notebooks/analyses/Coronavirus3DStructures.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Analyze Coronavirus 3D Protein Structures\n",
    "[Work in progress]\n",
    "\n",
    "This notebook analyzes available 3D protein structures for Coronaviruses in the [Protein Data Bank](https://www.wwpdb.org/)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import ipywidgets as widgets\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.dates as mdates\n",
    "import seaborn as sns\n",
    "from py2neo import Graph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.options.display.max_rows = None  # display all rows\n",
    "pd.options.display.max_columns = None  # display all columsns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Connect to COVID-19-Community Knowledge Graph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "graph = Graph(\"bolt://132.249.238.185:7687\", user=\"reader\", password=\"demo\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Get the list of pathogens in the Knowledge Graph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>taxonomyId</th>\n",
       "      <th>name</th>\n",
       "      <th>scientificName</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>taxonomy:11137</td>\n",
       "      <td>HCoV-229</td>\n",
       "      <td>Human coronavirus 229E</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>taxonomy:12131</td>\n",
       "      <td>HRV-14</td>\n",
       "      <td>Human rhinovirus 14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>taxonomy:12134</td>\n",
       "      <td>HRV-1A</td>\n",
       "      <td>Human rhinovirus 1A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>taxonomy:31631</td>\n",
       "      <td>HCoV-OC43</td>\n",
       "      <td>Human coronavirus OC43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>taxonomy:277944</td>\n",
       "      <td>HCoV-NL63</td>\n",
       "      <td>Human coronavirus NL63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>taxonomy:285949</td>\n",
       "      <td>CVCoV-civet020</td>\n",
       "      <td>SARS coronavirus civet020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>taxonomy:443239</td>\n",
       "      <td>HCoV-HKU1</td>\n",
       "      <td>Human coronavirus HKU1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>taxonomy:693998</td>\n",
       "      <td>RBCoV-HKU2</td>\n",
       "      <td>Rhinolophus bat coronavirus HKU2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>taxonomy:694009</td>\n",
       "      <td>SARS-CoV</td>\n",
       "      <td>Severe acute respiratory syndrome-related coro...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>taxonomy:766791</td>\n",
       "      <td>MKCoV-WD1127</td>\n",
       "      <td>Mink coronavirus strain WD1127</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>taxonomy:1263720</td>\n",
       "      <td>MERS-CoV</td>\n",
       "      <td>Middle East respiratory syndrome-related coron...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>taxonomy:1487703</td>\n",
       "      <td>RACoV</td>\n",
       "      <td>Rhinolophus affinis coronavirus</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>taxonomy:2697049</td>\n",
       "      <td>SARS-CoV-2</td>\n",
       "      <td>Severe acute respiratory syndrome coronavirus 2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>taxonomy:2708335</td>\n",
       "      <td>Pangolin coronavirus</td>\n",
       "      <td>Pangolin coronavirus</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>taxonomy:2709072</td>\n",
       "      <td>RaTG13</td>\n",
       "      <td>Bat coronavirus RaTG13</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          taxonomyId                  name  \\\n",
       "0     taxonomy:11137              HCoV-229   \n",
       "1     taxonomy:12131                HRV-14   \n",
       "2     taxonomy:12134                HRV-1A   \n",
       "3     taxonomy:31631             HCoV-OC43   \n",
       "4    taxonomy:277944             HCoV-NL63   \n",
       "5    taxonomy:285949        CVCoV-civet020   \n",
       "6    taxonomy:443239             HCoV-HKU1   \n",
       "7    taxonomy:693998            RBCoV-HKU2   \n",
       "8    taxonomy:694009              SARS-CoV   \n",
       "9    taxonomy:766791          MKCoV-WD1127   \n",
       "10  taxonomy:1263720              MERS-CoV   \n",
       "11  taxonomy:1487703                 RACoV   \n",
       "12  taxonomy:2697049            SARS-CoV-2   \n",
       "13  taxonomy:2708335  Pangolin coronavirus   \n",
       "14  taxonomy:2709072                RaTG13   \n",
       "\n",
       "                                       scientificName  \n",
       "0                              Human coronavirus 229E  \n",
       "1                                 Human rhinovirus 14  \n",
       "2                                 Human rhinovirus 1A  \n",
       "3                              Human coronavirus OC43  \n",
       "4                              Human coronavirus NL63  \n",
       "5                           SARS coronavirus civet020  \n",
       "6                              Human coronavirus HKU1  \n",
       "7                    Rhinolophus bat coronavirus HKU2  \n",
       "8   Severe acute respiratory syndrome-related coro...  \n",
       "9                      Mink coronavirus strain WD1127  \n",
       "10  Middle East respiratory syndrome-related coron...  \n",
       "11                    Rhinolophus affinis coronavirus  \n",
       "12    Severe acute respiratory syndrome coronavirus 2  \n",
       "13                               Pangolin coronavirus  \n",
       "14                             Bat coronavirus RaTG13  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "query = \"\"\"\n",
    "MATCH (p:Organism{type: 'Pathogen'})\n",
    "RETURN p.id AS taxonomyId, p.name AS name, p.scientificName AS scientificName\n",
    "\"\"\"\n",
    "pathogens = graph.run(query).to_data_frame()\n",
    "pathogens.head(20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Select a pathogen"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "style = {'description_width': 'initial'}\n",
    "pathogen_widget = widgets.Dropdown(options=pathogens['name'].values, description='Select pathogen:', value='SARS-CoV-2', style=style)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "674df75becb74e11832845169bc8a507",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Dropdown(description='Select pathogen:', index=12, options=('HCoV-229', 'HRV-14', 'HRV-1A', 'HCoV-OC43', 'HCoV…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(pathogen_widget)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "pathogen = pathogen_widget.value\n",
    "taxonomy_query = f\"name == '{pathogen}'\"\n",
    "taxonomy_id = pathogens.query(taxonomy_query)['taxonomyId'].values\n",
    "taxonomy_id =  taxonomy_id[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Selected pathogen: taxonomy:2697049\n"
     ]
    }
   ],
   "source": [
    "print(\"Selected pathogen:\", taxonomy_id)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Number of PDB Structures by Protein"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>protein</th>\n",
       "      <th>accession</th>\n",
       "      <th>uniprotChain</th>\n",
       "      <th>structures</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Host translation inhibitor nsp1</td>\n",
       "      <td>uniprot:P0DTC1</td>\n",
       "      <td>uniprot.chain:PRO_0000449635</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3C-like proteinase</td>\n",
       "      <td>uniprot:P0DTC1</td>\n",
       "      <td>uniprot.chain:PRO_0000449639</td>\n",
       "      <td>285</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Non-structural protein 7</td>\n",
       "      <td>uniprot:P0DTC1</td>\n",
       "      <td>uniprot.chain:PRO_0000449641</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Non-structural protein 8</td>\n",
       "      <td>uniprot:P0DTC1</td>\n",
       "      <td>uniprot.chain:PRO_0000449642</td>\n",
       "      <td>43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Non-structural protein 9</td>\n",
       "      <td>uniprot:P0DTC1</td>\n",
       "      <td>uniprot.chain:PRO_0000449643</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Non-structural protein 10</td>\n",
       "      <td>uniprot:P0DTC1</td>\n",
       "      <td>uniprot.chain:PRO_0000449644</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Spike glycoprotein</td>\n",
       "      <td>uniprot:P0DTC2</td>\n",
       "      <td>None</td>\n",
       "      <td>404</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Spike glycoprotein</td>\n",
       "      <td>uniprot:P0DTC2</td>\n",
       "      <td>uniprot.chain:PRO_0000449646</td>\n",
       "      <td>404</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Spike protein S1</td>\n",
       "      <td>uniprot:P0DTC2</td>\n",
       "      <td>uniprot.chain:PRO_0000449647</td>\n",
       "      <td>81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Spike protein S2</td>\n",
       "      <td>uniprot:P0DTC2</td>\n",
       "      <td>uniprot.chain:PRO_0000449648</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Spike protein S2'</td>\n",
       "      <td>uniprot:P0DTC2</td>\n",
       "      <td>uniprot.chain:PRO_0000449649</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>ORF3a protein</td>\n",
       "      <td>uniprot:P0DTC3</td>\n",
       "      <td>uniprot.chain:PRO_0000449650</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Envelope small membrane protein</td>\n",
       "      <td>uniprot:P0DTC4</td>\n",
       "      <td>uniprot.chain:PRO_0000449651</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>ORF7a protein</td>\n",
       "      <td>uniprot:P0DTC7</td>\n",
       "      <td>None</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>ORF7a protein</td>\n",
       "      <td>uniprot:P0DTC7</td>\n",
       "      <td>uniprot.chain:PRO_0000449654</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>ORF8 protein</td>\n",
       "      <td>uniprot:P0DTC8</td>\n",
       "      <td>None</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>ORF8 protein</td>\n",
       "      <td>uniprot:P0DTC8</td>\n",
       "      <td>uniprot.chain:PRO_0000449655</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Nucleoprotein</td>\n",
       "      <td>uniprot:P0DTC9</td>\n",
       "      <td>uniprot.chain:PRO_0000449656</td>\n",
       "      <td>44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>RNA-directed RNA polymerase</td>\n",
       "      <td>uniprot:P0DTD1</td>\n",
       "      <td>uniprot.chain:PRO_0000449629</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Helicase</td>\n",
       "      <td>uniprot:P0DTD1</td>\n",
       "      <td>uniprot.chain:PRO_0000449630</td>\n",
       "      <td>112</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Uridylate-specific endoribonuclease</td>\n",
       "      <td>uniprot:P0DTD1</td>\n",
       "      <td>uniprot.chain:PRO_0000449632</td>\n",
       "      <td>43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>2'-O-methyltransferase</td>\n",
       "      <td>uniprot:P0DTD1</td>\n",
       "      <td>uniprot.chain:PRO_0000449633</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>ORF9b protein</td>\n",
       "      <td>uniprot:P0DTD2</td>\n",
       "      <td>uniprot.chain:PRO_0000449657</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                protein       accession  \\\n",
       "0       Host translation inhibitor nsp1  uniprot:P0DTC1   \n",
       "1                    3C-like proteinase  uniprot:P0DTC1   \n",
       "2              Non-structural protein 7  uniprot:P0DTC1   \n",
       "3              Non-structural protein 8  uniprot:P0DTC1   \n",
       "4              Non-structural protein 9  uniprot:P0DTC1   \n",
       "5             Non-structural protein 10  uniprot:P0DTC1   \n",
       "6                    Spike glycoprotein  uniprot:P0DTC2   \n",
       "7                    Spike glycoprotein  uniprot:P0DTC2   \n",
       "8                      Spike protein S1  uniprot:P0DTC2   \n",
       "9                      Spike protein S2  uniprot:P0DTC2   \n",
       "10                    Spike protein S2'  uniprot:P0DTC2   \n",
       "11                        ORF3a protein  uniprot:P0DTC3   \n",
       "12      Envelope small membrane protein  uniprot:P0DTC4   \n",
       "13                        ORF7a protein  uniprot:P0DTC7   \n",
       "14                        ORF7a protein  uniprot:P0DTC7   \n",
       "15                         ORF8 protein  uniprot:P0DTC8   \n",
       "16                         ORF8 protein  uniprot:P0DTC8   \n",
       "17                        Nucleoprotein  uniprot:P0DTC9   \n",
       "18          RNA-directed RNA polymerase  uniprot:P0DTD1   \n",
       "19                             Helicase  uniprot:P0DTD1   \n",
       "20  Uridylate-specific endoribonuclease  uniprot:P0DTD1   \n",
       "21               2'-O-methyltransferase  uniprot:P0DTD1   \n",
       "22                        ORF9b protein  uniprot:P0DTD2   \n",
       "\n",
       "                    uniprotChain  structures  \n",
       "0   uniprot.chain:PRO_0000449635           2  \n",
       "1   uniprot.chain:PRO_0000449639         285  \n",
       "2   uniprot.chain:PRO_0000449641          31  \n",
       "3   uniprot.chain:PRO_0000449642          43  \n",
       "4   uniprot.chain:PRO_0000449643           8  \n",
       "5   uniprot.chain:PRO_0000449644          27  \n",
       "6                           None         404  \n",
       "7   uniprot.chain:PRO_0000449646         404  \n",
       "8   uniprot.chain:PRO_0000449647          81  \n",
       "9   uniprot.chain:PRO_0000449648           3  \n",
       "10  uniprot.chain:PRO_0000449649           6  \n",
       "11  uniprot.chain:PRO_0000449650           4  \n",
       "12  uniprot.chain:PRO_0000449651           5  \n",
       "13                          None           1  \n",
       "14  uniprot.chain:PRO_0000449654           1  \n",
       "15                          None           4  \n",
       "16  uniprot.chain:PRO_0000449655           4  \n",
       "17  uniprot.chain:PRO_0000449656          44  \n",
       "18  uniprot.chain:PRO_0000449629          19  \n",
       "19  uniprot.chain:PRO_0000449630         112  \n",
       "20  uniprot.chain:PRO_0000449632          43  \n",
       "21  uniprot.chain:PRO_0000449633          24  \n",
       "22  uniprot.chain:PRO_0000449657           3  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "query = \"\"\"\n",
    "MATCH (p:Protein{taxonomyId:$taxonomy_id})-[t:HAS_TERTIARY_STRUCTURE]->(:Chain)-[:IS_PART_OF_STRUCTURE]->(s:Structure)\n",
    "WHERE t.coverage > 0.2 // exclude polyproteins\n",
    "RETURN p.name AS protein, p.accession AS accession, p.proId AS uniprotChain, count(s) AS structures\n",
    "ORDER BY accession\n",
    "\"\"\"\n",
    "pdbs = graph.run(query, taxonomy_id=taxonomy_id).to_data_frame()\n",
    "pdbs.head(1000)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Number of structures by protein and structure determination method"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>protein</th>\n",
       "      <th>accession</th>\n",
       "      <th>uniprotChain</th>\n",
       "      <th>methods</th>\n",
       "      <th>structures</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Host translation inhibitor nsp1</td>\n",
       "      <td>uniprot:P0DTC1</td>\n",
       "      <td>uniprot.chain:PRO_0000449635</td>\n",
       "      <td>[X-RAY DIFFRACTION]</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3C-like proteinase</td>\n",
       "      <td>uniprot:P0DTC1</td>\n",
       "      <td>uniprot.chain:PRO_0000449639</td>\n",
       "      <td>[X-RAY DIFFRACTION]</td>\n",
       "      <td>284</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3C-like proteinase</td>\n",
       "      <td>uniprot:P0DTC1</td>\n",
       "      <td>uniprot.chain:PRO_0000449639</td>\n",
       "      <td>[X-RAY DIFFRACTION, NEUTRON DIFFRACTION, HYBRID]</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Non-structural protein 7</td>\n",
       "      <td>uniprot:P0DTC1</td>\n",
       "      <td>uniprot.chain:PRO_0000449641</td>\n",
       "      <td>[ELECTRON MICROSCOPY]</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Non-structural protein 7</td>\n",
       "      <td>uniprot:P0DTC1</td>\n",
       "      <td>uniprot.chain:PRO_0000449641</td>\n",
       "      <td>[X-RAY DIFFRACTION]</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Non-structural protein 8</td>\n",
       "      <td>uniprot:P0DTC1</td>\n",
       "      <td>uniprot.chain:PRO_0000449642</td>\n",
       "      <td>[ELECTRON MICROSCOPY]</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Non-structural protein 8</td>\n",
       "      <td>uniprot:P0DTC1</td>\n",
       "      <td>uniprot.chain:PRO_0000449642</td>\n",
       "      <td>[X-RAY DIFFRACTION]</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Non-structural protein 9</td>\n",
       "      <td>uniprot:P0DTC1</td>\n",
       "      <td>uniprot.chain:PRO_0000449643</td>\n",
       "      <td>[X-RAY DIFFRACTION]</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Non-structural protein 9</td>\n",
       "      <td>uniprot:P0DTC1</td>\n",
       "      <td>uniprot.chain:PRO_0000449643</td>\n",
       "      <td>[ELECTRON MICROSCOPY]</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Non-structural protein 10</td>\n",
       "      <td>uniprot:P0DTC1</td>\n",
       "      <td>uniprot.chain:PRO_0000449644</td>\n",
       "      <td>[X-RAY DIFFRACTION]</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Spike glycoprotein</td>\n",
       "      <td>uniprot:P0DTC2</td>\n",
       "      <td>None</td>\n",
       "      <td>[ELECTRON MICROSCOPY]</td>\n",
       "      <td>404</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Spike glycoprotein</td>\n",
       "      <td>uniprot:P0DTC2</td>\n",
       "      <td>uniprot.chain:PRO_0000449646</td>\n",
       "      <td>[ELECTRON MICROSCOPY]</td>\n",
       "      <td>404</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Spike protein S1</td>\n",
       "      <td>uniprot:P0DTC2</td>\n",
       "      <td>uniprot.chain:PRO_0000449647</td>\n",
       "      <td>[X-RAY DIFFRACTION]</td>\n",
       "      <td>56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Spike protein S1</td>\n",
       "      <td>uniprot:P0DTC2</td>\n",
       "      <td>uniprot.chain:PRO_0000449647</td>\n",
       "      <td>[ELECTRON MICROSCOPY]</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Spike protein S2</td>\n",
       "      <td>uniprot:P0DTC2</td>\n",
       "      <td>uniprot.chain:PRO_0000449648</td>\n",
       "      <td>[ELECTRON MICROSCOPY]</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Spike protein S2'</td>\n",
       "      <td>uniprot:P0DTC2</td>\n",
       "      <td>uniprot.chain:PRO_0000449649</td>\n",
       "      <td>[X-RAY DIFFRACTION]</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>ORF3a protein</td>\n",
       "      <td>uniprot:P0DTC3</td>\n",
       "      <td>uniprot.chain:PRO_0000449650</td>\n",
       "      <td>[ELECTRON MICROSCOPY]</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Envelope small membrane protein</td>\n",
       "      <td>uniprot:P0DTC4</td>\n",
       "      <td>uniprot.chain:PRO_0000449651</td>\n",
       "      <td>[SOLID-STATE NMR]</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>ORF7a protein</td>\n",
       "      <td>uniprot:P0DTC7</td>\n",
       "      <td>None</td>\n",
       "      <td>[X-RAY DIFFRACTION]</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>ORF7a protein</td>\n",
       "      <td>uniprot:P0DTC7</td>\n",
       "      <td>uniprot.chain:PRO_0000449654</td>\n",
       "      <td>[X-RAY DIFFRACTION]</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>ORF8 protein</td>\n",
       "      <td>uniprot:P0DTC8</td>\n",
       "      <td>None</td>\n",
       "      <td>[X-RAY DIFFRACTION]</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>ORF8 protein</td>\n",
       "      <td>uniprot:P0DTC8</td>\n",
       "      <td>uniprot.chain:PRO_0000449655</td>\n",
       "      <td>[X-RAY DIFFRACTION]</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Nucleoprotein</td>\n",
       "      <td>uniprot:P0DTC9</td>\n",
       "      <td>uniprot.chain:PRO_0000449656</td>\n",
       "      <td>[X-RAY DIFFRACTION]</td>\n",
       "      <td>41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Nucleoprotein</td>\n",
       "      <td>uniprot:P0DTC9</td>\n",
       "      <td>uniprot.chain:PRO_0000449656</td>\n",
       "      <td>[SOLUTION NMR]</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>RNA-directed RNA polymerase</td>\n",
       "      <td>uniprot:P0DTD1</td>\n",
       "      <td>uniprot.chain:PRO_0000449629</td>\n",
       "      <td>[ELECTRON MICROSCOPY]</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Helicase</td>\n",
       "      <td>uniprot:P0DTD1</td>\n",
       "      <td>uniprot.chain:PRO_0000449630</td>\n",
       "      <td>[X-RAY DIFFRACTION]</td>\n",
       "      <td>104</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Helicase</td>\n",
       "      <td>uniprot:P0DTD1</td>\n",
       "      <td>uniprot.chain:PRO_0000449630</td>\n",
       "      <td>[ELECTRON MICROSCOPY]</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>Uridylate-specific endoribonuclease</td>\n",
       "      <td>uniprot:P0DTD1</td>\n",
       "      <td>uniprot.chain:PRO_0000449632</td>\n",
       "      <td>[X-RAY DIFFRACTION]</td>\n",
       "      <td>43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>2'-O-methyltransferase</td>\n",
       "      <td>uniprot:P0DTD1</td>\n",
       "      <td>uniprot.chain:PRO_0000449633</td>\n",
       "      <td>[X-RAY DIFFRACTION]</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>ORF9b protein</td>\n",
       "      <td>uniprot:P0DTD2</td>\n",
       "      <td>uniprot.chain:PRO_0000449657</td>\n",
       "      <td>[ELECTRON MICROSCOPY]</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>ORF9b protein</td>\n",
       "      <td>uniprot:P0DTD2</td>\n",
       "      <td>uniprot.chain:PRO_0000449657</td>\n",
       "      <td>[X-RAY DIFFRACTION]</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                protein       accession  \\\n",
       "0       Host translation inhibitor nsp1  uniprot:P0DTC1   \n",
       "1                    3C-like proteinase  uniprot:P0DTC1   \n",
       "2                    3C-like proteinase  uniprot:P0DTC1   \n",
       "3              Non-structural protein 7  uniprot:P0DTC1   \n",
       "4              Non-structural protein 7  uniprot:P0DTC1   \n",
       "5              Non-structural protein 8  uniprot:P0DTC1   \n",
       "6              Non-structural protein 8  uniprot:P0DTC1   \n",
       "7              Non-structural protein 9  uniprot:P0DTC1   \n",
       "8              Non-structural protein 9  uniprot:P0DTC1   \n",
       "9             Non-structural protein 10  uniprot:P0DTC1   \n",
       "10                   Spike glycoprotein  uniprot:P0DTC2   \n",
       "11                   Spike glycoprotein  uniprot:P0DTC2   \n",
       "12                     Spike protein S1  uniprot:P0DTC2   \n",
       "13                     Spike protein S1  uniprot:P0DTC2   \n",
       "14                     Spike protein S2  uniprot:P0DTC2   \n",
       "15                    Spike protein S2'  uniprot:P0DTC2   \n",
       "16                        ORF3a protein  uniprot:P0DTC3   \n",
       "17      Envelope small membrane protein  uniprot:P0DTC4   \n",
       "18                        ORF7a protein  uniprot:P0DTC7   \n",
       "19                        ORF7a protein  uniprot:P0DTC7   \n",
       "20                         ORF8 protein  uniprot:P0DTC8   \n",
       "21                         ORF8 protein  uniprot:P0DTC8   \n",
       "22                        Nucleoprotein  uniprot:P0DTC9   \n",
       "23                        Nucleoprotein  uniprot:P0DTC9   \n",
       "24          RNA-directed RNA polymerase  uniprot:P0DTD1   \n",
       "25                             Helicase  uniprot:P0DTD1   \n",
       "26                             Helicase  uniprot:P0DTD1   \n",
       "27  Uridylate-specific endoribonuclease  uniprot:P0DTD1   \n",
       "28               2'-O-methyltransferase  uniprot:P0DTD1   \n",
       "29                        ORF9b protein  uniprot:P0DTD2   \n",
       "30                        ORF9b protein  uniprot:P0DTD2   \n",
       "\n",
       "                    uniprotChain  \\\n",
       "0   uniprot.chain:PRO_0000449635   \n",
       "1   uniprot.chain:PRO_0000449639   \n",
       "2   uniprot.chain:PRO_0000449639   \n",
       "3   uniprot.chain:PRO_0000449641   \n",
       "4   uniprot.chain:PRO_0000449641   \n",
       "5   uniprot.chain:PRO_0000449642   \n",
       "6   uniprot.chain:PRO_0000449642   \n",
       "7   uniprot.chain:PRO_0000449643   \n",
       "8   uniprot.chain:PRO_0000449643   \n",
       "9   uniprot.chain:PRO_0000449644   \n",
       "10                          None   \n",
       "11  uniprot.chain:PRO_0000449646   \n",
       "12  uniprot.chain:PRO_0000449647   \n",
       "13  uniprot.chain:PRO_0000449647   \n",
       "14  uniprot.chain:PRO_0000449648   \n",
       "15  uniprot.chain:PRO_0000449649   \n",
       "16  uniprot.chain:PRO_0000449650   \n",
       "17  uniprot.chain:PRO_0000449651   \n",
       "18                          None   \n",
       "19  uniprot.chain:PRO_0000449654   \n",
       "20                          None   \n",
       "21  uniprot.chain:PRO_0000449655   \n",
       "22  uniprot.chain:PRO_0000449656   \n",
       "23  uniprot.chain:PRO_0000449656   \n",
       "24  uniprot.chain:PRO_0000449629   \n",
       "25  uniprot.chain:PRO_0000449630   \n",
       "26  uniprot.chain:PRO_0000449630   \n",
       "27  uniprot.chain:PRO_0000449632   \n",
       "28  uniprot.chain:PRO_0000449633   \n",
       "29  uniprot.chain:PRO_0000449657   \n",
       "30  uniprot.chain:PRO_0000449657   \n",
       "\n",
       "                                             methods  structures  \n",
       "0                                [X-RAY DIFFRACTION]           2  \n",
       "1                                [X-RAY DIFFRACTION]         284  \n",
       "2   [X-RAY DIFFRACTION, NEUTRON DIFFRACTION, HYBRID]           1  \n",
       "3                              [ELECTRON MICROSCOPY]          19  \n",
       "4                                [X-RAY DIFFRACTION]          12  \n",
       "5                              [ELECTRON MICROSCOPY]          31  \n",
       "6                                [X-RAY DIFFRACTION]          12  \n",
       "7                                [X-RAY DIFFRACTION]           7  \n",
       "8                              [ELECTRON MICROSCOPY]           1  \n",
       "9                                [X-RAY DIFFRACTION]          27  \n",
       "10                             [ELECTRON MICROSCOPY]         404  \n",
       "11                             [ELECTRON MICROSCOPY]         404  \n",
       "12                               [X-RAY DIFFRACTION]          56  \n",
       "13                             [ELECTRON MICROSCOPY]          25  \n",
       "14                             [ELECTRON MICROSCOPY]           3  \n",
       "15                               [X-RAY DIFFRACTION]           6  \n",
       "16                             [ELECTRON MICROSCOPY]           4  \n",
       "17                                 [SOLID-STATE NMR]           5  \n",
       "18                               [X-RAY DIFFRACTION]           1  \n",
       "19                               [X-RAY DIFFRACTION]           1  \n",
       "20                               [X-RAY DIFFRACTION]           4  \n",
       "21                               [X-RAY DIFFRACTION]           4  \n",
       "22                               [X-RAY DIFFRACTION]          41  \n",
       "23                                    [SOLUTION NMR]           3  \n",
       "24                             [ELECTRON MICROSCOPY]          19  \n",
       "25                               [X-RAY DIFFRACTION]         104  \n",
       "26                             [ELECTRON MICROSCOPY]           8  \n",
       "27                               [X-RAY DIFFRACTION]          43  \n",
       "28                               [X-RAY DIFFRACTION]          24  \n",
       "29                             [ELECTRON MICROSCOPY]           1  \n",
       "30                               [X-RAY DIFFRACTION]           2  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "query = \"\"\"\n",
    "MATCH (p:Protein{taxonomyId:$taxonomy_id})-[t:HAS_TERTIARY_STRUCTURE]->(:Chain)-[:IS_PART_OF_STRUCTURE]->(s:Structure)\n",
    "WHERE t.coverage > 0.2 // exclude polyproteins\n",
    "RETURN p.name AS protein, p.accession AS accession, p.proId AS uniprotChain, s.methods AS methods, count(s.methods) AS structures\n",
    "ORDER BY accession\n",
    "\"\"\"\n",
    "pdbs = graph.run(query, taxonomy_id=taxonomy_id).to_data_frame()\n",
    "pdbs.head(1000)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Number of structures by protein and release date"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "query = \"\"\"\n",
    "MATCH (p:Protein{taxonomyId:$taxonomy_id})-[t:HAS_TERTIARY_STRUCTURE]->(:Chain)-[:IS_PART_OF_STRUCTURE]->(s:Structure)\n",
    "WHERE t.coverage > 0.2 // exclude polyproteins\n",
    "RETURN p.name AS protein, s.releaseDate AS releaseDate, count(s.releaseDate) AS structures\n",
    "ORDER BY protein, releaseDate\n",
    "\"\"\"\n",
    "pdbs_by_date = graph.run(query, taxonomy_id=taxonomy_id).to_data_frame()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "pdbs_by_date['cummulativeStructures'] = pdbs_by_date.groupby(['protein'])['structures'].cumsum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>protein</th>\n",
       "      <th>releaseDate</th>\n",
       "      <th>structures</th>\n",
       "      <th>cummulativeStructures</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2'-O-methyltransferase</td>\n",
       "      <td>2020-03-18</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2'-O-methyltransferase</td>\n",
       "      <td>2020-03-25</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2'-O-methyltransferase</td>\n",
       "      <td>2020-04-22</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2'-O-methyltransferase</td>\n",
       "      <td>2020-04-29</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2'-O-methyltransferase</td>\n",
       "      <td>2020-05-06</td>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  protein releaseDate  structures  cummulativeStructures\n",
       "0  2'-O-methyltransferase  2020-03-18           1                      1\n",
       "1  2'-O-methyltransferase  2020-03-25           2                      3\n",
       "2  2'-O-methyltransferase  2020-04-22           2                      5\n",
       "3  2'-O-methyltransferase  2020-04-29           2                      7\n",
       "4  2'-O-methyltransferase  2020-05-06           2                      9"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pdbs_by_date.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_date = pd.DataFrame(pdbs_by_date.pivot(index='releaseDate', columns='protein', values='cummulativeStructures').to_records())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Convert neo4j date object to datetime"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_date['releaseDate'] = df_date['releaseDate'].astype(str)\n",
    "df_date['releaseDate'] = pd.to_datetime(df_date['releaseDate'], infer_datetime_format=False) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Fill gaps by value from previous day"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_date.fillna(method='ffill', inplace=True)\n",
    "df_date['releaseDate'] = pd.to_datetime(df_date['releaseDate'], infer_datetime_format=False) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# TODO use a better color scheme and line styles"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = df_date.plot(figsize=(16, 8), x='releaseDate', title=f'Cummulative {pathogen} structures by release date', marker='o');\n",
    "ax.set_xlabel('Release Date');\n",
    "ax.set_ylabel('Structures');\n",
    "ax.fmt_xdata = mdates.DateFormatter('%Y-%m-%d')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Distribution of resolution by protein"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "query = \"\"\"\n",
    "MATCH (p:Protein{taxonomyId:$taxonomy_id})-[t:HAS_TERTIARY_STRUCTURE]->(:Chain)-[:IS_PART_OF_STRUCTURE]->(s:Structure)\n",
    "WHERE t.coverage > 0.2 AND ('X-RAY DIFFRACTION' IN s.methods OR 'ELECTRON MICROSCOPY' IN s.methods) \n",
    "RETURN p.name AS protein, s.resolution AS resolution, s.name, s.methods\n",
    "ORDER BY protein, resolution\n",
    "\"\"\"\n",
    "pdbs_by_res = graph.run(query, taxonomy_id=taxonomy_id).to_data_frame()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_res = pd.DataFrame(pdbs_by_res.pivot(columns='protein', values='resolution').to_records())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_res.drop_duplicates(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_res.drop('index', axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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QAisQGT0GVmvtgo7Hxpg4SWP9326z1raFszAAAAAgVhFYgcgIdJbgyyQ9JWmvJCNpqDHmM9baD8JWGQAAAADgtBboLMG/kXSltXabJBljxkp6XtK0cBUGAAAAADi9OQLczt0RViXJWrtdkjs8JQEAAAAAEHgL60pjzBOSnvZ/f6ekVeEpCQAAAIhtjGEFIiPQwHq/pP+S9GX5xrB+IOnRcBUFAAAAAEBAgdVa2yLpt5J+a4zJlDTE/xwAAABw2qGFFYiMgMawGmPmG2PS/GF1raS/G2N+G9bKAAAAAACntUAnXRpkra2T9HFJf7fWTpN0efjKAgAAAGIXLaxAZAQaWF3GmAJJt0p6I4z1AAAAAAAgKfDA+hNJ70jaZa1dYYwZKWlH+MoCAAAAAJzuAp106UVJL3b6frekT4SrKAAAAAAAAp10aawxZq4xZqP/+zONMQ+FtzQAAAAgNnm93miXAJwWAu0S/DdJD0pqkyRr7XpJnwpXUQAAAAAABBpYk6y1y094rj3UxQAAAAADAS2sQGQEGlgPG2NGSbKSZIy5RVJp2KoCAAAAYhjL2gCREdCkS5L+S9JfJY03xhyUtEfSHWGrCgAAAIhhBFYgMgKdJXi3pMuNMcnytco2SbpN0r4w1gYAAADEJLoEA5HRY5dgY0yaMeZBY8yfjDFXSGqU9BlJOyXdGokCAQAAgFhDYAUio7cW1qclVUtaIuk/JX1LUpykm6y1a8NbGgAAABCbCKxAZPQWWEdaaydLkjHmcUmHJRVZa4+EvTIAAAAgRjGGFYiM3mYJbut4YK31SNpDWAUAAMDpzuPxRLsE4LTQWwvrFGNMnf+xkZTo/95IstbatLBWBwAAAMQgugQDkdFjYLXWOiNVCAAAADBQEFiByOitSzAAAACAE9AlGIgMAisAAAAQpM6BldZWIHwIrAAAAECQ2tvbu3wMILQIrAAAAECQOodUugcD4UNgBQAAAIJECysQGQRWAAAAIEidW1UJrED4EFgBAACAILW2th593NbWFsVKgFMbgRUAAAAIUueQSmAFwofACgAAAASJwApEBoEVAAAACFLnLsGdHwMILQIrAAAAECQCKxAZBFYAAAAgSJ1DaktLSxQrAU5tBFYAAAAgSM3NzXI4XJIIrEA4EVgBAACAILW0tCgxIfXoYwDhQWAFAAAAgtTc3KIEf2Btbm6OcjXAqYvACgAAAASppbmZwApEAIEVAAAACFJTc7MSE9MkEViBcCKwAgAAAEFqbm5WQnyKjDEEViCMCKwAAABAkFpamuVyxcvtTiCwAmFEYAUAAACC4PF41NbWJrcrXm5XPIEVCCMCKwAAABCEjoDqcsXL5Y5nWRsgjAisAAAAQBA6AqrLFS+XM47ACoQRgRUAAAAIQkdAdbvj5aJLMBBWBFYAAAAgCMdaWOMIrECYEVgBAACAIBwdw+qMo0swEGZRCazGmHRjzL+NMVuNMVuMMedHow4AAAAgWG1tbZIkp9Mtp9Oltrb2KFcEnLpcUTrv7yXNttbeYoyJk5QUpToAAACAoLS3+wKqw+mSw+lSW0tblCsCTl0RD6zGmDRJl0j6D0my1rZKao10HQAAAEBfHG1hdbjldLiPfg8g9KLRJXikpEOS/m6MWWOMedwYkxyFOgAAAICgHesS7JTT6VJ7O4EVCBdjrY3sCY2ZLmmppAuttcuMMb+XVGet/f4J231O0uckKS8vb9oLL7wQ0ToBAACArtTX16u0tFQZGUPU3FSn1rYGjRw5MtplAQPazJkzV1lrp5/4fDQCa76kpdba4f7vL5b0HWvtdd3tM336dLty5coIVQgAAAB0b8GCBfr5z3+u2z7xS23a8p727l+ql176d7TLAgY0Y0yXgTXiXYKttWWSDhhjxvmf+qikzZGuAwAAAOiLjgYf4/9fpBuAgNNJtGYJfkDSs/4ZgndLuidKdQAAAABBORpQjZEMgRUIp6gEVmvtWkknNfcCAAAAAwuBFQinaMwSDAAAAAxYxhhJkrVeWeuVw8ElNRAufLoAAACAILhcvk6K1uuV1+uR0+mMckXAqYvACgAAAAShI6B6rUfWeo4GWAChR2AFAAAAgtARUL1er7xeugQD4cSnCwAAAAhCR0D1etvpEgyEGYEVAAAACEJcXJwkyeNpk8fTevR7AKFHYAUAAACCcHxgbVN8fHyUKwJOXQRWAAAAIAgdAbXd06p2T5vi4txRrgg4dRFYAQAAgCC43b6A6mmnSzAQbgRWAAAAIAgdAbXd00qXYCDMCKwAAABAEI52CW5vVTstrEBYEVgBAACAIBw3hrW9VQkJCVGuCDh1EVgBAACAIBztEtzeKk87LaxAOBFYAQAAgCAYY+R2x6m9vVVt7S2MYQXCiMAKAAAABCkuLk4eT5va29toYQXCiMAKAAAABCkuLk5tbc2y1ktgBcKIwAoAAAAEKS4uTq2tjUcfAwgPAisAAAAQpDi3W61tTb7HBFYgbAisAAAAQJDccW61tDT4HrvdUa4GOHURWAEAAIAgOZ1OeTxtRx8DCA8CKwAAABAkp9Mpj7ddkuRwcEkNhAufLgAAACBIDodDXo8vsA7EFtbm5mZ5vd5olwH0isAKAAAABKlzl+CB1sJaUVGhG2+8UT/64Y+iXQrQq4H16QIAAABigDFG1nqPPh5IysvLJUnLli+LciVA7wisAAAAQJC8Xq+cTvfRxwNJQ0NDtEsAAkZgBQAAAILk8XjkcLqOPh5Ijhw5Eu0SgIARWAEAAIAgeTxeOR2+wDrQWlg7B9aBFrZx+iGwAgAAAEHyer1yOAZmC2ttbW2Xj4FYRGAFAAAAgtTS0qL4+GRJUmtra5SrCU51dXWXj4FYRGAFAAAAgtTU1KSkxEGSfGuaDiSVlZVHH1dVVUWxEqB3BFYAAAAgSM3NzUpISJUxRk1NTdEuJyiHDh1WYcZw/+ND0S0G6AWBFQAAAAhSS0uz4tyJinMnDKgWVmutKirKNSRrlBzGoYqKimiXBPSIwAoAAAAEob29XW1tbXK74+V2J6ixsTHaJQXsyJEjampqUmZKngYlZ6m8vDzaJQE9IrACAAAAQeiYWTc+PlXx8Smqq6uLckWBKy0tlSRlpOQoIylHJSUlUa4I6BmBFQAAAAhCR2BNTExVQkKaampqoltQEDoCalZqvjJTclVykMCK2EZgBQAAAIJwNLAmpCkxIVU1NQNnLdODBw/KyCgzOVdZqfmqO1Kn+vr6aJcFdIvACgAAAAShI7AmJPhaWDu+HwhKSko0KDlTblecMlPzJEnFxcVRrgroHoEVAAAACEJ1dbUkKSlxkJIS09TY2KCWlpYoVxWY4gPFykrJlyRlpxZI8rW6ArGKwAoAAAAEoaKiQsY4tHLNK0pOzpIkHT58OMpV9c5aq+KDxcpK9QXWzJRcGWMIrIhpBFYAAAAgCIcOHZLT4VJl5X6lpmQdfS7W1dbWqqGh4WhgdTndykjJoUswYhqBFQAAAAhCRcUhOZwuSVJKSrb/uYpolhSQjpbUbH9glaTM5DwdLKaFFbGLwAoAAAAEoaKiQk6nW5KUkpx59LlY17GkTWZK3tHnslLzVFJSImtttMoCekRgBQAAAALU3Nys6uoqufyB1el0KyUlU6WlpVGurHclJSUyxig9Oefoc5kpuWpsalRdXV0UKwO6R2AFAAAAAtTRrdblij/6XFpq/oCYuKi8vFyDkjLl8ndnlqSM5NyjrwGxiMAKAAAABOhYYI07+lz6oHwVD4BxoL7Amn3cc+nJ2UdfA2IRgRUAAAAIUEdgdTqPBdZBafk6cqQu5rvVVpRXaFBS1nHPdQTWgTAGF6cnAisAAAAQoOLiYiUnZ8hhjl1Gpw8qOPparPJ6vaqsqtSgpMzjnk9wJynOFT8g1pHF6YnACgAAAARo9+7dyswYetxzmZlDJEl79+6NQkWBqaurk8fjUVpixnHPG2OUmpiuysrKKFUG9IzACgAAAASgvb1d+/fvV1Zm0XHPp6bkKC4uUbt3745SZb2rqqqSJKUkpp/0WkpCuqoqqyJcERAYAisAAAAQgOLiYrW3t58UWI0xyswYqj179kSpst5VV1dLklITBp30WkrCIFXX1ES4IiAwBFYAAAAgAB2BNCtz6EmvZWUWaffuPbLWRrqsgNT4A2lSfNpJr6UkpKnGH2iBWENgBRDzrLV6+umn9e6770a7FADAaWzHjh1yOt1KTy886bXsrGFqbGxQaWlpFCrrXW1trSRfOD1RUnyq6hvq1d7eHumygF65et8EAKKrvLxczzzzjCTpiiuuiHI1AIDT1ebNW5STPUJOx8mX0Hm5oyVJW7ZsUWHhyYE22mpqauR0OJXgTjrptRR/q2ttba2ysrJOeh2IJlpYAcS8hoaGaJfQLw0NDWptbY12GQCAfmhtbdWOHduVlzumy9cz0ocoLi5RmzdvjnBlgamurlZyQpqMMXpr9TN6a/UzR19L9o9rrWEcK2IQgRVAzOscWAda8KuoqNDHP/5xfeYzd0e7FABAP+zatUvt7e3Kz+s6sDocDuXmjIrZwFpTU6Nkf0tqafU+lVbvO/paRzfhasaxIgYRWAHEvCNHjnT5eCAoKSmRJFVVVcvj8US5GgBAX3UE0fxuWlglKS93jPbs2avGxsZIlRWwqsoqpXQxQ7AkpSakSyKwIjYRWAHEvI6JIqSB112pc+2dHwMABpbVq1crPb1ASUnp3W5TWDBB1nq1bt26yBUWoMOHDystMbPL11ITMyRJhw4dimRJQEAIrABiXsdi59LAu/vbufbKysooVgIA6Kvm5matW7dORUOm9LhdQd44xbkTtHz58ghVFpjW1lbV1NZoUFLXEyq5XXFKTkglsCImEVgBxLzOQW+ghb7O//hzIQAAx/N6vVq9erUOHDgQ7VJ6tG7dOrW1taloyNQet3M6XRpceIaWL18RU+uxlpWVSZIyUnK63SY9OSdml+TB6Y3ACiDmlZeXKy3DSPJNYjSQlJWVyZVgjj4GAByzceNGPfjgg/ryV74S7VJ6tGLFCrnd8SosGN/rtkVDp+jw4UPat29fr9tGysGDByVJWal53W6TlZKn4gPFkSoJCBiBFUDMKyk9qOQ0q6Rkx4C7+1t88IASc61cceboBEwAAJ+OG3mNDQ0xOzGd1+vV0qXLVJg/UU6nu9fti4b6ug0vXbo03KUFrCM856R2vz5sTlqhDh0+pKampkiVBQSEwAogprW3t6uivEJJqVJiilfFxQPn7q/H49HBgyWKT5fi0hVTd9sBIBZ07jXTecx/LNm0aZMOHarQ6FHnB7R9SnKWCvLG6r335sZMt+Ddu3crPTlLCXFJ3W6Tl150dFsglhBYAcS0kpISeTxepQ6SUgZJBw7sj5kLgN4UFxerva1dCZlSfIbVrt27BkztABAJnW9CdnRbjTXvvfee4twJGjFsWsD7jB1zsQ4c2K+dO3eGsbLAbd++QwUZI3rcpjBjuCTFTM1ABwIromLDhg3as2dPtMvAANDRKpmSLqWmS42NTTp8+HBUawrUjh07JEmJWVJittRQ3zDgxuACQDjt3btPyVlD/Y/3RreYLrS0tGjBggUaMXyG3O6EgPcbNeJcOZ1uvffee2GsLjA1NTUqLS3RkKxRPW6XlpihtKQMbdmyJUKVAYEhsCIqvvGNb+irX/1qtMvAALBr1y4ZI6UOktIyjz03EGzdulVOt1F8upSU63uOCwEA8GlpadG+/fuUWTRF8UlpR2/yxZIlS5aoqalJ48ZcFNR+8fHJGl50tt5//321t7eHqbrAbNq0SZI0LHtMj9sZYzQ0a4w2bNhIbyDElKgFVmOM0xizxhjzRrRqQHQxqB+B2LZtm1LTjZwuKc23rnlMXtR0ZcOG9UrMsTIOo4RMyek2Ry8cAOB0t337dnk9HqXljVJK7iht2rQ52iWd5M0331RqarYKCyYEve+4MRerrq5OCxcuDENlgVu3bp3crjgNzhzZ67Yjcifo8OFDTBKImBLNFtavSKKp4TTk9XqjXQIGCK/Xq63btio923en1+WW0jLMgGilrKur0759+5VU4PveOIwSc63Wrlsb1boAnFrq6ur0qU99Wtdee52uvfY6/ea3v412SQFbt26dJKO0/DEaVDBOpaUlMTVsYseOHVq/fr3OmHCljAn+krlo6BQNGpSnl156OaotlqtWrdaw7HFyBTDD8ai8SZKk1atXh7ssIGBRCazGmCGSrpP0eDTOj+iiZRWB2r9/vxobGpXRaZ3z9ByrzZs3xezyBx3Wrl0ra61SBh97LqVQ2r9vf8zOhAlg4Nm7d6+qq6vkGHGmbHquVq1aFe2SArZ8xQql5Y6QOyFFGUPPkCStXLkyylUd8/LLLyvOnaAJ42f2aX9jHDpz0jXavn2bNm+OTutxeXm5iosPaEzB5IC2z0rNV0ZKTkz9PQDRamH9H0nfkkRT22mooaEh2iVggNiwYYMkKbPTOueZuVJTU3PMT7u/atUqueKNkjqF7ZQhx14DgFDo6LoZP/1KuYdPUlVlpVpbW6NcVe+qqqq0fds2ZQzzrVmalDFYianZMbN26eHDh7VgwQKNH3eZ4ntYCqY348ZcrIT4ZL388sshrC5wy5cvlySNKZga0PbGGI3JP1Nr1qwZEO8jnB5MpLsoGGOul3SttfaLxpjLJH3DWnt9F9t9TtLnJCkvL2/aCy+8ENE6ET4tLS3av3+/JGn06NEyxkS5IsSq0tJSNTbWKzXj2HPWK9VVS9nZ2crIyOh+5yjbvXu35PIoLvX455urpJTkVOXn50enMACnlMOHD6u6pkbOrMGyLY3yHqnSsGHDFBcXF+3SelRbW6uKigolZQyW0+XrqtpSX6W25iMaOXKkHI7ozgt6+PBhVVdXKzNzqJwOV5fbzJrluza98cZP9XishsZqNTbWaPjw4XK7e++WG0olJSVqaW5R7qCh6ny19e/XfbXfcsPJtTe3NanySJkGDx6spKS+h3UgWDNnzlxlrZ1+4vNdfwLD60JJHzPGXCspQVKaMeYZa+2dnTey1v5V0l8lafr06fayyy6LeKEIj7Vr1+q3/jE2zz33nLKysqJcEWKRx+PRJz95izILGjXlguNfW/Ce0ZiRZ+mRRx6JTnG92LFjhx555BENuVTKGHv8DZnidVbNBxL14r9ejPiFC4BTz0MPPaQ1u4qVdMvX5KkrUcNLf9WDDz6oWL9u+trXvq69JVU6+9afyVjf78m6Izu19tWf6hvf+IY+8pGPRK22uro63X333RpcMEVXfOTm7vsDWv+8oe1Tez5ge7VeeeWruuKKj0Z0hYTGxkZ98pOf1IxRl2vSWdcd95q7/XVJUnbdmSft1+Zp1d9f/aKuuOpyffnLX45IrUBPIn77ylr7oLV2iLV2uKRPSXr/xLCKU1t9ff3Rx0eOHIliJYhl27ZtU0NDo3IKTn4tu8Bq/Yb1am5ujnxhAViyZIkkKXXoya+lFUlNjU1HuzsDQH/s3rNHyvT12HCk50rGEZPrmXZWVlamTZs2Kmf0ecf1skrNG6XEtJyor1368ssvq7mpWdPOujkkx0tOytDEcTP17rvvqqysLCTHDMSqVavU3t6uCYOnBbWf2xmn0fmTtWTJUibKRExgHVZEXOcxrIxnRXeWLVsmY6TswpNfyx0stbe1a+3atRGvKxCLFy9Scr6RK/Hk7u4pQySHy2jx4sVRqAzAqaSurk6Vhw/Lmem7s2ecLrkycmN+req5c+dKknLHHt99xhijnDEXaN26dVGbLbiurk6vvvqqRo6YocyMISE77tQpN0hy6Pnnnw/ZMXuzePFiJcWnaGgv6692Zfzgaaqqqhwwy8jh1BbVwGqtnd/V+FWc2jrPEhyrLWSIviVLlygzV4qLP/m1zDzJ7TZHWzJjSVlZmfbs2avUYV3PD+BwGaUMtlq8eBELswPol507d0qSnNnHpiM3WYXatn1HzP5+8Xq9emfOHKUPnqCE1OyTXs8bd5GstVFrZX3llVfU1NQUstbVDinJmZoQwVZWj8ej5cuWa2zBVDkdzqD3H1c4RQ7jiMl/Z3H6oYUVEdfS0tLlY6BDSUmJ9u3dpzx/l9pNK3xfHZxOKbvQasmSxTG3vE1Hy+mg4b7vS5ZYlSw5/sIxbbhUWVnFnWsA/bJt2zZJkjPn2PgDZ85Q1dZU69ChQ9Eqq0cbNmxQeVmZ8sZdLEnatehZ7Vr07NHXE9NylF44Xu+8Myfiobuurk6vvPKKRo6YoazMLsZ0dLJwydM6XLlPhyv3adYbP9XCJU/3evyzptwgyUSklXXTpk2qb6jX+MFn92n/pPhUFeWMIbAiJhBYEXFtbW1dPgY6LFq0SJKUX+T7vrbK99VZQZFUW1sXtbXturNw4UIlZhrFpfm6AzdX+r46Sy2SZES3YAD9smXLFrnSc2XiE48+58wdJknaunVrtMrq0ezZs+WKS1T2CN+4yvrD+1V/eP9x2+SNv1hlZaURH+v/8ssvq6mpSdMDaF2trNyn1rZGtbY1qqRsiyor9/W6j6+V9SMRaWVdunSpnE6XRuef0edjjCs8S3v37lV5eXkIKwOCR2BFVMVqlyVE14IPFig9yygppfttcodITqfRhx9+GLnCelFbW6vNmzd32x24gyvBKDnfaNGihRGqDMCpxuv1auOmzTJ5w4573pFdKONya9OmTVGqrHsNDQ1auHChckafK6e7i/EeftkjpssVl6jZs2dHrLaOsaujRpyrrMyisJ3nrAiNZV22dJlG5ExQvDux9427Ma7Q1zq7bNmyUJUF9AmBFUBMKSsr047tO5TfW+hzSzmDrT74YEHMdAtetmyZrLVKG9b7tmnDrPbvP6CSkpLwFwbglLN//3411B+RM3/Ecc8bh1OO3CKtW78+SpV1b/78+WptbVX++Et63M7pjlfO6HP14YcfRmxyxlDPDNydlOTMsM8YfPDgQRUfLNa4wqn9Ok52ar4yU3O1fNny0BQG9BGBFRHXeQr7aC8Mjtgzf/58SVLh8N63LRwuVVfXxMwSMUuWLFFcskMJJ88jcpKOULt06dLwFgXglLRu3TpJkqtw1EmvuQpGae+ePaqrq4t0WT16e/ZspWQNUUrOiF63zR9/iVpbWzVv3ryw19V5ZuDexq6GQrhnDF6+3Bcwx/YzsBpjNLZgqtatW8ckmYgq0gIirnNIJbCiM2ut5s59T5m5PXcH7pA3RHK5TUQuaHrT2tqqVatWKqXIe9xNme7EpRklZDgIrAD6ZPXq1XKmZcqRmnnSa87Bo2WtPRpqY8GePXu0Y/t25Y67OKDfkSk5I5ScOUSz33kn7LW99tprYZkZuDudZww+fPhwyI+/fNly5QwqVGZKbr+PNa5wqlrbWmN2GTmcHkgLiDgCK7qze/du7d9/QIUjAhvb7HRJeUN93YJbW1vDXF3PNmzYoJaWVqUGcXM+ZahXGzZuYD1iAEFpb2/X2nXr5Bg8tsvXnblFcsQlaPXq1RGurHuzZ8+Ww+FU3pgLet9Yvta9vPEXa8f27dqzZ0/Y6mptbdVrr72uoqFTI9K62mHK5Gvk9Xr12muvhfS4jY2NWrd+vcYVTA3J8YbnjFe8O4FxrIgq0gKAmPHee+/J4TAqDGAMaIchI6XGxqaoT72/YsUK/xqrge+TViR5Pd6YuqgEEPs2b96s5qYmuYZ0HViNwylH4SgtX7EiJiY3bG1t1XvvzVXm8LPlTkwNeL+8sRfK4XSFdfKlefPmqba2RlPOuCZs5+hKWmquRgybrjffeDOk3W1Xrlwpj6dd4wrPCsnxXE63RuVN1tIlS+X1ekNyTCBYBFYAMaG9vV1z576n3CFWcQmB75edLyUmOzRnzpzwFReAZcuXKjnfyuHqvatbh6Q8yRVvtHLlyjBWhoHk4MGD2rlz53Ff1dXV0S4LMWbFihUyDodcg8d0u41r6HgdPnRI+/fv73abSFm0aJHq64+oYMKlQe3nTkhR1vCz9e5774Vl3XZrrV566WVlZRVpcOGkkB+/N1MmX6v6hnq9++67ITvm4sWLlRSfoqHZ3b83gjVh8Nmqqq7S9u3bQ3ZMIBiuaBeA00/nu73crUOH5cuXq7a2TmODXOPcOKTBI71atWqVDh8+rOzsAGY8CrGDBw+q5GCpCs4Pbj/jMEoutFq23De7cCDjunDq2rBhg77xjW+c9HzaoEF69plnFBcXF4WqEIuWLF0mZ/4ImR7u7rmGjpfkm7182LAguq2EwVtvva3EtBylD5kY9L75Ey/Thl3LtXDhQn30ox8NaV1r1qzRvn17NfOSz0fl929e7hjl5Y7SSy+9rOuuu67fw6RaW1u1bOkyjS+cLqfDGaIqpXGDz5LD4dTChQs1fvz4kB0XCBQtrIi4ziE1FroqITbMnj1bCUkO5QTRpbbD0FG+91K0WllXrFghSUrtw9J9qUVSdVW1du3aFeKqMNC8/fbbkiTnR86T88qL5LzyIjnOGKO62lom58JRZWVlOrB/n5xFE3rczpGSLld2oZZE+b1TXFys9evXKW/8JTIm+MvO9MLxShqUpzfffCvktc2dO1fx8ckaMyrIu40hYozRpAlXqLS0RNu2bev38VatWqXGpkZNGjojBNUdkxiXrFF5k7RgwQdctyEqCKyIuM6BNVbWz0R0HTp0SMuXL9eQkV715QZzcpqUlS+9/fZbUWm1X7ZsmRLSHYpPC/4OfcoQ3387liHA6ammpkbzFyyQY+JoOUYPk2P4YN/XeVPlSE3WrFmzol0iYkTHzQvXsN5bKx1FE7Vl8xbV1taGu6xuvf322zIOp/LHX9yn/Y1xKG/8pdq0aWNIuze3tbVp8eIlGl50tpxOd8iOG6zhRWfL4XDqww8/7Pex5s2bp6T4FI3K77l781urn1FpzT6V1uzTE3N/prdWP9PrsScXnaeKinJt3ry533UCwSKwAoi6d955R9ZaDe3HkJuiMVJFxSGtWrUqdIUFoLGxUevXr1PK0L4FZXeSUVKOifqkUYiuV155RZ72djnOOP5DYBwOadJobdy4UVu3bo1SdYgli5cskSsjT85BOb1u6x42UdZ6o3ZDrLW1Ve/MmaOsYVMVl5Te5+Pkjb9IDofzaC+EUFi7dq0aGxs0ckRoWyODFR+frCGDJ+vDDz/sV+tlQ0ODlixeojOGniuno+cRf6XV+9TS1qSWtibtPbRVpdX7ej3+hCHT5XbF6b333utzjUBfEVgBRFV7e7veeON15RRKyYFPHnmS/CIpIdGhN954I3TFBWDlypVqb/corR9DxFKHWW3fvl2VlZWhK6yfdu3apS898CV94f4v6Av3f0HfefBBFo4Pk5qaGr06a5bMyKEy6Wknve4YP0qOhHg9+eSTkS8OMeXIkSPasGGDHEWBjQV1ZA+RM3mQFi9eHObKurZ48WIdqatT/sTL+nWcuMQ0ZQ4/W3PmvBuyJcwWLlyouLhEDR08OSTH649RI2aooqJCO3bs6PMx5s2bp9a2Vp01om8t2b1JcCdq4uBzNH/efP4tQMQRWBFxLtexO39ud/S64SA2LFy4UNXVNRrez3kcnE5pyGivli1bprKystAUF4CFCxfKnehQUl7fj9ERdhctWhSaokLg9ddf147tO7THU6E91Qe1ZvVqxlGGyTPPPKPm5mY5p53R5esmzi1NnaA1a9Ywo/RpbsWKFfJ6PHINDyywGmPkKJqglatWhWWW3d68PXu2ElOzlTGk/zPwFky4VPX1R0ISvq21Wrx4sYYNjW534A7Di6bJ4XD2+Wez1urtt95WfkaRBmeODHF1x0wbdakamxr1wQcfhO0cQFcIrIi4zjNdEljxyiuvKDnNKLcPky2daNg4SbIRG+/X3NysJUuXKHWYV8bR9xkmEzKNEjIcmj9/Xgir67uWlhYt+GCBNCZVjmsHy9w6TI5kd0iXXoDPrl279MYbb8gxYZRMxsmtqx0ck0bLMShVf3r0zyFrYcLAs2TJEjmTUuXMDXyGN/fwM9Ta0qK1a9eGr7AulJeXa93atcodd1GfJls6UfqQiUpMzdbbIViT9dChQ6qrq1N+Xtfr2EZaQkKKMtIL+zz53rZt27Rz106dM3JmWGc7Hp4zXjmDCvX665HtyQQQWBFxCQnHpuFPTEyMYiWIti1btmjr1q0aNs4qFP/GJiZJBcOkt95+Sw0NDf0/YC8WLlyo1pZWpY/u/7EGjfJq06bNEW0d7s7cuXPV2NAoM3GQJN/yO97xqVq5apUOHjwY5epOHR6PR7/57W9lEuPlOKfnbonG6ZS54CyVHizR888/H6EKEUva2tq0fMUKOYomBBUAnYWj5IhLiPg4+XfffVfWWuWNuygkxzPGoZyxF2rd2rWqqKjo17H27fON2czM6Pud0tbWRiUkJOimm25SQkKCWlsb+1VTRvoQ7d3b+1jSrrz66quKdydqyvAL+1VDb4wxmjHqcm3fvo0x9YgoAisiLikp6ehjAuvp7d//flFx8UZFIQh8HUZOkpqbmvXWW6FfAuFEs2e/rfg0h5Ly+3+sdP9cO++8807/D9YP7e3teuGfL8jkJkoFxz6f5ox0GachLIXQ888/r107d8pccLZMfO9rrDqGFsiMGa4XXnghJEtgYGBZv369mpua5BoWXPda43TJMWSsFi9ZGrFZ1K21mvPuu0ofPEEJqaFbGztv3EWy1vZ74p+OwJqRMaTPx2hpbdTVV1+t+++/X1dffbVa+htYMwaroqJcTU1NQe1XWVmpDz74UGeNuFjx7vBfU00dcZHi3Yl69dVXw34uoAOBFRGXnJzc5WOcXg4cOKBFixapaKyVK4Q9w9OzpOx86eWXXwpr18l9+/Zpw4aNSh/nDUkXrLgUo9Sh0ltvvam2trYQVNg3b775psrLyqXpGcf9XCbJJTtpkN577z3t3r07avWdKrZs2aJnn31WZnSRHCOHBryf84KzpKQEPfKLX6ixsX8XyBhYli1bJuNyyzU4+OnUXUUTVVtT3a9JfYKxadMmlZeVKW9sz62ruxY9q/rK/aqv3K91sx7RrkXP9rh9YlqOBhWM05x33+vXjLr79u1TUlKaEhP6PtNffFySZs+erccee0yzZ89WfFxS7zv1IDPdF56DXbrnjTfekNfj0XljruzX+QOV4E7U2SMu0QcffKDDhw9H5JwAgRUR17mFlcB6+vrnP/8ph9NoRD8nW+rKqDOkqqrqsE6//8orr8jhMsoMYf1Zk6SamlrNnz8/dAcNQlVVlZ586kmZIUlS0cmfTXN2pkyCU3/80x+jst7tqaKurk4//dnPpJQkOS+cFtS+Jj5O5rJzVVpaqj/84Q/9umjHwGGt1dJly+QsHC3Thzt8rqLxkkzElreZM2eOnO54ZY/s+f1df3i/PK1N8rQ2qbZ0m+oP9x7W8sZdqNKSg/1aD7SiokKpKf2YKU9SXFySmpub9eqrr6q5uVlx/QysaWm5knxjfwPV2tqqN15/Q+MGn6Ws1P79PME4b+yV8nq8ev311yN2TpzeCKyIuM6BtfNjnD7Kyso0d+5cDR1tFR+GHkzZBVJGjtHzzz+n9vb2kB+/srJS7747R+ljrFwJoZvgImWIlJhp9M9/vhDxQGit1R/++Ac1NTdLF+V22WpsEpyy52Vp86bNXKj0kdfr1X//6leqrKyU+ch5AXUFPpGjMFeOaZM0b968iC/jhOgoLi5WeVmZP3gGz5GQLFdekZYvXxHiyk7W1NSkBQs+UPbIGXK6E3rfIUjZI8+R0x2vOXPm9PkYDodDUqzd7PHV43Q6A95j3rx5qjtSp/PHXhWuorqUmZKr8YPP1ptvvhWV2adx+iGwIuLi4+OPPu68xA1OH88//7xkrEZ1vYpHvxkjjZ5sVVFxKCytrM8//7w8Xo9yzgztcY0xyp5ideBAccSXDXj77be1ZPESaUamTEYPIWpcmkxRsv72t79p7969EavvVPHcc89pxfLlMudPlSM3q8/HcZw1UY6hBXrssce0ZcuWEFaIWLRq1SpJkmtI37t0OIeM044d21VXVxeqsrq0YMECNTc3KX98eNYDdcUlKnvkDM2bN7/Pk+u5XC55vaG/mdkfHo9HUuDXRdZavfrqq8pLH6IRuRPCWVqXzht7pY4cqdOCBQsifm6cfgisiDhC6umttLRU7747R0WjrRLD2MCeO1hKzzZ69tlnQjomtLS0VG+99ZYyxkpxaaFfPmDQSCkxw+jJp54MS+twV3bt2qVHH33U1xV4SkaP2xpjpJl58rilnzz8E8ZRBmHp0qV6+umnZcYMk2Ni/2YaM8bIMfNc2eRE/fgnP1FlZWWIqkQsWrVqlZyDcuRIy+zzMZxDxspaqzVr1oSwsuNZa/XqrFlKzhyitPzgx9oGqmDiTLW0NPf5hqTb7ZbX6wlxVf3TEaADXe5v69at2r17t84dfUVYl7LpzojcCcodNFivvfZaxM+N0w+BFUBEPffcc5KsRve8ike/GSONneJrZe1P17ETPfHEE7LGq9yzQ3bI4xiHUe45VqUlpXrzzTfDc5JO6urq9KMf/0ieeEkfzQ/owsckuWQvz9PBgwf1q1//mvGsAThw4IAe+cUv5MjOlPPi6SG5wDQJ8XJccaFqjtTp4Z/+NKqTdSF82tvbtW79ejkG9+8mhzNniBzxiWENrOvWrdOe3btVeMblYQ1RaXkjlZY3Ui+//MrRlslguN1ueTyx9XnxBBlY33rrLcW7E3TmsAvCWVa3jDGaPmqmduzY0ef1Y4FAEVgRcZ1nbmXCkNPLwYMH9d5776lorFVCBIYv5xT6xrI+99yzIZkxeMOGDfrwww+VfaaVO7n3i7GSJVZNlVJTpbT7DauSJYG931OLpJRCo3/846mwdt9rb2/Xww8/rEOHD8temS+TFHjvBzM4Seb8HC1etEjPPPNM2Go8FTQ2NupHP/6xWmXluPICmRD2MjFZ6XJcco62bN6s//3f/w3ZcRE7tm/frpbmZrkK+9kq73DKkT9Ca9auDU1hXXj+hRcUnzRIeWPDH6IGT7lGZWWlfRo+kZmZqfqGqqMhMRbU1fnWls3I6LmXiyQ1Nzfrgw8+1KQhMxQfhnHCgZoy7EI5na6wTnCI2FJeXq7m5uaIn5fAioirr68/+pjuhKeXZ555RsYRvrGrJ+poZT18uLLf65t6PB79+c9/UlyqQzlTAtunuVLytvq+Gkp93wfCGKOC860aGhr05JNP9rnmnlhr9ec//1nr16+XLs2VyevD7Fdnpkvj0/Tss89GbWbjWGet1W9+8xsVFxfLfPQ8mZTQz4zuGFUkx5nj9cYbb+jdd98N+fERXRs2bJAkOQtG9vtYzoJRKistDctyJBs2bNDaNWs0eMrVcriCn0wsWNkjpik5c7CefvqZoFtZx48fr/b2VlVWBreETDiVV+xQamqaBg8e3Ou2y5cvV3Nzk6YMvzAClXUvKT5FY/OnaP68+fS0OQ1s3bpVd999tx78zoMRPzeBFRFXXV3d5WOc2oqLizVv3jwNG2uVEP61zY/KLpAyc42ee75/raxvvPGG9uzZq/wZXjlc4R8vlJBplDnRty5rONZOfPXVV/XWW29JZ2XIjEvr0zGMMTKX5MoUJOrXv/61tm7dGuIq+66srExLly7VsmXLonpjbNasWVq4cKEcM86UozB8y044ZkyWozBXv//DH5gM6xSzceNGOTNy5UhM6fexXAUjjh4zlKy1+tvjjys+OUMFEz8S0mN3xxiHhp3zcR08WBz0DckJE3yTFJVXRGZd2kCUH9qpCRPGB9SVevHixUpOSNXwnDCsCxekiUPPUVV1lbZt2xbtUhBme/bskSRt3tL3JaX6isCKiKuoqOjyMU5tzz//vBxOadSkyJ7XGGnMmVZVldV9HstaU1OjJ5/8u1IGS2kjQlxgD/KmSa4Eh/705z+F9O71qlWr9Je//EUakSJzbna/jmWcDumqAnkSjX74ox/GzOQ/33voIf3whz/UD37wAz399NNRqWHnzp3661//KlNUKMeZ48J6LuNwyPGR8+RxOfTTn/2MpSZOEdZabdy0WY7c4SE5niOrUA53XMhnll6wYIG2bd2qYefcLKc7vvcdQiRr+NkalD9GTz71VFA3pnJzc5WVmaWyGAmsLS0Nqq4+eDRI98Tr9WrVylUanX+mf3me4DW3NSkhIUE33XSTEhIS1NzW1KfjSNLYgikyMkdnssapa//+Yz0SOveWjAQCKyKupKTk6OODBw9GsZLTz6pVq8LSWtebiooKzZv3ftjWXe1Nx7qs/3rxX32aoOPJJ59UU3OTCi5QRGdjdMYb5Z7j1dYtW/X++++H5JilpaX66c9/JmXGywQ4yVJvTKJL9uoC1dYf8Y3VDMF44f4oLy9X8YEDMmMLpLQkLV+xPOI1tLa26he//KVsQpycl82IyPvGJCXKXDpDB/bvD1tXckRWcXGxGhvq5cwbFpLjGYdTjuwh2rw5dIG1paVFf3v8caVkFylv7EUhO24gjDEaecGnVVtToxdeeCGofSdOmqiysm2yNvpdWUvKfL1TJk6c2Ou2+/fvV92ROo3M7X3b7jS3Nerqq6/W/fffr6uvvlrNbX3vhZIUn6L8jCKtX7+hz8fAwLBz586jjyM90dZpHVittXr00UeZMCTC9u3bqyHpDiW4Hdq3b1+0yzltNDY26rvf/a6+9KUvRfzcs2bNktfr1ag+/Pu6aYVUV+X7WvyO7/tgGSONnGhVXlauJUuWBLXvnj17NHv228qcKCWkR37pgIyxUlKO0RP/93i/W81aW1v1k4cfVnNbi3R1vow7dP8EmKx4aWautm/bpscffzxkx+2LRYsWSZIcZ42U44yhKj5QfNyd4Uh44YUXdGD/fpmLp8skRK7FyTG0QI4Jo/TyK6/EVBdt9M327dslSc7coT1u17x4lpoXzwromI6codq1e1fIZpV++eWXdfjQIY284HaZPrb49Udq7kjljr1AL738ssrKygLe76KLLlJ9Q5UOHAxt9+i+2Lp9gdLSBmnSpN67IHXcdB6SNarP50twJ2n27Nl67LHHNHv2bCW4+zcL4pCsUdq5cwcTaZ7C2tvbtW3rNl0w2NcLYNOmTRE9/2kdWI8cOaJZs2bp6aef5kMWQXt271ZBqpSXIsZaRVDnSTYi+X5vaWnR22+/pfwiqS9DsGqrpPY231dVue/7vsgfKiWlOjRrVmAXdR3+/ve/yxlnlHtW387bX8YY5Z/r69Lc3/XunnrqKe3etUt2Zq5MWugnRTGjUqXJ6Zo1a5aWL498q6bke2+/M2eOHDlpMunJMqPyJWMiOhlRaWmpXvjnP2VGFclRVBix83ZwnDtFjqRE/fFPoe1Kjsjbvn27jMstR3puj9t5KkvkqSzpcZsOzpyh8rS3h+Tf39raWr3wz38qa/hZSi+M3njKETNukbW+33GBuuCCC5SWNkibt8zt0zmzsoYpzp2kOHeSCvMnKCurb63g9Q2V2rd/ta655uqAlrTZv3+/nA6nslML+nQ+SUpwJ6q5uVmvvvqqmpubleDuX9envEFD1dDQoKqqPv4DjZi3ZcsWtbS2aHr+GBUNytXq1asjev7Qza0/AJWXlx99XFtbq/T09OgV0wdz585VWlqazjnnnGiXErCamhrV1NapYIhTTofVpj27Za2NyqLXp5tovd8XL16shoZGTY7OUnFHGYc0dLRX69esV0lJiQoLew8Su3fv1rJly5Q7TXIlRO89mlxglDLY6l8v/ks33nij4uKCD5tbt27Vv196SZo4SGZE/ydv6Y45P1sqadZvf/dbPfH4E0pODv2suD3ZuHGj9u7ZI8fFvrvAJileZliO3nr7bd1xxx1KSAj/EhBPPfWUvJKc5wU4nXSImTi3zDmTtXP+Mn3wwQe67LLLolIH+m/Hzp1yZBXKOJwhO6Yz2zcL7c6dOzVmzJh+Hevll19Wc3OzJs24JRSl9Vl8SqYKJl2uefNm68477wxopt24uDhdddWV+ve/X1J9Q5VSkjODOudF59+lykpfL7Ebr3+oT3VL0pZt82Wt1TXXXBPQ9hUVFRqUnNXn8avhkJHsmwuhvLxcWVlZXW7z2GOPaffu3ZEsK2AjR47U/fffH+0yYtry5cvldDg0KXuYdteU6e3Nq1RfX6+UlPBdT3QWO+/2KDhw4ECXjweK//7v/9ZDD/X9l2Q0dHTLK0hzKD/V6Eh9AzMFR0jn93gku0cuWLBAickOZeVH7JTdGuKfMOnDDz8MaPtZs2bJ6TbKjvBEUV3JmSLV1dZpwYIFQe/r9Xr1xz/9UY4kly9QhpFxOqRLc1RdXa3nn38+rOfqyvMvvCBHYpzMmGM3JBxnDlP9kSO+WZHDrLS0VPPnz5eZOFomOQKLDXfDjC6SI2OQnnv+eXoQDVDWWu3atVuOrNC20pu0TDniEvo9Bq21tVVvvPmWsoafreTM3gNiuA2ZcpWMwxlUT5Rrr71W1nq1ddv88BXWA6/Xo63b5mvatGkqKAisxfTIkSNKiotMSAhUYryvnkhPxIPIsNZq4YcfakLmUCW54zUtf7Q8Hk/QQ6z647RuYe08+cyOHTs0efLkKFYTnL5MHBMLOiZZyk428lqHJI8OHjyozMzg7mwieJ0Hy+/cuVNnnnlm2M/Z2tqqVatWqnCEV7HQiJ6YIqVnGy1ZskS33XZbj9u2trZq/oL5Shth5YyPfvHJhVL8IIfefXeOrrjiiqD2XbJkiXbu2CkzM08mLvCWGu9C3yzejot67o54IpOXKDsmVa+++qo+/vGPR+zzvWHDBq1auVKOc8fIuI/9nKYgQ47CTD3/wgu6+uqrlZQUviD55ptvykpyTu5fy1V/GYdDZvJY7ftghTZt2qQzzojQ4scImfLycjU3NSoh1IHVOGQy87Wrn61dq1atUv2ROg2/+NIQVdY/cUnpyhw2VfPmzdfnP//5gFogCwsLNW3aNG3a/K6mTL5Wbnf4e2B0tmPXYtU3VOn66x8IeJ/W1la5HOFf5zYYbqevnp7mWaAFc+DasWOHSkpLdc2ZV0qSRqUXKCspTQvmzw/6eqSvTusW1k0bN2psVq6yk1MiPni4v2pra48+Hkh3zw8dOiQjKT1Rykg89hzCb9OmjSoaLKUkO0K+pEF3tm3bptbWNmVHfhhft7ILrLZt26bm5uYet9uwYYOam5ojuoxNT4wxShvu1YaNG9XQ0BDUvi+99JIcg+KksUGut1rZ4vvqAzMtS23tbXrzzTf7tH+wvF6v/vLXv8qRnCAzqejkemaMVl1trV588cWw1WCt1fvz5skMLehz66pn8Wq1v/7+0S/P4r6PEzKjhsq4XZo/f36fj4Ho6eg+6cjs+1jF7jgyC7Rnz55+XT+sX79eTlec0gf3fbba9tbjl1dpb+378iqSlDXsLNXW1gTVi+iuu+5SY1Od1m0Ifw+MzjyeNq1Y/W+NGjVa5513XsD7OZ1OeW1sNVp0jJV3OkPXdR2xY/bs2XI7XTqnYKwk3/XIhYUTtWrVqogtT3natrDW19drx86dumH0RFU1NWrdunXyeDwD5sPWOeTV1dVp0KBBUawmcHV1dUqKd8jpMEqJP/YcwquiokJlZeWacZbkdnm1du0aeb3esI+B6WjVTQ9vL9SgpGf5/nHdu3evxo/vfpKQjkXQk/vRldnTKiUkJOjqq6/W7Nmz5WntOST3JrlAOrTOq927dwfcI6SsrEybNm2SOTdbxhF4S7F3YYV02BdWvbMOSFnxQbW0mvQ4aXCS5rw7R3feeWfYx6m///772rF9uxyXTTqudfVoPXnpMqPy9eKLL+qaa65Rbm5wrcaB2LdvnyoPH5bzkr7PK2Ara2RLQ3MTz7jdUmGeli1frt7mBo/V8WWn89iyjkmRnBl5IT+2M7NATZuXqKKiQnl5fTt+cXGxEtPz5XD2/VKyvbVR1/iXV5Gkt+cu7POxJCnJ3zW5pKREw4cPD2ifCRMm6MILL9SK5W9q4oSPKikxMtdTG7e8pyNHDuu73/16UP8Wp6Sk6GBbbK1h39h6RJKUmpoa5UrCI1Z/P0rh/x1ZV1enue+9p3MLxumV7YslSXdO+oguK5qs13ct02uvvabPfvazYTt/h9O2hXXNGt8F++TcQk3OLVTdkSNRWZ+yr0pLS48+DmYa92hrbm5WnNN34Rrn/zeOBe7Db+XKlZKkwnzfV13dkeO6CIdLWVmZXG6j+Mj2supRsr+RsbfPTVlZmeKSHXLG9T1oeVp13Fp3nn4uTxrnv47q/PnvzdHZekcFOeapskVq9fq+Spr61tI6MkUV5RVhnyOgqalJjz/xuBw5g2TGdt+c7zh3jNqtV38L07I7HcvImIKcsBy/L0xBjirKy1VTUxPtUhCk3bt3y5WWJRMX+l+gjkzfnbg9e/b0+Rjt7e1yOHuf1bYnrrjjl1dxxfWvu35HPcGuBX3PPfeo3dOq1WuDm0W+r1paG7Vm7as666yzdfbZZwe1b1ZWluoaq2Kqd11to2924O4mXMLA9a9//UstLa26btQ52ldboX21vpslOUmDdG7BOL3+2msRmR36tG1hXbFihZLi4jQ6M0dN7W0yxmj58uU9trjEks4X26WlpRo3blwUqwlS9IcDnnaWLFms1BSH0tO8SkzwrUu6ZMkSjR07NqznraurU3yCkTGx8w9rx7Vf5271XWlpaZGjf9dicsb5utJIvv86+zlhrqMPN3m2bt0qR7JbNq2fP0xfFCYdraGo6ORuuqHy73//W9VV1XLeOKPHllyTmiidOUwfLFigm2+6SRMn9r0rY1f2798v43JKabEzIYrJ9N3lOHDgQI8zg5+urZixbPeePVJGeGarc3YKrMF0R+0sMzNTLdv61+rkiktUQ6VveRVJGpTRv+VVWuorj9YWjKFDh+qqq67SO+/M0eRJV2lQWuhbtTtbu+4NNTXX67777g1636FDh6qlrVm1jZVKT46N7kuHag/K7Xb3ubU+1p2uvx93796tV15+WRcOmaghqSe/124Zd6FWLtihRx99VN/73vfC2pPqtGxhtdZqxfLlOiO7QC6HQ6lx8RqVka2VK1ZEu7SAVVRUyOnv3jeQxoC6XC61+4deeLzHnkP4NDQ0aPXq1Soa7Jv4KCFeysuRPvzwg7Cf2+vt/2RLbf5utR1jnNr62UrZUU9v61MmJSXJ08/Gf2ecjlvrztnPeTK8rcdqC9T+A/vlzXBHZ+moQW4Zh1FxcXHYTlFTU6MX//1vmZF5MvnpvW7vmDpcjqR4PfF//xfyWqqqquRISoqpZbpMsi8AVFZWRrkSBKOlpUUlB0uOtoSGmolLlDM1o18trOPHj1dLQ40aqg6GsLL+qTm4WU6nS6NHjw5637vuuktut1uLlj4ThsqOqa0r17qNb2nmzJl9WlaoY5/iqtjpolpctVsjR4wcMMPq0Lv6+nr97Kc/VYo7UbdP7HpitbzkDN089gJ9+OGHev3118Naz2kZWPfu3auq6mpNyTvWdezM3AJt37FjwIynrK6uVkFqkuKczgG1LExCQoJaPb7Wttb2Y88hfBYvXqz2do+GDz323LAh0oEDxSFZOL4nSUlJamvr3zHa247vVtvez+N1BN7e1gctLCxUW5NX7U2x0zrc7P+oB7KGbIfKykopOTo3hYzDyCS7w9pdaNasWWppbpZj+qjAanK7pKnDtXHDhpBPttfU1CTbxfjZqHL7/u57m2QMsWXfvn2y1itniGcI7sxkFPRrpuALL7xQDqdTpZvnhbCqvvO0tahi+yKde+6MPs0EnpWVpbvuulP79q/W3n19n+ysJ9ZaLVzyD7ndLv3nf/5nn44xevRoJcQnaE/55hBX1zet7c06WLVbZ0xmJvJTRWNjo77//e+rtLRM/3XWdUrtoav+9aPO0Vl5o/TYo4/2adm9QJ2WgXXt2rWSpEk5x2beOyOnQNZarV+/PkpVBae+vl5JLqeS41w6cuRItMsJWGJiolrbvLLWqsUfXBMT+9cFCD2bN2+eUlMcyuk0tGREka+lcd688F5o5ObmqqXJ26+Q6XLr+DFO/ezZ2uj/uOTk9DzOcNIk3+Kr9bHTeKD6YikuPk6jRgUWziSpublFckfxV73boaam/s382Z329na98eabMsNyZDIC74Zrxg+RI94d1HqNAR03hlpWj4qd+y0IQscaqeEMrI7sQh0sPtjnmxmZmZm6/KMfVdmWBWqqi35Pr4Pr31Fr0xHdcsstfT7GzTffrKFDh2rRsn+ovb2f3Xm6sHf/au0/sFZ3331Xn8d7ulwunXX2WdpWujYmxrHuLNuodk+bZsyYEe1SEALV1dX69re+ra1btuqLU6/V+KyhPW7vMA598azrNDqjUL945JGwrXd+WgbWDRs2KCc5RdlJx1pYRmZkKc7p0oYNG6JYWeBaW1oU73Qo3uUMenKBaHK73bKSPFZHuwa73VEYW3eaqKys1OrVqzWi6PiuuYkJUmGeNHfue712je2PESN8a8LU9qOBzX1Ct1p3P7vVdtTSUVt3xo8fr8ysDFXHyFxs3narI3sdOu/c8xQXF/gfgrXe6I4bN+Fbemv9+vWqq62VGTc4uJLcTtlReVq8ZHFIf38mJCTItMXWchNq93VloSfLwLJr1y454hJk0sK3hrEze7Cs9farp83dd98tt8upnR8+1afPeUp2kZxxiXLGJWpQwTilZPdtrHtjTZkOrHldF1100dGbjX3hcrn0pS99SXV1h7RmXWi7OLa1t2jR0n+oqGiYbrzxxn4d68ILL1RtQ6X2H47+P1Dr9y1RWmoaaz2fAnbs2KEHvvSA9u7eo69M+5hmFAY2P06CK07fnPEJnZEzXL///e/12GOPqd3/b0+onHaB1VqrzRs3aWzG8a0rLodTozKytGnjxihVFpy2tjY5jZHTmJC/KSLBHP2/gbWO7EAzd+5cWWs1evjJr40eIR06dPhoj4NwmDRpkowxOhz4pLZhd7hUGjJkcI8T0EiSw+HQDdd/TPXFUlNl9N+j1dultmavbrjhhqD2czidkjeK9Xtt2MY1rV+/XjJGZsjJF/WeRVvlWbS1233N0Gy1trRq+/btIatn0KBBss2xNeu5bfLVk5YW5Bq8iKpt27bLZBXKmPBdpjmzfTd6+vMZyMnJ0X/+52dVfWCjDm6YE/T+oy68QylZRUrJKtKUGx/UqAvvCPoYXk+bts39XyUkxOuLX/xi0PufaOrUqbr00ku1Zv3rqqsL3fIxa9a9riNHDuuBB77U77k7LrroIsXHx2vNnvDPRdGTxpYj2layRpfNvIz5SAYwa61mz56tr/6/r8rT0KyHzr9NZ+cHNw48wRWnr02/WVeNOFuvvvqqvvWtb+nw4cMhq/G0C6wlJSWqrq3R2KyT1+Abm5mjXbt3h637WihZa2WMr1vnQAp89fX1cjuNHEZKcPkSa0NDQ5SrOjV5vV699dabyssxGtTFtWrRECkh3ujNN98MWw2pqamaNGmiyvYbxcLbtLVFqiqXzj//goC2v/7665WUnKTy5dH9nHlarQ6tcWjipIkBr7/aIc7t9nVpiBaPDapFOBjFxcVyDEryjUs9ga08IlvZ/XAJk+VbLzCUS+5kZmbK29oq29+B26HU6Pv3jOUmBo7W1lbt2r1Lzpyeu+L1l0lOlzMp5eia0311/fXX6/zzz9eepf9SbWnkW/x2LnxWRw7t1Te+/vWQvc8/97nPyeVyatGy0EzAVFdXobXr39Bll12mM888s9/HS0xM1MyZM7V+/xI1ttSHoMK+Wb37A7V72nTddddFrQb0T3Nzs371q1/pd7/7ncamF+rhi+7SiPS+TfbmdDh056SP6P6zrtPObTv0xfvv16pVq0JS52kXWNetWydJGp+dp6fXr9DT64/NDDw+O09er1cbB0Arq7VWRkZGAyuwlpaWKiPJIWOMBiVIDkdwa0oicKtWrVJpaZnGjfK9P5at9n11cDmlUcOtFi9eHNaZpmfO/IiO1FjVxMAkpQd3S16vNHPmzIC2T0tL05133KkjxVLd3vDW1pPyVVJ7k1ef/9zngx4nGRcfL7VH8XdEe/gCa319vWx8H+/q+xeCDuUNs9xc/43QI40hO2Z/2XpfLUdrQ8zbvXu3PO3tcuaGbykoyTfm2uQUadOWLf0+zje+8Q3l5+dry7t/VPOR0LWq9KZk0/sq2zJft912my64ILAbkYHIzs7WHXfcrr37Vml/cf/nNlm07Bm5XM4+T7TUlRtvvFFt7a1asfP9kB0zGO2edi3dMUdnnnmmhg8fHpUa0D8HDx7Ulx94QO/PfV83j71A3zr3E0qL799ayJJ0weAJ+vGFdyhVcfre976n5557rt/Dz067wLpixQplJiWrMCVN+2qrta/22Ay747Jy5XY6tXz58ihWGITO/WoHAK/Xqy2bN2lwmu/i2ekwKkxzaPPm2Jjp7lTz73+/qOQkx9HZgauqfV+dTRjjG+M4a1b4FkufOXOmEhLitbf73pkRYa20b7vR2LFjgpq06MYbb9SIEcNVutih9ubIB7+GMquqTdJ1113fp3Wi3S5XdLsEe2zYxqnHx8fLtPfxH0H/IPr4+PiQ1dOxBqGtj6FeI0calJyayuR2A0jHv4nOvGFhP5czb5hKDx7sdV3q3qSkpOgnP/6RXPJo8+z/UXtr+HuqVR3YqF0Ln9GMGefqM5/5TMiPf/PNN6uwcLAWLfmHPJ6+D73aX7xee/et0h133K7s7NCtmzpy5EhNnz5dS3a8o9b24CbOKsgYpnh3ouLdiRqeM14FGcG/19btW6TaxirdeuutQe+L6Fu3bp0eeOABVZYf0jfP/YQ+PvYCOXoYgvDMpve1r65C++oq9LPFL+iZTT3fKBmcmqUfXnC7zi8cr6eeekqPPPJIv+aMOK0Ca319vVasWKHp+UO6bKWIc7o0JbdQH37wgTyeGJs44wSVlZU6UNuggTQF5ObNm1VdU6sJecfeduNzjDZt3BjWZS9OR9u2bdPates0YYxXPQ0fTE2Rhg+V3njj9bDNNp2cnKyrr75GpXulxuj1XFLZAam+1uoTnwhuBkmXy6VvfvNb8rYYHfwwsj0aPK1WBxc4lJOTo/vuu69PxzAOh6LaH9vasM2em5+fL1vX1Le/kzrfBXUoF7o/FlhjqYW1QXm0rg4oGzZskDMtU47kQWE/lzPfN/lcKJZ4Kioq0kMPfU+N1SXa+t5jst7wXUc1VBVr67t/1rBhw/Tgg98Jyzj5uLg43X//F1RTW6oNm97p0zE8nnYtXvoPFRYO1s033xziCqXbb79dDc11WrbjvaD2u/bsO1WQPkwF6cN030e/p2vPvjOo/T3edi3YPEtjxozR9OnTg9oX0bd06VJ997vfVbojUT++8A5Nzhne6z77aivU1N6qpvZWba0q1r7a3sd3J7ji9IWp1+pTEy7RBx98oIe+91CfZyU/rQLrO++8o7a2Nl1S1P1A4ouLRqm6pkYLFy6MYGXBa21rU1ObR1YxupRCF15++WUluh3aW+XVKxt8dyunFznktTasLXyno2eeeVoJ8UbjAxgzf+ZEqampWS+//HLY6vnEJz4hY5zaGaXe9tZKOzcYFRTk6+KLLw56/1GjRumee+5R3V6pqn+95wJmrdXBhVJbvfSd7zzYp3UFJcnT3i45ovg7wukI28Rwo0ePlm1rl6qCvxNiy2skSWPGjAlZPenp6XI4HFIMBVbT2KzcXpZwQuzwer1av2GDHPkjI3I+Z85QGZf76HCp/po2bZoeeOABVe1fr52Lng3LDb7Wxhptevt3Sk1O1MMP/6TPvxsDMWPGDM2Yca5WrX1Fzc3B39TdvPV9VdeU6gtf+HxYhkZMmjRJ06ZN08Ktb6q5NXK/d1bumq/q+kO6++67B8w1KHzWr1+vhx9+WEUp2Xro/NuUm5Qe1vMZY3TdqBn6wtRrtX7Dej38k4f71Ch42gTW5uZmvfivf2lCdr6Gp3c/TfxZ+YNVkDpIzz37bMy3slr/10D4ZbFx40YtWrRIF480Kj9iVVLr68aXm+LQ1MEOvfzySyovL49ylaeGTZs2afnyFZo0ziqQnpiZ6b5W1pdffkk1NTVhqSk3N1fXXHONindKDVFYNrh0v1RbaXXHHXf2+U78Jz7xCU2bPk1lS01EZg2u3ibV7pLuuuuufi3T0NDYeMquwzplyhRJki0OfoC0La7UkKFDlZGREbJ6nE6nUgcNkm3q2x3ksGhsVmZm+JZGQWjt2bNH9UeOyFkY+LCF/jBOlxx5w7QmhLPFX3vttfrkJz+p0k3vq2TDuyE7riR52lq0afbv5W2t18MP/yQiY7Pvu+9etbU1a+2G4CYobGtv0Zp1szR58uSwrlF67733qrGlXh9ueSNs5+ispa1Z8ze/qjPOmKxzzjknIudEaBw+fFgP/+Rh5SYO0rdm3KKUuMgNFblwyETdc8YVWrlqpZ588smg9z9tAutzzz2n6poa3Tpxao/bOYxDt4yfor379oVt8duQsFZGGhCTLjU0NOg3v/6VMpMdmjn65LBw/USXjNejX//6VzF/kyDWWWv1xBOPKynRoQljA9/vrMlSS0uLnnvuubDVdvvtt8vpdGt7aG7kB8zrlbavNRoydIg+8pGP9Pk4DodD3/rmtzRoULqK5zrkaQ3f566p0qp0idFZZ52lT33qU30+jsfj0ZG6OikpessN2ESHKivDM+NWbm6uhg8fLrs3uEnDbEubbGm1Ljj//JDXlJaaKrXExtrY1lp5W1qVmpoa7VIQoNWrfTPjuQYH3vLfvHiWPJUl8lSWqOH1x9S8OLgeS67BY7Rv796QDs259957dcEFF2j3kudVuW9tSI5prdW2eY+r/tBePfid74S0d0RPhg8frssuu0wbN81RY2PgY303bX5PDY01+sxnPhPWhoXRo0dr5syZWrx9tmobwj+74cKtb6q+qVaf/ex9A6LBBD7WWv3h979XS1OT/t+0G5UcF/m1uWcOO1OXFZ2pF198UVuCnOzttAisO3bs0IsvvqiLi0ZpTGbvXaNmFBbpjNwCPfH4EyorK4tAhcHz+pe1cTscammJrXX/OvN4PPrlL3+psrIy3X6WU/Guk3+5ZSYZ3TzZofXrN+jxxx+PQpWnjkWLFmnTps2aMsmrLlb66FZ6mjRmpG8s68GDB8NSW1ZWlm666SYd3C3VVfe+fagU7/KNXb3v3vv6Pc4pPT1d3/vu99RS5+uuG46bRd42q+L3HUpLHaTvfOc7vi6mfXT48GF5PV6Z1CgG1lSXSsvCNxP4hRdeKFtWLdsUeEi0+w9LXhvSWUU7JCYkHJ3QqS9sa5sSEhJ00003KSEhQba1H0vkeL2S16uEhMhfmKBvVq5aJVdmflDjVz2VJVJrs9TaLE/pbt/3QXAN8d3dDNXyE5LvBt+3v/1tjRg5Utvm/q8aqvr/78r+VbN0ePcK3XfffWH57PbkzjvvlMfbpjXrXwto+9bWJq1d/5rOPvvsoJci64t7771XDofRu+tfDOt5ahsqtWjbW7r00ks1YcKEsJ4LobV69WotW75cN485XwUp0et1c/vEyzQoPll/+d+/BHUNdcoH1sbGRj3y80eUHp+oO8+YFtA+xhjdN+U8ydOuX/7iF2Ebf9VX1lq1t7fL6TBKi3OqOkYnLPJ6vfr973+vZcuW6aYzXBqZ1f3bbUaRUxeNcOjll1/Wv/71rwhWeepobW3V3/72V2UMMhrbh+FPZ50hORxWf/3rX0NfnN+tt96qpKREbVsTtlMcx+ORdm5waNy4sTo/RK1pkydP1t13363aXVLNzp63TciSHHG+r+QC3/e9KVkitdR69eB3HlR6enq/at23b5/vQXp4lpUJSLpbFeUVfZ5ooTfnnXeepOC6BdsDh5WalqZx48aFvB6ny9W/Sa5a23T11Vfr/vvv19VXXy31J7D66wjHhDQIvaamJm3YsEGOIUF0jwkBR1ahnEmpWrFiRe8bByEhIUE//tGPlJyUoC3v/EHtLV3Pnp2SXaSU7J6X8Dm8Z5X2rXxVl19+uW65JbiJ80JhyJAhuvzyy7Vpy1zVN/R+zbVh02w1NdeHZfbiruTm5urjn/i41u1bpAOVvfzD1A9z1v9LMrbPkwAiep5//nllJqbqyuFnR7WORFecbh5zvrZs3RLU2PlTOrBaa/U///M/Kikt0RfOvkDJcYEvX5CTnKJ7ppyrzVu29KmvdTgdOnRIXq9XcU6n8lISdaC4OOZCtcfj0e9+9zu98847umKsUxeN7P2C6abJLk0d7NATTzyhF154Iea7OseaV155RWVl5TrnLKu+NMolJUpnTrBaunRpSO+0d5aWlqZbb71N5cVSdQSW6tu/XWqs9+qee+4Nadel2267TZMmTVLZYqPW+u7fp4XnGyVmSYlZ0sjrjQrP77mGuv1W1dukT37yVk2dOrXfde7YscP3IDt0S7cEy2QnyFqrXbt2heX4o0aNUmJSkmxp4M32pqxWU6dM6VfrdXfa29ul/rzX4tyaPXu2HnvsMc2ePVuK68eSQP6fj6EWA8OaNWvkaW+Xa2hkW66MccgxZKxWrFwV8vdKbm6ufviDH6ilvlJb5/5V1p68DNWoC+/QqAvv6PYYjTVl2j7vbxozdqy+8pWvRK0b6h133CGv16PNW+b2uJ3H266NW97VOeec06elyPrqtttuU0Z6ht5eE57Jroord2n9vsX6+Mc/HtLZ1RF++/fv14YNG3TFsLPkdkavx1WHi4ZMUkp8ot54I/Bx16d0YH3llVe0YMECfXLCFE3MyQ96/wuGjNBHR4zViy++qA8++CAMFfZNxxiXJJdT47LS1Nraqo0bozT9aheam5v1k5/8RHPmzNGV45y6enxgd/cdxuiOs106e4hDf//73/XYY49xoRWgiooKPfvsMyoaLA0O/q1+1KRxUlqqQ3/+8x/7tV5WT2688UalpqZoR//XYu+RxyPt3uTQ5MlnhCT8deZ0OvXNb35TDuNWSYi6BntarUoXOjRs+DDdddddIajSNwGXIzNBJq4PLWytnuO6pqq1j5/FPF931HCtt+x0OjVq5EipOrCZgm1bu7xHGjV6dABTaPdBU1OTguqPfwIT51Zzc7NeffVVNTc3y/QjsBqHQ8YZvkmvEFrLly+XIy5BzvzhET+3q2iCGhvqgx5XFohJkybpC1/4vKr2r9OBNcFNXORpa9HWOX9SYkKcfvD974dlpt1A5efna8aMc7R1+3x5vN03Euzdu0qNjbW64YYbIlidlJSUpHvuvUcHDu/UxgPLQnpsa63eXvOs0tMz+jWvAqKjY+WTi4ZMjHIlPnFOl87NH6fly5YFfK15ygbWNWvW6G9/+5umFQzV9WPO6PNx7jxjusZk5ujXv/q1du/eHcIK+8Zaq7fefFNup0PxLqcm56Yr0e2KmQmiKioq9LWvflXLli3Vxye7dPV4V1B3Q50Oo9vPdunSUU7NmjVLP/zhD9XQ0HU3Ihzz6KOPyuNp04yz+nccp1M692yvDh4s1YsvhmcsTFJSkm6++eOqKA7vWNaDu6WmRq9uv/2OsNyRLygo0D3/cY+OHJDq9vb/eBWrpbZGr7721a+F5KKsvb1dGzdtlLegj62rLd7ju6a2nNwyEgiT5JIjIz5ky2Z0JTMzU46WAHuZ+Me6hnJ24M5q6+qk+Ch2wT6BiY9XXV1dtMtAL6y1WrpsmRyDx8hEoQXENWSsjMOhZctCG3Q63HDDDbr00ku1b8XLqi3dHvB+uxY/p4bqg3rwO9+JyIzAvbn++uvV0Fijvft8vZCysoYpK2vYcdts2jpXubm5UVmf9PLLL9fIkSM1Z/0/1eYJ3U3nTQeWa//hHbrnnv8I6zJCCI+tW7dqSFq20hNSol3KUZOyi9TS2qo9e/YEtP0pGVhLS0v1s5/+VAUpafrC2RfK0Y+LVbfTqS/PuERJTqd+9MMfqrY28BniwmHZsmXatn27MhPiZCTFu5yaOTxXH37wQdQD9fr16/XAl/5LBw/s1X3nugLqBtwVhzG68QyXbjnTpVWrVurLD3xJ+/fvD3G1p44PP/xQS5Ys0ZRJVqkh+F00pEAaUSQ999yzx8ZAhtgNN9yguDi39oRpTVNrpb1bjUaMGK6zzupniu/BjTfeqKFDh6h8hUPW2/dW1tY6q8pNRldddXXIupBt3bpVLc0tMoP7eHER7zi+a2p83/+58BYmaN369Wpr68d4zB54PB7ZQH/P+7vJhmMYRXt7u+rr6mSSYmiSo6SEkM7+ivDYuXOnqquq5CqKzkQ2Ji5RzvwRWrI0PIHVGKOvfOUrys3L0/b3/6r21t5b/Q/vWaWyLQt06623atq0wOYgCbdp06YpNydXm/zdgi86/y5ddP6xHjHVNSU6WLJJ1157bVTGjjudTn3+859XTf1hLdsRmiWF2j3tenf9vzR8+HBdccUVITkmIqumuloZ8bETViUpM8E3e311dWAtF6dcYG1oaNAPf/ADeVvb9NUZlyoxkIUoe5GRkKT/N+NSVVVW6uGHHw7bRVdvmpub9dijf1ZBapLSE479XNeNGaykOJf++Ic/RKULrbVWr776qr797W8rzluvr1zs1MS8/v+ivmCEU/ef71LN4TJ9+YEHtHjx4hBUe2qpqqrSH37/P8rONDqjh/ljlq2WKmt8X2/P9X3fk3PPllxOr371q/8Oy4V9WlqaPvKRj6p0r1F7GD5ONYelumqrm266OazjnZxOp+6777NqqfX2OgFTTyrW+o519913h6y2NWvW+Na9GtLHwBrnPK5rqvrSrdjPDElSa0uLtm7d2udj9GTf/v2yKQG2JCfFyTgdKi4uDnkdVVVVstbKJMdOC4RNSlRZBWtc92TBggW65957defddx/39f+++lVVVFREpIaOCY9cQyM35vFEzqIJOrB/X9jWRE9OTtZ3vv1tNddXafeSF3rctq3piHZ+8KRGjRodsiESoeB0OnXtddfqYMkm1dad/Oe0Zdt8OZ1OXXXVVVGozmfq1KmaPn26Ptj8mppa+99DbdXueaqqr9BnP/tZJnAboNIGDVJVc2DDZrrT1NZy3DChprb+rVBS1XxEkjRoUGAzop9SgdXj8egXv/iFDhw4oAfOuVj5KWndbvv0+hXaV1ulfbVV+umHc/T0+p5nxxuVka37pp6nDRs26M9//nNUJgT6y1/+ovLyCt0zZaSMjl2Ep8S59elJw7R5yxa99NJLEa2ptbVVv/71r/XYY49pQq70lYtdykvt/m31yoZ2Hay1Olhr9eeFrXplQ89haFS2Q1+91KWcxFb9+Mc/1tNPPy2vt29dE081Xq9Xv/rVr9TY2KCLz+15oqWqaqmtzfdVdsj3fU8SE6Tzp1vt2LEzbJOOXXnllWpvtyoNQ+N58S4pLj5Ol1xySegPfoLzzjtPw4YPU+VG06ffC+1NVrU7ja6+6mplZQUwjXCAVq1eLZOTKBMfAxcYg5Mk4w/RIVZeXq7iAwdkCgLr4mscDikvXctXLA/57/GjF/qpySE9bn+Y1CQdqghundrThbVWr7/+uh555BGV1jeqKjXr2FdKhrZu366vfu1rAXdZ64/ly1fIlTNEjqTorZnb0bob6tmCO5s4caJuueUTKtuyQDUl3d/A2r3kBbW3Nuqb3/yG3CFoeAilmTNnSpL27jv5zu++/as0depUZWZGb9kQSbrnnnvU1NqoRVvf7tdxWttbtGDzazrjjMlR6eKM0Jg2bZoOHjmsndXBLXnVWWN7y3HDhBrb+xdY5+/foLTU1IDXUz6lAuvf/vY3LV++XHefeY7OyCnocdt9tdVqam9TU3ubtlaWa19t703SFw0dqY+NPUNvv/12xIPhhx9+qLfeekvXjC7UuOyTg/iFQ3M0vTBTTz7597C1YpyopqZG3/rmN/Xee+/pqnFO3TPDpUR3z61ZJbVeNbdLze3Srkqrktrew2dGotF/XejSOUMdeuaZZ/Szn/0spteejZRnnnlGq1ev1oyzrNIDX7IvYMOHSuNGSS+++GJYWrcnTpyo7OwslYW417G1UnmxQ+fOODciY22MMfrYDR9TU6VVcx9mPq7ZKXk9Vh/72MdCVlNzc7O2b9smWxgbXVNNvFMmJyEs41jfeOMNyRiZUYHPNmZG56v4QLHWrw/tzF8dgdWkxE5gVUqymhobVV/fv7vrp5q6ujr94he/0J/+9Cc5hoxQ/M13K27mdce+PnKD4m64XVWNTXrgy1/W66+/HrabpUeOHNHWrVvliGLrqiQ5BuXImZoRtlniO9x5553Kyc3VroXPyHpP7hVWV7ZT5dsX6ZO33KIRI0aEtZa+yM/P19ChRdp/4PgbcDW1ZaqpLdO5554bpcqOGT16tC6++GIt3TFHjS19/+yv3PW+jjTV6D/+4zNRm50Z/XfVVVcpKzNTf1v/jhr72DKa5Io/bphQkqvvqw/M379B6w/t0a233SaXK7Ax+6dMYJ01a5ZeeeUVXTVyvC4fEfq19TrcMmGqZhQW6fHHH9eiRYvCdp7OSkpK9Nvf/EYjM1L1iYldr1VmjNG9U0crIyFOP/vpT3XkyJGw1lRRUaGvfvX/aeeObfrMOS5dNd7Vr7HCvXE7jT51lks3THJq4cKF+u6DD57WkzF9+OGHevbZZzV6hDR2VPjOM+NsKSfL6Je//EXIx0gbY3TeeeerstwolNeBddVSc6M3ohcNl1xyiRxOh2r70BBTt8doxMgRGjZsWO8bB2jnzp3yeDwyBYkhO2Z/2bwEbdu+LaTDFg4dOqRXZ70qMzJPJjXwn9WMLpAjOUFP/N8TIQ0hR7uPpsTOn7tJ8d20iVTX1ljn9Xo1Z84c3XvffVrwwQdyTbtI7itvlnGfPFGWIztPcTfdJU9Oof70pz/p69/4RliWZ1q/fr2s9co1OLCWhnAxxsgxeIxWr1kb1uFFCQkJ+vznPqeGqmKVbz/+Ospaqz1L/6mMjEx9+tOfDlsN/XXeeeeqpGyrWjuNxd3nD7CxEFgl3zI8LW1NWrp9Tp/2b/e0aeHWtzTlzCmaPHlyiKtDJCUlJelb3/62yhtr9JsVL/epO2+iO/64YUKJ7r4F1uUl2/Tkxnd11tSz9PGPfzzg/U6JwLpkyRL972OP6ez8IbpjcngH5juM0RemXaiRGVn6xSOPhGUK+M7a2tr0yM9/Lnna9cXpY+Tqod9ncpxLX5w+RpWVh/W73/0ubN2Wq6qq9I1vfF1Vh8r1hfNdmlIYmS6HxhjNHO3SXdNd2rxlsx763nd9Y+tOM1u3btUvf/lL5WYbnT+9f0s+9sbllGZeaOV0tOn7339IlZWVIT3+1KlT1d5mVRvCOWGq/L0yp0yZErqD9iItLU0TJ0xU/cHg/jI8rVYNFVbnn3d+SOs5elGdExstrJJkchLU1tqmgwcPhuR41lr98Y9/VJvHI8eM4JaoMW6ndM4obdu6TW++GdwyGz2pqqqSIyFeJsA7xhGR7AvPp/vES9ZarVixQl/8r//Sb37zGzUmpCjupjvlPvt8XzfxbpikFMVdc4vcl1ytrbv36Iv/9V/61a9+pbKyspDVtm7dOhmXW87crm9IR5KrcLSamxrDtm5yh4suukijx4zRgdWvH9fKWnNws2rLdujOO+9QYmLs3Pg50YwZM+T1elR88NiSgvv3r9XQoUOVn9+PteVCaMSIETp3xrlatvNdtfah++a6vYt0pKlGn/o0y9icCqZOnarvfOc72lVbpp8t/aeqmsLbsNWV9/au0Z/WvKFx48bp+z/4flBjogd8YN22bZse+fnPNTw9S1+cfpEcJvw/UpzTpa+fO1MZ8Yn6wfd/oJKSvvcJ781zzz2n7Tt26N4pI5WT3PvF56iMVN0yoUiLFi3SnDl9u6vWk7a2Nv3wBz9QdeUhff58p0ZkRf4tdNZgp+6a5tSWLVv129/+JirjiaOlpKRE3//+Q0qM9+gjF1m5InCvIDlJ+ujFXtXWVOmhh76nxsbGkB173Dhfb4jaPnSl7U5NpZSRkR7xJRDOPPNMNVVaedsDfz82HZJkpTPO6PvSW10pKSnxrb2aFAPjVzuk+8ahher35VtvvaVly5bJnDNaJi34rt9mbKHM0Cz95a9/Ddls2EeOHJGJoSVtJMkk+O6Cn65L21hrtWbNGn31a1/TQw89pP2HKuWeeZ3cH7tdjqy8gI5hjJFr3GTFffI+uSZP19x583XvvffqD3/4Q0harjds3CRHblFUlrM5kbNgpCTfGs7hZIzR7Z/+tJrqDunw3mNjQQ+ue0fpGRm68sorw3r+/po4caISE5N04OAGSZLH06bS8q0655xzolzZ8W755C1qbKnXhn1LgtrPWqulO97VyBEjwzrTPiLrkksu0Y9//GNVtNTpR4uf1d7ayEzI57VePbPpfT21ca5mnHOOHvnFL5ScHNzQmQEdWCsqKvTD7/9Aae44ff3cy5TgitzA/LT4BH3zvJnytrbq+w89FJbxQfv27dM///mCLhyao3MGBz4Zy9WjCzU2K01//ctfVFNTE9Kann/+eW3fsUO3n+XUsIzovX2mFDp1zQSnFiz4QPPmzYtaHZFUV1en733vu2ptadDll3iVGMHGs6wM6bILrPbs2aOf/exnIesulpOTo8TEBB3pYbWoQZmSy+37yszzfd+T+lqjkSPD2E+6G6NGjZKs1BLE2rLNVZ32DaGamhqZpODWQA67JN/FeKBT2Pdkx44devSxR2WGZslM7lurlDFGjsvOkMdl9OOf/CQkN2Kam5tlY6l1VVLHXa1AF2c/laxbt05f//r/Z+++w6Oqtj6Of9ekQoCEXkIvSpEOUhWxo1iveC3Ye6/X145ir/cKtmtFvfbesYGo9I50pEnvJYT0We8f5wxMQsrMZDLnHGZ/nicPmTPJ5Oc4mZx99t5r3cadd97JktVrSBpwLEnDLiOxbceIfjckJZWkPkeRcvblcEhnvvt+LBdffDGjR49m69bIrrrl5eWxauUKEhpEb0tAZfjS0kmokRGTWhh9+/alXv36bFw0AYDcrG1sX/MnJ590UlR6UVelxMRE2rVry9ZtqwDYvmMtRUWFUWtLFi2dO3emVctWTFv+S1jf9/fWZWzc+Tennnaqu/6OGJXWu3dvnv33v0lMS+XhyR8yb3PVFpXLLyrguRlf8sPKWZxxxhmMeOABUlPDP4H17IC1oKCAh0Y+RO7ebG7vM5j01PCWjuQU5Jcozxz+H/NGNWpxc+8j2bBhA08++WTUZ/reeOMNUhISOPewlmF9n0+Ei7u2Zu/evXzwQfml48Oxe/duPv74I7pn+ugSo2XA5Tm6XQJNM3yMefONg75ycFFREY8++iibNm3k6IF+0ssugF1lmjaBfj1hxowZvPHGG1F5TBGhSZNM9pazMqVTb6hVx/rof4J1uzw5e4QmTZpEJV84Gje2Cr3lh7HKJj8LUqulhlzWPVSFhYXgc9lJRoKVp7Jtknbu3MkDDz6APyUJ3+DOlTqZkuopcHRn1q1bx1NPP13p93BXntjFzwKUfZYvX86dd97JHXfcwaJVf5PU/xiSz76cxI7dkSi05ZAaNUkeeBzJ/7wc2h3Gt999z0UXX8xrr70W9sXrVatW4ff7SajftNK5okXqZrJ0WSX6dIUoISGBY485hp1rF5Cfs5sty6cByrHHHlvlPzsa2rZty/Ydf+P3F7F12+p9x9xERBhy0hDWb1/Fhh2hrySZtWICqanVOOqoo6ounOGY1q1b89yoUWQ2b8qzM75g7ubo1igJKCgq5N8zvmD25hVce+21XH311RG3RvLsgNVaKruUK7v3J7NWRtjfv7ewoER55siaQbav15DzOvVg6tSpfP995cqHB1u3bh1Tpkzh+NaNqJkS/sxxZq3q9Gtaj++/+y5qSzgnTpxIfn4Bg9tG/gc/t0CLXSjILYj8bMonwuA2PjZt3sLChQsjfhwv+PTTT5k9ezZ9eygN64f//fkFFHve8yPsfXpoW2jfFj755JOoVZJs2LAheTnReSsqLID8PD/160fwJFVSoCVNQRi/boU5ULt27agPdKpVqwb5LruIY+epzL60oqIiHnn0UbZt344c3wWpVvlZGF9mHXx92zFp4kQ+/PDDSj1W9erVkUh/uaqK3Tc8FhWznZabm8sLL7zAddddx9xFi0nscxTJZ19GYqceVbKv2FejFslHHE/ysEvRlofw8cefcPEll4S16iewHN1X2x37HgF8dRqxccP6mPScHzBgAKrKjjV/suPvubRs2cqRC46RaNu2LYWFBezctYGt21ZRrVq1fRcu3WTw4MEkJCQwb3Vo1f4LivJZuG46Rxwx0NX7iI3KqVu3Lk899RStWrVk9Kyv2bin8qufSnp34Xjmb1nNrbfeymmnnVapx4r5gFVEmonIeBFZJCILROSmcB9j27ZtfPzRx/Rv2oreTSJbDlY9MalEeebIlxMf37o97es15M033oxaEaDffvsNgEEtIt+Hd1TLhuTm5TFt2rSoZFq+fDnVkn1kpkd+cp1TSLELBTmVm2yhTT3rJfzXX1V/NdgpO3fu5J133qZ5JrRrHdlj5BcUf94rc07duzuk1xJefPGFqCwNzsjIIC9KtbPyc/c/ZqzVqFEDAH8YizWK8iC9VvSnyxs2bIh/Tz5a4KJB6y7riWnYMLR9g6UZM2YM8+bORY7ogNQve1a6aOJi2JYF27Io/Gq6dbsc0rkF0qYRb44ZU6lesXXr1sWfvRd10YoP3WNdQYlmj183Wr9+Pddedx1fffU1vg7dSD77cpK69EZisFXIVyuD5KNOIuWMC9ibmsbjjz/OM888E9Jqgg0bNoAIvlrO9u0M5kuvh9/vj0ll6bZt21KjRk12rJnP7k1/0bNnjyr/mdES2Mqxdesqtm5bRevWrfGV1xDdIbVq1aJHjx7MXxta7+m/Ns4nNz/HzK7GgRo1avDAgw+SmJzE+/bS/GhZvXszv6yey5lnnhmVPelO/GYVArepagegL3CdiHQM5wEmTJhAQWEBZ7bvEnGIaknJJcozR36lXkQ489Au7M7aHbWG2/PmzqVZehp1qkXe56htnZpUS0qMWq/BvLw8khOkUrNB1RIpdqGgWiUveqfY338w788aN24c+fkF9OwSeUXg5KTiz3tyJc7hEhOga0dl7dp1USnMUbNmTfLz/ERjRX1gZX/NmjUr/2BhSkxMJCEhAX8YF2H8hZAa5naGUASKWbExp/wvjCHdkIMvwRfxkrnp06fz0UcfIR2a4js0s/yftS0L8gutjw07rNvlEBF8gzriq53GY48/FvHe/xYtWliD1Z3uKXCk23YC0Ly58xVoq8ru3bu5/V//YsPWbSSffDbJA45FUsLbI5U/eRx533yw7yN/8riwc/jqNSTplPNI7N6PH3/8keeee67C79m2bRsJ1WshPue32QT40qyLQZHuyw3rZ/l8dOjQns3LJuMvKqRDhw5V/jOjpVmzZvh8PnbsWs/OXRui2pos2gYMGMDOPVvZtGtNhV+7ZN0sqlerHtNK+4Zz6tevz0knn8zszcvJLYzeufTU9UtISEjgvPPOi8rjxXzAqqobVHWW/XkWsAgo/+yjhLVr11IjJZVGNRzYyFeGtnWsJYhr1lT8ZhCKFSuW0zK99Apa785byepd2azelc1jv8/n3Xmlb5j2idC8VvWoladv2LAhu3OKyKnEMt7UJCl2oSA1qXJLITdnWVliXRE2llauXEladR8ZldjmmJxEsee9MgNWgMYN92errLS0NNQPpfSPD1tgBVu41eeiJTExIbz/Dr+QlBT9GaAuXbqQnJKMLo992frSqCq+Fdl07twloiVmWVlZPP3MM/jq1MTXv2r6bEtSInJMZ3ZnZfGf5/4T0X7Wjh2ta6/+9e7pearrN9O0WTNHLuLEyqeffsq2bdtJOvEsEiJcdaXbNuPfsGbfh26L7P+h+Hwk9RpIYtc+/PjjjxX2r7YqS7tr2aWkWMvHq6KYZGn+8Y9/cMQRR3D88cfTo4d3ZlgTEhKoU6cuO3dtIDd3T6VWj1S1ww8/HIBlG8qfwFBVlm2cR4+eParkb5PhTi1atEBV2ZWXHbXH3JGbRZ3adaL2t8fRtQsi0hLoDkwN5/tq165Ndn4eu/LcM3uwcY91Rb127dqVfqxdu3axc9duMmuW/kfs713Z5BQWkVNYxOJtu/l7V9kvsMxa1fh79eqoFITq0aMHCsxZ557lbrPX+fGJ0K1bN6ejVJm0tDTy8pVK1qqJqhx76W00BoaBAUyE28iLCVwcdGq/XkJiQlhFblQl4gIE5UlNTWXwUYORZXvQvS544azOxr8rnxNPOCGib3/99detysdHdUKqsJeT1KmJ9GrD5EmTmTQptP1ewZo0aUKTzExYFXmvWambgTSuv/+jbkbEj6W5ebBhC/369o34Mbxg5cqVJNSui6++e/aBJrSzLl6sWrWq3K/Ly8tDE11WEdfOk5cXfu/OSHTv3p17772X2267zbGLjZFq0KA+mzYtA3CkdkKo6tatS4sWLVm+qfxVUVuzNrB77w569uwZo2SGG6xfvx4RIT0ler9/tVNrsn3H9qhtlRSneliKSA1gAvCIqn5Wyv1XAlcCNGzYsGdwtdv8/HxWr15NjeQU6levEdHP/+Cbr1izccO+280aNeacoadG9FigbNyTRV5RIS1btar0CWh2djbr16+nUY1qVCvl5OyDb75nzcb9vZOaNWrIOUOHlPpYWfkFbN2bR4sWLaJSJn7N339TUJBHo5oSURHSj7/5lbUbt+y73bRRfYYNPSqiLIV+2Jil1KhR0zWNuqtCTk4Oa9eupXo1qB7hhfgvv/6VDRv2L+9q3Lgep51yVESPpUDWHigoEFpF4fWelZXFxo0bqZkBZa2K+/arXwE4+dSjyn2sgjzYuwdatmzpyNXh5SuW40v2kxT0nv/jl78CcPxpRx3w9Xk7oXpqjSop1FFQUGCdLFdLgBrhPxe/fvkjW4NmCus1acBRp0W4D2VHPkmSQMuWLcP+1sD7PdWSkRqhLfP89cvv2LJ+477b9Zs04qjTTgrtByrozmySxBdR3u3bt7Nt2zakdjoxaZRcDt2bC9l7ad68OSkpkW8vcbtt27axfft2pHZdJCGy3/vxX37Glg37ewTXb9yEwaedGXEm3bsH3bunwr+969atIye/EF/tyFcJjfvyY7as33+RpH6TTI4+bVjEj6eFhfh3bKRRo0YH9cx8NGzcuJGsLGslS9OmTV1dpGjLli3s2rWLxhktEBE++do6rz7rlHP2fU12XhY7s7c69jfUcMbq1avx+aFxWtl76T/49jPWbNz/HtmsURPOObns98jcwjw2ZO+gcePG+2p8hGLw4MEzVbVXyeOONIwTkSTgU+Dd0garAKr6CvAKQK9evbTk5u+33nqLMe+9y0ltO3JOpx74wtzc93WJYiRpBX66bg2/mm6hv4hXZ01m4tqV3HTTTRxzzDFhP0ZJTz/9NL+NH8fzQ3qRXMpgIK0g54Dbh21dWupjbd2by8s/zuLCCy/k/PPPr3S2BQsWcNttt9EjUzivR/h9HlNKtA9KKdhKsy1fhZ2jsEh5aXIhG7KTePXV1w7qJcGqyiOPPMIff/zOoH7QKoIVb1pY8vZW8nZ/EkEWmPUnzFsIl156aVRe75MnT+b5559n4EmQUa/0rwkUvN2UU37mlYthwTT48MMPHSm89OJLL5LQaBeZR+z/vdhjT7nOKfr0gK9f9oPQ+7ABnHvuuVWS54UXXuCrT79CTm+GNArvRMpfNx/Y/z1b6+bzGeEXcNPZ29EpW7n//vsZMGBA2N//8MMPM3HqFOTcgYg/tBOoQi0+M7RF8/jcvzbkn+nftQX/97O59dZbOSHMWeEdO3Yw/IILKGrTjMQjK+jBVIW0qAj/p9/ToUVLrrjiCsdyxMKuXbu44sor2eOHpKHnImnhX8jOo/jf2m0k8HVRZIOPopVLyf/lK4466qgKn/sHH3yQaYtXUP2s2yL6WQB78qzq+yeeeCJjx45lW57yfVFYO62KKdq2juzPPon4dzaevPbaa3zyifV3acyYMa6sEhwwfvx4XnvtNa45/iGa1GlJUuHXANTbvb8ezG9TX2HFtnl8+NGH7mzTZUTd4sWLeeyxx7ik83F0aVF2he6v84svyk3L99FlU9kXkf2azK3jP6Rlh3Y8+uijlc7pRJVgAV4HFqnqs5E+zgUXXMDQoUP57q+F/Gfqr2Tlh7d0pUV6baolJlEtMYn2dRvSIj38pbzb9mbz6MSfmbh2JRdffDEnnRTiFfxyrFu3jvHjxjGgWb1SB6vhqlc9lc4NMvji88/ZvbvyhUA6derE8OHDmbnWz3eLiqLeezYURX7l3VmFrNzm59Zbb4vZYFVV2bNnz76PaLULqoiIcPvtt9OhQwcmTIY/FxOVAkXhKiyCidOtweoJJ5zA2WefHZXHDVzBj6AV8gEK7LeBcK7mRVNSYmJ4e1iLiMrKh7JcfPHF1KtfHxm3Cc0Lb5Owb2ADfKc12/8xMPzfM92cC9O2MWDgAPr37x/292/evJk//vgD7ZCJpMbuar80q4evbi0+/ezTsN/jateuzcknnYQuWYk6WHzJv/Av/Huyo3Kh0u3S09N58IEHSMzPpeCbD/Dv3ulYlsK/FlEw7msOPfRQbrn55gq/vlatWmhu5f6WaH5OsSrwml+57VKam70vm1G+4B7aTlwkDUf79u0BWLe97H3V67av4ND2h5rBahz5/fffSfQl0LdJ+6g+rk989G/SgdmzZu1bhVCpx4tCpnANAC4AjhaROfZH2CM9n8/H9ddfzzXXXMO8LRu4a/w3zN4Y+hX0C7r0pkV6HVqk1+HeI47ngi6hXwlXVX77ezl3//ota7J3c9ddd0VlhiQvL4/HH3uM5AQfpx3arNKPFzCsYwuys/fw72efxR+Fdgvnn38+Q4YM4ZdlRXwxvwh/DEdP+YXKW9MLmbvezxVXXMGgQYNi8nOXLl3KzTffxD/+8Y99H2eccQYjRz7Ixo0bK36ASkpNTeWxxx6nf/8BzJgDv/yxfx9pKOrUhqQk66NRfet2OHbugu9+FpatgHPPPZebb745an/QAn/wo9HaJi8XqqdVI7EKei6GIiU1JewqwVW5VDMtLY2777wL9hTCLxtRf+x+V3VvIfLjRurWrcvNN0X2evn555+tgk2dovd+GAoRgY5NWb1qNcuWLQv7+8877zxSU1MpmjTLkYt6ujcHZi2kR48enipiUxkdOnTgiccfJ6WogMJvPsC/K/o9BStSuHgeBeO/pVOnTjz26KOkpla8hL1BgwYU7d2NVmITvyRXK1YFXpIrtyzVb/djdPOeTLcIvjgayv9vJzVq1Ii0tDTW71hV6v35hXls2b2edu3axTaY4ailS5fSMr0h1ZPKPxdpkd6AaonJVEtMpn2dprRIr/gidse6zfGrRqX4qxNVgv9QVVHVLqrazf74LpLHEhFOP/10nhs1iowG9XlmynhGT/+NHZW8WlmejXt288TkX3hl1iRatmvLCy++GJVeVfn5+Tz88MMsW7aMK7q3ISM1erMuLTLSOKdTCybZSy8rO2gVEW688UbOOOMMfl9RxBtTC0OuHNwk3UdqIqQmQpu6QpP00F+C2/cqz08sZMEmP9deey1nnXVWpP8JIcnLy2PChAnceef/ccMNN/D3qmX07ygc3dXH0V199GonTJ0ymUsuuYRHH32UmTNnRqUvaVlSU1O57777uOqqq9iwKYEvx/pYFWJR6j49oG6G9THkGOt2KPx++HMRfPWjUFCYxoMPPsjFF18c1V5zgf6Q0fi1zd0Ldes412+yRo2aYfVhLczXKi8y0qlTJ6695lp0dTY6eUvF3xAFWuCHsRtIyFMeGPFAxDM143/9FWlcGymjAF1VkjYNEZ+PCRPC702XkZHBpZdcgq7dhC5bFf1w5VBViv6Yhc+vXHfddXE1U9KhQweefeZpqicIhd9/hOZEr+JlRYpWLaPg9x/p1asnjz7ySMi/182aWRdj/Ds3VfCVZUts3Jr8tDp8+csE8tPqkNg4wobdNv/2TSSnpJgBawiCB6xu/10TEdq0acPGnatLvX/zrrWo6r7+skZ8KCwsJFEqPqcb3uloWtRqQItaDbin/zkM73R0hd+TaJ8rRuPc2JlpiChr27Ytz7/wAh9//DHvvfsef275mmHtu3FMq0PC3ttalkJ/Ed8uW8gXS/8kMTmZ6667jqFDh0blxH3Pnj2MfPBB5s6bx8VdW9OjcfQbiB/XujE7cwv49ttv2bt3L7feemulliL6fD6uvvpqMjMzeemlF3l2QiHDeybQonb5z8cZnRNZv8saMF83MPSfP39DER/M9YMvmQceuIu+VVT1Mjs7m+nTpzN58mSmTp1CTk4utdJ8HHmYj+5thZSk4v99vQ9Vpi/1M3Xy70yYMIGM9Fr0HzCQ/v3707Vr16gv9xQRzjzzTHr06MHTTz/F+Il/0bIZ9O0J1aJ8cXfnLvhjmrBlm9K/fz9uvPHGqFTBLql69erUqJnG3j2VP7nMzRZatC17D0ZVS6+Vzpqtob3n+AsUf2Fslt2dcsoprF27li+++AKtnoh0j/57TIAWKfy0ATbncvf990d8tX7jxo38vXo1vn5V08amIpKSBE1qM2ny5Ij2gJ5yyilMmDCBRZPmoI0bIDVjU/1Ul61CV63lwksvpWnTpjH5mW7SqlUrHnv0UW6+5RYKpvxK8uCTq/xnan4ehb//QNt2bbn//vvDWjUR6EtctHkNCfUi+/+V2v+0iL6vLP4ta2jbunWVVDA/2HitqnHr1q1ZvPB7/HrgxMXGndYV8FatWsU6luGgdu3a8d2Sb9mdt5daKdHtsDBn8wpEhNatK3cRDQ6SAStAUlIS5513HoMGDWLUqFG8NWcak9au5PJufcmslVGpx/5r+xZemzOFtbt3csQRR3DNNdfsmxWqrHXr1jHi/vtZv349V/VsR/9mVXNFU0QY1rE51ZMS+Hj8eDZt2sSIESMqvefilFNOoU2bNjz26COM/mMbJx7q4+h2CVG7UJBfqHy1oJBJq/y0bdOau++5l8zMyItJlGbdunVMnTqVKVOmMH/+nxQV+ame6qNtY6VDMx/NG0iZ/z01qwlHd03gyMOU5Rt8LF6Txc8/fs93331HSkoyPXr0pE+fPvTp04c6daI3SGjZsiXPPTeKjz/+mHfeeZsvxir9evppGYWVk6owfwnMnidUT0vjrrtuYNCgQVV69bhpZlO27FxSqcdQhezdVmsRp9SpU4fC+UJwb5vUMt4qCuwZ5aq4CFCaK6+8ku07tvPbhN8g2Yd0yoj6z1C/ouM2wupsbrzxxoj2rQbMmDEDAGleRiWuGJDm9Vg/aQkbN24MuxK5z+fjjjvu4KqrryZ/3GR8pxyNRHFlQml0Zxb+ibM47LDDqnwFipu1a9eOk086ia++/gY98gQkoeJTHc3PK1a4KC+MuhhFa1bgz83h2muuCXuJf+PGjUnPqE32hhUkd+wX1vdWBS3Ip2jLGjofGXmF5HjitQFry5YtyS/MY2f21gPu27xrLSkpKQd11wXjQEOHDuWbb77h7fm/cF2PoVE711u9ezM/rprNUUcdFZXznINmwBqQmZnJ448/zi+//MLLL73EPb9+x1kdunJS245hD6IKior4dPFcvv1rIfXq1mXkyJH06dMnalnnzJnDQyNHQmE+d/TvQPt66RV/UyWICEMPaUr9tFRem7WE66+7jocefrjSV9M6duzIiy+9zOjRo/huwm8s2qyc1z2RummVe9H/vcPPe7OL2LLHz1lnncVFF10UtRnL9evXM27cOH77bQKrV/8NQL10Hz3bKm2bJNCkrrVhPFSJCcKhTYVDm1oVjFdvVpZvKGDBvKlMnjwZEaF9+/YMGjSIwYMHR6U4Q0JCAueccw79+vXjySefYPzE5RzS2lruG+kWzr058NsUYcMma1b1pptuikkhiZYtW7Hy12Wo+on0vXLvHigsVFq0aBHdcGGoX78++Xv9+AvBl2j9hzTpV/p/UMGe/d8TCwkJCdzxrzvI2ZvD9N+nQ6IPOTR6s7uqik7YBH9lcemll3LyyZWb2Zo1axa+mtUgPYIrvvmFxQYfufmR9aKVpnX3ZYmkqF6jRo247dZbeeSRR2DKXBL6d48oRyi0oBD/z5NIS63GnXfeGfezY7Vq1UL9Rda+hlCeivy8fYWLAL78ZXzoP8xukh3JagkRoXevnoz7fRLqL0LK6u0VI4XrlqH+orjZ+1xZXmsXFfj7uHnXgTVfNu1aS/PmzaO65cdwv+bNm3PRRRfxxhtvUGdRDc7tcFSZg9ZQ9q0CbNiznaenfUat9HSuvvrqqOQ86AasYP0BOPbYY+nVqxejRo3ig4kT+XPzeq7tNZD0lP17ocqrDLw5O4tR039n1c5tDBkyhCuuuCKqV9ImTJjAk088QcO0FG7p35n6abHbrN8nsx4N0lJ5buoSbr3lFkY+9BCdO3eu1GPWrFmTu+66m759+/H86FE8MyGPMw7z0auZ74AXfkX7Vv2qjFtWxNglfurWqcPj995Bt27dKpUvYP78+bz77rvMmjULATLrC0d389G2iZBRwQD7lzlFLFlrzZzVrgENMoRjuh14cpGYILRpLLRpbJ3Eb9mVwF/rlaXrlvDyy4t49dVXOfLIIznvvPNo3jyCHjUltGjRgueeG8Xbb7/Nhx9+yLYdwtEDlRphvly3bINxf/go8idy663Xc/zxx8dsT06bNm0YO9ZPbjZUi7DA765t1r+BJXZOCMzu5mdBagUXFPN2Ff+eWEhKSuK+++7j3vvuY974uZAoSJvK91lUVfSPLbB4N+eddx7//Oc/K/V4RUVFzJ4zG21aJ7LVGvkFxQYfX/wyNrIgGWn40lIjHrACHHnkkcyfP58vv/wSaVAXX9vK/86XpKoU/T4d3bGLux95JO73Hu7atYuvv/mGhEZNkaQQL3ImpzB2rPU6GTt2LKSFPvj0NWkO4uODDz7g9ttvD/t9s3///vz8888UrV9OYtNDwvreaCtcOY/qaWl06dKl4i82PNerdP+Add0B923JWkffMAqQGgePs88+m61bt/LVV1+RXZDHJZ2PI7GUi2eh7FtdvmMDz874HElJ5NHHHo3apMdBfRklIyOD++67j1tuuYVlO7dx34TvWb1r+777L+jSu9TqwAu3bOS+Cd+zpSCXESNGcPPNN0d1sDpt2jQef/xxWmekcc/Aw8IerOYUWLMHp59+OqmpqeQUhD970CqjBvcdcRjpicK999zDX3/9FfZjlCQiHH300bz831doe0gH3p9dyPuzC8krLF6Q6YzOiZzRufRrJVm5yn8nF/LdoiKOOOIIXnr5v1EZrPr9fl588UVuu+02li6aw8BOPq4+OYHzjkqgVztfhYNVgM07lT05sCcH1myxbldERGiQIfTv6OPi43xcekIC3Vr7mfjHr1x99dV89VX4PWhLk5iYyKWXXsrIkSPZm5vCd7/4CKejxtr1MHa8UKtWPUaNGs0JJ5wQ0wISHTp0AGBHJWoC7dhinTw4uf8mUEAlb2fFX5u3C5JTkqlXL7ZLXlNSUnho5Eg6duyI/rwRXb2nUo+nqujUrTB/J2eddRYXXnhhpTMuW7aMvdl7kaYRLqNPTipWNZXkyE4qRQTNrM3sObMrVazuyiuvpGOnTvh/m45u2xnx45TF/+dS9K+/uejCC+nZs2fUH99LsrOzue/++9m1O4vEfhWfXAVIcgq5ubl88cUX5ObmIsmhz5z5aqaT2K0PP//8M++//37YmXv37k21atUpWDoz7O+NJs3PpWjVfAYdeaRjlda9pirbklWFtLQ06tape8CANSc/m6y9O6NyEd3wHhHh2muv5fzzz+e3NfN5cuonZOWHXwlz0rpFPDLlQ6pn1OSZZ5+lZcuWUct4UA9YwfqfcOKJJ/Lv//wHSU3h4T9+Ytn2ss+KZ21Yw5OTx1GnYQOef/75Su3BKs3u3bt54vHHaVarOrf160Bacvh/FPYWFhXruba3MLLqW3Wrp3DngI5UTxAefeQRCgoiL6sfrEGDBjz51FP7+rWO/qOQ7XsrHtyt2enn2d8KWb0zgVtvvZW77rp7X4/Oyho7dixffvklPdsKV5xoDSBrVg9vQJZXQLELBXkRPF31almzslec6KNlAz8vvPACCxYsCP+BytCnTx+effbfJCXX5IfxPrJCGIts2ATj/hBatGjFc6NGR/UNJlStW7cmNTWFbZEXymT7ZqFDh/aOXvFu1qwZIkLu9oq/NnebdbXbieVXqampPPzQw7Rt0wZ+3Iiur0SJ5tk7YPYOhg4dyuWXXx6VCx2zZs0CQDIjHbAmFht8EMH7bIBk1mVP1p5KXdRLTEzkvnvvJaNWLfw/TURzw+sbXh7/uk34p86lf//+UWmv5mW7du3ijv/7P5YuXUrS4JPx1WsYs5+d2HMACW078tZbb/HGG2+E1c4oOTmZo48eTOGqPyvdk7UyCpbPwV+Qz/HHH+9YBq/x2oAVoEXLFmzeXXxJcGCJsJNbagxniQgXXnghd9xxB3/t2siIP95lze7QZhH8qny46Ddemv0t7Tu0Z9To0fsu4EfLQT9gDWjbti3/ee45atery1NTxrFm94E92hZt3cSo6b/Tuk0bnn322SpZqvfDDz+wJzubK7q3ITUxsr0q1RMTis0eVI/wcQAyUpO5oHNL1q1fz9SpUyN+nJISEhK44IILeOihh9lVkMKoPwrZsLvsGYolm/28MLGQ5LQ6/Oe556I+w7dkyRJSkoWjuvpISozscfMKKHahIJIBa0BaqrUUOZAtmlq3bs2TTz6F+FL55Q8JbK8qVdYeGD9JaJKZyeOPP+FY4/OEhAS6dOnKto2RvSXl5cKubUq3blW3RzAUqampNMlsQs628r9OVcnb7qNtG+eWL6elpfHoI4/SpFFjGLsB3Rb+IEoX70KnbuWoo46KaguVmTNn4qtXC6nm/P6w4H2slVGnTh0eGDEC2ZtL0bjJaBR6YuuebHTcFJo2bcq//vUv17fVqEo7d+7ktttvZ/nKlSQdezoJrWK7tFZESBo0hIT2Xfjwww956aWXwhq0Dh06FC0sIH9x9P4Oh0NVKVgwkZatWu9b8WJUrEaNGrRq1ZrLL7/c6Sgha9myJVt2r6dRRnMa1y6+RNgMWI1jjjmGp595mqIUHyMnvc+8zSvL/fr8ogJGz/yKb5ZP46STTuKxxx8nPT36NXniZsAK1szfE08+SWpaGv+eOoG9BfsbJm7P2ctz03+jcWYTHn3s0SprNbF27VpqpSbTLD3yJcbVkorPHlRLqtzSnU710/dli7bevXvz7L//Q2JqLV6aVMTmPQeepP211c/r0wrJbNqcUaNHV0kPsAEDBpCXr3z8m59tWaGfRARLSaLYhYKUSkzkrd2qfDZRSUpK4vDDD4/8gcrQokUL7r77HnbsVGbOK/1rVOGPaeDzpfDQQw/HpL1KeXr16sWe3X6ys8L/3i3r9z+G09of2p68reW/tRZkQWGe3/EG7enp6Tz+2OOkV6+JfL8B3Rv69gJdtxedsJlu3btx++23R22mODc3l0WLFqGRzq5GmVRPwVe3JrNnz670Y7Vv354brr8eXbsJ/4z5lXosLSzC/9MkkhEefOABqlePbjsCLyksLOT+ESNYu34DSSf8g4QWzvSRFJ+PpIHHk3BYT7788ks++eSTkL+3devWdOvencIFf6BFkRUJq4zCNYsp2r6Rs/5xZlxf+AhXQkICL7/8EsOGDXM6SshatGhBYVEB/Q45npN6DAdg8+51pKak0qBBaEV1jINb+/bWLGmTZpk8O+Nzpq0vfWIltzCfJ6d9ysxNf3H11Vdz4403Vtkqt7gasII1aL33vvvYuncPHy7cfwLy5typFKgyYsSIqC1DLU2TJk3YnZvPhqycKvsZ4Vq6zdrs2Lhx4yp5/JYtW/LU08+QkJLG61OLyC3YP2Dcvld5c3oRjRtn8uRTT0e19Uuwww8/nNtvv50te1J444ciPp9YxF/r/RT5Qx+8piRR7EJBuAPWvAJlwWo/7/1axHvjiyAxnUceeaTKeiX26tWLk08+mUXLKHU/66o1sHEzXHnlVVX2/z4cgYH7pjXhf+/mtZCeUcvxASDAIYccQn62n4Lssl9bezdb/x56qDM9RoM1aNCAhx96mIQ8hR83WH1UK6BZBchPG2mamcn9990f1T9QixcvpqioCGkSm3Y/odDGtVmwcCGF5S1XCNGQIUM44YQT8M9ZhP/v9RE/jn/KbPxbtvN/d9wRl/1Wg33//fcsWbyYxCNPJKGJs3vwRISkvoPxtWzHm2PGsH17CPsDbOf8858UZe+mYMm0Kkx4IFWlYPYv1Ktfn8GDB8f0ZxuxF5hF3bR7/z7WzbvW0byFqRBs7Fe/fn2efuYZDm3fnhfnfHvATGuhv4j/zPiSZTvW83//93+cccYZVXqxKy5fmZ06dWLoKacwbtUyNuzZzeKtm5i9cS3DLxge9TXXJR1//PFUq5bKq7P/Ir8osr2n0ZSVV8Db81bRsEED+vWruh5wTZs25b77R7BtL3y1wDrpU1Xen12ILzGFhx6u+hm+4447jjFj3uLss//Jpj01+Gyinxe+Vr6dVsSStX7yCso/UW+QITSrz76PBhkV/2Jm5yp/rvTz2cQiXvjaz7fT/BQlNOCqq67ijTfH0LVr12j955XqwgsvJDk5mXkLix9XhXkLhWbNmnLCCSdUaYZQNW7cmBYtmoc9YC0qgi3rhH59+7vij2379u0B2FvOfty9m62CS25p0N6uXTtuu/U2dEMOOu3A/nzBtEjhp40kk8TIB0dGvQ/h0qVLAZAGVdvmKxzSMJ2C/Hz+/vvvqDzeddddR8tWrdBfp6HZ4e9Z9K9Yg3/hcoYNGxb1OgteNGvWLBLS65DYpr3TUQB70Nq9H0WFhcybV8YSl1J069aNDh07UjBnHFoYnZoSoShcs5jCTas5/7zzTLGlOBAorBTc2mbL7nVmObBxgLS0NB5++GFatGjBC7O/YfPenfvu+3DRbyzYuppbbrklJhe6nD+7c8h5551HQkICP61Ywtjli0ivVYvTTjutyn9u7dq1ue2221mxYw/PTV1CboQFk6JhV24+T05exM78Qu66++4qLx7QuXNnTj/9dKau9rNxt5/5G/0s3+rn8iuujNkMX0ZGBpdeeinvv/8BI0eO5IhBx7B6W3W+nOzn+a/8fPhbETOW+dlZyuzYMd0SOPeoxH0fpbW0UVU271QmLfTzv3F+Xvy6iO9n+NmZX4eTh57KM888w5tj3uLMM88kNbXqWxllZGRw/PEnsOpvIXgyedMW2L5TGTbsbFf1axw48Ai2b4a8MBYgbN0ABQXKgAEDqi5YGNq0aUNSUhLZ5QxYczYLhx5yqKue+8GDBzNkyBCYu6P8Ikyzt6Obcrj1llvIzMyMeo5169bhq56CpLqnmIlkWIPydesObAURiZSUFO695x4SFfy/Tgtrr6Pu2Yv+PoN2hxzCxRdfHJU8Xpeeno7mZDtasKgk/w7rwk84e7lEhEsvuYSiPbvIXzCxqqIVo+qnYPpYGjZqbIotxYm0tDTq1avPll3WCo+9eXvIytlpBqxGqdLS0hjxwAOQmMCYP38GYNmO9YxdOZNTTjklZu8bcTtgrV27Nn379uX3NcuZs2k9Rx9zTMwaQB9xxBHccsstLNyyi8f+WMD2nOhVjAzV2t17Gfn7fDbtzWfEAw/ErMjCOeecQ1JyEr+vLOK3FX4aNmzgyAxfYmIiffr04fbbb+ejjz7m6aef5sx/nIU/KZNxc/y88l0Rb/3kZ+piP1k5FZ9MbtutTPiziFfHKmN+KmLiQqVG3TZccOGFvPDCC/zvf+9y7bXXcthhh8V8f9CJJ55IkV/x+aCOvcpy+WpISUlm0KBBMc1SkSOOOAJV2BDGRNaG1VA9rTrduztbcCkgKSmJQw5pR87m0v8/+wuVnG1Kx44dY5ysYldffTX169dHfttS6tJg3ZkPs7Zz5KAjq+y1k5WVBS4arAL78mRlRbDBugzNmjXjmquvtqr8LgytArHVb3UGiQh33XmnmQ2znX766Yi/iIIfPkNzsp2OQ9G6VRRN/Jl2hxwSdj/TLl260KtXb2uWNQYD8IJlsyjctp5LL7nYvJ7iSIsWzfdVCt6y2xq4mpY2RlkaNWrEBRdewJ9bVrF42xo+XfIHtTNqc9lll8UsQ9wOWMHaM5dTUEChv4jevWPbLPmEE07ggQcfZFNuIQ9M+HPfPtJQNE9Po1piAtUSE2hftxbNwyzgNGP9Nh76fT7+5Go89fTTMS1Uk56eTr9+/Zm8yppdPf74ExyfZUpISKBz585cfvnlvPb667zxxhtcccUVZDRsy4Q//bz8bRFfTCoqte/qqk1+Ppjg5/Ufipi+VGhzaHduuukm3nvvPUaPfp7zzz+ftm3bOlrEok2bNjRp0pgEH/TpYS0HXrPOR9++/WIyyxuOli1b0rRpJhtWh/b1RUWwaY0woP8AVzVw79TpMHK2WoPTkvZuAfVbWxPcJjU1leuvux7/jjxYuPOA+3XyVlKSU7jm6muqLENCQgJEoYJuVNl5ov1eNWTIEHr06IFO+xPdU/HgRP9aja7ZwOWXXVYls9te1bJlS+695x58O7aS/9nbFK1cGtasNYDUbYCvcbN9H1I3/OIzWpBPwdQJ5H//CZmNG/HgAw9E9Jq54orL0fxc8mb9FPb3hkML8ymYPpa27dpx5JFHVunPMtylefPmbMvaiF/9bNltKgQbFTv55JNJS0vjkckfsmDr35x2+mlUq1YtZj8/ri+n9enTh379+pGcnMxhhx3myM9/btQoHhgxgscnLmR455Yc3apRhd93fpdW/L3Luop81xGh5/ar8vmiNXy1dC2HHnII948YQb169SLOH6nevXszYcIEAFc2uc/MzOSss87irLPOYt26dYwdO5Zvvvmat37KoXVj2Tf5s3uvsmYL1K1bh8suO4PjjjuO2rXdUygmQETo27cfX37xOQWFys5dkJPrp2/fvk5HO4CIMGjQUbz33rvk5kBqBe+FW9dDQb66bqa4Y8eOqF/J2QJpJVa7791o/evW1hF9+vShc5fOLJi9GO2QjiRa1zV1Sy6s2sM/L7qoyoqjgXUlV//IQQuLkEq07Iom3WkNJhs1qvj9ORwiwk033cTll19O4ZQ5JB5b9n5UzS9Ap86j3SGHcMopp0Q1x8Ggf//+jBo1iieefJJVP39JQmYLEg8fFHIv1uR+R0f8s9Xvp+ivhRTN/AP/nixOOOEErr766ogrN7ds2ZITTzyRsT/8QFLHfiRkVE3l1rw5v1KUvYtrrr7fFfv/jdhp1qwZ+YV57N67na27N5CUlET9+vWdjmW4WEpKCldddRVTpkwhJSXF2kIUQ3H9DpWRkcEDDzzA3XffHbPlwCW1aNGCUaNH06NHD96au4Ixc5ZTWAWzCzkFhTw3dTFfLV3L8ccfz9PPPOPIYBWKn6i3bt3akQyhyszM5LLLLuPtt99h6CmnsKeoHltzrI/CxEZcfPHFdiGns105WA3o2bMnRX5l8xbYYO+t7NGjh7OhyjBo0CBrWXDQLGt6HeujpPWrIM1Fy4EDAq/xQDXgYHs3QWbTTMfbCJVFRBh+/nD82QXw1/4lsDpvB6nVUqt8r3+XLl1Qvx9dW0Ez2xjS1ZtJSkqqkqrOjRo14uyzz0ZXrMG/qeyCV/65i/HvzeGG6683g4sytG7dmhdfeIFrrrmG1F3byPv8bfJ+/hL/9i1V8vNUlaIVSyj4bAwFE76ndeNGPPPMM9x6662VbjN08cUXk5KSSt7kr6OUtjj/nh0UzPuVI444wpEL9oazAis0tmVtZGvWRpo0yXR8tZvhfieccAIjRozgzjvvJCMjI6Y/O65nWN2iZs2aPDhyJGPGjOGjjz5iw55cbjj8EGokR2eJ45bsXP4zbQnrs3K49tprOfXUUx1dotq0aVNeffVVUlJSHLtQEK6aNWty/fXXw/XXOx0lIp06dcLn87Fxi59tO6BZs8yYv9mEqkWLFjRv3oyNq9fQyi762amUFftFRbB5rXDMMUe6bu9VRkYGjRo3Yu+mjcWOqyq5W3z0O9J9y4GDde3alabNmrJ2/Fp0eRb4gbV7Oe6UU6JeFbik7t27U7tuHXbOXYW2qO94T0jNzoNlGxg0aHCVLaEfNmwYX3/zDXtmzIeTjzowQ04uOn8pgwYNckUrJDdLSEjg9NNP57jjjuOTTz7hs88/J/fTt0hofQiJPfrjq135C7Wqin/lUopmT6Zo+xaaNmvORddezcCBA6N2MSEjI4MLhp/Pq6++SsHfi0hqHt0VGblTviVRhCuuuCKqj2t4w74B655N7MjeRLvD3FGx3jDKYi7TukRCQgKXXXYZd9xxB3/tzGbkb/PZuKfyvVr/2p7FyN/ns6NQeeSRRzjttNMcPwEEa/9Ew4ahLdUyKq9atWq0atWSLdtg63YfnTp1djpSuQYNOoptm6C8miNb1lvVgY844ojYBQtDxw4dyd3iK7aXLn83FOT6XbscOEBEuOjCizjkkENol5pJu+qZdOzUKSaV1BMTE7lw+AXoxp3o4uhU5Y2UquKfuAifCuedd16V/Zxq1apx9rBhVgGmzQfOLPsXLEMLixg+fHiVZTjYpKWlcdFFF/HO229zzjn/JHH9avI+GUP++G/xZ+2K6DFVlaI1Kyn4/G3yf/mKhtVSuPPOO3nlvy9z5JFHRn3m+7TTTqNJZib5k79GiyrfAzigcMMKClfM5eyzh5m/w3Gqbt26JCUlsT1rE9v3bIn6dgfDiDYzYHWZY445hieffJIcSeSh3+eHVYyppBnrt/H4xAVUT6/Nc8+Ncu0SUCM2Dj20PRs2QV6en3bt2jkdp1wDBw4EYGM5PVk3rraWA3fr1i02ocLUvn178vf6KQgqWppjr0z0wizZkUceyejRo3l+9PM8P/p5/v3ss1XepzrgxBNPpGu3buikJejmyAYX0aBzV6ErN3PJxRdXeZGjk046idRq1fDPX1Y8Q1ERLFpBn8MPN1U8I1CrVi0uueQS3nn7bYYNOwvf6mXkf/w6BdN/D6vXqX/ndgrGfkL+2E+ol+TjX//6F6+9+gqDBw+usqWUSUlJXHP11RTt2hK1Njfq95M/+Svq1qvP2WefHZXHNLzH5/PRoH4D1m1fQWFRgblwYbieGbC6UKdOnXhu1CjS69bjyUmLmLE+/L1cP63YwPPTltCmbTueGzUqZieahnu1bNly3+etWrl7+U+LFi1oktmYjWW0t/H7YfM6H/37D3DdcuCAwKA0J2j73N4tkJycVOz/hXEgn8/H3XfdRf269dDvZ6Pbw2snI3VrQnKi9dG4tnU7TP4l6/BPXcbAI47gH//4R9jfH660tDSOO/ZY9K/VFM2cT9G8JdbHT5Pw5+Ry6qmnVnmGg1l6ejqXX345b775JkcfdRSFc6ZQ8OkYijasLff71O+nYM5U8j8bQ/L2zVx11VW8/tprHHvssTHZ83f44YdbbW5m/Yw/Z0+lH69g6QwKt67jyisud12VeCO26jeoz99brZZaDRpUTWEvw4gWM2B1qSZNmvDv/zxHm7ZteX76Un5bvSmk71NVPl/0N/+bt5K+/frx5FNPuXavohFbwTNEbm+JISIM6D+Q7ZugIP/A+7dvgvw8P/37l11V1WmtW7fG5/ORE1RHJ3cbtG7TxhS3CEFGRgaPP/YY6dXT0G9moVtCn2lNGNAeOaQJckgTEk/tTcKA9mH9bP+CNfgnLKBHjx783x13xKzI0ZAhQ0hISMA/cwH+KXPwT5mD/r2explNXFdYzKvq16/PHXfcwVNPPUW96tXI//YDCuZNK7UNjublUvDDpxRO/40B/frx5htvcOaZZ8a8hdZVV10JhfnkzfyxUo+jBXkUzPiB9h06uK6yuhF79evXx69FAI4V4TSMULlzasIArCvCTzz5JA8++CCvz5pFkSqDW5a9z0BV+WTR33yzdB3HH388N998szkxNvYJvoKanp7uYJLQ9OvXj48//pgt66FJy+L3bVoLiUmJrj6JT05OplnzpmzbZk0Tqyq524R2vdy9HNtNMjMzeebpZ/i/O+9k69czkWM642sRWuuFcAepYO9Znf4XOnslffr04d577yU5OTnsx4lUmzZt+PzzzyksLL5fMSUlxbyXR1mXLl14+aUXefbZZ/n99wnoti34MoP6UPr9FM2fiezewU033cSQIUMcq//QvHlzTj75ZL7+5luSDxsYcZubvLm/UrR3N1dfNdIVtSwMZwV3NnBzlwPDADPD6nqpqak8+OCD9O7dm7fmrGDqurLbHnz313q+WbqOIUOGcMstt5gTHKOY4N6ZXjhZad++PTVqpLG5lLo7W9cLXTp3iWnT6ki0bdOO/O3W22xBFhTlq+tbOblNZmYmz/3nP7Rq3gL/D3Pwz1td6mxYZWlBIf6f56GzVzJkyBBGjBgR08FqQEpKCmlpacU+3Lrs3euqV6/O3XffzdChQyn6ayEFE77f//H7DyTs2cXIkSM56aSTHH/PHD58OCkpyeRN+z6i7/fvzaLwz98YOHCg64u+GbERvPrOrMQz3M78FfSA5ORk7rvvPu666y5enbWIBtUP3Hcya8N2PlqwmkGDBnHjjTeaPn3GAWrUqMFVV13lmebgCQkJ9OjRk+kz/kDVT+B8MScbsnYpvXuX0uvGZVq1asUvv/xCUR7kbt9/zAhP3bp1+fezz/LEE08wadIkZMcefAM7IAnReZ/TPbnoD3PQbVlcfvnlnHXWWY4PUIzY8Pl83HDDDZx77rkHzGzXqFGDGjVqOJSsuIyMDM4eNox33nmHos1rSGgQXl2K/DnjoKiQSy65pIoSGl4T3AvcXBQz3M6MajwiJSWFESNGkFG7Di/OWIY/aIZhe04er876i3bt2nL77bebwapRpjPPPNO1bWBK0717d3L2+skOKpa9dcP++9yuRQtriWHuDsjdaR0zlV4jk5qayn333cd5552HLl6HfjMTzcmr9OPqxp3o51NJzi5g5IMPMmzYMDNYjUP16tWjUaNGxT7cMlgNOOOMM6hRs2bYe1n92bsoWDSF4447jqZNm1ZROsNratYMvxidYTjFjGw8JD09nf+78042Z+ewde/+E7V3/1xJofi46667HVnCZhhVpWvXrgBsC6o5tn0T1KhZY99g0M0C1bnzdlofderUJi0tzdFMXubz+bjooou4++67SdiejX4+Dd0ReeVU/18b8H8zg/q1ajN61Cj69OkTxbSGEV1paWkMO+ssCtcspmhLOT2/Ssif+yuCVmkvYcN7zN8iw0vMgNVjOnfuzLHHHsvOvHwapKWyYkcWM9Zv55xzznF95VfDCFeTJk1Iz6jF9s37j+3Y6uOwTod5YiVBgwYNSExMIG8X5O8W014qSgYNGsSzzzxDzYQU9Mvp6IYdYT+Gf85K/L/8SccOHXl+9Ggz8214wimnnEL1tDTy5owP6ev9udkULJ7K0YMH06hR2UUbjfjj9hoQhhHM/Wd8xgGGDx8OCLWrJfPj8g1Ur1aNM844w+lYhhF1IkLHDp3YtdV6q8rPgz27/J4pGpKQkECDBg3Iz4LCLKFx4yZORzpoHHLIIYweNYpG9Rrg/24W/jVlF6QLpqoUTVuGf+oyjhw0iMcfe6zYXi7DcLO0tDROGTqUwpXz8e+u+DVfsHASWljA2WefHYN0hpeYPryGl5gBqwc1btyYbt268dvfW5i1cQdHDR5M9erVnY5lGFXikEMOYc9uPwX5sGvb/mNe0ahRY/J3Cfl7/TRs2NDpOAeVhg0b8u9nn6VFs+boD3Pwr9te4ff4Zy7fVwn4rjvvNNsoDM857bTT8CX4yF8wudyv06JCChdOoWevXp7YQmHEVsOGDRk4cCC3336701EMo0JmwOpRffv1Y0dOHnmFRfTr18/pOIZRZdq0aQNA1g7YvaP4MS9o0KABudutImleqdDsJRkZGTz5xBM0zWwKP85Ft5e9p9W/aC06cwXHHXecqaZueFbdunUZOGAAhUtnoIUFZX5d4eqFFO3dzWmnnhrDdIZXJCUlcd9993Hcccc5HcUwKmT+WntU8JLI9u3bO5jEMKpWoA3M7p2QtRNq184gPT3d0UzhqFev3r7P69at62CSg1d6ejqPPvIINaunoT/NRQsKD/ga3bIL/WMxPXr25JZbbjGDVcPTTjrpJPx5eylcNb/MrylYMp06devRq1evGCYzDMOIPvMX26OCS9Ob/VfGwax+/fqkpCSTvRuyd4nniuMEN2SvU6eOc0EOcg0aNOC+e+9Fd+3FP2Vpsfu0sAgdt4A6depw1513kpCQ4FBKw4iOrl27Uq9+AwqWzSr1fv/e3RSuXcLxxx1rXu+GYXieGbB6lNmzasQLEaFJkyZk74a9e4TMTG/1EQwesAZ/bkRf586dOf3009GFa9Gt+5v36vy/8e/cw6233GIu8BkHBZ/PxzFHD6Zo3VL8udkH3F+44k9Q5eijj3YgnWEYRnSZAavHmYIhRjxo0iSTrJ1CXq6fxo0bOx0nLMHN2U2j9qp3wQUXkFajBv6ZywGs5cFzV9O7d2+zNNI4qBxxxBGo30/h6oUH3Fe46k+aNmtuii0ZhnFQMANWD3viiSd49tlnnY5hGFWufv365GRbhYsaNGjgcJrwBA9SzdK8qpeWlsZpp56KrtqCZuWgf23En5vPueee63Q0w4iqtm3bUqduvQMGrJqXQ+GGlQzobwoyGoZxcEh0OoARuW7dujkdwTBiIrhYkdcKF6WlpTkdIe6ccMIJvPfee/in/4Vu2knjzCZ07NjR6ViGEVUiQp/De/PDL+PJXzRl33H/jk2gfg4//HAH0xmGYUSPGbAahuF6tWvXLvVzL2jYsCHDhw/33FJmL2vUqBGtWrdi5bKVABw55FRExOFUhhF9/fv35/vvvyf390+LHU/PyDAdBAzDOGiYAathGK4X3MbGa0VzRIQLLrjA6RhxZ+SDI1mxYgUiQpcuXZyOYxhV4vDDD+ejjz6isLB4K6e0tDQSE80pnmEYBwfzbmYYhutlZmYCUK1aNWrUqOFwGsMLGjRo4Ln9zoYRCS/1pTYMw4iEGbAahuF6mZmZfPbZZyQmJuLzmVpxhmEYhmEY8cIMWA3D8ARTvMgwDMMwDCP+mKkKwzAMwzAMwzAMw5XMgNUwDMMwDMMwDMNwJTNgNQzDMAzDMAzDMFzJDFgNwzAMwzAMwzAMVzIDVsMwDMMwDMMwDMOVzIDVMAzDMAzDMAzDcCUzYDUMwzAMwzAMwzBcyQxYDcMwDMMwDMMwDFcyA1bDMAzDMAzDMAzDlcyA1TAMwzAMwzAMw3AlM2A1DMMwDMMwDMMwXMkMWA3DMAzDMAzDMAxXMgNWwzAMwzAMwzAMw5XMgNUwDMMwDMMwDMNwJTNgNQzDMAzDMAzDMFzJDFgNwzAMwzAMwzAMVzIDVsMwDMMwDMMwDMOVzIDVMAzDMAzDMAzDcCUzYDUMwzAMwzAMwzBcyQxYDcMwDMMwDMMwDFcyA1bDMAzDMAzDMAzDlcyA1TAMwzAMwzAMw3AlUVWnM1RIRLYAq6vo4esBW6vosauaye4Mk90ZJrszTHZnmOzOMNmdYbI7w2R3hslethaqWr/kQU8MWKuSiMxQ1V5O54iEye4Mk90ZJrszTHZnmOzOMNmdYbI7w2R3hskePrMk2DAMwzAMwzAMw3AlM2A1DMMwDMMwDMMwXMkMWOEVpwNUgsnuDJPdGSa7M0x2Z5jszjDZnWGyO8Nkd4bJHqa438NqGIZhGIZhGIZhuJOZYTUMwzAMwzAMwzBcyQxYDcMwDMMwjJgTkTSnMxiG4X6JTgdwkoikqWq20zlCJSICnA+0VtWRItIcaKSq0xyOZriQiLQHTgMyAQXWA1+p6iJHg1VARDJUdafTOSIlIomqWmh/XgNoD6xQ1e3OJguNiNQHmgKFwEpV3eNwpLggIinAP4CWBP1tVtWRTmUKlZezA4hIfw7M/rZjgUJk/65ewYHZL3UqU6js5/w1oAbQXES6Alep6rXOJguNiCQADSn+vP/tXKLIiUh7VV3sdI6KiEiSqhaUOFZPVb3az9QTRKQ6cBvQXFWvEJF2wKGq+k0sc8TlDKuI9BeRhcAi+3ZXEXnR4ViheBHoB5xr384CXnAuTnhEpKGIvC4i39u3O4rIZU7nikTgv8GtROT/gA8AAaYB0+3P3xeRO53MFoKtIvKziFwmIhlOhwmHiFwMbBKRpSIyBJgHPAHMFZFzy/1mh9m/jz8Dk4GpWCeTf4rIGBFJdzZdXPgS6wJTIZAd9OEFns0uIu8ATwMDgd72h1f6I34JpAM/A98GfXjBv4ETgG0AqjoXONLRRCESkRuATcBP7H/OY3ryHmU/Oh2gPCIyWETWAutF5EcRaRl0t9uzrxSRFSIy1ekslfAmkIc1/gBYCzwc6xDxOsMaeKP8Cqw3ShHxwhtlH1XtISKzAVR1h4gkOx0qDGOwXvj32LeXAh8CrzsVqDwi0qOsu4BuMYwSicuATqVcjXwWWAA87kiq0CwC/oN1YeZJEfkDeB/4UlVznAwWgtuAQ4GawFygu6ouF5GGWCc37zsZrgJvABep6hIRORy4TlX7iMgVWL+jZzkbLzQiMgB4AGiB9TdOAFXV1k7mCkFTVT3R6RAR8nL2XkBH9WYFyuqq+n9Oh4iUqq6xFo7tU+RUljDdhDXDtM3pIKESkVFl3QVkxDBKJJ4ETlDVBSJyFvCTiFygqlOw8ruWqrZyOkMUtFHVfwYuuqtqjpT4xY2FeB2wevWNssBehqKwbzmQ39lIYamnqh+JyF0AqlooIm5+3qcDEyj9DTEjtlHC5geaAKtLHG+M+18zBfZSk29EpBpwCnAO8IKI/KCq5zkbr1xF9vKkrSKyR1WXA6jqJgfe38NVTVWXAKjqNBF52f78VRG5xdloYXkduAWYiTfe1wMmiUhnVf3T6SAR8HL2+UAjYIPTQSLwjYicpKrfOR0kAmvsZcFqX3i/EXvVmwesAXY5HSJMl2BdUM0r5T5Xr/4BklV1AYCqfiIii4DP7NVinrjQ5PHlzPn2uVhg7NGG0l9HVSpeB6xefaMcBXwONBCRR7BmPO51NlJYskWkLvtf9H1x95v+Iqw9NctK3iEiaxzIE46bgV9EZBnWH1eA5kBb4HqnQoVo38jOnlH9CPjIXpZ6ulOhQvS3iDyGNcO6WESeAT4DjsX9J8TLReQ+4BfgTGAOWH9o8dbfil2q6uol+2UYCFwsIiuxTgYCM8NdnI0VEi9nrwcsFJFpBJ2EqeqpzkUK2U3A3SKSBxSw/3mv5WyskFwNPIdVY2Et1tLO6xxNFLoVwK8i8i3FXzPPOhepQtOB+ao6qeQdIvJA7OOEpUBEGqnqRgB7pvUYrGXYbZyNVj4RGQy8A6TYqyOvVNVV9t0/AmWt5HOTEcBYoJmIvAsMAC6OdYi47MMqIvWw3iiPxXqD/xG4yQvLO+xCOsdg5f7F7QV0gtlLbEcDh2Fd1a4PnKWq8xwNVgZ76cmfgVmnEvedrqpfxD5V6ETEBxyOdUIgWCcF01XV1bNOInK7qj7tdI5IiEgtrJMuBZ7H2npwCdZM98Oq6tpBq71f+G6gI9Zy5sdVNcu+UNDBXn7leiLyOJCAdaEg+GRylmOhQiAiLUo7rqolV0m4jsezDyrtuKpOiHUWwxtEZERpx1X1wVhnCZWI1AFyVXWv01nCJSLHAlvsfc7BxzOwtq484kiwEIjIdODioOXMjwEXqOoUEZmtqt0djhgSe7KpL9a55BQnZobjcsDqVfY0/FpVzRORo4AuwNteqqgqIolYe/wEWFJyiYRR9USkhqn8ahysRGR8KYdVVY+OeZgQiEgtVd1tn1AewM3Vpb2c3cvErupaVp0Ft1+cARCRJ7EKt+Rgzd50BW5W1f85GixO2CtnDgPWqepmp/OEw0vZRWSuqnYNut0J62LqncB9qur6GVa7LsQcVc0WkeFYs8LPxfqCZFwOWL36Rikic7AKRLTEyv011sb/kxyMFTIRGQaMtWdt7sV60T/s9j+u9pWlB7CWQSjwBzDSCzPypRGRv1W1udM5DMMAEflGVYfay2mV4nvmXV0syuPZ/1DVgSKSRfF9cK5fVisir6jqlV67OBNMROaoajcROQNrq8ctwPjgk3u3EZH/qOrNIvI1peyddPMycrsmwWh7pi8dqxp8EVAHuF1VXVsQ0OPZZwBDA8uZ7WNNsZczq2pNx8KFSETmYY2TugBvYxVoPFNVS12dUmU54nTA6rk3SgARmaVWleA7gBxVHe2xJQXzVLWLiAzEWhbxNHC3qvZxOFq5ROQn4DcgcEHjfOAoVT3WuVTlE5Fby7oLuEdVS50RMQyvEpHhqvq/sl77Lt9fZhhxRUQWqGonEXkV+FRVx5acjXIbEempqjO9uIw88Hzbn9+MdQ5zuog0Ar5383mkx7N7djlzQNDY436sWe3XA8dimSMu+7ACSfa/JwHve2jZUoFYZaUvZH/Pr6Ryvt5tAnsnTwZeUtUvAS+05amjqg+p6kr742HcXyX4UaA2VvGf4I8axO/vvXFwS7P/LfmaD3y4mliG24WvEJHmYrUXcj0vZwcQkYEicon9eT0R8UQrChGpLiL3isgr9u12IjLU6Vwh+lpEFmOtGvtFrK4HuQ5nKpeqzrT/nYDV33yjqk4IfDibrkL5QZ8fB3wBEDzz52Keza6qPwcPVkUkSUS6Y1U+dv1g1ZYlVneP4cC3YnUrifnYI15nWB/HmlnNwSpKkwF844GZvo5YlfUmq+r79h/Vf6qqm3tq7iMi3wDrsIpd9cR6/qe5+YoqgIg8DczAqlYLVnXmTqpaauEFNxCRScANgT+wJe5bo6rNHIgVFvsE5gqsJfD7qtSq6qVOZQqVyW6ES0Rewmo5dbSqdhCR2sCPqtrb4WgV8nj2EViDpkNV9RARaQJ8rKoDHI5WIRH5EKt904WqephYrScmq2o3Z5OFxn6d7FbVIhGpDtTywiBERE7BWiGWrKqtRKQb1jYhNy8JHg88g3UONh5or6ob7boi81W1vaMBy+Hx7J5dzhxgz2Sfh1W083cRaY41y/12THPE44AVvPtG6WX283wiVuXdZSLSGOisqj86HK1c9h6nNKw3GcGaocy273blXicRORTYpqVUchORhqq6yYFYYbEH3b9Top+mqn7qWKgQmezOEZFDgJeAhvZJfBfgVHtlhGsFLbvat83D7UskAzyefQ7QHZgVlH2eeqAlj4jMUNVeXnzeAUTkMKyq5KmBY7E+CY6EiMwEjgZ+9cprxn5fHIXVc/g/qjrGPn4CcLyq3uZgvHJ5PLtnlzO7jZd660VbJnCciKQGHXP1G6WItMPa+1nyDd61hS2CqVVO/TMRaWBfoQFY7GSmUHhhU3xJWkornqD7XD9YtVVX1f9zOkSETHbnvAr8C/gvgKrOE5H3sArtuVmBvdQq0Ke6PtaspRd4OXu+qqqIBLKnVfQNLpJvz6oGsrchqJWTm9kz20dhnc98BwzBKmjo6vMwW6Gq7hKRir/SJVR1KdaEQcnjPwA/xD5R6LycnQOXM38M1nJmr7x+RKQvVkvKDljb+BKAPaqaHssccbmXzX6jHG1/DAaeBFy7lCPIm1gzB4VYud/GakjsCSJyqogsA1YCE+x/v3c2VcVEZEDgJMbep/Vs0IDbqDrfiIgnKmCXwmR3TnVVnVbiWKEjScIzCvgcaCAij2CdvD/mbKSQeTn7RyLyXyBDRK4AfgZeczhTqB7A6hjQTETeBX4BvHKx6SysnvIbVfUSrCqkKc5GCtl8ETkPSLD3DY8GJjkdynClnSIy1N63OgDr9zXQ4rGao8lC9zxwLrAMK/PlwAuxDhGXS4JF5E+sN8fZqtpVRBoCr6nqKQ5HK5eIzFTVniLyp6p2to/9rqpHOJ0tFCIyF2sZzc+q2l1EBgPnquqVDkcrlxQv6f0O8DoOlPSON0FLsfOAAjzQbiLAZHeOiHwPXI+1D7GHWM3aL1PVIQ5Hq5CItMc6iRfgF1Vd5HCkkHk8+3HA8VjZf1DVnxyOFDKx2q71xco+pbRtIG4kItNU9XB7ee1gIAtrP2Inh6NVyN7edA/WawasWb6HVNUTs9tG7Hh5OXNA0NaDfcveRWSSqvaPZY54XRKco6p+ESkUkVrAZsALy2pzRcQHLBOR67E2oDdwOFM4ClR1m4j4RMSnquNF5AmnQ4Wg0F4ydhpWs+TXReQip0Md7Ly4FDvAZHfUdcArQHsRWYe1kuN8ZyNVTETeUdULCNomEXTM1Tye/Ql7CfxPpRxzNRH5RVWPAb4t5ZjbzRCrtcerWPvl92BV3vWCk1X1HqxBKwBi9Zn/2LlIhht5fDlzwF4RSQbmiMiTwAb2V+WPmXgdsHr1jfJmoDpwI/AQ1myllwZOO0WkBlZP03dFZDPeWKoXXNL7SHGopHckxIMVX0WkvaouFpFSe3yp6qxYZwqVye4KqqrH2sv4faqaJd5oU1JsZsl+n+npUJZweTn7cRy4jHZIKcdcw669UR2oJ1YBycBmuFpAE8eChUiszXuPqepO4GURGYtV+HKes8lCdhcHDk5LO+Y6IpIC/IMDzwlGOpUpVF7O7nEXYG0hvR64BWiG9f8hpuJuSbD9RtlUVdfYt1virTdKz7JPIHOwXvjnA+nAu6q6zdFgFRCXlPSOhHiw4quIvKKqV4pVyr4kVdWjYx4qRCa786SUhuaB7RROZSqPfTHsbqy9QXvZP/jIB15R1bucylYRj2e/BrgWa3XV8sBhrF7VE1V1uFPZKiIiN2FdwG4CrA+6azfwqqo+70SucLj5d7IsIjIEOAk4G/gw6K5aQEdVdX3vYfviwC4OPCd4xrFQIfJydq+zi7s1L6+gZ5VniLcBK3jzjRL2rYX/F9CC4leXvHIi2QrYoKq59u1qWK0nVjka7CAmInPUIz35DKMy7D2UnbCK6P0r6K5awL/cvjdORB5z8wCvPF7MLlZPxNpYxaHuDLorS1W3O5MqPCJyg6qOdjpHJETkBWCMqk53OkuoRKQr0A0YCdwfdFcWMF5VdziRKxwiMl9VD3M6RyS8nN3LxCV9h+N1wOq5N0rYV7ToZQ68ujTTsVBhEJEZQH9VzbdvJ2NdyXZ1c3kRORN4Amu/sOChIjQi8jAwSVW/czpLuOzCFrdiXdW7Uqy2Toeq6jcOR6uQyR579h7z07Eqvn8VdFcW8IGqur6Kp4icChxp3/zV7c95MI9n7woEihf+rqpzncwTKvtv6NUEPe/Af1W1wLFQIRKRhcChwCqsvuaBv6uu7WUaICJJWHkPsQ8t8cJzDtZKGmC0qv7pdJZweTy7Z5czi0v6DsfrgNWTb5RenRkOKG22TzzQ5FxE/gJO8VLVywDxcMVXEfkQ6+LMhap6mD0jP9kLM8Ymu3NEpJ+qTnY6R7hE5DHgcOBd+9C5wAwvzFx6PPuNwJXAZ/ahM7CWM7t+5lJEXsOqp/CWfegCoEhVL3cuVWhEpEVpx1V1dayzhEtEBmG1FVyF9Te1GXCRqv7mZK5Q2Oe/bbGK0eXhkfNf8Hx2zy5nFpGpqtpHRGY7OWCN16JLrm9vUIavReRarH53+8qne2X5ErBFRE5V1a9g34yIF0rwb/LiYBU8X/G1jar+U0TOBVDVHHsPuheY7M5ZIyKfY/W8U6yeoDep6lpnY1XoZKCbqvoBROQtYDZWMRe383L2y4E+qpoNVoVgYDJWn3a3613igu84eyWW66nqahEZCLRT1TftAoE1nM4VomexWpIsgX3btd7HG4XGvHr+C97O3lRVD6gW7BHF+g5jFX6N+YqluByweviNMlAROHh/luKNljxgLV16V0Sex7oytga40NlIIZlhzzp9QfELBZ+V+R0Ok4Oj4mu+PbunACLShqDn3+VMdue8CbwHDLNvD7ePHedYotBlAIELkOkO5ohEBt7MLgTNeNife+UCTZGItFHV5QAi0pri/y2uJSIjgF5Yq93exJop/h/WhSa3SwouPqOqS+1lwq4lIrVUdTfWFglP8XL2IJNEpLMXlzMDN2C1cMrDujDzA1ankpiKywGrV98oVdULrRnKZP9R7StWaxtRVa+8+dTCqoB5fNAxZf8SMje6FWuZW2nLTRRrP4LbPQCMBZqJyLtYv5+XOJoodA9gsjulgaq+GXR7jIjc7FSYMDwKzLarNAvWvkQvzFCCt7O/AUy1Z+XB2gf9unNxwnI7MF5EVmA97y3wzu/qGUB3YBaAqq4XEa+sCJopIq8D79i3z8da6ulm7wFDsXIqxS/KuH3iw8vZAwYCF4uI55Yzq+perAHrPRV9bVWK1z2sc7DfKJ1cjx0qETlaVcfZxX8O4OaZPgARGa6q/xORW0u7X1WfjXUmwxtEpC7QF+vNfYqqemEJOWCyO0VEfgbGYF0JBms/5SWqeoxjoSogIj7gLKwWVL2xnvepqrrR0WAhOAiy9wVysU4oBfhNVWc7GiwEYvW6vRF4EeviuwCLVdUTqyFEZJqqHi52Gyqx2t5Ndut5WDC7gM51BL1mgBe98twbsefFPdsi8jX2SqvSxLpKcFzOsAL5qqoiEljyluZ0oAoMAsYBp5Ryn9tn+sAq+gPglaunBw2vVnwFEJFf7EHGt6UcczWT3VGXAs8D/8Z6f5xkH3MtVfWLyPWq+hHFKxy73kGQ/RlV7Yc90+cVqlpk14T4N+DFPvIfich/gQwRuQLrd/RVhzNVyL7IMVOt9iqeu9hu1yM4H2ilqg+J1Ve+kapOczhahbyY3ePLmZ92OkCweJ1hvR1oh7Wn6TGsN8r3vFAV0DDC4cWKryKSClQHxgNHsX/5Ty3ge1Xt4FC0CpnszrJnnd5S1eFOZwmXiNwH5AAfYlWvB7xRVM/j2R/EGvB9ph47IRKRR7D2C5d83j0x+BaR47C22gjwg6r+5HCkkNhbJe5S1b+dzhIuEXkJ8ANHq2oHEakN/Kguby8I3swuIt+o6lB7KfABy5lV1fXLme1JvZygonoJQIq9VDh2OTz2/lwpIpISWLLhxTdKEWmItVeoiaoOEZGOQD9VdfV+GxEZVd79qnpjrLKEK7DczZ498BwRmaGqvaR4OXJXtxISkZuAm4EmwPqgu3YDr6rq807kCoXJ7jwR+QGrDVW+01nCYZ/QlOSVExovZw+0/irCWhoM3mn9Nb6Uw6qqXqhRAFgzUBTvS+mFixzjsJa/T6P4hYKYLpGMRNASbM+cEwR4ObuXicgU4FhV3WPfroF1oaB/LHPE25LgyUAPEXlHVS8AXD9ILWEMVpGowMbnpVhXVl09YMX9xQjKFFjuBnhywIoHK76q6nPAcyJyg9dWPZjsrrAKmCgiX1H8ZNLVy/e8XFTP49k9u1VFVQc7nSFSInIVMBJrZt6PXYQGbxTQedDpAJVQYM+QBc4J6mM9/17g2exeXM4cJDUwWAVQ1T32drOYircBa7KIXAT0L62AkduLFwH1VPUjEbkLQFULRcT1JexV9a3g2yKSpnbPO4/4yV5G7rnlbni74ut/ReRGrIqjAL8C/1XVAucihcxkd856+8OHh/bN20uyr8Uq5KJYRYxeVtXccr/RBbycHcA+H9iXXVW/cDZRaOziaCPYn/0PYKSqbnM0WGhuBzp5qaBbgKpOEJFGwOFYz/t0LxQZs40CPgca2EvKzwLuczZSyLyc/UXs5cxYLWGygE+xZurdLltEegS2GohIT6wLTTEVb0uCB2Jd4TibA4tDqKq6ujCHiPwK/AP4yV4W0Rd4QlUHOZssNCLSD2s2uIaqNheRrsBVqnqtw9HK5eXlbuDdiq8i8hpWy6nABY8LgCJVvdy5VKEx2Z1nLzVU9Uj7LBH5COsk5n/2oXOB2qo6rOzvcgePZ38RaMv+qtL/BJar6nXOpQqNiPyEVaE28LyfDxylqsc6lyo0IjIWODPW++CiQUQuB+7HKoYpWIUxR6rqG44GC5GItAeOwcr+i6oucjhSyLya3cvLmUWkN/AB+7cKNQb+qaoxXT0ZVwPWABG5zO37PksjIj2A0cBhwHygPjBMVec6GixEIjIV64rYV0G/sPPVqrZnVAEppbpracfcqLQ3cw+9wZvsDhGRXlhbJwKzq7uAS2P9xzVcXn7ePZ59AXCY2idDdt2CP1W1k7PJKiYiM1W1Z4ljM1S1l1OZQiUi3bF+T6cStE3FzTUtAkRkCdA/MJNtXxSepKqHOpusYkFb4so95kYezz4V6I81G9/DXs78Y+Bc2O1EJIni7bNivuIq3pYEB+wWkZqqmiUi9wI9gIfU/b3XFmBdyQu8aJZgLXvzDFVdYy3l38f1S5rtX9Rr8NASSdlf8bWeWJX0giu+NnEsWHiKRKSNqi4HEJHWeOD1YjPZnfMGcK2q/g77Vta8Cbi9v+NsEemrqlMARKQPMNHhTKHycvYlQHMg0A+xGd5pEzNeRM5hf42FswhqR+Vy/8WaofwTj+xDDLKW4m1KsoA1DmUJV7ELMfae0J5lfK3beDm7Z5czi8iFJQ51FxFU9e1Y5ojXAet9qvqxfSJzAlavoZeBPs7GqtBkVe2BNXAFrGUGWANuL1gjIv0BFZFkrKbnXljO8RLWEskX7dsX2MfcvETyKvZXfA1ucbAbeMGJQBG4HeuEbAXWgLsF3tl/a7I7JyswWAVQ1T/EqgTrdn2AC0Uk0CqjObBIRP7EWtrs5gG3l7PXxcoaKH7SG5hsF+1ye+XXq7D6bL9j307A2m92K+6vdFyoqrc6HSJC64CpIvIl1h7W04Bp9vPuygJvdu2Tu4FqIrKb/Rex84FXHAsWAi9nD1DVd0VkJvuXM5/uleXMFN9nm4r13zALiOmANV6XBM9W1e4i8hjW0p/3gteVu429uT8Ta5/KeRSfLXtZVds7lS0cIlIPeA44Fuu/4UfgJrcXiPD4cjdPVny1r5zeiHWRIHgZiqsrHIPJ7jQR+TfW6oL3sU4m/wnswCpw4doelSLSorz7VXV1efc7yePZy60BoaoTYpUlntizTKuBrym+JNj1xQxFZER596uqa6sIi8hjqnqX0zki4fHsnl3OXJKIpAPvxPpiXrwOWL/BukJ2LNZyghxgmlsHIGJVNr4Y6AXMCLorCxij7q9u7Gn2LPawEkskP7Fnu13Nnsm+Gg8tZw4QkfHq0bYNJrtzpPTelAGqHupRaRgHK68XMwQQkZpYmfdU+MUuIiKnEnROoKrfOJknHF7NHii6FHQ7AWvCrKODsSJib5Obp6odYvpz43TAWh04EevFskxEGgOdVfVHh6OVS0T+oaqfOp0jXCIyGrtvVmncXmRBRI7B2gMXvETyUlUd52iwEIiHK77aV+DTObCdkCtnyIKZ7IZhGAcnETkMaxl2HfvQVuBCVV1Q9ne5g72y8HDgXfvQucAML8xcejF78HJmYC8lljO7OXuAiHzN/nN4H9AR+EhV74xpjngcsMK+qxsNCdrHq6p/l/0dzhORDKxS6oGrSxOwSqnvcixUCOwZ4oAHsfrG7aMl+rS6jYik2J/uWyIJ4IVlkh5fzlzaTJknZshMdufYy5VG4LH3ScOIJyIyA6tA2nuqutPhOGERkUnAPao63r59FPCoqvZ3MlcoRGQe0E1V/fbtBGC2y/eZA57P7uXlzMHbJgqB1aq6NuY54nHAKiI3YJ3QbGJ/dTq3F4ZARD7FamcTPFvWVVXPdC5VeNy8V7gsJZdylHXMjby8nNkwInEwvE8aRqjEanc3EGsGZKJXVkKISFusYm7/xNrq9CZWmw/Xn5R6/ELwPKxevdvt23Wwlta6+vwXvJ0dvLuc2S3itUrwTcChbi/2U4o2qvqPoNsPisgcp8JEyPV/jAKCil1VE6tnXHCxq+qOBQuPZyu+itXbbgT7T8b+wJopc/3vrcnuKE++T4pIX6w+2x2AZOyKry6v9Ap4M7tY/XqfwqpncRfWbN/hwFLgSnV/mztE5H5gGBCoY/GmiHysqg87GCskqvoXcI+I3AcMxXr+/SLyBvCcy4svrbBzB6ozDwdK25PrRo9itaEaj3VOcCTW698LPJu9lOXMN4nIAC/MuorImcATQAOs511woAp5vA5Y12A1k/eaHBEZqKp/AIjIAKyCUUbVOAGr2FVTILhMfRbWngRXs5fLdAXa4c2Krx8AvwGBwcf5WPsqj3UsUehMdud49X3yeeAc4GOsAnsXAm0dTRQ6L2Z/EevCTAYwCbhFVY+zaxa8CPRzMFuozgW6q2ougIg8jtVuwvUDVgAR6YJ1AfUkrCre72JdKBsHdHMuWYUuxdreFLhQ8BseuBAsIj6sVYV9sVqVCPB/qrrR0WAh8HJ228kUX878FjAbbwy4nwROUYfb8MTrkuDXsU7gv6V4OXXX9c4KJiJdsfoepduHdgAXqaqrm5yL1QMx8EKrjrXxHBy6ShMurxa7Am9XfBWRmaras8SxGaray6lMoTLZnSMi3bCWAwe/T16sqnMdCxWCwHMsIvMCS9xEZJJH9sV5Lnvw9hQR+VtVm5d2n5uJyPfAuYE9oHadi/+p6lAnc4VCrJ6UO4HXgU+DL6SKyGdmCX/VEJHfVPXIir/SfTye3bPLmUVkoqoOcDpHvM6w/m1/JNsfrmfPlg1X1a4iUgtAVXc7HCskqlrT6QyREJHhqvo/oKXYDcGDuf0Ch22SiDyPNyu+jheRc4CP7NtnYV1k8gKT3SGqOgfw3PsksFesNlRzRORJYAOQ5nCmUHkxe66IHI91YUNF5HRV/cIuMFLkcLZyBVXezwMWiMhP9u3jsJbwe8EwVV1R2h1uH6zaz/ewoAsFtYEPVPUER4OF5icRuZ0DzwncvAQ7wMvZPbucGZghIh8CX1B8ki+mLTXjcobVq0RknFcqdR4MROQqVf2vlNEkXF3cHDzAyxVf7Zn5NPafPCaw/4+Uq2fmTXbniEhDrJODJqo6REQ6Av1U9XWHo5VLRFpgFQJMBm7BGki9aO/1czUvZrdn4p/AWmZ4C3ANcBHWntYrVHWSc+nKJ8Ur7x9AXVx5v7SLv8G8cCG4tBl4D83Ke7b/rVez28uZzwJ+Z/9y5qleWc4sIm+WclhV9dKY5ojHAauI1AfuADoBqYHjbj+JF5FnsPYjfkzxq0sxvcphGIbhVvYyyTex2k50FZFErNYHnR2OZhhxr6wLwAEeuRA8EzhD7VaI9gWbz031faMsXl7O7BbxuiT4XawlBUOBq7Guqm5xNFFo6gDbgOCBtbJ/479RBexWMM9hbfZXYDJWgY5SlzO5yUFQ8dUwwlVPVT8Sq2E7qlooIq5d4mlXIx+BNdN3P3ADcCZWv+ebVHWDg/HKJVbbrM+A99VuneUVIjJGVS+2P7/IzbOSJYlIDayL7v/AKgqYDywHXlbVMQ5Gq5AXBqQhuAf4Q0Qm2LePBK50ME/IRCQVuJb95wS/Y71uch0NFgIvZ8fDy5lFpClWFfgB7D+PvElj3Is1XmdYZ6pqzxIFIiao6qCKvteIPyIyBXgBeN8+dA5wg6r2cS5VaOy9Nr8B/7MPnY+18d8rFV8NIywi8ivWifxPqtrDbrnyhFvf30VkLNYe4TTgPKwLqu8DpwHHquppDsYrl71E71PgbGAjVu4PVXW9o8FCUKLokif6ageIyJfA58DPWM99GlZ173uBdarqhSr2rjgJjpSI1MO6iC3AZFXd6nCkkIjIR1idDgLnBOcCtVV1mHOpQuPx7J5czgz7ziPfo3gbp/NV9biY5ojTAesUVe0rIj8Ao4D1wCeq2sbhaOUqY6bvZlX1Sv8vTxKRqSUHp4HXkFOZQuX1iq+GES4R6YF1InwYMB+oD5ylLq2mXkG12jmq2s2xcBUIHuiJyBFYJ5BnAouwZl1fcTJfeUpk99qAda6qdg26PV1Ve9t75RaqansH44XELSfBkRCRT7D6xo5Vu02JV5R87ZR1zI28nN3LSvs75MTfpnhdEvywiKQDt2Gd2NTCKrrgdu9hzfSdYd8+B+uqqutn+rzILjsOVtXUO7GeawX+iXeqpnqu4mvQ814qNy+hMdmdp6qz7Eqvgd7DS1S1wOFY5fEFff52Ofe5mqr+DvwuIjdgVav9J+DaASvQVERGYb1GAp/vo6o3OhMrJNli9xoWkVOA7QCq6hcRcThbqOqranAxlzEicrNTYcL0Mlbf1dEi8jEwRlUXO5wpVLNFpK+qTgEQkT7ARIczhcqz2T2+nHmriAxn/yrDc7G2J8ZU3M2witUe5kZV/bfTWcLl5Zk+L7KXcCjWCU1JXlnK4bmKr15+3k1254hIua0w3FqcTkRGAk+q6p4Sx9sCj6vqWc4kq5iIfKCq5zidIxIer7TbFXgVOARrFcGlqrrULih5rqqOKvcBXEBEfgbGUPwk+BJVPcaxUGGyJz7OxdrTugbr/8n/3HyBTEQWYV3M+9s+1BxrRYQf633etX1BPZ7dy8uZmwPPA/3sQxOxlu+vjmmOeBuwgtXqQ1UHO50jXCLyOFaj7eCZvhSsWVfPzIAYhmFEWxml9wNiXoLf8C4RaRHrk7F445aT4EjZBQ2HAxdgbSt7F2v2rLOqHuVgtHLZFY3L5Obn3+PZzXLmSorXAesjWH3iSlbrmuVYqBCUsWk7wPUzIF4jIker6riyZm7cOmNzMBGrIXs7iref+s25RKEz2Y1QeLxarWezA4hIPyAT+E1VN4tIF+BO4AhVbeZsurJ5/Xn3OhH5DGiPtf92jAZV8jY1IozSiMgYrCXAwcuZL1LVax0NFoIy6ufEvFNGvA5Yx5dyWNXlfViN2BKRB1V1RBkzN2bGpoqJyOXATVhtG+ZgvVlO9sLvqcnuHBF5FGuJ7U77dm3gNlW919FgZfB4tVovZ38Kq7XdHKAt8A3WHrNHgf+6eW+Zl5/3ALecBEcicDHb6RyGd3h8ObMrOmXEa9Gly0q+Kdpvnoaxj6qOsP+9xOksceomoDcwRVUHi0h7wCs9/Ex25wzRoLYeqrpDRE7CavnhRl6+auzl7CcD3VU1176osR7ooqrLHM4VCi8/7wGlFZF8HxcXkQxebVXayiuz6soox4lOB6gEUdV3gm7/T0Suj3WIeB2wfgKUvCL5MdCzlK814pyIpGD1dWxJ0O+Mqo50KlNFDpKKr7n2ySQikqKqi0XkUKdDhchkd06CnTsPQESqYe31dysvV6v1cvacwCyqfVFjiUcGq+Dt5z3AFSfBYTqlnPsU8MSA1d4L2k5Vf7bfHxNVNcvpXKHwanY3768ti7isU0ZcDVjtmYJOQHqJq2O1CNqrZRglfAnsAmYCeQ5nCdVMyqn4CnhhRcFaEckAvgB+EpEdWLMgXmCyO+d/wC/2Un4FLgXcvMfvX0Gfz3AsRWS8nL2NiHxlfy5Ay6DbqOqpzsQKiZef94BST4IDJ8luvKh6MKy2EpErgCuBOkAbrK0fLwOur87s5eweVfI88qqg+xR4KJZh4moPq4icBpwOnAp8FXRXFvCBqk5yIleoRESA84HWqjrSrrLXSFWnORztoCYi81X1MKdzxDOx+mqmYzVqz3c6TzhM9tgTkSFYJzEC/KiqPzgcKSJerlbr9uz2a7tMqjohVlmiye3Pe4CXi0iKyK2lHN4FzFTVOTGOExYRmQMcDkwN2gf9p6p2djRYCLyc3ai8uBqwBohIP1Wd7HSOcInIS1gbtI9W1Q72vpsfVbW3w9EOaiLyCjBaVf90OkskvFjxVUR8wDwvXigw2Y1webVaLXg7O4CIpGIVXVJguZuLLQXz+vPuZSLyHtAL+No+dDIwHaty8Meq+qRT2SoiIlNVtU+gcJeIJAKz3Fz0J8DL2cF7y5nFZZ0y4m1J8GjsYgUicm7J+z2w76OPqvYQkdmwb99NstOhDlYi8ifW6yURuEREVmAtCRZcXtUtoKyKr4CrK76qql9E5opIc1X9u+LvcA+T3VkiksX+ojTJQBKQraq1nEtVthLVav9PRIKr1bq6ErnHsyeyP+dqwIe1H/RN4B5VLXAyX3m8/LwfJOoCPVR1D4CIjMCqjXIk1jJK1w5YgQkicjdQTUSOw3rdfF3B97iFZ7N7dDnzIGAcpe/djvme7bgasOLdvR4BBSKSwP5Bd32sGVejagx1OkAUeLnia2NggYhMo3i/ZDfvLQsw2R2iqjWDb4vI6VjLyNzKy9VqvZz9KaAm0CowyyEitYCn7Y+bHMxWES8/7weD5kDwFokCoIWq5oiI2+tc3AlcBvyJtSfxO1V91dlIIfNy9uuwlzMDqOoyEWngbKTyqdXW0Qd8r6ofOZ0nrgasajfXFpHDVHW+03kiMAr4HGggIo8AZ+HeVg2eF7wPyL5Q0BDv/c54ueKrVwbWpTHZXUJVv7CLu7iVl6vVejn7UOAQDdoXpaq7ReQaYDHuHrB6+Xk/GLwHTBGRL+3bpwDvi0gasNC5WCF5QFXvB14F69xGRN5V1fMdzhUKL2fPU9V8qxTNvhUert+Taa+6uh4wA1aHvGwvpR0DvKd2g3kP+ARruUmgmMjpwCYnA8UDEbkBGIH1XAdmtBVw/ZJgPFzx1atFT8Bkd1KJ/TY+rL1mbj4x8HK1Wi9n1+DBatDBIhFx8+sFvP28H0BE2gDnAud4Yf+8qj4kIt8BA7Ge/6tVNbCCz+2Dp+YicpeqPmafB38MzHY6VIi8nN2zy5mxzh1vBz6k+KqrmFbyjsuiSwAi0g5rr8cwYBowRlV/dDZV+UTkW+D0wN4aEWkMfKOqpn9sFRKRv7D2D29zOktliMcqvnptL2Iwk9059h7EgEJgFfCqqm52JlH5xMPVaj2e/QvgM1V9u8Tx4cDZbh70efl5D7DPX/4JnId18fcxrP8fnixu6BViTfG9i7WsdjDWcs9/O5sqNB7P7sNaznw81kWOH7yynFlKr+itGuNK3nE7YIV9yzxPx1pquxvrRXR3rCtfhcretH0y8A+gGVZrntvdPtD2OhEZDxynqoVOZwmHHGQVXwN7EVX1bqezhMtkNyoiHq1WC97MLiKZWEVDctjfb7A3UA04Q1XXORgvJB593q/Amk1tirXM8CPgS1Vt5Wiwg5yI9Ai6mQT8F5gIvA6gqrOcyBUKL2cPEJGR9nLmwO0E4G2PLGd2hbgcsIpV/v0SrMHfT8DrqjpLRJoAk1W1haMByyEi1wEnAi2Bq9TlvWMPBiLyOnAo8C1WlWAAVPVZx0KFSETeBe7yasXXkkRkiqr2dTpHJEz2qiUi95dzt6pqTJuch6qsarWAF6rVejZ7gIgcDXTCumC9QFV/cThShbz8vItIPlal+tsCy2hFZEWsZ2vijX3hvSyqqq7tHODl7AEiMgZYUnI5s6o+4GiwEIhIEnANVhVsgF+B/8b6fSZe97A+D7yGNZuaEzioqutFxHVFjKR4k2rBml2dA/QVkb5eGDh53N/2R7L94SWerfjqwb2I+5jsjsgu5Vga1jKsuoArB6x4u1qtl7MDoKrjsFo3eImXn/cmWFuxnhWRhlgzrEnORgqfeKynpqoOdjpDpLycPcglwLsichceW84MvIT1O/qiffsC+9jlsQwRlzOsXiNWj68yqepBVdXTiJ6y9jp5ZI+Tp/YiBjPZnSUiNbFO2i/DOiF+xq35RWQZJarV2scTgMWq2s6ZZBXzcnYvO1iedxFpCpyDtUS4OvC5F7YeSFBPTVVtY9dEeVlVXdtTU0SGq+r/SkyA7OPmiQ+PZz8YljPPVdWuFR2ranE5wyoiA4AHgBZYz4HgwAbiUJkBqTNE5D+qerOIfE0pM0xemKX0wsC0HK+p6sTgA/bvrisHHiWY7A4QkTrArViVOt8CeqjqDmdTVcjL1Wq9nN3LDornXVXXYs8Ki9Vu7RyHI4XKcz01sVabgDUz7zVezv5Mids7gI72cQVcv5wZKBKRNqq6HEBEWgNFsQ4RlwNWrCsbt2AVWoj5kx6ug2Hg5FHv2P8+7WiKSvB4xdfRQI8QjrmRyR5jIvIUcCbwCtBZVfc4HClUC0XkwjKq1S52KFOovJzdyzz7vIvIkeXcXd5eRTfxXE9NVf2v/a/nJkA8nv1gWM78L2C8iKzAmuBrgbXEOabickmwiExV1T5O5wiViPRU1ZleXt5puIMXKr6KSD+gP3AzELzHoxZW9c6YLkMJh8nuHBHxYxVFK6T4yWNgBY0rL9J4uVqtl7N7mZefd/vCe0kKdAWaqmpCjCOFTUSeBHYCFwI3YPXUXKiq9ziZKxT27NhzQF+s530ycIuqrnA0WAi8mN3Ly5mDiUgKVvFRwdp2kFfBt0RdXM2wBq0lH29fjf+M4lVfXbmWXFVn2v+agalRKar6hYjc6XSOCiQDNbDen4KXAO0GznIkUehMdoeoqs/pDJGwBxd9SlSr/d4L1Wq9nN3LvPy8q+opwbdFZCBwD7ABuN6RUOG7E2t//J/AVcB36pGemsB7wAvAGfbtc4D3AS9M4ngxu2eXM5cowBisjYigMW4BGlczrF4tjS0if1LOchNV7RLDOIaHlFHxdZCq9nMoUshEpIWqrnY6RyRMdsMwjLKJyDHAfVjnNo+q6k8ORwqZl3tqlrbC0Atty8Db2b0oqABjA6zVV79gXRwbDPyqqmUNaKtEXM2wBtaSi0jrkksI7KUGbjXU6QCGZwVfzQ5UfD3NmShhe01EhqnqTgARqQ18oKonOBsrJCa7YRhGCSJyMtaM6i6snrETK/gWN2ouIndpiZ6aTocK0Xh7ldUHWBcL/gl8axesQ1W3OxmuAp7N7sXlzKp6CYCIfAN0VNUN9u3GWDPdMRVXM6wBIjJLVXuUODZTVXs6lSlcIjJUVb9xOkc8EJFDsDadB6pKA+DWGflgIjKgtIqvXjhJEJHZqtq9omNuZLIbhmEcyN5rvhaYi0eLSIpVbeldrCXBnuqpKSIry7nbtd0ywPPZp2AN8t63D50D3OCFejoiMl9VDwu67QPmBR+LhbiaYRWR9lj7PdJLLJWsBaQ6kypiIwEzYI2Nj4GXgVfxQFXpEjxZ8dXmF5Hmqvo37GvU7pUrbCa7YRjGgTxbNbVET83n2N9Tc4KI9HBrHZRgqtrK6QyR8nJ2rAnCd4Ju/09EvLJn+1cR+QFrsK1Yg+2YV/SOqwErVoWroUAGxZdKZgFXOBGoEsTpAHGkUFVfcjpEOIIqvtYvUZ2uFuD6Koy2e4A/RCRQbOxIrGbtXmCyG4ZhlFBe8Ui737Obebanpoj0Btao6kb79oXAP4DVwAMuX07r2exBPLucWVWvtyf5jrAPvaKqn8c6R7wuCe6nqpOdzlEZInK4qk5zOkc8EJEHgM3A5xSvKu3aNxi7BdJRwNVYs8MBWcDXqrrMiVzhEpF6WHs+BJisqlsdjhQyk90wDKM4u0DR2UAmMFZV54vIUOBuoJrZelA1RGQWcKyqbrd74X6A1ZKnG9BBVV1bCd7L2QO8vJzZLeJ1wHoI8BLQUFUPE5EuwKmq+rDD0UplNwMvuZwAEbkCyFbV95xJFh/KeKPxxBuMlyu+ShkN5lX1t1hnCZfJbhiGcSARGQM0A6ZhtSNZDfQD7lTVL5xLVjEv99QUkblq99IWkReALar6gH17jqp2czBeubyc/WBgz64+gVUtWHCot3m8LQkOeBWriM5/AVR1noi8B7hywArchrUsr6QPgF+xelMZVcTj+ya8XPH1X0GfpwKHAzNx8bKrICa7YRjGgXoBXVTVLyKpwFagbWC5p8t5tqcmkCAiiapaCBxD8W0ebh8LeDb7QbKc+UngFFVd5GQIV/+PrkLVVXWaVehtn0KnwoQgQVWzSh5U1SwRSXIiUDyxn+Nr2H/R4Ffgv6pa4Fio0NULDFYBVHWHiDRwME/I9MAG882w3jhdz2Q3DMMoVb6q+gFUNVdElnpksIqqBiY5HnQ6SwTexyoOtRXIAX4HEJG2WC2G3MzL2f8LHAv7Vi89zv7lzK8Arl/ODGxyerAK8Ttg3SoibbArX4rIWcAGZyOVK0lE0lQ1O/igiNQEkh3KFE9eApKAF+3bF9jHLncsUegOpoqva4GYllGPIpPdMAwD2ovIPPtzAdrYtwPLDLs4Fy00Hu2p+YiI/AI0Bn7U/fsBfVgDKNfycnasCafALOo/sQoWfQp8KiJznIsVlhki8iHwBcXruHwWyxDxOmC9DuvKRnsRWQesBIY7G6lcrwOfiMg1qroKQERaYvV0et3BXPGid2D/hG2ciMx1LE14PFvxVURGs39w7cO6IumJ591kNwzDKFUHpwNEwXtY519n2LfPwZoFdHVPTVWdUsqxpU5kCZeHs3t2OXOQWsBe4PigYwrEdMAal0WXAkQkDfCVttzWbUTkauAuoAbWCyUbeNxr7Va8yK5QN0xVl9u3WwOfqKoXepl6tuKriFwUdLMQWKWqE53KEw6T3TAM4+AkIlNVtU+JY1NUta9TmQx3EpF7gJOw9mo3B3qoqtrLmd9SVbe3cnKNuBywikgGcCHQkqArHKp6o0ORQiYiNbD+v7l+kH2wEJFjgDeBFViDvhbAJaoa88bJ4fJqxVe79cFbqurmlQ+lMtkNwzAOXiLyOLCT4j01U7BmXV3d8s6IPRHpy/7lzNn2sUOAGqo6y9FwIXBLZ5V4HbBOAqYAfwL+wHFVfcuxUBUQkcOx9ndMF5GOwInAYlX9zuFocUFEUoBDsQasi1U1r4JvcQUR+Tro5r6Kr6rq+oqvIvIDVmW6fKezhMtkNwzDODiZnppGPLG3lP0Lq9hod/vYfFWNaW0Lr6yfjrZUVS21j5YbicgIYAiQKCI/Ye2T+BW4U0S6q+ojTuY7WInI0ao6zu5BFayNiMR8w3kkPF7xdRUwUUS+wloCD7i7112QVZjshmEYBx2Pt7ozjHC5orNKvA5Y3xGRK4BvKF7xyq3LOM7CKnySAmwEmqrqbhF5CpgKmAFr1RgEjANOKeW+mG84jxIvVXxdb3/42N/3zitLQkx2wzCMEkTkT8p5P3FzleCDpKemYYTLFZ1V4nXAmg88hVVBNfDGqYBbl3EUqmoRsFdElqvqbgBVzRERfwXfa0RIVUfYn45U1WJLgETEE1dYPV7xdaGqfhx8QESGORUmTCa7YRjGgYba/15n//uO/e/5WJVI3exg6KlpGOEqrbPK+bEOEa97WJcDfTxULXUqMFhV94qIL9B0W0TSgfFeqVbrVSIyq+RzLCIzVbWnU5lC5eWKr2U87wcccyOT3TAMo2wiMrFkhdTSjrmJiMwNtLgTkReALar6gH17jqp2czCeYVQppzurxOsM6wLcfyUv2JGBIj+BwaotCbio9G8xKktE2gOdgPQS+1hrYRUwcjW74utxXqv4KiJDsMrAZ4rIqKC7auHAvolwmOyGYRghSRORgar6B4CI9AfSHM5UkYOhp6ZhRCRQ4dgp8foLVgTMEZHxFN/D6sq2NmVVpLVniD0xS+xRh2ItX8qg+D7WLOAKJwKFQ1WLRKS+iCR7rOLremAGcCowM+h4FnCLI4lCZ7IbhmFU7DLgDXulmAK7gEudjVSh94EJIrIVyAF+B7B7au5yMphhHOzidUlwqbOSbm5rYzhHRPqp6mSnc0RCRP4L9AA8V/FVRJJUtcD+vDbQTFXnORwrJCa7YRhGxUSkFta5qCcGfF7vqWkYXhWXA9ZgItLDvMkY5RGRVKyrwZ0IWgqsqm6/GhxoiVSSqurImIcJk4j8ijXblwjMAbYAE7zQkspkNwzDKJuINAQeBZqo6hC7v3w/VX3d4WiGYQQRkeuAd1V1p327NnCuqr4Yyxy+pyax9QAAEzVJREFUWP4wl3rN6QCG670DNAJOACYATbGWSXrBQlV9MPgDWOR0qBCl2xWxzwTetItcHetwplCZ7IZhGGUbA/wANLFvLwVudiqMYRhluiIwWAVQ1R04sC3ODFhBKv4SI861VdX7gGx72fjJQGeHM4XqrhCPuVGiiDQGzsbqmewlJrthGEbZ6qnqR4AfwC5kVORsJMMwSuETkX1jJbugZ3KsQ8Rr0aVgDzodwHC9AvvfnSJyGLARaOlcnIodJBVfR2Jdgf9DVaeLSGtgmcOZQmWyG4ZhlC1bROpi9wm394Z6Yh+rYcSZH4CPRORlrN/Xq4GxsQ4Rl3tYRaQV1n5EBRap6gqHIxkuJiKXA58CXYA3gRrA/ar6sqPByiEiXbGamY8E7g+6Kwurd+8OJ3IZhmEYhoj0AEYDhwHzgfrAMFWd62gwwzCKEREfcBVWKycBfgReU9WYroiIqwGrXY3uNaAXVjERAbpitXC4zN63ZRgHjYOl4quIzFLVHk7niITJbhiGUZyIpGAtAT4U61xsCeArq42fYRjxLd6WBI8CFgLnqKofwF6XfR/wPHChg9kMlxGRcquieqE1DPCTiBSr+CoiXqz46uW95ia7YRhGcZPti2ELAgdEZBZWGzbDMBwmIh+p6tki8if20v1gqtollnnibcA6QFUvDj6g1hTzSBExe7SMkmo6HSAK0lV1t72s+U1VHSEinpthBb51OkAlmOyGYRiAiDQCMoFqItKd/RfFagHVHQtmGEZJN9v/DnUyREC8LQn+S1XblnHfMlVtF+tMhlGV7CtjxwNvAffYRXTmxfrKmGEYhmGIyEXAxVhbs2YE3bUbeEtVP3Mil2EYxQW2BInIO6p6gdN54q2tzUQRuT+4PDOAiNwHTHEok+FyIvKkiNQSkSQR+UVEtorIcKdzhShQ8fUvr1V8FZEzRWSZiOwSkd0ikiUinthnbrIbhmEcSFXfUtXBwMWqOjjo4zQzWDUMV0m2LzD1t88Lin3EOky8zbDWAl7H2iMxB2tNdg9gFlbRJVNS3TiAiMxR1W4icgZwOnALVqXdrs4mO7iJyF/AKaq6yOks4TLZDcMwyiYijwJPqupO+3Zt4DZVvdfRYIZhACAiA4HzsXqyf1XiblXVS2OaJ54GrAEi0gboiLV3YoGqLnc4kuFiIrJAVTuJyKvAp6o6VkTmem3A6rWKryIyUVUHOJ0jEia7YRhG2URktqp2L3HMU3+jDCMeiMhlqvq60zniregSAPYAdTmAiAwNfG4YZfhaRBYDOcC1IlIfyHU4UyS8VvF1hoh8CHwB7Gt14JFlYya7YRhG2RJEJCXQxkZEqgEpDmcyDMMmIker6jhgR2lLgGN9ThCXA9YSRgLfOB3CcC9VvVNEngB2q2qRiGQDpzmdKwJeq/haC9iLVTQqQAEvDJxMdsMwjLL9D/hFRN7Een+5FKs4oGEY7jAIGAecUsp9MT8niMslwcFKW5ZiGCWJSH+gJUEXeVT1bccCGYZhGIaHiciJwLFYq39+VNUfHI5kGIZLxVuV4NJc5XQAw91E5B3gaWAg0Nv+6OVoqBB5ueKriDQVkc9FZLOIbBKRT0WkqdO5QmGyG4ZhVGgRMFZVbwN+F5GDofe5YRxURORREckIul1bRB6OeY54mmEVkSPtT/NV1bSxMUIiIouAjurBXxYvV3wVkZ+A94B37EPDgfNV9TjnUoXGZDcMwyibiFwBXAnUUdU2ItIOeFlVj3E4mmEYQdxSIC3eZlgvsT/+6XQQw1PmA42cDhGhTV4crNrqq+qbqlpof4wB6jsdKkQmu2EYRtmuAwYAuwFUdRnQwNFEhmGUJkFE9hVEc6pAWlwVXVLVS0SkA9BERGqo6p7AfSJyoqqOdTCe4V71gIUiMo3iVVNPdS5SyLxc8XWriAwH3rdvnwtsczBPOEx2wzCMsuWpar6IVbxeRBKxCrkYhuEuriiQFm9Lgm/Auqq3GOgG3KSqX9r3mf5fRqlEZFBpx1V1QqyzhMt+gykp5g2fIyEizYHngX5Yb5KTsH5nVzsaLAQmu2EYRtlE5ElgJ3AhcANwLbBQVe9xMpdhGAdyQ4G0eBuw/gn0U9U9ItIS+AR4R1WfM9WCDcMwDMMwqp5YU6uXY7XPEuAH4DUv1oowjIOdiLQA2qnqzyJSHUhQ1axYZoirJcFYT/AeAFVdJSJHAZ/Y/yPEyWCGe4lIX2A00AFIBhKAbFWt5WiwENjVXUdj7RVS4A+s2bK1jgYrh4jcX87dqqoPxSxMmEx2wzCM8omID5inqocBrzqdxzCMsgUXSAPaAJnAy0BMC6TFW9GljSLSLXDDHrwOxdqj2NmpUIbrPY+1j28ZUA3rqvDzjiYK3ZvAV0ATrDeZr+1jbpZdygfAZcD/ORUqRCa7YRhGOVTVD8y1tx8YhuFuriiQFm9LgpsChaq6sZT7BqjqRAdiGS4nIjNUtZeIzFPVLvaxSara3+lsFRGROararaJjbmX35bsJa9D0EfCMqm52NlVoTHbDMIzSicg4rJ7m09h/ccwrxQwNI26IyFRV7RPYOmkXSJsVOB+OlbhaElzeMkgzWDXKsVdEkoE5dqGIDUCaw5lC5cmKryJSB7gVOB+rGl0PVd3hbKrQmOyGYRgVetDpAIZhhGSCiNwNVBOR47AKpH0d6xBxNcNqGJGw9zhvwtq/eguQDryoqn85GiwEXqz4KiJPAWcCrwAvBLefcjuT3TAMwzCMg4W95/wyHC6QZgashlEOEUkA3lLV4U5niRci4sfqGVtI8b58glX8x7XFrkx2wzCMsonIH6o6UESyMO8zhuEJ9irD9li/s0tUNT/WGeJqSbBhhEtVi0SkvogkO/ELGikvV3xVVc8WgzPZDcMwyqaqA+1/azqdxTCMionIyVhVgZdjXVhqJSJXqer3Mc1hZlgNo3wi8l+gB1a13eDiEM86FqoCInJbKYfTsJZ11FXVGjGOZBiGYcQ5e598mVR1e6yyGIZRMRFZDAwNbIMTkTbAt6raPpY5zAyrYVRsvf3hAwJXhV19pUdVnwl8HlTx9RLgA+CZsr7PMAzDMKrQTKy/nwI0B3bYn2cAfwOtHEtmGEZpNpeo2bICiHnXADNgNYyKLVTVj4MPiMgwp8KEylR8NQzDMNxEVVsBiMjLwFeq+p19ewhwrJPZDMMo1QIR+Q6rxZ0Cw4DpInImgKp+FosQZkmwYVRARGapao+KjrmJqfhqGIZhuJWIzFTVniWOzVDVXk5lMgzjQCLyZjl3q6peGpMcZsBqGKWzr/ieBJwNfBh0Vy2go6oe7kiwEJiKr4ZhGIZbicgPwO/A/7D+Rg0HjlTVExwNZhiGK5mqkIZRtvXADCAXa99N4OMrwNV/VFXVp6rVVLWmqtYK+qhpBquGYRiGw84F6gOfA1/Yn5/rZCDDMA4kIk+KSC0RSRKRX0Rkq4jEvNWjmWE1jAqISJKqFjidwzAMwzAONiLSWFU3OJ3DMIwDicgcVe0mImcApwO3AONVtWssc5gZVsOogBmsGoZhGEaV+dbpAIZhlCnJ/vck4H2nWk+ZKsGGYRiGYRiGU8TpAIZhlOlruxdrDnCtiNTH2ioXU2aG1TAqUFoLGy+0tTEMwzAMD3gVrO03TgcxDKM4Vb0T6Af0slcc7gVOi3UOM2A1jIrdFeIxwzAMwzBCJCICLBaR14C1TucxDONAqrpDVYtE5BVVzVbVjbHOYJYEG0YZgtraZIrIqKC7amG1izEMwzAMI0wi0gc4DzgDqANcB/zL0VCGYVTEsT7JZobVMMrm2bY2hmEYhuE2IvKIiCwDHgX+BLoDW1T1LVXd4Ww6wzAqsNmpH2za2hhGBYLb2ohIbaCZqs5zOJZhGIZheIqIbAGWAP8BvlHVXBFZoaqtnU1mGEZ5RCRNVbOd+vlmhtUwKvaT3TS5DjAXeFNEnnU6lGEYhmF4TCPgEeBU4C8ReQeoJiJmi5phuJCI9BeRhcAi+3ZXEXkx1jnMgNUwKpauqruBM4E3VbUncKzDmQzDMAzDU1S1SFW/V9ULgbbAl8AkYJ2IvOdsOsMwSvFvrG1w2wBUdS5wZKxDmAGrYVQsUUQaA2cD3zgdxjAMwzC8TlVzVfUTVf0H1uD1B6czGYZxIFVdU+JQUawzmCUYhlGxkVh/SCeq6nQRaQ0scziTYRiGYXiKiFxYzt2mqIphuM8aEekPqIgkAzdiLw+OJVN0yTAMwzAMw6hyIjK6tMPAKUCmqpqJFMNwkf9v7/5D/arrOI4/X12T6dpdSSMw2yBCpOZYNgLdoFwhlVJBwoxZlvWHMNtMo6I/+m1/FmnUP/4KczGEoCwstWY2E2yt4R3R/mnYHwa21BxzLBnv/vie7/bd7Xt/2O4952w9HzC+3/M5557zun+M731/P7+SvB74LoOpcAEeArZW1XOt5rBglWaX5ALgdmA9g2+AdwHbqspNziVJ+h8kCbAZ+ALwZ+BWV+CX+iXJ+qp6fK62xeYcVmludzPYe/V84I3AA02bJEl6BZKcleTTDIrU9wJXV9Umi1Wpl8aNihjXtqgceiHNbUVVjRao9yS5qaswkiSdjpJsAbYBvwbeV1VPdxxJ0hhJLgUuA1YkuXnk1CQw0XYeC1ZpbgeTXAv8uDn+KM3y3pIkad5uB54FNgAPDEYFA4O5cVVVa7oKJukkZwOvYVArLhtpfxG4uu0wzmGV5pBkJfA94FIGc1h/z2AOq98MS5I0T0lWzXbez1WpX5Ks6sP/SwtWSZIkdSbJBHBNVd3XdRZJJyRZAXweeBuwZNheVRvbzOGQYGkGzfL7M36jU1VbW4wjSdJpLckksIXBAoY/Ax4GbgQ+B+wFLFilfrkP2AFcBdwAXAf8o+0Q9rBKM0hy3cjh14CvjJ6vqh+2m0iSpNNXkp8CzwNPAO8BXsdgrty2qtrbYTRJYyT5Y1W9I8lTwznmSX5bVe9qM4c9rNIMRgvSJDdZoEqSdEreXFUXAyS5AzgIrKyqQ93GkjSDl5vXvye5EngGuKDtEBas0vw4FEGSpFMz/OOXqjqW5IDFqtRr30yyHLiFwSrfk8Bn2w7hkGBpHpLsqapLus4hSdLpKskx4PDwEDgHeIkT29pMdpVNUn9ZsEozSHKIEz2r5zL4UAU/WCVJknSG6tvCow4JlmZQVcvmvkqSJEk6o+zuOsAoe1glSZIkSSdJsrqq9nWew4JVkiRJkjQqyS4GW0/dA2yvqhe6yPGqLh4qSZIkSeqvqtoAbAbeBOxOsj3JFW3nsIdVkiRJkjRWkgngw8BtwIsMFiD9UlX9pJXnW7BKkiRJkkYlWQN8ErgSeBi4s6r2JDkfeKKqVrWSw4JVkiRJkjQqyWPAHcD9VXVk2rmPVdW9reSwYJUkSZIk9ZH7sEqSJEmSAEgyBczYq1lVa1qMY8EqSZIkSTruquZ1S/M6HPq7GXip7TAOCZYkSZIknSTJ41W1fq62xeY+rJIkSZKk6ZYm2TA8SHIZsLTtEA4JliRJkiRN9yngriTLm+MXgOvbDuGQYEmSJEnSWEkmGdSN/+rk+RaskiRJkiSAJNdW1Y+S3DzufFV9u808DgmWJEmSJA0N56ku6zRFwx5WSZIkSdJxSSaArVX1na6zuEqwJEmSJOm4qjoGfLDrHGAPqyRJkiRpmiS3AsuBHcDhYXtV7Wk1hwWrJEmSJGlUkp1jmquqNraaw4JVkiRJktRHrhIsSZIkSQJgzHY2BRwEdlXVgbbzuOiSJEmSJGlo2bR/k8A64MEk17QdxiHBkiRJkqRZJTkPeKSqLmnzufawSpIkSZJmVVXPAWn7uRaskiRJkqRZJdkIPN/2c110SZIkSZIEQJIpBgstjToPeAb4eOt5nMMqSZIkSQJIsmpaUwH/rKrDneSxYJUkSZIk9ZFzWCVJkiRJvWTBKkmSJEnqJQtWSZIWWJJjSfYm2Zfk/iTnvoKfXZvkA/O4bl2S204tqSRJ/WbBKknSwjtSVWurajXwb+CG0ZNJJmb52bXAnAVrVe2uqq2nlFKSpJ6zYJUkaXH9DnhLkncn2ZlkOzCVZEmSu5NMJflTksuTnA18HdjU9NBuSrI0yV1J/tBc9yGA5n4/b95/tbnm0SR/TWIhK0k6I7gPqyRJiyTJWcD7gV82Te8EVlfVgSS3AFTVxUkuAh4CLgS+DKyrqhube3wL+E1VXZ/ktcCTSR4Z87iLgMuBZcD+JD+oqpcX8deTJGnR2cMqSdLCOyfJXmA38Dfgzqb9yao60LzfANwLUFV/AZ5mULBOdwXwxeZ+jwJLgJVjrvtFVR2tqoPAs8AbFuQ3kSSpQ/awSpK08I5U1drRhiQAo5uuZ573CvCRqto/7X7TC9KjI++P4We8JOkMYA+rJEndeAzYDJDkQga9pvuBQwyG9Q79CvhMmoo3ydtbzilJUmcsWCVJ6sb3gYkkU8AO4BNVdRTYCbx1uOgS8A3g1cBTSfY1x5Ik/V9IVXWdQZIkSZKk/2IPqyRJkiSplyxYJUmSJEm9ZMEqSZIkSeolC1ZJkiRJUi9ZsEqSJEmSesmCVZIkSZLUSxaskiRJkqResmCVJEmSJPXSfwCY/HPiwOShUAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1152x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(figsize=(16, 10))\n",
    "sns.violinplot(data=df_res, scale='width', ax = axes)\n",
    "#sns.swarmplot(data=df_res, size=3, ax = axes)\n",
    "\n",
    "axes.set_title(f'Resolution distribution of {pathogen} structures')\n",
    "axes.yaxis.grid(True)\n",
    "axes.set_xlabel('Protein')\n",
    "axes.set_ylabel('Resolution')\n",
    "\n",
    "plt.xticks(rotation=90)\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.7.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}