEGFAyRXHrBRRK0XETDIan+XqzBi/vOmRW9qCSVfSf2oIw2tQFBdpzc/s3wQQCAllGwL0nxxaRECIhRJ0Hexl4zNrmO4NUdjlYuwWqvJuHinBPk9DhgjzwsAlXhu+DoCVsDgQv0rHYYOyAZ3iukKa9zTkEWFAJZ1a9jRQs25xGwwPz3LmixeZG4tgeHRc18CO2yDAKtUwvBqrXwY5bS90OkJKmbZGSd20ItEzB+XnJKN7c4JSaRIMrgpAacl0teEt8m7ChdIBjYbTGuO7JmnZs3RVj2O7i4hboL4Tw6NT1lxy8w/JQcRKcNmZQDe1dGJR3eDEVkHFObnkpaZKRFpeWeQ9joHINNwhGaZ2fVUmMZmBAI9/aTWmZCSJbmg8+Km9HPqLExmSRf2WGrY+t57jf/8WnQd6kK7EEzDZ9SNbabtn+bZt0pW89seHmR1VSWI7YXP5O52YfpPN71tcYZ6LNW+a2EIQrpGYESi/KAlGAD9M3pjm0reu4tou3qBHBX4MjRd6LzOyykJLGsikw2y9ZKjVQ/OuegYfbOJ0RVfOc1HfxhdPHKfzG2/RtK2ePR/bxkTXVOYl9dcMJtdYWF6JFC54Behq7klFU5mqJ18Yes71o5sa+z6+45b3ZfpN9n18x21fB+BaSwSxIENQ2VPVsmheFMBMKobtOvh0kw2ltTzRuJ4Kn5JZSMwmGb2c09clKllpO3nJVjvpYCft25JhKleVU9FRxmTXNOV9GqEGjVCDq0hBUrKjspl1JYvnyg+2bmNnRTOd4XFeGrqCR8/KWdjS5dv9F/lh/xYO/eUJkBAvcjm3ZZLk+AS+hJfgnE6Fx8YTczNiaJlqs3RPciylIOG4N6n6XAZSaXu1hUOVa6gjpc3FFFYXZMbCecWf+efmL/HRuK2WQ39xguELY7i2SzKaIjGbYGZwlmuv3GDHhzex/qnlVZzfLWZTCTy6vmgu9RZ4eeY3HqHv1BCR8ShVayo4YQyT6B7OJkxsF8cSrKqqpiQQZCoZobWwgofr1qhz9pl5VdxCCPZVt3Jo9AagyJmu6WaSPr7SO1MN7D7Sn/m59pqBbkPvTgcjaKAbGv5hi/rrBhONDpYh09/NPJtAfYdnpwZ5z+5NJCI+kMnM33RTJ+51cNd6ee6/P0X/8AT/o/8NUgvaTMp1MDV9XqgjTVZQ7bWmR0c3lp4nPLrButIa3or241oOws0GgjVNU4p0lkPxdYkdt7GEy/Ce9OULcE2JLYACaD2l07vHJeVdkEwRZJTVBIKAYRKXlrIykkBCkiwVSC1tW6hJhOti2A5XZ0bZVtnItdlxpOMq0oKhYWo6m8rqFt1PLoJlipQ82T2NnXSoXFW+LOWvu8Xo1QmOffZM5vfoZIw3/+IEJb9VTHHtcumqWUgp+bvrR7keHoe0S8ewe4lPxSuozKnadaVkJhmjwPTmKUwttS4FRQ7JnDeNY+O9/NyGB/POmwvXcRm9MpG10ZMyS/J2JcX1hcsiGX1s1V7+5eppLkwMoSckHdNFXGuJsHAEc6RLyrXxpu9BSknMTvHGSCceTcfUdNYZldS/4jDTP0tpUwnbPriBylVLJ+I+0LKVQsPHK0PnkVJS0aNRd1lnQDiMFjt5zVUTglXFN1e3WMEKvtuwkzYXv3V10fFzX71M+33NGTJKaDAMcrHdsNAEj/zyvZz83DmGL4zhLfCw7vEO1j7avuicS0HZI00sOh6dihMenluSjPHG8HWmZyO4SQddeSVjB5QKjCfNyZnYrKwngHxOy5KQDKy1cCYk8fKbJMbT82xgUtL4qqTrOXGbc8L4LkHDAYmvxMcT/+nBJV/TXFjGz298KENi6Ur1cH78CvGZBMX1hez8oS2LiDCg1h4pxya5QFXGlq5SttM0hE8n6bdJFipbFd3UcR3JxW9fY8+Pbct738l/OpshwgBMz8zxl6+/ir3RR4Hp5f6aDnZW3j7p2n20n74Tg+imTts9RVwkX1HN1PTbqpc2F5RT4StgCkVcdV1lE/VAo7KzfWnoKq50MTWdYo+fD7ZupUIG8Rd7seJpYrOmoZvKWurt4sXBy7w6lN03Hxvv4cdW7WF9qbJ4Lq4ronl3PX0nhjKvmal36XwowlTUwfGotdB8IQwS7q24NdlgsnuaF3/3QGZvNHljmuELYzz93x5G0zUufOtqhggzj/5TQ0x0Ti05dx0+fRUpXDSXTGzBNSQ4qg9LnSyjRaaLcZJgpFVOhK3+K+yDivMwuVUy254m5WssTTib70o5XUo4yqK8oNNVBTwSZlZnrYUk4GiSULPLph1rb/mM5nFivBdbusStlNrHCEAodV0jrpYZ0+sEgTG1npzYDpF60HSBqeskkzaOuRQBTgndTKSiOco4ZF+nqeueV3XV7LSFbrpgS4+DkRKsPWKy5tGWZd3LQnQ80EJpUzF9JwYJD88y8NYwQmgk04VZhs/Iif05rN5fz4GCUP5JBCSCklSdesjTyVjen3VTreurHiznB39yH6Z/+YV8C5FybP6p80TGrg3g8tgwo68O0XJKrSnrNlUz0TuFbUoMK/us7aRNKmblra2lRCnn5qh1CgSrH2qj+T/U03WwlxP/eBZQ8d2MiqYm1PreayB2FXI5NJK2pVONUa0LHby6waqipePP04kof3XlEKFUHEdziexx0S2Qbpr0pIEbhN6noO4gTG4RRBrzWWWlV1xi1YJkaZrzj8R0NXbfu44THb2EUjGMbhNP9wKFYiEQAgoGJALBtg9uYMsH1tOTmOaFocuMxMLUBUp4rGoNpfr3hvrqClawghWsYAXfK/i+IMPkYu/evXz+85/niSee4Otf/zp/9md/xqc+9anv9mWtYAWkYilOf+EC/SeH0D06q/a3sum9a2+pCHAzbCqrZ19VCycm+rBdl5pAEc82b7mt9cv3MrZ/aBNv/M+jqmIzDdOf3cANRcN3TIZpK6rgw43b+ULfUeKFUDAuaDyrU+DR1MbeWV41kJKoV+yKerOE9zVv5jsDl7BcB13TuLe+jWhPD07AgycBruvieiWm32DfT+zIfMdrH+tg7WPvjo3VfKW+pmt5bcpO2JQ2lZCKpjLPUqbtQiAtv5newPccHWDHRzZTXFtIce2am37WQg/zlOMgkXnKROFUnLNTg+ytvkUwR6DUA2yXgohOZY9kstXJhBh1CyqOu6Qi6Q2gEGimhqblf/7cuCIXdB3qxSM1Wr/lMny/IF4FRgyqL8LujysiRdRKcXBEkQCk42a8gwc3O5QN6ISH57j+WveSlzt6eWIRGcaKW7z0+wfzlEQ8fpPKjjLeqgsx2eYALrhQdVpQna4Omb9/O+ngCZgkazWGdkpi1eCdgapTkuKe7OeEVwlVAT/Ph5H5cqmgZFFx1HNyDdT3uiDINNnq4pu1CZTefGM83RtSFdRLxHvXPtax7DFrzkriSokZMHHnUhnyj+PPUe5ZAG8om7g1zOznLJTQXQ42vmctI5fGs3YdArY+t4HCqiCDZxdXsddvUUHLqlUVfOAPnyQ0EMYTMCmsKuD6691cfz3bLlRV6imqVpUv209+vHMqQ4TJxY1Dfbckw4QGw4S7w9Q5BuWnVTuTEixsHNtFMwQ3DvUCikBkJWyk49JdkEKLGxl7KFCWDC/88zF2fWQT+qk+sB2EELi2i2M5uF6wDEnXxUEufHMKGr1E1zlUdWkYlqDlRUn/fZJkGYiYxINALyQjz2wmBNXX1YDSc6SfvR/b/s4pIwjy7C7mMR/UXV9ayw+0buPV4WuEkjGq/YWMRMNMJ+MYmo4tXS7PjPJMU9bGT+iKcDkvQyw0gdA19Rk5l13RVoqv0Mty8PAv3su5r15m+Pwoe0c8xGwYCofwjblUt6SI/1hiSVug5sIyNCF4aegKCzMow7EwnSd7Mv3xxj6bZIEKSNtJhymZJPWQoPUbGjL9XRR3SeKVOecRoMUlZeMm3GXsf01xtSI/mRpOMltdWzqg4S/xUbW6ksd/7QHOfuWSIhRKiZUmEtWsq2TnRzbjOpKh84q0ZCWsTNt0bQcpJWe/con2+1vylHneKYzFZvli92kGojPoQrCjopkPtGxBz5lTdFOnbV+W5Oa+dGNx5bArKRwT/NgTewAYjs7w2vA1ko7Ng7UdnJroZyoZRReC3ZUtPN24gSszo0wmoghNZCr9VhdXLalodCvIBddSdcOgrF+n4SNtbNrZwdj4IMe9l5gps3Pkt2WGrDKvzpJ0bAqrC0jGlHy3pis1H3V7kp7oFH8+9CbJNAFqYVVwzE5RYPrYVt7AmVQf7qxNVadG3UUd02+w5QNLW6V9sHUb0XiSa5OjWFp2EnD9kJhNUHjNpfINiWVBvAIcr8DxqMTM/HUky2CyzEamAE/6gnKq+6WpLH5Mj4GNRDrKqtSKWdiuTaqAvMSF1CHuWhhCY3tFExOJCIdGuki5DsUePx9o2UKxZ3nB5Iq2smW97u2iJ4cUNQ/XkfQc7Wfrc3emNgRwY3Yij7ACEHcsXh++zofaFVnx2swYX+k9SygZw6Pp3FfTzpON6rP2VbdyKTSc10wKTS8eR+PS6BCO7SKEwPDqJCDvvAuh6Wo8ic8kMLx6Zp0GAk3X2PTsOqykfVvlMZ9rUPA302yeAtcBKSM4P+Wm28zNkXAs7LQHasJxSTg2p+cGiIZNSmIaY1cneOWPDvHgz+1j6NwoidkEdZtraN3bqOwPNY0nmtZT/FqCzjeyC7m6izqpZoN4qbKz0zWN97dsoWiZbWsFK/huIDaTWGTzC0op0UrYWDGLA392jOm+GQBKGovZ/6m9eXaqBRVBHvqFe5b1eRErSV9kinAqwcXpYaYSEWbeY+MJuZSM6lT0aGiuWrt5C5fuzAOREFZaXSx38opVKvUHgEiu8MgylqmJKuj+gFB1G3n58PyN1mwrDO8Tyn7pZpBKgSPcDmsuBvjoXz67SA1xIebJh6v3t7HqgVashIUncPPBbGt5A1/rOZv5PXdLZ7sOKdfBFZLpdYKZtYK68xoNV1SSe7J7Ov9yXcnA6SxpRSK59pBFtETijbnMGgm+2H0GTQi2V9y8SODMFy9w6fnr2QMn4J5PNHIhqOy/q/2FPNu8+bZjoiYEH1+zjy92n1bqX8JkR2UTCcfm+EQvHqGRciS2ZbMlXkGVKMATNNnxkS28+tIZhjdAvMiheFZwz4Mtt/ys2yFqpTgw3Jl3zJWSFwYuZ8gwAPf+1G6qVnUz8NYIbonGxQ1jJFNJpQabVHt6PQGBCWjq83LPb97abvbyC52LigRCA2GGzo3SuL1ucTFIGtP9M0uSYZJ9UWhRP2sukEorSxqKjJwnemKBsNJrrahAsyS2B6QHojUwthsqzkFoNSTKcsn52fMICcFBSfENKL0CM6sVqaZwADRb4phKYcmMponLeRBIj0AGbx2PcB21to07Fo7rZgj9mYtIk6I1FyJ1EO6AkkGNuRbQPRqaX8clrTqSctIqvAvUckTuejL/Qeku6F4NS6ZVMt30d22qZ1Y0LGi97mX9va13tW6bR3lLaUaVO9Q/Q9ehProP9xGfTS4iZQcrghQXFhKaiSDnie5S3UhyWqnUtRVV8NrwgqIoAWurat8WEQage24yjwjj2ioWF6oXtJxSY8txbYjxD7g4miQ4pdF6QicQVnsUX5FJci6rwKx7dOxEvm2t4dWp31yDbup0PNBC/+khRi9PqDWoz8C1pVJTEQLTb2DuKYFEBE0IfLpBwlGDvCNdKn0FPNm4dF98fuASE4kIluuoQhORbqsuadVKFY9LlkLvewTZnZj6mxCQKhSUn5fMtYA0BMERSbEreSFwOfPcE0060UKHwgGYSw+vHr9JcQ8UTCnF0isvdeEv8bHh6TW0FVdwdrCf869f5/Dxw5xO6bTsaWD3j91esXcFK1jBClawgv8T8H05Gz722GPcf//9HDp0iN///d/nZ3/2Z9+2p+0KVvB2MV8JDUDM4vzXruDaLtt+YOOt37gAUSvF3147wkA0BEgMTeeRujV0fBcqC6UrGb06gRW3qFlXecugzO1Qv7mG9/7Wo8ydPc413zS6qWcSMwA1gaK7Ou/W6ia6DpwnFcupjNLU5sm7zOTmQtxX086OiibG4rOUe4Nocy5fsW5kT69paB6QjsRKWLdVE3gnUN5SSrAikCcjDdC0U1VXlbWUMDeeIxucTkCKHKl2ZXViETNtvj1wkc7wBCUeP/vrVrO9IqsiUukvzMjPg/JhB1XNlTvWztxCVhRU1VrH/c1cfVk9u9bjOiVDAmuDD79rwEszmOFsoEfTBamYha8g/3urWqU8zV3bxfSbBKdSrPqqxDVUlVLVqnLKmkoAmE5GM0kOx3YzMYtEocwQevLaSg4C5YuDc32nhkiEk7i2m7bQEOiGznSbZLpJwnz82BCM7YKykInZk00AI8H2Qd+zOkmpXpwoh/7HBW1flwRHIVKtLCHmXw/5icH5nbVrpH9eWIGVfo2rq/+Gt0nKV908Yecr8qrqQNvNCAqAIi5tfE+WJNU3N8218BgFppdt5Q34jfz+X+UvpMj0MUtCeVonbBzLJTAi0dPXuTCxW3oNpjZCqpSMtUypN0DzdAEv/+NBZsciVHaUs/WDGyjKCbQvBW+Bh6f/28MMXxgjNh2nZkMVRdUFSCkZuTxO18FeVd2mCdY/uSrPb13TtTy7t/5TQ4vOL13JwFvDrHt8eQpPS8rzokgAt8L8M9J0Dd3UM0S2+b9ZCYdUNIUnYKq2m25bUpMqGbCgOLX39CB79q+nJBggHI8re6N5MsucwMbh6pMuli+FT/eS3O4y1eKw7nkd/7DLmi8JRLWJaQukgPi9fkacOfyzigjjmfe5XtZTuT2iUzGuv97NVE8Iw2sgHRfHcjNBrPb7WjKv3V3Vwu6qFlwp+ZfuM4zF82X5E7bFt/sv8v6WrQRND96gh8YddfSfzH6/noCZV2EWKPOz9ydur1wzD2/Qw+4f2QrA2a9c4sI3rqaFFgQD08PEQnGe/n8eXvK9Fb4ghtAyY9Q8Kn0FmaB3MiCJ5ZBGpatIMXPVEqPMg4i7WAmbivMuVhFMrwOpCbxRQesJA7d86fFtOfAbJj/csYsvdp0m5EZwLZeyAY2O0QIe+Nk96IZGoNTPPZ/YedNzWPGcIOgShFTHcgkNhKlZ986uaVzp8tfnDzEVj6hElKlzYqKXgOHh6aabB6GrRkz8XkG8JIeomxA0hdR8cHVmlM9eP5axIADYX7uK3VUtBA0PPs1k6Pwo23pKOVgeJ+lxQAiqfIU817L1ju+jdW9jdi2Zhgede3esIxDwc3qHy6UiB9sWOL7c5ytBCjQHqosKKfcF2VreyKuJq7gGOLgIxyVoemgIlvLpq2+ScpYgwsz/LpSixvrSWn541W6Gzo8yODuCr8NLx/0tBMuXzgQWenz87Nb9/GPncd4aG0ATAk0X2JaDpydF8ysy83HCAse3IJEiABfmWsGYk1iFInNvufNeQjgk0wQMn6aI1ZquIVEB6aVk/610v3uiYT37a1czZyUo8wYWWzl8D2Dh3Jn9w92db+FYufB4xErwD53HsNLJk5Tr8OrQNcp9BeyqbGZVcRU/3LGb14avMZ2M0lpYwdONG/jaP7+JXW9nLi1lO3iCHsZy1AWXwvonVnH6CxcwfWZWMaiuiNKGYo5+5jRHPn2K+s017P3x7fhLFhcCSFfy6h8dItQfJqdrUtQLMx0aQmO+wB0dqPUVMW2pNauVTphlc90qMTdb5VIyotpCKmbxzT95A82r44sIeo8PMnxhjPv/fVZxcudHN+M6Lj3HBpCOpGVDLY80NPP6K28RisUoCZmY984hP3Rry6cVrOC7iWB5YEn70KKaAjwBkwN/miXCAMwMhDn86ZM8+esP3fFnHR3r5ht950k6NjHbQhcCV0pklYQKCDXaTDdprHndoHVP45LEYgD7ehQZkLierHWLsMGbI4hgRkAtkO9g0EzvXYSd3oflEmLm92dCEWJsP/mL7xxoqfTHulC1pfK2RJiRWJjn+y/RF5mm0hfk0fp1rCu9tSJvha+AKn8ho7FZHCkREkw0hCHwGR5cO4mBIIUFAoa3uJQPSvxzguK6BTEXoeImbpoUNVcliZZIkJJkJIUQYHgM3hy5cVMyTDKS5MpLXYuOy69P8Z//8EmSjoP/FmqyC1HlL+RTG/YTsRKYmkHSsfidt14EULZ4aWXCw3Pd8N9CPPHr+ynaV0F/0MBKqXuK1Op8buotfqW+nKB5dzGhyURk0ZodWDTH6YaWKYo6PHoDt2cMJ2VnmqCeAv84tL4Ij/+nvbctPMm1As47PqWOlzYW5/XLeZQ2LVZSAqjrMRmtT2UUWEARUeygUuXLri2USozUFWnFCiqFFakpMo/0wsR2gWZLOr4iGd8KI/eoYELeWs6Fji+niTdAVZo80/e0wPEqhZjyC5LqE+o18/ZCmasQgplUfMnvbao3xMnPnWPs2gTeAg+F768lFVx6ry09qo4oUQb9j2rMzho0tJbRZU1mVZU00P0a630VXImPZ61wYMGaK2cQAMp6NDxRiK0RSE31QeEIfHOCbV/30LqjkXt+e8dd2S7fDKVNJez64RJKG4s5+ren8/6mGRpNO+rYO5fgpdhF7GT24gumBN2vX2fjhhbkhTnqIz76SiLoHhXbaykoY1dZMxe/fY2+k4MYHp2OB1tpv/f2alC5WKgi6KRjIPOqSKNrXEbWORkLs2i5y7WHJFu+brLxsVUU1xep+0pfuuk1qGwvY2ZwFulK/CU+9v3EjkxBhW7qPPor9zN0fpTQQJjSxmJ0U2fowijeoIe2e5u44kxwvHsQUKRDQ9OxXYct5Q382Oo9i4oA53FheoiYncpbYyLIFBlm7tGr7k9KNXcgxPxWhrkWiNXlEHkSggndSleNqeOu7WKbUDIkqDsP7pYA4/U2kzVJpn4QynuVbe7Zr1ymdGclfz90gtGpGdxqF/FeaDlh4B7uR9M19t1BPGMFK1jBClawgu9XfF+SYQAef/xxDh06xPDwMAcPHuTBB5eWHl3BCv41MDcRXZS8ALj+Wvcdk2FeGrqcJsIACBzp8qWeM6wpqV6UjH43EQvFefWP3mRmSG32DZ/B/f9+Nw1ba2/zzpujqKaQD+3fx59eeoOonWX9dxRVsvoOVWHmoRsa655cxbmvXM47vubhNjyBu69u8BsmLYXpyppyKK4vJDyUn0wobSrGX+TDsV0GTg8xMzRLWUspDVtq7koR6FbQdI0Hf3YvB//XMSJpQkzVmnLWPtqB67hsfnY9I5fGSUUtlRQyNISer7JSVFOAv8LHH114lVBaInU0bvH5G6fwaDobc2wCfmzVHr7c8xZXZ8bwagYCgVfPn1KWI/2+/UObkBJuHOrFStisMiu4Z/9ONAFf+sbzOOkgpeE1MLw6qaiVl6iu3VBFy15F1Gne3UD34X58hV7slI10QQ9qPPAzezKfV+kvwKvpJF0nL/kQnBYZZZuKtjKm+2ZyqpGhoCJA6xK2QqloilQshZNDUNAMncmCON5Cb4YAIYQgMZsg3CCpHTVVQlhKDJ+O/lQ5ekkIPUr6PCpRN71eEByTRBe6M4j5/+WrVyDJEGGEyItbZZKXAnC9govTw2wpX9qPvbCqgOY9DfQeG8BOOggpEbpG8/vbiOk2Hjy8MHA5r2ro1aGrfHL9Axn7GVAKQj/Qto1/7DyBBXiCHoK6h/U3fAxsnSJRICmcEpiXUplYtOEINrxiEtqsU/lkLbXBEtYlyjj4e0cyhI2+E4OMX5/kfb/z2G0JeJquLRqThBDs+/gONjy9hvDQLKVNxRRU3Frd5WaBKsOz/GVU1eoKAqU+YiFVeeU6rrLe8eoc/bvTbHrP2iVVZkobiymuKyQ8PIeZHrPslIOmCzxBT5oko6WrYLMRmdIbgql1+Ulib0QQnBLMDszydOsG/v7skWxS1YWaIy4jTS4prxoX7DQBI14iCXVA0QUdj89EJLINr/6aSbO3kunembzrbt3T+LYTfOGROV74rdcz/d5O2iq5XeRFCEFpUzHt9y8OxGlCZBKaQMb2wpEuJyZ6uRQa4QMtW9hV1cK+j29HNzT60oSY1nua2PHRzUz3hBCaoHpt5V3bnXQd7M38nAxI+rfZnKob5fTxb/NQ81ruq8m3CfAbHh6qW83LQ1lLAg3BU40bKHaVepeeW5UsVAWcYzlKstxV448WVBV0zacsGi+BG9TwRQS6rlGz/s5IJr3HB7jychfJ2ST1W2rZ8oF1/PqOpxiKhiFkE1ilU/4Tpcue00y/SdPOevpODOYptGlpv3ehCYpqbk10uxscO3mV8XA4MzA6KQfDMTgz2X9LMkzd2irW/U0PEx0ukXIX/6yg6rpOy4+oceWFgct5RBiAQ6NdPFDbgU83Ofhnx+hPV1R3CIm7zs+9H9vBqqrquyLqt93bzOxYhCsvdmInHYLlfnb/6DYCJX66Zyc5MNKJ60iEK9GT4HjI9H/NhoCl85GdiqzUVljOC65DwrGycX0p+fWT3yDuWIo0skSeULjgcTVMj87VmTG2VTRSv7mG+s3Ls8u0XIcroVEMU8dNS5E7lkOqAawAmDE1bM2sTicdFwzzwlH35ZQunACz+nQaivjjuC5JTaKbGq4JToB0gFndmxACQ9fwGSZxO0vU8uoGXv2db4fvFFrvaeLGm315x4QmaN6z9Lx+OzQES29yvASAC9PDGSKM7TokbAtHSv7h/BH6Z/p56pGdbC6vZ3OOvcV45yTu5SjkOl5IpWR4s8+bx/qnVuMp8NB1oAfHcmne3UB4ZI7unHsePDvCob88zuO/tniPf/2NboYvjS9qvi3fgq4PQbROtRgjCR2HDZo1jYIfX0NvbIqzU4N5fVqC6kMxh2TUxQ1A935JpB4EDsFpjfbDBr3HBtj4zJqMbYvhNbjnEzvZ82PbcB1l8fTVX/kOWsyiHB1wufxCJ8GK4LItY1awgn9t6IbGzo9s5vCnT2XJ2YbGjo9uJhVNMXZ1sYXRRNc0sZn4TckqS2E6GeVrvecUac5VeyF7vnBjnuhvCubqoPwHGtn3xNJJvbmJKIVvJJDvExkLR4l6f6QBCtPc59IuZeWikv/zM8ftiTHChuAYJEvSNkva4rfZN7vteVUMF2wvaB7Ba7unmL5xiudatuZZz80jaqX4q8uHiKUT8wPRGf7++jE+uf4BmguXLmwIp+JIKdkaqOflyVnmV2dCupjoVNcVMhx3QQfNdDJk63CtS5HtZePT+VaVQghWPdjK5ReUAoplyoyahEjfl520mZq+OckxMhnL7OMWHpcOd0SEyUWBqciQo7FZXKRSubSy6/9EgWRuPMrVl7ro3mEhNRWzmkfUTvHW1AD31dydcm+VvxBT0/P2HAD16bnzppAys42X6bWgrgt8BSZ166tv+7nVayqZ7JpefDxt+7TpvWsZPDeSp6LRtKMuU0C0EJt2tDPz9XNMbBMkSiEwDt5pyeg9Ai29NBKoQgvNUioW8wQXqak1lWsq6yRQMYyqM5KyKzCxQ73Z1VD9xU3bE+U0h2QxDO4XzDdWqcPkVoF/UhIchLncrZ5QhVjdsxOLnnMqluLlPzhIdDKOk7IZqU0yEo3geLUl++o8pAHYkplim2qvjp3Mb6sSaK+spufGFAnh5Fs/uYoYlFHslZLSPsGqNw1u7LOVVVL6voQjaX1VjSOrH257R4kwuWi/v5nZ0TmuvtxFKm4jNKheVcnceIQnVq3j0ovXGapJ4BiS0iGN5lMGjuXw0u+8QSyUoAzwlIPdbrD/uW1sqG7g6GdO563Bxq9PkYqmll0UBNBcUEZtoIiRmBor5vdBVZ2KQaIUlUEzBIbHi51ysA0Xe72Xqd4ZimoLefw/PkjPkX6klDTvrKfn+CChNBnG9BuLFMOEJmjYWpsXD6rdkI0rb3EbODjSlbGA14Sg1BtQyqE3IcK4Uqq9Uy4xKndLkkO4l5npJZ+F7y4x5IXboDCygAiTUDbrIqBT3KdxoSRGQlfjhytgotVBT0nqjzn8w4FDjFUlFUFPU3GBvp02pYMaPUf72fOxbe94DHoFK1jBClawgn9r+L4lw9TVZbOHXV1dK2SYFXxXYceXrsS2EjbSvbOKwKuh0cXncV26ZifYVHZrj+V3Eqf++XyGCAPKjufwp0/ywT9+5m0poZT5gvzipoc5Nt7LdDJKW2EF2ysa71rdyXVc6jcr8snwhVFcR9K6r5HVD7Xd9TUuhX0/sYPX//gIidkkqbiF67joIxpnvniB0WsTTN3IlqPVbqjioV+8964TrDdDeWspWz+4gbe+fInwyBxD58YYvfIKBeUBdv3wVt77249x41Af8XCCkoYiLn37OpEJpRbjK/Zyz0/u5Gp4LEOEycWRse48MkyRx8/H19xDyrFxpcs/dZ3Mk9i/p7qN9qLbJ111U2fHhzcRnYwxeHaEye5pvvXrr7D1A+szSrRIFdzSPRq+Yi/3//Ru5sYjlDQWU7+pJtN/GrbUsvWDG7j4rasITeAJmGz70EbKW0txbBfpSnwekycbN/D1vvMZ9SGZdGk8q6ZDzdDY9sENmH6TS89fIzwyR2V7ORvfu2ZJaVjN1DJEmLkGGN0lSJS5eHwppKNj5Gw4PQEPPgSGRyWwG7bWsudj2zgS7ePC4AzeAg/SVfdqxS1EgYamuXhnlW92XuBlIXIrlEQmRplFTtG8qekMRcM3JcMAPPDJPZTUFdF3YpBYiaTnfpdO3wivnB2hpaCM3kg2ACelJJSM8bXec/y7tfdmjl/pGuDE61douWrhNntp29XAzq1r+BvzTcajOlLCjC6o3BOg/J8jCCHQTR3hCJ5s38DGNcoL/PDfnFoUQI3PJOg9Pvi2+nFRdcFt1WXm0fFgCwNn8n3tPUGT5t3LH3N1Q2P/z9/Dm399gpmhOZKRFLqpYScdug70MnRulPf+1qMZS7Z5CCF48Of28eZfnmC6bwbN1DAAM2hmUr+m31TtJuYSL4dUuUZNr47tdwi3qPP4w4L2IwZIGLk0zvhXJ1kdkoRXabgpl+KrLt4wzKzKJoqtuJVpN7EClyKXRfNVZXsZm59dx+FPn2L8+iRCEzTtrGPXD29Z9rO5GS59+xqpqJo/nZSjAj+uUj5p3t3A/k/tJTIR5cK3rjEzGKasqYRN71tLUU0hm8rqOTetsh5J18ZJV27OWyZ9pfcsa0qqKQr4ue+nd7PvE27mewKWndhfCnHb4mJomMH6BIVJiRmHa/stEkWqo4btBN/oO4+haeytas1772MN62gIlnJheghT19lV2awSx2Ww+f3ruPT8dcoGbKabVdBPM3RMn0npBRfNUd+NJ2DywM/s4fQXLzIzEIaI+t52fHTLHSWnuo/2c/ivTmZ+v/pyF1O9IZ78z/tVEubO3csA2Pvj2wDoOdpPMpJCM7QMOXX1w223tHG7W3S+0QOb84/ZSWUpJqXk0OgNjo13k3Rs1pfW8nTjRvyGIu60Hq/HODPMfLlf3ebqDAlzKYULR0om4hFm+8MZIgyAJgXa5QSxw1OI5+6+fW19bgMbnl5DYjZBsDyQCWxemB4ikUph6xLhKus8EYfAEFRekJi6wK2Bf5p8g9baSvoqonh0g6RjZ+S7k66d5dTlziPzhEpXEQiEof5Q5Lnzamoplfx4zE5lE0gGCB8kisBIAC5YRUJVU6arkHOvw9VVMkUmVSUygCm0dPJUgiURKdAFWD5JPJbE8ZFJtCAFQgOfMBAOSCSrl7Fm+V5B7foqdv3IFs599QqpaAp/iY8dH95EacPSVd+3Q3NhGVvLGzg7NZg5VmT6eKhOJUXn1+Bu+nubL4S3NckbwQEifzrDR3/5yTySeWw6TsGURnmfxlRzdg73xAX76/KTrUuh4/4WOu5vUZ+TtPnfP/U1pCPzVCPHrk4SmYguIpL2nxpCNzSsBYkvzYV1XwbHD5Sa+OcEQgomisP4+st5Zvsm+uZCTCejzGsUCXXjlFyQuBb07hFE6jJ/IVrmcuNemw0vmYSHZzNkmHnopo5uqvF0KeXBnqP9K2SYFXxPo3VfE2UtpfQeH0DTNVr2NFBYVYAVtzJ2t7kQmrit2slCXJsZyyNlLoIAw2/g0Q2CjaU3PX9iJoFmqb4uLTJqYgBju5RCQ/llCA5DYZ8k3L5EccHNINRerGBQ0vEvMLIHxu4R+YoXqNMtSpCmldmQEmmAkALdhng0yWm3D11o/GDb9kUfeXZqIEOEmYeL5Nh4N82FZUgpufJiJ9de6ybqJhl4WGO61EJoguIZneIhQbhOXYyZELQd0xDvkZAeqr1BD3bKwbVd2tfV8dgntuZZXM1j6w9sRGiC6693E+hPZdRxcpcJRQM3f4jFdUV4gmZmTT+P8rbSdyQeUhMowqcbzCXyz184oc492R0iutmjrCJtF6GJzNopYqUWnW+58BsmTzSs41v9FzPHDKHxdOPNSdZbyut5fuASSS2l4oDp45V9OpWrypdVrLXh6dUMXxjNs0PakEPGLKwu4D3/76N0HeglOh2jZl0VzbuW3rNGp2P0nRgkOKPhfznbl60AjO6FeT6ARLV/LQW2qZROyI3P5fzoprunJ6oU2WZb0yow6dNXXMjet+OBkX3qX83JF2ua6RAU9knmmrWcwh6BTzc5Nz3E/bX5RIz+U0PEZxI4KZvZRuh7VBFsRFKi+QWuyAmU5BIWNEXWkQZ0hyfx6AaOdJVNktDw6iYxJ0VFr85ovSLDOKbiNxgJ9Sy09HhTfUmj5rBLpM0l1OhiJNPPLq0CNFsraRcF1Kx999acQgi2f2gTgXI/J/7+LEITjF2Z4KUrE+z+sa1sniil+vBkWplIfRO2ZROZjGX6RcGUBlMWbkuEG9vGuHaqFxKKvC4E6B6DS89fvyMyjBCCT6y5h2/2XeDKzCjFQR/BswnKO1XDkJlCDwNN1xC2g0y6RGcSjA0p8uXG96xh74+rsfLsly/R/WafIosYGnNjUd74n0d57o+eWvYcZGo6n1z/AIfHbjAQmabCV8h9Ne2UeG/udZdybBWDkZDHernJEChsaL3hZWCtpeZLmd6O2PkEGRDU15fTz5yKf+bMraUDGtFyqdSsJXkFUBNNDlWvSwaLHCzI2IFqrlKdilRIPBOw1NS6ghWsYAUrWMH/afi+JcNEIpHMzxMTiytWVrCCf02UNBRTWBXMt6kBGrbV3nHlfIHpJbSE/UzBXcq73i2GLywm5aSiFpM3pqhZd3cqLqCqeIeiYeoCxeyvXbVkldRyMd45yZt/eYLolHpeDVtrWftoO3bKITGXwPCYmL6lzz/RNcWlI930+sKUtJRw3+a1eaoXC1HZXs4H/uhJvvofXlAexYaJY7mc+9plpEve54xcGqf/1BCtexcrjdwtUrEUh/7iBN2H+1SxkVRBBjsl0DTBob88wft+93E2vXdt5j0d97cwdm0S13apWadkmnvGe5c8f9xZmtA1//38u7X30j07yURijqaCMmoDy0/IXHmpi8GzI1gJCzvpgJQc+cxp1TfmLV8cl1TEYt0TzYxdm2To/CjeQg9WzKJ1X1aSedN717LmkXaiUzEKq1Ry5OjfnabnSD+uIxUB5ce3URAQHL5yHRnzUB/y4ymAgpYA6x5fRWVHOQOREFcftBiKJqgJzFGlx2hhcYJ2bjSK6TeZrLLpe4LMJjjh2tiWjSepKVUbTSPo87KvtYmx8VECJT5WP9RGnwzTH5kmYVuYmo6uqdcLTVAxCmgpivvAF4JECVl7JMiPQKZ/F056L+6q+JQwNBzppkUkBB5dx6Mb1N7GdszwGmz/0Ca2/eBG/uj8K8wmsnNq5+wEtuvgNzxY6QpxieTMZD+fuQofbd/F56+f5K2hfmiU0AD1l2xG//oGr3wqxYQdQeha5tInqiy2/bs12Edn0AyN9vtaaL8vWwKWnEuyFBI3Of5uoGFLLff8u52c/8YVIhNRqlZXsPOjm+/YGq68tZRnf/cJjv7taToP9OSR/OIzCW682cf6JxcnCYtrC3nmNx4hMhFlbjLKK79/KO/vQgjWPrWK78SvM900HzhxCE5A4YTATAgaLur4wgLdo9N1sBfXcfHMuVSOqYhM2tKcwLhktm1x+VzhtIZmuBkPdlAWQpufXUdBZZAn/tODxGcSaIaWkSe+U9hJmxuH+5junaGkoYix4Wkm2hxsIQlesTDTsUtNF4xcGKP3xCCn/zlrgzczOMvQhVHe+1uPsbm8nv3R1bw52oXtuhn1qvkqL0dKzk8N0VZUSXWg8B0jJw5FZ/j0lTeJORapnTbuRofay3qGCCN0ZQsDcGysZxEZBmBdaQ1tnjJCA2GKrKwFyJb3r2fd4x1Mj89xxO7n3MwQmhDsbO1gR0MtIzWjeAImrfc0ESjxU7uhmpFLY8RmEtRuqCJYdvPA3lK4kq4CzsVE5xST3dMEmwpwpLyrtYcn4OGBn9nDvk/sYKonRM/Rfqy4TdOOOpp3352yxe2gXUvgbRckgzntWsI6o4oDI508P3Apc/j4eC9TiSg/te4+NF1j/8/vY/TKBNP9M5Q2FlOzrjLTd+uDJRnLQFBzr+YKSlwvAzeyxIJcTN5YXM17pzB9BmbOmqQ/Ms1rw9dISkcluUyBa0uMJPhDUDgI3e+XxCstBBYDdh9OGAKmN8/HPq/KETKBXuEo6t28pL3u0fBoOnuWaL+3g0c3CBrefCtFoSqNb3xQ2VdUn5IEhiUzqzX0lJLhz32tZqetBex0QkITIAVSukqKfr6SWYKZADMOCY/AMDQMqWEJF8t1SFo2Rgpqrun0HrtG6/9V8Y4Tld8trH20g1UPthIPJwiU+t92tedH2neytbyBrtkJSj0BdlY2ZRQnN5XW8+2+C8xaVl770NLzRl9ZjJ5j/ax5OEvqqFpTgWZotB01KOtzmSm3McLQGPIyWzNN6c7ljUdj1yY48GfHScwmIa1U5w16MvsndwnLKMNjZKwFnRyVAKELEAKPrWHMakgkN/ZaTLW4XHVu8MbFfmr9RSScFEnHURL+DlSdkBhxcHSYbVG2lPNVvZoDkTKXRIHMs1dciJuS+t+egNoKVvCvguLaQra8fz2g1L1mU3GK/H5a9ihVzlw0727AG7yzdWAgR93W0DScJaxFjbSa6a1UN8paSkjV69l5wZWQQ6ic3CIIrYe2r0vqDymVKNcgq+oA4KT3U6ZgoY2S9MDYNkFoLThGTtX/fGZdArpKfgoXxcgEfJOwbbgEb2OQN0qGskoZlkPScXlrcoAPtm5dZMs3v/+WrsROqQSs7tGJpZXMLn/nOme+qIgY1x60CGsuWlTDW+hl3IgTNGDLNzzYXklgRpH/qqYKGaidUwpYQmB4DSqLC3h2/z2MX53kyktdyob0/pZM0YBuaGz/0CbmJqIkjvTT+LrD4H41DkohKJzUWD148ziJ4dHZ+UNbOPqZ0xmFIcNnsPMj+Sxly3W4HBoh4VisK6mhyLM8crRXN3hv82a+cO1kxqFYtwRNb6kvvqS+iNSETSKSzMQWNFPDE/SwtuT2Siy3wgO1q2guKOdiaAiPZrC9ooly380VR+N9UVYfMLjQKEkUSYyUoO6KQf1sgD2/vG159xv08PT/o6yAI5NRqtdWLiLDBkrUHu12OP+1K0SnYngLPMTDicwcb8ag7ohk+B5ljWT7VHewg+pnzQLdkgipbHNFjjhOcdY9nMZXJaO7BeF2tXYruyIp7IbR3ZAqFMy2KktM16O4MnpKvQ5AdyEQ1RT52QRNE5iajim0JS0s7ZSDY7nEKqD7vSLbrw0wXHX9TvpahUxbTKfhpNeZCWkhXfDqJl5dKaakIikGjvbSdB6KqnQiNarvj6zJqggr4iyUXpXohsZ0eZpSmybeuB41Jo1vg+P908jfeZmq8mLWPb6K8tZbq+UBJGaTnPr8eQbfGsb0m6x5pJ0NT6++6drCdVwufvPaoljzua9cZs/HtjHeOZW3nvMV+fJUkQEmmx3+yXMR2a2RfF+KskuSusNqz+DaKebGZF5cYDko8vj54VVZS8lIc5TzwStMdE3Rbku6CqLqfFJiJxyMlKB4NHsPV1/uYuN712J6DXqODyx+TuEko1cm7qioxW+YPFq/9vYvTMNnmJTGPaScVMamXDgyR+0l/5m3H9R57gN76Buf5K3uXhIem/AqQdhvIZOuao9CFTc11lXSPzbHrJVE00CXUHNFZ6LdwTEkLvnTkkzPJclSDXvBltzVQBjgjULTzvp/M/ubFaxgBStYwQreTXzfkmHeeOONzM8lJSXftetYwQpAVUjd/8k9vPGnR4lNq+B/eWspu3906x2f676aDv75xqm8Yw3BEloKyt+JS102fEU+rHhk8fHCuyfljMVn+ZurRwinEyQB3eRja/bSWqgkXV3pcmSsR1XMazp7qlpuqobjWA4H/vQYidlkRm2j62APNw73IjQNx3Lw+E0attWy7+M7CJaroHwqluLAnx3jUs8g3U+C6wUxPc7B4z380Prd7KxsJmIleG34Or1zU5R7g+yvW019sITQQJjkbCqvEsG1XVxbLiLdTHRNvaNkmNf+x2EGz40iXZlh/Uv10LAtFwNB/8lBNjy9JvMeTdeoXZ9PXFpbUo0uBM6C0oH1JbffULYVVdBWtLT87q0wdG4EO2nnbcDVfUgMj56pitBNjcmeEHOjqt1FJqK8+VcnkRLa7skSYjwBE0+ajHP0s6c5d74bzQ/+WY2BM8OEh2eZHYtQmL7FORK07G3k/n+vNuahZIy/vnKIZLpi/cbsJH995U1+cdPDVPrzpRCcKp2rz7rMVgpcQwU+9WTaF1pCSrhYVgqPYdDe5afrFeWXPjca4S19lPG9OrpHRxOCmJ3CZ5h4NYN99R089fNrOfG5c3Qf6mP9q4L+7TZTLVL5dFsqoKI23Sr4qidVdY+tpRNUEvymgcdjYrtOunhL0FRQyuZlqkiNxmeZSOT3cw2B5br4pMwQYdSZBeemhrgUGiFhWQikqnoRMLTRoWRIo394BBZy5QSw1s+TjywdAKzbVMPQucXku7ej3HE3aL+vOY+k83Zgp5wlg1fzxL2boaAySEFlkE3vW8uFb17NBLGq11bAIyVEunSIuSqA5IOZJoHHUQmEa+2SXW8VET0ZUmSMdEWfdKUyTE+j4qpgdp0gVpwdA4pHNUoGBWNbNaL7PCSkTaso5ZltuykoygZ8/SW5Ges7fyYv/u6BjN1SpNzl2n4b20y3r+2CppclRT1p2WBpc+HrVxZV2ifnUtw41MvG96zl6aYNPFi7ir++eojhaDjzzGVa3virvefQNY0i08eH2raz+i4C4v1nhrn2SpcKsO8q5Hj1OLOpOMJKB6sMjZF1DlKoIK4n4MlUUiZuQjK8/GInZ798CSflkCqA4CMVbNm/mjUl1XgCHmpaynmOcp4jv8/UtOePv0IT1G16Gwo34cSiY7Yp+dLYOXonZpAoG8MPt++geJkJi1yYXoOatZXvanXkPCpby1h9YJLuvQ7RMkWYqOo3eeb+zfyP668ven3X7ARjsVmq08TBmnWV1KxbfJ1PN27kM9cOY7lKytpK2DSc1Xn+716hbmN+e7K8EssnKax7Z+x3IlaCyUSUan8RX7hxmqSTH8SWhkCmoPIazK4RxCsX8CelsrwRiMw4ngs9OV/5LQhOQbJIEU8wBfXFpXykfSdFrueOA+EAEXtx20Ko5KPjh5EHBC3fAT0mSeQsbz2zUNQH4fb5twhMC6RHwzPlYuhgeUG4EikUgScYEhSMCsYDEhIOdpr5ZwgoHhU0HAJjyqbL7Cf+Vy5P/vS9mMa/jS2ybuq3tflbLjQhWF9ay/rSxXanQdPDj6/Zx2euHiFhq3am29mElatDdDJfVTBQ4mfHRzZz6n+fI3A9hc92EbqGDNoc/LPj3P9JScsS9pO5cGyXQ39xguRcEj2txCcdFyth4Ql4KG8tXVLhrePBFgbPjuAJmiTnXFxX2Wv6Cr0YPoNUNIWUksk2l/EOFwTo6aX7SHyW/XWrOXP0OlZcUnxdUnxFJRw0Q0Oaafux+Ws0VFX5mvtbllRUmEfDlpollRHa73ln1hUrWMG7DSklLw5e5vDoDZKuQ5WvkGef24TpM7lxuA+kVFaTH958+5MtwIbSWsq8AaaTMTyage26aTK/QKLIDprQWF9SQ8ctVLwiMsXAQwJpp4kqOhmeinDSQjEmjO8QNL8oqTwtGblvfk2cPomm9lk15zTG1rlpYk3mKeD6IZHH5VugjiHV3KknFClGCtA1wbFtYRwxM38aXF2No7olsZL2kg4u60tqeP7GhfS4kX5FXFBnqjXXtde6AbU2C9eq/bJrp4nrhsZclYVwIRjSSMUtnJTD6L/0seOBckJ7TeZkkraiCh6pW8vFb1zl/NeuZD77youdPPzL92XWaFO9IfpPDqGZGiU9gsIBSbQWPJagNG6y9uO3Jse239tMZXuZUu7y6LTsbszbN0wmIvzVlUOEU2p9oAvBh9p2sK1iefGSXZXNtARK+dIXDhIbjlHer2GklLVq271NXPud1ynfIZhqVs/RTbmsS5Zkba/fBpoLy25qW5ULK27x2h8fRotabLnoJeVxIeay7qE2dv/SNgyPzmQiwumJfhKOxcayupsq7S5lBXw3GO+cBBRh0/QaynY3jfKLUNQj6XlaEK/K9iE9lVZysdNrRU2kyTCSwl6oOZ5tzZqtSDV1R9TvkTro+nCaYOMlE9eYh+NJk8mAksuSwJjE2Ctw0uRsC4ljuexoXdwuGrfVoWmCsZ0LFZskNkp5MBABK5hWa9GzaiQZCDXqJB0bw1XFfp6ooOyKxE25+DptSka9SFeiD0tG9wjsgCJy1x6TeCcklRursIgxiFoXOd75MUQpN0aq4LQ7ydoDs/SdHOLxX3uAyo5bt8PX/+eRjDWWFbd560uKBLfxmTVLvj4ZSRGfWbzWTkZSlLeUsv/n9nH2K5dIzCZp3l1PUU0hJ//pXOZ1iaCke6+NaZoovrlkaiP4p6As7earCjzeHsGioCLIPZ9Q9q2O6/K1vnOcmujDdiW+WWg7ZmSUTwHspCInmV4DTRc4tptRe9I9GoK3f03LQel3Ekw+mCWF4yrFsWi9yJKsXCgbFFQMGxz9u9NYMZv4BpuhVQ7SVutHfODTlOpZpb+Qw2NqTC8wvUjpEvekGNngINJcS9eT7i9S4HhA6hLhgasfTpM4F2xf9BSsaqu/q7zDClawghWsYAXfj/i3Eem7Q7zyyit87Wtfy/y+YcPNZSpXsIJ/LZS3lvKBP3ySqe4QukenrLnkrs4zHxA4NNpFxEqytqSGJxrW37WN0N1i3RMdnPiHs3nHajdWUXIHEu2O7XLlxU56jw+ge3QuPZAk7M0mg2OOxRdunOY/bHkcTQi+0nOWExN9mb9fD4/zXOvWJSvrx69PkZhNqmqOmJWpQlI7aRchIBWzGL4wxut/coT3/OajALzxP4/Rd2KA4felq8SkqniyEjZf7znPupIa/uLyoQxBYDA6w+WZEX52/X5EanEVm9C09PY7H0U1d+kvsQQmu6eZ6JpW0ssL/yiVhZVh6stqI0UePz/Qup2v9p5Ne7bDupKaZUna3y3MgImdcpb8m2boGfUN13YJD82i6RquK9MWF5Lj//AWdRur8RXlE7GGIzN8pbCTxBPqewlOaaw6ZDDeOZVXVQzQe2yALR9YT1F1Aacm+jJEmHnY0uXERC/PNG3KO360aox4TgBVahLHi1LaSFceGUkwgxqXy0Js9XgwUwLblAyvd5BJie5Rai2mplNgevlk631MnR7nK7/7HZJzKcygiZGSrD5koiUKeWvjHHGRrkTJ0d11vaqKy7TUISlUBf6PduxOk1rmaCwoY2dFE7q2vCDBvIqGm5YL0ISGrml4dQNbupkEqky/xpY2GZttr7r3eSWBmTqXYFQnzOJ+UuW7eX9Ytb+VsasT9J9SljdCE2x+dt0tK7C/11G7voreY4urmXI9rG+Frc9toP3eZsauT1JYFaR6TSX/2HkckU7+ukY2COhIiSkEKcfmcmCa+qSzUPQlDyIhWfeizkQtpCoEhSGN0kGNsdUuA9sdPLoaT7uMEJ/rP8XPbdx/5w9gCZw9dp3u5DRBr8BMCvp2OEqGOy0QJTUYul/JZTspBweHqd4Qpt9cFPSKhbLBv6Dp4fGGdfz99eOASuZE7RSOdLFcF1PTkFLyj53H+U/bnsJv3F6efB49xwZ48y9PqJ932Yx7JrASqOerKUUKIUHzKWUoqee/f2Np3aJzTvWGOP3P5wEYXeXQv90GMcTJixNU+wrZdMzP7JWZDCmqace7Z49Yv7mGroO9ecf697hERChj0dU1O8HnOk/wMxsefNeu453Ajo9sJvRHb7LhJRvLJ9Ec2PvhbcQn44Sm57Bw0A0dw6tnyEox5/bS+YWml42ldVyfGCMylsC0JZNtDokiSfziMGUVASJTMXp3Oky2OUhdMFYyTMlMHWveRjXy8/2XODTaiSOVhPs8iXi+v8yjYFpQbJmEqm1Eztg7L+nu6BJT10mliTTzwVbdVhW5pEBPCdxKHRPwmRqaoTM2N8vRPzhGvC+Kv8TH5vevY/X+5dvWKbUmhdzhSDd0AqUekpEkY1sdbH860ZJWPMMU1AwaxDskRomZyV9aUyk2vGQiNbh2v0W8WIKUeCMaDS+52JpkbLVSBRHziVFXUn0UjEmXse0wsVVw0TPE0YPf4KNb9/yrWo/+W0B7USU/s+EB/vvpl7ESdl5lanm/RvU9ixN2ax9tJ1jm55U/OqRk7w0tsxa9/ELnbckwUz3TmWSO6TeRElxLVX5XdJRx30/vXvJ9jdvq2Pvx7Yo0CvhL/JQ0FBKbihMemcOK21h+SfdeoQheAuLYpFIOPsMkGk7Q9oIA1PrUwlHtNSURrrr+3CW1Zmise9/Nq4pHr0xw43AfZc0lhIfniM8kMP0Gax/rYNVDd66utIIVfDdwfKKX14avZ34fT8zxjz0n+I8fffJtJ9oMTeen193PCwOXuDE7SXNhGWtLqikwvMykEiQci9bCcjaV1d1yP3tyog/blAR1DzHbUrNe2lJCz5nSE+kthDecttxLW9HKtDBiYAzMWansloTAyfU9ErdYRKdfAirRr1kS3RXMVcqchPu8RYvAMRRZoN0tzey5clFlFlL5usXwDlUMgYTSa5KJi93IT28hFVU3NV+EkVGqkRLDq+OmVBLVils4SRvdoyM0QeJwiMaxMp76dRX/SMwmufita3mf7Vgub/3LRZ769YcACI/MpW9P4C3wkIpZFPW7aKZgy4+sZ9WDtx/LimoK2fgeNVamYimuvnKD6FSU6jWVvOzvyRBhQO1fvtp7lvWltXiXqRRcGSzip374SboO9TFeNklRTQGrH25j9MoEMiVpP2pSe9klUSgJTmtUljrw9LJO/Y5g8NxoHiHSk9LA0AiPzGF4dLpnJzMEa4DDY908Vr+Wxxpur/BytyioDDI7ouJahs8AocgW8zCjSjVFywmNaLaKcVRd0WhMFPDBn3iAawfewJyeJDrq5fJNtppCwODDArtAEdCkliYmmaAllPKS1BSRrPaopDAtsNjybcnQA4JEBWhJScUpm3IPsGAZHSwPsOMjmzjvnEWzyVHqAJBIAQ2vSmZWCWZWgYgqUpoTgPkOKlFFTbqEQEhQ3qNTeV1DOIqA7AGKagrQTR33xjQVAy5JHPSYuj/DZ/LQL9yL47j8j2MvMeVL5BFz5ok/s7US25QYlsul56+z/+f33fQ7mu6fyRBhcnH99e6bkmF8hV6C5f5FhTa+YhUvO/vlSxnL++7D/ez7xE7KW0uZ6lFql6EGF2Fo6F4d6cjMBmOmTVB2VYIm0I07s8NbCFdKeuYmsVyH9qJKTE3n4eIOZkfnuEGIlE8nXOsSnBYZO6fCqiCBMkUG1E2dVCSrFGwnNMpaSlSR0LuIqd4Q/m6bNcOSmVVq3C3uVnPJyEOC6WaJ0ARlQxqtJwyklMyNRTHKPIysVx1JSmU5pvt0dF3n8Yb1HB3vZs5KoCHwpNVsXU+6uCHdo3Q0tKSL7ZlXPBNIXWL7yShnIhRRydB1tjTU8dBT97yrz2MFK1jBClawgn9L+L4iwwwNDfHpT3+aP/zDP8wcq6qq4t577/0uXtUKVpCFpmtUrnr71SfbKhqXXSXzdnFsrIfXR64TTsXpKKrkfc2bqfIXsubhdnRD59qrN0jFLRq317FlGVKsuTj+92e4cUiRWxxd0rczhSlNtRFPYzoZYzIRwacbnJroX3SO14auLUmGMTw6UkqlGnAzg9S0b3SoP8x0/wwCGL0yjutKYtU5gbZ0JUrcTnFkrHuRUoblurw52sUH12zDV+wlEc5uygyvjuHL3yj6ir3EpmNce+0GrXsb79hqZSESs+rzlBz8YqKB66iq1+Y9y7Of2FHZxIbSWvoj0xR7/VT7b22p83bQe3yAsSsTWb95oUIRQiwOogTKfCRmlb92MpJU0tdAZDzCC7/9Bs/8xiN5Cjz/+8YpEgVu5kTRcpfeXTaN35r3gcgPpkbGIxRVFxC1l06ARhd4ik8loowkZvEWepHJFCnUc1YbU3X+jKe0K5EazNa4lPfrpAJS+WnnyPu7lsvk3Cxf+5PnkWG1UY42wNA+m1iNQPNpePU56r3F9I5P4Oppb20HHFN9lu1VxzQHNFPHEZLvDF7ilzc/uuzvZB7TiShRW8lJR6z5Kj0Nv2HyUN1qpJS8OnwtnRSXGfIUZB+tY4KW7g5mQnBfdTvfMrqJ5TzjxmAJHZ4yrrzUSWI2Sd2maqrXZJNquqHx4Kf2EhoIMzs2R0VrWUbJ6XZwpUtneIKkY7OquOqOiA7vJlrvaaL/9FCe4k3rPU3Ub1meiofruHQf7efGm31IV1J9Tw3hNTFSjp0hQmUgwUra4EqSxUt0rDRMv5GxKTOFwTP3b+XGm72Eh1Twe3yNixCCVCT73XWnxhmMhmgI3j0xyZEuX7hxmpNOD/aDNsKF+vO6Uu9AjWtCU4REOyBJlIB/GjVWaILkXAqhC4SmVG6kKxm7PkFoMJyRDN9QWsePdOzm4GgnfXPTuNLNhDwt11GBTyG4Fh5ja/nybXouPa8SB/Eil4l2lXAQbvb5OyYYKZBJl2dbNvJy6HpmfFlTXL1kcHvgzDAAKZ9kYLudTapYDgPxaZJotMQMpvtmOPBnx3jsPzywpGLJO4GtH9zAVE+I0EAYAOERRNYsJlb2RqaZTkQpu4Us/HcblR3lPPt7j9NzdAA7adO0vQ475fCd33qd4C6H6SYX13JxbAdvgZdC00tT8NZVvqOxWf7X5QMkHZtkMkWiWlV8GklJotAhXOPy3skyUs1FTIoBpKva6UwyliZfPZlnDXErdIUnODHRS8p1KPH4OZKuHJRSkkiksIWaQ4XIIcRICER1dv/IVmJXrzDBXN45Damxr6aNhGMzFp9VKjEJydhMWNk3AN45FYBNJmxlvZgA3aOsZwaETQU68ZkExz/7FgUVwUVqODfD+tIazkwOpImWWXh0HddxcVIOyaL0eiBnapECIhWSp6KtDLe5jMTCVBuFhL89SO8um2ipsoKouiAwxl38E6pfeoCG1yWje8EOCswEVB2V+EYkM+0wtis9ImgQ0hL8xeWDbCtv4tkWteZdgUJDsJQPtm/nq5fOkJI2woG6KzrbWlqo23Tz797wLA45xGdurYQG5K2PhRB4gx6klPiLfZkE7c2w6sFWVj3YinRVUuLk584xemlCnafAw/A2C8eQkJOkcpDE7BQzMs78aGZ4dJyUptapQq2vdCkyfcQQGqarcePSIOXbVi8iZ3Yd7OHo357JO7bxfWvZ/N61eUqSK1jB9zrOTC5hSeHYXA6NsKOyaYl33BlKvQE+2rEr87vruAydH8Xfl6C0oYKGxtolrVFyMTevKqLpFHo04vEUKeFkbFHm4VdiGCpxmLZLySUsxyth0idJ+cmyRDO4fXGJcJQKhauD5Sc9KS98nzpp4ZjGh57ZueR5+k4MUv6WS/EViFeAJwzeOYjrCWLTcRq21dFzpB/dFpQOaMoqVUsrIwjBlsYmHv5EEwf+9CiaqaEbeiapOtk1TXhkjuLaQmaGZ7N78RyE+mYyP+cWIWi6hq/Qi5SSlr2Ny7LjyUUsFOeF33o9k6i//J1OLv2oi1aQP1ckHJuBSIiO4uWvcw2vwdpH21n7aNa2L9e2KxDWCKhlLZ6G7PG4bXFmsp+ZVIy2wkrWllS/88VmN41JqX++M3ApQ4SZx2vD19hX3fauWaJvfGYto5dVLEYIgekz8Zf4sGK2UhOyHLzhtJpKuo/M96fqboOm+iAFRz7LjlAfCBgrcXC3VXD5rZYMeXQek5sgViUQGTfeeSmltOJMmgu16ssST86S1TcJFeclsWrwT0BJJ1xOdNHxwOIYYNWaSoJHNcJBiZ6UikyQ5rDVHZEUjEDBsKT+oOqflz8hcJ00MUcIpDvPn5NM+VLoq2FknVqIFo1ptJw0WPVgKxueXsNX/8N3GL82hZmz927cXkugxMc3f/1lGgZTGGskw/eJDAk9Mw7J7M9zE9FbfkdWfGklUSu29HFQe+TtH9qkVJTT8ab5Y2e+dDFDhFHntzn2d2d49vcep//UEFO9IQJNKSaCI6rQRie9zwYDgSeoo5s6dZvvntQfSsb4zNUjjCfUFx0wPNw3WcvBoU5Cdep5C7/G0CYVM2s8Z2B4dfb8+HaEEEz1hpjum8HwGqqoTkqEBlWrK951ZZhYKK6K2WI2lWfz/9Zx1CT+egpvoYnhaiQDktFVDpaEgpiDrZNRdAFwXQfNEZydGmQ8PoeUEicdd/bohmqLWtbO1sFNWx8p61+lQpPtZ64BugMew8Cj6exvXpostYIVrGAFK1jB/6n4niPDPPzww3f8nlgsxtDQEMPDKoEgpUpqCCH4zd/8TbRlVsCvYAXvFMKpOK6UlHoXJ2xnBsN0Hx1Aui4texrfNXWDifgcXt1YttfyUjg3NchXes9mfr8eHuevr7zJr259HFPT6XighY4HWu7q3PHZRJ7HuOYqb2dLs/PIMBqCgOEhlIzhLpHFDafimT6fi4qOMoLlfhJL2DzkYl4dRDqSRCyFECqA5JmVpDIcEAlCw9B1HKkCFDJNpBFCoBkaM6k4uqGx/+f28eZfnSQyEQUBTTvq2f6hjXQd6GVmaJZkJMVE1xSXnleVdRe+cZXH/+ODS8q8LxdVq8tVNTtgJeysCg6owL2u0fFAC8Gy5REIQHnh3o1lyJ0gPDKX2aAbXj2diFce3mbAxHWUvZR0JFVrKtj10c28+LsHVJWwO+/DrP6b7g1x9aVO6jbXEBoII+tMxhNziiCUozozU+fSUeLNVJfMQ/folLeqxOf60tpMojEXC60D5r3rhQCSEk2X+Z7z6cCna4AwNBBgpNTneiMC3RLIoMCRKgGbcGww4eIHJRXnoPQ69DwlcLzgmhLXdbFlim57CukXaHFlQ2T7svLdrqY2wR5bYHhVwnJUzhK1kgSXGUSL2yk+13WS6+Fx4mkFDV1oOFLiSolXM3hP00Y0IeianWAiEcmQZRZi/rrMhGBnfQvbH1hDu93MkbEbTCSitBaUs15W8O3//GqG1HXxW9fY8PRqtn8oX4WntLGY0sblK0+FkjE+ffVNJhMqwOTRdH6oYxfrSmoYiIaQEhoLStH+lVW1QBF8Hv7Fexm7NsnMUJjyllIq2m4vrz2P05+/wNWXleXWVJPDgeBVmNCwDBd8ZKpfBdkKIYCCyZvfq27qGF4D6Uqe+E8PUtlRzvonVjF2bYJU3Oby7DESifzv2U7YjPRN0bD+7uex4+O9nJ0aROjZZPTAVgcjLcGNLpCmAFdAXGLOxwsl6eCXGr/nBbgMr06oP8zLv3+QZ3/viUwAfHN5PRvL6vivp75JzNawZTbYbLsurnQxcpIsjnSZSkQpMn34bkKimlcsiJbmyIFbEseTvReAyhs6LQ2F/OdtTzIQCVFgehdZrs1jntA3W+3mkZqkK8HNyuDPP4Prr3e/a2QYf7GPZ37zEcauTpCYTVK1voJLV15cZKMHLCc39F1HoMTPhqeyKmev/8kRXMul6YxBotAiVipxLRevo/HRtbtuq6B1YKQzY01kpyPaUlP/CRcsv2S4PEG3G86qaNkSy3aJuXAlNLqsBOJbkwN8/sapzAoobqu1ik8zSM6lcB0X3UzbtUDmuxAulJy1OfzySXyFBv5SiFUoCW1Xh6DHZEt5w6K57fqhHt48eJFEKEGVDHB6yyypwux3Pq/kZiTzv/Qbb/YtmwyzM1xDZ2yUiNdS8xYSn25gaHpGot83pQiheZCSwuIADzy5NdO3Q/Eov7mvHzstQxb2S+aqYPWXNUROPy+9DiWdEicIfqljxdXfZjrm+6vATk+TQkouhoYZiYX5v7c8in+ZpKXvZzi2i25o3Fffwc7qZq5e7INxm/pnK29J9K9aXaEIVAsUAGs35LcV13EZfGuE8a4piqoKaL2niZL6IqrXVjJ2dSLzOiEE655Ytezrnl/n951UJeZW3MJOOUTKQVhCMaUWYCg1y/2bqxg7Pw6At8CLYzlUdpRjFwt69RlA9QUrZkHC5dpnLzJW3cdjv/oAgRK195Ku5OxXLi86/7WXu9h0k2ruFazguwVXurw8eJVj4z0kHZuNZXU827x52fuHdxKO7fL6Hx9m5NJ45ljV6nIe/b/vvyWJbFVxFUfHe9K/CWTCRTNh3pZonqRSfVLNaf6xtFXKfOghPdXZfojpaQLLbYRglr6B9OnmlxFL7DX0hFqvrzvqpfiHlo4DeAIqGarHoWAge4mapkh9Oz6yibnROSa7Q9QflDh7JOF2ZRm6uaKej3TsxK+bGF6DZCSpVEmkRDPSe+00Aaa4thDN0BYRYnL3XcW1hax9rJ2rL9/IHPMX+9j6gfV3/HgufvvaIsUKMW3j+jQ0I3/tVeK9+1jWPGo3VFFUW5BRQJnHmjRhJpyK82eXDmRU9g6MdLGtvJGPdixNUrpb1G+pxRMwF1m8tu5Ta8Gh6Myi9zhSMhafpcB8d9b61WsqeOI/PcjlFzuZ6JxESkUg9xV6Gbs+ycxgmMpui3CbzCi5AJT0C4IRje2b5mCwDwfB5yvqOVdShpASsVlS/YqgYBDCrRCvgqmtmtq3uSzaN4g0CaWoB8wcIoyrQ/f7BPGcJcP0eig9e5PYgyupv6AzV5VWPEw3s/LLUHUu250F6eIhO62U6EXFbuavy1WKHCm/Ggc0B6aaXWbqUmxs0Tj7lUsMjU+TqgP/uCKYGz6DsauTnPvaZab7wxB3qD4NiQqYzePtCMoGNHRbfVj16lsrmVS0ly8quANo3L5YYTQXLXsaKa4roudof+b3suYSTv7T2UWvTUVTzAyGWf2QUnmMWinOnXtJWfoKgcdvkoqlqO4x0E2NQKmPnR/dcsvPvxW+1nsuQ4QBiCQTfCt+FSfnlqTtYvgNZncY7N2xlabt9XgL1Hw00TWlyFv+bCGlEILIeH4fvxkmu6e5/lo3yWiKhq21tN/XvGwSTdWqclUM58pMLAJULEXTNRrWVRPqDzNb4XBtv41rqDF7XDpp4mW2H0kJSdtmND6LgYaV413tuIsJigD4NUxbI6HbmUJCyLZtV4ekZeO1Bd45Ae9eXeMKVrCCFaxgBf/m8D1HhnnjjTfuioEv04H5+fdKKfmlX/olfvInf/Idvb4VrOBWiFhJPn/jFNfDKnDTVFDKD3fszpBi+k8NcegvjuMq01suv9DJvo9vX7Kq4W4xHJ3hc10nmUhEEKiK+A+371i2vCyoANT1127w9egF/n/23js+jvO89v++U7ZhASx67wR7750SJVLNkiXbikucxEnsJHYS26l22i+5N7k3N3Z6nG5bseMk7pYtqzdKYu8FBAkQvfe62Dbl/f0xiwWWAEiwqRlHH0nY2Z0+85bnOc850TQDza0mAk9jRoTa4W7W3ED1/GyIjEaTSBtCCvLqFLpWWvGJqPMur88uwa+7casaPs2VpCgBUJmaPWubIYRg5y9v5qk/fAnbsBEKySQRHIKCoiqk5fvJLA9gxSxcPh3LsMg7ZdCeKDoV6F6N7fmVLM8o4MXWS4lgEjjy6GU5AcAJHjz6F/cx0jmGy6fjSXNz7ge1tBzvQNqSse5xVNdUEC88EuHc9y+y65Nbbvpaunwutv3CBg5/5RSqrmLGTIQicPl0hwwYJ+W83dBytD1xT3SvjhACM2YhFEGgMI0tH1tPdkUGlmHj9jvZim2/sIFX/ubQFBEG574aEZPDXzmFoiu4vDpGqiD2uJUkqw848quf3cHRr56aCigI2PDBVYl9LE7PZU9BNW90N2DjVGJsy6tkZWZywCHd5WVxei61fV2Ob7TtyPEKyzk2Gc+fi3i1UJ43jfQ+Z59Cgi+mMhQwiRrRRMGgGnXsufo2CcLZjiR2ksSvBDOuQCM8oEgB6lS1yGTez8TGDkaRmkA1oeVYByt2VjEf/Kj1gtOGSRmvTpMoQuDXXAghMKTNcDRMjtfPJ5bu4Om2Gg73TAVGE8ciwKvqrMzK477iZZQUOEG8DNWXZDf1+j8dSxBhJnHx2Xqq91SQegsksadazyeIMAAx2+J/Gk6SqnsYiDqBkmxPCj+3eOsdVT+6FvKWZJO35MakfC3DouH1ZqR0VE2aN1rYAoRh43XrRGJOgDVlSBDzORXxiqqgD9sUXtKwVdtRi8IhjpgxyyEQKw6JuGxrccK3XCiC/GWOdVPOd1wEU5MDj1pMoNZH4Mbj4AlcGu52tuVyjkWajmqLf1BhuExiKCZYTrJCE/H3YfJxkcRtN0ioYqkuDSEcxZiWY+0s2Tv9uXeu2dVqFBLwqDpL4wTAmqEunmw5x5gRQVcUduRV8WDpyhnHnh+3u/KNTPVBiiUQMYeUpoWg8ChkX7YILp+gUMmnIu3a97tieykXnrqMKzQtABYnNVqmjSuU3N9NSuTfKQgx9QwArM4s5sxgcnV4eWoWme63ryrMXBiPB0xdEcHK510Es2wsHe59aNW8qpD7pwVxhUpy8ise6HeXphCu64GruAJG1ESd53znhY5LSbk4gWN7Jiec9wVAiQl8QzA+eaukRBgwWA6eDkdpZdkrLur3WIxXCNyaglAEX6s/yoerNrJ2mtrg4l0VLN5VgW3ZHH3iNB31E4znmkkJBM8IpPckH/9ku3I9vPalozS83kyVkEyUKQifwrL3LOGk2s1oLIxHquQeFrj7bJqKpkV5AWHC+x/ZkVTl/XpDHdIjkNNIgLYKQ8sEeYcSl8P5ToIWlJjCSZK4U10owgkmJ/raafsLmlHODnawLW/+FlDvNrSf7uL0d2oY6x4nUJTG+g+uomh1PmvXLrruuuP9E7Sd6KBoTT5tpzqZdOoKFKWx9gNTNsbSlrz2D0fpONudWFb7/BXu/8O72PPrWzn97Qu0n+pC9+osvruC5dNIbfOFqjtEKzPq3HvXiCCUHfcVmXbPPapO1DZZ/fMrafp+I60nOtFcTgHA6keXM25F+NKJV+iLBLENG9WAiqMqii0Y6w5y7ge1bPv5DQDEwkaCNDkdRtgkNBK5JSL8AhZwu/FCx2Ve6Zqyyjk72MFoLMwnl+8GYH1WCS3jg0nreFRtBqHydqD1REcSEQYcC+TGg62JZO1sWJFRwMac0oSarD2pQGE740g1AuVPSbxBgVGo0nifja0mjwknrS5t9+SH6bg67Tg71Nndf5PWs9yQOiCwDZvQYGjWOU/+8jxUTZmh/Fq4Mg/NraG5NR74//by7J++SvvpLkpelhQfcGQK9ZRerC/EEHku0vL9dNdMzYls08aMWqQVOMRsb7qH5fdXJ1klKZrC2vcnj303/fRaitcV0l3TiyfdQ+X2UjypN06WGmwZnrGsoFalI89OIsOsziwi23Pr7aSiKtz7O7s4850aump68Qa8rLi/mvLNTjzr1a76BBFmEmcG29meV0lZ6vyLFSYGQzQdbiM2EaNobQH5S5PHki6fzt2f3c6RJ04z1j2O5lZZck8VS+5xnukCXxrtVxFiVCHIuYad8O1AdmUmZsQkNOT0V20nOhEKuPxuTDcMLgXVcGx4EU4Rm5WjsvvTWwn0v4gEvlFYxom0TKQQCClRUyWt+wXefggVOk++6YsHPK4a/qoRJwZi+hzSSP2HBaWvC7ydNiPVJBFhAMK5gol1s5OUs8oCuDotFn/bZnAZmB7HYjezW0H1aphRE9uWju22aZNxCQZXg4xNvvMOpBInLcQPedKe2nLDN3tO44rZhD8IIFCjUPyaJK05hhGKcfa7Fx1VVudril+VdJqCsSpACLJaFMpPO7HZ1Dw/qx6e22YRnCKa3Z/ayhv/fCwxpshdnM2GD62+zp2dLCZKLjDypLqTrLASy9M8ib9TdBefWLqDH7WepzU4RHZqKrtLq8jzamgejZJ1BTetbielpG6kN2mZbdiYXmdIpkxr7mzTRvoFlZvKk4oUpo+fpseCU+dhRd95vocDf3c4EZPvONNNX/0AOz6x6TprOnD73Wz5mbUc+9oZLNN2xpVCEChKpWpnOSvfs5TBpiH+9uTLceUWsOMxT3l1f4MzXwtbURRDoqiOsqyIK+CqiKviFgKXqrNj2SJe6riMKeO2S1ObQ42CYklCxPjaj1/lUw/uv6GCsgUsYAELWMAC3s1425FhYIrYcrPrbtiwgT/5kz/hoYceuo1HtYAFXB/fbz6TIMIAtAWH+a+G4/zairuQUnLq2xcSg24AJJz+Tg0V20pvi1S2LW2+Vn+U4fhkXgI1w12ktXt4tHz+zP3DXz5Jy9F2QvsNbK9NzLBwpbgSJI4kO5SbRHphKr4MD6HhqSBxUY1KRmYqoUI3lrRZm1XM3kKnclJXVB4rX8M3G08mqtJTNBcPl809CcxbksOi3eW0n3JUo8yYhRk1neQpji9yRmk6O395M0IINLfG9k9s5OC/niC/W+B+wWRklUrO8my2VFWxIbsUM2aRfV7SVTl1H7294OuMQDw2JxSRmHAc/NfjNB9xEoeWYWGEDaQt0b1TDIf+xpkewDeK8i0lFKzMo+lQK6e/dQErrloDkLske97V2m8qpie4plV2lG4qYs+vbk0cvzYtMFG+pYRNH13L8f88m0hoTI9H2oZNzI7hVtz4WyThaodII20JUrKzcBEl5QXk/7/7aD3RSWwiRvG6AtKumjg/VLqSHXmVdIVGyfemzWn/8eGqTfx7yys02kNgg3sYYmmTFYYiUVC4JrOQxyrWoq6QdJ7v4aSnB1P0kCJVwpaRUBwS09qH8HSrLnHViUpnw9ak7C0CJep4YVtxqW9Tc9aRCL4yeJzfHAhQnH19m7bzQ50z9mlLm1jEQBUKqktNJHEDbh8/Xb0Zt6pxsKfRUbeJr6MJhc+v20++79qT78GmWZ5/6QRKb4UMUzfaO2PZUHSCCTOWUPUZiEzwrcZTfHrlte0W3k6wDItY2CAWjBHMlli6iCtMCzRFwe9xY4Yttr+WwkS+xL0jk4KcDDqfrMeKmJgeDSMUc+R9fS40t02gOI2MkgCFq/Ko3D67UsX6UC4DkSDjOXEiTVRQdVgj9cFbI0EkFBeEwON3YcYsbMtmVWkxx709RG1H7UrGHBJYzxZB6cvTKqpsiYgTYiY/TyI2kVx5qQiFkpQMamJdM46jOi0XTVEZjYX5zyvHiFgmUkpMReFA9xVyvalszClLviaPr3Se374JsptVBiotdLdjN6WNSyp/aOMdBNWjc+zrZ1F0lUW7yq95PXwBL/f89k5Of+cCHUM9TOQ6ajFCEVhRk4JLU2OF0XwbY6PNK511bMwpvSUluPni0fI12NLmwlAXNpJFaTl8sGrDHd/vnUDOoqyEDRg4BCwEFFbPr/q2zJ9FW9BJ6GiKiilth8trg1AVdK/G4kAubQ3NjGVNRXYFjq/8ktTcObY8BSklg9Fk+XRdUXCcz6aIkMKUYAn0cFwhM758aDnknAHXBJhIJnIlmqXgcjtjEAm80HkpiQwzCUVVHDLpsIEagmiGs0JalyD3oqBpi4lUILtFJdClULHl+vadV15rpu7lRmeuJ8FX51yX1nM1lOd4KbmnlBU7FvHjL79IZDxKyUuS3s2CWLojjV94TJB7/1Sfcvo7NZzvuYJVHZdUF44qmqIp2H4LodhOGyGucicQAoHEilrkd7sYrYjFPZkmudgCXXHetaCRTNb8ScJwm2PHNtmujnSOceDvj/Ce/30P6YXXJpG2nezkjX85nlAaUFTBkgcWUbgij/xlOUkVuF01vUlEGIDx3iCXX2xg7ftWsO3nNyQIJjeL6j3lHPnqqcTn3LOSsXIFYQPxZlUVCi5Vw6+56Hqti55L/bh8OhVxGxBVUxg43EfpVyOkZQqilo2/E3RhQYqzkZ5pCXyXTyc1z894b3KlsifNPW+7xwUs4M3CsYSiyhSaxwfpDY+R501jS245I7Ewh3oaiNoWuZ5U3l+x9o5YkPZc7CMWijkkEOEQpnWPTn/D4DXJMEIIfqpyA7vyF9EeHOa/Th7GEgkhUSwP9K8X+F6UdK6wsNxxRYrpbM9r4vpxSmE424Qp8sCM1QxQTZjIhJYdNt6A5+rNANBxthvdpyODsUQMSXWpSYqAY71BemqdIgYBCWvBaDBG3SuNbPzwGmzLRtHVRHGIUBU0l0pPbR9Fqx2L1nUfWEnu4mzaTnXi8uos2l0+aztfsDyXguXXH79cC+n5qQw0JM//cppVNj6yjDrPEBHLZEVGAbsLrk+6nC9CXpuufSq9O1IpSgmQUTh1DTsmnLGcaVuY0kaJjwHaJ4bnTYYZaBripS++kSAZ1D53hdWPLmPNo8kVA7mLs3nvn+9nYiiEK8WF7p4Kyd9fsoKv1h1OUmDclV9Nmmv25+N2YbBlOMm6Fxx71ivlYwwvg6gvbmMUcwpxvOnO8chFHohm0ePycCpOhAGQQmBoAsUHwZK4rTM4hNhZyGdSdd5NxQIQxALQfB8s/U+H+JKEuAK8tnL2GEHbqS5HWTgkKThuJ5QZ0Z1YnzfDw5afXUfdS020neyg4KhEMZ355aR1UZKAcHxsGN81AIZpEc0HLey83JYb2u6BpV8Hl6UkyLeT66gGlL4ska+CN8NN2boiAg+mEyhKpWR94bziwHlLsnnsLx9gsGkI3acnrIBvBsvuq+b4f55NWlawMpdAUfL7XuLP4FdX7MGWckpJN3kqfFMQQuDV9GRr8jhx0TPhFPNM/+3i9LwEEaZjYpjhaIiyJZnkLs6mr34g8VvNoyUpgM6F8z+6lByTB5oOt7H6kWXzjj0t2l1B4ap8ui704klzU7gqL1G4cvmFK9Q8Xcf4g/H2Nr6OU8wnUA2ZeMaUBCfJKcFTzfgyBe4qqmKAEJdHe7GkjUCgqyp53lQeLl1J0IhyqNeZU6kiTpqxp/ofgNGAycVn6tj5y5vndV4LWMACFrCABbzb8bYjw/zcz/3cDf1eCIHP5yMQCLB06VK2bNlCdfX8pZMXsIDbhZhlUjscn0RKGRc3EbQFnQG7z9QI9s30g42Ox5gYCs+7OtCImpz65nmaD7eBEFRuL2XDB1ehuTVnX1dVtYBT2TVfMsx4X5CWYw55I6NdYSLTGU0bERPVpaIJhWW3wT5HURW2fXwjr3/paCJo4At4eXj/jjknd2uyiqlIzeLicDcuRWNFZgEe9doBuF2/spmap+sSFaVL7qmkfEsJ4dEIZtQkNTf5upesK+T9f/0gPZf7cafo5C5OVp7pqxug+LhCZp3OeK7EPS5I7xH0BnrhquYrMhal5VhH4vOkVLsZs9A8WmK7t6sy1J3iYtn+aorXFlD3ciPBgRD5S3NYtKcise+3Eyq2llDz47okSWYhBEv2ViGEIGIaHO1rpmNihDxvKluyy+l4tZ22011TscpZYpLSdiysqg5rjOWm0JsTQ9UU8n1p1I/28X/OPMeqzELu27H8mopJAbePwCxWZ9ORorv4QP4anv/ia5ghk+HFjrXR5LEJAbqlYLVG0PME3iwvi++u5HtnryAjjt2FNWmUjVNtpMXzbnrQkekWJshZip+0GBRcUuhfr6AqClEzFg80Of7Bwo5L/1qSmAt+WHeWX82+55rnA6ALhZg0CVtGkgKPYdjYloW7yaS2ppbNH12Ly+e8f/uLl8UD5uNY0kYBHilbc10iDEBaQSrBgdCM5ekFt1YF59c9DEentiuldI7tqlehY2KE0ViY9DeBSHA74PK50L060bEoriCJirXp/ij6uCQ8EkEZAeNyN/ouF+/9k300vN5CaDiMJ9XFeN8EZsyidGMRVTvLrqvKt2bvErr+dyfjPhPTDf5+QWqmj/J5JMCvhW15FZwb7IgfvhOo9GtuFpeVcqqxD5/iQkrJRDiCrcNoFZiHHJl7cN53RVWcAL8QqPpUgrV43cxq5cXpuVwe7cW0rXjxryBFdxGynIDckd4mxoxIgpgdsx0y5rnBjhlkmJQsHw//3/10XehhazDKSJmk2RxGjFgM/FMT7qCC4ldR41Wutc/WX5cMA5BTlcV9n7+L3abBK1111I/2kqp7KAv76B9sxcKidYPJwApweQe53DHEa91X+OTyXUnvXNQyea79IheGutAVlS255ewpqL4pBcZJeDWdn67eTNg0sKUkRX/n2sesfu8yei72JbU/Kx5YTFp+KrZl03ayk966AfzZPqp2lc+ofr6roJpLI90MRCZwxckwAoE3XUcAu/IXsbK4hAvtpwlJg/7VYHnB1yfYMJSF1339amohBKX+jATpBpwgPDJeOahBShcUHpK0POjc16S7q0As3SHDxFKdAL8dV5VShEAVCgORiVntJgF6h0Zovt8heE6+c8E8SbRExY7aYEuGyiTbZBGlG6+vQHf+yVon63h1LsSWBHtD1H+nHnVMsu3jGzjwd0cINEoCjU7QWBGCjJK0RNJgtGuMi0/XEShQ6Ku24vxNZ9uqrlIQcuPyRzBCBtY0QoaYbC9wyIX+JpUt6wo4nzbMmBVBEQoeVUsE/pdl5F/3vN6taHijdYaqom3aNB5qY/3jM9WyEr+xbI5/42zS+M62JJ1ne9j4wZkk9sHm2Unhg80z1QNuFiseXKTF08wAAQAASURBVMKZ7110qqqlJGVMZcULGq2bLMYrQFNVdEVFAIvbUrn4w8uJdS8+U094NMKOT2zi4rP1CATpfQpxkTlsLGzbRlEUfNMsSYUQbPzwal770tHEtRCKYMOHVif6hQUs4O2CSdu/qxEKR7F0mzErQtCIkuFOIcvt476SFeT77oyyYldNT8JaTcopG+D5JioLfOmEzBhamgsjHMNyTbVjQysg/ziE41wIYQI6U53n9Mxlolucxmi5FnFGOolvNeooUiRtc3pTqjnFC7aUDFXYhIRJ2iyh2dHOMRRFwZPmSeqnR7umiLyhodCMdhqcfnViaCou5E5xikOklAky4tWKbkWr8xPkmDuJFQ8tof10V5JdUP7yHHatXcbuO2BfOxoL848XDxCynP21TwxTO9zNb6y6hxTdRa4nlfqRPmL21DsQtU1814kzTceZ716cobZR89Rlqu+qSFjnTcds9tXV6bl8ZuVeTvS3ELFMVmYUviljkPHeoFMAEDWx4iq9g6sEg8ucZ26SIGK6wDWNv+DX3YgVO7nQ0zDrdm1tyrp3LiiGY0c0aXc7+c7YbogtdZEakYzoElVTHHKzIlBdKuX5sxOyIuNRFFXBk+rGtqeKw/w5KY6NTn+Qs9+rdQiuHoWmg23kH4eRajBSHTUpaxbuUeJVFgJbkVPT7slmQYXxUijo0RGqQFEFtiWTYinChshghCsHmsldnMWqR/bd0FhA1RRyr2OpNB8suacKRVOof6WJWMigZH3BDNLWdNwJS+kd+VW80HEp8VnVVXIGBLmnJVd2mRheJz6W70vjfRVriVkmX79yLFF4qgrBAz+7nA0XCui53E9Klo+l+xbNK350tV0aABJGe8ZvqBDLl+Fl0e7ypGUH//U4bSc6iQZjeIYk4WywNMc2HeGQrbCSOWH6OOSdknTtFkjVeaxSexWqrBT23LWUr9QdYige0/Jrbj5YuRFVUflo9Wa25JTzWs8VekJj9ARHIWo772t8265RGOkam/c5LWABC1jAAhbwbsfbjgzzxBNPvNWHsIAF3BSEEAgBZsR0KgFsiVAFqkdjIDJOpT8HX6aX0FAyWcXl0/FlzD8Je/SJ07QcnbIoqH+lCTNqsuMTm1DF7JMpbY7ls2FiMJQYPedfVggHFAbLnMpan6rz/sp1+PXbU51SuCKP9/3VA3Se70XVFYpW51+3MiLN5b0huXpVV1nz6PIZE7zJipbZ4PLplM7hwetKcYIivlEF3+jU8umS/ZOIhWJJwSlFdSpFLGNKWUfRFFY9cm1p1BtFaq7/ljx83yyk5aey65ObOfU/5wkOhPCkuVnz6HIKlucSs0z+qfZ1esJTk7dXL9Wy6EmJHhUIVVxFAkiGlKAZgr12BYuXV/LU+TMcGW5H0RRitskLHSOcHmjnV5fvJsd7a6SLotX5VG4upflIG6bXQgs7QR1FExCRIG0a+nt56o9e5P4/uIu0/FRUoRCxDIcIM/24VScIo9iQVeOQWrq3CmIaTsUMTiBFjTmT6dwrGrkV6Zx3DyTKGn09EM6+OkYr6WKc+WBzbjnPtddi2ta0yyuxXZB+GYoP2FwM19FyrJ3NH13L4rsrSXN5+c1V93BppIdxI8Li9DyypqnpTL4Hs5GyVr93GT2X+okGo1iGjVAEJesLySgNAI5tW/ORNvrqBvDn+KjeUzlnBeV07M5fxA9bzyctcykaylXtoSIELuXWlbneTDhKIQp6yCajHoaX4Eg4mzaWYVFwzCI8ajqVcx6VxjdaWbK3ijWPTbWDzUfbOf/DSxz7j9M0HWpjw4dWkVWeMec+M0rS2f97e6j5cR1jvePk7M5i9SPL0D23NpTMs1LYM1TEabWHcIpNdXYeD5SsSKglgZOgsTwkqgfrPyioelLijrfBqksBqSFUp1pQ1RXWPb5yVmJldSAXX5cLO779yeeh1O9Ufl4e6ZmhUGjYVlz1aCZUTaFk3VR/sQnoru3jJbMdruraQ8MzyarXglfTeah0JQ8RTzovhejWFTQ0dHF+4jQudep9ClsGL3Ze5meqpyz3/rvhBJdGpio9n2m/iClt7i269T7nTlSCv9lIyfTx8P/ZR+uJTkLDYQpW5JJd6TwHr33pKB1nHKUKKSU1T9dx92e3k1s9FYROdXn47Mq9nBvqZDgaojI1G11R6Y+MU+bPJMebynhvEDNqknsOcs461neKhKoPzd9W4uHS1Xy57hBRyyRmmcSwpioHpWMJ5OsGXx9MFIuk51dY4Im7WviCCrZmYWk2xKsxVRSyBnWe+oMXKd1YxMr3LEWbZuXYqA4j4+pTzgYdKz9LkfjS3NjxxMRlbQzDthJqKnMhPDrTMmY6bNOm7uVGtv78BsIjEc585wKW6exDd2ts/fn1id/21jnVoIFuhbx6ld7FcYKLZbMjp5y9j1fwRv8xxnqCSCRmxFEGjAVjTkVmnLSuKAq+QxH+7AuP8N9XTnBxuNtZLgT7ipZSnDJ3u/hux/TxatLy2LUVIicGQrPaA411jxMNRnH7k4lgc6nMpBfePmsIoQiWPVDNC+2X6Kt2rFEy2y3WXPSz+7GdnB3uRCJZlV7Aoa++kUgaT1oIthxtZ+OHVxOJP8OKpjgKRHGSi4wrzKx4cDGjXWPUPn+FYP8EuYuzuf+P7qLzbA/SlpRtLsY2bV7928MMtY2QUZLOmkeXk1Xxk/ucLeDtgVWZhZwZnCrikLaNOix5419egxSVy49JzBRACHrCYzSOD/CZlXuTxvu3AyOdY4SGIwhFONYS8f7HjFp0nOlm2b5FSQqrcyFV96BoCtIzTRoGQEi6twvcwxAJxBPf06cnltO/2q6pdRIWMRKkkIk52awQUPKCpOUBwMcsMhNT/5NATLFpHh+Y1YI6c1q7MJ2wmlkeSPydVZGJK0WfUkyNQ9GUhIJL+ZYSzj95yWnP4jt3+10UrJhZ4NTfOEh3TR/egIfyzcXzutY3ivSCVB78X/dQ/8q04p3d5bdE1r4WjvQ2J4gwkxgzIpwcaGVnThXL0vN4tas+6XtVKDSND7A+Z3bFzKsxNIv1k21JhttGZyXDzIV8X9o1lY/vBLIqMogGY1MEVksyWBJXecF5H6zJx8DlLF+RUeBYWHlAXbUbrasOc3L8GR9kKcaUUpFUmPnexOd1cbfKq18PhBRktyj0LTERJVPPYZY7hc255bOeS+GqPM5+7yKAYxeOE4cY7w1iGRa2KYmFRzj4bye4+zPbaDvRhRkx8XfC8FInBiONuHWmjJN1XM5BKQjUmMAQEjHpcZ2QJpXotkIsZGDb9lR7c1WcSgiQls1Q6widZ7vnRSSfRNCIcrK/lZFYmKq0bFZkFN40UaV6TwXVeypuaB3LtImMRvAGPEnqfjeDewqX4FI0jvU1Y9gWqzKL2F5WQu14HVmv92NWuliys4L1axchhOClzstJCuyWlDzdeZHfvXv/DVtn5izKTCghRVIkoQyblKB6zTjIXBiIBHm6rYbGsQG8EQW9fZRUU2KmQd5xSfN7BNa0MLFig3vMeaeMFEjphtyzTvwud0gnmAt6GPxDCikfc9Mw1ocmFFyKSqk/kw9VbUgowVqmjV4XZVN3BtmVVXzfd5H6UG+cgCURFmSdtOkZ6qPzfM+bQnRcwAIWsIAFLODtjrcdGWYBC3inQldUFslMzoacIJIETFVimAb/VnuQLK+f3R8op/HfLyVNinKXZPPcn76KbdmUbS5m5XuWzlkhEAvFaD3eMWN5y7EONn90LSX+DPK9aUkEAoBNufPXs8wsy0DzaBhhAytiUvKypCBdIXN9Lo/u25OU6LClpD04jKqIm04WuHwuKrbemrLAncRYb5DOc924UlyUbiwipyqL7MoMBpqSAx5L7q2asW5qnp+0Aj9j3U61jWVYCFWQlpNKbnU23nQPS+6pIrMs8Cadzdzoru2j7WQnqkulamfZLUmv3ghKNxRRsq6QyHjUseKKP/tnBzuSnmNpS8btKL3VKsU1GppLwzLsuBrEpOxo/LcSzKiBy6ehezX++5ef5Nz7TEyfo7QyKYffExrlby+8ymdX7SXHe/PqPEIR3PXpbSy+u5JTzU30u9ocf+OIDQikAO+oIDwa5dW/O0LJ+kKWLs6i0e6fZWMCqUDOSYfkAJBRLwkWQcfdAtMXlz4VUFirkq64iT01zmqPzrA/hj5oo0Ydr+2rNkxO6vyqN+8rXs4rHXU4VB2JMEGJk2+yayR6PJ8fHY9y7OtnyFuSTXphGqqisDIzmURmRE1O/c95mg63IaV0bK4+shqXbyoqkFWRgS/TS2Q86lR76Qr9VwZoPtbOxMAE9a82E+yfSARI615u4oE/uht/zrWD7zvyq3CpGkd6m4haJisyCgmaEU72tyX9bm1W8ZRVzzsEgcI0IqNRbNOm8pRkIARDJTbFmZlEf9SL/0o8OSclRsipihtoGiKrIoPx/gne+KdjSZYUoRMd9Fzq46e+9PAM9YvpyK7M5K5Pb7tt5zHcPsoLf/4asZCRUF1eus9L3hLnWV2SnkftSPe0Sk2BYkgsL/RtFJS8IsmuymTt+1ZQsDwXK2Yx3j9BVnlgRrJ1EhWp2azLKk5K+GS5U9gTl2I3LEfdQ14Vvcz3zr/6ObsyE92rzagQLbgNdnXuFBdmsYZomBn87JimHjIUmUgiwkziUE/jbSHDvJMx0DRE6/EOxvsnUHWVQFEaVbvKEsmK7tq+BBHGMm2MUIzIaISn/vAlKreXsvNXNidk7V2qxqarFIOmS+p3nOt2KlTT3A7ZT4CqK7MmTeZCWWomv7N6H+cGO3ix8xKaoRCZiCYIkLEATBTCkrYUzuWFMKfxUfKOyYTaWDAXh0gqpqrbTWmj1UbpqQ3Te3mAznPdPPS/7k2s70p3ITCmEneT/a1wEgBKnJAVsQzGY5E5LQUnkbMoi46zPTOSdtNhRi1sw2LNo8vJqc6i9XgHmktjeJ3Ct81aYmcvsDqzkJU5U7Z/Zac18upVQhk2i4rzeejedQDc+7ldnPlODeM9QSTQ2dpPy3bBWLmT4MiqExSeNrC7x4n0hfnYkm30hsbojwQp8We8YxTD7hTKNhdz5cBM65SyzddO3ngCHjSPhhlJvs+eNPesidXidYVkV2UyMM021JPmpmhNATVP16F7NMq3FM/Zrs8XvZsFfdlgGU6CfagKBjb5KEwNUJgaACAajBIaiWBFLZxMmMDl1QGVWNikYGUurccdO0lXisuppDdsXD6NzLIMImMRDv7r8UT731PbT/upLh74472omsLEYIin/vDFxPehoTC9dQO850/vJfU645oFLOBO4pGy1YzEwjSPOwxKZcCi7FWBkAo9hQbjhokW0RLvcMQyOdrXxEOlqxhuH2WwZZhAUVqCVHqzMKMOmdvlcxEZn2ZTJxy1qIvP1LP2/Suuu51cbyql/ow4wXFqrqgYMF4CuackQ8uvGks5koEoFigh5299TCC9YOtOgj92rW4hXvXffq/ASJu+cI6EtXBI2XWjvbOSYYrX5FO4Ko+uC1PWr2kFfpZOizvoHo0dv7SZV//2MGbEIXwIRVCyroCquBrhyvcsZWIgRPORNmxLkpqbwo5PbEoiv4JjPXjx6brE5/M/vMR9v78Hf/btb5tSc1LYMItS2O1ALBSj7VQXtmlTsq4wSSkUwLJtDNvilTMXaHj2POOFEtdWBelyxv9qPPk8XZXveggUp9FXP5i0TCjitpI67xSG20dnqAspk5xXOWXlInXI9PnZll/BPdPmEmmpWcRIVkJSUFhV66Upa4JI1jROmD31m8nt55yxGVqloKgCGbev0SOC9G6BYgvWH/WTt62a7tAo+b40tuZWzknKzyrPYPWjy7jwo8uJc/Ln+xlsGELa8Z2bDtn3yoFmKraU0HiolfwTNhMFji2najiFR9lnJHmnIbrVQ9duQXTcIQy5TTB1QVwK3CFsj0JKi8183eRtSzIxNFMZdy4MRSb4x9rXGI9bdx7ubWJNZhE/Xf3m2N/UvdLIuR/UEh2P4Q142PDBVVRsmx9RbDYIIdhdsGiGFdquT26Z9feXZ5nTyvjyHfkz47CzwYiaGGGDdY+vZKBxiCvVIXqWODdM9wqeHrzE+9PWzpuUZ9gW/3rpDUZjEayYxdBEDGtfnAQmwTUmUGKAd6qiwEYSS4Wq70tGFwmGVsB4GbiHBOVHTDI6nZiUK8VFb6XFUy01if1dGenjHw++zM56x9Kq/tUmRjvHHGVdl8qyzQX48nK4MtKHHoSsCxLvIFjC5qUvvsFHv/q+eVlyLWABC1jAAhbwbsYCGeZNhG3bHDp0iMbGRnp6esjIyKCkpIQ9e/aQknLng1/hcJiamhpqa2vp7+8nFouRkZFBeXk527ZtIxAI3PFjeLej4rROByqD5RZmvIJAjYGl2AwpIV72tPCJP9hN6+EObMvGilk0H5lSeTn/5CWCAyF2fHzjrNu3DEehxRaSrpUWAxU2UoGsFoVozED36vz8km18v/kM9aN9uBSVzbkVN5T4cvl0Vj28lCNfOTml5jAo8PVItGnlHF0To3z9ytGEZGOBL42PLd5GxnVsZd5JqHu5kePfOJsgWZz5Tg37P7+bu39jB2e+U0PH2W7cqS6W3rNoVv9yIQQ7PrGJV/7mEKOd40jbRigKsaCB2+9i2y9seHNPaA7UPF3Hme9MTbTqXmpkz69tpXjt/CvXbwVCETOUevrCySomMl4ZGEmfCtS4U1xYpkVafiqj3eMYoVjCnkxRFcyoxaF/P4kVNbE8jjypU6gkEkU8EcvgYE8Dj1WsvbVzEIKi1flcea0Jj99mIn+qHEiNQE6NQx7pqx9grHucseMWysMCW8iZ2wKGVgjcw5KMK04AKq0VlvyXZHyZgpLjIqNXpSo3j6z9GZz7Xi2eoKAg6CY8FkFakvQGGJ0WW1A1hXuLr98OjHaP88Y/HUOsjqGmShRdYBtTVZnKtKI6Ga+ybDvVxao5qrqP/+dZmg62Jj43HWzFDBvs+fUpQkXXhV6CfRPxZJMDM2rxyl8dRNVVYhMxECJBloqMRbn4XD1bfmbddc9nU05ZUqLatC1SNBen+tuRSNZllfBA6fUD6m83rHzPUnrrBpzKdCC/HtZ7CqmoLOHV1l6ujsOZMYv0wlQs0+alv3idodaRpKCnlJLIaITX//Eo+z+/Z8b++hsH6asfJDU3heK1BbdcDTaJc0/WJkmkA1x+qZGl9y4iNc/Pzy7ewn9dOc6JfseqQ8QkwnaSEaFch0x5/x/clSQ1Ph954w9VbWRDdilN44Nkun2szSrGFbdMK/IH6A6PEbEMbOkQidyKNm9SaTQYJTwaZcvPruPQl09ihE2EcKpgN3xw1fwvzjWQNwcxZ7pdwaTt09UIm8acljg/Cah/tYljXz9DLGRgRU2nbfHp1D5bz/7f20NGSTrDrSOA817EJmIJ0om0bDrOdHPu+xfnrb6me5x2TcQDlYnlN1hpnebysKtgEYd7mwjFJu/tlHaXSFHhSoSV3Ro9+VFsF6Q1g3fA+Vl6firKTxfSPtGBJW1M28YIGwhDEou/MtK26TzXS9upTko3OGSH3auX0zB8YupAHG4AuqoiLTuRBEjTPaS7r08cWf9Tq+hvHLomGUZzq4nrU7gij8IVebzScZnnGi9gxiyQku7+YWpCPqpzfEz0O+NQT1Dgi+ps/pjTpkeDMZ7/swNMDE4pMjXdJxnPcLKitgv6V0mwbIrPwI//6CX2/tZO8pfmkHeHrD/eaShYnsu6x1dy4UeXMKOOxeeax5YnKSTNBt2tseKBxZz7QW3S8lWPLJu1/1A1hXt/dxcNrzXT3zBIWl4qqq7w0hffSIw/zn6/ln2f20VmXDXuRjDZ3x0faMUVtwpBSoSq0GaOMhAJOhXuOAUG0rJJ7NiWxEIGuYvTEsnbodZRxnuDjiqoItA9GlbMpv/KIJ3ne1BUJel9H24fpf10F+Wbi2l4o2UGUdKMmDS81sy6D8xtPbWABdxppOhuPrl8N33hcRpOtlH3zRqnSEFAzOe8D2bMSuq/hqNhDn/lJI1vTI31SzcVsetXNt/0WDGzLEBKto/RrjGmD1XU+DvVeb5nXmQYgJ+q3MD/Pfschm3Hx5E2igl6CAZWCycxf9VhCstRsVAscA9BySFoeq8CUmJfb+gkHKWLyIwm8mpCjPNZSPAIDcOaPYOuqAp3f3Y7bSc76W8cIj0/lYrtpTNUGSu2lpD3L49Q/0oT4ZEI5VtLEqow4LSx2z++kfUfXEVsIkZqnn/GOHCsN8jFZ+qSloWGwpx/8hKbfnoNp759gdZjHSiaQtWuctY8tvxtafc20DTEy395MDG/OPHf58j6pSkFjJhlErEMpIQB3WT4fig5p2JHLVS0pIKNvBtQj13z2HJe/qtDSfaAi++uvCEikRmz6KntQ9EV8pfm3Lb51vXQ3zAYJ3aQ6PoyLkmCxQ7BRVEFXr+bFN3N59fun2FTfqinEa+mEzENbJw5Rorm4r2PbOflLx6ktTpG3xI7bj8UL7QxAQGZtVB4CDIyUhhcJRgPhvH2SCqOqyjxF2713mpWFC2Z9/mseXQ5VTvLGGgcIjXPz7knaxmoH0j+kS0Zbh9l3+d2M9gyzHhfkCXfsRgtkZg+SG2PW1crEDhn8YGfuotvP3cALaaS3iUYqLLpXmph+KAwmkLGa1GEMLCxk/czh4qxoipJ7+j18EpXXYIIM4lzQ53sHB9KIuHfCfRc6uf4188mPodHIhz695MEitPJKHlzCuj82uxkaL9+fZK0bdmc/vYF6l9txopZBErSKf7Fao6OX0CTAlVXUVSF4/0tVKfnzEpMnA0Xh7oYjTlqgUbEwNZJxBtVI6485o2TpuLtv1Qd1aGhFTCSeKQF0SxovEey7lkoXJTDhg+t5omRk4l9SVsSDUbpsiLU1IfRXzKwjKlnzYgYdB7rYv2jy7Ce6XPGucLZrQBiEzH66gcpWDH/Z24BC1jAAhawgHcjFsgwbwIsy+KLX/wi//AP/0BXV9eM7/1+Px/60If4whe+QEbG7ZVIbm9v51vf+hbPPPMMhw4dIhabPTkihOD+++/n85//PLt3776tx/CTBE0qVB7XKD+hcvYRA9OTPPMZN6KM50i2/Ow6bMvmO5/+8YxtNB9uY/1PrcSbNtMGxJvuIac6i5NpvfRWTwVN+lZJnh6o5YPpG8lw+/jFpTswbAtFiDmtk66FiaEQLr8bK+bMUlWX4kh5nu9JECT+u+FEgggD0B0a4/vNZ/nFpdtveH9vR0SDUU5983zS5NWR7K9hz69vmzeRJbsyk8rtpdQ8XQeIRNCo6VAb1XdVXDehcKcRC8W48KNLScts0+bMd2uuS4a5cqCZhjdasGIWZZuLWf7A4tsSFDvQVc/h3ibGYmE0RcWjak71uSJIGUzeftHqfPb97m7qDzRx5MunEjYp4FQWmhEnEZ3aCsOTCqoJVVvHBmEwOnHLxwzQc7mf+lebqRCSoaUwkS9wjUsyL4IIGs7EOB7U0mICNSLBO6VAMWnboNhguQUdd4OvV+KOC+SoBgTO27j9Nvf+7g6K1xTQeiJZKUpzqRhhk5IDEl8vjJUL1BgUtasEcgRcJ1by2peOMNo5Tq5XpW29iTQciwBpSTyDjoTrJCbvte6dfShhRM0kS7dJtJ/pJjIeTSiQXG0dBxALG06SadJQWUqMsIEaX2ekfXTGOtdDLGRw5UAT/qYJHikso3pPBb5M7zuSFFCwIpf7fm8Pl15sIDIWpWh1Hkv3LaLh9RY0j6OaxDSrFI/fRf6yXNpPdxEcCM2wAZp8J7pr+7BMO+k9PvafZ6h/uSnxObMswL7P7UoKFt8shuKkg6uPZbBlmNQ8P7qicm/xUmoGu7BMC0OamG6QikC1Bdt+e3MSEWa+EEKwOJDH4sBMpZa9hUu4PNKLpiiJ67Q6qyhhozQXbMvmxH+do+H1FmzTRvfpSYEoy7QxwiaWYXH62zVcOdCEETHJrc5i44fXkFOddc3tT0e+L40N2SWcGph6v3RF5d6iZYnPhb500l1eRmPJ79fSjPw78sybMYv+hkE8flfC4uztACklVw400xxvi/rqBxw7sUkyhpQYYRNVNzj3g1ru+vQ2AqVOYNc2k98jEW+/W090zpsMU7apiDPfrSEyllzdvnjv/O0ep2NlZiGvTlxOyMlLhNM3DKoYYQOP5iavVsGeFiD1pLrZ//t7OKf1wUQHqlBQVQXLMECCPjG9PZA0HmxNkGHWbanm0bNRnu+/RFSxcesai7zZXBzqJiJtEKDpGg+t3Div8WZwIOSo5CkiiZBnq07fJ1RlVrnzFy5fxDCNhPQ3QLs7SGZIp2xJLgjwZfpYtn9RQua88Y2WJCJMKN0mmGGjudQ4udwhlPetV9BdCjktFue+f5H8379rvrfjJwIrH1rC4rsrGO+bIC3PP28i1+r3LiO9MJXmI+0IVVC1o+ya40rdrbFsfzXL9lcTC8X43m88kzQGj03EOPXNC+z73V3zPnYjbHDiv87RcswhwI5/2ET1aXHLxql2MDrNBq/leAcun040aCSqyIWA0o1OciQly8cj/3cf3Rf7mBgMcfwbZxOV7ADSkpiGMUNxIdjvjDUjY1Fs28aMmNiWRFEFmkdLbiMWsIC3CEbY4Nw/nKHrQi/GhIEhTHSvTlqvQs/SmYSNrBGdxjeakpa1neikZW07lTvmr0w7HYqqsOdXt/LiF17HiBMaFE1JkEs96fNXiMrzpbE5p5xzQ52YtkksbhHoHpKMVIuZti3CUeAsf9qZf3mHASSeHgjlOInMJEmLq2HHf3MNMZjp/ilqSGCoBiszZrdnBud6lG8poXzLtZV0fQEva993bZKQJ9WdmH/Zlk3flUGEcFTbHELEzHX6Gwc5+G8nEop5ABefrsMyLDZ9ZH5joRuBZdoMtQ7j8bvnRXC/Gsf/82wS0d42bMb+u4Mlv5TL5dHeRHuvmHHikwqDZZL0boXRQgu8mkOUVlTuLpy//Ur+slwe/JO9NLzWTHTCoGRdwQ1Z4PTVD3DgH44QHXeeUX9OCvf81g7S8u+8skz+LKSMQCOYXkn/ejB9Em+nzX255TOIMLa06RwbxggZKFIidZCaJBiL8ncnXqZKURkutkEF3RYYwvleSsfeM/+YRAGKzgh+6af2YWlw5UdXaHN1oZVoVO+pSFJCmi/82SkJIpLH72Y2VoqiKZz61nnCYxFsy0ZVFNKbLUwfdOwRjMeFT3J6QZGCnOapfj2rFgLnnC3qnqhD8lYVpGVz9TRbiKTpBIqmsOaxZQRuQIm5c2Jk9uWhkTtOhmk+2jZjmbQlLcfabxsZxjJtrJg5Z3xhZ34VNcNdxGwLgUBXVLI9KazIuH7x3qUXGrj0fEPi80j7KBePj6CtUeGqOfHlkdlVumZDeNJ6TUqkJR1rLSGQmkxS6ZySJov/XIVwzlXkSCkwvVD9myvZvtRhyYQGpnI3ZsxKjDUNl0QxriJdSSd+Nt4XV1O+uuDuqqKMBSxgAQtYwAJ+UrFAhrnDGBkZ4T3veQ+HDh2a8zfBYJAvf/nLPPfcc/zoRz9i3brrV7vPBy+88AL333//zKTXLJBS8uyzz/Lcc8/xmc98hr/6q79KeKwuYP6o3F5K6/EOFFugWmCCow6jT13LSZsh25LEJowZ25C2dOQnZyHDAGz5xXU8f+7ZxIBaaAoun86ZwQ4eLluNL273Md3O6EYx2jmGZViYUacC14wJdI/OaNcYxWsL6A8H6YuMz1ivfrQXw7Zuad9vFww0DSex7SfRWzcwy6+vjcHmYVRt5jUZaBh6y8kwYz1B5z5fhZGOMaeCTpk9kne1msxwu1Mlu30OVaP54khvE8+0X0RKiaaomLZFSNr4dTeFKenktUaYfPg96W42xoNwAoFyNRFn2qEXHpKEcgXhHBK/96o6QggqUuefhL4W6l9pTJBZsmogq2Ym4UCLV/L5RhXSehQiVSqGdKSaATQT3D4XZszCipmMVUDOueTNRCdivPgXr7P9FzeyaHc5vgwPwf4Q0bDBeL6N6YHUTkFOjSTnoqNAoLkUjnz1FO/7qwfmlEcdbh9ltNN5r/PqFaSq0bPYwnRL0lsF+a/bSXFd27Zx+XTKN88enJWWjW3NfIekLRNVc0ORCVqKQvQtsshsU9BiwvHZtiWqW01YcExuTyIRiBtOtluGxQt//pojAx0n1pz4xllSsn0svruSdY+veltWN14LOdVZMwgUxWsL0P5bRaS6MaMm0pYousK6x51q81h4Mqmgztq+IcGKmajxfqS/YTBBhJFSYls2/Y1D1D535boB9/kgUJTGxMBMiejpwcHxQwN4mqIMlUssH0y+2NE0yT9HTvCLAxorMgtRb9OYJd+Xxm+s2suxvmZGY2Gq03PnFRS7/FIj9a9MJYTGe8aR0rH6EEIQHolw7OunsQzb8SmfTOif7WaobYRH/vy+OS0yLOlYN033hX+8cgNL0vO4NNJLqu5ma15FQtkAwAybbOnO4QW1CcMl0dwqBf4Aj5bdfkn6zvM9HPzXE46KCpC3NJu7Pr3tthCmbhWnv32B2mevAE6bFR2LolzVBkrbRkrJUKsjh1+wPJfCVXm0T0v8oIiENdKNSEzrXp19v7uLU9++QF/dAP6cFFY9vJTCFTdnmbWvaCl9E2OcnnDUkrQQlLxiYw4bCAGRsUjit0IIdK/GI3++n4zidDbGPLzWfYVQPBkoFIGISDKn82GFQPclJzruWbuS3fYyBiJBUnDx7O+8xEqhMVjm9FuZbQpqMAiPXPvYpZSc/W4N0paEMyT96wTBAjB8OGSWIcFefxXFZQWc/OZ5fOkeKneWIRTBhBlLIsLEN4jphqG2ET7wtw+huTUmBkP01g2QWRZgvD+Z6GpP3jYBmkclakgs3dle1wqLnuUWxpEhFjATLp8LlzdG05E2vAEvRavz59Vnlm0qpmzTzPYzFjLobxjEl+ElLd/PuR/U0nTYSbZUbi8lf1nOrGPT/obBGcuuhSNPnE6yl01tsBldZOBKmWbV6E6hwDfV56i6glAcazPbtJE45N+0/Kn2VVEVilbnM9Q2kkSEAVA0gRmRMxS4cuP9dVZ5wEl2xp9ly3KUP7Mrb29BzAIWcDO48NRlemr7UVQlQVo0wgZpnS6ym1VGlk4900vS88g8BbPNjLsu9iWRYXou9dF1oRdPqpuKHaVzxjkmkVWRweN//x5+8LvPMTEQmlLIELDsvurrnsdA05DT5+b5eXzVOvJ8aRypu0Kwz0AN2oxWiTixZRZISG9J/AlA4Ss2LQ8JDD9zEl1EnD8753ZhSn1jcuNCknXKJqdQhzcxJDDSMcqrf3uYYHwM7s9JYe37ls/6W1+GN8ladRINr7ew4YOrbqt6SXdtH4f+7QThEWcsU7y2gJ2fnLKmvB6MqMlg80xro+hwlPenrqQmkMd3m88gJJixqTjcRJbNxm+7GFgkyX5/IQG3l+15VTdso5xRnM6mn157Q+uAMzc+9O8nE0QYcAiUx75+hn2/e+cLFYtW5ZNVMdMGPDsez4gFIFgc4fXwGc79/VlUKfBnp7Du/Suo3FGK1m0SS3XIAPYkGQzJRBpc3mdiuOO2MUKDiIEtQA9C9belk7NXYHxggh/+9vOkF6RSvaeCx754/+07v7X51L3ShBE2EtZGiqYw2j3OUOuIY7sNoDpqb+17ITg5dBGC0UrBK0Yz6UWpjHaOY0QMzIiZGJJasWt4I0lwpTqKdIHidLKrMln54GIySgI3dA75vnQ6QzOLgQpuwMa3ZXzQUZg0YywL5LM1t+K2zZ9vFtKWnPluDXWvNGFGTHIWZbL1Y+sJFKczHotwtK+Zzv4hRoeDGLqFJWxHfQh4qGQl2jzizpPjyySMWVjmzDmdX5//3HVpIB+F89hismAiHluR0zqIWZSBvAMzuSoOIQYuv9jIlkXVqJrCYn8uJ/pb44Vp8fFoFPQrsxc424ZNwfJcus73MNadHKtPK0gle9GdJU0tYAELWMACFvBOwAIZ5g7CNE0ef/zxJCJMaWkpH/3oRykvL6e/v58nn3ySEycc+fGOjg4efvhhjh8/TmHh3NUh80UolFz9res6mzZtYufOnRQXF5OSkkJnZycvvPACBw8eBJxA9d/+7d8SDof5l3/5l1s+hp80DJRY9P6sj5GeMTxjglgq6D49UU2c502lLF5drrlU8pZm03s5OYTkz/aRXjB3BYgn24cecKPGk8yTAQhbSqKWmSDD3Ap0r44ZnkbUsSVGKIYeT2y5VXVWxU+XoiYl697J8GfPrjbgnyNZeS2k5afO8JAGZ1LyViMt34/mVmckHQIl6XMSYaSUXHr+yozlTYfbWPf4yhmWR9fCaCxMzLISwaZjfS2Ak8TzqjqmomJJmx15VTxYuhJzpUHH2W40t0bJugK0eHCsYGXujEpzVVdRXTa2YaFPOMGc7m0CywOaUFCFQpEvne15N15tNBuiwRiqrsyawJmEGbVweZ13dvFRnZFVAa4M9zkSxBKUGMSMGLpHx4pNeXQnIW5PdPb7tSzaXc6uT23lB//3Ba48IIlkOnLmwpSUHdLI6VSxTYkRMbAMi8GW4TkJWNPJRAJBwSWVgksqtmWjqArSJTFsR9kC6STH7v2dXXjSZq/QdPlcFKzIpbumL2l59qJMfBleTg+08a2GU8SkRWy3RXPYZNlLGql9CoquOvdWgKKrTpBKEQ6JKeBhxSzKAddCy7EOhuNqMrGJWIKMExmLcun5BoQQbPjQnfGtfzORkuVj2y9u5MQ3zjqJbkVQsbUkkTgoWpWP0GbXa9Z9OrlLspMIDH1xSWnbsolNxBLv19nvXaRiW+k1+6r5YPV7l9F7uT/pnancWUagyAnsRSdiHPvPs5QEbSY+DCHf5LELJI7N2b9fPkSKy4VHdWHYFssD+Txctoo01/UtW+ZChtvH/SU3RvZpPTal0mLH5fjBSXJOVmL11PZjXaU2gi2JjMdoOtjKmsecRIQZs+i7MoDtEbxmt1Az3IUiFDbmlPKe0lXo8b52bXYJa7NnktEs0+aF//c6w22jrFAE47ng1hQ++GubSbvNVoZG1OTgvxxPqsLtvTzAme9dnJeV2Z1ELGRQN03VSCgChHAS3HKqGFAojqLYJAlLCMFdn9lO0+FWjv3HaWIhE82tJhLb1XdVzNjXtRAoTuee39x5W87JpWr8/LLtbJzI4+WvHMPbayNsEKpItGsiLnohkfhzU8iIn1eqy8Onlu/mxY5LdIZG8FtpmN/pSaiPTVpGVW51nqnO8z3UvdxIbCJG8bpClu1fxNkf1DLeF0QA+cMqqq466juvNlG9uwJvYO7+34xaBAdChFIljQ8ITA9Y8e5DSEEkV/CS0krb3zWhTTjj25pn6tj1K5sJdAmGC5PbLS0EviEwMJkYDnPxx3U0HW5D2hLdq1F6FQkjZUjgDgosj8CImFhx9UbFdCoypYCuTdcvIPhJxMVn6jj9nZpE15FemMq+z+2+ofHeJJqPtHH0P04n2n3dqzlqcPEM8cVn6gkOTMwY08H8bPAmEZ2I0XayM2lZ2SmNBp9JqDCGZdh4IwpbXRWIaeIG1XsqEuOWyXGRK8WVUEuajrT8VFwpelJxg+bWHCLBtPlQ1a4ychc7Y6+JwTCqS51Sp8IZr4ZGZqrkLWABbzY6z01JQLpSXMRChpOEsyR3y3JK11bRHRsj35tGRVo2NY11s27HN60vOPXN89Q+NzVvrHm6jv2/tycx1psLqq7y4B/v5dwPaum51E9KppflDyy+Lpn0+H+epe7lxsTnSUXD1SXZPP31VzjzmIGQcVWQq6KhwgRtFsFQ75BjVxssgbFSx2LJnhSbAKdtjFsuydlyy9IhyyiRaTwaBUqflwSa4JLnCoUrb44kezM4/JVTCSIMOMSLyy82ULgqj64LvYnliq6waHc5PbX9M7ZhxSynjb5NNVBm1OT1fzya1J52nO3m3A9q2TjPOZqmq3jS3DOUthRNwRvwstZdwtPtNVhSYqlW3BIPvKMCxRZsTC9mz5Ktt+eEbgCj3eMJ9bDp6KntxzKsGyJh3ywe+X/38cwfv0zP5f6kaeLQSujaIRIPbs84VP7QRvaMc+SJ0ww2D1PUotKwy54iHSNRDBBSEvU6K6q2c4+FBMUWKKZMEAIGVwiGlthILURGa5jOf+ml5pk6tv3ChhuyEpoLJesKKd9cTMfZbmzLRiiOpbZlWhjGtLiNJZGZGsFiO6EirLlVNLdKzXAXv/WpPRz5pxP01PZPXaI5bJCY9r20oWJbKbs+ueWmi2/2Fi7m0nA3IWvq/VgWyKcibX4sunNd7fxnwzGkkCi6Sv1oH83jg3y0evN1163cXkbDay1JyyZjDLeK2ufqufhMfeJzf8MQL//1Ie76sz388+XXGRwbx4iYGF6nfXaZCj6/G6EI3uhpYFXW/NWXpiOnSWVsm8r0UJuuKGzOmf8cL8Pt4/0Va/lh63ksr4YZi2FNv72T80zhkFgQgCkoOA52ukJHVnLMUIsK1LMh2k91YkRMYt/vQNtiMJEVf2ajkuKX5ezxwfj+AoszuPdP9/DyH7/OSKczwcsoDfDQH+99R6ohL2ABC1jAAhZwu/GWkGH27t37pu1LCMHLL7/8pu1vOv76r/+al156KfH5Ix/5CE888QQu11Si6fd///f5+7//ez772c8ipaSzs5NPfOITPP3007ftOBYvXsynPvUpfuZnfobMzJls4D/6oz/iueee4yMf+QjDw041wL/+67/y6KOPcv/9t4+R/27HG90NPNV2AVKASh3blpT70xmNRYhYBksDeTxavjZpELrlZ9fx0l8eTNiEuFJ0tn9i45wkBACvplORmkXzeDK5It+bRsZtSnKJeFXE9CC0oquEhpygSZrLy/KMAi4OJ1cJbc4tT8jkj3SOIaVMJGHeaUgvTKN0UxFtJ6YF0gWsfHjpDW9r+QOLaTvZmZQszF6USdHq/NtxqLcEl8/F6vcu4/S3p1ReFE1hfVxJYjbYlpxVzl3azvL5JEfCpsE3G09yeaQHifP8fmTRpoRCCsSr2oWKjkplWja6oqKnq1TvmTlJ9WensPEjazj1zfOJhGDe0hyW3FvFa39/hLalBr3rQUXg0XQsISnwpfNrK+66LRUxgy3DGGGnQmhGAmdakGRSrcOd4iL8cBrtE8MoMSeQaLrA0kA1wTQskILRChgrh/QGSUYdCWsMVVeJTcQIDYcZah2mawNEMqcRBVRo227j/28bJTylAHL+h5e457d2zjoZTi9IJWdRJv0NyZXxRavz6b7YhxACl1eHuEVC1Y4ysiquXcm87Rc28Po/HWMgvs3MsgA7f2kTMcvkB83nmLBimLblnJgP6h6W/HraZoJXxuLWYuDy6ViGSv7SHEo3FlK1qzwh8T1fjHY5wQDbtpO83O14RXfD6y3vWDKMlJLui330NwySluenbFMRJRsK6bno+EXnL89NkCY9aW7yl+bSdKg16blUXSppeX62fmx90rZT4vLSsZCR9Exbhs2Rr5zk/j+8+5aOPbsyk4f+971cOdBEZDRK0Zr8JBWBodYRjLCTtLBc0ySGhUxUXRlYjETDqCJKiu7m3FAnfZFxPrtyKugz3D5K++lONJdG+bYSfIGbJ8rMhSQymRAzNbHjv7GmBV0ni4ulZRMLOVVeXRd6eONfHJWVy3cZBIudhJRQJEd6m0HCYxVrr3ks7ac6GW5zyF+KLUjvEYDN5Rcb2fwz1173RtF7uT+pb5tEx+mut5wMEx2PJlVrCgS6R3OeKRVk/CtFESiaYPV7p2ymVE2hencFBctzOfGNc3Se78Hl01m6r4rl91+/Kv1OI114yAy5sL22I8lt2xiW87dQBHJSfatthCNPnGLrx9YjhCDXm8pPTwt8145f4fS3zmNELNwpOqvfu4zCVfm0HO/gjX86lvhdf8MQDW+0MNI+mqigjRkWiqZgW5JoMMb3f+sZlt1XzfqfWjXrMesejbQCP01Fw9h6chJSCpCqIBo1GCiR5JwHC4ux7nEuv9xIVbObmpQwkXjuVI1C6QGBgoI34KHrfA+NB1sT2zPCJk0HW8mpzqLvyoCjkqUobOvK5bR3mBEdQCAsiRID4rpjkUxHielmLEbfrQgOTHDmuxeTkj2jXeOc/9GlG37HI2NRDn/1VMLGS0rJWE8Qza0mLFAA2k93U7G1JKmaVyiCNdPe0evBNu0ZZBo9Kqh80sYq0LF1hZQhQRuNXLS9rHzIkaUv21TMpo9GqfnxZcIjEXIWZbLpo2tx+WbaQ2kulY0fXsORr55K7Ev3aNz1me1Ex6ME+yfIXZydIMIAhEbCuLw6tktFWhKhOkm58MiCTdIC3nq4/FNxKkVV8KS6kbZk5yc3U7nN8QwpnyZhUrWrjEvPX0maF+pejeq7HSvA8d4gtVcVUESDMc7/sJbdn7o+6cAX8LLt5+dnTQyOpc90Igw4Y8lLzzew5rHllN1XwSn3FZASNYpjZREfK4qIRLUhMLPeA3CUP9NanX9zzkmaHxSEc3HUxoJgTyN4JsGC9EbJRJFg+uxLC0Jai/P3eHdw3ud4qwiNhGdVTxloGuZ9f/UAbac66arpxZvuYem+RWSWBrjw1OWEeugkClfl3VaSRs+l/llVk9tOds6bDCMUwcr3LOHkf59PWr747krcfhduYFd+NQe663Gn6EQnDKRpU3xeI395zls2bnWnuGbED6QtsUybb//6U+geneq7Klj9yLIZcUJL2relIE53a6x5bDlj/3wcACNiEMake/sUEQbASIWeLYKyVyRmzKT1ZCf+MZulIyq1j1hYukSYzvsCoFgSxRDY0w9PQKDBOde+ddC7JXHWdAckYS9wYIgXv/A6d39mOyXrbq1QVFEV7vrMNrou9DLYPEygKI2zT9bGbZevYrMIEAp40jxxcrmYPDTS8lPZ9StbeOoPXsS2JbFgdE4ejCtFR9WcoqJAcTp3/fq2WzqHHG8qn121l2N9LYzEQlSl5bAua35klM7zPXzr4iGiGZM3xcSd4uL8UCe94THyrqMuk7ckm60fW8/ZH1wkMhrFl+llwwdX3ZDN01yYPnafRGgozPM15xgxQ44CT3xILhWwsDGjjn1f+8TMdmw2VG4v5fS3LiQtS9M9/MrKbbzcW0/nxAj5vjT2FS27YTWoTbnlrMwsoj04RHBogq+2H8cUDplKFQq6oiIkeD0aXlNja1YZGz9dxkDLED9qOEdrTghbA9+IoOK4hmIJeusGqH+1CSSseEEnmGUTkQbedhCzi8Jg6dCzX+MvW18lFjHwbrWpPOTGFRZEx6J01/axaFf5DZ3bAhawgAUsYAHvRrwlZJgDBw68KazUq+WJ30yMjY3xF3/xF4nP69at42tf+xqaNvOSf/rTn+bKlSt86UtfAuCZZ57h0KFD7Nix45aOITc3ly9/+ct87GMfQ1WvPVG9//77+f73v8/evXsTajJ//ud/vkCGmSdsKTnQPcVoRwgUVdAfCfIHax/Aq+uzBtbTC9N49Av3032xF9u0KViZNy8Z2A9UrOeJ+sMMRJwKkgyXlw8vujV7mulweXTcqe5ExY+iKai6mjT5/mDVBp5tu8jZwQ40RWFjThn7i5YxMRjitS8dTQRZAiXp3PVrW2/K8/mtxs5f3kxdVQMdZ3twp+gsvqfqpqpT0gtSefCP93L5pQaC/SHylmRTvbfymqSnNxMrHlxCVmUmbSc60dwqlTvKrlmtp2rKrKpGvkwv6YXzU4p4qu08l0amqg97wmN8/coxVmcW8kpXPbblkBYUVeBzu1mcfv1quaX3VlG2sYjeun5SsnzkLHIk6Yu+/Bj/68SPcWM61ThCoMX3ORoLk+m5cbWf6eg4282Bvz/i2EoBSImiCmw73gfFJbEVfVJZQLLqZ1bwrdTLyGi8Mt0C1XCktaUJ7qgg7IKJOC8gWCKIBiQFx5zErVAE7lQncNZ+uouRRTZ2vOkQpmMlZGuS8VxJWiuJrHvLsQ4uv9jIsv2LZj2XPb+2jaNfO03nuR4UTaFqZxnrHl/pWAy1TUnzam51XglhX8DL3t/Y4Vh3SKfNA2gLDhE0ow4RZhISolgc7GnkQ/u2EShOo+VYh3Mcu8ooXnN9T+i5kDVpP3BVxGqSJGIa1m0fM1i2zamBNhrH+slw+9iaW0HgNqtySCl545+PcamunWgK+AcFuc8EyKnOouH1FmzTRnOrrHt8FUvvrcIyLPqvDODy6U5VvnAIGpqu8uD/uhfvVSo/JesLSc33E55esS4Emkelv2GIaDCK239jxKRJdE6McKi3kbFwmLQMQVaPk7DIWZRFSpZznVJzUhIhSu+AJJox8/5MKnxY0k7cw+7QGG3BYcpSM6l/pYlj/3kmce/P/fAS9/7WzhkWU7eKql3lCQUwIQSqrsarOaf6/srtpTS83oIZsxLHI+PnULymADNmJYgwUZ9kLN8G0wlITyr2nBxo5ZHy1YkxRWQ8yqXnr9BzqQ8jYpKW649vc+bzPNY7097wVuGewwrp7WCR5M9JwZ/tS6p+1twanjQ3oeEIUom3zaogJctHVvlMcp8/O4W7P7v9mraBbwUmlScmE1GJplSQTACw4cIPL9F5rocH/ujuxLs1ieX3VVN9VwXB3iAp2SmJhP/Fp2dW+/fVO20HigBbInGIcQjQXRq2Jbn4TD25i7MpXjt7e73hg6s5ce51h8AzxW5zjls4fag9bcpimTZXXm3G7XdR/U3JRAFYmsTf5Si6hEWE/DW5nGxsZjTfJrUHpOFY1CiqgqIpTiFmzEZP08jzpFLxtV7GAzaNjwhQp5I8ulcjz5+2QISZhsh4lLPfu4gRNmbMA3pq+66x5uzoqulNEGGARANoGTb6NB61bdqse3wluUuyaT/dhe7VWXx3BXlLcua9L2+6h9zF2QmFMyBu3SjwjSff4/pXGhNkGHDGk0vvrcIy7etWcVftLCO7KpO2k52oukL51msTLguW59LwWosz/pj2rBesvPXq9wUs4FaxdF8VfVdZAqcXpVG+eXa7SG+ah/t+fw8XfnSZwdZhAkXprHp4acL2cbBleFbVhKvtWKKWSdCIkuH23ZLK7NXHPoneOkfZpLwiD1dXAzGf0/RrUYfAIgHpAtuG8QoY6YbANE5NLBUGl4OZIvC3S4aXCIwAaDGHzGmmxO2RJqsWpkGYkHsehqOS0Qrna9cYlLwsE4SB6xUX3E5oLs0hsk4rDgBHBcaV4mLZ/mqW7U+e4+36lS0c+IcjBPuc2FNmWYAtP3trxJFJpdmGN1qwDZvMOa7BfC2SuiZGGY2FKd9bjjfd42zXtCnfXJKk6Pdg6QpK/RnUDHfhVjSWiRyKNwZuSv33dsEb8FC+pZjmI1Mqk7FQDEVTMSMWZsTi/JOOp+WaR6fsrA71NPJyZx1BM0quJ5X3lq+mOv3m+5KSDUUEStIZaR9Fc2uEAuasSkcTcW6KNG2C/Y6Sm9Zlk30WBtYkP//6GJS9KOm4GyJZjiJToB0yzzvv3dW/BxiphoKjEo90lKRulgwTGglz5UAz430T5C7OompHWaIYredyP6MdYyi6MmWTBHhiKnkxD2NKcsO1PKMAt6qh5ftxp7oda1jhjIevhhBOwY3mEqiKSixmcG6wg8HIBGWpmVSlzX8sMx0Bt4/7Sma3M5sLtmVz9D9OE96TrEoaCxt4Ut0MRiauS4YBRxWzalcZkfEonlT3bbNHs61ZOgig3wpNKV5OHzYqTBW+zXHc471Bznyvhr76QfzZPlY8tIRl9y2i/tVmrJhFoCSdbT+/nuxAJj8fuDWSEjjFqoWGH9L8LHZn02oOE8XClBLLNPCOCCqfF7gnbLzbIqR/PJWMknTuHQhz9ge1WDrokan3IBKMYcUsRNzOyz+o4Aop2NJGuJRZbbm69yiMVwuwTSLCJFQKowU2S17RyegSnPtBLVU7yxbUYRawgAUsYAE/8XjLbJKknH3Q827BN77xDYaGpqrrv/CFL8xKhJnEn/3Zn/HVr36VUMgJ2P/d3/3dLZNhtm/fzvbt2+f9+7vuuov777+fZ599FoBDhw4xOjpKevo7U9njzYQlbcaNmRV9lpQEzQh+19zJQlVTbjjJm+P189ur99EaHMSWkorULJTbGLyv3FlG48HWhAUNOAnTinhFGIBH1XmsYu2MCvXDXzmVVG000j7KwX8/wQO3qCDwVkDVFJbfv5jl9zuWLKNdY0wMhUjJvPGEdmqe/6Y8pN8s5C/NIX/p/Cfmmz+6lpf+8mDC01v3amz7xQ3znhifG+ycsWwgEmRZIJ9LF9townmG9IhgaVca6irmJcPsBJOSq2Q0l4qpgzrLBsLWzAq0G8WZ711MJB9dKS5s20YgWP3Yck7+9zmkLdFcatL75CryYo1KVE3FEAa2ArbmBFR9I0Cego7ANKeOb3CVIPe0xJaSzvIY4UKDxq//GKXfJLpxmke9DlpUgiIwUuSUtb0Ey7A49vXTFK/NJzV3JkHNG/Bw92e2O+oVQiSSQPs/v5tLLzTQVzeAPyeFZfdVX1fevOlQK2e+d5HQUJjU3BQ2fGh1ggyT4fIljQNk3PpJSGjs7uXpP3mZB/94b1KbcysoWVdIwUrHskmoCtKyEaqC5nYuWtnGotsaHJBS8kT9EepHp5KFR3ub+bWVd5HtuX3EwI4L3bzkamH4gXiwSELRiQFynh1MBI/NqMWJb5wld3EWKZleImNRxzM9Dttwgi1WzASS+ypVU7jnN3fy3c/+2AnKKM41UxQF1aWium5uGNk8PsC/XTqIZdtEgjGkbRNIVVj8jE7jwVYe/OO9pGT58OekULQ6n/bTXRS9BmNlJFcWTsulX518iFgGRtjg1LcvJCVizIjJyW+dv+190qJd5UTGotQ+V090PEbBylyyKjLorx90ZOZ3llN9dwWxkMGV15qT1lU0QcOhVs796BLBgQk0l4pUp87HmpY8tmwn0Y9w7JSe/7+vMdI5SnQ8BlLSU9uP7tWxDAt3SjIhZZIgeDuRvSiTzLIAQ60jScuX3HN77OduBaGRMC6/i3CjMx7X3Cq+TB/eNPcMO7vx3gl6LvfPSXh9OxFhAApW5uHL8BAadvpgRXXs5YCkoP7kazHSMcrBfz3Ofb9/14xt6W6NjNIA4Ch36D49iUAE8Xmb7fjZu1NcGGEj8VxqLhXVNdW/tp3snJMMU7y2gB2eZbzQcxmBjRGvnoR4sFtCIO5sJSf/ozjn50134x61HUKDBmiCUCE8XdWB7QW7QuIag4of27jHnFXbTnbgTnGjezRiIZPaZ6+g6iq+Ppu805LeTc5FEnFCzwM3aI/2bkJ/eJxn2i/SPD5IltvHeruAtn+qIxqMYYQNjIiJy6cnCFg3MxZ2pSSrqwhFIFSFq7vf7CrHTrF6T8WsaoDzxfaPO+p0Qy0jgENOH+uZqcBghGfXnJ+vnUF6QSqrrqMcaVs2NT+ucxK0lo0Vs9A8GkIIKreXUrr+5qT+F7CA24myjcXs/BVJ7bP1hEbCFK7IY93jK685v0vLT2XHL22a9bvJcf/M5VPFE8+1X+SNnkYM2yLD7eN95WtZErg5y6BJRcOr4Y8vr3+pkbKISuMOE1s4iXk1JJG6QJjOONpMgfZ7BJ5hiWcIwpnQ9OiUssXwEudvNQpSEZhxy73ksaic+lsBHzr5J1XC5yxCRgz3kMMrRThzx7kU1W4nei73039lkLR8PxXbSml8oyXp+6qdZeie2cf1GSXpPPr/7mOgeQhVV8mMjxluBpZpIwRc+NFlzv/wUmL5WG9w1rlYyd2l/E/DSWqGO3ErOlvyytlXtDQR+4pZJl+rP8qVMYfwpCsq76tcy71bds15DCszC1mZeeu29LcT235hA/7sFFqOtSNtiRk1k2IHAPWvNiXIMLXD3fywdUoBpy8yzn/UH+Fza/bftFWsqins//xuLr/QQNupTkIhCcyUotCDU2M0zaWge3SMsEHeCQvLCyPVAqmAdxCKX7bxjghWPqMzXgSta0xGyiQjPytIbwTTc/UMDhBg+xWEIZi4ajw6XwT7J3j2T19NqFY1H26j9XgH9/72LoQiWPXwUrov9jHaNYYRcuZaqkelYEUu+7es4qnBWhrG+hHA0kAe769wyF+qrrLxI6s58pVT6F7dUfZMmo86UOLzBlOXNN5ncrLhROK79dklfKjq9hUyXgvjfRNOLKZfMFw8LfZi2igISv3zJ+IpqnLb1VUrtpUkiF6T8KS5qcrPpa1zBHAuq2o6CsqT1rAKgv3FjlqgbdkMNg+julRSc/08/+evJWKU4ZEIr/3DUfZ/fjdr37eCWNi4recw0DTE4S+fZLB5GCNi4klRkO8HM8spShExMHRJ3dYYy59RaT7STv6yHBbtrmDpvipajncw1j1VrBIoSaP5SKtDtsKJwbtSXE5xjSHRPHqS8qFQHEXo4DIVxaMSMmOJx9HU4cpugzVPuWAonGTdvIAFLGABC1jATyreEjLMz/3cz70Vu31T8eSTTyb+Li8v55577rnm79PT0/nABz7A17/+dQCee+45YrFYkqXSm4G77747QYaxLIu2tjZWrbrzk/N3OnRFpSQlY4ZUY5ruIcc7P6WMG4UiBBWp8/OIvVHkL81h2y9u4Nz3LxIajpCam0LppmJajraTWRagaHX+rImhWCg2a6XoQMMQoeEwvoz5TTyklIRHI7hTXG+KR/L1MNw+yhv/fIzRrnEQTgX/jl/a+LaofH+rEChO57Ev3k/nhV5sw6JwVf6sEvJzQRUKBjOrGia6Jsj+ZohUnwvTLfGNCCw5Tl15IyseXDLLlq4PIQRLM/KpvcrWK+DyUuC7fiXM9TAa9+OdhBK3XSpalcfwjjI6zibv15PmZlFVEWkXahkzIqhejZh0EjHChli2wFANdKlgxv3oFdMJBPStg4F1CpZHOl7bNphr4773k6+KANMrEBZ07RIMrIbypyXu+GFK27EFWveBua2wJobCNB1qxTZtyjYVk1WRkVSJdj30Xxnk0JdPJoJD430TvP6PR3nPn+0jvSCVVJeHZRn5nBmIV8DFf6eYjkxseCTC5RcbbltwWFEV9v7GDtpPd9F+ppuOs93EJmIIIchfnsumn15zW/Yzicax/iQiDEDIMni1q57HK9fPsdaN4/XOBoaLp4gSUkD7JpuUVki3kod47ae6WPHg4nhlfDKkZc+Z2EzNTWHJ3qoZMsaLdpXfdEDl5c46rLgawKSdy0ihTTDThqEol19sSNhW3fcHd/Hal47SdLiNFV816dqlMFYBlgsUI17Jq4BLURNBdJ/mojItm5GmEczIzCTnQOPQjGW3AysfWsKKBxZjGtaclayLdpXTdqLTsULDsdQwYxaXX2jA5dWxoiZWzMIldbwjgnBAJiWKl2cUoCnOdW894QTQzIg5ZckkJbblJBps007YNwWK01i6b3ZFqFuBEIK9v7mD09+6QMe5btx+N0v3LWLx3srbvq8bxat/e5jhtlE8qW4s07nei++uSLY/nIZocA7t6bcYhm3xdFsNp+Pt5frsEh4sWcE9v7WTo187Q/+VQVw+nRUPLWGkY5TGgy1IU04KriTQVz84J6G3r36AY18/w0jHGC6fjitFTwRigUR1oqI5ijRuv5tY2MCMOVLl0xNYuvfaY4F7qpfTbA3TEhxCsQyiloVLUfEKlayDUdyjMklIYDIZJOInZMUsPGkeJJLmnQaWa8o2LZYq6dwtqPyxswXLcMgzihqvpJxkXkrIPQXePsloFRQuzuGR1ZspSgnM/8a8ixCxDP7l0hsJYn/IjNEw1sviNJW0iJJQEYiFDTyagqIqrHjoxsdkhSvzSMv3JxFSXD4dT6o7YbeWmpvC9o/fWpJoYjDE8W+cpfNcD64UncX3VLD0nkX4c/384LefTSRJJlGy/s4nRc9+7yIXn3FURBVVAZejrrD94xtvKbG8gAXcblRsLaFi6/zsN66HjJJ0yreW0HJ0SvFCdamsfsRJYp7ob+WVril13eFoiK9fOcrn19xHquv6trtXo2R9YULZYhKaW2XZfY7SSTQYI7NHxfeUoC8vhrQl4WzBaDXJCjaKo06Rfwz6N8y0eLFVh8jsDLXjGoZyeqcrkral57p56Hfvou1kJy3HOxhXHDZB/rIcNnx49QzVtmuh4Y0W6l9pIjYRo2R9Iavfu+y6/e7BfztB8zTbuYzSdJbtX0TrSWc8VLm9lNXXmecJRZBTdfOk6vBIhGNfP0PH2W4UTRALmUmEQxFXjCtalU/PpX7cfhfL9i/i9cJemgcd5UXDjvJyZx1iWiL8la66BBHG+Y3Fd5tPU52eS9pNPENvFVRdZe37V7D2/SvobxzkuT89MOM304mbpwbaZn5v25wd7GR3wc2P990pLnKXZHPhqcuk2ArpXQqjhcmKbrmnpUP2EALdoyMUgSvFhS4li88KzBqJpYNrAkKRGL13KQyVWURSnfGXEnPu9+gix67MvqpuUA+CP6yC5qgmnf72BUY6x8gsC7B036J52SXXPndlhq13T20/nRd6KF5TgDfdw3v+9F46znQxMRSiqbWXc9Eu2l3d1H15kAd2redDWzaiCIFfT95f1Y4ysiszaT3ewcRQmCuvNWFGLFSXim06Nj6TJPGxTToT6RbTTdJOD7SzMbuURbeg4jNfeNLcKJpC8TmNYLZBzCMTXL1NsXz8+lv7jqx8z1JCQ2GaDrdhmzZpBX62f3wj7qIULox00R0ZQZo2qgEpA4KUMYXqTaXsXrSUEn8GQ20jvPb3RxIkfk+6m/BIJGleIm3J5Zca2f2pLTMIZrcCy7B49W8PExoOJwqNZNDCdCnoIcdua1L5JpwN426DlJCk42wPi3ZX4Pa7efBP9tJ6rJ2xniCB4nSOPHHKiSXG1aVt006Q0St3ltF+qgvp0zHCzjPmSXVRva+KlrQmgpPFudOcv6SAwVKbFROZC0SYBSxgAQtYwAJ4i8gwTzzxxFux2zcN4XCYAwcOJD7fe++986o437dvX4IMMz4+zhtvvHFdEs3tRmpqMnFjYmLiTd3/OxmPlq/hK5cPEYorTWhC4X0Va9+xcuuLdpVTtaOMyFiE1//5eJJkfsHKXO7+7I4ZFZNCVWaV3RWKSCTkroeeS/0c+/ppxrqD6F6NZfdV31AS/nZD2jJJFhjp2OKc+taFG/Iwnwstx9ppPNiKbTkyvov2lL9j5CtVXaX0JhMIm3PKeL2nIWlZSUoGVr0zkXWHBO7Q1HXovth302QYgEfLVjMUmaAn7DBCUnU3H1m06bYoKmWWBxi8Su5bc6ukF6ay8SOrGe0aYzz+/GgeR0HHpWu8v2Id32g4RkRzyCwCgRvVUeiwIabYCTUcywVCg95NIhEwslUnmCRVp5JRjYKtx0kx8c8AsXTo2imoeCYegNIEkfGZSlaT6Dzfw4G/P5J4jy8+U8/mn13Lkr3zV3poPNw6QxbdtiTNh9tY+36n8v4XlmznC5EX6BwfcezYTHAHBXl1zkmPXEUyulUoqkLZpmLKNjlS78MdjgR06h2Qxe4Jz25H0xu+vefU4w/DLN10sEyQ3pS8THOrTAyF0dxaUkURimO1Mt2K52ps+bl1eNLcNB9tRwio3FHGqngi42YwEHESoVcTc6KpEv8QjE6rkFJUhbs/s52dv7yZ7/3mM6QfN7BPSLpXWAyU27gDbjwpHsZjTnIzVXfz4aqN6IqKPzdl1j4pLf/OkFTB6e+uKekuwLad4BZSEov/nSAaaArStDHCJosOuWjYaWAWONurSs3mfRVTxK3JfulqiWlpS9wpLpbcW4Xb7yI1z0/ZxqI7Ri71pnvmrAp/qzDYPJywdhOKQIurGDUfaadsUxGXnk/uf1SXUw36ZmK4fTRuAaNRvrUEb9rsAekfNJ/l5LSkx+HeJiKWwYeqNnL/H9yFETFRdQUp4dz3LzoB1HgDPFmbrro0bMvm1P+cRwKl6wsp31qCEIJYKMYrf3MokWSJhQwi41FHYSguxS0UR+2s4UBzQhHG5dNxeTWmJ/0UTWHR7vJrnrdL1fiV5bu5MtrHQCRIvjcNr+bCH1V54UevMayOJkhyQhFJVmOTywDC6ZKYL14ZKZzltgXBIgluBdUS2KYVt1xUEodpxRyymARSOyCjT6Uk6qboocA1j/vdjAuDnUkKlzJOquurhrQ+pyrVjJpYpk3eshzWPLr8htQEJ6GoCvf+7i7OfKeGrppevAEvKx5YTMXWEvobnWRnTlXWLSkxSSl55W8OMdLh9LfR8Rj1Lzfjz0phxYNL2PXJLbzxz8cShJjcxdls+NCdLfywLZv6V5M7ZUVVGOkYm/O9X8AC3i3Y8YmNFK7Ko/NcD950N4vvrkwoxiRI8dNg2DY1w11sy7txUq2qKez/3G5qn6unr95Rs1x+/2IyShyl46I1+Yz1NKCNSHK6nP6jY0985cn5t5zqPwEimcn7EDYoQkFP0bAsI+6x5BQhSEWCApMdjgC8qkbbfsHZH9fScbQraVsV20rJKJ6/CnP9q00c+9qZxOfa564w3DHKvb89twpK98W+JCIMwHDbKGWbinn/Xz84733fKl770hH6Gxwyuhm1iQWjaB4N3TNF5BEINnxwFYH4NekNj9F8vjbxvS1tTNvmte567i1aiiIEtcM9XA1LSupHe9mYUzbrsUTGoox0jpJWkHrblS5uB7LKM/BlegkNhZOWTyduWnMordtyZtHDjaL22frEPHHxIZ2epRbDhTZFFbls8BWR8wEPocFQkrIPOASX6j0VZFdlMtQ6gifdw3c6zzBcbjv2NpPTEOkofUyq/+lBMOLCqUoUig9IjAkTb6GXgYYhOs44hUWd53poOdrOg3+y97pFaSOdo7MuH+0cS6hyq5oTGzj0Rg0vFLUnCosGyyIM1R7jNxY/hH8Oq/f0glRWv9eZC695bBmXX2hguGOUrLIMyrYWO2R8v4untQbENLLWJJrHB98UMow7xUX1XRXUvdTI0u/CULGN6YHMXo1QuJOL43W3FGO7VaiawrZf2MD6n1pFbCJG6rTr/dlVezna2UT95Q5cLSZVIpOV9y0msywAxO2i/+lYkprleN8EtmnPUEeNBueOfd0sui/2ERmLzogzTL6aV8/PhXQI/dPnNbpbY9FuRwGx9UQHtmHHiw4cFU7bdIpidv3KZsq3lNB5voeOcz3kVGeSUZzOSOcYVw40kyEURvNtp9+aVNy0nH0qumDjh29v4dcCFrCABSxgAe9UvGU2Se9mXL58GcOYsiDYunXrvNbbti3Zr/LChQtvOhmmuTlZuj8v7+Ykan8SUeLP4PNr76NmuAtT2qwIFNxURdPbCUIRdJztmeG/3V3TR+vxDiq3J1uY6G6Niq0lMxQESjYUzquCIxqM8urfHU5U8hthk/NPXiIly8eiXeW3djI3icGW4SkizDS0Hu+4ZTJM7fNXOPU/U/K2PbX9jPWMJxQR3s14oGQFprQ50d+KaVssDeTz/oq19I/OVBYC8GXeWqAq4PbxG6v20hYcxrBNKlKzUZXbQ1Rb//gqXvmbQ4mkoZSSjNJ0nvzc89imTenGItY9vhIpIW9JNl01vZz+9gUyyzP4/Or9fK3xGI1jA2iIuNXJ9DTmFKQC4qpcttTivxSgxie8k3LD04W6x8vAdIMWdRKBk37Zs+HUt87PmNSf/e5FKneUzZnkjwajtJ/pRtEUStYVIufwf55OftAVld9ds58XTp7j5KkruIOQ26CiR50jnwx03CnMFnweik5wvK+FoBFlSSCPlRmFN0VOK55DXeB2qw7k5KbT2jaMnHZdhSJw2ckPiubRqNhWitvvwhvwOEnj+D1WNIW0/NRrVpWqusr6n1p125R6yvxZDEVDM0iSKQPO5+yqzBnr6B6Nu359Kwf/9QThkQhFNRpbtAJ2vmczukejLzxOyIxRkpKReLe9aR6W3FvFpeeuTG1IwJrH3jqCZXg0ghmbUnKZfCeUeGDM7XNhRExs0yIvJZXdWVVkbMpFEypZnmTiVu5iRx1OURWsac/ApJ1B5fZSsitnXsufBJiz+KmDE4Rc/d5lDLWO0HvZGdtoHo1tP79+RuD0TuLyS42c+K+ziWb+/JOXuPd3dpFVkSxXHrEMzgy2Y0QMx9pJgupSONvfznvL1uDV9IStwZEnTtHwWgsur07EjCaYMJpHRwjHIqw1rorTdqKTgaZhNv30GtpOdc2wiVFUhayKDJbsrSQ6EaNodT7+7BRWPrSE1uMdSEtSuqmI0FCY09+pYah1hEBxGmvfvyKRdLwWFCFYEshjCdPmGSmQkullvDeIHVcfMaPOebu8zjOtaip6ho5t2GjxvkIqgEsgbUBKtKiCx+vIiMeCVoJYoekqVtRCIqf1kQLNrdJ7eQDLtOdti/NuQ8hMtowUAIrAjL8SIl797XGp3P3p7TekBHg1UjJ97PzlzTOW51bfHrXLgYahBBFmOq4caGbFg0vIW5LNY3/5AINNQ+hefV7P661isrL3akhbEgsbeAPv7PniAhZwLSiqQtWOMqp2zCQmKHOMsedaPh+4/a451S9Xv3cZg83DdF3odRYIQXa7xvAya8qaD6ciPxDnzHoGIHqVk4gSkWRmpdAbHsPCBgvUmDOBM30gLImKgs/nRghB2DQ409RCatCxztBcKqquUvP0Zap2zk7YmA2Xnr8yY1l3TR8jHaMJAsnV6LsyMPvy+tmX3wmMdo8niDAwqTanYkatJDKMPyclyVorapnT/jYSnyOWyT9cPMAvLd1Bijb72M03x/ILTzn2TLZpIxSo2F7K5p9ee111nTcTiqqw+1NbeP2fjiUIMVmVGbi8Gk/94Yt40z2U7g1Qe9V6qhCszrp1y73pJBzFFhTWahTWwrZfrGLRunLAUcVoPdnBaOdUAYPLp7PigcWk5vk5N1DLkR+cZfgDdpwwBpMjL6kLMAEhUEzJkv+RjJeAVAWZAyqqKRA+gcujMTGYTAga75ug6XAbS++9tvpNZlkgMc5PXj7TFui1wUbkVd3wQJnF+dON7Hzg+iQCX8A7Y46cUeS8j9ktPUnKRZPIuo22ydfDpo+swZfh5eh/nCa7wbE2nVQJOf+jSxhRE1/AS/mW4rdM+drtd+H2J+/bq7m4u2wpd5fNbkM52jk2w/pS1RTMWawv57JvvSWIq/4f/zOjAYavOmRvH7hHnR8UrZk9Duf2T8XLFVVJfM6uzKBsczGHv3wyEWevf7kRVVccIpCURKJgZytOIV18kqMrCm6vxoc/ehf5uXdGUX4BC1jAAhawgHcaFsgwdwCXLiUz5Bctmp9MZXl5OaqqYlnWrNt5MzDd3ik/P5/y8vI3/RjeyfBoOmvTimg81Mr5phrSC1Kp3lOeNLB9p6G/YXD25VcGZ5BhADb/7DpUt0rTwVakdKSWN354fuSOtlNds1paNB9pe8vIMHMlRm61wt627CS1nUlcfrmRVY8sfddbMKmKwqPla3i4bBW2lOhx2w/fxiLO//ASwb4JzJiFtG00t3Zb7D2EEJSl3v7EcP6yHB7+P/toOtSKGTUJ9k/QdnKq8q/xjVai4zF2/spmXvjz1xhqHUl8l7c0m3s/voS2oENoSKh1CFAjwmG34OTNbTdTEtyT0qeKE0RK8GYmxT6Mye/BcjuLL/+cIOMyLK7T57QEsAwrKag1iVjIINg3MWvSqOtCD6996aiTqMUJZqyejWwgoHxLsuy6rqg8sGEtnjdCdF3sTSxPzfPfEUuXa6FzYoR/ufRGItB6vL+VTTllN2VrVJ6axZrMIs4NTdmxpOke7ipYfEvHON4/wcTABJllGbh8OjsKqrg43E0s6lQOKYogIyWFRx5aSe2TdYx1B8muzGDDB1cnbOrW/9Qqjnz1VKINUzSFjR9e/aYqUu0vXkbjWD9jMoKiq47VWq2KZ0I4dj73zq5ClL8sl/f91QMMtY44iie5U4HE3GmWhJZp03ayk8GWYQKFaWz7+EY6z3ajeTSqd5cnSCRvBZoPt+FOcRELGUjLTsjCT9qrCUXg8unkLingvt/bc81t5S/PoWJ7KU0HWx0bIFsiVAXNrVK+teQnlggDkLMoc9aq2rLNTqB3/+f3MNgyTGQsSm511puaBImFYpz5zoUkvmMsZHDqW+fZ//nke27aNpFwLCmwa0UtwlYUw7bw4hx3NBij6ZBT+a2oCt50D7GQgW3ZuFJ0R31LTR7P1L/SyIqHFjuS7jETK16NqLlVFFVBTGuzo8Eo4/0TpOaksGx/dWIb/uwU7v+Du27LdRnrDTLQNJwkYa7qjrJTelEq3nQPy/ZX40lzc+jfTzLWPY5vJC7fL2xQnD4vt0EgEKiaSnpRGoqmEBmNklGWTm51Nhd+dAlpS0cxyKM71yvg+YklwgAsz8jnmfaaqUdSOApXgc5kYuvSfYtuiQjzZmAuItz05aqmzNoPSCk5OdBGzVAXblVja24FlWm33l9obo38ZbkzrGTTCvyk5b95CbEFLODthg3ZpTNsRd2qxsqMW0/oXw3LtBP9/5kfXOTMt2pQNIF3SKPspKB9hYHlAS0E+Uck3ngIJO+kJFgisKYly7MuSj71vm38+MQpTvr7pqQAANeYk+wXSIjXcsQmYsiQhR2fn1kxC0UVN6yCdbXtyyTCY1ECc6wzfaw8n+V3ApYxs13WvVrCHg+c+eOOT2xMuibFKQEyXF4GoxNJxBhdUeicGOFA9xV25FfROJ5Mesj2+FkSmFnU11vXz9nvXUwcUyxscPHHdTQdbGXVI8tY+74Vt3yutws5i7J47Iv3M9g8jOZWOfrEaepfdQoXRzrGoLaPzZ8s5pzoJWpbpOke3lu+mkz3zSueXhruoXakm771FrInijY8Zamj6iq+aTabqq5y3+/dxaUXrtDfMEhqnp/l91WTmuen+Wgbp755nqjHnFJcskHYEqkIUMDt17ENyZJogEXbPLSd7kbTVef+x8NgwYHQrPPTse7gjGVXY/kDi2k72ZlEpileV0D+8pmqduNeY4aiLcCo25i58AaxK38RZwY6iFhT28rzprI688bVlW3L5vKLDbSd7ERza1TfXUHZxuLrricUQcm6As58Jzm+aEQMwiNhzj95CSEE5394ift+b0+SOsvbGbPN3RRVwZ3mQtEEtunc1JINhSy+AYXj+SJvSQ6eVBehURsUAfE4XukpBcULwxVg2Tb+Tknxq07MxZfpo2zz7DaEeUuzyShJZ3iazR84Y+/umt6kglPbsgkNh0GA4XPUoxVTYlsC4qkHoSn83LJtFGcuEGEWsIAFLGABC5jEAhnmDuBqdZXS0pmEgdmgqioFBQV0dHQA0NTUdJ01bi9+/OMfU18/5dn8gQ984LYnx0xzJtHh3QTLtHnpi28w0DRV+XL51Qbu+/0971gZbF+OF1vMlFv15Xhmv58KbPjwKjZ8OLk6Yj733rKsWfdlS+ste3ZSC/1kVKQz2JJsg1O+o+SWjskIG4TGw0mVBOBUj44PTpDu+slJygjAtOPXUoVdv7qZZ/73KxixGEIRCFtw9kcX2fXLm2+5TRpuH2W0c5SMsgzSC26fVYo3w82K9yzGtmy+8+kfz3iO2853cv7pWgbahhybFMvGDBuc62rl2De7yFjqZ8QbAylRTEnquCCSIqeCq0Jga6BLgY3AtC2kdPJ/3gFJep1kolQgbLA9gkiGRKiCqG6DDYrpxHiGlkKfWyZIlzMgICXXy3h/shqS5lJxp7tmPPO2ZXPoiZNMKDEGlzgSyJntFo0Hm1n7wRXUPHWZWNhwqjTft5K0Iv+s782uX9tM+6lO+huHSSvwU7GlBM2rvqnv/QttF4nGYknLTvQ0sz2nnDxv2hxrzY3Hy9exMj2fpvEBAq4UNuSU4FNnXsP5wDJtjv7HKVqOd4B0EmsbP7Saqp1lfLRqIwe6rzAUCVGRlsX+4mVke/yUr0tOJEzut3xbMenFqbSe6EBRFcq3FJOWn/qmXut0zc1nl9/FuaFOBobHCPQqpBQL0jb4Kd1UjOJSrnk8gVLnfsz2G8u0efVvDtJbP0XkzChJZ9/v7EoEzSbXswyLrppebMOiYGXem0JCNC0LNHClOQQFBNgxCyls7Hj7prk1Vr9v6bzuydZfWEfFzmK6a/oY75/A5dXJX5FD6bqid/2Y63rY+alNHPq3E4n2rHh1Pqsfm7qu6cWppOP0A2/mtepvGSQWM2b0/70NAzOOwyNUUrpsRtOT+xRvN8S6Q3RkhHiuvYbGkQHMe8MUXlLJbFOdikC/hpSS935hH09+7gVk/J+IX9K93CIUsInWHSfzokUsEkv0N6YpcKW4KN1cxEj3KAf/7QT9TYMoiuKQy35hwx1R7oqFozPHgAJUr8oDf3x3UoLsoT/dy2D3CHXtB3FZEUzbWU9DIIt0/L1+cquzWPPQMlwpOmbUTLzfsVicOCQckqyNzZL9lQy2D3P5pQYmBibIXZzDkr2Vb6tK8TuJDN3LQ0UreK79ImbcYmFdXjFb9+bR6ulA2pKyTcVU7ih727crmZXpuNL0GXaQRevzr3vsP2w5x7G+lsTnc31tfKhqI6tuQ6X9xo+u4sA/HGWsxyEc+wIetv7i+rnHYwtYwE8AVgUKGCpYyhvdVwhZBnneNB4pW4VbXHsceCPoONfN2e9dZLR7nPT8VBSXwnDbKEJ3lJmMmEFGrYr/giTmstEnnPmVjHc5rjFY9G0YXgKWV5DSJUlrFwyuH8J+YYjMDZKhIme+lXlJ4p1Q6Nzi2FVERpy5vj5s4+uUiW0CWDYYRoxYNDaDrDoX8lZmJxTeJuHy6mSUpc15vYrW5ZFW5Gekayxpneq95W9ae55akIK/wJes4qDB4nsrqNhRim3Y5C/LRXPNnPt9qGID/37poHPBAFUouBUVLJsrQ73sK1jCB8vW8npPI2OxMIvSc7i/ZAXSsjFJHlO0nGzHFja2LYmFo864R0A0HOPcU7X4c72Ub51f7PbNQkZ5Oj2X++lrHpwxbkx9JcTnf2M/40aEDLcP9Rbem2fbLvJGTwNSSiIpUXivpPxpm5ReHJtlBK//8xEe/JO9KIrC5ZcaGGoZJq0wjc0/t5aULB9G1OSNLx9zbJYsiSsK7kFBNCNu2RkGyy1QFIHbo7E+v5SHS1cRXD9BZ21vYpw6ifTC1CTr3MQ1qZj7eZ+Ey69z3/93F02H2wj2TZC7OIuS9YWz9rklqRlcGulOXigEK5eX3vI7EtA8fHLJTg71NjIYCVLqz2RHfhXYcir+NU8cfuJUkuVZZ20PW35m3XXtSQG8WR48GS5CcYtIaUuMqIHQBFJxrvvEaIjTP7jAjo+/vaxv54I73UXB6hw6L/QmLV/+YDVL9y1ioGkIf04KGcXpDLQOcvY7NfQ3DpGWn8qqR5ZSsu7m7N7Bsay78NRlQqNhpGmjeRRs22aiTEGt8LJvVRWxy0FqX7qCFo4Xt2kKGz+yComNac6MeQPs+Y2tnP3+Rbpr+vCme1h6TyUlmwo5892apDmSaZpIYRPJgKZHBKZ78r0RuC0Vl66jCFjsz5n1Gda0hVTgAhawgAUs4CcTQso5zEYXcNP43Oc+xxe+8IXE5+HhYQKBwLzWXb16NRcuXABgy5YtHD169E4c4gyMjY2xevVqWlsdtrHH46GhoYGiottblXPgwIHbur23G4Y7Rmk72Tljed6SbPKX3XlP2DsBM2pSf6ApSULf5dOpvqsyIa952/YVM7n0QsMMi5aSdYVJiRfblkTHo+geLamC+E7BCBt0nOthvDeIUAVZZQHyV+Sh3GA119WoP9BEOD4hnYTu1Vi2r/qGK8XeTei51E9v3Uw52crtpTddxSalpP10V1KlRVZFRsIz+nbBtmxqflzHbF1ram4K43HLLTNqMl4MoVxACASg6yoFET+x+iC2BgMVFpY2VVCVZrsJeZ0Aji0dm4cybyauVoOR1jGIq0tIBcZzbMK5gphiIeJF85Nw2yr3LJm78m2kY5S2U11J51CwPHfWKurIeJTzxxoYLHWq8gGQkNGhsGnXMgRO1ZHu1a8Z6DVtm6bxfvrC42hCpdgfoDhlpozxncQbPQ1EzJkVYKsyi8j33TgZZi6MREN0h5yAdIEvjYDbd501oL9xcErSPQ4hBEvvrcL1Jlq73E4YYYPWk51MDDo+375ML2Ubi29ZdWC4fZS2U51IARNZknCaRLGhOC2DxZVTY5rIeJSmw20YYeeeK5pC+abiO14N139lMEkFKbHvzcUE+0OoukKgJB3XT0gS/k5DSklkLIqqq28bRQsjbHDphYYZ/YQn3cOSuytn/P7M85cYKDIx45xqLQIZLYK8siwaM8Yw4skeM+qwHjNbFDxBp+NIyfSyaHcFDW+0MDEYwtKhb5GFjA/fVJeKDFtk1wkI24kKYE+ah/zlObSd7MQ24h2I4lg76F6dZfsW3ZFxSt0rjTMq3zNK0indMHM+MhINc6K/JfFZ2hLbtLGFRJMKQlfI8qawLFCAV9OTftffOMho1zhCEWSWBfAFPFx5vSVp/OnL8LJoV/lP1HgsZpmMxiL4NJ0U/Z2rajkxGKL1ZGeifU/LT6V0Y9E11X8ilsHB7sakRBxAiu5me97M9/JmIKUkNBRG2hJflu+W5xELWMC7BbaUmLaNS729sYXwWIQrB5oTypu2ZcdV0JxxtFAFUoKiCKTtvP1XxyFmgyfNTUZxGpfEAIZ3kkvq7COtyVHzDOUKbF3iHgV/O6iziLpo/z979x0fRZn/AfwzM9t303vvjSQkhN4RpQgWBD1Q9FDPs+t51lNP/amnnp7tbGfFXs+GeorSkd5DCwQICem9brbOPL8/lp1k2SQESLIb+L5fL15kJ7ObZ3ZnpzzP9/l+1QKSJsTDJ6T7bB6V7c0oaqmDyW6Fj0ID7WE7WF3HtWvs8KiTTvCwW0XUHamHscEEtV4FPk4Dq0KCr1KDQM3pZxI5FeYWM0q2lcvneJ9QA2JHRPWqP6na1IL8ujJHEGuniJBQrQ9ygk6eGcOp6kAtqg/UQhKljmsbON5HZ9nYhDFdZ23wJOe9zYnUBtVJywX1Rufzn/O9YQBULQz+x+dK8gIHQSkgNDUYzeUtsBg7JpAo1AqkTklAVUEt6osbXTKx2XVAcxIgHr9dVVo4hNapkTU5GQLXcU4u21XpMvlMUApIHBeL8j1VjvMmc1yg+oTqkTA2tk/Pn202MzZXHIXV7riO5ngO0Vp/ZEX0ft/qb1aTDQe6uHdQ6VXI6GU23ZbqNpRsKYN0PBuxaBMhOLPxnMbreQPnxJbmilZHP22sP8LSQ1y2yWax4+DyIy4ZqjiOQ/SwiOOl2hTwi/TpdVCio1RXicsyJgDm4Rq0So7jG2MM6ioJfuUcmAg4++mCEwO7LZPUk7qiBpTvrpIfS6Lj82tKBqx+gCTf5nDgOEAlKHq8fp0yZcopt4EQQgg5GwyKcFCj0Yg1a9Zg06ZNKCwsRGNjI9ra2mAwGBAQEIDU1FSMGTMGkydPhl4/MDdzPWlrc03bqNH0PiOIVqvt9nX6C2MM119/vRwIAwCPPfZYnwfCnAvMzd2kru1meX9qsZphl0T4q7XgudPPMqJQK5AyKQG1RxpgbjFD66dBcFJQnwfCAIBCpUDC6BiU5VfC0maFoOQRnBjkEgjTXNmKsvxK2M12xyBGrD+ihob362CFUqtEwpgYSJIjAKGv/lbU0HAc3Vgq35hxAofonIhzauClK+2N7V0vbzCddjBMc3mLW8rR+qON8A0zwDe87zLE8AIPn1A9WqpPOA/4qqEL1KG1xggmMdiVxwNhAIA5Zh/aRQnWIA5xOZFoKmuGXxPAolRQ+asRojVAp1ChzWZGrbkNKl6BMK0P6g81oqaiEZJzH1I6ykNEwxcip0ShvQ4ueX85DgpVz6d+/2g/qPQqNBxrAhMZ/KJ84dtNgIBCrUBzBOsIhAEADmiJdMy061zvuCd7GspRZ3a+ZzYUNFZBYgyxhoEr8+Kj1HQZDOPThwOC5cYmFDRWyp9IubERGQERiNL79/i85gr3WXGMMbRUtw3aUjilOyrkQBjA8f0u3VGBpAlxJ32uzWxH5b5qtNa0QaFWIDgxEEHxjuCp9iZHOuymKAkm/459v4hrhM6ok4OsyndXyQOlgGPwo3RnBTKm928wYnBSICxGq+P7JTEotUrEDIuAT6hhQFPWnys4joPWz7sy8ym1SgTFB6DuaEcWQY7jEJ7WdRrroCBf8IebYdM6Bur4ZglgEkob6mHSOsof8UoeglKAaBPRHsigaeOg1CoRnesI+IzIDMXRjcfQEmCXA2F4JQ8wQBIAUxDgU+vIJMNxjo7U8t1VroOCEoNolwCTDcb6dhh6GLw7XXEjo3FsWzlMzY5AYV2AFpY2K3YvKYDKoERYWggCoh3l+rQKBRyFKBgYYxCtIiTecT4VRQmwSqjnjNhVX4qxnTqDOZ6DUqOEaBMdgzkMaFLwbgOg7Y0mtNa09ek1grdTCQqEaAf/cUgfpEPGtGSYWswQlALUvQgaNdltboEwjuVWt2XtjaZeXSOdiOM46INOHgBLyLmG57g+D4QBgMZjzW6BMIAjeIUxJpe1YBwgKoDWWMDqzwEig7YOMJTDrXwKxwNaPw0sOgab89edStiawoCAA4Cuquc5hxx//Dk9zE1sMBuxr6FCbkKL3Yz2RB45qdHg7YBPiL5XpZsVKgHhGaGQmISddWVoMDbJvwvR+iAnMKrfy6VqfDVIm5oEc6sFvMCfUoByiMYHPioN2mwdfWo8xyHOEHRKbQiM8UPt4XpIYqfzPefogwE6JqB4G0OIDtzxgK3OfEL65nzdZrN0nP86/QmbrmPXdgZP1B6uh2SXXO6V7BY76ooa0FjWDOmENiragaA9gM3geCGtnYdSB5dAGACIygmHb7gBrdVGKDQCAmP9odQqETEkFEc3lR7v++sIXEMf3qsZlBpMiE5BRXszrKIdwRoDggYoSKy3bCZbl5OtbO29L+XkG2ZAxvRktFS1wWqyofqA++Sz3lwveRNBKSBmWCRihnW/TlNZs1upNtEmonhzqXz8VBUokTQhvlfHpcbSJrdlRn+GNqsJ/PGgayYxtAcwaJs5qNs4ONM6tTV03c95MgHHj13O0nK8wEESAdvxWxROdATkgDv+fQVDiq97STBCCCHkXOfVwTDV1dV48skn8cknn6C11X0g6EQ+Pj645ppr8Pe//x1hYe41YgeK2eya6UGl6v0FpVrdMehmMpl6WLPvPP744/jmm2/kx5MnT8b9998/IH/7bKP163rQVOc/cAMxVtGOXfVlaLY69h8VLyA7MOqMZv0otUpEZg3Md8oQokfa+Umwm+0QVIJLhL7NbEfJtjIw8XjHlcRQX9wIja96QAaE+3oGpz5Qh/RpyWipbIEkMfhF+EKp8erD8oBQapUQbRLY8bIHvOCYraU2nP7NeUuNscvlrdXGPhnoqmpvRpmxCXZJQnCaHhqrHeZGM8AAQS0gLC0E+mAdGkqaYG41w+7Sd+WIhmEAWm1m6AJC4Rtu6DJoxaDUwKB0HE+aKlpQVVADwBFI5pzlE5kVhuDkIFQX1oKXOs8UcfypKIP/SbdHF6CFLkB70vWgACSdo9PYZYvUHEySDQbh5IEkRpu1UyCM3EwUNlWj2WpCgFqHSJ0/+D7soZRECQ0lTcdnSCoRlBCAJJ9gNFraYZc6Okui9P59NjteYgyHW2pd+tQZgMPNtYjQ+fW4fd11dPM9zHL3Znar6FaKCwDa6oywme3dHgdba9pQV9SI5soWMMkRBGC3iCjbVQmOc2R40PioISrhEggDOAYhi1vrEa0PAJMY2uqMjiwSoiMbBic4eo/MLRZo+/GczfEconMdNevtZhFqg+qcD4A8F0UODYM2QIOWylbwCh5B8QHdDpJHZofB2m6FscHkmLHLGASlAMYDOD6LlxM4cLwj2FETpERCVDgMIXr5ukUfqEPa+UnIryhFO29yrM91zIQXnecJDpDAoNAoYDO7ptVmAJhdcpQ2tPVPaReNjxqp5yXC0maFJEo4sq5E7sC2tFpxbHs5FGoFfEL0UAtKROr9UG5sgmR3zGJm/PEOYQAccxzr22BBs9UEP5XjnNZaa8SxHeXygI+xoR2iXYIg8G4lCCxt7oEQZHDgeA46fy3sFjuMDe3Q+Kh7HDT2UaohcDxE5hoU5dxvnJrKW3BsW7k8KNVwrKnb7HmEEM9y9hl0DoTpCsdzaE5isB3vLmFKDsZIx2P/Qx0ZNnkFD0HBo73RhMAsX6AWbsEsksIlNkZ+/RMDGZjkGJCtO9IAQ7C+y2vBMmOTW4ieXZJg1ImIMZx6Bs0KYzMaLK7X37WmVtSYWhHWh1kwe6LxOfX7Kp7jkBcci131ZWiytEPgeMQaAuGv7sW9aicqvQqJY2NRsbcKLVVt4DgOvIKXA4H6owRkX1BqHH1xFXuq5XOPxk+DsPS+Oe8YlGo5uJgTOOD45Z+y07g9YwBEBrvFDiY6rkOdQUQAYG5zBBd3d0ejPH6rz2k4OajZ0mZB7eEGWNos0AXoEJIc6NIvI0kMJdvKIdkl+Z63pboNVftrEDW06+waTGJoLGuGsa79ePC5f69KXqoFBRJ8Ti24aiBp/TQQVAJEq+v1tz741AJsFWqFvJ+bmsxy6UbAcZwK6yYw3xNEm4j2BhMUGsUZTWyQTjj2M+bIJNl5/7W2O4KDYvJOr3SSVX88teZxzmOKRc+OB8M4nG7mV0EpIGliPGoL69HeZILaoEJAjB9aWkphht0xYZPjwThHUF9ecCyCNYM/uJ0QQgjpa1476vrdd9/hxhtvRENDg3zB77yg6BwR3XlZS0sL3njjDXzxxRd45513MGfOnAFvN+CeCcZqtfY6O4zF0jHboXOWmP7y9ttv4/HHH5cfJyUl4csvvwTP988A24QJE/rldb2FaJewqnQdqgvr5WU+IXrMWDC5x+wIZfmV2PfTQbTWGhGSHIjcuZnwizy9Domvi3agTusDwHEjaQOwRyHhgdyxUPB9P+NqIB1eW4yKhka35fpKf0z449m9b51Lflu3Fu2Vokvnok+4Dy68/MIeU9z3ZGfVXuzfc8hteWb8EGRNSDvttgLApuqj2FFSD/g7bjgbAKRGJSJtlYTK/bXgBR7Nyy0wZPvjipvmYskDSyG0iKicwLn0GAlKAZpaAdXftkCSJPhH+iLtgmQkjImB8ng5MGea16r9NTCVSfBtDXJL6ZoeOQRDJqVi6e+rEdfAUJxngzEYEOxAyGEBE1NG9Jh9o6msGaYWC4KTAuW/2x2JMWz0MaGxzSgPWAoqAVqNGlNyJ0GjOPkNf3FrHdYUdHTESEyC0WYFA2BVaVAKCSY/AdeljTvpa/WGJEpY8fw6GA+LAFSwAGgoMGHGg5Mx1nccttceQ6vdgjS/UKT7h/fZTMlmqwlLdzW4LbcByMkd4Tbg1lmCXxVWv7LRZZnGV40L504flAF01nYbKr5odJvhxnEcxo0Z12XgW8m2MqxbuhWwqaBtc5zfBKUA5fEZbMoiPSYsnADbSDu+ql6Oo5FNHa8rcFDr1WjjeUwY5ThXHPukDuZG16BjTuAxbtw46AO9Z+Y+kxiqC+sg2UWEpYX0agYwOQud70iTv2HxNjSWNoMzc9CJDFXDHcEaCo1CTm3vt1MBndYXo6YPdxt0CqwrxZdFO8AYg9VolYNIgvYzNIdwqBvCIKkAv1oBURu1MLQw2M12l7E+XsHDtE7C+PuyERDj12+bfHBlEWrq3CdDGGoCMOGyUQCAcYxha00Jlq7YCrHdjpZQAM6vCMdBqVFAqVEiM2MY4n0cHfy/v7UF/k2uMzPtZhs4joNwwjlvwqQJCEn03sER0rOdX+/FgeVHIIkSFGoLhs3LRGoXpcicNNWx+LFktzz4rBGUmJc21mXQ+YeHfoNfk+tgkW07j1ELRntNKTZCiENVSC1WPL8OllZLj+WPzEEcmlMcExNErSNTDI6XPjKHAIk/MAjHYyPVPioExgZgyvQJ2LpmCeyds4yAIXAfoK9iLhExal/HudjaZpWDYngFD6VOCb6QR8ykeMSPdi/Pc6RwI2xNNW7Lo6LTMSEy5ZTfjy+PbIOt3v19UIZFY0Lc0FN+vYH0ddEO1B/v4xIBHAKQFB2OSZGnUdLlUkfJpO1f7EZTeQs0vmpkzU5H2tS+KYnXLyY4gner9tdC669GxJCwPg2mtx0Lxe9Vhx0/m22QjCIidzFoqiV5P1aoBQhKAVajFZzAQ+2jhmgVYTfbwGoU8NeGQLTYYTXZ5EA0GecItkg7PwljrxuO9gYTlj61GqJJAQUUsAJoLbPhwr9PkMugVxbUoLS6zq2tyiINJtzadd/fmtc2ojXfBkAJC4D6fe2Y/uDkXmdw82ZxukRseG+7nNlI46vG+QvHwz/69K7Fx46RcHhtMSr3VkHjp0HqlESvCQgr3lyKzV/scpSABRCZqcWEW0aftG+qK41xzfh580r5sWgVYWu3QqlTQeiU7Vxfpe/VeEVFQDVWvbzBZVmr0g5jksKlX87SZkFIOQ//Zsff4AUeF1w2ASFJPd9X1B6ux8FVRbC0WBA5NByp5yV29IFOc103oq4UXxXtANARgDk0KBpzkoafdDsIIYSQc5FXjqB88803WLBgAURRdBmEYowhIiICYWFh0Ov1MBqNqKmpQUVFhcvz6+vrccUVV+DLL7/E3LlzB7r5MBhcL7TNZnOvg2E6Z4M58XX62ldffYVbbrlFfhwREYHffvutX7PqKBReucv1GYUCmHbfFBRvKkVdUQP8InyQOD6ux87RmsI6rHt9q9wxUrGrBg1Fzbj0n9Oh0p16JowDLTXACYPjRmZHmakFyX6DO1WiSq0Ez9yDIRRK5Vm/b50rWqvbUH+4EVqDFnarHZLIHLPwOAFqzcm/D5X7alC+pwoaHzWSxsfJGR7Spybj8OoS2DvNdFfplUidnHjG+87vtUUd3znGYDHakG+rgHmvBFUbIKgUUOmUqNxdC44VgNk5aCw8Ag9waMh0vgoHWIHQrRysrTYwu4SapnrUHWzEto/yMe5PI5A8KR4rn98gp7S1HB/IVBtU4AUejDEwkYHZHcdapUoJQ7MCWasUsCsZeDvAMw7qieout9lmsWPta5tQsacaAKDUKjD2T8MRN6LnetnT4zLxzdFdLhcUk6PSYND0LqAz3i8EerUGxuOlCCx2O5jAOYL3jr+vh9rqUWxsQLJfaE8v1SuleypQd6gRfKfaTtYWGw6tPIqRC3MxQ591xn+jK/6CHr4aHVpsrtnjfJUa+Gv1bumiO4sbFo1JN43B3h8PoK3OiLD0EOT9IRtag3eVf+ktha8C8SNiULKlzGV5bF4k9P5dB6Ls/qYAzOrIosQdPw9IVgaoHZ1LdqMd7ZIdW5qKoZoTCmWlCaIkghd4KFQKcDyHZL9Qed9XCgpYTjifCLwAc6MVfqEDMzv2ZFqr27DypfVoqXJMp9T4qjHlzrEISabB+XNRSEIQQuKC0FLa5kg1bwQStqlQkidCFBmYCAQdUyL8sIBKVottH+/GlDvHurzGsLA4HGitxbbyYkgSAJ5H0DEeYoCEmjx2vG4D0BIOmM5nyP5RgKDkIFocwY68godap4K9XcS+HwvdXr8vMRvr8pqPWZnLOSzDHozCX3jYTBzKx3Oolw/hHAS1EgFaPRL9Q+WSoczq/rpKlQoKjULeTgBInhyPiFTPZRolZ6Z4SxkOLD0CAODBQzJL2P7ZHoSnhSIw1r/L50yISkFKQBj2NlZAIyiRExQNQ6fscKJNhLHG5HL9AACShcHSbIXOt/8nshBCei86MwIjrhiK9W9vla8du8QD4DkwBQemgBwIA3CwBAGN6Qwh+Y5VrS12BMYEwE+rR9xmBYpG2iGqHJnJVM0MwXsc5QadI5Mcx0HBO65Dg9ID0FTe4sig4QxkYEDdoUYkj09wa9bQ4BgcbHUNBuAAZIfEnNb9a7DOF2iqdFseqvf16r6UVqsZuxorAIEHkyTYTHaIdgk/792NxCo94ke5BxKdTHRWBKL/EQFruxUKtcJtcsnpqC9uxP6lh2A8fq825MLUPi074xfq22/3KBcn5iA1MBz7GiuhUyiRqQiFGGpGw7Em5H+335Gp9/h7JCocmTWYlcFutINXCFCqlGB2gNkAjVYDq8kGSZQgKAWotEowxjB8QTayZqcDAHavPgB7u+hyPm2rbEfZjirEDo9E/nf7Uby5DLZWGwS1AopOkwGUqq77/qoP1qJiV43La9ra7Diw9DDG/WlEv7xvAylpbDwiMsJQtqsSSrUCMXmRcuDQ6VAogMzpqcicntqHrTxz5hYLNn+wC5JNkj/Lqr11OPDLYQy7/NT7aUISgjD6qmHY8d+9jsw6AgemApRKpUsKL7+w3h0HY3OiMHrhMOR/VwCr0QpdgAYXjUrDt0IBzGJHX2N4cBCm5kWjTl0HXYAWGdNTEJTgmtHrcHMtilrrEKDWIScwCnUF9Vj54np5bKCmoB51hQ047y9dTwobEZ4Ag0aLTdVHYRbtyAqMwNiwxB77lQghhJBzmdfd8ZSUlOCaa66RA2EYYxg5ciRuvfVWXHTRRQgKch8AqK+vx08//YQ333wTmzdvBsdxEEURV199NQoKChAX1/0M+P7g6+t6g9LY2Ah/f/9ePbepqUn+2cen/2rU//zzz7j66qshHS9DEhQUhGXLliEx0YtnQwwSgoJH0oS4HjMvdHZwZZFb2lxziwUlW8qRMsW9U+RkNAol2kX32rG6XmRo8HYxeZFQG1RuaetTJsd7pkGkzznLMnA8B6WmY5+1W+xgjPWYpWPb5/ko+PWw/Hjfz4WY/uAkBET7wSfUgOl/m4TdSwrQVN6CwDh/5FyaAZ3/mQ9ctFg7ghjtNhGSTQQDYNMDqjZAtNohqngICgE1hfUQlDyYKCFyHYOhDGiN46A0MwQcABTNVnQ+HEiiBHOzGWvf2Izqg7UutZ0VKgFWmwib2Q5e4GA/Poi3+4cCcDyHlCkJqDno6EBV2Bzvm9Zfg+jciC63Y8+SAjkQBgBsJjt++2ILDIYKtDIrknyDMSE8GWrB9dJhdGgCAlQ6bK0tgcgYcoKikBPkHkBTX9wIm8mGkJRglww/Cl7A/KTh+OzwVphFOyTGwHM8NILrMava1HpGwTCMMdQU1uPoxtIu96Wm8pbTfu3eEDges2Kz8OWRbXK/CwdgVmxmrzosEsbEIGHMqXf2eqsx1+ZBUPIo3uwIiIkbGYVR1+S6rWdutWDVyxtQe7jBkS3qhM+NSQwQAL8RQXhpzwo5qIqpOTCJg/L4uc9XqcHFsdny89Q+aliMVtitjpoqglKAQi241RT3pM0f75QDYQDHtcG6t7dizj9nUGmlc1TGjBQUbymT06SHFAmIaTegRjJCbeSgNnbsF2W7KmFtt7kEZPMcj4Upo2D/qhZ1khG6Rg6GBh75MzsyUzpfweoLKEb4QV1gRltVmzw2KNpFKFQK1Be7Z+rrSzF5kdj1zT63a+TY4VEuj5vKWxyp4+0SwjeLsOk5tMQDnAAE8FqkrFPihy+WISw9GDmXDUHsiCiU51e5vAYv8LjwkfPQeKwZbbVGhKUHIyxtcAeQn+uObSt3X8gcy7sLhgGAMJ1vt+VCBKUA/yhft+sFlU4JQ+jpl6Ml5GzEJIbqg7WwW0SEZ4Sc0aDtmfCL9IWgEuTrPRfHs7doawFVC2AKZsfTwjjwx8c1zUEdaV44nkPxplIMmZmCGKMBFW3NaA9g4ERA1ADFF3HIWqaCwuYoRxg3OhqxeZEITgpE0YZjyP92v1stGUM3pU6Gh8ShrL0Jm6uLIYFBIyhwcWw2QrSnN2lubFgCNtUcla+VAcBfpcXw4NjTer2BUlFeD6vVBp7nYDXa5OsCC2/Hmjc3Q6lRdls252ROZ/JZV+qLG/HrU6vlcly1hxtQnl+FWf83tU8CbQZCmn8Y0vw7BQFHAJHZ4Ti4/IjczwAAar0KvhE+kEQJzeUtctZKjnOUux56aQYSxsWivaEdReuPAQASxsUiYkjHfXzbCeVyGWOQREcJ9CO/F6OmsN4x0YcBNqMV0KmgOJ7FI2Vy132kjaVd38s3lfXvPf5A0vlrkTrl7O6zr9hX7VbaCHDc15xOMAwApE9LRsK4WDQea4ZPmB4b3t2Oqv0dWbd4gUPmrN5ni06/IBkpkxPk8sq8wCOiPRRrKg+hxtyKOEMgJkekwjdPA1zc8TxJlFC1vwbWdhs2+VVhR3PHxKBVFQeR/pvC7b6nbGclyvIrwQu8XBLapS3+4Uj3P73jHyGEEHKu8bpgmAcffBBms9mRqloQ8O9//9sle0lXgoKCsGjRIixatAhvvfUW7rjjDoiiCIvFgocffhiffPLJALXeISHB9eL82LFjbsu6IoqiS5ab/gpMWbVqFebNmwebzREw4evri6VLlyIzM/MkzyT9wWq0drnc0s3ykxkXloifju11WRZnCESk3v+0Xs+bKLVKXHDfRGz5ZBdqD9VD669B1kVpXaYVJoNTQIwfDME6tNW5ljCIHhbZYyBMa3UbCn477LLMarQi/7v9mHLHWNgtdtQeqoegFJAwJgapUxP7JBAGAJJ9Q3Cw2XEzLdkcJScEC6DpNJFPskkQFAL0QVpH6mG7BNgl+BcDfsUMgoJ3zLA6sZMWjq5X0Sbi8O/F4DhOfh8EpQClVgm7TYTNLILjOUcdYgnI/3Y/pj0wCSOvzsW+nw+ivdGE8IxQjFyYI3cknah0p+tMQWOAhP3n2aCsPQZBJeBwSy0Kmqpw65DJ4E/4LFL9w5Dq3/UM+vqGFmx8fSsajzQBcGS3mHTbaJeBxnT/cDw0bCYON9diS00xCpqq3D7vqDM4hplazFj5wno0lDRBtIuwGh0DxJ1LzgTFB/TwCn0jLzgG4Vof7KwvBQDkBsWc0Xb1tX2/FOLAssOwtFkRnRuOEVfmQBfQPzPdVTolxv95JMZc50jj210JtJ1f70XdkQbwCh6SzVk+raPEGK/gEZYejJphHIxNHedNJS9AwfGYHJGCaL0/hgREQNmpVGDcyCgU/HoYClXHpbDGT+01tdJFu4TKfe6p8dtqjGiuaDntlNhkcAuI8cOMhyZj38+FaKs1IiQlCHGjovG/R5dDEhnsAu9yjO/utBkGPfgjHVmqupswz2sFmBrN8teOSRJs7RIADv5RZz47ub3JhKMbSyHaRMSNiHIpEeoX4YPRi4Zh+xe7YTPZHUGe5yUgaaJrsHlQvD84joNar4LSLiFlHYN9FxCYHgjT3haYJEfAamt1G2oO1mH2kxegoaQJh1YVQRIZVDolhl85FAHRfgig79UZMbdYsOOrPSjLr4Rar0LaBclIvyDJI23p7lrnTAfk8+ZnY/UrG11KruTOyzyt1P2EnK3aao1Y8eI6tFQ6AnpVeiUm3TbGZTB8oNQerodSqwSTWMe9FufI2KLxVUOySxDtEhJWSjh0IYPl+O0AL3HgrY4bM02dM81Lx7GlZGs5VLODYTY1gRMhB9pYfYHKeDtiDikRPiQEYxblyUGpKZMScHD5EZhbOgJQNX5qJE2K77LtPMfhsvhcnB+ZhkaLCeE6X7dJCafCV6XFHVlTsLbyMKraWxCl98PkiBRoFX2XvaSvbflkFw6sOAxcaodZw5wJe8AB8KnhwUscDq44IgfD1Bc3omh9CUS7hPhR0QjPGJh9bv/SQ3IgjFNjaTPK86sQkxc5IG3oDyqdEsPnD8Xmj3fK+7hKp8SEm0Yi/7v9aKsxuj1HoRbgG2aAb5ih2/c/NDUIZbscfQ92qx02kx1gDPt/OQTJLkGpVYDjOKj0KthMNohWO3QBWqRPS0LmhV1nMgmM6/oazpOlfw6uPIIDvx2G+XjJm+ELsvusD+ps1V1WddUZZllS61UIz3D0P5131zgcWHYYlXurofHTIH1a0knLF51IUArQB3UEMobpfPGHbsoT2Uw2HFlfgvzvC2Bps8AYxLB/ug2qTqWa6sxGHPCV0HnaGmMMNpMNy//1OwSFAF7JY9jlWRgy49TL5BFCCCHEy4Jh2tvb8d1338kDYG+99Rauu+66U3qNm266CSqVCn/6058AAN9++y1MJhO02oG74ExPT3d5fOTIEUyePPmkzysuLoYodkTcn/g6fWHjxo245JJLYDY7OsB1Oh1++uknjBgx+NNGDlZROREu2RiconNOL7p7UkQKAA4bq4tgslsxJCACs2P7p+yHJwTG+WPmw1Mg2qVuB0/J4MXxHCbeOhprXtuE9gbHAFZQQgBGXp3T4/PqS5rcZ/sBaChuhGiXsOxfv6PucIO8/NCao5j16FSXG9jTdUncULx9YB2azCaINhGcnSFqlaMsEYNjMJLjOXA8h+ELhmL3kgLUHWmAZJcgScyR7ajVAklkAJj7djBAskuwtFig0ChcMubwCh6c6KjlzUQGu8UOXuDB8RxKd5Qj9/IsWNosKM+vgqDk0d5o6nYAU6V37XiozBDBBLjMXjzW1ohDzTWus8a6caytAd8e3YXi2logV0K4hkfUPgXMLRb8/p8tmPvChS4z5TSCElmBkYjS+6Ni3xqXckI5gVGI9zn90jC7vt6HhpImAHB0JChEWNtt0Pjy4DgOhhA9MgaoUyFS7++VwYkHlh/Gji/3yI9LtpSjubINFz1xfo+BaGfqZMdxZwYHpUYBiyjBkTqJQaVXY8iMZCRPTkBgrD/+vWel23M5jnOkHe4iU1HO3EwY69pxbEcFwByzcifcPMolQMqT+OPBbdZ210xvHM9BZfDeQQvS/4LiAzDp1tEAAGu7FUv/sRqiVYJkFyECEK081AY1YoZHQqntukM5Y3oyag/Vy48Dj/GozHQdxFGYAfPaeoiW41Pjj0+Y5+A4Jw2dk3FG21F9sBYrX1zfkdXs+wKMuS4PyRPj5XVSJicgfnQMmkqboQ/RdTmA4Bfpi7Tzk3BwxRHwCkdCdb1aBR+bCu0nzK5sqWpDeX4lRl2di+yL09FWZ4R/tB8FMvSRlS+vR32RI2OQpdWKrZ/sAscBaecPfEBMypQEFG045jLDVqEWkDD2zALoo4aG4+KnpqFofQkku4S4kdFuae8JOddt/TRfDoQBAKvRhvVvb3W79h8IvhE+joBJgxqSKIFJjhK8Kr0KtnYbBKUAQSmAbxWR+bkdhy/nYPLtuB9TNwCBBY6fOY6DQuM4XyjUAhCngapMBavRejzIxnG9bAnmEB8QjUm3jXFpi9Zfg5mPnIf9PxeiobQJgbH+yLwwFVrfnsue+qq08FX1TX9moFqPOfE931d7i8r9NTi4/Ag4cEjYrMCh8TZIgiMaRmnmELfdcc3unERWsrUMv/9ni3zcP7TqKIYvyMaQmf1fBqatzj0opKflg0nq1ESEpgWjdEcFlFol4kdHQ+Oj7jLTHsdzbhn8un7NJBzbVo6aw/WwHb/XEVQCwDGIVjt4BQeFSgGedwQ7a/zUuOLfF/X4mqEpwYgbFYWSLR2Z4TR+amTO7n3Gj9PBGEN5fhWqDtTCEKRDwrhYqPUqHFpzFFs+2iWvV7ypFE3lLf1+bz3YRWaFwTfc4JIdFejba0mFSkDW7DRk9fO+AQC1h+qx8uX1aKs1OjJ7chxaUhQAg6M/SsnL+4M5WgDQMSYk2kSINkm+n5NsErZ/vhsRQ0IREEMB/IQQQsip8qqev7Vr18JisYDjOAwfPvyUA2GcrrvuOvznP//Btm3bYLFYsGbNGsycObOPW9u99PR0KJVKOfPKxo0bcf3115/0eRs3bnR5nJ2d3c2ap2fnzp2YNWsW2tqOz9BRqfDdd99h4sSJffp3yMnVHq6Htd2GsPQQpJ6XgNrD9Sje5MgU4Iz2PpNZ35MikjEpIrmvmuuVKBDm7CPaRLTVtcMvyheX/Wsm6o40QKFW9Go2T3cBHn5RvijbWeESCAMA5mYLCpYdxogFQ8+43SFaHzyQMx0//rgJR3eWQXPQDqGjchI4gUdoWjBy5gxB1NBwhGeEoHhzGRpKGiFaRRQsPwKbydZRnr4rHADeMQopiZKcSpiJ0vFsMY7VJLsEm8kGlV4FpUaJ1f/e6JICtnx3FabcORYxw9xnqKWfn4R1nd4ni4GBEzi371qduQ1p6DkYxiza8N6BDTCJNscsOSVDebYIlYlDSJEAU5MZ9UWNCElxD3AJUOtw99Dzsa32GJos7UjyC0aGf9elnXqrfLdrR51ar4JoExGVE4GooeFIHBfTZ2myB6vClUVuy5pKm1F7uB6hKZ7LlqLWq2BqMoMXeGh81I79iTGcd9c4xI3o6GyN0PmhvL3Z7fmRuq7PpUq1ApPvGAtjQzusRhv8o3y9qvQQx3NIuyAJe3444LI8dkRUlwEBrbVGFK0vgc1kQ0xelNdkuCH9q3DVUTRXtEKlU8JqAiSbCMnO4B/jhzHX5nX7vLiR0Zh4C8P+Xw/B1GTG5KBY5KubcNBeByYx6GwKxK4FeIlBhGN8z3l6UqgVCE4KPOUZlCfa9tlul7T7TGLY/sVuxI+OccnqodQoujxXdDbqmlxED4tAxZ4qaP00SJwQh9WvbOxyXVOTY0a+1k8DrV/PA5Ck9+qKGuRAmM4OrjjikWCYkOQgTLxlFPK/24/mylYEJwZi+Pxs6APPPAjaN8yA3LmUUZWQ7px43Q0ALVWt+O+dP4FX8IgfHYNh8zIHpHRS/Kho7F96CE2lzY5AHMGRpXLiraOw5rXNaK1qBcDBN9wApcmGzKUS6hMktPjYoa5hCCriAZvjDMgkR5CMoBaQODYWNjiCSk/M7Glo4lF+tKrLsqw+IXqMXjSs37f7bNA5Q6J/JY/c75SoDbBCABDSoAQvOd7b6BzHfeLO/+51KzGye0kBUqYkQqnp330tLC3Erb8BAMLSz46Si/5Rvm79LUkT4tB4rAmFKzsy7Y24Kgc+YScv46XUKDDj4SlY//ZWHFpTDF7BQ1DwjkwYnB2iTULnhEVR2b2bKDjhplGIG1mBqoJa6IN0SJoYd9JgszO1/u2tOLrR0Zdrt9ix4b1tCIj1g6XVPdN3U2kzqg/WIfws2S9OlyRKKFp/DBV7q6EL1CLtvER5v+EFHhfcNxE7/rsXlXuroQ3QYsjMFMSPcp/cMhhs/GA7rEZbR+YoxqCoFeWfJbskT8ZJjA2Hxr8epibHpDDRJkGhUbidR0p3VrgEw4h2CaXby9FWa0RoajBCU6kfgBBCCOmKVwXDlJaWyj9fcsklZ/Ral1xyCbZt2wbAUaZoIOl0OkyePBnLly8HAKxYsaLLG+ETLVu2TP7ZYDD0aZBKQUEBZsyYgaamJgCAQqHAl19+ienTp/fZ3yAnZ2oyY+VL6+UMBSq9EhNvHY2JN4/C0Esz0FrThuCEQGh81T2/ECFnmSPrSrD9y92wtFqh0CiQfXH6Kc3U8I/yReK4WBRt6DjeCyoBQy/N6DLzEgA0l/dd/WgFL0C3z4aQYgUsNhFip15RXYAG5989Qf5eC0oBfpE+ENQCNr2/A4LAw8Zx4BgDuOPZXngedqu9UypoDkqNEoKSh9ZPI9fZFq0iBLVwfKTS8TdFmwRewSEwzh97fnQdSAcD9v3vYJfBMAljYyHaROxf6hgcjVbpUGIwudXZSPA5+c31/sZKmERHQGinpqE2QUJIkeNm/8RMNJ3pFKo+DehT+6jlTgUnQSkgZ04GghMD++zvdGZusaB4SxlEqx0xw6Pg24uOQU+ymWzdLLcPcEtcpU1LwuYPdgI4PiNXJSA4OdAlEAYApkaloaCpCkZ7R8djVkAk4n2C0FLdhoaSRhxZV4KawnoYQvTIPl5iTx+og75/doEzljNnCJQaBQ6vLYZoExE/OgZD5wxxW6+msA4rXlgnBxYU/HoYufMykX1x32cYJN7FeT3JHZ85y44fbCMyQ6E+STrx+NExLmUmJwBotphgtFigbmT4+ZuVYArHoAQYk5OEKbVKJI6LPaN2i3ZJbntnVqMNLVWtCIz1P+XXjMwKQ2RWmMtjt4EpDojMPnlmM3Lq+rrsa1+IGxmNuJGDc/CEkMFM4+t63W0z2WC32GFutYDneUfZkGYzJt4yut/bIigFzHhwEg6uKELdkQb4Rfog7fwkFK4qgrXN6phxzwGWNitiR0SidEcl/HfbYLBK4AUeaoMKIifCZrKDSQzaAA0m3jwahhA9IgvboSqxwxJ4/EaHAZpGDiFHeEiso6QPOT26AEcQA5MY7FbH+x/cyoMXePDq44EwuREYMjMFNosdrV2U7LGZ7GirM/Z7GcTMWamo2F2FxtKOwPyMmSmndT0zWHAch5ELc5F1UTqM9e3wj/I9pQA3XuARmhqM4s1lLq+p0imPZ8x1CIjxw9BLM9DeZDppiSFe4Af03F97uF4OhLGZbbCbHffNDSXNkGwieIXgVvbH1t71Pfe5ZO3rm1G6o0J+fGj1Ucx4aLL8fdEH6TDx5lEeal3faW8yobm8FYDjXo0d368NxRL0dQKMwUyejKMWFLggaQiC/qlDydYyWFotqDpQh4ougks7T+KyGK347Z9r0dTp2JM8KR5jr++6ZBMhhBByLvOqYJj6+o503VFRJ0+t2JPOz29ocI/Q729z5syRg2GOHj2KFStW4IILLuh2/ebmZnz99dfy45kzZ0Kt7puAiKNHj2LatGmora0FAPA8jw8++ABz5szpk9cnvbf9y90unf9Wow3r3tyCeS/Ogl+ED/wifDzXOEI8pLGsGRsXb5dnctnNduz87174R/vKM716Y9wNIxCRHYaK3VXQ+KqRMiURfhE+sLR1PRDT12ntfUINqNxfA8kuucSP2Ex2HFjmGJy2ttuw+pWNqD5Q6yh9ZHR0wqr0Skcnq+gonTTxxuE4uqkMVQU1jhmISkfHn81sh7HBBIVa4egYNNkgWkQotEqINhFMZBCUPKbePaHb4AZjg6nL5QCQPCkByZMSAABtNjPe2P876swdKWonhCUhUt99ZyJjDC1VbTDbOt5zhVrR0Zbj70t4Rgj8IrvO5tMfMqYnY+N7212WhSQH9lsgTO2Reqx4fp0cSLLz631u5T+8TczwKBxcfsRlmUqv9PiMxtQpiQCAg8uPwNJmRVROOPKucC/9F6wx4K/ZU7GltgRNlnYk+4YgTR2KZc+tReW+GphbLeDAQaVXoslsx+9vboFKr3IZPPc2HM8hc1YaMmf1HBi446s9Lhk2AGD3DwVImZIAjQ8F157N/KN9UbKl47Ez8L67bGkn46fWwk+thaiToPXXwNRkhlKrdBzDGQOvEBA3KhoZZ1hyQFDwMATr0FpjhCQx8AIHjuPAK/k+ydwBAENmpqJqfy1qCusAOL5Pwy7P9PrAxMEqNDXYkaHohEGeroJvCSFnt/Rpydj5373yY7tVBK8UwPMd2SZLtpZjxEJzv2dtABwDh50DhG0Wx70ZAJeyTcVbysBxHASV4z7LmXHTmZUQHKBQKRAQ4zjH7ltyAOkFClSFSmgPYtA0MAQcYuC1DHETo70q4+BglDA21pHdq6K1Y2YFxyF8SChyLhsCfZBODnIRgC5Lq6h0SviE6Pu9rWq9CrP+byrK8qtgrDMiLD3krA6E6ex0M+211RlhNdnB8YAkMXDHOwoUagUm3zkWkk2ESq9C5d5q/Pj3ZbBbRPhF+WDstcNPmjHwRMWbS1GytRyCkkfSxHhEDAk95fZ2pb64IyNe53sxyS6BVwoQrSIY68jsodQqEJ5xbmeFqT1S7xIIAzj6//b8eACTTygtN9iptEoo1ALsFhEKtQBbuyM7DMdxSF+lgHmyHqoEfwSodZgQlowQreMexdlnFJoSjMq91S4Zr1R6FRLGdAR7Ffx6yCUQBgAOry1G0sQ4j2b3JYQQQryRVwXD+Pp2dNyeaQBLY2PHRWnn1x0oV199NR555BG5HQ888AA2b94MhaLrt/zvf/872tvb5cd33nlnt68dHx+PkpISAEBcXByKi4u7XbeiogIXXHAByss76qa++eabWLhw4alsDukjZbsq3ZZZWq2oPdJwzqfKJOeuki1lbimNAaB4c9kpBcNwPIfEsbFIHOs6Yz0qOxzRuREu3z+fMAPSp/VtKbEhM1NwaM1Rl3JHHAeAc5QPAIC9Px1A9YFaub1gDDaTDRpfNTQ+ajCJQR+iQ8b0FPiEGlB/tMGldJIzKMb5fF4hQLKL4ABoDI5B74wZyYgYEgpTixm8godkl1za2dsOGINSg79mT8Wehgo0WdoRrw1E27o6LP10NbR+GqRPS3YpxVJX1ID1b29FS1UbRDUgXSGBNwhQHE9LbbfaEVatQvLkOOT9oW/LAJ5M8sR4cDyHA78dhsVoRXROBHLmumfY6CvbPt/tklGFSQzbP9+N+FHRA5IS/nTkzs1ES1UrKvc6UpJrfNWYcPMol3IlnpI6JVEOiumJr0qLC6I6BjvWv7sNlftqYDPbjx9jGKxGKzS+GoA5SkN5czBMb3WVYUOySWgqb6Fri7Nc2tREHFlXgrZOs6ED4/2RMCamh2ednKDgMea6PPz+xmbHYyUPpVaBSbeOQdTQ3qWq7wljDLogHWoPNwBgAMdBoVEgd3Ym1Ia+KVmn1Cgw46HJqCmsQ1t9O8JSg6EP6ptAG+JOoVZgws2jsO6tLbAaHQExIcmBGHa5e/AiIeTsljkrFQq1gEOrj8JmssNmssn3A06OoH47MPBddbC2Wd2CiBljsLRaofFVO0rEapSwtllgt4jyJAdBwaOt1oj87wow6ppcNJW3gJc4hFcoYT1kk2f9B8T6YeTCnIHerLOOWq9CSEow2mqNkERHBgWlWoGG4iYo1Aq3bC9587Ox9rVNLllFcuYOTDkuwBFYFZtHAaC9cfj3Ymx6f4cj6EyUIIkSVDol/CJ9MfTSDMTkOvqADq48gn0/F8rPay5vxcqX1mPuCxc6sjr1wq5v9rlkyz26sRTjbhiBpAlxZ7wdzsk9DMylXhovcFBqFGAik7PEq3RKjL9xZK/bfbZqrmjtZnnfZW32Fgq1AmkXJGPf/w5CoVIA4CBa7dD6a5ExPRnZl2T0WMItJCUIU+4ci91LCtBS1YqQ5CDkXZEFtaFjskvtofoun1tT6NlS14QQQog38qoRmYiIjkHPFStW4J577jnt11qxYkWXrztQ/Pz8cP/99+PBBx8EAOzYsQPXXnst3n//fSiVrhe/r776Kl5//XX58cyZM/ukRFJDQwOmT5+OoqIiedlLL72EP//5z2f82uT0qA3qLktOaHrR8V+yrQz7fzmE9kYTIoaEIvfyzJOmCSVkMOCFrmfN8X00m47jOUy+YwxKd1ag9lA9fMN8kDA2ps87Ivyj/TD8qqFY/9ZWeRljjgxQzsG9svyOgByO5yCoHDOGRLsEhVIAx3PyzMXo3Aic95dx2L+0EKYmMyKywmCsb0fZzo7XcGSUcZRWUmoVSJ6cIA88aX01GHHlUGz9NF8ONvIJ1SN3Xmavt0nJC8gLdgyqLntuLar218q/K91RgfPvmYCIzFCINhGrX9kop0QXLED8MqB6OgeLAdBoVRgbn4bZF2SDP0nJwP6SND4OSePPvNPrZJjEuqwZb223oam8pd+y0ZwplU6JC+6diObKVljaLAhKCHQMBniYTRJhlyRoFaf+fT268RgsrRa5U5wBcqcrL/BuGQwGK/9oP9QfbXRZxit4+IVTtrmzndqgxqxHp6Jw5RGU7qqEoOSRPCmhT2ajR+dEYO4Ls1CWXwlBKSA6N6LPguOKN5eh5mAdVAbl8VmzjlmSMXl9f88WmhqMvpn/S04mamg45r00GzWH6qDWqxAU37cZ+AghgwPHcUi/IBnpFzgmHpx4DwEAflE+8PFQpi5doBY+oXqXsjpMcgRbODM4CAoeCo0jMxoncBCUCig0jnNgxR5H2Yqg+ACU7aoEz/PypAaOB6beNf6kpQpJ75gaTS4lQZyaK9wDvmOGRWL24+ejaMMxiHYJ8aOiEZJ8ahlESP+zmWwu/RO84JjsE5EZhql/He+ybucS2E7WdhvKdlUiYezJS3Za263Yv7TQbXn+9/v7JBgmPCMEEZmhqNxX0zEJieOgUDuywQQlBmDy7WNgabMiJCnQayfFDKTurg3P1mvGYZdnQhegxdGNjn05YWws0s5PlM81JxOdG4Ho3O7vjwyheqCg1m25T2j/Z8QihBBCBhuvuhKbOHEieJ6HJElYtmwZNmzYgHHjxp3y62zcuBG//vorAEdJoEmTJvV1U3vlnnvuwW+//YZVq1YBAD799FOsW7cOV199NeLj41FbW4vvv/8eW7Z05DePiIjAO++80yd///XXX8e+ffvkx4Ig4LXXXsNrr73W69e48847e8xSQ05N2gVJ2P75bpdl4Rkh8D9JDePSnRVY+9pm+fGRdSWoPdKAi/9xgUtqX0IGo4Qxsdjz40HXDCYckDSx7wIXeIFH3IhoxI3o3/rRhiAdhBOzsXCAJDoeO2ZxdMyGUWqV4AQeAVG+MITokTIlwaXG9Yk3v7WH61Gxp1p+fY7jEJQQiIuePP/4bBNXaecnIWpoOCr2VkPjo0ZUbsRpBTjUHqp36cQW7SIkkWHrZ7tw8T+mofpArRwI4+RXzSPiFzUm/2MSDAo1NKcRzDAYcTwHQ6jeJVMD4AhO0Ad7f1YCR7k+zwdRiJKEn47twZbaEtgkEQk+Qbg8IU9OH3wyjhm+FvesUwxyua7+GHj3hNx5mVj18gaX407G9GRo/fu/9ADxPF7B49j2CjlDUM3BehStL8HUuyeccUCb2qDqlyBCZ3p0QSFAUHQE2JTtrERIEg1cDWYKlYDIzMGfcYsQ0nfGXJuHVS9vkDMC6IO0mHDTKI+1h+M4jL42D6v/vUHOEKPSKaFQK1yupXgFD17Bu5Wc1BwvCZM7dwhqCuvk4GpHectUGAagLM+5IigxoMsMiN0NnPtH+w14BtKzSeX+GhxYdhjmVguissMwZGZqnwdw1Bc3wm52nyDozJ7bl4x17Y4yZ10uFyEozyzIm+M4nHfXOBxZX4JjW8sdJbNFCTzPQ6VXYfwNI86Zclm9FRDjh5TzEnBo1VF5mcZPjaGXZHiwVf3HERyahPQLkvrl9YfMSEHJljKXSbcBMX6IpjKlhBBCiBuvCoYJDg7GpEmTsGbNGoiiiLlz5+Lnn39GXl5er18jPz8f8+bNgyRJ4DgOkyZNQlCQZzpVlUolvvnmG8yePRsbN24EAJSUlOCpp57qcv3IyEj88MMPiI7um8FaURTdHh85cuSUXuNMy1WRjllGgONClRd4HFxxBDaTDTF5kRh2+cmzNDhrWnfWUtmKir3Vp1RGhhBv5BNmwOQ7xmD7F7vRUtkGXaAWuXMzEZY2+Mp7GOvaoTKoIFpESKIEjnfMDDK3WgEAGdOSUXOwTl6f4zgEJwbgoid6F9gWkhyEC+6biH0/H0RbnRFhaSHIviS9y0AYJ0OIHqnnnbzETE/a6hyBHYwxWNttkGyO80t1QR1WvLAOQ2amdPk8QRAQrPHMrE9Pyrk0A+vf2eayLPW8RGh9KTiht5ZXHMD66o7Mdkdb67H44AbclzOtV9mFmspb5HMvx7lkrgYv8EgYE4O08/unU2qgRWaFYdZjU3F4raMcQczwSMRQB9g549Cao24DRVX7a1G6vRzxo8+sXFJ/Uem7Do5U6TwbNMkYw6E1R1G8qQwczyF5YlyvZh4TQgjpnk+oARc/NQ11RxogiQwhyYEen9ATMSQUlz1/IUp3VDgykw2PRMnmMmz+eKdcolapUcA33ABjXbvLc4fMcNz3BMT64+Knp6FoXYkjeCAnAhFDvCMXWUt1GxqKGxEQ4yeXchmMsmanoWxnpcuki8QJcV6baXMwK99dhVUvb5AnEtQdbkD1wTpMu79vJ5fqg3SOiQknzFfoatJI4thYt4yrKp2yx0wZnflG+EClV8FqtLosD4j1O2kgjGgTwSv4k2bwEJSCXNJXtEuo3FsNSZQQmRVGmWC6MWZRHmKHR6FybzW0AVokjY91Kf1Des8v0hcXPnIe9v96CG21RoSmBiNjerJXZPglhBBCvI3XXZk988wzGDduHDiOQ01NDcaOHYvbbrsNt9xyC1JSuh5sA4BDhw7hzTffxOuvvw6brSPt/dNPPz0Qze5WQEAAfv/9dzz77LN47bXXUFlZ6baOXq/HggUL8NxzzyEwkG7qzgZMYsj/fj8OriiCzWRDVE44Rl2TC32gzi0q3NRkxoENh8EkhtgRUdAHut8EOgfST2TpZjkhg010TgSicyJgbbdBqVH0SYkHTwhNDQYHzq3jIyzVUa83dkQUJt4yCvuXOkqeRWaFIffyzFPqEA5LC0ZY2sDW/w1NDQbHc7CbRTkQBnDMmKzcW4O4kVFu6cYBIHlS/IC201skjo+D1l+LQ2uOwm6xI3ZEVJ+kYu4tS5sVgkros5ImA83UYsaGo4chcqKjI+d4J2S9xYiS1nok+J58/1dplXIpNLtFBMcBvIKD2keNS5+eDt+zrIRQQIwfRi7M9XQziAfUHek6cL32SMOABcPYTDbUFTVCF6g9nl2qZymTE3BkbbFcwgwAlFoFEsZ5NvBk+5d7ULD0kPy4an8N2pvMyLww1YOtIoSQwY/jOK8rWaPxUSNlcoL8OHVqIoISA1CytRyCkkfi2Fio9Erkf1eAij1V0PhpMGRGCmJHRAFwBFC21RjhF+mL1PQQjwd0tjeasOenAziythimZjMUagV4gUfShDiM/dPwXpfl8JTaQ/XYt7QQxvp2RAwJReasNBiC9bj4qWk4uuEYjPXtCB8Sishsyj7WH/b9fNAto2bV/lrUFTX0afCRT6gB8aNjULyp1GV59kXpbuumTk2Esb4dB1ccgd0iwi/SB2Ouy+t1uWtBKWDElUOxcfF2edsUagEjrszp9jmNpc3Y8vFO1BTWQ+OrxpCZKciclda7v6fgex2oc66LzApDZBZ9l/uCX6Qvxl43HJIooWJPNY5tq0BEVmiXYwuEEELIuYxjjLGTrzawHn30UfzjH/8Ax3FgjMk3bQkJCcjJyUFoaCh0Oh3a29tRU1OD/Px8HD3qSLHnXJ8xhkceeQSPP/64JzfFhSiKWL9+PQ4fPozq6moEBAQgJiYGU6ZMgV5PqVTPJnt+PIBd3+xzWRYQ44eLnrzAZVnl/hqX9Ly8gsek20YjZlgk2htNKMuvhEqrRF1RIwp+PeTyXF7gcNkLF0Lnr+3fjSGEnJJNH+5wSfvqH+OH6X+bNOhrx+/7+SA2fbgTosWRgpXjeah9VOA4DonjY5F9SQY2f7ADVQW1UGoVSJ2ahNy5Qzw+8/Nc0lzRgg2Lt6PucAMUagHJkxIwfEH2oPoMitaXYOP7O7B9thk2LQMn8FAbVHKA3E0ZE5Dk27usUb88uQoVe6vBARBUAniBR+bsNORdkdWPW0DIwMr/fj92f1/gtnz0tcOQOuXMsoL1RtHGY9j84U455X1MXiQm3jLqpDNuK/ZUIf+7/WiubEVQQgCGXZ7l0Zne1nYrvv7L/9zS6at9VLj85dmD6jhKCCGkf5lazFj5wno5M5tCo8D4P49A7PAoj7THZrbjp78vQ3NVK6xtxydMcRzUBhV4gcfEW0cjflT/lgs+E7WH6vHbs2tdylQFxPhh1v9NpfPvAPnu3l/QdkIWJAD9su+IdgkHVxxB6fYKqHRKpJyX0GPGa7vFDmu7DbqA0+v7bK5sRcnWMghKAQljYrp9HbtVxHf3/QJzs8Vl+dg/DUfyxPjT+tuEDIT2JhOWPbsWLZVtABzjBaOuGYaUKQkneSYhhBBy7vC6zDAA8MQTT0AURfzzn/+UA2EYYygqKpKDXjpzxvM41+U4Dg899JBXBcIAjnIRkyZNwqRJfZtmknifQ2vc99PG0ma3WRVbP9klB8IAgGSXsOXjXbAardj0/g55xqzWT43AeH80FDcBAAQlj1HX5FIgDCFeaMyiPCSNj0P1wToYQvSIyYs8K9KUZs5Kg6nFgj1LCsDxnMtApyFED98wA6Y9MAk2ix2CgqeOywEmiRJWvrhe7sS0W0QcWHYYaoMSQy8d4uHW9Y7FaMWmD3dCsksIKuFRlS6CiRJsZhtUOhX8VVrE+/RuVvGhNUfljBl2qwjRJiJ5SgJy5w6O94KQ3kqdmojDa4vR3mCSl/lF+fS6vE/JtjLs/q4ALVWtCE4KRN78bIQk9e571t5kwsb3trsMXpXuqMC+nwsx9NKMHp8bmR2OyOzwXv2dgWBqtrgFwgCOLIx2ix0q3eAOaCWEENJ3dn6116VEod1sx4Z3tyEiMwxKzcB3s5ZsKUVbXbvreYwx2C0iVDoeFburvDoYZv/SQpdrCcDRf1axp5oybQyQsPQQtK0rcVnGK3iEpvZ9RidBwWPIjBS55NjJKNSKMyo55Bfhg6GX9HxdCgBluyrdAmEA4MjvxRQMQ7zarq/3yYEwACCJDFs+3YWY4ZHQ+FAJKkIIIQQAvHak6qmnnsKqVaswfPhw9DZ5DWMMI0eOxKpVq/Dkk0/2cwsJ6Z5oFbtc3jnwxdJmQXNFq9s6xvp2bP5wp0vqeFOzBTp/LS589DxMvmMM5r00C8mTKMKbEG8VkhyErNlpiB8VfVYEwjhlX5QG33CDSyCMxlftMuNEeTwlNxlY1QfrupzNd2T9MQ+05vTUHqqXz5/RuwUEFQvgGCDaJIRrfXFt6lgI3Mn3LdEmYud/9wJwlEvS+mmg8dWg8Viz16eIJ+RUaX01mPXoVGRfko6Y4ZHInZeJmQ9PgbIXgwY1hXX4/Y0taCpvgSQy1BTWY8Xz62BqNvfqb1fsrnYbvAKA0p0Vp7wdnuYTqocu0D3IPDDOnwJhCCGEuCjfXeW2zGayo6awrtvnMImhtboN1nZbt+ucLuPxgNgTSw07+1I1vt49GGrsFNDrsrze/d6G9I+cuUPgE9opYzkH5P0h65yagNe5HHRnXQVLE+JNKvfXuC2TbFKP5yRCCCHkXOOVmWGcJk2ahC1btmD79u348ccfsWnTJhQWFqKxsRFtbW0wGAwICAhAamoqxowZg4svvhjDhw/3dLMJQezIKBSuKHJZpvXXICSlY1aFQqOE2kcFS6vVZT1O4GC3ieDg2pFRVVCD8+4a13+NJoSQk1Ab1Jj59/Ow75dCNJQ0ISDaD0MuTOmXTjJnzWNzi4VqHvdGd3HDktdVw+yW1k8j/8xLHJI2KRC3Q4BPgi8uf/D8Xr9Oe4MJljar23JjXTus7VaoDd49IEDIqdL6a5A7N/OUn3dozVGwE44RNpMdRzeV9mq2rkqnPKXl3owXeIxeNAxrX9skD3qodEqMuibXsw0jhBDidTQ+aphb3DNIdBd0UrG3Gps+2AFjXTsEJY+085OQNz+7z4K0wzNCsPv7AihUgqNs4fEgGEHBQ6EWkDLZuydShWeEoP5oo/vyIaEeaM25SR+ow8VPT0fZzgqYmi2Iyg6DT5jB080aUFE5EVCoBZdJjAAQN9Iz5c8I6S1doNYlS6i8/DRLixFCCCFnI68OhnEaPnw4BbmQQWXYvCwY69pRnu+YMWQI1mHCzaNcMkQICh5ZF6Vj++e7XZ6bOiURhStdA2kAQB9EA8GEnK0q99egdIejZnbShDj4hHpvx5M+SIdRV+cCcNTfLs+vgtZfg+hhfVcOqr3JhOXP/S5nz+J4DqOuzkXq1MQ+ef2zUWhaMHQBGrQ3umZ0iB8T46EWnbqghACEpYeg+kCtvExh5TBi6snTWnemC9RCpVfBarS6L6cMD4TIbGZ7l8vt5t7NWo/KCYc+WAfjCVmp0s5POuO2eUJ0TgQue+FClO6oAC/wiB0eSccMQgghbtKnJ2PT+ztcloWkBCEoPsBtXXOrBWte3SgPsIs2CfuXHoJPqKHP7m3C0kKQdn4SDq44ArVBBZvZDkElIDYvErlzM70+qCFzdhoq99W4lJ4aemkG/CJ8PNeoc5Cg4BE30nvLafU3tUGFSbeNwab3t6O90Qxe4JA4IQ4ZvSznRIinZF6YijWvbXKZIBWeEYLgxEDPNYoQQgjxMhzrbQ0iQsgpa601wtZuQ0CMn1vKWqdj28txZF0JmMSQMDYWCWNisPaNTSjZUu6y3rg/j0DS+LiBaDYhZADlf7cfu5cUyI8FlYAL7p2A0NRgD7bq5Pb8eAC7vtknP/YNN2D63yZD66/p4Vm9s3HxdhxeW+yyjFfwmPvihdD6nvnrn60ajjVh/Tvb0FTaDF7BI3FcLEb9cdigKtVlM9mw58cDKMuvhNqgRvq0JMSNOPVO2YMrjmDLx7vkxxzPYdyfRyBxbGwftpaQwa1owzGsf3ur60IOuOiJCxAQ49er12itbsOOr/agcn8N9IE6ZM5KRSJdrxJCCDnLHf69GAeXH4GlzYLo3EjkzhvSZQDl4bVHsXHxDrfloanBmPHQ5D5tU+OxJtQeboBvhAFh6SGDqjyoMyuosaEd4RmhFAhDPEYSJTRXtELjp6a+BzJolO2qxIFlh2FutSBqaDiyLkqHUjMo5sATQgghA4KCYQjxQqJdQsGvh1C6owJKrQJpU5MQkxfp6WYRQvqYqdmMb+/5BZLdtQ51f3SO9qXWWiOWPPCrW3mN1KmJGP3HYWf8+t/e+4tbpgEAmHTb6HN6tlpvGevbodQqzvmMBjWFdTi6qRQczyFxXCzNjCLkBIwxbPssH4UriyCJDAq1gLw/ZA/azC6EEEKItzmyrgQb3t3mtjw8IwTTHpjkgRYRQgghhBBCyLmFQkQJ8UKCgkfW7DRkzU7zdFMIIf2oqbzFLRAGABpK3Gume5Oag3VugTAAXMrbnAldgLbLYBiqedw7VFbPITQ12OszLBHiSRzHYeTCXGTOTkNbrRH+UX5Q6ZSebhYhhBBy1ojJi4BKp4S13bUEYeIEyqJGCCGEEEIIIQNh8OTN74IkSfjPf/6D8ePHIyAgAAaDAVlZWXjkkUfQ0tLi6eYRQgghPfKL9AEvuKev9u9leQpP0Qd3HWzRV0EYmRemAie8LaFpwQhJDuqT1yeEENJB569FaEowBcIQQgghfUylU2Hq3eMREOu4v1PpVRh2eSaVwCaEEEIIIYSQAeJ1ZZKeeeYZ/Oc//wEAXHLJJXjttde6XM9ms+Giiy7C8uXLATjSfDtxHIeoqCgsX74cqamp/d9oQggh5DTt+GoP9v1cKD/mlTzOv3s8wjNCPdiqnjHG8Nsza1BTWC8v4wUO5987EeEZIX3yN8ryj9c8bjle83h2GpRaGqglhBBCCCGEDD4WoxUKtQKCYlDPSySEEEIIIYSQQcXrgmEyMzNRUFAAjuOwevVqTJw4scv1/va3v+G5554D4Ah+OTEYhjGG+Ph47N27FzodlQsghBDivcp2VaJ0ZwVUWiWSJ8XDL9LX0006KZvJhv2/HkLF3mro/LXImJGM0BQqSUMIIYQQQgghhBBCCCGEEEI8z6uCYerr6xESEgKO4xAWFoaKioou16uurkZCQgIsFgsAR/DLxRdfjLS0NOTn5+PXX3+Vlz/yyCP4v//7v4HaBEIIIYQQQgghhBBCCCGEEEIIIYQQ4kFelZszPz9f/nnKlCndrvfpp5/CbDbL2WC++eYbfPfdd/jnP/+JX375BS+88AIARxmHd999F14U70MIIYQQQgghhBBCCCGEEEIIIYQQQvqRVwXDlJSUyD9nZWV1u96SJUsAODK/TJ8+HZdeeqnL7//6178iIyMDAFBZWYm9e/f2Q2sJIYQQQgghhBBCCCGEEEIIIYQQQoi38apgmIaGBvnnkJCQLtcxmUzYtGkTOI4DACxcuLDL9WbPni3/vHv37j5sJSGEEEIIIYQQQgghhBBybqEM7IQQQgghZDBReLoBnZnNZvlnjUbT5TqbN2+GzWYD4MgMM3PmzC7XS05Oln+ura3tw1YSQgghhBBCCCGEEEIIIWc/iTEsLz+ADdVFMIs2DPGPwJz4ofBVaT3dNEIIIYQQQnrkVZlhtNqOC+jm5uYu11m7di0ARyBMRkYGgoODu1yvczCN0Wjsw1YSQgghhBBCCCGEEEIIIWe/lRUHsbz8ANrtVkiMYW9jBd4/uNHTzSKEEEIIIeSkvCozTOfSSAUFBV2us3TpUvnnCRMmdPtaTU1N8s+dg2wIIYQQQgghhBBCCCGEEHJym2uK3ZaVtzejpLUBDRYjDjXXwF+tw+jQePhRthhCCCGEEOJFvCozzNChQ+Wff/zxR9jtdpffFxYWYvPmzeA4DgBw3nnndftax44dk3/uHGRDCCGEEEIIIYQQQgghhJCTs0lil8v/d2wPPj+yDdvqjmF5+QG8tHsFakytA9w6QgghhBBCuud1wTBRUVEAgPLyctx5550QRcfFdktLC2688UYwxsAYg0ajwcyZM7t9ra1bt8o/JyYm9m/DCSGEEEIIIYQQQgghhJCzTHZgpNsyDa9AcWu9y7J20YaVFQcHqlmEEEIIIYSclFcFw3Ach9tuuw2MMQDAW2+9haioKIwdOxaxsbH4/fff5fUWLlwIPz+/Ll+nubkZmzdvBgDwPI+cnJyB2QBCCCGEEEIIIYQQQggh5CwxKyYLaX6h8uMAlRajQxOA49nbO6tsbx7IphFCCCGEENIjjjkjT7yEzWbDxIkTsWXLFnAcB8aY2/9hYWHYtWsXwsLCunyN999/H3/605/AcRyys7Oxa9eugd0IQgghhBBCCCGEEEIIIeQsUWdug0W0I0LnhzJjI17bt8ZtneHBsZifNNwDrSOEEEIIIcSdV2WGAQClUonffvsN8+fPlzPEdP4/MzMTy5Yt6zYQBgBeeeUV+eeeSikRQgghhBBCCCGEEEIIIaRnwRoDovT+4DkOsYZA5AZFu/xer1Dh/Kg0D7WOEEIIIYQQd16XGaazkpISrFy5ElVVVdDpdMjLy8OECRPAdZGC0enYsWN47LHH5Md33303srOzB6K5hBBCCCGEEEIIIYQQQshZT2IM+xorcKi5Fv4qLUaExMFXpfF0swghhBBCCJF5dTAMIYQQQgghhBBCCCGEEEIIIYQQQgghp8LryiQRQgghhBBCCCGEEEIIIaR/McbAJO+aK2u3ihDtkqebQQghhBBCzgIKTzeAEEIIIYQQQgghhBBCCCEDw2axY/vnu1G04RgYY0gYHYMRV+VApVN6rE1tdUZs+mAHKvfVQKFWIGVyPIZdkQ1BQfN5CSGEEELI6RlUwTB1dXU4cOAAGhsb0dbWBoPBgICAAKSlpSEkJMTTzSOEEEIIIYQQQgghhBBCvNqWD3eiaMMx+fGRdSWwWeyYfNsYj7SHMYZV/96IptJmAIDdbEfBr4ehUCuQOzfTI20ihBBCCCGDn9cHw+zduxfvvPMOfvjhBxw7dqzb9WJiYjBnzhzccMMNyMrKGsAWEkIIIYQQQgghhBBCCCHez2a2o3hLmdvy0u0VsLRZoDaoB7xN9Ucb5UCYzg6vLaZgGEIIIYQQctq8NsdgfX09Fi1ahJycHLz22msoKSlx1DA9/g+Ay+Njx47h1VdfRU5ODq699lrU19d7eAsIIYQQQgghhBBCCCGEEO8hiRIku+S2nEkMYhfLB4Ikdv13u1tOCCGEEEJIb3hlMMyRI0cwYsQIfPLJJ3Lgy4l6Wv7xxx9j5MiRKCoq6s9mEkIIIYQQQgghhBBCCCGDhlqvQviQULflISlB0PlrPdAiIDgxEPpgndvy+FExHmgNIYQQQgg5W3Csu6gSD2lqakJ2djbKy8vBcRwYY+A4DuPGjcPMmTORnZ2NkJAQ6PV6GI1G1NbWYu/evVi6dCnWr18vr88YQ0xMDPLz8+Hv7+/pzSKEEEIIIYQQQgghhBBCPM5Y3461r29CXVEjACAwzh+Tbh8DnxC9x9rUcKwJ697cguaKVnA8h5i8SIy7YQSUGoXH2kQIIYQQQgY3rwuGueGGG7B48WI5oGXy5Ml47bXXkJl58tqg+/fvxx133IFVq1aB4zgAwPXXX4933nmnv5tNCCGEEEIIIYQQQgghhAwazZWtAGPwi/T1dFNkLVWtUGgUHstSQwghhBBCzh5eFQzT3NyM8PBwWK1WAMD8+fPx8ccfQxCEXr+GJEm4+uqr8cUXXwAA1Go1qqqq4Ofn1y9tJoQQQgghhBBCCCGEEEIIIYQQQggh3oP3dAM6W7lyJSwWCwAgPDwc77zzzikFwgAAz/N45513EBERAQCwWq1YsWJFn7eVEEIIIYQQQgghhBBCCCGEEEIIIYR4H68Khjl27Jj88/z586HXn16NUr1ej/nz58uPS0tLz7hthBBCCCGEEEIIIYQQQgghhBBCCCHE+3lVMIwoivLPGRkZZ/RanZ8vSdIZvRYhhBBCCCGEEEIIIYQQQgghhBBCCBkcvCoYJjo6Wv7ZarWe0WvZbDb556ioqDN6LUIIIYQQQgghhBBCCCGEEEIIIYQQMjh4VTDM8OHD5Z937NhxRq/V+fkjRow4o9cihBBCCCGEEEIIIYQQQgghhBBCCCGDA8cYY55uRGeTJk3CunXr4OPjg/37959WVpfy8nIMGTIEbW1tmDhxIlavXt33DSWEEEIIIYQQQgghhBBCCCGEEEIIIV7HqzLDAMC///1vaDQatLW14fLLL0dDQ8MpPb+pqQlXXHEFWltbodFo8O9//7ufWkoIIYQQQgghhBBCCCGEEEIIIYQQQryN1wXDDBs2DB9++CG0Wi22bNmCYcOG4bPPPoPdbu/xeXa7HZ9//jmGDRuGTZs2Qa/X46OPPkJOTs4AtZwQQgghhBBCCCGEEEIIIYQQQgghhHiaR8okffTRRyddZ+/evXjxxRchSRI4joO/vz+mTJmC7OxshIaGQqvVwmQyoaamBnv27MHq1avR1NQExhgEQcDdd9+NzMxMAMAf//jH/t4kQgghhBBCCCGEEEIIIYQQQgghhBDiBTwSDMPzPDiO6/X6zib29Jye1hFF8RRbSAghhBBCCCGEEEIIIYQQQgghhBBCBiOFpxvQG70JnOlqHcbYKQXdEEIIIYQQQgghhBBCCCGEEEIIIYSQwc1jwTAeSEhDCCGEEEIIIYQQQgghhBBCCCGEEELOch4pk0QIIYQQQgghhBBCCCGEEEIIIYQQQkh/4D3dAEIIIYQQQgghhBBCCCGEEEIIIYQQQvoKBcMQQgghhBBCCCGEEEIIIYQQQgghhJCzxlkfDHPo0CFPN4EQQgghhBBCCCGEEEIIIYQQQgghhAyQszIYxmQy4aOPPsLkyZORkZHh6eYQQgghhBBCCCGEEEIIIYQQQgghhJABovB0A/rSli1bsHjxYnzxxRdobW0FYwwcx3m6WYQQQgghhBBCCCGEEEIIIYQQQgghZIAM+mCY+vp6fPzxx3jvvfewf/9+AKAgGEIIIYQQQgghhBBCCCGEEEIIIYSQc9SgDIZhjOHXX3/F4sWL8cMPP8Bms4ExBgByEAxjDBqNBrNmzfJkUwkhhBBCCCGEEEIIIYQQQgghhBBCyAAaVMEwxcXFWLx4MT744AOUl5cDgBwE48TzPKZOnYqrrroKl112GXx9fT3RVEIIIYQQQgghhBBCCCGEEEIIIYQQ4gFeHwxjtVrxzTff4L333sPq1avBGHMLgOE4DqNHj8ZVV12FP/zhDwgNDfVQawkhhBBCCCGEEEIIIYQQQgghhBBCiCdx7MTIEi+xc+dOvPfee/j888/R1NQEwJEFhuM4l5JIjDEkJyejsLDQg60lhBBCCCGEEEIIIYQQQgghhBBCCCHewKsywzQ1NeHTTz/Fe++9h/z8fADuATCCIGDmzJlYtGgRrrjiCnAc58kmE0IIIYQQQgghhBBCCCGEEEIIIYQQL+IVwTArVqzAe++9h++//x4WiwVARxCM8+ecnBwsWrQICxcuREhIiCebSwghhBBCCCGEEEIIIYQQQgghhBBCvJTHgmHKysrw/vvv4/3330dJSQkA9ywwISEhWLhwIRYtWoShQ4d6qqmEEEIIIYQQQgghhBBCCCGEEEIIIWSQ8FgwTHx8PBhjcuCLk0qlwsUXX4xFixZh5syZEATBQy0khBBCCCGEEEIIIYQQQgghhBBCCCGDjceCYSRJkssgcRyHUaNGYdGiRViwYAH8/f091SxCCCGEEEIIIYQQQgghhBBCCCGEEDKIeSwYxonjOCQkJOC6667D/Pnz4efn5+kmEUIIIYQQQgghhBBCCCGEEEIIIYSQQYr3dAMA4OjRo7jlllsQERGB+fPn43//+x8kSfJ0swghhBBCCCGEEEIIIYQQQgghhBBCyCDjsWCYZ599FmlpaWCMyf/MZjO+/vprXHLJJYiKisI999yD/Px8TzWREEIIIYQQQgghhBBCCCGEEEIIIYQMMhxjjHmyARs2bMC7776L//73vzAajS6/4zgOAJCVlYVrr70WV111FcLCwuTf8zwPjuOQlJSEwsLCAW03IYQQQgghhBBCCCGEEEIIIYQQQgjxPh4PhnFqa2vDF198gcWLF2PTpk0AHMEwjDE5KEYQBEybNg2LFi3CpZdeCq1WS8EwhBBCCCGEEEIIIYQQQgghhBBCCCFE5jXBMJ0VFBTg3XffxSeffILa2loA7oExvr6+aG5uBgAkJydTMAwhhBBCCCGEEEIIIYQQQgghhBBCCPHOYBgnu92OJUuWYPHixfj1118hSZIcFAN0BMj4+Pjg0UcfxYIFCxAVFeXhVhNCCCGEEEIIIYQQQgghhBBCCCGEEE/x6mCYzsrLy/H+++/jgw8+QFFREQDIWWKcOI7D+PHjsXDhQlx++eUIDAz0RFMJIYQQQgghhBBCCCGEEEIIIYQQQoiHDJpgmM5WrVqFd999F9999x3MZjMA9zJKCoUC06ZNw8KFC3HllVd6srmEEEIIIYQQQgghhBBCCCGEEEIIIWSADMpgGKfm5mZ88sknWLx4MXbu3AkALmWUnI9FUfRUEwkhhBBCCCGEkHNO5/LGhBBCCCGEEEIIIYQMtEEdDNPZrl278O677+Kzzz5DU1OTvJyCYQghhBBCCCGEEM+jABlCCCGEEEIIIYQQMlDOmmAYJ4vFgm+++QaLFy/GqlWrAICCYc5BjDEwxsDzvKebQggh5CznPOdwHEeDe4QQQgiA999/H2azGenp6YiKikJERAR8fHy6XV+SJDqPEkIIIYQQQgghhJA+ddYFw3R29OhRvP/++3jiiSc83RTiBSRJAgDqZPVSXc0SdQ4uE0IIIYQQQgaPuLg4NDc3IzQ0FIGBgQgLC0NUVBTi4uLkf9HR0QgNDYVare72dSiTDCGEEEIIIYQQQgg5XWd1MAw5N0mShKeffhppaWlISUlBXFwcAgICelwf8M4gmZaWFqxevRpDhgxBeHg4DAaDp5vU5yRJogw+hJBB6/XXX8d//vMfTJo0CTfccAPy8vI83SRCCCHEo1pbW+Hn5wee5+V7LQAQBAFarRYGgwEBAQEIDg5GeHg4oqOjER8fj7i4OMTGxiIqKgrBwcFed29GCCGEEEIIIYQQQgYXhacbQEhfO3DgAB599FEEBQVBq9UiMDAQkZGRSExMREpKClJTU5GcnIzo6GhotdouAzE6x4h5shN26dKlWLBgAQRBQFhYGNLS0pCVlYXs7GxkZGQgPj4ewcHBPc6m9HY8z6OoqAhlZWXw9fWF1WpFe3s7tFottFotgoKCXLbR2aFOATSEEG+watUq7N+/H/v378eMGTOQl5dHQX6EEOJhlF3Qs/bt2wcALoEwgONzaW9vR1tbG6qqquTlSqUSer0ePj4+CAwMREhICKKiopCeno7MzEykp6cjOTl5QLfhZKisEyGEkIHizZP4ziVnY7Y6umY++9BnSgghhLijYBhy1tm1axcAR1aV+vp6lJWVYc+ePRAEASqVClqtFr6+vggJCUFMTAySkpKQmpqKlJQUJCYmIjIystuLxoG+Ad29ezcAxyzKiooKVFRUYNWqVfLvVSoV4uPjMWTIEDlIJjU1FdHR0QgICHAbjPWGGzfnRbnFYsFHH32E77//HjU1NTh8+DCam5sBAIGBgQgODkZAQABiYmIwdOhQZGdnY8KECQgKCvJY28ngJkkSGGPy94JuDj3HbrdDoTg7LkHy8/MBAL6+vsjMzARA+xbpXxRsRfoLY8zlPDkYSZIEURShVCoBAKIoguf5s+K43NV33xuu7bsycuRIbN68Gfv378fWrVuxY8cOFBQUyNf6JxJFES0tLWhqakJpaSkAxzbpdDqEhIQgIyMDF198MebNm4eQkBCv6OTv6rPo/Hl4un3k7CCKorw/0T5FSO+JoghBEDzdjNN24nlusF6becP5uq90tS1nQx9T5zY7t4fOO55x4MABfPHFF4iNjUVSUhKGDRsGX1/fU34d+twIIYQQd1QmiZx17r//fjz//PMuyziOQ1e7Os/zUCgUUKlU0Ov18Pf3R1hYGOLj45GcnIy0tDQkJycjPj6+x1JL/eXaa6/FRx99BIVCAbvd7vK77rYJAPz8/JCcnIzMzEzk5uZi7NixGDZsGFQqlbyOJ25KnX9z1apVuPPOO3Ho0CFYrVYAkDsqnDdfJ1IqlYiJicHs2bNxySWXYPLkyV43mN75PT2bbvrPZfQ59r09e/Zg2bJlcvBhWloa/Pz8evXc7oIAPDU7W5Ik+TgUHx+PgwcPygOwxDt012kJDN5OZafOnfznaoYCb/gsvTUY4VSc7v7jjcEza9aswY033ogFCxbg1ltvRVhYmKeb1KcqKipQV1eH0NBQhIeHy8sHy/VKXV0d9u3bhx07dmDr1q3Iz8/HoUOH3O5zALhcUzuFh4fjhRdewJVXXjlgbe7KwYMH8cILL2Dq1KnIyspCamqqy31WZ2fDQNm5ztuCUGmf8j6UtcM7tbW1oa2tTc48BgyOa+bO5/SGhgasXbsW9fX1iIyMREZGBuLi4uT+wK62w9uOWZ2d7nfFarVCEASPBzg999xzSE9PR25uLmJjY7tdb7AcE1atWoXi4mKMGzcOKSkp3e43Z9t5xxvv3yRJwr333ouXX35ZXlZaWoqoqKhev0ZdXR327NkDq9Uqj234+/vLr+/t+yMhhBDSnygYhpx1SkpKsGnTJhw8eBB79uxBYWEhysvL0dzcDFEUXdbtKqCE4zjwPA+lUgm1Wi2n6+5caik9PR1xcXFITEzstuOzLyxfvhyFhYWoqqpCWVkZysvLUVVVhdraWjQ1NcFsNrs9p7sgGZVKhQsvvBBXXHEF5s6dC41G02/t7g5jDG+99RZuvfVW8DwPSZKgVCphs9kAoMugn66kp6fj3nvvxYIFC6DT6fq72b3iHJS02WxQKpWQJAltbW3QaDS92ke8ucNiMOrcMXTo0CF8+eWX2Lp1K3x9fZGWlobJkydjxIgR0Gq13c60ppvEvldfX4+//OUv+OyzzwAAubm52Lp16yl1ah05cgRVVVVy5ihnxyYw8J9bYWEh0tPTwfM8Jk6ciFWrVtG+44WMRiNKSkoQFBQ06AfG9+3bJ+9nPM8jPDwc48ePlwfFT9z/unrsvEagc86ZaW1thcFgOOXve0/ZPQDPdMr+/vvveOONN2AymTB58mRcfvnliImJ6fXzbTYbrFarW/lRTxwPH3/8cTz++OPw8/NDdnY2rrvuOlx00UUICQkZ0Hb0tR07duCNN97A4cOHUVFRgba2NoSEhGDq1Km44YYb5Mxk3oQx5jLDuKdz/dGjR5Gfn4/t27dj+/bt2LNnD8rLy13W4XkejDEIgoBHHnkEjzzySH9vQrfeeOMN3H777fJjQRCQkJCA7OxsDBs2DMOGDUN2dvZZMVB2Lmtvb/foveYzzzyD3377DTNmzEBubi6GDh2KyMjILtelzESEODQ1NeH999/H8uXLoVAooNPpEBAQgNzcXFx88cWIiIgA4P33+3V1dXj66afx6aefAnDcz1gsFkRGRmLWrFl4/PHHERoa2u3zTSYTzGYzDAaDPFnDE4PhW7duRVRUFEJDQ7udzNbT8ct53fzxxx+jvr4eQUFBuOSSS3o9maYvNTU1ITAwUH6sUqmQkpKCYcOGYfjw4Rg+fDiGDBnisk5nzusib8q6csEFF2DlypXy46SkJAwdOhQjRoxAXl4ehg4dKn9nTjSYzju9+b57+rqsqKgIkyZNQkVFBQICAnD33Xfj4Ycf7lXbN27ciEcffRQHDhyAUqmE3W6HIAhISkrC2LFjcfXVVyMtLW2AtoQQQgjxThQMQ84JjY2NOHz4MPbu3Ys9e/Zg3759KCoqQnV1Ndra2lzW7WoWonO5s9SSj48P2tra8Pzzz+Omm27q9/YzxtDc3Iy6ujpUV1ejoqIC5eXlcoBMRUUFqqurUVdXh5aWFreAEmfnMc/ziIqKwsUXX4xbbrkFQ4YM6fe2d/bZZ5/h6quvlgdKJEmCwWDAtGnTYDAYEBAQgNDQUIiiiNraWpSWluLgwYMoKyuTP6fOATN/+MMf8Mknn3hNhpidO3fiyy+/xDfffAPGGGJjY5GZmYkZM2Zg8uTJLoP23QW/dC4f4+0dNN7K+b41NTXh1VdfxT/+8Q/Y7Xa37/SMGTPw4osvIiMjo8s0ygcOHJBvRGNjY+USXd44i2SwWLZsGRYuXIi6ujpERUXh/vvvxx133HHSYDBRFLFu3To88cQTaGlpQUNDA1paWqDRaDB06FCcf/75mDdvHuLi4gZwa4CvvvoKCxYsAMdxuPHGG/Gf//xn0KfkPptUVlbivffew7Zt21BQUIDKykooFApkZmbioosuwsUXXzzg58FT5TyemUwmPPLII/j6669x7Ngx+fcqlQpRUVG4+uqrce+997qcZ5wsFgvq6uoQEhLSrwG8vbFnzx7o9XpERERAq9We8vOd36/ffvsNjz/+OCIiIrBo0SJcfPHF/dDant14441Yv3498vLy8NhjjyE5ObnXzzWZTCgvL4dOp+txcGCgXHfddfjwww8BALfffjseeeSRHoNHbDYb9u3bhy+++AINDQ3QaDRQKpXw9/dHbGwssrKykJub65Fj4Zw5c/DDDz/IweG+vr744x//iDvuuAMpKSkABk/pJOf3//XXX8eLL76Io0ePAnC9/tBqtcjMzMTTTz+N888/3+V5kiRhy5YtaGhoQFVVFQAgOjoaqampiI+PH/DtcXIGyDjvTbo7/zc1NWHHjh1YtmwZVq9ejZ07d8JqtYLjOCgUCnz55ZeYM2fOwDb+OOd3xhnc391kBB8fH6SkpCAnJwd5eXkYNmwYMjIyus04OpgGlrzVoUOHUFBQgKysLISGhsJgMJzW67S1tWH8+PHgOA45OTnyMXIgTZw4EevXr3dZptVqkZaWhtzcXHmwcsiQId2WUaCSF32v8zG2sbERa9aswdatW6HVapGSkoK8vDwkJye7ZO871wOQB+o9+Omnn3DTTTehrq5OnnDVWWxsLK666ircfffdCA4OPq2/MRB9NIWFhbj++uuxYcMGeZnzfOOUmZmJL7/80u1e5sMPP8Tvv/8u9yuFhYUhNzcX55133mlv8+kqLCzEeeedhyFDhshl6uPi4pCQkICYmBhERkZ2GzgCdASPCIKAjIwMHDx4EABQXV3tkSDnTZs2Ydy4cdDpdLBard1O5AsICMCQIUOQl5eHESNGYNiwYV6bQS4zMxMFBQVQqVRy1u4TOc87eXl5GD58OPLy8pCRkTFozjud76fVajUOHjyI8vJyMMYQHh6OyMhIua+vKwMVJPPWW2/hlltuAQBMmTIFX3zxRY8Bb4Djnuzf//437r//frdjhJNCoUBCQgJuvfVW/OUvf+mXthNCCCGDAQXDkLOOsxPRecHb0013WVmZnEFmz549OHDgAEpKSlBfXw+LxeKybucgGWdAxnfffYdLL720X7elp4ttm82GxsZG1NbWoqqqChUVFSgrK3MJkqmoqEBDQwPMZrPLxfGkSZPw4Ycf9vvgcefSSHfffTfy8/MBODqHFy1ahEWLFiE9PR16vV5e33ljaTabUVZWhgMHDmDVqlVYsmQJqqurAUDOKHPFFVfgyy+/7NdtOBlJkvDdd9/hhhtuQHNzc5frTJ8+HR999BFCQ0Pl96ShoQFbtmzBF198gYqKCkRERCAuLg4jR47E1KlTXd4TT99ADjZmsxl/+ctf8M477wCASwAW4JjBK4oicnNz8dFHHyErKwuAY4Dy22+/xUsvvYT29nb5OBAeHo7Ro0fjD3/4A8aMGQPg7P1c+rOz8i9/+QteffVVAMD111+PF198Eb6+vt2WsuF5Hs3NzXj++efx1FNPufzeGeTHcRz8/Pwwfvx43HPPPZg8eXK/tL0rDzzwAP71r3+B4zi88soruO222ygYxkusXr0ad999N3bt2gXAPdBVr9dj0qRJuPvuu3H++ed79fdZkiTMmDEDK1asAIAuOyu1Wi3uu+8+PPLII/L+197ejq+++gpr167Ftm3bUFFRAX9/f4waNQrTpk3DeeedN+AD4kFBQWhsbERSUhJSU1ORnZ2NrKwspKenIzY2FoGBgT0Ghjg79p966ik5K8RTTz2FBx980CWYdCAkJCSgpKQEfn5+2LFjBxISErpcz7lvtbe346effsKXX34Js9kMo9EIm80mD56NHj0aEydORFJS0oBtA+CYeZyTk4PKykqoVCps3boV2dnZbus5j8mbN2/Go48+inXr1sFsNrsFABgMBsTFxWHs2LG48sorMW7cOKjV6oHaHPlzcX4PnFkhdTod7rjjDjz44IPddt57oyVLluDGG29EbW2ty3e/c3kExhiysrLw7bffykFZ69atw3vvvYclS5agqakJgOOzCQkJQXp6OmbPno3LL7/c5brUk5z3bp2v05xtslgs2L17NxYvXoxPPvkE7e3tYIxh+PDh+Pnnnz0yIHbvvffit99+Q3t7uzwZ4UQ9lbQNCwtDRkYGhg0bhry8POTk5PQ4UNbf+nMfYIzJx+eB2M9uvfVWvPnmmxAEAeHh4UhLS0NWVhays7ORnp6OhIQEBAcHd/teO6/jdu3ahQkTJqC9vR2ZmZnYs2fPgF/jBQcHo6GhwWUArquBLue6mZmZcnaCYcOGISUlpdv2nm0lLwZaY2Mj3n77bfzzn/+ExWKB3W6XB8Y5jsO0adNw2223ycG63nCc9YRDhw4hPj5+QMrIfvjhh7j55pthsVjkgNcTM0MDgFqtxqJFi/Dwww+fUha8yspKhIaG9vsx4NixY3jwwQfx+eefu2RRBjr6wJz9enPnzsVXX30FnudRUFCA6667Dvv27YPRaHR5TZ1Oh9TUVFx55ZW46aabBuw66JNPPsEf//hH+XyoVquh0+ng6+uLwMBAhIWFISIiArGxsYiPj0dcXBxiY2MRFhYm94UBjuNVXFwcysvLodVqYTQaPRLY7MwKp1KpYLPZujzH93Tuj4mJQXZ2NkaMGIHhw4dj6NChAz6ZpzPGGFQqFURR7LbdPW1PSEhIl+cdbwz+W7VqFT7//HN8/fXX8n4IOL5TGo0GERERSEhIQGpqKlJSUpCcnIyYmJgByw5nMplwzTXX4Ntvv4UgCHj11Vdx880393jdYbfb8cYbb+Cuu+6CIAjyeaar4x7gCNJ66aWX8Mc//rE/N4UQQgjxXoyQc4QoisxutzO73c5EUex2PbPZzAoKCti3337L/u///o9dccUVbOjQoSw4OJgpFArGcZz8r6ioaAC3gDFJkuR/PTEajay0tJRt3bqVffHFF+yhhx5ieXl5crtVKhXjOI6NHDmyx/eiL/3hD3+Q/35QUBD7/PPPXX5vt9t7fL7JZGJ79uxhDz30EAsLC2Mcx8mfx7PPPssYYwO2LSdavnw58/X1ldvE87zLfiIIAuM4jo0fP54x5vgct2zZwsaPH+/y+87/fHx82D333MOsVqtHtqmvOffZw4cPsx9//JFt3ryZ1dXV9dvfe+aZZ+TPxPn+RkVFsREjRrDExESmUCjk/ee+++5jjDFWWVnJrrzySrfPwvlPqVSyyMhI9sADD7CGhoZ+a3t3+nv/bm1t7dfXr6+vZ7m5ufIxaOXKlYwx1uPxTBRF9te//lX+DNRqdbffL47jWHJyMtu+fXu/bkdn559/vtwu5/ac7Fjm7SRJYqIonvQ801vO/fa3335jH3zwAfvxxx9ZY2Njn7x2d3bs2MH8/Pxczncn7ivO/UitVrOPP/64V6/rfE+KiorYoUOHWHV19YB83s7vQOd9PyYmRj7Gdf63Zs0axpjjeHbppZe6/K7z8/38/Ni8efPYjh07XLatP9lsNrfvb+d/BoOB5eTksCuvvJI99dRTbMmSJWz//v2sqanJ7bWuvPJK+fNctmwZY2xgrwFsNpvc7sTExG7/tvN9Xbt2LcvNzWUajabLbVepVCwiIoJddNFF7KuvvmLt7e0Dti0ffvihvH/Mnz+/x+34/PPPXfY7pVLZ7efJcRwLCAhgr7zyivw96e/9zPm58DzP9Ho902q1Lvu/Wq1mOTk57I033mDV1dX92pa+0N7eztLT012OX9OmTWOXXXYZmzVrFktLS3N5vx988EHGGGO//vqr/LwTv/ud/40cOZLt2bOHMTYwx4C+8MEHH7jcA6xYscIj7SgvL2fbt29nP/30E3vrrbfYI488wq6//no2Y8YMlpOTwyIjI5lOp+vyfe/u81AoFCwlJYXNnTuXPfnkk+yXX34Z8HvNzs50n3AeF7ds2cIeffRR9tZbb7F9+/b1RdN6NGPGjB6PTxqNhqWnp7O5c+eyRx99lP33v/9lu3btYnV1dS7H8i+//FLez6677jrGGBvQ+zKTycTGjx/Phg4dyqKjo5mfn5/bNvE8zwRB6PHcmpiYyObMmcOefPJJ9vPPP7OysrIB24YT9fdxpq2tbUCuBTpfYzn3Eef/PM/Ln0dISAj761//esr3WDabzWP9Gn3B+TkfOXJE3g/Hjh3br39zyZIlLCUlxeWziI+PZ3PmzGETJ06U+486f1cuv/zyXn82paWlbMSIEWzMmDHszjvv7NdteeKJJ1z6HcePH8/efvtt9tZbb7GHHnqIjRw5Uv6dVqtla9euZXv27GHDhg3rtm+p8730448/zkwmU79ug9O9994rH3ed731X9/J6vZ6Fhoay1NRUNnbsWDZnzhx2++23s2effZb99NNP7MMPP5Sv6UaPHt3l3xqI6xhnX5FGo2FqtZrNnDmT3X333WzOnDksMzOTBQcHd3vu6e44rVKpWFZWFrv66qvZyy+/zH7//XdWX1/f79vCGGMHDhyQ9wtBEFhiYiK7/fbb2cKFC9mIESNYeHh4t/cuPZ13kpOT2dy5c9nTTz/Nli5dyioqKgZkezpz7g+tra3slVdekfsEuvt+CILA1Go1MxgMLCQkhCUmJrJRo0axuXPnsvvuu4+98847bM2aNezYsWP9cv+/d+9e+Rg2YcIEVlBQ4LIdnTn//jfffMOSkpLk/ch5j3/ZZZex66+/no0dO9atTyQhIYEdPny4z9tPCCGEDAYUDEPOeZ2DZHq6gWpoaGBbtmxhixcvZnfddRebNWvWALayZ70JkrHb7WzNmjVs+vTp8sW+UqlkTz/9dL+3b/369SwkJES+0fjpp5/k351qR4/ZbGavvvoqCw0NlW9kMjMz2ZEjR/q62b1y5MgRNmfOHPk9VSgULDExkV199dXsiiuuYFlZWfINsa+vL/vhhx/Yzp07WWxsrNxZc+INmTOgRqvVsoULF3Y5ENhfqqurmdFo7PPOBOcN28MPPyxv5+LFi/v0bzi1tbWx0NBQ+QY9KSmJLVu2jB04cIDt2LGDff/992zBggXyDWFwcDCrrKxkCxcu7LHzyPl6SqWSPfTQQ8xisfRL+3ujc2BfX3xWFouFzZw5k1144YXs9ttv74MWuvvll1/kQdTZs2f3GBDh3F9effVVufNSrVYzjuNYZGQku+CCC9j48eNZTEyMy+AGx3Fs1qxZrKWlpV+24UTh4eGM4xwBfv0Z3NXfrFbrSd8zURRPa39zHuNnzJjBgoKCWFBQUL8er0tLS9m4ceNcvrOjRo1il156KVu0aBGbNWuWy/nIuc7LL7/MGDt5cBZjjF144YUsISGB5eXlsfLy8n7bFsYY27Vrl8tAy7Bhw9gbb7zB1q5dyzZt2sTefvttNmbMGHlbLrnkEma32+Vgy84dsp07LZ0/+/n5sQ0bNvTrNjjt3btX7ow78VjbU4dqaGgomzBhArvpppvYa6+9xn7//XeWnZ0tv4YnghP37dsnn/fPP/98xlj3+86SJUuYUql0OYd01QHb+fGDDz44IIFWdrud3X777fJx7MMPP+x23a1btzK1Wu0WGD5s2DA2atQoFhsb67aPOR8//PDD/b4tjHV8LjzPs4kTJ7Jly5ax2bNnyx3Bzn/BwcHs5ptvZnv27JG/1ye7D/CEr7/+Wm6zn58fe+ONN1wG7oxGI/v2229ZQkIC4zjHwGtNTQ2Ljo52+RyCgoLcvmPOc2p0dLTHrqFPhfNzstls7IYbbpCPiX/605883LIOTU1N7MiRI2z9+vXs66+/Zv/+97/Zfffdx6666io2efJklp6efloDZatWrerXdu/atYsdPXqUNTY29irYo7ffE+cx7KGHHpK35V//+tcZtbU3QkNDux3w6ulcExAQwEaOHMn++Mc/srfffpvNnz9fXv+NN97o93Z3paysjG3bto1988037IUXXmB33nknmzt3Lhs/fjxLS0tjYWFhzGAwMEEQ5Ht75/mmp3sarVbLcnNz2bXXXitfUwz08c8ZfN2XAdiPPvooe/jhh9nLL7/cb/dplZWV7Oabb3YZXHT+i4qKYv7+/i77mkqlYvfcc88p3Se88MIL7K233mK//PJLvwbFvPDCC+yll15iq1evZsXFxX0WIOFs87Jly+SgwAsuuIAx1n+TBpx9XBzHMX9/f/bqq6+ygwcPsoaGBma1WtmRI0fY008/zYKCglw+u6uvvlreV7raD53bsnTpUvn656KLLnL5XV9qbW2Vg1o4jmN33XUXKykpcVvv0Ucflc/j99xzD7v11ltd9sXs7Gx2wQUXsPT0dPkeufMxwXnf099BV50nwnR3/O0uqE+pVDJfX18WExPDoqOj5d/n5OSwTz/9lC1ZsoTt2bNnwAJHGGPyd7/ztdRll13GPvvsM7Zt2za2ZMkS9tJLL7F77rmHzZ8/n02YMIElJyfLx4VTOSf9+c9/Zoz1b5DPhg0bXIJdeJ5nQ4cOZc8++yxbvXo1W7ZsGXvjjTfY3/72N3bNNdew8847j2VkZHQ5UfRk2yMIwoAFYTHWsW+/8cYb8jZ2bnNQUBBLSkpiERERLsHzJ/5TKBRMq9UyPz8/FhUVxeLi4thVV13V5+39+OOPmV6vZxzHsQceeKBX79W0adNcjmfjx49nv/zyi8u9QmlpKbvqqqtc1nvyyScZY4N/EhchhBByqigYhpx1epP95WQkSZJfZzBfIHYVJFNQUCBfNHMcx6ZMmcLMZnO/taFzZgee59kVV1xx2q/VeTteeeUVptVq5Q7lDz74wG2dgfD666/Lg/UGg4G9/vrrrL29nTU0NLCysjK2cuVKdvnll8vv94IFC+SbaI1Gw5RKJQsNDWVjx45lOTk5LDw83K0z4J///OeAbEtdXR2bM2cOe/jhh9mbb77JfvzxR7ZlyxZWVFTE6uvre7WfdPf+O7+PV111ldwR89tvv/Vp+50+/fRT+b2LjY1lpaWlXa7XOTDnwQcfdOnQnDVrFnvmmWfYgw8+yC6//HIWERHh1on03Xff9bjNfWnjxo1s06ZNrLm5udt1Oh+3env8c7b9wIEDzGAwMI7j2JAhQ/qkzSd64okn5E4jZwdcT6xWK0tNTXXpuJg5cyZbunSpy3rbt29nU6ZMcRnc+/nnnxlj/fvZtLS0yMe1hIQEdvToUVZXV3dG3xNPWbp0KZswYQK744472LPPPsu+/PJLtmnTJlZWVtbjYEJvBzIkSWLJycnyd6c/zzlvvPGG/HdCQ0PZ3/72N1ZZWSn/3mg0sh07drC77rpL7hDnOMfguDMQoKfvjyRJ8gys/t4WURTZ3//+d/lvTZo0iR09etStPbt375bP65GRkezll1+Wvzc8z7MJEyaw6dOns7lz57IJEya4Zc2Kj48fkI7kzz77TO6IU6vVLC8vj40dO5YFBwe7DSidrFP1xODEjz76iG3dupVVV1czm83W79viPM/wPC8HEHZ1vbhhwwY5EMP5fvv4+LBLL72UzZ49m+Xl5clBdZ07XTmOY3/729/6fTtaWlrYRRddxDjOEdTizKzl/D47/6+srGTz58+Xt1mv17PrrruO/f7772zXrl2srKyM1dTUsIMHD7JPP/2UXXXVVfJ2KxQKplKp2DfffNPv29P5c7nrrrsYY4yVlJSwv//97yw4ONjtPK7T6dhjjz3mcm71luNze3s7u/766+V9pvP+cOI+/s477zCe55m/vz+bN2+e/N0YM2YMe/LJJ9ny5cvZrl272C+//MIefPBBlpmZ6fJenMm1+UDqPCjZ+bjI2MB36Pc2W6fVamU1NTVs3759bMWKFezjjz9mzzzzDLvtttvYZZddJgeSBQQEuAwAqlQqefCmqqqq37ajurqaxcfHs9mzZ7NbbrmFPfvss+zTTz9lq1atYnv37mXl5eWstbX1tN5f5+e1cOFC+Ri/fPnyvt4EF21tbS7nibS0NLZgwQI2dOhQFhYW1u2AV08DeBzHsREjRrD77ruPvf3222zdunWsvLy8X8//PTEajayoqIitWbOGffDBB+y5555jl1xyicvxOTg4uMvZ/M6gma62sb8dOnSIbd26ldXW1p70mvF0AmQaGhrkAefw8PAzbW63XnnlFZf3MCcnh3300Uds48aN7Mcff2QvvPACmzx5stv768zcdbJ7tJqaGpeJNP3pxGuPpKQkNnv2bPa3v/2NffLJJ2zbtm2ndV3lXP+FF16Qv1uPPfYYY6x/gi+2b9/OfHx85G3pLoDQbrezXbt2sQkTJrjcNz733HPdvrbz2Pfiiy/Kn/ujjz7ab9vSefJIWlqanMWu8wQ+5/s7adIkxnGObJHOQP+RI0eyL774gh08eJA1NTWx1tZWtnPnTnb//fe7HBPS09PlzHD96ZZbbpH/ZlRUFFu8eDH79ddf2f3338/OO+88uR+tq38nHq+cQdZKpZIFBwez+Ph4lpeXxy666CI2b948OUtrfzp48CB77LHHWGpqqny97ufnxy688EL23//+V55c0tbWxkpLS1l+fj5btmwZ+/DDD9lTTz3FbrnlFnbxxRez4cOHs5iYmC4zyDnPl88//zxjrH8DlhobG9lHH33EJk2aJJ8flUolGzp0KHvmmWfkfjSz2cwqKirYvn372OrVq9nnn3/Onn/+eXbXXXexefPmsbFjx7KEhIQus5Y6jwFhYWH9th3dKS8vZ6NHj5bbEhISwh588EG2ZcsW9vvvv7Off/6Zff7553IA04IFC3oVvNzX182SJLH777+fcZwjWPWTTz456XOWLVsmB8847wE6Z9/rPHFOFEU2c+ZMuf2XXHLJoMiQSQghhPQ1CoYh54TeZn85Gedgs81m85rO8tOxdu1a+YY5MjJS7jDoj20qKSmRZ7dERESw1atXn/FrOjslnB0AHMex66+/fkDLCjhdeOGF8o3wc889x9ra2rpcb+rUqfJNoHPG4uzZs9mGDRtYXV0dO3DgAFu/fj1bvHgxmz17tssgRWJiIissLOz3bfnll19cbvL8/f1Zeno6mzZtGrvhhhvYP/7xD/bhhx+yZcuWsfz8fFZaWsqam5t71UHmvInPy8uTb4j74wbMbDazG2+8kXEcxwIDA9lTTz3FGOuY8S1JktzeqqoqFhsbK6fmdT7n66+/7jIbz5tvvumS4nfEiBHMaDT2+TZ0JScnR95/hg0bxhYsWMCefPJJtmTJElZYWNjjZ9DT8c/5uXz33Xfyzf4f/vCHPm+/xWJh11xzjdxhd7JAKEmS2Oeff+7yPUhISJAHhJw39U4FBQUsJSVF/mzuueeefs/cs3nzZrnDysfHh1144YXs+uuvd/ueHDt2rNffE0+dV+644w6588XHx4dFRkayzMxMNnnyZDZ//nx27733sldffZX9+OOPLD8/n9XW1vb4es7Px3msLi4ulgf64uPj5XX6WlNTkzywz3Ec+8c//iH/7sTZ7iaTiT399NPMx8dHPoanpaWxnTt3drtNJ25LQkJCv20LY44ABGegV0xMDPv6668ZY6zLAThnEIBGo2GBgYGM4ziWmprK3n//fflzEEWRVVZWst9++43NmzdPnj3O8zx74IEH+mUbOlu6dCmLioqSvzdjx45ln3zyCfvvf//LnnvuOXbnnXeyefPmsXHjxrGkpCTm7+/f5QBld2nWOc4RNJCens5uvfXWft0WZ5CvIAjszTffZIx1XJs494eysjL5uMfzPNPpdOzPf/4zy8/Pd8mMJUkS27FjB7v99ttdMpgEBwf3e0aIyspKeVBoxowZctps5zY4t+n777+X9/uwsDD20ksvdfuakiSxsrIy9sorr7C4uDiXc2Z/lxjp/Ln85z//cfldVVUV+8tf/uIyGOQ8v6SmprIXXnjBqzJ8lZSUyB34EydOZNu2bWOMuQZ9OD+nuro6l2tiZ0d9V9dZRqOR/e9//2PDhw+XP5vQ0FB24MCBgdmwM+Dc3pUrV8rZby699FKvKina2yCZtrY2VlxczAoKCtiqVavYyy+/zO688055MN+5b/b3YPhvv/3W5UBcVFQUGzlyJLv88svZvffey1555RX27bffsg0bNrDCwkJWU1Nz0vsu53vQ+dq/PwN7GHNcB1xzzTXy9XpQUBB77LHH2JYtW9i3337LXnjhBXb33Xez+fPns4kTJ7LU1FQWFBTU5Qz3E885nf/p9XoWHh7uEmzbX3oTHOIMBHaWu/v000/Z/fffzxYuXMimTp3KsrOzuyy15Ny2/gqE7+zmm29m8fHx7MILL2SLFi1ijzzyCHvvvffYihUrWGFh4WlnKHQ+3rp1q3w/N2LEiH7ZBqPR6BLocv3117Pi4uIu1/3mm2/kyRQnZiHs6p7AeV+zadMmObBj3LhxLr/rS01NTfJ1Y08lD318fFhOTg676qqr2NNPP91jCUsn5/Zdd9118rYvWbKEMdY/18z/+te/5EH8m2666aTrdy5X7cym5JxIcSLnOff6668fkG157LHH5OPwE0880eO6//znP12CyTMzM7stf2Iymdgnn3zCfH195ePde++91+ftP1FtbS275pprmE6nY4IgsMzMTLf3urW1lW3dupW9+eab7M9//jMbMWKEPEnnxGNxV1lknNvz448/MsYG5n569+7d7Oqrr3Zr46WXXso2bdrU7fNaWlpYcXEx27p1K/vxxx/Zm2++yf7+97+za6+9lk2bNo1lZmbK90rOftOB2J729nb2/PPPu2RP5ThHhuV333232+cZjUZWVlbGdu3axZYuXcoWL17MnnjiCXbjjTey/2fvu8Ojqra33zMzSSa9995JQkILUqRJlSoICAgogigiIiioYEGlSARBFLAgXAQFRLz6Q6RX6TWk9zbpvU+m7++PfHs7kwJBciaBO+/z5FFmzjmz19lt7bXetdaYMWNIjx49WCbfoUOH8i5HU/z6668s+MXa2prcvHmz1WvlcjkpLi4m8fHxOgSmhQsXkmeeeYb06dOHlTGKiopq13YqFAoyb948wnGNwXz3O/81NDSw6+k69tlnn93znvPnzzMCoJOTk4EMY4ABBhhgwP8kRDDAgMcIEyZMgK+vLyIiIhASEoLAwEA4OjpCIBC0eL1arQYAcBzH/u4FjuMgFArbvd36hFKpxMCBAxEREYGzZ8+isLAQcrmct9+LiYlBamoqAKBHjx4YNGjQQz+T9sEnn3yCoUOHguM4nDp1CkZGRg/97AfFtWvXoFarwXEc5s6dC3Nzc53vVSoVRCIRnnnmGVy7dg0lJSUAGsfqrl27YGdnBwCwt7cHAPTv3x8vvfQStm/fjnfffRcymQxZWVm4dOkSAgMDQQi57zj9t4iJiQEAmJqaQiaTobq6GtXV1UhJSWHXCAQCODg4wM3NDV5eXvDx8YGXlxc8PT3h4uICR0dH2NrawtLSEqampqytAoEAdXV1KCgoACEElpaWcHJyancZKisrkZycDAAIDg7GiBEjAEBnfotEjVufmZkZhg0bht27d0MqlcLJyQkfffQRJk+eDADQaDTQaDRs3r/yyiswNTXFnDlzAABxcXFISkpCr1692l2OpsjKygIAlJWVoaSkBHfv3mXficViODg4wMfHByEhIQgPD0fXrl0RHBwMV1fXe65/SqUSYrEYcXFxbD2MjIxs9/ZLpVI29t3d3eHm5gYAzcYz/XdBQQEOHjwIoLEf7O3t8e6778LZ2ZnNKe37unTpgkWLFmHJkiXgOA5nz56FsbFxu8uhjTt37gBoHFt1dXU4fvw4+057nnh6esLX17fFeWJhYQEzMzMmC19z+36IjY0F0Lg/yGQy1NXVobCwkH1vbGwMMzMzWFlZwc7ODo6OjjprgLe3N7y8vODi4gILC4tm+2lqairq6+sBAF27dgXQ2K/tvZ+mpqYiISEBADB8+HDMmDEDQONY194fCCEQi8VYsWIFxGIxli9fDqFQiNTUVHzwwQc4cOAALCwsdJ5Nx2ZycjKTJSwsjDdZACAzM5PN/SeffBJ9+/YFAJ2xTdsVERGB8PBwxMXFQS6XIyAgAOvXr8ekSZNACIFGo4FAIICLiwtcXFzQvXt31NXV4eTJkwCAixcvor6+vtke1l4ghGDUqFH4888/MW3aNKSnpyMmJgZXrlzBqlWrMGXKFNTX16O6uhqlpaUoLCxEQUEB8vLykJeXh/z8fBQWFqK8vBwlJSUt6i0cx0EqlSIlJQXOzs4AGvuej76h89/IyIiNaTrm6Xg4fvw4Tp8+DaBx31mzZg2WLl3KnkF1B4FAgB49euDTTz+Fi4sLoqKiIJPJUF5ejp07d2LIkCG87f0ajQbl5eVMFhMTEx1ZCCEAgAsXLqCqqgoAMG/ePCxevBjAPzqONjiOg7u7O9544w3Y29vjxRdfhFqtRnp6Oo4fP47x48e3uxwU2v0SHh7O2igQCODs7Ix169Zh/Pjx+OGHH3D06FHU1taC4zikpaXhs88+Q3x8PObNm4devXpBLBazedMRkEgkKCgoAAB069YNfn5+AKAznjmOg0qlgr29PQICAnDx4kUAjevfqlWr4OTkBLlczvoVaNR9xowZA1tbW4wdOxY1NTWoqKjA//3f/+Gdd97hVc9sL4hEIlhZWSE/Px/m5uaorKzkRaf8N2jt3anVahBC2HwxNzeHubk5kpKSUFNTgzt37uD333+HVCqFSCSCQCCAQqGAv78/r+2lc8bMzAwNDQ0ghECpVKKgoAAFBQW4desWu9bc3BzOzs7w8PCAt7c3OwO4u7vDyckJ9vb2sLKygoWFBYyMjMBxHBoaGlBYWAhCCCwsLNjazBesra2xZ88eODo6YvPmzaisrMShQ4fQtWtXTJkyBQCgUChQVVXV6l5TXFyMgoIC5OfngzQGj+n8Bt1rpFIpXFxceJWH/l7TcUXbpVQqYWJiwvpJIBBg2rRpmDRpEp5//nmo1WqUlpYiPz8fEokE2dnZyMnJQV5eHoqKilBVVYXU1FRedP+mOHHiBHJycpCTkwOO4yAWi2Fubg5ra2vY29vD2dkZ7u7u8Pb2ZuOLji0TE5MW12L6DoyNjREfHw+ZTAYA6N27Ny8y3Lx5E0lJSQCAoKAgfPrpp3B3dwfQuJ9q49lnn4Wrqytmz56NzMxMAMDHH3/M7CFN9RM6zuLj4yGVSgGA1zNmYmIiazc9AzYFPePExsayswKFk5MTgoKCEBYWhvDwcISEhMDPzw9OTk4wNTUF0KiXcxwHQggiIiLYM9sTCoWC6f4uLi5Mx7jXXta7d29ERUVh5syZkEgkAIDly5fD398fQUFBOtfScZecnKwXWehYcXFxYXp/a4iMjIRSqQTHcTAyMsLGjRvh7+/fouxisRgzZ87E0aNHceDAAQCNut306dNhZmbWrnJow8HBAXv27IG9vT22bNmCxMRELFy4EO+99x6ef/55WFpawsLCApGRkc3WoaKiIsTHx+POnTtYu3Yt6urqWlyT6fhtqo/zATrPw8PDsXfvXrz55pvYsGED/vjjDyiVShw+fBhHjx7FpEmT8NZbb6FPnz4691taWsLS0hLe3t7sM5VKherqalRUVKC4uBhFRUXIzMxk6xjfOplarYapqSnefvttvPDCC9i+fTt27dqF3NxcZGZmYv78+di+fTveeOMNzJw5U+dMbWZmBjMzM7i7u6Nbt27sHdXW1qKiogIlJSUoLi5Gamoq08v1BUIIkpOTUVFRAY7j8Oabb7I1lY4jbRuMsbExnJyc4OTkxM74QKMdi54/i4uLkZKSgmeeeaZd2yqXy1FRUQGgUY+h77g1W1lqaipOnDjBPvfx8cHs2bPv+RuRkZGwsrJCWVkZKioqdM4GBhhggAEGGPC/AgMZxoDHBvn5+Thy5AgEAgEIITAxMYGVlRVcXV0REBCA0NBQhIWFoUuXLvD394e5uXmLDhLqNALAnBSPE6hi7efnh4sXL0KpVEKlUgHg56B19epVZtAZMmQIOI57aOcUPQR4e3vD3t4e5eXlUKlUqKmpYeQSfSAtLY05hyIjI2Fvb9/swEKN3pGRkWhoaIBAIIBGo8GaNWt02koP9fRgtnDhQhw7dgzHjh0D0OgUUSqVvBJ+qEFcoVC0eo1Go0FJSUmLhAwnJyd4eHjokGQ8PDzg4OAAPz8/xMbGorq6GkAjeYEPFBYWMid+ly5dmKGh6TymhBxt48+oUaMwd+5cJqdAINC5j+M4TJgwASNGjMCpU6cgEAhw4sQJ9OrVi1fnUWFhIWprayESiVo0VsrlcuTn5yMvLw+XLl1in1taWsLFxQUBAQHMUBkaGorAwEBYWVlBKBSyeahNeOLD8FpXV8cO+JaWlq0SVagT+datW/j777912jR16lQAaOZ4pX01bNgwAI19S9ccPvvl9u3b7DdakuNe84Qa+ymRxM3NDc7OznBwcIC1tTUsLCz0aqCgBLLWQNfXqqoqZjTWdmTY2Ni06Mjw8PBAz549ER0dzfaZnj17svvbG7GxsYx0NWTIEOagaLrfUGM2IQRLly5FUVERNmzYAI7jcPToUWzcuBEff/yxzvih/ZyQkKAXWehvlZaWAmgkkzo6Ojb7PSpLQECAzuevvPIKxo4dy/6tvZapVCo4OjpixYoViI6ORmlpKXJzc3H9+nUMHTqUl3nDcRw0Gg169OiBs2fPYu7cuTh9+jS++eYbJCcnY/fu3fD09IS5uTnc3NyYQRUAamtrUV5ejoKCAsjlcqxbtw5nzpwBIQTBwcEwNjZmTj3aN08++WSzd9WeoI4XCwsLBAYGAvjnHdPfPHHiBBuPb7zxBl544QUA/xB0mo5LW1tbvP/++8jPz8e3334LoHFtzsjI4M0hbmdnh7KyMgCNBE8rKyud76ksdL0DGsm8VJdpuh5TUCLp888/j5MnT2LPnj2orq5GXFwcxo8fzxvJpKV+0W4jJcF26dIFoaGh2L17N7KysiAQCFBeXo7du3fjjz/+wBtvvIG3336bvY+OIMXk5eWxfdPFxaVVohrtI7pWAMD06dOZIb+lvUSlUqFfv36YNm0avvvuOwBg+llnJsPQdsnlcqaXnhLXAAEAAElEQVR/29raQiwWd2Cr7g9twmRDQwPKy8vx888/4/Lly4iJiUFubi6AxjWEEpyAxnH81FNP8dq2uLg4AI3OHisrK6jVakb4bIr6+npkZmYyZy2FnZ0dXF1d4eXlpUNiCAwMRFZWFusrvnR/bdC5+sUXX8DX1xdLly5FYmIi5s2bh5KSEixcuLBVh1d9fT3Kysogk8nwxx9/4MMPP4RKpYKLiwsiIyORnZ2NsrIy1NTUQCqV6jgz9Q1KkKHrW3R0NIDG+U7bRfcaSoLV1u8bGhpQXFyMvLw8JCYmMuc+XyCEICcnh62jhBDI5XLIZDKUlZUhIyMDQKO+ZmpqCktLS9jY2MDBwQEuLi7w9PRsRsCyt7dnDkygcb+kuhrVz9obV69eRWlpKTiOw4svvghXV1f2XUt7RL9+/fD1119j3rx5KCoqQnV1NRYuXIjz58/DwcGhxfU2Pj6eyUH7jI81mc5jpVIJd3d3DB8+HBqNBhKJBCUlJSgtLUV1dTWUSmWzezmOY+cc7bOnQCCAt7c3QkJCEBwcjOTkZGg0GhgZGcHHx6fdZQAa5y0ljrq7u7f5d5588kls374d48aNg5GRERITE/Hxxx/j+++/1yHEU1Jfbm6uXmSheqOHhwc7x7TmEKd9QwhB165d8fTTT993D1+yZAn2798PADh//jyvRBjgnzV58+bN8PPzw5IlS5CTk8PItwsWLNAJQKLt1167nnrqKWzYsAGEEDg7O+P27du4dOkSbt++jejoaMTExKC0tJS3ftFG03keGRmJzz//HP369cNPP/2EpKQkNDQ04PDhw8jMzMTzzz+PSZMmsbZROSnoOm5vbw97e3umu+oT2ucRR0dHLF26FN26dcPOnTtx5coVVFVV4e7du/jkk09w584dzJ49G926dYOxsTEj9muTSgQCAaytrWFtbQ1fX1/27JZsJnxCJpOhoaGBydW3b1+ddrY2T7TbyXEcI/x4enoCAMaMGdPubTU2NkZdXR2AxuC3puTKpjh9+jTy8/NhZGQEpVKJYcOGwd3d/Z529pqaGnTt2hXnz5+Hu7s7ioqKYG1t3e6yGGCAAQYYYEBnhoEMY8BjAxqtQhVHmUwGmUyGkpISxMTE4LfffmMGFltbW3h6erJolrCwMAQHB8Pb2xsCgaBFBVKj0TDFmBorH1VQwx4lV7Rm9GwPXL9+nf0/jYx42HdH7zc2NsYTTzyBY8eOwcrKCklJScwBpg/QTCocx6F79+4AWs8QQEksGo0GISEh6Nq1a7NoBO3/AsC0adMYGSYhIYH3MVdTUwOg0Xg6ceJEjBgxAgUFBUhOTkZ6ejpycnKYQbspZDIZJBIJJBIJrly5wj63traGs7Mz/P39UV9f38yJ3N4oKChgUe4eHh6wtLRs8TraT9nZ2eyzcePG3TMSXK1Ww8bGBkOGDMGpU6cgk8lQXFwMgF/nEc2spNFoIBaL4ebmBplMhtraWsjlcqhUqhZJMnV1dUhLS0NaWhobRyKRCDY2NvDw8EBwcDDCwsLQq1cvHcKGtmOgvWBkZMT6pbS0tFn2AQr678OHD6OiooId8MePHw87O7sW+4be09DQgNDQUCQmJsLZ2Rnl5eUs4xIfoO9MIBBg7ty5kEqlSE5ORk5ODiorK1u8R3ueXL16lX1uZWXVzNjv6ekJDw8P2NjYwMfHhzdZ6urq2JrFcRwWL14MT09P3Lx5EzExMUhLS2vVIKPtyEhPTwfQ3JHh5uaGzMxMCIVCqFQqFvXHx3zJzc2FVCqFiYkJPDw87kkookYwjUaDqKgoZGZm4rfffoNAIMDq1avRv39/jBw5stncjo+PZ//PpyxAIxGuoaEBJiYmcHd3b5VERolJlKgENO4f9Pqm7ROJRFAqlRg8eDCCgoJQWlqKvLy8exIh2wN07np4eODrr7/GmjVr8PPPP+PcuXMYMGAADhw4gH79+jFSMh2XNIqSGpI3b97MDOZr1qyBl5cXUlNTUVBQgMLCQty9exejR49uUfb2QFVVFcrLy8FxHFxdXZtlpKByXr58mc2dl19+mc3h1oyU1ID5+uuv4/jx48jOzsatW7dQWFjIGxlGIBDA3d2dZR3Ly8tDaGgo+562lTpnLC0tERwcrCNna8+l8oSGhsLc3Bz19fU62eLaG/frF224u7vjww8/xMsvv4wNGzbgyy+/1HnO6tWrsXPnTrz55ptYsGBBq7oEn1AqlSxLSH19favzn/ZRXl4emxdDhw6957Pp+/f29oaZmRmkUimMjIxACGm3vqHnprZm33wQZGdnM/KPnZ0dbxmt/g3oWVFbXoFAgPPnzyM1NRW//vorrl69CoVCwXRier1Go4G5uTnGjh2LsLAwTJw4kfdIappVgBCC0aNH49VXX0VdXR1SUlKQnp6O1NRUZGZmoqCgoEWnOABUVFSgoqKCkdGARr3P2dkZRkZGvOv+2tAmWyxatIg59oqKirBo0SLk5eVh3bp1ABplFwqF7B6arQdodJzR9fvZZ5/FZ599hjt37qCkpISdjfRB7rkfmpLara2tmTO1adYR7floamoKHx8f+Pj4YMCAAby3MzMzk60vtC0tOUYpof1eGQptbW3h5OTECFheXl6IiIjAtWvX2Fju0aNHu8ugVquRk5MDoDHbRbdu3dq0Xo4ePRorV67EihUrUF9fj8TERCxevBj79u1r5hQHgKSkJCYHtS/wocsoFArY2NigqqoKjo6OWLBgAcLDw5Gdnc3GOc2WlJeXh8LCQhQXF6OiooLZDLRB17CsrCxkZWXh6NGj7P0EBAQA4IdYKpVKGZnTysrqgYIJxowZg6ioKLz77rsQCoU4cOAAevTogeXLl+us5VlZWexsR+cXH7I0NDQwW4udnR0jqjTtf6ojp6Wlsc+GDBmi811T0PlPM3jV1NTA2NiYkTP4OsvQd6TRaFjGwOXLl6OwsBALFy5EVlYWoqKimN6oTWCm2QfPnz+P2tpaAI3ZX9zc3PDcc8/hueeeY9dS0kNHwNvbG0uWLMHTTz+N9evXY9++fZDL5bh9+zZu376NM2fO4J133sGgQYN03kdLwVoUHWlrtrKywsSJEzFmzBhs374dGzduREFBAXJycrB161acOHECb7zxBhYuXMjGWlvk0bdMpqamrH2Ojo6M+HE/u939SDJ8yGFsbMz02qKiIh39sGnbCgoK8NdffwFo1GMcHR3bRNCRy+VMx7G3t+/QOWOAAQYYYIABHQUDGcaAxwZyuRwREREQCoUoLS1FbW0tpFKpjuFOrVajrq4OdXV1yM3NZU57IyMjWFhYwNHRET4+PujSpQsjyQQEBMDJyemxyhDT0NDAMifY2dnxWk6EOozt7e2Zg6W93qW9vT0zEqhUKqbQ6yuqVTt1eGvGauoM0o6ipISd+5XWCA8PZwfL6urqViOw2wsODg7M+GZubo558+ZBIBCgvr4edXV1KCsrQ35+PjIzM5GWloaUlBRkZmYiPz+/1cMULbVECR10rPGV9rm2thaVlZXMOUzTNDcFHR/UcG9mZnbfLA/0czMzM+bYo1HjfK4P5eXlsLS0RG1tLXx8fLB+/Xr4+fkhJiYG6enpSEtLQ05ODkpKSlBVVQWpVAq5XM5S8mtDrVajvLwcZWVlOgQYapzmK4W9g4MDO+AnJCS0GsUtEAiQkZGB8+fPA2g84Ht7e2Pw4MEA7m18oBnBAMDGxgZSqZRXMgxNk25ra4vPPvsM5ubmqKioYBksMjMzkZqayhxJeXl5rc6Tmpoa1NTUsHkCgJW0AYCvv/4akyZN4kWOyspKeHt7o7CwEGKxGMHBwXjttdd0rsnIyEBMTAxu376NW7duISEhgZU8a4qmjgz6nuj6RdfK9l6j1Wo1I3NYW1u3OVMAnbubNm1CVlYW7ty5A4FAgBdffBF3795l84Fel5KSwtZJvmQBGuWh46Utzt6KigpUVVWB4zgEBgay6LXWQAmabm5uEAqFOqWk9LF/BgcHY+3atZDL5Th69Chyc3Px/PPPY/Xq1Zg1a1azvVHbAEmJVwAwadIkCAQClkqclheiRAg+ZKFjGgArXSOXy9n7EwgEKCkpYY68wMDANjlNhUIhNBoNwsLCYGNjwz7nc+8XCASYMGECYmJiIJfLsXHjRuzatQvAP7pUfX09vLy8kJKSwgiVbQHtQ3d3dzZ/lEpls7I97QXtfqHkodacVZRw5erqio0bN2LmzJn4+uuv8fvvvzOHS0FBAaKionDz5k3MnTsXAwYMgIWFhd50TBqZrlKp2H6tUCha1NnLysqQkJAAQghMTU3h4eFxz2fTd+Li4qJDhlQoFA/dN7SNrb137X3j3+hONTU1uH37NlQqFaytrRESEtKpStjSsVFeXs5KndHSKpRArX0dAPj6+sLT0xPz589H9+7d4evry3u0PgXNOAY0ng8HDBgAkUiEp59+GkqlEvX19aiqqkJxcTFycnKY3pmamsp0z5agVCqRl5cHgH/dvyXQ9zt16lQQQrB69WokJSVh/fr1SE9Pxw8//NAsExZtt5GREW7fvs3G6qBBg2Bpacl0UaBxnLdWWkbfKCgoYNlHWwsEaMkRqT0f+bZzFBQUsLOMr68vtmzZgoCAAFy5cgV37tzBnTt3kJSUxEgNTdFahkITExNYWFjA2tpaRy8NCQlpdxkUCgUjRNBMjkDb7A6LFi1CUlISvvnmGwgEAhw6dAhPPvkkXn/9dXYN7QNKHAL4zaZkaWnJ9IuCggJYWVnBzMwMoaGhOqRYmUyGiooKVlaMkmPy8vIYCbm0tBSVlZXMvkRB9RpKTuIjM4SJiQnLcCeVSpn+39Z9+u2338aNGzfw22+/AQA+//xzREREYNSoUZDJZBCLxUhMTGQluChBiQ9ZaFkzoHE8tHaWob+tXRqyraXB6uvrERISguvXr8PR0RH5+fn3PTO0B+j4fu655yAQCPDxxx8jNTUVGzZsQFZWFr7//vtm+iXtw+TkZGbTpfuITCZjZZMFAkGrNh99QHu+7t69G2+99RY2bNiAffv2gRCCo0eP4ujRoxg3bhxWrFiBfv36tbjmdqZgS41GA2NjYyxZsgTz5s3Dtm3b8O2330IikSAtLQ2LFy/Gl19+iffeew8vvPBCi7ppR8pDbbC0XTU1NQ8d9MG3PMHBwYiPj4darcaBAwfQv39/mJiYMGIuPav/8ssvLBscAAwePJjNi9Z0b47jIJPJGNnOwcFBb3qmAQYYYIABBnQmGMgwBjw2mDBhAp544gkUFhYiJyeH1cTOz89HUVERysvLUVVVhbq6OjQ0NOhEuiuVSlRWVrK62SdPnmQGFlpqyd/fH2FhYQgMDETfvn1ZhMujBKoIV1dXM0OSo6MjbG1tefm9iooKRkxwd3eHg4NDuz1brVZDLBYzw3JdXR07QOvr4KVtgKDZNFoyNGpfKxAIdMo/tATqvCkqKmL/bslg255oaGiAi4sLa29JSQk7PNI0p7QWsEajgUKhYJFYJSUlkEgkyMjIQGpqKtLS0pCVlYXCwsJmhiL6bz4i9rRhamp6zwOvQCCASqWCRCKBkZER3N3dWRrX1sYPPVxaWVnB2NgY9fX1EAgEzFDGF0QiEaytrVFbW4vq6mqW1l07g4tarUZ+fj6ysrJ0CBi5ubkspXtDQwOUSmWLxnsqM19G1/r6egQGBiI6Ohocx2H//v148803oVKpmIGevt/du3frZLgYPXo0i8JrqW+0M8NQI7WdnR2vRrGioiJIpVKWVYE6k8zMzODh4dHqPMnNzdVxJGVnZ6OwsLDFzCsajQbFxcVQq9U66brbG4QQtgfU1dWxVOMymYw5NP39/eHv749nn32W3dfQ0IDk5GRER0fj5s2biI6ORnJycqvRoiqVitfU4kKhkBlKlUolSzXc1shNT09PrFmzBi+++CJKS0tRXFyMWbNm4dSpU0wGqVQKiUTCe5p0oHG9oU4F7fJ4TZ0uVD4aFS4QCNjacD/ZVSoV3NzcWCRmS33HFwgh8PLywp49e7Bw4ULs3r0bOTk5+OijjyCXyzFz5kyddZXKXFBQgJKSEhBC4OLiwtZyoVDISJA0rTxfoPs58E+mg6YEAu3Sc3TPb0uJSIFAAIVCgfDwcNy9excikajVTFPtAZFIhGHDhmHjxo2QyWT4v//7P+zduxezZ89m79zc3BwDBgzAqVOnYG5ujsuXL7c5C59Go0F1dTVqa2shFAphbm7OWwk47X6hjrzWnFU0BT/Q+M579eqFNWvWICIiArt27UJaWhrUajUqKipw6NAhXLhwAa+99hqWLFnSZjLQw0J7nsfExODKlSvo379/s+tUKhX27dvH1j93d/c2lQ0lhLAsc0KhsN3K8y1evBhpaWno3r07/P39ERwcDD8/P3h4eMDIyKjFfZySk1rLIkOd9gKBAHFxcSwi1svLC127dn3oNv8btLS+KhQKHD9+HCkpKfj1119x69YtluGuqUy2traYMmUKQkNDMXbs2GbZn/RRmkuhUMDJyYnNk+LiYuYcF4lEEIlEMDU1hYODAwICAtC/f3+oVCrI5XLU1taioqIChYWFyMrKYnpNRkYGJBIJI5UB/8xDfWSGaQqBQIDp06fDyMgIy5Ytg0QiwZ9//olZs2Zh7dq1zYIZqPzJycmM+ErHmPZew2cgSVtBxwgtdUUIQVBQEIC27Tf6jNRXqVRwdnZGbW0ty0jbpUsXdOnShZWoBf4h9lHyNc1Q2FqUvEKhYCR/Sux3cXHhxcmnXYZVJBK1mYRH++mzzz5DamoqK/O4evVqPPHEE+jduzfT7SQSCcrKykAIYRkO+SJgurq6snlaUlLCzjLaZVyoXuXm5tashGVNTQ3Ky8tZFhlKksnNzUV5eTnu3LnDyE33ctQ+LGxsbBgpLzk5+YHLftASPnFxcUhNTUV5eTlWrlyJkJAQeHl5AWjMCknPajQrJB+yWFlZMRJhaWlpq7LQ/qGBfTTzcFvapVarmSz29vZ6J/WJRCJMnz4dIpEIK1euRHp6Oo4cOYKZM2ciKiqqxT1dOysnHUtGRkadhghL+4PudREREdiwYQNGjBiB3bt34+bNm6ivr8eJEyeQlpaGKVOm6JSz7IzQzrJmaWmJxYsX44knnsCPP/6IU6dOobi4GNnZ2fjwww9x4cIFzJkzB/369es0BAs6NkaPHo2vv/4aubm5rPTo/QISOwrPP/88Dh48CJFIhJ07d2LkyJGYPHky6wuBQID09HSsX78eVVVVrGTtCy+8wEr23ctWlpuby8pyOjo66u08Y4ABBhhggAGdCQYyjAGPDQQCATuoN62JXVRUhLy8PEaQycnJQUFBAUpLS1FeXo6amhrU19eziA/gHyOxdqmlP/74AxqNBkuWLMGmTZt4k6W+vh4mJibtHg1MjSnJycnMUUOjEfmAdrpsGj1N050+LOgBhqYrlkql942EbW/Qg7m5uTkzYrdWwoU6aUQiETvk3y8Fp7Yzje9U3Kamps3qYwMt9xeNVBKLxbCzs4Ovry/69OnDDOT19fWorKxkxDRqIP/zzz8ZQYWPiD3gnxS5MpkMcrkcQHOHAp0HSUlJIIRAqVTC3t6eGVHvZ3Ckzj2gMaqOL8cehYODA3NyFRQUsH6i6d2pU4+mCteOXK2vr29GwMjKykJBQQEqKioglUqZUVqtVvPmqKDpdqOjo6FUKrFhwwb069cPTzzxhM51d+/eZTXBgcb3+/zzz7fpHefm5jJynIODA69lLVpyPDSdK22dJ0VFRcjOzmb9Q0uSVVZWMgNlREQEb7I4OjrqGEKpA9XIyEjHEKZdKpCWQurRowd69Oih48goLS1FQkICbt68idjYWFy5cgUSiQRqtZrX1OIA2LysrKzUaXtb8fTTT+Ott97CihUrIBQKcebMGXz88cf4+OOPATRG69KIKtrvfMnCcRwz7ldXVzPHW2tkmL///htA4zymxtX7yS6Tydiz7Ozs9GoY5DiOkVp37doFLy8vfPrpp8jOzsY777wDuVyuk6GIOsJTU1MZ0Ul7L9Vn9OHt27cBNO7nv/zyC6qrq+Hn54du3brB398frq6uLEI/Ly+P6T9thVwuZw4Qvh0VGo0GgwYNwrRp07B7925UVlbio48+glAoxKRJkyAWi8FxHLp16waRSIS8vDzs27cPTz75JGtXS6VD6bisrKzEjRs3ADSuy3wSyGi2PoFA8K9Ky3h4eOCtt97CzJkzsWbNGnz33XfMKVhaWopPP/0U27dvx3vvvYe5c+fybkSOiIhAREQEYmNjUVRUhGXLlmHVqlXo0aMHrK2t2b743Xff4bPPPmP3qVQqxMfHY9CgQfdcn7Szkdna2rJMZA+L33//HaWlpbh8+TKMjIxYaRNnZ2d4enoiMDAQgYGBCAoKgo+PD5ydnXXISdqgDn06x3Nzc7Ft2zbmLOzfvz/vZYRaA32vZWVlyMjIwI4dOxAXF4eUlBTU1NSwNlMnvkAgQFBQEPz9/fHKK68gIiICLi4uzUgVdI3XR0ZSY2NjnaxjlIDYGpGC4zgYGRmxjKqurq4ICwuDRqOBUqlEQ0MDampqUFZWhtzcXGRmZmL16tVsb+ZL928LJk+ejICAAEyePBmZmZk4cuQIAGD16tU6Dn5a6iU7O5vtoXS/5ztD54OCzm+6JwH8lDptD1hZWbF9oqSkhI01hULB9hChUAgHBwcMHjxY5ywDAFlZWSxD4e3btxEXF4f8/PxmmaY0Gg1vBDkzMzN2zsjJyWnzeKCkXSsrK0RFRWHq1KnIyspCSUkJ3nrrLfz555+wtLSEUChEcnIyO5fRtY0vx62dnR07K1taWrIzdGtrT9MSIVZWVrCysmLBJEDj/lNeXg5CCKZNm8bIGnxmhaqpqYGjoyPq6uoYUe9Bz4AeHh744osvMG3aNMhkMkRHR+O9997D3r17IRQK2bkP4JfUJ5VK2R5QVlbWamAHHQ+0DKVIJGKBevfLcFtTU8Oyz9ja2nZYmcEpU6bAx8cHU6dORU5ODo4dOwaBQIBVq1Y1IxxpkxNpZp7OmLVb+927uLjghRdeQO/evbFt2zbs3r0bUqkUKSkpWLt2La5du4a3334bTz/9dAe2+P7Qzow8dOhQ9OvXD//5z3/w5ZdfIj09HcXFxfj5559x8eJFLF68GC+//DLvQXwUGo0Gf/zxB7y8vODo6MiyW2nbMHr16oXJkyfj22+/xY4dOzBixAhYWlq2iTCqb3Tv3h1du3ZFfHw8OI7D1KlTMXr0aAwbNgwODg6oqanBtm3bUFpayuyWPXv2xLhx49r0/Li4OLaHBQYG8hYQa4ABBhhggAGdGZ3rRG+AAe2Almpi+/r6wtfXFwMHDmTXVVVVobCwEBKJhJFkcnNzUVhYiMLCQtTU1ECj0aCsrIxF9IlEIigUivtm9ngYFBUVYfHixYiMjISvry+8vLzg5OQEa2trmJmZtZp6vC1QqVQwNjbGoUOHkJGRAaDR2M5HWRRAN0qXOr2p4eh+UaBtQV1dHYtoMjExaTdDfltQX1+PoqIicBwHNzc3FjkEgJWnoYd24B/ijIWFBTOq3q8fExMT2TP4HHPUmEodjECjA06j0TyQ4ZdGkZqbm8PJyQnBwcHsoJmUlIRTp05BLpfzFrEHgGUi0mg0zNDa1CFMx5qNjQ0++OADFBYWMsPp/QyOcrkcRUVF0Gg0sLS01DHu8gVPT08W8ajRaJhhTDtbBAUlLFBHirm5OYu81AZ13pSXl2Pt2rW4efMmgH+i3fjAkCFDWI3ygoICTJo0CRMnTkS/fv3g4+ODuLg4rF+/HgqFAiKRCBqNBsOHD8eAAQPuS1KSyWSIiYlBQ0MDxGIxAgICeCUpaa9trWWFag0tzZNBgwZBqVSyKP3y8nLk5+cjIyMDubm5vK3RdDxpl26gc1N7HrTmqNQmydBrHB0dMWTIEAz5/7Xrjx8/jvHjxwMAr2nSaduNjY1ZpHBTOe4FKsOiRYtw9+5d/PLLLxCJRPjyyy8RGRmJcePG6S1NOgV1IFZXV7ea+Yyu0W5ubggNDUVRUVGbyZN1dXWMcGNvb6834yWFUChkOtvHH38Md3d3LF++HJWVlVi0aBHy8/Oxdu1aAI1rr1gsRnx8fLNU6Xz2QUvQjsRNSkpi5XkEAgEsLS3h7u4OU1NT5tCimWraukbU19ez6EV7e3teiX1UD9m4cSOuX7+OpKQk5OTkYNasWZg8eTLmz5+P4cOHY8KECXj33Xexfv167NixA8HBwVi8eHGLz6RlRgDg6NGjrPSAn58fr8SF2NhYAI36YFsjpLVBx6KzszO+/vprLFiwAFu2bMFPP/3EiGPV1dVYtmwZpFIpPvjgA94i9gkh8PPzQ+/evREbGwuBQIBr165h4cKFGDp0KHx8fFBZWYmbN28iPj4elZWViIyMREFBAbKzs3Ht2jUMGjSoRZIv7Z/k5GRcvHgRQOMYbZqZ5N+gtraWGekVCgUUCgXq6+tRWlqKjIwMCIVCiEQiGBsbw8LCAra2tnB1dYWPjw8jyAQGBsLDwwPW1tYQCoVQqVSsJOcHH3yAq1evAmjMWjh16lQA+smioo2SkhKcPn0aCQkJ+OWXX5CZmcn0de2MChqNBn5+fpg0aRJCQ0Mxbtw4nbJEQHPiib6IffSdUYIn0PhO6+rqHjgbnUAggImJCUxMTGBjYwMvLy/07NkTOTk5WL16Na/ZOtoKtVqNbt264dy5c3jppZdw9uxZHDlyBBKJBLt370b37t3Z3MjKymIOfV9fX6aTdkbnK/APEVAoFPJavvFh4O3trZPljBKvW8qwQ/VKSoIVCATMljNx4kR2nUwmQ0JCArKzs7FlyxZcv34dAL/EC6pv1NTUsHNGW961SCRiQQeffPIJ5s+fD7lcjsuXL2P16tXYsGEDgEZ7ASXQUTn46kvtklQ2Njb3LZfTWlYv7e9FIhE7s9TW1rIMJA96VnoQKBQKREREICsrC8bGxjh58iTmzJnzwM8ZO3Ys3njjDXz++efgOA6HDh1CREQE3nvvPZ1y13zKUlZWBkdHRxQWFkIoFKK+vv6eZJVNmzahrKwMDQ0NbV5ftYNHXFxc9K77U2g0GkRGRuLSpUuYPXs2zp8/j7/++gt5eXn48ccfERERwco35uTkMLvh/Ug/nQUajYZl7Nm6dSuWLFmCTz/9FL/99hsaGhpw5swZnDlzBq+88gq+/fbbjm7ufUEIgUqlgqmpKRYuXIj58+dj69at+Oqrr5CTkwOJRIJly5bhhx9+wMGDB/WStS8uLg5TpkyBtbU1XFxc4O/vj6CgIAQFBcHX1xdubm5wdHTE/PnzcePGDZw4cQIff/wxvvjii05HhAEasx2uXLkSL730EuRyOTiOw4kTJ3DlyhXI5XJmgxCJRFCpVCyQBLi/HlxdXY1z585BLpfD3Nwc4eHhLdoSDTDAAAMMMOBxh4EMY8Bjh/vVxKbf29jYwMbGRidKTaVSoaysDDKZDNeuXcOlS5dw/PhxZGVl6TyPOsH4wJUrV3Do0CEcOnQIQGPGDmqk1lbuadkhS0tLiMXiVlOPa8PY2BjHjx/HTz/9xNL8Tpo0ibeIEG3jXF5eHpKSkmBlZQV3d/dWnavajtXWSDL0+8zMTBbRRMko+jJWJicnAwAz1F+7dg0eHh7w8PBoJpdMJkNxcTE4joOjoyMrR3S/cjzaUTB8kmFoO0QiEczMzCCVSqFWq1FWVgYnJ6eHejaVpbi4mEWc8ekME4lEOqWeFApFq6nMPT098dFHH7GoROD+jvOKigqWscfJyUkvNbbVajWLwLexsUFZWVmrmQZaqxOsTRIUCARwcHBgZct27NjBns9n+aqBAwdiwYIF+PzzzyEUClFYWIhvvvkGe/fuZUQsWp5OLpejV69eWLp0KZPhXmtBTk4Oc5C5urrq1LrnA5Q81F6OB5py39jYmK2RERER0Gg0raaGbw/Q8aKdhlsikbQ5g9e91nGFQgGxWIzLly/rJbU40Li20Pd19uxZLF26tM2/RR2X5ubmWL16NWJjY5GUlISamhq8//77GDp0KJKTk9lc4VsWACzDiEajYeSI1sbZ7NmzMXv2bABg77u19YzOm8LCQkaMdXFxaeak1Qe09/nZs2fDxMQEq1atQk5ODj777DOkpaVh586djBDSUqp0fRvETU1NGbFPGzRrWHV1NQQCAZtDKpWqTboJ7ZeioiLWL87Ozu1aYrI12NnZ4cCBA5g3bx7T3X777Tf89ddfCAwMxMiRI5mju7y8HCtWrMD169cxffp0REREwNHRkTlijIyMUFVVhb179+Lrr79mpKCnn36akYH5QHR0NIDGffLfOEua6pxhYWH45JNPEBkZiR9//BHR0dFsbrWWqam9QJ8ZFRWFU6dOsZKOWVlZ2LlzJ4DG+a2dNWjz5s14//33UVBQgG3btiEkJATjx4/XSd9Py9UBwM6dO1lGie7du7dL+deioiI4ODiAEIKKiopmRDWNRgO5XM6i9wsLC5GUlMRIMjRLoampKczNzSEWi2Fvb4+ioiI2LmnZoffeew99+/YFoN8I8RkzZiAxMREJCQlsPGiT3y0sLODj44OePXti/vz58Pb2vmfpto5yyNBx4eDgAD8/PxQXF8PIyAjV1dUPXZqRrndZWVnsvNlR5awoKPnS09MT//nPf7BixQrs27cPsbGxGDlyJHbt2sWiqxMTE1m7aWa+zkiGoe2hREAzMzMEBwcD6HyOYhMTE5SVlbF/36ssY2tnmaYZCsViMXr16oVevXrh0qVLuHz5MgD+iP1KpRKBgYG4ffs2RCIRLl++/EBnWjrXZ86cibt372LTpk0wMjLC5s2bERkZiRkzZujoOFQOvvrSzc0NK1euZO+N9smD7GutXVdSUsIy99jY2MDe3r59Gt0ElGhHn19RUYHo6GjMmTPnX2V+WLVqFW7cuIFz585BpVLhu+++g6enJ4qKiniXBWhcj1966SX8/vvvcHV1RUJCAp544olW+4SSxNoKmvWmoaEBpqamCAoK4j3DbWug5xt3d3fs2rULH374IX7++WfExMRg6NCh+OGHHzBx4kRkZ2eztSMgIICd1zrTekx1Ae02USIf0EhE8PHxwYoVKyCTyfDbb78xkiUN7OtMMmmTESloZjigMROzSCTCq6++CoFAgDVr1rCyPeXl5Sxwiy8dmYIGJ9GMOykpKTh69Cj73t7eHj4+PggLC4OXlxfu3LmDr776CjExMXjjjTfwxBNPwN7evlOUPaSYNGkSMjMz8eWXX6KyshIajQY1NTVMT1apVFCpVLC2tsayZcswZswYAPfO6MVxHGJjY3HixAkAjesG1Z8NMMAAAwww4H8NBjKMAf8TaI1UoVQqIRKJdMgAhBCcOnUKKSkpOH78OIv2EwqFrMQLNTTxAZpOnh4QGxoakJ6ejvT0dKbAAo1pTb28vBAQEMAiKX18fODi4gJra2tWR5emwC4rK8P27dvx3XffQSqVguM49OvXj9dUr3fv3mX//+OPP+KPP/6AtbU1iwKl7Q4ICIC3tzesrKxaVeTpIZP+v0gkQmJiIusTaqzUV4S4dtmjlJQU9O/fH0BjyaTg4GD07NkTPXv2xODBg1n0Dcdx6Nq1a7MSF9oHaDpWFQoFsrOzWRQMLS/CB2h7pk6dCkdHR6Snp8PT07PV1LwPArVaDZFIhNjYWBbNz+eYmzp1KoyMjHDlyhVYWVlBLpff84BLs3TcD9RAkZaWhsTERAAPboD6t1Cr1Rg1ahSuXr3K2nAvw1hT3ItURghhWQ0A/lPYr1ixAunp6fjvf//LPqurq4NQKGRZeeRyOezt7fH+++9jwIABAFo/4NN7Lly4gGvXrgFoXAv4drrQdNmmpqYsCwcfxh6BQMC7gUaj0WD9+vWIi4tDXl4eunbt+tClALTbTYmDAL9kK6CRHEHX00uXLqG4uJjV0G4L6L4bEBCAzZs3Y/z48VCpVIiLi8OaNWsQExPDruFbFrVajfHjx0MikaCsrAwpKSn3TIOsUqmYrtJWY2pCQgIkEgmARr3mQd4VHxCLxXjhhReg0WiwevVqZGVl4c8//8SUKVPw+eefo1u3bkhNTW1GEtWn8Vij0eC7775j2Zto6c2cnBzk5eWhuLgYUqmUEcIA4Nq1a3jjjTfa3M74+HjWLwEBAXrLehceHo4tW7bgs88+w4ULF1BbWwuZTIa4uDjExcWxqG+O49DQ0ID9+/fj+vXriIiIQGBgIGxsbFBfX4/CwkJkZ2fj3Llz7NlPPPEEpk2bxlvby8rK4OrqioaGBri5ubUbydvV1RWvvPIKRo0ahc2bN2PXrl1QKBTo06cPAP4dznZ2djh9+jTmzp2LS5cu6XynTcxZv349nnzySYwZMwaXL19Gbm4uVq1ahbKyMowYMYKR0I2MjBATE4Mvv/ySke4BYMKECe1CugoMDERJSQlKSkqQmpqK+Ph4xMXFITExEVlZWSgrK2MkA22o1WqoVCrIZDKd7BHaoOQfpVKJOXPmYOnSpXp3YNTX1+OXX34B0Lju0BJOKpUKvXr1wqRJk9ClSxeMHz9eJ+KWOvI7UyQyHbszZsxAeHg4MjIyYGxsrEOO/begZ7G4uLhmmbw6ElRmT09PrF27FsbGxti/fz/Kysrw/PPP46OPPsKyZcuQnZ3NiLV8Zkx8WAgEAqjVapa1gjr+gM5HhpFKpRg+fDhiY2OhVquRlZXFMgi2Ba2Rryl5+/bt27wHkBgZGaFHjx7Yv38/lEolTpw4gQULFjywE5vjOCxbtgx3797F2bNnIRQK8dprr2HAgAE6GUj4zvLj5eWFNWvWsH/Tufowv6dNhKNEWHq25MPZT9s6duxY5ObmIiUlBXFxccjIyHjgbGc0q+zXX3+NkSNHoqCgAHl5eVi+fDnLyMCnLECjzrF8+XLMmTMHUqmUBXu11idNS1e1Bu3gEZrd0MPDg/ezzP1A36GPjw/WrVsHkUiEgwcPoqKiAjNnzsQ333wDa2trFvzW0eTE1vZy2ha5XA4TExM0NDTg+vXrSE1NRW1tLaKjo3Hx4kUQQiASiSAQCNh8o2NK31kuKbQzddN2aP+bZkzLyMjAnTt3UFtbi8zMTJw/fx45OTmws7ODTCaDRqOBWq2Gk5PTfcdte4FmgaXvsqXvy8vLGfGb6pFnz55FTk4OevbsiR49eiA4OBgeHh5wdHSEtbU1TE1NHyob+8PAxMQEK1euRNeuXbFlyxZcvHiREWCoXd/Lywvr16/H2LFjAdw/aKyyshJ79+5les2AAQPg5ubGO1nJAAMMMMAAAzojDGQYA/6n0FThMzIyglQqRXp6OmJiYrBjxw5kZmaioKCg2X0qlQp2dnYYNmwYK7/CB+zs7NC1a1fmUKCH76aorKxEZWUlYmJi2Gccx8HV1RXe3t7w8PCApaUlampqkJaWhrKyMpSUlLDDQmBgID755BMmHx+KcGpqKoDGQ1Z9fT3q6+tRUFCA5OTkZlGgDg4OcHd3h5+fH8uA4+fnBw8Pj2aHEfr/cXFxzBlAjaz6OrTQzBC0f+jv1tfX486dOzplVCgIIZBIJDh58iQ8PDwQEhKiEzWijfT0dJYJoD2iddsCSuABcM+MKg8COq6SkpKY0bZ3794P/dzW4Orqitdeew2vvfZauz6XGihOnz6N9PR0AI3OPe3yWHzB398fx44dQ2VlJUpLS9vFUQE09k1BQQHKysr0lsLe2toahw4dwo4dO7Br1y4kJCSgrq4OhBAYGxtDLpejT58+WLduXZuM5EKhEKWlpTh48CCLkB8xYgSvNZAJIUhLSwPQuR0PbYVAIEDfvn3Rt29fNDQ06BAPH/a5ANi7Av6JDOfrXXl6euLjjz9GYWEhqqurkZSU9MAED9rukSNHYu3atXjnnXdgZGSE9evXw8zMjLWdb1mEQiEmTpyIiRMnQqlU6pSxawkPQmCibT569ChKS0sBAH379mWlmDoac+bMgYeHB2bPno3i4mKcOnUKH374Id555x1kZmY2I4nqc+4JBAJW9hFojI6sra1FVVUVysrKUFRUhLy8PEaQSU9PR3FxMSNb36ut9Lu//vqL9UtkZKRe+6Vfv3745ptvsGXLFuzYsUOnjAIhhJUNpeV3MjMzmfOOki0oCYjC2dkZO3fu5DUrjLm5OX799VdUVFSwrBbtodtS0qi3tze+/PJLLFmyBO+//z7LEKmPsRcQEIC9e/fi+PHjOHXqFJKSkpCbmwsjIyNMnjwZkydPxqhRowAAL730Ej7//HNUVFTg7t27mDdvHtzd3eHj4wNzc3NwHIfU1FRkZWWxtW7AgAEYPnx4u7SVvnMnJyc4OTkxQiv9Ljs7G8nJyYiNjUV8fDxSUlIgkUhQWVnZqjNDG87Oznj77bfxxhtvdEgkLyV3mpiYQKFQQKPRICgoCCtXrkR4eDhCQ0NhYmKik7EHaN2R3xng5OSEoUOHYujQoe3+7KSkJIhEIiiVyk5HKvH29sbmzZtBCMG+fftQV1fH9vkzZ84wYoW+z5cAkJ+fj+zsbHh4eMDKyuqejjl6JhEIBPD29u60JZ3s7e2xfft25OTkoKqqihHJH3adpiWsc3Nzm5VR4QOhoaHw9/dHdXU1KioqUFRU9K8Iqy4uLlizZg0mTpyIkpIS1NTU4LnnnkNmZiZzmPMdcKFdslogELRLyQx6Xr5z5w4jPuqDCDdp0iRMmjQJQGOGm38T0CMQCKBSqRAaGor169fj5ZdfhkKhQFFREdOx+QzoobC2tm7zWb+tc4fOszNnzjAbVs+ePTs8Y5c2PD098cUXX4DjOOzZswcNDQ348MMP4ejoyOTk04bUFmjv5ZQkkpKSgrt37zKSyIkTJ1i5SEpsp/dqE14sLCx0snl1FOheQYmFMpkMV69eRWlpKSoqKnDu3DncvXsXHMehoqKCEVAo8vPz2f9bWVnB09MTRkZGvBMtCCFYtmwZli1bhuLiYsTHx+PWrVu4ffs2YmJikJ6e3oxgpFarme01IyMDGRkZ+PXXXwE0niM8PDxaLLVES9aamJjoTfecMGECJkyYgLy8PJatlpLx+/fvr+OPuBdZjuM47Nu3D0eOHAHQuBc/88wzepHBAAMMMMAAAzojONJRFGQDDNAjtNOkUqUwOjoahw8fRkJCAo4dO4b6+vpmhxQjIyOYmJigT58+mDZtGkJCQtC7d29eleDS0lKUlZWhvLwchYWFyM3NhUQiQVZWFnJzc5nz+t84LI2NjaFQKODk5ITVq1dj/vz5vB1UiouL0aVLF6jV6mZOPO3IA23QKEtjY2OYmprC2tqalaIJDAxEUFAQAgIC4OXlBXd3d0ydOhX//e9/QQjB5cuX0a9fP70x3D/44AOcPn0aDQ0NKC8vR21tLRoaGnRKmlDjEjU0AWjWbwEBAYiIiGBpnkNCQuDp6Ynz589j5MiRUKlUmDp1KotE5RNtjS560GdyHIcePXow4lZqaiqvhsqWUru2B5RKJbp27cqc+xcvXsSTTz7Zrr+hL1BD+ZkzZzBq1ChoNBqMHDkSx48f5/V36XgghCA/Px8JCQm4e/cuiouL4ejoiKeeegq+vr6s3nxbnvXRRx/hq6++Qk1NDTw8PHDmzBleMymlpaUhODgYAoEATz75JC5cuGCIrGkFZWVlCAkJQXl5OWxtbZsZ0PiCSqVCeXk5BALBQ5X+qa6uxoIFC/DLL7/A2NgYSqWSpUmnZEU+0TRarz0hkUgQGhoKqVQKCwsLpKWltWne6QN0fUpJScGMGTNYljl3d3fU1taipqYGgYGBSElJ6RCn3/3mu1qthlQqRW1tLUpLSyGXy9GjR482OZokEgm6dOkCmUwGCwsL3L59m9f1rCm0ZSspKcEvv/yCI0eOICYm5r6EBTpWqZ5ja2uLPn364JtvvoG3t7de2s8XtPWjiooK2NnZdUg7GhoaUFlZCTMzMwiFQgiFQpiZmemcc37//XfMmDEDCoWC7bfa5xvtfnJwcMCZM2d4KV+pXdKEZj5sCfX19cjIyEBiYiJiY2ORmpqK4uJiyGQyWFlZwcbGBsbGxujTpw+ee+45uLm5AeA//X5L2LFjB1599VWYmZlBoVCAEAK1Wg1TU1NG4A8ODmYOc3d3d9jb28Pc3BwmJiadkhCjXUq4vdZS2jfdunVjmfSSk5N5JcQ9KLT3jpUrV2L9+vUAGh2UIpEI9fX1UCqVkEgk8PDw0Gvb3nrrLXz55ZestAPNpOrn58ccc6amprCzs8P+/fsxc+ZMcByHBQsWYNu2bY+ETtpeZEWO45CSkoInnngCtbW1CAoK0slI2N6oqalBQkICioqKUFpaiiFDhjzUuN61axdefvllVv6NlooNDQ1FfHx8pyQ23Qu0T7777jt8/fXXSExMxK5duzBnzhxex6V2ybqH/Q1KCliyZAm++uorCAQClu1DH7K0N7RLcM6aNQtnz54FAOzZswezZs3q4Nb9A+2xvnz5cnzxxRfsO1rG++TJkxg+fHiHzIu6ujpcvXoV5eXlKC4uxqVLl3D79m0YGRmhtLQUVVVVLd5nYmICU1NT1NXVoWfPnoiMjISdnR08PDwwfPhweHl5PXRG1n+LpKQkZGRkoL6+HtHR0Th79iykUikaGhqQlZXV6n2Wlpaor6+Hvb09Bg0aBDc3N9ja2mLAgAF6JfHfrxRaWloaYmJicPv2bdy6dYut3Q8Ce3t7eHt7o0uXLnB0dMTs2bP1QoprrzUmOzsbw4cPZ4ELc+bMwYYNG3gt92aAAQYYYIABnRmGzDAGPJZoqjwKhUIUFxdDIpHg0qVL+M9//oPS0lJWvoYepqgx0M/PDxEREejXrx9mzpwJe3t7vdXTdXR01HHeqVQqSKVSVFdXo7KyEiUlJSgoKEBubi6ys7ORk5OD3NxcFBUV6UTwaoMSMhQKBQYMGIDVq1dj8ODBAPiLanV2dkZFRQVycnJ0okCTk5ORm5uLioqKZk4VGnWsVCpRX1+PsrIyZGRk4MaNGxAKhTAxMYG5uTlsbW3h6uqKO3fuMAM/TTGqL8PExx9/jIULF7IyCbQv8vPzUVRUhPLyclRVVaG+vr5ZtgU63jQaDSuBpV02xtzcXCciSF+pxfl4d9ThEhkZCalUipycHN4z3fDhPFar1fjmm2+QlpYGgUCAgICAR5YIo42cnBxYWFigpqaGRbrzCdovHMfBw8MDHh4eLKJdG20xcnEch5iYGGzevJml4p42bRr8/Pzav+FaIIRg6tSpqKqqYlHvna38QWdBVVUVAgMDUVdXx6IP9WHAFIlE7ULssLa2xocffsgismit7rCwMAD8y9Lez6a6UXl5Ob788ksWtTts2DA4Ozt3GqcL1VmCg4Nx8OBBvPXWWzhy5Ajy8/NZ++h61RFtvlcqaqBR57S0tISlpSVz3N8L9N7i4mKsX7+eZZzr06cPAgMD9ep0oToVIQROTk547bXXMG7cOMTFxSE6Oho3b95ERkYG6urqGNlCKBQyPdXMzAz9+/dHcHAwRo4cyUp76UMGPn9D+7kdQYShspmamjaLeCeE6Ow/EydOxH/+8x98/fXXiI2NhVQq1SGf0/6NjIzEhg0bEB4ezsu7a2le0t+m7REKhTA3N0dERAQiIiIwffp0dm1VVRVqa2thaWnZomOlIxyRtJRt01JPDQ0NSEhIQEJCQrOStt7e3ggKCkKXLl1YpLGLiwtsbGxgZmYGY2PjDnWq8qEz0+e99dZbuHz5MhITEzsVEQYAy27FcRzWrVuHoKAgvPrqqzoBHGKxWO9EGAAseICOqWPHjrHv7Ozs4O3tDX9/f0RGRuL8+fPsO5qtUi6X85rFtj3QHmOO9l9iYiJqa2sBgLcSSRRWVlbo169fuz3vpZdewp07d7B9+3aIxWKWVYrK8ajFLdJ+nThxIiIjI5Gbm4tBgwbpfMcH2lMPpJn81q1bh8TERBb8BPxT7vVRIcJoY/78+YwI06tXLwwcOLCDW6QL7TV5w4YNCA4OxmuvvcYI5gDY+Uvfev/NmzfxzDPPwNjYWCfbS1NQkoiTkxMGDhwIV1dXODg4oG/fvujduzfkcjkj+HY0xowZg6ysLGRkZOgE82lDLBYzXT8yMhIhISGwsLBA165dMXToUFhYWEAoFD5U4MnDQFv3bVp6XiAQIDAwEIGBgZgyZQq7TiqVIikpiZ1poqOjkZyc3GoGVlpqiWb97tOnj17IMNprDCWX00DLtkKtVmP9+vU62XsWLVpkIMIYYIABBhjwPw1DZhgDHitos8NpVMfp06dx9OhRJCYm4uTJky3eZ2FhAY7jMHbsWEyaNAn+/v7NlFw+sma0hrYYpeVyOerq6lBdXc0iFPLz8yGRSFBYWIjCwkLk5+ejoqICHh4eGDFiBEJDQ/Hss8/qjdjTGqRSqU4UaEJCAtLS0lBYWIiamppm2VOaZuyhn1Gihb29PSsp0NGQSqUoKipCbm4uI8lIJBIUFBSgpKQE5eXlqKmpgVQqZfWPKbRJMkDjAVQmk+HUqVMYNmyY3mVpb8jlclRVVXWa7AMPgqtXr2LUqFGoq6uDubk5K9nxKEWGtYTbt2/j7NmziImJweuvv96uBt77Qdsp9qCHe6Ax68jbb7+N3377DUqlEq6urjh9+rTeyooBjWXSxGIx22/aG52FnPCwqK6uRm1tLTw8PB7JOXPo0CG8/PLLkMvlkMvleO2117Bt27ZHrn/ou9+/fz9WrFgBiUTC6o5Pnz6908qTkZGB999/H+fPn2dlidasWYOVK1fyNvfaC20Z7/SaX3/9Fe+99x6ysrJgZWWFzz77DK+99lqn7BdKBC4pKQEhBFZWVvD29mbE5Y7M3mFAI27cuIE//vgDV65cQWFhIXNYODo6ol+/fpgzZ06nMYjTch0AWtQHOsscuHbtGm7evImcnBykpKQgIyMDubm5jJDbVri5ubGysF26dEFgYCC8vb3h6OgIKysriMXidilZYkDbQcslffnllygsLERBQQHCw8MRExNz3+jz9oaXlxfy8vIe6B6O49C1a1f0798f3t7eCAwMhKenJ5ycnGBlZcWIV51hHrU3bt++jZ07d+LixYtYtGgRXn31VV5/r70ykNL9saKiAuPGjcO1a9dYFt8vvvgCS5cu5W0PValUzJbyqI8JPmW5e/cu1q9fj9TUVKSnp6OkpIRXohlfsiiVSuzYsQOxsbGIi4vDnDlz8PLLL/Oqnz2sLAqFAufOnWMZWHJycpCQkMBDS++PrVu3YvHixSyLtampKWpra8FxHPr06YOAgABYWFggMjISAwcOhKWlJYRCIRwcHFp8Hi3X05H6Me0TmvmIEAKZTAZPT09069YNVlZW8PHxwfDhw+Hn5weNRsNKQzeFNgmlM4Hqltq2ptbeeXFxMRISEpqVWmpql+Yry51arWbtbO/5X1BQgMTERBQXF2PmzJmGs5kBBhhggAH/0zCQYQx47JCTk4OMjAycPn0a+/fvR11dHSvN0JRU0b17d4SFhWHIkCGYPn06hEIhO+B2FsPrg4AQgoaGBsjlcmg0GpiZmcHU1BT19fUQi8UdkrmgpSjQ1lBaWoq0tDTEx8cjLi4OiYmJyMrKQklJSbMoTI7jWB36QYMG4fz58x1WLkH74NIaKioqUFhYCIlEguzsbGRnZyMvLw+FhYUstWpNTU2zUktA4wHm39QjN6D90NDQgGvXriElJQX19fV47rnn4OnpyfthUq1WPxaGSr6Qm5uLjIwMxMXFwdbWFrNmzeJ9HaCGlaZrWdPPH9RYrk/CZdPf5es39W1s4fP3tm/fjsOHDyMxMRGbNm3ClClT9FIPnY/nnz17FpcvX4ZEIkFERARmzpzJe7aLh5UlNzcX586dQ25uLuLi4rBixQp069atU5ZJ+reIjo5GbGwsioqKYGVlhQkTJsDd3b3df0cbbSXrPChx8XHql45Ce8ojk8lQXFwMpVLJG2GURuV2ZnJae0Cj0UCpVEIqlaKmpgalpaXIy8tDeno60tLSkJqaiqysLBQUFLQabd0STExM4OnpyVLxv/HGG7xn7NN2EvFVku9Rw9WrV5GVlYXk5GQEBgZi9uzZeiXDaDQa/PLLLygsLGRZR2lQRXl5eYtlkum6TLOKUJiamsLT05MRrwIDA+Hv7w83Nzc4ODjA3NwcVlZWesnaRdv5KEPbhtTesty6dQvz5s2DRCJBdXU1zp07h8GDB+t1X9N2xna0o/5h0Z6yUEJ/bW0ty0SsTxj6peMxd+5c7N69G8bGxiCEoH///nj99dfRo0cPmJubt2qno4SgzpY9lpZ8piWA3d3dMXfuXEycOBFWVlZwdXVtloUQeHz0vKYkmbaUWrp16xaio6N1sv/pA+09Zx63s5IBBhhggAEGPCgMZBgDHnloNBrIZDIcPXoUJ06cQHx8PK5fv97itZaWlrCwsMCzzz6LsWPHwtfXF8HBwTrXdLSCyCezvjORRe4XBap9nUQiQVJSEuLi4hAXF4eUlBRIJBLU1tZCKpVi4cKF2Lp1a6chMGmTf+5lXFapVCgpKWmx1FJxcTGKi4uhVqvvWbO3PVBcXAxzc3OIxeJH+nCbnJwMGxsbWFhYwMTE5LGMqm3r2HqQ59FnGfDgiI+Px19//YWGhgaYm5vDwsICHh4eiIiIgLe3d5ufo712KRQKAMDevXvh6+uLoUOH8tL2pujova89wacslZWVMDMz02uGNT7l0XdmlX8jS9O9XaFQwMjIqMPHK5/9ou9MBP+2X/5tZi8+8TitZcC/l4ca0IVCYTP9G2j/ff/s2bO4desWQkND4eTkBHt7e1hbWzP98nHqk5agVqshl8tRX1+PqqoqFBcXIycnB+np6UhJSUF6ejqys7NRVlbWpucdPXoUTz/9NM+tbg5tPRPofJHeD4K2Bix0RqhUKtTX16OmpgYVFRUoLi7WCaqgZZKLi4tRU1PzQM+2s7ODn58fHBwc8NNPP3VY6TeKh+2bjt6L2kuWW7duMXvHRx99BAsLi/ZoXjO8/PLLqK2txZAhQ1hgmJWVVYvXtlR6pDPBIMvjLYv2ftSRMvbq1QvR0dGMPELXm+DgYISEhCAoKAhdu3ZFWFgYfHx8WiztCHQeu8uhQ4fw3HPPwdTUFAqFghEpvby80LVrVwQHByM4OBgRERHw9/eHvb39PW21HS1Pe6Gj5pWzszOsra3Ru3dv9OrVC7169UJYWFirmYUIIazP2kqS0Wg0j6QuZIABBhhggAHtDQMZxoBHHuvXr8fGjRuZAbIl9OvXD926dcPIkSMxZswYCIVC5mTQt8NBH+isqSrbAmrQAu5NkpFKpcjKysKNGzfQs2dPdOvWrdMfxtp6oJdKpSguLkZWVhZkMhnGjBnDW5tKS0sxbNgw9OzZEwEBAQgICICXlxecnZ1hY2PD0mp39jmSn5+PoKAg9OjRA35+fggODoa/vz+8vb3h5OTULrLQsamvSKbff/8dRUVF6N69O3x8fODq6nrftj3u0b1NjUjaTnJ9kuGqqqrw1ltv4fjx4ygqKtL5TiwWw8fHB6NGjcLLL7/M6ptro6W16tq1a8jKysLJkydx8uRJFBYW4rfffsOkSZN4kUGpVCI2NhaEEHTp0uWBje6dhXwI6EcWfcrLtzz6NIjxJUtH7Pd894s+jeSG+f8POpMsAD/y6IMUMHbsWBw7dgwWFhawt7eHp6cnfHx84OfnB29vb3h4eMDJyQl2dnawtLRkOtnjDEIIVCoVZDIZy1RaUFCArKwslkWGllqqq6vTuZfPrJAVFRV47rnnEBERgYCAAAQHB8PPzw8eHh6tEsnbm4zdWdFZnJWtgZZJrqqq0imT3DSooqSkpFk53pbQUqaZ9kR8fDyys7Ph6+sLBwcH2Nra3nPe85l95WFw+fJlxMbGIigoCM7OznB0dHxkZbGwsGiWcdfBwQFhYWHo2bMnIiMj0b17dwQGBrZKltbOQtSRGT4MsujicZOls2SReRAd0czMDP7+/ggLC0P37t0ZucTNza3FbCuA/vedt99+G5s3b27TtSKRCL6+vggNDUVERAS6deuG4OBgeHl5wdLSssV7Ovs++iDQziLDB0GmqqqqVUKqtbU1QkJC0LNnT/Tq1Qs9e/ZEcHBwq6XaKJnncSmBZ4ABBhhggAF8wECGMeCRR/fu3REbGwugUVkXCARwdHSEhYUFpk6diqFDh8Lf3x+enp4693U24zcAZGdn49ixYyzCwM3N7YHu72xkkNTUVJw5cwaBgYFwc3ODh4dHq1Eh90LTUksdfSBub3Rk5OKpU6cwatQonc+MjY3h7u7O0moHBQUxI7mDgwOsrKwgFothbGzcrL0dNQaPHz/eImnIxMQEbm5u95XFyMhIZz3oDISy/v3749q1a3Bzc4OVlRVcXFzg4+ODwMBAlu7cy8ur1egj4B85HjXHxb1IL8A/JEa5XI6bN2+isrISycnJWLBgQauGmfZCfn4+3njjDfzxxx8AoBMl1hRjx47FV199BV9fXyYXlamyshIFBQX48ccfcffuXaSmpkIikQBoLCenVquRlpYGf39/XuS4efMm5syZg6SkJADAypUrsWbNmgd6hkKhQEFBAUxNTWFvb88Mmvo2gj1OsgCPlzwGWXRhkKX98TjJAjy68nh5eSEvLw8CgaBFB7u1tTVcXV3h7e0NX19f+Pr6wsvLi5VssbW1hYWFBUxNTTs9Afth0VqppYyMDCQnJyM/Px9Hjx7l7fdPnz6NkSNHwszMDBzHwczMDFZWVnB2doanpycCAwPZn6+vL5ydne8pS2ciYxNCUFdXh7i4OKSnp8Pc3Bze3t7w9/eHra0tu6aj29meIIRAKpWitrYWlZWVKCsrQ2FhIfLy8hhJpqVSS15eXsjOzua1bS+99BKOHDmCrl27wsnJCe7u7vDw8ICHhwfc3Nzg7OwMBwcHWFtb35cs2pF9NnnyZPz+++/o0qUL7O3t4ebmBnd3d3h6esLd3f2BZGkN+gi6qKmpYedG7fnaGinK19cX4eHh6NWrFyIjIxEeHg4PDw9e2vagMMhikEUfaGhowGeffcYC1XJzc1FUVITq6uoHeo6DgwOCg4MRHh6OiIgIhIWFISAgAE5OTnrXeX799VccP34cZWVlyM7ORmFhIcrLy1u0ZbQGKysrBAQEMHnCw8MRGBgIV1fXTkV0brp3dPRe0hSXL1/GwIEDIRaLoVaroVarGaGltf5wd3dHeHg4I5ZFRETAz8+v1d/QJpY97vq1AQYYYIABBtwPj249DAMM+P9IT08HAFYr28TEBM8//zwWLFgAZ2dnmJmZNbuHKoOdSRmWy+X47rvvEBUVBaAxhXF0dHQzEk9TNJWlrKwMlZWVzABuamrKyBb6dOzfvHkTS5cuRVxcHOzs7NC3b1+888476NGjxwM/qzXjKmXq61upz8zMhLOzM8zNzdvleS3Jp68+a6mkmEKhQFZWFrKysnDmzBn2uZWVFby8vBAQEMAIGT4+PvDw8IC7uzssLCw6bD7dvHmzxc/lcvl9ZQkKCmJZcQIDA+Hu7t4piHJxcXEAGqODCwoKkJKSgosXL8LIyAhisRiWlpaws7Nrkbjk6ekJExOTTiHH/aC9DlPSS9NxJBAIWIkBmUyGGzdu4MKFC6iurkZ9fT0yMjIAAMuXL+e1rWq1Gt9//z3++OMPGBkZQalUQqFQwMXFBQKBAOXl5ZDL5awsxV9//QVCCI4cOQKgca7Hxsbi1KlT2L9/P+7cuQORSMRqimv/DgDeiDAAcPDgQRQUFAAAQkND71uOQZuQdO7cOXz//feoqalhn7u5uaFnz54YPXo0fHx8eGt3S3icZAEeL3kMshhk4RuPkyzAoyuPnZ0dKisrW83SWV1djerqaiQnJ7PPBAIBHBwc4OHhoUOS8fDwgKur62NbakkgEMDExAQmJiawtbWFt7c3IiMjWaklWi6RL9y5cwdAYykehUKB+vp6lJaWIiMjA0KhECKRCMbGxrCwsICtrS1cXV11yNgBAQHw9vaGpaVlq5m7tM8x+uq3zMxM7Nq1C99++y1UKhUj7VtYWKBHjx6YPXs2nnnmGXAc98BBMZ2txIC27sxxHMzNzWFubq6TTahpqaWSkhJGkomNjUXXrl15b+fly5dRXl6OCxcusM9MTExgY2MDR0dHuLi4sIAZSpRxdXVlpdboWbuj3/vdu3cBQGf9AnRlcXZ2hru7O5ODkmSaykLR1A6ljzNbUVERunTpws4stbW1kMlkOtfQcU4IYWfow4cPs+/FYjG6d++OJ598Ek8++SR69+4NNzc3vdvWDLIYZNEHTE1N8f7776O+vh7V1dUoKytjGbkkEgkjHNIy501lpfKWlZWhrKwMly9f1vnc29sbISEh6NatG0JCQvD888/zbtucNGkSRowY0aYyfLW1tS0+o6amBnfu3GH6BIWLiwuCg4NZtuZnnnnmvvZsvqA9VqqqqmBjY8P2f7VaDY7jOrxEPH1/tE2urq4wMjJCYWFhi9ndOI5Dfn4+8vPzcfz4cfa5SCRCQEAAunfvjsjISJ1SSwYCjAEGGGCAAQb8A0NmGAMeaeTl5cHLywtGRkaMOa1Sqdj3Li4uCAgIQEREBKtX6+fnBycnp1YPUR1FkImNjcXEiRORnZ0NGxsbvPrqq/jss8/a1J6UlBTs3LkTf/75J8zNzWFhYQE7OzuEhYVh0KBBGDFiBAD9yVZbW4tXXnkFv/zyC4BGh+66deswderUTkVA+jdISUlBSEgIfH194e/vj/DwcHTt2hUhISHw9vaGg4NDqynGOyMOHDiAX375BVVVVZBIJCguLm6W1vZ+cHFxQVBQECIjIzF8+HD06dMHtra2eo2oPnXqFI4fP47y8nJkZmYiLy8PJSUlDyyLu7s7QkJCMHz4cIwYMQJhYWEwNjbW+7gtLi5uVhaJ/n7TbZtGeRgbG8PU1BTW1tZwdHSEl5cXwsLCEBERgZCQEPj4+MDExKRTzMF7kb2USiViYmIgk8mQm5uLY8eOISMjAwqFAqmpqc2MMjQKPTg4GElJSbxm/Tp+/DiWLVuGxMREAI0l+GbNmgUPDw+YmJigqKgIJ06cwMGDB6FWqyEQCGBubo59+/Zh2LBh+OKLL7Br1y5kZWXpODFotJyLiwtMTEzw9NNPY9KkSRg5ciQvcgCAj48Py0Tz7bffYu7cua0ahOiYqaiowMKFC3H48OEWjX1WVlbw8/PDiy++iJdffrndCIP3w+MkC/B4yWOQxSAL33icZAEeXXlUKhUqKiogkUiQnJyM+Ph4xMXFITk5GRKJRKd8A0Vrka9isRjOzs7w8PBgBBlKvra1tUW3bt0eKV27s2Hp0qXYsmULI/Vqo6U+oXqmSCSCqakpLC0tYW9v32oWSX1GhdM5cPLkSbz77ruIiYlh2fVakmXcuHHYtGkTAgIC9NZGfaItOr5cLkdNTQ2MjY1hbW3Na3ta0sdbm/eUfOXk5ARXV1dGKvH392fZMB0dHXltb0tQq9WtrjetyWJubg47OzsdWQICAlj2344af2q1GllZWSgqKkJ2djbLGkRLa1VUVLBAg6aOWO2ytBQ2NjYYNGgQZs+ejcmTJ7Pv9UHsMchikKUjQQhBQ0MDamtrUVVVhdLSUhQVFSEvL48RSnJyclBYWIiysrIWdaCWwHfpunuBlnTUJv3QDGP0j/ZjS6TdltbDn3/+GTNmzNCXCM1w+PBhREdH49q1a0hOToZAIICvry/8/Pzg6+uLgIAAeHp6wsnJCdbW1novET9//nzs3LkTpqam0Gg0CA8Px9ixY2FlZYWCggJkZ2ejqKgIxcXFKCsrQ3V1dYs6WmtuPWtra4SFhSEkJARjx47FxIkT9SCVAQYYYIABBnReGMgwBjzSOH/+PIYOHdrid60phUKhED4+Pjp1T7t06QIvL69WS/jow6m/efNmvP322wCAUaNG4fvvv4enp+c9jVoKhQL79u3DG2+80WokplAoRJ8+fbB27VoMHjyYt/ZrY/PmzYiKikJJSQns7Oywb9++h3bqlpeXo6SkBE5OTrC1te2wA/B///tfTJkypcXvxGIx/Pz8EBISgvDwcISFhSE4OBju7u4sEqGzQSaTsWjdsrKyFg/xBQUFKCsr0yGa3Qt9+vTB8uXL8eyzzwLQn8FCKpWirq4OVVVVOrJopwjPz89vsywBAQFYtGgRXn/9dWZY19fBWC6X4+TJk8jIyEBMTAxzJpWXlzczPrS21gkEAojFYtjb2yMwMBDjxo3DzJkzmSFZX6SYe703iUSCyspK1NTU4NKlS7hy5QoaGhqQm5uLtLQ0Jsf9DENCoRBz587Fd999x2s/vf7669i9ezcaGhowdOhQfPHFF+jWrZvONXK5HAcPHsRbb72F8vJyAMDixYsBAF999ZVOm9VqNdzc3DBu3Dh4e3tjyJAh6NevH+9z5uLFixg8eDA4joObmxuys7Pv+85kMhlGjRqFy5cvQ6PRwMjIiEUxNYVYLMaqVauwbNky3ufM4yQL8HjJY5DFIItBlgfD4yYPbV9JSQkyMzORmJiI+Ph4JCQkICUlBSUlJQ/0LGtra9jb2yMhIQEmJiY8tfj+aHoubM9zokql0kukcklJCdLS0hAfH4/Y2FgkJSUhKysLpaWlzYjkrZGxBQJBMzK2k5MTPD092fln+PDh90zf3x64desWZs2ahdTUVAgEAp3Suk0hFAoxZMgQbNq0CeHh4fd8LtXFkpKSkJKSgrCwMAQGBvKqP+ur//UBhUKB3377Denp6YiNjUViYiKysrJaJOy1BqFQCBsbG3h6eiI8PBzPPPMMhg8fDisrK72dYzQaDWJiYpCVlYW4uDjExcUhKSkJmZmZLUbutwahUAgHBwcEBARgxIgRGDduHCIiIjpFf1dVVbWYGaKwsBClpaXsrNbQ0NCMQEcxYcIEfPXVV/Dy8upQsoJBFl0YZNEvVCoVK4FYUVGB0tJSFBQUIDc3l5GD8vLyUFhY2KzUUu/evXH9+nXeZXuQMtoajYaRfioqKlgZvtzcXGZjy83NZWX4mu69ycnJCAoK4k2WpqD7Qnp6OtatW4dff/21VTs5hVgshoeHByP3BgYGws/PD+7u7nBwcIClpSVMTU15Ifru378fK1asYCR4oJEU/9prr+GVV16Bubk5MjIyIJFIUFRUxMYSzQ5TXFyM8vLyFgMAm9oIx44diz///FOvNk0DDDDAAAMM6GwwkGEMeKSRm5uLs2fPorKyEpmZmc2U8Qdh1ltYWLBMH7TuaVBQENzc3Hg3uMpkMkycOBEnT54EAOzatQtz5sy5r5Fn+/btWLp0KZRKJQQCAQQCQatO/t69e2PTpk148skneZFBG/7+/sjKygIAbNu2DfPnz39oQ09lZSXee+89FBUVwcHBAS+88ILeyD3aWLFiBaKiomBiYgK1Wt0mUoWtrS0CAwMRFhaG8PBwhIaGIiAgoF1LLbUn1Go1O8RXVlaytNr0EK9tvKisrNS5lxIXOI7DrFmzsHXrVlhaWnaQJM1laWqQaE0W7cPjgAED8Msvv8DV1bVDs6rIZDJkZGQgKSkJsbGxSEhIQGpqKgoKClBdXd1svWvJeWFpaYmPPvqIEe/0BalUitzcXAiFQkRHR+PcuXPMCHTt2jXIZDJGDmkKGpGsVqtZOSIjIyP06tULPXr0gLm5ORwdHTFw4EDe0/AGBwcjPT0dhBBcuHABAwcOBNAy4WvWrFnYt28fhEIhBAIBRCIRGhoaYGNjg4CAAPTt2xcvvvgiHB0d4ebmpjdjuFqtxurVq/Hpp5/C0tISr776Kj7//PN7Gt5UKhU+/vhjrFu3TsfRZGlpiYCAAJSVlSEvLw+EEJ1I8+PHj/Oa3eZxkuVxk8cgi0EWgyz/2/LcD9XV1SgoKEB6ejri4+MZSSYjI0PHcaFtsFer1bC1tWUOl47Qx/j63c7g2COEIDs7G8nJyYiNjUV8fDxSUlIYcbktWWSAxrOAmZkZ6urqsGfPHsyaNYvXdg8aNAhXrlxhpXO7d++OefPmwc7ODunp6Th//jxu3LiBmpoa1uaRI0fim2++ga+vb6vPpQ6j9957D59//jkA4PPPP8eyZct4kSM1NRV//PEHQkNDWWmdx6lMmEKhQHl5ORtjcXFxbIzl5ua2eE9LYywoKAhr1qxpNThFH1AoFCgrK0NOTg6SkpLYGvYgsjg4OOCDDz5ghHl9QZssdi9nuFqtRmlpKSsHk5WVBYlEgoyMDGRlZSE9PZ0FaNBz2rhx47B161Z4eXkZZDHI8ljI0ha0NSMXLbVUXl7OiA05OTnIy8vDzZs3MWXKFHz66ad60wdaI/K2RR6lUgmpVIrq6mpWhq+goICRnWjpqJSUFN7a3xT0vaWnp+Odd97BH3/8wb4zMTHRIS+2xZZrbW0NX19fuLi4YMyYMVi0aBEv7a6qqsLOnTuxc+dOnVJ8oaGhWLp0KWbPnq1DxKmrq2M2WjqO8vLykJeXh/z8fBQWFqK4uBhVVVU6QXSbNm3CkiVLOkWWaAMMMMAAAwzoMBADDHgMoFQqSVVVFZFIJOTu3bvk5MmT5McffySffvopmTdvHhk2bBgJDg4m1tbWhOO4B/pzc3MjvXr1Ihs2bOCt/Xfu3CGenp6E4zjSv39/kpWVRQghRKPRNLtWrVYTQgg5cuQICQ4OJhzHERMTE9besLAwMnr0aBIeHk4sLS0Jx3HEyMiIcBxHRo8eTaRSKW9yEELIuXPnCMdxRCAQED8/v3Z99vvvv8/knDhxIikrK2vX57cFQ4cOZe/UxMSEhIWFkfDwcGJlZfXAY8vLy4sMHz6cLF26lOzcuZNcuXKF5OfnE7lcrne5WhprTSGXy0lFRQXJysoit27dIkePHiXff/89eeutt8iYMWOIh4eHjnzGxsbk1VdfJSqVSg8S/IN/I8tff/1FNm/eTF5++WUSHh7OZBCJREQgEJBhw4aR+vp6PbT+H6jVaqJSqYhKpbqnTOXl5eTq1avkhx9+IG+++SYZPnw48ff3J2KxWKc/BAIBEQgExNLSkixbtox3eerq6khUVBSZO3cumTp1KgkLC2NzRyQS3XNumJubEzc3N+Ls7EwEAgERCoXE3Nyc/PTTT6SiooI9X58oKipi7fPx8SGE3HusnThxgq3NQqGQcBxHJkyYQPbt29fi9XRt5xtVVVVkwoQJhOM4Eh4eTk6fPk0IubcsV69eJWKxmMlhbGxMRo8eTY4dO0YkEgmprKwkt27dIosWLdKRt1u3bgZZ/kflMchikMUgy/+2PP8WxcXFJCEhgfz6669k6NChrM3aZxlCiN51S22cO3eO/P777+THH38kP/zwAzl48CC5cePGA+klLbVfIpGQPXv26E0faKueWVdXR2JjY8n+/fvJypUryYQJE0hoaCixtbVl/dNU3zQ2NiYcx5EbN27wKsOVK1fYb4rFYrJy5coWZTlx4gSJiIjQOTdPmzbtnn1G+2HWrFlMp6bzkg988sknhOM4YmVlRXx8fMjAgQPJ7NmzyUcffUR27dpFTpw4Qe7evUtycnJIZWVlh5wX+UBdXR3JyMggJ06cIJs3bybz5s0jffr0Ifb29s3GFj0/WFlZkXfffbejm94MTWWZO3cu6dOnD7Gzs9ORQ3veTJs2raObTQhp3GvUavV915/6+nqSnJxM9u3bR55//nni5ubGzmocx5EPP/xQTy1uHQZZDLK0N9piX/o3z6yvryfFxcUkPj6eFBcXt/tvNMWOHTvIli1bSGVl5QPf25Z30NDQQEpLS0lqaiq5e/fuv2jhvwcdVy+99BIbKyKRiAwePJh88cUX5Nq1ayQrK4sMHDiQ6Q0WFhbN9hljY+Nmus2sWbN4bXtDQwM5duwYmTJlCnFwcCACgYBwHEdcXV3J3LlzyaVLl+5px1epVKS8vJykpaWRy5cvk0OHDpEtW7aQ5cuXk1mzZpGwsDBy8+ZNQgg/Y9kAAwwwwAADHhUYyDAGPNaQyWSkrKyMpKenk+vXr5PDhw+Tb775hqxYsYJMnz6d9O/fn3h5eRFTU9MWHbLUecxxHJkzZw5v7dy+fTsz7qxcuZIolcpWr6XK6+DBg3WMQj4+PuTgwYMkPT2dFBQUkPT0dLJ3714SEBCgY/ijjlg+DK1KpZK8++67hOM4YmdnR9asWdOuvyWVSomLiwvrn5SUlHZ57oNA+31aWFiQkSNHkm+//Zb88ccf5JtvviHLly8nU6ZMIX369CFubm7MeN/WP7FYTCIiIsjQoUPJyZMn9S5fU7TlsFRbW0vS0tLI/v37ydSpU4mFhQWbNxzHkf/+9796aOn9cT9ZNBoNqaioIFevXiUff/wx6dKli44cX375pZ5aeu82UsfF/eZVVlYWOXz4MFmyZAnp0aMHMTU1ZfIIBALyww8/8NrWY8eO3Xe8CwQC4urqSuzs7EiPHj3I4sWLyYYNG8h3331HoqOjSV1dHbsuJCSkxd9Rq9V6OdSfOnWKtYUaru/ljMvKymLXC4VCMnLkSFJVVcXa3FFISkoiXbt2JRzHkcmTJ5OcnBxCSOvzo66ujixevJitTxzHkWeffZbU1ta2eO2qVatY/zo5OZGrV68aZPkflMcgi0EWgyz/2/K0Fdr7aHV1NcnOziYbN24k48ePJ6GhoUQkEhGhUMjOMe+99x4hhNzzrMQXzp07R6ZMmaLj2DYxMSHOzs4kMjKSvPnmm+TWrVtEo9G02m/an5eVlZHMzEyyc+dOMmbMGNKlSxfi6urKqwxVVVX3JIBQZyXVNe+FkpIScunSJfLtt9+S119/nTz11FPEx8eHmJubs/fDJ3FZoVCQZcuWMefV7Nmzdb5vqi+XlpaSqVOnsus5jiMffPABIeTeelnPnj2Z/lxUVMSbPOPHj9chfjX9s7a2Jl26dCGjRo0iCxYsIFFRUWT//v3kwoULJDExkRQUFJDa2toOJYq1F+iZ7NatW+SHH34gc+fOZeuB9tzj+yzTHtBoNKSoqIicP3+erFu3jgwdOpSt2bSv6TjsjE5Kuia01rYTJ04w+wjHccTGxobExsbquZVtg0EWgyz/6+jduzcxNTUl48aNI9999x1JT09n+hTd+zvjOtRW5OTkEFtbWzZW3n33XbYn0v/27t2bcFxj8FVSUhIpLy8nu3fvJs899xwxMzNrcf/dunWr3mTYvXs3CQ0N1fl9R0dHsmbNGh2dSqFQ3NemJJPJSFFREYmNje0QvdkAAwwwwAADOhsMZBgD/ueg0WhIXV0dKSwsJElJSeTvv/8mBw8eJF988QVZvHgxmTBhAunevTtxcnLSYYTv2LGDl/ao1WoWsWljY0N+/fXX+95z7tw51jahUEjEYjGJi4tr8dqCggKdw+OSJUtIQ0NDe4tBCCGkoqKCjBkzhnBcY9TpuXPnCCHtZ9hRqVRk1qxZTO7WMizwiQMHDpDu3bs3O5ysWrWKRVhUVlaSxMREcvHiRXLo0CGyadMmsnjxYjJu3DgSERHRYqRbS3979+7Vu3ztgaNHj+qQlnr16kUI6ZwGvnvh1q1bOlliQkJCOu0hUttx0dKhOD09nbz33ns6B/ygoCCSmprKW5tWr15NOI4jpqamxMTEhHh6ehInJyfi6+tL5s6dS9auXUu2bNlCzp8/T+rq6ohUKm0W8XLnzh02359++mkma0dg/fr1jNyydu1aQkjLZBjaPhqtTN/1nTt3CCEd48DTxsmTJ4mzszPhOI688847LTpOCflHjhMnTrCsPhzHkR49epC///6bEEJajErOz88nPXv2ZI4KSiLjY/4/TrI8bvIYZDHIYpDlf1ueltAaSeT06dPkq6++IiNHjiTGxsb3zB537NgxQoj+MsPQ9h48eJA5J4RCISNTNP3TPsu15NxTq9Xk8OHD5NNPPyXjxo1rRnwYNWoUr/KsWLGCREVFEUIeTB9pKxlbo9GQrKws8tdff5Ft27Y9dHvvhdLSUjJixAjCcRzp2bMnOXv2LCHk3npiRkYGGT16NHvflpaW7DzZ0vuorKxkZxpLS0t+BPn/GD9+/H0zJzb9EwgExNnZmfTs2ZM8++yz5O233yZff/01+eOPP8i1a9dIeno6KS0tJVKp9JE7h2mjrKyMnDx5ksycOVMnaj8wMJCkp6d3dPPajIaGBhIbG0vWrFlDvLy8GMnKwcGBXL9+vaOb12bQ9YCiqKiIODs7s/H7/fffd2DrHgwGWTonOoMsGo2GJCQkkNOnT5OEhARSWlr6r+0QKpWKKJXKVskm+lqfm2az7tmzJ1m3bh3Jzs5u19/piP1GpVKRrVu3Eo5rDBgdNGgQIaS5TkDtYW5ubqS6upp9npWVRT7++GOmi8+ZM4ccO3aMvPPOO3oJwtQe71KplHz//ffNSDEeHh5k48aNrWb2oXr2o7zfG2CAAQYYYACfEHV0mSYDDNAHiFZdTI7jYG5uDnNzc7i4uKBLly4AAJVKhfr6etTU1LC6p4WFhcjLy8Pdu3fx1FNP8dI2uVyOkpISAICrq+t969/W1NRg37590Gg0AABjY2O888476Nq1a4v1P11dXfH2229j4cKF4DgOZ8+ebbG+e3ugqKgIOTk5AICQkBAEBga227MJIRAKhZgwYQJ+/vlncByHM2fOYMaMGXqtezpt2jSMHj0av/zyC7Zt24bY2FiUlZXh008/xZ49e7B48WK8+OKLCAkJYfcolUo0NDSgtrYWFRUVKC0tRUFBAaunm5mZidzcXBQWFkIqlbL7evfurReZ2gOEEGg0GhBCMHr0aHzzzTeYNWsWpFIpCgsLcenSJQwYMKCjm9kmaDQaaDQa9OrVCzt27MC4ceNQXl6OsrIyHD9+HOPGjevoJjZDS3Wlyf+v1y0QCODv74/PPvsMw4cPx8SJEyGTyZCWloZr16616zzVxp07d1g7AGDMmDFYtWoV7OzsIJVKYWtr2+J9Go0GKpUKxsbGiImJYc8IDQ3VeZ6+cfv2bQCNNcu7du0KoHmNbeCf9iUlJYHjOBBC8MQTT6BHjx4AAJGoY1WvgoICVFZWAgAsLS1hamp6z+v/+usvZGRkQCAQQKPRYPr06YiMjAQAnfrVQOM+6ubmhn79+iE6OhoKhUKnPnd743GSBXi85DHIYpDFIMuD4XGTpym0dfWSkhJUVlbiP//5Dy5evIjs7GwUFhYCaL6vBgYGwsPDA3PmzEG/fv3g7e0NoHEv1gc4jkNlZSWWL18OiUQCY2NjKBQKGBsbQygUQiaTsWvFYjGqq6sxa9YseHh4oG/fvgAaz3opKSnYu3cvjh07hqKiIlRUVABo1N84joNIJIJSqURQUBCv8ty8eRO3bt3C888/Dw8PD2g0mhZ1yKbgOK7Fd051f3qNQCCAj48PfHx82rvpzVBQUACJRAIACA0NZWf6e8nj5+eHqKgo5OTkICkpCXV1dfj0008RFhaGiIgI9j7oeM3IyEBNTQ0AsOfzhd9//x0VFRWQSCRISUlBXFwc4uPjkZSUhJycHKjV6hbvKykpQUlJCaKjo9lnJiYmcHZ2hpeXF3x8fODr6wtvb294eXnB2toaoaGhMDc351We9oS9vT1GjBiBESNGYOTIkXj99dchlUqRnp6OEydOYOHChR3dxDZBLBYjPDwc4eHheOKJJ/DCCy+guLgY5eXlOHfuHJ544omObmKb0HQ9cHZ2xkcffYRFixZBKBTiwIEDmD9/fge2sO0wyNI50RlkiYmJwTvvvIOEhAQ4Ojpi8eLFeOmllx7oGTKZDGKx+L46iz5smaWlpaitrdX5zejoaERHR2PTpk0YMWIEnnvuOfTr1w/Ozs4AoLO/Pwj0ZZvVRlVVFbMdOTg4sL7Sbkt+fj4aGhogEAjg4eEBKysrtt/7+Phg1apVcHR0xBtvvMHsflFRUXppPx0jhBCYmprihRdeQGhoKPbu3YujR4+isLAQBQUFiIqKwvXr1/Hiiy9i4MCBOjJ0xHs3wAADDDDAgEcJBjKMAf8TaE0p1DbMikQiWFtbw9raGp6enuwauVyOuro62Nvb89I2qVSK8vJyAICNjQ1MTEyatQ0AM84lJibi5MmT7POgoKAWFX1tjB49GkKhEGq1Gnl5efc1sP9blJaWMlmcnZ1hZWV1z3Y9COj7oAZOtVoNhUKh852+YGVlhZdeeglBQUHYvXs3Tpw4gZKSEmRnZ2PdunW4ceMG5s6diz59+sDS0hJGRkYwMjKClZUV3N3d2XPkcjkjYJWXl6O4uBh5eXnIzs5GVlYWgoOD9SbTw0LbYEEIwcSJEzFq1Cj8/vvvKCwsxJ07dx4ZMoxAIIBAIIBSqUSfPn0wduxY7NmzB2VlZUhNTQWANjsPOhLaB2I6R4YOHYpFixYhKioKAoEAhw4dwowZM3ghaKSlpQEAc7jt3r0bNTU1+Pjjj5mzR61WM0cQBX3/QKPThsoSHh7e7m18EFDjiqmpKXOIUMdES8aHhIQE9v+UTNlZxo1SqYSxsTEqKytbNc7Rdh46dAhyuZz9e9asWa3uIfRZdnZ2MDU1RUNDA7uWrzX6cZIFeLzkMchikMUgy4PhcZFHo9E02xdVKhX+/PNPJCUl4ciRI7h+/TqMjIygVCqb3W9jY4Pp06cjPDwcw4YN44202xaoVCps3LgREokEHMdBoVBgwoQJGDhwIAICAiCTyRAdHY2//voLCQkJEAgEUCgU2LJlC/r27YuEhARs374dO3bsgEqlavZ8kUgEhUKBsLAwDBo0CHPmzOFVnsTERFRXV2PlypXYs2fPQ+skLd2vVqv1QlYqKipi5043Nzd27rwXCCEIDw/HunXrMGfOHNTW1iIlJQUfffQRfvrpJ1hYWABoHMNCoRAJCQlMj+3Zsyd/wqBxnjo6OsLR0RG9evVin8tkMpSUlCAzMxNJSUmIi4tDQkICUlJSWEBNU8jlckgkEkgkEly6dIl9bmdnB4FAgCtXriAgIIBXedoblHj1wgsv4M6dO9i+fTtUKhVOnDiB2bNnw9LSsqOb2CYQQqBWqzFixAgsWbIEq1atgkKhwNGjR7FgwQJYW1t3dBMfCPSMGRkZydZ0SiDrLOeetsIgS+dER8ly6NAhXLlyBVKpFDKZDD4+PizQ5X660+XLl3Ht2jVcvHgRhYWFsLCwQI8ePTBgwABERkbCw8ODlzbfC46OjkhISMDPP/+MI0eOIDY2ln1XXl6OAwcO4Pr16xg6dChmzpyJIUOGtGiP7myg/UH3SaCR+Kq9j9K2x8fHs3/7+/uz/xcKheyaBQsW4MaNG9izZw9WrlyJAQMGMHKQPkDfuYmJCZ588kk8+eST2L9/P6Kiolgg5qFDh3DhwgW8+uqrWLRoEZycnHTkNMAAAwwwwAADWoaBDGPAY4N/Y3hriwHYxMSEEVT4gEAgQHV1NYDGrC/3k+HUqVOQSCTsIDhixAh4e3u3qPjSg4FcLkdwcDASExPh7u6O4uJiXhT6srIyZpQ0NTVtV9IN7SsjIyN4eXlBIpHAysqqwxR+kUiEwYMHY/Dgwfjvf/+LtWvXIjo6GmVlZThw4ACOHj2K119/HW+++SY7nKjVah1CDx1bdnZ2LIJSo9GgoaFBJ9L0UQMdd35+fsxgwOcc4gt0Lnp4eLD5RuV41A6Z2pmxZs6ciaioKGg0GsTGxvKWqeTvv//G119/jR07diA/Px8KhQIHDhzAsWPHMG3aNMyfP1/HSNFSe2mUq7GxMcsM0xERL4QQpKenAwCsra2Z8cTIyKjZtdoOFDr+qQOlM0Tr0IwBCoUCubm5AFrfP48dO4bCwkJmIHrqqafg5ubW6rOpfPb29ix7mVqt5s0x9jjJAjxe8hhkMchCn22QpW14lOXRdgzRjHQAUFhYiJycHOzYsQPR0dHIyspiZx6O4xg5RCAQoGvXrvDx8cH8+fPRs2dP2NraQiwWN3u+vpGTk4MjR44AAMzNzTF79mxs27aNfU8IwZgxYzBs2DB8+umnuHz5MoDGzD1nzpzB66+/zojUVCcAgF69esHR0RFPPfUUJk+eDLFYfM8+bA/QbI0cx+HQoUMYPnw4XnjhhXb9DaoD7d+/H0888QTTl/iATCZjGXY0Gk2bzp0cx0GtVuOZZ57B4sWLsXbtWnAchz///BObNm3CRx99pHN9fHy8Tp91BMRiMby8vODl5YUhQ4awz2tqalBQUID09HTEx8ezv8zMTNTV1bHrKOlco9Gw98X3WOMD2mevxYsXY9u2beA4DpcuXWIkpkcBNBMUAMydOxerVq0CANy9e/eRkqMlBAUFISEhAQ4ODqioqICdnV1HN+lfwyBL54Q+Zfnll19Y1ubVq1ezwK576SO1tbVYs2YNtm/fjvr6ep3vzp07hy1btmDAgAF4//33MXz4cN7a3hpCQkLwzjvvYNSoUTh8+DD+7//+D1lZWcxOmZWVhZ07d2Lnzp0IDQ3Fiy++iAkTJiA4OLjT27+kUqnOHkfHBt3/AOgQgJpm4hMIBEwvfu6557Bnzx7k5ubi//7v//DKK6/oSQpdUFv3jBkzMGPGDOzevRubNm1CfHw8SktL2VhbvHgxli5d+siQQg0wwAADDDCgo9C5tRkDDHgACIVCEELQ0NDAjFbaqZub4l7f6RO2trYoKCgAAKSkpDBjeNNDlkAgQHl5OY4dOwagMVLRzs4OTz/99H1/Q6lUssw2tra2zBDd3hAKhVCpVDAyMkJFRUW7Otnp+1AoFCwjjFgs1ikrpG/QrBDPPvssbt++jT179rBSKDU1Nfjss88QHh6OVatWoaysDEKh8L6HSIFAAHNzc94yEekDVEY3NzeWglsmk3WK+fYgoHK4uroyEoxcLm8xsvdRAH3/QqGQRVnb29ujoaGBl9+ztbXF0qVL8c0332DixIlsTFdXV2P37t14++238eOPP6K0tJS1jxoqqHMuMTERQGM2JtrmjjDEpKengxACkUiEyspKPPPMM1iwYAE2b96MI0eOIDk5mRm8KEFGIpGwd07b3ln2He02UoeqWq0GIYSNb4lEgp9++glA41pnb2+PSZMmAQDrp5agUqmgUqkgl8shFothYWHBa4T44yTL4yaPQRaDLAZZ/jfk0T6zlJeX48cff8S7776LgQMHon///ti9ezfu3r3LIrlpGRpfX1+89957+PHHH3Hy5En88ccfGDt2LFxdXSEWi3Wyr3UUbt26hbi4OABAv379sHr1agC6meEsLS0xYsQIREVFQSgUQigUQiqV4tVXX2VEGCsrK3Tt2hVffPEF/vzzT0acX758Ofz8/ODm5sa7flBbW4sePXqAEAKZTIaoqChkZ2e3y7Np2wUCAdauXYtZs2ax8xpfMDU1ZbpZYWFhm/VDSiL75JNPMGrUKFZWdOPGjTrZV4F/9FCA/8wwDworKyt06dIF48aNw3vvvYeffvoJd+/eRWZmJmJiYnD48GFMnDiRyUvnkYeHB8zMzDq49Q8HsVgMf39/EEJgbm7ezOn8qIBmhQIaM2LRoKJHEdoBStbW1novxdeeMMjSOaFPWa5cuYKMjAxwHAdXV1e88MILzUpQtoTZs2djw4YNqK+vZ9dTvYC2/cKFCxg5ciS2bNnCW/vvBSsrKwwcOBAbNmzAnTt38P3332PkyJE6OgjHcUhKSsLatWsxb948fPbZZ4iPj2cBezTDVWewa2gHTVIyTGvlRpOSktj/t1T6kPaRubk5y/5Hsx13BGh7qH4/a9Ys7NmzB2+99Rb8/PyYbeqLL77AxIkT8fvvv9/zLGCAAQYYYIAB/+swkGEMeORBCMHu3buxaNEivPXWW1i/fj327t2LvLy8ZuU36PXAP1FSTRV4fSuParUa3t7ezOB94sQJne+1DeAHDhxgpTcIIRgyZAi6desGoGUHsTaBhBqgHRwceCuTRI3qSqUSRUVFANDuxAGlUomqqioAgIWFRYdmHNEuCwQA06dPx/fff48lS5bAz88PQqEQpaWl2Lp1K+bMmYNff/2VHdA6w8GRTxBCYGRkhLq6OnAcB1NT006RFeNBQQ3kdXV1EAgEMDU15S2Tir6gVCrh6OgIoJGwwhcZBmg0RIwbNw779u3DihUrWEpghUKBv//+Gy+99BJmzZqF06dP62RNAhrLrtXU1IDjOLi5ucHW1pa3dt4Pd+/eBdA45+vr63Hs2DHs2rULH3zwAebMmYMxY8bgqaeewrPPPosVK1YgKioKeXl5IITA3d2dkcKEQmGzdMNqtVqvJJm+ffuyTFQ3btzAzp07ddpGx/fvv/+OgwcPsnWuZ8+eGD16NIB7Oybr6uogkUgANKbjt7Gx4UmSx0sW4PGSxyCLQRaDLA+GR1UelUqFrKwsHDt2DBMnTsTgwYMxf/58bNiwgaWrp7CwsEBkZCReeOEFXLlyBZcvX8a6deswc+ZMODk5NdsH9VFq5364evUq+/9x48bBzs6uWUYd2u6wsDBMnDiRlYDMzMyEnZ0dnnvuOZw8eRIxMTFYsmQJxo4dC39/f0aooeBTTyaEwNnZGZ9//jnTyZOSkhi552F1EBpRPmTIEHz00UcghCAkJKQ9mt4qKNlGpVKhtrYWAJq909ZA9c1du3axckF1dXV4/fXXWQADAGRkZLB7+Jbn30JbZqrTx8bGYv/+/SgoKGAly6hMTaPhH0XIZDL4+voCaCz9wVegD99QKpVwcXEBADg5ObVa9qozg65btbW1LMDBxsbmkSRcGWTpnNC3LBqNBufOnQMAmJmZYcqUKbCwsLgv4Xjt2rU4fPgwRCIRK6soEomgVqthY2MDIyMjtl4LBAKsXLkSFy5c4EWG+4HaICwsLPDSSy9h7969+Ouvv/DSSy/B39+f6Zw1NTW4cuUK3n//fYwcORLLly/H1atX2Z7Smex7jo6OKC4uBgBUVVUx+wvwz56vnYmdrr3a+pw2sYYS1LUzy+gb1EZE2y8SidC9e3ds3LgRGzduRJcuXUAIQW1tLc6dO4etW7d2+gw+BhhggAEGGNCReLQ9egb8zyMjIwPTp09HSkqKTjpgoJH0MW3aNKxfv15HEeY4DomJibh58yY7jFhbWyMgIADdunXTu/IoEAgwadIkXLt2DQKBABs3bsS4ceOYgYcawPPy8vD555/ryDlr1iw4ODjc9zfy8/NZ9hk7Ozve0ieqVCrY2tqisrIS1dXV7V6OSaFQID09HTKZDKampnB0dGyxTIm+QQ9NIpEIvXr1Qq9evTB48GCsX78e169fR2VlJY4ePYqrV6/ihRdewNtvv81IAY9rXdeGhgZGiLK1tdVLnV0+0uhLpVLk5+cDaJTjUU49qk2Oo0ZjOzs7VoaAL2g0GojFYrz11lt4+eWXsXXrVmzZsoUZs06dOoWzZ89i1KhRePPNNzF8+HDmpKHQzqzSEfPlxo0bAKATgaZWq6FSqdDQ0ICKigpkZ2fjzp07EIlEEAqFLHJKKpXilVdegaurK0JCQhAYGAhvb284ODh0iCy+vr7o1asX0tLSYGRkhI8++gjJycmYPHkyvLy8YGNjg8OHD+OLL76AWq1mhrtp06axcgctzTM6/woLC1nfubq6wtPT0yDL/6A8BlkMshhkefzlqa6uxtKlS3Hs2DHmgNAGJbmGh4dj+vTpCA4OxqhRo5rpHXRv70xOFYpr164BaHSM9O3bF0Dz90xLPllbWzM9Ua1Ww9LSEu+//z6WLl2qcy2VV59kH9rGYcOG4fXXX8fGjRsBNJaBGDZsGJ5//vkHLptFx5ZKpcLPP/+MpUuXsoAFqrfxCbVaDWdnZxQXF0Mul6OkpISVpm0LKEHoyy+/xIwZM1BbW4uMjAy89NJL+PPPP5GTk4OSkhIQQuDi4tJpnMhNdWGhUIg7d+4gLi4Op0+fxp9//omGhgYolcpm9wHAyJEj9dre9gQdcwqFgmWHtbe35y3Qhy9QOWQyGSNy2dnZPZJlhSnS09PZ2c7V1fWRLvlkkKVzQl+y1NbW4ubNmwAAb29vjBkz5r73xMXFYf369WxPFAgE6NWrF1avXo2ePXsiJycH8fHx+PXXX3Hu3DnI5XI0NDRg7dq1GDx4MC9y3AtNbRCOjo4YPXo0hg0bhgsXLuDgwYM4cOCATtatoqIibNu2DXv37kWfPn3w3HPPYciQIfD19WUZ/4COy+ZHg0uBRj+B9npKg2DNzc1ZO+lYagmVlZWMnKgPezMNwKP9QvcI+m+5XA4TExM0NDTgwoULyMvLg1AoRPfu3ZGQkMAyD9ESiI+rjdkAAwwwwAADHhrEAAMeUSQkJJBu3boRjuOIQCAgxsbGhOM4nT9jY2OyfPlydk9tbS2ZP38+6dKli851JiYmxNfXl0ydOpX89ttvRKFQ6FWWv//+m1haWrL2uLm5kY0bN5Lz58+TK1eukH379pGRI0cSjuOISCQiHMeR3r17t/n5UVFRRCAQEI7jyLp164hGo+FFjszMTBIWFsbkOHr0KCGEEJVK9VDPpe2VSCRk8uTJhOM44ufnR06fPv3QbW5vqNVqnX8fPHiQPPHEEzrjzdTUlKxYsYIUFBR0UCvvj387Ruh92dnZZMqUKYTjOBIaGkri4uLas3kMbRlb/0YWek96ejqZMGEC4TiOhIeHk7///vuBn/WgUKvVRKVSEZVKxctc/f3334m9vT3hOI689tprDz0/2wo6N+rr68mFCxfIiy++SJycnNi8EAqFpGvXruTDDz8ktbW1JCoqiq3vn3zyCSHk4deSf4vLly+T999/n8yYMYP06NGDODo6EiMjo2Z7Dl1nBQIBEQqF7N8ikYiIxWJibW1NPDw8SHh4OBk1ahRZsGAB2bRpE/nzzz9JcnIyqaur04s8d+7cIV5eXjp7pY+PD4mMjGRjg+M4JuPEiRNJSUnJPZ9J+/enn34iPj4+hOM4MmPGDJKXl2eQ5X9UHoMsBlkMsjze8vz111+E4zgiFouJUChk7fPx8SHjx48n77zzDklISCAVFRU67eHrHMIHrKysCMdxxMXFRUeOpqD6ydixY9l7ePvtt0llZSUh5N/r1e0NjUZD6uvrSWRkJGtnt27dSEZGBiGk+TnmXs8hhJCUlBQya9YspvvQsTlt2jTeZKBISEggQUFBhOM44uDgQK5fv67TtgfBqlWriEAgYHrbtm3byJ07d9i5e8SIEe3d/AeGdt9UVlaS/Px8smnTJjJq1CgSGBioo4tSOTiOI56enqRXr14kKiqK3L17l1RVVemtvXzN9/PnzxN/f3/CcRyZPn06aWhoaPff0AZfspw+fZqtyzNnziRFRUXt+nyK4uLie65f/xb0fZSVlZHnnnuOcBxHbG1tyaFDh9r9tygMstwfBlkeDikpKSQiIoJwHEeeffZZkp2drdOWpmhoaCDLli1jdmWO48i4ceNIcXFxs2uVSiV58cUX2frs5OREkpOTeZPlQdB0/6+qqiI7duwgTz/9NLGzs2tm97C3tycTJ04k+/btI3K5vINa/Q/S09NJr169mK28aZvUajXZsGED068XLlxICGnsE7rGU6xYsYLtp+vXr+etzU3HlEajYZ8lJCSQ/fv3kx9++IEsX76cdO/enXh5eRFvb2+dftDWv1euXEkI6TibmQEGGGCAAQZ0dhjIMAY8kqiqqiKvv/46U2Q5jiNWVlakR48exNPTU0chtLKyIrGxsSQnJ4eMGDGCfU6NW03/HBwcyI4dO/RmtKS/8+677+q0QywWE29vbxIUFMQMi1RWX19fcuDAAULI/RXdoqIiMn78eCbb4cOHeZVj1KhRTIYhQ4aQ+vr6h362UqkkhDQSS9zd3QnHcWTkyJEkMzPzoZ/NFyiRQaVSkYSEBPLhhx+SiIgIYmZmRgQCATE1NSUDBgwg//d//9ch7fvvf/9LLly4QJKSkkhRURGpr69vlzFPx+PBgweZkXLChAnMGdDe2LJlC/nll1/IxYsXmSx1dXUPLQslxP3nP/8hHh4ehOM4MnnyZCKRSNqj2f8KlCDzMMZYpVJJVq5cyQ73W7dubedWth1VVVVky5YtbJxo/w0cOJAR64RCIfn1119Z+zsDqqqqyK1bt8iPP/5Ili1bRkaPHk2CgoKY06wpQUbbKaH9uZGRETEzMyP29vYkMDCQ+Pr6kj179uhFhu+//55YWVkx4o723qj9mYeHB0lJSbnv86gBaeLEiWyv2rp1q17IpY+TLIQ8XvIYZGkdBlnaB4+TLIQ8WvJQ0qpYLGZt7NGjB3n//fdJfHz8fe/XaDSdmhxTVFTE9uuePXu26Z7u3buzPf/mzZs8t/Df4++//ybm5ubsPPz666+3qR+0r9m9ezcJDg7WIWhxHEfmzZvXprHZHggPD2fvm56N/w2qqqrIxIkTmQyBgYFk9OjRxNTUlHAcR9577712bPX9QedGU5w5c4Zs3bqVjB8/nggEAjb3mv4ZGRmRadOmkaioKHLt2jW9t/1e37UHseTrr78m5ubmhOM48sEHH/CyhshkMiKTyVr9vj1k+fzzz9kY++STT3hzKA8YMICYmJiQQYMGkVdffZVs3bqVnD17lmRnZxOpVPrAz6M2DoojR46wsdizZ0+SlpbWns3XgUGW1mGQpX1w8uRJ4uLiQjiOI8uWLSM1NTUtXkfX6HPnzjHyDMdxJDg4mJw5c4YQ8o9NUKPRML0rLS2NhIeHM/3pq6++YjJ3Bmi3Q6VSkcTERLJlyxbSt2/fFgNQOY4jjo6OZOnSpeTixYttJtW2N2pqasj06dNZm/bt28fkoW369ddf2fc+Pj4t6qrXr18n1tbWTK5jx47x0l46vktKSsjJkyfJwYMHyZdffkkmTJhAAgMDSXBwMNvnqC6q/c7NzMyIhYUFEYvFpE+fPuStt94it27dIoS0ndhsgAEGGGCAAf9rMJBhDHgksX//fuLr66tjdDt16hRJSkoiCQkJZO/evSxrDMdx5I033iBr1qzRUR4tLS2Ju7s7MTMz0yGg0P8/ePCgXmXKzc0ls2fPZm3QVnZNTEx0DOIbNmy47/OoAnz8+HFiYWFBOI4jffv2Jbm5ubzK8d///lfHWL9y5UpSXV3dLs+mGTo4rjHDTWdD00N7U+zZs4d07dpVZxwuWrSIEKJf9j417vv6+pL+/fuTGTNmkBUrVpBvvvmGHD58mNy4cYNkZGSQsrKyexoBCfnncNm0/U8//TSTMSoqipcDWW5uLuE4jlhYWBBvb2/y5JNPPrQs2tlYamtrSb9+/Zgc27Zt4/1g+cEHH5B+/fqRd999l/z444/k1q1b94zgpG2mJJnWrtHO2EOjaF1dXf8fe+cd31TV//HPTdJ0Jt073bulpexhmQUZAooIggucuCeguBAQWfo4cKAgIiIKCoIbBNlQRinQvWfSnbRN05F1fn/0uedJ2qQUaNrS332/XnlBk5ub87nj3HO+5zvI+fPnLaLjWhi2tbm5maxZs4Y4Ozt3cBbh8/lEKBSSwsLCXmnn9SKVSsm///5LPvnkE/L444+TMWPGEIlEQg3dnTnJsAtJlox0a8+RI0eMosPbG1tGjBhBTp8+TQjpmoEuMzOTRsQxDEPy8vIsLYHSn7QQ0r/0cFrMw2npHvqTFkJuHT1z584lDNPmrM8+s319fcnw4cPJyJEjyZ133kmeffZZsm7dOvLjjz+SM2fOkOLi4msujFkyo8T1cOTIEXrc582bRwhpWyA31y6FQkEX0EQiUU829YZgI5/5fD4RCATkl19+6XR7VndDQwN54IEH6NiGdajx8PAg33//fY/Oac6ePUu2bNlClixZQt5///0b2gc7Jk1JSaHZYw0dlhmGoU7ZPYHhGLm6uppkZmaS1157jYwePZoGhZgaR0ZERJCxY8eSbdu2kcLCQqNAhJ68lw4cOEAee+wx8umnn5IDBw6Qy5cvk6qqqi45yVxrG0IIaWxsJBMnTiQM05Zt9VrX7Y2yY8cOMmnSJLJ06VLy8ccfkwMHDpCUlJTr0mJqO/b8yuVymsXA3t6e/PbbbxbRQQgxysRp+OLz+SQ4OJhMnz6dLFu2jOzYsYOcP3+eVFRUdDkA4dChQ2TYsGF0n8uWLbOYDk4Lp6UntGzfvp06fbz77rtm28ney6+88goNeGMYhqxZs8ZstlfW4e3RRx+lethnV2+PebrCiRMnyOLFi+lYx3AMYG9vT6Kiosibb77Za+0rLi4mBw4cIO+99x51BDV8phYXFxutE0gkErJkyRKyZ88easMxXGcYPXp0t2c8am1tJTt37iTPPPMMiY+PJ3FxcUZZIU292ICroKAgMn/+fPLcc8+RNWvWkOPHjxO5XE4qKio4BxgODg4ODo4uIOjtMk0cHDfCn3/+CZlMBgB47LHH8N5778HNzY1+Hh0djYSEBMyePRtXrlzBwYMHac3M2NhYLFiwAEFBQXBwcEBDQwOysrJw4MABXL16FTweD3q9Hh9++CEmTpwIV1fXHtEkkUjwwQcfwNPTE19//TWtuQ4AarUa1tbWEAqF+PDDD/Hggw8C+F8tUVPweDy0tLQY1XodM2YMJBKJRXVMmzYNU6ZMwd9//w0ej4e1a9dCKBTipZdeglgsBtBW451hGPrqDLZ+/fLly3H06FEAgKOjI2bPnm1RHddCr9d3aD/DMODz+dBoNKirq4OVlRXS0tJw9OhR1NfX09rmACAQCCAUCjFw4MAeb3tqaioAoLi4GEVFRTh79iz9TCwWw9vbGxKJBAEBAQgMDISfnx98fX3h7u4OFxcXODo6ws7ODgKBwOgYEELQ3NyMp59+GocPH6bv33vvvRapWXv16lUAbfdHSUkJSkpKcObMGZNaAgMDERAQAH9/f/j4+FAtYrEYdnZ2sLKyMjp/eXl5eP7553H+/HkwDAM7OzvMmTPH4rV3//77byQnJyMpKQlA23Xi5OQEX19fREREICYmBnFxcYiKikJQUBCsrKzA5/M77Eev1wNo6wfY8yCXy/HJJ58gNzcXABAfH4/BgwdbVI852LrShBDY2Nhg2bJlmDZtGrZs2YJffvkFlZWVtJ4zAGzcuBEPPvgg4uPjYWNj06M1qSsrK/Hvv/8iIiICwcHBcHJyAtB2vbPHmflvTWcfHx/4+PhgwoQJ9PsajQZFRUXIyMhAamoq0tLSkJOTg7KyMtTV1UGr1QIArXEdFxdncU1s+ydOnIhTp04hJSUFJ0+exOXLlyGTyeDv74+77roLCQkJcHd3p/1wZygUCrz//vtQq9Xg8XhISEhASEhIp88pTkv/18Np4bRwWvqvngsXLgBoe86xyGQyOkcD2p739vb2EIvFcHZ2hru7O7y8vCCRSODv7w9/f3/4+fnB29sbbm5u4PP5Fh9rdZXk5GT6/+HDhwMArK2tjbbR6XTQ6XQQCoXIzs5GfX09gLa5KPv5tc5Rb7F06VIcOnQIly5dAgC8/fbbGDp0qNm5IsMwOHbsGB555BEUFRXR60en02Hy5MnYunUr/Pz8eqz9ADBy5EiMHDnypvbBzv3j4+OxYsUKPPLII2hpaaHjM6DnxmYAoNVq8ddffyE9PR2//vorzp8/Dz6fT8eJLIQQODs7Y968eYiLi8PEiRMRERFhtA17/fVE38Xy3XffYe/evXBzc4OtrS1cXFzg7u4OHx8f+Pn5ITAwkM4vvb29IRKJzNoE9Ho9CCFUg06nw549e6hNICgoCOPGjbOIjp9//hlHjhzBmTNnIBQKIRaLTWph55deXl4Qi8VmtbDngsfjQaVSYfPmzfTeCwsLw8SJEy2iQ6lUorq6usP7DMNAr9ejsLAQhYWF+Ouvv+hn9vb2CA4ORnR0NGJjYxETEwOJRAIHBweoVCrU19ejtbUVx48fx9atW1FbWwsAiIiIwJw5cwB0bqfitHBa+qoWoO1e1Wg0sLa2RlNTEwQC08sm7Fhl3759aG5upvaN+fPnw97e3uR3rKysAACenp6wsbFBS0sLbGxsAPSMXQMw7ldZujpWGTNmDEaOHIlly5Zh37592L59O7Kzs8EwDJqbm5GVlYW//voLq1evtqQEs7DjylmzZtFnpuGY0t/fH4sXL8Yrr7yClpYWSKVSfP7559i9ezcqKyuh0Wjo9tbW1rj//vvh7OzcrW08e/YsHnzwQQgEAqNxBgB6TQgEAiQkJCAgIAAODg4YMWIExowZQ8fULi4u3domDg4ODg6O/y9wzjActyRHjx6FWq0GACxZsgRubm4dJkOBgYGYMmUKMjIyjBZ/P/vsM4waNcpofy0tLVi4cCE2btyIbdu2gcfjISkpCUeOHMG8efN6zJjv7u6OjRs3YuXKlTh9+jSuXLkCqVSK+vp6REZG4uGHH4a7uzv9jrk26fV68Hg87Ny5E4cOHQIAiEQizJo1i/6WpSaONjY2WLFiBZKSklBXVwc+n4+VK1di//79eP311zFjxgzY2tp20M4uKrefhPH5fKxYsQLr16+nbV64cGEHY19Pw06SFAoF7OzsUFpaipMnT6KoqAh1dXW4ePEiLl26RCc0ra2tRt/VarXQarUIDg4G0HOTX+B/TiSGzgYsDQ0NaGhoQHZ2Nn2Px+PBzc0N3t7e8Pf3pwZMJycn2NraQqPRwMrKCiUlJdiyZQuKi4vpNfb0008jICDAIjqKi4sBGC/CXI+WgIAASCQSuLm5wcbGBiqVCq2traipqcG+ffvQ0NAAoM0h5dVXX4Wnp6dFdBiSlZVl9LdOp0NtbS1qampw5coV+r6NjQ1cXV0RGBiIyMhIxMXFISYmBpGRkfDx8aHXZ0NDA5RKJSoqKrBq1Sr88ccf9Pv33HNPry7QGBqLBQIBBg0ahHfffRexsbH4/PPPkZWVRReZvvjiCxw+fBhPPPEEnnrqKdjZ2QH4X19nSf7++288/vjjiI6OxjPPPIPHH3+cXt/mHJHYe4vH48HKygphYWEICwvDnXfeSbdraGhAbm4uMjIycPXqVaSnp6OgoABhYWEW1cPCHntra+tOF5P0en2XrpO//voL3333HQDA29sbDzzwQPc19hr0Jy1A/9LDaTEPp6V76E9agFtLz7Jly5CamoqMjAwUFRWhuroazc3NHdqpVCqhVCohlUrp+1ZWVnBwcICjoyNcXV3pAjPrjO3v7w+JRAIvLy84Ojp2W5uvB9YZhs/nY9euXaivr0dwcDBiY2MRFhYGsVgMPp9Pz0NBQQGdn7LO7lqttk86w7COFOvXr8f06dMBAGlpaVi3bh0+/fTTDtvX1dXh/fffx3vvvQcej0cdSDw8PPDiiy/itdde62kJAP435mKdkm8U9rvz589HRkYG3n33XVhbW0Ov10MgECA8PLy7mmyS8vJyFBcX4+uvv0ZKSgry8vLoPIRhGDqu5PP5iIiIQGhoKB599FEMGjQI7u7u1Emr/Ry/N649dp5ZV1eHmpoalJaWAmjTYW1tTe97FxcXeHl5wcfHB/7+/ggKCqLOcR4eHrC2tqbnpbm5GQqFAtu2bcPatWvp/mbMmNHtC5Us7P2v0WjQ2tqK+vp6k1rEYjFcXV3h6ekJX19fqiUgIAA+Pj7w8PCAra0t+Hw+ampqkJmZiW+//Rbbtm0D0NbXz5kzB3Z2dhaZ17S0tOCVV16BUqlEaWkpysvLUV1dDYVCYRSow8IwDFQqFVJTU5Gamordu3fTz1xcXODi4gK9Xo+CggK6Pbuo++6772LIkCH0/e6G08JpsbQW4H9Or62trSgqKgIAau9qz9GjR1FUVEQdFkePHk3te6Zg2+zq6kr7db1e36OOs4Z9DPu77G9XVFSAYRi4u7tDpVIhLy8PNTU1EAqFEAqFkMlkKC0tRUtLC6ysrBAeHo7i4mKoVCpq95wyZUqP6LgW5o7nU089hZKSEnz66adQqVRobm5GWVkZ/Zw9L6tWrcLChQu7vV0ZGRkA2s6DQCBAZGQk4uPjIRQKERYWhjFjxsDb2xtAm8OnKXQ6nVHQGwcHBwcHB0fX4JxhOG45ZDIZNaZGRERQ45SpgeDkyZPx4YcfUoPdqlWrqCOM4YTDxsYGwcHBWLt2LZKTk3Hx4kUA/1ts74lBJmvoIoTAzs4OkydPRmJi4g0ZRHg8HmpqarB27VoapXHnnXciNjaW/pYl4PP50Ov1GDFiBL755hs8/vjjqKmpgUAgwJUrV7Bo0SJERERg0qRJSEhIQExMDPz9/Y2yW7S2ttLo1b1792Lr1q04deoUnWDGx8dj7dq1PT7wb2lpoROj48ePIzMzE0qlEunp6Th+/Dhts1wuN/qeoROMn58f1Go1JBIJxo0bh/j4eBpp2pNRsLNmzYKXlxfKysqQnZ2NnJwcFBUVoby8nDolGaLX61FVVYWqqiojhwyhUAiRSAS1Wg2lUgmg7Rpgj9P48eMtGhUyZ84cDBw4EOXl5cjLy0NOTg5yc3NRWFh43VrYzDDsIgYbAcTj8XDnnXdi+fLlFtPBUl9fD39/f7S2tqKurg7Nzc3QaDQdIkGBtuuK7QtPnz5N3xeJRBCJRPD09ISfnx8cHByQlZVFIw+FQiHUajUWL15MneP6Ei4uLnjqqaewaNEibNy4ER999BHNkpWbm4ulS5di48aNeO655/Doo4/Cy8vL4m06efIktFotrl69CqFQCKDzRUhT9zLbtxsuZojFYgwZMoQa9HoTdvHEMKMQS2d9E/u9AwcO4IUXXqCOaaNGjeq166s/aTFsV3/Qw2nhtFia/qTFsF19Vc9TTz1F/6/X61FUVITMzEy6wJWTk4PS0lIoFIoOjssajQYKhQIKhYIuNgGAra0tRCIRnJyc4OrqCl9fX9ja2mLDhg094pRsCDt2IoQgJSUFKSkpANrGiI6OjvDz80N4eDiio6MxduxYHD58mD7nJ02aBMA4k0x7Z9neXMRg552JiYl4+eWXsX79evB4PHzzzTdITEw0ysB54sQJvPXWWzh58qRRJPPEiROxevVqjBo1qtcy4HTn/Im9b1555RWkp6fjl19+AQCLjzXfeOMN7N27Fzk5OWbb5efnh/nz5yMmJgaTJ0/u0CbWiaIvLIwVFhYCQIeId6Atmyfr5J+fnw+gbUxsa2sLBwcHODk5wd3dHY6OjnBycoJAIICDgwNaWlpw4MABVFdX0wXpadOmYdGiRRbRQAiBWCxGVVWVSR3ttbCL9u21iEQi2NnZobW1FUKhEHw+H2fOnEFrayusrKyg0Wgwd+5cqsMS9gA22EqpVKK2thaVlZV0DllWVoaysjLIZDJUVlaipqYGdXV1JgNNGIaBXC6ntg7WNkMIgVarxZdffkkzdlgKTgunxdJaABjZXoqLi1FfXw9HR0f6jGD/lclk2LFjB/2Os7MzbV9njm1arRY6nQ6tra2wsbGhznKWghCCsrIySKVSFBYWQi6Xw8bGBjqdDmVlZSgsLERdXR3EYjHUajXKysqQk5MDW1tbiMVitLS00HEaj8eDra0tzTzOYvj8YW3OfZm1a9di3rx5+O6773D58mVUVVWhtbUVTU1NCAgIwKpVq5CYmGiRZyqbVZEQAp1OB7lcDpFIhJkzZyIgIABeXl5mnTzNBZBycHBwcHBwdA2GmEoNwMHRh/n7778xffp0MAyDBQsWYOfOnWYNcMnJyRg2bBgAwNfXF6WlpWazorATlg8++ADLly+HVqvFww8/jM8//7xDSuyehM2acj0D3paWFjz77LM04ggAzpw5c9NppDujtbUVfD6fOhHo9Xr89NNPWL9+PS5fvtxheysrK3h7e8Pd3R3Ozs5wc3ODp6cnBAIBrl69igsXLqC5uRmtra3U8Dps2DC8//77GDNmTI9khGC5evUqNm/ejLNnz6KoqAi2traoqKgwu72LiwuEQiEIIZgwYQLi4uLg6OiIsLAwJCQkUOMXmxK1t9BqtWhtbYVKpYJCoUBFRQWKioqQm5uLnJwc5OXlobi4GAqFwuw+DLN7GDqf3HPPPVi7dm2PlRboTAv7Kikp6eCs1F6HocOCUCjE008/jXfeeQdisbhHrjmVSoWysjIj556CggKUl5dDLpejsbGRpm43XFQx1GHqsc5mMdFqtZgxYwa2bdtmVFrOEtxItG77/i4nJwdfffUVduzYgZqaGrqds7MzYmJi8Pbbb9MFJ0sxYsQIXLhwAfb29jh27BiGDBnSbde0qVJLtyItLS1ITU3FpUuXcOLECTz11FNISEjosbIi3Ul/0gL0Lz2clr4Jp6XvYmk9hinoze2vsbER+fn5RlnQ8vLyUF5eDqVSadJx2RQqlapDZklLY2NjA41GY3KsBXTMsMgGBRBCcNddd2Hq1KkICQlBVFQUfHx8zP5OTziSdHbOVSoVxo8fTzNhDB06FD/99BP8/f2xYsUKfP7555DL5XT+IhKJsGTJErz44osQiUTQarVmy0hYEsNxprnSNDdKRUUF/v77b5w9exZeXl5YuXJlt+3bEKlUCj8/Pzr+Y+8HNzc3xMbGIjAwEIsWLUJ4eDhcXV2N5tl9ecxYVFRE5/OXLl3C1atXjTJDGdJeO0v70lBswIVer0doaCh27dqFoUOHWk7EfyksLMSVK1eQnJyM5ORkpKamXrcWQ9j7CAASEhKwdetWi2ceModOp6MZfCoqKiCTyajzglQqRXl5OSorKyGXy9HQ0GCky8bGBvb29hgyZAjWrFnTrfMjTgunpTe1XLlyBfPmzUNubi4EAgHeeustvPXWW0aa+Hw+vvrqKyxZsgSNjY0AgAkTJuDTTz9FVFRUp+2sr6/HO++8g48//hg+Pj746KOPcM8991hMz7lz5zBq1CiIxWJYWVmhvr7eyMnPVLkeU7TfTiAQ0MA4lUoFKysrODs74+zZs2YzmnQ37LMwMzMTly5dQnh4OEJCQq5ZRoj9XnNzM2QyGQ2ki4yMhIeHh0XbHBkZiZycHDruax/4ZmNjg6CgIMTExCA+Ph5xcXGIiIiAr68vzY7cnp4sIc7BwcHBwXErwznDcNxyvPfee3jzzTfBMAzWrFmD1157rYMRkR3c/v777zQCcu7cudi9e/c1DY6sAw2Px0NiYiIOHjxocU3mMFwkvZ6BrV6vp+mWMzIy0NTUhOeff96ik8cXX3wR3t7eiI2Nxe23306NdSdOnMC7776LEydO0KwbrDHL0PHAFIYTrunTp2PlypW9MqF/4YUXsGnTJppVg8Xa2ppGS0RERGDs2LFwdXWFj48PxowZg6CgIDQ2NkIkEpncb19cXCGEQKPRoKWlBUqlEjU1NZDJZCgoKKDOGfn5+ZBKpUZpb11cXGBlZQU/Pz+89tprmDp1qsXSPd+oltraWkilUiMthYWFKCsro9EtbDRGYGAgli5disGDBwPofaNzbW0tioqKkJeXh+zsbOTl5aGoqAhVVVWoq6tDU1MT1Go1dZJpD8MwEAqFWLJkCV5++WWLpRS3BHK5HL/++iu+/vprJCcnUwc5jUaD1atX44033rDovSQSiaBSqeDl5YXMzMxeK9fAwcHBwcHRlzGVBc0cVVVVyMnJQVpaGi21VFhYiJqamg5lFby9vSGVSnt03KxUKvHaa6+hsrISJSUlqKmpQUNDg1HWPnNzGDYjKYtIJIKXlxdCQkIQExOD2NhYxMTEIDQ0tFfGFIZOJGx7T58+jQkTJtBI5RdeeAHnzp3DuXPnQAihmgYPHoyPPvoICQkJdF99ySmj/dyyL7XNFEeOHMHkyZNhbW2N1tZWMAyDoKAgTJs2Dffff/81A1lYvd3tDGQJWltbkZWVhZSUFFy8eBEpKSnIyMhAfX29ye3NOfiPHz8emzZtQkxMjKWbbJbW1lZkZ2fj0qVLSE5OxqVLl5CZmUmzWV4LV1dXTJs2Df/5z38sHpzAYtgnXet6aWlpgUKhQFVVFSoqKlBVVYWGhgY0NjaCYRi0tLTA2dkZM2bM6LFFb0M4LZwWS8H2p1OmTME///wDoG0M8tJLL2HevHnw9/cHIQRffvkl1q9fj+LiYtp/b968GU888cQ1952VlYUXX3wRhw4dwqBBg7Bp0yaMHj3aYpq2bduGxx57zMjB0NHREQKBAHV1ddDpdBAIBAgODoZQKERdXR1aWloQHh4OiUQCgUAApVIJkUhEy4wLBAI0NzfDwcEBgYGB8Pf3R1NTE5qamiwagNke1hn36aefxubNm2Fvb4+tW7fi3nvvvakxoyXHm4MGDUJhYSEthdhVXFxcEBERgQEDBmDgwIEYMGAAQkJCaEBpe/qirZmDg4ODg6O34ZxhOG455syZg19++QUCgQD79u3DjBkzOhji2EHxmjVr8NZbb4FhGKxduxbLli0z6wzD7uPEiRMYP348AGDmzJk4cOBAT0m7ZQescrmcGnLCwsKQnZ3dYZuDBw/ihx9+wD///IPy8nKjz0xFUbFG17i4OMybNw9PP/00nJycLCeiE6ZNm4aDBw9CIBDAysoKo0aNQlxcHNzc3HDbbbdRhwk7OzuT1xZrWObz+bfk+QXazo1arUZTUxM1VNTX16O2thZyuRw8Hg8TJkyAn59fbzf1mrBa2MxDrMOMQqFAa2srBg0aBAcHh15rn+FiUmfZQvR6PXVUys3NRXZ2NoqLi1FXV0fTb7P30ZQpU3DnnXciKCgIAoHA4n3NqVOn8OWXXyI+Ph7h4eEYOXIk3N3db2qfCoUCn376Kd5//31aluvYsWMYO3asxfRUVlbC29sbPB4P8fHxtIQeh3EEEpu+2lQt9VuB/qQF6F96OC19E05L36Uv6ulqFjS21FJWVhauXr2KzMxMnDt3DgMHDsTu3bt7LQOJSqVCaWkp8vLyaPbEwsJCyGQyKBQKo6x9pkpbmlrU5/P5cHJygp+fHwICArBnzx6LnaempiYUFhYiKCjIbFSxVqvFypUrsWbNGrpgxi5m6vV62NjY4JFHHsHGjRt7PENPe+RyOebNm4e4uDiEhoYiIiICwcHBkEgkZo+hoZNMVxxH2GuWEGLRa279+vVYvnw5hEIhzRbi5uZGM2uyZU8lEgldcPTx8YGHh0en5+FGA2pulubmZtja2kKr1Rpd953NgeVyOdLS0nDp0iVcuHABqampkMlkUKvVEIvF8PDwgFAohIeHBx5//HFMnDgR9vb2Fp3LlJeXw93dnc6ZDPuwrmpJTk5GXl4eamtrYWNjAz8/P7i4uMDX1xdz587tsTKppaWlRvNzUxkEDPunzo6pRqPp1ecJp8U0nJbuJzk5GSNGjDCyTwoEAri4uMDBwQFSqdSo3NnEiRPx3Xffwdvb+5qZyPfs2YPly5ejsLAQ8+bNw/vvvw+JRGIxLY888gi2b98Oa2trqNVqhIaGYvTo0Rg6dCjNOqZSqaDVauHu7o7Q0FDY2NigqqoKAIwypfQ1B1jWxpqYmIijR49CIBDgn3/+wbhx47rcVraP7wlder0eFy9epEF6xcXFKC4uRlFREaRSKSorKzs4hAPmnUMBwN/fH5GRkRg4cCDi4uIQHR2NwMDAWyr4jYODg4ODo6foeWsSB8dNcuXKFQBtNeUjIyMBmJ9QsQuXfD4fAwYM6HRbdnBpWK87ODi4exrdBdjBOmvoZdOIq9VquLm5wcvLC/b29hAIBHB1de1Quqk3HWkyMzMBtKX9ZWvEsnrYf6dMmYLbbrsNWVlZSE5OxunTp3H+/HlUVVVBp9PBysoKbm5u4PP5UKlUGDNmDO666y7ExcUhLCysV3SxpKenU02tra0oKipCREQEnJ2dUVNTg6ysLPj6+kKv18PKygrW1tZGTjEMw/SK8b474fF4sLGxgY2NzTXTjvZ1DLWYozfvJ3OG6/aGfB6PB4lEAolEgrFjx9LtWlpaUFNTA41GA29v7w46La3t/PnzeOihh1BUVIRdu3YhPj4eH3744Q07w7CLec7Oznjrrbfw2GOP4eOPP8YHH3yAqKgoAJZLCZuWlkbbwPZDfc0I1BMYXjOsfsNjzjAMrKyskJ+fj8LCQri4uFAnwb5Gf9IC9C89nBZOi6XpT1qA3tXTlRJJhu0wF4jAjmt4PB54PB6Cg4MRHByM6dOn0+1YJ4GeHEsbLo7Y29sjMjKSzjtZTGXtKy4uRmVlZYesfe0XMfR6PRQKBWpra1FSUmLRxb8jR47g3nvvRXx8PM1QEx4ejrCwMISEhMDb25uWgrh8+TL++OMPmp2TEILQ0FCsXr0a9957b5fLWlmSS5cu4d9//0VSUhIYhoGdnR3EYjF1HAkLC6OvoKAgeHp6mh1bmyu1ZO6a7W6SkpIAgJbjYhgG1dXVqK6uptuw16BIJIKLiwvc3d3h6ekJiUQCf39/BAQEwM/PD97e3nQu3Vvj1Lfeegt33303Ro8ebbLMs6GTEXuMXVxcMHbsWKO5DADIZDJUVFSAYRiEhYUZBSpYeiz+xhtvYOrUqZg3bx7tB9r/3vVoqampQWtrK3Ww7ylKS0sREBCAOXPmYN68eZg8ebLJ4KJr2cfYz9l+SqfTdanv7044Lf+D02J5hgwZgi+//BJPPPEEbaNOp0NVVRWqqqogEAjA5/Oh0WggFouxadMmeHt7G2kxx759+2iptREjRli8LE9tbS0AQK1WQyAQ0PaxgX3tnWRZvYbtYp1O2gcxGj43e8N+xj5jUlNTAQBOTk7UPtTVvtawzZmZmVCpVHBycoK/vz+EQmG3tpfH42H48OH07+bmZiiVSqNSYmVlZSgqKqKOMjKZDDU1NWZLWZWUlKCkpASHDh2i79nZ2cHT0xNXrlzp1SA/Dg4ODg6OvsatvTrL8f8OvV6PgoICAG3pZQMCAgB0nHCwA9+rV68CaBsMRkREmNy2PZmZmdTzOi4urlvb3xk8Hg9XrlzB/v378eOPP6KxsRGEEMhkMrqNSCSCn58foqOjMXLkSCQkJGDQoEGwsrKikXu9Yfy6dOkSgLaoQnbyYWjcZnFwcMDQoUMxdOhQLF68GEDbOS0pKUFZWRkIIfD19UVwcLBRBp/eXHxWqVQoKyszakdBQQG++OILug2fz4ePjw+NToyMjERoaCj8/Pzg5uYGsVgMa2vrPuUQw6Z3Z50qbiaFqKkFGEvSfqLdnVqAno+i7KwdhnTFkM86+hhGF7W/fyypTSaT4eOPP0ZRUREAYOjQofjwww+pUfxG7mN2MY/F29sbS5YswahRo24628y1SE5Opv9nHSr/vyTUY41c7e8pHo+HpqYm5ObmQq1WIy8vD3/99RdkMhlaW1tx+vRp3HHHHfjtt9/6jONQf9IC9C89nBZOi6XpT1qAvqPH3CI30Hn2F0NMbdO+1BKPx+uVKPH2Yy5TWftcXV3h6upqlOFBr9dDKpWisLAQOTk5yMnJQV5eHqRSKaqrq41KLbFzt4EDB1pUy5kzZ9DS0oLk5GQ6vxIKhbC3t4ejoyM8PT3h7++P6OhoOq7S6XSwsbHB448/jrVr19LFMsOFsGtlMLQUhvNOtVoNlUqF6upq5Ofng8/nQyAQQCgUwsHBAc7OzvD29kZgYCDCwsIQHh6O0NBQBAQEQCQSXfMatPT8Zt68eXBzc0NBQQHy8/NRXV2N5uZmo230ej2USiWUSqWRXcDKygr29vZwcnKCi4sLPDw84O3tDT8/P+okI5FI4OXl1WPluHbu3Ilvv/0Wv//+O0aMGNHh82s5xhk6nvj4+MDHx4du0760lyU5cOAAtm/fDhsbG1pyu/3883q0GJZC6snnSUZGBgDgjz/+QFJSEkaOHIm7774bY8aMga+vLxiGoVmgTLXJ3LXfE45i7eG0/A9OS8+wcOFC2NnZYevWrbhy5QoaGhqoQwL775AhQ/Duu+8iKirqms4gPB4PUqkU+/fvp6XX77jjjm53uGjP2rVrMWTIEOzevRuZmZl0bPLtt99CLBZj7ty5uPvuu5GQkACRSGTSuaX9+biec2kp2L5UoVCgpqYGDMPA09PzhpyLWK2vv/46/v33XwgEAiQlJVkkKNPwuNra2sLW1hYeHh4IDw8H0Db+YrNxs6XEysvLUVpaSp1kSktLUV5eDoVC0WH/DMOgqakJ5eXlnCMMBwcHBwdHO/rOyiwHRxdgs7bw+XxqrDOVMpvH40Gj0aCwsBAMw8DV1RWBgYEAzA/S2fczMjKoM8ygQYMspKQNdiBcW1uLTZs24bPPPkNtba1RGkQrKytaZkepVCIjIwMZGRn4+eefAQBRUVG4//77MW/ePISGhlq0veZgM/DweDyaGcYcpgxEgYGB9PywGE64enMBoqamBpGRkdQpxlREpE6nQ2lpKUpLS3H06FH6vr29Pfz9/REeHo6IiAiEh4cjJCQEPj4+cHFxgb29PYRCYY/qUygUcHZ2NlvOqb1x+0ajfS0JwzC4dOkSvLy84OPj061a+gKm2tFZauFrXT89eX199913NColODgY77333k05wgBt12xaWho8PDwQGBgIa2truLm54c4777R4BBLrDMPn8xETE2Ox3+ltTJUPZM9XcXExWltbUV1djWPHjiElJYUuuObn59Nt9Xo9PReJiYk9K8CA/qQF6F96OC2cFkvTn7QAfVdPfn4+vvjiC9x2222IjIxEcHBwh6yILIbZX7riONHbDsks7HEzzBRibnzWXp+fnx/8/PyMskM0NTWhpKQE+fn5yMnJQW5uLkpKSpCSkoKJEydaVMu5c+eoJkIINBoNNBoNVCoVqqqqkJubCx6PB2tra9jY2MDa2hqtra1wdXVFXV0dduzYAQ8PD0RFRcHf3x/29vZmnUjY42BJ2HK7ppyT2Syera2tUCqVKC8vR2ZmJnWSsbW1hUgkgqurKw3ACA8PR3h4OC21JBQKe+waXLBgARYsWEDbblgmLC0tDdnZ2SgtLYVCoaAZklg0Gg3q6upQV1dHndCBtsU11hGI1WltbY2lS5daNNCHvZ4A4KGHHsKGDRtw5513dum75vqF3ghUqK+vh0KhAI/Hw/33349169bhmWee6fLv96V5GZstgRACqVSKvXv3Yu/evRgxYgTmzZuH+fPn02wWfR1OS9+kP2lpj0AgwIIFCxAXF4fDhw8jOTkZZWVlsLGxga+vL8LCwjB//nz4+/sD6Dz7DcMwaGxsxJo1a6BWq8EwDEaOHInw8HCL2zOio6Px9ttvY/HixTh69Ch++eUXJCUloby8HA0NDdi1axeSkpIwYcIEzJ07F4MHD4a9vT112AV61x7LotVq0dTUBDs7OwgEAtqmwsJCug1rD7/eclvsvmQyGS3Hbanr1txY0tDxSCQSQSQSwdfXl27DOv+ypeqrqqoglUpRUlJCnWTKyspQU1MDpVJJg1T7kkM/BwcHBwdHb8OQ/y8hzhz9gt27d2PBggVGRrqgoCDExsZiwIABiImJQVBQEMLCwpCZmYmYmBjweDyMHz8ehw8f7tJAMCIiAnl5eSCEoKmpqdNSKjcD25bs7GysWLECe/bsMWqbodOFtbV1hzr0rBOCTqeDnZ0d4uLi8Nxzz+HOO+/s8VruAwcORGpqKuzs7HD27FnExsZe96Supwyo1wubDrWhoQHl5eUoKipCbm4ujfQsKSkx6ZHfGW5ubggMDERERAR9BQQEwNPTk2Y7shSurq5Qq9W0puyQIUMwcOBAREZGmo0c0Ov1RosCfWEyJRQKodVq4eLigvDwcMTHx2Pw4MGIj4+/5bQAbeXf9u7di+joaHh5ecHT0xOurq5wcnLqNFqor903kZGR1Glxx44dmD9//k1nRGpubsaTTz6JAwcOwNHREU8++SReffVVAJY3zERERCA3NxcikQgpKSkIDg7u1RJaN0v7xT1DlEolqqurwefzkZSUhFOnTqG2thYVFRU4c+YM1Go1rYt+LY4fP44xY8ZY9Fj1Jy1A/9LDaeG0cFquj1tNz5dffomnnnqKOhi4urrC398fUVFRGDBgAGJjYxEZGWmUpc6UZsPsL335ucqOHYH/lXPqyne6kjmluroaVlZWJstKdBerV6/GqVOnUFJSQhe/DDGMAjf8m2EYWlKBLdPj5uYGiURCSy2xTiS+vr496hzPOvGkpaXh6tWryMzMRGFhIaqrq9HU1NSpPhYej0ez5Nja2sLR0REeHh7w8/NDREQEfH19MWnSJIuWTe5KuTGVSoX8/Hykp6cjNTUVaWlpyMvLQ3l5OZRK5TVLV7FBNsnJyRYN9GFtL0KhEGq1GjExMdiwYQOmTZtmsd+0BPn5+Rg4cCBaW1uh0+ng4eGB5cuX4/nnn+/T/ZQpfvnlF6xcuZJmSwb+dz0AgI+PD6ZPn467774bw4YNg6urK4C+N78EOC0Ap6Uv0NLSgvr6eri6ul63jWPXrl1YtGgRtFotPD098cYbb+DZZ5/tcduCTCbD4cOH8cMPP+DgwYMdPp84cSLmzZuHu+66y+IlnLoCe3yysrKwc+dONDU1ITg4GJ6enhg4cCB+/PFHvPPOO2AYBm+88QZWrVplch8s5o51RUUFRowYgdLSUnh7e9NSVt1JZ+N9U3Tl2mhfaqmqqgpZWVmIiIjA7NmzOWcYDg4ODg4OAzhnGI5biueffx6ffvqp0QTLFAEBAXBwcEB6ejoYhsHs2bOxa9cuaLXaDjVRgf85ppSUlCA+Ph51dXXw9fVFaWmpJeVArVbjgQcewM8//0xrszMMA19fX9xxxx0ICAiASqUCIQQqlYpGuBUUFKCiogL19fVGx8LV1RUrV67E008/3aOTKhsbG6jVavj6+iI7O9vkMe4vsBGVLS0tUCqVqKmpgVQqRUFBAXJzc5GdnY2CggJIpdIOaa6vxZgxY3D8+HELtbzNmCoSicx+LpFIEBMTg0GDBmHIkCGIjY1FaGio2clTd5Unul4aGhrg5OREI55NwZYTGzx4MAYPHoy4uDiEhIT0OS0sS5cuxQcffABPT084OjrC29sbvr6+kEgkkEgk8PX1hbe3N9zd3eHq6gqxWNzppLY3DEzHjx/HhAkTALQ5kVy9evWmSxuw5+TgwYO4//77IZfLERoaigMHDtBoF0vC5/NBCIGTkxO++uorxMbGwtPT06KLVj1BZWUlGIZBRUUFDh48iMLCQtTX1yM5ORk5OTmwsbFBa2uryecse0xsbW1hY2ODhoYGDBgwAIMGDYKrqytEIhFeeeWVHnPK7E9a+pseTgunhdPSP/U8/vjj+Prrr2FlZUVLBbRvk4ODAzw8PBASEoLo6GjExcUhJiYGYWFhcHZ2NrnfGym1ZAlkMhmOHz+OESNGdOoIcb1jR1OllnqaiooKZGVlIT09HVevXkVWVhYKCwtRU1ODlpaWLu3D0ImELbXk6+sLW1tbbNiwodcy6RFCTGZWYQMX2juMmbMn8Hg82NnZobGxETt27MADDzzQUxI6lAnrzMGouroa2dnZ9FxmZmaioKAANTU1HRyCAKCxsdGic3S9Xo/3338fr732Gn1PIpFg27ZtmDRp0i21ILdr1y489dRTNEOAo6Mj1q1bh4cfftjiJU0sQXJyMnbt2oWDBw/SsjaGhIWFYerUqXjwwQcxdOhQo8/6WhAAp6UNTkvPYCpDH0tX+jRW28WLF/Hqq6/SLNKzZ8/Gpk2b4OPj06v6CwsL8d133+GPP/5ASkoKHdPZ29sjPDwc06dPx/Tp0zFgwACIRCKjMUxPtZn9vZ9//hlPPfUUamtr4eDgQMceMpkMZWVlYBgGjz76KBYuXAhra2t4e3vDxcWl03Eva9sVCoU4efIkpk+fDpVKhcmTJ5t0FOpuXZYa87Lj05sNSuPg4ODg4OhvcM4wHLcUzz//PH766SfodDooFAqjTCmmsLKygk6ng16vh42NDQICAhATE4MBAwZgwIABCA8Ph0QigUgkgpWVFY4fP47bb78dGo0GU6ZMwV9//WURHeyAftWqVdiwYQM1WEVERODNN9/EtGnTaP1QQ5qamlBdXY2CggKkpKRg//79OHXqFNWq0WjA5/OxZcsWLFq0qNPJW3dRUlKCwMBA8Hg8DBs2DGfPnrXo7/VV9Ho91Go1mpqaUF9fj6qqqg7p0AsLC1FeXm4yMlGv12PWrFnYv3+/xdp45coVDBo0yKTx15xB2NraGsHBwYiLi6OOJTExMfDy8jL5G4bliSw1+bp69SoGDRoEOzs7qFSqDp+b0yIUChESEoLY2FgMGTIEgwYNwoABAzrVotPpemQSmZiYiKNHj1KnOEOsra3h7OwMDw8PeHt7UwcZiUQCHx8feHl5wd3dHc7OzrC3tzf7G5Z0kNHr9Vi5ciVWr14NkUiEZcuW4Y033ugW4w7bj40aNYqm+v/jjz8sHmmam5uLiIiIDufE3d0dYWFh9DkSHR2N4OBgeHh49FlHwKqqKpw9exbJycm03F5SUhKam5shFApNLpoY4uLigvr6egBt5yMmJgbPPfccBgwYgIEDB9Isap0523FaTNOf9HBaOC2cluvjVtVzxx134NChQzRTp2GWFxZTYzEejwcnJydaXoDNIsM+R62trbu1nTfKpk2b8MILLwBoa3NoaCgGDhyIoUOHYsiQIRgwYIDZaOkbKdNpabRabacZbbRaLQoLC5GZmUkzj2RnZ6OsrAx1dXUdxqWmzi07VsrOzkZYWJjFtBjS1exCKpUKBQUFJjOrNDQ0dHCsZzPiaDQanDt3DsOGDbNI+9lrpauOVF1ZNNPr9SguLjY6l6mpqdDr9UaZGyzJli1b8Pzzz6O1tRUA4OTkhO3bt2PWrFnXHRXfm/z1119YuHAhampqALTNI99//32ayQHo+1ktDOdhCoUCycnJ2LdvH37//XeUlZWZ/M7w4cNx33334Y477kBISEhPNrdTOC2clt6ivr4eOp0OYrHYyC50PXaOlJQUnDhxAkVFRcjJycGrr75qVEqxJ2nv0KJSqZCWlobff/8dv/76K3Jycmj/DQAxMTG44447cP/99yM2NrZX2gwAX331FV599VU67jWEPQ/29vbw8vKCm5sbDS5jA8wM7WZOTk4dbHwrV67EypUrAQBvvfUW/X93UVNTgyNHjmDQoEEICgoyGzR2o5kT+7JTGQcHBwcHR1+Bc4bhuKXIzMxEaWkpysvLUVZWhrKyMshkMshkMlRXV0Mul6OxsfG69uns7IyQkBAMGzYMMpkMv//+O3Q6Hd544w2sXr3aQkoAuVyOwMBANDU1Qa/XIyEhAfv374eLiwvdpiupk69evYp33nnHyIlCJBLh33//xZAhQyw+KP7jjz8wc+ZMMAyDhQsXYtu2bVCr1eDz+XSC9f95UK7VatHa2gqVSgW5XI6KiooOpZbY63fFihVYsWKFxdpSUFCA7777DkqlkqZKr6ysRG1tLRoaGq7pXGaIk5MTIiIiOpQn6okFpPLychw8eBB1dXUoKSlBWVnZLauFJS4uDmlpaSY/M+fcY29vDzc3N3h6etKJvp+fH80i4+npCTc3Nzg6Olp8gamxsREPP/ww9u7di/DwcGzatAmTJ0/u1v7ngw8+wLJly0AIwVtvvYUVK1ZYNML0559/xrx582i6d6DzKOKAgABERETQxT22/NmNpFHububOnYt9+/Z1mlENAMRiMZqamiAWizF69GgEBwdDKBRi5syZOHr0KN555x0AwOuvv4533323Q0ScTqez+MJff9IC9C89nBZOC6fl+rhV9Vy5cgXZ2dnIzc1Fbm4uioqKUFlZibq6OqhUKqjVami12g4L36zO9nqtra3h4uKCgIAAREZGIjY2lmb0CwwM7JY2Xw/PPPMMvvjiC9ja2prN8ujg4IDo6GjEx8dj2LBhGDRoECIjI806xd5IqSVL0dWFFqVSiby8POpEkp6ejry8PFRUVECpVJp0gLqe8feNUF9fD4FAYNb5+3ozq+Tk5FCHkYyMjA6llpRKZaeO5r1NV89la2trjzibsU47u3fvxvPPPw+5XA6gzZFk8+bNWLRoEd2uL9sHWEf8c+fOYcGCBSgqKqKfvfvuu3j99dcBdC0zRF9EoVBg9+7d2LNnD44dO0bfZ8+Js7MzBg0ahDvvvBNTp05FUFAQzT7WVQeunoLTwmmxBI2NjdiyZQtSU1Oh0+nQ2toKOzs7BAcHY/DgwUhISIBYLL6hfavV6j6bYaq5uRn79u3D7t278ddff3V4po8ZMwb33XcfEhMTERoaSt/viT79xIkT2LFjB4qLi1FRUQGFQkEDETsbR7OlKFm7mY+PD3x9feHj4wM3NzfY2tqisLAQmzdvRn5+PkQiEX7++WdMnjy5W9u/c+dOPPTQQxCJRBCLxQgMDERkZCTNnBgZGQkfHx+z32fPBWdj5+Dg4ODguHG4nGkctxRRUVFGpTFYB4PKykqUl5dDKpWirKwMUqkUUqkUFRUVqK6uRl1dnZF3OwvDMFAoFLh48SIuXrwIoM0gq9PpMHjwYIvpaGlpwbZt26jjTlhYGPbs2WPkCAOYNuAZGvgYhkFcXBy++OILxMbGYvXq1eDz+VAqlfjiiy/w+eefW3yixR43hmEQEBAAAGZ/s32k5K3K9USDCQQCarT18PBAZGRkh1JLtbW1yM/P75AytrsJDg7GihUr0NraCoVCgerqapSXl9PUoux9U15ejqqqKigUCpPOZQzDoK6uDufOnaOZOlj8/f0RFBSEO+64A0uWLLGIDm9vb2pMtaQWFxcXzJ49G1u2bLGIDkOuXr2KpqYmZGZmIiUlBRcuXEBKSgqysrLMOvg1NTWhuLgYxcXF9D0ejwdHR0e4u7vD29sbPj4+1EHGz88Pzs7OGDp0aLcb9hUKBW1HWFiYkXGku5gwYQK9944fPw4ej2dRwwvbt+n1eri5uUGn05k0tjAMA71ej8LCQhQWFuLvv/+mn9nb2yMoKMgoI1lYWBh8fX3h6OhokXab00IIgZWVFe2PVCoVGIbB0KFDERMTAwcHB4SGhiIxMRFisRh6vd4o8v3HH38E0HaNBQUFAei4oGnpTGT9TUt/08Np4bRwWv5/6Bk4cCAGDhxI/yaEQCaTobCwEDk5OdTZWyqV4sKFC/RZzefzTTpLqNVqVFRUoLy8HElJSQD+l/Hy9OnTGDVqVI8unqekpNB2mcum2NjYiPPnz+P8+fP46quv6Gfe3t6IiYmhWWQGDhxIS46amvtYejGdnTca/oap3zPlRCISiTBo0CAMGjTIaNuKigpkZ2dTJ5KsrCxkZGTA09PTYjpY1q9fDycnJyxbtgxarbaDs7G5BSJTmVXc3d3h7u6O2267zWi74uJiZGRkoKioyKKOMOvWrUNAQADi4+NvuPRnV84lj8frsaxL7H2+YMECqNVqrFy5EsXFxdBqtXjppZegUCjw0ksv9flFPLbPHDFiBL777ju89NJLuHjxIvh8Pt58801UVlbi448/tvhcpLthbTHOzs5YvHgxZs2ahVOnTmHfvn04e/YsysvLodPpIJfLceTIERw5cgQBAQGYM2cO7rvvPgwePLjHno/XgtPCabEUBw4cwGuvvYb8/PwOmdFsbW3h5eWF2267DY888ggSEhK6FPRi+Czui44w7HmztbXF/fffjzvuuAMpKSn45ZdfcPToURQUFKC5uRmnTp1CXl4e/vrrL8yZMwfTpk2Dm5tbj/SBY8eOpdl02DHjyZMn8eKLL9Lyhj4+Pqirq0NzczN1zNZoNKiurkZ1dTUyMzPp/uzs7CASiaDValFbW0ufp+PHj0d8fHy3tz85ORlAm/1SJpNBKpXi9OnT9HORSARPT0+EhIQgJiYGsbGxtLyoo6Njr98XHBwcHBwc/QEuMwzHLUVXnBD0ej3q6+tRW1uLiooKOtBkF8hlMhnNIMGmvDRFfn4+NSp3N2lpaXjuuedw/PhxeHt74/XXX8czzzxj0qjXVRobG/HMM8/gu+++A9A2USsoKLC4YXLWrFn4/fffIRAIIJFIMGDAAPj6+iIiIgLh4eEIDQ2Fn5+f2Vqt7Z17bhVj0vVwq6RSbmxsNOlcxmZgqqioQE1NjUnnMoFAYBT1ask6u105nl3VolAoaNYPoG3xhTVUqNVqWrrK0iXHOjOkVlRUID09HcnJybh48SKuXr2K3NxckxEwnZWIcnd3B4/Hw5kzZ+Dr69ut7U9LS8Ps2bORn5+PBx98EO+//z7c3d27Zd/ssTl79ixmzJgBhUKBcePG4ejRoxY1QN9+++04fPgwrKyssGrVKvj7+yM7O5tmIzN0tDJVysLcuQD+V2qJjXyfOHFih8Wm7kKn00EoFNJybC4uLli4cCHmz5+PgIAAtLa2QiKRmPwuIQRqtRrW1tYYPXo0kpKSYGNjg2PHjmH48OE9HhHbn7T0Nz2cFk4Lp+X/jx7DsjKdjd0LCgoQGhoKhmEgkUgwe/Zs5OTkQCaTQS6XQ6lUorW1FRqNpkOpJdZxpqKiwmxJIkvBltllHYynT58OQgiys7NRWloKhUIBjUbT4XudZY9jn/msk0xsbGy3jZOuB8PAhK7Mu7panker1aKmpsZs6dHuYvLkybh48SJSU1MhkUhu+lo3vO56MlhDpVLRDJSxsbG4cuVKj/xuT7N9+3YsW7YMtbW1IITA09MTb7zxBp544ok+uSBsjiNHjuC1116ji5n29vZ46KGHsG7duh7NJGop1Go1/vnnH+zevRt79+41mRHLzc0Nt912G+bNm4eEhARIJBIaENCXbDicFk7LjcA+F7/99lu88847KC4uBp/PB4/HM3reGz7n3d3d8dVXX+HOO+/skfL0PUF720praytOnTqF/fv34/vvv0ddXZ3R9p6enpg3bx7uuecejB492uL2MsDYBnjs2DFMnDgRAHDffffhs88+w7lz55Cfn4/c3Fzk5+dDKpWitrYWjY2NaGlpoU4y5jIvnzhxwiJ2mTvuuAN//fUXhEIhNBpNl8qL8vl8ODk5QSKRICwsDIMGDcLgwYMRFRUFiURCxyy3apYyDg4ODg6OnobLDMNxS2Eu0svwcx6PB2dnZzg7OxtlJ1Cr1TSDREVFRYcsMuzCZm1tLZqbmy3mCAMA586dQ0ZGBoC2VJN33XUXgBuP3CSEwMHBAW+//TZ+++03Okn5888/8fDDD3dHk83C1h/X6/UoKiqiE0ehUAgbGxs4OjrCzc0NEokEoaGhCA8PR3h4OIKCguDj42M2Ber1Gmt7ipKSEsjlcjg5OcHT0xO2trZmF+T7uhNM+/Y5ODjAwcEB/v7+dBvWuaympgYVFRW0JBFbZkmj0eDixYuQSqUA2jIrtba2YtKkSRZrd1eOdWdaqqurIZVKUVVVhYqKChQXF0OtVuPo0aPIysoCIYQaaMeNGwfAdOSlJTSxiw6GUdReXl7w8vJCYmKi0Xdyc3Nx9epVXLx4EcnJyUhNTUVlZaXRNmy71Wo1PUedpV+9Udi+FWi7BiwRSavX6+Hr6wuFQgE3Nzc0NDTccHrirsCWrRKJRHjiiSfg7OwMQggaGxtRW1uLqqoqk86Whk5jho5WLAzD0OikM2fOAACef/55iznD5OXlgRACnU4HgUAAnU4HmUyGpqYmo4U4tVoNKysro/uLYRgaTcw+s8RiMcLCwgBY/r5oT3/SAvQvPZyWNjgtlqM/aQFubT3m9s+OX1hHn8LCQvpZYmIiPvroIwBAXV0dioqKkJeXh+zsbOTl5dFSS6yDqUqlglgs7nFHmIaGBnh5eUEqlUKv16OhoQFJSUmYN28e5s+fDz6fj/z8fJSUlBjNIWtqajrNHpednY3s7Gz89NNPRp+fPXsWI0aMsJieH374AaGhoQgNDYWzs7PZTCLmHF7YsWh72pfnEQgEFneEAdqu9/r6erz++uvYsWPHTV/rpr7fE4uabHQ6n8+ncxVzi1l6vR4FBQVwcnKCSCTqsSwvNwM7j1m0aBEkEgnuueceNDQ0oLKyEkuWLIGNjQ0efvjhDnr76pw5MTERW7duxYIFC5CVlQWVSoXNmzfDwcEBS5cuhZubG4COdqm+DhuUJBQKcccdd+COO+7AunXrsHfvXuzfvx/JycloaGgAANTU1ODAgQM4c+YMJkyYgPvuuw+zZs3qMwuwnBZOy83AMAykUinWrl2L4uJiWiZZJBLBz88PCoUCUqmU3uPW1taorq7GE088AU9PT4wcObJPZokybJOpElTse+wzr337ra2tkZiYiMTERLz//vs4ePAg/vzzT/z++++orq5GZWUlvvzyS+zbtw+5ubkWf3ay7WN1VFVVwcvLC83NzRgwYAAcHR1x++23G32HLRGfl5dHsxcWFxdDLpdDo9FAIBCAEAIvLy+88847FrPJPPbYYxg2bBgKCwuNyos2NTWZddDR6/WQy+Wora3FlStX8PPPP9MSS+w9MmzYsD5zv3NwcHBwcPR5CAdHP0Wv19NXZzQ2NpKSkhJy4cIF8ttvv5FNmzaRjz76yKJte/DBB4lAICAMw5Dvv/++W/bJ6ly6dClhGIYwDEOeeOIJQgghWq22W36jPWq1mjAMQ3g8Hv1NHo9n9Df74vP5xNramohEIuLl5UUiIiLIuHHjyEMPPURWr15NfvzxR5KcnExqa2st0tYbQafTEUIIKSoqIq+99hoJDg4mkZGRZPjw4WTWrFnk3XffJYWFhdfcj0wmI9XV1UStVtP3LHVObpau3jeEEHL8+HGyePFiEhISQs+5jY0NYRiGHD58uAda2zmGWszp0ev15PDhw2T27NnE29ub8Hg8eq0yDEOOHTtGt+tNdDod0Wg0RKPR0OvSFEqlkuTk5JCdO3eSkSNHEoFAQHg8Hu1voqOjLdK+ixcv0vt88uTJ3bpv9tgfPnyYhIaGEoZhyMMPP0yqq6u79XcMaWxspP1ZZGTkNbfXarWkpqaGZGVlkWPHjpFdu3aRDRs2kBdeeIHMmTOHjB49moSGhhJnZ2fC5/M79I8//fSTxbS0tLSQr776ioSFhXX43enTp5N///33mvuorq6mx2PQoEEWa+u16E9aCOlfejgtxnBaup/+pIWQ/qfHEI1GQwghZO3atbR9q1evNvqsPXq9nkilUnLy5EnyzTffkJdeeom8+uqrhBDS6binu2lpaSH79u0jt99+O7Gzs6PnJCwsjLzxxht03N/S0kLKyspIamoq+ffff8nOnTvJ+vXryXPPPUdmz55Nhg8fTgICAoiDg0OH88swDLGysiIMwxCpVGoxLZWVlYRhGCISiYivry8ZPnw4ue+++8iaNWvI/v37SVZWFmlpaTH7fZ1OR7RaLdFqtb0+DiaEEJVKRa8nW1tb8u2333b7b7DX2q5du0heXl6375/lq6++olqWLl1KCOk4N2TbkpKSQsLCwsiCBQvId999Z7E2WQJWw/nz5+kYnn2tW7eu0+92dT7aE7A6iouLyYgRI4x0PPfcc6S1tbXT7/clLeYwbJ9WqyUpKSlkzZo1JC4uzmQfxjAMCQwMJK+88opF75UbgdPCabkRXn31VWoDYu/tX3/9lWRnZ5Pq6mqSnp5OvvjiCzJ06FCj5/iLL75ImpubLd6+7kSj0RidD4VCQYqKikhNTQ1paWkhOTk55NChQ2T//v3k0KFD5NdffyWfffYZeeWVV8jUqVOJh4cHYRiG2pgcHR17tP1sn9rQ0EAKCgrIpUuXSGVlJdWm1WqvOXasqqoiqamp5OLFi0Qul/dEsyk6nY6UlpaS48ePky1btpClS5eSu+++mwwbNowEBQURV1dXYmdnR6ysrEza1tnxg5ubG1m8eHGXbNIcHBwcHBwchHBlkjj+X9LbaQTDwsKQn58PAJBKpfD29r7pfbJRbJ9++imef/55AG3e54Z17LubtLQ0xMXFgc/nw9XVFb6+vigpKUFdXV2H8lOMibSPbPShQCCAjY0NHBwc4OLiAh8fHwQFBdEsMqGhoZBIJLCzs7OYlvYQg9IsixYtQm5ursnthEIhNm/ejEWLFtHvaDQaHDlyBN988w2USiWsra0hEokQEhKChISEDhk++jLEIJqkvr4elZWVOHv2LL799ltUV1cjJyfHZKr40tLSbi/FcyOYutfLyspw6dIlJCcnY/v27VAqlWhoaDAq2cWm5y8vL7d4qbEbRafT0ehr9u+Ghgbs378fGRkZSE5OxunTp6HVaiEUCtHa2oqZM2fiwIED3d6Wy5cvY/DgwQCAgQMHIiUlpdsja3/77TcsWrQICoUCzz//PNasWWORDDRAW03nYcOGgWEYTJs2Db///rvRtUS6GPnZ0tJCs+aw5boMM8lUVVUhIyMDly9fNspk1t1otVpkZmZix44d2Lt3L4qKigC0lTcLCwvDnDlzcO+99yIyMhJ8Pp/e96xmNgUxwzC45557sHv37l57jvYnLf1ND6eF08Jp+f+tx1CXQCDAPffcg3379kEgEGDv3r2YOXMmbZ/hmIvpQxkgDdmxYwfee+895OTk0PecnJywbNkyvPDCCyZLwKpUKsjlcppRUSaT0TKdbHbFqqoqVFdXg8/no6WlxWLtP3z4MG6//XZaissQgUAAOzs7uLq6wt/fHxERERgwYACio6MRHh5utkQX0HvZOysrKzF9+nSkpKQAAKKiovDHH38gMDDwpvdtON9Zs2YN3n77baSlpSEqKuqm922KxYsXY8uWLeDxePjmm2/w4IMPdhg3s39//vnnePbZZwEAzz33HD7++GOzY2xWR3l5Ob7//nv4+/vT89pbsPf82bNn8dxzz+HSpUvULrBw4ULMnTsXLi4u8PLygqura6dlhwz7jJ6G7dfKy8vxxBNP4I8//qA6EhMTMX/+fDg7O8PX1xeenp5wcXGxaAbLG6H98SMmMkWYeoYcOXIEP/74I44ePYqioiK6LcMw0Gq1OHTokEWzwpqC08Jp6U5qa2sxbtw4ZGRkwNraGs888ww2btzYoa/RaDS4cuUKli1bhmPHjoHP58POzg6lpaV97n5PS0ujfVRBQQHKysrQ0tICW1tb1NXVobi4mGb1tra2Rl1dHbKzs1FXVwcXFxcIBAJUVVXRctBslkI28y2fz6fn5/bbb8fff/9tMS2nTp2Ct7c3vL29O7UJG2a5Mzx37JiT/Lccem88Q3Q63TXHTU1NTSgtLaVZbHJzc1FYWAiZTIba2lrU1tbScvXsueXxeLjrrruwYcMGBAcH96QkDg4ODg6OWw9LetpwcPQ2rLf7iRMnyN9//03OnTtn0mv/erJh3Cxyudwo0qG7+eeff4i/vz9hGIbcd999Fs2e8N1331Ety5YtI4S0RaleuHCBbN++nbz88stk6tSpJCwsjIjFYpPe7KY83Xk8HrGysiJ2dnbE1dWVBAcHk8GDB5uNJrUUNTU1JDg4mLbRVEYHPp9PRCIROXfuHCGkLYvM1KlTjaJKDF9isZiMGzeuT2ROMYWpjDWXLl0ia9euJfPnzzeKkjV8CYVCYmVlRaZOnUq+/PLLXmi5MYaRIGq1migUCvLHH3+QBx54gIwaNcro/BnqcHJyIsHBweSxxx4jJ06c6EUFHTHXP50/f558//33ZOrUqcTNzY1GKZnS+PXXX1ukbRcuXCARERGEYRgSHBxMkpKSOm3z9aLT6ci6deuong0bNnTLfs3RlYhdU3T1WdLQ0EAKCwvJuXPnyP79+7ulzV2htbWV7Nq1q0NUK8MwZPjw4WTPnj1G27MR4x988AE9HitXriSE9H52q/6khZD+pYfTwmmxNP1JCyH9Tw87BmOz3ojFYpKTk0MIufa4QKfT0YwkvYXhb9fW1pKPPvqIxMTEGJ2X6Oho8uGHH3Ypo6VWqyVyuZzk5eWRM2fOkH379pEPPviAfPLJJ5aUQbZt20b4fD5xdXXtMDY097K2tiZubm4kOjqazJgxgyxdupRs376dnD9/vtPIaUvPoQ2zBNrZ2dG52SOPPNJtv19QUEDGjRtH921Jhg0bRhiGIXZ2duTixYuEkI4a2Hnv448/Ts/Nzp07CSHm73P23tuzZw89p3PnzrWUjC5jeP68vb3peF4oFJKAgAASHx9Ppk+fTh577DHyzjvvkK1bt5K//vqLXL58mchksl7PutD+3Fy6dIkEBwfTOTDDMMTBwYFIJBIyePDgPq3FHIYZbAkhpKKiguTm5pKqqirS3NxMjh8/Tl555RXi5+dHbThsv9LXMpBwWjgt18uxY8donzl06FCiVCoJIeb72hMnTlB7n2E/3pcYPHiwkQ2yvT3WnB3W3PjA1Gds//f8889bTEd1dTXx8fEhCQkJZPbs2eS5554jGzduJD///DM5d+4cKSsr69ROrNfriU6nMzlO6O2sXex491pZbGpra8nFixfJ9u3byZNPPkkCAwM72Pruu+++PjEH4ODg4ODg6MsIetsZh4PDkjD/9bheunQpCgoKYGdnh4sXL8LGxsbkdj1BRkYG/U22Hml3RnFKJBJad1cul9OsEZYgOTkZQFu99aCgIACAm5sb3NzcMHToUKNtpVIpsrOzkZaWhtTUVGRmZqK4uBg1NTXUu90QnU4HrVaLlpYWyOVy+Pv7QyAQ9FjEa1NTEz755BMUFhbS6IHAwEDMmjULTU1NSEtLQ1paGpRKJRobG7FlyxZERkbi7rvvxuXLlztkwWFRKpU4ceIEXnnlFXz22We47bbbLK6lM8h/IwjZf/l8Pmpra1FSUoITJ05g586dkMlkKC8vBwB6LFh9gYGBiIqKwvjx47FgwQI4Ojp2GtFnKdpfFzweD3l5ebhw4QLOnTuHH3/8EQqFokMWGzZ6ZPjw4Rg7diyGDRuGu+66C1ZWVj3a/s4gBtG3QFu2kYqKCnz77be4cuUKkpKSUFFRQbdnt+PxeHB2dkZoaCgeffRRi0aGxsbGIigoCDk5OSgsLMShQ4cwYsSIbtt/dXU1Ll26BADw8PBAbGxst+3bFGzfxjAMBgwY0OXvmXuWkHZRciKRCCKRqFuimbsK+W+99gULFmDBggXYunUr1qxZg+LiYgDAhQsXcO+992LDhg14+eWXsWDBAlhbWwMALl68CKDtmmKPR08+N9vTn7QA/UsPp4XTYmn6kxag/+kBQDOR5OXlAQBcXV3p8+5a7evtrDYAjDJuuLi44Mknn0RkZCS+/vprHDlyBHV1dcjMzMSGDRuQnp6ORYsWYciQIbCxsaGZMVkd7Nja2dkZzs7OCAkJofvWarUW1ZGQkIBvvvkGFRUVKCoqglQqRWVlJWpra1FfX4/Gxka0tLQYzVnUajVqa2shl8uRmZmJP/74AzweDzY2NnB0dISvry9CQ0MRExODmJgYREVFISAgoMPcurth/ht9npiYiGeeeQbvv/8+AGD37t1ITEzEfffdd90ZCdnxtVarxffff4+XXnoJdXV1ANqyuFqSzMxMAICjoyPNDNj+3mD/Zse/fD6fjuPN3SfsuczNzYWVlRU0Gg2GDRvW/QK6gOHcjGEY7NixA6dOnTKa92u1WpSUlKCkpARXrlyh24rFYri6usLDwwM+Pj7w9fWFv78/nJycMH369G7Jqnu9OtgMnJs2bcI///yDyspKAKBzy6amJqhUKkilUvpdU1r8/f3h4OCARYsWWSyKv7W1FY2NjRCJRBAKhZDL5SgqKoJarYZYLIZGo0F2djZkMhl0Oh1sbGwgl8tRUFAAmUxGs/bW1NQgNzcX9fX1kEgkcHBwoL9BCKHfNezXOC2clltNCwCcO3eO/n/KlClwcHCg2aAMYZ8bsbGxSExMxF9//QUHBwdkZ2djyJAhFm3j9dDU1ISUlBTw+Xzo9XoolcoujQ8N7XwAYGtrC3t7ezpe8PT0hL+/P6ysrFBfXw8HBwf4+Pjg/vvv73YNbN+bmpqK8vJyVFZWQq/XQyAQwNbWFmKxGE5OTnB3d4e3tzckEgkCAwMRGBgIf39/eHt7w8XFpdNMLL09Zjb1LCcGWWyY/2ZSd3FxgYuLC4YMGYKFCxcCAH744QesWbMGGRkZ4PP52L17N26//Xb6OQcHBwcHB4cJesrrhoOjt2hsbCS+vr7Uc7q3+fjjj6ln/VtvvUUI6d4ozqqqKuohPmbMmG7brynGjBlDGIYhNjY25NSpU4QQQuvKszXmO/NyV6vVJCsri/zyyy9k1apVZN68eSQ+Pp64ubnR+rPsa8aMGXT/PcH58+dJVFQU1ff4448TpVJJmpqaCCGE1NXVkc2bN9MIC4lEQt577z2jqImBAweSu+66i4wbN44EBATQyBY2gmLkyJGkoaGhR/S0x/A4sufo8OHDZPny5WTatGlmI0JsbGyIo6MjmT9/PtmxYwdJTk7ulfa3bzshhCiVSlJVVUW+/fZbMnPmTKP603w+3yiaxcPDg8TFxZGlS5eS8+fPE6lUanKfvYGp39doNOTw4cNk8+bN5LbbbiN2dnb0Omr/cnV1Jffddx9ZvXo1yc3NtXh72WiaZcuW0evfx8eHZoe5Gdjr9MCBA8TFxYUwDENuu+02kp+ff9P77oyuROzeLL0ViWR4fRUVFZG3336bhIaGGkV9+fr6koULF5JDhw4RQgiJjo4mDMMQkUhEMjIyaPt7m/6khZD+pYfTwmmxNP1JCyH9Rw/7+9nZ2XT8NX78eKPPbmV27txJIiMjjcZd7u7uZMWKFTSSnBDTY7mezERqCqVSSXJzc8mRI0fItm3byNtvv00efvhhMmXKFDJ48GASGBhIXFxciI2NTZeyyPD5fOLi4kLEYrHFM/ax6PV6olKpyNChQ2k7Bg4cSMeFXR3DG16nDzzwAL3H2LnavffeazENFRUV9PgNGTLkmtvb29sThmGIt7c3qa+v73RbVv/8+fPpvKens5EanoPi4mKyfft2Eh0dTUQikdE82VwGAnPvs8chNTW1R/UQQsjx48fJ22+/TUQiEb1G2HaKRCKT2WM702JpHVu2bCEzZ84kgwcPJv7+/mTgwIFkyJAhRjYxZ2dn4ujo2OGe7sq9b2jP8Pb2JoRYzkbDaeG0WFoLIYTcfffdhGEYYmVlRX799ddOf499frzwwguEYRji4uJCtm3bRgjpfTsSy7lz5wjDMMTR0dGoHwoICCDh4eHE09OTuLq6ktGjR5O5c+eSBQsWkJkzZ5JHHnmErF69mnz99ddky5YtZPPmzWTv3r3k8uXLRCqVktTUVJKammqUfVypVFpEN5vtZcWKFR2uIVPXkY2NDXFxcSGBgYFk8ODBZOrUqeSRRx4h77zzDtmxYwdJSkoi5eXldL+s3fpWwdDGzrY7MzOTjBs3jh6DqVOn0m05ODg4ODg4OsJlhuHot5D/elIXFBSgvr4eABAfHw8A1x051p0YZlNho7v0ev1Nt4fVy0aS6vV62Nvb0/9bItKSzXIjEoloBB2rw5QetsY80KbfysoKERERiIiIwF133UW3q6+vR25uLtLT05GRkYGjR4/i9ttvB9Bz3vunTp1Cbm4uAOCuu+7CypUrjSJVHB0dsXjxYgDAU089Bblcjk2bNgEABg0ahP/85z8ICQmhx72yshJHjx7FZ599hoKCAggEApw7dw67du2i+7Ek7PXBwufzUVRUhPLycvz+++/Ys2cPGhoaUF1dDcC4zjMAxMXFISwsDFOnTsXdd98NOzs7Gpnck/eTqQww58+fx9WrV3HixAn89NNPIITQWsaGEEIwZcoUjBo1CiNGjMCUKVM67Jv5b/RFb8BqY3+/sbERBQUF+Prrr5Geno5Lly6hrq6ORlyz8Pl8SCQShIWF4YknnkBUVBTCw8M7ZLdpfw10F+w+n3zySWzZsgV1dXUoLy/HihUrsH79egwcOPCGfp/tF+vr6/Htt99CoVAAACZOnGjxesg5OTng8XhwdXU1G7F7s/RUX9Yew+s7ICAAy5cvR2xsLD777DOcP38ezc3NkMlk2LFjB5KSkvDYY48hJycHAODk5AQ/P79ebb8h/UkL0L/0cFo4LZamP2kB+o8e9tl9+fJlAG3P/sjISKPPbkXYMdr999+PefPmYdu2bfj888+RmpqKmpoarFq1Cl999RVeeOEFPPbYY3B1de2wj54+N8QgsphhGDg4OCA0NJSOa1hqamogk8lQUlKCoqIiFBcXo7S0FJWVlaiurkZdXR3q6+vR0tJCM98w/40eV6lUNBNAT8AwDOzs7PCf//wH06ZNQ2trK65evYr//Oc/2LRp0zXH8IbH49tvv8XatWuRk5MDgUAArVYLrVaLRx55BMuWLbOYhrS0NNoWdv5sbq5eUVGBpqYm8Hg8SCSSax5ndh9s3wDgujIcdgc8Hg8XL17En3/+ie3bt6OoqAiAcebKsLAwPPDAAxg7diwuX76M9PR0pKWlITc3F1VVVSb329LSAgA0G60lMJyrEEJw4MAB/Pjjjzh06BDq6upolgUAsLOzw8SJEzF37lxMnDgRWVlZyMzMRFpaGtLT05GTk2NWCwCLZrrYsGED8vLywOfzodPpUFpaavQ5wzA0CxLQZtcghNDswl5eXvDw8IBGo4FcLodYLEZERARcXFygVqvR2toKHx8fSCQSal+zVP/GaeG0WFoLAFy9ehVAWyYUdsxi7nnCjmVkMhmANluYp6enxdp2I9ja2uKRRx5BbGws/Pz84O3tjZaWFmi1Wnh6eiIkJATW1taorq6GQCCAu7s7/W5ntj0fHx/6f/a5ZWgj7U7YrDwJCQmYP38+CgoKkJ6eDpVKZXJ7jUaDuro6KBQKahPn8XiwtraGWCyGj48PwsPDMXbsWMybN4+O0yxlI+tumP9mGjQkMjISL7/8MlJTU6FQKJCbm4vz589j+PDhvdRKDg4ODg6Ovg1DCDFdy4OD4xaHHcTv3r0bDzzwAHQ6HRYuXIhvvvnGZMrLniIuLg5paWmwt7dHSkpKB4MkAKPUlF0dmLN6v//+ezz44IMAgMWLF+OLL76wiLOCQqGAq6srGIZBTEwMnUBeL8QgDSRg2ommN7j77ruxf/9+AG0puOfOnWtkqGT/f/HiRTzwwAPU6Dh8+HBs3LgRY8aMMbnfjIwMPPnkkzh16hQAYPLkyTh48GCPlH/S6XQghOCXX37BiRMncPnyZZw+fdrktmKxGAKBAPfeey+mTJmC0NDQDiV2eqpkVXvkcjmUSiV+/vln/P7775BKpTQVf3snnsDAQLi5ueGee+7B7Nmz4ejoCA8PD7pNX5v41tbW4syZM0hLS8OOHTtQUFAArVbboZwVIQR+fn6YPn06YmJiMGfOnA4pw3vj/HzwwQdYunQphEIh1Go1xo4di/fff79D2bRrYXhunnjiCfzwww9QqVTw8vLCb7/9hiFDhljs/FVXV1ODlkQiQUlJSbf/Bktfuwa/+OILbNy4kS5csLDX3IQJE3DkyJHeadx10p+0AP1LD6elb8Jp6bvcSnrYOdarr76KjRs3gmEYfPLJJ3jmmWd6NRihu2Cf22q1GhcuXMCOHTvw999/QyqVQq/Xw9XVFePGjcMjjzyChIQEiMXiPvWsN5xzsU4hplCr1aiqqkJxcTGkUikqKiqQlZUFlUqFEydOoLS0FIQQWFlZQa1WIykpqccXX15//XWsW7eOlnD96aefjIIr2sOeB6VSiaeffhp79+5FS0sLXcx1d3fHhx9+iHvvvdei1+mGDRvw2muvgWEYrFq1Cm+88UaHe4Mdw//zzz+YMmUKGIbB/Pnz8f3331/zPqqrq0NUVBQqKyvh4OBAF58tQXvnkRMnTuCLL77A6dOnIZVKaZCBTqcDj8fDHXfcgWnTpuHhhx+mQRWG1NfXQyaT0aCY1NRUZGRkoLCwEEqlEgKBwGTAQ3fqqKysxMGDB7Fp0yZ6zbN9LdDmfLho0SLccccdSExMNLtPU1oyMzORnp4OBwcHyOVyi/UNzs7OaGhoAMMwsLKywsyZM+Hh4YGKigqo1Wp4e3sjMDAQjo6OaGxshFAohI+PD0JDQ8Hn89HQ0AChUIiAgAB4eXmhqakJSqUSdnZ2RuWQe6JP57RwWiythS29A7TZjlgHyc625/F4iImJQWZmJsRiMZKSkhAZGdmnnvfXAyGkg8OyTqfrEChmOH7oLcrLy5GWlobk5GQkJyfjypUryM/PN1mmnh3nGAaSAW39+EMPPYTXX3+d2gZvRdjrbfTo0UhKSgLQFtQ5evToXm4ZBwcHBwdHH6Vb8stwcPRB2LSWb775Jk0NuWnTJqPPegNra2vCMAxxcnIi27dvJ6dPnybZ2dlELpdfM71kZ6m1Ten99NNP6fe6m9OnT9P0v7NnzyaEdG9a0K6WWrIUEomEpmQuKSkxu119fT0ZPHgwTU355Zdf0s/at5s9D1988QVNQT5u3DhSXFxsGRH/pbKykvz555/k8ccfJ76+vsTZ2dlsOtrbbruNPPLII2Tfvn2ktbWVtLS00P305n1DCCFZWVlk/fr1ZNq0aUapzE2l1F2wYAH58MMPydmzZzvsp7d1GNLc3Ezq6urIP//8Qx544AEyevRomva1fWptsVhMQkJCyPz588k///xjVALJ8B7vzbSocrmczJgxg6aqZdu+aNEi8vfff19XWTCZTEYefvhho2OxcuVKo2vSUsjlcnL69Gly8OBBQkj3pzzuKymUWQzviYqKCrJu3ToSFBTUIc17eHg4+fPPP0ltbW0vtrZz+pMWQvqXHk5L34TT0ne5FfWw6ecnTZpEGIYh1tbW5OjRo4SQvvfs6y5++OEHMmTIEKMxm6OjI3n99ddJVVVVbzevSxjOu0xRXV1Nfv/9dzJu3DhiZ2dHeDweEQgEdMxqWCKqp5DL5UbHPTY2lpSWlnb6naNHj9J7yHCcffvtt3c63+tO5s2bR8tx7N+/nxDS8d5g76N169bRtq5Zs4YQcu3SHRcvXiR2dnaEYRgybNgwS8mgFBUVkV9++YUkJCTQ32VffD6f2NrakkcffZQcOnSIlhu+nr5Ao9GQyspKcvbsWYuWfEpKSiLr1q0jISEhHUq6CIVCEhISQlavXk3y8/Ppsb7ePk2j0ZDy8nKSk5NDCLHMnK2srMzoHFhZWZHBgweTzz77jJbZ0uv1pLm5+Yb235O2GU5L1+G03DhZWVm0n504cSIhhJDW1lai0Wg6LaXD2qL8/f1vWGtPodVqiVqtJhqN5pYbi2m1WnouzKHX60l2djbZs2cPWbZsGUlMTCReXl6dluGyt7cnc+bMIXl5eT2oxjLce++9RCAQEIZhyL///tvbzeHg4ODg4OizcM4wHP0WdpA/e/Zsauw6c+aM0Wc9TXFxMZ0w8vl84uXlRWJiYsikSZPIQw89RF5//XXy2WefkQMHDpDz58+T4uLiaxoXWQcZ1mhmqPf06dN0m+5m06ZNVMtbb71FCOlbTgY3g0qlohPiiIiIa27v6elJJ1VdcWzJz88n0dHRhGEYEhMTQ65cudIdzTbLm2++aXYS6OTkRIKDg8nbb79NDh48aLL9fWXCvGTJEtpugUBgZMSOjIwkkyZNItu2bSNFRUWkrq6Ofq+vtN8US5YsIZ6entRJrr0BmWEYEh8fT5YuXUp27NhBjcgser2+z+hj+5nCwkIyZcoUIx0CgYBIJBIyc+ZMsnbtWnL8+HGiUCiITqcjGo2GVFdXk4KCAlJcXEwuX75MnnvuORIeHk77GNZRqze03kz/qdPpzLa5ubmZZGZm3vC+LYlKpSK//vorueuuu4iTkxNd9GIYhvj6+pLly5cbGY50Ol2frU3dn7QQ0r/0cFo4LZamP2khpPf1dMXhgX3msYsQrq6uRCqVdlsb+hLtF5V27NhBBg4cSMdvDMMQd3d38ssvv/ReI7uIqTlcTk4O+eCDD8jDDz9MHenZOazheNXDw6PH28te14cPHyZCoZAIhULCMAx55plnTG6vUCjIG2+8QcelrAZPT0+ydu3anmw6Hd+KxWI6Dmx/n7Lz+jlz5tDj/uuvvxJCzM9r2HP47bffUn2LFy+2lAzKxIkTO8xdGIYhUVFR5NlnnyUFBQVG11dfmbe0Z+DAgXT+bzjHnDZtGvnwww+JSqUy2r6v6igtLSUzZ84kfn5+Rjo8PDzIrFmzyPfff9/hfjf8m7Ut9YVnIaeF09IT/Pjjj7T/evTRR81up9PpSGtrKyGk7fnIfichIYEQ0rsBSe05efIkycvL69BvtYe1VfSltncF1oZ0LecepVJJzp07Rz766CMyf/58EhoaSoRCodF1e++991InrluR5uZmsnTpUurgwzrZcnBwcHBwcHSEc4bh6PfExcXRwa5CoejVtuzdu5cu5Lc3JLIvBwcH4u/vT4YMGUJmzJhBFi9eTFatWkW2bdtGDh48SK5evUrKy8vNZkeIjY2leuVyucW0zJ8/n7b5+++/J4T0H2eYS5cuUWPYtGnTCCHmDV6NjY102/Dw8Gvum92Pv78/YRiGuLm5WTwScfLkyTSqjc/nE6FQSMaMGUOeffZZcuLECWpwbd/GvsaECRNolDGPxyNCoZDcc8895KOPPiIZGRkdtu/rk/rs7Gx6XthriM/nk+DgYDJs2DDy7LPPkqSkJJKfn0+/01fPTXsyMjLIAw88QBdP2me5MTSaDR8+nEyfPp1MmDCBeHh40D7S0Kg+fvx4ehx66rxe77HurP8rLS0lOTk5JCUlhaxdu5Y8+OCDZMKECeTBBx+82WZaFI1GQ7788kvaX7V/Vr300ks9Fkl9s/QnLYT0Lz2clr4Jp6Xv0ht6Lly4QGxsbMjUqVPJiy++SL7++mty+vRpIpVK6eIQi6FTeVRUVLe2o7cwHBO0H4cYRrH/9NNPJDw8nI5ThUIh+fHHHzvso7dhNRhqaWhoIOnp6WT79u1k7NixJDAw0MjJwXAs5+XlRSZOnEhef/11muWitzS89tprtI12dnZk3759RtsdP36cjB071sjBmmEYkpiYSANlenIOyx7HoKAgIy2Gi5Jse8LCwqjjzLWyibDfWbp0Kf2Nr776yqJaDO919riOGzeOfPjhh0Qmkxlt25fnZQ0NDUb9qLW1NbnvvvvIwYMHSWNjY28374a4fPkyWb58uVGmG/bl6elJnnvuOXL06NE+n9GCEE5LX6W/aGEdCdistl5eXmT8+PFkyZIl5IcffiBZWVkdnt+HDx+mfd/DDz9MCGlzSugLz/nq6mri4+NDEhISyOzZs8lzzz1HNm7cSH7++Wdy/vx5IpPJOtj/DGGfR325zzYF++zsLKNPRkYGee+996jNibXDWfpZaUnq6urIs88+SximLUvRn3/+2dtN4uDg4ODg6LMwhJgorMjB0U+oqqpCdHQ05HI5PD09UV5e3qvt2bNnD5577jm0traard/NGNSkZuHxeHB0dISLiws8PT3h4+MDX19fSCQS+Pr6wtvbGwEBAWhtbUVCQgJqa2vh4uKCmpoai2nZvHkzTp48CYVCgY8++gjh4eFdrpHbE7V/b4avv/4ajz/+OBiGwUsvvYT333+/Q5tZrcnJyRg2bBh4PB6mTp2K33//ndYRNodWq4WbmxsaGhrg5OSE2tpai9bd9ff3R1lZGXg8Hggh4PP5SExMxMyZM+Hl5QWJRAIvLy84ODjA3t4eQqGwT9Y6lkgkkMlkEAgEtIZxdHQ0xo8fD19fXwQEBCAkJATu7u5wcnKCnZ0drKys+qQWAPjll18wZ84cCIVCqNVq8Hg8+Pn5Ye7cuXjmmWcQEBBg9rukzZkVwP9qIfc1Ghsb8dFHH+Hzzz9HRUUFfZ/5b+1pvV7foa/j8/nQ6/WwsrKCWq0Gn8/H4sWL8dprr0EikfSJOtx6vd7sMW9paUFNTQ0IIbh8+TKOHj0KuVyO8vJyJCUlQalUwtraGq2trQCARx99FFu2bLF4m9nr5XquFcM+r6mpCZ999hk+//xzFBcXAwCtER8cHIx77rkHS5YsgVgstpgGlv6kBehfejgtnBZL05+0ALeWni1btmDx4sUd3re2tkZQUBCio6MRExODESNGoK6uDg888AAYhsGMGTNw4MCBm/79nsJwbAWgw5iePf51dXVISUlBXV0dZDIZjh07hrS0NDg5OaG4uBiVlZUghMDJyQlHjhzBoEGD+sQYxvD6YbWdP38ehw4dQmpqKg4cOAC1Wt3he0KhEDweD+PHj8edd96JqKgojB07tsfa3dmxU6lUGD9+PJKTkwEAQ4cOxU8//QR/f3+sWLECn3/+OeRyOaysrKDRaCASibBkyRK8+OKLEIlE0Gq1EAgEPaIjNzcXERER4PF4GDhwII4fPw6hUAihUNhhW71eT9sVEBCArKwsWFtbm903ez5nzJiBv/76C4QQXLhwAUOGDLGYnqSkJIwePRru7u6Ijo7GG2+8gQEDBsDLy8tiv2kJTp48iXHjxmHAgAEYPnw4XnvtNfj5+dHj3Rfu3a5iOE/R6/UoKyvDP//8g/379+PSpUuoqqqCTqeDvb09wsPDMWnSJMydOxfx8fH0etPr9QDQqT2jJ+C0cFp6gjFjxuD06dP0Hje3RBIcHIzY2FhMnDgRZ8+exY8//giGYfDll1/iscceM9qWEEJtHazto6f6kH///ReTJk2i9haBQABbW1uIRCI4OzvD3d0dXl5e8PPzQ0BAAIKCguDn5wcfHx+4uLj0SBt7kvbjOqDtWfzss8/in3/+AQD4+PigpKTEItejTqcDIQQ8Hq9b989eW0VFRXjxxRfx66+/Ij4+Hp9++ilGjx7dbb/DwcHBwcHRn+CcYTj6NZcuXcLEiRPR0NCA+Ph4XLp0qUcNXqbQaDSora1FcXExMjMzkZaWhtTUVGRnZ6OkpMTkd0w5yABthkknJye4ublBIpGAYRj8+++/0Gg0SEhIwIkTJ67pmHGjtLS0oLGxESqVCv7+/l2a3LFt2bdvH+rr6+Hi4oKJEydCJBJ1e/tuhqeffhqbN28Gj8fD119/jYULF3a4bti/t27diieeeAIMw+Dll1/Gxo0bzTr7sBOW3NxcDBkyBI2NjQgPD0dWVpbFtKhUKqPjy7ZLp9MZvefr64vQ0FCEh4cjMjISoaGh8PPzg5ubG8RiMaytrXv1vmEdhwxhJ/iG3ApaWJYvX47169fDysoKWq0WfD4fbm5ucHZ2BsMw8PHxgZ+fH0JCQhAeHo6QkBD4+/vD1dXV7D4Nj0dvGpcM+x2tVouffvoJ+/btQ0ZGBsrLy9HY2EgNA+aYPXs27r//fsyYMcPkIoGlYY1Y5hz3qqurYW1tjeLiYhw/fhx5eXmor6/HhQsXkJGRARsbG7S0tNDt2X6cNYjpdDps2rQJzzzzTE9JuiHYNjc1NSEpKQnffPMN/vnnH1RVVRltV1NT0+cNaP1JC9C/9HBa+iaclr5LT+t54oknsHXrVqP3zM1PDD+PiYnB888/Dx8fH4SHh1Pn676O4eL35cuXUVFRAZVKhdOnT+PUqVPQaDSor69HUVGR2X3Y2dnBzc0Nly9f7jCG7SlMLeKXl5ejsrIShw4dws6dO6FQKCCVSgF0PKdhYWEICwvDhAkTcP/990MkEsHe3h5AR0ehnsJwgRFoG++ePn0aEyZMACEEOp0OL7zwAs6dO4dz587RRSi9Xo/Bgwfjo48+QkJCQq9o+PnnnzFv3jxYWVlBJBJh2LBhcHZ2RkBAAAIDAxEYGAhfX1+Eh4ejqKgIkZGR1Anp8OHDdD+dOdJFRUUhJycHhBA0NjbCzs7OYnr++ecffPDBB1i1ahWGDx9O3+/rgS/tSUpKwu7du/Hiiy8aBSP01jVuCUpLS/H3339j586dOHnyZIfPJ0+ejAULFmDWrFl9/hnIaemb3KpaRo0ahezsbGg0GjQ3NxvZUwydftrDBpoFBwdj8uTJiImJwZAhQxAdHd1jTtWmeOedd7Bq1SqjdgLooMHa2hp2dnYQi8VwcXGBh4cHfHx84O/vj+DgYISHhyMgIABubm40GK0nnXosBfvsPHLkCBYtWgSpVAp3d3d89dVXuPPOO3ukDawdjL2+buQ5w37/77//xssvv4ysrCzcfffd+OCDDzoNquPg4ODg4Pj/DOcMw3FLcKPROEqlEhkZGcjOzoaPjw8mTZrUZ40zTU1NqKioQEFBATIyMpCamor09HTk5uaitrbW5HdMGaHZLBPPP/88Pvrooz5pxImNjUVGRgb4fD6dfPQlRo4cifPnz8PGxgZHjx7FiBEjOmzDXkeLFy/Gli1bjBxnzF1j7Ln47bffMGfOHGi1Wtx99934+eefLaaluLgY06ZNg0qlQllZWaeLFu2xt7eHv78/wsPDERERQR0y2KgRNotMT1xf5eXlePbZZ1FbW4u8vDyUl5ffkJawsDBERkb2qhaWGTNm4M8//4RAIIBWqzW5jZWVFYRCIY3mcXV1hbe3NwIDAxEaGoqwsDAazWPO2G0qGqYnYA317AJERUUF8vLykJaWhqtXr6KmpgYtLS1Qq9WwtraGSqWCjY0NZs6ciYSEBAQHB9MFl96kubmZZqk5duwYrly5gvr6emRnZ+P48ePQarXg8XhobGyk32E1G/bR9vb2UKlUEAqFGDJkCNzd3bF+/XpERERYrO1arRZHjx5FXl4eIiIi4OPjg8DAQNjY2NzwPjUaDbZu3Yr3338fhYWFAAAnJyfI5fLuarZJ+pMWoH/p4bR0Dqfl5ulPWoBbU88XX3yB48eP00wo1dXVqKuro5nO2sMumBg6XwNtWf7Cw8MRGxuLAQMGICoqCoGBgXBzc+sVx1dDqqurkZmZCbVajfT0dBw5cgQVFRVQq9XIysoymTEFAHV+5fP5SEhIgJ+fH8RiMYYNG4bx48f3yqKE4VzEcMHkyJEjuHr1Ko2Gbg877rr77rsxbdo0hIWFdcgs0tPjyqamJhQWFiIoKMjsWFer1WLlypVYs2YN+Hw+zSDJLmLa2NjgkUcewcaNG2Fra9sj7TYF6whvb2+PpqYmeiytra1hb28PR0dHODs7w8/PDw0NDTh69CgYhsHMmTOxY8cOAOh0kbWkpASDBg2CQqGAl5cXZDKZRfWoVKo+E2RwM6jVatr/9FVb0Y1iyo6Wnp6OXbt24a+//kJ6ejo0Gg2AtmdGTEwMZsyYgSlTpiAiIgK2trbUAa23F8I5LZwWS3D06FGUlZWhsLAQpaWlkEqlqKqqglwuR0NDA1QqldmxjjmnYDc3NwwYMACDBw/G0KFDER8fj7CwMPD5fItnmjp8+DC2bduGgoICpKenQ6VSmdyOdeYxbD+Px4O1tTXEYjF1Yh47dizmzZtHA7JupUxZ1+Lxxx/H119/DQBYu3YtXn311W7df35+Pp588klMnjwZ8fHxiIuLM5s57XqzCbHPqsceeww//PADmpubsWLFCixdutSiTrAcHBwcHBy3MpwzDMctzfUMxG/FQTshBHV1dZBKpcjJyUF6ejpSU1ORmZmJgoICNDc3d/gOmwJ6586duO+++/qcbqVSiejoaEilUlhZWZmdWPYmjo6OUCqVsLe3x+rVqxEfHw83Nze4ubnBwcGBpky2srKijjO2trY4ceIEhgwZYtYBiZ2wrF27Fm+++SYIIVizZg2WL19uMS06nQ5VVVVoaGhAeXk5ioqKkJubi5ycHOTl5aGkpAQKheK69unm5obAwEBERETQV0BAADw9PS1m8CeEQKVSoampCXK5HBUVFd2uJTIykjqXDBs2zCI6DPnyyy9x8eJFlJWVQSqVQi6XU2OLVqs16yDD4/EgEAiMDOdsdqjg4GDqJBMYGAhvb+8+bdBVKBSor6+Hg4MD3NzcOnze0/2XVqsFwzAoLCzE8ePHUVtbi7KyMpw8eRI5OTmwt7e/Zvk5NtMPIQSurq64++67IRQKERkZiVGjRsHNzQ1arRaBgYEWc75ij9vmzZuxfv16KJVKuhD01FNPwdvb+4b2a9i3qdVqfPHFF3jrrbfg4+ODrKwsizhf9ictQP/Sw2m5NpyWm6M/aQFubT16vR719fWoqalBZWUlZDIZpFIpXThis43U1taivr6+gxMMYH7RiC21NGDAAEgkEixevNiijqKmWLNmDX766SfIZDL6nG+fgdDQeZktl2BnZ4fw8HAkJibC19cXDMMgMDCwR9tujpKSEhQWFuKPP/6gWTnZAIv25SDi4+MRERGBqVOnYs6cOeDxeHQxpbedA3777Tfce++9iI+Ph5eXF82YGBYWhpCQEHh7e0MgEECtVmPOnDn4448/jM5VaGgoVq9ejXvvvbfXg0RGjRqFc+fOdXjf1KKkYUZBd3d3REVFUUcZNpNMQEAAfH194ezsDGtra5w/fx5jx46FWq3GpEmTcOjQIYtpMTVGN1zI66tlXLtKf9ICGAcpAG1ZV1NSUvDrr7/i119/RWFhoVF/FxcXh1mzZuG+++5DZGRkbzXbJJwWToslUSgUKC8vR0lJCYqKilBcXIySkhJUVFSguroaCoUCSqUSTU1NRvYatp9o35cbUlRUBH9//56SAqAtqCwtLQ3JyclITk7GlStXkJ+fb7KN5jLhODk54aGHHsLrr78ODw+Pnmq6xWDHNe+88w7ee+89aLVafPDBB3jppZe69Xe+++47LFy40Og9Ozs7REZGYtCgQRg6dCgGDRqEqKgos9nSOysp9u+//2LmzJloaWkBIQTJyckYNGhQt2rg4ODg4ODoT3DOMBx9Gq1WixMnTsDT0xMuLi4QiUSws7Prc5lOehrDUktZWVlITU1FWloasrKyUF5eDo1Gg5SUFAwcOLC3m0phjUkpKSlISEhAc3MzBg8ejIsXL/a6kdWQqqoqeHl50Qw7LB4eHggICKDG19DQUISGhmLixIloamqCh4cH0tLSTC7os7A677//fvz4448ghODgwYOYPHlyT0gD0HYeNBoNWlpaoFQqUVNTA6lUioKCAuTm5iI7OxsFBQWQSqUmna06Y8yYMTh+/LiFWt4RQy0NDQ2ora01qaWsrMyoXM21iIqKQnp6eo86YtTU1KCoqAh5eXnIzs5GXl4eiouLUVVVhbq6OjQ1NUGtVkOr1ZpM0wu0lYiysrKCjY0NHBwc4OzsDF9fX9ja2mLNmjW9bmQyNAyZMiob1h83PPY9eR42bdqEt956C3Z2dmhsbDTK9tIetu6zVquli2I+Pj6oqanBTz/9BACYOXMmDhw40CNtb09eXh4SExNRWloKAJgyZQo++eQThIWFddtvaDQanDx5Eo2NjZg1a5bFFpn6kxagf+nhtFwfnJbrpz9pAfqfHrVaDYVCgerqalRUVEAqlUIqlVJH3/LychphbeqZaspB5o8//sC0adN61HEhLi4OaWlp4PP5dE6iVqthb2+P+Ph4iMVieHl5YdKkSRgwYAB4PB78/f1NLl5cq8yipfn999/x22+/4fLly7hw4YLJbezs7ODo6Ii5c+ciMTGROoQb0tuOIyyGZUXZuZRQKKQO4Z6envD390d0dDTy8vKwfft2MAwDa2trPP7441i7dm2HKOn20dc9RXl5OVJSUpCSkoKLFy/i6tWrNJNTe3g8Hvh8PhiGMZqTCgQC2NnZQSQSwdnZGe7u7vD09ERUVBTS0tKwd+9e6PV6vPrqq1i7dq1F9Xz//fe0lJazs7PJbdj7AUCPH++u8sYbbyA8PBwxMTEICQm5pbXcCI2NjdizZw9++uknHD582MiZ0draGuPGjcP8+fMxYcKEPl9+g9PSN+nLWgzvaXM2BzbATCaTUQeZ4uJilJWVoaKiArW1tairq0NjYyNaWlo6lK5mnYTYDDmWxLAEj7lxCCEEubm5uHLlCi5cuIBLly4hPT0dlZWVZvdrZ2eHqVOnYv369QgJCbFU83uUzz//HMuXL4dSqcT69euxZMmSbrU7vfTSS/j444/p+AXoWK6KxcPDg5baGjp0KAYOHIiwsDCj54xKpaKO2lu2bMHGjRtRXl4OHo+HUaNG4eTJk30uGJaDg4ODg6MvwTnDcPRpzp8/jzFjxmDo0KE0hXhAQAAkEgk8PT3h5uYGJycnODg4XDPl8f+HQWFTUxMqKyuRn5+PxMTEPqWXNV5+//33HY+mnAAAx8lJREFUWLhwIfR6PR555BFs3boVWq22z6RXPnz4MG6//Xaj98xFsrJZeBiGgaurK9566y34+fnB398fbm5uEIvFsLW1hZWVldEkZvjw4bh06RL0ej2kUukNRwJ3J3q9Hmq1Gk1NTaivr0dVVRVKSkqQn5+PnJwc5ObmorCw0GR5InZCNmvWLOzfv793BBhgTkteXh5yc3M71QIA8+bNw48//mhxJ61r1V3W6XRmnXtqamrQ0NCAlpYWaDQamomkPWxkbHZ2drcutFkCwz7a0DGmJ5k8eTKOHDnS4X1ra2u0trbCw8MDQ4cOhZOTE1xdXTFhwgTExMSAYRg4OTnBzc0NX3/9NR5//HEAwJtvvolVq1ahpaXlpspf3AiLFi3Cd999BwCIjo7GyZMn4eTkdFP7zM/Ph1qthpeXF5ycnHrs/PQnLUD/0sNp6RxOy83Tn7QAt76e9pkrzKFSqSCXy1FZWYny8nIjBxmpVIqKigrU1NRAoVB0yBJZXl4OT09Pi2kwhYuLC+rq6sDj8SAWi3HPPfdg/vz5cHd3h7u7u9m09lqtljos9JW55pgxY3D69GkAbeNAvV5PF19GjRqF6OhozJo1C5MmTYJQKKRj3b4UnGDIxIkTcezYMfD5fOrE0h62tIONjQ2amprQ2toKX19fTJw4EaNGjYKHhweioqLg7+9vtgRnb5UV1el0yM3NRUpKCo3cT09PN5uJkJ1Ttl9Qs7Gxoc4aGo0Ge/bswT333GOxdrMBJA4ODhCLxfD19UVoaChiYmIQExODyMhIBAYG0oyq7TE8l71Z5kUmk0EikUAgEEAoFMLJyQk+Pj4ICwtDdHQ0BgwYgIiICAQFBfV5LdcL2272vq+ursbZs2exd+9enDlzBqWlpdQRy9vbG7fddhvuuecezJw5s1fLjZmC08JpuRHYYJ32dgfDIJ7OnN5Y+2tpaSmKi4tRVFSEkpISyGQyVFVVoba2Fo2NjaipqUFERAQyMzN7xdHUcBzABvSYorGxEZmZmThz5gySkpJw8eJFlJSUGNma5s2bh6+++qrTkn23Cl988QWeeeYZAMAnn3yCZ599tlv3/9JLL+GXX36hGd+bm5uvO5tQSEgIIiMjERAQACsrK2RnZ+PSpUuor6+n4+fw8HB8+umnmDRpUp8Zi3JwcHBwcPRFOGcYjj7N5s2b8fTTT3d4397eHp6enkZpgv39/eHj4wMPDw+4urrC0dER9vb2sLKy6vQ3uMFiz8AaWJcvX44NGzaAEILPP/8cTz75ZJ8yvp4/fx4fffQRGhsbqcOEXC43uz3rxGM4qRGJRPDz86PlasLDw+k1CgDjx49HZWUlzT7Rl9FqtWhtbaWLGqbKE8lkMlRXV2PFihVYsWJFbzfZLNfSUlxcjIsXL2LdunVYtmxZjxsqrpU5hUWlUqG0tJQ69+Tk5Bhdq42NjR1KLZmLQOltrhU9nZ+fDwC4fPkyJBIJRowYYdH2+Pj4oKKiAnw+H/b29pgyZQp8fX0RGxuLUaNGgcfjQSQSmXRgY/uxhQsX4rvvvgOfz8fOnTt7JS1/Wloa4uLiALSVAtuzZw/Gjx9/08+7zMxMvPTSSxCJRHBxccHrr79u8ei9/qQF6F96OC3XhtNyc/QnLUD/02NIVxwJ9Ho9GhoaaKklwywyMpkMpaWlUKlUSElJ6almAzDOCqnT6Wj0bmBgIOLj42nWlAEDBiA4OBguLi4mdfaWM0V7RCIRVCoVdR4RiUR49tlnMW3aNMTExHRwvtJqtZ0ujvU2q1evxqlTp1BSUoLy8nI0NDQYfd6+5BP7N8MwEAgEYBgG9vb2EIlEtKwoW2opPDwcwcHB8PX17bG5aFcXJRsaGpCRkYGUlBRcuHABly9fRnZ2ttnMnWwmGYFAgObmZos7wrMBJO3LiQH/y17j6uoKf39/REREYMCAAYiOjkZ4eDgkEonZ/fZ0eaK///4b06dPN/t5ey2RkZGIiYnpk1rMYapvau+k0J6TJ09i165d2LVrF5RKpdFnjo6OWL16dbcvHHcFTgunxdKYc5C51meGGJZaKikpQVpaGsLDw/Hss8/2maxrrB3GMEtae02ZmZnYv38/PvroI1RXV9NgwC+//JIG/1i6fZbKwtXU1IR169bh3XffhZOTE7Zu3Yq77767W3+joqICJSUlKC0tRWFhIYqLi6lDOJtNSKVSdcgmZK5clSFsJnM3Nzds2LABixYt4tY2ODg4ODg4rkHfSMXAwWGGs2fPmnxfpVKhoKAABQUFRu+7urrCy8sL/v7+NIuMn58fvL294e7uDmdnZ4jFYqNSSz01WNRqtZDJZHBxcYGDg8N1f7+3U27fLOxxTk9Pp+8NHjzY6LO+wPDhw7F9+3Y0NTVBLpdTpwPDmsFlZWWorKyESqUycoJhUSqVyMjIQEZGhtH7bDYj1rmmt8rWXI+xXiAQQCAQwN7eHh4eHoiMjOxQaqm2thb5+fkYOnSopZvegeuZ8HVFS3l5OYKDgwF0HgV0s5iq/WvOqNLeSGFvb4/IyMgO109NTQ2Ki4uNHJWys7Ph6OhoMR3XgymnNzZ9r1wuh1KphFqtxunTp3HmzBkolUqUlJQgOTkZarWalhuy1CS/qamJOsIAbdfLqFGj8OCDD8LV1bWDFnORn1euXAHQlk0mOjqa6uwpCCH49ddf6d/Tpk3D+PHju8XpMDw8HAzDYO/evQAAd3d3vPvuuze1z87oT1qA/qWH09I1OC03Tn/SAvQ/Pe0x95wzHHPyeDw4OTnByckJoaGhdBu1Wo26ujpUVFTQspY9adC/evUqbYchRUVFKCoqMnpPKBQiODgYMTExiIuLw8CBAxEeHg4/P78+kXGkqqoKKpWKOvYwDIOGhgZs27YNmZmZCAsLQ0REBGJjYxEUFARXV1eT2Tn7imMPALz11lv0/xUVFcjKykJ6ejquXr2KrKwsFBYWoqamxujaYf9lz2lrayvkcjmKi4uRkpLSodQSW1Z0w4YNiImJsageUw4wphYlxWIxRo4ciZEjR+Kpp56i28pkMly9ehWXLl3ChQsXkJqaioKCAupkw5bhsHRGyNLSUnpPNzQ0GM2JtVotGhoa0NDQgMLCQlpGVygUQiQSwcPDA8HBwYiKiqKOJaGhoXB2djY5/7Jkf1BTUwN3d3fY2tpCoVCgqanJqIRLV7Swkftd0WJJ2h+nzhbt2bax9wmb9UalUqG6uhqhoaH4+OOPsXjxYqxevRp///03NBoNhEIh6uvraTkVSy3sc1o4LZbWArSVevP19YW/vz+8vLxgZ2fXqU0G6GizMRXQ5OzsDGdnZ2oLYLcDLGtjuh7MlVEy1BIVFYWoqCjcc889ePbZZ/HPP/8AAFauXIlHH33UolpMta+rwWOdwV6zFRUVSEtLA9CWlSgiIuLmG90OLy8veHl5Yfjw4fQ91uZkLpuQXC5HfX09zXJnDrVajbFjx2LlypUYN24cgL4xZuPg4ODg4OjLcJlhOPo0x44dQ1JSEoqLi5GTk4OCggLIZDKTtVY7K2Xj7u4OHx8f+Pv7IygoCP7+/pBIJPDy8oK7uzuNpLxWFpmb4ciRI3jnnXdgb28Pd3d3LF++3GhydCOwhrNbKR1vbGwsTQ9aX18PkUjU203qEmzpHaVSierqahQVFSE/Px8lJSUoLCxESUkJpFIpampqrlkL2MbGBi0tLbRMVF+nLxnEb4auRvL0RDva/75hbeeu3M+sFsPvmEKn06Gurq6DM4clMeXoY0h5eTmsra2Rnp6OEydO0LJPSUlJkEql9P5gYSOQcnNzLVqfurKyEvfeey9OnDhh9H5MTAyWLl2Khx56iL7XmVHO1taWpufPzs6GnZ2dxdpsipqaGtx33304fPgwoqKi8MEHH2Dq1KndloHr3LlzGDVqFIC2BdisrKyb3qc5+pMWoH/p4bR0HU7LjdGftAC3tp5Tp05BLBbD3d0dDg4OsLGxoRk3boSullrqKa43K6QpnJ2dERYWhtjYWMTFxSE2NhahoaHw8vLq0VKwpsq9mqOvO/awXCtzjVarRWFhITIzM5Gamoq0tDRkZ2ejrKwMdXV1HQIXTNkM+mpZUdZBhh3vm+sr2FJLly9fxpkzZ6BQKGg5NkuRm5uLpKQkmmlTKpWisrIStbW1qK+vR2NjI1paWkzaZwzPAY/Hg42NDXVKMiy1FBUVhYCAAIuWGq2trUVmZibKy8uNIve7U8uAAQMQFhaG0NBQCIVCi2kBALlcDkIIXFxcQAhBTk4OqqqqYGVlBQcHB1qCVy6X0zJq7HsNDQ0QiURQq9U0k4GTkxOsrKxQU1MDvV4Pa2trtLS0YO/evZg9e7ZFHZX6u5bq6moIBIJOtRQWFqK+vh5isRitra2clm6koqICQUFBCAoKgpOTE7y8vODr60szjwcEBMDX1xceHh5mn+NdySID9B0HmBuB1XfkyBEsWrQIUqkU7u7u+Oqrr3DnnXda7HfZEvSRkZEICgqCr6+v2eN4PVm42HH3Dz/8gNdeew2lpaWYNWsWtm/fftNlS6/Vts6uA7lcTrMJGQZhstljmpuboVKpoFQqkZiYiJkzZyIhIQFubm7d3mYODg4ODo7+CpcZhqNPM378eIwZMwatra1oamqCQqFAVVUVzX7AvoqKilBdXW1yHxqNBjKZDDKZDBcvXqTvs6WWAgMDYWdnh1dffRW33XabRXQ0NTXhs88+o/XbBwwYYBQReS1KS0vx+++/o76+Hi4uLnBzc0NMTAwiIiJuqUwxMpkMhYWF0Ov18PLygkgkumVSObLGLRsbG7i7u1NHJp1Oh+bmZtTX16O8vBz5+fl08lJUVITS0tIORnV2oX/IkCE9rqOkpARyuRxOTk7w9PSEra2t2XPQF51gmpuboVQqUVpaipqaGri4uCAwMBDu7u7X/G5vO8EYtuOTTz7BgAEDEBsbC3d3d7NROWxkYnsnGXNa2kfL8Pl8izrCmFrEYyf59fX1ANr6v8OHDyM7OxsNDQ1ISUnB+fPnYWVlBZ1O1yHipf3fbNp3SzrCAG2Zm44dO4aDBw/i3Xffpf11eno6Fi1ahI8//hgvvvgi5s6da9YoX1paitbWVvB4PEgkkh53hAGAwsJCWl6KNcAD6LZnBRvddP78eWg0GiQnJ1usL+tPWoD+pYfT0nU4LTdGf9IC3Np6xo4dCxcXFwQFBSEkJAQREREIDQ1FYGAgPD094ezsDHt7e1hbW3dJT2dZZHpjnHa9WSFNoVAocP78eZw/f97ofYlEgsjISFpuaeTIkTcdDNEZYrEY8+fP75Jjj1qtRlZWFrKysmhWIaDvOPawtP9NdmEJaBsjCgQChIWFISwsDLNmzaLbKZVK5OXlIT09HampqUhPT0deXh4qKiqgVCqNHBu0Wi0YhrG4I8z1ls3pbOHP0Pmcz+fTrJHz58/v9nabgj3mLI2NjbQkhOE8mC2nK5fL0dDQ0CHaXa/Xo6mpCU1NTSgvL6f2Gh6PB0dHR2i1Wrz55ptYunSpRXS4uroiISHB6L3GxkaUl5ejtLSU9gGmtKhUKqOMUua0WFtbo7W1Fa+//jreffddi/R1mZmZeOedd2BtbY3S0lKaLcjZ2RnNzc0oKSmBRqOBlZUVrK2tO5RqNuUkxjAM5HK5USky1o4xcOBAuk13w2nhtFhaC9CWFa61tRV5eXnUaVIgEMDW1hYikQjOzs5wd3eHt7c3JBIJAgMDERgYCD8/P/j4+NCSieba11dsTzcLqyExMRFTp07F119/jerqamRlZVnMGaa6uhpr1qyBWCyGjY0NnJyc4OHhgcDAQISGhhqNQ11dXbvkJMNuw+fz0dTUhK1bt6K0tBRA27qDpbIpm8t21j7DjYuLC1xcXIyy02m1WsjlcurY6+fnB5FIRO8zDg4ODg4OjuuDc4bh6PPw+XzY2dnBzs4Obm5uCAsLw+jRo6HVatHS0oLGxkbqRV1QUEBLhLBZO9pPwljal1p6/vnnLaYhPT0df/zxBwDAx8cHb7zxBoRC4TUNIXV1dVi9ejV++OEH1NfXG9UIt7GxQVRUFB544AE8/PDDFvFi725qa2vh7++PrKwsBAUFAWhbTO8Nw2p3wefz4eDgAAcHB/j6+mLo0KG09A5rVC8rK0NeXh6Ki4upQf3ixYtG6TItAZu9ori4GJs3b8aePXsgFAohFovposn999+PwMBAk99nr83y8nJYWVnB0dGRTrq6K5L5enScOXMGn376Kfbt2wcrKyv8H3v3HR9F1f0P/DOzJXXT26b3Sq+CUhRFUYrCg4CKomJBbNgb8gj2go1iQVAfUJGvFVAEFBSQGloSEtLLJptNTzZ9y/39kd9cs2kEyCZhPe/XKy9ld3b2nmzJzJ1zz/H09IS3tzeGDh2KefPm4corr+SPafvZamhoQE5ODmQyGby8vHjp6r7o2VxcXIxHH32U/9vFxQVxcXEYNmwYhg8fjmHDhiEmJgYODg4dfjbaToB3p9WStchkMj7xq1AocPr0afz999+oqqpCTk4O9u7di5KSEtjb26OqqsrisQaDAYIgQKFQwGg0ws7ODs3NzZDL5Rg9ejSCg4OhVCoRExODsWPHAuidC2XXXHMNIiIisHnzZmzevBmpqakwmUw4ceIEli5dikOHDuGOO+7AoEGDYG9vz18PuVzOWy0wxngbq978rAAtf2+kstLDhg2Dj49Pj+3bZDIhJCQEgwcPxpEjR1BYWIjk5GQMHz7cKq+NLcUC2FY8FEv3UCwXzpZiAS7deDIyMgC0JHtUVFQgMTGR3yeTyeDv74+wsDBER0cjOjoa4eHhCAoKgre3N1xdXeHg4AClUtmtY62+vGikVCqhVCrh5uaG8PBwTJgwoUeqQmo0Gmg0GuzevRsAMG/ePGzatMlqcdhSYg/wzwWj1u+fri4sSReXZDIZVCoVhg4diqFDh1psW1xcjLNnzyI5ORlJSUlIS0vDmTNn4Ovra9VYzjX2rrbpaD9dtVqSyWRW/zy1rYjg7OyMyMjIdgt+ysrKUFRUZPEeLCgogE6nQ2lpKaqqqlBdXY3GxkaLRQCMMZ5s4uLi0uuxtE326SqW4uJilJWVoby8nCf7SEkyrRMApFa81rB7925s2bKl3e2FhYX8/wVBgMFg6LDNc0dJCqIo8uqloijC19cXBoMBVVVVFEs3USz/6E+xAODVYFuP1Ww28+obRUVF/HY7Ozs4OjrCxcUFHh4e8PX1hVqtRlhYGOLi4hAdHY2QkBD+XXWpLPjrLmk+IyAggFdSs0aVK2luTmpfVF9fj5qaGpSUlCA9PR0HDx6EXC6HnZ0dT1hqfRwqVeCSKty1/jspfc/rdDrcdddd2Lt3LwRBgFKpxOzZs632etXV1cFkMsHJyYnPCXVnYZuU7Ovj49PunIESYQghhJALc+legSb/atIFVIVCAZVKBbVajYSEBN4nu6GhATU1NSgtLYVGo0F2djbS09O7bLUkrTywhu3bt/PnmzBhAubMmcPj6Exubi4efvhhbNu2DcA/lUmklVSNjY04ceIEsrOzUVBQgGeffbZb1TH6UnR0NLZt24bCwkJ+ongplwztjHRS1XpSffz48RaT6iUlJfxiuTVIEwoHDx7EggUL+MWM1rZu3YoVK1bgo48+woIFC/hJu8FgwO+//44NGzZAr9fzk82IiAhcccUVmDRpUq9d3Jfi+Pbbb/HQQw+htLQUoiiiubkZdXV1yM/PR2JiItatW4fly5fjueee4yeXWq0WP//8Mz755BNUV1fDzc0Nrq6uCAsLw/jx4zFr1qxOy8BbU0pKCoCWSRWDwYCamhocPnwYhw8fttjO398fCQkJGDFiBIYPH47BgwcjIiKi01Lx59tq6WJt374dmZmZqKiowF9//YXTp09DoVCgsrKy3fdr67ZHrSdQpOo3jDHMnTsXzz77LBwcHGAymRAUFNQuzt6YVBJFEZGRkXj++ecxduxYvPXWW9ixYweAlspKa9euxU8//YSHHnoIDzzwAFQqFR9n6+pjrVf19KaSkhKenOTl5dWjE1XS71+Kt3X/d2uwpVgA24qHYukeiuXC2VIswKUbT2pqKoD2F7GAluOOgoICFBQUWLQYdHJyQmBgICIiIniSTFhYGPz9/eHp6QmVSnXRrZZ6Q09WhZSMGzfO6uO2lcQeoP0Fo84qq3R1YUlKIJeOj/38/ODn54cJEybw7YxGI8rKyqwWR0VFBW6++WYMGjSIr2oPDw9HYGAgFApFt6o9nuuz0lUbJWs4V1sQ6X4vLy94eXlh0KBBfLvm5mZe7bewsBDFxcVIS0tDXV0d/vrrLxQUFFh857RNaOpvsWi1Wmi1WlRXV+P48ePIz8/Hrl27kJub22Ec1vjea52o2BGpWmV9fT0YY/z7uLGxEXq9HoGBgQgLC4OrqyuampqgUCh4uxgnJyfU1dXByckJYWFhnS6koVgolkslFgBYsGABfHx8kJKSgsTERKSlpaG+vr7DbaUEncrKSuTl5QFo+Rw7OjrySsVjxozB3LlzMWTIEAiC0CeLrqxF+tvi4+MDBwcH6PV6GI1GqyX9JCUlAQBPkATAf6dNTU1oampCTU0NCgsLkZKSAplMBoVCAQcHB7i4uMDLywv+/v68qo+vry/q6+uxd+9e/PXXX6ioqOAL455++mkEBAT06Pil38uRI0fw6aefwtHREaGhoRgyZAhGjRrV6RxkV8cy/fl4mRBCCLlUUDIMsSmiKMLOzg52dnZwc3NDcHAwhg8fDpPJxC+eV1VVQafTIS8vD5mZmUhNTYVOp7NaIol0oRgAvL29eSJMRydH0kFuWVkZ3njjDWzbto1PFBsMBjQ2NvIeu01NTZDJZKiursZ7770Hs9nM/9uXJ11dHajb2dkhPDzcYpWHrZwgdkfbSXVrEgQB5eXluO2225Cbm8ufX5oQlhiNRjz88MOIj4/HqFGjkJeXh/vvvx979uyxKPsskVZZLl26FJMmTbJqDFIcOTk5WLx4MS+h2zYGoGV1xIsvvoiRI0fi2muvxd9//40HH3wQJ0+ebLftnj17sH79ejz77LN45ZVXsGDBAqvH0VpeXh5EUYSjoyNqamos7pNOgBljvL3brl27+P2iKCIqKgqDBw/G8OHDMWLECCQkJMDHx6fTVkvWOnFetGgRNBpNp/eLogiFQoGmpiYEBQVh8ODBUKlU8Pf3x5gxY3Ddddfh2muvxYEDByCXyzF//vx2KzB7u6qKRPq9XXnllbjyyivx66+/4rXXXsP+/fsBtLR8e/bZZ7Fq1SosXrwYd955J3x9fXHq1CkALQk/UjJMb05cmM1m6PV6AICbmxscHBx6dP/S97W/vz8cHBwsqpX1dJy2FAtgW/FQLN1HsVwYW4oFuLTjiYuLw6pVq1BRUYHs7Gzk5uZCo9GguLi4y+qbZ8+exdmzZ/HLL7/w2z09PRESEtKuxL2vry88PT151T5Jf538v5iqkCNHjuyTMV+qiT1ff/01rzbS9v0h6SjhRdJZgkhHrZb8/PysFAVw/Phx/PHHHzh06BC/gOri4gJfX18EBQXxKiRRUVEICwuDr69vpxfFzrfVUm/qTlKSTCaDUqlEYGAgAgMDAbRUXDl8+DDeeustlJSU8MUQUsUGa1cg6khXv3+psq20ACYkJARBQUFITEyEl5cXvv32W+Tl5fEqPVKCWUxMjNXGq1AocPnll/M2tMHBwQgNDYWLiwsaGxvh4OCA4OBgREVFgTGG2tpaXgFHpVJBr9ejsbERXl5eff6eolgolt4QGRnZrjq4VqtFUlISjh8/jmPHjuH06dPIysrqcP4JaKk+LCUF79u3D2+++SamTZuGd955p12VLFsgCAI/nnVwcOjx11H6+7148WLMmTMHubm5SExMxL59+3DgwAGUlZVZLLKSmEwmGI1GNDQ0oKKiArm5uRBFsV3ltdbMZjNmzpyJF198sUdjAFp+T01NTbj//vv5fKQoivjll18uaDFef/i8EEIIIbaAkmFIv9b6wPViDgBlMhkcHBzg4OAALy8vREZGWrRa6uiif09JTU1Feno6AGDMmDEYMWIEgI7jMZvNkMlk+PTTT/HVV1/x2w0GA78o6+npCb1ej7/++stigvmLL77A1VdfjalTp1pt8ripqQnffvstgoOD4eXlBQ8PD6hUKjg6OvITl66et79Oatui+vp6fPDBB8jJyeFVQkJDQzF9+nTU19cjOTkZycnJ0Ov1qK2txaefforY2FjMnDkTJ0+e7PSkUXrvPf7441i9ejUuv/xyq8ZRXV2Nd999F+Xl5RBFEUqlEiNGjMCcOXNgNptx6tQp/PXXX8jMzAQArF69GgMHDsQtt9yC/Pz8TvcrVY559tlnYW9vj7lz51o1jtbGjRuHDRs2oLi4GLm5uSgsLIROp0N5eTmqqqpQV1fHT/KlKlhAy0m+yWTiF5i+/fZbvk+VSoXY2FgMGzYMI0aM4K2WHB0drfK5KyoqgkajgZ2dHYxGI0RRhMFggLOzM0aPHg1PT094enpi9OjRuOyyy8AYg7u7Oy/xKo1JSqZxdnbmSXKtE/r6IhEG+Od7zGw2QxAETJkyBYMGDcIPP/yADRs24MSJEwBaykK/9dZbOHDgAJYsWcIrwzg6OvIJ7978zhNFka9oky7KSXH0VOKhdBGmoaGBV2myBluKBbCteCiW80OxnD9bigW4tOORLtBLrWmrqqpQXl6O4uJiFBYW8qQJqbJISUlJp89dXl6O8vJyHD9+nN8mtVoKDw/HoEGDcNlll2HSpEnw8fHhfz8vhfOH/lAV8kL058SekpIS3HrrrXB2doaLiwsCAgIQGRmJhIQEJCQkIDY2FqGhobCzs+tWwkvr91BvL8aQ3vNGo5Ev0CktLUVWVhZkMhnkcjmUSiWcnZ3h7u4OtVqN0NBQREVFITo6GpGRkQgJCbGoRtha6zZRvVEd8nxJcxytZWRkYOvWrUhOTsaPP/6IqqoqyOVy/rpJFQG8vb3h7OzcF8NuR/rOlhaY1NXV4dSpU/jyyy+RmpqKgwcPwmQyWSxuMBqNUCgUiI+Pt9p5GQA888wzcHZ25u2+pIvD3a0SplKpoFKp+GNbv49aJ5v1xnuLYvkHxWI90neN9JmUyWRQq9VQq9WYPHky344xhoyMDJw6dQpHjx7F8ePHkZyczBP3WhNFEb/++itycnLw8ccfY8yYMb0SizROae7CGn/j6uvrodVqAbQkdvv7+/f4c0hkMhl8fX3h6+uL0aNH44EHHgDQUm1Yp9Ph7NmzOHz4MPbu3YukpCRUVVW1a83VUSKMTCaDyWSCUqnEo48+iiVLlvCkmZ583zU2NuKDDz7AyZMnIZfL4e7ujs2bN2PixIkXtd/m5martKcihBBC/i0oGYb0a50dkPZEkox0kdna/TYTExOh0+kAAGPHjuUnjx2NWyaToaamBhs3boRer4dSqURzczOWLFmCp556yqKX+aOPPootW7bg0UcfRXl5Oaqrq7Fv3z5MnTrVaieQR44cwR133IGQkBD4+PggJCSElzYNCAiAr68vvLy84Obmxkugt9ZR+eH+NllnK1JSUnhPZ4VCgfnz52PlypU8May6uhrffPMNnnrqKej1euzYsQPh4eE4ceIEf00GDRqEsLAwVFZWIjc3F0VFRXxC7/Tp03jiiSewc+dOPqlhDcnJydi+fTuAlgmUhx9+GC+99BKMRiPkcjkMBgN27NiBxx57DFlZWXxVYX5+PgRBgKurK0aOHIm4uDjU1dUhPT0dZ8+eRWlpKWQyGXQ6HV588UVMmDABarXaanG01rYHfW1tLYqLi5Gfn88vKuXl5aGyshL79u1DVVVVpytbpYkOvV6Po0eP4ujRo/j444/5/Y899hjefvvtHo9BKl0rTQZPnDgRH374IVQqFRhjfKVnW60nvhoaGpCXlwdBEODt7Y2QkBCLmPqD1mMJCAjAgw8+iPj4eKxZswY7duxAfX09KisrsX37dvz55598lbyXlxfCw8M7TSqzJimRqqqqik8s9uQF14aGBv4cHh4e7b7ne5ItxQLYVjwUS/dRLBfGlmIBLv14pKoi0qICoGX8DQ0N0Ov1qKioQFlZGbRaLQoKCiySZLRaLcrLy8/ZaunPP//Ehx9+CFEUMW3aNDz22GMYN27cJX2u0JtVIXtKf0nsOX36NICWC3C1tbUoKirC0aNHAbRU4HN0dISnpyeCg4MRExODAQMGID4+HtHR0QgMDOz0s9UXlVWkC4gdfQZat33Q6/XQarVITU3lSTIODg5QqVTw9PREQEAAwsPDefsxqdWSUqnsV58T6ffb+iKzXq9HQUEBjh49ivXr1/PzHuCfZCXpYqavry/i4+Nx2WWX9XoFz9bafkeLoojCwkIcPXoUJ06cwJdffoni4mLexrp1Ah9jDLGxsbjqqqswbNgw3HjjjVYda0REBICWhCuZTMZ/pAvkrRMPWrd/6kjbc87ePjejWCiW3tDV3wjpOE1KfpO+c2fPns230+v1OH78OP7++2/s3bsXp06d4gkyycnJeOyxx/D999/32hxTR/NF59turyPS93hxcTGSk5MBAGq12qqVrjpjb2/P56AnT56MpUuXgjEGvV4PjUaDpKQkHDhwAHv37kVxcTFPxLS3t0d9fT2USiVmzpyJG2+8EePHj4dCobDKnPSpU6fwwQcfAABcXV3x6KOPYuLEiRd93H/s2DGUlZXBz88PHh4eNll9iBBCCLEmSoYh/darr76KwMBABAQEwM/PD97e3nBzc+tysudcJ129zWw2IysrCyaTCa6uroiNjT1npYMtW7bwKhfNzc0YPXo0li5dCjc3N4sLyUqlErfeeisOHjyIjz76CGazGQcPHoRWq7XaCZdU4lGa5JYmJAHAyckJfn5+CAwM5AkywcHB8Pf3h4+PDzw9PeHq6gonJyeegNTV69hfXsNL1f79+5GRkQEAuPHGG/HSSy9ZrKpzdXXFfffdB6Cl3U1FRQU+/PBDAC39zFeuXImIiAh+sqbT6bBnzx6sXr0a2dnZkMvlOHz4ML766iu+H2v466+/kJOTAwD4z3/+gyeeeAJAyyQ40JLoM23aNDQ2NmLOnDmoqanB+vXrAQCTJ0/GmjVrEBQUxFsGVFZW4uDBg/jggw9w8OBBKBQKZGZmYt26dVi6dKnV4mit9QokQRDg7OzMS8BLpFWFERERqKqqglKpxC233IKmpiZoNBqUlZWhsrISer3eoh0C0PK5srOzQ2NjI5+06mlpaWkAwJ/7999/x3333YelS5fi6quv7vRx0oQZAP7+FAQBYWFhAHr2wqC1XHXVVbjqqqvw66+/YsWKFTh06BCAlqQmabWRvb09qqqq4Obm1uvjU6lUPJEyLS0NTU1N3V6x1xXpPVtRUcH/Rnl7e8PLy+ui990ZW4oFsK14KJZzo1guji3FAthOPK2P0UVRhJOTEz8HkEhVRaqrq1FRUYGSkhIUFRVZJP1KrZakcvttn+OXX37BiRMn8PDDD+Ohhx6y+uIFcm69ndhTUFAAURTh5uaGmpoai1XfRqMRNTU1qKmpQU5ODv78808AgFKphEqlgo+PD8LDwxEXF4eEhATEx8efs9WSNc893333XTz77LPIyMhAcnIyTp8+jdTUVOTk5KC0tJRXi2pNqrrQ2NiIyspK5Ofn49SpU7zNkIODA1xdXeHj44OgoCDExMQgICAAV199tUVL4t4ktTcVBIEf0x85cgQ7d+5EUlISfvrppw4rR8nlcoiiiIkTJ2LGjBmIi4vD+PHj+yCCFlIcoiiioaEBBoMBe/bswTfffIOcnBwcOXIEQPsWxK6urvD29sbEiRNx6623IigoiJ/jSKw9xyGdI7d+vrbzT5fKPAvF0j/ZUiwdkRJgWpMSfqS/FaIoQqVSYcKECZgwYQIWLFiAXbt24e2330ZycjIUCgUOHz6MtWvXYvny5b0y7qVLlyIoKAixsbEICwtDQEBAp5XCzicpVEooOXz4MBITEwG0LO4KCAiwWiznQxAEuLi4ID4+HvHx8ZgzZw6Af44TSkpKUF9fDw8PD6jVat7Ku/Xje9rWrVtRVFQEAJg+fTqefPJJABef9FVfX48bb7wRarUa0dHR+PjjjxEdHX3R4yWEEEL+NRgh/VBaWhoTBIH5+vqy6OhoNn78eDZnzhz2+OOPs5UrV7Jvv/2W7d+/n2VkZLDKykpmMpm63J/ZbGZms7mXRv+PhoYGdttttzFBEFhkZCTbu3dvl9uXl5ezG264gQmCwARBYM7Ozuzbb7/t8jHZ2dnM0dGRCYLAFAoFq6ys7MEILN133318bN398fT0ZAMGDGDXX389e+CBB9gbb7zBvvrqK7Znzx6WnJzMCgsLmV6vP+drSM7PTTfdxF8D6T3U+ncs/f/Ro0dZTEwM33b06NHsr7/+6nS/KSkpbNy4cXz7yZMnt9t3T5o6dSp/rq1btzLGmMVnWXre5ORkNnToUL7tNddcw5KSkjrdb1VVFZs5cybffuLEiVaN41zMZjMzmUzMZDJZxCeKIhMEgYWFhTHGGDMajaygoIAdOnSIbd68mb3xxhvsgQceYDNmzGCXXXYZi4qKYj4+PszFxYUJgsAOHz5slfFqtVr22muvscGDB/PfoUwmYyEhIezBBx9khw8fZo2NjTy21oxGI2OMsc8//5wJgsBEUWSPPvqoxX39mdFo5DFVV1ez9evXs5EjR/LfgyiKzN7eno0fP56tWbOG5eXl8cf2xt+h3bt3Mzs7OyYIAvPw8GDl5eU98tzSZ2P37t1s0KBBTBAENm3aNJaVlXXRY+6MLcUiPZ+txEOxnBvFcnFsKRbp+Wwpnra6E0dDQwMrLS1lmZmZ7NChQ+ynn35iq1evZk8//TSbNWsWGzJkCPPw8GCiKPLjH5VKxX744QfrB0D6nfT0dPbll1+yN998s8NjXUdHR/4+afvT+naZTMacnJyYv78/GzlyJJs3bx57+eWX2Q8//MDS0tJYQ0NDn8VoNptZdnY2++WXX9jrr7/ObrvtNjZy5Ejm6+vLlEpll3G1/pHJZEylUjFBENj//ve/Xh1/W0VFRezEiRPsjTfeYAMHDmSBgYGdjj86OprdcMMN7O2332ZarZbV1tby/fTmOVlHz5WcnMw2btzIFi1axFxdXZmDg0OHv3dBENjll1/OnnnmGbZly5Z2+2p7bmctaWlp7NSpU0yn013Q+ZTZbGZGo7FfzMlQLP+gWC4tdXV1bP78+fw7wsvLi+n1eqs/r06nY4IgMFdXV+br68tiYmLYuHHj2Pz589lLL73EvvnmG3bs2DFWVlbW5X5MJlOHr1FdXR276qqreFzvvvtun8yvXwp0Oh0bM2YMv56xc+dOxljP/E1rampiCxcu5K/DCy+8wBjrnTkmQgghxBZQZRjSL504cQJASxWHkpISXkEAaCmN6ObmBm9vb/j5+cHf3x+BgYEIDAyEv78/r0Ti4eEBJycnAB1ne7MeaLV0LgaDAdXV1Xzc0ooJ1mb1mbRy6vTp09i9ezdfZTRgwADMmjWry+fw9vaGt7c38vPz4ejo2OFqq57y8MMP45prrkF+fj7S09ORnp6O7OxsFBUVwWAwdPiYiooKVFRUICUlhd+mUCjg5eWFgIAABAcHIywsDMHBwQgMDIRarYabmxsCAgKs2n7H1klVexwdHXHZZZcBsFyJIP1/dHQ0/5wAwF133YVx48YBaF+lgzGG+Ph43HLLLTh69Cgv6Z2fn4/g4GCrxCGtPlGpVBg6dCgAy8+rNL7WVWwAYMGCBRgwYECH+zQajXB1dcXNN9+MPXv2oKqqCgaDAenp6X22sqL1ihzWqnIKYwyiKPK2QzKZjH/fjR49mj9earVUUFDA2yxlZWVh0KBBVhmvn58fnnnmGYwcORIrV67E7t27YTAYkJ+fj9WrV+OPP/7Aww8/jDvuuIO3apDeT1J80msrCAIGDhxolXFaQ+uVby4uLliwYAGioqKwbt06bN++HZWVlWhubsa+fftw+PBh7Nu3D4sXL8bll1/eK6vkRowYATc3N149aNmyZfjwww8v+rml123r1q3Izs4GAAwYMAA+Pj4XPebO2FIsgG3FQ7GcG8VycWwpFsD24mmrO3G0brUkVa6TWi1VVlby9iObN2/GgQMHIJPJUFtbi+effx4TJkyAu7u7tcMg/Uh32ooWFBSgqKgIpaWlqKioQE1NDerr63nbGqDlPVZfX4/6+npotVocO3YMQMs5hKurK4xGI1544QW+ctuapNX40vML/786YlhYGKZMmcK3q6urQ3Z2NlJSUpCUlITk5GRkZmZCq9WipqbGohoJ0PI9IMXcG60rWleAkeY1duzYgd9//x2nT5/Grl27Onyco6MjAGDmzJmYMmUKoqKiMHz4cIttWv9+rE06NxFFEbW1taitrcW2bdvw/fffo6CggM9ftP1+8/f3h4eHB6ZPn46bb74ZHh4e/Fyt9RxTb8Vx/PhxvPTSSyguLoanpycWLVqE66+//pwViVvrqLoH0PsVeykWSxTLpcNoNMLR0RFPPPEETp48ieTkZJhMJnz33Xe44447rPrc0ndVfX09r4aSnp6OgwcPQi6Xw97eHs7OznB3d4e/vz/CwsIQHR2NqKgoREZGIigoCE5OTu3m/hhj0Ol0uOuuu7B3715eoXz27Nk28ZpZQ1JSEr9+ceWVV/I5zIv9W2AymaBUKjF27FisX78ejDGkpqZe9HgJIYSQf5Xeyroh5Hy8+OKLnVYa6WxFlEqlYiEhIWzkyJFs+vTpbNGiReyVV15hn3/+Odu1axdLSkpixcXFrKmpqVdjueKKK5ggCMzNzY3t27ePMdZ55vbjjz/OBEHgK8FWrFjBGOu6WkJ+fj6vJhMdHc1Onz7d80G0YTQaWX19PSstLWUZGRls//79bNOmTey///0vu/XWW9moUaOYj4/PeVeRcXZ2ZjExMSwhIYH99ttvVo/DVtXV1fHPSkxMzDm39/X15a9B6yoWncnKymLx8fFMEASWkJDATp061RPDbqempobHER8ff87tvby8eIWkwsLCc25fXFzMV1FHRUWxEydO9MCoL570ed+yZQuP/95777W4T6ok01erQNo+79atW9nYsWPbfaYjIyPZmjVrmMFg4NtKMUjbOzg4sIMHDzLG+q4yT0/Zvn07GzFiRIffb1OnTmV79uyxevUbg8HAli5dygRBYHK5nDk7O7MNGzb0yPNqtVrm4eHBY9q/f38PjLhzthQLY7YVD8XSPRTLhbOlWBizvXguRneOXT766CN+DCSKotUq3ZH+q7vHuqWlpezUqVNs69at7MMPP2RPPPEEmzNnDps4cSJLSEhgAQEBzNnZmcnlcov5BFEUebWmjz76yGpxVFVVWVQ8aUuK02g0nvP7oKSkhO3fv5999NFHbPHixezKK69koaGhzMnJicfW1XP1pLy8PLZ371725JNPsoiICH4e1vr3K/176NChbO7cuezzzz9ner2e1dXV8f30dVXIw4cPszVr1rA5c+YwBwcHplAoOp2ruOGGG9grr7zCdu/e3W4/fRnHo48+yqvUuLq6suTk5C63lz5TJpOJ5eXlsQ0bNrDbb7+d3X333ezll19mv/zyi0W14d4836RYKJZLmfQ98Mgjj/DvDWlO15rzHO+//367ufK238Otb1coFMzR0ZF5enqysLAwNnLkSDZjxgx2//33s5dffpl9+umn7P3332c33XQT8/T0tKiEtWzZMqvFYQtee+01/rt+++23e3z/mZmZbODAgUwQBDZs2DCWkpLS489BCCGE2CpKhiH9UnV1NUtMTGRbtmxhL774IrvppptYbGwss7e3P68EGblczjw9PVl0dDQbN24cu/nmm9ljjz3GVq5cybZs2cL27NnTrYvmF2PUqFF8PFKZ747a1Rw/fpxfmBcEgcXGxrK///673fZtZWVlsSlTpjBBENiYMWN6JRmmI2azmTU3N7OamhpWVFTEkpOT2c6dO9nHH3/MnnjiCTZ9+nSWkJDASzifK9lpz549fL/k/Bw/fpz/LqdMmcIY6/w9VFtby7eNjo4+576l/QQHBzNBaCn9mp+f33ODb+XIkSN8bFOnTrV4/rZaJ87ExcWdc9/S+yooKIgJgsDc3d1ZQUFBzw3+IkiJI8888wyP6YMPPmCMdT3R2lmrJWtq/Vw6nY6tXbuWDR8+3OLz7OXlxW688Ub2448/sqqqKv5Yd3d3JggC8/HxOWfJ3v6u9fvSbDazjRs3Wnz3y2QyZm9vz1xcXFhGRobVxiG9FsnJySw6Opo/v6enJ9uwYYNFMqhUAvlc75XWLaGmTZvG4xk+fLjV4mj9vLYQS+vntoV4KBaKhWI5P7YWj7VILRQYY6ysrIzNmDGDCULLgofXXnutj0dH+oPuHus2NTWxgoICtn//frZ582b2/vvvs0WLFrHbb7+dhYaGMplMZpEMY81kq2effZa98cYbjDFmkRx+LtLn4VxtRcxmM8vJyWHbt29nq1evvujxnsvWrVvZvffea3Gc2/ZHakv1yCOPsJ9//pmlp6e3209fJ8CvW7eOXXHFFSwkJMTivEW64CsIAgsODmaXX345W7lyJTt79qzF+Upfj19iNBqZr68vT/Zas2ZNt8ZWW1vLXn/9debq6trpPNrcuXNZWlpaL0TRgmKhWC510u/l1Vdf5a3V3nzzTas/r9FoZMXFxezQoUNs9erVbO7cuSwoKKjD9m6dJclIfxe7mqudNWtWv/nu64+MRiN74IEHmCAIzNvbm23btq1H9y/97hMSEvhrmZqa2qPPQQghhNgySoYhl4ympiZWWFjI9u/fz9atW8ceeeQRNmnSJIs+1N1NkrG3t2dBQUFMrVazzz77zKrjXrJkCT+5uOyyyzrd7r777mP29vZ8zEuWLGHV1dWdbi9NAJ44cYINGzaMCULLaqWsrKwej+FimEwm1tjYyCorK1leXh5LTExkP/74I1u5ciW7//772VVXXcVCQ0Pb9UUvLy/v66FfstatW8ff/48//jhjrH0ShfT+OXbsGH9/3nDDDYyxc0/uGQwGPqnh7u5utcSL1quCn3rqKcZY+ziksR46dIhvO2PGDIv7OtPY2MgTMtzd3Xs+gAskxXjNNdcwQRCYnZ0d++OPPxhj/WfitSt//PEHmzNnTruJr4CAAPbcc8+x8vJyptfr+ftu8ODBfT3kHmc0GtmRI0fYgw8+yEJCQpgoinwC0dp9w6ULLR9++GG7v33Tpk3j76W2pIsuHX2es7Ky2H/+8x++H7VazT799FPGmHXfk7YUC2O2FQ/FQrFQLOfH1uKxJuk46JVXXuGx0Upk0pXWiSMdKS0tZdu2bWMTJkxgjo6O/LhMSnyw5rHZ1Vdfzdzc3HjS/cV+NltXkemLz7lU9VYQWqpxtk4eGTt2LFu4cCH7+eefWX19vcXr0dcVYNpqXclRqVTyWBwcHNjcuXPZypUr2dGjR9s9rr/F8csvv/A4oqOjWWNj4zkfYzKZ2G233WYxX6ZUKplSqbT4bNjZ2bEBAwawnTt39kIkFAvFYjs+/vhj5uLiwgRBYK+//jprbm7uk3E0NDSw3Nxc9ttvv7Hly5ezq666inl7e3dYAaujuXNp/sLOzo49/fTTrLi4mDFGCxY709DQwG6//XYmCC2LW//880/GWM//vqZMmcJfr/42/08IIYT0Z5QMQy55er2eZWRksB07drCVK1eyO++8k40aNcqiXHhHB/rSCcCWLVusOr4///yTX/QVRZH95z//sVj9UFFRwb7//ns+LmlsJ0+e7Nb+v/vuO96S6N5777UoN9qfddRq6auvvmIvvvgiu+eee/p6eJe0RYsW8ffc559/zhhrvxJR+venn37K33tPPPEEY6zzST7pJC49PZ1X+OlOG6YLtXDhQh7Hxo0bGWPtJ5ClONauXcvjeOaZZxhj544jNTWVOTs7M0EQeBum/nDxSBqfWq1mgiAwDw8PptFo+nhU52/r1q1s5MiR7b5//fz8LC7e3XzzzYyx/jO53NPvgR9//JFfOPDw8OjRfXdGiuHNN9+0uGAhCC0r7MeMGcOWLVvGtm/fznJyctp9P9TU1LDq6mpWUFDAnnnmGRYeHm4xSbZkyZJeicPWYrG1eCgWioVi+XfHYy0dtRnYsGFD3w6K9GsdHUOmp6ezd955h9155508+b1t8oYgtFQotCZ/f38mCAKbP3++1Z6jN4+hpXMnuVzORFFkrq6u7Pnnn2f79+/vcB7CYDD0i/OrtqTWUq3jWLFiBcvKyurwwnVvV9/sjubmZt5m28XFhS1dupQx1vX7oa6ujv8Nar0QS/q3t7d3u2rMI0aMsHoFVYqFYukvVXovltls5nNTMpmMV/jtL8xmM6uurmYpKSnsm2++YQ899BAbOHAg8/b2Zp6enszHx4cFBwczLy8v5u/vzx588EG2e/du/r3Yn74D+5va2lp2ww03MEEQ2KBBg9iBAwcYYz37O6uurmYLFixggtBSpVuqJk8IIYSQc5ODkEucs7MzIiMjERkZiWuvvRYAYDabUVVVBY1Gg7NnzyIlJQVJSUlITU1FTk4OmpqaYDQaAQCDBg2y6viGDRuGadOmYevWrRBFET/++CPS09MxdOhQBAUF4dSpU9i2bRsAgDEGhUKBWbNmYfDgwWCMQRCETvfNGMOxY8dQVlYGAIiJiYGrq6tV45GeF0CXYzsXmUwGBwcHODg4wMvLC5GRkbj88sthMBj4a0MuzPHjxwEASqUSsbGxAAC53PLrXnrtjh49yv89YMCALvcrvR/T0tLQ2NgIAOd8zMU4ceIEAMDOzg4REREdbiOKIoALiyM1NRVNTU0A/vkekN7bfUkQBNTX16O4uBiCIMDHxwcBAQF9PaxuM5lMkMlkmDp1KiZNmoRvv/0Wq1atQmJiIgBAp9Php59+4ttHR0f31VA7JIoiTCYTamtr4ejoCIVC0eV3ntlshiAIFvexlmRjiKKIGTNmYMaMGfjss8+QmpraazEAwP333w9BELB27Vrk5OQAAGpra3Ho0CEcOnQI9vb2UKvVcHV1hYeHBzw9PeHt7Q2TyYQDBw4gMzOTf9YFQQBjDAsXLsSSJUt47NJzUSz/vngoFoqFYvl3x2Mt0t/TzMxMfltCQkJfDYf0Q9KxvPRfmUwGvV6PgoICHD16FOvXr0d+fj7y8vIAtHz2BEHg55i+vr6Ij4/HZZddhgULFlhtnPX19dBqtRAEAf/3f/+Hq6++GrfffnuPPofZbIZMJsPXX3+NUaNGdXrO1BNKSkpQV1cHpVIJk8kEQRBQU1OD9evXIzU1FVFRUYiJicHAgQMRFhYGT0/PduegQM/MJVwMrVaL+vp6KJVK/n1ZU1ODF198EZ9//jmioqIQGxuLoUOHIiEhAWFhYfDw8OhwX30ZS3l5OY4dOwagZR5o8uTJnY5FivPAgQN45ZVXAMDiPPipp56CSqVCeXk5jh8/jm+++QZlZWVQKBRITEzEBx98gDfffJNioVgu2VjajlXS08dL9fX1/G+Pm5sbXFxcenT/F0sQBLi4uCA+Ph7x8fGYM2cOAMBoNKKmpgYlJSWor6+Hh4cH1Go1RFGEQqGweDzpmJOTE0pLSwEA2dnZsLe37/HnaGho4Mcynp6eFu9lQgghhJxDb2beEHI+rJFxLrVaOnDgAPvss8/YY4891uPP0ZoUw759+1hkZCRfHdB6FYS0olPqWT527FjeV7uz34F0e1FREW+R5O3tzfbu3Wu1WOrr67u832w28x/St6SSrM7Ozuzdd99le/bsYUlJSUyr1TK9Xs+am5v5yo7Ro0czQRCYo6MjO3bsGGOs88oY0gqgV199la/wefXVV60Wh7Riz8HBgT300EPss88+Yzt37mTJycmsqKiI6fV6/r6UPgdOTk68qlJn70UpjuXLl/M43nrrLcZY31eGuZj2Vf2V0Whk+/fvZ3fffTfz8/NjMpmMyWQyvhp+9uzZLCMjg28vrbzsLdLvvK6ujr3//vts5syZbN68eWzx4sVs5cqVLDk5uVv7MRgMFu85KQ4pFoPB0Gft37766is2bNgw/ndGJpPx8tSd9Q1vXdJa+u/jjz/Oampq+iQGW4zF1uKhWCgWiuXfHU9PKyoqYjt37mTLli0753kI+XdoXY1AOr46fPgwW7FiBbv55pv5Z6ntj52dHXNwcGBTpkxhH330EW9dYG3FxcX8HEUQWipR5uTk9Mi+Wx9zvvzyy0wURXbmzJke2Xdndu3a1eHvt7PfeVxcHPvPf/7Dli9fzn766SeWmprKamtru4ypN+YSdu7c2eGYO2uxbWdnx+Lj4/tdLAcPHmShoaFMEAR29913s5KSki63b2pqYnfeeSev0CGdh2VnZ7fbtqCggI0fP57/DgYPHtxlvBeLYqFYrB3LuZjN5ouqACU95uzZsxbVQfbv39/TQyX9mDS/KggCO3ToUI/tV3p/ZWRksMmTJ/NrBydOnOix5yCEEEJsHSXDkH6nL0+ArEGaqNu1axdPiGn7I10UHj16NC+l2J2LwRs3buT7GD9+vNX6nRuNRnb//fezV155hX3xxRds165dLDk5mel0OtbU1NTlYylBpnfpdDo+adf6Pebr68tGjRrFbrvtNvbSSy+xTZs2scOHD/OEE19fX1ZaWtrlvqUJ6FtuuYVPFlqrv3NRURGPo+3EpK+vLxs9ejS77bbb2PLly9nmzZuZg4MDv+9cvYylz9bNN9/M9/3HH390+ZjeciHtqy4lP/30ExsxYgR/LVv3oV60aBHLzMzsk3GdOnWKRUdHtyvZLI3xP//5DysrK2v3uGPHjrHXXnuNvfzyy+yNN95g69atY/v27WvX2qI/MBgMbOPGjWz69Om8XUDryX/p4mtHf6NuvPFG3qqsP7ClWBizrXgoForF2mwpFsZsLx5CelJHx+VFRUXsxIkT7I033mADBw5kgYGBnSYzREdHsxtuuIG9/fbbTKvVWswzWDvxWhr77t27maOjIx/bXXfd1Wls5ys7O5tNmDCB79vaDh8+zObNm8emTZvGBgwYwDw9PTv8burqx8PDg40ePZotXLiQffDBB2zPnj2soKCgV4+dz5w5w1544QX24IMPsilTplyysWzYsIG3rXrttdc6nZeR3mu//fYbi4iIsPh8HDx4kDHW8nkwGo3MZDLxRTM//vgj8/X15efY27dvt9gfxUKxXEqxSObOncsee+wx9umnn7K9e/ey/Pz8LudZWo+7q3FJ+/jkk094TLNnz74k212TC9Pc3MxbvYuiyB5++OEe27f03tuxYweLiopigtDSbtxW2osRQgghvYHaJJF+55lnnkF5eTkSEhKwaNGiTkvSXipEUYTZbMbVV1+NtLQ0bNy4EX/88QfS09NRUVGByspKyOVy3H///bjjjjsQEhLCH9cRqfzo2bNn8b///Y/ffu2118LZ2fmcrZUuRGpqKj7++GPY2dnByckJnp6e8PX1hb+/PwICAhAYGIiAgAAEBATA19cXXl5ecHFx4SWpO8L6uDyyrTp9+jQAoLm5md8mCAJKSkpQUlLC2wkBgEKhgMFggCAIMJvN+PrrrxEUFITg4GD+Gjo4OEChUEAURchkMgBARkYGL01urTZJycnJ7eJoG8uRI0fa3dfY2IgVK1YgPDwcERERCAoK6jAWoOPy+9Z8P+p0OiiVSt56p6PPuPT859P26VIgfW9Nnz4d06dPx6ZNm/Duu+/yll7Nzc349NNPsWPHDsyZMwdz587F4MGDe2VsycnJmD17Nn9fy+Vyi1ZtJpMJP/zwA+zt7fH5559DJpOhvLwcixYtwsGDB1FYWGixPzc3N8THx2PevHm488474ejo2CtxdMZkMsFsNkOhUODWW2/F1KlTcebMGSQmJmLfvn04efIkKioqYDKZ4OjoCC8vL15ud8aMGbj66qsxcOBAeHh48BZYFAvFQ7FQLBQLxUOItUjvaaFVK6QdO3bg999/x+nTp7Fr164OHycdc82cORNTpkxBVFQUhg8fbrGNdA5q7VZiwv9vyzRp0iQsXrwYb7/9NgBg8+bNmDRpEm655Zbz/uxKvwuj0YhNmzZhyZIlqKqqAgBERUVZIwwLo0aNwueff476+npUVFSgoKAAmZmZyM/PR25uLvLy8qDRaKDT6VBXV9fhPiorK3HkyJF253GBgYGIjY3FkCFDEBsbi8suuwzx8fFWiSMuLg7Lly+HwWCwWizx8fGIiorClClTcP3111slDqltlUKhgIeHB5RKZYfbSeeXP/zwAzQaDf9cLVq0CEOGDAFg+XmQ5q9mzJiBt99+m597GwwGq8RBsVAsvREL0NIibfPmzbC3t4dcLoejoyPc3Nzg6+uLkJAQREVFITo6GlFRUQgJCYGnp2enfytMJhMA8O9wmUwGjUaDTz75BCUlJQCAa6655pJqd00ujkKhwLRp0/DZZ58BALZu3YpnnnkGarX6ovctfcYOHjwIrVYLABg8eDC8vLwuet+EEELIvwUlw5B+59NPP0VzczNCQ0Px8MMP9/VweoQoimCMQSaT4bbbbsN1110HrVaLhoYGBAYGws/PD3K5/LwuxK9atQoHDhwA0DKhc91111lr+EhMTATQMgFXUVGBiooKZGRk8Pvt7e3h5uYGb29v+Pr68gSZwMBA+Pv7w9/fHz4+PvDw8ICTkxOAjpMOKEHm4rm4uGDu3Lmora1FTk4OtFotKioqOtzWYDDwPu5lZWV45JFHAAAqlQpBQUGIjIzkEwKhoaEIDg4GABQUFMBsNsPR0bFHTuw6EhgYiOeffx5VVVXIyspCQUFBl7FIiVc1NTVYs2YNv71tLFI8SqUSBQUFYIzBxcUFPj4+VomjtenTp8PHxwfx8fGIjIxEWFgY1Go1PD094ezsDDs7OwAtEymnTp0CANjZ2fFkmEv5cyFNIkmT+HPnzkVcXBw2btyIn376Cbm5uTCbzcjNzcVbb72FY8eO4d5778Xs2bOtOq7y8nKsXr0aGRkZPDnMxcUFgwcPRnl5OXJzc1FTUwOz2YzvvvsOCxcuRFhYGObOnYtDhw7x2Fr3aq6qqsLff/+N06dPo6qqCo8//rhV+kV3l0wms7jQ4urqijFjxmDMmDF48MEHAbT0CC8oKIBOp4NcLkd4eDg8PDwskivNZnOfX2y1pVgA24qHYqFYrM2WYgFsLx5CepJMJkN+fj5ycnKwfft2fP/996iurkZ5eTmAf46JpXPHIUOGICYmBtdddx1mzZoFURR5YkzbhJPePJ6Wy+VgjOG///0v9uzZg8TERNTX1+PNN9/EZZddhvDwcJ4wfi7S5z49PR0rVqzApk2bLJK4hw4d2gsRAUqlEkqlEm5ubggPD8eECRNgNpvR3NwMvV6P0tJS5ObmIisri7+G+fn5KCwsRFlZWacXuzUaDTQaDXbv3g0AmDdvHjZt2mS1OARBsHosO3fuRGVlJa6//vpuv87dZTab0djYCABwcnKySOTvSG1tLX744QcYDAYwxmBvb49bbrml03MUaaxqtZqf60jzBj39GaJYKBZrxyJJSkoC0PJ3obGxEbW1tSgpKUFGRgb+/vtvyOVy2Nvbw9nZGR4eHlCr1QgPD+fzSJGRkQgKCoKTkxP/u9Lc3IzKykoUFBRgwYIFOHPmDERRhLu7O2bOnGmVOEj/FRQUhNDQUOTm5iI3NxfPP/881q9fD5PJ1OVi0e6or6/H+vXrUVdXB0EQMHr06D6dZyKEEEIuNZQMQ/qV4uJiNDc3QxAEuLu7Q6VSdbn9/v37ERkZCT8/v/N6HpPJBMYYP9nqDdJBr0wmg6+vL3x9fS9oP6IoYvPmzVi9ejW/bd68eXzllDVOHKUEhLZVOiRNTU0oLi5GcXExP8EEwE8ifXx8oFarefUYqZKMn58fvL294e7uDqVSeUlf7O8vznfFXkeTGnq9HmfOnMGZM2csbpeq/kjvh9jYWKvF0VMr9jqLxd3dHXq93iKOnp6kbK11VZ7t27fz2318fBASEsJXIEVGRiIyMpJXxlGpVOesFnUpaf09OGzYMAwbNgzjxo3DO++8g7///htAS5zSyt/Zs2db9XXZtWsXduzYAaAlOezhhx/GHXfcwS82pqSk4K233sJff/2FxsZGrF+/HhERETh06BBf8ebq6gpvb29UVlaivLwcZrMZSqUStbW1WLp0Kby8vHDfffdZZfxttb5A2tDQgOTkZFRXV8PDw4NXfJK2MxgMvOKTXC5HWFgYwsLCLPbX+ju5t99/thQLYFvxUCwUi7XZUiyA7cVDiDVt27YNW7duxcmTJy0qWrbm4OAAV1dXzJ49G5MmTUJsbGy7yijS8WNfJ4sJggBHR0esXLkSU6ZMQVNTE06fPo2VK1fiww8/POdnWPr+EAQBX3zxBV577TWkp6fzJBij0Yi77roLTz31VC9F1J4oirC3t4e9vT28vb35vITJZEJDQwOqq6uh1WqRlZXFz+Fyc3O7XOwwbty43g4DQM/HMmHCBAA9P0cjLbYCWhLxpUSxzqoN7dy5EyUlJfx9M3HiRPj4+HR5nmUymRAWFsa36ex8m2KhWPp7LJL8/HwAHX8ezWYzmpqa0NTUhOrqahQWFiI5ORkymQwKhQIODg5wcXGBu7s73N3d4enpCS8vL8hkMhw9ehSHDx+22NfLL78MT09Pq8ZD+heTyYShQ4di+vTp+OCDDyCTyfDFF18gPDwcL7zwAoCW94a0ULa7+5TJZGhsbMSaNWug0WgAAAMHDsRVV11ltVgIIYQQW0TJMKRfaZ1IERcXB6DzC9QFBQUYP348AOC6667DL7/80u3n6etJMcByYvx8GI1GNDQ04K677kJ+fj4qKiqwaNEiq2aEP/roo5g/fz6Kiopw9uxZJCcnIzk5GWfOnEFubi6ampraPUYQBNTW1qK2tpafdAItv3tXV1feakmtViMwMJC3sxk2bJjVSiL/W1hrlZtOp4NOp4O9vT0MBgMveWst1lyxV1lZCQcHh15bSdn6u03SU+2relNaWhocHBzg5uYGe3v7ixqD9N1+00034aabbsKWLVuwYsUKngg0adIkAP+s+rWGX3/9FUVFRQCABx98EC+88IJFqdnQ0FCMGzcO06ZNw759+/Dnn3/ypJ3hw4fj3nvvRVhYGBQKBWpra5Geno7vv/8e+/fv5/v44IMPcP311yMoKMhqcUgEQcDJkyfx+uuv48CBA/w9DrQkXg0cOBBTp07FjBkz2pW5bv177g+JibYUC2AZz/79++Ho6Mjj8fb2xqBBgy6ZeGzptbHV14Vi+Wc//YGtxUOINb3xxhu8+qlcLofZbOYV+MaMGYP4+HhMnz4dV199NZRKJT+vb3uBtr8lio0bNw4PP/wwXn/9dYiiiI8//hhXX301brzxxk4fI80X6PV6PPDAA/juu+/Q2NgImUwGo9EIb29vvPvuu5gzZ06/mN9oSyaTwdnZGc7OzggICMCIESN40p+02EGj0SAzMxN5eXl8ocOxY8cwcuTIvh6+hfONRYpj7NixAKzz/d16UUtWVhYfp0Q618rLy8O3337LH+Pj49Pl+04iVQUBAA8PDygUih4aeXsUC8Vi7VgAYOHChViwYAEqKyuRnZ3NW1MeOHAAZWVlFuORmEwmPgdbUVGB3NzcDvctVbcRRREPPPBAry2GIf2H9Nl45plnsG3bNmRnZ0Mul+PFF1/EiRMnsGzZMgwaNKjd4xhjFomvEoPBwD8Tn332GV588UUALdXA58yZw+cL+9vxDiGEENJvMUL6kddff50JgsBEUWSvvPIKY4wxo9FosY3JZGKMMbZz504mCAKTyWTs5ptv7nDbtiorK9nQoUPZhAkT2NNPP22FCHpHQ0MDKy8vZ5mZmSwjI6PPxtHU1MQKCwvZ/v372bp169ijjz7KJk2axIKCgpggCB3+iKLY4e0qlYqJoshWrVrFGGPMbDb3WVz/Jkajken1eqbRaNjRo0fZN998w15//XW2aNEiNmXKFDZgwADm6enZ4Wu2Zs2avh6+hQuN5aOPPmKMWfc9d/jwYTZv3jw2bdq0Ln+n0o9CoWAKhcLiNhcXF5aQkMBmzJjBnnjiCfbJJ5+wXbt2seTkZFZUVMT0ej1rbm62ahy+vr4sNja2x8ZgNpv597bRaGTp6enslVdeYU5OTuydd95hjP3znW8NwcHB/Pd79uxZPiZp/NJzv/baa8zBwYFvO2bMGJaYmNhhPFVVVWzZsmXM0dGRyeVyJggCW7duHb/fWpqamtiGDRuYTCbr9H0lk8mYn58fu+2229ipU6esNpaLZUuxMGZb8VAs/RPF0n/ZWjyEWJuzszMTBIHJ5XImiiJzdXVlzz//PNu/fz+rrKxst73BYLDqsWJPqqioYMOHD+ef/YEDB7KCgoIuH7Nnzx4WFhbW7jx68uTJLD8/v5dGbn0mk4k1NDSwkpISlpycfM55nf6sdSzWPPbfsmULc3d3Z4IgMC8vL7Zr1y7GWMs5lcFg4Nu9/fbbfDtBENj06dP5HFJX4ysuLmbz589ngiCw2NhY9vvvv1MsFMslG8u5NDQ0sLy8PPbbb7+x5cuXs6uuuop5e3u3m5Pp6sfPz4+tWrWK1dfXM8ZoTvPf7LvvvmN+fn78OF+hULCEhAT2xBNPsD/++IPpdLpuvT/S09PZ/fffz5ycnPj77Pbbb2cajaYXoiCEEEJsCyXDkH5l1qxZ/ELwzz//zBhrfyFUOul65ZVX+KTQ66+/zhjrPBlGOsg8cuQIs7OzY4IgsISEBGuFcdEu5ckfxhjT6/UsMzOT7dixg61cuZLdeeedbNSoUczDw6PTE0fpdfnxxx8ZY3Ti2JfMZjNrampilZWVLCsri/3555/ss88+Yy+++CK744472KRJk5irqys7duxYXw/1nLqK5a677mKDBg1iycnJvTKW1uPYu3cvW7duHXvxxRfZ7bffziZMmMAiIiL4BYDz+fH19WWjRo1i8+fPZ88//zz79ddfrTL+oqKiLscwevRodtttt7Hly5ezr776ih04cIBlZGSw0tJSVl9ff17fa59//jlPTrGWkpISPv7o6Ogutz1w4ACzt7fnFzN37NjB7zMaje2+r5qbm9nVV1/N979s2TKrX6jZtGkTnySRy+UdXnhtfRFlxowZTKfTWXVMF8qWYmHMtuKhWCgWa7OlWBizvXgIsSadTsfPC1t/XtRqNZs5cyZ7+umn2fr169nRo0dZWVlZp/tpndjcX0jj2b17N1MqlUypVDJBENjixYs73L6yspI9//zz/EKa9Lvw9fVlr732Wm8OnfRTBoOBhYSE8L8fcXFxFucoer2e/fDDDywwMNBivuWbb77pcr/SezUxMZGNGTOGCYLAJk6cyJKSkigWiuWSjeVCmM1mVl1dzVJSUtjmzZvZQw89xEaOHMkiIiJYZGQkGzZsGBs+fDgbO3YsW7lyJUtPT7d4LPl3+/DDD5larW53rO/r68smT57MHnnkEbZ27Vq2ZcsW9scff7ATJ06w4uJilpWVxT777DN27733soiICH4cIAgCi4+PZydPnuzr0AghhJBLErVJIv3K6dOnAbT0AY+NjQXQeUnZxMREAC2lCBMSErrc1mw2QyaT4cyZMzCZTAD+6d/cn0glDn/66SdUV1fDw8MDV111FVQqVYfbswtstWRtUgnhiIgIXHvttQBaYquqqoJGo8HZs2eRkpKCpKQkpKamIicnh7daGjhwIAAqBd+XhA7aE40fP96iPVFJSQn/jPZn3YmldVsca7JWy6e2rZZuvfVWXHfddT0+fql9UVtCq3ZPR44c4bcrFAoEBAQgLCwM0dHRiI6ORnh4OG+J1lW7pzvuuKPHx9+W1LpKEATeKquzHuguLi78O0qtVvPvNaB92z3GGBQKBebMmYN9+/ahubkZZ8+eRWNjIxwdHa0SS2pqKtauXYv6+nrIZDKYTCbExcVh0qRJMBgMOHPmDFJSUlBRUQFBECCTyfDzzz8jNDQU7733Xr8qr2tLsQC2FQ/FQrFYmy3FAthePIRYmzQX0NzcbHF7cXExfvjhB4vblEolwsPDkZCQgEGDBmHw4MGIjo5GUFAQnJycOtw/+/9tx/riPFMQBDDGMGnSJDz22GN44403IIoiNmzYgEmTJuGmm27i2/71119YunQp9u3bB7lczluVXHXVVVixYgXGjBnT6TEr+feQy+VYunQp7rnnHgiCgLS0NEyZMgUhISGIi4uDUqnEtm3b+JxRc3MzrrjiCkyfPr1b+z9x4gRvCRMTEwO1Wk2xUCyXbCwXQhAEuLi4ID4+HvHx8bj55psBtMwZ1NbWoqGhAU5OTh3O19J8JnnwwQfh5eWF5557jr/P5XI5SkpKsGvXLuzevRvOzs5QqVRwcHBAY2MjCgsL+bxhbW0t35fZbEZISAg2bdqEwYMH91FEhBBCyCWu7/JwCLFkNpt5pnRYWBhrbm7ucDtpdb2UIe3q6nrOEpxSRYLHH3+cZ2R/9tlnVoiiZwwYMICJosgUCgUrKSnp6+FYldRq6cCBA2zDhg19PRxC+oWLaV+1du1aq4zpzJkz7IUXXmAPPvjgOcfQ1c+52j1VV1ezhoYGq8TQ2ltvvcVX6Sxfvpwx1nlbvt9++42Pf+bMmR1u29aZM2f4Kp7x48dbIYJ//Pe//2Vubm48nlWrVrHCwkKLv6MVFRVs5cqVzMXFhccSGRnJTpw4YdWxnS9bioUx24qHYqFYrM2WYmHM9uIhxNrOt61oRz8eHh5s9OjRbOHCheyDDz5ge/bsYQUFBRYtPXpDV1UBamtr2YgRI/iYR44cyXJzc5nZbGZLly7lcUvVY1xcXNjy5ctZTU0NY4z1eiyk/9Lr9eyuu+7iVQPatqS2s7Pjt7m5ubHTp093e9/Tpk3jLWLWr19v9erFFEv3UCyE9H+tK9RVVlay5cuXswEDBnRaGVKaN5I+ZzKZjN/v7OzMrrrqKqbVavs4KkIIIeTSRskwpN84e/YsP+ibOHEiY6zzSSSTyWSROHOuCSHpgub111/PDygTExN7NoAeUlNTw8uMKpXKvh4O02g0rLS0tK+HQci/Xn9oX9V2DD3d7mnMmDFs1qxZbN26dVaLQTJnzhwmCC1t+X744QfGWOdt+d544w0+YfHyyy8zxjpPhpH2cfDgQR7btddea6UoWowcOZJPnLz99tusqamJ32c0GvmYzGYz27hxI3N0dGzX8qm/lHK2pVgYs614KBaKxdpsKRbGbC8eQnqDtY4zg4KC2DXXXMOefPJJ9tlnn7GUlJRei8lkMvHPvPS5379/P1MoFEwulzNBENijjz7KxowZw+cqpO+C4cOHs3379lnsi5DWtFotu+eee5iHh0e7i6vST0REBNu6dStjrHvvofT0dP5YZ2dnlpeXZ+0wGGMUy7lQLIRcWqTPQn19PTt+/Dh7/fXX2aRJk3j7pLY/0jGBIAjM39+f/ec//2Hffvst3x+dFxBCCCEXTmDs/9eKJaSPbdmyBXPmzIEgCLjnnnvw0UcftSv/y/5/+c2zZ88iLi4Ooihi/Pjx+OOPP7rVMiguLg7p6elgjKG2ttZqLSsuhDT+EydO4IorrkBDQwOGDRuGY8eO9WkZ5Hnz5kGr1SI+Ph7Lli2Dr69vn4yDENKxjtpX9fb3RU+0e2rttttuw5dffmnVFhFxcXE4e/YsnJ2dceTIEcTGxrZ7PqPRCLlcjjlz5mDLli2QyWT4v//7P8yYMaPTsUnf119++SUWLFgAALj//vuxZs0aq8Sh0+kQGBgIk8kELy8vaDQaKJXKTrdnjGHixInYv38/GGN499138dBDD/WLVhy2FAtgW/FQLBSLtdlSLIDtxUNIX+rp40yg5Rx306ZNVhlvfX09cnJyEBYW1ul8g9FoxEsvvYRXXnmFt1ETBAGCIMBsNsPe3h533XUX3nrrLTg4OFhlnMS2HDt2DP/3f/+HpKQkVFRUAGh5n40fPx6LFy9GeHh4l4+X5qN0Oh1WrFjBz12uvfZa/Prrr73auo9i+QfFQojtKSkpwZkzZ/jxS1ZWFkpLS+Hu7o7Y2FjEx8fD398fw4YNgyiK3breQQghhJCuyft6AIRIjh07xv9/4MCBHW5jNpshk8lw8uRJAC0nU7GxsRb3dUaj0aCkpASMMajV6n6VCAP8M/4zZ86gqakJADBkyBAA//Q37wvffvstGGPIysrCW2+91WfjIIR0TBRF2Nvbw97eHt7e3v1iDPHx8QBaEkMaGhpQXV0NrVaLrKws5ObmIi8vD7m5uSgoKIBWq+WTaZJx48YBsG6v7czMTACAh4cHwsLCOnw+aTLu1KlTAAAHBwfExcV1a2wpKSkQBAGMMf5dbg2nT5+GyWQCAIwfPx5KpbLLBEpBEHDTTTdh3759UCgUKCws7DeTjrYUC2Bb8VAsFIu12VIsgO3FQ0hf6unjTOCfY01r+P333zFnzhwMGTIEfn5+iIiIQHR0NKKiohAREQG1Wg25XI6lS5fi5MmT2L59O+RyOYxGIxhjiIyMxIoVKzBnzhyYzWarjZPYlhEjRmDEiBEwGAyoq6tDc3MzfHx8uv146dxm69at+OKLLwAAAQEBmDt3rlXG2xWK5R8UCyG2wWw2gzEGURTh4+PT6Wen9fmCdC2AEmEIIYSQi0fJMKTfOH78OABAoVDwCa62SSDSvxMTE/ltAwYM6HK/0uqC9PR01NXVdesxfSk5OZnHOWLECAB9d+Cr0WjAGINMJkNAQACcnJy63H7t2rVISEhAXFzceV+U78vqN4QQ65DJZHB2doazszMCAgIwYsQIMMZgMBhQX1+PiooKaDQaZGZmIi8vDxqNBseOHcPYsWMBWO+7Lysri3/nuLi4oLq6Go6OjnB2drbYTlqFk5GRAaAlcSY0NLTLsUm3nzlzht82dOhQK0TRQkokFUURw4cP73JbaUWRWq3mj2lubgaAfrESz5ZiAWwrHoqFYrE2W4oFsL14COmPzvc4s/Wx5siRI602rr///huNjY1ITEzkx5tKpRJOTk5wdXWFr68vgoODER8fz8+ZTSYT7O3tcc899+C1117jC3ekz790EU0QBPpOIF1SKBRwc3M7r8dI762DBw/iww8/5PNmV111FSZPngwAffK+o1goFkJsRVfvb6k6nCiKkMlklARDCCGEWAElw5B+Izk5GQDg7OzMk1Xkcsu3qHRAKE0wKxQKvm1nB4nSY5KTk3nZ5HNNSvcFafwpKSn8tmHDhlnc19tOnz4NoGXyLSYmBkDnSSu5ublYvHgxgJbf79GjR7v1HJWVlXB3d6dEGEL+JQRBgFKphFKphJubG8LDwzF+/HiLEvheXl5WHYP03aZQKJCcnAy1Wo2goCDExcVhwIABGDhwIKKjoxEfH8+TAkVRRGhoKJRKZZdlaqVJjuzsbH6bVE3GGqTkUFEUkZCQAKDzvxlSBTKNRgOg5e+jv78///++ZkuxALYVD8VCsVibLcUC2F48hFwqunOcKbUVtZbDhw8D+CeBxWAw8EoKJSUlyMjIgCiKsLOzg729Pezs7NDU1ARPT09UVVXhyy+/hI+PD+Li4hAcHAwnJ6cOL6LRxTLSU6T3UHNzM6ZNm4YhQ4YgOzsbDz30EE/UvFRQLP2TLcVCSGtZWVnIysrii0Lt7e3Pex9t58Pp7zohhBDS8ygZhvQLtbW10Ol0EAQB9vb2SElJgbe3N9zd3eHi4gIHBwfIZDIoFAoA/6y4d3Z2RlRUVLeeQ0q2Af6puNKfSBNcOTk5vLWGdAG1r1ZISNV6APCJ/LakFaupqakAWi4uS9ueq9pLWVkZBgwYAEEQMHLkSPz88889OHpCyKWkdQl8aztw4AAAoLGxEUDLxYT8/Hzk5+fjt99+49s5OTnBw8ODb2NnZwetVguFQgFPT892kxRSkkxRURF0Oh0YY/D19W1XcaYnnU8LJ+miifTdbjabERER0eVjepMtxQLYVjwUC8VibbYUC2B78RByqevNtqJXXnklFAoF8vPzodVqUVNTY3G/dK7f0NCAhoYGCIIAQRCg1WqxefNmfPvtt3BycoJKpYKXlxcCAwN5q6Xo6GiEh4cjICCAFpOQHjdhwgRMmDABRqMRDQ0NUKlUfT2kC0ax9E+2FAshAPDWW2/hk08+gYODA/z9/S0WWMXExCAoKAgeHh70N5sQQgjpY5QMQ/qFtLQ0AC2JFMXFxbj99tvh5+eHkJAQhIaGIiQkBAEBAQgICIBCoUBJSQkEQYCvr69FSfG2Wq+mTEtL4xNPgwcP7p3AzlNRURFycnJgNpvh5+cHlUrVZQUCa5NWtcpksk4r8EjJMFK1HqPR2O3WVZmZmdDr9WhoaIBWq7W4jxBCrMXBwQGRkZEwGAwoKyvjZZpbEwQBdXV1qKurg0KhgNlsxq5duxAQEABPT09ER0cjISEBAwcORHx8PCIiIuDp6QmVSoXc3FzU1tYC6DyRsCeYzWZkZWUBADw9PREZGcnH3hHpdukirb29/Tkv0vYWW4oFsK14KBaKxdpsKRbA9uIhhJyfpUuX8v8vLi5GWloaUlJScPr0aaSlpSEnJwdlZWUWSdnSf6UWaU1NTaioqEBeXh5OnDjRrtVSQEAAHBwc8Oabb1r1WJP8O8nlcptJUqBY+idbioX8u0nXM5qbm3mVmG3btvH7VSoVIiIiEB8fj4EDByIhIQFRUVFQq9VwcXHpq2ETQggh/zqUDEP6hYMHDwJoSaRgjEGj0fB+3hJHR0f4+/tbrLBXKBTYv38/7Ozs4O3tDVdXVzg5OUGpVAJomUCWyWQwmUzIz8/niRbSasv+pry8HMHBwUhLS0NYWBiAluoqbdtF9RZpUt7R0ZGXku5sUl56reRy+TnLwUsTfikpKWhqagIAjB07tucGTgghXbjvvvswdepUFBcXo7CwEAUFBSgsLERRURGKi4tRVlaGqqoqfkFCarEHtHyvlZeX4+DBg/xvl3R7UFAQBg8ejLq6OphMJgDWbcuXkZEBALxy2uHDh+Ht7Q21Wg1HR8d220urkdLT0wEAbm5u/G9NX19wtaVYANuKh2KhWKzNlmIBbC8eQsj5MRqNEEURoijCz88Pfn5+mDhxosX9OTk5SE1NRVJSEpKTk3H27FloNBpUVVXBaDRa7K+jVks5OTkwGo147733ejc4QgghhHAVFRX8ukNbgiBAr9fj5MmTOHnypMV9vr6+iI6O5lVk4uLiEBYWBh8fn16plkwIIYT821AyDOkXpImipqYmVFZWdrhNfX09MjMzAbQkwZhMJpw+fRo33XQTfHx8EBISwivJBAUFQa1Ww9PTE8HBwcjKykJFRQUA8NWZ/VF0dDS2bduGwsJCniHeV1VSjEYjcnNzIQgCPD09ERISAqD9pLw0vqSkJADdKwcvSUpK4okx0gVjmvQnhFhbYGAgAgMD+b9NJhOqq6tRWloKnU6HwsJCFBYWQqPR8CSZkpISlJeXo6amxqLqGPBPuXup1RLQsrLfZDJZNRlGSlhUKpXIyMjA2LFjIQgCQkJCEBMTg4EDB2LAgAGIiYlBcHAw1Go1CgoK+EWa4OBgODg49IuKXLYUC2Bb8VAsFAvFcn5sLR5CyPlpu5DFbDbzY0dRFCGXyxEVFYWoqChMnz6db6fX65GZmYmUlBQkJSUhJSUFmZmZKC4uhl6vtzj+NBqNEASh2y2jCSGEENKzzGYzXnjhBRQXF/NFvVqtFsXFxSgvL0d1dbXFwiqFQgHGGEwmE3Q6HXQ6Hfbt28fvl8vlCAkJQWxsbLvzBXd39z5bKEsIIYTYAvorSvqFO+64A1OnTkV5eTk0Gg2ysrKQnp6O9PR05OTkoLCw0OIAUvp/aYV+eXk5UlNT+f1yuRze3t7w9/dHVFQU6uvreRniIUOG9GpsbXXV9sjOzg7h4eEIDw/nt/XVJLi0OlUQBISFhUEmk3U4dlEUYTAYeOKMl5cXgoOD+WM7It2emprKLyJLrwslwxBCrK31xQSpgpiHhwc8PDwQExPD75NK1JeWlkKr1aKoqIhPchQWFqK4uBglJSWorKxs12qpN/7mSJVpGhoaeCyMMeTm5iI3Nxe//fYb39bR0RFRUVG8uhpjDL6+vgBakoH6+oKrLcUC2FY8FAvFYm22FAtge/EQQrqPMQbGmMVnt7N2ztIP0FIhSqVSYejQoRg6dKjFtsXFxTh79iySk5ORlJSEtLQ0nDlzhn9XEEIIIaT3iaKIm2++GUBLkmpVVRVKS0tRXFxsMXek1WqRn5+PY8eOdTlPbjQaeaul7du38/vc3Nzg7u6OQ4cOwdvbu1diI4QQQmyNwNoubyakHzCZTGhubkZdXR2qqqqg0+mQn5+PjIwMpKenIyMjA3l5eSgpKen2PpVKJZqbm/H666/jqaeessq4m5qa8O233yI4OBheXl7w8PCASqWCo6Njtyazu0qU6W1ff/01br31VgiCgEWLFmHVqlUwmUy8lDvwz3hTUlIwcOBAiKKIK6+8Ert27epWLNHR0cjKygJjDA0NDbCzs7N2WIQQ0qm2STKdqa2tRXl5OXQ6HbRaLa8io9Fo2rVaamhosNr3+sqVK7F161bU1dWhuLgYFRUVqK+vb7eddCFWIlVXM5vN8PHxQXh4OC/PGx8fj4iICPj4+HTYzsNabCkWwLbioVgoForl/NhaPISQCydVhREEgf90hTEGs9kMoOU7orM5BKPRiLKyMvj5+fX4mAkhhBDSPeea+66trQVjDN9//z3uvPNOXtVt0qRJyM7ORnFxMUpLS1FZWckT6Vtrfb4gHR8QQggh5PxRMgy5ZDDGYDQa0dTUBL1ej4qKCmi1WuTk5PAEmaysLOTn50Ov17d7vFwuh9FoxO+//44rr7zSKmPct28fJkyYgJCQkHatmwICAuDr6wsvLy+4ublBpVKdsw9oXybHPPbYY3jvvfcgiiJWr16N++67r10yjPTvjRs34vbbb4cgCFi8eDE++OCDdttKpJjy8vIwZMgQVFdXIygoCHl5ef0qGYgQQiTSoVJX309Sq6WysjIUFxejoKAADQ0NWLhwodXGVVtbi7KyMpSUlKCoqMgiKUcqzysl5TQ3N7d7fNsLsa1vDw4ORmxsLAYPHoywsDDccMMNFq2lKJZ/TzwUC8VCsfy74yGEdN/XX3+NyMhIREZGwt3dvcNtupvw0lrbVkt0zkwIIYT0P50tsPrss89wzz33QBAEPP3003j11VeRkZGB8vJyi/MFqWW3TqdDWVkZ9Ho9mpqaEB4ejszMTGqjSgghhFwgSoYhlzyz2QyDwYCGhgbU1NSgrKysw1ZLxcXFaGxsRFFRkdVWUH344Yd45JFHIIpiu4xtJycn+Pn5ITAwkCfIBAcHw9/fHz4+PvD09ISrqyucnJygUCi6fJ7eSBq58sor8eeff8LOzg67du3CFVdcAaPRaNGjVPr3kiVL8P7770MURaxZswb33ntvp8kw0oH7zp07MW3aNBgMBkyZMgXbt2/v9DGEENJTdDodlEolHB0doVAoLngiobtVZHpb26QcqTyvNLmi1Wqh0+lQXl6Ompqadn+rOrsI+8033+Dmm2/u1aRFW4oFsK14KBaKhWI5P7YWDyGkvZKSEvj5+cHZ2RkuLi4ICAhAZGQkEhISkJCQgNjYWISGhnZaDZUSXgghhBDbYjAYoFAocOedd+KLL76ATCbDl19+iXnz5rXbtnWrJZ1Oh8LCQmi1Wpw5cwYDBgzAY489RskwhBBCyAWSn3sTQvo3URRhZ2cHOzs7uLm5ITg4GMOGDeuw1ZJWq7VqKeGUlBQAHZcurKur470/W/Pw8IBarUZwcDBCQ0MREhKCoKAgqNVqeHt7w93dHS4uLhatlnpjYiw5ORkA4OzsjOjoaACwSIRpLTExEUBLefeEhIQuxyhN8CUnJ8NoNAIARowY0eVjCCGkp0yfPh0+Pj6Ij49HZGQkwsLCoFar4enpCWdnZ9jZ2UEul5/z++hc33FdbdOT2j6fTCaDh4cHPDw8+Hc3ADQ2NqKyshKlpaXQarUWPawLCwtRXFyMkpISVFZWoq6urt3zDB8+3Oox2VIsthYPxUKxUCz/7ngIIed2+vRpAEB9fT1qa2tRVFSEo0ePAmg5j3Z0dISnpyeCg4MRExODAQMGID4+HtHR0QgMDOz04tb5tloihBBCSP8g/W0/deoUAMDOzg7x8fEAWpLlW8/zy+VyeHl5wcvLC3FxcXwf9fX1FsmyhBBCCDl/VBmG9Ftt21JcCiulzpw5g9TUVOTn5/OqNNnZ2SgqKoLBYOj2fhQKBby8vBAQEIDg4GCEhYUhODgYgYGBUKvVcHNzQ0BAAFQqlVXiqK6uhru7O0RRhLe3N9577z34+PjAx8eHJ+c4ODjwKi7e3t4oLy+Hl5cXUlJS4O3t3Wm2ulT95e6778bnn38Oxhh+/PFHTJ8+nVa1EkKsSlqx25bU1i46OhpRUVGIiopCcHAwfH194ebmBicnJyiVykuqclV3k3Jqa2tRXl7OE0Zbt/MoKiqCVqtFZWUlioqKemPYHbKlWADbiodioViszZZiAWwvHkLIPzZs2IB77rkHbm5uqKmp4Qs/uqJUKqFSqeDj44Pw8HDExcUhISGBJ2131WqJzpsJIYSQS4OjoyMaGxuhVqtx9uxZODs7d7ptf61CTAghhFzKKBmG9EvdndyR+m13t9d2b+moKk1eXh4yMjL4T25uLkpLS89rv05OTggICIBcLsfKlSsxefJkq4z/0KFDGDt2LOzt7dHY2Nhhck5QUBDCwsKgVCoxZcoUCIKAuLg4XlGmM1IyzOWXX47Dhw/DbDYjOzsboaGhVomFEEIkv//+O6655hqL2zprPaFQKODv74/w8HBER0cjOjoa4eHhCAoKgpeXF08KvJhWS32hbaJpR1q38ygsLER9fT1uuOGGfnfhxZZiAWwrHoqFYrE2W4oFsL14CPk3ysjIwKFDh1BcXIzc3FwUFhby9mfV1dWora1FY2Njh8edrY9HRVGEvb09XF1d27VaiouLQ0hICOzt7Xs7PEIIIYRcgMLCQgQFBUEURQwfPhyHDx/u6yERQggh/zqUDEP6rSNHjqC0tBQlJSXIyspCVVUVfH19ERkZidjYWMTExMDR0bGvh9ltjDEYjUY0NjaitrYWFRUVKCoqQk5ODjIyMpCeno6srCzk5+ejtra20/1IE2V//PEHJk6caJUJ8HfeeQdPPvnkObdzcnKCq6srioqKIAgCoqOjsWzZMqhUKqjVanh4eMDV1ZVXVZCYTCaEhYVBo9FAoVCgqampR8dPCCEdOXLkCN577z3U1tYiJycHWq0WFRUV57UPlUqFoKAgREZGIioqCtHR0bzVkoeHB1QqVbdbLV2MtLQ0ODg4wM3NDfb29heVlNPXK49sKRbAtuKhWDpGsfQcW4oFsL14CCEXrra2FsXFxcjPz0deXh5yc3NRUFCAoqIilJaWoqKiAjU1Naivr+/W+bAoinB1dYXRaMQLL7zQrfN1QgghhPQNqWL6jh07cP3110MQBNx666348ssv+UJRQgghhPQOSoYh/YrZbMY333yD//3vfzh79ixyc3MBtPTYNpvNMJvNfFtHR0cMHToUo0aNwrXXXouRI0d2Wka4PzObzTAYDGhoaEBNTQ3KyspQUFCA7OzsLlstlZWVwcPDwypj2rx5Mz766CNUVlYiJycHer2+y+2VSiVMJhNMJhMAwN3dHf7+/ggKCkJISAhCQ0MRFBQEX19fhIWFIS8vD9OnT0ddXR3i4+ORnJzcaVslQgjpSc3Nzaivr0dFRQUKCgqQmZmJ/Px85ObmIi8vDxqNBjqdDnV1dee1X6nVUkxMDIKDg3HFFVfguuuus1IUgJ+fH9zd3RETE2O1pJzuVCroCbYUC2Bb8VAs54diOX+2FAtge/EQQrqPMcYXqnT1+SwrK0NRUZHF8WdBQQF0Oh1KS0tRVVWF6upqNDY28vNraX8KhQLNzc1Yu3Yt7rvvvl6JixBCCCHnz2g0Qi6X45VXXsHSpUshCAJeffVVPP3005QMQwghhPQySoYh/UZKSgqeffZZ/PbbbzAYDBBFETKZjP+/KIoWk78mkwmMMSgUCkRERODaa6/F/PnzMWzYMACXfh/tjlot5efnIy0tDVqtFp988onVnttgMKC2thZ6vb7byTnnIpfL4e3tjZCQEDDGkJiYCKPRiFtuuQUbN26kEwFCSJ8xm81obm6GXq9HaWkpcnNzeaWunJwc5Ofno7CwEGVlZd3+3rv11lvxv//9zyrj1Wq1CAgI6PA+Hx8fhIaG8ouwkZGRCAkJgY+PD9zc3Hilrv7yfWtLsQC2FQ/FQrFYmy3FAthePISQiyclyADoMkmmubkZJSUlyMvLQ2FhIYqLi5GWloa6ujr89ddfKCgo4HMfzc3NOHToEEaNGtWboRBCCCHkPEjJMDNnzsSPP/4IuVyOH3/8Eddffz0tCCWEEEJ6GSXDkD4lHfzt27cPTz/9NA4dOsQTX4xGI2QyGWQyGZqbm/ljZDIZGGP8sVJiDNAywbRixQo899xzAPouIcaaKzYNBgOMRiMcHBx6fN9d6Sw5R2rxlJGRgby8PJSUlHRrf/b29mhsbMQ777yDJUuWXPLJS4QQ22MymdDQ0IDq6mpotVpkZWXxFbxSqfvOWi2tWbMG999/v1XGtWvXLlx77bXtbpfa6LWlUCgQEBCAsLAwREdHIzo6GuHh4QgKCoKXlxdcXFzg4OBwUe08LpQtxQLYVjwUC8VibbYUC2B78RBCrEeazwDQYRJcWVkZDh8+jLfeegtHjx5FY2MjXxxkNptRU1MDZ2fn3h42IYQQQjrRdl5bum4RERGBnJwcuLi4IDExERERETQHTgghhPQySoYhfUY68CsqKsLNN9+Mv//+G0qlEs3NzfDz88PYsWORkJCAMWPGQK1Wo6amBllZWcjLy8PZs2dx8uRJpKWl8f21TqKZM2cOPv74Y7i4uPTaAWZDQ0OXCSqtP2q2csDLGIPRaERTUxNqa2tRXl4OrVaLnJwcniQjVVforNXSn3/+iXHjxtGJACHkksAYg8Fg4K2WNBoNMjMzkZeXx9ssHTt2DL///juGDx9ulTGkpqbiq6++QlVVFbKysrpMyumKSqVCUFAQIiMjO2zn4ezsDAcHB8jlcqvEAdhWLIBtxUOxtEex9CxbigWwvXgIIdbTUVXUjIwMbN26FcnJyfjxxx9RVVXVYbtob29v6HS63h4yIYQQQvBPNTc3Nzc4Ojp2mbRuMBhgZ2cHAAgLC0N6ejpVgiSEEEL6ACXDkD4jJT/ceeed2Lx5MxobGwEACxYswO23346JEyeecx9ZWVnYtm0bfv75Z+zbtw9Go5Gvvnz66afx0ksvQalUWjmSlsmsBx98EEFBQQgMDIS/vz/UajW8vb3h5ubW5RisWUWmr5jNZhgMBjQ0NKCmpgZlZWXQaDTIysrirZby8vKQnZ2NkpISeHl59fWQCSHkorRutVRSUoLY2FirTnK0TcopKChAZmYm8vPzefUajUYDnU6Hurq689q3j48PwsPD4e/vjylTpuDuu++2UhQtbCkWwLbioVi6h2K5cLYUC2B78RBCeoY099F6AYher0dBQQGOHj2K9evXIz8/H3l5eQDAq8BIcwW+vr6Ij4/HZZddhgULFiAqKqrPYiGEEEL+zXbt2oXXX38dI0eORGBgIAIDA+Hr6wsvLy+4ublBpVJBqVRCFEVkZmYiOjoaoihi4sSJ2L17N0wmE690TwghhJDeQckwpE+lpaUhISGB//vee+/F22+/DScnJ5jNZou+2mazmU8GtT1oTEpKwqpVq/D111+jvr4eZrMZSqUSP/zwA6ZMmWL1OJKTkzFo0CDY2dnByckJnp6e8PX1hb+/PwICAhAYGIiAgAAEBATwA2QXF5cus8dtMUmmbaul8vJyjB49uq+HRQghNqN1Uk5paSlyc3N5ha6cnBzk5+ejsLAQZWVlMBgM59zfbbfdhi+//LJPelrbUiyAbcVDsXSOYukZthQLYHvxEEK6p3UFGOnzeuTIEezcuRNJSUn46aefLFpCS6SLaBMnTsSMGTMQFxeH8ePH9/bwCSGEENLGkiVL8P777/N/Ozk5wdfXF0FBQQgJCUFoaCgCAgIwYMAA7N27F88//zwEQcC9996LtWvXdrlvqppOCCGEWAfVViZ9pqamBl988QVP+hg5ciQ/KOxoYrerid6BAwfi448/xujRo3HPPfdAFEU0Nzfj/fffx/jx4+Hk5GS9QAAkJiYCaDloraioQEVFBTIyMvj99vb2cHNzg7e3N3x9fXmCjFRFxt/fHz4+PvDw8OBj7ejgty8TZKSVaa1fh64SlDoik8ng4OAABwcHeHl5ITIy0qpjJoSQfxtRFGFvbw97e3t4e3sjPj4eQMvFmIaGBlRXV0Or1SIrK4tXKcjNze20nce4ceMA9M3fHVuKBbCteCgWioViOT+2Fg8hpGNtL2LJZDJotVrodDrs3LkTGzduRGVlJQoLCwG0/wxHRUUhKioKV155JW699VaoVCo+P0DJb4QQQkjfO3DggMW/6+rqkJ2djezsbIvb21ZB1+l02L59O+zs7KBWq+Hu7g4XFxeLVkt0bE8IIYRYB1WGIX3m6NGjePDBB3H06FFERUXhtddew8yZMzvsn30uravIrFq1Cg8//DAvLZyeno6IiAgrRdHi3XffxeOPP97p/VJJ5LacnZ3h4eEBHx8fqNVqXj1GqiTj5+cHb29vuLu790q7p860ntQzGAwwmUywt7fvdFtpezqIJ4SQ/qttOw+NRoPMzEzexuPYsWP46quvLCq49Ve2FAtgW/FQLP0TxdJ/2Vo8hPwbtJ7DkM6Fd+zYgd9//x2nT5/Grl27OnyclOgyc+ZMTJkyBVFRURg+fLjFNrZYMZYQQgi5VD3xxBM4cuQINBoNCgsLO6zqKF0HEEURMpkMZrMZJpMJCoUC3t7eCAgIQHBwMEJDQxESEsKvAbRutdTZvDshhBBCzh8lw5A+8/777+Oll15CVVUV7r77brzxxhvw8PC44P1Jk04FBQWYMWMGTp48CXt7e7zwwgt47rnnenDkHT93eXk5ioqKcPbsWSQnJyM5ORlnzpxBbm4umpqa2j2mswQZmUwGV1dX3mpJrVYjMDAQQUFB8PLywrBhw/hq0t7S2NiIbdu2ITMzE3V1dSgoKIBOp4OrqytCQkIQGxuLYcOGISYmhg7WCSHkEte6nYeXl9clffHFlmIBbCseiqV/olj6L1uLhxBbI7U92759O77//ntUV1ejvLwcwD+JLNL5/5AhQxATE4PrrrsOs2bNgiiKcHR0BIALWhxECCGEkN5RX1+P+vp6VFZWoqSkBLm5ucjMzER6ejoyMzORm5uL0tLS89qn1GopMDAQoaGhCA8Ph7u7O+bMmQMfHx8rRUIIIYT8e1AyDOkzs2bNwk8//QSz2YytW7fihhtuuOh9SqWDX3rpJbz00ksAgDvuuAMbNmzok7LCzc3NKCsrQ05ODtLS0pCcnIykpCSkp6dDo9F0+JiuqsjU1dXhgw8+wOLFi3ulj2hZWRnWrVuHTz75BDU1NRYl2jsaZ2BgIIYOHYqpU6di8uTJCAkJser4CCGEEEIIIYSQvrJt2zZs3boVJ0+exNGjRzvcxtHREa6urpg9ezYmTZqE2NhYREVFWWxDbZAIIYSQSw9jDEajEY2NjaitrUVFRQWKioqQk5ODjIwMpKenIysrC/n5+aitrT2vfWdnZyM0NNQ6AyeEEEL+ReR9PQDy73X48GGYzWYAwNixY3tkn1JySEhICJycnFBXVwdBENDU1AQ7O7seeY7zoVQq4e/vD39/f1x++eX89traWuh0OmRmZuLMmTNISkpCSkoKMjMzUVlZ2eG+mpubwRhDYGBgr4x97969eOyxx3Dy5EkALRVrFAoFL/8otUGSfhhjKCwsRFFREY4cOYJt27Zhzpw5mDp1KlxcXHplzIQQQgghhBBCSG954403cODAAQCAXC6H2Wzm8xxjxoxBfHw8pk+fjquvvhpKpZJXfWlbAYYSYQghhJD+z2w287lwoGV+XKFQQKFQQKVSQa1WIyEhAWazGQaDAQ0NDaipqUFpaSk0Gg2ysrJ4kkx2djaKioo6bLUkk8koEYYQQgjpIVQZhvQJnU4HtVoNQRAQGxuLlJSUHt3/vn37MH/+fOTn52PmzJlYvXo1fH19e/Q5eprZbEZVVRU0Gg3Onj2LlJQUJCUlITU1FTk5ObzVUmZmJsLDw602BlEU8cMPP+DJJ59Edna2xYSeg4MDvLy8UF5ejvr6etjZ2UEURRiNRn7gLpPJYDKZ+D4XLVqEV199Fa6urrTajRBCCCGEEEKIzVCpVKirq4NMJoPZbIZKpcKDDz6IKVOmICEhAW5ubhbbG41GiKJI58WEEELIv5DJZEJzczPq6upQVVUFnU6HvLw83mopKysLKSkp8Pf3R1paGs2lE0IIIT2AKsOQPpGcnMz/f/DgwQB6tixwaGgob+lTVVUFJyenHtmvNYmiCA8PD3h4eGDQoEGYPXs2gH9aLeXm5iI9Pd1qiTCMMYiiiOTkZLz00ks8EcZoNCIuLg6zZs1CVFQUBg4ciLCwMDQ3NyMnJwfZ2dlIS0vD0aNHceTIEYu+6AqFAmvXrsXJkyexZcsW+Pv700E8IYQQQgghhJBLXklJCerq6qBUKmEymSAIAmpqarB+/XqkpqYiKioKMTEx/Bza09MTcnn7aThpjZq12yATQggh5MI99NBDuPnmmzFu3LgL3odMJoODgwNfcBoZGYmxY8fyVkt1dXXQ6XR87pyODQghhJCLR8kwpE8kJiby/4+PjwfwzwRQT1AqlaitrYUoimhsbISzs3OP7butwsJC2NnZwcvLyyr7b91qqafaSXXlueeeQ2pqKoCWVWv//e9/ccsttyAyMrLdtt7e3hg1ahT/N2MMO3fuxObNm7F582Y0NDRAqVTi4MGDePLJJ7F69ep2K+MIIYQQQgghhJBLzenTpwG0LGBprbi4GD/88IPFbUqlEuHh4UhISMCgQYMwePBgREdHIygoqNPFO5QkQwghhPQfq1evRmpqKt555x2+uLcntG215OfnZ3EfIYQQQi4OJcOQPiElw8hkMgwaNKjH9itVHcnJyeH/dnFx6bH9d+SJJ56AVqtFfHw8li1b1u/bMXVGEAQcOXIE27Zt471PV6xYgWeeeQaiKLbriQq0TM5JP4IgQBRFXHvttRgwYACGDBmCVatWITMzEzKZDF9//TWmTJmC2267rQ+jJIQQQgghhBBCLp6Liwvmzp2L2tpa5OTkQKvV8gq1bTU3NyMtLQ1paWn47rvv+O3u7u68AuugQYMwcOBAREZGws/Pr8MqMoQQQgjpfVqtFgDwxx9/4I477sCWLVsQFRXVoxXQpTl2qqhOCCGE9CyB9WQ5DkK6KSYmBhkZGXB2dsbhw4cRFxfXI/s1mUyQyWTYsGED7r77bgDA4sWL8eGHH/bI/jsik8nAGENAQADS0tIuiZZMHamoqMB///tfrFq1CjKZDFdffTV+/fVXAODJLufrl19+wR133MFbJw0ePBh//vmn1ROUCCGEEEIIIYQQa2tubkZ9fT0qKipQUFCAzMxM5OfnIzc3F3l5edBoNNDpdKirqzuv/QYGBiI2NhZDhgxBbGwsLrvsMl5VlxBCCCG9688//8SVV17JF4wGBQVhy5YtGDVqFL8ecTFaJ9U0NTXBzs6uJ4ZNCCGEEFBlGNJHMjMzAbRUI/ntt9+Qn58PPz8/eHl5QaVSwd7eHgqF4oJLAaampkIQBDDGerRsYVsajQaMMchkMgQEBJwzEWbt2rVISEhAXFwcvL29z+u5euLAuiupqan466+/ALS0rnrssccA4IIy3KUcu+uvvx4fffQRZs+eDVEUcfr0aWRmZmLYsGE9O3hCCCGEEEIIIaSXKZVKKJVKuLm5ITw8HBMmTIDZbEZzczP0ej1KS0uRm5uLrKws5OfnIycnB/n5+SgsLERZWRkMBkOH+9VoNNBoNNi9ezcAYN68edi0aVNvhkYIIYSQ/2/kyJF4/vnn8c4776CxsREFBQWYPXs2Vq9ejalTp170/kVRRElJCY4cOYJNmzbh66+/7oFRE0IIIQSgZBjSBzIyMsAYg729PfR6PU+6cHd3R3BwMKKiovhPaGgo/Pz84OHhAScnJ9jZ2XVZKlhKnklJSeG3DR061GqxSD3CzWYzYmJiAHSetJKbm4vFixcDAIYPH46jR4926zkqKyvh7u5u1UQYANi/fz+ys7MBABMnTsTYsWMB4IJKM0qJSNK+rr76auzevRsKhQKbN2/GsGHDLrjaDCGEEEIIIYQQ0l+Jogh7e3vY29vD29ubV3QxmUxoaGhAdXU1tFotsrKyeAWZ3NxcFBQUdNpqady4cb0dBiGEEEL+P0dHRzz99NMwmUx477330NzcjIKCAjz00ENoaGjA7Nmzu72vtgtPCwsL8fnnn2PPnj34448/MGrUqA63I4QQQsiFoWQY0uuOHz8OAGhsbLS4vbKyEpWVlTh16hS/TRAE+Pv7IzQ0FFFRUYiOjkZERASCg4Ph4+MDV1dXODo6QqlUQhRFfoCYk5PD99FTLZi6igUAEhISOtxGOnBNTU0FACgUCr7tuaq9lJWVYcCAARAEASNHjsTPP//cg6O3tG/fPl66efr06Rfd7kkQBJhMJnh6emLy5MnYvXs3DAYDioqKALT8Xqyd4EMIIYQQQgghhPQHMpkMzs7OcHZ2RkBAAEaMGAHGGAwGA2+1pNFokJmZiby8PN5m6dixYxg5cmRfD58QQgj512KMwdnZGa+++ioAYOXKlTAYDMjLy8Nzzz0HURQxa9asLvchXSMQRRGNjY1IT0/HqlWrsHPnTuTn5/PtrLmwlxBCCPk3omQY0uv8/f2xYMECVFdXIycnB0VFRSgrK4PZbG63LWMMhYWFKCwsxIEDB/jtDg4OCAgIQEREBKKjoxEdHY3Q0FBERERAr9ejuLgYjDH4+fnB0dHRarEkJiYCaJnUGjBgAAC0q3YiHegeO3YMAGA0Gvm2nZEek5mZCb1ej4aGBmi1Wov7etrRo0fBGIMoijwD/WJJ4wwODoanpyfKy8sBAHq9HiqVqkeegxBCCCGEEEIIuRQJgtCu1dL48eMtWi2VlJQgNja2r4dKCCGE/GtJVdAFQcCrr74Kb29vPP744wCArKwsLFy4EAqFAtOnT7eohi79vzTnXlZWhj179uCbb77B1q1bYTQaLfYPAKNHj+a3EUIIIeTiUTIM6XVjx47FoEGDoNfrUVFRgZKSEhQVFSE/P9+iPHBxcTFqamo63EdDQwMyMzORmZmJ3377jd/u4eEBd3d3XuHkXEknF0uqYuPo6Mgnpzo7UJWSYeRyOa8M09m20sFvSkoKmpqaAIC3LbKGwsJClJaWQhAEDBgwoMcSVaT4QkND4eTkhPLyctTU1KCuro6SYQghhBBCCCGEkA60bbVECCGEkL7Veh5/yZIlUKvVuPPOO9HU1ITq6mrceOONWLduHe666y6Lx5hMJtTU1OCdd97B/v378ddff/H7ZDIZTCYT/P39MXbsWCxcuBBXXnllu+cjhBBCyIWjZBjS62QyGVxdXeHq6orAwEB+e1NTE2pra1FdXY3y8nLodDpoNBqeJCOVCC4pKWnXYklSUVGBiooKODg4wGAwYPjw4VaLw2g0Ijc3F4IgwNPTEyEhIQDaH6hK1VGSkpIAtFS1kVo3neugNikpiSfGSLFY40A4OTmZ//+gQYMAnLuF0/kIDQ1FWVkZAKCqqooSYQghhBBCCCGEEEIIIYRckubOnQuj0YjnnnsOGo0GAPDQQw+hsLAQS5cuRXNzM06ePIlNmzbhf//7H/R6PZ9vN5lMEEUR3t7eWLJkCS6//HKrLoQlhBBC/s0oGYb0G3Z2drCzs4OnpyfCw8MBtFRIaWhogF6vR2VlJcrKyqDVaqHRaCyqyBQWFqK8vBwmkwlAS+UYwLo9NtPT0wG0JKeEhYVBJpNZlEGUiKIIg8HAE2e8vLwQHBzMH9sR6fbU1FReJnHIkCFdPuZiSFVrACA+Pr7H9282m1FfX89/F05OTj3+HIQQQgghhBBCCCGEEEJIb7jtttvQ2NiIF198EaWlpWhoaMCHH36IwsJC1NXVYdOmTXxbacGsvb09BgwYgHvuuQczZ86Em5sbgJ5dmEoIIYSQf1AyDOl3WieUCIIAR0dHODo6wtfXl29jNBpRX1+Pmpoa3mpJq9XyKjL5+fk4efKkVSvDSC2SGGO8RZLZbLY4aJViaZs4I4pih4kzEungOCcnh1eGiYmJsVosiYmJAFqq9kiVYXoi6cZsNkMURWRmZvJ/u7u7W9xHCCGEEEIIIYQQQgghhFwqpLnthQsXQi6X4+GHH0ZtbS3KysrwySefAPhnjt9sNsPFxQWzZ8/GlClTcOONN1rsizFGiTCEEEKIlVAyDOl3OkvCaJ08IpfL4eLiAhcXly5bLUVERFhtnEePHuXjHThwYIfbSMkxJ06c4DFILZLaJs5IpDjz8vJQWloKxhiCgoJgZ2fXZQLNxZASexwcHHi7p54gJfKkpaXx26Kjo3ts/4QQQgghhBBCCCGEEEJIb5ISXf7++2+Ul5fDyckJtbW1UCgUEAQBJpMJJpMJAQEBuP/++3HDDTfwyu9tF4laY76fEEIIIS0oGYb0K10le3TnoLCjVkvWIiW4KBQKJCQkAPgn+UMi/VuqvCI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+      "text/plain": [
+       "<Figure size 1800x1500 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Set up the plot\n",
+    "sns.set_context(\"paper\", font_scale=1)\n",
+    "np.random.seed(1)\n",
+    "\n",
+    "fig, ax = plt.subplots(\n",
+    "    nrows=2, dpi=300, figsize=(6, 5), sharex=True, constrained_layout=True\n",
+    ")\n",
+    "sns.despine(bottom=True, left=True)\n",
+    "\n",
+    "for i in np.linspace(0, 1, 6):\n",
+    "    ax[0].axhline(y=i, color=\"gray\", linestyle=\"-\", lw=0.75, alpha=0.5)\n",
+    "\n",
+    "for i in np.linspace(0.2, 0.8, 4):\n",
+    "    ax[1].axhline(y=i, color=\"gray\", linestyle=\"-\", lw=0.75, alpha=0.5)\n",
+    "\n",
+    "\n",
+    "plot_dict = dict(\n",
+    "    palette=[\"#984ea3\", \"#66c2a5\", \"#fc8d62\"],\n",
+    "    zorder=1,\n",
+    "    x=\"Dataset\",\n",
+    "    y=\"Value\",\n",
+    "    hue=\"Type\",\n",
+    "    alpha=0.9,\n",
+    "    legend=True,\n",
+    "    size=1.5,\n",
+    "    jitter=0.4,\n",
+    "    hue_order=[\"Contra\", \"Ipsi\", \"Hom\"],\n",
+    ")\n",
+    "\n",
+    "# Top panel\n",
+    "parc = \"dkt\"\n",
+    "df = pd.read_csv(f\"./data/dwi_ipsi_{parc}.csv\", delimiter=\" \")\n",
+    "df = df[df.Dataset != \"Dataset\"]\n",
+    "df.Value = df.Value.astype(float)\n",
+    "uniques = np.unique(df.Dataset)\n",
+    "\n",
+    "for i in datasets:\n",
+    "    if i not in uniques:\n",
+    "        df.loc[df.index[-1] + 1] = {\n",
+    "            \"Dataset\": i,\n",
+    "            \"Atlas\": \"DKT\",\n",
+    "            \"Type\": \"Ipsi\",\n",
+    "            \"Value\": np.nan,\n",
+    "        }\n",
+    "\n",
+    "df.Dataset.replace(replace, inplace=True)\n",
+    "df.sort_values(by=[\"Dataset\", \"Type\"], inplace=True)\n",
+    "sns.stripplot(data=df, ax=ax[0], **plot_dict)\n",
+    "ax[0].get_legend().remove()\n",
+    "\n",
+    "# Bottome panel\n",
+    "parc = \"dkt\"\n",
+    "df = pd.read_csv(f\"./data/func_ipsi_{parc}.csv\", delimiter=\" \")\n",
+    "df = df[df.Dataset != \"Dataset\"]\n",
+    "df.Value = df.Value.astype(float)\n",
+    "uniques = np.unique(df.Dataset)\n",
+    "\n",
+    "for i in datasets:\n",
+    "    if i not in uniques:\n",
+    "        df.loc[df.index[-1] + 1] = {\n",
+    "            \"Dataset\": i,\n",
+    "            \"Atlas\": \"DKT\",\n",
+    "            \"Type\": \"Ipsi\",\n",
+    "            \"Value\": np.nan,\n",
+    "        }\n",
+    "\n",
+    "df.Dataset.replace(replace, inplace=True)\n",
+    "df.sort_values(by=[\"Dataset\", \"Type\"], inplace=True)\n",
+    "sns.stripplot(data=df, ax=ax[1], **plot_dict)\n",
+    "ax[1].get_legend().remove()\n",
+    "\n",
+    "plt.setp(ax[1].get_xticklabels(), rotation=80, ha=\"right\", rotation_mode=\"anchor\")\n",
+    "\n",
+    "dx = 15 / 300.0\n",
+    "dy = 0 / 300.0\n",
+    "offset = mpl.transforms.ScaledTranslation(-dx, dy, fig.dpi_scale_trans)\n",
+    "# apply offset transform to all x ticklabels.\n",
+    "for axes in [ax[0], ax[1]]:\n",
+    "    for label in axes.xaxis.get_majorticklabels():\n",
+    "        label.set_transform(label.get_transform() + offset)\n",
+    "\n",
+    "\n",
+    "# Deal with legends\n",
+    "handles, labels = ax[1].get_legend_handles_labels()\n",
+    "\n",
+    "legend = fig.legend(\n",
+    "    handles[:],\n",
+    "    [\"Contralateral\", \"Ipsilateral\", \"Homotopic\"],\n",
+    "    ncol=1,\n",
+    "    loc=\"center right\",\n",
+    "    bbox_to_anchor=(1.23, .55),\n",
+    "    bbox_transform=plt.gcf().transFigure,\n",
+    "    markerscale=3,\n",
+    ")\n",
+    "\n",
+    "\n",
+    "for axes in ax:\n",
+    "    length = 0\n",
+    "    axes.xaxis.set_tick_params(length=length, width=1)\n",
+    "    axes.yaxis.set_tick_params(length=length, width=1)\n",
+    "\n",
+    "\n",
+    "ax[0].set_ylabel(\"Ratio of Total Streamlines\", fontsize=9)\n",
+    "ax[1].set_ylabel(\"Absolute Correlation Coefficient\", fontsize=9)\n",
+    "ax[0].set_title(\"Homophyly and Homotopy Metrics for DKT Parcellation\", fontsize=13)\n",
+    "ax[1].set_xlabel(\"\")\n",
+    "\n",
+    "fig.savefig(\"./figures/figure4.pdf\", bbox_inches=\"tight\", dpi=300)\n",
+    "fig.savefig(\"./figures/figure4.png\", bbox_inches=\"tight\", dpi=300)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "m2g",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.19"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/docs/paper/figures/figure3.pdf b/docs/paper/figures/figure3.pdf
new file mode 100644
index 000000000..37b837e5e
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diff --git a/docs/paper/figures/figure3.png b/docs/paper/figures/figure3.png
new file mode 100644
index 000000000..2c04a9c3d
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diff --git a/docs/paper/figures/figure4.pdf b/docs/paper/figures/figure4.pdf
new file mode 100644
index 000000000..2d026854d
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diff --git a/docs/paper/figures/figure4.png b/docs/paper/figures/figure4.png
new file mode 100644
index 000000000..499d5dfbb
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diff --git a/docs/paper/index.rst b/docs/paper/index.rst
new file mode 100644
index 000000000..60239ee44
--- /dev/null
+++ b/docs/paper/index.rst
@@ -0,0 +1,20 @@
+***********************
+Manuscript Figures
+***********************
+.. toctree::
+   :hidden:
+
+.. _figures:
+
+The outputs of the datasets analyzed in the manuscript is located in the `open-access AWS S3 bucket <https://registry.opendata.aws/open-neurodata/>`_, and described in `our website <https://neurodata.io/mri/>`_.
+
+Figures
+======
+
+.. toctree::
+   :maxdepth: 1
+   :titlesonly:
+
+   figure2
+   figure3
+   figure4
diff --git a/docs/paper/ipsi-calc.py b/docs/paper/ipsi-calc.py
new file mode 100644
index 000000000..ccea672e8
--- /dev/null
+++ b/docs/paper/ipsi-calc.py
@@ -0,0 +1,419 @@
+import numpy as np
+import os
+import csv
+import networkx as nx
+from matplotlib import pyplot as plt
+import m2g.utils.cloud_utils as cloud_utils
+from m2g.utils.gen_utils import as_directory
+from graspologic.utils import pass_to_ranks, import_edgelist
+from argparse import ArgumentParser
+
+import json
+from plotly.graph_objs import *
+
+
+def load_avg_ptr():
+
+    localpath = "/discrim-data/diffusion"
+    datasets = [
+        "SWU4",
+        "HNU1",
+        "NKIENH",
+        "XHCUMS",
+        "BNU1",
+        "BNU3",
+        "IPCAS8",
+        "NKI1",
+        "NKI24",
+        "MRN1",
+    ]
+    dsize = [382, 298, 192, 120, 114, 46, 40, 36, 36, 20]
+
+    atlas = "Desikan_space-MNI152NLin6_res-2x2x2"
+    # atlas = 'Hammersmith_space-MNI152NLin6_res-2x2x2/'
+    # atlas = 'AAL_space-MNI152NLin6_res-2x2x2/'
+
+    # localpath = '/discrim-data/functional'
+    # datasets = ['SWU4','HNU1','NYU2','XHCUMS','UPSM1','BNU3','IPCAS7','SWU1','IPCAS1','BNU1']
+    # dsize = [429,300,300,247,230,144,144,119,114,106]
+    # atlas = '_mask_Desikan_space-MNI152NLin6_res-2x2x2_mask_file_..m2g_atlases..atlases..label..Human..Desikan_space-MNI152NLin6_res-2x2x2.nii.gz'
+    # atlas='_mask_Hammersmith_space-MNI152NLin6_res-2x2x2_mask_file_..m2g_atlases..atlases..label..Human..Hammersmith_space-MNI152NLin6_res-2x2x2.nii.gz/'
+    # atlas='_mask_AAL_space-MNI152NLin6_res-2x2x2_mask_file_..m2g_atlases..atlases..label..Human..AAL_space-MNI152NLin6_res-2x2x2.nii.gz/'
+
+    # Calculate total size of scans
+    tot = sum(dsize)
+
+    for idx, dset in enumerate(datasets):
+        # get list of all files belonging to atlas:
+        connectomes = os.listdir(f"{localpath}/{dset}/{atlas}")
+
+        if idx > 0:
+            for name in connectomes:
+                if "mean-ptr" in name:
+                    mean_files.append(f"{localpath}/{dset}/{atlas}/{str(name)}")
+                if "variance-ptr" in name:
+                    var_files.append(f"{localpath}/{dset}/{atlas}/{str(name)}")
+
+        else:  # itterate through list and calculate mean and variance of edgelist
+            mean_files = [
+                f"{localpath}/{dset}/{atlas}/{str(name)}"
+                for name in connectomes
+                if "mean-ptr" in name
+            ]
+            var_files = [
+                f"{localpath}/{dset}/{atlas}/{str(name)}"
+                for name in connectomes
+                if "variance-ptr" in name
+            ]
+
+    # Create the weighted averages for mean and variance
+    list_of_means, verts = import_edgelist(
+        mean_files, delimiter=" ", return_vertices=True
+    )
+    if not isinstance(list_of_means, list):
+        list_of_means = [list_of_means]
+
+    mean_connectome = np.array(
+        [array * (dsize[idx] / tot) for idx, array in enumerate(list_of_means)]
+    )
+    mean_connectome = np.atleast_3d(mean_connectome)
+    mean_connectome = np.sum(mean_connectome, axis=0)
+
+    return mean_connectome
+
+
+def ipsi_calc(connectome, atlas, pipeline="dwi", verbose=False):
+    if "DKT" in atlas:
+        ipsi = []
+        contra = []
+        hom = []
+
+        with open("/DKT_space-MNI152NLin6_res-1x1x1.json") as f:
+            des_dat = json.load(f)
+
+        r, c, _ = connectome.shape
+        for j in range(r):
+            for i in range(c):
+                if (
+                    des_dat["rois"][str(i)]["label"]
+                    and des_dat["rois"][str(j)]["label"]
+                ):
+                    if i > j:
+                        if (
+                            des_dat["rois"][str(i)]["label"][0]
+                            == des_dat["rois"][str(j)]["label"][0]
+                        ):
+                            ipsi.append(connectome[j][i])
+                        elif i == j + 41:
+                            hom.append(connectome[i][j])
+                        elif (
+                            des_dat["rois"][str(i)]["label"][0]
+                            != des_dat["rois"][str(j)]["label"][0]
+                        ):
+                            contra.append(connectome[j][i])
+
+        # r,c,_ = connectome.shape
+        # for j in range(r):
+        #    for i in range(c):
+        #        if i>j:
+        #            if j+35==i:
+        #                hom.append(connectome[j][i])
+        #            elif i<35 and j<35:
+        #                ipsi.append(connectome[j][i])
+        #            elif i>=35 and j>=35:
+        #                ipsi.append(connectome[j][i])
+        #            else:
+        #                contra.append(connectome[j][i])
+
+    if "Hammer" in atlas:
+        ipsi = []
+        contra = []
+        hom = []
+
+        r, c, _ = connectome.shape
+        for j in range(r):
+            for i in range(c):
+                if i not in {43, 48} and j not in {43, 48}:
+                    if i > j:
+                        if (i == j + 1) and (j % 2 == 1):
+                            hom.append(connectome[j][i])
+                        elif i % 2 == j % 2:
+                            ipsi.append(connectome[j][i])
+                        else:
+                            contra.append(connectome[j][i])
+
+    if "AAL" in atlas:
+        ipsi = []
+        contra = []
+        hom = []
+
+        r, c, _ = connectome.shape
+        for j in range(r):
+            for i in range(c):
+                if i > j:
+                    if (i == j + 1) and (j % 2 == 1):
+                        hom.append(connectome[j][i])
+                    elif i % 2 == j % 2:
+                        ipsi.append(connectome[j][i])
+                    else:
+                        contra.append(connectome[j][i])
+
+    hom = np.array(hom)
+    ipsi = np.array(ipsi)
+    contra = np.array(contra)
+
+    if pipeline == "dwi":
+        tot = np.sum(ipsi) + np.sum(hom) + np.sum(contra)
+        tot_ipsi = np.sum(ipsi)
+        tot_hom = np.sum(hom)
+        tot_contra = np.sum(contra)
+
+        mean_ipsi = np.mean(ipsi)
+        mean_hom = np.mean(hom)
+        mean_contra = np.mean(contra)
+
+        if verbose:
+            print(f"Total = {tot}")
+            print(f"Ipsilateral = {mean_ipsi} ({tot_ipsi/tot})")
+            print(f"Homotopic = {mean_hom} ({tot_hom/tot})")
+            print(f"Contralateral = {mean_contra} ({tot_contra/tot})")
+
+        if max([mean_ipsi, mean_hom, mean_contra]) == mean_ipsi:
+            ans = "i"
+        elif max([mean_ipsi, mean_hom, mean_contra]) == mean_hom:
+            ans = "h"
+        else:
+            ans = "c"
+
+        return (
+            ans,
+            mean_ipsi,
+            mean_hom,
+            mean_contra,
+            tot_ipsi / tot,
+            tot_hom / tot,
+            tot_contra / tot,
+        )
+
+    elif pipeline == "func":
+        # Because the functional connectomes are correlation, there isn't any normalizing to be done
+        tot_ipsi = np.mean(ipsi)
+        tot_hom = np.mean(hom)
+        tot_contra = np.mean(contra)
+
+        if verbose:
+            print(f"Ipsilateral = {tot_ipsi}")
+            print(f"Homotopic = {tot_hom}")
+            print(f"Contralateral = {tot_contra}")
+
+        if max([tot_ipsi, tot_hom, tot_contra]) == tot_ipsi:
+            ans = "i"
+        elif max([tot_ipsi, tot_hom, tot_contra]) == tot_hom:
+            ans = "h"
+        else:
+            ans = "c"
+
+        return ans, tot_ipsi, tot_hom, tot_contra
+
+
+def main():
+    parser = ArgumentParser(
+        description="This is an end-to-end connectome estimation pipeline from M3r Images."
+    )
+    parser.add_argument(
+        "output_dir",
+        help="""The local directory where the output
+        files should be stored.""",
+    )
+    parser.add_argument(
+        "pipeline",
+        help="""Pipeline that created the data""",
+    )
+    parser.add_argument(
+        "--input_dirs",
+        action="store",
+        help="""The directory with the input dataset
+        formatted according to the BIDS standard.
+        To use data from s3, just pass `s3://<bucket>/<dataset>` as the input directory.""",
+        nargs="+",
+        default=None,
+    )
+    parser.add_argument(
+        "--ptr", action="store", help="whether to pass to ranks", default=False
+    )
+    parser.add_argument(
+        "--atlases",
+        action="store",
+        help="which atlases to use",
+        nargs="+",
+        default=None,
+    )
+    result = parser.parse_args()
+    input_dirs = result.input_dirs
+    output_dir = result.output_dir
+    pipe = result.pipeline
+    PTR = result.ptr
+    atlases = result.atlases
+
+    for input_dir in input_dirs:
+        title = input_dir.split("/")[3]
+        atlases = result.atlases
+        # Inputs needed:
+
+        if "s3://" in input_dir:
+            # grab files from s3
+            creds = bool(cloud_utils.get_credentials())
+
+            buck, remo = cloud_utils.parse_path(input_dir)
+            home = os.path.expanduser("~")
+            input_dir = as_directory(home + "/.m2g/input", remove=True)
+            if not creds:
+                raise AttributeError("""No AWS credentials found""")
+
+            # Get S3 input data if needed
+            if pipe == "func":
+                if atlases is not None:
+                    for atlas in atlases:
+                        info = f"_mask_{atlas}"
+                        cloud_utils.s3_get_data(
+                            buck, remo, input_dir, info=info, pipe=pipe
+                        )
+                else:
+                    info = "_mask_"
+                    cloud_utils.s3_get_data(buck, remo, input_dir, info=info, pipe=pipe)
+            elif pipe == "dwi":
+                if atlases is not None:
+                    for atlas in atlases:
+                        info = f"{atlas}"
+                        cloud_utils.s3_get_data(
+                            buck, remo, input_dir, info=info, pipe=pipe
+                        )
+                else:
+                    info = ""
+                    cloud_utils.s3_get_data(buck, remo, input_dir, info=info, pipe=pipe)
+
+        # DEF LOAD and AVERAGE NON-PTR CONNECTOMES
+        if PTR:
+            mean_connectome = load_avg_ptr()
+
+        if pipe == "func":  # Because CPAC makes terrible output directory trees
+            atlases = os.listdir(f"{input_dir}")
+
+        # Write the dataset being analyzed
+        # with open('results.csv', mode='a') as result_file:
+        #    result_writer = csv.writer(result_file, delimiter=" ")
+        #    result_writer.writerow([f"{title}"])
+
+        for atlas in atlases:
+            # ACTUAL IPSI-CALC OCCURING HERE
+            connectomes = os.listdir(f"{input_dir}/{atlas}")
+
+            results = []
+            ipsi = []
+            hom = []
+            contra = []
+
+            # just for percentage of edges calculation
+            ipsi_t = []
+            hom_t = []
+            contra_t = []
+
+            for cons in connectomes:
+                node_weights, nodes = import_edgelist(
+                    f"{input_dir}/{atlas}/{cons}", delimiter=" ", return_vertices=True
+                )
+                connectome = np.array(node_weights)
+                connectome = np.atleast_3d(connectome)
+
+                if pipe == "dwi":
+                    spread, i, h, c, i_t, h_t, c_t = ipsi_calc(
+                        connectome, atlas, pipeline=pipe, verbose=False
+                    )
+                    ipsi_t.append(i_t)
+                    hom_t.append(h_t)
+                    contra_t.append(c_t)
+
+                else:
+                    spread, i, h, c = ipsi_calc(
+                        connectome, atlas, pipeline=pipe, verbose=False
+                    )
+
+                results.append(spread)
+                ipsi.append(i)
+                hom.append(h)
+                contra.append(c)
+
+            if "DKT" in atlas:
+                a = "dkt"
+                atlas = "DKT"
+            elif "Hammer" in atlas:
+                a = "hammer"
+                atlas = "Hammer"
+            elif "AAL" in atlas:
+                a = "aal"
+                atlas = "AAL"
+
+            if pipe == "func":
+                ipsi_t = ipsi
+                contra_t = contra
+                hom_t = hom
+
+            with open(f"{pipe}_ipsi_{a}.csv", mode="a") as result_file:
+                result_writer = csv.writer(result_file, delimiter=" ")
+                result_writer.writerow([f"Dataset", f"Atlas", f"Type", f"Value"])
+                for idx, i in enumerate(ipsi_t):
+                    result_writer.writerow([f"{title}", f"{atlas}", f"Ipsi", i])
+                for idx, i in enumerate(hom_t):
+                    result_writer.writerow([f"{title}", f"{atlas}", f"Hom", i])
+                for idx, i in enumerate(contra_t):
+                    result_writer.writerow([f"{title}", f"{atlas}", f"Contra", i])
+
+            print(f"{atlas}:")
+            print(f"Ipsi count: {results.count('i')}")
+            print(f"Mean: {np.array(ipsi).mean()} and STD: {np.array(ipsi).std()}")
+            print(f"Homotopic count: {results.count('h')}")
+            print(f"Mean: {np.array(hom).mean()} and STD: {np.array(hom).std()}")
+            print(f"Contra count: {results.count('c')}")
+            print(
+                f"Mean: {np.array(contra).mean()} and STD: {np.array(contra).std()}\n\n"
+            )
+
+            if pipe == "dwi":
+                print(
+                    f"Ipsi Mean: {np.array(ipsi_t).mean()} and STD: {np.array(ipsi_t).std()}"
+                )
+                print(
+                    f"Homotopic Mean: {np.array(hom_t).mean()} and STD: {np.array(hom_t).std()}"
+                )
+                print(
+                    f"Contra Mean: {np.array(contra_t).mean()} and STD: {np.array(contra_t).std()}\n\n"
+                )
+
+            # Print info to csv file
+
+            with open("results.csv", mode="a") as result_file:
+                result_writer = csv.writer(result_file, delimiter=" ")
+                result_writer.writerow(
+                    [
+                        f"{atlas}",
+                        f"{results.count('i')}",
+                        f"{np.array(ipsi).mean()}",
+                        f"{np.array(ipsi).std()}",
+                        f"{np.array(ipsi_t).mean()}",
+                        f"{np.array(ipsi_t).std()}",
+                        f"{results.count('h')}",
+                        f"{np.array(hom).mean()}",
+                        f"{np.array(hom).std()}",
+                        f"{np.array(hom_t).mean()}",
+                        f"{np.array(hom_t).std()}",
+                        f"{results.count('c')}",
+                        f"{np.array(contra).mean()}",
+                        f"{np.array(contra).std()}",
+                        f"{np.array(contra_t).mean()}",
+                        f"{np.array(contra_t).std()}",
+                    ]
+                )
+
+
+if __name__ == "__main__":
+    main()
diff --git a/tutorials/AWS_batch_setup.md b/docs/tutorials/AWS_batch_setup.md
similarity index 100%
rename from tutorials/AWS_batch_setup.md
rename to docs/tutorials/AWS_batch_setup.md
diff --git a/tutorials/Batch.ipynb b/docs/tutorials/Batch.ipynb
similarity index 100%
rename from tutorials/Batch.ipynb
rename to docs/tutorials/Batch.ipynb
diff --git a/tutorials/Overview.ipynb b/docs/tutorials/Overview.ipynb
similarity index 92%
rename from tutorials/Overview.ipynb
rename to docs/tutorials/Overview.ipynb
index be2201677..8e8bcf6b2 100644
--- a/tutorials/Overview.ipynb
+++ b/docs/tutorials/Overview.ipynb
@@ -88,13 +88,19 @@
     "try:\n",
     "    print(f\"Your fsl directory is located here: {os.environ['FSLDIR']}\")\n",
     "except KeyError:\n",
-    "    raise AssertionError(\"You do not have FSL installed! See installation instructions here: https://fsl.fmrib.ox.ac.uk/fsl/fslwiki/FslInstallation\")\n",
-    "    \n",
+    "    raise AssertionError(\n",
+    "        \"You do not have FSL installed! See installation instructions here: https://fsl.fmrib.ox.ac.uk/fsl/fslwiki/FslInstallation\"\n",
+    "    )\n",
+    "\n",
     "# AFNI\n",
     "try:\n",
-    "    print(f\"Your AFNI directory is located here: {subprocess.check_output('which afni', shell=True, universal_newlines=True)}\")\n",
+    "    print(\n",
+    "        f\"Your AFNI directory is located here: {subprocess.check_output('which afni', shell=True, universal_newlines=True)}\"\n",
+    "    )\n",
     "except subprocess.CalledProcessError:\n",
-    "    raise AssertionError(\"You do not have AFNI installed! See installation instructions here: https://afni.nimh.nih.gov/pub/dist/doc/htmldoc/background_install/main_toc.html\")"
+    "    raise AssertionError(\n",
+    "        \"You do not have AFNI installed! See installation instructions here: https://afni.nimh.nih.gov/pub/dist/doc/htmldoc/background_install/main_toc.html\"\n",
+    "    )"
    ]
   },
   {
@@ -138,7 +144,9 @@
     "\n",
     "print(f\"Your input and output directory will be : {input_dir} and {out_dir}\")\n",
     "\n",
-    "assert op.exists(input_dir), f\"You must have an input directory with data. Your input directory is located here: {input_dir}\""
+    "assert op.exists(\n",
+    "    input_dir\n",
+    "), f\"You must have an input directory with data. Your input directory is located here: {input_dir}\""
    ]
   },
   {
@@ -181,7 +189,7 @@
    ],
    "source": [
     "# Specify base directory and paths to input files (dwi, bvecs, bvals, and t1w required)\n",
-    "subject_id = 'sub-0025864'\n",
+    "subject_id = \"sub-0025864\"\n",
     "\n",
     "# Define the location of our input files.\n",
     "t1w = str(input_dir / f\"{subject_id}/ses-1/anat/{subject_id}_ses-1_T1w.nii.gz\")\n",
@@ -219,13 +227,13 @@
    "outputs": [],
    "source": [
     "# Use the default parameters.\n",
-    "atlas = 'desikan'\n",
-    "mod_type = 'prob'\n",
-    "track_type = 'local'\n",
-    "mod_func = 'csd'\n",
-    "reg_style = 'native'\n",
-    "vox_size = '2mm'\n",
-    "seeds = 1\n"
+    "atlas = \"desikan\"\n",
+    "mod_type = \"prob\"\n",
+    "track_type = \"local\"\n",
+    "mod_func = \"csd\"\n",
+    "reg_style = \"native\"\n",
+    "vox_size = \"2mm\"\n",
+    "seeds = 1"
    ]
   },
   {
@@ -254,7 +262,9 @@
    "source": [
     "# Auto-set paths to neuroparc files\n",
     "mask = str(atlas_dir / \"atlases/mask/MNI152NLin6_res-2x2x2_T1w_descr-brainmask.nii.gz\")\n",
-    "labels = [str(i) for i in (atlas_dir / \"atlases/label/Human/\").glob(f\"*{atlas}*2x2x2.nii.gz\")]\n",
+    "labels = [\n",
+    "    str(i) for i in (atlas_dir / \"atlases/label/Human/\").glob(f\"*{atlas}*2x2x2.nii.gz\")\n",
+    "]\n",
     "\n",
     "print(f\"mask location : {mask}\")\n",
     "print(f\"atlas location : {labels}\")"
@@ -316,7 +326,25 @@
     }
    ],
    "source": [
-    "ndmg_dwi_pipeline.ndmg_dwi_worker(dwi=dwi, bvals=bvals, bvecs=bvecs, t1w=t1w, atlas=atlas, mask=mask, labels=labels, outdir=str(out_dir), vox_size=vox_size, mod_type=mod_type, track_type=track_type, mod_func=mod_func, seeds=seeds, reg_style=reg_style, clean=False, skipeddy=True, skipreg=True)"
+    "ndmg_dwi_pipeline.ndmg_dwi_worker(\n",
+    "    dwi=dwi,\n",
+    "    bvals=bvals,\n",
+    "    bvecs=bvecs,\n",
+    "    t1w=t1w,\n",
+    "    atlas=atlas,\n",
+    "    mask=mask,\n",
+    "    labels=labels,\n",
+    "    outdir=str(out_dir),\n",
+    "    vox_size=vox_size,\n",
+    "    mod_type=mod_type,\n",
+    "    track_type=track_type,\n",
+    "    mod_func=mod_func,\n",
+    "    seeds=seeds,\n",
+    "    reg_style=reg_style,\n",
+    "    clean=False,\n",
+    "    skipeddy=True,\n",
+    "    skipreg=True,\n",
+    ")"
    ]
   },
   {
diff --git a/tutorials/Presentation.ipynb b/docs/tutorials/Presentation.ipynb
similarity index 97%
rename from tutorials/Presentation.ipynb
rename to docs/tutorials/Presentation.ipynb
index 2558d8fa0..4b4abe7a0 100644
--- a/tutorials/Presentation.ipynb
+++ b/docs/tutorials/Presentation.ipynb
@@ -57,8 +57,9 @@
    ],
    "source": [
     "import warnings\n",
-    "warnings.filterwarnings('ignore')\n",
-    "warnings.simplefilter('ignore')\n",
+    "\n",
+    "warnings.filterwarnings(\"ignore\")\n",
+    "warnings.simplefilter(\"ignore\")\n",
     "\n",
     "# Standard Library\n",
     "import os\n",
@@ -82,29 +83,31 @@
     "from ndmg.utils.bids_utils import NameResource\n",
     "\n",
     "# Inputs\n",
-    "dwi = '/mnt/labbook/input/data/BNU1/sub-0025864/ses-1/dwi/sub-0025864_ses-1_dwi.nii.gz'\n",
-    "bvals = '/mnt/labbook/input/data/BNU1/sub-0025864/ses-1/dwi/sub-0025864_ses-1_dwi.bval'\n",
-    "bvecs = '/mnt/labbook/input/data/BNU1/sub-0025864/ses-1/dwi/sub-0025864_ses-1_dwi.bvec'\n",
-    "t1w = '/mnt/labbook/input/data/BNU1/sub-0025864/ses-1/anat/sub-0025864_ses-1_T1w.nii.gz'\n",
-    "atlas = 'desikan'\n",
-    "mask = '/ndmg_atlases/atlases/mask/MNI152NLin6_res-2x2x2_T1w_descr-brainmask.nii.gz'\n",
-    "labels = ['/ndmg_atlases/atlases/label/Human/desikan_space-MNI152NLin6_res-2x2x2.nii.gz']\n",
-    "outdir = '/mnt/labbook/output/untracked'\n",
-    "vox_size = '2mm'\n",
-    "mod_type = 'det'\n",
-    "track_type = 'local'\n",
-    "mod_func = 'csa'\n",
+    "dwi = \"/mnt/labbook/input/data/BNU1/sub-0025864/ses-1/dwi/sub-0025864_ses-1_dwi.nii.gz\"\n",
+    "bvals = \"/mnt/labbook/input/data/BNU1/sub-0025864/ses-1/dwi/sub-0025864_ses-1_dwi.bval\"\n",
+    "bvecs = \"/mnt/labbook/input/data/BNU1/sub-0025864/ses-1/dwi/sub-0025864_ses-1_dwi.bvec\"\n",
+    "t1w = \"/mnt/labbook/input/data/BNU1/sub-0025864/ses-1/anat/sub-0025864_ses-1_T1w.nii.gz\"\n",
+    "atlas = \"desikan\"\n",
+    "mask = \"/ndmg_atlases/atlases/mask/MNI152NLin6_res-2x2x2_T1w_descr-brainmask.nii.gz\"\n",
+    "labels = [\n",
+    "    \"/ndmg_atlases/atlases/label/Human/desikan_space-MNI152NLin6_res-2x2x2.nii.gz\"\n",
+    "]\n",
+    "outdir = \"/mnt/labbook/output/untracked\"\n",
+    "vox_size = \"2mm\"\n",
+    "mod_type = \"det\"\n",
+    "track_type = \"local\"\n",
+    "mod_func = \"csa\"\n",
     "seed_dens = 1\n",
-    "reg_style = 'native'\n",
+    "reg_style = \"native\"\n",
     "clean = False\n",
-    "skipeddy=False,\n",
-    "skipreg=False,\n",
-    "buck=None,\n",
-    "remo=None,\n",
-    "push=False,\n",
-    "creds=None,\n",
-    "debug=False,\n",
-    "modif=\"\",\n",
+    "skipeddy = (False,)\n",
+    "skipreg = (False,)\n",
+    "buck = (None,)\n",
+    "remo = (None,)\n",
+    "push = (False,)\n",
+    "creds = (None,)\n",
+    "debug = (False,)\n",
+    "modif = (\"\",)\n",
     "\n",
     "assert all(map(os.path.exists, [dwi, bvals, bvecs, t1w, mask, labels[0], outdir]))\n",
     "\n",
@@ -218,7 +221,7 @@
     "    \"reg_anat\": \"anat/registered\",\n",
     "    \"fiber\": \"dwi/fiber\",\n",
     "    \"tensor\": \"dwi/tensor\",\n",
-    "    \"conn\": \"dwi/roi-connectomes\"\n",
+    "    \"conn\": \"dwi/roi-connectomes\",\n",
     "}\n",
     "\n",
     "label_dirs = [\"conn\"]  # create label level granularity\n",
@@ -246,7 +249,7 @@
     "\n",
     "if vox_size != \"1mm\" and vox_size != \"2mm\":\n",
     "    raise ValueError(\"Voxel size not supported. Use 2mm or 1mm\")\n",
-    "print('DONE')"
+    "print(\"DONE\")"
    ]
   },
   {
@@ -317,7 +320,7 @@
     "    sts = Popen(cmd, shell=True).wait()\n",
     "    print(sts)\n",
     "    print(f\"eddy correct time: {time.time() - ts} seconds.\")\n",
-    "print('DONE')"
+    "print(\"DONE\")"
    ]
   },
   {
@@ -372,7 +375,7 @@
     "    )\n",
     "np.savetxt(fbval, bvals)\n",
     "np.savetxt(fbvec, bvecs)\n",
-    "print('DONE')"
+    "print(\"DONE\")"
    ]
   },
   {
@@ -417,10 +420,9 @@
     "start_time = time.time()\n",
     "dwi_prep = mgu.match_target_vox_res(dwi_prep, vox_size, namer, sens=\"dwi\")\n",
     "print(\n",
-    "    \"%s%s%s\"\n",
-    "    % (\"Reslicing runtime: \", str(np.round(time.time() - start_time, 1)), \"s\")\n",
+    "    \"%s%s%s\" % (\"Reslicing runtime: \", str(np.round(time.time() - start_time, 1)), \"s\")\n",
     ")\n",
-    "print('DONE')"
+    "print(\"DONE\")"
    ]
   },
   {
@@ -481,7 +483,7 @@
     "    \"%s%s%s\"\n",
     "    % (\"Preprocessing runtime: \", str(np.round(time.time() - start_time, 1)), \"s\")\n",
     ")\n",
-    "print('DONE')"
+    "print(\"DONE\")"
    ]
   },
   {
@@ -642,7 +644,7 @@
     "            \"s\",\n",
     "        )\n",
     "    )\n",
-    "print('DONE')"
+    "print(\"DONE\")"
    ]
   },
   {
@@ -689,12 +691,13 @@
     "# Compute direction model and track fiber streamlines\n",
     "from dipy.tracking.streamline import Streamlines\n",
     "from dipy.tracking.local import BinaryStoppingCriterion\n",
-    "from dipy.reconst.shm import CsaOdfModel    \n",
+    "from dipy.reconst.shm import CsaOdfModel\n",
     "from dipy.tracking.local import LocalTracking\n",
     "from dipy.data import get_sphere\n",
     "from dipy.direction import ProbabilisticDirectionGetter\n",
     "import tempfile\n",
-    "tempfile.tempdir = '/tmp'\n",
+    "\n",
+    "tempfile.tempdir = \"/tmp\"\n",
     "\n",
     "dwi = dwi_prep\n",
     "gm_in_dwi = reg.gm_in_dwi\n",
@@ -704,7 +707,7 @@
     "stream_affine = np.eye(4)\n",
     "dwi_img = nib.load(dwi)\n",
     "data = dwi_img.get_data()\n",
-    "        \n",
+    "\n",
     "print(\"Creating WM tissue classifier...\")\n",
     "wm_in_dwi_data = nib.load(wm_in_dwi).get_data().astype(\"bool\")\n",
     "tiss_classifier = BinaryStoppingCriterion(wm_in_dwi_data)\n",
@@ -715,9 +718,9 @@
     "mod = CsaOdfModel(gtab, sh_order=6)\n",
     "mod_fit = mod.fit(data, wm_in_dwi_data)\n",
     "\n",
-    "print('Running local tracking...')\n",
-    "pdg = ProbabilisticDirectionGetter.from_shcoeff(mod_fit.shm_coeff, \n",
-    "    max_angle=60.0, sphere=sphere\n",
+    "print(\"Running local tracking...\")\n",
+    "pdg = ProbabilisticDirectionGetter.from_shcoeff(\n",
+    "    mod_fit.shm_coeff, max_angle=60.0, sphere=sphere\n",
     ")\n",
     "streamline_generator = LocalTracking(\n",
     "    pdg,\n",
@@ -744,9 +747,9 @@
     "trk_hdr[\"voxel_to_rasmm\"] = trk_affine\n",
     "trk_hdr[\"voxel_order\"] = \"RAS\"\n",
     "trk_hdr[\"pad2\"] = \"RAS\"\n",
-    "trk_hdr[\"image_orientation_patient\"] = np.array(\n",
-    "    [0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n",
-    ").astype(\"float32\")\n",
+    "trk_hdr[\"image_orientation_patient\"] = np.array([0.0, 0.0, 0.0, 0.0, 0.0, 0.0]).astype(\n",
+    "    \"float32\"\n",
+    ")\n",
     "trk_hdr[\"endianness\"] = \"<\"\n",
     "trk_hdr[\"_offset_data\"] = 1000\n",
     "trk_hdr[\"nb_streamlines\"] = streamlines.total_nb_rows\n",
@@ -761,7 +764,7 @@
     "        \"s\",\n",
     "    )\n",
     ")\n",
-    "print('DONE')"
+    "print(\"DONE\")"
    ]
   },
   {
@@ -800,7 +803,7 @@
     "start_time = time.time()\n",
     "for idx, label in enumerate(labels):\n",
     "    print(f\"Generating graph for {label} parcellation...\")\n",
-    "    \n",
+    "\n",
     "    # align atlas to t1w to dwi\n",
     "    print(\"%s%s\" % (\"Applying native-space alignment to \", labels[idx]))\n",
     "    labels_im_file = mgu.reorient_img(labels[idx], namer)\n",
@@ -853,15 +856,17 @@
     "\n",
     "import matplotlib\n",
     "import numpy as np\n",
-    "import networkx as nx \n",
+    "import networkx as nx\n",
     "import matplotlib.pyplot as plt\n",
     "from graspy.utils import ptr\n",
     "from graspy.plot import heatmap\n",
     "\n",
+    "\n",
     "def plot_graph(g):\n",
     "    heatmap(g)\n",
     "    plt.show()\n",
     "\n",
+    "\n",
     "mat = ptr.pass_to_ranks(np.array(nx.to_numpy_matrix(g1.g)))\n",
     "plot_graph(mat)"
    ]
@@ -924,7 +929,7 @@
     }
    ],
    "source": [
-    "np.sum(np.diagonal(quad_2) > 0)/np.sum(np.ones(len(quad_2)))"
+    "np.sum(np.diagonal(quad_2) > 0) / np.sum(np.ones(len(quad_2)))"
    ]
   },
   {
@@ -944,7 +949,7 @@
     }
    ],
    "source": [
-    "np.sum(np.diagonal(quad_3) > 0)/np.sum(np.ones(len(quad_3)))"
+    "np.sum(np.diagonal(quad_3) > 0) / np.sum(np.ones(len(quad_3)))"
    ]
   }
  ],
diff --git a/tutorials/QA_Tractography_Tutorial.ipynb b/docs/tutorials/QA_Tractography_Tutorial.ipynb
similarity index 99%
rename from tutorials/QA_Tractography_Tutorial.ipynb
rename to docs/tutorials/QA_Tractography_Tutorial.ipynb
index d2fa1a825..15aeec8b1 100644
--- a/tutorials/QA_Tractography_Tutorial.ipynb
+++ b/docs/tutorials/QA_Tractography_Tutorial.ipynb
@@ -52,14 +52,14 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "#Change the stream_path manually, the stream_path is the path for the input streamline.trk file.\n",
-    "stream_path = '/Users/zhenhu/Desktop/qa_fiber/streamlines.trk'\n",
+    "# Change the stream_path manually, the stream_path is the path for the input streamline.trk file.\n",
+    "stream_path = \"/Users/zhenhu/Desktop/qa_fiber/streamlines.trk\"\n",
     "\n",
-    "#Change the qa_out_path manually, the qa_out_path is the path for the output QA imgae file.\n",
-    "qa_out_path = '/Users/zhenhu/Desktop/vertical'\n",
+    "# Change the qa_out_path manually, the qa_out_path is the path for the output QA imgae file.\n",
+    "qa_out_path = \"/Users/zhenhu/Desktop/vertical\"\n",
     "\n",
-    "#Change the brain_path manually, the brain_path is the path for the reference brain scan, in order to get the scan volume..\n",
-    "brain_path = '/Users/zhenhu/Documents/NDD/Downloads/ndmg_outputs2/sub-0025864/ses-1/dwi/preproc/eddy_corrected_data_reor_RAS_res.nii.gz'"
+    "# Change the brain_path manually, the brain_path is the path for the reference brain scan, in order to get the scan volume..\n",
+    "brain_path = \"/Users/zhenhu/Documents/NDD/Downloads/ndmg_outputs2/sub-0025864/ses-1/dwi/preproc/eddy_corrected_data_reor_RAS_res.nii.gz\""
    ]
   },
   {
@@ -75,7 +75,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "def combine_plot(qa_out_path,brain_path):\n",
+    "def combine_plot(qa_out_path, brain_path):\n",
     "    \"\"\"\n",
     "    Combine the nine plots produced by qa_tractography function into one image.\n",
     "\n",
@@ -86,57 +86,72 @@
     "    brain_path : str\n",
     "        Path for the reference brain scan, in order to get the scan volume.\n",
     "    \"\"\"\n",
-    "    \n",
-    "    #Get the scan volume of the brain.\n",
+    "\n",
+    "    # Get the scan volume of the brain.\n",
     "    brain_ref = nib.load(brain_path)\n",
     "    brain_ref_shape = brain_ref.shape[0:3]\n",
-    "    \n",
-    "    plots_list = ['Rotate_Z_axis_000000.png','Rotate_Z_axis_000001.png','Rotate_Z_axis_000002.png',\n",
-    "                 'Rotate_Y_axis_000000.png','Rotate_Y_axis_000001.png','Rotate_Y_axis_000002.png',\n",
-    "                 'Rotate_X_axis_000000.png','Rotate_X_axis_000001.png','Rotate_X_axis_000002.png']\n",
-    "    y_labels = [\"Rotate with Z axis\",\"Rotate with Y axis\",\"Rotate with X axis\"]\n",
-    "    x_labels = [\"angle=0\",\"angle=120\",\"angle=240\"]\n",
-    "    \n",
-    "    #Temporary list to store the image nparray:\n",
-    "    im_arr=[] \n",
-    "    \n",
-    "    fig= plt.figure()\n",
-    "    plt.title(f'QA_tractography. Scan volume = {brain_ref_shape} \\n\\n', fontsize=60,fontweight='bold')\n",
+    "\n",
+    "    plots_list = [\n",
+    "        \"Rotate_Z_axis_000000.png\",\n",
+    "        \"Rotate_Z_axis_000001.png\",\n",
+    "        \"Rotate_Z_axis_000002.png\",\n",
+    "        \"Rotate_Y_axis_000000.png\",\n",
+    "        \"Rotate_Y_axis_000001.png\",\n",
+    "        \"Rotate_Y_axis_000002.png\",\n",
+    "        \"Rotate_X_axis_000000.png\",\n",
+    "        \"Rotate_X_axis_000001.png\",\n",
+    "        \"Rotate_X_axis_000002.png\",\n",
+    "    ]\n",
+    "    y_labels = [\"Rotate with Z axis\", \"Rotate with Y axis\", \"Rotate with X axis\"]\n",
+    "    x_labels = [\"angle=0\", \"angle=120\", \"angle=240\"]\n",
+    "\n",
+    "    # Temporary list to store the image nparray:\n",
+    "    im_arr = []\n",
+    "\n",
+    "    fig = plt.figure()\n",
+    "    plt.title(\n",
+    "        f\"QA_tractography. Scan volume = {brain_ref_shape} \\n\\n\",\n",
+    "        fontsize=60,\n",
+    "        fontweight=\"bold\",\n",
+    "    )\n",
     "    plt.xticks([])\n",
     "    plt.yticks([])\n",
     "    plt.axis(\"off\")\n",
     "\n",
     "    j = 0\n",
     "    for i in range(9):\n",
-    "        #Load in the nine images into a nparray one by one.\n",
+    "        # Load in the nine images into a nparray one by one.\n",
     "        im_arr = np.array(Image.open(qa_out_path + \"/\" + plots_list[i]))\n",
-    "        #Change the background of the image into black:\n",
-    "        im_arr = np.where(im_arr<=0.01, 255, im_arr) \n",
-    "        ax = fig.add_subplot(3,3,i+1)\n",
-    "        ax.imshow(im_arr,interpolation=\"none\",alpha=0.9)\n",
-    "        \n",
-    "        #Set the X labels and Y labels\n",
-    "        if i<3:\n",
-    "            ax.set_title(x_labels[i],fontsize=60,fontweight='bold')\n",
+    "        # Change the background of the image into black:\n",
+    "        im_arr = np.where(im_arr <= 0.01, 255, im_arr)\n",
+    "        ax = fig.add_subplot(3, 3, i + 1)\n",
+    "        ax.imshow(im_arr, interpolation=\"none\", alpha=0.9)\n",
+    "\n",
+    "        # Set the X labels and Y labels\n",
+    "        if i < 3:\n",
+    "            ax.set_title(x_labels[i], fontsize=60, fontweight=\"bold\")\n",
     "        if i % 3 == 0:\n",
-    "            ax.set_ylabel(y_labels[j],fontsize=60,fontweight='bold')\n",
+    "            ax.set_ylabel(y_labels[j], fontsize=60, fontweight=\"bold\")\n",
     "            j = j + 1\n",
     "        plt.xticks([])\n",
     "        plt.yticks([])\n",
-    "            \n",
-    "    fig.set_size_inches(40, 40, forward = True)\n",
-    "    fig.savefig(qa_out_path + \"/\" + 'qa_tractography.png', format='png')\n",
     "\n",
-    "    #Delete the Nine images which used to generate the qa_tractography.png \n",
+    "    fig.set_size_inches(40, 40, forward=True)\n",
+    "    fig.savefig(qa_out_path + \"/\" + \"qa_tractography.png\", format=\"png\")\n",
+    "\n",
+    "    # Delete the Nine images which used to generate the qa_tractography.png\n",
     "    for plot in plots_list:\n",
     "        if os.path.exists(qa_out_path + \"/\" + plot):\n",
     "            os.remove(qa_out_path + \"/\" + plot)\n",
     "        else:\n",
-    "            print('No such file generated from streamlines window. Please check if the streamline.trk files is generated from the pipeline correctly or not')\n",
+    "            print(\n",
+    "                \"No such file generated from streamlines window. Please check if the streamline.trk files is generated from the pipeline correctly or not\"\n",
+    "            )\n",
+    "\n",
     "\n",
-    "def qa_tractography(stream_path,qa_out_path,brain_path):\n",
+    "def qa_tractography(stream_path, qa_out_path, brain_path):\n",
     "    \"\"\"\n",
-    "    Visualize the streamlines, \n",
+    "    Visualize the streamlines,\n",
     "    then using window.record to get nine snapshots of the 3D streamslines.\n",
     "\n",
     "    Parameters\n",
@@ -148,11 +163,11 @@
     "    brain_path : str\n",
     "        Path for the reference brain scan, in order to get the scan volume.\n",
     "    \"\"\"\n",
-    "    \n",
-    "    #Use window to visualize the streamlines\n",
+    "\n",
+    "    # Use window to visualize the streamlines\n",
     "    r = window.renderer()\n",
-    "    \n",
-    "    #Load the streamline.trk file\n",
+    "\n",
+    "    # Load the streamline.trk file\n",
     "    streamlines_mni_load = nib.streamlines.load(stream_path).streamlines\n",
     "    streamlines_mni_in = Streamlines(streamlines_mni_load)\n",
     "    streamlines_actor = actor.line(\n",
@@ -164,17 +179,47 @@
     "        fake_tube=True,\n",
     "        opacity=0.3,\n",
     "    )\n",
-    "    \n",
+    "\n",
     "    r.add(streamlines_actor)\n",
-    "    #window.show(r)\n",
-    "        \n",
+    "    # window.show(r)\n",
+    "\n",
     "    showmng = window.ShowManager(r)\n",
-    "    #window.record function can rotate the 3D-image, then get the snapshot of the specific angle. \n",
-    "    window.record(r,cam_pos = (70.03, 64.97, 269.80), cam_view = (0,1,0),path_numbering= True,out_path = qa_out_path + '/Rotate_Z_axis_', az_ang=120,n_frames=3,reset_camera=True,size=(600,600))\n",
-    "    window.record(r,cam_pos = (70.03, 64.97, 269.80), cam_view = (1,0,0),path_numbering= True,out_path = qa_out_path + '/Rotate_Y_axis_', az_ang=120,n_frames=3,reset_camera=True,size=(600,600))\n",
-    "    window.record(r,cam_pos = (70.03, 64.97, 269.80), cam_view = (0,0,1),path_numbering= True,out_path = qa_out_path + '/Rotate_X_axis_', az_ang=120,n_frames=3,reset_camera=True,size=(600,600))\n",
+    "    # window.record function can rotate the 3D-image, then get the snapshot of the specific angle.\n",
+    "    window.record(\n",
+    "        r,\n",
+    "        cam_pos=(70.03, 64.97, 269.80),\n",
+    "        cam_view=(0, 1, 0),\n",
+    "        path_numbering=True,\n",
+    "        out_path=qa_out_path + \"/Rotate_Z_axis_\",\n",
+    "        az_ang=120,\n",
+    "        n_frames=3,\n",
+    "        reset_camera=True,\n",
+    "        size=(600, 600),\n",
+    "    )\n",
+    "    window.record(\n",
+    "        r,\n",
+    "        cam_pos=(70.03, 64.97, 269.80),\n",
+    "        cam_view=(1, 0, 0),\n",
+    "        path_numbering=True,\n",
+    "        out_path=qa_out_path + \"/Rotate_Y_axis_\",\n",
+    "        az_ang=120,\n",
+    "        n_frames=3,\n",
+    "        reset_camera=True,\n",
+    "        size=(600, 600),\n",
+    "    )\n",
+    "    window.record(\n",
+    "        r,\n",
+    "        cam_pos=(70.03, 64.97, 269.80),\n",
+    "        cam_view=(0, 0, 1),\n",
+    "        path_numbering=True,\n",
+    "        out_path=qa_out_path + \"/Rotate_X_axis_\",\n",
+    "        az_ang=120,\n",
+    "        n_frames=3,\n",
+    "        reset_camera=True,\n",
+    "        size=(600, 600),\n",
+    "    )\n",
     "    showmng.exit()\n",
-    "    combine_plot(qa_out_path,brain_path)"
+    "    combine_plot(qa_out_path, brain_path)"
    ]
   },
   {
@@ -201,7 +246,7 @@
     }
    ],
    "source": [
-    "qa_tractography(stream_path,qa_out_path,brain_path)"
+    "qa_tractography(stream_path, qa_out_path, brain_path)"
    ]
   },
   {
@@ -231,11 +276,11 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "stream_path = '/Users/zhenhu/Downloads/streamlines.trk'\n",
+    "stream_path = \"/Users/zhenhu/Downloads/streamlines.trk\"\n",
     "\n",
-    "qa_out_path = '/Users/zhenhu/Desktop/vertical'\n",
+    "qa_out_path = \"/Users/zhenhu/Desktop/vertical\"\n",
     "\n",
-    "brain_path = '/Users/zhenhu/Documents/NDD/Downloads/ndmg_outputs2/sub-0025864/ses-1/dwi/preproc/eddy_corrected_data_reor_RAS_res.nii.gz'"
+    "brain_path = \"/Users/zhenhu/Documents/NDD/Downloads/ndmg_outputs2/sub-0025864/ses-1/dwi/preproc/eddy_corrected_data_reor_RAS_res.nii.gz\""
    ]
   },
   {
@@ -257,7 +302,7 @@
     }
    ],
    "source": [
-    "qa_tractography(stream_path,qa_out_path,brain_path)"
+    "qa_tractography(stream_path, qa_out_path, brain_path)"
    ]
   },
   {
diff --git a/tutorials/Qa_skullstrip.ipynb b/docs/tutorials/Qa_skullstrip.ipynb
similarity index 99%
rename from tutorials/Qa_skullstrip.ipynb
rename to docs/tutorials/Qa_skullstrip.ipynb
index be348a8e8..464f85446 100644
--- a/tutorials/Qa_skullstrip.ipynb
+++ b/docs/tutorials/Qa_skullstrip.ipynb
@@ -54,7 +54,7 @@
    },
    "outputs": [],
    "source": [
-    "#import packages\n",
+    "# import packages\n",
     "import warnings\n",
     "\n",
     "warnings.simplefilter(\"ignore\")\n",
@@ -62,7 +62,7 @@
     "import nibabel as nib\n",
     "import numpy as np\n",
     "import os\n",
-    "from PIL import Image, ImageDraw,ImageFont\n",
+    "from PIL import Image, ImageDraw, ImageFont\n",
     "import matplotlib.pyplot as plt\n",
     "from m2g.stats.qa_skullstrip import gen_overlay_pngs"
    ]
@@ -88,7 +88,8 @@
    "outputs": [],
    "source": [
     "def gen_overlay_pngs(\n",
-    "    brain, original, outdir, loc=0, mean=False, minthr=2, maxthr=95, edge=False):\n",
+    "    brain, original, outdir, loc=0, mean=False, minthr=2, maxthr=95, edge=False\n",
+    "):\n",
     "    \"\"\"Generate a QA image for skullstrip.\n",
     "    will call the function plot_overlays_skullstrip\n",
     "\n",
@@ -124,7 +125,7 @@
     "    fig = plot_overlays_skullstrip(brain_data, original)\n",
     "\n",
     "    # name and save the file\n",
-    "    fig.savefig(f\"{outdir}/qa_skullstrip__{original_name}.png\", format=\"png\")\n"
+    "    fig.savefig(f\"{outdir}/qa_skullstrip__{original_name}.png\", format=\"png\")"
    ]
   },
   {
@@ -147,7 +148,9 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "def plot_overlays_skullstrip(brain, original, cmaps=None, minthr=2, maxthr=95, edge=False):\n",
+    "def plot_overlays_skullstrip(\n",
+    "    brain, original, cmaps=None, minthr=2, maxthr=95, edge=False\n",
+    "):\n",
     "    \"\"\"Shows the skull-stripped brain (green) overlaid on the original t1w (magenta)\n",
     "\n",
     "    Parameter\n",
@@ -183,8 +186,7 @@
     "    if original.shape != brain.shape:\n",
     "        raise ValueError(\"Two files are not the same shape.\")\n",
     "    brain = pad_im(brain, max(brain_shape[0:3]), pad_val=0, rgb=False)\n",
-    "    original = pad_im(original,max(brain_shape[0:3]), pad_val=0, rgb=False)\n",
-    "\n",
+    "    original = pad_im(original, max(brain_shape[0:3]), pad_val=0, rgb=False)\n",
     "\n",
     "    if cmaps is None:\n",
     "        cmap1 = LinearSegmentedColormap.from_list(\"mycmap1\", [\"white\", \"magenta\"])\n",
@@ -237,26 +239,28 @@
     "            plt.yticks([])\n",
     "\n",
     "            # Set the frame invisible\n",
-    "            ax.spines['top'].set_visible(False)\n",
-    "            ax.spines['right'].set_visible(False)\n",
-    "            ax.spines['bottom'].set_visible(False)\n",
-    "            ax.spines['left'].set_visible(False)\n",
+    "            ax.spines[\"top\"].set_visible(False)\n",
+    "            ax.spines[\"right\"].set_visible(False)\n",
+    "            ax.spines[\"bottom\"].set_visible(False)\n",
+    "            ax.spines[\"left\"].set_visible(False)\n",
     "            ax.imshow(atl, interpolation=\"none\", cmap=cmaps[0], alpha=0.9)\n",
     "            ax.imshow(\n",
     "                opaque_colorscale(\n",
     "                    cmaps[1], image, alpha=0.9, vmin=min_val, vmax=max_val\n",
     "                )\n",
     "            )\n",
-    "            if idx ==3:\n",
-    "                plt.plot(0, 0, \"-\", c=\"magenta\", label='skull')\n",
-    "                plt.plot(0, 0, \"-\", c=\"green\", label='brain')\n",
+    "            if idx == 3:\n",
+    "                plt.plot(0, 0, \"-\", c=\"magenta\", label=\"skull\")\n",
+    "                plt.plot(0, 0, \"-\", c=\"green\", label=\"brain\")\n",
     "                # box = ax.get_position()\n",
     "                # ax.set_position([box.x0, box.y0, box.width, box.height*0.8])\n",
-    "                plt.legend(loc='best', fontsize=15, frameon=False, bbox_to_anchor=(1.5, 1.5))\n",
+    "                plt.legend(\n",
+    "                    loc=\"best\", fontsize=15, frameon=False, bbox_to_anchor=(1.5, 1.5)\n",
+    "                )\n",
     "\n",
     "    # Set title for the whole picture\n",
     "    a, b, c = brain_shape\n",
-    "    title = 'Skullstrip QA. Scan Volume : ' + str(a) + '*' + str(b) + '*' + str(c)\n",
+    "    title = \"Skullstrip QA. Scan Volume : \" + str(a) + \"*\" + str(b) + \"*\" + str(c)\n",
     "    foverlay.suptitle(title, fontsize=24)\n",
     "    foverlay.set_size_inches(12.5, 10.5, forward=True)\n",
     "    return foverlay"
@@ -286,8 +290,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "original = r'/mnt/f/JHU/ndd/dataset/part_of_SWU4/sub-0025629_ses-1_T1w.nii.gz'\n",
-    "outdir  = r'/mnt/f/JHU/ndd/dataset/output1/sub-0025864/ses-1/'"
+    "original = r\"/mnt/f/JHU/ndd/dataset/part_of_SWU4/sub-0025629_ses-1_T1w.nii.gz\"\n",
+    "outdir = r\"/mnt/f/JHU/ndd/dataset/output1/sub-0025864/ses-1/\""
    ]
   },
   {
@@ -349,7 +353,9 @@
    ],
    "source": [
     "%matplotlib inline\n",
-    "gen_overlay_pngs(brainfile, original,outdir, loc=0, mean=False, minthr=2, maxthr=95, edge=False)"
+    "gen_overlay_pngs(\n",
+    "    brainfile, original, outdir, loc=0, mean=False, minthr=2, maxthr=95, edge=False\n",
+    ")"
    ]
   },
   {
diff --git a/tutorials/Tractography_Directional_Field_QA_Tutorial.ipynb b/docs/tutorials/Tractography_Directional_Field_QA_Tutorial.ipynb
similarity index 99%
rename from tutorials/Tractography_Directional_Field_QA_Tutorial.ipynb
rename to docs/tutorials/Tractography_Directional_Field_QA_Tutorial.ipynb
index 9ab8d70ab..fb0398829 100644
--- a/tutorials/Tractography_Directional_Field_QA_Tutorial.ipynb
+++ b/docs/tutorials/Tractography_Directional_Field_QA_Tutorial.ipynb
@@ -21,14 +21,14 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "#general imports\n",
+    "# general imports\n",
     "import os\n",
     "import nibabel as nib\n",
     "import numpy as np\n",
     "import matplotlib.pyplot as plt\n",
     "from scipy import ndimage\n",
     "\n",
-    "#dipy imports\n",
+    "# dipy imports\n",
     "from dipy.reconst.shm import CsaOdfModel\n",
     "from dipy.reconst.csdeconv import ConstrainedSphericalDeconvModel, recursive_response\n",
     "\n",
@@ -59,10 +59,10 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "#change this location\n",
-    "output_folder = '/Users/wilsontang/Desktop/m2g_outputs/sub-0025842/sub-0025842/ses-1/'\n",
+    "# change this location\n",
+    "output_folder = \"/Users/wilsontang/Desktop/m2g_outputs/sub-0025842/sub-0025842/ses-1/\"\n",
     "\n",
-    "#standard outputs\n",
+    "# standard outputs\n",
     "bvals_loc = \"dwi/preproc/bval.bval\"\n",
     "bvecs_loc = \"dwi/preproc/bvec.bvec\"\n",
     "preproc_data_loc = \"dwi/preproc/eddy_corrected_data_reor_RAS_nores.nii.gz\"\n",
@@ -75,7 +75,7 @@
     "# load nifti image as numpy\n",
     "preproc_im = nib.load(f\"{output_folder}{preproc_data_loc}\").get_fdata()\n",
     "\n",
-    "# load white matter masks \n",
+    "# load white matter masks\n",
     "wm_mask = nib.load(f\"{output_folder}{wm_mask_loc}\").get_data().astype(\"bool\")"
    ]
   },
@@ -100,25 +100,26 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "#build gradient table\n",
+    "# build gradient table\n",
     "gtab = gradient_table(bvals, bvecs)\n",
     "\n",
-    "#running model\n",
+    "# running model\n",
     "csa_model = CsaOdfModel(gtab, sh_order=6)\n",
     "\n",
-    "#selecting sphere\n",
-    "sphere_mod = get_sphere('repulsion724')\n",
+    "# selecting sphere\n",
+    "sphere_mod = get_sphere(\"repulsion724\")\n",
     "\n",
-    "#extracting peaks\n",
-    "csa_peaks = peaks_from_model(model=csa_model,\n",
-    "                             data= preproc_im,\n",
-    "                             sphere=sphere_mod,\n",
-    "                             relative_peak_threshold=.5,\n",
-    "                             min_separation_angle=25,\n",
-    "                             npeaks = 5,\n",
-    "                             normalize_peaks=True,\n",
-    "                             mask = wm_mask\n",
-    "                            )"
+    "# extracting peaks\n",
+    "csa_peaks = peaks_from_model(\n",
+    "    model=csa_model,\n",
+    "    data=preproc_im,\n",
+    "    sphere=sphere_mod,\n",
+    "    relative_peak_threshold=0.5,\n",
+    "    min_separation_angle=25,\n",
+    "    npeaks=5,\n",
+    "    normalize_peaks=True,\n",
+    "    mask=wm_mask,\n",
+    ")"
    ]
   },
   {
@@ -150,7 +151,9 @@
     "peak_dirs_csa = csa_peaks.peak_dirs\n",
     "peak_values_csa = csa_peaks.peak_values\n",
     "\n",
-    "qa_tensor.create_qa_figure(peak_dirs_csa,peak_values_csa,'Tractography_Model_Peak_Directions','csa')"
+    "qa_tensor.create_qa_figure(\n",
+    "    peak_dirs_csa, peak_values_csa, \"Tractography_Model_Peak_Directions\", \"csa\"\n",
+    ")"
    ]
   },
   {
@@ -182,41 +185,42 @@
     }
    ],
    "source": [
-    "#build gradient table\n",
+    "# build gradient table\n",
     "gtab = gradient_table(bvals, bvecs)\n",
     "\n",
-    "try: \n",
+    "try:\n",
     "    print(\"Attempting to use spherical harmonic basis first...\")\n",
     "    csd_model = ConstrainedSphericalDeconvModel(self.gtab, None, sh_order=6)\n",
     "\n",
-    "except: \n",
-    "    #build response\n",
+    "except:\n",
+    "    # build response\n",
     "    response = recursive_response(\n",
-    "                    gtab,\n",
-    "                    preproc_im,\n",
-    "                    mask =wm_mask,\n",
-    "                    sh_order=6,\n",
-    "                    peak_thr=0.01,\n",
-    "                    init_fa=0.08,\n",
-    "                    init_trace=0.0021,\n",
-    "                    iter=8,\n",
-    "                    convergence=0.001,\n",
-    "                    parallel=False,\n",
-    "                )\n",
+    "        gtab,\n",
+    "        preproc_im,\n",
+    "        mask=wm_mask,\n",
+    "        sh_order=6,\n",
+    "        peak_thr=0.01,\n",
+    "        init_fa=0.08,\n",
+    "        init_trace=0.0021,\n",
+    "        iter=8,\n",
+    "        convergence=0.001,\n",
+    "        parallel=False,\n",
+    "    )\n",
     "\n",
-    "    #build csd model\n",
-    "    csd_model = ConstrainedSphericalDeconvModel(gtab,response,sh_order=6)\n",
+    "    # build csd model\n",
+    "    csd_model = ConstrainedSphericalDeconvModel(gtab, response, sh_order=6)\n",
     "\n",
-    "#extracting peaks\n",
-    "csd_peaks = peaks_from_model(model=csd_model,\n",
-    "                             data= preproc_im,\n",
-    "                             sphere=sphere_mod,\n",
-    "                             relative_peak_threshold=.5,\n",
-    "                             min_separation_angle=25,\n",
-    "                             npeaks = 5,\n",
-    "                             normalize_peaks=True,\n",
-    "                             mask = wm_mask\n",
-    "                            )\n"
+    "# extracting peaks\n",
+    "csd_peaks = peaks_from_model(\n",
+    "    model=csd_model,\n",
+    "    data=preproc_im,\n",
+    "    sphere=sphere_mod,\n",
+    "    relative_peak_threshold=0.5,\n",
+    "    min_separation_angle=25,\n",
+    "    npeaks=5,\n",
+    "    normalize_peaks=True,\n",
+    "    mask=wm_mask,\n",
+    ")"
    ]
   },
   {
@@ -248,7 +252,9 @@
     "peak_dirs_csd = csd_peaks.peak_dirs\n",
     "peak_values_csd = csd_peaks.peak_values\n",
     "\n",
-    "qa_tensor.create_qa_figure(peak_dirs_csd,peak_values_csd,'Tractography_Model_Peak_Directions','CSD')"
+    "qa_tensor.create_qa_figure(\n",
+    "    peak_dirs_csd, peak_values_csd, \"Tractography_Model_Peak_Directions\", \"CSD\"\n",
+    ")"
    ]
   },
   {
@@ -266,7 +272,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "qa_tensor.plot_directions(peak_dirs_csa,peak_values_csa,0,0,size=(300,300))"
+    "qa_tensor.plot_directions(peak_dirs_csa, peak_values_csa, 0, 0, size=(300, 300))"
    ]
   },
   {
diff --git a/tutorials/Tutorial_For_QA_Registration.ipynb b/docs/tutorials/Tutorial_For_QA_Registration.ipynb
similarity index 99%
rename from tutorials/Tutorial_For_QA_Registration.ipynb
rename to docs/tutorials/Tutorial_For_QA_Registration.ipynb
index c204d3652..50e61c5d4 100644
--- a/tutorials/Tutorial_For_QA_Registration.ipynb
+++ b/docs/tutorials/Tutorial_For_QA_Registration.ipynb
@@ -32,6 +32,7 @@
     "from matplotlib.colors import LinearSegmentedColormap\n",
     "from nilearn.plotting.edge_detect import _edge_map as edge_map\n",
     "import matplotlib.pyplot as plt\n",
+    "\n",
     "%matplotlib inline"
    ]
   },
@@ -54,7 +55,7 @@
     "):\n",
     "    \"\"\"\n",
     "    A function to create and save registered brain slice figures.\n",
-    "    \n",
+    "\n",
     "    Parameter\n",
     "    ---------\n",
     "    mri: nifti file\n",
@@ -62,16 +63,16 @@
     "    atlas: nifti file\n",
     "        the reference brain file used in each registration step.\n",
     "    outdir: str\n",
-    "        directory where output png file is saved. \n",
+    "        directory where output png file is saved.\n",
     "    loc: int\n",
-    "        which dimension of the 4d brain data to use \n",
+    "        which dimension of the 4d brain data to use\n",
     "    mean: bool\n",
-    "        whether to calculate the mean of the 4d brain data \n",
-    "        If False, the loc=0 dimension of the data (mri_data[:, :, :, loc]) is used \n",
+    "        whether to calculate the mean of the 4d brain data\n",
+    "        If False, the loc=0 dimension of the data (mri_data[:, :, :, loc]) is used\n",
     "    minthr: int\n",
-    "         lower percentile threshold  \n",
+    "         lower percentile threshold\n",
     "    maxthr: int\n",
-    "         upper percentile threshold \n",
+    "         upper percentile threshold\n",
     "    \"\"\"\n",
     "    atlas_data = nb.load(atlas).get_data()\n",
     "    mri_data = nb.load(mri).get_data()\n",
@@ -88,9 +89,21 @@
     "\n",
     "    fig = plot_overlays(atlas_data, mr_data, [cmap1, cmap2], minthr, maxthr, edge)\n",
     "    # name and save the file\n",
-    "    fig.savefig(outdir + \"/\" + \"{\"+get_filename(mri) +\"}\"+ \"2\" +\"{\"+get_filename(atlas)+\"}\"+ \".png\", format=\"png\") \n",
-    "    \n",
-    "    #plt.close()"
+    "    fig.savefig(\n",
+    "        outdir\n",
+    "        + \"/\"\n",
+    "        + \"{\"\n",
+    "        + get_filename(mri)\n",
+    "        + \"}\"\n",
+    "        + \"2\"\n",
+    "        + \"{\"\n",
+    "        + get_filename(atlas)\n",
+    "        + \"}\"\n",
+    "        + \".png\",\n",
+    "        format=\"png\",\n",
+    "    )\n",
+    "\n",
+    "    # plt.close()"
    ]
   },
   {
@@ -108,29 +121,29 @@
    "outputs": [],
    "source": [
     "def plot_overlays(atlas, b0, cmaps=None, minthr=2, maxthr=95, edge=False):\n",
-    "    \"\"\" \n",
-    "    A function to plot the overlay figures of registered and reference brain slices. \n",
-    "    \n",
-    "    Parameter \n",
-    "    --------- \n",
-    "    atlas: str, nifti image, numpy.ndarray \n",
-    "        an object to open the data for a registered brain. Can be a string (path to a brain file), \n",
-    "        nibabel.nifti1.nifti1image, or a numpy.ndarray. \n",
-    "    b0: str, nifti image, numpy.ndarray \n",
-    "        an object to open the data for a reference brain. Can be a string (path to a brain file), \n",
-    "        nibabel.nifti1.nifti1image, or a numpy.ndarray. \n",
-    "    cmap: Colormap objects based on lookup tables using linear segments. \n",
-    "    minthr: int \n",
-    "        lower percentile threshold  \n",
-    "    maxthr: int \n",
-    "        upper percentile threshold \n",
-    "    edge: bool \n",
-    "        whether to use normalized luminance data \n",
-    "        If None, the respective min and max of the color array is used.  \n",
-    "    \n",
-    "    Returns \n",
-    "    --------- \n",
-    "    foverlay: matplotlib.figure.Figure \n",
+    "    \"\"\"\n",
+    "    A function to plot the overlay figures of registered and reference brain slices.\n",
+    "\n",
+    "    Parameter\n",
+    "    ---------\n",
+    "    atlas: str, nifti image, numpy.ndarray\n",
+    "        an object to open the data for a registered brain. Can be a string (path to a brain file),\n",
+    "        nibabel.nifti1.nifti1image, or a numpy.ndarray.\n",
+    "    b0: str, nifti image, numpy.ndarray\n",
+    "        an object to open the data for a reference brain. Can be a string (path to a brain file),\n",
+    "        nibabel.nifti1.nifti1image, or a numpy.ndarray.\n",
+    "    cmap: Colormap objects based on lookup tables using linear segments.\n",
+    "    minthr: int\n",
+    "        lower percentile threshold\n",
+    "    maxthr: int\n",
+    "        upper percentile threshold\n",
+    "    edge: bool\n",
+    "        whether to use normalized luminance data\n",
+    "        If None, the respective min and max of the color array is used.\n",
+    "\n",
+    "    Returns\n",
+    "    ---------\n",
+    "    foverlay: matplotlib.figure.Figure\n",
     "    \"\"\"\n",
     "    plt.rcParams.update({\"axes.labelsize\": \"x-large\", \"axes.titlesize\": \"x-large\"})\n",
     "    foverlay = plt.figure()\n",
@@ -149,18 +162,42 @@
     "        z = [88, 103, 107]\n",
     "    else:\n",
     "        brain_volume = b0.shape\n",
-    "        x = [int(brain_volume[0] * 0.35), int(brain_volume[0] * 0.51), int(brain_volume[0] * 0.65)]\n",
-    "        y = [int(brain_volume[1] * 0.35), int(brain_volume[1] * 0.51), int(brain_volume[1] * 0.65)]\n",
-    "        z = [int(brain_volume[2] * 0.35), int(brain_volume[2] * 0.51), int(brain_volume[2] * 0.65)]\n",
+    "        x = [\n",
+    "            int(brain_volume[0] * 0.35),\n",
+    "            int(brain_volume[0] * 0.51),\n",
+    "            int(brain_volume[0] * 0.65),\n",
+    "        ]\n",
+    "        y = [\n",
+    "            int(brain_volume[1] * 0.35),\n",
+    "            int(brain_volume[1] * 0.51),\n",
+    "            int(brain_volume[1] * 0.65),\n",
+    "        ]\n",
+    "        z = [\n",
+    "            int(brain_volume[2] * 0.35),\n",
+    "            int(brain_volume[2] * 0.51),\n",
+    "            int(brain_volume[2] * 0.65),\n",
+    "        ]\n",
     "    coords = (x, y, z)\n",
-    "    \n",
+    "\n",
     "    atlas = pad_im(atlas, max(brain_volume[0:3]), 0, False)\n",
     "    b0 = pad_im(b0, max(brain_volume[0:3]), 0, False)\n",
-    "    x = [int(max(brain_volume[0:3]) * 0.35), int(max(brain_volume[0:3]) * 0.51), int(max(brain_volume[0:3]) * 0.65)]\n",
-    "    y = [int(max(brain_volume[0:3]) * 0.35), int(max(brain_volume[0:3]) * 0.51), int(max(brain_volume[0:3]) * 0.65)]\n",
-    "    z = [int(max(brain_volume[0:3]) * 0.35), int(max(brain_volume[0:3]) * 0.51), int(max(brain_volume[0:3]) * 0.65)]\n",
+    "    x = [\n",
+    "        int(max(brain_volume[0:3]) * 0.35),\n",
+    "        int(max(brain_volume[0:3]) * 0.51),\n",
+    "        int(max(brain_volume[0:3]) * 0.65),\n",
+    "    ]\n",
+    "    y = [\n",
+    "        int(max(brain_volume[0:3]) * 0.35),\n",
+    "        int(max(brain_volume[0:3]) * 0.51),\n",
+    "        int(max(brain_volume[0:3]) * 0.65),\n",
+    "    ]\n",
+    "    z = [\n",
+    "        int(max(brain_volume[0:3]) * 0.35),\n",
+    "        int(max(brain_volume[0:3]) * 0.51),\n",
+    "        int(max(brain_volume[0:3]) * 0.65),\n",
+    "    ]\n",
     "    coords = (x, y, z)\n",
-    "    \n",
+    "\n",
     "    labs = [\n",
     "        \"Sagittal Slice\",\n",
     "        \"Coronal Slice\",\n",
@@ -190,7 +227,7 @@
     "            else:\n",
     "                image = b0[:, :, pos]\n",
     "                atl = atlas[:, :, pos]\n",
-    "                \n",
+    "\n",
     "            if idx % 3 == 1:\n",
     "                ax.set_ylabel(labs[i])\n",
     "                ax.yaxis.set_ticks([0, image.shape[0] / 2, image.shape[0] - 1])\n",
@@ -199,33 +236,41 @@
     "                image = edge_map(image).data\n",
     "                image[image > 0] = max_val\n",
     "                image[image == 0] = min_val\n",
-    "            #Set the axis invisible\n",
+    "            # Set the axis invisible\n",
     "            plt.xticks([])\n",
     "            plt.yticks([])\n",
-    "            \n",
-    "            #Set the frame invisible\n",
-    "            ax.spines['top'].set_visible(False)\n",
-    "            ax.spines['right'].set_visible(False)\n",
-    "            ax.spines['bottom'].set_visible(False)\n",
-    "            ax.spines['left'].set_visible(False)\n",
-    "            \n",
+    "\n",
+    "            # Set the frame invisible\n",
+    "            ax.spines[\"top\"].set_visible(False)\n",
+    "            ax.spines[\"right\"].set_visible(False)\n",
+    "            ax.spines[\"bottom\"].set_visible(False)\n",
+    "            ax.spines[\"left\"].set_visible(False)\n",
+    "\n",
     "            ax.imshow(atl, interpolation=\"none\", cmap=cmaps[0], alpha=0.9)\n",
     "            ax.imshow(\n",
     "                opaque_colorscale(\n",
     "                    cmaps[1], image, alpha=0.9, vmin=min_val, vmax=max_val\n",
     "                )\n",
     "            )\n",
-    "            \n",
-    "            #set the legend\n",
+    "\n",
+    "            # set the legend\n",
     "            if idx == 3:\n",
-    "                plt.plot(0,0,\"-\",c=\"pink\",label='registered')\n",
-    "                plt.plot(0,0,\"-\",c=\"green\",label='reference')\n",
-    "                plt.legend(loc='best',fontsize=12,bbox_to_anchor=(1.5,1.5))\n",
-    "            \n",
-    "    #Set title for the whole picture\n",
-    "    a,b,c = brain_volume\n",
-    "    title = 'QA For Registration. Brain Volume:'+ str(a) +'*'+ str(b) + '*' + str(c)+'\\n'\n",
-    "    foverlay.suptitle(title,fontsize=24)\n",
+    "                plt.plot(0, 0, \"-\", c=\"pink\", label=\"registered\")\n",
+    "                plt.plot(0, 0, \"-\", c=\"green\", label=\"reference\")\n",
+    "                plt.legend(loc=\"best\", fontsize=12, bbox_to_anchor=(1.5, 1.5))\n",
+    "\n",
+    "    # Set title for the whole picture\n",
+    "    a, b, c = brain_volume\n",
+    "    title = (\n",
+    "        \"QA For Registration. Brain Volume:\"\n",
+    "        + str(a)\n",
+    "        + \"*\"\n",
+    "        + str(b)\n",
+    "        + \"*\"\n",
+    "        + str(c)\n",
+    "        + \"\\n\"\n",
+    "    )\n",
+    "    foverlay.suptitle(title, fontsize=24)\n",
     "    foverlay.set_size_inches(12.5, 10.5, forward=True)\n",
     "    foverlay.tight_layout()\n",
     "    return foverlay"
@@ -278,7 +323,7 @@
     "mri = r\"/Users/xueminzhu/Desktop/test/t1w_aligned_mni.nii.gz\"\n",
     "atlas = r\"/Users/xueminzhu/Desktop/test/MNI152_T1_2mm_brain.nii.gz\"\n",
     "output_dir = r\"/Users/xueminzhu/Desktop/test\"\n",
-    "reg_mri_pngs(mri, atlas,output_dir)"
+    "reg_mri_pngs(mri, atlas, output_dir)"
    ]
   },
   {
@@ -327,7 +372,7 @@
     "mri = r\"/Users/xueminzhu/Desktop/test/desikan_space-MNI152NLin6_res-2x2x2_reor_RAS_nores_aligned_atlas.nii.gz\"\n",
     "atlas = r\"/Users/xueminzhu/Desktop/test/nodif_B0.nii.gz\"\n",
     "output_dir = r\"/Users/xueminzhu/Desktop/test\"\n",
-    "reg_mri_pngs(mri, atlas,output_dir)"
+    "reg_mri_pngs(mri, atlas, output_dir)"
    ]
   },
   {
diff --git a/tutorials/Tutorial_of_QA_for_FAST.ipynb b/docs/tutorials/Tutorial_of_QA_for_FAST.ipynb
similarity index 98%
rename from tutorials/Tutorial_of_QA_for_FAST.ipynb
rename to docs/tutorials/Tutorial_of_QA_for_FAST.ipynb
index 811fae855..894ea9942 100644
--- a/tutorials/Tutorial_of_QA_for_FAST.ipynb
+++ b/docs/tutorials/Tutorial_of_QA_for_FAST.ipynb
@@ -75,7 +75,7 @@
     "    FAST (FMRIB's Automated Segmentation Tool)\n",
     "    segments a 3D image of the brain into different tissue types (Grey Matter, White Matter, CSF, etc.)\n",
     "    Mark different colors of white matter, gray matter, cerebrospinal fluid in a '3 by 3' picture, i.e. QA for FAST\n",
-    "    \n",
+    "\n",
     "    Parameters\n",
     "    ---------------\n",
     "    csf: str\n",
@@ -87,25 +87,27 @@
     "    outdir: str\n",
     "    the path to save QA graph\n",
     "    \"\"\"\n",
-    "    \n",
+    "\n",
     "    # load data\n",
     "    gm_data = nb.load(gm).get_data()\n",
     "    csf_data = nb.load(csf).get_data()\n",
     "    wm_data = nb.load(wm).get_data()\n",
-    "    \n",
+    "\n",
     "    # set Color map\n",
-    "    cmap1 = LinearSegmentedColormap.from_list('mycmap1', ['white', 'blue'])\n",
-    "    cmap2 = LinearSegmentedColormap.from_list('mycmap2', ['white', 'magenta'])\n",
-    "    cmap3 = LinearSegmentedColormap.from_list('mycmap2', ['white', 'green'])\n",
-    "    \n",
-    "    \n",
+    "    cmap1 = LinearSegmentedColormap.from_list(\"mycmap1\", [\"white\", \"blue\"])\n",
+    "    cmap2 = LinearSegmentedColormap.from_list(\"mycmap2\", [\"white\", \"magenta\"])\n",
+    "    cmap3 = LinearSegmentedColormap.from_list(\"mycmap2\", [\"white\", \"green\"])\n",
+    "\n",
     "    overlay = plt.figure()\n",
     "    overlay.set_size_inches(12.5, 10.5, forward=True)\n",
-    "    plt.title(f'Qa for FAST(segments a 3D image of the brain into different tissue types)\\n (scan volume:{gm_data.shape}) \\n', fontsize=22)\n",
+    "    plt.title(\n",
+    "        f\"Qa for FAST(segments a 3D image of the brain into different tissue types)\\n (scan volume:{gm_data.shape}) \\n\",\n",
+    "        fontsize=22,\n",
+    "    )\n",
     "    plt.xticks([])\n",
     "    plt.yticks([])\n",
-    "    plt.axis('off')\n",
-    "    \n",
+    "    plt.axis(\"off\")\n",
+    "\n",
     "    # Determine whether the input data types are consistent. If they are inconsistent, an error is reported.\n",
     "    if gm_data.shape != csf_data.shape:\n",
     "        raise ValueError(\"GM and CSF are not the same shape.\")\n",
@@ -113,7 +115,7 @@
     "        raise ValueError(\"GM and WM are not the same shape.\")\n",
     "    elif wm_data.shape != csf_data.shape:\n",
     "        raise ValueError(\"WM and CSF are not the same shape.\")\n",
-    "    \n",
+    "\n",
     "    # Set the 3D matrix cutting position in three directions\n",
     "    shape = csf_data.shape\n",
     "    index = [0.35, 0.51, 0.65]\n",
@@ -121,17 +123,16 @@
     "    y = [int(shape[1] * index[0]), int(shape[1] * index[1]), int(shape[1] * index[2])]\n",
     "    z = [int(shape[2] * index[0]), int(shape[2] * index[1]), int(shape[2] * index[2])]\n",
     "    coords = (x, y, z)\n",
-    "    \n",
+    "\n",
     "    # Set labels for the y-axis\n",
     "    labs = [\n",
     "        \"Sagittal Slice\",\n",
     "        \"Coronal Slice\",\n",
     "        \"Axial Slice\",\n",
     "    ]\n",
-    "    \n",
-    "    \n",
+    "\n",
     "    var = [\"X\", \"Y\", \"Z\"]\n",
-    "    \n",
+    "\n",
     "    # Generate 3 by 3 picture\n",
     "    idx = 0\n",
     "    for i, coord in enumerate(coords):\n",
@@ -152,40 +153,40 @@
     "                gm_slice = ndimage.rotate(gm_data[:, :, pos], 90)\n",
     "                wm_slice = ndimage.rotate(wm_data[:, :, pos], 90)\n",
     "\n",
-    "            # set y labels    \n",
+    "            # set y labels\n",
     "            if idx % 3 == 1:\n",
     "                plt.ylabel(labs[i])\n",
-    "            \n",
+    "\n",
     "            #  padding pictures to make them the same size\n",
-    "            csf_slice = (csf_slice*255).astype(np.uint8)\n",
-    "            gm_slice = (gm_slice*255).astype(np.uint8)\n",
-    "            wm_slice = (wm_slice*255).astype(np.uint8)\n",
+    "            csf_slice = (csf_slice * 255).astype(np.uint8)\n",
+    "            gm_slice = (gm_slice * 255).astype(np.uint8)\n",
+    "            wm_slice = (wm_slice * 255).astype(np.uint8)\n",
     "            csf_slice = pad_im(csf_slice, max(shape), 0, False)\n",
     "            gm_slice = pad_im(gm_slice, max(shape), 0, False)\n",
     "            wm_slice = pad_im(wm_slice, max(shape), 0, False)\n",
-    "            \n",
+    "\n",
     "            # hide axes\n",
     "            ax.set_xticks([])\n",
     "            ax.set_yticks([])\n",
-    "            ax.spines['top'].set_visible(False)\n",
-    "            ax.spines['right'].set_visible(False)\n",
-    "            ax.spines['bottom'].set_visible(False)\n",
-    "            ax.spines['left'].set_visible(False)\n",
-    "            \n",
+    "            ax.spines[\"top\"].set_visible(False)\n",
+    "            ax.spines[\"right\"].set_visible(False)\n",
+    "            ax.spines[\"bottom\"].set_visible(False)\n",
+    "            ax.spines[\"left\"].set_visible(False)\n",
+    "\n",
     "            # display image\n",
     "            ax.imshow(csf_slice, interpolation=\"none\", cmap=cmap1, alpha=1)\n",
     "            ax.imshow(gm_slice, interpolation=\"none\", cmap=cmap2, alpha=0.5)\n",
     "            ax.imshow(wm_slice, interpolation=\"none\", cmap=cmap3, alpha=0.3)\n",
-    "           \n",
+    "\n",
     "            # Legend of white matter(WM), gray matter(GM) and cerebrospinal fluid(csf)\n",
     "            if idx == 3:\n",
-    "                plt.plot(0, 0, \"-\", c='green', label='wm')\n",
-    "                plt.plot(0, 0, \"-\", c='pink', label='gm')\n",
-    "                plt.plot(0, 0, \"-\", c='blue', label='csf')\n",
-    "                plt.legend(loc='upper right',fontsize=15,bbox_to_anchor=(1.5,1.2))\n",
-    "                \n",
+    "                plt.plot(0, 0, \"-\", c=\"green\", label=\"wm\")\n",
+    "                plt.plot(0, 0, \"-\", c=\"pink\", label=\"gm\")\n",
+    "                plt.plot(0, 0, \"-\", c=\"blue\", label=\"csf\")\n",
+    "                plt.legend(loc=\"upper right\", fontsize=15, bbox_to_anchor=(1.5, 1.2))\n",
+    "\n",
     "    # save figure\n",
-    "    overlay.savefig(f\"{outdir}\", format=\"png\")\n"
+    "    overlay.savefig(f\"{outdir}\", format=\"png\")"
    ]
   },
   {
@@ -213,7 +214,7 @@
    "outputs": [],
    "source": [
     "# change this path to your own m2g output_dir when you want to run in your own computer\n",
-    "output_dir = 'd:/Downloads/neurodatadesign/output_data/flirt/sub-0025864/ses-1/'\n",
+    "output_dir = \"d:/Downloads/neurodatadesign/output_data/flirt/sub-0025864/ses-1/\"\n",
     "\n",
     "# The following input path will be generated automatically\n",
     "csf_input = f\"{output_dir}anat/preproc/t1w_seg_pve_0.nii.gz\"\n",
@@ -314,11 +315,11 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "cmap1 = LinearSegmentedColormap.from_list('mycmap1', ['white', 'blue'])\n",
+    "cmap1 = LinearSegmentedColormap.from_list(\"mycmap1\", [\"white\", \"blue\"])\n",
     "\n",
-    "cmap2 = LinearSegmentedColormap.from_list('mycmap2', ['white', 'magenta'])\n",
+    "cmap2 = LinearSegmentedColormap.from_list(\"mycmap2\", [\"white\", \"magenta\"])\n",
     "\n",
-    "cmap3 = LinearSegmentedColormap.from_list('mycmap2', ['white', 'green'])"
+    "cmap3 = LinearSegmentedColormap.from_list(\"mycmap2\", [\"white\", \"green\"])"
    ]
   }
  ],
diff --git a/docs/usage.rst b/docs/usage.rst
new file mode 100644
index 000000000..0c7c91e32
--- /dev/null
+++ b/docs/usage.rst
@@ -0,0 +1,202 @@
+================================
+Usage
+================================
+
+
+.. contents:: Table of Contents
+
+
+Execution
+================================
+
+
+.. _Dockerhub : https://hub.docker.com/r/neurodata/m2g/
+.. _documentation : https://docs.docker.com/
+
+In order to share data between our container and the rest of our machine in Docker, we need to mount a volume.
+Docker does this with the -v flag. Docker expects its input formatted as: ``-v path/to/local/data:/path/in/container``.
+We'll do this when we launch our container, as well as give it a helpful name so we can locate it later on.
+
+The ``neurodata/m2g`` Docker container enables users to run end-to-end connectome estimation on structural MRI or functional MRI right from container launch. The pipeline requires that data be organized in accordance with the BIDS spec. If the data you wish to process is available on S3 you simply need to provide your s3 credentials at build time and the pipeline will auto-retrieve your data for processing.
+
+If you have never used Docker before, it is useful to run through the Docker documentation_.
+
+**Getting Docker container**::
+
+    $ docker pull neurodata/m2g
+
+Structural Connectome Pipeline (`m2g-d`)
+----------------------------------------
+
+The structural connectome pipeline can be ran with::
+
+    $ m2g --pipeline dwi <input_directory> <output_directory>
+
+We recommend specifying an atlas and lowering the default seed density on test runs (although, for real runs, we recommend using the default seeding -- lowering seeding simply decreases runtime)::
+
+    $ m2g --pipeline dwi --seeds 1 --parcellation Desikan <input_directory> <output_directory>
+
+You can set a particular scan and session as well (recommended for batch scripts)::
+
+    $ m2g --pipeline dwi --seeds 1 --parcellation Desikan --participant_label <label> --session_label <label> <input_directory> <output_directory>
+
+The outputs of the pipeline are organized as described `here <diffusion.html#pipeline-outputs>`_.
+
+
+Functional Connectome Pipeline (`m2g-f`)
+----------------------------------------
+
+The functional connectome pipeline can be ran with::
+
+    $ m2g --pipeline func <input_directory> <output_directory>
+
+or::
+
+    $ m2g --pipeline func --parcellation Desikan <input_directory> <output_directory>
+
+You can set a particular scan and session as well (recommended for batch scripts)::
+
+    $ m2g --pipeline func --parcellation Desikan --participant_label <label> --session_label <label> <input_directory> <output_directory>
+
+The outputs of the pipeline are organized as described `here <functional.html#pipeline-outputs>`_.
+
+
+
+Running both `m2g-d` and `m2g-f`
+--------------------------------
+
+Both pipelines can be run by setting the `pipeline` parameter to `both`::
+
+    $ m2g --pipeline both <input_directory> <output_directory>
+
+
+Demo
+=====
+
+To install and run a tutorial of the latest Docker image of m2g, pull the docker image from DockerHub using the following command. Then enter it using `docker run`::
+
+    $ docker pull neurodata/m2g:latest
+    $ docker run -ti --entrypoint /bin/bash neurodata/m2g:latest
+
+Once inside of the Docker container, download a tutorial dataset of fMRI and diffusion MRI data from the `open-neurodata <https://registry.opendata.aws/open-neurodata/>`_` AWS S3 bucket to the `/input` directory in your container (make sure you are connected to the internet)::
+
+    $ aws s3 sync --no-sign-request s3://open-neurodata/m2g/TUTORIAL /input
+
+Now you can run the `m2g` pipeline for both the functional and diffusion MRI data using the command below. The number of `seeds` is intentionally set lower than recommended, along with a larger than recommended `voxelsize` for a faster run time (approximately 25 minutes). For more information as to what these input arguments represent, see the Tutorial section below.::
+
+    $ m2g --participant_label 0025864 --session_label 1 --parcellation AAL_ --pipeline both --seeds 1 --voxelsize 4mm /input /output
+
+Once the pipeline is done running, the resulting outputs can be found in `/output/sub-0025864/ses-1/`, see Outputs section below for a description of each file.
+
+
+
+Working with S3 Datasets
+========================
+
+**m2g** has the ability to work on datasets stored on `Amazon's Simple Storage Service <https://aws.amazon.com/s3/>`_, assuming they are in BIDS format. Doing so requires you to set your AWS credentials and read the related s3 bucket documentation. You can find a guide `here <https://github.com/neurodata/m2g/blob/deploy/tutorials/Batch.ipynb>`_.
+
+
+Example Datasets
+----------------
+
+Derivatives have been produced on a variety of datasets, all of which are made available on `our website <http://m2g.io>`_. Each of these datsets is available for access and download from their respective sources. Alternatively, example datasets on the `BIDS website <http://bids.neuroimaging.io>`_ which contain diffusion data can be used and have been tested; `ds114`, for example.
+
+
+Command-Line Arguments
+======================
+
+Below is the help output generated by running **m2g** with the ``-h`` command. All parameters are explained in this output. ::
+
+    $ docker run -ti neurodata/m2g -h
+
+    usage: m2g [-h]
+            [--participant_label PARTICIPANT_LABEL [PARTICIPANT_LABEL ...]]
+            [--session_label SESSION_LABEL [SESSION_LABEL ...]]
+            [--pipeline PIPELINE] [--acquisition ACQUISITION] [--tr TR]
+            [--push_location PUSH_LOCATION]
+            [--parcellation PARCELLATION [PARCELLATION ...]] [--skipeddy]
+            [--skipreg] [--voxelsize VOXELSIZE] [--mod MOD]
+            [--track_type TRACK_TYPE] [--diffusion_model DIFFUSION_MODEL]
+            [--space SPACE] [--seeds SEEDS] [--skull SKULL] [--mem_gb MEM_GB]
+            [--n_cpus N_CPUS]
+            input_dir output_dir
+
+    This is an end-to-end connectome estimation pipeline from fMRI and diffusion
+    weighted MRI data.
+
+    positional arguments:
+    input_dir             The directory with the input dataset formatted
+                            according to the BIDS standard. To use data from s3,
+                            put the bucket and directory location as the input
+                            directory: `s3://<bucket>/<dataset>` downloaded file
+                            will be stored in ~/.m2g/input. If directory already
+                            exists it will be deleted.
+    output_dir            The local directory where the output files should be
+                            stored.
+
+    optional arguments:
+    -h, --help            show this help message and exit
+    --participant_label PARTICIPANT_LABEL [PARTICIPANT_LABEL ...]
+                            The label(s) of the participant(s) that should be
+                            analyzed. The label corresponds to
+                            sub-<participant_label> from the BIDS spec (so it does
+                            not include "sub-"). If this parameter is not provided
+                            all subjects should be analyzed. Multiple participants
+                            can be specified with a space separated list.
+    --session_label SESSION_LABEL [SESSION_LABEL ...]
+                            The label(s) of the session that should be analyzed.
+                            The label corresponds to ses-<participant_label> from
+                            the BIDS spec (so it does not include "ses-"). If this
+                            parameter is not provided all sessions should be
+                            analyzed. Multiple sessions can be specified with a
+                            space separated list.
+    --pipeline PIPELINE   Pipline to use when analyzing the input data, either
+                            func, dwi, or both. Default is dwi.
+    --acquisition ACQUISITION
+                            Acquisition method for functional data: altplus -
+                            Alternating in the +z direction alt+z - Alternating in
+                            the +z direction alt+z2 - Alternating, but beginning
+                            at slice #1 altminus - Alternating in the -z direction
+                            alt-z - Alternating in the -z direction alt-z2 -
+                            Alternating, starting at slice #nz-2 instead of #nz-1
+                            seqplus - Sequential in the plus direction seqminus -
+                            Sequential in the minus direction, default is alt+z.
+                            For more information:https://fcp-
+                            indi.github.io/docs/user/func.html
+    --tr TR               functional scan TR (seconds), default is 2.0
+    --push_location PUSH_LOCATION
+                            Name of folder on s3 to push output data to, if the
+                            folder does not exist, it will be created.Format the
+                            location as `s3://<bucket>/<path>`
+    --parcellation PARCELLATION [PARCELLATION ...]
+                            The parcellation(s) being analyzed. Multiple
+                            parcellations can be provided with a space separated
+                            list. If not parcellations are defined, will use all
+                            parcellations from neuroparc.
+    --skipeddy            Whether to skip eddy correction if it has already been
+                            run and the files can be found in output_dir.
+    --skipreg             Shether to skip registration of the parcellations if
+                            it has already been run and the files can be fround in
+                            output_dir
+    --voxelsize VOXELSIZE
+                            Voxel size : 2mm, 1mm. Voxel size of both parcellation
+                            and reference structural image to use for template
+                            registrations.
+    --mod MOD             Deterministic (det) or probabilistic (prob) tracking
+                            method for the dwi tractography. Default is det.
+    --track_type TRACK_TYPE
+                            Tracking approach: local, particle. Default is local.
+    --diffusion_model DIFFUSION_MODEL
+                            Diffusion model: csd or csa. Default is csa.
+    --space SPACE         Space for tractography: native, native_dsn. Default is
+                            native.
+    --seeds SEEDS         Seeding density for tractography in the m2g-d
+                            pipeline. Default is 20.
+    --skull SKULL         Special actions to take when skullstripping t1w image
+                            based on default skullstrip ('none') failure: Excess
+                            tissue below brain: below Chunks of cerebelum missing:
+                            cerebelum Frontal clipping near eyes: eye Excess
+                            clipping in general: general,
+    --mem_gb MEM_GB       Memory, in GB, to allocate to functional pipeline
+    --n_cpus N_CPUS       Number of cpus to allocate to either the functional
+                            pipeline or the diffusion connectome generation
\ No newline at end of file
diff --git a/m2g/__init__.py b/m2g/__init__.py
index 3c8b3f0e8..967d65108 100644
--- a/m2g/__init__.py
+++ b/m2g/__init__.py
@@ -6,13 +6,11 @@
 """
 
 # naming convention for __version__ : major.minor.bugs
-__version__ = "0.3.0"
+__version__ = "1.0.0"
 
 
 # to call `m2g.graph`, etc
 __all__ = ["graph", "preproc", "register", "track"]  # modules
 __all__.extend(["functional", "scripts", "stats", "utils"])  # subpackages
 
-# import everything listed in __all__
-# TODO: maybe still change this
 from . import *
diff --git a/m2g/functional/m2g_func.py b/m2g/functional/m2g_func.py
index e79309b1e..4de4860c2 100644
--- a/m2g/functional/m2g_func.py
+++ b/m2g/functional/m2g_func.py
@@ -1,7 +1,6 @@
 import os
 import shutil
 import subprocess
-import sys
 
 import numpy as np
 import regex as re
@@ -93,7 +92,7 @@ def make_script(input_dir, output_dir, data_config, pipeline_config, mem_gb, n_c
     with open(cpac_script, "w+", encoding="utf-8") as script:
         script.write(
             f"""#! /bin/bash
-        python3.7 /code/run.py --data_config_file {data_config} --pipeline_file {pipeline_config} --n_cpus {n_cpus} --mem_gb {mem_gb} {input_dir} {output_dir} participant
+        python3 /code/run.py --data_config_file {data_config} --pipeline_file {pipeline_config} --n_cpus {n_cpus} --mem_gb {mem_gb} {input_dir} {output_dir} participant
         """
         )
 
diff --git a/m2g/preproc.py b/m2g/preproc.py
index a832d8fc0..51c2857e2 100644
--- a/m2g/preproc.py
+++ b/m2g/preproc.py
@@ -8,8 +8,6 @@
 TODO : depracate or change name. Add other preprocessing functions created from m2g_dwi_pipeline.
 """
 
-import subprocess
-
 # package imports
 import numpy as np
 
diff --git a/m2g/register.py b/m2g/register.py
index a096e646d..0e933a728 100644
--- a/m2g/register.py
+++ b/m2g/register.py
@@ -9,15 +9,12 @@
 
 # standard library imports
 import os
-import subprocess
-from argparse import ArgumentParser
 from pathlib import Path
 
 # package imports
 import nibabel as nib
 import numpy as np
 from dipy.io.streamline import load_trk
-from dipy.tracking import utils
 from dipy.tracking.streamline import deform_streamlines
 from nilearn.image import load_img, math_img
 
diff --git a/m2g/utils/gen_utils.py b/m2g/utils/gen_utils.py
index 452c94ffb..f962f9e99 100644
--- a/m2g/utils/gen_utils.py
+++ b/m2g/utils/gen_utils.py
@@ -12,7 +12,6 @@
 
 # system imports
 import os
-import re
 import shutil
 import subprocess
 import sys
diff --git a/m2g/utils/reg_utils.py b/m2g/utils/reg_utils.py
index d3192344f..a976e00b1 100644
--- a/m2g/utils/reg_utils.py
+++ b/m2g/utils/reg_utils.py
@@ -5,10 +5,6 @@
 Contains small-scale registration utilities.
 """
 
-# standard library imports
-import os
-import subprocess
-
 # package imports
 import nibabel as nib
 import nilearn.image as nl
diff --git a/requirements.txt b/requirements.txt
index 7455a1eae..f3f23a7b0 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,45 +1,8 @@
-hyppo==0.1.3
-awscli==1.15.40
-virtualenv==20.13.4
-cython==0.29.28
-vtk==9.1.0
-pyvtk==0.5.18
-pandas==1.3.1
-ipython==7.32.0
-scikit-image==0.19.2
-scikit-learn==0.22.1
-joblib==0.17.0
-graspologic==0.2.0
-regex==2022.3.15
-setuptools==57.5.0
-numba==0.52.0
-networkx==2.4
-click==6.7
-future==0.16.0
-lockfile==0.12.2
-nibabel==3.2.2
-nilearn==0.4.1
-nipype==1.1.2
-nose==1.3.7
-patsy==0.5.0
-prov==1.5.0
-psutil==5.6.6
-python-dateutil==2.7.3
-numpy==1.20.1
-scipy>=0.13.3
-dipy==1.1.1
-boto3==1.7.37
-matplotlib==3.1.3
-plotly==1.12.1
-duecredit
-pybids==0.12.0
-configparser>=3.7.4
-fury==0.5.1
-pytest==7.1.1
-pathlib2==2.3.7.post1
-requests==2.21.0
-traits==4.6.0
-PyBASC==0.4.5
-pathlib==1.0.1
-yamlordereddictloader==0.4.0
-xvfbwrapper==0.2.9
\ No newline at end of file
+awscli==1.29.54
+boto3==1.28.4
+dipy==0.16.0
+graspologic==3.3.0
+networkx==3.1
+nibabel==5.1.0
+nilearn==0.10.0
+matplotlib==3.7.1
\ No newline at end of file
diff --git a/setup.cfg b/setup.cfg
new file mode 100644
index 000000000..3b7b870c9
--- /dev/null
+++ b/setup.cfg
@@ -0,0 +1,61 @@
+[metadata]
+name = m2g
+version = 1.0.0
+
+description = Neuro Data MRI to Graphs Pipeline
+long_description = file: README.md
+long_description_content_type = text/markdown
+author = neurodata
+author_email = support@neurodata.io
+url = https://github.com/neurodata/m2g
+keywords = connectome, mri, pipeline
+license = Polyform Noncommercial
+classifiers =
+    Development Status :: 5 - Production/Stable
+    Intended Audience :: Science/Research
+    Programming Language :: Python :: 3
+    Programming Language :: Python :: 3.8
+    Programming Language :: Python :: 3.9
+    Programming Language :: Python :: 3.10
+
+[options]
+packages = find:
+include_package_data = True
+python_requires = >=3.8, <3.11
+install_requires =
+    awscli==1.29.54
+    boto3==1.28.4
+    dipy==1.7.0
+    fury==0.9.0
+    graspologic==3.3.0
+    networkx==3.1
+    nibabel==5.1.0
+    nilearn==0.10.0
+    matplotlib==3.7.1
+    pybids==0.15.6
+
+[options.entry_points]
+console_scripts =
+    m2g = m2g.scripts.m2g_bids:main
+    m2g_dwi_pipeline = m2g.scripts.m2g_dwi_pipeline:main
+    m2g_cloud = m2g.scripts.m2g_cloud:main
+
+[options.package_data]
+templates = *.json
+
+[options.packages.find]
+exclude =
+    tests
+
+[options.extras_require]
+dev =
+    black
+    ipykernel>=5.1.0
+    ipython>=7.4.0,!=8.7.0 # https://github.com/spatialaudio/nbsphinx/issues/687#issuecomment-1339271312
+    isort>=5.9.3
+    nbsphinx>=0.8.7
+    numpydoc>=1.1.0
+    pandoc>=1.1.0
+    sphinx>=4.2.0
+    sphinxcontrib-rawfiles>=0.1.1
+    sphinx-rtd-theme>=1.0.0
diff --git a/setup.py b/setup.py
index 0d0466e9e..d112525fb 100644
--- a/setup.py
+++ b/setup.py
@@ -1,84 +1,6 @@
-#!/usr/bin/env python
-"""
-setup.py
-~~~~~~~~
+# Copyright (c) NeuroData and contributors.
+# Licensed under the Polyform License.
 
-on package install:
-- generates metadata
-- installs json files for use in m2g_cloud
-- installs `m2g` script keywords to the command line
-- ensures python version
-- installs m2g dependencies
+import setuptools
 
-Use `pip install .` to install the package.
-Use `pip install -e .` to install the package in developer mode.
-See our README for more details on package installation : https://github.com/neurodata/m2g/blob/staging/README.md
-"""
-
-from setuptools import setup, find_packages
-from m2g import __version__
-
-# initial setup
-kwargs = {}
-
-# add metadata
-kwargs.update(
-    dict(
-        name="m2g",
-        version=__version__,
-        description="Neuro Data MRI to Graphs Pipeline",
-        author="Derek Pisner, Alex Loftus, Greg Kiar, Eric Bridgeford, and Will Gray Roncal",
-        author_email="dpisner@utexas.edu, aloftus2@jhu.edu, gkiar@jhu.edu, wgr@jhu.edu, ebridge2@jhu.edu",
-        url="https://github.com/neurodata/m2g",
-        download_url="https://github.com/neurodata/m2g/tarball/" + __version__,
-        keywords=["connectome", "mri", "pipeline"],
-        classifiers=["Programming Language :: Python :: 3.7"],
-    )
-)
-
-# add utility info
-kwargs.update(
-    dict(
-        packages=find_packages(),
-        package_data={"templates": ["*.json"]},
-        include_package_data=False,  # only include the m2g_cloud template jsons
-        entry_points={
-            "console_scripts": [
-                "m2g=m2g.scripts.m2g_bids:main",
-                "m2g_dwi_pipeline=m2g.scripts.m2g_dwi_pipeline:main",
-                "m2g_cloud=m2g.scripts.m2g_cloud:main",
-                "m2g_bids=m2g.scripts.m2g_bids:main",  # for backwards compatibility
-            ]
-        },
-        python_requires=">=3.6",
-    )
-)
-
-# add requirements
-kwargs.update(
-    dict(
-        install_requires=[
-            "nibabel",
-            "numpy",
-            "dipy>=1.0.0",
-            "scipy",
-            "boto3",
-            "awscli",
-            "matplotlib",
-            "nilearn",
-            "vtk",
-            "pyvtk",
-            "fury==0.5.1",
-            "requests",
-            "plotly",
-            "pybids>=0.9.0",
-            "scikit-image",
-            "networkx>=2.4",
-            "configparser>=3.7.4",
-            "pytest",
-        ]
-    )
-)
-
-# run setup
-setup(**kwargs)
+setuptools.setup()
\ No newline at end of file