analysis.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "af3ffeb2-b9ef-4eb5-be42-71969701aed3",
   "metadata": {},
   "outputs": [],
   "source": [
    "from CPT.model import *\n",
    "from CPT.utils import *\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "cc301b76-6870-4545-968d-3781b4b8664f",
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "dbabfc42-d910-4a21-a394-a35d7d6cbe76",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define global variables\n",
    "all_genes = ['CALM1', 'TPK1', 'MTHR', 'SUMO1', 'UBC9']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f1a67511-b1d8-486a-a307-8e56fea94199",
   "metadata": {
    "tags": []
   },
   "source": [
    "### Regression performance on DMS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "eee280de-8cb0-4cd3-b567-884322deb993",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loaded 1962 variants in CALM1\n",
      "Loaded 3424 variants in TPK1\n",
      "Loaded 11947 variants in MTHR\n",
      "Loaded 1801 variants in SUMO1\n",
      "Loaded 2721 variants in UBC9\n"
     ]
    }
   ],
   "source": [
    "# Load DMS data\n",
    "feat_mat, label = load_data(all_genes, binarize = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "1ba9bdcc-f27d-4ce1-bcb2-c80503a1903f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test on gene CALM1\n",
      "All selected features: ['esm_1v', 'evolutionary_index_ASM', '100verte_mut_freq', '30mammal_mut_freq', 'vanilla_protmpnn_norm', 'conditioned_mt_score', 'BLOSUM1', 'Z1', 'BLOSUM7']\n",
      "Test on gene TPK1\n",
      "All selected features: ['esm_1v', 'evolutionary_index_ASM', '100verte_mut_freq', '30mammal_mut_freq', 'vanilla_protmpnn_norm', 'conditioned_wt_score', 'BLOSUM1', 'Z1', 'ST6']\n",
      "Test on gene MTHR\n",
      "All selected features: ['esm_1v', 'evolutionary_index_ASM', '100verte_mut_freq', '30mammal_mut_freq', 'vanilla_protmpnn_norm', 'conditioned_wt_score', 'BLOSUM1', 'ST6', 'VHSE3', 'MSWHIM2']\n",
      "Test on gene SUMO1\n",
      "All selected features: ['esm_1v', 'evolutionary_index_ASM', '100verte_mut_freq', '30mammal_wt_freq', 'vanilla_protmpnn_norm', 'conditioned_wt_score', 'PP1', 'Z5']\n",
      "Test on gene UBC9\n",
      "All selected features: ['esm_1v', 'evolutionary_index_ASM', '100verte_mut_freq', '30mammal_mut_freq', 'vanilla_protmpnn_norm', 'conditioned_wt_score', 'BLOSUM7', 'KF5', 'VHSE7', 'KF10']\n"
     ]
    },
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CALM1</th>\n",
       "      <th>MTHR</th>\n",
       "      <th>SUMO1</th>\n",
       "      <th>TPK1</th>\n",
       "      <th>UBC9</th>\n",
       "      <th>Avg</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>CPT-1</th>\n",
       "      <td>0.29700</td>\n",
       "      <td>0.33150</td>\n",
       "      <td>0.63690</td>\n",
       "      <td>0.37200</td>\n",
       "      <td>0.57800</td>\n",
       "      <td>0.44308</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>esm_1v</th>\n",
       "      <td>0.31811</td>\n",
       "      <td>0.31443</td>\n",
       "      <td>0.58013</td>\n",
       "      <td>0.39565</td>\n",
       "      <td>0.55790</td>\n",
       "      <td>0.43324</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EVE_scores_ASM</th>\n",
       "      <td>-0.29145</td>\n",
       "      <td>-0.29295</td>\n",
       "      <td>-0.60559</td>\n",
       "      <td>-0.31403</td>\n",
       "      <td>-0.57433</td>\n",
       "      <td>0.41567</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>evolutionary_index_ASM</th>\n",
       "      <td>-0.29114</td>\n",
       "      <td>-0.29389</td>\n",
       "      <td>-0.61464</td>\n",
       "      <td>-0.31599</td>\n",
       "      <td>-0.57464</td>\n",
       "      <td>0.41806</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100verte_gap_freq</th>\n",
       "      <td>-0.04541</td>\n",
       "      <td>-0.25709</td>\n",
       "      <td>0.20035</td>\n",
       "      <td>0.01087</td>\n",
       "      <td>-0.02229</td>\n",
       "      <td>0.02271</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100verte_wt_freq</th>\n",
       "      <td>-0.01560</td>\n",
       "      <td>-0.13102</td>\n",
       "      <td>-0.19360</td>\n",
       "      <td>-0.12562</td>\n",
       "      <td>0.03904</td>\n",
       "      <td>0.08536</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100verte_mut_freq</th>\n",
       "      <td>0.21690</td>\n",
       "      <td>0.16231</td>\n",
       "      <td>0.37367</td>\n",
       "      <td>0.34208</td>\n",
       "      <td>0.26276</td>\n",
       "      <td>0.27154</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30mammal_gap_freq</th>\n",
       "      <td>0.04414</td>\n",
       "      <td>-0.18624</td>\n",
       "      <td>0.09755</td>\n",
       "      <td>-0.04367</td>\n",
       "      <td>-0.01413</td>\n",
       "      <td>0.02047</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30mammal_wt_freq</th>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.07664</td>\n",
       "      <td>-0.11220</td>\n",
       "      <td>-0.09827</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.09570</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30mammal_mut_freq</th>\n",
       "      <td>0.21239</td>\n",
       "      <td>0.13859</td>\n",
       "      <td>0.37505</td>\n",
       "      <td>0.30426</td>\n",
       "      <td>0.29887</td>\n",
       "      <td>0.26583</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>conditioned_wt_score</th>\n",
       "      <td>-0.04451</td>\n",
       "      <td>-0.12713</td>\n",
       "      <td>-0.29195</td>\n",
       "      <td>-0.10397</td>\n",
       "      <td>-0.27310</td>\n",
       "      <td>0.16813</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>conditioned_mt_score</th>\n",
       "      <td>0.21125</td>\n",
       "      <td>0.29118</td>\n",
       "      <td>0.49426</td>\n",
       "      <td>0.26407</td>\n",
       "      <td>0.45350</td>\n",
       "      <td>0.34285</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vanilla_protmpnn_norm</th>\n",
       "      <td>0.16844</td>\n",
       "      <td>0.27814</td>\n",
       "      <td>0.52639</td>\n",
       "      <td>0.27075</td>\n",
       "      <td>0.39979</td>\n",
       "      <td>0.32870</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ca_protmpnn_norm</th>\n",
       "      <td>0.18220</td>\n",
       "      <td>0.27265</td>\n",
       "      <td>0.51319</td>\n",
       "      <td>0.26859</td>\n",
       "      <td>0.37546</td>\n",
       "      <td>0.32242</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ca_only_protmpnn_norm</th>\n",
       "      <td>0.17373</td>\n",
       "      <td>0.24065</td>\n",
       "      <td>0.50822</td>\n",
       "      <td>0.26609</td>\n",
       "      <td>0.36579</td>\n",
       "      <td>0.31090</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          CALM1     MTHR    SUMO1     TPK1     UBC9      Avg\n",
       "CPT-1                   0.29700  0.33150  0.63690  0.37200  0.57800  0.44308\n",
       "esm_1v                  0.31811  0.31443  0.58013  0.39565  0.55790  0.43324\n",
       "EVE_scores_ASM         -0.29145 -0.29295 -0.60559 -0.31403 -0.57433  0.41567\n",
       "evolutionary_index_ASM -0.29114 -0.29389 -0.61464 -0.31599 -0.57464  0.41806\n",
       "100verte_gap_freq      -0.04541 -0.25709  0.20035  0.01087 -0.02229  0.02271\n",
       "100verte_wt_freq       -0.01560 -0.13102 -0.19360 -0.12562  0.03904  0.08536\n",
       "100verte_mut_freq       0.21690  0.16231  0.37367  0.34208  0.26276  0.27154\n",
       "30mammal_gap_freq       0.04414 -0.18624  0.09755 -0.04367 -0.01413  0.02047\n",
       "30mammal_wt_freq            NaN -0.07664 -0.11220 -0.09827      NaN  0.09570\n",
       "30mammal_mut_freq       0.21239  0.13859  0.37505  0.30426  0.29887  0.26583\n",
       "conditioned_wt_score   -0.04451 -0.12713 -0.29195 -0.10397 -0.27310  0.16813\n",
       "conditioned_mt_score    0.21125  0.29118  0.49426  0.26407  0.45350  0.34285\n",
       "vanilla_protmpnn_norm   0.16844  0.27814  0.52639  0.27075  0.39979  0.32870\n",
       "ca_protmpnn_norm        0.18220  0.27265  0.51319  0.26859  0.37546  0.32242\n",
       "ca_only_protmpnn_norm   0.17373  0.24065  0.50822  0.26609  0.36579  0.31090"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Table: performance on DMS genes\n",
    "feat_selected = {}\n",
    "corr = {}\n",
    "corr['CPT-1'] = {}\n",
    "\n",
    "for test_gene in all_genes:\n",
    "    print('Test on gene', test_gene)\n",
    "    train_genes = list(set(all_genes) - set([test_gene]))\n",
    "\n",
    "    # select features using training genes\n",
    "    feat_selected[test_gene] = select_feat_by_category(feat_mat, label, train_genes, \n",
    "                                                       model = 'regressor', tol = 2e-3, verbose = False)\n",
    "    \n",
    "    # predict on the test gene\n",
    "    cpt = CPTRegressor()\n",
    "    cpt.train(feat_mat, label, train_genes, feat_selected[test_gene])\n",
    "    pred = cpt.predict(feat_mat[test_gene][feat_selected[test_gene]])\n",
    "    corr['CPT-1'][test_gene] = round(stats.spearmanr(pred, label[test_gene])[0], 4)\n",
    "\n",
    "corr = pd.DataFrame(corr).transpose()\n",
    "corr['Avg'] = corr.mean(axis = 1)\n",
    "corr_mat = cross_validate(feat_mat, label, all_genes, model = 'regressor',\n",
    "                          feat_performance = True).drop(['transfer_model'], axis = 0)\n",
    "corr_mat = corr_mat.loc[['esm_1v'] + eve_feats + msa_feats + struct_feats + mpnn_feats]\n",
    "corr_mat = pd.concat([corr, corr_mat])\n",
    "corr_mat"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "44258377",
   "metadata": {},
   "source": [
    "#### Test on more genes from ProteinGym"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "9a14103e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Predict more genes in ProteinGym?\n",
    "extra_genes = ['KCNH2', 'SCN5A', 'SC6A4', 'RASH', 'SYUA', 'PTEN', \n",
    "               'VKOR1', 'A4', 'P53', 'MSH2', 'TPOR', 'BRCA1', 'YAP1']\n",
    "\n",
    "for gene in extra_genes:\n",
    "    feat_mat[gene] = pd.read_csv(f'./data/additional_dms/feat_mat_{gene}.csv', index_col = 0)\n",
    "    label[gene] = pd.read_csv(f'./data/additional_dms/score_{gene}.csv', index_col = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "96240b7f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Selecting features in the category 100verte_msa\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 7/7 [00:02<00:00,  2.90it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best features: ('100verte_mut_freq',) Avg metric on DMS: 0.43916\n",
      "Selecting features in the category 30mammal_msa\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 7/7 [00:02<00:00,  2.94it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best features: ('30mammal_mut_freq',) Avg metric on DMS: 0.43848\n",
      "Selecting features in the category mpnn\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 7/7 [00:02<00:00,  2.88it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best features: ('vanilla_protmpnn_norm',) Avg metric on DMS: 0.44035\n",
      "Selecting features in the category struct\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 3/3 [00:01<00:00,  2.58it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best features: ('conditioned_wt_score',) Avg metric on DMS: 0.43922\n",
      "0 ['BLOSUM1'] 0.44357\n",
      "1 ['BLOSUM1', 'ST6'] 0.44641\n",
      "2 ['BLOSUM1', 'ST6', 'VHSE6'] 0.44772\n",
      "All selected features: ['esm_1v', 'evolutionary_index_ASM', '100verte_mut_freq', '30mammal_mut_freq', 'vanilla_protmpnn_norm', 'conditioned_wt_score', 'BLOSUM1', 'ST6', 'VHSE6']\n"
     ]
    },
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A4</th>\n",
       "      <th>BRCA1</th>\n",
       "      <th>KCNH2</th>\n",
       "      <th>MSH2</th>\n",
       "      <th>P53</th>\n",
       "      <th>PTEN</th>\n",
       "      <th>RASH</th>\n",
       "      <th>SC6A4</th>\n",
       "      <th>SCN5A</th>\n",
       "      <th>SYUA</th>\n",
       "      <th>TPOR</th>\n",
       "      <th>VKOR1</th>\n",
       "      <th>YAP1</th>\n",
       "      <th>Avg</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>CPT-1</th>\n",
       "      <td>0.44860</td>\n",
       "      <td>0.56860</td>\n",
       "      <td>0.39270</td>\n",
       "      <td>0.42080</td>\n",
       "      <td>0.56080</td>\n",
       "      <td>0.51350</td>\n",
       "      <td>0.47560</td>\n",
       "      <td>0.57190</td>\n",
       "      <td>0.12080</td>\n",
       "      <td>0.30390</td>\n",
       "      <td>0.44760</td>\n",
       "      <td>0.53960</td>\n",
       "      <td>0.54890</td>\n",
       "      <td>0.454869</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>esm_1v</th>\n",
       "      <td>0.43529</td>\n",
       "      <td>0.44976</td>\n",
       "      <td>0.29482</td>\n",
       "      <td>0.40018</td>\n",
       "      <td>0.55259</td>\n",
       "      <td>0.45422</td>\n",
       "      <td>0.40489</td>\n",
       "      <td>0.54230</td>\n",
       "      <td>0.13549</td>\n",
       "      <td>0.26653</td>\n",
       "      <td>0.39967</td>\n",
       "      <td>0.47378</td>\n",
       "      <td>0.44075</td>\n",
       "      <td>0.403870</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EVE_scores_ASM</th>\n",
       "      <td>-0.34342</td>\n",
       "      <td>-0.51509</td>\n",
       "      <td>-0.02333</td>\n",
       "      <td>-0.39441</td>\n",
       "      <td>-0.45182</td>\n",
       "      <td>-0.35965</td>\n",
       "      <td>-0.48025</td>\n",
       "      <td>-0.51973</td>\n",
       "      <td>-0.08233</td>\n",
       "      <td>-0.15615</td>\n",
       "      <td>-0.35242</td>\n",
       "      <td>-0.46381</td>\n",
       "      <td>-0.61665</td>\n",
       "      <td>0.366080</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>evolutionary_index_ASM</th>\n",
       "      <td>-0.34342</td>\n",
       "      <td>-0.51581</td>\n",
       "      <td>-0.02826</td>\n",
       "      <td>-0.39429</td>\n",
       "      <td>-0.45070</td>\n",
       "      <td>-0.36404</td>\n",
       "      <td>-0.48051</td>\n",
       "      <td>-0.52316</td>\n",
       "      <td>-0.08465</td>\n",
       "      <td>-0.15515</td>\n",
       "      <td>-0.35242</td>\n",
       "      <td>-0.46536</td>\n",
       "      <td>-0.61665</td>\n",
       "      <td>0.367260</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100verte_gap_freq</th>\n",
       "      <td>0.40694</td>\n",
       "      <td>-0.13666</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.11625</td>\n",
       "      <td>0.63874</td>\n",
       "      <td>0.08372</td>\n",
       "      <td>0.09417</td>\n",
       "      <td>0.20864</td>\n",
       "      <td>0.14001</td>\n",
       "      <td>0.15293</td>\n",
       "      <td>0.03194</td>\n",
       "      <td>0.12481</td>\n",
       "      <td>-0.17067</td>\n",
       "      <td>0.140900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100verte_wt_freq</th>\n",
       "      <td>-0.26228</td>\n",
       "      <td>-0.42326</td>\n",
       "      <td>0.14871</td>\n",
       "      <td>-0.24381</td>\n",
       "      <td>-0.28606</td>\n",
       "      <td>-0.27595</td>\n",
       "      <td>0.04578</td>\n",
       "      <td>-0.31523</td>\n",
       "      <td>0.00902</td>\n",
       "      <td>-0.05549</td>\n",
       "      <td>-0.13236</td>\n",
       "      <td>-0.21957</td>\n",
       "      <td>-0.53608</td>\n",
       "      <td>0.195890</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100verte_mut_freq</th>\n",
       "      <td>0.12902</td>\n",
       "      <td>0.35669</td>\n",
       "      <td>0.03142</td>\n",
       "      <td>0.14522</td>\n",
       "      <td>0.08369</td>\n",
       "      <td>0.11979</td>\n",
       "      <td>0.01903</td>\n",
       "      <td>0.23295</td>\n",
       "      <td>0.06214</td>\n",
       "      <td>0.05274</td>\n",
       "      <td>0.23124</td>\n",
       "      <td>0.24640</td>\n",
       "      <td>0.16544</td>\n",
       "      <td>0.144290</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30mammal_gap_freq</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.03888</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.13148</td>\n",
       "      <td>0.32798</td>\n",
       "      <td>0.10798</td>\n",
       "      <td>-0.05327</td>\n",
       "      <td>-0.10774</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.16243</td>\n",
       "      <td>0.09576</td>\n",
       "      <td>0.05007</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.018420</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30mammal_wt_freq</th>\n",
       "      <td>-0.12259</td>\n",
       "      <td>-0.32339</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.15667</td>\n",
       "      <td>-0.29898</td>\n",
       "      <td>-0.06172</td>\n",
       "      <td>-0.03278</td>\n",
       "      <td>-0.25351</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.06260</td>\n",
       "      <td>-0.24962</td>\n",
       "      <td>-0.19417</td>\n",
       "      <td>-0.06870</td>\n",
       "      <td>0.165880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30mammal_mut_freq</th>\n",
       "      <td>0.05700</td>\n",
       "      <td>0.25054</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.04734</td>\n",
       "      <td>0.03044</td>\n",
       "      <td>0.02878</td>\n",
       "      <td>0.04751</td>\n",
       "      <td>0.09252</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.02429</td>\n",
       "      <td>0.14787</td>\n",
       "      <td>0.14808</td>\n",
       "      <td>0.04505</td>\n",
       "      <td>0.083580</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>conditioned_wt_score</th>\n",
       "      <td>-0.31218</td>\n",
       "      <td>-0.30810</td>\n",
       "      <td>0.08401</td>\n",
       "      <td>-0.22421</td>\n",
       "      <td>-0.38295</td>\n",
       "      <td>-0.24094</td>\n",
       "      <td>-0.21965</td>\n",
       "      <td>-0.22017</td>\n",
       "      <td>-0.04511</td>\n",
       "      <td>-0.11188</td>\n",
       "      <td>-0.06918</td>\n",
       "      <td>-0.04740</td>\n",
       "      <td>-0.27378</td>\n",
       "      <td>0.182430</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>conditioned_mt_score</th>\n",
       "      <td>0.35218</td>\n",
       "      <td>0.38792</td>\n",
       "      <td>0.29080</td>\n",
       "      <td>0.34044</td>\n",
       "      <td>0.27954</td>\n",
       "      <td>0.36347</td>\n",
       "      <td>0.44065</td>\n",
       "      <td>0.46115</td>\n",
       "      <td>-0.05639</td>\n",
       "      <td>0.25419</td>\n",
       "      <td>0.35434</td>\n",
       "      <td>0.35238</td>\n",
       "      <td>0.42527</td>\n",
       "      <td>0.326610</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vanilla_protmpnn_norm</th>\n",
       "      <td>0.27046</td>\n",
       "      <td>0.43791</td>\n",
       "      <td>0.31881</td>\n",
       "      <td>0.25626</td>\n",
       "      <td>0.44565</td>\n",
       "      <td>0.47245</td>\n",
       "      <td>0.37484</td>\n",
       "      <td>0.48646</td>\n",
       "      <td>0.04403</td>\n",
       "      <td>0.22275</td>\n",
       "      <td>0.35942</td>\n",
       "      <td>0.50327</td>\n",
       "      <td>0.41517</td>\n",
       "      <td>0.354420</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ca_protmpnn_norm</th>\n",
       "      <td>0.30828</td>\n",
       "      <td>0.42245</td>\n",
       "      <td>0.31461</td>\n",
       "      <td>0.25781</td>\n",
       "      <td>0.43951</td>\n",
       "      <td>0.48217</td>\n",
       "      <td>0.36590</td>\n",
       "      <td>0.48757</td>\n",
       "      <td>0.09929</td>\n",
       "      <td>0.07586</td>\n",
       "      <td>0.29910</td>\n",
       "      <td>0.50311</td>\n",
       "      <td>0.41811</td>\n",
       "      <td>0.344140</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ca_only_protmpnn_norm</th>\n",
       "      <td>-0.04255</td>\n",
       "      <td>0.41879</td>\n",
       "      <td>0.15635</td>\n",
       "      <td>0.25208</td>\n",
       "      <td>0.36956</td>\n",
       "      <td>0.43178</td>\n",
       "      <td>0.37740</td>\n",
       "      <td>0.40113</td>\n",
       "      <td>0.12342</td>\n",
       "      <td>-0.00940</td>\n",
       "      <td>0.13769</td>\n",
       "      <td>0.25589</td>\n",
       "      <td>0.41680</td>\n",
       "      <td>0.253000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                             A4    BRCA1    KCNH2     MSH2      P53     PTEN  \\\n",
       "CPT-1                   0.44860  0.56860  0.39270  0.42080  0.56080  0.51350   \n",
       "esm_1v                  0.43529  0.44976  0.29482  0.40018  0.55259  0.45422   \n",
       "EVE_scores_ASM         -0.34342 -0.51509 -0.02333 -0.39441 -0.45182 -0.35965   \n",
       "evolutionary_index_ASM -0.34342 -0.51581 -0.02826 -0.39429 -0.45070 -0.36404   \n",
       "100verte_gap_freq       0.40694 -0.13666      NaN  0.11625  0.63874  0.08372   \n",
       "100verte_wt_freq       -0.26228 -0.42326  0.14871 -0.24381 -0.28606 -0.27595   \n",
       "100verte_mut_freq       0.12902  0.35669  0.03142  0.14522  0.08369  0.11979   \n",
       "30mammal_gap_freq           NaN  0.03888      NaN -0.13148  0.32798  0.10798   \n",
       "30mammal_wt_freq       -0.12259 -0.32339      NaN -0.15667 -0.29898 -0.06172   \n",
       "30mammal_mut_freq       0.05700  0.25054      NaN  0.04734  0.03044  0.02878   \n",
       "conditioned_wt_score   -0.31218 -0.30810  0.08401 -0.22421 -0.38295 -0.24094   \n",
       "conditioned_mt_score    0.35218  0.38792  0.29080  0.34044  0.27954  0.36347   \n",
       "vanilla_protmpnn_norm   0.27046  0.43791  0.31881  0.25626  0.44565  0.47245   \n",
       "ca_protmpnn_norm        0.30828  0.42245  0.31461  0.25781  0.43951  0.48217   \n",
       "ca_only_protmpnn_norm  -0.04255  0.41879  0.15635  0.25208  0.36956  0.43178   \n",
       "\n",
       "                           RASH    SC6A4    SCN5A     SYUA     TPOR    VKOR1  \\\n",
       "CPT-1                   0.47560  0.57190  0.12080  0.30390  0.44760  0.53960   \n",
       "esm_1v                  0.40489  0.54230  0.13549  0.26653  0.39967  0.47378   \n",
       "EVE_scores_ASM         -0.48025 -0.51973 -0.08233 -0.15615 -0.35242 -0.46381   \n",
       "evolutionary_index_ASM -0.48051 -0.52316 -0.08465 -0.15515 -0.35242 -0.46536   \n",
       "100verte_gap_freq       0.09417  0.20864  0.14001  0.15293  0.03194  0.12481   \n",
       "100verte_wt_freq        0.04578 -0.31523  0.00902 -0.05549 -0.13236 -0.21957   \n",
       "100verte_mut_freq       0.01903  0.23295  0.06214  0.05274  0.23124  0.24640   \n",
       "30mammal_gap_freq      -0.05327 -0.10774      NaN -0.16243  0.09576  0.05007   \n",
       "30mammal_wt_freq       -0.03278 -0.25351      NaN -0.06260 -0.24962 -0.19417   \n",
       "30mammal_mut_freq       0.04751  0.09252      NaN  0.02429  0.14787  0.14808   \n",
       "conditioned_wt_score   -0.21965 -0.22017 -0.04511 -0.11188 -0.06918 -0.04740   \n",
       "conditioned_mt_score    0.44065  0.46115 -0.05639  0.25419  0.35434  0.35238   \n",
       "vanilla_protmpnn_norm   0.37484  0.48646  0.04403  0.22275  0.35942  0.50327   \n",
       "ca_protmpnn_norm        0.36590  0.48757  0.09929  0.07586  0.29910  0.50311   \n",
       "ca_only_protmpnn_norm   0.37740  0.40113  0.12342 -0.00940  0.13769  0.25589   \n",
       "\n",
       "                           YAP1       Avg  \n",
       "CPT-1                   0.54890  0.454869  \n",
       "esm_1v                  0.44075  0.403870  \n",
       "EVE_scores_ASM         -0.61665  0.366080  \n",
       "evolutionary_index_ASM -0.61665  0.367260  \n",
       "100verte_gap_freq      -0.17067  0.140900  \n",
       "100verte_wt_freq       -0.53608  0.195890  \n",
       "100verte_mut_freq       0.16544  0.144290  \n",
       "30mammal_gap_freq           NaN  0.018420  \n",
       "30mammal_wt_freq       -0.06870  0.165880  \n",
       "30mammal_mut_freq       0.04505  0.083580  \n",
       "conditioned_wt_score   -0.27378  0.182430  \n",
       "conditioned_mt_score    0.42527  0.326610  \n",
       "vanilla_protmpnn_norm   0.41517  0.354420  \n",
       "ca_protmpnn_norm        0.41811  0.344140  \n",
       "ca_only_protmpnn_norm   0.41680  0.253000  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Table: performance on additional DMS genes\n",
    "feat_selected = {}\n",
    "corr = {}\n",
    "corr['CPT-1'] = {}\n",
    "\n",
    "feat_selected_dms = select_feat_by_category(feat_mat, label, all_genes, \n",
    "                                            model = 'regressor', tol = 2e-3, verbose = True)\n",
    "\n",
    "# predict on the test gene\n",
    "cpt = CPTRegressor()\n",
    "cpt.train(feat_mat, label, all_genes, feat_selected_dms)\n",
    "\n",
    "for test_gene in extra_genes:\n",
    "    pred = cpt.predict(feat_mat[test_gene][feat_selected_dms])\n",
    "    corr['CPT-1'][test_gene] = round(stats.spearmanr(pred, label[test_gene])[0], 4)\n",
    "\n",
    "corr = pd.DataFrame(corr).transpose()\n",
    "corr['Avg'] = corr.mean(axis = 1)\n",
    "corr_mat = cross_validate(feat_mat, label, extra_genes, model = 'regressor',\n",
    "                          feat_performance = True).drop(['transfer_model'], axis = 0)\n",
    "corr_mat = corr_mat.loc[['esm_1v'] + eve_feats + msa_feats + struct_feats + mpnn_feats]\n",
    "corr_mat = pd.concat([corr, corr_mat])\n",
    "corr_mat"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "05c20d26-ba1b-493a-ad47-5f778b576d1c",
   "metadata": {},
   "source": [
    "### Classification performance on ClinVar"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "f55ec1f4-c10c-4499-b156-64f120c847cc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loaded 785 positive variants and 785 negative variants in CALM1\n",
      "Loaded 1370 positive variants and 1370 negative variants in TPK1\n",
      "Loaded 4769 positive variants and 4779 negative variants in MTHR\n",
      "Loaded 720 positive variants and 721 negative variants in SUMO1\n",
      "Loaded 1088 positive variants and 1089 negative variants in UBC9\n"
     ]
    }
   ],
   "source": [
    "# Load DMS data and binaize\n",
    "feat_mat, label = load_data(all_genes, binarize = True, quantile = 0.4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "97de0343-4b2e-4c2d-bbd0-e0f57ebab050",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loaded 785 positive variants and 785 negative variants in CALM1\n",
      "Loaded 1370 positive variants and 1370 negative variants in TPK1\n",
      "Loaded 4769 positive variants and 4779 negative variants in MTHR\n",
      "Loaded 720 positive variants and 721 negative variants in SUMO1\n",
      "Loaded 1088 positive variants and 1089 negative variants in UBC9\n"
     ]
    }
   ],
   "source": [
    "# Load cross-gene imputed DMS data and binaize\n",
    "feat_mat_xgi, label_xgi = load_data(all_genes, binarize = True, quantile = 0.4, cross_impute = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "138845c3-4f7f-4b62-8ebd-09b044695305",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Selecting features in the category 100verte_msa\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 7/7 [00:13<00:00,  1.91s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best features: ('100verte_mut_freq',) Avg metric on DMS: 0.78502\n",
      "Selecting features in the category 30mammal_msa\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 7/7 [00:03<00:00,  1.81it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best features: ('30mammal_mut_freq',) Avg metric on DMS: 0.78406\n",
      "Selecting features in the category mpnn\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 7/7 [00:04<00:00,  1.46it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best features: ('vanilla_protmpnn_norm',) Avg metric on DMS: 0.78406\n",
      "Selecting features in the category struct\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 3/3 [00:02<00:00,  1.41it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best features: ('conditioned_wt_score',) Avg metric on DMS: 0.78364\n",
      "0 ['BLOSUM1'] 0.78655\n",
      "1 ['BLOSUM1', 'ST6'] 0.7887\n",
      "2 ['BLOSUM1', 'ST6', 'VHSE6'] 0.79004\n",
      "All selected features: ['esm_1v', 'evolutionary_index_ASM', '100verte_mut_freq', '30mammal_mut_freq', 'vanilla_protmpnn_norm', 'conditioned_wt_score', 'BLOSUM1', 'ST6', 'VHSE6']\n"
     ]
    }
   ],
   "source": [
    "# Select features based on DMS experiment labels\n",
    "feat_selected_dms = select_feat_by_category(feat_mat, label, all_genes)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "439832e7",
   "metadata": {},
   "source": [
    "#### Load ClinVar benchmark dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "15716ccd",
   "metadata": {},
   "outputs": [],
   "source": [
    "x = pd.read_csv('./data/since2017_one_star_clinvar_x.csv', index_col = 0)\n",
    "y = pd.read_csv('./data/since2017_one_star_clinvar_y.csv', index_col = 0)\n",
    "# pred_VESPAl = pd.read_csv('./data/since2017_one_star_clinvar_VESPAl_pred.csv', index_col = 0)\n",
    "# pred_GEMME = pd.read_csv('./data/since2017_one_star_clinvar_GEMME_pred.csv', index_col = 0)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "6f0e4e99",
   "metadata": {},
   "source": [
    "#### Run benchmark"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "40f27c0c-ad21-4294-ab73-bf6472f514d7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Positive features: ['evolutionary_index_ASM', 'EVE_scores_ASM', 'PP1', 'KF1', 'KF2', 'KF4', 'KF5', 'KF6', 'Z1', 'Z2', 'Z3', 'Z4', 'Z5', 'F5', 'T1', 'T2', 'T3', 'VHSE2', 'VHSE3', 'VHSE4', 'VHSE6', 'VHSE7', 'VHSE8', 'ProtFP2', 'ProtFP3', 'ST1', 'ST2', 'ST5', 'ST6', 'ST7', 'ST8', 'BLOSUM1', 'BLOSUM2', 'BLOSUM5', 'BLOSUM9', 'BLOSUM10', 'MSWHIM1', 'MSWHIM3', '100verte_gap_freq', '100verte_wt_freq', '30mammal_gap_freq', '30mammal_wt_freq', 'conditioned_wt_score']\n",
      "Negative features: ['esm_1v', 'PP2', 'PP3', 'KF3', 'KF7', 'KF8', 'KF9', 'KF10', 'F1', 'F2', 'F3', 'F4', 'F6', 'T4', 'T5', 'VHSE1', 'VHSE5', 'ProtFP1', 'ProtFP4', 'ProtFP5', 'ProtFP6', 'ProtFP7', 'ProtFP8', 'ST3', 'ST4', 'BLOSUM3', 'BLOSUM4', 'BLOSUM6', 'BLOSUM7', 'BLOSUM8', 'MSWHIM2', '100verte_mut_freq', '30mammal_mut_freq', 'conditioned_mt_score', 'vanilla_protmpnn_norm', 'ca_protmpnn_norm', 'ca_only_protmpnn_norm']\n"
     ]
    }
   ],
   "source": [
    "# Determine features positively/negatively associated with variant effect using DMS results\n",
    "auroc_feat = {}\n",
    "for gene in all_genes:\n",
    "    auroc_feat[gene] = {}\n",
    "    for feat in feat_mat[gene].columns:\n",
    "        auroc_feat[gene][feat] = roc_auc_score(label[gene], feat_mat[gene][feat])\n",
    "auroc_feat = pd.DataFrame(auroc_feat)\n",
    "auroc_feat['Avg'] = auroc_feat.mean(axis = 1)\n",
    "pos_feats = auroc_feat[auroc_feat['Avg'] < 0.5].index.to_list()\n",
    "neg_feats = auroc_feat[auroc_feat['Avg'] >= 0.5].index.to_list()\n",
    "print(f\"Positive features: {pos_feats}\")\n",
    "print(f\"Negative features: {neg_feats}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "4ba80c6c-66f1-4f22-bd6b-89e436bf9d7e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Features selected on DMS ['esm_1v', 'evolutionary_index_ASM', '100verte_mut_freq', '30mammal_mut_freq', 'vanilla_protmpnn_norm', 'conditioned_wt_score', 'BLOSUM1', 'ST6', 'VHSE6']\n",
      "Features w/o msa ['BLOSUM1', 'ST6', 'conditioned_wt_score', 'vanilla_protmpnn_norm', 'esm_1v', 'evolutionary_index_ASM', 'VHSE6']\n",
      "Features w/o struct ['BLOSUM1', 'ST6', '100verte_mut_freq', 'esm_1v', '30mammal_mut_freq', 'evolutionary_index_ASM', 'VHSE6']\n"
     ]
    }
   ],
   "source": [
    "# Construct prediction matrix\n",
    "pred = pd.DataFrame(index = y.index)\n",
    "\n",
    "cpt = CPTClassifier()\n",
    "# Transfer model predictions\n",
    "features = feat_selected_dms\n",
    "print('Features selected on DMS', features)\n",
    "cpt.train(feat_mat, label, all_genes, features)\n",
    "pred_clinvar = cpt.predict(x[features])\n",
    "pred['Transfer'] = 1 - pred_clinvar.values\n",
    "\n",
    "# pred['VESPAl'] = pred_VESPAl\n",
    "# pred['GEMME'] = -pred_GEMME\n",
    "\n",
    "# Transfer model predictions without MSA features\n",
    "features = list(set(feat_selected_dms) - set(msa_feats))\n",
    "print('Features w/o msa', features)\n",
    "cpt.train(feat_mat, label, all_genes, features)\n",
    "pred_clinvar = cpt.predict(x[features])\n",
    "pred['Transfer_no_msa'] = 1 - pred_clinvar.values\n",
    "\n",
    "# Transfer model predictions without structural features\n",
    "features = list(set(feat_selected_dms) - set(struct_feats + mpnn_feats))\n",
    "print('Features w/o struct', features)\n",
    "cpt.train(feat_mat, label, all_genes, features)\n",
    "pred_clinvar = cpt.predict(x[features])\n",
    "pred['Transfer_no_struct'] = 1 - pred_clinvar.values\n",
    "\n",
    "# Individual features\n",
    "for feat in list(set(pos_feats) - set(aa_feats)):\n",
    "    pred[feat] = x[feat]\n",
    "for feat in list(set(neg_feats) - set(aa_feats)):\n",
    "    pred[feat] = -x[feat]\n",
    "\n",
    "feat_list = pos_feats + neg_feats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "0f7f25f0-7e0f-4400-90c4-9656dcbaaaaa",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "274 genes used to calculate per gene AUROC out of 1298 genes\n",
      "Calculating...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 274/274 [00:04<00:00, 57.25it/s]\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Transfer</th>\n",
       "      <th>Transfer_no_struct</th>\n",
       "      <th>Transfer_no_msa</th>\n",
       "      <th>EVE_scores_ASM</th>\n",
       "      <th>evolutionary_index_ASM</th>\n",
       "      <th>esm_1v</th>\n",
       "      <th>100verte_wt_freq</th>\n",
       "      <th>conditioned_mt_score</th>\n",
       "      <th>vanilla_protmpnn_norm</th>\n",
       "      <th>ca_protmpnn_norm</th>\n",
       "      <th>ca_only_protmpnn_norm</th>\n",
       "      <th>100verte_mut_freq</th>\n",
       "      <th>conditioned_wt_score</th>\n",
       "      <th>30mammal_wt_freq</th>\n",
       "      <th>30mammal_mut_freq</th>\n",
       "      <th>30mammal_gap_freq</th>\n",
       "      <th>100verte_gap_freq</th>\n",
       "      <th>n</th>\n",
       "      <th>n_pos</th>\n",
       "      <th>n_neg</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Global_AUROC</th>\n",
       "      <td>0.9343</td>\n",
       "      <td>0.9301</td>\n",
       "      <td>0.9254</td>\n",
       "      <td>0.8896</td>\n",
       "      <td>0.8860</td>\n",
       "      <td>0.8742</td>\n",
       "      <td>0.8649</td>\n",
       "      <td>0.8257</td>\n",
       "      <td>0.7813</td>\n",
       "      <td>0.7812</td>\n",
       "      <td>0.7649</td>\n",
       "      <td>0.7454</td>\n",
       "      <td>0.7194</td>\n",
       "      <td>0.7101</td>\n",
       "      <td>0.6195</td>\n",
       "      <td>0.4869</td>\n",
       "      <td>0.3345</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Avg_per_gene_AUROC</th>\n",
       "      <td>0.9339</td>\n",
       "      <td>0.9323</td>\n",
       "      <td>0.9247</td>\n",
       "      <td>0.9193</td>\n",
       "      <td>0.9196</td>\n",
       "      <td>0.8765</td>\n",
       "      <td>0.8649</td>\n",
       "      <td>0.8438</td>\n",
       "      <td>0.7648</td>\n",
       "      <td>0.7626</td>\n",
       "      <td>0.7458</td>\n",
       "      <td>0.7529</td>\n",
       "      <td>0.8073</td>\n",
       "      <td>0.7027</td>\n",
       "      <td>0.6254</td>\n",
       "      <td>0.4781</td>\n",
       "      <td>0.3371</td>\n",
       "      <td>53.9927</td>\n",
       "      <td>28.0401</td>\n",
       "      <td>25.9526</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ABCA3</th>\n",
       "      <td>0.9479</td>\n",
       "      <td>0.9375</td>\n",
       "      <td>0.9375</td>\n",
       "      <td>0.9844</td>\n",
       "      <td>0.9844</td>\n",
       "      <td>0.6667</td>\n",
       "      <td>0.8594</td>\n",
       "      <td>0.8958</td>\n",
       "      <td>0.9635</td>\n",
       "      <td>0.9479</td>\n",
       "      <td>0.8854</td>\n",
       "      <td>0.9115</td>\n",
       "      <td>0.7396</td>\n",
       "      <td>0.8438</td>\n",
       "      <td>0.8620</td>\n",
       "      <td>0.4740</td>\n",
       "      <td>0.5312</td>\n",
       "      <td>28.0000</td>\n",
       "      <td>12.0000</td>\n",
       "      <td>16.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ABCA4</th>\n",
       "      <td>0.9515</td>\n",
       "      <td>0.9497</td>\n",
       "      <td>0.9382</td>\n",
       "      <td>0.9466</td>\n",
       "      <td>0.9466</td>\n",
       "      <td>0.7832</td>\n",
       "      <td>0.9423</td>\n",
       "      <td>0.9141</td>\n",
       "      <td>0.8893</td>\n",
       "      <td>0.8745</td>\n",
       "      <td>0.8235</td>\n",
       "      <td>0.9160</td>\n",
       "      <td>0.8646</td>\n",
       "      <td>0.9137</td>\n",
       "      <td>0.7161</td>\n",
       "      <td>0.5062</td>\n",
       "      <td>0.3337</td>\n",
       "      <td>283.0000</td>\n",
       "      <td>16.0000</td>\n",
       "      <td>267.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ABCC8</th>\n",
       "      <td>0.9736</td>\n",
       "      <td>0.9665</td>\n",
       "      <td>0.9648</td>\n",
       "      <td>0.9437</td>\n",
       "      <td>0.9489</td>\n",
       "      <td>0.9049</td>\n",
       "      <td>0.9102</td>\n",
       "      <td>0.8398</td>\n",
       "      <td>0.7817</td>\n",
       "      <td>0.8398</td>\n",
       "      <td>0.8486</td>\n",
       "      <td>0.7447</td>\n",
       "      <td>0.8697</td>\n",
       "      <td>0.5625</td>\n",
       "      <td>0.5625</td>\n",
       "      <td>0.6294</td>\n",
       "      <td>0.3512</td>\n",
       "      <td>79.0000</td>\n",
       "      <td>8.0000</td>\n",
       "      <td>71.0000</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WFS1</th>\n",
       "      <td>0.9048</td>\n",
       "      <td>0.9351</td>\n",
       "      <td>0.8939</td>\n",
       "      <td>0.9589</td>\n",
       "      <td>0.9589</td>\n",
       "      <td>0.5455</td>\n",
       "      <td>0.9177</td>\n",
       "      <td>0.8658</td>\n",
       "      <td>0.7619</td>\n",
       "      <td>0.7359</td>\n",
       "      <td>0.7056</td>\n",
       "      <td>0.6667</td>\n",
       "      <td>0.7771</td>\n",
       "      <td>0.6656</td>\n",
       "      <td>0.5455</td>\n",
       "      <td>0.3831</td>\n",
       "      <td>0.3874</td>\n",
       "      <td>53.0000</td>\n",
       "      <td>11.0000</td>\n",
       "      <td>42.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WWOX</th>\n",
       "      <td>0.7347</td>\n",
       "      <td>0.7551</td>\n",
       "      <td>0.6939</td>\n",
       "      <td>0.6735</td>\n",
       "      <td>0.6327</td>\n",
       "      <td>0.7347</td>\n",
       "      <td>0.5000</td>\n",
       "      <td>0.6327</td>\n",
       "      <td>0.3265</td>\n",
       "      <td>0.4082</td>\n",
       "      <td>0.3469</td>\n",
       "      <td>0.5714</td>\n",
       "      <td>0.7551</td>\n",
       "      <td>0.5714</td>\n",
       "      <td>0.5000</td>\n",
       "      <td>0.4286</td>\n",
       "      <td>0.3980</td>\n",
       "      <td>14.0000</td>\n",
       "      <td>7.0000</td>\n",
       "      <td>7.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ZC4H2</th>\n",
       "      <td>1.0000</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>0.9500</td>\n",
       "      <td>0.6250</td>\n",
       "      <td>0.9000</td>\n",
       "      <td>0.9000</td>\n",
       "      <td>0.8750</td>\n",
       "      <td>0.8500</td>\n",
       "      <td>0.5000</td>\n",
       "      <td>0.9250</td>\n",
       "      <td>0.5000</td>\n",
       "      <td>0.5000</td>\n",
       "      <td>0.3750</td>\n",
       "      <td>0.7125</td>\n",
       "      <td>14.0000</td>\n",
       "      <td>4.0000</td>\n",
       "      <td>10.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ZEB2</th>\n",
       "      <td>1.0000</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>0.9988</td>\n",
       "      <td>0.9988</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>0.9051</td>\n",
       "      <td>0.8872</td>\n",
       "      <td>0.9292</td>\n",
       "      <td>0.9471</td>\n",
       "      <td>0.9327</td>\n",
       "      <td>0.6392</td>\n",
       "      <td>0.9528</td>\n",
       "      <td>0.5823</td>\n",
       "      <td>0.5190</td>\n",
       "      <td>0.4873</td>\n",
       "      <td>0.1948</td>\n",
       "      <td>101.0000</td>\n",
       "      <td>79.0000</td>\n",
       "      <td>22.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ZMIZ1</th>\n",
       "      <td>0.6750</td>\n",
       "      <td>0.7375</td>\n",
       "      <td>0.6625</td>\n",
       "      <td>0.7500</td>\n",
       "      <td>0.7500</td>\n",
       "      <td>0.6875</td>\n",
       "      <td>0.5812</td>\n",
       "      <td>0.7375</td>\n",
       "      <td>0.5750</td>\n",
       "      <td>0.4625</td>\n",
       "      <td>0.7000</td>\n",
       "      <td>0.5750</td>\n",
       "      <td>0.8000</td>\n",
       "      <td>0.5000</td>\n",
       "      <td>0.5000</td>\n",
       "      <td>0.6250</td>\n",
       "      <td>0.8062</td>\n",
       "      <td>24.0000</td>\n",
       "      <td>20.0000</td>\n",
       "      <td>4.0000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>276 rows × 20 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                    Transfer  Transfer_no_struct  Transfer_no_msa  \\\n",
       "Global_AUROC          0.9343              0.9301           0.9254   \n",
       "Avg_per_gene_AUROC    0.9339              0.9323           0.9247   \n",
       "ABCA3                 0.9479              0.9375           0.9375   \n",
       "ABCA4                 0.9515              0.9497           0.9382   \n",
       "ABCC8                 0.9736              0.9665           0.9648   \n",
       "...                      ...                 ...              ...   \n",
       "WFS1                  0.9048              0.9351           0.8939   \n",
       "WWOX                  0.7347              0.7551           0.6939   \n",
       "ZC4H2                 1.0000              1.0000           1.0000   \n",
       "ZEB2                  1.0000              1.0000           1.0000   \n",
       "ZMIZ1                 0.6750              0.7375           0.6625   \n",
       "\n",
       "                    EVE_scores_ASM  evolutionary_index_ASM  esm_1v  \\\n",
       "Global_AUROC                0.8896                  0.8860  0.8742   \n",
       "Avg_per_gene_AUROC          0.9193                  0.9196  0.8765   \n",
       "ABCA3                       0.9844                  0.9844  0.6667   \n",
       "ABCA4                       0.9466                  0.9466  0.7832   \n",
       "ABCC8                       0.9437                  0.9489  0.9049   \n",
       "...                            ...                     ...     ...   \n",
       "WFS1                        0.9589                  0.9589  0.5455   \n",
       "WWOX                        0.6735                  0.6327  0.7347   \n",
       "ZC4H2                       1.0000                  1.0000  0.9500   \n",
       "ZEB2                        0.9988                  0.9988  1.0000   \n",
       "ZMIZ1                       0.7500                  0.7500  0.6875   \n",
       "\n",
       "                    100verte_wt_freq  conditioned_mt_score  \\\n",
       "Global_AUROC                  0.8649                0.8257   \n",
       "Avg_per_gene_AUROC            0.8649                0.8438   \n",
       "ABCA3                         0.8594                0.8958   \n",
       "ABCA4                         0.9423                0.9141   \n",
       "ABCC8                         0.9102                0.8398   \n",
       "...                              ...                   ...   \n",
       "WFS1                          0.9177                0.8658   \n",
       "WWOX                          0.5000                0.6327   \n",
       "ZC4H2                         0.6250                0.9000   \n",
       "ZEB2                          0.9051                0.8872   \n",
       "ZMIZ1                         0.5812                0.7375   \n",
       "\n",
       "                    vanilla_protmpnn_norm  ca_protmpnn_norm  \\\n",
       "Global_AUROC                       0.7813            0.7812   \n",
       "Avg_per_gene_AUROC                 0.7648            0.7626   \n",
       "ABCA3                              0.9635            0.9479   \n",
       "ABCA4                              0.8893            0.8745   \n",
       "ABCC8                              0.7817            0.8398   \n",
       "...                                   ...               ...   \n",
       "WFS1                               0.7619            0.7359   \n",
       "WWOX                               0.3265            0.4082   \n",
       "ZC4H2                              0.9000            0.8750   \n",
       "ZEB2                               0.9292            0.9471   \n",
       "ZMIZ1                              0.5750            0.4625   \n",
       "\n",
       "                    ca_only_protmpnn_norm  100verte_mut_freq  \\\n",
       "Global_AUROC                       0.7649             0.7454   \n",
       "Avg_per_gene_AUROC                 0.7458             0.7529   \n",
       "ABCA3                              0.8854             0.9115   \n",
       "ABCA4                              0.8235             0.9160   \n",
       "ABCC8                              0.8486             0.7447   \n",
       "...                                   ...                ...   \n",
       "WFS1                               0.7056             0.6667   \n",
       "WWOX                               0.3469             0.5714   \n",
       "ZC4H2                              0.8500             0.5000   \n",
       "ZEB2                               0.9327             0.6392   \n",
       "ZMIZ1                              0.7000             0.5750   \n",
       "\n",
       "                    conditioned_wt_score  30mammal_wt_freq  30mammal_mut_freq  \\\n",
       "Global_AUROC                      0.7194            0.7101             0.6195   \n",
       "Avg_per_gene_AUROC                0.8073            0.7027             0.6254   \n",
       "ABCA3                             0.7396            0.8438             0.8620   \n",
       "ABCA4                             0.8646            0.9137             0.7161   \n",
       "ABCC8                             0.8697            0.5625             0.5625   \n",
       "...                                  ...               ...                ...   \n",
       "WFS1                              0.7771            0.6656             0.5455   \n",
       "WWOX                              0.7551            0.5714             0.5000   \n",
       "ZC4H2                             0.9250            0.5000             0.5000   \n",
       "ZEB2                              0.9528            0.5823             0.5190   \n",
       "ZMIZ1                             0.8000            0.5000             0.5000   \n",
       "\n",
       "                    30mammal_gap_freq  100verte_gap_freq         n    n_pos  \\\n",
       "Global_AUROC                   0.4869             0.3345       NaN      NaN   \n",
       "Avg_per_gene_AUROC             0.4781             0.3371   53.9927  28.0401   \n",
       "ABCA3                          0.4740             0.5312   28.0000  12.0000   \n",
       "ABCA4                          0.5062             0.3337  283.0000  16.0000   \n",
       "ABCC8                          0.6294             0.3512   79.0000   8.0000   \n",
       "...                               ...                ...       ...      ...   \n",
       "WFS1                           0.3831             0.3874   53.0000  11.0000   \n",
       "WWOX                           0.4286             0.3980   14.0000   7.0000   \n",
       "ZC4H2                          0.3750             0.7125   14.0000   4.0000   \n",
       "ZEB2                           0.4873             0.1948  101.0000  79.0000   \n",
       "ZMIZ1                          0.6250             0.8062   24.0000  20.0000   \n",
       "\n",
       "                       n_neg  \n",
       "Global_AUROC             NaN  \n",
       "Avg_per_gene_AUROC   25.9526  \n",
       "ABCA3                16.0000  \n",
       "ABCA4               267.0000  \n",
       "ABCC8                71.0000  \n",
       "...                      ...  \n",
       "WFS1                 42.0000  \n",
       "WWOX                  7.0000  \n",
       "ZC4H2                10.0000  \n",
       "ZEB2                 22.0000  \n",
       "ZMIZ1                 4.0000  \n",
       "\n",
       "[276 rows x 20 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get per gene AUROC\n",
    "roc_by_gene = get_per_gene_auroc(y, pred)\n",
    "\n",
    "# Add global AUROC\n",
    "roc_by_gene.insert(loc = 0, column = 'Global_AUROC', \n",
    "                   value = [np.nan, np.nan, np.nan] + \\\n",
    "                           [float(roc_auc_score(y, pred[feat])) for feat in pred.columns])\n",
    "roc_by_gene = roc_by_gene.sort_values('Global_AUROC', ascending = False).round(4)\n",
    "\n",
    "# Transpose the dataframe\n",
    "roc_by_gene = roc_by_gene.transpose()\n",
    "roc_by_gene"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "5bcf3ec4",
   "metadata": {},
   "source": [
    "#### High quality Clinvar set and benchmark against REVEL\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "104ab047",
   "metadata": {},
   "outputs": [],
   "source": [
    "hq_clinvar = pd.read_csv('./data/since2017_one_star_REVEL_clinvar.csv').drop('ClinicalSignificance', axis = 1).drop_duplicates().dropna(axis = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "f1576252",
   "metadata": {},
   "outputs": [],
   "source": [
    "hq_clinvar['mutant'] = hq_clinvar['GeneSymbol'] + '_' + hq_clinvar['mutant']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "ab6817bb",
   "metadata": {},
   "outputs": [],
   "source": [
    "hq_y = pd.DataFrame(index = x.index)\n",
    "hq_y = hq_y.merge(hq_clinvar[['mutant', 'label']], left_index = True, right_on = 'mutant', how = 'inner')\n",
    "hq_y = hq_y.set_index('mutant')\n",
    "hq_x = x.loc[hq_y.index]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "97f6baee",
   "metadata": {},
   "outputs": [],
   "source": [
    "hq_x = hq_x.merge(hq_clinvar[['mutant', 'REVEL']].set_index('mutant'), \n",
    "                  left_index = True, right_index = True, how = 'left')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "1cc81c9f",
   "metadata": {},
   "outputs": [],
   "source": [
    "hq_y.to_csv('./data/since2017_one_star_REVEL_clinvar_y.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "9d38ed22",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Features selected on DMS ['esm_1v', 'evolutionary_index_ASM', '100verte_mut_freq', '30mammal_mut_freq', 'vanilla_protmpnn_norm', 'conditioned_wt_score', 'BLOSUM1', 'ST6', 'VHSE6']\n"
     ]
    }
   ],
   "source": [
    "# Construct prediction matrix\n",
    "pred_hq = pd.DataFrame(index = hq_y.index)\n",
    "\n",
    "cpt = CPTClassifier()\n",
    "# Transfer model predictions\n",
    "features = feat_selected_dms\n",
    "print('Features selected on DMS', features)\n",
    "cpt.train(feat_mat, label, all_genes, features)\n",
    "pred_clinvar = cpt.predict(hq_x[features])\n",
    "pred_hq['Transfer'] = 1 - pred_clinvar.values\n",
    "\n",
    "# pred_hq['VESPAl'] = pred_VESPAl.loc[hq_x.index]\n",
    "# pred_hq['GEMME'] = -pred_GEMME.loc[hq_x.index]\n",
    "\n",
    "# Individual features\n",
    "for feat in list(set(pos_feats + ['REVEL']) - set(aa_feats)):\n",
    "    pred_hq[feat] = hq_x[feat]\n",
    "for feat in list(set(neg_feats) - set(aa_feats)):\n",
    "    pred_hq[feat] = -hq_x[feat]\n",
    "\n",
    "feat_list = pos_feats + neg_feats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "ca3321b4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "101 genes used to calculate per gene AUROC out of 407 genes\n",
      "Calculating...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 101/101 [00:01<00:00, 57.72it/s]\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Transfer</th>\n",
       "      <th>REVEL</th>\n",
       "      <th>EVE_scores_ASM</th>\n",
       "      <th>evolutionary_index_ASM</th>\n",
       "      <th>100verte_wt_freq</th>\n",
       "      <th>esm_1v</th>\n",
       "      <th>conditioned_mt_score</th>\n",
       "      <th>100verte_mut_freq</th>\n",
       "      <th>conditioned_wt_score</th>\n",
       "      <th>30mammal_wt_freq</th>\n",
       "      <th>vanilla_protmpnn_norm</th>\n",
       "      <th>ca_protmpnn_norm</th>\n",
       "      <th>ca_only_protmpnn_norm</th>\n",
       "      <th>30mammal_mut_freq</th>\n",
       "      <th>30mammal_gap_freq</th>\n",
       "      <th>100verte_gap_freq</th>\n",
       "      <th>n</th>\n",
       "      <th>n_pos</th>\n",
       "      <th>n_neg</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Global_AUROC</th>\n",
       "      <td>0.9187</td>\n",
       "      <td>0.9007</td>\n",
       "      <td>0.8848</td>\n",
       "      <td>0.8787</td>\n",
       "      <td>0.8728</td>\n",
       "      <td>0.8699</td>\n",
       "      <td>0.7898</td>\n",
       "      <td>0.7625</td>\n",
       "      <td>0.7402</td>\n",
       "      <td>0.7280</td>\n",
       "      <td>0.7201</td>\n",
       "      <td>0.7198</td>\n",
       "      <td>0.7131</td>\n",
       "      <td>0.6326</td>\n",
       "      <td>0.4832</td>\n",
       "      <td>0.3183</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Avg_per_gene_AUROC</th>\n",
       "      <td>0.9358</td>\n",
       "      <td>0.9111</td>\n",
       "      <td>0.9154</td>\n",
       "      <td>0.9168</td>\n",
       "      <td>0.8645</td>\n",
       "      <td>0.8814</td>\n",
       "      <td>0.8357</td>\n",
       "      <td>0.7357</td>\n",
       "      <td>0.7979</td>\n",
       "      <td>0.6801</td>\n",
       "      <td>0.7028</td>\n",
       "      <td>0.7012</td>\n",
       "      <td>0.6942</td>\n",
       "      <td>0.6120</td>\n",
       "      <td>0.4651</td>\n",
       "      <td>0.3675</td>\n",
       "      <td>18.1881</td>\n",
       "      <td>12.7228</td>\n",
       "      <td>5.4653</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AASS</th>\n",
       "      <td>1.0000</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>0.7500</td>\n",
       "      <td>0.7500</td>\n",
       "      <td>0.9688</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>0.6875</td>\n",
       "      <td>0.8125</td>\n",
       "      <td>0.6250</td>\n",
       "      <td>0.7500</td>\n",
       "      <td>0.6875</td>\n",
       "      <td>0.6250</td>\n",
       "      <td>0.3750</td>\n",
       "      <td>0.7500</td>\n",
       "      <td>0.4375</td>\n",
       "      <td>0.5312</td>\n",
       "      <td>10.0000</td>\n",
       "      <td>8.0000</td>\n",
       "      <td>2.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ABL1</th>\n",
       "      <td>0.9399</td>\n",
       "      <td>0.9765</td>\n",
       "      <td>0.9455</td>\n",
       "      <td>0.9465</td>\n",
       "      <td>0.9352</td>\n",
       "      <td>0.8770</td>\n",
       "      <td>0.8507</td>\n",
       "      <td>0.7324</td>\n",
       "      <td>0.8545</td>\n",
       "      <td>0.7113</td>\n",
       "      <td>0.8479</td>\n",
       "      <td>0.8685</td>\n",
       "      <td>0.8254</td>\n",
       "      <td>0.6056</td>\n",
       "      <td>0.6066</td>\n",
       "      <td>0.2216</td>\n",
       "      <td>86.0000</td>\n",
       "      <td>71.0000</td>\n",
       "      <td>15.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ACTN1</th>\n",
       "      <td>0.9556</td>\n",
       "      <td>0.7556</td>\n",
       "      <td>0.9556</td>\n",
       "      <td>0.9333</td>\n",
       "      <td>0.7778</td>\n",
       "      <td>0.8889</td>\n",
       "      <td>0.7333</td>\n",
       "      <td>0.6667</td>\n",
       "      <td>0.4889</td>\n",
       "      <td>0.5000</td>\n",
       "      <td>0.7333</td>\n",
       "      <td>0.8667</td>\n",
       "      <td>0.7556</td>\n",
       "      <td>0.5000</td>\n",
       "      <td>0.4667</td>\n",
       "      <td>0.4444</td>\n",
       "      <td>18.0000</td>\n",
       "      <td>3.0000</td>\n",
       "      <td>15.0000</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WDR4</th>\n",
       "      <td>1.0000</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>0.9545</td>\n",
       "      <td>0.8636</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>0.8636</td>\n",
       "      <td>0.5909</td>\n",
       "      <td>0.6818</td>\n",
       "      <td>0.5455</td>\n",
       "      <td>0.6364</td>\n",
       "      <td>0.5000</td>\n",
       "      <td>0.0909</td>\n",
       "      <td>13.0000</td>\n",
       "      <td>11.0000</td>\n",
       "      <td>2.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WDR73</th>\n",
       "      <td>1.0000</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>0.6667</td>\n",
       "      <td>0.6667</td>\n",
       "      <td>0.5833</td>\n",
       "      <td>0.9167</td>\n",
       "      <td>0.4167</td>\n",
       "      <td>0.4167</td>\n",
       "      <td>8.0000</td>\n",
       "      <td>6.0000</td>\n",
       "      <td>2.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>XYLT2</th>\n",
       "      <td>1.0000</td>\n",
       "      <td>0.9167</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>0.9583</td>\n",
       "      <td>0.8750</td>\n",
       "      <td>0.9583</td>\n",
       "      <td>0.6667</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>0.9167</td>\n",
       "      <td>0.9583</td>\n",
       "      <td>0.6667</td>\n",
       "      <td>0.5417</td>\n",
       "      <td>0.6250</td>\n",
       "      <td>14.0000</td>\n",
       "      <td>12.0000</td>\n",
       "      <td>2.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ZIC1</th>\n",
       "      <td>0.8889</td>\n",
       "      <td>0.6667</td>\n",
       "      <td>0.7778</td>\n",
       "      <td>0.7778</td>\n",
       "      <td>0.7222</td>\n",
       "      <td>0.8889</td>\n",
       "      <td>0.6667</td>\n",
       "      <td>0.5833</td>\n",
       "      <td>0.8056</td>\n",
       "      <td>0.5000</td>\n",
       "      <td>0.6667</td>\n",
       "      <td>0.8889</td>\n",
       "      <td>0.8889</td>\n",
       "      <td>0.5000</td>\n",
       "      <td>0.3333</td>\n",
       "      <td>0.2222</td>\n",
       "      <td>9.0000</td>\n",
       "      <td>6.0000</td>\n",
       "      <td>3.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ZMIZ1</th>\n",
       "      <td>0.6750</td>\n",
       "      <td>0.6000</td>\n",
       "      <td>0.7500</td>\n",
       "      <td>0.7500</td>\n",
       "      <td>0.5812</td>\n",
       "      <td>0.6875</td>\n",
       "      <td>0.7375</td>\n",
       "      <td>0.5750</td>\n",
       "      <td>0.8000</td>\n",
       "      <td>0.5000</td>\n",
       "      <td>0.5750</td>\n",
       "      <td>0.4625</td>\n",
       "      <td>0.7000</td>\n",
       "      <td>0.5000</td>\n",
       "      <td>0.6250</td>\n",
       "      <td>0.8062</td>\n",
       "      <td>24.0000</td>\n",
       "      <td>20.0000</td>\n",
       "      <td>4.0000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>103 rows × 19 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                    Transfer   REVEL  EVE_scores_ASM  evolutionary_index_ASM  \\\n",
       "Global_AUROC          0.9187  0.9007          0.8848                  0.8787   \n",
       "Avg_per_gene_AUROC    0.9358  0.9111          0.9154                  0.9168   \n",
       "AASS                  1.0000  1.0000          0.7500                  0.7500   \n",
       "ABL1                  0.9399  0.9765          0.9455                  0.9465   \n",
       "ACTN1                 0.9556  0.7556          0.9556                  0.9333   \n",
       "...                      ...     ...             ...                     ...   \n",
       "WDR4                  1.0000  1.0000          1.0000                  1.0000   \n",
       "WDR73                 1.0000  1.0000          1.0000                  1.0000   \n",
       "XYLT2                 1.0000  0.9167          1.0000                  1.0000   \n",
       "ZIC1                  0.8889  0.6667          0.7778                  0.7778   \n",
       "ZMIZ1                 0.6750  0.6000          0.7500                  0.7500   \n",
       "\n",
       "                    100verte_wt_freq  esm_1v  conditioned_mt_score  \\\n",
       "Global_AUROC                  0.8728  0.8699                0.7898   \n",
       "Avg_per_gene_AUROC            0.8645  0.8814                0.8357   \n",
       "AASS                          0.9688  1.0000                0.6875   \n",
       "ABL1                          0.9352  0.8770                0.8507   \n",
       "ACTN1                         0.7778  0.8889                0.7333   \n",
       "...                              ...     ...                   ...   \n",
       "WDR4                          1.0000  1.0000                0.9545   \n",
       "WDR73                         1.0000  1.0000                1.0000   \n",
       "XYLT2                         1.0000  1.0000                0.9583   \n",
       "ZIC1                          0.7222  0.8889                0.6667   \n",
       "ZMIZ1                         0.5812  0.6875                0.7375   \n",
       "\n",
       "                    100verte_mut_freq  conditioned_wt_score  30mammal_wt_freq  \\\n",
       "Global_AUROC                   0.7625                0.7402            0.7280   \n",
       "Avg_per_gene_AUROC             0.7357                0.7979            0.6801   \n",
       "AASS                           0.8125                0.6250            0.7500   \n",
       "ABL1                           0.7324                0.8545            0.7113   \n",
       "ACTN1                          0.6667                0.4889            0.5000   \n",
       "...                               ...                   ...               ...   \n",
       "WDR4                           0.8636                1.0000            0.8636   \n",
       "WDR73                          1.0000                1.0000            1.0000   \n",
       "XYLT2                          0.8750                0.9583            0.6667   \n",
       "ZIC1                           0.5833                0.8056            0.5000   \n",
       "ZMIZ1                          0.5750                0.8000            0.5000   \n",
       "\n",
       "                    vanilla_protmpnn_norm  ca_protmpnn_norm  \\\n",
       "Global_AUROC                       0.7201            0.7198   \n",
       "Avg_per_gene_AUROC                 0.7028            0.7012   \n",
       "AASS                               0.6875            0.6250   \n",
       "ABL1                               0.8479            0.8685   \n",
       "ACTN1                              0.7333            0.8667   \n",
       "...                                   ...               ...   \n",
       "WDR4                               0.5909            0.6818   \n",
       "WDR73                              0.6667            0.6667   \n",
       "XYLT2                              1.0000            0.9167   \n",
       "ZIC1                               0.6667            0.8889   \n",
       "ZMIZ1                              0.5750            0.4625   \n",
       "\n",
       "                    ca_only_protmpnn_norm  30mammal_mut_freq  \\\n",
       "Global_AUROC                       0.7131             0.6326   \n",
       "Avg_per_gene_AUROC                 0.6942             0.6120   \n",
       "AASS                               0.3750             0.7500   \n",
       "ABL1                               0.8254             0.6056   \n",
       "ACTN1                              0.7556             0.5000   \n",
       "...                                   ...                ...   \n",
       "WDR4                               0.5455             0.6364   \n",
       "WDR73                              0.5833             0.9167   \n",
       "XYLT2                              0.9583             0.6667   \n",
       "ZIC1                               0.8889             0.5000   \n",
       "ZMIZ1                              0.7000             0.5000   \n",
       "\n",
       "                    30mammal_gap_freq  100verte_gap_freq        n    n_pos  \\\n",
       "Global_AUROC                   0.4832             0.3183      NaN      NaN   \n",
       "Avg_per_gene_AUROC             0.4651             0.3675  18.1881  12.7228   \n",
       "AASS                           0.4375             0.5312  10.0000   8.0000   \n",
       "ABL1                           0.6066             0.2216  86.0000  71.0000   \n",
       "ACTN1                          0.4667             0.4444  18.0000   3.0000   \n",
       "...                               ...                ...      ...      ...   \n",
       "WDR4                           0.5000             0.0909  13.0000  11.0000   \n",
       "WDR73                          0.4167             0.4167   8.0000   6.0000   \n",
       "XYLT2                          0.5417             0.6250  14.0000  12.0000   \n",
       "ZIC1                           0.3333             0.2222   9.0000   6.0000   \n",
       "ZMIZ1                          0.6250             0.8062  24.0000  20.0000   \n",
       "\n",
       "                      n_neg  \n",
       "Global_AUROC            NaN  \n",
       "Avg_per_gene_AUROC   5.4653  \n",
       "AASS                 2.0000  \n",
       "ABL1                15.0000  \n",
       "ACTN1               15.0000  \n",
       "...                     ...  \n",
       "WDR4                 2.0000  \n",
       "WDR73                2.0000  \n",
       "XYLT2                2.0000  \n",
       "ZIC1                 3.0000  \n",
       "ZMIZ1                4.0000  \n",
       "\n",
       "[103 rows x 19 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get per gene AUROC\n",
    "roc_by_gene_hq = get_per_gene_auroc(hq_y, pred_hq, min_neg_sample = 2, min_pos_sample = 2)\n",
    "\n",
    "# Add global AUROC\n",
    "roc_by_gene_hq.insert(loc = 0, column = 'Global_AUROC', \n",
    "                   value = [np.nan, np.nan, np.nan] + \\\n",
    "                           [float(roc_auc_score(hq_y, pred_hq[feat])) for feat in pred_hq.columns])\n",
    "roc_by_gene_hq = roc_by_gene_hq.sort_values('Global_AUROC', ascending = False).round(4)\n",
    "\n",
    "# Transpose the dataframe\n",
    "roc_by_gene_hq = roc_by_gene_hq.transpose()\n",
    "roc_by_gene_hq"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "297fd7f1-5fd3-434c-8040-2faed5a8c003",
   "metadata": {},
   "source": [
    "### Analyze ROC curve"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "a6167637-af41-403c-a01b-0947aed4bf32",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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ztOgLoZ0hIBraDoFmrRv8fByNlSQjQtQjhfZCTuec5kT2CeIz4wHYc2YP/mZ/Vh9bTXJ+cpXPeWnYpRTYCuge0h2L3UKnZp3oEdqDCK8I3Ax135pxLqvdwYm0PNLOtm4cSc1lz+lMTqbnE5+SS3Zh5SYN8/Nww8fdiNmoJyW7kOhmXrQI9KRrcz/ah/sSG+pDqK907qxX8tK0RCPxb20SMJ0ODq6E3JTy6zgTkbOTnjdrC+FdteQlpIM2+Zd/CwiObfJDZRsaSUaEqGNKKZLykjiUfojl8ctRKJYdqd4kgH3D+5JjzaF7SHcKbAX0DO1JG/82uBvd8Tf742f2q+Hoqy81p5Ct8WkcSc3l5z2JJGQWkJxdeOGKZwX7mHHT62jRzJOWQd5EBXrQIcyXHtEB+Hm4NqkSFchPh/SjWivHgRXaoxQ3D61fR2X6I0Vcok1pHtxem6ejWVtt9IokG42KJCNC1KLU/FT+SvmLg2kHicuIY/2p9ShUuet9FInyiSLAPQCHw0FsYCwZhRl0CepClE8UXYO70sy9mctbNc6Xnmthz+ksMvItnEjL51BSNjtPZpBvsZNQRifQ80U380QpiPT3IMDLjV7RgbQI9KRDhC+hPmaM8gilfnI44EwcxP+m9eFIj9f6cHgEaI9WKuLZDMy+YPaBwFbalOf+MRDcDvyiZMRKEyLJiBA1JLMwk1XHVrHy6EoKbAX8lXKBv4iBK5pfQY4lhyExQ/A2eXNp6KWEeYXV28cJSim2H88gPjWXY2dyWfZ3AkdScytdX6eDCD8PbukRSZtQH2JDfWgT4i2TbtV3OSlweDWkxWt9OTybweE12sJseeX0Tzp33RWjB9jyIfZ6cPeFVoMguq/2SEUIJBkRolqsDiunc06zJ3UPa06s4beTv1XY2lE0eVeUTxQ9QnswJGYIge71e3Ikm93BXyczWbErgdOZ+ZxMz+fvkxfuPNgu1JtWQd7EBHkR4qM1pUf4e9CluR+R/jJUsl5SSnuccnq7NsX56R3aXBzHNoAlu3LncPeDLreByVub6tw/SlstNrA1+ITWbvyiwZNkRIhKsNgtbDi9gXUn1/HNwW+co1bKcnu72wnyDKJXaC9a+bWimUezOo62euKSs/ng1zgOJGazP7HiL6AeLfyJ8PcgNtSHqEBP2oX6EOnvgZ9n/Xp0JCqQlQA7voCUfXBsI2SfvnCd6Mu1Cb/cPLR5OAJbao9XvEOhnj02FA2LJCNClCG9IJ1lR5axI3kHhzMOczjzcInj5yYiXYO6EuYVxl0d76JbcLd6+4gFiuflOJ6Wx8bDZ9gcn8bvB1Mw6nXYylkUxd/TDR0wrl8MnSL8uLxNEB4mGR5Z7+WlaZ1Fj23QRq3knQFrHuRnQNZp7bFJKTpt3g3PZlrS0WG4tpBbYGut06gQtUSSEdGkWe1WdiTv4K+Uv9iXto9Vx1aVW9bT6EmoVyh9w/tySeglXBt9bY2vf1LTTqTlsWT7KQ4mZbNsVwJ+Hm5k5peem6MoEWkT4k1UgAfXdAyjU4QvnSJ8peNofWbJgxObIDUODv2sLUF/YJn2qMSSU7lzdB2lLVffZrAkHMJlJBkRTYZSipM5J/nx8I+sOLrCOY9HRa5vdT3tAtoxKGoQMb4x9brVw2p3sOd0Fn/EpfLz3iT+OpFRqsy5iUjvloG0bOZFM28TIy+JpEWgp8wuWp/ZbdoU54dWwZo3ILeCOWeKEhGdHpRDe5QSdZnW6hEQow2V9QoGr4bxCFE0fpKMiEYroyCDTQmb2JSwiW8PfVthWV+TL0EeQfQM7UnX4K4MiBxQ7/t6WGwOft2fxMxfDqHX6dibkFVu2Ru6hhPp70HHCF+uah+Cj7s836+3lIK0I7D/R225+vwMOL6x/FEroCUY0f2hMBua99KSjZYDwexdV1ELcVEkGRGNSkJOAt/FfceqY6uIy4grs0yMbwyt/VvTOagzAyIH0Dagbb1/3JKcVcCaA8nsOpWJ3QFLtp/EYnegyujmYTLq6dMykG7N/bm6QwiXtAio+4BFxfLStGGyZw5B+jFtuGxuirawW2H5SaVT/8chvLu2ZL1vpMzHIRo8SUZEg2d1WPli7xdM3za9zOOB7oEMjRnKNdHX0Cmok0tWja0OpRQrdifyy74klmw/VW65lkFe9I4JZEyfFtLHoz6yFsC+H2DDB9r7xL8vXEdn0Fo3HDZtyfrmvSBmgDZ6RUatiEZIkhHR4OTb8tmcsJlvD33LhlMbsDgspcrc1OYmxnUaR2v/1i6IsPrSci188+cJPlp7GIdDlVqbpWWQF5H+HgxqH0LHcF96twyUCcPqi7w0yDimdSaNW6XN1ZF6sOI67n7aNOdFy9cHxGj9OvyaS9IhmhRJRkSDcCzrGEvjlnIy+yQrj64ss0yPkB681O8lWvq1rOPoqi8918J3O06xdOepcicU69HCnzF9orn5kkj0kni4llLacNmj67Vhsju/AltBydlGy+PfAlpfBe2HQ2QP8Kzfk94JUZckGRH1VnpBOgsOLGDN8TXsS9tX6rhRb+SWtrdwTfQ19AjpUe/WaimLUoqdJzLYdiydN5bvo5ypPege5U/P6AAmXdMOb7P8Z+oSDofW0nHyT9jysZZ0JO66cD2ju7auSkh7bdn65pdqq8hKvw4hyiV/y4l6w+awsSlhE+tOrmPPmT1lru3SM7Qnd3W8i0FRg+p9p9NzZeZb+WD1IT5bX/Zw4n6tm9El0o/bekXRJkRGQNSprARI2Q/HN2kLu2Wd1PblpVZcz91PW2PFYIL212tJh19k3cQsRCMjyYhwqbSCNNadXMcvx35h7cm1ZZa5Kuoq7mh/B5eFX1av5/k436GkbOZtOMrBxGz+PJZe4pjZqGfsZdHcdEkknSP9XBRhE5GbCqe2g70QcpK1lg43Dzj4k5Z4VEbLgRDSATrcqHUmleXrhahRkowIl/j56M/8eORH1pxYU+pYM/dmDGg+gFvb3UrXoK4NJgFJy7XwxaZjfL3lOEpBYlZBqTLeZiO//fNKmnnLl1mtsBacnZH0EPw5V1va3l66g3OZWg3SHsWEdtLWXYm4RJsG3Wiq3ZiFEJKMiLqhlCI1P5W5u+fyw+EfyLKUnEuhW3A3bml7C9e3uh6ToeH85Z+UVcD0nw+y40Q6B5NKT79tMuoZ3CGEh69sIy0gNSnjuNZ/4+Sf2nZ2Ahz7o+I6BjO0uEwbpVKQqa0wGxCjJR5eQXUSthCibJKMiFp1JOMI96y8h/TC9FLHTHoTL/d/mWExwzDoG8405Da7g6U7T/PWyv0kZxeWOn5Nx1Dt1SGUAK+Gk1jVS0ppM4/uWgR7f9ASkMouaR97nZZs9HsMfMNrNUwhxMWRZETUis0Jm5m1cxY7kneUOhbjG8NL/V6iZ2hPF0R2cZbuOMUTC3eW2j/h8pbc1qs5saE+DeaxUr2ScVxLNvZ+r/XHOLpO21+0tkp52lwDPqHgH609Vglqq23Ln4EQDYokI6LG2Bw2Zmybwed7Py91bFjLYbxw2Qv4mHxcENnFSc4u4N2fDrJsVwI550xCFtPMk5l3XEL3KH/XBdeQKAVZp+DIb9qolYJMOLC84unPixIRzyDtEUtUHwjtCOGXyCJvQjQikoyIGvHRzo/48K8PS+wL9wpnYveJ3Nj6xgbXWnAqI5+3Vu7neFoeO45nlDgW7ufOV/dfRssgL9cEV18ppa2vknUa8tMgPx3i12mLvsX/duH6QWfn4giOhfY3gHeoNv25Z5A2+qWB/Q4JISpPkhFxUX478RtT1k8p0SG1hU8L/jPsPwR5NLxOgXtOZ3LHx5tKTcMO0CXSjw/v7EFUoKcLIquHCrJg66ew82ttwbeqMLpDzOUQOwz8Y7RHLNLSIUSTJcmIqDKHcvDzsZ+Zv3s+e87sce7vGtSVN694kyifKBdGV3WHU3KYtnw/v+xLKnVsXN9oRvdpQfswXxdE5mIOuzY0dvvn2tL0hdlwYrM2b4e9dMfdEryCtZYSr2BtnZX210NAtDYxmLnhPaoTQtQuSUZEpeVYcrhz+Z0cyTxSYn+fsD7c3/V++oT3cVFkVReXnMMXm44xf8PRMo8/O6w9/zewYS2yVy1KgTUf0uO1vhzH/tAer1xomGwRN09tKfvYodpib8HttUcqQghRBZKMiAoppTiccZjn/3i+RCsIaDOjPtX7KSK9G8YU2IU2O1OW7GbF7gTyLPYSx/Q6GHtZNI9c1YYQH3cXRVgHHHY4vhH2L4ekXcUdSSvi21xr1Wh5BfiEaecIbq/NSOoRIH05hBAXTZIRUa7/7v0vb219q9T+4a2GM6XPFLxNDWMNlTM5hdz3+Z+lOqK2DPJi7GXRXNsxtHH2A8k8qc3LkX4MkvfAnu+h8AKJR+urtccqEd21OTqiL5cZSIUQtU6SEVFKXHoc//z9n8RlxDn3+Zp8mdJnCtdEX9MgZki12R0s2HqCf63cT3ZByc6ot/ZszoMDWzeuBemU0h61bP8ctv/3wou8AfS4G9oN1TqSmnxA33AWHhRCNC6SjAintII03tr6FsuOLHPuax/Yns+u/Qw/c8OZynzV3iTu//zPEvuCvM2M6dOChwa2xsPUcGZ7LcVWqA2fPbUdUg9o/Tzy0yH1YPlrsPhFaY9YIi6BoHYQ1Vv6dQgh6hVJRgQO5eCzXZ/xwY4PnPu83Lx4oOsDjO80vsHMEbJydwIPfrG9xL7bejbn/ita0SbYG72+YXyOEvLTtUctJzbDHx9c+DGLzgC9xkOrK7WVZt2b4CggIUSDI8lIE/fNgW94ddOrJfZdE30Nr/Z/FS+3hjGp1097EvnXiv0cSc117usS6ceLwztyaUygCyOrIluhlnjs/BKyk7R+HulHyy6r02sLv3W4QVvorVlbrcVDkg8hRAMkyUgT9u3Bb0skInfE3sHTvZ/GqK//vxaHU3KY/rM2Rfv5Vjw+gA7h9fRLuTAHkvfCkbXFI1nyM7SRLRWJ7AnR/aDNYK3Fo4G0VgkhRGXU/28dUeOyLdncveLuEh1Uv7nhGzo06+DCqCpn27E0Xl+2j+3njYzp3TKQp4e2p2d0gGsCK4/dCqe2wcGVcHyTNqz2QqL6gLsfdButbfs1jKHTQghRXZKMNDE7knfw2K+PkVGYAUD/iP58cNUHuBncXBtYBc7kFPL1luPMWnOYfGvJ+UFG927BgwNbEd2sHjxSUgoKMmDfj3DoZ9j3Q/llW/QDvUFbZTa4A4S0B59waNZGWj2EEE2OJCNNhNVuZfq26Xyx7wvnvkcveZT7utyHXlc/h3QqpfjXygPM/u1wif16Hbx0YydG926Bm8FFsVvz4cQWbQG4ff+D1EOAKr+8fzSEd4W+j2itHZJwCCGEkyQjTUCBrYAxy8dwKL14MbP/Dvsv3UO6uy6oC4hLzuHOzzaRlKWtgaLTwZjeLbihawR9W9fRgmq5Z7Ths0l7IDsRHFb4ayHkJFZczyMAQjpqi8D5RUHHEZJ8CCFEBSQZaeSyLFn0/7q/8/3o9qN56tKn6m0n1ZxCG08v/rtEx9RIfw9W/2Mg7m61OD+IrVAbPrv6VSjMgpT9la/b5XZtxtKoPhAcKwvBCSFEFdXPbyRRI/ac2cMdP97hfP9Kv1e4qe1NLoyoYpl5Vrq98rPzfVSgB+/e1p3eLWtoeK7NorVqZJ7UWjuO/aGNZkk7Uv4Q2iLN2kBgKwjtBJ5B0HUUeAfXTFxCCNHESTLSSL2//X0+3fVp8ftB7zOoxSAXRlQ+i83B+6sP8e81xaN7IvzcWfOPKzFeTJ8Qu03rSPrnXIhbdeHyBhN4h2qJRttrIKwLmOpBx1ghhGjkJBlpZLIsWUxaM4ktiVsACHQPZPbg2fV22O75s6aaDHpeG9mZ2y+Nqt4Jk/fB1jnaXB7H/ii7jLsfWAu0hMMzEGKugBaXgW+krM8ihBAuIMlII2KxW7h7+d0cztRGn9zY+kZe6/9avZzOPS45hwc+/7PErKk3XRLJtJu7VK5vSPI+bX2W3YshKwHOxGkdTM+n02uvq57XFoULipWEQwgh6hlJRhqJQnshgxYOItuaDcDYDmN56tKn6lUiopRi7h9H+WLTMeLPSUJMRj3fPdyPThEVLManFCT+DVs+0ebxKMio+GKdb4We4yDqMjDW/1WGhRCiKZNkpBHYnrSd8T+Nx6EcALxx+RsMbz3cxVGVNO+PeP6z4ShHz+Q59wX7mLm1Z3P+eW1s2YvY2W1ay0f879p6Lefza6H16YjsobV6NGujzVbq3nBWGBZCCCHJSIP31b6vmLZlmvP9U5c+Va8SkVV7k3js6x0lZk5tFeTFzDu607W5f+kKDjv89TWsmgp5qaWPh3eH2Ougy63QrHWtxS2EEKLuSDLSgK0/tb5EIjLjyhkMjh7swohKevl/e5j3x1Hn+wg/dz65uxedI89pubDmw18LIGEnHN8MKftKnyisK/QaD11ukzk8hBCiEZJkpIFKzU/loV8eAsDT6Mnvd/yO2WB2cVSaXSczefTr7SUeyThX0rXb4ODPsPd7SNqtJSFladYW+j0KXW8HN4+6CVwIIYRLSDLSQN3+v9ud219f/3W9SUQ+33iUF7/f43zfJsSbZSONmJffBtY8rRNqWXwjoc3V0GEEtLxCOp0KIUQTIslIA/TaptdIyU8B4K0r3qKVfysXRwQFVjsPfbGNNQdS8COHGw0bmNDBQUzc5/B5GRX0btD6KrjkTmg7BNzc6zxmIYQQ9YMkIw3Mtwe/ZeGBhQBcFXUVw1oOc3FEkG+x0+XFH3nC+C3z3L8vPhB3XsFWV8KAf8hwWyGEECVIMtKAWOwWXtr4EgA+bj5Mv3K6awMCCnPS+e+bE4lzX1b6oMEM17wC7a8H/2rOqCqEEKLRk2SkgVBKMerHUc73P9z0AwZ9La5ieyHJ+2HROMwp+3ng3N+iyF4waIrW/0MIIYSohGrNiz1r1ixiYmJwd3enT58+bNmypcLyM2fOJDY2Fg8PD6Kiopg0aRIFBQXVCrgpUkrx+JrHicvQnns82O1BgjyCXBPMwZ/hg57wYR9I2e/cvcxvDDxzAu5fLYmIEEKIKqlyy8jChQuZPHkys2fPpk+fPsycOZMhQ4Zw4MABQkJCSpX/6quveOaZZ5g7dy79+vXj4MGD3HPPPeh0OqZPd/1jhobg+T+eZ82JNQCMbj+aid0n1m0Adhuc2Aw/TSkxFDddeTPTdgtnOtzNv8f2qtuYhBBCNBpVTkamT5/O/fffz/jx4wGYPXs2y5YtY+7cuTzzzDOlym/YsIH+/fszZswYAGJiYhg9ejSbN2++yNCbhoX7F/LD4R8ArcPqlD5T6u7iJ7bAuulwcEWpQ09Z7+cb+yCCfcysG3VJ3cUkhBCi0anSYxqLxcK2bdsYPLh4lk+9Xs/gwYPZuHFjmXX69evHtm3bnI9yjhw5wvLly7nuuuvKvU5hYSFZWVklXk3R1sStvLb5NQBuaXsL7131Xt1cWCnY/AnMuaZkIhJ1Gfab59C68Au+sQ8CYPGDfSu3yq4QQghRjiq1jKSmpmK32wkNDS2xPzQ0lP3795dZZ8yYMaSmpnL55ZejlMJms/Hggw8yZUr5/8KfNm0aL7/8clVCa5Te2voWAEadkecve772L2i3wprXYedXkJNUvH/om9D7AfJtMHbOZuwqHYBpN3chuplX7cclhBCiUatWB9aqWLt2LW+88QYffvgh27dvZ8mSJSxbtoxXX3213DrPPvssmZmZzteJEydqO8x651D6IfanaQneU72fwqivxYFP1gJYcCe8FgrrZxQnIj3vgeeS4LKHSMy20uHFlWw7piUiDw5szejeLWovJiGEEE1Glb7hgoKCMBgMJCUlldiflJREWFhYmXVeeOEF7rrrLu677z4AunTpQm5uLg888ADPPfccen3pfMhsNmM214/pzV3B5rBx5/I7AWgb0JbR7UfX3sV2fAHfn9chNqwr3DQbQjsBYHcoLpu22nn4hq7hPDUktvZiEkII0aRUqWXEZDLRs2dPVq8u/mJyOBysXr2avn37llknLy+vVMJhMGh9DJRSVY23SXhpw0vk2/IB+Gevf9bORVLj4PWIkolIZE94Yjc8uM6ZiCiluOrdtc4ij1/dln+P6YFer6uduIQQQjQ5VW77nzx5MuPGjaNXr1707t2bmTNnkpub6xxdc/fddxMZGcm0adrS9sOHD2f69Olccskl9OnTh7i4OF544QWGDx/uTEpEsT1n9vD9YW1K9Zvb3kzfiLKTvGpL2qONkNm9uOT+Z0+B2bvELqvdQaepP2GxOQC4tWdzJl3TrmbjEUII0eRVORkZNWoUKSkpvPjiiyQmJtK9e3dWrlzp7NR6/PjxEi0hzz//PDqdjueff55Tp04RHBzM8OHDef3112vuUzQid/x4h3P72d7P1tyJE3fD9w9Dwl/F+3wj4dL7oP8TcF7r1Ym0PK5+9zcsdi0RGdwhhHdu61Zz8QghhBBn6VQDeFaSlZWFn58fmZmZ+Pr6ujqcWvNn4p+M/0lrYXqm9zPc2eHOiz+pUvDDo7Djv8X7wrtrK+Ze9TyUMaX8voQshr23zvn+7r7RvDKi88XHIoQQokmp7Pe3rE1TTyiluH/V/c73Y9qPqYmTwpe3Qtwv2ns3TxgxCzrfXG6Vo6m5jJz1h/P9izd05N7LW158LEIIIUQ5JBmpJ979811sDhsAs66ehU53kR1E89Lgo36QnaC9d/OCp46Am3u5VZbuOMVTi//GYnfg7+nGe3dcwsB2wRcXhxBCCHEBkozUAwk5Cfxn738AuLH1jVzR/IqLO+Gp7fDpoOL3fR+BwS+Dofw/7vWHUnli4U7n+8UP9qVNiM/FxSGEEEJUgiQjLuZQDu5ZeY/z/Ut9X6r+yexW+N8TsPOL4n0jPoRLKu57svd0FmPnaGsFeZuNrP3nlQR5N915XoQQQtQtSUZc7PeTv3M69zQAH1z1AW4Gt+qdqDAb3mkH1rzifeNXQnTFQ4NfX7aXT9fFO9//9s8raSaJiBBCiDokyYiLffr3pwAMiBzAlVFXVu8kyfvgw8uK30f1gbt/qLB/CMCMVQdLJCI/Pnq5JCJCCCHqnCQjLrQ1cSt/p/4NwC3tbqneSeJ+gS/OqTvqS+hwQ4VVLDYHbyzfx/wNRwHQ62D/q8MwGWt9qSIhhBCiFElGXOiFP14AINA9kKtbXF31E/z+Dvx6dsFBozvc9h+IHVphFaUU7Z5f4XzfIdyXefdcKomIEEIIl5FkxEVWH1/NqZxTAHx49YdVq5yfAf8dCad3FO97dDv4RV6w6oxfDjm377u8Jc/f0LFq1xZCCCFqmCQjLvLEmicA6BDYgU5BnSpfMeFv+HhA8fuQjnDfL2DyumBVq93B+6u1ZKRf62aSiAghhKgXpG3eBX459otz+4XLXqh8xd/fLpmIDHsbHvyjUolIVoGVG95f73z//uhLKn9dIYQQohZJy0gdy7HkMGntJAAuCbmELsFdKlfx6Hr49bXi95UYtlvEandw/3/+5EBSNgCvjugk84gIIYSoNyQZqWPv/PmOc3v6ldMrVynzJMy/vvj9fauhea9KX/Pl/+1hc3waALPG9OD6ruGVriuEEELUNklG6lB6QTrfHvoWgAe7PUiQR9CFK6UcgE+uLH7/0AYIrXwfk+92nOSLTccBuLNPC0lEhBBC1DuSjNSh+XvmO7cf6vbQhSukH4VZvYvf3/tzlRKRMzmFTFr4l/P9KyM6V7quEEIIUVekA2sdybflszRuKQCjYkeh113g1hdkwnvdit/fOg9a9Kn09ZRS3DNvq/P9D4/0x6C/yJWAhRBCiFogyUgdefzXx0krSMPbzZtHL3n0whV+OKfMyI+g881Vut6khTvZdSoTgGeGtadrc/8q1RdCCCHqijymqQMr41eyMWEjAO8MfAc/s1/FFZb/E/Z+r22P/RbaDK7S9f676RhLd2qL73WO9OXBga2rHLMQQghRVyQZqQOvb34dgGbuzegf2b/iwl+PgQPLtO32N1Q5Edl6NI0Xlu4GwNNkYOnDF7ieEEII4WLymKaWnco5RUZhBgC3tru14sIpB4oTEdD6iVTRA5//6dze+MzVGA3yRyyEEKJ+k2+qWvbJ3584tx/u/nD5BZWCz0cWv3/mBBhNVbrW9ztPkZ5nBeCLCX3w83SrUn0hhBDCFSQZqWVLDi0B4KY2N1U8gubzEZCt9fNg5Efg7lul62QVWHl8wU4Amgd4cHnbSsxhIoQQQtQDkozUom8PfuvcntRzUvkFd3wB8b9p2xGXQPcxVbqOze5g9CebnO+/nyj9RIQQQjQckozUopc2vgTAsJbDCHAPKLuQtQC+f0TbDm6vTfVeRTN+Ocie01kAjO8fQzNZd0YIIUQDIqNpasn83fOd27e3u738ggvGAErbHvcj6A1Vuk6hzc6sNYcBmDS4HY8PblvFSIUQQgjXkpaRWqCU4t1t7wLQuVlneoWVs6jd8qfg8NmWkMEvgXdwla/1y95k5/ZDV8p8IkIIIRoeSUZqwZbELc7tZ/o8U3ahk3/Clo+1bTdP6P9Eta415btdAIzsHoHJKH+cQgghGh759qoF/9nzHwDaBrSlW3C30gUcDlh6dpiv2RemnAZd1deNeWP5PjLztaG8N/doXu14hRBCCFeSZKSG5VnzWHdqHQBDY4aWXejQT5B6QNse90O1EpFCm51Pfj8CaEN5r2hX9Uc8QgghRH0gyUgN++2kNkTXqDMyofOE0gX2L4ev79C2Ww3ShvJWw/Pf7XZuf3lf5VfzFUIIIeobSUZq2H/3/heAm9vejOH8kTE5ybBgtLbtHQa3zq3WNdJyLSzadhKAUb2iiG7mVe14hRBCCFeTZKSG7UvbB8C1MdeWPvjdg8XbE34Cz8BqXWPc3OIOss/d0KFa5xBCCCHqC0lGatD+tP3YHDaA0h1XM44XD+Md+i8IiKnWNbYfT2fXqUwAXhnRCV93WX9GCCFEwybJSA36945/A2DUG3E3upc8uPje4u2e46p9jf/77zbn9l2XRVf7PEIIIUR9IclIDfoz6U8A+kX0K3kg7Qic3Kptj10Cbh7VOv+uk5mkZBcC8NGdPdBVYxSOEEIIUd9IMlJDdqXsIteaC8Dr/V8veXDPUu1nUDtoc3W1r/H+r4ec29d2Cqv2eYQQQoj6RJKRGvLZrs8ACPMKw9/dv+TBjdrjG1oNqvb5f92fxKq9SQC8d0d3DHppFRFCCNE4SDJSQ3498SsAo2JHlTxgzYe8M9p21/OOVcG8P44C0DrYixHdI6t9HiGEEKK+kWSkBuxOLZ6A7IZWN5Q8uOwfxduRPap1fqUU6w6lAvDoVbIqrxBCiMZFkpEa8L/D/3Nuh3md05fDkgs7v9S2+z9erWnfAVbvK16Zd3DH0GqdQwghhKivJBmpAauOrQJgWMywkgc2fFC8ffnkap9/6g97tPN3DsPbbKz2eYQQQoj6SJKRi5RnzSMlPwWAG1qf84hGKVg7TdsO7w4e/tU6/4m0PE5l5APwxOB2FxGpEEIIUT9JMnKRlsYtdW4PiBxQfCCpuB8Jo/5b7fM/+vUOAAx6He1Cvat9HiGEEKK+kmTkIr23/T0AxnYYW3ISsp1fF2/7t6jWuS02BztPZAAwuneUTHImhBCiUZJk5CLkWfPIs+UBMKb9mJIHM45pP6Mvr/b5/7Vyv3P72WGyIJ4QQojGSZKRi/D7qd8B8DH50NynefEBuxUO/axtX/GPMmpWzpz18QC0CvbCSzquCiGEaKQkGbkI606uAyDfmn/eI5ovwW4BNy9o0a+c2hU7lJTt3P5iQp+LilMIIYSozyQZuQiZhZkADIwaWLxTKfjf49p27DBwcy+j5oXNWhMHQJdIPyL8q7ewnhBCCNEQSDJSTVaHlc0JmwG4qc1NxQc2zirevvLZap1bKcXSnacBuKN3VLVjFEIIIRoCSUaq6cu9X1JgL8DbzZt+kec8iln9svazRV8IalOtc/9+dup3gGs6yIyrQgghGjdJRqrpvR3akN5uId1w07tpO3PPaH1FAIZOq/a5H/lqu3M7xLd6j3mEEEKIhkKSkWrIt+Vjc9iA8x7R/Ppq8XZYt2qde94f8WQXaOe+tWfzC5QWQgghGj5JRqph4f6Fzu3BLQZrG1kJsG2etj3iQ9BX79a+/L+9AET6e/D2rV0vKk4hhBCiIZBkpBp+PqbNIRLiGYJBb9B2Hlmr/fRsBt3HlF3xAr7584Rze9rNXWTGVSGEEE2CJCNVlFmYya7UXQCM6ziu+MC+/2k/2wyGaiYRy/5OAKBPy0CuaBd8UXEKIYQQDYUkI1X0zYFvnNu3x95efODYeu2nT3i1znsiLY/fDmqr/z52ddtqxyeEEEI0NNVKRmbNmkVMTAzu7u706dOHLVu2VFg+IyODiRMnEh4ejtlspl27dixfvrxaAbva9mRtpEu4VzjuxnNGuhRoE6AR2bNa533oy20ARPi507dVs4uKUQghhGhIqrzgycKFC5k8eTKzZ8+mT58+zJw5kyFDhnDgwAFCQkJKlbdYLFxzzTWEhISwePFiIiMjOXbsGP7+/jURf537O+VvAC6PPGcBvIS/i7dbXlHlc/55NI3dp7IAGHVpC/R66SsihBCi6ahyMjJ9+nTuv/9+xo8fD8Ds2bNZtmwZc+fO5ZlnnilVfu7cuaSlpbFhwwbc3LT5OGJiYi4uahfKsmhJQ+/w3sU7d36p/fQJBw//Kp/z7Z8OANAh3JdHr6reRGlCCCFEQ1WlxzQWi4Vt27YxePDg4hPo9QwePJiNGzeWWeeHH36gb9++TJw4kdDQUDp37swbb7yB3W4v9zqFhYVkZWWVeNUHSinndvuA9sUHTv6p/fSr+rwghTY7m+PTABgUGyytIkIIIZqcKiUjqamp2O12QkNLTlEeGhpKYmJimXWOHDnC4sWLsdvtLF++nBdeeIF3332X1157rdzrTJs2DT8/P+crKqp+rM+SXpju3A7zCis+cOpsMtL++iqf8+c9Sc7te/rHVDc0IYQQosGq9dE0DoeDkJAQPvnkE3r27MmoUaN47rnnmD17drl1nn32WTIzM52vEydOlFu2Lq2IX+HcdnZezUsrLtD6qiqfc9G2kwBc1yWMEB+Z+l0IIUTTU6U+I0FBQRgMBpKSkkrsT0pKIiwsrMw64eHhuLm5YTAYnPs6dOhAYmIiFosFk8lUqo7ZbMZsNlcltDrxw+EfAOgXcc7CeGteP7uhg/CqTQFvsTn4/exw3hu6RtREiEIIIUSDU6WWEZPJRM+ePVm9erVzn8PhYPXq1fTt27fMOv379ycuLg6Hw+Hcd/DgQcLDw8tMROqz/Wn7gfOSkUytZQNj1Vs1Plwb59we0qnsZE4IIYRo7Kr8mGby5Ml8+umn/Oc//2Hfvn089NBD5ObmOkfX3H333Tz77LPO8g899BBpaWk8/vjjHDx4kGXLlvHGG28wceLEmvsUdUAphUNpCdUlIZdoO20WOLhS277jiyqdz2p3MPOXQwD4ebhhkI6rQgghmqgqD+0dNWoUKSkpvPjiiyQmJtK9e3dWrlzp7NR6/Phx9OcsEhcVFcVPP/3EpEmT6Nq1K5GRkTz++OM8/fTTNfcp6sCfSX86t9sFtNM2Dv+q/TSYoNWgKp3vnZ8POLdfvKHjRccnhBBCNFRVTkYAHnnkER555JEyj61du7bUvr59+7Jp06bqXKreWLB/gXPb2Xk1/vezO/xAbyijVvk+/u2Ic/uWnlUfEiyEEEI0FrI2TSUVrdTbO+ycyc52f6v97P9Elc6193TxvCnzx196saEJIYQQDZokI5Vw7mRnt7S9RdvIPQM5Z+dWaXVllc439Yfdzu0rY0tPoS+EEEI0JZKMVELRFPAAg1qc7RuyfnpxgdBOlT7X5iNn2HpUmzzt+es71Eh8QgghREMmyUglnMk/A4Cb3g0Po4e2c+O/tZ+BrUFX+ZEwry7bC4BBr+Pe/i1rNE4hhBCiIZJkpBLWnFgDgNVh1XZYC4oP3jCjSuc6naHVvbp9iKxDI4QQQiDJSKXEZWiTkzlbRZY+VHwwun+lz3P8TB5puRYA/m9gqxqLTwghhGjIJBmphCOZ2jDcsR3Gajv2LNF++oSDofKjo+f+Ee/cviQqoMbiE0IIIRoySUYqISEnAYBuwd2Kp38HuKn8xf7KsvWotqje/w1sJY9ohBBCiLMkGbmAAlsB6YXa6Jduwd1g7/fFB1sOrPR5TmXks+fs/CIjukXWaIxCCCFEQybJyAXsObPHue1n9oMNH2hvLhlbpVE0N3/4BwBB3iY6RvjWaIxCCCFEQybJyAX8dPQn57bOboFs7ZEN3cZU+hx/Hk0jKasQgCevja3R+IQQQoiGTpKRC9iauBWAfhH9YPvnxQeiepdTo7SvNh93bt/Ru0WNxSaEEEI0BtVaKK+psNqtzmG9j/V4DOaO0A60GQwGt0qfZ9W+JABG946q8RiFEEKIhk5aRirw+8nfndsdvFtAbor2ZtBzlT7HntOZZBfYALhvgMwtIoQQQpxPkpEKZBRmAOBj8kEfv07bafKBiEsqfY6lO045t1sHe9dkeEIIIUSjIMlIBZLzkwEY2HwgJJ8dVRPUttKjaJRSfLpOm+hMHtEIIYQQZZNkpAKH0g8BEOwRDMc3azuj+1W6/l8nM53bz1/fsUZjE0IIIRoLSUYq8GfinwB4unnCMW2eENz9K11/yfbi2Vq9zNJXWAghhCiLJCPlUEo5Z14N9woHa552ILLy/UUWbD0BwK09m9d4fEIIIURjIclIORJyE5zbwyIGFB+I7FWp+qcz8rHYHADc279ljcYmhBBCNCaSjJSjaLKzYI9gTOnFq+3i7lep+nPWa3VaBXvJ9O9CCCFEBSQZKcfxbG3W1CifKEjcXXygEiNpsgqszmRkVC8ZRSOEEEJURJKRchxMPwhA24C2kJ+m7fSv3FTuc9YVt6Tc1Te6xmMTQgghGhNJRsqRVZgFgK/JF7b/V9sZe12l6i7dqU10Nr5/DJ4mGUUjhBBCVESSkXJsT94OQK+wXpB2WNvp2eyC9Qptdo6d0UbejOgeWWvxCSGEEI2FJCNlSMlLcW538mtXfKBF3wvW3Xs6y7ndrXnlOrsKIYQQTZkkI2Uo6rzqZ/bDL/VQ8YHmFx7W+7+/tCHB/p5u6Co5bbwQQgjRlEkyUoa9Z/YCYHPY4OAKbWdIR3DzuGDdLUfPANAyyKvW4hNCCCEaE0lGynA4Q+sjMrD5QDixRdtpyb1gvdxCG7tPaY9pnrw2ttbiE0IIIRoTSUbKYDaYi9+c1CY/Y9CUC9bbm1DcX6Rf6wt3dhVCCCGEJCNlOlOgPWpp5RUBDpu2M+byC9Y7mJTt3Jb+IkIIIUTlSDJShl+P/wqAR35m8U7fCw/T/fOotrBe9yj/2ghLCCGEaJQkGSmD1WEFwL3gbEuHwVSpaeA3HtZaVCL9L9zRVQghhBAaSUbOU2gvdG5fka21dOATdsF6dociu0BLYm7pKZOdCSGEEJUlych50gvSndvBNru2Edj6gvXiknPItWjlB7QNrpXYhBBCiMZIkpHz5FqLh/Aa9v2gbVRi5tV1h7RZWwM83XAzyG0VQgghKku+Nc+TnJcMQLhXOOgM2s6gthes9/HvRwDpvCqEEEJUlSQj5zmepU0Fn5KXDEX9R9oNrbBOgdVOSrZW9uYezWs1PiGEEKKxkWTkPEVzjLRxP9vvQ28Ek2eFdfacLh4CfH2X8FqLTQghhGiMJBk5z6F0bWG8gQ6TtqPzLRes8+t+7dFOgKcber1MdiaEEEJUhSQj5ylasTc46YC2o2gG1gos3XEagEtjAmstLiGEEKKxkmTkPAfTDwIQbvLRdoR2qrD8mZxCTmXkA3BzD5lfRAghhKgqSUbK0TxNayGh5ZUVllt7IMW5PbSz9BcRQgghqkqSkXOcyT/j3HZOeOYTWmGdXae0zqttQrxrLS4hhBCiMZNk5By7U3c7t32U0uYZ8YmosE7RejTXdb7wlPFCCCGEKE2SkXOcztU6onro3bQdga1AX/EtOpCkLabXLsynVmMTQgghGitJRs6RlJsEQEej39k9qsLymXlW5/YV7WQ9GiGEEKI6JBk5R0JuAgD+1rMzr8YOq7D8jhPaonrNvEz4urvVamxCCCFEYyXJyDlOZJ8AoFmm9rgG/+gKyy/YopXPKrBWWE4IIYQQ5ZNk5By+Jl8AAu0ObUdATIXlbQ7tMU5UYMXTxQshhBCifJKMnCM+Mx6AVtazLR0hHSos/8s+rY/J7b2iajUuIYQQojGTZOQcqfmpAJjV2Y6r3pUbrtsyyKu2QhJCCCEaPUlGzlJK4VDa45mWRS0jBmO55e2O4pE2XSL9yi0nhBBCiIpJMnKW1WHFprRF8YJtdojsVWH59DyLczvEx1yrsQkhhBCNmSQjZxXYC5zbZqXAp+JHNAcStcnO9DowGuQ2CiGEENUl36JnpeWnObeNAO7+FZbffkybY8RR8bxoQgghhLiAaiUjs2bNIiYmBnd3d/r06cOWLVsqVW/BggXodDpGjhxZncvWqtM5p53bOgAP/wrLL9lxCoDRvWUkjRBCCHExqpyMLFy4kMmTJzN16lS2b99Ot27dGDJkCMnJyRXWO3r0KE8++SQDBgyodrC1qWjCM+ccI8Htyy2rlCI+NReAvq2Daj02IYQQojGrcjIyffp07r//fsaPH0/Hjh2ZPXs2np6ezJ07t9w6drudO++8k5dffplWrVpdVMC1RafTAWcf0QCYyp/ILLvQ5twe2FbWpBFCCCEuRpWSEYvFwrZt2xg8eHDxCfR6Bg8ezMaNG8ut98orrxASEsKECRMqdZ3CwkKysrJKvGrbwfSDAFxWcHZdmqB25Zb9aXeic9vPU9akEUIIIS5GlZKR1NRU7HY7oaGhJfaHhoaSmJhYZp3169czZ84cPv3000pfZ9q0afj5+TlfUVG13y/DoDNoGw679tOv/Gtm5staNEIIIURNqdXRNNnZ2dx11118+umnBAVVvm/Fs88+S2ZmpvN14sSJWoxSU9QyEmO1gdGjwg6sn288BsCEy1vWelxCCCFEY1f+FKNlCAoKwmAwkJSUVGJ/UlISYWGl5+U4fPgwR48eZfjw4c59DofWQdRoNHLgwAFat25dqp7ZbMZsrtuJxMwG7Xr5eh3oK74tzbxNHE/Lw2SUkdFCCCHExarSt6nJZKJnz56sXr3auc/hcLB69Wr69u1bqnz79u3ZtWsXO3fudL5uvPFGBg0axM6dO+vk8UtlFXVgDbPZwVHxY5gdxzMA6NuqWW2HJYQQQjR6VWoZAZg8eTLjxo2jV69e9O7dm5kzZ5Kbm8v48eMBuPvuu4mMjGTatGm4u7vTuXPnEvX9/f0BSu13tb1n9gLg43BAeLdyyznOmeUsWKaBF0IIIS5alZORUaNGkZKSwosvvkhiYiLdu3dn5cqVzk6tx48fR69veI8vfEw+pBWkYQNIP1puuR0nMpzbbUK8azssIYQQotGrcjIC8Mgjj/DII4+UeWzt2rUV1p0/f351Llnr9DotgQqz26HtFeWW+2mPNmqof5tmuMmaNEIIIcRFk2/Ts/KseQCYlILssocpAxxJ0WZelURECCGEqBnyjXpWUp42QshNKYjqU245pbQ+I+3DfOskLiGEEKKxk2TkrKJJz/wcDnDYyi23JV5b3bddqPQXEUIIIWqCJCOA3WHHrrSZV70cCqL7l1lOKeVclyY2zKfO4hNCCCEaM0lGAIvD4tx2UwrcPMost/tU8Ro5MpJGCCGEqBmSjFDceRXOdmD1L3sytiOpOc5ts9FQ63EJIYQQTYEkI5RMRtwAvEPLLLf+UCoA3Zr71UFUQgghRNMgyQhwpuAMAIH2syv2lvOY5nRmPgA+7m51EpcQQgjRFEgyAhTaCwFIM1T86OVAovaYpluUtIwIIYQQNUWSEeBwxmEAuhYUQmDpVYQB7A5FoVVrOenW3L+uQhNCCCEaPUlGKJ4K/qSbEXKSyyyz61Qm2YU29DoY1D6kLsMTQgghGjVJRoA8m9aBtW9+ATRrVWaZPaczAfAyG2UqeCGEEKIGybcqEJceB4BBKYjsVWaZXSe1ZCTcz73O4hJCCCGaAklGgGYezQDINBjAml9mmQVbTwBwY7eIOotLCCGEaAokGQGsDisAbS0WaHVlhWWjm3nVQURCCCFE0yHJCGA7uzCemwL0pYf3puYUOrevjA2uq7CEEEKIJkGSESDHqs0fYlQKzKUXwFuy/aRzWyY8E0IIIWqWJCNAekE6AN4OB3gEljqelKW1jLQKkkc0QgghRE2TZAQosBUAEGy3g9FU6vi6QykAXBpTOlERQgghxMWRZAT4O/VvAMxKgW9kiWNKKQ4maY9x2oWVfoQjhBBCiIsjyQgQaPLVNnR68AgocSw1x+LcvrVn87oMSwghhGgSJBkBkgvSAAg1epcaTbP7VKZz289DOq8KIYQQNU2SkXN4GErPrnrsTC4AOl1dRyOEEEI0DU0+GVFKObe9Mk+WOv7LPm3hvHF9Y+oqJCGEEKJJafLJiF3ZndvG5r1LHY9L1jqvuruVngxNCCGEEBdPkpFzkhGDoWSfEKUUiVnasN8O4TKSRgghhKgNkow4zklG9CWTkWNn8pzbV8aG1FlMQgghRFPS5JMRm7I5t422whLHNhw+A0DLIC8ZSSOEEELUkiafjORb853bBuUocWxvgjas16iXoTRCCCFEbWnyyUiBvcC5rQ9qV+JYToHWatIi0LNOYxJCCCGakiafjORatXlEmtnsYDSXOPbbwbNr0rSUNWmEEEKI2tLkk5GMwgwAMg16KMwpcSw9zwpAkLf5/GpCCCGEqCFNPhkpGk3jZ3eAd/GImawCq3N7YLvgOo9LCCGEaCqafDJyKucUAC1sVjinz8ihpGzndrCPtIwIIYQQtaXJJyNmg5ZoxLmZQG907j93jhEhhBBC1J4mn4w4zg7n7VlQUGLF3txCbSRN8wAPl8QlhBBCNBVNPhkpmg5eDyVaRg6naKNs+rcOckFUQgghRNPR5JORolV7DVAiGfl1v7Zab49o/7oPSgghhGhCmnwyUtQyolMK7Np08GdyCjmepvUZ6RTh57LYhBBCiKagyScjRX1GDADeoQDsOpXpPN4pwtcFUQkhhBBNR5NPRkr0GTG6A7DxiLZA3qDYYHQ6WZdGCCGEqE1NPhkp6jOiV4BBW5l310mtZaRPq2auCksIIYRoMpp8MnI06ygAepSzA+uGw1rLSPswH1eFJYQQQjQZTT4ZCXAPAOCEmxHMvjgcynnM39PkqrCEEEKIJqPJJyNFfUY6FVpAb2RvQpbzWMdw6bwqhBBC1LYmn4xY7dqCeGalPaZZvU+bX6RVsBcmY5O/PUIIIUSta/LftvvT9gHgpgC9wTm/iLvRUEEtIYQQQtSUJp+MNDMHApBq0IPeyLJdpwEYdWmUK8MSQgghmowmn4w47BYA2lisKIOJAqs2CZqX2VhRNSGEEELUkCafjMSfHdrroRR7Uwqd+6/tFOqiiIQQQoimpcknIwqtJcRiMLHzRAYAzbxM+Lq7uTAqIYQQoulo8smIl8EDAD8MbDqSBkCQt9mVIQkhhBBNSpNPRhxnh/Z6KDv7z84xEuwjyYgQQghRV5p8L03H2cc0ems+h9JzALiuS7grQxJCCCGaFGkZcWgzsOr8iofyXhoT4KpwhBBCiCanyScjRdPBQ/EkZ1GBnq4JRgghhGiCqpWMzJo1i5iYGNzd3enTpw9btmwpt+ynn37KgAEDCAgIICAggMGDB1dYvq4VzTPiOOdWuLvJ7KtCCCFEXalyMrJw4UImT57M1KlT2b59O926dWPIkCEkJyeXWX7t2rWMHj2aNWvWsHHjRqKiorj22ms5derURQdfE+wOrQOrMe8MAKG+0nlVCCGEqEtVTkamT5/O/fffz/jx4+nYsSOzZ8/G09OTuXPnlln+yy+/5OGHH6Z79+60b9+ezz77DIfDwerVqy86+Jpw1KqNoEl3jwbAzdDkn1wJIYQQdapK37wWi4Vt27YxePDg4hPo9QwePJiNGzdW6hx5eXlYrVYCAwPLLVNYWEhWVlaJV23xO3sLTutCAAj0MtXatYQQQghRWpWSkdTUVOx2O6GhJadKDw0NJTExsVLnePrpp4mIiCiR0Jxv2rRp+Pn5OV9RUbW3aJ1Bp92CIHs+AM0DPGrtWkIIIYQorU6fSbz55pssWLCA7777Dnd393LLPfvss2RmZjpfJ06cqLWY7Er7eYowAHpFl99iI4QQQoiaV6VJz4KCgjAYDCQlJZXYn5SURFhYWIV133nnHd58801++eUXunbtWmFZs9mM2Vw3HUkdaNmI1aYDoJm3PKYRQggh6lKVWkZMJhM9e/Ys0fm0qDNq3759y6331ltv8eqrr7Jy5Up69epV/Whrgf1sMmJXWjIiw3qFEEKIulXl6eAnT57MuHHj6NWrF71792bmzJnk5uYyfvx4AO6++24iIyOZNm0aAP/617948cUX+eqrr4iJiXH2LfH29sbb27sGP0r1FLWMJGSeXaNGkhEhhBCiTlU5GRk1ahQpKSm8+OKLJCYm0r17d1auXOns1Hr8+HH0+uIGl48++giLxcKtt95a4jxTp07lpZdeurjoa0DR/KsOpcUsHViFEEKIulWthfIeeeQRHnnkkTKPrV27tsT7o0ePVucSdcZ6tmVEoT2mCfeTZEQIIYSoS016hi+bw1b8RmmPZ9zdmvQtEUIIIepck/7mtZxdlwZA73ADQKfTuSocIYQQokmq1mOaxsJ6dl0aAJSeMN/y5z4RQtQch8OBxWK5cEEhRL3m5uaGwXDxAz8kGTnLoQzEBHm6MBohmgaLxUJ8fDwOh8PVoQghaoC/vz9hYWEX9WShSScjRX1GjEqhB5RybTxCNHZKKRISEjAYDERFRZUYeSeEaFiUUuTl5ZGcnAxAeHh4tc/VpJORXGsuADadDjednVB5TCNErbLZbOTl5REREYGnp7RECtHQeXhoI1CTk5MJCQmp9iMb+WfJWcnKn57RAa4OQ4hGzW7XZvYxmWTZBSEai6J/WFit1guULF+TTkbU2ecygXY7dvR4mGT2VSHqgoxaE6LxqIn/npt0MuJA60CnU2DFSEwzLxdHJIQQQjQ9TToZKWoZ0QF2pSfEp25WChZCCCFEsaadjJydCl6PwoYBL3OT7s8rhCjHPffcg06nK/UaOnQoFouFoKAg3nzzzTLrvvrqq4SGhmK1Wpk/f36Z53F3b9qd59u3b4/ZbHYupHqu+Ph4xowZQ0REBO7u7jRv3pwRI0awf/9+Z5mi+7hp06YSdQsLC2nWrBk6na7UUiU1EVtMTAwzZ84stf+ll16ie/fuJfYlJiby6KOP0qpVK8xmM1FRUQwfPpzVq1dXKy6Av//+mwEDBuDu7k5UVBRvvfXWBets3bqVq6++Gn9/fwICAhgyZAh//fVXiTLffPMN3bt3x9PTk+joaN5+++1qx1hZTToZcZztTKcDMvEiwNPNtQEJIeqtoUOHkpCQUOL19ddfYzKZGDt2LPPmzStVRynF/Pnzufvuu3Fz0/5+8fX1LXWeY8eO1fXHuaC6mpRu/fr15Ofnc+utt/Kf//ynxDGr1co111xDZmYmS5Ys4cCBAyxcuJAuXbqQkZFRomxUVFSpP4PvvvvuolaHryi2qjh69Cg9e/bk119/5e2332bXrl2sXLmSQYMGMXHixGqdMysri2uvvZbo6Gi2bdvG22+/zUsvvcQnn3xSbp2cnByGDh1KixYt2Lx5M+vXr8fHx4chQ4Y4O5+uWLGCO++8kwcffJDdu3fz4YcfMmPGDP79739XK85KUw1AZmamAlRmZmaNnnf36c2q8/zOavBn7VXs09/W6LmFEKXl5+ervXv3qvz8fFeHUiXjxo1TI0aMKPf433//rQC1bt26EvvXrFmjALVv3z6llFLz5s1Tfn5+1Y5j0aJFqnPnzsrd3V0FBgaqq6++WuXk5DiPz5kzR3Xs2FGZTCYVFhamJk6c6Dx27NgxdeONNyovLy/l4+OjbrvtNpWYmOg8PnXqVNWtWzf16aefqpiYGKXT6ZRSSqWnp6sJEyaooKAg5ePjowYNGqR27tzprLdz50515ZVXKm9vb+Xj46N69Oihtm7dWunPdM8996hnnnlGrVixQrVr167EsR07dihAHT16tMJzAOr5559Xvr6+Ki8vz7n/mmuuUS+88IIC1Jo1ayodU2ViU0qp6OhoNWPGjFL7i+5lkWHDhqnIyMgSf1ZF0tPTqxyXUkp9+OGHKiAgQBUWFjr3Pf300yo2NrbcOlu3blWAOn78uHNf0e/uoUOHlFJKjR49Wt16660l6r3//vuqefPmyuFwlHneiv67ruz3d5NuGVHWQgD0Cgp1MtRQiLqmlCLPYnPJS9XgLIddunTh0ksvZe7cuSX2z5s3j379+tG+ffuLvkZCQgKjR4/m3nvvZd++faxdu5abb77Z+Tk++ugjJk6cyAMPPMCuXbv44YcfaNOmDaBNvz9ixAjS0tL47bffWLVqFUeOHGHUqFElrhEXF8e3337LkiVL2LlzJwC33XYbycnJrFixgm3bttGjRw+uvvpq0tLSALjzzjtp3rw5W7duZdu2bTzzzDPOVqALyc7OZtGiRYwdO9bZArJu3Trn8eDgYPR6PYsXL3YOCy9Pz549iYmJ4dtvvwXg+PHj/P7779x1112ViqWqsVVWWloaK1euZOLEiXh5lR4k4e/v79weNmwY3t7e5b46derkLLtx40auuOKKEsPkhwwZwoEDB0hPTy8zltjYWJo1a8acOXOwWCzk5+czZ84cOnToQExMDKA92jr/saGHhwcnT56s1Ra8Jt1JwlGYCWiPaSL8ZAImIepavtVOxxd/csm1974yBE9T5f8K/PHHH0s1+U+ZMoUpU6YAMGHCBJ588knef/99vL29yc7OZvHixbz//vsl6mRmZpY6z4ABA1ixYkWF109ISMBms3HzzTcTHR0NaElQkddee41//OMfPP744859l156KQCrV69m165dxMfHExUVBcDnn39Op06d2Lp1q7OcxWLh888/Jzg4GNAeU2zZsoXk5GTMZq2D/zvvvMPSpUtZvHgxDzzwAMePH+ef//ynM+Fq27bthW6l04IFC2jbtq3zS/aOO+5gzpw5DBgwAIDIyEjef/99nnrqKV5++WV69erFoEGDuPPOO2nVqlWp8917773MnTuXsWPHMn/+fK677jrnZ6mqC8VWWXFxcSilKpWQfvbZZ+Tn55d7/NwkLzExkZYtW5Y4Hhoa6jwWEFB63iwfHx/Wrl3LyJEjefXVVwHtz+unn37CaNT+WxgyZAiTJk3innvuYdCgQcTFxfHuu+8C2u9gUdJS05p0y4jDkgeADpkHXghRsUGDBrFz584SrwcffNB5fPTo0djtdr755hsAFi5ciF6vL9X64OPjU+o8n3322QWv361bN66++mq6dOnCbbfdxqeffur8F3BycjKnT5/m6quvLrPuvn37iIqKciYiAB07dsTf3599+/Y590VHR5f48v7rr7/IycmhWbNmJf6FHh8fz+HDhwGYPHky9913H4MHD+bNN9907q+MosShyNixY1m0aBHZ2dnOfRMnTiQxMZEvv/ySvn37smjRIjp16sSqVatKnW/s2LFs3LiRI0eOMH/+fO69995Kx1Kd2CqjKi1wkZGRtGnTptxXURJaXfn5+UyYMIH+/fuzadMm/vjjDzp37sz111/vTILuv/9+HnnkEW644QZMJhOXXXYZd9xxB0CtLt/QpFtGyNXm03dgICGz/GxUCFE7PNwM7H1liMuuXRVeXl7Oxx5l8fX15dZbb2XevHnce++9zJs3j9tvv71UK4her6/wPOUxGAysWrWKDRs28PPPP/PBBx/w3HPPsXnzZoKCgqp8vrKc/xghJyeH8PDwMkeiFD1eeOmllxgzZgzLli1jxYoVTJ06lQULFnDTTTdVeK29e/eyadMmtmzZwtNPP+3cb7fbWbBgAffff79zn4+PD8OHD2f48OG89tprDBkyhNdee41rrrmmxDmbNWvGDTfcwIQJEygoKGDYsGFVTh6qEpuvry+ZmZml6mdkZODn5wdoLQ86na7E6J/yDBs2rMJHQdHR0ezZsweAsLAwkpKSShwveh8WFlZm/a+++oqjR4+yceNGZ2Lx1VdfERAQwPfff88dd9yBTqfjX//6F2+88QaJiYkEBwc7R/yU1RpVU5p0MuLQaX8YRuWge5S/a4MRognS6XRVelRS302YMIErr7ySH3/8kQ0bNtT4kEidTkf//v3p378/L774ItHR0Xz33XdMnjyZmJgYVq9ezaBBg0rV69ChAydOnODEiRPO1pG9e/eSkZFBx44dy71ejx49SExMxGg0Vtg8365dO9q1a8ekSZMYPXo08+bNu2AyMmfOHK644gpmzZpVYv+8efOYM2dOiWTk/HvQvn17NmzYUObxe++9l+uuu46nn3662uukVDa22NhYtm3bVqr+9u3biY2NBSAwMJAhQ4Ywa9YsHnvssVIJX0ZGhjOxq8pjmr59+/Lcc89htVqd+1etWkVsbGyZj2gA8vLy0Ov1JWZMLXp//iraBoOByMhIAL7++mv69u1b7UdelVJh99Z6orZG0/y5+T1tNM0nXdRtszfU6LmFEKU15NE0Q4cOVQkJCSVeKSkpJco5HA7Vpk0bFRAQoNq3b1/qPPPmzVO+vr6lzpOQkKDsdnuFMWzatEm9/vrrauvWrerYsWPqm2++USaTSS1fvlwppdT8+fOVu7u7eu+999TBgwfVtm3b1Pvvv++Mq3v37mrAgAFq27ZtavPmzapnz55q4MCBzvOfPwKkqN7ll1+uunXrpn766ScVHx+v/vjjDzVlyhS1detWlZeXpyZOnKjWrFmjjh49qtavX69at26tnnrqqQo/i8ViUcHBweqjjz4qdWzv3r0KULt371Y7duxQN954o1q0aJHas2ePOnTokPrss8+Ul5eXeuWVV5x1APXdd985Y05JSXGOMklPT6/SaJrKxqaUUn/88YfS6/XqtddeU3v37lW7du1SU6ZMUUajUe3atctZ7/DhwyosLEx17NhRLV68WB08eFDt3btXvffee2X+nlRGRkaGCg0NVXfddZfavXu3WrBggfL09FQff/yxs8ySJUtKjK7Zt2+fMpvN6qGHHlJ79+5Vu3fvVmPHjlV+fn7q9OnTSimlUlJS1EcffaT27dunduzYoR577DHl7u6uNm/eXG4sNTGapkknIxs3vK06z++srvmki7rz0001em4hRGkNORkBSr3KGkb5xhtvKEC99dZbpY7NmzevzPMAKiEhocIY9u7dq4YMGaKCg4OV2WxW7dq1Ux988EGJMrNnz1axsbHKzc1NhYeHq0cffdR5rLJDe8+XlZWlHn30URUREaHc3NxUVFSUuvPOO9Xx48dVYWGhuuOOO1RUVJQymUwqIiJCPfLIIxf88128eLHS6/Ulrn+uDh06qEmTJqmUlBT12GOPqc6dOzuHDnfp0kW98847JZK3c5OR85WVjAwcOFCNGzfuomIr8tNPP6n+/furgIAA1axZM3XllVeq3377rVS906dPq4kTJ6ro6GhlMplUZGSkuvHGG6s15LjIX3/9pS6//HJlNptVZGSkevPNN0scL/p9O9fPP/+s+vfvr/z8/FRAQIC66qqr1MaNG53HU1JS1GWXXaa8vLyUp6enuvrqq9WmTRV/P9ZEMqJTqgbHt9WSrKws/Pz8yMzMxNfXt8bO+/Pvr/CP+EV423VEFs5m8UP9auzcQojSCgoKiI+Pp2XLlk1+1lHhOtHR0bz88svcc889rg6lUajov+vKfn836dE0584sYrE7yi0nhBCicdizZw9+fn7cfffdrg5FnKNJJyP2bG2tgSCrnjYh1Z8yWAghLtbx48crnPDq+PHjrg6xSiqawOuNN95wWVydOnXi77//rtVhqqLqGk839mpwmLQExFNZMBnkF1MI4ToRERHOWU/LO96QVDQyJDAwsI6jEfVd005G0KYXzsMD9yrOOSCEEDXJaDRWa/6R+qpoWKgQldGkmwOKxlXrlA4/D1mxVwghhHCFJp2M2JXWMqJDh8nYpG+FEEII4TJN+hvYoWwA6BTSZ0QIIYRwkSb9DZxsyz27pcNo0FVYVgghhBC1o0knIyalffx0Nxu5hTYXRyOEEEI0TU06GSkaPxNgMdEmxMelsQghhBBNVZNORpRNGwPvpvTo5CmNEKKB2LNnD7fccgsxMTHodDpmzpxZq9fbuHEjBoOB66+/vtSxtWvXotPpyMjIKHUsJiamVGxr1qzhuuuuo1mzZnh6etKxY0f+8Y9/cOrUqWrHN2vWLGJiYnB3d6dPnz5s2bLlgnVmzpxJbGwsHh4eREVFMWnSJAoKCkrErtPpSr0mTpxY6lxKKYYNG4ZOp2Pp0qXV/hxNWdNORgqzATAqB828TBcoLYQQ9UNeXh6tWrXizTffJCwsrNavN2fOHB599FF+//13Tp8+Xe3zfPzxxwwePJiwsDC+/fZb9u7dy+zZs8nMzOTdd9+t1jkXLlzI5MmTmTp1Ktu3b6dbt24MGTKE5OTkcut89dVXPPPMM0ydOpV9+/YxZ84cFi5cyJQpU5xltm7dSkJCgvO1atUqAG677bZS55s5cyY6+RftxalwGb16orZW7Z2/+HbVeX5nNfb9S9X2Y2k1em4hRGkNddVeu92u3njjDRUTE6Pc3d1V165d1aJFi5zH09LS1JgxY1RQUJByd3dXbdq0UXPnzlVKKRUfH68AtXDhQnX55Zcrd3d31atXL3XgwAG1ZcsW1bNnT+Xl5aWGDh2qkpOTqxxbdHS0mjFjRol9H3/8sQoPDy+xsq1SSt14441q/PjxVTp/dna28vb2Vvv371ejRo1Sr7/+eonja9asUYBKT0+vMLYTJ04ok8mknnjiiTKvU1b9yujdu7eaOHGi873dblcRERFq2rRp5daZOHGiuuqqq0rsmzx5surfv3+5dR5//HHVunVr5XA4SuzfsWOHioyMVAkJCRWuHtyY1cSqvU27ZQRtweJ83DHKOgVC1D2lwJLrmlcVFiyfNm0an3/+ObNnz2bPnj1MmjSJsWPH8ttvvwHwwgsvsHfvXlasWMG+ffv46KOPCAoKKnGOqVOn8vzzz7N9+3aMRiNjxozhqaee4r333mPdunXExcXx4osv1shtve222zhz5gxr1qxx7ktLS2PlypXceeedVTrXN998Q/v27YmNjWXs2LHMnTsXVY3F3hctWoTFYuGpp54q87i/vz9w4TV6zl3bxmKxsG3bNgYPHuw8j16vZ/DgwWzcuLHcWPr168e2bducj3OOHDnC8uXLue6668osb7FY+OKLL7j33ntLtIDk5eUxZswYZs2aVSctVI1Z054O/pz/oCQXEcIFrHnwhovWXJlyGkxeFyxWWFjIG2+8wS+//ELfvn0BaNWqFevXr+fjjz9m4MCBHD9+nEsuuYRevXoBWn+D8z355JMMGTIEgMcff5zRo0ezevVq+vfvD8CECROYP39+jXy0gIAAhg0bxldffcXVV18NwOLFiwkKCmLQoEFVOtecOXMYO3YsAEOHDiUzM5PffvuNK6+8skrnOXToEL6+voSHh1dY7kJr9EDx2japqanY7XZCQ0NLHA8NDWX//v3l1h8zZgypqalcfvnlKKWw2Ww8+OCDJR7TnGvp0qVkZGRwzz33lNg/adIk+vXrx4gRIyqMV1xYk05GilpGUDppGRFClCkuLo68vDyuueaaEvstFguXXHIJAA899BC33HIL27dv59prr2XkyJH069evRPmuXbs6t4u+PLt06VJiX0X9HKrqzjvv5P777+fDDz/EbDbz5Zdfcscdd1RptdoDBw6wZcsWvvvuO0BbP2fUqFHMmTOnysmIUqpS/SrqYo2etWvX8sYbb/Dhhx/Sp08f4uLiePzxx3n11Vd54YUXSpWfM2cOw4YNK7FY4Q8//MCvv/7Kjh07ajXWpkKSEbTp4A166XwkRJ1z89RaKFx17UrIyckBYNmyZaUWfzObzQAMGzaMY8eOsXz5clatWsXVV1/NxIkTeeedd4ov51a8/lXRl/L5+4rWy6oJw4cPRynFsmXLuPTSS1m3bh0zZsyo0jnmzJmDzWYr8SWslMJsNvPvf/8bPz8/fH19AcjMzHQ+aimSkZGBn58fAO3atSMzM5OEhIQKW0eOHz9Ox44dK4xrypQpTJkyhaCgIAwGA0lJSSWOJyUlVfjY5IUXXuCuu+7ivvvuA7SkMDc3lwceeIDnnnuuRMJ27NgxfvnlF5YsWVLiHL/++iuHDx8u9ZlvueUWBgwYwNq1ayv8DKKkJp2MWO3F/+GH+7m7MBIhmiidrlKPSlypY8eOmM1mjh8/zsCBA8stFxwczLhx4xg3bhwDBgzgn//8Z4lkpK65u7tz88038+WXXxIXF0dsbCw9evSodH2bzcbnn3/Ou+++y7XXXlvi2MiRI/n666958MEHadu2LXq9nm3bthEdHe0sc+TIETIzM2nXrh0At956K8888wxvvfVWmUlRRkYG/v7+VXpMYzKZ6NmzJ6tXr2bkyJGAtgDq6tWreeSRR8qtn5eXV6qFyGDQZp46vz/MvHnzCAkJKTWs+ZlnnnEmM0W6dOnCjBkzGD58eIXxi9KadDJiO7tQHujwMjfpWyGEKIePjw9PPvkkkyZNwuFwcPnll5OZmckff/yBr68v48aN48UXX6Rnz5506tSJwsJCfvzxRzp06FBrMVksFvbu3evcPnXqFDt37sTb27vEI44777yTG264gT179jj7fVTWjz/+SHp6OhMmTHC2bhS55ZZbmDNnDg8++CA+Pj7cd999/OMf/8BoNNKlSxdOnDjB008/zWWXXeZ8XBUVFcWMGTN45JFHyMrK4u677yYmJoaTJ0/y+eef4+3tzbvvvlvlxzSTJ09m3Lhx9OrVi969ezNz5kxyc3MZP368s8zdd99NZGQk06ZNA7RWo+nTp3PJJZc4H9O88MILDB8+3JmUgJbYzJs3j3HjxmE0lvyOCAsLK7P1pUWLFrRs2bLS8Yuzan6QT82rraG9M7+6QXWe31nd/t6gGj2vEKJsDXVor8PhUDNnzlSxsbHKzc1NBQcHqyFDhqjffvtNKaXUq6++qjp06KA8PDxUYGCgGjFihDpy5IhSqnho744dO5znK2s47Lx585Sfn1+l4ik65/mvgQMHlihnt9tVeHi4AtThw4fLPMeaNWvKvMYNN9ygrrvuujKPbd68WQHqr7/+Ukppf65Tp05V7du3Vx4eHqply5bqgQceUCkpKaXqrlq1Sg0ZMkQFBAQod3d31b59e/Xkk0+q06dPV+qzl+WDDz5QLVq0UCaTSfXu3Vtt2rSpxPGBAweqcePGOd9brVb10ksvqdatWyt3d3cVFRWlHn744VLDi3/66ScFqAMHDlQqDmRob6ljlf3+1ilVjTFadSwrKws/Pz8yMzOdzydrwowvb2Cu7RidMkNY8NjqGjuvEKJsBQUFxMfH07JlS9zd5dGoK61Zs4abb76ZI0eOEBAQ4OpwRANW0X/Xlf3+btJDSDKVFQC9zJwnhGhili9fzpQpUyQREfVCk+4okW63AGDR11wPdiGEuFje3t7lHluxYgUDBgy46Gu8/fbbF30OIWpKk05GfJQRdGCzSzIihKg/KhpNcv7wYiEagyadjNjRkpBwt/L/FSKEEHWttif9EqK+adJ9RixnW0Skz4gQQgjhOk06GXGcnYHVZnNxIEIIIUQT1qSTkQKbNumZp6lJP60SQgghXKpJJyO6s5/ebq+4nBBCCCFqT5NORqxnO7D6uZtcHIkQQgjRdDXpZOS4oeDslnRgFUI0LImJiVxzzTV4eXmVWjlWiIamSScjYXatRaRQLz1YhRBl0+l0Fb5eeukll8Q1Y8YMEhIS2LlzJwcPHqyz606bNg2DwVDmpGkvvfQS3bt3L7X/6NGj6HS6EvOnKKX45JNP6NOnD97e3vj7+9OrVy9mzpxJXl5etWIrKChg4sSJNGvWDG9vb2655RaSkpIqrJOUlMQ999xDREQEnp6eDB06lEOHDpUo83//93+0bt0aDw8PgoODGTFiBPv37y/zfGfOnKF58+bodDoyMjKq9TmaoiadjBSNpgmifi9hLoRwnYSEBOdr5syZ+Pr6ltj35JNPOssqpbDV0fC8w4cP07NnT9q2bUtISEi1zmGxWKpcZ+7cuTz11FPMnTu3Wtcsctddd/HEE08wYsQI1qxZw86dO3nhhRf4/vvv+fnnn6t1zkmTJvG///2PRYsW8dtvv3H69GluvvnmcssrpRg5ciRHjhzh+++/Z8eOHURHRzN48GByc3Od5Xr27Mm8efPYt28fP/30E0oprr32WuxldDicMGECXbt2rVb8TVrNr99X82pr1d57ZndVned3Vm//554aPa8QomwNddXeIuevrFu0+u7y5ctVjx49lJubm1qzZo2Ki4tTN954owoJCVFeXl6qV69eatWqVSXOFR0drV5//XU1fvx45e3traKiotTHH3/sPF5YWKgmTpyowsLClNlsVi1atFBvvPGGsy7nrNZbtCJtenq6mjBhggoKClI+Pj5q0KBBaufOnc5zTp06VXXr1k19+umnKiYmRul0uip9/rVr16rIyEhlsVhURESE+uOPP0ocLzr/+c5fuXjhwoUKUEuXLi1V1uFwqIyMjCrFpZRSGRkZys3NTS1atMi5b9++fQpQGzduLLPOgQMHFKB2797t3Ge321VwcLD69NNPy73WX3/9pQAVFxdXYv+HH36oBg4cqFavXl1qVebGrCZW7W3SLSP5Zz++uz3fxZEI0TQppciz5rnkpWpwwfJnnnmGN998k3379tG1a1dycnK47rrrWL16NTt27GDo0KEMHz6c48ePl6j37rvv0qtXL3bs2MHDDz/MQw89xIEDBwB4//33+eGHH/jmm284cOAAX375JTExMQBs3bqVoUOHcvvtt5OQkMB7770HwG233UZycjIrVqxg27Zt9OjRg6uvvpq0tDTnNePi4vj2229ZsmRJhdPOl2XOnDmMHj0aNzc3Ro8ezZw5c6p1v7788ktiY2MZMWJEqWM6nQ4/Pz9nOW9v7wpf69atA2Dbtm1YrVYGDx7sPFf79u1p0aIFGzduLDOOwsJCgBIrzer1esxmM+vXry+zTm5uLvPmzaNly5ZERUU59+/du5dXXnmFzz//HL2+SX+1Vku1JtiYNWsWb7/9NomJiXTr1o0PPviA3r17l1t+0aJFvPDCCxw9epS2bdvyr3/9i+uuu67aQdeYs78wOfrylzUWQtSefFs+fb7q45Jrbx6zGU83zxo51yuvvMI111zjfB8YGEi3bt2c71999VW+++47fvjhBx555BHn/uuuu46HH34YgKeffpoZM2awZs0aYmNjOX78OG3btuXyyy9Hp9MRHR3trBccHIzZbMbDw4OwsDAA1q9fz5YtW0hOTsZsNgPwzjvvsHTpUhYvXswDDzwAaI9mPv/8c4KDg6v0GbOysli8eLHzi33s2LEMGDCA9957r8KF/cpy6NAhYmNjL1juxhtvpE+fin8/itbqSUxMxGQylerMGxoaSmJiYpl1i5KVZ599lo8//hgvLy9mzJjByZMnSUhIKFH2ww8/5KmnniI3N5fY2FhWrVqFyXS232FhIaNHj+btt9+mRYsWHDly5IKfTZRU5fRt4cKFTJ48malTp7J9+3a6devGkCFDSE5OLrP8hg0bGD16NBMmTGDHjh2MHDmSkSNHsnv37osO/mKps31GvDykz4gQovp69epV4n1OTg5PPvkkHTp0wN/fH29vb/bt21eqZeTcvgU6nY6wsDDn36X33HMPO3fuJDY2lscee+yC/Sj++usvcnJynJ03i17x8fEcPnzYWS46OrrKiQjA119/TevWrZ1JVvfu3YmOjmbhwoVVPldlW6V8fHxo06ZNhS8PD48qX7+Im5sbS5Ys4eDBgwQGBuLp6cmaNWsYNmxYqdaNO++8kx07dvDbb7/Rrl07br/9dgoKtBGZzz77LB06dGDs2LHVjqWpq3LLyPTp07n//vsZP348ALNnz2bZsmXMnTuXZ555plT59957j6FDh/LPf/4T0P6FsGrVKv79738ze/bsiwz/4hR1YDUaZAZWIVzBw+jB5jGbXXbtmuLlVfIfNE8++SSrVq3inXfecX5h3nrrraU6jLq5uZV4r9PpcDi0+Y969OhBfHw8K1as4JdffuH2229n8ODBLF68uMwYcnJyCA8PZ+3ataWOndtacH6slTVnzhz27NmD0Vj896XD4WDu3LlMmDABAF9fXzIzM0vVLRpVUvT4pV27duWORjnXl19+yf/93/9VWGbFihUMGDCAsLAwLBYLGRkZJT5vUlKSs/WoLD179mTnzp1kZmZisVgIDg6mT58+pRJMPz8//Pz8aNu2LZdddhkBAQF89913jB49ml9//ZVdu3Y5/2yKkq2goCCee+45Xn755Qt+1qauSt/CFouFbdu28eyzzzr36fV6Bg8eXO4zuY0bNzJ58uQS+4YMGcLSpUvLvU5hYaHzWR5ozYO1oahlxKB3u0BJIURt0Ol0NfaopD75448/uOeee7jpppsALVE4evRolc/j6+vLqFGjGDVqFLfeeitDhw4lLS2NwMDAUmV79OhBYmIiRqPR2bekpuzatYs///yTtWvXlrh2WloaV155Jfv376d9+/bExsZy8uRJkpKSCA0NdZbbvn077u7utGjRAoAxY8Zwxx138P3335fqN6KUIisrCz8/vyo9punZsydubm6sXr2aW265BYADBw5w/Phx+vbte8HPWJQoHTp0iD///JNXX3213LJKKZRSzu+pb7/9lvz84r6HW7du5d5772XdunW0bt36gtcWVUxGUlNTsdvtJX7JQHsmV16Wm5iYWGb58p7hgTaOvS4ySWcyYpBkRAhRc9q2bcuSJUsYPnw4Op2OF154wdniUVnTp08nPDycSy65BL1ez6JFiwgLCyt3grPBgwfTt29fRo4cyVtvvUW7du04ffo0y5Yt46abbir1L/2qmDNnDr179+aKK64odezSSy9lzpw5vP322wwZMoTY2FhGjx7Na6+9RlhYGNu3b+f555/n8ccfx2AwAHD77bc7WxWef/55rr32WoKDg9m1axczZszg0UcfZeTIkfj4+ODj41OpGP38/JgwYQKTJ08mMDAQX19fHn30Ufr27ctll13mLNe+fXumTZvmTBQXLVpEcHAwLVq0YNeuXTz++OOMHDmSa6+9FoAjR46wcOFCZ4wnT57kzTffxMPDw9n38fyEIzU1FcD5mE5cWL3s8vvss8+SmZnpfJ04caJWrtPHuzfDbC2Ijbq0Vs4vhGiapk+fTkBAAP369WP48OEMGTKEHj16VOkcPj4+vPXWW/Tq1YtLL72Uo0ePsnz58nJHauh0OpYvX84VV1zB+PHjadeuHXfccQfHjh0r9Q/C882fPx+druyZqC0WC1988YWzteF8t9xyC59//jlWqxWj0cjPP/9MixYtGD16NJ07d2bq1Kk8/vjjJVoadDodX331FdOnT2fp0qUMHDiQrl278tJLLzFixAiGDBlSybtU0owZM7jhhhu45ZZbuOKKKwgLC2PJkiUlyhw4cKDEo6SEhATuuusu2rdvz2OPPcZdd93F119/7Tzu7u7OunXruO6662jTpg2jRo3Cx8eHDRs2VHt+F1GaTlVhfJvFYsHT05PFixczcuRI5/5x48aRkZHB999/X6pOixYtmDx5Mk888YRz39SpU1m6dCl//fVXpa5b1GSXmZmJr6+MfBGioSooKCA+Pp6WLVuWGE4pXGvq1Kn89ttvZfY3EeJCKvrvurLf31VqGTGZTPTs2ZPVq1c79zkcDlavXl3uM7m+ffuWKA+watWqSj3DE0IIUftWrFjBW2+95eowRBNW5WEkkydPZty4cfTq1YvevXszc+ZMcnNznaNr7r77biIjI5k2bRoAjz/+OAMHDuTdd9/l+uuvZ8GCBfz555988sknNftJhBBCVMuWLVtcHYJo4qqcjIwaNYqUlBRefPFFEhMT6d69OytXrnQ+kzx+/HiJZ5r9+vXjq6++4vnnn2fKlCm0bduWpUuX0rlz55r7FEIIIYRosKrUZ8RVpM+IEI2D9BkRovGp8z4jQgghhBA1TZIRIUSdawANskKISqrqHDplkXnQhRB1xs3NDZ1OR0pKCsHBweXObSGEqP+UUlgsFlJSUtDr9c6FA6tDkhEhRJ0xGAw0b96ckydPVmt6dCFE/ePp6UmLFi3KnZCvMiQZEULUKW9vb9q2bYvVanV1KEKIi2QwGDAajRfdyinJiBCizhkMBuc6JUIIIR1YhRBCCOFSkowIIYQQwqUkGRFCCCGESzWIPiNFcxJkZWW5OBIhhBBCVFbR9/aF5hZqEMlIdnY2AFFRUS6ORAghhBBVlZ2djZ+fX7nHG8TaNA6Hg9OnT+Pj41OjkyRlZWURFRXFiRMnZM2bWiT3ue7Iva4bcp/rhtznulGb91kpRXZ2NhERERXOQ9IgWkb0ej3NmzevtfP7+vrKL3odkPtcd+Re1w25z3VD7nPdqK37XFGLSBHpwCqEEEIIl5JkRAghhBAu1aSTEbPZzNSpUzGbza4OpVGT+1x35F7XDbnPdUPuc92oD/e5QXRgFUIIIUTj1aRbRoQQQgjhepKMCCGEEMKlJBkRQgghhEtJMiKEEEIIl2r0ycisWbOIiYnB3d2dPn36sGXLlgrLL1q0iPbt2+Pu7k6XLl1Yvnx5HUXasFXlPn/66acMGDCAgIAAAgICGDx48AX/XESxqv5OF1mwYAE6nY6RI0fWboCNRFXvc0ZGBhMnTiQ8PByz2Uy7du3k749KqOp9njlzJrGxsXh4eBAVFcWkSZMoKCioo2gbpt9//53hw4cTERGBTqdj6dKlF6yzdu1aevTogdlspk2bNsyfP792g1SN2IIFC5TJZFJz585Ve/bsUffff7/y9/dXSUlJZZb/448/lMFgUG+99Zbau3evev7555Wbm5vatWtXHUfesFT1Po8ZM0bNmjVL7dixQ+3bt0/dc889ys/PT508ebKOI294qnqvi8THx6vIyEg1YMAANWLEiLoJtgGr6n0uLCxUvXr1Utddd51av369io+PV2vXrlU7d+6s48gblqre5y+//FKZzWb15Zdfqvj4ePXTTz+p8PBwNWnSpDqOvGFZvny5eu6559SSJUsUoL777rsKyx85ckR5enqqyZMnq71796oPPvhAGQwGtXLlylqLsVEnI71791YTJ050vrfb7SoiIkJNmzatzPK33367uv7660vs69Onj/q///u/Wo2zoavqfT6fzWZTPj4+6j//+U9thdhoVOde22w21a9fP/XZZ5+pcePGSTJSCVW9zx999JFq1aqVslgsdRVio1DV+zxx4kR11VVXldg3efJk1b9//1qNszGpTDLy1FNPqU6dOpXYN2rUKDVkyJBai6vRPqaxWCxs27aNwYMHO/fp9XoGDx7Mxo0by6yzcePGEuUBhgwZUm55Ub37fL68vDysViuBgYG1FWajUN17/corrxASEsKECRPqIswGrzr3+YcffqBv375MnDiR0NBQOnfuzBtvvIHdbq+rsBuc6tznfv36sW3bNuejnCNHjrB8+XKuu+66Oom5qXDFd2GDWCivOlJTU7Hb7YSGhpbYHxoayv79+8usk5iYWGb5xMTEWouzoavOfT7f008/TURERKlfflFSde71+vXrmTNnDjt37qyDCBuH6tznI0eO8Ouvv3LnnXeyfPly4uLiePjhh7FarUydOrUuwm5wqnOfx4wZQ2pqKpdffjlKKWw2Gw8++CBTpkypi5CbjPK+C7OyssjPz8fDw6PGr9loW0ZEw/Dmm2+yYMECvvvuO9zd3V0dTqOSnZ3NXXfdxaeffkpQUJCrw2nUHA4HISEhfPLJJ/Ts2ZNRo0bx3HPPMXv2bFeH1qisXbuWN954gw8//JDt27ezZMkSli1bxquvvurq0MRFarQtI0FBQRgMBpKSkkrsT0pKIiwsrMw6YWFhVSovqnefi7zzzju8+eab/PLLL3Tt2rU2w2wUqnqvDx8+zNGjRxk+fLhzn8PhAMBoNHLgwAFat25du0E3QNX5nQ4PD8fNzQ2DweDc16FDBxITE7FYLJhMplqNuSGqzn1+4YUXuOuuu7jvvvsA6NKlC7m5uTzwwAM899xz6PXy7+uaUN53oa+vb620ikAjbhkxmUz07NmT1atXO/c5HA5Wr15N3759y6zTt2/fEuUBVq1aVW55Ub37DPDWW2/x6quvsnLlSnr16lUXoTZ4Vb3X7du3Z9euXezcudP5uvHGGxk0aBA7d+4kKiqqLsNvMKrzO92/f3/i4uKcyR7AwYMHCQ8Pl0SkHNW5z3l5eaUSjqIEUMkyazXGJd+FtdY1th5YsGCBMpvNav78+Wrv3r3qgQceUP7+/ioxMVEppdRdd92lnnnmGWf5P/74QxmNRvXOO++offv2qalTp8rQ3kqo6n1+8803lclkUosXL1YJCQnOV3Z2tqs+QoNR1Xt9PhlNUzlVvc/Hjx9XPj4+6pFHHlEHDhxQP/74owoJCVGvvfaaqz5Cg1DV+zx16lTl4+Ojvv76a3XkyBH1888/q9atW6vbb7/dVR+hQcjOzlY7duxQO3bsUICaPn262rFjhzp27JhSSqlnnnlG3XXXXc7yRUN7//nPf6p9+/apWbNmydDei/XBBx+oFi1aKJPJpHr37q02bdrkPDZw4EA1bty4EuW/+eYb1a5dO2UymVSnTp3UsmXL6jjihqkq9zk6OloBpV5Tp06t+8AboKr+Tp9LkpHKq+p93rBhg+rTp48ym82qVatW6vXXX1c2m62Oo254qnKfrVareumll1Tr1q2Vu7u7ioqKUg8//LBKT0+v+8AbkDVr1pT5d27RvR03bpwaOHBgqTrdu3dXJpNJtWrVSs2bN69WY9QpJW1bQgghhHCdRttnRAghhBANgyQjQgghhHApSUaEEEII4VKSjAghhBDCpSQZEUIIIYRLSTIihBBCCJeSZEQIIYQQLiXJiBBCCCFcSpIRIYQQQriUJCNCCCGEcClJRoQQQgjhUpKMCCGEEMKl/h/abRo7AOCwPAAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Figure: analyze ROC curve\n",
    "spec_at_sens = {}\n",
    "sens_at_spec = {}\n",
    "for m in ['EVE_scores_ASM', 'esm_1v', 'Transfer']:\n",
    "    fpr, tpr, _ = roc_curve(y, pred[m])\n",
    "    spec_at_sens[m] = {}\n",
    "    sens_at_spec[m] = {}\n",
    "    for sens in [0.9, 0.95, 0.99]:\n",
    "        spec_at_sens[m]['spec at sens '+str(sens)] = 1 - min(fpr[tpr > sens])\n",
    "    for spec in [0.9, 0.95, 0.99]:\n",
    "        sens_at_spec[m]['sens at spec '+str(spec)] = max(tpr[fpr < (1 - spec)])\n",
    "    auc = round(roc_auc_score(y, pred[m]), 3)\n",
    "    plt.plot(fpr,tpr,label = m + \", \" + \"AUC=\" + str(auc))\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "35c1b932-cca8-400e-b894-bb0cf0dd8523",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>EVE_scores_ASM</th>\n",
       "      <th>esm_1v</th>\n",
       "      <th>Transfer</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>spec at sens 0.9</th>\n",
       "      <td>0.689396</td>\n",
       "      <td>0.498661</td>\n",
       "      <td>0.809410</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>spec at sens 0.95</th>\n",
       "      <td>0.548100</td>\n",
       "      <td>0.273181</td>\n",
       "      <td>0.674701</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>spec at sens 0.99</th>\n",
       "      <td>0.237640</td>\n",
       "      <td>0.070214</td>\n",
       "      <td>0.371625</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   EVE_scores_ASM    esm_1v  Transfer\n",
       "spec at sens 0.9         0.689396  0.498661  0.809410\n",
       "spec at sens 0.95        0.548100  0.273181  0.674701\n",
       "spec at sens 0.99        0.237640  0.070214  0.371625"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Table: specificity at high sensitivity\n",
    "pd.DataFrame(spec_at_sens)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "7b57e716-29ae-4eb1-8f63-c9a52b726cec",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>EVE_scores_ASM</th>\n",
       "      <th>esm_1v</th>\n",
       "      <th>Transfer</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>sens at spec 0.9</th>\n",
       "      <td>0.677369</td>\n",
       "      <td>0.746809</td>\n",
       "      <td>0.816054</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sens at spec 0.95</th>\n",
       "      <td>0.512282</td>\n",
       "      <td>0.639942</td>\n",
       "      <td>0.698066</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sens at spec 0.99</th>\n",
       "      <td>0.229787</td>\n",
       "      <td>0.392360</td>\n",
       "      <td>0.446325</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   EVE_scores_ASM    esm_1v  Transfer\n",
       "sens at spec 0.9         0.677369  0.746809  0.816054\n",
       "sens at spec 0.95        0.512282  0.639942  0.698066\n",
       "sens at spec 0.99        0.229787  0.392360  0.446325"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Table: sensitivity at high specificity\n",
    "pd.DataFrame(sens_at_spec)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "d5b5ac7a",
   "metadata": {},
   "source": [
    "#### High quality ClinVar set and benchmark against REVEL"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "5a035499",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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uZl6V3qdmp+KXa78gNTsVAOCscMaMTjMQ6hcK2YPhtc4KZ1jJrQCgRJPMTa3rtvZ2xs7pPSCX16EvwqJakIrUcnCyMCICwwg9JIQQuDZ2HHJOny73WFNrQDRCg6sZV/H7jd/x0Z8f6T1GBhmeaf0MpjwyBS5KF73HCFEURIprQ+psE0xZAcSUmg0GDyICwwhZgL7ht5qcHL1BpCJTrGeqMrH/6n58c+EbXLpbPKKlsVNjdPDoAACwtbJFN59uaOvWFp4OngbLm5OvRrZKLQWRoiaZOhFCgPKnQwfYX4OIzMYwQtXG2OG3zY8chvzBqBZT1nYRQiAhIwG//fMb7qvuY+2ZtdI+pZUSLgoX9G3cF7O7zIaN3MbgNXLy1SXe618Bd+f0HnBQ1tI/PvpGuZQ3HbpMxloOIjJbLf3blB4mpswBYtepE6zq1zc6gFzPvI70vHQkZCRg47mNiE+P1znmuXbPYWKbiQabX4rKmK1S6w0epQX51YO9wsqo8tUoxnRC5XToRFQFGEaoSgmNBokjnzRqhVvAtJqQ6GvRmHlwpjQnSJHOnp0R4BIAK5kVhjcbjrZubQ2Xz8gQUnKislo3OsbYkTCcDp2IqgjDCFU6qU+IEEgcMRKqa8XDZSsy/BYAsvOzEZ0UjXmH52ltb+TYCP39+2Ny28ll1oCUVDwyRjuE6JshtVYFEGM6onKUCxFVI4YRqlSGakIUfn4I2PaD2SEEAC7euYgn/+9JrW09GvbAp30/hY2V/j4gOuV70CdECOhMVlYjR8aYOn16ecNw2QmViCyAYYQqjdBokDBwkFZNCFBYGxLww1bIKlC9f+b2GYzdPVZ6P7L5SLR1a4uRzUcaHRwM1YTUqJExxg6vNRVDCBFZEMMIVVhRB9WSTTJFNSGQyUzqB6LPvzn/4oPjH0jv3wx+E6NbjtYpQ8lRMLpl1K0JAR6SycosMYV66WYYNsEQkQUxjJDZDI2SUfj5ocme3RWqCSkp8mAkzv17Dgq5ApsGbUKbBm20ymDsKJgiJadtr9S+IOasOFsZAcOc6dMZPojoIcIwQiYra6huZTTJlBR9LRqnbp0CAKzqv0oKIuaEEKASa0KqcsXZ8rBWg4hqGYYRMlp5IaQio2RKy9fk48tzX+Lz2M8BAL0a9kKb+h2RrSowOBGZvlEwpVVKTYgQwPow4Prxil2nJE6hTkR1GMMIGcXQ2jGVHUIAYOPZjVj19ypk5Rf27+jq3Q1Xzg1H61/26T2+2kfBqLIMBxFzV5xlwCCiOoxhhMpUNGdI6bVjKjuE/HX7L/x16y/sSdyDs/+eBQDYyG3gdG8M9v/aCoBK55wqDyH6+oAUTY1eZFY8oLAvfs9QQURkMoYR0lFy0jJ9TTLNjxw2acp2QzJVmbidfRu56lw8t+855KpzpX3dfboj/kJvxN9wlraVboapkonIigKIMX1AvNoBDm4MH0REFcQwQloMNccUMXXtGH3WnVmHg9cP4u/bf2tN5e5h54EgryC0btAaI5uORdvonwFU4zwgpvQFKZoanUGEiKjCGEbqOKkW5IHSzTGA9joyFZkzJP5uPL6M+xI74ndI2+QyOVwULrCxssHsR2ejh89j0pwgRaplhVwhgKw03SBiqA8Im2OIiCoNw0gdVl4tSPMjhyG3szM7gOQW5CL2dix2XtmJhIwEnEnTbvJY3X81gr2CYSW3kmZHnZqs3Um1tbdz1a2QW1aTTFFfEIYOIqIqxzBSh4ns7Eptjjlz+ww+Pf0p8tR5AIDTt3Sv3d2nO8a2GosuXl1ga20LIQSy8grKnB21SiYlK6tPiG9X9gUhIqpGDCN1lHjQObVIUS1IEVNrQ06mnMSz+57Vu8/e2h6PNX4MU9pNQRPXJtL9s/IKdOYLMTg7qjmzm5ZkbIdUrs9CRFTtGEbqkJL9QzQ5OdIoGWWrVmZ3Ss3Oz8Ynpz7BNxe+kbaNaD4CvRoWDn9t6toUfs5+0rox5U1apjU7qhCFc3pU5eymJfuEsEmGiMgiGEbqCKHRIHHkkzrDdAEUzhdixpfwpbuXMPKnkVrbVvZbiV6NiufhEELgyVXHEHPtrsHrSPOF2MghK6iCFWlL4lTqREQPHYaROkBoNEgYOEhaUbcku06dILO313NW2fYk7sHrh16X3req3wrrwtbBWeGstYJutkptMIhoTVomBLCml3HNKBUJDwwfREQPHYaRWk4IgcSRT0pBROHnh4BtP0hfyOaMlPny3Jf46M+PpPf/7flfDGoyCACkUTH6Fq/7881QrZExUp8QjQb4LAi4c0X3ZmxGISKq9RhGajEhBNR37khNMwo/PzTZs7tCK+oKIbD+7HoAQJ9GfRDeJhxBXkHSKrr6RsUAQJBfPTRwUBSGj6LOqPkonl69KIjUbwo8f4jNKEREdQjDSC2lr49IwLYfKhRE1Bo15h2ehzu5d6C0UmJpn6WwsbLR2y+k5KgY4EEtCADk3TfcF6R+U2Dan0AFykhERDUPw0gtpK+PiLl9Q4okpCdg+anlOHD9AABgcJPBsLGyAaDbL0RnVAxQ/lTrRdOrM4gQEdU5DCO1iBACIjsbiSNG6vQRqcjquqdvncakvZOgFmrIZXK81fUtjGg+AkBxH5Eif74ZWtwcU1iowiYZVbZ2EOGoFiIiesCsf4auXLkS/v7+sLW1RXBwME6cOFHm8cuXL0dgYCDs7Ozg6+uLmTNnIjc3t8xzyHhCCGiyspA4YiQudg7SCiJN9uyG3MHBrCCSr8nH2r/XYurPU6EWhaNjlvZeipEtRkImk0EIodVHpLW3s3a/kLz7wOqewEIf4KNmxReeFQ88/zugdCycYIyTjBER1Wkm14x89913iIyMxKpVqxAcHIzly5cjLCwMFy9ehIeHh87x33zzDebMmYP169ejW7duuHTpEiZOnAiZTIalS5dWyoeoywytL6Ns1QoBP2w1u49IRl4GXj34Ko6nFNZmdPbsjBWPrYCTwkm6779ZKmnUTHEfEVnZTTKcap2IiEqRCSFE+YcVCw4OxqOPPorPPvsMAKDRaODr64vp06djzpw5OsdPmzYN58+fR3R0tLTt1VdfxfHjx3H48GGd4/XJzMyEi4sLMjIy4OzsbEpxaz1NVhYudg6S3hetsFuRZpkTyScw+efJAAA7aztMbjsZ4W3CYWttW3hPreG7AnbIw59vhBavrKvK1q4J4fBcIqI6ydjvb5NqRlQqFWJiYjB37lxpm1wuR2hoKI4dO6b3nG7duuGrr77CiRMn0KVLFyQkJGD37t2YMGGC3uMBIC8vD3l5eVofhnQJjQaJI4pnQG1+5LDZ07oXSchIwNT9U6X373Z/FwP8B0jvNRqBfkt/Q2LafdgjD1sU76CN/BrwsYELzopnTQgREZXJpDCSlpYGtVoNT09Pre2enp64cOGC3nPGjh2LtLQ09OjRA0IIFBQU4IUXXsC8efMM3mfRokV45513TClanVN6xExF1pcpcuzmMa0g8mL7F/G43+PS+6IgcjXtHnYp3igMIWVhkwwRERmhykfTHDx4EAsXLsTnn3+O4OBgxMfHY8aMGXjvvfcwf/58vefMnTsXkZGR0vvMzEz4+vpWdVFrjNJBROHnV9g/xMwv/ez8bLz4y4s4deuUtO37wd+jVYNWWvcc+Wk0UtMyEa2YhybylOILGJqmnU0yRERkBJPCiJubG6ysrJCamqq1PTU1FV5eXnrPmT9/PiZMmIDnnnsOANCuXTtkZWVh6tSpeOONNyDX08FSqVRCqVSaUrQ6Q9/07hWZVTU7Pxtjd43FlYzCGVCfDnwaw5oN0woi0GigWd0L29PPALYlTi6aLZWjYYiIqAJM+gZTKBTo3LmzVmdUjUaD6OhohISE6D0nOztbJ3BYWRWuT2Ji31kCIHJyKm1699hbsQj+JlgKIsv7LMcbXd9AW7e2D25WODxXfBYEq9RSM6Z6tSucLVXpyCBCREQVYnIzTWRkJMLDwxEUFIQuXbpg+fLlyMrKwqRJkwAAzzzzDBo2bIhFixYBAIYMGYKlS5eiY8eOUjPN/PnzMWTIECmUkHkqMr37ytiVWPXXKun9+Fbj0c+vX+EbIQBVFsSGAZClnEFR1EjQeGFW/RX44cVukLE2hIiIKonJYWT06NG4ffs23nrrLaSkpKBDhw7Yu3ev1Kk1KSlJqybkzTffhEwmw5tvvokbN27A3d0dQ4YMwQcffFB5n6KuMiMM5BTk4Pn9z+P0rcJ5Sdo0aIO3Qt5C6watCw/QaIA1vYASIQQAzmn8MN1pOX55uS9kcoYQIiKqPCbPM2IJnGekWMl5RQJPxUBu4nozX8R+gc//+hwA0NGjI74c8GVxx1eNBvgsqHgFXRSGkFGqBfD3csfOl3tqrzdDRERUhiqZZ4QsSwiBq+MNz89SnkxVJlb/vRoA0N+vP94IfsNgEEnQeGGwaiE8G9THyZd7wl5hVaFhw0RERIYwjNQQQgio79yROq8qW7WCzM7OpGtsv7wdaqGGq9IVH/f+2GAQuS7zQT/VEgjIsfPlnsUzqxIREVUBfsvUAEKjQeLIJ6UgAqBwyncTaiq+Pv81PvrzIwDAI+6PFPYHUWUVdlZd3UsriPTKKQwirb2dYa9gJ2MiIqpaDCMPuaJ5RUoGEbtOnSAzoa/I6VunsfjEYun96+2nFa6mm6I9XPeG3Ae9sguDiNbCd0RERFWIYeQhJ7KzteYVCdj2g8mL4BXViADAkWvX4fxZV51j1J7t0OPabCmIREf2ZmdVIiKqFgwjD7HSC+EFbPsBcgcH488XAvF3L+Pv238DAL5IuQVnTYnBUw+mcdcIIHTFSQhkAwB2Tu/BIEJERNWGYeQhVXrad2WrVkY3zWiEBrsTd+PdY+8ipyAHANApNxfdcnKLp3CXyQAbe2gEClfh/bcwiLCfCBERVTeGkYdU6WnfjVkI7/Ldy1hxegUOXD+gtd1Zrcb7t+9A7tUOmHoIeDApXdEqvIlpWQDAfiJERGQRDCMPqxJz0Rkz7fuav9dgxekVWtvcCtRYk3ILfk6+ULx2TVrQTgiBbJUag1cc1goi7CdCRESWwDDyENKZ3KycmoqEu1e0gsjzdzPQKycHj+SpHmz4vXBBuwfXfnLVMcRcuysdzyBCRESWxDDyECrZRFPe5GYF6ny8+ONIQAYoNALR12/AVaMpPsC3a2GNyAPZKrVWEGnt7cwOq0REZFEMIw+jEk00hiY3yynIwbZL27Dj0lbclKkBACtSb8PVow0waW9xbYqNvU7TTJE/3wxFAwcF+4gQEZFFMYw8ZEoP5zXURPPab7Pw2z+HpPf9s7LRbdpZwMFN6xwhBLLzCjBq1THEJWdK21t7OzOIEBHRQ4Fh5CGidzivniYalVqFmNQY6f2Ht9IQVr+d3iBSun8IUNw0wyBCREQPA4aRh4jObKv6hvMKgcS0ONzPz4KjRoMj1/6BfFa83iDyb5ZKp3/IlhdCuAIvERE9VBhGHhKlR9DoDOcVonBhuw0DcOXeFcDDDc1VqsK5Q0oFEY1GYPCKw1rNMuwfQkREDyuGkYdEyVoRndlWhQDWhwHXjwMArri6AACaWDsBkw7p1IiUDiJBfvUYRIiI6KHFMPIQKF0rojWCRgggK00KIgCQ4NwAQAGahcyUZlMtus6/WSopiBTNqMpmGSIiepgxjDwEDNaKlKoRAQDMikf8/meBzKto4tr0wWGFw3ZLj5jZOb0HHJT8X0xERA83flNZWOmhvFq1IvnZ2kHEtytuywSuZRaOtmnu2tzgiJkgv3pc8I6IiGoEhhELKndl3hKTn2FWPDT29XHg8g8QEPB39oe7vTuyVQUcMUNERDUaw4gFlbkyrxDAhgHFByvsMef3udhzdQ8AwNHGUTqsCEfMEBFRTVT2UrBUtcpamVeVBaScKfzZqx3OZSRKQcRV6YrRLUdDCIFRq45Jp7A2hIiIaiLWjFhImdO+azTA6l7S28xxW/D0tscBAO3c2uGbJ74BAGSrCqQOq629nWFnwz4iRERU87BmxALKnPZdCGBNL+DOFQBAvldbPPXzJOncuV3mlrhO8TW3vBDCWhEiIqqRGEYsoMy+IiWbZ+o3xfEnFuPG/RsAgFlBs9DOvR2A4llWizCHEBFRTcUwYmFafUVKNc/g+UO4kH4RAOCidMH4VuMfHCbQb+lvSEzLAsAmGiIiqtkYRiyt5OiZEs0z8GqHWwVZ2HhuIwDgxfYvwkpuJU33XhREimZZZRMNERHVVOzAagklO3sULYCnytZqnsHUQ3jup+HIyMsAAIT5hwEAcvLVWtO9R0f2hlzOIEJERDUXw0g10xlFs2kYkHpS65i85/bjpf1TkJiRCKCwr4ibnVvh+SVyzM7pPRhEiIioxmMzTTXSGUUT2AKyFO0gAt+ueOHQLJxIOQEAGNl8JMLbhEvnl5xXhC0zRERUG7BmpBqVXBBP4eeHgAFpkKU+2DkrHlDYY+nfq/Dng34i/s7+WBCyQDq/ZBMNO60SEVFtwZqRaiKEwNXxE6T3AZs3QZZ6tvCNVzvAwQ1br+7BhgdBpE+jPtg2dJtWx1TOK0JERLURa0aqSclaEWXLlpBtCiveOWkvBIClfy4FAIxtORZzg+dqn88mGiIiqqVYM1INSteK+Pe8CtndhMI3Xu0AhQMSMxNxL/8eACCiY4TONdhEQ0REtRVrRqpByRlXla75kGXeBGSQhvBCJsOL+18EADzi/gicbJzKvB6baIiIqDZhzUh1KNHZw79fWmETi1c7YNqfgFyOC3cu4GbWTQDAW13f0gkaQghkq9TSe+YQIiKqTVgzUsVKN9FAhsKRMw5ugEyG1KxUjPq/UdLuFvVa6Jz/5KpjiLl2t5pKTEREVL1YM1LFtDquuuZDZiUAhb1UvfHfk/+Vjv160Nc6tSLZKrVWEAnyq8f+IkREVKuwZqQK6XRc7ZcGmXc7wMZe2qa0UgIAGtg2wCPuj+icX3IEzZ9vhqKBg4L9RYiIqFZhzUgV0qkVsRbApL1SrYgQAjsTdgIApnecrnN+6RE0DCJERFQbMYxUEYO1IgoHadsPl3+Qfg71C9VzjeKfOYKGiIhqK4aRKlJerQgAvHPsHQBAj4Y94KJ00T6fk5wREVEdwTBSBfTWisiglSgu3rko/fxm1zd1rsFJzoiIqK5gGKkCWpOcuVsV1oqU8vqh16WfGzo21L0Gm2iIiKiOYBipZEIIaHJypPf+vf4pnuTMxh4FmgI8v/95JGQUTgff2bOzzjU0GoHBKw5L75lDiIioNuPQ3kokhMC1seOQc/p08caiIPGgv8jZ22dx9OZRAIC/sz9WPLZC5xqDVxxGYloWADbREBFR7ccwUolETo5WELFzyyuc5MyreBTNjvgdAIDGTo2xfdh2WMu1/xdkq4r7igS4OWDn9B5soiEiolqNzTRVpPmBn+HX79/CJpYHtSIpWSnScN5HvR7VCSKlR9DsnN4DcjmDCBER1W4MI1VEbmdX3NfjwQ/P7HlG2v9yp5d1zik9gsZeweYZIiKq/RhGKpPQHTVT5Mb9G0jOSgYAzAqahfq29cu8FEfQEBFRXcEwUkmERoPEESOLN+Rna+1f/ddq6efwNuHlXo85hIiI6gqGkUoghEDiyCehunYNAKD0sIHss3Za+7fHbwegf06R4uOqtpxEREQPI4aRSlBykjOFX2ME9L1WXLPh2xWX7v8jHftF6Bf6r1Gq8yoREVFdwTBSGUpUaQR891VxEJkVDzy7FynZqQAABxsHBLgE6L0Ep38nIqK6imGkgnT6iqBEZw+FPRIzr2Lar9MAAAHO+oNIaey8SkREdYlZYWTlypXw9/eHra0tgoODceLEiTKPT09PR0REBLy9vaFUKtGiRQvs3r3brAI/TIRGg4SBg4r7irRqBZmtUuuYoTuGSj8PbjrYqOsyhxARUV1i8gys3333HSIjI7Fq1SoEBwdj+fLlCAsLw8WLF+Hh4aFzvEqlQv/+/eHh4YGtW7eiYcOGuHbtGlxdXSuj/BZTutOqws8PAVu3QLa2t3RMWs6/0s+vdn4V41qNq/ZyEhERPexMDiNLly7FlClTMGnSJADAqlWrsGvXLqxfvx5z5szROX79+vW4c+cOjh49ChsbGwCAv79/xUr9EBDZ2SU6rfqhyZ7dkOVnAylnCg/waoft1/ZKx49rXXYQ4UgaIiKqq0xqplGpVIiJiUFoaGjxBeRyhIaG4tgx/SNBfvrpJ4SEhCAiIgKenp5o27YtFi5cCLVabfA+eXl5yMzM1Ho9TIQQuDp+gvQ+YNsPhT1FVvcqPmjSXsTcOgUAaNugLWzkNmVejyNpiIiorjIpjKSlpUGtVsPT01Nru6enJ1JSUvSek5CQgK1bt0KtVmP37t2YP38+Pv74Y7z//vsG77No0SK4uLhIL19fX1OKWeVKDuVVtmoFmZ0dsKYXcOdK4QEPFsZLuV/4TIY2G2roUgC0F8fjSBoiIqprqnw0jUajgYeHB9asWYPOnTtj9OjReOONN7Bq1SqD58ydOxcZGRnS6/r161VdTLP5f/U/yApyiptn6jcFph7CjaybuJJRGE6cFc4Gzy9dK8KRNEREVNeY1GfEzc0NVlZWSE1N1dqempoKLy8vved4e3vDxsYGVlbF/9pv1aoVUlJSoFKpoFAodM5RKpVQKpU62x9KMhmAEh0+nj8EjQxYfGKxtCnYO9jg6Vwcj4iI6jqTakYUCgU6d+6M6OhoaZtGo0F0dDRCQkL0ntO9e3fEx8dDo9FI2y5dugRvb2+9QaRGKtn7VCbD4RuHcfD6QdjIbfC/gf+Dm52bUZdhrQgREdVFJjfTREZGYu3atfjyyy9x/vx5vPjii8jKypJG1zzzzDOYO3eudPyLL76IO3fuYMaMGbh06RJ27dqFhQsXIiIiovI+RXUrGT6EADYM0NqdklXYV6SbTzd08Ohg9KWYQ4iIqC4yeWjv6NGjcfv2bbz11ltISUlBhw4dsHfvXqlTa1JSEuTy4ozj6+uLffv2YebMmXjkkUfQsGFDzJgxA7Nnz668T1GNSo+kQfa/WsN5YWOPM2mF763lZT9ejqIhIiIyI4wAwLRp0zBt2jS9+w4ePKizLSQkBH/88Yc5t3roaI2kcbcqXJ23qEZj0l5AJoOjjSMAIOleUpnX4no0REREXJumQvx7/aO1Oi8UDgCAX5J+AQD0btTbwJm62F+EiIjqKrNqRugBGQqH8j5/qDCIyGS4fu+61GekVf1WZZ7O/iJEREQMI6YrPW/784cApaP09ty/5wAU9hd53P/xMi7D/iJEREQAm2lMotN5FdCp0vg16VcAQIi3/qHORdhfhIiIqBDDiAm0Oq+65kNmpbu63fHk4wAABxsHo6/L/iJERFSXMYyYokQTjX+/NJ1+HmfTzuJO7h0AwMQ2E429FPuLEBFRncYwYiSdJho9AWJZzDLp5zZubcq8FvuLEBERFWIYMZLeJpoHk5wVOZFyAgD7ixAREZmCYcQMUhPNg0nOSnv90deNvhb7ixARUV3HMGKOouxQIkRk5GVIPzewa1Dm6ewvQkREVIxhpJJcy7wm/VzPtp7B4zQagcErDldHkYiIiGoEhpFKcjbtLACgdYPWBo8RojCIJKZlFR7L/iJEREQMI0YrPfNqqc6r/+b+C6DsMJKtKu64GuDmgJ3Te7C/CBER1XkMI0bQO/Nqqc6rRfOLeNp7GrxGyeG8O6f3gFzOIEJERMQwYgSRna07rLdUjcaxm4VBQ6ZvAhLoDue1V7B5hoiICGAYKVfpWhF9M6/mFOTgxv0bAAArefkhg8N5iYiIijGMlENnsjNr3cnOrqRfkX5+svmT5V6TOYSIiKgYw4gJypvsrL5tfbjauuo9t3T/VyIiIirEMGIKPZOdAUBmXmFfEHtre+jDtWiIiIgMYxipBH/d/gsA0M6tnd79XIuGiIjIMIaR8pTTvqIRGuy9uhcA4OPoU+7l2HmViIhIG8NIGfTOL1Kq8+qP8T8iISMBANDRo6OB6xT/zBxCRESkjWGkDDojaayETufVIzePAChsounVqJfONbgWDRERUdkYRowkjaQpEURu3r+JfVf3AQDGtRqn0/zCtWiIiIjKxzBiLD3NK0V9RQCgg0cHnf0lO65yLRoiIiL9GEbKUk7n1ePJxwEATzR5Ag0dG5Z5OteiISIi0o9hxIDyOq9m5GXg6M2jAIBhTYfpnF+6rwgrRIiIiPRjGDGgvM6rRbUiABDsHax9LvuKEBERGY1hxAj6Oq+++turAABHG0fIZdqPkX1FiIiIjMcwYowycsTgJoN1trGvCBERkfEYRsyQkpUi/Ty903StfaXXoWGFCBERUdkYRswQkxoj/eyscNbal63iOjRERESmYBgxw+q/VwPQHUVTulaE69AQERGVj2HERDGpMUjMSAQAhPqFau0rvTqvvYK1IkREROVhGDFEz4RnGqHBKwdeAQC0bdAWfXz7GDydtSJERETGYRjRw9CEZymqTKTnpQMAIoMiy7wGcwgREZFxGEb0MDTh2b38+wAAF6ULHvV61JJFJCIiqjUYRspRcsKzP5L/AAD4OPhYtlBERES1CMNIeUo0t2SqCjunFjXVlFbOunpERESkB8OIMR4skPdr0q8AgCFNh+gcUnpYLxERERmHYUSf0lUcDxbIi0+PBwBk52frnMLJzoiIiMzDMFKK3pE0MhnUGjWs5dYAoDOkl5OdERERmY9hpBS9I2kAJGclo0BTAAAI8gzSOoeTnREREZmPYaS0Ek000kgaAGfTzgIAmrk2g5XcytAprBUhIiIyEcNICTpNNDJInVf/TP0TAKC0Uuqcw1V6iYiIzMcwUoKhyc4gk8FF6QIAcFI4aZ3DjqtEREQVwzBiQMnJzkpq4tJE+pkdV4mIiCqOYcSQUpni/L/ndQ5hx1UiIqKKYxgxUoYqA0Dhyr36sFaEiIjIPAwjRhBC4FrmNQDA4/6Pl9hefAxzCBERkXkYRozw5bkvkZGXAWu5Ndq7twfA6d+JiIgqC8NIWbzaocBKgY9jPgYAOCucobBSANDtL8JRNEREROZhGCnLpL04lvyH9HZ1/9V6D2N/ESIiIvMxjJRFJkNWfhYAoJNHJ7Ss39LQYURERGQmhpGSSq/WC0CgcFtZU8ATERGR+RhGHtC7Wi8KF8gDABeFi9ax7LxKRERUOcwKIytXroS/vz9sbW0RHByMEydOGHXe5s2bIZPJMHz4cHNuW6UMrdabnpsOAKhnW086lp1XiYiIKo/JYeS7775DZGQkFixYgFOnTqF9+/YICwvDrVu3yjzv6tWrmDVrFnr27Gl2YatLydV6f/vnNwCAlUx/4GDnVSIioooxOYwsXboUU6ZMwaRJk9C6dWusWrUK9vb2WL9+vcFz1Go1xo0bh3feeQdNmjQxeNxD40G2EEIgPS8dANDBo4P+Q5lDiIiIKsSkMKJSqRATE4PQ0NDiC8jlCA0NxbFjhvtQvPvuu/Dw8MDkyZONuk9eXh4yMzO1XtXOqx0uZ93Andw7UFop0d+vf/WXgYiIqA4wKYykpaVBrVbD09NTa7unpydSUlL0nnP48GFERUVh7dq1Rt9n0aJFcHFxkV6+vr6mFLNyTNqLDFVhCGro2FCa7IyIiIgqV5WOprl37x4mTJiAtWvXws3Nzejz5s6di4yMDOl1/fr1KiylASXaX2Sll/AlIiKiSmNtysFubm6wsrJCamqq1vbU1FR4eXnpHH/lyhVcvXoVQ4YMkbZpNIWr3lpbW+PixYto2rSpznlKpRJKpdKUolUcJw4hIiKyCJNqRhQKBTp37ozo6Ghpm0ajQXR0NEJCQnSOb9myJc6cOYPY2FjpNXToUPTt2xexsbGWaX4xJD+n+GfPNoCNveXKQkREVIeYVDMCAJGRkQgPD0dQUBC6dOmC5cuXIysrC5MmTQIAPPPMM2jYsCEWLVoEW1tbtG3bVut8V1dXANDZbnklakbG7wBkMmkkjY2VjfaRrEQhIiKqNCaHkdGjR+P27dt46623kJKSgg4dOmDv3r1Sp9akpCTI5TVrYlchBK5OfK54w4P+Indz7wIAvB28tY7l7KtERESVx+QwAgDTpk3DtGnT9O47ePBgmedu3LjRnFtWKZGTg7wLlwA8mH3VzlZrv1xWHK44+yoREVHlqllVGNWgcPbVwpqR7PxsAIDSqrgzbckmGs6+SkREVHEMI6WVyBZpOWkAgAZ2DQDoNtEwhxAREVUcwwhgsEfqtcxrAABfp8JRP2yiISIiqnwMI4DBYb0JGQkAgCYuhevpsImGiIio8jGMlPZgWK9KrcI/9/8BAAS4BLCJhoiIqIowjJT2IGVcy7wGjdDAztoO7nbubKIhIiKqIgwjBhz65xAAIKcgR6c5hk00RERElYdhxACVRmVwH3MIERFR5WEYMeDinYsAgIEBAy1cEiIiotqNYcSAO7l3AAANbBtYuCRERES1G8OIATfv3wQAdPPpBoCL4xEREVUVhhEDctW5AICGjg25OB4REVEVYhgpj4wzrxIREVUlhhE9CjQFyMjLAADISz0iDuslIiKqXAwjeiTdS5J+buTUSGsfcwgREVHlYhjR407OHelna7m1BUtCRERU+zGM6JGclQwAeNTrUQuXhIiIqPZjGNGjQFMAALC3trdwSYiIiGo/hhEiIiKyKIYRPXYl7tJ6zwnPiIiIqg7DiB521nYAgHuqe5zwjIiIqIoxjOghQ+H43cFNB3PCMyIioirGMGICTnhGRERU+RhGAJ1OIfmafACAtUx7jhHmECIiosrHMCIE8NVwrU23s28DANzs3CxQICIiorqFYSQ/G0g9V/zexg6p2akAAE8HTwsVioiIqO5gGCklT5OP9Lx0AICnPcMIERFRVWMYKeXf3DQAgEKugLPCmXOMEBERVTGGkVIKNGoAgMJKAQCcY4SIiKiKMYyUkluQC6BwrhHOMUJERFT1GEZKSc0q7Lzq5eiltZ1zjBAREVUNhpFSbucWDutVypVa/UWYQ4iIiKoGw0gpd3PuAgD+uf8P+4sQERFVA4aRUu7m3QEAPOoZzP4iRERE1YBhpJR9V38GAPi7BEjb2F+EiIio6jCMlNLkQQjRCI20jTmEiIio6jCMlJKhKmyaaerSzMIlISIiqhsYRkpJSL8CGWRo6sowQkREVB0YRvTwcvCCn1NA+QcSERFRhTGM6CGXyTmsl4iIqJowjOjRwNaNw3qJiIiqCcMIAJRambeTR5D0M4f1EhERVa06H0aEELga7aa17fl2EdLPzCFERERVi2EkJxd56TYAgERPIM8GeGr1HxYuFRERUd1R58NISW+Nt0KLei3ZX4SIiKgaMYyUgf1FiIiIqh7DSCmJaVnSz8whREREVY9hpJTcAjUANtEQERFVF2tLF+BhxSYaIvOp1Wrk5+dbuhhEVMVsbGxgZVXxf7gzjBjAHEJkOiEEUlJSkJ6ebumiEFE1cXV1hZeXV4X+Ac8wQkSVpiiIeHh4wN7enrWLRLWYEALZ2dm4desWAMDb29vsazGMEFGlUKvVUhBp0KCBpYtDRNXAzs4OAHDr1i14eHiY3WTDDqyliAInSxeBqEYq6iNib29v4ZIQUXUq+jNfkX5iDCOlFqbR5HlZqBxEtQObZojqlsr4M1+nw4gQAlcnPqe9TWPDYb1ERETVqG6HkZwc5F24BABId9Mgr3CJGg7rJSIiqkZmhZGVK1fC398ftra2CA4OxokTJwweu3btWvTs2RP16tVDvXr1EBoaWubx1UoUN9H89p88aTwvcwhR3TFx4kTIZDKd14ABA6BSqeDm5obFixfrPfe9996Dp6cn8vPzsXHjRr3XsbW1reZP9HBp2bIllEolUlJSdPYlJiZi7Nix8PHxga2tLRo1aoRhw4bhwoUL0jFFz/GPP7QXMM3Ly0ODBg0gk8lw8ODBSi+bv78/li9frrP97bffRocOHbS2paSkYPr06WjSpAmUSiV8fX0xZMgQREdHm1UuAPj777/Rs2dP2NrawtfXF0uWLCn3nJMnT6Jfv35wdXVFvXr1EBYWhr/++kvrmH379qFr165wcnKCu7s7Ro4ciatXr0r7Dx48qPf3WN8zqkwmh5HvvvsOkZGRWLBgAU6dOoX27dsjLCxMGtpT2sGDBzFmzBgcOHAAx44dg6+vLx5//HHcuHGjwoWvsPwc6cc79q6FPwgOMCKqawYMGIDk5GSt17fffguFQoHx48djw4YNOucIIbBx40Y888wzsLEprFZ1dnbWuc61a9eq++OUS6VSVct9Dh8+jJycHDz55JP48ssvtfbl5+ejf//+yMjIwLZt23Dx4kV89913aNeunc48Nb6+vjr/D7Zv3w5HR8cqKZsprl69is6dO+PXX3/Fhx9+iDNnzmDv3r3o27cvIiIizLpmZmYmHn/8cfj5+SEmJgYffvgh3n77baxZs8bgOffv38eAAQPQuHFjHD9+HIcPH4aTkxPCwsKkjqWJiYkYNmwYHnvsMcTGxmLfvn1IS0vDiBEjdK538eJFrd9jDw8Psz6L0YSJunTpIiIiIqT3arVa+Pj4iEWLFhl1fkFBgXBychJffvml0ffMyMgQAERGRoapxS2T+u5tERfYUsQFthTPb58g2m5sK5p+8J7Iysuv1PsQ1QU5OTkiLi5O5OTkWLooJgkPDxfDhg0zuP/vv/8WAMTvv/+utf3AgQMCgDh//rwQQogNGzYIFxcXs8uxZcsW0bZtW2Frayvq168v+vXrJ+7fvy/tj4qKEq1btxYKhUJ4eXlp/T187do1MXToUOHg4CCcnJzEqFGjREpKirR/wYIFon379mLt2rXC399fyGQyIYQQd+/eFZMnTxZubm7CyclJ9O3bV8TGxkrnxcbGij59+ghHR0fh5OQkOnXqJE6ePGn0Z5o4caKYM2eO2LNnj2jRooXWvtOnTwsA4urVq2VeA4B48803hbOzs8jOzpa29+/fX8yfP18AEAcOHDC6TMaUTQgh/Pz8xLJly3S2Fz3LIgMHDhQNGzbU+n9V5O7duyaXSwghPv/8c1GvXj2Rl5cnbZs9e7YIDAw0eM7JkycFAJGUlCRtK/rdvXz5shCi8HfM2tpaqNVq6ZiffvpJyGQyoVKphBDFv9emlL2sP/vGfn+bVDOiUqkQExOD0NBQaZtcLkdoaCiOHTtm1DWys7ORn5+P+vXrGzwmLy8PmZmZWq+qVqApKPzBtEdCRAYIIZCtKrDISwhRfgGN1K5dOzz66KNYv3691vYNGzagW7duaNmyZYXvkZycjDFjxuDZZ5/F+fPncfDgQYwYMUL6HF988QUiIiIwdepUnDlzBj/99BOaNWsGANBoNBg2bBju3LmD3377Dfv370dCQgJGjx6tdY/4+Hj88MMP2LZtG2JjYwEAo0aNwq1bt7Bnzx7ExMSgU6dO6NevH+7cuQMAGDduHBo1aoSTJ08iJiYGc+bMkWqBynPv3j1s2bIF48ePl2pAfv/9d2m/u7s75HI5tm7dCrVaXea1OnfuDH9/f/zwww8AgKSkJBw6dAgTJkwwqiymls1Yd+7cwd69exEREQEHBwed/a6urtLPAwcOhKOjo8FXmzZtpGOPHTuGXr16QaFQSNvCwsJw8eJF3L17V29ZAgMD0aBBA0RFRUGlUiEnJwdRUVFo1aoV/P39ARQ+R7lcjg0bNkCtViMjIwP/+9//EBoaqvP/tUOHDvD29kb//v1x5MgRk5+NqUxqk0hLS4NarYanp6fWdk9PT602vrLMnj0bPj4+WoGmtEWLFuGdd94xpWgVdjs3DQAgCpyr9b5EtVVOvhqt39pnkXvHvRsGe4Xxf73t3LlTp8p/3rx5mDdvHgBg8uTJmDVrFj799FM4Ojri3r172Lp1Kz799FOtczIyMnSu07NnT+zZs6fM+ycnJ6OgoAAjRoyAn58fgMIQVOT999/Hq6++ihkzZkjbHn30UQBAdHQ0zpw5g8TERPj6+gIANm3ahDZt2uDkyZPScSqVCps2bYK7uzuAwmaKEydO4NatW1AqlQCAjz76CDt27MDWrVsxdepUJCUl4bXXXpMCV/Pmzct7lJLNmzejefPm0pfs008/jaioKPTs2RMA0LBhQ3z66ad4/fXX8c477yAoKAh9+/bFuHHj0KRJE53rPfvss1i/fj3Gjx+PjRs3YtCgQdJnMVV5ZTNWfHw8hBBGBdJ169YhJyfH4P6SYSAlJQUBAQFa+4u+d1NSUlCvXj2d852cnHDw4EEMHz4c7733HoDC/1/79u2DtXXhn4WAgAD8/PPPeOqpp/D8889DrVYjJCQEu3fvlq7j7e2NVatWISgoCHl5eVi3bh369OmD48ePo1OnTuV+TnNVazXA4sWLsXnzZmzfvr3MTl1z585FRkaG9Lp+/XqVl+1Wzm0AgCbftcrvRUQPl759+yI2Nlbr9cILL0j7x4wZA7Vaje+//x5AYd85uVyuU/vg5OSkc51169aVe//27dujX79+aNeuHUaNGoW1a9dK/wK+desWbt68iX79+uk99/z58/D19ZWCCAC0bt0arq6uOH/+vLTNz89P68v7r7/+wv3799GgQQOtf6EnJibiypUrAIDIyEg899xzCA0NxeLFi6XtxigKDkXGjx+PLVu24N69e9K2iIgIpKSk4Ouvv0ZISAi2bNmCNm3aYP/+/TrXGz9+PI4dO4aEhARs3LgRzz77rNFlMadsxjClBq5hw4Zo1qyZwVdRCDVXTk4OJk+ejO7du+OPP/7AkSNH0LZtWzzxxBNSCEpJScGUKVMQHh6OkydP4rfffoNCocCTTz4pfZbAwEA8//zz6Ny5M7p164b169ejW7duWLZsWYXKVx6Takbc3NxgZWWF1NRUre2pqanw8ip7srCPPvoIixcvxi+//IJHHnmkzGOVSqWU1KtLviYfQshZM0JUSexsrBD3bpjF7m0KBwcHqdlDH2dnZzz55JPYsGEDnn32WWzYsAFPPfWUTi2IXC4v8zqGWFlZYf/+/Th69Ch+/vlnrFixAm+88QaOHz8ONzc3k6+nT+lmhPv378Pb21vvSJSi5oW3334bY8eOxa5du7Bnzx4sWLAAmzdvxn/+858y7xUXF4c//vgDJ06cwOzZs6XtarUamzdvxpQpU6RtTk5OGDJkCIYMGYL3338fYWFheP/999G/f3+tazZo0ACDBw/G5MmTkZubi4EDB5ocHkwpm7OzMzIyMnTOT09Ph4uLC4DCmgeZTGZUy8DAgQPLbAry8/PDuXPnAABeXl56v2eL9unzzTff4OrVqzh27Bjkcrm0rV69evjxxx/x9NNPY+XKlXBxcdEamfPVV1/B19cXx48fR9euXfVeu0uXLjh8+HC5n7EiTAojCoUCnTt3RnR0NIYPHw6gsL0yOjoa06ZNM3jekiVL8MEHH2Dfvn0ICgqqUIGrUmEQ4WRnRJVBJpOZ1FTysJs8eTL69OmDnTt34ujRo/jwww8r9foymQzdu3dH9+7d8dZbb8HPzw/bt29HZGQk/P39ER0djb59++qc16pVK1y/fh3Xr1+Xakfi4uKQnp6O1q1bG7xfp06dkJKSAmtra6lPgT4tWrRAixYtMHPmTIwZMwYbNmwoN4xERUWhV69eWLlypdb2DRs2ICoqSiuMlH4GLVu2xNGjR/Xuf/bZZzFo0CDMnj3b7DVQjC1bYGAgYmJidM4/deoUAgMDAQD169dHWFgYVq5ciZdfflkn8KWnp0vBzpRmmpCQELzxxhvIz8+Xtu/fvx+BgYF6m2iAwv6Ycrlca46sovcajUbrmJKKnmPRMfrExsZWaBE8oxjdXfaBzZs3C6VSKTZu3Cji4uLE1KlThaurq9Rze8KECWLOnDnS8YsXLxYKhUJs3bpVJCcnS6979+4Zfc/qGE3TeU0b0fLzwcJv9k6OpiEyQ00eTTNgwACtv5+Sk5PF7du3tY7TaDSiWbNmol69eqJly5Y619mwYYNwdnbWuU5ycrLW6AV9/vjjD/HBBx+IkydPimvXronvv/9eKBQKsXv3biGEEBs3bhS2trbik08+EZcuXRIxMTHi008/lcrVoUMH0bNnTxETEyOOHz8uOnfuLHr37i1dv/QIkKLzevToIdq3by/27dsnEhMTxZEjR8S8efPEyZMnRXZ2toiIiBAHDhwQV69eFYcPHxZNmzYVr7/+epmfRaVSCXd3d/HFF1/o7IuLixMAxNmzZ8Xp06fF0KFDxZYtW8S5c+fE5cuXxbp164SDg4N49913pXMAiO3bt0tlvn37tjTK5O7duyaNpjG2bEIIceTIESGXy8X7778v4uLixJkzZ8S8efOEtbW1OHPmjHTelStXhJeXl2jdurXYunWruHTpkoiLixOffPKJ3t8TY6SnpwtPT08xYcIEcfbsWbF582Zhb28vVq9eLR2zbds2rdE158+fF0qlUrz44osiLi5OnD17VowfP164uLiImzdvCiGEiI6OFjKZTLzzzjvS71FYWJjw8/OTRiotW7ZM7NixQ1y+fFmcOXNGzJgxQ8jlcvHLL78YLG9ljKYxOYwIIcSKFStE48aNhUKhEF26dBF//PGHtK93794iPDxceu/n5ydQuACM1mvBggVG36+6wkjzj15mGCEyU00OI/r+jtI3jHLhwoUCgFiyZInOvg0bNui9DgCRnJxcZhni4uJEWFiYcHd3F0qlUrRo0UKsWLFC65hVq1aJwMBAYWNjI7y9vcX06dOlfcYO7S0tMzNTTJ8+Xfj4+AgbGxvh6+srxo0bJ5KSkkReXp54+umnha+vr1AoFMLHx0dMmzat3P+/W7duFXK5XOv+JbVq1UrMnDlT3L59W7z88suibdu20tDhdu3aiY8++kgrvJUMI6XpCyOlv4PMKVuRffv2ie7du4t69eqJBg0aiD59+ojffvtN57ybN2+KiIgI4efnJxQKhWjYsKEYOnSoWUOOi/z111+iR48eQqlUioYNG4rFixdr7S/6fSvp559/Ft27dxcuLi6iXr164rHHHhPHjh3TOubbb78VHTt2FA4ODsLd3V0MHTpUGp4uhBD//e9/RdOmTaUh5n369BG//vprmWWtjDAiE6ISx8BVkczMTLi4uCAjIwPOzpXXp0Nz9zYuhvQCAEx41QrpaaNQkBFkck98IgJyc3ORmJiIgICAOj/rKFmOn58f3nnnHUycONHSRakzyvqzb+z3d92dVEMI4Kvh2ts0/AuUiKimOnfuHFxcXPDMM89YuihkorobRvKzgdRzWptEgflTCxMRGZKUlFTmhFdJSUmWLqJJyprAa+HChRYrV5s2bfD333/rdNKkhx/bIkrQFDijtbezycMCiYjK4uPjI816amh/TVLWyJCyZtcmMoRhpASR74ItL4RoDY0iIqooa2trs+YfeVg1bNjQ0kWgWoZ1WQ8ULtNjBeYQIiKi6sUwQkRERBbFMEJEREQWxTBCREREFsUwQkRERBbFMPIA5xghIiKyDIaRB4RQWroIREQ4d+4cRo4cCX9/f8hkMixfvrxK73fs2DFYWVnhiSee0Nl38OBByGQypKen6+zz9/fXKduBAwcwaNAgNGjQAPb29mjdujVeffVV3Lhxw+zyrVy5Ev7+/rC1tUVwcDBOnDhR7jnLly9HYGAg7Ozs4Ovri5kzZyI3N1er7DKZTOcVERFhdjmpYhhGiggrTnhGRBaXnZ2NJk2aYPHixfDy8qry+0VFRWH69Ok4dOgQbt68afZ1Vq9ejdDQUHh5eeGHH35AXFwcVq1ahYyMDHz88cdmXfO7775DZGQkFixYgFOnTqF9+/YICwvDrVu3DJ7zzTffYM6cOViwYAHOnz+PqKgofPfdd5g3b550zMmTJ5GcnCy99u/fDwAYNWqUWeWkSlDmMnoPiSpZtTfvvlC/4SKt2tt+2XBxP5er9RKZq6au2qtWq8XChQuFv7+/sLW1FY888ojYsmWLtP/OnTti7Nixws3NTdja2opmzZqJ9evXCyGESExMFADEd999J3r06CFsbW1FUFCQuHjxojhx4oTo3LmzcHBwEAMGDBC3bt0yuWx+fn5i2bJlWttWr14tvL29tVa2FUKIoUOHikmTJpl0/Xv37glHR0dx4cIFMXr0aPHBBx9o7T9w4IAAIO7evVtm2a5fvy4UCoV45ZVX9N5H3/nG6NKli4iIiJDeq9Vq4ePjIxYtWmTwnIiICPHYY49pbYuMjBTdu3c3eM6MGTNE06ZNhUajMaucdV1lrNrLmpEHhMaaE54RVSYhAFWWZV4mLEa+aNEibNq0CatWrcK5c+cwc+ZMjB8/Hr/99hsAYP78+YiLi8OePXtw/vx5fPHFF3Bzc9O6xoIFC/Dmm2/i1KlTsLa2xtixY/H666/jk08+we+//474+Hi89dZblfJYR40ahX///RcHDhyQtt25cwd79+7FuHHjTLrW999/j5YtWyIwMBDjx4/H+vXrIcxYyH3Lli1QqVR4/fXX9e53dXUFUP4aPSXXtlGpVIiJiUFoaKh0HblcjtDQUBw7dsxgWbp164aYmBipOSchIQG7d+/GoEGD9B6vUqnw1Vdf4dlnn+Xs2xbE6eCJqGrkZwMLLbTmyrybgMKh3MPy8vKwcOFC/PLLLwgJCQEANGnSBIcPH8bq1avRu3dvJCUloWPHjggKCgJQ2N+gtFmzZiEsLAwAMGPGDIwZMwbR0dHo3r07AGDy5MnYuHFjpXy0evXqYeDAgfjmm2/Qr18/AMDWrVvh5uaGvn37mnStqKgojB8/HgAwYMAAZGRk4LfffkOfPn1Mus7ly5fh7OwMb2/vMo8rb40eoHhtm7S0NKjVanh6emrt9/T0xIULFwyeP3bsWKSlpaFHjx4QQqCgoAAvvPCCVjNNSTt27EB6ejomTpxYZrmoajGMFNHYWroERFTN4uPjkZ2djf79+2ttV6lU6NixIwDgxRdfxMiRI3Hq1Ck8/vjjGD58OLp166Z1/COPPCL9XPTl2a5dO61tZfVzMNW4ceMwZcoUfP7551Aqlfj666/x9NNPm7Ra7cWLF3HixAls374dQOH6OaNHj0ZUVJTJYUQIYVStQnWs0XPw4EEsXLgQn3/+OYKDgxEfH48ZM2bgvffew/z583WOj4qKwsCBA2vcYoW1DcPIA0JtZ+kiENUuNvaFNRSWurcR7t+/DwDYtWuXzuJvSmXhCLuBAwfi2rVr2L17N/bv349+/fohIiICH330UfHtbGykn4u+lEtv02g05n0WPYYMGQIhBHbt2oVHH30Uv//+O5YtW2bSNaKiolBQUKD1JSyEgFKpxGeffQYXFxc4OzsDADIyMqSmliLp6elwcXEBALRo0QIZGRlITk4us3YkKSkJrVu3LrNc8+bNw7x58+Dm5gYrKyukpqZq7U9NTS2zY+/8+fMxYcIEPPfccwAKQ2FWVhamTp2KN954QyuwXbt2Db/88gu2bdtWZpmo6jGMPCAEHwVRpZLJjGoqsaTWrVtDqVQiKSkJvXv3Nnicu7s7wsPDER4ejp49e+K1117TCiPVzdbWFiNGjMDXX3+N+Ph4BAYGolOnTkafX1BQgE2bNuHjjz/G448/rrVv+PDh+Pbbb/HCCy+gefPmkMvliImJgZ+fn3RMQkICMjIy0KJFCwDAk08+iTlz5mDJkiV6Q1F6ejpcXV1NaqZRKBTo3LkzoqOjMXz4cACARqNBdHQ0pk2bZvD87OxsnRoiK6vCUZKl+8Ns2LABHh4eeoc1U/XiNzAR1VlOTk6YNWsWZs6cCY1Ggx49eiAjIwNHjhyBs7MzwsPD8dZbb6Fz585o06YN8vLysHPnTrRq1arKyqRSqRAXFyf9fOPGDcTGxsLR0VGriWPcuHEYPHgwzp07J/X7MNbOnTtx9+5dTJ48WardKDJy5EhERUXhhRdegJOTE5577jm8+uqrsLa2Rrt27XD9+nXMnj0bXbt2lZqrfH19sWzZMkybNg2ZmZl45pln4O/vj3/++QebNm2Co6MjPv74Y5ObaSIjIxEeHo6goCB06dIFy5cvR1ZWFiZNmiQd88wzz6Bhw4ZYtGgRgMJao6VLl6Jjx45SM838+fMxZMgQKZQAhcFmw4YNCA8Ph7U1vwotrvIH+VS+6hja23bRiyIrj0N7icxVU4f2ajQasXz5chEYGChsbGyEu7u7CAsLE7/99psQQoj33ntPtGrVStjZ2Yn69euLYcOGiYSEBCFE8dDe06dPS9fTNxx2w4YNwsXFxajyFF2z9Kt3795ax6nVauHt7S0AiCtXrui9xoEDB/TeY/DgwWLQoEF69x0/flwAEH/99ZcQovD/64IFC0TLli2FnZ2dCAgIEFOnThW3b9/WOXf//v0iLCxM1KtXT9ja2oqWLVuKWbNmiZs3bxr12fVZsWKFaNy4sVAoFKJLly7ijz/+0Nrfu3dvER4eLr3Pz88Xb7/9tmjatKmwtbUVvr6+4qWXXtIZXrxv3z4BQFy8eNHsslGhyhjaKxPCjHFc1SwzMxMuLi7IyMiQ2jArTJUFzbsNcXFrYfvmUxP74njkp7BXMCETmSM3NxeJiYkICAiArS07hFvSgQMHMGLECCQkJKBevXqWLg7VcmX92Tf2+7tuzzPy0McwIiLT7d69G/PmzWMQoRqjzlYDCCFwNdqt/AOJiCqRo6PhRTn37NmDnj17VvgeH374YYWvQVSd6m4YyclFXnrh0LtETyDPum5XEhFR9ShrNEnp4cVEdUWdDSMlvTXeCsjkNMBEVPWqetIvopqI1QFERERkUQwjREREZFEMIw8I9cM9UyQREVFtxTDygCbP8FoHREREVHUYRh7wr1cPdjZW5R9IRERElYph5IGPRz1q1BLYREREVLkYRh7wtPewdBGIyAImTpwImUwGmUwGGxsbBAQE4PXXX0dubq7WcUXHlH5t3rwZP/zwA6ysrHDjxg2992jevDkiIyMBAH369NF7nRdeeEHrXjt27KjwZ/vnn3+gUCjQtm1bnX1Xr16FTCbTO+9Jnz598Morr2htO336NEaNGgVPT0/Y2tqiefPmmDJlCi5dumR2+bZs2YKWLVvC1tYW7dq1w+7du8s9Z+XKlWjVqhXs7OwQGBiITZs2mXzdbdu24fHHH0eDBg0MPgOqXgwjEtaKENVVAwYMQHJyMhISErBs2TKsXr0aCxYs0Dluw4YNSE5O1noNHz4cQ4cORYMGDfDll1/qnHPo0CHEx8dj8uTJ0rYpU6boXGfJkiWV/rk2btyIp556CpmZmTh+/LjZ19m5cye6du2KvLw8fP311zh//jy++uoruLi4YP78+WZd8+jRoxgzZgwmT56M06dPY/jw4Rg+fDjOnj1r8JwvvvgCc+fOxdtvv41z587hnXfeQUREBP7v//7PpOtmZWWhR48e+O9//2tW2akKVMkSfpWsKlbtVd+9La3Y23lNG5GWlVlp1yaqi2rqqr3h4eFi2LBhWttGjBghOnbsqLUNgNi+fbvB60RGRormzZvrvX5wcLD0vnfv3mLGjBlllqm8exlDo9GIJk2aiL1794rZs2eLKVOmaO3Xt+KwvjJmZWUJNzc3MXz4cL33Kb0arrGeeuop8cQTT2htCw4OFs8//7zBc0JCQsSsWbO0tkVGRoru3bubdd2yngEZrzJW7WXNCABRYAc7aztLF4OoVhFCIDs/2yIvUYHFyM+ePYujR49CoVCYdN7kyZNx+fJlHDp0SNp2//59bN26VatWpLocOHAA2dnZCA0Nxfjx47F582ZkZWWZfJ19+/YhLS0Nr7/+ut79rq6u0s+Ojo5lvko2RR07dgyhoaFa1woLC8OxY8cMliUvL09nVVg7OzucOHEC+fn5Zl+XLI/TwQNgEw1R5cspyEHwN8EWuffxscdhb2Nv9PE7d+6Eo6MjCgoKkJeXB7lcjs8++0znuDFjxsDKSnvUXVxcHBo3bozWrVuja9euWL9+PXr16gUA+P777yGEwNNPP611zueff45169ZpbVu9ejXGjRtndJnLExUVhaeffhpWVlZo27YtmjRpgi1btmDixIkmXefy5csAgJYtW5Z7bHl9L0ouIZ+SkgJPT0+t/Z6enkhJSTF4flhYGNatW4fhw4ejU6dOiImJwbp165Cfn4+0tDR4e3ubdV2yPIYRIqrz+vbtiy+++AJZWVlYtmwZrK2tMXLkSJ3jli1bpvOvbh8fH+nnZ599FjNnzsSKFSvg5OSE9evXY9SoUXByctI6Z9y4cXjjjTe0tpX+Aq2I9PR0bNu2DYcPH5a2jR8/HlFRUSaHEVNqmap63Z358+cjJSUFXbt2hRACnp6eCA8Px5IlSyCXs6K/JmMYASCzzrZ0EYhqHTtrOxwfa36nyYre2xQODg7SF+n69evRvn17REVF6TSveHl5lfmF+/TTT2PmzJn4/vvv0atXLxw5cgSLFi3SOc7FxaVKv7i/+eYb5ObmIji4uGZKCAGNRoNLly6hRYsWUi1FRkaGzvnp6elwcXEBALRo0QIAcOHCBYSEhJR5X0dHxzL3jx8/HqtWrQJQ+CxTU1O19qempsLLy/AElHZ2dli/fj1Wr16N1NRUeHt7Y82aNXBycoK7u7vZ1yXLYxgBIHJ9yj+IiEwik8lMaip5WMjlcsybNw+RkZEYO3Ys7OyMDzZOTk4YNWoU1q9fjytXrqBFixbo2bNnFZZWv6ioKLz66qs6tSAvvfQS1q9fj8WLF6N+/fpwc3NDTEwMevfuLR2TmZmJ+Ph4KYQ8/vjjcHNzw5IlS7B9+3ade6Wnp0v9RkxppgkJCUF0dLTWEOL9+/eXG3gAwMbGBo0aNQIAbN68GYMHD5ZqRipyXbIchhEAYD9eIiph1KhReO2117By5UrMmjVL2p6enq7T98DJyQkODsVrW02ePBk9e/bE+fPnMXv2bL3Xz87O1rmOUqlEvXr1pPeJiYk6X+7NmzfXupc+sbGxOHXqFL7++mudfh5jxozBu+++i/fffx/W1taIjIzEwoUL4enpia5du+Lff//Fe++9B3d3d4wYMQJAYa3RunXrMGrUKAwdOhQvv/wymjVrhrS0NHz//fdISkrC5s2bAZjWTDNjxgz07t0bH3/8MZ544gls3rwZf/75J9asWSMdM3fuXNy4cUOaS+TSpUs4ceIEgoODcffuXSxduhRnz57VGlJtzHXv3LmDpKQk3Lx5EwBw8eJFAIW1KqxBsZDKH+RT+ap6aG+nT8JEVl5+pV2bqC6qTUN7hRBi0aJFwt3dXdy/f18IUTjcVt9r0aJFOucGBgYKKysrcfPmTZ19vXv31nudsLAw6RhD9/r999+l/Rs2bND7eaZNmyZat26td19ycrKQy+Xixx9/FEIIUVBQID799FPRrl07YW9vLxo1aiRGjx4tEhMTdc49efKkGDFihHB3dxdKpVI0a9ZMTJ06VVy+fFnvvYzx/fffixYtWgiFQiHatGkjdu3apbU/PDxc9O7dW3ofFxcnOnToIOzs7ISzs7MYNmyYuHDhgsnX3bBhg97nu2DBArM/S11WGUN7ZUJUYAxcNcnMzISLiwsyMjK0qvkqQpOehotdC6tPx7/kh99f2Al7BSuKiMyVm5uLxMREBAQE6Ay/pMqTmJiIFi1aIC4uDs2bN7d0cYjK/LNv7Pc32yeIiGqQ3bt3Y+rUqQwiVKuwKoCIqAaJiIiwdBGIKh1rRgAIjWkzLRIREVHlYRgBgIKyx8YTERFR1WEYAcDp4ImIiCyHYYSIiIgsimGEiIiILIphhIiIiCyKYYSIiIgsimEEAGx0V60kIrKUlJQU9O/fHw4ODtIidES1GcMIAHE/wNJFICILkMlkZb7efvtti5Rr2bJlSE5ORmxsLC5dulRt9120aBGsrKzw4Ycf6ux7++230aFDB53tV69ehUwm01rUTwiBNWvWIDg4GI6OjnB1dUVQUBCWL1+O7Oxss8qWm5uLiIgINGjQAI6Ojhg5ciRSU1PLPCc1NRUTJ06Ej48P7O3tMWDAAFy+fFnrmDVr1qBPnz5wdnaGTCZDenq6znVOnTqF/v37w9XVFQ0aNMDUqVNx//59sz4H6ccwQkR1VnJysvRavnw5nJ2dtbaVXLFXCIGCgoJqKdeVK1fQuXNnNG/eHB4eHmZdQ6VSmXzO+vXr8frrr2P9+vVm3bPIhAkT8Morr2DYsGE4cOAAYmNjMX/+fPz444/4+eefzbrmzJkz8X//93/YsmULfvvtN9y8eVNaWVgfIQSGDx+OhIQE/Pjjjzh9+jT8/PwQGhqKrKws6bjs7GwMGDAA8+bN03udmzdvIjQ0FM2aNcPx48exd+9enDt3DhMnTjTrc5ABVbCAX6Wr6lV7O7w/jav2ElVQTV21t8iGDRuEi4uL9P7AgQMCgNi9e7fo1KmTsLGxEQcOHBDx8fFi6NChwsPDQzg4OIigoCCxf/9+rWv5+fmJDz74QEyaNEk4OjoKX19fsXr1aml/Xl6eiIiIEF5eXkKpVIrGjRuLhQsXSueixEqy4eHhQggh7t69KyZPnizc3NyEk5OT6Nu3r4iNjZWuuWDBAtG+fXuxdu1a4e/vL2QymUmf/+DBg6Jhw4ZCpVIJHx8fceTIEa39RdcvLTExUQAQp0+fFkII8d133wkAYseOHTrHajQakZ6eblK5hBAiPT1d2NjYiC1btkjbzp8/LwCIY8eO6T3n4sWLAoA4e/astE2tVgt3d3exdu1aneOL/n/fvXtXa/vq1auFh4eHUKvV0ra///5bAKjQisW1SWWs2suaESKqEkIIaLKzLfISlbgY+Zw5c7B48WKcP38ejzzyCO7fv49BgwYhOjoap0+fxoABAzBkyBAkJSVpnffxxx8jKCgIp0+fxksvvYQXX3wRFy9eBAB8+umn+Omnn/D999/j4sWL+Prrr+Hv7w8AOHnyJAYMGICnnnoKycnJ+OSTTwAAo0aNwq1bt7Bnzx7ExMSgU6dO6NevH+7cuSPdMz4+Hj/88AO2bdum1WxijKioKIwZMwY2NjYYM2YMoqKizHpeX3/9NQIDAzFs2DCdfTKZDC4uLtJxjo6OZb5+//13AEBMTAzy8/MRGhoqXatly5Zo3Lgxjh07prcceXl5AKC1iqxcLodSqcThw4eN/jx5eXlQKBSQy4u/Lu3s7ADApOtQ2cxaKG/lypX48MMPkZKSgvbt22PFihXo0qWLweO3bNmC+fPn4+rVq2jevDn++9//YtCgQWYXutIJG0uXgKjWETk5uNips0XuHXgqBjJ7+0q51rvvvov+/ftL7+vXr4/27dtL79977z1s374dP/30E6ZNmyZtHzRoEF566SUAwOzZs7Fs2TIcOHAAgYGBSEpKQvPmzdGjRw/IZDL4+flJ57m7u0OpVMLOzg5eXl4ACr/0Tpw4gVu3bkGpVAIAPvroI+zYsQNbt27F1KlTARQ2zWzatAnu7u4mfcbMzExs3bpV+mIfP348evbsiU8++QSOjqYtl3H58mUEBgaWe9zQoUMRHBxc5jENGzYEUNihV6FQ6HTm9fT0REpKit5zi8LK3LlzsXr1ajg4OGDZsmX4559/kJycbNyHAfDYY48hMjISH374IWbMmIGsrCzMmTMHAEy6DpXN5JqR7777DpGRkViwYAFOnTqF9u3bIywsDLdu3dJ7/NGjRzFmzBhMnjwZp0+fxvDhwzF8+HCcPXu2woWvCK1/ORU4W64gRPRQCwoK0np///59zJo1C61atYKrqyscHR1x/vx5nZqRRx55RPpZJpPBy8tL+nty4sSJiI2NRWBgIF5++eVy+1H89ddfuH//vtR5s+iVmJiIK1euSMf5+fmZHEQA4Ntvv0XTpk2lkNWhQwf4+fnhu+++M/laxtZKOTk5oVmzZmW+imogzGFjY4Nt27bh0qVLqF+/Puzt7XHgwAEMHDhQq5ajPG3atMGXX36Jjz/+GPb29vDy8kJAQAA8PT1Nug6VzeSakaVLl2LKlCmYNGkSAGDVqlXYtWsX1q9fL6XFkj755BMMGDAAr732GoDCf0Xs378fn332GVatWlXB4psvp0At/ezjag87GyuLlYWoNpLZ2SHwVIzF7l1ZHBwctN7PmjUL+/fvx0cffSR9YT755JM6HUZtbLRrXGUyGTQaDQCgU6dOSExMxJ49e/DLL7/gqaeeQmhoKLZu3aq3DPfv34e3tzcOHjyos69kbUHpshorKioK586dg7V18VeCRqPB+vXrMXnyZACAs7MzMjJ0p0EoGn1S1PzSokULXLhwodx7fv3113j++efLPGbPnj3o2bMnvLy8oFKpkJ6ervV5U1NTpdojfTp37ozY2FhkZGRApVLB3d0dwcHBOgGzPGPHjsXYsWORmpoKBwcHyGQyLF26FE2aNDHpOmSYSWFEpVIhJiYGc+fOlbbJ5XKEhoYabLc7duwYIiMjtbaFhYVhx44dBu+Tl5cntfcBhVWIVWl632aQybhYHlFlkslkldZU8jA5cuQIJk6ciP/85z8ACoPC1atXTb6Os7MzRo8ejdGjR+PJJ5/EgAEDcOfOHdSvX1/n2E6dOiElJQXW1tZS35LKcubMGfz55584ePCg1r3v3LmDPn364MKFC2jZsiUCAwPxzz//IDU1FZ6entJxp06dgq2tLRo3bgyg8Iv76aefxo8//qjTb0QIgczMTLi4uJjUTNO5c2fY2NggOjoaI0eOBABcvHgRSUlJCAkJKfczFgWly5cv488//8R7771nxJPRVfS5169fD1tbW63mO6oYk8JIWloa1Gq11i8iUPg/yFASTklJ0Xu8oXY+oHCs+zvvvGNK0SqGOYSIjNS8eXNs27YNQ4YMgUwmw/z586UaD2MtXboU3t7e6NixI+RyObZs2QIvLy+DE5yFhoYiJCQEw4cPx5IlS9CiRQvcvHkTu3btwn/+8x+T/6VfUlRUFLp06YJevXrp7Hv00UcRFRWFDz/8EGFhYQgMDMSYMWPw/vvvw8vLC6dOncKbb76JGTNmwMqqsHb5qaeewvbt2zFmzBi8+eabePzxx+Hu7o4zZ85g2bJlmD59OoYPHw4nJyc4OTkZVUYXFxdMnjwZkZGRqF+/PpydnTF9+nSEhISga9eu0nEtW7bEokWLpKC4ZcsWuLu7o3Hjxjhz5gxmzJiB4cOH4/HHH5fOSUlJQUpKCuLj4wEUhjMnJyc0btxYCmefffYZunXrBkdHR+zfvx+vvfYaFi9ezAnpKtFD2eA1d+5cZGRkSK/r169X+j3s67vj9KZI7Fs9Hk0btq306xNR7bR06VLUq1cP3bp1w5AhQxAWFoZOnTqZdA0nJycsWbIEQUFBePTRR3H16lXs3r3bYB8EmUyG3bt3o1evXpg0aRJatGiBp59+GteuXdP5x15pGzduNFjzq1Kp8NVXX0m1DaWNHDkSmzZtQn5+PqytrfHzzz+jcePGGDNmDNq2bYsFCxZgxowZWjUNMpkM33zzDZYuXYodO3agd+/eeOSRR/D2229j2LBhCAsLM/IpaVu2bBkGDx6MkSNHolevXvDy8sK2bdu0jrl48aJWU1JycjImTJiAli1b4uWXX8aECRPw7bffap2zatUqdOzYEVOmTAEA9OrVCx07dsRPP/0kHXPixAn0798f7dq1w5o1a7B69Wq8/PLLZn0O0k8mTBgDp1KpYG9vj61bt2L48OHS9vDwcKSnp+PHH3/UOadx48aIjIzEK6+8Im1bsGABduzYgb/++suo+xZV62VkZMDZmZ1NiR5Gubm5SExMREBAgNZwSrKsBQsW4LffftPb34SoMpT1Z9/Y72+TakYUCgU6d+6M6OhoaZtGo0F0dLTBdruQkBCt4wFg//79RrXzERFRxezZswdLliyxdDGIymTyaJrIyEiEh4cjKCgIXbp0wfLly5GVlSWNrnnmmWfQsGFDLFq0CAAwY8YM9O7dGx9//DGeeOIJbN68GX/++SfWrFlTuZ+EiIh0nDhxwtJFICqXyWFk9OjRuH37Nt566y2kpKSgQ4cO2Lt3r9RumZSUpNXu2a1bN3zzzTd48803MW/ePDRv3hw7duxA27bsp0FEREQm9hmxFPYZIXr4sc8IUd1U7X1GiIiIiCobwwgRVSpT59wgopqtMv7Mm7VQHhFRaUUrm968eRPu7u5QKBSc2ZioFhNCQKVS4fbt25DL5VAoFGZfi2GEiCqFXC5HQEAAkpOTcfPmTUsXh4iqib29PRo3blyhhQMZRoio0igUCjRu3BgFBQVQq9Xln0BENZqVlRWsra0rXAvKMEJElUomk8HGxkZn1VoiIkPYgZWIiIgsimGEiIiILIphhIiIiCyqRvQZKZokNjMz08IlISIiImMVfW+XN9l7jQgj9+7dAwD4+vpauCRERERkqnv37sHFxcXg/hqxNo1Go8HNmzfh5ORUqZMoZWZmwtfXF9evX+eaN1WIz7n68FlXDz7n6sHnXD2q8jkLIXDv3j34+PiUOQ9JjagZkcvlaNSoUZVd39nZmb/o1YDPufrwWVcPPufqwedcParqOZdVI1KEHViJiIjIohhGiIiIyKLqdBhRKpVYsGABlEqlpYtSq/E5Vx8+6+rB51w9+Jyrx8PwnGtEB1YiIiKqvep0zQgRERFZHsMIERERWRTDCBEREVkUwwgRERFZVK0PIytXroS/vz9sbW0RHByMEydOlHn8li1b0LJlS9ja2qJdu3bYvXt3NZW0ZjPlOa9duxY9e/ZEvXr1UK9ePYSGhpb7/4WKmfo7XWTz5s2QyWQYPnx41RawljD1OaenpyMiIgLe3t5QKpVo0aIF//4wgqnPefny5QgMDISdnR18fX0xc+ZM5ObmVlNpa6ZDhw5hyJAh8PHxgUwmw44dO8o95+DBg+jUqROUSiWaNWuGjRs3Vm0hRS22efNmoVAoxPr168W5c+fElClThKurq0hNTdV7/JEjR4SVlZVYsmSJiIuLE2+++aawsbERZ86cqeaS1yymPuexY8eKlStXitOnT4vz58+LiRMnChcXF/HPP/9Uc8lrHlOfdZHExETRsGFD0bNnTzFs2LDqKWwNZupzzsvLE0FBQWLQoEHi8OHDIjExURw8eFDExsZWc8lrFlOf89dffy2USqX4+uuvRWJioti3b5/w9vYWM2fOrOaS1yy7d+8Wb7zxhti2bZsAILZv317m8QkJCcLe3l5ERkaKuLg4sWLFCmFlZSX27t1bZWWs1WGkS5cuIiIiQnqvVquFj4+PWLRokd7jn3rqKfHEE09obQsODhbPP/98lZazpjP1OZdWUFAgnJycxJdffllVRaw1zHnWBQUFolu3bmLdunUiPDycYcQIpj7nL774QjRp0kSoVKrqKmKtYOpzjoiIEI899pjWtsjISNG9e/cqLWdtYkwYef3110WbNm20to0ePVqEhYVVWblqbTONSqVCTEwMQkNDpW1yuRyhoaE4duyY3nOOHTumdTwAhIWFGTyezHvOpWVnZyM/Px/169evqmLWCuY+63fffRceHh6YPHlydRSzxjPnOf/0008ICQlBREQEPD090bZtWyxcuBBqtbq6il3jmPOcu3XrhpiYGKkpJyEhAbt378agQYOqpcx1hSW+C2vEQnnmSEtLg1qthqenp9Z2T09PXLhwQe85KSkpeo9PSUmpsnLWdOY859Jmz54NHx8fnV9+0mbOsz58+DCioqIQGxtbDSWsHcx5zgkJCfj1118xbtw47N69G/Hx8XjppZeQn5+PBQsWVEexaxxznvPYsWORlpaGHj16QAiBgoICvPDCC5g3b151FLnOMPRdmJmZiZycHNjZ2VX6PWttzQjVDIsXL8bmzZuxfft22NraWro4tcq9e/cwYcIErF27Fm5ubpYuTq2m0Wjg4eGBNWvWoHPnzhg9ejTeeOMNrFq1ytJFq1UOHjyIhQsX4vPPP8epU6ewbds27Nq1C++9956li0YVVGtrRtzc3GBlZYXU1FSt7ampqfDy8tJ7jpeXl0nHk3nPuchHH32ExYsX45dffsEjjzxSlcWsFUx91leuXMHVq1cxZMgQaZtGowEAWFtb4+LFi2jatGnVFroGMud32tvbGzY2NrCyspK2tWrVCikpKVCpVFAoFFVa5prInOc8f/58TJgwAc899xwAoF27dsjKysLUqVPxxhtvQC7nv68rg6HvQmdn5yqpFQFqcc2IQqFA586dER0dLW3TaDSIjo5GSEiI3nNCQkK0jgeA/fv3GzyezHvOALBkyRK899572Lt3L4KCgqqjqDWeqc+6ZcuWOHPmDGJjY6XX0KFD0bdvX8TGxsLX17c6i19jmPM73b17d8THx0thDwAuXboEb29vBhEDzHnO2dnZOoGjKAAKLrNWaSzyXVhlXWMfAps3bxZKpVJs3LhRxMXFialTpwpXV1eRkpIihBBiwoQJYs6cOdLxR44cEdbW1uKjjz4S58+fFwsWLODQXiOY+pwXL14sFAqF2Lp1q0hOTpZe9+7ds9RHqDFMfdalcTSNcUx9zklJScLJyUlMmzZNXLx4UezcuVN4eHiI999/31IfoUYw9TkvWLBAODk5iW+//VYkJCSIn3/+WTRt2lQ89dRTlvoINcK9e/fE6dOnxenTpwUAsXTpUnH69Glx7do1IYQQc+bMERMmTJCOLxra+9prr4nz58+LlStXcmhvRa1YsUI0btxYKBQK0aVLF/HHH39I+3r37i3Cw8O1jv/+++9FixYthEKhEG3atBG7du2q5hLXTKY8Zz8/PwFA57VgwYLqL3gNZOrvdEkMI8Yz9TkfPXpUBAcHC6VSKZo0aSI++OADUVBQUM2lrnlMec75+fni7bffFk2bNhW2trbC19dXvPTSS+Lu3bvVX/Aa5MCBA3r/zi16tuHh4aJ3794653To0EEoFArRpEkTsWHDhioto0wI1m0RERGR5dTaPiNERERUMzCMEBERkUUxjBAREZFFMYwQERGRRTGMEBERkUUxjBAREZFFMYwQERGRRTGMEBERkUUxjBAREZFFMYwQERGRRTGMEBERkUUxjBAREZFF/T/O9bHtoNGEoAAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "spec_at_sens = {}\n",
    "sens_at_spec = {}\n",
    "for m in ['EVE_scores_ASM', 'esm_1v', 'REVEL', 'Transfer']:\n",
    "    fpr, tpr, _ = roc_curve(hq_y, pred_hq[m])\n",
    "    spec_at_sens[m] = {}\n",
    "    sens_at_spec[m] = {}\n",
    "    for sens in [0.9, 0.95, 0.99]:\n",
    "        spec_at_sens[m]['spec at sens '+str(sens)] = 1 - min(fpr[tpr > sens])\n",
    "    for spec in [0.9, 0.95, 0.99]:\n",
    "        sens_at_spec[m]['sens at spec '+str(spec)] = max(tpr[fpr < (1 - spec)])\n",
    "    auc = round(roc_auc_score(hq_y, pred_hq[m]), 3)\n",
    "    plt.plot(fpr,tpr,label = m + \", \" + \"AUC=\" + str(auc))\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "8352be09",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>EVE_scores_ASM</th>\n",
       "      <th>esm_1v</th>\n",
       "      <th>REVEL</th>\n",
       "      <th>Transfer</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>spec at sens 0.9</th>\n",
       "      <td>0.675329</td>\n",
       "      <td>0.501519</td>\n",
       "      <td>0.704354</td>\n",
       "      <td>0.746878</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>spec at sens 0.95</th>\n",
       "      <td>0.502531</td>\n",
       "      <td>0.269997</td>\n",
       "      <td>0.569693</td>\n",
       "      <td>0.592980</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>spec at sens 0.99</th>\n",
       "      <td>0.243672</td>\n",
       "      <td>0.080661</td>\n",
       "      <td>0.264934</td>\n",
       "      <td>0.263584</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   EVE_scores_ASM    esm_1v     REVEL  Transfer\n",
       "spec at sens 0.9         0.675329  0.501519  0.704354  0.746878\n",
       "spec at sens 0.95        0.502531  0.269997  0.569693  0.592980\n",
       "spec at sens 0.99        0.243672  0.080661  0.264934  0.263584"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Table: specificity at high sensitivity\n",
    "pd.DataFrame(spec_at_sens)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "a81b6ded",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>EVE_scores_ASM</th>\n",
       "      <th>esm_1v</th>\n",
       "      <th>REVEL</th>\n",
       "      <th>Transfer</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>sens at spec 0.9</th>\n",
       "      <td>0.683944</td>\n",
       "      <td>0.729456</td>\n",
       "      <td>0.713021</td>\n",
       "      <td>0.774968</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sens at spec 0.95</th>\n",
       "      <td>0.489254</td>\n",
       "      <td>0.620733</td>\n",
       "      <td>0.589128</td>\n",
       "      <td>0.643489</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sens at spec 0.99</th>\n",
       "      <td>0.230088</td>\n",
       "      <td>0.396966</td>\n",
       "      <td>0.257901</td>\n",
       "      <td>0.415929</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   EVE_scores_ASM    esm_1v     REVEL  Transfer\n",
       "sens at spec 0.9         0.683944  0.729456  0.713021  0.774968\n",
       "sens at spec 0.95        0.489254  0.620733  0.589128  0.643489\n",
       "sens at spec 0.99        0.230088  0.396966  0.257901  0.415929"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Table: sensitivity at high specificity\n",
    "pd.DataFrame(sens_at_spec)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3849e635-6a8e-437b-b999-c6bf9fcdac53",
   "metadata": {},
   "source": [
    "### Number of training genes vs performance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "e50b886a-3a9d-4cab-b242-acf7294ccdd6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "274 genes used to calculate per gene AUROC out of 1298 genes\n",
      "Calculating...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 274/274 [00:08<00:00, 30.64it/s]\n"
     ]
    }
   ],
   "source": [
    "# Get the prediction matrix\n",
    "pred_ntrain = pd.DataFrame(index = y.index)\n",
    "features = feat_selected_dms\n",
    "cpt = CPTClassifier()\n",
    "\n",
    "pset_genes = powerset(all_genes)\n",
    "for genes in pset_genes - {()}:\n",
    "    genes = list(genes)\n",
    "    n_genes = len(genes)\n",
    "    # Transfer model predictions trained on each individual gene\n",
    "    cpt.train(feat_mat, label, genes, features)\n",
    "    pred_clinvar = cpt.predict(x[features])\n",
    "    pred_ntrain['Transfer_' + str(n_genes) + '_' + '+'.join(genes)] = 1 - pred_clinvar.values\n",
    "\n",
    "# Compare with EVE and ESM-1v\n",
    "pred_ntrain['_EVE'] = x['EVE_scores_ASM']\n",
    "pred_ntrain['_ESM-1v'] = -x['esm_1v']\n",
    "    \n",
    "# Get per gene AUROC\n",
    "roc_by_gene_ntrain = get_per_gene_auroc(y, pred_ntrain)\n",
    "\n",
    "# Add global AUROC\n",
    "roc_by_gene_ntrain.insert(loc = 0, column = 'Global_AUROC', \n",
    "                   value = [np.nan, np.nan, np.nan] + \\\n",
    "                           [float(roc_auc_score(y, pred_ntrain[feat])) for feat in pred_ntrain.columns])\n",
    "roc_by_gene_ntrain = roc_by_gene_ntrain.sort_values('Global_AUROC', ascending = False).round(4)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "cf351793-0137-4ba6-9842-0aaefdf2dd93",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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/x0HbNsR5PMluTQl1S6lvHbCeBOjfTbc2bdIEjgeq42dGbUlbHGeAv38bZRaUQV9PgpoaAX26mrWZ/lPjNHY8EHXKroGBAdzc3JCYmCgvk8lkSExMrHe9Zy0jIyPY2tqiuroaBw4cgI+PT4P1ZTKZwjpRosby6meNbwKG48LKF/FNwHCd/DGvbJqnsjNV2nQLGGX3StO1e6oqU98tBmKmu+ns3y8RkRja6jgD/PXbaO4oBzysluH+wxo8rJG1qf6Teol+H9LAwEBs27YNu3btQmZmJt59911UVFRg1qxZAIAZM2YobHp06tQpxMXFITc3Fz/99BNefPFFyGQyvP/++/I6wcHBSE5OxtWrV3H+/HkEBwcjKSkJ06ZN03j/iLRBZkHjN7nRlg1xlCbZWpRQNxXXARMRaUZbHWdqtfX+k/qIvobU19cXN2/eRFhYGAoLC+Hq6or4+Hj5Rkd5eXnQ0/s7b37w4AFCQkKQm5sLU1NTjB8/Hrt374alpaW8TnFxMWbMmIGCggJYWFhgwIABSEhIwAsvvKDp7hG1egkZhXhYI2t0fW3ZEKexOyTr4nQrr37WWt8HIqLWrq3txP+4tt5/Uh/RE1IAmDdvHubNm6f0taSkJIXnI0eOxIULFxpsb/v27eoKjUjnNWZX1lratCGOk9RU6VrKRxPqx9fO1k430qZ1skREJI7GjDO6TFf6r4snprWN6FN2iUhc9Z3hNGinh61vaO/Uz7mjHZWWP5pQc7oRERE1VX1r9rXlxG1z6UL/2/I64NakVVwhJSLx1HeGs6pGhs3HcnTqTOHjOwdzuhERETVV7Zp9Xd+Jvz660P+GTkzrym8fbcCElKiNU3avVeCvf5C1eQprfTsHPzrI6Mp0IyIiEpEg/DWGCoLSHep1mbbvWcAT060Dp+wStXGP7sqqJ6n7urZOYW3MIKML042IiEgcnO6p/ZykpkrLeWJas5iQEpH8XquG7fSVvq6NZwobM8jwFilERNRU3IdA+/HEdOvAKbtEKtLl3dh0aQqrsqnIygYZbZ9uRERE4uB0T+2nC+tgdQETUiIV6PptQhqbxGkDDjJERNSSdOkkblvGE9PiY0JKpAJd342toSROG68Mc5AhIqKWoksncYnExISUSAVtYXqOsiRO168MExGROLTxZGctzsQhUg8mpEQqaKvTc3T9yjAREWmeLpzs5EwcouZjQkqkgrY6PactXBkmIiLNau0nO5OTk0V9/xEjRoj6/kSawoSUSAVtdXpOW70yTERELae1n+wcOXKkqO8vPHr2m0iHMSElUpE2Tc9R19nd/zO7hdTr2Xh0aJQAGNHHqcH34NldIiKqD092EhHAhJRIp7X02d0l+xp+nWd3iYioPq19Gczx48dVqp965RYOncnH9T/vw66jMSYOtMWQHp1aKDoi3cGElIiIiIg0rrUvg1Fllk9CRiE2/ZAGGPWAxAb4A0B0FtB/sJPWzKoiEgsTUiIdpurZXSIiIk3SpmUwDWntGzQRtWZMSIl0GNdwEhERtbzGbNCkzfdcJWpJemIHQERERESkzZykpkrLazdoqr3n6rk/SnG/qkZ+z9WEjEJNhknUKjEhJSIiIiJqhrmjHSGRKJY9ukFTQ1N6ido6JqRERERERM1Qu0GTi50lTAz04WJnia3T3eQbNLX2e64SiYlrSImIiIioRbSldZMNbdCkq/dcbUvfL7UcXiElIiIiIrXjusm/PWlKrzbi90vqwoSUiIhIi0VHR8Pe3h5GRkbw8PBAampqvXWrqqqwcuVKODg4wMjICC4uLoiPj1eoExERAXd3d5iZmcHKygoTJ07ExYsXW7obpIO4bvJvT5rSq434/ZK6cMouERGRltq/fz8CAwMRExMDDw8PREVFwcvLCxcvXoSVlVWd+iEhIdizZw+2bdsGZ2dnJCQkYNKkSThx4gQGDhwI4K/7FwcEBMDd3R3V1dVYtmwZxo4diwsXLqBDhw6a7iJpMa6bVKQr91ytxe+X1IVXSImIiLRUZGQk/P39MWvWLPTt2xcxMTEwMTHBjh07lNbfvXs3li1bhvHjx6Nnz5549913MX78eHz66afyOvHx8Zg5cyb69esHFxcX7Ny5E3l5eUhLS9NUt0hHPOlWKKTd+P2SujAhJSIi0kIPHz5EWloaPD095WV6enrw9PTEyZMnlR5TWVkJIyMjhTJjY2OkpKTU+z6lpX9txNKxY8d62ywrK1N4EAG6uW6S/sbvl9SFCSkREZEWKikpQU1NDaRSqUK5VCpFYaHyTUW8vLwQGRmJS5cuQSaT4ejRo4iLi0NBQYHS+jKZDAsXLsTw4cPxzDPPKK0TEREBCwsL+cPOzq55HSMAf20Y47MpBX1C4+GzKUUrN4rRxXWT9Dd+v6QuXENKRETURmzYsAH+/v5wdnaGRCKBg4MDZs2aVe8U34CAAPz+++8NXkENDg5GYGCg/HlZWRmT0kckJyerfEzqlVv45Ei2/PmpXCD15M9YPNYJQ3p0UqmtESNGqPz+6qRr6yZJEb9fUodWkZBGR0fj448/RmFhIVxcXLBx40YMGTJEad2qqipERERg165dyM/PR+/evbFu3Tq8+OKL8joRERGIi4tDVlYWjI2NMWzYMKxbtw69e/fWVJeIiIhaVOfOnaGvr4+ioiKF8qKiIlhbK/+B2KVLFxw6dAgPHjzArVu3YGNjg6CgIPTs2bNO3Xnz5uG7775DcnIyunXrVm8choaGMDQ0bF5ndNjIkSPV1tbSfaofIwiC2t6fiKgliD5lt3aHwPDwcKSnp8PFxQVeXl4oLi5WWj8kJARbt27Fxo0bceHCBcyZMweTJk3CmTNn5HVqdwj85ZdfcPToUVRVVWHs2LGoqKjQVLeIiIhalIGBAdzc3JCYmCgvk8lkSExMxNChQxs81sjICLa2tqiursaBAwfg4+Mjf00QBMybNw8HDx7E//73P/To0aPF+kBE2ksXppVT6yARRD515uHhAXd3d2zatAnAX4OpnZ0d5s+fj6CgoDr1bWxssHz5cgQEBMjLXnnlFRgbG2PPnj1K3+PmzZuwsrLC8ePHGzV1paysDBYWFigtLYW5uXkTe0ZERNqutY8H+/fvh5+fH7Zu3YohQ4YgKioKX331FbKysiCVSjFjxgzY2toiIiICAHDq1Cnk5+fD1dUV+fn5+PDDD3HlyhWkp6fD0tISADB37lzs27cP33zzjcLMIgsLCxgbGz8xptb+mWlaU6bsLov7DTk3655Ed7QyxZpJ/VVqS+wpu6SbEjIK8c5uxZ23JRIgZrobp/CSXGPHA1Gn7NbuEBgcHCwvE2uHwMrKSvlz7hBIRETawNfXFzdv3kRYWBgKCwvh6uqK+Ph4+UZHeXl50NP7ezLUgwcPEBISgtzcXJiammL8+PHYvXu3PBkFgC1btgAARo0apfBesbGxmDlzZkt3Sec0JSEM7eSEOXvS8OglA4kECJvuhhH8sU+twOZjOXXKBAHYnHSZCSmpTNSEtKEdArOyspQeU7tD4IgRI+Dg4IDExETExcWhpqZGaf3G7hC4YsWK5nWGiIhIBPPmzcO8efOUvpaUlKTwfOTIkbhw4UKD7XHNofhqdy/dnHQZl4ruopfUDAGjHLh7KbUa2UXlSssvFd3VcCSkC1rFpkaq4A6BREREpOu4eym1Zk5SU5z7o7ROeS+pmQjRkLYTdVOj5uwQWFFRgWvXriErKwumpqYN7hB47NixJ+4QaG5urvAgIiIiIqK65o52hESiWCaRAAGjHMQJiLSaqAkpdwgkIiIiItIutdPKXewsYWKgDxc7S2yd7sZp5dQkok/ZDQwMhJ+fHwYPHizfIbCiogKzZs0CgEbtECiTyfD+++/L2wwICJDvEGhmZobCwr+2oW7sDoFERERERFQ/TisndRH9PqS+vr745JNPEBYWBldXV5w9e7bODoEFBQXy+rU7BPbt2xeTJk2Cra0tUlJS6uwQWFpailGjRqFr167yx/79+zXdPSIiIly6dAlTpkxRuot7aWkppk6ditzcXBEiIyIiEpfo9yFtjXgPNSIiAtQ3Hrz99tuwtLTE+vXrlb7+wQcfoKysTH7LFW3GMZSIiAAtuQ8pEVFrkJBRiM3HcpBdVA4nqSnmjnbkNCRSq+PHj2PPnj31vv76669j6tSpGoyIiIiodRB9yi4RkZgSMgrxzu40nPujFPeranDuj1LM2ZOGhIxCsUMjHZKXlwcrK6t6X+/cuTOuX7+uwYiIiIhaByakRNSmbT6WU6dMEIDNSZdFiIZ0lYWFBS5frv9vKicnh9NbiYioTWJCSkRtWnZRudLyS0V3NRwJ6bIRI0Zg48aN9b7+z3/+E//3f/+nwYiIiIhaByakRNSmOUlNlZb3kpppOBLSZcHBwTh8+DBeffVVpKamorS0FKWlpTh16hReeeUVJCQkIDg4WOwwiYiINI4JKRG1aXNHO0IiUSyTSICAUQ7iBEQ6aeDAgfj666+RnJyMoUOHomPHjujYsSOGDRuGn376CV999RUGDRokdphEREQax112iahN8+pnjZjpbticdBmXiu6il9QMAaMcMJa77JKaTZgwAdeuXUN8fDxycnIgCAKcnJwwduxYmJiYiB0eERGRKJiQElGb59XPmrd5IY0wNjbGpEmTxA6DiIio1eCUXSIiIg2orq7Gxx9/jEGDBsHU1BSmpqYYNGgQPvnkE1RVVYkdHhERkSh4hZSIiKiF3b9/Hy+88AJOnjwJT09PjBgxAgCQmZmJDz74AN9++y2OHDkCIyMjkSMlIiLSLCakRERELWzt2rW4fv06zpw5gwEDBii8du7cObz88stYu3YtPvzwQ3ECJCIiEgmn7BIREbWwL7/8EpGRkXWSUQBwcXHBJ598gn379okQGRERkbiYkBIREbWwa9euYciQIfW+/uyzzyIvL0+DEREREbUOTEiJiIhamLm5OYqLi+t9vbCwEGZmZhqMiIiIqHVgQkpERNTCRo8ejTVr1tT7+tq1azF69GgNRkRERNQ6cFMjIhUlZBRi87EcZBeVw0lqirmjHXkPSyJqUHh4ODw8PPDss88iMDAQzs7OEAQBmZmZ+Oyzz3DhwgX88ssvYodJRESkcUxIiVSQkFGId3anyZ+f+6MUc/akIWa6G5NSIqpX3759cfToUbz55puYPHkyJBIJAEAQBDg7O+PIkSPo16+fyFESERFpHhNSIhVsPpZTp0wQgM1Jl5mQElGDnn32WWRkZODs2bPIzs4GADg5OcHV1VXcwIiIiETEhJRIBdlF5UrLLxXd1XAkRKStXF1dlSahv/76KwYPHqz5gIioybiMh6j5uKkRkQqcpKZKy3tJuTsmET1ZeXk57t+/r1B29uxZeHt7w8PDQ6SoiKgpapfxnPujFPerauTLeBIyCsUOjUirMCElUsHc0Y74/0u/5CQSIGCUgzgBEZFWuH79OoYOHQoLCwtYWFggMDAQ9+7dw4wZM+Dh4YEOHTrgxIkTYodJRCpoaBkPETUep+wSqcCrnzViprthc9JlXCq6i15SMwSMcsBYTs8hogYsXboUDx48wIYNGxAXF4cNGzbgp59+goeHBy5fvoxu3bqJHSIRqYjLeIjUgwkpkYq8+llzfQgRqSQ5ORlxcXF49tln8frrr8Pa2hrTpk3DwoULxQ6NiJrISWqKc3+U1innMh4i1XDKLpEaJWQUwmdTCvqExsNnUwrXkRARAKCoqAg9evQAAFhZWcHExATjxo0TOSoiag4u4yFSD5US0hs3bmDJkiUoKyur81ppaSmWLl2KoqIitQVHpE24uQERNURPT0/hvw0MDESMhoiaq3YZj4udJUwM9OFiZ4mt0924jIdIRSpN2Y2MjERZWRnMzc3rvGZhYYG7d+8iMjIS69atU1uARNqC9yglovoIggAnJydI/v/llPLycgwcOFAhSQWAP//8U4zwiKiJuIyHqPlUSkjj4+MRExNT7+szZsyAv78/E1Jqk7i5ARHVJzY2VuwQiFpEcnKyqO8/YsQIUd+fiJpPpYT0ypUrePrpp+t9vVu3brh69arKQURHR+Pjjz9GYWEhXFxcsHHjRgwZMkRp3aqqKkRERGDXrl3Iz89H7969sW7dOrz44ovyOsnJyfj444+RlpaGgoICHDx4EBMnTlQ5LiJVcHMDIqqPn5+f2CEQtYiRI0eK+v6CIIj6/kTUfCqtITU2Nm4w4bx69SqMjY1VCmD//v0IDAxEeHg40tPT4eLiAi8vLxQXFyutHxISgq1bt2Ljxo24cOEC5syZg0mTJuHMmTPyOhUVFXBxcUF0dLRKsRA1Bzc3IKL6lJWVKX3U1NSIHRoREZGoJIIKp5Zeeukl2NjYYNu2bUpff+utt3Djxg388MMPjQ7Aw8MD7u7u2LRpEwBAJpPBzs4O8+fPR1BQUJ36NjY2WL58OQICAuRlr7zyCoyNjbFnz5469SUSicpXSMvKymBhYYHS0lKl62WJ6pOQUch7lBLpEHWNB3p6evL1o4/S19dHjx49sGTJEvj7+zcn1FaDY2jbwim7RFSfxo4HKk3ZXbJkCV544QVYWFhg6dKlkEqlAP7azn79+vXYuXMnjhw50uj2Hj58iLS0NAQHB8vL9PT04OnpiZMnTyo9prKyEkZGRgplxsbGSElJUaUrRC2CmxsQkTLHjh1TWn7nzh2kpaVh6dKlaNeuHWbNmqXhyIiahwkhETWXSgnp6NGjER0djQULFuCzzz6Dubk5JBIJSktL0b59e2zcuBHPP/98o9srKSlBTU2NPLGtJZVKkZWVpfQYLy8vREZGYsSIEXBwcEBiYiLi4uKaNe2psrISlZWV8ufKbmtDRETUVA2ts/Px8YG9vT02btzIhJSIiNoclRJSAHjnnXcwYcIEfPXVV8jJyZFvZf/qq6+iW7duLRGjgg0bNsDf3x/Ozs6QSCRwcHDArFmzsGPHjia3GRERgRUrVqgxSiIiosYbOXIkFi5cKHYYRKJJyCjE5mM5yC4qh5PUFHNHO3LGEVEboXJCCgC2trZYtGhRs9+8c+fO0NfXR1FRkUJ5UVERrK2V/yPUpUsXHDp0CA8ePMCtW7dgY2ODoKAg9OzZs8lxBAcHIzAwUP68rKwMdnZ2TW6PiIhIFaWlpbCwsBA7DCJRJGQU4p3dafLn5/4oxZw9aYiZ7saklKgNaFJC+p///AdffPEFsrOzAQBOTk6YOnUqXn31VZXaMTAwgJubGxITE+WbDslkMiQmJmLevHkNHmtkZARbW1tUVVXhwIEDeP3115vSFQCAoaEhDA0Nm3w8aTduyEBEYqqqqsLHH38MDw8PsUMhEsXmYzl1ygQB2Jx0mQkpURugUkIqk8kwZcoU/Oc//4GTkxOcnZ0BABkZGfD19cVrr72GL774QulOgvUJDAyEn58fBg8ejCFDhiAqKgoVFRXydTQzZsyAra0tIiIiAACnTp1Cfn4+XF1dkZ+fjw8//BAymQzvv/++vM3y8nLk5Pz9j9uVK1dw9uxZdOzYscH7qFLbxHuoEVFL+8c//qG0vLS0FBkZGZBIJPjpp580HBVR65BdVK60/FLRXQ1HQkRiUCkh3bBhA3788Ud8++23mDBhgsJr3377LWbNmoUNGzaotA7G19cXN2/eRFhYGAoLC+Hq6or4+Hj5Rkd5eXnQ0/v7dqkPHjxASEgIcnNzYWpqivHjx2P37t2wtLSU1/n1118xevRo+fPa6bh+fn7YuXOnKl0mIiJqtvqm49rZ2eGVV17BtGnTOGWX2iwnqSnO/VFap7yX1EyEaIhI01S6D+mAAQOwcOFCzJ49W+nr27dvx4YNG/Dbb7+pLUAx8B5qbQun7BJRfTgeqI6fGakqIaMQc/ak4dFfpBIJsHW6G+/lTaTFGjseqJSQGhsb4+LFi/VOe7127RqcnZ1x//591SNuRTiYEhERoJnxoKysDHv37sX27dvx66+/tsh7aBLHUGqKhIxCbE66jEtFd9FLaoaAUQ5MRom0XGPHA5Wm7BobG+POnTv1JqRlZWUwMjJSLVIiIqI26NixY9ixYwfi4uJgYWGBSZMmiR0SkWi8+llzAyOiNkqlhHTo0KHYsmULtmzZovT16OhoDB06VC2BERER6Zr8/Hzs3LkTsbGxuHPnDm7fvo19+/bh9ddfV2lDQCIiIl2hUkK6fPlyjBo1Crdu3cKSJUvg7OwMQRCQmZmJTz/9FN988w2OHTvWUrESUQvgzciJWt6BAwewfft2JCcnY9y4cfj0008xbtw4dOjQAf3792cySkREbZZKCemwYcOwf/9+vP322zhw4IDCa0899RS++OILDB8+XK0BElHL4c3IiTTD19cXH3zwAfbv3w8zM+4cSkREVEulhBQAJk2aBC8vLyQkJODSpUsAACcnJ4wdOxYmJiZqD5CIWg5vRk6kGW+++Saio6ORlJSEN954A76+vnjqqafEDouIiEh0KiekAGBiYlLv5gv5+fmwtbVtVlBEpBm8GTmRZmzduhVRUVH46quvsGPHDixcuBBeXl4QBAEymUzs8IiIiESjp66GCgsLMX/+fPTq1UtdTRJRC3OSmiot583IidTP2NgYfn5+OH78OM6fP49+/fpBKpVi+PDhmDp1KuLi4sQOkYiISONUSkhv376NKVOmoHPnzrCxscE///lPyGQyhIWFoWfPnjh9+jRiY2NbKlYiUrO5ox3x+F4qEgkQMMpBnICI2ohevXphzZo1uH79Ovbs2YN79+5hypQpYodFRESkcSolpEFBQThx4gRmzpyJTp06YdGiRZgwYQLS09Pxv//9D7/88gt8fX1bKlYiUjOvftaIme4GFztLmBjow8XOElunu/Fm5EQaoqenB29vbxw6dAjXr1+Xl7/00ksoKCgQMTIiIiLNUGkN6eHDh7Fz5048//zzmDdvHnr27AlXV1esWbOmpeIjohbGm5ETtQ5WVlby/05OTsb9+/dFjIaIiEgzVLpCeuPGDfTp0wcAYG9vDyMjI0yfPr1FAiMiIiIiIiLdplJCKggC2rX7+6Kqvr4+jI2N1R4UERERERER6T6VE9IxY8Zg0KBBGDRoEO7fvw9vb2/589oHERERaUZ0dLR81pKHhwdSU1PrrVtVVYWVK1fCwcEBRkZGcHFxQXx8fLPaJCIiag6V1pCGh4crPPfx8VFrMERERNR4+/fvR2BgIGJiYuDh4YGoqCh4eXnh4sWLCmtSa4WEhGDPnj3Ytm0bnJ2dkZCQgEmTJuHEiRMYOHBgk9okIiJqDokgCILYQbQ2ZWVlsLCwQGlpKczNzcUOh4iIRCLWeGBmZoZz586hZ8+eDdbz8PCAu7s7Nm3aBACQyWSws7PD/PnzERQUVKe+jY0Nli9fjoCAAHnZK6+8AmNjY+zZs6dJbT6OYygREQGNHw9UmrJLRERELW/ZsmXo2LFjg3UePnyItLQ0eHp6ysv09PTg6emJkydPKj2msrISRkZGCmXGxsZISUlpVptlZWUKDyIiosZSKSF96qmn0LFjxzqPHj16wMvLC0ePHm2pOImIiHTC7t27MXz4cNjY2ODatWsAgKioKHzzzTfyOsHBwbC0tGywnZKSEtTU1EAqlSqUS6VSFBYWKj3Gy8sLkZGRuHTpEmQyGY4ePYq4uDj5PU+b0mZERAQsLCzkDzs7uwbjJiIiepRKa0ijoqKUlt+5cwdpaWmYMGECvv76a3h7e6sjNiIiIp2yZcsWhIWFYeHChVi9ejVqamoAAJaWloiKimrxvRk2bNgAf39/ODs7QyKRwMHBAbNmzcKOHTua3GZwcDACAwPlz8vKypiUEhFRo6mUkPr5+TX4uqurKyIiIpiQEhERKbFx40Zs27YNEydOxNq1a+XlgwcPxpIlS1Rqq3PnztDX10dRUZFCeVFREaytrZUe06VLFxw6dAgPHjzArVu3YGNjg6CgIPla1aa0aWhoCENDQ5ViJyIiqqXWNaQTJkxAVlaWOpskolYgIaMQPptS0Cc0Hj6bUpCQoXzqHhE17MqVK/LdbB9laGiIiooKldoyMDCAm5sbEhMT5WUymQyJiYkYOnRog8caGRnB1tYW1dXVOHDggPzKbHPaJCIiagq1JqSVlZUwMDBQZ5NEJLKEjEK8szsN5/4oxf2qGpz7oxRz9qQxKSVqgh49euDs2bN1yuPj49GnTx+V2wsMDMS2bduwa9cuZGZm4t1330VFRQVmzZoFAJgxYwaCg4Pl9U+dOoW4uDjk5ubip59+wosvvgiZTIb333+/0W0SERGpk0pTdp9k+/btcHV1VWeTRCSyzcdy6pQJArA56TK8+imfwkdEygUGBiIgIAAPHjyAIAhITU3FF198gYiICPzrX/9SuT1fX1/cvHkTYWFhKCwshKurK+Lj4+WbEuXl5UFP7+9zzw8ePEBISAhyc3NhamqK8ePHY/fu3QobKD2pTSIiInVS6T6kj25a8KjS0lKkp6cjOzsbycnJcHNzU1uAYuA91Ij+1ic0HverauqUmxjo48LKF0WIiEhzWmI82Lt3Lz788ENcvnwZwF/3Bl2xYgXefPNNtbQvNo6h1FYlZBRi87EcZBeVw0lqirmjHXniltq0xo4HKl0hPXPmjNJyc3NzvPDCC4iLi0OPHj1Ui5SIWjUnqSnO/VFap7yX1EyEaIi037Rp0zBt2jTcu3cP5eXlsLKyEjskImqm2uUttWqXt8RMd2NSSvQEKiWkx44da6k4iKiVmjvaEXP2pOHRuRQSCRAwykG8oIh0gImJCUxMTMQOg4jUgMtbiJpObZsaCYKAw4cP49VXX1VXk0TUCnj1s0bMdDe42FnCxEAfLnaW2DrdDWM5wBKprKioCG+88QZsbGzQrl076OvrKzyISDtlF5UrLb9UdFfDkRBpn2ZvanTlyhXs2LEDO3fuxM2bN+Hp6amOuIioFfHqZ80zvERqMHPmTOTl5SE0NBRdu3aFRCIROyQiUgMubyFquiZdIa2srMTevXvx/PPPo3fv3lizZg0CAwNRXFyM7777TuX2oqOjYW9vDyMjI3h4eCA1NbXeulVVVVi5ciUcHBxgZGQEFxcXxMfHN6tNIiIiTUhJScHevXvx7rvvYuLEifDx8VF4EJF2mjvaEY+fX+LyFqLGUSkhTUtLw9y5c2FtbY2oqChMnDgR169fh56eHry8vJq0m97+/fsRGBiI8PBwpKenw8XFBV5eXiguLlZaPyQkBFu3bsXGjRtx4cIFzJkzB5MmTVLYcEnVNomIiDTBzs4OKmxuT0RagstbiJpOpdu+tGvXDvPnz8ecOXPQu3dveXn79u1x7tw59O3bV+UAPDw84O7ujk2bNgEAZDIZ7OzsMH/+fAQFBdWpb2Njg+XLlyMgIEBe9sorr8DY2Bh79uxpUpuP45b1REQEqH88OHLkCD799FNs3boV9vb2zQ+wFeIYSkREQAvd9mXMmDHYvn07iouL8cYbb8DLy6tZ618ePnyItLQ0BAcHy8v09PTg6emJkydPKj2msrISRkZGCmXGxsZISUlpcptERESa4Ovri3v37sHBwQEmJiZo3769wut//vmnSJERERGJQ6WENCEhAdevX0dsbCzeffdd3L9/H76+vgDQpMS0pKQENTU1kEqlCuVSqRRZWVlKj/Hy8kJkZCRGjBgBBwcHJCYmIi4uDjU1NU1us7KyEpWVlfLnZWVlKveFiIjoSaKiosQOgYiIqFVReZddOzs7hIWFISwsDEePHkVsbCzatWsHHx8fvPrqq3j11VcxaNCglogVALBhwwb4+/vD2dkZEokEDg4OmDVrFnbs2NHkNiMiIrBixQo1RklERFSXn5+f2CEQERG1Ks26D+kLL7yAffv24caNG5g/fz4OHz4Md3f3Rh/fuXNn6Ovro6ioSKG8qKgI1tbKF4F36dIFhw4dQkVFBa5du4asrCyYmpqiZ8+eTW4zODgYpaWl8sf169cb3QciIiJVXL58GSEhIZgyZYp8s73Dhw8jIyND5MiIiIg0r1kJaa2nnnoK8+fPx5kzZ3D69Gl5+dy5c1FSUlLvcQYGBnBzc0NiYqK8TCaTITExEUOHDm3wPY2MjGBra4vq6mocOHBAvl1+U9o0NDSEubm5woOIiEjdjh8/jv79++PUqVOIi4tDeXk5AODcuXMIDw8XOToiIiLNU0tC+qhHp+vu2bPniesxAwMDsW3bNuzatQuZmZl49913UVFRgVmzZgEAZsyYobBBUe0gnpubi59++gkvvvgiZDIZ3n///Ua3SUREJIagoCB89NFHOHr0KAwMDOTlzz//PH755RcRIyMiIhKHymtIVdGYO8r4+vri5s2bCAsLQ2FhIVxdXREfHy/flCgvLw96en/nzQ8ePEBISAhyc3NhamqK8ePHY/fu3bC0tGx0m0RERGI4f/489u3bV6fcysqqwRlFREREuqpFE9LGmjdvHubNm6f0taSkJIXnI0eOxIULF5rVJhERkRgsLS1RUFCAHj16KJSfOXMGtra2IkVFREQkHrVP2SUiIiLlJk+ejA8++ACFhYWQSCSQyWT4+eefsWTJEsyYMUPs8IiIiDSOCSkREZGGrFmzBs7OzrCzs0N5eTn69u2LESNGYNiwYQgJCRE7PCIiIo1rFVN2iYiI2gIDAwNs27YNoaGh+P3331FeXo6BAweiV69eYodGREQkihZNSKdPn85bqBARET3m6aefxtNPPy12GERERKJrdEL622+/NbrRAQMGAAC2bNmiekREREQ6KjAwUGm5RCKBkZERHB0d4ePjg44dO2o4MiIiInE0OiF1dXWFRCKp91Yuta9JJBLU1NSoLUAiIiJdcebMGaSnp6Ompga9e/cGAGRnZ0NfXx/Ozs7YvHkzFi9ejJSUFPTt21fkaImIiFpeoxPSK1eutGQcREREOq/26mdsbKx8SUtpaSneeustPPfcc/D398fUqVOxaNEiJCQkiBwtERFRy5MI9V3ybMPKyspgYWGB0tJSroElImrD1D0e2Nra4ujRo3WufmZkZGDs2LHIz89Heno6xo4di5KSkma/nxg4hhIREdD48aBZmxpduHABeXl5ePjwoUL5yy+/3JxmiYiIdFJpaSmKi4vrJKQ3b95EWVkZAMDS0rLOuEpERKSrmpSQ5ubmYtKkSTh//rzCulKJRAIAXENKRESkhI+PD2bPno1PP/0U7u7uAIDTp09jyZIlmDhxIgAgNTUVTk5OIkZJRESkOXpNOWjBggXo0aMHiouLYWJigoyMDCQnJ2Pw4MFISkpSc4hERES6YevWrRgzZgwmT56M7t27o3v37pg8eTLGjBmDmJgYAICzszP+9a9/iRwpERGRZjRpDWnnzp3xv//9DwMGDICFhQVSU1PRu3dv/O9//8PixYtx5syZlohVY7j+hYiIgJYbD8rLy5GbmwsA6NmzJ0xNTRVe/+OPP2BjYwM9vSadNxYVx1AiIgIaPx40aaSrqamBmZkZgL+S0xs3bgAAunfvjosXLzalSSIiojbD1NQUAwYMwIABA+okowDQt29fXL16VfOBERERaViT1pA+88wzOHfuHHr06AEPDw+sX78eBgYG+Pzzz9GzZ091x0hERNSmcAN8IiJqK5qUkIaEhKCiogIAsHLlSkyYMAH/93//h06dOmH//v1qDZCIiIiIiIh0U5MSUi8vL/l/Ozo6IisrC3/++Seeeuop+U67RERERERERA1p1n1IAeD69esAADs7u2YHQ0RERERERG1HkzY1qq6uRmhoKCwsLGBvbw97e3tYWFggJCQEVVVV6o6RiIioTeFsIyIiaiuadIV0/vz5iIuLw/r16zF06FAAwMmTJ/Hhhx/i1q1b2LJli1qDJCIiaku4qREREbUVTUpI9+3bhy+//BLjxo2Tlw0YMAB2dnaYMmUKE1IiIqLHVFVVwdjYGGfPnsUzzzzTYN0LFy7AxsZGQ5ERERGJp0kJqaGhIezt7euU9+jRAwYGBs2NiYiISOe0b98eTz/9NGpqap5Yl/syEBFRW9GkNaTz5s3DqlWrUFlZKS+rrKzE6tWrMW/ePLUFR0REpEuWL1+OZcuW4c8//xQ7FCIiolah0VdI//GPfyg8//HHH9GtWze4uLgAAM6dO4eHDx9izJgx6o2QiIhIR2zatAk5OTmwsbFB9+7d0aFDB4XX09PTRYqMiIhIHI1OSC0sLBSev/LKKwrPOb2IiIioYRMnThQ7BCIiolal0QlpbGxsS8ZBRESk88LDw8UOgYiIqFVp0qZGtW7evImLFy8CAHr37o0uXbqoJSgiIiJddefOHXz99de4fPkyli5dio4dOyI9PR1SqRS2trZih0dERKRRTUpIKyoqMH/+fPz73/+GTCYDAOjr62PGjBnYuHEjTExM1BokERGRLvjtt9/g6ekJCwsLXL16Ff7+/ujYsSPi4uKQl5eHf//732KHSEREpFFN2mU3MDAQx48fx3//+1/cuXMHd+7cwTfffIPjx49j8eLF6o6RiIhIJwQGBmLmzJm4dOkSjIyM5OXjx49HcnKyiJERERGJo0kJ6YEDB7B9+3aMGzcO5ubmMDc3x/jx47Ft2zZ8/fXXKrcXHR0Ne3t7GBkZwcPDA6mpqQ3Wj4qKQu/evWFsbAw7OzssWrQIDx48kL9+9+5dLFy4EN27d4exsTGGDRuG06dPqxwXERGROp0+fRrvvPNOnXJbW1sUFhaKEBEREZG4mpSQ3rt3D1KptE65lZUV7t27p1Jb+/fvR2BgIMLDw5Geng4XFxd4eXmhuLhYaf19+/YhKCgI4eHhyMzMxPbt27F//34sW7ZMXuett97C0aNHsXv3bpw/fx5jx46Fp6cn8vPzVesoERGRGhkaGqKsrKxOeXZ2NvdhICKiNqlJCenQoUMRHh6ucFXy/v37WLFiBYYOHapSW5GRkfD398esWbPQt29fxMTEwMTEBDt27FBa/8SJExg+fDimTp0Ke3t7jB07FlOmTJFfVb1//z4OHDiA9evXY8SIEXB0dMSHH34IR0dHbNmypSndJSIiUouXX34ZK1euRFVVFQBAIpEgLy8PH3zwQZ3bqREREbUFTUpIN2zYgJ9//hndunXDmDFjMGbMGNjZ2eHEiRPYsGFDo9t5+PAh0tLS4Onp+XdAenrw9PTEyZMnlR4zbNgwpKWlyRPQ3Nxc/PDDDxg/fjwAoLq6GjU1NQprcwDA2NgYKSkpStusrKxEWVmZwoOaLyGjED6bUtAnNB4+m1KQkMHpaETUtn366acoLy+HlZUV7t+/j5EjR8LR0RFmZmZYvXq12OERERFpXJN22X3mmWdw6dIl7N27F1lZWQCAKVOmYNq0aTA2Nm50OyUlJaipqakz/VcqlcrbfdzUqVNRUlKC5557DoIgoLq6GnPmzJFP2TUzM8PQoUOxatUq9OnTB1KpFF988QVOnjwJR0dHpW1GRERgxYoVjY67rWnKRhupV27hkyPZ8uencoHUkz9j8VgnDOnRSaW2RowYofL7ExG1RhYWFjh69ChSUlLw22+/oby8HIMGDVI4MUtERNSWSARBEMR68xs3bsDW1hYnTpxQmOr7/vvv4/jx4zh16lSdY5KSkjB58mR89NFH8PDwQE5ODhYsWAB/f3+EhoYCAC5fvozZs2cjOTkZ+vr6GDRoEJycnJCWlobMzMw6bVZWVqKyslL+vKysDHZ2digtLYW5uXkL9Fy7SCQSUd9fxD9RImrjysrKYGFhwfFABfzMiIgIaPx40OgrpN9++22j3/zll19uVL3OnTtDX18fRUVFCuVFRUWwtrZWekxoaCjeeOMNvPXWWwCA/v37o6KiAm+//TaWL18OPT09ODg44Pjx46ioqEBZWRm6du0KX19f9OzZU2mbhoaGMDQ0bHT/iIiImioxMRGfffaZ/ARpnz59sHDhQl4lJSKiNqnRCenEiRMbVU8ikaCmpqZRdQ0MDODm5obExER5+zKZDImJiZg3b57SY+7duwc9PcWlr/r6+gDqXknr0KEDOnTogNu3byMhIQHr169vVFyk6Pjx4yofsyzuN+TcrKhT7mhlijWT+qsjLCIirbN582YsWLAAr776KhYsWAAA+OWXXzB+/Hh89tlnCAgIEDlCIiIizWp0QiqTyVokgMDAQPj5+WHw4MEYMmQIoqKiUFFRgVmzZgEAZsyYAVtbW0RERAAAvL29ERkZiYEDB8qn7IaGhsLb21uemCYkJEAQBPTu3Rs5OTlYunQpnJ2d5W2SapqyhjO0kxPm7EnDo+cIJBIgbLobRvRTfvWbiEjXrVmzBp999pnCSdf33nsPw4cPx5o1a5iQEhFRm6PSpkYPHjzAjz/+iAkTJgAAgoODFdZetmvXDitXrqyzw21DfH19cfPmTYSFhaGwsBCurq6Ij4+Xb3SUl5encEU0JCQEEokEISEhyM/PR5cuXeDt7a2wO2FpaSmCg4Pxxx9/oGPHjnjllVewevVqtG/fXpXuUjN49bNGzHQ3bE66jEtFd9FLaoaAUQ4Yy2SUiNqwO3fu4MUXX6xTPnbsWHzwwQciRERERCQulTY1iomJwffff4///ve/AP7a0bZfv37ynXWzsrKwdOlSBAYGtky0GsINGYiICFD/eDB16lQMHDgQS5cuVSj/5JNP8Ouvv+LLL79s9nuIjWMoEREBLbCpEQDs3bsX77//vkLZvn375JsF7dmzB9HR0VqfkBIREbWEvn37YvXq1UhKSpLvLv/LL7/g559/xuLFi/HPf/5TXve9994TK0wiIiKNUekKadeuXXHy5EnY29sDALp06YLTp0/Ln2dnZ8Pd3R2lpaUtEavG8OwuEREB6h8PevTo0ah6EokEubm5zX4/MXAMJSIioIWukN65c0dhzejNmzcVXpfJZAqvExER0d+uXLkidghEREStit6Tq/ytW7du+P333+t9/bfffkO3bt2aHRQREVFbZm5u3ugrpNHR0bC3t4eRkRE8PDyQmpraYP2oqCj07t0bxsbGsLOzw6JFi/DgwQP56zU1NQgNDUWPHj1gbGwMBwcHrFq1qs6t1YiIiNRBpYR0/PjxCAsLUxi4at2/fx8rVqzASy+9pLbgiIiI2qLGJn/79+9HYGAgwsPDkZ6eDhcXF3h5eaG4uFhp/X379iEoKAjh4eHIzMzE9u3bsX//fixbtkxeZ926ddiyZQs2bdqEzMxMrFu3DuvXr8fGjRvV0jciIqJHqbSGtKioCK6urjAwMMC8efPg5OQEALh48SI2bdqE6upqnDlzRn7LFm3F9S9ERASINx6YmZnh3Llz8k0D6+Ph4QF3d3ds2rQJwF9LZ+zs7DB//nwEBQXVqT9v3jxkZmYiMTFRXrZ48WKcOnUKKSkpAIAJEyZAKpVi+/bt8jqvvPIKjI2NsWfPnifGzjGUiIiAxo8HKl0hlUqlOHHiBPr06YOgoCBMmjQJkyZNQnBwMPr27YuUlBStT0aJiIi0wcOHD5GWlgZPT095mZ6eHjw9PXHy5EmlxwwbNgxpaWnyab25ubn44YcfMH78eIU6iYmJyM7OBgCcO3cOKSkpGDdunNI2KysrUVZWpvAgIiJqLJU2NQL+2iEwPj4ef/75J3JycgAAjo6O6Nixo9qDIyIiIuVKSkpQU1NT50SwVCpFVlaW0mOmTp2KkpISPPfccxAEAdXV1ZgzZ47ClN2goCCUlZXB2dkZ+vr6qKmpwerVqzFt2jSlbUZERGDFihXq6xgREbUpKl0hfVTHjh0xZMgQDBkyhMkoERGRGkkkkhZpNykpCWvWrMHmzZuRnp6OuLg4fP/991i1apW8zldffYW9e/di3759SE9Px65du/DJJ59g165dStsMDg5GaWmp/HH9+vUWiZ2IiHSTyldIiYiIqGU1ZnuHzp07Q19fH0VFRQrlRUVFsLa2VnpMaGgo3njjDbz11lsAgP79+6OiogJvv/02li9fDj09PSxduhRBQUGYPHmyvM61a9cQEREBPz+/Om0aGhrC0NBQ1S4SEREBaMYVUiIiImoZhw8fhq2tbYN1DAwM4ObmprBBkUwmQ2JiIoYOHar0mHv37kFPT3Ho19fXB/B3ElxfHZlMpnI/iIiInoRXSImIiDQkMDBQablEIoGRkREcHR3h4+OD5557rtHt+fn5YfDgwRgyZAiioqJQUVGBWbNmAQBmzJgBW1tbREREAAC8vb0RGRmJgQMHwsPDAzk5OQgNDYW3t7c8MfX29sbq1avx9NNPo1+/fjhz5gwiIyMxe/ZsNXwCREREipiQEhERaciZM2eQnp6Ompoa9O7dGwCQnZ0NfX19ODs7Y/PmzVi8eDFSUlLQt2/fJ7bn6+uLmzdvIiwsDIWFhXB1dUV8fLx8o6O8vDyFq50hISGQSCQICQlBfn4+unTpIk9Aa23cuBGhoaGYO3cuiouLYWNjg3feeQdhYWFq/jSIiIhUvA9pW8F7qBEREaD+8SAqKgo//fQTYmNj5e2VlpbirbfewnPPPQd/f39MnToV9+/fR0JCQrPfTwwcQ4mICGj8eMCEVAkOpkREBKh/PLC1tcXRo0frXP3MyMjA2LFjkZ+fj/T0dIwdOxYlJSXNfj8xcAwlIiKg8eMBNzUiIiLSkNLSUhQXF9cpv3nzJsrKygAAlpaWePjwoaZDIyIiEgXXkBIREWmIj48PZs+ejU8//RTu7u4AgNOnT2PJkiWYOHEiACA1NRVOTk4iRqk9EjIKsflYDrKLyuEkNcXc0Y7w6qf8ljdERNQ6MSElIiLSkK1bt2LRokWYPHkyqqurAQDt2rWDn58fPvvsMwCAs7Mz/vWvf4kZpsYlJyerfEzqlVv45Ei2/PmpXCD15M9YPNYJQ3p0UqmtESNGqPz+RESkHlxDqgTXvxAREdBy40F5eTlyc3MBAD179oSpqana2hZbUz4ziUTSwlE1jD+FiIjUj2tIiYiIWpk9e/bg3r17MDU1xYABAzBgwACdSkaJiIhUxSm7REREGrJo0SLMmTMHL7/8MqZPnw4vLy/o6+uLHZbojh8/rvIxy+J+Q87NijrljlamWDOpvzrCIiIiDWBCSkREpCEFBQWIj4/HF198gddffx0mJiZ47bXXMG3aNAwbNkzs8ETTlDWcoZ2cMGdPGh6dbSuRAGHT3TCCGxsREWkNTtklIiLSkHbt2mHChAnYu3cviouL8dlnn+Hq1asYPXo0HBwcxA5Pq3j1s0bMdDe42FnCxEAfLnaW2DrdDWOZjBIRaRVeISUiIhKBiYkJvLy8cPv2bVy7dg2ZmZlih6R1vPpZ8zYvRERajldIiYiINOjevXvYu3cvxo8fD1tbW0RFRWHSpEnIyMgQOzQiIiKNY0JKRESkIZMnT4aVlRUWLVqEnj17IikpCTk5OVi1apX8vqRERERtCafsEhERaYi+vj6++uor+e66d+/exeeff47t27fj119/RU1NjdghEhERaRQTUiIiIg3Zu3cvACA5ORnbt2/HgQMHYGNjg3/84x/YtGmTyNERERFpXquYshsdHQ17e3sYGRnBw8MDqampDdaPiopC7969YWxsDDs7OyxatAgPHjyQv15TU4PQ0FD06NEDxsbGcHBwwKpVqyA8ujc8ERGRBhUWFmLt2rXo1asXXnvtNZibm6OyshKHDh3C2rVr4e7uLnaIREREGid6Qrp//34EBgYiPDwc6enpcHFxgZeXF4qLi5XW37dvH4KCghAeHo7MzExs374d+/fvx7Jly+R11q1bhy1btmDTpk3IzMzEunXrsH79emzcuFFT3SIiIpLz9vZG7969ce7cOURFReHGjRsck4iIiNAKpuxGRkbC398fs2bNAgDExMTg+++/x44dOxAUFFSn/okTJzB8+HBMnToVAGBvb48pU6bg1KlTCnV8fHzw0ksvyet88cUXT7zySkRE1BIOHz6M9957D++++y569eoldjhERESthqhXSB8+fIi0tDR4enrKy/T09ODp6YmTJ08qPWbYsGFIS0uTJ5e5ubn44YcfMH78eIU6iYmJyM7OBgCcO3cOKSkpGDdunNI2KysrUVZWpvAgIiJSl5SUFNy9exdubm7w8PDApk2bUFJSInZYREREohM1IS0pKUFNTQ2kUqlCuVQqRWFhodJjpk6dipUrV+K5555D+/bt4eDggFGjRilM2Q0KCsLkyZPh7OyM9u3bY+DAgVi4cCGmTZumtM2IiAhYWFjIH3Z2durrJBERtXnPPvsstm3bhoKCArzzzjv48ssvYWNjA5lMhqNHj+Lu3btih0hERCQK0deQqiopKQlr1qzB5s2bkZ6ejri4OHz//fdYtWqVvM5XX32FvXv3Yt++fUhPT8euXbvwySefYNeuXUrbDA4ORmlpqfxx/fp1TXWHiIjakA4dOmD27NlISUnB+fPnsXjxYqxduxZWVlZ4+eWXxQ6PiIhI40RdQ9q5c2fo6+ujqKhIobyoqAjW1tZKjwkNDcUbb7yBt956CwDQv39/VFRU4O2338by5cuhp6eHpUuXyq+S1ta5du0aIiIi4OfnV6dNQ0NDGBoaqrl3RERE9evduzfWr1+PiIgI/Pe//8WOHTvEDomIiEjjRL1CamBgADc3NyQmJsrLZDIZEhMTMXToUKXH3Lt3D3p6imHr6+sDgPy2LvXVkclk6gyfiIio2fT19TFx4kR8++23YodCRESkcaLvshsYGAg/Pz8MHjwYQ4YMQVRUFCoqKuS77s6YMQO2traIiIgA8NfW+ZGRkRg4cCA8PDyQk5OD0NBQeHt7yxNTb29vrF69Gk8//TT69euHM2fOIDIyErNnzxatn0RERERERKRI9ITU19cXN2/eRFhYGAoLC+Hq6or4+Hj5Rkd5eXkKVztDQkIgkUgQEhKC/Px8dOnSRZ6A1tq4cSNCQ0Mxd+5cFBcXw8bGBu+88w7CwsI03j8iIiIiIiJSTiLUznMlubKyMlhYWKC0tBTm5uZih0NERCLheKA6fmZERAQ0fjzQul12iYiIiIiISDcwISUiIiIiIiJRMCElIiIiIiIiUTAhJSIiIiIiIlEwISUiIiIiIiJRMCElIiIiIiIiUTAhJSIiIiIiIlEwISUiIiIiIiJRMCElIiIiIiIiUTAhJSIiIiIiIlEwISUiIiIiIiJRMCElIiIiIiIiUTAhJSIiIiIiIlG0EzuAti4hoxCbj+Ugu6gcTlJTzB3tCK9+1mKHRURERERE1OKYkKpJcnKyysekXrmFT45ky5+fygVST/6MxWOdMKRHJ5XaGjFihMrvT0REREREJCYmpGoycuRItbW1dJ/qxwiCoLb3JyIiIiIi0gSuISUiIiIiIiJR8Aqpmhw/flzlY5bF/YacmxV1yh2tTLFmUn91hEVERERERNRqMSFVk6as4Qzt5IQ5e9Lw6GxbiQQIm+6GEdzYiIiIiIiIdByn7IrIq581Yqa7wcXOEiYG+nCxs8TW6W4Yy2SUiIiIiIjaAF4hFZlXP2ve5oWIiIiIiNokXiElIiIiIiIiUTAhJSIiIiIiIlEwISUiIiIiIiJRMCElIiIiIiIiUTAhJSIiIiIiIlEwISUiIiIiIiJRtIqENDo6Gvb29jAyMoKHhwdSU1MbrB8VFYXevXvD2NgYdnZ2WLRoER48eCB/3d7eHhKJpM4jICCgpbtCREREREREjST6fUj379+PwMBAxMTEwMPDA1FRUfDy8sLFixdhZWVVp/6+ffsQFBSEHTt2YNiwYcjOzsbMmTMhkUgQGRkJADh9+jRqamrkx/z+++944YUX8Nprr2msX0RERERERNQw0a+QRkZGwt/fH7NmzULfvn0RExMDExMT7NixQ2n9EydOYPjw4Zg6dSrs7e0xduxYTJkyReGqapcuXWBtbS1/fPfdd3BwcMDIkSM11S0iIiIiIiJ6AlET0ocPHyItLQ2enp7yMj09PXh6euLkyZNKjxk2bBjS0tLkCWhubi5++OEHjB8/vt732LNnD2bPng2JRKL+ThAREREREVGTiDplt6SkBDU1NZBKpQrlUqkUWVlZSo+ZOnUqSkpK8Nxzz0EQBFRXV2POnDlYtmyZ0vqHDh3CnTt3MHPmzHrjqKysRGVlpfx5WVmZ6p0hIiIiIiIilYg+ZVdVSUlJWLNmDTZv3oz09HTExcXh+++/x6pVq5TW3759O8aNGwcbG5t624yIiICFhYX8YWdn11LhExERqZW6NwYEgPz8fEyfPh2dOnWCsbEx+vfvj19//bUlu0FERG2UqFdIO3fuDH19fRQVFSmUFxUVwdraWukxoaGheOONN/DWW28BAPr374+Kigq8/fbbWL58OfT0/s6xr127hh9//BFxcXENxhEcHIzAwED587KyMialRETU6rXExoC3b9/G8OHDMXr0aBw+fBhdunTBpUuX8NRTT2m6e0RE1AaImpAaGBjAzc0NiYmJmDhxIgBAJpMhMTER8+bNU3rMvXv3FJJOANDX1wcACIKgUB4bGwsrKyu89NJLDcZhaGgIQ0PDJvaCiIhIHI9uDAgAMTEx+P7777Fjxw4EBQXVqf/oxoDAX7dJmzJlCk6dOiWvs27dOtjZ2SE2NlZe1qNHjxbuCRERtVWiT9kNDAzEtm3bsGvXLmRmZuLdd99FRUWFfHCdMWMGgoOD5fW9vb2xZcsWfPnll7hy5QqOHj2K0NBQeHt7yxNT4K/ENjY2Fn5+fmjXTvS72xAREalVS20M+O2332Lw4MF47bXXYGVlhYEDB2Lbtm31xlFZWYmysjKFBxERUWOJnqn5+vri5s2bCAsLQ2FhIVxdXREfHy/f6CgvL0/himhISAgkEglCQkKQn5+PLl26wNvbG6tXr1Zo98cff0ReXh5mz56t0f4QERFpQkttDJibm4stW7YgMDAQy5Ytw+nTp/Hee+/BwMAAfn5+ddqMiIjAihUr1Ns5IiJqMyTC4/NcCWVlZbCwsEBpaSnMzc3FDoeIiETSmseDGzduwNbWFidOnMDQoUPl5e+//z6OHz+uMA23VlJSEiZPnoyPPvoIHh4eyMnJwYIFC+Dv74/Q0FAAfy2nGTx4ME6cOCE/7r333sPp06eVXnlVtlO9nZ1dq/zMiIhIcxo7hop+hZSIiIhU11IbA3bt2hV9+/ZVOK5Pnz44cOCA0ja5DwMRETWH6GtIiYiISHWPbgxYq3ZjwEevmD6qMRsDDh8+HBcvXlSok52dje7du6szfCIiIgC8QkpERKS1AgMD4efnh8GDB2PIkCGIioqqszGgra0tIiIiAPy1MWBkZCQGDhwon7L7+MaAixYtwrBhw7BmzRq8/vrrSE1Nxeeff47PP/9ctH4SEZHuYkJKRESkpVpiY0B3d3ccPHgQwcHBWLlyJXr06IGoqChMmzZN4/0jIiLdx02NlGjNm1gQEZHmcDxQHT8zIiICGj8ecA0pERERERERiYIJKREREREREYmCCSkRERERERGJggkpERERERERiYIJKREREREREYmCCSkRERERERGJggkpERERERERiYIJKREREREREYmCCSkRERERERGJggkpERERERERiYIJKREREREREYmCCSkRERERERGJop3YARC1dgkZhdh8LAfZReVwkppi7mhHePWzFjssIiIincBxlqht4xVSogYkZBTind1pOPdHKe5X1eDcH6WYsycNCRmFYodGRESk9TjOEhETUqIGbD6WU6dMEIDNSZdFiIaIiEi3cJwlIiakRA3ILipXWn6p6K6GIyEiItI9HGeJiAkpUQOcpKZKy3tJzTQcCRERke7hOEtETEiJGjB3tCMkEsUyiQQIGOUgTkBEREQ6hOMsETEhJWqAVz9rxEx3g4udJUwM9OFiZ4mt090wlrv/ERERNRvHWSLibV+InsCrnzW3nyciImohHGeJ2jZeISUiIiIiIiJRMCElIiIiIiIiUTAhJSIiIiIiIlG0ioQ0Ojoa9vb2MDIygoeHB1JTUxusHxUVhd69e8PY2Bh2dnZYtGgRHjx4oFAnPz8f06dPR6dOnWBsbIz+/fvj119/bcluEBERERERkQpE39Ro//79CAwMRExMDDw8PBAVFQUvLy9cvHgRVlZWderv27cPQUFB2LFjB4YNG4bs7GzMnDkTEokEkZGRAIDbt29j+PDhGD16NA4fPowuXbrg0qVLeOqppzTdPSIiIiIiIqqH6AlpZGQk/P39MWvWLABATEwMvv/+e+zYsQNBQUF16p84cQLDhw/H1KlTAQD29vaYMmUKTp06Ja+zbt062NnZITY2Vl7Wo0ePFu4JERERERERqULUKbsPHz5EWloaPD095WV6enrw9PTEyZMnlR4zbNgwpKWlyaf15ubm4ocffsD48ePldb799lsMHjwYr732GqysrDBw4EBs27atZTtDREREREREKhH1CmlJSQlqamoglUoVyqVSKbKyspQeM3XqVJSUlOC5556DIAiorq7GnDlzsGzZMnmd3NxcbNmyBYGBgVi2bBlOnz6N9957DwYGBvDz86vTZmVlJSorK+XPy8rK1NRDIiIiIiIiqo/oU3ZVlZSUhDVr1mDz5s3w8PBATk4OFixYgFWrViE0NBQAIJPJMHjwYKxZswYAMHDgQPz++++IiYlRmpBGRERgxYoVdcqZmBIRtW2144AgCCJHoj1qPyuOoUREbVtjx1BRE9LOnTtDX18fRUVFCuVFRUWwtrZWekxoaCjeeOMNvPXWWwCA/v37o6KiAm+//TaWL18OPT09dO3aFX379lU4rk+fPjhw4IDSNoODgxEYGCh/np+fj759+8LOzq453SMiIh1x9+5dWFhYiB2GVrh79y4AcAwlIiIATx5DRU1IDQwM4ObmhsTEREycOBHAX1c3ExMTMW/ePKXH3Lt3D3p6iktf9fX1AfydfQ8fPhwXL15UqJOdnY3u3bsrbdPQ0BCGhoby56amprh+/TrMzMwgkUia1DdVlJWVwc7ODtevX4e5uXmLv58m6XLfAN3uH/umvXS5f5rumyAIuHv3LmxsbFr8vXSFjY0Nx1A10eW+AbrdP/ZNe+ly/1rrGCr6lN3AwED4+flh8ODBGDJkCKKiolBRUSHfdXfGjBmwtbVFREQEAMDb2xuRkZEYOHCgfMpuaGgovL295YnpokWLMGzYMKxZswavv/46UlNT8fnnn+Pzzz9vVEx6enro1q1by3S4Aebm5jr3h19Ll/sG6Hb/2Dftpcv902TfeGVUNRxD1U+X+wbodv/YN+2ly/1rbWOo6Ampr68vbt68ibCwMBQWFsLV1RXx8fHyjY7y8vIUroiGhIRAIpEgJCQE+fn56NKlC7y9vbF69Wp5HXd3dxw8eBDBwcFYuXIlevTogaioKEybNk3j/SMiIiIiIiLlRE9IAWDevHn1TtFNSkpSeN6uXTuEh4cjPDy8wTYnTJiACRMmqCtEIiIiIiIiUjNR70NKfzE0NER4eLjCOlZdoct9A3S7f+yb9tLl/uly36hpdPlvQpf7Buh2/9g37aXL/WutfZMI3MueiIiIiIiIRMArpERERERERCQKJqREREREREQkCiakREREREREJAompI00c+ZMSCSSOo8XX3wRAHDu3Dm8/PLLsLKygpGREezt7eHr64vi4mIAwNWrVyGRSKCvr4/8/HyFtgsKCtCuXTtIJBJcvXq13hgePHiAmTNnon///mjXrh0mTpzYUt1tUH2fxfPPP4/OnTtj7dq1So9btWoVpFIpqqqqsHPnTqVtGBkZabg3TZOcnAxvb2/Y2NhAIpHg0KFDYoekNhEREXB3d4eZmRmsrKwwceJEXLx4Ueyw1GLLli0YMGCA/P5bQ4cOxeHDh8UOq0WsXbsWEokECxcuFDsUtfjwww/r/Hvh7OwsdljUSBxD/8YxlGOotuIYqr1a+xjKhFQFL774IgoKChQeX3zxBW7evIkxY8agY8eOSEhIQGZmJmJjY2FjY4OKigqFNmxtbfHvf/9boWzXrl2wtbV94vvX1NTA2NgY7733Hjw9PdXaN1Up+ywOHDiA6dOnIzY2tk59QRCwc+dOzJgxA+3btwfw1015H2/j2rVrmu5Kk1RUVMDFxQXR0dFih6J2x48fR0BAAH755RccPXoUVVVVGDt2bJ2/ZW3UrVs3rF27Fmlpafj111/x/PPPw8fHBxkZGWKHplanT5/G1q1bMWDAALFDUat+/fop/HuRkpIidkikAo6hf+MYyjFUG3EM1W6tegwVqFH8/PwEHx8fpa8dPHhQaNeunVBVVVXv8VeuXBEACCEhIUKvXr0UXnNychJCQ0MFAMKVK1eaHM/FixcFAEJmZqZCeWRkpNCzZ89GtdvU967122+/CQCEn376SaH82LFjCrHFxsYKFhYWaotJTACEgwcPih1GiykuLhYACMePHxc7lBbx1FNPCf/617/EDkNt7t69K/Tq1Us4evSoMHLkSGHBggVih6QW4eHhgouLi9hhUBNxDG34vWtxDNU9HEO1C8dQcfAKqRpYW1ujuroaBw8ehPCEu+i8/PLLuH37tvysREpKCm7fvg1vb+9mx+Hk5ITBgwdj7969CuV79+7F1KlTm91+Y/Tv3x/u7u7YsWOHQnlsbCyGDRvWqqYHUOOUlpYCADp27ChyJOpVU1ODL7/8EhUVFRg6dKjY4ahNQEAAXnrpJdGvALWES5cuwcbGBj179sS0adOQl5cndkikBhxD/8YxVPdwDNUuHEPFwYRUBd999x1MTU0VHmvWrMGzzz6LZcuWYerUqejcuTPGjRuHjz/+GEVFRXXaaN++PaZPny4fbHbs2IHp06fLp+A017Rp0/DFF1/In2dnZyMtLQ3Tpk1TS/u16vssAODNN9/Ef/7zH5SXlwMA7t69i6+//hqzZ89WaKO0tLROG+PGjVNrnNQ8MpkMCxcuxPDhw/HMM8+IHY5anD9/HqampjA0NMScOXNw8OBB9O3bV+yw1OLLL79Eeno6IiIixA5F7Tw8PLBz507Ex8djy5YtuHLlCv7v//4Pd+/eFTs0aiSOoX/jGNo2cAzVLhxDRST2JVpt4efnJ3h6egqXLl1SeNy6dUtep6SkRPjqq6+ExYsXCz179hQsLS2F3377TRCEv6cbnTlzRvjtt98EU1NToaCgQDA1NRXOnz8vnDlzRmG6Ud++fYUOHToIHTp0EF588UWl8Sib8lNQUCDo6+sLJ0+eFARBEMLCwoRBgwZp9LMoLS0VjI2Nhe3btwuCIAjbtm0TTE1Nhbt378rbiI2NFczMzOq08ccff6g1Vk2ADk83mjNnjtC9e3fh+vXrYoeiNpWVlcKlS5eEX3/9VQgKChI6d+4sZGRkiB1Ws+Xl5QlWVlbCuXPn5GW6NN3ocbdv3xbMzc11aqqYLuMY2vjPgmOo7uAYqj04hoqrnZjJsLbp0KEDHB0d6329U6dOeO211/Daa69hzZo1GDhwID755BPs2rVLoV7//v3h7OyMKVOmoE+fPnjmmWdw9uxZhTo//PADqqqqAADGxsaNjtHa2hrPP/889u3bh2effRb79u3Du+++2/hONlJDn4W5uTleffVVxMbGYvbs2YiNjcXrr78OU1NThXp6enoNfp4krnnz5uG7775DcnIyunXrJnY4amNgYCD/u3Nzc8Pp06exYcMGbN26VeTImictLQ3FxcUYNGiQvKympgbJycnYtGkTKisroa+vL2KE6mVpaQknJyfk5OSIHQo1EsfQv3EM1X0cQ7ULx1BxccpuCzEwMICDg0O9u6rNnj0bSUlJdabg1OrevTscHR3h6OjYqN0DHzVt2jTs378fJ0+eRG5uLiZPnqxy/M315ptvIiUlBd999x1OnDiBN998U+MxUNMIgoB58+bh4MGD+N///ocePXqIHVKLkslkqKysFDuMZhszZgzOnz+Ps2fPyh+DBw/GtGnTcPbsWZ0aSAGgvLwcly9fRteuXcUOhVoAx1COodqKY6h24hgqLl4hVUFlZSUKCwsVytq1a4dffvkFX375JSZPngwnJycIgoD//ve/+OGHH5Ru3w4A/v7+eO2112BpaalSDBcuXMDDhw/x559/4u7du/Kzwq6urvI6//jHP/Duu+/i3XffxejRo2FjY6PSezRGfZ9F586dAQAjRoyAo6MjZsyYAWdnZwwbNqxOG4Ig1GkDAKysrKCn17rPlZSXlyucVbpy5QrOnj2Ljh074umnnxYxsuYLCAjAvn378M0338DMzEz+HVlYWKh0paE1Cg4Oxrhx4/D000/j7t272LdvH5KSkpCQkCB2aM1mZmZWZ41Shw4d0KlTJ51Yu7RkyRJ4e3uje/fuuHHjBsLDw6Gvr48pU6aIHRo1EsfQv3EM5RiqjTiGaq9WP4aKOV9Ym/j5+QkA6jx69+4tXL58WfD39xecnJwEY2NjwdLSUnB3dxdiY2Plxz+6/kWZx9e/1Kd79+5K43jc66+/LgAQduzY0YxeK9fQZ/GoNWvWCACE9evX12kjNjZWaRsAhIKCArXHrG61W/A//vDz8xM7tGar73t59O9ZW82ePVvo3r27YGBgIHTp0kUYM2aMcOTIEbHDajG6tP7F19dX6Nq1q2BgYCDY2toKvr6+Qk5OjthhUSNxDP0bx1COodqKY6j2au1jqEQQnrDHOhEREREREVELaN1zOoiIiIiIiEhnMSElIiIiIiIiUTAhJSIiIiIiIlEwISUiIiIiIiJRMCElIiIiIiIiUTAhJSIiIiIiIlEwISUiIiIiIiJRMCElIiIiIiIiUTAhJWqGq1evQiKR4OzZs2KHIpeVlYVnn30WRkZGcHV11ch72tvbIyoqqtH1k5KSIJFIcOfOnRaLiYiIWjeOoX/hGEptHRNS0mozZ86ERCLB2rVrFcoPHToEiUQiUlTiCg8PR4cOHXDx4kUkJiYqrTNq1CgsXLhQbe95+vRpvP32242uP2zYMBQUFMDCwkJtMRARkWo4htbFMZRI85iQktYzMjLCunXrcPv2bbFDUZuHDx82+djLly/jueeeQ/fu3dGpU6cmtyMIAqqrqxtVt0uXLjAxMWl02wYGBrC2tm6zP3iIiFoLjqGKOIYSaR4TUtJ6np6esLa2RkRERL11PvzwwzpTb6KiomBvby9/PnPmTEycOBFr1qyBVCqFpaUlVq5cierqaixduhQdO3ZEt27dEBsbW6f9rKwsDBs2DEZGRnjmmWdw/Phxhdd///13jBs3DqamppBKpXjjjTdQUlIif33UqFGYN28eFi5ciM6dO8PLy0tpP2QyGVauXIlu3brB0NAQrq6uiI+Pl78ukUiQlpaGlStXQiKR4MMPP6zTxsyZM3H8+HFs2LABEokEEokEV69elU8BOnz4MNzc3GBoaIiUlBRcvnwZPj4+kEqlMDU1hbu7O3788UeFNh+fbiSRSPCvf/0LkyZNgomJCXr16oVvv/1W/vrj04127twJS0tLJCQkoE+fPjA1NcWLL76IgoIC+THV1dV47733YGlpiU6dOuGDDz6An58fJk6cqPSzqrVt2zbY2dnBxMQEkyZNQmRkJCwtLRXqfPPNNxg0aBCMjIzQs2dPrFixQuGHxJP6Azz5O/7666/Rv39/GBsbo1OnTvD09ERFRUWDsRMRtTSOoRxDG8IxlDRCINJifn5+go+PjxAXFycYGRkJ169fFwRBEA4ePCg8+ucdHh4uuLi4KBz72WefCd27d1doy8zMTAgICBCysrKE7du3CwAELy8vYfXq1UJ2drawatUqoX379vL3uXLligBA6Natm/D1118LFy5cEN566y3BzMxMKCkpEQRBEG7fvi106dJFCA4OFjIzM4X09HThhRdeEEaPHi1/75EjRwqmpqbC0qVLhaysLCErK0tpfyMjIwVzc3Phiy++ELKysoT3339faN++vZCdnS0IgiAUFBQI/fr1ExYvXiwUFBQId+/erdPGnTt3hKFDhwr+/v5CQUGBUFBQIFRXVwvHjh0TAAgDBgwQjhw5IuTk5Ai3bt0Szp49K8TExAjnz58XsrOzhZCQEMHIyEi4du2avM3u3bsLn332mfx57Weyb98+4dKlS8J7770nmJqaCrdu3RIEQZC/1+3btwVBEITY2Fihffv2gqenp3D69GkhLS1N6NOnjzB16lR5mx999JHQsWNHIS4uTsjMzBTmzJkjmJubCz4+Pko/K0EQhJSUFEFPT0/4+OOPhYsXLwrR0dFCx44dBQsLC3md5ORkwdzcXNi5c6dw+fJl4ciRI4K9vb3w4YcfNro/T/qOb9y4IbRr106IjIwUrly5Ivz2229CdHS00u+HiEhTOIZyDOUYSq0BE1LSarWDqSAIwrPPPivMnj1bEISmD6bdu3cXampq5GW9e/cW/u///k/+vLq6WujQoYPwxRdfCILw92C6du1aeZ2qqiqhW7duwrp16wRBEIRVq1YJY8eOVXjv69evCwCEixcvCoLw12A6cODAJ/bXxsZGWL16tUKZu7u7MHfuXPlzFxcXITw8vMF2Ro4cKSxYsEChrHaAO3To0BPj6Nevn7Bx40b5c2WDaUhIiPx5eXm5AEA4fPiwwns9OpgCEHJycuTHREdHC1KpVP5cKpUKH3/8sfx5dXW18PTTTzc4mPr6+govvfSSQtm0adMUBtMxY8YIa9asUaize/duoWvXro3uz5O+47S0NAGAcPXq1XpjJSLSNI6hHEM5hlJrwCm7pDPWrVuHXbt2ITMzs8lt9OvXD3p6f/9vIZVK0b9/f/lzfX19dOrUCcXFxQrHDR06VP7f7dq1w+DBg+VxnDt3DseOHYOpqan84ezsDOCvtSq13NzcGoytrKwMN27cwPDhwxXKhw8f3qw+P27w4MEKz8vLy7FkyRL06dMHlpaWMDU1RWZmJvLy8hpsZ8CAAfL/7tChA8zNzet8bo8yMTGBg4OD/HnXrl3l9UtLS1FUVIQhQ4bIX9fX13/iZ3bx4kWFYwDUeX7u3DmsXLlS4fvx9/dHQUEB7t2716j+POk7dnFxwZgxY9C/f3+89tpr2LZtm06t1yIi7ccxVD04hnIMJdW1EzsAInUZMWIEvLy8EBwcjJkzZyq8pqenB0EQFMqqqqrqtNG+fXuF5xKJRGmZTCZrdFzl5eXw9vbGunXr6rzWtWtX+X936NCh0W22pMfjWLJkCY4ePYpPPvkEjo6OMDY2xquvvvrETSNU/dyU1X/8O2sJ5eXlWLFiBf7xj3/Uec3IyKjB+Gr786TvWF9fH0ePHsWJEydw5MgRbNy4EcuXL8epU6fQo0cPNfeIiEh1HEPVg2Po3ziGUmMxISWdsnbtWri6uqJ3794K5V26dEFhYSEEQZDvSqfO+5798ssvGDFiBIC/Ng5IS0vDvHnzAACDBg3CgQMHYG9vj3btmv6/nLm5OWxsbPDzzz9j5MiR8vKff/65zhnLJzEwMEBNTU2j6v7888+YOXMmJk2aBOCvgePq1asqvV9zWVhYQCqV4vTp0/LPuaamBunp6Q3eJ6537944ffq0QtnjzwcNGoSLFy/C0dGxyfE15juWSCQYPnw4hg8fjrCwMHTv3h0HDx5EYGBgk9+XiEidOIY2DsfQv3EMJXXglF3SKf3798e0adPwz3/+U6F81KhRuHnzJtavX4/Lly8jOjoahw8fVtv7RkdH4+DBg8jKykJAQABu376N2bNnAwACAgLw559/YsqUKTh9+jQuX76MhIQEzJo1q9EDWq2lS5di3bp12L9/Py5evIigoCCcPXsWCxYsUKkde3t7nDp1ClevXkVJSUmDZ1179eqFuLg4nD17FufOncPUqVNVOrutLvPnz0dERAS++eYbXLx4EQsWLMDt27cb3PZ+/vz5+OGHHxAZGYlLly5h69atOHz4sMIxYWFh+Pe//40VK1YgIyMDmZmZ+PLLLxESEtLo2J70HZ86dQpr1qzBr7/+iry8PMTFxeHmzZvo06dPsz4TIiJ14hjaOBxDOYaSejEhJZ2zcuXKOv/Y9+nTB5s3b0Z0dDRcXFyQmpqKJUuWqO09165di7Vr18LFxQUpKSn49ttv0blzZwCQn5GtqanB2LFj0b9/fyxcuBCWlpYKa20a47333kNgYCAWL16M/v37Iz4+Ht9++y169eqlUjtLliyBvr4++vbtiy5dujS4liUyMhJPPfUUhg0bBm9vb3h5eWHQoEEqvZ86fPDBB5gyZQpmzJiBoUOHwtTUFF5eXgpTgh43fPhwxMTEIDIyEi4uLoiPj8eiRYsUjvHy8sJ3332HI0eOwN3dHc8++yw+++wzdO/evdGxPek7Njc3R3JyMsaPHw8nJyeEhITg008/xbhx45r1mRARqRvH0CfjGMoxlNRLImhigjkRkZrJZDL06dMHr7/+OlatWtXo4/z9/ZGVlYWffvqpBaMjIiJqvTiGUmvCNaREpBWuXbuGI0eOYOTIkaisrMSmTZtw5coVTJ06tcHjPvnkE7zwwgvo0KEDDh8+jF27dmHz5s0aipqIiEh8HEOpNWNCSkRaQU9PDzt37sSSJUsgCAKeeeYZ/Pjjj09cQ5Kamor169fj7t276NmzJ/75z3/irbfe0lDURERE4uMYSq0Zp+wSERERERGRKLipEREREREREYmCCSkRERERERGJggkpERERERERiYIJKREREREREYmCCSkRERERERGJggkpERERERERiYIJKREREREREYmCCSkRERERERGJggkpERERERERieL/ATzkU732T0HaAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 1100x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Figure: Performance with different numbers of training genes\n",
    "df_ntrain = roc_by_gene_ntrain.iloc[:-3, :2]\n",
    "df_ntrain['Number of training genes'] = df_ntrain.index.to_series().str.split('_', expand = True)[1]\n",
    "df_ntrain['Number of training genes'] = pd.Categorical(df_ntrain['Number of training genes'], \n",
    "                                                       ['ESM-1v', 'EVE', '1', '2', '3', '4', '5'])\n",
    "df_ntrain = df_ntrain.sort_values('Number of training genes')\n",
    "df_ntrain_mean = df_ntrain.groupby('Number of training genes').mean().reset_index()\n",
    "order = df_ntrain_mean['Number of training genes']\n",
    "\n",
    "fig, axes = plt.subplots(1, 2, figsize=(11, 4))\n",
    "sns.scatterplot(ax = axes[0], x = \"Number of training genes\", y = \"Global_AUROC\", data = df_ntrain_mean, marker = '_', s = 500, color = 'k')\n",
    "sns.stripplot(ax = axes[0], x = \"Number of training genes\", y = \"Global_AUROC\", data = df_ntrain, order = order, zorder = 0)\n",
    "sns.scatterplot(ax = axes[1], x = \"Number of training genes\", y = \"Avg_per_gene_AUROC\", data = df_ntrain_mean, marker = '_', s = 500, color = 'k')\n",
    "sns.stripplot(ax = axes[1], x = \"Number of training genes\", y = \"Avg_per_gene_AUROC\", data = df_ntrain, order = order, zorder = 0)\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "62aebcca",
   "metadata": {},
   "source": [
    "#### Add more proteins from ProteinGym"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "c58189f3",
   "metadata": {},
   "outputs": [],
   "source": [
    "extra_genes = ['KCNH2', 'SCN5A', 'SC6A4', 'RASH', 'SYUA', 'PTEN', \n",
    "               'VKOR1', 'A4', 'P53', 'MSH2', 'TPOR', 'BRCA1', 'YAP1']\n",
    "\n",
    "for gene in extra_genes:\n",
    "    feat_mat[gene] = pd.read_csv(f'./data/additional_dms/feat_mat_{gene}.csv', index_col = 0)\n",
    "    label[gene] = pd.read_csv(f'./data/additional_dms/label_{gene}.csv', index_col = 0)\n",
    "    feat_mat[gene] = feat_mat[gene].loc[label[gene].index]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "5929e261",
   "metadata": {},
   "outputs": [],
   "source": [
    "# We experimented on adding more genes from proteinGym. \n",
    "max_subsets = 30\n",
    "\n",
    "# Function to check if a subset contains the standard 5 training genes\n",
    "def is_subset(subset, test_set):\n",
    "    return all(item in test_set for item in subset)\n",
    "\n",
    "extra_gene_subsets = []\n",
    "\n",
    "np.random.seed(0)\n",
    "for i in range(1, len(all_genes + extra_genes) + 1):\n",
    "    all_combinations = list(itertools.combinations(all_genes + extra_genes, i))\n",
    "    subset_combinations = [comb for comb in all_combinations if is_subset(comb, all_genes)]\n",
    "    \n",
    "    remaining_combinations = [comb for comb in all_combinations if comb not in subset_combinations]\n",
    "\n",
    "    np.random.seed(0)\n",
    "    sampled_combinations = random.sample(remaining_combinations, \n",
    "                                         min(max_subsets - len(subset_combinations), len(remaining_combinations)))\n",
    "    \n",
    "    # Combine sub_a_combinations and sampled_combinations\n",
    "    extra_gene_subsets.append(sampled_combinations)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "56135d6b",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Number of genes: 100%|██████████| 18/18 [03:32<00:00, 11.81s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "274 genes used to calculate per gene AUROC out of 1298 genes\n",
      "Calculating...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 274/274 [02:04<00:00,  2.20it/s]\n"
     ]
    }
   ],
   "source": [
    "# Get the prediction matrix\n",
    "pred_ntrain_extra = pd.DataFrame(index = y.index)\n",
    "features = feat_selected_dms\n",
    "cpt = CPTClassifier()\n",
    "\n",
    "for n_genes, subsets in enumerate(tqdm(extra_gene_subsets, desc = 'Number of genes', leave = True)):\n",
    "    n_genes += 1\n",
    "    for genes in tqdm(subsets, desc = 'Subsets', leave = False):\n",
    "        genes = list(genes)\n",
    "        # Transfer model predictions trained on each individual gene\n",
    "        cpt.train(feat_mat, label, genes, features)\n",
    "        pred_clinvar = cpt.predict(x[features])\n",
    "        pred_ntrain_extra['Transfer_' + str(n_genes) + '_' + '+'.join(genes)] = 1 - pred_clinvar.values\n",
    "\n",
    "# Get per gene AUROC\n",
    "roc_by_gene_ntrain_extra = get_per_gene_auroc(y, pred_ntrain_extra)\n",
    "\n",
    "# Add global AUROC\n",
    "roc_by_gene_ntrain_extra.insert(loc = 0, column = 'Global_AUROC', \n",
    "                                value = [np.nan, np.nan, np.nan] + \\\n",
    "                                [float(roc_auc_score(y, pred_ntrain_extra[feat])) for feat in pred_ntrain_extra.columns])\n",
    "roc_by_gene_ntrain_extra = roc_by_gene_ntrain_extra.sort_values('Global_AUROC', ascending = False).round(4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "b9c0337b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2400x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Figure: Performance with different numbers of training genes\n",
    "df_ntrain_extra = roc_by_gene_ntrain_extra.iloc[:-3, :2]\n",
    "df_ntrain_extra['Number of training genes'] = df_ntrain_extra.index.to_series().str.split('_', expand = True)[1]\n",
    "\n",
    "df_ntrain['Training set'] = 'Only contains the five CPT-1 training proteins'\n",
    "df_ntrain_extra['Training set'] = 'Contains additional training proteins'\n",
    "df_ntrain_extra = pd.concat([df_ntrain_extra, df_ntrain])\n",
    "df_ntrain_extra['Number of training genes'] = pd.Categorical(df_ntrain_extra['Number of training genes'], \n",
    "                                                             ['ESM-1v', 'EVE'] + [str(i+1) for i in range(18)])\n",
    "                                                             \n",
    "df_ntrain_extra = df_ntrain_extra.sort_values('Number of training genes')\n",
    "df_ntrain_mean_extra = df_ntrain_extra.groupby('Number of training genes').mean().reset_index()\n",
    "order = df_ntrain_mean_extra['Number of training genes']\n",
    "\n",
    "fig, axes = plt.subplots(1, 2, figsize=(24, 8))\n",
    "sns.scatterplot(ax = axes[0], x = \"Number of training genes\", y = \"Global_AUROC\", data = df_ntrain_mean, marker = '_', s = 500, \n",
    "                color = 'blue')\n",
    "sns.scatterplot(ax = axes[0], x = \"Number of training genes\", y = \"Global_AUROC\", data = df_ntrain_mean_extra, marker = '_', s = 500, \n",
    "                color = 'orange')\n",
    "sns.stripplot(ax = axes[0], x = \"Number of training genes\", y = \"Global_AUROC\", data = df_ntrain_extra, order = order, zorder = 0, \n",
    "              hue = 'Training set')\n",
    "sns.scatterplot(ax = axes[1], x = \"Number of training genes\", y = \"Avg_per_gene_AUROC\", data = df_ntrain_mean, marker = '_', s = 500, \n",
    "                color = 'blue')\n",
    "sns.scatterplot(ax = axes[1], x = \"Number of training genes\", y = \"Avg_per_gene_AUROC\", data = df_ntrain_mean_extra, marker = '_', s = 500, \n",
    "                color = 'orange')\n",
    "sns.stripplot(ax = axes[1], x = \"Number of training genes\", y = \"Avg_per_gene_AUROC\", data = df_ntrain_extra, order = order, zorder = 0,\n",
    "              hue = 'Training set')\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "2d15ea47",
   "metadata": {},
   "source": [
    "#### Add more proteins from ProteinGym (alternative plot)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "d4b8d086",
   "metadata": {},
   "outputs": [],
   "source": [
    "extra_gene_subsets = []\n",
    "\n",
    "np.random.seed(0)\n",
    "for i in range(1, len(extra_genes) + 1):\n",
    "    all_combinations = list(itertools.combinations(extra_genes, i))\n",
    "    subset_combinations = []\n",
    "    \n",
    "    remaining_combinations = [comb for comb in all_combinations if comb not in subset_combinations]\n",
    "\n",
    "    np.random.seed(0)\n",
    "    sampled_combinations = random.sample(remaining_combinations, \n",
    "                                         min(max_subsets - len(subset_combinations), len(remaining_combinations)))\n",
    "    \n",
    "    # Combine sub_a_combinations and sampled_combinations\n",
    "    extra_gene_subsets.append(sampled_combinations)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "0749d09d",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Number of genes: 100%|██████████| 13/13 [01:58<00:00,  9.15s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "274 genes used to calculate per gene AUROC out of 1298 genes\n",
      "Calculating...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 274/274 [01:25<00:00,  3.20it/s]\n"
     ]
    }
   ],
   "source": [
    "# Get the prediction matrix\n",
    "pred_ntrain_extra = pd.DataFrame(index = y.index)\n",
    "features = feat_selected_dms\n",
    "cpt = CPTClassifier()\n",
    "\n",
    "for n_genes, subsets in enumerate(tqdm(extra_gene_subsets, desc = 'Number of genes', leave = True)):\n",
    "    n_genes += 1\n",
    "    for genes in tqdm(subsets, desc = 'Subsets', leave = False):\n",
    "        genes = list(genes)\n",
    "        # Transfer model predictions trained on each individual gene\n",
    "        cpt.train(feat_mat, label, genes, features)\n",
    "        pred_clinvar = cpt.predict(x[features])\n",
    "        pred_ntrain_extra['Transfer_' + str(n_genes) + '_' + '+'.join(genes)] = 1 - pred_clinvar.values\n",
    "\n",
    "# Get per gene AUROC\n",
    "roc_by_gene_ntrain_extra = get_per_gene_auroc(y, pred_ntrain_extra)\n",
    "\n",
    "# Add global AUROC\n",
    "roc_by_gene_ntrain_extra.insert(loc = 0, column = 'Global_AUROC', \n",
    "                                value = [np.nan, np.nan, np.nan] + \\\n",
    "                                [float(roc_auc_score(y, pred_ntrain_extra[feat])) for feat in pred_ntrain_extra.columns])\n",
    "roc_by_gene_ntrain_extra = roc_by_gene_ntrain_extra.sort_values('Global_AUROC', ascending = False).round(4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "29f9bfd1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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PsD6tw80wXJLJupGdCnvnQGEutBgGsc09HZHjjq6AmRPVDgiAjNMw/0kwF0GfhzwbmxBCCCGEuCJk5hUy+auNpOeWVOnak5TB5K82suKJqwny9/NgdG6SlWJjwgJZ5yShLIQQQghRSZJQFkJcEfyMBl4a3Y6HBjXj2LlsGsWGERcZbPP49glRtE+IcmOE1TO8fR2+XnOMHafTrcYn9W5I3egQD0XlGiM61OHaNnHsPJ1GWJA/reuUKkUe3QAm/QaLnoPjqyAoCrrcBoOe81zAVZF2CjZ9AakHoXZr6HYnRNXzdFTusf9PmH0HFOWq5wufgQH/gmv+7dm4HLXuw5JkcmlrP4ReD4DR6P6YnC07FTZ/BUnbILohdL8LYptVfJ4QQgghhHCLeTvPWCWTiyVn5LFkXwrD29fxQFRu1rAPHJhXfjykBsS1cX88QgghhBA6JwllIcQVpXZEMLUjbCeS9SrQ38h3U3sxbc0xluxPITTQjxs7JzC2a4KnQ3OJQH8j3RrFaE/W6wK3/6H61xr9wItLlWtK3gXTRkDepcUBB/6EzV/DHfOhVkvPxuZqWSnw690lyeRiK16HFkOhXlfPxFUZ549oj2clQ0EWBHtvL3aHpCfCl0PUzutiW6bBrbOhUT+PhSWEEEIIIUqkZuVXac6ndJ0M27+DlL3W4wOfhQDfWnQthBBCCOEOklAWQggfER7kz4MDm/PgQB2VB3YlP53+iVvyUkkyuVjuBVj2Coz/1jMxuZqpEBb8GzZPA3OB9jF75ugjoRzfHi5oJJVrNIYgH+hXt/od62QyqAUAC5+Fu5d5JiYhhBBCCGGlZ5OaNud625nzKUERMPxNmDUZclLVWEQd9XldRwpNZv7ancz2k2nUqxHCjZ3rUSPYAPvmwunNEJUAHSdAqI0F10IIIYQQTqLTu+1CCCGEjzq2Qnv86HK3huFWi5+HjZ/ZP8agk1LR/R6Bg/OhKM96/Oon9bdbXstRGz+fSVshL0P/O7CFEEIIIUo5eT6HlMw8WteJJCxIP7fQujeKYUSHOszbecZq/JaeDWge5wOLHB1RmAs/3V6STAbIPAPf3QSP7NbF59bMvEJu/WKDVWurD5ceZEbMV7RNnV9y4Mo34bY5ULeT22MUQgghxJVDP5+GhdCTonzAAP6Bno5ECKE3obHld4AWj/uiogLY8k3Fx7Ud4/pYnKFuZ5jyJ6x8W/UYrtEIej8ArUd6OjLnCIuF84fKjweGS+lAIYQQQviMtJwCHv5xOysOngNUNahHBjfnrquaeDgyx73/t84MbFmbv3Yn42eEkR3qMrLDFdA72WyGw4tgw6eQfa78fF467PlVlcT2cl+sOmaVTAa4mFPEf3J7MjuoVEI59yL8+TjctcjNEQohhBDiSiIJZSGc6cIxmP8kHFoIBj9oPQquex3Ca3s6MiGEXnS9HZa9XH682xS3h+IWBVlQkGl73mCEa57W12r7el1hwveejsI1Qmpoj7e/CfwC3BuLEEIIIYSLPPnzrsvJZICs/CJenrePZrXDubqlPq7v/YwGxnZNYGzXBE+H4j5mE8yaBPv/AGC9uRUfFo1hn7kBjQzJ3OP/B0P8tkDOeQ8H6pgl+89qjm+2tCTNEka0Ibtk8PRGyD4PYVdISXMhhBBCuJ0klIVwlsJc+GYUpJ9Szy1m2POL2sl1zyrfKHUqhHC9qx6DrGTY+i2YCsAvCLrfCb0e8HRkrhEaA7EtIfVA+bn6PeHGz9QuX+F5ORfg8BLtuZim7o1FCCGEEMJFzmfls3BvsubczE2ndJNQviLt/e1yMnl2UT+eLLqboku3Ps9bori7sCUf8D6jmgzwZJQOC/b30xz3w4Q/JutBg58s8BRCCCGES0lCWQhn2TOnJJlcWvIuOLoMmg50e0jCt6xc6dl/v39/z/77VwyjH4x4G65+Ci4eh5gmKunqy659AWbeCuaikrHQWEkme5vkXWDKtzG3072xCCGEEEK4SEZeEWaL9tzFnAL3BiMq5+ACAPIsATxVNPVyMrm09/xuZ1S9ru6OrErGdKnH5hMXy40PMW4m3JBnPdjyOl30ha6Q2QRr3oVtMyA3DZoNUhWrYhp7OrLqyb2oWiJF1IXarTwdjRBCCFElklAWwlkuHrc9d+EYyOYtr3U2I483Fxxg8b6zBPkbGd25Ho8MakFIoPZqYE8Z4OFF1BYbN1WEi4TFqseVoOV1cMdC2PAJpJ1UJaN73w9RV1B5Pj2w9/3w1e9VzgVY9opaNGYwQJvRMPBp26W/hRBCCKF7DWNCqR8TwqkLueXmrmpeywMRCYcFhACwyNyVQrR36x7Oi8RktuBn9P4qchO6N2DX6XRmbj51+Xq8Y0IUL9XJhV0G4NJg3c4w8r8ei9Op/ngUtn5T8nzXT3BsFdy3Vr/lvFe+pR5Fl95TGl0F477R7+sRQghxxZKEshDOUqej7Tk99f4sLTdNrfC1mKHFUJ/cJZlbYGL8p+s4cT7n8tinK45yMDmTr6f08GBkQlxhErpCwueejkLYU7MptBgGB+dbjweEQpfJnonJlcxmmD4GzmwvGdv0OSRugalLpZWFEEII4aOMRgPPjWzL/d9todBUsqq2ZVwEt/Zq6MHInODIUljxpqo8E9MY+j4M7W/ydFTO0/FvsOVr0i1hNg8JDjDqIpkM6mfxtbEduHdAU3acTiOhRghdG8YA/eCa+yBxq1rY2aCXp0N1jowktTO5rKxklWS+6jH3x1Rd+/+EpS9Zjx1fBXMfggnfeyYmIYQQoookoSyEs7QYCgnd4fQm6/GWw9VuO73Z9zv8cg8UZqvn/sEw6j11geZDft+RZJVMLrbswDl2J6bTrl6UB6LStmKFk77Q1m9hxw/lx/s+Ci2GOOkfEUL4pBs/h7/+Bbt/VuWv4zvAsNf0X4JOy+FF1snkYklbVS/p5oPdHpIQQggh3OPaNnH88dBV/LDxJCmZeXRvFMP4bvUJC9LxbbRjK2HGTWC51Hs3eSf8fCeYCqHTBM/G5iwNesGQl7lq0SdQZAaM5Q7JLzRzJj2XOlEh7o+vknYnpvPz1tPk5Ju4plUtOtcvVSUnpol6+JJzB0p+PstK2efeWJxFK0EOcPAvyE69cqqSCSGE8Ak6/iQshJcx+sFtv8Lqd1Uy1ugP7cdC7wc9HVmlLD+Qwo8rdvBu0h0EU6o/VFEe/PaAKs0TVc9zATrZwbOZNucOpWR6VULZaT2M1z8PDc+XHzfnQX9JKAs3MhWqMte7flL/3WqE2iURFOHpyIQtwZEw5mPV57sw17fLtNm7aXVuvySUhRBCCB/XMj6C569v6+kwnCZp2WfsLepIA8NZWhgTSyZWvuk7CWWAPg/RsOMtdP1kLVvOlZ+2ACsOnONvPRq4PbTKmL7+BM/9tvtyqeuZm09xXbt4PrqlC0ad7LCutJrNwGBUVfLKim3u/nicIS9de9xiVnOSUBZCCKEjklAWwpmCImDQs+qhQ79tT+SRmdu5ybic4ICC8geYi2Dvb6q3qY9oVjvc5lzz2j6Y1DIVQo5GMhkgK8W9sQhtF46pldmxzVWJYV/2y1TY82vJ85S9qgzfHQvBTz6ieLXAUPXwZbVaVm1OCCGEEMKLmM0Wnv1tNz8eHI8JPwD6G3fwUcD7RBhy4cIRMBX51OfvdcmwPyMA0N7tGh7s3a81PbeQ/5u373Iyudhfu5NZdiCFQa3jPBOYC2Uc3cSsxWvoaOxEd9NW68nQmvptsdNsIJxYXX48pqnv7TAXQgjh88rXfhFCXJEsFgvvLDqIxQL+Ni66ADBpJJp1Zu2RVN5csJ8vVh2lT9RF6oeUf039W9Tyqt3JTuMXAAk2ekM36uveWIQ1UyH8PBXe7ww/3AwfdIVZk6Ewz9ORuUbybutkcrHELar8lxCe1nwIxLcvP16nEzQd5PZwhBBCCCGqYsaGE3y34eTlZDLASnNHXi66VT2JaaLfZHJeBsz7J7zWAF6pA7PvJCvlJHdP30x2vvZ9jRqhAQz28oTspmMXyC3Ujn/5AY1t1zqXsvIrRn6+i5ePNueW7Ef4sOgGzlmiMPsFQssRcPufEOHd3zObuk9VbYJK8wuC4W+AwUd3mgshhPBZOv3EKIRwtvTcwsu9hJeYulDoP40AQ9kLGAO0HuX+4JzEbLbw9x+38cfOM5fH3iaP1/w/Z4WxE4vMXQkKCmJMj2Y8em0LD0bqYte+ANPHqDLmxUJioP8TnotJwKq3YdesUgMW2DtH9aYd/LyHgnKhpG225xK36vq9RvgIox9MmgtLXlSLHwwGaDsGBj4LRlmTKYQQQgh9+Gnzac3x30x9eNn/KwKu+qebI3KiHyZY7/7cPZsFh0xk5o3TPDwy2J/PJ3UjOMBPc95bRNjZQW1vzlusXFmJgwuy+OrbHA6augKQB7zMfbzMfbSKtvDigD6wH/VwkNPahTlDcCTcsQB2zoST6yAiXu229vVqZEIIIXyS938KEUK4RXiQPzVCA7iYU0gKNXihaBIv+E/Dz1CqxtLg/+j6Q++8XWeskskAuQTzetEEVgU9rF6rMQD674ZAH357bNgH7l4OGz+D80fUDrye90J0fU9HVn0FObDsFdjxg/rvFkNVAr1GI09HVrFt32mPb//eNxPKNRpWbU4IdwqNgVHvqofQP7NJLd7Z/LVq/9D4Khj0HNTp6OnIhBBCCJfJzi/SHM8jiKIbPiGg83g3R+QkpzZqlhLOyc60eco9/ZvSrVGMK6Nyiu6NYmhUM5Tjlxb9F/MzGrixS4KHonLcgAGVOToceFBz5iww4H+V//fLlgr3uMBQ6DZFPYQQQggdk+0VQggA/P2M3N6n8eXnM0zXck3BO7xe9DeOd3wMHtgI/R71YITVt3DvWc3xJGLZZbnUu8ZcCAfnuzEqD6ndGkb+FybPhaGv+EYyOTMZvhgM6z5UiYKiXLXD9+vhqhSatyuwceMj3/YNEV1rdJV2Eic8Htrd5P54hBC+b/6TatFRZhKY8uHwYpg2Ei4e93RkQgghhMtc3bK25nivJjGEdNFhMtligb1zYf5TmtMDjDsxoJ1RHNRG+/8Lb2M0GvhsUjea1Aq7PBYZ7M/b4zrSrHa4ByMTQgghxJXMh7fgCSEq66GBzTAYYNra41zILsC/ZhPaDhlGow51PR2aUwT42e5PE0hhyRO/IDdEI5xq89eqd5ZFY/V9RqIqL9VjqvvjqozmQ1Sc5cavdX8s7mAwwMTZ8NcTsO8PMBdB02vgujcgSG6SCA/LSoEt38C5fRDbEloMg/1/wIUjENcOut6udi/r3ZFlsOz/IPOMel3D/k/1UfRF2edhy7Ty4/kZsOkLGPKy20MSQggh3OGBa5qy/GAKR89lXx6LDg3g2ZFtPBhVNcx9ELbNsDndwJjC39sV8d7uAKvxu/o1plV8pKujc5oWcREseWwAW0+mkVNQRLeGMYQEenep7mIrVlTiYIuFRd+/zaKsJiQYznHKUpsTxANwbdNQpg6WSjJCCCGEtzBYLF5XCMTjMjIyiIqKIj09nchI/XzYFMJZTGYL2QVFRAYHVHywjqw4eI7JX20sN97ccJpFQZf6BweEwWN7ISTavcGJqrt4At7vBBaz7WN63A3D33RbSFWSdhK+GqYS4MXC42HKn7ouNe+QwjywmCAwrOJj9cxUCNmpEBYLfr71/upTUg/D18Mg+1ypQQOU3ukSURfuXADRDdwdnfOselv1hy7N4Ae3/wkNe3kmJldK3AKfD9SeazEMbtFY0CMqJNdNwtvJz6i40u07k8F3G06QlJZHoJ+B0EB/msWFM65rfWpF6HAhtb2/58ViW8K9q9iSmM3vO85gsVgY1q4OvZvWdE+MonIKcrDMGIvh5NrLQ0tMnfml9gO8ec8YQn25HZkQQgjhBSpzzSR/lYUQ5fgZDT6XTAYY0KIWD1zTlI+XH8F8KS9Q13CeDwI+UE8CI2Ds55JM1pu9v9lPJgPU1sHq++gGcN9a1TM5ZS/UagmdJvrGLsiKBAR7OgLXW/1fWPuBKsceVgv6PgJ9tHuFCQ9b+lKZZDJQtmxiZhKseB1u+MhtYTlVXrramVyWxaR2/Ty02f0xuVpME/APhqK88nO1W7s/HiGEEMLFFu5J5v7vtlJkLvkc06RWGM+NakN0aKAHI6uGo8ttzwWGQ/txcM3T4B9E14ZBdG2o/2sps9nC6sOppGbl071RDPVjQj0dknMte8UqmQwwyG8bg1rthsBxHgpKCCGEEFokoSyE0JSdX8TcHUkcTsmiZVwEozrW1U15JXseH9qKCT0asHBPMnuTMoj0j+dk8As0TwC/lkMgKMLTIYrKspjsz0c3VDcW9CAkGnrf7+kohLPNfwrW/6/kefY5WPg0BEdCl0mei0toO7LMseMOL3VtHK50coMqM6/l/CFY877qS1+3M7S5Afx1etO5tJAa0P0uWPeh9XhAqCqHvWs2tL7eN16rEEKIK57FYuHlefuskskAR89l8/Wa4zx6bQsPRVZNIXYSxKPeg/Y3uS8WNziems2UaZs4lqrKlRsNcGe/xjw9QgcLph21c5aN8Zlw7QvujUUIIYQQdklCWQhRTmJaLjd/upbTF0t28fxv/lZmPjiIuBr6722alJbH2wsPkl2gEpFfEUy/ZrF82SYUHRb9Eq2vh8UvUG4HIUC7m2DIS/rvyWs2g9Ho6Shcw1df27mDsP4j2P8nZKdoH7P+E/0nlI8shU1fqp7DDXtDrwcgIs7TUVVPSBTkpztwXLTLQ3GZimJf9GzJf699Hyb/ru/XW2zIyxBZFzZ/DVln1W7lwhzY9q161OkEk+dCcJSnIxVCCCGq5dSFXE5eyNGcW3M4Vb8J5bZjYNF/yn9WC42FViM8E5MLPTZr++VkMoDZAp+vOkbXhjEMaxfvwcicSKt6DEBhrnvjEEIIIUSFfPAOrhCiul7/a79VMhngeLY/b38xzTMBOdmTv+y8nEwutvpwKrM2nfJQRKJaajZVSWMMJWNGf7jhf3DTlyp5oEdmEyx/Dd5sBi/GwNfD1a5CX2A2w8q34K2W6rV9OQSOr/Z0VM5zZid8fg1smWY7mQyQcdptITlVbpr6WVz9X5g+Bvb/Aac3wpr34IvBkFWqXPTF4+rY/CxPRVt5jib5O9/m2jhcKaE7RNRx7NjknbDmXZeG4zYGA/R+QJX0jm8PpgLr+TPbVWl6IYQQQuciQ/zxNxo052LCdFyNIyQabpmpqlAVq9kMJv4EASEeC8sVTp7PYevJNM25OdsS3RuMK7UYpj3ecrh74xBCS1YKLH1ZXff+/jAk7/Z0REII4VGyQ1kIJzmcksXyAymEB/lzXfs6RIXotwfxkr1JmuOLz9eExK1Qr4ubI3Keo+eyOHouW3Nu0b4UbuvdyL0BCefo85C64Nw/TyWT21wPUQmejqp6FjwNGz4ueX5iDUwfDXevgFo63VFQbMkL1gmqUxvg2zEw7mtIO6FKz7e+Xr87Ile8DgUOJFATurs+Fmdb/rr63hVq73gh/SRs/Ewl7X65Gw4tUOOBEXD1k/roG933UUg7BdtmXCqpb4QaDSDtpOrXbgyAbndAz3vhwlE4uAD8g6D1DRBW09PRO8ZggNvnwZfXqr7eFTnwFwx+3uVhuU1ehnpP1XLgLxj4jHvjEUIIIZwsOjSQYe3i+WPnmXJzE3o28EBETtSwN/x9u1oIZjBCnY7qs42PKTDZbu2UX1RB2yc9GfwfOLVefdYuVqMRDHrW5ilCuEV6olownVnqHum272DCj9B8sOfiEkIID5KEshBO8Pr8/Xy8/Mjl5y/9sZdPb+tGv+axHoyq6oIMRWRrvD0EU6ASPzpOKAcF2O4DHezvO0UbsvKLWP3nd0QfmUuYv4WYbmOp12eCb5YWLhbdEMJrq2RA8i7o+DdoMsDTUVVNbhps+br8eGEObPwURrzt9pCcpiAbNn1RftxcADMnljyf/xT0uAcuHgX/YNUHu9kg98VZHacc3Ene7zHXxuFsO2fB8v+r+LhT6yH1YEkyGaAgU/WNjm0BLYa4LkZn8POH699XCfDzhyGmKUTVU0nmXbMgaTvkpMIvU2H37JLz5j8FY7+E1iM9FrpDigrU69j7m/pvR/jpqCGExaJe2765YPCDdmOhVktY8QacWHsp+T9KzVk0bsb6B7s/ZmfZ9wesehvO7YdareCqf3j/z6MQQgiX+b8b25NfZGbxvrNYLBAR7M+jg1twTcvang6t+ozGy/clLBYLF7MLCA/yJ1An1/QrV1Z8jMUSTkxGXZLSy5eErt+4oUNfw5b+/at+rtNFJcD962HXbDh3AGq3Up/fAsM8HZlwVEE2mAr1uyDcltX/tU4mA5gLYdFzklAWQlyxJKEsRDVtOHqej5cfoTYXsWDgHNFkF5h4ZOZ21j45UDcXNKWNaWzmywPlx0f7rYEIfZcdqhcdQo/GMWw8dqHc3JjO9TwQkfOl5xay4L9TGV/wa8ng4iWcOfAnde78znOBuZLZrJKRB+eXjO34HgY9p26o601Gou1eUucPuzcWZ8tMdmz3bkEWrC6VON/xAwz4F1zzb9fF5iwRdSD7XMXH5aS6PhZn2vqtY8eF1FTJPM2v8Y33J5SLRdZVvdsW/weOrQRTEeTa2c1blAe/3gtN9ntv33ZTEXx3ExxbUbnzCrNVolYPu3/m3KfeL4rtmoXq8mMuGVt1QN2kLNCoWNLhZldHWLGUKtwhPrEWlr6k/tsfuLgR5v4Nsp6Bhn0q97Vqe9NdZiGEEFUVGRzA55O6kZiWS0pGHi3jIwgN9K3bgPN2nuGthQc4lppNRLA/t/ZqyD+ubYG/n3ffhxng0LpnA9BZc+al7+Glavz7Fks1TnaFwDDoOtnTUYjKyjoHf/5TtUAyF0GDPjD8TYhv5+nInMNWRaOUPZBzAUJj3BuPEEJ4Aa/4JPnRRx/x5ptvkpycTMeOHfnggw/o0aOH5rGFhYW8+uqrfPPNNyQmJtKyZUtef/11hg0r6bnx8ccf8/HHH3P8+HEA2rZty3PPPcd1113njpcjrjBbNq3h18AX6GxUSZ4N5lY8UXg3J7Li2Xjsgi53Kf9j3GAOvf4ZKwtbXR4bbNzC32tugpYfejAy53h7XEemTNvE4RSV1PIzGrizX2Oua+9gP0kvN2fJam7Ln2PVUhigzqk/MB9bg7FxX4/E5VSnNsHFYxDfQa1gPrTAOplcbPlr0HkShNdyf4zVEd1QlQguyCw/F6fzi7PIerZ3BlZk5VvQ9Xbv74vd8x747YGKj8spv7DFqzlSGtngB23HwN5ftecd+RreIiNJ9feuTOK/IBOOLIE2N7gururY91vlk8mgFrLs+gk6jHd+TM50apN1Mvkyc/mhgmy1+COzuBSoATrdAt3vdGWEjllcxeoa9TXGDj0Fhyr5dW7xtrvMQgghqqNedAj1on2rvzDA+qPneeiHrZgv/dnKzCvi4+VHMJstPDW8tWeDE+JK8MPNkLil5PnJtfDt9fDgZt9ItobZuI8UECY76IUQVyyPJ5RnzpzJY489xieffELPnj159913GTp0KAcOHKB27fJleJ555hlmzJjB559/TqtWrViwYAFjxoxh7dq1dO6sVu4lJCTw2muv0bx5cywWC9988w033HAD27Zto23btu5+icKXFWQz6dDDhBtLkgI9jfuZHvAqAwv0W5I2NDyCbx8awe6fXuJI8gWaG07TpnF9uH42+Ad6OjybHC/5FMqzXfuzJzGdtNxCWsZHUis8qFolo8B7ykblHVqG0aB9Mzh77WdE7PuNfP8w/jQOYEtWTVrERTCmcz0ignXQ9zvnAvwwQZXULdZuLITa6FtqKlCrStuOdkt4ThMUrnrNLn+1zIQBGvTySEhOExCsLr7yMyp/rsUEx1ZBRy/YQWhP51vVz+qKN7QXBQBg0F9J9sYDIGWv7Xm/ALjuTdXDvEYjuHi8/DFNrnZRcC6w6Qv97SKvyNHlVT93/lOq9Lw371Ku7OvLSoHrP1Rlrut2hthmLglLCCGEcLftp9LYeuIidaNDGNS6NgFevmO3Kr5ec+xyMrm07zac5NFrWxBsp92Vp62oxPq++bvP8NWa45pzL1zfltZ1Ip0TlBCVcXKDdTK5WM552P69up+hd93v1F6M23miaqMjhBBXII8nlN955x2mTp3KlClTAPjkk0+YN28eX331FU8++WS546dPn87TTz/N8OGq7O59993H4sWLefvtt5kxYwYAo0aNsjrnlVde4eOPP2b9+vWSUBbOtWcO4UXld5g1MJ7jhtDd9Gg8SuMknajdinYPfEe7zGTAABFxno6oQo6VjSpmAKKd+u97S9koS0gMpGvPRRyaA0AQMNLyHssK7+U5c18+XXGUmff0IqFGqNvirJIFT1snkwF2/wxNB9o+R68rY1uPUjusKf2DZYE/H4cWw1TyTq/qdITjq6p2rq3FA97m3AE7yWTUBXZME/fF4wzhFfwdMF3qJ3XgT2h/M6x+R/WYKhbbUu3e1ouzeyp/TlAkNPXiXt/Z1UiQ56SqBTqN+jkvnqqwWw76LIQUVeKLFcGaZ2DcN2BOgpSkik9xRznowVXYRT73YTh/sPx4bEsY9W61QxJCCKEPhSYzD32/jfl7ki+PNYgJZcadPWlQ08uv9SopMS1Xczwrv4j03EKvTihXZjH66qw0gk9qVzaq1TyD/l0koSw8IO2k7bkVr0HL66BmU/fF4wptboAhr8DKNyAvHYwBqj3OtdUpOC+EEPrm0YRyQUEBW7Zs4amnnro8ZjQaGTx4MOvWrdM8Jz8/n+DgYKuxkJAQVq9erXm8yWTip59+Ijs7m969e9v8mvn5+ZefZ2RUYdeUuDJl2L7xeH/XUF32Ty4nIt7TEYhKajNgHMk/vku84aLd4wIMJl4K+JqF+d1ITIM3Fxzgvb9p92jyCqYilTzWkn4a/ALVjuTSohtCQw8nP6pq6wysk8mXZJ5R/VybeXHSqiL9/1m1hHJ0A2h6jfPjcbYds2D7DO25ViOhy2T99BEu7ciSio/Jz4BDC9Wj/xPqwjszCRr0hs63QbBObnhln4PsSpbn9g+BMZ94b/9kgHw7ixwckXGm4mNcraJy0Fpln+06CUsr8b7ijnLQVUla930JZt6K9d8NgxqXnshCCHHF+G79CatkMsDJCzk8PWcX0+/s6aGoXKNDQjS7E8vfv6sbFUxsuO/sHuyQEGVnLtp9gbjL6c1wcp1qTdJqpKpwJbxPvS6ojRoan43zM2Hu32HKPHdH5Xx9HlQ7lc8fhoi6EKaTBe5CCOEiHk0op6amYjKZiIuz3vESFxfH/v37Nc8ZOnQo77zzDv3796dp06YsWbKEX375BZPJuhfjrl276N27N3l5eYSHh/Prr7/Spk0bza/56quv8sILLzjnRYkri53Ss0276DjZo1OVKRvly/q3rsec3p/Qbv0/aMZpu8dGGXLoZjzIanN7lu5LcVOEVWQxlU8YF8tI0p5LOwGzp8DojyFQRyvyN3wGmz63PV+U575YXKHJ1TD8LVj8PBSoXuY0vhoGPgOnN0JQBARHqxK7GZd+huPawdgvwei9Ow0uW/ma7bkmV+szmQxQlF/xMaVt+hz6/F0tBGhzg36SyT/fDbtmVu6cwHC4ZzXUbOyamJylWr8/Bqjf3WmhCCdrPRJunqEqA6Tsh9qt4Kp/QKsRno5MCCGEG/2xU3vx1+rDqaTlFBAd6r0trCrrnv5N+HPXGdJyCq3GHxvSEj+jF7foqKShbePpGJbGjuxoq/GxfqtoVhAHdPNIXE5nNsHPd8KeX0vGIurAbXPU5xrhXWo2VaWft9lYSH1iNWSe1UW1wwoFhEB8e09HIYQQXsHjJa8r67333mPq1Km0atUKg8FA06ZNmTJlCl999ZXVcS1btmT79u2kp6cze/ZsJk+ezIoVKzSTyk899RSPPfbY5ecZGRnUr1/p7Q3iStT4Kmg+FA4tsB5vPw7qdPBMTFcwp/YwTtwKm7+CrLNq4UC3OyCkhhP/AdcaPWwYOQMHc3zFFzRa8y+7x+ZY1OrtkEAvT9T5B6lduYcXl58rTkpq2TtHleod/obLQnOq42vgr8dtzwdGQGMf2G3WYyp0mghnd0NYLYi5lIir310lLrfNUEnk2Baq/Hf3Ozwbb2WkJ9qeC7y0e7UoH7ZNh0OLVU/pTrd4/67zltephL+jci/CkksL9hb/B67/QL1Ob7btu8onk0G9Bx1ZDDWnOj8mZ2o5vOp9lDv8TfXG9jRHykHvnwfbf4DcSuwyD42F8d96d4/oirQeqR5CCCGuWLbqaFgsaPYb1rOGNcOYc39fPllxhG0n06gTHcztfRpxdcvang7NqQIK0vnO8iRf+A9moakbQRQyxm81E/0Ww8YLkOAjCeVtM6yTyaCqc819EO7SuAcgPG/UB3BiHVw4YuMAH3vTEUII4dmEcmxsLH5+fpw9e9Zq/OzZs8THa5fZrVWrFnPmzCEvL4/z589Tt25dnnzySZo0se5DGBgYSLNmzQDo2rUrmzZt4r333uPTTz8t9zWDgoIICvKdcjjCzW6eAVumwb65YDBCuxtVWU+hX3t+hdl3qh2xoEq3bv8e7lykq368oYH+NOo6FOwklI+a49lqaQ7A2K4J7gqt6oa9BtNGQlZyxceWtv07da5RB2Xot023M2mEEW+rHbzbf4D1H6neRXW7wNVP2q2a4JUCQ6F+D+sxUxHMGGtdEvvoUsi9oEpl60F0Q0g9oDFhUAsiFvwbCrLBVGrH7+7ZMOg5taPQW/W8Bw4uKN/H3BHmIvj9EWg+BMJinR6a06x9r+rnXjjqvDiqw16P4QZNIaGRKtlWGQ37Qt8JFfQvvsTV5ZUd+fq1+4OxjlrI4KjcNIjpoXYgCCGEEDp1Xbt4tpwo3/qoV5MYYsJ8Z3dysUaxYbw21scX82edI9ycwSP+v/CI/y/Wc/YWsurN3jna46c3qRZXUTq4X3GlMRqh133wp8Z1ev2e0kJPCCF8kEcTyoGBgXTt2pUlS5YwevRoAMxmM0uWLOHBBx+0e25wcDD16tWjsLCQn3/+mfHjx9s93mw2W/VJFsJp/AOh593q4aPMZgtrjqRyJi2PTg2iaREX4emQXMdsgoXPliSTi50/DBs/h6vt7/b1OjGNocV1cPCvclNJlhjuK3wEMDC0bRwPD2ru9vAqLbY5PLgJds5UCznO7nbsvIIsMBeCUQeLh3LTbM+NmwZtb1A/i6Uv2o4ugxNr4Y75l3oZ6di+37T7K694A7pO0UfPoj4PwtyHyo8HhqnEsS3LX1ev0VsXrgSGwe3z4MA8OLURwuMhtIba8bprNhWuQDflw8H50PlWd0RbNfl2qh1UpE5H58VRHRX1GA699KgM8yJYssixY93RY9gR27+r3PGxLSSZLIQQQvdu692Q1YdTWX7g3OWxOlHBvDJGyrXq0sn1alGmLWUX5+qZxUs+Q4rK6Xo7HF5ifc8pPB5Gve+xkEQVmYrgwJ9wbj/UaqWqW/nprritEMLFPP6u8NhjjzF58mS6detGjx49ePfdd8nOzmbKlCkATJo0iXr16vHqq68CsGHDBhITE+nUqROJiYk8//zzmM1mnnjiictf86mnnuK6666jQYMGZGZm8v3337N8+XIWLFigGYMQQpvFYmH2ltO8PG8f6bklfYnGdK7HW+M6+kZfovxM2PEjnNmhkq8N+0H6Ke1jj63UX0IZ4MbP4K8nYPfPqs9wVAPoMZWcZpN5JCWHZrXDaa6nRQLBkapc8sbPHD+nYT9VMlsPaja1PVeUB2YzrHqn/JwpH9a8B+O/cV1s7nBMI5kM6vWd2gCthrs3nqroMkm9t6x+B7JTISgSGvSBQ/Ptn1f8Glte5544q8LPX/VDbnMDJG5RieSgCOj3KGz4BApz7J/v7TeKQmIgw8ZOj4g6kJMKpsLyc4Hh0Ga0S0MTlWSoTEUKA1z9lMtCEUIIIdwlyN+PaVN6sO7IebadukjdqBCGtYsnOMDL2xtVw+wtp5m1+RSZeUX0bxHLPf2b+sZu7MyzMOMmKMjUno+sBz3vdW9MrtR2tFooXVa9brI72Zv5BcAtP6rWXac2QGRdda0oCzX1JTsVvrkeUvaUjNVuC5N/18eifiGE23g8oXzzzTdz7tw5nnvuOZKTk+nUqRPz588nLi4OgJMnT2IsVaI0Ly+PZ555hqNHjxIeHs7w4cOZPn060dHRl49JSUlh0qRJnDlzhqioKDp06MCCBQu49tpr3f3yhNAti8XC33/czu87ksrN/botkS4NormtdyP3B+ZMmWfhq6Fw8RgAK0wd+MpiItH0Jp2Mh7nPby5NjWdKjg+J9kyc1RUcCWM+geFvqTK7Eer9tRnQLC7Ks7FVR1gtSD1Y8XFBUTDkJdfH4yzFvYS1pB6AvDTILP97CaiVpHpmNqkS3raE1XJfLNXV+wHocrv6XtVoBH/a6Ytdml5e49oPYeHTlT/P4OU3M9NP257LTdNOJoO6mRcQ7JKQKs2RHsMAC56FpM2Of91e96t+5nrRdQrMd2ARWIM+cNVj0FyuE4QQQviO3k1r0rup7ycBXvtrP5+sKOnfuu9MBov3nuW3B/sRHuTxW57Vs+N728nktjfC0P+7fG3vEzrfpiofle6jHFEHbvjQYyGJSmjUVz2EPi150TqZDOr50hdhVDXaQgkhfI5XfLp68MEHbZa4Xr58udXzAQMGsHfvXrtf78svv3RWaEJcsVYcPKeZTC42d0eS/hPKK9+8nEz+1dSXxwrvw4JawHLYVI8Fpm7MCXyuJKnszX0/HREUrh6+otsdcGKNxoQBsEBoTVWi5+on9bWiOa6d7bnYlhAcBeFxkHVWY14HZcvtmXMfHFmiPRfXHup3d288VVWQDQueVtUPinLV962mA9+b+PaQ0M318VVXVgoseaFq557eCJ1vcW48zmI2QV75noOXFeXangsMc348VeVIj2FTERzbA+ZKXArUH+n6/sjO1ONuVUL/xFrbx0TUUWXcjZXZzSyEEEIIb5Calc9Xq4+VGz9yLpuft5xmcp9G7g/KmTLO2J5rcz1E1nFfLO5g9FMtnno/qD6/RdSB1iN9c6drfhas/QD2/wFGf2g/Ti1QlfLCwlP2zdUe3/ubJJSFEFbk7okQQlPpnktaCorMborEhQ4tBMBsMfB20bjLyeRimYTxqWlkycD5I+hGYZ5KZi19Wa3wtbWrTs/a3wSD/gOBxeW6i0uwXyqpm3Me9s5VF2h60qAXNNRY2RvTFJpeA1u/gdptys8bA6DPw66Pz1VS9qve2FpiW8CE790bT3X8eg9s+bokAZl6AA78QcnPqIYGfeBvP7glvGo7ulyVz68Kb34vMvpBQhX70Gn9znqzLV+pvvKOiqgDjXWUTAaVJJ7yF0yeB00Govn71/cRSSYLIYQQOrXvTAYFJu37EjtOpbk3GFdo0Et73FCNz6x6kNAN+v4dOozzzWSy2QTTx8CK1+DsbjizXVV++uUuT0cmrmS2Kol5e4UxIYTb6ewuuxDCXSoqDzWkbbybInGhoEgALhDBaUttzUN2mkv1sw3SSZ/h9ESYNuLy7msAajSGup1V/8/6vdTOrXCdlNa156rH1Gs5vBh+mlx+Pj8dtk6HAQ6WG/YWE36EpS/Brp9UAq7VSOhwM/yvl0qUFwsIUTsNE7qp/p962cGrJWmb7bkWwyC6gftiqY6Lx2HfHzYmNfoHR9SBm76Chn1cGZVzVee9sCjPeXFURcpK+/PdR8OF9WCpxKKpRv0gOL/irw3es8P3QvndPHbduRAMdhZEeLPG/dTj6HJY9iokbYXohtDnQeh6u6ejE0IIIUQV5BeZ7C6Cr1fDBxKRra9Xi05Plqm20us+iKrnmZhE9R2cr6o2lbXnV7jqH6pqlRDu1u5G2PiZxvhY98cihPBqklAWQpRzPDWbzg2iMRrArJH/6N6wBnf0tdPnVS863QILniKSbCLJJoPyJUsTDKUuUjtNdGNw1bDoOetkMqjnxWPHVqrdy3ct9o2eS0Hh9lcupx13WyhOExwJw99Uj2KfD7ROJgMU5sLQV6H3/e6NzxXsJYz1kkwGSDuFZuLYlswzsOFTfSWUmw6C8HjISq78uSfXOz+eylg8oOJjKlsh37QQFi907NhbKvGz4UrxHRw80AA3fKSv30FbmlytHkIIIYTQvftmbGXp/hTNubBAP27uXt/NEbmAnz/c+jNs/kolIQNCoePNkuDRu8St9uckoSw8YeAzkLTderFDQg8Y+LTHQhJCeCdJKAshLjt6LotHZ+24XB6qVngQWQVF5BaYAIgM8efRwS2Y3LsRRqNOdyqV1mok7PudwJNrmeS3kA9NY6ymDZi5w+8vwAAD/qX69+jBflu7I0tJPwnrP4JrX3R9PO4Q316VtjYXlZ+r28X98ThbRhIkbtGe2/+HfhPKZhMcWgTppyChO9TppEp+lRZWGzqM90R0VVOVft3754HFop8doP6BMOEHmDVZvZdURn6ma2ISjtv/p6qAUJGIenDHX1CjoetjEkIIIYRw0JYTF20mkxvVDOWtcR1JqBHq5qhcJDBUVVXp86CnIxHOcO6Aqppji3zuFp4SHKWqUh1bqX5Oa7VULY/0co9CCOE2klAWQgBgMluYMm0TJ87nXB47l5VPWJAf70/oRHxkCN0a1vCNRDLA6ndhyYtgUcnyR/1nY8TCN6YhpBNOY8MZ/uE/iz5+++HGz1X/Hr1wtGfw8TWujcOdIutC97tgwyfW47EtoOPfPBOTM104bnvOL8BtYThV2knVO+r84ZKxFtepi+j989TigMb94bo31MWNXlw4Wvlz/AK850LNkbLNAAHAhPfh7B7IvQAr3gJMFZ/XuJ39f8PVJaEHr7A/n3IA5j0G2Cl57RekqgfENndqaG6ReghmTXKsf3KX23zrplZ6oupBf+EY1OkInW+FkGhPRyWEEEKIMlZW8HF0/u488k7FaM51jEsg52QMKyu55rG0/l7SoUT4mAtH4ctrIS9dez6+PTR2oJqSEK5iMECTAeohhBA2SEJZCAHAqkPnrJLJxbLzTaRk5HN9R9/p0bNg1Tq++rOQM7xFJ8NhHvSfQwtjIo8FzObv/r+QTQiR5GCo3w0G/wGN+no65MppfxNsmVbxceE+UO66tGGvqVWU279XuyCbXwt9H4HA8qXMdWXvXJg9xfZ8u5vcF4sz/fGodTIZ4OBfcN2bMPoTlVAOjvRMbNVRld8rbypb50hJaC2OVhUs+gsW/2V73tUloStKWK+bBblGwKg9b/SH0f+DNjdDxhlY+SYcXaYWPXS+Dbrd4T2LA7Rsm+5YMjmunerP5yuStsE3N0D+pRt4u2apHmF3LIDIOp6NTQghhBBWBlT4cbTOpUd5738P71fz37d4SYcS4WPWfWQ7mdxqJIx4x7uvI4QQQggkoSyEuCQ1q8Dm3PnsUnOZZ+H0JoiIh4RubojMuX7ceJIn510A2gBw0hLHsoLO/Br4HM2MSfg3HUBUva6XdmY18misVTb4BUjZB6c22D+u+53uicddDAaVzOl2h6cjcR5TEfz1L+1S3qB22Omlt3dpuRfh8BLtud0/Q8+73RuPM5XtX16RhO6+U3reF2Sn2p83F8HmadB8CHw1RO20L5a0Te08GPqKS0OslqxztufqdFKJ5Prdof14VWLRVyx4uiSZXCztBKx6G0a85ZmYhBBCCCHElePMDttzQ1+BCB9b8C+EEMInSUJZCAFAz8YxGA1g1liN26tJTfUfS16CNe+V7G6q2xn+9oNudveYzBbeW3Ko3HgmoXxmGsEbxs+hx93QargHonOikGjV++T4Gjh/CGo2g20zYNdPKhkSGgsDn4FmgzwdadUcXQ5HLu0I7HAzRPnO7vlyUvZCZpL2XIO+cMNH7o3HWSwWwMbS/6I8t4bidMtetT9v8IOWw6DJIKjdEhr1c09cjqqoJHSxtJMw50GwlNnt2ugq6Psw/DBBeyds61HQy4t7fjfqp3bK23NyLWz60jqZXGzjZ6oyQngtl4RXbY36wY7vtefOH1Y7dgOC3RuTqxXmwQkbLR4OL3ZvLEIIIYSo0AoHPo5eyM7n6zXH2Xz8AmYLdGkQzZS+jakd6WOfY65k5w5CTqpa9OgLCx1rNFabM8oKDIew2u6PR4iyzGa1SNoA1OkMxktVuywWOLNdbXio1wWMfp6MUgjhYZJQFkIAUD8mlDv7NebzVda76wa1qk3/5rGq7O6qMrt4krbBb/fDbb+6MdKqO5+dz5l07WTVLnNjCIqCJle7NyhXatS3pFx3o34w5BXIPgcxTcA/0LOxVYXZDL/cpXawFlv+Gtw8HVoM9VxcznTxOKz7n1q9XKMRtLnB9rHemrByRGiMSjweX1V+7twBOLMT6nRwf1yOsNf/12SCzB0QYuf8up1g4L2Xnpgd71lczNU9hh39+otvhhwL5T5K7lsPvf4D2RpzACnnXf8aqqPrZFU2P2WP7WMCQlXvaC2mArUQJNxL+061vwn+egIKssrPFWSpvuaTftPn3whb/AIgIAwKs8vPSQ9lIYQQwus41sM4iNHXtSS/yITFAsEBkuDwGZnJ8NPtcHKdeh4cpaqwdbPTBkoPet0He34tv+i2+52+kTAX+mQqhL2/qZ/N46tKyrLXaAQ3fgFZZ2Hug6rKHEBkPdUCypfunQohKkUSykKIy54e0YZujWL4bXsi+YVmhrSN48YuCRgMBtjxg/ZJR5apD/wR8e4NtgqiQwKJDPYnI698+eCGxlQY/REHL5rIL0ynTd1I/Iw+1r8mrKZ66NW+udbJZABTPsz9Ozy6WyUNQCV6Fv3Huq/pNf8G/yD3x1wZqYfhy8ElH9RPrVevNzACCjI9G5srjHgbPh9U/rUV5cKf/1S77L1RRT2GK+wlvLLqfYrB9T2GHVFUAEeW2pi0qF2f/sHau81jGrs0tGoLioA75qs+9Nu/g3P7yx/T8W8Qauu91AA1GroywurxD4IWw2D3bO35k2th+wzfah1g9INOE2DTF+XnOt/q/niEEEII4TRB/pJIxmKBpK2qKktCN++/7q3Iz3eVJJNBJbj+eBTi2kL9Hp6Lq7rqdYFbZsKSF9QC8tCa0H0qDHjC05G5Vn4mrHgddv8KFpNaNH/1kxBSw9ORVWzbd7DhE0g/rX72BvxLfR99RUE2fDsaTm8sP3fxOHxzPRTlWI9nJMIPt8Aju/R5f7EoH05vVlW56naRvuVCVIEklIUQVoa2jWdo2/LJ4by8XBaYenPOEk1P437aG4t3MlvUhxAdCPQ3cnufRry/9LDVuJ/BwrCR4xix6Dx7ktRuwbpRwbw2tgP9W+h4F6ivOWCjDG1WsvpA2LA3ZJyBaSNKkrI552HNu5CRBGM/d1uoVbLq7ZK4i5kLoUCjbDDAKY0P/XpSqyXENIXk7eXnTm2A7PP6vEC5EpzepHbi2lK2mkUxYwD0vFd7zpsER0Lfv0O7sfDrvapcssUEGKDVCBjysvpd3fAp5GdYn9t6lFrN7c16PwB75oDFRm/2/X/6VkIZVJ/yrLOw7w/Aon4We0yFbnd6OjIhhBBCiKpL2QezJkPqAfU8NBZGvas+k+rRhaPaVaywwNZv9J1QBtV2rNkglfz3D7oyklnfjVeLVott+AROroepS727dPK6j2DBv0ueH5wPx1bCXUsgrg2cP6I23mSnQuP+0Pp68NNZmmXj59rJ5GJlk8nFCrPVAuWe97gmLlfZ9wf8/rAqpQ8Q2wLGfaO+n0IIh+nsnU4I4QkHkjOZdGIKZwtL3jJGG1fzTsDHGGs1h5pNPRhd5TwyuAUBfkamrT3O+ewCWsVH8Oi1Lfi/P/dx4nzJh6Wk9Dzunr6Z5f+8hvgo6cPkFeyttC6e2/pN+aQsqA+7g56D6Aq3j3pO6VXYjshKhtw0fZdstVXay2D03osxWz2GzWb4aZJaxKAlIAwGPw/x7VwWmttUateDAbBAfAcY/B/9rOje/LXaKW++lHQ1+EHTgeqmx/LXVMJ10hxY8Iy6QRIYDh0nqMSlt6vXBW74AObcpz2v910tWgLD4OYZcOGYWm0f1xbCpVedEEIIIXTMbILvb4a0EyVjOanw0xR4aLP3L3LUonUtf3kuzW1huFzAFXKP6dgq62RysTPb4eACaDXc7SE5xFQIq94pP16YA2s/UAs2fppcssh6y9fQeABMnK2v1kGHqlEVzt7vqje6eAJmT7FeGJ96EH64Gf6+3bsXNwjhZbz0bq0Qwps8PnsHZ/Os3y7mmPvRz3CQm0Y87KGoqsZoNPDQoOY8cE0z8ovMhAT6serQOatkcrG8QjO/bDvN/Vc380CkopwO41XCuKzYFlC3s/rv1EPa51rMaqVzp1tcF191RdaFi8cqPq6YxQx7ftH3TsIO47UvMBsPUH1qvZGt/r95GXA+HZsfrXILoMEodQGz/DU4vhrCYlXPrK63uypa16jXVe0uv3DEgYMtcN2b0PNul4flNGmnYN4/Lu1KvsRigsOLSp5v+hJu+wXu+AsKc8EvUF8XoZ1ugS3fqNL6ZXUY7/543CWmsfeXXRdCCCHc5HhqNrM2nyI1K5+ejWsysmMdnyshfSw1m58uvcZeTWoyskNdAv2Nng7LOY6ttE4mFzMXwo6ZcPW/3B9TdcW1h7BakH2u/FzTa9wfj6ielL125vZ4b0I5K6VkF2tZiVtUe7WyFbuOrYCdP0KXSa6Pz1mqc8+l6UDnxeEOO2dqV1lLO6m+d3p7PUJ4kCSUhRB2nbqQw87T6Zpzf9R9mJsa20iueDmj0UBIoLpYvpBtu3TrRTtzws0a9YOBz6hkXPGuwcgEGDetpFRUXFvbvUHnPwWNrvLeXco9pqrSupWRbWM3rKekrKzc8QmNocOgSytji3sDG+HMIvioiUp6tRrh+Nezlex1h6AIiG6gLkg0WVQ/3l/vLbk4zUxSJZcyklSfb70wGGD8N2pHREZixceXLQvt7fbNtU4maynMhgVPw9QlEBDinricbczHMOOmkoUBBiP0ul/1NRNCCCGET1u2P4V7ZmyhoMgMwKzNp/l+40lm3Nnz8nWy7mScgeSd6jN57dYs2XeW+2ZspcBU8hp/2HiS6Xf2JDhAp6+xtLy0qs15M/9AGPoq/DKVkuvDS/IyPRKSqIaUA7bnYlu4L47KCoqwPZeVrPp6azm4QF8J5Y5/s140XVZwDcjT2IkcWlMtqNYTexUOfKn6gRBuIAllIUSVWfx9o0xPz8Y18TcaKDJbys31aRbrgYiETf0fh063qt3GwdFqFWHp0shdJqu+plnJ5c/NS1P9eoa+4q5oK6ftGLUSdsUbtlfDlrV9htqh7C29hhcPqNp5mjn+HDj9hno46pbyv8NuYzBA/ydg7oPa82G1Vckvre/tuo+gz0P2L1y9TXx7eHgHHFmqKgMsfNr2sU10tpvA4uDPUeJmyM/U1/ettJyLkJ9V8twYoKsWFkIIIYSoGrPZwrO/7b6cTC625cRFftx0kil9dVbNw2JRi4c3fX554bG58TU8l/Tg5WRysU3HLzJr8ykm9W7kgUCdrNFVKqmjteuu6SD3x+MskXUol0wGWP5/0PlWCK/l9pBEFeRcgK3TtOfC46FlJRaOu1vmGdtzRfm25/R2Xdj+JjizA9b/r2TTRkiMqhjX6Ra1OOfHCaq3eWk552HaCLhnpX6uH5sOhPUflR83GGH/PPVe2nI4GH2kgoUQLiS/JUJUxvHVMPNW+HSAKod58binI3K5+jGhtK8XpTk3on0dN0fjGvFRwTxwTfmy1oNbx3F1C7lY8TqRdVRJ1qBw1QPl0wHq9/L78TB9DNRuY/vc5F3ui7Mqet4Dj+2DThMdO/7icfjrcZeGJCqhy20w5pPyO1YNRmjcH7ZM0z6vIKv8RZoe+AVAi6HQ50Hoa6P9QcdbIKGre+OqrubXOnacwV9dSM++Q5U+0xNToXrfzD5baiwf/ngMztopTSeEEEIIfSnMg0X/gTebw8vxMPNWTh7ayemLuZqHLzugUWrY2239FjZ8XJIQAQ4eOUxiunbiZ9n+FHdF5lphsTDoufLjbW6AZjpOKB+ysWPSVABHl7s1FFENhxbZrvoUEm29McDbBNpJDPsHQ0J37bmOE1wTjysNeQke2QXjp8Odi+Ffx2D8NGgxBGq3gruXg5/GZqKCLLWZQy+aDYI2o8uPW8yqyuHMifDLXY4vLvdmiVtVkjzzbMXHClEFXvzuLYSX2f0L/Hyn+mMDcGY77PkV7lri8/343ripA7d9uZHUrJILspGBWxmbsw/Mj/nECq5Hr21Bl4Y1+G1bInlFJga3juP6jnUxFJdSFt5l3+8wa5L176MjauqgH7Z/INTrAtu/c+z4vXNVD1dvKLs7eEXFx5hNl3pdGyC2uXr/KMiB78baPqfHPdB2tLOidK2OE6DVKPX9O7ZSlYNK3GK7FDuo1bBRXlqK3VHXvggN+8Lqd+HsbijMUTf1zu1Tu5j10pPo4kn42sFeXpYitaL7zA71ezhxln5e57GVquR6ORbYNQvinnd3REIIIYRwhV/vgb1zSp7v+536JzYQzUukUT5pEhGkw9uEGtdNYWgnzAHCgwNcGY179XkIEnqo3q2FudBiGLS+vqQllB7Z2+Wptx2gV7JC27+DFOa4L46qiKoLEXW0dyq3GgED/gU/3qKue0Elma/5NzSpYsU2T4usC22u155LTwRTnvbcuX2ui8nZDAa46WvYf6O6l7/3t5L7icV2/wydb9Nvv/asFPhhgqqkBqoCWZ8HYfDzHg1L+B4dflIUwgMsFlj6Uvk/NjnnYc17MOpdj4TlLq3rRLJqYhR/ff0iKeZIehj308V4GJahPlgMfMbTITrFgBa1GCA7kj3L0R68y/8JwZXsb+0XBC262P83PNmDt7T242Hl2zYSPmWYC9VuQ29IKFf0/9/xNfDr/ZB+Sj2v0Qhu/AKCaoEh3nap7w2zoe/LEBjq1HBdJihc7TbvcTfMvK3kQtOWzrdBaIx7YnOlFkPh5Do4ubZkLGkbzBgL476xfZHqLYry4ctrHS85X5q5EJa+rJ+EcpGNmwKgdjIJIYQQQv/OH1E3zcvwy0nh8bhNPH22/OeWm7oluCMy58ov31u3vjGV3sY9rDO3LTc3rqsOX6M9DXqqh94kboUlL6i2QGG1VCun/v9U1ciWv6Y+X5cWUUffO6+vNG1Hwx82qljVLv976XXGfQPfjLQuKR9VH4a8rK7d71sDpzZCdio06OUb1/NaoutDQBgUZpefq9Xa/fFUh9GoKjiYi1RSWcvhxfpNKM99qCSZDOo9dPV/Ib4DtLvRc3G5Ws4FOLEGgiKhUT8w+nk6Ip8nCWUhHJGdarsc6amN7o3FQ0I2/48bjSvLF8rf8Jnqa+sf5JG4hA1mE+ycBQcu9QJpP179Ud3xI1w8phZHRNVXHyrajPaeFcyO9uCNuvSolBzYfJv9QzzZg7e04Ei4/Q9Y+CwcnK++d1q9sUD1zgqOdG98VZGbBj/8DfIzSsYuHoevhpRfrFNWXjqkHoS6nVwYoAts/hL2/257PjAMet4HVz/pvphc6exeWPth+XGLGWbdphYf9ffiEu37ftfuv15aXFvIOgfZGqUSE7eo9149XMA0ugoCw1WpsrJaXuf+eIQQQgjhfKmH0OxFC9xYP4dZgdHsOJUGQJC/kb8Pas41LWu7Lz5naTYYUsq37HinwTruLerDjtPpAAQHGHl4UAv6yyJyz7twFL65HgouLQbISlY9knNSYfibMPK/8Oc/SxZBRtSFCd+rljtCH0Ki1QJyrVaBWslJb9OgJzy0FbZNh7RTqopcxwlq8Xix+j08F5+7BEVAr3th1dvW44ERahG9HgXbuZkYHO22MJwqOxUOLtCe2/GD7yaUN36u7psWXaqIEN0QJvyg7tsIl5GEshCOCI5UfywLyq983XghhHteXEiXBjV4eHBzOiREuz8+d7CVUM9PV3+4ouq5Nx5hm8UCP01WyZFiu38uf1zSNtg3V+2OvEEjCSQ8q2ZTddFsNquE//4/YM791gnZkBgY9prnYqyMvXOsYy9WUTIZwOAHEfFOD8nltn5rf37cN4736/V2pzerFdxldxKUtvRlaHEdxLdzX1yVceFYxcd0nwrbZmgnlIOi9JFMBvW5Zvhb8NsD1r3NukzSb6k2IYQQQlir1RIwoJVUDqnXjt9u6svuxHTOZebTuUE00aGBbg/RKXrdr653MxJLxoKiqDPyGX5L6Mqu0+mkZufTub6OX6Ov2fiF5v01tnyjPo8uf9W6ok5+pmPXjcK7aFQPAODkevfGUVXR9VUp6yvdwGchNBY2fa7KKjfsCwOfVves9Kjx1RDVANJPWo8b/VUCPWUf1NbZ7uuCbGwtIFNzPihxK/z5OFavO+2EqhL40Bbv2TjlgyShLIQj/IOg2+2w9oNyUx/mDOKiuZAl+1NYe+Q8vz3YlxZxPtjXJb69dp/a8Dj1EN7j6HLrZHJFtk2HHlOhTkeXheQwR3rwAmz+WvX5tMc/BG75Ue3Q1rPiHuUNeqvk8bGVcGQZZJ+F3Asw90EY9T7U6eDZOCuSe7Hq59Zqqc+E8sUTtudqNoemPlSybdF/7PfJKrb3N+9NKMe3tz/f5XboejscX21dSqpYfrpqg9HXRmk3b9NpAiR0V++lBdmq517jqzwdlRBCCCGcJaYxtBsLu2dbj/sHQ1u1W6ldvUqXffIuJ9bBrEkli/0M/qrayvA3IbIOAO0TdP4aNZy6kMOM9Sc4lppNm7qRTOzZkFoROqoad/6Q9rgpX5W7Lr04AFTyeeFzMGWe62NzhaPLVbW44j7X7ceB3xVwSz44WrUK1BrXo6wUtWg8aRtEN4Bud0JsM09H5XoGA/S+Xz18gZ+/2rwxazJcOKLGjAFqcfz8f6nnrUfB2C/1U42zRkNVglyrr3XzIe6Pxx12zkQziX7hiKomq8dWEDpxBfz1EsJJBj2vdn5umQYFWSQTy5sFN7HSXJKEyy008dnKo7w1zgsSc1VRkKNuLCduVX+MOk2EpO1waKGa9w+Fohzrc/o/fmV8ENaTYw72IS57jjcklB3tYTy0D/gnqMSy1spmgKEvQJ3BzovNkzZ9AfP/rS6wy0raBtPHwMM7rMsveZumA2Hx81U7V48XnEX59hOso94vWSygd2YTnFjt2LEGL37NzYdAvW7lk8WB4TBxNjTsDdt/KH9TtrRV76gy5v46WcgS20xW3QshhBC+TGvxd1EebPwUBj/v9nCcqigfZt6qyiQXsxTBgT9hyIuei8vFdpxKY+IXG8jKLwJg4d6zfLfhJD/f24cGNUM9HJ2Darcpuc9UWkAonNmhfc6J1VBUoJ/P2cVWvqkqNRXbO0c9/vaD71wP2tJlEiz+j8Z4Ba3IvE3KPrUT8vgq6/HNX8PEn2RRrrcryIEjS8BUqPqwB0ep/46so5KPBr/yldb2/a7KfOvpWnnE2/DdOOuS8vW6Qve7PBeTK9nbea2Hsvo6JlkgIRzl5w9DX4GBz5B89gx9PtyDuVxDYTh41kZyy9tln4evr4PUAyVjy19Vf2SLGYxQt7PqHxlVT/XLaDfW/bEK+8JiK39OaBXO8SQ/fxjykvpwl3MB8tJgxZtwah1EJkDPe6HDOE9H6Rwp+2DeP7FZvgbUTZS9c6Dzre6KqvLqdISuU2DL15U/N0KHVRAyk7UXABT79nq1g76Zjhc9ZJ5VO3KPLlflocxFFZ/TdrSro6o6oxFu+1VdOO6bq8ZajVTvMwEh6vnGz+x/jbw01QMuuoFLQxVCCCGEcIithXA7ftR/QvnwYutkcjGLCXbNhgFPuD8mN3j1r32Xk8nFzmXm8/7SQ/rZ3NBjKmz9pnwVq573wJGlkH6q/DlBkeqaQ0+yUmD56+XHD85XCfWWw9wfkzOdO6gWv6edgLpdoPud1vej+jyk5rZ+q64VDUbocDMMeNJzMVdW9nmYNlL7vaYoFxY+DfdUYVOHcI/DS2D2Heo6HSAgDAY9B8teKWnJVroFVGk7fvT+hHJGkvodPLsHajaDiT/D0aWQfU5VYMs+B7OnQFQCdLuj4qpsetJiqKq4WVZwlKrwKFxGZ3+JhfACASHUiG9IRMhh0nPL94psEhvmgaCcYM1/LyeTMywhbDc3o6Y5g7bGUiVbLWZIPQT/2K/6Sgjv1H48LPs/KMhy7PjQmtDmetfG5CoBIWpxQ1Q9GD/N09FULKUKFxpbvoEQO31pi6WugZQKkliO7gB3lVHvqg99vz2gXfrKlq63uyoi1wmPg8AI2zvozYUw7x/w0DZ9rkzPvQhfXqtuEDjC4AfXvuj9vYiCI+HaF9RDS9ZZ++f7BUFYbefHJYQQQghRFbYq5hTmaI/rib1qQL7w+jSYzBbWH72gObfmsEbCy1tFJcAdC1R562MrIayWSnb0mApR9WHeY+XP6Xyr/q6bTqwpv/Ox2NHl+k4oH1updkMW97o+OF8tErhjIaQdV+2f6naGkf+FAf+CcwdUz92oBI+GXWnbv9NOJhc7s0NdG4fUcF9MwjH5mfDT7SWJY1A7V+c/BTjQk92Rll6elHoIvhpqfW9t3UeABWKaqoR4UanXsPVbuHmGaguhZ2aTWniUl6GqzJWudmH0hxHvlGwIEC4hCWUhqiDI34+7+jXm7UUHrcYD/YzcdVUTD0VVTYcWAfBF0XW8XTSOXIIB6GQ4xCeB7xJvuLRytCALjq/R9wdfXxdeCyb8qJJ2xckeg5/2qruQGFXKNVCnCyH0ZvGAqp1X34Fjzn0Ciz+xf8wtdnY5u0vL6+DeNfDzXSVlkoMiVDWE4ovRsjZ8pnq96unnNCBYJcLXfWD7mIvHYcnz0OsB/e3C3vKN48lkDGq1ep8HXRqSWzToBbt/tj3fdoz63gshhBBCeIOW113qM1h2fIT636IClfDS0+fsYnU7266SU/z69CznAmz+Ek6uh4g60P1O/Op2JiokQHNzQ41QnZWCrtUSxmlUr+p2h7pO2vCpqvhkMEK7m2CQRulkb3ZslWqZZ0tojNtCcYmFz5S/fs9IhM8GWCdg294IN34OTeLdG5+zXDhqf95gVNUde90PNRq5JSThoAPzrZPJlzmQTAbvT7wu+z+NjRqX7vkV94YuzVwE859UfdwNBpeH51SFeYAF0k/DjLGl7nUbofX16m9kcCR0+NuV0dfcwyShLEQVPTiwGaFB/ny95hjJ6Xl0aVCDx4a0oF29KE+HVjWB4aw1teHlIuteJtstzXm44AFmBpXq+eLNfVqF0vgq+Pt2OLsb/INUmZcFT1kfY/RXq0drNfdIiFWWcwE2fQkn10J4vLrgrN/d01GJyoisA1PmqYuzvHTV02bLNLh4VPXNKtsH/MA8WPgsjHzHI+FW2ZCXYM+vkHHa9jFr3oN1H8P4b6DVcPfFVl1l+wzbZVGlohv2UQlXPRvwL/V+Wlwyq7TYFjD6Y7eHJIQQQghRVqHJzLL9KVyMfYBe4QdomLW9ZDK6IfR9GOY8oEpiF+VBw34w7FWo08FjMVdKdirMuFE7mdzxFji3HzLPqOpI/kHuj6+6slPhi8Fw8VjJ2I4f4Kav+VuP5ny6onySa0IPR1Yh64DBoK6j+j6ivo81GupvV+uGz+Cvx23P+wVBxwnui8fZ8tJt97ouu5t3zy+qj6teFxdXVCLYYlaLH7b/ALfP08976JXA1oYFR4TU8P5y12V7ejvi4nFIO6neV/UgI0n1Lz/wF2ABv0Dr76vFrNqV/e0Hfd1P0zmDxWLxgu1K3iUjI4OoqCjS09OJjIz0dDhCuMemL3j018P8ar5Kc3pl4CM0MKaoFXd6LdF6Jfv5Ltj1U/lxoz/U7wVXP6mS0N4u54K6sC692s5gVCte29+k5jOSVCklbyxxUpWS1wDrP4V9c2zPd58K7W6s+Ot4uuR1acdXqxJnpzba7zVcLCAMnjoFRj/Xx+ZMZ3bA18MrLkEfEgOP7dPP7tb5/4b1H1XunGbXwq02+vjpyYVjsP5jOLJELYaIrKNuCHWZDP462xkiRDXJdZPwdvIzKq5EB89mcvtXG0lKVzddDQa4q1k2T9fdqnaFth8HMyeqkrulhdSABzapilfebvELsFpjsWlgOBRkc3mXVlhtuGUm1Ovi1vCqbclLsOqt8uPRDSl4YCv/nrOHX7clYjJbCPI3cnvfRjw5rBUGve0680X5WfB2K9utjyLqlrSC0quiAni9kSof7Ig6HfXbZzg/Cz7tr73js6yw2moRfOtRro/LFYryVVnkg/PV/bQON+v3tQBkJsN/29kuO2/PXcsgwcv/bnzUUy26qax/HddHiXazCf7XC1IPVnxsXFv1+xdaE7pOhsZedO9TJypzzSQ7lIUQStc7yFz2LWi34yGDEKjRWPVbkGSy/uz7Q3vcXKTKDk8fA1P+hPo93BtXZW34tPwHeYtZlVs6shR2zlIfFoOjoP8T3rcKtqoJ3ev7Q6uF8P047fmje2Hgu1UOy63MZrVbdcFT6nvnqMJsVRZbbwnlOh3h0d3w3Xg4vdH2cbkX1O9is8Hui606ut0Bm7+y7skDgBGbJaTy0l0dlXvENIbhb3g6CiGEEEIITX//YdvlZDKAxQKfHwqjZ+9HGdwmDpJ3l08mg+oDun0G9HvUfcFW1fHV2uNlF3Fmp8DsO+Chrfq6j2Hr9aWdIDDzFG+N68gTQ1ty6mIuTWuFEa23ctelpSfC4UVqAXHL6/RfEe/MdtvJ5KbXwC2zwU/nt+P9A6HTLbDpc8eOLypwbTyuFBQOU/6ClW+qnZB56bZ3vmanwMxb1Xvo4OfdGma1mU2qJ/axFSVj+35XlQKufcFjYVVLRDx0uU3dt6iMq/7p/clkUMnTqjizE5pUsR2fOx1c4FgyGeDsHmCP+u/ds1Xv9m53uCy0K52OPk0JIVzKaKT/VVdrTsWHQqtJ78PVT8H6/8GCp+HsXvfGJ6rHWMEFi7lQld71difWaI9nnoHt35WsPMxLh4VPq3LDvsLeqvqkLaq3lrfLPAufXgXz/1W5ZDJAo6v0s3u3rJAaMPT/Kj6uot9TbxLbDCb+BPGXSnr5B6sdui2G2D5HL8lyIYQQQgidOng2k/3J2smsuTuS1H8U9x7UcvG484NyhbBK7KK+eAwSt7guFlcIi9UeNwZASDQAtSOD6dqwhr6TyWveg3fbw+8Pwy93wX/bwNEVFZ/nzez9bMa20n8yudiQl1S/UsOlBd+B4WoTipbgSJg2En65G05tcl+MzhIRB30eUruVHSmjvPYDVTlPTw78ZZ1MLrbmXdUe4cKx8nPeLmk7bJ1euXNCY6FWK7USy5tdOAYn1lbtXHufAbxJdeJc/AIUlt38IJxFEspCiMvGd6tPt4bWZS8C/Aw8P7oj/mvfhV/vVkm7dR/CJ/1gp0YJZeF9zCao3ari41L2uT6W6qrMjQOo/EpEbxYWC41trCI0F8FPU8Ck0UPMm8z/l+rrXVnBUTDk5YqP8ybHVqoVvu93hpm3qVqDNRrZPj6ijupdpyeNr4J7V8ETx1TZpOvfV9+n4Kjyx8a2gF73uj1EIYQQQogrSaHJ9qLNy3PxHVTbIC11O7sgKheo7M6jqpQ89aRuU7TH292oj1KljjizAxY9BxZTyVheutpRXnSpJZLFoiqRbfhUXV/pQa2W2td1Rn+1W9JXBITAjZ/CY3th6lL4x364ZRaEx1kfZwyAUxtUv9edM+GrobD3N8/EXB1bpjle4ttcBCfXuTQcp7OXnNw+Az4boI97hqVt/Kzy7/05qWpxy5//dE1MznJyHZdbO1SWXjaIRTeo+rl5aZd2LevUvj/gs2vglTrqf21VHfUQH1kWJYRwhuAAP76b2pPfd5xh3ZHz1AwPZHy3+jRLmgtHl1kfbDHBX0+ofhp63TV4pfjtATjtwCrQ2q1dH0t1dbvj0q5jBz84ZaW4NBy3u/59+KQ/5GuUDs5MUju4vbV0TVG+KpnkqJ73qhsKNRqpna+RdVwWmtMdmA8/TijZhX3hqCrXM+JtWPICZJ+zPj6kBtz0tX5Xq4fGlPx3bHNVUnDNu3B4CfgFQPvx6nc3MNRjIQohhBBCXAlax0fSICaUkxdyys0NbRuv/iO6vvp8veVr6wNiW6rPbXrQfDAMfwuWvaJKdRv8VPsmrSROeBwkdHd/jNXRbLC6dlj6imqNYzBCmxvUWGl56bDhM3W/JjgKukxSZaP1YNds7fGcVJU8rtcVZoyFpK0lcw36wMRZEBThnhiratzXajdu8X20iLow7FXV59PXpOyFzV+ra9zohpd6mJdSNqFnMcH8p6D19WrRtV5Udrdk2cS6N8pOhfUfq/tIuWn2j81LhxWvw7hp7ojMOdJOVv3cTV9Cr/uhZlPnxeNMYbWrfu6Gj9Xr6jHVefG4giO/QzWbw/lD2nOV3ZDkCilVWAh1Yi0sfUn9tz9wcSPM/RtkPQMN+1Tua1W17WIFdHrnUgjhKkH+ftzUNYGbuiaUDK5crH1w7gVI2gYNe7snOFF5qYdgxw8VH2cMgL4Puz6e6mp8FVz/QUlSzuAHba6HlP1wTmO1ZCOd7fisSI1G0P4m2Pyl9rzZi3coW8yOl7nWc58eUDe2yr5WUz7s/wMe2Q0H/7pUMsqivqctrvOtZGtYrNqprLdd5UIIIYQQOmc0Gnjzpg7c+c1msvJLrg1GtK/DqI51Sw4c8Y5Kbm3/XvUdbj4E+j2mr8+kPaZC59tUj8XwOAivrRZTb/+u5Bj/ELjhf2qRo950vws63Vry+iLK3FwvyIavh1tXgDrwJwz6D1z1mHtjrQp714bmInXNXzqZDHByLSx/DYa+4trYqiu8NkyaA2mn1E65Wq31u3jYnk1fwrxSP2uO7srNSFSPqISKj/UWdbvA7p8dO7ZWa2jY17XxVFfOBfhisGoJ4KjDi1UVwFajINwLknUVqdtZ7YyvEotK7HlrQrnpNWoRUZ7GZhNHLHtF/Y3x9KIOuwnXbAg3gslGD3a/IOh7Kyx6Fijz96ReNyg8ASkVLARxUcL1ssVV3PBTX2Ps0FNgI3du0y2uKd3ug3/NhBBOp1W+9PJcpPviEJWXtM32XHCUKpFctzNc/S+1olsPutwGHW5Wq9DCaqmLtcNL4Pvx1gnV0FoqMelr2lyvnVAOifHuBHpACDQdDIcXVnxs6R2vemM2Q/JO7bmkbaqiQ9sx7o1JCCGEEEJcMXo2qcnKJ65h7vZEzmcX0LdZLL2a1LQ+yGhUCVlv36FUkYBgqNOh5Pno/6nd14cXq17D7cep60W9Kvv6Stv+vXY7oZVvQvc77d/H8QatR6l2amUFRalWT7/co33e7l+8P6FcLLo+2pkBH1CYB0ursYA438Hy0d6iy22w6YuKE7DxHeDmGZ5P1FVk4+far8VgtL3YIz8T/ngU/npSvde2v8m1MVZXr/vU+2ROatXOj4h3bjzOZPRTSdMjS6p2fu5F1cO+3yNODavSKkq41rU3mQP7H7XxFrvSsWSuixKuvk4SykJcQVZWteVM3j1w4gDlygzXbA4H2sIBx75Mfxcv/KmUcwehKA/i2qmLaV9VkGV7rk5HmFyJEsTexD/QulyUqYDyZbAtGmM+oMnV0ONu1Q+mmH+w+kDvH+SxsBzi7+DOgOiGro3DlYxG1etFq7xS6R7KpzbB0hfhxDq146D7nWoBhC+/HwkhhBBCCLeICQvk9r6Ny0/kXFD9SwuyocVQ1a7E1zToqR6+ztZu0MIcOLNTVffyZg16QZ+HYO0HJWN+gXDDh2qnfOneyqXZGhfulXpQVS2sKr21zguOgjsXqiTczlmQbaO9WofxUEMH9zNsvX9YzGpBx7EVts815atqEE0HevdmAINRJV6rIrSmen2eZG/3buYZSFoEIQ5WAdSy9W1o0cX2vKt3714JBtv5PbJl7sNw/mD58diWMOrdaofkDJJQFuIKMqDKrVVbAX9qT72tPazF4g25vdRD8POdcGaHeh7dUF2wNPbRP5SRdkoI2ZvTm0XPgbnMhWVOqvqwX7bPlC8Y/iZ0ugUOLlT9o9qNLV8Czduc2Qn751V8XFQCtBrh+nhcqfdD8NfjGuMPqP89dxC+vV7d7AHIOK1KuuVehCEvuS9OIYQQQghx5Ti0GGZNgsJLOwMXPg1X/RMGPevZuETVRNSp2pw3GfKyqj52YL5KIrcbW7IrsM0N1uXLi7W5wb0xCm3hte3vZrUnvoM+kq5lhddWu+PrdIJf7tI+Ri+/e5F2tn4W5KjFHhu/gKJc7WOK8lSJ/c63uiY+Z1j/P8g6W7VzW15X9WS0s1S0w7baxQ9y7P8b7ti9ay/hmnVO/Z7ZKnldVpOrYcC/nBKW01QlKd/3JZh5K9YbpAxq3EuS/JJQFkL4joqa3ZtN8NMUyDkHIZfG8o/AL2Pgpq8gpEb1/n0veWO30rA3BISV3DQorfRuST3LPq9Wx2o54WAPH70xm+HicbhwRK3iTm3n/Qnlk+srPqZ+Txj7pT57nJXW825Vfn3t+2rlaI1G6oNt8c2PDZ+UJJNL2/QF9H9cf60ELBbVH3rfH2D0h/ZjPb+aVwghhBBClCjMg1+mlr8uXPWW6p98Jezo9TVdb1dla0351uNNrobYZp6IqGri26udV/t/h20zVLKu2SAY/DwkboFz+0sd2wGufspTkbpGQY66bjy1AWq3UdeDIdGejqpiEfHQ+nrYO8f+cTUaW5dWDo2FGz5yaWgu13qkShxnnrEej6gLrUZ6JqbK6nan9oINgDPbYepitWBgzXu2v4ZX7Bqy49TGqp/bbLDz4hC22buPvvsVyDLjcPoy8aR33pevrNYjVdn81e9Ayn6o3Qqu+odXbbyRhLIQV5AVVai0oCuO9EeoeelhJQfWjK7+v++NvReCImD4GzD3ofIrR5f/n1rppecV6dmpsPw1wIBmeWtvT7Lak58J+/+E7TPg/FGIb6d2vsY0hoXPwJ5fSo7dNh2ueRoGPOG5eCti73vRoDfcNkd/Za/s6X0/9LxX3bQLDLfuoWRrAURhDmQk6S+h/NsD1hej22dAv8dg8H88F5MQQgghhChxfJXt8rR75+gvoZyyXyXhzuyEmEbQ6wG1mPpKEtsc/vYd/PUvtdDYYISWw+H6Dyo+15uknYJvRlknHRv0homz4d7Vahdk6kGo3VaVaff0rkFHFOaqRcR7flVJt44TVNsqvzK34c/uhc8HluwCPbJUnXfbr/qoonfDh2ox+J5f1YLqqAbqnkRGkrq2bT9O3cc4sVY9IuKhzWgICvd05NUTEKLuX8x9EE5vUmMJPdT/H3q5p5HQVVUtzDhdfs7oBzt/ggN/2T7fGKjeb7yZQ7vFNe4lJnT3joUB9nbvpiXCrzZ2yTvECNe+AAndqvE1XOzEmsodH1nPNXF4QuuR6uGlJKEsxBXEq3oYi8qraAe2LfUawLV/V6vPy9r8BjRqWVJWyh5vW+lVmAtfD4dUO028u091XzzOcv4I/P53tbu6dH+ojNNwcL7t81a8rsoN2Std5EktrrNdEuvcfv1ceFWG0agWdZQV11bd1CsrMAKiq123yL1ObdRe2bzmXeg62XcqIQghhBDC56XnFPL7ziQuZBfQt1lNujb04t6QlWao4pwXSt4NXw2Fgiz1/OwutRB3wg8q4eiLTIWw73fVwiuujbq28vOH5teqnXQXj0NQJISVWz3v/RY8ZZ1MBtXfdc27MPAZ/ZW4tlhgxljrZEjyTtjzM9y1xPrYWZPKlxQ2F6nxx4+q60lvFhQBY7+A696AvDTIS4ffHy5pMXdyHdzwP2jYRz18QephtQgn9aBKPHa8RVXnimnk6cgqb8AT6t5TWUV5qtRwqJ33k1Hvev/7TY+71ffKbll2i6r2kHpY/S62vA563ucdVfPs3YPNWg651UjrXfM0dHms6ue7RSU/m/S42zVhiHIkoSyE8B0VNbv//mbIz9Ceu/ZltULPmzmyA9seW3mqDTc7dr637cDeNdt2Mjmkhrr4bOXlKybLWvaqSgxr7bauiLkIjq2Cjg5+P93NP1Dd5MhLKz/n7aWSnCn7PASGqVLlZXvB9H5AzenJkaXa4xYzHF2uLs6EEEIIIbzc5uMXmDJtE5l5RQC8swhGd6rLO+M7YTTqLOGqpfFVKjmQc778XNvRbg+nWla9VZJMLmYxwdKXfDOhnJmsdvCWrnIU1x4mz4XQGFUJKaax5+KrDlOhWgygZc17avFA7wfUz69eHFmivbPu9GbY/j10ukU9z0yG84e0v0buRTizDep5+T2qYqExqvXRF4Ot32NObYAZY+Chrd6RoKuuNe/DIo0Kf7Vaw+TfIbyW+2Oqjq6TIScVlr5ivZmhWG6a9nkthkHniS4NzSka9oYbP4fFL0D6SdvHdZwADXq5Ly5nyDhT8TH2JO9yThyu1LAfnFitPdfxFrXIqiATwmqrVgHtbnRvfFcwSSgLIXxHRTto/RMgTaPUrF8gdH7EuiSt8H72PgBd9wZ0GO++WJzh4EJY8Vr1voa391pqdyNs/qr8uN5WnVdV0jb4drR1Ut3oDzWbQY+p0L06JYs8xF7v+er2pRdCCCGEcAOLxcI/f9pxOZlcbM72JAa2juP6jl5aAagy/IPUjfVZk0olYw1qh1r9Hh4NrdJOb9YeT94FRQVqIasvWfx8+ZY5Z3fBsv+DERpVyPTGYNROZpkK4OBfcGiB6ifpRf0j7TpqZ6PD5q9KEsoV7b4zePnu5LL2/Kq9YCXtJBxaqJ/vny2ph7WTyQDn9sHSF/VXbh4gqr727x+o8fgOaod9sdiWMPJdt4RWIUeqOMbVhgkfqLYA8/4B5kLreb8A8EuvWkVIT1ZxrN8Dm63/HKGH95duk2Hdh1BYZgFZ/V4w5mMY8TZkn1NVGn1hwYqOSEJZCHHl6DJJ9Z4tq9f9+kgmV7QD257cNJg9RZWuKS0kBsZ9rZLqehPTxPZcbAv3xeEsO36o3vmRCdB0kHNicZWBz8KpTeoGSDGDH/gH++bNn7Lm/aP8Dm1zEbS/SZ/JZIB2Y9WK38Js6/Gw2tDcB3eICCGEEMLn7DuTyfHzOZpz83ef8Y2EMkCzQfDoHtg3FwqyofkQqNnU01FVXlR9SD9Vfjw8zjevJ/b9bmN8rv4Tyn4Bqk/knl9tH2Mxw9KX9ZOQ1Gp5VCz1EJjNqpR1RJza2XpuX/njQmOhTieXhegSR5bbnsus5m5KV6hsAnHePyCkyPb8kVmQMs7xr+eOZKQjr3HP5/Zf16D71S7m84choq7qFZ13SD0q4urXWNkqjrZa7K4YVrV/35NVHGs2VTvFD9rpc22PHnbzRtaF236Bvx6/VEbfAIGh6u/G4cWq3UNgQ09HeUWShLIQ4srR6wG1OnLjl8ClFXgNesOAJz0alsOq+2Fs9E8w5wHITFLPY5rCTV/p70KlWMebYfU7kHXWerxxf6jbySMhVUthbsXH2BLbAsZNU320vFloDIz/Bj7uU7K4wWKCjZ+qm1qjP/JsfK6UdQ4St2jPHfhLlejRo7BYmPA9/HpfmfeWL32zL7YQQgghfI6fnZLWfl7Yw3RlFTZScfEEHF+tFlI3uhqiG8Ceqv37/T24KQuAXvfCybXlx3ve4/5Y3MHgpz1u9PJrP0cNew1S9msnVoul7FXXywEh7ourqnpMVbvHtXYO5qXBnl/UgmKAv30H/+tl3QrJYITx3+pj00Oxiydg3xzb8w28sH9yZZORQdhuIwdATuW+pjuSkY7GY+91bbql5L+TAQfyyJd5W9s8X1O/R9UTyvHtnRuLqzToCaM/gS8GQWGOum94fJX6PDP2i5L3UuFWPvLpQwghHGA0AkYuJ5MBTq6Dj3vDXUsgrKanInOPpgPhkV0qqeXnD3W76OsipayQGnD7PFjwNBxepHa5th8HQ172dGRV02Jo5T4MBkXByHfUysS6nV0Xl7Nt/ab8TnmAnT/CoGchIt79MbmDf6C6GaRVTspfBzdG7GlytW+9twghhBDiitIyPoIWceEcPJtVbm5UhzoeiMi+AZXMgygNLz2qz+LpHEGbG2DUe7DiDchIVNeFPe+Dfo95ODAXaXcjbPlae9wX5KVDfDtVLjkvHUz55Y8Jj1PX+3oQUgO6ToEtGq2eQO04b9Bb9QLf94d1MhnUjuw9v0Kjvq6P1VmWvaLi1hJRB+LauDcecWVwpIrjmV2w+ye4cEzttNbS+bZSpeh1JLpB1c/dPw/6POS8WFxp9TsqmWzFohbutBsr9548QBLKQgjHFWSr3aCR9VQPJr05sRY2flJ+/OIx1Utqyjz3x+Rufv5qhZeviG0OE2eBqUit5PXCHQQO6zRRlS07srTUoAHajoYhr6jd9Rs+Vqt/63WB3g/qs0Td+SPa4+Yi9dp8NaEcHKVKEh3QeJ/peLP743E2X3tvEUIIIcQV5d2bO3P71xtJyVTJLIMBJvduxJC2PvrZVO+63q6SANmpKoHni6Wuiw3+j+oPnViqd3TDfvqptGbP+SPwxbWQn27/uJ736itp0OMu2wllixm+HAIZp22fv/17GPqKZ++7VaYk9Pn1tssmx8Z6Z3/ayrSU+/V+SDtme752G7j2BQgMr35czuTIa8w+rxYElK4Q4B8C1zwNCV3V8xNrYf3HJQnZuPaqwlp4LefHXBkV/YwcXgJzni21qF8jDeYXCCEtILITBEc6O0LXaj1KJZXTTlqPh9eGrBT75xp11HM4aZv2+IUjkJ+h7rUJt5KEshCiYmYzLH0RNn4OBVmq726/R6Hv3z0dWeXsmWN77sRq2PSFfvuYXunKlnouylcfHgtzoMk1+th97h8IE2er8sfHV6lSwh0nQFSCmo+qBw17ezZGZ4hvD/v/KD/uH6wWCPiykf9VZaGLPxAbjCU3w4QQQgghhMe0qRvJqn9dw5J9KZzPLqBP05o0reVlyYFLVlSUI7hwHHb8AOf2q8+bWcnax/W4F9reoG5GF+ZBTBPvb6FTmtFP9aH1dSE14K7FcGwlpB6EuLbQ0AtLCFfFug+1k8kGo0q8hsRAr/vV/Sc9iWtruz9yYJj9ZDJAYbba0OHJhHJlyjeHXXpoWlP58tLg+nLJjiSsT6yFVe9A6gUotPHeeP1H0OVW58bmLI4kXH+8D4pysU4RFcLvL6r3noi6quS8xVRyzPF9UPgRTF2q8UW9yPLXtCvElWYqgCUvwIZP4fY/9HVPyj8IJs2FP/+pvpcGAzS7Fka8BSfXwy93o1l63+CvNq7oRXRD1cO7rNCa3reI4wqho0+KQgiPWf0OrP5vyfPcC7DoWZXw0lNZkIpWtK58W5UmMtroUST04dRG+HEiZF9akecXpFb39pjq2bgcYfSD1iPVw1d1uwM2f1W+93WPu1WPZV8WEQd3L1cf7tNPQ72uENPY01EJIYQQQgggyN+P4e29r8R1WXZ7GKcegs8HgiUDYi+N2Vpb2+Ya2H8NJG1VzyPqqgWQLYc5MVrhFAYDNBmgHr4kabv2uMUM96xW14e7flJtrhr2hpbD9XO/ZuwX8N04taC4WN0ukH6q4nNrt/X9a2Nvd2wlTB+jKqnZUr+n9yaTK2I2wdy/X0oma7CYVRn6nPPa84lb4MwOqNPRdTFW15ntjh+blQzzn4JbZ7ssHJeIaQy3/gz5mYABgi4lWKMbqJ27syZZt5wz+sPoj/VVGbD3A5cqOZZJjve6Tz9/D3yMJJSFEBXb+JntcT0llNuMhg0aJa+LZSZB5hm1CjYgRF8llUAlqNa8ByfWqWR/VAKk7AMs6rX3vEetYNv5k+pjm5cOTa+BPg/rYwevI0yFMPO2kmQyqB5Mfz6uVnHHtfVcbEIJrw13LFA9z44uV6sKu06+sqoDNOjl6QiEEEIIIYQvWvuBKgFpR5IlhkTq0GL7b0Rd3F0ykZmkbj4/uBFqNHJtnKL68jJg5Zuw9ze1o7ftGOj/T7UD1tMcLXEcFQwXNRJ2/qGQvASWvlyS8NrxHtTppEoL+1VQ4tzV5ZIdEd8O/vY9rHwDDvypxooXb9jjFwjXvuja2BxRUbnkC8fgr8fVTmotjQaossh+Ok34rHjDdjLZ6KfKXBfkwtutoH4PGPAvfd1vOrO94p3yFck+55RQXKZGI1XVwVGHF6tqHQE66ddeWlBE+bEWQ+Gfh2DPL5C4Tf18tr9Jf4tVmg1SC3SW/Z8qcx0aq9ogXPVPT0d2xTJYLBYX15DQn4yMDKKiokhPTycyUmf184VwNrMZXqyhPRdRB/6x373xVNeCp1VZpYqE1FA9aq/6hz4Sy5nJ8NnVKiFuS7NroW5ndUFTWs3mqlSN3vqFaDm8GGaM1Z7r+4i6+BRCCCGEU8h1k/B28jMqrjifXW2z32CuJZDHC+/hT3NPzBgJooC7/P7k8YBZ1gcOeBKuecr1sYqqM5vhy2ut+yoDNLpKlW31tO89fA/F1eWSK5J6CH6aAmd3OX5OeBy0vA66T1XJaG924Rh83Ee1GLPl5u/0XXnttQZqE4ajgiJh6jKIbea6mJzp7B71PayqgDD4xz7v7l+79VuY+5Djx/sFwb8TwU9H/YWvNHnpqsy17Ex2uspcMxndFJMQQq+MRkjooT1Xv6d7Y3GGoa/AmM8rPi73Iix9Sa341YMNn9hPJgMcXgRr3i0/fv4QbP/OJWG5XVF+1eaEEEIIIYQQQu9immiPB0byaoNP+cPcG/OlW4H5BPKRaTSzisqUUc5JdXGQbnBmB/xwC7zRFD65Crb5yPVuscOLyieTAY6vguOr3R+PKGE2w/c3V5xMjqyn/jcwQu22e3gHjHrP+5PJAOs/tp9MhpLWTsm7VUuyN5rCJ/1Ukk8PbL2X2pKfoVoDJm4FPezdi2sLcdX4WRv4tHcnkwHajYWmAx0/vu0YSSZ7u+AoSSZ7ASl5LYSo2KDn1K5PU6mEXFCUKumiR6kHHD92/f+g36Pe/6HitMbFpBZTQfXO93aN+6sLsoLM8nOtRrg/HiGEEEIIIYRwl173qxLIZUq1Fva4h9krQwFTuVN+NF3DeP9S5W2bXO3aGF3t3AH4ejgUZKnnOanw2/1q0XifBz0bm7Mk20lWJu+CRv3cF4uWisolaynMVTsEjUbISoGfJmsfV783DH6uevG50onVqixrRbrdoX5f/YP0lyA5u9v+fEIPlbA8fwS+vq6kDH9Oqtoxmp0KVz3m+jiro8/fYfaUyp1z4E/1iG0B46Z5fwnssV/C9+Mg7aRjx/sFQauR0O12de/N2/0wAY7ZeC8Kr63eZ4oldIdhr7onLlc4shQOzFfvJx3GQ3x7T0ckfJgklIUQ5W38XPVHzjyren0OfBruWqx2wZ4/rP4w9bofajb1dKRVs3W648fmXlS9iby9x3B0g+qdH5XgnDg8LSgCRr4Dc+6zvonS7Q5ofJXn4qqszGRY8pK6GPEPVh8Ir35S9fYWQi8uHgezSb9/K4QQQghx5chKUUmS6Ib6/uyS0A0mzISlL6pdumG1oOc9FPZ4mJzFizRPSSO85EmzwdByuJuCdZF1H5Ykk0tb/V/ocTf4V9B/Vw9qNLY9V9MLSu5Wt4dxbaD21yo5W1anh7yjR7ItuRcrPsbgp3qZBoa6Ph5XiGkCJ9ZozwVFwW2/qP9e/z/tnu5r3lP3FL25V227GyE/C/54GCzmyp2belDtUv/7dvDz4tRL7Vbw0DY4uhxyzkOtVmoDzt45sH9e+eNN+VCUp49k8sn1tpPJAC2Gq5/NQ4vUvcOUffBhd1Wm/ZpnILyW+2Ktrt8egG0zSp6v/QB6TIVr/q3aOepZQY5q27jzJ7VBqtVw/X1/fJAXv6sJITxi+WuwvNSqrEML1AfFu1fA6P95Li5n2fAZZJ91/PioBvr4A9z9Ltg5s9xKdIcEhELX250eksd0GK9WF+6aDYXZ0GKYWhihF4W5akV96VXNa96FlL0w8SePhSWEw1IPwa/3lpThi2sH138A9bp4Ni4hhBBCCC0Ln4H1n4C5UD1vcR2M/VwtVtWj5oPVoygf/ALBYCAU6Nwgmm0n08od3q92AdS9Xl03dRivv92SZZ3doz2ekwpZydVfjO1pR1fAn49rz4XHQ9NB7o3HVUb/D74fD+f2q+cGP+j9ALS5wbNxVaRhX/V7Z6s6XECYWgRfo5Fbw3KqXvfBrp9UcrG0wHCVKF/wb9Uiz9ZO+rw0yEj0/sU7XSepOFe8Vvlz00+pRG3zwU4Py6n8/K1jrNtRle225eB8KMiGwDDXx1YdFe2i3zWrfNn2gizYMg32/wn3b4CwGJeF5zTHVlknkwGwqE1iW76FAY9D/396JDSnmHkrHFlS8nzLNDixDu5d7RuLw3RKEspCiBIFObDuI43xLNjwMYx42/0xOdP2H+AvGxdetlz9pCq55O3qdYHx02HRc6onsn8wWExgKrR/Xnx7GPZaSX8bvUneBTt+VCsLmw1W5XeMfur1DKjk99pb7P5Zu0TWoYWQtB3qdnJ3RMIVTEWw7zc4skwtWuk0Ua0Q1jtTIUy/EdJLlc06uxtm3Kj6gnl7nyUhhBBCXFm2fqt285R28C94owmExuq6UlCu2Z93F+1nzrZE8ovMdEyIJiTAj9zCkrLX9cLg/nHDIeEBD0ZaRsrK6p1fIxwuaCy0DgyDnINQcNz++d68+zXnAvx4i/YObFA7CM1FYPSBG+01GsL961VP6KyzKkEZXd/TUVUsLFbtDFz8vPV4va7Q+0FoNkhdExXmqg0dO2eqxGyL62DQsxBZ1yNhV0pcW7j1F1jyApzaoDYp1Gyhdrdu/lIds/VbCLGRkAuMgIh498VbHVc/qe7RnD9U+XMzk50fjzvUaGh7zmJS9469PaFcs7n9eXs9wLNT4H89VNLS239ODy2wPWfKg6Uvqd/Xlte5LyZnSdxinUwulnpAtfboMM79MQlAEspCiNLST2uXowFV/kPvFtnpsxMQpm4Y5J4Hg1H9we3zd1VOQy9aDVePrBS1mv71RoCdhHJAGNy1RPXY0KOt0+H3v5eUH9r6LbQcATdP1/eqenu/a+cOSELZF5gK4btxcHRZydi6j+DGz9SKbj07uMA6mVws9yLs/gW6VbIPlRBCCCGEK9lqh2QqgMwkVSko9SBM+MGtYTnDA99vZen+kh6RKw6eo250MBNa1+HMvnW0z1rDhKKl1PgyFzpPhJHvesd11OIB1f8amnnHHFh+bcXn3mKp/r/vKnt+tZ1MBvWZOzvFd1paGQz6al1VrN+jqmrajh9U8q3lddD2Ruvyxz/drnZ7FtvxPZxcC/et00cp7EZ9YfibMGuSanWUvL38MbkXAANQ5neqx1TvT0gWMxig0wRY8mLlz63ZxPnxuNre39RCB1viO+ij3HDj/moRR+KW8nMGP5UYtyf7HKx80/s3VgWGV3zMthn6TCjbuzeasgeQhLKnSEJZCFEiqp5aKViQWX4utoX743Gm/Cx1YWXLXYshro374nGl8Nrqf2NbQPJO28cVZqtSNXpMKOdnqTJKZXvZHJin+g63HuWZuJyhVks7czr/PRTKrtnWyWRQFzR/PaF+dvX4O1ksy05LAXtzQgghhBCekGXnGrHYgT9VGeW4tq6Px0n2JKVbJZOLJaXl0erCMp7L+0/JHUELanFuzWbQ92G3xikqKS/d/nxwtOqbLTyvUT/1KMtUBPOftE4mF7t4HHbPhi6TXB5etZkK4YcJqiS0XaWSyRF1oefd0Edn7zNndlT+HL9AqNPR+bG40sUTMPvOkvYPZRkDof041U7B2+9ZGAwwcTYs+g/s/KGkemO97qrEd+lWj7YcWujaGJ2h/ThY8Yb9BHlumtvCcSp7eQi95yh0ThLKQogSgWHQ6161Cqu0gFDVI0XPyiYeS/MLhNqt3ReLu1z1D/hpsu352m0gVAc9QbScWm97N/2hhfpOKLe7CVa+BWknrMebDoS6nT0Tk3Cuw4u0x3POq35FDXu7Nx5nati3anNCCCGEEO6WleL4greUfbpKKB88q7FIvHju+Am1abCsbd95R0J58IqqnzvnAbh4tPx4x4nQ5daqf11v0WywKjNsS+8HvT/R4yuqWpp97QdqkYqtKvqJCyGhUcVfx9Ol2Y8udyCZXMagZ6HTLS4Jx2XyM2H/vMqf1+ch/ezCLrZrlu1ksl+QKqm/6FlVuWP0J9BiiFvDq7TQGLjhA/UorahA9QA/f9j++YERrovNWWo2hTGfwLx/2L5H6u19vG2p3wMa9oMTq63HazSGtmM8E5MAJKEshCjrmqdVP8+Nn6t+Hw17wzXP2N81qQfBkarnzqkN5edaX69Wr/matqOBb2DF65Cy13rOGACD7VyIerugSNtzaafVDtBdP6mVk61GQJfJ4K+TPlKBoTDlT1VSaf+f6oZAh5th4NOejkw4S5CdC5NgOz/belC7FXS9HbZMsx5vOUKf5eqEEEII4bu2TIOiXMeOrdXKpaE4W/Patj9vNjcfB63K1rZuRrtbVRNlmcmQdBLNW52nDsMwL+6N7IjUQ2oHa7ub1C7W0oKjYMCT0Pt+j4RWbcm7YPELcGyF6rvb9Xbo/7h1iWhvU53S7PZaQWd+B4u/q/hreLo0e0W75bXMfQiiG2jv3PZWhXmqL7mWqAQY/hb4h8Dqd+DkOgiPhx53qRZ6emAqguOrVB/v7FQ7x+WX/HfOebV55ZHdEFbT9TE6Q+nXWa87DPiXKgV9erP6HKC1CUkvix86jIeWw2Hdh2pzSulFAfEdoPtdnoutuib8oO6N7vpJ7TJvNQIGPw8BtlbkCHfw4r/MQgiPMBig9wPq4Wuu/xC+Hg4550rGareD69/3XEyu1na0emScgQ2fQNJWiGqgetbouRdvQneIblh+Fy/AseVwdGnJ86PLVDmpibP1s3AgKkH10y3r6HJY9qr6PkY3VL+n0pNWfzpNLJ9wBajTSVc7X2wa+a5aSbp7trr4bj0KOvnAjhAhhBBC+JZz+x07rsUwiG/n2licrF29KAa0qMWKg+esxuvHhDCqTjAc0TipmU53MRULCAGjv3byx96CZE+ozO5WU4FKEhxfVTLWoBVE1gO/ANUrtLiSlaNf19O7W0tLOwXTRpQkKLOSYcVr6n9HvefZ2IRtjQeoan+mAsfPMRfBX0/CfasrPtZbhNdSSTmtdnIdJ5T0pm16tVvDcorErTDzNsg4rZ77Bzt+bmEO7PlF3Vv0dolbLr1OjR31oTXhxk9Vcrm4DL3BCJ1v01elzqBwuPpJVQJ76zdqgVWD3mpzih56stsSHAkj3lIP4TUMFovFw0uavE9GRgZRUVGkp6cTGellHzqFENWTnwW7f4a0k1Cvq7o5YDR6OipRFfMeh00aSVdbJv6s31IvAKc2wtfXlb9BMux1Vape6MvGz2Hx81CQpZ7HtYObZ0BMY4+GJYQQlSHXTcLbyc+osGvlm7D0ZY0Jg2r7FBgGHf8G1/xbl7thcgtMvLPoAL9uSyK/0MS1beJ4fFhL6uQfVwm8nPMlB4fGwp2LoGYTj8XrFD/dDnt+LT8+9ktof5Pbw7Hpew8vdPb07tbSFj8Pq/9bftwYAI/thfDabg/JIVUpeZ2bphJbFo1FD9GN4NoXHH+93rAoYO2HsLBMJbVm10LSduuNHGX94yBExLk0NKc6sQ6+u6nk2h1UC7kpf6oKj3pkKoR3O0BmUtW/xuDnod+jTgvJJUyF8G57yDxj+5jAcHh0j2qBceGoWuQf3cB9MQrhBSpzzSQ7lIUQV5agcOhqp6+w0I/I+Modf2KNvhPKa97TXm2/5l3ocbcsjNCbHlPVatFTG9VFaEJXT0ckhBBCCHFl6XI7bPgMslPKjN8G13+geYqehAT68fSINjw9ok2ZmdZw/waY/yTs/U2Vx8xJhS+vhZu+gibVKOXraSPeUYnyY5eSfX6B0ORq2Pe7Gus0ERr09GiIoozUQ9rj5kJV4ttbE8pVTei2nQqbPrceC4mBCYshsk7143KnPg+qXZC7ZkFhriq722IonN0DXwzWbilgDNDfjsmGveH+9bBtOqQnQkI3aDdW3+2qjq6oXjIZoLmX91AGVeXPXjIZ1EKBfXOhyyT9t3sUwg0koSyEEEKf2t6odhRo9TrRElHJBLSnFeaqsl/hcapUd+pB7eMyz0B+un5Xxl7JgiP1vchBCCGEEELPwmvBHfNh6UtweCmERKkbyn29fMeVM5gL1Q300r0Wc1Jh1m3w6F61EFuPQmNg8u+Qsg8unlB9TQ8tLJnf+i0Mf9PzZVoHr3D82O9v1u5v7R8Mt2nsxtab2m1g/x/lx/2CIKap++Nxteteh6h6qgVSzkW1gGPgM/pLJhdL6Fp+cXR8O7Uo5xeN3q1tx0CQ7R7vXiu6vqpWsWWa2lH/xyNQqxUMeEIll/Wm9G7rsgLD7c8D9H5QH+26Knodl4/Ldm0cQvgQSSgLIYTQp5jGqqfSvH+CKd/+sQGhqpeIHhQVwKJnYet0KMyGmCb/z959R0lRpX0c/05gAmkAyTmDCIKgIgZMKIoBI2YRw66umDAsrqi77mtY0+KadRVdMSNmBREDoigSVUQBQaIkgRniwIT3jxJwnB5gUlf38P2c00f63qrm14cGp/qp+9yglVDdPSMXlas3htSMqMeUJEmS4tnk+at55ev1rN44kIMP+zund2tCekpS2LGi47vXIu99uikz2EcyltpD70RObh5vTlvChzOXkZqcyMldG3No2z3hl29g4Vd/ODofPvxH0Mo8zKJWcVa3NusL018sPL7XCbHR9rg0Nq6Bnz6KPLfvhVBlj6jGiYrEpN86jKXAgi8gvQZkrw07Vdnb+3RY+SN8/p/t39e07hXc0BGvJj8Lb1+1/fmKH2DEhZCcDu37hJerJFr0DHJHWkW+oyJsWo1gq64Wh5RbtDLV4tDg5pucTTs+rpEd46RdZUFZkhS/up4P7Y6DOWNgyVT46rHIx7U9NrhbPR6MvhG+/u/256vmwiv94cQH4Mf3C3/xc8gg211LkiRJxfDy1wsYPPJb8n/bSvaD75fx2pTFvPynA0irtBsUlbdEKCJsm9sQvRyllJ+fz6XDp/DhzGXbxt6YtoQrj2zDoHUfRz5p81pYNAlaHR6llKV0xBD4+XPIXLB9rFpDOPLW8DKVlQ9ugsWTCo/X6xS0Ks/NgaQK9tX1tBfhnasg53c3xU95Dvo+DPucE16u8nDEEOh+GSz7NrgRvnbrsBOVzucPRB7/4j/xV1CuXAt6/x+8e23xzkvLiJ9iMvz2Pu/47X3uYO/4qnG0p7cUMr+BliTFtyp7BHeYt+1d9DF120cvT2lsygouJgvJhxlvwvlvBRfWqRlQvxOc/Djsd1G0U0qSJElxa9OWXO5474dtxeStpi9cw4jJi8IJFW1FXTslJgerCOPEJ7NWFCgmb/XIx3NYlriDLY/i5WZjgIzGcNnn0OfeYNXuMf+Cv0wIOnbFs7xc+Pa1yHPLvoUXz4D/dAn24928HvJ2caurWDZrNLxxacFiMgD58MEQ2LKTVZTxqMoewb7Jn90btG//8jHYHD83rWyTnx/c7B/J0m/h+X7w+qUwf0J0c5VG1k72Fo5kzXxYNa/ss5Sn/S6CS8dD84Mjz9ftADWbRTeTFMcq2G1ekqTdVotDoUZTWLOg4HhSCux9RjiZimvDr0W37174FTTrAee/Gd1MkiRJUgUyY0kmmRu3RJz74qeVnHvAbvDFcoPOcOAV8MWDBcd7/QOqNwwnUwl8MWdlxPGcvHy+rNGHvgn/gfzcgpMNugTvP56kVQ9/3+eylr1u521oMxfCE4cH18jpNWG/i+GwG4O20fFmzcJgRXZRNq6CZd9B432jl6msrV8Jcz4MvoNp2xtSqgTd136/CnbWKPj2FbjgXaiUHl7W4kpIgHodg5sd/mjzOpg9Ovj19Jfg+H/DvgOim6+4tmyCiU+U7Nyd/b2NRfU7wtmvwvBTYMHviv7JaXD07eHlkuKQBWVJ2h3k5QU/2K/4IdiLt9WRFa9NcmISnPUyvNp/+17DVerA8UPj527DjCbBqoC8nMJzm9fBhlXxdTe9JEmSFGNqVk4p0VyFc/T/QfsTYOZbwTVIx1Ohwd5hpyqWWlVSi55r2AJOeQLe/yts+K3w3Hg/OO3pKKUrI6vmBYWf5TOhTnuo0SRogU0+dDgJOp0ef9f2P4+Hl89lhy1ot9p6w/XG1TDunqCYdfT/lWu8MvfR7fDZfYVvbvijeL7WnzQM3r9h+xZdaRlw0mPBnuV/tHhysC/4vhdGN2NpHTZ4Fz63+fDhrcGihpTK0UpWfBt+heys4p9Xq2Xw71A8SqkM/d+G926AKc8Gfx9zNsGrFwT/X2gTP905pDDFxE8cDz/8MM2bNyctLY3u3bszceLEIo/dsmULt912G61atSItLY3OnTszatSoAsfceeed7LffflSrVo26dety0kkn8eOPP5b325Ck2LN6fvDD0t3N4YXTYczN8Pxp8N8jgguyiqZeB7h8IlzyEVzwHlzzPex5fNipdl1ScrDKOpKEpOCLHkmSpBjkdb3iRcs6VeneonDhJjEBztyviJ/FK6qm3aH37XDUP+KumAxw8j6NSKtU+KvNZntU5sBWtaHTaTBoJlw0JrhOvPjDoq+3Yk3mYvj4DnikB3z5CMz9GL56FEb/DX58F358D17/U/CIJznZQQGnpN9HTBoWXy2T534C4+7eeTG5Rc+gWBdP8nKDNt6f3A3vDtpeTAbYlAkjLym6aDnvs+hkLEt7Hg/njIDmh0C1BlC1buTjNmXC0m+im624qtbbyb7BCcFilN+rVCVYsJGQUJ7Jylfmou3F5K2yM2HEgKBrgqSdCr2g/PLLLzNo0CBuvfVWpkyZQufOnenduzfLly+PePyQIUN4/PHHefDBB/n++++59NJLOfnkk5k6deq2Yz799FMuv/xyvvzyS8aMGcOWLVs4+uijWb9+fbTelqRYtHI2jLgI7m0Hjx4Mk+LszuTiWjwZHj0IJj4e/ED7e0umBnfJViQbV8Oov8HQvWHEhfDTR5FX+sa6HgMjj7c7Nmh1JkmSFGO8rle8efDsfejRco9tz/eoksJ9/TrTqXFGiKlUXPUz0nji3K40yti+snyv1BU83egdkpZMDgaSU6DJ/lCnXUgpS2D8UBjaCT79F+Rs3PGx374KiyZHJVaZmPsJrF9R8vM3r4P1kf/fEpO+fXXXjmtzdPnmKGur5sFD+8IL/eCT2yE/wh7Xm3dQoKtSu/yylac2veCCd+DaH4IOD0WpUid6mUoiKRl6Xh9hPAX2uwQGToJLvwi6APYYGGyHcOUUaHlo9LOWpUlPRb65Iztre9tySTuUkJ+fvwv9RcpP9+7d2W+//XjooYcAyMvLo0mTJlxxxRUMHjy40PENGzbkpptu4vLLL982duqpp5Kens7w4cMj/h4rVqygbt26fPrpp/Ts2XOnmbKyssjIyCAzM5Pq1f3yXqoQMhfBY4cE+9L8Xs8b4Igd7GMTz4YdB/PHFz1fpQ5cPyd6ecpTbk6w6vqX6QXHWx4O578RSqQSy8uDt6+EqcPZ1kqpQRc459Wi74CVJCkEXjdpq1i8rgc/o9q5+b+uZ/WGLezZoBqpyXG4L+vubvKz8Ond5GYu5ntakJafTZvExcFcQlLQxnSvk0KNWGyLJ8OTRxTvnAMuh2PuKJ88ZW3m27+1DS6hKnVh0PeQVKnsMpWnkX+Cb17e+XE1W8JVU3d+XKx45nj4eRdWGdduByv/0F0kIQn+/CnU71Q+2aLll+nBHt9/LFC26Bm0Vo4HM16HLx+DrMXBjTc9ry+8MrmiWPY9PHl40XtAn/w4dD4zupmkGFGca6ZQVyhv3ryZyZMn06vX9h71iYmJ9OrViwkTJkQ8Jzs7m7S0tAJj6enpjB9fdNEkMzNYmVerVhzvRSGpdCY+UbiYDEHrqOy10c9T3nJzdlxMrmhmvV+4mAxBW7AFX0U/T2kkJkLfh4I7Qk95Ega8H1xsWUyWJEkxyOt6xbNme1ShS5MaFpPj0Yw3ghtxsxaRlJBPp4S524vJEBR5xtwS3LAbT74bWfxzpjwLG9eUeZRy0fIwSKla8vMPvSF+iskA7frs2nFrfynfHGVp7dJdKyYnp0O/54I/862q1odTn4zPYvKmLNjwu+8VG3QO9mn/fevoVkfAqXHUDXGvk+Gi0XDNd8ENOFuLyfn5sHYZbCmi+BqPPrmz6GJyUiq0Piq6eaQ4FepmjCtXriQ3N5d69Qr27K9Xrx4//PBDxHN69+7N/fffT8+ePWnVqhVjx45l5MiR5OZG3osiLy+Pq6++moMOOoiOHTtGPCY7O5vs7Oxtz7OySrApvaTYtvS7yOOb1wWteuJwv6gdSkyCtIzCra5/r0Pf6OUpa3l5wRcEWy8ki/rzBVj2XbA3WLyp3Tp4SJIkxbBYua4Hr+2l3cqXj+z8mDXzIWtR/OybDJC7pfjnbF4XtFbe/5Kyz1PWUqvBCQ/A63/etS2qMhpDYiWo3hD2/1P8rTjf80TodPrOW1836hadPGUhbyf7QUOwCvm4+6BuOzj/TVg9Hzatgbod4uuGAAgK6O8MChYy5OdBk+7Be6vfKdinvUNfWP49pNeMr39rijLjdfjwH7B6XnDzR9fz4ajb4u/P7Y/mf1H0XJ97oMoeRc9L2ib0PZSL64EHHqBNmza0b9+elJQUBg4cyIABA0hMjPxWLr/8cr777jteeumlIl/zzjvvJCMjY9ujSZMm5RVfUlj2KKIwl5QaXKBUNAkJwQ99RWm4DxwxJHp5ykr2Wnj7arizMfyzDvzvJFg2o+g/X4A9WkUrnSRJknZBeVzXg9f20m5lzcKdH5OUCmk1yj1KmdpzB/uy7sia+WWbozx1Og2unBrszZqYUvRxPf8K18yAiz8MVrrGWzEZgg5kxw+F0/8HB18LzQ4ufExCMhz216hHK7GMRsF3SpG0OhKOvCXYb3efc7aP12wWrOiNt6Lk+l/huZPhx3e37xO98Cv4X1/YuDp4nlQpeG8VoZg8/wsYcWFQTIbgZpUvH4EPbg43V1moWi/yeJW60K1/dLNIcSzUgnLt2rVJSkpi2bJlBcaXLVtG/fr1I55Tp04d3njjDdavX8/8+fP54YcfqFq1Ki1btix07MCBA3nnnXf4+OOPady46ILRjTfeSGZm5rbHwoW78EOppPiy/5+gUpXC413Ph8oVtG3eETdD57ODO0MhuKu32cFw9itw8UfB3ZPxZsSFMHkYbFkP5ActrZ85Hpr1gJotCh9frUFw92x+ftSjSpIk7Q5i5boevLYvT6NnLKXvQ+PZ8+ZR9H1oPKNnLA07knZ3jbru/Ji9+0FanO2f3uIQ6H5p8c+LpxWuEBTfet8Ol34WrPiM5Iv/wOM94Z5WcE9LePYEWDU3ujlLY8tGePuqIP+r58OMkbDfxcF3NbVaBu2f2x8PF74f7LsbT054ACr/YUVnqyPhrJfgkGuhZvNQYpWZtUvhhTOCP7vl3xee3/ArfLOTVefx6KvHthfOf2/Ks7B5ffTzlKX9Loo83v3P0c0hxblQW16npKTQrVs3xo4dy0knnQQErazGjh3LwIEDd3huWloajRo1YsuWLbz22mv069dv21x+fj5XXHEFr7/+Op988gktWkQoMvxOamoqqamppX4/UoU2ZyxMfzH4AaLNUdDlHEiOo783tVvD+W/A+3+FJVOCOwgb7x/8oPt7m7Jg8jMw/3OoUhu6DYDG+4aRuPSSU+HkR4O7Q9fMh9pt47t4vnwmzP6g8PjGVUH7qAvegQ+GwMx3fmudlR/sQzT8FGh+SFBIT6kc9diSJEkVWaxc14PX9rti+phxxT5n5i+ZvPT1QhKAdgBr4LEfv2H1fk3Ys0FGsV6r81FxVjSJd5syg711M5oEKyUrkp7Xw08fwZYNhecSKwWrYI+9O/q5ysKx/wpaBM96f9eOb9QN2pdwZXPYEpMgc1HkuZyN8Mv07c/njQu6lF0xOT5Wur57HUwbvv356nkw4oKCx2xcDfX2imqsMtGgM1w5Db57LfjepekB0PLwoFteRfD86bD0mx0fk1kBb1or6u/ilg3B/tEpERbqxIv9LoL1K2DCw5CdBZUqB2MHDwo7mRRXQi0oAwwaNIj+/fuz7777sv/++zN06FDWr1/PgAEDADj//PNp1KgRd955JwBfffUVixcvpkuXLixevJi///3v5OXlccMNN2x7zcsvv5wXXniBN998k2rVqrF0aXDnbEZGBunp6dF/k1K8++w+GHvb9uc/vhfsqXHu65AU+j8ju27LxqA9MgT7Es3/HIZ2gkrpwQ/D3S+Dj2+H5TO2nzPtBTjpUeh8ZjiZy0L1BsEj3q2at+O5jMZw+jMw/FSY82HB+Z8/C35oPPT6co0oSZK0O/K6Pn50XnFo8c9JhjN7FDG5orivZuegqNi8Ad67Hr59BXI3B6tBj/pnfLYMLkrDLnDRGPj8AVj6bbDV0f5/hqp1g0c830wN0PbonReU6+wZ/Jn2uDy+vpv5vbeugKzFu378mvnBd1Id+pZfprKwYRV88/LOj5v/efCd25G3lH+mspZWHfYdEHaKsjf/i50XkyF+F5/sSOP9YPHkwuPVGwX7mMe7wwbDgVfAmgXBe4q3DhZSDAj9p40zzjiDFStWcMstt7B06VK6dOnCqFGjqFcv6Gu/YMGCAvsobdq0iSFDhjB37lyqVq1Knz59eO6556hRo8a2Yx599FEADjvssAK/17Bhw7jgggvK+y1JFcv6X+GTfxUenzcOfngb9jo5+plK6oMhkJtdcCxvC2RvCQqOP4+n0Bcc+XnBXiEdT42PO2ArsvodISExcvudBnsH/81eF6ymj2TmmxWroLwpK1ixnZ8PbXrFZwtzSZJUIXhdL8WYd66Bb3635/iaBcH2QRmNK1YRpH5HOPXJsFOUj73PgK+egBUziz4mpUpQIIlXa5fCggnFPy+zGAXosKxfEXzftCtmvB6fBeWKKmvJzo9pvD+0O678s0Rbj8uDVefr/3C32OE3Bd0EKoKUKlB3z7BTSHEr9IIyBHsiFdUK65NPPinw/NBDD+X77yPsXfA7+e6VKZWdhV8VLsJuNffT+Ckob1yzC3cYFvFvx/rlQbvlrUVLhaNGU9jnXJjyv4LjtVrB3r+tIE9I+K3onFv4/IQK1OLth/dg5J9g89rgeXI6nPgg7H16uLkkSdJuy+v6+DC9zqfFPueJcT+xeM3GQuONaqTzp56tivVanYv9u6vY1v8K340oPJ6fC1//t2IVlCuylCow4D146eyii66LJ8GsMbDqp6Albbtjd49CSVF7LseSmi2gSp3ChTnFvkbdgAQifkdYswXsc07Q4TBeuwLsSI2mcPHYYP/yhV8Fq3j3/xO0PjLsZJJiRAX8l09SmapSewdzdaKXo7QqVYZKVWDL+hKcnACV9yjzSCqB44cGbb2mvRDsedK2d7B3VmrVYD6lSjD243uFz+14alSjlpuNq+G1iwruFZazEd64DJofXDHam0uSJKlclGQP4xMbtuXS4ZP5fY0/IQH+cmY3Ou9VvwzTqUysXw55OZHndmXlnWJH5Vpwwbvw+KGw7NvIx7x01vaVsGP/AYdcB0feHL2MpVGtPjTtEblgnpwOGY3g1zkFx/c8ERp3i06+0khOCVZ1vnP1zo/tcFJ5pwnX+l9hw69Qq2V8FGFrtYB9L4RJTxUcT04Pis1tj93+HVRFVLMZHHdf2CkkxagKtFxLUrlosj/U61h4PCkVupwd/TwllZwS3EVYEm17BxcyCl9iEvT4C1w2Hq7+BvrcE+yP9Xt97oXabQuOtT0muIO0Ivjh3YLF5K3ytsD3b0Q9jiRJkiq23nvV57Fzu9G5SQ0qpyTRuUkNHj+3G0dbTI5NtVoVffN30wOimyVKtuTm8e43v/DvMbN4e/oSNudE2CYpXiUmBXt+FuWPbZU/uxcWRdgDNVad8J9gFeTvtToSrp8Dl02AY+6CJgdA0wPh2HvgtGHh5CyJfQfAOa8FBciG+0C3AcHfz99reiAccm04+cpb9joYcRHc2wYe3g/uahpsKRcPHUiOuw/6Plzw+9CcjUH3h6eOgiVTw8smSSFKyLePVCFZWVlkZGSQmZlJ9epuzi6xZgG8dgks/DJ4Xr0xHH9/UGiNJ1s2BXeHfvtqEXdsJ8ABf4FvXoYNK4PnbY6Gkx8L7gxW/MjLDfYXXrMguHBrsn/YicrOpKeDPdEi6fUPOPjqqMaRJO2+vG5SrPMzqt3W5Gfg7asKjmU0hT99vOMuZHHo13XZnPPfr/hh6dptY63qVOHFPx1A3WppISYrQzmbYdgxsHgXC8UHXglH/7N8M5WlLZvgh3eCFfRND6hY1+9/lJsDs0YFbcrr7w0tDwtaPlREIy6K3H6/4T4wYBRUioO/n08cFrl43P54OPP5qMeRpPJQnGumOOgzISl0NZrCRaODH6KmDIdfZ8O3IyC1OjTrEXa6XVcpLSgOH3UbZC4K9lT+8rGghVL9jnDoX4M9h3rdCsu/h8q1oUaTsFOrJBKTgj/LiqjtMZB4fYSbIhKCixpJkiRJu7duFwTX8V8/BeuWQbODoMflFa6YDHD/mFkFiskAP61Yz92jfuTe0yvIrt3JKXD+WzDxCZg1OtjqqW4HmPBg5OMTk6Kbr7TW/gJr5gfF1kqVw05TvpKSYc/d4Lp9/a9Fd1BbMjX4LB90ZVQjFVteXtErkXf15g5JqmAsKEvaNRtXw6sDYPW87WPfvgLH3Q/7XRRerpKoWjd4NOoaXGj/UXJqcMekFIuqN4Ted8L7NwC/azJyxBCo3Tq0WJIkSZJiSKsjgkcFN3rG0sjj3y2tOAVlCPZsPWRQ8ADYshGmDQ++q/mjvU6JbrbSmPK/YDV9/m9tyj+5I+gcd8yd4eZS6Wz4tei93AFmvh37BeXExKAde9biwnMZjaOfR5JigAVlaXeyfFzJz536PGyaDel/GP94EDRuDkmVdv4adXuW/PeXtF33P0Grw2HGG5CfC3ueCPU6hJ1KkiRJkqIqsYh2wUlJFbSN8FaV0uHUp+CV/rD5txXaCUlBx7UGe4ebbVetXwnvXre9mLzVl4/AshlwxnBIc7uCuFSrJVSpC+uXR55PjJOSxAF/gQ9uijB+WfSzSFIMiJN/vSWViQ8PLd35RXV//rjXrp1/tlu2S2Wmdhs49PqwU0iSJEmKRYsmw/j7g62earWEA6+A1rt47R5H+nZpyJOfzSs0fmLnhiGkibLWR8Kg7+GHd2HLBmhzdHxt2zV7DORmR56b92mwcvn0YdHNpLKRlBzs4/36nyPPdzo1unlKqsflwWd0wsPBquvqjaDn9dAxTvJLUhmzoCxp95OTDRMegm9fC1rwtD8eup4XtKzZlZXWkiRJkiTFqkWTYFif7cW6NQtg7qfQ73/Q4cRws5Wxq3u15ZtFmXw1b9W2sW41N3Fd8iswcx606xN/ewoXR1p16HJW2ClKZmffv3z/JmxYBZVrRSePysb6X+GblyBrCfS4AiYPg83rts93PA26DQgvX3EkJMAh18KBV8LGNcFnsSL/eyJJO2FBWdqd9Pq05OdOfT7YnyeS4+6HunuW/LWj7dUL4Mf3tj8f/yOMvw/SawV3H/a8LrRoUbEpE+Z/AanVoOmBwb4wkiRJkrQ7WzUP1q+Aeh0hpXLYaUpn3L0RVn7mwyd3VbiCcpXUZF7+cw++/nkVP878ltaT/sEBGyfD1wSP5ofAOSOgUlrYUfVHbXtDanXIzoo8n58bnwXlzRuC7gDfjQTygy2qel4XfAdT0S2aBM+dAtmZ28eaHgg9/hL8+9p4P6jfKbx8JZVUCarWCTuFJIXOgrK0OynNHsZHHgDffgLrlhYcb9oDOhbRwiYWLZ5csJj8extXwUf/DPYi6nF5dHNFy9dPwQdDgnZYELQ+O/OF+LohQJIkSZLKyoZVMPISmPNh8DwtA464Gfa/JNxcpfHL9Mjjy2dAbk7QjraC2a95LfZ770bInVFw4ufPghWS7nkae1KrwWlPw0vnRG59ndEEarWIfq7SeqFf8Lnb6vOhsGACXDg6+O8P70JyGnQ6Heq2Dy1muXjn6oLFZIAFX0D74+DAgaFEkiSVHZelSdo1ySlw6WfQ5ZzgDtLKtaHHwOBO3ziRm5fPS5//yHmbB3Pu5ht5PucIcvIj/DP45aPRDxcNM9+BdwdtLyYDrJoLL58H+e5vLUmSJGk39Obl24vJEHR0eu+6oEV0vCqqCJfRpGIVk5d+Cwu+hJzNsPrnoGAeyYe3wtBO8MHNkL02qhG1E22OgismQ9V6BccTkuCo2+KvvfDP4wsWk7da+BW8eBYMOzbYgu2ze+GRA2DS09HPWF4yFwV/JyMpamGHJCmuVKCfIiWVu6p14aRHgkccuublabw1PQPYG4DxeZ0Yl7c3j6cMLXhg5sKgwJqQEPWM5WbpdzCiiD1qfp0Ni76GJvtHN5MkSZIkhWntUvjx/chzU56FlodGN09ZOfAKmP95hPEro5+lPPz6E7zaf3vxqkqdYFV5UXKyg32kv/hPUNi7cHTFut6PdzWawOVfBcXV+ROgWn3Y90Jo1DXsZMVXVEEVYNYf/63Jh1E3QoeT4q+tdyRJqUACEGHBQrIt5yWpInCFsqTdwjeL1vDW9CWFxkfn7c+kvLYFBxt2rXgXl5/eBbmbi57fvC56WSRJkiQpFmxcTcTiBwStsONVu2Ph1KdgjzbB8+qN4Jh/Qfc/hZurLOTnBys9f1+4W78C3r022J91ZxZ+BT99VH75VDLpNeGQa+HcEdD3ofgsJkOwrVhx5GyqOJ/HqnWg1eGR5/Y+I7pZJEnlwhXKknbNxtUw5X+weArUaAr7Dij+D8oh+vrn1UXP5bVj38RZwZPEZDhiSJRSRdGCL4ueS8uAJgdEL0t5+ulj+HZEUDxv3wf27AuJ3jslSZIkKYLabYNia9biwnMtD4t6nDLV6bTgsWVjsDqwotw0veBLWPlj4fG8LdBgH8heBytm7vg1ln0HrY8sn3zavbXuBXX3Ktx+Pb0WbCziJpVK6eWfK1pOfBCe77f9/Sckwn4Xw979ws0lSSoTFpQl7dzaZfDUUbBm/vaxr/8L574GzQ4ML1cx1K9edHudek3bQs5esEeroDVYRWz9XK1+cNd2JMfeAymVo5unPHx0O4y7e/vzb1+BjqfBaU+Fl0mSJElS7EpMgt53wGsXQV7O9vF6HYOWuxVBRSpWwW+ryouQnwt/mRDsYTtrdLBXbSR7tC6fbFJiEpz/Boy+Cb5/I1hRv+fx0P1SePpYIK/g8VXqBEXoiiKjMVz2ebCX9Npfgq4BRe3pLkmKOxaUJe3c+H8XLCYDbNkAb10JA7+Oizude3WoS8OMNJZkbiowXqdaKsdecBOk3BJSsijZ/0/w1hWFxw/4C3SuAK2H1iyEz+4rPP7diGA1ffODo59JkiRJUuzb66Tg5uLJz8C6ZdDsYNjnXEitGnYyRdL0AEhOh5yNhedaHRF8P9GiJzQ7KGglvPz7gsfU7QBtj4lO1vL2yzcw/aVgC6s2R0O7PnboigVV68KpT8LJjwP5kJ/32/cxf2ivX7kO9HsOklPDSFl+EhKgxSFhp5AklQN/ypCKY/MGmPIcvP9XmPgkbMoMO1F0zBgZefzX2fDgvrBqXnTzlEBqchL/u6g73ZrV3DbWpUkNhl/UnfSUpBCTRUnX8+HIWyGtRvA8pRocPAiOvj3UWGVm3rjgbvRIKsp+RJIkSZLKR/1OcNx9cMZwOOBSi8mxrHItODLCDeFtegd7R2+VmATnvwl7nxm0/E5OC/ZxPf/NYC7eTRoGj/eELx+GKc/Cy+fAiAsgL2+npypKEhODz9pn98H0FylUUO56PjTrEUo0SZJKwhXK0q5atxyGHQu/ztk+9tn9cME7wd3MFdmOCuer5sDrf4aLPohenhJqXbcqr112IEszN5GXn0/DGhWs9Vck014MLly2bAgusAdOhuzMoAV2SpWw05Wd9Jo7mKsVvRySJEmSpPLV4y/QqCtMewE2r4e2vWGvUwoXiqvWhVMeDx4VyabMoKXyHwuU378Jsz+AdhVkBXZFMXV45PFvXoJeFbxbniSpQrGgLO2qT+4sWEwGWLsExtwCZz4fTqZoySti5edWC7+CVXOhVsvo5Cml+hlF76dcobw/GL56dPvzRV/Dj+/BhaMhOSW8XOWhzVFQvRFkLS44npwOnU4PJ5MkSZIklbXl48L9/ev2DPf336rpAcHj9375BjIXQoMukNEolFhRMf8L2LI+8pwF5dizKauI8Qrc9XDRpGBhTpP9oUrtsNNIksqIBWVpV/04KvL4rFGQnx8X+wiXWLMeQUvhHdkSYf8ihSdzEUx8ovD4kinw/Ruwd7+oRypXSZXg7FdgxABYOSsYq9YQ+j4E1eqFm02SJEmSysqHh4b7+5+dv/Njom3DKnj5PJg/PniekAT7XwLH3BW/39XM/hC+fz34dYeToU2v7XOp1Yo+L616+eZS8bU+Ama8HmH8yOhnKW+Zi+DFs2DpN8HzpBToeQMcen24uSRJZcKCsrSrKhXRHjk5PX4vUHbV4TfBwomQsynyfM3mULdDVCOV2IZVMOkpWDwFMhrDvhdB3fZhpyp7iycXvafwwokVr6AMUL8jDPwalkyD3C1BC7SKsDeWJEmSJKlo7123vZgMwbXwV49BvY7Q9bzwcpXUH7uNTR0O3S+DY+8Knjc9EGq2gNXzCp6XkBTsGa3YcsTNwarydcsKjs/+EN6+Gnr9HdJrhBCsHIz88/ZiMkDuZvj4/6Bhl6CznCQprllQlnZV57OCH4IKjZ8R/SzR1vQAuPhD+Pw/MOt9yF77u8kEaNg12LcotWpoEXfJ2qXw36Mgc8H2scnPwtkvQ6vDw8tVHqrvoL1X9YbRyxGGhl3CTiBJkiRJ5aPXp2EniC3Z64K9gyOZ9kL8FZSXfV+wmLzVV49C1/OhXgdITIQzX4CXz4VVPwXzqRnQ5+6KecN8vNujFVw2ASY9DZ8Phc3rgvEt62HyMFj+PVz0QagRy8Tq+QVv7Pi9ac9bUJakCsCCsrSrDroKls8o2Kam1RHBnYS7g/qd4NQnIWczPLwfrP75t4l8mDES1q+AC94JM+HOff5AwWIyQG42fDAELvs8nEzlpfG+0GhfWDyp4HhqdehyTjiZJEmSJEmlEyt7GMeK3M2QlxN5bmvhLp789NGO5+r91h2uXge4YnLQgWzzWmjaA1KqRCejiq/KHlCjaeTP5MKv4OfPoflB0c9VljYXsa/3zuYkSXEjMewAUtxIToHTn4HLJ8JpT8Ofx8F5r+9475qKaPbo3xWTf+fnz4IWPrGsqH2gl30H61dGN0s0nPUitDsOEn77p75BZzh3pHsKS5IkSZIqhsq1oFG3yHNte0c3S1nYUevjP84lJEDT7tC6l8XkePDr7KLnVs6KXo7yUqc91GgWea7N0dHNIkkqF65QloqrTrvgsbta9v0O5mZAswOjl6W4Ku8ReTw5vWJefFWtC2e9ABtXQ042VKsfdiLtTE52sNfXjDeCLwc6nATd/wzJqWEnkyRJkqTYdOw98NzJkJ25faxeJ+gxMLxMJbXniTDqRsjOKjy34ofo51HZqduh6Lk6FaBVeWIiHH8/vHQO5GzaPt7sYNgnzlrPS5IisqAsqXhqt97BXNvo5SiJbhfAvAj7TXU+AyqlRz1O1KTXDDuBdtVL58CcMdufL54crP4/59XwMkXb2mVB14CazYO9piRJkiRpRxp3g4Ffw7ThsGZhsGK502nxeZ2fVh0OGwyj/1Z4bsIj0P1SyGgc/VwqnZ8+gh/eDbocZq8tPD9iQLAvdqOu0c9Wllr3Cjo7Tnse1i2H5gdDh76QVCnsZJKkMmBBWVLxtD8B9mhTuFVPw67QIsb3cup4StCu+7P7gz2GEhJhr5Oh951hJ4uuvLygdfkv04OiXYe+8XmhXdH8/HnBYvJWsz+A+ROgWY/oZ4qm/HwYfRNMfALytgRj7frAKU9CatVws0mSJEmKbdXqwSHXhp2ibKxfEXk8PxfmfQZdzopuHpXOuHvgo//b8TFrf4FX+sNV0yAxKSqxyk3NZnB4hBsiJElxz4KytKuy18HkZ2Dux8GKz67nx34BtTwkp8AF78CYW+D7tyAxOSjU9vp70KI31h0yCPa/BFbMguoNoXqDsBNFV/baoBXYoq+3j310O1zwdlBcVngWT9rB3OSKX1CePAy+fLjg2I/vwQdD4IShoUSSJEmSpKhLr1X0XFFbeSk2rVsBn969a8dmLoAFE4JVvZIkxSALytKu2LwBnjkOfpm2fezbV+HYu4P9TSuqLZtg3N0w7UXYsh7a9IYjb4YaTeGUJ4JHPEqtFrTE2h2NH1qwmAzBRcuoG+GsF0OJpN9kNNnB3G7Q0mzq8Mjj37wMfe6xRZYkSZKkQF4ezP0IMhdD432h3l5hJypbe58Bn9wJWzYUHM9oCq2OCCeTSmbhl5C7edeP//3ew5IkxZjEsANIcWH6CwWLyVt99H/ByuWKauQl8Nl9sHYJbMqEb1+BYX1gU1bYyVRSM9+OPD5rFORkRzeLCmp/PNRoVni8RrOg9XNFtykz8viWDcW7AJckSZJUUF4ubFgVFGLjXeZiePRAGH4qvH1l8OvXLgneY0VRrR6c+TxU/92NxXX3gnNegaTdaG3Q5vXx/51bcVaUp9WAZgeVWxRJkkrLgrK0K37+PPJ4dlawD21FtGIWzHyr8HjmwmDFoOJTUXvxJCQBcdCyvCJLToHz34RWRxL8WSRA617Q/61grqJr3SvyeNMDIaVKdLNIkiRJFcWER+D+DnB3CxjaEb7+b9iJSueda2DFzIJj374Ck54OJ095aXUEXP0NXPIxXPYF/OULqLtn2KmiI3MxvHgW3Nk4eAw/FVbNDTtVyTTtAXUi/Lkl/OEr+cRKcPy/oVJ6dHJJklQCu9FtbVIpVK1bsrl49scLtN9bvoM5xbaOp8BH3xce73Di7lG0jHW1WsB5I7ev1k3LCDdPNB08CGaNhtXzto+lVofe/xdeJkmSJCmeTXoaRt+4/XnWYnj3WkipCp3PDC9XSW3KhDljIs99OwL2vyS6ecpbYhI06hp5Lj8f1q+EtOqQnBrdXOUpLxeeOwlWzto+NudDeLYvDPwaKqWFFq1EEhKC7cVeuxgWTwrGqjcOiscplWH2B8G2bJ36Qc0IHcskSYohFpSlXdH1/OAu3rycguPND4HabcLJVN7qtC/ZnGLbgVfCoklBi+ut6nWCY+4KL5MK250KyVtVqwd//hSmvQBLpgUX0137Q0ajsJNJkiRJ8WnCw0WPx2NBOS8X8oto2523JbpZwjTjDRj7j2DVbko16NYfev0dkiqFnaz0Zn9QsJi8VeaCYAuvvU+PfqbSqtUCLhkLK+fA5nVQv9P27nHNDw43myRJxWBBWdoV9faC04bBqMHBHb0kQOsj4aTHwk5Wfuq0C/Z0/eGdguPVG8fnhacCyalw9suwaDL8MhVqtghaaSXY7loxIC0DDrgs7BSSJElSxbBmQRHj86Obo6xUrhVsibPgi8JzbXpv//XG1cF+0VV+2792w6rt58ejdSuCjmJpGTD/CxgxYHthffNamPBQ8PyYO8PNWRaK+swCrPk5ajFKLScbJj8TFMGTUqDTadD5LL97kSTFNQvK0q7qcCK0Pw5W/ABpNXaPVXOnPgWf3gXTX4LN66Ftbzji5qClkuJb427BQ5IkSZJUMTXsCgu/jDwer46/H549EdYvLzj+xYOwbimsmgdzPwHyg/eZkACLJwfHtDg0aDW8R6topy6ZxZPh3etgyRRISIJ2xwad8yKt0p78LBwxBFKqRD9nWdrRZzNePrf5+cEe0D+N3T7201hYMAFOfDC8XJIklVJCfn5+ftghYk1WVhYZGRlkZmZSvbqFM+2G1iyEDSuhboeKtRePJEmSyozXTYp1fka125v7KQw/peD2XUkp0P9taHpAeLlKK3stjL0NJj5R/HNrNIWBk4MVv7Fs3Qp4qFuwb/TvVaoCW9ZHPufq76BGk/LPVt5eOBNmvV9wrPkhwec2Hlb4zv4Qnj818tzlX0OdttHNI0nSDhTnmskVypK227AKXr802LOGfKi8Bxx1G+xzbtjJJEmSJElScbQ8FAa8D1/8B1bMgrp7wkFXQaM4WelZlNRq8Ouckp27ZgH8+B7sdVKZRipz018sXEyGoovJlWtD9Yblmyla+v0PvnoUvhsJ+bnQoS/0GBgfxWQIViIXZeGXFpQlSXHLgrKk7d64DGaP3v58w6/w5kDYo3V8370sSZIkSdLuqMn+cMbwsFOUvcxFJT83a3HZ5SgvO3p/KdWCvZN/b8NKeO1iOPW/kJhUvtnKW3JKcOPDQVeFnaRkqjcoeq7aDuYkSYpxiWEHkCqC/Px8lmVtYsPmnJ0fHKsyF8Os0REm8mHyM9FOo/K0ZCq8dA78uyM8czz8OCrsRJIkSZIk7brG+4VzbrQUmTEBOp0CqRFaUs4YCdNeKNdY2gUdT4P0moXHa7WCVkdEP48kSWXEgrJUSqNnLOXwez+h+x1j6frPMdz0+rds2pIbdqzi2/ArUMSW6utXRjWKytGSafD0sfDDO5C5EH7+DF48A755NexkkiRJkiTtmoOvgbSM4p/X7rhg1Xas69AXGkZqTZ4Pk5+F7KzI5814vVxjlYuNa2DlbMjZHHaSspFeA857HervvX2s+SFw7mvxv3pckrRbs+W1VArTFq7hL89PITcvKMRu2pLH818tYEtuHnef1jnkdMVUpz1UqQvrlxeea9Ez+nlUcqvnwzevwKY10PpIaHn49r2Gxv8bcjYWPueTO2Dv06MaU5IkSZKkEqndBi75ONgfevEUqNkM9v8zrPgBvnsN8nKgXZ+ggDfzneCaeK9TYL+Lwk6+a5JToP9b8OVjwZ7Pq+YG1/g7Ey/7DAPkZMOowTD1ecjNhip14LDBsN/FYScrvYb7wKWfwZqFkJi84zbYkiTFiYT8/PwiliTuvrKyssjIyCAzM5Pq1SO0kJF+M+iVaYycUnjvnZSkRCbedCQ1KqeEkKoUpr8Mb1wK+Xnbx+ruBReOgrQIfxfy8iDRRgcxZebb8OoAyNuyfazjqXDKf4M/q//sE1yIRnLjIkitFp2ckiQp7nndpFjnZ1RShbBxDfyrOUV2lfu9vg/DPueWc6Ay8v5f4avHCo9nNIa2x8LBVwe/liRJ5aY410xWgqRSWLImwkpPYHNuHivXZUc5TRnofAZcNAb2OQ/aHgNH3x65mDzzHXj0ILitJgztBBOfDCevCsrJhrevKlhMhuDu7B/fC35ds0Xkc6vWg0pVyjefJEmSJEkqnl1dddzpdOh8VvlmKStbNsGU5yLPZS6Cr5+E/x4Fa5dFN5ckSSqSLa+lUujatCZfzl1VaHyPKik0rRWnxbnG+waPoswZCy+fy7Y7Y9csgPeug/x86P6nqERUERZO/G0v7Ah+fB/2PB4OHAg/fUShO5sPvMLV5pIkSZIkxZq0DGjdC+aMKTy39xnBXr3NDoRGkfZcjlGb18GW9Ts+Zu2SoLB8xJDoZJIkSTtk9UAqhQsOak796mmFxq85qi0pyRX0r9cX/yFim6XPH4h6FP1BcuHP4jaVfptrdQT0+x/U7RA8r9YQet8RFJQlSZIkSVLsOf5+qNWq4Fi744IW1wcOjK9iMkCV2rBHm50ft3hy+WeRJEm7xBXKUinUrZbGG5cfxJOfzWXivFXUrZbKuQc04/D2dcOOVn5+LWL/3axFQcuiSjsoaqp8Nd4X9mgNv84pPNep3/ZfdzgxeORkQ3Jq9PJJkiRJklQe1v8Ka+bDHq2CFb0VTY2mcPlEmD0a1iyERt2gyX5hpyqdo/4BL58H+blFH1OjafTySJKkHbKgLJVS/Yw0bj6+Q9gxoqd+R8hcUHh8jzYWk8OWkACnPwsvngmZC4OxpBQ44mbI2QTTX4am3aFm82DOYrIkSZIkKZ7l5sD718PU4ZC7GSpVhgMuC66Dd3Xv4XiRlAztjws7RdlpfxxcPAYmPhlsr7Z+ecH5pBTY75JwskmSpEIsKEvasV++gW9fhdwtwQ/7Bw+COR8GF2q/d+hfw8mngup3hCunwbxPYOOaYMXy65fCmJu3H9OgC/R9COp3CiejJEmSJEll4dO7YNLT259v2QCf3QfVG8J+F4eXS7umUTc4uVvQ8W7MzTD1+WBv5bp7wdG3Bd9xSJKkmJCQn58fYTPU3VtWVhYZGRlkZmZSvXr1sONI4ZnwCIy+seDYfpdAp9Phs3uDYnOtFsH+uxXpLtmK5NkTYd6nESYS4Nh/Qfc/Rz2SJEmqGLxuUqzzMyrtBu5uBRtWFh6v1wkuGx/9PCqdnGzYvB4q1wo7iSRJu4XiXDO5QllSZGuXwZhbCo9//SR0PhPOeTX6mVQ8a5cWUUwGyIcPhkDHU6FK7ajGkiRJkiSp1PLyIheTAdaviG4WlY3kVLfnkiQpRiWGHUBSjPrpI8jbEnlu1qjoZlHJ5GTveD53c/DnLEmSJElSPMndAm9dUfR8i57RyyJJkrQbsKAsKbKUyjuYqxK9HCq5ms2g3k72G/LPUpIkSZIUbz65C6YNjzyXXguq1YcH94V72sDrl8GaBdHNJ0mSVMFYUJYUWZujofIehccTK0HH06KfRyVz/FBIqRp5rkodaN0rqnEkSZIkSSq1yc9EHq+8R3Cd+8V/4NfZsH45TH8Bnj4GNqyKakRJkqSKxIKypMgqpcMZz0PVetvHUjPglCegRpPwcql4muwHV02Hvc+ApJTt41Xrw5kvuDeRJEmSJCn+bFxd9Nx3rxUey1oMU4tY0RzPvnsNnjgM7moGz54IP38ediJJklRBJYcdQFIMa9YDrpkB88YF++226GmL5HhUpXZwI8Bx98PPnwWF5RY9IalS2MkkSZIkSSq+lofBT2MLj9frCPM+jXzOshnlGinqpg6HNy/f/nzepzD/CxjwHjTZP7xckiSpQrKgLJWBj39czrDPf2bx6g3s07Qmlx3WilZ1imgzHKvy82HS0zD9RcheB22PhoOuhsq1oPWRYadTWUitCu2ODTuFJEmSJMW2dcvhl+mQ0Rjq7hl2mjIx85cslmZtonPjGtSqkrLzE2LdUf+AxZNgU+b2sSp1oef1wU3x5Bc+p3brqMWLinH3FB7L2wLj/w1nvRj9PJIkqUKzoCyV0muTF3Htq9O3Pf9pxXo+mLGUNy4/iJbxVFR+7zr4+r/bn6+YCbM+gEs+gpTK4eWSJEmSJClaxtwCEx4JCnMQrIQ9/RlIrxlmqhJbuS6bvwyfwsSfg/2DU5IT+cthrbi6V9uQk5VS/U5w2RfBjfErZwcrk/cdAFXrQsdTCre9Tq8F+5wfTtbysHk9rP458tzy76MaRZIk7R7cQ1kqhby8fO4fM6vQeNamHB7/dG4IiUpozcLgIuyPVsyE70ZEP48kSZIkSdE2/SX4/IHtxWSAuZ/Au9eFFqm0Br/2zbZiMsDmnDyGfjibUd8tDTFVGcloDEfeAmc8B4f9NSgmA/R9BA68MigiJyZDm95BG+hq9cLNW5YqVYbqjSPP7dEmulkkSdJuwRXKUimsXJ/N4jUbI859szgz4nhM+mU65OdFnls8BbpWoLt4F0+Gif+FrEXQaF/ofmnFuqiUJEmSJJXM1OGRx79/E7IfCLYRiiMr12Xz0Q/LI86NmLyIY+qvhSnPBi2+m/aAzmdCpfQopywHldLg6H8Gj4oqIQEOvjroNldgPAkOuiqUSJIkqWKzoCyVQkZ6JaqlJbN2U06hucY14+girGazHc+tng9j/wGzRgcXl3ufAYffFH+tsH94F145H/J++/OaNy64A/2SsVC9YbjZJEmSJEnhys6KPJ63BbZsjLuC8obsXPIibCUMsHb1Mnj0WMjdHAx883JQXO7/Tty9z93W/pdAUgpMeAhWzYMGneGwG6HFIWEnkyRJFZAtr6VSSE1O4vwehYuxiQlw4UEtQkhUQvU7QYuehcfTakCHvvDMccH+Q5vXwfoVwcXKiAFRj1kq+fnwwc3bi8lbrV0Cn90P344I9pBeFUetyiVJkiRJZad1r8jjDbpA1TpRjVIWmtRKp2WdKhHnDlv3/vZi8lZLpkbeDkuxq1t/GPg13LIyuFm+TRGfYUmSpFKyoCyV0qCj2nHFEa3JSK8EQMs6VXjo7K70aLVHyMmKqd//YO8zg7tbAZoeCOe/CT99BJkLCx8/axQs/S66GUtj3TJY9VPkuUlPw2sXwbvXwn+6wod/j2o0SZIkSVIM6DEQ6uxZcCylKhz7r3DylFJCQgK3ndiR1OSCX/91qpfKeVtejXzSnA+jkKyMzP8Cnj4G/l4D7msPn90HeUVs5yVJkqRSScjPzy+i+c3uKysri4yMDDIzM6levXrYcRQncnLzWL85d1thOW5t2RTcpZz222f/veth4hORjz1tGHQ8JXrZSmPzBri7JeRE3vO6kPPfhJaHlWskSZKkeOZ1k2Kdn1GVyOb1QfvnRZMhozHscy7UaBJ2qlJZ8OsGXpm0kGVZm9iveS1ObJVI2n86RD64zdFwThHF5liybAY8eQTkbCo43vksOPmxcDJFU15u8PjmZfhpLKRWhy7nQNPuYSeTJElxpDjXTO6hLJWR5KREMtIrwKL/SmnBY6u6exZ97I7mYk1KZehy1q637/pupAVlSZIkSdrdpFSBfS8MHhVE0z0qc13vdtsHMhcXfXBqnNx88eWjhYvJANNfhKY9glbQFU1+Pnz+AHz1GKz9BVKqwea12+enPAt97g32VpYkSSpjFaD6JalcdeoHNZsXHm9/fHwVlAF63xncXZ742yry1Iyij83LjU4mSZIkSVLsyM+HnM07Py4eFPVeVs3dwTlx0jL61zlFz425BXKyo5clWj79F3x4a1BMhoLF5K0+/DtsyopqLEmStHtwhbKkHUutChe8Bx/fDj++B5WqQOczoOcNYScrvkpp0PdhOOqfsG45ZDSBh/aFtUsKH9vhxOjnkyRJkiSFIzcHPrkj6Gq1cTU02hd6/R1aHBJ2suLL3QIf//ZeNq0J3kuHvlC1LjQ7COq0C260zttS+Nx6e0U9brHl5UZenbzVpjWw9FtovG/UIpW7nGz48pGdH7d5HSz6GlofWf6ZJEnSbsWCsqSdy2gEJ+3ChUu8qFwreACc/Ci8eDZsWb99vtsF0LZ3KNEkSZIkSSEYfSNMfGL788WT4PnT4JKPoV4R+w3Hqvf/CpOe2v588aTgAZCQBAdeAfsOKPh+AarWD66HY92Hf4clU3d8THrNqESJmg2/wqbMXTt26/cdkiRJZSgmWl4//PDDNG/enLS0NLp3787EiROLPHbLli3cdttttGrVirS0NDp37syoUaMKHDNu3DhOOOEEGjZsSEJCAm+88UY5vwNpN7BiVrBH0dTnK1b7pJaHwTXfBfsMHTEk+LLghAfCTiVJkiRJipaNa2Dys4XHczYVLrrGug2rYOpzRc/n58LnQ6HFYXD07VCnPVStB53PggtHQZXaUQpaQpvXw9dP7fiY5ofAHq2ikydaqtQN/px2pv7e0HCf8s8jSZJ2O6GvUH755ZcZNGgQjz32GN27d2fo0KH07t2bH3/8kbp16xY6fsiQIQwfPpwnn3yS9u3bM3r0aE4++WS++OIL9tkn+IFp/fr1dO7cmQsvvJBTTjkl2m9JqnjG3AKf/67IOmownPlCfLb+iqRyLdj/krBTSJIkSZLCkLUEcovYc3dH+w3HoqwlkLsLe0B/9yqc/gwcOLDcI5WpdcsKdhj7o2YHw6n/jV6eaElKhp7Xw3vX/WEiAcgPftloXzjt6WgnkyRJu4nQC8r3338/l1xyCQMGDADgscce49133+Xpp59m8ODBhY5/7rnnuOmmm+jTpw8Al112GR9++CH33Xcfw4cPB+DYY4/l2GOPjd6bkCqyeeMKFpMBsrNg5CVw9XfBRY0kSZIkSfGqZnNIzYDsCC2FG+wd9TilUqsFpFYPrtt3JGcXis6xqHpjqLxH0AL6jzr1g1OfjH6maNn/kqCV91ePQebiYI/og6+BvJzgz7xu+7ATSpKkCizUltebN29m8uTJ9OrVa9tYYmIivXr1YsKECRHPyc7OJi0trcBYeno648ePL3GO7OxssrKyCjwk/WbG65HH1/4CC7+MbhZJkiRJkspaSmU4+KrC45X3gO6XRj9PaaRUgQOv3Plx7fuUf5bykJwCh/xxlS6QVgMO/1vU40Rdp9Pg4g/h2plwxnPQqCs02d9isiRJKnehLi1cuXIlubm51KtXcA+QevXq8cMPP0Q8p3fv3tx///307NmTVq1aMXbsWEaOHElubm6Jc9x555384x//KPH50m4rPz/sBJIkSZIkbTNuXEnPvBaadIYf3gn2VK7fEfY8C6Y3Ktar9OxZ0t+/DB16PWQ0CvYaXrMANq4KVrFu1fZY2PvM8PKVVo+/QNW68NXjsHYpNO0OPW8IVmdLkiSpXMRdr9oHHniASy65hPbt25OQkECrVq0YMGAATz9d8j1CbrzxRgYNGrTteVZWFk2aNCmLuFL869AXJkX4+1W1PjQ9IPp5ylPO5uBuZ0mSJElSXBry5xJXlIEU4JTtT5//CfipWK8wbmYsVJSBLmcHj9wtMOFhmPK/oKi85wnQ6zZISgo7Yel0Oi14SJIkKSpCLSjXrl2bpKQkli1bVmB82bJl1K9fP+I5derU4Y033mDTpk38+uuvNGzYkMGDB9OyZcsS50hNTSU1NbXE50sVWsvDoMdAmPDQ9rGUanDKE5BUKbRYZeqnj2HsP2DJVKhcO9iXqOf1kBjnF9iSJEmStJsZd/OhISeIkU5e8yfAL9Pg21dh8eTt4xMegg2r4ORHQ4smSZKk+BPqHsopKSl069aNsWPHbhvLy8tj7Nix9OjRY4fnpqWl0ahRI3Jycnjttdfo27dveceVdl+9b4ezXoI2vaHjafCnT6Fl2BfpZWTJVHj+9OC/ABtWwid3BgVmSZIkSZLiyZZNMPxUGHYMjBpcsJi81fQXYOl30c8mSZKkuBV6y+tBgwbRv39/9t13X/bff3+GDh3K+vXrGTBgAADnn38+jRo14s477wTgq6++YvHixXTp0oXFixfz97//nby8PG644YZtr7lu3TrmzJmz7fm8efOYNm0atWrVomnTptF9g9r95OfDjNfh+zeC5x1Ogr1OhoSEMFOVzttXw+Rh25/PfAtOezpolRXvvnwM8rYUHv/6KTh0MKRUjn4mSZIkSVLJ9Po07AThmvAgzPlw58ctmBDsEy1JkiTtgtALymeccQYrVqzglltuYenSpXTp0oVRo0ZRr149ABYsWEBi4vaF1Js2bWLIkCHMnTuXqlWr0qdPH5577jlq1Kix7ZhJkyZx+OGHb3u+dX/k/v3788wzz0TlfSn+jCvNNku/N34ozP5g+/P3n4Q28+Hga3Z4Ws8Y2WapkAmPFCwmA+RuhtcvhVZHQEqVcHKVlVVzI49vXgfrlkGtFtHNI0mSJEkqubqxenEdJd+9vmvHVYu81ZwkSZIUSUJ+fn6MbO4SO7KyssjIyCAzM5Pq1auHHUdREvYC4pj8m5ifD/9qDpvWRJ4/Y3j8r1J+91r4+r+FxyvXhkEzITkl+pkkSZLigNdNinV+RrVbeqQHLP9+x8dkNIUrp0BSpehkkiRJUkwqzjVTqHsoS4pxWUuKLiYDJFSAf0J6XA5pGYXHD7nWYrIkSZIqnNmzZ3PWWWeRlZVVaC4zM5Ozzz6buXOL6OIjVWQbVsGvP0FeXthJSqdD3x3PN9oPznvdYrIkSZKKJfSW11Ks+LS02yxlLYG3r4TN6yPPH3IdtD6ylL9JlKVWg8RkyMspPJeYHLS8jne1WsKFH8C4e2DhV1CtAXT/M3Q6LexkkiRJUpm75557aNKkScS7zzMyMmjSpAn33HMPjz76aAjppBBkr4V3roEZrwfXvhlN4eh/wl4nhZ2sZA68EuZ9BvPHR55v2xtqt45uJkmSJMU9C8rSb0q9h/Grf4MGYyLPVd4Dzj0A4m3Ba1p16NQPpr9QeO6Q66BSevQzlYe67eG0p8JOIUmSJJW7Tz/9lOHDhxc5369fP84+++woJpJC9uZA+P6N7c8zF8CIC6FGU2jUNbRYJZZSGc5+Be5sBETYW2vGa3Do9VGPJUmSpPhWAfrVSjFidhHF5IREOGcEpFSJbp6y0uce6HAS8Nsm08npcMQtcPiNYaaSJEmSVAILFiygbt26Rc7Xrl2bhQsXRjGRFKK1S2HmW4XH83Nh0tPRz1NWEhKK3qJq+Q/w1hWQkx3dTJIkSYprrlCWykpKVdi8rvB49UbxeVfzVqlVod+zQUvvtb9AnfbxWxyXJEmSdnMZGRn89NNPNGvWLOL8nDlzIrbDliqkdcsgv4g9k9cujW6WspRSOWht/eN7ESbzYcr/IDktuIFckiRJ2gUWlKWtlo8r3fkdDoRvXyk8vmePXXvtuqXtuV3OqjcMHpIkSZLiVs+ePXnwwQc54ogjIs7/5z//4ZBDDolyKikktdtBei3YuKrwXNMD+G5xJmO+X0Z6ShIndG5IoxpxtO1Tn3tg5Sz4dU7k+anD4ajbKs5WVpIkSSpXCfn5+RE2VNm9ZWVlkZGRQWZmpndm705eSAj39z/bv4qSJEmKH143xaepU6fSo0cPjj/+eG644QbatWsHwA8//MDdd9/Nu+++yxdffEHXrnHcZek3fka1SyY9De9cU3CsVkvua/YID07YXmhOTkzgvn6d6dulUZQDlkJuDjy4D6xZEHn+6u+gRpPoZpIkSVLMKM41k3soS5IkSZK0m9hnn30YMWIE48aNo0ePHtSqVYtatWpx4IEH8tlnn/HKK69UiGKytMv2vRDOewM69IWmB8Khf2Va7xEFiskAOXn5/G3kt6zdtCWcnCWRlAxtjy16/t1ro5dFkiRJcc2W19JWvT4NO4EkSZIklbvjjz+e+fPnM2rUKObMmUN+fj5t27bl6KOPpnLlymHHk6Kv1eHB4zfvvzcz4mHrN+cyfvZKju3UIFrJim/dchh3L8z+AFKrQrs+kFYDNq0pfOzs0bBoEjTeN9opJUmSFGcsKEtbxfoexpIkSZJURtLT0zn55JPDjiHFpEpJRTf029Fc6LLXwdPHwKqfto8t/RbqdYxcUAZYMtWCsiRJknYqhn8KliRJkiRJZS0nJ4d77rmHrl27UrVqVapWrUrXrl2599572bIljtr5SuXkhM4NSUgoPF6rSgqHtK0d/UC76puXChaTt1r2XdHn1GxebnEkSZJUcbhCWZIkSZKk3cTGjRs56qijmDBhAr169aJnz6BT08yZM/nrX//KW2+9xQcffEBaWlrISaXwtKtfjb+fsBf/9+73bMnNByAjvRIPnb0PqclJIafbgSXTip5LqQab1xYcq7MntDqyXCNF1fIf4NtXIWdT0Oq7+UFhJ5IkSaowLChLJbX6ZxhzC/w4CiqlQ+cz4chbIKVK2MkkSZIkKaK77rqLhQsXMnXqVPbee+8Cc9OnT+fEE0/krrvu4u9//3s4AaUY0f/A5vTp1IBPflxOekoSR7avR3pKDBeTARJ20Iiw8b6QnBrsrUwCVKkDa5fAowfCQVdBl7OiFrNcTHoa3hkEBDcAMOEh2O8SOO7eUGNJkiRVFLa8lkpiUxYMOw6+fxNys4O9iL56DF45P+xkkiRJklSkl156ifvvv79QMRmgc+fO3HvvvbzwwgshJJNiT51qqZy+bxOO37th7BeTAdofV/TcHq3g7JfhT+MgMRnWLYVNmbBiJrxxKXx6d/RylrUNq+D9wWwrJm/19ZOw4KtQIkmSJFU0FpSlkvjmZchaVHh8zofwyzfRz1PGZi1byz/ensHAF6bw3ISf2bg5N+xIkiRJksrA/Pnz2X///YucP+CAA1iwYEEUE0kqM22OhuqNIs916hf896Pbghvj/+jTf0HO5vLLVp5++ijyewKY9X50s0iSJFVQFpSlkvh1zg7mZkcvRzkYPWMpx/3nM4Z9/jPvfPMLN785g9Me+4J12TlhR5MkSZJUStWrV2f58uVFzi9dupRq1apFMZGkMpOQAGe9BNUbbx9LrARH3QZNuwfPF30d+dy8HFgypfwzlodK6TuYqxy9HJIkSRWYBWWpJOp2KNlcjMvLy+e2t79nS27BNlEzlmTx/JfzQ0olSZIkqawcfvjh3HHHHUXO33XXXRx++OFRTCSpTDXYG66aDueMgFOfgkHfB3skb5NQ9LkLviz3eOWida9gT+g/SkiCTqdFP48kSVIFZEFZKolOp0GtloXH9zwB6u4Z/TxlZO7KdSxeszHi3GezV0Y5jSRJkqSyduutt/LBBx9wwAEH8Morr/DNN98wffp0XnrpJbp3784HH3zArbfeGnZMSaWRlAxtjgq+u6hat+Bc/Y5Fn7d5ffnmKi/JqXDGcKjyu/daqQr0fTjydzeSJEkqtuSwA0hxKaUKXPAefHIn/Ph+0F6p85lwyLVhJyuV6mmVSEiA/PzCcxmVK0U/kCRJkqQy1aFDB8aMGcNFF13EmWeeSUJCsFoxPz+f9u3b88EHH7DXXnuFnFJSuekxEOaNizxXp110s5SlpgfANTNg7ieQswlaHgZp1cNOJUmSVGFYUJZKqnoDOPE/YacoU3Wrp3F4u7p89EPhPdXO3K9JCIkkSZIklbUDDjiAGTNmMG3aNGbNmgVA27Zt6dKlS7jBJJW/NkdDw66F90veozXseWI4mcpKcgq0PTrsFJIkSRWSLa8lFXDv6Z05uHXtbc+rpiZzy/EdOKRNhP2IJEmSJMWtLl260K9fP/r161egmDxp0qTwQkkqXwkJcN5I6HYBpFaHSpVh7zOg/ztBQVaSJEmKwBXKkgqoVSWF4QO6MfeL11gxayIdq62nSr1TgRZhR5MkSZJURtatW0dSUhLp6enbxqZNm8bNN9/Me++9R25ubojpJJWr9JpwwgPBQ5IkSdoFrlCWVFB+Przan5Zj/0T3hf+lyvcvwvOnwUf/F3YySZIkSaW0cOFCevToQUZGBhkZGQwaNIgNGzZw/vnn0717d6pUqcIXX3wRdkxJkiRJUgxxhbKkgn4aCz+8U3h83L1QswXs3Q+SKkU/lyRJkqRSu/7669m0aRMPPPAAI0eO5IEHHuCzzz6je/fu/PTTTzRu3DjsiJIkSZKkGGNBWVJB88YVMZEPb/4Fxt4G57wKDfaOaixJkiRJpTdu3DhGjhzJAQccQL9+/ahfvz7nnHMOV199ddjRJJWnVXNh8RSo0Qya7Bd2GkmSJMUZC8qSCqq8x47n1y2FVy+AKyZDQkJUIkXN5g2QlAJJ/tMoSZKkimnZsmW0aNECgLp161K5cmWOPfbYkFNJKjd5efDO1TDlf0B+MNZ4PzjrJahSO8xkkiRJiiPuoSypoL3PgEqVd3zMqp9g8eTo5ImGhV/DU0fDHQ3gX83gvRtgy8awU0mSJEnlIjExscCvU1JSQkwjqVxNHgZTnmVbMRlg0dfw7rWhRZIkSVL8KVZBecmSJVx33XVkZWUVmsvMzOT6669n2bJlZRZOUgiq1YczX4DEneyTnLMpOnnK2+qf4bmTYOFXwfPN62Di4/Dm5WGmkiRJkspFfn4+bdu2pVatWtSqVYt169axzz77bHu+9SGpgvjm5cjjP7wDm9dHN4skSZLiVrH6ut5///1kZWVRvXr1QnMZGRmsXbuW+++/n3/9619lFlBSCFodDj0Gwuf/jjxfpS403j+6mcrLpGFBEfmPZrwOR90GGY2jn0mSJEkqJ8OGDQs7gqRo2rIh8nheDuRuBqpENY4kSZLiU7EKyqNGjeKxxx4rcv7888/nkksusaAsVQSHXANzP4ZfphUcT6wEJzwAyRWkLd7qeZHH8/NgzQILypIkSapQ+vfvH3YESdHUrg8s/bbweNMekF4z+nkkSZIUl4pVUJ43bx5NmzYtcr5x48b8/PPPpc0kKRakZcDFH8LMt2DmO7BxNTTpDl3Ph4xGYacrOw26wPdvFh5PToM67aMeR5IkSSpPkbawAqhSpQpJSUlRTiOp3PUYCLNGF7xZPL0WHHt3aJEkSZIUf4pVUE5PT+fnn38usqj8888/k56eXibBJMWApErQ8dTgUVF1uwAmPQ2ZCwuOd78UKrt3nCRJkiqWGjVqkJCQUGg8KSmJFi1acN1113HJJZeEkEyKQZvXB92rUquFnaTk0qrDRWOCG6kXT4YaTaDzWV7vSpIkqViKVVDu3r07zz33HD179ow4/7///Y/9968g+6pK2j1UrgUXjobP7gtafKfXhK79oZutACVJklTxfPzxxxHH16xZw+TJk7n++utJTk5mwIABUU4mxZC1S+Hda+HH94OCcsvDoM89ULtN2MlKJjkF9j49ePxe7pZg9fK6ZdDsIKhrly5JkiRFVqyC8nXXXcdRRx1FRkYG119/PfXq1QNg2bJl3H333TzzzDN88MEH5RJUkspNRiM4/v6wU0iSJEnl7tBDDy1yrm/fvjRv3pwHH3zQgrJ2X3l58NwpsHzG9rG5H8OzJ8IVkyClSnjZytLK2cH7zFywfazbBXD8UIjQxUCSJEm7t8TiHHz44Yfz8MMP89BDD9GwYUNq1qxJrVq1aNiwIQ8//DAPPvggRxxxRHlllSRJkiRJ5ejQQw9lzpw5YceQwjP344LF5K3WLoEZr0c/T3l5/dKCxWSAyc/AtyNCiSNJkqTYVqwVygB//vOfOf7443nllVeYM2cO+fn5tG3bltNOO43GjRuXR0ZJkiRJkhQFmZmZZGRkhB1DCs+aBUXPrZ4fvRzlafXPsHhS5LnvXivcGluSJEm7vWIXlAEaNWrENddcU9ZZJEmSJElSSLZs2cI999xD9+7dw44ihadR15LNxZO83B3M5UQvhyRJkuJGiQrKr776Ki+++CKzZs0CoG3btpx99tmcdtppZRpOkiRJkiSVnVNOOSXieGZmJjNmzCAhIYHPPvssyqmkGNKgM3ToC9+/WXC8yQHQpnc4mcraHq2g7l6RW3vveUL080iSJCnmFaugnJeXx1lnncWrr75K27Ztad++PQAzZszgjDPO4PTTT+fFF18kISGhXMJKkiRJkqSSK6qddZMmTTj11FM555xzbHktnfoUNN4fvhsBuTlBkfXAgZCYGHaystP3IRh+CmxcvX2s3XHQ5ZzwMkmSJClmFaug/MADD/Dhhx/y1ltvcfzxxxeYe+uttxgwYAAPPPAAV199dVlmlCRJkiRJZWDYsGFhR5BiX1KloIB84MCwk5SfRl3hqunw3UhYtwyaHQQtDgk7lSRJkmJUsW6tHDZsGPfcc0+hYjLAiSeeyN13383TTz9dZuEkSZIkSVJ0ZGVl8eijj7LvvvuGHUVSNKRlwL4D4LDBFpMlSZK0Q8UqKM+ePZtevXoVOd+rVy9mz55d6lCSJEmSJCk6Pv74Y8477zwaNGjAP//5T7p37x52JEmSJElSDClWy+v09HTWrFlD06ZNI85nZWWRlpZWJsEkSZIkSVL5WLx4Mc888wzDhg1jzZo1rF69mhdeeIF+/fqRkJAQdjxJkiRJUgwp1grlHj168OijjxY5//DDD9OjR49Sh5IkSZIkSWXvtddeo0+fPrRr145p06Zx3333sWTJEhITE+nUqZPFZEmSJElSIcUqKN9000089dRT9OvXj4kTJ5KVlUVmZiZffvklp59+Ok8//TQ33XRTeWWVJEmSJEmlcMYZZ7DPPvvwyy+/8Oqrr9K3b19SUlJK/boPP/wwzZs3Jy0tje7duzNx4sQij92yZQu33XYbrVq1Ii0tjc6dOzNq1KhSvaYkSZIkqfwUq6B84IEH8vLLL/Pxxx/To0cPatasSa1atTjooIP4+OOPefHFFznooIPKK6skSZIkSSqFiy66iIcffphjjjmGxx57jNWrV5f6NV9++WUGDRrErbfeypQpU+jcuTO9e/dm+fLlEY8fMmQIjz/+OA8++CDff/89l156KSeffDJTp04t8WtKkiRJkspPQn5+fn5xT9qwYQOjR49m9uzZALRt25ajjz6aypUrl3nAMGRlZZGRkUFmZibVq1cPO47i1eYNsH45VGsIyaW/4z8WrVyXTU5uPvUz3DtdkiRpd+N1U/zauHEjr7zyCk8//TRfffUVvXv35t1332XatGl07Nix2K/XvXt39ttvPx566CEA8vLyaNKkCVdccQWDBw8udHzDhg256aabuPzyy7eNnXrqqaSnpzN8+PASvWYkfkYlSZIkqWjFuWZKLslvULlyZU4++eSIc4sXL6ZRo0YleVmpYsjLhbG3wdf/hc3roHJt6HkdHHBZ2MnKzKLVGxj82reMn7MSgL0bZ3DHyZ3o2Cgj5GSSJEmSdiY9PZ3+/fvTv39/Zs+ezbBhw5g0aRIHHXQQxx13HKeddhqnnHLKLr3W5s2bmTx5MjfeeOO2scTERHr16sWECRMinpOdnU1aWsGbUtPT0xk/fnyJX3Pr62ZnZ297npWVtUvvQZIkSZK0Y8Vqeb0jS5cu5YorrqBNmzZl9ZJSfBp3L3w+NCgmA2xYCaMGw7cjQo1VVvLy8un/9MRtxWSAbxZlcu5TX5G5cUuIySRJkiQVV5s2bbjjjjtYuHAhw4cPZ8OGDZx11lm7fP7KlSvJzc2lXr16Bcbr1avH0qVLI57Tu3dv7r//fmbPnk1eXh5jxoxh5MiR/PLLLyV+TYA777yTjIyMbY8mTZrs8vuQJEmSJBWtWAXl1atXc9ZZZ1G7dm0aNmzIf/7zH/Ly8rjlllto2bIlX3/9NcOGDSuvrFJ8mPhE5PGvHo9ujnIyfs5KflqxvtD4mg1beGva4hASSZIkSSqtxMRETjjhBN544w0WLly4bfy4447bVugtKw888ABt2rShffv2pKSkMHDgQAYMGEBiYunueb/xxhvJzMzc9vj9+5AkSZIklVyxWl4PHjyYL774ggsuuIDRo0dzzTXXMGrUKBITE/noo4844IADyiunFB9yNgcrkiNZW7ZfwoRlaeamoueyip6TJEmSFB/q1q277dfjxo1j48aNRR5bu3ZtkpKSWLZsWYHxZcuWUb9+/Yjn1KlThzfeeINNmzbx66+/0rBhQwYPHkzLli1L/JoAqamppKam7vT9SZIkSZKKp1i3/77//vsMGzaMe++9l7fffpv8/Hy6dOnCO++8YzFZAkhOgYb7RJ5r0j26WcpJt+Y1i55rVvScJEmSpIonJSWFbt26MXbs2G1jeXl5jB07lh49euzw3LS0NBo1akROTg6vvfYaffv2LfVrSpIkSZLKXrEKykuWLGHPPfcEoHnz5qSlpXHuueeWSzApbh15CyRWKjiWmgE9rw8nTxlrVacqZ+1feC+yA1vtwWFt60Y4Q5IkSVJFNmjQIJ588kmeffZZZs6cyWWXXcb69esZMGAAAOeffz433njjtuO/+uorRo4cydy5c/nss8845phjyMvL44Ybbtjl15QkSZIkRU+xWl7n5+eTnLz9lKSkJNLT08s8lBTXWh0BF4+BLx+FVXOhQWc44C+wR6uwk5WZO07uRNemNXlr+hKyc/LovVd9zunelMTEhLCjSZIkSYqyM844gxUrVnDLLbewdOlSunTpwqhRo6hXrx4ACxYsKLA/8qZNmxgyZAhz586latWq9OnTh+eee44aNWrs8mtKkiRJkqInIT8/P39XD05MTKRjx47bisrffPMN7du3JyUlpcBxU6ZMKduUUZaVlUVGRgaZmZlUr1497DiSJEmSFHO8bto9VKtWjenTp2/b3zie+BmVJEmSpKIV55qpWCuUb7311gLPt+5vJEmSJEmSJEmSJEmqeEpVUJYkSZIkSRXX3/72N2rVqhV2DEmSJElSiBJ3fogkSZIkSaponnvuOQ466CAaNmzI/PnzARg6dChvvvnmtmNuvPHGAnsbS7uFX6bD7A9h4+qwk0iSJEkxoVgF5Zo1a1KrVq1CjxYtWtC7d2/GjBlTXjklSZIkSVIZefTRRxk0aBB9+vRhzZo15ObmAlCjRg2GDh0abjgpLFm/wH97weM94flT4b49Yfy/w04lSZIkha5YLa+Luqhcs2YNkydP5vjjj2fEiBGccMIJZZFNkiRJkiSVgwcffJAnn3ySk046ibvuumvb+L777st1110XYjIpRG9cCou+3v48ZyN8+Heo1wna9AotliRJkhS2YhWU+/fvv8P5Ll26cOedd1pQluJd9lpYNQ9qNIH0mmGnkSRJklTG5s2bxz777FNoPDU1lfXr14eQSApZ5iKY+0nkuWnDLShLkiRpt1ameygff/zx/PDDD2X5kpKi7aPb4d528PghwX/fuwHycsNOJUmSJKkMtWjRgmnTphUaHzVqFHvuuWf0A0lhy15b9NymrOjlKANZm7Yw5vtlfDFnJbl5+WHHkSRJUgVQrBXKO5OdnU1KSkpZvqSkaPr6KRh39/bnudkw8XGoXAsOGxxeLkmSJEllatCgQVx++eVs2rSJ/Px8Jk6cyIsvvsidd97Jf//737DjSdFXux1kNIXMBYXn2hwV/Twl9OLEBdz29vds3BLcGN6kVjpPnLcvezaoHnIySZIkxbMyXaH81FNP0aVLl7J8SUnRNOnpyONfPxXdHJIkSZLK1cUXX8y//vUvhgwZwoYNGzj77LN59NFHeeCBBzjzzDPDjidFX2IiHHcvJP1hoUTj/aHrjreAixUzf8nib69/u62YDLBw1UYuHT6ZPFcqS5IkqRSKtUJ50KBBEcczMzOZMmUKs2bNYty4cWUSTFII1q8oejw/HxISoptHkiRJUrk555xzOOecc9iwYQPr1q2jbt26YUeSwtW2N/zlS5g6HNYth+YHQ8dTIDk17GS7ZOSUReRHqBvP/3UDk+avZv8WtaIfSpIkSRVCsQrKU6dOjThevXp1jjrqKEaOHEmLFi3KJJikEDQ7CGaMLDze/GCLyZIkSVIFVblyZSpXrhx2DCk27NEKet0adooS2bA5t8i59ZtzophEkiRJFU2xWl5//PHHER9vvvkmd999d4mLyQ8//DDNmzcnLS2N7t27M3HixCKP3bJlC7fddhutWrUiLS2Nzp07M2rUqFK9pqTfHHYjpNcsOFapCvT6eyhxJEmSJJWPZcuWcd5559GwYUOSk5NJSkoq8JB2S/n58P2b8OqA4PH9m0Rc8hujjmgfuctAtdRk9m/u6mRJkiSVXLFWKO9Ifn4+o0aN4qmnnmLEiBG7fN7LL7/MoEGDeOyxx+jevTtDhw6ld+/e/PjjjxHbbQ0ZMoThw4fz5JNP0r59e0aPHs3JJ5/MF198wT777FOi15T0mzpt4dLxMPEJWPod1G4D+/8puENbkiRJUoVxwQUXsGDBAm6++WYaNGhAgh2JJHj7Spjyv+3PZ4yEbhfACQ+EFqk4jmhflxM6N+Tt6Uu2jSUlJvD3E/eiSmqZfQUoSZKk3VBCfn7pbrWcN28eTz/9NM888wwrVqygV69evPPOO7t8fvfu3dlvv/146KGHAMjLy6NJkyZcccUVDB48uNDxDRs25KabbuLyyy/fNnbqqaeSnp7O8OHDS/Saf5SVlUVGRgaZmZlUr159l9+LtM38CbBkCtRoCm2PgaRKYSeSJEmSypTXTfGtWrVqfPbZZ3Tp0iXsKOXGz6iK5Zfp8HjPyHOXjof6naKbp4Ty8/P5ZNYKPv5hOVVTkzmlayNa160WdixJkiTFoOJcM5Xo9sTs7GxGjBjBU089xfjx48nNzeXee+/loosuKtZF2ubNm5k8eTI33njjtrHExER69erFhAkTivy909LSCoylp6czfvz4Ur1mdnb2tudZWVm7/B6kAnKy4aVzYM6Y7WO1WsL5bwbFZUmSJEmKAU2aNKGU95dLFcu8cUXPzf00bgrKCQkJHN6uLoe3s0OfJEmSyk6x9lCePHkyf/nLX6hfvz5Dhw7lpJNOYuHChSQmJtK7d+9i3/G7cuVKcnNzqVevXoHxevXqsXTp0ojn9O7dm/vvv5/Zs2eTl5fHmDFjGDlyJL/88kuJX/POO+8kIyNj26NJkybFeh/SNl8+UrCYDLBqLrx3fTh5JEmSJCmCoUOHMnjwYH7++eewo0ixofIeJZuTJEmSdgPFKih3796d1NRUvvzyS77++muuvPLKQoXb8vbAAw/Qpk0b2rdvT0pKCgMHDmTAgAEkJhbrrRRw4403kpmZue2xcOHCMkys3cqM1yOPz/4AstdFN4skSZIkFeGMM87gk08+oVWrVlSrVo1atWoVeEi7nT1PhPQIn/30WtDhxOjnkSRJkmJIsVpeH3nkkTz11FMsX76c8847j969e5OQkFDi37x27dokJSWxbNmyAuPLli2jfv36Ec+pU6cOb7zxBps2beLXX3+lYcOGDB48mJYtW5b4NVNTU0lNTS3x+5C2sWWcJEmSpDgwdOjQsCNIsSW1KpwzAl7/E/w6Jxjbow2c8jikVAk3W3nasCp4f8l+LyZJkqSiFaugPHr0aBYuXMiwYcO47LLL2LhxI2eccQZAiQrLKSkpdOvWjbFjx3LSSScBkJeXx9ixYxk4cOAOz01LS6NRo0Zs2bKF1157jX79+pX6NaVS2+skWPpN4fFWRwYXp5IkSZIUA/r37x92BCn2NO4GAyfBsu+C5/U6QikWUsS02WPgg5thxUyoVAX2OReO/qeFZUmSJEVU7D7RTZo04ZZbbmHevHk899xzrFixguTkZPr27cvf/vY3pkyZUqzXGzRoEE8++STPPvssM2fO5LLLLmP9+vUMGDAAgPPPP58bb7xx2/FfffUVI0eOZO7cuXz22Wccc8wx5OXlccMNN+zya0rl5oDLoeXhBcdqNIPj7g0njyRJkiQV4aeffmLIkCGcddZZLF++HID333+fGTNmhJxMClFCAtTvFDwqajH5l+nw4llBMRlgy3qY+Di8e224uSRJkhSzirVC+Y+OOuoojjrqKFavXs3w4cN5+umn+de//kVubu4uv8YZZ5zBihUruOWWW1i6dCldunRh1KhR2/ZmXrBgQYH9kTdt2sSQIUOYO3cuVatWpU+fPjz33HPUqFFjl19TKjeV0uD8N2DeOFg8GWo0hfYnQHJK2MkkSZIkaZtPP/2UY489loMOOohx48Zx++23U7duXaZPn85TTz3FiBEjwo4oqbxMfBLythQen/4SHHUbVHYfdUmSJBWUkJ9ftpu+Tpkyha5duwLwl7/8hdtuu43atWuX5W9R7rKyssjIyCAzM5Pq1auHHUeSJEmSYo7XTfGtR48enH766QwaNIhq1aoxffp0WrZsycSJEznllFNYtGhR2BFLzc+oVIT/9YW5n0Seu2wC1OsQ1TiSJEkKR3GumYrd8npnthaTAYYPH05WVlZZ/xZSbNm8AVbODv4rSZIkSXHg22+/5eSTTy40XrduXVauXBlCIklR07Br5PH0mlCrRXSzSJIkKS6UeUH598p48bMUW/Lz4eM74b528NC+wX8/vjMYlyRJkqQYVqNGDX755ZdC41OnTqVRo0YhJJIUNd3/DFXrFx7veQNUSo9+HkmSJMW8ci0oSxXaV4/Bp3dB9m+r8LOzgucTnwg3lyRJkiTtxJlnnslf//pXli5dSkJCAnl5eXz++edcd911nH/++WHHk1SeqtWHS8bCfpdA3Q7Q8jDo9xz0+EvYySRJkhSjksMOIMWtiU8WMf5EcLevJEmSJMWoO+64g8svv5wmTZqQm5tLhw4dyM3N5eyzz2bIkCFhx5NU3jIaw3H3hp1CkiRJccKCslRSa5cWb1ySJEmSYkRKSgpPPvkkN998M9999x3r1q1jn332oU2bNmFHkyRJkiTFGAvKUkk1PQB+GhthvEf0s0iSJElSCTRt2pSmTZuGHUOSJEmSFMPKtaB87rnnUr169fL8LaTwHHETLPgStqzfPlapChz+t/AySZIkSdIuGDRoUMTxhIQE0tLSaN26NX379qVWrVpRTiZJkiRJijUJ+fn5+bty4DfffLPLL7r33nuXOFAsyMrKIiMjg8zMTAvi2rEVP8KXjwb/rdMODvgL1GkbdipJkiSp3HndFN8OP/xwpkyZQm5uLu3atQNg1qxZJCUl0b59e3788UcSEhIYP348HTp0CDltyfgZlSRJkqSiFeeaaZdXKHfp0oWEhASKqj9vnUtISCA3N7d4iaV4VacdnDA07BSSJEmSVCxbVx8PGzZs2xcHmZmZXHzxxRx88MFccsklnH322VxzzTWMHj065LSSJEmSpDDt8grl+fPn7/KLNmvWrMSBYoF3MUuSJEnSjnndFN8aNWrEmDFjCq0+njFjBkcffTSLFy9mypQpHH300axcuTKklKXjZ1SSJEmSilYuK5TjvUgsSZIkSZICmZmZLF++vFBBecWKFWRlZQFQo0YNNm/eHEY8SZIkSVIM2eWCciTff/89CxYsKHSBeeKJJ5YqlCRJkiRJKj99+/blwgsv5L777mO//fYD4Ouvv+a6667jpJNOAmDixIm0bds2xJSSJEmSpFhQooLy3LlzOfnkk/n2228L7KuckJAA4B7KkiRJkiTFsMcff5xrrrmGM888k5ycHACSk5Pp378///73vwFo3749//3vf8OMKUmSJEmKAYklOemqq66iRYsWLF++nMqVKzNjxgzGjRvHvvvuyyeffFLGESVJkiRJUlmqWrUqTz75JL/++itTp05l6tSp/PrrrzzxxBNUqVIFgC5dulC7dm3y8vJCTitJkiRJClOJVihPmDCBjz76iNq1a5OYmEhiYiIHH3wwd955J1deeSVTp04t65ySJEmSJKmMVa1alb333rvI+Q4dOjBt2jRatmwZxVSSJEmSpFhSohXKubm5VKtWDYDatWuzZMkSAJo1a8aPP/5YdukkSZIkSVJotm5xJUmSJEnafZVohXLHjh2ZPn06LVq0oHv37tx9992kpKTwxBNPeNeyJEmSJEmSJEmSJFUQJSooDxkyhPXr1wNw2223cfzxx3PIIYewxx578PLLL5dpQEkxKjcH1i2DyntApbSw00iSJEmSJEmSJKkclKig3Lt3722/bt26NT/88AOrVq2iZs2aJCQklFk4STFq4pMw7p6goJyaAd3/DIfdCIkl6qIvSZIkSZIkSZKkGFWigvLvLVy4EIAmTZqUOoykOPDda/DeddufZ2fCuLuDVcqHXBteLkmSJEllzpvGJUmSJEklWk6Yk5PDzTffTEZGBs2bN6d58+ZkZGQwZMgQtmzZUtYZJcWSrx4v3rgkSZKkuJWfnx92BEmSJElSyEq0QvmKK65g5MiR3H333fTo0QOACRMm8Pe//51ff/2VRx99tExDSoohmYsjj69bBrlbIKlSdPNIkiRJKpYtW7aQnp7OtGnT6Nix4w6P/f7772nYsGGUkkmSJEmSYlGJCsovvPACL730Escee+y2sb333psmTZpw1llnWVCWKrLG+8L3iwqPN+hsMVmSJEmKA5UqVaJp06bk5ubu9Fi3t5IkSZIklajldWpqKs2bNy803qJFC1JSUkqbSVIs63k9pFQrOJaYDEfcEk4eSZIkScV200038be//Y1Vq1aFHUWSJEmSFONKtEJ54MCB/POf/2TYsGGkpqYCkJ2dze23387AgQPLNKCkGFO/I1zyEXzxH1j6LdRqCT0uD1YuS5IkSYoLDz30EHPmzKFhw4Y0a9aMKlWqFJifMmVKSMkkSZIkSbFmlwvKp5xySoHnH374IY0bN6Zz584ATJ8+nc2bN3PkkUeWbUJJsadOW+j7UNgpJEmSJJXQSSedFHYESZIkSVKc2OWCckZGRoHnp556aoHn7qskSZIkSVJ8uPXWW8OOIEmSJEmKE7tcUB42bFh55pAkSZIkSVG0Zs0aRowYwU8//cT1119PrVq1mDJlCvXq1aNRo0Zhx5MkSZIkxYgS7aG81YoVK/jxxx8BaNeuHXXq1CmTUJIkSZIkqfx888039OrVi4yMDH7++WcuueQSatWqxciRI1mwYAH/+9//wo4oSZIkSYoRiSU5af369Vx44YU0aNCAnj170rNnTxo2bMhFF13Ehg0byjqjJEmSJEkqQ4MGDeKCCy5g9uzZpKWlbRvv06cP48aNCzGZJEmSJCnWlKigPGjQID799FPefvtt1qxZw5o1a3jzzTf59NNPufbaa8s6oyRJkiRJKkNff/01f/7znwuNN2rUiKVLl4aQSJIkSZIUq0rU8vq1115jxIgRHHbYYdvG+vTpQ3p6Ov369ePRRx8tq3ySJEmSJKmMpaamkpWVVWh81qxZbmclSZIkSSqgRCuUN2zYQL169QqN161b15bXkiRJkiTFuBNPPJHbbruNLVu2AJCQkMCCBQv461//yqmnnhpyOkmSJElSLClRQblHjx7ceuutbNq0advYxo0b+cc//kGPHj3KLJwkSZIkSSp79913H+vWraNu3bps3LiRQw89lNatW1OtWjVuv/32sONJkiRJkmJIiVpeP/DAA/Tu3ZvGjRvTuXNnAKZPn05aWhqjR48u04CSJEmSJKlsZWRkMGbMGMaPH88333zDunXr6Nq1K7169Qo7miRJkiQpxiTk5+fnl+TEDRs28Pzzz/PDDz8AsOeee3LOOeeQnp5epgHDkJWVRUZGBpmZmVSvXj3sOJIkSZIUc7xuUqzzMypJkiRJRSvONVOJVigDVK5cmUsuuaSkp0uSJEmSpBCNHTuWf//738ycORMIbhS/+uqrXaUsSZIkSSpglwvKb7311i6/6IknnliiMJIkSZIkqfw98sgjXHXVVZx22mlcPlowHwAAeeJJREFUddVVAHz55Zf06dOHf//731x++eUhJ5QkSZIkxYpdbnmdmJi4ay+YkEBubm6pQoXNtliSJEmStGNeN8W3xo0bM3jwYAYOHFhg/OGHH+aOO+5g8eLFISUrO35GJUmSJKloxblm2rUqMZCXl7dLj3gvJkuSJEmSVNGtWbOGY445ptD40UcfTWZmZgiJJEmSJEmxqlh7KG/atIkPP/yQ448/HoAbb7yR7Ozs7S+WnMxtt91GWlpa2aaUJEmSJEll5sQTT+T111/n+uuvLzD+5ptvbrvmlyRJkiQJillQfuaZZ3j33Xe3XVw+9NBD7LXXXqSnpwPwww8/UL9+fQYNGlT2SSVJkiRJUpno0KEDt99+O5988gk9evQAgj2UP//8c6699lr+85//bDv2yiuvDCumJEmSJCkG7PIeygCHHHIIN9xwAyeccAIA1apVY/r06bRs2RKA4cOH8/DDDzNhwoTySRsl7rMkSZIkSTvmdVN8a9GixS4dl5CQwNy5c8s5TfnwMypJkiRJRSvONVOxVijPmTOHTp06bXuelpZGYuL2bZj3339/Lr/88mLGlSRJkiRJ0TRv3rywI0iSJEmS4kTizg/Zbs2aNQX2TF6xYgXNmzff9jwvL6/AvCRJkiRJil/Vq1eP2xXKkiRJkqSyUayCcuPGjfnuu++KnP/mm29o3LhxqUNJkiRJkqTwFWOXLEmSJElSBVWsgnKfPn245ZZb2LRpU6G5jRs38o9//IPjjjuuzMJJkiRJkiRJkiRJksJTrD2U//a3v/HKK6/Qrl07Bg4cSNu2bQH48ccfeeihh8jJyeFvf/tbuQSVJEmSJEmSJEmSJEVXsQrK9erV44svvuCyyy5j8ODB21pfJSQkcNRRR/HII49Qr169cgkqSZIkSZIkSZIkSYquYhWUAVq0aMGoUaNYtWoVc+bMAaB169bUqlWrzMNJkiRJkqTwJCQkhB1BkiRJkhSyYheUt6pVqxb7779/WWaRJEmSJEkxZGtnMkmSJEnS7isx7ACSJEmSJCk2vf/++zRq1CjsGJIkSZKkEJV4hbIkSZIkSYpPgwYNijiekJBAWloarVu3pm/fvhx88MFRTiZJkiRJijUWlCVJkiRJ2s1MnTqVKVOmkJubS7t27QCYNWsWSUlJtG/fnkceeYRrr72W8ePH06FDh5DTSpIkSZLCZMtrSZIkSZJ2M3379qVXr14sWbKEyZMnM3nyZBYtWsRRRx3FWWedxeLFi+nZsyfXXHNN2FElSZIkSSFLyM/Pzw87RKzJysoiIyODzMxMqlevHnYcSZIkSYo5XjfFt0aNGjFmzJhCq49nzJjB0UcfzeLFi5kyZQpHH300K1euDCll6fgZlSRJkqSiFeeayRXKkiRJkiTtZjIzM1m+fHmh8RUrVpCVlQVAjRo12Lx5c7SjSZIkSZJijAVlSZIkSZJ2M3379uXCCy/k9ddfZ9GiRSxatIjXX3+diy66iJNOOgmAiRMn0rZt23CDSpIkSZJClxx2AEmSJEmSFF2PP/4411xzDWeeeSY5OTkAJCcn079/f/79738D0L59e/773/+GGVOSJEmSFAPcQzkC91mSJEmSpB3zuqliWLduHXPnzgWgZcuWVK1aNeREZcfPqCRJkiQVzT2UJUmSJElSkYYPH86GDRuoWrUqe++9N3vvvXeFKiZLkiRJksqOBWVJkiRJknYz11xzDXXr1uXss8/mvffeIzc3N+xIkiRJkqQYZUFZkiRJkqTdzC+//MJLL71EQkIC/fr1o0GDBlx++eV88cUXYUeTJEmSJMUYC8qSJEmSJO1mkpOTOf7443n++edZvnw5//73v/n55585/PDDadWqVdjxJEmSJEkxJDnsAJIkSZIkKTyVK1emd+/erF69mvnz5zNz5sywI0mSJEmSYogrlCVJkiRJ2g1t2LCB559/nj59+tCoUSOGDh3KySefzIwZM8KOJkmSJEmKIRaUJUmSJEnazZx55pnUrVuXa665hpYtW/LJJ58wZ84c/vnPf5KTkxN2PEmSJElSDImJgvLDDz9M8+bNSUtLo3v37kycOHGHxw8dOpR27dqRnp5OkyZNuOaaa9i0adO2+bVr13L11VfTrFkz0tPTOfDAA/n666/L+21IkiRJkhQXkpKSeOWVV/jll1946KGH6NixI0888QTdu3enc+fOYceTJEmSJMWQ0AvKL7/8MoMGDeLWW29lypQpdO7cmd69e7N8+fKIx7/wwgsMHjyYW2+9lZkzZ/LUU0/x8ssv87e//W3bMRdffDFjxozhueee49tvv+Xoo4+mV69eLF68OFpvS5IkSZKkmLW11fXnn39O//79adCgAffeey+HH344X375ZdjxJEmSJEkxJCE/Pz8/zADdu3dnv/3246GHHgIgLy+PJk2acMUVVzB48OBCxw8cOJCZM2c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      "text/plain": [
       "<Figure size 2400x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Figure: Performance with different numbers of training genes\n",
    "df_ntrain_extra = roc_by_gene_ntrain_extra.iloc[:-3, :2]\n",
    "df_ntrain_extra['Number of training genes'] = df_ntrain_extra.index.to_series().str.split('_', expand = True)[1]\n",
    "\n",
    "df_ntrain['Training set'] = 'Only contains the five CPT-1 training proteins'\n",
    "df_ntrain_extra['Training set'] = 'Only Contains additional training proteins'\n",
    "df_ntrain_extra = pd.concat([df_ntrain_extra, df_ntrain])\n",
    "df_ntrain_extra['Number of training genes'] = pd.Categorical(df_ntrain_extra['Number of training genes'], \n",
    "                                                             ['ESM-1v', 'EVE'] + [str(i+1) for i in range(13)])\n",
    "                                                             \n",
    "df_ntrain_extra = df_ntrain_extra.sort_values('Number of training genes')\n",
    "df_ntrain_mean_extra = df_ntrain_extra.groupby('Number of training genes').mean().reset_index()\n",
    "order = df_ntrain_mean_extra['Number of training genes']\n",
    "\n",
    "fig, axes = plt.subplots(1, 2, figsize=(24, 8))\n",
    "sns.scatterplot(ax = axes[0], x = \"Number of training genes\", y = \"Global_AUROC\", data = df_ntrain_mean, marker = '_', s = 500, \n",
    "                color = 'blue')\n",
    "sns.scatterplot(ax = axes[0], x = \"Number of training genes\", y = \"Global_AUROC\", data = df_ntrain_mean_extra, marker = '_', s = 500, \n",
    "                color = 'orange')\n",
    "sns.stripplot(ax = axes[0], x = \"Number of training genes\", y = \"Global_AUROC\", data = df_ntrain_extra, order = order, zorder = 0, \n",
    "              hue = 'Training set')\n",
    "sns.scatterplot(ax = axes[1], x = \"Number of training genes\", y = \"Avg_per_gene_AUROC\", data = df_ntrain_mean, marker = '_', s = 500, \n",
    "                color = 'blue')\n",
    "sns.scatterplot(ax = axes[1], x = \"Number of training genes\", y = \"Avg_per_gene_AUROC\", data = df_ntrain_mean_extra, marker = '_', s = 500, \n",
    "                color = 'orange')\n",
    "sns.stripplot(ax = axes[1], x = \"Number of training genes\", y = \"Avg_per_gene_AUROC\", data = df_ntrain_extra, order = order, zorder = 0,\n",
    "              hue = 'Training set')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "812e8926-a540-4f5c-85b5-a36a89160613",
   "metadata": {},
   "source": [
    "### Test within-gene imputation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "9bc3920b-81f3-4178-b364-c900c8f3109c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fraction of variants without EVE scores: 0.1466363071827779\n"
     ]
    }
   ],
   "source": [
    "# Select mutations where EVE scores are not available\n",
    "print(f\"Fraction of variants without EVE scores: {x['eve_na'].mean()}\")\n",
    "y_eve_na = y[x['eve_na'] == True]\n",
    "x_eve_na = x[x['eve_na'] == True]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "455e15e6-00d0-4fe8-b441-6a4a098a7055",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Selecting features in the category 100verte_msa\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 7/7 [00:03<00:00,  1.88it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best features: ('100verte_mut_freq',) Avg metric on DMS: 0.78211\n",
      "Selecting features in the category 30mammal_msa\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 7/7 [00:04<00:00,  1.68it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best features: ('30mammal_mut_freq',) Avg metric on DMS: 0.7812\n",
      "Selecting features in the category mpnn\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 7/7 [00:04<00:00,  1.47it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best features: ('vanilla_protmpnn_norm',) Avg metric on DMS: 0.78523\n",
      "Selecting features in the category struct\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 3/3 [00:02<00:00,  1.33it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best features: ('conditioned_mt_score',) Avg metric on DMS: 0.78552\n",
      "0 ['BLOSUM1'] 0.78851\n",
      "1 ['BLOSUM1', 'ST6'] 0.79029\n",
      "All selected features: ['esm_1v', '100verte_mut_freq', '30mammal_mut_freq', 'vanilla_protmpnn_norm', 'conditioned_mt_score', 'BLOSUM1', 'ST6']\n"
     ]
    }
   ],
   "source": [
    "# Reselect features without EVE\n",
    "feat_selected_dms_no_eve = select_feat_by_category(feat_mat, label, all_genes, base_feat = ['esm_1v'], with_struct = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "1a90ba7e-fb21-44b9-bfa2-7f60cafe278a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Features selected on DMS ['esm_1v', 'evolutionary_index_ASM', '100verte_mut_freq', '30mammal_mut_freq', 'vanilla_protmpnn_norm', 'conditioned_wt_score', 'BLOSUM1', 'ST6', 'VHSE6']\n",
      "Features w/o eve ['esm_1v', '100verte_mut_freq', '30mammal_mut_freq', 'vanilla_protmpnn_norm', 'conditioned_mt_score', 'BLOSUM1', 'ST6']\n",
      "Features w/o eve w/o mpnn ['BLOSUM1', 'ST6', '100verte_mut_freq', 'esm_1v', '30mammal_mut_freq']\n",
      "Features w/o msa ['BLOSUM1', 'ST6', 'conditioned_wt_score', 'vanilla_protmpnn_norm', 'esm_1v', 'evolutionary_index_ASM', 'VHSE6']\n",
      "Features w/o struct ['BLOSUM1', 'ST6', '100verte_mut_freq', 'esm_1v', '30mammal_mut_freq', 'evolutionary_index_ASM', 'VHSE6']\n"
     ]
    }
   ],
   "source": [
    "# Construct prediction matrix\n",
    "pred_eve_na = pd.DataFrame(index = y_eve_na.index)\n",
    "\n",
    "cpt = CPTClassifier()\n",
    "# Transfer model predictions\n",
    "features = feat_selected_dms\n",
    "print('Features selected on DMS', features)\n",
    "cpt.train(feat_mat, label, all_genes, features)\n",
    "pred_clinvar = cpt.predict(x_eve_na[features])\n",
    "pred_eve_na['Transfer'] = 1 - pred_clinvar.values\n",
    "\n",
    "# Transfer model without EVE\n",
    "features = feat_selected_dms_no_eve\n",
    "print('Features w/o eve', features)\n",
    "cpt.train(feat_mat, label, all_genes, features)\n",
    "pred_clinvar = cpt.predict(x_eve_na[features])\n",
    "pred_eve_na['Transfer_no_eve'] = 1 - pred_clinvar.values\n",
    "\n",
    "# Transfer model without EVE without structural features\n",
    "features = list(set(feat_selected_dms_no_eve) - set(struct_feats + mpnn_feats))\n",
    "print('Features w/o eve w/o mpnn', features)\n",
    "cpt.train(feat_mat, label, all_genes, features)\n",
    "pred_clinvar = cpt.predict(x_eve_na[features])\n",
    "pred_eve_na['Transfer_no_eve_struct'] = 1 - pred_clinvar.values\n",
    "\n",
    "# Transfer model predictions without MSA features\n",
    "features = list(set(feat_selected_dms) - set(msa_feats))\n",
    "print('Features w/o msa', features)\n",
    "cpt.train(feat_mat, label, all_genes, features)\n",
    "pred_clinvar = cpt.predict(x_eve_na[features])\n",
    "pred_eve_na['Transfer_no_msa'] = 1 - pred_clinvar.values\n",
    "\n",
    "# Transfer model predictions without structural features\n",
    "features = list(set(feat_selected_dms) - set(struct_feats + mpnn_feats))\n",
    "print('Features w/o struct', features)\n",
    "cpt.train(feat_mat, label, all_genes, features)\n",
    "pred_clinvar = cpt.predict(x_eve_na[features])\n",
    "pred_eve_na['Transfer_no_struct'] = 1 - pred_clinvar.values\n",
    "\n",
    "# Individual features\n",
    "for feat in list(set(pos_feats) - set(aa_feats)):\n",
    "    pred_eve_na[feat] = x_eve_na[feat]\n",
    "for feat in list(set(neg_feats) - set(aa_feats)):\n",
    "    pred_eve_na[feat] = -x_eve_na[feat]\n",
    "\n",
    "feat_list = pos_feats + neg_feats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "777b2730-6adb-4d04-b2da-69b354008453",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "17 genes used to calculate per gene AUROC out of 828 genes\n",
      "Calculating...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 17/17 [00:00<00:00, 34.17it/s]\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Transfer</th>\n",
       "      <th>Transfer_no_struct</th>\n",
       "      <th>Transfer_no_msa</th>\n",
       "      <th>Transfer_no_eve</th>\n",
       "      <th>Transfer_no_eve_struct</th>\n",
       "      <th>esm_1v</th>\n",
       "      <th>EVE_scores_ASM</th>\n",
       "      <th>evolutionary_index_ASM</th>\n",
       "      <th>100verte_wt_freq</th>\n",
       "      <th>100verte_mut_freq</th>\n",
       "      <th>...</th>\n",
       "      <th>conditioned_wt_score</th>\n",
       "      <th>ca_only_protmpnn_norm</th>\n",
       "      <th>ca_protmpnn_norm</th>\n",
       "      <th>vanilla_protmpnn_norm</th>\n",
       "      <th>30mammal_mut_freq</th>\n",
       "      <th>30mammal_gap_freq</th>\n",
       "      <th>100verte_gap_freq</th>\n",
       "      <th>n</th>\n",
       "      <th>n_pos</th>\n",
       "      <th>n_neg</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Global_AUROC</th>\n",
       "      <td>0.9215</td>\n",
       "      <td>0.9169</td>\n",
       "      <td>0.9111</td>\n",
       "      <td>0.9096</td>\n",
       "      <td>0.8919</td>\n",
       "      <td>0.8650</td>\n",
       "      <td>0.8517</td>\n",
       "      <td>0.8497</td>\n",
       "      <td>0.8242</td>\n",
       "      <td>0.7314</td>\n",
       "      <td>...</td>\n",
       "      <td>0.6663</td>\n",
       "      <td>0.6525</td>\n",
       "      <td>0.6307</td>\n",
       "      <td>0.6260</td>\n",
       "      <td>0.6239</td>\n",
       "      <td>0.5073</td>\n",
       "      <td>0.4757</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Avg_per_gene_AUROC</th>\n",
       "      <td>0.9216</td>\n",
       "      <td>0.9160</td>\n",
       "      <td>0.9139</td>\n",
       "      <td>0.9033</td>\n",
       "      <td>0.8889</td>\n",
       "      <td>0.8487</td>\n",
       "      <td>0.9016</td>\n",
       "      <td>0.8995</td>\n",
       "      <td>0.8001</td>\n",
       "      <td>0.7252</td>\n",
       "      <td>...</td>\n",
       "      <td>0.7446</td>\n",
       "      <td>0.6632</td>\n",
       "      <td>0.6177</td>\n",
       "      <td>0.6293</td>\n",
       "      <td>0.6056</td>\n",
       "      <td>0.5685</td>\n",
       "      <td>0.4544</td>\n",
       "      <td>26.0</td>\n",
       "      <td>15.4118</td>\n",
       "      <td>10.5882</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                    Transfer  Transfer_no_struct  Transfer_no_msa  \\\n",
       "Global_AUROC          0.9215              0.9169           0.9111   \n",
       "Avg_per_gene_AUROC    0.9216              0.9160           0.9139   \n",
       "\n",
       "                    Transfer_no_eve  Transfer_no_eve_struct  esm_1v  \\\n",
       "Global_AUROC                 0.9096                  0.8919  0.8650   \n",
       "Avg_per_gene_AUROC           0.9033                  0.8889  0.8487   \n",
       "\n",
       "                    EVE_scores_ASM  evolutionary_index_ASM  100verte_wt_freq  \\\n",
       "Global_AUROC                0.8517                  0.8497            0.8242   \n",
       "Avg_per_gene_AUROC          0.9016                  0.8995            0.8001   \n",
       "\n",
       "                    100verte_mut_freq  ...  conditioned_wt_score  \\\n",
       "Global_AUROC                   0.7314  ...                0.6663   \n",
       "Avg_per_gene_AUROC             0.7252  ...                0.7446   \n",
       "\n",
       "                    ca_only_protmpnn_norm  ca_protmpnn_norm  \\\n",
       "Global_AUROC                       0.6525            0.6307   \n",
       "Avg_per_gene_AUROC                 0.6632            0.6177   \n",
       "\n",
       "                    vanilla_protmpnn_norm  30mammal_mut_freq  \\\n",
       "Global_AUROC                       0.6260             0.6239   \n",
       "Avg_per_gene_AUROC                 0.6293             0.6056   \n",
       "\n",
       "                    30mammal_gap_freq  100verte_gap_freq     n    n_pos  \\\n",
       "Global_AUROC                   0.5073             0.4757   NaN      NaN   \n",
       "Avg_per_gene_AUROC             0.5685             0.4544  26.0  15.4118   \n",
       "\n",
       "                      n_neg  \n",
       "Global_AUROC            NaN  \n",
       "Avg_per_gene_AUROC  10.5882  \n",
       "\n",
       "[2 rows x 22 columns]"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get AUROC matrix\n",
    "# Get per gene AUROC\n",
    "roc_by_gene_eve_na = get_per_gene_auroc(y_eve_na, pred_eve_na)\n",
    "\n",
    "# Add global AUROC\n",
    "roc_by_gene_eve_na.insert(loc = 0, column = 'Global_AUROC', \n",
    "                   value = [np.nan, np.nan, np.nan] + \\\n",
    "                           [float(roc_auc_score(y_eve_na, pred_eve_na[feat])) for feat in pred_eve_na.columns])\n",
    "roc_by_gene_eve_na = roc_by_gene_eve_na.sort_values('Global_AUROC', ascending = False).round(4)\n",
    "\n",
    "# Transpose the dataframe\n",
    "roc_by_gene_eve_na = roc_by_gene_eve_na.transpose()\n",
    "roc_by_gene_eve_na.iloc[:2, :]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "c4022ca6-30d2-4411-ae3d-6b434c4e6cd8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 700x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Figure: within-gene imputation of EVE scores improves performance\n",
    "auroc_g_na = roc_by_gene_eve_na.loc['Global_AUROC', \n",
    "                                    ['Transfer', 'Transfer_no_eve', 'esm_1v', 'EVE_scores_ASM']].to_list()\n",
    "auroc_p_na = roc_by_gene_eve_na.loc['Avg_per_gene_AUROC', \n",
    "                                    ['Transfer', 'Transfer_no_eve', 'esm_1v', 'EVE_scores_ASM']].to_list()\n",
    "bars_na = ['Transfer with imputed EVE', 'Transfer w/o EVE', 'ESM-1v', 'EVE imputed']\n",
    "x_pos_na = np.arange(len(bars_na))\n",
    "width = 0.4\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(7, 4))\n",
    "rects_g_na = ax.bar(x_pos_na - width/2, auroc_g_na, width, label = 'Global AUROC')\n",
    "rects_p_na = ax.bar(x_pos_na + width/2, auroc_p_na, width, label = 'Avg per gene AUROC')\n",
    "\n",
    "ax.bar_label(rects_g_na, labels=[f'{x:,.3f}' for x in rects_g_na.datavalues], padding = 5)\n",
    "ax.bar_label(rects_p_na, labels=[f'{x:,.3f}' for x in rects_p_na.datavalues], padding = 5)\n",
    "\n",
    "ax.set_xticks(x_pos_na)\n",
    "ax.set_xticklabels(bars_na, rotation = 30, ha=\"right\")\n",
    "\n",
    "ax.set_ylim(0.755, 0.925)\n",
    "\n",
    "plt.legend(bbox_to_anchor=(1, 0.65))\n",
    "plt.xlabel('')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "af9781a7-2e28-4359-9db4-c1947c786aef",
   "metadata": {},
   "source": [
    "### Test cross-gene imputation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "eb278de7-f827-47c5-bdf6-158b93d651ea",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Selecting features in the category 100verte_msa\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 7/7 [00:04<00:00,  1.71it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best features: ('100verte_mut_freq',) Avg metric on DMS: 0.78211\n",
      "Selecting features in the category 30mammal_msa\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 7/7 [00:04<00:00,  1.41it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best features: ('30mammal_mut_freq',) Avg metric on DMS: 0.7812\n",
      "Selecting features in the category mpnn\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 7/7 [00:05<00:00,  1.17it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best features: ('vanilla_protmpnn_norm',) Avg metric on DMS: 0.78523\n",
      "0 ['BLOSUM1'] 0.78816\n",
      "1 ['BLOSUM1', 'ST6'] 0.79002\n",
      "2 ['BLOSUM1', 'ST6', 'VHSE1'] 0.79086\n",
      "All selected features: ['esm_1v', '100verte_mut_freq', '30mammal_mut_freq', 'vanilla_protmpnn_norm', 'BLOSUM1', 'ST6', 'VHSE1']\n"
     ]
    }
   ],
   "source": [
    "# Reselect features without all EVE-related features (EVE and conditioned scores)\n",
    "feat_selected_dms_no_eve_related = select_feat_by_category(feat_mat, label, all_genes, \n",
    "                                                           base_feat = ['esm_1v'], with_struct = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "69a28d91-90af-4fc3-9235-7942d53e2443",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Selecting features in the category 100verte_msa\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 7/7 [00:03<00:00,  1.82it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best features: ('100verte_mut_freq',) Avg metric on DMS: 0.7832\n",
      "Selecting features in the category 30mammal_msa\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 7/7 [00:04<00:00,  1.61it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best features: ('30mammal_mut_freq',) Avg metric on DMS: 0.78305\n",
      "Selecting features in the category mpnn\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 7/7 [00:04<00:00,  1.52it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best features: ('vanilla_protmpnn_norm',) Avg metric on DMS: 0.78537\n",
      "Selecting features in the category struct\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 3/3 [00:02<00:00,  1.39it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best features: ('conditioned_wt_score',) Avg metric on DMS: 0.78555\n",
      "0 ['BLOSUM1'] 0.78792\n",
      "1 ['BLOSUM1', 'ST6'] 0.78955\n",
      "All selected features: ['esm_1v', 'evolutionary_index_ASM', '100verte_mut_freq', '30mammal_mut_freq', 'vanilla_protmpnn_norm', 'conditioned_wt_score', 'BLOSUM1', 'ST6']\n"
     ]
    }
   ],
   "source": [
    "# Reselect features without all EVE-related features (EVE and conditioned scores)\n",
    "feat_selected_dms_eve_xgi = select_feat_by_category(feat_mat_xgi, label_xgi, all_genes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "3852d2fc-dd8c-4172-9105-6f3a8a88a6e6",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load weighted KNN imputers fitted on the 5 DMS genes\n",
    "imputer = pd.read_pickle('./data/imputer_5dms.pkl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "dfff92f1-d55c-4f5e-aa51-07d7827836f2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Imputing with weights fitted on DMS genes...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 5/5 [00:43<00:00,  8.64s/it]\n"
     ]
    }
   ],
   "source": [
    "# Calculate imputed values for mutations without EVE scores\n",
    "x_impute = []\n",
    "print('Imputing with weights fitted on DMS genes...')\n",
    "for gene in tqdm(all_genes):\n",
    "    x_eve_na[eve_feats] = np.nan\n",
    "    x_eve_na[struct_feats] = np.nan\n",
    "    for feat in eve_feats + struct_feats:\n",
    "        x_eve_na = imputer.loc[feat, gene].transform(x_eve_na)\n",
    "    x_impute.append(x_eve_na.copy())\n",
    "x_eve_na = sum(x_impute) / len(all_genes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "e960c7b1-4722-4436-9a0d-abc60d06c3d3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Features selected on DMS ['esm_1v', 'evolutionary_index_ASM', '100verte_mut_freq', '30mammal_mut_freq', 'vanilla_protmpnn_norm', 'conditioned_wt_score', 'BLOSUM1', 'ST6', 'VHSE6']\n",
      "Features w/o eve ['esm_1v', '100verte_mut_freq', '30mammal_mut_freq', 'vanilla_protmpnn_norm', 'BLOSUM1', 'ST6', 'VHSE1']\n",
      "17 genes used to calculate per gene AUROC out of 828 genes\n",
      "Calculating...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 17/17 [00:00<00:00, 59.02it/s]\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Transfer</th>\n",
       "      <th>Transfer_xgi_model</th>\n",
       "      <th>Transfer_no_eve_related</th>\n",
       "      <th>EVE_scores_ASM</th>\n",
       "      <th>evolutionary_index_ASM</th>\n",
       "      <th>conditioned_mt_score</th>\n",
       "      <th>conditioned_wt_score</th>\n",
       "      <th>esm_1v</th>\n",
       "      <th>100verte_wt_freq</th>\n",
       "      <th>100verte_mut_freq</th>\n",
       "      <th>30mammal_wt_freq</th>\n",
       "      <th>ca_only_protmpnn_norm</th>\n",
       "      <th>ca_protmpnn_norm</th>\n",
       "      <th>vanilla_protmpnn_norm</th>\n",
       "      <th>30mammal_mut_freq</th>\n",
       "      <th>30mammal_gap_freq</th>\n",
       "      <th>100verte_gap_freq</th>\n",
       "      <th>n</th>\n",
       "      <th>n_pos</th>\n",
       "      <th>n_neg</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Global_AUROC</th>\n",
       "      <td>0.9109</td>\n",
       "      <td>0.9093</td>\n",
       "      <td>0.9046</td>\n",
       "      <td>0.9008</td>\n",
       "      <td>0.9002</td>\n",
       "      <td>0.8907</td>\n",
       "      <td>0.8855</td>\n",
       "      <td>0.8650</td>\n",
       "      <td>0.8242</td>\n",
       "      <td>0.7314</td>\n",
       "      <td>0.7168</td>\n",
       "      <td>0.6525</td>\n",
       "      <td>0.6307</td>\n",
       "      <td>0.6260</td>\n",
       "      <td>0.6239</td>\n",
       "      <td>0.5073</td>\n",
       "      <td>0.4757</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Avg_per_gene_AUROC</th>\n",
       "      <td>0.8958</td>\n",
       "      <td>0.8932</td>\n",
       "      <td>0.8842</td>\n",
       "      <td>0.8932</td>\n",
       "      <td>0.8935</td>\n",
       "      <td>0.8745</td>\n",
       "      <td>0.8442</td>\n",
       "      <td>0.8487</td>\n",
       "      <td>0.8001</td>\n",
       "      <td>0.7252</td>\n",
       "      <td>0.6755</td>\n",
       "      <td>0.6632</td>\n",
       "      <td>0.6177</td>\n",
       "      <td>0.6293</td>\n",
       "      <td>0.6056</td>\n",
       "      <td>0.5685</td>\n",
       "      <td>0.4544</td>\n",
       "      <td>26.0</td>\n",
       "      <td>15.4118</td>\n",
       "      <td>10.5882</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    Transfer  Transfer_xgi_model  Transfer_no_eve_related  \\\n",
       "Global_AUROC          0.9109              0.9093                   0.9046   \n",
       "Avg_per_gene_AUROC    0.8958              0.8932                   0.8842   \n",
       "\n",
       "                    EVE_scores_ASM  evolutionary_index_ASM  \\\n",
       "Global_AUROC                0.9008                  0.9002   \n",
       "Avg_per_gene_AUROC          0.8932                  0.8935   \n",
       "\n",
       "                    conditioned_mt_score  conditioned_wt_score  esm_1v  \\\n",
       "Global_AUROC                      0.8907                0.8855  0.8650   \n",
       "Avg_per_gene_AUROC                0.8745                0.8442  0.8487   \n",
       "\n",
       "                    100verte_wt_freq  100verte_mut_freq  30mammal_wt_freq  \\\n",
       "Global_AUROC                  0.8242             0.7314            0.7168   \n",
       "Avg_per_gene_AUROC            0.8001             0.7252            0.6755   \n",
       "\n",
       "                    ca_only_protmpnn_norm  ca_protmpnn_norm  \\\n",
       "Global_AUROC                       0.6525            0.6307   \n",
       "Avg_per_gene_AUROC                 0.6632            0.6177   \n",
       "\n",
       "                    vanilla_protmpnn_norm  30mammal_mut_freq  \\\n",
       "Global_AUROC                       0.6260             0.6239   \n",
       "Avg_per_gene_AUROC                 0.6293             0.6056   \n",
       "\n",
       "                    30mammal_gap_freq  100verte_gap_freq     n    n_pos  \\\n",
       "Global_AUROC                   0.5073             0.4757   NaN      NaN   \n",
       "Avg_per_gene_AUROC             0.5685             0.4544  26.0  15.4118   \n",
       "\n",
       "                      n_neg  \n",
       "Global_AUROC            NaN  \n",
       "Avg_per_gene_AUROC  10.5882  "
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Construct prediction matrix\n",
    "pred_eve_na = pd.DataFrame(index = y_eve_na.index)\n",
    "\n",
    "cpt = CPTClassifier()\n",
    "# Transfer model predictions\n",
    "features = feat_selected_dms\n",
    "print('Features selected on DMS', features)\n",
    "cpt.train(feat_mat, label, all_genes, features)\n",
    "pred_clinvar = cpt.predict(x_eve_na[features])\n",
    "pred_eve_na['Transfer'] = 1 - pred_clinvar.values\n",
    "\n",
    "# Transfer model predictions\n",
    "cpt.train(feat_mat_xgi, label_xgi, all_genes, feat_selected_dms)\n",
    "pred_clinvar = cpt.predict(x_eve_na[feat_selected_dms])\n",
    "pred_eve_na['Transfer_xgi_model'] = 1 - pred_clinvar.values\n",
    "\n",
    "# Transfer model without EVE-related features\n",
    "features = feat_selected_dms_no_eve_related\n",
    "print('Features w/o eve', features)\n",
    "cpt.train(feat_mat, label, all_genes, features)\n",
    "pred_clinvar = cpt.predict(x_eve_na[features])\n",
    "pred_eve_na['Transfer_no_eve_related'] = 1 - pred_clinvar.values\n",
    "\n",
    "# Individual features\n",
    "for feat in list(set(pos_feats) - set(aa_feats)):\n",
    "    pred_eve_na[feat] = x_eve_na[feat]\n",
    "for feat in list(set(neg_feats) - set(aa_feats)):\n",
    "    pred_eve_na[feat] = -x_eve_na[feat]\n",
    "\n",
    "feat_list = pos_feats + neg_feats\n",
    "\n",
    "# Get per gene AUROC\n",
    "roc_by_gene_eve_na = get_per_gene_auroc(y_eve_na, pred_eve_na)\n",
    "\n",
    "# Add global AUROC\n",
    "roc_by_gene_eve_na.insert(loc = 0, column = 'Global_AUROC', \n",
    "                          value = [np.nan, np.nan, np.nan] + \\\n",
    "                                   [float(roc_auc_score(y_eve_na, pred_eve_na[feat])) for feat in pred_eve_na.columns])\n",
    "roc_by_gene_eve_na = roc_by_gene_eve_na.sort_values('Global_AUROC', ascending = False).round(4)\n",
    "\n",
    "# Transpose the dataframe\n",
    "roc_by_gene_eve_na = roc_by_gene_eve_na.transpose()\n",
    "roc_by_gene_eve_na.iloc[:2, :]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "8d9cc4fe-b1b0-4d79-bcae-12462cea11f8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fraction of variants without EVE scores: 0.8533636928172221\n"
     ]
    }
   ],
   "source": [
    "# Mutations with EVE scores\n",
    "y_eve_av = y[x['eve_na'] == False]\n",
    "x_eve_av = x[x['eve_na'] == False]\n",
    "print(f\"Fraction of variants without EVE scores: {(~x['eve_na']).mean()}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "b4ae9b1f-a9a8-4299-8974-cf52ed808ea9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Features selected on DMS ['esm_1v', 'evolutionary_index_ASM', '100verte_mut_freq', '30mammal_mut_freq', 'vanilla_protmpnn_norm', 'conditioned_wt_score', 'BLOSUM1', 'ST6', 'VHSE6']\n"
     ]
    }
   ],
   "source": [
    "# Construct prediction matrix\n",
    "pred_eve_av = pd.DataFrame(index = y_eve_av.index)\n",
    "\n",
    "cpt = CPTClassifier()\n",
    "# Transfer model predictions\n",
    "features = feat_selected_dms\n",
    "print('Features selected on DMS', features)\n",
    "cpt.train(feat_mat, label, all_genes, features)\n",
    "pred_clinvar = cpt.predict(x_eve_av[features])\n",
    "pred_eve_av['Transfer'] = 1 - pred_clinvar.values\n",
    "\n",
    "pred_eve_av['EVE'] = x_eve_av['EVE_scores_ASM']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "3128c3c6-76b0-45bc-8033-39f90222a0f0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Imputing with weights fitted on DMS genes...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 5/5 [04:29<00:00, 53.85s/it]\n"
     ]
    }
   ],
   "source": [
    "# Calculate imputed values for mutations with EVE scores\n",
    "# ! This step is relatively slow and takes ~10min on a laptop\n",
    "x_impute = []\n",
    "print('Imputing with weights fitted on DMS genes...')\n",
    "for gene in tqdm(all_genes):\n",
    "    x_eve_av[eve_feats] = np.nan\n",
    "    x_eve_av[struct_feats] = np.nan\n",
    "    for feat in eve_feats + struct_feats:\n",
    "        x_eve_av = imputer.loc[feat, gene].transform(x_eve_av)\n",
    "    x_impute.append(x_eve_av.copy())\n",
    "x_eve_av = sum(x_impute) / len(all_genes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "b4c0e8f0-80f2-470f-8376-ad00547ae026",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Transfer model with imputed eve related features\n",
    "features = feat_selected_dms\n",
    "cpt.train(feat_mat, label, all_genes, features)\n",
    "pred_clinvar = cpt.predict(x_eve_av[features])\n",
    "pred_eve_av['Transfer_eve_imputed'] = 1 - pred_clinvar.values\n",
    "\n",
    "cpt.train(feat_mat_xgi, label_xgi, all_genes, feat_selected_dms)\n",
    "pred_clinvar = cpt.predict(x_eve_av[feat_selected_dms])\n",
    "pred_eve_av['Transfer_eve_imputed_xgi_model'] = 1 - pred_clinvar.values\n",
    "\n",
    "# Transfer model without eve related features\n",
    "features = feat_selected_dms_no_eve_related\n",
    "cpt.train(feat_mat, label, all_genes, features)\n",
    "pred_clinvar = cpt.predict(x_eve_av[features])\n",
    "pred_eve_av['Transfer_no_eve_related'] = 1 - pred_clinvar.values\n",
    "\n",
    "pred_eve_av['EVE_imputed'] = x_eve_av['EVE_scores_ASM']\n",
    "pred_eve_av['ESM-1v'] = -x_eve_av['esm_1v']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "7671b112-ab1f-4b00-8380-2233caca6b4b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "230 genes used to calculate per gene AUROC out of 1246 genes\n",
      "Calculating...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 230/230 [00:01<00:00, 119.95it/s]\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Transfer</th>\n",
       "      <th>Transfer_eve_imputed_xgi_model</th>\n",
       "      <th>Transfer_eve_imputed</th>\n",
       "      <th>Transfer_no_eve_related</th>\n",
       "      <th>EVE_imputed</th>\n",
       "      <th>EVE</th>\n",
       "      <th>ESM-1v</th>\n",
       "      <th>n</th>\n",
       "      <th>n_pos</th>\n",
       "      <th>n_neg</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Global_AUROC</th>\n",
       "      <td>0.9331</td>\n",
       "      <td>0.9218</td>\n",
       "      <td>0.9184</td>\n",
       "      <td>0.9176</td>\n",
       "      <td>0.9094</td>\n",
       "      <td>0.8890</td>\n",
       "      <td>0.8723</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Avg_per_gene_AUROC</th>\n",
       "      <td>0.9364</td>\n",
       "      <td>0.9188</td>\n",
       "      <td>0.9176</td>\n",
       "      <td>0.9158</td>\n",
       "      <td>0.9077</td>\n",
       "      <td>0.9223</td>\n",
       "      <td>0.8747</td>\n",
       "      <td>49.613</td>\n",
       "      <td>23.6261</td>\n",
       "      <td>25.987</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    Transfer  Transfer_eve_imputed_xgi_model  \\\n",
       "Global_AUROC          0.9331                          0.9218   \n",
       "Avg_per_gene_AUROC    0.9364                          0.9188   \n",
       "\n",
       "                    Transfer_eve_imputed  Transfer_no_eve_related  \\\n",
       "Global_AUROC                      0.9184                   0.9176   \n",
       "Avg_per_gene_AUROC                0.9176                   0.9158   \n",
       "\n",
       "                    EVE_imputed     EVE  ESM-1v       n    n_pos   n_neg  \n",
       "Global_AUROC             0.9094  0.8890  0.8723     NaN      NaN     NaN  \n",
       "Avg_per_gene_AUROC       0.9077  0.9223  0.8747  49.613  23.6261  25.987  "
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get AUROC matrix\n",
    "# Get per gene AUROC\n",
    "roc_by_gene_eve_av = get_per_gene_auroc(y_eve_av, pred_eve_av)\n",
    "\n",
    "# Add global AUROC\n",
    "roc_by_gene_eve_av.insert(loc = 0, column = 'Global_AUROC', \n",
    "                          value = [np.nan, np.nan, np.nan] + \\\n",
    "                                  [float(roc_auc_score(y_eve_av, pred_eve_av[feat])) for feat in pred_eve_av.columns])\n",
    "roc_by_gene_eve_av = roc_by_gene_eve_av.sort_values('Global_AUROC', ascending = False).round(4)\n",
    "\n",
    "# Transpose the dataframe\n",
    "roc_by_gene_eve_av = roc_by_gene_eve_av.transpose()\n",
    "\n",
    "roc_by_gene_eve_av.iloc[:2, :]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "3dc91a43-6d62-4d12-9c72-44ca490d26f9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "230 genes used to calculate per gene AUROC out of 1246 genes\n",
      "Calculating...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 230/230 [00:01<00:00, 136.55it/s]\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Transfer</th>\n",
       "      <th>Transfer_eve_imputed_xgi_model</th>\n",
       "      <th>Transfer_eve_imputed</th>\n",
       "      <th>Transfer_no_eve_related</th>\n",
       "      <th>EVE_imputed</th>\n",
       "      <th>EVE</th>\n",
       "      <th>ESM-1v</th>\n",
       "      <th>n</th>\n",
       "      <th>n_pos</th>\n",
       "      <th>n_neg</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Global_AUROC</th>\n",
       "      <td>0.9331</td>\n",
       "      <td>0.9218</td>\n",
       "      <td>0.9184</td>\n",
       "      <td>0.9176</td>\n",
       "      <td>0.9094</td>\n",
       "      <td>0.8890</td>\n",
       "      <td>0.8723</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Avg_per_gene_AUROC</th>\n",
       "      <td>0.9364</td>\n",
       "      <td>0.9188</td>\n",
       "      <td>0.9176</td>\n",
       "      <td>0.9158</td>\n",
       "      <td>0.9077</td>\n",
       "      <td>0.9223</td>\n",
       "      <td>0.8747</td>\n",
       "      <td>49.613</td>\n",
       "      <td>23.6261</td>\n",
       "      <td>25.987</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    Transfer  Transfer_eve_imputed_xgi_model  \\\n",
       "Global_AUROC          0.9331                          0.9218   \n",
       "Avg_per_gene_AUROC    0.9364                          0.9188   \n",
       "\n",
       "                    Transfer_eve_imputed  Transfer_no_eve_related  \\\n",
       "Global_AUROC                      0.9184                   0.9176   \n",
       "Avg_per_gene_AUROC                0.9176                   0.9158   \n",
       "\n",
       "                    EVE_imputed     EVE  ESM-1v       n    n_pos   n_neg  \n",
       "Global_AUROC             0.9094  0.8890  0.8723     NaN      NaN     NaN  \n",
       "Avg_per_gene_AUROC       0.9077  0.9223  0.8747  49.613  23.6261  25.987  "
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get AUROC matrix\n",
    "# Get per gene AUROC\n",
    "roc_by_gene_eve_av = get_per_gene_auroc(y_eve_av, pred_eve_av)\n",
    "\n",
    "# Add global AUROC\n",
    "roc_by_gene_eve_av.insert(loc = 0, column = 'Global_AUROC', \n",
    "                          value = [np.nan, np.nan, np.nan] + \\\n",
    "                                  [float(roc_auc_score(y_eve_av, pred_eve_av[feat])) for feat in pred_eve_av.columns])\n",
    "roc_by_gene_eve_av = roc_by_gene_eve_av.sort_values('Global_AUROC', ascending = False).round(4)\n",
    "\n",
    "# Transpose the dataframe\n",
    "roc_by_gene_eve_av = roc_by_gene_eve_av.transpose()\n",
    "\n",
    "roc_by_gene_eve_av.iloc[:2, :]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "c5d3b3b9-2f68-4701-8624-c9507d27ba2c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1800x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Figure: Cross-gene imputation of EVE scores improves performance\n",
    "auroc_g_na = roc_by_gene_eve_na.loc['Global_AUROC', \n",
    "                                    ['Transfer_xgi_model', 'Transfer', 'Transfer_no_eve_related', 'esm_1v', 'EVE_scores_ASM']].to_list()\n",
    "auroc_p_na = roc_by_gene_eve_na.loc['Avg_per_gene_AUROC', \n",
    "                                    ['Transfer_xgi_model', 'Transfer', 'Transfer_no_eve_related', 'esm_1v', 'EVE_scores_ASM']].to_list()\n",
    "auroc_g_av = roc_by_gene_eve_av.loc['Global_AUROC', \n",
    "                                    ['Transfer', 'Transfer_eve_imputed_xgi_model', 'Transfer_eve_imputed', 'Transfer_no_eve_related', 'ESM-1v', 'EVE', 'EVE_imputed']].to_list()\n",
    "auroc_p_av = roc_by_gene_eve_av.loc['Avg_per_gene_AUROC', \n",
    "                                    ['Transfer', 'Transfer_eve_imputed_xgi_model', 'Transfer_eve_imputed', 'Transfer_no_eve_related', 'ESM-1v', 'EVE', 'EVE_imputed']].to_list()\n",
    "bars_na = ['CPT-ie with imputed EVE', 'CPT-1 with imputed EVE', 'CPT-ne w/o EVE', 'ESM-1v', 'EVE imputed']\n",
    "bars_av = ['CPT-1 with true EVE', 'CPT-ie with imputed EVE', 'CPT-1 with imputed EVE', 'CPT-ne w/o EVE', 'ESM-1v', 'EVE true', 'EVE imputed']\n",
    "x_pos_na = np.arange(len(bars_na))\n",
    "x_pos_av = np.arange(len(bars_av))\n",
    "width = 0.4\n",
    "\n",
    "fig, axes = plt.subplots(1, 2, figsize=(18, 4), gridspec_kw={'width_ratios': [2, 3]})\n",
    "rects_g_na = axes[0].bar(x_pos_na - width/2, auroc_g_na, width, label = 'Global AUROC')\n",
    "rects_p_na = axes[0].bar(x_pos_na + width/2, auroc_p_na, width, label = 'Avg per gene AUROC')\n",
    "rects_g_av = axes[1].bar(x_pos_av - width/2, auroc_g_av, width, label = 'Global AUROC')\n",
    "rects_p_av = axes[1].bar(x_pos_av + width/2, auroc_p_av, width, label = 'Avg per gene AUROC')\n",
    "\n",
    "axes[0].bar_label(rects_g_na, labels=[f'{x:,.3f}' for x in rects_g_na.datavalues], padding = 5)\n",
    "axes[0].bar_label(rects_p_na, labels=[f'{x:,.3f}' for x in rects_p_na.datavalues], padding = 5)\n",
    "axes[1].bar_label(rects_g_av, labels=[f'{x:,.3f}' for x in rects_g_av.datavalues], padding = 5)\n",
    "axes[1].bar_label(rects_p_av, labels=[f'{x:,.3f}' for x in rects_p_av.datavalues], padding = 5)\n",
    "\n",
    "axes[0].set_xticks(x_pos_na)\n",
    "axes[0].set_xticklabels(bars_na, rotation = 30, ha=\"right\")\n",
    "axes[1].set_xticks(x_pos_av)\n",
    "axes[1].set_xticklabels(bars_av, rotation = 30, ha=\"right\")\n",
    "\n",
    "axes[0].set_ylim(0.755, 0.945)\n",
    "axes[1].set_ylim(0.755, 0.945)\n",
    "\n",
    "axes[0].title.set_text('ClinVar variants with no EVE scores') # 6566\n",
    "axes[1].title.set_text('ClinVar variants with available EVE scores') # 45751\n",
    "\n",
    "plt.legend(bbox_to_anchor=(1, 0.65))\n",
    "plt.xlabel('')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ffc6128a-20be-4f48-bae8-1a4777bfb0d3",
   "metadata": {},
   "source": [
    "### Baseline performance with no supervision on DMS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "60c7868e-afbb-486e-93ff-5b92a2a40333",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loaded 785 positive variants and 785 negative variants in CALM1\n",
      "Loaded 1370 positive variants and 1370 negative variants in TPK1\n",
      "Loaded 4769 positive variants and 4779 negative variants in MTHR\n",
      "Loaded 720 positive variants and 721 negative variants in SUMO1\n",
      "Loaded 1088 positive variants and 1089 negative variants in UBC9\n"
     ]
    }
   ],
   "source": [
    "# Reload data\n",
    "feat_mat, label = load_data(all_genes, binarize = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "a90f1060",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Remove columns that will disrupt quantile normalization\n",
    "x = x.drop(['100v_na', '30m_na', 'eve_na'], axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "9551c178-3b10-4225-93b2-eb3db7179897",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AUROC by averaging quantile normalized esm-1v and EVE\n",
      "274 genes used to calculate per gene AUROC out of 1298 genes\n",
      "Calculating...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 274/274 [00:00<00:00, 436.81it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AUROC by averaging quantile normalized selected features\n",
      "274 genes used to calculate per gene AUROC out of 1298 genes\n",
      "Calculating...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 274/274 [00:00<00:00, 497.51it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AUROC by fitting transfer model with only esm-1v and EVE\n",
      "274 genes used to calculate per gene AUROC out of 1298 genes\n",
      "Calculating...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 274/274 [00:00<00:00, 572.95it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AUROC by averaging quantile normalized random features\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 100/100 [00:01<00:00, 52.21it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "274 genes used to calculate per gene AUROC out of 1298 genes\n",
      "Calculating...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 274/274 [00:30<00:00,  8.98it/s]\n"
     ]
    },
    {
     "data": {
      "image/png": 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GsGHDBiZPnkzp0qVxcHDg1Vdf5datW4/cr1y5chn/NplMAJmC4Yfh7u5+z1HlOypWrEjFihV566236NOnDy+++CKbN2+mcePGZvneeOMNateuzaFDh3jjjTcylVOjRg2io6ON925ubjluZ6dOnXjvvffYv38/f/31F2fPns201v9uS5cuZciQIUyZMoU6derg5OTEJ598wq5du8zy3X1d4fa1fRzX1dbW9r7X1crKiu7du7NgwQLGjh3LvHnzaNy4MSVLlsyUt0SJEjkK/uH2Z/Tvn1MRERERkeedAvEnKDU1lYULFzJlypRM62zbtGnD119/TZ8+fahbty6enp4sW7aMtWvX8tprrxkBWfny5bGzs+PMmTNm09BzYvv27XTv3t1Ya56UlMTp06eN4z4+PqSmpnLgwAFjlDUuLo4rV648Qq9vl7tnzx6ztL+/f1zKly8P3J5J8HcVKlSgQoUK91y77eDgkG3Qfy9FixalYcOGLF68mL/++otmzZrdd+fw7du3U7duXd566y0j7e8zGrJTtmxZUlNT2bdvnzE1PTY2lqtXrz5UH/6uR48efPDBB6xcuZKIiAi+/PLLx1KuiIiIiIiYUyD+BH3//fdcuXKFnj174uLiYnasffv2hIeHG89v7ty5M2FhYRw7doxNmzYZ+ZycnBgyZAiDBg0iPT2d+vXrk5iYyPbt23F2diYoKOie9Xt7e7Ny5UoCAgIwmUyMGjXKbHS1bNmyNG3alN69e/P555+TK1cuBg8ejIODgzHK/TAGDBhAgwYNCA0NJSAggJ9++om1a9fmqMyrV69meia1k5MTefLkoW/fvhQuXJiXXnqJokWLEh8fzwcffEDBggWN9fN/99NPP5GSkpLjEVy4/YVFXFyc8f7UqVNER0eTL18+s8efBQYGMmbMGG7dusXUqVPvW6a3tzcLFy5k/fr1lChRgkWLFrFnzx5KlCiR43b5+Pjg7+/Pf/7zHz7//HNsbGwYOHBgpkeOZSU1NTXTdTWZTGazAEqUKMFLL71E7969sbOzu+ej2y5evMiNGzfM0vLnz59pNF9ERERERLKmNeJPUHh4OE2bNs0UhMPtQHzv3r0cPHgQuB3UHTlyhCJFilCvXj2zvBMmTGDUqFGEhIRQrlw5/P39+eGHH7IN4kJDQ8mbNy9169YlICAAPz8/s/XgAAsXLsTNzY0GDRrQtm1bevXqhZOTk/HIsIdRr149wsLCCA0NxdfXl3Xr1jFo0KAcldmjRw88PDzMXtOnTwegadOm7Ny5k9dee40yZcrQvn177O3tiYyMJH/+/FmWlydPngcKwgH27t1L1apVqVq1KgDBwcFUrVqV0aNHm+V79dVX+eOPP7h+/Tpt2rS5b5n/+c9/aNeuHR07dqR27dr88ccfZqPjOTVv3jwKFy5Mw4YNadeuHb17987RM7wPHz6c6boWL148U76ePXty5coVOnfufM/75ePjk6msffv2PXBfRERERESeV6aMO8/REgF+++03PD092bhxI02aNHls5fbq1YujR4+ydevWx1amPHuuXbuGi4sLngOXY2WnXdNFROTJ0a7pj8+d39+JiYk4Ozs/7eaI/Ctoavpz7qeffiIpKYlKlSoRHx/P0KFD8fLyokGDBo9U7uTJk2nWrBl58uRh7dq1LFiwgFmzZj2mVouIiIiIiDy7FIg/51JSUhg+fDgnT57EycmJunXrsnjx4kde77t7924+/vhj/vzzT0qWLMlnn33Gm2+++ZhaLSIiIiIi8uzSGvHnnJ+fH4cOHeL69etcuHCBiIiILNcOP6jly5dz8eJF/vrrLw4fPmxsSiciIiLyTzJz5ky8vLywt7endu3a7N69+555U1JSGD9+PKVKlcLe3t7YC+duW7ZsISAggMKFC2MymVi1alWmcrp3747JZDJ73Xl0rYg8HxSIi4iIiMhzadmyZQQHBzNmzBj279+Pr68vfn5+XLx4Mcv8I0eOZPbs2UyfPp0jR47Qp08f2rZty4EDB4w8ycnJ+Pr6MnPmzPvW7e/vT3x8vPH6+uuvH2vfROSfTZu1iYjFaLM2ERGxlJxs1la7dm1q1qzJjBkzAEhPT8fT05MBAwYwbNiwTPkLFy7MiBEj6Nevn5HWvn17HBwc+OqrrzLlN5lMREREZHq6Svfu3bl69WqWo+X/RNqsTeTx04i4iIiIiDx3bt26xb59+2jatKmRZmVlRdOmTdmxY0eW59y8eTPT4z0dHBzYtm3bA9cfFRVFoUKF8PHxoW/fvvzxxx8PXIaIPLsUiIuIiIjIc+fSpUukpaXh5uZmlu7m5kZCQkKW5/j5+REaGsrx48dJT09nw4YNrFy5kvj4+Aeq29/fn4ULFxIZGclHH33E5s2badGiBWlpaQ/dHxF5tmjXdBERERGRHPj000/p1asXZcuWxWQyUapUKXr06MHcuXMfqJzXX3/d+HelSpWoXLkypUqVIioqiiZNmjzuZovIP5BGxEVERETkuVOgQAGsra25cOGCWfqFCxdwd3fP8pyCBQuyatUqkpOT+d///sfRo0dxdHSkZMmSj9SWkiVLUqBAAeLi4h6pHBF5digQFxEREZHnjq2tLdWrVycyMtJIS09PJzIykjp16tz3XHt7e4oUKUJqaiorVqygdevWj9SW3377jT/++AMPD49HKkdEnh2ami4iIiIiz6Xg4GCCgoKoUaMGtWrVYtq0aSQnJ9OjRw8AunXrRpEiRQgJCQFg165dnDt3jipVqnDu3DnGjh1Leno6Q4cONcpMSkoyG9k+deoU0dHR5MuXj2LFipGUlMS4ceNo37497u7unDhxgqFDh1K6dGn8/PwsewFE5KlRIC4iIiIiz6WOHTvy+++/M3r0aBISEqhSpQrr1q0zNnA7c+YMVlb/fwLpjRs3GDlyJCdPnsTR0ZGWLVuyaNEiXF1djTx79+6lcePGxvvg4GAAgoKCmD9/PtbW1hw8eJAFCxZw9epVChcuTPPmzZkwYQJ2dnaW6biIPHV6jriIWIyeIy4iIpaSk+eIS87oOeIij5/WiIuIiIiIiIhYkAJxEREREREREQtSIC4iIiIiIiJiQQrERURERERERCxIgbiIiIiIiIiIBSkQFxEREREREbEgBeIiIiIiIiIiFqRAXERERERERMSCbJ52A0Tk+XNonB/Ozs5PuxkiIiIiIk+FRsRFRERERERELEiBuIiIiIiIiIgFKRAXERERERERsSAF4iIiIiIiIiIWpEBcRERERERExIIUiIuIiIiIiIhYkAJxEREREREREQtSIC4iIiIiIiJiQQrERURERERERCxIgbiIiIiIiIiIBSkQFxEREREREbEgBeIiIiIiIiIiFqRAXERERERERMSCbJ52A0TkORRSFOxMT7sVIiLyvBib+LRbICJiRiPiIiIiIiIiIhakQFxERERERETEghSIi4iIiIiIiFiQAnERERERERERC1IgLiIiIiIiImJBCsRFRERERERELEiBuIiIiIiIiIgFKRAXERERERERsSAF4iIiIiLy3Js5cyZeXl7Y29tTu3Ztdu/efc+8KSkpjB8/nlKlSmFvb4+vry/r1q0zy7NlyxYCAgIoXLgwJpOJVatWZSonKSmJ/v37U7RoURwcHChfvjxhYWGPu2si8g+kQFxEREREnmvLli0jODiYMWPGsH//fnx9ffHz8+PixYtZ5h85ciSzZ89m+vTpHDlyhD59+tC2bVsOHDhg5ElOTsbX15eZM2fes97g4GDWrVvHV199RUxMDAMHDqR///6sXr36sfdRRP5ZTBkZGRlPuxEi8ny4du0aLi4uJA5zwtnO9LSbIyIiz4uxifc9XLt2bWrWrMmMGTMASE9Px9PTkwEDBjBs2LBM+QsXLsyIESPo16+fkda+fXscHBz46quvMuU3mUxERETQpk0bs/SKFSvSsWNHRo0aZaRVr16dFi1a8MEHHzxID58o4/d3YiLOzs5Puzki/woaERcRERGR59atW7fYt28fTZs2NdKsrKxo2rQpO3bsyPKcmzdvYm9vb5bm4ODAtm3bHqjuunXrsnr1as6dO0dGRgabNm3i2LFjNG/e/ME7IiLPFAXiIiIiIvLcunTpEmlpabi5uZmlu7m5kZCQkOU5fn5+hIaGcvz4cdLT09mwYQMrV64kPj7+geqePn065cuXp2jRotja2uLv78/MmTNp0KDBQ/dHRJ4NCsRFRERERB7Ap59+ire3N2XLlsXW1pb+/fvTo0cPrKwe7E/r6dOns3PnTlavXs2+ffuYMmUK/fr1Y+PGjU+o5SLyT2HztBsgIiIiIvK0FChQAGtray5cuGCWfuHCBdzd3bM8p2DBgqxatYobN27wxx9/ULhwYYYNG0bJkiVzXO9ff/3F8OHDiYiIoFWrVgBUrlyZ6OhoJk+ebDZVXkT+fTQiLiIiIiLPLVtbW6pXr05kZKSRlp6eTmRkJHXq1Lnvufb29hQpUoTU1FRWrFhB69atc1xvSkoKKSkpmUbRra2tSU9Pf7BOiMgzRyPiIiIiIvJcCw4OJigoiBo1alCrVi2mTZtGcnIyPXr0AKBbt24UKVKEkJAQAHbt2sW5c+eoUqUK586dY+zYsaSnpzN06FCjzKSkJOLi4oz3p06dIjo6mnz58lGsWDGcnZ1p2LAh7777Lg4ODhQvXpzNmzezcOFCQkNDLXsBRMTiFIiLiIiIyHOtY8eO/P7774wePZqEhASqVKnCunXrjA3czpw5YzZyfePGDUaOHMnJkydxdHSkZcuWLFq0CFdXVyPP3r17ady4sfE+ODgYgKCgIObPnw/A0qVLef/99wkMDOTy5csUL16ciRMn0qdPnyffaRF5qvQccRGxGD1HXEREnopsniMu96fniIs8flojLiIiIiIiImJBCsRFRERERERELEiBuIiIiIiIiIgFPTeBeEJCAs2aNSNPnjxmG2k8a+bPn//A7TeZTKxatcqi9T/OOuX+unfvTps2bZ52M0REREREJIceeyBuMpnu+xo7duzjrjJHpk6dSnx8PNHR0Rw7duyptOHfoGPHjmbXb+zYsVSpUuWxlN2oUaMsPzN37xy6efNmXnrpJfLly0fu3Lnx9vYmKCiIW7duARAVFYXJZCJv3rzcuHHDrPw9e/YYZd5PfHw8nTt3pkyZMlhZWTFw4MDH0r+n5c41yeqVkJDAgAEDKFeuXJbnnjlzBmtra1avXg3c++d76dKlluySiIiIiMgz7bE/viw+Pt7497Jlyxg9ejSxsbFGmqOjo/HvjIwM0tLSsLF58k9RO3HiBNWrV8fb2/uhy7h16xa2traPsVXPHgcHBxwcHJ5Y+b169WL8+PFmablz5wbgyJEj+Pv7M2DAAD777DMcHBw4fvw4K1asIC0tzewcJycnIiIi6NSpk5EWHh5OsWLFOHPmzH3bcPPmTQoWLMjIkSOZOnXqY+rZ0xcbG5tpp9NChQrRs2dPZsyYwc8//0zdunXNjs+fP59ChQrRsmVLI23evHn4+/ub5XuWZ5mIiIiIiFjaYx8Rd3d3N14uLi6YTCbj/dGjR3FycmLt2rVUr14dOzs7tm3bxokTJ2jdujVubm44OjpSs2ZNNm7caFaul5cXkyZN4o033sDJyYlixYrxxRdfGMdv3bpF//798fDwwN7enuLFixMSEmKcu2LFChYuXIjJZKJ79+4AXL16lTfffJOCBQvi7OzMSy+9xC+//GKUeWe098svv6REiRLY29tn2ec707W///57fHx8yJ07N6+++irXr19nwYIFeHl5kTdvXt5++22zgPHKlSt069aNvHnzkjt3blq0aMHx48czlV2sWDFy585N27Zt+eOPPzLV/91331GtWjXs7e0pWbIk48aNIzU1NUf36/vvv8fV1dVoV3R0NCaTiWHDhhl53nzzTbp06WLW1zv/HjduHL/88osxMnrnuZgAly5dom3btsbI9Z1R1fvJnTu32WfI3d3dCB5//PFH3N3d+fjjj6lYsSKlSpXC39+fOXPmZPpyICgoiLlz5xrv//rrL5YuXUpQUFC2bfDy8uLTTz+lW7duuLi4ZDr+448/Ym9vz9WrV83S33nnHV566aV7lhsaGkqlSpXIkycPnp6evPXWWyQlJRnH71zb9evXU65cORwdHfH39zf7cistLY3g4GBcXV3Jnz8/Q4cOJadPICxUqFCma2tlZUWVKlWoVq2a2fWC21+UzZ8/n6CgILMvy1xdXTOVc6+fDRERERERyeyprBEfNmwYH374ITExMVSuXJmkpCRatmxJZGQkBw4cwN/fn4CAgEwjl1OmTKFGjRocOHCAt956i759+xqj7Z999hmrV69m+fLlxMbGsnjxYry8vIDbU5L9/f3p0KED8fHxfPrppwC89tprXLx4kbVr17Jv3z6qVatGkyZNuHz5slFnXFwcK1asYOXKlURHR9+zT9evX+ezzz5j6dKlrFu3jqioKNq2bcuaNWtYs2YNixYtYvbs2Xz77bfGOd27d2fv3r2sXr2aHTt2kJGRQcuWLUlJSQFg165d9OzZk/79+xMdHU3jxo354IMPzOrdunUr3bp145133uHIkSPMnj2b+fPnM3HixBzdixdffJE///yTAwcOALenfhcoUICoqCgjz+bNm2nUqFGmczt27MjgwYOpUKEC8fHxxMfH07FjR+P4uHHj6NChAwcPHqRly5YEBgaaXdsH5e7uTnx8PFu2bMk2b9euXdm6davxGVqxYgVeXl5Uq1btoeu/o0mTJri6urJixQojLS0tjWXLlhEYGHjP86ysrPjss884fPgwCxYs4KeffmLo0KFmea5fv87kyZNZtGgRW7Zs4cyZMwwZMsQ4PmXKFObPn8/cuXPZtm0bly9fJiIi4pH71LNnT5YvX05ycrKRFhUVxalTp3jjjTceutybN29y7do1s5eIiIiIyPPuyc8Jz8L48eNp1qyZ8T5fvnz4+voa7ydMmEBERASrV6+mf//+RnrLli156623AHjvvfeYOnUqmzZtwsfHhzNnzuDt7U39+vUxmUwUL17cOK9gwYLY2dnh4OCAu7s7ANu2bWP37t1cvHgROzs7ACZPnsyqVav49ttv6d27N3B7pH3hwoUULFjwvn1KSUnh888/p1SpUgC8+uqrLFq0iAsXLuDo6Ej58uVp3LgxmzZtomPHjhw/fpzVq1ezfft2Yzrw4sWL8fT0ZNWqVbz22mt8+umn+Pv7G8FamTJl+Pnnn1m3bp1R77hx4xg2bJgx0luyZEkmTJjA0KFDGTNmTLb3wsXFhSpVqhAVFUWNGjWIiopi0KBBjBs3jqSkJBITE4mLi6Nhw4aZznVwcMDR0REbGxvjut6te/fuxtTwSZMm8dlnn7F79+5M05rvNmvWLL788kuztNmzZxMYGMhrr73G+vXradiwIe7u7rzwwgs0adKEbt26ZTnlukWLFsyfP5/Ro0czd+7cRwoo72Ztbc3rr7/OkiVL6NmzJwCRkZFcvXqV9u3b3/O8u9eae3l58cEHH9CnTx9mzZplpKekpBAWFmZ8jvr37282VX/atGm8//77tGvXDoCwsDDWr1+fo3YXLVrU7H3x4sU5fPgwAJ07d2bw4MF88803xoyRefPmUb9+fcqUKWN2XqdOnbC2tjZLO3LkCMWKFctUZ0hICOPGjcuUXvFGOFYZuXPUbhERkeyc/rDV026CiMgDeSoj4jVq1DB7n5SUxJAhQyhXrhyurq44OjoSExOTaUS8cuXKxr/vTHm/ePEicDvoi46OxsfHh7fffpsff/zxvm345ZdfSEpKIn/+/Dg6OhqvU6dOceLECSNf8eLFsw3C4faU6jvBE4CbmxteXl5ma+Ld3NyM9sbExGBjY0Pt2rWN4/nz58fHx4eYmBgjz93HAerUqZOpH+PHjzfrQ69evYiPj+f69evZthugYcOGREVFkZGRwdatW2nXrh3lypVj27ZtbN68mcKFCz/U2vq771eePHlwdnY2+n8vgYGBREdHm71eeeUV4HYAPG/ePH777Tc+/vhjihQpwqRJk4wR+b974403mD9/PidPnmTHjh1Zjlbffd3u3hQuO4GBgURFRXH+/Hng9pcorVq1uu9a6Y0bN9KkSROKFCmCk5MTXbt25Y8//jC7T3//HHl4eBjXLDExkfj4eLPPhI2NTaafp3vZunWr2XVds2aNcczV1ZV27doZ09OvXbvGihUrjC8a7jZ16tRM96hw4cJZ1vn++++TmJhovM6ePZujtoqIiIiI/Js9lRHxPHnymL0fMmQIGzZsYPLkyZQuXRoHBwdeffVVYyfsO3LlymX23mQykZ6eDkC1atU4deoUa9euZePGjXTo0IGmTZuaTQW/W1JSEh4eHmZTsO+4O5j6e1vvJau23a+9j0tSUhLjxo0zRkjvltN1u40aNWLu3Ln88ssv5MqVi7Jly9KoUSOioqK4cuVKlqPhOfEw/XdxcaF06dL3zVOkSBG6du1K165dmTBhAmXKlCEsLCzTyGuLFi3o3bs3PXv2JCAggPz582cq6+7lBn8fVb+fmjVrUqpUKZYuXUrfvn2JiIgwWx//d6dPn+bll1+mb9++TJw4kXz58rFt2zZ69uzJrVu3jA3psrpmOV0Dnp0SJUrc94uCnj170qRJE+Li4ti0aRPW1ta89tprmfK5u7tne4/usLOzM2aciIiIiIjIbU8lEP+77du30717d9q2bQvcDi5Pnz79wOU4OzvTsWNHOnbsyKuvvoq/vz+XL18mX758mfJWq1aNhIQEbGxsjLXkllSuXDlSU1PZtWuXMTX9jz/+IDY2lvLlyxt5du3aZXbezp07zd5Xq1aN2NjYHAdGWbmzTnzq1KlG0N2oUSM+/PBDrly5wuDBg+95rq2tbaYdyy0pb968eHh4mK1tvsPGxoZu3brx8ccfs3bt2izPf5TrFhgYyOLFiylatChWVla0anXvaXH79u0jPT2dKVOmYGV1eyLK8uXLH6g+FxcXPDw82LVrFw0aNAAgNTXV2N/gUTVu3JgSJUowb948Nm3axOuvv57jL6JERERERCTn/hGBuLe3NytXriQgIACTycSoUaMeeOQ4NDQUDw8PqlatipWVFd988w3u7u73HAFs2rQpderUoU2bNnz88ceUKVOG8+fP88MPP9C2bdscT/d9WN7e3rRu3ZpevXoxe/ZsnJycGDZsGEWKFKF169YAvP3229SrV4/JkyfTunVr1q9fb7Y+HGD06NG8/PLLFCtWjFdffRUrKyt++eUXDh06lGljt3vJmzcvlStXZvHixcyYMQOABg0a0KFDB1JSUu47Iu7l5cWpU6eIjo6maNGiODk5PdII6PXr10lISDBLs7OzI2/evMyePZvo6Gjatm1LqVKluHHjBgsXLuTw4cNMnz49y/ImTJjAu+++m+Vo+P3cGSlPSkri999/Jzo6GltbW+NLErgdiI8dO5aJEyfy6quv3rffpUuXJiUlhenTpxMQEMD27dsJCwt7oDbB7Z3ZP/zwQ7y9vSlbtiyhoaGZdm+/l4sXL2Z6tnr+/PmNUXiTycQbb7xBaGgoV65cueej265evZrpHjk5OSloFxERERHJoaeyRvzvQkNDyZs3L3Xr1iUgIAA/P78HHuFzcnLi448/pkaNGtSsWZPTp0+zZs0aY/Tx70wmE2vWrKFBgwb06NGDMmXK8Prrr/O///0PNze3x9GtbM2bN4/q1avz8ssvU6dOHTIyMlizZo0RGL3wwgvMmTOHTz/9FF9fX3788UdGjhxpVoafnx/ff/89P/74IzVr1uSFF15g6tSpZpvV5UTDhg1JS0szdkfPly8f5cuXx93dHR8fn3ue1759e/z9/WncuDEFCxbk66+/frCL8Ddz5szBw8PD7HVnw7datWqRlJREnz59qFChAg0bNmTnzp2sWrXqnl8W2NraUqBAAUwm0wO1o2rVqlStWpV9+/axZMkSqlatavYsbbgdXNeqVYuDBw/ed7d0AF9fX0JDQ/noo4+oWLEiixcvNh6v9yAGDx5M165dCQoKok6dOjg5ORkzSbLj4+OT6dru27fPLE/37t1JTEykQoUKmfYnuKNHjx6ZyrnXFyEiIiIiIpKZKeNxLUAVEcnGtWvXcHFxwXPgcqzstGu6iIg8Hto1/cm68/s7MTHxgfbUEZF7+0eMiIuIiIiIiIg8LxSIi4iIiIiIiFiQAnEREREReS7MnDkTLy8v7O3tqV27Nrt3775n3pSUFMaPH0+pUqWwt7fH19c306a5W7ZsISAggMKFC2MymVi1alWmcjIyMhg9ejQeHh44ODjQtGlTjh8//ri7JiLPGAXiIiIiIvKvt2zZMoKDgxkzZgz79+/H19cXPz8/Ll68mGX+kSNHMnv2bKZPn86RI0fo06cPbdu25cCBA0ae5ORkfH19mTlz5j3r/fjjj/nss88ICwtj165d5MmTBz8/v0xPMhGR54s2axMRi9FmbSIi8iTkZLO22rVrU7NmTeNRrenp6Xh6ejJgwACGDRuWKX/hwoUZMWIE/fr1M9Lat2+Pg4MDX331Vab8JpOJiIgI2rRpY6RlZGRQuHBhBg8ezJAhQwBITEzEzc2N+fPn8/rrrz9oV58KbdYm8vhpRFxERERE/tVu3brFvn37aNq0qZFmZWVF06ZN2bFjR5bn3Lx5E3t7e7M0BwcHtm3bluN6T506RUJCglm9Li4u1K5d+571isjzQYG4iIiIiPyrXbp0ibS0NNzc3MzS3dzcSEhIyPIcPz8/QkNDOX78OOnp6WzYsIGVK1cSHx+f43rvlP0g9YrI80GBuIiIiIjI33z66ad4e3tTtmxZbG1t6d+/Pz169MDKSn8+i8ij0/8kIiIiIvKvVqBAAaytrblw4YJZ+oULF3B3d8/ynIIFC7Jq1SqSk5P53//+x9GjR3F0dKRkyZI5rvdO2Q9Sr4g8HxSIi4iIiMi/mq2tLdWrVycyMtJIS09PJzIykjp16tz3XHt7e4oUKUJqaiorVqygdevWOa63RIkSuLu7m9V77do1du3alW29IvLvZvO0GyAiIiIi8qQFBwcTFBREjRo1qFWrFtOmTSM5OZkePXoA0K1bN4oUKUJISAgAu3bt4ty5c1SpUoVz584xduxY0tPTGTp0qFFmUlIScXFxxvtTp04RHR1Nvnz5KFasGCaTiYEDB/LBBx/g7e1NiRIlGDVqFIULFzbbXV1Enj8KxEVERETkX69jx478/vvvjB49moSEBKpUqcK6deuMjdTOnDljtv77xo0bjBw5kpMnT+Lo6EjLli1ZtGgRrq6uRp69e/fSuHFj431wcDAAQUFBzJ8/H4ChQ4eSnJxM7969uXr1KvXr12fdunWZdmQXkeeLniMuIhaj54iLiMiTkJPniMvD03PERR4/rREXERERERERsSBNTRcRERERkUeWlpZGSkrK026GyFOTK1curK2tc5RXgbiIiIiIiDy0jIwMEhISuHr16tNuishT5+rqiru7OyaT6b75FIiLiIiIiMhDuxOEFypUiNy5c2cbgIj8G2VkZHD9+nUuXrwIgIeHx33zKxAXEREREZGHkpaWZgTh+fPnf9rNEXmqHBwcALh48SKFChW67zR1bdYmIiIiIiIP5c6a8Ny59TQUEfj/PwvZ7ZegQFxERERERB6JpqOL3JbTnwUF4iIiIiIiIiIWpDXiImJxh8b54ezs/LSbISIiIpIjJpOJiIgI2rRpk6P83bt35+rVq6xateqh6zx9+jQlSpTgwIEDVKlS5aHLkX8mBeIiIiIiIvLYeQ37waL1nf6w1QOfk5CQQEhICD/88AO//fYbLi4ulC5dmi5duhAUFPTMrH3/+uuv6dKlC3369GHmzJlmx+bPn8/AgQOzfLzc3V8w3An878ibNy+VKlXigw8+4MUXXzQ77/Lly4wfP56IiAji4+MpUKAA/v7+jB07lmLFipnlTUhIYOLEifzwww+cO3eOQoUKUaVKFQYOHEiTJk0e30V4xmhquoiIiIiIPHdOnjxJ1apV+fHHH5k0aRIHDhxgx44dDB06lO+//56NGzc+7SbmWHh4OEOHDuXrr7/mxo0bj1TWxo0biY+PZ8uWLRQuXJiXX36ZCxcuGMcvX77MCy+8wMaNGwkLCyMuLo6lS5cSFxdHzZo1OXnypJH39OnTVK9enZ9++olPPvmEX3/9lXXr1tG4cWP69ev3SO181ikQFxERERGR585bb72FjY0Ne/fupUOHDpQrV46SJUvSunVrfvjhBwICAu557q+//spLL72Eg4MD+fPnp3fv3iQlJWXKN27cOAoWLIizszN9+vTh1q1bxrF169ZRv359XF1dyZ8/Py+//DInTpx44H6cOnWKn3/+mWHDhlGmTBlWrlz5wGXcLX/+/Li7u1OxYkWGDx/OtWvX2LVrl3F8xIgRnD9/no0bN9KiRQuKFStGgwYNWL9+Pbly5TILsN966y1MJhO7d++mffv2lClThgoVKhAcHMzOnTsfqZ3POgXiIiIiIiLyXPnjjz/48ccf6devH3ny5Mkyz712v05OTsbPz4+8efOyZ88evvnmGzZu3Ej//v3N8kVGRhITE0NUVBRff/01K1euZNy4cWblBAcHs3fvXiIjI7GysqJt27akp6c/UF/mzZtHq1atcHFxoUuXLoSHhz/Q+ffy119/sXDhQgBsbW0BSE9PZ+nSpQQGBuLu7m6W38HBgbfeeov169dz+fJlLl++zLp16+55jV1dXR9LO59VWiMuIiIiIiLPlbi4ODIyMvDx8TFLL1CggDG1u1+/fnz00UeZzl2yZAk3btxg4cKFRoA5Y8YMAgIC+Oijj3BzcwNuB69z584ld+7cVKhQgfHjx/Puu+8yYcIErKysaN++vVm5c+fOpWDBghw5coSKFSvmqB/p6enMnz+f6dOnA/D6668zePBgTp06Zbbe+0HUrVsXKysrrl+/TkZGBtWrVzfWcv/+++9cvXqVcuXKZXluuXLlyMjIIC4uDoCMjAzKli37UO34t9OIuIiIiIiICLB7926io6OpUKECN2/ezDJPTEwMvr6+ZqO89erVIz09ndjYWCPN19fXbLO3OnXqkJSUxNmzZwE4fvw4nTp1omTJkjg7O+Pl5QXAmTNnctzeDRs2kJycTMuWLYHbXyQ0a9aMuXPn5riMv1u2bBkHDhxgxYoVlC5dmvnz55MrVy6zPBkZGdmWk5M8zzONiIuIiIiIyHOldOnSmEwms8AZoGTJksDtadZPWkBAAMWLF2fOnDkULlyY9PR0KlasaLaOPDvh4eFcvnzZrL3p6ekcPHiQcePGYWVlhbOzM8nJyaSnp2Nl9f/HYe/sou7i4mJWpqenJ97e3nh7e5Oamkrbtm05dOgQdnZ2FCxYEFdXV2JiYrJsT0xMDCaTidKlSwO3p/cfPXo0x/15nmhEXEREREREniv58+enWbNmzJgxg+Tk5Ac6t1y5cvzyyy9m523fvh0rKyuzqe6//PILf/31l/F+586dODo64unpyR9//EFsbCwjR46kSZMmlCtXjitXrjxQO/744w++++47li5dSnR0tPE6cOAAV65c4ccffwTAx8eH1NRUoqOjzc7fv38/AGXKlLlnHa+++io2NjbMmjULACsrKzp06MCSJUtISEgwy/vXX38xa9Ys/Pz8yJcvH/ny5cPPz4+ZM2dmeY2zepza80SBuIiIiIiIPHdmzZpFamoqNWrUYNmyZcTExBAbG8tXX33F0aNHsba2zvK8wMBA7O3tCQoK4tChQ2zatIkBAwbQtWtXY304wK1bt+jZsydHjhxhzZo1jBkzhv79+2NlZUXevHnJnz8/X3zxBXFxcfz0008EBwc/UPsXLVpE/vz56dChAxUrVjRevr6+tGzZ0ti0rUKFCjRv3pw33niDyMhITp06xbp163jrrbfo2LEjRYoUuWcdJpOJt99+mw8//JDr168DMGnSJNzd3WnWrBlr167l7NmzbNmyBT8/P1JSUsyeYz5z5kzS0tKoVasWK1as4Pjx48TExPDZZ59Rp06dB+rvv40CcRERERERee6UKlWKAwcO0LRpU95//318fX2pUaMG06dPZ8iQIUyYMCHL83Lnzm3sDF6zZk1effVVmjRpwowZM8zyNWnSBG9vbxo0aEDHjh155ZVXGDt2LHB7ZHnp0qXs27ePihUrMmjQID755JMHav/cuXNp27Ztlru7t2/fntWrV3Pp0iXg9rrvhg0b8p///IcKFSrw9ttv07p1a7788sts6wkKCiIlJcXoX/78+dm5cyeNGzfmP//5D6VKlaJDhw6UKlWKPXv2GNP74fZU//3799O4cWMGDx5MxYoVadasGZGRkXz++ecP1N9/G1OGVtGLiIVcu3YNFxcXEhMTcXZ2ftrNERERkRy43+/vGzduGDt029vbP6UWivxz5PRnQiPiIiIiIiIiIhakQFxERERERETEgvT4MhGxvJCiYJd5PZOIiDxmYxOfdgtERCQLGhEXERERERERsSAF4iIiIiIiIiIWpEBcRERERERExIIUiIuIiIiIiIhYkAJxEREREREREQtSIC4iIiIiIiJiQQrERURERERERCxIzxEXEREREZHHb6yLhetLfKjTduzYQf369fH39+eHH354zI2SnAgJCWHkyJF8+OGHvPvuu2bHxo4dy6pVq4iOjjZLP336NCVKlODAgQNUqVKFqKgoGjdubBwvUKAANWvW5KOPPqJSpUpm5549e5YxY8awbt06Ll26hIeHB23atGH06NHkz5/fLG9cXBwTJ05kw4YN/P777xQuXJgXXniBwYMHU6NGjYfus0bERUREROSJmjlzJl5eXtjb21O7dm1279593/zTpk3Dx8cHBwcHPD09GTRoEDdu3DCOjx07FpPJZPYqW7ascfzy5csMGDDAKKNYsWK8/fbbJCY+XKAm/27h4eEMGDCALVu2cP78+afdnIeWkpLytJvw0ObOncvQoUOZO3fuI5cVGxtLfHw869ev5+bNm7Rq1Ypbt24Zx0+ePEmNGjU4fvw4X3/9NXFxcYSFhREZGUmdOnW4fPmykXfv3r1Ur16dY8eOMXv2bI4cOUJERARly5Zl8ODBj9ROBeIiIiIi8sQsW7aM4OBgxowZw/79+/H19cXPz4+LFy9mmX/JkiUMGzaMMWPGEBMTQ3h4OMuWLWP48OFm+SpUqEB8fLzx2rZtm3Hs/PnznD9/nsmTJ3Po0CHmz5/PunXr6Nmz5xPtqzx7kpKSWLZsGX379qVVq1bMnz/fONa5c2c6duxolj8lJYUCBQqwcOFCAP78808CAwPJkycPHh4eTJ06lUaNGjFw4MB71jl27FiqVKnC7Nmz8fT0JHfu3HTo0CHTF0Vffvkl5cqVw97enrJlyzJr1izj2OnTpzGZTCxbtoyGDRtib2/P4sWLs6zv6NGj1K9fH3t7e8qXL8/GjRsxmUysWrXKyHP27Fk6dOiAq6sr+fLlo3Xr1pw+fdo43r17d9q0acPkyZPx8PAgf/789OvXzyz4v3nzJkOGDKFIkSLkyZOH2rVrExUVdc/rcMfmzZv566+/GD9+PNeuXePnn3/O9pz7KVSoEO7u7lSrVo2BAwdy9uxZjh49ahzv168ftra2/PjjjzRs2JBixYrRokULNm7cyLlz5xgxYgQAGRkZdO/eHW9vb7Zu3UqrVq0oVaoUVapUYcyYMXz33XeP1E4F4iIiIiLyxISGhtKrVy969OhB+fLlCQsLI3fu3Pcc+fr555+pV68enTt3xsvLi+bNm9OpU6dMo+g2Nja4u7sbrwIFChjHKlasyIoVKwgICKBUqVK89NJLTJw4kf/+97+kpqY+0f7Ks2X58uWULVsWHx8funTpwty5c8nIyAAgMDCQ//73vyQlJRn5169fz/Xr12nbti0AwcHBbN++ndWrV7Nhwwa2bt3K/v37s603Li6O5cuX89///pd169Zx4MAB3nrrLeP44sWLGT16NBMnTiQmJoZJkyYxatQoFixYYFbOsGHDeOedd4iJicHPzy9TPWlpabRp04bcuXOza9cuvvjiCyPQvCMlJQU/Pz+cnJzYunUr27dvx9HREX9/f7OR5E2bNnHixAk2bdrEggULmD9/vtkXF/3792fHjh0sXbqUgwcP8tprr+Hv78/x48fvey3Cw8Pp1KkTuXLlolOnToSHh2d7/XIiMTGRpUuXAmBrawvcni2zfv163nrrLRwcHMzyu7u7ExgYyLJly8jIyCA6OprDhw8zePBgrKwyh82urq6P1D6tERcRERF5ipJvZTzBwpOfXNlAnjx57nv81q1b7Nu3j/fff99Is7KyomnTpuzYsSPLc+rWrctXX33F7t27qVWrFidPnmTNmjV07drVLN/x48cpXLgw9vb21KlTh5CQEIoVK3bPtiQmJuLs7IyNjf78lf8vPDycLl26AODv709iYiKbN2+mUaNG+Pn5kSdPHiIiIozP35IlS3jllVdwcnLizz//ZMGCBSxZsoQmTZoAMG/ePAoXLpxtvTdu3GDhwoUUKVIEgOnTp9OqVSumTJmCu7s7Y8aMYcqUKbRr1w6AEiVKcOTIEWbPnk1QUJBRzsCBA408WdmwYQMnTpwgKioKd3d3ACZOnEizZs2MPMuWLSM9PZ0vv/wSk8lk9MPV1ZWoqCiaN28OQN68eZkxYwbW1taULVuWVq1aERkZSa9evThz5gzz5s3jzJkzRv+HDBnCunXrmDdvHpMmTcqyfdeuXePbb781/j/o0qULL774Ip9++imOjo7ZXsesFC1aFIDk//v/75VXXjGWrhw/fpyMjAzKlSuX5bnlypXjypUr/P7778YXCHcve3mc9D+RiIiIyFPkGPLnkys85OH+kM2pOyOH93Lp0iXS0tJwc3MzS3dzczObKnq3zp07c+nSJerXr09GRgapqan06dPHbGp67dq1mT9/Pj4+PsTHxzNu3DhefPFFDh06hJOTU5btmDBhAr17936IXsq/VWxsLLt37yYiIgK4PcuiY8eOhIeH06hRI2xsbOjQoQOLFy+ma9euJCcn89133xmjrCdPniQlJYVatWoZZbq4uODj45Nt3cWKFTOCcIA6deqQnp5ObGwsTk5OnDhxgp49e9KrVy8jT2pqKi4u5hvgZbdZWGxsLJ6enkYQDpi1F+CXX34hLi4u08/OjRs3OHHihPG+QoUKWFtbG+89PDz49ddfAfj1119JS0ujTJkyZmXcvHkz0+Znd/v6668pVaoUvr6+AFSpUoXixYuzbNmyh15KsnXrVnLnzs3OnTuZNGkSYWFhmfJk939XTvM8CgXiIiIiIvKPERUVxaRJk5g1axa1a9cmLi6Od955hwkTJjBq1CgAWrRoYeSvXLkytWvXpnjx4ixfvjzTH+/Xrl2jVatWlC9fnrFjx1qyK/IPFx4eTmpqqtkIdkZGBnZ2dsyYMQMXFxcCAwNp2LAhFy9eZMOGDTg4OODv7/9E23VnKvycOXOoXbu22bG7A2HIflZKTuurXr16lmvMCxYsaPw7V65cZsdMJhPp6elGGdbW1uzbty9TG+83sh0eHs7hw4fNZqqkp6czd+5c42fZ2dk5y40Wr169CpDpy4kSJUrg6uqKj48PFy9epGPHjmzZsgWA0qVLYzKZiImJMZYX3C0mJoa8efNSsGBB40uFo0ePUrVq1Xv24WEpEBcRERF5ipLezzyC+9iMiH9yZedAgQIFsLa25sKFC2bpFy5cMBuhu9uoUaPo2rUrb775JgCVKlUiOTmZ3r17M2LEiHuu1SxTpgxxcXFm6X/++Sf+/v44OTkRERGRKZCQ51dqaioLFy5kypQpxtTrO9q0acPXX39Nnz59qFu3Lp6enixbtoy1a9fy2muvGZ+jkiVLkitXLvbs2WMsi0hMTOTYsWM0aNDgvvWfOXOG8+fPG18C7Ny5EysrK3x8fHBzc6Nw4cKcPHmSwMDAR+qnj48PZ8+e5cKFC8bMlD179pjlqVatGsuWLaNQoUI4Ozs/VD1Vq1YlLS2Nixcv8uKLL+bonF9//ZW9e/cSFRVFvnz5jPTLly/TqFEjjh49aqzf/+2338z6ALB//37s7e3vuySlX79+hISEEBERQdu2bcmfPz/NmjVj1qxZDBo0yGydeEJCAosXL6Zbt26YTCaqVKlC+fLlmTJlCh07dsz0f8/Vq1cfaZ24NmsTEREReYry2Jqe3CtPnif6yo6trS3Vq1cnMjLSSEtPTzceE5SV69evZ/qD984I272miiYlJXHixAk8PDyMtGvXrtG8eXNsbW1ZvXo19vb22bZXnh/ff/89V65coWfPnlSsWNHs1b59e7MNwzp37kxYWBgbNmwwC4ydnJwICgri3XffZdOmTRw+fJiePXtiZWVlrLW+F3t7e4KCgvjll1/YunUrb7/9Nh06dDC+oBo3bhwhISF89tlnHDt2jF9//ZV58+YRGhr6QP1s1qwZpUqVIigoiIMHD7J9+3ZGjhwJYLQxMDCQAgUK0Lp1a7Zu3cqpU6eIiori7bff5rfffstRPWXKlCEwMJBu3bqxcuVKTp06xe7duwkJCbnns9nDw8OpVasWDRo0MLv+DRo0oGbNmsY98PPzw8fHh06dOvHzzz9z8uRJvv32W0aOHMk777yTaQT+brlz56ZXr16MGTPG+P9jxowZ3Lx5Ez8/P7Zs2cLZs2dZt24dzZo1o0iRIkycONG4PvPmzePYsWO8+OKLrFmzhpMnT3Lw4EEmTpxI69atc3YT7kEj4iIiIiLyxAQHBxMUFESNGjWoVasW06ZNIzk5mR49egDQrVs3ihQpQkhICAABAQGEhoZStWpVY2r6qFGjCAgIMP7gHjJkCAEBARQvXpzz588zZswYrK2t6dSpE/D/g/Dr16/z1Vdfce3aNa5duwbcnmp7vz/c5TEa+899bnt4eDhNmzbNNK0ZoH379nz88cccPHiQypUrExgYyMSJEylevDj16tUzyxsaGkqfPn14+eWXcXZ2ZujQoZw9ezbbL35Kly5Nu3btaNmyJZcvX+bll182ezzZm2++Se7cufnkk0949913yZMnD5UqVbrvY9GyYm1tzapVq3jzzTepWbMmJUuW5JNPPiEgIMBoY+7cudmyZQvvvfce7dq1488//6RIkSI0adLkgUbI582bxwcffMDgwYM5d+4cBQoU4IUXXuDll1/OlPfWrVt89dVXvPfee1mW1b59e6ZMmcKkSZPIlSsXP/74I8OHD6dTp078/vvvlChRgnfeeYfg4OBs29W/f39CQ0P55ptv6NChA97e3uzdu5cxY8bQoUMHLl++jLu7O23atGHMmDFmo/O1atVi7969TJw4kV69enHp0iU8PDyoW7cu06ZNy/G1yYop40mvQhcR+T/Xrl3DxcWFxGFOONvd/5tiERF5DP4hgdCMGTP45JNPSEhIoEqVKnz22WfG2tdGjRrh5eVlPAYpNTWViRMnsmjRIs6dO0fBggUJCAhg4sSJxjTQ119/nS1btvDHH39QsGBB6tevz8SJEylVqhRwe51548aNs2zLqVOn8PLyetJd/lcxfn//387zd7tx4wanTp2iRIkSmnXA7Z26ixQpwpQpU+652djYsWNZtWoV0dHRlm3c/9m+fTv169cnLi7O+JmRxyenPxMKxEXEYhSIi4hY2D8kEJdnmwLxeztw4ABHjx6lVq1aJCYmMn78eKKiooiLizN7tv3dLB2IR0RE4OjoiLe3t7H5Yd68edm2bZtF6n/e5PRnQlPTRUREREREHtLkyZOJjY019kTYunXrPYPwp+HPP//kvffe48yZMxQoUICmTZsyZcqUp92s555GxEXEYjQiLiJiYRoRl8dAI+IiOZfTnwntmi4iIiIiIiJiQc9dIG4ymVi1atUTr+fo0aO88MIL2NvbU6VKlSde3/Po9OnTmEymJ76+JioqCpPJxNWrV59oPfdiqX4+jH9y20RERMRyNMlW5Lac/iw8VCC+Y8cOrK2tadWq1cOc/lTFx8fTokWLJ17PmDFjyJMnD7GxsWbPznxUlvoiQf69FDyLiIjI45IrVy7g9vPfReT//yzc+dm4l4farC08PJwBAwYQHh7O+fPnKVy48MMUkyMZGRmkpaVhY/N49pVzd3d/LOVk58SJE7Rq1YrixYtbpL4HdevWLWxtbZ9oHY/73omIiIjIP4u1tTWurq5cvHgRuP1MapNJ+8DI8ycjI4Pr169z8eJFXF1dsba2vm/+B46QkpKSWLZsGXv37iUhIYH58+czfPhwADp37kxaWhrLli0z8qekpODh4UFoaCjdunUjPT2djz76iC+++IKEhATKlCnDqFGjePXVV4H//9zHNWvWMHLkSH799Vd+/PFHPD09CQ4OZufOnSQnJ1OuXDlCQkJo2rSpUVd8fDxvvvkmP/30E+7u7kycOJHhw4czcOBABg4cCNweUY6IiKBNmzacPn2aEiVKsGLFCqZPn86uXbvw9vYmLCyMOnXqGOXOmTOH8ePH88cff+Dn58eLL77I+PHj7zlV+c5/Pvv27WP8+PGMGTOGsWPHcvbsWQYPHsyPP/6IlZUVL774Ip9++qnxLMs9e/YwfPhwDhw4QEpKClWqVGHq1KlUq1YNwMjXtm1bAIoXL87p06fp3r07V69eNRspHzhwINHR0URFRQG3n9FZsWJFbGxs+Oqrr6hUqRKbNm3i0KFDvPvuu2zdupU8efLQvHlzpk6dauz0+O233zJu3Dji4uLInTs3VatW5bvvviNPnjyZ+v0o987Ly4vevXsTFxfHN998Q968eRk5ciS9e/c28uzevZv//Oc/xMTEULFiRUaMGJGpDZs3b+bdd9/ll19+IV++fAQFBfHBBx8YXwY0atSISpUqYW1tzYIFC7C1teWDDz6gc+fO9O/fn2+//RY3NzemT5+e5cyJ5ORkPDw8mDt3rvGZBVi1ahWBgYEkJCTg5OSU6bzsruOXX37JlClTjGebvv3227z11luZyrkju/uWnp7O5MmT+eKLLzh79ixubm785z//YcSIEZQoUQKAqlWrAtCwYUPjc5JdO3JyD0REROT5cmeg604wLvI8c3V1zdHg7wMH4suXL6ds2bL4+PjQpUsXBg4cyPvvv4/JZCIwMJDXXnuNpKQkHB0dAVi/fj3Xr183gseQkBC++uorwsLC8Pb2ZsuWLXTp0oWCBQvSsGFDo55hw4YxefJkSpYsSd68eTl79iwtW7Zk4sSJ2NnZsXDhQgICAoiNjaVYsWIAdOvWjUuXLhEVFUWuXLkIDg7O0X8II0aMYPLkyXh7ezNixAg6depEXFwcNjY2bN++nT59+vDRRx/xyiuvsHHjRkaNGnXf8uLj42natCn+/v4MGTIER0dHUlJS8PPzo06dOmzduhUbGxs++OAD/P39OXjwILa2tvz5558EBQUxffp0MjIymDJlCi1btuT48eM4OTmxZ88eChUqxLx58/D398/2W5a/W7BgAX379mX79u0AXL16lZdeeok333yTqVOn8tdff/Hee+/RoUMHfvrpJ+Lj4+nUqRMff/wxbdu25c8//2Tr1q3Zrnt4mHsHMGXKFCZMmMDw4cP59ttv6du3Lw0bNsTHx4ekpCRefvllmjVrxldffcWpU6d45513zOo9d+4cLVu2pHv37ixcuJCjR4/Sq1cv7O3tGTt2rNl1GDp0KLt372bZsmX07duXiIgI2rZty/Dhw5k6dSpdu3blzJkz5M6d26yOPHny8PrrrzNv3jyzQPzO+6yC8Oyu4+LFixk9ejQzZsygatWqHDhwgF69epEnTx6CgoIylZfdfQN4//33mTNnDlOnTqV+/frEx8dz9OhR4HYwXatWLTZu3EiFChWMmRHZtSMn9+Dvbt68yc2bN433165du29+ERERefaYTCY8PDwoVKgQKSkpT7s5Ik9Nrly5chyjPXAgHh4eTpcuXQDw9/cnMTGRzZs306hRI/z8/MiTJw8RERF07doVgCVLlvDKK6/g5OTEzZs3mTRpEhs3bjRGnEuWLMm2bduYPXu2WSA+fvx4mjVrZrzPly8fvr6+xvsJEyYQERHB6tWr6d+/P0ePHmXjxo3s2bOHGjVqALdH97y9vbPt05AhQ4z17uPGjaNChQrExcVRtmxZY2R0yJAhAJQpU4aff/6Z77///p7lubu7Y2Njg6Ojo/FtyFdffUV6ejpffvmlMWI+b948XF1diYqKonnz5rz00ktm5XzxxRe4urqyefNmXn75ZQoWLAjk/FuWv/P29ubjjz823n/wwQdUrVqVSZMmGWlz587F09OTY8eOkZSURGpqKu3atTOm2FeqVCnbeh703t3RsmVLY/T1vffeY+rUqWzatAkfHx+WLFlCeno64eHh2NvbU6FCBX777Tf69u1rnD9r1iw8PT2ZMWMGJpOJsmXLcv78ed577z1Gjx6NldXtLRF8fX0ZOXIkcDtg/fDDDylQoAC9evUCYPTo0Xz++eccPHiQF154IVP/3nzzTerWrUt8fDweHh5cvHiRNWvWsHHjxiyvR3x8/H2v45gxY5gyZQrt2rUDoESJEhw5coTZs2dnGYjfCZTvdd88PDz49NNPmTFjhnF+qVKlqF+/PoDxOcqfP7/Z5yi7duTkHvxdSEgI48aNy5Re8UY4Vhm5szhDRESyc/rDZ2+PHnk+WFtbP/BAkcjz6oE2a4uNjWX37t106tQJABsbGzp27Eh4eLjxvkOHDixevBi4PY33u+++IzAwEIC4uDiuX79Os2bNcHR0NF4LFy7kxIkTZnXdCabvSEpKYsiQIZQrVw5XV1ccHR2JiYnhzJkzRttsbGyMadwApUuXJm/evNn2q3Llysa/PTw8gP8/tSY2NpZatWqZ5f/7+5z45ZdfiIuLw8nJyeh3vnz5uHHjhtH3Cxcu0KtXL7y9vXFxccHZ2ZmkpCSjj4+qevXqmdq0adMms3tRtmxZ4PYad19fX5o0aUKlSpV47bXXmDNnDleuXMm2nge9d3fcfR9MJhPu7u7GfYiJiaFy5cpmz+K7e/nAnTx16tQxW5dUr149kpKS+O2337Ksx9ramvz585sFxm5ubsC9p1fVqlWLChUqsGDBAuD2lyzFixenQYMGWea/33VMTk7mxIkT9OzZ0+w+fPDBB5l+Ju7I7r7FxMRw8+ZNmjRpkuX5WclJO3JyD/7u/fffJzEx0XidPXs2x20SEREREfm3eqAR8fDwcFJTU802Z8vIyMDOzo4ZM2bg4uJCYGAgDRs25OLFi2zYsAEHBwf8/f2B2wEZwA8//ECRIkXMyrazszN7//c1yEOGDGHDhg1MnjyZ0qVL4+DgwKuvvsqtW7cepAtZuntHuztBXHp6+iOXe7ekpCSqV69ufElxtzsjlEFBQfzxxx98+umnFC9eHDs7O+rUqZNtH62srDJNF89qWtDfr2lSUhIBAQF89NFHmfJ6eHhgbW3Nhg0b+Pnnn/nxxx+ZPn06I0aMYNeuXcY646w87L37+86CJpPpsd+He9XzoJ+BN998k5kzZzJs2DDmzZtHjx497rkxyf2u452p73PmzKF27dqZzstKdvft5MmT92z3vdz52XyQduSEnZ1dpp9tEREREZHnXY4D8dTUVBYuXMiUKVNo3ry52bE2bdrw9ddf06dPH+rWrYunpyfLli1j7dq1vPbaa0aQU758eezs7Dhz5ozZNPSc2L59O927dzfWmiclJXH69GnjuI+PD6mpqRw4cMAY+Y2Li8vRCO79+Pj4sGfPHrO0v7/PiWrVqrFs2TIKFSqEs7Nzlnm2b9/OrFmzaNmyJQBnz57l0qVLZnly5cpFWlqaWVrBggU5dOiQWVp0dHS2W+ZXq1aNFStW4OXldc+dzU0mE/Xq1aNevXqMHj2a4sWLExERQXBw8H3L/nu/7nfvcqJcuXIsWrSIGzduGCOyO3fuzJRnxYoVZGRkGEHx9u3bcXJyomjRog9UX3a6dOnC0KFD+eyzzzhy5EiWU8jvdr/rWLhwYU6ePGnMHMlOdvfN29sbBwcHIiMjefPNNzMdv7Mm/O7PkZubW7btyMk9EBERERGR7OV4avr333/PlStX6NmzJxUrVjR7tW/f3pieDrd3Tw8LC2PDhg1mf9Q7OTkxZMgQBg0axIIFCzhx4gT79+9n+vTpxjTfe/H29mblypVER0fzyy+/0LlzZ7MRy7Jly9K0aVN69+7N7t27OXDgAL1798bBweGRHqEwYMAA1qxZQ2hoKMePH2f27NmsXbv2gcsMDAykQIECtG7dmq1bt3Lq1CmioqJ4++23jWnT3t7eLFq0iJiYGHbt2kVgYCAODg5m5Xh5eREZGUlCQoLxJcNLL73E3r17WbhwIcePH2fMmDGZAvOs9OvXj8uXL9OpUyf27NnDiRMnWL9+PT169CAtLY1du3YxadIk9u7dy5kzZ1i5ciW///475cqVe6C+Z3fvcqJz586YTCZ69erFkSNHWLNmDZMnTzbL89Zbb3H27FkGDBjA0aNH+e677xgzZgzBwcHG+vDHJW/evLRr1453332X5s2b3zfQz+46jhs3jpCQED777DOOHTvGr7/+yrx58wgNDc2yvOzum729Pe+99x5Dhw41ln3s3LnT+BktVKgQDg4OrFu3jgsXLpCYmJijduTkHoiIiIiISPZyHJ2Eh4fTtGlTXFxcMh1r3749e/fu5eDBg8DtoPPIkSMUKVKEevXqmeWdMGECo0aNIiQkhHLlyuHv788PP/xw36nOAKGhoeTNm5e6desSEBCAn5+f2XpwgIULF+Lm5kaDBg1o27YtvXr1wsnJyWxN64OqV68eYWFhhIaG4uvry7p16xg0aNADl5k7d262bNlCsWLFaNeuHeXKlaNnz57cuHHDGCEPDw/nypUrVKtWja5du/L2229TqFAhs3KmTJnChg0b8PT0NB4/5efnx6hRoxg6dCg1a9bkzz//pFu3btm2qXDhwmzfvp20tDSaN29OpUqVGDhwIK6urlhZWeHs7MyWLVto2bIlZcqUYeTIkUyZMiXLx3rdT07uXXYcHR3573//y6+//krVqlUZMWJEpqnZRYoUYc2aNezevRtfX1/69OlDz549jY3ZHreePXty69Yt3njjjfvmy+46vvnmm3z55ZfMmzePSpUq0bBhQ+bPn3/Pn4ns7hvAqFGjGDx4MKNHj6ZcuXJ07NjRWPNuY2PDZ599xuzZsylcuDCtW7fOUTtycg9ERERERCR7pozsnkX1DPvtt9/w9PRk48aND7RxVXZ69erF0aNH2bp162MrU549ixYtYtCgQZw/f96Y7i33d+3aNVxcXPAcuBwrO+2aLiLyMLRruljand/fiYmJ91xiKSIP5oEfX/ZP9tNPP5GUlESlSpWIj49n6NCheHl53XM365yaPHkyzZo1I0+ePKxdu5YFCxYwa9asx9RqedZcv36d+Ph4PvzwQ/7zn/8oCBcRERERkQfyeBfOPmUpKSkMHz6cChUq0LZtWwoWLEhUVFS2m5ZlZ/fu3TRr1oxKlSoRFhbGZ599luUmWPJ8+Pjjjylbtizu7u68//77T7s5IiIiIiLyjPlXBeJ+fn4cOnSI69evc+HCBSIiIihevPgjl7t8+XIuXrzIX3/9xeHDh+nTp89jaK08q8aOHUtKSgqRkZE4Ojo+7eaIiIj8Y8ycORMvLy/s7e2pXbs2u3fvvm/+adOm4ePjg4ODA56engwaNIgbN25kmffDDz/EZDIxcOBAs/QbN27Qr18/8ufPj6OjI+3bt+fChQuPq0siIk/EvyoQFxEREZGnY9myZQQHBzNmzBj279+Pr68vfn5+xmahf7dkyRKGDRvGmDFjiImJITw8nGXLljF8+PBMeffs2cPs2bOpXLlypmODBg3iv//9L9988w2bN2/m/PnztGvX7rH3T0TkcVIgLiIiIiKPLDQ0lF69etGjRw/Kly9PWFgYuXPnZu7cuVnm//nnn6lXrx6dO3fGy8uL5s2b06lTp0yj6ElJSQQGBjJnzhzy5s1rdiwxMZHw8HBCQ0N56aWXqF69OvPmzePnn39m586dT6yvIiKP6l+1WZuIiIiIJaXfynoa9ZOUnJxs8Trz5Mlz3+O3bt1i3759ZnunWFlZ0bRpU3bs2JHlOXXr1uWrr75i9+7d1KpVi5MnT7JmzRq6du1qlq9fv360atWKpk2b8sEHH5gd27dvHykpKTRt2tRIK1u2LMWKFWPHjh288MILD9pVERGLUCAuIiIi8pDOTn3V4nU6TrV4lWT3tNtLly6RlpaGm5ubWbqbmxtHjx7N8pzOnTtz6dIl6tevT0ZGBqmpqfTp08dsavrSpUvZv38/e/bsybKMhIQEbG1tcXV1zVRvQkJCDnomIvJ0aGq6iIiIiFhcVFQUkyZNYtasWezfv5+VK1fyww8/MGHCBADOnj3LO++8w+LFi7G3t3/KrRURebw0Ii4iIiLykDwHfWvxOmMm+Fu8zuwUKFAAa2vrTLuVX7hwAXd39yzPGTVqFF27djUeCVupUiWSk5Pp3bs3I0aMYN++fVy8eJFq1aoZ56SlpbFlyxZmzJjBzZs3cXd359atW1y9etVsVPx+9YqI/BMoEBcRERF5SFa2lh+pzW699tNga2tL9erViYyMpE2bNgCkp6cTGRlJ//79szzn+vXrWFmZT860trYGbk+Fb9KkCb/++qvZ8R49elC2bFnee+89rK2tqV69Orly5SIyMpL27dsDEBsby5kzZ6hTp85j7qWIyOOjQFxEREREHllwcDBBQUHUqFGDWrVqMW3aNJKTk+nRowcA3bp1o0iRIoSEhAAQEBBAaGgoVatWpXbt2sTFxTFq1CgCAgKwtrbGycmJihUrmtWRJ08e8ufPb6S7uLjQs2dPgoODyZcvH87OzgwYMIA6depoozYR+UdTIC4iIiIij6xjx478/vvvjB49moSEBKpUqcK6deuMDdzOnDljNgI+cuRITCYTI0eO5Ny5cxQsWJCAgAAmTpz4QPVOnToVKysr2rdvz82bN/Hz82PWrFmPtW8iIo+bKSO7bTBFRB6Ta9eu4eLigufA5VjZ5X7azREReSad/rDV026CPGfu/P5OTEzE2dn5aTdH5F9Bu6aLiIiIiIiIWJACcRERERERERELUiAuIiIiIiIiYkEKxEVEREREREQsSIG4iIiIiIiIiAUpEBcRERERERGxIAXiIiIiIiIiIhakQFxERERERETEgmyedgNE5PlzaJwfzs7OT7sZIiIiIiJPhUbERURERERERCxIgbiIiIiIiIiIBSkQFxEREREREbEgBeIiIiIiIiIiFqRAXERERERERMSCFIiLiIiIiIiIWJACcRERERERERELUiAuIiIiIiIiYkEKxEVEREREREQsSIG4iIiIiIiIiAUpEBcRERERERGxIAXiIiIiIiIiIhakQFxERERERETEgmyedgNE5DkUUhTsTE+7FSIikpWxiU+7BSIi/3oaERcRERERERGxIAXiIiIiIiIiIhakQFxERERERETEghSIi4iIiIiIiFiQAnERERERERERC1IgLiIiIiIiImJBCsRFRERERERELEiBuIiIiIiIiIgFKRAXERERkQc2c+ZMvLy8sLe3p3bt2uzevfu++adNm4aPjw8ODg54enoyaNAgbty4YZbn3LlzdOnShfz58+Pg4EClSpXYu3evcbx79+6YTCazl7+//xPpn4jIk2TztBsgIiIiIs+WZcuWERwcTFhYGLVr12batGn4+fkRGxtLoUKFMuVfsmQJw4YNY+7cudStW5djx44ZQXVoaCgAV65coV69ejRu3Ji1a9dSsGBBjh8/Tt68ec3K8vf3Z968ecZ7Ozu7J9tZEZEnQIG4iIiIiDyQ0NBQevXqRY8ePQAICwvjhx9+YO7cuQwbNixT/p9//pl69erRuXNnALy8vOjUqRO7du0y8nz00Ud4enqaBdklSpTIVJadnR3u7u6Pu0siIhalqekiIiIikmO3bt1i3759NG3a1EizsrKiadOm7NixI8tz6taty759+4zp6ydPnmTNmjW0bNnSyLN69Wpq1KjBa6+9RqFChahatSpz5szJVFZUVBSFChXCx8eHvn378scffzzmHoqIPHkaERcRERGRHLt06RJpaWm4ubmZpbu5uXH06NEsz+ncuTOXLl2ifv36ZGRkkJqaSp8+fRg+fLiR5+TJk3z++ecEBwczfPhw9uzZw9tvv42trS1BQUHA7Wnp7dq1o0SJEpw4cYLhw4fTokULduzYgbW19ZPrtIjIY6ZAXERERESeqKioKCZNmsSsWbOoXbs2cXFxvPPOO0yYMIFRo0YBkJ6eTo0aNZg0aRIAVatW5dChQ4SFhRmB+Ouvv26UWalSJSpXrkypUqWIioqiSZMmlu+YiMhD0tR0EREREcmxAgUKYG1tzYULF8zSL1y4cM+126NGjaJr1668+eabVKpUibZt2zJp0iRCQkJIT08HwMPDg/Lly5udV65cOc6cOXPPtpQsWZICBQoQFxf3iL0SEbEsBeIiIiIikmO2trZUr16dyMhIIy09PZ3IyEjq1KmT5TnXr1/Hysr8z847U8kzMjIAqFevHrGxsWZ5jh07RvHixe/Zlt9++40//vgDDw+Ph+qLiMjTokBcRERERB5IcHAwc+bMYcGCBcTExNC3b1+Sk5ONXdS7devG+++/b+QPCAjg888/Z+nSpZw6dYoNGzYwatQoAgICjIB80KBB7Ny5k0mTJhEXF8eSJUv44osv6NevHwBJSUm8++677Ny5k9OnTxMZGUnr1q0pXbo0fn5+lr8IIiKPQGvERUREROSBdOzYkd9//53Ro0eTkJBAlSpVWLdunbGB25kzZ8xGwEeOHInJZGLkyJGcO3eOggULEhAQwMSJE408NWvWJCIigvfff5/x48dTokQJpk2bRmBgIHB7BP3gwYMsWLCAq1evUrhwYZo3b86ECRP0LHEReeaYMu7MBxIRecKuXbuGi4sLicOccLYzPe3miIhIVsYmPu0WyD+M8fs7MRFnZ+en3RyRfwVNTRcRERERERGxIAXiIiIiIiIiIhakQFxERERERETEghSI54DJZGLVqlVPvJ6jR4/ywgsvYG9vT5UqVZ54ff8G3bt3p02bNk+7GVn6J7dNRERERESeHosF4jt27MDa2ppWrVpZqsrHJj4+nhYtWjzxesaMGUOePHmIjY01ezbno7LUFwn/Bv+G4Ll79+6YTCZMJhO5cuXCzc2NZs2aMXfuXNLT083yenl5YTKZWLp0aaZyKlSogMlkYv78+UbaL7/8wiuvvEKhQoWwt7fHy8uLjh07cvHixSfdLRERERGRfw2LBeLh4eEMGDCALVu2cP78+SdaV0ZGBqmpqY+tPHd3d4s8FuPEiRPUr1+f4sWLkz9//ide34O6devW026C5JC/vz/x8fGcPn2atWvX0rhxY9555x1efvnlTD8bnp6ezJs3zyxt586dJCQkkCdPHiPt999/p0mTJuTLl4/169cTExPDvHnzKFy4MMnJyRbpl4iIiIjIv4FFAvGkpCSWLVtG3759adWqldkIW+fOnenYsaNZ/pSUFAoUKMDChQsBSE9PJyQkhBIlSuDg4ICvry/ffvutkT8qKgqTycTatWupXr06dnZ2bNu2jRMnTtC6dWvc3NxwdHSkZs2abNy40ayu+Ph4WrVqhYODAyVKlGDJkiV4eXkxbdo0I8/dI8qnT5/GZDKxcuVKGjduTO7cufH19WXHjh1m5c6ZMwdPT09y585N27ZtCQ0NxdXV9Z7XyGQysW/fPsaPH4/JZGLs2LEAnD17lg4dOuDq6kq+fPlo3bo1p0+fNs7bs2cPzZo1o0CBAri4uNCwYUP2799vHPfy8gKgbdu2mEwm431WI78DBw6kUaNGxvtGjRrRv39/Bg4cSIECBfDz8wPg0KFDtGjRAkdHR9zc3OjatSuXLl0yzvv222+pVKkSDg4O5M+fn6ZNm94zULty5QqBgYEULFgQBwcHvL29zYLC7Pr/d9l9VgAOHz7Myy+/jLOzM05OTrz44oucOHGCsWPHsmDBAr777jtjRDkqKipH7UhLSyM4OBhXV1fy58/P0KFDycmTAVesWEGFChWws7PDy8uLKVOmmB338vJi0qRJvPHGGzg5OVGsWDG++OKLbMu1s7PD3d2dIkWKUK1aNYYPH853333H2rVrzX7+AAIDA9m8eTNnz5410ubOnUtgYCA2NjZG2vbt20lMTOTLL7+katWqlChRgsaNGzN16lRKlCiRbZtEREREROQ2iwTiy5cvp2zZsvj4+NClSxfmzp1rBCmBgYH897//JSkpyci/fv16rl+/Ttu2bQEICQlh4cKFhIWFcfjwYQYNGkSXLl3YvHmzWT3Dhg3jww8/JCYmhsqVK5OUlETLli2JjIzkwIED+Pv7ExAQwJkzZ4xzunXrxvnz54mKimLFihV88cUXOZpmO2LECIYMGUJ0dDRlypShU6dOxkjj9u3b6dOnD++88w7R0dE0a9aMiRMn3re8+Ph4KlSowODBg4mPj2fIkCGkpKTg5+eHk5MTW7duZfv27Tg6OuLv72+MTv/5558EBQWxbds2du7cibe3Ny1btuTPP/8EbgfqAPPmzSM+Pt54n1MLFizA1taW7du3ExYWxtWrV3nppZeoWrUqe/fuZd26dVy4cIEOHToY/ejUqRNvvPEGMTExREVF0a5du3sGpaNGjeLIkSOsXbuWmJgYPv/8cwoUKACQo/7/XXaflXPnztGgQQPs7Oz46aef2LdvH2+88QapqakMGTKEDh06GKPJ8fHx1K1bN0ftmDJlCvPnz2fu3Lls27aNy5cvExERcd9ru2/fPjp06MDrr7/Or7/+ytixYxk1alSmQHnKlCnUqFGDAwcO8NZbb9G3b19iY2NzfA/veOmll/D19WXlypVm6W5ubvj5+bFgwQIArl+/zrJly3jjjTfM8rm7u5OamkpERESOvmQAuHnzJteuXTN7iYiIiIg872yyz/LowsPD6dKlC3B7ymxiYiKbN2+mUaNG+Pn5kSdPHiIiIujatSsAS5Ys4ZVXXsHJyYmbN28yadIkNm7cSJ06dQAoWbIk27ZtY/bs2TRs2NCoZ/z48TRr1sx4ny9fPnx9fY33EyZMICIigtWrV9O/f3+OHj3Kxo0b2bNnDzVq1ADgyy+/xNvbO9s+DRkyxFjvPm7cOCpUqEBcXBxly5Zl+vTptGjRgiFDhgBQpkwZfv75Z77//vt7lufu7o6NjQ2Ojo64u7sD8NVXX5Gens6XX36JyWQCbgfUrq6uREVF0bx5c1566SWzcr744gtcXV3ZvHkzL7/8MgULFgTA1dXVKPdBeHt78/HHHxvvP/jgA6pWrcqkSZOMtLlz5+Lp6cmxY8dISkoiNTWVdu3aUbx4cQAqVap0z/LPnDlD1apVjet/Z8QeYNmyZdn2/245+azMnDkTFxcXli5dSq5cuYDb9+cOBwcHbt68aXatcnIfpk2bxvvvv0+7du0ACAsLY/369fe9tqGhoTRp0oRRo0YZ7Thy5AiffPIJ3bt3N/K1bNmSt956C4D33nuPqVOnsmnTJnx8fO5bflbKli3LwYMHM6W/8cYbDB48mBEjRvDtt99SqlSpTBsGvvDCCwwfPpzOnTvTp08fatWqxUsvvUS3bt1wc3PLsr6QkBDGjRuXKb3ijXCsMnI/cPtFRMQChv3A6Q+fvT19RESeJU98RDw2Npbdu3fTqVMnAGxsbOjYsSPh4eHG+w4dOrB48WIAkpOT+e677wgMDAQgLi6O69ev06xZMxwdHY3XwoULOXHihFldd4K5O5KSkhgyZAjlypXD1dUVR0dHYmJijBHx2NhYbGxsqFatmnFO6dKlyZs3b7b9qly5svFvDw8PAGMkPTY2llq1apnl//v7nPjll1+Ii4vDycnJ6He+fPm4ceOG0fcLFy7Qq1cvvL29cXFxwdnZmaSkJLNR/0dRvXr1TG3atGmT2b0oW7YscHuNu6+vL02aNKFSpUq89tprzJkzhytXrtyz/L59+7J06VKqVKnC0KFD+fnnnx+o/3fLyWclOjqaF1980QjCcyK7diQmJhIfH0/t2rWNc2xsbDJ9Hv8uJiaGevXqmaXVq1eP48ePk5aWZqTd/VkzmUy4u7s/9OZoGRkZxpcJd2vVqhVJSUls2bKFuXPnZhoNv2PixIkkJCQQFhZGhQoVCAsLo2zZsvz6669Z5n///fdJTEw0XndPfxcREREReV498RHx8PBwUlNTKVy4sJGWkZGBnZ0dM2bMwMXFhcDAQBo2bMjFixfZsGEDDg4O+Pv7AxhT1n/44QeKFCliVvbfN1C7e2MpuD1qvWHDBiZPnkzp0qVxcHDg1VdffSybjt0dyN0JbP6+I/WjSkpKonr16saXFHe7M9IdFBTEH3/8waeffkrx4sWxs7OjTp062fbRysoq0/TilJSUTPn+fk2TkpIICAjgo48+ypTXw8MDa2trNmzYwM8//8yPP/7I9OnTGTFiBLt27cpyHXGLFi343//+x5o1a9iwYQNNmjShX79+TJ48OUf9/3vb4P6fFQcHh0znZedB2/G4/f1LA5PJ9NCftZiYmCzvg42NDV27dmXMmDHs2rXrvtPq8+fPz2uvvcZrr73GpEmTqFq1KpMnTzamtt/Nzs7OIhsdioiIiIg8S55oIJ6amsrChQuZMmVKpmnEbdq04euvv6ZPnz7UrVsXT09Pli1bxtq1a3nttdeM4KN8+fLY2dlx5swZs2noObF9+3a6d+9urDVPSkoy22DLx8eH1NRUDhw4YIz8xsXF3XcENyd8fHwyrcV+0LXZANWqVWPZsmUUKlQIZ2fnLPNs376dWbNm0bJlS+D2pmJ3b5wGtwO5u0dY4XYAeejQIbO06OjobEeKq1WrxooVK/Dy8jLbyOtuJpOJevXqUa9ePUaPHk3x4sWJiIggODg4y/wFCxYkKCiIoKAgXnzxRd59910mT56co/7fLSeflcqVK7NgwQJSUlKy7KutrW2ma5WTdnh4eLBr1y4aNGgA3P7s79u3z2y2xd+VK1eO7du3m6Vt376dMmXKYG1tfd++PoyffvqJX3/9lUGDBmV5/I033mDy5Ml07NgxR7NC4Pb1KlWqlHZNFxERERF5AE90avr333/PlStX6NmzJxUrVjR7tW/f3pieDrd3Tw8LC2PDhg3GtHQAJycnhgwZwqBBg1iwYAEnTpxg//79TJ8+PcsRuLt5e3uzcuVKoqOj+eWXX+jcubPZSGLZsmVp2rQpvXv3Zvfu3Rw4cIDevXvj4OCQ5fTdnBowYABr1qwhNDSU48ePM3v2bNauXfvAZQYGBlKgQAFat27N1q1bOXXqFFFRUbz99tv89ttvRh8XLVpETEwMu3btIjAwMNOor5eXF5GRkSQkJBhfMrz00kvs3buXhQsXcvz4ccaMGZMpMM9Kv379uHz5Mp06dWLPnj2cOHGC9evX06NHD9LS0ti1axeTJk1i7969nDlzhpUrV/L7779Trly5LMsbPXo03333HXFxcRw+fJjvv//eyJuT/t8tJ5+V/v37c+3aNV5//XX27t3L8ePHWbRokbH5mZeXFwcPHiQ2NpZLly6RkpKSo3a88847fPjhh6xatYqjR4/y1ltvcfXq1ftey8GDBxMZGcmECRM4duwYCxYsYMaMGcbeAo/i5s2bJCQkcO7cOfbv38+kSZNo3bo1L7/8Mt26dcvynHLlynHp0qVMjzK74/vvv6dLly58//33HDt2jNjYWCZPnsyaNWto3br1I7dZREREROR58UQD8fDwcJo2bYqLi0umY+3bt2fv3r3GxlGBgYEcOXKEIkWKZFo3O2HCBEaNGkVISAjlypXD39+fH374IdtHJoWGhpI3b17q1q1LQEAAfn5+mUYoFy5ciJubGw0aNKBt27b06tULJycn7O3tH7rf9erVIywsjNDQUHx9fVm3bh2DBg164DJz587Nli1bKFasGO3ataNcuXL07NmTGzduGCOz4eHhXLlyhWrVqtG1a1fefvttChUqZFbOlClT2LBhA56enlStWhUAPz8/Ro0axdChQ6lZsyZ//vnnPQO0uxUuXJjt27eTlpZG8+bNqVSpEgMHDsTV1RUrKyucnZ3ZsmULLVu2pEyZMowcOZIpU6bQokWLLMuztbXl/fffp3LlyjRo0ABra2uWLl2a4/7/XXaflfz58/PTTz+RlJREw4YNqV69OnPmzDFGx3v16oWPjw81atSgYMGCbN++PUftGDx4MF27diUoKIg6derg5ORkzMS4l2rVqrF8+XKWLl1KxYoVGT16NOPHjzfbqO1hrVu3Dg8PD7y8vPD392fTpk189tlnfPfdd/cdbc+fP/89p++XL1+e3LlzM3jwYKpUqcILL7zA8uXL+fLLL42NFkVEREREJHumjJw+h+g58dtvv+Hp6cnGjRtp0qTJYyu3V69eHD16lK1btz62MkWeNdeuXcPFxQXPgcuxstOu6SIi/1TaNV3uduf3d2JiYo6WC4pI9izy+LJ/sjujo5UqVSI+Pp6hQ4fi5eVlrPV9WJMnT6ZZs2bkyZOHtWvXsmDBAmbNmvWYWi0iIiIiIiLPquc+EE9JSWH48OGcPHkSJycn6taty+LFix/o8VZZ2b17Nx9//DF//vknJUuW5LPPPuPNN998TK0WERERERGRZ9UTf474P52fnx+HDh3i+vXrXLhwgYiICIoXL/7I5S5fvpyLFy/y119/cfjwYfr06fMYWisiIiLyzzFz5ky8vLywt7endu3a7N69+775p02bho+PDw4ODnh6ejJo0CBu3LhhlufcuXN06dLF2LekUqVK7N271yxPTEwMr7zyCi4uLuTJk4eaNWty5syZx94/EZEn5bkfERcRERGRB7ds2TKCg4MJCwujdu3aTJs2DT8/P2JjYzNtHAuwZMkShg0bxty5c6lbty7Hjh2je/fumEwmQkNDAbhy5Qr16tWjcePGrF27loIFC3L8+HGzx2qeOHGC+vXr07NnT8aNG4ezszOHDx9+pI12RUQsTZu1iYjFaLM2EZFnQ042a6tduzY1a9ZkxowZAKSnp+Pp6cmAAQMYNmxYpvz9+/cnJiaGyMhII23w4MHs2rWLbdu2ATBs2DC2b99+381tX3/9dXLlysWiRYsetFvykLRZm8jj99xPTRcRERGRB3Pr1i327dtH06ZNjTQrKyuaNm3Kjh07sjynbt267Nu3z5i+fvLkSdasWUPLli2NPKtXr6ZGjRq89tprFCpUiKpVqzJnzhzjeHp6Oj/88ANlypTBz8+PQoUKUbt2bVatWvVkOioi8oQoEBcRERGRB3Lp0iXS0tJwc3MzS3dzcyMhISHLczp37sz48eOpX78+uXLlolSpUjRq1Ijhw4cbeU6ePMnnn3+Ot7c369evp2/fvrz99tssWLAAgIsXL5KUlMSHH36Iv78/P/74I23btqVdu3Zs3rz5yXVYROQx0xpxEREREXnioqKimDRpErNmzaJ27drExcXxzjvvMGHCBEaNGgXcHvGuUaMGkyZNAqBq1aocOnSIsLAwgoKCSE9PB6B169YMGjQIgCpVqvDzzz8TFhZGw4YNn07nREQekAJxEREREXkgBQoUwNramgsXLpilX7hwAXd39yzPGTVqFF27djUe51qpUiWSk5Pp3bs3I0aMwMrKCg8PD8qXL292Xrly5VixYoVRr42NTZZ57qwzFxF5FmhquoiIiIg8EFtbW6pXr2628Vp6ejqRkZHUqVMny3OuX7+OlZX5n57W1tYA3Nk7uF69esTGxprlOXbsmPFoWVtbW2rWrHnfPCIizwKNiIuIiIjIAwsODiYoKIgaNWpQq1Ytpk2bRnJyMj169ACgW7duFClShJCQEAACAgIIDQ2latWqxtT0UaNGERAQYATkgwYNom7dukyaNIkOHTqwe/duvvjiC7744guj3nfffZeOHTvSoEEDGjduzLp16/jvf/9LVFSUxa+BiMjDUiAuIiIiIg+sY8eO/P7774wePZqEhASqVKnCunXrjA3czpw5YzYCPnLkSEwmEyNHjuTcuXMULFiQgIAAJk6caOSpWbMmERERvP/++4wfP54SJUowbdo0AgMDjTxt27YlLCyMkJAQ3n77bXx8fFixYgX169e3XOdFRB6RniMuIhaj54iLiDwbcvIccXl+6DniIo+f1oiLiIiIiIiIWJACcRERERERERELUiAuIiIiIiIiYkEKxEVEREREREQsSIG4iIiIiIiIiAUpEBcRERERERGxIAXiIiIiIiIiIhakQFxERERERETEgmyedgNE5PlzaJwfzs7OT7sZIiIiIiJPhUbERURERERERCxIgbiIiIiIiIiIBSkQFxEREREREbEgBeIiIiIiIiIiFqRAXERERERERMSCFIiLiIiIiIiIWJACcRERERERERELUiAuIiIiIiIiYkEKxEVEREREREQsSIG4iIiIiIiIiAUpEBcRERERERGxIAXiIiIiIiIiIhakQFxERERERETEgmyedgNE5DkUUhTsTE+7FSIiIjkzNvFpt0BE/mU0Ii4iIiIiIiJiQQrERURERERERCxIgbiIiIiIiIiIBSkQFxEREREREbEgBeIiIiIiIiIiFqRAXERERERERMSCFIiLiIiIiIiIWJACcRERERERERELUiAuIiIiIvKIZs6ciZeXF/b29tSuXZvdu3ffM29KSgrjx4+nVKlS2Nvb4+vry7p168zyfP7551SuXBlnZ2ecnZ2pU6cOa9euzbK8jIwMWrRogclkYtWqVY+zWyLyhCgQFxERERF5BMuWLSM4OJgxY8awf/9+fH198fPz4+LFi1nmHzlyJLNnz2b69OkcOXKEPn360LZtWw4cOGDkKVq0KB9++CH79u1j7969vPTSS7Ru3ZrDhw9nKm/atGmYTKYn1j8RefxMGRkZGU+7ESLyfLh27RouLi4kDnPC2U5/MIiIyDNibOJ9D9euXZuaNWsyY8YMANLT0/H09GTAgAEMGzYsU/7ChQszYsQI+vXrZ6S1b98eBwcHvvrqq3vWky9fPj755BN69uxppEVHR/Pyyy+zd+9ePDw8iIiIoE2bNg/Ywfszfn8nJuLs7PxYyxZ5XmlEXERERETkId26dYt9+/bRtGlTI83KyoqmTZuyY8eOLM+5efMm9vb2ZmkODg5s27Yty/xpaWksXbqU5ORk6tSpY6Rfv36dzp07M3PmTNzd3R9Db0TEUhSIi4iIiIg8pEuXLpGWloabm5tZupubGwkJCVme4+fnR2hoKMePHyc9PZ0NGzawcuVK4uPjzfL9+uuvODo6YmdnR58+fYiIiKB8+fLG8UGDBlG3bl1at279+DsmIk+UAnEREREREQv69NNP8fb2pmzZstja2tK/f3969OiBlZX5n+Y+Pj5ER0eza9cu+vbtS1BQEEeOHAFg9erV/PTTT0ybNu0p9EBEHpUCcRERERGRh1SgQAGsra25cOGCWfqFCxfuOV28YMGCrFq1iuTkZP73v/9x9OhRHB0dKVmypFk+W1tbSpcuTfXq1QkJCcHX15dPP/0UgJ9++okTJ07g6uqKjY0NNjY2wO215o0aNXr8HRWRx0qBuIiIiIjIQ7K1taV69epERkYaaenp6URGRpqt586Kvb09RYoUITU1lRUrVmQ7xTw9PZ2bN28CMGzYMA4ePEh0dLTxApg6dSrz5s17tE6JyBNn87QbICIiIiLyLAsODiYoKIgaNWpQq1Ytpk2bRnJyMj169ACgW7duFClShJCQEAB27drFuXPnqFKlCufOnWPs2LGkp6czdOhQo8z333+fFi1aUKxYMf7880+WLFlCVFQU69evB8Dd3T3LEfdixYpRokQJC/RaRB6FAnERERERkUfQsWNHfv/9d0aPHk1CQgJVqlRh3bp1xgZuZ86cMVv/fePGDUaOHMnJkydxdHSkZcuWLFq0CFdXVyPPxYsX6datG/Hx8bi4uFC5cmXWr19Ps2bNLN09EXkC9BxxEbEYPUdcRESeSdk8R/zfTs8RF3n8tEZcRERERERExIIUiIuIiIiIiIhYkAJxEREREREREQtSIC7yGCQkJNCsWTPy5MljttHKs2b+/PkP3H6TycSqVaueSHtERERERP6NFIjLM8VkMt33NXbs2KfSrqlTpxIfH090dDTHjh17Km0QEREREZFngx5fJs+U+Ph449/Lli1j9OjRxMbGGmmOjo7GvzMyMkhLS8PG5sl/zE+cOEH16tXx9vZ+6DJu3bqFra3tY2yViIiIiIj8E2lEXJ4p7u7uxsvFxQWTyWS8P3r0KE5OTqxdu5bq1av/v/buPyjqOo/j+GsR2YUAfxQBEog/Sa/EwB+hzp16OCjpEJ2K052/LnUsOTudzuhECTRtOlOpPI/K08vqtBSJUQTNcjrL9ETwUjlORaXzQMpMA09R9nt/NO60h6YIfFeX52NmZ9rPfr7ffX3mPdvy9vtjZbVatWvXLh07dkwJCQkKDAyUr6+v+vbtqw8//NBpv+Hh4Vq0aJF+/etfy8/PT2FhYXr99dcdr9fW1io5OVnBwcGy2Wzq2LGjFi9e7Nh248aNeuutt2SxWDRp0iRJ0rfffqspU6YoICBA/v7+Gjp0qA4cOODY5/PPP6/evXvrzTffVKdOnWSz2a655quni2/evFkRERHy8fHR6NGjdeHCBf3lL39ReHi42rVrp5kzZ6qurs6x3dmzZzVhwgS1a9dOPj4+GjFihI4cOVJv32FhYfLx8VFiYqLOnDlT7/0/+OADRUVFyWazqXPnzkpPT9eVK1caVjgAAAAADjTicDspKSl68cUXVVJSol69eqm6ulrx8fHasWOHioqKNHz4cI0aNUrl5eVO27388svq06ePioqK9NRTT+nJJ590HG1/5ZVXlJubq/fee0+lpaV65513FB4eLkn6+9//ruHDh2vs2LGqqKhQZmamJGnMmDGqqqrS1q1bVVhYqKioKP385z/XN99843jPo0ePauPGjcrOzlZxcfF113ThwgW98sorWrdunfLz87Vz504lJiYqLy9PeXl5Wrt2rbKysrRhwwbHNpMmTdK+ffuUm5ur3bt3yzAMxcfH6/Lly5KkPXv26IknnlBycrKKi4s1ZMgQLVy40Ol9//a3v2nChAl6+umndfjwYWVlZWnNmjV64YUXbqoWly5d0vnz550eAAAAQEvHqelwOxkZGRo2bJjjefv27RUZGel4vmDBAm3atEm5ublKTk52jMfHx+upp56SJD377LNatmyZPv74Y0VERKi8vFzdunXToEGDZLFY1LFjR8d2AQEBslqt8vb2VlBQkCRp165d2rt3r6qqqmS1WiVJS5YsUU5OjjZs2KBp06ZJ+v5I+1tvvaWAgIAfXdPly5e1cuVKdenSRZI0evRorV27VqdPn5avr6969uypIUOG6OOPP1ZSUpKOHDmi3NxcffrppxowYIAk6Z133lFoaKhycnI0ZswYZWZmavjw4ZozZ44kqXv37vrss8+Un5/veN/09HSlpKRo4sSJkqTOnTtrwYIFmjNnjtLS0m5Yi8WLFys9Pb3e+AMXV8nD8Lnh9gAA3A5OuDoAALfDEXG4nT59+jg9r66u1jPPPKMePXqobdu28vX1VUlJSb0j4r169XL899VT3quqqiR9f3S5uLhYERERmjlzprZt2/ajGQ4cOKDq6mrdfffd8vX1dTyOHz+uY8eOOeZ17Njxhk24JPn4+DiacEkKDAxUeHi40zXxgYGBjrwlJSXy9PRU//79Ha/ffffdioiIUElJiWPOD1+XpJiYmHrryMjIcFrD1KlTVVFRoQsXLtww93PPPadz5845Hl9++eUNtwEAAADcHUfE4Xbuuusup+fPPPOMtm/friVLlqhr167y9vbW6NGjVVtb6zSvdevWTs8tFovsdrskKSoqSsePH9fWrVv14YcfauzYsYqNjXU6FfyHqqurFRwcrJ07d9Z77Yc/D/b/Wa/nWtl+LG9Tqa6uVnp6uh577LF6r13vmvYfslqtjjMCAAAAAHyPRhxu79NPP9WkSZOUmJgo6fvm8sSJEw3ej7+/v5KSkpSUlKTRo0dr+PDh+uabb9S+fft6c6OiolRZWSlPT0/HteRm6tGjh65cuaI9e/Y4Tk0/c+aMSktL1bNnT8ecPXv2OG33+eefOz2PiopSaWmpunbtak5wAAAAoAWgEYfb69atm7KzszVq1ChZLBbNmzevwUeOly5dquDgYD300EPy8PDQ+++/r6CgIKej2z8UGxurmJgYPfroo3rppZfUvXt3/ec//9GWLVuUmJhY7/T5ptatWzclJCRo6tSpysrKkp+fn1JSUhQSEqKEhARJ0syZMzVw4EAtWbJECQkJKigocLo+XJLmz5+vkSNHKiwsTKNHj5aHh4cOHDiggwcP1ruxGwAAAICbwzXicHtLly5Vu3btNGDAAI0aNUpxcXGKiopq0D78/Pz00ksvqU+fPurbt69OnDihvLw8eXhc+yNksViUl5enn/70p5o8ebK6d++ucePG6eTJkwoMDGyKZd3Q6tWrFR0drZEjRyomJkaGYSgvL89xSvvDDz+sN954Q5mZmYqMjNS2bduUmprqtI+4uDht3rxZ27ZtU9++ffXwww9r2bJlTjerAwAAANAwFsMwDFeHANAynD9/Xm3atFHob9+Th5W7pgMA7gwnXnzE1RFc6ur397lz5+Tv7+/qOIBb4Ig4AAAAAAAmohEHAAAAAMBENOIAAABAE1ixYoXCw8Nls9nUv39/7d2797pzL1++rIyMDHXp0kU2m02RkZH1bpq6cuVK9erVS/7+/vL391dMTIy2bt16zf0ZhqERI0bIYrEoJyenKZcFoBnQiAMAAACNtH79es2ePVtpaWnav3+/IiMjFRcXp6qqqmvOT01NVVZWll599VUdPnxY06dPV2JiooqKihxz7rvvPr344osqLCzUvn37NHToUCUkJOjQoUP19rd8+XJZLJZmWx+ApsXN2gCYhpu1AQDuRDdzs7b+/furb9++eu211yRJdrtdoaGh+s1vfqOUlJR68zt06KC5c+dqxowZjrFf/OIX8vb21ttvv33d92nfvr3+8Ic/6IknnnCMFRcXa+TIkdq3b5+Cg4O1adMmPfroow1Y4Y/jZm1A0+OIOAAAANAItbW1KiwsVGxsrGPMw8NDsbGx2r179zW3uXTpkmw2m9OYt7e3du3adc35dXV1WrdunWpqahQTE+MYv3Dhgh5//HGtWLFCQUFBTbAaAGagEQcAAAAa4euvv1ZdXZ0CAwOdxgMDA1VZWXnNbeLi4rR06VIdOXJEdrtd27dvV3Z2tioqKpzmffHFF/L19ZXVatX06dO1adMm9ezZ0/H6rFmzNGDAACUkJDT9wgA0GxpxAAAAwGSZmZnq1q2b7r//fnl5eSk5OVmTJ0+Wh4fzn+cREREqLi7Wnj179OSTT2rixIk6fPiwJCk3N1cfffSRli9f7oIVAGgMGnEAAACgEe655x61atVKp0+fdho/ffr0dU8XDwgIUE5OjmpqanTy5En985//lK+vrzp37uw0z8vLS127dlV0dLQWL16syMhIZWZmSpI++ugjHTt2TG3btpWnp6c8PT0lfX+t+eDBg5t+oQCaDI04AAAA0AheXl6Kjo7Wjh07HGN2u107duxwup77Wmw2m0JCQnTlyhVt3LjxhqeY2+12Xbp0SZKUkpKif/zjHyouLnY8JGnZsmVavXp14xYFoFl5ujoAAAAAcKebPXu2Jk6cqD59+qhfv35avny5ampqNHnyZEnShAkTFBISosWLF0uS9uzZo1OnTql37946deqUnn/+edntds2ZM8exz+eee04jRoxQWFiYvvvuO7377rvauXOnCgoKJElBQUHXPOIeFhamTp06mbBqALeKRhwAAABopKSkJH311VeaP3++Kisr1bt3b+Xn5ztu4FZeXu50/ffFixeVmpqqsrIy+fr6Kj4+XmvXrlXbtm0dc6qqqjRhwgRVVFSoTZs26tWrlwoKCjRs2DCzlwegifE74gBMw++IAwDuRDfzO+LujN8RB5oe14gDAAAAAGAiGnEAAAAAAExEIw4AAAAAgIloxAEAAAAAMBGNOAAAAAAAJqIRBwAAAADARDTiAAAAAACYiEYcAAAAAAATebo6AICW52B6nPz9/V0dAwAAAHAJjogDAAAAAGAiGnEAAAAAAExEIw4AAAAAgIloxAEAAAAAMBGNOAAAAAAAJqIRBwAAAADARDTiAAAAAACYiEYcAAAAAAAT0YgDAAAAAGAiGnEAAAAAAExEIw4AAAAAgIloxAEAAAAAMBGNOAAAAAAAJqIRBwAAAADARDTiAAAAAACYyNPVAQC0HIZhSJLOnz/v4iQAAOBmXf3evvo9DqDxaMQBmObMmTOSpNDQUBcnAQAADXXmzBm1adPG1TEAt0AjDsA07du3lySVl5fzRe5i58+fV2hoqL788kv5+/u7Ok6LRz1uH9Ti9kEtbh/nzp1TWFiY43scQOPRiAMwjYfH97elaNOmDX9U3Sb8/f2pxW2Eetw+qMXtg1rcPq5+jwNoPD5NAAAAAACYiEYcAAAAAAAT0YgDMI3ValVaWpqsVquro7R41OL2Qj1uH9Ti9kEtbh/UAmh6FoPfIQAAAAAAwDQcEQcAAAAAwEQ04gAAAAAAmIhGHAAAAAAAE9GIAwAAAABgIhpxAE1qxYoVCg8Pl81mU//+/bV3797rzl2zZo0sFovTw2azmZjWvTWkFpL07bffasaMGQoODpbValX37t2Vl5dnUlr315B6DB48uN5nw2Kx6JFHHjExsftq6Gdj+fLlioiIkLe3t0JDQzVr1ixdvHjRpLTurSG1uHz5sjIyMtSlSxfZbDZFRkYqPz/fxLTu65NPPtGoUaPUoUMHWSwW5eTk3HCbnTt3KioqSlarVV27dtWaNWuaPSfgTmjEATSZ9evXa/bs2UpLS9P+/fsVGRmpuLg4VVVVXXcbf39/VVRUOB4nT540MbH7amgtamtrNWzYMJ04cUIbNmxQaWmp3njjDYWEhJic3D01tB7Z2dlOn4uDBw+qVatWGjNmjMnJ3U9Da/Huu+8qJSVFaWlpKikp0apVq7R+/Xr9/ve/Nzm5+2loLVJTU5WVlaVXX31Vhw8f1vTp05WYmKiioiKTk7ufmpoaRUZGasWKFTc1//jx43rkkUc0ZMgQFRcX67e//a2mTJmigoKCZk4KuBEDAJpIv379jBkzZjie19XVGR06dDAWL158zfmrV6822rRpY1K6lqWhtVi5cqXRuXNno7a21qyILUpD6/H/li1bZvj5+RnV1dXNFbHFaGgtZsyYYQwdOtRpbPbs2cbAgQObNWdL0NBaBAcHG6+99prT2GOPPWb88pe/bNacLY0kY9OmTT86Z86cOcZPfvITp7GkpCQjLi6uGZMB7oUj4gCaRG1trQoLCxUbG+sY8/DwUGxsrHbv3n3d7aqrq9WxY0eFhoYqISFBhw4dMiOuW7uVWuTm5iomJkYzZsxQYGCgHnjgAS1atEh1dXVmxXZbt/rZ+KFVq1Zp3Lhxuuuuu5orZotwK7UYMGCACgsLHadMl5WVKS8vT/Hx8aZkdle3UotLly7Vu3zJ29tbu3btatasqG/37t1OtZOkuLi4m/5/GgBOTQfQRL7++mvV1dUpMDDQaTwwMFCVlZXX3CYiIkJ//vOf9cEHH+jtt9+W3W7XgAED9O9//9uMyG7rVmpRVlamDRs2qK6uTnl5eZo3b55efvllLVy40IzIbu1W6vFDe/fu1cGDBzVlypTmithi3EotHn/8cWVkZGjQoEFq3bq1unTposGDB3NqeiPdSi3i4uK0dOlSHTlyRHa7Xdu3b3dcxgFzVVZWXrN258+f13//+18XpQLuLDTiAFwmJiZGEyZMUO/evfWzn/1M2dnZCggIUFZWlqujtTh2u1333nuvXn/9dUVHRyspKUlz587Vn/70J1dHa/FWrVqlBx98UP369XN1lBZp586dWrRokf74xz9q//79ys7O1pYtW7RgwQJXR2txMjMz1a1bN91///3y8vJScnKyJk+eLA8P/pwFcOfxdHUAAO7hnnvuUatWrXT69Gmn8dOnTysoKOim9tG6dWs99NBDOnr0aHNEbDFupRbBwcFq3bq1WrVq5Rjr0aOHKisrVVtbKy8vr2bN7M4a89moqanRunXrlJGR0ZwRW4xbqcW8efM0fvx4xxkJDz74oGpqajRt2jTNnTuXJvAW3UotAgIClJOTo4sXL+rMmTPq0KGDUlJS1LlzZzMi4weCgoKuWTt/f395e3u7KBVwZ+HbA0CT8PLyUnR0tHbs2OEYs9vt2rFjh2JiYm5qH3V1dfriiy8UHBzcXDFbhFupxcCBA3X06FHZ7XbH2L/+9S8FBwfThDdSYz4b77//vi5duqRf/epXzR2zRbiVWly4cKFes331H6wMw2i+sG6uMZ8Lm82mkJAQXblyRRs3blRCQkJzx8X/iYmJcaqdJG3fvv2mv+8BiLumA2g669atM6xWq7FmzRrj8OHDxrRp04y2bdsalZWVhmEYxvjx442UlBTH/PT0dKOgoMA4duyYUVhYaIwbN86w2WzGoUOHXLUEt9HQWpSXlxt+fn5GcnKyUVpaamzevNm49957jYULF7pqCW6lofW4atCgQUZSUpLZcd1aQ2uRlpZm+Pn5GX/961+NsrIyY9u2bUaXLl2MsWPHumoJbqOhtfj888+NjRs3GseOHTM++eQTY+jQoUanTp2Ms2fPumgF7uO7774zioqKjKKiIkOSsXTpUqOoqMg4efKkYRiGkZKSYowfP94xv6yszPDx8TF+97vfGSUlJcaKFSuMVq1aGfn5+a5aAnDH4dR0AE0mKSlJX331lebPn6/Kykr17t1b+fn5jhu6lJeXOx1ZOnv2rKZOnarKykq1a9dO0dHR+uyzz9SzZ09XLcFtNLQWoaGhKigo0KxZs9SrVy+FhITo6aef1rPPPuuqJbiVhtZDkkpLS7Vr1y5t27bNFZHdVkNrkZqaKovFotTUVJ06dUoBAQEaNWqUXnjhBVctwW00tBYXL15UamqqysrK5Ovrq/j4eK1du1Zt27Z10Qrcx759+zRkyBDH89mzZ0uSJk6cqDVr1qiiokLl5eWO1zt16qQtW7Zo1qxZyszM1H333ac333xTcXFxpmcH7lQWw+C8KgAAAAAAzMI14gAAAAAAmIhGHAAAAAAAE9GIAwAAAABgIhpxAAAAAABMRCMOAAAAAICJaMQBAAAAADARjTgAAAAAACaiEQcAAAAAwEQ04gAAAAAAmIhGHAAAAAAAE9GIAwAAAABgIhpxAAAAAABM9D/5XmIcyt5LKAAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Figure: baseline performance by averaging quantile normalized features\n",
    "# Quantile normalize the features\n",
    "# https://stackoverflow.com/questions/37935920/quantile-normalization-on-pandas-dataframe\n",
    "rank_mean = x.stack().groupby(x.rank(method='first').stack().astype(int)).mean()\n",
    "x_qnorm = x.rank(method='min').stack().astype(int).map(rank_mean).unstack()\n",
    "\n",
    "# Get global and per gene auroc for avg 2 models and avg selected features\n",
    "pred = pd.DataFrame(index = x.index)\n",
    "pred['avg_2models'] = (-x_qnorm['esm_1v'] + x_qnorm['EVE_scores_ASM']) / 2\n",
    "pred['avg_feat_selected'] = (x_qnorm[list(set(feat_selected_dms).intersection(set(pos_feats)))].sum(axis = 1) -\n",
    "                             x_qnorm[list(set(feat_selected_dms).intersection(set(neg_feats)))].sum(axis = 1)\n",
    "                            ) / len(feat_selected_dms)\n",
    "cpt = CPTClassifier()\n",
    "cpt.train(feat_mat, label, all_genes, ['esm_1v', 'EVE_scores_ASM'])\n",
    "pred_clinvar = cpt.predict(x[['esm_1v', 'EVE_scores_ASM']])\n",
    "pred['transfer_2models'] = 1 - pred_clinvar.values\n",
    "\n",
    "print('AUROC by averaging quantile normalized esm-1v and EVE')\n",
    "g_auroc_avg_2models = roc_auc_score(y, pred['avg_2models'])\n",
    "p_auroc_avg_2models = get_per_gene_auroc(y, pred[['avg_2models']]).iloc[-1, 0]\n",
    "\n",
    "print('AUROC by averaging quantile normalized selected features')\n",
    "g_auroc_avg_feat_selected = roc_auc_score(y, pred['avg_feat_selected'])\n",
    "p_auroc_avg_feat_selected = get_per_gene_auroc(y, pred[['avg_feat_selected']]).iloc[-1, 0]\n",
    "\n",
    "print('AUROC by fitting transfer model with only esm-1v and EVE')\n",
    "g_auroc_transfer_2models = roc_auc_score(y, pred['transfer_2models'])\n",
    "p_auroc_transfer_2models = get_per_gene_auroc(y, pred[['transfer_2models']]).iloc[-1, 0]\n",
    "\n",
    "\n",
    "# Get global AUROC for avg randomly selected features\n",
    "print('AUROC by averaging quantile normalized random features')\n",
    "g_auroc_rand_feat = []\n",
    "pred_rand_feat = pd.DataFrame(index = x.index)\n",
    "for i in tqdm(range(100)):\n",
    "    feat_random = random_feat_by_category(i)\n",
    "    pred_rand_feat[i] = (x_qnorm[list(set(feat_random).intersection(set(pos_feats)))].sum(axis = 1) - \n",
    "                         x_qnorm[list(set(feat_random).intersection(set(neg_feats)))].sum(axis = 1)\n",
    "                        ) / len(feat_random)\n",
    "    g_auroc_rand_feat.append(roc_auc_score(y, pred_rand_feat[i]))\n",
    "    \n",
    "# Get per gene AUROC for avg randomly selected features\n",
    "p_auroc_rand_feat = get_per_gene_auroc(y, pred_rand_feat).iloc[3:, 0]\n",
    "\n",
    "# Plot\n",
    "auroc_global = [roc_by_gene.loc['Global_AUROC', 'Transfer'], \n",
    "                g_auroc_avg_feat_selected, \n",
    "                pd.Series(g_auroc_rand_feat).mean(), \n",
    "                g_auroc_transfer_2models,\n",
    "                g_auroc_avg_2models]\n",
    "auroc_avg_per_gene = [roc_by_gene.loc['Avg_per_gene_AUROC', 'Transfer'], \n",
    "                      p_auroc_avg_feat_selected, \n",
    "                      pd.Series(p_auroc_rand_feat).mean(), \n",
    "                      p_auroc_transfer_2models,\n",
    "                      p_auroc_avg_2models]\n",
    "bars = ['Transfer model', 'Averaging features selected on DMS', 'Averaging features randomly selected',\n",
    "        'Transfer model with ESM-1v and EVE', 'Averaging ESM-1v and EVE']\n",
    "err_global = [0, 0, pd.Series(g_auroc_rand_feat).std(), 0, 0]\n",
    "err_avg_per_gene = [0, 0, pd.Series(p_auroc_rand_feat).std(), 0, 0]\n",
    "y_pos = np.arange(len(bars))\n",
    "width = 0.4\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "rects_g = ax.barh(y_pos - width/2, auroc_global, width, xerr = err_global, label = 'Global AUROC')\n",
    "rects_p = ax.barh(y_pos + width/2, auroc_avg_per_gene, width, xerr = err_avg_per_gene, label = 'Avg per gene AUROC')\n",
    "\n",
    "ax.bar_label(rects_g, labels=[f'{x:,.3f}' for x in rects_g.datavalues], padding = 5)\n",
    "ax.bar_label(rects_p, labels=[f'{x:,.3f}' for x in rects_p.datavalues], padding = 5)\n",
    "\n",
    "plt.legend(bbox_to_anchor=(1, 0.65))\n",
    "plt.yticks(y_pos, bars)\n",
    "plt.xlim(0.5, 1)\n",
    "plt.rcParams[\"figure.figsize\"] = (5,4)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fb729a11-6fb0-4da0-8757-ff7db7875430",
   "metadata": {},
   "source": [
    "### Analyze human beneficial adaptation variants"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "941e55ab-35c1-4940-afb3-ee6f49db8e18",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Ojrh48SKA+lGcefPmYcCAAXB0dISdnR0uXrzY5Hq62NjYCInV7Xu9fZ+VlZW4du0aRo8erXXM6NGjhWunp6cL0/luCwkJ0eovk8nw888/w87OTthuJ8EZGRlCvzu/5o1jaezs2bMYOnSokFgREZHp4sgVEVEH8/f3h0Qi0Vm0oi1uvxsF1L/rA0Brytn06dMRGxuLn376CWq1Grm5uYiIiAAAJCUlYfLkyVixYgXCw8PRrVs37N27Fxs3btS6hq7pi20xb948HDt2DBs2bICfnx+sra3x/PPPQ6lU3vPYO+8TqL9XfSeqN2/exFNPPYW1a9c2+ex24tpcLM1N87O2ttZrjEREZLg4ckVE1MGcnJwQHh6Obdu2oaqqqsnn5eXler1enz59MGbMGGzfvh07duxAWFiY8P7ViRMn4OXlhcWLF2PYsGHw9/dHdnZ2m69VV1eHP//8U9hPT09HeXk5BgwYAAA4fvw4pk2bhmeffRZDhgyBVCpFVlaW1jksLCygUqladV0HBwf07NlTeLfrtuPHj2PgwIEAgH79+iE1NRUKhUL4PDk5Wav/Aw88gPPnz8Pb2xt+fn5aW1sTzICAAJw9exalpaVtOp6IiIwHkysiIhFs27YNKpUKISEh+O6773D58mVcvHgRW7ZswciRI/V+vRkzZmD//v04cOAAZsyYIbT7+/sjJycHe/fuRUZGBrZs2YIDBw60+Tpdu3bF7NmzcfLkSaSkpGDatGn4y1/+Iky/8/f3x/79+3H27FnIZDK8/PLLTUZ8vL298euvvyI/P79V65TNnz8fa9euxb59+5Ceno5Fixbh7Nmz+Mc//gEAwrVef/11XLx4EUePHsWGDRsANIz4vfXWWygtLcVLL72E5ORkZGRk4OjRo4iIiGh1wnfbSy+9BKlUigkTJuD48ePIzMzEd999h6SkpDadj4iIDBenBRKRSSvMybh3JxGu4evri9OnT2P16tX45z//iYKCAri4uCA4OBgfffSR3mOcOHEiIiMjYW5uLpQHB4Cnn34ac+fORWRkJBQKBZ544gksXbpUq6x4a9jY2GDhwoV4+eWXkZ+fj9DQUHz22WfC55s2bcL06dMxatQoODs7Y+HChaisrNQ6x8qVK/H3v/8dffr0gUKhaPHUv7fffhsVFRX45z//iaKiIgwcOBD//ve/4e/vD6B+dOv//b//hzfffBNBQUEYMmQIYmJi8PLLLwvvYd0e/Vq4cCHGjRsHhUIBLy8vjB8/XnhHrbUsLCzwww8/4J///Ccef/xx1NXVYeDAgdi2bVubzkdERIZLotH3hHUiog5UU1ODq1evwsfHR6tQgaEvImyKbq9zpe9pje3pyy+/REREBCoqKvhuFImmue9jRGR8OHJFRCapd+/eSLt4sVXTyu6Hs7Nzp06sjMXu3bvh6+sLDw8PyGQyYQ0rJlZERKQPTK6IyGT17t2bCQ9pkcvliImJgVwuh7u7O1544QWsXr1a7LCIiMhEcFogERk1TqchImPH72NEpoPVAomIiIiIiPSAyRUREREREZEeMLkiIiIiIiLSAyZXREREREREesDkioiIiIiISA+YXBEREREREekBkysiIgMmkUhw8OBBAEBWVhYkEgnOnj0rakzN2blzJxwdHdv9OomJiZBIJCgvL2/3axEREbUGFxEmIpOVk5ODkpKSDrmWs7NzqxcslsvlWL16NQ4dOoT8/Hy4uroiKCgIc+bMwSOPPNKkv6enJwoKCuDs7Nyi82/cuBGrVq1CQUFBk7VzqqurIZVKsWrVKrz99tutiltso0aNQkFBAbp16yZ2KERERFqYXBGRScrJycGAAf1RXX2rQ65nY2ONixfTWpxgZWVlYfTo0XB0dMT69esxZMgQ1NbW4ujRo3jrrbeQlpbW5Bhzc3NIpdIWx/Tqq68iOjoa+/fvx8svv6z12bfffgulUolXXnmlxee7TalUwsLCotXH6YuFhUWrvg5EREQdhckVEZmkkpISVFffwhfv/A0Deru067Uu5hTjlfe+RklJSYuTq1mzZkEikeDUqVOwtbUV2gcNGoTp06frPCYrKws+Pj44c+YMgoKCkJiYiIcffhg//vgjFi5ciAsXLiAoKAg7duxAv3794Orqiqeeegrbt29vklxt374dEyZMgJOTExYuXIgDBw4gLy8PUqkUkydPRkxMDLp27QoAWL58OQ4ePIjIyEisXr0a2dnZUKvVzd7bwYMHMX/+fOTm5mLMmDH49NNP4enpKXz+f//3f1ixYgUuXLiAnj17YurUqVi8eDG6dKl/JEkkEnzyySc4dOgQjh49Cg8PD2zcuBFPP/00AAj3XVZWJkxD/OSTT7By5Upcv34d4eHhCA0NxcqVK4Wpg7fv4Z///CeWLl2KsrIyPPbYY/jkk09gb2/foj8zIiKie2FyRUQmbUBvFzzQ10PsMLSUlpbiyJEjWL16tVZidVtr31tavHgxNm7cCBcXF7zxxhuYPn06jh8/DgCYMWMGnnzySWRnZ8PLywsAkJmZiV9//RVHjx4FANjb22Pnzp3o2bMnUlNTMXPmTNjb22PBggXCNa5cuYLvvvsO+/fvh7m5ebOxVFdXY/Xq1di9ezcsLCwwa9YsvPjii0I8v/32G6ZMmYItW7YgNDQUGRkZeP311wEAy5YtE86zYsUKrFu3DuvXr8fWrVsxefJkZGdnw8nJqck1jx8/jjfeeANr167F008/jR9//BFLly5t0i8jIwMHDx7E999/j7KyMvztb3/DmjVrsHr16lZ9vYmIiJrDghZERB3sypUr0Gg06N+/v17Ot3r1aowZMwYDBw7EokWLcOLECdTU1AAAwsPD0bNnT+zYsUPov3PnTnh6egrvdS1ZsgSjRo2Ct7c3nnrqKcybNw9ff/211jWUSiV2796NoUOHIiAgoNlYamtr8cEHH2DkyJEIDg7Grl27cOLECZw6dQpAfdK0aNEiTJ06Fb6+vnj00Ufx7rvv4uOPP9Y6z7Rp0/DSSy/Bz88P7733Hm7evCmco7GtW7fisccew7x589C3b1/MmjULjz32WJN+arUaO3fuxODBgxEaGopXX30VCQkJLfgKExERtQyTKyKiDqbRaPR6vjuTHXd3dwBAUVERgPr3tKZOnYqdO3dCo9FArVZj165diIiIgJlZ/SNg3759GD16NKRSKezs7LBkyRLk5ORoXcPLywsuLveeXtmlSxcMHz5c2O/fvz8cHR1x8eJFAIBMJsPKlSthZ2cnbDNnzkRBQQGqq6t13pOtrS0cHByEe2osPT0dISEhWm2N9wHA29tbawqgu7t7s+ckIiJqC04LJCLqYP7+/pBIJDqLVrTF7XejgPr3lQBovRM1ffp0xMbG4qeffoJarUZubi4iIiIAAElJSZg8eTJWrFiB8PBwdOvWDXv37sXGjRu1rqFr+mJb3Lx5EytWrMBzzz3X5LM7KxreeU+37+tu73m1RHuck4iI6E5MroiIOpiTkxPCw8Oxbds2vP32200Sl/Lycr2uF9WnTx+MGTMG27dvh0ajQVhYmPD+1YkTJ+Dl5YXFixcL/bOzs9t8rbq6Ovz555/CyFF6ejrKy8sxYMAAAMADDzyA9PR0+Pn53ccdaevXrx+Sk5O12hrvExERdQQmV0REIti2bRtGjx6NkJAQrFy5EgEBAairq8OxY8fw0UcfCdPo9GXGjBmYOXMmgPp3rm7z9/dHTk4O9u7di+HDh+PQoUM4cOBAm6/TtWtXzJ49G1u2bEGXLl0QGRmJv/zlL0KyFRMTgyeffBK9e/fG888/DzMzM8hkMvz3v//FqlWr2nTN2bNn46GHHsKmTZvw1FNP4aeffsJ//vMfYRSPiIioozC5IiKTdjGn2CCv4evri9OnT2P16tX45z//iYKCAri4uCA4OBgfffSR3mOcOHEiIiMjYW5ujgkTJgjtTz/9NObOnYvIyEgoFAo88cQTWLp0KZYvX96m69jY2GDhwoV4+eWXkZ+fj9DQUHz22WfC5+Hh4fj++++xcuVKrF27Fl27dkX//v3x2muvtfneRo8ejfj4eKxYsQJLlixBeHg45s6diw8++KDN5yQiImoLiUbfb1YTEXWgmpoaXL16FT4+Plrv7Bj6IsLUvmbOnIm0tDT89ttvYodCdE/NfR8jIuPDkSsiMkm9e/fGxYtpKCkp6ZDrOTs7M7ES0YYNG/Doo4/C1tYW//nPf7Br1y58+OGHYodFRESdDJMrIjJZvXv3ZsLTSZw6dQrr1q3DjRs34Ovriy1bttzXVEMiIqK2YHJFRERGr/Gix0RERGLgIsJERERERER6wOSKiIiIiIhID5hcERERERER6QGTKyIiIiIiIj1gckVERERERKQHTK6IiIiIiIj0gMkVERF1iJ07d8LR0fG+zrF8+XIEBQUJ+9OmTcOECRNafHxiYiIkEgnKy8vvKw5Tp4+vk0ajweuvvw4nJydIJBKcPXsWY8eOxZw5c/QWpy6N/460RlZWlhArEVFbcJ0rIjJZOTk5KCkp6ZBrOTs7t2nB4qSkJDz44IMYP348Dh061A6RtV1iYiIefvhhlJWV3XdSRA3Gjh2LoKAgxMXFiR1Kuzpy5Ah27tyJxMRE+Pr6wtnZGfv370fXrl3FDq1Znp6eKCgogLOzMwD+GyCi1mNyRUQmKScnB/0H9Met6lsdcj1rG2ukXUxrdYL12WefYfbs2fjss89w7do19OzZs50ibD9KpRIWFhZih0F6Ultbq5fzZGRkwN3dHaNGjRLanJyc9HLu9mJubg6pVCp2GERkxDgtkIhMUklJCW5V38LU1VOxcM/Cdt2mrp6KW9W3Wj1KdvPmTezbtw9vvvkmnnjiCezcuVPr89tTsxISEjBs2DDY2Nhg1KhRSE9P1+r3//7f/8Pw4cNhZWUFZ2dnPPvss8JnCoUC8+bNg4eHB2xtbTFixAgkJiYKn2dnZ+Opp55C9+7dYWtri0GDBuHw4cPIysrCww8/DADo3r07JBIJpk2bBqB+5CUyMhJz5syBs7MzwsPDAQCbNm3CkCFDYGtrC09PT8yaNQs3b95s1ddk4cKF6Nu3L2xsbODr64ulS5e26od9tVqN2NhY+Pj4wNraGoGBgfj222/veszvv/+O0NBQWFtbw9PTE2+//TaqqqqEzz/88EP4+/vDysoKbm5ueP7555s91/Xr1/HSSy/Bw8MDNjY2GDJkCL766ivh82nTpuGXX37B5s2bIZFIIJFIkJWVpfNc3t7eeO+99zB9+nTY29ujd+/e+Ne//qXVJzc3F3/729/g6OgIJycnPPPMM1rnS05OxqOPPgpnZ2d069YNY8aMwenTp7XOIZFI8NFHH+Hpp5+Gra0tVq9erfV5VVUVHBwcmnwdDx48CFtbW9y4caNJ7NOmTcPs2bORk5MDiUQCb29vANCaFpiWlgYbGxvs2bNHOO7rr7+GtbU1Lly4AAAoLy/Ha6+9BhcXFzg4OOCvf/0rZDKZ1rXWrFkDNzc32NvbY8aMGaipqdH59bytrKwMkydPhouLC6ytreHv748dO3YA0J4WeLd/A/f6e3a3axCRaePIFRGZNKmPFL0HtH66Xkf4+uuv0b9/f/Tr1w+vvPIK5syZg+joaEgkEq1+ixcvxsaNG+Hi4oI33ngD06dPx/HjxwEAhw4dwrPPPovFixdj9+7dUCqVOHz4sHBsZGQkLly4gL1796Jnz544cOAAxo8fj9TUVPj7++Ott96CUqnEr7/+CltbW1y4cAF2dnbw9PTEd999h4kTJyI9PR0ODg6wtrYWzrtr1y68+eabQhwAYGZmhi1btsDHxweZmZmYNWsWFixYgA8//LDFXxN7e3vs3LkTPXv2RGpqKmbOnAl7e3ssWLCgRcfHxsbiiy++QHx8PPz9/fHrr7/ilVdegYuLC8aMGdOkf0ZGBsaPH49Vq1Zh+/btKC4uRmRkJCIjI7Fjxw78+eefePvtt/H5559j1KhRKC0txW+//dbs9WtqahAcHIyFCxfCwcEBhw4dwquvvoo+ffogJCQEmzdvxqVLlzB48GCsXLkSAODi4tLs+TZu3Ih3330X77zzDr799lu8+eabGDNmDPr164fa2lqEh4dj5MiR+O2339ClSxesWrUK48ePx7lz52BhYYEbN25g6tSp2Lp1KzQaDTZu3IjHH38cly9fhr29vXCd5cuXY82aNYiLi0OXLl2QmZkpfGZra4sXX3wRO3bs0Eosb+/feZ7bNm/ejD59+uBf//oXkpOTYW5u3qRP//79sWHDBsyaNQsPPvggzMzM8MYbb2Dt2rUYOHAgAOCFF16AtbU1/vOf/6Bbt274+OOP8cgjj+DSpUtwcnLC119/jeXLl2Pbtm148MEH8fnnn2PLli3w9fVt9mu6dOlSXLhwAf/5z3/g7OyMK1eu4NatpiPcd/s3cK+/Zy29BhGZHiZXREQi+eyzz/DKK68AAMaPH4+Kigr88ssvGDt2rFa/1atXC4nBokWL8MQTT6CmpgZWVlZYvXo1XnzxRaxYsULoHxgYCKB+auSOHTuQk5MjTDecN28ejhw5gh07duC9995DTk4OJk6ciCFDhgCA1g+lt6dwubq6NnnfxN/fH+vWrdNqu7NQgbe3N1atWoU33nijVcnVkiVLtM4xb9487N27t0XJlUKhwHvvvYcff/wRI0eOFO7n999/x8cff6wzuYqNjcXkyZOF2P39/bFlyxaMGTMGH330EXJycmBra4snn3wS9vb28PLywtChQ5uNwcPDA/PmzRP2Z8+ejaNHj+Lrr79GSEgIunXrBgsLC9jY2LRo+tnjjz+OWbNmAagf1Xv//ffx888/o1+/fti3bx/UajU+/fRTISHfsWMHHB0dkZiYiHHjxuGvf/2r1vn+9a9/wdHREb/88guefPJJof3ll19GRESEsH9ncgUAr732GkaNGoWCggK4u7ujqKgIhw8fxo8//qgz7m7dusHe3v6e0+xmzZqFw4cP45VXXoGFhQWGDx+O2bNnA6gfUTx16hSKiopgaWkJANiwYQMOHjyIb7/9Fq+//jri4uIwY8YMzJgxAwCwatUq/Pjjj3cdvcrJycHQoUMxbNgwABBG1RozNzfX+W+gJX/PWnoNIjI9TK6IiESQnp6OU6dO4cCBAwCALl26YNKkSfjss8+aJFcBAQHC/7u7uwMAioqK0Lt3b5w9exYzZ87UeY3U1FSoVCr07dtXq12hUKBHjx4AgLfffhtvvvkmfvjhB4SFhWHixIla12tOcHBwk7Yff/wRsbGxSEtLQ2VlJerq6lBTU4Pq6mrY2Njc85wAsG/fPmzZsgUZGRm4efMm6urq4ODg0KJjr1y5gurqajz66KNa7UqlstmESCaT4dy5c/jyyy+FNo1GA7VajatXr+LRRx+Fl5cXfH19MX78eIwfPx7PPvtss/ejUqnw3nvv4euvv0Z+fj6USiUUCkWL77+xO/8sJBIJpFIpioqKhNivXLnSZOSopqYGGRkZAIDCwkIsWbIEiYmJKCoqgkqlQnV1NXJycrSOuZ0ENCckJASDBg3Crl27sGjRInzxxRfw8vLCQw891Kb7utP27dvRt29fmJmZ4fz580KiKJPJcPPmTeHv6m23bt0S7u/ixYt44403tD4fOXIkfv7552av9+abb2LixIk4ffo0xo0bhwkTJmi9F3YvLfl7dr/XICLjxeSKiEgEn332Gerq6rQKWGg0GlhaWuKDDz5At27dhPY7q6vd/sFTrVYDgNZUvcZu3rwJc3NzpKSkNJmWZWdnB6B+RCI8PByHDh3CDz/8gNjYWGzcuFEYPWiOra2t1n5WVhaefPJJvPnmm1i9ejWcnJzw+++/Y8aMGVAqlS1KLpKSkjB58mSsWLEC4eHh6NatG/bu3YuNGzfe89jb9wvUT5X08PDQ+uz2yIeuY/7+97/j7bffbvJZ7969YWFhgdOnTyMxMRE//PADYmJisHz5ciQnJ+usHrd+/Xps3rwZcXFxwvtnc+bMgVKpbNE9NNa4sp5EIhH+7G/evIng4GCtxPC221MNp06diuvXr2Pz5s3w8vKCpaUlRo4c2SSexn+eurz22mvYtm0bFi1ahB07diAiIqLJFNa2kMlkqKqqgpmZmTAydvv+3N3dtd4RvO1+Kvc99thjyM7OxuHDh3Hs2DE88sgjeOutt7Bhw4YWHd+Sv2f3ew0iMl5MroiIOlhdXR12796NjRs3Yty4cVqfTZgwAV999VWT38Y3JyAgAAkJCVpTum4bOnQoVCoVioqKEBoa2uw5PD098cYbb+CNN95AdHQ0PvnkE8yePVuoAKhSqe4ZR0pKCtRqNTZu3Agzs/paSV9//XWL7uG2EydOwMvLC4sXLxbasrOzW3z8wIEDYWlpiZycHJ1TAHV54IEHcOHCBfj5+TXbp0uXLggLC0NYWBiWLVsGR0dH/PTTT3juueea9D1+/DieeeYZYbqnWq3GpUuXhHeIAMDCwqJFX9OWxL5v3z64uro2O7p3/PhxfPjhh3j88ccB1BfAaOvyBK+88goWLFiALVu24MKFC5g6dWqbY7+ttLQU06ZNw+LFi1FQUIDJkyfj9OnTsLa2xgMPPAC5XI4uXbo0O61uwIABOHnyJKZMmSK0/fHHH/e8rouLC6ZOnYqpU6ciNDQU8+fP15n46Po30NK/Zy29BhGZFiZXREQd7Pvvv0dZWRlmzJihNUIFABMnTsRnn33W4uRq2bJleOSRR9CnTx+8+OKLqKurw+HDh4Wqe5MnT8aUKVOwceNGDB06FMXFxUhISEBAQACeeOIJzJkzB4899hj69u2LsrIy/PzzzxgwYAAAwMvLCxKJBN9//z0ef/xxWFtbCyNejfn5+aG2thZbt27FU089hePHjyM+Pr5VXxd/f3/k5ORg7969GD58OA4dOiRMm2wJe3t7zJs3D3PnzoVarcaDDz6IiooKHD9+HA4ODjqTgYULF+Ivf/kLIiMj8dprrwlFPY4dO4YPPvgA33//PTIzM/HQQw+he/fuOHz4MNRqNfr169fsPXz77bc4ceIEunfvjk2bNqGwsFArufL29sbJkyeRlZUFOzs7ODk5CQlpa0yePBnr16/HM888g5UrV6JXr17Izs7G/v37sWDBAvTq1Qv+/v74/PPPMWzYMFRWVmL+/Pl3He28m+7du+O5557D/PnzMW7cOPTq1atN57nTG2+8AU9PTyxZsgQKhQJDhw7FvHnzsG3bNoSFhWHkyJGYMGEC1q1bh759++LatWtCEZdhw4bhH//4B6ZNm4Zhw4Zh9OjR+PLLL3H+/Pm7FrSIiYlBcHAwBg0aBIVCge+//174O9+Yrn8DLfl71pprEJFpYXJFRCZNflVucNf47LPPEBYW1iSxAuqTq3Xr1uHcuXMtOtfYsWPxzTff4N1338WaNWvg4OCg9R7Mjh07sGrVKvzzn/9Efn4+nJ2d8Ze//EUoZqBSqfDWW28hLy8PDg4OGD9+PN5//30A9cUZVqxYgUWLFiEiIgJTpkxpUi7+tsDAQGzatAlr165FdHQ0HnroIcTGxmqNKNzL008/jblz5yIyMhIKhQJPPPEEli5diuXLl7f4HO+++y5cXFwQGxuLzMxMODo64oEHHsA777yjs39AQAB++eUXLF68GKGhodBoNOjTpw8mTZoEoH762f79+7F8+XLU1NTA398fX331FQYNGqTzfEuWLEFmZibCw8NhY2OD119/HRMmTEBFRYXQZ968eZg6dSoGDhyIW7du4erVq20qeGBjY4Nff/0VCxcuxHPPPYcbN27Aw8MDjzzyiDCS9dlnn+H111/HAw88AE9PT7z33ntaBTdaa8aMGdizZw+mT5/e5nPctnv3bhw+fBhnzpxBly5d0KVLF3zxxRd48MEH8eSTT+Kxxx7D4cOHsXjxYkRERKC4uBhSqRQPPfQQ3NzcAACTJk1CRkYGFixYgJqaGkycOBFvvvkmjh492ux1LSwsEB0djaysLFhbWyM0NBR79+7V2be5fwP3+nvWmmsQkWmRaDQajdhBEBG1VU1NDa5evQofHx9YWVkJ7cayiDCRMfn8888xd+5cXLt2jQtH61Fz38eIyPhw5IqITFLv3r2RdjGtze+XtJazszMTKzJZ1dXVKCgowJo1a/D3v/+diRURUTOYXBGRyerduzcTHiI9WLduHVavXo2HHnoI0dHRYodDRGSwOC2QiIwap9MQkbHj9zEi09H68kRERERERETUBJMrIiIiIiIiPWByRUQmgTOcichY8fsXkelgckVERq1r164A6quZEREZo9vfv25/PyMi48VqgURk1MzNzeHo6IiioiIA9QurSiQSkaMiIro3jUaD6upqFBUVwdHREebm5mKHRET3idUCicjoaTQayOVylJeXix0KEVGrOTo6QiqV8hdDRCaAyRURmQyVSoXa2lqxwyAiarGuXbtyxIrIhDC5IiIiIiIi0gMWtCAiIiIiItIDJldERERERER6wOSKiIiIiIhID5hcERERERER6QGTKyIiIiIiIj1gckVERERERKQHTK6IiIiIiIj0gMkVERERERGRHhhEcrVt2zZ4e3vDysoKI0aMwKlTp5rtu3//fgwbNgyOjo6wtbVFUFAQPv/8c60+06ZNg0Qi0drGjx/f3rdBRERERESdWBexA9i3bx+ioqIQHx+PESNGIC4uDuHh4UhPT4erq2uT/k5OTli8eDH69+8PCwsLfP/994iIiICrqyvCw8OFfuPHj8eOHTuEfUtLyxbHpFarce3aNdjb20MikdzfDRIRUYtpNBrcuHEDPXv2hJmZQfz+z2Dw2UREJI5WPZs0IgsJCdG89dZbwr5KpdL07NlTExsb2+JzDB06VLNkyRJhf+rUqZpnnnmmzTHl5uZqAHDjxo0bN5G23NzcNn8P7ygffPCBxsvLS2NpaakJCQnRnDx58q79y8rKNLNmzdJIpVKNhYWFxt/fX3Po0KEWX4/PJm7cuHETd2vJs0nUkSulUomUlBRER0cLbWZmZggLC0NSUtI9j9doNPjpp5+Qnp6OtWvXan2WmJgIV1dXdO/eHX/961+xatUq9OjRo0Vx2dvbAwByc3Ph4ODQijsiIqL7UVlZCU9PT+H7sKFq7awLpVKJRx99FK6urvj222/h4eGB7OxsODo6tviafDYREYmjNc8mUZOrkpISqFQquLm5abW7ubkhLS2t2eMqKirg4eEBhUIBc3NzfPjhh3j00UeFz8ePH4/nnnsOPj4+yMjIwDvvvIPHHnsMSUlJMDc3b3I+hUIBhUIh7N+4cQMA4ODgwAcYEZEIDH3a26ZNmzBz5kxEREQAAOLj43Ho0CFs374dixYtatJ/+/btKC0txYkTJ9C1a1cAgLe3d6uueftrwmcTEZE4WvJsMsoJ7fb29jh79iySk5OxevVqREVFITExUfj8xRdfxNNPP40hQ4ZgwoQJ+P7775GcnKzV506xsbHo1q2bsHl6enbMjRARkdG5PesiLCxMaLvXrIt///vfGDlyJN566y24ublh8ODBeO+996BSqToqbCIi6gCiJlfOzs4wNzdHYWGhVnthYSGkUmmzx5mZmcHPzw9BQUH45z//ieeffx6xsbHN9vf19YWzszOuXLmi8/Po6GhUVFQIW25ubttuiIiITN7dZl3I5XKdx2RmZuLbb7+FSqXC4cOHsXTpUmzcuBGrVq1q9joKhQKVlZVaGxERGTZRkysLCwsEBwcjISFBaFOr1UhISMDIkSNbfB61Wq01ra+xvLw8XL9+He7u7jo/t7S0FKZZcLoFERHpm1qthqurK/71r38hODgYkyZNwuLFixEfH9/sMZxVQURkfESfFhgVFYVPPvkEu3btwsWLF/Hmm2+iqqpKmMc+ZcoUrYIXsbGxOHbsGDIzM3Hx4kVs3LgRn3/+OV555RUAwM2bNzF//nz88ccfyMrKQkJCAp555hn4+flplWonIiJqi7bMunB3d0ffvn213vsdMGAA5HI5lEqlzmM4q4KIyPiIvs7VpEmTUFxcjJiYGMjlcgQFBeHIkSPCdIucnBytevJVVVWYNWsW8vLyYG1tjf79++OLL77ApEmTAADm5uY4d+4cdu3ahfLycvTs2RPjxo3Du+++26q1roiIiHS5c9bFhAkTADTMuoiMjNR5zOjRo7Fnzx6o1WrhmXbp0iW4u7vDwsJC5zGWlpZ8bhERGRmJRqPRiB2EoamsrES3bt1QUVHBKYJERB3IWL7/7tu3D1OnTsXHH3+MkJAQxMXF4euvv0ZaWhrc3NwwZcoUeHh4CO8D5+bmYtCgQZg6dSpmz56Ny5cvY/r06Xj77bexePHiFl3TWL42RESmpjXff0UfuSIiIjI2rZ114enpiaNHj2Lu3LkICAiAh4cH/vGPf2DhwoVi3QIREbUDjlzpwN8OEhGJg99/m8evDRGROFrz/Vf0ghZERERERESmgMkVERERERGRHvCdKyIyKAqFAikpKVptwcHBrJpG1MnV1NQgOTlZq2348OGwsrISKSIioqaYXBGRQUlJScHWw1vh4ecBAMi/ko/ZmI1Ro0aJHBkRiSk5ORlbD21Fr769AAB5l/IwG7MRGhoqcmRERA2YXBGRwfHw84BvkK/YYRCRgenVtxf8HvATOwwiombxnSsiIiIiIiI9YHJFRERERESkB0yuiIiIiIiI9IDJFRERERERkR4wuSIiIiIiItIDJldERERERER6wOSKiIiIiIhID5hcERERERER6QGTKyIiIiIiIj1gckVERERERKQHTK6IiIiIiIj0gMkVERERERGRHjC5IiIiIiIi0gMmV0RERERERHrA5IqIiIiIiEgPmFwRERERERHpQRexAyAiIiK6U01NDZKTk7XaZDIZVJYqkSIiImoZJldERERkUJKTk7H10Fb06ttLaDuTdAbug9zRD/1EjIyI6O6YXBEREZHB6dW3F/we8BP28y7liRgNEVHLMLkiIoNWV1uH1NTUJu3BwcGwtLQUISIiIiIi3ZhcEZFBK8wqRFp5Gi5bXhba8q/kYzZmY9SoUSJGRkRERKSNyRURGTwXLxf4BvmKHQYRERHRXbEUOxERERERkR4wuSIiIiIiItIDJldERERERER6wHeuiIiIyOjU1dZBJpM1aR8+fDisrKxEiIiIyEBGrrZt2wZvb29YWVlhxIgROHXqVLN99+/fj2HDhsHR0RG2trYICgrC559/rtVHo9EgJiYG7u7usLa2RlhYGC5fvtzMGYmIiMjYyK/K8X/n/g8HLh8Qtq2HtiI5OVns0IioExM9udq3bx+ioqKwbNkynD59GoGBgQgPD0dRUZHO/k5OTli8eDGSkpJw7tw5REREICIiAkePHhX6rFu3Dlu2bEF8fDxOnjwJW1tbhIeHo6ampqNui4iIiNqZi7cL/B7wE7ZefXuJHRIRdXKiJ1ebNm3CzJkzERERgYEDByI+Ph42NjbYvn27zv5jx47Fs88+iwEDBqBPnz74xz/+gYCAAPz+++8A6ket4uLisGTJEjzzzDMICAjA7t27ce3aNRw8eLAD74yIiIiIiDoTUZMrpVKJlJQUhIWFCW1mZmYICwtDUlLSPY/XaDRISEhAeno6HnroIQDA1atXIZfLtc7ZrVs3jBgxotlzKhQKVFZWam1EREREREStIWpyVVJSApVKBTc3N612Nzc3yOXyZo+rqKiAnZ0dLCws8MQTT2Dr1q149NFHAUA4rjXnjI2NRbdu3YTN09Pzfm6LiIiIiIg6IaOsFmhvb4+zZ8/i5s2bSEhIQFRUFHx9fTF27Ng2nS86OhpRUVHCfmVlJRMsonagUCiQkpKi1RYcHAxLS0uRIiIiIiLSH1GTK2dnZ5ibm6OwsFCrvbCwEFKptNnjzMzM4OfnBwAICgrCxYsXERsbi7FjxwrHFRYWwt3dXeucQUFBOs9naWnJH+6IOkBKSgpSv12HIT71/05Tr8oBLMCoUaPEDYyIiIhID0SdFmhhYYHg4GAkJCQIbWq1GgkJCRg5cmSLz6NWq6FQKAAAPj4+kEqlWuesrKzEyZMnW3VOImofQ3ykGDXYC6MGewlJFhEREZEpEH1aYFRUFKZOnYphw4YhJCQEcXFxqKqqQkREBABgypQp8PDwQGxsLID696OGDRuGPn36QKFQ4PDhw/j888/x0UcfAQAkEgnmzJmDVatWwd/fHz4+Pli6dCl69uyJCRMmiHWbRKSDslaF9NRUrbbU1FTUmdWJFBERERmzmpoanWudcXFp6iiiJ1eTJk1CcXExYmJiIJfLERQUhCNHjggFKXJycmBm1jDAVlVVhVmzZiEvLw/W1tbo378/vvjiC0yaNEnos2DBAlRVVeH1119HeXk5HnzwQRw5coT/qIgMzIWcQuTlxsM1z0doy75wFQV9+6Lv8L4iRkZ0b9u2bcP69eshl8sRGBiIrVu3IiQkRGffnTt3Cr80vM3S0pLrLxLpWXJyMmTfrEdgn4aZEbIMOYD5CA0NFS8w6jRET64AIDIyEpGRkTo/S0xM1NpftWoVVq1addfzSSQSrFy5EitXrtRXiETUDirKK2BuXgpbJ0ehzdy8FBUVFeIFRdQC+/btQ1RUFOLj4zFixAjExcUhPDwc6enpcHV11XmMg4MD0tPThX2JRNJR4RJ1KoF9pAgN8Ll3R6J2IPoiwkTUuVnZWKG7tLuwWdlwhJkM36ZNmzBz5kxERERg4MCBiI+Ph42NDbZv397sMRKJBFKpVNgaLxlCRETGj8kVERFRKyiVSqSkpGgtVm9mZoawsLBmF6sHgJs3b8LLywuenp545plncP78+btehwvcExEZHyZXRERErVBSUgKVStWqxer79euH7du34//+7//wxRdfQK1WY9SoUcjLy2v2OlzgnojI+DC5IiIiamcjR47ElClTEBQUhDFjxmD//v1wcXHBxx9/3Owx0dHRqKioELbc3NwOjJiIiNrCIApaEBERGQtnZ2eYm5ujsLBQq72wsFBYyP5eunbtiqFDh+LKlSvN9uEC90RExocjV0RERK1gYWGB4OBgrcXq1Wo1EhISWrxYvUqlQmpqKtzd3dsrTCIiEgFHroiIiFopKioKU6dOxbBhwxASEoK4uDhUVVUJa1lNmTIFHh4eiI2NBQCsXLkSf/nLX+Dn54fy8nKsX78e2dnZeO2118S8DSIi0jMmV0RERK00adIkFBcXIyYmBnK5HEFBQThy5IhQ5CInJwdmZg2TQ8rKyjBz5kzI5XJ0794dwcHBOHHiBAYOHCjWLRARUTtgckVERNQGkZGRiIyM1PlZYmKi1v7777+P999/vwOiIiIiMTG5IiIiIiKTpaitQ5pM1qR9+PDhsLLiwvWkX0yuiIiIiMhkpeUUo7RsL6A6J7TJMuQA5iM0NFS8wMgkMbkiIiIiIpM2wLMHQgN8xA6DOgEmV0RERGQS6mrrIOP0r06jpqYGycnJWm0ymQyDVSqRIiJickVEREQmQn5VjoulF5Fpkym05V3Kw2zM5vQvE5ScnAzZN+sR2Kdh8e4rv56HWz9nAH7iBUadGpMrIiIiMhku3i7we4A/WHcWgX2kWtP9ZBkFIkZDBJjduwsRERERERHdC5MrIiIiIiIiPWByRUREREREpAdMroiIiIiIiPSAyRUREREREZEeMLkiIiIiIiLSAyZXREREREREesDkioiIiIiISA+YXBEREREREekBkysiIiIiIiI9YHJFRERERESkB0yuiIiIiIiI9IDJFRERERERkR4wuSIiIiIiItIDJldERERERER6wOSKiIiIiIhID5hcERERERER6UEXsQMgIiIiIrqXmpoaJCcnC/symQyDVSoRIyJqyiBGrrZt2wZvb29YWVlhxIgROHXqVLN9P/nkE4SGhqJ79+7o3r07wsLCmvSfNm0aJBKJ1jZ+/Pj2vg0iIiIiaifJycmQfbMeOP05cPpzXPn5KxSXlIgdFpEW0Ueu9u3bh6ioKMTHx2PEiBGIi4tDeHg40tPT4erq2qR/YmIiXnrpJYwaNQpWVlZYu3Ytxo0bh/Pnz8PDw0PoN378eOzYsUPYt7S07JD7ISIiIqL2EdhHitAAHwCALKOgzedR1NYhTSZr0j58+HBYWVm1+bxEoidXmzZtwsyZMxEREQEAiI+Px6FDh7B9+3YsWrSoSf8vv/xSa//TTz/Fd999h4SEBEyZMkVot7S0hFQqbd/giUjv6lRqlMnLkHk2EwBQklcCF1sXkaMiIiJTkpZTjNKyvYDqnNAmy5ADmI/Q0FDxAiOjJ2pypVQqkZKSgujoaKHNzMwMYWFhSEpKatE5qqurUVtbCycnJ632xMREuLq6onv37vjrX/+KVatWoUePHjrPoVAooFAohP3Kyso23A0R6cO16woMqsvFgOz6efU3iq8i29Zc5KiIiMjUDPDsIYyC0d01ft/tNo70NSVqclVSUgKVSgU3Nzetdjc3N6SlpbXoHAsXLkTPnj0RFhYmtI0fPx7PPfccfHx8kJGRgXfeeQePPfYYkpKSYG7e9Ie02NhYrFix4v5uhoj0ppezJQL61v8yRH5VjmyR4yEiIurMkpOT8f6+Y/Dw7S+05WemYS7Akb5GRJ8WeD/WrFmDvXv3IjExUStrfvHFF4X/HzJkCAICAtCnTx8kJibikUceaXKe6OhoREVFCfuVlZXw9PRs3+CJiIiIiIyEh29/+A4ZJnYYBk/U5MrZ2Rnm5uYoLCzUai8sLLzn+1IbNmzAmjVr8OOPPyIgIOCufX19feHs7IwrV67oTK4sLS1Z8IKIiIiIOj1dUwBlMhlUqm4iRWRcRC3FbmFhgeDgYCQkJAhtarUaCQkJGDlyZLPHrVu3Du+++y6OHDmCYcPunUHn5eXh+vXrcHd310vcRERERESm6PYUwK+Tcxu2n/5ESUmx2KEZBdGnBUZFRWHq1KkYNmwYQkJCEBcXh6qqKqF64JQpU+Dh4YHY2FgAwNq1axETE4M9e/bA29sbcrkcAGBnZwc7OzvcvHkTK1aswMSJEyGVSpGRkYEFCxbAz88P4eHhot0nEREREZExaDwF8Fpmy2ohkAEkV5MmTUJxcTFiYmIgl8sRFBSEI0eOCEUucnJyYGbWMMD20UcfQalU4vnnn9c6z7Jly7B8+XKYm5vj3Llz2LVrF8rLy9GzZ0+MGzcO7777Lqf+EXUghUKBlJQUrbbU1FQMUKtEioiIiIiofYmeXAFAZGQkIiMjdX6WmJiotZ+VlXXXc1lbW+Po0aN6ioyI2iolJQWp367DEJ+G9yczjl+Aq78zAF/xAiMiIiJqJwaRXBGRaRriI8WowV7CfmqmXMRoiIiIiNqXqAUtiIiIiIiITAWTKyIiIiIiIj1gckVERERERKQHTK6IiIjaYNu2bfD29oaVlRVGjBiBU6dOtei4vXv3QiKRYMKECe0bIBERdTgmV0RERK20b98+REVFYdmyZTh9+jQCAwMRHh6OoqKiux6XlZWFefPmITQ0tIMiJSKijsTkioiIqJU2bdqEmTNnIiIiAgMHDkR8fDxsbGywffv2Zo9RqVSYPHkyVqxYAV9fLkdARGSKWIqdiIxOXW0dUlNTtdqCg4O5UDh1CKVSiZSUFERHRwttZmZmCAsLQ1JSUrPHrVy5Eq6urpgxYwZ+++23jgiViIg6GJMrIjI6hVmFSCtPw2XLywCA/Cv5mI3ZGDVqlMiRUWdQUlIClUoFNzc3rXY3NzekpaXpPOb333/HZ599hrNnz7b4OgqFAgqFQtivrKxsU7xERNRxmFwRkVFy8XKBbxCnVpHhu3HjBl599VV88skncHZ2bvFxsbGxWLFiRTtGZhhqamqQnJys1SaTyaCyVIkUERFR2zG5IiIiagVnZ2eYm5ujsLBQq72wsBBSqbRJ/4yMDGRlZeGpp54S2tRqNQCgS5cuSE9PR58+fZocFx0djaioKGG/srISnp6e+roNg5GcnIyth7aiV99eQtuZpDNwH+SOfugnYmRERK3H5IqIiKgVLCwsEBwcjISEBKGculqtRkJCAiIjI5v079+/f5N3BJcsWYIbN25g8+bNzSZMlpaWneY9wl59e8HvAT9hP+9SnojREBG1HZMrIiKiVoqKisLUqVMxbNgwhISEIC4uDlVVVYiIiAAATJkyBR4eHoiNjYWVlRUGDx6sdbyjoyMANGkn/aurrYNMJmvSPnz4cFhZWYkQEZFhaTw1VyaTQaXqJmJExo3JFRERUStNmjQJxcXFiImJgVwuR1BQEI4cOSIUucjJyYGZGVc7MQTyq3JcLL2ITJtMoS3vUh5mYzbXGyNC/dTc9/cdg4dvfwCA7Pc/IfUPgL/IcRkrJldERERtEBkZqXMaIAAkJibe9didO3fqPyBqlou3i9a0QyLS5uHbH75DhgEArmXqrnpKLcNfqxEREREREekBkysiIiIiIiI9YHJFRERERESkB3znioiIiIiIWqWuVslKnDowuSIiIiIiolYpys1EWlkp0pVOQlt+ZhrmAp26EieTKyIiIiIiajWXXr5ClUGqx+SKiIiIiAxK44VtgfrFbQerVCJFRNQyTK6IiIiIyKAkJydD9s16BPaRCm1Xfj0Pt37OALhmGRkuJldEREREZHAC+0gRGuAj7MsyCkSMhqhlWIqdiIiIiIhID5hcERERERER6QGTKyIiIiIiIj1gckVERERERKQHTK6IiIiIiIj0gMkVERERERGRHjC5IiIiIiIi0gOuc0VERERE1AnU1NQgOTlZq00mk0Gl6iZSRKbHIEautm3bBm9vb1hZWWHEiBE4depUs30/+eQThIaGonv37ujevTvCwsKa9NdoNIiJiYG7uzusra0RFhaGy5cvt/dtEBEREREZrOTkZLy/7xi+Ts5t2H76EyUlxWKHZjLalFxlZmbqLYB9+/YhKioKy5Ytw+nTpxEYGIjw8HAUFRXp7J+YmIiXXnoJP//8M5KSkuDp6Ylx48YhPz9f6LNu3Tps2bIF8fHxOHnyJGxtbREeHo6amhq9xU1ERMZHn88vIiJj5OHbH75DhgmbS8/eYodkUtqUXPn5+eHhhx/GF198cd8Jy6ZNmzBz5kxERERg4MCBiI+Ph42NDbZv366z/5dffolZs2YhKCgI/fv3x6effgq1Wo2EhAQA9aNWcXFxWLJkCZ555hkEBARg9+7duHbtGg4ePHhfsRIRkXHT5/OLiIiosTYlV6dPn0ZAQACioqIglUrx97///a5T+ZqjVCqRkpKCsLCwhoDMzBAWFoakpKQWnaO6uhq1tbVwcnICAFy9ehVyuVzrnN26dcOIESOaPadCoUBlZaXWRkREpkdfzy8iIiJd2pRcBQUFYfPmzbh27Rq2b9+OgoICPPjggxg8eDA2bdqE4uKWzdssKSmBSqWCm5ubVrubmxvkcnmLzrFw4UL07NlTSKZuH9eac8bGxqJbt27C5unp2aJrExGRcdHX84uIiEiX+ypo0aVLFzz33HP45ptvsHbtWly5cgXz5s2Dp6cnpkyZgoKCAn3FqdOaNWuwd+9eHDhwAFZWVm0+T3R0NCoqKoQtNzdXj1ESEZGhEfv5RUTU3mpqavDbb79pbfWVAVVih2bS7iu5+vPPPzFr1iy4u7tj06ZNmDdvHjIyMnDs2DFcu3YNzzzzzF2Pd3Z2hrm5OQoLC7XaCwsLIZVK73rshg0bsGbNGvzwww8ICAgQ2m8f15pzWlpawsHBQWsjIiLTdb/PLyIiQ8fKgOJo0zpXmzZtwo4dO5Ceno7HH38cu3fvxuOPPw4zs/pczcfHBzt37oS3t/ddz2NhYYHg4GAkJCRgwoQJACAUp4iMjGz2uHXr1mH16tU4evQohg0bpvWZj48PpFIpEhISEBQUBACorKzEyZMn8eabb7bldomIyETo6/lFRGQMblcGvO1aZpqI0XQObUquPvroI0yfPh3Tpk2Du7u7zj6urq747LPP7nmuqKgoTJ06FcOGDUNISAji4uJQVVWFiIgIAMCUKVPg4eGB2NhYAMDatWsRExODPXv2wNvbW3iPys7ODnZ2dpBIJJgzZw5WrVoFf39/+Pj4YOnSpejZs6eQwBERUeekz+cXERFRY21Kro4dO4bevXsLv+m7TaPRIDc3F71794aFhQWmTp16z3NNmjQJxcXFiImJgVwuR1BQEI4cOSIUpMjJydG6zkcffQSlUonnn39e6zzLli3D8uXLAQALFixAVVUVXn/9dZSXl+PBBx/EkSNH7uu9LCIiMn76fH4RkX7U1NQgOTlZq00mk2Ew3w0iI9Sm5KpPnz4oKCiAq6urVntpaSl8fHxa/aJcZGRks9MAExMTtfazsrLueT6JRIKVK1di5cqVrYqDiIhMm76fX0R0/5KTkyH7Zj0C+zS8G3/l1/Nw6+cMwK/D4lDU1iFNJtNqGz58OH85T63SpuRKo9HobL958yb/AhJRs1QaNUrLyoWKnJU3KmFrr/v7CVF74POLyDAF9pEiNMBH2JdldHzFzrScYpSW7QVU5/4XgxzAfISGhnZ4LGS8WpVcRUVFAagfGYqJiYGNjY3wmUqlwsmTJ4UiEkREjVWUV6C6OgsXr0sAAAVlBehtaS1yVNQZ8PlFRC0xwLOHVpJH1FqtSq7OnDkDoP43f6mpqbCwsBA+s7CwQGBgIObNm6ffCInIpFjZWKG7tDsAwNLK4h69ifSDzy8iIuoIrUqufv75ZwBAREQENm/ezPWgiIjIKPD5RUREHaFN71zt2LFD33EQERG1Oz6/iIioPbU4uXruueewc+dOODg44Lnnnrtr3/379993YERERPrA5xcRmbrmytmrVN1EiqjzanFy1a1bN0gkEuH/iYiIjAGfX0RkahonUzKZDD9eKICn36CGtt//hNQ/AP5iBNiJtTi5unMqBadVENGdFAoFUlJStNpSU1MxQM01g0h8fH4RkalJTk7G+/uOwcO3P4CGRMp3yDChz7XMNLHC69Ta9M7VrVu3oNFohFK22dnZOHDgAAYOHIhx48bpNUAiMnwpKSlI/XYdhvg0LACZcfwCXP2dAfiKFxhRI3x+EZGp8PDtLyRTTKQMR5uSq2eeeQbPPfcc3njjDZSXlyMkJAQWFhYoKSnBpk2b8Oabb+o7TiIycEN8pBg12EvYT82UixgNkW58fhERUXsya8tBp0+fFlar/vbbbyGVSpGdnY3du3djy5Yteg2QiIhIX/j8IiKi9tSm5Kq6uhr29vYAgB9++AHPPfcczMzM8Je//AXZ2dl6DZCIiEhf+PwiIqL21Kbkys/PDwcPHkRubi6OHj0qzFMvKiriwoxERGSw9Pn82rZtG7y9vWFlZYURI0bg1KlTzfbdv38/hg0bBkdHR9ja2iIoKAiff/75fd0LEXUONTU1+O2337S2+jLrLBpliNqUXMXExGDevHnw9vbGiBEjMHLkSAD1vwUcOnSoXgMkIiLSF309v/bt24eoqCgsW7YMp0+fRmBgIMLDw1FUVKSzv5OTExYvXoykpCScO3cOERERiIiIwNGjR/VyX0Rkum5XBvw6Obdh++lPlJQUix0a6dCmghbPP/88HnzwQRQUFCAwMFBof+SRR/Dss8/qLTgiIiJ90tfza9OmTZg5cyYiIiIAAPHx8Th06BC2b9+ORYsWNek/duxYrf1//OMf2LVrF37//XeEh4e37WaIqNO4szIgwOqAhqxNyRUASKVSSKVSrbaQkJD7DoiIiKg93e/zS6lUIiUlBdHR0UKbmZkZwsLCkJSUdM/jNRoNfvrpJ6Snp2Pt2rXN9lMoFFAoFMJ+ZWVli2MkIiJxtCm5qqqqwpo1a5CQkICioiKo1WqtzzMzM/USHBERkT7p4/lVUlIClUoFNzc3rXY3NzekpTX/2+SKigp4eHhAoVDA3NwcH374IR599NFm+8fGxmLFihX3jIeIiAxHm5Kr1157Db/88gteffVVuLu7QyKR6DsuIiIivRPz+WVvb4+zZ8/i5s2bSEhIQFRUFHx9fZtMGbwtOjoaUVFRwn5lZSU8PT07KFoiEkNNTQ2Sk5O12uqLV3QTKSJqrTYlV//5z39w6NAhjB49Wt/xEBERtRt9PL+cnZ1hbm6OwsJCrfbCwsIm0w3vZGZmBj8/PwBAUFAQLl68iNjY2GaTK0tLS1haWrY5TkPU7A+Olqx6RgQ0FK/w8O0vtMl+/xNS/wD4ixgXtVybkqvu3bvDyclJ37EQERG1K308vywsLBAcHIyEhARMmDABAKBWq5GQkIDIyMgWn0etVmu9U9UZJCcnY+uhrejVt5fQdibpDNwHuaMf+okYGZHhYPEK49amUuzvvvsuYmJiUF1dre94iIiI2o2+nl9RUVH45JNPsGvXLly8eBFvvvkmqqqqhOqBU6ZM0Sp4ERsbi2PHjiEzMxMXL17Exo0b8fnnn+OVV165rziMUa++veD3gJ+wuXi6iB0SEZHetGnkauPGjcjIyICbmxu8vb3RtWtXrc9Pnz6tl+CIiIj0SV/Pr0mTJqG4uBgxMTGQy+UICgrCkSNHhCIXOTk5MDNr+P1lVVUVZs2ahby8PFhbW6N///744osvMGnSJP3dHBHplaK2DmkyWZP24cOHw8rKSoSIyBi0Kbm6PQ2CiIjImOjz+RUZGdnsNMDExESt/VWrVmHVqlV6uzYRtb+0nGKUlu0FVOeENlmGHMB8hIaGihcYGbQ2JVfLli3TdxxERETtjs8vImqNAZ49EBrgI3YYZETavIhweXk5vv32W2RkZGD+/PlwcnLC6dOn4ebmBg8PD33GSEREpDd8flFjuqoYApz+RUSt16bk6ty5cwgLC0O3bt2QlZWFmTNnwsnJCfv370dOTg52796t7ziJiIjuG59fpIuuKoZ5l/IwG7M5/YuoFepqlZB18vfU2pRcRUVFYdq0aVi3bh3s7e2F9scffxwvv/yy3oIjIiLSJz6/qDm3qxgSUdsV5WYirawU6cqGJS/yM9MwF+g0v6hoU3KVnJyMjz/+uEm7h4cH5HL5fQdFRETUHvj8IiJqXy69fLXW6eps2rTOlaWlJSorK5u0X7p0CS4uXK+CiIgME59fRETUntqUXD399NNYuXIlamtrAQASiQQ5OTlYuHAhJk6cqNcAiYiI9IXPLyIiak9tSq42btyImzdvwsXFBbdu3cKYMWPg5+cHe3t7rF69Wt8xEhHdVV1tHVJTU3HixAmtTaFQiB0aGRg+v4iIqD216Z2rbt264dixYzh+/DhkMhlu3ryJBx54AGFhYfqOj4jongqzCpFWnobLlpeFtvwr+ZiN2Rg1apSIkZGh4fOLiIjaU6uTK7VajZ07d2L//v3IysqCRCKBj48PpFIpNBoNJBJJe8RJRHRXLl4u8A3yFTsMMmB8fhGRWHStpXZ7doWlpaXQJpPJoFJ169DYSL9aNS1Qo9Hg6aefxmuvvYb8/HwMGTIEgwYNQnZ2NqZNm4Znn3221QFs27YN3t7esLKywogRI3Dq1Klm+54/fx4TJ06Et7c3JBIJ4uLimvRZvnw5JBKJ1ta/f/9Wx0VEhqFOpUaZvAyZZzOFrSSvBKo6ldihkRFpj+cXEVFLJScn4/19x/B1cq6wrfzwC6zecVCr7euf/kRJSbHY4dJ9aNXI1c6dO/Hrr78iISEBDz/8sNZnP/30EyZMmIDdu3djypQpLTrfvn37EBUVhfj4eIwYMQJxcXEIDw9Heno6XF1dm/Svrq6Gr68vXnjhBcydO7fZ8w4aNAg//vijsN+lS5tmPxKRAbh2XYFBdbkYkN3wG78bxVeRbWsuYlRkbPT9/CIiai0P3/5aJcqvZabB3NaxSRsZt1aNXH311Vd45513mjyYAOCvf/0rFi1ahC+//LLF59u0aRNmzpyJiIgIDBw4EPHx8bCxscH27dt19h8+fDjWr1+PF198UWsItbEuXbpAKpUKm7Ozc4tjIiLD08vZEgF9ewhbb1drsUMiI6Pv5xcREZEurUquzp07h/Hjxzf7+WOPPQaZTNaicymVSqSkpGi9RGxmZoawsDAkJSW1JqwmLl++jJ49e8LX1xeTJ09GTk7OXfsrFApUVlZqbUREZDr0+fwiIiJqTqvmy5WWlsLNza3Zz93c3FBWVtaic5WUlEClUjU5n5ubG9LS2j4kOmLECOzcuRP9+vVDQUEBVqxYgdDQUPz3v/+Fvb29zmNiY2OxYsWKNl+TiIgMmz6fX0TUdroKO8hkMgxW8T1aMg2tSq5UKtVd318yNzdHXV3dfQd1Px577DHh/wMCAjBixAh4eXnh66+/xowZM3QeEx0djaioKGG/srISnp6e7R4rERF1DGN4fhF1BsnJyZB9sx6BfaRC25Vfz8OtnzMAP/ECI9KTViVXGo0G06ZNa/Z9p9Ys2Ons7Axzc3MUFhZqtRcWFkIqlTZzVOs5Ojqib9++uHLlSrN9LC0t7/oOFxERGTd9Pr+I6P4E9pEiNMBH2JdlFIgYDZF+tSq5mjp16j37tLTSkoWFBYKDg5GQkIAJEyYAqF+DJCEhAZGRka0J665u3ryJjIwMvPrqq3o7JxERGRd9Pr+IiIia06rkaseOHXq9eFRUFKZOnYphw4YhJCQEcXFxqKqqQkREBID6B52HhwdiY2MB1BfBuHDhgvD/+fn5OHv2LOzs7ODnVz+UPG/ePDz11FPw8vLCtWvXsGzZMpibm+Oll17Sa+xERGQ89P38IiIi0kXUBaAmTZqE4uJixMTEQC6XIygoCEeOHBFeOs7JyYGZWUNBw2vXrmHo0KHC/oYNG7BhwwaMGTMGiYmJAIC8vDy89NJLuH79OlxcXPDggw/ijz/+gIuLS4feGxERERERdS6ir64bGRnZ7DTA2wnTbd7e3tBoNHc93969e/UVGhHdB5VGjdKycuTm5gptlTcqYWt/93/DRERERMZK9OSKiExTRXkFqquzcPG6RGgrKCtAb0suAExERESmickVEbUbKxsrdJd2F/YtrSxEjIaIiIiofZnduwsRERERERHdC0euiIiIiIj0qKamBsnJycK+TCaDStVNxIioozC5IiIiIiLSo+TkZLy/7xg8fPsDAGS//wmpfwD8RY6L2h+TKyIiIupU6mrrIJPJhH2ZTAaVpUrEiMgUefj2h++QYQCAa5lpIkdDHYXJFREREXUq8qtyXCy9iEybTADAmaQzcB/kjn7oJ3JkRGTsmFwRERFRp+Pi7QK/B/wAAHmX8kSOhoyForYOaXeMet42fPhwWFlZiRARGRomV0TUagqFAikpKcJ+amoqBqg5pYaIiExbWk4xSsv2AqpzQpssQw5gPkJDQ8ULjAwGkysiarWUlBSkfrsOQ3ykAICM4xfg6u8MwFfcwIiIiNrZAM8eCA3wETsMMlBMroioTYb4SDFqsBcAIDVTLnI0REREROLjIsJERERERER6wOSKiIiIiIhID5hcERERERER6QGTKyIiIiIiIj1gQQsiIiIiahc1NTVITk4W9mUyGQaruHQHmS4mV0RERETULpKTkyH7Zj0C+9Qv3XHl1/Nw6+cMwE/cwIjaCZMrIiIiImo3gX2kwrpQsowCkaMhal9854qIiKgNtm3bBm9vb1hZWWHEiBE4depUs30/+eQThIaGonv37ujevTvCwsLu2p+IiIwTkysiIqJW2rdvH6KiorBs2TKcPn0agYGBCA8PR1FRkc7+iYmJeOmll/Dzzz8jKSkJnp6eGDduHPLz8zs4ciKijlVXq4RMJsNvv/0mbDU1NWKH1W44LZCITFJdbR1SU1O12oKDg2FpaSlSRGRKNm3ahJkzZyIiIgIAEB8fj0OHDmH79u1YtGhRk/5ffvml1v6nn36K7777DgkJCZgyZUqHxExEJIai3EyklZUiXekEAMjPTMNcAKGhoeIG1k6YXBGRSSrMKkRaeRouW14GAORfycdszMaoUaNEjoyMnVKpREpKCqKjo4U2MzMzhIWFISkpqUXnqK6uRm1tLZycnNorTCIig+HSyxe+Q4aJHUaHYHJFRCbLxcsFvkG+YodBJqakpAQqlQpubm5a7W5ubkhLS2vRORYuXIiePXsiLCys2T4KhQIKhULYr6ysbFvARNSuFLV1SJPJtNpkMhlUqm4iRURiYnJFRETUgdasWYO9e/ciMTERVlZWzfaLjY3FihUrOjAyImqLtJxifHc9TZj2BgCy3/+E1D8A/iLGReJgQQsiIqJWcHZ2hrm5OQoLC7XaCwsLIZVK73rshg0bsGbNGvzwww8ICAi4a9/o6GhUVFQIW25u7n3HTkTt4/a0t9ubS8/eYodEImFyRURE1AoWFhYIDg5GQkKC0KZWq5GQkICRI0c2e9y6devw7rvv4siRIxg27N7vHlhaWsLBwUFrIyIiw8ZpgURERK0UFRWFqVOnYtiwYQgJCUFcXByqqqqE6oFTpkyBh4cHYmNjAQBr165FTEwM9uzZA29vb8jlcgCAnZ0d7OzsRLsPap2amhokJyc3aR8+fPhdp3gSUefB5IqIiKiVJk2ahOLiYsTExEAulyMoKAhHjhwRilzk5OTAzKxhcshHH30EpVKJ559/Xus8y5Ytw/LlyzsydLoPycnJ2HpoK3r17SW05V3Kw2zMNtmy0kTUOkyuiIiI2iAyMhKRkZE6P0tMTNTaz8rKav+AqEP06tsLfg/4iR0GERkovnNFRERERESkB0yuiIiIiIiI9IDJFRERERERkR6Inlxt27YN3t7esLKywogRI3Dq1Klm+54/fx4TJ06Et7c3JBIJ4uLi7vucREREREQtUVunwsXc6/jt3FVhu5J/HSqVWuzQyECIWtBi3759iIqKQnx8PEaMGIG4uDiEh4cjPT0drq6uTfpXV1fD19cXL7zwAubOnauXcxIRERE1VldbB5lMptUmk8mgslSJFBEZguzCcpyq7I7ifCeh7fcCczg5VYgYFRkSUZOrTZs2YebMmcK6IPHx8Th06BC2b9+ORYsWNek/fPhwDB8+HAB0ft6WcxIRERE1Jr8qx8XSi8i0yRTaziSdgfsgd/RDPxEjI7H1cJXC17+vsH/uv2m4caMSOTnZQltpaSmcrLjwd2ckWnKlVCqRkpKC6Ohooc3MzAxhYWFISkoymHMSdXYKhQIpKSlabampqRig5m9vici0uXi7aJVdz7uUJ2I0ZKhu1iihVhcABeeEtkp5Jsxsu4sYFYlFtOSqpKQEKpVKWHDxNjc3N6SlpXXoORUKBRQKhbBfWVnZpusTmaKUlBSkfrsOQ3ykQlvG8Qtw9XcG4CteYERERAbCzqorers1JFMONpYiRkNi4iLCAGJjY7FixQqxwyAyWEN8pBg12EvYT82UixgNERERkWESrVqgs7MzzM3NUVhYqNVeWFgIqVTazFHtc87o6GhUVFQIW25ubpuuT0REREREnZdoyZWFhQWCg4ORkJAgtKnVaiQkJGDkyJEdek5LS0s4ODhobdR5KBQKnDhxosl251RRMix1KjXK5GXIPJuJzLOZKMkrgaqO74ARERGRuESdFhgVFYWpU6di2LBhCAkJQVxcHKqqqoRKf1OmTIGHhwdiY2MB1BesuHDhgvD/+fn5OHv2LOzs7ODn59eicxI1lpKSgvf3/Qh3n4bKPwVXL2EugFGjRokXGDXr2nUFBtXlYkB2MgDgRvFVZNuaixwVEVHnVlNTg+TkZK02mUyGwSr+8os6D1GTq0mTJqG4uBgxMTGQy+UICgrCkSNHhIIUOTk5MDNrGFy7du0ahg4dKuxv2LABGzZswJgxY5CYmNiicxLp4u7TFz6DHhA7DGqFXs6WCOjbA0B9yeTse/QnIqL2lZycDNk36xHYp+FVjCu/nodbP2cAfs0fSGRCRC9oERkZicjISJ2f3U6YbvP29oZGo7mvc5Jp01U2PDg4GJaWrNpDRETU3gL7SBEa4CPsyzIKRIzm/tTWqXAxtwy/nbsqtOWXVELloRYxKjJ0oidXRPrUeIpf3uULCE9NxZAhQ4Q+SqUSQP07ekD9mk1qtV3HB0tEZMKamyKmsuQUMTIO2YXlOFXZHcX5TkLbuRt2cK1WihgVGTomV2Ry7pziV3D1Er45nobzt7oJn6ee+BHmVvYY+MCI/+0nw80vAH1EiZaIyDQlJydj66Gt6NW3l9B2JukM3Ae5ox/6iRgZUcv1cJXC17/hnWy7P8/dpTcRkyvqBJx7+Wi9T1Vw9RLMbR21EjAiItK/Xn17we+Bhndt8i7liRgNEVH7E60UOxERERERkSnhyBUR3TeVRo3SsnKtBbgrb1TC1v7eBWiIiIiITAWTKyK6bxXlFaiuzsLF6xKhraCsAL0trUWMioiIiKhjMbkio6Wr7Dor/4nHysYK3aXdhX1LKwsRoyEiIiLqeEyuyGg1LrsOsPIfEREREYmHyRUZtTvLrgOs/EdERB2rrrYOMpmsSfvw4cNhZWUlQkREJCYmV2Q0Gk8DFHsKoK5picHBwbC0tGxVHyIiMl7yq3JcLL2ITJtMoS3vUh5mYzZCQ0NFjIyIxMDkioxG42mA7TkFUFVXi9TUVK22xklR43gKrl7CXACjRo1qVR8iIjJuLt4uWut5EVHz6mqVJj3ay+SKjMqd0wDbcwpgUe5VfFNWhvO3ugnX0pUUNZ6WqEtL+hARERF1BkW5mUgrK0W60kloy89Mw1zAJEZ7mVwRNcO5l4/ekyJdI2IApwoSERGJrbZOhYu5Zfjt3FUAQH5JJVQeapGjMk0uvXzhO2SY2GG0CyZXZJBMtcx64xExgFMFiYiIDEF2YTlOVXZHcX79iMq5G3ZwrVaKHBUZGyZXZJAMrcy6rhGntiZ77TEiRkREhqumpgbJyclN2k3lHRNT0sNVCl//+p897P48J3I0ZIyYXJHBMqQy67pGnLimlnGpq63jlEwiEkVycjK2HtqKXn17CW2sKGja1GoNqm5UIicnGwBQWloKJysHkaOijsDkiqiFGo84meqaWrpK3g9Qq0SMSD8KswqRVp6Gy5aXhbb8K/mYjdmckklE7a5X316sKNiJ3KxRQq0uAArqR78q5Zkws+0uclTUEZhcEemJPqcOiiklJQWp367DEB8pACDj+AW4+jsD8BU3MD1w8XKBb5Dx3wcRERk+O6uu6O1Wn1A52HCGRGfB5IpIT0xp6uAQHylGDfYCAKRmykWOhoiIiMg4MLki0qPOMnWQiIiaV1dbp7VIqkwmg8rS+KdX30lXkQ6ZTIbBKuO4z8Zl1wGWXif9YHJFJDJd0wlZZIGIyHjJr8pxsfQiMm0yAQBnks7AfZA7+qGfyJHpT3JyMmTfrEdgH6nQduXX83Dr5wzA8N8ta1x2HWDpddIPJldkEHQVUTC2d5XaqvF0Qq57RURk/Fy8XYQCFnmX8kSOpn0E9pEiNMBH2JdlFIgYTevdWXYdYOl10g8mV2QQGq9rZazvKrXVndMJdY1kARzNIiIiIjJ0TK7IYNy5rlVnfldJV2EMUxnNUqk0yC2uwblL1wEA+ddrcEthJuwDgLxMgR721mKFSEREpHeN170CuPaVqWJyRWSAGhfGMBXFlUpctRgAxXV/AEBqdQUsJfaou94wLSNdAjiVdt7kmoiITE/jda8Arn1lqphcEVGHcnBygodPbwCA9Z/psHboLuwDwKWMEoDvExMRkYm5c90rgGtfmSozsQMgIvEoFAqcOHFCa0tNTUWd2jhK6RKJadu2bfD29oaVlRVGjBiBU6dONdv3/PnzmDhxIry9vSGRSBAXF9dxgRIRUYfhyBVRJ5aSkoLUb9dhiE9DKd2M4xfg6u8MwFe8wIgM3L59+xAVFYX4+HiMGDECcXFxCA8PR3p6OlxdXZv0r66uhq+vL1544QXMnTtXhIjJ0DReC+u24cOHw8rKSoSIiEgfmFwRdXJDfKQYNdhL2E/NlIsYTceqq63jGmPUJps2bcLMmTMREREBAIiPj8ehQ4ewfft2LFq0qEn/4cOHY/jw4QCg83NT0HhRWVNcOFefGq+FBdSXbJ+N2QgNDRUxMiK6H0yuiKjTKswqRFp5Gi5bXgYA5F/Jx2zMNvqqjNS+lEolUlJSEB0dLbSZmZkhLCwMSUlJeruOQqGAQqEQ9isrK/V27vaQnJyMrYe2olffXgBMc+FcfbtzLSzqfFhB0DQxuaIO13jBYKBzLRqsD7q+hgBHXdrCxcsFvkGcAkktV1JSApVKBTc3N612Nzc3pKWl6e06sbGxWLFihd7O1xF69e1l8gvnEukLKwiaJiZX1OEaLxgMdL5Fg++Xrq+hqayFRUT1oqOjERUVJexXVlbC09NTxIiISN9YQdD0MLkiUdy5YDDQuRcNbqvGX0ND03jBYAAovVELddN3/YmMirOzM8zNzVFYWKjVXlhYCKlU2sxRrWdpacmRaCIiI2MQpdhbU84WAL755hv0798fVlZWGDJkCA4fPqz1+bRp0yCRSLS28ePHt+ctELUrVV0tUlNTtcqlq9tQLr1x6fX2LLteXKlEirIfEq73FbYcSW/cUvAFdzJuFhYWCA4ORkJCgtCmVquRkJCAkSNHihgZUfuoqanBb7/9prXJZDKoVPx+TtSY6CNXrS1ne+LECbz00kuIjY3Fk08+iT179mDChAk4ffo0Bg8eLPQbP348duzYIezzt39kzIpyr+KbsjKcv9UNgO5plLcTsDs1fgercen19i67fueCwUD9osFEpiAqKgpTp07FsGHDEBISgri4OFRVVQnVA6dMmQIPDw/ExsYCqC+CceHCBeH/8/PzcfbsWdjZ2cHPjwUNyLAlJydD9s16BPZpGJm98ut5uPVzBsC/v0R3Ej25am05282bN2P8+PGYP38+AODdd9/FsWPH8MEHHyA+Pl7oZ2lpqdfpGdR2jYsvsHhF2zj38hGmAeqaRtk4AWvuHaw7S693prLrRPo0adIkFBcXIyYmBnK5HEFBQThy5IhQ5CInJwdmZg2TQ65du4ahQ4cK+xs2bMCGDRswZswYJCYmdnT4ZKAMee2rwD5ShAb4CPuyjAIRoyEyXKImV20pZ5uUlKT1gi8AhIeH4+DBg1ptiYmJcHV1Rffu3fHXv/4Vq1atQo8ePXSe09jK3RqbxsUXWLyi/dyZgFHr6Vr3CmAVRtItMjISkZGROj9rnDB5e3tDo9F0QFRkzLj2FZHxEzW5aks5W7lcrrO/XN7wG/jx48fjueeeg4+PDzIyMvDOO+/gscceQ1JSEszNzZuc0xjL3Rqq5sqsu3n1ueuoC9FtapUKReVKoRCGvEyBHvbWHXLtxuteAVz7iog6Fte+IjJuok8LbA8vvvii8P9DhgxBQEAA+vTpg8TERDzyyCNN+rPcrf6wzDrdr+rKclw2H4iE6/V/h9IlgFNpxyXkXPeKiAyJrqmChjBN0JjU1qlwMbcMv527KrTll1RC5aEWMSoyVaImV20pZyuVSltd/tbX1xfOzs64cuWKzuSK5W71i2XWO586lRp512/prey6rWNDIYxLGSWA8t7XL5OXIfNsw1SakrwSuNi6tC0AIiID0XiqIKcJtl52YTlOVXZHcb6T0Hbuhh1cq+/xcCFqA1GTqzvL2U6YMAFAQznb5uaxjxw5EgkJCZgzZ47QduzYsbuWv83Ly8P169fh7u6uz/CJDJqu6oGpqanoV3v/pXNVGjVKy8qRm5sLALiUW4hU9MGN6w1TWXIkFXDqoLLr164rMKguFwOyk4W2G8VXkW3bdBowEZGx4VTB+9fDVQpf/4ZZNXZ/nhMxGmqsrlZpsMVcWkv0aYGtLWf7j3/8A2PGjMHGjRvxxBNPYO/evfjzzz/xr3/9CwBw8+ZNrFixAhMnToRUKkVGRgYWLFgAPz8/hIeHi3afRB2tcfVAALhwOg0v9CjG2KH3N+2torwC1dVZuHhdAgC4fqMEdr0Hi1p2vZezJQL6NhStkV+VI7tDIyAiMn41NTVITk7WapPJZBjcxjWt1Go1SkvLkZ1T/x25tKwMLtbd7ztOMi1FuZlIKytFurJhdDE/Mw1zAaMbpRU9uWptOdtRo0Zhz549WLJkCd555x34+/vj4MGDwhpX5ubmOHfuHHbt2oXy8nL07NkT48aNw7vvvsupf2TS1GoVKsvKhNGksrIyOPXsrTVFs6ioCLh1tblTtIqVjRW6S+sfkF0tuurlnEREJC59r2lVXlGBmupspF2vn0mQXZQNN1uJvsIlE+LSyxe+Q4aJHcZ9Ez25AlpXzhYAXnjhBbzwwgs6+1tbW+Po0aP6DI/IKFRUVKKqsgQosK/fL7iMKrUZev0v2QLqEy6VJV/gJSKi5ul7TSsrW2s4uTv97//z7utcpk6t1qDqRiVychrmXpSWlsLJykHEqKg1DCK5IiL9cLC2hKerIwDADBLUluUDBQ1zmCsLrqDCsUKk6IiIiNqXsVcGvFmjhFpdABQ0vBNWKc+EmS2nUhoLJld0Xxqva5Wamgq12k7EiOhOdlYWQrIFAOettafG1tapkJZbhhP/bfgNWca1UnT35Tfx23QtLMxFhYnIVDR+x+p+3q8yBKZQGdDOqit6uzU8hx1s+LwxJkyu6L40XteKa1oZNrVKheyiCiGZSrqQi+yunii44yF0srQbuluXixSh4Wm8sDAXFSYiU9L4Hav7eb/KULAyIImJyRXdtzvXteKaVoatsqwEJ2vdYfG/ZOpMpS1ce3eHT5+Gh2h6dhGASpEiNExcWJiITNmd71jd7/tVpH98D8u4MLki6mS69XARkin7lNR79NY9dTC7qALuPYxj/joR6V9z5bpVlsY7ncxY1NXWmcx6QNQyfA/LuDC5ohZr/H4VwHesOoOcogokVTpqTx2sdkcIzosYFRGJKTk5GVsPbUWvvr2EtjNJZ+A+yB390E/EyEyf/KocF0svItMmU2jLu5SH2ZhtFOsBadTAjRs3hHWvAK591RJ8D8t4MLmiFmv8fhXAd6zE0nhNKwC4caMSDu30S0snFzetqYNn07IhL1Tg3KXrAIDSG7VQu7bPtYnIMPXq2wt+DzR8X8i7xBLbHcXF20Xray+GxosDAy1LkhS3FChUVSHteprQxrWvyJQwuaJWufP9KoDvWIml8ZpWAFB1PR/WLvef4TQuepFfUgm1u0arz43y6yiS9EfC9fpEO0dSASdF+0wHUqtUKCpXCokcAMjLFOhhb90u1yMiMgW6pm4C+ps+2HhxYKDlSZKFjYWw7hXAta/ItDC5IjJSd65pBQC2ll31cl6dRS8cm5awtevuBA+f3gAA6z/T9XJtXaory3HZfKCQyAFAugRwKmViT0TUHF1TN7MvZONh2cMIDAwU2u6n9PqdiwPX7zNJImJyRURNtLboRXuzdWxI5ADgUkYJYDxLlhARiULX1M3/O/d/Wu9rnUk6AzcvBxhz6XUiQ8LkisgINH7Hqj3fryIiItOkqlNBA02TdrWqofprW9+lIqJ6TK6IjEDjd6z09X4VEREZP13l2XWVxi8tKEWf8gz0z24Y+r+am4lyex9hX9e7VFeLrsJGfVMr4aqsrIS9g3aipqsSoK5+jbW0gmDjxK+w5DpKSgDpuatCn/ySSqg8uFQIiYfJFZGRuPMdK329X0VERMZPV3n25krje0mtMbS/s7B/VnZNK7GpT4astN6lMoMZCisLtSr8FZbLYWmlXVhIVyVAXf0aa2kFwcaJX8qVSyiwDEC2o4XQ53SZFdy7K+56PaL2xOSKiIiIyMg1Ls/e0tL4jROb5pKhxhX+LKx0r7PU0n73Os7SOq+ZUbCGxE8CM1ja28FW6iD0MbO2Rm1tXYuuSdQemFxRsxovGswFg8mY1KnUKJOXIfNs/W9yS/JK4GLrInJUREQdQ1WnQmlWKa6cviK0lReVQ9Or6RS9OxObliZD7a2lo2BmXcxgfcfSHGbmnWO9LLVag6oblcj5X/JZUlIC81sqYR8ASktL4WTl0NwpqJ0wuaJmNV40mAsGkzG5dl2BQXW5GJBdv87LjeKryLY1v8dR91ZXW4fUVO0Kikpl/fsLFhYNU1OCg4NhaWkYP6QQkenQlTTJs+ToYtNFqy1TlokHupZovV+VXpEPhZPxrBHY1lGwzuBmjRJqdQFQcA4AUJRzGVYO3YGChq9XpTwTZrYsRNLRmFzRXd25aDAXDCZj08vZEgF9ewCofych+x79W6IwqxBp5Wm4bHlZaJMlymBua47BwwcDAPKv5GM2ZmPUqFF6uCIRUQNdRSnSM9PgZGeG/p4N0+HSK/Ih9dF+v+rkH0xOTImdVVf0dqtPnmysusLmjn0AcLDhn7cYmFwRGZjGZdcBll43NC5eLvAN8hX28y/no0u3LlptRETtpXFRipN/WELqaMZEisgAMLkiMjCNy64D4pZeV0MDZW0tKisqhLa6OhXMRInGOOiaOghwqiAZp5qaGiQnJ2u16SrzTWRUNICythYVFeVCU21trc51wIhag8kVAWhavAJgAYuOonOBYGsLoew6IG7p9braOtSaV6P4VonQVlNbgy4qG9FiMnS6pg5yqiAZq+TkZGw9tBW9+vYS2por80361/gdq+aKUlDr1NbVok55CyXVDc+2W8oaWLPSIN0nJlcEoGnxCoAFLPShceJUVlYGJ2t7rT7GsEBwF3MzWNs1zEs07yTVmO5H46mDRMasV99ebSrzTfev8TtWxlaUwiDoGKWqq6uDhXnnrDRoLOpqlU0WxwaA4cOHw8rKcN+VYHJFgjuLVwAsYNFaut6VysvLg0StEBKnGwVXYG7r2ORYLhBMRETNufMdK75L1Xq6RqmUtQp01ahFjKr9NS7XDhhXefai3EyklZUiXdlQATE/Mw1zAYSGhooX2D0wuSLSk+belXJ2cW1InKwsUXmjksUq7pNapUJRuRLnLl0HAMjLFOhhz9/kEhFR/ajUvUapJOb3vzSHoWtcrh0wvvLsLr184TtkmNhhtAqTq06KCwS3jztHoICmo1BVNQqo1PlAQcMwt6FNA2xcwMIQi1dUV5bjsvlAJFyvn8aaLgGcSg17pFVXkQsWuCCiO7Vm4V/STa3SQFmn7HSjVM2xY3n2DsfkqpPiAsHisbMynGIVujQuYGGoxStsHZ3g4dMbAHApowRQ3uMAkTUucsECF0TUmM41rPiOVauZdZF0ulEqMhxMrjqB5ioBunn14QLBpNOdBSxYvEJ/7lXkQte/VY5uEZmm5kapevey4npVREaMyVUnwEqARECdSo0yeRkyz2YKbSV5JXCxdRExKm0pKSnYengrPPw8AHB0i8iUcZTq7upUGuQU1+BMWsP0vvzrNbilMBPaSm8oATexIryHRhUKDWUNLWMvcmEMmFyZIF3vU905SgVwpKq1dFUCvH69BOY1ddrrU7EwhcG6dl2BQXW5GJDdsBjqjeKryLY1rOkiHn4eLOFOZER0jUAV5xbDte+936W9swogwFGqO5VUKpHdtR/qSv2Ftv9Wl8PKzB6a/7XlSsrRQ2mYi1k3rlBoKGtoGXuRC13l2Q2tNDuTKxPE96nuX+NkqnFJdQAoyrkEK3tHoLcjAMMrTEFN9XK2REDfHsK+/Koc2XfpT0SGS1dSI8+So4tNlzYlOi05v65z6RqBul6Yif9WVsLD06PZuFio4t4cevRAL19vYd/61CXYODoKbdanRPhFsY41s5oblTIzNxfe/WrxGlqtOH9bNS5yYWdlgRt3jGYZ8khW4/LshliancmVibpzzSqOUrWeroV97yypDtQXorC+oziF2IUpGlf5UyhrYV6rFPaB+m/YVhpNs8cAhlkdsL00nirYkdMEdVUPTE1NhaqLYf4WlsjQ6JxWl5kGJzsz9PdsGCFonOgALUu4dJ2/okT3aHfTEairGFiVj/7ZfzYbF6cANtA1BdBQp/zpWjNLn6NS7X1+XRqPZhn6SJahl2dnckWdnq4pfzduVMLB2nASp5ZoXOWvqqYKGsuuwj4AVCurYVFTIyRTNbcUqDU31+pjqNUB20PjqYIdOU2wcfVAAJAlySAdKIU//O9yJFH7qampQXJyslabTCaDytIwk35d0+qkjmZ3TXSA5pOke50/98o1nC0oa9EIVG8XS62Ff5vGxSmAt+maAmjIU/7uHJGq39dv4af2Pr8ud45msVz7/WFyZeSaqwTINavqteRdKV1T/gxtil/jEabGI1C3Na7yZ2bWsA8AEgA1dTVaZdbtYKPVxxirAzZeVBho+cLCd04V7Ohpgo2rB+Zfzu/AqxM1TaZkMhkScxLRu39voe1M0hm4D3JHP/TrsLhaMuWvNdPq7kx0gKZJUkun7V0rUWBgrXaixhEo/dA1BZDEwaIX98cgkqtt27Zh/fr1kMvlCAwMxNatWxESEtJs/2+++QZLly5FVlYW/P39sXbtWjz++OPC5xqNBsuWLcMnn3yC8vJyjB49Gh999BH8/Y3/t8G6ilX8cL4AHr79G9r4jpWg8fQ+QPe7Uo2n/FlbdmlRMtNRGo9KNR6BAlo+ne/OhMsYEyldGi8qDLRtYWFjqChIhkPfzy4xJCcnY+uhrejVtxeAhkTK7wE/oU/epbx2jUFXIpUpy8QDXUvuOuXvfpKaxklSa6btNU7UOALVOoY+BbCurq5NFf7uPO72vlmjYxv3MZQKgo3pKnpRUZCJKrW5wSVcugpcAOIWuRA9udq3bx+ioqIQHx+PESNGIC4uDuHh4UhPT4era9ORgxMnTuCll15CbGwsnnzySezZswcTJkzA6dOnMXjwYADAunXrsGXLFuzatQs+Pj5YunQpwsPDceHCBYOqJtIWzRWr6AyVABuPQpWVlcHJ2v6ufRpP7wNa9q5U42TmlrIa1nWtn+/c0hGnlrhzVKrxCBTQuabz6XLnosIAkHapEEXXG0azWjKSpauiYLk8E6k3KtGzZ08AQFF2EcxszZiAdXLt8ewSS6++vYRkSp+JVEsLTuhMpCryIfW5+5S/+01qOG1PHIY8BVCt0kBZpxTed6pW3kLXWzX3TJoaHwcAyloFumrUd+1zX+9SNSp8oaxVoouyBYUwWlgwo3HRC4kEqC1vVGXQABKuxgUuAPGLXIieXG3atAkzZ85EREQEACA+Ph6HDh3C9u3bsWjRoib9N2/ejPHjx2P+/PkAgHfffRfHjh3DBx98gPj4eGg0GsTFxWHJkiV45plnAAC7d++Gm5sbDh48iBdffLHjbu4+dabFfxsnRY2n7gFNp+9VFFxGldoMve7S536m92klM21c3b0lSVpLClHoGpVqPOXPVEah9KXxaNZFjRp1mRfQ5x5TBxtXFDx18ioG3WxIuC5npKGHvTkG9Gz4QbBxAqavZEtX0Qvg3gsLczHi9qfvZ1dHaO/3qRonUy0ZfQKaT6TINDQeqSq9oYS9n+FOATTrIhHed5JAcs+kSddxgO6fGxr3MTOXtGjE63b7nf1u1dxC1y7mDaXeb1UDFlZasepKDhsfp6ufrkRNWauEjRXQzeqOnzXUtciV5+Lk5VNCU1bOJXjUmXVowmVoBS5ETa6USiVSUlIQHR0ttJmZmSEsLAxJSUk6j0lKSkJUVJRWW3h4OA4ePAgAuHr1KuRyOcLCwoTPu3XrhhEjRiApKclgkitdP/wolfUPIAsLCwD6m/Kn670jXaM++tCSd5yKi4txq7gMXe64/4tpabDuClR1r08hiq5eQJlVd9y6Y0pGdmEO+kmlWqNQt4qzUXXlhLBfUZgNN6m70Kfx9D6gafLS0tEkVV3dXc/TXFJk0ShJa3yexkUldBWi6OyjUm1152jW6T/TcRnaUwcbJ1zNjW7dmXCdOmkJN0fzuyZgjZMtoOmIl64RsMZt538/jwvWF7SKXuSk5eCR1EcwZMgQAE2/bwD13zt+yv4Jnn09AehejFhfCVhnTOTa49nVERpPAQSAP3//ExYOFjD/3w+CukaWWtrWOJlqyejT7TZ90TXlTF6mgLN9+81aaclit/qM4X7usa2xtuS45tou5d9EpdMQYaTKUEapWqolSVNbtWTE6679zOwaEkFz86axNpcc3nGcrn66EjVdbRVVVXCws4Cv+U2h7dqtalQVZ+Pm5YbvhSW5abh5R8JVUlIC81sqrQRMV1tbkzKxpwqKmlyVlJRApVLBzU17oq2bmxvS0tJ0HiOXy3X2l8vlwue325rr05hCoYBCoRD2K/73g29lZWUr7kbb0qVL7/p5QUEBTl8tga1jwwPmel4GJBZWcHL1EPbte/qgvKohtmt5eZBfL0dxYQEAIO/iGZhZ2wn7utoyZH9AVVeL8pwLQp/cjEuw6uGB68VFLT5PW691Nf0iutrYoTIvvb5P2gWYWVqhqii94euRcRU2dnZINa8BABTKr0FiVYGbpxvuXVmYj5xb1TiWUJ+A5efkwszSEqmptUKf0sJrUNy61aTPUU210Cc/LwcWpddxVHOrfj83B+UVN4RjAKCwoABdrK1w6nj9N9HrBYWoLL+Jo78omz9Po30AkBfkw/KG3V3PIy/Ih6WdHVJl9dMTK65fx60aBVJlDV+f222njtd/sygtKoFFVY2wr6vN0PqIff3SohJY2Noh/1qZ0Ke85DpOqnog+4f636LLCx2RX5SLUkXDec5cLoO9lQRF1TKd+3e2WZypn1J1LrMcitrrOH8tR+iTk1MOGwsJzsv+q3NfV1tJ0Q1ct3VCbUXD3/H8K/k4UXUC3V3rp2tcz78Os65mwv7tNntXexRdrf/3XVFSgbhLcdi3b5/Qp6CgACmXU2DTrT5pr66oRrB/MNzd3dEaus4Tv65+qlxb3P6+qxHxHcd7aY9nly76fjZVVVUh7WQaCjIbvodnnM5AD1UFUk/Xj5Dm5VTA2lKC1HPnhT4tbavIqcAV564wM6//d1BQUoWbN2+hqPqs0OfM5VLYW5nppU1XnxPnS1Bh5YmU6ju+xxY6IL8oH6WK1p2rpW26rpmXa4EuVtY4W6NscQwtbWvrPd5PrC05rtm2UlvYmd9CXl4pAKCmRoHi/AL88etpoU9ZcRGqqm61uq2tx4lx/ub6WFTdguxMw+sJ5aXXUV2jbHU/fZ7/dh9dxzVus6hRIvtCw/e9m2XFUNTUIDW1IUEsyM+F+fVK/FZ5DQBw9dL/fi7Mb/g5R1dbXkY6LJ2uoOR/P6sCQP7F0zCztkXh/753Nt6/3fZ/VZXo7tqw/EJ1RQk+em+x1i8ZW6NVzyaNiPLz8zUANCdOnNBqnz9/viYkJETnMV27dtXs2bNHq23btm0aV1dXjUaj0Rw/flwDQHPt2jWtPi+88ILmb3/7m85zLlu2TAOAGzdu3LgZyJabm9vWR0u7a49nly58NnHjxo2bYW0teTaJOnLl7OwMc3NzFBYWarUXFhZCKpXqPEYqld61/+3/FhYWav0GtrCwEEFBQTrPGR0drTVdQ61Wo7S0FD169IBEcu/3WCorK+Hp6Ync3Fw4OBh/mUpTux+A92QseE/GoT3vSaPR4MaNG1pTKg1Nezy7dLnfZ9O9mOLfzebwXk0T79U0GeK9tubZJGpyZWFhgeDgYCQkJGDChAkA6h8eCQkJiIyM1HnMyJEjkZCQgDlz5ghtx44dw8iRIwEAPj4+kEqlSEhIEJKpyspKnDx5Em+++abOc1paWjZ5P8DR0bHV9+Pg4GAwfwn0wdTuB+A9GQvek3For3vq1q2b3s+pT+3x7NJFX8+mezHFv5vN4b2aJt6raTK0e23ps0n0aoFRUVGYOnUqhg0bhpCQEMTFxaGqqkqowDRlyhR4eHggNjYWAPCPf/wDY8aMwcaNG/HEE09g7969+PPPP/Gvf/0LACCRSDBnzhysWrUK/v7+Qin2nj17Cg9BIiKi+6HvZxcREZkG0ZOrSZMmobi4GDExMZDL5QgKCsKRI0eEF39zcnJgZtZQaGDUqFHYs2cPlixZgnfeeQf+/v44ePCg1johCxYsQFVVFV5//XWUl5fjwQcfxJEjR4x+jSsiIjIM7fHsIiIiE3DPt7LonmpqajTLli3T1NTUiB2KXpja/Wg0vCdjwXsyDqZ4T51RZ/pz5L2aJt6raTL2e5VoNAZc75aIiIiIiMhImN27CxEREREREd0LkysiIiIiIiI9YHJFRERERESkB0yuiIiIiIiI9IDJVRtlZWVhxowZ8PHxgbW1Nfr06YNly5ZBqVRq9Tt37hxCQ0NhZWUFT09PrFu3TqSIW2b16tUYNWoUbGxsml2sUiKRNNn27t3bsYG2QkvuKScnB0888QRsbGzg6uqK+fPno66urmMDvQ/e3t5N/kzWrFkjdlitsm3bNnh7e8PKygojRozAqVOnxA6pzZYvX97kz6N///5ih9Uqv/76K5566in07NkTEokEBw8e1Ppco9EgJiYG7u7usLa2RlhYGC5fvixOsHTfLl26hGeeeQbOzs5wcHDAgw8+iJ9//lnssNrNoUOHMGLECFhbW6N79+4mvw6mQqFAUFAQJBIJzp49K3Y4etfSn8mMlSk9H+8mNjYWw4cPh729PVxdXTFhwgSkp6eLHVarMblqo7S0NKjVanz88cc4f/483n//fcTHx+Odd94R+lRWVmLcuHHw8vJCSkoK1q9fj+XLlxv0opFKpRIvvPAC3nzzzbv227FjBwoKCoTNkB9M97onlUqFJ554AkqlEidOnMCuXbuwc+dOxMTEdHCk92flypVafyazZ88WO6QW27dvH6KiorBs2TKcPn0agYGBCA8PR1FRkdihtdmgQYO0/jx+//13sUNqlaqqKgQGBmLbtm06P1+3bh22bNmC+Ph4nDx5Era2tggPD0dNTU0HR0r68OSTT6Kurg4//fQTUlJSEBgYiCeffBJyuVzs0PTuu+++w6uvvoqIiAjIZDIcP34cL7/8sthhtasFCxagZ8+eYofRblryM5mxMsXnY3N++eUXvPXWW/jjjz9w7Ngx1NbWYty4caiqqhI7tNYRuRS8SVm3bp3Gx8dH2P/www813bt31ygUCqFt4cKFmn79+okRXqvs2LFD061bN52fAdAcOHCgQ+PRh+bu6fDhwxozMzONXC4X2j766CONg4OD1p+dIfPy8tK8//77YofRZiEhIZq33npL2FepVJqePXtqYmNjRYyq7ZYtW6YJDAwUOwy9afxvXq1Wa6RSqWb9+vVCW3l5ucbS0lLz1VdfiRAh3Y/i4mINAM2vv/4qtFVWVmoAaI4dOyZiZPpXW1ur8fDw0Hz66adih9JhDh8+rOnfv7/m/PnzGgCaM2fOiB1Sh2j8M5mxMrXnY2sUFRVpAGh++eUXsUNpFY5c6VFFRQWcnJyE/aSkJDz00EOwsLAQ2sLDw5Geno6ysjIxQtSbt956C87OzggJCcH27duhMeLl0pKSkjBkyBC4ubkJbeHh4aisrMT58+dFjKx11qxZgx49emDo0KFYv3690UxrVCqVSElJQVhYmNBmZmaGsLAwJCUliRjZ/bl8+f+3c+dBUVx5HMC/wzEDA8PNAKIBkYAoahAVrw0qrGCqPFYXjfEAZTEaFV1vYtR4lnE9klBJdK0saqKJoTyXNQhBxiV4YWRQBEdFQFTGExA0AsJv/7DodWDAQUcGzO9T1VV29+s3v/da+vXr1/2uoF27dvDw8MCECRNw/fp1Q4ekN/n5+VCr1RrnzNraGgEBAW36nP1R2dvbw9vbG7t27cKjR4/w9OlTbNu2DXK5HP7+/oYOT6/OnTuHmzdvwsjICH5+fnBxccGwYcOQnZ1t6NBei9u3byMqKgrfffcdpFKpocNpUfXvydqiN7V91FVZWRkAtLnzaGLoAN4UV69eRWxsLDZu3ChsU6vV6Nixo0a6uht4tVoNW1vbFo1RX1atWoUhQ4ZAKpUiKSkJH330ESoqKhAdHW3o0F6KWq3W6FgBmuepLYiOjkbPnj1hZ2eHEydOICYmBsXFxdi8ebOhQ3uhe/fuoaamRus5uHTpkoGiejUBAQHYsWMHvL29UVxcjJUrV+JPf/oTsrOzIZPJDB3eK6v7u9B2ztrK3wz7P5FIhF9++QWjRo2CTCaDkZER5HI5EhMT22w71Zhr164BePZd5ObNm+Hu7o5NmzZh0KBBuHz5cpu7iWsKESEiIgLTp09Hr169UFBQYOiQWoy2e7K26E1sH3VVW1uLuXPnYsCAAfD19TV0OM3CI1f1LFmyROuEDc8v9f9D37x5E6GhoQgLC0NUVJSBIm/cy5SpKcuWLcOAAQPg5+eHxYsXY9GiRfjHP/7xGkvQkL7L1Bo1p4zz5s3DoEGD0L17d0yfPh2bNm1CbGwsKisrDVyKP6Zhw4YhLCwM3bt3R0hICI4cOYLS0lL89NNPhg6N/YHoeg0hIsycORNyuRxpaWk4c+YMRo0aheHDh6O4uNjQxdCJrmWtra0FACxduhRjxoyBv78/4uLiIBKJEB8fb+BS6EbXssbGxqK8vBwxMTGGDvmlvYn3ZEw3M2fORHZ2dqueMK0xPHJVz/z58xEREdFkGg8PD+Hft27dwuDBg9G/f/8GE1U4Ozvj9u3bGtvq1p2dnfUTsA6aW6bmCggIwOrVq1FZWQmJRPLS+TSHPsvk7OzcYOYdQ5yn+l6ljAEBAXj69CkKCgrg7e39GqLTHwcHBxgbG2v9WzFk/euTjY0NvLy8cPXqVUOHohd15+X27dtwcXERtt++fRvvvPOOgaJi9el6DTl27BgSEhJQUlICKysrAMDXX3+N5ORk7Ny5E0uWLGmBaF+NrmWt6yx26dJF2C6RSODh4dFmXt1tznk9efJkg3a5V69emDBhAnbu3Pkao9QPfd6TtUV/hPZRm1mzZiEhIQH//e9/0b59e0OH02zcuarH0dERjo6OOqW9efMmBg8eLDz5MjLSHAjs168fli5diurqapiamgIAkpOT4e3t3aKvWjSnTC9DqVTC1ta2xTpWgH7L1K9fP6xduxZ37tyBXC4H8Ow8WVlZaTTALe1VyqhUKoVXe1o7sVgMf39/pKSkCLNO1tbWIiUlBbNmzTJscHpSUVGBvLw8TJo0ydCh6EXHjh3h7OyMlJQUoTP18OFDnD59+oUzjbKWo+s15PHjxwDQoA0zMjISRnpaO13L6u/vD4lEApVKhYEDBwIAqqurUVBQADc3t9cdpl7oWtYvv/wSa9asEdZv3bqFkJAQ7N27FwEBAa8zRL3R5z1ZW/RHaB+fR0SYPXs2Dhw4AIVC0eDTmjbDsPNptF03btwgT09PCgoKohs3blBxcbGw1CktLSUnJyeaNGkSZWdn048//khSqZS2bdtmwMibVlhYSJmZmbRy5UqytLSkzMxMyszMpPLyciIiOnz4MG3fvp0uXLhAV65coa+//pqkUiktX77cwJE37kVlevr0Kfn6+tLQoUNJqVRSYmIiOTo6UkxMjIEj182JEydoy5YtpFQqKS8vj77//ntydHSkyZMnGzo0nf34448kkUhox44dlJOTQ9OmTSMbGxuNGRzbkvnz55NCoaD8/HxKT0+n4OBgcnBwoDt37hg6NJ2Vl5cLfysAaPPmzZSZmUmFhYVERLR+/XqysbGhQ4cO0fnz52nkyJHUsWNH+v333w0cOWuuu3fvkr29PY0ePZqUSiWpVCpasGABmZqaklKpNHR4ejdnzhxydXWlo0eP0qVLlygyMpLkcjk9ePDA0KG9Vvn5+W/sbIG63JO1VW9a+9iUGTNmkLW1NSkUCo1z+PjxY0OH1izcuXpJcXFxBEDr8rysrCwaOHAgSSQScnV1pfXr1xsoYt2Eh4drLVNqaioREf3888/0zjvvkKWlJVlYWFCPHj1o69atVFNTY9jAm/CiMhERFRQU0LBhw8jc3JwcHBxo/vz5VF1dbbigm+G3336jgIAAsra2JjMzM/Lx8aF169bRkydPDB1as8TGxtJbb71FYrGY+vTpQ6dOnTJ0SC9t3Lhx5OLiQmKxmFxdXWncuHF09epVQ4fVLKmpqVr/bsLDw4no2XTsy5YtIycnJ5JIJBQUFEQqlcqwQbOXlpGRQUOHDiU7OzuSyWTUt29fOnLkiKHDei2qqqpo/vz5JJfLSSaTUXBwMGVnZxs6rNfuTe5c6XpP1la9Se1jUxo7h3FxcYYOrVlERG14Dm3GGGOMMcYYayXa/gupjDHGGGOMMdYKcOeKMcYYY4wxxvSAO1eMMcYYY4wxpgfcuWKMMcYYY4wxPeDOFWOMMcYYY4zpAXeuGGOMMcYYY0wPuHPFGGOMMcYYY3rAnSvGGGOMsVbE3d0dn3/+uaHDeKMMGjQIc+fOFdb1Ucd8npg23LlirJ6IiAiIRKIGS2hoqJAmKysLI0aMgFwuh5mZGdzd3TFu3DjcuXMHAFBQUACRSARjY2PcvHlTI//i4mKYmJhAJBKhoKCg0TiePHmCiIgIdOvWDSYmJhg1atTrKC5jjLFmiIiIeO3X44yMDEybNk1YF4lEOHjwoE7Hpqam4r333oO9vT2kUim6dOmC+fPnC22RQqHQaNucnJwwZswYXLt2rcE+bYtCoWjwm/v378fQoUNhb28PkUgEpVKph1p4verXcVN27NgBGxubV8qD/XFw54oxLUJDQ1FcXKyx/PDDDwCAu3fvIigoCHZ2djh69Chyc3MRFxeHdu3a4dGjRxr5uLq6YteuXRrbdu7cCVdX1xfGUFNTA3Nzc0RHRyM4OFh/hWtBVVVVhg6BMcbaHEdHR0il0mYft23bNgQHB8PZ2Rn79u1DTk4Otm7dirKyMmzatEkjrUqlwq1btxAfH4+LFy9i+PDh6Nevn0a7N3bs2AbtYf/+/Rv87qNHjzBw4EB89tlnL11mXeizTXnZOtZ3HuwNRIwxDeHh4TRy5MhG9x84cIBMTEyourq60TT5+fkEgD755BN6++23NfZ5eXnRsmXLCADl5+e/dEwqlYoAUG5ursb2zZs3k4eHR6N5ffXVV+Tp6UkSiYTkcjmNGTNG2FdTU0OfffYZderUicRiMXXo0IHWrFkj7D9//jwNHjyYzMzMyM7OjqKioqi8vLxBnGvWrCEXFxdyd3cnIqLr169TWFgYWVtbk62tLY0YMUKj7KmpqdS7d2+SSqVkbW1N/fv3p4KCAp3qhjHGWtKL2giFQkG9e/cmsVhMzs7OtHjxYo324uHDh/TBBx+QVColZ2dn2rx5MwUGBtKcOXOENG5ubrRlyxbh3wCExc3NTevvFhUVkVgsprlz52rdX1JSQkTPrrcAhHUiot27dxMAunTpUrPKWl9d25eZmfnCtHV5f/rpp+Tg4EAymYw+/PBDqqysFNIEBgbSzJkzac6cOWRvb0+DBg0iIqILFy5QaGgoWVhYkFwup4kTJ9Ldu3eF4yoqKmjSpElkYWFBzs7OtHHjxibruK5+pk2bRnK5nCQSCXXt2pX+/e9/C/X1/LJixQqteRQWFtKIESPIwsKCZDIZhYWFkVqtFvavWLGCevToQbt27SI3NzeysrKicePG0cOHD4U08fHx5OvrK7SzQUFBVFFRoeMZYK0Bj1wx1kzOzs54+vQpDhw4ACJqMu2IESNQUlKCX3/9FQDw66+/oqSkBMOHD3/lOLy8vNCrVy/s3r1bY/vu3bvxwQcfaD3m7NmziI6OxqpVq6BSqZCYmIh3331X2B8TE4P169dj2bJlyMnJwZ49e+Dk5ATg2ZPJkJAQ2NraIiMjA/Hx8fjll18wa9Ysjd9ISUmBSqVCcnIyEhISUF1djZCQEMhkMqSlpSE9PR2WlpYIDQ1FVVUVnj59ilGjRiEwMBDnz5/HyZMnMW3aNIhEoleuI8YYa0k3b97Ee++9h969eyMrKwvffPMNvv32W6xZs0ZIM2/ePKSnp+Pw4cNITk5GWloazp0712ieGRkZAIC4uDgUFxcL6/XFx8ejqqoKixYt0rpf22ttdczNzQG0/NsGKSkpyM3NhUKhwA8//ID9+/dj5cqVGml27twJsViM9PR0bN26FaWlpRgyZAj8/Pxw9uxZJCYm4vbt2xg7dqxwzMKFC3H8+HEcOnQISUlJUCgUTdZxbW0thg0bhvT0dHz//ffIycnB+vXrYWxsjP79++Pzzz+HlZWVMHq3YMECrXmMHDkSDx48wPHjx5GcnIxr165h3LhxGuny8vJw8OBBJCQkICEhAcePH8f69esBPPtsYPz48Zg6dapQL6NHj37hvQZrZQzdu2OstQkPDydjY2OysLDQWNauXSuk+fjjj8nExITs7OwoNDSUNmzYoPF06vmnd3PnzqUpU6YQEdGUKVPo73//O2VmZr7yyBUR0ZYtW6hTp07CemOjWXX27dtHVlZWGk/J6jx8+JAkEglt375d67H//Oc/ydbWVuMJ2n/+8x8yMjISyh4eHk5OTk4aTx6/++478vb2ptraWmFbZWUlmZub09GjR+n+/fsEgBQKRdOVwBhjrUBTozkff/xxg+vdV199RZaWllRTU0MPHz4kU1NTio+PF/aXlpaSVCptclQFAB04cKDJuGbMmEFWVlYvjL/+yNWtW7eof//+5OrqqnHtflFZtWnuyJWdnR09evRI2PbNN98IdUX0bOTKz89P47jVq1fT0KFDNbYVFRURAFKpVFReXk5isZh++uknYf/9+/fJ3Ny80To+evQoGRkZkUql0hprXFwcWVtbN9j+fB5JSUlkbGxM169fF/ZfvHiRANCZM2eI6NnIlVQq1WiDFy5cSAEBAURE9NtvvxEAfnOjjeORK8a0GDx4MJRKpcYyffp0Yf/atWuhVquxdetWdO3aFVu3bkXnzp1x4cKFBnlNnToV8fHxUKvViI+Px9SpUxuk6dq1KywtLWFpaYlhw4bpHOf777+PgoICnDp1CsCzUauePXuic+fOWtP/+c9/hpubGzw8PDBp0iTs3r0bjx8/BgDk5uaisrISQUFBWo/Nzc1Fjx49YGFhIWwbMGAAamtroVKphG3dunWDWCwW1rOysnD16lXIZDKhjHZ2dnjy5Any8vJgZ2eHiIgIhISEYPjw4fjiiy9QXFyscx0wxlhrkZubi379+mmMvA8YMAAVFRW4ceMGrl27hurqavTp00fYb21tDW9v71f+bSJq1oh/+/btYWFhIXwvvG/fPo1rtza7d+8WruOWlpZIS0t7pZh79Oih8c1Sv379UFFRgaKiImGbv7+/xjFZWVlITU3ViKOuzcvLy0NeXh6qqqoQEBAgHGNnZ9dkHSuVSrRv3x5eXl4vXZbc3Fx06NABHTp0ELZ16dIFNjY2yM3NFba5u7tDJpMJ6y4uLsJkWD169EBQUBC6deuGsLAwbN++HSUlJS8dEzMME0MHwFhrZGFhAU9PzybT2NvbIywsDGFhYVi3bh38/PywceNG7Ny5UyNdt27d0LlzZ4wfPx4+Pj7w9fVtMJPSkSNHUF1dDeD/r2fowtnZGUOGDMGePXvQt29f7NmzBzNmzGg0vUwmw7lz56BQKJCUlITly5fj008/RUZGRrN+tynPd74AoKKiAv7+/g1eXwSefQwMPHvdJTo6GomJidi7dy8++eQTJCcno2/fvnqJiTHG3nReXl4oKytDcXExXFxcXpg+LS0NVlZWkMvlGjf7TRkxYoRGp0WXyZlelbY2Zfjw4Vonz3BxccHVq1eb/Rv6av90YWpqqrEuEolQW1sLADA2NkZycjJOnDiBpKQkxMbGYunSpTh9+jQ6duzYYjGyV8MjV4zpgVgsRqdOnRrMFlhn6tSpUCgUWketAMDNzQ2enp7w9PRsdmM1YcIE7N27FydPnsS1a9fw/vvvN5nexMQEwcHB2LBhA86fP4+CggIcO3YMb7/9NszNzZGSkqL1OB8fH2RlZWmUMT09HUZGRk0+EezZsyeuXLkCuVwulLFusba2FtL5+fkhJiYGJ06cgK+vL/bs2dOsemCMMUPz8fHByZMnNb6RSU9Ph0wmQ/v27eHh4QFTU1ON76bKyspw+fLlJvM1NTVFTU1Nk2n++te/QiwWY8OGDVr3l5aWaqx37NgRnTp10rljBTx7QPf8NfxVOyVZWVn4/fffhfVTp07B0tJSY/Snvp49e+LixYtwd3dv0KZYWFigU6dOMDU1xenTp4VjSkpKmqzj7t2748aNG42mEYvFL6x/Hx8fFBUVaYy65eTkoLS0FF26dGny2OeJRCIMGDAAK1euRGZmJsRiMQ4cOKDz8czwuHPFmBaVlZVQq9Uay7179wAACQkJmDhxIhISEnD58mWoVCps3LgRR44cwciRI7XmFxUVhbt37+Jvf/tbs+LIycmBUqnEgwcPUFZWJryi+LzRo0ejvLwcM2bMwODBg9GuXbtG80tISMCXX34JpVKJwsJC7Nq1C7W1tfD29oaZmRkWL16MRYsWYdeuXcjLy8OpU6fw7bffAnjWiTMzM0N4eDiys7ORmpqK2bNnY9KkScKkF9pMmDABDg4OGDlyJNLS0pCfnw+FQoHo6GjcuHED+fn5iImJwcmTJ1FYWIikpCRcuXIFPj4+zaorxhhrKc9fj+uWoqIifPTRRygqKsLs2bNx6dIlHDp0CCtWrMC8efNgZGQEmUyG8PBwLFy4EKmpqbh48SIiIyNhZGTU5Ct97u7uSElJgVqtbvQ1sQ4dOmDLli344osvEBkZiePHj6OwsBDp6en48MMPsXr16tdVHXjw4AGUSiVycnIAPJvmXalUQq1WN3lcVVUVIiMjkZOTgyNHjmDFihWYNWsWjIwavz2dOXMmHjx4gPHjxyMjIwN5eXk4evQopkyZgpqaGlhaWiIyMhILFy7EsWPHkJ2djYiIiCbzDAwMxLvvvosxY8YgOTkZ+fn5+Pnnn5GYmAjgWf1XVFQgJSUF9+7dE16nf15wcDC6deuGCRMm4Ny5czhz5gwmT56MwMBA9OrVS5dqxOnTp7Fu3TqcPXsW169fx/79+3H37l1uD9saQ3/0xVhrEx4e3mDaVQDk7e1NRER5eXkUFRVFXl5eZG5uTjY2NtS7d2+Ki4sT8njRR726TmhRfwreuqW+sWPHEgD617/+1WR+aWlpFBgYSLa2tmRubk7du3envXv3CvtrampozZo15ObmRqampvTWW2/RunXrhP26TsVeX3FxMU2ePJkcHBxIIpGQh4cHRUVFUVlZGanVaho1ahS5uLiQWCwmNzc3Wr58ufBBM2OMtSaNtRGRkZFE9HJTsffp04eWLFkipKk/ocXhw4fJ09OTTExMGp2KvU5ycjKFhISQra0tmZmZUefOnWnBggV069YtItI+FXtTZdVlQou4uDitdVI3ZXlTeS9fvpzs7e3J0tKSoqKi6MmTJ0Ka+tOn17l8+TL95S9/IRsbGzI3N6fOnTvT3LlzhYlEysvLaeLEiSSVSsnJyYk2bNjwwqnY79+/T1OmTCF7e3syMzMjX19fSkhIEPZPnz6d7O3t9TIV+/O2bNkinNOcnBwKCQkhR0dHkkgk5OXlRbGxsY3WIWudREQ8vyNjjDHGmCE8evQIrq6u2LRpEyIjIw0dTouJiIhAaWkpDh48aOhQGNMrntCCMcYYY6yFZGZm4tKlS+jTpw/KysqwatUqAGj0tXLGWNvCnSvGGGOMsRa0ceNGqFQqiMVi+Pv7Iy0tDQ4ODoYOizGmB/xaIGOMMcYYY4zpAc8WyBhjjDHGGGN6wJ0rxhhjjDHGGNMD7lwxxhhjjDHGmB5w54oxxhhjjDHG9IA7V4wxxhhjjDGmB9y5YowxxhhjjDE94M4VY4wxxhhjjOkBd64YY4wxxhhjTA+4c8UYY4wxxhhjevA/8e/nZM+EeskAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 1000x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Figure: ancestral alleles of human beneficial adaptation variants\n",
    "ancestral_x = pd.read_csv('./data/ancestral_x_knn10.csv')\n",
    "ancestral_x = ancestral_x.set_index('mutant')\n",
    "\n",
    "# Get predictions\n",
    "features = feat_selected_dms\n",
    "\n",
    "cpt.train(feat_mat, label, all_genes, features)\n",
    "pred_clinvar = cpt.predict(x[features])\n",
    "pred_ancestral = cpt.predict(ancestral_x[features])\n",
    "\n",
    "df_ps = pd.concat([x[['esm_1v']], ancestral_x[['esm_1v']]])\n",
    "df_ps['transfer model'] = logit(1 - pd.concat([pred_clinvar, pred_ancestral]).values)\n",
    "df_ps['variant class'] = pd.concat([y.iloc[:, 0].map({1.0: 'ClinVar pathogenic', 0.0: 'ClinVar benign'}),\n",
    "                                    pd.Series('Ancestral alleles at nearly fixed sites', index = ancestral_x.index)])\n",
    "df_ps.columns = ['ESM-1v scores', 'Logit CPT-1 predictions', 'Variant class']\n",
    "df_ps['Log 100vertebrates mutant frequency'] = pd.concat([x[['100verte_mut_freq']], ancestral_x[['100verte_mut_freq']]])\n",
    "df_ps['Log 30mammals mutant frequency'] = pd.concat([x[['30mammal_mut_freq']], ancestral_x[['30mammal_mut_freq']]])\n",
    "\n",
    "# Plot\n",
    "fig, axes = plt.subplots(1, 2, figsize=(10, 4))\n",
    "sns.histplot(ax = axes[0], data = df_ps.reset_index(), x = \"ESM-1v scores\", \n",
    "             hue = \"Variant class\", stat = \"density\", common_norm = False)\n",
    "sns.histplot(ax = axes[1], data = df_ps.reset_index(), x = \"Logit CPT-1 predictions\", \n",
    "             hue = \"Variant class\", stat = \"density\", common_norm = False)\n",
    "sns.move_legend(axes[0], \"upper left\", bbox_to_anchor=(0.65, 1.35))\n",
    "axes[1].legend([],[], frameon=False)\n",
    "plt.show()\n",
    "\n",
    "fig, axes = plt.subplots(1, 2, figsize=(10, 4))\n",
    "sns.histplot(ax = axes[0], data = df_ps.reset_index(), x = \"Log 100vertebrates mutant frequency\", \n",
    "             hue = \"Variant class\", stat = \"density\", common_norm = False, bins = 50)\n",
    "sns.histplot(ax = axes[1], data = df_ps.reset_index(), x = \"Log 30mammals mutant frequency\", \n",
    "             hue = \"Variant class\", stat = \"density\", common_norm = False, bins = 50)\n",
    "sns.move_legend(axes[0], \"upper left\", bbox_to_anchor=(0.65, 1.35))\n",
    "axes[1].legend([],[], frameon=False)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "25074c46-b9b4-4f4e-81a5-0874dc3707a3",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.10"
  },
  "vscode": {
   "interpreter": {
    "hash": "949777d72b0d2535278d3dc13498b2535136f6dfe0678499012e853ee9abcab1"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}