Calculate_RPKM.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "counts = pd.read_csv('data/severity_study/GSE147507_RawReadCounts_Human.tsv',sep='\\t',index_col=0) \n",
    "length = pd.read_csv('data/severity_study/genes_length.csv',sep=',',index_col=0) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SYMBOL</th>\n",
       "      <th>GENEID</th>\n",
       "      <th>len</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>DDX11L1</td>\n",
       "      <td>ENSG00000223972</td>\n",
       "      <td>1735</td>\n",
       "    </tr>\n",
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       "      <th>2</th>\n",
       "      <td>WASH7P</td>\n",
       "      <td>ENSG00000226210</td>\n",
       "      <td>1817</td>\n",
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       "      <th>3</th>\n",
       "      <td>WASH7P</td>\n",
       "      <td>ENSG00000227232</td>\n",
       "      <td>1351</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>WASH7P</td>\n",
       "      <td>ENSG00000262435</td>\n",
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       "      <th>5</th>\n",
       "      <td>FAM138A</td>\n",
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      ],
      "text/plain": [
       "    SYMBOL           GENEID   len\n",
       "1  DDX11L1  ENSG00000223972  1735\n",
       "2   WASH7P  ENSG00000226210  1817\n",
       "3   WASH7P  ENSG00000227232  1351\n",
       "4   WASH7P  ENSG00000262435  1817\n",
       "5  FAM138A  ENSG00000237613  1219"
      ]
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     "execution_count": 7,
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    }
   ],
   "source": [
    "length.head()"
   ]
  },
  {
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   "outputs": [
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       "  </thead>\n",
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       "      <th>DDX11L1</th>\n",
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       "      <td>11</td>\n",
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       "         Series1_NHBE_Mock_1  Series1_NHBE_Mock_2  Series1_NHBE_Mock_3  \\\n",
       "DDX11L1                    0                    0                    0   \n",
       "WASH7P                    29                   24                   23   \n",
       "FAM138A                    0                    0                    0   \n",
       "FAM138F                    0                    0                    0   \n",
       "OR4F5                      0                    0                    0   \n",
       "\n",
       "         Series1_NHBE_SARS-CoV-2_1  Series1_NHBE_SARS-CoV-2_2  \\\n",
       "DDX11L1                          0                          0   \n",
       "WASH7P                          34                         19   \n",
       "FAM138A                          0                          0   \n",
       "FAM138F                          0                          0   \n",
       "OR4F5                            0                          0   \n",
       "\n",
       "         Series1_NHBE_SARS-CoV-2_3  Series2_A549_Mock_1  Series2_A549_Mock_2  \\\n",
       "DDX11L1                          0                    0                    0   \n",
       "WASH7P                          44                   68                   43   \n",
       "FAM138A                          0                    0                    0   \n",
       "FAM138F                          0                    0                    0   \n",
       "OR4F5                            0                    0                    0   \n",
       "\n",
       "         Series2_A549_Mock_3  Series2_A549_SARS-CoV-2_1  ...  \\\n",
       "DDX11L1                    0                          0  ...   \n",
       "WASH7P                    33                         65  ...   \n",
       "FAM138A                    0                          0  ...   \n",
       "FAM138F                    0                          0  ...   \n",
       "OR4F5                      0                          0  ...   \n",
       "\n",
       "         Series15_COVID19Lung_1  Series16_A549-ACE2_Mock_1  \\\n",
       "DDX11L1                       0                          0   \n",
       "WASH7P                        0                          0   \n",
       "FAM138A                       0                          0   \n",
       "FAM138F                       0                          0   \n",
       "OR4F5                         0                          0   \n",
       "\n",
       "         Series16_A549-ACE2_Mock_2  Series16_A549-ACE2_Mock_3  \\\n",
       "DDX11L1                          0                          0   \n",
       "WASH7P                          11                          7   \n",
       "FAM138A                          0                          0   \n",
       "FAM138F                          0                          0   \n",
       "OR4F5                            0                          0   \n",
       "\n",
       "         Series16_A549-ACE2_SARS-CoV-2_1  Series16_A549-ACE2_SARS-CoV-2_2  \\\n",
       "DDX11L1                                0                                0   \n",
       "WASH7P                                 2                                6   \n",
       "FAM138A                                0                                0   \n",
       "FAM138F                                0                                0   \n",
       "OR4F5                                  0                                0   \n",
       "\n",
       "         Series16_A549-ACE2_SARS-CoV-2_3  Series16_A549-ACE2_SARS-CoV-2_Rux_1  \\\n",
       "DDX11L1                                0                                    0   \n",
       "WASH7P                                 5                                   12   \n",
       "FAM138A                                0                                    0   \n",
       "FAM138F                                0                                    0   \n",
       "OR4F5                                  0                                    0   \n",
       "\n",
       "         Series16_A549-ACE2_SARS-CoV-2_Rux_2  \\\n",
       "DDX11L1                                    0   \n",
       "WASH7P                                     6   \n",
       "FAM138A                                    0   \n",
       "FAM138F                                    0   \n",
       "OR4F5                                      0   \n",
       "\n",
       "         Series16_A549-ACE2_SARS-CoV-2_Rux_3  \n",
       "DDX11L1                                    0  \n",
       "WASH7P                                     8  \n",
       "FAM138A                                    0  \n",
       "FAM138F                                    0  \n",
       "OR4F5                                      0  \n",
       "\n",
       "[5 rows x 78 columns]"
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     "execution_count": 8,
     "metadata": {},
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   "source": [
    "counts.head()"
   ]
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   "execution_count": 9,
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       "                                      0\n",
       "73      Series16_A549-ACE2_SARS-CoV-2_2\n",
       "74      Series16_A549-ACE2_SARS-CoV-2_3\n",
       "75  Series16_A549-ACE2_SARS-CoV-2_Rux_1\n",
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       "77  Series16_A549-ACE2_SARS-CoV-2_Rux_3"
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     "execution_count": 9,
     "metadata": {},
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   ],
   "source": [
    "pd.DataFrame(counts.columns)[-5:]"
   ]
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  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "length = length.drop_duplicates(subset=['SYMBOL'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = length.groupby(['SYMBOL'])['len'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SYMBOL\n",
       "A1BG         3999\n",
       "A1BG-AS1     3374\n",
       "A1CF         9603\n",
       "A2M          6384\n",
       "A2M-AS1      2948\n",
       "            ...  \n",
       "ZYG11A       4694\n",
       "ZYG11B       8206\n",
       "ZYX          3267\n",
       "ZZEF1       15888\n",
       "ZZZ3         9008\n",
       "Name: len, Length: 21568, dtype: int64"
      ]
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     "execution_count": 12,
     "metadata": {},
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    "x"
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   "execution_count": null,
   "metadata": {},
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   "source": []
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   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(21797, 78)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "counts.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "genes = length.SYMBOL\n",
    "lst3 = [value for value in counts.index if value in genes.values]\n",
    "counts = counts.loc[lst3,:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(20851, 78)"
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     "execution_count": 15,
     "metadata": {},
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    "counts.shape"
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       "      <th>CDY1</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CSPG4P1Y</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GOLGA2P3Y</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GOLGA2P2Y</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 78 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           Series1_NHBE_Mock_1  Series1_NHBE_Mock_2  Series1_NHBE_Mock_3  \\\n",
       "CDY1B                        0                    0                    0   \n",
       "CDY1                         0                    0                    0   \n",
       "CSPG4P1Y                     0                    0                    0   \n",
       "GOLGA2P3Y                    0                    0                    0   \n",
       "GOLGA2P2Y                    0                    0                    0   \n",
       "\n",
       "           Series1_NHBE_SARS-CoV-2_1  Series1_NHBE_SARS-CoV-2_2  \\\n",
       "CDY1B                              0                          0   \n",
       "CDY1                               0                          0   \n",
       "CSPG4P1Y                           0                          0   \n",
       "GOLGA2P3Y                          0                          0   \n",
       "GOLGA2P2Y                          0                          0   \n",
       "\n",
       "           Series1_NHBE_SARS-CoV-2_3  Series2_A549_Mock_1  \\\n",
       "CDY1B                              0                    0   \n",
       "CDY1                               0                    0   \n",
       "CSPG4P1Y                           0                    0   \n",
       "GOLGA2P3Y                          0                    0   \n",
       "GOLGA2P2Y                          0                    0   \n",
       "\n",
       "           Series2_A549_Mock_2  Series2_A549_Mock_3  \\\n",
       "CDY1B                        0                    0   \n",
       "CDY1                         0                    0   \n",
       "CSPG4P1Y                     0                    0   \n",
       "GOLGA2P3Y                    0                    0   \n",
       "GOLGA2P2Y                    0                    0   \n",
       "\n",
       "           Series2_A549_SARS-CoV-2_1  ...  Series15_COVID19Lung_1  \\\n",
       "CDY1B                              0  ...                       0   \n",
       "CDY1                               0  ...                       0   \n",
       "CSPG4P1Y                           0  ...                       0   \n",
       "GOLGA2P3Y                          0  ...                       0   \n",
       "GOLGA2P2Y                          0  ...                       0   \n",
       "\n",
       "           Series16_A549-ACE2_Mock_1  Series16_A549-ACE2_Mock_2  \\\n",
       "CDY1B                              0                          0   \n",
       "CDY1                               0                          0   \n",
       "CSPG4P1Y                           0                          0   \n",
       "GOLGA2P3Y                          0                          0   \n",
       "GOLGA2P2Y                          0                          0   \n",
       "\n",
       "           Series16_A549-ACE2_Mock_3  Series16_A549-ACE2_SARS-CoV-2_1  \\\n",
       "CDY1B                              0                                0   \n",
       "CDY1                               0                                0   \n",
       "CSPG4P1Y                           0                                0   \n",
       "GOLGA2P3Y                          0                                0   \n",
       "GOLGA2P2Y                          0                                0   \n",
       "\n",
       "           Series16_A549-ACE2_SARS-CoV-2_2  Series16_A549-ACE2_SARS-CoV-2_3  \\\n",
       "CDY1B                                    0                                0   \n",
       "CDY1                                     0                                0   \n",
       "CSPG4P1Y                                 0                                0   \n",
       "GOLGA2P3Y                                0                                0   \n",
       "GOLGA2P2Y                                0                                0   \n",
       "\n",
       "           Series16_A549-ACE2_SARS-CoV-2_Rux_1  \\\n",
       "CDY1B                                        0   \n",
       "CDY1                                         0   \n",
       "CSPG4P1Y                                     0   \n",
       "GOLGA2P3Y                                    0   \n",
       "GOLGA2P2Y                                    0   \n",
       "\n",
       "           Series16_A549-ACE2_SARS-CoV-2_Rux_2  \\\n",
       "CDY1B                                        0   \n",
       "CDY1                                         0   \n",
       "CSPG4P1Y                                     0   \n",
       "GOLGA2P3Y                                    0   \n",
       "GOLGA2P2Y                                    0   \n",
       "\n",
       "           Series16_A549-ACE2_SARS-CoV-2_Rux_3  \n",
       "CDY1B                                        0  \n",
       "CDY1                                         0  \n",
       "CSPG4P1Y                                     0  \n",
       "GOLGA2P3Y                                    0  \n",
       "GOLGA2P2Y                                    0  \n",
       "\n",
       "[5 rows x 78 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "counts.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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>SYMBOL</th>\n",
       "      <th>GENEID</th>\n",
       "      <th>len</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>25210</th>\n",
       "      <td>CDY1B</td>\n",
       "      <td>ENSG00000172352</td>\n",
       "      <td>2675</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25211</th>\n",
       "      <td>CDY1</td>\n",
       "      <td>ENSG00000172288</td>\n",
       "      <td>2625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25212</th>\n",
       "      <td>CSPG4P1Y</td>\n",
       "      <td>ENSG00000240450</td>\n",
       "      <td>795</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25213</th>\n",
       "      <td>GOLGA2P3Y</td>\n",
       "      <td>ENSG00000172297</td>\n",
       "      <td>1653</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25214</th>\n",
       "      <td>GOLGA2P2Y</td>\n",
       "      <td>ENSG00000239533</td>\n",
       "      <td>1506</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          SYMBOL           GENEID   len\n",
       "25210      CDY1B  ENSG00000172352  2675\n",
       "25211       CDY1  ENSG00000172288  2625\n",
       "25212   CSPG4P1Y  ENSG00000240450   795\n",
       "25213  GOLGA2P3Y  ENSG00000172297  1653\n",
       "25214  GOLGA2P2Y  ENSG00000239533  1506"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "length.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "#counts['len'] = length['len'].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "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>Series1_NHBE_Mock_1</th>\n",
       "      <th>Series1_NHBE_Mock_2</th>\n",
       "      <th>Series1_NHBE_Mock_3</th>\n",
       "      <th>Series1_NHBE_SARS-CoV-2_1</th>\n",
       "      <th>Series1_NHBE_SARS-CoV-2_2</th>\n",
       "      <th>Series1_NHBE_SARS-CoV-2_3</th>\n",
       "      <th>Series2_A549_Mock_1</th>\n",
       "      <th>Series2_A549_Mock_2</th>\n",
       "      <th>Series2_A549_Mock_3</th>\n",
       "      <th>Series2_A549_SARS-CoV-2_1</th>\n",
       "      <th>...</th>\n",
       "      <th>Series15_COVID19Lung_1</th>\n",
       "      <th>Series16_A549-ACE2_Mock_1</th>\n",
       "      <th>Series16_A549-ACE2_Mock_2</th>\n",
       "      <th>Series16_A549-ACE2_Mock_3</th>\n",
       "      <th>Series16_A549-ACE2_SARS-CoV-2_1</th>\n",
       "      <th>Series16_A549-ACE2_SARS-CoV-2_2</th>\n",
       "      <th>Series16_A549-ACE2_SARS-CoV-2_3</th>\n",
       "      <th>Series16_A549-ACE2_SARS-CoV-2_Rux_1</th>\n",
       "      <th>Series16_A549-ACE2_SARS-CoV-2_Rux_2</th>\n",
       "      <th>Series16_A549-ACE2_SARS-CoV-2_Rux_3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>DDX11L1</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WASH7P</th>\n",
       "      <td>29</td>\n",
       "      <td>24</td>\n",
       "      <td>23</td>\n",
       "      <td>34</td>\n",
       "      <td>19</td>\n",
       "      <td>44</td>\n",
       "      <td>68</td>\n",
       "      <td>43</td>\n",
       "      <td>33</td>\n",
       "      <td>65</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>12</td>\n",
       "      <td>6</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FAM138A</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OR4F5</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OR4F3</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 78 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         Series1_NHBE_Mock_1  Series1_NHBE_Mock_2  Series1_NHBE_Mock_3  \\\n",
       "DDX11L1                    0                    0                    0   \n",
       "WASH7P                    29                   24                   23   \n",
       "FAM138A                    0                    0                    0   \n",
       "OR4F5                      0                    0                    0   \n",
       "OR4F3                      0                    0                    0   \n",
       "\n",
       "         Series1_NHBE_SARS-CoV-2_1  Series1_NHBE_SARS-CoV-2_2  \\\n",
       "DDX11L1                          0                          0   \n",
       "WASH7P                          34                         19   \n",
       "FAM138A                          0                          0   \n",
       "OR4F5                            0                          0   \n",
       "OR4F3                            0                          0   \n",
       "\n",
       "         Series1_NHBE_SARS-CoV-2_3  Series2_A549_Mock_1  Series2_A549_Mock_2  \\\n",
       "DDX11L1                          0                    0                    0   \n",
       "WASH7P                          44                   68                   43   \n",
       "FAM138A                          0                    0                    0   \n",
       "OR4F5                            0                    0                    0   \n",
       "OR4F3                            0                    0                    0   \n",
       "\n",
       "         Series2_A549_Mock_3  Series2_A549_SARS-CoV-2_1  ...  \\\n",
       "DDX11L1                    0                          0  ...   \n",
       "WASH7P                    33                         65  ...   \n",
       "FAM138A                    0                          0  ...   \n",
       "OR4F5                      0                          0  ...   \n",
       "OR4F3                      0                          0  ...   \n",
       "\n",
       "         Series15_COVID19Lung_1  Series16_A549-ACE2_Mock_1  \\\n",
       "DDX11L1                       0                          0   \n",
       "WASH7P                        0                          0   \n",
       "FAM138A                       0                          0   \n",
       "OR4F5                         0                          0   \n",
       "OR4F3                         0                          0   \n",
       "\n",
       "         Series16_A549-ACE2_Mock_2  Series16_A549-ACE2_Mock_3  \\\n",
       "DDX11L1                          0                          0   \n",
       "WASH7P                          11                          7   \n",
       "FAM138A                          0                          0   \n",
       "OR4F5                            0                          0   \n",
       "OR4F3                            0                          0   \n",
       "\n",
       "         Series16_A549-ACE2_SARS-CoV-2_1  Series16_A549-ACE2_SARS-CoV-2_2  \\\n",
       "DDX11L1                                0                                0   \n",
       "WASH7P                                 2                                6   \n",
       "FAM138A                                0                                0   \n",
       "OR4F5                                  0                                0   \n",
       "OR4F3                                  0                                0   \n",
       "\n",
       "         Series16_A549-ACE2_SARS-CoV-2_3  Series16_A549-ACE2_SARS-CoV-2_Rux_1  \\\n",
       "DDX11L1                                0                                    0   \n",
       "WASH7P                                 5                                   12   \n",
       "FAM138A                                0                                    0   \n",
       "OR4F5                                  0                                    0   \n",
       "OR4F3                                  0                                    0   \n",
       "\n",
       "         Series16_A549-ACE2_SARS-CoV-2_Rux_2  \\\n",
       "DDX11L1                                    0   \n",
       "WASH7P                                     6   \n",
       "FAM138A                                    0   \n",
       "OR4F5                                      0   \n",
       "OR4F3                                      0   \n",
       "\n",
       "         Series16_A549-ACE2_SARS-CoV-2_Rux_3  \n",
       "DDX11L1                                    0  \n",
       "WASH7P                                     8  \n",
       "FAM138A                                    0  \n",
       "OR4F5                                      0  \n",
       "OR4F3                                      0  \n",
       "\n",
       "[5 rows x 78 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "counts.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "def calculate_rpkm(counts,len_):\n",
    "    sum_= np.sum(counts.values,0) #Total mapped reads per sample \n",
    "    normalized_by_depth = np.divide(counts,sum_)\n",
    "    normalized_by_len = np.divide(normalized_by_depth.T,len_)\n",
    "    rpkm  = normalized_by_len*10**9\n",
    "    return rpkm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SYMBOL</th>\n",
       "      <th>GENEID</th>\n",
       "      <th>len</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>DDX11L1</td>\n",
       "      <td>ENSG00000223972</td>\n",
       "      <td>1735</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>WASH7P</td>\n",
       "      <td>ENSG00000226210</td>\n",
       "      <td>1817</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>FAM138A</td>\n",
       "      <td>ENSG00000237613</td>\n",
       "      <td>1219</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>OR4F5</td>\n",
       "      <td>ENSG00000186092</td>\n",
       "      <td>2618</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>OR4F3</td>\n",
       "      <td>ENSG00000230178</td>\n",
       "      <td>995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25210</th>\n",
       "      <td>CDY1B</td>\n",
       "      <td>ENSG00000172352</td>\n",
       "      <td>2675</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25211</th>\n",
       "      <td>CDY1</td>\n",
       "      <td>ENSG00000172288</td>\n",
       "      <td>2625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25212</th>\n",
       "      <td>CSPG4P1Y</td>\n",
       "      <td>ENSG00000240450</td>\n",
       "      <td>795</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25213</th>\n",
       "      <td>GOLGA2P3Y</td>\n",
       "      <td>ENSG00000172297</td>\n",
       "      <td>1653</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25214</th>\n",
       "      <td>GOLGA2P2Y</td>\n",
       "      <td>ENSG00000239533</td>\n",
       "      <td>1506</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>21568 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          SYMBOL           GENEID   len\n",
       "1        DDX11L1  ENSG00000223972  1735\n",
       "2         WASH7P  ENSG00000226210  1817\n",
       "5        FAM138A  ENSG00000237613  1219\n",
       "6          OR4F5  ENSG00000186092  2618\n",
       "9          OR4F3  ENSG00000230178   995\n",
       "...          ...              ...   ...\n",
       "25210      CDY1B  ENSG00000172352  2675\n",
       "25211       CDY1  ENSG00000172288  2625\n",
       "25212   CSPG4P1Y  ENSG00000240450   795\n",
       "25213  GOLGA2P3Y  ENSG00000172297  1653\n",
       "25214  GOLGA2P2Y  ENSG00000239533  1506\n",
       "\n",
       "[21568 rows x 3 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "length"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "length.index= length.SYMBOL\n",
    "length = length.loc[lst3,:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "rpkm = calculate_rpkm(counts,length['len'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(78, 20851)"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rpkm.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "rpkm = rpkm.T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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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>Series1_NHBE_Mock_1</th>\n",
       "      <th>Series1_NHBE_Mock_2</th>\n",
       "      <th>Series1_NHBE_Mock_3</th>\n",
       "      <th>Series1_NHBE_SARS-CoV-2_1</th>\n",
       "      <th>Series1_NHBE_SARS-CoV-2_2</th>\n",
       "      <th>Series1_NHBE_SARS-CoV-2_3</th>\n",
       "      <th>Series2_A549_Mock_1</th>\n",
       "      <th>Series2_A549_Mock_2</th>\n",
       "      <th>Series2_A549_Mock_3</th>\n",
       "      <th>Series2_A549_SARS-CoV-2_1</th>\n",
       "      <th>...</th>\n",
       "      <th>Series15_COVID19Lung_1</th>\n",
       "      <th>Series16_A549-ACE2_Mock_1</th>\n",
       "      <th>Series16_A549-ACE2_Mock_2</th>\n",
       "      <th>Series16_A549-ACE2_Mock_3</th>\n",
       "      <th>Series16_A549-ACE2_SARS-CoV-2_1</th>\n",
       "      <th>Series16_A549-ACE2_SARS-CoV-2_2</th>\n",
       "      <th>Series16_A549-ACE2_SARS-CoV-2_3</th>\n",
       "      <th>Series16_A549-ACE2_SARS-CoV-2_Rux_1</th>\n",
       "      <th>Series16_A549-ACE2_SARS-CoV-2_Rux_2</th>\n",
       "      <th>Series16_A549-ACE2_SARS-CoV-2_Rux_3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>DDX11L1</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WASH7P</th>\n",
       "      <td>1.419095</td>\n",
       "      <td>1.289233</td>\n",
       "      <td>0.763253</td>\n",
       "      <td>1.83724</td>\n",
       "      <td>1.056917</td>\n",
       "      <td>0.826847</td>\n",
       "      <td>1.95627</td>\n",
       "      <td>2.21594</td>\n",
       "      <td>2.25467</td>\n",
       "      <td>1.464444</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.582988</td>\n",
       "      <td>0.458366</td>\n",
       "      <td>0.35774</td>\n",
       "      <td>0.993138</td>\n",
       "      <td>0.829595</td>\n",
       "      <td>1.464681</td>\n",
       "      <td>0.742496</td>\n",
       "      <td>1.32132</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FAM138A</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OR4F5</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OR4F3</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 78 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         Series1_NHBE_Mock_1  Series1_NHBE_Mock_2  Series1_NHBE_Mock_3  \\\n",
       "DDX11L1             0.000000             0.000000             0.000000   \n",
       "WASH7P              1.419095             1.289233             0.763253   \n",
       "FAM138A             0.000000             0.000000             0.000000   \n",
       "OR4F5               0.000000             0.000000             0.000000   \n",
       "OR4F3               0.000000             0.000000             0.000000   \n",
       "\n",
       "         Series1_NHBE_SARS-CoV-2_1  Series1_NHBE_SARS-CoV-2_2  \\\n",
       "DDX11L1                    0.00000                   0.000000   \n",
       "WASH7P                     1.83724                   1.056917   \n",
       "FAM138A                    0.00000                   0.000000   \n",
       "OR4F5                      0.00000                   0.000000   \n",
       "OR4F3                      0.00000                   0.000000   \n",
       "\n",
       "         Series1_NHBE_SARS-CoV-2_3  Series2_A549_Mock_1  Series2_A549_Mock_2  \\\n",
       "DDX11L1                   0.000000              0.00000              0.00000   \n",
       "WASH7P                    0.826847              1.95627              2.21594   \n",
       "FAM138A                   0.000000              0.00000              0.00000   \n",
       "OR4F5                     0.000000              0.00000              0.00000   \n",
       "OR4F3                     0.000000              0.00000              0.00000   \n",
       "\n",
       "         Series2_A549_Mock_3  Series2_A549_SARS-CoV-2_1  ...  \\\n",
       "DDX11L1              0.00000                   0.000000  ...   \n",
       "WASH7P               2.25467                   1.464444  ...   \n",
       "FAM138A              0.00000                   0.000000  ...   \n",
       "OR4F5                0.00000                   0.000000  ...   \n",
       "OR4F3                0.00000                   0.000000  ...   \n",
       "\n",
       "         Series15_COVID19Lung_1  Series16_A549-ACE2_Mock_1  \\\n",
       "DDX11L1                     0.0                        0.0   \n",
       "WASH7P                      0.0                        0.0   \n",
       "FAM138A                     0.0                        0.0   \n",
       "OR4F5                       0.0                        0.0   \n",
       "OR4F3                       0.0                        0.0   \n",
       "\n",
       "         Series16_A549-ACE2_Mock_2  Series16_A549-ACE2_Mock_3  \\\n",
       "DDX11L1                   0.000000                   0.000000   \n",
       "WASH7P                    0.582988                   0.458366   \n",
       "FAM138A                   0.000000                   0.000000   \n",
       "OR4F5                     0.000000                   0.000000   \n",
       "OR4F3                     0.000000                   0.000000   \n",
       "\n",
       "         Series16_A549-ACE2_SARS-CoV-2_1  Series16_A549-ACE2_SARS-CoV-2_2  \\\n",
       "DDX11L1                          0.00000                         0.000000   \n",
       "WASH7P                           0.35774                         0.993138   \n",
       "FAM138A                          0.00000                         0.000000   \n",
       "OR4F5                            0.00000                         0.000000   \n",
       "OR4F3                            0.00000                         0.000000   \n",
       "\n",
       "         Series16_A549-ACE2_SARS-CoV-2_3  Series16_A549-ACE2_SARS-CoV-2_Rux_1  \\\n",
       "DDX11L1                         0.000000                             0.000000   \n",
       "WASH7P                          0.829595                             1.464681   \n",
       "FAM138A                         0.000000                             0.000000   \n",
       "OR4F5                           0.000000                             0.000000   \n",
       "OR4F3                           0.000000                             0.000000   \n",
       "\n",
       "         Series16_A549-ACE2_SARS-CoV-2_Rux_2  \\\n",
       "DDX11L1                             0.000000   \n",
       "WASH7P                              0.742496   \n",
       "FAM138A                             0.000000   \n",
       "OR4F5                               0.000000   \n",
       "OR4F3                               0.000000   \n",
       "\n",
       "         Series16_A549-ACE2_SARS-CoV-2_Rux_3  \n",
       "DDX11L1                              0.00000  \n",
       "WASH7P                               1.32132  \n",
       "FAM138A                              0.00000  \n",
       "OR4F5                                0.00000  \n",
       "OR4F3                                0.00000  \n",
       "\n",
       "[5 rows x 78 columns]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rpkm.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "rpkm.to_csv('data/severity_study/RPKM_3_all.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Find correlation between some genes and phenotype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Selecting phenotypes with COVID19 infection\n",
    "cols = rpkm.columns\n",
    "phenotypes = ['Series1','Series2','Series5','Series6','Series16','Series7']\n",
    "\n",
    "#phenotypes = ['Series1','Series2','Series5','Series6','Series7','Series16']\n",
    "phenotypes_idx = [x for x in cols if x.split('_')[0] in phenotypes] \n",
    "rpkm=  rpkm.loc[:, phenotypes_idx]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Series1_NHBE_Mock_1',\n",
       " 'Series1_NHBE_Mock_2',\n",
       " 'Series1_NHBE_Mock_3',\n",
       " 'Series1_NHBE_SARS-CoV-2_1',\n",
       " 'Series1_NHBE_SARS-CoV-2_2',\n",
       " 'Series1_NHBE_SARS-CoV-2_3',\n",
       " 'Series2_A549_Mock_1',\n",
       " 'Series2_A549_Mock_2',\n",
       " 'Series2_A549_Mock_3',\n",
       " 'Series2_A549_SARS-CoV-2_1',\n",
       " 'Series2_A549_SARS-CoV-2_2',\n",
       " 'Series2_A549_SARS-CoV-2_3',\n",
       " 'Series5_A549_Mock_1',\n",
       " 'Series5_A549_Mock_2',\n",
       " 'Series5_A549_Mock_3',\n",
       " 'Series5_A549_SARS-CoV-2_1',\n",
       " 'Series5_A549_SARS-CoV-2_2',\n",
       " 'Series5_A549_SARS-CoV-2_3',\n",
       " 'Series6_A549-ACE2_Mock_1',\n",
       " 'Series6_A549-ACE2_Mock_2',\n",
       " 'Series6_A549-ACE2_Mock_3',\n",
       " 'Series6_A549-ACE2_SARS-CoV-2_1',\n",
       " 'Series6_A549-ACE2_SARS-CoV-2_2',\n",
       " 'Series6_A549-ACE2_SARS-CoV-2_3',\n",
       " 'Series7_Calu3_Mock_1',\n",
       " 'Series7_Calu3_Mock_2',\n",
       " 'Series7_Calu3_Mock_3',\n",
       " 'Series7_Calu3_SARS-CoV-2_1',\n",
       " 'Series7_Calu3_SARS-CoV-2_2',\n",
       " 'Series7_Calu3_SARS-CoV-2_3',\n",
       " 'Series16_A549-ACE2_Mock_1',\n",
       " 'Series16_A549-ACE2_Mock_2',\n",
       " 'Series16_A549-ACE2_Mock_3',\n",
       " 'Series16_A549-ACE2_SARS-CoV-2_1',\n",
       " 'Series16_A549-ACE2_SARS-CoV-2_2',\n",
       " 'Series16_A549-ACE2_SARS-CoV-2_3',\n",
       " 'Series16_A549-ACE2_SARS-CoV-2_Rux_1',\n",
       " 'Series16_A549-ACE2_SARS-CoV-2_Rux_2',\n",
       " 'Series16_A549-ACE2_SARS-CoV-2_Rux_3']"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "phenotypes_idx"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Ignoring biopsy samples s  we dont know the viral load\n",
    "# Also ignoring samples with drug pertubagen\n",
    "phenotypes = ['Series1','Series2','Series5','Series6','Series16','Series7']\n",
    "mic = [2,0.02,2,0.2,2,2]\n",
    "mic = np.concatenate([[x]*6 for x in mic])\n",
    "healthy = [1,1,1,0,0,0]*6"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Genes of interest \n",
    "genes =  ['GPX4','AIFM2']\n",
    "# SKO genes\n",
    "genes = ['SLC27A4', 'SLC7A9', 'SLC6A14', 'SLC2A13', 'CTH', 'DHFR', 'DTYMK',\n",
    "       'AGXT', 'SLC7A11', 'SGMS1', 'GUK1', 'RRM2B', 'ISYNA1', 'CMPK1',\n",
    "       'PEPD', 'PLD2', 'CRLS1', 'SLC5A7', 'RRM1', 'RRM2', 'SLC3A1',\n",
    "       'SLC3A2', 'SLC5A3', 'TYMS', 'PTDSS1']\n",
    "labels = [x.split('_')[2] for x in cols]\n",
    "df = rpkm.loc[genes,:].T\n",
    "df = df.iloc[:-3,:]\n",
    "df['Healthy'] = healthy\n",
    "df['MIC'] = mic\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "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>SLC27A4</th>\n",
       "      <th>SLC7A9</th>\n",
       "      <th>SLC6A14</th>\n",
       "      <th>SLC2A13</th>\n",
       "      <th>CTH</th>\n",
       "      <th>DHFR</th>\n",
       "      <th>DTYMK</th>\n",
       "      <th>AGXT</th>\n",
       "      <th>SLC7A11</th>\n",
       "      <th>SGMS1</th>\n",
       "      <th>...</th>\n",
       "      <th>SLC5A7</th>\n",
       "      <th>RRM1</th>\n",
       "      <th>RRM2</th>\n",
       "      <th>SLC3A1</th>\n",
       "      <th>SLC3A2</th>\n",
       "      <th>SLC5A3</th>\n",
       "      <th>TYMS</th>\n",
       "      <th>PTDSS1</th>\n",
       "      <th>Healthy</th>\n",
       "      <th>MIC</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Series1_NHBE_Mock_1</th>\n",
       "      <td>8.370931</td>\n",
       "      <td>0.034224</td>\n",
       "      <td>16.132253</td>\n",
       "      <td>0.239516</td>\n",
       "      <td>1.767959</td>\n",
       "      <td>1.834309</td>\n",
       "      <td>4.076906</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.281388</td>\n",
       "      <td>4.388232</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>14.724413</td>\n",
       "      <td>5.137662</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>20.966303</td>\n",
       "      <td>1.306875</td>\n",
       "      <td>16.306338</td>\n",
       "      <td>19.884740</td>\n",
       "      <td>1</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Series1_NHBE_Mock_2</th>\n",
       "      <td>9.189262</td>\n",
       "      <td>0.150278</td>\n",
       "      <td>15.471448</td>\n",
       "      <td>0.301883</td>\n",
       "      <td>1.900359</td>\n",
       "      <td>2.832357</td>\n",
       "      <td>4.434773</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.831688</td>\n",
       "      <td>4.710846</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>16.163852</td>\n",
       "      <td>4.371199</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>21.526785</td>\n",
       "      <td>1.392438</td>\n",
       "      <td>15.239549</td>\n",
       "      <td>20.486327</td>\n",
       "      <td>1</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Series1_NHBE_Mock_3</th>\n",
       "      <td>8.421790</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>18.716537</td>\n",
       "      <td>0.481269</td>\n",
       "      <td>1.848376</td>\n",
       "      <td>1.995776</td>\n",
       "      <td>3.242316</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.600419</td>\n",
       "      <td>4.384043</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>17.506507</td>\n",
       "      <td>5.117508</td>\n",
       "      <td>0.018437</td>\n",
       "      <td>19.994205</td>\n",
       "      <td>1.543137</td>\n",
       "      <td>14.545007</td>\n",
       "      <td>19.082212</td>\n",
       "      <td>1</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Series1_NHBE_SARS-CoV-2_1</th>\n",
       "      <td>12.375658</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>37.035560</td>\n",
       "      <td>0.137143</td>\n",
       "      <td>1.504896</td>\n",
       "      <td>2.025565</td>\n",
       "      <td>4.420134</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.150319</td>\n",
       "      <td>3.714585</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>13.731418</td>\n",
       "      <td>4.289865</td>\n",
       "      <td>0.060042</td>\n",
       "      <td>20.023677</td>\n",
       "      <td>1.247889</td>\n",
       "      <td>12.815461</td>\n",
       "      <td>16.042552</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Series1_NHBE_SARS-CoV-2_2</th>\n",
       "      <td>8.733299</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>35.407318</td>\n",
       "      <td>0.332781</td>\n",
       "      <td>1.507327</td>\n",
       "      <td>2.062280</td>\n",
       "      <td>3.496955</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.226100</td>\n",
       "      <td>4.500599</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>15.234023</td>\n",
       "      <td>5.343542</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>19.715215</td>\n",
       "      <td>1.529315</td>\n",
       "      <td>14.862689</td>\n",
       "      <td>18.020674</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 27 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                             SLC27A4    SLC7A9    SLC6A14   SLC2A13       CTH  \\\n",
       "Series1_NHBE_Mock_1         8.370931  0.034224  16.132253  0.239516  1.767959   \n",
       "Series1_NHBE_Mock_2         9.189262  0.150278  15.471448  0.301883  1.900359   \n",
       "Series1_NHBE_Mock_3         8.421790  0.000000  18.716537  0.481269  1.848376   \n",
       "Series1_NHBE_SARS-CoV-2_1  12.375658  0.000000  37.035560  0.137143  1.504896   \n",
       "Series1_NHBE_SARS-CoV-2_2   8.733299  0.000000  35.407318  0.332781  1.507327   \n",
       "\n",
       "                               DHFR     DTYMK  AGXT   SLC7A11     SGMS1  ...  \\\n",
       "Series1_NHBE_Mock_1        1.834309  4.076906   0.0  1.281388  4.388232  ...   \n",
       "Series1_NHBE_Mock_2        2.832357  4.434773   0.0  1.831688  4.710846  ...   \n",
       "Series1_NHBE_Mock_3        1.995776  3.242316   0.0  1.600419  4.384043  ...   \n",
       "Series1_NHBE_SARS-CoV-2_1  2.025565  4.420134   0.0  1.150319  3.714585  ...   \n",
       "Series1_NHBE_SARS-CoV-2_2  2.062280  3.496955   0.0  1.226100  4.500599  ...   \n",
       "\n",
       "                           SLC5A7       RRM1      RRM2    SLC3A1     SLC3A2  \\\n",
       "Series1_NHBE_Mock_1           0.0  14.724413  5.137662  0.000000  20.966303   \n",
       "Series1_NHBE_Mock_2           0.0  16.163852  4.371199  0.000000  21.526785   \n",
       "Series1_NHBE_Mock_3           0.0  17.506507  5.117508  0.018437  19.994205   \n",
       "Series1_NHBE_SARS-CoV-2_1     0.0  13.731418  4.289865  0.060042  20.023677   \n",
       "Series1_NHBE_SARS-CoV-2_2     0.0  15.234023  5.343542  0.000000  19.715215   \n",
       "\n",
       "                             SLC5A3       TYMS     PTDSS1  Healthy  MIC  \n",
       "Series1_NHBE_Mock_1        1.306875  16.306338  19.884740        1  2.0  \n",
       "Series1_NHBE_Mock_2        1.392438  15.239549  20.486327        1  2.0  \n",
       "Series1_NHBE_Mock_3        1.543137  14.545007  19.082212        1  2.0  \n",
       "Series1_NHBE_SARS-CoV-2_1  1.247889  12.815461  16.042552        0  2.0  \n",
       "Series1_NHBE_SARS-CoV-2_2  1.529315  14.862689  18.020674        0  2.0  \n",
       "\n",
       "[5 rows x 27 columns]"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(36, 27)"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [],
   "source": [
    "#sns.jointplot(x=\"GPX4\", y=\"AIFM2\", data=df, hue=\"MIC\");\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/ali.kishk/anaconda3/lib/python3.7/site-packages/statsmodels/tools/_testing.py:19: FutureWarning: pandas.util.testing is deprecated. Use the functions in the public API at pandas.testing instead.\n",
      "  import pandas.util.testing as tm\n"
     ]
    }
   ],
   "source": [
    "import seaborn as sns; sns.set()\n",
    "import matplotlib.pyplot as plt\n",
    "ax = sns.scatterplot(x=\"SLC7A11\", y=\"MIC\", hue=\"Healthy\",data=df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### PCA of read counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Remove phenotypes with other viruses\n",
    "for i in ['Series3','Series4','Series8','Series9']:\n",
    "    counts = counts.drop(columns =counts.columns[counts.columns.str.startswith(i)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "# Separating out the target\n",
    "y = [x[:-2] for x in counts.columns]\n",
    "y = [x.split('_')[0] for x in counts.columns]\n",
    "# Separating out the features\n",
    "x = counts.T.values\n",
    "# Standardizing the features\n",
    "x = StandardScaler().fit_transform(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.decomposition import PCA\n",
    "pca = PCA(n_components=2)\n",
    "principalComponents = pca.fit_transform(x)\n",
    "principalDf = pd.DataFrame(data = principalComponents, columns = ['PC1', 'PC2'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "metadata": {},
   "outputs": [],
   "source": [
    "finalDf = pd.concat([principalDf, pd.DataFrame(y)], axis = 1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "'c' argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with 'x' & 'y'.  Please use a 2-D array with a single row if you really want to specify the same RGB or RGBA value for all points.\n",
      "'c' argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with 'x' & 'y'.  Please use a 2-D array with a single row if you really want to specify the same RGB or RGBA value for all points.\n",
      "'c' argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with 'x' & 'y'.  Please use a 2-D array with a single row if you really want to specify the same RGB or RGBA value for all points.\n",
      "'c' argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with 'x' & 'y'.  Please use a 2-D array with a single row if you really want to specify the same RGB or RGBA value for all points.\n",
      "'c' argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with 'x' & 'y'.  Please use a 2-D array with a single row if you really want to specify the same RGB or RGBA value for all points.\n",
      "'c' argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with 'x' & 'y'.  Please use a 2-D array with a single row if you really want to specify the same RGB or RGBA value for all points.\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib import cm\n",
    "\n",
    "fig = plt.figure(figsize = (7,7))\n",
    "ax = fig.add_subplot(1,1,1) \n",
    "ax.set_xlabel('Principal Component 1', fontsize = 15)\n",
    "ax.set_ylabel('Principal Component 2', fontsize = 15)\n",
    "ax.set_title('2 component PCA', fontsize = 20)\n",
    "targets = np.unique(y)\n",
    "colors = cm.rainbow(np.linspace(0, 1, len(y)))#gist_ncar \n",
    "for target, color in zip(targets,colors):\n",
    "    indicesToKeep = finalDf[0] == target\n",
    "    ax.scatter(finalDf.loc[indicesToKeep, 'PC1']\n",
    "               , finalDf.loc[indicesToKeep, 'PC2']\n",
    "               , c = color\n",
    "               , s = 50)\n",
    "ax.legend(targets)\n",
    "ax.grid()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Z-score\n",
    "z = np.abs(stats.zscore(x))\n",
    "print(z)\n",
    "z.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "18242\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/ali.kishk/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:2: RuntimeWarning: invalid value encountered in greater\n",
      "  \n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([], shape=(0, 21797), dtype=float64)"
      ]
     },
     "execution_count": 189,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "threshold = 3\n",
    "print(len(np.where(z.T > 3)[1]))\n",
    "mask = (z[:, 0] > 3) & (z[:, 1] > 3)\n",
    "z[mask]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/ali.kishk/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:1: RuntimeWarning: invalid value encountered in less\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([], shape=(0, 21797), dtype=float64)"
      ]
     },
     "execution_count": 190,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x[(z < 3).all(axis=1),:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0, 0, 'Series1_NHBE_Mock_1'),\n",
       " Text(0, 0, 'Series1_NHBE_Mock_2'),\n",
       " Text(0, 0, 'Series1_NHBE_Mock_3'),\n",
       " Text(0, 0, 'Series1_NHBE_SARS-CoV-2_1'),\n",
       " Text(0, 0, 'Series1_NHBE_SARS-CoV-2_2'),\n",
       " Text(0, 0, 'Series1_NHBE_SARS-CoV-2_3'),\n",
       " Text(0, 0, 'Series2_A549_Mock_1'),\n",
       " Text(0, 0, 'Series2_A549_Mock_2'),\n",
       " Text(0, 0, 'Series2_A549_Mock_3'),\n",
       " Text(0, 0, 'Series2_A549_SARS-CoV-2_1'),\n",
       " Text(0, 0, 'Series2_A549_SARS-CoV-2_2'),\n",
       " Text(0, 0, 'Series2_A549_SARS-CoV-2_3'),\n",
       " Text(0, 0, 'Series5_A549_Mock_1'),\n",
       " Text(0, 0, 'Series5_A549_Mock_2'),\n",
       " Text(0, 0, 'Series5_A549_Mock_3'),\n",
       " Text(0, 0, 'Series5_A549_SARS-CoV-2_1'),\n",
       " Text(0, 0, 'Series5_A549_SARS-CoV-2_2'),\n",
       " Text(0, 0, 'Series5_A549_SARS-CoV-2_3'),\n",
       " Text(0, 0, 'Series6_A549-ACE2_Mock_1'),\n",
       " Text(0, 0, 'Series6_A549-ACE2_Mock_2'),\n",
       " Text(0, 0, 'Series6_A549-ACE2_Mock_3'),\n",
       " Text(0, 0, 'Series6_A549-ACE2_SARS-CoV-2_1'),\n",
       " Text(0, 0, 'Series6_A549-ACE2_SARS-CoV-2_2'),\n",
       " Text(0, 0, 'Series6_A549-ACE2_SARS-CoV-2_3'),\n",
       " Text(0, 0, 'Series7_Calu3_Mock_1'),\n",
       " Text(0, 0, 'Series7_Calu3_Mock_2'),\n",
       " Text(0, 0, 'Series7_Calu3_Mock_3'),\n",
       " Text(0, 0, 'Series7_Calu3_SARS-CoV-2_1'),\n",
       " Text(0, 0, 'Series7_Calu3_SARS-CoV-2_2'),\n",
       " Text(0, 0, 'Series7_Calu3_SARS-CoV-2_3'),\n",
       " Text(0, 0, 'Series15_HealthyLungBiopsy_2'),\n",
       " Text(0, 0, 'Series15_HealthyLungBiopsy_1'),\n",
       " Text(0, 0, 'Series15_COVID19Lung_2'),\n",
       " Text(0, 0, 'Series15_COVID19Lung_1'),\n",
       " Text(0, 0, 'Series16_A549-ACE2_Mock_1'),\n",
       " Text(0, 0, 'Series16_A549-ACE2_Mock_2'),\n",
       " Text(0, 0, 'Series16_A549-ACE2_Mock_3'),\n",
       " Text(0, 0, 'Series16_A549-ACE2_SARS-CoV-2_1'),\n",
       " Text(0, 0, 'Series16_A549-ACE2_SARS-CoV-2_2'),\n",
       " Text(0, 0, 'Series16_A549-ACE2_SARS-CoV-2_3'),\n",
       " Text(0, 0, 'Series16_A549-ACE2_SARS-CoV-2_Rux_1'),\n",
       " Text(0, 0, 'Series16_A549-ACE2_SARS-CoV-2_Rux_2'),\n",
       " Text(0, 0, 'Series16_A549-ACE2_SARS-CoV-2_Rux_3')]"
      ]
     },
     "execution_count": 191,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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7jDFzwrwXhNv3AKuBD4bbLwRuC//9izBN+PfV4f7D240xpwC7rLWRmz1FREREWl2UwQS/AH4O3A9sAPqttUuBi4EfAg8DGxkZKHAe8FVjzEbgQODqcPvlwGXGmIeBucBnw+2LgZOMMQ+F+1wRbr8G2C/cfjVB0CciIiLSNjL5fCr7cB0BPKY+aiIiIuK7oj5qRwKbSv7WjAKJiIhIa8rlclx11WKtkZsQBWoiIiISWW/vMqx9RGvkJkSBmoiIiESSy+Xo61tBPp+nr2+FatUSoEBNREREIuntXUY+H0xpOjQ0pFq1BChQExERkUj6+/sYGAgWGRoYGGDNmr4mlyj9FKiJiIhIJN3d8+jqChY16urqYs6ceU0uUfopUGszGq0jIiKuenoWkMkEoUNHRwc9PQuaXKL0U6DWZjRaR0REXGWzWebNO41MJsO8eacxcWK22UVKPQVqbUSjdUREZLR6ehZgzNGqTUuIArU2otE6IiIyWtlslsWLr1JtWkIUqLURjdYRERFpLQrU2ohG64iIiLQWBWptRKN1JGkaZSwiMjoK1NqIRutI0jTKWERkdBSotRmN1pGkaJSxiMjoKVBrMxqtI0nRKGMRkdFToCYiDaFRxiIio6dATUQaort7Hp2dnQB0dnZqlLGIiAMFaiLSED09CxgaygOQz+fVL1JExIECtTaj6RIkSZlMs0sgItLaFKi1maVLl7Bx48MsXbqk2UWRlOvtXUYmjNQymYwGE4iIOFCg1kZyuRz9/UGH7jVr+lSrJg3V39/H4OAgAIODgxpMICLiQIFaG1m6dAlDQyPTJahWTRpJS5aJiIyeArU2cs89q0vS/f2rq+wpMnpaskxEZPQUqIlIQ2jJMhGR0VOg1kZOPnluSbq7e26VPUXGhpYsExEZnUw+n292GRrhCOCx7dtfGp7HSYLBBFdeeRn5/BAdHR1cc823VcshIiLSZB0dGSZPPhDgSGBTyd+aUSBpjmw2O9yhe86ceQrSREREPNfV7AJIshYuPJ9nn93KwoXnN7soIiIiUoeaPkVERESaSE2fIiIiIi1IgZqIiIiIpxSoiYiIiHhKgZqIiIiIpxSoSUvL5XJcddViLTAvIonQNUeSpkBNWlpv7zKsfYTe3mXNLoqItAFdcyRpCtSkZeVyOfr6VpDP5+nrW6EnXBFpKF1zpBkUqEnL6u1dRj4/BMDQ0JCecEWkoXp7lzE0NAjA4OCgrjmSCAVq0rL6+/sYGBgAYGBggDVr+ppcIhFJs/7+PgYHRwI1XXMkCQrUpGV1d8+jqytYBa2rq2t4HVMRkSjiDgw4/vjZJekTTphdZU+RsaNATVpWT88CMpngEO7o6KCnZ0GTSyQirUQDA6QVKFCTlpXNZpk37zQymQzz5p3GxInZZhdJRFqEy8CADRvWlqTXr19bZU+RsaNATVpaT88CjDlatWkiEovLYKTu7nl0dnYC0NnZqe4WkggFatLSstksixdfpdo0T2lyUPGVy2Cknp4FdHSMBGp6QJQkKFCTluZzIOBz2ZKiPkDiK5fBSOpuIc2gQE1ams+BgM9lS4ImBxWfuQ5GUncLSZoCNWlZPgcCPpctKZqQWHzmWjum7haSNAVq0rJ8DgR8LltSNCGx+M6ldkxdGiRpCtSkZfkcCPhctqRoQmLxnUvtWLt3aZDkKVCTluVzIOBz2ZKiCYklbdSlQZpBgZq0LJ8DAZ/LlhSNkEufdm/2U5cGaQYFatKyfA4EfC5bkjRCLl3avdlPXRrSyfcHEAVq0tJ8DgR8LltSNEIuPdTsp5UJ0sr3BxAFatLSfA4EfC5bUnx/UpXo1OwXPHwNDeUByOfzbf0Qlhat8ACiQE1EGsb3J1WJTs1+gUym2SWQsdQKDyBdUXYyxvwj8IEw+XNr7d8ZY84AvgKMB75vrf1suO9M4HrgVUAf8BFr7YAx5jBgCXAwYIHzrLUvGWMmAt8DXgdsAz5grd1ijNkXuAE4AdgJnGut3Tgmn1pEGq78SbWnZ0Fb1y62uu7ueaxadRcDAwNtO5K5t3cZmTBSy2Qy9PYu45JLLmtyqWQ0Kj2A+Pab1q1RCwOyvwCOA2YCxxtjPgTcCJwDHA3MMsacHWZZAnzUWjsDyACXhtuvBa611h4FrAcWh9s/D6y21h4NXAd8Pdz+MeDlcPsngJtH8TklpKYoSUorPKnG1c7nj0YyBzf1wcFBAAYHB9u2VjFNWmEqpShNn5uBT1prX7HW7gEeAWYAv7fWPmatHSAIzhYYYw4Hxltr7w3z3hxu3weYB/ygeHv473cQ1KgB3AqcHe4/vN1a2wdMDWvlZBTUFCVJSWNTWTufPxrJrMEEadQKDyB1mz6ttQ8V/m2MeQNBE+g1BAFcwWbgNcAhVbZPAXaEQV3xdorzhE2kO4CpNV7riYifjcmTD4y6a1vYvn07fX3Lh5uiFi26hEmTJjW7WJJSp59+OrfffvtwU9kZZ5zB1KkTml0sZ9u3b2f16hVtff4sWnQxW7c+HX721v0tXS1adDErVtwJBIMJ2vV7SJOpUyfw9rf/BT//+c95+9vfzhve4F99UKQ+agDGmDcBPwf+FhggqFUryABDBDV0+QjbCbcX9ilW77Ui2779peEROgI33njzcA3Hnj17uP76m7xri5f0OOus93DHHb8EgifVs846h23bXmxyqdzdeOPNDA2NNOW25/mzL5/+9OcYHKSlf0tXudzLJcfA9u0vMTi4T5NLJaN11lnv4dFH/9jUa1RHR6Zq5VKkUZ/GmDnAXcCnrbXfAZ4EphftMg14usb2rcBBxpjOcPv0cDvAU+F+GGO6gAnA9hqvJY7WrFlFPj8ytPzuu1c1uUTN0c79jJKUtqayNDblSjxLly6pmZbW5PtUSlEGE7wW+DHBqMul4ea1wZ/M68Pg61zgNmvt48CuMLADuCDcvgdYDXww3H4hcFv471+EacK/rw73H95ujDkF2GWtjdzsKXubPHlqzXQ1aQts2rmfUdLSNOlvK3Q6lsa6557VJen+/tVV9hQZO1Fq1D4FjAO+Yox5wBjzAHBx+N8PgYeBjYwMFDgP+KoxZiNwIHB1uP1y4DJjzMPAXOCz4fbFwEnGmIfCfa4It18D7Bduv5og6JNR2L59W810NWkKbJKc3DBtAa4L359U42iFTscikj5RBhN8HPh4lT8fW2H/3wAnVtj+OPC2CtufA95dYfsu4KJ65ZPoTjjhJO6+e+Vwetask+rmSdtcWJWmjGhUP6PiALf9+jKlT6Epd/nyX6aiKVeC69s3vvEVrrzybyL9niefPLfkGtrdPbeBpRMJaGUCqSltc2El1c+oFZYlkfjS1JQr8VsLFi48v6RWdeHC8xtZvLbnc6tEkmVToNZGNmxYW5Jev35tlT1HpK0DdVL9jNIW4EogTU257c7lYSqbzQ5fM+bMmafjoMF87naTZNkUqLURl8ka09aBOql+RmkLcF35/EQs7c31YWrhwvM56qg3qjatwXxulUi6bArU2khPzwI6OkYCtShBSto6UCc1ZYRrgJu2wMblqTNt34H4yfVhSrWqyfC5VSLpsilQayMuQYrvc2G53NST6GfkGuD6XNUfl+tTZ5q+A/FX2loL0sbnVomky6ZArc3Mn38m48aNZ/78v4icx+cO1C439SSeiF0C3CCwCZb4WrVqecvXKLk8dfrc3CHpkrbWgrTxOZBOumwK1NrM8uV3smvXTpYv/2XkPL5W9ft+U48b4Pb2Lit5Smv1GiWXp06fmzskXc3Srq0FafoOfOZzIO1aNtdjR4FaG/E9sInL95t63AA3bUt8uTx1+tzcIelrlnZpLUjbd+Arn7vduJbN9dhRoNZGfA9s4krbTT1tS3y5PHX63NzR7nK5HKtWpadpHuI/TKXtYdd3Pne7iVu20Rw7CtQawNcbZ9oCm7Td1NO2xJfLU6fPzR3trrd3GYOD6Wmad5G2h13fuXS7Ser+G7dsozl2FKg1gK83zrQFNmm7qc+ZcyqZTAaATCbDKaecWjeP70/4cZ86fW7uaHdpa5p3kbaH3TTy9f47mmNHgdoY8/nGmbbAJm039Z6eBXR2jgTSUX4f35/wXZ6IfW7uaGeuTfNpkraH3bTx+f47mmNHgdoY8/nGmbbABtJ1U89ms5x66nwymQynnjo/0u+Txid8X0cZtzvXpvk0SdvDbtr4fP8dzbGjQG2M+X7jTFNgk0Zxfx894UtSXJrm0yabzTJ7djcAs2d362HCMz7ff0dTUaJAbYx1d88ruZj5duNMW22Fr/0RXMX9fXx/wvd1YI3E19OzoOShwLdjLSmvvLI7/P8rTS6JlPP9wdW1okSB2hibP//Mkg63cVYASEKabpyu/RHS9B343py9dOkSNm58mKVLlzS7KDJKwbEWr2k+bXK5HOvXrwXgvvvuTcU1JE18f3B1rShRoDbGli+/s6RGLc4KAElIUw2Ua3+ENH0H4G9zdi6XG256WLOmTze1FHBZgi5Nli5dwtDQyDVHDyB+8f3B1ZUCtTHW399XUqPmUxt52iasdOmP4POoIFe+NmcvXbqkJJDWTa31uSxBlyb33LO6JN3fv7rKntIsvj64joYCtTHmcxt52iasdPmufR4V5Lu4Tca6qaVL8KB3F/l8npUr70rFQ46kj68PrqOhQG2M+dxGnrYJK12+a59HBfkubU3GEk9v77KSc6cdj4OTT55bku7unltlT5Gxo0BtjPncRp62CStdvuvu7nl0dnYC0NnZ6VWNp6skBke4NBnrppYud9+9siS9evXKivul2cKF55c8HC5ceH6TSyTtQIFaA/jaRp7GCSvjftc9PQtKahV9+41cuNR0xQ3uXJqMFy48n44O3dTSotDNoFq6HWSzWWbNmg3ArFmzvXoQl/RSoNYAvraRp3HCSpfvOozTUsF1cETc4M6lyTibzXLCCYWb2knenQ8Sz8svv1wz3X4yzS5AS0nTtEhJU6DWRjRhZRCgdHSMBKut3s/GpaYrCO7ijf51HSSz7777hf/fN9L+4q9DD31tzXQ70Dxq7tTH1Z0CtTbiOmFlmp6E+vv7GBwcBGBwcLDlBxO41HS5dAp3GbiRy+VYu7YfgLVr+1Nx/LSzyy//eEn6iis+0aSSjJ241zbNo+YmjdMiJUmBWgP4HNi49J9L05OQz9OnuHD5PC6jf10GbmgqlHQ54ogjh2vRDj30tRx++BHNLdAYiHtt05QzbnQtGB0Fag3gc2ATt09X2p6EfJ4+xYXL53Ed/Rs3yNdUKOlz+eUfZ/z4/VNTmxb32pYv6+BanpbKdC0YHQVqYyxtgU1v7zKGhkaaCn0MPuPwefoUFy6fx3X0b9wgP221lxLUql1//XdTU5sWt5bn4INfXZJ+9aunNaRsaaNrwegoUBtjaaviTVufLvB3+hRXcT9PUqN/01Z7KeniUstT/uCdyz3XkLKlja4Fo6NAbYylrYr3+ONnl6QL0y20Ml+nT3EV9/P09Cygs7Pxo3/TVnsp6eJSyzNnTulDTRqmOEqCrgWjo0BtjKmK138+D/ZIQjab5dRT44/+dZG22ktJD5danvnzzyxL/0VDypZGuha4U6A2xtJWxbthw9qSdGEOIV+4BF1Lly5h48aH23pofVIXzbTVXra7ND3kuNTyLF9+Z0m3geXLf9noYqaGrgXuFKiNsbRV8XZ3zyu5MPlWQxh3hG0ul6O/P2iOXrOmLxU3HBe6aIoLn0e0u3AZyVw8tU2rd22R1qBArQHSVMU7f/6ZJRcmn6r6c7kcq1bFm2HfdcLKNNUkuNJ30N7SNqIdNJJZWoMCtQZIU22Fz1X9vb3LGByMN8O+64SVaatJcKHvoL2lbUS7i6Bry8j1MA0P4+I/BWpSk89V/S4z7LtIY01CXPoOJG0j2l1ks1kmT54CwJQpU1PxMC7+U6AmNflc1e8yw/7JJ88tSXd3z62y5wjVJKTzO1BTbjw+XwuSksvl2LJlMwCbNz+tY0cSoUBNavJ5FKvLDPsLF55f8nkWLjy/bh7VJKTzO1BTbjw+XwuSUt6ntZ1HjktyFKhJTdlsltmzuwGYPbvbq6p+lxn2s9nscE3AnDnzIn0e1SQk+x0kUdOlptz40jai3UVhxHhBGh5YxH8K1BrA5yYVn7ptnFIAACAASURBVMsWV0/PgpLgIeoT/tlnv5Px4/fn7LPfFfl9oL07ECdZm5JETVcam3KTkKYR7RD/elgYMV4tLdIICtQawOcmFZd5x9au7Qdg7dp+rwK84Ak//gz7y5ffya5dOyOPYM1ms8OLLx988LS2rElIqjYlqZquNDblJiFNI9rB72u1SIECtTHmc5OKS9l8r3mI+4Tv8h3kcjm2bt0CwNatW7z6TZM0f/6ZjBs3vqFz6SV1vKk5W1yuBdOmTS9JT59+SKOKJzKsrQK1JJr9fA5sXMrme81D3Cd8l+8gyDMyDYhPv2mS4tZEQvxzLqnjTR3jxeVacOWVn6yZFmmEtgrUkljj0efAxqVsaat5cPkOfP5Nk+JaUxz3nEvqeFPHeDdp6uPqcl4fdNDEsvRBDSmbSLG2CdRyudzwidjINR59Dmy6u+fR2dkJQGdnZ6Sypa0jvcvv4/NvmpTe3mUMDQ0CMDg4GKn2weWcS3Lm97R1jE9CEg+7SXE5r3t7l5VcQ9u1dl2S1TaB2tKlS0qquRt1ofG5SaWnZ0FJE16UsvnekT7uE77L7+P6m6at9mFwcCRQi1L74HLOZbNZDj44meMtbR3jGy2Xyw1PT9HIh92kuJzXLueB79J0nUqrtgnUXNd4jHsQ+96kUhyoReF7R/q4o7Zcfh/X39TnEWVxj+vjj59dkj7hhNlV9hzhcs7lcjmeecbf462dLV26ZHg6ikY+7CbF5bxOY+26z9cpCbRNoObK5SBOqkkl7s3WpVO8a0d6nyctdfl9khhdmqS4x/Urr+yumR4rQXk0cMNHrg+7Pos7kjlti7L7fp2SQNsEai5rPCZ5ELsENnFvtmvWlC5aHmURc9eO9D5PWurS5JXE6NKkuBzXGzasK0mvX7+uyp4jXM45DdyQJLnMqZjNTgr/Pcm7FpO4fL5OyYi2CdTOPvudZen6s9K7HsQuQYrLRLRxb7Yui5i7VPVr0lK/y5bUxXnhwvPp6Ii3rmoam5bSwiXw9pn7nIrPALB16zMtXwPl83VKRrRNoLZ8+Z1l6fpPUC4HsevJn8REtC6LmLt0uE1y0tK4o1iT4nPA4XJcu9yks9ks3d3x1lX1eTBOu3N52AV/O6u7jGS+5ZYbaqZbjc/XKRnRNoGay2K6rsO33SZUbfxEtHPmlC5aHnUR87gdbpOctLS4c7NPN3WfAw6X49qldgzir6uazWaZPbsbgNmzu1u+aSlNbrvtZ2Xpn0bK52tndZcRnOvW3VOSXrv2nip7tgafr1Myom0CNZebk+vw7SQmVHX5PPPnn1mWjt6BNk5H+iSf0kYGOjTsLZy4jhRNovbBpUO0S+0YuK1mIH5yHcXra2d1l5HMaeP7LAUSaJtAzSXoSmr4dlJBpEvzL8TvSJ/UU1rp9AD5hk4X4BJAuayNmUTtg+tcZXFrx1y7Aaxd2w/A2rX9Xt3Y212h9qlaupK0dVZP41qfmvjZf20TqLk+OcQ9iJOaUNW1SbJYo5okk3pKS3K6AJcZ2ePWJiVV++A6V1ncz+PSB8gljySjUAtbLV1Jkp3V4z5MbdiwtiS9fv3aKnuOeN/7Fpak3//+D0UvoIijyIGaMeZVxpjfGWOOCNNnGGMeNMb83hjz+aL9Zhpj1htj/tsYc70xpivcfpgxps8Ys9EY8xNjzIHh9onGmJ8bYx4J/z4t3L6vMea74fZfG2OOGu2HdXlyiFublOSEqnE/z1veMrMkfcwxx0XK58Lnp7RNmx5j0aILePzxTZH2z+Vyw1OZ3H33Kq8GiLhwmassl8uxatVy8vk8q1Ytj/R5XPoApXHm97QYN258zXQlSXaDiFsb7TIY6cc//kFJ+kc/+q/4BfWMr30IZUSkQM0YMxu4G5gRpscDNwLnAEcDs4wxZ4e7LwE+aq2dQbBI5KXh9muBa621RwHrgcXh9s8Dq621RwPXAV8Pt38MeDnc/gngZsfPOCypJWOCvg4ZZs06KXKeJILIJ554vCQdNVBxkcR37TpdwDXXfJmdO//MNdd8OdL+QS3aSGATpVbNpWYoqdoHl/fp7V3GwMAeAPbs2RPp87j0AVK/IX/t3PnnmulKkuoGETwYxXuQcFlS76mn/lQz3Wp87kMoI6LWqF0KXAE8HaZPBH5vrX3MWjtAEJwtMMYcDoy31t4b7ndzuH0fYB7wg+Lt4b/fAXwv/PetwNnh/sPbrbV9wFRjzGGxP+EoufRNWrLkJvL5IW655cYGliy+LVuerpluNS7TBWza9BhbtmwGYPPmpyMFqy5Nxi41Q0nVPri8j8tkyXuvZvBKjFKKb1z6ZyXVDSJ4kBh5+IhaOxR3ENL48fuXpPfff/8qe7aGtPUhTKuuKDtZaxcBGGMKmw4BNhftshl4TY3tU4AdYVBXvL3ktay1A8aYHcDUGq/1RJQyA0yefGDUXau69dabsPYRbr/9J1x55ZV193/00UeHn7KeeupPvPjiNl73uteN+fu4OPTQQ3nqqaeG0695zWuYOnVC3Xzbt2/nX/7lX/jMZz7DpEmTGlI2FzfddHtJesWK2/nbv/3bmnn+7u++WpK+9tqvcuONtQPqwhQgxel639ucOXP41a9+NZw+5ZRT6uZZtOhiVq9eAQS1D4sWXcKkSfV/n7gWLbqYvr7lQNDPKMr7TJ48ueTYmTJlSt3P8+tf31eS3rBhXYQ8pSseRMkjyTj88MOGH3IAjjji8Ei/zaJFF7N169MNO54heDAqrh1bs6aPv/u7T9bMc+utN9HRkWFwMDgPolx7d+/eVZLetWtXSx+f99yzuiTA7e9fXfd7A3/vCWkVKVCroINCW1AgAwzF2E64vbBPsXqvFdn27S8xNOQ+b0Mul+OOO35JPp/njjvu4Kyzzqn7RPiFL/xrSfqqq77AF7/4tTF/n0K+b3zjK1x55d9E2n/q1GklN9upU1/Ntm0v1s13440387vf/Y7rr7+JSy65rO7+SVmxYkVJevny5Vx88Udq5in+/ABPPvlk3e8gk8mULGKfyWTq5tm1a6AsvSfCd70vJ57Yzd13r2T27G4GB/eJ9PvEty8HHzyNp576EwcfPC3S+2zZsqUkvXnz5rp58mXVFfl8vm6ek0+ey4oVdzI0NERHRwfd3XMb9B1IXOvXry9Jr1u3LtJvk8u9zJ49g2zf/hKDg/s0pGyTJk0paYacNGlK3bLdddddJbXev/rVr1i48OKaeSo9tLXy8XnyyXNZteouBgYG6Orqiny++XpPaGUdHZmqlUuuoz6fBIrrwacRNItW274VOMgY0xlun85IM+pT4X6EAw8mANtrvFZiXKqFXfowuFY/xx2J+NvfPlCSfvDBB6rsOcKl70faFCZ6rZauxGVEGYw0F8ZpJozbPB8sgxN/1Gdcxx9/Ykn6hBNOrLLniKDfUGlaWpvLiOm4XFZd0az8bn0I1a8tea6B2lrAGGNeHwZf5wK3WWsfB3YZY+aE+10Qbt8DrAY+GG6/ELgt/PcvwjTh31eH+w9vN8acAuyy1kZu9hwLLp2uDz30tTXTY/U+uVxuuO/UmjV9DTtZXPt+JMFlMMHMmceXpI877vgqe47ufVxGlOVyueGA7r777o38m8YdtRU8GMQb9ZnkOo8RZn2QMRA3wHcJvHO53PD1rJHXqTlzTi2ZxDnKqiuald+tD6H6tSXPKVCz1u4CLgZ+CDwMbGRkoMB5wFeNMRuBA4Grw+2XA5cZYx4G5gKfDbcvBk4yxjwU7nNFuP0aYL9w+9UEQV+iurvnlZz8UW62l1/+8ZL0FVd8ItL7xL2pL126pGT5pChPqy4X2jVrVpXc1KN0IodkZth3WdbowAMn1ExXe5/i4yDK+/T0LKCjY+Q3jXITcPlNXZ5uXR4Mgu8g3ne9YUNpf7P169dV2XNEb++yku9aN4HGiVvTte+++9VMV3uP4pt6o2rVXM43lyBl2rRDaqZbUdwZB7SQe/JiBWrW2iOstZvCf99lrT3WWmustX9trc2H239jrT3RWnuUtfZca+3ucPvj1tq3WWvfaK09y1qbC7c/Z619t7X2TdbaOUWvv8tae1G4/a3W2l+P5QePYv78M0uClCgzzB9xxJHDJ++0aYdw+OFH1M3jMkx8bCZ7rV91UX7xymajdRxNaob9wvQNs2adFOlC69Ikmc1mh9dJPeWUUxs2N57Lb+oyDYhLk0/wHcRfQiouzaOWDJearvXr7y1J33ffvVX2HJHUpNTZbJapU6cCMHXqwQ2bi/KiixaVpC+55NIqe7aOuFMpqck4eW2zMoGL5cvvLHm6jzoj+2GHHQ4QKUgrSGKtyr1rOOoHKVu3PlOSLsxoX4trHwaXWrhCX67yqSCqcam9hKBG6aij3hh5MXJIZtJfl8DGtckn7nfgUoOb5DxqSdT6+sqlpmvy5Kk1081UvNrGli2bI/+mcYOU8vMragtDmqjJOHkK1GqoNOS7nlwux/33bwDg/vvXR56Rvrhfjk99hlyWjXHtwxC3Fi6Xy/HAA4XvekPkSS6Lm0ujXmRcJvB94YXnefzxTbzwwguR9nf5TV1Wm3Cd2yrud+DSVJakdp6R3aWm69lnt9ZMV5LUdSoIPONNSu0iyWXrfJXNZpk9uxuA2bO7tZB7AtoqUIv7BO1Sxes6I31x36QoAaHLZK+TJk0uSU+ePKVuHpcLrevgiLi1cLfcckPNdCWuC5K7uPbar7Nz55/593+vPT1LgUs/MNfVJpKo7XNpZnYdLRuXRq7FN2XKwTXTlbhcp1wktY6xSDO0VaAW9wnapYrXpSnKpbln+fI7y9L1m2Wfe257SXr79mfr5nG50Lo0L7rUwq1bd09Jeu3ae6rsOSKXyw1P2vnMM9GbSOLatOmxkomPowRQ2WyWWbMKfe5mRwoifV5twmUwTlJNn+0+cs3lAcxlCgzX7iNxVZrfrBGSHP3sq1wux9q1/QCsXduvh5wEtE2g5vIE7dJElNSNJqknSJeAsKdnwfBEw1EHRyQ1ksilxtPFtdd+vSQdtVZtRLQ5Klymg4Fkmv1cBuMkpd1Hrrk8gBUG1BREmQLDpfuIi8KDYbX0WEmqhtBn7f6Q0wxtE6i5Hly+NhG5NMu6NmMWi3qhjTsXVlIjiVynG4nLZeJjl3nUXKaDSWoSY5cgP6mmz3Yfueb6AFYrXYnr4J24kqrpcvne0qbdH3KaoW0CNdeDK24HategK+7FzKVZtvx1ozwRuzdjxptQ1eXzTJ1a2kfm4INfXTdPUiPXXBZvdplH7YgjjhyuRTv00NdGGmmc1CTGhXVLC/r6VlTZc0RSNdLtPnItqRp5l6mHXCRV07VmTemDnW+jPpMYydzuDznN0DaBWlJPdt3dpa8bNehKYrLGm276Vkn6hhu+GalsxcFDlLK51Fq5fJ4XX9xRkt6xo/7oSpd+NhD0OVu06ILInfV3795dM12J64iy97zn/QC8970fiLR/UrWKhb6a1dLN1O4j11z6D5Y/OEQdWZnE1ENJ1XRNnDipZrrZkujS0O4POc3QNoGaS78pF/Pnn1mWrt8vx3W6hLjNsuVzopWnqym+qUfhOklu3M9zwgknlaRnzTqpyp4jXPrZAFxzzZfZufPPXHPNlyPtn+QySD/+cbAoyI9+9F+R9k+qVtElUEuq6ROK11WNNgdfmrj0H3SdkBni1a67SKqma9u2Z2qmmympkcyu9ytx1zaBGiRz87zttp+VpX8aKd/8+Wcybtz4WB2uXeb2isvlKdplklxI5vO4BNKbNj02PFJ08+anI9WqufSZccnjMrrUtVYxCUnVfOdyOe67r9AfcG3bjVxLqgYqqcEEPk/Gm5QkO/kn0XdbRrRNoJbUGoKuzVfLl9/Jrl07G9o5db/9xtVMV+LyeVwmyXXhUvvicoMqr0WLUqvmNqoufh9Cl9GlLgtYJyWpZpWk1qD01d13ryxJr169suJ+xconVz722PqTK+89IfPMKnuOjuvDR9w+XS5zUSYlyU7+STxUy4i2CdRc1xBMonNmUlXWheWWCvbseaXKnqOT1Agsl/6ALp2oC7VpBZs315+rzKVm9Tvfua4kffPN11XZc4TL6NKengV0do50Bo4aDCVxLmSzWV796sZPSNzuM8wXOoNXS1dSfh48/XT98+CJJzaVpR+vvOMouXZpiNuny2UuyqSok396tU2g5nJTh/gnssv6hknN7VWoQSiIMimkS9CV1Ais8hGBUfqoJTWq0CUQcAkIXeZRy2aznHrqfDKZDKeeOj9yMJRER+VgzcbChMRb2q5JMikvv/xyzXQlLpMruxzTLly6NORyOVauvIt8Ps/KlXe1/LGmTv7p1TaBWlInssv6hq61fUlwaY5z7f8St8bGpQbKxYknnlySnj375Cp7Js9lHjWI38ckqVrf3t5lRedC46YO2fuBqnGLv/vIJcAfP358zXQl06YdUjM9VlyuOcGxFm+amvKHGp9Gfbp28k+iplxGp20CNZemKJcT2aXfVFK1PC5cgiHXOZri1ti4PK2vX39vSfq+++6tsueIc855X1n6/XXzuNREugSERxxxJAcccAAABxxwQKR51CB+H5OkOionNXXI3g9U+zbkfXzlEuDv2rWrZrqSadOml6QPOSRaoBY3eHAZ9enST6+8qdOngTjg1sl/6dIlbNz4cNv102wlbROouTRFuZzISQZdcef2cuESDLn0lUiqxuZVrzqoZroSl6f10gXVM5EWWL/wwr+qma4kl8sNz9G2e/fuyN9b3GMnqY7KSY3eu+++0nVhy9eNTbuDDppYlq5/HpRPzxNlup7f/vaBkvRvfnN/hNLFf2hzOW4Kc1dWS7eiuA9guVxu+Fxes6ZPtWqeaptAzWVOJ5cOty5c546KO7dXUlz6SiTVT2/r1q0105WMfhb3aPPPZbNZZs48HoDjjjs+0sW2tKkw+vd27bVfZ+fOP0deg9RlglQXSU0dktS57ave3mV0dIycoz6t1+jy0OZy3Ozc+eea6UqSWlM0Ka6jn9Vcmqy2CdRcpoxw6XDr0rTmMtDBZW4vF4WLebV0JS59JZLrp1ceNNUPolxqSctvfPFvhNGmNHFpKnSZey2pBdaPOaZ0yocoU0C4cDm306S/v69kxZEo55vLNdSlC4BLM7vL5NcufF5tw4Xr6OckBhbJiLYJ1ApD/gvK+05UUuj7Uy1dicsSIy6jF13m9nLhUjaI31fC53565TPXR5nJ3qXPTC6X44EHNgBw//3rIz2tuqwC4TL3WlITpCY1nYNLZ/o06e6eV1KjFuXh0CVQW7jw/JLa9ShdAFya2V3O0fK1gaOsFSzJdVOREW0TqJUfTLncc3XzFC4W1dKVuCwx8p3vXF+Svumm+h32kxr27trpOm5fCZcLrQuXi/OGDetK0uvXr6uy5wiXAOqWW26oma7EZRUIl7nX9l5gfWXdPC6SOq7PP/+SkvSFF364Ie/jK5c1fF36gWWzWWbNCh66Zs2aHel64NLH1eUcLb8nPP/883XzpI1rjWcS3VRkRNsEai5V46ec8raS9Ny5b6u432i5zE+UFNf+c3H7MJS/bmF5n9riP+EvWHBuSfqDH6z/hO9i27byvnD1A/byDu1r19bv4O5Sy+HSP6t8zr3ChXqsldd0T5/emOkc9j7e6ndRSJMXXni+LP1C3Tyj7z8YrTk/qfnAygcPdHa2ze1wWHkNZ9QaT1+nk0qr9jsyY3CZe81lfqakllxy4dokGXfI996BQP3JeDs64n9vy5b9Z0n6+9+vXz6Xp06XEXIu9l6ip36fLpeaYpc8Lg477Iiy9OENeZ/RDxBpbS7N3y7HdC6XGw6K77vv3kgPbi59XKdOPbgkHaWmfNeunSXpnTt3VtkzvVwCdp+7qaRV2wRqSa0L6dJUeOyxby1JF0b+1ZLU6KOXXnqxZrqSXC43fCOMOuR776fb+p/HJbgrr9mKUtPl8tRZWKKpWroSl9okl9pYl/cZN258zfRYKZ/O4cEHH6iy5+gkNYrVVy7N3y6B2tKlS0qaWKM+uMXt4+rStUXcAnZJXtsEai6dZ12eul1GfR544ISy9IF18yQ1s3qhc3vB/fdvqLLnCJeL88EHx38i9ln5OqpR1lW96KJLS9IXX3xplT1HuPTpcqm1GjduXEk6yqz0LlzOUxdJjWL1VVIrBiS1pmpSoz7TxiVgd+0OI+7aJlArfzKL8qTmUsXrMurT5cD3eWZ1l4uzz0/E5YFmo2bw/tnPflyS/ulPe+vmcRmZ/OCDpZOORpmENKnfx+U8hfh9IpMaxQp+zjnlumJAXK7TWSQx/YNLc2nauIx+7u6eN9zi0dnZ2Xa10c3QNoEaQNxuQi4jEbdu3VIzXYlLLYLPTzUuF+c5c0rXEI2ypmhSkurP9NBDD5akf/e7B6vsOWLPnj0105W4PEwk2Y+yuKYrqrg3dpfpU1y5BB0uwV2cPOVNylFXDEhCLpdj1arl5PN5Vq1aHunzuLRkvPjijpL0jh31+2eljcvo556eBcNdVTo7O7X4ewLaJlDr7V1G8b0l2rqd8Yd8u/Sbchkqn7anmp6eBXR17QPAPvvs49XJ7/KbvulNx5Sk3/zmY6rsOTrlSwFF6Xjt8jCxzz771EyPlWCy05FALcp56jKvU1JLVbnOOeUS3MXJUz5qt1ETt7r0pe3tXcbAQPDAsWfPnkifx2VpODWX7v3QGeWBJZvNMnt2NwCzZ3dHnoJJ3LVNoOYyE3dSXEbe+PxU47qawamnzieTyXDqqfNb/uQvzB1VcOKJ3Q15H5dpQFwCz1deeaVmeqy4rK/rMpN9UktVuZTNJbjzdRLSvUclz6yy5wiX2k6XQULi3oew0LrUqOuAlGqbQM2lv5nLtAwuoxddRt64DGFPyt6rGUQb6BDky6Tiyfa7372xJP2d79SfxFjcgnyXmeyTqk1xKZtLcBes+TryPr5MQrp3Z/Un6+ZxmSzaxX33lc5TWD6PoVTmMuWKjE7bBGrl00q8/PJLdfOcffY7y9LvqpvHZfSiy8gbCEaujRs3PjUj1m688Zvk80PccMM3m12UUfN5TUCXYCgp5XNZRZnbymUm+6S4lM0luPO1xcClxnfr1vh5XBQm1a2WHks+DiiBvYPiKA/8rlOuiDt/rtAN9pvf/LokHWWaiZ/85Idl6R/UzfPss8+Wpes3qbiuO7hs2a3s3PnnvSZxbTaXvn2bNj02fFHfuvWZhi0y7yKpOeuS4tL0WT6quHzUcTO5zGS/bl1/SXrt2v4qeyZfNpfgLm4T496/pz+jxpPqP1c+4W15eiz5uoj59u3P1kxXktSUKzKibQI1l8kaXZb0KXSCLYgyCs9l5E3pAt4bvHtSi+trX/tizXQzJbXKgM/27qPWmLVYXbh0A3AJVpMqm0twt2nTH0vSjz32xyp7BpLqc7jffvuVpcdV2XOEywhjn1d38bX/oCtdD5PXNoGaz1asKJ/T6c4qe464/vr/qJluJpfqdJcmkqQkdVN3kdSKAb6LO5N9UktiQfyyuQR3vnam3717d1l6V908LsGdz9K2iLnLWsEyOgrUaip/KmvMU9reNXf1m2FcVgxIikt1urjVChQ6kFdLt4tsNsvixVdFHlQzfvz+Jen999+/yp6lXPoaxS0bxA/u4vK56XPXrl1l6ShNkv7WqKVtEfOkamNlhAK1GsqXzSlPy9hJ21O0C5cmhYEBfwct+Kx8fdPp0w+NlC+pvkZxg7vJk6eUpWvPC5e2m21hlGyBT7XeWsRcRkuBWg1JdjZtd3v2lDdF1e/bJ37foHz2xz8+WpL+wx9+XzePz32Nypf0yuW2N6kkozdz5vEl6eOOO77KniLtQYFaDeXz92SzkxvyPi5NXvvss2/NdDNNmlT+dD+lyp4jPGqpEKnIZX6zpPjcjzKuAw+cUDPdanxe7k9agwK1GvZeMaAxT9BvetNbStJRlhvas+eVmulmeu65+H3UXCYXFnHlMuWKy/xmSYk70bbPc+m5rNvpM5+X+yt/iJ4ypTFLqcno+HN2eiipp9RNmzaVpR9ryPv47PWvf0NJ+g1vOKpJJZF24DIhsc8T62az8Wbz97kGbuLESTXTrcbn5f6ee660iVwDv6JLchJjBWoeeOmlHSXpF1/cUWXP9Fqy5KaS9C23XN+kkohUFsxvFrTRZzIZr2645TfYKBNt+2rr1i01060mm80Ot5q8+c3HNHS5v7jBg8sAJp/nrEvS0qVL2Ljx4URWZlCgJl5Icl4rERfZbHa4qWjKlKlera+bJj7X9rmy9mEAHnnkoYa+TxLBw7HHvrUknYbBHnED3FwuR39/0PVhzZq+hteqKVATEYkgl8vxzDNB7c6WLZu9GvUp/nrwwd8Mr1m7c+dOfve7BxvyPrlcbrjfZCODh7irYLSCuNPuJL3eqQI1EZEIli5dMtw0lM/ntRi1RPKNb3y5JH311V9qyPsEx2fjg4fyALB8apix5NIPzKV2bNWq5eTzeVatWh4pX9LrnSpQExGJQItRi4uXX365ZnqspPH4dGnKjZunt3fZ8Lyde/bsiVSr5jIYCdwHIChQE5GWl+QILBGJz6Wma82aVQDcffeqSPlc+o4V3qPg7rtXVdlzhOv0Nq59CBWoiUjLS2Jpp7e8ZWZJ+phjZlbZUyR5vs9FGTdIcelq4NJ3bMKEg2qmKylfBmzWrPrLgrkEngUK1ESkpSW1tNNTT/2pLP1kQ95HxMXZZ7+zLP2uJpVkb7lcjrvvXgnA6tUrG9YPzCXPtm3P1ExXsn37trJ0/SXbRtPHVYGaiLS03t5lDA0FfUQGBwcbVqu2bdvWkvTWrfUv6KBm2Xa377771kyPldtu+1lZ+qcNeR8X5UFJlCDFZZoW175jcf3hD6VrBT/66H/XzVNoki2Is7KJAjURaWn9/X3DF+TBwcEGLu1UPrFntIk+k2iWFX8dcMCBJelGrV3q82CC0QQpaTGa+QEVqIlIS9u779hxDXmf8gnYo0zIJVCEdgAAIABJREFUHgz9v4t8Ps/KldGG/hfyqRYuHcqnryhftmms+DxRsEvZXNbjTeuqCQrURKSlbdz4SFn64Ya8j8tyO8HQ/8JC7tGG/gPccssNbNz4MLfccmP8gkpbcjk+feayWk3avoMCBWoi0tJ27Hi+JP3CC89X2TN5hQ7UBatXr6y4X7FcLse6dfcAsHZtv2rV2pRqVaVAgZqISIN0dHTWTFdyyy03lKVVq9aOrr/+P9i48WGuv/4/ml0UaTIFaiIiDbJz559rpisp1KYVrF3bP6ZlEv/lcjkeeGADAPffv0G1am3O60DNGHOuMeZhY8zvjTFXNLs8IiIijVZei6ZatfbW1ewCVGOMORT4AnA8sBvoN8assNY2pqdwiq1evZJVq5ZX/dvcuW8bkzxJqVW2scwjbvRdp0+1vnXNvhYkKcnvoFCbVnD//Ruq7CntwNtADTgDWG6tfQ7AGPMD4P3AP0d9gW984yv88Y+P8cILlauNP/WpjzFx4kQOP/wILrjgw3z3uzfy+OObeP7559smz09+8iNWrVre8Dyf//w/AChPSvMAPP74JoCG5rnggg9X3KeWwmdxfR/fAk+XzwOl33UlhetHnDyFa0Gj38fnPOXfgcsxmpTyY6eSaveROHmKuQS4acozFpUeGV+Hrxpj/h44wFr72TC9CDjRWntZhOxHAI9dcMEFPPNM/dnDp08/lC996Wo+9amPsXnzU5HKpzzKozwjeYDI+48mzznnvHe4s/3u3a8wOFh5yP7+++8PwIUX/hU/+cmPEnkfIJE8Lp8Hkvl9lCfasdPZ2cV++wUrFEQ9Dgp5Lrzwr5g7922sXr0ydp6krh8uZUtrnlrndXGeU089jcmTDwQ4EthUvJ/PNWodQHEUmQFizeA3fvx49ttvP/bs2VNxgr399tuPrq4uDjlkGlOnTuCQQ6axY8fzDAwMKM8Y5znggAMAlCeleWBkmoxG5pkwYdzwJJa15rIs7DNhwrjhz+L6Pl1dneTzQxXzFL9PcTpq2VzyuHweKP2ud+/evdf+hfNaeUaXJ8oxmsnEPw4KeSZMGMfUqRNivU8hT/mxU+vzlF8L4uQpP38qBSpdXZ1VP0+a8tS6fhTnCYO0inyuUbsImGutXRSmFwMZa22Ups8jgMe2b3+JoaHg85133vv22ul73/thzRdJW57yfFH2d8mjz6M8lfLpu/Ynj35PfQfK41eejo5M1Ro1n0d9/go43Rgz1RizP/A+4HbXFyv/EqOcYGnLkxR9HuVx5fP7pOkYTdv3nLbfMyk+f9fKM8Lbpk9r7VPGmM8AK4B9geutteuaXKyW53Ix8vkClrbPkzZJfNff+94PnWpWk+BSNt8/T7uL+x0kdQz4fNzI6Hjb9DlKR1DW9Cki6eXzDSqJ5nnxW1LHgI6b1lWr6VOBmoiIiEgTtWofNREREZG2pkBNRERExFMK1EREREQ8pUBNRERExFMK1EREREQ8pUBNRERExFMK1EREREQ8pUBNRERExFMK1EREREQ8pUBNRERExFPeLso+Sp0QLMkgIiIi4rOieKWz/G9pDdSmA2SzBzS7HCIiIiJRTQf+ULwhrYuy7wfMAjYDg00ui4iIiEgtnQRB2n3A7uI/pDVQExEREWl5GkwgIiIi4ikFaiIiIiKeUqAmIiIi4ikFaiIiIiKeUqAmIiIi4ikFaiIiIiKeUqAmIiIi4ikFaiIiIiKeUqAmIiIi4ikFahUYYzLGmAkVtk+LmH+iMeatxpj9x750oxN+tkkNeu1zjDFXGmP+R9n2y2rkeYMx5pDw34uMMVcbYz7QgLLNNMa8yxhzUNn2d9bIc4AxZt/w32cYY/7GGDO7zco21RjzFmNMR9n2t0bMb4wx7zXGvHasyzZajTwXfKbzVED3uVaiJaTKGGNOA/6TYL3Q+4ELrbVPhX/7tbV2rxuUMeZY4FrgOeBqYAnwOPBqYKG19p4xKtuxwHeA1wK9wF9ba1+sU7bXAv8nLNt1wE+B8cA24P3W2kcq5DmsVjmstU9UyPN/gBOAR4AFwKestUvqlO2vgSsJ1ji7CzgM+BFwDnC3tfaqWuWIyhjzceAy4I8Ea8Cea61dXqdsHwKuIVhz7dvA+4HbgLcD11hrr2+Dsn0Q+ArBsbMv8D5r7e/qlO104JYwz5eBzwP9BMfGx621Px2jsiVyLjiW7bUEv0+hbP9qrR0M//Yza+1eQUcYmPwtkAOWAv8FvAW4G1hkrX26Qh6dpzHPBZffxvGzHAT8U+F9Ct9x+LdvW2urBsVJ0H3OqVyxf1NjTBdwMcF5/Uvgm4yc1/9f4XPV0zXawvvMGPMratQaWmvnV9j8b8CpwKMEF85Vxpi51trNQKbKS30T+BzwOuAnwMnW2t8YY94I3ACcXKFsNwFVo2Rr7YcrbL4W+GvgQeAqYIUx5m3W2pdqlO1mgov+4cBK4EPW2jvCE/Va4LQKeX4OvAF4usLr5sPPWe4dwHHW2gFjzNXAL40xu621y2qU7cPAGwlO9IeAKdbaXcaY6wkWpt3rBmCMeTrcv1wGyFtrOyv8bREwy1r7Z2NMN/ADY8wHrbWra5Tt04ABDgXWAdOttTljzFXAamCvYMjzsv0OqPTkWyhbpd/0fwMzrbXbwtqTO4wxZ1prH65Rti8C8xk5F15vrX3CGDMd+BnBBbS8bN6eC8aYf6hWrrBs/1xh840EN8EHCa4LPzXGvNtaO0Dwm1XyHYLg6Vjg4wQB7hLgg8C3gHdVyKPzNP65EPu3cTwGbgJ+G5bh08aYeUU38hOqvM+Fdd7nlir5dJ+LeW47ftexf1OCh4fxwMHAYoJz9gsED0nfBM6rVY6CVAdqwP8FbiW4AOQi5um01v53Ib8xZjfBxWwO1Q+48dbaOwCMMR+z1v4GwFr7sDFmfJU8dwNfJThJdkUs2/7W2hXhvy83xnwJ+H/GmLfXyDPZWvstEzRdXVQop7V2hTHmy1XyzCE4GC+31q6JWLYM4fdjrf192FRxpzFmG9W/tw5gt7X2cWPMl6y1xd9DtWNzFrAc6AkDhkistX8O/99vjFkI/Jcx5sxaZbPWbge2G2O+a60tHD8vtWjZPkRQ07AQ+FOMsm0L//9fxpg88Is650KXtdYC1hizvFCrY63dbIzZp0oen8+FfQhuGl8GhiKWbbK19iYAY8y7CQKDJQTffTVHWmvfE35Hf7LWfjvcfpMx5soqeXSexj8XXH4bl2PgSGvte8P3+QXwM2PMl621n6R6sHE68D6CgKNS4F0xUEP3OYh/brt81y6/6Sxr7VtM0Dz8hLX2M+H2fzLG3F/j85RIdaBmrb3TGPOvwF/GqGreYoz5X8B/WmtfsNZ+zQR9M+4EslXybDPGXGqtvc5aezQE/SYIqvG3VCnbDcaYNxD8+J+OWLYdxpizgDustXlr7aeMMd8Dfkjl2hKAl8NakDvDJx/C8r0HeLlK2XYYYy4lOPGj3gCWASuNMZ+01q6z1j5kjFlAUHW9X5U8PyR4kjvNWvu5sFzHElRdf79K2Z4Km2L+ieCpJIrVxpilwD9bax+21vYZY64AfkXQnFPJmvC7vcBae2lYttcT1Bj1tWDZfmuM+d8EzY9Ry7bRGPNF4Gpr7ZPW2mUm6L/SB4yrkuf3xph/AT5rrT0rLNs0gpqPis0Pnp8Li8PawJettV+MWLY9xpg3WWsfstbmw6f3240x36T6NXePMcZYa60x5oyish1HleBA5ykQ/1yI/ds4HgMYY6ZZa7dYa3caY94L9IXnYMVAyFp7kTEmS9CcfGPU99F9Doh5brt+13F/U2DIGDPFWvusMeb8otd5DTHGCLTDYIKvAF+Lsf8lQDdB8w0A1tq/I3hiOahKngvYu9r3VOAkgvbpav6RoM9HVB8BPhO+X8GFBH06KjVzAFwK/L0xpsNa+wJAeGH++/BvFYUX8Vqdi6eV7f9PBNXiLxZtWwMcT1BlXCnPPxDc0AeLNu8C/tGGzQnlecJ8v4gRbAB8lOACdGDRa/yIoDmp2g3ucoILRfFN8lXh63y02ht5XrZbgL+KUbYPE/T9MUWvcQ3wSYK+H5VcBOwqK9uM8HVqvbe35wLwN8DmGGX7G4Kn7XMBrLV7gHcD0wiaECv5a4JmuE470g/wHIKm4o9VeyOdp7HPBZffppAvzjHwOWBD+BsSHm9vB94LHFMj3/8EXDrB6z4X/9yO+11/jvi/6eeA+8Pz+vawXGcCGwiaQqPJ5/Nt/d+MGTO+7XGez8XYd0rcPC7vU5Tn18qTujw/8zjP52Lsm/S5cFmV7ftW2DazVp6yffebMWNGR7338eTYaak8o/1tohwDM2bMmDBjxoxJZds6ZsyY8Z7w3+90eJ/YecJ8us/FzFPpu3b5TWfMmLF/WTpb/BpRftN2qFGrp1onQB/yvDvqjtbaZ+PmcXmfItXa5JWndfNU6+juQx6fz4WPVCnHKxW2PVArT9m+u8tqiurmqcDn461peUb721SwVx5r7YvW2ufKtg1Za38cJisNQqjHJQ/oPueSZ6/v2uU3tWG/y6J0ruw16v6mCtT85vPFz2VeF+VRHtc8Pp8LPufx+Tf1NY/Pv6dLHt/5+t15Uy4Fan7z9UImkjSfzwWf80h8Pv+eaTwGfP3uvCmXAjURERERTylQ86h6s8X4/L0pT/ryiBuff1Of86SNvusW1haBmjFmrzmfjDEzw3/e2cw8dUSeKLIReYwxJ5alxxdNHlhxEs4k8pj6a9T9a7PyhH/fawi5GZm0tKl56vhOM/MYY/a6yIdzO0H1YzSRPHU838w8Ok+d8uxbvq1Ite85dp46xjQQ0n1uzPN4E9y2xVqfxpjlwDvCSerGEyxLca619pBm5TFu64Ylkqfo7/9NMMvzPcaYvwT+HVhura06H1aj8xi3NeoSyVP2ef7SWvuoMeYtBMvYvGitPaNZeYzb+nmJ5CnKW5i4dChMfxRYbK2ttBRRInlMsFbfFQTrW/7YBssZFf72ORtOANuMPEV/13ka8zw1xjxBMEfdzdba+6p95jHIU75UUR7YCWwEHrWlqzw45wnz6T4X8z5ngrVvP2uDJcQwwcTG37bWvssYM67K75NInmJtUaNGsC7ZbcaYHuB3wETgzU3OcxPwAsFF5uPGmG8X/a3aEOek8hS8E7jOGPMTgmVKLqx1IU8oT2GNuikEi9yuCg96qP5kklSegosJljz5OsGyTd+oFXAllKewft4M4BWC9fMKE4tW+zxJ5SnYBtxqjDnOGHMPwaLfpzQ5z7eA4wjW07zFBLOQF1Qb7p9UngKdp/HP06OAe4F/NcY8aIz5lKkwae8Y5DmHYNLTmeF/iwkC8puA/zWGeUD3OZf73CRgnTHmjSZYOWAdsAKgRvCUVJ5hbVGjBmCMuYhggdT3Wmt/3uw8xpj7rbXHhf8eT7Bg9QPW2k8W/61JeQ4rSh5GsB7axwgOLmy4fmOT8pSU2RjzCYIZ7+cAK6s8dSeSpyz/McDtBE+aK2vtm0SeCp/nS8BbCWbWXlfv2GlknrL8/0hwY7rUhmsy1tPIPMaY31hrjw3/PZVgOaObbLDkTrXzJ6k8Ok9HeZ6GeXqAqwmWTvoV8Clr7aNjkccYs4ag9un5MP0qglq504ENhd98tHmK8uo+FzFPUd6FwHeBZ4Fua+1jET5/InkKUh2oGWNWMDL0NUMQ9e8AHgew1v7/7J153G1j3f/f53AkQzIUkiisT+ohRympJFIpyoNoIEOZVUJI5NBorFAikVAcJDKkzMrwMxP68KAiSpFZiPP747v2Oevsvdaezn1fa5373J/Xa7/utdde73Xva+11rWv6DmvXyNwErGf77/n7hYjcdKcBm1Q9lBIx9+XlKRuRTrNdZhuVirkAOIc8R12+72BiVP1q26+vkXmJme8DCuWbZrsjX2FC5nLCVudC29PyfacACwKyrRqZE9rKsx5wH3luUNvb1MjcBqxu++n8/VJEOqOvArtX1J9UzHg9HYLJj1ke2Bz4FPGcPgH4JZFS6fu2Vxgh5i5gRefptyRNAm50JOq+2fYqs8qMt3PDMflxWxMBZ48EVgRWAj7nGcGPa2OKGtNJ2Ynp4yYzN0jayfbZth9XJKL9Nd3zho06U3y4SZpk+4X8YfEy20/VyRA56g4hkgCflZ9nz3wk/bU6GdsdpgSSJrQ6LHUyROT0Y4FXAz/L930GOIzosNTJXNbjfZ3MkcCNef252JFs/EPAhUQZa2PG6+nQDITR+0+BdW3/pbD/fEUuxpFizgQukTSVMDXaGPiVwg6tKnfooMyUivN0U0qmke1crh2I3/NPAJI+AvwKWLYBzAwNkzdsdntlWbZUlmUH5duvz7LsZ1mWLV430ytvWJ1MfszHsyy7Ld9eLsuy+7Ms+1gTmDZ+1SHuiVFjsixbK8uyP+TbyrLs3izL1mgC08Yvkf9drAlMfp/unG8vlWXZge158upgsixbIcuy17XtWyDLsl3rZvJjxuvpgEyWZROyPN9nlmXLZ1n2kayQV3WkmPzY9bMs+36WZYdnWfbhfN/qWZYtPMLMeDs3ODOhZN9Cve6DFEzxNdZn1Fo6GTg1334QuJJYK/5AnYxzb7iWJJ1re32ip12qVEyu/YD35+e4R9JbCYPdsxvAFHUcYQc1iEaTOZyYRcK2FR5yJwGrNYAp6nxgVc/In1c3cwpwW779JDGbcBIxo1AbY/vu4nvN8CL7XtU/SMXkGq+ngzP7Am+StBexRHYH8Zz+4ggzEMvrZ5AvHUta0/YVo8CMt3MDMsC9kspWIjqW82tgpmtO8fpcxPYxMD3R8Y8JL6EmMEVVukTXxMxj+x+tN7YfprfnXiqmqMbEu8k1r+0/tt7k092TGsIU1bTrtoztrwLYfsL2vsByDWGKalqC6/F6OjizIbANYW92ssNb+l0jzUj6AeGRfSARPuIAeiwhDsPkGm/nBmfWAt6Xvz4AHA0c3xBmuuaUGbVnJa1n+wIASe8Hnm4IU1STHmQAv5f0C2IGYhqwGXB1Q5iirh/g2BTMnyQdRIwypwGfBO5qCFPUgwMcm4KZJmkl27cBSHoj8EJDmKLG6+nsX08nOuKArQ/sK2kiMP8oMB8gnGie7eM7zQoD4+3cwEybrSHAIZKuB75RN1PUnNJR2wE4WdJJ+fv7gS3qZCT9T3E2JNeHu508FVPQzkTE8e2JmFhXEnGyamMU4R5+YvvO1j7b23Y7eSqmoM8SQSB/QZTnCqAXO6qMIqjrKbb/3dpn+yPdTp6KKWgP4HeSHsjfv4rwsquNkbSI7Ufbdn+u28lTMQWN19PB6+nFkv4IPEPUm8sJ79GRZu5l8E7GMAyMt3MDt3OS1iy8nQC8GXh5E5iixnR4jnYp4gFNs31/3Yyke4h4KscDv7D9RB/nTcK08f9DTNfORcQl6ulOPJqMpK8RjeojwE+AU13teZaUaeNfBayel+fq4pJRHYyk44GPAJcQDdtFfZw7CdPGz0O4rj8P3GX7uToZSf8CLibqz2/d3bM2KdPGj9fTAetp/px+wPZLklbps/wDMfkM5DuBq4DpgU1dEgpmVpiS7zjezvVXhksLb6fl5zjYduWsbCqmqDmioyZpOWLWYTmiN/sXYDPblctEKRhJ7yZGIusTD+mf2L68R1mSMDm3BWEb8SvCnvFjwDdsV66tJ2TeSRjSr08EnDzehbQ7NTMfJB4Y1+TlWQP4rO1z62QUgSA3zMuzArFkeoJLApamZnLuVcBRhMH6XERnb8duHdbRZiTNB2xE1J+sUJbKYJWpmAI7Xk8HZPJOxpFEp/O/hE3Yrrb/OcLMlmX7bZ84kkzOjbdzQ7RzJedZ1vafm8TMKR213wHH2D4jf78psJPttepm8uPmIdLEbAEsTywdfatuRtLNwDq2H8nfL0aMoitTjKRiCuwkomJ+EljZ9hvrZhS2Bx9vNbKK5Om/dElwy9RMgV2cMIrehMgP+qG6GUm/JGYRjiUagO2BNR2eW7UyObck8GkKMzi2f143M15PB2ckXUkEQz2R6HRuQ8S5qlwuG5J5Xdn+HgOdgZmcG2/n+mQkZYSTxiPA3rafkrQg4dm8i+35Ss6bhCnTnOL1uVjrpgKwPZXIvdUEBtvP59xBwLXAbg1h5mo9lHP+X8BLDWFaWoMIpLoq/QUwTcFMKs6E2L6X3nUtFdPSvISNxMuYEW28buYNtg91eGI+bvtgYJmGMNh+yPahREfgLiIyfROY8Xo6OPMK20fZfjK/B74LLNXj3MMwl+ff43JicHAvEdB2pBkYb+cGYX5KBEleDNhP0tpE0vt3kYegqZHp0JziTPCcpFVt3wigiP/zTBMYSW8mRs+bEhWyV+LdZAxwi6TvEbYfEMbrt9TNSJrMjLK0GrIvuEty21RMrr8qIqO3yvM58jQrdTL5DMhmRJkWJWYGNrT9QN1MrmmSlnZu85LPLPTjwTnqjKRXAh/Py7Q4kXWhND1RaobxejpMPb1K0ua2T87P8RHgpi7HD8W4LYWVpLcTThkjyuQab+f6Zxaz/aV89u124nm1u+1TS45NzXRoTumo7QqcKelRYqljEeKC1cYogiZ+mnDvPhFYu4+p7SRMQdsS07bHEzM1lwA71clIupOYnTkReI873Z5rYwr6LGHL8lWiPBcD29XJKHIirk7YF+3t3sEzkzEF7QdcLelaov68g97XYFSZfFlnc2Km5mxgP/e2l0rCFDReTwevpxsB20s6lpgVnC8/32eoyJc7JDOTbP8/hcNN3xqAGW/n+meehph9kzQvsaTfK7RRKqZDc4SNGky3XciIB4xtP18no3BtPt72pWWf18k0WZLWsX1x4f2CxJLMY3UzTZakzwKneTDP1SRMG/8q4O1E/bnWEVS1NkZhl3Q8MNV5wvQ+zp2EabLG62lI4ZXaUissw6K21xlJpsCOt3P9HX+j80Ttkm6yPbkpTJnGdEet7YbvkO0D62IK7AQiLs06xAznpcCRtittP0abkXQfXeyJbHekvUjFFNg3EGlMliceZn+mt7fSqDKSLqk6T16etetiCuyrgB8Q98FcxH2wg3t7SY4ak89IdCvPz9r3pWLa+PUo1B/bPVMnjSYzXk+HZxRetvsz4zpfQsxiVnaSh2T2L7xthWU41Z0x84Zmxtu5wRlJfyFm1icQGSD2aytL2fMjCVOmsb70uS/wKHAW8BD9BRFMxbR0CPFwOT7ntiZsUnatkbkFmAycS3g59bOckIpp6RgiDk3R8+hYIlVHXcwiwJLA6USZ+oksnopp6RjCQPlzxGh4O8LmqJvH42gzPwUeJsIqPM/M9WcaYadVFwOApD2JXKCn5NxXFUE2v1kjM15Ph2eOIuyqtiGu87bAj+ge7HVgxvYB7fskfZIIbzFSzHg7NzhzCRFmpX0bqp8FqZgOjfUZtUWJsACb5LtOB850wXOpLqbA3gJMbvX4Jc0N3GZ7xTqZfAlhQ8Ioc0Gicp5uuzIdUCom5zqmkSXdanvlOhmFO/ZmRIfkHqJhu6DbckIqJududlv4Dkm32V6pLkbSKnlZ1iU6BacBF/UYOSdhCuytwDucp/XJZ1du6FHnRp0Zr6fDMZJusf2Wtn132H5Tl/8xMFNxnidsv2KkmPF2btaYLueaYntKE5gxHZ7D9iO2j7G9LmFoOAmYKul8SVvVyRQ0NzMn0p4beLFuxuGCfpLtDYgH9BPAGZIqAwimYnI9J2nV1hsN4K00moztu2x/3fY7gG8SYQKulPTTuplc0yQtXShP316So8XYvtn2V2y/jUhWvC7w/yT9SNJadTIFTfTMuRf/QwQ9rZUZr6dDMxMVHrat419JH7/NEEyZBpmJ6smMt3OzzFTpo01hxvrS53TZfljSz4DHCVfn/YmlkLqZU4BLFWlDIAI1dg2ImZBp2RptRIQMeAXwy4YwjfNwKkrSXMBriThLi9HHwzkR0zjPyqIcKVWul/Qe4DuEJ+QCDWAulnQmM+rylsRSRjelYsbr6eDM4cB1ks7Jj98A+HaP/zEMU6ZhlrH6YsbbueHauQqNaId6VpgxvfQJ00c9GxLTtRnwa2LK/pq6mQL7IcL4cSJwie3z6mQUkdE3ysvyaiLY4hm2b+1y7iRMG980D6dJwAeIRuy9RNLq04EL62ba+EZ5VubHTgDWzMu0HnBzXqZfu8JYOxVT4HYA1mZGaIpjbFfOqIw2M15PZ41R5C19b378ZbZv6+N/9MWo2vB+AvAV2/OOBFNgx9u5WWAqzjPdY7NuZkzPqCliOmVEXKJv9HkzJWFybmHClfw3wG/y5ZfbG8A8kL/OJGx5pgGrKGx8qjxVUjFI2hr4o+3rgNslfZseEdwTMf8kRphnErNHrUTfq0vC5THFUjEoomI/ZPtO4DxJXyCWhy4uOz4VI+lo4ENE8NCpwJ62ewXQTMLk3FzAy2wfDRwt6U3A3T06XCmY8Xo6ICNpfdvnaoYH8JP538mSJpeVZRiG7rMkVbNwwzDj7dwsMLOLxnRHDfhg/ndXYFdJrenDCVQHKEzCKKJpn094pfwm370ucIqk9cpGq6kYIiH0NGBhOr2mqjxVkjCSPk8sUxVDLVwAHCZp3ryRq4UhZmemER5y7fFyphGzJLUwkjYDvgF8orD7H8Cxkva03ZGiJhVD5Np8pFCeb0ma/qHLwz8kYSS9HvgtsBczlvm+BLxX0gdcklQ5FcN4PR2GWY3wXn0fnaoq/8CMc89NScsCSxBexvfZ/nfJOYZmco23c8O1c7OPpk2bNke/sixbtQ4my7KLsyxbq+S4D2ZZdlHFOZIwfZZnSh1MlmU3Z1n2ipLjFsuy7JaKcyRh+izPdnUwWZZdn2XZUiXHLZtl2fUV50jFLNPtlR+zRE3MOVmWfarkO2+dZdmvKsqThOnzPpjlOjcMM7vX01l9ZVm2fJZlN2VZ9o8sy17I68WjWZb9pv0emxVmgO8z3s4NXpaTm8IkuWmb/Mqy7MY6mG7nyLLs5kEcKDPuAAAgAElEQVT/70gys/F1u2mIazBizPh1G5PXrfI7Z1l266DlHEmm4detkfdblmXzZFm2Q5ZlG2ZZ9oq8E/RElmWXZVm2QsV5hmEuzrLsHfn2OlmWfSvLsrnz8/xmpJjZ/D6orZ3LsmyhLMu+l2XZmVmWbd722bEV50nClL3GdHiOPlWXZ8ckSR3XP983T8U5UjH9qK7r9l9Jr24/SNLiRAT8MqVi+lFd122CpA6vRkV8rKr7IBXTj2qrp0OcPxXTj8br6cw6ilgS2x64HPgT8G7gHCJobpmGYRazfS2AI8XVerb/a/tHhIf2SDH9arydm1knEHa+Pwe+qMjf2tLbKv5HKqZD4x21UXSV7sFcTrg0t2tf4PqKc6Ri+lFd1+0o4HxJ75P0SknzSurnQZuCGaY8qZiTgNNyWygAJL2WeIBMrThHKqYf1XXdblLkL51JuQH7PRXnSMX0o/F6OrPWsL0x8DHgtbZ3tX2r7cOBV5YcPyzzb0lbSHq5pG2AByXNLWkTIgbdSDH9arydm1mvt71/bjO7JrCcpMPyz6o6qKmYDo11Z4Im6yvEA2ZLwjj8P8BbCcPrqgB4qZjGyvbPJM1LxOx5bb77XuBQ26UP81RMk2X7cEmLAX+U9DzhKTof0dCV5uhLxTRcXwYuz+vPjUT9WQ1YBnh/zUxj1eB6+kLOPS/pgT6LMwzzOWI25WjgBmAr4A2Ek01VyqlhmKarse2cpCVs/932s5I2Aq6QtA9dOqipmHaNd9Rqku0nJa1JeBJNBl4CfmD7yrqZpsv2sYQX4aLAS314RSVjmizb+0j6JvBG4j640/Z/msA0Vbb/nnuUfYKoPy8nOgWnV5UpFdN0NbSeTqvY7qaBGdv/B7yn5KNNSvYNzTRdDW7npgA3SNrJ9tm2H1fEX/s1UJWqLBXTqVkxUBwLr2EMnEeLyWry0BryujXGI6YuQ9khr9slTWGyLDt3iPMmYUrO0aR6Wovn7pDXbbyezvzZS1mWvZj/bX+9OFJM014Nqz9ThjjviDNZli2YZdkibfsmZlm2Yd1M+2tMz6hJWs0RBLHss81tnwxsXAdToY8SPfBBNOJMboD5BSICdSuezz3AabZPBbC9eR1MhRqT6kPShnSWZ6rtqwFsl8U4S8KU6DV9HJOEUWQxWAK43YUk6ZJWtX0j8Pm6mBLtABzb86hRZhQJp7cC/k3EYPsRsBLwe2AvR37O9jqXhKlQ7fXU9sB22cMwqs4y0DpnhxnAMEyBXQVYGrjC9uOF/evbPpeS9icVU6JGtHO2nyy+l3Su7fWBX9XNtGusOxNMt1GQdHXbZ7sB2L63JqZMtT/Ich0GrAAcBPwBOI7IobajpP1qZhorSV8hAi5eSyyRXAP8DThe0rZ1MhVqxP2mCJJ7M+FwcKciTU9LxwHY/n0dzDDlScgcS6TK2YlII/ZnYun0H0Rnqk6m0ZL0tbbXfpL2kPSREWImAXsSnqcTSl5lGoZB0heJ5+Z2gBWZQVo6EDrbn1RMhZpSf9rVmIFru8b0jBoz/1DtedKqfsRUTJm6jqgSMmvbfguApAuJ0dO7FcmIbwW+XiPTIdvt0fnrYjYDJtueJukE4Hzba0v6MdGZ+nGNTJnW6/O40Wb2AVax/U9JmwIXSlrX9h1U159UTJmG6ZyMBrOa7ZUkzQf81fZX8/0HSLqpZqZDDaqnAMsTg8RW8u6NCc/Kd0t6r+09Z4WxvZ8iR+rTtg/u83sPzOT6HPEbPSNpDeAMSZvl9llV93UqpkxNaefa1dQO5JifUetmBFplFJqKQdJKkpbIt98OfEDhll2pRMzcmhGbaEnCaw8iHk1V3sEkjKT5JB2alwNJ35X0pKQrJJXGGkrFEJ30VhleDiyabz9FGLfWxkiaKGmX1iySIv/m7yT9TNIr6mQAbP8z/zuVSFFzfn6dK+tPKkbSYpIWyreXBRaQtE7V8QmZlyQt5shXOn3pUREOperZnoRpeD0FELCW7SNsH0HESVvM9obMSJU0q8xuwENdvkOZhmHIfxtsX0XMdk7N62C3upCEaWI7p5ln01v6cI/zJ2HKNNY7ao2VpC0Iz4/XSnoDkbD6cWADVSz7pWKAQwhPldOAq4BDJC1PJLg9rGbme0Qn5c+SPgx8ClgV+AERAqJO5qfAHyQdBFwKnCDpdUQcn5/XzHybaFiekvQuYrbyS8AfgSNrZv4k6eC84cf26cTvfwWweJ2MpA8S9+PKiiCq1xFeZd9Q9dJ0Eoawv7lJUisRNZLWJUI7VNXtVEyT6ylE3tLiitI8QCtQc1W7OBBj+wnbJ3X5Dh0ahgGulHSqpDfl57gC2Bm4iLAnq41pcDt3tqRrJW2vfABpu1cHORXTobHeUVtG0vGSji9uK5aLXlcz8yViGvl6IkbOpbb3JZa1PlEnY/unhE3K6cD7bf8C+Cux3HZCnQzwTts72X6YCEA51fbdtk8jRry1Mba/A+wB/BPYzfb3gEeBz9j+Zp0MMYrb2JHc++PAGbYvypdY3l4zsw0Rb236dbV9JLB7XsY6mQOAd+fLO1sCtzkM5z9IteNBEsb2WYBsv1jYfR2wou1z6mRocD3NdRRwvaRDJB2el+doSbsSZhezzEh6Yz7bd66k0yUdkA+oKjUMA+wC/I4ZnUZs/xLYgLD9rZNpZDtnezmi3q9K2Kv+TNJ7K86dlCnTWLdR262wfXnbZ5fVzExsLcMQsV9aXo7PS5XPl1QMtu8C7irs+rztqlmulEyxsViLML5tqSq9SCoG2xcRI8yWNsg7oJVKxLxou7WcvBYx89VS1YAtFfOM7Y5Rr+1fSXLNzLy278631yYi3mP7CZWkrEnM4LAX6rDFkVTpJZiIaXo9PULSpUQw4ReBTWzfLmkF4IezyiiWrE/LX+cRS4MrA9dJ2tR2ezsxFJNrSds/Kfm+10k6omamse2cw3Ho95LmITxEd5P0Q+AU29+qk2nXmO6o2T6xfZ+kRWw/WjcDTMt/uAWAdxKjfRRBG6vy2iVhyh7IwA6S5oeBXMtHnAEeUdgfzE/kv7soP9daQFXU8CSMpM+U7D5Q0qS8PD+riwGeyUfmCwIrEiNjJK1MdXqaVMwNxIgTSUfaLs4gndL6rCZmgqQJhC3gu4nRMfk9+vKK8qRiprOF7UnAhwiP4DqZxtbT/PO5idWOf+Xlequkt1bUnWGYA4AP2r6h7Rw/JZbbywLbDsNALPm17uszHemuWjqU8vs6FdPYdq4l288TjhEPEg4TuwFdO1CpmJbGdEdNYZj7FcKN/HQiBpAk3Q9s5jwBbh0MEQ7gmnz7fNv3Ktydv5V/VqZUzErE6PQY8vQp9HART8jsSow4Fwd2sv20pH2JeGxVrvWpmB0Ir7BfF8qwIDHCmwaUPdBTMfsAVwOvAKbYflTSjkR+vK0qypOKKf7e7+ryWR3MWcDZxMP+5nwGZWUiHMHpNTMA2D6g+F7S14lnUJ1Mk+sphC3nMsCdzDCEr6o7wzALtXe4AGz/v9ZAdIQYmPnefUOXz+pgmtzOIenNwKeBTYnUYycAO1Ydn5Ipakx31ICfAPcDbyYq9PeJH+39+fbqdTG2fyDpOiL45gX57qWAHzlstzqUkPm4wltmS2B723+StGH7g7om5jbgTW27TyUM1Z/sJNIxxIh3f6IDup0jDMRNtreuOD4ZY/syRaL0+Ww/lu++EXhPYcmtFoaZPcbaH/j9eFqPGmN7iiKUxxLMaJDXIeKxVS0TJmG6aAGqbWOTMA2vpxBLiivaHiTx+CDMC70PGREGGhzdoKntnKS9iI7T/MCJRKiov5adOzVTprHeUXuj7U0UNh732z483/9LSV+tmQG4A7jJdivp70mSXibpK7a/XSdj+3hJlwPHSTqzSxmSMpJeDnwGeMT2Gfk5/k/SeoQXaYc7dCrGYWz9NUWMoXMkfYseuQFTMfmy2vuARwjvUGxfK+l/JF1ouyO8QCqmTYM0nEkYRyiP4vvvAkj6JDNiatXC5J/fx4zyTCS8Ew+pLFACpsn1NNedRKM+iAfeIMyCkt5D+UzTAiX7hmVmBzWxnfsf4Iu2Lx2gHKmYDo31jlrrB3tJ0j/aPquaqk3CSNqeGPU9pQi6eYOkTQhbhCeZ2Qg7KdOS7XskvZ9wz39Z1XGJmROJ5YeFFDHYziCmkd9NZDiok2mV5ypFuIWjgFd3OzYh80PCI/Plkj5PGKsfCnyWKGudzKIKu7sJhW3y94vUyUj6GLE0/wjwsbwT8A7gCGBZSjpQqZiC1ipsTwMes11lD5iKaXo9nQ+wpD8C05Peu3vqtUGYv1E9E/q3LvsHZQCW1Ax73+L2BKJjWRvT1HbO9hY5N4EwJVmH6A9dChzpQnq51EyZxnpHbZKkpYnR3zz5dqvjVOURlIrZE1gNeD2wl6THCI+Q/aleU0/CSFrKduvBsDwRuuAISUvbvr9OJi/LCkTDeh7wZeBCYHnP8PqphZE0obAsMjfRaJwuaX7bT9fJEIbfbyY6dCcAexM2las6IvPXyVxCzMK1b0M80OpkDga2JzoD+0q6i7DDO5LqQU4SRuVOJa3PBnFEGXGGBtfTXH0ZcQ/L2F5r0JMPw+T6ETPam+I2FNIb1sQ0tp3LdQjR9hyfl2fr/By7NoCZrrHeUVuACGbZ0hVVB9bAPG37FuAWReqfi4Gsx6g2FfNrYNV8RHI4cCbRCd1T0p6tJYaamMcc4R8ezjvEOzli+XRTKuaGvDxrEgbO1+TlOUrSFo4AkXUxj9t+ihhxrgh80/b3e5QnCePu9ni1MsDzts8GkPQQ8XD9H0ecuLqZ93X5rMrIPRXT5Hra+t6Dqm+mW+cWBvLm7srk+ztseiXN7RlhcmpjaHY7BxGce3JrZkvSecBtDWGma0x31Gwv21SGmeP//BvYorW23gCmpb0JI/C/AChsoS4lZnDqYooPy38M8VAeTaal7wDr2b4Zwv0XmAq8pUamWJ6H++hwpWSQdABwue1L8vcnAn+2vX/NTLERegb4SN4R7aYkTLeOp8J2qzaG5tfTYqdjEuEocCXdB9mDMKk6xEial5jR+pUjODGEffS/CWej5+piaH47NzfxWz5XeP9i9eFJmZngMSuVx+maLvcf22vEGWZ+wDzV502VimnpP8xsOPswvUeVo820lpYnAnO1LTPjco+aVExLkyiMlmxbXYKWJmKK1/P5HudNykg6kOhcFpdPvgEcLulrFXUuCdNWntZsYS+lYgCQtAFRjgWIe3QuIvZapd1iAqbR9dT2TJ0ihafyd8uOHZLZ3d3jaI4UA2ED+jR5zMJcnybssw4Gvlgj0/R27hTgUkktG9BPUp2GLzUzXWO6o8bMa+jbU72OXgezgqRLSraBSgPVVMxCku4gHn4HA7tKeiux3v/7ivKkYhYgsj+0rnlxNDuNzvg+KZnlJF2Ql+crRJ7GNxDBS/9UUZ5UzCqSWiO4CcVtYJrtsqCQqZgNifQv00fktu9WeDteQ7mBdSpmGUVquPbtFr9NjUxL3wW2JX7/bxLl7BZ3KwXT5HraIdv3SXpjP8f2ydwl6WIidNPv3F9Ij2EYgDWBVVwwTLf9pKSdifAudTJNbuew/S1JNxJG/hMJU43zKsqSlClqTHfUimvq6hGfKzUDrN/HMbUwtpeTtAhhnDlvvnsJwkj36JqZZau+tyLwcG0MsCixHLIaM0Z4axBLWVXLR0kY25Wzc1WzcKkYIu1Ux7KJ7ackVY2MUzHd0sNVNaSpmJYes32ppHcRQVP3ygdAtTENr6co8jC3rusEIovGH6uOH4J5HbAR0ak9RtJJwAm27+vyL4ZhIO7rMo/DFyRVzWqnYhrbzklaGJjL9m+A3yiyWdzeBKZdY7qj1qZGxWdyRd42hYH4DnQ+rJMxOfco0WFqvT9PkUz2ZOBTdTIlZXl7XpZN6TPe0GgwDqPaG/NXa9/Jkv5AePDtUxdTUZ7XEKlMPkefwU5HiXla0nK272njlgeq3NeTMC5JD5czywLb1ckU9KykjIjztVY+m1CZ5zIxM11Nqae5LitsTyOyP/yu/NDBGdvPEM+wkyUtSSwRniXpEeAntjuWvYZhcj0i6W2OhOTTJeltwLN1Mk1t5yRNBs4nBra/yXevC5wiaT3bt5acKwlTpjmpo9ZYSXoleXR+YEm6uxOPM3H8AsSDbEciHMTJRI632pmcmwhskJdnHfIk2w1hPkiU58PEEvNONTPfBn4r6RvATYTN4tuIJfCqgNGpmGJ5mvqb7kvYjm1BOOZsTyyf1c40tZ6WdYwl7U046IwYk3MPAYdKOpW4z06gh33SgMy+RODr44jB23+I2fYdgc1rZqarYW3JocAnbV/W2mH7q5KuICIQvL9GpkMTpk0bZtJo9pBmdnf+OrBf8XP3dpEeNSbnVidu8o2JNX4Bb7BdmfpkTmfyEUprtHwd4eW4n+1luvyPJEzOLUXMgGxDjLoXJGKIVS5fpGAUwUA/S9gYvUDMBmxpe+ku/yMJk3MfImYCJxOzW9cBh9i+sAFMI3/TLudZ2Pa/62SaXk8rzvWE7VeMJJN3Gj5OdCIXJzw3T7T94AgzKwN7MOO+vh74ru3K5dyETOPaEkk32i5LIo+km22vUhdTprE+o1b00mkPcNmPi/SoMZJuBp4iYod91fYDku7rcSPO8QwRQ2wq8BbnHl2Sei31JWEknU14FZ4NfAK4Cri3R+OchCFy0Z4FbGz7pvw8vZaWUzG07Dfa90v6gO3SxN8pmCb/popo51MoCTdCzBLWxtDgetpFE3of0h+jyNu6OWE7ejbRebyy24mGYVrKl9BmisMmaX5J29k+ti6mwW3JJEkT3WZzl89mdwtsn4Lp0JjuqLnZgTTvAVYhEmvfoQhy2Wt6c5yBjwFbATdLupBIwNwrjEUqZingASIN0L9sT5PUqzypmD2I8pwp6TSiPL2UiplJkl5F2HRsT6QUe22NTJN/0wMYPNxIKqbJ9bRKI2mT/HkiCv0nXZ0tZCSYDkl6C3FPbw4YKO10JWKa2pZcTgwy2gca+5LnKK6R6dBYX/o8vsvH02x/ti4m5xYlpre3JhqJeYB13GasOc5UcpsTD+mViEbkh7YrvWlSMJJWysvyaeBBwnj+zbb/3uV/JGEK3DY590piGfB4d4nflJBZi1jG2pB4yO4A/NxdYiKlYJr6m0q6lbZwI/n+BYBrbJclMU/CFI5pVD3VzJ6bRU0APmW7I9fwMEyBfRczvLOvt/2HqmNnkZmXmIndgfAIfxFY3xVG9omZxrUlkhYkjPyXJpZJ/wO8lUh399Gy51QqpkxjvaO2Zcnu5Yl8cNfafm9dTMk5ViEatk8SkdJXG2d6Mzk3Oec2s91XQvPRZiTNTRiEb0V4+Zxn++NNYArcR3NuLfdhmzNajKQvEbZZzxPLWFOB39p+fZfzJmFKyrIB0RC8n8F+nxFnJN1ke3LFOUo/S8VUHFt7Pa14Vk+Xyx0GhmHmJZxA3gRcTXQaVgXuADa03eElOQyTc98nbPT+H3FPnwPc2qMuJGFKztGYtkSxpP8+CvZ2fSxPJ2E6NG3atDnmlWXZF7IsezjLsi9mWTahSUyBnZRl2cZNZ7Ism9IEJsuyiVmWvTXLsldmWTapz/MnYQrs4lmW7Z5vb9cUJsuyRbMsm5Bl2asHKMuIM1mWPZtl2alZlq3Zqi9Zlt3b45xJmC7nenXrWtfFZFn2+yzLlivZv3yWZTfUyZQc26h6mmXZD7MsW23A36JvJsuyw7IsOzLLsrkL++bJsuzoLMsOHykmP+bBLMt+lWXZZ7IsWyjf16suJGG6nKtx7Vy/7VMdzJi2UWtJEbX9hPztGrb/r25GYUy4E3CZ7T9K+gLhKXcjYatQG1OlfGnozPztRwkD46SMIubVqcSa/0VEJPJXE+lsPgl0LBOkYrqU5x9EmhWIJYOe9h8jzeT2WEcDRxFlORP4APB3Iljkw+3nS8UQ9lmfBr4HLC5pKmEz1k2pGCStD9xh+15JGxJerTcSdlp1Mo0NazIb1NNrge8ovJR/BpzUy2xgQOYDRBLu6TlcbT+fP39vY+YAx7PCQCyrfZiYgT1Kkd1gfknz2K4KRJuEmc3aub7apzqYYY0uZxvlP9jVRIOxVp8drhTMt4mlqqdym4SvA18iIhYfWTPTj0bMQ2pA5ggiNs35hE3KAsAKxBLRwRXnSMX0o7qu25GE8er1hOv/ZCLG0OZEWcuUhLH9qO0jHW7sH86/+yRJt0sqjb2WipG0B9EJmFcRmuAUwiNvUeCQOhlHCpqdiYbzSqIjsRWwi+2pdTI0vJ7aPtH2Osy4D66SdG7eQR4JZmKxw1U4xwtE2JoyDcNg+0Xbv7a9EZEy63Iid/KDkkqvQSqG2audq+vZ3FNjekZN0uXA24mK/BiwhaTpn7s8JloShqjsk23/V9KuwBm2LwIuknRnRZFSMf2orkwPS9k+FUDSukR5/kvkyatKGZOKGaY8qZg32f4EgKT1gKm2nwCuVmQOKFMqZrps30LkfP0yM2zbflgjswXwTtvPSPoOcI7t43K7k6qUS6mYynAjLUmaYntKDUzj66kiqfrmxMzb/wG/BDaVtJHtz8wi86Skt+T3WZFfBaiKVzcMM5Ns/4uYMf6eIm/yVjUzs1M797U+j0vOjOmOGnBv/notna73VTHRUjEvFkZPaxEjgpa65URMwTRZE2AmA80fFN5XJYhOxTRZxY7b2kQ6p5bmq5kp0w9sb8eMJfC6mGmO1D4Q98EPARzhM6rOmYrpR3Ut5zS6nkr6PZFX+GfAhzwjBtvPgL+NALM/kf5pfyIQ79xEpoR9aYtDNotMVfnOtb0+EWeuTqax7ZzCy/qftv+uSDv2AUmvsl0ZxSEV064x3VFzH/HN2keCqRjgGUmvI6KPr0ieMy5f9nii4jSpmCbrVkl7EUncnwP+IGkeIn7XNTUzTdZfJG1GdJbmI89bKGlzqhMEp2LK9LYBjh1N5r+KSPELEMu4vwWQtAzQsUyVmOlHdS3nNL2efs158N6i8sZ+8VllbF8oaVuik3U0MYC5hgjnUWo3NwzTRT1nrxMxjWznJG1BLI9uImk+4GLg+8AGkpay/fW6mDKN6Y5an6prxLkPYdP2CmCK7Ucl7UiMqraqOEcqph9VLseMMrMzMVpagnBZf0nS94jK+YmKc6Ri+tFjNTE7E7GlFice/M9LOpwIA/HhinOkYsrUFHuR7xDxj+YGjrP9kCKC/LeIQLB1Mv2orqX2ptfTP0s6CFiMwj1ge5uRYmxfTDTMfWsYpkJNqT9Nbee+RMQG/Gc+g3mp7X3zTv5NROeqLqZTw7jWjqVXlmU31cXkrtevLLx/R5ZlKxTer1oHk2XZQlmWfS/LsjOzLNu87bNjK8qXhOnzWk+pg8mybMf878uyLPt6lmU3ZFl2TZZlexVd7utgKs6zcJZlEwvv+wnpkYp56xC/x6gwWZa9JsuylQvvP5xl2VqF90vUxfTx3W9sMDOlLibLspuyLDsky7KtsizbsvXqcZ6+mSzC0RyQZdnahX0nZll2QJfzD8x0Odcw98qoME1s57Isu7mwfVmWZTsU3t9eUY4kTNlrfEatvhEnDpfm5wvvr2075Dgi4GFq5gTCHfxKYG9Ja+Z2PFC9VJSK6Ud1zZJuSyxZHEpE4/8sMeLcGfgRM9trpWY65M6E2j3DgIwGo3Ct/wKRKWAJ4l69R9JpLUPxdqVi8u//IJEpoPX+/LZDzqezziVhZnPVGQ5hgu0vD3ieQZhUqbrIl9S+TdzXi1O4r4FDbL9YFwONbeem5bNaCxB2gNvkZVyUCO1SplRMh2ZHY/I5SXVNX7/e9v62zwTWBJaTdFjFsamZflT3tP+awJa2b3YkJt8WeHePc6Viuqmu63YYEVLhICLu1XFEeIodJe1XcY5UTD+q+37rprpMFPpRndftKkn/m3fe+9UgzIbApnnnGwDbdxPeopuOIAPRsXsA+BCxhL4b8SxYmbCHqpPpR3XcB8cR9n8XAec7YheuDZyXf1amVEyHxjtqzVZts32SlgBwpC3ZCHi/pH26nT8V04fqum6LSHoH8GdgucL+11EdBykV04/qum5r297Z9gXEg39T22cQcZE+XXGOVMww5UnCSLpV0ju7AbY3r4PpU8mvm6SXJL1IzOqeCbwg6cXC/g4NwxBeiM+177T9FNV1dBgGYCXbh9i+M3dY29aR3/LTRDy5Opl+lPw+sP0D4vecwoxO8FLAj2wfWganYso03lFr9oizLk0BbpD0MQDbjxOjqI2I0VOdTJN1PBGpfTXguwCStiaCv1bFzknFNFlzK6K9QwTHbYXxmIdqj8dUTJO1CHCMpCMlLdYwppGyPdH2XPnf1vZctidSET5mGAZ4WtJy7TsVmRReGkEGYIIUMVwUoSBaHZLW8n6dTJN1B3CBI6Awtk8CfiHpKw1gZtIc31EbZiSYiqlLts8G3kjYjrX2PUQE9d2kTqbJsn2g7Y/afg2wWb77ImBF22fVyTRchxAd9tOAq4BD8sbpdmakxKqLabIeBt5BePj+UdLRkt6b28TUzTRakq5uez+RGOiMFNNKu7W1pFUkvVERouYCYul9pBiAvYErJF0LXAjsK2lFInn6XjUzjZSk7YFHgX8oAvYiaRPgLipm11MxZRrTzgSSugYJdHmWgSRMn6rNhsP2k5KWU0SX/zlhnzAZ2LHqJKmYPlTrLGk+Kl5d0s+B/YDJknawXRlIMhXTQ7WEDrH9U0lXEbOo+9q+O+8ETLb9aNkJUjF9qs56+iywn6RDiIj5XwXeKum5vCPfoVRMH0peTyVdQgRERVJxluq/wDll8DCM7fPyZdF9iDRXLxFBbHexfeFIMTl3gaSMsL+82/bjioC/r7NdukSYiulTddSfPYkVidcDe0l6jHA62Z9q27FUTIfGdEcNWAfYGJhK5w9blTEgCSNpNdvXlX1pSZvbPjk/Z3KmoBOAHxM3VkYYjx4BrBhAF6gAACAASURBVFFx/KgzqvDcA6Z77pXY2CRhepTnyCGuwYgyipyE7eWZavvqvDxr18XYvosYZbb0edtdZ7lSMYpE80sAt9t+qbB/VdulSZ8TMcVYXk8Q2Qx+WDhXmZIwkuYm4lf9mwje+yNgJeD3wF62nyypc6POtO49Sd+3/cWK8s6kYZicS5LeK+ceZ+bZvUNt797j+yVhFCmwlgauyPnW/vVtn0tJ+5OAedqRqusWST8mYtdl+f1dpVRMh8Z0R832lpIWBn7vPtM1pGKImaNVIabUbReNdXcDTrZ9b01MS/PaPknSccAptq+U9LIe5Rpt5jDCpuggYnn0FiKkweclreDySM+pmGHKk4RR2EOsTjQCHyU8kZ4Hjpd0uO0f18iU2dXtIGl+iKXeGpnNgMOJ5Yt5JG1s+4/5x8cBq9r+fR0MXQJW2/5nxUepmGOBlwOvJmZ7zwO+CXyc6EyVLfuMOlNY/bihbCXE3VdM+mb61IiEGpFU1uZ8NG+TSgPyJmS+CGxHpFj8saRPeUZ2hwOBc9vbn0RM0Qnk38AWzm3IuigV06E5wUZte8IYtmlMceZt3i6f1cG09KKkjYH1gXMVRv9VXk6pmNnBQ7CJ120zInL70cD/Au93eB2tTnTY62RWImaL5iHuyfZXncw+wCq2VyI6AhdKelP+Wa1MPlJHYTrwaUkTJB0r6TpJpXEIUzFENPZPEhkpXmv7q7bvsH0A8KYamfd1ea1V8T+GYfrRSC35PUo8A24BLs9fTxe2y5SK+RzxG21AOImdLOk9XcqSiiku1T7VZ+cpFdOhMT2jBtON0/t2g03ITKvYLnufmmlpOyIFxk6OlDafpHcw1dFm5pb0atsPM6C3XwJmmPKkYubNy/A0MQOxaL7/Kao9ypIwtj8uaRtgS2B723+StGHe2JYqFZNz/8z/TpU0DThf0rvoUn9SMbkaZ6IAvCRpMdv/UhjEAyDptVRPEIw64y55mSW9vGz/MEyfGqnA6XtIuoAIjvsV25dJ2tX2iVUnScXk3DP536skfQKYKmndsrIkZFZQ2B62b7fO1WGekZDp0JjvqMEMo2vajNUdMWBqZZos27dJ+jrwJklzEZXzvpqZlufeVcS13lvhuXcx1csIqZhhypOK+SmRsPpC4IPACYokxmcT92udDLaPl3Q5cJykM6uOq4H5k6SDgSNsP2D7dEXsvyvonKFOzbTUuKV2oo7cJGlZh90VeaN5MjFDXSeDpA2IDscCxIzLXMxYQh0xJpVsXyzpJuBHktanj6j3iZgrJZ0KHJjPdF4haWfCS72KTcGs3+N7lykV06E5YekTYiQ4kc6RYJ3MMpKOV6z7T9+WdAIRuLROBphuN3MOEXV6UeDq4qi1Dsb2TwnnjdOJZbVfAH8lPPdOqJMZpjypGNvfAfYA/gnsZvt7xFLGZ2x/s06mwN5DBM5cHOjVaUjFbAM8B6jAHwnsTpSxTqalxi21O8LEyDOnFbqOCCFT5SmZhMn1XWBX4E7ClOFUwhmsm4Zhksn2o7Y3Jb7fQw1hdgF+R3RuW/wviaXqP9TF2L687EXMvm1f9g9SMWWaI2bUaOaIs2iv076+f1nNTEt7EcsaV9h+WNJkYoRycp2MG+whmKup1+2i/POWNsg7oJVKwUhayvbf8rfLEx2TIyQtbfv+OhngGdsd6aVs/0qSa2ZaauJSO7afUYkDh6RSx42UDPCY7UsVS8sL2d5LUq/QH8Mw3TQi4UlU8OyXtA7wZmKW8R3uzHeZlAGWtP2T9p22r5NUNYmRigFA0ivJzSEIM5eeYTNSMS3NKR214khwvyFGjyPOuGRdX9Ii7hLPKRVT0IuOOGetcz2kmeMIJWfKHsg0xEOwoCZet7JYfwdKmpSz/cYHHHEG+DWwqiIQ5OFEmp6JwJ6S9nQ4cdTF3MAMr+kjbRdDZJxCeZL0VAzQ2KX2loqG3JOIzCNVjXpK5llFTLA7gbUUtkO9Avj2zUj6uu398sb5KGA9Ig3UWcCeLg81MjCT6xjivt6ZSFf0E+J6HCPpONtH1cj8mhn39Zm2iyEyDqX8vk7CSFqdiNe5MXAz8CoiJtyTJedPyrRrTumoNW7EKWkh4CvAP4jltd/Gbt0PbFY2QknFFHS7pF2ASYoYNTsRN1o3jTazEuFpdQwz8t9189pLybTUxOu2AxGo8teFMixIeK5VxQdMxbS0N/Ae238BkPQt4FKgrAOViin+3u/q8lkdDDB9CXxfwl5qDWIJfA9HjMRaGbc5auQdvd92K08iZl/C3mwL4n7Ynuh4dNMgzEcI790jCZOJ5YhBwS5EHfjfEWKK2hZYy/YjAIqVneuITl9dTPHefUOXz5Iykm4mHJzOBL5q+wFJ9/XocCVhyjRH2KjZvg34OvBcYSR4a83MTwj7mDcTa+jHAQsTS5Xfr5lpaWcigeyzRH7JJ4jOQG2M7Y8TS37vIYLPHgD83fYB7Q/r1Mww5UnIvAc4mvjt93R4sv3V9tYuiX+UmGnpP8xs8/IwvT3jRpsp7m9/4NfNtNRaAn/S4aU8mRicNYFp1wL0sI1NwThshTa1/Zzt1YA32N6j2wmHYYC32N7H9mMO264DCfvlkWQmKQJ0P0LYObb0PNXe2amYpkY3uIdYflwJeHPeXveqZ6mYDs0RM2oNHXG+0fYm+Y1/v+3D8/2/lPTVin+RigHA9tPEg7jvh3EKxs31EGxxjbtuDmPrr0laAzgnn0Xq+sBIxQALKWx9JgIHA7sq8uLtT0SYr5MpakTCKYwC07il9pYk3ceM8kwkOvCH1M1IWoYYtC5LDC5+Lmkb238eIWaJvD14QNLqtq/Jz7EaMVAo0zAMwL+IGTiIWa2tJK1NlL9qZjkV00jZ3ljSooRTyHeA1xKBpt/miigNqZgyzREdNZpp3P0CgO2XJP2j7bOq6d0kjKQbba+aP4TbR/rTbHe4O6diWrJ9j6T3E+75fXv7jSYzm1y3qyR9kHjQ9hVWYLQZ28tJWoTIidcKRbEEkfj56DoZYFGF3d2Ewjb5+6oA16mYlpq41N7SWoXtaYRBfq/0OSmYY4gOxkGEWcgviOXFNUeI2ZtYwl4Y+DKwsaRd8+1NK84/DIPt90HYs+QsxIzX12yfVycDLKkZ9r7F7QlE3auNcSzdHkE4FK1CeF5fIOnP+YxpbUy75pSOWhNHnJMkLU2M/ubJt1sdpyqj1iSM7Zbh5WTnUcl7KRWjBnsINvy6TfCMxMlzEyPg0yXNn8/M1cbkZXqU6DC13p8n6b3EIOdTNTKXEPZ17dsQdm1lSsW0tDMxk99aAr+ECOtRG6Nyp5LWZ4M4oow4k2sx27+VdFB+v/5YYSTfTX0z+f9t/9/HE3HyqgI/D8y08UXv4OuBT0i6ynZlEOMEzI+Y0d4UtyE6vmVKxUyX7ZuBL0janQiv1VOpGJhzOmpNHHEuQASzbOmKiuPqYFo6FVhxgONTME32EBymPKmYG4jyrAmcRuTgnAgcJWkL22X3RSpmutQwV3l3iUpfpVRMgW3cUjszdzTbVeVUkoqB8OB8bX4Mkt7NzHZXI8VMVx+zgrPMSHojcU9/hohf2MsOeVQZl9jzSprbdmV2lxSMwhRoJ+Ay23+U9AXCSeJGIs1cbUyZ5pSOWuNGnLaX7e+rp2cKuiOfQr6WKFPrnN0a21RMEz0EZ6U8qZjvAOvlI7vWMsZU4C11Mmqwq7ykA4DLnSd5lnQi8Gfb+9fJqMFL7d06nhrBVE3DMLl2A84FllN45i1CJHLvpr6ZbjN9+fceaHawism5ScAmhMf1W/Lv+DyQFWa362LmJWa0fuUITgxhH/1vYDvbHR3dRMy3gTcSgZvfRTgBbkyE8TiSGMS1KxXToTmio9bEEafK43QVz9VvbK8RZwpahBnJh1uaBnTLT5aKaaKHYEtNvm6TgNtab2w7H/V106gyarCrvKQDiUapuHzyDeBwSV+rqHNJmFTL5sMwLSlRqqZBGUcg1NUIb8q5gD/Zfr5bWQZk1iEa5KmUe/GWdbqGYQD+Rnj0fw+4wPZ/JN1b1XlKzBxK5P39XWHfp4HDCIeeL9bEfJi4n/+rsAM8w3mgbkl3VpQlFdOhMd1Ra/KIk5kr4vb0sY6ekAFmGI8OogRM4z0EG3rdllMkVJ5IDCS+IekNxIzvn2pm7gFWIVzY75D0EP25vadgNgRWK47Ibd+tiJF4DVA20EnFtNTEpfaWvkss9ewOfJMo5/x1Md0GrqrIZDAMY3tLSQsDv7d9fI/vPjST6yRiZm8h4NWS+vHATMWsCazigo2dw457Z6rNglIwLxaWRdciZr5aqhqEpmI6NKY7ak0ecbqwpi5pQ/eOzZWMKRzf4Y4ODOzCPpKMm+0hOHB5EjKLAivn5Wl1TtYAngGqlo+SMG62q/yLZUsttp+S9EIZkJBpqclL7alSNfXLpBzsbk/ca4NoYMb27pL2JALmbk3Y1KKwrz3LM+dBTcoQ93WHI4TtFyRVzUamYJ6R9DoiGPeK5DNxklYm4lGWKRXToTHdUSuoySNOaG58ptF2YR+KcXM9BIcqTwomH9XdmL9a+06W9Adgn/xVC5Mf01RX+aclLedI5D5dkpanOshnKqalJi+1j2qqpkGZlINd2w8RS3J9axgm514EziFiF76KyJ6wH2Hkv1SNzCNlAyFJb6PQ2a+B2Qe4GngFMMX2o5J2JFZMtqr4H6mYDs0pHbUmjzibrFF1YZ9FpnEeggU1/bpNBDbIy7MO8eBtBAPlLuySptieUgPzbeC3kr4B3ETYLL6NeNBWBYxOxbTK0cSl9pZGO1XTrDCjPtiVtBywOjHbfQyRzWHHLjO4QzFF2f4nMdt1uMJcA0nH2t6uBmZfolN3HDF4+w8x274jUJa3NAlj+zJJrwfms/1YvvtGwnHs7rwsq9q+MTVTpjmlo9a4Eadm9vBZtO19P15Bo8YUlMqFfSBGDfYQHKY8qRhJSxH5aLfJmQWJzBX31c2UyfYLhOE/REdqSmomn0V9kRgZH0HMbl0H7GL7QkqUimlJzVxqb5XrcuDy/O1qkha2/e9u5UnFJNIJwI+J+yojPEePIMwBRpIple0b8s231cHYvkbSh4A9iFylLxGx1z5g+48V50rFPE94rbbet+e9Po62ZO6pmHbNER21ho44i8e2B7jsJ2bQaDItfYnBXdhHlVGDPQSHKU8qRtLZhFfh2cAngKuAe3t0uJIwfardCy4ZY/s3wG/a90v6gO3SxN+pmFyNW2qXNIHo8HaEGyFmCTuUgqlhsDuv7ZPymZ5TbF8pqVdWlGGYxsqR73qmayZpfknb2T62TqaHanvmtGuO6Kg1ccTphgfSlLQ+cAcxfbw30cE7j4LtUU1Mkz0Em3zdlgIeIBIq/8v2NEm9ypOK6UeNsONU2OVsTSytvYxwSOiqBEwTl9oPYMBwI4mY1IPdFyVtDKwP7CfpY0CZ0f2sMrOFJL2FuKc3Bwz07EClYkrUiGcOzCEdNZo54uzmfj3N9mdrZPYANiPsslYkOgJfJDouhwC71sW4wR6CDb9ub5O0EtH4XyHpQSIEyRK2/15xDZIws4MkrUUE+tyQeLDuQAzEamdo5lJ7I8Oa9DNwVZud4jBMQdsRM987OVIKfhL4XI/TDcM0VopgtJ8g7uWViU7n+vlyda3M7KK+43jM5lqstXRge5rtHxNeGHUyl5e8/kZ4Ey5fM7MF8F7bd+THnWP7OCLlxQdrZrD9iO0jbE8G1gVOJDz3rquZafp1u832bsSs14FEPLh7JZ1eN9NUSfqSIjDl94kAvisBf7d9osO2rTamoNYS+Ar5EvjPgS/UzFSGGwEGDlEywkwv9Z2DsRdj+zYiGv1zkuYCvpIv0VVqGKYP1bKMJ+n7wH2E3diRwOLETHu3DlcSZnbSnNJRa9yIM38AT38RQQS3B/YiAuPVxhAzbc/k2+8jt5tx9wjUqZiZZPtm218AXkPMfCFpSk3MbHHdbP/X9lm2Pwa8nph5QFI3D68kTBf1iqM1Wsy3gFuITvC3HJ5ava5zKqa1BP4osQR+cL59Er2XzUebeVrhvdh+np4hShIwvTRinRpJmxHezt8nYgxeLanKc3Fopg/9rvcho8J8nIiCcCZwrsPGt9d9nYrppXEbtcRqnHF3S4qo7Sfkb9ew/X89/kcK5r+KkBQLEK7hv83PsQxQlRg3FVMqN8BDkNnzuv2DSLMCsWTQ05ZjpBlJCxHLtz8gHDi+S3QIbgB2t/0v2zM1VKkYYlbw00TanMUlTSVsxropCdPkpXZmg7AmXTSSdkZ7Ed6aV9h+WNJk4CIiFmOVBmLyev8dIkTF84S5Teu+3sb2Pbb3rIMBlibSKG0NHCXpYmB+SfO4Ov1WEkYRR3Fp4jo/Xti/vu1zCW//Wph2jfkZtQaPOJH0BSIY3pnAWn12uFIw3yHCUVwDHOewk9gUuDgvW51MP6prJDR+3YZjTiUauseJZYv7iQHOHUQdKlMSxvajto90ZB/5cP7dJ0m6XdJOdTI0eKnd9nnAzkTDeSUx27EVEW5kap1MYr3ogqe4I6Btr5m+QZmTgUuJ+/mo/P2rgB8S5hq1MbZftP1r2xsBbyBMbx4CHpRU+qxKwUj6InAKYQ9oScUQWgfm57y3DqZMY3pGrckjTkmXA28nolA/BmwhafrnLncTT8LYPkPSVYTNXcs24ingc7Yvaz8+JdOnavHWGb9uQzNL2l4PQNKbbG+R77+zy5JPKma6HOnhdpX0ZWI2dSuikaqLaV8C/2HOTyvW8ZqYynAjLanEAD8Vk1C3S9qF6HSvAuxEdb7KYZn5nYefkLSk7Vag39Mk7VszM122/0XMGH9PESB3qxqZzxFOKM9IWgM4Q9Jmtq+kevCZiunQmO6oESPBd+YX6TvkI0FF7J0q+5VUzL3567V0ut5XuXynYrD9IPBg4f35ZcfVwTRZ49dtKP1b0rq2fwfcJGmy7ZsU3qPP1MyU6QeO6Otn9jxydJnZbqm9TX2ZG9TAjKQ95M7EUuGzwPFEeI/de5xrUOZBSds6nNeulLSe7QskfRD4V81MhySda3t9Ysm0NqY1+LB9laRPAFMlrUuXwWcqpl1jvaPW2BGnE7mJD8OMa1yJtQNwtqSniLA2V0q6m1hW+d+amTL1HbF9lJnWEvjczLwE/i0ivlidTD9KvtQu6eu298s7nkcB6xHeoWcBe9p+0p22jQMzLdl+GvhK/upLQzCfA05S2OndT8zGPkHEM9yoZqZMrxng2NFirpR0KnCg7TtsX6GICXgRMFfNTIfGekdtfMQ5ekyTVZeH4Oyux3ofMvKMbQNvlLQqETLmFODvwB9cEnohJVOhRniDDbMEnorpU3UstX+ESCJ+JPBXYDnCVnsXYnWhrMM+MCPpRturSnqp7f9PIAb2HY30MAyAIz7huoq4j8sR7frf3cX2KRVToSbUn10Iu8YFWjts/1LS/VQ7oaRiOjTWO2rjI87RY2qRwnPvAMKL5izbJxc+O9b2diUj4iRMkyVpR9tHK1LR7EsYrLdmBQ5zhNJYuw6moMeBSUSg6B8BB0nawTPyCNbJFDVM8NFRYcaX2ofWWzzDRhHgQEm3jxTjcAoBmOywOeypYZg2/hEiI0jjmDat1wBmSc+wsZsu29dJOqJmpkNjuqM2PuIcVaYunUAEBL0S2FvSmg47HqheKkrFNFnbAkcTTiWvBD5LdNB3JjorZZ2CVExLJxDJqDcAViCSUR9JfwmsR4WRNJEI6rohsAQRluAeSafZPrXs5KmYcQ2lJRRxyh6QtLrtVmy/1YjQHiPFtHQq4WA2iAZi1JZ7tF3unb90NJn5iBAqGxJBaKff18AhtjtSYyVifk2eCF3SmbaLITIOpTxJeiqmQ2O6owbjI84xqNc7XLCRdD5wrqTDbO9O9cxgKmZ20JrEiP0lAEnbAnc2hCkmo/65B09gPRrMYcA8RFq4TYigtA8Cn5e0gu2v18jM7qrDRGFv4F3AwsCXgY0l7Zpvb1rBD8NM/9+SvkaEDXm2tdP2FSPIrEPE4ppK57OpymEsFXMMcCvwISIywgPE6tNuREDfXWpiit//DV0+q4Pp0JjvqI1r7El53kjbz0raiMgpuQ9dZgZTMQ3WIpLeAfyZsC+5O9//OqpT7aRiWkqVwHoQZm3bbwGQdCERtPLdks4hGoayDlQqprGSdCuwve2rq44pMTcYdSaf9WnvUBwPHNEaVJTwAzMFLUI4mL2vsG8aULX8PzBje0tJCwO/t90tt3NyBlipsFw8RdK1tt8h6dNUD9xSMNMqtsvep2Y6NOYD3s7mGjeK79QU4Ia8ccUR6flDhNfRyjUzTdbxhPHqakREfiRtDVwPfK1mpqXtCMPtnRxBPvtNYD2azNySXp1vLwnMl2/PQ7WjUCqmyVoEOEbSkZIWaxgzk2w/0UeHayjG9vtKXt06aUMxRFrARQYpQyJmghShDxRhcFqdk9byfp3MbKMJ06bNjpMD45qTJWlBYJLtRwv7JgIftf2rOpnZQZLmt/20pKWBZx0BIpvCvB54ExHA9HW276uTkbQVMZt1FbA6sQx2HZEFYortE+pimixJNxLLhfsQdotnEbZXV7s6pc+oM6lsswrsMsBxwLLAe4hE9tvY/vNIMk2VpPWAnxIz7EsDnwH+RkRG2M72BXUwkh4ibGkBdixsTyBmaDtCe6RiyjTeUatJTTYAnR2kSMS8OvEQO4YIi7Kj7evrZpqsivJ09XhMyGxGeIq+nDDsvxXYwwWP2zoYSRkxi3qL7bslzQMsUOzA18U0VcpDTeTbrwA2Jwy93wo8V9GojToj6US62FnZ3qbkfwzMFNjfAIcTtoerEjO3W9hecySZnGvkM1HhQb8CcLftxxWB4LHdzexkVBlJ+1edJ2c6ojWkYso0bqNWn5psADo7qOW591EgI4xGj6A/b7/RZpqssvL06yU52syoJ7AehrF9F3BXYdfnbR9WdmxqpsGa/qyx/QQRBPyHAJJeVReT0DarpcVs/1bSQXmH4ceKgKcjzUBDn4m52UixE3eowymrUqPNVHSq5rZdaWaQiinTeEetJqV6YMziQ6bJKnruneLBvf1Gk2mymnzdXrT9ZG5qgiMeYV8JrEeLUXjftWsHSfP///buPkiyqj7j+HdFIolGUTCCkKC1yX3EFAEkpEyC4oKKEkKMCL4goiiioIkaKmKCqHkxIKZ8KWMFWLF0FVHxJesSFBUVEgF5xxLzlIWaaFg0QiqAS9zsMvnj3B56Z3qmZ8ade08zz6fqFj29+8zSt+b2nL7n/M6vzf5VX5nKvXSuP7D9Xz1nTgKOnSu3HTMA90rak3bNlKSDgHGbKy8lAxVe25JG/c45sv2dxBx3MJc9I2knyt3Az9r+TPv0pyX9N2WqdNb57iozSgZq/erqDWOpbzI1q7FCcBLUfN66aGC92Mw+wNMob7aDqtVVzF9a31WmWm43bF3MNFmHmY2UPawW83oWnWm9HtgArJZ0I2Uh/tHLkIE6r+07KevF/pb7O5IcCnxtnu/fReadwE+BLw49dyxlm5x3AH/aY2aWVH32yPZG24t+w+giMwFqrBCcBDWft1OAPbi/GfVdlEFUbxnbR1OmSp8CfLydyrjd9tvmWl/SVWZCfJDye2bmNFmvGUmrJR0raZWkcyVdI2nejawXm5F0BGUAcSDll/KdwDrg+u2ZGVLdtW371PbPXwD8u+0PAXfa/lD7uK/MU4HX2L5n6HvcTXlvePocr7urzCwpJuhZrQtAJ4EqqxCcFDlvi9deP2uBTwEvt71/LZmaSfqG7d9pp8mutn2epOtsH9BnRtLllHVWd1HuYL0ZOMv2nGuzFpORdCpl49XjKTNXV1LunuwHbLH9uu2RGfE9qry2JT2K0pnkP4Bn2h67xdFyZiTdMNe1NdefdZUZJXfU+lflJ87aqVTurafsOr0LcKWkeXtvdpWpWY3nTWWLBSTdJ2nr0HGfpJFTKl1lBmzfSvkE/BhgQWsUu8pUbniabMMSptaWK7OT7XWUNmIftX0F48/3YjLHAQfbvgV4EbDe9lrgtcBh2zEzrcZre8D2nbaPoWw+u3Hca+kgc8eou6Htc/eO+PtdZmbJQK1/y/2G8fNkajao3Lvb9o8pdwjfVEmmZtWdN2/bjHqHoeNBtnfoMyNpj6Evfx34L+C9KnvDjdRVZkJUNx3XWu7B4JTtTe3jNZQ7T/NuL7HEzLDqrm2VfqiDx4cCvwncoNK9ZKSOMqcD6yX9laTnSHqWpDdTBqCn95yZJQO1/tX6ibN2W9u5fmB6se+Cqv06yNSs5vO2lObjy535HICk51EWBD+e0jD7a+1zfWaqZ/ublI18fyZpB+BNtm+uILPcg8EtknZWqd7cn7Lx6mAz27m2ZlhKZliN1/Y5ACrbi7ybMiX5I0o3iVE9OzvJ2L6K0mnmcZSfm7Pax8+0fdmof6CrzCip+uzfKynrHU522SZgoW8YXWRqVmOF4CSo+bx10cB6qZnTgKfY/ncASW8HvgJcVEGmWpq9ufCVkha7IfF2z9j+pqS/Bp44NLCbd53VIjNnUn7eHwysbd9zjwHeDsxVHLKUzLCar+0TgafZvgNAZS3hNcD7+sq0A/ltNoSX9FBJr7R97qhv3lVmptxR61nFnzhrV12F4ISo+bwNmlGfRvnF9DZKz9UaMv/Ltmtefsz4pspdZWpW3XQcLP/aLNsXtf8/h9se/NzfA7yiXYKyXTIz1Hht76jSdu8Ott0LbjNz34XrKjNN0r6S3k+59k4c9/e7zECqPns34pNgFW1zIgIk3Up5438Q8Hnbr5N0APAWYKPtk/rKTAJJ19g+UEMVbpK+aXufPjMqhSUHU7pT7C9pd+BLtn9znn9j0ZmVTtJXKG2doJyrl0o6BDgbuNj2rI2eO8zsRNnO41WU1m1bgSNsz7lfW1eZmTL12b8q2+bUSm1vP5Wd5Ic/ZayiLMadtSi8q0zNJuG8BXL7vAAAEGdJREFUqaMG1ovJ2F6tUvJ/ILBT+/RuwBe4v8FyL5kJUet0XFddMJZdzde27TVtVsAj26d/Bpxh++JRr6eLjKT3AMcA36C0tlsP3DxmwNVJZpQM1PpXXducmnnbyr2basrUbELO2zmUT8BnURYCf4zSi3a+ZtTLnnFpiv6Foa8vlnQw5UPOi/rMTIBTKHfyB9NklwHz9mzsKPOAWa86Cde2bQ99eS3wAklf9zz71i1z5mjKGtVPAxva343jphe7ysySgVr/av3EWbsLKVVxNWZqVvN566qB9ZKaXkvambIZ6UnA7pS7clVkamX7p5S1Ygve0qajTFcDyC7VfG0j6QmUn+mXUDouvKfHzK8ChwMvA94n6cvAQyX9gu3Nc3zrrjKzZKDWv1o/cdau5grBmtV83rpqYL2ojKQnA68GjqJ8uHk0ZTf2u/vO1Krm6TjobgDZsequbUk7As+jrM/al9LDdDPQeI794brI2N5K2RLnc5J2BV5M2RLnNknn2/7zvjKjZKDWs4o/cdZuULm3Zui5KeCQCjI1q/m8ddXAesGZ9s/vobR0+kvbP5T0vTEDrk4yNat1Oq7rdZcdq/Ha/k/gXyl7m11i+38lfXeuAVfHGQBs/6TNvVulgOeltWQGMlDrSe2fOGs3WDxaY6ZmtZ43lWbUt1AW059G+SVwMeMbWC935lZKr8V9KHcSNjJ+u4yuMpOgqum4B/J61Uqv7XWUD0GPAH5F0kL2A+wqsw1JG2wfAVxXWybbc/RM0r6Lvfi7ytRMFVYIToIaz5s6amC9lEyb2wU4lrLOZE/gF4BDbV87z+vvJFM7SZ8CbmIRU2tdZCR92/aiBoNLyXSpxmu7/fs7UDo6vAx4Zvv08cBn2qnB3jIz8tcPDcoXpKtM7qj1r6pPnBOkygrBCVDjeTsO+F3bmySdSduMWtIqyt2vUbrK4LLL+XspvTf3A04ALpH0fdsH9pmZADVOx8EDc71qjdf2YJ3WekrPy0dTrsM3Uxb579FnZoZVC/g7/WSmpqZy9Hg0TfOppmnOaJrmsKZpnjo4asjUfDRNc2373xuGnruxhkzNR43nbfj5pmmubprm+KGvv91nZp7Xt2PTNEe1j99aUybHgs7rV0Ycl23vTMevqbpre8z3OaD977k1ZJqm2W0Jr6GTTO6o9a/WT5y1q7JCcALUeN62qGxJ8TAW2cC6g8xItv+PsvAf4EjGt6DqLFOTWqfjHqDrVWu8tudke7BG67e7zkj6JeDvgOcAj6FUiN4q6ePA2aOmSrvKjJKBWs8qXQA6CaqrEJwQNZ63rhpY/7xNr+dS75RJ/6qcjnuArlet8dqu1TmUNorPoqxb/SHlveENlKnS1/SYmSVN2XsmaS9JX5T0HUm7SbpM0uNqyNSqrdy7k1K594728TrGV/ste6ZmtZ43d9TAeimZBVpKRVZXmb7tavtSANtTts8DHl5BZjCwu4dtB3bzWUqmE7Ve2xXbx/bZtr9t+63AiW3RzrHA03vOzJKBWv+6esOo9k1mMdrKvbdQ+iHuTdli4QJKWfbZfWZqVvt5s32b7ZuHvv5n21+d7zV1lYmfS63TcV0NIJdd7dd2pVZJpZ+ipH24/0PQbpTpyT4zs2Sg1r9aP3HW6jjgYNu3UPofrre9FngtcFjPmZrlvEUfBtNkv9FOk10A/EkFmQfSetVJv7b7WDpwGnC5pKsp/XVPl7Q3pXn6G+fId5WZJQO1/tX6ibNWU7Y3tY/XAJ+HMvisIFOznLflM+fWHhVkelP5dFxXA8guTPq1/cWuM7YvARpKa8W9bX8J+DdKy7ZLRoW7yoySYoL+ZQHo4kxchWAlct6WQNIjKJ+K/4GybOBdlAHBdcCf2f6J7Rf3kamZtt1ceDBNNthc+Gxg3IbEy5nppAtGh6q9tts/O5PSZ3ozZbnN4Of6BNu3eka/y64ytv8HGN5M+p225+1/3VVmpgzUetTVG0blbzKLNekVgn3JeVuaC4EbgP8B3g98j3LX5kjKXZtn95ipWZWbGHc1GOxYzdf2Ryg/vz8APtl+fTjl5/pDwEF9ZCSdP+J7HCnpkQC2T5j5h11lRsnUZ0+yAHRpPPkVgr3IeVuy3W3/hUtz9CfaPsOlgussSpunPjM1q3U6rqa1WdtF5df2Q22fa3sz5Wf8A7bvtf1xyu+gvjJ3AkdQ2o59rT1+OvR4lK4ys3W5c3KObXYnvqlpml9qH5/ZNM3H2ser5tlZvZNMjhw5ytHuPv+M9vHapmn2bx/v0zTN1X1maj6aprm2aZqdm6bZs2mazU3T7N4+v1fTNN/sK9N3R4uVdjRNs6FpmhPbx3/fNM2z28eHNU3zlZ4zhzZNc2XTNE9rv75hvtfSZWbmkanP/sz8JPh+KJ8E22rePjMRUbwK+CdJg21trpD0HeDRwB/3nKlZrdNxK37dZcdeAayT9DeUacnXSbqLsvHrc/vM2P6ypBuAf2yXB+0w7sV0lZkpA7X+VLsANCIK2waeIOlJwK8DHwVuB/7V9siq6a4yNbN9kaSvU7YFGuxbN5gm+2qPmay77JDt24FnSNoFWE05h7fb/m7fmTZ3J3CMpJcD+yzwNXWSGbZqauqBXDVfL0nPA95J+YFab/vk4Yt/1Jx/V5mI2Jak1cCTKRtF/yPlQ8+rfH8fwd4ysTiSHsvQwE7S4cCmuQaDS81EvSQdaPua9vGhwB9QqkU/Y/vqPjOjZKDWo67eMPImE/HzkXQ5cB5wF6XK7wzgLNu/13cmolaSXjLfn9ue1R2ni4yk620/SdIplGUHH6BsiHs85a7p+0b8G51kRsnUZ49s3wbcNvT1P9eSiYht7GR7naS1wAW2r5D0kEoyEbU6FDgK+ASzuwlMMbqNYVcZgBOBp9m+A6C97q4B5htAdZWZloFaRMR4WyUdRSm1f7OkPwK2VpKJqJLt49s9w/7F9qg9xfrK7CjpQcAdbNuhZzNwX8+ZWbKPWkTEeK+krC852fZG4IWUSrMaMhE1O4nSCaemzE+A/wBEe1dL0iHA14GLes7MkjVqERELIOnxwBMpm6r+mu3v1ZKJiMVT2aPqkbavkvT7wM62L64hMywDtYiIMSQ9n9JH8Bcpu7PfDJxq+yN9ZyJqN1TNfAFwDqWa+dW2r+07M5R9CPAC4KSFFu90lcnUZ0TEeG+kDJzutv1jyi+AN1WSiajdBynjjSOBBngD8N4aMpKeIOldlIK70ymDvHl1lRnIQC0iYrytLn04AWjXj41bDNxVJqJ2O7V7dv4h8FHbVwALqoBejoykHSW9UNLXgKsoHUA2A81cW2Z0lRklA7WIiPG+Jek1lCqu/SSdS9mtvoZMRO2Gq5k3LKECentn/hM4Bng3sJvtFwP32p5vLVhXmVkyUIuIGO8UYA/gXuB8yoa0J1eSiahdbVXT64ADgNcCx7etp8bpKjNLigkiIiJiWdVWNS1pB8qg7mXAM9unj6e0dxp5566rzEwZqEVEzGGoBcx9lB3OB1YBU7Z36CsTMSlqr5qW9GjgOMoAalfbeyzgNXWSAWBqaipHjhw5csxzNE2zb62ZHDlqP5qmub5pml9umuaG9uvdm6b5Vg2ZEd/jgPa/59aSyRq1iIjxLqw4E1G7iamatn1d+/C3a8mk12dExHi3SDoDuJqy0B8A25dXkImo3TbVzJQCmUVVQC9jpnoZqEVEjPcoYE17DEwBh1SQiajdKZS1Y4Nq5suAP6skU70UE0REREQMGRT41JDJHbWIiDEk7QWsBR4HPIXS/uUE29/vOxNRqwmvml5VSyYDtYiI8c4BzgbOAn4EfAz4MPDUCjIRVRq6U7S/7ZtqyizAF2vJZKAWETHerrYvlXRW2/7lPEmnVJKJqN2FwN61ZNo712dS1rNtpnwYOhC4jnIH+1bbf95HZpQM1CIixrtX0p600yqSDgJ+Vkkmona1VU1/hNLe6QfAJ9uvDweOBD4EHDTie3eVmSUDtYiI8V4PbABWS7qRUp15dCWZiNrVVjX9UNvnAkja3fYH2uc/Lun0Ob53V5lZMlCLiJiHpCOAWyhTFqdRfglcDFzfdyZiEtheM/5vdZq5TdKJts8DrpD0bNuXSDoM+EnPmVmyPUdExBwknQo8n9Kb78HAlcCfAvsBW2y/rq9MxKSorWpa0m6UKcnfokxL7g/cBfwQeK7t74z43p1kRskdtYiIuR0H/K7tTZLOBNbbXitpFeXuV5+ZiElRVdW07duBZ0jaBVhNGQvdbvu7c33jrjKjpNdnRMTcpmxvah+vAT4P0FZk9p2JmBS72r4Uys90OxX48L4ztu+w/Q3bX1/o4KmrzLDcUYuImNsWSTsDD6NMW1wK01MsW3rOREyKqqqmJb1kvm9i+8N9ZUbJQC0iYm5nUpo6PxhYa3ujpGOAtwNv6zkTMSlqq5o+FDgK+ASzOwNMUaZL+8rMkmKCiIh5SHosZUrl5vbrw4FNtr/adyaidkPVzD/g/mrmK4G32v6/vjKS1gOftX3+Il5LJ5mZMlCLiIiI7a7mqmlJuwPH2n7nIl5PJ5mZMlCLiIiI7U7STWxbzbyX7RcOqpltz2r31FVmkmSNWkRERCyHmdXM74dSkSmp7wySVgNPpuy3dg6lkOfVtq/tOzMs23NERETEctgiaee2EnNRFdAdZAA+SBkHHQk0wBuA9455TV1lpmWgFhEREcthUM18FdtWM38ZeEfPGYCdbK8D/hD4qO0rgIeMeU1dZaZloBYRERHbne2LgN8DDrd9cvv0PcAr2oFLb5nWVklHAUcAGyT9EbB1zMvqKjMtxQQRERGx4kjah3bvNduflnQh8PbBFjl9ZoZloBYRERErkqTHA0+ktG37NdvfqyUzkKnPiIiIWHEkPR9YD7wH2AW4UtKLa8gMy0AtIiIiVqI3Uta23W37x5SK0TdVkpmWgVpERESsRFtt3z34wvZG4L5KMtOy4W1ERESsRN+S9BpgR0n7ASdTtvmoITMtd9QiIiJiJToF2AO4FzgfuIsyiKohMy1VnxERERGVytRnRERErBiSrrf9JEn3AcN3q1ZR+obu0FdmlNxRi4iIiBVH0r62b6oxMyxr1CIiImIlurDizLTcUYuIiIgVR9KngJuAqykL/QGwfXnfmWFZoxYREREr0aOANe0xMAUcUkFmWu6oRURERFQqd9QiIiJixZG0F7AWeBzwFOAC4ATb3+87MyzFBBEREbESnQOcDdwD/Aj4GPDhSjLTMlCLiIiIlWhX25cC2J6yfR7w8Eoy0zJQi4iIiJXoXkl70m5GK+kg4GeVZKZljVpERESsRK8HNgCrJd1Iqc48upLMtNxRi4iIiBVF0hHAncCBwDvax+uA6/vOzJSBWkRERKwYkk4F3gLsBOwNnEapxHwEZdF/b5lRMlCLiIiIleQ44GDbtwAvAtbbXgu8Fjis58wsGahFRETESjJle1P7eA3weSgVmRVkZkkxQURERKwkWyTtDDwM2B+4FKY3pt3Sc2aW3FGLiIiIleRM4EbgKmCt7Y2SjgG+TFnw32dmlvT6jIiIiBVF0mMpG9He3H59OLDJ9lf7zsyUgVpEREREpTL1GREREVGpDNQiIiIiKpWBWkRERESlMlCLiIiIqNT/A+rDmc0l1bwyAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "plt.figure(figsize = (10,7))\n",
    "ax = sns.boxplot(data=counts)\n",
    "ax.set_xticklabels(ax.get_xticklabels(), rotation=90)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "43"
      ]
     },
     "execution_count": 192,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.cluster import DBSCAN\n",
    "outlier_detection = DBSCAN(min_samples = 2, eps = 3)\n",
    "clusters = outlier_detection.fit_predict(counts.T)\n",
    "list(clusters).count(-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1,\n",
       "       -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1,\n",
       "       -1, -1, -1, -1, -1, -1, -1, -1, -1])"
      ]
     },
     "execution_count": 193,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clusters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1,  1, -1,  1,  1, -1, -1,  1,  1, -1, -1,  1,  1,  1,  1,  1,  1,\n",
       "       -1,  1,  1,  1,  1,  1,  1,  1,  1, -1, -1,  1, -1, -1, -1,  1,  1,\n",
       "        1,  1,  1,  1,  1,  1,  1,  1,  1])"
      ]
     },
     "execution_count": 194,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import IsolationForest\n",
    "import numpy as np\n",
    "np.random.seed(1)\n",
    "clf = IsolationForest( behaviour = 'new', max_samples=6, random_state = 1, contamination= 'auto')\n",
    "preds = clf.fit_predict(counts.T)\n",
    "preds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Series1_NHBE_Mock_3', 'Series1_NHBE_SARS-CoV-2_3',\n",
       "       'Series2_A549_Mock_1', 'Series2_A549_SARS-CoV-2_1',\n",
       "       'Series2_A549_SARS-CoV-2_2', 'Series5_A549_SARS-CoV-2_3',\n",
       "       'Series7_Calu3_Mock_3', 'Series7_Calu3_SARS-CoV-2_1',\n",
       "       'Series7_Calu3_SARS-CoV-2_3', 'Series15_HealthyLungBiopsy_2',\n",
       "       'Series15_HealthyLungBiopsy_1'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 195,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "counts.columns[preds==-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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