main.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Discovering telomerase inhibitors with machine learning\n",
    "\n",
    "Telomerase is the enzyme that repairs the protective caps of chromosomes. It is active in 90% of cancer cells, but not in most normal cells. Inhibiting telomerase could be a promising strategy for cancer treatment. In this notebook, we will build a machine learning model to predict whether a compound is a telomerase inhibitor.\n",
    "\n",
    "We will focus on small molecules, excluding other types of compounds such as peptides. From a machine learning perspective, small molecules are easier to work than more complex compounds. From a drug discovery perspective, small molecules also have several advantages:\n",
    "\n",
    "> Small molecule telomerase inhibitors are an attractive approach because it should be possible to intervene at multiple levels within the pathways that activate telomerase. Both known and unknown signaling pathways converging on either promoter are candidates for inhibition, and selective active site inhibitors also have considerable potential. Additionally, the telomere itself and various other targets that we discuss later in the chapter should be amenable to drugging. Small molecules are likely to demonstrate favorable bioavailability and frequently have the manufacturing advantages of cost-efficient, reproducible, and scalable synthesis. Importantly, they are also easily amenable to classic medicinal chemistry for lead optimization, and small molecule pharmacokinetics can easily be established in early trials ([Keith, 2008](https://www.researchgate.net/publication/278717635_Targeting_Telomerase_Therapeutic_Options_for_Cancer_Treatment)).\n",
    "\n",
    "We have [built](data.ipynb) a dataset of biologically active compounds that could or are known to have telomerase-blocking activity. We will train a classifier on numerical features extracted from the SMILES structures of these compounds. We will use the best performing model to screen a separate chemical library for potential telomerase inhibitors, which could be used as leads for drug discovery."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "%pip install -qU numpy pandas scikit-learn xgboost scipy matplotlib seaborn pubchempy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from pathlib import Path"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "RANDOM_STATE = 42\n",
    "np.random.seed(RANDOM_STATE)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data preprocessing\n",
    "\n",
    "We will start by loading the dataset and performing basic preprocessing and exploratory analysis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "DATA_DIR = Path(\"data\")\n",
    "\n",
    "DATA_PATH = DATA_DIR / \"data.csv\"\n",
    "SCREEN_PATH = DATA_DIR / \"screen.csv\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>smiles</th>\n",
       "      <th>label</th>\n",
       "      <th>MaxAbsEStateIndex</th>\n",
       "      <th>MaxEStateIndex</th>\n",
       "      <th>MinAbsEStateIndex</th>\n",
       "      <th>MinEStateIndex</th>\n",
       "      <th>qed</th>\n",
       "      <th>SPS</th>\n",
       "      <th>MolWt</th>\n",
       "      <th>...</th>\n",
       "      <th>fr_sulfide</th>\n",
       "      <th>fr_sulfonamd</th>\n",
       "      <th>fr_sulfone</th>\n",
       "      <th>fr_term_acetylene</th>\n",
       "      <th>fr_tetrazole</th>\n",
       "      <th>fr_thiazole</th>\n",
       "      <th>fr_thiocyan</th>\n",
       "      <th>fr_thiophene</th>\n",
       "      <th>fr_unbrch_alkane</th>\n",
       "      <th>fr_urea</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>BIBR 1532</td>\n",
       "      <td>C/C(=C\\C(=O)NC1=CC=CC=C1C(=O)O)/C2=CC3=CC=CC=C...</td>\n",
       "      <td>1</td>\n",
       "      <td>12.254174</td>\n",
       "      <td>12.254174</td>\n",
       "      <td>0.062600</td>\n",
       "      <td>-1.077657</td>\n",
       "      <td>0.687718</td>\n",
       "      <td>11.320000</td>\n",
       "      <td>331.371</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Costunolide</td>\n",
       "      <td>C/C/1=C\\CC/C(=C/[C@@H]2[C@@H](CC1)C(=C)C(=O)O2)/C</td>\n",
       "      <td>1</td>\n",
       "      <td>11.559391</td>\n",
       "      <td>11.559391</td>\n",
       "      <td>0.082500</td>\n",
       "      <td>-0.217658</td>\n",
       "      <td>0.362512</td>\n",
       "      <td>36.352941</td>\n",
       "      <td>232.323</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>EGCG</td>\n",
       "      <td>C1[C@H]([C@H](OC2=CC(=CC(=C21)O)O)C3=CC(=C(C(=...</td>\n",
       "      <td>1</td>\n",
       "      <td>12.721634</td>\n",
       "      <td>12.721634</td>\n",
       "      <td>0.052570</td>\n",
       "      <td>-1.181149</td>\n",
       "      <td>0.211503</td>\n",
       "      <td>17.090909</td>\n",
       "      <td>458.375</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>BMS-582949</td>\n",
       "      <td>CCCNC(=O)C1=CN2C(=C1C)C(=NC=N2)NC3=C(C=CC(=C3)...</td>\n",
       "      <td>1</td>\n",
       "      <td>12.493042</td>\n",
       "      <td>12.493042</td>\n",
       "      <td>0.067470</td>\n",
       "      <td>-0.124329</td>\n",
       "      <td>0.559723</td>\n",
       "      <td>13.300000</td>\n",
       "      <td>406.490</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Hypericin</td>\n",
       "      <td>CC1=CC(=O)C2=C(C3=C(C=C(C4=C3C5=C2C1=C6C(=CC(=...</td>\n",
       "      <td>1</td>\n",
       "      <td>13.357201</td>\n",
       "      <td>13.357201</td>\n",
       "      <td>0.043065</td>\n",
       "      <td>-0.483063</td>\n",
       "      <td>0.123350</td>\n",
       "      <td>12.789474</td>\n",
       "      <td>504.450</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3022</th>\n",
       "      <td>Icatibant Acetate</td>\n",
       "      <td>CC(O)=O.NC(CCCNC(N)=N)C(=O)NC(CCCNC(N)=N)C(=O)...</td>\n",
       "      <td>0</td>\n",
       "      <td>15.342188</td>\n",
       "      <td>15.342188</td>\n",
       "      <td>0.002684</td>\n",
       "      <td>-1.667813</td>\n",
       "      <td>0.019985</td>\n",
       "      <td>21.729167</td>\n",
       "      <td>1364.600</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3023</th>\n",
       "      <td>Plerixafor (AMD3100)</td>\n",
       "      <td>C1CNCCNCCCN(CCNC1)CC2=CC=C(CN3CCCNCCNCCCNCC3)C=C2</td>\n",
       "      <td>0</td>\n",
       "      <td>3.625948</td>\n",
       "      <td>3.625948</td>\n",
       "      <td>1.039362</td>\n",
       "      <td>1.039362</td>\n",
       "      <td>0.353778</td>\n",
       "      <td>22.888889</td>\n",
       "      <td>502.796</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3024</th>\n",
       "      <td>Sodium Hyaluronate</td>\n",
       "      <td>[Na+].CC(=O)NC1C(C(O)C(CO)OC1OC2C(O)C(O)C(O[*]...</td>\n",
       "      <td>0</td>\n",
       "      <td>11.570592</td>\n",
       "      <td>11.570592</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-1.759350</td>\n",
       "      <td>0.266010</td>\n",
       "      <td>45.814815</td>\n",
       "      <td>401.300</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3025</th>\n",
       "      <td>Fosinopril Sodium</td>\n",
       "      <td>[Na+].CCC(=O)OC(O[P](=O)(CCCCC1=CC=CC=C1)CC(=O...</td>\n",
       "      <td>0</td>\n",
       "      <td>14.211981</td>\n",
       "      <td>14.211981</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-3.616382</td>\n",
       "      <td>0.107598</td>\n",
       "      <td>20.650000</td>\n",
       "      <td>585.654</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3026</th>\n",
       "      <td>Strontium Ranelate</td>\n",
       "      <td>O=C1CN(CC(=O)O[Sr]O1)C2=C(C#N)C3=C(S2)C(=O)O[S...</td>\n",
       "      <td>0</td>\n",
       "      <td>12.127403</td>\n",
       "      <td>12.127403</td>\n",
       "      <td>0.037674</td>\n",
       "      <td>-2.417788</td>\n",
       "      <td>0.424243</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>513.493</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3027 rows × 213 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                      name                                             smiles  \\\n",
       "0                BIBR 1532  C/C(=C\\C(=O)NC1=CC=CC=C1C(=O)O)/C2=CC3=CC=CC=C...   \n",
       "1              Costunolide  C/C/1=C\\CC/C(=C/[C@@H]2[C@@H](CC1)C(=C)C(=O)O2)/C   \n",
       "2                     EGCG  C1[C@H]([C@H](OC2=CC(=CC(=C21)O)O)C3=CC(=C(C(=...   \n",
       "3               BMS-582949  CCCNC(=O)C1=CN2C(=C1C)C(=NC=N2)NC3=C(C=CC(=C3)...   \n",
       "4                Hypericin  CC1=CC(=O)C2=C(C3=C(C=C(C4=C3C5=C2C1=C6C(=CC(=...   \n",
       "...                    ...                                                ...   \n",
       "3022     Icatibant Acetate  CC(O)=O.NC(CCCNC(N)=N)C(=O)NC(CCCNC(N)=N)C(=O)...   \n",
       "3023  Plerixafor (AMD3100)  C1CNCCNCCCN(CCNC1)CC2=CC=C(CN3CCCNCCNCCCNCC3)C=C2   \n",
       "3024    Sodium Hyaluronate  [Na+].CC(=O)NC1C(C(O)C(CO)OC1OC2C(O)C(O)C(O[*]...   \n",
       "3025     Fosinopril Sodium  [Na+].CCC(=O)OC(O[P](=O)(CCCCC1=CC=CC=C1)CC(=O...   \n",
       "3026    Strontium Ranelate  O=C1CN(CC(=O)O[Sr]O1)C2=C(C#N)C3=C(S2)C(=O)O[S...   \n",
       "\n",
       "      label  MaxAbsEStateIndex  MaxEStateIndex  MinAbsEStateIndex  \\\n",
       "0         1          12.254174       12.254174           0.062600   \n",
       "1         1          11.559391       11.559391           0.082500   \n",
       "2         1          12.721634       12.721634           0.052570   \n",
       "3         1          12.493042       12.493042           0.067470   \n",
       "4         1          13.357201       13.357201           0.043065   \n",
       "...     ...                ...             ...                ...   \n",
       "3022      0          15.342188       15.342188           0.002684   \n",
       "3023      0           3.625948        3.625948           1.039362   \n",
       "3024      0          11.570592       11.570592           0.000000   \n",
       "3025      0          14.211981       14.211981           0.000000   \n",
       "3026      0          12.127403       12.127403           0.037674   \n",
       "\n",
       "      MinEStateIndex       qed        SPS     MolWt  ...  fr_sulfide  \\\n",
       "0          -1.077657  0.687718  11.320000   331.371  ...         0.0   \n",
       "1          -0.217658  0.362512  36.352941   232.323  ...         0.0   \n",
       "2          -1.181149  0.211503  17.090909   458.375  ...         0.0   \n",
       "3          -0.124329  0.559723  13.300000   406.490  ...         0.0   \n",
       "4          -0.483063  0.123350  12.789474   504.450  ...         0.0   \n",
       "...              ...       ...        ...       ...  ...         ...   \n",
       "3022       -1.667813  0.019985  21.729167  1364.600  ...         0.0   \n",
       "3023        1.039362  0.353778  22.888889   502.796  ...         0.0   \n",
       "3024       -1.759350  0.266010  45.814815   401.300  ...         0.0   \n",
       "3025       -3.616382  0.107598  20.650000   585.654  ...         0.0   \n",
       "3026       -2.417788  0.424243  17.000000   513.493  ...         0.0   \n",
       "\n",
       "      fr_sulfonamd  fr_sulfone  fr_term_acetylene  fr_tetrazole  fr_thiazole  \\\n",
       "0              0.0         0.0                0.0           0.0          0.0   \n",
       "1              0.0         0.0                0.0           0.0          0.0   \n",
       "2              0.0         0.0                0.0           0.0          0.0   \n",
       "3              0.0         0.0                0.0           0.0          0.0   \n",
       "4              0.0         0.0                0.0           0.0          0.0   \n",
       "...            ...         ...                ...           ...          ...   \n",
       "3022           0.0         0.0                0.0           0.0          0.0   \n",
       "3023           0.0         0.0                0.0           0.0          0.0   \n",
       "3024           0.0         0.0                0.0           0.0          0.0   \n",
       "3025           0.0         0.0                0.0           0.0          0.0   \n",
       "3026           0.0         0.0                0.0           0.0          0.0   \n",
       "\n",
       "      fr_thiocyan  fr_thiophene  fr_unbrch_alkane  fr_urea  \n",
       "0             0.0           0.0               0.0      0.0  \n",
       "1             0.0           0.0               0.0      0.0  \n",
       "2             0.0           0.0               0.0      0.0  \n",
       "3             0.0           0.0               0.0      0.0  \n",
       "4             0.0           0.0               0.0      0.0  \n",
       "...           ...           ...               ...      ...  \n",
       "3022          0.0           1.0               3.0      0.0  \n",
       "3023          0.0           0.0               0.0      0.0  \n",
       "3024          0.0           0.0               0.0      0.0  \n",
       "3025          0.0           0.0               1.0      0.0  \n",
       "3026          0.0           1.0               0.0      0.0  \n",
       "\n",
       "[3027 rows x 213 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv(DATA_PATH)\n",
    "data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We noticed that the dataset contains infinities and `NaN`s. We will clip the columns to the 1st and 99th percentiles to remove outliers and replace `NaN`s with 0s."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>smiles</th>\n",
       "      <th>label</th>\n",
       "      <th>MaxAbsEStateIndex</th>\n",
       "      <th>MaxEStateIndex</th>\n",
       "      <th>MinAbsEStateIndex</th>\n",
       "      <th>MinEStateIndex</th>\n",
       "      <th>qed</th>\n",
       "      <th>SPS</th>\n",
       "      <th>MolWt</th>\n",
       "      <th>...</th>\n",
       "      <th>fr_sulfide</th>\n",
       "      <th>fr_sulfonamd</th>\n",
       "      <th>fr_sulfone</th>\n",
       "      <th>fr_term_acetylene</th>\n",
       "      <th>fr_tetrazole</th>\n",
       "      <th>fr_thiazole</th>\n",
       "      <th>fr_thiocyan</th>\n",
       "      <th>fr_thiophene</th>\n",
       "      <th>fr_unbrch_alkane</th>\n",
       "      <th>fr_urea</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>BIBR 1532</td>\n",
       "      <td>C/C(=C\\C(=O)NC1=CC=CC=C1C(=O)O)/C2=CC3=CC=CC=C...</td>\n",
       "      <td>1</td>\n",
       "      <td>12.254174</td>\n",
       "      <td>12.254174</td>\n",
       "      <td>0.062600</td>\n",
       "      <td>-1.077657</td>\n",
       "      <td>0.687718</td>\n",
       "      <td>11.320000</td>\n",
       "      <td>331.371</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Costunolide</td>\n",
       "      <td>C/C/1=C\\CC/C(=C/[C@@H]2[C@@H](CC1)C(=C)C(=O)O2)/C</td>\n",
       "      <td>1</td>\n",
       "      <td>11.559391</td>\n",
       "      <td>11.559391</td>\n",
       "      <td>0.082500</td>\n",
       "      <td>-0.217658</td>\n",
       "      <td>0.362512</td>\n",
       "      <td>36.352941</td>\n",
       "      <td>232.323</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>EGCG</td>\n",
       "      <td>C1[C@H]([C@H](OC2=CC(=CC(=C21)O)O)C3=CC(=C(C(=...</td>\n",
       "      <td>1</td>\n",
       "      <td>12.721634</td>\n",
       "      <td>12.721634</td>\n",
       "      <td>0.052570</td>\n",
       "      <td>-1.181149</td>\n",
       "      <td>0.211503</td>\n",
       "      <td>17.090909</td>\n",
       "      <td>458.375</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>BMS-582949</td>\n",
       "      <td>CCCNC(=O)C1=CN2C(=C1C)C(=NC=N2)NC3=C(C=CC(=C3)...</td>\n",
       "      <td>1</td>\n",
       "      <td>12.493042</td>\n",
       "      <td>12.493042</td>\n",
       "      <td>0.067470</td>\n",
       "      <td>-0.124329</td>\n",
       "      <td>0.559723</td>\n",
       "      <td>13.300000</td>\n",
       "      <td>406.490</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Hypericin</td>\n",
       "      <td>CC1=CC(=O)C2=C(C3=C(C=C(C4=C3C5=C2C1=C6C(=CC(=...</td>\n",
       "      <td>1</td>\n",
       "      <td>13.357201</td>\n",
       "      <td>13.357201</td>\n",
       "      <td>0.043065</td>\n",
       "      <td>-0.483063</td>\n",
       "      <td>0.123350</td>\n",
       "      <td>12.789474</td>\n",
       "      <td>504.450</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3022</th>\n",
       "      <td>Icatibant Acetate</td>\n",
       "      <td>CC(O)=O.NC(CCCNC(N)=N)C(=O)NC(CCCNC(N)=N)C(=O)...</td>\n",
       "      <td>0</td>\n",
       "      <td>15.342188</td>\n",
       "      <td>15.342188</td>\n",
       "      <td>0.002684</td>\n",
       "      <td>-1.667813</td>\n",
       "      <td>0.026224</td>\n",
       "      <td>21.729167</td>\n",
       "      <td>1364.600</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3023</th>\n",
       "      <td>Plerixafor (AMD3100)</td>\n",
       "      <td>C1CNCCNCCCN(CCNC1)CC2=CC=C(CN3CCCNCCNCCCNCC3)C=C2</td>\n",
       "      <td>0</td>\n",
       "      <td>3.625948</td>\n",
       "      <td>3.625948</td>\n",
       "      <td>1.000995</td>\n",
       "      <td>0.874735</td>\n",
       "      <td>0.353778</td>\n",
       "      <td>22.888889</td>\n",
       "      <td>502.796</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3024</th>\n",
       "      <td>Sodium Hyaluronate</td>\n",
       "      <td>[Na+].CC(=O)NC1C(C(O)C(CO)OC1OC2C(O)C(O)C(O[*]...</td>\n",
       "      <td>0</td>\n",
       "      <td>11.570592</td>\n",
       "      <td>11.570592</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-1.759350</td>\n",
       "      <td>0.266010</td>\n",
       "      <td>45.814815</td>\n",
       "      <td>401.300</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3025</th>\n",
       "      <td>Fosinopril Sodium</td>\n",
       "      <td>[Na+].CCC(=O)OC(O[P](=O)(CCCCC1=CC=CC=C1)CC(=O...</td>\n",
       "      <td>0</td>\n",
       "      <td>14.211981</td>\n",
       "      <td>14.211981</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-3.616382</td>\n",
       "      <td>0.107598</td>\n",
       "      <td>20.650000</td>\n",
       "      <td>585.654</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3026</th>\n",
       "      <td>Strontium Ranelate</td>\n",
       "      <td>O=C1CN(CC(=O)O[Sr]O1)C2=C(C#N)C3=C(S2)C(=O)O[S...</td>\n",
       "      <td>0</td>\n",
       "      <td>12.127403</td>\n",
       "      <td>12.127403</td>\n",
       "      <td>0.037674</td>\n",
       "      <td>-2.417788</td>\n",
       "      <td>0.424243</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>513.493</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3027 rows × 213 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                      name                                             smiles  \\\n",
       "0                BIBR 1532  C/C(=C\\C(=O)NC1=CC=CC=C1C(=O)O)/C2=CC3=CC=CC=C...   \n",
       "1              Costunolide  C/C/1=C\\CC/C(=C/[C@@H]2[C@@H](CC1)C(=C)C(=O)O2)/C   \n",
       "2                     EGCG  C1[C@H]([C@H](OC2=CC(=CC(=C21)O)O)C3=CC(=C(C(=...   \n",
       "3               BMS-582949  CCCNC(=O)C1=CN2C(=C1C)C(=NC=N2)NC3=C(C=CC(=C3)...   \n",
       "4                Hypericin  CC1=CC(=O)C2=C(C3=C(C=C(C4=C3C5=C2C1=C6C(=CC(=...   \n",
       "...                    ...                                                ...   \n",
       "3022     Icatibant Acetate  CC(O)=O.NC(CCCNC(N)=N)C(=O)NC(CCCNC(N)=N)C(=O)...   \n",
       "3023  Plerixafor (AMD3100)  C1CNCCNCCCN(CCNC1)CC2=CC=C(CN3CCCNCCNCCCNCC3)C=C2   \n",
       "3024    Sodium Hyaluronate  [Na+].CC(=O)NC1C(C(O)C(CO)OC1OC2C(O)C(O)C(O[*]...   \n",
       "3025     Fosinopril Sodium  [Na+].CCC(=O)OC(O[P](=O)(CCCCC1=CC=CC=C1)CC(=O...   \n",
       "3026    Strontium Ranelate  O=C1CN(CC(=O)O[Sr]O1)C2=C(C#N)C3=C(S2)C(=O)O[S...   \n",
       "\n",
       "      label  MaxAbsEStateIndex  MaxEStateIndex  MinAbsEStateIndex  \\\n",
       "0         1          12.254174       12.254174           0.062600   \n",
       "1         1          11.559391       11.559391           0.082500   \n",
       "2         1          12.721634       12.721634           0.052570   \n",
       "3         1          12.493042       12.493042           0.067470   \n",
       "4         1          13.357201       13.357201           0.043065   \n",
       "...     ...                ...             ...                ...   \n",
       "3022      0          15.342188       15.342188           0.002684   \n",
       "3023      0           3.625948        3.625948           1.000995   \n",
       "3024      0          11.570592       11.570592           0.000000   \n",
       "3025      0          14.211981       14.211981           0.000000   \n",
       "3026      0          12.127403       12.127403           0.037674   \n",
       "\n",
       "      MinEStateIndex       qed        SPS     MolWt  ...  fr_sulfide  \\\n",
       "0          -1.077657  0.687718  11.320000   331.371  ...         0.0   \n",
       "1          -0.217658  0.362512  36.352941   232.323  ...         0.0   \n",
       "2          -1.181149  0.211503  17.090909   458.375  ...         0.0   \n",
       "3          -0.124329  0.559723  13.300000   406.490  ...         0.0   \n",
       "4          -0.483063  0.123350  12.789474   504.450  ...         0.0   \n",
       "...              ...       ...        ...       ...  ...         ...   \n",
       "3022       -1.667813  0.026224  21.729167  1364.600  ...         0.0   \n",
       "3023        0.874735  0.353778  22.888889   502.796  ...         0.0   \n",
       "3024       -1.759350  0.266010  45.814815   401.300  ...         0.0   \n",
       "3025       -3.616382  0.107598  20.650000   585.654  ...         0.0   \n",
       "3026       -2.417788  0.424243  17.000000   513.493  ...         0.0   \n",
       "\n",
       "      fr_sulfonamd  fr_sulfone  fr_term_acetylene  fr_tetrazole  fr_thiazole  \\\n",
       "0              0.0         0.0                0.0           0.0          0.0   \n",
       "1              0.0         0.0                0.0           0.0          0.0   \n",
       "2              0.0         0.0                0.0           0.0          0.0   \n",
       "3              0.0         0.0                0.0           0.0          0.0   \n",
       "4              0.0         0.0                0.0           0.0          0.0   \n",
       "...            ...         ...                ...           ...          ...   \n",
       "3022           0.0         0.0                0.0           0.0          0.0   \n",
       "3023           0.0         0.0                0.0           0.0          0.0   \n",
       "3024           0.0         0.0                0.0           0.0          0.0   \n",
       "3025           0.0         0.0                0.0           0.0          0.0   \n",
       "3026           0.0         0.0                0.0           0.0          0.0   \n",
       "\n",
       "      fr_thiocyan  fr_thiophene  fr_unbrch_alkane  fr_urea  \n",
       "0             0.0           0.0               0.0      0.0  \n",
       "1             0.0           0.0               0.0      0.0  \n",
       "2             0.0           0.0               0.0      0.0  \n",
       "3             0.0           0.0               0.0      0.0  \n",
       "4             0.0           0.0               0.0      0.0  \n",
       "...           ...           ...               ...      ...  \n",
       "3022          0.0           1.0               3.0      0.0  \n",
       "3023          0.0           0.0               0.0      0.0  \n",
       "3024          0.0           0.0               0.0      0.0  \n",
       "3025          0.0           0.0               1.0      0.0  \n",
       "3026          0.0           1.0               0.0      0.0  \n",
       "\n",
       "[3027 rows x 213 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.iloc[:, 3:] = data.iloc[:, 3:].clip(\n",
    "  lower=data.iloc[:, 3:].quantile(0.01),\n",
    "  upper=data.iloc[:, 3:].quantile(0.99),\n",
    "  axis=1,\n",
    ")\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>smiles</th>\n",
       "      <th>label</th>\n",
       "      <th>MaxAbsEStateIndex</th>\n",
       "      <th>MaxEStateIndex</th>\n",
       "      <th>MinAbsEStateIndex</th>\n",
       "      <th>MinEStateIndex</th>\n",
       "      <th>qed</th>\n",
       "      <th>SPS</th>\n",
       "      <th>MolWt</th>\n",
       "      <th>...</th>\n",
       "      <th>fr_sulfide</th>\n",
       "      <th>fr_sulfonamd</th>\n",
       "      <th>fr_sulfone</th>\n",
       "      <th>fr_term_acetylene</th>\n",
       "      <th>fr_tetrazole</th>\n",
       "      <th>fr_thiazole</th>\n",
       "      <th>fr_thiocyan</th>\n",
       "      <th>fr_thiophene</th>\n",
       "      <th>fr_unbrch_alkane</th>\n",
       "      <th>fr_urea</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>BIBR 1532</td>\n",
       "      <td>C/C(=C\\C(=O)NC1=CC=CC=C1C(=O)O)/C2=CC3=CC=CC=C...</td>\n",
       "      <td>1</td>\n",
       "      <td>12.254174</td>\n",
       "      <td>12.254174</td>\n",
       "      <td>0.062600</td>\n",
       "      <td>-1.077657</td>\n",
       "      <td>0.687718</td>\n",
       "      <td>11.320000</td>\n",
       "      <td>331.371</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Costunolide</td>\n",
       "      <td>C/C/1=C\\CC/C(=C/[C@@H]2[C@@H](CC1)C(=C)C(=O)O2)/C</td>\n",
       "      <td>1</td>\n",
       "      <td>11.559391</td>\n",
       "      <td>11.559391</td>\n",
       "      <td>0.082500</td>\n",
       "      <td>-0.217658</td>\n",
       "      <td>0.362512</td>\n",
       "      <td>36.352941</td>\n",
       "      <td>232.323</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>EGCG</td>\n",
       "      <td>C1[C@H]([C@H](OC2=CC(=CC(=C21)O)O)C3=CC(=C(C(=...</td>\n",
       "      <td>1</td>\n",
       "      <td>12.721634</td>\n",
       "      <td>12.721634</td>\n",
       "      <td>0.052570</td>\n",
       "      <td>-1.181149</td>\n",
       "      <td>0.211503</td>\n",
       "      <td>17.090909</td>\n",
       "      <td>458.375</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>BMS-582949</td>\n",
       "      <td>CCCNC(=O)C1=CN2C(=C1C)C(=NC=N2)NC3=C(C=CC(=C3)...</td>\n",
       "      <td>1</td>\n",
       "      <td>12.493042</td>\n",
       "      <td>12.493042</td>\n",
       "      <td>0.067470</td>\n",
       "      <td>-0.124329</td>\n",
       "      <td>0.559723</td>\n",
       "      <td>13.300000</td>\n",
       "      <td>406.490</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Hypericin</td>\n",
       "      <td>CC1=CC(=O)C2=C(C3=C(C=C(C4=C3C5=C2C1=C6C(=CC(=...</td>\n",
       "      <td>1</td>\n",
       "      <td>13.357201</td>\n",
       "      <td>13.357201</td>\n",
       "      <td>0.043065</td>\n",
       "      <td>-0.483063</td>\n",
       "      <td>0.123350</td>\n",
       "      <td>12.789474</td>\n",
       "      <td>504.450</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3022</th>\n",
       "      <td>Icatibant Acetate</td>\n",
       "      <td>CC(O)=O.NC(CCCNC(N)=N)C(=O)NC(CCCNC(N)=N)C(=O)...</td>\n",
       "      <td>0</td>\n",
       "      <td>15.342188</td>\n",
       "      <td>15.342188</td>\n",
       "      <td>0.002684</td>\n",
       "      <td>-1.667813</td>\n",
       "      <td>0.026224</td>\n",
       "      <td>21.729167</td>\n",
       "      <td>1364.600</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3023</th>\n",
       "      <td>Plerixafor (AMD3100)</td>\n",
       "      <td>C1CNCCNCCCN(CCNC1)CC2=CC=C(CN3CCCNCCNCCCNCC3)C=C2</td>\n",
       "      <td>0</td>\n",
       "      <td>3.625948</td>\n",
       "      <td>3.625948</td>\n",
       "      <td>1.000995</td>\n",
       "      <td>0.874735</td>\n",
       "      <td>0.353778</td>\n",
       "      <td>22.888889</td>\n",
       "      <td>502.796</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3024</th>\n",
       "      <td>Sodium Hyaluronate</td>\n",
       "      <td>[Na+].CC(=O)NC1C(C(O)C(CO)OC1OC2C(O)C(O)C(O[*]...</td>\n",
       "      <td>0</td>\n",
       "      <td>11.570592</td>\n",
       "      <td>11.570592</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-1.759350</td>\n",
       "      <td>0.266010</td>\n",
       "      <td>45.814815</td>\n",
       "      <td>401.300</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3025</th>\n",
       "      <td>Fosinopril Sodium</td>\n",
       "      <td>[Na+].CCC(=O)OC(O[P](=O)(CCCCC1=CC=CC=C1)CC(=O...</td>\n",
       "      <td>0</td>\n",
       "      <td>14.211981</td>\n",
       "      <td>14.211981</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-3.616382</td>\n",
       "      <td>0.107598</td>\n",
       "      <td>20.650000</td>\n",
       "      <td>585.654</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3026</th>\n",
       "      <td>Strontium Ranelate</td>\n",
       "      <td>O=C1CN(CC(=O)O[Sr]O1)C2=C(C#N)C3=C(S2)C(=O)O[S...</td>\n",
       "      <td>0</td>\n",
       "      <td>12.127403</td>\n",
       "      <td>12.127403</td>\n",
       "      <td>0.037674</td>\n",
       "      <td>-2.417788</td>\n",
       "      <td>0.424243</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>513.493</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3027 rows × 213 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                      name                                             smiles  \\\n",
       "0                BIBR 1532  C/C(=C\\C(=O)NC1=CC=CC=C1C(=O)O)/C2=CC3=CC=CC=C...   \n",
       "1              Costunolide  C/C/1=C\\CC/C(=C/[C@@H]2[C@@H](CC1)C(=C)C(=O)O2)/C   \n",
       "2                     EGCG  C1[C@H]([C@H](OC2=CC(=CC(=C21)O)O)C3=CC(=C(C(=...   \n",
       "3               BMS-582949  CCCNC(=O)C1=CN2C(=C1C)C(=NC=N2)NC3=C(C=CC(=C3)...   \n",
       "4                Hypericin  CC1=CC(=O)C2=C(C3=C(C=C(C4=C3C5=C2C1=C6C(=CC(=...   \n",
       "...                    ...                                                ...   \n",
       "3022     Icatibant Acetate  CC(O)=O.NC(CCCNC(N)=N)C(=O)NC(CCCNC(N)=N)C(=O)...   \n",
       "3023  Plerixafor (AMD3100)  C1CNCCNCCCN(CCNC1)CC2=CC=C(CN3CCCNCCNCCCNCC3)C=C2   \n",
       "3024    Sodium Hyaluronate  [Na+].CC(=O)NC1C(C(O)C(CO)OC1OC2C(O)C(O)C(O[*]...   \n",
       "3025     Fosinopril Sodium  [Na+].CCC(=O)OC(O[P](=O)(CCCCC1=CC=CC=C1)CC(=O...   \n",
       "3026    Strontium Ranelate  O=C1CN(CC(=O)O[Sr]O1)C2=C(C#N)C3=C(S2)C(=O)O[S...   \n",
       "\n",
       "      label  MaxAbsEStateIndex  MaxEStateIndex  MinAbsEStateIndex  \\\n",
       "0         1          12.254174       12.254174           0.062600   \n",
       "1         1          11.559391       11.559391           0.082500   \n",
       "2         1          12.721634       12.721634           0.052570   \n",
       "3         1          12.493042       12.493042           0.067470   \n",
       "4         1          13.357201       13.357201           0.043065   \n",
       "...     ...                ...             ...                ...   \n",
       "3022      0          15.342188       15.342188           0.002684   \n",
       "3023      0           3.625948        3.625948           1.000995   \n",
       "3024      0          11.570592       11.570592           0.000000   \n",
       "3025      0          14.211981       14.211981           0.000000   \n",
       "3026      0          12.127403       12.127403           0.037674   \n",
       "\n",
       "      MinEStateIndex       qed        SPS     MolWt  ...  fr_sulfide  \\\n",
       "0          -1.077657  0.687718  11.320000   331.371  ...         0.0   \n",
       "1          -0.217658  0.362512  36.352941   232.323  ...         0.0   \n",
       "2          -1.181149  0.211503  17.090909   458.375  ...         0.0   \n",
       "3          -0.124329  0.559723  13.300000   406.490  ...         0.0   \n",
       "4          -0.483063  0.123350  12.789474   504.450  ...         0.0   \n",
       "...              ...       ...        ...       ...  ...         ...   \n",
       "3022       -1.667813  0.026224  21.729167  1364.600  ...         0.0   \n",
       "3023        0.874735  0.353778  22.888889   502.796  ...         0.0   \n",
       "3024       -1.759350  0.266010  45.814815   401.300  ...         0.0   \n",
       "3025       -3.616382  0.107598  20.650000   585.654  ...         0.0   \n",
       "3026       -2.417788  0.424243  17.000000   513.493  ...         0.0   \n",
       "\n",
       "      fr_sulfonamd  fr_sulfone  fr_term_acetylene  fr_tetrazole  fr_thiazole  \\\n",
       "0              0.0         0.0                0.0           0.0          0.0   \n",
       "1              0.0         0.0                0.0           0.0          0.0   \n",
       "2              0.0         0.0                0.0           0.0          0.0   \n",
       "3              0.0         0.0                0.0           0.0          0.0   \n",
       "4              0.0         0.0                0.0           0.0          0.0   \n",
       "...            ...         ...                ...           ...          ...   \n",
       "3022           0.0         0.0                0.0           0.0          0.0   \n",
       "3023           0.0         0.0                0.0           0.0          0.0   \n",
       "3024           0.0         0.0                0.0           0.0          0.0   \n",
       "3025           0.0         0.0                0.0           0.0          0.0   \n",
       "3026           0.0         0.0                0.0           0.0          0.0   \n",
       "\n",
       "      fr_thiocyan  fr_thiophene  fr_unbrch_alkane  fr_urea  \n",
       "0             0.0           0.0               0.0      0.0  \n",
       "1             0.0           0.0               0.0      0.0  \n",
       "2             0.0           0.0               0.0      0.0  \n",
       "3             0.0           0.0               0.0      0.0  \n",
       "4             0.0           0.0               0.0      0.0  \n",
       "...           ...           ...               ...      ...  \n",
       "3022          0.0           1.0               3.0      0.0  \n",
       "3023          0.0           0.0               0.0      0.0  \n",
       "3024          0.0           0.0               0.0      0.0  \n",
       "3025          0.0           0.0               1.0      0.0  \n",
       "3026          0.0           1.0               0.0      0.0  \n",
       "\n",
       "[3027 rows x 213 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.impute import SimpleImputer\n",
    "\n",
    "imputer = SimpleImputer(strategy=\"constant\")\n",
    "data.iloc[:, 3:] = pd.DataFrame(imputer.fit_transform(data.iloc[:, 3:]), columns=data.columns[3:], index=data.index)\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ipc        1.161637e+23\n",
       "BertzCT    5.729426e+02\n",
       "MolWt      2.280084e+02\n",
       "Name: std, dtype: float64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe().loc[\"std\"].nlargest(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We see that the `Ipc` feature has an incredibly high standard deviation compared to the other features. We will standardize the features later, so we will not worry about this for now.\n",
    "\n",
    "We will now drop the non-numeric columns and split the dataset into training and testing sets. Considering the extreme class imbalance, we will use stratified sampling to ensure that the training and testing sets have the same proportion of positive and negative examples."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "label\n",
       "0    2381\n",
       "1      40\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X = data.drop(columns=[\"label\", \"name\", \"smiles\"])\n",
    "y = data[\"label\"]\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, stratify=data[\"label\"], random_state=RANDOM_STATE)\n",
    "y_train.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we can proceed with dimensionality reduction and visualization. We will only perform PCA since we found that to give the best results while experimenting with different feature selection/extraction methods. We will, of course, standardize the features before applying PCA."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# from sklearn.feature_selection import SelectFromModel\n",
    "# from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "# selector = SelectFromModel(RandomForestClassifier(n_estimators=100, random_state=RANDOM_STATE))\n",
    "# selector.fit(X_train, y_train)\n",
    "\n",
    "# X_train = pd.DataFrame(selector.transform(X_train), index=X_train.index, columns=X_train.columns[selector.get_support()])\n",
    "# X_test = pd.DataFrame(selector.transform(X_test), index=X_test.index, columns=X_test.columns[selector.get_support()])\n",
    "# X_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.decomposition import PCA\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.pipeline import Pipeline\n",
    "\n",
    "scaler = StandardScaler()\n",
    "pca = PCA(random_state=RANDOM_STATE)\n",
    "\n",
    "pipeline = Pipeline([\n",
    "    (\"scaler\", scaler),\n",
    "    (\"pca\", pca)\n",
    "])\n",
    "pipeline.fit(X_train)\n",
    "\n",
    "X_train_pca = pd.DataFrame(pipeline.transform(X_train), index=X_train.index, columns=[f\"PC{i}\" for i in range(1, X_train.shape[1] + 1)])\n",
    "X_test_pca = pd.DataFrame(pipeline.transform(X_test), index=X_test.index, columns=[f\"PC{i}\" for i in range(1, X_test.shape[1] + 1)])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will keep 95% of the variance in the data, which will reduce the number of features from 1875 to 50."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "We need 70 (out of 210) components to explain 95% of the variance.\n"
     ]
    }
   ],
   "source": [
    "threshold = 0.95\n",
    "cum_expl_var = pd.Series(pipeline.named_steps[\"pca\"].explained_variance_ratio_.cumsum())\n",
    "n_components = (cum_expl_var >= threshold).argmax() + 1\n",
    "print(f\"We need {n_components} (out of {X_train.shape[1]}) components to explain {threshold:.0%} of the variance.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cum_expl_var.plot(xlabel=\"Number of components\", ylabel=\"Explained variance ratio\")\n",
    "plt.axvline(n_components, color=\"red\", linestyle=\"--\")\n",
    "plt.axhline(threshold, color=\"red\", linestyle=\"-\", linewidth=0.5)\n",
    "plt.xticks(ticks=list(range(0, len(cum_expl_var) + 1, 50)) + [n_components])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2421, 70)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Select the first n_components\n",
    "X_train_pca = X_train_pca.iloc[:, :n_components]\n",
    "X_test_pca = X_test_pca.iloc[:, :n_components]\n",
    "X_train_pca.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It helps to visualize the data in 2D to get a sense of the structure of the data. We will use t-SNE for this purpose, which attempts to preserve the local structure of the data by mapping similar data points in the high-dimensional space to nearby points in the lower-dimensional space."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.manifold import TSNE\n",
    "\n",
    "tsne = TSNE(n_components=2, perplexity=50, n_iter=1500, random_state=RANDOM_STATE)\n",
    "X_train_tsne = pd.DataFrame(tsne.fit_transform(X_train_pca), index=X_train.index, columns=[\"t-SNE1\", \"t-SNE2\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='t-SNE1', ylabel='t-SNE2'>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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hDLIs88fKDFb8k41eLzF0QAgP3t0KN43I9r0FlJbqGTYwxKk8nVrSMit5eWY8p5IrAKMa9I3XRPLMxBjUjejY7izzfj7ND4tTre779qNepnYXX/+UxIIl5q04RBFmv9ldESm8yHClquuSC3VNmjSJ66+/ntGjR5tt379/Pzqdzmx7x44dadmyJTt3Wi+TnDlzJv7+/qZ/oqKizuncFRQUFC4lJEliwuR9zP7mFKeSK0hJr+KXpWncNGEn5eV6hvYPYczoCJeMHp1O4pmXj3A6taLedWD52izm/Xyac/0sLssyv63IsLn/1Q/iATidWmFh9IBxrq++H48kXVI+A4V6XFKGz6+//sqBAweYOXOmxb7s7Gzc3NwICAgw2x4eHk52drbV8aZPn05JSYnpn7Q0yy+5goKCwuXKj4tTrVZ4lVcYeOPDYw0ac9ueAnLyLDuhyzL8/HsaV96ylZfeiSMlrWGVZY7Q6WW75fxZOVr++ieLB5/eb/OYkjI9J5LKz8X0FM4Dl0yOT1paGk8//TTr1q3Dw6NpVDbd3d1xd3dvkrEUFBQUziWyLBN/ooyUtEoiwz3o2dX/nCT81mfVeusPjQD7jxQ3aMz0zCqLLun1MRhktu3J50BsET982rfJVZU1agEPd5FqO0KO7392wuE4er2SDH2pcskYPvv37yc3N5fevXubthkMBrZs2cLnn3/O33//TU1NDcXFxWZen5ycHCIiIi7AjBUUFBSahqKSGqa9dZT4hLqeWK2jvJj1Wjciw89duwV7lU6GBoR6dDqJlPRKh2XykgRVVQZ+XZbOMxOdT5h2BkEQGNw/iI1bGya2CMY8n/ZtfZtwVgrnk0sm1DVq1ChiY2M5dOiQ6Z++fftyzz33mP5fo9GwYcMG0zkJCQmkpqYyaNCgCzhzBQUFhcbx1kfHSThpHlpJy6jkxbePntOcmK4dbSeJNnPR4DIYZF58J461G52TGDFIsO9wkUvXcIbTqRXsPdi4cW8Z0ww3zSWzfCqcxSXj8fH19aVr165m27y9vQkODjZtf+ihh5g6dSpBQUH4+fnx5JNPMmjQIKsVXQoKCgrni7iEUlauy6K8XM+1I8MZ3C/Y6TBVZnYVe6ws1AYJTiVXEH+ijC4dzk0V6pRH27FjbyEGK4rMUx+PcWmsHXsL2O1Ea4paBME5RWdX+XDuSSoqG96yY1C/IKZMdO21K1xcXDKGjzPMnj0bURS59dZb0Wq1XHPNNcydO/dCT0tBQeEyRZZl3vgonvWb68Iqm7bnExrsxm/f9kejUTkcI69Aa3d/Tp6WLh0aPVWrhIV6MH9Ob179IJ6UtCoAQoPdeO6J9vTvFeTgbHN27C1ApRKsGlHWkGUYM6pp0xSKims4HFdic/91I8M5mVROYnKFzWPG39C8SeekcP65pA2ff//91+xvDw8PvvjiC7744osLMyEFBYXzgl4vkZuvxddHc068Ak3Fpu15ZkZPLXkFNUyefpivP+xt5Sxzopp5IYpYVEHVEt3Sq7HTtEvb1j78Mrc/siwjyzS4X5YzHi5BMI5vMMiMHBbKmNFNa/jYC50JArRv68OwgSG89G6czeNCQ5SCmEudi/eOoaCgoHAWsizzx+pM5i9MoaRUhyjA8EEhPPtEDIH+bo4HOM98uyDZ5r64hDIkSXZoSAQFunHdqAjWrM+mfj6xKEJMtA/LVmei0YiMGBJqNyensQiCQGOKyIYPCmHF31lW97Vq4cnECdHEHitFkmSGDAihl4tVa8UlOgQB/P00Vvf/sSqDj79KtHm+LEP/XkFEhrsTFuJGbn6N2X5RgE7t/Wgd5e30nBQuThTDR0FB4aIg8XQ5ZeV6unbyQ6O2nji6fG0Ws+stXpIMW3blk5ZZxfw5fVCdB9VfVygsrrG/v6iGkGDHHoRnHzcqGq9cl20KFUkSJJwqJ+FUOYIAi/9MZ/xNzXnyobbnvMy9IQzoHcjo4aGs35KHKBg/O1EwelA+eacHIUHuDB8UavVcg0Hm4NFi8vK1tIv2IaZNXRuLo8dL+ejLE5xMMoan3NxErh0ZxrOPxaBSGb9HZeV6Pv8uye783NxE4k+U8sSLSZSU6iz2N2/myRsvdGroy1e4iFAMHwWFy5xV67L5flEyhcU1+Plq+N9tUdx2Q4vzdv0dewt448NjpoRTUYTxN7Zg8kNtzY6TJJkffk2xOF86k+S7+0Ahg/sFN2outVo5x0+WEeCvYWj/YNzdHefh2CIsxN2qAGAtfr7O3YLdNCJPP9qOI/HFnE6tsjJv439/W57BwN5B9O/tWv7N+UAQBF6Z2okh/UNYtzmHqioDfXsGcsuYZvj5WvfSAKSkVfL8m7FkZlebtvXrGcjb0ztTUFjD5OkH0evrjq+pkVixNpttuwv4/buBuGlEDsQWU6Ozr7ujqzH277LGHTc154kH2l50hrVCw1AMHwWFy5jPvzvFr3+mm/7OL6xhzjenOJ5YzstTrHfLbkoysiqZ9tZRM10XSYJf/0wnwF/D/25radpeUqYjv9C2B2XV+uxGGT6VVQamv3OU/YeLTQJ7vj5q3nu5Kz26+DdozBefbM/E5w9Z3dc6ygs3N+eNqt9WpFs1es7m03mJ/Dy3v9Pjnk9UKoGrrgjjqivCnDpeb5B59vUj5OSZJ3jvP1LEh3NP4uWpMjN66lNYpGPO1yd5YXIHnDFX7KVcr9uc61IDVoWLG0WIQEHhMqWyysDi5elW963dmEN+of1qoqbgk29P2RSz+3mJeRNJL0/7z2lH4oobNZfPv0vkYKxxjNo5VVToeeHNWIsWB6kZlbw8M44R47Yw8tatvPnRMbJzqzmbLh39uesWS++Zj7eKz2f2sDsfSZLZvDOfZaszycip4odFlt4ua6SkV/0n+khJksz0t2LJztVafEckCTZszWXjtlzrJ5/h3x3GxPI+PQJwd2v4cldYrKO03DL8pXBponh8FBQuMHq9xJZdBew/XISHu8io4WF0bn/uklRrWfdvjl0F3eVrMnnonuhzOoekFNtlw+Vnaa04WrhqVYaLimvYtD2PikoDvbr506WDn8OcF63WwJoNORaVU5IMFZUGNu/I47ozpdXZudVMfPYglVV6DBKAzIYtuew7XMyPn/YhMMA8yXrSg22546YWzF+UTFGJjiH9g7l+dITdOf27PY83PjqGTue6ASPLkJNXTWS4p8vnXizIssydE/eYhbfORpKgrNy+Ho/uTHjL20vN1MdjmPlJgt12GfY4dqKMARdhCFHBdRTDR0HhAlFaruNIXCnfLEgiKaXSlD+weHkGE8a35NF7o5Fl+Zwlqjoa9nz4DAL8NGTnWvcsqa3kUwQFaCgstv7kHRLkzj//5vDOnAQkSUYQjIvj4H5BvD29i0lpV6s1sGZjDrsPFKJRi4wYGkrHdj7o9NZfsUrEFGLT6SQ++PwEZRXm8RWDBMUlNSxbk8mDd7W2nFuwOy9Mdk5s5+9N2bz1sfVcE2dx5B272Fm3Jdeu0eMs9dtKXD86ghaRHrz50XGL0JkzeHs1PNdL4eLi0v51KChcgsiyzPyFKSz4PRV9vcW2vrDbT7+lsmRFOlqtRNdOfjx8T2t6dw9s0nlcfWU4s+aetPn0O27MuRdqe+Cu1kx766jVfUN6+5Hw+iekzf8dfUkpgUP7cv+IO/h4s/UqKLVa4O2Pj5tKvmtf1859hbz50TFeerojsiwzefohTiZVIAggABu35eHpYdubZJCgXbQ3VdUGnph2iJM2unJLEuw/XMyDdzn98i145uXD7Dtc3PABgMgwd5sl3ZcKv/ye6vggB4gCPPeEucJyZZWhQUYPGCvDFP4bKIaPgsI5pqrawKZtuWTnaenXM5ATSeV8b6U6yfI8o5v+SHwpT798hI/f6EY/F9Vy7eHhoeLuW6P45fc0i33XXxVBUOC518UZ0j+YG6+JYMXf5l3AoyI9GLPlYxK37jUp9+Vv2IHHhp206jeFlID2FmMlpVivnpJl+Hd7Prv372RQ3yAST1eYttfafLXvtS3c3QR+XZZm0+gBYzWaj7fzt9TcfC1/b8qhoKiGDm19KC7VNdroEQT48PVujRrjYkBqeEcJAKKae/LKlI5EtzTX3KnN+WkIX/1wmkF965Lns3Or+XNNJqdTK4kIc+fGayJp29rHzggKFwuCfC473F1ilJaW4u/vT0lJCX5+5z7HQuG/TVZONW9+dIzYY6Vm2+2p8NrDTSOwYemwJg99rfs3h3m/JFNQVIO/n4Z7b2/Jzdc1a9JrOCIts5IFS9KozsxhaGgxMV4lxD/9tuWBoki6XzQLBk47r/MLCtAgCAIFRfZ1ed6c1pmRQ61r0dRny858Xv0g3szj11g8PEQWzu1HWOi569buDGUVer75KYmExHLCQtx5dEI0LZu7pi7tSGzQHg/c2dJmbto7s4+zxskmqdbw9lIREebBuLGRfPpNEjq9hCQZq9VkSebV5zoxerhzFWsKTYsr67di+NRDMXwuLWRZJvZYKYfjSvD1UXPl4FAC/C8OF39ZuZ57Ht9NYXHTuscfursVD1jJIbnUqSkoYs/YRyjZF+vU8e9fPRdJVIMsozFo0ancHSctNRIPD5FqO56hzu19+frDXg4N07JyPTdN2EFNAxKXh/QP4v47WvHbigx27C2gotKAj7eK0cPDeOqRdhe8Y/iRY8VMfvGwhWE/6YE23DUuyulxZFlm7P92UFLq+u+nRTNPfv3aejn/xm15vPp+vMtjOouHh8iKnwbj5ankA51vXFm/lVCXwiWJVmvg2deOcCiuzpvy8Vcnee25TowaduGfuNZsyG5yowfglz9S/5OGz96bHnPa6DGIamRBBFlmxPHf2dTp9nM8OyPRUV4cO2k71PXI/1o75Y3bsiu/QUZPu2hv3pneBbVa5LXnjDd2g0G+aET1ZFnmhdePWvVmfvF9EteODLeoeLOFIAgs/3EgU1+L5VBsCa5U56dnVrF1Vz679heiVguMGBJKjy7G9hfDB4UQ3dLLrqhkY6iulth7qIgrBoWck/EVmgbF8FG4JJn2dpyZ0QPG8NFrHxzDTSMwbKDjcMO5JP5E2TkZt0b733PQlh8/RfHuQ04dKyEQF9kfWRBpmxdL7/TNbOp4Kwjn3tMxfFCITcPH10dF357OJZ+XNyBJ9tF7WzNhfCuL7efD6JFlGb1eRuPAmxSfUGYhQVCfD75IINDfnZT0SqKaezJuTDOzqiswavN89OVJSsv0CAJ0ivFhxYJBLPkrgx8XO5/wPP2dONN7s3RlJjddG8lzTxjbfsyb3Ye3Pj7Glp35DQo5O0LnQCFa4cKjCBgqXNQUFdeQml5pdjNJSy9n3yHbXZanvxPPH6syzsf0bBLgpzknkRevi7yktrrawO4Dhew+UEhFhR69wbGhVnHK+QWtws2PTe3HAdA5cw8qSU+7vFiExmbDOkCjhq9/Sra6z8tT5KsPHIe4aunR1XkVaE8PkWcfj7Fq9JxrtDUSX/+UxHV3bWfEuK3c/dge1m22LRiYV2C/WmrrrkJWrsvicFwJazZk8/CUA2zanmfa/8eqdF774BilZUbDUJYh/kQ5tzywyyyp2FkMBtlUKbl8bRY79hYCRj2ot1/swobfh/Hxm90Qm3AVVIkCvbsFNN2ACucExeOjcFGSV6Dlvc9OsHu/8Wbl76vmwbtbM+76Zjw5w3FI5JNvTnHF4FCCz0NlkjWuGxXO7yub3vi6746W1KblXWyNKNdsyGb214lmKscCMKBvEE/c34Y2rax3tfZu59yibhBEDkcNpdLdGOZx01cjyhKjjy0ma2ArKtz8EABBlpHEpjMQRRH0VuwqUYQXJrXn+qvsixGCMfzy659pHIwtwd9PjZtGcBjuat/Gm/mf9G3M1F1GkmSyc6tx04h8+OVJtu8pMMkCpGZU8caHx9DWGBh7VaTFuT27OTboaj0shjPv5/ufnWBI/2A0aoFP51lvIqrTySxY4pxqtS1E0dh2Ykj/OgNKoxHp3yuID1/rxrS3YtE1IDJdK4ZY23T1/jtbnpdqSIXGoXh8FC46dDqJJ186zL6DhaZtJWV6Zn+dyIdzT9rt11SLQZLZtrvhpauNpUM7X555tGG9fR69txXhoZZaNZ3b+7LnQBFX3LSF0bdtY+anCRQ56P59vjgSX8I7cxIsWjvIwJ79hTz2wkEys837TCWnVfDPvzmc0Abi08Vcb6VG5U65uz9yvS5LoixR7FmXO5Ea3AEQCKzK55GtrzMyYSmRJclo9A0XvhME4z8qETq19+HucVHGsncrNookGb9njoyexNPlPPD0fv76O4uU9Epij5U6leNzx83OJwM3Bdt253PHo3sY/8gebr5/F9t2F1h93d8sOG3Vkxfg54a/n2vP0uUVeg7HlZCdW223wm3X/kKb+5xBkqCyyrpl0793EKsWDiGmjXXD3BZBgRr69wokONCNzh38eOOFTtx/5/n3zCm4juLxUbjo2Lq7gPRM680Yl6/NcmoMQaBBcv9NyW03NEejEZj1xUmnzxEEmDC+NRPGt2bf4SJ+/ysDN43IkP7BvPdZAgaDjCQbwxBrNmRz6Ggx33/S94JXkSz5KwOVyJkWDuZIsjEE9tvyDJ6Z2I7qagOvf3iMbbsLTMe07vYM9xa+SUlBJes63cnxiN7Iggq/qgKuOPEnnbP2UKP24HhEH9M5+1teQe+UjQRUF+Kpr8RTV0FmQBuQXc+xEAW46spQPNzVhIe6o9PJbNtTwJZd+TYFHgUBU1jGHnO/T0JbYzB5O5ypo+0Y48M1I8JdeAWN49DRYqa/E+fU3AqLdOTkVdM8wrIlxguT2zPjXdeqpiRJRhTtG4+GRkYyBQF6dw+wud/LU833n/Rl07Zc3vjomFnjU+HMv+q/Nx3b+fDlBz3RaC7u0LOCdRTDR+Gi42RSOWqV4FR+iC1kGfr3blql44YwZlQE3y5IorjUuTu3Rz0F4b49Aunbw/gaZsyMQzLIZsmYkgQZWdX882/OedfdOZu0jEqrRk8tkgQHYo15WZ/OS2TH3gKz/amlGr4c8R6G8kqKtSpj1RZQ6hHEXz0eQqdy40iLIejUdRo1kqjh+8EvM/LEH3TM2se/MbcYP/gGJDoLgoCHu5qnH23H0zMOc/R4qUMjQJahSwdfu8fo9RJ7Dxa51P5DEOCW8/x5Lvg9zekeVgLg42V96bhiUCgTJ0Tz9U+nnbqup4eK7p398fRwbEC0ivIiLb3SqQqv+q9FECAowI3rR0c4PG/E0DDat/Xl659Os3WX0WM8bGAIj94bTVCgG4VFWkKC3PFwYr4KFy9KqEvhoiMk2A1DI7tLj7u+mcuiaecCjUbk4zftd+GuRRCgVQsvflycwr7DRciyzMnT5SxYksqufYVWDQtRhENHS5p41q4T3dLbYYWRr4+ayko9q601A5Ugv1BHUY3GZPQAphVsbZf/Gb05Z+EV7Efwq9OJe/MXKjz8G6zlY5CM7/WKtZnEHnNs9NTiTGsIV6ckyzDz0xMcP3luKgOtcexEqVMVTqJozNmy97rvvb0lrz3f0e44qjMf8VMPt8XTQ8XJ07ZlAgA0Gvjgla60aGbuZQoNdjONV/v96xTjQ48udflGsgwFRTVMevEQFZWOPXTNIz15c1pnNv4xjI1/DOPNaZ1p0cwTL08VLZp5KUbPfwDF46Nw0XHV8DC+/D6Jaq3rIYvWUZ7ceUuUU09354v2bX25YlAIm3fazzmSZTh+spyTp8oxSBAYoKGoWGdX6VkQBHy8nbsRF5foyM6tJjzU3Wk9FWe57YbmrN9iu+IHIPF0BSdPl7uuVmzHcigu1TNqWChenirWbcmzeVzL5p6kZ1XZfB9VIkSEebhUMg2OW12o1SKD+wezY2+BS6XTKlHgzzWZvBjjXGPTxpCeWeWwD1Vt8m5EmAcvTLJsF3I2o4eFkZRcyYIlqaaqKUmCiFB31GqBVlHejL+xOX3OeDRT0+3r6gzsE0zzSE9+ntuPQ0eLycrVEt3Si04xviSnVbJxax5V1Qb69Aige2d/bpqw02KMpJRKnn3tCF/N6u1w/nDxFQ8oNB2K4aNw0eHnq+H9V7ry4ttHHS4s9VGpBPr2DLJacXKhuWtcC4eGTy21np2iM13I7S2YBoPsMBckr7CaZ1+LJSm5bnEZPjCYl6d2cio3qEYnsWlbHofiSvDxUnH1iHBios17EnXp4Mfrz3fiw7knKCu3HtarqDTw9MtHHF7PFQQBlq7MYPrTHU2GojU+eacHvt5qNu/Ms9r53CAZ35ONW20bT9ZY+lcGaRmVxJ8oJSunGjc3ke6d/Ll1bDNT/sekB9twJL6E8go9koRTISWDJFskgzc1siyzYm0Ws7+x3ai2FpUaosI9adnci4TEMkKD3ewaBoIgMHFCNNeOCGfLrnwMksyQfsHEtLHeyyoizH6bjacebguAKAoWzXqjW3rz0D11icmr12fbfGg6eryMgkItwUHWG90qXB4ooS6Fi5I+PQJZ+GV/XHnmkiSZAicqvi4EXTv6M+F2yyodUTSGgFx9uFSdSQZ94K5WdOtku4y4slLPPY/tNTN6ALbsKuCV9+IcXqe0TMfDUw7w1sfHWb0+m9+WZ/DAU/tZ+IdlY9NRw8JY8dNgbrzWtuHZlL2pwGhApKQbDYQPX+uG2kq47eF7WhEabMzLuGZEBJMebGMWllOJMHxQMBUVrmfQrtuSyztzEli2Ootd+4vYsrOAz+cncdX47Rw6WgxAVDMvfvq8L3fc1AJ3N9GpMJpKhDbnuOHlGx8eY9bck2aJvLbQ6Yzv8/a9BUx/J4453zjXR6tVlBf33t6S++9oZdPoAWPFYkwbb1MIrBZRNDayjQy3TKS2xfFE+yHCnY2sEFO49FEMH4WLltAQd0Y40fCxPu3bXrzdkR+d0IY3p3UmLMT4tCkIMKRfMNeODHdY1VKfsBB37r61Bd9/2oeH7m5t99hFy9KprLL+9Lv7QJHN6rlavllwmuQ0Yzdzg0E25V7N/T6JpJQKi+MNBtkicflcohKNYSwwSgisXjSY225oTuf2vgwfGMyCL/py/52tzc6565Yo/vxxILfd0ByVyujt2bargFlzna++c4ReLzP11SPo9cb3PiTIGF6scVLV1yDBX39n8cS0gxw4Yluss6Ekp1Ww3k5o0Ba13selKzM5dqLU/sEuIAgC773clTatzUvKe3YNYMYU18J9jnL7/Hwujn5+ChcOJdSlcFHzwuT2JCWXk5zu2O3v66Nm7NUXT26PNUYODWXEkBAKi3V4uot4eanZvqeAJSucEzsURbh2ZDiP3mu9+/TZbHdghJxOrbBIGK1FlmXWbrJMRAajx2nD1lzatDKfx+8rM8gvOH9eN0mGcWObm/728lQ7pZ+k1UosW51JreBzI3PprVKjk/l7Uw7Xnwm97tpnXRfH5hxrJI7El/LUjCO8+mxHrr6y6crbnf2+2UKlEti8M59O7ZuumXN4qAfz5/Qh/kQZ2bnVtGrhRbto1x9kbrw2ks++S7RaAq9RC/TrdeGrPRUuLIrHR+GixsdbTZvWPg5l5VUqgS/e60mg/8WvmioIAsGBbnidKQke2CeImDbeToW71CqBcWOcL3X2dpDD4yi3QmsrwVzAQqwQcJjgbEnDLQ6VSuD5STF0bGe/pNwaazflIMuy1au7aYQm64GVUi9pV6MRG9zG5JNvEpu0B1RNTROMVVhA9or1FG7bh9xETa8EQaBLBz9GDQtrkNED4KYRbXqJnp8c41TpvMJ/G8Xjo3BRU6OT+HdHnsOKmFYtvIhu6Zry6sWCSiXwyds9+OTbRP7eZN9w8HBXERLsfGLm2KsjOWij3D0sxM1u3oUgCPTo4s+R+BKL999gkOnVNcDiHGvGkH3OWAKy7HLdt8Eg89HcRE6cKueZR9uhVjv/HFdYVIMoCEhWTB+dTmblL4PIyqnm2IkyNmzLIy9fS26+Fr1Bdslr06ueaN6oYWHsPtCwsFVJmZ4TSeV06dA0HpbrRoWzZmNOg84VZImRsYuJWrOZ/We+GJ6tmtPnt8/w792lSebXWK6+IoLoKB8WLUsjKaWC1lFe3HZDC7p2bDoPlcKli+LxUbio0eskp8qAb7ru4qvkcgU/Xw2vTO3E95/0RqO2bQCUVbjWUGjU8DCiW1rPeXCUHwTw6L3RCIJg5nETRaNw3+B6fY90OonvfjlNZnYD20XUN3pcsCwMkszytVl8+aNzgnkARcVa9h0usiqQKQgQ08YHfz83Osb4ccv1zfl8Zk8WfzuAHz/rS896DUaDAuznini4i/yyJJU3PjzGoaPFXH1lGAP7BDk9z7Ox971wld7dA2kX7fqDgiDAwKS/6ZO6yazcsCo9i93XPoC+zL4ez/kkpo0Prz7biR8+7cvrz3dWjB4FE4rho3BR4+WldhgGun50xHlXuj1XxLTxZeJ9lkJ9tQgCpKRZ1zyRZRnprGSVmhqJrBxLY0QQYN4vyabu1bbo3tmfL97rQZ/ugbhpBAL8NNx5SxSz3+phqqDKzK7irsf28P2vrmng2MRFz48sw7LVmU55myqr9Ix/ZA+pNnLGZNlYKWeNli28+Ozdnvy1YBC/fzeA5T8NYtZrXW32eNLWSByKK2Xj1lwmTz/MH6syee+Vrjx2n3P5WfWJCHNvcOjHFl9/2JshA5zveu7nq6ZTjA9X5Gy0rLY0SNQUlXDs2+VNOkcFhXOBIMuuOG7/25SWluLv709JSQl+fsrTwcXC3oOFPPtaLAjmmjYtW3jy1rTOtD3HZb/nG63WwPhH9lBQZJkkLAowuF8w773S1bQtK6eaT75JZNseYyKzKMKgvsE8/UhbTiVXMP0d22Xr82b3blCOTH0mPneQ+ITSRmTrNA2/zO1Hqyj7FT0ffXmSZaszbe5vaBKxLMtk5VRz/GQZ8xelkJpRaeGpFEWY+15P/Pw05ORWM/vrRFIzjAZYm1bedOngy1//ZFuMrVYLfPxGNwv9mrOvL0k0KDdp/ZZc3p5t7E9lTWNIAEJC3Pl+Th98NXrW+ve0Oo5BUHEo5moeXDfTZsK8gsK5wpX1W8nxUbjo6dcriE/f7cH3i1I4erwUP181N14TyT23tcRN899zWrq7q7hySAh/rMy0MCYk2VipZTDIqFQCRSU1PPTMPkrriQZKEmzfU8Dho8U8/oBt75Hx2MaZK+mZVcQlNF1Zc0NRqwSCgxwntu/ab7/KzdtGDypHCIJAswhP1GqRZBseOUmCx144BECzcA+mPNaOVi28EEWB8FB3BEFg1PAwfl2WRmJyBRq1QJtWPgQFaNh1oAiNRqRrRz8z4cDKKgMzZh5l/+FiJAm8vVQ8cGcr7rzF+c7uo4eH0b9XIFt25lNeqadVCy+27ipg8848BEHgysGh3H9nSwL8NciyGveIELTZlmKcomwg1z2U739N5pWpnVx7AxUUziOK4aNwSdCzawCfvBNwoadx3vDyVCGqBKuhKFmG3AItkWEe/Lk608zoqU95pYGExHI0GsFqp/pAfw3t7SQ3O0Nx6fkXjKxtn1D/7+tGh+Pj7fh25kgv6WRSOUP6Ox/+ORtHocNasnKrefGtOL7+sBcdY+o8brWNaSVJZuanCazZkGPy4ixcmsb1oyOY9mR7RFFAkiTuemyPmWhnRaWBz+cnUVlt4MG7Wjs9bz9fDWOvrsuTG9Q3mBcmW7amEASBNlMf5tgL75ltlwSRKo03ceH9SNyjCAQqXNz89x6XFRTOAXq9xIatubz/WQIffnGC7Xvyaeoosd4gk3i6nJS0Sob0D7a7iP7yu1E5eetu+x6M/UeKePx+o9fn7NSZKwaHNLpsO7qlN26a89PTSBShc3sfBsgnGZC0li6Zu1EbtAwbFMLTDzvW7gG4bqT9MFaIE14jexyJL3bqOFkGBFi0zFIBG2Dd5lzWbDBWXRkMsum7sGp9NhvOtNVYtT7bplL5gt9SG+3Ns0X00/eRN/p29EJdWXiBdwQL+z2LTm3sxaWgcDGjeHwUFBxQVW3gmVeOEHe8LqTz59osvL1UPD8phtHDXc8Jqao28NNvKaxen0NllZ6oZl7k5FdTUmqs2mrZwpOQYDebYoCbd+bx3BMxDo0vjVrk2pHhLFiSatHH6s81WciyzPOTGt4I09tLzV3jolxu7ukqomj01gzf9g2BB/6ltnOrKsCPga9/53TH7AnjW7JoWToVlZZeMg930WWl8PpUVup579MTTh9vMMgkJFqvglqzIdvCswVG79bqDdlcdUUYG7fa7v2m08tkZFUR5UDFuCEIokiXd6cyffpgwkvTqNJ4k+vbAs5U/40eFtbk12wIf2/K4dN5iabfVOsoL96d0ZmWze1Xs2XmVLFrXyHNIz0Z0LvhVXgKFy+Kx0dBwQG//J5K/HHLPJaKSgOvzzrudPPRWvQGYzuDX5amUVBUQ1W1xImkctMNGiAto8rm07wgS3hXl2Ko1jKwr/0bsyjCT79ZGj21LF+bzankxpUgP3R3ax6/PxpPj6a9nYwZFU54qDveXioG9wvmKXkNQYe3GneeyRzWl5bz1/iXWfZXKtm5jkvpRVHkj+8H0CzCXAvJ20vFh693a3COD8C2PQXoXOxF5u9n/Xql5XqratKSDGVlxs/SkRCft/e5E+rr2zOQkde2ISW4I/kBUSAICIIxd+n+O61XxZ1PNmzL5a2Pj5v9ppLTKpkweT/FJdZ/VzqdxKPPHmD8w3v4+KtEnn0tlqtu38rhOOs6WAqXLkpVVz2Uqi4Fa9z64C5y8rQ297dp5c1Pn/d1erzNO/OZ8a7jBqEWyDJ9Ujcx+NRqfGpKET3caDbhVqZlDKXcYFtTxs1NoKbG9s984oRo7r29penvlLRKMnOqaNHMk6hmznsMDAaZo8dLeH3WMfIa2bYiwF/Dnz8MNIkSanPyWR811KzkKCWoA6u73Euxd52HoXc3f2a91g13d/uLvizLHDtZxrETZQT4axjaP9jhOY5YuiqD2V8517yzFh9vFct+GGRhxHz+3Sl+W5FutTLs1rHNSEmv4khcic0u5EEBGlYsGOzSXFxFlmW27S5g/ZZcqqoN9O0RyPVXRTTKeGwqbr5vJ/k2HhyuGRFmNfn6mVcOs+9QscV2lSjw9+LBeHg07evKyavmcFwJ3l5q+vYMxN1N8UM0BqWqS0GhCamqtq8Pk5RSgd4gW+0Mbo0DR4pQ2Uhctkf/5HWMSvjd9LdUXUP6vMVMG3yatwIfosZKAjNg1+iBuhLo4hIdr8+KZ9/hYtO+gX0Cee25zvj6OL5VqFQCPboE8Mf3AzkcV0JmdjWHjhbb7PdldQxRAAFeerqDmRJzTX6RmdFT4B3B4r5PYxDMF4sDsSW8/uExZs7oij0EQaBzez86N2Gvqe6d/R0fdBblFQbWb87lhmvMBThvv7E5q9ZnU1GpN713KhHc3UWWrLBdjg9G4+idl869grIgCAwbGMKwgSHn/FquYsvoATgcZ+m9rdYarBo9YBTJnL8olSccVEg6iyTJfDbvFL+vzDB9pX191LzxfCf6K6G184JiYiooOKBfz0C7mnpeXipULvySPBrgWVBJOgafWm25Q5Ko3LaLyJIUl8esZfiZhevVD+I5GFtstm/X/iLue3Ifh+OKLU+0gSAI9OwawJjREfTpEWjX6Kl9X1u39KJ/70BuGdOMHz/rw+B+5pVVXm2iUPnU5WbsazkCGQEEyzd+664CSsuth/bOJTHRPlwxKMQl/UW1SiA5zbLLfXioB19/2IuhA4JRqYy9w4YMCEFvJ5Tm6SEyYmgIv383gG6dXDfC/kvY6+3nYSUkm5FlvwnyiaQyl+cgyzJrNmbzvyf2csXNWxj/yG6Wrsrg978yWPJXhpleUnmFnmlvHyWvwLZnWaHpUAwfBQUH3HdHK5uVS6IIN10Taaat4ojRV4S57O3xryzAU29DsRkIzk+ye74tj839d7SkRTNPTqdWcOBIMQYrRkpuvpZJLx5m/qJkl+YMcOXgEIKD3Kwahj7eKjrG+PLik+356bO+fPxGd56Z2I7WUZbJpypPD9pNe9T0d45fFJJo24C0F5o8l7z2fCf+d1tLU2l9ZLgHM57pQKC/9VCkQZIJt9EotmVzL959qSub/hjGv8uGMemBNja9emBMe3prWhfCQuw3nr0c6NbJtifv1uubW2xz1Kw36ixBxvwNO9hz/cNsaHMlu665n5zV/1qcs2hZOu/MTiAlvRKDQSYzu5rZXyXy3cJki2Nl2RgqXr3eUsBSlo3VnkePl6DVutoLT8EaSqhLQcEBbVp5881HvXnvsxMcO2F88qtVuO3dLcCpnlf1iYn24cG7WzF/oW0vjYB53/Jydz9kBAQr+sgCUOFmX33ZIMl0aOdDdk41NTqJ5pGePHJPa4YMMHp7HD3xAsxfmMKIIaEuNYN1d1cx583uTH3tiCnvRxBgzOgIXpjU3qVy+rbTJiJo1Jz64Bv8qwrI9I9Gtmb8yDJhQfb7aJ0r3DQiEydE8+i9rTEYZFO4Lq9Ay7c/J5s95QuCsZLsGgdK0bXaQ46MOb2+6bq3X+q8M70Ldzy6x6J6r3tnP24ZY9nexttLTccYH46ftEz0FwR45H91bUbSfljKkUdeQlCpkA0GqjNzKNi4k86zXyZ68r2AUVhy/hkD5+wsWmsVhbXXyT7rMz5+sow3PzpmUvj28lQx8b5oq8abgvMoho+CghO0be3Dtx/1prhEx7878igr19O9sx/dO/u75O0BY4z/xFllzGe3CjjbvKnReLG9zXUMPr0WUa5b4CQEtBpPToV2s3vNykoDJ06VM2xAMO9ayX9xpsWAKMK/2/NcMnwAfvsrnbyCGtNrFATYtC2P229o7lL/KUEQaPvsw0Q/dR/y7FW8tdW6x6d1fjwkhUGvzi7N01l0Oomd+wrJL9QS08bHTE25slLPmo05HD1eir+vhmtHhdOxnS9339qSrNxq/vq77one30/DO9M74+/nnJHWoZ399yoiXPH01BLg78aKBYOZvyiZnXsLcXMTuX1sM64ZGWHznDlv9+D+J/eRnVtnfKhEoxfPz9f4GRmqqol/9l0AZMMZA+aMm/T49Fm0mHALGj8fTiaV2Uw8t4XBAFVVet77LIHY+BJ8fdQcP1lu1ky3ssrA7K8SCfDTMOoikQ24FFEMHwUFFwjw13BzIxui7jtUZOqrVYsztZXb2o2lQ+4hQsszMQgioiyhU7vze+9J6FWOhfdkGbbsKuDEqTLatzX3ELWO8qZPjwAOxhbbzMkRBIEanWs382MnSk2Lfe1rlCRjMukX85OY/VZ3l8YDEDUaurfz5Irv/mBzzC11iUKyTGhZOrcd/BJZP9TlcZ3hxKkynnvjKIX1+qh16+TH+692pbpa4vEXDpKTp0UUje/X7yszmPxQG+68OYppkzsw4fZWxB4zLmr9egaaJXA7wttLTffOvhyJt55v8vaLDTf0Sst1GPQyAf4alw35ixV3N5HH72vD43aa/tbHx0vN798N5EBsEdt2FRAR5sEtY5qhqdcWp3hfLPpS6/IPUrWWl+9ZxOmI7rRx8eGglvVb8hweIwjwy9I0xfBpBIrho6Bwntmxr7BBVV2yqGbekFdpl3eUsLI0ytwDSIjoQ43a+Sd9QYAj8aUWhg/Amy905o0Pj7HnYJHVcw0G2WVBtx17C1GJAoazRGkkyWgA1uikBvVbC75yAEPSn6Vn2hYOtRhGlZsPnbP2ElmailtYMH7nwNuj00k8+3qshSZS7LFS3vskAbVaJP9McqrReDS+5i++S2LYgBCaR3oSGe5BZCM8M5/P7MWkFw8Se6zO+FGJ8PoLnYhp43qz2ZS0Sj7+6iT7jxQD0LaVN08+0pa+PWw3RL0YqNFJ/Px7Kr/9aRSjFEVo39aHpx5ph5+Pmg+/PElCYjlqtcCVQ0KZ8mhbNBrnigp6dwukdzfrr19UG5dMvaCmxCsEz5pyvHR1hlBmvo5MqsnMdqwp1VBkGVLTref7KTiHYvgo/KfYe6iQT745RXZeNcGBbjxxfxuuGNxwNd5zgSsVYBYIIolh3UkMc91TAsabpq1EZ38/DR+/2Z2N23J548NjyFKdcrAgwNABwfTo4mK1kACWgbv6+xqGW1AAHd95jmPPz2Rw6nowGIw5F4JAlzkvmxaopmTnvkKbQpBbdhVY5GXVIoiwcVuemVZSQxFFgS8/6E2NTiIlrYKQIDcCA9wdn1gPnc7AO7MTOBRXTGGxzszDl5RawbOvxvLlrJ5NWurflMiyzMsz49ixt64nmEGCYyfLefyFQxZh4xVrs9i5t4Df5g1AY8fDVlyiY9nqDLbvKQBB4Oorw7j9huZmHjC/vl3Z1/0WtoRegVbjBbJETO5hro37GZVkICXQsr/ZuSA8VAlrNgbF8FH4z/DTbyl8syDZ9HdGVjUzZsZzy5hInn28YTckSZLYsrOA8go9wweFmGL9jeGKwaEsXp7R6HHs0TrKk7SMKrMqrdpk2mED7DfhHDk0jKjmXixYksqho8X4+Wq44apIbh3bzOUwyPCBIXy/yDKJWxShf++gBnl7amnzzP14t29N8ucLqExKxbdbB9o88wBBQ/o0eEx7JDpQuLblvxMEgeomrsZx04gN8vD8uz2Pl9+Lt7nf2ENM5uclqVZzwS4GjsSXmhk9Z2MtbJxXUMPPS9J44C7rqtI5edU8POUARSV1hu3xk2UsWprGj5/3Nf3ul67JYV2z6+pOFEQSQ7vza98peGuLkVTnJ6l+/E1KcnNjUAwfhf8ENTrJzOipz7LVWTx8T2v8/VxrQPnHqgxmf51oupG+99kJBvYJ5MPXG+ZtqaVta2/GjApn9Yac2pZTFk+pDSE0xJ12rb2Z9GAbPD1UTJ5+mKycatQqY6hJrRZ444XOeDmhrBsT7cObLzQ+XBTTxofbb2zOkhUZptcqiuDtqWbyg40XhAsfcyXhY65s9DjOUFLaMG0gg0G+KEJH+QVau0ZPLQYJ4hJc1605Xxw4UtSg38u6LTk2DZ9vf042M3pqySusYdpbR/nyg14YDDI//WbZk04WVeT5tSCPFq5NqIGMv6k5N54leKngGorho3BO2LIrn2MnyujS0Zeh/c+9suvyNfY9KPN+SeHZx2OcHu/YiVI+ttJ+YNf+It6dc5yXnuno8hzTMiuZ83Uiew4UIQPNIjwIC3ZHrRFo09KL3xwo8jripac70K9n3QL789x+/Ls9jxOnyggOcueaEeEEBzau+7g9amoMqFSgUpnnUjz1cFt6dg1g9fpsikt09Ojqz21jmxMW4lqI5kJRVq7ngafNq32cRRBgUN8gena98IKCv61Id+o4QTC2vLhY8fRsYGsRO4bSxq25NvfFHislK6cajUag2IpxdD65ZUwkTz3c7oLO4b+AYvgoNCkpaRVMfP4g5RV1rn1fHzXfftybFpGOS6YbiqNFqaDItUXrnTnHbe5buynHZcOnqKSGx184RGmZznT/zcqpJjdPy9cf9iIpxVK911kEAQL9NfQ6a3F1dxO5ZkQ414xw3D2+RieRlFKBh7tIqxZeLoW0PpuXyJK/Mky5It7eKma/0Z3OHfzOzE/gikEhXDGo4QZw3j9bSfvhDwS1iqgHbiP4ygF255ieVcnSlZnIskxUM0/iE8qQZBjSP5grB4fYrKbKzq3mx8UpbN1VgCjCyGFh7N5f4LLRI4oQEerBDddEcufNLczmmpVTzZIV6RgkmVuub0brFg2rAHKV44mWrRqsIctwUyMrF88lI4aE8sV8+4Kd1hg51Haun6NCg8Tkcvr3DESjEdDZEZE8lzQLd+eZR51/eFOwjWL4KDQpZxs9YHxinvjcAVb9MgRZltEbZL76IYmN2/LQ62V6dvVn6sQYAhvhjbhuVLjdvJnr7Oh3WMOeWJyzfafq89ffWZSW6sw6bsuyMVFz/qLTCFZaLzhCJRqTj93cRN54obNLpdFnz23uD0mUlRs7WbeO8uLlqR3p2M5xDsncH05ZvO8VFQYefe4gK34aRFAjPUz6snK2D7uT8riTpm2Zi/7Cv38PBq3/CZWnZZLnK+/FsWl7vtXx1m3OpU/3AGa93s0ivyi/QMvDU/ZTWlbXGf33v1zPxRJFuG98Kx66p7XFvrdnH2ftxhzT30tXZjKobxCzXrOvw9QUtGruw4Ejjo2fsVdFXNShlPBQD559PIYP55602OftpaKy0mDh3An013Cfna7xnTv4EXvM9nuTmVWF+4AQxoyK4K9/shp0D2gsPboGuCT4qWAbxfBRaDK27c63MHpqKSnVc+O9Oygs1iEKmBkAm7bns21PAQu/7EdkeMO8Qu2ifQkNcSMv37I5oY+3yuVGip4eKqqqm+7uduxEGZKVB0WDBNv3WC8fd8SgvkF06+zPdSMjGmxgbN2Vz/ufnzDblppRyVMzDvPrV/0djrv4T9vhk9dmxfPZuz3NtlVU6Fm9IZuKKj3jxjTDz9f++IcfnWFm9NRSsucwCa99QucPppltX/hHqk2jp5b9R4r56+8sbh1rniC6YGkqxaV6u+eejZtGQG+QTQuhKEJIkDvjxjajpEzH1z+eJi6hlAB/DR3aepsZPbXs3FfIg0/v490ZXR22TmgMN10XybI1tsOpD9/dimGDQmjb2nlRyQvFzdc1o3tnfxb+kcqxk+V4uqu4ckgIN13bjKycKmZ9cZJTyeWo1SJD+gfxwuQOdiu6JoxvyfNvHLW5/4fFKfj5apj0YFuOJ5aRkGg/0f1ccOVFVp16KSPIcmNTKv87uNLWXsGSeT+f5ofFlsl/ztKlgy9ff9i7wedXVOp5eOp+0jLqNDTCQtyY93FvggJdyydZ8lc6n3xzyuq+Nq28+Onzfi6N996nCazZkG21F1ZD6dbZj8/e6dFgTw/Ao88d4LgVo0wU4JF7ox2WYA+9YbPNfd5eKv5eXCck+NbH8fy9yVygrVmEBz9/0Rc3N8u8DX15BX8H97XpYlN5e3Jt8SGzbTdN2ElBke3O3LUEB7mx/MdBpr91Oomrxm+z2wTUGvff0ZLKKgObtuchy3DlkFDuvb0l+QVaHn3uoEtaTf5+Gr6b3fucGj9rNmTz7pwEM4+ISgVz3+tJl44XPg/pQiFJMjfdt9OmXEEtXTv5oddJHD9Phk9tEveoYaG89lwnU/sSBUtcWb8Vj49Ck9G1Y+OMxWMnG1dJ4u2lZtFXA8jNr+b4yTLaRfvQLKJhHqTe3QJQqwQzuXgwdnae+14Pl8cbMzqClessGxA2htj4Um6+fxfz5/RucGPK0ymVVj1RCHA6teF5R2AMwdXy428pFkYPQGZ2NfdO3sfibwZY7KvJL7IbVzRUVCHLsln+TJmTXdkLCmv4ZWkq99xqNOz+XJPpstGjUQvcf2cr1GqRpx4xTzh98Jn9LgtUlpXrWPhHGlMfO3d5HNeNimDogBC27MwlNaOKoQNCLplO7nsPFbF8TSY5+Vo6xfhy2w3Nadncq0nGFkWBaZPb89I7ccYQtI3jjtoJhzU1zSM8aNPam9HDwxgxJFQxepoQpTu7QpMxsG8wfr4Nt6WbyvcYFuLB8EGhDTZ6qqoNPDTlgIXRA8aFw8fH9bBS987+9O0R0KD52KO4RMftD+1m+dqGVYRFhNn2hDkjkhYcZPu9uOuWuvLen5fY9gRmZFWTkmapROvRLAyVl+3P0KtdK4sE5+Ag5z173/yUbGo9scpKV2x7eHqIfPqudW9bbl41BYWOvU5nI0kYxfPOMb4+aq6/qhmP39/2kjF6Fv6RxpRXjpiqRZevyeT+J/dzJL6kya4xdEAIcz/oSWTExSEO+NQj7Zg5oyujhoUpRk8Toxg+Ck3KvI96E+DXMOOnZYtzV/XlCt8uOG3z6f+vtVk0NDrcmMotexgkmPXFSaa/c9SqsWaP22+01B4RBGMl1qioMmInv86ua+7n6JNvUGYl12bu+z2xVlzVqoUXd4+rC5M5ypfaecBywRfd3Gj7wqM2z+l0Vn4PwMNWEoptYZBkdh80CuFl5zrfYiC6pSfrlgyzaTSUVriWJ1Sf+l4ye+TmaXn+9SNcPX4b1965jVffj6fcSW/XpUZ+oZavfjRWcdU6AA2SsRv9h3NPNPj3aI2uHf254epIxAu4MooiRLf0YmAf19rDKDjPJWP4zJw5k379+uHr60tYWBg333wzCQkJZsdUV1czadIkgoOD8fHx4dZbbyUnxzKZUOHc0SzSk+/m9GHkMNdLl198ssM5mJHrxB637c7WG2RT9ZOrlDbwPGfZuquAOx/dzcp/spw2gG64OoK7b42i/gOll6eK14fkk3DdHaR99xsFG3eSOm8xW/veRM7KjWbnN4/w5J/FQxkxNIQAfzVhIW5Mfbwdv3xpngOlUdt/Yo2yIXXQbvpjtHnuYQR1XQ6Q6O1Jt3nvEnHDKIvjrxkRzn13tMTZB+Sffkvlg89PUFnlvLKyj7d9jZvWUd4Nqr4RBbjmSsfSA1k51Yx/dDc79xdRWWWgvMLAxm15jL13J7v2FzSpIXC+ORJfwrS3jnLz/Tt59LkDrN2Yw469BVYjnpIMSSmVDdJXssfVV57b5p/dOvnZ7dYyqG8wc97uoVRwnUMumeTma6+9ljvvvJN+/fqh1+t56aWXOHr0KPHx8Xh7G3UwHn/8cVatWsUPP/yAv78/kydPRhRFtm/f7tQ1lOTmxvPj4hTm/ZLsctjqf7dF8ZiTXZTPNdPeimX7HtuS+JuWDbNbIWKLx54/SPyJUoelsJ3b+1JUUkNWTsNv6MMGBvPO9C5Ou8jzCrQcOlqCh7tI387ebI25Al1RqXn8URBwCwlkVMoWRI1rAnezvzrB0lVZVvdp1AIb/xhmV5fHUFVNWdxJVN6e+HRs61BnSFtjYOuufLbszGfjNvtVXq4gCPDYfdGm3CCA9VtySThVxoihoXSOMd43PvsukcV/WpbC+/mq+WP+AKa+dsTUZb22yrFjjC+fvtMDLwcCfZOnH+LQUdshnutGhTP9qQ7nLTwiSTKH40ooLK6hfVsfopo1LO9m2+58XnonDkEwenRqE3v79wpgz8Fim+d5uIu8MrUjPboE8Nc/WSScKiM40I3rR0dYbcbrDF/MP8WiZc4JPrqCIMCir/qjVgus3ZhNTr6WHp39GTowhIKCGvx81QQGnDuR0f8yrqzfl4zhczZ5eXmEhYWxefNmhg8fTklJCaGhoSxcuJDbbrsNgOPHj9OpUyd27tzJwIEDLcbQarVotXWLS2lpKVFRUYrh00B27iuwWxJqj4/f6EZ/Fzt/nysST5dz/1P7re5r39aH+XMa1gtq78FCprwaa1Nuf8TQEF6e0gl3N5HyCh1PvXSEE0kNrx754NWuDO5nvy+XNfI37WT31ffb3D9w488ED3Otqk2WZR54ej+Jp83DfYIAn73bg55dA1yep7PsPVTEc6/HIklyo/PIWrXw5OsPe+PjrWb3gXyeez3ObExfbxW/zRuAr4+GbxacZvGf6WhrjJZu5/a+zHqtG/5+GiRJZvueAv7dnodOLzOgTxCjh4fh7kSoa8QtW9A5SMR+7blOXHXFufVcgDF8O/3to2TU60Y+YkgI42+OIjmlgqBANwb0DnRYeShJMuMf2W3Te+NMiwo3jWB6X8QzxtNzT8RwcwPEGBf+kcaXPyQ1Wd5hLZFh7iz5znItUmg8l0VVV0mJ8YknKMi4WO7fvx+dTsfo0aNNx3Ts2JGWLVvaNHxmzpzJG2+8cX4mfBmwYm2WqR+Ts6hECA/zoM9F0MuolnbRPtx7e0sWnJWQ6+uj5uPX64TmJElm94FC9h0qws1NZOSwMGKibWug9OsVxHsvd+HLH5JISa9CwFgee9UVYQzqG0xkeF1SpY+3hu/m9Obg0RLWb85FqzXQpaMf6/7NIfa44+o3lUpg6+6CBhk+ss5+SE7WW99/JL6EhUvTOJFUTkSYO+Oub86oYaEIgoAgCPzwaV8SEkv54POTlFXoGdo/iIkT2uDu3sAWBFaQJJkDR4o4frKcyAgPhg8MoV/PQKZNjuHdT044HsAO7m4CX87qhY+3Gq1Wz7OvxVkcU1Zh4K6Je1j5yxAevTeaR++NpkYnoVYJZh4YURQYNjDEZX0psNt5wTi2AGs3Zp9zw0enk5j66hEKi80TuTdtzzfTUgoOdOODV7vSwY4gZkZ2ld2Q1ZD+wWzbbT/xu6aeonJtpHf2V4kMGxjicquWoABNkxs9ALfdoDQXvRi4JA0fSZJ45plnGDJkCF27GjsIZ2dn4+bmRkBAgNmx4eHhZGdbr9iYPn06U6dONf1d6/FRaBi5BVqXFU0jIzz54NWuF108e+KEaMZeHcH3C5MpKtUxpF8wN18XiXgm61FbIzHtzVj2HS42zl2GBUvSeOBO64q9tQwdEMKQ/sGUlulxcxPx9LC96AuCQO9uAfTuFmDaNu765ixdmc7sr61rDNlClmWqqgx4eqochokCB/dG5e2JoaLKYp/a15vAAT0ttm/Zmc+MmcYwhSQZVZCPxJeSnFrBw/+LNh3XoZ0f39nxmFVX65nzzSm27ylAkmX6dA/k2cfa4e/veOEqKqnhkakHzBZQdzeR2W91Z9X6HJeN8rPR1sgYzngUZrx3zOZxxaV6MnOqaHZGjLMxHeit0aW9L4fjbeehSTKUNSLB2lm27ykg34nqtcLiGp59PZY/vh9o871wFDq+YlCIQ8PHGgZJZuuufJe9Ppom/szAqFN2pXcKe254k4oTp/Hp1JboZx4g5ErFA3S+uSQNn0mTJnH06FG2bdvWqHHc3d1xd780GiVeCnSK8SMxqQKDVWEYczp38OWx+9rQs4v/RVuq2TzCk5endrK679dlaew/UgyY9/n5/tcU+vQIsBu6EQQBf7+GN4G8dWwLunbwZ8bMOLJttNYwGGSGDQhGp5N48e2j7DlYhCwbK0auHBLK6891NBlxZ6P28abTBy9ydNJrCCoVssFgVLkzGOj04XRTiXmtx2vzznzWb84904LDOEbtV+Cn31K56bpmhAY7/p1VVem59cHdZkngG7flsX1PAb99299hqfrUV45YeA20NRJPzTiMLMlN0mYgK7eawAA3jiXY13PZd6iIG685N1WKLz3TkXse32szgV0UMTOWzxWZOdVOGZOybJRd2La7wGa/rIgwD2LaeHMqucJiPI1aYHA/o1RGaZlrBp0ggFbr+gfv7t60hk9ggIbpMSfYN6buN1WVkknuqn/pPm8mUfeNa9LrKdjnkqnqqmXy5MmsXLmSTZs20aJFXSluREQENTU1FBcXmx2fk5NDRIRrfZoUGsb4G5ujUglWy5vPpl1rb3p3C7hojR5HrFqfbdMVPnn6YZ5//QgTJu9j/MO7mf31SZfKpZ2hQ4wvS74bwOw3u+Pnoza957X/HT4wmIF9gnhoyn52HyiqM0gk2Lg1jymvxNodv9Wjd9J/9XeEjB6CZ3QLQq8awoC139PywdsBo2H1ynvxPP/GUVavz6baxuIiybD3oHMtOeZ8e8pq5Zu2RuLt2babxgLkFVRz8rR1uQC9Xnb6Cd6eDpUgGBuPAvh4239mbKyYpz2aR3qy6Ot+dLNyDZUIvj6a8xJSaR3l5ZIxmZJuX85h2pMd8HBXmUrJVWecoVMfi8HfT8Mj9TyHziLL0K+X82H0k6fL+eDzEyxcmobaQSWiKwT5qTn+wnvGORkMZv+Nn/oOhqqmvT8o2OeSMXxkWWby5MksW7aMjRs3Eh1t/iPo06cPGo2GDRs2mLYlJCSQmprKoEGDzh5O4RzQsoUXn7zTnbatHXebHtK/4Z26LwYqHIQSdu4vIimlgsycapatzuShZ/aTmW0ZOmoMgiDQr1cgv3zZjwnjW9IpxpdeXQOY/nQH3nyxCwmnykhKsRQGBGO/qtx8+1VjoVcNpf/KbxlydB29l31NyKjBpn1/b8ph805jHoejxc/ZBWT7btvVV4fj7AvV1W9TYo3QYHenStwnP9gGjcb6gaOHh5p6l02cYHsRVqmgTatz2+8qMtyTL2f1YtHX/c50mxfQqAWuGBzKNx/1IsQFIUdXkGWZyko9er3EgN5BtGzuicrJVWTnXvuhqo7tfPl5bj/+d1tLBvcLYuxVkcyb3ZsbzjRMvWVMMx5/INrug9XZTsyxV0XQppXj+xHA5h15PPTMflatz+ZIfKnLytu2EAQY0qIKQ6X137++tJziPYeb5FoKznHJhLomTZrEwoULWb58Ob6+vqa8HX9/fzw9PfH39+ehhx5i6tSpBAUF4efnx5NPPsmgQYOsJjYrnBu6dfLnh0/7klegJTOniqmvHEFbY34DGdgniEF9L44KrobSq1sAW3bmW2/3cBaSBOUVen5cnML0pzs2+VwCA9x45H/RZk/E5RV6vv3ptN3ztu7Kt2jUWZ/YYyXM/T6J2GOliCIMHxjC5IfaEhHmwT//OqePpdEIDHTys7b3XjpKNHW0uA0dEEzi6XL2HjLmZNVWeAUHatDrZdq39WXogGA+nHsSnc76xY7El7BzXwGD+gZz5ZBQ2rZO5VSypRfjk7e623gNMqdTKzEYZKJbeaNugry2qGZevD29i0m7x1H+VmPYsDWXeT8nk5ZZhUYjcN3IcN55qQsffnHCbs5RLZnZjr0aYSHuPHqvbaPynnEtufOmFnwxP4m1m3KorDQQ4K9hwviWtG3tw4IlqRw7UUpQoBs3j2nGLU7m9tToJN777MQZI974XjZVcnP3zv6M7V3BQTvHCKqmS/BXcMwlY/h8+eWXAFx55ZVm27///nvuv/9+AGbPno0oitx6661otVquueYa5s6de55nqgDGJ+zQYHcWftWfxX+ms/tgEV6eKq4dGc5N10Se9xCXJMlIktyohp71ue/OVmzbXYDk5FOhQYKtuwuY3gTXLi7RkZxmLBW21qtozYZsZn6a4NATExJsO2H4xKkynnrpsClfS5KMhlJcQik/fd7PYSPQ2tyPZx+PwddbzeLlaSxdmUlllYE2rbyY8mgM0WcZK727BfDvDuten/oVQbIsc/BoCZu25SIDN14TSfs2vrRp5W1VHVsQICzUA7VaoF20D6II/n5ujBwaamoIWlRSw20P7jKrDDqbnLwaXnwrjq9m9aRTe2OS9vI1mfz0WwqVVQY6d/Dl5SmdrOYzHYwt5r1PE0xl38GBbkyZ2I4rhzRNx+1zafCAMdfqtQ/qErp1OpmV67I5mVTB1x/2IiunmgOxRbz/maW6dy2enk2z3KhUxt5oZ/dHA+jRpZuVM6xTo5OIjS9Bb5DR66UGC5PawsdbxR03t6Bze1/cmrujDvCjqrSahPBelHkEEFaWQXRBPB6hgQQMcL3/n0LDuWR1fM4FioDhf4/SMh1f/XiavzfloK2R6BTjy6MTounXMxBJkhtlgO3YU8ALbzmvWxQUoGHFgsGOD7SB3iDz2bxE/lyTZXLDd+/sxwuT2tM80hONRiQjs5I7Ju51OJabRmD970NtJjjPmBnHtl35Ft3kBQEmPdiGf7fncdROWX2XDr5MmRhDxxhfnppxmANnEsHrj/PxG93o16vOG1RUVMOtD++mpsb8oioRfvyiL61beKPXS0x9NZYDsebjDekfxItPdeC+J/dRWFTXukEQwNNdpLJaQiUaDdAAfw2fvtPDzEu08I805n6fZPP11M1F4IohIbz5QmeHx9ZyMqmch6fst/pefvFeT7p3vrj7ZcmyzF2P7SU903qoZtZrXRnU1yibcPX4rVRWWbe4H7/fXPjxQrJlZz7vfZZgSpZ20wh2jV5nCfTX0L2zPx3a+bB1V4Gp8bIoQNdIAwnJ1WjdvBEkA7KoIrQsnfeeiKTD+JGNvvblzmWh46Og4AidTuLJlw5zOrWuUiQhsYyprx7Bz1dNSamesBA3hg8M4a5xUU415axFWyOxdpPz7VBEEUYPb5yuyne/nOaPVZlmLvgj8aX8b9I+RBFGDg2lotLxU6sgwOsvdLZp9AAciSuxWKhriY0vpV+vILuGz203NKdjjC97DhRaGD1gDCO88eFxVv5SZwgGBrqx+Jv+vP3xcY4cK0GWoUM7H6Y/1YHWLYxGyi9L0yyMHoDtewr5c3Umy74fxLY9+Rw4XExYqDt/rskiK8foZal9PcUlOma8G8vCrwaYPCVpNhb1szFIMicSLUUlU9Iq+Wz+KfYfNiaSNwv34IkH2tC7WwDPvHzY6nspYKwOvNgNn4pKg02jR6USiDteajJ85s3uw/+e2GvhbYxu6WXWtPZ8Icsyew4WsXVXPpJk1AMKCXJjxkxz4cmmMHrAGOp/c1pn7nliL5lZde+ZJMORTBW4Gb/HsmgMbeX5tuCxRQI3FSVy3x0tFdXm84Ri+Cj8Z9myK98iB6M2j6Sk1Ggg5ObX8PvKTH5fmUmfHgHMnNHVYcsAgC9/SGLT9jy7x9S2IhAEaBbhyYTxrRr2QgCt1sCSFRk28w4kyRiOcMaD9cOnfWjb2nbyrVZrsDmOAPj5abj7lhb88GuK1fmoVQJXDjaGcBYvty37X1yqo7ikhoB6Gj2hwe588o5tt//SlZZtIGqZvyiFtq19GD4whCsGhRKXUMqXP1jPc0rL1HI4rsQkOxAa5Jy8gChC+Fkd7U+nVvDQlANmnqrUjCpefDuOgX2CKLFRgi3J2KxEayryCrTM+/k0m7bnI0kyg/sF8/D/WlsNkdrC3U1EoxGs5j5JkoxfPWmGls29WPXLED75NpF9h4vwdFdx07WRjL+puV1D+2x0OgPJaVV4e6mIDPdoUCjPYJB586NjbNiaZ9IJW/F3Fp4e4jkRJwQYOTSE3QcKbRqK1tDpZf5YncH2vQV8N6c3fj4Nl7pQcA7F8FG4pDkYW8z8RckcPVaKj7easVdHcv8dLXF3VxEbX4pKJThdnbH/cDEvvxfHx29YT06tRas1sOLvLLObp1qvpU3+UaLz4wkL1hB4390cKQ+iWivRr2cgY6+OwNur4T+3nHytzZLxWiTJuBA5YuqrR7huVAR33hxFgL/5TbaouIYnXjxkM4dHko3htWVrsogM97CasPrSMx1M5eOlZfY7hu/YV0BpmZ5m4Z4M7hdkMwfrZFI567bkUlxqezxZhpfejWPogCDemtaFtAzrFW21LFyWRs+uAZSW6VizMdfusbVIEowbY54wO39hskV4rpZd+233fAPM1LobgizLrN6Qw5+rM8kv1NK5vR/33BZF5/Z+lJbpePS5gxQWak0ep3+357FjbwFjr46kdZQXo4aF4etj/3up0YhcMyKc1euzzTw5AkaPT6soL7buyqdNK2+aR3ri66Pm5SkNS+LX1ki8M/s4m7bnmX5fwYEaXn++E726uabuvn5LLhu2Gh9O6t8DqqqbQNDJFgIs+iPN5dMkCbJzq1m+Jot7b784woH/ZRTDR+GSZe+hIp599QicUQsuKtHxy9JU4o6XMuft7vh4q1wuzdhzoIhTyeV2PSIlZXqzhc6zpowJu94nsDIXGQEhU0SYupH/Pf8IHd99rsGvr5aExDKmvWVfd8cVCop0LPwjjQ1b85j3cW8zMcUvf0giI8v206ogwGfzTlFWrjd7a93dRQb0DuSJB9rSol6ndTcH7SjenXPC5BkLC3Fn9pvdaRVl7o344Vdj41tjNZbj17dtdyEL/0ij0kHYL+64sRLpt+Xp5OQ5rjgSBbj/rlZcMdg8IXnnPvvGjT1ub6TezpxvElm6MtPUy2rb7ny27S5g1mtdOZ5YRn6B1uxzkmSo1kosXWn0Hn4x/xTvv9KV3t3tGxWTHmjLyaRyEhLLTdeSAU8PFc++WvfdvGJwCC9P6WihSC7LMrn5WtzdVBbGdi16g8ykaQc5flYosaBIx9MvH+HHz/oS3dK50nSAf/7NQcBxi4+m5KO5JymrMDToXFmGPQcKFcPnPHDJ6PgoKJzNVz8mIWOuIyNJcCC2mH2Hirh6RHiD1HofmnKAdDvegkB/Dd5edTf24SdXEFCVjwCIyAiS8cZ3ata3FO894voE6lFVbWDKq0coLLbvOXEVSYKcvGqWrEivt01m3ZZcu++ZLENpmd7CntRqJfr2CDQzesCY6+JwLmfGKijU8uLbR6lfbxF/opR5vyQDuKSrsmJtFp5Oeti2nMn/sEbHGB9mTOnAhPFRjL4ijFOnK1iwJJXikrrPw5E4oq0ozeB+QS736tp7qJCHntnP9fds586Ju1m6MhOos+8NEkiyzCfzTrHvcJFNu792e7VWYvo7cVRX21+sfX3UvPViZ3y8VGZjnl0JtXVXPrO/Mq/s2rwznzse3cOtD+5m7P928PSMw6Rlmv++9h4s5KYJOyyMnlokyXVPSlFxzXk1egSBBhs9tWg0YpPpBynYRjF8FC5JtDUSCYnlVm/sKpXAwaPFtGzuxbNPxCAIxtwMZ1MM9HqZOx/by6Jl1m+0Go3IHTfXJWruazWSP3s8QnpAW7PjBLWKzCVrLM4/drKMtz8+zotvH2XJinS7Ccn/7sijtEzfJO0WzkY6U2Jf97dsU8PGEYKASdCwPjdf63yPJINkTDKOPVanCfPPv7kN6uNWXKpjuAOjYmh/Y0WZzXwmAfx9NZSU6vjpN6OHbMuufL5ZcJp7J9VVOV03MtzmNUQRWrXwsjB+2kV7u1QZBrBwaSpTXokl4VQ5JaV60jOte6lk2ZhsHZ/guJmtLBuTl7c7EBcE+OX3NKocGEiSBH9vyjWFOPcdLmLGu3FmIdGDscU89txBk9GUllnJC28eNeXd2WLLrnwLBfSTSeWs2ZDNvsNFVgyG8yOZcbZqemPYc7CIa+/czhfzT6G1ET5VaDxKqEvhkkSlElCrBfR6y4ValmW8zmiG3HxdM/r1DGTdllzKynWsWJvldIz/i/lJdGjrYzUMUF8Zt8AnkkKvMBLCe3PDke/omrXnzB4B6Swp+hkz49hcT6tm2+4Cvl+UwvxP+pg0ZeqTk6t1KU/JVeobFWq1SJeOfsQnlLqc/Fm/T5fBILN0ZQa/r8ygoLAGT0+RKhslztaon19UWal3OVYhCNCpvVHXZ9jAYLbusr6or1qfS3mlxOB+wZxKrrB4zbIMPbsG8MX8JNPrqqWkTMecbxL58PVu3H9XqzOLsqUS9pMPteX60RH8uTaLzTuNXsERQ0K56dpIPOw0qD2bmhoDX/1oX5DybBzlhNXHXu5ULVt3F9is9KuPQZLJydfi56vhx8UpFv28JNkYLr7/qX3Mebs7y9dkOZWbVl5h4IGn9/PNR70I9Hfj5Zlx7DtcbNrfLMKDWa92M4VKK6vObaNWUQBPTxXNIz25/cbmvDsnoUnGrao28Ouf6aRnVTFzRtcmGVPBHMXjo3BJolYJjB4eZtWLI8swalhdHkbzSE/uv6MVD90dTQ8XSodVosCKv7PMthkMMsUlNcz+2tydX1ue+k/nu9ALRqNL1usJuWqI6ZglK9LNjJ5aSsv1vPGh9W7fraO8zpnRIwjm7xPAYxOiEQXb/dY6tvOx+hwtYOygDfDRlyf5dN4pMrOr0dZIVJ8xejzcRVQqgZAgN7vtI9q3qcuv6tk1wGrTW1E0Jr1aQ5bh/juMeRJvv9iFh+5pjaeH9Vvd1l35HDpaQptW3hZP7j26+COK1v0GkgS79xdSWWXAz0fDz1/048G7W9EswgN/XzU9uvjxydvduP3GFnh5qbl7XBRfz+rFV7N6ccfNLawaPbn5Wr7+6TRTXzvCu3OOE3usrk3H4uUZTqmEN5QuHRzrljnreVOrBCor9Rw4UsTxk2U2vZU5eVqemHaI+BOlThlUYAytfbsgmQ+/PGkha5CTW82zrx8x/V6sPUg0HTLSGW+Zm0akd7cABvQJahKvDxi/w1t3FXDytPXQn0LjUDw+Cpcskx5oQ1xCKWkZVcabsixjkOCZie1oFmGea7LnQCEvvxdPZZXzMXiDJHMgtpgNW3Pp1yOA7xalsGqd7YacCAJajTcnwnrQOe8gQUP7En79CNPu787kqlgj9lgpWq0B97OSgYcOCCYy3J2sHPt9tQA8PUSXKlY6tvPl1uvNk2t7dQvg03e7s/irPSScKKU6OJLWLb3p3tmPbp38aRbhwYx340jNqDJ5SAQB2rf14frREaRmVFoYi7XrtbeXmqXfD0StEpjzzUmWrjTXJBIEoxZR83p5QqOGh7FwWRqp6ZWmBVQUjUbp+692Q6s18Pl3pzh2stw0hizDM6/E0inGlw9e68oDd7bi1rHNuPF/Oy06mkuSsRXFF+/1IOFUOdt25SOKAlcOCWXM6AgW/5mOrQxZGUhOq6Bzez88PFQ8eFdrHryrtdPvf31OJpUzafohqqsNSJLx9a3ekMPTj7Tl9htbsHp9luNBGsG3C07j7aXmqivCGDog2Gr5+OhhoSxenu4w7Ko3yEx60bneU0XFOopczF/bsjMPSbbsEWeQIDtXy95DRQzsE8QtY5qZeYRcpfa7ZGOv6f/iE0qZ+losrz/XkUOxxWhrJPseU1lCbdBhENWoZAN6lW3tnrjjpcREn9u+b5cjinJzPRTl5ksPrdbAxm15HDlWir+vmmtGhFtUfhSX6Lj1wV3U6BzckOygOuOud+Z0n5oSPhueRpupDyF6uFNWocfdTcWoW7faPW/tr0Osdv3+d0ceL8+Mt3uur7fK6cRKUTS2krh2ZARarYHVG7I5eaqc4CA3hgYXUvTy65QfSwTAq01L3Kc+yxeHg0lJMyaktm3tTbdOfhw/WYaoEhgxJJSbr2uGp4eK5WszmfWF7bYFv8ztR6soL/QGme9+Oc1vK9LRamVUotHImfZkB9zdzL0z+YVa3vr4OIeOliBJMu2ivXnuifZmXopvfz7Nj4tTLa7n461i5c+DOZlUziPP2u6WNGNKB64bGWGx/XhiGQ9POWDzPE8PFV/N6mm3CtARBoPMvZP2kpZZZfH9FEX4eW4/7n7MsRp3Y6kNSY29KoJpT7a3MH5Ky3Q89vxB0zztGwbnDpUKDHa+6s9PiuGma5shyzKffHuK3/+yrf1kC39fNa9M7cgX3ydxOrUS4y/fvjvn4ze6ER7qwc9LU1m7McfqexNWmsZtBz7Hv7rItO14eG9WdH8Ig8rSg/nWi50Z0URtTf7rKMrNCv9ZqqoNlJTqCAp0w00j4u6u4rpREVw3ynLRqmX9ltxGGT2A0654gHI3f4KeuJZFq7P58dcUp7wwgoBVoweMT5S1Jd/WuHJwiNXEYlt07+LPTdc2IzW9kiemHaKkTIcgGG/riwwSY0tC6IrR8Kk8nUb5k1PQDnoJ/Izho9OpFWRkV/HTZ30tPGtnlzGfjecZcUi1SqBjjB/CGXeKQTImMqtVAtOe7GAKq1RXG3h5ZjxHjxsbpYoCnEyqYMlfGXSK8UUUBWRZtlnxU15h4NdlaVxrxaipT3iI9W7mHdv5cvWVYfzzr3Wdn6pqA/c/uZ97x7fknnEtWLgsnb835VBZqUetFik9U/bfOsqL5x6PodtZodaM7ComPneA4hIbIocSbN/jOPG4Kaj1oKxcl81VV4TRp4d5bpufr4Z5H/dm5bpsdu0vxN1dJDmtkrQM58X6moIAfw1lZXqbasu1Dz6CIPDMo+0YOiCY9z5JIDvPsde0lpIyPZ07+PHT530pKtZy4707QLD/3U5Jr6R/7yBmPNORrbvyKT/rQURl0HHnvjl41piHr9rnHGTkiaWs63SnaVvt/WDwJd7M+WJFyfFRaDLWb8nlwWf2c9Xt27jvyX2s2ZhNUzkUq6sNzPriBGPu2s5tD+1m7D07+H5RslNJkfmFWlR2kkr69gzgsfuiLfRjGsO8n5P56ofTToeehg4ItrlPkux3Lt+yK98lo27s6EgA3v/8BKXlOuQzYQODBDICq7pM4Ghkf4o9g0GWkYF+p9eZzaemRmLpqkyLsYf0D8bdXbR4NhZF6NrRj7AzBkZWTjWvvh9vETZcvSHHTO35txXpxCWUmq5ba4Cu35zLljPGXnGJzm7Lge17CwkJdmdIf8scDFGAqGaeJgVna8x4piMBfrbVdGXgp99Sue+p/SxYkkp2rpbScgOFxTr0ehmDQeZUcgVPvHiIA7F1T/qyLPPk9EM2jR4wLoD//Ot8a5SmQKUSbDaL9fJSM/6mFnz8ZndmzuhKeRM39nSGiFAPbh3b3PKzFKFzB1+6dTJ/2u/bI5Df5w9k/I3OayYJGI1zQRDOKIs7Xirrh2hbNPO02N8h5yDeNWWIZ/mNRWR6pm9Fbag5c01jPtw7L3WxCH0rNA2K4aNgk9T0Sr77JZlPvk1k8858i/yI+vz6ZxqvzzrGyaRyqqoNJKVU8M7sBH78zRh+yC/Q8tffWaz4O4u8AuefvGp5bdYx/vonC92ZKq7KKgPfLUzh25+THZ4b08bH5twjwz2Y/WZ3/ndbS37+oi9XDA5pkgTF1RuynT42KFDD6891srm/V7cAu+c7yrkQhLrE1JFDQ7nqijAKimo4HFdiea4gIKnU/NXjYb4c/i5/9ngESVARUZpicc2Ek5bl0t5eal6Z0hFRFFCJxhChIICvj4YXn2pvOm71BttGcf22FP9szrVq1IkibNhq9MI48jJ5e6uoqjaQX1BjMZbGTWTmy13stvpQqQSaR3o6/F5k52odaiB98HldGDDuRBm5+fa73Muy0cPVVHTt6EtEmDveXiq78g72fuv1adnCy2mZiKZAAAb2CWLifW0Yf2NzNJq6D2VQ32A+eKWbzfYWj9/fxpSAb++zFEXo3zsQrzM6UKIo4oXWblxPFKB/7zrvzK1jLY0sv+oCJMH6m6U26HjlkebcNS6KqY/FsPT7gfR28LtXaDhKqEvBKktWpPPJt6cA401iyYoMOrT14dN3e1i0XqisMjDvjAFSe2+o/e8Pi1I4dbqcf3fUeSVEAR7+X2une1clpVTYdPcvXp7OvbdFmW5S1hg+MIQWzTzJyq6yCFk9eFcr041SEASG9Au2WnnlCr27+3PgSInjAzHeZIcNCLH7ZNe3Z0Cj8ilaNveiskqPRiMQ6K8h2wmVYgAEgeMRfXDXVdIhxzw/RiVCSLD1pMwrh4Ty/ScePP9mHLl5WmN2hAAZWVW0jjKGIfILtGfed8sXlV9Yg04nodGIaG0kkksSJp0TDw8VzSKst88AyM+v4fq7d1CjsxxLq5WITygzzeubBaf5bXk62hoJd3eR28c2Y+J9bbnhmgiT56kxpGdWUVllwMtTxfETjscL9NdQXKprslyavj0DefieaABenxXPpm15Fr8Jg0FmUB/nQix3jWvB4bec+667ytll8GD8tsSfKAVZ5smH2/HAXa1Jz6oiJNCNkGDr4cpaNBqjF+XEqTIOxZVw6nQFq9ZbPqB4eqh4ZmI7s233XOXLt+tsG6mjhoehrlf1du2IcE6nVLBoWbrpsyvyb44oW/8+qwP8GHF9O0a5KU1KzweKx0fBguS0CpPRA3ULbsKpcubOP2Vx/OG4YpuVTnqDzKbt5qEYSYZvFiSzc59zuQsJp2wLsdXUSKQ6yDHQaEQ+f7eHsdz0zLZAfw3PPRFjkRs0angYUVbc1LYICqgLgQgCDBsQTEKiY+G4unOsP3rqDTIFRTXGBdhNxQ1XRzTYE5WSXkleQQ2Z2VqWrc5kwqR95BVoCQ+1v1AAyILIkRZD2NtqpNl2gwQ32RAn1OkkHn3uEDlnjB4whqOmvRXHqnXG6qR20T42w5QGg8zY/+0gJ7+awf2CrHoUBAH696pbnN9/pStqtfU3KCm1wqrRUzvOxm3Gfk5PvnSIn35LpVprzAerrpZY8Hs6z78Ry5hREVw3yrZQoSvUzjOmjeOEaFFs2gTiPt0DTP//0D2t8fRUoar3/goC9OkRwJD+lqFXg0Fm574CPv/uFPMXJZOaXsnQ/iE8+3iMWWPfpvIAXTnIugDlrv1FJsVqH281Hdv5OjR66tO+rS/Xj45g/RbreVueHiqahZvfA+57ejDjh7hZ/TDCQtyZ9GAbs22CIPDEA21ZMm8A0ya35+UpHfl41cN4t2+NoLJ8yGkz9UFExeg5bygeHwULFi61LQ2/ekMOz0/uYPpbb5D58ockl68hirBsdSaD+trObaklOMD+DSEwwHE345Bgdz54tRvFJTrKK/REhHuYPaHV4u4m8vVHvXjhjaMcPe74ify+O1sxamgo+QVaolp489HcEy6J9RkMsll+jyzL/LY8g5+WpFJSqsNNI3L9VRE8dHdr0jOrLbRLXEWSjZ6SJ6cfpn+vQHKcSPiURDVJYd1NXidBgOtGGZtWrvg7k2tGRDCgd6DJiJvzTaJNQ/ijL09y/VWRXDMinB9+TaGkTGc1PFRRaeCex/by89y+rN+SR3m5zuSZEEWjF+u6UeGcTCqnoKiGdtHerFwwiK9+Os3huBJ8vNUkpVRQUWlwGH7S6SVOp1ZwMNa652LnvkJy87XMeKYjo4eFMe2to06Hgs6mWyc/3M60uOjRJQAfLxXllbZLlNpF+1BQVGRz/9nYkzRQnUkoryWqmRfz5/Rh4R9p7D5QhJenimtHhnPr2OYWmj3V1Qaeez2WQ3ElqFTGhPL5C1OY/FAb7rw5iutGhhN/sgw3jUCHtr7EnyilpFTP+i05bN6R71JxQC2pGVVWK7hkGf76J8tMPd1V9h0utqmMnF9Yw6nkctq39TXb/tT0wTxSbeCn31LZd6gQQRAY2DeIcWOa2+w/FhHmwQ3XRJr+HvD3jxy6/3kKNxtFTkUPd6KfeYB20yY2+LUouI5i+FzGVFcbSEqtwNtTTcsWnqaF60SSbdEsnV7m/c8S2He4GB9vNTFtvElKsd8F2xqSZBRsc4bePQIJC3Env9A8h0IlQu/ugYSHOi9UFuCvsXmTqsXPR8NXs3pxOK6EFX9n8vcm2527QwLdCPB3O5MAaVS3dUZortaIGNo/mAH1cgN+/j2Nr3+qU+it0UksX5tJSlol14wIIyTYzWaFkSvIMuw+4PyC+urUDpRXGpBkmfWb81i9vi7hdt3mPGLaePPtR71Rq0X+3ZFnc5wanUxpmQ4/Xw1fvN+TF944SrqNpqjVWomtOwv4bnZvfvwtlW278lFrRK4aHsrIoaE8Me0Qp5KN+S8C0K9XIDNnGBNCTyWXc9+T+x2+LkGAIf2C+WaBfVXk5WszmTihDQP6BDH3g5489vxBl9uIuLmJvPqsedfyj9/qwcTnDlj16ri7i0x+sC27D+xz+hrV1RLhoe7kFZj/VgQBbjkjOVCfZhGePPdEexzx42+pHIk3Gob1BTU//y6Jvj0CaRftY5aT0qNLAPmFWrbvyTcLZtZWJ7q7CWhr7P9QAgM0nEqxvq/ECaVpe8gOfqS2dnt6qJg4IZqJE6It9lVW6lm3JZfYYyUE+rsx/sYWhJ5VLejZIoJB6xdQeToNbW4hPp3aovFTdHrON4rh8x/jQGwxv/2ZTnJaJc2beXDb9c0Z1M/Sq7L4z3S+W5hsEvSLaePNK1M70aaVN4EODIPV67NNT3An7RhJ9jA+ffo6PhBjdcX7r3bl2TPNOmtj/y1beDFjSgfHAzSQHl386dHFnxqdzOYdeWYLiSgay3vPfm8dufkFwMNDpG20D9eNDOf6qyJNibXaGokFSyy1aGobrzbW29MYDh4tYdqTHVi0LM2qJ+xkUgVvfXycN17o7HSLiZbNvbh2VBjzfraxugFrNmVz+00tmDa5PdMmGxdovV7izol7zLxVMsY+R/97Yi+/fNWfGif6HNX20Rp7dSR7DtjvsK6p5wHp3N6P9m18bDbUrI8ggLenikF9g3j60RgLo7tze1++ntWLp18+bPLUCAKIgsArUzoSfUZR2tlwV3CQG7Pf6s70d+JMuktgVOh+/IE2ds60z6p1WVaNAZUo8PemHNqdJbJXVFLDI1MPUFBUY/a78XBX8ca0TtTUSMywo03VMcaH/r2DrAoQqkTjb7Mx9O4RgEYjWO1NFxzoZvF6HJGQWMaTLx02E0hdtCydKweH8PLUjniclcPnFR2FV3RUwyav0GgUw+c/xJoN2bwzJ8FkGKRnVbF7fxHRLb1468XOpgTONRuy+ew781ydU8kVPPnSIRZ/M4ARrSrZe8jaFYwiXg1xW9fHeGOHO25y3lUdE+3D7/MHsm13ATl51US39KZ/r0C71Ti17D9cxMp12RSV1NC5vR/jxjRzKSfg+SdiyMmtJv5EmWkR8vPRMOvVrqawBRjL5s8W3zsbjZsxFBF3rBQfLzU9uvibPpeMrCqXlKXPJ7X9s/6wUsJey4ateTzzqJbhg0L46x/rVW1ubiJ+vnWLf9eO9oXGEpMq+HFxCv+7raUp/LJ9T4HVvlgAWblafluebpZzYo2IMHeuHRHOHTdH4eWp4rrREeyy4wELDfHg+TdiqajU06dHIKOvCHXK8JFlY/L/rTe0sOlp7NzBjz9/HMTfm3I4ebqCkCA3xoyKIPJMZ/uQIDfyCuxXf8EZr86YZrRs7sXPX/QlLqGMgqIaYqK9LfSWXMVmOE6QLTq0g7EY4myjB6Baa+DEqXLuHheFn6+a0jLLc/181HzzYS/KKw0s/jOdouIaszCnKApMGN/S7Jy//83m6x9Ok19Ug5tGZNTwUJ5/oj1qtfXfo5+PhkkPtGXON4mm+6VKJSBLMlMfj7EaBreFwSAz/Z2jVn+7/+7I598d2xg2IJh3XuqMeD5L4BRsoig31+NSVm7Wag3cOGEnFTZuUD5eKn6e24+QYHfufnwPqenWwwtPP9ASn0fv4PtOj5Hp36au7rN+f4JG0jrKi2cfj3FYpt0U/LA4hXk/J6MSjQm5ogjenmrmftDTQuHZHrIsc/BoCYlJZxSOB4SYGTmVVXpuuX+Xzfe/WYQHeQVa9Hq5rrpNBC9PFT993o+wEHcKi2q4ccLORr3ec4Eown13tOKhu1sz+ratDptfdu3oR0JiKTorEi8vPdOeMaPqch5kWeaKm7Y4DA/edG0kz08yenx+/j3VbsPOiDB3rh0Zzg+/WnrPannigTbcPa7uiVtvkBn3wE4KiyxDKKHBRsOjdoEUBPDzVVOt1aN1IlqrEmH4oFDeetG1buy1/PVPJu9/ZlsNu5arrghjxjMdbC72jWHKK4fZf6TYanjvpac7MGa0eZHAA0/vs1mG37WjH1/N6sXB2GKefyOWaq1keqDw9lIx9/06Jezs3Gq+mJ/E5p1Gj2v3zn48fn8bunWq8/gsWpZmaiRbn+iWXiz4op/d17X3YCG/r8wkI6uKtq29uePmFnRu79q9/9DRYiZPd9yiY1DfIGa91s2lsRWcx5X1WzE//yPEJZTaXHQBKqoMLF2VgSzLpNkwegDm/pjCtuBB3LbvM3yrz7j/m8jo6Rjjw+B+QXh4iKxcl8UxJ8p5G0NGVpWpzL72iVGSoKxCz0dzT7g0liAI9O4WwPibWjBqWJiFZ+fLH5Jsvv+hwW4M6B2EZJDNq9skYxLvj4uNIcegQDcG9Q0yq7K50NQaZzddazRWfH0cO4njT5QiCCLt23ojnlGE9vNV07u7P0v/yuTV9+M5HFcMGN/X5pGOc7SWr80i50wZvqOqu6LiGsIcePSKSsw9KGqVwILP+9G3R0DdNrXAiCEhJm9L7aIvy1BSajR6ah/g7f00DFLDQ8IAY6+K5L7xLS2uoVELjL0qnKmPteOnz/vy2nOdGmz0ZOdWc/R4KaVl1nNnHry7NQKCWXNZY6jQk1HDwyyO93BXWX1PasX5AHp29TczmETR+Hv45JtTaLXG31JEmAdvvdiZjUuHseH3ocx9v5eZ0SNJMt/8ZN0IPp1aybbd9qUp+vUK4v1XuvLz3H688UJnl40eMKo8O8POfYWUVzQuN0mhaVBCXf8RHIV8ZBmOxJciCAKhIe42E4t1ksDmmFtIDO1OmccZyfpGGjyCYIzJH44rQRTOLASnyvnn31xef74To4ZZ3jibgi27bN/0DsWVUl6hw8fbcUWYM+zcaztHJL+whsNxxVZDhLIMy9dms2pdDteMCOfpR9ry4tvVJKdVmrxUF5KuHf147on2hAQZDYlhA0PshrvgjMIyEu3b+jJ/Tl++W5jM94tSTNpGJ0+Xs3FbHi8+2Z6xV0cSGeFJWqZjbaG4hDIqqwyo1YLdFh4ajcjGbfbVjtu0svT2+ftpmPN2D2p0EtVaA77eamZ/nYhKJZgl9J79Wj09RHp1C6C8XEfs8TKLfBxBAL0k8fFXJ+nbI5DB/YOdCqXsPlDIgiWpnEwqJyzEnckPtSEyzIOs7Go6xPjSuYOfWag1Nb2Sd+YcJ+FMCK5jjC+vTO1A80jbiuSFRTW8Pfs4ew4aw3xqlcAtY5ox6cE2ZkZUt07+fPpud77+6TRH4ktx04hcPSKMiROirYZ3R18RRuwxywcbWTbuAzgYW2z6Lsly3fPVwaPF/PpnOvfdUafzpVaLqK2sVkcTSkyiptb4659shg6wXhbfVHRu72urj60FR+JKGNz/3M5HwTEX0bOlQmPo0sEPfzuy+oKAKcfgdkfS7YJARkBbHDXlcxZZNnqkkOsWcoMEyDDri5M2NVYai6ME16WrXG9eaAt7Nz1BADeNaNd+1Btk1mzM5oMvTvLDp3344NWuPHRPawb3u3C9etzdRD58rZuZkXDXLVFOfSsMkvEmv2x1Bt8vslR9BmPZe2WlnmtHOKePM39hMvdO2se0t+LshsZ8vdXEHrfvYVm3OdfC61OLm0bEz0eDIAhOVW5VVUvs2FtIama11SRkWYbcPC3L12bx0rtxPDn9EFXV9nO51m3O5dnXYjkSX0JFpYHTqZV8Ni+Jg7El3HFLFD27BpgZPZnZVdw7eR9xCWXoDTJ6g8zR46Xc8/g+snOtG5WyLPPc67HsP1yX26Q3yPy+MsNqlVuPLgHMfb8X//45nA1Lh/Likx0I9LcuNXHj1ZH06xkA1OblGLcP6htk+rz//jfXomy+9v1a46Ty+doN9iscNTa0nZqS0GB3pzWeIhuZa6XQNCiGz38EjUbkxSfb21xcZdnYdRmMScXjrrcuPmdCEJokn6cWnU62MA5koLxCT2y8feXX1IxKjp0oNbm/ncVav5z6rFrnXA+k7NwqnppxmFG3bmHUrVuZ8uoR8gvMFxNrgm+1qFQCxxPLHVbmSBIcOFLMiaRyBvcLZsL4Vrw7oyu3jGnG2Q69UBuqya7SraPt6hVtjWSR8BsZbgw9uGnsfzcEAfz8NHz+nW2Np2qtxKfzTuHu4dz3LDXDOdmE/MIah4/f+w8V8dzrsQ57yQ3pH2TT23M2xSW2wxiSVFcGHne8lJ9/t51/pDfIfPZdoum8+iz5K4OsHEtD5r3PTlidp94g8/7n1sO6h46WcCKp3MKrKMvw+8pMm4n2tT2s7KHRiMx6vTtvvGD06I4eHsZbL3bmvZe7mjxJlZUGmyKW9sL29dl9wL4I6l3jzk/l1LQnO3DjtZF2b5lBARqX8goVzh1KqOs/xLCBIcyf05vXZx0jJb3K+GR+Jmnwf7dFMfCMDL0oCjz1SDs2bsuze7NGlsBGb5mmxNaysnxtJp/NO2VKpnXTCDx6bzR33mL/ZpaVU0211kDn9vbL5W3lM9QnO7eKOyfuRW9yp8vsPVjE+Ef2suS7AQQHGg2Qh+9pzd+bcqzesPV2XPHWSEgsN+UaqFUCzz4ewwN3tuJkUhlV1RLRrbxoEenB/ybtJz2zqlHKvo48I6s3ZNO1oy9LVmSwekM2VdUGggPdGHt1BDqdTEWlno3bLEOKsmzseH7USrijPivXZXMkvsSpkm1ndXN0epnO7X2IP2FbQdsgGd/nQ0dLzJLsk9MqOHaijAB/DX17BNCvZwDDBgSzdXfTdUiXZKMkxCP/s9SCAUjLqLSaZF3L/iNFjL3KmHNVVW3gr3+yOGRH6sDWZ5CcZtuQrKmRTNWTZ5NXoGX5mkxOJJUTGuzODVdHWpWmUKsERg0LsxnK7tnNn3+3W2o+qUSjerQzWCtHryUkyI0uHc5PkYpKJfDCpPZMndiOz787xe8rzcPBbhqBOW91Py9zUXCMYvj8x4hp48svX/YnIbGMHXsLEEWB4YNCLG5gu/cX2jd64LwYPd5eKrNkxVo+m5fI4uXmoaganczn85Pw89VYVJEA7NhbwCvvxZsUWWudVrYWVGdKfGd+esKq4VKjk5j1xQnee7krYNT0+XluP2Z/dZId+wqRJJmIUHcyc7QuGyZBgZbenKBANwb0CWbh0lTennOcaie7vjuDm0aw2d18175Cxj2w22xbVo6WP1Zlmf6ODPew6oXYsNW2kGF90rMaZ7xZ4+lH2jL5pcN2F0aA06kV9OoWgLZG4u2Pj7Fpu6URJ4pGxeVaJeimwN44jiQR3N1UZ8bQ88S0QySlVNh9/2xVUEeE2U4AV4kCwUGW38MTp8qY/NJhqquNitgq0Zh4/vykGJstTGxx3cgIlixPJyun2qxcHUEgKaWC+5/ch5eXivBQD/r3DmTUsDCz8B4YQ2drN+ZYzYUrKKrh9od28/gD0Ywcem7yCM9GrRZ5ZmIM945vxbyfT5OdV023jv787/aWFnNXuHAohs9/lA7tfOnQzrrHI/F0OW9+dOw8z8hIrSFSm7g7ZWKMxY3+eGKZhdFTnx9+TbEwfFIzKnnhzaNm2xwtprWVSvaw57E4eNZTdmiwO+/O6Gr6e87Xify5JtPp9gYCxgTbQX3r8nr0eollqzNZuS6btIxKmwZKY/DxVlNYrGtwI1RbOSTO4qoCsiPCQ93p0tGfxd/0553ZCew/UmznWGNF2RfzT1k1emrnF3uslHbRXiQlVzqlzG0PlWjeM+tsmkV40r6tD4mnyy3eG3d30ZT3teSvDE47MHoABthoONqvVxARYcZCh7PFOa+6Mgw/H8ucwQ++OGkyeqAuZ2/214kMHxRiM+fHGl6eKr78oBff/pzMus256HQSarVAtVY6Sw2+lHWbc/n9rww+e6eHWUPiCXe0YsvOAiqr9FZDdlm51bz6/jGjEXRDw1tcuEpwoBvTnjx34qoKjUMxQS8z9AaZF960LrZljabW2+rXM5CwEDeimntx/50tGTE01OKY+QuT7Y6RmVNtkRvw9uzjLs1jcL8grrfiNTobezF7R5kpzSI9MLiwSnp7q/ngtTpRRFmWmTEznk++PcWp5IpzYvQAFJ3x/DnK27GFo4W3CVPFnMLTU+R0agW79hcxZnQEo4aGWnyPRdHYXHJAnyBKy3QsW22/Ug0g8XQlwcGNqwIUBEAwelvWbMimstJYCn0quZxFy9JYuiqD/AIt05/ugJenyiT2qTqTIPzS0x3wPrPwb9ya59AI8/FS8cIk6y0p1CqBj9/obuH5HNA7iKmPxVgcn5uv5fjJMquGql4vs2NvIXqDzJIV6dz/1D7GPbCLtz46RlJKhen82GMlFBXXJZUHBrjxwuT2rFsylPE3Nbdb6JCQWM4r78eb5TI1j/Bk3pzeXDsqAm8v26KVX35/Gt05KqJQuPRQPD6XGfsPFzndI8vbW6SiouluFmOvjmDn3kIKimpQqXT88Gsqf/2TzSdvdzepF4P93AMwasmcXb6fnGr/nNee78j+Q8XIGDuoD+obbLWi5Gx6dg1g137rper9ell/kq7l2hHhzPs5mWqt/UaZAP17BTBzRlfc60nb7ztczPY9TZdbYotaw8VRWKix458vklOruHdSXX8rNzeR6ChvTqXUCeqFh3rwwatdUasEXn4/zuk5+vu4kZffcC0WTw+RyiqJpasykST4+KuTdOngZ9aaYc7XiUyZ2I5FX/Vn5bpsEpPLCTuTS9OyRV1puq3EYDBqEF07IpynHm5r5iHJyKoiN7+axNMV/LEqk+y8alpEejDh9pa0bulFu2gfq6X+YGzmao+aGgOvvR/P5p11nrP1W3LZuD2PLh38OHTUWMQginDNiHCeezzG7Pu+aXu+w9/J7gNFvPp+PG9O62z6/TaP8GT6Ux1Iz6zicJz1QokancSp5Aqn2+Qo/LdRDJ/LjLRM2+KFZ3Pvba0ID/VgzjeJjW4K2K9XABu35lJ5pnN57VNbUXENL8+MZ8EXfU2VIs0jPMjMth0+GW+lHN/dXbTrxRraP4SrhjtXclqfl57uwO0P77bo5OzpIfL8JMun4vr4+WqY/VZ3Xnkv3mRsiqKxxLa2QaNaJXDv+Cgeutsy0XXX/kKXtHwC/dUUldgWUzv72mfT2BDOxUpNjbH7+rszulBRqSc02J1eXQNQqQR27C3gwGH7VYX1SUy2rkbsLLXf/9oFvqpasuhHJcvw8VeJdO7gx723t8QWwwaGkJqRatVYeOrhtoy7vu53snh5Gt/8lGy1I3lyWhWnU1OZ/FAbm0YPQGSYB83CPcjKtV627+OtNjN6wPjdNUiyyegB42v/e6OxonLGM/WatjppfW7emc9NE3by+ANtzLy2jrzTbg5ypxQuHxTD5zLCYJBZ5kB8rhYBo5t8zYZsIkLdkCWJ0vKGJ3buPVhsdbskGT08CafK6XgmJ+mWMc3Ze8j68e3b+jBhfCuL7ffcGmWzdDo4UGPRlbo+O/cV8Nff2eQXaenc3o/bb2xO8zPu/6BAN36fP4AP555k/+EiBAT69Q7kucdjzHpO1VJeoWfNhmxOnmltMWZ0BEvmDeDo8VLKK/R07uCLr4+Go8dK0NZIdO3oZwpdnI1aJTjb7xPArtEDxnXFy1OFtsY5pdn/EoIAsfElTHqwrdn2FX9n2TjjwvPzklTent7FYvvp1Ap+/j2V/YeLEUUBWa5TBBcE42+k1iDIy6/m/qf3U1Jq+zOvPXfeL8nceE0zm33ORFHg6UfbMf3towj12nfIMtxxcws2bXMumR2MRvbfm3J47L42psrIK4eEsnh5ulM5X8WlOmZ+koCbRuSqM4KIY6+K5GCsdSPW20tFdEvbQo4KlxeK4XMZsXN/ASnpjrVQRBE0GoEPv0w8D7MysmFLLtFRXri7qxg+KIQH727F/IXmwnc3XhPBC5OtJwzeeXMUazbkcOqsJ3JRhE/e7mHzuvMXJTN/YYqpD9OxhDJW/pPF5zN7mtzigf5uvGNlATqbtMxKJk07RFGJzqQsvGBJGi9Mbs+N15gnUvfsGuBwvBFDQvllaZrD4wDT/O0hy46NI7UKEASrlWzu7qJRedsgn7N8I1sIZ1pfNNQrJYPVRp/5hY6bfzpCrbb+fjWWs7/LYOwC/sS0Q+j1kpkn0MtTRfNID0YNC+PWsc1NIaSJzx+ya/TUp7pa4mRSGT26BNg8Zkj/YL54vye//J7G8cQyQkPcGTemGVHNPHnshUOuvDxjI+XMKpPhc/etUfy7PZ/sPOseJWvMX5hsMnxGDQ9j0bI0Ek9bvm8vT+3gUHtI4fJBMXwuI+KOl9mV36/FzU1s0nJpZ1i0LJ2/N+Uw5+0etGnlzYN3teb60RHs2l+IKAgM6hdkaptgix8/68vqDdn88GsK1VqJfj0DePrRdlarU8CodltrXJn6MGEU1nvzo2Ms/Kq/S6/hg89PUFyqQ5ah/lv8wecn2Lgtlzlv2TbArNExxpc7bmput8KtlqaqjBrU17ZmjVYr0b9XAGmZVWTnul6m7wy2DDgPd2NX+4YiSdCmleUTf5cOvpxILHNoUNlrkSEZZL6b3Zu//smivFLPsRNlZGQ1rtINjKGjs/nqxyR0esniPaqsMvDUw+3MNIlOnS53Op+vFi9Px0tC5/Z+PHJva0RRoHWUF4Ig8Mp78S5dp5bw0LrfdKC/G/Nm9+aPVRls2ZnP6dOl6GXRlB2vMtTQI2MHMTkHATgR3osjhiFoayTc3UTUKoFvP+rND4tTWLXOqDkV3cqbqRPb0b6tktujUIdi+FxG+PupbSZEqlUCj90Xjd4g2+18fS4pLtXxyntx/Dy3H4IgEB7qYdIGOXGqjK9+PM2p5HKaRXhy6/XN6N090GKMMaMiGDPKcbUWYDdxODWjikNHi+jZ1fIa1igqrrHpZgfYd6iYJ6YdYu77Pe2OE59Qys9L06ipMXD1leFMerAN/XoG8vWCZJuNLp3x9jjLiVO2Rf8A9tgIWTYWXx81Op1ETBsf4hJKLV5PY4yeWvYfLuauW6LQ1NNTuf2GFqxal422Rvo/e+cdHkXZdvHfzJb03huB0HvvTUREFBAFKYogdhAsoIiABX1VFFApIlgARZEugkqRIr33HiCE9N77lnn/GLLJZmtCgqic6/q+V3ZnZ5/szs5znvs59zkWiVzblp6kpBZb1MfpJXjj/bMsntWGoABHMrNKGPDkwVseb706xnobnU7i2E2BfkUoFAIHj2cYE58b9uuRRFEWCVd8z4rYdySNOQuvGKpnvt5qHhsQwl4ruXhgShxFEdq19CLQ3zig1sNdxZgRtYnfeZQr+ANyiJdSX8ITR+YQlB0NgIRAnfSLtEg8xEuvOZOYBXXDXXhiSBjPjaxj0RyyPM5dymbJihtcupJLiUaPo4OIn68DBYU6snO01A13ZuRjtejczrIr+138M3FX7fUfwn09/M2GmYoiPNQnkOGPhLFlp30xDvagsoVlvR5uxBVy6Yrx5Lv/SDrPTjzBn7tTuBKVz75Dabw87YxdLcjWYKtg8fXyaACibuQz68tInn/9BO/NumC2c8RW9hLAmQvZJn+bJEkkpxax91AaL7x+gudfP8meg2kcOp7J+3MuMfS5I7Rq7snSuW0txlRIdnACe20JktNufeunsnBxVrB+aSe2r+1O8yYe1ZQQZ4rjZ7L47U/jDKjQYCfmf9TSoC8DqB/hwqSxdZn1TjM2/diZuf9raTPfLiNTw7gpJ0lMLsLLU03tsFvPZKrYZi4IIFrqRJRMc6nq1bYvHkEQwMlRwbuvN7a6HXQhMoep/ztvtGWYllHCV99ft+lV5etjXK3V6+HE2UzmfXsVbblusWvReQx7cjdbkvzLBicItI7dQ1D2DW66ASAiIQB+2TGoDuwmO0fDqfNZTHr3LFt32b6HbdyayItvnOLIyUxy8rQUFevJytFyJSqf+MQiOUrnUg5vzDhnd27YXfxzcJf4/Ifg6+3AO5MaoVAICAKGdtCGdd0Y+1QEKWnFNlvJbaFpQzcWz27NmBHh9Ormx73dKp9EnFWug0ynk5i9MNIo66hU2zD/22vk5lVdqNvFxkruekwBR05kMOblY/y6JZELl3PZvieVl6ac4se1xvqjQH9H/H2tb8UB/LGjTEy7fU8KA0cdZPDTh3nrw/Ocv2xabUlKKWbGbNlscoSZqA5RBBcXBf3vt17lUogCH05tQv0Il2r3ZroVODmJvPR0hCFQNtlCx1B1QBBg21+mk2LjBu5881kbNi3vzMYfOjP7vRZciy5gxS9xLF8dQ3JqEQPuD6LdzdBNS0hNK+H5ScfJzCoxK8CvLFo0MXY0F0WBe7r4ojDz/en0Evd0NfbEiqjtSpiVvDoHB5E+PfwY+1QEq77uaLPVe+WGuCrnFk9/rQER4c5Gnk5aLazZGG+oMKemF/PC6yeJzzS9ABolHqf8UkUnKPirwSPMvfczzoZ2AcqqnvO/u2ZEpioiN0/L54uv2Bxz6fkWLIm66wH0L8Pdra7/GHp396dVM0+2704hO1dD88budGzjjSgKtiMsbMBBLTJjchMC/R0NGTk6ncSFyMMkpxTb1aGkEAUaltuPv3o9z6woFWRvjuOnM01u+PYiNNjJ6jaRs5OCj+ZeNttOvuj7aMKCnenZRX5vURR46ekI3v3UuiN2Tq5cGdq1P5X3Ztnnnn3wqOwjNGRACOmZJazcEGcggQF+jnzwZhMa1HWlUT03vlwaRaGZtn6NVuLg0Qzmf9SK71ff4Of1cXa9d02jsFDPpwuu8OmCK/Ts4kt4qJM8udYA+ZEkzH42pfDyVLNjTwrvzb5oIF8nz2axZlM8M6c1ZfZ7LXh75nn2HU63SM6ysrVs2JzAmBG1ycrR8O2P0QabBXdXRaU6I79fdYMXR9cx0qeMfSqC0+ezSc8sMXJAHzkkjPSMEuYsPElWtoZGDVx5cVQdFn7airFvnCSunOZIqRQYM7wWo4aGk55ZQn6+DlcXy12PpbhyzdRJ2h7UqeWMWqWo4MYsQ5Jg/e/xjBkRzi9/xMu5fGaqTiJlb5zu7M/61mNJcw02e2xWtoaoG/lE1HblxGm5otOkgZuhgnbkZEalPKuyczRcj8m/qxP6F+Eu8fkPwsdLzbBBpvbtwYGOBPg5kJxqXRCpVgty2nq5e0dwoCPTXm1ksmevUAjMea8Fr0w/bVcHzSMPBRllVRUUWq/olJjxJbGEoiItnyy4wp5DaWg0eoL8HWnawI2zl8zrWnp28WW1FWHxtI8vMPyRUEY8EoaPl5re3f1Jyyhh/rfXLL6mWSOZEH77U7Td49bqJCRJQhQFxj4VwfBHQrl0JRdnJyX16rjcdPgVGNQvmM8XW+7E+3N3Cm9OaICft0O16oKqC7sPpKFQ1Ny4RBE6tbNsOllYqGXGnIsmpEaS4O1PLrB5ZTeGDwpl7yHL2jAJuark6KjAz8eBNd90IDaxCGcnBZ8uiOTcJeuhreVx7HQmYydn881nbQz+Ov6+Djw2MIQV62LJzdPi6qJkcP8QklOLeP29s4bXxiUWsnNPKvM+asnKrzty9XoeZy/mUL+OC80ae5CQVMgr085w4mbsiqeHiuefrGPSfVgeQQGOlfIBA3B3VTLr3eYcPZlp8ZgSjURCchEnz1nWyEX6tyIw+wbJbqEs7zQZrai2agkeE1/AGzPOkZ5Zds9p0sCNlk09zHoZ2UJpPtpd/Dtwl/jcBSBXT0pT26d/fB5BuOnTgXwzf/ShIAbcH4SjgwIfbzWT3j3L2XI5VhqNDi8P85dTeJgzKxd3YOCTByiwIFJ1c1Uy7OFQE8M2W6GQLmY6X8xBq5N4YtwxI1IXn1REfFIR7m5KcnKNCVbLph70uzfAKvEBWLUhjm27kvl6ThsC/R0Z9nAoMXH5/LrFVBfg56OmX+8Aikv03KjElqKHm9JIe+GgEln/ewKHjmcgSTIRfaRfMBOerWe1Y69Eo2fF+li+WlYz4vX2rT0pKtKTlFJIWrqmSkUbnZmvW6UUbpI/0+cWz2rF5p3JbNhs3Y9HIYKHh5rHBlrOa1r7W4JF0lWikTh8PJ3unXxp1cydU+csE5jYhCIWLo1Cuqm7UTuIeLipyMi0TPzVKtEkrkGvl6MgflwbwzuTGgPy9u6aTfGG32VOnpYlP98wPSGymHj6zAv89mMX6tVxpV4dVwCKinS89NZp0jPKfgtZ2Ro+XRDJnoOpXLqah1arp0UTD14fVx9/X3kxM7h/CEesEBhzmPN+cwL9HY26typCFGSRtKe75TiQE2H30CzhEDsbDkEnqqySHl9vNbO+vGKiu7sQmcvFK7mV2koVRQgPdaZW6K1rtu7izsFd4vMfx77D6Xy59Bqx8YUoFQL3dvfj/TebsGlrIpev5eHno+bRh0IYcH+gYfJ9cfJJk/DO1HQNT796go3Lu5gYoMXEF/DC6ydNSI+APGn/b0pTOrTxNhsh4WjFeBDkLhB78Nu2RIuVLJ1OYvJL9dm2OwWVUmTowyF0bueDTifZrIxIEmTnaFmyIpqpN11o33ipIZ3b+zBrQSTpmXL45z1d/Bj/TAQuznJnnaOj/ZYBzzxRphfR6yXGvHKc+HLO1iUlEqt+jScjS2M1ad3JUeSH1TF2vWdVEBVdwOLZrRn2/JFq3anSaiWL59t3JJ3xz9Rl98E0MrNMt2qdHBWo1QI9Ovvx1LBaBs8Yc0hMsl7NyMzWIAgCajtW/4YYEK2ERqsjP986gbcUB6HTSxy/6eyclFLE2t9kIl76ediaxLOyNSQmFxEUUFaJ3bEvlVQLbe6HjpcRmwNHM3js2SMsnduGiHBXunbwYfigUFnrYyca15crnG1behEU4EhKapHR1rEoyn5VXh5qhj1suZqm0hXxfcfJlKhdbb5nu1aebN2ZYvaaKW/0aO2zK/WNcnRQMO3VRnc9gP5luEt8/sOY/WUkG7aUrZS1Ook/d6dw7lIOy+a1M+vgGp9YaDGxvKhYzy9/xPPE4LKqjSRJjJ180qwIWaUSWPVNR6v+PC2beODhriInx7iCINxcJTa2M3vHnKi1FPkFOjq392Hgzdb5UigUAvf18GPbX9YdaXV6id0H05j6atlj3Tr40u0HXwqLdCgVglELtSgKDLg/iHWb4m36xygUAo8+VFalOHg8w4j0lMefu1N4pF8gv2w234XS//4gmxWsW0FevpZtfyUjVbM62drZ1m6KJzTI2SzpAbnbrmVTL8aOrmPWabs8Wjb3ZOM2yx08HdrI22SWrv9bgbWPzM1Vvk2fPJdVJeH37IWRNG/iwYD7g/DxUnMlKg+lQrDZiQXyomD6zAus+Er2tBr/TF3qR7gye2GkTYsBhaL8fwvMea85b8w4a3T9tmrmyRs3Q1RbNfNk4P2Bpt+BpMetdhCJybY9icKCnfD1VqOw8feVfo51ajkTl1iITiehUomEBTsREuiIUilSJ9zF8Jndxb8LlSI+p0+fZtOmTXh7ezN06FB8fcs6dnJycnj11VdZsmRJtQ/yLqofO/elGpGeUkgSJCQVse2vZAb1CzZ5/nqMdW+Qk2ezjIjPsdOZFp1jSzQSZy5kcW83yxlap85lERzgYJQVJoqgVIhMf62RXUGjIPsUWYOlZye/1JDjp7ONtAKVgaWojOefrMPlq7mcuWB5EhVFuP8ef6PH9h607pfSpKEH0XGFJp5CLZu4M2JQaI0RH1GUu5Bk12oBvRW6Yu+kaw8Ki/T8dSDVqjHnkZNysOUXVhy8Ae7t6scXi6+aJenhoU74eau5HpNvNROusigtJFgjNA/ejJ9wcqiazuToyUyOnspkxfpY5n/YEj8ftdWA04qIiSuksEhnuJb79grg3m5+nL6QTdSNPOZ9Yz4qpkcn447OWqHO/Ly4AyfOZpGaVkxEuAsN6xkvXCZPaEiPrn4s+SmapNRiaoU48cTgMPYdTudXM/er8vD2VPHJO824GJlr9/X1vylNCQ+7G2XxX4Pdja3btm2jQ4cOrFy5kk8++YRGjRqxa9cuw/OFhYV8//33NTLIu6h+rNlovVx99oJ5oaGtlu30zBLS0stWZhcjrRvibfjD/Apbq5OY9WUkr71zlktXZeO+0kmicztvflzYjrYtTc0FdTeFwBUxZIBlHxYPd5WJz0gpHB0VrPm2A316+hutYMtDIQr07Gy7bf/0+SwmvXeGfiP28fCTBwykx9lJJNBffv/SVnNBAD8fB55/0tiIrXT1bwkLl0WZNVI8fSGHZyeepHEDNxRmvJxuFZIe7uvhh6SXrE46ggAN67la9CSqLG7avFitMun1ctK9LdKuUoks/LSV2Q6nG3GFjBx3lHW/xVuTl9g13vJhmeGhzlZ/U6IoV7WeevkYsQmFODlW3otAL8mfQVGRjo++uMz99/ijUAqV+juKKuhlVCqRdi29GDowjOdG1jY5vk4tZ6ZPbGzyuCgKtGvpRb/egSakpxSd2njz9Zw2bPyhMws+bkXndj48YMOUVKWEdUs6UivEmXu6+uHno7Zp2+DhpiQ40NH6QXfxr4Tdv6L33nuP119/nXPnzhEdHc3kyZMZOHAgW7Zsqcnx3UUNIT7Rup7BzcK2QP0IV3y8LU9a16LzGT3hmOH8jRtY34oyp224dCWHB0fsN6zwSue00v89ezEHvwpE5cTZLF584yQ9B+2hz2P7mL0wkpy8sipRj85+ho6q8hCQc3ysQa1WMKhfMD07+5pMiqIoO2I//Xhtq+fYfTCVl6ac5vDxTHLzdEZ6p4JCPUkpxTzyYBBBAY54uCvp0NqLL2e2Mvk7hz1iWZwLWNzyAZmUXonKI6ACyaoWCPDxvEjW/mbdVFKSoF4dV35Y0I5WzUy/j8pCkuCBewPs0mDExlu/5iVJ4svvoiwK6hOTZaPJWyE+kgRzP2jOd5+34ceF7Vj+ZTv69PS3+F3o9ZCSVszV6/l8vfy6wYJBFDHr52MNer3s5lxYpOejqU1xtLOC5OaqxNPD+H6w/0g6z086Qa9H97BxayIjh4QxamgYj/YP5vMPWvDDgnY4VGMaevNG7nh7Wt6q1GihtG7roBaZ/1ErI1PK8ij9+p5+orbRFvRd/HcgSHZuyHt4eHDixAnq1i1LN16xYgXPP/88K1eupH379gQHB6Mz15bxD0FOTg4eHh5kZ2fj7n7rN+U7Gc9POsEFK9WYj6Y1NSlVlyI2oYBnXzthcYJQiHLS8ozJTQDoMXC3RS3LiEdCjRKzc/I0PDzqoE2fjYWftDIYvJ06l8XL004jScbixVohTiyb185wc5MkiR/XxbLhjwQKCnXUre3CK8/Vo36EdcHkL38kMOcrY8MzAQgNduServ4MGRBiVQcgSRK9Htlb6e0dtUrg28/aElHBgXfpz9F8VyHA1V6IIjz8QDA+Xiq+/alq56gOOKjFKrUVl4dChAb1XBnxSBjfr4oxG+pZHqIIbi5KunbwYfTwcEIqOCOfu5TDi2+cvKUxWRzrza24wQ8F0+cefwL8HA2kNitbw7MTj5OSVmxXK//k8fVJTi0mOVXeLtJq9Xz30w10dm5fLZ3Xlvp1XCks0rH/SDp5+VqaNHDl1bfPmnQ3Akx7tSH9ylVc/tydwozZF40EwoIgb8u+babKc6vIy9eSk6vhwNEMvvjavF2Dv68D65Z0NCHAsQkFpGeUsOdgGr9tT6KgQEdQgCOjh9Wifx/Lrft38c9DZeZvuzU+Dg4OZGVlGT32+OOPI4oiw4YNY86cOVUa7F38PejYxssq8XF1LlsNanUS+4+kcy06Dz9vB+7t7seWlV15dbrsA1KROuv0GHVnjB4ezlIzLbeiAAMq+IasWBdrl7lY+eiNr5dHm0wYkiRvT2zYnGBoYRYEgSeH1OLJIcYt89aQnaPhCzPeOBKQV6Dj+Sdr26w2bN+TUiVNS4lGYtKMs/yytJPR42NG1CYowJGFS6PIzZe9XKxVespDr4fomHyuRdvuWLOGW00kv1XSA9C9ky8+3mre+eSiXdUrvR6yc7Vs3ZXM3sPpLPmirVG305kL2XZ9Jv6+DqRl2CYpMvl2RqfTk5RajCDAut8TWPe7XBXr0cmHt15phKeHim8/a8vP62/w03rrGiyFQiDyWh6vj2tg9Hj/PkEcOJaOXi9HVbz4xkmziw0vDxV1bmpanBwV3NejTEO2+tuOvPfpRY6eykSnk/D1VjPhmQh69yjT4Ol0EguXyj5V5X/3kgRbd6XwxOBaBs+hW0VOrobPFl1h575U9Hq58uToIJrNVXv80TCzv8OwYGfCgp1p1cyT8c/UpaREj4ODeLdL6z8Ou4lPq1at2LVrF23btjV6fPjw4UiSxOjRo6t9cHdRc7ivRwBLV5pvbS69YQOkpRfzyvTT3IgrNKxa5393jVnvNqNubRdOnc82Lyotd195ekQ4+QVaI2Gto4MsTi59n1KcOpdlc+zeXioje/2zFy0bn/3yR4JV75byiIkr4MCxdAQEunX0ISTIif1H0y2upDOzNOzYm8LBoxmcv5yLr4+aQf2C6d3dz+jGuvuA9a4wa0hNKyY+sZCQoLLqxL7DaXw09zICAjq9VCnHbVGUzSb/3JNaZdLTIMKV9MySKgu+bcFWqzHAcyNrc293P0a8cBSoHIHT6aGgQMtP62KMCISbi9LmeRwdROZ91JIlK6LZsTcVnU6iQV1Xenb2YdmqGLRaCYUoi7eDAhzR6fUkJBeZPe++I+m8PfM8X/yvJZ4eKgY8EGKT+IAs7C9FcmoRl67m4emu5MHegYYFwfBHQlmxPs7wWZYSupeerotSaZ4lujormf1ecyRJQqc33xCQlFpk0UldEOTmhuogPnq9xKR3z3L5Wq7hsysVnXt5qgxEXwD8fB3Q6vTk5GlIzyjBw01lMEGVJInL1/LIzdVQP8LNZMuuFFE38ln3WzzXY/IJCXLi0QeDadzg3131/y/DbuIzduxY9uzZY/a5ESNGIEkS33zzTbUN7C5qFuFhznRu583hExlGN2VRlMNMS8W+M+dHEnfTrbWU4BQW6Zjyv/O8+3pjVm80vVErRHk1WwpBEHj52XoMHRjKyXNZODoo6NjG22y7vLe3A2C5EiUKMGVCQ6ObskJhufqQmGy+9bs8JEniq2VRhokC5LyfMcPDTUzlKmLG7EuIgkxAEpIKOXUum8tXc4227zzcb03Im5ZRYiA+Wp3Epwsib35n0s3x238uvR66tPdh35H0Srlel4dGq6dVMw927K06obMGf181yanmJ1dRBFcXJY88FMyWHcl2kSRz0Onh4LEMo8d6dvXl88VXrX7njw8OIzTIiXcmNWbqKw3RaCWcHBUUl+gpKNSzcWsCRcV6GtZxpUNrL5avibV4rlLR9bXoPOrWdiXA18Hm36PTSdzT1RetTuKT+ZfZvKPMpiHI34GPpjWjfoQrY5+KoFaIM2s2xZOcWkzd2i6MHBJmMWlckiTOXsxh98E09HqJLu28adfKy6QyYq2zTJIw+5uuCk6ezeLiFfP3AZ1OIjTIkfgkOdctJa2YBd9FsXBplOFe1rGtN088GsqcRVcNZqEKhUCfnn707ubPpau5FBTqaN3cE0kPUz86D4J87vOXctmyM5nprzWiby/LHad38c+F3Rqf/wL+Do1PZnYJK3+JY/+RdBQKgd7d/RgyILTabiDWkF+g5ZP5kezan2pYFfbtFcDrY+vj4KAgI7OEgaMOWnz9B282Zu/hdLb9lYIoyN0jgiB3Qk2ZUJ97u/lXqqSckVnCxHfPcPW6ea2Gm6uSL2e2MlpRSpLEM6O3E5lh2c1VpRJo09yT4YNCad/aNLJg1/5U3p55wexrXZ0V5NlwjzaHAfcH0qGNF/d08SM2vpDHxx6t9DlKsX1tN4MQ9UJkDs9PqroOxcNNSbYZHUd5NGvkbjVaYdTQWvTu7sfoCcerPA5LcHZS8NPCdqRnabgRV8DFyFy27Ewi76YBYP0IF14aU5fT57PZfTDVbP6TvagV6mTwpynF7oNpvPvpBfQ62TSx9O7o5qpkUL9gdDo9Fy7n4u2lZkDfINq38kKvl5j4zhmOnynb9lWIGLaabN1h33ujEffd3E56e+Z5du23bFkwsG8Qb7xUn0/mR5okzZeOc/3STjg5KtBq9fz8Sxzrf5fNLevWdmHM8HC6V9DuSZLE7IVXTNrF69Vx4ZvP2qCqUCF6eeppTp/PMjIiFJA7vX79obPNzkN7sGJ9LIu+j6pyVVIsjXHHcjWwtAqmVAo3u0GNn3dyVLBxeWeLlhR3cWehRjQ+R44coW3btigs9PQWFxfz66+/MnTo0MqN9j+MzKwSnp14gtT0Mr1A1I18dh9MY+HMVjhU0bfDXrg4K3n/zSakZRSTlFJMSKAjXp5l1YnyXVHmkJ2rZfprjejYxptf/kjg0tVctFqJwkId7356iZ9/iWPWO82NzpmUUsT+o+kgyW3ppcGBkiTx5gfnuH7DPOkJ9Hdg2dy2uLqWlaqzT13kxPCX6R+dzMr2r5HgGWGstrwJjUbi8IlMDp/IpFaIEx9MaULd2mWC5o1bEi1qO6pCegA2bUti07YknBwjmfu/FvTr7c/mHSkmx5VmU1maHLt19DHqvrnVZYot0gPg66NmxKOhZoNM3VyVPDYwBGcnJf6+alLSqne7q6hIx6T3zuHiLHL2orziV6kE+vcJZMQjoeQX6nhl2hmKinW39FkIAvS9RyYbMfEFbNqaSHxiIb4+Dnw2ozkXInPJyCyhXh1X2rX0JD1LwyvTTlNUrEOvl4nNzn2pPDeyNg3ruXHsprtyKcwF21pCeUfxGZObcGr0QYuaLXdXBcUaid+3m7eByM3T8tf+VPr1DuSjuZf5c3eK4XO6EpXHWx+e5+2JxpWMPYfMe+RcvZ7P+CmnWDy7jdHjk8c3YNyUU2RklqBQCAYrgWmvNbwl0pOXr2Xj1kQOHc8g3Q4NlTXoJWwG3Zae31K1uLBIx4kzWXTtYL5Kdhf/XNhd8VEoFCQmJuLvL4vh3N3dOXXqFBEREQAkJyff7eqyAzqdxNXreWg0en7eEMeeg2lmb+Cvj6tv1kDwdqJEo2fgkwcMq+2KWDq3LfUjXNHrJR5/8YiJlkEhQvvWXsx+rwUgp02XD+eUJBg9rBbPPlGbc5dyGDv5lNn3EQXY9GNnoy0jTXYuu+r3RpOTCzo9ekHkXFAHfm8+xmqOD8hVheVftiPATxa2jhp/9JYqB7agVglsXN6ZzTuT+fqH6wbHWw8PJTk5WosTeKe2XnzydnODSaNWq2fVr3Es/uF6jQeM9ujsQ5vmHny/KpasHA0KhUCH1l5MHt8AX28HPvvqCuv/sN66fisorSCWx4uja7N5ZwoxcQVVJj2lBLdpI3e++KAFJ85k8daH54w6AkEW/8+Y3ATXm1lw4948yblLOSafuwA81CeQzTuTreakWUO3jj7MnN4MgOJiHfc9ts/q3zf04WBW/2r5sx/8UDAP9wtm1PhjZp93clJQr7YL7m5KRg8LZ9nKGxw4mmH2WIBG9d2Y9U4zowVMfoGWLbuSuXItD19vNf3uCzTpkqsMsnM0vPDGScO2uiWU+jbdroDdmdOb0q2jbY+uu/j7USMVn4r8yBxfurtrZh1HTmQwc34kKRZyckohCLJPxt9NfNQqkdHDwvlyibEzqyhCp7behjbwU+eziUs01dLo9HL2T3JqEdGxBXzzY7TJMd+viqFBXVfWmtEKlUIvyavi8sQn4edNaLJyDLOVKOnxyU+2SXpAXsmt/z2BsU/JpL1ZIw9uxBba3Q5cWZRoJD6YfYkDN0NFS5Gdbbn6EujvYCCMIP+23p55gX1H0m+56mMP9hxMR6uV8PJUodXpCQl0Yvgjofh6O5BfoOXXrTVHesCU9AD8uCa2UhU4UYRuHXwID3PG21PFlesFaLV6OrX1pldXPyTgf59fMjuJHj6RyeT3z/LlzFbk5GotO2wLssD4Vr6U8n43WTkam6f68y/TymF5eHqqOHkuyxBkWhGFhTpDwPD+I5YJTykir+byzqcXmf9RmfO1i7OSwQ9ZNgWtLH5cG0OCjaw0wISg1iTUapHWzT1vz5vdxW1FtWZ13Sktgl9++SWzZs0iKSmJli1bMn/+fDp06GD7hTWI6Nh8Jn9wzu4W4MtXcxnyzCFUKpEGdV3p092PTu19bUYvVDeGDwpFrRb5YVUM6ZklODqI9L8/kK7tffjw80vExBeQk2t9Sywto8Sq3fy8b65ZDBAFeQLzq+Bum38tBkGpQNKUkQePogz5rmjjOpQkOF9OwzJ8UChbdiUjaaUaW0nuP2Z7gilFqWtzeZw6l83ew+YDHGsK5asAl67m8fLUMzSu78bEsfXMpqjXNCq77SgAgf6OvDAqwuzzh09kmI2nKMWZCzmcuZBjM5m7Xh1Xjp7KMvtc+Q4kS3hiSBggC9evx+TbbKnPtEKYAdb/noBaJVZbUKxeksXGMXEF1AqtmXiHHXur3mVYUxj/tBwqfBf/PvzrvtVVq1YxceJEFi1aRMeOHfniiy/o27cvly9fNmzT3U7o9RJHT2WycEmU3aVwSYKMcjfL2PhCduxJxdtTxcy3m9HkNrZZCoLA4IdCeKRfMLl5WpwcRb5adp3X3jlr9zmux+STmm6Z2FgjPQABvg7MnHeZlk096X9/IO6uKlzq1kLS6ojyacKZ0C7kq93xzUtEodegU1jvohIE2UfmRmwB4WHO1Ap1Zv5HLZn79VWr3ka3C5IEAx8w9jc6cjLDah5VRbg4K9Bo9Wg1kqF6UtUOqPK4eCWXT+ZH3tpJqggHBwEnByVZOfa17+v0coBsUkoRgf6m0QSx8da3NwUBLl3JpWVTDxrUdeVKVJ7J5ydJMOD+IFxdlHzzY7QJabFlNdConisNItw4ezGb6R9fsNsiwFI1B2SbhZpYHiWmFNlNfIqLdfy5O4WjpzNxclDQu4c/7Vp6cuFyDl99f53U9GLCgp0Z/0wEtcNcKpUddjsgitChtWkkzl38O2C3xkcURXbu3Im3t9wV06VLF1avXk1oqOyRkpaWRp8+ff52jU/Hjh1p3749CxYsAECv1xMWFsaECROYMmWK1ddWt8ZHq9Uz/WN5e6K64OqiYN2STn/bSuToyYxKkR6Qt22aNXRnexXbn8t3jPn5OLB4dms8lSW8e89M9oTej6DXIYkKs8JmW7i3mx/TJzZCfdPdOStbw9lL2bz1v/NVGuutoHTSfOTBICa+WN+ogrr052iWrrxh96r4ySFh9L8/iO9XyfoNlUrgvh7+5BVo+WN7mR6lOshQdcJatWPEI6HUj3Dl/TmXKnVOQYCHHwjitRfqG/RSqenFjBx3hPwC6x/ou683pk9Pf85cyOaV6afR62SPm9Jr8rGBIbzyXD0Azl/O4cPPLxEbX2hXtUWtFvlzdTfyC7QMfvowxSW6Gql6VJcuZs23HY0MH/PytWTnavD3cTCKfsjJ0zB+ymmibsjVK0GQCXuThm5cuGy6sJj+WiMuRObw6+aESonCKwXpptpZsD+iws9HzbolnYzMUu/izkWNaHwAevfubaTj6d+/PyBf2JIk/e1bXSUlJRw/fpy33nrL8Jgoitx3330cPGjall1cXExxcVm1ISfHcgtvVfDr1sRqJT0Aefk6duxNZWDfv8du/c/dKVZXm+aQlFLMkP5uVSY+5duC0zNKWLQsitHDw9kTer/8uHiz66kK19+u/akE+jswbozsu+PpoaJrex9CAh1JSC66baSgZVN3GtV3o2FdN7p38jX8lnJyNeTma+nR2a9SMRUFRVpCgpyY+mojk+deeDKC6Nh8fLzVnDidxayFV8yc4e+Btck5OraAmPhCRg8NY8uuZItePxUhSbBhcyJBAY48MVh27V7/e7xBZG4Jri4KuneUO3paNPHgu8/b8vMvsZy7mIOPt5qBfYPo07Osity0oTsp6cV2/TZcXRTM+7AlCoXA1l3Jt9ylZgmCIDcYBPg6sHlHcpUcxEVRFmCnZRRz5kI2RUU6/tyTzJnzOegl2Vdp5JAwnhgsuyf/sCqG6Fi5O7O835Q50gMwc/5lVn3dgd0H0sjIKqmZ35wggASNko7RqY6egGee4JMF1q/71PQSDp/IsOh9dBf/XNhNfK5fv16T46gWpKWlodPpCAgwNp0KCAjg0iXTVeLHH3/MjBkzamw8my20nN4qYhNqrgPJFgqKdJXWDggCdOngzYIKIunyKI0cKJ34LFUidHqJnftTqRPuUqnIBUsRC5IEqzbEkZFZwtCHQ2lQ1w2dTuLpx2vz4eeXqk0nYQtnzudw+rxMvJUKAbWDSEmJDu1NOYeXh4quHbzZf0Te8ir1mbGEXl0tb+u6uykRRYEbcQV06+RLUmoRP62LtfnZl4e5z14UQaUUKC6pmU+t1HBw/5F0QoOdKh2bsWZjvIH4nDfToVUeLs4KPnm7GY7lPFwiwl2YZoZIloe91+Pr4xrQoK7sPp6QXGRwe65uSJIs8H70oRC8vdR8v8q8WzvI37uTo4J2LT3ZezjdIJfr3M6bhKQii12XeflaFn1/HUmCJx+rxba/kitVXdJqJS5fzWPJ3Las+jWOrTuTyMzWVH/1SxC44d2Idx9TstdCp2qFwzl3Kecu8fkXwm7iEx4eXpPj+Fvw1ltvMXHiRMO/c3JyCAsLq7bzV9UDxhaOn8ni6VePU1yip0s7b4Y/EmY1JLM60aa5J39ZMVirCIUoOwXXCnGhaUM3zptZ9SmVArPeaca3P0UbnvfzdSAt3byXh04nVbp1eECfQDZuSzL7Op0etuxKYcuuFJo3dicmvoDsHNt+N9WJ8qPS6iS0Fa6dzGwN+49k8ORjYWTlaMnJ0XDqXDbZZoTl9SNcadnUw+ixrBwNRcU6srJKmD7zAkkpcqVTFGHwQyFsW9WVQ8cz0UvQvpUXH8+9xJ5DlquV5r6XLu19yMgq5sLlPPv/8CoiLqGQyu5ApGWUoNdLiKKAp4faInEOC3Zkydx2Fo3rzl3OZubcSFLTi/H0UDHphfp0aCtLALq292HPwVSrWzaiCL9tSzTkZIWHOle5Fd4elFatnhtZB51OYuWGOCPCKIoyQVIpRT6Y0oSObbxJTS8mIamIkEBHpn50nigbAbAAP62LYdig0CrlsGXnavDxUjPuqQjGPRVBiUZPTFwBzk4KfvsziR9WWyZslUGh2pU3/nQkLtHyIqw8PNwsJ8LfxT8XdhOfmBj7LrxatewPgKxu+Pr6olAoSE5ONno8OTmZwMBAk+MdHBxwcHAweby60L6VF4lJhdW+bx15tWxiiY0vYNtfKXz7eRuTTqCaQL/egazaEEd8kvUoCIUoE4oAf0cmjq1PWkYxQf4OJsRHoRBo3cyDy1F5RltVTg6i2UlJFh1606WDj9n2eHNwd1XSsrknv2y23FlWitI23zsVf+5OYc23cgp1RmYJ7826wImzclaZKELXDj588GYTw1bZxSs5vPXhedIs5Cvp9bBmUzzeXmqefKzst/vRtGZ8+MUlo0gEWzh5NtOmZsYW1GrR7iiNyuphQ4IcDXqN/n0C2bnP/NbrqGHhFknP0p+jjbYc8wt0THzvLIP6BfH6uAY892Rtjp7KoLBQZ/F3r9dDUjlB//09/Vnw3VWKimuG/CQkF+PnK2tzXhwdwfNP1iElrRilEo6cyOLq9Tz8fB3oe0+AIePKz8cBPx8HrlzPs1vwn5evIzm1iI5tvdm93zr5q4genY29ctQqkXp1ZLuM55+sw0N9AjlwJJ3ktGIuXcnl9PnsKm+J2bp3lUKhEOjd4/Y3xNxFzcNu4lO7tvkU6vLaHkEQ0Gpv70q5PNRqNW3btmXHjh0MGjQIkMXNO3bsYPz48bd9PCMeCWXbX8kUFtWMaBHkm2hWdgk/ro3htRfq18yblIOTo4L7ewWYTVsHuVLTqqkH7m5KGtVz495ufuglGD3hGEkpZrx+dBLHTmeZtAPHJhQaVuSlmiKFKNvivzC6DvXruDKwbxAbtyba3JqZ8GxdenbyJTjQkeSU4hrz67kdSEoppqBQh4uzEm8vNfM+akVmVgmZ2RpCgpyMPGHSM0t4ftJJuyaI736K5rsV0eh0EmHBTrw+rj7TXm3EpBfrsWVXMhmZGrJySlj/u2XyaA/pUakENBrLA9JUslogCBAaLH+vgiAQEuRIUkoRBYWm53lySBmx69DGmzHDw1m68obRNuvDDwTxgIV8Jp1Ob1FntWFzIs+PrE2tEGeWzm3Hyg2xbN2VbNb8UyEKNKwrT+qSJPHurItmSY+DWsDPR0Vc4q25Y586l4Wvt5rgQEcEQUAUBUOX24P3BRJ1I589B1KZ9+1VklKKuB5TgCgK9OrqR7NGbjbOXgZRBA93FU+PCOfg0QxKSsrInyjKZKao2PR7ubebn83KSkigkyFs+Lc/Ezl1znIwsS1Y+z2UTnGiKPDOpMa3rZJ+F7cXdnd1nT592uzjkiSxcuVK5s2bh6urKykp1s21ahqrVq1i9OjRLF68mA4dOvDFF1+wevVqLl26ZKL9qYiacG6+EVvANz9eZ//RdBSigJurkvTMkmonQr7eajZ837l6T2oG+4+k8+YH56weIwow/+OWtGzqCcCGzQnMroKAViFCk4bu5BfoyMvX0qaFJ08OqUV4mNxSq9dLbN2VzKZtiWRkamjSwJXsXC3HT2eh1UkE+KkZ+1SEIQcpKaWID7+4xMmzVb9p/t1wdBDZsqqbXX5OM2Zd4M89VQ8SnfNeMzq2LdM3rN4Yx7xvrlXpXC7OCoY9HMpjA0MYNPpgtemASkN135nUGACNRs/wF46YWCQolQLfz2tLeJhxcnhsQgF7Dqah18talnp1XLkYmcOcRVfIytZQr44rvbv54uvjyNmL2Xy9PNriWPr3CWTKyw0N/45LKGTUhGNotXoj/ZQoCCye3ZpG9d3YtDXBpsi2ulA7zJk3Xmpg2AYtLtbx7qcX2GfBxFAUwctDbVeLvSBAr25+vD+5CSDf935YfYNDJzJxchTp2yuAYQ+HMmfRFXbvT0Ork3BQizz8QBAv3+yKsxeFRTqGPXeYrBzLOqBaYU7ExNo2RAS5svNgb3+6dvTlwuVcPNxU3NfT/y7p+YehMvP3LYWUbt++nSlTphAZGcnEiROZNGkSbm72rxBqCgsWLDAYGLZq1Yp58+bRsWNHm6+7HZEV+QVaZs67bDWIsCoI8HNg3ZJO1XpOcxj/1imbZWZRlFfUs99tDsAjTx0k1cJWiy00iHBlydy2lXqNXi+RnlHC5ahcHB0UtGrqgbJc0GJCUiGPjz3C31icrBJEEQb3L2uftoWBow6QkWmf5405+Ps6sH5p2TWVnlHMw6MPVfo8a77tQKC/o6EyfPhEBlM/PE+JRo8oyq3Ole0ULA+VSqB2qDNDHw7lRlwBP641n4jeo5MPH01rZvE8xcU6xr55kshr5vUsjo4CRUWWR9m9ow8fTzc+/5kL2Xy6IJLomwnhwYGOvPZCPUKCnNh9II2f1t0gL//2OfepVALLF7QnNNiJud9cZe3GeKufuyhAaIiT3KJv5cAmDd2Y/W5z3G+TJiY6Np+3/neeWDMRFz5eany91Vy+Zp/WTBRh9NBwnnmidjWP8u9DYZGOPQfTSM0opmt7H+rUcrH9on84aqydvRQnTpzgzTffZO/evTz77LP88ccff4s5oCWMHz/+b9nasgcuzko+mNLURCtQEa2aedCskRv7j2SQl68lOMCRiNqu/GImH0kUoXd3v5ocNgC/bkmwq8Ss18PlK7IuIDomv8qkRxTB27tyqy5Jkvh+1Q2+XxVj6JLx9FAx9qk65ORocXNT0ru7P0MfDmXFOtMQzjsZ7Vt58cKTdew+XleJjidzqBit4uPtwAP3BrBlp/26Hy8PFZ8vvopGI1EvwoX+9wXKobbLOrF9TwoZmSU0rOfGp19G2nQ4tgSNRuLK9Xw+/OIyTo6WfVoOHbfunj3/22sWSQ9glfQADB5gGjHTookHy79sR0JSETqdRGiwEz+sjuGNGef+Fg8ljUZi3e/xjH0qgo1bEm2STb0kC3wbdHdlx55Uk+Mb1HXlhVF16NDa67bamdQOcyE02JG4RFNClp5ZYrfJJQASPHCv9d2AfxL+2p/Ke7MuGu5/i5ZdJ6K2M1/PbmMUePxfRqWIz7Vr15g6dSrr1q1j6NChXLhwwRBSeheVw+D+IRYN6QQB6tV25cXRdXlxdF3D4zqdRFp6MXsPpxtumoIAIUFOhjbd6kBxiR6dTsLZqexHciUqj1lf2leSFwTwvSm0Xvub5QwuW9DrZVfcymDzjiQTQpmVreHjuWVuw5/Mj6R+hAsNIlyIjLLdrXI7ICB7q7Vq6mEQKxueE+Tog5nTm6JS2X/jatvSk537qreyOGpoLXYfSKOwqEy74uQoolYJ5OWbCnozszWG6IujpzL5eX0cIUGO1KnljINagYODSES4C/16B7ByfVylBcsVYc2bRy/B8dOZ1K/rirurcWWiqEjHxq22xe+W4OOlol1Lb6PH0tKLOR+Zi5uLgpZNPVEoBE6czTIE9f5dxpGXruSSl6e1q/tKFMHNVcl7bzRhwrMlJKUUodPqKSrWEx7mbAj6/Ttw5kKO2c9Q7lKz/uEqRPl6EEWBKS83ICSo6gGrdxISkwuZPvOCyeNR0QXc/9g+Rg8P55nHa9/+gd1hsJv4jBs3ju+++45evXpx7NgxWrVqVYPD+vfD3U3FuDERLPjOtK1SFAQe7mc64SsUAh+81ZQde1LYsTeF4hI5cHFg36BqcXJOSili3rfX2HcoDb0kpzK/NCaC1s09+f3PJLsjEyQJHn1QXv1WdhUoCDIJ0EvwxOAwenSyz0NDp5PYtT/VLr2EJEHktfybviUiY5+qw4LvoiixIrq9VVRsnS4lrgoF6HTQrLE7z42szdSPTB2jJQkyMmUC0bOL/ZW9l8bUvSXiE14ho0qSJCZMPW1EekAmG96ejgQHOnPxZqVPrRIsfp7xiUXElwu1/WN7Ms7OCsLDnG8Ka2smfVurlXhl+hlUKoEnHg3jmSfKGjaycjR2kS6FCF6eatIyyqqYDeu6snh2K8O/dTqJ+d9dZf1vCYZz+vmo+d9bTflje5Kh4/HvwrXreaxYH4ubi4JcG342ej306SlXQ3y81HeU7sXVWUm+GcuQUpNmS+jawZvGDdxxcVJwb/d/h5ZHkiR+3ZLI3G+uWjxGL8HSn2+QkFTI2xMb38bR3Xmwe7ZctGgRjo6OpKSk8PTTT1s87sSJE9UysP8Chg8KQxQEvvo+ytDp4uaqZOorDS3uySoVAn17BdDXQudJVZGXr2Xs5JNkZJYYbtaRV3N5dfoZFn7aiuTUIru9Rgb3D6b//bJ9wJABIWa35+BmJcNDiVYnISCApgQ3lZaurTx4+Ilm1AqxLxdIp5OYPvM8e634zpiDJMnVrfW/J95ypcEWKk7kkiTrJ+pHuLFoVmuUCoG0jGKzXUAgf1Z/7kmhc3sfQ7yGLVyyU+NgDqII773RxOixk2eySM8wv20Zn1TEzOnNUKtF8vK1zPnqSqVyzwoKdMTEFzDl5QZEx+Tzx/ZkcqwEiN4KNBqJZati8PRQM2SAnDDu7aVGpQSNjbcsJeSlHUbmsHJDLGs3GV/z6ZklTHznDE0auP2tpAegoEjPql/jjDoALeG+nv63ZRu9Knjo/kCW/nzDbH6aJfj7qvlgSlO7f0P/FKz/I4HPF1kmPeWxdVcKL4yKwN+35u1P7lTYTXzefffdmhzHfxZDHw7loT6BnLmQjVIp0rKpx9/yo/xjexJpGcZ28XoJREHi88VXiLRzEnV3VVKvtqthJR0e6kzPLr7sPmBaeRg7ug4Hj2fIXVaSBAhkF6pYvTuPgL3vE7zyHZQutsnPXwdSK016SqHXy1EIbVp4cupcVo1lJYHpDVkvydsO8QkF7DuSjlol4uhgvuVXkuCv/WkkJJ1k/kct7arwZWVXXlslCNCyqQfenmomvnMGURTo3d2PUUPDOX42y+prj5/JZMgAmRBUJXRSr4f4xELGP1OP5k08mfbReUMmlr1wc1HywL3+rNlknmyXx0/rYgzER60SGflYLZb+bNmvTBRljV5fK3oQSZJYtcFUO6bXQ0GhDqVKrLGKVmUgSXIHXN1wZ2LiC9GU04N5e6po08KTfr0DK6Xd+X17IstW3iArWzYjfObx2vS5p+a0M088Gsbpc9kcP5OFQiEg6SWr18rwR0IYOzoCheLfRXq0OomllYizAdhzMM1w7f8XcZf43AFwcVb+7bbo5y/nmO2s0evh0hX7Kwc5eVo+WRCJo6PCkGP04VtNWbUhjpUbYsnN0xLg58DY0RHo9FJZa3m5m6uEyJKizjR5azbN5r1j8z137U+9ZaHoI/2CuBiZa+Q9Ul0o1WJZwhPjjtl9rqvX8/hhdQxjn4ogPrGQIyczCA50MjtBNWlYuc5EUYRHHgxm595Uzl7INnwOazfFs/WvZHpWMJmrCCensttJt46+XL6aV7lOLQnDFljPzr7MercZ366IJvJqHgpRwMtThV4vkZllfltKoYC3JzWiS3sfhg0K4/iZLP7an8rRk5lm4yBS00vQaPSGgM2nhtUmM6uEDZvNR82EBDoxY3JjE31QeWi1EhkWRNoKUcDfR427m4rsHM3fHg6r00NcYhF//NyV6Jh8SjR6aoU64+VR+a2fOV9F8ssfZRqpuMQiZsy5xJXr+YwbUzM6UAcHBZ9/0IIjJzM5diqT6Nh8Dh3PtHh8gwi3fx3pAUhJLaqcmBuoeg/lvwO3LAzZvXs3+fn5dO7cGS8vr+oY0138DfBwVyGIAlSDdb4gwLKV0UYBjsMGhTJskPH2wISppyyeIM/Rk/MrttP40zdROFovySbdYpiop4eK7p18WRLhyk/rZOM5ayZ7VUF1TXJ6PfyxI5HjpzO5VM7B28lRwWfvN6d547Koivp1XOnawYeDR9NNiELzxu6cvZgjp5VLcuJ4/TquSHqJ7FxjfxS9BNk5WjZutZ49d61crMGj/YPZsjOJuET7XHIBEOQKYSnCgp1JTJK/W61OMuoONJhb3iS8bZp78ObLDQkJlHVJgf6OPHRfIEXFOovdXD5eapTKMrKoUAi8Pq4hL4yqy/EzGaSmldC+jRcZGSW4OCtpUNfVZvVDqRTw93Uw6YgD+W9oUNeNEY/UYu43Vzh4zPIkfbtQms/VuIFMklPSirh0NYfwEGcjImsNOTklRqSnPH7+JZYxw2vZfa7KQhQFOrX1plNbb5b8HG2V+PzdRLOm4OqqrHRltEv7/3b+mN1X4yeffEJeXh4ffPABIJd0+/Xrx7Zt2wDw9/dnx44dNG3atGZGehc1in73BrD+d9vbA/ZAkuBGXCE6nSRPrBYgRxPIW1zmUKwV0GbnWiU+GZklREZVTctSOmmOf7ouSqVIWLAzUyY0xNNdxYr1sdW6HVFaTRNF+X11txDjlpWtJTPL+G8uLNIx/q3TbF3ZBUdH+WctSRLvTmrEtz9Fs3FrIkXFery9VIx6LJxHHwri9PkcduxNoahYT7uWXtzb3Y+nJhyr8t998Gg6Lz8rdyG6u6pYPLsNy9fGsOGPBLPbdxWhVokM6Fsm6p857zI5ueZFNwoRGtd3I9DfkYcfCKJNCy+uReex4Ltr5ORqaNLQnfvvCaBzW28W3dw+rDjxDRsUapbIuLkquadLGWmvHepCZnYJ87+7xo49KQhA757+jBxSy6Q6IggCTwwJM9FbiKL8mfTp4Y+zs5JZ77YgL0/Dteh8omIKUKkE1v0Wz5Xb2GWoEDFU8a5ez2P6x+eNiGqLJu58+FZTvDytV4A2bLHcDSdJ8MeOZAb3r/ltlQYRrlafb9/as8bH8HfA3VVeuO0+aF8jg0rJf76t3W7is2rVKt58803Dv9euXcuePXvYu3cvjRs3ZtSoUcyYMYPVq1fXyEDvombRuIE745+J4MvvohBEEBBuKdrB3U1plfQA3NPFz2xoKZKEUq8hwEWH2td6FXHH3pQqT9SeHiqmv9aIjm2M25AH9w/h1y2J5Bdoq438lH6SSqXIfT38aFjXjc8X2ydGNDmXha9Fp5N44/1z3NPFj5i4ArbsSia/QEfd2i5Me60hbZp74eaqNORVtW7uSevmnkbncLiFG2L56gnIVcTxT9dl/NN1iY7NZ8eeVLbsSiIx2bQaEuCn5p1JTQyCy7SMYk6dt+wZpdFCZFQeLZp48NufSUz96Dx5+TpDV+AfO5L5bNGVMsfkcq8VRXhsQAjDB1kWKJdHTq6GUeOPGfkMrdoQz5+7U/jxy/Ympn2PPhhMdo6GH9fEGDrbwkOdmTG5Cc7OSko0er5Zfp1fNidQVKTHzVXJiEdCGfZwKP/7/LJdYyrFfT382LE3tdLVDEGQv5/nnqxDemYJL75x0oScnrmQw7MTT7BycQfDdqA52PqN3IJHbqXQsY03nh4qsrJNt33atfTE2/PfK+Z9fVx99hxKs+s60Olg07ZERg/79wWP2wu7ic/169dp0aKF4d9//PEHQ4YMoWvXrgBMnz6dxx57rPpHeBe3DcMHhdG9ky+79qVSXKJn5YY4CgsrX5oQhLJ2dmt4bGAIK9bHlE0o5cyJekT+SoM3n0NQWJ+Ic3K1drfZl4dCFOh7T4AJ6QE5oHHRp635csk1DhyzbnpXWZSU6GnWyJ0H7wuqMvGxhpNns00iOaJu5PP2zIt8MKUJvbpa79C5/x5/rl7Pq/REKgpYDXSsHebCM0+48PTj4aRllKAQBZwcRSKj8nBQK2hQ19VAyACTlnlz0Ggkfv7FWEgsSWUks/yEXPrYiEdCGf5IWKVamBf9EGXWXDEjU8O3P0Uz8UXjjDxBEHh6RG2GDgzlSlQebq5K6tZ2MVSXPp57me17UgyfcW6elq+XRzNySFiltywefjCI7XbGkqjVIs5OCrw8VfTo5Mvgh0Lw9lKzbOUNixW55NRidh9MMyTJm8PAB4IM3kTm8EBv04DomoBKJTLnveZMeu8MWdlllcJG9VxNOhT/bfDylLPY4u3YWpbA7maVfyvsVnpptVqjJPODBw/SpUsXw7+Dg4NJS6tes7S7uP0ICXRi5JBackdGFZOJ7+nia9dqQqkUWbm4Ix2auyIKepAknEtyeejaakaPbUXtCaNsnqNpI/dKkx4AnV7iwfss35DDw5z59N3mrF/aEQ+36i0Lb92VjFIh4OJye8rNpRPsou+jbK6+H30oxJDlVBlE1HZhaIUW7+jYfL5efp1Vv8ZSdNMsTxAE/Hwc8PZS4+SkpGVTTxrVdzMiPQBBAU5GBprVhe17Uirt2/KXlXiZHXstZxO6uihp3dyTenXKtEFxCYX8uTvFLLFc/WscEbUrFy1w5Vo+Tz8u/9as6XZLPat+WdqJ5Qva89zIOoYk9svXrNsOXLicY/V5b081/Xqb79569KFgXKvBY8xeNKznxvqlnfnwrSa88lxd5n/Ukm8+a4Onx+2J0vg7McSMc7g5iKKAr88/37voVmD3FVm3bl327NlDREQEMTExREZG0qNHD8PzcXFx+Pj8twVT/zY8/Xg4B46lk2YlcsLLQ0WH1l44OysIC3GmdXNP6texvtdeHi4uSj77uB0AxSnplKRn4VznfpuC5lJ0aO1FkwZuXLqaa/e2lCjCxBfrExFue5Lx93XkuScjqhSyagml+VljhoebNbCsKcQnFpGRVcK3P0WzfU8KOq1EWIgz77/ZxCAqdlCLfPFBC/46kMbCpddISbOzJb5c55per+elKac5e7FswlzwXRSvPFfX0O5eHnsOprHnUBo+XmpGPlYLNxclSoXAC6PqVHtVLM2CD1EpCgq0xMQX4u2lNmy7lVhxOM63YQBYEdZIRolG4up1+zU+oghR0flMebkhrZt5snlHEgnJhZw6Z0pUpJsC9eNnsujU1rjK6WsjFsacxkej0aNQCAbCOu3VRtSPcOWndTHk5Gjx9FAxelg4g/rZNxlXJ9QqsVJGn/8GlGj0LF9tPqOuInQ6if59KueI/2+D3cTnpZdeYvz48ezdu5dDhw7RuXNnmjQpKx/u3LmT1q1b18gg7+Lvga+3A9/Pa8fYN08SE2c+6fj9KU1o3cyzWt7Pwd8HB//KkWdRFPjs/RZ8tSyKzTuSKdHoCQ5wJCOrxGz5/uEHgnhqeDh+PqbESquTWP1rHL/8kUBGZgn1I1wZPawWV6LyKr2d5qgWKDKTQC4Kcr7RybNZHD6eiVIBWjNzZ9cO3uy3kJpdVYgCPDXhGJnltgGuRefzxNijfPG/FrRrKeupZB2SPz27+HLf4L12tfdfu5HPpm2JDB0YyvtzLhmRHpAn3i++vkbrFl7UvUk48/K1jBx31IiM/LQultdeqMfg/iEM7h9CdEw+v2yuepRERVhKtdfpJL796Tqrfo03EJ2ObbyY+mojQgKduHbDPCHR6iSibuTbRaIBPN2rr/IgSeDvJ1/HpXqt+d9dNUt8SlFgZut6YN9gi11ZoiBvf5bi8IkMFv9wnchreTioRfr1DuCFURG4uSoZOjDUpOp3F7cHB46kW7RRKA+lUuD1sfWpb0MI/m+H3cTnueeeQ6FQsGnTJnr06GHi65OQkGDV0fku/pnwcFfx5ceteOGNkyQkGe8fP/9k7UqRnqL4ZBJ/2Ya+uBi/+7rh3rLRTddmbAqhrcHVRckbLzXgtRfqUVKix8lJQXGxji++uca+w+mUlOhpXN+V50dF0LSct02JRs/yNTFs3JJIbp4GF2clmeWEkRcic3hjxjk6tqm8TcP4Z+vxxeKrJv4xgiAQHurMhKmnTV7j6CgyuH8IQ/qH4OfjwF8HUvl47iXyC25dYa0QISzEiehY8wR26kfn2baqm9FjKqXI9ImNmDH7ks3zSxLsPZjG0IGh7LKyNbRwyTXmzJC1gq++fcZsBebzxVfp2sGbQH8nJo1rwMP9gnl/9kWiYgpsjsMWOrQ2/10uW3WD5WuMV8zHTmUy8Z0zNKjnapH4CALsP5JuN/Fp1cyTAD8HUtOLjSNMqLyziiRB23Li9Lx8rdXOTFGEhhEu/LYtkXOXcnB3U/LAvYHUj3Bl0th6zPnKuLomCLIvUmke1+ETGbz+3lnD88UlejZuTeRCZC5fz2ljkVTehTF0OgkJyyS8KkhJL7bqZTZmRDgBvg506+j7n9j2swVBul2S+38AKhNr/1+DVqtn7+F0zl3Mxs1NxYD7gyqllYhe+BPnX/sfmY4+HA3vzXXvRhS7+1AgOSCI0L2TL+OeirhtYYGSJDFuymnOXrCdNu/lqap0cviXM1sB8OmCy9y4WS0L9Hfg5Wfr8sFnlyyGaXp7qZjwTD2DB9J3P0WzdGXlXFnLozQXqlaIEzl5GiPRZ0Xs3tDdrMHb2YvZzPvmKtei89HpJbOt+IIArZt5MO+jVnQbsNvie9QKdWLFVx0oLtHTe/Bei8f16enPu6+X5QkdOJrO5PfPWTy+Isw5YHt5qFj1TUcT7VBxiZ4BIw+YrYYAuLqI5OVbJp+tmnrg5akmPNSJAX2DDERBkiSDpUP5tvkrUXm89vYZsnI0hkqis5PC4vtbgiDAyCFhvDBKNgjcdySNKR+YZr2VonsnH6Ki84lPKkLuGZDfe+KL9Xj0oRCysktY8Usc0TH5NKzrxiMPBht0QADPTjzB5au5ZifXD99qctu2l7RaPTm5WtxclVa7ze40JKUU8dmiKxw8loEkQevmHox/ui4N67nd8rmPn87klelnzD7n7KRg049d7IooqU7o9RIZWSU4OylrRK9XEZWZv29JdfbQQw/x7bffEhT0394v/C9AqRTp1dXPZleQOWQfP8f5V94n2S2U5R0noxFVICrk9hUBJD3sPZTG6XPZ/LCgndHNtiaQla3hk/mX7CI9AJlZGvre48/WvywLWUshCBDg50Dzxu6IosCPC9sTl1iIXidXXE6dz7aaIJ6RqWHG7Ius2RTH4lmt6dHZt0rEp2dnH2rXcqGoSEfjBu707OzLoKcOWn2NVgflm+gkSSIusRA3VyWLZ7eR/561MSz+4brp5CdBz5vXhlIpoNWaX0+F3iS2WVnWtTZJKcbVxY5tvGnRRDZdLH1vcytcUYQ3xjXgoT6BbPsrmSU/3yAtvRidTiaw+w6ncX+FGIW0jGKLpEMQsEp6AE6dz5bb6AX4+Zc45sxozqWruaz8JY60jBL8fNSMeCSMxwaGIAgC9SNcWbekI7sPppGYXETtMGd0Ool3Pr1o9X3MISlFtgbQavVcvW69U6eoWEfizc9VJq/yh/f54qt0budDUIAjTw8PZ/naGK5E5bH2t3hGD62Fg4MCrU7i0hXz+iSFQuDMhewaJz5ancSyn6NZvTGegkIdzk4KHhsYwpgRte/4alNWdglPjDtKcTkyfvJsNi+8cZKlc9tazGa0F21aeNKgrivXrueZbE2PeCT0tpOezTuS+Hr5dVLTSxBF6NXVj1dfqFclV/CawC0Rnz179lBYaL50fhd3UYrYZesQlAq2NxqGtpT0gFGOg14POXkafvkjgWeeqF3tYyjR6Nm+O4VNN8v8la1zvvR0BPf19GfmvEjSMy1P2q4uSv73VlOD6FMQBMKCy9yIRTvvzxcu5/Lz+lgeH1yL4YNCWWkm/8lS67NCFPjfW01NzPnat/Ky2PqsUGB0czx9PtukWjXxxfo8+lAI2/ekcC06v4yAAE0autH/Zpdc314B/P6neZfnl56WqxO+PmqrpfnGDYxXwQqFwOz3WrBsZTS//ZlEfoGOZo3ceeaJ2vj7OHDkZAZqtUi3DmWl/LiEQqPt2esxBbw/5xJpGSU8/miY4XEvd5VFsiZJWNRhVTyuNP/qzQ/OGaWGp6aXMO/ba2Rma3hhVB1A9ksqT8C0Oomw4GhiE+y/n0oSuLgoOH46k/dmXTTapi0POQxYxYkz2WYbAARB7k5r0sCN1945Y6jo7TuSzk/rYvj8/Ra0bu6Jo6NIkRnSLkkSbm6V3z65dCWXNZviiI4pICzEiSEDQmjWyHxHoV4v8er005w6V7ZYKSjU8cPqGLJzNLw+rkGl3/92QZIkXp522oj0lEKrlfjmx2g+mnprxr+CIDBnRnNmzotk/xE5t9DRQWT4oNDb7tez7a9kPvyizI9Kr5djha7HFLBkbts7gqTe0laXm5sbp0+fJiKiZrJYbjfubnXVDI4Pe5kbG3fz2X3zbB4bFODA4lltqrXqU1Sk45Xpp82bJdqAKEKrpp7M+6glACfOZPLyNNOSskop8OzI2gzqF2w1QFSj0fPgEwfs8kcKDnRk9TcdkSSJ3QfS+H17EkVFWlo18yIrR8PGrYlmBddqlciOdd1MiE9RsY6+w/aZ3ap6YVQdnnysFiAThlHjj6HV6g3EqrSisXhWa2rXcmHT1kT2HExDEGUjyv59Ag3mh3q9njdmnOPwibL4AIVCYMqEBvQr5+nyzqcX2LnXlIgpFAJbfu5iiDnQ6SRycjW4uCjtDvDNytbw8OiDZj8fRweRjT90xvnm95RfoGXQ6INmK3EKUb4m4xJNTRcrC6VS4NfvO+NhQeCck6th1sJIdu9Pq5SXjzWUaufen9yYaR9fsHjME4NDWbkh3mwHm4Na5M81Xfni62ts2JxgQp4EAVZ+3cEQF2IPdu1P5d1PLiCI8nZb6ZZsj84+1A51plc3fyMB7gefXWTrLvMVV1GA9cs64et95xkUSpLEC6+f5EKk5XuPi7OCrRX0dVV9r2Ons/hjRxIFBVq6d/Kl7z0Bt3U7UJIkhj9/hPgk835CH01tSg8bmX9VRWXm70p/IjExMQYvkPDwcFQq+UcsSRIxMZaTje/ivwvPds2sp3SWQ2JyMSNePMwVG2X7ymDNpnirNx5rcHJU4O2lYtH3UUTdyKdNCy8+mNIEv3I+GA0iXFk0qzVPDK5lMzVdpRKZ+rJ9q9PCIh15+XLo64zZFzl4LIPYhCJ8vNX07eVvdlJXiHBPV1+zUQyODgrWL+tMWLBTucdEXnmuroH0AKz7PR6dTm80+cq+kgI/b4jDyVHB0IdDWTCzFfM/asXg/iEG0lNUpOOXzYkIokD7Vp7c18OPqa80YNf6bkakB+C91xvRtYNxa7Wbq5KFn7TEyUmJJEms3RTPoKcOMuDJgzwwfD9zvrpil7nhuUvZFrvwior1XCpn4Pb79iSL249ykOetkx6QV/eXr1q+Dt3dVHzwZlN++6mLhRCXykEhyvoOB7XIut8SCAlyNPsz1N1sMLDUtl9comfnvlSef7IODevKlTilQkAUZdIx+aUGlSI9Go2e2V9GopcwfEel2zN7Dqbzw5pYxrxynEnvnSE5pYh3Z12wSHpArnpeqWJsTU3jjx1JNu89FT2sLCG/QMuXS67R/4kD9B68l8kzzhquJ0mS+PTLSF57+ww796Zy6FgGM+dFMv6tU3b9XqoLhYU6i6RHoRC4aGG79Haj0ltdderUITExEX9/f86dKxMbZmRkUKdOHXS3EkJ0FzaRk6th1a9x7DmYhigK9Ormx2MDQmxOuH8nwsYMIerzpdTKuEysZz0k0brQraBQz8dzL7Pki7bV8v7lXXIrA1GA/AKd3KUkSfy4NpaXn6vL0IGh9OjkS2xCAWqVSHAlbvoAvbr5M9dNxacLIi3eJAAa1nVl8vtnOXcpx7DKTssoYfbCK0waW59HHwpm/e8JRoGdXp5qw3aKOfh4qvl5cQer47tyzVQnAPIkdfpcNpNnnCU7V0urZh483C+IG7EF5OXrCApw4KMvLhMTX2iYYCUJ8vJ13N8r0GSVJYoin7zdnNw8DcfPZBHk72gQeup0ElP+d46D5ZyzS0r0/LolgYSkQkNnWHlIkqxDiY4tIC/fsogbwNmx7Bo8c1OjczvaPNxcbf9O3d1UdO/kw/4j6XZZCViCTi+7QgOcOp9luEZKrxeQ/92upScKpfU1cGx8Iff1ULJoVmsOHE3nzIVs3FyV9OkZQFCAo9XXXovOY/ueFAoL9bRp4Ymri5JsCxls5XH4eCaDnzls1996p2hHKmL9b7bzD1vZYRiq0eh5edpprkTlGb67QycyOHo6k68+bU1mVgmbboYIlyf8FyJz+fmXWJ4eUbtK468s1A4y0S42Q6L1eglvzzujo6zSs6UkSWZXk3l5eTg6Wv8B3MWtISdXw3MTT5CYUmS4+KNu5LNrXypffdr6tijnqwK1rzdXxs6k8ZIfSXQPRwNlOh8zkCTZUj0hqbDSpMIczP0I7UFpxaP8jWTeN9fo1MabWqHO1A6ruiCxbUsvVn3TkV37U3h7pqmoVRThgXsDeG+W+VbyJT9H88vSTnRo48W2XSnk5slEZFC/YItbKbag0eg5eiqTDCvC4/TMEg6dyECvl9v9V6yLNduGXZ5EHDqewY49KXi6K1m4LIrYBJnstW7mIVcR6rlxTwVh7IzZF4xITyn0ejh8IpNLV3NpVK4bJjO7hPFTTnMjrqzl3az4WYDAAEca1C3bRnF2ltOtq2AAbhbubkry8o1z3kQRggOdaFS/bMwHj6Xz7Y/RpGeWEBzoyPin69Lkpt3Cqy/UJzIqj6SUYiOiUlWUvt7dTUlEuAvnL+Xg5qpiYN8gRj5Wi+sx+SxZYVlE36mdXJlTKAS6d/Kleyf7tiuWr5HF8ApRDlFb+1s8dWo5235hJeDupqRhvTvTlybXBgFXKAQmvSRXgIuKdCz5OZpjp7Jwdlbw5GO1DJE6ew6lcfmqcVVL/k4lvvspGg93ldnrRJJkp3h7iE9cQiErN8Ry8mw2nh4q+vcJpG+vALsrUiBXAvvfH8gvfxhviQqCvNV7X8+qpQFUN+wmPhMnTgTkcvfbb7+Ns3PZxavT6Th8+DCtWrWq9gHeRRnWbIwnMbnIZAviWnQ+z7x2nJZNPXj0wWAa1L319sjqxK9bElhxBGg2EiQJQdLJ26U2tr/mf3eN0+flLYumjdx5Y2x9gqpAhLq292bVr/FVHL0xRBH+3JPCM4/Xrpbz9erqz7uvS3y++KohiTzQ34H3Jzfh1Plsi5NeZpaG9MwSunXwpVuHW98zX70xjoVLokx8h8yhdDz2VkgEAb5fHU1MnHF16/CJTI6fzuLLT1oZ+StFx+azc5/1+JsLl3OMiM/Et88YkZ7y45OrHAKSXsLBQcE7kxpx+EQGW3elkJevJcBPfUuVFZCvi8b13Rg5pBZanZ6PvoiksEiH8ma7uouzkg/ebGJYNC5cGsWK9WW+QWkZJTz/+kleH1ePQf1C8Pd1YPmX7dm+O4ULV3K5fDWHK1H5t1yVys7R8sZLDagVYkw+GtVzIyzY0UBKyyM02JHG9Suvebx0NZfFP1wHMAo8jo4tqNYKW0W7gDsJ/j4OJv5n5THnvWb4eqlJSiky6fo6de4svbr68sGUphw7lWn29Xo9HD+dRZcO3hY/T3OC9Iq4EpXH2DdPoimR0OklYuLlBodT57J565WGNl9fHi+MiiDqRj4nz5ZVUgVBDn8+fDwDCejY2rvGu3etwW7ic/LkSUCu+Jw9exa1umzQarWali1b8vrrr1f/CO/CgL2HLYseY+MLiY0v5LdtSbRo4s6Cj1siin+/x8Xp89nGcQ+CgCTYd9ntPZRu+O8jJzIZ/uJRvp/fjtphlVsxjngkjM07ksnJs11etwcFBdVznlL06RlAn54BaDR6RBGDl86NuAKLK32FKODmUj3bm7v2pzLvm2vVci5zkCRMSE8ptDqJRd9HMf+jVobHVm+0TVLLxyjEJxZyxUrUQ6e2Xnh5qKkV6sxD9wXy/eobrN1kvAWhUgpotFKlJ2RRlIXhjz8aRnpGCW9/cp6zF8t0DMGBjjz6YDD97gs0bEdnZWuMSE95fL74GgP7BiGKIk6OCgb0DWJA3yBKNHq+WHyVTdsSbY7P1t9gqbX5uy/aMmHqaaPKQsO6rgZhf2Xx51/JZh3Pq3tLMTNLw5BnDvHE4FoM6hd0R5Gg7p19OXXesm1G4wYyoZz8/lmzXV+79qdx+HgGcYmWu/3UaoF2Lb3MZsopRPn6t4acXA1T/nfOiCCVfke/b09iUL8gwzjtgbOTgs/eb8Frb5/h1LlsxJvX48oKgcJd2nvzv7ea2t2wUJ2w+865a9cuAMaMGcPcuXPvdj39Dci3c8I9cyGHzxdfY9LY+rYPrmF886MZz5cqQqeT+OCzi3z3eeW0P74+Diyb346Z8y5z9FSm0Xgqu42g18vuuzWBit0XPTv78tmiqxQV60zGfG93P0NXkjwuibWb4ti4NYkSjZ52rbwY91QErnaQo++sJGvfDpw8m41WJxnaXDOs2AUAODuJdGlfFm0Sl2Dd0VmlEpn6aiNAFj1XJD0AGq2Ej5eKnl382Lor2agd3RoG3B/IE4NrUVSkZcwrx01ayuMSC7kanW+kwVu90dSaoBQ6ncTug+kmfllqlcjk8Q0YM7wWL087Y7XtPTjAkdT0Eko0xhe2KMqVnVKDxYpwdlLy3edtycrWcD02nzphznjegnYmr0BH5f2oq4aklGLmfHWFnFzNbW/ftoY+Pfz58rtrJgtWQYAu7X1wcVai1eqJumH5Gl6+NsZq1Sgs2JkHegWw7rd4o8WSQgRHRwVPPmb8eZy7lMOu/aloNHoEJNb9bjkSRiEK7D+SXiniczEyh2WrbhisBywt1g8czeD5SSf49vPb3+Jeaaq1dOnSu6Tnb4AkSeRVIhBxy07zPiq3E5IkccbKasccRBGrk3Xk1ap1b/j7OvDZ+y3Y9UsPls5ry/Iv2zH3f815bGAo9SPs1+oEBTgaTbrmcC06j7f+d457H93L/UP38emCSDJtGPaZg7Ozko+nN8XRQf6Zlpoq16vjyqvP1zMcp9dLPP3qceZ9G0V0bAEJSUVs3JLIw6MOkpJmuyMpNv7WoyAsQRCwKwk6L6+MMDRtaH2rdta7LYyqFrVtmL/FxhcaDBHNtc+XIj1Tw4C+Qaz8uoORkaMlCICPl8NNn5YzZn109HrYvCOZ7Jyy54qKrf+OCwotL3D8fB35cWF7q693dVEy5eUGiIJ8zYg3rQhcnJW8OcF2R6Gnh4rWzTxvifSALNq93b0uP6yOsXuBeDvg7aVmyssNEcp9FyB/H1ev5zFy3FGm/M+y2zZAfr6WnFzLzvEhQU44Oir48pNWDB0Yio+XGjdXJff18Oebz9oQerOLU5Ik5n17lRffOMnaTfH8ujnRKukBkJAQ7SQlkiQx68tInpt00u6cwavX89l3yPq2dk3g798LuQu7kJWtMXRo2ANzAZ23G4Ig4Ohov+C6dpgzv/3U1TDRm4MEpKZXvb1YqRCoX8eVOrVcaNvSmwnP1KV9a2/bL7yJBTNbWs0VuxFbwAtvnGT/kXRKNHoKCnVs3JrIsOePkJJmedVmCe1aevHLss5MmdCApx+vzZwZzfn2szZGAuY1m+LNpnoXl+iZ/rH1myqAVw3ttatVAs88Hs7H02ybs60oVwbv1zvQ4gpwYN9AWlboggnwcyQk0HJjxY24Ap6beILU9GKDjsoSzlzIJvJanl0TtgQE+jty7lKO1ZZlnV4ycqLu3yfQ4rECmAi9K0KhEGjSwDI5HDwghPvvCeD7Be0YMjCUXt38eP7JOqxY1J66tW+fCPje7v7UruVM+R33mt59Ly6x7WBd3Sgq0rHo+ygeHXOIgU8e4N1PL5CVXbbQefC+QH78sh3hYc6G6odeD8mpxUTHFnDouHWS0L61l1U9TKP68nfq7qpi/DN1+fWHzmz+uStvT2pspOU6diqT1Te1jjqdZKS7sgS9HnrYKWTfuS+VX7dUPlD4wNF02wdVM+4Sn38IHB0Vlbpp2NM2ezvQr3eAXeN+8L4Als5ti7urkjYtPK0eu3BJlNnHJUmSO43Wx/LrlgSjVbY19OhkXyK8Wi0S4Gu9c/GHNTcoLtablHcLCnU8/sJRq67PluDqoqT//UGMHhZOxzbeJl0WG7dabpm1xzdj+KDqT9QOD3NmxaIOPDW8No3ruzNkQLDV43fuKfNp8fV2YObbzXCqQJrv7ebHxBeNt29LPcXmf9wSVxfzJFuvh+xcDSt/iaWjDb2Dq7PSZis8lFUm7+nqZ/PGLQoYbS9FhLvSupn5FuYH7wuwy5riw6lNzS4Qmjd254FesiN0nVouTHimLjMmN+HJx2rd9pZvB7XIlx+3YmDfYJxu3r9aNfXgISvErzrg5nr7WqaLinQ8Me4oP66NJSWtmIwsDTv2pvLImENGupzcfJ3V7SxLcHAQ6XdvIInJ5hdNarVAl3be/LQuhk8WRLJifSyZN0nXybNZTP3oHE+MPcrUj87x8y9xcnddJTD04RDcXJV23Ut/3Wy7db8iBOz3MapO3Bmz413YhJOjgp5d/Phrf6pdmpmRQ8JsH3Qb8OwTdTh9PttsRaIUSoWAu6vKoHEJC7FOLs5cNN0+K9HoeXvmBfYfSTfodr5YfJXpExvRu7v1FsqmDd0Z0DfQ4INhCf6+1p1htVo9uw+mWe6uKNEz56tIOrT2RqkU6NJeDo3UaCXatPCscp6OJeM5sE9I+tiAUK5dz2fzzmS739OSNkqhEJj1bjPatfQyuqG98lw9Tp/P5kqU+esgq0Ipv1NbbzYu78yBo+nk5Wtp2dTDyD7gmx+vs+rXOIqKZEF4l/Y+rP22I8+/fpKYeFP9i14PW3el8OzIOni4mfeRUasEunf0scv+wN1NxeOPhjFh6imTNuOKuLe7n0ki9twPW/D54qts3pFMcYkeFycFwwaFMsZK23FBoY7la26weUcyhUW6m544Ci5E5qHV6vHzcaBeHRfOXcqhRRMPJEliy85k1v0WT0p6MQ0i3Bg5JKzGNGrm4OGu4vVx9Xl9XH1On89i+sfnOXG2ctvfYCzYLq9drqh9qx3mUu3t8tawfG0MyammFWiNRuKdmedZMrcdAPuPpJsVeluDm6uSTu28WbHesjGwKAg889oJior1iKKAXi+xbOUNhg4MYdmqGIMjdlxCQaU7Fx9+IIi9B9MMVaJ2LT15fVwDw9ZZeSQmF1oVcVuCBPTsUjNOztZwl/j8A5CVXcLug2l0aOXFlWu5xCVa3jIRBHj0oWAef7SWxWNA9pdY82sc2bka+tzjT7OGtk20qgI3VyXfzGnDXwdS+fCLy2bzkLQ6Ca9yxlaSZH0FYM6J9IdVNwwr79IJWaOVeH/2JZo2dCfQ3zKZEgSByS81oENrb9b/Fs/Jc+Z/wG2aW/6MSjR6nppwzGbr6J6D6ew5aL5CoFQKuLsqadvSi8cfDTOy7LeGti29+G2bedLmZ4e+RqEQmPZaI54aHs6OvSkUFOq4t5sfUz86bwjBNH4/T06czjJ5XPYe8kfSy918rZp5GDpsBEG4qRkxT3yKi/UGj7CklCLW/x7PpSu5+Po4MLBvkBHp+XRBJBu3lpXU9XrYdzidZyeeoFaoM3EJhWYFlVk5GvoO28eEpyNYsvIGuXll15FSAR9MaYqzsxJnZ/B0V5FlYZXr76umSzsfFi41X3ksD19vNW+8VJ+zE2aQsGIjuoJCHIL8afi/iUwaO5BJY+1z8dZq9bw6/TSXruaWGdgdz0CpEOVIjZRiUtOKOXsxh1/+kD+biuT0yMkMDp/I4H9vNaVnDcUGWML1mHwmTD1dZT+iurVduHo9HwE5sPaBewP4dIFsGaAQBbQ6CXdXFe+90fi2dnVts+IoHRmVj1arR6kUZX1eJXXeBYU6du5NtUqWior1BlJYelxhoY7vV8tkqZTsVJb0hIc6mWxbnTibxbg3T7JiUQeDDrOoSMeaTbF8vfxGlZpY7uvhR6e29ksNqgt3ic8dDEmSmPbRBfaUE38pFTCgbyBJKcWkpBUT4Kumd3c/3N3UaLR6urT3wdHBuq7mp3UxLFp23fA7XPdbAnVru/DtZ61RqarfBFGlEmlUz9XqD7i85XzzRtbF83n5WgqLdEZbIRs2m2/zlZBXvU8Nt97pIQiCIX1++54U/vf5JROStnFrErVCnRk+yLSaturXOLOVhspAq5XIyNKwfU8Ku/alMmdGc9q2tL41AzB+TATb/koxW/l57YV6Jo9dvZ7H9Rv5NKjrSng5QhES5MSooWWf0/IF7Xh9xjlO31zJiSL06enP9Ncase63BL74+qp8Qxfklay7q4o/tifz+59y5UilFJg4tj4D7g8CIDfP8jafJMGGzQk0rOvGy9NPU1KiR6+XBaHb/kph/DMRDB8URmGhlk3bzOsI4hKL6N7ZlwNHLWsm9HqY910U65d05NjpLCKj8vD1VtP3ngDyC3Rs3pGEh7uKenVcOGaG3AGoVQo22KFlCPR3YNGs1py4dzg5x8tc7otiEzk9+g2KE1OpO+kZs6/VaPSs3RTHteh8mjR0x81VYaIj0utBI+kNQbIVJ7eKJKP03/O+uUr3jj63bYuhRKO3SnrssRCY/lojggOdEAUMusEOrb3Y9lcKCUmFhIc506eHv1Gn4+2AzgaT02ollEro0dmXZasqF+lkb3Wo4mcnGf6feQjlnhZFkPTGh7dq5oFeJyEKxgsIvR4yszVs3pHEYwND+XVzAp8tvlIpAfugfoFcicrH00NNn57+3NvN72+xH7ilkNJ/G+60kNLZCyPZsNn8DfaHBW2JCK+8UPHy1Ryeee2k2ed6dPLho2nNKn1Oe3Dvo3so0Vi/1H5c2I7aYS7o9RKPjDlIeoblfeW133U0VHEkSaLHw3vM3jyVCoEBfYMq1dqv00k88tRBMrJM399S0OTw5w9brcRVFoIA4aHOLP+yndUbQ2JyERu3JhIVncvla/mkZ5YgSXKl57UX6xsJE6Nu5PPq9NNGf1edWs7M+7ClkS9OReh0egqL9Lg4K4zGcj0mn21/JZObp0WrkyxWnYY+HMLxU1lcu2F5u7MUHu5KcvO0JpOkKML6pZ24dCXXahdMn55+aHUSu2wYID42MIRXnpNJYYlGz4efX2JHuY4vZycFBRaCZJs3dudCZK7ViUkU4Z1JjWlaFMmJh8yTG0Gtol/uGYQKIrhT57J49e0zRsRbFMsS4KsDKxa1NzExrCn8uTuFGbNN3cnLIzzUyUDgykMU5E7KlV93uKP8eUox68vL/LrF/HXv661mw/edDf+e+/VV1myKv23xKNagUsqfZce23gzuH8y5izmkZRTT795AmjX2oPfgPRSXmA5SFOH+ewLo1dWXNz+w3ThRERHhziyb165GSHeNhpTexe2BXi/x+5+WNSeLll2v1PkkSeLHNTd44Y1TFo/Zd6Rm1PX7DqfZJD2CIDuQgix2K52UzMHVRYFPuS4HQRCoH+Fq1ghaq5OMYgLsQdSNfLOkB+QV3LHTpi6qtv6+ykKSZIdbc/qBUhw5kcHjLx7hp3UxHDwmR02oVSILPm7JL8s6G5Ge/AItz008YfJ3XY8pYPzU01hb/ygUIq4uSpOJp04tF14YFcHr4xqYjZcoxepf44mKsU16QHYWNreILt3OcrHhS+TmqmTGG00YO9pyXhlAXLnq3Lc/RbNzn3Gbe1GRzqwoX6USCPS3nQKu18N7sy7y3dRNFhffUomG7HKVIPl1el5754xJtVGvr97J8naaxl26kmO1wSHI34GfvurA5PH1ZbHrzctMFEGpEpn6asM7kvQAjBtTFydH839cxcXWy8/V5eNpTenW0Ye6tV0I9Hcw+Vxul+esTi+x5tuOtGnhyaR3zrJkxQ02bk3ixcmnGP7CETRmJAmlKNHomfv11Sq9b9SNAvbX0DxTGdwlPnco8gq0Vi++GBumbeWRklbM0GePsOiHaLMam1Lo9TLhqm6YIwoVIUkYtb737OxHqIU06ZZNPDh/Ocdosn56RLjJxKAQ5e2G3t2stwdXhK2bj7mbcI8a0kxYap3XaPS8/9kltDpJ/t4k+fsr0eh5b9YFomPyjUS6639PsCjavRFbwOVrt9YCbMun6FYnbUGQb9YtGntYFYE/MbgWoigw8IEgq+drctMrSKuT2PBHgsn4Sj/PitBoJPYdzrB7GyK9yHqHkaA2fv6PHcloqplEl4coQoMIV6uat+qGp4fa6tbLEzcbMQb2DWbZ/LYMejCYLu29Gf5IGD9+2Y6WTT1vz0CrABdnJau/7UjbFp43ozPkCtWsd5uZ5JkJgpxz9vG0Znw/vx2D+4eYzda6HdDr5dy0ed/I5opSufeOSyi0uC2p18teWAnJVbMUEQTZKf7vxl2Nzx0KZ0cFomDZ9dKS+2pFSJLEK9NOk5hiextGpRJqpATZvLG7Wbfciu/dvaPcVl5YpOODORctbh3tP5rB/qMZNKrvyqx3m+PloaZbR19mTG7MV8uiSEopRhCgUzsfJo2tj4ODyNGTGWzZKcdWtGjiwcC+QRbDPOvUkldjyanFJjcitUqkQ2tT3c2ox2qxeUeS3Y6/tiAKUC/CFT8f89WF0xeyyTJjmCdJkJahYeRLx3ByFBk1NJyRQ8I4c8F6x0VsfKFR9hXIniibdySx73AaCAI9O/nS994As9UCJ0dFtf3t5iBJ0LmtNwqFwDuTGjHt4wsmxzRr5EZCUhH+vg64uapo3dyDk2Y6iBQ3tz9BFoJa2tKyBFlfJlJUrLc5UV0JaEXvy2vMPqdwccKjZWOjx6JjbC9oFAoBSZIJr6+PmrR0+ywSxJtOvtZMDCVJ4o8dyfx2U0fV//5AHuwdeEsVl769Avj2p2izKbAtm3rw8ANlVgd1a7uaWBbc6fDyUDP3w8rFeuh0Ej+tM40tKd+5VtMkaPWvlh3EawoC1Cixtxd3Kz53KJRKka4dLPvLjBtjvZQPctl82HOHrdrbl8dD91lfJVcV93bzR6m0fOMUBHjr5Ya4u8lE5PPFV+zadrt8NY+Z8yIN/+7d3Z/V33Tkl2Wd2PxzVz55uxn+vg4s/uE6r71zlj/3pHDwWAZfL7/OqPHHjEzlykMUBd6c0BCFQjD4XpRWXl57oZ5ZZ2lvLzU/LWxP1w7eqFSCrfxVqxAEUDuIvDHO8gRgLtenIgqL9Cz+4Tor1scSHGCdKIcEGT9fVKTj5amnmL3wCodPZHL4eAafLIjktbfPmK0cPdg7wOZ4bhWRNwXwPbv4sWJRe7p28MHDXf4uBODilTwmTD3NGzPOUaLR89n7LWjWyHSbU6eTeOzZw8z95ioOagFvr8r7vhQW6a2aCJYiy9mPPfUfBkyLHs2/+sDk+HYtPa2eb/rEhjz8QBD33xPA1Fca4upsuxnBx0tFtw7ejB4azoqF7WlYz/y4tVo9T4w7ysdzL3P2Yg5nL+bw8dxInhh3FK226oao/r4OvPdGY4OupHRt1aaFJ5/NaH7HbmPVJPLytWYXLiBXqqtqbVEZVPf2fCmUCgFvT/O/Kb0Endvd/i6uirgrbi6HO03cXFys4/nXT3ItukwfIQgw9qkIHn/Utk/P2zMv2F1W7NHJhw+nNq2xm1BkVA7PTTxp0gEQUduFDyY3NnQX5eRpGPDEQbtcRUuxYVknfC1URq5cz2PMy8dNHleI8gT6/ptNLJ43OjaftZsSuBadR1CAI488GEzzxva1/RcU6nj2tePEJZaVjUUBBBEcHRQ4Oijo3M6bgiIdcQmFCIJMNpycFLRs6sngh4IJtpJEn5Or4eFRB61uh5bCzVXJlx+3ZNQE088BZCH0+qWdjL77VRviWLDkmtlV55NDwnhhdAQJyUXEJxZQr44rHm5KBj99mFQ7qw+WYC07rXaYs1Fcw9XreTxl5rsVBOh7jz/RcYVcupKLo4OISiWQl2+ceSYnRgcTEujE3BoMaQVoc2MnnWN24K0uxrV+HZrMmYpXp1Zmjx3w5AEyzWjMwoKd+HlxB6PHeg/ea9V3qO89/kx9tZFVt/FSfL74Cut+M1+ZHfxQMK/dYiUmO0fDrv2p5N6surZo4v6fIj0JSYVodRJhwU7odBL9Rhwwa83xT0bPzj68/2ZTUtOLeXbiCXJzNYZuQ1GABvXc+HJmqxohdpWZv+8Sn3K404hPKc5ezGbnvlQ83FQ89nAoLk72tZz3eHi3Xb4ZCz9pSYsmnrc2SDsgSRKbtiay+2AatUKdeHpEbROX1agb+Ywaf6xS5x3QJ5D+fYNo0sDN5Eb67Y/XWb4m1iyRUigEdq7rbtekUBVk52j4YfUN/tydgkYr0bWDD2OGhxMSZJnQVAYLl15jxXr7ytVrv+vI4eMZzP7qitHk7+Gm5Lsv2hDobzym518/wYXLll2fK3Y9tWnhwczpzXj0qUM3wymrH85OCrat7mb494LvrrFmU7wFvY3sCWTr7qZUCPz6Qyc2bk1i8Q+VaxioLGqFOrHiK5m4FBTqiIkrwMNdRVCFalx6RjFjJ5800lHUqeXMok9bm4i7H3v2EIlW9BbrlnS0e1v8wRH7ybEQi+PuquSPn7vadZ67MMa2v5KYOS/SUGFRKgReejqC1PRifv4l7m/v8LoVODmKPNQnCGcnBQ/eF0BoUFmnYHJqESvWx7L/SDpqlcj99wQw9OFQnO2cvyqLyszfdzU+/wA0b+xhd6WhPGyRHkGANyc0uC2kR34/gYEPBDPwAcvxBQF+DqhUQqX2gX/fnsSmP5N4oFcAU19tSF6Blv2H0yks0skRERZ4jV4n1Wh2tIe7ignP1mPCs5Y71G4Fzk4Ku7QACoWAg1rg4X7B9Ojsy4bNiSSmFNGxjZdFV2udja2NirqYE2eyefODc/z4VXtemnKKeDP6rFvVLfh6y518GZkl/L49iT2H0iyL8SX7/OK0Oon4pCJGDgmrUeIjV6ECkCSJpStv8NPaWEOlplUzD6a/1sggOPbxdmD1t52IiS/galQejRu6EeRvniyPeCSMzxaZ77CJqO1iN+kBrFaO7HGzvgtTnDyXxftzLhs9ptVJzP3mGu+81pB7u/kZ2ShY03XeiRgyIIQXRkWYfS7Az5HXXqjPay/ccDBTHgAAavxJREFUeZqtu8TnXwxbE83n7zenXSvT/daiIh1nL+WgEKFZY4/b2vrq4qxkYN8g1v9u2mljCaU3ii27kvlzT7JdhlqCAM0buVkMw/wnIDNbY3CttQadTmLY80d5ang4Ix4JZcwI62aOAM2beHD5mn0t6KU4eTYbrUZPmoUQWUkyNk+rLJ4dWYeLkTm8Mv0MRUU6o04UE1RiC8XPxwFBEGjS0I1LkbnVNvEIgjyR6fTQrJE7wx4O5edf4liy4obRcWcvZPPKtNP89FV7lMqy31qtEGebXjuD+gVz+ny20eQJEBzowNezWldqvLXDnC1294WH3b4YiH8TPvzsksXnvvj2Gn+s6Mozjxdw8UouHu4qPpl/+Za3i28nHn3IegbfnYq74uZ/MVpZCEIE+aZc0Zk2IamQl6edps/Qfbz29hlennaGQaMOsvvA7W0/HP90XQbcH1QlTwt7XUQlCc5czOXpV44buUb/k9CwrptN0lOKgkIdC5dGGWzos7I1nDibRUy8+S6inp0rZwFQijmLr5o1PitF8M0U9crSzeGDQunV1ZcPPrtEUbFObsEtfZvy7EeqnOGNKMDFm47Izz5eG4lKcSaL8PZUcX+vAPrcE8B7bzRm/kctUalEs508Oj3EJxXZ5W+SlFLE+cs55OTJGiBRFJgxuQnL5rVl+KBQBvULYvGsVqz+ppORPYQ9eO1Fy5VJcw7gd2EbKRYWAQA5N/PiaoU607dXAJ3aetO7u/9t8/KpDqxYd/s7w6oDdzU+5XCnanyqiuycEh564qDF5/v1DmDaq40AiIkv4OlXj5vNmhJF+O7ztnZnR1UXMjJLuBFXwI34fFasiyMhqerOyF4eKpycFCbnEARwdBBZ+Enr2/733SqKi+Vk6NS0YruzePx81HTv5MuvWxIN2pjmjd15743GRtsiGo2efo/vt5k9VhEqFWisBDl3ae/N0yNqs+dgGivWx9okbp3aevPOxIa4u6stipnLIyD7Bi7FOUT5NZWV5HZi1TcdCAl04uCxdBb/cN1qqK4t9OjswxsvNTBJQ8/MLmHASPO/R1GEMcPDLYaUpmeW8OHnlzhyUvbEUioFBj8UzNgxde2uWur1EtGxBeh0EhHhLma1bfuPpDNz3mUyb3YceXmomPJyQ6sdpndhGb0e2WOxAUEUYM/GnkaPZWVreOH1EyQkF/0jtD+hQY6s/Lrj3z0M4K7G5y6Qu5nefP+c1WOkcnPaN8ujrU5y636PZ8qEhtU1PLvg7aXG20tN6+aeDHoghOISHdnZGlb9GseaTfGVCjzML9RSp5YLSSlFRq+TJLk1ecwrx+nQ2ouprzS02CFWE9DqJA4dSyc+sYhaoU50aO1tVWx99XoeP66NIStbQ7tWnsz7sAWfL77GoWMZSMitw/UjXDh8PNMsqUhNL+GXCoZ9Fy7nMPGdM/ywoL3hvVUqkZefrcunC65U6u+xRnoAQoOcaFTfjQVLrtkkPaIAvbv7cehEJs0auVNUbLuc51ySS5+LK1nuMZkCB/t1cQu+u8bH05rRuZ0Pjeq58fDog1UK1JS3twQT0gPg6qzE0UH2/6kIvV6+Rs1Br5eY9O4Zrt8oq85ptRKrf41HpRJ5cbSpxiI3V8MPa2JITS+mS3sfvD1VfLLgConJMvH39VYz8cX6JsabXTv4sOnHLhSVyK6Yla0a/RdQUKDl+9UxbNmZTEGRjrYtPHnm8dpmF07tWnly8Jh5A1dzjvKeHiq+/bwtc7++ypZdydU+9uqGm+s/k0L8M0d9F1ZRUKjj5amnybXQoVGKjKyyMmxpsrk56PWywd3fDQe1An8/BY8PrsXmncmGUrE90OskIqNyrU5mx09n8srbZ/hhfrtq6/QqKtLx8y+xbN6ZTGGhjg6tvRg1NJzwMDlF/NW3TxsMFyUJQoOd+OKDFmaddRd8d42VG8pKy8dOZ/H9qhh+mN+OtKElbNmZjFYrUVSss0oqKq4kdXq4EVfIlP+d4+r1PJQKgft6+vP4o7XwcFcxe+EVs+3V5uDgIFr1GOrXO5CDx9I5dc66oWLpuT78okwY+sC9ATg5ihRaIejXfRqzudmTDDs6l6Xd3rFrzGBsHPjn7pQqr7YlCf46kEbUjXwiwl2MnlOpRFo397QY77F1ZzLjnzbdUjp1LstsBUoC1m6K56lh4UYEZd3v8Xyx6KpBS7V9j+lWdVpGCdM/Ps9Xs1rTtKHp6tjxNvjI/BOh1ep5ZfoZLl8ru5ccOJrOkROZfPVpKxOPpPfeaMKg0QdNrlmVSmD2e+ZzEd1clVy6armj8k7CiEdr/d1DqBLuXt3/Quzcm2KT9AAcOZnFnK+uoNNJVid6QYCwkOppwa4OHD+dWWlDNa0OPN1VVvUbOr0c33DwePVkyWi1cu7S0p9vkJBURGa2nLz+7MQTXIvO460Pz5GaJpPP0ok2MamQt2eauhJfuJxtRHpKUVik55mJJxg7+RS/bUti884kE6GrvTh0PIPU9BISU4r5aV0s4948SYfW3mxa3oXta7rx5cxWfPd5G5o2ML8lKAjQqqnlKsvwQaHUq+PCwqVRVsdRK8QJB7VgUhnZujOZ8FAbIltRQYxPI47U7kP9WvZ3NBUUag0dYqnpxQbjyqri/KUcs4/n5Vv+XWZmaykqMn3+ekyBxeu2qFhPSlrZAiY5tYjPy5EeaxAEWPlL1TUauXka3pt1gceeOczoCcdYsT6WggL7FyP/ROw+mMbFK8YLKL0etDo93/0UbXK8i7OSX7/vTPPGxuSybrgLWTnmP6uCQh3RsfZHEv1daFLflXsrGQd0p+Au8fmX4UpUHstW3bB94E1s2JzAslU3bJYsBz8UcqtDqxacu5TN+3MuUVBY+X2Iti29bLd+ixAVXXV9R3nsOZTO2Ys5Rl1COr2szZkx+xLXYwpMtDk6PVy8kktUhSRza349pZUvnV6q0vZMKcp/Nnq9POHO/eYqc7+5yo9rY/DyUNGwnhsvPFXXZCIWRVCIAodPmJb1vTxUTHu1IS89HUFevo7rNmIZ2rfyokQjmXxXEhBjZ+XxfGhnouKL7BaKpmdqDKHA9SNc7RaNW4K7hTgUW6punWR6QIC/g8XrVqEQ8C4X2PutmcnX4nvpqbKwf+a8S/QbcYDte1JJTCniWnQ+C5dG8dykk+T/i8nP8dNZZheJer1ps0gp9h2R7wPlERmVx/i3Tv+jiWJiShE6e8WFdxjuEp9/EWLiCxj75kmrid4VIUnw07pYi69RKQU+eLPJHSP8XbMxHkUVZQe/bknE39dUe1EeOj34+d66xkevl9h/OM3sSl0vYUJsKiI907ilNdOCvX1N47dtSfzyewI/rInh8bFHWb0xjjbNPfl4elPCgsuqgAqFYNZT5/kna7Ppxy70u5n3pFaLVslIy6YeKJSCxYpLQaHObjGvTle57rHla2I4cDSdTm29CQ50RGFhnCFBjrRv5Wn1XJYqXw/eG2jxNe6uSrPmpJ3aeOPvaz7J+4FeAUYRKpZiWMxBFMu67CqDb3+8zm9/mtef3IgrYM2m+Eqf858CS0nsIG/NmsMPq2NM7gN6vdy8sW13isnxzk4KWjf3qJbuwppEZraWpSvtX2TfSbhLfP5F+PmXODQltoMTK6LEijnZ6GG1uKfrnVPOvBFXYLFlPTjQwSYpSssowdtTRctmpjcWUZT31+/pUvW/V5IkVm+MY9Dog2z9q2paEVHERB/S1kaGU02iNAEeYN4317gek0+3Dr6sWNSeDcs6MXFsPTQayaz/za9bEinfOOqgFrmni59ZYiMK8O7rjYgId7FYcVGrBbt8iKA00d2uQwFISC5i8vvnGPLMYYYNCqVdq7IwWk8PFS89HcGGZZ1YubgDbVt6WZ2YLBn+9b8/0CLZmDTOfMu4Uiny2fvNCaqg++rUxptXnjd+TbNG9nej6vWyAZ29yM3Tsv73eL5fHWP1uJ17TSfzfwvu6+Fv1ilcFGUNWkVIktxJZ+4+oFQIFhdALz9XD6dbFJY7qOULVBDk/1MoBLp1tN2dFx5qv6xhZxW31f9u3CU+/yKcOptVqRu9LSgVgsV96L8L4aHOZsmNQhRo1siDHWu70//+QIvhjXo9ZGRpuLerH53bGd8EPNxVzJnR/JZuOD+ujWXeN9fIsFMMXBGCAAPuD8LHy7gyNeKRMBwtrChvJxSiwLa/5IlNEAR8fRxISi6yWIVJTi02IdYvP1eXAH+5qiYHwcp/9xsvNcDf15He3f0thhyWlEjsO5xmEqpqDr27+9G+lXWCYg4FhTq+WHyVF0bVYdOPnfl5sUzwRjwShu9Ns8OG9dwsklpPD5XR9lN5CILATwvb0a93AE6OChQKgVohTnw2ozm9u1sOeq0d5sLPizsw78MWvDOpET8saMen7zY3sf8fPbSWxUDg8p+DUiEwbkwEXdrb16Z++nw2g58+xGeLrtok81o78uP+qWjcwJ3Rw2RBb/kQ4zq1XMwSckEQTH7LpdDrJfwtVJfr13Hl+/ntrGrmbKG4RDI0TXRs4yUvUl6sb3P790ac/Y0s/4SWe3O429X1L4KHu4q4xMJKX4wODiIlZipFWp1ERC1nklOL8PNRI94BzlqPDQwxK97VSxKD+4egVIpMmdCQKRMa0n3gbrOfhUIhEB1bwKfvNOPq9TwuXcnF01NFx9beqG7Bpbq4WMfyNdZXw9bg6CDy6EPBPP9kHZPnzl/KoU9Pf/YeTiMrWyajggD3dvMjO1fD8dNZt+UmpJck/tiRxOWrufTu4U/fXgEE+DlaDJV1c1WirtAh5OvtwA/z27F9bwoXLsuOtf3uDaDWTeGyk6OCkY/VYp6F4NCLV/JskpnQYEdeH9eA+K9+4Ofr5znl05ZipRM6RycKBNsrWlGEXzYn8uZ4Uz8egLYtPKlTy9msXqlBXVeGPXeYvHwtrZp5MmZ4uFHrskqlYNqrjZj2qs1hVBiTQJsWXlaPcXRU8vXs1kx896xR+nfndt68PbERx05noddLtGvphaeHfan0JRo9Uz86Z3eg5r3d75wKcU3guZF16NzOh+27Uygo1NK6uSf3dve3GLw5ZEAIXy+/bhKQq1SKZqtEpQgKcOSDKU0Y/PRhNFr7KvkVQ35LX3P4RCYlGj0Bfo6MHV2HL5eaj2fxcFchChKZ2fYteP+p3/Vd4vMvQv8+gZyz0E1iDc88Hs5CMz8EpQI+/VL2cRFFuRX5rZdvr5dPRTRr5ME7kxox56sr5N8Mw3R1UTBpbAOTtlwvTxUZmaaVF0mSDLlP9eq4Uq9O9eiXYhMKTTKs7IVaBb/92MXENyUzq5gxr54grZyNvUIBE56J4MHeQTg7Kyku0fPjmhg2bUskO6cEBwcFefll4ygfEyGKN82NqzRK+UaanlFCRmYJR05msvtAKm+90pBF30dRXKw32u4SBBjSP8QkOLakRM/yNTHsO5KOWi3y2IBgA+kpRaSF6ITy47CEpg3dmP9RS65//CVX//cl7YH2/AaAVlRysPkQTtbtY7h+zEGng/gEyyvfo6cyLIq0j5QTeB86ls7hExks+LglzRpVffV+ITKHn9bGEHWjgJAgR559ojaN6pvf1mpQ143ffuxC1I08EpKLaNnEwxAGXJUunCMnMsi2s/Lr4qxg+KCwSr/HPw3NGrnbva044tEw4hIK+X17kuExpVKgbh1nlqy4wcC+QdSLcCU6Jh+lUsDLQ0VKWgn+vg54ear5cGoT3vnkAoVF+nIVHE9CgpzYtT+NkhI9Deq6MXJIGJPePWt2DJIkk5+BfYMYNiiM7XtTuXzV9Df21LBa5Bfo7BLJe3moGDnkn9nOfte5uRz+6c7Ner3Eh19cYuuuFLnzQJLQ6WHEI6FoNHrW/pZg8poAPwcKCnXk5mlRKATD/rVCYT7+oW8vf96e2Lim/xSbKC6W88RADnE1t9patvIG362INpkklUqBVV93qFSAoz1ITS/mkacOVem1TRu4snhOW5PHhz13mHgzjtWCABuXdzaqRmh1EouWRbH2tzi0Fuap0u4rS26yVcH7bzbBx0vFtI8ukJVTRjT79grgrZcbGOVPZedqGPbsYZME9+aN3Vn4SSsDSVr0fRQ/rjWNd7CFurWdWfJFO0QRNrs2Ryoxv+XYbOEMXIc8ygdzLnLiTJaJPkkhwqAHg80GLG7YnMDshfYbO4oitGziwfyPW1XmTzFg665kPjCT+TR2TARPPFrzJOP37Ul8PPeyzeN8vNT8sKAdHpY62v7jiEsoZOtfyfy0LtaqrrIUoiDrhia+WB+dXmLPoTRyc7U0a+xOkwam81NichGPPXvY4vneeqUhD90XyOyFV/htW6KRjs7dVcFTI2rz2IAQdDqJd2ddYPcB87YeSgXc19OfV56rf0cZGN51bv6PQhQFpr/WiEceDOHA0XQUCoEenX2pX8cVSZII8HPkhzUx5OZpcVALeHqoSUkrNhCD0q6cB3r5s2WXeYHitr9SeHN8A9Tqv9fR1cFBQbuW1sv+I4eEER2bz/Y9qYaqh6ODyLuvN6520gNy2GXblp5V0lo984Tp9lZ0bL5Z0gPyCm7dpnieHVn2uu9+us6qDXFWqzl6PZYTzS3AWrCoKMLeQ2m8+3pj1i/rxNGTmeTkaWjWyJ2wYFPPnbdnXjAhPQBnL+aw/vd4BvcPBeCRB4MtEh+lQu6Mq9i6LwjQv08QCoVAXuR1i6QHIHHdVjo9N5ynhoebtCELAgiiwKMPmgp/s3M0fPG1+TR0S9Dr4dS5bJt+WeZQXKxj5nzzpOOrpVEM6BOAu5v1TsVbRTMzBoelEASZtD7/ZB1aNfOs0XH80xEa7MSBo+loNPbdHPQS/LEjmR17U+ja0ZdhD4eaNZssRaC/A3XDXbgek2+GyAt0aefNe7MvsH23qVTAwUHB0IHyb0+pFPjwrWb8sT2JOV9dMRLqd2rrxf+mNP3HO3rfJT7/MgiCYLYMKwgCIx4NY8SjYeh0Eucv5zDuzVNGx5QSoL8OpFk8vyTB5at5NG9S9bL97YJSKfLeG00YNTSfk+eycHVW0q2jDy7ONXfZT3u1ES9PO02clW2SivDzUdO+tSmJi7bhd3O9nMlZUZGONRvjq7yFZQ1bV3VlxItHSDezbQiy7gdArRJtZjqdPpdl8bn1fyQaiE+AnyMTnolg/nfGZocKBXw4tSmfLbpKanqxgfyIItQJc+GhPkHycW7GXXEVobr5fKtmnrwzqRFzv75K9k0/JAH5On9x8kn69wlk7FN1DPq2g8cyqiTeVaoEuzyF0jNL2LormbT0YupHuOLqrECjsfx+cxZdZcYbTSo9nsogPMyZ3t392Lkv1aR6OvapCB6/DVWnfwNS0orNbi/ZQnGJxM69qezal8r7bzahl4UuW0EQeGN8A16ZdhqNVo9eL1cudXqY8Gxd1A4Kdphx8QY5ymbvoTS6dyqLMHnwvkB6d/dj35F0snM0NGnoTqN6pjEb/0TcJT7/QSgUAqfPZ5sI4UphLkuoPPx8anaFWd2ICHcxaQ+vKfj7OvDjl+3YfySdazfy0ekknBwVfPdTtMXtpSkvNzDRwQAmupeKaFJOMJuSVmzze6ssRFHWMjg7K+nYxpvNO5NNJj69Hrra2RlUerwlFFUQzw4bFEbXjj4s+/kGSSnFNKrvxrNPhOPoqKRJA3d+3hDHnoNpKESBe7v7MezhUEOXk1OQP+oAX0qSzZP4elPHGf77/nsC6NXVjy+XXmPtpgR5tayTyM3T8vMvcZy7nMNXn7QGZIfeykIU5TZoc99xeRw8ls7Uj86j00qIooBWJ+FpY9tox55UYmKP8c3nbQ2ddYVFOr77KZozF7OpE+bM2DEReLrf2m922muNCApwZP0fCRQU6PDzUTNqaDiD+gXd0nn/S9BU0m2+IiQJZi+8QveOPkbbx+XRrJE7P3zZjvW/JxB5NRd/PwcG9g2mZVMPDhxJs6qN27kvxYj4gFwJ6t3d/5bGfSfiH0F8oqOj+eCDD9i5cydJSUkEBwczcuRIpk2bhlpd9oM+c+YML730EkePHsXPz48JEyYwefLkv3Hkdy5cnBUWfwSlAjpz8PVWExhw58RX3IlQKkV6dvGj500/oJNns6xqary9zLe0RoS7EBbsRKyZ6pFKJTC4f9lWjJenGoUoWOyusoXSlWHpd68QQVQIjBtTlx9W32DzjmSTapIAtGruUSnBrK+PmtT0ErPPVbT1BwgNcma6GU2Zl6eacU9FMO4p04DOUrRds4CDvR43MfMJeXIQHq2NqyQKBfy6OdHsec5eyOH0+WxaNvWgQ2tvRAGznkXG5xOQJNn/KMjfkbFmgkTLo6BQxzufXDBUd/Q39RfZubZtEa5cz+fjuZd4e2JjTp/LZPzUM4bf74XLufy+PZkpLzegf5+qkxT1zTDU50bWobhYh5OTwiaR+68gPqmQuPgCrkTns2NPKilpxdSr48LIIbVoX84LKsjfkUB/B5JS7DeYrYjsHA2XruZaFcqHBDox4Zm6Jo/7eFs3Zr1VcvxPwt/fn2wHLl26hF6vZ/HixZw/f57PP/+cRYsWMXXqVMMxOTk53H///YSHh3P8+HFmzZrFe++9x9dff/03jvzORa9ufmb1BgoRenb25bmRtU2eUykFPpvR4jaM7t8FW7lp4988SY+Hd/PiGyc5eqqsIygrW8ObL9cnKMBYj+TqomDRp62N9tndXJV06+hd5TF6eqgI9HfAy1OFr7ea3j38+fazNuTkavh6ebTZLbRBDwYx+70WFlef5vDycxZM+hQCLz9rerO+FXh3bk3vqL8IHt4f53rheHZoSbtfFtFqyScmx16IzLVKTtf/LrsR+/s6MHqY7NdS8dfTIMKFzSu68PWc1gzqF8R9Pfx5fVx9ls1vZ9HXpxRfL79uNnzV3taTnfvkLYxX3j5r9jUz50VSUlK1jsPyUCgEnJ2Vd0kP8u968oyzDHvuCJPeO8eiZde5EpVHdo5sL/Ha22f4c3eZw7UoCkx80VQsX3lU7bNvWM8NVxfL2pxRQ/+ZHVpVwT+i4vPAAw/wwAMPGP4dERHB5cuX+eqrr5g9ezYAP/30EyUlJSxZsgS1Wk3Tpk05deoUn332Gc8///zfNfQ7Fl4eaqa/1oj351wEQBTk0npQgCOvvlAPX28HenX145sfr5OWUUKLJu6MGVEbR4d/tqjt70CTBm5WqwT5N3PHLkTmMPGdM7w9qSEr18cRGSW7uipE6NzOiyYN3GnW2J32rcwTnOxKpNVXRKl+RxTl7pyxT0VQUKDjs0Xmu5dEUTaCrNhNd/hEBt/9FM2lK7m4uioZcH8QY4aXpYf36urH25MaMe+bq4YW6fBQZz58q4nFytetwDE4gNbL5yBJEqfOZbP1YjZufyTQq6ufkY+NLfImlnOafvrxcCJqu7D+93iSU4ppUNeVEY+GGYSnTdxUZrtuLOFCZA5rbzHmQaOR2Hs4zar+aNH31y0Sz7uQcT0mn7SMEurUcsbXRoXk/TkXOXwiw+oxH8+7zD1d/VDdvL66tPfh+VG1+fqH6EqPTUC26CjvCVVZzJzejJennTbZcn5uZG28PP87FZ9/BPExh+zsbLy9yyaAgwcP0qNHD6Otr759+/LJJ5+QmZmJl5epeLS4uJji4rKyY05O5T1w/sm4r4c/zRq5s2VXMllZGho3cOOern6GyaxWqDMfTGn6N4/ynw9fHwce7R/M2k2mdgLlUXoz+mDOZaNVu04PB49lcvBYJp3beVMnzAVfH+ObcmZWCafOZd/yWEszhD5dEMnRU5kWJ1K9HpN8twNH05n8/jkDycvJ1fLz+lguRuYy98MWhipB33sC6HtPAJIk3ZbKQWGRjnGTT3Llelk8wBeLr/De5DKhaKN6bjioRYtRE8MeDjX8tyAI9OrqZ1FkWlms2Rhv1/aZm4uC3HzzVRtXFwXb/jKfn1WKqJjqCd+9XYiOzWfDH4lEx+UTGuTMIw8GUbe2Zc8tnU4i8louJRqJRvVccajEIi0lrZh3Prlg8EETBeh3XyCTxtZHbcbUNC6hkIPHrJMekJ3GN25JNNqWHjm4VtWIjwiTJzSwO6vOHFo182TT8i4sXBZF5LU8fH3UvDiqjtXP9d+IfyTxuXr1KvPnzzdUewCSkpKoU8e4JTggIMDwnDni8/HHHzNjxoyaHewdjkB/R54aZl/20V1UHROeqYefjwOrNsSRkaXB2UlBYZHO7LaEpe0NpbaIqztPM+7QJb5ZNRAP9zLyY6+rrj3Q6eHQces3dYX4//buO76p8vsD+OfezKYj3XuXtlA2ZS9ZAqKiqChTUQQZKijIUH+AigLiQnHgAFFREPArgoMhCo6CrEIplN29Z7oz7v39ERoaMpq0adO05/169aVNbpInlzQ5eZ7znMMY7PD4ZOt1MLd9gHM8cDqpFKfOlRqUH7g96FGUq5B8SQEnqQBdOsmb9AZf30tvnNcLegDtc1yx7gJ+2NIfPjeDyOfmdMDa9y8b3H5Ab88mfctuiLHtx8bIZEKTgY+AZfDH36Z3YwJA3x6G74Gt1e9/5WHV+hTd38Lps6XY81s2Vi+L0+XO1Xc6qRSvv5uiC8adZQLMfyIK48c0nNfEcTyeX3EOGVm3dklyPPDzwVw4SVksnG24PJWRbX7HZX0nE0v0Ah+WZRAWIkNahuX3IRQAH73Z06qZRFPkbiK7F6K1N7vm+CxbtgwMw5j9SUnRL9yVlZWFsWPHYuLEiZg1a1aTHn/58uUoKyvT/WRkWF8wjRBLCAQMpj4Yij1fDcAfPwzBY4+EWt5DiucRXHwFYBgUuIUgW+iLaRP24sS+swCAkjIlXn/PsMBdU/C8+fwSVgBMrNfgsrJKjetpxpsxCgQMzpqZjeJ5Hlu+S8X4RxPwwivn8fTys3jw8WM4k1TahGegVVOrwX9njN8PzwN7fr01C3fPnQF4//VuiI50hsxJAF9vMeY/EYn1K7s2eRzmhATJTHaCry8mygX9jJQ9cHMV6lXqNkbAApMmBJs9pjWoqtbg8203sPLNFL3XEnfz9bhmwyUob6uDk51bjcUrk5BfeGsGsrJKgzc3Xsa/J4wX4avvdFIpUjOqjNbe+vHXbFRVGS4hBwVYvsGDNRLAFxQar89lytwZkTYJeoiWXWd8Fi1ahBkzZpg9JjLy1m6I7OxsDB8+HAMHDjRIWvb390denv5Ub93v/v7+Ru9bIpFAIrF9XgEhpjAMA5GIwcghvvjkS+P9cozJ9NT/1lkq9cTyjdn4vkcwVn6UiaQLLbdM6+MlxqoXOiEs5NZ2e5GI1av8XR/P8ZCZaBoLaIu0ffFtmt5lJaVKLF6VhB2f9jVY1jOlqkqNIwmFKC5VomMHV/Tq5o7kBlq4bP8xE49PDtcl+vfq5oEtG3pb9Hi28tA9Qfjj74a7XGs0HI6fKTG4XNFAbpdIxGDT+p6toteeORWVasxbmmiyYzkAVFRp8OEX1/D0zChdX72f9udArTHsZcWywHf/y2iwEevfx0zPlKnVQHZ+NTqE68/4hQbJ4O0pRmGx8R2K9fW9LVi9cFmBqmrzW9tv31m7cfN1nDpXitXL4uxePLYtsGvg4+PjAx8fy9bJs7KyMHz4cMTHx2PLli0Gf8QDBgzASy+9BJVKBZFIm7R48OBBxMbGGl3mIsSePNzFEJvJKdFT9y5Yb4qIZwWoYaT4cs1hnElvuTobQiGDrR/EG1QLFotYjBzig9+P5ht+c2ZgthbI9v9lGLzRczxQq+Tw2Tc3sHxBRwDamSGliodYxBgsk50+V4Jlq5NRVa3R1aeKi3XFk1PCzT6fmloO3/6QgekT7bejpXtnOV5cEIt3N101umTJskBUuDP+PWEY9DTkicmheHxyuEPswtqxJxOpGQ3nIe3+ORvpWdUYNtgbh48W4OKVcqP1oTgOuJFehaoqtcnt9/mFNUZb+dR3PbXKIPABgMcnh2H9h+Zbl0SFO2PMMP1GpOkNdD8fPdwXapUGh/++NVvF88C/J4rx+ILT2PZxH7O3N4bntUVrk1O0TYGH9veCrF4hV47jcSOtEi7OQvj52r6qfWvjEDk+WVlZGDZsGMLCwvDWW2+hoODWt6O62ZwpU6bglVdewcyZM7F06VKcP38eGzZswLvvvmuvYRNi0ulzJZYFPXWMvGmzPIeLVysAcfMFPnX1feqCibmPRZpskfDMzCikXClHela1ro4NzwNLn4mFr7fpWZvs3BqTy2o/H8rDncP88N+ZEuz5NRuVVRqEBDrh8clhGH3zA6WqSo1lq5N1QUPdh2DKlXL89kceXJ2FKK80PSvy2+Fck4FPYVEtjp0uBsMwGBDv2eC29MYaN8ofwwf7IPF8KRTlKly8Uo7CIiWEIgaHjhTgWqr1icksA4wfG+gQQQ8AHP6rwGyBy/pOJJbolX4wpbRMhdGP/IPwEBnmPBaBwf30C/Stftd8DzIGMDmrM35MAD7ZesNkuYqHxwfhiSnhBu0dQoPNL5MN6eeN/1t7weh1aZlVOJNUip5d3c3eR33VNRosX30eJ8+W6r5gOEkFeOPFOLAsg9ffS0F+4a3n6O4mwuplcehhxWM4GocIfA4ePIirV6/i6tWrCA7WX6eu67Eql8tx4MABzJ8/H/Hx8fD29saKFStoK3s7p1ZzOJNUiqpqDbp2kjfbB5e1zLUhMOq2GR8A4BkWFRUqoPHlexrUs6s7svNqEOTvhIfvC8KA3qaXDTzcxdjyfm/8+U8Bki8pIHcVYewIvwbzIQL9pSY7nQPA8tXn9Tq/Z2RX49W3U6BUcrhndAD2H8lHVbXhTAnHAQeP5CM8RGY28Kms1uBschk6xbjq7eDZuiMNX2xL1T2ugGUw7/FIPHJ/8+TKOEkFuvM7Zrg/KirVGP9ogu65WIO5uSupoS3ZrUlji29aIi2zCstWJ2Pd/3XRa6ty5Xq52dvxAIJNvH4ZhsGWDfGYtzRRL78oPNgJm97qCWdn41W3O0W7IiTICRlZhjM/ft4So8vF9e3/M8+qwGfT1hs4fTNfru4LRk2tBktePW+0dlWpQoVnXzqLnZ/3a7OzP9SdvR5H785O9J06W4KV6y+itExbo0bAMpg0IRhzHouw+7dgRbkK4x9NsKzvU92faL0xMxwHAa+CSK1EtaT5dhzt+qIf/JvxzU+t4TF70WlcvmZ9DyM3VyGcZQLk5JmvhGuuEvnt9/fcU9G48w5f/P1fIZa9lmz0uPdf74Ze3Zp/+fxIQiFeesP4GBoyfow/Fj5lfCt2a/XRlmvY/mOm1UGepRgGiI5wweYN8brLJsxIMFlJHNA2/ty+qW+DdZ5upFfgWmoV4mJcEejfcOJzXkENnltxTm/ZKzTICR+t64FrqZVY8PI5k7ed+mAI5pqpWF6fWs1hzCP/WDe7fNOwgd5YvdxxyplQd3bS7hUW1WLJq+f1doBoOB7bdmcgwE+K++8KtOPoADdXEWZOCcemr25YVMMlqOQKsj2iwDM3e1GpKuBcW4YCtyY2iOQ4gOEBsHqBFcsC0ZEuzRr0AMDhv/IbFfQA2qTehhJ7AcurHyvK1XjlrYvw85Hgf7/kGO1lJ2CBH3/NaZHAp7GhubNMgOfnNq3eiz1MnhCC3/8q0Gs+a0s8D1y+XgG1htedm+kTQ/HOJ1dN3mb9yq4WVSaPCHVBRKjltXD8fKTY9lEfJF9SIDu3BmHBMsTeLA/Rq5vIbE2pKQ9a/jdfXcM1KugBgEuN/Lt0BI7zdYAQK/zyey5UasOdHgyAHT9m2mVMt5s+MRSvLo3TveGZxDDI8oyBtLYCgSXX4FeWhmqRTC/oYRigXy/rP4ynH1+Lu89/DQBgGe3JYllAKhVg6dMxVt+ftQ4eyW/2xzCGNRETCFgGO37MRH5BjdEPX42Rwo3NJb67u0FlbEtMnhDicEEPoF0q/fzdXhg11DaFIY1xkgr0SgeMvi3x+HYJJxveDt9YDMOgS0c5Rg/z03sPYBgGq17oaDTwfXJqGOSu5hvX1pdXYD6R2hx5Aw1yHRnN+JA26er1CqMfXDyA3Hzramg0pxGDfdA/3hOjH/67wWOrpXJUS403J+R5IPmS9VvaXZTlCMk9jnBPDtlPLEdhmRodIlxw35gAi7eRN5Zaw+PcBfPVpoVCbZK0xsr6jCwLgDc+k8ayQICfFFk5hq8DDcfjRkYl4mLckJ5ZbZB3IhAwiO1g2Td7RYUKyloOXp7iRi2tOsuEWDi7A9ZtNCyqaAzLABPHB9l1h1pT8DwPD7kYpWVqo7NtTcWywD2j/fX+LUpKzW9HP5lYiikPtPz5HNLfBzs+74sPN19HanolvL0keOrRCKtr+Rw80nCZBFMea8O9uyjwIW0Kz/NY/W4KDv9jujZHcGDr6i4vcxKgR2c5EpMb13KCZbRv6pVVlkcHDM8hoCwVHhoFgqbdh5FrXoDEpxmzpI3497+iBsd8ew5U3S6zhowe5offDhtv4cBxQFZODRjAoPmqgAVCAmV45P5gHDySr5cfxDLaGaGH6hVuNCY7txpvf3wF/50uAQ8gKECKp5+IwpD+3mZvZ8y9YwKw57dspFw1v+zAstp6QM886Vi9uKprNHhpTTJOJpaA4wCphAV3s6t9U/Tu7o4zSWXgeB4sq60v1TnWDbOm6Vf39/I0v9nBzdV+H5GBfk54vQk5NoeO5mH3PvM94FycWVRUGp7ssSP8GvV6dRQU+JA25fs9Wdj/h/nlky4dW1/i+uL50XjyudOoqTX/js8y2mUoD3cRsnJqtEFTFzn+PdFw36A6DAPI5RKs2TABkeGPghG0TEE0tZrDhcsK5OTXIjLMGaeTSqz+Zu/lJUF+A0tNAha4dFUBsYiB0szuOWPXaDigZxc5OoQ7461VXfH2x1eQma1dLggNluGF+dEIDZIZuaVWZZW2CF9xqVJ3/1k5NVj+ejLefa0b+jSibcTi+TF48rnTZo/hODS8ZNrK8DyPqfNO6P17NvT6t9Q7r3ZDUYkSv/+Vj/IKNbrFydGnh4des1kAkDkJEegvRXau8VlgS1peGJORXYUTZ0ogFrMY3NdbryFuSzh6rBCr1puv5i4SMtj5+QAcOpqHg0fyUVisREigE+bMiEB0hGO9lqxFgQ9xGBoNj5NnS5CZU43QQCfEdzd8I/t6V3qD92PqTc5eOI7HwSP5UNfbxioSMRg+yBunk8q09VwEDDQcD6lUgLdWdUXXTm5QKjmIRCx27c1Cwslik0m8gpvBBcMC8d08MKivF8aO8IOLc8v9+f/5TwHWvH9Jb4ZHKmGNRx9mNBT0MIw2eEnNqNZVADB1XhhGu3R1+6zSxs3X8fm2VLy6NA7ffdJHOzvEAoF+0gaXrH47nIeiEqXRKsJfbk9rVODTsYMr5jwWgU+2Gq/0zbKAp7sYwwY61jf0Q0fzG/z3bAyBgAHLMvDxkmDS/Q0nAq96oRPmvHDGaAD+46/Z6BDhAjcL82p4nsd7n17F7n3ZuteeUHgFi+ZE495GBlGNseFT0wnbgDbXccWijnB1EWLCuCBMGGd+FrOtocCHOITs3Go8vzIJmdnVuiWKsBAZ3nmlK/x8bu08qtu6bk5VjQbnUxT461gh0jOrIJawGNrfG3cM9LFLUujOn7KwdYd+wKbR8DiZWIqvN/ZGwqkS3EivhJ+PBHfe4QdXF+2fbV336ehIF5Mf7lIJi3tG+8PHS4Ixw/yaPW/HmAuXFfi/tRcMYhxbfbuvr+483P5fY1iWwfgxAcjJqzHotF1Ty2Hpa+exe3N/q5ZGU66Ug2WA20uxcBxw8XLj24pMeygU998ViG9/SMfx0yW4dqMC6psxZHSkC1a90MmqbuStwe9/NZx/Uhc8CAQMOnZwQfIl83V3AG1SeH0aDY/zKQpUVqnRKcYVHnL95a24GDdsfi8eq99JwdXbCkX++W8hcvJq8enbPS3K09p3IBe7b1aCrnvtqdU81m28jNgOLoiJapmZlPp1hW4nEjH4/J1e7a4je30U+JBWj+d5vPhGMnJytUsOdZ8paRlVmDjzOGQyIUYN9cXMKWHw8hCjqMR0wiLLAFk51Zjzwhm9y38/WoBucVl4//XuEApZqFQcfvk9F4eO5kOp4jGwtycm3B0INxfbTlnzPI9tPxjOUnEcUFyqwj8nijFulPFec3V6dJEjLtYVKUZK9z/2SJjdk12/35Np1cSOUGg4C2MpS2v2ANoPxGEDvbFoZZLR63ke+HDzNbyyJM7ix/cwUyBTqeLx93+FGNy3cTMzLs5CzJ4eidnTtX2trqVWwt1NpNczrSlqajTILaiBh1zcIjt6nM30bwOAN17qjNz8GkjE2i8m7nIRvtmVgS3fpZpcwnSWCfDSgludx5MvKbBi3QXdTjyBgMGUB4Ixe7p+La/wEBkKigyDBY4DLl4px+lzpYjvbjhbp6hQ4Y13U3AisRRqDWeyBIFAwGDfwVw830KBT11ekzH+vtJ2HfQAFPgQB3DpWgWu3jBesp/jtR8CP+3Pxn9nijFpQjA+3Hzd6LEMow2aTNV+OXdBgZXrL+KlhR3x0hvJOJFYovsgvXhJgR9+zsa8xyMwYrCvrkFiU1XXcCguMT5LJRQwSMs0XdG4DsMweGtlV7z18RX8+Y+27L+zTIBpD4Vi2kNNrPNjA+aqMhvT2KAHMB/01P1b1tVNGjPcF3Gxbkar19Yx1zDTmLtH+uO7HzJMXr/mvUv4casHRKKmzc64OAvRvbPxHX6mXLlegc+3peJkYgkkYhajh/vhiSlhcJEJseW7NHz7QzpqldpzERnujDsGeGP0MF+EBNomsLrdtIdCTObjuToLMaSfl8Esy/SJoZg+MRTpWVUoLqnFqbOlOPxPITRqHoP6emLmlHBdD6oyhQrP/d851NTrf6bR8Ph6Zwa8PCR6SeqFxUqUmXhfYFnte9DtgU9WTjWmzT9hURV2jYa3qKGprfTqKseJxFKj19m7hllrQIEPafUKixvOA+A4ICdPu1Nn0v3B2LEnU+9DUCRk0LmjGxLPm985deTfQpxJOqYLjurug+OBohIlXnvnEl5/7xKmPBCCxyeFYd/BXBxJ0O4gG9rfG/fc6W/Qm8ccqYSF3FVo9E1XreEbbPdQx81VhFeXxEFRoUKZQgU/H2mrqdobHOBkptcUj8aX6rMMy2rzZHr38MDJsyVwdRZizHA/jBrqC4YxP0vkY6bH2O2upVbg/c+umQ2+ysrVeOGV83j3tW4tWj38yo0KPPXCGajVHDhO2wD2h5+zcPpcKQbEe2LbbcHa9dRKXE+txJbv0vD45DDMbKDZa2NEhLrgvrEB2PNbjt7lLAO8/mKc2fMTGiRDaJAMPbp4YObUCKPH7P8jD9U1GqP/Hl98m4p7R/vrlgfdXIQQCIzPknAcYCz//7V3LlrceoZlgJjIlptleXVpHB5+8jjKK/V3TcbFuOLh8e0rn8eY1vHOSIgZUWGWvWHwPHA6qRQsa/hBJhAwCA5wsiiHp7yBasAcB3yzKwNT557Ae5uu4vS5Upw+V4oNn17F0y+e1fuG2RCWZTDxvmCDHqQsC7i6CDFqiHXF3NxcRAgJlLWaoAdAA9u/m+fDn2Vv/dfZSYhlz8Zi9vQI3Xbmj7+8jmdfOoujx4rQt6fphOM5j1rWGiAnrwZzlyTizPnSBo89ebYUp841fJwtbfkuVRf01OE47YzW7UGP4W3TcOKM5bsGrfHC/Bi8/ao2WT8k0Akjh/hg95b+NqmMnZVbDYGJSpXlFWo89cIZVNzs4aZdLvcx+WrcuiMdeQU1erc/n9JwvhGgfQ3KZIIWTW52dRHhp68HYsakUESFO6NTtCteXBiLT9/uZfd2Pa0BzfiQVi/AT4pRQ3203ZvNJqsCxaVKo1u7a2o5nDxbordzyhRLF1pyb9uRwgO4dLUcP/6WbdFukjrTHwpFUbESe37N1j0/Hy8JXn+xs27a3pH16CLHs09G4cMt1/QKETK8RteCw9aG9PeGRsMjNsoF48cGQu4mwjMvJuJM0q0Zv6ISJRLPl+Gp6eHIzq1GRrb+br/Z08MRE2VZ0L3zp0zU1Gos2prPskDCyWL0NpIz0lxOnS1tdG0chgH2HcxFn57NU+epX09P9GuG+w4OcDLb+PR6aiWeWnwatUoOri5C5BfWmPzbr6xU47v/ZWLhbG2dJJXK8pPZKdoVL8yPgVcLN0gWiVg8OTUCT5qYEWvPHP9dlbQLyxd0hIuzEPsO5prMAeE4IOWK6UJvufm1kLuJoFCorN1FbTGeB/74u9CqwEcgYLBobjQefTgUFy6XQ+4qRNdOcggcsO3A7Q78mYePNl9HYYkS3TL/RmhhCvLdQuBdmY3o/HP4qv8ylMh8tHvtbcTFWYiVizvpZr1S0ytx380u5/XVBQKbv0vDnq8H4Mq1Chw6kg83NxGmPhAMNzfLP6jOXVRYHFhwHKBQNLz70JakUoFVBS7r4/mGKxy3RmNG+OGLb1NRUWn8eXM8kHazSWhuvvnldA2nbV9RF/h4uIvg7ysxeTuGAb7e2BsuzkK77KQk5rWe+XBCzJCIWSyeF4PdX/RDXIzxnRGsBa/mlYs6IiSoeSs385ZuK7qNj5cEdwzwRo8u7m0i6Nn/Rx5efTsFhTd32bnVlCAu7yRGXt6N7lkJkKkqMeTKTzYNegDtTE39pb4nFp4ye7xKzeNMUhniu3tg6bOxmDsj0qqgBwA85CKLXn91MrKtS/huqnEj/awa3+06t8Kinw1xcxHhvdXd4SS1zesrJ7cGh//WbsFnGO2XFVOemByG8FBnCnpaKQp8iEP5ZncGUq4aX1u35Bt3Tn4Ntn3cB5++3RNPPRpuNGmxPhdn65ZiGAYYOsCxCsk1B57n8clW/d11uW5hEPD6/0iFLgEA17iZCGN6dJHjgbtv5RRlZleZrd5cRyRsWqB5z53+Vi0ltVSj0zrTHgrVq3dlrYhQZxuOpuV07OCKhU/Zpo0HxwOr3ryAqze0s8oDenth45ruCPK/dV7dXIVYsagjHp8cbpPHJM2DAh/iMDQaHnsP5DSpj88vh3Jx6GgBGGi3dTbU/LKiUgOhFQvCPt4STBhH20XLFGoUFOkvj1z16YoPh76BP2IeQJVI+0GqEkrA2mjhkWWB5277kLt8zXyPK0CbeBrfzb1Jjz10gDceuT8YAAwS1Y1p6XyP737IQE5e4yuWf7Tlusm6MLbAcRzWb7yMUQ/9hSHjj+DuKf9gx4/mk64tdecdfojt4NKkGa86DMvgx1+zdb/36OKOHZ/1w99778Dfe+/AL98OarDjO7E/yvEhDkOl4lBT07Rqv8mXypF86SIAoGO0q0W9otTmN3npKSlRorpaA+c2kJTcFE5S1vDcMgwUMm8cjxiNFP94zEh4HdHqLJxgbXOuIsOcse6DyygoqoWvjxT3jPJH317uDd5u+bOxTa56zDAMnpkZhbtH+ePAH7n4Znem2eMfb4bt4abkF9biq+8bbuViTmGxEhevKNClo3W1gyw1d0miXlXmsnI1PvjiOvKLlHhmZlST7lssYvHB693x7Q8Z2P9HHgqKlBZtcjBGo+GRmVPdpPEQ+6MZH+IwJBLWpp3VU66Ua/tF2ZBGw+NXE13B2xOJRID+8cZ36vAMi1KZD4re/gp3/7rBZo959UYlLlwuR0GREskpCqzbeBkfbbkBdzfTgdXGN7ph+CDrSgaYExnmjCemRph9XfXsIsegPl42e8yGnEwsMbsb0lKW1qyx1oVLCpOtKHbuyYTSih1UpshkQjw5LQLPz41udNADaGcVw4Mdc9mP3EKBD3EYDMPgyanhNr3Pqmrb9otiWabF8zdaq5ef6wiBmXeYYylK/HqkECZKrcDNtekzQb//VYD/W9QRbq6GMzorF3VEj66231IuFrGYMC7Q6JKX3FWI2dMjsHVHGr7cnorffs/FhcuKRifEW0JoQf6Sq4v5GS9nmQBxsc2T4Lxnf47J6zgeOHbKdjWEvv0ho0lLXhwHjB/bcvV4SPNo3/PxxOGMGuoLlZrDZ1+nmm3EBwDeniIUFrfstmGNhkdUOH0jBLTVpLvFyXHGRLVskfBmkFjXdfY2ajWPze/1wrLV55Ff2Ljt1AIWOJlYil++HYwr18tx8Gg+YiJdMHKIb7MWcps9PQLFpUq9lgwBflKEh8gwd2miQbXooAApXlsaZ7KJZWFRLXgA3p5iq8fdv7cnRCLG5IxNZJgMn78bj8oqNT7ech2//J6nW6as++8zM6MgETfP9+SG7lfmZLvHTc2oalKOIACcu1CKyDD6G3dkFPgQh3PXCH+MGeaH4hIl/vdLNrYayV/wkDc96HF3E6BUYUUVZgaQu4kwephvkx63LRk51Ndo4MMwwMghvlCqOPx+1PB2LAtEhMrwwivnzTadbQgP7SwcAERHuiI6smWaRIpELP7v+U6YOSUcl69VwMNdhMvXKrDhs2vacd0Wg2Tl1OCZF89i9+b+cHG+9bacdLEMb398RderrkOEM56fE41ucZbn2ri5iLBobjTWvn/ZIO9qxGAfvDA/BmIRC7FcjBcXdsTQgd74/scsZGRXITzEGZMnBKNvr+YpXggAkx8Ixg8/Zxu9Tixi0Kuru80eK8jfCWVlqiYt/V262nDCPGndKPAhDollGXh7STBregS8PMX44ttUlCnUEAoZ9OvlgZAgJ2z/X1aj759hgOoa694dA/2leHNF13af2FzfuFH+OPxXAU4nleqWfngeiIt1w/13BaCqWoNvdqWjukZj0E4hONDJZO6HVMLCw12Mqiq1yeaSdfdzx0D7lRcI9HdCoL82L+2tj66YmtwCAFRWabDk1XOYMC4Ix06VoLpGg39PFOntprqWWomFL5/Dlg3xVnVlv+fOAHQId8Ge/TnIL6hBhwgXTBgXCH9fwy3ug/t6N7qDfGME+Dph/NgA/PSb4ZLX4nkxYG2xHeumh+8Lxop1F5p0H8215EdaDsM35+Kyg1EoFJDL5SgrK4ObG724Hdmk2ceRmdP47buN8eX78egQ0XKNCB2FSsXh4JF8HEkoBM/zGNLPG6OH++mWOC5fK8fr713SNTJ1dxNh3uOR2P5jBq6nmS70t+erAfDyEGPJq+eRcLLIaDPKB8YF4nkzheZa0r3T/kVJmWWzkCwL8JzxIEnAMrh3jD8Wz4ux7QDt7PDf+fhyexpKy1QIDnTCMzOj0CnG9u/D23an4/NvUqG6WQHeWHNSF2eB0YrPUgmL/TsGt4kCo22NNZ/f9NWUtEk5DZSgtyWWBXp2daegxwSRiMW4Uf4YN8rf6PUxUa748v14ZOZUo6aGQ0SoDEIhi517zc/YcTfXK15d0gkfb72On/bnQKXiIRAwCA1ywpPTwjG0f+spJtm1kxv++a8IGnM5JjwPMIzZPBQNxyP5ksLm47O3EYN9MWJw8y8TT30wFPeOCcDZ82UQi1l07yzHueQynE4qhcxJgJFDfCFzEmDmc6f18gidZQJ8uLYHBT1tAM341EMzPm3HyAf/Qq3Stju2TBna3wvLFsTCzUXUIo/XlvE8r0ve3bU3C+99etXocR5yEX76eoBeom+tkkNFpRpyNxGErfDD6eJlBeYsSQSn4ZtUspFlgf7xnnhzRVebjY0Yl3ypDGfOlSIqwgUDerdcCQJiPZrxIe1aZZUaXePccPpsqU3qlwBATJQLwoNlcHEWYNKEEDg5CZCVUwM/Hwl8qB9Pk504U4zPtqXi4uVyuDgLce+YADw6MRS7f85CRpZhwbgXF8Ya7G6SiFlIxC1bEdkanWLc8O5r3bDxi2vaitI3Z3esxXGAolyNqmoNZE7N092eaHWOlaNzbPMUbST2QzM+9dCMj2PjeR7f7MrAlu1pUJqY7ZFJWYSFyJBytcJoTogpXh5i7PlqgN5l6VlV2HcwBzfSKhHk74QZj4TC3Z2CIGslnCzCklfPgwF0gSrLAt3i5Hj31a74eOsNHPgzH7W1GkSGOWPB7A6IM5P7cfpcCX78NQe5BTWIiXTFxHuDrEoEbm61Sg5zXjiDK9fLtUk8jQh+WBYYNtAHry6Ns/0ACXFANOND2qXfDudh01c3jF7HskCnGFfcOzoAb35w2aqgBwB8vPRnEvb/kYfX3kmpd0kJdu3LRkiAFK+/1Bl+PlLa3WWhT7Zq/83qz85xHJB4vgyJ58vw7JMd8OyTljWa/P6nTLz/2TUIWEDDAZeuVODnQzl455Vu6GnDbdFNse9gDq5erwDAaGsYNQLHAX/8U4CColqacSTESlS5mbQZ23ZnmPzyzHFAcko51r5/uVHLX4/cH6L7/5IyJd7YkGL0uIycGjz69CmMm/wvXn37IsorrGj01Q5V12hwLbXSaCAqEDAmix8aU1KqxIebtR3h6xKINRwPtZrHmx9ebtbqyNb4858Cm7Rl5Xkgi/pGEWI1+kpK2oys3GqrZ3Is0THaBSOH3NoddOTfwga7ums4HoeO5iM3vwYfru3RrFWCHZlIyEAoZKBWG/7D8TyvV8zPmJy8Guw9oJ1BOXexzGgHcZ4HMrKqkZapLchnb7ypnPtG5PwE+BnW4SGEmEeBD2kzAvykSM9s3DdghgGkEgF48AYd4FOuVGDXvmw8PD4YgHaWwhIcB5y7oEDSRQX8faWorlYjJMjJpgXZHJ1QyGLkEB8cPJJvuIWbB0YO0TYQLSpRYtuudBw9VgiGYTBisA86dnDBq2+nQMPxFrUheGHVeWxc2x1+PvYNFgb398LZC2WGQboVQQ/LAoP7etn9uRDiiCjwIW3G5PtDsG7j5UbdNjjQCfePDcAHX1w3ev03u9Lx0D1BYFnrS+g/v+Icamq1n8wiEYOZk8MxbWJoo8bZFj09MwopV8qRllkNgYABz/PgeWDJ0zHw85GipFSJJ58/jaLiWl2A8+0PGbp+V5bO8uUX1uC1d1KwcU0Pi44/k1SKnw/loqRUebPSdCC8PJq2a6xWycFDLoaXhxiFxbe14rBixqdfL0+8uLBjk8ZCSHtFgQ9pM+4Z7Y/8wlps3ZFmUR7Ps7Oi4CwTIsBXgh5d3PHZN6kQChiojSyXFJeoUF2jgbNMiNgOrujVVY7TSZbln9QFPQCgUvH45KsbcHUV4r6xgRY/t7bMQy7Glvd7489/CnA+RQG5qwhjhvshOFDb6mHHnky9oAewLuCpo7mZMJ2VW42gm20kTPl6Zzo2fXVDlyR9IrEEu/dl4eN1PRu9Q+zCZQWWvHIepQoT1ZvNBD0sC9w3NhD9enkgNFiG0KDWs0uNEEdDgQ9pMxiGwcyp4bjvrgAseOks0kwse7EsEBnmrJvBqRPgJzWaIwJoq7ZKJbdqprzzajcsePksziY3roLuZ9+kUuBTj1jEYvQwP4we5mdw3b8niprcUbu+klIVgvydoNbw+OtYIY6dKoZQwGDYIB/07u6O7LwafHpzd2BdkjTHARWVarz36RW8+1p3qx+zVslhySvnoajQD3pYFvB0F+sasYYGy5CWod+mg2G0vemmPhhitLeWPfA8j9PnSnHhcjnkbiIMH+QDVxf6OCGOgV6ppM3x9pTgm4/64EhCIXb+lInM7GqUlaugVms/RAb19cKSp2P0gh5Am0/y4eZrBg0zGQZ46N4gvVL1QiGLD9f2RGZ2NdZtvISMrGpUVqlQXcPrlmDq/mtMqYU9m4i25YWtiEUMwkNkqFVyeGFVEk4nler+Xff8loO7R/kjPERm9N+O44ATiaWorFJbXarg7+OFRmd6OE4biP301QC4OAshEDB455Mr+PHXWw07naQCrFzcqdUEPZVVaixelYSkiwpdT7ENn13F6mVxVN2YOAQKfEibxDAMhg30wbCB2uTYWiWHnLxqyN1E8JAbz9Nwlgnxzqvd8OLrybpv4AAwZrgfHp8UZvQ2wYFO+OCNHgC034LPnC/DlWsV8PYS48vtabiRbrzJplBIu7wsNWqoLy5fs67gpCkP3xcMF2chtu1Ox5nzpQCgN8v386FcjBrqY3a7ualZQXOKipUmA2ENx6OqRgMPd+3rcvG8GEx+IARnz5dB5iRAv3hPOElbT4XmDzdf0/UKq/uCoFRyeHnNBfywpT/kbtS6hbRuFPiQNu9EYjE++PwaMnNq4CQV4O5Rfpg7I9LoFvPOsW7YvaU/Tp8tQVm5GnGxrg3mg9RhGG3ic13ys1rN31bk8JaBfTwb/XzamwfGBeJoQiGSLirA3Kz5x/GAu1yI0jLzdZJEQgYqNa9rNfLoxFCcSCzBV9+nGw1CGAY4fqrE5HUxUS5wc7X+g71DpIvJwM3FWWhQhDDI38ni111LqlVqsO9ArkEOHc8DShWHw38XYMI4WsIlrRsFPqRN++1wLla/e0n3u1LJ4dsfMnEmqRSfvRNv9DZCAYO+vZoemIwZ7oe/jhfiz38K9S7395Vg5WJqNWApiUSA91/vjkN/5eOf40VgWQZDB3gjJNAJ85YmQqniDIIKlgE6RLjgo3XdUV6pgbubCCIRi01fXcfXOzNMPhbPA+WVpoOpZ5+MatRz6NlFjrgYV6RcLTfIV5r6YAjENlzOs1R5hRr7DubgbHIZnGVCjB3hh97d3U3WnCqvUOGhmcdNbhxgWQYlZUrjVxLSilCvrnqoV1fbM/rhv1FVbbzuzvqVXVokJ+HiZQW+3pWB2loNxozww+g7DBN4W7vC4locPJKP0jIV4mLdMKivV6vogH4ttQJffJuGhBNFUNUrgti7uztWLOoEz3rbz1MzKjFt3skG79PUkpRUymLv1wNxJqkUHM+jZxd3q3J9FOUqrP/oCo78UwCO1ybMT3soFNMeCmnxApcFRbV4avEZFBTVgucBActAw/GY8mAI5s2INHqb+x9LMNyCf5uW+psi5HbWfH5T4FMPBT5tS05+NSbO/M/k9QN6e2L9yq4tOCL70Gh47DuYg30HclGqUKF7ZzmmPBCCyDDDKsY8z+NMUilOJJZAIhZgxGAfXE+rxKq3LkKj4cGyDDQaHlFhztjwene4y1tPPkeZQoX0rCp4e0rg5SnGrr1Z+OVQLiqq1Ijv5g6eBw78mW/2PlgWJneQScQsWJbRFbCUiFk882QU7r/LuqUdRbkKpQoV/HykkIjtU8zytbcv4tDRfN2utfq+eLcXYju46l128bICsxadMXufMZEu+OydXnqbANIyqvDljjRUVKoxYrAP7hrpb5PxE3I7alJKCADw9p+RaA3WbLiE3/7I0/2ek1eD3w7nISbKGauXdUagvxM0Gh4aDYdJT/2H/MJb3+o/35aqLS/DaxuJ1yX23sioxPufX8WKRZ1a+NmYJncToaubHBzHY9HKJJw8eytX59CRfIuSo81tm69Vcga/v/XRFYQEOiG+u4fF43RzFTUqT8hWeJ7H4X8KjAY9AgGDP/4pMAh8LOmZ9t7qbnpBz9sfX8H/fsnW/Z5wshiffHUD333cBzJq4EvsiGrnkzYrwE8KmZPp3TAP3N32kzAvXFboBT31Xb5WiclP/Ydp807gjvuPYuRDf+sFPXX4m0FPfRwH/P5XAWprLWvf0ZKOnSrGiUT9BGUNh0Y1p20IywK79mbZ/o6bmbmdaSrVrYioskqNpItlEDSwqSwmylkvmDt9rkQv6KlTVKzEstfPWz9gQmyIwm7Spi2eF41X3zbcWdUpxqVd5CIcO1Wsqz5sjIYDUm8WzLO6ErKGR15BLa6nVUIoZBDf3cPu264rKtVIOFEEgYBp1LZza3EckOlgHdIZhkGfHh44kVhiMMOl0fDoH+8Jnuex+bs0bNudAeXNmS5zdalemB+r9/vn36aafPwzFlY8J6S5UOBD2rTRw/zg5SHG+59fQ2Z2tXY7+53+mPNYhL2H1iIELGO2Jk1TSCQsHn3mpK6zusxJgKXPxGDkEN9mekStvIIa7PwpC2eSSuHmKsRdI/0hdxXioy9v4FpqJQDtlveWwDDaJTaO4w0KYrZmcx6LxNnkM1CpOF1QzDBA/3hPxHf3wPd7srDluzS925gLjH89nItO0beWx8rMFOikrFJib5TcXA8lN5O2xtKdTLbCMsDmDfHoEOHSLPefnlmFpxafQWW1GhynfbzmWMKyVliwE95+pVurqa5sidSMSny9Mx2nzpXC1VmIu+/0x7AB3tiyIx37/8jTBbSWcJYJsH/HYN3vr71zEfv/MJ5ILpWyOLRzSJPHT0h91nx+U44PIW1YWLCsRXsoMSyj127B1j7acl0X9ACtI+gBgMzsarz4ejIc6XtkeIgzli/oiMXzonHv6ABk5VTjoSf/w88Hc60KegCgukaj19x3/uNRYE18ukx9IKQpwyakySjwIaQNq6jUoLzCfHVjW9JoeOTkNU/OC8fx+PekbRuW2oqGAy5fr8ClaxX2HorFMrOrMWn2cSx7LRnvf34N//ul8QErxwELXz6r2/nm6SHGh2t76JU7EAgYTLo/GI9PDm/q0AlpEsrxIaSN4Xkeew/kYudPmcgrqG3RxxawQHioYX2gtkAsZvDgPUH47odMk8cUFNai421bwVsjnuex/PXzKCi03esj8XwZdv6UiWkPhQIAunaSY983A5GTVwNFuQodIpwhENB3bWJ/9CokpA1RqTRYue4C3tx4GTfSq0xWrW4ODAOwAgYTrCzoZymWZTCwjxfs9dkZF+2KR8YHw1wOs7GikK3RxSvluJFeZXK3X2P9dtiwdEKAnxSxHVwp6CGtBr0SCXFAHMfj3IUyHE0oRF5BDXiex4p1FzD8gb9x+LbeYC3Fx0uCt1d1RXBg8zXXnDcjEjKZUJc/UheE+PlITN/IBlgWcHERwdtLgjEj/HB7hwmWBYYN9EZQQOtrLGpMYVHz9NRqyUCbkMaipS5CHMz1tEq8+Pp5ZObUANDOtPh6S1p8Wet2j9wfhF7dLK9g3BihwTJ8+X48du3NwumkUshdRbhrpD9GDPbGh1uu4/s9zVNMkOO0pREAYPG8GEglLPbuz4Vaw0MgYDB2uB+ee6pDszx2c4iOtP2uO4YB+vVq3n9/QmyBAh9CHEitksPCl8+iVHGrTgrPw+5Bj0DAID2zZQr5+flIMf8Jwy7pgX5OZovsNcXIod4YNtAbgLZH16K5MZg9PRL5hTXw8ZbAzaX19CyzRICfFHfe4YtDRy1r5WEJqYTV5fcQ0prRUhchDuRoQiGKS1UtvrNpzqPh2Pv1AAT4Ga9Tw3O83WvYDOzj2WzF8XLzag0KFLq6CBEV7uJwQU+dZc/G4oG7AyEWaZ+XRMxi8oTgBm8XFqxdzqu/3BcX44ov3ot3mKU+0r45XOBTW1uLHj16gGEYJCYm6l137tw5DBkyBFKpFCEhIXjzzTftM0hCmklWbrVeI8iW8slXqfjfL9l4eHyQwXUMA4hELMaNsm/n7UB/Jzw+KQwATNaQaazkS+WorGq5sgAtQSJm8dxT0di3bRB2fNoXP28biPlPRMHD3Xwgl51bg1WLO+Kxh0Mx/4lI/LClPz59uxdCg2QtNHJCmsbhAp8lS5YgMNBw14hCocDo0aMRFhaGU6dOYf369Vi1ahU+/fRTO4yStGU8zyMrpxppGVXgWriCXmiQrEV6UBmz+bs0jBrqi4fvC9Lb2SR3FeHNFV3g5SG2y7jqmzk1HOv+rwv6x3siJtKlweaabq5C+HiJEegnNUhYvl1xafMkBNubzEmAoAAnSG/2WVs0x3yukobjkZNfiyenRWDyhBD4ejdvYjkhtuZQOT6//vorDhw4gN27d+PXX3/Vu27btm1QKpXYvHkzxGIxOnfujMTERLzzzjuYPXu2nUZM2orC4lrk5tdCUa7Cx19ex410bWNPPx8JnnuqA6LCXfDviUKkZVbhTFIZ8gtr0SHCBdMnhqJ/vKfNxjGknxf8fCQoKKo1WO5iWTT7EtiOPZl49skOmDwhBEkXFXCWCdCrqztEotbzHWpQXy8M6qttQDtp9nFdEvjt3OVC7PtmkO73NRtS8PMh453sBQLA16t9fMAPG+SLV17gsXK9YXNfAGAZBkXFbTMIJO2DwwQ+eXl5mDVrFn788UfIZIZTqgkJCRg6dCjE4lvfOseMGYN169ahpKQEHh6Guw1qa2tRW3srKVShUDTP4IlDuZFeib37c5BfVIuQQCdcT6vEvyeKjeaP5BXUYtnqZKP3k3SxDItXJeH/nu+IMcP9bDI2kYjFhtXdseLNC7h8s0qwUMDgwXsC8dikMHz/YyaSUhTgNDzOJpfptXSQSlgM6OOBP/4uavTj11WB9vGSYMRgnyY9l5bw+ORwvPaO8Q/wSffrt06YMyMSv/2Rb3RGbfyYAEgk9u0835JGDvXDlzvSkZpRZfC6V2t4dIhwjHpFhBjjEIEPz/OYMWMG5syZg969eyM1NdXgmNzcXERE6Hfc9vPz011nLPBZs2YNXnnllWYZM3FMB4/k47W3L4JhGHA836Rk2brZl41fXMPIIT4QCm0zKxIc6ITN78XjelolSsuUiAp3gdxNm5fx5LRbfwNFJUoc/DMP+UW16NFFjkF9vDF/WWKTdj6NHta8nddtbcxwP6RcLcfOn/S3uY8a6oOpD+oHPh5yMTat74mlryWhqES7a45hgLtG+mHh7OgWG3NrMWNSGFa+eVHvMpYFvDwlGDnUsV4HhNRn18Bn2bJlWLdundljLl68iAMHDqC8vBzLly+36eMvX74czz//vO53hUKBkBBqoNdeVVapsfb9S9pZEhtuDyopUyEtswpR4batnaKtEmz6m7eXhxiTJtx6PfM8j+RLikY/tcgwGbp3dm/cje1owawOeHxSGPb8mg21hsfdowNMLlt1jHbFnq8GIr+wBoXFSoQFy+Asc4jvhzY3cogvqqs12PTVDZSUaQPBnl3csfSZGDhJ28/sF2l77PoXvWjRIsyYMcPsMZGRkTh8+DASEhIgkei/WfXu3RtTp07F1q1b4e/vj7w8/fX5ut/9/Y3vNpFIJAb3SdqvY6eKdU0WbU3aCpZJGIaBs0yAikrLquuKhAxUah4ClsGgfp54ZUlcM4+w+bi5ijD94TCLj/f1lsLX277b81uDe0YHYOwIP2Tn1kAmE8Dbk94vieOza+Dj4+MDH5+G8wTef/99rF69Wvd7dnY2xowZgx07dqBfv34AgAEDBuCll16CSqWCSKSd9j948CBiY2ONLnMRcrvaWtsHPSyrnZkJ9G8dH6J3j/LHzr1ZDSZBi8Us9n49oN3OdpBbhEIWocG0VZ20Ha1nK4YZoaGh6NKli+4nJiYGABAVFYXgYG3BrSlTpkAsFmPmzJlITk7Gjh07sGHDBr2lLELM6dXNvcEtzdZgGMBJKsBLCzuCseUdN8HMqRGIi3EDALP1gKY+EEJBDyGkTWoz72xyuRwHDhzA/PnzER8fD29vb6xYsYK2shOL+ftKMen+YHz3v8wm31dcrCsG9/XCvaMD4OFu//o2dWROAny4tgeOny5GYnIZnCQsSspUOPBnHioqNfCQizD1oRA8cl/DFXwJIcQRMTzfXEXeHY9CoYBcLkdZWRnc3NzsPRxiB0kXyzB3SaLVt/P3kSCmgwviu3lg/Bh/iET2z+mxhkbDo7pGA5mTwKA1AyGEtHbWfH47xFIXIS3lq+/TG3W7vMJaHE0ogkrNOVzQA2iXvVychRT0EELaPAp8CKnn4uXyRt2ubt5009YbKKvXOZ0QQkjrQoEPIfV4ejSt07ZKzePk2RIbjYYQQoitUeBDSD1THmh6AUsBLRcRQkirRYEPIfX07uGBAU1oKioRs+jTk+pGEUJIa9VmtrMT0hSlZUosXpWElKvaxp8MA/j7SsBx2kakptR1RBcIAI0GWDQ3murfEEJIK0bv0KTd4zgOk+f8h/KKW60ceB7IyavF+DH+kLuJ8e3uDGhutjqXiFlMeTAEwwb5YM+v2bieWokAfykmjAvUFQckhBDSOlEdn3qojk/79N6nV7Brb7bR61gW+PW7Qait5XDuQhmcnATo1dUdIhGtEhNCSGthzec3zfiQdo3jePz0W46Z64Hs3BpER7pg2KCG+8oRQghp3SjwIe3atbRKKFXmJz29PVtPywl7Ka9QI6+gBr4+Eri5NG3LPwCcTynD59+kIjG5DDKpAHeN8scTk8MoP4oQ0uzoXYa0ayqV+Tblcjdhq+q11dJqlRw2fHoVvxzKhVrDQyBgMG6kHxbM6gCptHEVqpMuluHp5WfBcTx4HlBUqPH9nkycOluCVS90gotMCG8viY2fCSGEaFHgQ9q16AgXuLkIoahQG73+1SWdW3hErcubGy/h4J/5uJnXDY2Gx76DubiWVol1L3dpVFD46dc3oNHoz7LxPHD1RiWmzTsJAOjeWY6lz8QgNEjW5OdACCH1UYYmaddEIhbPzIoCoN3CXt+UB0IQ39295QfVSuQX1uJAvaCnDs8DFy6V475HE/DtDxn1LueRm1+DwiLT2/8BIPF8WYOPnXSxDPOXJqKi0nhA2hYVFNXi4mUFyk0E4YQQ26AZH9Lu3TXCH94eYmzbnYEb6VUI8JNi4vggjBzia++h2dWN9EqY2/PJ8cBHW67jp9+yMeoOX/x+tAAZ2dUAgE7RrnhhfjRiolwb9dgcB5QqVNj/Rx4evCeoUffhKErLVHhjQwr+PVEMABAKGUwYF4j5j0dCKKTvpoTYGgU+hADo09MTfXo2vmJzW+RjYZ5NZk4Nvtyu39X+0rVyzF+WiK8/7AOBgMGOPZk4mVgC3nxKlR6WZXD5eoU1Q241yhQqHPorH8UlSsREuWJQXy8IBYatTKqq1Zj53Cm9IplqNY9de7MgFDCY/0RUSw6bkHaBAh9CiFGRYc7oHOuGlCsKaKwIWADtjE11DYeHZh5v9OPzPA8fB9xRd/x0MV58PRlKFQeWZaDR8AgPleH91d3h6XHr+VRVqfH4wlNGK4PzPLD752w8PikMMtrpRohN0TwqIcSk15bFISzEucUflwHAgMFdo/xb/LGborJKjZfXaIMenocuiTs1vQpPPn8KWTnVumN37ctCdm6NyftSKjnk5Ju+nhDSOBT4EEJM8vWWYOsH8XhxQQwELfhuIZaweGVJJwT5O7Xcg9rA0YRCVNdwRnOj8guVmP70SWzblY6KSjUO/11gNoeKZQFvT9rWT4it0RwqIcQshmEwblQA/HykWLfxstlZCmstnB0JiUQIoZCBq7MQhcVKyJwEGNjHCy7Ojvf2VFauAsvAYCdcHaWSw8dbb+DTb1IbLIw5cogv5G5NLxZJCNHneO8shBC7iO/ugaVPR2PtB5eRk2d+y7olBAIGI4f4takCkZ1j3UwGPfVpNDzyCmpNBkmhQU5YPC/a9gMkhFDgQwgxxPM8Tp8rxV/HiwDwGNLPCylXy/Hxl6k2e4xpD4W0qaAHALp0dEN8d3ecOltq0fFiCQtlLacX/IQFO+GLd+MbXRmbEGIeBT6EED0aDY9X376I3/8q0F1mqnu9NeoKRHp7ijFzSjjuvtOxEpctwTAM1r7cBS+9cR7/nSlt8HiRgIFawIBT60/7VFVrKPAhpJlQ4EMI0bP/jzy9oMcSIhEDlYlmrx5yIbZs6N1u+m85SQXo3NHNosCnvFJjUDE8M7sab310GW+81KV5BkhIO0eBDyFEz6+Hc62+jdxNhJISFTRGElYefTis3QQ95RVqfPtDBr79Ib3hg+twHLpkH0eX7ARIVDVI9e6EUzUjUDAnGuUVari5UNNWQmyJAh9CiJ7yCpXVt7n3Tn9s250JHjy4m8UOBSzg4y3BOAerxdNYVdUazF1yBulZVbpz0CCex4Cr+3DHtX3gwIAFD39FOrpn/o050zXIE2iriffp4YHlC2Lh600BECFNRXV8CCF6+vTwsOp4Hy8xpj4Yio/W9UB8N3cwjHbp685hfvhkfU84t5PKw/sO5CAt04qgBwAYBglRd+OGVyew0M6WseDgpKpEn+T/6Q47fa4Ez76YCLXayhLahBAD7eMdiRBisYfvC8HufdlQmsjZqW9Abw+8uLAjpFIBOka74t3XuoPjeDCMNtG3PUk4WWy2IKEpPMNgf9wUzPp7JQQ3m5mxPIfYvDPYd/MYDaftifb38SIMG+Rju0ET0g7RjA8hRI+PlwSfrO8JV+eGdxWNHxMID7n+lnSWZdpd0AMAYjFrkKhsEYZFibMfNgx/C/+FjYQudrotiBIIGFy94ZhNWwlpTSjwIYQYiIlyxd5vBmLscF+zx0kl9BZSZ8Rgn0bN+NSpFbvg906P4ECnSUgK6IuTYcP1ruc0PHwox4eQJqN3LUKIUUIhi+ULYuEkNf424eYiQI8u7i07qFZs5FBfDOrr1eT7OR02Avu6P4kjsQ/qLmMYwMlJgJFDzAeihJCGUeBDCDFJIGCxfmVXiIS31nAYBhAKgBWL4yAS0VtIHaGAwRsvdsYrSzohKty2He2lEu2/gyP2LyOktWF4vimTs22LQqGAXC5HWVkZ3Nzc7D0cQlqN/MJa7D2Qg/TMKgT6S3Hv6AAEOljn9JakUnF4fMEppGZU2ew+J44PwoJZHWx2f4S0JdZ8ftPXB0JIg3y9JZg5Jdzew3AYIhGLsSP8sOmrG03K+6lv509ZuHe0PyLDXGxzh4S0UzRPTQghzaBfL0+jQQ/DAL4+jUtS/manFRWhCSFGUeBDCCHNIDrSBWOH+wEA6jKkWFYb+Cx7OgZjR/hZfZ/pWdU2HCEh7RMtdRFCSDNZviAWHaNdsee3bBSXqtA5xhWPPhKKLh3l6NvLEwN6e2DlmykW39+laxU4dbYE8d2tq65NCLmFkpvroeRmQkhLe3fTZezel2PRsQwDRIQ6Y+sH8e2ySCQhpljz+U1LXYQQYkdPP9EBQoFlQQzPA9fTKlFcan0jWUKIFgU+hBBiRyIRi2ULYqy6jaWBEiHEEAU+hBBiZ2OH+2Pjmu6IjnQGwwBSE9WyWRbo0VkOuZuohUdISNtByc2EENIK9Ojiji0beoPneTAMg+0/ZmDjF9chYBloOB4sC8ichHh+brS9h0qIQ6PAhxBCWpG6pOVJ94egc6wb9h7IRXGJEp2iXXHfXQHw9qRGpYQ0BQU+hBDSSnXtJEfXTnJ7D4OQNoVyfAghhBDSblDgQwghhJB2gwIfQgghhLQbFPgQQgghpN2gwIcQQggh7QYFPoQQQghpNyjwIYQQQki7QYEPIYQQQtoNCnwIIYQQ0m5Q4EMIIYSQdoMCH0IIIYS0G9Srqx6e5wEACoXCziMhhBBCiKXqPrfrPsfNocCnnvLycgBASEiInUdCCCGEEGuVl5dDLjff2JfhLQmP2gmO45CdnQ1XV1cwDGPv4TgMhUKBkJAQZGRkwM3Nzd7DaRPonNoWnU/bovNpe3ROm4bneZSXlyMwMBAsaz6Lh2Z86mFZFsHBwfYehsNyc3OjP1gbo3NqW3Q+bYvOp+3ROW28hmZ66lByMyGEEELaDQp8CCGEENJuUOBDmkwikWDlypWQSCT2HkqbQefUtuh82hadT9ujc9pyKLmZEEIIIe0GzfgQQgghpN2gwIcQQggh7QYFPoQQQghpNyjwIYQQQki7QYEPsYna2lr06NEDDMMgMTFR77pz585hyJAhkEqlCAkJwZtvvmmfQTqA1NRUzJw5ExEREXByckJUVBRWrlwJpVKpdxydU+t8+OGHCA8Ph1QqRb9+/fDff//Ze0gOYc2aNejTpw9cXV3h6+uL+++/H5cuXdI7pqamBvPnz4eXlxdcXFzw4IMPIi8vz04jdixr164FwzBYuHCh7jI6n82PAh9iE0uWLEFgYKDB5QqFAqNHj0ZYWBhOnTqF9evXY9WqVfj000/tMMrWLyUlBRzHYdOmTUhOTsa7776LTz75BC+++KLuGDqn1tmxYweef/55rFy5EqdPn0b37t0xZswY5Ofn23tord6RI0cwf/58HDt2DAcPHoRKpcLo0aNRWVmpO+a5557D3r17sXPnThw5cgTZ2dl44IEH7Dhqx3DixAls2rQJ3bp107uczmcL4Alpol9++YXv2LEjn5yczAPgz5w5o7vuo48+4j08PPja2lrdZUuXLuVjY2PtMFLH9Oabb/IRERG63+mcWqdv3778/Pnzdb9rNBo+MDCQX7NmjR1H5Zjy8/N5APyRI0d4nuf50tJSXiQS8Tt37tQdc/HiRR4An5CQYK9htnrl5eV8dHQ0f/DgQf6OO+7gFyxYwPM8nc+WQjM+pEny8vIwa9YsfP3115DJZAbXJyQkYOjQoRCLxbrLxowZg0uXLqGkpKQlh+qwysrK4OnpqfudzqnllEolTp06hVGjRukuY1kWo0aNQkJCgh1H5pjKysoAQPd6PHXqFFQqld757dixI0JDQ+n8mjF//nzcfffdeucNoPPZUijwIY3G8zxmzJiBOXPmoHfv3kaPyc3NhZ+fn95ldb/n5uY2+xgd3dWrV/HBBx/gqaee0l1G59RyhYWF0Gg0Rs8XnSvrcByHhQsXYtCgQejSpQsA7etNLBbD3d1d71g6v6Zt374dp0+fxpo1awyuo/PZMijwIQaWLVsGhmHM/qSkpOCDDz5AeXk5li9fbu8ht3qWntP6srKyMHbsWEycOBGzZs2y08gJ0Zo/fz7Onz+P7du323soDisjIwMLFizAtm3bIJVK7T2cdkto7wGQ1mfRokWYMWOG2WMiIyNx+PBhJCQkGPSW6d27N6ZOnYqtW7fC39/fYEdC3e/+/v42HXdrZuk5rZOdnY3hw4dj4MCBBknLdE4t5+3tDYFAYPR80bmy3NNPP419+/bh6NGjCA4O1l3u7+8PpVKJ0tJSvVkKOr/GnTp1Cvn5+ejVq5fuMo1Gg6NHj2Ljxo3Yv38/nc+WYO8kI+K40tLS+KSkJN3P/v37eQD8rl27+IyMDJ7nbyXiKpVK3e2WL19OibhmZGZm8tHR0fykSZN4tVptcD2dU+v07duXf/rpp3W/azQaPigoiJKbLcBxHD9//nw+MDCQv3z5ssH1dcm4u3bt0l2WkpJCybgmKBQKvffMpKQkvnfv3vy0adP4pKQkOp8thAIfYjM3btww2NVVWlrK+/n58dOnT+fPnz/Pb9++nZfJZPymTZvsN9BWLDMzk+/QoQM/cuRIPjMzk8/JydH91KFzap3t27fzEomE//LLL/kLFy7ws2fP5t3d3fnc3Fx7D63Vmzt3Li+Xy/k///xT77VYVVWlO2bOnDl8aGgof/jwYf7kyZP8gAED+AEDBthx1I6l/q4unqfz2RIo8CE2Yyzw4XmeP3v2LD948GBeIpHwQUFB/Nq1a+0zQAewZcsWHoDRn/ronFrngw8+4ENDQ3mxWMz37duXP3bsmL2H5BBMvRa3bNmiO6a6upqfN28e7+HhwctkMn7ChAl6gTox7/bAh85n82N4nudbfH2NEEIIIcQOaFcXIYQQQtoNCnwIIYQQ0m5Q4EMIIYSQdoMCH0IIIYS0GxT4EEIIIaTdoMCHEEIIIe0GBT6EEEIIaTco8CGEEEJIu0GBDyGEEELaDQp8CCGtzrBhw7Bw4cIGj/vss8/QvXt3uLi4wN3dHT179sSaNWt0169atQoMw2DOnDl6t0tMTATDMEhNTQUApKamgmEYoz/Hjh0DAOTk5GDKlCmIiYkBy7IWjY8Q0vpQ4EMIcUibN2/GwoUL8eyzzyIxMRH//PMPlixZgoqKCr3jpFIpvvjiC1y5cqXB+zx06BBycnL0fuLj4wEAtbW18PHxwcsvv4zu3bs3y3MihDQ/ob0HQAgh9c2YMQNHjhzBkSNHsGHDBgDAjRs3EB4ernfcTz/9hIcffhgzZ87UXda5c2eD+4uNjYWvry9eeuklfP/992Yf28vLC/7+/kavCw8P141n8+bN1jwlQkgrQjM+hJBWZcOGDRgwYABmzZqlm3UJCQkxOM7f3x/Hjh1DWlpag/e5du1a7N69GydPnmyOIRNCHAgFPoSQVkUul0MsFkMmk8Hf3x/+/v4QCAQGx61cuRLu7u4IDw9HbGwsZsyYge+//x4cxxkc26tXLzz88MNYunSp2cceOHAgXFxc9H4IIW0LBT6EkFavc+fOukDkrrvuAgAEBAQgISEBSUlJWLBgAdRqNR577DGMHTvWaPCzevVq/PXXXzhw4IDJx9mxYwcSExP1fgghbQvl+BBCWr1ffvkFKpUKAODk5KR3XZcuXdClSxfMmzcPc+bMwZAhQ3DkyBEMHz5c77ioqCjMmjULy5YtwxdffGH0cUJCQtChQ4fmeRKEkFaBAh9CSKsjFouh0Wh0v4eFhVl0u7i4OABAZWWl0etXrFiBqKgobN++vemDJIQ4JAp8CCGtTnh4OI4fP47U1FS4uLjA09MTLKu/Mj937lwEBgZixIgRCA4ORk5ODlavXg0fHx8MGDDA6P36+fnh+eefx/r1641eX1RUhNzcXL3L3N3dIZVKAUC39FVRUYGCggIkJiZCLBbrAi5CSOtHOT6EkFZn8eLFEAgEiIuLg4+PD9LT0w2OGTVqFI4dO4aJEyciJiYGDz74IKRSKX7//Xd4eXmZvW9TScujRo1CQECA3s+PP/6ou75nz57o2bMnTp06hW+//RY9e/bEuHHjmvx8CSEth+F5nrf3IAghhBBCWgLN+BBCCCGk3aDAhxBCCCHtBgU+hBBCCGk3KPAhhBBCSLtBgQ8hhBBC2g0KfAghhBDSblDgQwghhJB2gwIfQgghhLQbFPgQQgghpN2gwIcQQggh7QYFPoQQQghpN/4frHb57rxcrY8AAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "X_train_tsne.plot.scatter(x=\"t-SNE1\", y=\"t-SNE2\", c=y_train, cmap=\"coolwarm\", colorbar=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We wee some patterns in the data (e.g., positive examples missing from the central region of the plot), but the data is still quite scattered. This is somewhat of a good sign, as it suggests that our dataset is reasonably diverse (both the negative and the positive examples) and not trivially separable. We can verify this by performing clustering on the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dist (k= 2): [2134  287]\n",
      "Dist (k= 3): [1044 1220  157]\n",
      "Dist (k= 4): [ 171 1099  152  999]\n",
      "Dist (k= 5): [ 131 1052  203  985   50]\n",
      "Dist (k= 6): [123 862 121 766  50 499]\n",
      "Dist (k= 7): [102 844  98 210  48 346 773]\n",
      "Dist (k= 8): [ 99 744 377 192 166  49 709  85]\n",
      "Dist (k= 9): [ 99 708 154 194  27  50 381  91 717]\n",
      "Dist (k=10): [ 99 678 151 200  27  49 371  98 694  54]\n",
      "Dist (k=11): [ 99 638 146 207  27  50 311  86 646  41 170]\n",
      "Dist (k=12): [ 88 585 141 362  27  47 143  71 355  41 168 393]\n",
      "Dist (k=13): [ 85 579 141 301  27  45 158  69 321  42 166 365 122]\n",
      "Dist (k=14): [ 91 462 139 260  27  42 120 105 433  43 159 178 297  65]\n",
      "Dist (k=15): [ 89 530 136 247  27  40  92  30 389  43 158 184 260  71 125]\n",
      "Dist (k=16): [ 95 510 156 210  27  38  88  19 390  41 149 175 232  68  85 138]\n",
      "Dist (k=17): [ 95 411 119  70  26  38  84  19 483  41 173 182 226  66  81 135 172]\n",
      "Dist (k=18): [ 95 410 118  69  26  38  82  19 482  41 174 181 233  60  78 135 171   9]\n",
      "Dist (k=19): [ 95 407 128  67  26  26  81  24 461  41 176 181 232  59  74 134 179   9\n",
      "  21]\n",
      "Dist (k=20): [ 80 395 109  67  26  26  82  21 455  44 155 168 231  46  78 136  78  10\n",
      "  21 193]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.cluster import KMeans\n",
    "from sklearn.metrics import silhouette_score\n",
    "\n",
    "inertia = []\n",
    "silhouette = []\n",
    "\n",
    "# kmeans_data = X_train_pca[y_train == 0]\n",
    "# kmeans_data = X_train_pca[y_train == 1]\n",
    "kmeans_data = X_train_pca\n",
    "\n",
    "ks = range(2, 21)\n",
    "for k in ks:\n",
    "  kmeans = KMeans(n_clusters=k, random_state=RANDOM_STATE)\n",
    "  kmeans.fit(kmeans_data)\n",
    "  inertia.append(kmeans.inertia_)\n",
    "  silhouette.append(silhouette_score(kmeans_data, kmeans.labels_))\n",
    "\n",
    "  print(f\"Dist ({k=:>2}): {np.unique(kmeans.predict(kmeans_data), return_counts=True)[1]}\")\n",
    "\n",
    "plt.plot(ks, inertia, marker=\"o\")\n",
    "plt.xticks(ks)\n",
    "plt.xlabel(\"Number of clusters\")\n",
    "plt.ylabel(\"Initial\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There is no elbow point, which suggests that the data is not easily separable. This is consistent with the t-SNE plot, as well as the rather low silhouette scores:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(ks, silhouette, marker=\"o\")\n",
    "plt.xticks(ks)\n",
    "plt.xlabel(\"Number of clusters\")\n",
    "plt.ylabel(\"Silhouette score\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We see the same trend when considering the positive and negative examples separately, which you can check by rerunning the code with the appropriate lines uncommented.\n",
    "\n",
    "With data preprocessing complete, we can now move on to building and evaluating machine learning models to predict telomerase inhibitors."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model selection\n",
    "\n",
    "We will consider the following models:\n",
    "\n",
    "- Support vector machine\n",
    "- Random forest\n",
    "- XGBoost\n",
    "\n",
    "These models are known to perform well on a wide range of classification tasks, especially complex ones like this.\n",
    "\n",
    "We will first train the `scikit-learn` gradient boosting model, which is a simpler version of XGBoost, to get a baseline performance. Keeping in mind that accuracy is not a good metric for imbalanced datasets like ours, we will mainly focus on the precision, recall, and F1 score (the harmonic mean of precision and recall) of the models."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-1 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-1 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-1 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-1 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-1 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GradientBoostingClassifier(random_state=42)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;GradientBoostingClassifier<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.ensemble.GradientBoostingClassifier.html\">?<span>Documentation for GradientBoostingClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>GradientBoostingClassifier(random_state=42)</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "GradientBoostingClassifier(random_state=42)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import GradientBoostingClassifier\n",
    "\n",
    "model = GradientBoostingClassifier(\n",
    "    n_estimators=100,\n",
    "    random_state=RANDOM_STATE,\n",
    ")\n",
    "model.fit(X_train_pca, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00      2381\n",
      "           1       1.00      0.93      0.96        40\n",
      "\n",
      "    accuracy                           1.00      2421\n",
      "   macro avg       1.00      0.96      0.98      2421\n",
      "weighted avg       1.00      1.00      1.00      2421\n",
      "\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.99      0.99      0.99       596\n",
      "           1       0.20      0.10      0.13        10\n",
      "\n",
      "    accuracy                           0.98       606\n",
      "   macro avg       0.59      0.55      0.56       606\n",
      "weighted avg       0.97      0.98      0.98       606\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import classification_report\n",
    "\n",
    "y_pred_train = model.predict(X_train_pca)\n",
    "print(classification_report(y_train, y_pred_train, zero_division=np.nan))\n",
    "\n",
    "y_pred_test = model.predict(X_test_pca)\n",
    "print(classification_report(y_test, y_pred_test, zero_division=np.nan))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We see that the gradient boosting model performs quite poorly on the test set, managing to detect only 1 out of 10 positive examples while making many incorrect guesses. Hence, we should adjust our expectations for the other models accordingly.\n",
    "\n",
    "We will now perform hyperparameter tuning using grid search with 5-fold cross-validation. We will use the F1 score as the scoring metric for the grid search. In reality, we care slightly more about precision than recall, because we wouldn't want to waste time and resources on false positives. However, we still want a sensitive enough model that can detect as many telomerase inhibitors as possible, so we will go ahead with the F1 score as the main evaluation metric and a wide range of hyperparameters to explore (including the `class_weight` parameter to account for the class imbalance)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from xgboost import XGBClassifier\n",
    "\n",
    "param_grid = {\n",
    "  \"svm\": {\n",
    "    \"C\": [0.1, 1, 10],\n",
    "    \"kernel\": [\"linear\", \"poly\", \"rbf\", \"sigmoid\"],\n",
    "    \"degree\": [2, 3, 4],\n",
    "    \"class_weight\": [\"balanced\", None],\n",
    "  },\n",
    "  \"rf\": {\n",
    "    \"n_estimators\": [50, 100, 200],\n",
    "    \"max_depth\": [3, 5, 7],\n",
    "    \"criterion\": [\"gini\", \"entropy\"],\n",
    "    \"class_weight\": [\"balanced\", None],\n",
    "    \"max_features\": [\"sqrt\", \"log2\"],\n",
    "  },\n",
    "  \"xgb\": {\n",
    "    \"n_estimators\": [50, 100, 200],\n",
    "    \"max_depth\": [3, 5, 7],\n",
    "    \"learning_rate\": [0.01, 0.1, 0.3],\n",
    "  }\n",
    "}\n",
    "\n",
    "models = {\n",
    "  \"svm\": SVC(random_state=RANDOM_STATE),\n",
    "  \"rf\": RandomForestClassifier(random_state=RANDOM_STATE),\n",
    "  \"xgb\": XGBClassifier(random_state=RANDOM_STATE),\n",
    "}\n",
    "\n",
    "def tune_model(model, param_grid, X=X_train_pca, y=y_train):\n",
    "  grid_search = GridSearchCV(\n",
    "    model,\n",
    "    param_grid,\n",
    "    cv=5,\n",
    "    scoring=[\"f1\", \"precision\", \"recall\"],\n",
    "    refit=\"f1\",\n",
    "    n_jobs=-1,\n",
    "  )\n",
    "  grid_search.fit(X, y)\n",
    "\n",
    "  return grid_search"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "results = {name: tune_model(model, param_grid[name]) for name, model in models.items()}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "svm: {'C': 0.1, 'class_weight': 'balanced', 'degree': 3, 'kernel': 'poly'}\n",
      "svm: 0.21242236024844718\n",
      "rf: {'class_weight': 'balanced', 'criterion': 'entropy', 'max_depth': 3, 'max_features': 'sqrt', 'n_estimators': 50}\n",
      "rf: 0.06713286713286713\n",
      "xgb: {'learning_rate': 0.1, 'max_depth': 3, 'n_estimators': 100}\n",
      "xgb: 0.04444444444444444\n"
     ]
    }
   ],
   "source": [
    "for name, result in results.items():\n",
    "  print(f\"{name}: {result.best_params_}\")\n",
    "  print(f\"{name}: {result.best_score_}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The performance of all the models leaves much to be desired. However, it explains why we're getting better results with PCA than by using random forests to select features. Clearly, random forests cannot be trusted to identify the most important features for this dataset, so an unsupervised method like PCA is probably more reliable.\n",
    "\n",
    "Somewhat surprisingly, the SVM outperforms the random forest model and even XGBoost. On the other hand, SVMs are known to perform well on high-dimensional data (with the right kernel, of course, which in this case is the polynomial kernel), so this is not entirely unexpected."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test\n",
      "====\n",
      "\n",
      "SVM\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.99      0.97      0.98       596\n",
      "           1       0.16      0.30      0.21        10\n",
      "\n",
      "    accuracy                           0.96       606\n",
      "   macro avg       0.57      0.64      0.59       606\n",
      "weighted avg       0.97      0.96      0.97       606\n",
      "\n",
      "Random Forest\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.99      0.98      0.98       596\n",
      "           1       0.19      0.30      0.23        10\n",
      "\n",
      "    accuracy                           0.97       606\n",
      "   macro avg       0.59      0.64      0.61       606\n",
      "weighted avg       0.97      0.97      0.97       606\n",
      "\n",
      "XGBoost\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.98      1.00      0.99       596\n",
      "           1        nan      0.00      0.00        10\n",
      "\n",
      "    accuracy                           0.98       606\n",
      "   macro avg       0.98      0.50      0.50       606\n",
      "weighted avg       0.98      0.98      0.98       606\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(\"Test\\n====\\n\")\n",
    "\n",
    "print(\"SVM\")\n",
    "print(classification_report(y_test, results[\"svm\"].best_estimator_.predict(X_test_pca), zero_division=np.nan))\n",
    "\n",
    "print(\"Random Forest\")\n",
    "print(classification_report(y_test, results[\"rf\"].best_estimator_.predict(X_test_pca), zero_division=np.nan))\n",
    "\n",
    "print(\"XGBoost\")\n",
    "print(classification_report(y_test, results[\"xgb\"].best_estimator_.predict(X_test_pca), zero_division=np.nan))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train\n",
      "=====\n",
      "\n",
      "SVM\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.99      0.98      0.99      2381\n",
      "           1       0.35      0.70      0.47        40\n",
      "\n",
      "    accuracy                           0.97      2421\n",
      "   macro avg       0.67      0.84      0.73      2421\n",
      "weighted avg       0.98      0.97      0.98      2421\n",
      "\n",
      "Random Forest\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      0.98      0.99      2381\n",
      "           1       0.36      0.78      0.49        40\n",
      "\n",
      "    accuracy                           0.97      2421\n",
      "   macro avg       0.68      0.88      0.74      2421\n",
      "weighted avg       0.99      0.97      0.98      2421\n",
      "\n",
      "XGBoost\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00      2381\n",
      "           1       1.00      0.88      0.93        40\n",
      "\n",
      "    accuracy                           1.00      2421\n",
      "   macro avg       1.00      0.94      0.97      2421\n",
      "weighted avg       1.00      1.00      1.00      2421\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(\"Train\\n=====\\n\")\n",
    "\n",
    "print(\"SVM\")\n",
    "print(classification_report(y_train, results[\"svm\"].best_estimator_.predict(X_train_pca), zero_division=np.nan))\n",
    "\n",
    "print(\"Random Forest\")\n",
    "print(classification_report(y_train, results[\"rf\"].best_estimator_.predict(X_train_pca), zero_division=np.nan))\n",
    "\n",
    "print(\"XGBoost\")\n",
    "print(classification_report(y_train, results[\"xgb\"].best_estimator_.predict(X_train_pca), zero_division=np.nan))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Judging by the train vs. test F1 score of the XGBoost model, it seems to be overfitting the training data. We can plot the confusion matrices to see where the models are making mistakes:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1500x500 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import ConfusionMatrixDisplay\n",
    "\n",
    "fig, axs = plt.subplots(1, 3, figsize=(15, 5))\n",
    "\n",
    "ConfusionMatrixDisplay.from_estimator(\n",
    "  results[\"svm\"].best_estimator_,\n",
    "  X_test_pca,\n",
    "  y_test,\n",
    "  cmap=\"Blues\",\n",
    "  colorbar=False,\n",
    "  ax=axs[0],\n",
    ")\n",
    "axs[0].set_title(\"SVM\")\n",
    "\n",
    "ConfusionMatrixDisplay.from_estimator(\n",
    "  results[\"rf\"].best_estimator_,\n",
    "  X_test_pca,\n",
    "  y_test,\n",
    "  cmap=\"Blues\",\n",
    "  colorbar=False,\n",
    "  ax=axs[1],\n",
    ")\n",
    "axs[1].set_title(\"Random Forest\")\n",
    "\n",
    "ConfusionMatrixDisplay.from_estimator(\n",
    "  results[\"xgb\"].best_estimator_,\n",
    "  X_test_pca,\n",
    "  y_test,\n",
    "  cmap=\"Blues\",\n",
    "  colorbar=False,\n",
    "  ax=axs[2],\n",
    ")\n",
    "axs[2].set_title(\"XGBoost\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "On the test set, the random forest model performs better than the SVM, achieving marginally higher precision. However, the cross-validation scores suggest that the SVM is more consistent and generalizes better to unseen data. We will use the SVM model to screen the chemical library for potential telomerase inhibitors.\n",
    "\n",
    "Before we proceed, it will be interesting to visualize where the SVM model is making mistakes. We will again use t-SNE to visualize the predictions of the SVM model on the test set:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "tsne = TSNE(n_components=2, perplexity=50, n_iter=1500, random_state=RANDOM_STATE)\n",
    "X_test_tsne = pd.DataFrame(tsne.fit_transform(X_test_pca), index=X_test.index, columns=[\"t-SNE1\", \"t-SNE2\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "X_test_tsne.plot.scatter(x=\"t-SNE1\", y=\"t-SNE2\", c=y_test, cmap=\"coolwarm\", colorbar=False, title=\"Performance of the best SVM model on the test set\")\n",
    "\n",
    "y_test_pred = results[\"svm\"].best_estimator_.predict(X_test_pca)\n",
    "plt.scatter(X_test_tsne[(y_test == 1) & (y_test != y_test_pred)][\"t-SNE1\"], X_test_tsne[(y_test == 1) & (y_test != y_test_pred)][\"t-SNE2\"], c=\"black\", marker=\"x\", label=\"False positive\")\n",
    "plt.scatter(X_test_tsne[(y_test == 1) & (y_test == y_test_pred)][\"t-SNE1\"], X_test_tsne[(y_test == 1) & (y_test == y_test_pred)][\"t-SNE2\"], c=\"green\", marker=\"o\", label=\"True positive\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It's hard to make sense of the plot without having the decision boundary but we're unlikely to learn much from it anyway. For the sake of completeness, we will now retrain the SVM model on the entire dataset and use it to screen a different dataset for potential telomerase inhibitors."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Screening and conclusion\n",
    "\n",
    "We will load the chemical library and preprocess it in the same way as the training data. We will then use the SVM model to predict whether each compound is a telomerase inhibitor. Of course, we will not be able to verify the predictions, but we can at least get an idea of the potential telomerase inhibitors in the library."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>smiles</th>\n",
       "      <th>name</th>\n",
       "      <th>MaxAbsEStateIndex</th>\n",
       "      <th>MaxEStateIndex</th>\n",
       "      <th>MinAbsEStateIndex</th>\n",
       "      <th>MinEStateIndex</th>\n",
       "      <th>qed</th>\n",
       "      <th>SPS</th>\n",
       "      <th>MolWt</th>\n",
       "      <th>HeavyAtomMolWt</th>\n",
       "      <th>...</th>\n",
       "      <th>fr_sulfide</th>\n",
       "      <th>fr_sulfonamd</th>\n",
       "      <th>fr_sulfone</th>\n",
       "      <th>fr_term_acetylene</th>\n",
       "      <th>fr_tetrazole</th>\n",
       "      <th>fr_thiazole</th>\n",
       "      <th>fr_thiocyan</th>\n",
       "      <th>fr_thiophene</th>\n",
       "      <th>fr_unbrch_alkane</th>\n",
       "      <th>fr_urea</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Nc1nc2[nH]nnc2c(=O)[nH]1</td>\n",
       "      <td>Azaguanine-8</td>\n",
       "      <td>10.962593</td>\n",
       "      <td>10.962593</td>\n",
       "      <td>0.049583</td>\n",
       "      <td>-0.387731</td>\n",
       "      <td>0.430316</td>\n",
       "      <td>10.545455</td>\n",
       "      <td>152.117</td>\n",
       "      <td>148.085</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>CN(C)C(=N)NC(=N)N</td>\n",
       "      <td>Metformin hydrochloride</td>\n",
       "      <td>7.067778</td>\n",
       "      <td>7.067778</td>\n",
       "      <td>0.113426</td>\n",
       "      <td>-0.214167</td>\n",
       "      <td>0.248785</td>\n",
       "      <td>8.222222</td>\n",
       "      <td>129.167</td>\n",
       "      <td>118.079</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>CC(=O)OCC(=O)[C@@]1(O)CC[C@H]2[C@@H]3CCC4=CC(=...</td>\n",
       "      <td>Isoflupredone acetate</td>\n",
       "      <td>16.884033</td>\n",
       "      <td>16.884033</td>\n",
       "      <td>0.085680</td>\n",
       "      <td>-1.997379</td>\n",
       "      <td>0.679307</td>\n",
       "      <td>47.066667</td>\n",
       "      <td>420.477</td>\n",
       "      <td>391.245</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>CCCc1ncc(C[n+]2ccccc2C)c(N)n1</td>\n",
       "      <td>Amprolium hydrochloride</td>\n",
       "      <td>5.985117</td>\n",
       "      <td>5.985117</td>\n",
       "      <td>0.587225</td>\n",
       "      <td>0.587225</td>\n",
       "      <td>0.830783</td>\n",
       "      <td>10.555556</td>\n",
       "      <td>243.334</td>\n",
       "      <td>224.182</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NS(=O)(=O)c1cc2c(cc1Cl)NCNS2(=O)=O</td>\n",
       "      <td>Hydrochlorothiazide</td>\n",
       "      <td>11.633654</td>\n",
       "      <td>11.633654</td>\n",
       "      <td>0.014270</td>\n",
       "      <td>-4.069104</td>\n",
       "      <td>0.654458</td>\n",
       "      <td>18.235294</td>\n",
       "      <td>297.745</td>\n",
       "      <td>289.681</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1677</th>\n",
       "      <td>CN(C)CC[C@H](CSc1ccccc1)Nc1ccc(S(=O)(=O)NC(=O)...</td>\n",
       "      <td>ABT-737</td>\n",
       "      <td>13.350173</td>\n",
       "      <td>13.350173</td>\n",
       "      <td>0.140221</td>\n",
       "      <td>-4.426475</td>\n",
       "      <td>0.057731</td>\n",
       "      <td>14.035714</td>\n",
       "      <td>813.446</td>\n",
       "      <td>768.086</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1678</th>\n",
       "      <td>O=c1c(O)c(-c2ccc(O)c(O)c2)oc2cc(O)ccc12</td>\n",
       "      <td>Fisetin</td>\n",
       "      <td>12.082041</td>\n",
       "      <td>12.082041</td>\n",
       "      <td>0.087182</td>\n",
       "      <td>-0.653875</td>\n",
       "      <td>0.510622</td>\n",
       "      <td>10.857143</td>\n",
       "      <td>286.239</td>\n",
       "      <td>276.159</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1679</th>\n",
       "      <td>CC1(C)CCC(c2ccc(Cl)cc2)=C(CN2CCN(c3ccc(C(=O)NS...</td>\n",
       "      <td>Navitoclax</td>\n",
       "      <td>14.177964</td>\n",
       "      <td>14.177964</td>\n",
       "      <td>0.001862</td>\n",
       "      <td>-6.052872</td>\n",
       "      <td>0.104649</td>\n",
       "      <td>18.338462</td>\n",
       "      <td>974.634</td>\n",
       "      <td>919.194</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1680</th>\n",
       "      <td>Cc1c(-c2ccc(N3CCc4cccc(C(=O)Nc5nc6ccccc6s5)c4C...</td>\n",
       "      <td>A1331852</td>\n",
       "      <td>13.522989</td>\n",
       "      <td>13.522989</td>\n",
       "      <td>0.025570</td>\n",
       "      <td>-1.061671</td>\n",
       "      <td>0.185260</td>\n",
       "      <td>24.187500</td>\n",
       "      <td>658.828</td>\n",
       "      <td>620.524</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1681</th>\n",
       "      <td>CN(C)CC#Cc1ccc(OCCCc2sc(N3CCc4cccc(C(=O)Nc5nc6...</td>\n",
       "      <td>A1155463</td>\n",
       "      <td>14.580479</td>\n",
       "      <td>14.580479</td>\n",
       "      <td>0.004160</td>\n",
       "      <td>-1.103020</td>\n",
       "      <td>0.131321</td>\n",
       "      <td>12.468085</td>\n",
       "      <td>669.804</td>\n",
       "      <td>637.548</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1682 rows × 212 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                 smiles  \\\n",
       "0                              Nc1nc2[nH]nnc2c(=O)[nH]1   \n",
       "1                                     CN(C)C(=N)NC(=N)N   \n",
       "2     CC(=O)OCC(=O)[C@@]1(O)CC[C@H]2[C@@H]3CCC4=CC(=...   \n",
       "3                         CCCc1ncc(C[n+]2ccccc2C)c(N)n1   \n",
       "4                    NS(=O)(=O)c1cc2c(cc1Cl)NCNS2(=O)=O   \n",
       "...                                                 ...   \n",
       "1677  CN(C)CC[C@H](CSc1ccccc1)Nc1ccc(S(=O)(=O)NC(=O)...   \n",
       "1678            O=c1c(O)c(-c2ccc(O)c(O)c2)oc2cc(O)ccc12   \n",
       "1679  CC1(C)CCC(c2ccc(Cl)cc2)=C(CN2CCN(c3ccc(C(=O)NS...   \n",
       "1680  Cc1c(-c2ccc(N3CCc4cccc(C(=O)Nc5nc6ccccc6s5)c4C...   \n",
       "1681  CN(C)CC#Cc1ccc(OCCCc2sc(N3CCc4cccc(C(=O)Nc5nc6...   \n",
       "\n",
       "                         name  MaxAbsEStateIndex  MaxEStateIndex  \\\n",
       "0                Azaguanine-8          10.962593       10.962593   \n",
       "1     Metformin hydrochloride           7.067778        7.067778   \n",
       "2       Isoflupredone acetate          16.884033       16.884033   \n",
       "3     Amprolium hydrochloride           5.985117        5.985117   \n",
       "4         Hydrochlorothiazide          11.633654       11.633654   \n",
       "...                       ...                ...             ...   \n",
       "1677                  ABT-737          13.350173       13.350173   \n",
       "1678                  Fisetin          12.082041       12.082041   \n",
       "1679               Navitoclax          14.177964       14.177964   \n",
       "1680                 A1331852          13.522989       13.522989   \n",
       "1681                 A1155463          14.580479       14.580479   \n",
       "\n",
       "      MinAbsEStateIndex  MinEStateIndex       qed        SPS    MolWt  \\\n",
       "0              0.049583       -0.387731  0.430316  10.545455  152.117   \n",
       "1              0.113426       -0.214167  0.248785   8.222222  129.167   \n",
       "2              0.085680       -1.997379  0.679307  47.066667  420.477   \n",
       "3              0.587225        0.587225  0.830783  10.555556  243.334   \n",
       "4              0.014270       -4.069104  0.654458  18.235294  297.745   \n",
       "...                 ...             ...       ...        ...      ...   \n",
       "1677           0.140221       -4.426475  0.057731  14.035714  813.446   \n",
       "1678           0.087182       -0.653875  0.510622  10.857143  286.239   \n",
       "1679           0.001862       -6.052872  0.104649  18.338462  974.634   \n",
       "1680           0.025570       -1.061671  0.185260  24.187500  658.828   \n",
       "1681           0.004160       -1.103020  0.131321  12.468085  669.804   \n",
       "\n",
       "      HeavyAtomMolWt  ...  fr_sulfide  fr_sulfonamd  fr_sulfone  \\\n",
       "0            148.085  ...         0.0           0.0         0.0   \n",
       "1            118.079  ...         0.0           0.0         0.0   \n",
       "2            391.245  ...         0.0           0.0         0.0   \n",
       "3            224.182  ...         0.0           0.0         0.0   \n",
       "4            289.681  ...         0.0           2.0         0.0   \n",
       "...              ...  ...         ...           ...         ...   \n",
       "1677         768.086  ...         1.0           1.0         0.0   \n",
       "1678         276.159  ...         0.0           0.0         0.0   \n",
       "1679         919.194  ...         1.0           1.0         1.0   \n",
       "1680         620.524  ...         0.0           0.0         0.0   \n",
       "1681         637.548  ...         0.0           0.0         0.0   \n",
       "\n",
       "      fr_term_acetylene  fr_tetrazole  fr_thiazole  fr_thiocyan  fr_thiophene  \\\n",
       "0                   0.0           0.0          0.0          0.0           0.0   \n",
       "1                   0.0           0.0          0.0          0.0           0.0   \n",
       "2                   0.0           0.0          0.0          0.0           0.0   \n",
       "3                   0.0           0.0          0.0          0.0           0.0   \n",
       "4                   0.0           0.0          0.0          0.0           0.0   \n",
       "...                 ...           ...          ...          ...           ...   \n",
       "1677                0.0           0.0          0.0          0.0           0.0   \n",
       "1678                0.0           0.0          0.0          0.0           0.0   \n",
       "1679                0.0           0.0          0.0          0.0           0.0   \n",
       "1680                0.0           0.0          1.0          0.0           0.0   \n",
       "1681                0.0           0.0          2.0          0.0           0.0   \n",
       "\n",
       "      fr_unbrch_alkane  fr_urea  \n",
       "0                  0.0      0.0  \n",
       "1                  0.0      0.0  \n",
       "2                  0.0      0.0  \n",
       "3                  0.0      0.0  \n",
       "4                  0.0      0.0  \n",
       "...                ...      ...  \n",
       "1677               0.0      0.0  \n",
       "1678               0.0      0.0  \n",
       "1679               0.0      0.0  \n",
       "1680               0.0      0.0  \n",
       "1681               1.0      0.0  \n",
       "\n",
       "[1682 rows x 212 columns]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "screen_data = pd.read_csv(SCREEN_PATH)\n",
    "screen_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PC1</th>\n",
       "      <th>PC2</th>\n",
       "      <th>PC3</th>\n",
       "      <th>PC4</th>\n",
       "      <th>PC5</th>\n",
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       "      <th>PC62</th>\n",
       "      <th>PC63</th>\n",
       "      <th>PC64</th>\n",
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       "      <th>PC67</th>\n",
       "      <th>PC68</th>\n",
       "      <th>PC69</th>\n",
       "      <th>PC70</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-6.435865</td>\n",
       "      <td>-1.165214</td>\n",
       "      <td>3.444639</td>\n",
       "      <td>-4.015028</td>\n",
       "      <td>-3.586400</td>\n",
       "      <td>-0.972146</td>\n",
       "      <td>-1.330753</td>\n",
       "      <td>4.754884</td>\n",
       "      <td>-1.929031</td>\n",
       "      <td>-0.872592</td>\n",
       "      <td>...</td>\n",
       "      <td>0.408643</td>\n",
       "      <td>0.564708</td>\n",
       "      <td>-1.028492</td>\n",
       "      <td>1.442688</td>\n",
       "      <td>0.114789</td>\n",
       "      <td>-0.602954</td>\n",
       "      <td>0.098020</td>\n",
       "      <td>-0.572900</td>\n",
       "      <td>-0.401485</td>\n",
       "      <td>-0.684371</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-7.223818</td>\n",
       "      <td>2.634289</td>\n",
       "      <td>2.219258</td>\n",
       "      <td>-4.102018</td>\n",
       "      <td>0.398284</td>\n",
       "      <td>2.820498</td>\n",
       "      <td>0.264503</td>\n",
       "      <td>2.276806</td>\n",
       "      <td>0.564471</td>\n",
       "      <td>0.903378</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.185200</td>\n",
       "      <td>-1.076500</td>\n",
       "      <td>-0.154501</td>\n",
       "      <td>0.300433</td>\n",
       "      <td>1.113195</td>\n",
       "      <td>-0.221557</td>\n",
       "      <td>2.550697</td>\n",
       "      <td>0.815038</td>\n",
       "      <td>0.813637</td>\n",
       "      <td>0.055411</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.980497</td>\n",
       "      <td>4.538316</td>\n",
       "      <td>-6.554852</td>\n",
       "      <td>0.743492</td>\n",
       "      <td>-2.567119</td>\n",
       "      <td>-1.304740</td>\n",
       "      <td>-4.504168</td>\n",
       "      <td>0.035021</td>\n",
       "      <td>2.961456</td>\n",
       "      <td>0.391823</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.903900</td>\n",
       "      <td>0.078594</td>\n",
       "      <td>0.314244</td>\n",
       "      <td>-0.210039</td>\n",
       "      <td>0.259596</td>\n",
       "      <td>-0.753362</td>\n",
       "      <td>0.152769</td>\n",
       "      <td>-0.170915</td>\n",
       "      <td>0.261304</td>\n",
       "      <td>0.248686</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-5.380038</td>\n",
       "      <td>-1.842459</td>\n",
       "      <td>0.095300</td>\n",
       "      <td>-1.456840</td>\n",
       "      <td>-1.683904</td>\n",
       "      <td>0.274600</td>\n",
       "      <td>1.390494</td>\n",
       "      <td>2.500195</td>\n",
       "      <td>-1.538728</td>\n",
       "      <td>-1.201845</td>\n",
       "      <td>...</td>\n",
       "      <td>0.749690</td>\n",
       "      <td>-0.384907</td>\n",
       "      <td>-0.333796</td>\n",
       "      <td>0.571784</td>\n",
       "      <td>0.036849</td>\n",
       "      <td>-0.940803</td>\n",
       "      <td>-0.243061</td>\n",
       "      <td>-0.494785</td>\n",
       "      <td>0.398642</td>\n",
       "      <td>-1.059548</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-3.728620</td>\n",
       "      <td>0.304802</td>\n",
       "      <td>1.917586</td>\n",
       "      <td>-2.877411</td>\n",
       "      <td>-1.171531</td>\n",
       "      <td>-1.276258</td>\n",
       "      <td>0.473734</td>\n",
       "      <td>0.027714</td>\n",
       "      <td>4.291011</td>\n",
       "      <td>1.587343</td>\n",
       "      <td>...</td>\n",
       "      <td>0.108182</td>\n",
       "      <td>0.869715</td>\n",
       "      <td>0.381031</td>\n",
       "      <td>0.294130</td>\n",
       "      <td>0.879353</td>\n",
       "      <td>0.703216</td>\n",
       "      <td>0.252689</td>\n",
       "      <td>0.497328</td>\n",
       "      <td>-0.583170</td>\n",
       "      <td>-0.131496</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1677</th>\n",
       "      <td>9.305070</td>\n",
       "      <td>-9.837719</td>\n",
       "      <td>-0.443627</td>\n",
       "      <td>2.005294</td>\n",
       "      <td>-0.441083</td>\n",
       "      <td>0.364780</td>\n",
       "      <td>1.460171</td>\n",
       "      <td>-3.615066</td>\n",
       "      <td>3.215915</td>\n",
       "      <td>0.665244</td>\n",
       "      <td>...</td>\n",
       "      <td>0.402348</td>\n",
       "      <td>-0.507237</td>\n",
       "      <td>-0.997598</td>\n",
       "      <td>0.048516</td>\n",
       "      <td>-1.193661</td>\n",
       "      <td>-0.958192</td>\n",
       "      <td>1.629432</td>\n",
       "      <td>-1.901187</td>\n",
       "      <td>1.595384</td>\n",
       "      <td>0.969078</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1678</th>\n",
       "      <td>-3.157728</td>\n",
       "      <td>-1.204468</td>\n",
       "      <td>1.523460</td>\n",
       "      <td>-0.373145</td>\n",
       "      <td>6.847182</td>\n",
       "      <td>-3.987455</td>\n",
       "      <td>-4.480237</td>\n",
       "      <td>1.324709</td>\n",
       "      <td>-1.404899</td>\n",
       "      <td>1.800692</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.444657</td>\n",
       "      <td>0.518785</td>\n",
       "      <td>-0.418380</td>\n",
       "      <td>-0.171931</td>\n",
       "      <td>0.329601</td>\n",
       "      <td>0.282524</td>\n",
       "      <td>0.216190</td>\n",
       "      <td>-0.149364</td>\n",
       "      <td>0.204443</td>\n",
       "      <td>0.372099</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1679</th>\n",
       "      <td>12.579355</td>\n",
       "      <td>-8.781502</td>\n",
       "      <td>-1.834660</td>\n",
       "      <td>2.354436</td>\n",
       "      <td>-2.889661</td>\n",
       "      <td>-1.398139</td>\n",
       "      <td>4.083722</td>\n",
       "      <td>-3.961008</td>\n",
       "      <td>6.569989</td>\n",
       "      <td>2.217482</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.307355</td>\n",
       "      <td>0.233143</td>\n",
       "      <td>-0.221776</td>\n",
       "      <td>0.244149</td>\n",
       "      <td>-0.811210</td>\n",
       "      <td>-2.531757</td>\n",
       "      <td>1.711749</td>\n",
       "      <td>-0.374385</td>\n",
       "      <td>1.911767</td>\n",
       "      <td>0.525103</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1680</th>\n",
       "      <td>7.813546</td>\n",
       "      <td>-6.343183</td>\n",
       "      <td>-4.741265</td>\n",
       "      <td>2.409620</td>\n",
       "      <td>-4.448208</td>\n",
       "      <td>-1.000367</td>\n",
       "      <td>-1.520703</td>\n",
       "      <td>1.694106</td>\n",
       "      <td>-0.749610</td>\n",
       "      <td>0.830364</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.313976</td>\n",
       "      <td>1.186002</td>\n",
       "      <td>0.121576</td>\n",
       "      <td>0.347491</td>\n",
       "      <td>0.469063</td>\n",
       "      <td>0.580393</td>\n",
       "      <td>0.107885</td>\n",
       "      <td>-0.181348</td>\n",
       "      <td>1.594031</td>\n",
       "      <td>-0.602466</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1681</th>\n",
       "      <td>6.257111</td>\n",
       "      <td>-7.971505</td>\n",
       "      <td>-0.029602</td>\n",
       "      <td>1.550161</td>\n",
       "      <td>-1.328804</td>\n",
       "      <td>-1.433166</td>\n",
       "      <td>-0.556121</td>\n",
       "      <td>-1.801222</td>\n",
       "      <td>-0.704170</td>\n",
       "      <td>0.057440</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.630263</td>\n",
       "      <td>0.769126</td>\n",
       "      <td>-0.487230</td>\n",
       "      <td>1.288012</td>\n",
       "      <td>0.165724</td>\n",
       "      <td>0.217653</td>\n",
       "      <td>0.217278</td>\n",
       "      <td>0.671736</td>\n",
       "      <td>1.753214</td>\n",
       "      <td>-1.098941</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1682 rows × 70 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            PC1       PC2       PC3       PC4       PC5       PC6       PC7  \\\n",
       "0     -6.435865 -1.165214  3.444639 -4.015028 -3.586400 -0.972146 -1.330753   \n",
       "1     -7.223818  2.634289  2.219258 -4.102018  0.398284  2.820498  0.264503   \n",
       "2      1.980497  4.538316 -6.554852  0.743492 -2.567119 -1.304740 -4.504168   \n",
       "3     -5.380038 -1.842459  0.095300 -1.456840 -1.683904  0.274600  1.390494   \n",
       "4     -3.728620  0.304802  1.917586 -2.877411 -1.171531 -1.276258  0.473734   \n",
       "...         ...       ...       ...       ...       ...       ...       ...   \n",
       "1677   9.305070 -9.837719 -0.443627  2.005294 -0.441083  0.364780  1.460171   \n",
       "1678  -3.157728 -1.204468  1.523460 -0.373145  6.847182 -3.987455 -4.480237   \n",
       "1679  12.579355 -8.781502 -1.834660  2.354436 -2.889661 -1.398139  4.083722   \n",
       "1680   7.813546 -6.343183 -4.741265  2.409620 -4.448208 -1.000367 -1.520703   \n",
       "1681   6.257111 -7.971505 -0.029602  1.550161 -1.328804 -1.433166 -0.556121   \n",
       "\n",
       "           PC8       PC9      PC10  ...      PC61      PC62      PC63  \\\n",
       "0     4.754884 -1.929031 -0.872592  ...  0.408643  0.564708 -1.028492   \n",
       "1     2.276806  0.564471  0.903378  ... -0.185200 -1.076500 -0.154501   \n",
       "2     0.035021  2.961456  0.391823  ... -0.903900  0.078594  0.314244   \n",
       "3     2.500195 -1.538728 -1.201845  ...  0.749690 -0.384907 -0.333796   \n",
       "4     0.027714  4.291011  1.587343  ...  0.108182  0.869715  0.381031   \n",
       "...        ...       ...       ...  ...       ...       ...       ...   \n",
       "1677 -3.615066  3.215915  0.665244  ...  0.402348 -0.507237 -0.997598   \n",
       "1678  1.324709 -1.404899  1.800692  ... -0.444657  0.518785 -0.418380   \n",
       "1679 -3.961008  6.569989  2.217482  ... -1.307355  0.233143 -0.221776   \n",
       "1680  1.694106 -0.749610  0.830364  ... -1.313976  1.186002  0.121576   \n",
       "1681 -1.801222 -0.704170  0.057440  ... -0.630263  0.769126 -0.487230   \n",
       "\n",
       "          PC64      PC65      PC66      PC67      PC68      PC69      PC70  \n",
       "0     1.442688  0.114789 -0.602954  0.098020 -0.572900 -0.401485 -0.684371  \n",
       "1     0.300433  1.113195 -0.221557  2.550697  0.815038  0.813637  0.055411  \n",
       "2    -0.210039  0.259596 -0.753362  0.152769 -0.170915  0.261304  0.248686  \n",
       "3     0.571784  0.036849 -0.940803 -0.243061 -0.494785  0.398642 -1.059548  \n",
       "4     0.294130  0.879353  0.703216  0.252689  0.497328 -0.583170 -0.131496  \n",
       "...        ...       ...       ...       ...       ...       ...       ...  \n",
       "1677  0.048516 -1.193661 -0.958192  1.629432 -1.901187  1.595384  0.969078  \n",
       "1678 -0.171931  0.329601  0.282524  0.216190 -0.149364  0.204443  0.372099  \n",
       "1679  0.244149 -0.811210 -2.531757  1.711749 -0.374385  1.911767  0.525103  \n",
       "1680  0.347491  0.469063  0.580393  0.107885 -0.181348  1.594031 -0.602466  \n",
       "1681  1.288012  0.165724  0.217653  0.217278  0.671736  1.753214 -1.098941  \n",
       "\n",
       "[1682 rows x 70 columns]"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "screen_descriptors = screen_data.drop(columns=[\"name\", \"smiles\"])\n",
    "screen_descriptors = screen_descriptors.clip(\n",
    "  lower=screen_descriptors.quantile(0.01),\n",
    "  upper=screen_descriptors.quantile(0.99),\n",
    "  axis=1,\n",
    ")\n",
    "screen_descriptors = pd.DataFrame(imputer.transform(screen_descriptors), columns=screen_descriptors.columns, index=screen_descriptors.index)\n",
    "screen_descriptors_pca = pd.DataFrame(pipeline.transform(screen_descriptors), index=screen_descriptors.index, columns=[f\"PC{i}\" for i in range(1, screen_descriptors.shape[1] + 1)]).iloc[:, :n_components]\n",
    "screen_descriptors_pca"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "label\n",
       "0    1656\n",
       "1      26\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "screen_data[\"label\"] = results[\"svm\"].best_estimator_.predict(screen_descriptors_pca)\n",
    "screen_data[\"label\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        vertical-align: middle;\n",
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       "\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>smiles</th>\n",
       "      <th>name</th>\n",
       "      <th>MaxAbsEStateIndex</th>\n",
       "      <th>MaxEStateIndex</th>\n",
       "      <th>MinAbsEStateIndex</th>\n",
       "      <th>MinEStateIndex</th>\n",
       "      <th>qed</th>\n",
       "      <th>SPS</th>\n",
       "      <th>MolWt</th>\n",
       "      <th>HeavyAtomMolWt</th>\n",
       "      <th>...</th>\n",
       "      <th>fr_sulfonamd</th>\n",
       "      <th>fr_sulfone</th>\n",
       "      <th>fr_term_acetylene</th>\n",
       "      <th>fr_tetrazole</th>\n",
       "      <th>fr_thiazole</th>\n",
       "      <th>fr_thiocyan</th>\n",
       "      <th>fr_thiophene</th>\n",
       "      <th>fr_unbrch_alkane</th>\n",
       "      <th>fr_urea</th>\n",
       "      <th>label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>CN1CCc2cccc3c2[C@H]1Cc1ccc(O)c(O)c1-3.CN1CCc2c...</td>\n",
       "      <td>R(-) Apomorphine hydrochloride hemihydrate</td>\n",
       "      <td>10.260322</td>\n",
       "      <td>10.260322</td>\n",
       "      <td>0.016275</td>\n",
       "      <td>-0.037993</td>\n",
       "      <td>0.216641</td>\n",
       "      <td>20.350000</td>\n",
       "      <td>534.656</td>\n",
       "      <td>500.384</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>224</th>\n",
       "      <td>CN1CCc2c(c3ccccc3n2Cc2ccccc2)C1.CN1CCc2c(c3ccc...</td>\n",
       "      <td>Mebhydroline 1,5-naphtalenedisulfonate</td>\n",
       "      <td>2.520833</td>\n",
       "      <td>2.520833</td>\n",
       "      <td>0.972620</td>\n",
       "      <td>0.972620</td>\n",
       "      <td>0.227285</td>\n",
       "      <td>15.285714</td>\n",
       "      <td>552.766</td>\n",
       "      <td>512.446</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>260</th>\n",
       "      <td>c1ccc2[nH]c(-c3cscn3)nc2c1</td>\n",
       "      <td>Tiabendazole</td>\n",
       "      <td>4.443796</td>\n",
       "      <td>4.443796</td>\n",
       "      <td>0.840231</td>\n",
       "      <td>0.840231</td>\n",
       "      <td>0.657283</td>\n",
       "      <td>10.857143</td>\n",
       "      <td>201.254</td>\n",
       "      <td>194.198</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>321</th>\n",
       "      <td>NCCNCCNCCNCCN</td>\n",
       "      <td>Tetraethylenepentamine pentahydrochloride</td>\n",
       "      <td>5.319533</td>\n",
       "      <td>5.319533</td>\n",
       "      <td>0.706404</td>\n",
       "      <td>0.706404</td>\n",
       "      <td>0.248876</td>\n",
       "      <td>10.615385</td>\n",
       "      <td>189.307</td>\n",
       "      <td>166.123</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>530</th>\n",
       "      <td>Cc1cc(C=Cc2ccc3cc(N(C)C)ccc3[n+]2C)c(C)n1-c1cc...</td>\n",
       "      <td>Pyrvinium pamoate</td>\n",
       "      <td>2.308375</td>\n",
       "      <td>2.308375</td>\n",
       "      <td>1.180201</td>\n",
       "      <td>1.180201</td>\n",
       "      <td>0.143965</td>\n",
       "      <td>11.482759</td>\n",
       "      <td>765.062</td>\n",
       "      <td>708.614</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>544</th>\n",
       "      <td>CN(C/C=C/c1ccccc1)Cc1cccc2ccccc12</td>\n",
       "      <td>Naftifine hydrochloride</td>\n",
       "      <td>2.334537</td>\n",
       "      <td>2.334537</td>\n",
       "      <td>0.942575</td>\n",
       "      <td>0.942575</td>\n",
       "      <td>0.639079</td>\n",
       "      <td>11.545455</td>\n",
       "      <td>287.406</td>\n",
       "      <td>266.238</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>612</th>\n",
       "      <td>Nc1c2ccccc2nc2ccccc12</td>\n",
       "      <td>Aminacrine</td>\n",
       "      <td>6.114630</td>\n",
       "      <td>6.114630</td>\n",
       "      <td>0.815926</td>\n",
       "      <td>0.815926</td>\n",
       "      <td>0.558615</td>\n",
       "      <td>10.933333</td>\n",
       "      <td>194.237</td>\n",
       "      <td>184.157</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>642</th>\n",
       "      <td>C=C1CC[C@H](O)C/C1=C/C=C1\\CCC[C@]2(C)[C@@H]([C...</td>\n",
       "      <td>Cholecalciferol</td>\n",
       "      <td>10.023784</td>\n",
       "      <td>10.023784</td>\n",
       "      <td>0.171925</td>\n",
       "      <td>-0.171925</td>\n",
       "      <td>0.499104</td>\n",
       "      <td>37.642857</td>\n",
       "      <td>384.648</td>\n",
       "      <td>340.296</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>673</th>\n",
       "      <td>Oc1cc(O)c2c(c1)O[C@H](c1ccc(O)c(O)c1)[C@@H](O)C2</td>\n",
       "      <td>Cianidanol</td>\n",
       "      <td>10.180343</td>\n",
       "      <td>10.180343</td>\n",
       "      <td>0.142543</td>\n",
       "      <td>-0.933439</td>\n",
       "      <td>0.509593</td>\n",
       "      <td>20.619048</td>\n",
       "      <td>290.271</td>\n",
       "      <td>276.159</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>679</th>\n",
       "      <td>O=C(c1cc(O)c(O)c(O)c1)c1ccc(O)c(O)c1O</td>\n",
       "      <td>Exifone</td>\n",
       "      <td>12.089798</td>\n",
       "      <td>12.089798</td>\n",
       "      <td>0.223898</td>\n",
       "      <td>-0.858266</td>\n",
       "      <td>0.357576</td>\n",
       "      <td>10.400000</td>\n",
       "      <td>278.216</td>\n",
       "      <td>268.136</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1080</th>\n",
       "      <td>NCCCCCCCCCCN</td>\n",
       "      <td>1,10-Diaminodecane</td>\n",
       "      <td>5.395080</td>\n",
       "      <td>5.395080</td>\n",
       "      <td>0.854339</td>\n",
       "      <td>0.854339</td>\n",
       "      <td>0.522778</td>\n",
       "      <td>10.500000</td>\n",
       "      <td>172.316</td>\n",
       "      <td>148.124</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1093</th>\n",
       "      <td>CC[n+]1c(/C=C/C=C2/N(C)c3ccccc3C2(C)C)sc2ccccc...</td>\n",
       "      <td>AC-93253 iodide</td>\n",
       "      <td>2.382204</td>\n",
       "      <td>2.382204</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.406318</td>\n",
       "      <td>16.888889</td>\n",
       "      <td>488.438</td>\n",
       "      <td>463.238</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1128</th>\n",
       "      <td>Cc1c2ccncc2c(C)c2c1[nH]c1ccccc12</td>\n",
       "      <td>Ellipticine</td>\n",
       "      <td>4.275863</td>\n",
       "      <td>4.275863</td>\n",
       "      <td>1.200894</td>\n",
       "      <td>1.200894</td>\n",
       "      <td>0.486410</td>\n",
       "      <td>11.684211</td>\n",
       "      <td>246.313</td>\n",
       "      <td>232.201</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1158</th>\n",
       "      <td>CCCc1c(-c2ccc(O)cc2)nn(-c2ccc(O)cc2)c1-c1ccc(O...</td>\n",
       "      <td>1,3,5-tris(4-hydroxyphenyl)-4-propyl-1H-pyrazole</td>\n",
       "      <td>9.728438</td>\n",
       "      <td>9.728438</td>\n",
       "      <td>0.194101</td>\n",
       "      <td>0.194101</td>\n",
       "      <td>0.436223</td>\n",
       "      <td>10.931034</td>\n",
       "      <td>386.451</td>\n",
       "      <td>364.275</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1175</th>\n",
       "      <td>Cc1nccc2c1[nH]c1ccccc12</td>\n",
       "      <td>Harmane</td>\n",
       "      <td>4.271326</td>\n",
       "      <td>4.271326</td>\n",
       "      <td>1.054352</td>\n",
       "      <td>1.054352</td>\n",
       "      <td>0.568427</td>\n",
       "      <td>11.214286</td>\n",
       "      <td>182.226</td>\n",
       "      <td>172.146</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1228</th>\n",
       "      <td>O=C(/C=C/c1ccc(O)cc1)c1ccc(O)cc1O</td>\n",
       "      <td>Isoliquiritigenin</td>\n",
       "      <td>11.839243</td>\n",
       "      <td>11.839243</td>\n",
       "      <td>0.100404</td>\n",
       "      <td>-0.369838</td>\n",
       "      <td>0.582503</td>\n",
       "      <td>10.736842</td>\n",
       "      <td>256.257</td>\n",
       "      <td>244.161</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1265</th>\n",
       "      <td>CCCCCCCCCCCC(=O)c1c(O)c(C(=O)CCCCCCCCCCC)c(O)c...</td>\n",
       "      <td>YM-26734</td>\n",
       "      <td>13.854692</td>\n",
       "      <td>13.854692</td>\n",
       "      <td>0.020670</td>\n",
       "      <td>-0.766417</td>\n",
       "      <td>0.045227</td>\n",
       "      <td>15.207547</td>\n",
       "      <td>730.983</td>\n",
       "      <td>668.487</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1268</th>\n",
       "      <td>O=c1c(O)c(-c2cc(O)c(O)c(O)c2)oc2cc(O)cc(O)c12</td>\n",
       "      <td>Myricetin</td>\n",
       "      <td>12.165302</td>\n",
       "      <td>12.165302</td>\n",
       "      <td>0.099877</td>\n",
       "      <td>-0.966848</td>\n",
       "      <td>0.370750</td>\n",
       "      <td>10.956522</td>\n",
       "      <td>318.237</td>\n",
       "      <td>308.157</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1313</th>\n",
       "      <td>COc1ccc(-c2cc(=O)c3c(O)c(OC)c(OC)cc3o2)cc1O</td>\n",
       "      <td>Eupatorin</td>\n",
       "      <td>12.465943</td>\n",
       "      <td>12.465943</td>\n",
       "      <td>0.014691</td>\n",
       "      <td>-0.453694</td>\n",
       "      <td>0.750638</td>\n",
       "      <td>10.680000</td>\n",
       "      <td>344.319</td>\n",
       "      <td>328.191</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1363</th>\n",
       "      <td>C[C@H](Cc1ccc(O)c(O)c1)[C@@H](C)Cc1ccc(O)c(O)c1</td>\n",
       "      <td>Nordihydroguaiaretic acid from Larrea divarica...</td>\n",
       "      <td>9.541867</td>\n",
       "      <td>9.541867</td>\n",
       "      <td>0.096472</td>\n",
       "      <td>-0.107389</td>\n",
       "      <td>0.636536</td>\n",
       "      <td>13.727273</td>\n",
       "      <td>302.370</td>\n",
       "      <td>280.194</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1385</th>\n",
       "      <td>Oc1cc(O)cc(/C=C/c2ccc(O)c(O)c2)c1</td>\n",
       "      <td>Piceatannol</td>\n",
       "      <td>9.321635</td>\n",
       "      <td>9.321635</td>\n",
       "      <td>0.024812</td>\n",
       "      <td>-0.198235</td>\n",
       "      <td>0.483345</td>\n",
       "      <td>10.888889</td>\n",
       "      <td>244.246</td>\n",
       "      <td>232.150</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1430</th>\n",
       "      <td>c1cnc2c(c1)ccc1cccnc12</td>\n",
       "      <td>1,10-Phenanthroline monohydrate</td>\n",
       "      <td>4.346852</td>\n",
       "      <td>4.346852</td>\n",
       "      <td>0.977407</td>\n",
       "      <td>0.977407</td>\n",
       "      <td>0.496980</td>\n",
       "      <td>10.857143</td>\n",
       "      <td>180.210</td>\n",
       "      <td>172.146</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1572</th>\n",
       "      <td>O=C1c2c(O)cc(O)cc2OC(c2ccc(O)c(O)c2)C1O</td>\n",
       "      <td>(?)-Taxifolin</td>\n",
       "      <td>12.200745</td>\n",
       "      <td>12.200745</td>\n",
       "      <td>0.078366</td>\n",
       "      <td>-1.604735</td>\n",
       "      <td>0.500531</td>\n",
       "      <td>20.318182</td>\n",
       "      <td>304.254</td>\n",
       "      <td>292.158</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1583</th>\n",
       "      <td>C[C@]12C=CC(=O)C=C1CC[C@@H]1C2=CC[C@]2(C)[C@@H...</td>\n",
       "      <td>U-74389G maleate</td>\n",
       "      <td>14.030275</td>\n",
       "      <td>14.030275</td>\n",
       "      <td>0.039440</td>\n",
       "      <td>-1.257037</td>\n",
       "      <td>0.276843</td>\n",
       "      <td>31.018868</td>\n",
       "      <td>726.919</td>\n",
       "      <td>672.487</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1652</th>\n",
       "      <td>CC(=O)c1c(O)c(C)c(O)c(Cc2c(O)c3c(c(C(=O)/C=C/c...</td>\n",
       "      <td>Rottlerin</td>\n",
       "      <td>13.404842</td>\n",
       "      <td>13.404842</td>\n",
       "      <td>0.015077</td>\n",
       "      <td>-0.840709</td>\n",
       "      <td>0.216438</td>\n",
       "      <td>13.789474</td>\n",
       "      <td>516.546</td>\n",
       "      <td>488.322</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1678</th>\n",
       "      <td>O=c1c(O)c(-c2ccc(O)c(O)c2)oc2cc(O)ccc12</td>\n",
       "      <td>Fisetin</td>\n",
       "      <td>12.082041</td>\n",
       "      <td>12.082041</td>\n",
       "      <td>0.087182</td>\n",
       "      <td>-0.653875</td>\n",
       "      <td>0.510622</td>\n",
       "      <td>10.857143</td>\n",
       "      <td>286.239</td>\n",
       "      <td>276.159</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>26 rows × 213 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                 smiles  \\\n",
       "49    CN1CCc2cccc3c2[C@H]1Cc1ccc(O)c(O)c1-3.CN1CCc2c...   \n",
       "224   CN1CCc2c(c3ccccc3n2Cc2ccccc2)C1.CN1CCc2c(c3ccc...   \n",
       "260                          c1ccc2[nH]c(-c3cscn3)nc2c1   \n",
       "321                                       NCCNCCNCCNCCN   \n",
       "530   Cc1cc(C=Cc2ccc3cc(N(C)C)ccc3[n+]2C)c(C)n1-c1cc...   \n",
       "544                   CN(C/C=C/c1ccccc1)Cc1cccc2ccccc12   \n",
       "612                               Nc1c2ccccc2nc2ccccc12   \n",
       "642   C=C1CC[C@H](O)C/C1=C/C=C1\\CCC[C@]2(C)[C@@H]([C...   \n",
       "673    Oc1cc(O)c2c(c1)O[C@H](c1ccc(O)c(O)c1)[C@@H](O)C2   \n",
       "679               O=C(c1cc(O)c(O)c(O)c1)c1ccc(O)c(O)c1O   \n",
       "1080                                       NCCCCCCCCCCN   \n",
       "1093  CC[n+]1c(/C=C/C=C2/N(C)c3ccccc3C2(C)C)sc2ccccc...   \n",
       "1128                   Cc1c2ccncc2c(C)c2c1[nH]c1ccccc12   \n",
       "1158  CCCc1c(-c2ccc(O)cc2)nn(-c2ccc(O)cc2)c1-c1ccc(O...   \n",
       "1175                            Cc1nccc2c1[nH]c1ccccc12   \n",
       "1228                  O=C(/C=C/c1ccc(O)cc1)c1ccc(O)cc1O   \n",
       "1265  CCCCCCCCCCCC(=O)c1c(O)c(C(=O)CCCCCCCCCCC)c(O)c...   \n",
       "1268      O=c1c(O)c(-c2cc(O)c(O)c(O)c2)oc2cc(O)cc(O)c12   \n",
       "1313        COc1ccc(-c2cc(=O)c3c(O)c(OC)c(OC)cc3o2)cc1O   \n",
       "1363    C[C@H](Cc1ccc(O)c(O)c1)[C@@H](C)Cc1ccc(O)c(O)c1   \n",
       "1385                  Oc1cc(O)cc(/C=C/c2ccc(O)c(O)c2)c1   \n",
       "1430                             c1cnc2c(c1)ccc1cccnc12   \n",
       "1572            O=C1c2c(O)cc(O)cc2OC(c2ccc(O)c(O)c2)C1O   \n",
       "1583  C[C@]12C=CC(=O)C=C1CC[C@@H]1C2=CC[C@]2(C)[C@@H...   \n",
       "1652  CC(=O)c1c(O)c(C)c(O)c(Cc2c(O)c3c(c(C(=O)/C=C/c...   \n",
       "1678            O=c1c(O)c(-c2ccc(O)c(O)c2)oc2cc(O)ccc12   \n",
       "\n",
       "                                                   name  MaxAbsEStateIndex  \\\n",
       "49           R(-) Apomorphine hydrochloride hemihydrate          10.260322   \n",
       "224              Mebhydroline 1,5-naphtalenedisulfonate           2.520833   \n",
       "260                                        Tiabendazole           4.443796   \n",
       "321           Tetraethylenepentamine pentahydrochloride           5.319533   \n",
       "530                                   Pyrvinium pamoate           2.308375   \n",
       "544                             Naftifine hydrochloride           2.334537   \n",
       "612                                          Aminacrine           6.114630   \n",
       "642                                     Cholecalciferol          10.023784   \n",
       "673                                          Cianidanol          10.180343   \n",
       "679                                             Exifone          12.089798   \n",
       "1080                                 1,10-Diaminodecane           5.395080   \n",
       "1093                                    AC-93253 iodide           2.382204   \n",
       "1128                                        Ellipticine           4.275863   \n",
       "1158   1,3,5-tris(4-hydroxyphenyl)-4-propyl-1H-pyrazole           9.728438   \n",
       "1175                                            Harmane           4.271326   \n",
       "1228                                  Isoliquiritigenin          11.839243   \n",
       "1265                                           YM-26734          13.854692   \n",
       "1268                                          Myricetin          12.165302   \n",
       "1313                                          Eupatorin          12.465943   \n",
       "1363  Nordihydroguaiaretic acid from Larrea divarica...           9.541867   \n",
       "1385                                        Piceatannol           9.321635   \n",
       "1430                    1,10-Phenanthroline monohydrate           4.346852   \n",
       "1572                                      (?)-Taxifolin          12.200745   \n",
       "1583                                   U-74389G maleate          14.030275   \n",
       "1652                                          Rottlerin          13.404842   \n",
       "1678                                            Fisetin          12.082041   \n",
       "\n",
       "      MaxEStateIndex  MinAbsEStateIndex  MinEStateIndex       qed        SPS  \\\n",
       "49         10.260322           0.016275       -0.037993  0.216641  20.350000   \n",
       "224         2.520833           0.972620        0.972620  0.227285  15.285714   \n",
       "260         4.443796           0.840231        0.840231  0.657283  10.857143   \n",
       "321         5.319533           0.706404        0.706404  0.248876  10.615385   \n",
       "530         2.308375           1.180201        1.180201  0.143965  11.482759   \n",
       "544         2.334537           0.942575        0.942575  0.639079  11.545455   \n",
       "612         6.114630           0.815926        0.815926  0.558615  10.933333   \n",
       "642        10.023784           0.171925       -0.171925  0.499104  37.642857   \n",
       "673        10.180343           0.142543       -0.933439  0.509593  20.619048   \n",
       "679        12.089798           0.223898       -0.858266  0.357576  10.400000   \n",
       "1080        5.395080           0.854339        0.854339  0.522778  10.500000   \n",
       "1093        2.382204           0.000000        0.000000  0.406318  16.888889   \n",
       "1128        4.275863           1.200894        1.200894  0.486410  11.684211   \n",
       "1158        9.728438           0.194101        0.194101  0.436223  10.931034   \n",
       "1175        4.271326           1.054352        1.054352  0.568427  11.214286   \n",
       "1228       11.839243           0.100404       -0.369838  0.582503  10.736842   \n",
       "1265       13.854692           0.020670       -0.766417  0.045227  15.207547   \n",
       "1268       12.165302           0.099877       -0.966848  0.370750  10.956522   \n",
       "1313       12.465943           0.014691       -0.453694  0.750638  10.680000   \n",
       "1363        9.541867           0.096472       -0.107389  0.636536  13.727273   \n",
       "1385        9.321635           0.024812       -0.198235  0.483345  10.888889   \n",
       "1430        4.346852           0.977407        0.977407  0.496980  10.857143   \n",
       "1572       12.200745           0.078366       -1.604735  0.500531  20.318182   \n",
       "1583       14.030275           0.039440       -1.257037  0.276843  31.018868   \n",
       "1652       13.404842           0.015077       -0.840709  0.216438  13.789474   \n",
       "1678       12.082041           0.087182       -0.653875  0.510622  10.857143   \n",
       "\n",
       "        MolWt  HeavyAtomMolWt  ...  fr_sulfonamd  fr_sulfone  \\\n",
       "49    534.656         500.384  ...           0.0         0.0   \n",
       "224   552.766         512.446  ...           0.0         0.0   \n",
       "260   201.254         194.198  ...           0.0         0.0   \n",
       "321   189.307         166.123  ...           0.0         0.0   \n",
       "530   765.062         708.614  ...           0.0         0.0   \n",
       "544   287.406         266.238  ...           0.0         0.0   \n",
       "612   194.237         184.157  ...           0.0         0.0   \n",
       "642   384.648         340.296  ...           0.0         0.0   \n",
       "673   290.271         276.159  ...           0.0         0.0   \n",
       "679   278.216         268.136  ...           0.0         0.0   \n",
       "1080  172.316         148.124  ...           0.0         0.0   \n",
       "1093  488.438         463.238  ...           0.0         0.0   \n",
       "1128  246.313         232.201  ...           0.0         0.0   \n",
       "1158  386.451         364.275  ...           0.0         0.0   \n",
       "1175  182.226         172.146  ...           0.0         0.0   \n",
       "1228  256.257         244.161  ...           0.0         0.0   \n",
       "1265  730.983         668.487  ...           0.0         0.0   \n",
       "1268  318.237         308.157  ...           0.0         0.0   \n",
       "1313  344.319         328.191  ...           0.0         0.0   \n",
       "1363  302.370         280.194  ...           0.0         0.0   \n",
       "1385  244.246         232.150  ...           0.0         0.0   \n",
       "1430  180.210         172.146  ...           0.0         0.0   \n",
       "1572  304.254         292.158  ...           0.0         0.0   \n",
       "1583  726.919         672.487  ...           0.0         0.0   \n",
       "1652  516.546         488.322  ...           0.0         0.0   \n",
       "1678  286.239         276.159  ...           0.0         0.0   \n",
       "\n",
       "      fr_term_acetylene  fr_tetrazole  fr_thiazole  fr_thiocyan  fr_thiophene  \\\n",
       "49                  0.0           0.0          0.0          0.0           0.0   \n",
       "224                 0.0           0.0          0.0          0.0           0.0   \n",
       "260                 0.0           0.0          1.0          0.0           0.0   \n",
       "321                 0.0           0.0          0.0          0.0           0.0   \n",
       "530                 0.0           0.0          0.0          0.0           0.0   \n",
       "544                 0.0           0.0          0.0          0.0           0.0   \n",
       "612                 0.0           0.0          0.0          0.0           0.0   \n",
       "642                 0.0           0.0          0.0          0.0           0.0   \n",
       "673                 0.0           0.0          0.0          0.0           0.0   \n",
       "679                 0.0           0.0          0.0          0.0           0.0   \n",
       "1080                0.0           0.0          0.0          0.0           0.0   \n",
       "1093                0.0           0.0          1.0          0.0           0.0   \n",
       "1128                0.0           0.0          0.0          0.0           0.0   \n",
       "1158                0.0           0.0          0.0          0.0           0.0   \n",
       "1175                0.0           0.0          0.0          0.0           0.0   \n",
       "1228                0.0           0.0          0.0          0.0           0.0   \n",
       "1265                0.0           0.0          0.0          0.0           0.0   \n",
       "1268                0.0           0.0          0.0          0.0           0.0   \n",
       "1313                0.0           0.0          0.0          0.0           0.0   \n",
       "1363                0.0           0.0          0.0          0.0           0.0   \n",
       "1385                0.0           0.0          0.0          0.0           0.0   \n",
       "1430                0.0           0.0          0.0          0.0           0.0   \n",
       "1572                0.0           0.0          0.0          0.0           0.0   \n",
       "1583                0.0           0.0          0.0          0.0           0.0   \n",
       "1652                0.0           0.0          0.0          0.0           0.0   \n",
       "1678                0.0           0.0          0.0          0.0           0.0   \n",
       "\n",
       "      fr_unbrch_alkane  fr_urea  label  \n",
       "49                 0.0      0.0      1  \n",
       "224                0.0      0.0      1  \n",
       "260                0.0      0.0      1  \n",
       "321                8.0      0.0      1  \n",
       "530                0.0      0.0      1  \n",
       "544                0.0      0.0      1  \n",
       "612                0.0      0.0      1  \n",
       "642                0.0      0.0      1  \n",
       "673                0.0      0.0      1  \n",
       "679                0.0      0.0      1  \n",
       "1080               7.0      0.0      1  \n",
       "1093               0.0      0.0      1  \n",
       "1128               0.0      0.0      1  \n",
       "1158               0.0      0.0      1  \n",
       "1175               0.0      0.0      1  \n",
       "1228               0.0      0.0      1  \n",
       "1265              14.0      0.0      1  \n",
       "1268               0.0      0.0      1  \n",
       "1313               0.0      0.0      1  \n",
       "1363               0.0      0.0      1  \n",
       "1385               0.0      0.0      1  \n",
       "1430               0.0      0.0      1  \n",
       "1572               0.0      0.0      1  \n",
       "1583               0.0      0.0      1  \n",
       "1652               0.0      0.0      1  \n",
       "1678               0.0      0.0      1  \n",
       "\n",
       "[26 rows x 213 columns]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "screen_data[screen_data[\"label\"] == 1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "While we're not able to perform wet lab experiments to verify the predictions, a quick literature search reveals that many of the compounds have aging-related effects, and some, like [myricetin](https://pubmed.ncbi.nlm.nih.gov/25965778/), are indeed telomerase inhibitors! This suggests that our model has some predictive power and could be used to guide further experiments. While not all of the compounds are telomerase inhibitors, our model has reduced the search space from 1656 to just 26, which is a significant improvement.\n",
    "\n",
    "To summarize, we have built a machine learning model to predict telomerase inhibitors and used it to screen a chemical library for potential telomerase inhibitors. Despite its modest performance, the model has identified several compounds that could be used as leads for drug discovery.\n",
    "\n",
    "The poor performance of the models suggests the need for more data and perhaps better features. In particular, we could consider using more advanced featurization techniques like graph neural networks to capture deeper structural information about the compounds. We could also try more advanced models like deep neural networks to see if they can learn better representations of the existing molecular fingerprints.\n",
    "\n",
    "However, the fact that simple traditional machine learning models can be useful in computational drug discovery is already a promising result. And more than anything, it's exciting to be able to contribute to medical research from the comfort of our laptops."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Acknowledgments\n",
    "\n",
    "This project is heavily inspired by the [Discovery of senolytics using machine learning](https://www.nature.com/articles/s41467-023-39120-1) paper by Vanessa Smer-Barreto et al. The authors used machine learning to find potential senolytics (compounds that selectively kill aging cells) and then validated the predictions in vitro, discovering three powerful new senolytics.\n",
    "\n",
    "The simplicity of their ML pipeline was quite surprising, hardly requiring any domain-specific knowledge or machine learning skills what we covered in this course, so we wanted to replicate their work with a different target. We did not use any of their code, but our analysis naturally converged to theirs. However, we would like to thank the authors for sharing their data, which included the Prestwick and LOPAC libraries that we used for our screening, saving us the trouble of purchasing them."
   ]
  }
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