3. Tabula Sapiens Specificity Metrics.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "8423dcf7",
   "metadata": {},
   "source": [
    "# 1. Pre-requisites\n",
    "## 1.1 Import packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "c2368ab5",
   "metadata": {},
   "outputs": [],
   "source": [
    "import gzip\n",
    "import json\n",
    "import warnings\n",
    "\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "import networkx as nx\n",
    "from networkx.drawing.nx_agraph import graphviz_layout\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import pronto\n",
    "from pyspark.conf import SparkConf\n",
    "from pyspark.sql import SparkSession\n",
    "from pyspark.sql.functions import arrays_zip, explode, map_keys, col\n",
    "import pyspark.sql.functions as f\n",
    "from scipy.stats import gaussian_kde\n",
    "import seaborn as sns\n",
    "from IPython import display"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "90f1fac9",
   "metadata": {},
   "source": [
    "## 1.2 Read in Tabula Sapiens median expression dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "636818dc",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>21.089552</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.993362</td>\n",
       "      <td>2.679963</td>\n",
       "      <td>44.480511</td>\n",
       "      <td>41.907898</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.362435</td>\n",
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       "      <th>mast cell</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>143.721497</td>\n",
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       "    <tr>\n",
       "      <th>myeloid cell</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>4.754292</td>\n",
       "      <td>5.817049</td>\n",
       "      <td>30.588665</td>\n",
       "      <td>NaN</td>\n",
       "      <td>13.951584</td>\n",
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       "      <td>3.565895</td>\n",
       "      <td>19.759800</td>\n",
       "      <td>29.607697</td>\n",
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       "      <th rowspan=\"5\" valign=\"top\">UBERON:0018707</th>\n",
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       "      <td>NaN</td>\n",
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       "      <th>smooth muscle cell</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>12.087816</td>\n",
       "      <td>2.875839</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>55.026272</td>\n",
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      "text/plain": [
       "                                            ENSG00000227232  ENSG00000238009  \\\n",
       "tissue         cell_type                                                       \n",
       "UBERON:0000016 b cell                                   NaN              NaN   \n",
       "               endothelial cell                         NaN              NaN   \n",
       "               mast cell                                NaN              NaN   \n",
       "               myeloid cell                             NaN              NaN   \n",
       "               pancreatic acinar cell                   NaN              NaN   \n",
       "...                                                     ...              ...   \n",
       "UBERON:0018707 plasma cell                              NaN              NaN   \n",
       "               plasmacytoid dendritic cell              NaN              NaN   \n",
       "               smooth muscle cell                       NaN              NaN   \n",
       "               t cell                                   NaN              NaN   \n",
       "               vein endothelial cell                    NaN              NaN   \n",
       "\n",
       "                                            ENSG00000233750  ENSG00000268903  \\\n",
       "tissue         cell_type                                                       \n",
       "UBERON:0000016 b cell                                   NaN              NaN   \n",
       "               endothelial cell                         NaN              NaN   \n",
       "               mast cell                                NaN              NaN   \n",
       "               myeloid cell                             NaN              NaN   \n",
       "               pancreatic acinar cell                   NaN              NaN   \n",
       "...                                                     ...              ...   \n",
       "UBERON:0018707 plasma cell                              NaN              NaN   \n",
       "               plasmacytoid dendritic cell              NaN              NaN   \n",
       "               smooth muscle cell                       NaN              NaN   \n",
       "               t cell                                   NaN              NaN   \n",
       "               vein endothelial cell                    NaN              NaN   \n",
       "\n",
       "                                            ENSG00000269981  ENSG00000241860  \\\n",
       "tissue         cell_type                                                       \n",
       "UBERON:0000016 b cell                                   NaN              NaN   \n",
       "               endothelial cell                         NaN              NaN   \n",
       "               mast cell                                NaN              NaN   \n",
       "               myeloid cell                             NaN              NaN   \n",
       "               pancreatic acinar cell                   NaN              NaN   \n",
       "...                                                     ...              ...   \n",
       "UBERON:0018707 plasma cell                              NaN              NaN   \n",
       "               plasmacytoid dendritic cell              NaN              NaN   \n",
       "               smooth muscle cell                       NaN              NaN   \n",
       "               t cell                                   NaN              NaN   \n",
       "               vein endothelial cell                    NaN              NaN   \n",
       "\n",
       "                                            ENSG00000241599  ENSG00000279457  \\\n",
       "tissue         cell_type                                                       \n",
       "UBERON:0000016 b cell                                   NaN              NaN   \n",
       "               endothelial cell                         NaN              NaN   \n",
       "               mast cell                                NaN              NaN   \n",
       "               myeloid cell                             NaN              NaN   \n",
       "               pancreatic acinar cell                   NaN              NaN   \n",
       "...                                                     ...              ...   \n",
       "UBERON:0018707 plasma cell                              NaN              NaN   \n",
       "               plasmacytoid dendritic cell              NaN              NaN   \n",
       "               smooth muscle cell                       NaN              NaN   \n",
       "               t cell                                   NaN              NaN   \n",
       "               vein endothelial cell                    NaN              NaN   \n",
       "\n",
       "                                            ENSG00000228463  ENSG00000237094  \\\n",
       "tissue         cell_type                                                       \n",
       "UBERON:0000016 b cell                                   NaN              NaN   \n",
       "               endothelial cell                         NaN              NaN   \n",
       "               mast cell                                NaN              NaN   \n",
       "               myeloid cell                             NaN              NaN   \n",
       "               pancreatic acinar cell                   NaN              NaN   \n",
       "...                                                     ...              ...   \n",
       "UBERON:0018707 plasma cell                              NaN              NaN   \n",
       "               plasmacytoid dendritic cell              NaN              NaN   \n",
       "               smooth muscle cell                       NaN              NaN   \n",
       "               t cell                                   NaN              NaN   \n",
       "               vein endothelial cell                    NaN              NaN   \n",
       "\n",
       "                                            ...  ENSG00000198886  \\\n",
       "tissue         cell_type                    ...                    \n",
       "UBERON:0000016 b cell                       ...        19.339554   \n",
       "               endothelial cell             ...        21.089552   \n",
       "               mast cell                    ...        26.537643   \n",
       "               myeloid cell                 ...        19.736168   \n",
       "               pancreatic acinar cell       ...        20.173513   \n",
       "...                                         ...              ...   \n",
       "UBERON:0018707 plasma cell                  ...        10.436072   \n",
       "               plasmacytoid dendritic cell  ...        32.792404   \n",
       "               smooth muscle cell           ...        59.357460   \n",
       "               t cell                       ...        28.703005   \n",
       "               vein endothelial cell        ...        28.743824   \n",
       "\n",
       "                                            ENSG00000210176  ENSG00000210184  \\\n",
       "tissue         cell_type                                                       \n",
       "UBERON:0000016 b cell                                   NaN              NaN   \n",
       "               endothelial cell                         NaN              NaN   \n",
       "               mast cell                                NaN              NaN   \n",
       "               myeloid cell                             NaN              NaN   \n",
       "               pancreatic acinar cell                   NaN              NaN   \n",
       "...                                                     ...              ...   \n",
       "UBERON:0018707 plasma cell                              NaN              NaN   \n",
       "               plasmacytoid dendritic cell              NaN              NaN   \n",
       "               smooth muscle cell                       NaN         3.291515   \n",
       "               t cell                            161.340042        19.775871   \n",
       "               vein endothelial cell                    NaN              NaN   \n",
       "\n",
       "                                            ENSG00000210191  ENSG00000198786  \\\n",
       "tissue         cell_type                                                       \n",
       "UBERON:0000016 b cell                                   NaN         5.395704   \n",
       "               endothelial cell                         NaN         8.993362   \n",
       "               mast cell                                NaN        13.669518   \n",
       "               myeloid cell                             NaN         7.315608   \n",
       "               pancreatic acinar cell                   NaN         4.997244   \n",
       "...                                                     ...              ...   \n",
       "UBERON:0018707 plasma cell                              NaN         3.812207   \n",
       "               plasmacytoid dendritic cell              NaN        10.851281   \n",
       "               smooth muscle cell                       NaN        19.427898   \n",
       "               t cell                                   NaN        10.623112   \n",
       "               vein endothelial cell                    NaN        12.842116   \n",
       "\n",
       "                                            ENSG00000198695  ENSG00000210194  \\\n",
       "tissue         cell_type                                                       \n",
       "UBERON:0000016 b cell                              3.489092        13.395752   \n",
       "               endothelial cell                    2.679963        44.480511   \n",
       "               mast cell                           4.014368              NaN   \n",
       "               myeloid cell                        4.754292         5.817049   \n",
       "               pancreatic acinar cell              3.565895        19.759800   \n",
       "...                                                     ...              ...   \n",
       "UBERON:0018707 plasma cell                         1.975962         1.906330   \n",
       "               plasmacytoid dendritic cell              NaN              NaN   \n",
       "               smooth muscle cell                  3.201281         6.694295   \n",
       "               t cell                              4.768910        36.772465   \n",
       "               vein endothelial cell               3.532256         6.673464   \n",
       "\n",
       "                                            ENSG00000198727  ENSG00000210195  \\\n",
       "tissue         cell_type                                                       \n",
       "UBERON:0000016 b cell                             40.021950              NaN   \n",
       "               endothelial cell                   41.907898              NaN   \n",
       "               mast cell                          60.740822              NaN   \n",
       "               myeloid cell                       30.588665              NaN   \n",
       "               pancreatic acinar cell             29.607697              NaN   \n",
       "...                                                     ...              ...   \n",
       "UBERON:0018707 plasma cell                        11.519205              NaN   \n",
       "               plasmacytoid dendritic cell        26.677158              NaN   \n",
       "               smooth muscle cell                 88.981812        12.087816   \n",
       "               t cell                             35.843376       359.533264   \n",
       "               vein endothelial cell              55.026272         5.575330   \n",
       "\n",
       "                                            ENSG00000210196  \n",
       "tissue         cell_type                                     \n",
       "UBERON:0000016 b cell                             12.686798  \n",
       "               endothelial cell                    6.362435  \n",
       "               mast cell                         143.721497  \n",
       "               myeloid cell                       13.951584  \n",
       "               pancreatic acinar cell              3.796115  \n",
       "...                                                     ...  \n",
       "UBERON:0018707 plasma cell                        17.227077  \n",
       "               plasmacytoid dendritic cell              NaN  \n",
       "               smooth muscle cell                  2.875839  \n",
       "               t cell                              6.805990  \n",
       "               vein endothelial cell              13.300036  \n",
       "\n",
       "[769 rows x 39431 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tabula_sapiens = pd.read_pickle('final_tabula_adjusted_median_drop_na.pkl', compression = 'gzip')\n",
    "\n",
    "tabula_sapiens"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1e03debc",
   "metadata": {},
   "source": [
    "## 1.3 Reformat Tabula Sapiens for specificity analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "c532a6fd",
   "metadata": {},
   "outputs": [],
   "source": [
    "#reset index\n",
    "tabula_sapiens = tabula_sapiens.reset_index()\n",
    "\n",
    "#merge together strings from tissue and cell type columns\n",
    "tissue_cell_type = tabula_sapiens['tissue'].astype(str) + '/' + tabula_sapiens['cell_type'].astype(str)\n",
    "\n",
    "#insert new column into dataframe\n",
    "tabula_sapiens.insert(0, 'tissue_cell_type', tissue_cell_type)\n",
    "\n",
    "#remove previous tissue and cell type columns from dataframe\n",
    "tabula_sapiens = tabula_sapiens.drop(columns = ['tissue', 'cell_type'])\n",
    "\n",
    "#set tissue_cell_type as index\n",
    "tabula_sapiens = tabula_sapiens.set_index('tissue_cell_type')\n",
    "\n",
    "#tranpose table so genes are rows\n",
    "tabula_sapiens = tabula_sapiens.T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "f5fdfd9c",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>tissue_cell_type</th>\n",
       "      <th>UBERON:0000016/b cell</th>\n",
       "      <th>UBERON:0000016/endothelial cell</th>\n",
       "      <th>UBERON:0000016/mast cell</th>\n",
       "      <th>UBERON:0000016/myeloid cell</th>\n",
       "      <th>UBERON:0000016/pancreatic acinar cell</th>\n",
       "      <th>UBERON:0000016/pancreatic alpha cell</th>\n",
       "      <th>UBERON:0000016/pancreatic beta cell</th>\n",
       "      <th>UBERON:0000016/pancreatic ductal cell</th>\n",
       "      <th>UBERON:0000016/pancreatic pp cell</th>\n",
       "      <th>UBERON:0000016/pancreatic stellate cell</th>\n",
       "      <th>...</th>\n",
       "      <th>UBERON:0018707/macrophage</th>\n",
       "      <th>UBERON:0018707/mast cell</th>\n",
       "      <th>UBERON:0018707/myofibroblast cell</th>\n",
       "      <th>UBERON:0018707/nk cell</th>\n",
       "      <th>UBERON:0018707/pericyte cell</th>\n",
       "      <th>UBERON:0018707/plasma cell</th>\n",
       "      <th>UBERON:0018707/plasmacytoid dendritic cell</th>\n",
       "      <th>UBERON:0018707/smooth muscle cell</th>\n",
       "      <th>UBERON:0018707/t cell</th>\n",
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       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>15937.101562</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>ENSG00000268903</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSG00000198695</th>\n",
       "      <td>3.489092</td>\n",
       "      <td>2.679963</td>\n",
       "      <td>4.014368</td>\n",
       "      <td>4.754292</td>\n",
       "      <td>3.565895</td>\n",
       "      <td>10.730105</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.254530</td>\n",
       "      <td>20.681417</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>2.864313</td>\n",
       "      <td>5.147031</td>\n",
       "      <td>2.396611</td>\n",
       "      <td>4.926093</td>\n",
       "      <td>2.856090</td>\n",
       "      <td>1.975962</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.201281</td>\n",
       "      <td>4.768910</td>\n",
       "      <td>3.532256</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSG00000210194</th>\n",
       "      <td>13.395752</td>\n",
       "      <td>44.480511</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.817049</td>\n",
       "      <td>19.759800</td>\n",
       "      <td>75.670105</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.384467</td>\n",
       "      <td>21.783213</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>10.472430</td>\n",
       "      <td>2.486131</td>\n",
       "      <td>11.702745</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.794537</td>\n",
       "      <td>1.906330</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.694295</td>\n",
       "      <td>36.772465</td>\n",
       "      <td>6.673464</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSG00000198727</th>\n",
       "      <td>40.021950</td>\n",
       "      <td>41.907898</td>\n",
       "      <td>60.740822</td>\n",
       "      <td>30.588665</td>\n",
       "      <td>29.607697</td>\n",
       "      <td>66.148064</td>\n",
       "      <td>91.813042</td>\n",
       "      <td>147.611801</td>\n",
       "      <td>144.078552</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>31.003387</td>\n",
       "      <td>79.626801</td>\n",
       "      <td>42.324898</td>\n",
       "      <td>29.035542</td>\n",
       "      <td>64.616829</td>\n",
       "      <td>11.519205</td>\n",
       "      <td>26.677158</td>\n",
       "      <td>88.981812</td>\n",
       "      <td>35.843376</td>\n",
       "      <td>55.026272</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSG00000210195</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.958794</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>33.565372</td>\n",
       "      <td>NaN</td>\n",
       "      <td>27.510916</td>\n",
       "      <td>8.583153</td>\n",
       "      <td>26.636036</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12.087816</td>\n",
       "      <td>359.533264</td>\n",
       "      <td>5.575330</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSG00000210196</th>\n",
       "      <td>12.686798</td>\n",
       "      <td>6.362435</td>\n",
       "      <td>143.721497</td>\n",
       "      <td>13.951584</td>\n",
       "      <td>3.796115</td>\n",
       "      <td>52.290524</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.081991</td>\n",
       "      <td>38.245033</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>5.277477</td>\n",
       "      <td>14.290271</td>\n",
       "      <td>3.949566</td>\n",
       "      <td>5.115552</td>\n",
       "      <td>5.220804</td>\n",
       "      <td>17.227077</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.875839</td>\n",
       "      <td>6.805990</td>\n",
       "      <td>13.300036</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>39431 rows × 769 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "tissue_cell_type  UBERON:0000016/b cell  UBERON:0000016/endothelial cell  \\\n",
       "ENSG00000227232                     NaN                              NaN   \n",
       "ENSG00000238009                     NaN                              NaN   \n",
       "ENSG00000233750                     NaN                              NaN   \n",
       "ENSG00000268903                     NaN                              NaN   \n",
       "ENSG00000269981                     NaN                              NaN   \n",
       "...                                 ...                              ...   \n",
       "ENSG00000198695                3.489092                         2.679963   \n",
       "ENSG00000210194               13.395752                        44.480511   \n",
       "ENSG00000198727               40.021950                        41.907898   \n",
       "ENSG00000210195                     NaN                              NaN   \n",
       "ENSG00000210196               12.686798                         6.362435   \n",
       "\n",
       "tissue_cell_type  UBERON:0000016/mast cell  UBERON:0000016/myeloid cell  \\\n",
       "ENSG00000227232                        NaN                          NaN   \n",
       "ENSG00000238009                        NaN                          NaN   \n",
       "ENSG00000233750                        NaN                          NaN   \n",
       "ENSG00000268903                        NaN                          NaN   \n",
       "ENSG00000269981                        NaN                          NaN   \n",
       "...                                    ...                          ...   \n",
       "ENSG00000198695                   4.014368                     4.754292   \n",
       "ENSG00000210194                        NaN                     5.817049   \n",
       "ENSG00000198727                  60.740822                    30.588665   \n",
       "ENSG00000210195                        NaN                          NaN   \n",
       "ENSG00000210196                 143.721497                    13.951584   \n",
       "\n",
       "tissue_cell_type  UBERON:0000016/pancreatic acinar cell  \\\n",
       "ENSG00000227232                                     NaN   \n",
       "ENSG00000238009                                     NaN   \n",
       "ENSG00000233750                                     NaN   \n",
       "ENSG00000268903                                     NaN   \n",
       "ENSG00000269981                                     NaN   \n",
       "...                                                 ...   \n",
       "ENSG00000198695                                3.565895   \n",
       "ENSG00000210194                               19.759800   \n",
       "ENSG00000198727                               29.607697   \n",
       "ENSG00000210195                                     NaN   \n",
       "ENSG00000210196                                3.796115   \n",
       "\n",
       "tissue_cell_type  UBERON:0000016/pancreatic alpha cell  \\\n",
       "ENSG00000227232                                    NaN   \n",
       "ENSG00000238009                                    NaN   \n",
       "ENSG00000233750                                    NaN   \n",
       "ENSG00000268903                                    NaN   \n",
       "ENSG00000269981                                    NaN   \n",
       "...                                                ...   \n",
       "ENSG00000198695                              10.730105   \n",
       "ENSG00000210194                              75.670105   \n",
       "ENSG00000198727                              66.148064   \n",
       "ENSG00000210195                                    NaN   \n",
       "ENSG00000210196                              52.290524   \n",
       "\n",
       "tissue_cell_type  UBERON:0000016/pancreatic beta cell  \\\n",
       "ENSG00000227232                                   NaN   \n",
       "ENSG00000238009                                   NaN   \n",
       "ENSG00000233750                                   NaN   \n",
       "ENSG00000268903                                   NaN   \n",
       "ENSG00000269981                                   NaN   \n",
       "...                                               ...   \n",
       "ENSG00000198695                                   NaN   \n",
       "ENSG00000210194                                   NaN   \n",
       "ENSG00000198727                             91.813042   \n",
       "ENSG00000210195                                   NaN   \n",
       "ENSG00000210196                                   NaN   \n",
       "\n",
       "tissue_cell_type  UBERON:0000016/pancreatic ductal cell  \\\n",
       "ENSG00000227232                                     NaN   \n",
       "ENSG00000238009                                     NaN   \n",
       "ENSG00000233750                                     NaN   \n",
       "ENSG00000268903                                     NaN   \n",
       "ENSG00000269981                                     NaN   \n",
       "...                                                 ...   \n",
       "ENSG00000198695                                3.254530   \n",
       "ENSG00000210194                                4.384467   \n",
       "ENSG00000198727                              147.611801   \n",
       "ENSG00000210195                                9.958794   \n",
       "ENSG00000210196                                4.081991   \n",
       "\n",
       "tissue_cell_type  UBERON:0000016/pancreatic pp cell  \\\n",
       "ENSG00000227232                                 NaN   \n",
       "ENSG00000238009                                 NaN   \n",
       "ENSG00000233750                                 NaN   \n",
       "ENSG00000268903                                 NaN   \n",
       "ENSG00000269981                                 NaN   \n",
       "...                                             ...   \n",
       "ENSG00000198695                           20.681417   \n",
       "ENSG00000210194                           21.783213   \n",
       "ENSG00000198727                          144.078552   \n",
       "ENSG00000210195                                 NaN   \n",
       "ENSG00000210196                           38.245033   \n",
       "\n",
       "tissue_cell_type  UBERON:0000016/pancreatic stellate cell  ...  \\\n",
       "ENSG00000227232                                       NaN  ...   \n",
       "ENSG00000238009                                       NaN  ...   \n",
       "ENSG00000233750                                       NaN  ...   \n",
       "ENSG00000268903                                       NaN  ...   \n",
       "ENSG00000269981                                       NaN  ...   \n",
       "...                                                   ...  ...   \n",
       "ENSG00000198695                                       NaN  ...   \n",
       "ENSG00000210194                                       NaN  ...   \n",
       "ENSG00000198727                                       NaN  ...   \n",
       "ENSG00000210195                                       NaN  ...   \n",
       "ENSG00000210196                                       NaN  ...   \n",
       "\n",
       "tissue_cell_type  UBERON:0018707/macrophage  UBERON:0018707/mast cell  \\\n",
       "ENSG00000227232                  182.832504                       NaN   \n",
       "ENSG00000238009                22025.464844                       NaN   \n",
       "ENSG00000233750                15937.101562                       NaN   \n",
       "ENSG00000268903                  156.842438                       NaN   \n",
       "ENSG00000269981                         NaN                       NaN   \n",
       "...                                     ...                       ...   \n",
       "ENSG00000198695                    2.864313                  5.147031   \n",
       "ENSG00000210194                   10.472430                  2.486131   \n",
       "ENSG00000198727                   31.003387                 79.626801   \n",
       "ENSG00000210195                   33.565372                       NaN   \n",
       "ENSG00000210196                    5.277477                 14.290271   \n",
       "\n",
       "tissue_cell_type  UBERON:0018707/myofibroblast cell  UBERON:0018707/nk cell  \\\n",
       "ENSG00000227232                                 NaN                     NaN   \n",
       "ENSG00000238009                                 NaN                     NaN   \n",
       "ENSG00000233750                                 NaN                     NaN   \n",
       "ENSG00000268903                                 NaN                     NaN   \n",
       "ENSG00000269981                                 NaN                     NaN   \n",
       "...                                             ...                     ...   \n",
       "ENSG00000198695                            2.396611                4.926093   \n",
       "ENSG00000210194                           11.702745                     NaN   \n",
       "ENSG00000198727                           42.324898               29.035542   \n",
       "ENSG00000210195                           27.510916                8.583153   \n",
       "ENSG00000210196                            3.949566                5.115552   \n",
       "\n",
       "tissue_cell_type  UBERON:0018707/pericyte cell  UBERON:0018707/plasma cell  \\\n",
       "ENSG00000227232                            NaN                         NaN   \n",
       "ENSG00000238009                            NaN                         NaN   \n",
       "ENSG00000233750                            NaN                         NaN   \n",
       "ENSG00000268903                            NaN                         NaN   \n",
       "ENSG00000269981                            NaN                         NaN   \n",
       "...                                        ...                         ...   \n",
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       "ENSG00000210196                       5.220804                   17.227077   \n",
       "\n",
       "tissue_cell_type  UBERON:0018707/plasmacytoid dendritic cell  \\\n",
       "ENSG00000227232                                          NaN   \n",
       "ENSG00000238009                                          NaN   \n",
       "ENSG00000233750                                          NaN   \n",
       "ENSG00000268903                                          NaN   \n",
       "ENSG00000269981                                          NaN   \n",
       "...                                                      ...   \n",
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       "ENSG00000210194                                          NaN   \n",
       "ENSG00000198727                                    26.677158   \n",
       "ENSG00000210195                                          NaN   \n",
       "ENSG00000210196                                          NaN   \n",
       "\n",
       "tissue_cell_type  UBERON:0018707/smooth muscle cell  UBERON:0018707/t cell  \\\n",
       "ENSG00000227232                                 NaN                    NaN   \n",
       "ENSG00000238009                                 NaN                    NaN   \n",
       "ENSG00000233750                                 NaN                    NaN   \n",
       "ENSG00000268903                                 NaN                    NaN   \n",
       "ENSG00000269981                                 NaN                    NaN   \n",
       "...                                             ...                    ...   \n",
       "ENSG00000198695                            3.201281               4.768910   \n",
       "ENSG00000210194                            6.694295              36.772465   \n",
       "ENSG00000198727                           88.981812              35.843376   \n",
       "ENSG00000210195                           12.087816             359.533264   \n",
       "ENSG00000210196                            2.875839               6.805990   \n",
       "\n",
       "tissue_cell_type  UBERON:0018707/vein endothelial cell  \n",
       "ENSG00000227232                                    NaN  \n",
       "ENSG00000238009                                    NaN  \n",
       "ENSG00000233750                                    NaN  \n",
       "ENSG00000268903                                    NaN  \n",
       "ENSG00000269981                                    NaN  \n",
       "...                                                ...  \n",
       "ENSG00000198695                               3.532256  \n",
       "ENSG00000210194                               6.673464  \n",
       "ENSG00000198727                              55.026272  \n",
       "ENSG00000210195                               5.575330  \n",
       "ENSG00000210196                              13.300036  \n",
       "\n",
       "[39431 rows x 769 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tabula_sapiens"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "1e7965ab",
   "metadata": {},
   "outputs": [],
   "source": [
    "tabula_sapiens.to_pickle('tabula_gini_input.pkl', compression = 'gzip')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b2ba2852",
   "metadata": {},
   "source": [
    "## 1.4 Read in GTEx data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "48b8bfaf",
   "metadata": {},
   "outputs": [],
   "source": [
    "GTEx = pd.read_pickle('uberon_id_GTEx_gene_ensemblid.pkl', compression = 'gzip')\n",
    "\n",
    "#transpose dataframe to match Tabula Sapiens\n",
    "GTEx = GTEx.T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "c1b6dc23",
   "metadata": {},
   "outputs": [
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       "                 UBERON:0000473  UBERON:0002046  UBERON:0000995  \\\n",
       "ENSG00000223972        0.166403        0.000000        0.000000   \n",
       "ENSG00000227232        4.702530        6.272550        7.190010   \n",
       "ENSG00000243485        0.054223        0.000000        0.000000   \n",
       "ENSG00000268020        0.000000        0.000000        0.000000   \n",
       "ENSG00000240361        0.053578        0.039058        0.016333   \n",
       "...                         ...             ...             ...   \n",
       "ENSG00000198695     1676.590000     2520.410000     3644.000000   \n",
       "ENSG00000210194        2.871770        5.795670       10.370100   \n",
       "ENSG00000198727    15337.800000    19868.500000    15025.000000   \n",
       "ENSG00000210195        0.443781        0.000000        0.000000   \n",
       "ENSG00000210196        1.057760        0.982817        0.808096   \n",
       "\n",
       "                 UBERON:0000996  UBERON:0000178  \n",
       "ENSG00000223972        0.000000         0.00000  \n",
       "ENSG00000227232        5.745540         2.64743  \n",
       "ENSG00000243485        0.000000         0.00000  \n",
       "ENSG00000268020        0.000000         0.00000  \n",
       "ENSG00000240361        0.027464         0.00000  \n",
       "...                         ...             ...  \n",
       "ENSG00000198695     2237.430000       960.29100  \n",
       "ENSG00000210194        5.724510         3.49736  \n",
       "ENSG00000198727    12803.400000      2305.49000  \n",
       "ENSG00000210195        0.000000         0.00000  \n",
       "ENSG00000210196        0.737385         0.00000  \n",
       "\n",
       "[44219 rows x 50 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "GTEx"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "955935a8",
   "metadata": {},
   "source": [
    "# 2. Gini Coefficient\n",
    "## 2.1 Define Gini coefficient function\n",
    "\n",
    "[Gini coefficient](https://en.wikipedia.org/wiki/Gini_coefficient) is a metric that originates from economics and describes the level of inequality between factors. It ranges from 0 (perfect equality) to 1 (maximum inequality). The application to this dataset could reveal differences in median expression across our geneset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "c4740ecd",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Adapted from https://github.com/oliviaguest/gini/blob/master/gini.py\n",
    "def gini(row):\n",
    "    \"\"\"Calculate the Gini coefficient of a Pandas row.\"\"\"\n",
    "    assert min(row) >= 0\n",
    "    if (max(row) == 0):\n",
    "        return np.nan\n",
    "    # # Values cannot be 0:  (disabled because zeroes are filtered out already)\n",
    "    # array += 0.0000001\n",
    "    # Values must be sorted:\n",
    "    array = np.sort(row)\n",
    "    # Index per array element:\n",
    "    index = np.arange(1,array.shape[0]+1)\n",
    "    # Number of array elements:\n",
    "    n = array.shape[0]\n",
    "    # Gini coefficient:\n",
    "    return ((np.sum((2 * index - n  - 1) * array)) / (n * np.sum(array)))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "121011de",
   "metadata": {},
   "source": [
    "## 2.2 Calculate Gini coefficient for each gene in Tabula Sapiens"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "18906554",
   "metadata": {},
   "outputs": [],
   "source": [
    "tabula_sapiens = pd.read_pickle('tabula_gini_input.pkl', compression = 'gzip')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "58f6ea03",
   "metadata": {},
   "outputs": [],
   "source": [
    "#define the genes as index for the gini_df\n",
    "gini_df = pd.DataFrame(tabula_sapiens.index)\n",
    "\n",
    "#rename column as ensemblid and set as index\n",
    "gini_df = gini_df.rename(columns = {0: 'ensemblid'})\n",
    "gini_df = gini_df.set_index('ensemblid')\n",
    "\n",
    "#fill in NaN with zeros and apply gini function across each gene\n",
    "gini_df['gini'] = tabula_sapiens.fillna(0).apply(gini, axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "4c0a148b",
   "metadata": {
    "scrolled": false
   },
   "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>gini</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ensemblid</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>ENSG00000254081</th>\n",
       "      <td>0.998700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSG00000234569</th>\n",
       "      <td>0.998700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSG00000226387</th>\n",
       "      <td>0.998700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSG00000215002</th>\n",
       "      <td>0.998700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSG00000203434</th>\n",
       "      <td>0.998700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSG00000107223</th>\n",
       "      <td>0.369096</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSG00000122566</th>\n",
       "      <td>0.368352</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSG00000165119</th>\n",
       "      <td>0.365086</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSG00000198258</th>\n",
       "      <td>0.364446</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSG00000251562</th>\n",
       "      <td>0.324208</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>39431 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                     gini\n",
       "ensemblid                \n",
       "ENSG00000254081  0.998700\n",
       "ENSG00000234569  0.998700\n",
       "ENSG00000226387  0.998700\n",
       "ENSG00000215002  0.998700\n",
       "ENSG00000203434  0.998700\n",
       "...                   ...\n",
       "ENSG00000107223  0.369096\n",
       "ENSG00000122566  0.368352\n",
       "ENSG00000165119  0.365086\n",
       "ENSG00000198258  0.364446\n",
       "ENSG00000251562  0.324208\n",
       "\n",
       "[39431 rows x 1 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#sort Gini values starting with the maximum values\n",
    "gini_df.sort_values(by = 'gini', ascending = False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "37a2748c",
   "metadata": {},
   "source": [
    "Here, it appears that there is a maximum for the Gini coefficient of 0.998700 for our analyses."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "216e50d3",
   "metadata": {},
   "source": [
    "## 2.3 Plot histogram of Gini coefficient values for all genes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "00db0013",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='gini', ylabel='Count'>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.histplot(data = gini_df, x = 'gini', bins = 200)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0b3fcea5",
   "metadata": {},
   "source": [
    "We see a spike of highly specifically expressed genes. However, many genes will have a low total expression which is close to zero but not exactly zero meaning they wouldn't be filtered out by the zero-expression filter step. These genes will often erroneously produce very high Gini coefficients by chance; in order to check, the Gini coefficients vs the maximum median values for the gene can be plotted."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d0bfcc5f",
   "metadata": {},
   "source": [
    "## 2.4 Gini coefficients vs adjusted median expression (max values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "1d9031c9",
   "metadata": {},
   "outputs": [],
   "source": [
    "def gini_vs_expression(x, y):\n",
    "    # From: https://stackoverflow.com/a/20107592\n",
    "    xy = np.vstack([x, y])\n",
    "    z = gaussian_kde(xy)(xy)\n",
    "    plt.set_cmap('jet')\n",
    "    fig, ax = plt.subplots()\n",
    "    ax.scatter(x, y, c=z, s=2)\n",
    "    ax.set_xlabel('log10 expression across all tissues')\n",
    "    ax.set_ylabel('Gini coefficient of gene expression')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "a1b3da58",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "gini_df['maximum_adjusted_median_expression'] = tabula_sapiens.max(axis = 1)\n",
    "gini_df['log_max'] = np.log10(gini_df.maximum_adjusted_median_expression)\n",
    "gini_vs_expression(gini_df.log_max, gini_df.gini)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d61dfbfd",
   "metadata": {},
   "source": [
    "We don't need a threshold here as we already have used a minimum expression threshold of 1 CPM to calculate the median expressions."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1a602fbe",
   "metadata": {},
   "source": [
    "# 3. Human Protein Atlas specificity metrics\n",
    "\n",
    "The HPA specificity metrics have two dimensions: **specificity** and **distribution**.\n",
    "\n",
    "## 3.1 HPA specificity categories\n",
    "\n",
    "| Category        | Description                                                                                                                                                                                |\n",
    "|:----------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|\n",
    "| Enriched        | nTPM in a particular tissue/region/cell type at least four times any other tissue/region/cell type                                                                                         |\n",
    "| Group enriched  | nTPM in a group (of 2-5 tissues, brain regions, single cell types or cell lines, or 2-10 blood cell types) at least four times any other tissue/region/cell line/blood cell type/cell type |\n",
    "| Enhanced        | nTPM in a one or several (1-5 tissues, brain regions or cell lines, or 1-10 immune cell types or single cell types) at least four times the mean of other tissue/region/cell types         |\n",
    "| Low specificity | nTPM ≥ 1 in at least one tissue/region/cell type but not elevated in any tissue/region/cell type                                                                                           |\n",
    "| Not detected    | nTPM < 1 in all tissue/region/cell types                                                                                                                                                   |\n",
    "\n",
    "## 3.2 HPA distribution categories\n",
    "\n",
    "| Category           | Description                                                                     |\n",
    "|:-------------------|:--------------------------------------------------------------------------------|\n",
    "| Detected in single | Detected in a single tissue/region/cell type                                    |\n",
    "| Detected in some   | Detected in more than one but less than one third of tissues/regions/cell types |\n",
    "| Detected in many   | Detected in at least a third but not all tissues/regions/cell types             |\n",
    "| Detected in all    | Detected in all tissues/regions/cell types                                      |\n",
    "| Not detected       | nTPM < 1 in all tissues/regions/cell types                                      |\n",
    "\n",
    "## 3.3 Apply single-cell HPA definitions to Tabula Sapiens data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "425978a0",
   "metadata": {},
   "outputs": [],
   "source": [
    "THRESHOLD = 1.0\n",
    "\n",
    "def hpa_specificity(row):\n",
    "    expr = sorted(row)\n",
    "    if expr[-1] < THRESHOLD:\n",
    "        return 'Not detected'\n",
    "    if expr[-2] == 0 or expr[-1]/expr[-2] >= 4.0:\n",
    "        return 'Individually enriched'\n",
    "    for i in range(2, 6):\n",
    "        if expr[-i-1] == 0 or expr[-i] / expr[-i-1] >= 4.0:\n",
    "            return 'Group enriched'\n",
    "    mean = sum(expr) / len(row)\n",
    "    if 1 <= sum([e / mean >= 4.0 for e in expr]) <= 10:\n",
    "        return 'Individually enhanced'\n",
    "    return 'Low specificity'\n",
    "\n",
    "def hpa_distribution(row):\n",
    "    expr = sorted(row)\n",
    "    if expr[-1] < THRESHOLD:\n",
    "        return 'Not detected'\n",
    "    num_detected = sum([e > THRESHOLD for e in expr])\n",
    "    if num_detected == 1:\n",
    "        return 'Detected in single'\n",
    "    if num_detected < len(row) / 3:\n",
    "        return 'Detected in some'\n",
    "    if num_detected < len(row):\n",
    "        return 'Detected in many'\n",
    "    return 'Detected in all'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "e9927932",
   "metadata": {},
   "outputs": [],
   "source": [
    "#fill NaNs with zeros to account for undetected expression in cell types\n",
    "gini_df['hpa_specificity'] = tabula_sapiens.fillna(0).apply(hpa_specificity, axis = 1)\n",
    "gini_df['hpa_distribution'] = tabula_sapiens.fillna(0).apply(hpa_distribution, axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "3d803cbd",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>gini</th>\n",
       "      <th>maximum_adjusted_median_expression</th>\n",
       "      <th>log_max</th>\n",
       "      <th>hpa_specificity</th>\n",
       "      <th>hpa_distribution</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ensemblid</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>ENSG00000227232</th>\n",
       "      <td>0.994666</td>\n",
       "      <td>22025.464844</td>\n",
       "      <td>4.342926</td>\n",
       "      <td>Group enriched</td>\n",
       "      <td>Detected in some</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSG00000238009</th>\n",
       "      <td>0.960035</td>\n",
       "      <td>22025.464844</td>\n",
       "      <td>4.342926</td>\n",
       "      <td>Low specificity</td>\n",
       "      <td>Detected in some</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSG00000233750</th>\n",
       "      <td>0.988349</td>\n",
       "      <td>22025.464844</td>\n",
       "      <td>4.342926</td>\n",
       "      <td>Individually enhanced</td>\n",
       "      <td>Detected in some</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSG00000268903</th>\n",
       "      <td>0.981275</td>\n",
       "      <td>22025.464844</td>\n",
       "      <td>4.342926</td>\n",
       "      <td>Low specificity</td>\n",
       "      <td>Detected in some</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSG00000269981</th>\n",
       "      <td>0.994798</td>\n",
       "      <td>22025.464844</td>\n",
       "      <td>4.342926</td>\n",
       "      <td>Group enriched</td>\n",
       "      <td>Detected in some</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSG00000198695</th>\n",
       "      <td>0.496340</td>\n",
       "      <td>60.438301</td>\n",
       "      <td>1.781312</td>\n",
       "      <td>Low specificity</td>\n",
       "      <td>Detected in many</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSG00000210194</th>\n",
       "      <td>0.841466</td>\n",
       "      <td>143.026062</td>\n",
       "      <td>2.155415</td>\n",
       "      <td>Low specificity</td>\n",
       "      <td>Detected in many</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSG00000198727</th>\n",
       "      <td>0.447636</td>\n",
       "      <td>485.283173</td>\n",
       "      <td>2.685995</td>\n",
       "      <td>Individually enhanced</td>\n",
       "      <td>Detected in many</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSG00000210195</th>\n",
       "      <td>0.914414</td>\n",
       "      <td>1245.440186</td>\n",
       "      <td>3.095323</td>\n",
       "      <td>Low specificity</td>\n",
       "      <td>Detected in some</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSG00000210196</th>\n",
       "      <td>0.789843</td>\n",
       "      <td>363.563232</td>\n",
       "      <td>2.560580</td>\n",
       "      <td>Low specificity</td>\n",
       "      <td>Detected in many</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>39431 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                     gini  maximum_adjusted_median_expression   log_max  \\\n",
       "ensemblid                                                                 \n",
       "ENSG00000227232  0.994666                        22025.464844  4.342926   \n",
       "ENSG00000238009  0.960035                        22025.464844  4.342926   \n",
       "ENSG00000233750  0.988349                        22025.464844  4.342926   \n",
       "ENSG00000268903  0.981275                        22025.464844  4.342926   \n",
       "ENSG00000269981  0.994798                        22025.464844  4.342926   \n",
       "...                   ...                                 ...       ...   \n",
       "ENSG00000198695  0.496340                           60.438301  1.781312   \n",
       "ENSG00000210194  0.841466                          143.026062  2.155415   \n",
       "ENSG00000198727  0.447636                          485.283173  2.685995   \n",
       "ENSG00000210195  0.914414                         1245.440186  3.095323   \n",
       "ENSG00000210196  0.789843                          363.563232  2.560580   \n",
       "\n",
       "                       hpa_specificity  hpa_distribution  \n",
       "ensemblid                                                 \n",
       "ENSG00000227232         Group enriched  Detected in some  \n",
       "ENSG00000238009        Low specificity  Detected in some  \n",
       "ENSG00000233750  Individually enhanced  Detected in some  \n",
       "ENSG00000268903        Low specificity  Detected in some  \n",
       "ENSG00000269981         Group enriched  Detected in some  \n",
       "...                                ...               ...  \n",
       "ENSG00000198695        Low specificity  Detected in many  \n",
       "ENSG00000210194        Low specificity  Detected in many  \n",
       "ENSG00000198727  Individually enhanced  Detected in many  \n",
       "ENSG00000210195        Low specificity  Detected in some  \n",
       "ENSG00000210196        Low specificity  Detected in many  \n",
       "\n",
       "[39431 rows x 5 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gini_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "d71242ad",
   "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>gini</th>\n",
       "      <th>maximum_adjusted_median_expression</th>\n",
       "      <th>log_max</th>\n",
       "      <th>hpa_specificity</th>\n",
       "      <th>hpa_distribution</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ensemblid</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>ENSG00000273443</th>\n",
       "      <td>0.998700</td>\n",
       "      <td>22025.464844</td>\n",
       "      <td>4.342926</td>\n",
       "      <td>Individually enriched</td>\n",
       "      <td>Detected in single</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSG00000256159</th>\n",
       "      <td>0.998700</td>\n",
       "      <td>22025.464844</td>\n",
       "      <td>4.342926</td>\n",
       "      <td>Individually enriched</td>\n",
       "      <td>Detected in single</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSG00000257756</th>\n",
       "      <td>0.998700</td>\n",
       "      <td>11192.734375</td>\n",
       "      <td>4.048936</td>\n",
       "      <td>Individually enriched</td>\n",
       "      <td>Detected in single</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSG00000256512</th>\n",
       "      <td>0.998700</td>\n",
       "      <td>22025.464844</td>\n",
       "      <td>4.342926</td>\n",
       "      <td>Individually enriched</td>\n",
       "      <td>Detected in single</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSG00000256226</th>\n",
       "      <td>0.998700</td>\n",
       "      <td>22025.464844</td>\n",
       "      <td>4.342926</td>\n",
       "      <td>Individually enriched</td>\n",
       "      <td>Detected in single</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSG00000108848</th>\n",
       "      <td>0.468295</td>\n",
       "      <td>157.542084</td>\n",
       "      <td>2.197397</td>\n",
       "      <td>Individually enriched</td>\n",
       "      <td>Detected in many</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSG00000124795</th>\n",
       "      <td>0.460936</td>\n",
       "      <td>292.550934</td>\n",
       "      <td>2.466202</td>\n",
       "      <td>Individually enriched</td>\n",
       "      <td>Detected in many</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSG00000164346</th>\n",
       "      <td>0.438628</td>\n",
       "      <td>87.399033</td>\n",
       "      <td>1.941507</td>\n",
       "      <td>Individually enriched</td>\n",
       "      <td>Detected in many</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSG00000119707</th>\n",
       "      <td>0.428070</td>\n",
       "      <td>113.630234</td>\n",
       "      <td>2.055494</td>\n",
       "      <td>Individually enriched</td>\n",
       "      <td>Detected in many</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSG00000141002</th>\n",
       "      <td>0.411006</td>\n",
       "      <td>75.779129</td>\n",
       "      <td>1.879550</td>\n",
       "      <td>Individually enriched</td>\n",
       "      <td>Detected in many</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>7547 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                     gini  maximum_adjusted_median_expression   log_max  \\\n",
       "ensemblid                                                                 \n",
       "ENSG00000273443  0.998700                        22025.464844  4.342926   \n",
       "ENSG00000256159  0.998700                        22025.464844  4.342926   \n",
       "ENSG00000257756  0.998700                        11192.734375  4.048936   \n",
       "ENSG00000256512  0.998700                        22025.464844  4.342926   \n",
       "ENSG00000256226  0.998700                        22025.464844  4.342926   \n",
       "...                   ...                                 ...       ...   \n",
       "ENSG00000108848  0.468295                          157.542084  2.197397   \n",
       "ENSG00000124795  0.460936                          292.550934  2.466202   \n",
       "ENSG00000164346  0.438628                           87.399033  1.941507   \n",
       "ENSG00000119707  0.428070                          113.630234  2.055494   \n",
       "ENSG00000141002  0.411006                           75.779129  1.879550   \n",
       "\n",
       "                       hpa_specificity    hpa_distribution  \n",
       "ensemblid                                                   \n",
       "ENSG00000273443  Individually enriched  Detected in single  \n",
       "ENSG00000256159  Individually enriched  Detected in single  \n",
       "ENSG00000257756  Individually enriched  Detected in single  \n",
       "ENSG00000256512  Individually enriched  Detected in single  \n",
       "ENSG00000256226  Individually enriched  Detected in single  \n",
       "...                                ...                 ...  \n",
       "ENSG00000108848  Individually enriched    Detected in many  \n",
       "ENSG00000124795  Individually enriched    Detected in many  \n",
       "ENSG00000164346  Individually enriched    Detected in many  \n",
       "ENSG00000119707  Individually enriched    Detected in many  \n",
       "ENSG00000141002  Individually enriched    Detected in many  \n",
       "\n",
       "[7547 rows x 5 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#genes that are highly specific to one cell type\n",
    "gini_df[gini_df.hpa_specificity == 'Individually enriched'].sort_values(by = 'gini', ascending = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "62723d81",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tissue_cell_type\n",
       "UBERON:0018707/bladder urothelial cell    22025.464844\n",
       "Name: ENSG00000273443, dtype: float32"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tabula_sapiens.loc['ENSG00000273443'].dropna()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b58801fe",
   "metadata": {},
   "source": [
    "Here we see the gene with the top Gini result for the single cell type enriched group is only detected in one cell type (bladder urothelial cell), as demonstrated by the hpa_distribution categorisation, which has the maximum expression of it (max threshold for Tabula Sapiens count data)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "fbc1499c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tissue_cell_type\n",
       "UBERON:0002108/plasma cell                          1.296758\n",
       "UBERON:0000017/plasma cell                          1.379401\n",
       "UBERON:0010032/keratinocyte                         1.493335\n",
       "UBERON:0010033/keratinocyte                         1.537262\n",
       "UBERON:0001911/plasma cell                          1.672193\n",
       "                                                     ...    \n",
       "UBERON:0002108/intestinal tuft cell                24.690405\n",
       "UBERON:0000059/intestinal tuft cell                25.366571\n",
       "UBERON:0000016/pancreatic pp cell                  29.211235\n",
       "UBERON:0002385/cd8-positive, alpha-beta t cell     36.399769\n",
       "UBERON:0001831/cd4-positive, alpha-beta t cell    157.542084\n",
       "Name: ENSG00000108848, Length: 578, dtype: float32"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tabula_sapiens.loc['ENSG00000108848'].dropna().sort_values(ascending = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b4e537fd",
   "metadata": {},
   "source": [
    "Again, we see the gene is expressed in a lot of cell types which is why the hpa_distribution is 'Detected in many' and the Gini score lower however the hpa_specificity is 'Single cell type enriched' as it has a much larger expression in a single cell type (cd4-positive, alpha-beta t cell) than in others."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f460e62c",
   "metadata": {},
   "source": [
    "## 3.4 Comparison of HPA: bulk vs single-cell"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "35e07e37",
   "metadata": {},
   "source": [
    "### 3.4.1 Calculate Gini and HPA metrics for GTEx"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "9163b838",
   "metadata": {},
   "outputs": [],
   "source": [
    "#filter out zero expression across all tissues\n",
    "GTEx = GTEx.loc[(GTEx != 0).any(axis = 1)]\n",
    "\n",
    "#define the genes as index for the gini_df\n",
    "GTEx_gini = pd.DataFrame(GTEx.index)\n",
    "\n",
    "#rename column as ensemblid and set as index\n",
    "GTEx_gini = GTEx_gini.rename(columns = {0: 'ensemblid'})\n",
    "GTEx_gini = GTEx_gini.set_index('ensemblid')\n",
    "\n",
    "#apply gini function across each gene\n",
    "GTEx_gini['GTEx_gini'] = GTEx.apply(gini, axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "2f328fae",
   "metadata": {},
   "outputs": [],
   "source": [
    "GTEx.to_pickle('GTEx_counts_zero_filtered.pkl', compression = 'gzip')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "1ca93dda",
   "metadata": {},
   "outputs": [],
   "source": [
    "#calculate HPA metrics for GTEx data\n",
    "##use tissue specific definitions\n",
    "THRESHOLD = 1.0\n",
    "\n",
    "def hpa_specificity(row):\n",
    "    expr = sorted(row)\n",
    "    if expr[-1] < THRESHOLD:\n",
    "        return 'Not detected'\n",
    "    if expr[-2] == 0 or expr[-1]/expr[-2] >= 4.0:\n",
    "        return 'Individually enriched'\n",
    "    for i in range(2, 6):\n",
    "        if expr[-i-1] == 0 or expr[-i] / expr[-i-1] >= 4.0:\n",
    "            return 'Group enriched'\n",
    "    mean = sum(expr) / len(row)\n",
    "    if 1 <= sum([e / mean >= 4.0 for e in expr]) <= 5:\n",
    "        return 'Individually enhanced'\n",
    "    return 'Low specificity'\n",
    "\n",
    "def hpa_distribution(row):\n",
    "    expr = sorted(row)\n",
    "    if expr[-1] < THRESHOLD:\n",
    "        return 'Not detected'\n",
    "    num_detected = sum([e > THRESHOLD for e in expr])\n",
    "    if num_detected == 1:\n",
    "        return 'Detected in single'\n",
    "    if num_detected < len(row) / 3:\n",
    "        return 'Detected in some'\n",
    "    if num_detected < len(row):\n",
    "        return 'Detected in many'\n",
    "    return 'Detected in all'\n",
    "\n",
    "GTEx_gini['GTEx_hpa_specificity'] = GTEx.apply(hpa_specificity, axis = 1)\n",
    "GTEx_gini['GTEx_hpa_distribution'] = GTEx.apply(hpa_distribution, axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "67561641",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
       "    .dataframe tbody tr th {\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>GTEx_gini</th>\n",
       "      <th>GTEx_hpa_specificity</th>\n",
       "      <th>GTEx_hpa_distribution</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ensemblid</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>ENSG00000173357</th>\n",
       "      <td>0.980000</td>\n",
       "      <td>Not detected</td>\n",
       "      <td>Not detected</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSG00000244551</th>\n",
       "      <td>0.980000</td>\n",
       "      <td>Not detected</td>\n",
       "      <td>Not detected</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSG00000266707</th>\n",
       "      <td>0.980000</td>\n",
       "      <td>Not detected</td>\n",
       "      <td>Not detected</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSG00000267726</th>\n",
       "      <td>0.980000</td>\n",
       "      <td>Not detected</td>\n",
       "      <td>Not detected</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSG00000256769</th>\n",
       "      <td>0.980000</td>\n",
       "      <td>Not detected</td>\n",
       "      <td>Not detected</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSG00000143252</th>\n",
       "      <td>0.111678</td>\n",
       "      <td>Low specificity</td>\n",
       "      <td>Detected in all</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSG00000005075</th>\n",
       "      <td>0.109183</td>\n",
       "      <td>Low specificity</td>\n",
       "      <td>Detected in all</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSG00000134809</th>\n",
       "      <td>0.108472</td>\n",
       "      <td>Low specificity</td>\n",
       "      <td>Detected in all</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSG00000131495</th>\n",
       "      <td>0.100672</td>\n",
       "      <td>Low specificity</td>\n",
       "      <td>Detected in all</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSG00000157823</th>\n",
       "      <td>0.097210</td>\n",
       "      <td>Low specificity</td>\n",
       "      <td>Detected in all</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>43848 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 GTEx_gini GTEx_hpa_specificity GTEx_hpa_distribution\n",
       "ensemblid                                                            \n",
       "ENSG00000173357   0.980000         Not detected          Not detected\n",
       "ENSG00000244551   0.980000         Not detected          Not detected\n",
       "ENSG00000266707   0.980000         Not detected          Not detected\n",
       "ENSG00000267726   0.980000         Not detected          Not detected\n",
       "ENSG00000256769   0.980000         Not detected          Not detected\n",
       "...                    ...                  ...                   ...\n",
       "ENSG00000143252   0.111678      Low specificity       Detected in all\n",
       "ENSG00000005075   0.109183      Low specificity       Detected in all\n",
       "ENSG00000134809   0.108472      Low specificity       Detected in all\n",
       "ENSG00000131495   0.100672      Low specificity       Detected in all\n",
       "ENSG00000157823   0.097210      Low specificity       Detected in all\n",
       "\n",
       "[43848 rows x 3 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "GTEx_gini.sort_values(by = 'GTEx_gini', ascending = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "7961c157",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "UBERON:0000473    0.089572\n",
       "UBERON:0002190    0.000000\n",
       "UBERON:0001150    0.000000\n",
       "UBERON:0006631    0.000000\n",
       "UBERON:0006566    0.000000\n",
       "UBERON:0001225    0.000000\n",
       "UBERON:0001293    0.000000\n",
       "UBERON:0002107    0.000000\n",
       "UBERON:0002048    0.000000\n",
       "UBERON:0006330    0.000000\n",
       "UBERON:0011907    0.000000\n",
       "UBERON:0001323    0.000000\n",
       "UBERON:0000992    0.000000\n",
       "UBERON:0000007    0.000000\n",
       "UBERON:0010414    0.000000\n",
       "UBERON:0002367    0.000000\n",
       "UBERON:0036149    0.000000\n",
       "UBERON:0004264    0.000000\n",
       "UBERON:0001211    0.000000\n",
       "UBERON:0002106    0.000000\n",
       "UBERON:0000945    0.000000\n",
       "UBERON:0002046    0.000000\n",
       "UBERON:0000995    0.000000\n",
       "UBERON:0000996    0.000000\n",
       "UBERON:0003889    0.000000\n",
       "UBERON:0004648    0.000000\n",
       "UBERON:0006920    0.000000\n",
       "UBERON:0004550    0.000000\n",
       "UBERON:0002369    0.000000\n",
       "UBERON:0000947    0.000000\n",
       "UBERON:0001621    0.000000\n",
       "UBERON:0007610    0.000000\n",
       "UBERON:0001255    0.000000\n",
       "UBERON:0001876    0.000000\n",
       "UBERON:0009835    0.000000\n",
       "UBERON:0001873    0.000000\n",
       "UBERON:0001870    0.000000\n",
       "UBERON:0009834    0.000000\n",
       "UBERON:0001954    0.000000\n",
       "UBERON:0001898    0.000000\n",
       "UBERON:0001882    0.000000\n",
       "UBERON:0001874    0.000000\n",
       "UBERON:0006469    0.000000\n",
       "UBERON:0002038    0.000000\n",
       "UBERON:0008367    0.000000\n",
       "UBERON:0012249    0.000000\n",
       "UBERON:0000458    0.000000\n",
       "UBERON:0001159    0.000000\n",
       "UBERON:0001157    0.000000\n",
       "UBERON:0000178    0.000000\n",
       "Name: ENSG00000173357, dtype: float64"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "GTEx.loc['ENSG00000173357'].sort_values(ascending = False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8b49c40f",
   "metadata": {},
   "source": [
    "Here, we see that the reason the genes are 'Not Detected' for both HPA specificity and distribution is because the gene has an expression that is below the threshold of 1 CPM which we applied to match the Tabula Sapiens adjusted median counts. It has a high Gini score however since the expression is present in one tissue only and this score does not take a minimum expression threshold into consideration."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "221ec12d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='GTEx_gini', ylabel='Count'>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.histplot(data = GTEx_gini, x = 'GTEx_gini', bins = 200)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8168f0ae",
   "metadata": {},
   "source": [
    "### 3.4.2 Gini coefficients vs maximum gene expression for GTEx (TPM)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "098fc241",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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FLBRx7ELGVymAGhTBK0DRo6D76vOsEQChTScSzITyHSDAkikS+LGw8ZHAxsTAIUQMG5MEAcDCxCGHGHEC2PiRCAIkSGIQI4iPJDY+pHsRP3EkBklM/FhIBBZJklguq7CVgcmRmD4HKQXkJXAiFqQk5IsMfYUMv9cql34m3gdueJ6XplGW+w/SNEWra/oXmCJj9cnxzHPNgFYAUtqxrH0RW0FzCr0hG03AMz4v7UjW6pke08dCZNQdcByz3Y3edttouncPsM8+M9GM5Ze//Iry8iB5eT6XCUAoZBCJOBx8cHdefXV/nnpqDS+8sB6AUaOKueKKzzjttPexbUk0quzxb721keJik2g0hWkK+vcv4MorZ5FMSubMOY7HHlvKo48uwzAgHrcJhbZPvh57bCKTJg3k1FNfw+83aWlJ0Nwcp7g4uNXciy8ew4YNLZx55sj0saefPoZXX13BMccM3u51OsKnn27g0083cPHF4wiFfP/y+dl4662VTJr0FJWVuaxf//37GToZQQc4+ug+/Oxnw9h11zImT+5Jc3N7n8KcOQVccsk61q2zyc/PY/NmwU9/anPvvYK6OvAlVRRORQ9oFBBuwhW1AUuQqgsQXZdLRZ9NSGHSRBEb6U4cPwNYQaGopaaskPJ4M6ZrFhI+oK9aQ7SBqIDIRsUEAigFpArFBAwyvkjIyH6aXWnZT8uCWpjX9N4vPOZrTSO0PxG0CVhJuDqQynTfa4dEwN2M5jLaspAgzS99cZCGJBkUmKSQhJAIDBwsEkTIwQFCRLGxXL+sWtBPkjABNEFz8GG5xCxAhDCFmEj8pEjiYLhEz49N0BXZk/hJigBCOoT8CRKJOKlk0DVfOeAYat8lZLQcDa8DxkExwbB779qM5iPjT4ijaGzCHYuRMd373b9MoE/mn5ev1k1pmq6fvRYs2m3GT8bsY+D3+0kkNIdR/gQhDEpKkhxxRDGPPaYT/xWxDwTAth0KC4MUFEjq62MEAhZNTQ7JpINlwapVYaR03KAKrRalyMkxaGxMpvdzySWD+etflzBzZg3BoEVNTTT96M48sz+/+tVshADHkZimYI89yvn00xoaG5P4fIJAwOK++yZwyCGvAVBVFaauLoZhCBxHsnBhgxKGOspGcmEYAtM0ME1BNJpi9eom1qxpori4y1Zze/cu5Mknj253rLw8l7PP3mXbF9gOjjzyCVpb4+Tk+LjoonHfag0vfD4DKSV+v/nNk78FOhlBB8jN9fHPf+63zfH+/UO8+ebWYWAnngjTpsGll0KvXpnj6+rgjGfhw1ZcTcEg2ZpHndMNu59JvtHMXGs3imkkSJyECGJYJrGem+lr1oAEOQREI+lIHqsX5C+EvrVQtRmSSchxFJ35CkU/FBHNCKOaBkGGdOiIoiSZQNmADwytDUjPQBBFM4ysBSDDBLw/TDeM1Ct8SjccVQpI5itamzIEKeFDIpEYpLCIEyDpRgXFXZu/IvNKMlWkzSLuhjBZpEggSWESS2/GxHFNbgoSB4GND8v1LEQIIN05Qihx2/TZOB5zQ1pKD7ub0Bw15j5gbdMvQNFhrS14hW794DUhD6A0DdyxFBlLjbbESM+5jmeePmerQBztsFFPp6jIpKbGcTebwDBsKisNpAxRX4/7j7XcTZv8+tflHHFEIaEQjBs3F9tWpqWcHB+vvLIrixa1csgh5YwYkc/mzTGuvXYxAOPHVzBpUg+OPLIbwaDJK69s4sMP6zn++J7U1sbp0eMFnnlmXwoKfLS1Jbn88i+YPLkPP/vZILp1y8E0BQMHFjJp0jssX97IypWNJJNJNmwI8/LLh5NMOgwbVsK1147jgAN6UF0dZvjwUpJJm08+2cRee3XdptQ9ZcogevQ4hRUrGvD5THbddWsmsDNw0knDeeONFey3X+/vZb2DDurHypWXUlq6c3wRnYzge0QikaKqKkZTU5BevSy+mA+TL4D6BMixZGI384BSkCFByvLTLMoIySRCwEoGESTOFlGJYTgkK4J0S20mry6BDIHoAVggatQaucUwyACaoSmmooD2QQmw1WRMQNpaoS052n3Rn4xAbwHFeZAXUlYRcly6HiIj+Wvrgd+9nyCZ4BX9WkbGKSFQEnUApB53/6SreRiGScIIEBcB4gQRSFKu2iAROCjHbpIgSSySWLSRQwp/2gyk8gp8JN3zDCSt+IkTwsGgjTxsTNefIFxLuomFQwITW5ppCddxBARTKopIQzPDFvezFsx04I12GGtarKX3BO0DdIKktSsrV31sa6C9zyHlOV+bkLQK55qGhF9pg7bLGISAYFAQjeoNQE2Ng2kmsW0YPtxi0SJBdTVAkjffTBIICPx+CIdNHCfFrbfW8uc/VzNhgh87E7WJYRi89FINDz20kWuvXUlNzUF06xZIj++6axGGIfD7TU49tQ/PPLORL75o4IYb9uW00z6hoSHBgw+uIBpNISWkUjBuXDmHHtoDgFmztnD//Ut55ZVDiMVsLr74I3JzLU44oT9+v8n06esYP/5Z7rhjP/bbL1OFZsqUV3jxxZUceGAv3n33ODqCEII99+zGnnt263B8Z+Huu4/63tfs1avwe19To7MfwfeIX/86wlNPJbj2WiXqvf0xVNdAvAUSa4BFwHKUnb9ZkGoJIB31LzCwSWHgI0UtqoxGLRWs8fcl1sdP87gAMgTSRHmF80F0R5mLVBBE2lKgIzgHonzN+p+ch0rlLgf6AbujmEQ+UGJAvyLIVflbWJZrz9ccQieLVbiL5JGxl+e7F8z3/Gk7dzFIVwPABzIP2vINIvkWSdMgaVqEjRBSCNrIJUwOMfwYpEhiYiOIY7lOY8VZouRg43cjhAQpTCIup4pjuccsEviJEaSNAmx8RMjFdomkgyBCCFtaJGwf0VgIYYBh2ji2CQkjI/0DpMDQtE8/E/1gtfSu/wleSV4/Oz1Hc11HmXtKyhRRT0MzDB9pE1r6uGaiwMEHQdBj6rYs+O1v9UR3mybYttrIokWaiwsGD/ZhmiaTJhXS2uoyPiASkSQSEImoixQXW3z99R6ccUYl995bRTIpKS31EQgYNDRk1JEJE8rS7w87rAuDB+cgZYpTTvmYgw/ugpTwyCNrcByJlHDLLbty+eXDiURSTJw4nfHjX+Wiiz7l9NM/IDfXx8MPH8hdd+3Hhg1tXH31Zxx//JvMmrWZk06ajhfLlinz0OefV3PGGW/yySdVfBvceecXlJffyksvLftW5/8voFMj+B6weXOcnByTq64KEQ5HuOIK9QsdWAmHjIX9DoHrnnUdvDFUVrAJTo1FQ1UXuh+6mjaRT1C2EhMWWyhlOQMZIFZTIutI4MMqSJLYG0QUfDVguJKpkMBQkD2geDnkuHagdUug1ILuEhrjSrjsEQJbgiXA7wcjB8KtEI1CaaEiJo4JwgLRBeWgjqEIWR/cdEAymkGR+1pBxjxdRjrPQBagtAE3iqa1uw/bb2I7AhuLqAhiGjYOJi3k00gpEgOBQxKTGCE3qawIiUOKAAl8rilHEfM2crHdpLIofhKE0rEyTZRiuIlRDj4cjxifSCeWCZy0rUuSivuQSYsSAxqkSBP4nrnQKwGfaL+p1zmszWU6d8Il+j0KYWMtGeKeVHNzc6HSgtWbYMMWxSjTwT6aYVikHdElxdCwmYy/14BlyyGsHUKOMg3efXfmn2MY0mUCXhuTSgKbObML9fU2Z5yhE6LaOx6uv74nhx6acZquWBFNz+nTJ8gJJ3zByy/XpMfPP/8rjjuuJz//+RweeGANBSoHkMbGJDNmbFHPNSUYMaKIZcua+d3vvqJ79xz69ctj+vSN6XWamxNcf/3n7L9/Nw48sAc///nHvPnmeior/bS2JpkypT9eTJ16GHvv/RStrUkeeWQJ8+bV8vXXp/Ov4umnl1BfH+Wll5YxefK/7hj+X0AnI/iOWLQozNixX5Cba7Fp03hmzy6kpsbm8ccj/PSnQSzL4Jzj4IwJ8PDL0GUN3HUrnPgWJGOCoN+i5vP+JAxJZGwOG43+VFBNXOSzhGHsIz6iWRbRm3UMEiuQIYF9rIT5YKxHZRvHQZQBQyA4S/20BwwApwFENyg1IVkNThOYQoVuWnnqtaAE8oMg3Ph1cwAqWVrTkCgqo9gkY5MOodSKXBSB0wTfDUqRrg/X6QLCBhGCeACSfh8CScTIIeLG9sfx00ARSQL4PO7tOAEaKMV2nQw2PpIuMY/jI4VFM0W0UJTOJhYuoU9hUk8JDiYmJlFCru9BOUUT+Glx8rGliSEcHAfslCARC2LHfCAFDTEjY+pJwoYG2OAS8jS0OUybfrQN32USt58M1z8PizbQzn8SboXV7jypbf4yM542CbmfWzwRSkJAt0poa0o/qrTjeMuWzMbKy5Ns2aJUGtNMYhhJkkkljKxenWKPPfzst18OX3wR59BD85kxowm/X/Dee8OIRh2i0Yxt6KyzuvHuuzU4DnzySTMZxqEwebIyu7z3Xg3JpMNhh/Xk5Zc30NKSpL4+U0Nj0aImhIDcXJPRo0sYPryIyy8fSW6uxfDhxaxZ08xvfvMZd9yxgKamczjrrCEsWtTA3/42nqOO6k0w2J5cjRvXlaOPHsArr6yiX79Crr56d74Npk2byLPPLuHMM0d9q/P/F9CZUPYd8cQT1Zx66lcIAc3NB5Of72P8+M3Mnh1n4MB8TLOExx6DPzwMz74KBCFUBtFdwcgFpyswRsWC9thrJSrkMUY/sZYubKGIRkqpYz8+osyuIy789IpWUbaoWTl0JYgvUSX8HJTpqRlkf2A9iIiyzcsUJFaAGQCzEGQCDFVVA5GLkv57kwlL9EYcFpJx+gZRdZN0PlIJkAOylxp3THAKoS03gB2wcBxDSepCRejkEKFOlCKAADHqKaaNAkxsDBzXpGOSwkcjpbSRT5AobeSTwE8bea6NX9JEETFCxPETIEnMrTPUQAkJMnaTGipwMJUnQUoi5BBO5SClQSpuEIsFsJOhjHAcMaHNhIjIRFLqEhIJMjkDUTIVHjSTjJHODhYx1y+C+15HBcXIlKbQKd+aoehoIjLP2LDBSZCpS6QTzLxxwh6bPkC3bik2bdKL67AvHUtm4/M5JJM2999fyosvtpCfLxkyxOKvf91EOGxTWKg4oBCQTCaJRJJuvSIAiRA2u++ex+zZjQwcmMuyZYeyZk2Y99+v4eSTe7FqVSvHHjuTww7rxp57ljJt2gpmzaolGDRZtWrKVtm4AMuWNTJ58lscdVQf/vKXvbYa11iypJ7lyxs5+uj+bvSSRGSFD0UiSQ488Cksy+Ddd0/cion8GNGZULYT0b9/CL9fEAqZBINKXFy7NorjpBg0KM5BB8HYsdBvD1V3xUlBtFlFADlhVEx+TwHlkmhDPjnFzaSEnxVyID3YCELQQBnr6UnAjIE0WJnTn+qRbYxashxhgOwGFLtSfSGwzrU79wTWKiIk/BAchJLiy1CMQycWDCMTEVSHohc9ULkCWgAMoYrp5aJoS9C1+9so7cCVTJ0iFRnUFsglTgBpCFL4SAifG+WjHLjSJdgNlCAxMHCIumMmKWopw8bCh00CP1FyiGNRT4nrABau41c9c28hujC5blwR1FOsTD8SYk6ApOMnkggghYVhOiQTAexwDtgGJA0QDtRaWxd90zXedH2gVrxJtJkxTaQ9p2K7kr/WCnzQuxzWbXI/O2AI6FIKm7zWGgk+A5LaZKThRgwJw123A1lu0yYTb72K/v0tVq3SnEOSTCpz0eOPN/PBB7oIUoYLxWIOUqr4/kmTSvn00yYcJ8mWLQkMQ9CjRw719aqGyKpVYaqro/Trl0e/fsphNXJkMStWTErvZ6+9yjjwwLcYNKiQ/HxlY2xuTmCagmDQ5KKLZtKnTz5Llmy3Mg0Au+/+BKmUw2OPHcGUKYO2YgIAq1c3MWeOMktt2NDCwIEl37jujxmdjOA7Yo89ivj6630oLvbh86lf6+GHh5g6tZXjj89l8WJle++bD9f+Gm58APbZDbqOhD+9rX7M3VfBxoWC+vUVtO4doriyHpmCdaGe9JQbMUWKpQzBxKaX2IApHcLBPObuMpxhTcsJmkmE49KDImAgiHlkiL5ESa/lZCTR3igmNJhM3D+o8hA5ZEwT+cAQ91gryJibHVyp1rKL3C+RhFQONIdy2OKvxHRF1ISwMJFpZ64DtJCPRYomt6CcdE1BzRQSIEEjRemCcilSNFGIiUPSdRbrQnIpfG68EETxU0cFYXJwMBXTkQHaEiFyzBg+adCUyCERz1FJY0jspIETyVUcTRNq28pI5LpWh/4c8RzXxFc7JCAjlXulc50/oU1KrlC+bjOZSqMSHFsR/fT6errWNLyRRO7zlpoRufvYZTRUVUFtrdICunUTbNqkJq1aZWMYFo6jUpT9fklxcYr161P07m1RVRVzC7gpX8LRRxdRVubjoou6MHBgiLPPXsCTT1ZjGIL58/emtNTHIYd8RH6+oK0twXnnfckhh1Tw8MOrefTR8YwYUYQX11wzl/XrI6xfH6GqKoIQMGTIs/j9Br/85TDuv38JAMcd159Bg9qfm41Ro8qYO7eG/v23PW/48DLuu+8wLMvoZAI7gE5G8D1g8GAlBc2e3cT55y/kiiv68MtfdmXoUBVWU1+vAtBPPjKfM49V0ktrBP70oCKqG7UZZohBYlketa1BCEB1vyZq6IKQktHiK/JppZ4SBollFNJGmwgyo3g/8gpbGbNuEQXhMFSAaAY5AWU2SqKk/XpUrSLthOyHqhyqJUpd46YHqpxCjLR/QOqksJ5KAo32EDhCEPMFwRCEY0GShkXSDBIzc1SikGvAaXXTqh0MIm5Z6Sghmil2aZjE56Z8tVBAApNWCjFc6llPmRuRGSVOgAghYuQQcU1EYUL4iFJLdxUUKqHNyQdD5w+YNERKQDrEGgvx5auaPHbSxIn4wI2YQQIxoXIFNNHVGcUCzusBD3wNjnbqps/JvA/6YWglzFvtGdd+Wsc15WuNwaM5YKh11m1Sjvq0lO81G0Emz2CrqqQwahQcfDD89a8ghIOUDpvadfeQaDOwaQquv76Qa6/djBBw8sl5PP10K7YN/fr56Ns3h+efr8VxBDfe2Jt3363n2Wc3IyVcckkvBg/O4623trBkSRvJpNIK3nuvhrlza9m8OcZzz61vxwiqqsKcfno/ZsyoZsKESnr0yGHlyhZsWxKL2Ywf3wXLUprBxx9vok+f/O0mTn388UlI2b60RDaEEJx11shtju8IDjvsCb74oppPPjmDoUPLvvmE/2J8IyMQQgSAKai4kfR8KeX/7bxt/fehoSHOccd9zsaNNr/97XJqagyOOaaMu+8exgMPtGFZcMIJAQ4+OMAnn8DGKigxocH9sQd8cFYZhCpMbrNV6OKm2u50L99IyjH5nHGsMAcylCW0UEAv1pNwM2Mrjc0s69tXRaqkqhDNUCqa8ScdZaNOkaminSQT7aPr3xSjHL55pEsapKuMlikTliEgXBCkNRgiJNrYEuiCdKN2CEJY5OFgEiCGiU0jxcTx4SOFTYBWimjFYjOVVFCP7eYGRAm49FKFfG5GOR4jhJCYGNgYSDbSAx8JGinB7yl1LDFopISUNPGRIuYEiNi5mE4SpEE0GiQeyVFSf8IkWZPnElmpyk0L1CtkTDxaCtcEOwr31ZCxyetxTagFkICYhE0tYDjKBNjOp+oobap9RjDtq5hK14+AG/rp9R044A+AE1M5Hrl5sMsQ+OQjNX/hAqivU09EelSFESMsVq6MkUwKHnqogJoam+7dDV5/vQUwkDLFE080MGhQgObmFKtXh1m9WiWkhUIGjz9ezZQp5Rx4YCkDBuRw221DEUJw6KEV/O53Q7n++vk4DsRiNj//+Qg2bgxz0UWD0re3YUOYwYNfwDAEa9YcR3m5G1E3sJB5847B7zfo16+AZPIC+vefxvnnv09LS4LLLtulw98ZKCK/vYzi7wNSSmbO3IDjSBYsqOlkBMDLKEvxHLZR4eR/DStWNLDHHlMZPryMmTNP79AGmY0ZM2qorm4G/Bx+eC8efbSOWMyhtFRwySUhNm602WsvP5EI7H8wCB/IMsCBkAELHoD+XdVaN8QFA16S1FplrD7QRzLhxzJS5JctYpkYTA2VfM4eTEBRgRpRgYNBObXU+8owylI4UYOuLQ0IKZXDrxzaRgfBhsLlcYywRJaBqAC7SGA4bnWeIiAGshTaKoI0FBTjs6NIYRG2lIbTKAuUtC0UAVc1kkwE0EYuq+lHCh9+EgSIUE+JWyVUECNEM6UEiaQjgBopw0CSxMRA4ieBzvYNk+P6ApTGIDGJYdBKDo70gTTYbJdjWCYWKSJ2kFTMTziSjy0lwpSQEBA3lPQvXQdwo1DHvfb9ILgdcDKhoBEyph4tqWsi7/XFurxpy2Z3rjepI5wZB896ml57x9zzpIQTfgIzPoH6KnU8NwDjxsDb70E4DI2NmfmOVGahYNAmFtMlJVRocCzm0L274PTT1f9v5swo5eUm/ftbrF4dRwjBihUJTFPi90MioUo8RKMpfvGLlaxcGeWttzJlEu6/fw0LFrTwhz8M5+GHV7F2bZgrrxzKb387rJ2U/vLL67jgglkkkzaBwNakZsiQonaf99qrCxs2rGTUqH9fIbdtQQjBxx+fzpIldUyZ8sNsJvN9YkcYQQ8p5eE7fSc/ICxdWkckkuTzz6uxbYllfTMj+MlPunLuuX0ZNaqQCy4YwMUXtzF8eA4zZjTz/vubWLIkxeuvC447rojdxsLyNRA2ICcIKx6BilK48Rm47x0o2A1qLAHCILE6B7rbJKNBEik/ppFEGgJHmEQJECRBKwW0UEiV6MEefEaAONWhLmwKdSHlmASdBAPsFSQCqkhbw1CTwmSYsC9ITagMv5HCH08QCsepyy0hz2wiIvOJ+YIESBAxipCYREUAH0k2iwqaKUUiyKWVLXQhhY8i6om7ZhtQTWaWMQgLm5Qr3euw0TA5rmM3RNT1H/hIEXcTv4KEsd0OZK0UuPnFCdpkLi1uzWvDUVFGKRmEBCSlj2TSIFJX7Jq7BNKXgjqLdBqzNudIFIH2OmGbUIRfZ/G2kTnHaw7KBN+0dypnl3zQUUaa8OsiUNrEA+21Bq2lJRRhf+ZlMkxHQmMNzKiB88+AhYthxeKtv4eOIxFCphu8FBQoj351Ncybl2DUKB9HHLHRre4pMQwVxHDGGSUEg4KJE/N56qk6dt01h0svXQ4kuOuu1ZSXq+ickhKLn/98vnuu5NBDK5k1q4axY1V28bPPruXRR1dxxx27c8EFs9i8WdnO1q8/Nq0NbAuPPXYYjz566HYFryefXMzVV8/kjjsOZuLEAdtd77ti7NiujB3bdade44eCbwwfFULcB/xDSrng37Ol7ePfET4qpeSZZ5bQr18R48Z9t9T0Xr2+YMMGC8MwmTAhn+ZmQU2Nn6amMtrCgiuvNPnDLQZPvAZn3wTxMpT9fgiZmv5dHQiC2S1OqFcrJWYDBUYzfXxrsN1Kk3vyOXH8tJJPDzYwmvnYGKrGpoRKNpFPGyYO9ZTiJKDFX0yAOJWoAPQayomSQ5AIbRTgF3GKaGYdPQmTRw5R4m7JZwVBHSXpCJ2Um+UbJRcThxrKcdxM35jr/A2i7CP1FBEljyQWCXwEiGFjESFIOO1X0HRYRf3UijIcaZJMGZg4xON+4vFcDCtFtLYAmfCBZdMrIFifNDI2/maRtsNbLaptZrr8s4580jkALaQ7rKV7BuhkMM0cIEPkgSP7wutf0a55ULvwUG+jGd00TDuhdUiot5qp1jS8vQlk5tUA1i2GESOgudn7bVMqRo8eSTZvdujaFebPD3DwwY20taVYvTqKaUK/fg5LlqiH4/c7SAlXX11GZaXJAQfk8bOfLWPx4lbKygyWL1c3HAw6xOMOw4fnsmhRE4ZhY9tanZLstlsJX3xxBD16PENVVYTrrhtN9+4hrrlmHuefP4ibbx5LPG7z2GPL2WuvSoYN+3YO3IMOeor33lvPsccO4vnnJwNQWxvhvvvmc+yxgxg69D+vTfxQsb3w0R1hBItRbsU1ZH46Ukr5H8m++KHlEXwT7rmnmrvvruGkkyq54YYm4nGJCsWpAAS9ewtOPNFi6sdQ2whYYPYAezDp0hFWN0j1lohcm5zdmpBS4CQkZYX1jDAWqZIQJHCwqGALJTSQRwQbQYgY5WyhnlLyaKEPG2iglEKaMZDkEKaEOmwsmilEJ141U4xAUkIdNXRx7fl+HCwk0m0haZDAT4AYBg5VdCVMASYOAocoIXKJUEcxDpa7R4MGSgFVQTSOjzbyCBAlTAGNFJLremBjBHCkScoxiMoAmBaOI6irK8OxLUiJTD2gmMfpWw+u3UoR0ibaV/XUlVR1KHvKc1xL77rRjIaD28aSjNlHJ9xqJuEl9JqQayKuCbqeF/esaXvOsz3redfUGkQKBg+Ev94Mp52WzQiS7V4rK1MMHOiwYkWE+voEqZT3AWQcIUIkkFKZk1RtIrXpYFAQi6kC5pYlKC31MXXqLtx99wpefVWXc5Acckg5N9wwir32Kuepp1Yzbdoq7rprT/r0ycMwlCbR0pJg2rRlXHHFp1RW5rBxY/sM4DlztlBdHebII/tuVyNYtqyehx5aQP/+RTz22GJuv/0g7r9/Pvfc8xV+v0m/fkXcdddB7LtvL0xTMGtWFcOGlVFUtH1tBKC2Nsz69c2MHfvvrUv078J3ZQS9OzoupVz3PeztX8Z/GyPw4p57GnnxxVb698/j00/zWbgQdt/dZNYsg5xSiLhdvPYYCRX7w6trUWaEAIoVRyV0TyJGJfAH4+Tmx8iRYboYmygyWjFwKKKRPFqooB7DE2toYeMg6EFVep6fJFGCNFFALm3U0oUgUYJECZFA9QnOQzWM1Jm+yppoY5BHCwn8VNGDgCrdRgxlToriI0wBYdcUZJCkiWIEqsx0mFy38UyQOCH8xEg6FjGRA1ISSYXwG4o6NidKkFIQawlhO4J0U4OIocI9UyjiH0IlgalbVvSuEfrnwqowiqi2kYnnT5FpyKAbvwj3mNdhrEv1exO4NC31SOrtCLZNRiNxzUaGAEczgFjWfM1g4mC6+QGO7mHsZT5eRtEO2lZF1qKa0+iNesOW9A3qCyhuZZoJQiGTa67pzVVXLU2ve/fdI5k4sYKePV91G85Y3HvvWE4+uU/2Zrj33iVccMEnHHpod0aMKODWW7/mwguH8dhjKzjmmD5Mm3YwAO+8s56PP97EH/7wGaYpeP31YzjwwF5brZeN0aOn8vXXtZxzzkhOPHEoRx31PImEynswDJvzztuFUaMquPTStxk+vJx58879xjV79ryN2towTz99HJMm/e/5Bb5TQpmUcp0QYjSwr3voIynl/O9zgz8WHHFEiFmzttClC8yZU8zbbyeZOLEJCBKpD3HAroIPV8Hsz2D3OBy0G3zQpJrPsBSoELDSh2yySHT3kSgtoCFlEh8UoNHXQrmsIUe0sU70ZjmD6c4GulBDDlHayCFAknpKKKSZWsowSREhh3oq8BMnSJw2t3l8jABJLGxMwuQTIIFAus7bEBFySNEr3QwmSYIUEh+SOkqpc6X+AloIk0urmz0cIkoDRVg4pKSqFioRtFBAm8zHSKZIyADCEMRTIRIRH6ZfUUE7ZUHKVGE5CQHNZsakkhBue0kyBN01tdSHYVcD5m4hE+Wj63Jn00fdulfXVcpuE+kpO5GGXsNr9094xl1GkQ499WoA2jRlZI5dfjbcdrc73y18F7AgHiajqWRHH6UXU5wnJwcikQRCGAjh4DjqBrp3NxHC5IQTClmxIs7NN1fy179W8cgjtekbtW2IRm0KC30EAoJ4HMDm4ovnsWxZ73QfgFWrjtym3X/mTOU1f+edKsLhOD6fQUtLkl69gjzxxFI2bGjhvfeOZdKk10kkbLp0yaO1NUafPgUdrueFbTtce+1ePPPMUn7969258MI3se04QpgMGFDI6tUN5OX5KS3NwbblDpduLiwMsHlzG/n5gW+e/D+GHQkf/QXwM+AF99BjQoj7pJT/2Kk7+x/Euecu5d132xBCMHZsHn/+s8C2tfcyxPvvgFkG+ODzz4EtgANj9od5dUAbiC4qoYo6PzJfrRtuzscpMFnd0I8B5cvwmzZCqNr7dVSSSxiLJF3YTBN5bKQbeTQjsYgTIo8wMYIYqG5d9ZQQJZc8WmmmgBxibKQ7jmsKEoBFijB56H7BmyhEVwNV0UB+bCzqKQdsQiRISZ+aJ3zEHN00XaV/xQlip0yicRXZYvkTpKJ+Yo1FqmCR5aj5joBan9sbwDX/6L4J2nnrSboy4nBYd3j6K/eY13YP5PohrCVz71raF6BLa3gFZzyvUTLMYlsJZdrSodvESbWucMCUbtMZR332+eD0KbBuHTzzojpNGHDRWfDOdBUmqlFSAg0N6v2xxzp88YXDxo3KzBOJOAwebBIKSRKJFIsXKw4SjxvU1AxoZ37ZssWrAkFhoY+2tgT33rsO21aF6hxH1WR6//1aQDVKiUZTvPvuJg48sGu7aKFZs7bw9NOryM016d07n8ceO4glSxrYtKmNJ59cguPA559vYfnyRiZP7svcubW8/vrR9OtXsEMRej/96Ws8/fQybr31AAYPLuXzzzdhWQY33LAfv/rVHqxb10zfvkUIIdh3356UleV845oAc+acR1NTjMrKvB2a/7+EHYkaOgfYQ0oZBhBC/AmYBXQyAhezZlVx5JHPc/zxg7n33sO2Oe/EE8uZOTNMWZmPsWPzMIxGtGfxlluKuOUWQVudmpvbU1VCTjow7wOU8zgCRdWqbWGtzw2D7CZJLioiMiAHDKhNdKEsVItfJgnLHCIij3pRhoGDjYFq+6gkJJMEPVHVHxsoJkooXZotSIwGimijgBARt86/wEa1hqygkBQGba55RzutU1jECJGSllsjFJIigO1YtCVyiaRyCfmi2JalQkIb83Bsk5zcNrBVK0lpC1obiiHhGvENC7aAz5CkIgIZFxmTC2TCM93GMSEbEs1QaKnKq0/rx6zNQR5iHtY00BvZozUDXVVUz9dMRhNzbU3x7gXSZqA+xbC6mq1KT4Dai0yq2vwI8FmQjKntXX0z1NTAcw/AP+6BDz+A227NnB8MwrPPwLvvwu23qwu/8ILeYKY3cffufkaOTHL77em0aerqElx++SZuu607UkqqqxNccUV31yns0NZmc9ZZldx++xoWLWpVt+3Ar389kJaWJDfcMJSHHlrDlCk92Guv19m0KcoZZ/TjZz8bxN57q6Yva9a0YlkGOTk+Fi1SfQL69MknEkmycGEd//znQhIJm5EjH8EwYMWKs+nRI58dRUNDHNMUNDQoX9KsWWeydGk9U6YMwTAE/foVp+d26bLjRD0QsH6UTAB2jBFo5VXDK+N0Apg1axMtLXFef33Vduede253zj0301jj2WfLuPjiBkaMyOWBB7bgOOVoETRcA/0rYZ0fUlFUD4Me0BhG2cPzgeFATJAMCERDAFnq0JRbTrJ3EBFIYfhsegQ2Epa5JAgQExZdxRaaKcRPgjxgDf3cyv2qwXuUQgCCLvFPutkAbeSRSytxAm4GcAGLEUiZIoUqJmdIm9pUKSERVSmyhspydSQ0R8pwHB+OI2hrLVB9GFICLBthC9oaK5S0r1vkppyMdN4AJARJR2TMOS5RFwk3UcsNuyQBUVfy1sl6OuErO3krTdB1e0ntB/A6eL3E22PvT8PDWIZ3h4OHwzMfqf7Cq6s962h4zfj6mglIunt2HHj1TWV6eXU6rF6lGtA4njUmHgV9+kB+vlu/Kj2muZFg4kSLPfeU3HJLazrTWM958sl6zjmnmF/+cjUzZjRz/vmVbNwYJpl0EMJm/vwmpkwp59xze3HPPWsZOjSf3/8+U575qquGARCPqwc6bdpyHntsBS+/fAhHHtmLk07qT0GBn2HDitLn3HHHV7zyyhomT+5Hfr7qVGaaBpalGtq0tsZ32CTz/POT+OqrmnSzmZEjKxg5smKHzu1Ex9gRZ/HlwBmAq6gyGXhYSvn3nbqzbeCH6CyORpM8/PBC9tmnByNHln/j/L/+dTXXXLOcO+8cxs9+1ovjj6/nueeigIVllZFKmW53GZHpHCNQdYN0p70gqjx0N1QAEkBIKtZegsoUFpLS/hvRwfIWSRwpKDPqMYVNCXVIBM0UUkZ9uvG7QJJwE8Rwc3tjhBDYtMk81yQkSEkfwk0qA4gnTaKxPKR0g/Oljc+yaWvOx/Q5WIEkjm2QiLmdauJufH8CxQQcMv18Y2JrO7ziVBkzTatnPEn7nsG4c3UkkHbSQqb2jyb07nVyLNfXrMczVbEze7BpZ3pqVwbCZVJBIBbznK+1Ae2LQM0NmBBvA59U2kA7n66uNpqV7Xz6T+HFFyESgZwcaG2V7LqrZN68uEvsU5SXO1x3ncnllzeQSoFp2khp4ziqvKllwbhxAWbNUmWhjzuulFWrmpk7tzm9CctyeOKJXTn++K0jaLZsiTJ9ehVDhhRy3XVzefvtDQC8995POOCAjiNuCgvvpbVVFawrKQmwYMGphEImQggmTnyejz/eyJtvHs8hh/Tp8PxOfHd8V2fxrUKID1AdEAVwlpRy3ve7xf9uhEI+LrxwzDbHq6sjHHTQdIYOLeS55w7g/fcbSKUcpk+v5Wc/68UNNxTw2WcRNm6Mkkq5GUeOVGGPIZGRHmOZAJf0f24Lno5ZQkUYNaLEyJCgdUMhocoIlmUTSQSJx3Lw5SUIWVGWMRQfSYLEWCfySQmTfJoIkiRCLhYJBAaqdaRBSvoIOzkIA1KOT5VyTkAgmART0hbOA2khDAchIJXyEWvLB8cglZKk2tySpYZLIaVQ5R10qYco6gY9xdjS8f6QIc5x2jd/1wRaS/2mO0e3j9RSu9fEo52+HjNPRDeVgUyDGVT/ZkeAaYOtGY3XnOTdrwMxbxCOyzRUzLXn+kDcjSpKZvsWNDPI0rt32QUeeYT0JMOAU0+Ff/zD4A9/gL/8RV24tjbGb38r6dHDJBCAjRsTnHBCHrffXskzzzQyd26EPfYIsGBBI4ceWsy0aQMJhUyi0RS/+c0ypk1bTyolGDWqvblmxozNLFzYxPPPr2HWrBrOO28Qr7xyMFdd9QU9euRskwkA3H33/kybtpT339/A4MHFmKagoEBpAOvWtWCagqqq1m2e34mdi222qhRCFLivJcBa4DHgUWCde6wTO4gvv6xn5cpWXnppPfG4zdSpI+nbN8BLL21i+vQa2tpSbN7chhAxiot1XKFLnaolbIbuJcBmSC5FEcwYymRSiyoA0gqsRxWWawBWCFgNiUUFNM+rpLmxgLbmIhKxAFuaKtkcVtnAUXLcSp4WkVSITcmebHB6EZN+qu0uVCcq2WKX0CwLaSOPxlgxtS1lNEfySUQDtIULqK8vpW5lVxLVRSQ255II+4m1+bCjPre8M9BgQJsB9QbU+KDKB40GhIXafzPKtt6IKoXdgJLwvURVRwW1qbkVEoxW99wIGeYQ8ZzrJa7eUtFJNW54TC5Cz4MMs0hAuV+tZ3gzib1agVdj8Pog3OO7DFD+ijRTcuD5P0NFUWbeIXt77jPlnurNJQD23cf7rXJobpY8+6xDUZFk5EjvT1li2zBunJ8VK2KEw5K33mojP9/knHPKuOuuXqxdG6GtLcULL9RyzDHzuf/+jTQ1pVi4sJnW1hTxuMOHH9ZnruZIDj/8PX7967mUlgZwHNhzz3L8flWt9MMPN9La6g2Vao9TThnM9OmTePPNiXzxRRXDhz+cLoL36aen8sYbx3PGGSO2eX4ndi62pxE8ARyFqjHktR/pGLV+O3Ff/1M44oju/PnPYxkwoIBg0CIYtJBSVYGsrU1w4IFBTj+9kNxcg7/9rZDXX3d4803JffepOEErYrHFrSQZEBDX1p7ecMBIeH8tquR0HtAG/kLlaFZxp0A9pHLyoDgFpsRuyiPcmIvVpxbDkNTHi8gPtRG3lRNZCkFjS08M00lHcaSSAsdWDd7TPX2TJuTEESkDYqpENBJkTS4YIMOoCmkGuDakDNG0UczLK03b4GTH72upWod1ajOLgBrdLMYbKp8koxl4pHR0W0dcxcL9Rjsu4c7zwXvXwe43kPmGuwR4S6N6TZeE9kYOaeLv9T94QjuFgNot0KSjkFxcdQfU1JM2Tb3zieeeQfWm9qyZm+s6lt0LK7s/dOsGu+wSJhh0KCxUpqL8fMFf/lLIkiWqbnZZmcmZZ+YSCs3jD3/oxmWXVfL007XpxadPr+Xtt2s5++xKPvhAbcpxJL/5zdfMnl3DJZcMZMyYYqZM6cUXX9Rz662788ILByCEoKUlwW23fY3PZzBzZjVHHtmbqVMX8+67G7jjjv0oLQ0xffpabr11Dn/5y34EAhZSynaNYrp2zaNr1x+nk/aHgm0yAinlUe5r33/fdv67IKXktNNeZu3aJl577USKizuOV7Ysg8suG57+vGhRE8OH+5k0qYxTT+2OEIIHH8yo1ZMnG4TDDg8+aJOXB81hYAOQ7wbS5KlqpTedAtc8gvo9V6CISAEklqk5dHFfIwLqBIQt9dlUppiWhgJkSwgcCAeKyK1swTAk4XDIJfgC01IEx3FMHMdExi1FSVt9LhEOqYqZmvFocwy40rFr9gnQnlhqgq1iUbdO4vI6er21fNxrVZhQE/fMJ2uOS1SLc6Gxvv26th7XazvQloS9r4dCHzS3kYkYai9ot7fjZ8fy688ewVgmoKotaxwIt9AeNlshLxfaXGvJNdfA1IeUc/iEExyeekodTyajfP219+aTRKNgWQ733tuM49jU19vcd1+EWEzy1lstnHJKMePG5bF4se6uIxDCpqIiSNeuARobI8RiqvroQw+tYcWKVmbOPIinntpnqz0WFPj529/Gs3JlMwcdpAIhLr74Q+Jxm3326caFF47kyis/4quvaunWbQ5Tpx7O6tXnUFQU3KFQ0U78e7AjeQR7A19JKcNCiNOAXYG/SynX7/Td/cDR0hLnyScXYVkGn3++icMO67/d+Y4jefLJVZx//heEwwIhNnHllYN5/fV6LrtsOXvvXUYwGGDatP6ceqrJnntCWRkcfgR89hnQpkIOKYNRu8CixZBoAbfsvmoeo2O6wqhIoyKU3b0KKDIUwfUDjkRGQpDjSuqtfqI5eSAlTmsILAczN0Yy4UAkiLRBGBLCPpW8pfscREX72HydHqDNGtr565XwdWZsAN1lXsFHJhvXSxg1A9HOU9PVBjxzci2Ix1WwUZpQA43NZIi1Ng15zUR6fUeF6jZ7k8x07wYdyeRZN32eF3b7YwZuUph3vnvN6gYytNubw6DXSYERylz3d9drH4Nk8eLMRUIhdTM+nyCZtBkwwOLkk31ccMEWEgll3zIMaGhIYRjw859XMGHCPJYujVBUZGLbDvvvX0IkkuKyy/pyyy1DGDfuPebNa+bCC/vzwgsbueKKTMSQlJLXXlvPgAEFDB2qwjQvv3x0u8dw8817Mn36eiZPVkaDP/95P26/fS5XXLEbzc1xPvtsE4cd9q/Jl7ff/jl1dRF+//v9MM1tWrQ78S2xI1FDXwOjgVEoH8GDwLFSygk7f3tb44cWNTRt2tesW9fM1VfvjWVt/wv6wgtrOOWU90kkHKTM5dBDu/LmmxM46qh5vPVWA1KaWFYOzz03kEmTSthjj3rmzk2x334lrF5jsHa9qYi9BeSBWQl2obu4hSLweaiooQrP8WJwC3wqxpDrjunPMfc74JeQL0mLwY6tQjxjRqYAHrjJVqJ9EpWWzr1OVE1MvaUUtJSvbe36cl5zjPcVMpFCeMYinnGXoFqulJ7SPgC9B31tb/2gZNZ7VCe5lDdHwCuwdpTJ6/U/6GfgMgsBfPYP2PNcVE8IzUS8oalRz3F3rfJC2G0QvDmdjLPcc13DkDiOjWFIfvYzePjhVuJxOPdcPw880IQQEsOIKa3Hk8Kcm2tz5ZVduPrqLowbN4e5c1sAB8OAhQv3ZOjQPJqbk9xxx0omTCijd+9cevdWiVjensAvvriWU055H9MUtLae8S9L9ccc8yKvvbaK004bxtSpP9mhczZtaqVnz39gmoL33juNffbp+S9dsxMK37VncUpKKYUQk4DbpZQPCiHO+H63+N+LM84Ytc2xm276jHffXc/jjx9B9+75DB9eTG6uj1Gj8pk4sQ/jx3chL+95olGBELmAjxEjghxyiKLuK1faCGHx/vuk2ysSdkNKDbB1IpUPRahzgFwIJCDegCLyOhJHS+oR969ELUcLYLnmm7hL3H0oUTZpKiN3Ap38rK4X86ylCbomoNosYpAhjAZuVJN7TGfrepyj6XO1VOx1ynozV3R9IC+jcF9T3kqf2aV3vATehqAFMS39u9FIKXcf+X5o1Z3HBJnIpO3JTFmhrhI47NceJqD36WWeGgJyAhAJQ201vFlF+7aUHjgO7L23QzTqUFJiu+UfVEVRUN8TO72+DqFK4fMZ/Pa3XYhGbW6/fQCzZzfzm9+swHEcxo79lLVrJ/Dgg2v53e+W0K1bkI0bFZE+77xPmDp1JS+/fBA/+UkP+vdXkUQjRhR/K9POqFFlvPTSinZh1hs2tLBmTRP77tuzwzW7dMnj5JOHUVcXZcyYyn/5mp34ZuyIRvAh8BZwFrAfKk7lKynlyJ2/va3xQ9AIPvlkPb/85XSuv34/jjpq8DbnhUK3k0w6/POfB3HeeRmG0bfvc6xb18Zhh/Xirbdq9WyE8HHDDQPZbbcyNm9OMmFCMYsW2fzf/xl8+aWy/1R2gb4jBJ/NRxFzrYTko0w12i+gm9FbKBNMHoqohVA5Bm4nQH8eJErJJF2l3Ll+MlKxtzJm1H2viZQO09REThN4LyHV0CGZggxj0ozB6xfwEk+vPd87x6Y9I4l7juvztjVXv3c8c73Xy4ZJ+/vTe9HPQa8LGROWHvMWp3NNPvoaPieTSJa+Nz1X5z/YICRu5zK9SW+rNBu/X2IYUZW7gIMQMSzLIZlUi40d6+f11/uzalWEffedh+PAr37Vnb//fR2pVJJQyGDNmv1Zvz7Cccd9xr77lvDOO9X88peDePzx5Sxd2swFFwzmkEO6MnFiLxxHYhjiGxnBU08t5YYbPuOuuw5sV0guHk+1a1RTWvo3wuEkzzxzLEcfPaijpTrxPWB7GsGOGNtORH21z5FSbkalMf3le9zffx3uuGM2X3yxiT/84ePtznvwwUM58cRBrFrVxKpVTenjkyb1pKIiyFVXDeOXvxzE4MHF9O2bh5SqhsvEicu44II1rF8fYeLEAIMGZeIgt2wGS5s1Gmlvk9YmE52MpO3b2cS4mTRBSrSgHNHVZIhQGyp8U4enakeut5pmGKVNeDN24+71W93xqOeYN5TTcec0kdE2vBE52X/6HO+1dA6At3Q0nvke6X+7knzqG8ZRa6Q76GqGkP0+23TkvbblOabhQNIbbanP12tqholiAqGQqkOkoJ0wCief7CeRXkv5AlIprerAJ58MorLSx+WXr0hnIW/eHEOIFELA11/vTWVlgHPOmUNVVZhIJEVtbZwnnljP668fzIMP7s2DDy7hxBPf49VX12OaBmvWtPLqq2txnG0/vDvumMfSpQ088ED7VibZ3cq6d8/HcSSVlbl04j+DHWEErSiT0EdCiEHALsCTO3VXP3D8/vf7c+qpI7nttu03bjvllKFEozZ//vOXXHjhuwBEoykeeGAJzc1RAgGDW28dw9Klh5Kbq37lq1eH6dHDTyLhcOaZK1m2LMbGje3tCY11MLCbe6geNxMXjtsfRYCbUUS6kQxj0EmjuktWCxnCpM09TWRi8lvJMABN1MNkiLwOu0y55zTRngHEyMTzJ8k4jHWCmCaUWmLWTlMdCqq1D33cK1XrP6+T2htq6okaAjISvNfur1+9RFqCbxs9022vBpDNpLzMyobyYs8evXC1CUNL/3jO1/fn1Yg8Gs6uu8J++6lNm2aGs1sWPP54BMfRG09h27Zr15ccdVQegYD6ma9dq1SVgw8u4o9/HEBhoUmvXgHOO28+b7yxhaVLm7Btm1WrWrnlllE89dRe9OqVx09/2p8RI5RjuF8/ZRoaP/4Fjj9+Oo8/vjy9l40bW9myJZz+fPfdB3PJJbtw0017d/xQXcydey719Zezxx7dtzuvEzsPO8IIZgIBIUR3YAbKRPTwztzUDx1Dh5bz2GPHsvvu3/zFPfXUoXTrlsvppw/j2WdX8OyzKwmFLOJxm/fe25Sed//9Izj//J5ce+0A2tqUmLtuXYzRo5fzm9+YBIMxhFBi38aNKVYsIUNc3YJrr71GhphqwtKEIsgtMKUSDurmHo+STuJKEyJvoxXbs76WxnWjFTehi2QmvLEdEc6ulS9VojMRz3WyNYkoGZ+D1hL0Wvq6Xvu/Dt/0mniyJO60GQe2tvNr2z+eOY6H4Hv23s4Pkp3fkD2eyjQYSq/t3btbkC69d+896eu5a+6yiyL0AJ98AjNnQn6+YPz4zCZTKdWWMi/PYNgwg5yczMOwLJuf/7yc++/fTDD4KYWFPkwTrryyN8mkpLExxfr1cd5/v4FrrlnKtdcOBSQ5OWpzQ4cWqFsQgtmzJxEOn8mIESqXdNiwEhwH+vdX/qw1a5oZNGgqAwY8REODsh+OHl3OP/5xIMXFQd5+ew2pVAdOD1R49Y+x9PMPCTvCCISUMgIci2pZeQyq3Nk3nyjE4UKIZUKIlUKIKzsYLxRCvCqEmC+EWCSEOOtf2/4PH1OmDKSq6nzGj+/OKadM59xzZ7D//hVICY8/voq5c+t5/fWNXHrpPFavbqJbtwD33tuP7t1NQiE/QggGDDBZsiSfwYPjQBvNzUmKCxxKcmhH3CJtKGLahNIUIrRrnv78F/DB52SybrXWUEeGuGlTjpbEdTmHJjJSfsozloCQlvR1boAu3Kal+jZwtFlJm4g0MU/QnrB6GYo33NNLbJOZa7eDnfVqkrHpZEvw2dK3S5wdmeELpTrHqSNGk81U6GBM/7q8zEOA6Yf8HM+6AgIhmDBeVSHVx5evgAfuhwJFj0kmHVpbk3z0kVetcTAMSTyeZPHihNsBT0UXpVLw1ltNnHfeauJxSUlJkE2b9qKqKkpjY4qDDy5j9Oh8SkstDMPht78dwvvvH8CcOfVcc81XVFVph5BiBt5S0zNmTCQcPpfx47u447Sb68XEic9z1FHP87vffcT3iRUr6rn55pls2dL2zZM7sV3sUPVRIcRewKmoktTgMZlu5yQTuAs4BNgIfCGEeEVK6W25fTGwWEo5UQhRDiwTQjwupdx2rvp/Kbp2zWXMmHISCYcbbhhHaWkOkyf3Zq+9XgfAcXKQ0mDLljj33FNFVVUMvx+mTevPMcdsYskSbdcpAqCxIaDoQC5KQo+iHMSFZCT+EIooF2SO27onr5/2RDLuLu8VzEzaJ3lpgqZr+bi+iqi39o83usfjGE3nOmhYdGw7z9YmvGt6pXB9XraJ2jvmtbtnR/90pBmg9quH8gJQ38y24d2LV/vIdpxnRQ0lIhC3QRiuEzgF8SR8MhuKC6HW9QVHWuHMc6B3D2hp8d6c92YNkkn13ejXz6KqCmzbdhvROG6fYpPycpNjjy2ksvIjhIDiYovf/743OTmC88+fS1NTlFWrwuyxRylHHtmdurooe+/9OvffP55DD1Wa71df1fGzn33AJZeM4IwzhuDz2NH69Clk5cqzsSyD4uL2zWoKCwMkkw533DGX66/feysfwbfFGWe8xGefbWTVqkYeemhSh3OmTp3HjBlruP32wykt3bG+BD9G7IhGcBlwFfCilHKREKIf8P4OnLc7sFJKudol7E8B2f8tCeQLJULkodyT2aTgfwKhkMXnn5/IV1+dzLBhxdxzzz7sv39X8vJUyv0hh5QjZZJ//GM5q1e3ACkSCbjggo0sWZJddyGJquEgydWF2HRcurbJe+3mMZT03+IeayYjmetM4CgZ2z5kfAMx2jt+PZoAUTIE32MaadfDV8MbsaNNUXEQXpOLdoJnm5m863vDQDX0MS+h95pk8BzThN+gffaw/iV4nLSmATm6r3E2srUD72u2HyHLtyHdPUlvPgUq/LW2jvaOZ1s1qTEMvTno1y9jMoIUXbsanHpqgGRSMniwH/3gR40KMGNGE1KmaG2N8Pjjm7EstbhtJ7j88kWce+58dduGIDfXJBSy+M1vhrF8eTPr14d5/PHV6ducNm0ZX35Zyy23zO3wkZSUBFm7tpkNG5qZPTtj9rzhhr0JBAzicZv993+M+vrINh7qv4ZTThlJRUUuU6YM3eaciy9+gyefXMizzy7e5pxO7ED4aHqiELm6Oc0Ozj8OOFxKea77+aeoBjeXeObkA6+g2q7kAydKKV/f3ro/hPDR7xOWNQ0pJYFAkERCMGBAIcuXSyxLIGWQESNKWLBAYNsBVCmAHKQsJBMfKjBywFHDSvLXOQU6v6AIJekb7mm483RymkVGWndLBpFtsvVK89oEBO3DNDVRcwlcj1zYqCVqvZ7XqqGJc7Z+6dUANLTW4v3sDdvUe8kmyinPmD7uNQt556TomIB3BA+h7tCslfSMezuTed/ra+qwXW/NouxwWMDvh3vvhQsuiBKPQ1GRQ1NTG5WVAiFibN6c4qCDAlRWSo44IpcpUwrIyfksvUnLggEDTJYuVUWaCgqgrS2G40AgYLBq1WF07x5i+PDXWLy4ibFji3j55QPo3l1F86xf38rvfvcFe+xRjm1Ljj22P2VlwbRmoJPF1HcXXnhhMj/5icq2nzNnM7vt9iA+n8Hzz09h4sR/T5jorbd+yttvr2bq1El07Zr/b7nmDxXfKXxUCLGXEGIxsMT9PFoI8c8duW4Hx7K5zmHAV6iq+rsAd+qqp1l7OE8I8aUQ4sva2trs4f9qXH/9aHJyfESjKcaPL+WZZ/bi6KMruP32odx2W3/q6lLYtjK8V1bCkUf66NUriRAxKirU43QiKPOQDbRAvgPjhpGR6t1on4CXaGVHBnVkP/dK3prA6VBQTUh11I+OPHKZRMiBjQ2eNbQjuKO1s+312RFA3kxhPd+bMYznXP1+W/BqBF6TUUfztvUN1nu1s9535Gi2Id/ra3D3aAA5BmmnuTDB0vkU+r4EBDwMOZGAjRsdQm5Jq379JD6fj+HDDTZvVpuZMaOVJ59soLHRJhQyGTAgY6ZJpSCRcDBNGDAgh1tvHU6fPjnk5Zn8/veDueWWRTQ3J7jiiqGMHFnEuHGlDBr0An/720IikRS9euVz9dW7cvnlH3PppR/So8dUxo17Jr1+Xp4Px5Hk5KhXr4lo7NguPPzwUfz613t+YymW7xOXXz6et9467UfPBL4JO5JQNhs4DnhFSjnGPbZQSjniG87bC/i9lPIw9/NVAFLKP3jmvA78UUr5kfv5PeBKKeXn21r3v10jePjhxSxe3MiNN+5JIKAkqdmz63jwwZVcffUI+vRRVGPLlgT9+39GOOwA+ViWYNy4UmbNSgABTDOPgw4K8fbbfhAGVkCQ0klaJgwYCisjKGLmlpcQOSBDKK3AcI8HaZ/gpevmeUtKaGLpNeOYZCRZLyPBM8drl3fIaBteAuwluNk5D3jW1dqAzh/wMpJsY2KSrWFnTtfL+pysXgCawWQzv47MQPq8bB+KnqMd2o7ns35+2f0KUhD0QSxMRhtw3JIX2lSXXjgTuuT3x0gmJePGpfj880j6QkLEufHGCq65ppJHH63h9NOXIoSDZQnefXckhx76OamURAibvfcu4vrrB3LUUTOJxyU33jiCq64aihCC0tInaGiIYxiKIc2efTS77vosqZR0cxocystDVFWdDcD998/nwguns+ee3Xn11Slb+Qo68Z/Fd00oQ0q5IevQthRmL74ABgoh+goh/MBJKDOQF+uBg9xNVgKDgdX8D6G6uo299nqUK654j2g0xdlnz+Dvf/+KN99UyTjJpM0ee5Rx3317ppkA4I5JfD7BP//ZnTFjQixerKM4bE44wceMGQAJhEiqBCJtsknCypVw2uHg1+0eQRUt8xI+b9eubEldRwfpKCBvMTitCej3XilZS++esFafNwIpW2r2EltvSQkbRpR61tTwag7QsUepI0leJ2d5Tu1Q6jezJnl+IWJb53jvxcMQ/F5m6jLG0UMhqKV8Cd3KAVt1NMv11oDCLZlB+4iczIYkffoYSGnz5ZftL3zlleVcdZUqNvXTn1YQDo/nkUcG8/zzwxg4MIdkUvUrSKUk69Y1c/jhHzJiRBFdugS49tp5/Pa38wD429/GUVkZwjRVWepNmyLYtno4hx3Wk7ffnsScOSe2ewRCqP11MoH/LuwII9gghBgPSCGEXwjxK1wz0fYgpUwBlwDT3fnPuM7mC4QQF7jTbgTGCyEWoHIUfiulrPtWd/IDxYwZ6/jyy838/e9zCAZNzj9/BLvuWsa4cRUMGvQExcUPuc5hsG2HadPW8MUX9SSTklQqiZQpiopM5s2L0tKi7Cvjx/uJx0X6RykduPB8EB4/Mg489rCbOdwCg0rJ5ADopC6dWKbj+lOesZjnuCcGvp3zFteJ6ZVuob2zWroZtC5h3bWSrePwpecanrH1DWxttvKWcsjOKJZujH5HSm4HJqCkJBNi6tVOsjUVzyUA+nrL3XjmmKZnUiLTv0B4mN/CpW4HM/fchB6zIa4T9bTpyr2/0lLNJPSmTCzLYcUKAVhutrByzBx9dA5PPdVI795L2HvvBfTp8wV9+szmzDOXMGnSPKZMmYth2BhGihNP7MoRR1Ri25KJE7tRUaH6ZKxfr8IxzzxzIGvWHJdOduzbt4Dy8hDg8MYba3nzzXV06ZLJBj733FF89dWZvPPOCe2f2w76ITvxn8OOMIILUGGe3VFhoLu4n78RUso3pJSDpJT9pZQ3u8fukVLe477fJKU8VEo5Uko5Qkr52Le6ix8wjj12EL/4xVgef/wohBCMHVvGF19s4oIL3mXjxjCplGT58iYAnn9+I+ed9yX77vsexx77Ofn5NsOH57JgQSupVIxQKMXcuT155pkKli5NYVkxxoxxME3B3Xe50SjaJKOJkpsTsHwlmaifRjKMQNcS0lK+ZgiaEHmldU91TR1x5ERoT9i1M1SbjPTxpPIXz11LhgFkO2ezfAgt2iySbY7Rn712HveaoiOzkHeu9xvvTSjL7j2g181mCA6sqfKMe/5sL70T7jYFSNcH0LMU1epSM08bDt4bSgvU53b5VrY6Vwior4dwuzANg9GjzXaaQihksWRJL/70pzKqqlJUVaX49NNW1q2LU1urwkuFsJk9u5njj++G48DTT1dz5JE92LjxaK66aggLFjQDKZ55ZjVvvLFRbcOWpFIOUkpOPPFtDjmkB+PHd0FKyciRpe0fsRAMH15GKJSuhcFXX20mN/fPHHjgtn/azz23hJEj7+PDD9dtc04ndi62ywjcXIC/SylPlVJWSikrpJSnSSnr/037+6+B40j++c8veOWVZe2O5+T4+OtfD+Skk1SIW319FNMUNDTEOfXUfjiOzcknv0NbW5JRo4rIz7fYdddSvv66hebmBOvWtfD44zWAJBJJ8vnnrYwcuZ6VK+v56U8N5s1LYNttgNPeNKElch3d4yNj7tEhpXEo8IZuSghlLA+ZP60ldCTFQyYLWYeTkrUGcPmh8NjZbnkFrXFItjbtZF9Dr+ORnNv9ecNSySLGHWFbjmTh+fOxtZbgnZcd5eRkxnw+jwlJa1GATMKGLZ713HNmzIRzTlcO5Tv+7DqMPfdeWQkdCdRz5pj4/ZrbCrp183HmmfVUVPg4/PBcpIwDKgmsqMjknXdG88c/DuKII8q4/vqBCOFgGA65uSa//OU8AoHnOPHEPoDKVJ4/vwFQDuAlS47n1FP78tVXdTz++Aree28y8fhFnHbakO0+aoBlyxpwHMmXX1Zvc86tt85m4cJa7rmn47DUTux8bJcRSCltoNy18XdiO3jvvTVcdtlbHHPM0zQ3x7Y579e/Hsf77x/PUUcN4OGHl5JMptwyAZIhQwqoqzuGTz45gNNP745hmITDDgcdVIAQMH58AaeeWkosliKRSDB1aiuZcByb0093KUYbKhNYm3YaXfOEJqiawMWgpY20LZ9WiLaSIfy6rISXuUQ9x7UPwdNiGbLe2ypXIBGF4+9CtYb0+gK8voHscFENTThl1nFvnsD2oM/bju9gK4ew1qwk7XMOtgcbelW4lbS9/hNor314NJraOnjxVWhtg9ZW1RfZBLp2UfM2b97GhZButVGlNaxaZTN7doK33opSU6NUPCEM/vSnPvzjH/0pKjK45poVvPFGHUuWhPnww705++zufPVVA3PmNGCagq5dQ/j9fgoLg/ziF8PSV+vRI5eLLhqBz2fQp08efr/ZLplsezj++KE89tgkZs06c5tz7rzzMM47bww33jhhh9bsxPePHYkauhfVlewVFLkAQEp5687dWsf4oUYN1dSEOeCAafTuXchrr53SLh2/I8ydW8PEia9xyCE9+cMfxtO1a8bW2tycpLj4VQzD4Be/GMSf/jSEyy9fyQsv1PHKKyMYP34T8bgO8RFk6kq7Naf1b9REZR4H1NRQHkR97jTdqMZHpjeBz3PMm2WsCaFXMtdSeLZ5BzWnMAT5JsRsqGv1rIN7DctzPf1Zj9F+vhBKom4XMaQZiUb211gT/o4ifrKRbZbyRiUlPHNwexV46ytlMzPY2mEOhHwQbVJze3WDRBNs3uKZZ8PI4bBiidr2yOHw+extbVZrAsn0Z9Nsw++HqqqexOMO1123kWnTajGMBLZtM3p0iLlz65ESjj++kv33L+Tii9Xv6M47d6WsLMCTT66hri7O88/vS2WlCh+rro4wdepyTjihLwMGFG7jAf5n8PnnVRQUBBgypOw/vZX/CnzXqKFNwGvu3HzPXyc8qKjIZdGii3jjjVO3yQSefnoJEyY8yaJFdey6awVVVWfz8MOH0NSU4OmnV2LbDo2NcWKxJMce25VRo/I46aSuTJkyj6lTq6mqivP883V88EFvzj038y8wTdBhQP37y4x07YZ85uaoOXEddKRt1LjzdO0hyBA/6R7Tfx6nbbu700TUlfQt9zXPhKoGt0SDdjJnawzewnPZYx6iLVNQ5CdD+L1MIOtcw6s9ZOctbAtZUvq2HMeGoXogtzMNaQaZbULKCoWNav8K8JufuUzAy6wkLFwEl/4c7BR4ZR1vLkHmf603KygsFFx+eSFff92NwkKDQEAwf34Mx5H07x/CtiVz5rQgJQwdmssf/ziEbt2UEOH3Gxx8cCWFhSavvLKRzz+vY82ajEPiN7/5nGuv/ZKzz565nQf478e8edVMmDCVMWPu3a4G3okdwzcyAinlDVLKG4DbgFs9nzvxL+Laaz9m5swNPPCASutvaUlQWxtl331f4vTTZ3DffYvp2/c5+vZ9Hr9f8tVX9Vx55UJeeaWWtrYkQljccks1Bx64hLVrdQaZxLZVDLlhCNatcymuJuQJCDeA3QZOgkwPAV3tEzICpk4Oy46ddx3J5+wGRsKVzr09DzwELeVGLlVtUWPSawbS5iRvKQjNJGBrYu3xQzTFMucEjA7mu0R//0HwDR1DO0a2H8CAivz2xx0J690SEMEgGT9FJpin3flAptNYknQDnkuuJZNT4TGHFebDn/8CiaTSgtxITH7728wc1X0sw3XKy21aW6P85S9hhg7dRH7+CkpKlvHFF1Fs20dxsYFpQkmJWqyhIUm/fjlMntyNxsbJ1NZO4o03qpg8eSY+n8Fhh1XywQeZ8hDHHdeHiooQp5wygGTS5rXX1lJXlylG959CcXEI01R1jb6v2kU/ZuxIZvFubnjn18ACt1Lo2J2/tf893HnnwRx33CB+/vOxRCJJ+vR5mF69piKExLYlgwYVk0pJHAcWLGjCMKCmJkpZmRJvlQPQJhqVvPdeq1uWWongubkp+vZVouP+EzwUUhMgD0H2a2Jvu76DGJloIR2C6j0mQUhl33e8iVKwdTZwMjPWrk0jtDfpZPsDvBqJhjfHwfNNjXub2GT5Ft5bkhV9s6Pw+gBcQl7b2v6zt2d60Jsj4NVmOnKWa+1MawluhFM61FXAhAnw0jOZc+yU+/yAPfcknU2sJliApEsXQV6e44aPSlIpm0hEcyYfkGLVqji2DT/7WS+OOaaCO+8cxvr1EZ54YgOhkMl5583m17+eSyolyc+3eO21DVx11VxGjHieoUOfZcyYMjZvPpULLhjKzTfP5dhjpzNx4pvtHt0jjyziwgvfpbX131crsk+fImpqfs3atZcRDHYygu+KHZGdHgIuklL2kVL2QYWOTt2pu/ofxWGH9eXZZyfTr18Rti1JJh1sW9LYGMe2JePHV7JgwdEcf3w/li5tYtSoAhYvbqOuzkv5EowYYXH11ZWUlGhRPko43MrllydYv97iwQcNgi7hCObBxWfCn66Hu/6gpMxEGFUGwgdF3iQmj70/V0uyOhTUga/X07HNXRP8RAfj2URZRzJ5wzw9SWQdlmrQa+m/7N4C3xey8waywka9zbiaWsjUaPImjXn3a0DAG2aR5UD2rrdiFWzYAOPGtl/HMGD6dKUJqHBRxfksy+Tuu0O89loRpaVbp2b37WtRVmYybFgOY8bkccwxFTz33K48/PB6Bg9+mzPO+JK//nUFgwcXYNtw1VXDmTPnCLe4nWTRokZWrmzh00+3pPcyYEAhyaRKLpsxY2P6+FlnTee++77mhRdWbOPBfr8IhxPsu+9Ujj76R90f63vFjrDSVl0CAkBK+bEQonV7J3Tim5Gf7+f9949h06Ywzz23huHDi6mqCjN69IsEgyaplKC83I/jZEoH6M7uCxe2snBhyvVFBAA/RUWS/fbzU18v+d3vkvTq6aOgQHDoYfDMS6qUwuCRruMVIAp9B8Lize7yARTBd78RYW12df3PUsKc7DBvL3HuILkr/V6FsStoc4jlHvc2svcyFuE57l17e7VpvdpBR+UqAFN8Q4ipZoDuGn1KYa0b+SidrHF9P9t6L9ykMhOCJsQTIA3Uv1FrD26+xqb18NOzyJiaNLNw4PbbM8vusw98/DF0724wbpzJ3Llxmpo0ZzQYMyaXOXN68MQT9fz0p6t47z1lH7zrripuvbUfb75Zg5QghGTMmCJ+8pMu/PrXw8jLU9+t6uoT2H//6fh8cOKJfTj22D6sXdtKS0uC004bhJSSs89+n8MOe414/Dyqq8OYrqq0997dtvNgvz+sWNHAZ59tRErJhg3N9O9f8m+57v8ydoQRfO5GDj2J+radCHwghNgVQErZGfz7LdDUFGP//Z/Bth3mzz+dQYOK+fjjzTiOJBJJsXLlFP72t2VAA6GQ5C9/Gc4VVyxzE3xU7GdlZS6XXVbBVVc10dSUYsKETUyY0JUXX9RXkXw5RygiL5R9O78CWlwNftVa0pYGIwFOkIykrh2UKfe91/maWT6N/KAKgQTShDJgQTzlOdZRoTd9XCewaees972G931H/gQvgXbcWj2aibjXMw1tZ+8AWTZ7HFhbS8e5BPqatuezAXuMgNlfk2ZeukdwLNtZru9X71v7GLYTDjtwIHz8sVpk3TrJ/fenmDcvim0bGIaD4zgsWCBpbXU48sgiDANs22TXXQu55prebmTQWDZvjnHxxX3SBLy6Osqzzy7jnHP6U1mZw+LFx6Sv2daWZMSI57FtyaefTiQSUeHOeXk+TNOgtTXhNrKHoqJvV1aioSFKKuVQUZH7zZOB0aMr+fvfDyMQsDqZwPeEHWEEu7ivv8s6Ph71NT7w+9zQfwMcR35jeOg3wTAEPp+B40i3Rjzss08XXnjhYP7610UcddR7LF8e5thje3LXXeOorAwyfHg+ZWV+DjlkMZs3pxg2zGLVqhSOoyhpQ4ND9+42QphI6WCaEgcLmYJgCHr2gxXrSZuQfX6Iu/Z+Ryeb+dw/b2nqOFs7Qz2SevdiqKpxxzyO47hHei8MQbvgjoyCowihtzyDXqMjE1F2SOg2IGSmVo83sifxTXkH3vW9Woom3JoZehmXx9m9tIqtNRHvvXj6RAjTXV6AP6ByLbylJbKxZo1+pzjIH/5gk0rZCAGlpRZ1dQlSqRQDB65l/fo+bm9jyW9/24vBg1VTlhNO2FpqP/PMWcyaVceKFa1MnbqX2mbSYcuWCBs2hMnNtWhpSZKb66Nr1xwsS9Cnj4paGzq0lNmzTyEYVJnOl1/+HpMmDWDChF7be8ppNDZG6dv3dlIph8WLL6J376JvPEcIwcUX775D63dix7AjUUMHbOfvR8cEPv54PcHgTUya9N3sk4mEwzHHDOChhw6lX78iz4jgo49UI3GAL7+s54YbvgZg//1LGTEin7ffHsFpp5Xzf//Xiy1bdAwmgMGddzYhZQy/36ZnT4m0VQRRLAorvvJcxoa2OrauFOq4dEy3m9QmDK/ZJYsQVzWwNSHH815CczTzPi3xe+P1d9Tm7w3z9CLbvr/ND+1Rlh0I7U0e070aPBL/Vu+ztJTm1qxzvDDJ5E44rqnJZbCO+2/0+To4z0UqpRdRN5RICAzDh5SC2lrcDGRJTY3NnXc2MmBAEMsSDB0aarfO6tVhiopeYezY93AcySmn9KGiIsixx/Z0x1soKppG795PccABr+P3GyqKScData3061fAL34xMr3e6NHlDB5cwt13z+Pvf/+Sk056lZdeWkEstj07noLjSGzbcbOPNzFixD958MFvNjLMnLmOp55a2FnH6HvCjkQNPSqEKPR87i2EmLFzt/XDxeLFqh/CnDnbTpnfETz44AIefngRl176frvjBxzQhXPOGcQtt+zK3/8+lurqKPfcs9KNBlEYOTKXRx8dxPvvh3n11Xp69EgwdKjpOvq0fSHM2rUAqfYRPjqeXeuCXvOEK+269CQzF88cb6cwjW2ZbLzRRO5x0+sP8Nr/d/D3nOOp3OnFsK4weXuxbB0wGr/pSXbLhjePQJec0J+9eRjZzMB7PaBndxjYk7T2IAwwNbF3GY4lIeUyyFEjoKIcBg+GHE9nxUxdIYPDD9cLCAIBA8uS7niGmz/6aB3r1jm8/voQ+vULMHTopwwY8AnNzSlWrWojHnf4+usmfvrTT3jnnc2sWzeZRCLFffctY9OmMLatotekhC1bYkgJGzeGueKKT1m+vJnzzvuQZcsaaGyMsWSJKkcxadJAhg0rJRSyOOGEl7nuum/uUVxamsOyZZewZMnFvP/+WhYtquW22z7b5vzq6lYeeGAuBx44jTPPfIn331/7jdfoxDdjR0xDHwOzhRCXowrP/Rq4Yqfu6geMs88eQ3FxkDFjun6ndY45ZiDPPbeCk04anD62fHkjr766juuuG0m3bnnE4zbr10cYNCif3Nyt/1V77JFLMCjo1cvPp5+GAYMBAwpYuRISCY931iWC6R65CbYuvqZ7F2gTkKY1cVTPAm9yVrbE60b9BHwq7DFtlxcZAqaZi60/eHIBDFxbukCZTrZT0CQ/AJH41scXV0Ntc+aziYqIqg+TMT3pa7t72qaZyBPW2c4vIrJevfO9zvAE6vnZbn0hTymMIQNgyYL2e0lpwdmAOV+q4zXa1KanStAPeu1agWGoZxYOS847L4ennmp2+xqrzc+fr2L9L798A8lkhKVLlUq2ZEkbBx9cwRNPjKOmJsoFF6j05RdeWM9pp83ENAXvv38Yb7xxOAUFPvr2zae6OkJ1dYQDDujGDTeM45Zb5hKNJhkz5mmKivzU18d49dWJHHpoLxYuPIeLL36bu+/+iqFD2xel2xa6dy8A4Mor9wHgpz8dtc25xxzzNHPmVFNenoMQgkGDduwandg+dqhVpRBiH1Sf4jpgjJSywwoo/w78UEtMfFdUVbXRu/djbmlpOPHEwfzpT7vz+99/xciRJVx66RCsDjKlli6NMmHCampqFBW/8cYyZswI8MEHDhBCCD9Sx0GaZJrRaGKeR8YxrNtdBlClKbT0qwmz10fgbTzvSUAzhFtPiPbH2/kYIGNaMdX6fh8kvPZ4bULJRnYOgRceS0RlAYT8sNZb1Ny7/+3B8czRGdVeU5ZmDinPHD1Pa1LQLvQ2XRIKlceR0Mm7EkhAz0rYsJoONZf2N6ik/yefFJx0knLsmKaDlIrQSxl3zSXedPDW9MZNM8URR5Ty6qu7MXduPbvt9haWJdi48RhOPvlDamvjfPjh4RQXZ/cqhXXrWujSJYfa2hhDhjyKaRoUFQWorg4zffokDjigR3puLJbaKfH9v/zldO64YzZPPDGFE08c/r2v/7+M79qq8qeoXILTgYeBN4QQo7/XHXaCLVuiaelZCIM5c+r4v//7iocfXskVV3zBX/7ScfPtP/1pEzU1mZIAN9/cxGefNaD7VAYCWbYZnVEsYWA/V5jVRE87iN1M4nbmIK9k7PUbQPvs222FVm6LwEl1/YSXaej1O4oM6qiMNFvP3dKSxQSgPYHfHryhq1rad4/lBCFXl7vwaggG7Zmb4Znj9TcYKnM4vR8HJh4Jcz+DvNwO7rkd1KCUsGaN6hJmWQa2bfDznxfy9tsV/OIXBRhGx6pOTo6BYUi+/FKpTs8/X4WUJj16FFBREWLGjMP5+utJHTKBf/5zAX36PE5BwYPk5vqYPHkAOTk+pk49iJUrT2/HBICdluR1222HkUpdt10m8Mkn6znllOdZvryzSPKOYkcSyqYA+0gpn5RSXoXqTzBt527rx4UPPtjIHns8Tf/+uaxceQpPP30IM2b8hLPOGkhBgRKNBwzIZ8mSFpqa2mdvXnxxJXvvnctDD5VRWmoRi0liMV2hTRKLaQrlgdugZkR/l+5r84lXevcSec0QvAlfWkr2MoTsZLDsb1d2CKbbFMbx+CfazeuIeWQ7aT2vhgn+7dGfHXVIe+9ZX8tSUVaRFISz/SMdMQQj67jXLxLIvDdNOPd0KCyAkSPpkFE98QScfHLGzDZ6NJSUOCSTNik3jfqll2LccUcrzc02jiPJdNyRFBQo215xscW99w5n5sw9Adi8OQYIhg7NZ9GiJubNa9jmI9myRZmakkmHzz7bwuOPr2Tz5hjTpi2lVy/lcW9piXPhhe/y+OMdCy3/Kt58cwW9e/+dadO+andciO1z80svfYsnn1zIzTf/sOoj/ZCxI1FDk6WUNUKIXPfz50Bn7Nb3iKamOIYhaG1N0r9/Iccf34/Gxjj33ruYDz44jHD4FHJzfYwc+RaDBr3B009XMWTIe8yYUctuu+Xx8cfDOemkMhoavFEa3qJBLvXTxNYNH33xbUi5jWuAjBTu/s4sL5HXxz3j20PPUsgNgE9rGWbGmmRpRuNlKLA14cwWbDuy0bvvy/JUDaLENweq7DiyNI6kVwvQDmQt/XvnahOU13zmJ6NdaT+LoYj7qWeDPwdmZVUb9fvhjTcUE3j8cUnv3io34uyzDU44wdODFMm6dXFeeSXM1KmeokxIcnIE06YNwDCgujqOlAYDB6p4/bq6OKYJOTkWu+32JnvtNZ1ly1p49dV1jBr1PH/+83wOPvh1Lr30U37/+3Hcf/8E3n77KPbZpwsDBigt4ne/U6QgmbSZOnUR99+/gLPOevs7PPQMHn30a9avb+aBB/61VKXrrtuPPffs3hli+i9gR8pQ7wU8CORJKXu5ZqHzpZQX/Ts2mI3/VR/B559vduu8GwwfXsoRR7zFW29tpEuXEHPmTObZZzdy2WVfATBuXBe++KKFk07qxpNPqlCZ1atjPPVUM9dc04w3zMWychk+vIj5811KJVBESZen1oQs5B4z3eOWZ64mcvp9dqKXhnZKC9J1cvKC0Lat4pAuIQ2EIK6zjKG9Ld9bA6ijSCMPn/Nrs8v2vtIdKEjbhF5H81O3V4CTJF1HqU85rN1Ae3OW1p68RfxAme2jtNeqWskU5HOfWa+esH69eo4TJ0JREfTvb/O736kJQkjKy5PU1GS85jk5USIRzbkTGEaSv/2tnN/8Zg2O42Db6sEsWLAbI0bkAVBXF2P06OnU1kYBh0DAYPnySZx00gxmzlRuQNNUCXjh8Jnk5LRXtxYurOfyyz/iF78YzfXXz2b+/DqGDSvkmGP6c8MN43fwIXeMBx+cy/33z2X06Eouu2xPhg4t/07rdWL7PoIdMeT9HTgMt/G8lHK+EGK/7297/5uQUvLBB2sZNKg0HRWxPYwYUUpl5YOkUg6ffHIcv/rVSKZP38jmzVGOOeZdTj5ZdYMKBg3uvns099yzliOPrERKyZYtSUaM+JpEAhQ19yGEQ5cuIYQQLFvmsXFIKC+FWt1gxodyHutIIj8ZJuAlwMKNOqL9sXbQl/CUYmjnM8ie677GY7T/JmZH+WSfk/3ZnZPQ1xVsXYrCa+v/V6Ht/aaKivKag4TX7+A1qbn5CH4DjJRqTt/Od2DAhN3g4/fdkhdm5tyjJ6leBPPmwSuvuEsLfaM2UkJNTfvqdsocZNC1q8kDD5RxwgnruOKKjThORt3Kzzfo1y+TUxAKWdTVxUkmbUxTcMcdu9G1a4jbbtuTI4+czpYtUQ49tAdHH917KyYAcMcd83nnnQ2sXNlEKiUxTcF11+3J8ccP/BYPuT2uv/4DqqtbOfzwAZ1M4N+AHfLoSCk3ZNnlvik/80ePp59exBlnvERpaYhNm7aOtpVStrN1mqZBTo5FS0uCUMhi110rGDOmlLlz6zn00G707ZuHYcAee5QydmwRM2c28MgjG3n44THssksxAH6/oLTUR9StElxdrQvjRPC2kKjdhJL6s5OpJO0jYLKibGQcxSikssc725K8PTH4kW0VpOwo/l4Tcr0XTVi9TAkydYq8PoUs/8LuA+GLVW7YpedevFM12pXC2B70ifq5pGBdg+ezvgf3mnm5SpqOtmad68DeY+HDD8k4lHWFWAl3/iMjiesLS+lNOfZyG3VMZZc7lJfDscduVhnj6RtP0a2bRUmJQf/+n3DzzX0YN64IcDj//AHU1yttYs89y/jooy3ss08FGzacTF1djC5dVDLDrFnVNDYmOOKIXunv7WWX7cJzz61g7dpWzj57KOeeO4IxY8o499y3efHFFVxzzR5cfnmHAug34u67f8JTTy3iZz/b9Vud34l/DTuiJG8QQowHpBDCL4T4FbBkJ+/rvx5du+bhOJJevQq3GnvvvbX4/X/k5JNfTB8LBExWrz6DZ545nDvumM9LL61h2bIGTjqpDzfeOI5Jk7qzZctk3n13fwAKCixsG/LzLc48cwmxWIJo1GHSJB9Dh1rU1ycRogVv2E9uLuR4y7l4iT60t9nrPgHepDJNd2w3RHR70r73HOF5n22a8UbobIuxdKQVeAl/B+aez9egqodmJYB1dIltMoHs6KEOtCBDQElB1nxX8m+Lug1pshvZWPDJfNJ0WrjjhqdUT+5WZXc6cpjozTgcdVSQxYvLWbAgSjyunRAmBQU+LrywO8uX787SpWFqamKcc84CdtttJscd9wl33rmCggI/l18+lP32e4uDD57OM8+sxbKMNBN45pmVjB//EhMnvsHrr69L76CsLEhTUwIpVfTSnnt24ZVXVjN16kIaGuJMm7ZoGw/2m3H00UN44okpO6RNd+K7Y0cYwQWo0tPdgY2o2kMX78Q9/U9gwoQ+NDT8hk8+OXursQULahACZs+uanc8N9fHL3/5Mffcs5Cf/3wm4XCSZ55Zk06jLy318/bbm3n88bXss08JJSU+iot9jB6dh3QjUBYtilJTo4iGlJm6ED5finBYEtGRpt46P94oGb/nuCZ82yL629EIAjonQRNjk621AC/x9xB4kV05dEdt+h1FKX0Xk5BG9h49GkpKQkOYrRmZ4Tmm6TK0b8LjPl8pwB9s34ksHlfO4gy8NTwMDEMVmhsxQmAYDqGQwddfJz2JZ2rx3FyL3/++O7m5Fn/5ywD3u+SQTEp+8pNulJb6eeihley33zvU1dnYtmT+/HoCgYfIz3+YE0+ckW5EIyXpCCGAvDyL0aPL6Nu3gBtu2IOTTnqLe+9dyMCBxYweXcbjjx/5Lz/qTvxnsEMJZT8k/Dc4i6PRJOed9yp9+hRx441bl2NKJm2ef34pu+/ejX79ituN3XvvAu6+eyG77lrJ1KmrEELwxReTGTu2jJkzazj44A9IJiU+Xw6pFFx33UBuuGEIra0pZsxoYb/9VKP7v/ylkTvvjNHaGiJDifNIU8WgeiGISjKztp5CwH2vSx34PK8dSfda8tVE0Gu+yUa209nrl9BStZ5nbnW2+yA977Ovsy1tZXvQ95J9rtdp7Hj+Okow8yaZ6ePaERwjk2yWAhEBGVUMvCCg/A/D+sFns7K2ZUjy8x1aWhLon2tOTopYLElRkU1Dg0r6MAybkhKbtjabv/+9jGuvXU9dXQzDgEsvLWfq1HU0N6eAGIGAwdtv70mXLj5Gj36DeNwmFDL49NPD+OMfv+Kpp1YDkJtr0dp6Bh99tInBg4uprMyhri5KcXGASZPe5K23NvCPf+zDEUf0YsCARzAMwUcfTWGPPbp8i3/A94+HHprHp59u4NZbD6OgYOv8iB8TvmvP4k78i1AFsRZx880f0di4dVs/n8/kpJOGb8UEAM4/fyRffXUy//znfvTvX0BpaYBDD32D115bR69euelKpQMH+vjjH4dyxRX9AcjLM5k8uYSSEoviYov99w9x9tl+LEtRK5/PY1rwOmK15UhrBd4IH51Uti2rhFcKdsMh25Va8Er22RJ5R988w/O6I/JJRyYm7+fv69udrQl4r+XVFrZ1v9ndzwQUFLn+iwAMHQZtberv6wVshcGDIRp1MAxBv35w7bUGf/iDhWlCQ0PmYUkJhx6aR9eugnvuaWSXXZStyXEkt99eTXFxpshRr145hEKC/v3z6N07Bylh+PAiRo4sTic37rJLCe+8cwR33LGIxYubqagI8eyzq6isfJhDDnnVjVJSDe57987nT38az9VX78a4cZXf9kl/7zjvvFd5+OGveOGFTmv29tDJCHYC9tuvNyefPIJrr92P4uLQN5/QAT77bAvr1jVSV9dGQ0OMm26aR58+ubS1Hcf69Ufxyit7c8ghZZx66lzuvXc9OTnvs/fen5NIOBxxxHIOO2w9d93VwAEHKEqeTEpKSpL4fJCXQ0Z6dfsIW96YdzzvvdKw7pKVbbPvyPGrxywY3BuEzqwFMKCyEApyoLKYDGPqKARhe74Dk22HO3gI8g73MM6y8W/TnJSdQZx9fvZ5njLYGi1hOHR/dXzhYjBzlfM9koTDj2h/+pIluBFhJrvtJrjppji/+pU2A0mEsDj++BBjxvh44okW1qxJsmBBnF/8oov7f3WYMKGQ554bmW5numJFG3vs8Qlz5zazcqWqvDdnTiPRqM1RR/UiFLK47LIRhEIWv/rVbC65ZBYrV7bwf//3JY4D8+bVccklIzEMwZ13LmDGjA2sWNHMhReO3KpEe2NjjDlzNv9HKoVef/0EDjigLz/5yXePZPpfRmezz52AUMjHI48c853WGDWqFNtWdWWklCxd2gJAW1uK0aOn09iYpLDQT3OzxZw5YRIJh1mz4uTkzMK2LSCHYNBi9uwkKkkAWluTJJMBkkmpvJzaHCOhMAfqW1FESxeag4zWIICkxwGbFV6a5g/6uD4PWFbtNoRxAJ+6dH0bjOoLc9fR3pmcXQ+oozDVjuDtB+yxv4Nb2fObsC1Glh0S6m10o4NyvOGqbvmJIT1h6UrPWj7Uc9Wd4GyoqidtPoq5EVkiAftOgPffc3tFeDZj25IXXlCbSSbVJvr1E0ybVsCECTVuCKkPcPD7k0yatIqSEj/HHlvISScVU1+fYtOmSLpgnWGA32/w4YeH8PTT65g8uSe5uRaXXz6Syy9XZaaj0RT77tsFv9+kd++8dF/iUMiia1dlM6yoyOGss2ZQVdVGZWWIG27Ys91jHD/+MVatamLq1CM49dR/b32g66+f8G+93n8rvpERCCEqgVuAblLKI4QQw4C9pJQP7vTd/YhRXBxgr70q+frrBg47rDe9euVRXR0hmZS0tiqq09aWBAyiUcf9YRtuyKES9e+6qyt//WuKxYsT9OsXorHRpK7OpYqOgCSY+Yq21degaIin/IGnSkGGUNqob40DQ3vD6lpVeqFNh4mKrD/33MIQNLQBhmtiFzB3rWfdbfkBsgh0mqF4x701/DtiHDqKNtuZu41rtDsms97rtbY3B7dBjQ9lXtPXs8iY2hxYtArFo8Oko7ekhGuuU5+HDNG9iiXLlysuNHasxerVNrW16qIbNiQ488xmV9rW3NcmGlUb6t07yCWXVLD77nOQEvx+k9xci48/3gPLEgwcqJLL9t67Aikld9+9FIALLhiMEIJQyOK99zJO3wceOIDzz/+Am27anb326sLmzWeQl+fjvvsW8tBDizvMIcjP9yOlJD9/O2VlO/EfxY5kFr+JalZ/jZRytBDCAuZJKUdu98SdhP8GZ/H3halTl1BdHaG+Psntty+isjKHs88ezBFH9GTZslYuvXQRbW2KOhUW5nDjjQO54ooNJJOSSy7pytixpZx1Vg35+RZtbQWAhZRZnt9cEAGQ2lGsCbJ+r/1r2gzjJ10Z1PS7yVB+2hF0ITyagyaStue9V9L2RvZARtuwsuaTdS5sTdyhfX/k9jlXmT+vvd7zdpu/BG8Jbn0Nr+/EW0Qu6ZnjzTDWdZpcJ3K+Aa26JloclWGMZ14chg+HK6+ECy6QhMPqAsXFcRobbSorJaefbvC3v8VwHG3n83YSSnHGGXlccUUJP/nJMjZvjlFaKlm0aBy5uSbB4Nacd/HiJkaPfhmA+fMnMWxY0VZzpJQsW9bE/Pl1TJ26jH/8Yx8GDmw/L5GweeSRxey+exdGjSonkbCprY3QvXt24opCY2OUoqLgN9YQ6sR3w3d1FpdJKZ/BlYOklJ7q6p3YWaitjXLOOR/w+99/SU6OieOoWjF//OMCiot9nHVWP555Znfy8y0KC01OOaWSyy5bhWGk/p+98w6Tosra+O9WVcfJM+ScgxJERAEFATNmXXN2za5xjd+qq7usac1rWtOa1qwrioKiIoqiKCCS40gchmHyTMequt8ft253zTAEd1c3zXmefrq76tatW9XV59z7nnPew+DBeSQS8PnnylFdX+9kok22qYBlqFlox7awzx7N9vn5gPyRMp44OhmqWZTNwI6eT6C5c7V5OKeGgppx+vjH1mTF4MfuhW+bfvmjjZrLjsJY2YER0Mfqd79vwDtvSZHyQ0T9JXv9eRQ0O86G+gRZQ2mAaKHc7+LFcMYZ0NioYJxhwxxqa9XNKC+X3HdfCtdtySuufrRPP23k0UcrKCtLEwhYxGIOZ5+9PGMEpJTU1WVDrzp0CLPXXm3o1SuPo4/+mFmzymlsTLPvvu8wcODrLF9ew+23z2Pw4Fc599xP+eCD9Tz5pHLCVlTEuOWWr7jpptk89dRCLr74I8aOfZU2bR7lmmtmbtcIPProt5SU3McFF7y/o1+gVX5i2RUfQaMQogQNAQsxEqj9SUfVKpSUhDn++F6UlcW45poh3Hzznpxwwqc0NKTp3Vv9qQ47rD2bNx/Mp59WctxxC3Fdg2TSZOHCGAsXxlmyZBDRqEE4bPL99y7XXx/is88sbrvNdyJPuZeth9NOUrDLt0vJ4tlaobVUQlHDRH7nqQlLytkxp49fSbYU+7+9Uo8tiX8q3zze3w/j/CPih7n82zypbFDnsXXymJ9Ez0e3sY0xiqCitByQ+p9o0jQs1ney9u0lriswTYnjOF7tCsmppwbJy7N46aVG9t/fpH//KA8/XMHatQmeeCLGQQfl88EHlRiGy+ef12R6Pf/8eTzzzFoef3wPCgsNzjvva+Jxh86dA6xd28hLL60hGBTMnr0FKeHccz/jkENUzePBg4tp2zbCiSf25pFHvueKKz7DcVT97WefPZDc3CDdu+excGElL764lC1bGnj88YO3KXD/ww+1WJbBDz+0qpR/peyKIbgaxTPUWwjxBdAW+MVPOqpWwTAEr79+CFOnruWSSz7nzjtHMnduJRUVCV55pZQzz+zDunWN9OjxgTfbDxEKhejcOciaNWpGOGjQci69tBP33NOIZQkuuihKhw7NNKxNRhk/8RrUxcjCQRrS8R/iTzTD964VXUuRNJCdte9sLSmafdZQjvBI5VpS7N4xeTlQH2t2fn9ilx7n3yO6z+Z1CPxQE2SVvL5Pza9Z32/teLbIKv4gyjBo3iHfcYMHw9y5avCOAyUlgspKhU+tXCm4774cVq1KUVwseOCBOs9XZGJZkmBQXXy/frm8+uoAkkmHbt0+YuvWGKYpKC2Ncc8932N7XvWLLx5IeXmMa64ZRIcOEU47rTeTJ6/lvPP6c9ZZ/TjmmJ4MGFBIMGjSo8cLrFvXgGEIwmGDI47oyQkn9OW00wYQi6V56qmFXHnlx7zxxnImTuzFmWcOanJb//CHcRx4YE9Gjuz8o3+SVvnnyU4NgZRynhBif6A/6hFfLqXcZs7SKj+NXHTRZ6xb10CPHnn06ZPH5s1xunfPRUrJqad+Q9bF43DllV25++4yOneOsnGjgevC3/6mPLThsME338A999RRXByhqsqb4nvOTKsY6mpRM3+bphTKnsITBoQjkBSePvWiZYJBT0E3h0O0+CEbzRPULLIos6+589hnGFLNnbV6JeIpzPok5EXUe5Nz+42LPv7vMQjbw4/0rN+v8PX16RwNr3QlAZrSeviztq1mx/hkwQIYPjxMPJ4gkYD8fIPKShfThHnzbM4/v5Jly/xUp8oyjx2by9atjYCgf/8chgzJZdGiOrZsSQEmF17YjZNO6sxnn23iyy8rOP/83lx/fVNl/cIL45t8HzKkJPO5okLVMy4sDNKtWy5/+tNYgkET15X87ndfEQya3HXXGL78sowjjui9za0LBEwOPrjXdm5sq/xcsqvho3sDPbz2ewohkFI+/5ONqlUyctttI3j66aWccUY/fv/7QhIJh2jU4pFHVvLll1sBAyEUfXVNjY1lCfLy4LXXeiEEdO8eYebMOO3aBZg0KUE6bVFVZSGEVCUsPWVsJ8g6gvVM1Q+5pGDK/XDM71XED5BxGqfSZDOTW4qr11z8WnxPXTSkYueBbIRScwhGK2/D107vo+k561O+7RYts5CaXpc/1hg0X634+st8trfTXn8PkTUE+l6nffv1PWxhbEOGCCZPDrPPPg2UlgJYXlRRioEDgyxfnvZoRSTBYJiBA+Gvf+3OE0+UsXlzkrq6JL/85TKeeKI/p5/embKyJBdf3IO9956OEILy8uNo106FGjc2polGre06cCdNmsujjy4iHlcO6srKBLW1KT77bBMnntiXBQsquPfeuUgpWbz4LK69NhtS+s03ZXTpkkfHjipiacuWxkwN4lb518hOncVCiBeAe4D9gBHe6++jFGyVHy1nnz2Azz8/lgEDijAMkaEDLi5WAetjxhSyYcPBfP75vvTrl8MZZ7Tj44+H0Lt3mNNPX8uxx66itlZy1lk1rFwZR7PJmabMctt4jKIZJeZXSN5/Mz8Pbn4G7BT0bOO182Py28P6NeeQhoaspm337QfDu5GFVJpnJzfPWWgeOUQLbfyiDVQz+btdB80NnX8V5HeG6/unaTq0gbNpOl5/bWZ9vK+spZ9/6M47oXNnQUGBPsAhGATLcpkypWnRh1TKZcGCFJMmlfG7322ivNzl889ree65zZSWxnnhhT356KNRFBUFMU3lR9LlJZ96agW5uX/lkku+Ys2aOmbPLt/mNjz77DLKymLeilTwwANjuO66YYwe3ZETT5zKnDnl/OIXfTn11AH06VOYOW7q1DWMGfMSgwY9g5SSP/5xNh06PMAll0zbxR+gVX4K2ZUVwV7AbvI/jZTov1xOOaU748a1o23bENOmVXDGGd9TU+NiWRbnnNOBv/2tDilh48Y0d9yxBchBreRU2IptO9h2SMV6psiQzYkETLoZ/joNlqwjo9TqYjBvKZADa8tRCqy5EvRBO+3ywQ3A1phv0P6ZvadQp6+mKVTk788vO5ssNoeUmpe+NJttaz7z1krYhmHdYP56b3tLT73fN+CPqPLvN8nAboVFUFOptgmdlOeNd989YPZcb5WVhOJOULWeDJyU1NCdBVVV0K4d3H57iOOOi+O6BqmUsiy9ehmsWaOXctmC08XFBum0JJ2G0aMLmDChgLw8gzfeKOOII9rRqVOEESOKmDu3mrvvXkr37hH+8IcFAMybV8mQIW/hOJKpUw9h3LhOrFxZw/jx7xAMCsaO7UhdXZI+fQo466yBFBaGeOSR73nzzdW8994PNDZevM2tKywM4bqSNm1UrYxNmxqwLINNm+q3adsqP5/sSh7B68DlUsqyn2dIO5b/pTyCXZGPPqrgkEPm4LrKD7D//m3p0CGXF1+sYMiQXFauNLBtcN08pATbLkZKPS0PEQgJIjlQByAgGoVkvpe0pfnqBCrT2KApAZ2ezZpki6JZvm06f0grfn9egF8Bb0+R7kz8xqF52Knf/6A/S7b1SzTrLmM7BAxsB0vK2NbB7e/PabZNczd5cFpmn67vnCC78nJRyWQOTZLKMm3SZEjqhICSElizBt59N80558TJzVUx+FLC22/nc/zx5TiOpKhIMmiQyahRYS6/vJAePeZj2w4dOwbYtGlvRo+ezTff1PLrX/fkhht6Ulz8t4yvyTQdHMdBCIf77tuLBx9cxKZNjTz99FhOP70vL720grPO+gTblhiG4NRTe/HKKys54IAupFKqhnJOToDDD+/Jr341pMX7XFkZJy8vSDBokk47fP75evbZpxM5Oa0JZz+l/MN5BMASIcQHQoh39GsXT3yoEGK5EGKVEOKG7bQZJ4T4TgixWAgxc1f6/U+RysoYgwc/xiGHvEAikea5575j4cJtl9labNtlxoy11Dfxdu5YGhsdTFNSVGTw1VejmDZtT3bfPYrjmNTVuSxf3p/Fi/uRm2tgmsLLQFUeyj897GIA8QYyDspYHBytkLRC817Cc/R2bUNTVuTmEUT6s579m83e/ZAPZLNu/TkB0BRu8cNGO8tNaL6v+f6WVhw0tSVS+sJgW1o3t7RC8a82dJU17UTW99EPjWnfih9O8h+no46EGs/WrZKBA10+/dRl770D9OunurEsWLYs6ySurnb4/PMkd99dzsiRa7jiinYAJJMu5eUphg3Lx3EkgwfnUlgY5J57hiIEhMOC4cOLGTSoiBtuGMQFFwxgyJBiUinJOed8xpw5WzjhhN7cc89oRo9uz6mn9mHEiHbYtkvHjlE+/3wTs2dv5v77x2zXCACUlEQIBtWPHAiYTJjQY7tGIJVy+OijNTQ2bq/CUav8M2RXVgQtknVIKXeotIUQJrACOAhVx+Ab4BQp5RJfm0LgS+BQKeU6IUQ7KeWWHfX7n7QimDnzBw466AUcR3LffQdz7bXTycsLUVl5XYvtb775c+6662v2378r06ef1GRfaWk9Bx74PsOHt6GgIIennlrNPvuU8NVXh/LBB1u44ILFDByYy9Spe3HLLeuZNGkTICguDrFkye7k5BisWeOyxx6NSGnSp49JRUVUJSgJIJ9srkAOajYfJhtKGoazjoS/fQ11cbLYt84q1qsCvc2/OmiO7/ujf/wZv37RxzTPLtbin743F/92t4XtWmf+mEL3/uQ6fyiqjzLCEOBq+mn/rD/lOyZJ1jns1YawXLAbstssCbkBqNnsO5+rO9Mpy+lMx716SUpLY776E9ryxBHCoUcPlZywbl2SUaPy+PzzIaTTLoGAusnptMvZZ39NJGLyxBMjEAJuvHEetbUpvv22nG+/3YplCUpKwhQXh5g//1hCIdM71uGdd0oZM6YTr7++CsOAiy4anHH82rbLE098z4ABxUyY0O1H3HAlV145jUcf/YZDD+3DO++c8qOPb5Ws/EM1i6WUM4UQ3YG+UsqPhBBRdm3hvjewSkq5xhvEK8DRwBJfm1OBt6SU67xz7dAI/KfJmDHdefDBQ+nQIZdu3QqIRAKMH99ju+07dszxZlfblKdizpwtrFvXwJo19WgcZt68KkDFlW/YkGDdugQrVjRy110b0GEpdXUunTqtZOLEPN59tytz5uSSSEgKCiyG+CdteoYfJZP9in+SJuG56agnRvGaNX0KfJm2mf6arxB87KOZCBtdtN7vUNXnbx6j7x+rNgT+5C1vnJl9/ogj2NZ4ePt2ZFOajN+fE+A/jzdGtyWjZsC+A+GLpWRXBDpSyLvmow+ENyeTuf8P3gyXXuXrvxmUpXw92QsvLnYpLRUeQaH/RkikhNJSG8NQ2eXDh+dy660r+eMfS3n22cGccEJHvv++htdeW4/rShYurOGHH+rYsqUByxJMmXIAUkpycwMceOD7VFUlqKlJ0r69wgjvumsuv/vdN+yxRxvmzMlOXnQp1smTV3HllZ8ipSSRuALTVDfr//7vMz74oJQ33zyGHj0KWrzl8XiahoYU6bRLly75O/uFWuUfkF2JGjofeAP4s7epM/D2LvTdGVjv+77B2+aXfkCREOJTIcRcIcSZ2xnDBUKIb4UQ31ZUVOzCqf89xDAEF188gmOPHcjw4Z2orb2BN944MbM/FmuajnHJJXuyefOlPPfc4Xz00VoOPfRNFiyooLIyzscfryc/38I0lcbOzTV5551xABxwQAmuqwqcvPhimWIXBcD2wgth5sw0xx5bTqdOkv32C9ClC3Tt6rXz49ktzaC1MtWwhqfwX77ct02/t6RR/asDP0wTQK06mu+DrMFpLv7VgR9u8hui5kao+fF+6MncBSOgj/O/+8fi3xdgm3OPHAxBf16Gvh9e5vabn5C51kgEKrd67cI0uwfqBu21V/YGWBbMm+dyxBE5PP10MYahliKm2ZSIKRg0CYcFDz20iTvu2Eg87vDJJ4rsaI89CjnnnJ4EgwZz5lR5OQaCCRM6MX58Rw49tCvDhpXwyisT+OCDwzJGAKBPn0Js26V//6LMto8/Xkco9CCnn/4+w4e3p1OnXI44olfGCAA8+OC3LFiwhWnTVAGc+vokI0f+hQMP/CvptDJyl1zyHs8/v4BjjhnAI49MbOlXaZV/kuxK1NClqNn91wBSypVCiHa7cNyOUFT/+YcDB6Bck7OFEF9JKVc0OUjKJ4AnQEFDu3Duf3v57W9n8rvfzeKOO8Zxww37Zra3a6dWAzfc8Dlz524hGPyS6dM3kkg4CGFx9dXDOPronowYUYJpGvzhD8uoq0szaVJf7r13Lbffvg4dsxkOG3TvHmXlSkl9vcPbb8fZunUrV17ZnhNPdBg/XjBhgkV1A0z5yLMBKdQvYQJxmlYsgyaRP6c8SjbUsaVQz+b8QH6MH7Kz3SAZMrZIAOJp337dtz+PwC8tha36oSet+P2z9RZgpkEdYdlmsHc1rlTQdDXi95lorN/r697JcNXR8Ox7UF1PU8OZ8I1JqBrHU2fSxGCZLrQrgbIytfGbb1TuiJQOtm1iGDbdu1vMnq04LvbYI8Azz5Sw554rMoNKJJqmRN9//wDOOqsLUkouu2wBc+bUkk5n05kDgSCffrqVp59exZAhBYwb9x7DhpUwZ84xTW7D8OHt+PrrE9lrr6xK+P77CqSUvPdeKTffPJIffjhvm9v38stHMnPmek47bTcAli2rZN68zbiupKysgW7dCujVq5h02mXgwDatOQY/seyKszgppcx4ajz20V1RxhuArr7vXYBNLbSZJqVslFJuBT4Dhu5C3//xsnTpVgIBg6VLK1vcf+edYzj00B6cfvpAAMJhk2uuGcq11w7hiiu+YvjwKfz+94u56abF3H33SsrLG+jVK9fjpLcJBCQ//LAnu+2WixBQVASGAfvsE2TKFBvXtfnyS3jvPRi+h8K2u3VCGQH/LFcrKR1BpLdp5e4pegPf95CvrVasehbsJ33zz849H0Nc+rb5najNn1S/Uxgwm8/UtbL1n9Mv/nOYsGjLLhgB/3j0d2iaeNf83njw1f2TobqRpiJQ90rfG1Tb2fPJlhI1wBFQ1izGQEodv+sihMG336aZOzeN6wq++y7Jww/XcM45Jb4jlAU+/vgSevUKcPPNpRxxxFzKypI89lgpS5bUc/rpPcnLU5XPwmGDVMrhiiu+ZunSGkxT8MMPypFh2y633z6Xxx9fxNChLzF27JusXl2bOdOll+7B8OHtqalJcvTRk71nsqkceWQfzjprUEbB77VXRx544GDuuGM8xx33Bnfe+SU33zyW6urruf32A3byw7TKPyq74iy+G6gBzgQuAy4Blkgpf7OT4yyUs/gAYCPKWXyqlHKxr81A4GHgENS8cA5wspRy0fb6/U9yFu9I6uuTfPLJDxx4YE9ycoLU1upU/W1pKJcsqSIvL0DXrnksWFDFnntOwXX902uDN94YTc+e+ey117dIadChQ4DS0pG8/349a9faHHtsPmPHbqW83GXChBKmTQOtsfv1g8uugatvhrQOETVQTuMwWaUUBCwUbbV2Intho6YFToCsEdAwiR+28a8a/E5g/Qj6VwItvWt/QnPxz/L9kUx+fF1PiP2UDs2dxX6fw/Yki7ptG5aqqSEcmkZcpXzHaJpp/T3ma5cGGnztE2AkwdXVTj0fsMoKT/g6T/oGrzisDcMmHJbEYrqdOnmbNqZHOeFgGDZPPTWIWCzFmjWN3H777rzyyg+cd97XOI4kPx+iUYuzzurNHnsU06lThI8/3kTPnrmcf/6npNMOhYUmti1Zvvx0OnXKzdymN95YzgknvIdpwpQpx3LooT2b3MbHH5/PpZdOp3v3AtasuTCz/d57v+Laaz+mqChCZeXVO/kxWuXHyI6cxbtiCAzgl8DBqMf8A+CpXUkwE0JMBB5A/X2fkVL+QQhxEYCU8nGvzbXAOai/1FNSygd21Od/iyHwy9atMXr3fgTXhZUrL6ZDh+wfKpGwKS2tZd26esaO7UI4bHLeebN55plSQCCESffuUUpLj+SCC5bw5JOb0Ro2GMzDdQMUFppUVgaRUk3Xb7wxlzfeyGHlSh9O40G8RgTMKAwZCssqoVEnm3mKXYRBBsjSUfihHx1ppBV+uNl+/2xfi57p+wvB4/usj/sxgKC/rdNsmz/O36/Im7fZ2fkcXzt/9JDfqPkVvr+YvU1Tva0VfRKl133fOxZD+WqPDiMDN0l0cenevR1Wr9ZLjWyH0ajLkUfmMnNmNVu2pMjJcb3qYurEQtj06AHr18dp08YiL08we/b+lJcnOfLIT+ndO5fddsvhySdXEos5nH56L8rLG5k+fSNHHNGV8vJ6evXK56mnxiEl2xSdcV3JgQe+werVNXzxxcl06ZKlob7vvjncffccystjGIbAca7N7KuqinPLLTOZOLEPEyf22cmP8NNIKuXw3HPfsffenRk6tMO/ZAw/hfyjUUMu8KT3+lEipXwfeL/Ztsebff8j8Mcf2/d/sqxeXc3Uqas488wh5OeHSKUc0mkXKdVD6Jfx4//GV19tBqBfvwKWLz+Tp58ezV137cmKFXV8+20NEyd2BFTkkD9cJuVRdVZV6emwwiEefzzFiy/mcvTRKtksE+seAGmBk4K5i1AhpSbZCCIB0iU7028Ox2gjIGharEbDJc3rIstm+5vP7P39Nwmj3Ino4/yRTVpJ63GkycI3drPjDC8UdEfhpX7oS9+/Zn1ktlkQMRX+nyGe05xIOuxWGxY/pGTBls0+bqcWrj0vTyCEQV6epK5OWxxBLGayfHmCzZvVb9+nTy5Dhgiee24LIMnJUVnnti3YvDnF5s0Ow4d/xLp1jYwf347bbhvEuHFTSaddioqCHHRQJ+LxNIsWVXHWWf34xS96cvnlnzNhwju8/fZh2xgCwxDsvXcHZs/exNy55U0Mwa23fkljY4pOnXI477ymSHBxcYSHHz50Bzf+p5dnnpnPZZdNpbAwTEXFtTs/4L9AdqVU5UK2nR/VAt8Ck6SULYPcrbJdOeGEN1mwYAulpTXce+9BdOqUx6JFF+C6km7dmobS6QgKACnhnXfW0rNnHoMHF/OrX83jtdfWk5trkZ8f4qOPqr0QQgCXU04p4uWXG3FdwbBhAebPBxBUVwc58shGXDcHMLnnPrjGA/qkDWYEsHzhkDpL2J817Dlju5ZAtWhWqlKvFnSkEbScQKaVv05g868G/ApRK8jmUTo+4xAwIN1C6GamnQ791P35Q0Gb01KIXSSk85+reWip35fgQCjgGQLtxPbBU5blq2XgNm3j6DBTAcUFUOX923r2tCgvt+nUyeSKK/JpbEzzq18lyFoZh+++05AR1NY6bNigLv7kk9sxcGCQW29diWUZ2La6wfG4jZTwySfljB1bhhAWBx7YhmBQcP75n1FcHGLDhlMwTQMpJY88shjDgBkzNnLaaf2a3JqvvtrEyy8vI5VymDVrI0cfnZ3dP/HEIXzwQSl33DGWTZsaeO+9lUyc2OffxiE8YkQn8vKCHHLItmyp/62yK1FDU1GP+Eve95O99zrgWeDIf/6w/rvliCP6smpVNRMm9Mhs69WrqMW2n356HIsXV7FuXT2maXHSSZ8AUF9/Fps2xbEsQVlZnPx8k27dwlRWpqmvFzgOFBaCxnS+/15PQZV2VJWtBAMHSuIx0aQCmQ0KeYiybQin/q96TuH1cW+bVuxBsvkH/lDOEE1n+Lov6e3TFbuaK+DmSr+lME7Tcy+0pEc0j5FfwZrNtrcEP/kd2S4cOhCm+TNgmo0xGvBYVP0+Cl/SXI2+T954M3TU2ggYZO+1NlYpIBeEDbLRq/kMFBQIrrhCcOWV8P77LmefbVFQYCCEgZSQn69XB8pid+8O4XCajz+OEQ4HOfHENnzxRQ2GIRBCcsMNfRg3rojOncMMHz7dMwyQTkumT68AUhiGS3V1kjVr6unbtwAhBK+8ciBz527luON6bXNLxo9/HceRnHPOIH7721FN9p188kAOPrgH/fo9RmVlnFDIZPLkE/5tFO/w4Z2oqrr+Xz2Mn1V2xUfwhZRy35a2CSEW/ty1i/8bfQS7KkuXVrPXXpPZbbdC5sw5mrq6NPPmVTNmTBseeKCU+nqbW27px1VX/cDDD5fx5JN9EMLioouqvNwCE9MMMXp0PgsWhKirCxIMghACN+QpU+0gFt7nQrKx/gAFZGsXa+WuHctaeerwU8jO4P3FbpqLXi2kaVr/VyNafuzeH4UE2dm3H59vLlqx+nF9Dfv4Z/EtOYq9PgMWpJPbbtf9WAIcW62omuzTDmJ//3q/V50s4+/F+5xG+Qn0eFMoJ7JePTkwaZLLTTepNOVAAJ59Nsz69QlycgSXX17mSziTfPttd/7613Luv7+MAQNCrFihooAMwyESEZSWjqOwMMDq1Q0MGjQVgH79Inz/fS2GAYMH55JIpFi+vJaTT+7Fyy9P2PY2SUkq5WYyjo877h2+/HITn356IgMGFG/TvrS0hoEDHyeVcgiFTObPP48BA9ps065V/nnyD/kIgFwhxD5Syq+9zvZGRZfDj0vSb5V/UAYOLKK+/iyEUMq7oCDI+PHtWbOmkeuuU7Vj77prM0IILrmkK6tXJxk1KoJt63CWEH36RPjiC3DdJBAklRKYphfxo5AiAIQJwZCnn9JklXnzUExJ1h+gI4W0+MMt9Yw84GsrPD3vXwVoSMlfGVvPtJvnIOhzQFNMviVpnmFsZT9KbSh2sCJJ64il5r4Az1DZzQ2UDUbA8zPotv76CBo20yuTsLdPQ2x+4+ZbCeUVQH0V3H67vggT207RrZvBqaeq7Nv7769gzRqHYFBw4YWFxOOQSKgBXHRRJ2bODDBrVjWVlSmOPLIDhYUBamtTPP30av70p+EUFwc4/vjPAOjZM5fvvjual19exa23zuPcc/uRSjkcfvj7JJMO06YdTjQa4NRTP+C111by1luH07dvAcGgwVtvHdWiEVD9FvL55yp/dM89OzRJNmuVn192xRCcBzwjhNDKvx74pRAiB7jjJxtZq7QohrHtlHry5M0ZXvhkUmnIxx6rwDDguedysSxIp5VWdhzp4d9Zje04EDJV5TGtqGQOJHXGiAM920H7zvCVzgTRVcyiNHUM+xWpSXZ14aet9hkT6Ydm9LuGmPRsOEjTWXZLM38/lKPFh/ln3i2a8AZlutL+Cr+yB/bqCt/q/PiWztssecw/Hlf6zuevVGZ7Po1U0/ZYqNUUZAOAbO+YHCAOk34P114F6XT2wHDY5PjjGxg3zuDll/OYMaMrn38e5/rry/nTnyp45JEKXFdFDP3xj2uxbZdTTmnPI4+sJRJRCvipp9Zw113LKC4OsmzZRAxD+UmuumoAAKec0odTTunDtGnrKSh4nkQijWVJVqyoJSfH4ssvyzAMwbJlVbzyylJefXUFa9fWM3v29vmBRozotN19rfLzyk7NsJTyGw/+2QMYJqUc4m1rlFK+9pOPsFV2Kk8/vQ6AvDyTESNyyMsz6N8/yHHHFfHWW404DhiGAqZXr9Z4g09zCSjIAVMrQk1prxWiAaU18O0asrH8EbIQkB/+0cdrh7B/VaBpI/xOX3/IKb7t+PpoTkHhXxnQwrH65ec+Es0+G9Apv9nxGqLyFbNZ5k/k8o/Nv635tTb3NWjDBhlDJ/yht/5r0aumqO94/W5B505www3w8ceCW2816NBBcfJs2SJ47TWbjRtdunULcNpp+bRpoy7CddUEolOnAOXlKaqrbQ44oA2LF+/HM88oZPfQQzvSr18eF13Uh5KSELHYydTWnsg++5Tw/vsbMsP78styUimHNm3C3H//aPr0yWe33V5k/foGfvnL3bj66mFcccUwRo3qyE037cOPkdWrq5kyZSWtpU9+ftmpj+DfTf6XfQS27fLqq2sYOrSYQYOyS+5XX93IzTcvp7Q0oYrNeGE7QggMw8JxwuipeyhkkUpFEcLCdcN07SowQrC2DHLaQqM/4kX7C3KBPLLKtMDbpxWnpqEwUMYgSFMlrtedfoWL77NW0Boz90Mizfn+NU6ut/lXIs3DT/EK3vv79M32exZC6RbfNj+0JFvYpqX5CqCZv6CJP8LOtsn1Vg+NSZDebD8gIN3o69NFJZnZQFxdTp6hVhCXnwMP3AvVVXDIgfD23xQ237lzPdXV6gKvvDLE/fcrmpIzztjIiy9WAhIhXG66qZjKyjQjRuRw5pkdWlxdxmI269bFGDAgn/r6NG3avIxhCKZPP5j99mvPhg0N9Or1VwwDXnnlIIqKQowf/xZSwmuvHcoJJ/Rt4YbtmrRpcx8NDSn+8pcjOeWU3f/uflqlZflHfQSt8jPI/PmbqaqKc8ABPbfb5sUXV3HhhbPIzQ1QWXlGZvtJJ3Vm/Pg2jB49m0DAJBCIsnBhGikV7JPFGExSqTBSmh6UJKmsFOS3BwQ01kB+e6izaeow9j8luri9xrb9M3UdDeOfefuqljVpq1cGfgUeomkNX5p990NHzenp/XkKOntYQsofeoqvjatWORnIqiUfwfbmSM3b6XFZkCOUom9yPk/Bjx2iSn1+ODd7zrTuz+/zyAZ3IfFov+Pw27tRxlLAB9Ng4kRV1L62NoSyHrB0qbJc++23ni++iANhhEjSvj384Q8bME1B377dqKuzKSzMwoOTJ2/i1FPnEIng+QtG8OKLK3BdSTBo0LWrMi5FRSEKCoI0NKQ56aTp5OUFePrpAwgEjH/ICADstlsbvvpqI716Ff5D/bTKj5ftQkNCiBO89+1rplb5p0hdXZKRI5/liCNe5euvN2633eDBxQQCBrvvXsyMGU0LxrVrF+LSS3uyenWKzZtjZL2R2ZCdQAAfTTEEAoJYDDZv8Jo5MHx3lBGAzOzb1AlPBlj+HAEbtRrQEIf/OL9/AO+79idoyEb3paEYDR2FfJ/9ldAgqyQ1M6c+t98/4IenzGbvWprnAGij5jduLUU4abF8bcm+J7SBNJu9DHj/O/hwEU2NIWRXU9pH0xzW8htiX8jrjBmqfKXraipTi8suiyCl5OuvszWMr7++LRs2DOGII4po397i2mtXc/DBC7jrrh9YsqSBhx5aw//93yJisSSVlSp8yXEkn3yiEhlfeGEM3bsrF2FOToB1687g3XcPAyActjjrrAGcfrryJaxaVc3TTy8kHvdbc1i0qIIxY/7KSy9tLwYXZs48g8bG69hnn+Ykxa3yU8uOfAQ3eu9v/hwD+V+WcNiia9d8gkGTjh1zt9tu+PA2bNhwCl9/XcnEiR/xzTdbM/u2bElSWZkmnZZUV6ewLIFpCizLRLGEqOS0xx6LUFDgIARYlo3wz6SBGd+QnW1HgKAKi8QBQ4KtHcQh1GpBi99PoDFurcy0P0HrK328Vtj65fcf+PH6kHd8gKbKVR/jT3bDd7wfkvKvWvyGApoakubn39E/pAVfheOLTOpU3Oy8kqZ9a2Oo+/BWVEF/qK0HTYkcsjQVTRzTGu9S1vSuu+qwrDJsO0A4DKbpsGRJkjFjVvHww73p0CGIbRts2ZLgxhtXceSR87nqqsWsW6cMgGHAtGn7s25dA65rMWFCZwoLg2zZEs+cMRKxWLiwih498ti4sZFrrpmd2Xf44W9z4YUfce+9c5vcqmeeWcisWRv57W9nbfd2CiEIBFoik2qVn1p2BA1VCiFmAD1bKk0ppTzqpxvW/5YEgyYrV16M68qdhtFFIhadOkUpL49z9tmzeeaZUWzcmOaEE76lS5c8DAPy800eeaQPRUVB0mnJ2WdXUVkpiUYFl12WVrQSOMTjnsZJQUlbsAqgvB6wYcAAxTWEQyYqKByEUK7KJM6IP3y0JVjF7xD1O4i1QtMKXEcDhbztCV9/fqWZbrZdn0u38WP6fuhJh2P64SUtPiqINlGoioOrJ9R67NvzGTRfNeiQUAkXT4A/vg91Md+163uir0GvDHz9pyAbKeWFzkptEGPNr1MQCgmSSXVxn3+eznSqSIMF77xTB6Q577y1fPddI6ZpcNVVXZk0qZQxYwpYs6aahgaHO+8cysiRxey/fzteeaUUgE8/LWfGjI10757LypXHZy7z2mtne8+rYP36BmbM2MDNN3/FbruVsH59PSNHdmxyWy6/fDhlZQ2cc87PmnbUKrso23UWCyGCwJ7AC6gQ0iays1KVP5X8NzqLVWWoLQwYUEIotHO3jetKhgyZwuLFdVx4YR/22qsDF1+8ENsOY5qCjh2j7LtvMcFgkC5dItxxh5rNtWljsXVrPtk4xjCGIQjnQMeucP3/wcW3QCQKsYgX/hjyXsVkZ/n5KF0TIQvR6NVALk2pKPwZx3pm7A8nbYkB1EUpQO089ieUad40/2Obana8P2TUT/ym+9D7mpO/+Q2ZzfazZKTvXfelj23u6NbvPqdx5nz+saV84/FxyB08GD6cqRLW2kagbK13b3w5B5blYNvqS25uAw0NLvvvDzNn1pC1hAlKSkxGjDBp3z7IX/7SFyEEW7Yk6d79AxIJm27dIpx3XjeuuqofgYBBQcFLHl+VZL/92vHZZxOpq0vxwAPfU12dYO3aBk47rS+HHdaNffZ5jUWLqhgxol2TSmWt8u8jf5ez2KtB8JUQYrSUskIIkac2y4afaqD/q/KHP8zid7+bxRFH9OFvfzthp+0NQyn7xYvr2HvvEs45pxsDBuRy4YUrWLkyRlmZzauv1gAGkYhJQUERtbWCY4+N8vzzDiNHhhg8OMioURLHFZx5PqwuhU8+hslPw9FXeVw7GkYp8E6sHar+Wbz+rLONm1NB+OEYbVj8EvT60SsGjbFLFPSUJGsAtEHxRwHpPporZb8D2J8f4FfIzRdfhm+/h1DkBaA+4TsGtl0F6Nl+85wF/yrJv91/jD6vX3TimYBZ89UmOwFltWThsRTk5UB9Ndi26syyJH36WHz3XYpvvvEPVFmcykqHDz9swDDgmWf6snRpPU89tYEXXhjOjTcuYtOmRm69dRGNjWnOPbcXhqF4kGbPPoI99lBRao8/vpjbbvuGdu2ilJWdlRmy6RWEKC5uSqO+fHklf/rTPC65ZBi77daaOfzvKruSztdeCDEfWAQs8UpKDvqJx/U/JZrEy7J2PbuyrCxOICDYvDnBW29tYt26OIccUkBp6b4MHpyL1oTxuEuvXkECAeja1eDTT/OZMyfIww+7nHGGpGILtGkHBOGVyXDEOZCj+YL0LF7rkzRN6xhrxemPk9fQj/YhhL3vEbI0FIbvmABZ/4HpO1YbkDBZ+MTy9aXb+53NLSlZ/3c/pt+8jd9gaTGhPtXsOP+xzZV5c6ezNpJ6zPp6tR9Fj7l5tJXPCR5zyUJv3hgMz8DU1/gHr5hkFy5UncRi8NRT7X0DEkAa14Vg0EAIOPHE77j//h84//zF9O5dwAkndKV9+xD337+UESOmkU5LDENQXBzCNA0WLqxkxYoaunTJ5Zxz+uOXF188hJNO6kP//nmsW1eX2X7NNZ/yyCPzufLKj/lflC+/XM9ZZ/2N1aur/tVD2aHsiuZ5ArhaStldStkN+LW3rVX+SXLjjaNZtOgCbNugR4/HKS2t2ekxH344gVdfHUNZmc3JJ8/hzDPn8uCDa5k8uZwTT2wDSAIBwcCBOTQ2JgiFDG65JcF++9WRTqtpr+sKfn0NbPVnzhpQ5w9/dIAYRLXyCpKFZwTZpDKtxLVi0+KHgizfu1bwWlE2j6AJkSmKM6AtmH5DY6BgKn+9g+YJax4pXpOEtyBZR7V2Pjc3ds0jh/yRO8378ye3+Y/3+0X80JnXZlh33z3Q4aV6LPja+ukpJBkj0qYNTUNsAY21OU4WMysoCDFmTIhw2KRtW70kU7kCxx23mGHDVEZdfX2aDz6oIBQK8re/7QeoOhi27XLEEV3p2VNRSJ955sc8/fQyhg1rwxVXDGly9k8+Wc+rr67ioYcWctVVn2a2X3LJMNq3jzJz5nqeempB80H/18ull77H889/zx13bN9J/u8gu2IIcqSUM/QXKeWnNI0XaZV/UIQQ9OtXwpQpq9i0qYFvv92c2feb38xm4MAXWbZMzSg+/bSMfv3e5MMPN3LssV0ZMqQAx5EMH17I0KH5HHBACeed156amhF8/fVgli6FFSsEDQ0KgHYcybhxaSZMsMBP+6szhrVi9BOhRSGZghP2UVDBNpm00HTWju+zVsD4jtMKPEyWuE6vDPRsWBuCICxzwNWJav7oIL/B8StiC8J+g+Afl7+tVtj+DGl/JJO+jmCzY/Q98Ndl0C9fVnLz6KNIEAwT5utEXQ2/aTTFb8gsEF5ortAGyOtrSzmYOXD0cb5rylgKdXIh4Ne/rmb+/DSpFFx3XUd+8Yu25OcrjOztt7fSrVse06btxbPPDuXQQ9tyxRW92GefNtx3354cckgHSkqCnH56LwDWrq0nFnPJzQ3w/vvr2GuvN/wnZ+vWBKYpCIVMTjllQGb7YYf1Yu+9O2LbkmnTSvlfkxtvHMPw4R254ILh/+qh7FB2hX30b8A8lNMY4HRgLynlMT/t0FqW/0ZnsZZPPlnL0qWVXHjhHhmYqKTkCWpqkuyxR3suumh3vvyymmefXcVee5XwzTeKATyZVAyOqZRLnz6z2bgxxdSpQ+ndO5e+fVcjpdZquQgR5IMP8rj11iBfziarwHJQCq8E7UfOZhcXKcUiC8gqzQKy9BAa+ghkTpNVklqJ6ZwCf9imFq2EdVUvf4ik/mz7Xg7KSBk0rfyVJKPA25iw1V8j2O9T0P4DPxupJrbzO419nEQ7YiZtUi9BO3z1ZxeEVPfPtbP7hetlF+uX9kOkyDKQuijHsX7XhtmrSWCacMEp8NhjekAuiqbUJhCIY9u6pGWKMWNC/P737Rg37nv0cuKQQ4qYNm0oAKtWNXDaad8wcWI7brvtO6SEo4/uzNtvjwPg4IPfY/r0dQih/AFDhpQwd27Wn+U4Lh9/vJ7LLvuElSurueaa4dx99/4AbN7cwBtvrODEE/vTrt0/bw65bl0tmzc3sPferXkHuyL/aGbxucBtwFve989QpSVb5Z8sEyZ0Z8KE7k22vfbaYVxzzSzmz9/Kr341i1WrTqewUE1RH3xwKSNGtOPUU+dw6aW9adMml/XrVYTQ7Nl1HHxwMcGgJqJTmmnIkBBjxwZYuFBt6dMPVpYCQaWsjtoPPlkE9f5ZvwQpIC8X6v0RQQKltLTRcMni97At95AWPzYfktltAaGUnUm2bKPfAWv59vnxdA1l6bBUYKv0fdcGSEcj+WP6mzF8Rk2INZB1Mvt9BH4ntB6zfyUifX3rlUAapHf//Elt0m3Wt3Zk63snUfSOeox+57Pn+HYcmD4T3wHCK0zk4DgWBx5oUl2dZv58weefxzjllPWUlJhUVipr99lnNdTWpikoCPD225v45ptqVq1qYNCgQhYurGHGjCzR0hln9OWjj9YRDls88sh+dOoUZeTI17jjjtGMH98F23Z5/vnFrFhRDcD06esyx3bokMuvfrUnLUkiYWMYgmDwx+UP2LbLoEGPkk67TJ16GuPG9fhRx7dKU9kpNCSlrJZSXi6l3NN7XSmlrP45BvffJPfc8wUTJjzHhg11O2/sk0TC5rvvKpFSctVVQ+jaNYeJE7vywAMruPLKedx660LWro3z298uY489cjIU1T17hrnttk307OnnhY6waBG8/HKK++6DPn1g5WpAgGHD7gNhwxaUAtdcPTpU1IL6RrKQiF/5apZSDfHoaJ3mkTQaesr4FLypsu7HlJAjlXHIk2o1olcnzZ282hD5IKSMEdLKVRshDdE07wvfNq9dTOdN+J3H2rHt95Po7GcNPenr9TuBmyWQCb9Du7lT2Zf8liGki5A1YrpIkAPkKIipIB9WLc1eihDQti2AYpj94gublSsFjqNuWFlZmrq67I8Tj9sce6zC7c8+uzvnn9+D3/62Pz/80ED//nlMmTI+0/cZZ/TjjDP6EY/bPPzwIu677zu+/rqce+6ZB8DUqT/w2msrADjttAFMmXIMLUkiYfPBB6U0NqbYtKmedu3up2vXh2hoSLXYfntiGIKSkiiuKykqCu/8gFbZobRyDf1McuutM4nHbSZPXsall+69w7ZLl1ZyxhlTOeOMgfzmN3PQGqOkRD3wQ4YUEgyqmsTDhxczfXod8Ti8+eYm1q8fw/ffN3Daaeuprtbxmlo7OhQVGYwebXH77bByJRlF4zqwqNRrWkiGDiJTQjmPpli54fvux8+1AtQGQVNENHHKSg8LlxBywKuWljEoukhvQPrCP4WCT+JkncvaGOmQVK/iF5A1FhrW0bNpyIab6hWH3qahF4usMzZK05h/P+7ffJs2Gjr8029wEt6p/GMxaRqu6l2TRH02BMi4ByF5P6XwCOvcoKotnVmJuQLLgp49TbZsUfGlsZgahBABpEwSDjskk/gGYDB7tpqYtGkT4s9/3pN7711KY6NNaWkjBQVqEiGl5Pe/n49pBjj99L5ccskgwmGT4uIQ11+vsO+xYzuz776d2H33Evbcsz3HH/8uTz11MIMGNQ0ZvfLKj3nmmYUceWQfJk3aj3Ra1euOxdLk5jate7wjMQzB8uW/Ih5PU1DQagj+UWk1BD+TPPHEEUyfvoZTTtl5ZuXrr69g3rwtbNrUyB57tOXLL7dgGPDCCyvo16+Qo4/uSV3didTWpikqCvLhh1XMn1/P3nsX0blziD595pJIaOWvwfAA3bu7fPRRLkVFJs89553MD91opgJ/cpR2ImulnvLe83wD1gZAH6tnytovoGfj2ggIwJBguWoVIKRS/sLDcUyhFGQEiAtwPI2qI3A85W4GwdElILVijfjG71fsfrhGG4dQszYWWd+Dzm/Q0BLqcyQEibQH7fgI7IBtVxr+yJ6wb4xxmq4ItGhfhdfOTXjHqXwwAAqKIN0AjXGvTrRnyHJzwTAkc+aYCBFh2DDJvHmqMyHSSGmTSJi0aRPmkktKeOqpjVRUpJg0qSe//OV3WJbgoYcGsWpVHa4LqZTLHnu8j22fyvr1Ddx223wMAz777AhGjWoPwMsvZ4vMFxdHmDHjRAB69nyKH36o44UXlnDXXWMBxacVCpl0756Pbbt06pTDwIFtmDXrLMJh6+/yHQSD5o+GlFqlZdkpNCSE2HdXtrXKjuXUU4fwl78cQ3FxZKdtHcdlwIAinnjiIGbN+gV1dedx2mn9WLiwhuOP/5jJk9fSrt2bdOv2N77+eitz545j9eoDmDy5grPOWkQioTTKoEEWffqECATgsMPCrF0bZMCAeqR0Oe88CATB8kMZCZSe1ti6zu7Vs/q4tz3KtlCNhjGg6UpBRwBp5W9KCDsQdsGSYNkQsBGRJEZOEjMnAaatVgoBB/IcyHcg14WwzBZuMcDxRxH5MXj9XUM5/jZ+fiLDNzbIQjDNI4E05BOBeBCkhoaiKPgqQjbrWp9D5zz4VxB4bXK9Y/zRQNr4aKop/7l1XkUD1NRCo+07h+fQjsWgrg6kDCClw7x5Wa/32LE5mU6vvroTt93WnQ0bRvH224NYtqyWZ55Zz5NPruPTTyvZtEk7LpRvw7YdunbN5dxz+3HMMT0YNqyEncmTTx7EGWcM5IADuhKPp1myZCvt2z9M795P8Ktf7UnbtlGefvp71qypYfjwjuy+e9ud9tkqP63sSvjon3ZxW6v8E6S+PsXvfvc1q1bVZqI7c3ODxOMq5dZx4JFHllFXlyKZdPj0U+XQGz16Ns88s4Hnn99Ex44Wpilo2zZIWZmDbcO0acqJ7DiwebPDk09CUSHYcTLUxhovl9AU39YzXx0ZlCI7U9azfd1eT9A05KJX7UJ6Sk94ClMihCQQShEIpzCDaYIhG9NysKJJjFAKEbDBdNSxhteh4a0g9Ll00po2Vrr0gl4d5JA1CNA03DXcrI3pe+k6DDr3QJ+jpXwCvaoK0pS22wIR9h3j/7dpKMjvBNb31G9Amudh+CKVgr71vJuhx2iavmwYMGRImFtvbc8tt7TjhhvUbP7JJzdy5JHf8de/quenc+cwo0cXs2RJHYahjjNNweLFNQwd+hbFxSEOPbQTV1zxBfX1O8bzDzywO8OHt+Xww9/kiCPepLY2iZRQU5MkkbCpr0/hulBbm9xhP63y88l2oSEhxChgNNBWCHG1b1c+TSO4W+WfKHl5Qa65Zi9Wrqxm7Ngume2HHtqVN99cw4gRHejcOYrONjrpJBVlVFOjwOZwWHD77T244IK1zJxZz/vvD2TSpFpmzcrGQH71lWTePJdQyNNCGoP2c/ILsiGlOjQz3xuMhln0bDrh7fM7UiGruPwzZX0ioWb8puVpMOFiGFn8RkoBrkRYNq5tekrRVX6DIFBngGlkMXdNSKc51yJsy0mkr00PQ/i2+aOCdF0EvH50YpevYlvm2jVs479OX5awAGTS1xdqjKbjMZVqyEj3o7c5ZGs/WL5+bVQoqfTcCxqCylynxrMCHHNMiNWra3jooRrAJhRKc/TR+ey5Z5SFCxs8CMggFpvIZ59VMHDgh5x9dnf69cvBcVy6dcth9uwtLFlSQ2lpPfF4HMMQjBnTkdNP78eOJBBQ2fKBgMGoUZ354ovTKCmJ0LZtDt9/fw61tUmGDWu/wz5a5eeTHZHO7Q+MAy4CHvftqgfelVKu/MlH14L8N+cR7Iq4rqSk5CVqatLk5Fg0NJyOlJIuXT5i06YkQ4YUsHq1RSoFo0cXMnNmmpycMImEwHF08YBCwGDQYMGS5aaaTRYCuaregK2pINp574Uo0jmtAEOofAPt28sjSx+hZ9mGf5vn9A16M/lQCjNgYwVshOEiBBiGizDU9bmOMgSGIZHSwHEE0jGw0xbY3kltAQ2m55QV2dwCzVWUJqu8tVNWf9ehmrJZWxcFf2mbqf8a+rPG7/1wj/Tt8ziAMm1M3z6tnyELu0lf+6TvuK2+MVR726tRhkJfYyOKidTzE5hA+/YOmzYl0c4Jw0jhujX4nQzvvNOdI48sYPr0Si68cCnnntuRm27qzZ57fsz8+bUAVFcfzqRJi2hoSHHUUV2YNWsz++/fkS++2MTs2eW89NKBtG27Y4hTSsnKldX06FGAZRktVkNrlZ9XdpRHsCsJZd2llGt/kpH9HfK/bgi+/76KoUPfAeC3v92DW2/dg3jcITf3fYQQPPDAIK6/vgzXBdPMobERslPmfLL8CgqzsYIC2yCrzA2yUUMWCh7JBaMYXA23hIAisk5jTfmgfQWaxkF4n7VDOJpWnw2wgimsgNKwQrhYwRRIk0AgjZQuCFPFxLuQTpukEiqmNdXgDVICMW9BqyN0XNFUqUJWUetVjf9x1wlefgUN2VVFgqzCj/n60QZQQ2r+UFGHrENdK/a0b7tLy2Oq9+2r8u1vIKvwG30vbfjqVbv99oVnn3Hp0yeBtjzDhsGJJwpmz44zc2Y19fUu48eHmDKlL127fk5VVZrdd8+htraReDxJRYVautx+ez9+85sFSCkJhyXxeLYa3o+VSZO+5JZbZnHHHWP505/mYVkGCxeeQ15ec/bBVvmpZUeGYFd8BCEhxBNCiA+FEJ/o1z95jP+z8sMPNVx++QcsWFC+88bAgAEFdOuWQ3FxkIsuUsvzSMRkypS9efDB3bnoom4MGKDSfxsb/emwGsjX4UFK7BRNnbsaGtFKzXt3dfCRhn502KM+RitRf7IWZLH9gFTOYQPMQBLTshFCIgyXQChJMOgQDiexTIeApWAitR+sgEMkJ0Ew7BAtrlfOZCONKIwph3LGce0ZHL3y8Ie26hm6fzbvf9d4vL4+/Vlj89pP4CfE08YSmlZX01Tc2iGt6TO0kfSPSYs/ES/f95v4kuQykJPOtPbxO836Avr3l76DDPLzXQ46KJcXX+zARx/1QMoEn35aw9NPb6FPnwiuC4sWNbB+fZyKCgfDgFDI4IgjOtO7dy6FhRYnn9yDf0TmzCnDNA1mz95ERUWcjRvrufji6dxyy+ek0y2la8PDD8+hTZu7eeutpS3ub5V/vuzKimABChqai0+DSCnnbvegn1D+21YEZ5wxmRdfXMTIkZ2YPfscKipiFBWFW2QifemlFdxww1ds2OBiWQZvvz2BUaPaceWV83n11U3stVcbPv54NHl5XyMEtG+fS35+gMWLdRaXnrKH0IB/5y6C3HawfD1K0ekEMr14yPW2t/MGkU+2kH2QLNWEXh34FWIYtQKISAikEWEbBFiBJMGwmv4apkM4lFIhKkIgPP4FKQ2VkSsNJBLXtbDTypgJQ5JOWmqVIMGtC4P2I+gQTQ3VJFFpvZqaQosfltErA88oCOnh+n6qC53PoP0B/kgjPeP3+x30TL+llYmNmun7YSMNVcW8vmp8x9YDtd4xaRR8VQeWCT2KYNUKfS61HDJN6NOnnuXLU0SjLrFYnDPPzME0kzz33BbAJRJJYBiCE05oy7PP/qAu0bDJyRG4borf/34IV1yR5Qz6e6WqKs4HH5Ry+OG9mTp1DaeeOiVT0Gby5OM4/PDe2xyz//7P8vnnazn99CE8//yx//AYWkXJP7oisKWUj0kp50gp5+rXP3mM/7Ny7rlD6d+/mMsuG8E776ykY8dHmDDhFQCefnohJ544hYoKVZj8qaeWsn59IyNGFHDzzUPZbbcCbrppIc8//wPJZJovvthKTY3N++8PZPjwPDZvtlm2LEF2+q4C/IVwMYw0EybAmP3gy+lkQxG1gtPRh3qG7ym5XkXZsXduR9aR2bw+sT+0UwKWUuBCSAxLdSiEJBhIYeJgCheLNIZIEyZORMQJGUkiRiMhkSTHipETqsM0UwghMS1vAiOAnCQUxiCaVP4IS0KuDQFXzcCjUmUqF3irBe3U1uPUkUM5apsMkDV4Ors56r08A1mQ532OQl4eymB6x2fsbUsRQ9LbVkTTKCV9n3Q0kd8Z7c/z0NccAteC/cejostctUMIies6rF9v0rWrSSymsu5eeKGWQw4ppmNHi+JiVau6vt5l2rRqbrihD7/+dW9OOaUDtbVJ6usd3nxTUURIKRk3bgqdOr3EunUNfPddBatW1bCrUlwc4ZRTdiM/P8Sxx/Zj+PD25OUF2XPP9owc2anFY5599mjuuutA7rnn4F0+j1+2bo1RV9cakfRjZFcSyt4VQlwC/I1s+g1SyqqfbFT/QzJ+fA+WLbsYgL/8ZSGGIdi6VSn+X/3qExIJh4qKGB9+eDw9euQxZkwHnntuAmed9SW33fYdV189mNxci8LCMBdc0IdAwGDq1Cpmz64HQhiGR0xDGq2tpZRICZ94AF/v3hAKQFJH3eSRjQvTUSwJtW1NOcpxnA8bU2TZRLWS0tCvP67MmxlLVyBMG9NUSjwQSGIaatorhMQijYnj6UwH6RkL5SwwsAxJbjBBwg5gBByM/BTJeAQRcnEdCxlKg5vCjofVSsRyIW6BNLyoJQlBAfVSXVeByEJg2gfgjxLSkUg5qFm48O5NAdRqzN+Gem1Q/NcvUP8WHb0U8O5hiqzBjKJm+7r2g/YF6PunISv9vQiMBrVakQ64KXj6ebLJbyiK6ooKl1jMJBbLrvalFJx++no2bRpC+/azEUIZjd13z+XKK3vRv/+H2LZNYWGAMWPa8NBDihto2rT1zJy5GcMQHHXU+yxZUoFlGaxceTqdO6uswsbGNPfdN5fi4hB7792BESOalqnUEgyazJlzJgALF27hwgvf46qr9mHffbs2adezZxHXXvv3pSqtWVPNoEGPEg5brFt31Y/KVv5fll0xBGd579f6tkmg1z9/OP/bcvbZg+jXr4gBA1TSTufOOaxeXcenn25g6tS1vPjiShxHEgiY1NWlMAxB//651NcfD0As5tCu3Wc0NjoUF0cQwqCyUmkSy3Jx3SSuq/8YWQ6iP9wFVgmIqEdnkCYLEenVQAo169UKPk52JqvF/9mADJWEpTYYRgq8KCEhHExTL0EcLGwMJAYuBhIXQdizLjYOaRFCIEBKwlYCAaQCQQIBm1QqjJQ2rhNASokQCdIaRspLI5OWmj5LQ0FQuVJFGvkzfyMiCycZKMWqC/Hol9+prP0PGhbTDKJ5KIWulXjc11b7HvR5DRS05jl8VSY12axnDUPpCCPhOex19FaMLBQFdOokOOQQk2eftZFSe8GDXqc2juOycGGSs85qT3W1zV139aSiIkVNTYpYzMF14d13R3HIIVlF/tln5QgBOTkGCxZsBVxcV9Kjx3PMnn0Ce+3VnueeW8ytt87GdV1CIVi16gK6dPGnnm8rkybN4s03l1NW1sgXX5y1w7Y/RpJJGykhlXJwHHfnB7QKsAuGQErZ8+cYSKuAEIJ9983mDgwcWMKaNXWMG9eVAw/sypFH9qCkJESnTjnMnHkYK1bUMXRoEQcf/Clr1ybYsMElFlNZSlVV/qllHrYtMU1Jhw4mmzereMriYpNEQhBLQb/OUBeCDVVko2QKycbmh1BKzp9BrEM1Eygj4U+00srMsztmOIEZAITAsSV5+amsP1nYBLCRSEwvrMfEUYofCOASoBGJgSMMbEzVTkowQ5iRBFKC49o4toFtmoSiCWL1URASM5rETggwkwghkakgwnSRmGB7yQ4RR/E6+xV8imw8f0h48fterCYiq5C91VIm6kpHDGk/g6buAKWTNamf7dunfQRhlC9A921799b1zqONkw6LhcwqY9MmyV/+AmCRk+PQ2AjFxQZVVcofk5sLiUSaV16pwDAkU6ZswHHgiSd2589/3oMLLviGo4+eRXn50RQUqB/uxhuH0qFDhMGDi7j77vkceWR3/u//viCVctm8Wa1cJ0zoRo8e+WzcWEcwCAcc8ArPPjuRUaOa0kPX1SU59dR36devmKuv3odNmxq46aZ/LknBwIFtWbDgIiIRq5WD6EfIrjiLo8DVQDcp5QVCiL5AfynllJ9jgM3lv81Z3FzSaYfrr59Ju3ZRrrpqL5YsqWLo0LbbjcNevz5Gjx5TkNJCiAAlJVHq6gSpVAApDSIRi3g8FyFMjjuuiJkzI9TVwb33hgiHg5x/vgATrByw81GKSDuDdR5BLtk8glwUvq0x7qivjXYug4e9Sy+nwMHMbcQ0AeFimmly8xIIJKZIEyaJgUPA87gqqFytBgxcJBKHIK4X5mNj+cL+w7jSwpUCqcNKhYJC0ukst4N0BbGGMEhTxd0H1GzRSVm4jsqkdVMWpExwhIp2CrvgSLADygCkUd/D3m+hXS8JsvkJQd8+vb/B94PVkg1h1Q5lh4wDmBTKCNved71Cq/Xab1FDG9gFFn9FxqFt1KscDAAh4gwdmqBHD7j88jCHHVZKMplECPjss94cdNACAgGX+nqVbV5QIJgzZ1/22utDSkpCLF9+WIbD55NPNjF16np+85s9KCxUuNfatXWsXVvPmDGdEL7iRrbtMmzYsyxatJVLLtmDRx5pivFPmbKK4457m3Tapb7+ylbY5meWf7QewV9QEUOjve8bgNeBf4kh+G+Qp5+ex4cfruGRRybSpk20yb4vv9zIww/Pw7ZdRo7sxLhx3XbYV9euUZ5+egQVFQkqKyUrV8b58stGRo4sYNy4Eh57rIblyyXdupm8+66qeHLuuSVccYUgHPYgEgF2jGyYYwxoTxYf145grbRc1Ow4jyw5nEU2Vl5TOhiAsCFkYxgqaQzhYgYkrgQTFyFcT1UbSGwszxgIJGYmsB4EaVwMLBzihHAJYGKTTwpXGCRkgCRRpDCQCAwhsQyJROBKA8OSFBQ3kIwHSKeCngKTBKNJUskApuliBtO4joHTGPHsh/T+ITZIMPJVUpbbGAbHzK6MQq6ClqCpotfbNGykZ/z6PmaimsjmYmiHsZ/kT8NKXnb3uN3hpDHwtA2LF8O+e8FH7/ifCoOFC9N8953kk0/iVFT049xz1xMISBoaXDZvHk2/fl9QX6/8R42NNgUFQaqqjvXoJbLK/eSTP2Hr1gRt24a57rqhAHTvnk/37vk0F8syeOaZw3jxxSVcd922DLsHHtiDCy4YSt++Ra1G4N9MdmVF8K2Uci8hxHwp5TBv2wIp5dCfZYTN5L9hRZCT8wcSCYeHHz6Miy8e0WRfLJbmpJMmM21aKaYp+OqrM9hjjx2n4juO5Fe/+o4XXiijsVGTA6lXmzZBtm7NxTCE5x8Ics45JbzwQhDbNpUh8CbOBe0gVghpA4woyByQOiS0hKyCb+ttKyAbaqqziHXETVR6OLkDBSkCgTRmwEEYEiuQJhSMYwlJQCQJerN/kxQBHASSACkP+lY+AxCksDKGIo1FmjDajEiEigSVJg4Bb8UgMmCT7VoIQ+JIE8cF6RokE2GsgMLSk/Gogo2kgesKpOsipIErTYIRFSNhmC7SFTiOiZ0yMAMuTsrEdQ1IeJm2aS+vQQpwDZXk5gBbRTbUVKJm+zrDOE7G8YyDqkCm223xtWlA+RMawYpDfh6s+VjVJbjsMnj4Yf1ftunVq57S0gQ9egh++UuDm28uR0qHQCDN0qVDmDy5nL/+dTMXX9yRAQNy2G+/lsnkfv/7+bz88mry8wPk5AR4772DCYdbSYv/E+UfDR9NCSF07ANCiN74ooda5cfLnXceyGGH9eHYYwdusy8aDfD668dQVBRGCAXt7Ezmz6/hz39eRWNjgpISQW6uznhS0TjHHJPnkZI5COHy/PNxjj7aA6X1pNuCYAjSHse/mwLZQDYUUjuOs/7d7GxVh5n6aw5nqouZCCGxbRMpXQLBJIGA7XHZ2Z7iVy8LFwNbhZF602oDBxMXAweLpOdQhhApBDYC6eWKuVjYBIWNKRwVhSRU0pohJGEjQYA0UdFI0LQJBhzy8hqIhJNEwinyCuqJ5MQxAzY5eY0UtmmgoG0d0fxGhCG9BDcwLZdQJEE4J4lluYSiaaIFjYhIHMIJiNqIvBQiJ61CVUMORF1o5yW74bunmijPIpveoX0MWvQKQecoeA5724aqSijcDSYcBU885T8gzpo1FhddVMCBBxq89FItar5n0KlTgKqqJMce25bf/74Hr7++KVN3oLw8Qffu77L33h9i2wo6u/nmYTz66Gjmzt3KZ5+VUVpa3+IzaNsud975FW+8sWynz2ur/PvJrpj23wLTgK5CiL8C+wJn70rnQohDgQdRj/JTUso7t9NuBPAVcJKU8o2W2vw3yWWX7cNll+2z3f3hsEVp6YUkk84u0VbPn18FuJimpLz8YNatS3DTTaUEgxa/+103Zs5M8PbbMUIhQf/+OXz/vWDaNINg0KGoSCAMwaFHwjufgZUG279IbEQpnwRNSegE2VBTf4JVRoSXJJb9LgxXcQoJb54vJA4GQS+MRuAS8Obxao7vYOAiPLdxCAfbly6c44XnJAhiY2EivWqZMY9CKIDlrTCEgDTCWyc5xIhiyjRBbHUmwyBGlGjUbXItlpkkJ5jGdixSTlD5iA0JpoPtGphmimDQxihQaxPHMRBCEG/0aFw9Uj0pAiAsjyfIO0EIqCALu2koSIfrCu+eN5CNEtJhuho+smDGJzSlzvCs85NPJoEGbNulc2eLgw6K8sILG9h77/kAdOyYoqwsyUMPlfLkk0NZubKezZsTbNoUZ9myOq677hs+/HATjpNm8OAiLrpoIAMGFLb4DE6duoZbbpmFbbs0NvYmEgm02E6L68pMNb1W+dfLrkQNTRdCzANGop6wK6SUW3d2nBDCBB4BDkL5Fb4RQrwjpVzSQru7gA/+jvH/10pOTpCcXazVceCB7dltt3wmTGhPZWWK0tJG+vQJc9ddG+jTJ0wyGUEIl3A4wPffKwKdZDIXKaG8HAoL4YU3PH5/AdF8r2SjR3JaFIVqTTehDYDm7ddFVrSz1LfGzLckdSEHmQ5ghOJYFkjXxDBTmMLNRAgpkRmQB5THwML1AB+BwMFEYmGTIuitCzyc31t+hEh7of8ejxJpb8FjIjG9yCQTE4cgCUzPP6F6cSkyqnGkIC4iODKAQBIJqko1AdMhRNpzSgtkQBAI1WIIqbwbliBNBEeaSCkIhpPU1+Tier4EkZPETQQ8ymoXdOZ4CQom0vxB2k+gQ0rzycJEhcAmsuyqXlaykQ+FAi48W3DH7QBRLKuWQAAGDAgSi5m89FIHbr/9BxwHdAxrWZli2zvxRBUuuu++bXjuuX349ttKhg59D9fVJzbIzw9yySW7IaXk7LOnU1pax+TJR2QcyHvv3ZHdd2/DwIHFO4WOli+vZM89n6J//xLmzv1lqzH4N5Ad0VAPkFIuE0Ls6W0q8967CSG6SSnn7aTvvYFVUso1Xn+vAEcDS5q1uwx4ExhBq+ySuK5kwYKt7LZbMaGQSc+eubzxxr4UFQUYM+YLSktjRKMRkskQN920nrPP7o6UeDzyKiC+S5ckPXtGmTFDUFMDwgSrnYKpY3EQhkfqaUDNOkBHAsZQT42fklq//N8NSYONUnrC9eimJabl4mJiSBdXgOUF5ytlrJB+j4gaUNCQwMVEGw6B9KAj1wOOAtgECXgIl8AkRooQApccbwqdwvL6MnCAiJexECcKuASQGAICIkWIFAiU4vfOHieMRGAKh4DHRpcSQaQ0cTAJ4uAIB1cCwkAISTQvjpSqoLxjQ2NjREUk5XqJbS5QL5SCt1AGQUcaaboMmyxNVJJsprIuFJRQVBNVW+DOuyA/X1JXJ7HtMGPGxJgxQwUIHHDAGi69NBcQHHRQAccdV8jFFy9ScJdpUFmZ5A9/WMJRR3WmY8cQpikIhSzicZs+ffKYOvUQAGprUzz//DIsy+DrrzdzyCHdAWjfPof588/epee3tLQG14UlS7Zi2y6BQCur/b9admS6rwYuAO5tYZ8EJuyk787Aet/3DUATPEQI0Rk41utru4ZACHGBNxa6ddtxFM3/gkyaNJff//5bJk7szuTJhzF79lbGj5+BlCapFIBBbW2WWGfixBy+/TbFxIl5vPCCTVmZwfr1Lp07Z2dihlAv7bAMheGiE+GB90FqJ6dGqeIoWuRivIxd1AxWh217uLbrCnBdRMDGNMFOWwQCNsKQmEaz5QOgMRmRcfMmCHrGQfsCQBIm5enIAIqJSGKR8pS2gYkgSJo0wWxgjxeeo53RSY+YLUTSA6EsHEwMXAKksTEJelo5SYAIMSIkcD0uDgubRnKwRSAT6ppDI9IwSEuLJCEcw0ZK9RezLBDhNNIxIWlmawvEJaSFWgkAw6OweC0kilGwkU4+0/BbNiI2k1/QORdKKxQSV1enHTg2ixfjBQmkqamRrFjh0tg4kmjUxLZdFi6sJRKxGD++hNtvX8oDD6zg1VfXs2bNRGIxm7Fj2zFrVjmnntqLvDwV5VNYGOLhh8fywQfr6dOnYEeP6XblkEN68frrx9GtWz4XXPA+M2b8wAcfnEr//juvftYqP41s1xBIKS/w3sf/nX23tN5rHqL0AHC9lNLZ0fJQSvkE8ASoqKG/czz/9jJr1nouv/wDbrppP447bvuEX6GQiZQQDCpFqt91lSrLEkSjFoMHR7n22u4cfXQRJ5xQBMBdd8Frr6VYsMDlkksEY8fCmjXQpw+s0KQhDZAIwQMvQF4x1OuZqc6c1Q5NGzU7jaOwbK2sXFS9gIAE28AI6Z9MIAzlwNYqX7mCk5hNIoXIRApl/QQ63lJ6RsFAeoUElOozsZC4uDhYGF5GV5ikWoFoXwFkzEqaoOewThPEIY1JI2HyaCBBEIcwAkku9URIIRE0EiVIioDn1LaxCJHAxaCBfAVFCe/3CJg0uLnUO3mYhk1uUQ1CQF15ibo/FormIiFVlJGEuZqEVtNaqAk9WGAVKt9z41aa0FGUVpAprhMICCIhh7o6KCwMUlVl4LoGublw5ZVtiUbV7Pu99yp48skNOI5k0qS+HHVUJ15+eS39+uVw7LGfMXXqRi/v4GB69myaJVxRkeD993/gu+8qWLr0dKLRHfsDmosQgiOO6AvA668vJZl0+PLLDa2G4F8oO40aEkJcKoQo9H0v8riHdiYbAD+JSBcUwumXvYBXhBA/AL8AHhVCHLMLff9XymOPzWX+/HLuvnv2DtudfnofZs48mpdfPoh02mX48GJKS4/gnXdGYRiKoeyee/rQq1eYCy5YSffu89iyRU0ta2pcbropxscfJ7nuOhg4UNK1O6zaBFJnEWu9nYJ6zdBZrb5nkqe009gPZTSQLV6vJ6ae8rfTAtsWuK6awauwZddT6SqBzMT1zf3TzXwGYGBnoCKLFFGSRIkTIoGJ7W13CREnSpwoMSIkySGGicxQWARJE6KRIioooYowKS9k1aaYWsKkyCdOIdUUUE0ujd6oJLnUEvBuUoQExdQQJkUYmwLqKKCBQmrJo54ASfKNetoHNlNk1pIXbMAKOITyGyBog3DVPSrxXvlkI4a0f0CH5ApVVrTRn70NahUWht2GQ4cOkE5J6upCgMXIkRahkKCoKI8NGwYyerRyOn37bR0nnriQYNDg2GPbEwoZDB5cyDvv7Mfbb69n2rQyQBUH+tOflgNw661z+eUvPyORsBk+vB2OI1m/voFrr/1iZ481Tz/9PW3b/olXXmmOCsP775/M3XdP4NRTd99pP63y08muRA2dL6V8RH+RUlYLIc4HHt3Jcd8AfYUQPYGNwMnAqf4GfvoKIcSzwBQp5du7NvT/Pvntb8cQDJpcfPGe222zYUMD/fu/iBBw8cXDuOeeRTz44Eguv3wQ1123CNe1adMmh0suWYJt60wkyVtvVXHRRe15/PEkK1cqB+zXX2t2NQtTT/p0aCM0TRDTdXk1Vl2HmrXmkoE1Mgoqjlo5AKQkjm0QDKkcAsexsEwXWwQIkPZgH9fLIDY8X4GKEwpmCOiEF6GqHBCWVyhBetsUYWfMs0umFymEFw+kBhUggU0wo9DzqfcKl1kZX4PpGR+JSZA4EWIEsD0YSWT8FPo8jUQIeuR4Ki866nk4IIBDIQ04CJIEiYsopuEQIYGVm6IxYNNYVaTKb0rANiBPqPuqqSQMlKLXSXsm2epl2svtZRYHcqCiHB3kDRi88ILNxo1dWLw4ySuv1PPLX+ZjWYKlSxsBQSgU5I03ss9a167KUEgJffvmIQTcfPNgKiri/O5387Esg1NO6c0RR/Tk178exr33zmfQoOLtPqtaXnttGZWVcd58cwUnn7xbk31jx3Zj7NhWuPdfLbtiCAwhhJBe5pkX5bPTtEAppS2E+BUqGsgEnpFSLhZCXOTtf3yHHfyHiuO4VFcntskY3hXp16+Ev/zlSNatq6Nt2z/RrVs+c+acgWka1NUlqavzU1fCwoXVWJZg/vxKAI4/vhMffriF8ePb8NprW+jXL4fSUoN0WvD113GGDGnkpJNC3H13gkQiSDwOelE4YSQM3Qf+/FdIBrMkmBnFX0i2pGORty+GUvg621gTpzUpWmOB1AYHpGMo2mRP/YdIkSKMS4qAx6KmlbXIsLypFYOVMQxeSGYGYNKnkx7M43rHCFwv+SFIGockNmYTCEr5DZJESeAiCJDCxCVFyOM7gqDnnHYwPVd1gDAxcohnbpGLST7VOASooA2uN7UXSILYpHGQwsCUDtKS2AQIdtpCvCFMojEX4gbUWYrTSBvelO++e3WKMySymhTPARGEBd94+00vgQ2JlC5//nOCSZM2IQTk5xucckoep57aAcdxmTKlnJdf3sQppyg66EDA4PHH9+bGG79jzZpG9t+/LYMHT+aCC/pyzTVD2LChkX33VcmNf/zjfkyaNIpQaOeO3ieeOISXX17KmWe2zvr/XWVXMov/CPRAFaeRqBrG66WUv/7JR9eC/LtnFh966ItMn76GV1/9Bb/4xW47P6AF+fTTdRx66Ou4rqSq6nLCYYsuXZ6gpibJrFkn0amTyhQOhSymT9/IxIldyc1ViufVVzeQk2MyalQJxcUBRo1azNdfJxAiiBBQWbkbS5a42LbgN78RzJrlTeMDIMKoKpAFKIXu1RQIFEOwxIMlQGUWa46hHBQnka6oVUQ2qkigMowLEoiATTiaxDAllpUiFExjiZQHy4BFirCXESBwCJEkSMqL7bG9mb+Tme2Dop2wvNwBm2AGNtJK3vVCHwOkMbEJeTi/iyBJGBeTPGqxvXBUiXIqCxQldtKLFDI8dtSA56/Ai2JqJEqSSAbOMj2oy0bQQB715GFjYZGihmKSXiSTiUOlW0ycPKQU1Nfkkq4NQ8xUnNablL+ABFCOsnt1qIzjOu8FBKshVeNdbJX3EkBCYgibjh3j1NQkaGxsIBx2WbCgG+XlCebPb0SIJFdeuZRw2GTLlgnEYg5t26of+Jpr5nLffcsoLraoqoozenRbJk8+gC1bEgwcWPh3PdPNRUrJd99toU+fwtaylT+T/KNcQ9cDFwIXox6zD4GndnjE/7CUlTVgmoLy8oadN96O7L9/V1566Ug6dswhNzeIbbuZsn627dKpU26m7S9+0ZPbb/+ewsIgo0d34IwzvsF1Yfr0/RgzpoSVKxOA62HygjvvrOHuu9XU/tFH2zFrlqdWHZB6Ap4NPAEL0glIbyHr8alDGQLNg+8tH8wQOHrVIFGZtS4QCyAKbW/qLrDtAJFgSlWxFNnA0bRHDRElmVH/IU+xK+VueE7jLF21UtogSKITGrShCOISwMb1VgGml4vgIFQWskd9nfYczFEacTy1b+EQoA4HkzzqCHqK3yaQWbEUUItFJQbQqMrpkCCHXOKkCSGoJ0EIAbShymujfrsSUc0GGSFgSAqKaqlMC2QwAAFDhZTqmsSFKJoJB0XpUUeGddT0hZiGw5DQOR5S4MoAW7bUk04rWtQXXsiluFjQv78q/3jTTR0YMiSPCRNK6NPnE6qr08yZM4YhQ/I5/vhuPPTQcior1YonGjUZOPBt6upSTJ16EDk5JtOmreOTTzbw618P5dBDu/3oENBnn13EhRd+wKBBbZg37+wfdWyr/PNlVxLKXOAx79UqO5EZM85i0aItjBnz9+OeQggmTuzFpZdO54MPSrn11v1YvvxcqqsT9O1b1KTtK6+s4ZZbVKbo998fQ48eUWpqHA488CuGDy9k+vRhXHJJKYmEQTQa4qGHVGw7hLjpJp3GimYmyJKiQbawu1dgRZe4yWTA6olcA1CoeNjQhGp1QBuh2DpTJoYjQBq4DhimJGkHEKaDFHihog4BjzIiScALETVxPUDGn7Cg/QpZkFznI7iZPlRsUJoQLrYXbaRpKwLYBEjiYhEljomN7ZkTBSZJCqgn7l2g9lNEiZFDHS6CBFFyaCBGHiYOERpxMakhnxh5FFMJ3tirKKSaEkqoohwTmzCmkBTSgESQFAGiOQkaa0IQdKGDgA2eOdMlQGPe71CA+tc2QjwC4RxIVHtGIAflQ/AkEjFJpxV+9MILDcyapeoSuy5s3Zrgu+8SLFq0kWhUTTLicfW+zz5tuOWWwSxaVMXbb6/l4483k5cXQEo1Edlvv6nYdgopJTNnlnHIIV2ZNu2InT7XTz65gFTK4ZJLhhGNWriu/NERR35Zt66WhoYUu+3W9u/uo1WU7Cih7DUp5YlCiIVsG/aJlHLITzqy/1ApLo4wdmz3f7ifWbM28NxzizxCuT1p0yZKmzZN6Sa++mozkybNRUqXaNRi4MB8unaNsnJlJWDy7be1hEKS9ettNm1KYRi2IkjzcJ/KSq3NVdKT1FNpbRA0r773X3XKYegwWFCHyhvwRxXaKGWlnZq6AItQn23XwolJwtEkrmtg2xa2aZGSEkM4WBmaCeUOFiS82KEAJmnFVIoqWGNmfAgy4zNVEUF2xmsQIO3RWiteIpVVrJY4Aa8/SGN50UYBkjgECJMg4lWTKaCeXBoQQAqTKHFcDAwcXCwaiZBLBaAJMUws0qSoQwCVtMXFoJgaL+ksjcQgRYAaijIrJyEhGE6TEBJHGmq11V5CrbcyyCNbt1j/fF70llGEgoS8SK5QEeRbUFkpqa/PBzYDLu+8U08g4PKnP/Xm0ENzOeQQVW3WtuGbb8aQSrkMGqSyBA1DcNNNgwE4/vhP+Nvf1vHgg/twwAEd6dAhQklJiMpKl169oqxeXef5rlqWsrIGJkx4gy5dcpkxoxQhBPvt14WTThrIyJGd6NBhF9Pnm0l9fZLddnsEx5F8/fV5DBmyY2LGVtmx7GhFcKX3vnNT3yr/dHn55aU4jsQwBLW1yRadz5ddNoulSyvp3r2AW2/di+7d36a+3sQ0JV265NCtW5RevaJ06GCxaVOKvDzFPb9unUMwKIlEBHV1aQwjgON6VsCvwKWq1eLoUFEXFqwG2nj7G1EGw/COCeCtBGiabWwKcAUyFSIlIBBO49gmZlhnCpskCWLgYnl4PB4ar1cARsY1bOB6mbxNs48h4PkA1Oesg1q3C5EmQgNpj2bCRRAkRQF1Xit1nhRhLNLkU+9BQS4l1COQhIlh4JAkQi71SqH7RhYlhkkEiSBCI43kYpImhxgWDjYNOBRgICmmkmpRgEsAw5REChpo2FoEARscC4qF0uMpVPJeFVk6ay+5LBYHkQciBW4jJAVsrYX27WDLFhcooWvXGoYNk6xfn+YXvyikXTuLo49uw733rsc0oVu3COFwFtpZvbqeiRNncPjhnXjjjfHU1aUzhWrKy2OUl8cxTcGjj+5Pbq7FwIFNV6l+mTdvC6tX17BiRTVjx3bFcVx69Mjn1FPfQQjBc88dvt1jdySWZRCJBGhsTG2XmDEeT5NOu+Tnt/ogdiY7yiPQ9QYmSSnXNn/9HIP7b5d77vmCk09+g9raxDb7Xn5ZYbk5OQFKSlomnjvssK4IIaipSWLbgoqKJI2NjXz++VhKS8fz2WcjiURMvv12EKFQgNpaB8dRGb2plENtra0oEhTsjhdPCUCuR2rm2ChIIqX2TxzkfU+glL7m09d+BY/2IANluELVCLZVqUgnqYobOI6KAbIxkK6OFTJRtcqyah8ENqHMXF+5f10viBN0epjhRQKZuIRJeT0lCXl5BgZpIsQJkSKHOMVU05Yq8qnzfBCSCClySNCeDXSgnBBJwsSJ0kiQJG2pIEqMMCmCXg5DN9aRSx3tKaMjmyigJpO81o4tdGMt+dSTQyMAYRIUU0U7yrBwaMtWojQQFjEKcyoxwgmPi8hVcZx+kr8uZJlKNe23BJmDorbwHPbShYMPgYICl759Yd26NJMnS+bPd1mwIElpaZLbbuvDt9+OoLx8LOGwyS9+8RXh8Nt88UUlf/zjElasqOf++5ezfn2M/PwsfNOuXYTx4zviOJIPP9xAmzYRcnK2D+8cemgP/vjHsUyefDQzZpzMZ5+dyrp19bz++nJefXUZzzyzkIKCB7jxxpnb7WPGjB+46qoPM7W8ASKRAKWlV7Bhw9X07bttIloiYdOz54N06HAPq1ZVbbO/VZrKjlYEQSHEWcBoIcRxzXdKKd/66Yb13y+O43LddR9hmgZDh7bnuuv2xTSzdvmNN45m5sz1/OY3o7Y7o1mzph4pJXV1SYqKLB57bG/69Mll1KjsH+PhhzfwxhsVJJMAgo0bQWlozYHsKVwPwkER9tCgm2imChWYz/vzULkDulqZC2yFUFtI6kSyOrKMpZZ3cJ2AAgmGJBkPYFo28VgUM5AkaNpYKGOoom6CJDEJoSuWSVxvvm9kPkk0S6mmlVO2zPbBTDLjUM6nzksEM4kSy5iZiHdeBwMLGxOHYuoRQIyIZ1AccqnDIUAQmygxgoTIJ4aDoIgqttIWB4M8GiigjnLae1nNEPaS00rpRT5J4oQzUVG1FJAUQdJSFcMpKKqmtqYYt1BAvoTvRNZA+1Zm6HwPbXA1TbgNgSCce47ghF8EaGyMc/LJ3spLBjj11A1UVsbp08dixYpBmefkyy8rsW2Xs876lpNPztYs7t79bc47rzdPPjlS3VMhGDCgkBkzyrj//kXcdddCLrpoAI89tl+Lz6hpGlxxRdO8mN13b8P11++DYQhKS2toaEjz7rurueOO/b3nuoZf/nIqp5wykAsu2IPTT59MWVkDxcURbr55TKafHRW3Sacd6uuVH6O+vpU1f2eyI0NwEXAaKm7hyGb7JNBqCP4BMU2DO+88kMce+4abb55BWVkDDz10WGb/xIm9GTmyEw8+OJcjj+zdYnGa22/fh0TCoX//Io45pnsTQ7J2bYyOHcPceGMpDQ0u2RqSCgO++mqT774LsnZtipUrg4qTRu8PqAcj5ee38cewa4ew3g8ka2lat6DR6ywzWRQQMiFkIJMCEbRVN3aIFCZhEccxXK/2gEuCXFLYhEgTJoEmnHAxPO4f7RRWDmOZMRI6W1likPb8A2lCpLFIYeF41NNAJu/AJkCKPOo9/lJlRNqz2cP+IYiN43NoF1GLi4WFJEQck81YuCQJUUs+HdlEkrCXfxDABAqp9ag0bCoJedeWIohNSMRxpYkjrQyghQDaSigSKiWzFrUqWKV/KO9nbUS5epS7h3ASTj0dtmyGc86JkJMDjY3qgK1bVS6Keiay0qlTHmVlLqtXx3jnnS088sgI/vKX1Xz3XRWLF9cCEIvZfPDBRm64YSgffbSRFStUHOuWLfFtns0diWEI+vdvw/nnf8juuxfSs2cBf/mLevarquIceeSbLFlSyfffV3DCCQO48sq9ef75hRxzTP9dPkdeXoiFCy+mri7JHnt0+FHj+1+UHRmCjlLKi73KZE/8bCP6H5LrrtuXNWuqeeqpeZkasX65666v+eMf5/Dii4tZsuSXXHXVpwSDJnffPRbDEHTrlscbbxzS5Jh43KZfv/fZsCHOvvu2Z/DgPGbPbiBbJb2RU04p5u67ixBC8NVXklGjwHWkgnGC4KYhpesQOL5oIVBOywKyATsJspnIVSjHZpBMDkJGpIC0VAlTjkosy3COSkjJELiCgJEi4uFTiiMoQdqrUazgH+mpbxszQzlhYWNkEtIkBgZJoiS9aCIFPIU8Ja7gJYcIDZ7fIE6uh/2HPGoKbTBMbHJkIxY2UkBMRnClICoSNJBHQCZJiQghz9kcEknyPGdxOe1xMcijHgeTOCGSRLBIU01B5goKqSaFIEEOOcF6UmEL6YJbHUTRwkqwRNbvUgKRNMS3kmUjTaCczA3Q2AD1tYANffvCIYeEeOutBNllRYpQyGTu3EY2bYpx7bUricVsLEuwxx5F3H337owf35bzzuvN++9vYt99VVTOgQd+wOzZFZSUhDj44A6UljbQvXsOL7447sc89gB88sk60mmXefO2EAgYlJermcOUKatZvlxBObW1SQYNeoaNGy/h2mtH/ehz9Oq1fd9FqzSVHRmCG1G1iS/CI3xrlX++PPLIRK66amSLOOeRR/bhpZeWcvbZg1i4cCuPPbYAKeG88wYzYEDLqf0VFUk2bVIztJqaNDff3JVvv12B46Q55pgi3nvP5qWXGigqqqBNmyjffmuRKTCsQ3A8fjfhQn4+3HU1XPSgty/pvaJkic8cVJKZTnxSeIhSWmHUU2agPJmuBMPCFmGkIwjmqtl+2gphEyTkJrANxfapVH7Io4FIIHEIe7N5BxMbJ9O1P8dYoVs6Gc0hSBrhGQ1Fa5Emj5hXEMcljxiaayhEIxFspFQ1k8NujHYJFRnUEIqQ68SJxhRjaVd7K44piOcYCCS1Ip+4GUIaRdgiQDvKiRGhngIsbCIkSBHCAPKox0CSTwN15BClkRViAMVGDaHcJOuS3aE4CeEAlAWU/tbOeFORlmbcJEUoFi+vjLIbhIF94esZUFvr8sQT3lKBajR50YYNMUaP/p5USing7t3DfPHFMEaMKOSHHxpZs6aBXr1y2W23AixL3de1a1VuTGVlkpdfXktRUYg5c46hujrFEUdM5aCDunDDDQoGklLusM7AffeNY+TITsRiSSoqYhx8cM/MM/+LX/SnbdsIf/7zAgKBbd2YqZRDfX2SkpIfn73fKi3LjgxBpRBiBtBTCPFO851SyqN+umH974hpGvTv36bFffvt14X16y8GVPz2mWfuRiBg0rdv4Xb769Yth3ffHcPSpfVccEEv8vICnHRSO8rLk3TosAg1fRc8/HAM7XrNcEZotMBLL5ASamvhkt9nDsuSi8TJFqyPo+AJ/b+Mee3qvFPkkC3JqJOeXAsnZpFwLMxgilCoASkFUghcaREXOeR4oZsqfDPkof1JBBAmjU3QYxptxEKS8jB8tVhRa4UwaW+NILz8gZRXvyBOrgc5BbwiCwFSFFOLLYPkyxrCIkUomSTgKI1bvCmGmfSuOaJeliOxEmrZ1C5egxuALRGHtGUSM6KERAIHkwQR2lBBMVXUkUuApJdc5hAiRZIQ7dlMmehE2PQwbWlC2FUsrsVC3eMGsn6CIFlnvQmmBMdjIV26Cm6bBCWFadaoiiBo63zBBSbvvBNj69YsVfl553UiL8+kb98PWbu2lkDA4NlnR3DGGV9SXBxk06bjmDr1IJ56ajmPProCx5HU19vU1aWZNm0tM2duYtasMr74ooxBg4r44x/ncfbZA/nzn8czadI3lJSE+dWvhmbOF41aNDam2Wuvjowd2yWz3TAE9fVJJk9eSf/+xcya1YSeDIA993yK5curmDHjdPbbr+s2+1vlx8uODMHhwJ7AC7Rck6BVfgb5+utNPPjgXMaO7cKkSfvSsWPuNm3mz9/KddfN4brrhnDQQV2YOLETEyc2bfPaa1UoaKARyCcQkBgGJJMa+DGzpD0ueCiLqiugs4x9fmXpkuUY0hXK/GRoOgcshoItNCtpiVDtbZSCExLbtqjZmk8ktxGCQcJWEikt4iLqKW9FFpH0+H8MXFQpGRuJ4WHxSYwsgOUR1unY/pTnIHaJes7hqJcrECZJEQ0KOJPKMRwkRo4dx0hKzKRU9Bs2mA1kHeoxFf/v1kJER1x5TKCdE1tpLLBwwrXEZZhcGlkremLh0uBlHps4lNMBmwBFxEh7mQ85Hnle24LNbKnuoqKn0kLdrw4omgkPAiLk3dMKsmG/OttYwr33gnAtQiEbiJNIqB/mkUc6cued7Tj22OXMnFlF584G113Xg4cfXs2aNQ1IKQmFRGY2bnnV1IYMKeahh0YxbFgbXnppDdddNwgpJaNHd+DMM/vx3XdbmTJlHVOmrAMMPvlkA7Nnb+YPf/gWx5Ece2xvOndWz+/jj3/PddfNJBSyaGy8PPO7vfXWCt5/vxSARYu2ZqjV/VJREcM0BVVVP8430Srblx3VI0gBXwkhRkspK4QQOVLKxu21b5Vtpb4+yR13zGLChJ4ceGCvv6uPK6/8mK++KuOVV5bywANzOfPMwTzxxPe88srhjBypyMLuvXchH320iZqaFPn5ISZNWsS++7bj+ut3QwjBhx9WcsUVq/BP5yMRi4aGNPn5Bscck2bIkCDXXINaDWgHryaSs8kaAhekdk7qEgFpstWzNOeQzjHww00uSmm1RdEmGALZ2QIhcIVLoxMgHWlARg2iwThJGSQhwl7WLwRIkyBIhCRJIgRpyOQY2J5DNu4RzClCuySajML1/ASgvAgCgxAJ8rz8AEtK8u0aIjKNYbrkVqQR2gDGFFSfqclQ6V1TCIyIuu4MZZcLRhxyUza1XS1MHHKJ0YGNuBispK/Hm5SiLVXUk4tAEqWBBFE6somtohjHCpJXXI3rGjSE23mGWWSzjP2Om2I1JidF9h9drfZLDBKJIEVFFomEyr7esMHl66/r+OIL9UOWlcUZNWoeM2YMpaIiSThssHZtjP32a8fy5UdSXByipibFBRd8yaBBhYwY0Zb33z+IJ55YxmGHTSMYNFiz5iSWLq3m4IPfxbYllmVw7bXDGTasLfvv35k2bcJ07KiSxz7+eB2//vVMDMPgpJOaOoDjcRXxFQgY/N//jaSoKExzWbDgfNaurWWffTpvs+/fRdaureHee2dzzjl7MGxYx50f8C+WXeEa6iOEmIlyHXYTQgwFLpRS7kpNgv9pefbZ77jrri949NFvqKm54e/q48YbR3LttTNYtaqG/v2LefrpRaxbV8+7767JGILrrhtCTU2KX/96MOPGfUQi4TJlyiYOP7wzDz74A99/n0JKB8NI4Lp5gEtdXQIIUV/v8tprKQoKvJWGppQIkS08o7/rCCJ/yKKBgn50mcUoyqHcjqxPIUrWaCRRRiIg1LkqA9kM5Zw4QpgkkhFsxyQUSlEjCigW1QghvKBQXctYUksBQWKYnts14CWipbFoIOIlfykcxSKNiYMD5NOILnUDNkKqJLaIm6S4OoHQdBuGWglQhTJqLlBDtmrYVpThLABRjDJuFsgugIS8siT1UUEqL0C+UUtahGlLBWkP5sqhlrT3FyygjiraehxIKQrNaqQh2FzfEbNjHCcagY1C1S3Q5UKV35dQCIwwxBNqLKRQEUaehEJw5535LFvWQEmJydNPV/LII1vp3j3KwIEWH36YoLw8yaefVnHRRb0ZOfITysuTpFIOr7yymvPO682YMW14++11vPHGWgIBwfnn9+PJJ5fgOJJUyqGiIsFrr5Vy1VVD+e67rZx1Vn9OPbUvQgimTz+myTNdU6Oy4mxbMmfOZsrLGznttCnU1CQYNaojlmWw994dufXWbEjqI498y1/+soAFC8p5992TOPTQ3tv7y/xbyC23zOD557/nq682MGfO+f/q4exUdsUQPAAcArwDIKVcIIQY+1MO6r9FDj64N/37l3D00bse9tZcjjqqL0cd1ZdYLE0kYvHVV2Wcfvr7fPLJemKxNNFogCFDSpgy5RAqKxP07JnD0qX1ALzyykaefXY9UgYQIsDEiW34+GOXeFwBLR98UMCllyYpL7d56y2PYU6LVxidCGpV0IBSPIVs41QGlJLU9QtAzZo7+NolyNYrqCPrb5BCMZQiIWbghAxEwMW2A9h2kEAoSWMgQg4JDKF8GjFyMmGcLgEaycUlTtCjpVAU10EvAzmFqi+Q5cYOkSREkgBJ8ogRJkFuMk5OwjMCXvirqPOuO02WaqMOZRT0terrqSITPSXiICIg2kkKG+MUVsWpaJ9HKirpJtazht5IoANbaCSXNCGP2iJGjCgmkjZUUSHaUZRTw6ZK76YXepFdUe/+dgI2onJENISix68JAV1BQYHFyJGSc84p5G9/a+CUU7biug6WZVJXZ3Pttd2ZM2cLRx89j0jEYNKkfjz77FrKyhoQAr76ait33rkHo0a1Y86cCpJJl2eeWUk6rZZBpmnwzDPLefzxZar6nHA46qgeTZzFd945h0mTvubII3sxa9ZG2raNsnlzjKVLK5k+fS2ffroOwxDce+94Ro/uwoQJWZqWzZsbuPzyD3FdiWkKvvlm07+9ITjnnGF8/fVGLr98n503/jeQXTEESCnXN4sAcLbXtlWy0r9/G5YsufSf0pcm52rXLsoPP9Tzww/1zJlTxrhxWXK7Qw+dzsqVNQQCAWzb4L77lrDvvh1ZtsxmyxabqVMrGD++Cx99pDI0v/wyxubNgmRS0rVrnPLyELYdUMWLtUJJQl6RV6kshZoR55MtpZgGEfSgEU2FYKnjqEDN9gWZGbaX/qskoI5nk1DhkWYIW1o40STBnDTCcEknA8TNKI5pEiRBHnEcTOrJIZfGjF9ARRLpesJ6m/Con1UZyhBJ8mjweIcMBdFIiStNwnYCDM/lUaPwf7RjGNSOld51gDJsAkWnEULN0jUUthUoAlEC0kM2oskk6WgA03Vob2z2OFUFXdjIanphEyJEknrvhkWEOoErBYqm1RtLA9lEPRO1Aqj07mu+91n7D5JqPFu2uAwbluK44wwuvtj0+KYcKipSVFTEWbkyTjiss3Yl++3Xhj/9aSWLF9dz4IEdeeqpvTFNg7KyGMmky777tiWRcJk7V9XIdBxJbW2KYNBg3Lj2uK7LMcf0bPL8vv9+KfG4zdSppdTVpTjppP7MnbuZmpokPXsW8OKLR1BdnWDs2K7bRBu1a5fDSScNZO3aOs48czBnnfXvT3M2blwPli371b96GLssu2II1gshRgNSCBEELgeW/rTDapXtSe/ehRQXh9i6NcFHH61j3LhuVFerZXyPHrnMnVvJ3XcP4aablpCfH+Djj0fz/vuVHHvsAhwHZszYzB57tGXtWsltt1Vy0klFdOuWzx//CGCTmytoaLCUg9JT6PUVZB2TBkoRpVCzzjDIahRwKFCwUA4Z2II4Skm2p2l5S4FSWA3ePvBKYppII0TKkpimRBiS+nqL/MI6hBTEEJjCpd6L8s8hQJCUR2GtQ0AlIRLUk0OEJBGSnuM44PkYVPWzHFQ4ZNBNYpuKr8dMgqglGxZrk53xa54fyDrHy8gq3QqUYQijDFwuiDTIAOREUoQraijLK8IIgyFUWc5C6ujFGhYziGKqkEhiRLAJUyi3Umt3RwYk5KagwVLkT7pmtC5WE/F9N8j6bzwOKCEMXDfERx8lWbCgimwHAAaFhUE2bFBYUiLhMmrUTFzPS/vRR1t4551NBIOSNWsUxfoTT+zLgAEFfPzxJg4+eCquK3n77bWk0y6rVtXRr18eW7cm6NIll2XLqnjllZXce+/+fPPNZgYPbsPrr6/g9ddXUleXJBazufLKT/nmm9NwXcm7765iwIAS+vXLhkcbhuCll479Ef+Sf72UllZTV5dk6ND/jGS2ndYsRuURXAp0RuU37uF9b5V/kVx11XC6d8/jyCN709CQplevF+nR4wUmTRpKdfWpDB5cSDKZYsuWRmbP3spRR7VlyZJRHHhgG1wXrruuDYah6CXee6+O11+vQgiF6TQ0eHMDzYevVwY6Y1jXLbbJBvBrhW97x8VQK4cYSjnFUYpUZ8dWe++O9yrz+pBCvWwL2RDBSSjntusI0imDNAFsTFypyrs0kk8NBTge5pT0qhc4mRI2QRKEvWErDiMLl0bycTyjIQEpBKEqCFaBsZUs02es2TWkm12/622vJOsMr/f26eusA+E5mM0tLh0qKgmSwJGKYg9UWct2bMJE0pZqiqihCBW+2i5YQTAaxwonVJRVgVT3PA8V9evd95ICsrrdi9gKFsBpp4Hh/ctralKsWqV/OP0jq9l3zFsQWJYkHDYpLFSduS48+uhKiovD9OmTzw03DGK33QoxDMH48R2JRAykhDFj2rPffu1Ys6aGadPWc9RR7wNw7rkf87vfzeG++75jn306st9+nRkxogOVlXESCQXXxePq/fXXl3Hiie+wzz4vtPjc/6dIQ0OKQYMeY+TIp5g/v+xfPZxdkp0aAinlVinlaVLK9lLKtlLK06WUlT/H4FqlZfm//9uHqVOP47rrPuPll5eSSNgkEg677/5aJitTS7t2Cpvo3z+HqVOH8Kc/9aZ37zBPPNER03RpaEjzww8OUibJ4h4+8cohZmbxGvPXRkFDJ7p0ok5PcFHKvw726gpCh5FWoRRrPVns3UZV4ir3tlcCmwxkRYD0pjzSlfnUrOtETWUBFQ1taUjnqJoKUgFBdeSRwiRFkAYiSEzqKSDl5RkEcL00KtuLMYJcYl5SmoVlOBj6OgzfNW1BGa8ylGLfgkrc2oJiBV0FrEWtBMq9a497bbZ670u89p4IoHvdRqJ2jPZsJkIDSSwsVJlMfWtVRbQ0hiFBCmQsAA1eiG8B2Rl/IRCGygb1nnEWJ1VOwQsvwNNP491YByn10g1M02L48By++GI3Tj21PYYBhmGxefNhVFcfyahRalYei9mcfPIsVq+O89RTq/j2W1X0YNGiakpK1PP13nvr+fLLTeoaBRx/vMLwjzuuN3l5QT75ZC2jRr3Mq68u5+ST+3P//eOYMuVY7rtvf95+W6Uk9e5diOtKdt+95byaXRUpJddfP51TTnmDWCy98wN+hKxbV0tFxY6DJy3LIC8viBBih3xI/06yU0MghOgihPibEGKLEKJcCPGmEKLLzo5rlZ9W/vKXxXz2VUMhVgAASJpJREFU2UZuvPELr/gIOI5kzJjJDBiQz2uv7UfnzmHOP/8rkkml2V5/fQtXX72CMWO+5YEHNuA4Wrv7p/N1hMM2maVAGvWqI2sAbO+QJNmZv55F+2bCuEAjROtB6hWAzkx2UcqzlqyjU2cm69VB3MjWP5YCOx3AcSxq4kUea5BASlVLuIE80oTQlQl0xWNVnibg0VxL4kQ9A6BYjQKkMaQkXQiyhizeXutdY726BtJk8yH07fIUbua9AmU4dLhtzGu/CcT/t3feYVJUWR9+T+cJPTky5ChJEEVFFBUwu4JiXMxpXXWNa17zurvm7Jo+w6qLilnRFRMqIoIYEFDiDEOYnKdzd93vj1s1PcCQXGXGpd7n6ae7q6urTvf03FP33HN+Z5n5OTw6tN8tUo1PRSig1ux/QJsoXncqSKeZIClEcZOa3oQzIwKZUX0ua5wMkczusvDSNlM7+zQ480w4/fT2ZcjgdLoBRSIR4/vvo3z+eYBbbunP0KF+zjuvB36/nqnceedI+vZNZe3aFgxDoZSiqirE+++v47XXShk16nXWrg3icEAioW8iUFSUyr33jgXg229raGmJEgoZGIbC7/fgdju54IKRHHZYHy69dHdyc1N47bVlDBmSx+jRxcybV8FXX63n51JfH+LOO+fw6qs/8sUX5T/7OBuzZEkNgwY9xIABDxIIbL4Hg8/norT0YioqLu9QMaArsi2hoafRGUPd0OGht81tNp3IBReM5PjjB7Y1BO/TJw2nUzeYaWiIcPPNC1m3Lsznn9ew334fkZ39OjNnrsfrFaJR4fPPrZFaxxEOOABSUwHihMNJuV/iINbAbYVEVLvnCp1vbw2Glj+x9kvAZ8tIhodMtVIazMcxtDNpMbdXogfTkHnMFmlzDkZDGrGGVKIhL1V1RVS1FlBr5FJvZNNKGk2kEyCdKvIIkIIThYGLWgoIkGbqDIlZlGsQwUer4SMtGMRVaaaKWkVxLVhqDNqOKMkBPoa+yrcktxvNzxZEr3lUop3HGvNxrX5dmsC5GqRRL65b6qg9WUM2DRRS2dZ5wUuMAvO508zbdReHk4V6lsCf2aCGbNrCU4dNhpXfwMBu8MwzmB8kA3CQmZnguuvSyMlxkpPjwueDSZOWMHt2K6ef3o2GhhgtLfrCYuzYfN54Y19cLgepqS4++WQCt966G+efvwsffrgO0AN/QYGVWiY888yBLFp0QtvPJy1NdzYbNiyX8vJzOOIIXU+jlGL16maUUvz+929zwglvccUVn7B0aZ2pStou/7UDlFLcf/9c/va3zzEMtcFrubmp3Hjj/pxyygj226/XZo6w/WxBMWMTUlLcZGZuWgPRVdmW5vXfKaVGbm3bjqKrN6/vDFaubKJ793RWrWqmpSXKnnsW8tBDP3Httd8zdGgW8+Y1YhjgdqcRjzvRo1uKWQSlH++6q4eFC/0k+0+m4nJ5zP0BB+y7L+x/KDw6Deqa0QOitTBshY1c6EHJSTIVIQ0dtojSJs3Qtq91ShdJgVSvud1Nsh4hQ5lCdgakxHCn6iuy9IwW3K4YmTTjkxAZNCMocqgnn2o8pupoPnUUUYETAycxhrNEfywVY1BrKZ6GBI5qM1toLTrkI+jBfT16nLNmPY20yXUDyfCZoFM789AhG7f5PWSgi+gG6c+Y8EHLAC91qdlEcdMgOZSaKaUV5LOcwSRwEsPFPPYkanipD2YTjvmIfOeHdS5oNCUnEujwUwDtpGqACvB64KQJ8MxjkFzoqSEpHduMiEIpPbVzu+PEYsmpztCh6TzxxG60tsZZsqSRiROLGDo0C4D331/LUUfNJJFQrFt3EkuWNDB+/NuAYu7co9lrr+QC6VVXfcHdd38LKPx+Ydq0Izj00D5cdtks7r//Wy67bBRgcNdd83nooYkcemgffvqpnsMP77tFraLS0gYGDHgQEWHOnDMZPXrHFJetWdNEaqr7N6lz9N82r68VkZOBaebzk9ATaJsuQr9+mQAbdIq68MJduPXWRcydq6ucRo3KobJSqKyMc+qpBaxf72TmzEYcjjgOh2LRIrMxcdugoRgxwsGCBeak0RBmfw5fzoWSPlBnZaxAsq2B9X/bTHKumYO+SjZTGfGgZwJWpW49erC31hw8JFMxY+jZgQsICaQriDkg3Y3RPYbDrYjHXLhccRRCWPkQ0ZfFmTRQRz6g6MsyYjipoogs6ulOA62kIxjkq0qUgJFiZmlaY6E1W7Fi8QGSMwPM+xjJZRUrWydCMkTWA+0EMszPV6/fI5mQHo3Q4o4Tc3vw00oazcRxk0aAuJlna/VmdojC72ulqT4PepF0RFbIqn1ozRSZjUXhmWf1ZhGhV68gZWXWdKJdGbT5xevoopXPq/jxxxb22+9jUzwuwYQJRTz11F6ceuosxo4tpKgolX79/OTnp3DgganMnj0Jp1M2cAIAt966N6WljUyfvpTGRnj22SUcemgfmpoiOBxCY2OUJ544iFtv3Q+fTw9H/fplszV69MjkqKMGEQjEGDasYKv7/1L06JG5w861I9kWR3Am8BBwL/pXM8fcZtOFWbmymepqnR7pdAoOh5uKimaUcpKT4+Tmm/vy2GNVHHdcLnPmRLjssvUo1USPHtmUliYwDMW334bRg4PC7XYQiwmJOJSvQl+xm/FyrxMiDthloJ4+/7icZOjCSlLJQQ/yLeiBTNAFZwY6zOJErxn40TMIS6DOaqVgoDe4gICTRFkaqihE0EghEvYQ8KTicsTISEnFJVGqSFBChbkCovV9FEIr6TiIE8OFgwRpEiaY5sPtieN3RPT0P5/kLKCaDbOhrHAQJNcMHGjHYJj3VvewBpJhnHw2KDhzGFAYaGBlRhrNjgwypZVqcmklm76sppTeOHCSQhinGMTjAk4DlAM8os+RD65aiFszj1XaTsmCgW74qR7wQl4GDB3qpKzMS9JzecjICFFY6GGZuX5RWOjhnHNKqKgI8NxzpUSjCpdLiMcVa9e2Mm3aSmbNqmTu3BoCgdM3+L2NHVvMxx+vYenSBgYNSg7kHo+TJ56YSI8e6TQ0hLjxxn0A+Oc/J3LWWcMZPVrnDltOYFtxuRy89toJW9/xVyAQiHLPPV8yblwv9t+/92b3mzlzJXffPYc77jioy6eRbkvWULlS6igzY6hAKTV5Z2pV+dZbP5GR8XduueXTTjn/4sXVLFu2/ROwhobkYtZjj+3FlCklgOB0wg8/NDNgwGfcffdKolGDbt0UaWlOHA4oLY1hGAYeTxylwuhwQsxcCFT4vOYCcj16UG+GiHn/0yL4cRnJVFNrrSBMW+aNO4J+f6t5aGvdwRpom9Ax9SqSUg7W+oKVmVMPBAVjXSqJeh9GwoEynMTiPkJRH0oJAfyU0YMW0gmTwjq604SfBG7W0t3sOpBOEB+I4IgZSFyPs7jQtQ0xkouxlsxG+1JKK4OqfZptAu0A2ncfrTU/q9UoKwLSAs5oArcRIY7LVFZ1mmsCWi47gZBGCygIJVIRZxzcMW1Pgz5u3BLCs/oSACoGP60yz+WAmhpYtMjLI49Y0zBFZqaTlpYoq1bFufbafgwe7KOpKchf/7qK11+vZ/hwPx6Pg9mzxwMxliypx+l0cvLJ/Xj88bGb/N4++KCcI454m1GjXiQeN/jyywo++WQtAJmZXu6+e3+eeupQevXKALSD2Gefbrjdm/bh+G+ZMuVlCgruZPHi6q3v/DN4+unvuOmmT5k8+aUt7nfNNR8xc+Yq7r77y1/Fjl+SbckaelZEsto9zxaRp35Vq7oQH3ywikAgxjvvLNv6zr8wy5fXscceTzBy5KNUVydT1sLhODNnlm4xNW6PPfKYMWMC77wzgbPOGsD48XlMmZLHI48M48cfW4lGo8RiUU48cTFHH72YMWMSHHKIVSKbwOEIoZQOsxxySBTDiKNUgnCo3ZqSVQdgmAtp1iBvDezt8+5DQDPEqtADo7XAHEAvqNaTHFDR+1LKhouvAdpl7gi0Cqx2E6/PIBF1oZQQiXqRhAIFUXxt6qQJXLTix9kmTaF7CMTEQ7NKJ0RKW1uGDdLsfeirb2vAz2r32Qw2jLJYISXrFiSpCBqmLWwkEUg4IOR3kuoKU0gFPoKEcZNOMzGcBPESJJ1cGkmY4tnKMMuJ842kzIUTBvaBDEvuIxXcLn1vzVA8XjjyyARLlzoYNMhJ9+5Ojj7ahWEI2dkuzj23gPr6KOGw/jDp6U7mzz+IlpZjyMnR9QQiUFKSyrPPHsDUqQPafgK1tSGuu+5LLr30c2Ixg169/FRVBTnggNc47LC3WLy444uYH36o4amnFhKL/fIiBe+/v4LGxjDffPPr5PBPmNCHvn2zOOOMkRtsX7BgPUOHPsxDD80D4I47JnLEEQO44op9fhU7fkm2ZT62q1Kq0XqilGoQkd1+PZO6FrfeOp6BA3M58siBO/zcqaluXC4HbrcDrzd55XTBBR/w3HOLmTJlIM8+ewSfflrOrFnr2HPPIiZN6t+23+GHJ7Xar7jiez77rAbDUJx0UgkPPFBKLKaortYzhy++CNDYqMMG6eleAgFr5BM++KD9yq+TpIopgJASg1SfuYAcMl/2t9u9fW9za8CMmgoN2VATMvdLQc8I/CR1iSrRA7GVZWSgF2Otq28EwopoXTru3BZwu6kP51Gcth5DOQiLiwb8pvRzjNX0oiflxHEwnEU4zcypmMulY/ktoKIgfvM8FSR1kszTtc0MrNRa2r1mrS1YfZ3rzM9lpZPm6v0cDeDOUHidMRo9uQhCdyqowqCaQtzm1EOXnUVQ1pWzVdyXS9vCdXYGLKs1bXSC8sJBu8EH7+s/WzymePhhnQl27bXZ3Hqrl0MOKcXlclJcLPTp8zV/+lMx771XSVaW8MwzuyIi1NZGGDnyXdxuF5MmlXDkkfr3tH59gClT3mfcuGIaGoI8+eQSlIL0dDeLFk0lFIpTWJhKMBinoKDjRdUJE16koSFCLGbwhz+M7HCfn8snn5zG999XcdJJw3/R41oMHpzP8uUXbbL9xRcXs2RJLffdN5cLL9yTCRP6MmHCz1Md3tFsiyNwiEi2UqoBQEQsNZmdgqwsH3/6U+cIR5WUZLB+/WU4HEJaWnLw7dnTTzxu0KNHBhde+AFPPfUDhqGLgWKxSzrMtrj22sFEIgkuvXQQ++6bz6pVQV59tYI99/QxcGA2o0dncs45q1EKIpFm9t8/j1mzYogIhpGge/cEa9cqDj1UmDlTYRimvJsoQiEhZOW0Q/JK2BoDzDXK7BRosAZ6AVUBngySC6xetBOphcxe0BQj6VCslNX2C8/d0YNspYAHYvXpqJIwMYeHOmcuqb4ADSofh4jZHyyFQqqoIY8QKVRQg5MYqRImQ5pRPlNewonOfLJUVq3zppHsxWxpJqW222YN0qkkHaJVv2Uthpv/OdIAniKDDCOEeBIoXOaSRC4KB8VU0kAWlRSTTgivI0yqv5XQulRUoyQX1IGvrAvfsLY5bkDcB92LYW2pbgrndDpJJCJ8/nmEYcPWs3RpK7m5DhoatPNftSrEypWtGIbikEO+Ys2ag4jHFUoJbreLhx7am9RUbfzMmWuYP7+ar7+u5tVXD+aVV1YyYEAml18+yvytulm9+nSU0vIQG1Ne3ozTKbhcwu67b9qL+79l9OiSXyyLKBZLcPHF/yE728df/zp+i5lMl18+hmg0wQknDP1Fzr0j2ZYB/W5gjoi8gv45Hw/c9qtaZdOG3+/dZNuNN+7LhRfuTk6OjxtumI1hKHJzUznuuMHmwK0wDK0Jn0gYjBnzFgsXNnHUUT0ZO1ZXIy1bpheSP/mkhueeG0FOjofFi5t5+OEqnE7FQQd5mDMnQHa2k+XLC6iuFh54IITfH2XsWBe33GIQi+lwTFuMImIu5prZN0cdAu/MBMOUsPa0ogdNS0/fC43LoaQbrLPWMM2Cteb1JAXrWsz960hWz2YBZeiMowz0AmrMSbw+BbINamsKSUtvJDVrHVHxkkKYOE6UqTnkIkEZ/cxGNQ0E3enk5tThD0dxNZnntQb4HHRNgKFtJhM9Q7EWtKMkO7FZMwbrs+SQXCtJM7dbY0kLVPTIIocGVtGXGG58hHETIYIXFzFAtKJnVBEO+rQCqfV+d/K7Jpvk7CQKn/zHPK8bBvUVvlmQyu67t/D558kOQwUFwtKlMZSC++8fgFJR3nuvikgkzpVX/sAddwxnyZIjcTiEwkKrMTUcd1w/vv++jr33LuSoo/pSX3/uJr9REdls3v1LL/1IVVWQrCwfe+yxZa3+5uYIX3xRzvjxffB6d9z1p2EoJk9+kcWLaygv1+nXf/jDHvTsufmsoaKidO6//9AdZuMvyVbrCABEZAgwHv0L+kgpteTXNmxz2HUEG6KUorIyQFFRmtbsjyUYOvQFKiqCLFx4EiIO+vR5GavctL7+JJYta2Xq1HmsXKlHjpNOKmbvvQu55JJlKAUlJWlUVzsZMSKdO+/syRFHlKGUk1BIB9APPjiHmTOt/3I9QPTr52LlSt27MiPDgVIOWqwB3HIOBsnBUiA9H1pNSQrJgcxMaLSK0nzowc0SVBP01bXXPFZPkmmTVr1BHnqgLE6AW5dB+zxBemavJsdVj6Doxnpyqac7a03rWxjFDwD4E/UMji7DEQFvucJZT1JesRVYBGZjM/28vdKANQh7SM4K0kybPaZ9vc3vIgtUnn595bASFEK15FNPDhUUsYxdTIXVVJYwnKhysjbenZr6QlTEiar2Qalo51RPslBvvWnfSvDG9EJyz3x47mlwueJcd10zs2YFUCrBiBExVq5sRqkoKSmw114+XnttBNOnr+PUU3VHsVjsaJxOIRyOM39+DS+8sJILLhjC8OEd98veVioqWrn44o+YPHkAv//9kC3ue+ihz/Pxx6VceOGe3HPPIf/VebeHpqYw2dm343I5GDeuF0OG5HP//YducUawcmU9BQVpHV68dQX+2zoCzIG/0wZ/m80jIhu0r4xEEpSXtyAirF8fYObMtVhtx373u55kZ3t56KFvWbmyEWtldNq0Neyyi5+cHDetrQZ7753Ja681UlvbyjffBExBMmskd/DFF1pitF8/FwMH6lDRkUca3H+/7lbT3KyrTF0uRTzu4JSp8MprZuMUraKAKPh0Oux+CJCAcw+Fo46AIy4m2f8gAKTrLmCGk2Qbxji66MtKP7XSPCNAQPTOPRJ6kpLwsibYC196CKcowpJKFW6zP3EDQlpbMXFYUlEOIZrqwpERx+E0kDr04nYGUIyenUCyQQ9op5WClYKvnZ8VEoqhHYCHZJtLM6zU2tuJmzBhfNSSZ9aoxUi0SWQ4ieAgiJ8sVzOJbCe1y3roMFoY7WhiOgNJVZvnazR/By448jDYdySsWQO33RZg0SJITfUQjYZobIzS3Kx1tltbI8yYEWDt2giTJhVz3nl9GDkyk3//u5RevdKYOvUj1q7VH3b58iY++uiI7fyVbkhxcTovvzxpm/bt2zebmTNX0qfP1msLfkkyM328+urxlJc38ac/7dUW4lJK8cILP1BYmMZBByV7Irz//gp+97tp9OiRwcqVF+9QW38JdppY/85CerqHr746npqaEGPHduPaa+fhcAj77JPH9OkTALjuuiE8/3yZ+Q5d3XXXXctYufIQ8vP11Yzf/xllZVEeemgVIj6U0pe6TicEAgYiAc4+O5Pp0+t49FE/06dbcRLrmAYeT5j0dBfTXzZQyqkT3g0BgWFD4XeHwbjhMPdrePJhaGlGD/AukEIzISeqY9xtMfZC9FWvpXtkyUDkktQ9yhRY6Qa/gadPGKWERpVNtqOJICmkk6CVdHxESeBgEUPpQTnZjigN7mwcAqEeCYrKG1GZ5qdqRYd5rFlAummL0LZISyPaSVkhLcs2q9NbVN8rN8R7Q8ytOyS7MYiZvckieFEYVFNAFB9xszWcAMHGLHAKJER/drPoTkKg9GSMA8dBzSJYtATema5vDuDoSV6qq4PU1UUZOdLDE0/ks8ce32EYQq9eKUyZkkfPnj7C4QQFBW4qKwPccssPJBKKREI7gR490rj44mEb/N7C4TgNDeEOe2n/Ejz88OH8/e8TOkWu4eijB2+y7bPPVnPWWW+RSBjU1l5JVtaGdm1DgKVLYjuCLko0muDYY19GKXj11ePxeLY933rEiPy2x//+90Tee6+c447rh9fr5Omnl7JgQR17753DvHkNDB+ezeLFAYLBOE8+uZLLLhvECy+so7VVx6dLS0MMGeJizJg0HnqoP7ffXsdNN1XTp4+Lu+9upLYWzjmnHp9P0MFz67LdQTCoCAY3qrgyfHi9sGI5hEKwfr35nyPCjJeTu91xHdz2f9DYTLLArA7tBEz9HxFQlhKnpRMUBZYBuQ5oEiItGZCqiIyqJ+ZwUkO+1iSSFDxEKaaVRnJwESOOm25SRRohxFDabJ/WvxNLcrsAPfuwah4sTSQPerCvN+1NNW22Csny0c7CAYkSvW9aNEKTxyAoKW2qqAZOCqijCt2GtIBKVjCQxnAWMcO5YYFdMxABI2aeKxXK6qFnFklHCRgJcLncTJgA06YZjByZQu/eVvhCKCsLcc89qwiF4uy2m4+bb16M2y3k5Hjo1SuNJUtidO+eyqJFx26y+Lvbbs+zYkUjM2ceQ3l5M5de+jF33XUAZ575yzSPEZEuodlTWdlKa2uUQYPy6NbNT7dufvz+ZALHIYf0Z/Hi8yks/HUc4q+N7Qi6KCtW1PPeeysAWLasbqtl9ImEwRVXzEIE7rzzwLZ/2JKSdM4+ewhNTREGD57GTz814nS6+de/DuD3v9eppvfcs4wrr/yOa69dzLXX/gh4EPHg87kJhQyWLGlh2bIAa9aEyM/3AxFWrYpwxBEpzJgRQ8RBLAa9e0coK2vf41LrSYjoKbXXG+aMM9xkZ8dwOFzcdpsVS3Ez5Rgnr73uaKtGvuE6CMXNQ+SiB9EYyRi8AcqSWLB6AWDuG0bPLDLNCtwmoW51AbHeXjIc9Sjx06oyaJUM3Mwjh0YSuKgnjx9kOJmqiSxnPZECN91c9bjEQFWBVJMMW4XRMwQFWAKXLmAXkmmuFlbWkIFe2zD/lA4UDiNGg6sbCRw0kUUMDyFSSKOFdRTRQiZBI41gKB1xG4gzgapxJKUuMtqdrxlKK6F0pfk8FWjR3uCllxQQY+jQFC68MB2XC9LSHASDBmlpDgIBobExRnZ2Jv37p3HQQUU8+ODuAHzyyTpeemkFVVXBtgb0tbUhHnjgG+rqQqZURIQHH1xAY2OY119f/os5gq5AKBRj0KCHiEbjzJ17NqWlHYd+fitKox2xWUcgIrOVUvuKSAvtNWzNn7VSKuNXt24nZvDgPG67bTxKwdCh+Vvdf8mSOh58cAFKwRlnDGfYsA3fU1bWwsqVzYjA6NH5TJyYTK877bRevPPOOr76qo5gMI7PJwwdmsHhh3fj1lu1M4rH3cyc2cBVV6WTm+skP9/Fiy+W8MUXIQ47bC2JBDgcliSFk7y8GOeem05KigOPR3H11SEikRCvvhqipsaNxwMibpQSXC4nH30Up0d3B+Xl+icZanFAiiSzjMwKfQlBn95w9D5w79tgWP971sQDklfDLWj1UhTxsjTqQ17cAyL4vFESpqZnPfk0koOPMH5aCOMlIbmAwu8Lsqo4hX5V63CmG6ga04ZitIOxupPtgb46b19qYa6FIOhFby/QB1QOGG6IOl2E3R7CjjSqKSRs5trGcFFHHm6zdU4TmcQiVnxaUCGn/nyZJLWOrP9O67ml12SdX1nTJSeLF9czalQdt9ySSyymzEVhePvt3bnppkWcdFIp9967KxdckIx/n3baR6xZE2D9+gBvvXU4ALffPo+7715A376ZzJw5hQ8+KOW776rJzU3h0UcP3vwP9TeIiOByOYhG9f3/Ipv9VEqpfc17v1Iqo93NbzuBXx8R4corx3LVVWO3mKlgMXhwLn36ZJFIKP7zn1WbvD5iRB7PPTeB118/lC+/nGRKB2tyc718/PGBOJ36PNFojEceGcbNN/dn6NB0XC4xQz8J7rqrDMNoYP78QaSnOxk+3E1eHjgccUpLo0CQjIwo112Xyt//Xk51dRNXXpnKBRfoIHZ9fYLsbEU0qi/jDzjAwdVXC42NccrLgyRFexT7WOUbChzmAum/boCVr8B9z4BhVSXXgLMUPBHapBfaZCHioDWKHNDspuqHHtQ15ZKmmvER0AM/TlpJp4pCaimgkiIqpYCQ+FAiVGVnEPMLgVFOYnlgpAED0M6pJ3oWUoge8K31AbONJ9lAAah+oFJAJbQERNQhlLp70+TIxE8jbiK0kEot2bSQTgvpNJCLXwUIhDN0mq615mD1gbAa/GiPpn831mK2lV2lzM/fhp6q+P0uPvxwKF6vTh/V2WdhEgnV1o/A4oADdJjqvfeS2v6TJ/enZ08/5523KyNHFjBiRAEiwuGH96WkxM//Ej6fi1WrLmLVqosYOnTHCdztSLbJvYmIU0S6iUhP6/ZrG2azeR5+eB6HHvo869Y1t21zuRwMGJCN2+3gww9Xk5PzAM8/v3iD951wQn+++GI9fv+jzJy5YcOOUCjO558fSN++qYwalc3IkVnU1UWpqWmmsFDx3nu7cfjh2SQSBs3NceJxRXNznMGD51FXV0mvXq1ccUUeYNDc3MI337TicBi89lod69dH+dvfUjnjDDfXXZdGXV0eTz/t5/rr3cycmUZWllVRpnUonE4Dv7+VJd8106+Pgc8NRgQkABVmv5KeuSSLy4KQqIboOvSVeS06XFOPFmJbh061bATq3LR+m8/CFXtRobpRQTGlqi9r6UYLfuK4EBRh/HzNntRIFgFvGoFcD625aTTs5ic6FmL5oEaAarvqRl+B9wQGo51EFtALlOkMcEIiHar8eazx9cFFwpSeLqGWfJwYNJJrttnU5dhxXPjTmgFFIuDUshoB9HpJrfnHs0JjHu1oECAOXgErJiUSo6RET1OUcnHRRdmMHZvJDz/sxbff7sWtty6mvDxIfr6TwsINu2rdfvsY9t+/mGuvHdW2bezYEsrKzuHPfx4NwMEH9yEavZxnnz2CE054jQEDHmH16qYt/Yx/U2Rm+igu/t9ycO3ZFq2hP6Hlvj4AZpi3d7bl4CJyqIgsFZEVInJ1B69PFZGF5m2OiIzYTvt3Sq699iM++GAVr7yyYUbvSy8dxfvvH4eItMVqN+bdd8sIheJ8+qluLPLvfy9n8uT/kJb2DFdc8RVr1rSwYEENjY0xqqoiNDbGWLcuxE03fcf++2ficiUYOtRHVpabysoIkYiBYcCaNWHKyiwlujC77+6mpESxbl2EP/xhNUOG/MjTT6/jvvtWIyKcfnoqN9+cjtstLF4cw+kEKw3opJNitLRAYyOcenKUOV/AEYcAUbjuajjmGHj0FrNweT1JMbdm9GBv3btIykDUAQ1mxk0IqHSxcsUQfqodTHUkl2VqIE2kU0k+6yjGwEEcJ6voyw8yjJXO3loUWhyEXR4iPdFX+yNA7QqqO6hCvbCsHKCGQmJ3iA8B1Rd9hZ4L4Vw3TW49oRZRVFFEDbk0kUUN+bSQRggvteQQVQ4qE4UYCRextakYK1K1E7DCQlZIyNJyaicN7hCIBHW/4pycKEq1sm5dstrNmmUOGJDKLrukceGF/cjIcFBVFebKK78HYM6cKj7+eD3FxWnMmjWZm2/ec4u/SxEhkTB49dWfWL26iXnzfn6XMZsdy7YsFl8MDNrePsUi4gQeBg5CL93NF5G3NipGKwX2N/WLDgMeBzpHz+E3xIMPHs6MGcs30VJJT/dw4IG9GDAgh2nTljB16qbFOm+8cQQzZ5Zz6qm7EIslmDr1Q0DLU1RUBCgsdDJgQDZ5eV4KCnzccMMgbrppEbNn1zJjxjhGjcpk990zUUqx226ziEQUEyYU0b17Cq+9VgcI2dlOrrlmOXvtlcuaNQbvvFOLVZabnb1pD9cbb0ynpibI55/HCYViXHFFJmlpBgsWJDjtNA+9esGwwTDjLT3+vf461NdDdDmmLDW6mKyRpPQDMGkYvLkY6IMO3bRfwHVBIuqhqS6foKTSpDKo8hUxWr6iiGp+ZBBeYqQQooS1KIeDOlVABo0UuyqJiZsSXw0p4ZjW/x8MziYgAQ5TXynSDYLuNHzRGMQN4qkOIm4PMeVCiRAkhVJ6ESQdBwZRvITw694K+EjgIRTy0hzMRLwJVMKlU6WsamZL5ttNsp7CDQR1JtHxUyAccNCvn3DvvWCVbXs3qneqrY0wf34D9947kpdeWs0llwxi/vxqxo59G4cDvvvuaIYP37aFUKfTwXvvnciSJbVMnrzj9blsfh7b4gjWoOft28uewAql1CoAEXkRmES7wjSl1Jx2+89Fq8fYAG+88RNnn/0W118/josv3nuD1049dQSnnrr5yVP37n6uuKJjf9q/fxb9+2fxyCMLqagIUlSUQmVliJEjs5g4sQd33vkDSiXaso6uuWYXCgo89O2bTlqai4kTtUSFUnqhERSpqQleeGENhiGccEI3evZM4Z571lJW1kxubozaWicej3DTTUVccUUJixa18pe/lNKvn4899shgyZI477yznvx8NytW7EpenpNHH9X2vvpqgNNOa+bcc/04HD4Mw4nHA59+an0iaRsIHS5w1EBcARnw5hz9MmVo57AW3XC1h/meFIX4YhhRBy3xTJTh4quUfdjf8Qk5ZmVWBA9BUvDjoUFyiOJGXKBwUltcQF6ikrCk4XLESOsWQpRCBYW4z4mYs5xqdwotpJNCgIik0CJ+YrhoJotmsoibBWQtpJmFZEJE6ewhh1N7L9XsSArtLUfPAtoL3llNc8zFY4lAWppi6okG06aZsSIzvSgSiROJGHi9OiDwt78t5f77V1JU5KWiQheLvfzyKkBhGAmuumou06cfTFpae/XAzXPQQX056KDfhtiajWZbHMEqYJaIzCA5CUcpdc9W3leCdiIWa9ny1f5ZwHsdvSAi5wLnAvTsuXMsT7z11lLq60NMn75kE0ewJZRSPP309+TmpjBp0qAO96muDnLhhZ/icjm44459effdcv761z3x+Vy8884axo4tpHfvF7nmmhH84Q+DOeecfgQCMWIxA7dbDx4iwnff7c/06RWcf35vHnhgNZ99Vs999w3E73cxYEAql1++mGAwznXXDWDPPbP53e9ymD27gUmTvqGhAcCF07mOSy7pBcRoaIiTl7dhvcQFF9RRVZXg00/DgAORbHOhWYA0HaQ3HFAFA4bC0kb0oN+CDt0odC8AP3rxdj1tzd2pFZTDS6LQjWOXMM0tGTjEYG1KdyJ4yZNaErioJRcPC0glRD3d8EmINMIESUG5ClHKiYMYKUQQUTSn+fERxomBIQ4a0JIMDXhZSX9CpBLBSxMZNJJNDA9xHNSRSyN+IspLGB91kWzC0VSMqEC9W4e1Gkl2UQOdHbSSZMUykK2gIQRPPwlPP2m1g9MxI7fbwZlnZrU5AYAnnyxDBCZOTC6ETpnSm4kTC/nww3W89145gwY9zzffnLBZNVGb3zbb4gjKzZuHDfWHt0ZHqS4d1t2JyIFoR7BvR68rpR5Hh43YY489fqO1e9vH7bdPZPDgPKZM2bIWy8bMmbOW8857j0RCUVl5Mfn5aZvsk5eXwtSpg6ioCHDWWYO55JLk7GLx4mM555zPWb26lQceWMSiRbUcdlhPpkz5mKKiFFasOB6n08H69UGOPnoWw4ZlkpMzgJtvHsC//lXKHXcs4bbbhnPOOd1YuLCBN96o4thj8xk5Uot1PfJIOQ0NMfx+N4WFPnr08FFV1YrPB7/73aYaNjU1rVj6DE6nwbHHRnjpJWtgM4X9wz6GDnNQukZvdmdCLIy+hHGincAqdJtHF7ofcdB83iTgFlTcAQKN1XksLNkNlzNOT1XKcMdiFPADI+hBOfXksoJB7MY3pBKmkUyKpJooKaylG7XkkkaIQiqJ46SU3qQTwkeYnxhIM1kAKBy4iRPGh4GDICm6u7LhZgUDUQiBukyiDX6dCRWVZJMf0KF+q6jNyg5K6NcbLY2nGFhSH7rWI8E++wjffNPATTeFOeaYfHbdNZ2hQzNYsKCBiy9OSpg7nQ6ysrxtNSBVVUEWL663HcH/KNskOvezDiwyBrhJKXWI+fwaAKXU3zfab1fgdeAwpdRWu7/YonNbpq4uyH77PUd+fioffTR1u/Oef/yxnpkzy6moiPDpp+uYO7eKffYp4ptvGnE4hPLyE4lEEsyfX89xx31OIqEIBk/E5RLc7uk4ncLzz+/NCSf05N13K5k06UuGD8/g/PP78vjjpVx//RBef72a88/vxR57ZNLUFCMr631E4LnndmPq1A3lg08+uZx3320B3CQSipYWA48nk0jEhy58c+Byhfn00wxaQy5mzYafyuC1j0j2QXCbt17orB6rv7Dld9KATAV7gKQkSM1rIh70Upi3BofLYADLyZRmCqjCaV7LCAl8ROjGelJoReEilaBZISy0koaHKBm0ohDqyKYVc5EYg3J6EiSNOA4q6EYCF3EclBs9CJj7tVZnEmlK146g2pSeXoy+N2saqIQ8F6Q7Yf1yiAZh4gj48F3AiKNTqwwcjhaOOcbLK69YPTgDiEQpLnZz+eUlGEaMBQsaefjhUTgckJXlpaUlyscfryORSFBfH+Gss4ZsUyqzTddkS6Jzm3UEInKfUuoSEXmbDq7klVJHbeWkLnSx/wR0At984PdKqcXt9ukJfAycutF6wWaxHcGvS7du/0dNTZgHHhhHcXEaV101l/vuG0tWlo+8PB8HHvgmNTUhPvrod8yf38TAgRkccYQevC+77Fu+/rqeV14ZS0GBj+nT13LyyfPp2TOVeFxRVhbkqqsG8o9/DOP77xuYOPFTDjmkiNdfrycYTDB5cgEiBo88shtFRZvKCowfX8onn4Rwu+NcdFE3/vKXbAYMaKa5WfHoo2mccYZeBW1ugb/eDXc+QZvcNaBTOgvRef8GOlRUQNIh+IACBf31+3K6rcebFjYbSBoMYSHdZZ0ZyNHNJbOpI4Ug6YSI48BNDJepZVRHLk5ipBEigg8XURK4qCOHBnKpI4cwKfpISiiPl9BKOobDQbg5ndaqHIgJlIlu81kLsdXotYBW9BV/mTb9r+fCvTdDXS0443DEwQk+/TREU5PVAayF/v0jrFjRCChcriCJRBT9/x9CJITTKQwdmsYPP9QwffqBHHNMr1/qZ2XTBfi56qPPmfd3/ZyTKqXiInIh8D763/EppdRiETnPfP1R4Ab0v+Uj5pVGfHOG2uwY9tuvG2+/XcbIkXmMGVPM5MnJRT/DUITDCUTAMODSSzcU5brnnt02eH7ccd3p1y+NlSubqKmJsmBBC3/6k65YnTevnoaGKP/+92qOPbYX9fWKN99ch1Kw1145XHXVpusbp5+ewaxZDcRi8PXX9WRl5XHvvW6uu66BtLTkiJ+WqrjgTLj7IcGwcvy96BWrdcAQ2hrJ0wz0I7mGYDWrHwAtK7NxDqnB4Yph4GCRGsFiRpBDHd1kLVk0U002MVIREqQQpoAa0mghQBqNZJJNI1FTFyNIFs1k4iZBNblESNWzByOF1fFeRA2vDgmtysUIunQO6Gr06loMYpUk23laxXOAKwYlfkiY6waJuOLttw30v7deJN5vPy+ff2718DXIzvbQrZub77+vw+t1ct55Aykra2XZsjqcTgfr1rXX2N4+Vq1q4JBDnmfs2B4888zkn30cmx3HrxYa+rWwZwTbTyQSZ86ctYwZ0x2fb9vlpd58cwXnn/8Rt946ljPP1KqTNTUhamvDDB68qSzwqlXNrFzZxMSJ3dtCCLNnVzF+/PuAorb2JDIy9DJTNJpg4sSP+fzzakaPzuG11/anZ88ZgPDhh/sxfnzHnatmzmzmzTcb+eMf8xk2LIXf/34d06Y1M2ZMCnPm9KaiwmDw4CZSUmC/calMf8uNzykMHCIsNJVNSUWnMuSgHYTDfJ6GDrsUgAxQKAGyFJ60MO6erWRlNSMosmggHPOS5dYB+u6yzuyL7ESIEyaFFMKkEqSSArMgOIYCgqQQJQUvEb2IphSrwn0IxtOI1PmJB73QqKBWkovDAfQsoJRkukYVer3AWu8AHrkZrv4ztDbBmL3DfPFFFIhw3nlO/u//qonHw2Yf6hgiusF0LDahraJ8zZoAL79cSu/e6dx337e0tsb49NPJbX+zbeX55xdyxhlvIgLR6PXb9V6bX4+fNSMQkUlAd6XUw+bzr9AaigBXKqVe+cUttflV+OMf3+P55xdx0klDefbZDSN6q1c3cd11n3PGGcOZMGHDUMCLLy6loiLAv/61pM0R5OenkJ+fQkeMGjWdcDjBHnvks9deBdx11z4sXlxPLKZnET5fMiMoFjN4+um9mD59DUcf3YPu3VOZMWNfQqHEZp0AwMEHZ3DwwRmm7WFWrKikZ0/FWWfptNbqaoPmZoOmJpj+cgDIIgwsnA9DRsKKGohWoAfXHPTagR+96GolzQRg1wHC9wZQDlGVQizmwDkkjtNl0BjLBHEQc3sIkUpzIoNc6kh3BIlIClHlI4qPCB5ayESZeRNx3IgycIhBM+mkEKY5mkF9ZTGJkCcZgBWBkCQF7SyFU1NcrhioqIWUMISsVprAHfdBPAzdSyAzUxcWiLjweuPEYgZOp4cTTnDyxhv1TJqUx5FH5jB16pe0tMR55ZV9OP74WcyfX8vJJ/fhyy+rcDiE5cub2H33rWtdtef444dSWdnKyJFF2/U+m85jS5eHVwIntnvuBUajr5ueBmxH8BuhqCidREJRVLRpBtH99y/ghReW8NVX61m+fMOWg3feOY5Bg7KZOnVTXfaO6N8/k++/r2Xu3CrmzKnkmmtGMXZsISkpwu6757RJabe2xujd+0XC4QSLF0+hVy9dun/YYR23LZw/v46XX17Nn/88GL/fzaJFTeyxRw4vv1zN/PkBwMFVV63irLPyGTHCxbBhCX74Qbd5gRDZ2Snk5AiPPgDjjkAPsA3gLoaY1VQ+1ZT6N4AWCHwC08+H4740Bd++9dFcVohzZAinAxKtbhp7x/H6I1QnClkf64HEoqQ6wvRIX0tDPJOYKsTniOJ2xYkrByLQEM/Eo2LUtRYi8QSt1dngNIXk1ptqqWbvYdaZj63wVhxohop63dgn2qyL14wW/VpZBbgcivXr4eqrYdasCLvsYrDvvj4eeCBOSopw9939eeEFHXYrKwtwyilrcDqFhQubOPjgbnz3XT1HHtmTww/vRiAQZ9SovG3627fH43Hy5z/vs93vs+k8tuQIPEqp9nUAs83q4joR2XREsemy3HbbAVx00WgKCzf9s51xxnC+/HId558/apPXunf3c9NN2/4PPW/eFFpaolx55Zd0755OXl4KeXkpBAKnbZBtEosZBAIxotEYr79eyiWXbFmy+JRT5rB0aTOGoVi0qIVPPqnmxhuHcvbZA5g1q4l584JMnaqvWq+/fg0tLbVMm9abqVOjGEYLxx0XobbWzaWXGqS40wlFHKCEr56Fgy+A2iZgDeRHobIbILBiPZx4D3r9wIeWqGhykqhLJ9Fb29XUmo+rXxCny8DpiREM5NASE6rXdyelezOJqItI2ItXRVARBynFrSQMF4E6P6rFC1VmlXCG6LqHGLAcBnlg6VqS3dh0TEnfWkCa4c8nw/33QawG7UTMns/duhusXRvissuaCYcTfPONwYgRWTidQVpbDV55pZ4LL9RX6r16pXLnnSNobo4zenQOe+2Vy80370Y4HOfGG79i2LAcO0toJ2FLWUMrlFL9N/PaSqVUv45e+7Wx1wj+N7j66rnccce35OR4qa09c4v7PvDATzz44DKee24f7rprKW+8sY5jj+3OjBkV3HPPSM45J/lT7NbtGyorY/z97z0oLMzgyScD3HNPNnvv3QqAUg4cnlSmPefi+OMdHDAZPv2OZJFZOjq1oQQtW5Fu3lvJN0VAD0xpCwVOBbuYA7kvDgEXKHBmRTFigqo3ZSF8QCRhLlw7ddaPA/28QnTop46kNpIl/t6MdghRdDFcFRCAyQfBO29A3NIXMheRL7ggwmOPtbLnnopvvw0TjbaQkhIjEGhBKejdW/j3v4cwZkzWZr/vV19dwUknvU88bhAOn79dTZFsui5bWiPYUpL5VyJyTgcH+wMw75cyzmbn5A9/GMK4ccXcdNPore570UW7sHz5Uey9dx4vvjiGFSuOIBxOEAzGmTFjQ2GzV18dQM+ewrXXLsEwmpg9u5DU1ARnnWXQs6cAgiSieDz6AsgZBWnVYzN16MG2ybwPAOvgQGXKO1sq2avR1bzrBcod8JHAVwI/uXW6pxISa72oCo8uBIuCIyBQ6YIql37fKoEfBRYLrGCDRV8SaHntNeZ9mXm+SvP8MXjjFZAYuMx2lShFVlaY9etbgRAFBTBvXi4ORxil4KijcvD7oawsyD//uXaL3/e4cSXstVchf/zjcNsJ7CRsaUZQALyBvtb4xty8O3qtYLJSqmpHGLgx9ozABqCyMsT06Ws47rgeFBUlF6/D4QQpKZ8AMGFCNh9+uDuFhQuprY1z/fVFDBtWQDwO48bBmDFNlJe7cTrT6dFLKKsBPGa4Jgu9kOxCp5paWbTt6+sd5vYoOhPJQGf5ZJnbstCzhwAclgHvmb0CHHEwKsz9zWpgIFkdvBI9Y/CS7IMcRstOK3DWQcLqzBZXDBlssGSJzgI6+OAwM2dGOPfcVHJzQ9xxx3r69PEyf/5QbrttBXffXUZuroPq6oNpaYkxfXoZe+2Vx7nnzqZfvwz+9a/97XDQ/yg/q6Cs3ZvHA0PNp4uVUh//wvZtF7YjsNkat9yykpkz63nppeGUlPjw+b4mEhFEIsycOYSJE7M47rgGXnlFp9s4nakkEj7wgicbJuwvLFkDq+sAN2RmwpDRsOtweHmeFrVrWYd2BMXoMI4fnVOXQDsPMyKkCgAFj4yB2gj8/U3IE9h7F5i+GqsFQ7IFpqA7n5mhKImDajL3seQkgiQXj+OJtg1ebwSlGolGFS5XmIceKuS880qBBCIBIIJS0KOHj9WrJ3LhhXN57LFlDBzoZ8WKRuJxg6amU/H7PVRVBQiF4vTunfkr/7VsdhQ/NzQEgFLqY6XUg+atU52Ajc22cMMN/Zg9ezQlJbo6edw4HxDG6VQ0N+sRtrY2iB4gFY895sLlSuBKGNQug3dfBKMaqINcN/wwHebcC4+eCfWPwum7klzItRQbWtADeByKHOBpgZcmQ69l4F4AKS2QVQOhhbBmIUx/GfgaWIgO+TShQ0ClkO0wszgaIScBrAVfDFxOyEyFNLeeuBCHlJQwbrfizDNdfPllBm634HQq+vf3csYZ+QwapDWrlVIoBfn5HpYsORARYd99C005CQdDhmTw5JP74fd7aGmJMmDA0wwe/Aw//rhd6vM2v1Hs5vU2//O8//5QqqpirFkTYfRonar6j3/kcv31tfzlL7mMG+dmyBCDvDz4+uswgwe7qa1wQRQuuAByM+GYKVBQAA8/BJkhIAyeFDOFMw6T94c1rTCsB0x7C4wEBCuhdx6srYfu2XD0vSQrg83ewo40s+VmQKevuuLQuBxUANIyoaFcnyscAoIQckE0CocdBqedBqNHp/Dkky0ceSTstpuXlpZ+G4R2EokQTmeEMWMyETF47rndSE/X//YnndSXgw4qpqDgGVwuBz16aEE5Ed3QBmiTI7f538Z2BDa/CQKBKKef/h59+mRyxx0HbNd7RYSiIg9FRckK2b328jFzZrL9xZgxDv71r1bOOaeW4mInj/2zB++9BxdeABddBK+/BgiceQZ8Ngccy+Dk4+Hkk2HlejjzcD14JhLw4UtQXQcjesPHE6G6AR5+BsYPgre+RjuDEDgisOBJaAxA/2LIStPvP/JMmPMlFPhhXTVEE7QpjO6yC7jd8Oc/w/jx8OabAe65p4K77lIEg7vgcm04cA8enM6qVSFKSoQ1a0K43Ru+npvr49prR7FmTSv77qvrONLTPaxadRbhcIJu3dK367u2+Y2ip4y/ndvuu++ubHY+3n13pXK771Rwu2poCP3ixw+HE+r99wPK6SxV++67foPXnn1Wt3cfOFCpSESp8rVKPfCkUnX1mx6nolopR1+lXAOUeuEVpcrKlcofppT0Uco/TKn7XlCK3ZRiV6WG/k6pK69V6qTTlAoEksdYtUopV5pSbp9Sc79SasqxSh12mFJVVUolEhueb9myiMrNXar2379MGYaxwWutrTF1zz3L1Jdf1iq3+yXldL6knnpqpWpujqg993xDjR8/Q0Ui8V/oG7Tp6gBfq82Mq50+sG/vzXYEOyehUEydffZ76q9/nfOLH/uNN6qUw/GBOuGEhaqxMaHicWOTfWKxbT/eYy8o5SlSypum1O33KeUqVopeSl3+V/36XY8p5euv1H1PKCWpSrn9Ss38MPn+5malevRUqribUvUdOJtt5c47lyqH41VVUjJDTZu2Wl100QLV0hJVX39do9zuJ5XT+aQqK2v++Sew+U2xJUdgh4ZsfhP4fC6eeOLQX+XYS5cGcTqFJUtaycxM5k98+20rb79dz4UXFpOTs2GbxlWr4hx3XBOHH+7h1lv9G7x2ytFw7WUQEDhsAnh9MGQQHHSAfv3yc/UNoLEKytfCfmOT7/f7YXWZfry5TM7HH6/iwQereOqpvowe3XH4Zty4PPLyvEyZUsKJJ/bkxBN1d79Ro3J54IF9SEtztcl72Ozc2OqjNjs98bjBjBm17LlnJsXFyc7uAwcuYMWKMFdfXcLf/tZ7g/c8/HCQiy5qITVVaGkpYGOamyEchtLSOGef3coll/iorRVuvjnC44/7OPnk7VP03JhddvmepUvDXHJJEffea/cNsNk6/1X6qI3N/zoul4NJkwo2cAIAp59eQEmJhyOP3LSF5imn+Lj44lT+/e+MDo+ZkaGzjJ57LsKiRQb33hvm/ffjRCLw8cfxtv1CIeNn2fzUU3254IJCrriiY6E+G5vtwZ4R2Nh0QCxmMHt2NXvumUda2s+PoK5Zk+Af/whzyileevZ08NZbcU44wU12tnDbbbX85S813HprHn/5y/ZJPdvYbC8/t0OZjc1Oy7XXfsf99//EIYd04+23D/jZx+nRw8nDDydVX887LxkSWrw4gsul721sOhPbEdjYdEBxcQrxuKK4eNPeyb8Ujz9ezPHHZzBxoq3qbtO52KEhG5vNUF0dJj/fa4uw2fxPYIeGbGx+BgUFv95swMamK2FnDdnY2Njs5NiOwMbGxmYnx3YENjY2Njs5tiOwsbGx2cmxHYGNjY3NTo7tCGxsbGx2cmxHYGNjY7OT85srKBORGmD1DjhVHlC7A87zc+nK9nVl26Br29eVbYOubV9Xtg06375eSqkORa1+c45gRyEiX2+uCq8r0JXt68q2Qde2ryvbBl3bvq5sG3Rt++zQkI2Njc1Oju0IbGxsbHZybEeweR7vbAO2Qle2ryvbBl3bvq5sG3Rt+7qybdCF7bPXCGxsbGx2cuwZgY2Njc1Oju0IbGxsbHZybEewBUTkThH5SUQWisjrIpLV2Ta1R0SOE5HFImKISJdISxORQ0VkqYisEJGrO9ue9ojIUyJSLSKLOtuWjRGRHiLyiYj8aP5NL+5smyxExCci80Tke9O2mzvbpo4QEaeIfCsi73S2LRsjImUi8oOIfCciXa6zlu0ItswHwDCl1K7AMuCaTrZnYxYBxwCfdbYhoP8RgYeBw4AhwEkiMqRzrdqAZ4BDO9uIzRAHLldKDQb2Bi7oQt9dBBivlBoBjAQOFZG9O9ekDrkY+LGzjdgCByqlRnbFWgLbEWwBpdRMpVTcfDoX6N6Z9myMUupHpdTSzrajHXsCK5RSq5RSUeBFYFIn29SGUuozoL6z7egIpVSFUuob83ELekAr6VyrNErTaj51m7culWUiIt2BI4AnO9uW3yK2I9h2zgTe62wjujglwJp2z9fSRQaz3xIi0hvYDfiqk01pwwy7fAdUAx8opbqMbSb3AVcCRifbsTkUMFNEFojIuZ1tzMbs9D2LReRDoKiDl65TSr1p7nMdeur+wo60zTz3Vu3rQnTU5b1LXTl2dUQkHXgVuEQp1dzZ9lgopRLASHOd7HURGaaU6hJrLSJyJFCtlFogIgd0sjmbY6xSar2IFAAfiMhP5gy1S7DTOwKl1MQtvS4ipwFHAhNUJxRdbM2+LsZaoEe7592B9Z1ky28OEXGjncALSqnXOtuejlBKNYrILPRaS5dwBMBY4CgRORzwARki8rxS6uROtqsNpdR6875aRF5Hh1G7jCOwQ0NbQEQOBa4CjlJKBTvbnt8A84EBItJHRDzAicBbnWzTbwIREeD/gB+VUvd0tj3tEZF8K2NORFKAicBPnWpUO5RS1yiluiuleqN/cx93JScgImki4rceAwfTdZwoYDuCrfEQ4EdP5b4TkUc726D2iMjRIrIWGAPMEJH3O9Mec2H9QuB99GLny0qpxZ1pU3tEZBrwJTBIRNaKyFmdbVM7xgKnAOPN39p35hVuV6AY+EREFqKd/QdKqS6XotmFKQRmi8j3wDxghlLqP51s0wbYEhM2NjY2Ozn2jMDGxsZmJ8d2BDY2NjY7ObYjsLGxsdnJsR2BjY2NzU6O7QhsbGxsdnJsR7CTIiKtW99rs++90FQXVSKS1267iMgD5msLRWTUL2PtjkFE9hCRBzrbjh2FiDwjIseaj2d1pGArIpeISGq75+92NRVem/8e2xHY/By+QBcVrd5o+2HAAPN2LvDPHWWQqXz6X6GU+lopddEvYc/PRUS6WrX/JUCbI1BKHa6Uauw0a2x+FWxHsJNjXsXfKSKLTL30E8ztDhF5xNSff8e8EjwWQCn1rVKqrIPDTQL+ZapVzgWyRKS4g3MeLCJfisg3IjJdRNJFpJeILBeRPPPcn5v79RbdE+JZc5bxinWFamq83yAis4HjOjquud8/RGSJ+f67zG3HmZ/5exH5zNx2gJha9iKSIyJvmO+ZKyK7mttvEt3XYJaIrBKRDh2HiPxTRL6WjfT7RWS0iMwxzztPRPwicrpp79toYbLNnXv/dsVm35rvLRaRz8xti0Rkvw5suUFE5puvPy4iHWlCdfQZLgK6oYvJPmn3neeJrpadYX6ORe1+Nx19120zD/N5a7vHV5i2LbS+p80d2+ZXRCll33bCG9Bq3k9B911woisgy9GVpMcC76IvFoqABuDYjY5RBuS1e/4OsG+75x8Be2z0njy0xkqa+fwq4Abz8dnAK8AVwGPmtt5o4bqx5vOngD+3O/+VWzoukAMsJVk8mWXe/wCUbLTtAOAd8/GDwI3m4/HAd+bjm4A5gNc8Zx3g7uD7zTHvncAsYFfAA6wCRpuvZaD1vk5H6zTlbOXcb7f7HtLN916OFiC0zuXfnC3m4+eA35mPn7H+pqaNe3Tw3o3/xmXm554CPNFue+YWvuu282z02zsY3dBd0L+zd4BxHR27s/9f/tdv9ozAZl9gmlIqoZSqAj4FRpvbpyulDKVUJfDJNhxrW9RH90Y3rflCtKzxaUAvAKXUk2hJj/OAP7d7zxql1Bfm4+dN2yxe2spxm4Ew8KSIHANYmlFfAM+IyDnoAXRj9kUPmiilPgZyRSTTfG2GUiqilKpFyzIXdvD+40XkG+BbYKhp2yCgQik13zxus0r2u/hAKWX1Stjcub8A7jGv1LPM984HzhCRm4DhSvcy2JgDReQrEfkB7ViGdrDP9vIDMFFEbheR/ZRSTWz+u94cB5u3b4FvgF3QYcWOjm3zK2I7ApvNhQm2KXywEduiPiroQW+keRuilDoLwAz5WM1/0tu9Z2Nn0v55YEvHNQfLPdGqnpOB/wAopc4D/mLa+52I5HZg58ZY542025ZgIxVfEemDdmQTlO5uNwOtiikdfJaNP8dmz62U+gd61pQCzBWRXZSWMh4HrAOeE5FTN7LFBzyCviIfDjxh2vJfoZRaBuyOHrT/LiI3bO67Rku4O0x7BD0zsj7n39v9zforpf6vo2P/t/babBnbEdh8BpwguvFIPnpQmQfMBqaIjtcXosMmW+Mt4FTR7A00KaUqNtpnLjBWRPqDHvxFZKD52u3ong83oAcsi54iMsZ8fJJp28Z0eFxznSBTKfUueuFzpPl6P6XUV0qpG4BaNnRg1vcy1dz3AKBWbXt/gAz0wN5kfneHmdt/ArqJyGjzuH7peHG4w3ObNv+glLod+BrYRUR6obX4n0Crl26cqWUN+rXmd3Es20cLepa2ASLSDQgqpZ4H7gJGbe67RoeTdjcfT0J3OAMtTnimJNdySkSkoKNjb6fNNttJV8tQsNnxvI5WL/0efbV6pVKqUkReBSag5XKXobtlNUHbIuKV6LWDhSLyrlLqbPSawuHACnRY4IyNT6aUqhGR04FpIuI1N/9F9KLyaHQMPCEiU0TkDHRI6kfgNBF5DFhOB9lImzsueiB707wyFuBS87U7RWSAue0j8/Pv3+6QNwFPi1bcDKJDTduEUup7EfkWWIxeE/jC3B41Fz4fFC3nHEJnX23M5s59iYgciJ6FLEF3zDsRuEJEYkArsMGMQOn+AU+gr67L0KGk7eFx4D0RqVBKHdhu+3D0d2gAMeCPaIfR0Xf9hLl9Hvq7Dpi2zRSRwcCX5vp1K3Ay0L+DY9v8itjqozabRUTSlVKtZthkHnqQrtzBNvRGL+AO25HntbHZmbBnBDZb4h3RxUMe4NYd7QRsbGx2DPaMwMbGxmYnx14strGxsdnJsR2BjY2NzU6O7QhsbGxsdnJsR2BjY2Ozk2M7AhsbG5udnP8Hdf1lauWBNfgAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "GTEx_gini['maximum_median_expression'] = GTEx.max(axis = 1)\n",
    "GTEx_gini['log_max'] = np.log10(GTEx_gini.maximum_median_expression)\n",
    "gini_vs_expression(GTEx_gini.log_max, GTEx_gini.GTEx_gini)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "caaed1d7",
   "metadata": {},
   "source": [
    "There are 2 clusters with the highest population of data points; most of these occur for the highest Gini coefficient in the cells with small total expressions (this will occur by chance) and the other at a lower Gini score with average total expressions across these tissues. This is different to Tabula Sapiens where most data points have a high Gini coefficient regardless of the maximum adjusted median expression across the cell type but the Gini score does appear to increase as the maximum adjusted median expression increases."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "309bd217",
   "metadata": {},
   "source": [
    "### 3.4.3 Merge Gini dataframes together from GTEx and Tabula Sapiens"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "b1d20b16",
   "metadata": {},
   "outputs": [],
   "source": [
    "gini_df = gini_df.drop(columns = ['log_max', 'maximum_adjusted_median_expression'])\n",
    "GTEx_gini = GTEx_gini.drop(columns = ['maximum_median_expression', 'log_max'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "0b27e7ba",
   "metadata": {},
   "outputs": [],
   "source": [
    "GTEx_gini.to_pickle('GTEx_gini.pkl', compression = 'gzip')\n",
    "gini_df.to_pickle('tabula_gini_df.pkl', compression = 'gzip')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "550e4d77",
   "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>gini</th>\n",
       "      <th>hpa_specificity</th>\n",
       "      <th>hpa_distribution</th>\n",
       "      <th>GTEx_gini</th>\n",
       "      <th>GTEx_hpa_specificity</th>\n",
       "      <th>GTEx_hpa_distribution</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ensemblid</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>ENSG00000227232</th>\n",
       "      <td>0.994666</td>\n",
       "      <td>Group enriched</td>\n",
       "      <td>Detected in some</td>\n",
       "      <td>0.279425</td>\n",
       "      <td>Low specificity</td>\n",
       "      <td>Detected in many</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSG00000238009</th>\n",
       "      <td>0.960035</td>\n",
       "      <td>Low specificity</td>\n",
       "      <td>Detected in some</td>\n",
       "      <td>0.458660</td>\n",
       "      <td>Not detected</td>\n",
       "      <td>Not detected</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSG00000233750</th>\n",
       "      <td>0.988349</td>\n",
       "      <td>Individually enhanced</td>\n",
       "      <td>Detected in some</td>\n",
       "      <td>0.623261</td>\n",
       "      <td>Not detected</td>\n",
       "      <td>Not detected</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSG00000268903</th>\n",
       "      <td>0.981275</td>\n",
       "      <td>Low specificity</td>\n",
       "      <td>Detected in some</td>\n",
       "      <td>0.647153</td>\n",
       "      <td>Individually enhanced</td>\n",
       "      <td>Detected in many</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSG00000269981</th>\n",
       "      <td>0.994798</td>\n",
       "      <td>Group enriched</td>\n",
       "      <td>Detected in some</td>\n",
       "      <td>0.658953</td>\n",
       "      <td>Individually enhanced</td>\n",
       "      <td>Detected in many</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSG00000198695</th>\n",
       "      <td>0.496340</td>\n",
       "      <td>Low specificity</td>\n",
       "      <td>Detected in many</td>\n",
       "      <td>0.291177</td>\n",
       "      <td>Low specificity</td>\n",
       "      <td>Detected in all</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSG00000210194</th>\n",
       "      <td>0.841466</td>\n",
       "      <td>Low specificity</td>\n",
       "      <td>Detected in many</td>\n",
       "      <td>0.364488</td>\n",
       "      <td>Low specificity</td>\n",
       "      <td>Detected in all</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSG00000198727</th>\n",
       "      <td>0.447636</td>\n",
       "      <td>Individually enhanced</td>\n",
       "      <td>Detected in many</td>\n",
       "      <td>0.236466</td>\n",
       "      <td>Low specificity</td>\n",
       "      <td>Detected in all</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSG00000210195</th>\n",
       "      <td>0.914414</td>\n",
       "      <td>Low specificity</td>\n",
       "      <td>Detected in some</td>\n",
       "      <td>0.612010</td>\n",
       "      <td>Individually enhanced</td>\n",
       "      <td>Detected in many</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSG00000210196</th>\n",
       "      <td>0.789843</td>\n",
       "      <td>Low specificity</td>\n",
       "      <td>Detected in many</td>\n",
       "      <td>0.427757</td>\n",
       "      <td>Individually enhanced</td>\n",
       "      <td>Detected in many</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>35469 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                     gini        hpa_specificity  hpa_distribution  GTEx_gini  \\\n",
       "ensemblid                                                                       \n",
       "ENSG00000227232  0.994666         Group enriched  Detected in some   0.279425   \n",
       "ENSG00000238009  0.960035        Low specificity  Detected in some   0.458660   \n",
       "ENSG00000233750  0.988349  Individually enhanced  Detected in some   0.623261   \n",
       "ENSG00000268903  0.981275        Low specificity  Detected in some   0.647153   \n",
       "ENSG00000269981  0.994798         Group enriched  Detected in some   0.658953   \n",
       "...                   ...                    ...               ...        ...   \n",
       "ENSG00000198695  0.496340        Low specificity  Detected in many   0.291177   \n",
       "ENSG00000210194  0.841466        Low specificity  Detected in many   0.364488   \n",
       "ENSG00000198727  0.447636  Individually enhanced  Detected in many   0.236466   \n",
       "ENSG00000210195  0.914414        Low specificity  Detected in some   0.612010   \n",
       "ENSG00000210196  0.789843        Low specificity  Detected in many   0.427757   \n",
       "\n",
       "                  GTEx_hpa_specificity GTEx_hpa_distribution  \n",
       "ensemblid                                                     \n",
       "ENSG00000227232        Low specificity      Detected in many  \n",
       "ENSG00000238009           Not detected          Not detected  \n",
       "ENSG00000233750           Not detected          Not detected  \n",
       "ENSG00000268903  Individually enhanced      Detected in many  \n",
       "ENSG00000269981  Individually enhanced      Detected in many  \n",
       "...                                ...                   ...  \n",
       "ENSG00000198695        Low specificity       Detected in all  \n",
       "ENSG00000210194        Low specificity       Detected in all  \n",
       "ENSG00000198727        Low specificity       Detected in all  \n",
       "ENSG00000210195  Individually enhanced      Detected in many  \n",
       "ENSG00000210196  Individually enhanced      Detected in many  \n",
       "\n",
       "[35469 rows x 6 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#merge gini dataframes together on index\n",
    "##number of genes has decreased as retains only genes which are shared across the two dataframes (i.e. both have gini values)\n",
    "gini_merged = pd.merge(gini_df, GTEx_gini, left_index=True, right_index=True)\n",
    "gini_merged"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "5afc2c61",
   "metadata": {},
   "outputs": [],
   "source": [
    "gini_merged.to_pickle('merged_gini_HPA_metrics.pkl', compression = 'gzip')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4453854b",
   "metadata": {},
   "source": [
    "### 3.4.4 Specificity similarity matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "0daa57c5",
   "metadata": {},
   "outputs": [],
   "source": [
    "gini_merged = pd.read_pickle('merged_gini_HPA_metrics.pkl', compression = 'gzip')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "5d56e21f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='GTEx_hpa_specificity', ylabel='hpa_specificity'>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "categories = ['Not detected', 'Low specificity', 'Individually enhanced', 'Group enriched', 'Individually enriched']\n",
    "cross = pd.crosstab(gini_merged.hpa_specificity, gini_merged.GTEx_hpa_specificity)\n",
    "cross = cross.reindex(categories, axis = 'columns')\n",
    "cross = cross.reindex(categories[::-1], axis = 'rows')\n",
    "sns.heatmap(cross, cmap = 'rocket_r', annot = True, fmt = 'g')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3aff3090",
   "metadata": {},
   "source": [
    "### 3.4.5 Distribution similarity matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "dd3a7edf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='GTEx_hpa_distribution', ylabel='hpa_distribution'>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "categories = ['Not detected', 'Detected in all', 'Detected in many', 'Detected in some', 'Detected in single']\n",
    "cross = pd.crosstab(gini_merged.hpa_distribution, gini_merged.GTEx_hpa_distribution)\n",
    "cross = cross.reindex(categories, axis = 'columns')\n",
    "cross = cross.reindex(categories[::-1], axis = 'rows')\n",
    "sns.heatmap(cross, cmap = 'rocket_r', annot = True, fmt = 'g')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "852e838a",
   "metadata": {},
   "source": [
    "We don't have any not detected since we removed these genes that had zero expression across all tissues and cell types and also for Tabula Sapiens there are none in detected in all since this is single-cell data grouped by cell type so it is unlikely the gene will be detected in all."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8263d237",
   "metadata": {},
   "source": [
    "### 3.4.6 Gini similarity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "0921cc03",
   "metadata": {},
   "outputs": [],
   "source": [
    "def gini_vs_gini(x, y):\n",
    "    # From: https://stackoverflow.com/a/20107592\n",
    "    xy = np.vstack([x, y])\n",
    "    z = gaussian_kde(xy)(xy)\n",
    "    plt.set_cmap('jet')\n",
    "    fig, ax = plt.subplots()\n",
    "    ax.scatter(x, y, c=z, s=2)\n",
    "    ax.set_xlabel('GTEx Gini coefficients')\n",
    "    ax.set_ylabel('Tabula Sapiens Gini coefficients')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "631191f2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "gini_vs_gini(gini_merged.GTEx_gini, gini_merged.gini)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "655b7f97",
   "metadata": {},
   "source": [
    "This plot is as expected for the Gini values since Tabula Sapiens is expected to have high Gini values from looking at single cell data so the log curve is observed when compared against the GTEx gini values."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.10.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}