diff --git "a/notebooks/plot_experimental_results.ipynb" "b/notebooks/plot_experimental_results.ipynb"
--- "a/notebooks/plot_experimental_results.ipynb"
+++ "b/notebooks/plot_experimental_results.ipynb"
@@ -18,7 +18,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 127,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -33,7 +33,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 130,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -42,233 +42,191 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 128,
+   "execution_count": 22,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "cv_train: (15, 52)\n"
+      "cv_train: (15, 52)\n",
+      "test: (9, 34)\n",
+      "hparam: (3, 7)\n",
+      "majority_vote: (6, 28)\n",
+      "ablation: (84, 22)\n",
+      "xgboost_cv_train: (15, 24)\n",
+      "xgboost_test: (9, 20)\n",
+      "xgboost_hparam: (3, 7)\n",
+      "xgboost_majority_vote: (3, 19)\n",
+      "cellsonehot_cv_train: (15, 52)\n",
+      "cellsonehot_test: (9, 34)\n",
+      "cellsonehot_hparam: (3, 7)\n",
+      "cellsonehot_majority_vote: (6, 28)\n",
+      "cellsonehot_ablation: (84, 22)\n",
+      "aminoacidcnt_cv_train: (15, 52)\n",
+      "aminoacidcnt_test: (9, 34)\n",
+      "aminoacidcnt_hparam: (3, 7)\n",
+      "aminoacidcnt_majority_vote: (6, 28)\n",
+      "aminoacidcnt_ablation: (84, 22)\n"
      ]
-    },
+    }
+   ],
+   "source": [
+    "active_col = 'Active (Dmax 0.6, pDC50 6.0)'\n",
+    "test_split = 0.1\n",
+    "n_models_for_test = 3\n",
+    "cv_n_folds = 5\n",
+    "\n",
+    "active_name = active_col.replace(' ', '_').replace('(', '').replace(')', '').replace(',', '')\n",
+    "report_base_name = f'{active_name}_test_split_{test_split}'\n",
+    "\n",
+    "# TODO: Maybe a function to get the experiment dataframes would help...\n",
+    "# def get_experiment_df(experiment_name, report, splits=['random', 'uniprot', 'tanimoto']):\n",
+    "#     for split in splits:\n",
+    "#         df = []\n",
+    "#         for report_name in ['cv', 'ablation', 'hparam', 'majority_vote']:\n",
+    "#             filename = f'reports/{report_name}_report_{report_base_name}_{split}.csv'\n",
+    "#             df.append(pd.read_csv(filename))\n",
+    "#         report[experiment_name] = pd.concat(df)\n",
+    "#     return report\n",
+    "\n",
+    "reports = {}\n",
+    "for experiment in ['', 'xgboost_', 'cellsonehot_', 'aminoacidcnt_']:\n",
+    "    reports[f'{experiment}cv_train'] = pd.concat([\n",
+    "        pd.read_csv(f'reports/{experiment}cv_report_{report_base_name}_standard.csv'),\n",
+    "        pd.read_csv(f'reports/{experiment}cv_report_{report_base_name}_target.csv'),\n",
+    "        pd.read_csv(f'reports/{experiment}cv_report_{report_base_name}_similarity.csv'),\n",
+    "    ])\n",
+    "    reports[f'{experiment}test'] = pd.concat([\n",
+    "        pd.read_csv(f'reports/{experiment}test_report_{report_base_name}_standard.csv'),\n",
+    "        pd.read_csv(f'reports/{experiment}test_report_{report_base_name}_target.csv'),\n",
+    "        pd.read_csv(f'reports/{experiment}test_report_{report_base_name}_similarity.csv'),\n",
+    "    ])\n",
+    "    reports[f'{experiment}hparam'] = pd.concat([\n",
+    "        pd.read_csv(f'reports/{experiment}hparam_report_{report_base_name}_standard.csv'),\n",
+    "        pd.read_csv(f'reports/{experiment}hparam_report_{report_base_name}_target.csv'),\n",
+    "        pd.read_csv(f'reports/{experiment}hparam_report_{report_base_name}_similarity.csv'),\n",
+    "    ])\n",
+    "    reports[f'{experiment}majority_vote'] = pd.concat([\n",
+    "        pd.read_csv(f'reports/{experiment}majority_vote_report_{report_base_name}_standard.csv'),\n",
+    "        pd.read_csv(f'reports/{experiment}majority_vote_report_{report_base_name}_target.csv'),\n",
+    "        pd.read_csv(f'reports/{experiment}majority_vote_report_{report_base_name}_similarity.csv'),\n",
+    "    ])\n",
+    "    if experiment != 'xgboost_':\n",
+    "        reports[f'{experiment}ablation'] = pd.concat([\n",
+    "            pd.read_csv(f'reports/{experiment}ablation_report_{report_base_name}_standard.csv'),\n",
+    "            pd.read_csv(f'reports/{experiment}ablation_report_{report_base_name}_target.csv'),\n",
+    "            pd.read_csv(f'reports/{experiment}ablation_report_{report_base_name}_similarity.csv'),\n",
+    "        ])\n",
+    "\n",
+    "for k, report in reports.items():\n",
+    "    print(f'{k}: {report.shape}')\n",
+    "    # display(report.head())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<!-- # # Load the data\n",
+    "# reports = {\n",
+    "#     'cv_train': pd.concat([\n",
+    "#         pd.read_csv(f'reports/cv_report_{report_base_name}_random.csv'),\n",
+    "#         pd.read_csv(f'reports/cv_report_{report_base_name}_uniprot.csv'),\n",
+    "#         pd.read_csv(f'reports/cv_report_{report_base_name}_tanimoto.csv'),\n",
+    "#     ]),\n",
+    "#     'test': pd.concat([\n",
+    "#         pd.read_csv(f'reports/test_report_{report_base_name}_random.csv'),\n",
+    "#         pd.read_csv(f'reports/test_report_{report_base_name}_uniprot.csv'),\n",
+    "#         pd.read_csv(f'reports/test_report_{report_base_name}_tanimoto.csv'),\n",
+    "#     ]),\n",
+    "#     'ablation': pd.concat([\n",
+    "#         pd.read_csv(f'reports/ablation_zero_vectors_report_{report_base_name}_random.csv'),\n",
+    "#         pd.read_csv(f'reports/ablation_zero_vectors_report_{report_base_name}_uniprot.csv'),\n",
+    "#         pd.read_csv(f'reports/ablation_zero_vectors_report_{report_base_name}_tanimoto.csv'),\n",
+    "#     ]),\n",
+    "#     'hparam': pd.concat([\n",
+    "#         pd.read_csv(f'reports/hparam_report_{report_base_name}_random.csv'),\n",
+    "#         pd.read_csv(f'reports/hparam_report_{report_base_name}_uniprot.csv'),\n",
+    "#         pd.read_csv(f'reports/hparam_report_{report_base_name}_tanimoto.csv'),\n",
+    "#     ]),\n",
+    "#     'majority_vote': pd.concat([\n",
+    "#         pd.read_csv(f'reports/majority_vote_report_{report_base_name}_random.csv'),\n",
+    "#         pd.read_csv(f'reports/majority_vote_report_{report_base_name}_uniprot.csv'),\n",
+    "#         pd.read_csv(f'reports/majority_vote_report_{report_base_name}_tanimoto.csv'),\n",
+    "#     ]),\n",
+    "#     'xgboost_cv_train': pd.concat([\n",
+    "#         pd.read_csv(f'reports/xgboost_cv_report_{report_base_name}_random.csv'),\n",
+    "#         pd.read_csv(f'reports/xgboost_cv_report_{report_base_name}_uniprot.csv'),\n",
+    "#         pd.read_csv(f'reports/xgboost_cv_report_{report_base_name}_tanimoto.csv'),\n",
+    "#     ]),\n",
+    "#     'xgboost_test': pd.concat([\n",
+    "#         pd.read_csv(f'reports/xgboost_test_report_{report_base_name}_random.csv'),\n",
+    "#         pd.read_csv(f'reports/xgboost_test_report_{report_base_name}_uniprot.csv'),\n",
+    "#         pd.read_csv(f'reports/xgboost_test_report_{report_base_name}_tanimoto.csv'),\n",
+    "#     ]),\n",
+    "#     'xgboost_hparam': pd.concat([\n",
+    "#         pd.read_csv(f'reports/xgboost_hparam_report_{report_base_name}_random.csv'),\n",
+    "#         pd.read_csv(f'reports/xgboost_hparam_report_{report_base_name}_uniprot.csv'),\n",
+    "#         pd.read_csv(f'reports/xgboost_hparam_report_{report_base_name}_tanimoto.csv'),\n",
+    "#     ]),\n",
+    "#     'xgboost_majority_vote': pd.concat([\n",
+    "#         pd.read_csv(f'reports/xgboost_majority_vote_report_{report_base_name}_random.csv'),\n",
+    "#         pd.read_csv(f'reports/xgboost_majority_vote_report_{report_base_name}_uniprot.csv'),\n",
+    "#         pd.read_csv(f'reports/xgboost_majority_vote_report_{report_base_name}_tanimoto.csv'),\n",
+    "#     ]),\n",
+    "# } -->\n",
+    "(Legacy code here as comment)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Print CV Folds Parameters\n",
+    "\n",
+    "Print the dataset characteristics for each fold set per experiment."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
     {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>model_type</th>\n",
-       "      <th>fold</th>\n",
-       "      <th>train_len</th>\n",
-       "      <th>val_len</th>\n",
-       "      <th>train_perc</th>\n",
-       "      <th>val_perc</th>\n",
-       "      <th>train_active_perc</th>\n",
-       "      <th>train_inactive_perc</th>\n",
-       "      <th>train_avg_tanimoto_dist</th>\n",
-       "      <th>val_active_perc</th>\n",
-       "      <th>...</th>\n",
-       "      <th>val_roc_auc</th>\n",
-       "      <th>test_loss</th>\n",
-       "      <th>test_acc</th>\n",
-       "      <th>test_f1_score</th>\n",
-       "      <th>test_precision</th>\n",
-       "      <th>test_recall</th>\n",
-       "      <th>test_roc_auc</th>\n",
-       "      <th>split_type</th>\n",
-       "      <th>train_unique_groups</th>\n",
-       "      <th>val_unique_groups</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Pytorch</td>\n",
-       "      <td>0</td>\n",
-       "      <td>616</td>\n",
-       "      <td>155</td>\n",
-       "      <td>0.798962</td>\n",
-       "      <td>0.201038</td>\n",
-       "      <td>0.514610</td>\n",
-       "      <td>0.485390</td>\n",
-       "      <td>0.376475</td>\n",
-       "      <td>0.516129</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.904333</td>\n",
-       "      <td>0.661766</td>\n",
-       "      <td>0.802326</td>\n",
-       "      <td>0.817204</td>\n",
-       "      <td>0.716981</td>\n",
-       "      <td>0.95</td>\n",
-       "      <td>0.860326</td>\n",
-       "      <td>random</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Pytorch</td>\n",
-       "      <td>1</td>\n",
-       "      <td>617</td>\n",
-       "      <td>154</td>\n",
-       "      <td>0.800259</td>\n",
-       "      <td>0.199741</td>\n",
-       "      <td>0.513776</td>\n",
-       "      <td>0.486224</td>\n",
-       "      <td>0.377372</td>\n",
-       "      <td>0.519481</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.924493</td>\n",
-       "      <td>0.658051</td>\n",
-       "      <td>0.755814</td>\n",
-       "      <td>0.764045</td>\n",
-       "      <td>0.693878</td>\n",
-       "      <td>0.85</td>\n",
-       "      <td>0.858152</td>\n",
-       "      <td>random</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>Pytorch</td>\n",
-       "      <td>2</td>\n",
-       "      <td>617</td>\n",
-       "      <td>154</td>\n",
-       "      <td>0.800259</td>\n",
-       "      <td>0.199741</td>\n",
-       "      <td>0.515397</td>\n",
-       "      <td>0.484603</td>\n",
-       "      <td>0.376572</td>\n",
-       "      <td>0.512987</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.931139</td>\n",
-       "      <td>0.611611</td>\n",
-       "      <td>0.779070</td>\n",
-       "      <td>0.781609</td>\n",
-       "      <td>0.723404</td>\n",
-       "      <td>0.85</td>\n",
-       "      <td>0.863587</td>\n",
-       "      <td>random</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>Pytorch</td>\n",
-       "      <td>3</td>\n",
-       "      <td>617</td>\n",
-       "      <td>154</td>\n",
-       "      <td>0.800259</td>\n",
-       "      <td>0.199741</td>\n",
-       "      <td>0.515397</td>\n",
-       "      <td>0.484603</td>\n",
-       "      <td>0.376068</td>\n",
-       "      <td>0.512987</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.924557</td>\n",
-       "      <td>0.693243</td>\n",
-       "      <td>0.732558</td>\n",
-       "      <td>0.747253</td>\n",
-       "      <td>0.666667</td>\n",
-       "      <td>0.85</td>\n",
-       "      <td>0.847826</td>\n",
-       "      <td>random</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>Pytorch</td>\n",
-       "      <td>4</td>\n",
-       "      <td>617</td>\n",
-       "      <td>154</td>\n",
-       "      <td>0.800259</td>\n",
-       "      <td>0.199741</td>\n",
-       "      <td>0.515397</td>\n",
-       "      <td>0.484603</td>\n",
-       "      <td>0.377543</td>\n",
-       "      <td>0.512987</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.927257</td>\n",
-       "      <td>0.561546</td>\n",
-       "      <td>0.779070</td>\n",
-       "      <td>0.781609</td>\n",
-       "      <td>0.723404</td>\n",
-       "      <td>0.85</td>\n",
-       "      <td>0.878804</td>\n",
-       "      <td>random</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>5 rows × 52 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "  model_type  fold  train_len  val_len  train_perc  val_perc  \\\n",
-       "0    Pytorch     0        616      155    0.798962  0.201038   \n",
-       "1    Pytorch     1        617      154    0.800259  0.199741   \n",
-       "2    Pytorch     2        617      154    0.800259  0.199741   \n",
-       "3    Pytorch     3        617      154    0.800259  0.199741   \n",
-       "4    Pytorch     4        617      154    0.800259  0.199741   \n",
-       "\n",
-       "   train_active_perc  train_inactive_perc  train_avg_tanimoto_dist  \\\n",
-       "0           0.514610             0.485390                 0.376475   \n",
-       "1           0.513776             0.486224                 0.377372   \n",
-       "2           0.515397             0.484603                 0.376572   \n",
-       "3           0.515397             0.484603                 0.376068   \n",
-       "4           0.515397             0.484603                 0.377543   \n",
-       "\n",
-       "   val_active_perc  ...  val_roc_auc  test_loss  test_acc  test_f1_score  \\\n",
-       "0         0.516129  ...     0.904333   0.661766  0.802326       0.817204   \n",
-       "1         0.519481  ...     0.924493   0.658051  0.755814       0.764045   \n",
-       "2         0.512987  ...     0.931139   0.611611  0.779070       0.781609   \n",
-       "3         0.512987  ...     0.924557   0.693243  0.732558       0.747253   \n",
-       "4         0.512987  ...     0.927257   0.561546  0.779070       0.781609   \n",
-       "\n",
-       "   test_precision  test_recall  test_roc_auc  split_type  train_unique_groups  \\\n",
-       "0        0.716981         0.95      0.860326      random                  NaN   \n",
-       "1        0.693878         0.85      0.858152      random                  NaN   \n",
-       "2        0.723404         0.85      0.863587      random                  NaN   \n",
-       "3        0.666667         0.85      0.847826      random                  NaN   \n",
-       "4        0.723404         0.85      0.878804      random                  NaN   \n",
-       "\n",
-       "   val_unique_groups  \n",
-       "0                NaN  \n",
-       "1                NaN  \n",
-       "2                NaN  \n",
-       "3                NaN  \n",
-       "4                NaN  \n",
-       "\n",
-       "[5 rows x 52 columns]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\\begin{tabular}{rlrrrllllll}\n",
+      "\\toprule\n",
+      " \\textbf{Fold} & \\textbf{Study split} &  \\textbf{Train size} &  \\textbf{Val size} &  \\textbf{Test size} & \\textbf{Train active \\%} & \\textbf{Val active \\%} & \\textbf{Test active \\%} & \\textbf{Leaking Uniprot \\%} & \\textbf{Leaking SMILES \\%} & \\textbf{Avg Tanimoto distance} \\\\\n",
+      "\\midrule\n",
+      "             0 &             standard &                  560 &                140 &                  78 &                   49.6\\% &                 50.0\\% &                  55.1\\% &                      79.1\\% &                      8.8\\% &                          0.379 \\\\\n",
+      "             1 &             standard &                  560 &                140 &                  78 &                   49.6\\% &                 50.0\\% &                  55.1\\% &                      80.0\\% &                      8.4\\% &                          0.379 \\\\\n",
+      "             2 &             standard &                  560 &                140 &                  78 &                   49.6\\% &                 50.0\\% &                  55.1\\% &                      80.2\\% &                      9.3\\% &                          0.379 \\\\\n",
+      "             3 &             standard &                  560 &                140 &                  78 &                   49.8\\% &                 49.3\\% &                  55.1\\% &                      79.3\\% &                      8.6\\% &                          0.379 \\\\\n",
+      "             4 &             standard &                  560 &                140 &                  78 &                   49.8\\% &                 49.3\\% &                  55.1\\% &                      79.3\\% &                      7.1\\% &                          0.379 \\\\\n",
+      "             0 &               target &                  594 &                108 &                  76 &                   51.3\\% &                 41.7\\% &                  53.9\\% &                       0.0\\% &                      1.0\\% &                          0.390 \\\\\n",
+      "             1 &               target &                  559 &                143 &                  76 &                   47.2\\% &                 60.1\\% &                  53.9\\% &                       0.0\\% &                      0.9\\% &                          0.390 \\\\\n",
+      "             2 &               target &                  556 &                146 &                  76 &                   46.8\\% &                 61.6\\% &                  53.9\\% &                       0.0\\% &                      1.4\\% &                          0.390 \\\\\n",
+      "             3 &               target &                  592 &                110 &                  76 &                   50.3\\% &                 47.3\\% &                  53.9\\% &                       0.0\\% &                      1.2\\% &                          0.390 \\\\\n",
+      "             4 &               target &                  507 &                195 &                  76 &                   53.8\\% &                 39.5\\% &                  53.9\\% &                       0.0\\% &                      1.2\\% &                          0.390 \\\\\n",
+      "             0 &           similarity &                  601 &                100 &                  77 &                   48.8\\% &                 57.0\\% &                  53.2\\% &                      51.1\\% &                      0.0\\% &                          0.407 \\\\\n",
+      "             1 &           similarity &                  568 &                133 &                  77 &                   50.9\\% &                 45.9\\% &                  53.2\\% &                      53.5\\% &                      0.0\\% &                          0.407 \\\\\n",
+      "             2 &           similarity &                  572 &                129 &                  77 &                   49.5\\% &                 51.9\\% &                  53.2\\% &                      51.0\\% &                      0.0\\% &                          0.407 \\\\\n",
+      "             3 &           similarity &                  563 &                138 &                  77 &                   50.4\\% &                 47.8\\% &                  53.2\\% &                      54.0\\% &                      0.0\\% &                          0.407 \\\\\n",
+      "             4 &           similarity &                  500 &                201 &                  77 &                   50.2\\% &                 49.3\\% &                  53.2\\% &                      50.6\\% &                      0.0\\% &                          0.407 \\\\\n",
+      "\\bottomrule\n",
+      "\\end{tabular}\n",
+      "\n"
+     ]
     },
     {
-     "name": "stdout",
+     "name": "stderr",
      "output_type": "stream",
      "text": [
-      "test: (9, 34)\n"
+      "/tmp/ipykernel_1899217/2599982738.py:35: FutureWarning: In future versions `DataFrame.to_latex` is expected to utilise the base implementation of `Styler.to_latex` for formatting and rendering. The arguments signature may therefore change. It is recommended instead to use `DataFrame.style.to_latex` which also contains additional functionality.\n",
+      "  print(tmp.to_latex(index=False, escape=False))\n"
      ]
     },
     {
@@ -292,3574 +250,470 @@
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
-       "      <th>train_loss</th>\n",
-       "      <th>train_loss_step</th>\n",
-       "      <th>train_loss_epoch</th>\n",
-       "      <th>train_acc</th>\n",
-       "      <th>train_acc_epoch</th>\n",
-       "      <th>train_f1_score</th>\n",
-       "      <th>train_f1_score_epoch</th>\n",
-       "      <th>train_precision</th>\n",
-       "      <th>train_precision_epoch</th>\n",
-       "      <th>train_recall</th>\n",
-       "      <th>...</th>\n",
-       "      <th>train_avg_tanimoto_dist</th>\n",
-       "      <th>test_len</th>\n",
+       "      <th>train_active_perc</th>\n",
+       "      <th>val_active_perc</th>\n",
        "      <th>test_active_perc</th>\n",
-       "      <th>test_inactive_perc</th>\n",
-       "      <th>test_avg_tanimoto_dist</th>\n",
-       "      <th>num_leaking_uniprot_train_test</th>\n",
-       "      <th>num_leaking_smiles_train_test</th>\n",
        "      <th>perc_leaking_uniprot_train_test</th>\n",
        "      <th>perc_leaking_smiles_train_test</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
        "      <th>split_type</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>0.190532</td>\n",
-       "      <td>0.307893</td>\n",
-       "      <td>0.190532</td>\n",
-       "      <td>0.939547</td>\n",
-       "      <td>0.939547</td>\n",
-       "      <td>0.940150</td>\n",
-       "      <td>0.940150</td>\n",
-       "      <td>0.930864</td>\n",
-       "      <td>0.930864</td>\n",
-       "      <td>0.949622</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.376806</td>\n",
-       "      <td>86</td>\n",
-       "      <td>0.465116</td>\n",
-       "      <td>0.534884</td>\n",
-       "      <td>0.381147</td>\n",
-       "      <td>34</td>\n",
-       "      <td>44</td>\n",
-       "      <td>0.832685</td>\n",
-       "      <td>0.102464</td>\n",
-       "      <td>random</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>0.188614</td>\n",
-       "      <td>0.163636</td>\n",
-       "      <td>0.188614</td>\n",
-       "      <td>0.940806</td>\n",
-       "      <td>0.940806</td>\n",
-       "      <td>0.941760</td>\n",
-       "      <td>0.941760</td>\n",
-       "      <td>0.926829</td>\n",
-       "      <td>0.926829</td>\n",
-       "      <td>0.957179</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.376806</td>\n",
-       "      <td>86</td>\n",
-       "      <td>0.465116</td>\n",
-       "      <td>0.534884</td>\n",
-       "      <td>0.381147</td>\n",
-       "      <td>34</td>\n",
-       "      <td>44</td>\n",
-       "      <td>0.832685</td>\n",
-       "      <td>0.102464</td>\n",
-       "      <td>random</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>0.189793</td>\n",
-       "      <td>0.201865</td>\n",
-       "      <td>0.189793</td>\n",
-       "      <td>0.942065</td>\n",
-       "      <td>0.942065</td>\n",
-       "      <td>0.942786</td>\n",
-       "      <td>0.942786</td>\n",
-       "      <td>0.931204</td>\n",
-       "      <td>0.931204</td>\n",
-       "      <td>0.954660</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.376806</td>\n",
-       "      <td>86</td>\n",
-       "      <td>0.465116</td>\n",
-       "      <td>0.534884</td>\n",
-       "      <td>0.381147</td>\n",
-       "      <td>34</td>\n",
-       "      <td>44</td>\n",
-       "      <td>0.832685</td>\n",
-       "      <td>0.102464</td>\n",
-       "      <td>random</td>\n",
+       "      <th>similarity</th>\n",
+       "      <td>49.950387</td>\n",
+       "      <td>50.376493</td>\n",
+       "      <td>53.246753</td>\n",
+       "      <td>52.049611</td>\n",
+       "      <td>0.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>0.543100</td>\n",
-       "      <td>0.583436</td>\n",
-       "      <td>0.543100</td>\n",
-       "      <td>0.809463</td>\n",
-       "      <td>0.809463</td>\n",
-       "      <td>0.820265</td>\n",
-       "      <td>0.820265</td>\n",
-       "      <td>0.776256</td>\n",
-       "      <td>0.776256</td>\n",
-       "      <td>0.869565</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.375305</td>\n",
-       "      <td>85</td>\n",
-       "      <td>0.541176</td>\n",
-       "      <td>0.458824</td>\n",
-       "      <td>0.394830</td>\n",
-       "      <td>0</td>\n",
-       "      <td>6</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.011658</td>\n",
-       "      <td>uniprot</td>\n",
+       "      <th>standard</th>\n",
+       "      <td>49.714286</td>\n",
+       "      <td>49.714286</td>\n",
+       "      <td>55.128205</td>\n",
+       "      <td>79.571429</td>\n",
+       "      <td>8.428571</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>0.649270</td>\n",
-       "      <td>0.631336</td>\n",
-       "      <td>0.649270</td>\n",
-       "      <td>0.767263</td>\n",
-       "      <td>0.767263</td>\n",
-       "      <td>0.795964</td>\n",
-       "      <td>0.795964</td>\n",
-       "      <td>0.708583</td>\n",
-       "      <td>0.708583</td>\n",
-       "      <td>0.907928</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.375305</td>\n",
-       "      <td>85</td>\n",
-       "      <td>0.541176</td>\n",
-       "      <td>0.458824</td>\n",
-       "      <td>0.394830</td>\n",
-       "      <td>0</td>\n",
-       "      <td>6</td>\n",
+       "      <th>target</th>\n",
+       "      <td>49.904115</td>\n",
+       "      <td>50.042054</td>\n",
+       "      <td>53.947368</td>\n",
        "      <td>0.000000</td>\n",
-       "      <td>0.011658</td>\n",
-       "      <td>uniprot</td>\n",
+       "      <td>1.141854</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
-       "<p>5 rows × 34 columns</p>\n",
        "</div>"
       ],
       "text/plain": [
-       "   train_loss  train_loss_step  train_loss_epoch  train_acc  train_acc_epoch  \\\n",
-       "0    0.190532         0.307893          0.190532   0.939547         0.939547   \n",
-       "1    0.188614         0.163636          0.188614   0.940806         0.940806   \n",
-       "2    0.189793         0.201865          0.189793   0.942065         0.942065   \n",
-       "0    0.543100         0.583436          0.543100   0.809463         0.809463   \n",
-       "1    0.649270         0.631336          0.649270   0.767263         0.767263   \n",
-       "\n",
-       "   train_f1_score  train_f1_score_epoch  train_precision  \\\n",
-       "0        0.940150              0.940150         0.930864   \n",
-       "1        0.941760              0.941760         0.926829   \n",
-       "2        0.942786              0.942786         0.931204   \n",
-       "0        0.820265              0.820265         0.776256   \n",
-       "1        0.795964              0.795964         0.708583   \n",
-       "\n",
-       "   train_precision_epoch  train_recall  ...  train_avg_tanimoto_dist  \\\n",
-       "0               0.930864      0.949622  ...                 0.376806   \n",
-       "1               0.926829      0.957179  ...                 0.376806   \n",
-       "2               0.931204      0.954660  ...                 0.376806   \n",
-       "0               0.776256      0.869565  ...                 0.375305   \n",
-       "1               0.708583      0.907928  ...                 0.375305   \n",
-       "\n",
-       "   test_len  test_active_perc  test_inactive_perc  test_avg_tanimoto_dist  \\\n",
-       "0        86          0.465116            0.534884                0.381147   \n",
-       "1        86          0.465116            0.534884                0.381147   \n",
-       "2        86          0.465116            0.534884                0.381147   \n",
-       "0        85          0.541176            0.458824                0.394830   \n",
-       "1        85          0.541176            0.458824                0.394830   \n",
-       "\n",
-       "   num_leaking_uniprot_train_test  num_leaking_smiles_train_test  \\\n",
-       "0                              34                             44   \n",
-       "1                              34                             44   \n",
-       "2                              34                             44   \n",
-       "0                               0                              6   \n",
-       "1                               0                              6   \n",
-       "\n",
-       "   perc_leaking_uniprot_train_test  perc_leaking_smiles_train_test split_type  \n",
-       "0                         0.832685                        0.102464     random  \n",
-       "1                         0.832685                        0.102464     random  \n",
-       "2                         0.832685                        0.102464     random  \n",
-       "0                         0.000000                        0.011658    uniprot  \n",
-       "1                         0.000000                        0.011658    uniprot  \n",
+       "            train_active_perc  val_active_perc  test_active_perc  \\\n",
+       "split_type                                                         \n",
+       "similarity          49.950387        50.376493         53.246753   \n",
+       "standard            49.714286        49.714286         55.128205   \n",
+       "target              49.904115        50.042054         53.947368   \n",
        "\n",
-       "[5 rows x 34 columns]"
+       "            perc_leaking_uniprot_train_test  perc_leaking_smiles_train_test  \n",
+       "split_type                                                                   \n",
+       "similarity                        52.049611                        0.000000  \n",
+       "standard                          79.571429                        8.428571  \n",
+       "target                             0.000000                        1.141854  "
       ]
      },
+     "execution_count": 4,
      "metadata": {},
-     "output_type": "display_data"
-    },
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "cols_to_show = {\n",
+    "    'fold': 'Fold',\n",
+    "    'split_type': 'Study split',\n",
+    "    'train_len': 'Train size',\n",
+    "    'val_len': 'Val size',\n",
+    "    'test_len': 'Test size',\n",
+    "    'train_active_perc': 'Train active %',\n",
+    "    'val_active_perc': 'Val active %',\n",
+    "    'test_active_perc': 'Test active %',\n",
+    "    # 'train_unique_groups': '',\n",
+    "    # 'val_unique_groups': '',\n",
+    "    'perc_leaking_uniprot_train_test': 'Leaking Uniprot %',\n",
+    "    'perc_leaking_smiles_train_test': 'Leaking SMILES %',\n",
+    "    'test_avg_tanimoto_dist': 'Avg Tanimoto distance',\n",
+    "}\n",
+    "# print(reports['cv_train'][cols_to_show].to_markdown(index=False))\n",
+    "# Print a subset of columns (that contain the string \"perc_\") as percentages in format: .1%\n",
+    "tmp = reports['cv_train'][list(cols_to_show.keys())].copy()\n",
+    "for col in tmp.columns:\n",
+    "    if 'perc' in col:\n",
+    "        tmp[col] = tmp[col].apply(lambda x: f'{x*100:.1f}\\\\%')\n",
+    "    if 'dist' in col:\n",
+    "        tmp[col] = tmp[col].apply(lambda x: f'{x:.3f}')\n",
+    "# Rename columns\n",
+    "tmp.rename(columns=cols_to_show, inplace=True)\n",
+    "# Rename studies\n",
+    "tmp['Study split'] = tmp['Study split'].replace({\n",
+    "    'random': 'Standard',\n",
+    "    'uniprot': 'Target',\n",
+    "    'tanimoto': 'Similarity',\n",
+    "})\n",
+    "tmp = tmp[list(cols_to_show.values())]\n",
+    "tmp.columns = [f\"\\\\textbf{{{col}}}\".replace('%', '\\\\%') for col in tmp.columns]\n",
+    "# Print to LaTeX\n",
+    "print(tmp.to_latex(index=False, escape=False))\n",
+    "\n",
+    "# Print the average active % for each study split (for train val and test sets)\n",
+    "tmp = reports['cv_train'].groupby(['split_type'])[['train_active_perc', 'val_active_perc', 'test_active_perc', 'perc_leaking_uniprot_train_test', 'perc_leaking_smiles_train_test']].mean()\n",
+    "tmp = tmp * 100\n",
+    "tmp"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Plot (Raw) Datasets Information"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "ablation: (84, 23)\n"
+      "PROTAC-DB: (5388, 89)\n",
+      "PROTAC-Pedia: (1203, 43)\n"
      ]
-    },
+    }
+   ],
+   "source": [
+    "data_dir = 'data'\n",
+    "\n",
+    "protac_db_df = pd.read_csv(os.path.join(data_dir, 'PROTAC-DB.csv'))\n",
+    "protac_pedia_df = pd.read_csv(os.path.join(data_dir, 'PROTAC-Pedia.csv'))\n",
+    "print(f'PROTAC-DB: {protac_db_df.shape}')\n",
+    "print(f'PROTAC-Pedia: {protac_pedia_df.shape}')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "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>test_loss</th>\n",
-       "      <th>test_acc</th>\n",
-       "      <th>test_f1_score</th>\n",
-       "      <th>test_precision</th>\n",
-       "      <th>test_recall</th>\n",
-       "      <th>test_roc_auc</th>\n",
-       "      <th>train_len</th>\n",
-       "      <th>train_active_perc</th>\n",
-       "      <th>train_inactive_perc</th>\n",
-       "      <th>train_avg_tanimoto_dist</th>\n",
-       "      <th>...</th>\n",
-       "      <th>test_avg_tanimoto_dist</th>\n",
-       "      <th>num_leaking_uniprot_train_test</th>\n",
-       "      <th>num_leaking_smiles_train_test</th>\n",
-       "      <th>perc_leaking_uniprot_train_test</th>\n",
-       "      <th>perc_leaking_smiles_train_test</th>\n",
-       "      <th>majority_vote</th>\n",
-       "      <th>model_type</th>\n",
-       "      <th>disabled_embeddings</th>\n",
-       "      <th>test_f1</th>\n",
-       "      <th>split_type</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>0.726923</td>\n",
-       "      <td>0.604651</td>\n",
-       "      <td>0.673077</td>\n",
-       "      <td>0.546875</td>\n",
-       "      <td>0.875</td>\n",
-       "      <td>0.717391</td>\n",
-       "      <td>771</td>\n",
-       "      <td>0.514916</td>\n",
-       "      <td>0.485084</td>\n",
-       "      <td>0.376806</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.381147</td>\n",
-       "      <td>34</td>\n",
-       "      <td>44</td>\n",
-       "      <td>0.832685</td>\n",
-       "      <td>0.102464</td>\n",
-       "      <td>False</td>\n",
-       "      <td>Pytorch</td>\n",
-       "      <td>disabled e3</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>random</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>0.697167</td>\n",
-       "      <td>0.616279</td>\n",
-       "      <td>0.535211</td>\n",
-       "      <td>0.612903</td>\n",
-       "      <td>0.475</td>\n",
-       "      <td>0.671739</td>\n",
-       "      <td>771</td>\n",
-       "      <td>0.514916</td>\n",
-       "      <td>0.485084</td>\n",
-       "      <td>0.376806</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.381147</td>\n",
-       "      <td>34</td>\n",
-       "      <td>44</td>\n",
-       "      <td>0.832685</td>\n",
-       "      <td>0.102464</td>\n",
-       "      <td>False</td>\n",
-       "      <td>Pytorch</td>\n",
-       "      <td>disabled e3</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>random</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>0.654254</td>\n",
-       "      <td>0.639535</td>\n",
-       "      <td>0.643678</td>\n",
-       "      <td>0.595745</td>\n",
-       "      <td>0.700</td>\n",
-       "      <td>0.714131</td>\n",
-       "      <td>771</td>\n",
-       "      <td>0.514916</td>\n",
-       "      <td>0.485084</td>\n",
-       "      <td>0.376806</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.381147</td>\n",
-       "      <td>34</td>\n",
-       "      <td>44</td>\n",
-       "      <td>0.832685</td>\n",
-       "      <td>0.102464</td>\n",
-       "      <td>False</td>\n",
-       "      <td>Pytorch</td>\n",
-       "      <td>disabled e3</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>random</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>NaN</td>\n",
-       "      <td>0.616279</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>0.629630</td>\n",
-       "      <td>0.425</td>\n",
-       "      <td>0.689674</td>\n",
-       "      <td>771</td>\n",
-       "      <td>0.514916</td>\n",
-       "      <td>0.485084</td>\n",
-       "      <td>0.376806</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.381147</td>\n",
-       "      <td>34</td>\n",
-       "      <td>44</td>\n",
-       "      <td>0.832685</td>\n",
-       "      <td>0.102464</td>\n",
-       "      <td>True</td>\n",
-       "      <td>Pytorch</td>\n",
-       "      <td>disabled e3</td>\n",
-       "      <td>0.507463</td>\n",
-       "      <td>random</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>0.744749</td>\n",
-       "      <td>0.593023</td>\n",
-       "      <td>0.653465</td>\n",
-       "      <td>0.540984</td>\n",
-       "      <td>0.825</td>\n",
-       "      <td>0.709239</td>\n",
-       "      <td>771</td>\n",
-       "      <td>0.514916</td>\n",
-       "      <td>0.485084</td>\n",
-       "      <td>0.376806</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.381147</td>\n",
-       "      <td>34</td>\n",
-       "      <td>44</td>\n",
-       "      <td>0.832685</td>\n",
-       "      <td>0.102464</td>\n",
-       "      <td>False</td>\n",
-       "      <td>Pytorch</td>\n",
-       "      <td>disabled poi</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>random</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>5 rows × 23 columns</p>\n",
-       "</div>"
-      ],
       "text/plain": [
-       "   test_loss  test_acc  test_f1_score  test_precision  test_recall  \\\n",
-       "0   0.726923  0.604651       0.673077        0.546875        0.875   \n",
-       "1   0.697167  0.616279       0.535211        0.612903        0.475   \n",
-       "2   0.654254  0.639535       0.643678        0.595745        0.700   \n",
-       "3        NaN  0.616279            NaN        0.629630        0.425   \n",
-       "4   0.744749  0.593023       0.653465        0.540984        0.825   \n",
-       "\n",
-       "   test_roc_auc  train_len  train_active_perc  train_inactive_perc  \\\n",
-       "0      0.717391        771           0.514916             0.485084   \n",
-       "1      0.671739        771           0.514916             0.485084   \n",
-       "2      0.714131        771           0.514916             0.485084   \n",
-       "3      0.689674        771           0.514916             0.485084   \n",
-       "4      0.709239        771           0.514916             0.485084   \n",
-       "\n",
-       "   train_avg_tanimoto_dist  ...  test_avg_tanimoto_dist  \\\n",
-       "0                 0.376806  ...                0.381147   \n",
-       "1                 0.376806  ...                0.381147   \n",
-       "2                 0.376806  ...                0.381147   \n",
-       "3                 0.376806  ...                0.381147   \n",
-       "4                 0.376806  ...                0.381147   \n",
-       "\n",
-       "   num_leaking_uniprot_train_test  num_leaking_smiles_train_test  \\\n",
-       "0                              34                             44   \n",
-       "1                              34                             44   \n",
-       "2                              34                             44   \n",
-       "3                              34                             44   \n",
-       "4                              34                             44   \n",
-       "\n",
-       "   perc_leaking_uniprot_train_test  perc_leaking_smiles_train_test  \\\n",
-       "0                         0.832685                        0.102464   \n",
-       "1                         0.832685                        0.102464   \n",
-       "2                         0.832685                        0.102464   \n",
-       "3                         0.832685                        0.102464   \n",
-       "4                         0.832685                        0.102464   \n",
-       "\n",
-       "   majority_vote  model_type  disabled_embeddings   test_f1 split_type  \n",
-       "0          False     Pytorch          disabled e3       NaN     random  \n",
-       "1          False     Pytorch          disabled e3       NaN     random  \n",
-       "2          False     Pytorch          disabled e3       NaN     random  \n",
-       "3           True     Pytorch          disabled e3  0.507463     random  \n",
-       "4          False     Pytorch         disabled poi       NaN     random  \n",
-       "\n",
-       "[5 rows x 23 columns]"
+       "Index(['PROTACDB ID', 'PROTAC SMILES', 'Active/Inactive', 'Best PROTAC',\n",
+       "       'Cells', 'cLogP', 'Comments', 'Curator', 'Dc50', 'Dmax',\n",
+       "       'E3 Binder SMILES', 'E3 Ligase', 'Ec50 of Ligand Cells',\n",
+       "       'Ec50 of PROTAC Cells', 'exit_vector', 'Hbond acceptors',\n",
+       "       'Hbond donors', 'Ic50 of Ligand', 'Ic50 of PROTAC', 'Ligand Name',\n",
+       "       'Ligand SMILES', 'Linker', 'Linker Type', 'linker_ha', 'linker_no',\n",
+       "       'linker_rb', 'MW', 'Off Targets Reported', 'PATENT', 'Ligand PDB',\n",
+       "       'Ligand ID', 'Pubmed', 'PROTAC Name', 'Proteomics Data Available',\n",
+       "       'Secondary Pubmed', 'Status', 'Target',\n",
+       "       'Tested A Non Binding E3 Control', 'Tested Competition With Ligand',\n",
+       "       'Tested Engagement In Cells', 'Tested Proteaseome Inhibitor', 'Time',\n",
+       "       'TPSA'],\n",
+       "      dtype='object')"
       ]
      },
+     "execution_count": 6,
      "metadata": {},
-     "output_type": "display_data"
-    },
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "protac_pedia_df.columns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "hparam: (3, 7)\n"
-     ]
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c3256f8bf45e46b3b22e86a2cd329f98",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "PROTAC-DB:   0%|          | 0/5388 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
     },
     {
      "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>hidden_dim</th>\n",
-       "      <th>learning_rate</th>\n",
-       "      <th>smote_k_neighbors</th>\n",
-       "      <th>use_smote</th>\n",
-       "      <th>dropout</th>\n",
-       "      <th>use_batch_norm</th>\n",
-       "      <th>split_type</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>128</td>\n",
-       "      <td>0.000925</td>\n",
-       "      <td>10</td>\n",
-       "      <td>True</td>\n",
-       "      <td>0.216350</td>\n",
-       "      <td>True</td>\n",
-       "      <td>random</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>32</td>\n",
-       "      <td>0.000649</td>\n",
-       "      <td>4</td>\n",
-       "      <td>True</td>\n",
-       "      <td>0.092638</td>\n",
-       "      <td>False</td>\n",
-       "      <td>uniprot</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>64</td>\n",
-       "      <td>0.000771</td>\n",
-       "      <td>13</td>\n",
-       "      <td>False</td>\n",
-       "      <td>0.304535</td>\n",
-       "      <td>True</td>\n",
-       "      <td>tanimoto</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f3ce4a6e83274c32918790a30b6f6061",
+       "version_major": 2,
+       "version_minor": 0
+      },
       "text/plain": [
-       "   hidden_dim  learning_rate  smote_k_neighbors  use_smote   dropout  \\\n",
-       "0         128       0.000925                 10       True  0.216350   \n",
-       "0          32       0.000649                  4       True  0.092638   \n",
-       "0          64       0.000771                 13      False  0.304535   \n",
-       "\n",
-       "   use_batch_norm split_type  \n",
-       "0            True     random  \n",
-       "0           False    uniprot  \n",
-       "0            True   tanimoto  "
+       "PROTAC-Pedia:   0%|          | 0/1203 [00:00<?, ?it/s]"
       ]
      },
      "metadata": {},
      "output_type": "display_data"
-    },
+    }
+   ],
+   "source": [
+    "# Canonize SMILES column in both datasets\n",
+    "from rdkit import Chem\n",
+    "from rdkit.Chem import AllChem\n",
+    "# Import tqdm for jupyter\n",
+    "from tqdm.notebook import tqdm\n",
+    "\n",
+    "def canonize_smiles(smiles):\n",
+    "    mol = Chem.MolFromSmiles(smiles)\n",
+    "    if mol is None:\n",
+    "        return None\n",
+    "    return Chem.MolToSmiles(mol)\n",
+    "\n",
+    "tqdm.pandas(desc='PROTAC-DB')\n",
+    "protac_db_df['Smiles'] = protac_db_df['Smiles'].progress_apply(canonize_smiles)\n",
+    "tqdm.pandas(desc='PROTAC-Pedia')\n",
+    "protac_pedia_df['PROTAC SMILES'] = protac_pedia_df['PROTAC SMILES'].progress_apply(canonize_smiles)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "majority_vote: (6, 28)\n"
+      "PROTAC-DB unique SMILES: 3270\n",
+      "PROTAC-Pedia unique SMILES: 1178\n",
+      "PROTAC-DB unique targets: 323\n",
+      "PROTAC-Pedia unique targets: 79\n",
+      "PROTAC-DB single SMILES: 2451 (45.5%)\n",
+      "PROTAC-Pedia single SMILES: 1153 (95.8%)\n",
+      "PROTAC-DB single targets: 78 (1.4%)\n",
+      "PROTAC-Pedia single targets: 15 (1.2%)\n",
+      "Shared SMILES: 807 (24.7%) of PROTAC-DB, (68.5%) of PROTAC-Pedia\n",
+      "Shared targets: 0 (0.0%) of PROTAC-DB, (0.0%) of PROTAC-Pedia\n",
+      "\\begin{tabular}{lrrrrlrlrl}\n",
+      "\\toprule\n",
+      "\\textbf{Dataset} &  \\textbf{Length} &  \\textbf{Unique SMILES} &  \\textbf{Unique Targets} &  \\textbf{Shared SMILES} & \\textbf{Shared SMILES \\%} &  \\textbf{Single SMILES} & \\textbf{Single SMILES \\%} &  \\textbf{Single Targets} & \\textbf{Single Targets \\%} \\\\\n",
+      "\\midrule\n",
+      "       PROTAC-DB &             5388 &                    3270 &                      323 &                    1222 &                    22.7\\% &                    2451 &                    45.5\\% &                       78 &                      1.4\\% \\\\\n",
+      "    PROTAC-Pedia &             1203 &                    1178 &                       79 &                     832 &                    69.2\\% &                    1153 &                    95.8\\% &                       15 &                      1.2\\% \\\\\n",
+      "\\bottomrule\n",
+      "\\end{tabular}\n",
+      "\n"
      ]
     },
     {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
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-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>test_acc</th>\n",
-       "      <th>test_roc_auc</th>\n",
-       "      <th>test_precision</th>\n",
-       "      <th>test_recall</th>\n",
-       "      <th>test_f1_score</th>\n",
-       "      <th>train_len</th>\n",
-       "      <th>train_active_perc</th>\n",
-       "      <th>train_inactive_perc</th>\n",
-       "      <th>train_avg_tanimoto_dist</th>\n",
-       "      <th>test_len</th>\n",
-       "      <th>...</th>\n",
-       "      <th>val_active_perc</th>\n",
-       "      <th>val_inactive_perc</th>\n",
-       "      <th>val_avg_tanimoto_dist</th>\n",
-       "      <th>num_leaking_uniprot_train_val</th>\n",
-       "      <th>num_leaking_smiles_train_val</th>\n",
-       "      <th>perc_leaking_uniprot_train_val</th>\n",
-       "      <th>perc_leaking_smiles_train_val</th>\n",
-       "      <th>cv_models</th>\n",
-       "      <th>model_type</th>\n",
-       "      <th>split_type</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>0.825581</td>\n",
-       "      <td>0.847826</td>\n",
-       "      <td>0.777778</td>\n",
-       "      <td>0.875000</td>\n",
-       "      <td>0.823529</td>\n",
-       "      <td>771</td>\n",
-       "      <td>0.514916</td>\n",
-       "      <td>0.485084</td>\n",
-       "      <td>0.376806</td>\n",
-       "      <td>86</td>\n",
-       "      <td>...</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>Pytorch</td>\n",
-       "      <td>random</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>0.813953</td>\n",
-       "      <td>0.868478</td>\n",
-       "      <td>0.815789</td>\n",
-       "      <td>0.775000</td>\n",
-       "      <td>0.794872</td>\n",
-       "      <td>617</td>\n",
-       "      <td>0.515397</td>\n",
-       "      <td>0.484603</td>\n",
-       "      <td>0.377543</td>\n",
-       "      <td>86</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.512987</td>\n",
-       "      <td>0.487013</td>\n",
-       "      <td>0.373853</td>\n",
-       "      <td>40.0</td>\n",
-       "      <td>70.0</td>\n",
-       "      <td>0.910859</td>\n",
-       "      <td>0.176661</td>\n",
-       "      <td>True</td>\n",
-       "      <td>Pytorch</td>\n",
-       "      <td>random</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>0.611765</td>\n",
-       "      <td>0.614827</td>\n",
-       "      <td>0.675676</td>\n",
-       "      <td>0.543478</td>\n",
-       "      <td>0.602410</td>\n",
-       "      <td>772</td>\n",
-       "      <td>0.506477</td>\n",
-       "      <td>0.493523</td>\n",
-       "      <td>0.375305</td>\n",
-       "      <td>85</td>\n",
-       "      <td>...</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>Pytorch</td>\n",
-       "      <td>uniprot</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>0.411765</td>\n",
-       "      <td>0.549610</td>\n",
-       "      <td>0.400000</td>\n",
-       "      <td>0.173913</td>\n",
-       "      <td>0.242424</td>\n",
-       "      <td>693</td>\n",
-       "      <td>0.484848</td>\n",
-       "      <td>0.515152</td>\n",
-       "      <td>0.377092</td>\n",
-       "      <td>85</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.696203</td>\n",
-       "      <td>0.303797</td>\n",
-       "      <td>0.359625</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>8.0</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.014430</td>\n",
-       "      <td>True</td>\n",
-       "      <td>Pytorch</td>\n",
-       "      <td>uniprot</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>0.705882</td>\n",
-       "      <td>0.823761</td>\n",
-       "      <td>0.772727</td>\n",
-       "      <td>0.459459</td>\n",
-       "      <td>0.576271</td>\n",
-       "      <td>772</td>\n",
-       "      <td>0.518135</td>\n",
-       "      <td>0.481865</td>\n",
-       "      <td>0.372540</td>\n",
-       "      <td>85</td>\n",
-       "      <td>...</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>Pytorch</td>\n",
-       "      <td>tanimoto</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>5 rows × 28 columns</p>\n",
-       "</div>"
-      ],
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/tmp/ipykernel_1899217/285611913.py:62: FutureWarning: In future versions `DataFrame.to_latex` is expected to utilise the base implementation of `Styler.to_latex` for formatting and rendering. The arguments signature may therefore change. It is recommended instead to use `DataFrame.style.to_latex` which also contains additional functionality.\n",
+      "  print(shared_stats.to_latex(index=False, escape=False))\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Count the number of unique SMILES in both datasets (and as percentage)\n",
+    "protac_db_unique_smiles = protac_db_df['Smiles'].nunique()\n",
+    "protac_pedia_unique_smiles = protac_pedia_df['PROTAC SMILES'].nunique()\n",
+    "print(f'PROTAC-DB unique SMILES: {protac_db_unique_smiles}')\n",
+    "print(f'PROTAC-Pedia unique SMILES: {protac_pedia_unique_smiles}')\n",
+    "\n",
+    "# Count the number of unique targets in both datasets (and as a percentage)\n",
+    "protac_db_unique_targets = protac_db_df['Target'].nunique()\n",
+    "protac_pedia_unique_targets = protac_pedia_df['Target'].nunique()\n",
+    "print(f'PROTAC-DB unique targets: {protac_db_unique_targets}')\n",
+    "print(f'PROTAC-Pedia unique targets: {protac_pedia_unique_targets}')\n",
+    "\n",
+    "# Count the number of entries in both datasets with a SMILES that appears only once (and show the percentage compare to the total)\n",
+    "protac_db_single_smiles = protac_db_df['Smiles'].value_counts().value_counts().get(1, 0)\n",
+    "protac_pedia_single_smiles = protac_pedia_df['PROTAC SMILES'].value_counts().value_counts().get(1, 0)\n",
+    "print(f'PROTAC-DB single SMILES: {protac_db_single_smiles} ({protac_db_single_smiles / protac_db_df.shape[0]:.1%})')\n",
+    "print(f'PROTAC-Pedia single SMILES: {protac_pedia_single_smiles} ({protac_pedia_single_smiles / protac_pedia_df.shape[0]:.1%})')\n",
+    "\n",
+    "# Count the number of entries in both datasets with a target that appears only once (and show the percentage compare to the total)\n",
+    "protac_db_single_targets = protac_db_df['Target'].value_counts().value_counts().get(1, 0)\n",
+    "protac_pedia_single_targets = protac_pedia_df['Target'].value_counts().value_counts().get(1, 0)\n",
+    "print(f'PROTAC-DB single targets: {protac_db_single_targets} ({protac_db_single_targets / protac_db_df.shape[0]:.1%})')\n",
+    "print(f'PROTAC-Pedia single targets: {protac_pedia_single_targets} ({protac_pedia_single_targets / protac_pedia_df.shape[0]:.1%})')\n",
+    "\n",
+    "# Count the number of shared SMILES between the two datasets\n",
+    "protac_db_unique_smiles = set(protac_db_df['Smiles'].unique())\n",
+    "protac_pedia_unique_smiles = set(protac_pedia_df['PROTAC SMILES'].unique())\n",
+    "shared_smiles = protac_db_unique_smiles.intersection(protac_pedia_unique_smiles)\n",
+    "print(f'Shared SMILES: {len(shared_smiles)} ({len(shared_smiles) / len(protac_db_unique_smiles):.1%}) of PROTAC-DB, ({len(shared_smiles) / len(protac_pedia_unique_smiles):.1%}) of PROTAC-Pedia')\n",
+    "\n",
+    "# Count the number of shared targets between the two datasets\n",
+    "protac_db_unique_targets = set(protac_db_df['Target'].unique())\n",
+    "protac_pedia_unique_targets = set(protac_pedia_df['Target'].unique())\n",
+    "shared_targets = protac_db_unique_targets.intersection(protac_pedia_unique_targets)\n",
+    "print(f'Shared targets: {len(shared_targets)} ({len(shared_targets) / len(protac_db_unique_targets):.1%}) of PROTAC-DB, ({len(shared_targets) / len(protac_pedia_unique_targets):.1%}) of PROTAC-Pedia')\n",
+    "\n",
+    "# Assemble a dataframe of these statistics (and corresponding percentages)\n",
+    "shared_stats = pd.DataFrame({\n",
+    "    'Dataset': ['PROTAC-DB', 'PROTAC-Pedia'],\n",
+    "    'Length': [protac_db_df.shape[0], protac_pedia_df.shape[0]],\n",
+    "    'Unique SMILES': [len(protac_db_unique_smiles), len(protac_pedia_unique_smiles)],\n",
+    "    'Unique Targets': [len(protac_db_unique_targets), len(protac_pedia_unique_targets)],\n",
+    "})\n",
+    "# Count the number of entries with a SMILES that is shared\n",
+    "protac_db_shared_smiles = protac_db_df['Smiles'].isin(shared_smiles).sum()\n",
+    "protac_pedia_shared_smiles = protac_pedia_df['PROTAC SMILES'].isin(shared_smiles).sum()\n",
+    "shared_stats['Shared SMILES'] = [protac_db_shared_smiles, protac_pedia_shared_smiles]\n",
+    "shared_stats['Shared SMILES %'] = shared_stats['Shared SMILES'] / shared_stats['Length']\n",
+    "# Count the number of entries with a SMILES/target that is single\n",
+    "shared_stats['Single SMILES'] = [protac_db_single_smiles, protac_pedia_single_smiles]\n",
+    "shared_stats['Single SMILES %'] = shared_stats['Single SMILES'] / shared_stats['Length']\n",
+    "shared_stats['Single Targets'] = [protac_db_single_targets, protac_pedia_single_targets]\n",
+    "shared_stats['Single Targets %'] = shared_stats['Single Targets'] / shared_stats['Length']\n",
+    "\n",
+    "# Convert all percentages to percentages in a for loop\n",
+    "for col in shared_stats.columns:\n",
+    "    if '%' in col:\n",
+    "        shared_stats[col] = shared_stats[col].apply(lambda x: f'{x*100:.1f}\\\\%')\n",
+    "\n",
+    "# Print bold headers in Latex\n",
+    "shared_stats.columns = [f\"\\\\textbf{{{col}}}\".replace('%', '\\\\%') for col in shared_stats.columns]\n",
+    "print(shared_stats.to_latex(index=False, escape=False))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
       "text/plain": [
-       "   test_acc  test_roc_auc  test_precision  test_recall  test_f1_score  \\\n",
-       "0  0.825581      0.847826        0.777778     0.875000       0.823529   \n",
-       "1  0.813953      0.868478        0.815789     0.775000       0.794872   \n",
-       "0  0.611765      0.614827        0.675676     0.543478       0.602410   \n",
-       "1  0.411765      0.549610        0.400000     0.173913       0.242424   \n",
-       "0  0.705882      0.823761        0.772727     0.459459       0.576271   \n",
-       "\n",
-       "   train_len  train_active_perc  train_inactive_perc  train_avg_tanimoto_dist  \\\n",
-       "0        771           0.514916             0.485084                 0.376806   \n",
-       "1        617           0.515397             0.484603                 0.377543   \n",
-       "0        772           0.506477             0.493523                 0.375305   \n",
-       "1        693           0.484848             0.515152                 0.377092   \n",
-       "0        772           0.518135             0.481865                 0.372540   \n",
-       "\n",
-       "   test_len  ...  val_active_perc  val_inactive_perc  val_avg_tanimoto_dist  \\\n",
-       "0        86  ...              NaN                NaN                    NaN   \n",
-       "1        86  ...         0.512987           0.487013               0.373853   \n",
-       "0        85  ...              NaN                NaN                    NaN   \n",
-       "1        85  ...         0.696203           0.303797               0.359625   \n",
-       "0        85  ...              NaN                NaN                    NaN   \n",
-       "\n",
-       "   num_leaking_uniprot_train_val  num_leaking_smiles_train_val  \\\n",
-       "0                            NaN                           NaN   \n",
-       "1                           40.0                          70.0   \n",
-       "0                            NaN                           NaN   \n",
-       "1                            0.0                           8.0   \n",
-       "0                            NaN                           NaN   \n",
-       "\n",
-       "   perc_leaking_uniprot_train_val  perc_leaking_smiles_train_val  cv_models  \\\n",
-       "0                             NaN                            NaN        NaN   \n",
-       "1                        0.910859                       0.176661       True   \n",
-       "0                             NaN                            NaN        NaN   \n",
-       "1                        0.000000                       0.014430       True   \n",
-       "0                             NaN                            NaN        NaN   \n",
-       "\n",
-       "   model_type  split_type  \n",
-       "0     Pytorch      random  \n",
-       "1     Pytorch      random  \n",
-       "0     Pytorch     uniprot  \n",
-       "1     Pytorch     uniprot  \n",
-       "0     Pytorch    tanimoto  \n",
-       "\n",
-       "[5 rows x 28 columns]"
+       "<rdkit.Chem.rdchem.Mol at 0x7f415d7f5a10>"
       ]
      },
      "metadata": {},
      "output_type": "display_data"
     },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "xgboost_cv_train: (15, 24)\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",
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-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
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-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>model_type</th>\n",
-       "      <th>fold</th>\n",
-       "      <th>train_len</th>\n",
-       "      <th>val_len</th>\n",
-       "      <th>train_perc</th>\n",
-       "      <th>val_perc</th>\n",
-       "      <th>train_active_perc</th>\n",
-       "      <th>train_inactive_perc</th>\n",
-       "      <th>train_avg_tanimoto_dist</th>\n",
-       "      <th>val_active_perc</th>\n",
-       "      <th>...</th>\n",
-       "      <th>perc_leaking_uniprot_train_val</th>\n",
-       "      <th>perc_leaking_smiles_train_val</th>\n",
-       "      <th>val_acc</th>\n",
-       "      <th>val_roc_auc</th>\n",
-       "      <th>val_precision</th>\n",
-       "      <th>val_recall</th>\n",
-       "      <th>val_f1_score</th>\n",
-       "      <th>split_type</th>\n",
-       "      <th>train_unique_groups</th>\n",
-       "      <th>val_unique_groups</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>XGBoost</td>\n",
-       "      <td>0</td>\n",
-       "      <td>616</td>\n",
-       "      <td>155</td>\n",
-       "      <td>0.798962</td>\n",
-       "      <td>0.201038</td>\n",
-       "      <td>0.514610</td>\n",
-       "      <td>0.485390</td>\n",
-       "      <td>0.376475</td>\n",
-       "      <td>0.516129</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.886364</td>\n",
-       "      <td>0.146104</td>\n",
-       "      <td>0.838710</td>\n",
-       "      <td>0.917167</td>\n",
-       "      <td>0.816092</td>\n",
-       "      <td>0.887500</td>\n",
-       "      <td>0.850299</td>\n",
-       "      <td>random</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>XGBoost</td>\n",
-       "      <td>1</td>\n",
-       "      <td>617</td>\n",
-       "      <td>154</td>\n",
-       "      <td>0.800259</td>\n",
-       "      <td>0.199741</td>\n",
-       "      <td>0.513776</td>\n",
-       "      <td>0.486224</td>\n",
-       "      <td>0.377372</td>\n",
-       "      <td>0.519481</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.920583</td>\n",
-       "      <td>0.188006</td>\n",
-       "      <td>0.883117</td>\n",
-       "      <td>0.959291</td>\n",
-       "      <td>0.860465</td>\n",
-       "      <td>0.925000</td>\n",
-       "      <td>0.891566</td>\n",
-       "      <td>random</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>XGBoost</td>\n",
-       "      <td>2</td>\n",
-       "      <td>617</td>\n",
-       "      <td>154</td>\n",
-       "      <td>0.800259</td>\n",
-       "      <td>0.199741</td>\n",
-       "      <td>0.515397</td>\n",
-       "      <td>0.484603</td>\n",
-       "      <td>0.376572</td>\n",
-       "      <td>0.512987</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.914100</td>\n",
-       "      <td>0.144246</td>\n",
-       "      <td>0.876623</td>\n",
-       "      <td>0.917975</td>\n",
-       "      <td>0.894737</td>\n",
-       "      <td>0.860759</td>\n",
-       "      <td>0.877419</td>\n",
-       "      <td>random</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>XGBoost</td>\n",
-       "      <td>3</td>\n",
-       "      <td>617</td>\n",
-       "      <td>154</td>\n",
-       "      <td>0.800259</td>\n",
-       "      <td>0.199741</td>\n",
-       "      <td>0.515397</td>\n",
-       "      <td>0.484603</td>\n",
-       "      <td>0.376068</td>\n",
-       "      <td>0.512987</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.876823</td>\n",
-       "      <td>0.162075</td>\n",
-       "      <td>0.883117</td>\n",
-       "      <td>0.937384</td>\n",
-       "      <td>0.858824</td>\n",
-       "      <td>0.924051</td>\n",
-       "      <td>0.890244</td>\n",
-       "      <td>random</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>XGBoost</td>\n",
-       "      <td>4</td>\n",
-       "      <td>617</td>\n",
-       "      <td>154</td>\n",
-       "      <td>0.800259</td>\n",
-       "      <td>0.199741</td>\n",
-       "      <td>0.515397</td>\n",
-       "      <td>0.484603</td>\n",
-       "      <td>0.377543</td>\n",
-       "      <td>0.512987</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.910859</td>\n",
-       "      <td>0.176661</td>\n",
-       "      <td>0.883117</td>\n",
-       "      <td>0.939241</td>\n",
-       "      <td>0.858824</td>\n",
-       "      <td>0.924051</td>\n",
-       "      <td>0.890244</td>\n",
-       "      <td>random</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>5 rows × 24 columns</p>\n",
-       "</div>"
-      ],
+      "image/png": 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",
       "text/plain": [
-       "  model_type  fold  train_len  val_len  train_perc  val_perc  \\\n",
-       "0    XGBoost     0        616      155    0.798962  0.201038   \n",
-       "1    XGBoost     1        617      154    0.800259  0.199741   \n",
-       "2    XGBoost     2        617      154    0.800259  0.199741   \n",
-       "3    XGBoost     3        617      154    0.800259  0.199741   \n",
-       "4    XGBoost     4        617      154    0.800259  0.199741   \n",
-       "\n",
-       "   train_active_perc  train_inactive_perc  train_avg_tanimoto_dist  \\\n",
-       "0           0.514610             0.485390                 0.376475   \n",
-       "1           0.513776             0.486224                 0.377372   \n",
-       "2           0.515397             0.484603                 0.376572   \n",
-       "3           0.515397             0.484603                 0.376068   \n",
-       "4           0.515397             0.484603                 0.377543   \n",
-       "\n",
-       "   val_active_perc  ...  perc_leaking_uniprot_train_val  \\\n",
-       "0         0.516129  ...                        0.886364   \n",
-       "1         0.519481  ...                        0.920583   \n",
-       "2         0.512987  ...                        0.914100   \n",
-       "3         0.512987  ...                        0.876823   \n",
-       "4         0.512987  ...                        0.910859   \n",
-       "\n",
-       "   perc_leaking_smiles_train_val   val_acc  val_roc_auc  val_precision  \\\n",
-       "0                       0.146104  0.838710     0.917167       0.816092   \n",
-       "1                       0.188006  0.883117     0.959291       0.860465   \n",
-       "2                       0.144246  0.876623     0.917975       0.894737   \n",
-       "3                       0.162075  0.883117     0.937384       0.858824   \n",
-       "4                       0.176661  0.883117     0.939241       0.858824   \n",
-       "\n",
-       "   val_recall  val_f1_score  split_type  train_unique_groups  \\\n",
-       "0    0.887500      0.850299      random                  NaN   \n",
-       "1    0.925000      0.891566      random                  NaN   \n",
-       "2    0.860759      0.877419      random                  NaN   \n",
-       "3    0.924051      0.890244      random                  NaN   \n",
-       "4    0.924051      0.890244      random                  NaN   \n",
-       "\n",
-       "   val_unique_groups  \n",
-       "0                NaN  \n",
-       "1                NaN  \n",
-       "2                NaN  \n",
-       "3                NaN  \n",
-       "4                NaN  \n",
-       "\n",
-       "[5 rows x 24 columns]"
+       "<rdkit.Chem.rdchem.Mol at 0x7f415d7f5930>"
       ]
      },
      "metadata": {},
      "output_type": "display_data"
     },
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "xgboost_test: (9, 20)\n"
-     ]
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<rdkit.Chem.rdchem.Mol at 0x7f415d7f5af0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
     },
     {
      "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>test_acc</th>\n",
-       "      <th>test_roc_auc</th>\n",
-       "      <th>test_precision</th>\n",
-       "      <th>test_recall</th>\n",
-       "      <th>test_f1_score</th>\n",
-       "      <th>model_type</th>\n",
-       "      <th>test_model_id</th>\n",
-       "      <th>train_len</th>\n",
-       "      <th>train_active_perc</th>\n",
-       "      <th>train_inactive_perc</th>\n",
-       "      <th>train_avg_tanimoto_dist</th>\n",
-       "      <th>test_len</th>\n",
-       "      <th>test_active_perc</th>\n",
-       "      <th>test_inactive_perc</th>\n",
-       "      <th>test_avg_tanimoto_dist</th>\n",
-       "      <th>num_leaking_uniprot_train_test</th>\n",
-       "      <th>num_leaking_smiles_train_test</th>\n",
-       "      <th>perc_leaking_uniprot_train_test</th>\n",
-       "      <th>perc_leaking_smiles_train_test</th>\n",
-       "      <th>split_type</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>0.767442</td>\n",
-       "      <td>0.879348</td>\n",
-       "      <td>0.700000</td>\n",
-       "      <td>0.875000</td>\n",
-       "      <td>0.777778</td>\n",
-       "      <td>XGBoost</td>\n",
-       "      <td>0</td>\n",
-       "      <td>771</td>\n",
-       "      <td>0.514916</td>\n",
-       "      <td>0.485084</td>\n",
-       "      <td>0.376806</td>\n",
-       "      <td>86</td>\n",
-       "      <td>0.465116</td>\n",
-       "      <td>0.534884</td>\n",
-       "      <td>0.381147</td>\n",
-       "      <td>34</td>\n",
-       "      <td>44</td>\n",
-       "      <td>0.832685</td>\n",
-       "      <td>0.102464</td>\n",
-       "      <td>random</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>0.790698</td>\n",
-       "      <td>0.884239</td>\n",
-       "      <td>0.711538</td>\n",
-       "      <td>0.925000</td>\n",
-       "      <td>0.804348</td>\n",
-       "      <td>XGBoost</td>\n",
-       "      <td>1</td>\n",
-       "      <td>771</td>\n",
-       "      <td>0.514916</td>\n",
-       "      <td>0.485084</td>\n",
-       "      <td>0.376806</td>\n",
-       "      <td>86</td>\n",
-       "      <td>0.465116</td>\n",
-       "      <td>0.534884</td>\n",
-       "      <td>0.381147</td>\n",
-       "      <td>34</td>\n",
-       "      <td>44</td>\n",
-       "      <td>0.832685</td>\n",
-       "      <td>0.102464</td>\n",
-       "      <td>random</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>0.767442</td>\n",
-       "      <td>0.882065</td>\n",
-       "      <td>0.692308</td>\n",
-       "      <td>0.900000</td>\n",
-       "      <td>0.782609</td>\n",
-       "      <td>XGBoost</td>\n",
-       "      <td>2</td>\n",
-       "      <td>771</td>\n",
-       "      <td>0.514916</td>\n",
-       "      <td>0.485084</td>\n",
-       "      <td>0.376806</td>\n",
-       "      <td>86</td>\n",
-       "      <td>0.465116</td>\n",
-       "      <td>0.534884</td>\n",
-       "      <td>0.381147</td>\n",
-       "      <td>34</td>\n",
-       "      <td>44</td>\n",
-       "      <td>0.832685</td>\n",
-       "      <td>0.102464</td>\n",
-       "      <td>random</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>0.447059</td>\n",
-       "      <td>0.499443</td>\n",
-       "      <td>0.482759</td>\n",
-       "      <td>0.304348</td>\n",
-       "      <td>0.373333</td>\n",
-       "      <td>XGBoost</td>\n",
-       "      <td>0</td>\n",
-       "      <td>772</td>\n",
-       "      <td>0.506477</td>\n",
-       "      <td>0.493523</td>\n",
-       "      <td>0.375305</td>\n",
-       "      <td>85</td>\n",
-       "      <td>0.541176</td>\n",
-       "      <td>0.458824</td>\n",
-       "      <td>0.394830</td>\n",
-       "      <td>0</td>\n",
-       "      <td>6</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.011658</td>\n",
-       "      <td>uniprot</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>0.517647</td>\n",
-       "      <td>0.486065</td>\n",
-       "      <td>0.567568</td>\n",
-       "      <td>0.456522</td>\n",
-       "      <td>0.506024</td>\n",
-       "      <td>XGBoost</td>\n",
-       "      <td>1</td>\n",
-       "      <td>772</td>\n",
-       "      <td>0.506477</td>\n",
-       "      <td>0.493523</td>\n",
-       "      <td>0.375305</td>\n",
-       "      <td>85</td>\n",
-       "      <td>0.541176</td>\n",
-       "      <td>0.458824</td>\n",
-       "      <td>0.394830</td>\n",
-       "      <td>0</td>\n",
-       "      <td>6</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.011658</td>\n",
-       "      <td>uniprot</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
+      "image/png": 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",
       "text/plain": [
-       "   test_acc  test_roc_auc  test_precision  test_recall  test_f1_score  \\\n",
-       "0  0.767442      0.879348        0.700000     0.875000       0.777778   \n",
-       "1  0.790698      0.884239        0.711538     0.925000       0.804348   \n",
-       "2  0.767442      0.882065        0.692308     0.900000       0.782609   \n",
-       "0  0.447059      0.499443        0.482759     0.304348       0.373333   \n",
-       "1  0.517647      0.486065        0.567568     0.456522       0.506024   \n",
-       "\n",
-       "  model_type  test_model_id  train_len  train_active_perc  \\\n",
-       "0    XGBoost              0        771           0.514916   \n",
-       "1    XGBoost              1        771           0.514916   \n",
-       "2    XGBoost              2        771           0.514916   \n",
-       "0    XGBoost              0        772           0.506477   \n",
-       "1    XGBoost              1        772           0.506477   \n",
-       "\n",
-       "   train_inactive_perc  train_avg_tanimoto_dist  test_len  test_active_perc  \\\n",
-       "0             0.485084                 0.376806        86          0.465116   \n",
-       "1             0.485084                 0.376806        86          0.465116   \n",
-       "2             0.485084                 0.376806        86          0.465116   \n",
-       "0             0.493523                 0.375305        85          0.541176   \n",
-       "1             0.493523                 0.375305        85          0.541176   \n",
-       "\n",
-       "   test_inactive_perc  test_avg_tanimoto_dist  num_leaking_uniprot_train_test  \\\n",
-       "0            0.534884                0.381147                              34   \n",
-       "1            0.534884                0.381147                              34   \n",
-       "2            0.534884                0.381147                              34   \n",
-       "0            0.458824                0.394830                               0   \n",
-       "1            0.458824                0.394830                               0   \n",
-       "\n",
-       "   num_leaking_smiles_train_test  perc_leaking_uniprot_train_test  \\\n",
-       "0                             44                         0.832685   \n",
-       "1                             44                         0.832685   \n",
-       "2                             44                         0.832685   \n",
-       "0                              6                         0.000000   \n",
-       "1                              6                         0.000000   \n",
-       "\n",
-       "   perc_leaking_smiles_train_test split_type  \n",
-       "0                        0.102464     random  \n",
-       "1                        0.102464     random  \n",
-       "2                        0.102464     random  \n",
-       "0                        0.011658    uniprot  \n",
-       "1                        0.011658    uniprot  "
+       "<rdkit.Chem.rdchem.Mol at 0x7f415d7f5bd0>"
       ]
      },
      "metadata": {},
      "output_type": "display_data"
     },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "xgboost_hparam: (3, 7)\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>eta</th>\n",
-       "      <th>max_depth</th>\n",
-       "      <th>min_child_weight</th>\n",
-       "      <th>gamma</th>\n",
-       "      <th>subsample</th>\n",
-       "      <th>colsample_bytree</th>\n",
-       "      <th>split_type</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>0.098975</td>\n",
-       "      <td>9</td>\n",
-       "      <td>0.013528</td>\n",
-       "      <td>0.000218</td>\n",
-       "      <td>0.851506</td>\n",
-       "      <td>0.630858</td>\n",
-       "      <td>random</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>0.025540</td>\n",
-       "      <td>3</td>\n",
-       "      <td>0.051810</td>\n",
-       "      <td>0.007490</td>\n",
-       "      <td>0.514577</td>\n",
-       "      <td>0.883731</td>\n",
-       "      <td>uniprot</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>0.000391</td>\n",
-       "      <td>10</td>\n",
-       "      <td>0.027727</td>\n",
-       "      <td>0.013472</td>\n",
-       "      <td>0.649744</td>\n",
-       "      <td>0.511964</td>\n",
-       "      <td>tanimoto</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
+      "image/png": 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G7I3LMpddU1yjZZQQQjIzM0UiEYfDuX79Oothhw0bBqBTp0700De9FBUVdejQ4Z2Fksvl2tnZffLJJ05OTl5eXj4+Pl26dBkwYMDQoUNbtmwJwNra+tmzZ6b+BJTegoKCwN7boZkzZ5bFrNpL7cuWCS1xG3Mz5haRonhlfKo6VaPTZGuzjQ9e4cvod999VxbzcS9fvmTaRO3atUu/kWo12baNhIWRJKMODqm4tFrt9u3bz507Fxsbm5yc/PTp09zc3A9+S/j7+wPo2bNn+SRJsSU5OdnCwsLMzCwxMZGVgAUFBcysWlhYGCsBCSHZ2myPeA/chssDl3QN+4ePVewympaWxuPxuFxuXFwc68F37twJoHr16syCjNIKCiJXrpAXL0j//uTvv1nPqrLKzMxkJrgPsngmNVX2Bg8eDGAsKwc3/r+LFy9yOByBQMDKrFqWNss93h234frQNUOTYXzAt1XsBU+BgYHR0dEjRoxgSh7runXrdu7cuc8//3zw4MESiYRZSCyRSNRq9TSRyCMlBWo1ZDIoFFCrIZFg7VqcOoXDhwHgyBFoNBg8uCwSq5S2bNkyfvz42rVrx8fHMyWV+sg9ePDA09PT0tIyOTm5Xr16LEZm+v926tSJeVFhcJwsbVbnR50fqh425Te94HzBwcKBxST/URa1uXw8fPjQ3NzcwsKiLF4EhYWFPX36NDk5mc/n16hR4+2/t+Nt276jM/y6daRfv9chYmLIoUOsJ1aJ6XQ6X19fAMunTjV1LlSpfPXVVwCmlsG/18uXL5ntTDt37jQ4SGZmpm+Ab7279TzjPVmZA/0vFfhutGXLlvfu3WMai5w9e7ZNmzbMJgrjnTt3rlu3bnXq1FmwYMGECRNq1KjRr18/Zo9w8XbSLtWq1bO0BI8HsRgCAfh82NlBIMC8eWjXDs7OCAnBzp0Qi1lJqYpITEzMmDSp85UrOHMGn39u6nSo97l165a3t7dQKHz8+LG9vT3r8Xfv3j1ixAg7O7sjR474+Pjo23X32bNnX3zxxePHj7sHdt8Ttae6eXXWM/xH2VXosubt7c3hcPz8/FasWAEgMDCQxcgAQkNDmeeUAwcO6DFYpyOnTpGdO0lWFrlyhU6P6m3RIgIQFxdixJHUVDn44osvAMybN4/1yOfPn//tt98IIW5ubsyOOHNzcycnp969e8+ePXvnzp2xsbEFBQXviZCWlsa8p2rVqtXLly9Zz7CEClxGL1++zLxf2rdvH5/P53A459k4Xu3IkSMAHBwcwsLCALi7uxu4ST8yknC5ZNAg41OqWtRq4uZGADJ/vqlTof7T/v37mbXVeXl52dnZV65cYSuyVqt1cXEBsH//fmaKvH79+m+vO7awsHBzcwsICFi0aNGhQ4cSEhK0Wi0T4cmTJw0bNgTQunXrV69esZXYe1TgMkoIWbBgAQBXV9eFCxcCcHZ2fv/PqA8qKiry8PAAEB4ezjynnDp1ysBYT58SKysCkOPHjUmpKrp+nXC5xNyc3L1r6lSod9u3bx9TRu/cuVOjRo1atWqxddO3detW5nt53rx5ADp06EAIUalU9+7di4mJCQ4O7tu3b+PGjc3MzEoUVktLSw8Pjz59+jAvMz777LP8/HxWUvqgil1G1Wp1s2bNACxYsKBFixYAgoODjQm4a9cuAA0aNGDqcvv27Y3Kb+1aApD69UlZ7hGunCZOJABp25YUFpo6FeodtFots3Fz/PjxXbp0ATB69Gjjw6rVauZGcuvWrcwm/T///POdX6nRaB48eHDw4MGQkJCAgIBmzZoVF1ZHR0cPD49yq6GkopdRQsi1a9e4XK6lpeX+/fvNzMzMzc3v3LljWCiNRsPMp2zevJl5mrh48aJRyRUVkXbtCEDoq2d9SaWkbl1Sty55/NjUqVDvlpiYyEym7dq1SyAQsDKrFh4ezsykTZs2DYCfn1/pxyoUilu3bjH9cWbPnm1kJnqp8GWUEDJu3DgAn376KbOjqU2bNoUG3cJs3ryZmSJgNul/+eWXLCQXF0csLAiXS65eZSFalXL3LpFKyenTZOVK+rf3cQoJCQHg4uLCtFhr2LAh0/reMHK5nJlJ27lzJ1OX7+ozq/PXX3/t379/27ZtAAICAgxOwwAfaBhRIaxateqXX365fv36oEGD6tWrd+vWrY0bN06ePFmvICqVatmyZQBmz57NtKth/s8wlocHpk9XnDq1eNmypT//XKLfPvU+LVogMhKFhRgxAitWQKVCrVp49QoqFaRSKJVQqSCR7LWwuJ+VlZ+fr1arZTKZXC5Xq9UqlWrVqlXt27c39Weo5IKDgw8dOhQfH6/Valu2bHn37t2lS5cuX77csGgRERFZWVlt27a9evWqUqkcNGgQM1NXSmPHjr1x4wYztZqcnGxYDgYqz5pddg4fPgzAxsaGmdwUiUSpqal6RWB6HXp5eTH1t1/xKnqjFRYUNHVxAbB8+XK2YlYVAwa8/kV6Opk0iXh6vr3loa+399v/V3M4HEdHR8MeSii9sDWrlpeXx6z73rt3r6WlpZmZWUJCgl4Rhg8fDoDpMSYQCAw+BcMAlaSMkjd6BgcEBADo0aNH6cfKZLLiLRPMIirjW3696dKlS8we4UePHrEYtvLz9yfMN0NaGpk2jYweTTp0IF26kIAAMmwYGTeOzJhxaNWqFStWbNiwYdu2bQcPHjx16tThw4dr164NYN26dab+AFXChAkTAHh7e0+aNAmGzqoFBwcD6NKly9ChQwGMGTNG3wjM4+OcOXOYf/3y7BZWecpoRkYG0zM4OjqaeUFU+mXzzHt5Hx+fwMBAAMOGDWM9vZEjRwLo2LEj7bynh927ydKlJDmZjB9P9GmEePr0aQDMBpuyy45iSKVSZpF8eHg48549PDxcrwjFzfP37t3L3Nvq+zRJCNm/fz9zI8VsKWYW8JePylNGCSEbNmwA4ODgwLzvs7e3z83N/eCogoIC5mkiJiam7DbpF+8Rjo6OZj14ZXb1KomMJA8e6Dtu4MCBALp3714WSVElMJtWimfVhEKhXnWQeRvRt29f5ply8uTJBuRw584dAM2bN2f6n5bn6YqVqowWFRX5+PgAmDhx4ueff/7JJ5/cunWrNAMTExOXL1/OfOONHz++jNLbs2cPgFq1ahm5R4AqjZycHGYZ9r59+0ydS5VQPKvGfB+VflatuHl+TEwMh8MRiUSZmZkGJCCXyzkcDo/HY95xTZs2zYAghqlUZZQQkpCQwExuHj16VCqVvv+Lc3NzMzMzHz9+fOfOndDQUC6Xy+fzy3RKZdq0abGxsWUXn3rT9u3bAdSoUSMnJ8fUuXz05HIikRgToPgkrp9++omZVSvlDzBmRnXIkCFdu3YFMHfuXINzYOYWoqKiAPTq1cvgOPqqbGWUEPLDDz8AqFOnztixY0eNGhUQENCtW7eOHTt6eXm5uro2aNCA6dX0zte7vXv3NnX6FGt0Ol3nzp3B0gabymzVKjJtGgkOJrNmGROGeUvu4ODw448/cjicUm4pzM/PX7x48b59+wCIxWJjdsF36tQJQPF2UoPj6KsSllGFQmFra1uaJrK2trb29vZOTk6enp4NGzbs3bv3ixcvTJ0+xabk5GS2NthUWjIZKT5Gado0YsSLgeJZtW+++aY0y54kEklWVtbjx4/v3bvXtm1bAEuXLjX46oSQ8ePHMy+4mKVXarXamGilVxmW35dw4MABiUQiEAjWr18vEomsrKysrKz4fL6NjY1QKOTz+ba2tnw+XyAQmDpTqsw5OzvPnz9/3rx5EydOjIuLo//o75Cbi+JuofXqITMTjRoZFonL5UZFRbVq1SoqKio/P9/GxubNPRFSqbSgoECtVufl5anV6oKCgjfH2tvb8/n8MWPGGPNRnJ2dAaSmpjo6OqampqampjZp0sSYgKVVPtW63Gg0GicnJwD03HOKwWywgXGTbpWZVkv69CFaLdHpyJAhxOjOct27d69Tp05pio+1tXXNmjWdnJzc3d0dHBwAdOjQwZjtpCdOnADQvXv37t27AzheXs3VKtvdaFRUVGpqavPmzQfTQ5AoAIC5uXlUVFS7du1WrVoVEBDAlNQKR6fTvXk3p1KpcnJyXrx4MX78+LdbxunH3ByzZ2PqVHC5GDoUxh0hIZfL79y5k52dHRgY2KpVq3c+Bdra2vJ4PJFI9ObAZ8+eff7553/88Uf37t3PnDlT4k9LiWlUmpyc3Lt37zNnzpTfltDyqdblo6CggHlVd+zYMVPnQn1cDNtgk5pKduwghJCnT8nVq2TPHsJsnvjlF+Nv2v5FLpePHj26a9euAQEBXbt29fX19fLycnZ2rl+/vp2d3XtaMbDwIkWhILNnk++/JwcOkIAAotdZD29hdrJ89tlnBox99OgR8/3bpUsXwxYFajQaKyurFi1a/PjjjwDGjRtnQBADVKoyypwm0rp1a7pTiCpBLpc3aNAAQERERCmH6HTk0iXSpAm5e5dcvUo2bCBDhrzemzpnDtF/l837fP6hg6c4HI6dnZ2Dg4OTk1OrVq3atWv36aefMveh+h1y8za5nABEKCSLFxt56EBubi6z5un33383LEJiYiKzlbN79+4qlSEviJjvfaZbW6NGjQxLQ1+V56FeKpWuWrUKwPLly405kZWqlEQiUbdu3aKior7//vuEhAR7e3uVSlX8roPLrff332vkcshkUKuRnw+FAhoNoqMxcSJCQzFlyus4ixYBwJUrGDeOtdxevHhx8+ZNDocTGBjYunVrpjjqdDoul6vVapm6UFBQUFBQoFKpig/6XrBgwbVr18aNGzdlypSuXbsafiS1UAgOB0olhEIAUCgM/iDLly+XSCQ9evT44E+F/+Lq6vr777937NhRoajXr5/5zz+Dx9MvQnp6+urVqyMjIy0sLJiKXB7Kp1qXA2a5KHPkAEWVUNzL8p0/YmvX9nr7tGyAbNlCtm0jR4+SMWNe343K5UShIDNmsHk3OnbsWACDBw8mhDBn2JTG2bNndTodc65cUFCQURkIhQQgEREEIIY+CL948UIoFHI4nBs3bhiVDCEPHjyvWVMHkL59iUZT2lFpaWkTJkxgDhDlcrlff/21wR3c9VVJ7kZfvnwZEREBgHmup6gSmF6WrVq1UqlU8fHxbm5uw4cPF4vFfD5fJBKJxTUsLWFtDZEIfP4/Z2ZfvoxHj9CvH3766XUcgQBcLt46YM1wKSkpO3bsMDMzYw4Wc3Z21mg0IpHI2tqaz+dbW1uLRCI+ny8WiwUCQfGLGqFQ6OnpyeFwNm/e7OnpuX379kGDBjF7DQwhEqGg4PWnMvRudO1aXps24+vUyWFWgBrDza3u+fPo3BnHjmHQIBw48IG/8LQ0rFo1fdu2jRqNxszMbOjQofPnzy+npU6M8qnWZW369OkA6B4k6p0kEkmJXpbx8fGlGZiXRzIyCCEkK4ucPk3Cwl7//qNHrB3/PGTIEABjx441OALTIM7Z2VlpaE46dzdSs3rR7q1FzV0KpxlyY5uWRng8YmZGHj40LIV3uHuXVKtGAOLvT/7/0M93XHfyZMLjEV/ffVwuNyAgQN8upayoDGU0IyODeZoot3t4qmJhzpjs0qXLsGHDAAQGBuobISmJmJsTkYjlN0v379/ncrk8Hs+YTg5ardbT0xPAfEPfDj186BYbi+zsqNhYJCd3NSDCyJEEICNHGnb9/3TjBhGLCUDenrRITSVjxhALCwIQc3MycaIsKSmJ5cuXWmUoo8wOsIEDB5o6Eepj9GYvSzMzM8N6WRJChgwhAOnWjc3c+vTpA2Cq0SceXr9+ndn+qNfhRcUSEtrGxiInZ0tsLBIT9T4Nl/kZY2FRJscPXrtG7O3JgQPk8GFCCPn7b/LLLyQw8J8COnIkSU5m/7p6qfBl9MmTJ3o9plFVTXZIyPctW3711Vf9+vUDMGnSJMPi5OSQmjUJQNjaH8e8nReJRH///bfx0b799lsAbdu2NaDzfFJSx+Iy+vChp77D/f0JQCZO1HdcaSkU5EZ6xKwAABQJSURBVMYN0rAh+esvcvs2mT+fWFoSLpcEBBDT3YD+S4Uvo8xjmgFHDlBVwrNnhM8nXK704MGg5s0N7mXJiI4mAKlenWRns5Aa80Zo3rx5LMQiJD8/v27dugDWr1+v79hHj3rFxiInJyo2FvfvN9ZrbFwc4XIJn0+eP9f3snq4cYOEhZGBA0lsLFmzhkRHG9NBhX0V4009s/tNqVRKpVKVSqVQKGQymVqtTkxMjImJsbS0ZCa/KKqkxYuhUmHIEJtt27Y+eLBk+XJj1hKOGoWYGJw/j5kzsWOHUXldvnz5woULtra2zNtR41lbW0dGRvbu3Ts4OLhPnz6l6XBWjMtldl7qAOh0+r2pt7REz55o0gR16+o1Tm9WVhgwALt2wdERo0aV7bX0Zuo6/o/bt2+7ubm1bdv2zcagvA+tvnV0dPT29jZ17tRHKTmZWFgQMzOydy8BiFhs/BbOJ0+Ii0tq69bdz507Z0ycdu3aAQgNDTUynxIGDBgAoGfPnnqNkkh+ycmJUijuvnq1Pz//UmmGqNWkUycil5OiIhISQvbsIX/9RQghFy+Ss2f1z/tDbtwgGzcSQkivXmTNGvbjG+ljuRstKioaOHDgs2fPNBrN239a3MvAxsaGx+MVL6YrLCw8duxYenr67du3vby8yj9t6qMWEgKtFmPHYssWAJgxw8i+GwAaNEBg4KE5c858882jv/76S8js/NHTyZMnr127VrNmze+++87IfErYsGHDxYsXT58+ffjwYX9//1KOEot75ubGZGdHmJvXdHD44e0vyM+HSgW5HHI51GpIpWjcGEVFWLYMS5YgLQ12dq/Xm0okeNd3sLHq1EG7dgCwYQMyM9mPbyQOIcTUOQDA7t27R4wYYW9vv379eolEAkCj0XA4HEKIXC4vfqJXq9VyuZx5os/Pz79+/frcuXNXr17t4eERGxv7ng4OVJXz4AE8PWFhgagojBqFGjWQmgpra+MDFxYWtm3b9u7du7NnzzZguwchpFWrVvfu3YuIiJg8ebLx+ZQQFRU1YcKE2rVrx8fHW1lZyeVy5jsoPz+/oKCgSROtufkrnU5FiLKwUEKIWqdT8PlNFIqb1auPFIm8tdqMlJQ+Op2SEFVhYR4hap2uYMAA7dOn/7rl2rgRf/0FGxsMGYLwcLRsiZs34eCAlBQMHoyBA1n+UOfP4/lzBAYiORl7977ekvsRMfHdMCGEEI1G06hRIwA7d+4s0cz1/fLz8xUKBTN21apVpv4c1MckOJgAZMoU0rIlAQirZ9bfvHmTWWB0+/Ztfccyp2XUr19fpVKxmBIhRKFQSCQSnU7n7e3N4XDeue01NrZZbCxK/JeS0l+rzXr+fPrjxwOVyqS3v8DbW1GjBnFyIs2bk9atSZcu5MABMn48kUrJoEFk5EgSHk6uXSOEkKNHyf797H4sQgg5d46sXEnS0silS2TBAvbjG+mjeKjfvn3748ePXV1dhwwZYmZm9vnnn1taWr7dnfDN7XE8Hs/W1lYgEJibm2/ZsqVLly4hISH9+vVrZGjjbqqyWbYMHTogNxcREahbFxMmsBi7TZs2kyZNCg8PHz9+PLNms/iP3m4M+uarUalUyuxaXrBgAY/HU6vVH5z9L72IiIjVq1evX7++YcOGd+7c0Wq15ubmb36/8Pl8LreRWNyMw+FzuUIzMxsul8/lWgkEzczNa9WtuyYnZ5Na/bhp01gOh8/lCszMxFwun8sVXb9e8loqFS5ehI0NvvoKZ8+y9QneJy4OAgFycsrjWnozdR0nSqWSWahxmFlfaxBm2VOnTp1oizyKEEIiI8mMGWTWLJKdTQ4fJkeOsH4FmUzm6OgIoHr16s7OzvXq1Xt/Y9Bi9erVs7W1zc7ODgkJcXJy+uD5taWUl5dXYsPr/fv3Sz88J2fLs2dT0tKCiooMaT7/5rddWXwLnjtHtm8nhJCkJHo3+i6bNkWlp6d7eXn179/f4CDh4eFnz569dOnS3r17mZJKVV0XLiAvD6tWIS0Nc+Zg+/ayuIiVlVVYWNiYMWNevXr16tWr4t/ncDhvP0i9+Wr0yJEjEokkNDT01q1bqampwcHBGzZsMD6fVatW5ebmdunS5cyZMxqNJjAwsHnz5qUcq1LFq9VPRCJvjebJ06dBdeos5fH0e6p7c/6gLLpU1q4NS0sAsLGBuzv78Y1l2iqen08aNCjq2PF/Z878ZmSoHTt2AKhevXpWVhYruVEV1dKlpLi7woABZXqplJSUkydPJiUlpaWl5ebmlma6My4uzsLCgsvlMreNXC73ypUrRqZh5IbXvLyjsbFISemXmPhZbCxksstG5lPVcE1bxNeuRVoat7DQt1s3Q3t8/b+RI0d27dr11atXM2fOZCU3qqKqVev1opiiIpTxQpRGjRr17t3bxcXF0dGxNMucAXh4eEydOlWn04WFhc2YMUOn0wUFBanVamPSWLZsmUwm69Onz+HDh4uKisaPH9+wYcPSD+dyhQB0OgWzDl/fFfiUKe9Gc3OJrS0BiKEnDpT05MkT5iSss2WxApiqKPLzib8/2byZjBtHfvnF1Nm8Q/HykmXLljVt2hTA4sWLDY6Wnp4uEAi4XG5MTAyHwxEIBOnp6XpFkMn+iI1FYqJPSkr/2Fjk5hr+lqJqMmUZnTWLAKRHDzZjMuv4GjRoYMwxrVSFp9WS5GTC0tubsnDx4kWm5O3bt4/D4fB4PIN76wQFBQEYMmTIl19+CWDOnDn6RlAo7sTGIj6+xZMnw2Nj8fLlTsMyqbJMVkZfvCBCIeFwiNEnDvyLVqtt1aoVgFmzZrEZl6LYNnz4cACdOnVi6qCvr68B60ySk5MtLCzMzMz27t0LQCwWv9J/w6tSmfTXHzbJ11pnPvj+8VGXvKRt+kao4kxWRr/9lgBl8gLg1q1bBi+Npqhy8/Lly1q1agGIjIysU6cOgC1btugbZPDgwQDGjh3LnCK3ZMkSQ1JJTycAqVOHfP89AQjdyaInk20GTUnBokUIDkbTpuwHnz59+rp169zd3U+dOlVYWMgcplhQUJCfn69SqUpsjys+bTEzM1Mul69Zs6Znz57s50RRb2H2QFevXn358uXjxo0Ti8UPHz5kzmovjQcPHnh6elpaWkZGRo4aNapGjRqpqanWBmx4lUhgZwexGFOmYPFiLFyIkBC9g1Rl5Vy2f/6Z+PoSnY6cPEn27Surq8jl8tq1a1tZWRnwFyIWi2UyWVllRlH/xkxoDhs2jOmEHxAQUPqxX331FYCpU6cyE1lr1641MAmN5nUr+RUrCEDohJieWF5+X7z7LT+/UKl0USggk0GlgkwGhQIqFRo0QKNG+Okn2Nuze+V/sbS0tLCwUKlUNWrUsLGxEYvFPB7PysrK2tqax+PZ2Ni8uT2OECIQCJhV0z/88ENycvKCBQvWrl1bhvlR1P/bsmVL8+bN9+zZEx0dffHixUOHDh0/fpwpqe9369atkydPikQiNze38PDwOnXqTDB4w6uFBSwsoNWCzweMOqq+ijK+EoeFhb29Yq5p0/88+Pvnn8mwYWTHjjK8G42KigLg4uKi1Wpnzpw5YsSIgICAL7/80tfX18vLq3iVH3OkdbG4uLjipdFXr14tq+Qo6t9WrlwJwNHRkfmFq6trUVHRB0fduXOnQ4cOwcHBzJKpqKgoo5Jg1h6uX08AMmqUUaGqHhbuRrVabV5eHvPr4t1v9evXt7GBtTWsrMDjQSyGUAge7/VPu5AQ9O+P4GDjL/4OKpVq6dKlAJYuXWpubn7kyJHU1NT3fL2dnR2zdY8Q4unpOW3atJUrV06YMIF23qPKx7Rp0w4cOHD79u2srKxp06Z99913XO6H98W0bNny8uXLW7duTUhIaNy48ejRo41KIiIChYVo1QqnT0OfzvkUWOk3KpVKdTodU4k++MW//w4AHTtizRq4u8PX93VhZdG6deumT5/u4eFx9+5dLpd76NAhhULx5r5mKysrHo8nFosFAgH/rcsrlUp3d/fHjx+vWLFi9uzZLCdHUe8SFxfXpk0bnU4XHh7erl07hUKhVqslEolSqSx+Bcq8I1Wr1TKZTC6Xq9VqqVR69+5dnU4XHR09ysiDNZ4+xdy5aNIE8fEIDYWTEzsfrIow1W1wRgbx8yP9+7McViaT2dvbA/jFiO0rxUujHz16xGJuFPUeo0ePfmeH0PfjcDjM5JWxl58yhTDb8J8+Jd9+a/zHqVJM2eHpjz8gkeDYMfTty1rM8PDwrKwsHx8fYxYtderUafjw4bt27Ro7dixTUlnLj6L+w8qVK9PT069fv+7i4sI8JzGvHIRC4dvvSIVCIZ/PZxrudevWrTSTAB+QmYn69QGgXj1kZRn/caoUUx4ismEDJk2CgwPi42Fry0JAiUTi5OSUl5d38eLFTp06GRPq1atXzZo1y87O3rFjx8iRI1lIjqI+ZsuX47PP0KEDrl3DhQuYP9/UCVUkpiyjOh18ffHnn/j2W7DRcRFz585dsWJFt27dzpw5Y3y0PXv2DB8+vFq1agkJCcxuE4qqtBQKLFkCjQYWFliwACKRqROqSEx8pN2DB/DyQmEhLl+Gj49RoXJycpycnORy+fXr1729vVlJz8/P79SpU0OHDt2zZw8rASmKqnxM3G+0eXPMnAmdDitXXn/n0cqlt2TJErlc3r9/f7ZqKIANGzZYWVnt3bv31KlTbMWkKKqSMVu4cKFpM2jfHomJS48fH2Zpaenr62tYkGfPno0aNYoQsm/fPnv2Nkgxy2DPnTt39erVoKCgEsv1KYqiYPK7UQB8PiZN6gBg6dKlCQkJhgVZtGiRWq0eOnSoh4cHq9lh6tSpXl5eT58+NfnPG4qiPk4mnhstFhQUtH37dl9f399//13fBUbJyclubm4cDichIaEsDlhmlkYXFRXt2rVr6NChrMenKKpCM/3dKGPNmjV16tS5fPnydv3Pcfzhhx8KCwvHjBnDeg29fPnyxo0bmzZtypTps+VzIDdFURWK6Q9YZojF4qCgoMWLF0+dOlWlUjk4OKjV6v9qDKpUKiUSiVqtVigUUqm0oKCAx+PNnz8/Ozv70qVLAwcOZCUlQsjMmTNv3rypVqtzc3OLiopat27NSmSKoiqTj6WMAjh//jwAjUYzadIkvQZaW1srlcr09HQ/P7+8vDxXV9cWLVoYn8/x48dv3rzp4OBQVFT07Nkzd3f37777zviwFEVVMh/L3OiJEyf69OljZ2dXq1atpKQkW1vbHj16WFtbFzcGZbbHMR1ChUKhjY0Nn8+3srKysrIKDg7evn27t7e3t7f3jz/+2KZNm2vXrpmZmRmTj06na9WqVVxc3Nq1a8PCwrKysk6cOOHn58fW56UoqvIw4X7+Yjqdjrl/XL16NXOCwrFjx0o/XCqVMqPCw8OZ47nXrVtnZErMentHR8dFixYBaNu2rQHHjVEUVRV8FGU0JiYGQP369Zk+oa1bt9a3Zh05cgSAjY3Nrl27AAiFwsePHxucj0ZDfHw6A9i0aVO1atUAXLhwweBoFEVVbqYvo4WFhU2aNAGwYcOG6tWrAzh//rwBcfr16wegd+/ezCum7t27G5xSVBSxtFQHBEQvWZLh4zOia9cvDQ5FUVSlZ/oymrF3b2dHRxcXl/nz5wPo0KGDYXFevHhha2sLYMeOHTVq1ACwz6BTSpRKUq8eAcjWrcTKinA45Pp1o5s5UhRVeZm6jKpUpH59YmHxfOfOjo0bA/jzzz8NDrZx40YADg4O69evB1CjRo2cnBx9g6xZQwDi4UEmTyYA6dPH4HQoiqoSTL38PjISz56hSZO6cXGX0tNjv/22ffv2BgebMGHCZ599lpmZGR8f37lz55cvX86aNUuvCHI5wsIAYMYMbN0KLheLFhmcDkVRVYMpa7hcTuztCUB27iRCIeFwyJ07RoZMTEzk8XhcLnffvn3Mscl6zbQuXkwA4uNDgoIIQIYMMTIdiqIqP5PejUZEICsLbdvi6lUUFODrr9GypZEhXV1dZ8+erdPpFi5cOGfOHELIxIkTlUplKYd3744uXfDNN9i5E2ZmWLDAyHQoiqr8TFdGJRKsWQMAU6YgOhpmZggJYSXwvHnzmjVrlpSUVFRU1LJly0ePHi1ZsqSUY9u0wfnzaN4cXl4IDISrKysZURRVmZmujG7ejNxcdO6MX3+FRoNRo9C0KSuBLS0tIyMjORzOihUrZs2aZW5urlAoSjNQp0P//lCp4OmJPn0QEcFKOhRFVXKm2wyq0WDLFtSqhcGDYW6OxEQ0bMhi+PHjx2/ZsuXTTz+NiYlp+N+RpVKo1ZDLIZOhdm34+aFXL4SEYPhw7N7NYjoURVVaJmpNcvs2jh5FrVr44gssW4aCAnZrKICwsLCTJ09ev369R48eHh4eCoVCpVJJpVKlUqlSqTic5MxMs4KCfw3ZsAEeHsjLQ1ISu7lQFFWZmaKMPnmCdeuwdSsePsScOdi2rSwuYmtrO3bs2MWLFyclJSW9VRfr1SNMDbW2Bp8Pa2uIRLC2BoAFCzBtWllkRFFU5WSKMnrxIoYNg0CA1q0hkZTddRYtWuTj4/Pw4cO6desKhUIej2dnZ8fn8wUCga0teLySh8gWFeHyZVSrho4dceFC2eVFUVSlYoq50b17wedjwAAAGDAAR46UdwL/raAAQiEIQUEBPamboqhSMcXdaK9eGD0a9va4fx+tWpkggf8mFAIAh0NrKEVRpWWiN/V5efj9dzg44NNPTXB1iqIo9nws3e8piqIqKFO3JqEoiqrgaBmlKIoyCi2jFEVRRqFllKIoyii0jFIURRmFllGKoiij0DJKURRlFFpGKYqijELLKEVRlFFoGaUoijIKLaMURVFG+T8oPoPsAk/tzQAAA9l6VFh0cmRraXRQS0wgcmRraXQgMjAyMy4wOS41AAB4nIWTa2wUVRTH79zZnd2672232wdtp6XdR8tu2132zWMuFNQFRVAeFRpvS2smFg0mpiQGImW1RZCUENAPkEAVS8W0BBOCsbSdm2gTNYGgNSoqiUE/aG1IiEkTWht3zqYtboPc5Ob+zn/O/M+5ZzJ3RwZuo/SyoIW1Ir3XpPdBTkA0fXK8HnnSJ48FCU6NIMnqyXMLQtYDXUbg5xzmEh7qNP/CgvWiGlnCvFdWjUyMsQ6JajwXPkLWg4wfKf+3tg5BU5ibT1jcbVbbVnAU5hwWO/2Pw0PGbgNL7oFhRlV4YCSLLbLuaUAc4rCIeRnzGqrRilpBxoKO6vRIl0NzHvPgHAM1GGVsNFGTWcZmC7VYZayxydhmp/ZcMTcP2R0yduSjfCdyFqCCQlRYhIqKUfEStKQElZSi0jJUJiKxHJVXoIqlHlxRKVZWoSoXcrll7PZQj1fG3mpUXaPH1cvS24d8ftFfK+PaOlpXL+P6AA0EaXA5CoZkHArTcIR6ozQak3FRHMUTyIFpwomsWhpzUauBRvw0EpBxnk6dNnbYbRpeEHQ5BqtWMJrMFqtBKCgsiiecgtvjjcZcQm1dfSDi1wYDkXDIeQun5zn/Q8zEbyvad5Ir1WD2/pRibOpfrfKfU7ls1ncO+Fl7Gfvw9LlhlUeMEbZyzDaistu9hh3ce21U5RPlz7NjZz8Hnk60sQ/aI4rKL4YPsOpUAvj+zh1sJvImcGvHbjbxxSDw8WaJxZYdAu7pDLLWwYvAld3FrPf9fuChPVbW5t0PfG1jCbNU1QC7Hg+xd1fUAo/+tZb9dqMHet76TZRd50sklbd9so5V6l8BnnwvztCJXuCJ70W2tbgcuHPYweojHcD+rmnl06dfB64Z+1VpSXozPjcvK380+4BTZ04rd1Z1Au/atF3pS6aAd9xYqkwdD2X0r7tHD/THgDf6UiObnrgJvXH6s6ub9v0IrNXw0thnv6xS+dJkizTTMQt8K3xU+uG7BpinWfOStGWzAHe8VDMkDe9bDvrliXHpSMMM8Nq9mOx3/A3c8IyF5Fk2ZPKTbnJsaj1wQVuANF+9AzmpF5LkucM/Abe2bSDjA09BTm/jk2T6QhN838nGFrK+8VvI2f7ayyTcdR70bScpSVx/FfofHWwn7T02uOOp8U5yr8+R4YbDJLrubci5V3iScF9uAQ58fIb0jd8F/ufnU+S8yQv+u0uPkjeEi8DNVw8R7qsU1HK+VUX2NPGgDxwxke6hXOCuXVcUM/sIau3c/LuSvHIBOP9fcwQn5KOscokAAAUmelRYdE1PTCByZGtpdCAyMDIzLjA5LjUAAHicfVfbbt02EHz3V+gHLHCvJB8TOyiKIjbQpP2HAn3s/6MzlMOjoIuey0KS51Cj3dnh+ung6/fX3/7659gvfX16Oo72P5855/Gntdaevh48OD5/+eXXt+Pl+6fPP668vP/x9v3bke3IxG/w/hn76fv71x9X5Hg5nvOc7r0BeDafHutgvR4/1eP9eO5n92ljHnKahHkFNK44T/XRNAHUEG9eAP0CTpfZ/XgGoA9XLZBBpMjp3lKEUHG1GAU0F1RPMY+YODy9qwwroH1B7UwzmUloGJafBXQcb/i7n6ZdHFz1HBlIVAGda9U4h0YfQujUOXD0X6i0Dwah6gEGDtrgWiULj/2+KKgk0/kcp6dkVhUQ/UhCUxt4Hqzbso9Z5UvsI7cqU3COdc2sjxLrTMM8u0IlRqjmHFFVTOIqLiQQSNOzocyNeiigeUGHidrKbXYX6xW0f5A1pLSv5HYbrVfVlfGRBKRWJxWL5cNKsvNj2QixkENPD7OoGGhbZdDTIpvNw84uU72EyseqmdqmHMHear0ioFfB8IegwEAgpOusHks/2gvq7+huO4f3WrO6qtWh1CZKplOkRSVZXcXquHu48/HTQ7JErloFEpWjkWfHk7VK29p5d8fddUDaUCBso6y/rkLZaenQIJDSFL+qkKtOSE7M4Ckkk7Msvl1lguzbQBrRg63NVmXJVpUaXApVCqgkLVqZT2OR0CeujkMA55hlOs1wczQfCqNsTzdrWT250QHR/R59ic0nPLh6cGOB4D7R3JNse45Rtqgl7g1Li8RizFVjo1SGYiwQbAqaHLrWhDzLXjYWKLGmdfacnKAJo66QLNA4JdTbWMiW+FTe3xZyBgSUrD703meVTWeB0Bqw847MEtrGiKpCviqERveZYzHtfbbSTt2Ob/BJKB22s6h2iVY6JPz+nVBUU8NJQOFOVnKNCxrD7Uoq3Tqr/kAd32Co4JpUEQhkiJSyd7oeoMOhYeOqBgZWpnVc0GapQobY1yOqDvF5Ibs1SXaIdrdeZTVYKslTdYzGrjNDzqqkxioVfMTMoU60X0e3VImKVakBm9NM3nQoVq32MpB/+RsOjZxaTPbd6Ja9Mtzwa9GO3QGtjDxhe4SrVNBg9vsJTxpohGfuKMO0SlTk9fhmM6DaZRXdS8+LfknV3OdchcJ+wnmhgI6l/+Gwm9XR0dFUZQLmZbmZHe6MjrXmQ6riZ7tsHFOBDO437MJyzS9vrz8NgddY+Pn97fUxFvKtj+EPJ4c9Rjzn9zHI8R2PaQ03P/IxkTm+/TF1Ob7jMVkJvvMxPTkv3UckZRC5DUK+rmxyGHecQTY/IUEE2RSFDBFks0R78BSlvQ0iziCbq5AsIJstrMIZZBPGViEMeh8RhEE3ZV2UgXnkcyVUsZvf9nVh0E0Z+7cw6KaszCyCbsocsBl0U8a+Kwy6OStTjKCbs5IzR4L7finH2g1v+6Iw2ObMTY/BHjIgZwTbnI2cEWxzhk8qg23ORs68sjkbOSPY5mzkjGCbs1EZDPctxFfYnJ15ZticnZwRfHOG8fMUrn7z9+vK5uzMM69szk7OCL45O6XBsDk7tcGwOTs5I8TdS50hNucgZ4TYnIOcEWJzDsqZVzbnWA2Hz6PpKGeE2Jw5dfHZY3OOJecO87nZ0FJLbM5BbSDk3VacWt10ktLIdpNqrg7st55fjO/1S78gj7bNxfie08zryiM7NKq7LfH8x3+6OH76F0Oq0Xw+ZfCiAAACf3pUWHRTTUlMRVMgcmRraXQgMjAyMy4wOS41AAB4nC2TOY4bQQxFr+JQAroL3MmC4KgTR6PAoeGoU2NOMIf3Z9UICqRfXB/J633zfT/OW+7Px/W8Hz/fz1vvz/u+RZ94eUPk64Ov6+NxXe/1baOPCy54/91/lgA3fOzWP5+//uLH9Q/+8rwu/vH1OGNMszxokOHHnMfrzJE2dR48lF0NyhxiRQpFnLcwjWceJ/yyTLj9mIcZRYtsol6tyWA1aHhL4SXpCFVpyTVYWrKhknycMip8dgb2UYIQkKbM+nZ0ET9OQ0wu3Y7CYcfpw4IjdkYSrbaiyO+6UCC3kaouaY4UNNySxCxfEkk4oit6I9IllbJo1xBpq3HEUgElSKlFuROKecOBres28uYkA7p+F6Ueh47kKb5NIoQPb+47DFC6JJycU3aRYJgJr7JcnBJ4iBXKZCZfCrlbpwqDHxRH7igISej5BQwVUnEAh9bs8nRomAgUpn55oRn3idKBX2OuagCPakKZtOMQZh+CpKhcnVAOsJpYNw6EE3FAfhVKw1Spp4GJmWe0MpPaCZN2Mu8lyajqwWIfPLyR0ixswQujB5RaNsDjcAvYKFigMAQqjKIGuxgvhaJLrDG9l0gHACIuiJp7LoGqGwV1trmiZM6eMTc4rFIr7LyEXDNXLI3mNvEC4iaAXYs2AjpbYcKZYwllNNtG4aZbIo2Ggbvy3gsoqWCAfUnTLjCGSPWsVLFpEDBO1UZMCbbLQnUuWuJptG0wx+rkKqVtVIgrK3kEctqSnKXPukRXqokmUBaPSo0NR/cYHMdkC2hZ6DpfTzBeuxSRbthSJau9gbgynDHuxsD0eH79B8Gk9EuZ9Et5AAAAAElFTkSuQmCC",
       "text/plain": [
-       "        eta  max_depth  min_child_weight     gamma  subsample  \\\n",
-       "0  0.098975          9          0.013528  0.000218   0.851506   \n",
-       "0  0.025540          3          0.051810  0.007490   0.514577   \n",
-       "0  0.000391         10          0.027727  0.013472   0.649744   \n",
-       "\n",
-       "   colsample_bytree split_type  \n",
-       "0          0.630858     random  \n",
-       "0          0.883731    uniprot  \n",
-       "0          0.511964   tanimoto  "
+       "<rdkit.Chem.rdchem.Mol at 0x7f415a5be570>"
       ]
      },
      "metadata": {},
      "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "xgboost_majority_vote: (3, 7)\n"
-     ]
-    },
+    }
+   ],
+   "source": [
+    "# Draw some PROTAC molecules using RDKit\n",
+    "from rdkit.Chem import Draw\n",
+    "\n",
+    "# Draw the first 10 PROTACs from PROTAC-DB\n",
+    "protac_db_mols = [Chem.MolFromSmiles(smiles) for smiles in protac_db_df['Smiles'].unique()[:5]]\n",
+    "protac_db_mols = [mol for mol in protac_db_mols if mol is not None]\n",
+    "protac_db_mols = protac_db_mols[:5]\n",
+    "for mol in protac_db_mols:\n",
+    "    display(mol)\n",
+    "# img = Draw.MolsToGridImage(protac_db_mols, molsPerRow=5, subImgSize=(200, 200))\n",
+    "# img"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Bar Plots of Performance Metrics"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from typing import Optional, List, Dict"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "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>test_acc</th>\n",
-       "      <th>test_roc_auc</th>\n",
-       "      <th>test_precision</th>\n",
-       "      <th>test_recall</th>\n",
-       "      <th>test_f1_score</th>\n",
-       "      <th>model_type</th>\n",
-       "      <th>split_type</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>0.779070</td>\n",
-       "      <td>0.880978</td>\n",
-       "      <td>0.723404</td>\n",
-       "      <td>0.850000</td>\n",
-       "      <td>0.781609</td>\n",
-       "      <td>XGBoost</td>\n",
-       "      <td>random</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>0.447059</td>\n",
-       "      <td>0.487179</td>\n",
-       "      <td>0.481481</td>\n",
-       "      <td>0.282609</td>\n",
-       "      <td>0.356164</td>\n",
-       "      <td>XGBoost</td>\n",
-       "      <td>uniprot</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>0.717647</td>\n",
-       "      <td>0.831081</td>\n",
-       "      <td>0.842105</td>\n",
-       "      <td>0.432432</td>\n",
-       "      <td>0.571429</td>\n",
-       "      <td>XGBoost</td>\n",
-       "      <td>tanimoto</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
       "text/plain": [
-       "   test_acc  test_roc_auc  test_precision  test_recall  test_f1_score  \\\n",
-       "0  0.779070      0.880978        0.723404     0.850000       0.781609   \n",
-       "0  0.447059      0.487179        0.481481     0.282609       0.356164   \n",
-       "0  0.717647      0.831081        0.842105     0.432432       0.571429   \n",
-       "\n",
-       "  model_type split_type  \n",
-       "0    XGBoost     random  \n",
-       "0    XGBoost    uniprot  \n",
-       "0    XGBoost   tanimoto  "
+       "Index(['train_loss', 'train_loss_step', 'train_loss_epoch', 'train_acc',\n",
+       "       'train_acc_epoch', 'train_f1_score', 'train_f1_score_epoch',\n",
+       "       'train_precision', 'train_precision_epoch', 'train_recall',\n",
+       "       'train_recall_epoch', 'train_roc_auc', 'train_roc_auc_epoch',\n",
+       "       'test_loss', 'test_acc', 'test_f1_score', 'test_precision',\n",
+       "       'test_recall', 'test_roc_auc', 'model_type', 'test_model_id',\n",
+       "       'train_len', 'train_active_perc', 'train_inactive_perc',\n",
+       "       'train_avg_tanimoto_dist', 'test_len', 'test_active_perc',\n",
+       "       'test_inactive_perc', 'test_avg_tanimoto_dist',\n",
+       "       'num_leaking_uniprot_train_test', 'num_leaking_smiles_train_test',\n",
+       "       'perc_leaking_uniprot_train_test', 'perc_leaking_smiles_train_test',\n",
+       "       'split_type'],\n",
+       "      dtype='object')"
       ]
      },
+     "execution_count": 11,
      "metadata": {},
-     "output_type": "display_data"
-    },
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "reports['test'].columns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "cellsonehot_cv_train: (15, 52)\n"
+      "Pytorch performances:\n",
+      "Metrics: ['Accuracy', 'ROC AUC']\n",
+      "Metric: Accuracy\n",
+      "Metric: ROC AUC\n",
+      "Plotting performance for main part of the paper...\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>model_type</th>\n",
-       "      <th>fold</th>\n",
-       "      <th>train_len</th>\n",
-       "      <th>val_len</th>\n",
-       "      <th>train_perc</th>\n",
-       "      <th>val_perc</th>\n",
-       "      <th>train_active_perc</th>\n",
-       "      <th>train_inactive_perc</th>\n",
-       "      <th>train_avg_tanimoto_dist</th>\n",
-       "      <th>val_active_perc</th>\n",
-       "      <th>...</th>\n",
-       "      <th>val_roc_auc</th>\n",
-       "      <th>test_loss</th>\n",
-       "      <th>test_acc</th>\n",
-       "      <th>test_f1_score</th>\n",
-       "      <th>test_precision</th>\n",
-       "      <th>test_recall</th>\n",
-       "      <th>test_roc_auc</th>\n",
-       "      <th>split_type</th>\n",
-       "      <th>train_unique_groups</th>\n",
-       "      <th>val_unique_groups</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Pytorch</td>\n",
-       "      <td>0</td>\n",
-       "      <td>616</td>\n",
-       "      <td>155</td>\n",
-       "      <td>0.798962</td>\n",
-       "      <td>0.201038</td>\n",
-       "      <td>0.514610</td>\n",
-       "      <td>0.485390</td>\n",
-       "      <td>0.376475</td>\n",
-       "      <td>0.516129</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.918667</td>\n",
-       "      <td>0.625937</td>\n",
-       "      <td>0.767442</td>\n",
-       "      <td>0.782609</td>\n",
-       "      <td>0.692308</td>\n",
-       "      <td>0.900</td>\n",
-       "      <td>0.816304</td>\n",
-       "      <td>standard</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Pytorch</td>\n",
-       "      <td>1</td>\n",
-       "      <td>617</td>\n",
-       "      <td>154</td>\n",
-       "      <td>0.800259</td>\n",
-       "      <td>0.199741</td>\n",
-       "      <td>0.513776</td>\n",
-       "      <td>0.486224</td>\n",
-       "      <td>0.377372</td>\n",
-       "      <td>0.519481</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.916047</td>\n",
-       "      <td>0.564669</td>\n",
-       "      <td>0.720930</td>\n",
-       "      <td>0.744681</td>\n",
-       "      <td>0.648148</td>\n",
-       "      <td>0.875</td>\n",
-       "      <td>0.845652</td>\n",
-       "      <td>standard</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>Pytorch</td>\n",
-       "      <td>2</td>\n",
-       "      <td>617</td>\n",
-       "      <td>154</td>\n",
-       "      <td>0.800259</td>\n",
-       "      <td>0.199741</td>\n",
-       "      <td>0.515397</td>\n",
-       "      <td>0.484603</td>\n",
-       "      <td>0.376572</td>\n",
-       "      <td>0.512987</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.898903</td>\n",
-       "      <td>0.564599</td>\n",
-       "      <td>0.732558</td>\n",
-       "      <td>0.752688</td>\n",
-       "      <td>0.660377</td>\n",
-       "      <td>0.875</td>\n",
-       "      <td>0.825543</td>\n",
-       "      <td>standard</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>Pytorch</td>\n",
-       "      <td>3</td>\n",
-       "      <td>617</td>\n",
-       "      <td>154</td>\n",
-       "      <td>0.800259</td>\n",
-       "      <td>0.199741</td>\n",
-       "      <td>0.515397</td>\n",
-       "      <td>0.484603</td>\n",
-       "      <td>0.376068</td>\n",
-       "      <td>0.512987</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.910717</td>\n",
-       "      <td>0.566597</td>\n",
-       "      <td>0.755814</td>\n",
-       "      <td>0.764045</td>\n",
-       "      <td>0.693878</td>\n",
-       "      <td>0.850</td>\n",
-       "      <td>0.836413</td>\n",
-       "      <td>standard</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>Pytorch</td>\n",
-       "      <td>4</td>\n",
-       "      <td>617</td>\n",
-       "      <td>154</td>\n",
-       "      <td>0.800259</td>\n",
-       "      <td>0.199741</td>\n",
-       "      <td>0.515397</td>\n",
-       "      <td>0.484603</td>\n",
-       "      <td>0.377543</td>\n",
-       "      <td>0.512987</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.908692</td>\n",
-       "      <td>0.538316</td>\n",
-       "      <td>0.802326</td>\n",
-       "      <td>0.808989</td>\n",
-       "      <td>0.734694</td>\n",
-       "      <td>0.900</td>\n",
-       "      <td>0.856522</td>\n",
-       "      <td>standard</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>5 rows × 52 columns</p>\n",
-       "</div>"
-      ],
+      "image/png": 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",
       "text/plain": [
-       "  model_type  fold  train_len  val_len  train_perc  val_perc  \\\n",
-       "0    Pytorch     0        616      155    0.798962  0.201038   \n",
-       "1    Pytorch     1        617      154    0.800259  0.199741   \n",
-       "2    Pytorch     2        617      154    0.800259  0.199741   \n",
-       "3    Pytorch     3        617      154    0.800259  0.199741   \n",
-       "4    Pytorch     4        617      154    0.800259  0.199741   \n",
-       "\n",
-       "   train_active_perc  train_inactive_perc  train_avg_tanimoto_dist  \\\n",
-       "0           0.514610             0.485390                 0.376475   \n",
-       "1           0.513776             0.486224                 0.377372   \n",
-       "2           0.515397             0.484603                 0.376572   \n",
-       "3           0.515397             0.484603                 0.376068   \n",
-       "4           0.515397             0.484603                 0.377543   \n",
-       "\n",
-       "   val_active_perc  ...  val_roc_auc  test_loss  test_acc  test_f1_score  \\\n",
-       "0         0.516129  ...     0.918667   0.625937  0.767442       0.782609   \n",
-       "1         0.519481  ...     0.916047   0.564669  0.720930       0.744681   \n",
-       "2         0.512987  ...     0.898903   0.564599  0.732558       0.752688   \n",
-       "3         0.512987  ...     0.910717   0.566597  0.755814       0.764045   \n",
-       "4         0.512987  ...     0.908692   0.538316  0.802326       0.808989   \n",
-       "\n",
-       "   test_precision  test_recall  test_roc_auc  split_type  train_unique_groups  \\\n",
-       "0        0.692308        0.900      0.816304    standard                  NaN   \n",
-       "1        0.648148        0.875      0.845652    standard                  NaN   \n",
-       "2        0.660377        0.875      0.825543    standard                  NaN   \n",
-       "3        0.693878        0.850      0.836413    standard                  NaN   \n",
-       "4        0.734694        0.900      0.856522    standard                  NaN   \n",
-       "\n",
-       "   val_unique_groups  \n",
-       "0                NaN  \n",
-       "1                NaN  \n",
-       "2                NaN  \n",
-       "3                NaN  \n",
-       "4                NaN  \n",
-       "\n",
-       "[5 rows x 52 columns]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "cellsonehot_test: (9, 34)\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>train_loss</th>\n",
-       "      <th>train_loss_step</th>\n",
-       "      <th>train_loss_epoch</th>\n",
-       "      <th>train_acc</th>\n",
-       "      <th>train_acc_epoch</th>\n",
-       "      <th>train_f1_score</th>\n",
-       "      <th>train_f1_score_epoch</th>\n",
-       "      <th>train_precision</th>\n",
-       "      <th>train_precision_epoch</th>\n",
-       "      <th>train_recall</th>\n",
-       "      <th>...</th>\n",
-       "      <th>train_avg_tanimoto_dist</th>\n",
-       "      <th>test_len</th>\n",
-       "      <th>test_active_perc</th>\n",
-       "      <th>test_inactive_perc</th>\n",
-       "      <th>test_avg_tanimoto_dist</th>\n",
-       "      <th>num_leaking_uniprot_train_test</th>\n",
-       "      <th>num_leaking_smiles_train_test</th>\n",
-       "      <th>perc_leaking_uniprot_train_test</th>\n",
-       "      <th>perc_leaking_smiles_train_test</th>\n",
-       "      <th>split_type</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>0.409341</td>\n",
-       "      <td>0.762586</td>\n",
-       "      <td>0.409341</td>\n",
-       "      <td>0.811933</td>\n",
-       "      <td>0.811933</td>\n",
-       "      <td>0.828808</td>\n",
-       "      <td>0.828808</td>\n",
-       "      <td>0.780000</td>\n",
-       "      <td>0.780000</td>\n",
-       "      <td>0.884131</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.376806</td>\n",
-       "      <td>86</td>\n",
-       "      <td>0.465116</td>\n",
-       "      <td>0.534884</td>\n",
-       "      <td>0.381147</td>\n",
-       "      <td>34</td>\n",
-       "      <td>44</td>\n",
-       "      <td>0.832685</td>\n",
-       "      <td>0.102464</td>\n",
-       "      <td>standard</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>0.392072</td>\n",
-       "      <td>0.457777</td>\n",
-       "      <td>0.392072</td>\n",
-       "      <td>0.801556</td>\n",
-       "      <td>0.801556</td>\n",
-       "      <td>0.821470</td>\n",
-       "      <td>0.821470</td>\n",
-       "      <td>0.765217</td>\n",
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-       "      <td>0.866499</td>\n",
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-       "   train_loss  train_loss_step  train_loss_epoch  train_acc  train_acc_epoch  \\\n",
-       "0    0.409341         0.762586          0.409341   0.811933         0.811933   \n",
-       "1    0.392072         0.457777          0.392072   0.801556         0.801556   \n",
-       "2    0.404924         0.757534          0.404924   0.797665         0.797665   \n",
-       "0    0.449669         0.444385          0.449669   0.797954         0.797954   \n",
-       "1    0.455240         0.706470          0.455240   0.792839         0.792839   \n",
-       "\n",
-       "   train_f1_score  train_f1_score_epoch  train_precision  \\\n",
-       "0        0.828808              0.828808         0.780000   \n",
-       "1        0.821470              0.821470         0.765217   \n",
-       "2        0.815166              0.815166         0.769575   \n",
-       "0        0.821267              0.821267         0.736308   \n",
-       "1        0.816327              0.816327         0.733198   \n",
-       "\n",
-       "   train_precision_epoch  train_recall  ...  train_avg_tanimoto_dist  \\\n",
-       "0               0.780000      0.884131  ...                 0.376806   \n",
-       "1               0.765217      0.886650  ...                 0.376806   \n",
-       "2               0.769575      0.866499  ...                 0.376806   \n",
-       "0               0.736308      0.928389  ...                 0.375305   \n",
-       "1               0.733198      0.920716  ...                 0.375305   \n",
-       "\n",
-       "   test_len  test_active_perc  test_inactive_perc  test_avg_tanimoto_dist  \\\n",
-       "0        86          0.465116            0.534884                0.381147   \n",
-       "1        86          0.465116            0.534884                0.381147   \n",
-       "2        86          0.465116            0.534884                0.381147   \n",
-       "0        85          0.541176            0.458824                0.394830   \n",
-       "1        85          0.541176            0.458824                0.394830   \n",
-       "\n",
-       "   num_leaking_uniprot_train_test  num_leaking_smiles_train_test  \\\n",
-       "0                              34                             44   \n",
-       "1                              34                             44   \n",
-       "2                              34                             44   \n",
-       "0                               0                              6   \n",
-       "1                               0                              6   \n",
-       "\n",
-       "   perc_leaking_uniprot_train_test  perc_leaking_smiles_train_test split_type  \n",
-       "0                         0.832685                        0.102464   standard  \n",
-       "1                         0.832685                        0.102464   standard  \n",
-       "2                         0.832685                        0.102464   standard  \n",
-       "0                         0.000000                        0.011658     target  \n",
-       "1                         0.000000                        0.011658     target  \n",
-       "\n",
-       "[5 rows x 34 columns]"
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-      "cellsonehot_hparam: (3, 7)\n"
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-       "   hidden_dim  learning_rate  smote_k_neighbors  use_smote   dropout  \\\n",
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-       "   test_acc  test_roc_auc  test_precision  test_recall  test_f1_score  \\\n",
-       "0  0.651163      0.722826        0.604167     0.725000       0.659091   \n",
-       "1  0.790698      0.852717        0.761905     0.800000       0.780488   \n",
-       "0  0.458824      0.619844        0.500000     0.260870       0.342857   \n",
-       "1  0.529412      0.583612        0.650000     0.282609       0.393939   \n",
-       "0  0.658824      0.715653        0.750000     0.324324       0.452830   \n",
-       "\n",
-       "   train_len  train_active_perc  train_inactive_perc  train_avg_tanimoto_dist  \\\n",
-       "0        771           0.514916             0.485084                 0.376806   \n",
-       "1        617           0.515397             0.484603                 0.377543   \n",
-       "0        772           0.506477             0.493523                 0.375305   \n",
-       "1        693           0.484848             0.515152                 0.377092   \n",
-       "0        772           0.518135             0.481865                 0.372540   \n",
-       "\n",
-       "   test_len  ...  val_active_perc  val_inactive_perc  val_avg_tanimoto_dist  \\\n",
-       "0        86  ...              NaN                NaN                    NaN   \n",
-       "1        86  ...         0.512987           0.487013               0.373853   \n",
-       "0        85  ...              NaN                NaN                    NaN   \n",
-       "1        85  ...         0.696203           0.303797               0.359625   \n",
-       "0        85  ...              NaN                NaN                    NaN   \n",
-       "\n",
-       "   num_leaking_uniprot_train_val  num_leaking_smiles_train_val  \\\n",
-       "0                            NaN                           NaN   \n",
-       "1                           40.0                          70.0   \n",
-       "0                            NaN                           NaN   \n",
-       "1                            0.0                           8.0   \n",
-       "0                            NaN                           NaN   \n",
-       "\n",
-       "   perc_leaking_uniprot_train_val  perc_leaking_smiles_train_val  cv_models  \\\n",
-       "0                             NaN                            NaN        NaN   \n",
-       "1                        0.910859                       0.176661       True   \n",
-       "0                             NaN                            NaN        NaN   \n",
-       "1                        0.000000                       0.014430       True   \n",
-       "0                             NaN                            NaN        NaN   \n",
-       "\n",
-       "   model_type  split_type  \n",
-       "0     Pytorch    standard  \n",
-       "1     Pytorch    standard  \n",
-       "0     Pytorch      target  \n",
-       "1     Pytorch      target  \n",
-       "0     Pytorch  similarity  \n",
-       "\n",
-       "[5 rows x 28 columns]"
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-      "aminoacidcnt_cv_train: (15, 52)\n"
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-       "      <th>...</th>\n",
-       "      <th>val_roc_auc</th>\n",
-       "      <th>test_loss</th>\n",
-       "      <th>test_acc</th>\n",
-       "      <th>test_f1_score</th>\n",
-       "      <th>test_precision</th>\n",
-       "      <th>test_recall</th>\n",
-       "      <th>test_roc_auc</th>\n",
-       "      <th>split_type</th>\n",
-       "      <th>train_unique_groups</th>\n",
-       "      <th>val_unique_groups</th>\n",
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-       "  </thead>\n",
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-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Pytorch</td>\n",
-       "      <td>0</td>\n",
-       "      <td>616</td>\n",
-       "      <td>155</td>\n",
-       "      <td>0.798962</td>\n",
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-       "      <td>0.516129</td>\n",
-       "      <td>...</td>\n",
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-       "      <td>0.750000</td>\n",
-       "      <td>0.687500</td>\n",
-       "      <td>0.825</td>\n",
-       "      <td>0.832609</td>\n",
-       "      <td>standard</td>\n",
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-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Pytorch</td>\n",
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-       "      <td>617</td>\n",
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-       "      <td>0.800259</td>\n",
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-       "      <td>0.909459</td>\n",
-       "      <td>0.742713</td>\n",
-       "      <td>0.779070</td>\n",
-       "      <td>0.786517</td>\n",
-       "      <td>0.714286</td>\n",
-       "      <td>0.875</td>\n",
-       "      <td>0.850543</td>\n",
-       "      <td>standard</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
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-       "      <th>2</th>\n",
-       "      <td>Pytorch</td>\n",
-       "      <td>2</td>\n",
-       "      <td>617</td>\n",
-       "      <td>154</td>\n",
-       "      <td>0.800259</td>\n",
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-       "      <td>Pytorch</td>\n",
-       "      <td>3</td>\n",
-       "      <td>617</td>\n",
-       "      <td>154</td>\n",
-       "      <td>0.800259</td>\n",
-       "      <td>0.199741</td>\n",
-       "      <td>0.515397</td>\n",
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-       "      <td>0.908017</td>\n",
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-       "      <td>0.732558</td>\n",
-       "      <td>0.747253</td>\n",
-       "      <td>0.666667</td>\n",
-       "      <td>0.850</td>\n",
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-       "      <td>standard</td>\n",
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-       "      <td>NaN</td>\n",
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-       "      <th>4</th>\n",
-       "      <td>Pytorch</td>\n",
-       "      <td>4</td>\n",
-       "      <td>617</td>\n",
-       "      <td>154</td>\n",
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-       "      <td>0.947342</td>\n",
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-       "      <td>0.802326</td>\n",
-       "      <td>0.795181</td>\n",
-       "      <td>0.767442</td>\n",
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-       "      <td>0.857609</td>\n",
-       "      <td>standard</td>\n",
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-       "      <td>NaN</td>\n",
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-       "</table>\n",
-       "<p>5 rows × 52 columns</p>\n",
-       "</div>"
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-       "  model_type  fold  train_len  val_len  train_perc  val_perc  \\\n",
-       "0    Pytorch     0        616      155    0.798962  0.201038   \n",
-       "1    Pytorch     1        617      154    0.800259  0.199741   \n",
-       "2    Pytorch     2        617      154    0.800259  0.199741   \n",
-       "3    Pytorch     3        617      154    0.800259  0.199741   \n",
-       "4    Pytorch     4        617      154    0.800259  0.199741   \n",
-       "\n",
-       "   train_active_perc  train_inactive_perc  train_avg_tanimoto_dist  \\\n",
-       "0           0.514610             0.485390                 0.376475   \n",
-       "1           0.513776             0.486224                 0.377372   \n",
-       "2           0.515397             0.484603                 0.376572   \n",
-       "3           0.515397             0.484603                 0.376068   \n",
-       "4           0.515397             0.484603                 0.377543   \n",
-       "\n",
-       "   val_active_perc  ...  val_roc_auc  test_loss  test_acc  test_f1_score  \\\n",
-       "0         0.516129  ...     0.894500   0.770433  0.744186       0.750000   \n",
-       "1         0.519481  ...     0.909459   0.742713  0.779070       0.786517   \n",
-       "2         0.512987  ...     0.904304   0.639202  0.790698       0.785714   \n",
-       "3         0.512987  ...     0.908017   0.749724  0.732558       0.747253   \n",
-       "4         0.512987  ...     0.947342   0.720424  0.802326       0.795181   \n",
-       "\n",
-       "   test_precision  test_recall  test_roc_auc  split_type  train_unique_groups  \\\n",
-       "0        0.687500        0.825      0.832609    standard                  NaN   \n",
-       "1        0.714286        0.875      0.850543    standard                  NaN   \n",
-       "2        0.750000        0.825      0.845109    standard                  NaN   \n",
-       "3        0.666667        0.850      0.838587    standard                  NaN   \n",
-       "4        0.767442        0.825      0.857609    standard                  NaN   \n",
-       "\n",
-       "   val_unique_groups  \n",
-       "0                NaN  \n",
-       "1                NaN  \n",
-       "2                NaN  \n",
-       "3                NaN  \n",
-       "4                NaN  \n",
-       "\n",
-       "[5 rows x 52 columns]"
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-      "aminoacidcnt_test: (9, 34)\n"
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-       "   train_loss  train_loss_step  train_loss_epoch  train_acc  train_acc_epoch  \\\n",
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-       "1    0.295044         0.491786          0.295044   0.886189         0.886189   \n",
-       "\n",
-       "   train_f1_score  train_f1_score_epoch  train_precision  \\\n",
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-       "1        0.950125              0.950125         0.940741   \n",
-       "2        0.944099              0.944099         0.931373   \n",
-       "0        0.802484              0.802484         0.780193   \n",
-       "1        0.886335              0.886335         0.885204   \n",
-       "\n",
-       "   train_precision_epoch  train_recall  ...  train_avg_tanimoto_dist  \\\n",
-       "0               0.929114      0.924433  ...                 0.376806   \n",
-       "1               0.940741      0.959698  ...                 0.376806   \n",
-       "2               0.931373      0.957179  ...                 0.376806   \n",
-       "0               0.780193      0.826087  ...                 0.375305   \n",
-       "1               0.885204      0.887468  ...                 0.375305   \n",
-       "\n",
-       "   test_len  test_active_perc  test_inactive_perc  test_avg_tanimoto_dist  \\\n",
-       "0        86          0.465116            0.534884                0.381147   \n",
-       "1        86          0.465116            0.534884                0.381147   \n",
-       "2        86          0.465116            0.534884                0.381147   \n",
-       "0        85          0.541176            0.458824                0.394830   \n",
-       "1        85          0.541176            0.458824                0.394830   \n",
-       "\n",
-       "   num_leaking_uniprot_train_test  num_leaking_smiles_train_test  \\\n",
-       "0                              34                             44   \n",
-       "1                              34                             44   \n",
-       "2                              34                             44   \n",
-       "0                               0                              6   \n",
-       "1                               0                              6   \n",
-       "\n",
-       "   perc_leaking_uniprot_train_test  perc_leaking_smiles_train_test split_type  \n",
-       "0                         0.832685                        0.102464   standard  \n",
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-       "2                         0.832685                        0.102464   standard  \n",
-       "0                         0.000000                        0.011658     target  \n",
-       "1                         0.000000                        0.011658     target  \n",
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-      "aminoacidcnt_hparam: (3, 7)\n"
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-       "</div>"
-      ],
-      "text/plain": [
-       "   hidden_dim  learning_rate  smote_k_neighbors  use_smote   dropout  \\\n",
-       "0         256       0.000519                 13       True  0.491166   \n",
-       "0         256       0.000540                 14       True  0.476270   \n",
-       "0          64       0.000326                 13      False  0.066941   \n",
-       "\n",
-       "   use_batch_norm  split_type  \n",
-       "0            True    standard  \n",
-       "0            True      target  \n",
-       "0            True  similarity  "
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "aminoacidcnt_majority_vote: (6, 28)\n"
-     ]
-    },
-    {
-     "data": {
-      "text/html": [
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-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>test_acc</th>\n",
-       "      <th>test_roc_auc</th>\n",
-       "      <th>test_precision</th>\n",
-       "      <th>test_recall</th>\n",
-       "      <th>test_f1_score</th>\n",
-       "      <th>train_len</th>\n",
-       "      <th>train_active_perc</th>\n",
-       "      <th>train_inactive_perc</th>\n",
-       "      <th>train_avg_tanimoto_dist</th>\n",
-       "      <th>test_len</th>\n",
-       "      <th>...</th>\n",
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-       "      <th>val_avg_tanimoto_dist</th>\n",
-       "      <th>num_leaking_uniprot_train_val</th>\n",
-       "      <th>num_leaking_smiles_train_val</th>\n",
-       "      <th>perc_leaking_uniprot_train_val</th>\n",
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-       "      <th>model_type</th>\n",
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-       "      <td>0.514916</td>\n",
-       "      <td>0.485084</td>\n",
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-       "      <td>0.813953</td>\n",
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-       "      <td>86</td>\n",
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-       "      <td>0.373853</td>\n",
-       "      <td>40.0</td>\n",
-       "      <td>70.0</td>\n",
-       "      <td>0.910859</td>\n",
-       "      <td>0.176661</td>\n",
-       "      <td>True</td>\n",
-       "      <td>Pytorch</td>\n",
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-       "      <td>0.505882</td>\n",
-       "      <td>0.530658</td>\n",
-       "      <td>0.571429</td>\n",
-       "      <td>0.347826</td>\n",
-       "      <td>0.432432</td>\n",
-       "      <td>772</td>\n",
-       "      <td>0.506477</td>\n",
-       "      <td>0.493523</td>\n",
-       "      <td>0.375305</td>\n",
-       "      <td>85</td>\n",
-       "      <td>...</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
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-       "      <td>NaN</td>\n",
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-       "      <td>0.391304</td>\n",
-       "      <td>0.439024</td>\n",
-       "      <td>693</td>\n",
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-       "      <td>0.515152</td>\n",
-       "      <td>0.377092</td>\n",
-       "      <td>85</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.696203</td>\n",
-       "      <td>0.303797</td>\n",
-       "      <td>0.359625</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>8.0</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.014430</td>\n",
-       "      <td>True</td>\n",
-       "      <td>Pytorch</td>\n",
-       "      <td>target</td>\n",
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-       "      <th>0</th>\n",
-       "      <td>0.670588</td>\n",
-       "      <td>0.719032</td>\n",
-       "      <td>0.695652</td>\n",
-       "      <td>0.432432</td>\n",
-       "      <td>0.533333</td>\n",
-       "      <td>772</td>\n",
-       "      <td>0.518135</td>\n",
-       "      <td>0.481865</td>\n",
-       "      <td>0.372540</td>\n",
-       "      <td>85</td>\n",
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-       "      <td>similarity</td>\n",
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-       "  </tbody>\n",
-       "</table>\n",
-       "<p>5 rows × 28 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   test_acc  test_roc_auc  test_precision  test_recall  test_f1_score  \\\n",
-       "0  0.802326      0.842391        0.767442     0.825000       0.795181   \n",
-       "1  0.813953      0.858152        0.815789     0.775000       0.794872   \n",
-       "0  0.505882      0.530658        0.571429     0.347826       0.432432   \n",
-       "1  0.458824      0.501115        0.500000     0.391304       0.439024   \n",
-       "0  0.670588      0.719032        0.695652     0.432432       0.533333   \n",
-       "\n",
-       "   train_len  train_active_perc  train_inactive_perc  train_avg_tanimoto_dist  \\\n",
-       "0        771           0.514916             0.485084                 0.376806   \n",
-       "1        617           0.515397             0.484603                 0.377543   \n",
-       "0        772           0.506477             0.493523                 0.375305   \n",
-       "1        693           0.484848             0.515152                 0.377092   \n",
-       "0        772           0.518135             0.481865                 0.372540   \n",
-       "\n",
-       "   test_len  ...  val_active_perc  val_inactive_perc  val_avg_tanimoto_dist  \\\n",
-       "0        86  ...              NaN                NaN                    NaN   \n",
-       "1        86  ...         0.512987           0.487013               0.373853   \n",
-       "0        85  ...              NaN                NaN                    NaN   \n",
-       "1        85  ...         0.696203           0.303797               0.359625   \n",
-       "0        85  ...              NaN                NaN                    NaN   \n",
-       "\n",
-       "   num_leaking_uniprot_train_val  num_leaking_smiles_train_val  \\\n",
-       "0                            NaN                           NaN   \n",
-       "1                           40.0                          70.0   \n",
-       "0                            NaN                           NaN   \n",
-       "1                            0.0                           8.0   \n",
-       "0                            NaN                           NaN   \n",
-       "\n",
-       "   perc_leaking_uniprot_train_val  perc_leaking_smiles_train_val  cv_models  \\\n",
-       "0                             NaN                            NaN        NaN   \n",
-       "1                        0.910859                       0.176661       True   \n",
-       "0                             NaN                            NaN        NaN   \n",
-       "1                        0.000000                       0.014430       True   \n",
-       "0                             NaN                            NaN        NaN   \n",
-       "\n",
-       "   model_type  split_type  \n",
-       "0     Pytorch    standard  \n",
-       "1     Pytorch    standard  \n",
-       "0     Pytorch      target  \n",
-       "1     Pytorch      target  \n",
-       "0     Pytorch  similarity  \n",
-       "\n",
-       "[5 rows x 28 columns]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "active_col = 'Active (Dmax 0.6, pDC50 6.0)'\n",
-    "test_split = 0.1\n",
-    "n_models_for_test = 3\n",
-    "cv_n_folds = 5\n",
-    "\n",
-    "active_name = active_col.replace(' ', '_').replace('(', '').replace(')', '').replace(',', '')\n",
-    "report_base_name = f'{active_name}_test_split_{test_split}'\n",
-    "\n",
-    "# TODO: Maybe a function to get the experiment dataframes would help...\n",
-    "# def get_experiment_df(experiment_name, report, splits=['random', 'uniprot', 'tanimoto']):\n",
-    "#     for split in splits:\n",
-    "#         df = []\n",
-    "#         for report_name in ['cv', 'ablation', 'hparam', 'majority_vote']:\n",
-    "#             filename = f'reports/{report_name}_report_{report_base_name}_{split}.csv'\n",
-    "#             df.append(pd.read_csv(filename))\n",
-    "#         report[experiment_name] = pd.concat(df)\n",
-    "#     return report\n",
-    "\n",
-    "# Load the data\n",
-    "reports = {\n",
-    "    'cv_train': pd.concat([\n",
-    "        pd.read_csv(f'reports/cv_report_{report_base_name}_random.csv'),\n",
-    "        pd.read_csv(f'reports/cv_report_{report_base_name}_uniprot.csv'),\n",
-    "        pd.read_csv(f'reports/cv_report_{report_base_name}_tanimoto.csv'),\n",
-    "    ]),\n",
-    "    'test': pd.concat([\n",
-    "        pd.read_csv(f'reports/test_report_{report_base_name}_random.csv'),\n",
-    "        pd.read_csv(f'reports/test_report_{report_base_name}_uniprot.csv'),\n",
-    "        pd.read_csv(f'reports/test_report_{report_base_name}_tanimoto.csv'),\n",
-    "    ]),\n",
-    "    'ablation': pd.concat([\n",
-    "        pd.read_csv(f'reports/ablation_zero_vectors_report_{report_base_name}_random.csv'),\n",
-    "        pd.read_csv(f'reports/ablation_zero_vectors_report_{report_base_name}_uniprot.csv'),\n",
-    "        pd.read_csv(f'reports/ablation_zero_vectors_report_{report_base_name}_tanimoto.csv'),\n",
-    "    ]),\n",
-    "    'hparam': pd.concat([\n",
-    "        pd.read_csv(f'reports/hparam_report_{report_base_name}_random.csv'),\n",
-    "        pd.read_csv(f'reports/hparam_report_{report_base_name}_uniprot.csv'),\n",
-    "        pd.read_csv(f'reports/hparam_report_{report_base_name}_tanimoto.csv'),\n",
-    "    ]),\n",
-    "    'majority_vote': pd.concat([\n",
-    "        pd.read_csv(f'reports/majority_vote_report_{report_base_name}_random.csv'),\n",
-    "        pd.read_csv(f'reports/majority_vote_report_{report_base_name}_uniprot.csv'),\n",
-    "        pd.read_csv(f'reports/majority_vote_report_{report_base_name}_tanimoto.csv'),\n",
-    "    ]),\n",
-    "    'xgboost_cv_train': pd.concat([\n",
-    "        pd.read_csv(f'reports/xgboost_cv_report_{report_base_name}_random.csv'),\n",
-    "        pd.read_csv(f'reports/xgboost_cv_report_{report_base_name}_uniprot.csv'),\n",
-    "        pd.read_csv(f'reports/xgboost_cv_report_{report_base_name}_tanimoto.csv'),\n",
-    "    ]),\n",
-    "    'xgboost_test': pd.concat([\n",
-    "        pd.read_csv(f'reports/xgboost_test_report_{report_base_name}_random.csv'),\n",
-    "        pd.read_csv(f'reports/xgboost_test_report_{report_base_name}_uniprot.csv'),\n",
-    "        pd.read_csv(f'reports/xgboost_test_report_{report_base_name}_tanimoto.csv'),\n",
-    "    ]),\n",
-    "    'xgboost_hparam': pd.concat([\n",
-    "        pd.read_csv(f'reports/xgboost_hparam_report_{report_base_name}_random.csv'),\n",
-    "        pd.read_csv(f'reports/xgboost_hparam_report_{report_base_name}_uniprot.csv'),\n",
-    "        pd.read_csv(f'reports/xgboost_hparam_report_{report_base_name}_tanimoto.csv'),\n",
-    "    ]),\n",
-    "    'xgboost_majority_vote': pd.concat([\n",
-    "        pd.read_csv(f'reports/xgboost_majority_vote_report_{report_base_name}_random.csv'),\n",
-    "        pd.read_csv(f'reports/xgboost_majority_vote_report_{report_base_name}_uniprot.csv'),\n",
-    "        pd.read_csv(f'reports/xgboost_majority_vote_report_{report_base_name}_tanimoto.csv'),\n",
-    "    ]),\n",
-    "}\n",
-    "for experiment in ['cellsonehot', 'aminoacidcnt']:\n",
-    "    reports[f'{experiment}_cv_train'] = pd.concat([\n",
-    "        pd.read_csv(f'reports/{experiment}_cv_report_{report_base_name}_standard.csv'),\n",
-    "        pd.read_csv(f'reports/{experiment}_cv_report_{report_base_name}_target.csv'),\n",
-    "        pd.read_csv(f'reports/{experiment}_cv_report_{report_base_name}_similarity.csv'),\n",
-    "    ])\n",
-    "    reports[f'{experiment}_test'] = pd.concat([\n",
-    "        pd.read_csv(f'reports/{experiment}_test_report_{report_base_name}_standard.csv'),\n",
-    "        pd.read_csv(f'reports/{experiment}_test_report_{report_base_name}_target.csv'),\n",
-    "        pd.read_csv(f'reports/{experiment}_test_report_{report_base_name}_similarity.csv'),\n",
-    "    ])\n",
-    "    reports[f'{experiment}_hparam'] = pd.concat([\n",
-    "        pd.read_csv(f'reports/{experiment}_hparam_report_{report_base_name}_standard.csv'),\n",
-    "        pd.read_csv(f'reports/{experiment}_hparam_report_{report_base_name}_target.csv'),\n",
-    "        pd.read_csv(f'reports/{experiment}_hparam_report_{report_base_name}_similarity.csv'),\n",
-    "    ])\n",
-    "    reports[f'{experiment}_majority_vote'] = pd.concat([\n",
-    "        pd.read_csv(f'reports/{experiment}_majority_vote_report_{report_base_name}_standard.csv'),\n",
-    "        pd.read_csv(f'reports/{experiment}_majority_vote_report_{report_base_name}_target.csv'),\n",
-    "        pd.read_csv(f'reports/{experiment}_majority_vote_report_{report_base_name}_similarity.csv'),\n",
-    "    ])\n",
-    "\n",
-    "for k, report in reports.items():\n",
-    "    print(f'{k}: {report.shape}')\n",
-    "    display(report.head())"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 129,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['standard', 'standard', 'target', 'target', 'similarity', 'similarity']"
-      ]
-     },
-     "execution_count": 129,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "reports['cellsonehot_majority_vote']['split_type'].to_list()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Print CV Folds Parameters\n",
-    "\n",
-    "Print the dataset characteristics for each fold set per experiment."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 131,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\\begin{tabular}{rlrrrllllll}\n",
-      "\\toprule\n",
-      " \\textbf{Fold} & \\textbf{Study split} &  \\textbf{Train size} &  \\textbf{Val size} &  \\textbf{Test size} & \\textbf{Train active \\%} & \\textbf{Val active \\%} & \\textbf{Test active \\%} & \\textbf{Leaking Uniprot \\%} & \\textbf{Leaking SMILES \\%} & \\textbf{Avg Tanimoto distance} \\\\\n",
-      "\\midrule\n",
-      "             0 &             Standard &                  616 &                155 &                  86 &                   51.5\\% &                 51.6\\% &                  46.5\\% &                      82.5\\% &                     11.2\\% &                          0.381 \\\\\n",
-      "             1 &             Standard &                  617 &                154 &                  86 &                   51.4\\% &                 51.9\\% &                  46.5\\% &                      84.0\\% &                     10.2\\% &                          0.381 \\\\\n",
-      "             2 &             Standard &                  617 &                154 &                  86 &                   51.5\\% &                 51.3\\% &                  46.5\\% &                      83.8\\% &                      9.4\\% &                          0.381 \\\\\n",
-      "             3 &             Standard &                  617 &                154 &                  86 &                   51.5\\% &                 51.3\\% &                  46.5\\% &                      82.3\\% &                     10.4\\% &                          0.381 \\\\\n",
-      "             4 &             Standard &                  617 &                154 &                  86 &                   51.5\\% &                 51.3\\% &                  46.5\\% &                      83.8\\% &                     10.0\\% &                          0.381 \\\\\n",
-      "             0 &               Target &                  560 &                212 &                  85 &                   54.5\\% &                 40.6\\% &                  54.1\\% &                       0.0\\% &                      1.1\\% &                          0.395 \\\\\n",
-      "             1 &               Target &                  627 &                145 &                  85 &                   51.7\\% &                 46.2\\% &                  54.1\\% &                       0.0\\% &                      0.8\\% &                          0.395 \\\\\n",
-      "             2 &               Target &                  662 &                110 &                  85 &                   50.6\\% &                 50.9\\% &                  54.1\\% &                       0.0\\% &                      1.2\\% &                          0.395 \\\\\n",
-      "             3 &               Target &                  546 &                226 &                  85 &                   48.4\\% &                 56.2\\% &                  54.1\\% &                       0.0\\% &                      1.5\\% &                          0.395 \\\\\n",
-      "             4 &               Target &                  693 &                 79 &                  85 &                   48.5\\% &                 69.6\\% &                  54.1\\% &                       0.0\\% &                      1.3\\% &                          0.395 \\\\\n",
-      "             0 &           Similarity &                  660 &                112 &                  85 &                   51.5\\% &                 53.6\\% &                  43.5\\% &                      57.7\\% &                      0.0\\% &                          0.420 \\\\\n",
-      "             1 &           Similarity &                  589 &                183 &                  85 &                   49.7\\% &                 58.5\\% &                  43.5\\% &                      56.4\\% &                      0.0\\% &                          0.420 \\\\\n",
-      "             2 &           Similarity &                  616 &                156 &                  85 &                   54.2\\% &                 42.3\\% &                  43.5\\% &                      57.3\\% &                      0.0\\% &                          0.420 \\\\\n",
-      "             3 &           Similarity &                  598 &                174 &                  85 &                   52.8\\% &                 48.3\\% &                  43.5\\% &                      56.5\\% &                      0.0\\% &                          0.420 \\\\\n",
-      "             4 &           Similarity &                  625 &                147 &                  85 &                   50.7\\% &                 56.5\\% &                  43.5\\% &                      57.0\\% &                      0.0\\% &                          0.420 \\\\\n",
-      "\\bottomrule\n",
-      "\\end{tabular}\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/tmp/ipykernel_2173070/2599982738.py:35: FutureWarning: In future versions `DataFrame.to_latex` is expected to utilise the base implementation of `Styler.to_latex` for formatting and rendering. The arguments signature may therefore change. It is recommended instead to use `DataFrame.style.to_latex` which also contains additional functionality.\n",
-      "  print(tmp.to_latex(index=False, escape=False))\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>train_active_perc</th>\n",
-       "      <th>val_active_perc</th>\n",
-       "      <th>test_active_perc</th>\n",
-       "      <th>perc_leaking_uniprot_train_test</th>\n",
-       "      <th>perc_leaking_smiles_train_test</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>split_type</th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>random</th>\n",
-       "      <td>51.491559</td>\n",
-       "      <td>51.491412</td>\n",
-       "      <td>46.511628</td>\n",
-       "      <td>83.268223</td>\n",
-       "      <td>10.246743</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>tanimoto</th>\n",
-       "      <td>51.808814</td>\n",
-       "      <td>51.817503</td>\n",
-       "      <td>43.529412</td>\n",
-       "      <td>56.976186</td>\n",
-       "      <td>0.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>uniprot</th>\n",
-       "      <td>50.715931</td>\n",
-       "      <td>52.699394</td>\n",
-       "      <td>54.117647</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>1.168248</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "            train_active_perc  val_active_perc  test_active_perc  \\\n",
-       "split_type                                                         \n",
-       "random              51.491559        51.491412         46.511628   \n",
-       "tanimoto            51.808814        51.817503         43.529412   \n",
-       "uniprot             50.715931        52.699394         54.117647   \n",
-       "\n",
-       "            perc_leaking_uniprot_train_test  perc_leaking_smiles_train_test  \n",
-       "split_type                                                                   \n",
-       "random                            83.268223                       10.246743  \n",
-       "tanimoto                          56.976186                        0.000000  \n",
-       "uniprot                            0.000000                        1.168248  "
-      ]
-     },
-     "execution_count": 131,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "cols_to_show = {\n",
-    "    'fold': 'Fold',\n",
-    "    'split_type': 'Study split',\n",
-    "    'train_len': 'Train size',\n",
-    "    'val_len': 'Val size',\n",
-    "    'test_len': 'Test size',\n",
-    "    'train_active_perc': 'Train active %',\n",
-    "    'val_active_perc': 'Val active %',\n",
-    "    'test_active_perc': 'Test active %',\n",
-    "    # 'train_unique_groups': '',\n",
-    "    # 'val_unique_groups': '',\n",
-    "    'perc_leaking_uniprot_train_test': 'Leaking Uniprot %',\n",
-    "    'perc_leaking_smiles_train_test': 'Leaking SMILES %',\n",
-    "    'test_avg_tanimoto_dist': 'Avg Tanimoto distance',\n",
-    "}\n",
-    "# print(reports['cv_train'][cols_to_show].to_markdown(index=False))\n",
-    "# Print a subset of columns (that contain the string \"perc_\") as percentages in format: .1%\n",
-    "tmp = reports['cv_train'][list(cols_to_show.keys())].copy()\n",
-    "for col in tmp.columns:\n",
-    "    if 'perc' in col:\n",
-    "        tmp[col] = tmp[col].apply(lambda x: f'{x*100:.1f}\\\\%')\n",
-    "    if 'dist' in col:\n",
-    "        tmp[col] = tmp[col].apply(lambda x: f'{x:.3f}')\n",
-    "# Rename columns\n",
-    "tmp.rename(columns=cols_to_show, inplace=True)\n",
-    "# Rename studies\n",
-    "tmp['Study split'] = tmp['Study split'].replace({\n",
-    "    'random': 'Standard',\n",
-    "    'uniprot': 'Target',\n",
-    "    'tanimoto': 'Similarity',\n",
-    "})\n",
-    "tmp = tmp[list(cols_to_show.values())]\n",
-    "tmp.columns = [f\"\\\\textbf{{{col}}}\".replace('%', '\\\\%') for col in tmp.columns]\n",
-    "# Print to LaTeX\n",
-    "print(tmp.to_latex(index=False, escape=False))\n",
-    "\n",
-    "# Print the average active % for each study split (for train val and test sets)\n",
-    "tmp = reports['cv_train'].groupby(['split_type'])[['train_active_perc', 'val_active_perc', 'test_active_perc', 'perc_leaking_uniprot_train_test', 'perc_leaking_smiles_train_test']].mean()\n",
-    "tmp = tmp * 100\n",
-    "tmp"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Plot (Raw) Datasets Information"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 132,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "PROTAC-DB: (5388, 89)\n",
-      "PROTAC-Pedia: (1203, 43)\n"
-     ]
-    }
-   ],
-   "source": [
-    "data_dir = 'data'\n",
-    "\n",
-    "protac_db_df = pd.read_csv(os.path.join(data_dir, 'PROTAC-DB.csv'))\n",
-    "protac_pedia_df = pd.read_csv(os.path.join(data_dir, 'PROTAC-Pedia.csv'))\n",
-    "print(f'PROTAC-DB: {protac_db_df.shape}')\n",
-    "print(f'PROTAC-Pedia: {protac_pedia_df.shape}')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 133,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Index(['PROTACDB ID', 'PROTAC SMILES', 'Active/Inactive', 'Best PROTAC',\n",
-       "       'Cells', 'cLogP', 'Comments', 'Curator', 'Dc50', 'Dmax',\n",
-       "       'E3 Binder SMILES', 'E3 Ligase', 'Ec50 of Ligand Cells',\n",
-       "       'Ec50 of PROTAC Cells', 'exit_vector', 'Hbond acceptors',\n",
-       "       'Hbond donors', 'Ic50 of Ligand', 'Ic50 of PROTAC', 'Ligand Name',\n",
-       "       'Ligand SMILES', 'Linker', 'Linker Type', 'linker_ha', 'linker_no',\n",
-       "       'linker_rb', 'MW', 'Off Targets Reported', 'PATENT', 'Ligand PDB',\n",
-       "       'Ligand ID', 'Pubmed', 'PROTAC Name', 'Proteomics Data Available',\n",
-       "       'Secondary Pubmed', 'Status', 'Target',\n",
-       "       'Tested A Non Binding E3 Control', 'Tested Competition With Ligand',\n",
-       "       'Tested Engagement In Cells', 'Tested Proteaseome Inhibitor', 'Time',\n",
-       "       'TPSA'],\n",
-       "      dtype='object')"
-      ]
-     },
-     "execution_count": 133,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "protac_pedia_df.columns"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 134,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "63dc03fdd7324a3399eb19ed7f46d3c8",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "PROTAC-DB:   0%|          | 0/5388 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "ea699ebfd89c48f0b521c5005132ff9b",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "PROTAC-Pedia:   0%|          | 0/1203 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Canonize SMILES column in both datasets\n",
-    "from rdkit import Chem\n",
-    "from rdkit.Chem import AllChem\n",
-    "# Import tqdm for jupyter\n",
-    "from tqdm.notebook import tqdm\n",
-    "\n",
-    "def canonize_smiles(smiles):\n",
-    "    mol = Chem.MolFromSmiles(smiles)\n",
-    "    if mol is None:\n",
-    "        return None\n",
-    "    return Chem.MolToSmiles(mol)\n",
-    "\n",
-    "tqdm.pandas(desc='PROTAC-DB')\n",
-    "protac_db_df['Smiles'] = protac_db_df['Smiles'].progress_apply(canonize_smiles)\n",
-    "tqdm.pandas(desc='PROTAC-Pedia')\n",
-    "protac_pedia_df['PROTAC SMILES'] = protac_pedia_df['PROTAC SMILES'].progress_apply(canonize_smiles)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 135,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "PROTAC-DB unique SMILES: 3270\n",
-      "PROTAC-Pedia unique SMILES: 1178\n",
-      "PROTAC-DB unique targets: 323\n",
-      "PROTAC-Pedia unique targets: 79\n",
-      "PROTAC-DB single SMILES: 2451 (45.5%)\n",
-      "PROTAC-Pedia single SMILES: 1153 (95.8%)\n",
-      "PROTAC-DB single targets: 78 (1.4%)\n",
-      "PROTAC-Pedia single targets: 15 (1.2%)\n",
-      "Shared SMILES: 807 (24.7%) of PROTAC-DB, (68.5%) of PROTAC-Pedia\n",
-      "Shared targets: 0 (0.0%) of PROTAC-DB, (0.0%) of PROTAC-Pedia\n",
-      "\\begin{tabular}{lrrrrlrlrl}\n",
-      "\\toprule\n",
-      "\\textbf{Dataset} &  \\textbf{Length} &  \\textbf{Unique SMILES} &  \\textbf{Unique Targets} &  \\textbf{Shared SMILES} & \\textbf{Shared SMILES \\%} &  \\textbf{Single SMILES} & \\textbf{Single SMILES \\%} &  \\textbf{Single Targets} & \\textbf{Single Targets \\%} \\\\\n",
-      "\\midrule\n",
-      "       PROTAC-DB &             5388 &                    3270 &                      323 &                    1222 &                    22.7\\% &                    2451 &                    45.5\\% &                       78 &                      1.4\\% \\\\\n",
-      "    PROTAC-Pedia &             1203 &                    1178 &                       79 &                     832 &                    69.2\\% &                    1153 &                    95.8\\% &                       15 &                      1.2\\% \\\\\n",
-      "\\bottomrule\n",
-      "\\end{tabular}\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/tmp/ipykernel_2173070/285611913.py:62: FutureWarning: In future versions `DataFrame.to_latex` is expected to utilise the base implementation of `Styler.to_latex` for formatting and rendering. The arguments signature may therefore change. It is recommended instead to use `DataFrame.style.to_latex` which also contains additional functionality.\n",
-      "  print(shared_stats.to_latex(index=False, escape=False))\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Count the number of unique SMILES in both datasets (and as percentage)\n",
-    "protac_db_unique_smiles = protac_db_df['Smiles'].nunique()\n",
-    "protac_pedia_unique_smiles = protac_pedia_df['PROTAC SMILES'].nunique()\n",
-    "print(f'PROTAC-DB unique SMILES: {protac_db_unique_smiles}')\n",
-    "print(f'PROTAC-Pedia unique SMILES: {protac_pedia_unique_smiles}')\n",
-    "\n",
-    "# Count the number of unique targets in both datasets (and as a percentage)\n",
-    "protac_db_unique_targets = protac_db_df['Target'].nunique()\n",
-    "protac_pedia_unique_targets = protac_pedia_df['Target'].nunique()\n",
-    "print(f'PROTAC-DB unique targets: {protac_db_unique_targets}')\n",
-    "print(f'PROTAC-Pedia unique targets: {protac_pedia_unique_targets}')\n",
-    "\n",
-    "# Count the number of entries in both datasets with a SMILES that appears only once (and show the percentage compare to the total)\n",
-    "protac_db_single_smiles = protac_db_df['Smiles'].value_counts().value_counts().get(1, 0)\n",
-    "protac_pedia_single_smiles = protac_pedia_df['PROTAC SMILES'].value_counts().value_counts().get(1, 0)\n",
-    "print(f'PROTAC-DB single SMILES: {protac_db_single_smiles} ({protac_db_single_smiles / protac_db_df.shape[0]:.1%})')\n",
-    "print(f'PROTAC-Pedia single SMILES: {protac_pedia_single_smiles} ({protac_pedia_single_smiles / protac_pedia_df.shape[0]:.1%})')\n",
-    "\n",
-    "# Count the number of entries in both datasets with a target that appears only once (and show the percentage compare to the total)\n",
-    "protac_db_single_targets = protac_db_df['Target'].value_counts().value_counts().get(1, 0)\n",
-    "protac_pedia_single_targets = protac_pedia_df['Target'].value_counts().value_counts().get(1, 0)\n",
-    "print(f'PROTAC-DB single targets: {protac_db_single_targets} ({protac_db_single_targets / protac_db_df.shape[0]:.1%})')\n",
-    "print(f'PROTAC-Pedia single targets: {protac_pedia_single_targets} ({protac_pedia_single_targets / protac_pedia_df.shape[0]:.1%})')\n",
-    "\n",
-    "# Count the number of shared SMILES between the two datasets\n",
-    "protac_db_unique_smiles = set(protac_db_df['Smiles'].unique())\n",
-    "protac_pedia_unique_smiles = set(protac_pedia_df['PROTAC SMILES'].unique())\n",
-    "shared_smiles = protac_db_unique_smiles.intersection(protac_pedia_unique_smiles)\n",
-    "print(f'Shared SMILES: {len(shared_smiles)} ({len(shared_smiles) / len(protac_db_unique_smiles):.1%}) of PROTAC-DB, ({len(shared_smiles) / len(protac_pedia_unique_smiles):.1%}) of PROTAC-Pedia')\n",
-    "\n",
-    "# Count the number of shared targets between the two datasets\n",
-    "protac_db_unique_targets = set(protac_db_df['Target'].unique())\n",
-    "protac_pedia_unique_targets = set(protac_pedia_df['Target'].unique())\n",
-    "shared_targets = protac_db_unique_targets.intersection(protac_pedia_unique_targets)\n",
-    "print(f'Shared targets: {len(shared_targets)} ({len(shared_targets) / len(protac_db_unique_targets):.1%}) of PROTAC-DB, ({len(shared_targets) / len(protac_pedia_unique_targets):.1%}) of PROTAC-Pedia')\n",
-    "\n",
-    "# Assemble a dataframe of these statistics (and corresponding percentages)\n",
-    "shared_stats = pd.DataFrame({\n",
-    "    'Dataset': ['PROTAC-DB', 'PROTAC-Pedia'],\n",
-    "    'Length': [protac_db_df.shape[0], protac_pedia_df.shape[0]],\n",
-    "    'Unique SMILES': [len(protac_db_unique_smiles), len(protac_pedia_unique_smiles)],\n",
-    "    'Unique Targets': [len(protac_db_unique_targets), len(protac_pedia_unique_targets)],\n",
-    "})\n",
-    "# Count the number of entries with a SMILES that is shared\n",
-    "protac_db_shared_smiles = protac_db_df['Smiles'].isin(shared_smiles).sum()\n",
-    "protac_pedia_shared_smiles = protac_pedia_df['PROTAC SMILES'].isin(shared_smiles).sum()\n",
-    "shared_stats['Shared SMILES'] = [protac_db_shared_smiles, protac_pedia_shared_smiles]\n",
-    "shared_stats['Shared SMILES %'] = shared_stats['Shared SMILES'] / shared_stats['Length']\n",
-    "# Count the number of entries with a SMILES/target that is single\n",
-    "shared_stats['Single SMILES'] = [protac_db_single_smiles, protac_pedia_single_smiles]\n",
-    "shared_stats['Single SMILES %'] = shared_stats['Single SMILES'] / shared_stats['Length']\n",
-    "shared_stats['Single Targets'] = [protac_db_single_targets, protac_pedia_single_targets]\n",
-    "shared_stats['Single Targets %'] = shared_stats['Single Targets'] / shared_stats['Length']\n",
-    "\n",
-    "# Convert all percentages to percentages in a for loop\n",
-    "for col in shared_stats.columns:\n",
-    "    if '%' in col:\n",
-    "        shared_stats[col] = shared_stats[col].apply(lambda x: f'{x*100:.1f}\\\\%')\n",
-    "\n",
-    "# Print bold headers in Latex\n",
-    "shared_stats.columns = [f\"\\\\textbf{{{col}}}\".replace('%', '\\\\%') for col in shared_stats.columns]\n",
-    "print(shared_stats.to_latex(index=False, escape=False))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 136,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<rdkit.Chem.rdchem.Mol at 0x7f9df8c2b450>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<rdkit.Chem.rdchem.Mol at 0x7f9df8c2b3e0>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<rdkit.Chem.rdchem.Mol at 0x7f9df8c2b290>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<rdkit.Chem.rdchem.Mol at 0x7f9df8c2aea0>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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G7I3LMpddU1yjZZQQQjIzM0UiEYfDuX79Oothhw0bBqBTp0700De9FBUVdejQ4Z2Fksvl2tnZffLJJ05OTl5eXj4+Pl26dBkwYMDQoUNbtmwJwNra+tmzZ6b+BJTegoKCwN7boZkzZ5bFrNpL7cuWCS1xG3Mz5haRonhlfKo6VaPTZGuzjQ9e4cvod999VxbzcS9fvmTaRO3atUu/kWo12baNhIWRJKMODqm4tFrt9u3bz507Fxsbm5yc/PTp09zc3A9+S/j7+wPo2bNn+SRJsSU5OdnCwsLMzCwxMZGVgAUFBcysWlhYGCsBCSHZ2myPeA/chssDl3QN+4ePVewympaWxuPxuFxuXFwc68F37twJoHr16syCjNIKCiJXrpAXL0j//uTvv1nPqrLKzMxkJrgPsngmNVX2Bg8eDGAsKwc3/r+LFy9yOByBQMDKrFqWNss93h234frQNUOTYXzAt1XsBU+BgYHR0dEjRoxgSh7runXrdu7cuc8//3zw4MESiYRZSCyRSNRq9TSRyCMlBWo1ZDIoFFCrIZFg7VqcOoXDhwHgyBFoNBg8uCwSq5S2bNkyfvz42rVrx8fHMyWV+sg9ePDA09PT0tIyOTm5Xr16LEZm+v926tSJeVFhcJwsbVbnR50fqh425Te94HzBwcKBxST/URa1uXw8fPjQ3NzcwsKiLF4EhYWFPX36NDk5mc/n16hR4+2/t+Nt276jM/y6daRfv9chYmLIoUOsJ1aJ6XQ6X19fAMunTjV1LlSpfPXVVwCmlsG/18uXL5ntTDt37jQ4SGZmpm+Ab7279TzjPVmZA/0vFfhutGXLlvfu3WMai5w9e7ZNmzbMJgrjnTt3rlu3bnXq1FmwYMGECRNq1KjRr18/Zo9w8XbSLtWq1bO0BI8HsRgCAfh82NlBIMC8eWjXDs7OCAnBzp0Qi1lJqYpITEzMmDSp85UrOHMGn39u6nSo97l165a3t7dQKHz8+LG9vT3r8Xfv3j1ixAg7O7sjR474+Pjo23X32bNnX3zxxePHj7sHdt8Ttae6eXXWM/xH2VXosubt7c3hcPz8/FasWAEgMDCQxcgAQkNDmeeUAwcO6DFYpyOnTpGdO0lWFrlyhU6P6m3RIgIQFxdixJHUVDn44osvAMybN4/1yOfPn//tt98IIW5ubsyOOHNzcycnp969e8+ePXvnzp2xsbEFBQXviZCWlsa8p2rVqtXLly9Zz7CEClxGL1++zLxf2rdvH5/P53A459k4Xu3IkSMAHBwcwsLCALi7uxu4ST8yknC5ZNAg41OqWtRq4uZGADJ/vqlTof7T/v37mbXVeXl52dnZV65cYSuyVqt1cXEBsH//fmaKvH79+m+vO7awsHBzcwsICFi0aNGhQ4cSEhK0Wi0T4cmTJw0bNgTQunXrV69esZXYe1TgMkoIWbBgAQBXV9eFCxcCcHZ2fv/PqA8qKiry8PAAEB4ezjynnDp1ysBYT58SKysCkOPHjUmpKrp+nXC5xNyc3L1r6lSod9u3bx9TRu/cuVOjRo1atWqxddO3detW5nt53rx5ADp06EAIUalU9+7di4mJCQ4O7tu3b+PGjc3MzEoUVktLSw8Pjz59+jAvMz777LP8/HxWUvqgil1G1Wp1s2bNACxYsKBFixYAgoODjQm4a9cuAA0aNGDqcvv27Y3Kb+1aApD69UlZ7hGunCZOJABp25YUFpo6FeodtFots3Fz/PjxXbp0ATB69Gjjw6rVauZGcuvWrcwm/T///POdX6nRaB48eHDw4MGQkJCAgIBmzZoVF1ZHR0cPD49yq6GkopdRQsi1a9e4XK6lpeX+/fvNzMzMzc3v3LljWCiNRsPMp2zevJl5mrh48aJRyRUVkXbtCEDoq2d9SaWkbl1Sty55/NjUqVDvlpiYyEym7dq1SyAQsDKrFh4ezsykTZs2DYCfn1/pxyoUilu3bjH9cWbPnm1kJnqp8GWUEDJu3DgAn376KbOjqU2bNoUG3cJs3ryZmSJgNul/+eWXLCQXF0csLAiXS65eZSFalXL3LpFKyenTZOVK+rf3cQoJCQHg4uLCtFhr2LAh0/reMHK5nJlJ27lzJ1OX7+ozq/PXX3/t379/27ZtAAICAgxOwwAfaBhRIaxateqXX365fv36oEGD6tWrd+vWrY0bN06ePFmvICqVatmyZQBmz57NtKth/s8wlocHpk9XnDq1eNmypT//XKLfPvU+LVogMhKFhRgxAitWQKVCrVp49QoqFaRSKJVQqSCR7LWwuJ+VlZ+fr1arZTKZXC5Xq9UqlWrVqlXt27c39Weo5IKDgw8dOhQfH6/Valu2bHn37t2lS5cuX77csGgRERFZWVlt27a9evWqUqkcNGgQM1NXSmPHjr1x4wYztZqcnGxYDgYqz5pddg4fPgzAxsaGmdwUiUSpqal6RWB6HXp5eTH1t1/xKnqjFRYUNHVxAbB8+XK2YlYVAwa8/kV6Opk0iXh6vr3loa+399v/V3M4HEdHR8MeSii9sDWrlpeXx6z73rt3r6WlpZmZWUJCgl4Rhg8fDoDpMSYQCAw+BcMAlaSMkjd6BgcEBADo0aNH6cfKZLLiLRPMIirjW3696dKlS8we4UePHrEYtvLz9yfMN0NaGpk2jYweTTp0IF26kIAAMmwYGTeOzJhxaNWqFStWbNiwYdu2bQcPHjx16tThw4dr164NYN26dab+AFXChAkTAHh7e0+aNAmGzqoFBwcD6NKly9ChQwGMGTNG3wjM4+OcOXOYf/3y7BZWecpoRkYG0zM4OjqaeUFU+mXzzHt5Hx+fwMBAAMOGDWM9vZEjRwLo2LEj7bynh927ydKlJDmZjB9P9GmEePr0aQDMBpuyy45iSKVSZpF8eHg48549PDxcrwjFzfP37t3L3Nvq+zRJCNm/fz9zI8VsKWYW8JePylNGCSEbNmwA4ODgwLzvs7e3z83N/eCogoIC5mkiJiam7DbpF+8Rjo6OZj14ZXb1KomMJA8e6Dtu4MCBALp3714WSVElMJtWimfVhEKhXnWQeRvRt29f5ply8uTJBuRw584dAM2bN2f6n5bn6YqVqowWFRX5+PgAmDhx4ueff/7JJ5/cunWrNAMTExOXL1/OfOONHz++jNLbs2cPgFq1ahm5R4AqjZycHGYZ9r59+0ydS5VQPKvGfB+VflatuHl+TEwMh8MRiUSZmZkGJCCXyzkcDo/HY95xTZs2zYAghqlUZZQQkpCQwExuHj16VCqVvv+Lc3NzMzMzHz9+fOfOndDQUC6Xy+fzy3RKZdq0abGxsWUXn3rT9u3bAdSoUSMnJ8fUuXz05HIikRgToPgkrp9++omZVSvlDzBmRnXIkCFdu3YFMHfuXINzYOYWoqKiAPTq1cvgOPqqbGWUEPLDDz8AqFOnztixY0eNGhUQENCtW7eOHTt6eXm5uro2aNCA6dX0zte7vXv3NnX6FGt0Ol3nzp3B0gabymzVKjJtGgkOJrNmGROGeUvu4ODw448/cjicUm4pzM/PX7x48b59+wCIxWJjdsF36tQJQPF2UoPj6KsSllGFQmFra1uaJrK2trb29vZOTk6enp4NGzbs3bv3ixcvTJ0+xabk5GS2NthUWjIZKT5Gado0YsSLgeJZtW+++aY0y54kEklWVtbjx4/v3bvXtm1bAEuXLjX46oSQ8ePHMy+4mKVXarXamGilVxmW35dw4MABiUQiEAjWr18vEomsrKysrKz4fL6NjY1QKOTz+ba2tnw+XyAQmDpTqsw5OzvPnz9/3rx5EydOjIuLo//o75Cbi+JuofXqITMTjRoZFonL5UZFRbVq1SoqKio/P9/GxubNPRFSqbSgoECtVufl5anV6oKCgjfH2tvb8/n8MWPGGPNRnJ2dAaSmpjo6OqampqampjZp0sSYgKVVPtW63Gg0GicnJwD03HOKwWywgXGTbpWZVkv69CFaLdHpyJAhxOjOct27d69Tp05pio+1tXXNmjWdnJzc3d0dHBwAdOjQwZjtpCdOnADQvXv37t27AzheXs3VKtvdaFRUVGpqavPmzQfTQ5AoAIC5uXlUVFS7du1WrVoVEBDAlNQKR6fTvXk3p1KpcnJyXrx4MX78+LdbxunH3ByzZ2PqVHC5GDoUxh0hIZfL79y5k52dHRgY2KpVq3c+Bdra2vJ4PJFI9ObAZ8+eff7553/88Uf37t3PnDlT4k9LiWlUmpyc3Lt37zNnzpTfltDyqdblo6CggHlVd+zYMVPnQn1cDNtgk5pKduwghJCnT8nVq2TPHsJsnvjlF+Nv2v5FLpePHj26a9euAQEBXbt29fX19fLycnZ2rl+/vp2d3XtaMbDwIkWhILNnk++/JwcOkIAAotdZD29hdrJ89tlnBox99OgR8/3bpUsXwxYFajQaKyurFi1a/PjjjwDGjRtnQBADVKoyypwm0rp1a7pTiCpBLpc3aNAAQERERCmH6HTk0iXSpAm5e5dcvUo2bCBDhrzemzpnDtF/l837fP6hg6c4HI6dnZ2Dg4OTk1OrVq3atWv36aefMveh+h1y8za5nABEKCSLFxt56EBubi6z5un33383LEJiYiKzlbN79+4qlSEviJjvfaZbW6NGjQxLQ1+V56FeKpWuWrUKwPLly405kZWqlEQiUbdu3aKior7//vuEhAR7e3uVSlX8roPLrff332vkcshkUKuRnw+FAhoNoqMxcSJCQzFlyus4ixYBwJUrGDeOtdxevHhx8+ZNDocTGBjYunVrpjjqdDoul6vVapm6UFBQUFBQoFKpig/6XrBgwbVr18aNGzdlypSuXbsafiS1UAgOB0olhEIAUCgM/iDLly+XSCQ9evT44E+F/+Lq6vr777937NhRoajXr5/5zz+Dx9MvQnp6+urVqyMjIy0sLJiKXB7Kp1qXA2a5KHPkAEWVUNzL8p0/YmvX9nr7tGyAbNlCtm0jR4+SMWNe343K5UShIDNmsHk3OnbsWACDBw8mhDBn2JTG2bNndTodc65cUFCQURkIhQQgEREEIIY+CL948UIoFHI4nBs3bhiVDCEPHjyvWVMHkL59iUZT2lFpaWkTJkxgDhDlcrlff/21wR3c9VVJ7kZfvnwZEREBgHmup6gSmF6WrVq1UqlU8fHxbm5uw4cPF4vFfD5fJBKJxTUsLWFtDZEIfP4/Z2ZfvoxHj9CvH3766XUcgQBcLt46YM1wKSkpO3bsMDMzYw4Wc3Z21mg0IpHI2tqaz+dbW1uLRCI+ny8WiwUCQfGLGqFQ6OnpyeFwNm/e7OnpuX379kGDBjF7DQwhEqGg4PWnMvRudO1aXps24+vUyWFWgBrDza3u+fPo3BnHjmHQIBw48IG/8LQ0rFo1fdu2jRqNxszMbOjQofPnzy+npU6M8qnWZW369OkA6B4k6p0kEkmJXpbx8fGlGZiXRzIyCCEkK4ucPk3Cwl7//qNHrB3/PGTIEABjx441OALTIM7Z2VlpaE46dzdSs3rR7q1FzV0KpxlyY5uWRng8YmZGHj40LIV3uHuXVKtGAOLvT/7/0M93XHfyZMLjEV/ffVwuNyAgQN8upayoDGU0IyODeZoot3t4qmJhzpjs0qXLsGHDAAQGBuobISmJmJsTkYjlN0v379/ncrk8Hs+YTg5ardbT0xPAfEPfDj186BYbi+zsqNhYJCd3NSDCyJEEICNHGnb9/3TjBhGLCUDenrRITSVjxhALCwIQc3MycaIsKSmJ5cuXWmUoo8wOsIEDB5o6Eepj9GYvSzMzM8N6WRJChgwhAOnWjc3c+vTpA2Cq0SceXr9+ndn+qNfhRcUSEtrGxiInZ0tsLBIT9T4Nl/kZY2FRJscPXrtG7O3JgQPk8GFCCPn7b/LLLyQw8J8COnIkSU5m/7p6qfBl9MmTJ3o9plFVTXZIyPctW3711Vf9+vUDMGnSJMPi5OSQmjUJQNjaH8e8nReJRH///bfx0b799lsAbdu2NaDzfFJSx+Iy+vChp77D/f0JQCZO1HdcaSkU5EZ6xKwAABQJSURBVMYN0rAh+esvcvs2mT+fWFoSLpcEBBDT3YD+S4Uvo8xjmgFHDlBVwrNnhM8nXK704MGg5s0N7mXJiI4mAKlenWRns5Aa80Zo3rx5LMQiJD8/v27dugDWr1+v79hHj3rFxiInJyo2FvfvN9ZrbFwc4XIJn0+eP9f3snq4cYOEhZGBA0lsLFmzhkRHG9NBhX0V4009s/tNqVRKpVKVSqVQKGQymVqtTkxMjImJsbS0ZCa/KKqkxYuhUmHIEJtt27Y+eLBk+XJj1hKOGoWYGJw/j5kzsWOHUXldvnz5woULtra2zNtR41lbW0dGRvbu3Ts4OLhPnz6l6XBWjMtldl7qAOh0+r2pt7REz55o0gR16+o1Tm9WVhgwALt2wdERo0aV7bX0Zuo6/o/bt2+7ubm1bdv2zcagvA+tvnV0dPT29jZ17tRHKTmZWFgQMzOydy8BiFhs/BbOJ0+Ii0tq69bdz507Z0ycdu3aAQgNDTUynxIGDBgAoGfPnnqNkkh+ycmJUijuvnq1Pz//UmmGqNWkUycil5OiIhISQvbsIX/9RQghFy+Ss2f1z/tDbtwgGzcSQkivXmTNGvbjG+ljuRstKioaOHDgs2fPNBrN239a3MvAxsaGx+MVL6YrLCw8duxYenr67du3vby8yj9t6qMWEgKtFmPHYssWAJgxw8i+GwAaNEBg4KE5c858882jv/76S8js/NHTyZMnr127VrNmze+++87IfErYsGHDxYsXT58+ffjwYX9//1KOEot75ubGZGdHmJvXdHD44e0vyM+HSgW5HHI51GpIpWjcGEVFWLYMS5YgLQ12dq/Xm0okeNd3sLHq1EG7dgCwYQMyM9mPbyQOIcTUOQDA7t27R4wYYW9vv379eolEAkCj0XA4HEKIXC4vfqJXq9VyuZx5os/Pz79+/frcuXNXr17t4eERGxv7ng4OVJXz4AE8PWFhgagojBqFGjWQmgpra+MDFxYWtm3b9u7du7NnzzZguwchpFWrVvfu3YuIiJg8ebLx+ZQQFRU1YcKE2rVrx8fHW1lZyeVy5jsoPz+/oKCgSROtufkrnU5FiLKwUEKIWqdT8PlNFIqb1auPFIm8tdqMlJQ+Op2SEFVhYR4hap2uYMAA7dOn/7rl2rgRf/0FGxsMGYLwcLRsiZs34eCAlBQMHoyBA1n+UOfP4/lzBAYiORl7977ekvsRMfHdMCGEEI1G06hRIwA7d+4s0cz1/fLz8xUKBTN21apVpv4c1MckOJgAZMoU0rIlAQirZ9bfvHmTWWB0+/Ztfccyp2XUr19fpVKxmBIhRKFQSCQSnU7n7e3N4XDeue01NrZZbCxK/JeS0l+rzXr+fPrjxwOVyqS3v8DbW1GjBnFyIs2bk9atSZcu5MABMn48kUrJoEFk5EgSHk6uXSOEkKNHyf797H4sQgg5d46sXEnS0silS2TBAvbjG+mjeKjfvn3748ePXV1dhwwZYmZm9vnnn1taWr7dnfDN7XE8Hs/W1lYgEJibm2/ZsqVLly4hISH9+vVrZGjjbqqyWbYMHTogNxcREahbFxMmsBi7TZs2kyZNCg8PHz9+PLNms/iP3m4M+uarUalUyuxaXrBgAY/HU6vVH5z9L72IiIjVq1evX7++YcOGd+7c0Wq15ubmb36/8Pl8LreRWNyMw+FzuUIzMxsul8/lWgkEzczNa9WtuyYnZ5Na/bhp01gOh8/lCszMxFwun8sVXb9e8loqFS5ehI0NvvoKZ8+y9QneJy4OAgFycsrjWnozdR0nSqWSWahxmFlfaxBm2VOnTp1oizyKEEIiI8mMGWTWLJKdTQ4fJkeOsH4FmUzm6OgIoHr16s7OzvXq1Xt/Y9Bi9erVs7W1zc7ODgkJcXJy+uD5taWUl5dXYsPr/fv3Sz88J2fLs2dT0tKCiooMaT7/5rddWXwLnjtHtm8nhJCkJHo3+i6bNkWlp6d7eXn179/f4CDh4eFnz569dOnS3r17mZJKVV0XLiAvD6tWIS0Nc+Zg+/ayuIiVlVVYWNiYMWNevXr16tWr4t/ncDhvP0i9+Wr0yJEjEokkNDT01q1bqampwcHBGzZsMD6fVatW5ebmdunS5cyZMxqNJjAwsHnz5qUcq1LFq9VPRCJvjebJ06dBdeos5fH0e6p7c/6gLLpU1q4NS0sAsLGBuzv78Y1l2iqen08aNCjq2PF/Z878ZmSoHTt2AKhevXpWVhYruVEV1dKlpLi7woABZXqplJSUkydPJiUlpaWl5ebmlma6My4uzsLCgsvlMreNXC73ypUrRqZh5IbXvLyjsbFISemXmPhZbCxksstG5lPVcE1bxNeuRVoat7DQt1s3Q3t8/b+RI0d27dr11atXM2fOZCU3qqKqVev1opiiIpTxQpRGjRr17t3bxcXF0dGxNMucAXh4eEydOlWn04WFhc2YMUOn0wUFBanVamPSWLZsmUwm69Onz+HDh4uKisaPH9+wYcPSD+dyhQB0OgWzDl/fFfiUKe9Gc3OJrS0BiKEnDpT05MkT5iSss2WxApiqKPLzib8/2byZjBtHfvnF1Nm8Q/HykmXLljVt2hTA4sWLDY6Wnp4uEAi4XG5MTAyHwxEIBOnp6XpFkMn+iI1FYqJPSkr/2Fjk5hr+lqJqMmUZnTWLAKRHDzZjMuv4GjRoYMwxrVSFp9WS5GTC0tubsnDx4kWm5O3bt4/D4fB4PIN76wQFBQEYMmTIl19+CWDOnDn6RlAo7sTGIj6+xZMnw2Nj8fLlTsMyqbJMVkZfvCBCIeFwiNEnDvyLVqtt1aoVgFmzZrEZl6LYNnz4cACdOnVi6qCvr68B60ySk5MtLCzMzMz27t0LQCwWv9J/w6tSmfTXHzbJ11pnPvj+8VGXvKRt+kao4kxWRr/9lgBl8gLg1q1bBi+Npqhy8/Lly1q1agGIjIysU6cOgC1btugbZPDgwQDGjh3LnCK3ZMkSQ1JJTycAqVOHfP89AQjdyaInk20GTUnBokUIDkbTpuwHnz59+rp169zd3U+dOlVYWMgcplhQUJCfn69SqUpsjys+bTEzM1Mul69Zs6Znz57s50RRb2H2QFevXn358uXjxo0Ti8UPHz5kzmovjQcPHnh6elpaWkZGRo4aNapGjRqpqanWBmx4lUhgZwexGFOmYPFiLFyIkBC9g1Rl5Vy2f/6Z+PoSnY6cPEn27Surq8jl8tq1a1tZWRnwFyIWi2UyWVllRlH/xkxoDhs2jOmEHxAQUPqxX331FYCpU6cyE1lr1641MAmN5nUr+RUrCEDohJieWF5+X7z7LT+/UKl0USggk0GlgkwGhQIqFRo0QKNG+Okn2Nuze+V/sbS0tLCwUKlUNWrUsLGxEYvFPB7PysrK2tqax+PZ2Ni8uT2OECIQCJhV0z/88ENycvKCBQvWrl1bhvlR1P/bsmVL8+bN9+zZEx0dffHixUOHDh0/fpwpqe9369atkydPikQiNze38PDwOnXqTDB4w6uFBSwsoNWCzweMOqq+ijK+EoeFhb29Yq5p0/88+Pvnn8mwYWTHjjK8G42KigLg4uKi1Wpnzpw5YsSIgICAL7/80tfX18vLq3iVH3OkdbG4uLjipdFXr14tq+Qo6t9WrlwJwNHRkfmFq6trUVHRB0fduXOnQ4cOwcHBzJKpqKgoo5Jg1h6uX08AMmqUUaGqHhbuRrVabV5eHvPr4t1v9evXt7GBtTWsrMDjQSyGUAge7/VPu5AQ9O+P4GDjL/4OKpVq6dKlAJYuXWpubn7kyJHU1NT3fL2dnR2zdY8Q4unpOW3atJUrV06YMIF23qPKx7Rp0w4cOHD79u2srKxp06Z99913XO6H98W0bNny8uXLW7duTUhIaNy48ejRo41KIiIChYVo1QqnT0OfzvkUWOk3KpVKdTodU4k++MW//w4AHTtizRq4u8PX93VhZdG6deumT5/u4eFx9+5dLpd76NAhhULx5r5mKysrHo8nFosFAgH/rcsrlUp3d/fHjx+vWLFi9uzZLCdHUe8SFxfXpk0bnU4XHh7erl07hUKhVqslEolSqSx+Bcq8I1Wr1TKZTC6Xq9VqqVR69+5dnU4XHR09ysiDNZ4+xdy5aNIE8fEIDYWTEzsfrIow1W1wRgbx8yP9+7McViaT2dvbA/jFiO0rxUujHz16xGJuFPUeo0ePfmeH0PfjcDjM5JWxl58yhTDb8J8+Jd9+a/zHqVJM2eHpjz8gkeDYMfTty1rM8PDwrKwsHx8fYxYtderUafjw4bt27Ro7dixTUlnLj6L+w8qVK9PT069fv+7i4sI8JzGvHIRC4dvvSIVCIZ/PZxrudevWrTSTAB+QmYn69QGgXj1kZRn/caoUUx4ismEDJk2CgwPi42Fry0JAiUTi5OSUl5d38eLFTp06GRPq1atXzZo1y87O3rFjx8iRI1lIjqI+ZsuX47PP0KEDrl3DhQuYP9/UCVUkpiyjOh18ffHnn/j2W7DRcRFz585dsWJFt27dzpw5Y3y0PXv2DB8+vFq1agkJCcxuE4qqtBQKLFkCjQYWFliwACKRqROqSEx8pN2DB/DyQmEhLl+Gj49RoXJycpycnORy+fXr1729vVlJz8/P79SpU0OHDt2zZw8rASmKqnxM3G+0eXPMnAmdDitXXn/n0cqlt2TJErlc3r9/f7ZqKIANGzZYWVnt3bv31KlTbMWkKKqSMVu4cKFpM2jfHomJS48fH2Zpaenr62tYkGfPno0aNYoQsm/fPnv2Nkgxy2DPnTt39erVoKCgEsv1KYqiYPK7UQB8PiZN6gBg6dKlCQkJhgVZtGiRWq0eOnSoh4cHq9lh6tSpXl5eT58+NfnPG4qiPk4mnhstFhQUtH37dl9f399//13fBUbJyclubm4cDichIaEsDlhmlkYXFRXt2rVr6NChrMenKKpCM/3dKGPNmjV16tS5fPnydv3Pcfzhhx8KCwvHjBnDeg29fPnyxo0bmzZtypTps+VzIDdFURWK6Q9YZojF4qCgoMWLF0+dOlWlUjk4OKjV6v9qDKpUKiUSiVqtVigUUqm0oKCAx+PNnz8/Ozv70qVLAwcOZCUlQsjMmTNv3rypVqtzc3OLiopat27NSmSKoiqTj6WMAjh//jwAjUYzadIkvQZaW1srlcr09HQ/P7+8vDxXV9cWLVoYn8/x48dv3rzp4OBQVFT07Nkzd3f37777zviwFEVVMh/L3OiJEyf69OljZ2dXq1atpKQkW1vbHj16WFtbFzcGZbbHMR1ChUKhjY0Nn8+3srKysrIKDg7evn27t7e3t7f3jz/+2KZNm2vXrpmZmRmTj06na9WqVVxc3Nq1a8PCwrKysk6cOOHn58fW56UoqvIw4X7+Yjqdjrl/XL16NXOCwrFjx0o/XCqVMqPCw8OZ47nXrVtnZErMentHR8dFixYBaNu2rQHHjVEUVRV8FGU0JiYGQP369Zk+oa1bt9a3Zh05cgSAjY3Nrl27AAiFwsePHxucj0ZDfHw6A9i0aVO1atUAXLhwweBoFEVVbqYvo4WFhU2aNAGwYcOG6tWrAzh//rwBcfr16wegd+/ezCum7t27G5xSVBSxtFQHBEQvWZLh4zOia9cvDQ5FUVSlZ/oymrF3b2dHRxcXl/nz5wPo0KGDYXFevHhha2sLYMeOHTVq1ACwz6BTSpRKUq8eAcjWrcTKinA45Pp1o5s5UhRVeZm6jKpUpH59YmHxfOfOjo0bA/jzzz8NDrZx40YADg4O69evB1CjRo2cnBx9g6xZQwDi4UEmTyYA6dPH4HQoiqoSTL38PjISz56hSZO6cXGX0tNjv/22ffv2BgebMGHCZ599lpmZGR8f37lz55cvX86aNUuvCHI5wsIAYMYMbN0KLheLFhmcDkVRVYMpa7hcTuztCUB27iRCIeFwyJ07RoZMTEzk8XhcLnffvn3Mscl6zbQuXkwA4uNDgoIIQIYMMTIdiqIqP5PejUZEICsLbdvi6lUUFODrr9GypZEhXV1dZ8+erdPpFi5cOGfOHELIxIkTlUplKYd3744uXfDNN9i5E2ZmWLDAyHQoiqr8TFdGJRKsWQMAU6YgOhpmZggJYSXwvHnzmjVrlpSUVFRU1LJly0ePHi1ZsqSUY9u0wfnzaN4cXl4IDISrKysZURRVmZmujG7ejNxcdO6MX3+FRoNRo9C0KSuBLS0tIyMjORzOihUrZs2aZW5urlAoSjNQp0P//lCp4OmJPn0QEcFKOhRFVXKm2wyq0WDLFtSqhcGDYW6OxEQ0bMhi+PHjx2/ZsuXTTz+NiYlp+N+RpVKo1ZDLIZOhdm34+aFXL4SEYPhw7N7NYjoURVVaJmpNcvs2jh5FrVr44gssW4aCAnZrKICwsLCTJ09ev369R48eHh4eCoVCpVJJpVKlUqlSqTic5MxMs4KCfw3ZsAEeHsjLQ1ISu7lQFFWZmaKMPnmCdeuwdSsePsScOdi2rSwuYmtrO3bs2MWLFyclJSW9VRfr1SNMDbW2Bp8Pa2uIRLC2BoAFCzBtWllkRFFU5WSKMnrxIoYNg0CA1q0hkZTddRYtWuTj4/Pw4cO6desKhUIej2dnZ8fn8wUCga0teLySh8gWFeHyZVSrho4dceFC2eVFUVSlYoq50b17wedjwAAAGDAAR46UdwL/raAAQiEIQUEBPamboqhSMcXdaK9eGD0a9va4fx+tWpkggf8mFAIAh0NrKEVRpWWiN/V5efj9dzg44NNPTXB1iqIo9nws3e8piqIqKFO3JqEoiqrgaBmlKIoyCi2jFEVRRqFllKIoyii0jFIURRmFllGKoiij0DJKURRlFFpGKYqijELLKEVRlFFoGaUoijIKLaMURVFG+T8oPoPsAk/tzQAAA9l6VFh0cmRraXRQS0wgcmRraXQgMjAyMy4wOS41AAB4nIWTa2wUVRTH79zZnd2672232wdtp6XdR8tu2132zWMuFNQFRVAeFRpvS2smFg0mpiQGImW1RZCUENAPkEAVS8W0BBOCsbSdm2gTNYGgNSoqiUE/aG1IiEkTWht3zqYtboPc5Ob+zn/O/M+5ZzJ3RwZuo/SyoIW1Ir3XpPdBTkA0fXK8HnnSJ48FCU6NIMnqyXMLQtYDXUbg5xzmEh7qNP/CgvWiGlnCvFdWjUyMsQ6JajwXPkLWg4wfKf+3tg5BU5ibT1jcbVbbVnAU5hwWO/2Pw0PGbgNL7oFhRlV4YCSLLbLuaUAc4rCIeRnzGqrRilpBxoKO6vRIl0NzHvPgHAM1GGVsNFGTWcZmC7VYZayxydhmp/ZcMTcP2R0yduSjfCdyFqCCQlRYhIqKUfEStKQElZSi0jJUJiKxHJVXoIqlHlxRKVZWoSoXcrll7PZQj1fG3mpUXaPH1cvS24d8ftFfK+PaOlpXL+P6AA0EaXA5CoZkHArTcIR6ozQak3FRHMUTyIFpwomsWhpzUauBRvw0EpBxnk6dNnbYbRpeEHQ5BqtWMJrMFqtBKCgsiiecgtvjjcZcQm1dfSDi1wYDkXDIeQun5zn/Q8zEbyvad5Ir1WD2/pRibOpfrfKfU7ls1ncO+Fl7Gfvw9LlhlUeMEbZyzDaistu9hh3ce21U5RPlz7NjZz8Hnk60sQ/aI4rKL4YPsOpUAvj+zh1sJvImcGvHbjbxxSDw8WaJxZYdAu7pDLLWwYvAld3FrPf9fuChPVbW5t0PfG1jCbNU1QC7Hg+xd1fUAo/+tZb9dqMHet76TZRd50sklbd9so5V6l8BnnwvztCJXuCJ70W2tbgcuHPYweojHcD+rmnl06dfB64Z+1VpSXozPjcvK380+4BTZ04rd1Z1Au/atF3pS6aAd9xYqkwdD2X0r7tHD/THgDf6UiObnrgJvXH6s6ub9v0IrNXw0thnv6xS+dJkizTTMQt8K3xU+uG7BpinWfOStGWzAHe8VDMkDe9bDvrliXHpSMMM8Nq9mOx3/A3c8IyF5Fk2ZPKTbnJsaj1wQVuANF+9AzmpF5LkucM/Abe2bSDjA09BTm/jk2T6QhN838nGFrK+8VvI2f7ayyTcdR70bScpSVx/FfofHWwn7T02uOOp8U5yr8+R4YbDJLrubci5V3iScF9uAQ58fIb0jd8F/ufnU+S8yQv+u0uPkjeEi8DNVw8R7qsU1HK+VUX2NPGgDxwxke6hXOCuXVcUM/sIau3c/LuSvHIBOP9fcwQn5KOscokAAAUmelRYdE1PTCByZGtpdCAyMDIzLjA5LjUAAHicfVfbbt02EHz3V+gHLHCvJB8TOyiKIjbQpP2HAn3s/6MzlMOjoIuey0KS51Cj3dnh+ung6/fX3/7659gvfX16Oo72P5855/Gntdaevh48OD5/+eXXt+Pl+6fPP668vP/x9v3bke3IxG/w/hn76fv71x9X5Hg5nvOc7r0BeDafHutgvR4/1eP9eO5n92ljHnKahHkFNK44T/XRNAHUEG9eAP0CTpfZ/XgGoA9XLZBBpMjp3lKEUHG1GAU0F1RPMY+YODy9qwwroH1B7UwzmUloGJafBXQcb/i7n6ZdHFz1HBlIVAGda9U4h0YfQujUOXD0X6i0Dwah6gEGDtrgWiULj/2+KKgk0/kcp6dkVhUQ/UhCUxt4Hqzbso9Z5UvsI7cqU3COdc2sjxLrTMM8u0IlRqjmHFFVTOIqLiQQSNOzocyNeiigeUGHidrKbXYX6xW0f5A1pLSv5HYbrVfVlfGRBKRWJxWL5cNKsvNj2QixkENPD7OoGGhbZdDTIpvNw84uU72EyseqmdqmHMHear0ioFfB8IegwEAgpOusHks/2gvq7+huO4f3WrO6qtWh1CZKplOkRSVZXcXquHu48/HTQ7JErloFEpWjkWfHk7VK29p5d8fddUDaUCBso6y/rkLZaenQIJDSFL+qkKtOSE7M4Ckkk7Msvl1lguzbQBrRg63NVmXJVpUaXApVCqgkLVqZT2OR0CeujkMA55hlOs1wczQfCqNsTzdrWT250QHR/R59ic0nPLh6cGOB4D7R3JNse45Rtqgl7g1Li8RizFVjo1SGYiwQbAqaHLrWhDzLXjYWKLGmdfacnKAJo66QLNA4JdTbWMiW+FTe3xZyBgSUrD703meVTWeB0Bqw847MEtrGiKpCviqERveZYzHtfbbSTt2Ob/BJKB22s6h2iVY6JPz+nVBUU8NJQOFOVnKNCxrD7Uoq3Tqr/kAd32Co4JpUEQhkiJSyd7oeoMOhYeOqBgZWpnVc0GapQobY1yOqDvF5Ibs1SXaIdrdeZTVYKslTdYzGrjNDzqqkxioVfMTMoU60X0e3VImKVakBm9NM3nQoVq32MpB/+RsOjZxaTPbd6Ja9Mtzwa9GO3QGtjDxhe4SrVNBg9vsJTxpohGfuKMO0SlTk9fhmM6DaZRXdS8+LfknV3OdchcJ+wnmhgI6l/+Gwm9XR0dFUZQLmZbmZHe6MjrXmQ6riZ7tsHFOBDO437MJyzS9vrz8NgddY+Pn97fUxFvKtj+EPJ4c9Rjzn9zHI8R2PaQ03P/IxkTm+/TF1Ob7jMVkJvvMxPTkv3UckZRC5DUK+rmxyGHecQTY/IUEE2RSFDBFks0R78BSlvQ0iziCbq5AsIJstrMIZZBPGViEMeh8RhEE3ZV2UgXnkcyVUsZvf9nVh0E0Z+7cw6KaszCyCbsocsBl0U8a+Kwy6OStTjKCbs5IzR4L7finH2g1v+6Iw2ObMTY/BHjIgZwTbnI2cEWxzhk8qg23ORs68sjkbOSPY5mzkjGCbs1EZDPctxFfYnJ15ZticnZwRfHOG8fMUrn7z9+vK5uzMM69szk7OCL45O6XBsDk7tcGwOTs5I8TdS50hNucgZ4TYnIOcEWJzDsqZVzbnWA2Hz6PpKGeE2Jw5dfHZY3OOJecO87nZ0FJLbM5BbSDk3VacWt10ktLIdpNqrg7st55fjO/1S78gj7bNxfie08zryiM7NKq7LfH8x3+6OH76F0Oq0Xw+ZfCiAAACf3pUWHRTTUlMRVMgcmRraXQgMjAyMy4wOS41AAB4nC2TOY4bQQxFr+JQAroL3MmC4KgTR6PAoeGoU2NOMIf3Z9UICqRfXB/J633zfT/OW+7Px/W8Hz/fz1vvz/u+RZ94eUPk64Ov6+NxXe/1baOPCy54/91/lgA3fOzWP5+//uLH9Q/+8rwu/vH1OGNMszxokOHHnMfrzJE2dR48lF0NyhxiRQpFnLcwjWceJ/yyTLj9mIcZRYtsol6tyWA1aHhL4SXpCFVpyTVYWrKhknycMip8dgb2UYIQkKbM+nZ0ET9OQ0wu3Y7CYcfpw4IjdkYSrbaiyO+6UCC3kaouaY4UNNySxCxfEkk4oit6I9IllbJo1xBpq3HEUgElSKlFuROKecOBres28uYkA7p+F6Ueh47kKb5NIoQPb+47DFC6JJycU3aRYJgJr7JcnBJ4iBXKZCZfCrlbpwqDHxRH7igISej5BQwVUnEAh9bs8nRomAgUpn55oRn3idKBX2OuagCPakKZtOMQZh+CpKhcnVAOsJpYNw6EE3FAfhVKw1Spp4GJmWe0MpPaCZN2Mu8lyajqwWIfPLyR0ixswQujB5RaNsDjcAvYKFigMAQqjKIGuxgvhaJLrDG9l0gHACIuiJp7LoGqGwV1trmiZM6eMTc4rFIr7LyEXDNXLI3mNvEC4iaAXYs2AjpbYcKZYwllNNtG4aZbIo2Ggbvy3gsoqWCAfUnTLjCGSPWsVLFpEDBO1UZMCbbLQnUuWuJptG0wx+rkKqVtVIgrK3kEctqSnKXPukRXqokmUBaPSo0NR/cYHMdkC2hZ6DpfTzBeuxSRbthSJau9gbgynDHuxsD0eH79B8Gk9EuZ9Et5AAAAAElFTkSuQmCC",
-      "text/plain": [
-       "<rdkit.Chem.rdchem.Mol at 0x7f9df8c2aff0>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Draw some PROTAC molecules using RDKit\n",
-    "from rdkit.Chem import Draw\n",
-    "\n",
-    "# Draw the first 10 PROTACs from PROTAC-DB\n",
-    "protac_db_mols = [Chem.MolFromSmiles(smiles) for smiles in protac_db_df['Smiles'].unique()[:5]]\n",
-    "protac_db_mols = [mol for mol in protac_db_mols if mol is not None]\n",
-    "protac_db_mols = protac_db_mols[:5]\n",
-    "for mol in protac_db_mols:\n",
-    "    display(mol)\n",
-    "# img = Draw.MolsToGridImage(protac_db_mols, molsPerRow=5, subImgSize=(200, 200))\n",
-    "# img"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Bar Plots of Performance Metrics"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 137,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from typing import Optional, List, Dict"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 138,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Index(['train_loss', 'train_loss_step', 'train_loss_epoch', 'train_acc',\n",
-       "       'train_acc_epoch', 'train_f1_score', 'train_f1_score_epoch',\n",
-       "       'train_precision', 'train_precision_epoch', 'train_recall',\n",
-       "       'train_recall_epoch', 'train_roc_auc', 'train_roc_auc_epoch',\n",
-       "       'test_loss', 'test_acc', 'test_f1_score', 'test_precision',\n",
-       "       'test_recall', 'test_roc_auc', 'model_type', 'test_model_id',\n",
-       "       'train_len', 'train_active_perc', 'train_inactive_perc',\n",
-       "       'train_avg_tanimoto_dist', 'test_len', 'test_active_perc',\n",
-       "       'test_inactive_perc', 'test_avg_tanimoto_dist',\n",
-       "       'num_leaking_uniprot_train_test', 'num_leaking_smiles_train_test',\n",
-       "       'perc_leaking_uniprot_train_test', 'perc_leaking_smiles_train_test',\n",
-       "       'split_type'],\n",
-       "      dtype='object')"
-      ]
-     },
-     "execution_count": 138,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "reports['test'].columns"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 139,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Pytorch performances:\n",
-      "Metrics: ['Accuracy', 'ROC AUC']\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1200x400 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Metric: Accuracy\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 600x400 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Metric: ROC AUC\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 600x400 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Plotting performance for main part of the paper...\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1100x500 with 2 Axes>"
+       "<Figure size 1100x500 with 2 Axes>"
       ]
      },
      "metadata": {},
@@ -3914,9 +768,11 @@
     "        plt.annotate(value, (x, y), ha='center', va='center', color='black', fontsize=10, rotation=90, alpha=0.8)\n",
     "\n",
     "    plt.savefig(f'plots/{title}.pdf', bbox_inches='tight')\n",
-    "    plt.savefig(f'plots/{title}.png', bbox_inches='tight')\n",
+    "    # plt.savefig(f'plots/{title}.png', bbox_inches='tight')\n",
     "    if show_plot:\n",
     "        plt.show()\n",
+    "    else:\n",
+    "        plt.close()\n",
     "    \n",
     "    # print(combined_data.to_markdown(index=False))\n",
     "\n",
@@ -4125,11 +981,191 @@
     "\n",
     "print('Pytorch performances:')\n",
     "plot_performance_metrics(\n",
-    "    df_cv=reports['cv_train'],\n",
-    "    df_test=reports['test'],\n",
-    "    df_test_majority=reports['majority_vote'][reports['majority_vote']['cv_models'].isna()],\n",
-    "    title=f'summary_performance-best_models_as_test',\n",
-    "    show_plot=True,\n",
+    "    df_cv=reports['cv_train'],\n",
+    "    df_test=reports['test'],\n",
+    "    df_test_majority=reports['majority_vote'][reports['majority_vote']['cv_models'].isna()],\n",
+    "    title=f'summary_performance-best_models_as_test',\n",
+    "    show_plot=False,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### PyTorch Plots"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Pytorch performances:\n",
+      "Metrics: ['Recall', 'ROC AUC', 'Precision', 'F1 Score', 'Accuracy']\n",
+      "Metric: Recall\n",
+      "Metric: ROC AUC\n",
+      "Metric: Precision\n",
+      "Metric: F1 Score\n",
+      "Metric: Accuracy\n",
+      "Plotting performance for main part of the paper...\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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XzdVS7biytKyRr1W8jsvJyazYcdQlI0P1w+ro6AgjI6Nyak3FUBHes5ycJ8VeN1dHdXTkHNMkCAbF+9pwcnIqUbuI1OVdOyt35MgR/Pjjj5g3bx4CAgJw69YtjBo1ClOnTsWECROKfE159KFilKQPFfM9XZLvTzEq0/fn25gDYjAHSo45UDpvaw4Utw+VvMj86quv8M0336BXr4KzGLVq1cL9+/cxbdo09OvXD3Z2dgCAuLg42NvbK18XFxeHOnXqFLnNw4cP49q1a1iyZAm++uortG/fHoaGhujRowf++OOPIl+jq6sLXV1daXeOiIioHFlZWUFTUxNxcXEq8+Pi4pT968smTJiAjz/+GJ9++imAgn45MzMTgwcPxv/+9z9oaBS+c4Z9KBERiSH5PZlZWVmFOixNTU0oFAoAgJubG+zs7HDw4EHl8rS0NJw+fRqBgYGFtvf06VMMGzYMCxcuVF5+m5dXcAlfXl4e5HK51LtARET0VtLR0YGfn59KH6pQKHDw4MEi+1Dg1f0yAA5ORkREZULyM5kdO3bEDz/8ABcXF3h7e+PChQuYNWsWPvnkEwCATCbD6NGj8f3336N69erKR5g4ODigS5cuhbY3depUtG/fHnXr1gUABAUF4auvvsKAAQPwxx9/ICgoSOpdqNQW3x5X7HVzs1QvlVx5dzJ0inmp5CD3aSVqFxERFU9oaCj69esHf39/NGjQALNnz0ZmZiYGDBgAAOjbty8cHR0xbVrB93DHjh0xa9Ys1K1bV3m57IQJE9CxY0dlsUlERCQlyYvMOXPmYMKECfj8888RHx8PBwcHfPbZZ5g4caJynbFjxyov1UlJSUHjxo2xZ88e6OnpqWzr6tWrWL9+PS5evKic98EHH+DIkSNo0qQJatSogTVr1ki9C0Qq5uxPKfa6uU9V78tdeDgVOnr5r1hb1YjWZiVoFRG9q3r27ImEhARMnDgRsbGxqFOnDvbs2aMcDOjBgwcqZy7Hjx8PmUyG8ePHIyYmBtbW1soDwkRERGVB8iLT2NgYs2fPxuzZs1+5jkwmw5QpUzBlypTXbsvHxwc3b95UmaehoYF58+Zh3rx5UjSXiIiowhk+fDiGDx9e5LIjR46oTGtpaWHSpEmYNGmSGlpGRERUBkUmEVFlU5Kz2WLwbDYRERFVBiwyJaSta4D2o1epTBMRvW1Kcm+2GLw3m4iI6N3EIlNCMpkMOnrFey4mERERERFRZST5I0yIiIiIiIjo3cUik4iIiIiIiCTDy2WJ6JW09TXRdXY9lWkiIiIiotdhkUkkoco2+JNMJoOOAb8miIiIiKj4+OuRSEIc/ImIiIiI3nW8J5OIiIiIiIgkwyKTiIiIiIiIJMMik4iIiIiIiCTDIpOIiIiIiIgkw4F/6JX4+AoiIiIiIiopFpn0Snx8BRERERERlRQvlyUiIiIiIiLJsMgkIiIiIiIiybDIJCIiIiIiIsmwyCQiIiIiIiLJsMgkIiIiIiIiybDIJCIiIiIiIsmwyCQiIiIiIiLJsMgkIiIiIiIiybDIJCIiIiIiIsmwyCQiIiIiIiLJsMgkIiIiIiIiybDIJCIiIiIiIsmwyCQiIiIiIiLJsMgkIiIiIiIiybDIJCIiIiIiIsmwyCQiIiIiIiLJaJV3A4iIiIjEiF8zsNjrZubIVaYTNgxHlq5m8V4cYFOSZhERvbN4JpOIiIiIiIgkwyKTiIiIiIiIJMMik4iIiIiIiCTDIpOIiIiIiIgkwyKTiIiIiIiIJMMik4iIiIiIiCTDIpOIiIiIiIgkwyKTiIiIiIiIJMMik4iIiIiIiCTDIpOIiIiIiIgkwyKTiIiIiIiIJMMik4iIiIiIiCSjVd4NIKKSEwQBmZmZymlDQ0PIZLJybBERERERUQEWmUQVUGZmJjp37qyc3rp1K4yMjMqxRUREREREBXi5LBEREREREUmGRSYRERERERFJhkUmERERERERSYZFJhEREREREUmGRSYRERERERFJhkUmERERERERSYZFJhEREREREUmGRSYRERERERFJhkUmERERERERSYZFJhEREREREUmGRSYRERERERFJRqu8G0AlJwgCMjMzldOGhoaQyWTl2CIiIiIiIqICLDIroMzMTHTu3Fk5vXXrVhgZGZVji4iIiIiIiArwclkiIiIiIiKSDItMIiIiIiIikgyLTCIiIiIiIpIMi0wiIiIiIiKSDItMIiIiIiIikgxHlyV6S8SvGVjsdTNz5CrTCRuGI0tXs3gvDrApSbOIiIiIiEqEZzKJiIiIiIhIMiwyiYiIiIiISDJlUmTGxMTgo48+gqWlJfT19VGrVi2cO3dOuVwQBEycOBH29vbQ19dHcHAwbt68qVyek5ODjz/+GCYmJvDw8MCBAwdUtj9z5kyMGDGiLJpORET01ps7dy5cXV2hp6eHgIAAnDlz5rXrp6SkYNiwYbC3t4euri48PDywa9cuNbWWiIjeNZLfk5mcnIygoCC0aNECu3fvhrW1NW7evAlzc3PlOjNmzMDvv/+OlStXws3NDRMmTEBISAiuX78OPT09LFq0COHh4QgLC8Pu3bvx4YcfIi4uDjKZDHfv3sXixYtVilYiIqJ3xbp16xAaGooFCxYgICAAs2fPRkhICCIjI2FjU/ie69zcXLRu3Ro2Njb4559/4OjoiPv378PMzEz9jScioneC5EXm9OnT4ezsjOXLlyvnubm5Kf9fEATMnj0b48ePR+fOnQEAq1atgq2tLbZs2YJevXohIiICnTp1gre3N6pWrYqvvvoKiYmJsLa2xtChQzF9+nSYmJhI3XQiIqK33qxZszBo0CAMGDAAALBgwQLs3LkTy5YtwzfffFNo/WXLliEpKQknT56EtrY2AMDV1VWdTSYioneM5JfLbtu2Df7+/ujevTtsbGxQt25dLF68WLn87t27iI2NRXBwsHKeqakpAgICEBYWBgDw9fXFiRMnkJ2djb1798Le3h5WVlZYvXo19PT08P7777+xHTk5OUhLS1P5IyIiqshyc3MRHh6u0odqaGggODhY2Ye+bNu2bQgMDMSwYcNga2sLHx8f/Pjjj5DL5UWuD7APJSIicSQ/k3nnzh3Mnz8foaGh+Pbbb3H27FmMHDkSOjo66NevH2JjYwEAtra2Kq+ztbVVLvvkk09w+fJleHl5wcrKCuvXr0dycjImTpyII0eOYPz48Vi7di3c3d2xbNkyODo6FmrHtGnTMHny5ELzY2JiStRZGuRnlWT3S+3hw4xir5uZmakyHRMTA0NDw2K9VjfFskTtKq2HDx+qJY46qCsHEjUL5/GrZGnmA4hWTj/RdEC2ZvE+zrop6rkKgDlQciX5HhDjXf8eSE9PL+8mlFpiYiLkcnmRfeiNGzeKfM2dO3dw6NAh9OnTB7t27cKtW7fw+eefIy8vD5MmTSryNRWtD+X359uL35+lwxwoOeaAehS3D5W8yFQoFPD398ePP/4IAKhbty6uXr2KBQsWoF+/fsXahra2NubOnasyb8CAARg5ciQuXLiALVu24NKlS5gxYwZGjhyJjRs3FtrGuHHjEBoaqpxOS0uDs7MzHB0dS3SpbVZESrHXFcPJyazY62ZkqH6IHB0dYWRkVKzX5uQ8KUmzSs3JyUktcdRBXTlgJY8p9rqZL52BsJQ/gqG8eM/JzDHLK1G7Sos5UHIl+R4Q413/HnjXzsopFArY2Nhg0aJF0NTUhJ+fH2JiYjBz5sxXFpkVrQ/l9+fbi9+fpcMcKDnmgHoUtw+VvMi0t7eHl5eXyryaNWsqC0E7OzsAQFxcHOzt7ZXrxMXFoU6dOkVu8/Dhw7h27RqWLFmCr776Cu3bt4ehoSF69OiBP/74o8jX6OrqQldXV4I9IiIiejtYWVlBU1MTcXFxKvPj4uKU/evL7O3toa2tDU3N54VUzZo1ERsbi9zcXOjo6BR6DftQqswEQVC5KszQ0BAymawcW0RSiF8zUD2BAgoPsEaFSX5PZlBQECIjI1XmRUVFoUqVKgAKBgGys7PDwYMHlcvT0tJw+vRpBAYGFtre06dPMWzYMCxcuBCampqQy+XIyys4kpiXl/fae0qIiIgqEx0dHfj5+an0oQqFAgcPHiyyDwUK+uVbt25BoVAo50VFRcHe3r7IApOossvMzETnzp2Vfy/fhkRE4kleZH7xxRc4deoUfvzxR9y6dQtr1qzBokWLMGzYMACATCbD6NGj8f3332Pbtm24cuUK+vbtCwcHB3Tp0qXQ9qZOnYr27dujbt26AAo6y02bNuHy5cv4448/EBQUJPUuEBERvbVCQ0OxePFirFy5EhERERg6dCgyMzOVo8327dsX48aNU64/dOhQJCUlYdSoUYiKisLOnTvx448/KvtlIiIiqUl+uWz9+vWxefNmjBs3DlOmTIGbmxtmz56NPn36KNcZO3YsMjMzMXjwYKSkpKBx48bYs2cP9PT0VLZ19epVrF+/HhcvXlTO++CDD3DkyBE0adIENWrUwJo1a6TeBSIiordWz549kZCQgIkTJyI2NhZ16tTBnj17lIMBPXjwABoaz48hOzs7Y+/evfjiiy9Qu3ZtODo6YtSoUfj666/LaxeIJFeSSyUzc1SvgkvYMBxZusW7L5eXShIVj+RFJgC89957eO+99165XCaTYcqUKZgyZcprt+Pj44ObN2+qzNPQ0MC8efMwb948SdpKRERU0QwfPhzDhw8vctmRI0cKzQsMDMSpU6fKuFVEREQFJL9cloiIiIiIiN5dZXImk4iIiIjobWSgo4ElHzmrTBORtFhkEhER0TuDBQbJZDIYFvceTCIqFRaZRERE9M5ggUFEVPZ4+I6IiIiIiIgkwyKTiIiIiIiIJMPLZYkqIN5TRERERERvKxaZRBUQ7ykiIiIiorcVT38QERERERGRZFhkEhERERERkWR4uSwRlTtBEJCZmamcNjQ0hEwmK8cWkboxB4iIiCoPFplvifg1A4u9bmaOXGU6YcNwZBX3/rwAm5I0i0gtMjMz0blzZ+X01q1bYWRkVI4tInVjDhAREVUevFyWiIiIiIiIJMMik4iIiIiIiCTDIpOIiIiIiIgkwyKTiIiIiIiIJMOBf4ioTKTMTin2upl5mSrTqfNTka+dX6zXmo02K0GrSJ2YA0RERO8mnskkIiIiIiIiybDIJCIiIiIiIsmwyCQiIiIiIiLJsMgkIiIiIiIiybDIJCIiIiIiIslwdFkiKncGWgZY1XaVyvS7KH7NQPUECrBRT5wSYA4QERFVHiwyiajcyWQyGGoblnczqBwxB4iIiCoPXi5LREREREREkmGRSURERERERJJhkUlERERERESSYZFJREREREREkuHAP1TuBEFAZmamctrQ0BAymawcW0RERERERKXFIpPKXWZmJjp37qyc3rp1K4yMjMqxRUREREREVFq8XJaIiIiIiIgkwzOZVCZSZqcUe93MvEyV6dT5qcjXzn/j68xGm5WwVUREREREVNZYZBIRERG9RUpyoLa0eKD27aaOHACYB2+zip4DLDKp3BloGWBV21Uq00REREREVDGxyKRyJ5PJYKhtWN7NICIiIiIiCXDgHyIiIiIiIpIMi0wiIiIiIiKSDItMIiIiIiIikgyLTCIiIiIiIpIMi0wiIiIiIiKSDItMIiIiIiIikgyLTCIiIiIiIpIMn5NZARnoaGDJR84q00RERERERG8DFpkVkEwmg6GuZnk3g4iIiIiIqBCeAiMiIiIiIiLJsMgkIiIiIiIiybDIJCIiIiIiIsmwyCQiIiIiIiLJsMgkIiIiIiIiybDIJCIiIiIiIsmwyCQiIiIiIiLJsMgkIiIiIiIiybDIJCIiIiIiIsmwyCQiIiIiIiLJsMgkIiIiIiIiybDIJCIiIiIiIsmwyCQiIiIiIiLJsMgkIiIiIiIiybDIJCIiIiIiIsmwyCQiIiIiIiLJsMgkIiIiIiIiybDIJCIiIiIiIsmwyCQiIiIiIiLJsMgkIiIiIiIiybDIJCIiIiIiIsmwyCQiIiIiIiLJlHmR+dNPP0Emk2H06NHKeU+fPsWwYcNgaWkJIyMjdOvWDXFxccrlSUlJ6NixI4yMjFC3bl1cuHBBZZvDhg3DL7/8UtZNJyIieivNnTsXrq6u0NPTQ0BAAM6cOVOs161duxYymQxdunQp2wYSEdE7rUyLzLNnz2LhwoWoXbu2yvwvvvgC27dvx4YNG3D06FE8evQIXbt2VS7/4YcfkJ6ejvPnz6N58+YYNGiQctmpU6dw+vRplaKViIjoXbFu3TqEhoZi0qRJOH/+PHx9fRESEoL4+PjXvu7evXv48ssv0aRJEzW1lIiI3lVlVmRmZGSgT58+WLx4MczNzZXzU1NTsXTpUsyaNQstW7aEn58fli9fjpMnT+LUqVMAgIiICPTq1QseHh4YPHgwIiIiAAB5eXkYMmQIFixYAE1NzbJqOhER0Vtr1qxZGDRoEAYMGAAvLy8sWLAABgYGWLZs2StfI5fL0adPH0yePBlVq1ZVY2uJiOhdVGZF5rBhw9ChQwcEBwerzA8PD0deXp7KfE9PT7i4uCAsLAwA4Ovri0OHDiE/Px979+5VngmdMWMGmjdvDn9//zfGz8nJQVpamsofERFRRZabm4vw8HCVPlRDQwPBwcHKPrQoU6ZMgY2NDQYOHFisOOxDiYhIDK2y2OjatWtx/vx5nD17ttCy2NhY6OjowMzMTGW+ra0tYmNjAQDffPMNhg4dCnd3d7i6umLp0qW4efMmVq5cibCwMAwZMgT79u2Dv78/Fi9eDFNT00Jxpk2bhsmTJxeaHxMTU6LO0iA/q9jripGo6aiWOLopJmqJE2/4+su2pJDxMKPMYwDMgdJSRw4A6skD5kDpvK05kJ6eXkYtKXuJiYmQy+WwtbVVmW9ra4sbN24U+ZoTJ05g6dKluHjxYrHjsA8tWmX67LAPLZ3KlAMA+9DSeNdzoLh9qORFZnR0NEaNGoX9+/dDT0+vVNswNTXFmjVrVOa1bNkSM2fOxOrVq3Hnzh1ERkZi0KBBmDJlSpGDAI0bNw6hoaHK6bS0NDg7O8PR0REmJsVPjqyIlFLtQ0lZyWPUEifHLE8tcWwybco8hpmTWZnHAJgDpaWOHADUkwfMgdJ5W3PgXTorl56ejo8//hiLFy+GlZVVsV/HPrRolemzwz60dCpTDgDsQ0vjXc+B4vahkheZ4eHhiI+PR7169ZTz5HI5jh07hj/++AN79+5Fbm4uUlJSVM5mxsXFwc7OrshtLl++HGZmZujcuTO6du2KLl26QFtbG927d8fEiROLfI2uri50dXUl3TciIqLyZGVlBU1NTZUR2YFX96G3b9/GvXv30LFjR+U8hUIBANDS0kJkZCTc3d0LvY59KBERiSF5kdmqVStcuXJFZd6AAQPg6emJr7/+Gs7OztDW1sbBgwfRrVs3AEBkZCQePHiAwMDAQttLSEjAlClTcOLECQAFBWteXsERhLy8PMjlcql3gYiI6K2ko6MDPz8/HDx4UPkYEoVCgYMHD2L48OGF1vf09CzUJ48fPx7p6en47bff4OzsrI5mExHRO0byItPY2Bg+Pj4q8wwNDWFpaamcP3DgQISGhsLCwgImJiYYMWIEAgMD0bBhw0LbGz16NMaMGQNHx4LrrIOCgvDnn3+iTZs2WLRoEYKCgqTeBSIiordWaGgo+vXrB39/fzRo0ACzZ89GZmYmBgwYAADo27cvHB0dMW3aNOjp6RXqk59dRfTyfCIiIqmUycA/b/Lrr79CQ0MD3bp1Q05ODkJCQjBv3rxC6+3duxe3bt3Cn3/+qZw3fPhwnDt3DgEBAWjQoAEmTZqkzqYTERGVq549eyIhIQETJ05EbGws6tSpgz179igHA3rw4AE0NMr0MdhERESvpZYi88iRIyrTenp6mDt3LubOnfva14WEhCAkJERlnoGBAdavXy91E4mIiCqM4cOHF3l5LFC4z33ZihUrpG8QERHRC3iok4iIiIiIiCTDIpOIiIiIiIgkwyKTiIiIiIiIJMMik4iIiIiIiCTDIpOIiIiIiIgkwyKTiIiIiIiIJMMik4iIiIiIiCTDIpOIiIiIiIgkwyKTiIiIiIiIJMMik4iIiIiIiCTDIpOIiIiIiIgkwyKTiIiIiIiIJKNV3g2obOTyfCTFRUMQFLCwcYaWtk55N4kqqdTMHNx4mAyFAvBwMoOlsV6ZxJHnK/AkJhWCIMDSwRRaOpplEoeIiIiIKgcWmRKKvnUZO5ZNgUIuh0Ihh4aGJtr1/QZVvQPKu2mlxgKj5NRxoOH4tUeYtfkinKyMkC9X4GFiBoa9Vxtt/VwkjfPgWiw2/3IMCrkCCrkCGpoa6DSqMdzrOUka55ns3GwoBAUMdQ3LZPvqlpuTDUGhgK5+2e1Pdm4+FAoBhnraZRYDAHKf5kFQCNA1KNsDZ5UtB4iIiN5FLDJFUCgU0NB4fsXx4X/+QIf+4+HiUQcAcOnEdhxYNxuDp/wtadwn6U8xb8cVXLqbCIUAeLtY4PMOPrC3kPZHmboKjCcZT/D74d9xKfoSFIICPg4+GN5iOBzMHCSNow5ldaAhKycfBrrPP65/HorE7581hbO1EQDgdGQsft1ySXSRqVAI0NCQKaf3Lz2LLqFNUcXHDgBwfm8kdi84heGLPhAV52X3Eu9h2p5puBl/EzKZDFUsq+DrkK9Rw7aGpHHUJfHxPexaNQ3x0TcBmQxWdlXQ9uOvYeci3f7ci0/DzH8u4NbjVABAFRtjfNm1LjwczSSLAQAJD1Kw7bfjiL3zBDKZDFbOZug4Igj21awkjVPZcoCIiOhdxiJThNU/f442vUJh6+IBAFDI82FiYaNcbmxug/z8PMnj/rLpIjydzfBxqxrIzxew7fRdTFsfjt+HNBW13fIqMGbsm4GadjUxIHAA8hR52HJxC77f9T3mfThP0jhlQV0HGobPP4pPQ7zQqKY9AEBLUwOpmTnKIjM5IxdamuJvsV4xdgfaDW0Ee3dLAIBcroCJ1fODF6bWhpDnyUXHedmsA7PQpU4XtKjRAnnyPPxz/h/8tPsnLO+/XPJY6rDv71mo27QLPP1aQJ6fh/BD/2DXqp/wyXjp9ue3rZfRqaEbmvk4IF8uYNPJ25i58TwWj2wpWQwA2D3/JPzbe8KrsRvkeXKc3n4d2347gc/mdJE0TmXLARIvMy0Jl05sR6P2/STfdr5cgdjkLJgb6Up+FcC9y48RfT0OGcnZkGnIYGZrhOoNnGHpYCpZjKTMJEQ8jkBSVhIAwMLAAjXta8LC0EKyGG+D7Mw03LkaBu+AENHbuv04FTcfpcLXzRL2Foa4F5eGbafvQhCARl72qF/d5s0beYMbYffgXs8J2rrq+XldGfIgPy8XMg0NaGoWvGfJCTG4GrYbacnxMLGwRa3AdjCzEn/SITdfDg2ZTPlb6dGTTOw9/wDxqdmwNdNHSD0XyU/WvOivCXvw3ojGMLMxknS7b3MOsMgUoVX3kdi75mc4VfNF446fILB9P6ya/hksbJyhkOcjKe4BWvUYKTrOvJ1XMKB1TejrFPxzPUrKwKQP60NXu+DS1fcDqyJ0yQnRcdRVYMw5PAefBn0KfR19AEBMSgymdJoCXS1dAEC3ut0wav0o0XHUQV0HGn7sF4g52y9j3/loDO9YC0Pb++CHdeegEAC5QgGZTIavutYVHSdkcEPsmnsSLt62aNanLpr09MXSMdth6WgCRb6AxJhUhAwSf/n3+K3jMarlKFgbWwMAUrNT0ci9EfS09aCnrYcAtwBsvbhVdBx12bxwPIJ7jIKxecH+ZGekolrtRtDW0YO2jh7cvANw4bi4/Zn012kM71gb1qYFn5u0zFwEetpB77/vhfoetth2+q64HQGw4ceDCPmsIUwsCz77WWk5qN7AGdq6WtDW1UI1PyeE774hOk5lywGSXmZaEk7uXiW6yFx//CY6N6wKXW1NyBUKLNkbga2n7kCuEKAhkyG4jhNGdfYVfaAuMyUb6388iMe3Cs76C4IAWzcLRJ66j0N/hiOgkzda9fMXFSM7NxuzDszC4cjDkMlkMNY1BgCk56RDEAS09GyJ0OBQ6GmXzT366paeHI/df80QXWQev/YIP6w7ByM9beTlKzCpTwNM/fssajiaQaYhw4Q/T2Fst3po6SvuSq2NM45AV18bNRu7oU5wdTh6WIva3qtUpjz4Z+5Y1G32PmrUbYaY21ewbs4YWNg4w9KuCu5eO43wQxvQfcTPcKzqIyrOtytPoVOAG5r6OODa/ScYuzwMTlaGcLE2xpmoeGz89zZ+GtAI3i7iirOoMw+KnP/gehxunY2GiXVB3+rRQNyVZxUhB1hkiuDg5oWPvpqPMwfW4s/pQ9Csy2AMnLAKj+9dhyAIsHOpofzRKYaViR6GzzuGT0O8EFjTDs1rOWLEgmNo4GGLfIUC/15/jFYivxgB9RUY1kbWGLJ6CAY3HYwg9yC0qNECQ9cMRUPXhshX5OP4reMI9gwWHUcd1HWgwc7cAD/0bYjDlx/iy6X/okvDqlgRGoxHTzIhVwhwtjZSHnQQw9HDGgNmdkDY5qtYNmYHWvbzw9C57yMmKgGCQoB9dStl8SFGsGcwxvwzBp19O6Nr3a7oUqcLBq4aiNpOtZEvz8eF6Avo4ddDdBx18aofjPVzxqBOk86o17wr6jbrguU/DIRztdpQyPNxP+oC6rcUtz8t6zjh6+Un0THADV0auqFTQ1cMnnMYtVwtIZcLuHAnAR80riZ6X7ybuWP1xL3wa+eJ+h1qwr+9JxaP3AoXbzvI5Qrcu/wYDTt7i45T2XKASi4+5vZrlyfFRUsSZ+m+CLSu6wJdbU1s+vcO9p5/gJGdfOHpbIbbj1KxcPc1bDp5Gz2aVBcVZ9+SMzAyN8CYv9pAU0sTB1eeQ05mLgb+0hH3Lj/Gpp+PwNjCAA06epU6xh9H/sCN2Bv48f0f4efiB02Ngu99uUKO8w/OY87hOfjj8B/4ss2XovZFXXKyM1+7PDcnW5I4fx+9ib4tPfFhcw8cuRyDqX+fRbcgd3zUouBy/H9O3MKGE7dEF5kAENDZB1GnH2DF/ihYO5vBN7g6arVwh4GEg/NVpjyIf3gL1o5VAQDHty9F3SZd0KLb58rlx7cvxdEti/Bh6O+i4tx+nIqqdiYAgOUHbqBjgCuGtHteuK44EIEle6/j10GNRcXZMO2Q8iDTy/YuOQ0AkMlk+HaTuINnFSEHWGSKpKGpiYYhfVCjXnPsX/srdE/vRcvuI2FsJt39Sj2aVEcTbwfM2X4F+y48wOcdaqGGozku3UuEoBDwaYgXmnqLv5RAXQVGr/q90LR6U/x26DfsubYHI1uMhKedp/KezM+afIZmHs1Ex1EHdR1oeKZFbSfUr26LRXuu4aul/2JUZ19Uk/ASLADQ0NRA0Ae14RXkit0LTuHy4dsI+TQAxpYGksVoXqM5/F39sej4Igz7exi+CP4CM7rOwMWHFyFXyNG7fm/UtK8pWbyyVqNec7jW9MfRLYuw+udhaN3rC3QfNgPRNy9CoZCjQevesHcVtz/NfBzhX80GS/Zex6iFxzGyky+m9bPCpbuJkCsE9GhaDZ5O5qL3xSvIFVXrOODwqnCs+Hon2g0JRO9JbXD/WiwUcgUave8DBwmOzle2HKCSWzVtECCTAUX8GFPOl8kKLxPh8JUYDGxTU3n/uqtNwY/OtcduiS4yb59/iL7T2isHx2rxcT380udvhAwOgGtte7T+pAH+3XBZVJF57OYx/NjlR9RyrKUyX1NDE/Vd6+OrNl/h2y3fVojiAgDmfNXx9f/GEuXAw8QMZQHZrJYDpv9zHkH/3X4CAEFe9vjzUKToOABQL8QDTXr64vGtRFw8cBPH113C4T/Pw6O+M+q08UDVOuJ/r1WmPFAo5BAUBd8BSXHRaPnBcJXlPg3b4vzhjaLjyBUCFP9910QnZGBoe9Uzo23qumDzyTui47jXdYRMQ4b3hgfB0ExfOX9at1X49NdOsHYxEx0DqBg5wCJTpIRHd5EcFw0rB1f0GPEzrp7ag7W/joJ/q+6o27SLZHHsLQzxY7+GOHgxGl8u/RfvB1bFZ229IZO4A1ZHgQEADmYOmN51OvZH7Mfo9aPRrV43DG02VPL9UQd1HGgAgDORcXiQkI6q9qYIfb8OLt1NxE8bzqO+hw36tfKU5EwmAMTfT0bSo1RYu5jjw8ltcPnQLaz6324EdPKGf3tPSWIAgJGuEUKDQ3El5gqm7ZkGfxd/fBL0SYW4vKcouvpGaNM7FA9vX8HuVdNQxdMfjTt+Am0d6fbHUE8bozr74uq9J5ix8TzqVbNG/1aeyktmpaJnqIN2QwMRfT0O2347ATdfezTvU0/y+4wqWw5QyegZmqBZl8Fw8ahX5PInsfewacH/JIn1rGuJT8kqdDmcl4sFYpNff0atODS1NVX6sGdnM+RyBQDAydMGKfEZomIIggBtzVffQ6qloVXkGZS3lY6eARqGfAS7KkX3LSkJMdi3dpboOAa6WkjLyoWduQEysvOgEASkZecql6dm5UJf4u83+2pWsK9mheAB9XEj7D4uHriJtVP2w8TKUPTYFpUpD+xda+L2lZOwtHOBmZUDEmJuw8bp+VU5CQ9vQ8/QWHQcTydznLoRCxdrY9hbGOBObBrc7Z8fpL8dmwojffGjp/ea2Bqnt13Dsq92oO3ghqhe31n0NotSEXJA/Egh77CzB9dj9cyhOHNgLdb8MgKXTuyAT8O26PPVPDy+F4HVPw9DQoz4oyLPpGXlolUdZ/wxpCluP07FqIXHcSc2VbLtAwUFxo2we1AoBHw4uQ086jtj1f9249wu8fdgvSw1OxWta7bG/D7zcSv+Fob9PQy3E15/+dTbKOHRXURdOAZBIUePET/DvVYjrP11FC4c2yJZjAW7r+LnTRcQGZOC37Zewl+HI+HrZoV5nzeDjpYGPp97FGej4kTHOb31GpaP3YmwzVex8ptduLAvErVbVsOAGR3wKCoBK77eifh7yRLsUcG/f2RcJNys3LCozyIY6Bhg8F+DcerOKUm2r27ZmamIfRAJawc3fPzNIujoGWDVT4Nx55p0+5OWlYuomBS42Zlg3ufNYKCrhc/nHcWZSPH/9i/KSn+Kx7cSYV3FHAN/6QhdAx0sCd2GW+EPJY1T2XKASsbWuToyUp7A1NKuyD8jU6uiz3KWwq6z97E57A60NDWQlpWrsiw7Nx/aWuIP0jnXtMGxtReQ+zQP8nwFjvx1Hma2RsrLJLPSnkLfSNyP2IZVG2LWgVm4GX+z0LKb8Tcx++BsBFYNFBVDnZ4VEy4edYr8s6tSQ5IcqOtujT+2X8bBi9GYuekC6lWzxrJ9EXiQkI7ohAws2Xtd9L14AIo8UK6tq4Vazd3x8fdtMeSP9+HTtKroOJUpDxq/9wlO7f0L/+5cAU//lji8aT6Ob1+K62cP4N8dy7FnzUzUbfq+6Dj9gj3x99GbWHXoBlrUdsTC3Vex4kAEDl16iFUHb2DW5ovo3NBV/A4BCOjkje7jWuLQqnDsmncSeTn5kmz3RRUhB3gmU4SzB9ah69BpcPGoi9Qnsfhn7tfwbfweDIxM0b7vONyLOIdtSydj4MSVouKcv52An9aHIzUrF5bGevhfL3+M6VoXF+8kYtr6cDTwsEVfCc5knd56DUfWXIBNFTMkP05Hi4/roW6bGqjm74QDy85ixdHbaD+0EWxcxV2SF34/HD/s+gEp2SmwNLLEpPcmYWzIWFx4cAHf7/weDas2RP9G/ZUDAb3Nzh5cj393LIOVQ1WkJMSgSadB8G38Hqr6BOLIpnm4fmY/2vQeo7zfoLT2X4jGtH6B8HA0Q1pWLkYtPI6PWtSAtpYG+gfXRPPajvht62XU97AVFSds81X0HN8KrrXskRKfgbWT96NumxowMNFDp9FNcOfiI2yaeQRD5or7wj8QcQA/7/8ZhjqGyMnPwbh249C/UX+09GyJWQdmFVxG3XLkWzE6WnFcP3sA+9b8DB09Q+Tn5aB933EI6tAfnn4tsX/tLFw9tQetuo+EoUnp9+fQpYf4dctFGOhqIzdfjrHd6qFvS080r+WI37ddxt4LDzCsQy1YiLzv5+rRO9g57yR09bWRn5uPTqOboGmvOvBq/N/VDQdvoc2gABiZ6795Y69R2XKASs63cSfk5b76njtjcxu0/Wis6DjWpvrYHX4fAKCtpYGbj1JR2+35lSYX7yTC2Ur8iI+t+tfHmu/24Zc+BaOJa+tpodvY5srlidEpqNVC3H3TI1uOxA+7fsBnf30GYz1jmOmbAQBSslOQkZOB+lXqY2RL8eMAqEtN/2Dk5+W8crmhiQUC2/UVHWdQiBdm/HMev2+7DK8qFhjf0x8rDtzAoN8PAwAcLAwR2qWO6DhvOnNk4WCC5h8Vfea+JCpTHjhW9UG3z3/CkU3z8fheBADg9N7VAAAjU0sEte8Hvxbin2rg7WKB7/s2xKLd13DjYcHB8r+PFhRolsZ6+LhlDXRt5C46zjN2VS3xyc/v4cCys1jyxTYIkPasYkXIARaZIhR8mRQctZJpFD4p7FrTH32/WSQ6ztztV9CjSTV0DHDDuZvxWLjrKn4f0hR1qlph7ufNsPpwFIbOPYJlo1uJiqOuAuP3Q7+jZ/2e6FKnC87eO4t5R+Zh3ofzUNelLhZ+tBCrTq3CoD8HYdWAVaLiqIO6DjToaWshNjkLHo5mSEjNhraWar652piIvlkdKMjpZ0diX3yczTNV6zhg4KyOouMsPrEYY9uMRUvPloiMi8SMvTMQ5B4EFwsXzO4xGzsu78DwtcOxZuAa0bHU4fi2xQjpMxY1/Vsi9kEk9vw1A9VqB8HSzgW9Rs/GpRM7sPqX4Rg8ufT7s3RfBMa8XxfNazsiKiYFv2y+gMCadnCxNsbPA4Ow6+w9jF50AqvGiBs06/Bf4XhveBC8m7jh8a1E7PjjX3g0cIGVkxk+/r4tLuyLxMpvdmLYQnGdfmXLASo5jzpNXrtc39AEPg3bio7z15etX7vc08kctV3F395gbmeMwb91RnREPOR5cjjWsIaByfODPr6txN3zCQAmeiaY3nU67j+5j2uPryE5s+DHsrmhOXwcfOBiIW7ESnXzbfzea5cbmlggqEN/0XEsjPXw04BGKvOGvVcLXRtVRU6eHM7WRtAs4ndcSQ1b+AEMTMv+cv/KlgeOVX3Q58u5yEpPQUriIwiCAkamljC1tH/zi0vA28UCv33WBCmZOXiclAWFIMDSWA925tLeEvaMtq4W2g0NRNSZB7h/JRYGJtKdPKkIOcAiU4T6wT2waf43sHaqhuT4h2jScWChdbR1xCdUUsZTNKhhC11tTfhXt8HC3deUy3S0NDGgdU20qO0oOo66CownmU8QWDUQulq6qO9aH/OOPn8epo6WDj5t/ClaeYormNVFXQcaPmntiZkbz2PezivIyZPjq27iH1dSlIZdfLB26gHYupkj6VEamvcpfMRVivvynuY9hbNFwX0KDqYOyMlXPZL9Xu33EOQeJDqOuuTlPIWFbcH+mFk5ID9XdX98G7+HarXF7c/TvHw4/Xe2xcHCEDkvPU6ofX1XBNYU3yHnPc2HpWPBYCjmdsaFLvOp26aG6KHXgcqXA1RxeUlwmeQz2rpakgzs8iZVLKugimWVMo9T2Un9XESpn4H4JpUtDwyMzWBgbAbgzWeFxTAz1IWZofqulvNo4CJJv1mUtzkHWGSK0CC4F1xr1v9v4J+qsLQrmwRq6GmHqX+fQ6CnLa7eT0IDj8IPDHa1NREfR00FRiP3Rvhu+3cIdA/E1ZirCHAt/FgUNys30XHUQV0HGlrVcUZ9D1s8SsqEk6URjPSlfXj4M4Hv+8C9riMSY1JhU8UMVk5mZRInxCsE4zaPg6+TL6LiotC6ZuEzDeaG4kdKVRfvgBBsnD8OLtV9EfsgCl4NCu+PoYm4/Wld1xnj/zyF2m6WuBmTila+hQcTMDcSn2u1Wrhj3dQDcPGxQ+ztJ6jVrPDlQy+OmFdalS0HqHQyUp/g4vGtiLl9BRmpSZBpaMDM0h7VfBvDJyAEGprSDGiWl6/AyYjHiIhORlLGUwCAhZEearqYo5GnfaGrQ0pj7+LTqNmoCly87URv63Xy5Hk4cesErj++jqTM/x7AbmgBb3tvBFULeu1gIG+73JxsRJ4/jJSERzA0tURN/5bQN5RmBPVTN2IR+TAF/tWt4V3FEhduJ+Cff29DIQho7GWPDvVdJYnzotynebh+4h5SYtNhZK4Pr6Zukj3KpDLnAQDMGtUG/cctgaW9dAVUTp4chy/H4Nr9J3iSngOZrOCgbWBNO9RzL5tnmr7LOcAiUyQbR3fYOEp3DXdRQrvUwc6z9xCdmIFWdZyUQ69LTV0FxldtvsL2y9vxIOkBgmsGo71P+zKJow4NgnvBrWYDJMU9KNMDDQBgYqADE4PCg0a8eAZaCjau5qLvu32Tz5t/Dl9nX0QnRaOtd1vUd61fpvHKWotun8O5ui+S4qLh3bAt3GpKvz9D2vnA19UK0YnpaFPXBf7VCx9skkLrTxqgio8dnsSkwrdlNVStK/4qiaJUthygknt8/wY2zPkSZtaO0NLWRUpCDGrWbwV5fh6ObJ6Pq2G70e3zn6CrL+5sU8yTDHy78hSepD2Fp7O58gzGrcep2HHmHqxMI/BD34ZwtBR3FurcrgiE774Bcztj+AZXR+0W1UTfu/yyh8kP8fWmr5GYkYia9jVhblDwXX0z/ia2XdoGa2Nr/PT+T3AyF/+8R3VYNrU/eof+Bn1DU6Qlx2Ptr6PwNCsdFjbOSEl8hLDdf6LPl3/AzErc2eGdZ+/hj+1XUNXOBFtO3cGw92rhj+1X0NTHAZoaMizYdRU5eXLR9+QtGL4Zfae1g4GxHtISM7Hq2914mpEDS0dTJMem4/j6S+g/vQPM7cSNllqZ8uDwxnlFzhcEBU7vX6M8yPDiszNLI+ZJBr5eHobcPDm0tTSRmJaN+h42iHyYjO1n7iLIyx7f9vATfdk0c+A5Fpki3Ys4h5jbV+BU3RdVatRD9M1LOL1vDeT5ufBq0Bq1AsUXUNpaGugSKH40suJQR4GhramNrnW7lmkMdbJ2rCp6YJ83yc2XY/n+G4h8mIyAGrbo2bQ6Vh+JwrpjBTetN/S0xahOvjDUK9ujVmmJmTj29wW8N0L8/Z9B7kFA2R6fUSuxl8MWR2BNOwSibM+SACizy3peVtlygErm8MZ58GvxgfKeu2tn9uHC0S346Kt5yM5Mw/rfx+DEjmVo1X2EqDhztl2Gq40x5n3erNB3ZObTPMz45zz+2H4F0/qLH4mx96TWuHk2Gqe2XMXR1RdQzc8RdVp7wN3PqcjbUEpq9sHZBaMxf7QIhrqqxXdmTiam7ZmG3w79hpndZoqOpQ5JcQ+g+O8RL8e3LoaRqRX6jVsMXX0j5D7NwpbFE3Fi+1K8N2CCqDhbwu5gRMdaaF/fFRfvJGL8qlMY3M4bnQIKrprydDbHhuO3RBeZT2JSIcgLLvM8/Gc4jC0M8OmvnaBnqIOc7Dxs/Okwjqw+j/fHiHsWeGXKg/DD/8Da0b3wwSRBQFLsA2jp6EpyIH3ezquoX90GIzvVhkwmw7pjN3H53hP8PqQpHiZmYNzKMKw+EoW+LcU9qo058BwfYSLCtTP7sHH+ONy+GoYtC8fjSthubF40HsZm1jC1dMD+tbMReeFomcXv+8sBPEwU98ytl6UnZeHomgv4a8IeLBi2GQtHbMG67w/g4v4oZUcglfj0eGTlZhWany/Px6XoS5LGKitxD6KQkvhIOX3tzD6s+WUEFozvgTWzRiLi3CFJ4izbH4Ejlx+ihpM59l+Ixpztl7Hr7H2M6lQbX3TxRVRMClYckP4xMy/LTs/B5cNl/5iZpMwkrAwTN1iSuuXl5uDh7StIfHyvyGXXTu8Vtf341GykZj6/b/HKvSeYtj4cXyw+gZ82hOPagyRR239RXk4+Lh64iR1zTmDtlP1YO3U/9i4+jbuXHr35xRKpiDlAJRMfHQXvgDbK6Zr+wYiLvonMtCToG5qgWZfPECVBH3rtQRIGtK5Z5EE4Qz1t9A+uiav3n4iOAwA2VczR5tMAjFrWE52/aIL8PDk2/HQIcz7dgCN/nUfSozRR27/66CoGBg0s9KMSAAx1DfFJo09wJeaKqBjl5dHd62jUvh909QvOKOvoGSCofT/E3LkqetuxyVnKqz/qVLWCXCGgtqulcrmvmxXiUgr/HhHjYWQCmvSqAz3DgiuQdPW10aSXLx7eiBe97cqUB006fYqcp5kIbNcXvUbPVv7JNDTR9uOv0Wv0bPQc9avoOJfvJqJbkLuyYO3ayB3nbyUgLSsXTlZGGNreB/svRIuO86J3PQd4JlOEcwc3oPn7Q+DXohvu3wjH5oX/Q+OOA+HfsjsAwNLOBeGHN6JGXXFHKzaHFf2szfjULOw7/wDm/13b/b7Is52PbiZizaS9MLc3gbaOJpJi0+DTpCrk+QocWHEOlw7eQq+JwdAt4pLNkniS8QTjt45HZFwkNGQaaOXZCqNajYKBTsHoXmlP0xD6TygOfnFQVBx12P3XdLTo+jnMrBxw+d+dOPTPHNRq1AFeDVojKS4a+9b8jPy8p6LPaB+/+hhffVAP9dyt0SnAFf1/PYhJH9ZHo/8GejEx0MGvWy5h2Hu1RMWJOvPgtcuTY9NFbb+4kjKTsOrUKvQL7KeWeGIlxT3Ahj/GIj05HpDJ4FS1Ft77ZAKMTAt+xOQ+zcTuv2bAOyCk1DG+//ssPmzugYaedjgZ8RiT15xFwxq28HaxQMyTDHy55F9M+rA+GnqKO9OZ9CgNaybtRV6uHFo6mkhLzEQ1Pyc8upmA8D034BlQBV3GNIWGZtkeo6xoOUAlp29khszUJ8pLIbPSkyEo5NDRK/jRZG7jiKdZ4r9zDPW0EZuc9cqxC2KTsyS/CkRTSwNejd3g1dgNqQkZuHTwFi4fvImTm67g202lz2kjXSM8Tn38ynELHqc9hpGuegefEevZj/78vBwYmlqqLDMys0ZWeoroGCYGOohLyYaNmQES07KhEATEp2QrcyIuJavI21FK5dn+5MoLXS5tbGmIrNSnokNUpjwIaPMhXDzqYteqaajqE4imnQdBU1P68sRITxvZuc8HssvJk0MhCND87woDN1sTJKW/+nE6JcIcAMAiU5TkhIdwr1VweU0VTz/I5XK41Hg+WE5Vn0Cc3v+36DgLdl2FlYleoUttBAE4cPEhNDVlkEEmusg8sOwMGnTyRtNedQAAV47cxrldERgw4z1kp+dg9aS9OLrmAtp8WnignpJYdHwRZDIZ5n04D5k5mVh0fBFCN4RiRrcZMNEz+W/fym5UMSmlJMTAzLrgnrWLx7eiRbfhKkOy21WpgVN7V4suMtOycuBkWfDDy97CEBoyGRxeGBXP0dJI5SxXaW2Ydggymey1778Ul63cTnj92dDoZGmPJpa1Y1sXw8rBDR9/vRA5Wek4tHEu1swagV6jfoWJhbhnlz5zLz4dVWwK7uFYe/QmPmldEz2bPn8kwtZTd7DqUKToInPf0tNwr+eEtkMaQiaT4eSmK3hwLRYDZryHJ49S8fd3+3Fiw2Xl90RpVbYcoJKr7tsY+9f+iqZdPoOWtg7Cdq+CU7XaysHSkuKiYWQq/tEi7fyqYMbGC+jT3AN13a2U92SmZObgwu1ErDkahc4Ny26wOVNrIzTtVQdNevri7qXHorbV3qc9ftrzEz5u+DHqudRT3oeVnJWM8w/O46/Tf+H9OuIfXK9O638PhYamFnKfZiE5LhrWDs//LdKS4qBvKH5gw0BPO8zafBGt6zrj1I1YBNdxwsLd1wpqAZkMi/dcg181ae5zXz1xLzQ1NZCbnYukR6mwqfL8FqTU+AzoG4sfoK2y5YG9a018PHYBDqz/DX9O/wwd+o+XPEa9atZYuPsaRnaqDW1NDSzbHwF3e1PlAab41GzJRpxlDhRgkSmChqYWFPLnR0W0tLSho/v8iIWmlhbyc8UfrWjvXwU3Hibjm+5+yh+ZANBu4nb82L8hXG3EfwEDwOM7Seg46vlzy7ybVsWOOf8iIzkbRub6aNnXH9t/PyG6yAx/EI6pnabC067guvc5jnMwecdkjNkwBj9/8DMAaQoZddDS0UN2ZipMLe2QnpIIe1fVa/ntXWsi7Ym4HxUAYGNqgOsPkmFjZoAbD5MhkwE3HqYoj8LeiE6GlYn4ASaMzA3Q9rOGqBFQ9D15sXeeYNmXO0THGfTnoFcWs8/mV5QcAICYO9fQY8TPMDAyhYGRKboO+RH71/6Kv38diZ6jfoW2jviR5DQ1ZMqjsLHJWaj/0ijT9avbYsne66Lj3L8ah09/baB8/xt09MLR1ReQlf4Ulg6maD2wAfYvPSO6yKxsOUAl1/i9T7An9Qk2LxwPQSGHg5s32vcb93wFmQxNOw8SHadfsCf0dDSx4cQtLNpzTWWZuZEuejaphh5NxD/D0tTaCDLNV+esTCYT/XiTT4I+gb62PtadW4f5R+crPyOCIMDC0AK96/dGr/q9RMVQp8B2fVWmtXVVvytvXw2DU7XaouMMDPFCnvwqjlyJgZeLBYZ1qIUtYXcwafUZyBUCarlaYkDrmqLjNOlZ54UpZ2jrqp4hv3UuGs5e4g88VrY8AAouj27fdxwizh3ChjlfQhCkvUXr0xBvfLf6DAb9fhgAYG2qj+8+bKBcnpqZi+6NxQ8SwBx4jkWmCObWjngSFw0L24If5EN+2AAdvecPdE1NfAxjc/FHxkZ19sWJa4/x7cpT6NHEHZ0bls0gM4YmushIzlKOeJWZkg2FQgFdg4IPiIW9MZ5miD9blpmTCSO956fwdbR0MKXTFHy3/TuEbgjF/9r9T3QMdXHzaoCLx7ah7Udfwbl6bUReOAobp2rK5ZHnj4geFQ8A2tevgp83XcCe8PuIepSKwW29seJABB4mZkAmA3acuYduQeK/HO3dLRF7+8kri8w3neUsLhM9EwxuOhj1nAs/JgcA7j25h/9trTh5kJ+XAw2N549akMlkaNM7FAfW/4a1s0dLclS2tqslDl+OQVU7U7g7mOLS3Seoavd8aP+LdxMlOdCgZ6iD3OznB8/yc/KhUCig+d/lsTZVzJGRnC06TmXLASo5HT0DdBo4CXm5Of9dJqv6QHQpR2nu2bQ6ejatjsdJmUj6rx+zMNKV9DmJwxd9INm2Xqd3g97o3aA3HqU8QlLWf48tMLCAg1nZP59Tas8GfXqV5u8PkSSOvo4WvuhSR2Ve9ybV0KmhG/LlCskul37TwbdW/aXL6cqUBy+q6d8Sju4+iIu+CRMJfkM/Y26ki98+a4KHiRnIlyvgbG2kMpJsUx9p3jfmwHMsMkUIaNMHevrPi6WXR8aKfRAJD5H3Yz7T2NseNZzMMHPjBZyOjMeXXetIst0XeQS4YPeCU2jVzw+a2po4sf4SXLztlM/GfBKTBiMLgzds5c3szexxN/EunM2fP+dPU0MT33X8Dt9t/w7jtox7zavfLk07D8bfs0Zg7a+jYevigfBDGxB98xIs7VyQHPcQj+5dR5fBU0TH6RbkDnMjXUREJyPEzwUtajvBzc4EKw/cQE6eHO83qooPm3mIjtOwizfycvJfudzc3hgfTW0rOk512+p4kvEEdqZFX9qZkZNRYS6ZBgBLWxfERkcWep5XcI9RAIDNC8UXS5+08cKYJSfwJO0pfKpYYsX+CETFpMDZyggPEzNw9OojjOwk/oi/m689Diw/i3ZDGkJTSxNH/joPOzdL5b3YaYmZMDQTf2a2suUAlV5RzxIuqzPZ9haGkhaWxVUW++Ng5vDW/JgsC+q4mkFXWxO62tI8i/VNymp/KmMemJjbwMTcpkz6ACer8rtP8V3LARaZInjUafLa5QFtPpQ0nrWpPqYPCMTaYzfx+byjECDth69Zn3rI+ONfrP/hEBQKBZxq2KDT6Of7KJMBLT72Ex2noWtDbL+8HU2rN1WZ/6zQnLR9EhIzEkXHUQdjMyv0/WYRTu/7G7evhkEQBMTev4H05Hg4VvVB79DfYV9F3HDYz7T0dUJL3+fPO/J1s8KsQeIfJfKiNz1EXEdPG1V8xD9Co1PtTsjOe/XZMBsTG4wNGSs6jrpU822MG+cOwbtBm0LLgnuMgqBQ4NKJ7aJiVLExxu+fNcWKgxHYcPwWnubJcejSQ2jIZKjhZIZve/ghyMteVAwAaNXPHxumHcLCEVsgk8lgYmmAD8a1VC7PSnuKhl18RMepbDlAJZefl4vj25ci9v4NVPVuiIA2vRG2+0+c3r8GAFCtViO07hUq+jmZE/48jWa1HNDE26FMC4r8XDmOrD6PRzcTUc3fCY261sKJ9ZdwclPBCI/V6zuj/dBA0YPnvSgxIxHbL29HTEoMLA0t0aFWB7hYqOcRRFJQVw68LDEtG7vO3kfMk0xYGOuhnb8LXKzFPbcQKJ8cACp2HjAHpPE25gCLzApGJpOhdzMP+FWzwdX7T2BpJP6MwjO6+tro+lVz5OXkQ6EQoKuvevmIVA9lH9h4IJ7mFX2vqqaGJiZ3nIyE9ARJYqmDnoExmnUZjGZdBpd3UyqMJtVff4DGRM8Ebb3FnzFVl4YhfV67vHWvL9C61xei4zhYGuLbHv4QBAHJGTkQAJga6EBLwpFeDc300X96Bzx5lAp5ngJWTqYqI8nWbOQqSZzKlgNUcse3LcGN8EPw9G+Fa6f3Ii05DneuhqFNr1DINDTw747lOLF9KVr1GCkqzpmoOJy7GY+5O66geS1HtPOvAg9HM2l24gWH/wrH9RP34N3EDZcP3UJaQiZunotGu6GB0JDJcPTvCziy+gJCBpV+XIO2v7fF2k/XwszADPcS72H42uEwMzBDNetqOHXnFLZe2oq5vefC3bpiPIBWXTnQcfIO/Plla5gZ6uJefBq+WHQCpoY6cLc3xZnIOOw4cxe/fdZE5RaE0lBHDgCVKw+YA6VTEXKARWYZOrZtMTLTktHuI+mPxns4mpVJJwlAeXlsWdHU0CzyuT4vLn/VJXRUtGX7riM5IwdjutYt0zjndt1AdtpTNBE56AuJI5PJYGEs3QGmolg6iOtoid4k6uJRtO87DlU8/VC3aWcsmfwxOg+aguq1gwAA+oam2LvmZ9E/LgFg/vBmCL+ZgL3nH2DXuftwszVBO38XtKztBGOJzijcCLuPTqMaw83XAX7tPDH/803o9nUL5T3u+iZ62DX3X1E/LnPzc5WXEC75dwl8nXwxpdMUaGpoQqFQ4IfdP2DpiaX48f0fJdmnsqauHMjNV+DZlZfL90WglqslJn1YH5oaGlAoBPz0TziW77+BqR+L++Gvjhwo2J/KkwfMgdLuz9ufA2X7oLN3XEZKoiQji77JyYjHkj9Atijndt3A8bUXyzzOv7f+xd5r4h5c/7a4cGwLTu4q+wfKJ6Y9RWyytA+SLkrkqfu4fPhWmcepTDkAqCcP1PU9EHn6AXOAJJGdkQpzm4JbAMysHCCTaagMlGZu44jsjBRJYpka6KJbkDsWjWiB3z5rAk9nc6w4cAMfztyHH9efw4Xb4q+eyUrLgYVDwYjf5nbGkMlkyoH0gILB8zLTJHoOH4Cb8TfR078nNP8bdExDQwO96vdCVHyUZDHKmjpz4Jlbj9PQvXE15aAvGhoy9GhcHTcfiY+j7hwAKn4eMAfEe1tzgEVmGWrfdxx6jvq1zOMs3RuBnzddKPM46iowFh1fhBn7ZpR5HHWIungMV0+X/Q/lsR/Uw8yBQWUep8+UEAxbWPYjKFamHADUkwfq+h44vCocO+b8W+ZxKlsOUGHG5raIuVPwSJHH9yIgk8kQez9CufzR3QgYmVlLHtfTyRyjO/vi77FtMLxjbSSkPsU3K8JEb9fUyhAPbxQUq4+iEiCTAY9uPh9fICYqESaW4gbPk8lkyoFDZJAVuirIUMcQ6U/TRcVQJ3XmwLPxVmQywFBP9YotQz0tZGTniY6hjhwAKlceMAdKpyLkAC+XrQSWjm755pUk0GdKiFrirBxQ9mf+1KXnyFnl3YQKqTLlAKCePFDX98CQuep5uHNlywEqzLfxe9jz13RcCduFuAdRaP7+EBzfvgxJcQ8hkwEXj2+Df6seZRZfT0cLIfVcEFLPBdEJGaK3VzfEAzvmnMClAzfx+HYiWg2oj6OrzyPpUSoAGc7vuYGAzuIGzRIEAR8v/xgyyJCdl43bCbdV7rl6lPoIFoYWIvdEfdSZAwN+PQiZTIbsnHzciU1TuffuUVImzI0Lj3JcUurIAaBy5QFzoHQqQg6wyCxDmWlJuHRiOxq171feTaFKLin9KXaevY+PW9YQva38PDmiTj9ATGQCMlIKRv80MtOHo6cNPBo4Q0tNw70TUeXm37I7DIzN8fjuddQKbIea/q1g5VAV/+5cjrzcp/Br8QEahnwkOk4tV0tov2FwLGdr8Y81COjkDUNTfcREJcC3VTV4N60KmyrmOLrmAvJy8tGgkzeCuot7zNDLIy47mqkOyHf98XU0qfb6QbXeJurKgTHv11GZdnjpMTYR0cmSjM6tjhwAKlceMAdKpyLkAIvMMpSZloSTu1eVSZH5OCkTj5IyYWmsB1dbE8m2G3c3CY9vP0EVHzuY2xkj/n4ywnffAISC52i615NmhFkAiHgcgWuPryEp878HyBpawNveGzXta0oWQx3iH95CXHQUnKvXgZmVAxIe3cXFY1sgCAKq+zaGm1eDMm9DckYO/jocKbrITHqUhr+n7EdGUhYcPKxhaFowuEzs3SSc3xsJY0sD9JrQWnm/gdS+WP8Fvg75ukIO/KSuPFAoBGhoFH7OlkIhIDEtGzZm4i7DuRF2D+71nMp8ALBXqcg5QCXnVT8YXvWDldMuHnXg4vGbpDF+VsOtBM/4NKsKn2ZVldNVfOzQ98d2km3/TSMu923YV7JY6qKOHGhT7/WPcviohfgDtM+UdQ4AlS8PmAMlVxFygEWmCPExt1+7PClOmkE4ft92CZ+GeMNAVws5eXLM+Oc8Tlx/PqBQbVdLTP4oAAYifxTeCLuHTT8fhZ6hDuR5cnzwTUtsnHEEDtWsINMA1v1wAJ1GNlH54JRGcmYyJm2fhKuPrsLG2EZ5Oj8pMwnz0ufBx8EHkztOhrmhuag46hB14Ri2L5sCXQMjyPNy0WXwVGxd+h3sXGpAQ0MDm+Z/i3Z9v1H58iyNO7Gpr13+MFH8pV4AsGfhKdi4mOHTWR0LPcMpJysX22Yfx55Fp/Dhd4WfB1kS/94u+p6+KzFXEHYnDDYmNgCAIHf1/TgUQx15kPk0D7O2XMTpG3Ew0NVCh/qu+Kilh3LggpTMHHz8ywHsndpJ1L5snHEEuvraqNnYDXWCq8PRQ/p74oDKlwNERET0HItMEVZNG1Rw9/CzMZFf9Gy+rPAZh5IquAzSEwa6Wlh9OBI3HiZj+oBG8HQyw+3HqZix8QL+PhqFgW28RMX5d8MVNO1VB427++La8bvYOOMIAjp5o0lPXwDAqS1XcWrrVdFF5uxDs6EQFFjRf0WhB8U+SHqAmftmYvah2ZjccbKoOOpwau9faNShPwLbfoSIc4ewdel38G/ZHY3aFRxBOntgHc4eWCe6yBw696gUzX2j6BvxGDCjQ5EPCdY10EGzD+th+dc7RceZsHUCZDKZcvjtF805PAdAwU3tB784KDqWOqgjD1YevIG7sWkY+0E9ZDzNw5ojUbj5OAWTejeAtpa0Y7gFdPZB1OkHWLE/CtbOZvANro5aLdxhIOFjUypbDpD0yvIxYC9S1yOgDv8ZjsyUbLw3onGZxVh8YjGSM5MLXUpXUTEHSqcy5QFzoHTehhxgkSmCnqEJmnUZDBePekUufxJ7D5sW/E/SmKci4/BpiBfqVLUCAHhXscSQdj5YvPea6CLzyaNU+DQtKCC9Grti2+zjyuf6AECNhlVwfN1FUTEA4Oy9s/it52+FCkwAcLFwwfAWw/HFevEPrleHpPhoeNVvBQDw9GuBXaumoXrt518c1es0keTRFcb62vg0xBt1//t3f9n9+HRM+Ou06Dh6BtpIjc+ATZWizyKnxKdDz0BbdJz6rvWhIdPA2DZjVc5Yt57dGos/WgxXK1fRMdRJHXnw7/VYjP2gLnzdCnIgqKYdJvx5GhP/Oo3JH0l7SXa9EA806emLx7cScfHATRxfdwmH/zwPj/rOqNPGA1XrOLx5I29Q2XKApJeRkoj05Pgyj5OY9hQJqdllHif9SRbSnmSWaYzE9ETEp5f9e6YuzIHSqUx5wBwonbchB1hkimDrXB0ZKU9galn0vUM52RlFn+UshWcnRJPSn8LtpXswq9qZSPLB0NXXRnZ6DsxsjfE0IxcKhQLZGc+f5ZOd9hQ6euILDB1NHWTmvPoDlp2bDR1NaR6OXdZ09AyRnZkGU0t7PM1Kh6CQIzszTbk8OyMV2nrih6qu7mCGJ+lPYWte9LYynoofdhsA6rT2wLbfjqNxD1+41raHoak+ACAzNRv3Lj/Gvxsuw7+D+Htmp3edjg3hGzBkzRCMajkKjdwbid5meVJHHqRl5cDmv38PADA11MW0/oH4dtUpjF91Gl908RW1/aLYV7OCfTUrBA+ojxth93HxwE2snbIfJlaGGL5I3KNsKlsOkPTa9x2nljhjPyj6QLHUOo0u+0E4xrVTz3umLsyB0qlMecAcKJ23IQdYZIrg27gT8nJfXdwZm9ugrUSn91ceuAFdHU1oyGR4kv5UZbCftOxc6Eow4qerrz32LDoF/w41EXHiHqrWccDhP8Px3oiggsvWVoXDqaaN6DjNazTHT3t+wrDmw1DPpZ7y2T6ZOZk4/+A85h2dh5ae6nkcg1hVatTDgXW/oV6z93Hj/BG41vTH8W2L0fajryGTAUe3LIRjVW/RcTrUd8XTvPxXLrcx1S80clppNPuwLrT1tHBq81UcWH5W+QwmQRBgZKaPwK4+CHy/lug4ANDdrzvqONfBj7t+RNidMAxrPkyS7ZYHdeSBjakBohMyYP/CiHiGetqY1i8Q41aGYfKas2J3AwCU/+Yv0tbVQq3m7qjV3B1Jj9Jw+ZA0z8utTDlApZOVkYqrYbvx6O41ZKYVDAJnaGIBBzdv+DRsCwNjM0nipGbmYO/5B7j+IBnJ/x08NTfShZeLOdrUc4GZofhHFwBAVtpTXDp4Ew9vJCDzv9G5Dc304eRpjdotqysHUxMjNTsVu6/uxrVH15CU9d/AeQYW8HbwRlvvtjAzMBMdQ52YA6VTmfKAOVA6b3sOsMgUwaPO649G6BuawKfh60d/Ko5arpaI/m9gFxcbY8SlqBa2Z6LiJRlhtlW/+gUDuywIg5OnDd7/sjmOrrmAhSO2QCaTwdzOGO8NE3+24fNmn0MhKDB151TIBTm0NArSMF+RD02ZJtr5tMOQpkNEx1GH5u8Pwc6VP2L/2l/h6O6Djp9MxInty7D8+/6ATAYzKweE9PlKdJzG3q8fVtvYQOeNI6cVV6OutdCoay0kx6arfDma2xlLsv0XVbepjgUfLcDcI3Mx6M9BRd6fVxGoIw/qVbPG3vMP0KCGrcp8A10t/Ni3IcZJ8DB5AG/8N7BwMEHzj6Q74ltZcoBK7vG9CPwz92to6+jCpYYfzG2cARSMzH7+6Cac2f83ug2bDvsqnqLi3HiYjG9XnoKetibqulvByargcSXJGU+x9dRdrDt+Cz/2bYgaTuIGm3sUlYC/p+yHtq4WXGvbw9KxoF/OSM7G2Z03ELbpKnpNbA2H6kXf9lAcEY8j8PWmr6GrrQs/Fz84mxe8Z0lZSdh0YRP+Pvs3pnedDk87ce+ZujAHSqcy5QFzoHQqQg6wyKwA3jT8esvajmhd11l0HCNzfXw4WXXU0JBBAWjQ0Qt5OfmwcjKFxhueNVYcOlo6CA0OxWdNPkNUXJTK0RcPWw/lmc2KwNDEAj1G/Kwyr1WPkfBr+QHycnNgaesCDc2K+VxJczvjMiksX6arpYvQ4FD8e/tfXIy+CFN90ze/6C2jjjzo27IGnqQ/LTq+njam9Q/ErcevH4W4OIYt/AAGEh1lLa7KkANUcgc3zEGNes3QuldooTPogiBg/9pZOLRhDvp8OVdUnHk7rqCpjwNGdapdZJzftl3GvJ1X8dtn4i5j27vkNGo2ckW7oYFFxtk9Pwz7lpxG/+kdSh1jzuE5aObRDKHBRb9nsw7MwpzDczC3t7j3TF2YA6VTmfKAOVA6FSEHpB2S8B10/uhm7Fo1DRHnDgEArp3Zh2VT+2PplH44tm0xFHK55DGzc/OxJ/wBlu+PwNZTd2Cop61yr1Zp7V18Gg+uxRaab25nDJsq5pIUmC8y1DVEXZe6aFS1EXLycxD+IBz7ru9Darb4H8rqcnD974i+dbnQfDMrB1g7uElWYN58lILHSc/vYz1wMRqjFx1Hn5n78MXiEzhyOUaSOABwbmcEts0+jmvH7wIArhy5jQXDN2PBsM04/Gc4FHKFZLGeyc7NRmp2KnS1dHEk6kiFygFAPXlgbKCjcsXCy98DcoWgHBRIjNNbryH6epzo7ZRURc8BKrmEmNvwa9G9yEu0ZTIZ/Fp0R/xD8Zdm34lNQ9dGVV8Zp2ujqrgtwQGa+HvJaNDJ65VxGnTyQtzdJFExbifcRne/V79n3f2641a8NJezqwNzoHQqUx4wB0qnIuQAi0wRwnb/iePbliAv9ykOb5qH0/v+xuGN81CzfjC8A9rgyr+7ELZ7leg4n/52CGlZuQCA+NRsDPr9MBbuvorztxOw6lAkPv3tkEoBUlrndkXgrwl7Mf/zTTi56QoykstmlK3+K/orf0DGp8fjk1WfYN6ReQi/H44VYSswYMUAPEp5VCaxpXbh2Bas++0LLJn8MU7v+1t5L4HUft50AY+TswAAu87dx29bL8HD0Qy9m3mghqMZft1yEXvCH4iOc2L9JRz+6zzycvNxYNkZnNx0BfuXnYFPs6qo1cK9YKTR9ZdEx3k5BwasHFBhcwBQTx68/D0weE7l+h6o6DlAJWdoYoHY+xGvXB57PwIGxuKfl2xurIvIhymvXB75MAVmRuLvxTI008ejm4mvXP7oZqLoe7EsDC0Q8fjV71nE4wiYG7z9z5h+hjlQOpUpD5gDpVMRcoCXy4pw9dQetPvoa3jUbYr4h7fw5/QhaPfx1/Bq0BoAYGHrgmNbFiLovQGi4kQnZkCuKLhPadm+67Ay0cPC4c1hqKeNrJx8TFlzBssPRODbHv6i96n3pNa4eTYap7ZcxdHVF1DNzxF1WnvA3c8JGhrin/kJFDwLU6EoOBu2+PhiWBlZYfHHi2Gka4Ss3CxM3DYRS/9digkdJkgSr6x1HzYDt6+G4ezBdTixYxmqegegdqP2cPNuCA0NaY7jPHqSCcf/BnzZceYehrb3Qfv6rsrlHo5m+PtoFNr6ibsv89KhW+g4Mgiega6Iu5uEpV9uR8eRjVGrmTsAwNLRFIdWnUOz3uKeI/VyDlgbW2NJ3yUVNgeAss+Dl78HLI31sGBY5fkeqAw5QCXj36oH9v79C2IfRKFKjXrKH5JZ6cm4H3keV07uRLP3xd+f/0GQO2ZvvYSbj1JQt6q18odkSkYOLtxJwO5z9zGorfgB2hp29saueWGIvf2kYHRus/9G504pGJ37wv6baNVP3Oezh18P/HLgF0TFR6GeSz3lj8jkrGScf3AeO6/srDBjGgDMgdKqTHnAHCidipADLDJFyEx7ArsqNQAANk7VAJkM1k7VlMttnasjI/XVRzNKIyI6GSM71Ybhf48SMdDVwscta+DH9ecl2b5NFXO4+TqgVf/6iDx1H5cO3sSGnw7B0FQfvi2roXbLarBwED/I0DPXH1/HF8FfwEi34AZsAx0D9Avsh+93fS9ZjLJm5VAVVTz90Oz9Ibh58TiuntqNLYsmwsDYDD4N28KnYVuY2ziJiqGrrYnUrFzYmhsgMTW70I3pnk7miP3vTKcYGcnZsK9WcMmlrZsFZJDB1tVCudzO3RLpSdKe2aoMOQCoJw+e4fcAVQb1mr0PfUNThB/+BxePb4OgKLi9RKahCVvn6mj70dfw9GshOk7nhlVhaqCLTSdvY/vpe1D8N7iUhkyG6g6m+LJrXTSr5Sg6jn+HmtA30cOZ7dcQvjtSeRBFQ0MDdu4W6DgiCF6N3UTFeL/u+zDVN8U/5//BtkvbIP/vPdPU0ER1m+r4OuRrtKgh/j1TF+ZA6VSmPGAOlE5FyAEWmSIYmFgg8fE9mFjYIik+GoKgQFLsfVg7FCTPk9h7kpziB54/JzM3Tw5LY9XT7FYm+kjNzCniVaWnqaUBr8Zu8GrshtSEDFw6eAuXD97EyU1X8O2mfqK3/+wa8pz8HFgaWqosszayRkpWiugY6qapqQVPvxbw9GuBtKQ4XAnbjaun9uD0/r/x5ZyDorZd38MWO87cQ+j7dVDLzRLHrz2Cu/3zwVGOXo1RebRFaRmZ6SExOgWm1kZ48igVgiAg8WEqbKoU5HFidIpkQ29XxhwAyjYP+D1AlU1N/5ao6d8Scnk+sjMKLp/WNzKFpqa0P0+a13ZE89qOyJcrkPrfZeemBjrQknisAe8mbvBu4gZ5vgJZaQUDdRmY6EFTS7o4LT1boqVnS+TL85WXnJvqm0JL4vdMXZgDpVOZ8oA5UDpvew68Ha2ooGr6t8LuVT+hWu0g3I86jwbBPXFk83xkZ6ZBJpPh1N6/4FGnmSSxxi47CS1NDWTl5CM6MUNlAJC4lCyYGOhIEqcoptZGaNqrDpr09MXdS48l2WboP6HQ0tBCVm4WopOj4Wb1/KhOXFocTPSlO0tSHkwsbBHUoT8ate+H+zfCRW9vYJua+GLxCYxZcgLVHc2w8d/buHT3CVysjfAwMQMR0cn47sMGouN4N62Kbb+dgEcDZ9y7/BgNu/jg4IqzyE5/CplMhn//uQzPwCqi4wCVPwcA6fOA3wNUWWlqasHI1PLNK4qkpalR6ABNWdDU0oCxhUGZxtDS1IKlUdm/Z+rCHCidypQHzIHSeVtzgEWmCEEdBkBLWxeP715H7UYdENDmQ1g7VsOxrQuRl5sDd59A0fdjAkCfFh4vTNlCT0f1n+1UZBx8XriksbRMrY0g03z1/VYymQxV6ziIjtO3YV+VaT1t1Q962J0w1HasLTqOOphY2L521F2ZTAbXmuKvvbcy0cf8z5tj7bGbOB0ZC0EAIh8mIyE1G94uFvh1kLfoZzsBQNPedaGlo4mYyATUae2BRt1qwdbNAodWnUNeTj6q+zuj2Yfi7scEKlcOAOrJA34PEBERUUXBIlMEDQ0NBLb9SGXes1P+Uurb8vUPUh0swY3KADB80QeSbOdN+jfq/9rlQ5pVjJvVAWDwlL/VFstIXxufhnjh0xCvMouhoSFD4+6+KvOeXfYhpcqUA4B68oDfA0RERFRR8BEmREREREREJBkWmURERERERCQZFplEREREREQkGRaZREREREREJBkWmURERERERCQZFplEREREREQkGRaZREREREREJBkWmURERERERCQZFplEREREREQkGRaZREREREREJBnJi8xp06ahfv36MDY2ho2NDbp06YLIyEiVdZ4+fYphw4bB0tISRkZG6NatG+Li4pTLk5KS0LFjRxgZGaFu3bq4cOGCyuuHDRuGX375ReqmExERVQhz586Fq6sr9PT0EBAQgDNnzrxy3cWLF6NJkyYwNzeHubk5goODX7s+ERGRWJIXmUePHsWwYcNw6tQp7N+/H3l5eWjTpg0yMzOV63zxxRfYvn07NmzYgKNHj+LRo0fo2rWrcvkPP/yA9PR0nD9/Hs2bN8egQYOUy06dOoXTp09j9OjRUjediIjorbdu3TqEhoZi0qRJOH/+PHx9fRESEoL4+Pgi1z9y5Ah69+6Nw4cPIywsDM7OzmjTpg1iYmLU3HIiInpXSF5k7tmzB/3794e3tzd8fX2xYsUKPHjwAOHh4QCA1NRULF26FLNmzULLli3h5+eH5cuX4+TJkzh16hQAICIiAr169YKHhwcGDx6MiIgIAEBeXh6GDBmCBQsWQFNTU+qmExERvfVmzZqFQYMGYcCAAfDy8sKCBQtgYGCAZcuWFbn+6tWr8fnnn6NOnTrw9PTEkiVLoFAocPDgQTW3nIiI3hVlfk9mamoqAMDCwgIAEB4ejry8PAQHByvX8fT0hIuLC8LCwgAAvr6+OHToEPLz87F3717Url0bADBjxgw0b94c/v7+b4ybk5ODtLQ0lT8iIqKKLDc3F+Hh4Sp9qIaGBoKDg5V96JtkZWUhLy9P2S8XhX0oERGJoVWWG1coFBg9ejSCgoLg4+MDAIiNjYWOjg7MzMxU1rW1tUVsbCwA4JtvvsHQoUPh7u4OV1dXLF26FDdv3sTKlSsRFhaGIUOGYN++ffD398fixYthampaKPa0adMwefLkQvNjYmJK1Fka5GeVYI9LL1HTUS1xdFNM1BIn3rDoy7aklPEwo8xjAMyB0lJHDgDqyQPmQOm8rTmQnp5eRi0pe4mJiZDL5bC1tVWZb2trixs3bhRrG19//TUcHBxUCtWXsQ8tWmX67LAPLZ3KlAMA+9DSeNdzoLh9aJkWmcOGDcPVq1dx4sSJEr3O1NQUa9asUZnXsmVLzJw5E6tXr8adO3cQGRmJQYMGYcqUKUUOAjRu3DiEhoYqp9PS0uDs7AxHR0eYmBQ/ObIiUkrU9tKykqvn3pgcszy1xLHJtCnzGGZOZmUeA2AOlJY6cgBQTx4wB0rnbc2Bd/ms3E8//YS1a9fiyJEj0NPTe+V67EOLVpk+O+xDS6cy5QDAPrQ03vUcKG4fWmZF5vDhw7Fjxw4cO3YMTk5Oyvl2dnbIzc1FSkqKytnMuLg42NnZFbmt5cuXw8zMDJ07d0bXrl3RpUsXaGtro3v37pg4cWKRr9HV1YWurq6k+0RERFSerKysoKmpqTIiO/D6PvSZn3/+GT/99BMOHDigvA3lVdiHEhGRGJLfkykIAoYPH47Nmzfj0KFDcHNzU1nu5+cHbW1tlQEHIiMj8eDBAwQGBhbaXkJCAqZMmYI5c+YAAORyOfLyCo4g5OXlQS6XS70LREREbyUdHR34+fmp9KHPBvEpqg99ZsaMGZg6dSr27NlTrHENiIiIxJD8TOawYcOwZs0abN26FcbGxsr7LE1NTaGvrw9TU1MMHDgQoaGhsLCwgImJCUaMGIHAwEA0bNiw0PZGjx6NMWPGwNGx4DrroKAg/Pnnn2jTpg0WLVqEoKAgqXeBiIjorRUaGop+/frB398fDRo0wOzZs5GZmYkBAwYAAPr27QtHR0dMmzYNADB9+nRMnDgRa9asgaurq7JfNjIygpGRUbntBxERVV6SF5nz588HADRv3lxl/vLly9G/f38AwK+//goNDQ1069YNOTk5CAkJwbx58wpta+/evbh16xb+/PNP5bzhw4fj3LlzCAgIQIMGDTBp0iSpd4GIiOit1bNnTyQkJGDixImIjY1FnTp1sGfPHuVgQA8ePICGxvMLlebPn4/c3Fx88MEHKtuZNGkSvvvuO3U2nYiI3hGSF5mCILxxHT09PcydOxdz58597XohISEICQlRmWdgYID169eLaiMREVFFNnz4cAwfPrzIZUeOHFGZvnfvXtk3iIiI6AVl/pxMIiIiIiIienewyCQiIiIiIiLJsMgkIiIiIiIiybDIJCIiIiIiIsmwyCQiIiIiIiLJsMgkIiIiIiIiybDIJCIiIiIiIsmwyCQiIiIiIiLJsMgkIiIiIiIiybDIJCIiIiIiIsmwyCQiIiIiIiLJsMgkIiIiIiIiybDIJCIiIiIiIsmwyCQiIiIiIiLJsMgkIiIiIiIiybDIJCIiIiIiIsmwyCQiIiIiIiLJsMgkIiIiIiIiybDIJCIiIiIiIsmwyCQiIiIiIiLJsMgkIiIiIiIiybDIJCIiIiIiIsmwyCQiIiIiIiLJsMgkIiIiIiIiybDIJCIiIiIiIsmwyCQiIiIiIiLJsMgkIiIiIiIiybDIJCIiIiIiIsmwyCQiIiIiIiLJsMgkIiIiIiIiybDIJCIiIiIiIsmwyCQiIiIiIiLJsMgkIiIiIiIiybDIJCIiIiIiIsmwyCQiIiIiIiLJsMgkIiIiIiIiybDIJCIiIiIiIsmwyCQiIiIiIiLJsMgkIiIiIiIiybDIJCIiIiIiIsmwyCQiIiIiIiLJsMgkIiIiIiIiybDIJCIiIiIiIsmwyCQiIiIiIiLJsMgkIiIiIiIiybDIJCIiIiIiIsmwyCQiIiIiIiLJsMgkIiIiIiIiybDIJCIiIiIiIsmwyCQiIiIiIiLJsMgkIiIiIiIiybDIJCIiIiIiIsmwyCQiIiIiIiLJsMgkIiIiIiIiybDIJCIiIiIiIsmwyCQiIiIiIiLJsMgkIiIiIiIiybDIJCIiIiIiIsmwyCQiIiIiIiLJsMgkIiIiIiIiybDIJCIiIiIiIsmwyCQiIiIiIiLJsMgkIiIiIiIiybDIJCIiIiIiIsmwyCQiIiIiIiLJsMgkIiIiIiIiyZRbkTl37ly4urpCT08PAQEBOHPmjHJZaGgoLCws4OzsjNWrV6u8bsOGDejYsaO6m0tERPTWeF0fWpQNGzbA09MTenp6qFWrFnbt2qWmlhIR0buoXIrMdevWITQ0FJMmTcL58+fh6+uLkJAQxMfHY/v27VizZg327duHGTNm4NNPP0ViYiIAIDU1Ff/73/8wd+7c8mg2ERFRuXtdH1qUkydPonfv3hg4cCAuXLiALl26oEuXLrh69aqaW05ERO+KcikyZ82ahUGDBmHAgAHw8vLCggULYGBggGXLliEiIgLNmzeHv78/evfuDRMTE9y9excAMHbsWAwdOhQuLi7l0WwiIqJy97o+tCi//fYb2rZti6+++go1a9bE1KlTUa9ePfzxxx9qbjkREb0rtNQdMDc3F+Hh4Rg3bpxynoaGBoKDgxEWFobPP/8cixYtQnJyMu7cuYPs7GxUq1YNJ06cwPnz5zFv3rxixcnJyUFOTo5yOjU1FQCQlpZWovZmZ5Zs/dJKz8pVS5zs9Jw3rySBtKdl/75ppKnnGAlzoHTUkQOAevKAOVA6b2sOPOsHBEEoi+aUqTf1oUUJCwtDaGioyryQkBBs2bLllXHYhxatMn122IeWTmXKAYB9aGm86zlQ7D5UULOYmBgBgHDy5EmV+V999ZXQoEEDQRAEYdKkSYK7u7vg4+MjbNq0ScjJyRF8fHyEc+fOCXPmzBE8PDyERo0aCVevXn1lnEmTJgkA+Mc//vGPf/wr8i86OrpM+7uyUJw+9GXa2trCmjVrVObNnTtXsLGxeWUc9qH84x//+Me/1/29qQ9V+5nM4vjuu+/w3XffKacnT56M4OBgaGtr4/vvv8eVK1ewY8cO9O3bF+Hh4UVuY9y4cSpHbhUKBZKSkmBpaQmZTFbWu/BWSktLg7OzM6Kjo2FiYlLezaFywBwg5gAgCALS09Ph4OBQ3k15a7EPLYyfHWIOEHOg+H2o2otMKysraGpqIi4uTmV+XFwc7OzsCq1/48YN/PXXX7hw4QKWLVuGpk2bwtraGj169MAnn3yC9PR0GBsbF3qdrq4udHV1VeaZmZlJui8VlYmJyTv7waACzAF613PA1NS0vJtQKiXtQwHAzs6uROsD7ENf513/7BBzgJgDxelD1T7wj46ODvz8/HDw4EHlPIVCgYMHDyIwMFBlXUEQ8Nlnn2HWrFkwMjKCXC5HXl4eACj/K5fL1dd4IiKiclSSPvSZwMBAlfUBYP/+/a9cn4iISKxyuVw2NDQU/fr1g7+/Pxo0aIDZs2cjMzMTAwYMUFlvyZIlsLa2Vj4XMygoCN999x1OnTqF3bt3w8vLi0dWiYjonfKmPrRv375wdHTEtGnTAACjRo1Cs2bN8Msvv6BDhw5Yu3Ytzp07h0WLFpXnbhARUSVWLkVmz549kZCQgIkTJyI2NhZ16tTBnj17YGtrq1wnLi4OP/zwA06ePKmc16BBA4wZMwYdOnSAjY0NVq5cWR7Nr7B0dXUxadKkQpdA0buDOUDMgYrvTX3ogwcPoKHx/EKlRo0aYc2aNRg/fjy+/fZbVK9eHVu2bIGPj0957UKFxM8OMQeIOVB8MkGogGO4ExERERER0VtJ7fdkEhERERERUeXFIpOIiIiIiIgkwyKTiIiIiIiIJMMisxiaN2+O0aNHK6ddXV0xe/bs175GJpNhy5YtomNLtR0iIqLywD6UiOjdU6mLzI4dO6Jt27ZFLjt+/DhkMhkuX75c4u2ePXsWgwcPFts8Fd999x3q1KlTaP7jx4/Rrl07SWO9SnZ2NiwsLGBlZYWcnBy1xKyIZDLZa/++++47UdsuyQ+izz77DJqamtiwYUOpY1LpMA+osmMfWjwrVqxQfu41NDRgb2+Pnj174sGDB4XWvXbtGnr06AFra2vo6urCw8MDEydORFZWVqF1L1y4gO7du8PW1hZ6enqoXr06Bg0ahKioqDe26e+//4ampiaGDRtWZHtf9fi3or57Nm7ciObNm8PU1BRGRkaoXbs2pkyZgqSkpDe241Uxyvu788V4JiYmqF+/PrZu3VpovezsbEyaNAkeHh7Q1dWFlZUVunfvjmvXrhVaNy0tDf/73//g6ekJPT092NnZITg4GJs2bcKbxth80++vV+1X//790aVLF5V5t27dwoABA+Dk5ARdXV24ubmhd+/eOHfu3OvfFDViDhRWGXOgUheZAwcOxP79+/Hw4cNCy5YvXw5/f3/Url27xNu1traGgYGBFE18Izs7O7UNk7xx40Z4e3vD09Oz3I/8CoKA/Pz8cm3Dqzx+/Fj5N3v2bJiYmKjM+/LLL9XSjqysLKxduxZjx47FsmXL1BLzdXJzc8u7CWrFPCjau5YHlRn70OJ79vmPiYnBxo0bERkZie7du6usc+rUKQQEBCA3Nxc7d+5EVFQUfvjhB6xYsQKtW7dW+ezs2LEDDRs2RE5ODlavXo2IiAj89ddfMDU1xYQJE97YnqVLl2Ls2LH4+++/8fTp01Lv1//+9z/07NkT9evXx+7du3H16lX88ssvuHTpEv78889SbfNt+e5cvnw5Hj9+jHPnziEoKAgffPABrly5olyek5OD4OBgLFu2DN9//z2ioqKwa9cu5OfnIyAgAKdOnVKum5KSgkaNGmHVqlUYN24czp8/j2PHjqFnz54YO3YsUlNTX9sWqX5/nTt3Dn5+foiKisLChQtx/fp1bN68GZ6enhgzZkyptys15kBhlTIHhEosLy9PsLW1FaZOnaoyPz09XTAyMhLmz58vJCYmCr169RIcHBwEfX19wcfHR1izZo3K+s2aNRNGjRqlnK5SpYrw66+/KqejoqKEJk2aCLq6ukLNmjWFffv2CQCEzZs3K9cZO3asUL16dUFfX19wc3MTxo8fL+Tm5gqCIAjLly8XAKj8LV++XBAEodB2Ll++LLRo0ULQ09MTLCwshEGDBgnp6enK5f369RM6d+4szJw5U7CzsxMsLCyEzz//XBnrdZo3by4sWLBAmD9/vtC6detCy69evSp06NBBMDY2FoyMjITGjRsLt27dUi5funSp4OXlJejo6Ah2dnbCsGHDBEEQhLt37woAhAsXLijXTU5OFgAIhw8fFgRBEA4fPiwAEHbt2iXUq1dP0NbWFg4fPizcunVL6NSpk2BjYyMYGhoK/v7+wv79+1Xa9fTpU2Hs2LGCk5OToKOjI7i7uwtLliwRFAqF4O7uLsycOVNl/QsXLggAhJs3b77xPXmT5cuXC6ampirzFi9eLHh6egq6urpCjRo1hLlz5yqX5eTkCMOGDRPs7OwEXV1dwcXFRfjxxx8FQSjIqxdzoEqVKq+NvWLFCqFhw4ZCSkqKYGBgIDx48EBl+avel2de9+/5cs4LgiB07txZ6Nevn3K6SpUqwpQpU4SPP/5YMDY2Vi57Xa4/s23bNsHf31/Q1dUVLC0thS5dugiCIAiTJ08WvL29C+2rr6+vMH78+Ne+H+WJecA8qIzYhxavDy3q8//7778LAITU1FRBEARBoVAIXl5egr+/vyCXy1XWvXjxoiCTyYSffvpJEARByMzMFKysrJSfh5clJye/si2CIAh37twR9PX1hZSUFCEgIEBYvXr1G9v7zIvv1+nTpwUAwuzZs0vVjuIor+/Ol/MiLS1NACD89ttvynk//fSTIJPJhIsXL6q8Vi6XC/7+/oKXl5egUCgEQRCEoUOHCoaGhkJMTEyhWOnp6UJeXt5r34c3/f56ub3PPMtXQSjIMW9vb8HPz69QjgmCNP9eZYE5UKAy5kClLjIFQRC++uorwd3dXZkEgiAIy5YtU34BP3z4UJg5c6Zw4cIF4fbt28Lvv/8uaGpqCqdPn1au/7oOUi6XCz4+PkKrVq2EixcvCkePHhXq1q1bKBmmTp0q/Pvvv8Ldu3eFbdu2Cba2tsL06dMFQRCErKwsYcyYMYK3t7fw+PFj4fHjx0JWVpYgCKpJlZGRIdjb2wtdu3YVrly5Ihw8eFBwc3NT+cHXr18/wcTERBgyZIgQEREhbN++XTAwMBAWLVr02vfp1q1bgq6urpCUlCQ8efJE0NPTE+7du6dc/vDhQ8HCwkLo2rWrcPbsWSEyMlJYtmyZcOPGDUEQBGHevHmCnp6eMHv2bCEyMlI4c+aM8j0qSZFZu3ZtYd++fcKtW7eEJ0+eCBcvXhQWLFggXLlyRYiKihLGjx8v6OnpCffv31duq0ePHoKzs7OwadMm4fbt28KBAweEtWvXCoIgCD/88IPg5eWlsq8jR44UmjZt+tr3o7he/nL866+/BHt7e2Hjxo3CnTt3hI0bNwoWFhbCihUrBEEQhJkzZwrOzs7CsWPHhHv37gnHjx9X/iCLj49X/jh6/PixEB8f/9rYTZo0Ef744w9BEAShW7duwpQpU1SWv+59edO/Z3GLCxMTE+Hnn38Wbt26pSxMXpfrgiAIO3bsEDQ1NYWJEycK169fFy5evKjsIKKjowUNDQ3hzJkzyvXPnz8vyGQy4fbt2699P8oT84B5UFmxD31zH/ry5z8uLk5o0aKFoKmpKWRkZAiCUJC/AAoV4M+0bt1a8PX1FQRBEDZt2iQAEE6ePPnKmK8zYcIE4YMPPhAEQRDmzJkjtGzZ8rXtfdGL79fIkSMFIyOjYh2kLq3y+u58cT/z8vKEX3/9VQAgzJ8/X7lO7dq1hTZt2hT5+tWrVyt/18jlcsHc3FwYPHhwqd6DN/3+erm9L3qxwHhTjr2tmAOVNwcqfZEZERGhUswIQsGPso8++uiVr+nQoYMwZswY5fTrOsi9e/cKWlpaKkcudu/e/cpkeGbmzJmCn5+fcnrSpEnKDuZFL25n0aJFgrm5ubLTEgRB2Llzp6ChoSHExsYKglCQbFWqVBHy8/OV63Tv3l3o2bPnK9siCILw7bffqhw17dy5szBp0iTl9Lhx4wQ3N7dXdjYODg7C//73vyKXlaTI3LJly2vbKQiC4O3tLcyZM0cQBEGIjIwUABQ6u/lMTEyMyg+e3NxcwcrKSvllJdbLX47u7u6FPtxTp04VAgMDBUEQhBEjRggtW7ZU+cH2ojflzTNRUVGCtra2kJCQIAiCIGzevFlwc3NTbvdN78ub/j2LW1y86kj7i17O9cDAQKFPnz6vXL9du3bC0KFDldMjRowQmjdv/sY45Yl5wDyorNiHvrkPfXYm1dDQUDAwMFCeRRk5cqRynbVr1xbqB180cuRIQV9fXxAEQZg+fboAQEhKSnplzFeRy+WCs7Ozsi9NSEgQdHR0hDt37qi0tzhFZrt27YTatWuXuA0lUV7fnQAEPT09wdDQUNDQ0BAACK6ursKTJ0+U6+jp6RX6/nvm2Y/5devWCXFxcQIAYdasWW+MW5Q3/f561t43FRjr1q0TAAjnz58vVTvKC3Og8uZApb4nEwA8PT3RqFEj5b1Kt27dwvHjxzFw4EAAgFwux9SpU1GrVi1YWFjAyMgIe/fuLfKG/aJERETA2dkZDg4OynmBgYGF1lu3bh2CgoJgZ2cHIyMjjB8/vtgxXozl6+sLQ0ND5bygoCAoFApERkYq53l7e0NTU1M5bW9vj/j4+FduVy6XY+XKlfjoo4+U8z766COsWLECCoUCAHDx4kU0adIE2trahV4fHx+PR48eoVWrViXan6L4+/urTGdkZODLL//f3n1HVXG0cQD+0a5c6qUpCHwiRYqFqrEENRjUmBBj7JoogiaCMTYsMUbBrhGJJYkl0hJ7I0aDsStiQ+ViQ4wUKzaUKCCK8H5/cFhZ6gUplvc5h6O7Ozszuzt3Z2d3ZzYA9vb2kMlk0NLSQmJiorDv5HI5VFRU0KlTpzLja9y4MT7++GPh+P/111949uxZqb4yNSE7OxvJycnw9fWFlpaW8Dd79mwkJycDKOygLZfLYWtri2+//RZ79uypVlqhoaHo1q0bDA0NAQA9evTAf//9hwMHDgCofL9UdDyrouTxAiov63K5vMKyMmLECKEf0fPnz7Fu3Tr4+Pi8Uj7rEpeDQu96OXhbcB1aeR0KANra2pDL5Th9+jSCg4Ph4uKCOXPmlApHlQz+oWiY8uzduxfZ2dno0aMHAMDQ0BCenp7V6q/9Kvmojro8dwJASEgI5HI5oqOj4eDggN9++w36+vqiMLV9vBS5/lJUXR+v2sBloNDbUgbe+kYmUDh4wdatW/HkyROEhYXByspKuOj68ccfsWTJEkyePBkHDx6EXC5Ht27danTwiuPHj2Pw4MHo0aMHdu7cifj4eHz//fe1NkBGyQtGJSWlCgvqP//8g1u3bqF///5QVVWFqqoqBgwYgGvXrmH//v0AAKlUWu76FS0DAGXlwmJWvPDn5eWVGbZ45Q8AAQEB2L59O+bOnYuYmBjI5XK0bNlS2HeVpQ0Aw4cPx4YNG/D06VOEhYWhf//+tTLoRFZWFgBg9erVkMvlwt+FCxeEzuEuLi5ITU3FrFmz8PTpU/Tr1w99+vSpUjpFJ6Rdu3YJx0tDQwMPHz4ULiQq2y+KHLOSJ6uyjlnJ46VIWa8sbS8vLzRo0ADbt2/HX3/9hby8vCrvo/rE5YDLwduG69CK61Cg8LdibW0Ne3t7jB8/Hm3btoWfn5+wvFmzZgAKG7plSUxMFMIU/Xv58uUq533NmjV4+PAhpFKpcF74+++/ERERIWyDjo4OsrOzS21TZmYmAEBXV1fIR0pKSrn1dU2rq3NnEWNjY1hbW6Nr167CtUHxmwnNmjWr8HgVhTEyMoJMJqvW8VLk+gsovIlR1sAxmZmZouMFVK/cvC64DLxdZeCdaGT269cPysrKWLduHSIjI+Hj4wMlJSUAQGxsLHr27IkvvvgCjo6OsLS0VGh48CL29va4ceMG0tPThXnFR5sCgGPHjqFJkyb4/vvv4ebmBhsbG1y7dk0URiKRID8/v9K0EhISkJ2dLcyLjY2FsrIybG1tFc5zSWvWrMGAAQNEP2i5XI4BAwZgzZo1AIBWrVohJiamzMpGW1sbFhYWoh9DcUZGRgAg2kdyuVyhvMXGxsLb2xu9evVCy5YtYWxsjLS0NGF5y5YtUVBQgMOHD5cbR48ePaCpqYlff/0Vu3fvrrWnIY0aNULjxo2RkpICa2tr0V/Tpk2FcDo6Oujfvz9Wr16NjRs3YuvWrcJQ8GpqapWWg7///htPnjxBfHy86HitX78e27ZtQ2ZmZqX7paLjCRQes+LHKz8/HxcuXKh0HyhS1lu1alVuWQEAVVVVDB06FGFhYQgLC8OAAQMUupnwuuBywOXgbcN1aNVNmTIFGzduxNmzZwEATk5OsLOzQ0hISKnGXUJCAvbt24eBAwcCALp27QpDQ0MsXLiwzLiLGoMlZWRk4M8//8SGDRtE54T4+Hg8evRIeOJja2uLFy9elKqHi/JadKE6aNAgZGVl4ZdffqlSPqqrrs6dZWnTpg1cXV1FT58HDBiAffv2ISEhQRS2oKAAISEhcHBwgKOjI5SVlTFgwACsXbsWt2/fLhV3VlZWuaPlK3L9BRQeszNnzojWzc/PR0JCgnC8nJyc4ODggODg4DJvitT08aoNXAbesjJQP2/p1j1fX1/S09MjFRUVUd+PcePGkbm5OcXGxtKlS5do+PDhpKOjI7zfTFT5oAUODg7k6elJcrmcjhw5Qq6urqJ3p//8809SVVWl9evX09WrV2nJkiWkr68vegd97dq1pKmpSfHx8XT//n3Kzc0lIvE72NnZ2WRiYkK9e/em8+fP04EDB8jS0rLUoAXF805ENGbMGOrUqVOZ++XevXukpqZG0dHRpZb9/fff1KBBA8rIyKAHDx6QgYGBMEDIlStXKDIyUhggJDw8nNTV1WnJkiV05coVOnPmDC1dulSIq23btuTu7k6XLl2iQ4cOUZs2bcrsk1ly5KtevXqRk5MTxcfHk1wuJy8vL9LW1hYdD29vbzI3N6ft27dTSkoKHTx4kDZu3CiKZ+rUqSSRSMje3r7M/VBdJfsSrF69mqRSKS1ZsoSSkpLo3LlzFBoaSsHBwUREFBwcTOvWraPExERKSkoiX19fMjY2FkYBs7GxIT8/P0pPTy+3P07Pnj3L7B+Un59PxsbGwiAwFe2Xyo7nihUrSENDg3bu3EmJiYk0YsQI0tHRKdUXr/gIkUSKlfWDBw+SsrKyMODLuXPnhFEVi1y5coVUVFRIRUWFTpw4UfmBqGdcDkJEeXhXy8HbjOvQTuXum/L6OPbr148+/vhjYTo2NpY0NDTos88+o5MnT9K1a9do06ZNZG5uTu3btxfyTEQUFRVFampq5OXlRXv37qXU1FSKi4ujiRMnlts/NCQkhExMTMrsq9avXz9hMCAioq5du5KjoyPt27ePUlJSKDo6mmxtbUvFPWnSJFJRUaGJEyfSsWPHKC0tjfbt20d9+vQpd9TZqqiPcydR2f3biq55bt68SURET58+pffee4/Mzc1p06ZNdO3aNTp16hR99tlnpKmpScePHxfWzcjIIDs7OzIzM6OIiAi6ePEiXblyhdasWUPW1tZljuqp6PUXEdG6detIKpXSzz//TFeuXKH4+Hjy8fEhXV1doT8xUeGIwNra2tS+fXvatWsXJScnU0JCAs2ePbvGBjysaVwG3t4y8M40Mo8dO0YAqEePHqL5GRkZ1LNnT9LS0qKGDRvStGnTaMiQIQpXkESFg2u8//77JJFIqFmzZrR79+5ShXfixIlkYGBAWlpa1L9/fwoJCRH9qHJzc6l3794kk8lqZPj14iqqIBctWkQymazMgT+ePXtGMplMGM45ISGBunbtShoaGqStrU3u7u6ikR5XrFhBtra2pKamRiYmJjR69Ghh2aVLl6hdu3YklUrJyclJGKK+skZmamoqffDBBySVSsnc3JyWL19e6ng8ffqUxo0bRyYmJiSRSMja2ppCQ0NF8SQnJxMAWrhwYZn7obrKurhYu3YtOTk5kUQiIT09PerYsSNt27aNiAoHnnByciJNTU3S0dGhLl26iDpo79ixg6ytrUlVVbXMobfv3LlDqqqqtGnTpjLz4+fnR87OzkRU+X6p6Hg+f/6c/Pz8SF9fnxo2bEjz5s0rc8CXko0LosrLOhHR1q1bhX1kaGhIn3/+eal43N3dy/yMxeuIy0FIqTy8i+XgbcZ1aKdy9015jczjx48TANFIu+fOnaPevXuTvr4+qampkZWVFU2bNo2ys7NLrR8XF0eff/45GRkZUYMGDcja2pq++uqrcj+/1bJlS/L39y9z2caNG0kikQiDhD169Ii+/fZbsrKyIqlUSjY2NjRp0iTRvii+bseOHUlbW5s0NTWpVatWNHPmzFr7hEltnjuLlNXAKCgoIDs7O9GAY9nZ2fT999+TtbU1qampkb6+vnCToqTMzEyaMmUK2djYkEQioUaNGtGHH35I27dvL7PhX5Xrr6L94urqStra2tSoUSPq0aMHJSQklFo3KSmJhgwZQo0bNyaJREJNmjShgQMHvjaDwZTEZeDtLQNKRK9ZL1HGakFMTAy6dOmCGzduoFGjRvWdHVYJIoKNjQ38/f0xfvz4+s4OqydcDhhjjLE3k2p9Z4Cx2vTs2TPcv38fgYGB6Nu3Lzcw3wD379/Hhg0bcOfOHQwbNqy+s8PqCZcDxhhj7M3FjUz2Vlu/fj18fX3h5OSEyMjI+s4OU0DDhg1haGiIVatWQU9Pr76zw+oJlwPGGGPszcWvyzLGGGOMMcYYqzHvxCdMGGOMMcYYY4zVDW5kMlaOjIwMNGzYUPRdTvZ6GTBgAIKDg+s0zbooF4GBgXBycnrleMLDwyGTyV45ntfJihUr4OXlVd/ZYIxVguvQ1x/XoRXjOvQV1elYtoy9QcaNG0fDhw8vc1nXrl1JWVmZTp06Vce5qnvZ2dk0ZcoUsrS0pAYNGpChoSF17NiRoqKi6jtrdP78edLT06PMzMw6S7NkuUhNTSUApKysLHxbq8jt27dJRUWFAFBqaqrCaTx58oQePHjwynnNycmhu3fvCtMzZswgR0fHV4pzy5YtZW5rEWtraxo3blyl8VQ3L8+ePaPGjRvTkSNHqrwuY6zucB1aiOtQMa5D3506lJ9kMlaGnJwcrFmzBr6+vqWWXb9+HceOHcM333yD0NDQWs/L8+fPaz2NiowcORLbtm3DsmXLcPnyZezevRt9+vRBRkZGraWp6Da3aNECVlZW+OOPP2otL8VVVC5MTU1LDS4VEREBU1PTKqejpaUFAwODaucTAPLy8iCVStGwYcNXiqekTz/9FAYGBoiIiCi17MiRI7h69WqZ+6emSCQSDBo0CEuXLq21NBhjr4br0Je4Dn2J69B3rA6t9WYsY2+gzZs3k5GRUZnLAgMDacCAAZSYmEi6urqUk5NDRIUfvwVAiYmJovCLFy8mS0tLYfr8+fPUvXt30tTUpIYNG9IXX3whfCCbqPDD5aNGjaIxY8aQgYEBde7cmYiIgoODqUWLFqShoUFmZmbk5+dX6sPZq1atIjMzM5JKpfTZZ59RcHBwqY8cR0VFkbOzMzVo0ICaNm1KgYGBlJeXV+6+0NXVpfDw8Ar3V25uLk2aNInMzMxIIpGQlZUV/fbbb8LyQ4cOUevWrUkikZCxsTFNnjxZlGZ521zZviIiCgoKovfff7/C/NWUsspF0V3YadOmkY2NjWhZs2bN6IcffhDdhX3x4gX5+PiQhYUFqaurU7Nmzeinn34SrVfyDmV+fj4FBQWRqakpSSQScnR0pOjo6FJ52LBhA3Xs2JEaNGhAYWFhoo9ch4WFEQDRX1hYGA0bNow+/vhjUfrPnz8nIyMj0TEsbvz48aW2lajwQ/bvvfceERFdu3aNPv30U9LU1CRtbW3q27cv3blzp8K8EBV+JN7X15cMDQ1JW1ubPvjgA5LL5aJ0Dh8+TBKJRPjtMcZeL1yHvsR16EtchxZ6V+pQbmQyVoZvv/2WunfvXmp+QUEBNWnShHbu3ElERK6urhQZGSksd3Nzo2nTponWcXV1FeY9evSIjIyM6LvvvqPExEQ6e/YseXp60gcffCCE79SpE2lpadHEiRPp8uXLdPnyZSIiCgkJoQMHDlBqairt37+fbG1tyc/PT1jv6NGjpKysTD/++CMlJSXRzz//TPr6+qIK8siRI6Sjo0Ph4eGUnJxMe/bsIQsLCwoMDCx3X9ja2lK/fv3o8ePH5Ybp168fmZub07Zt2yg5OZn27dtHGzZsICKimzdvkoaGBvn7+1NiYiJt376dDA0NacaMGRVusyL7iogoOjqaJBIJ5ebmlpu/mlJWuSiqnE6dOkWGhoYUExNDREQxMTFkZGREp06dElWQz58/p+nTp1NcXBylpKTQH3/8QRoaGrRx40YhzpIV5OLFi0lHR4fWr19Ply9fpkmTJpGamhpduXJFlAcLCwvaunUrpaSk0O3bt0UVZE5ODk2YMIGaN29O6enplJ6eTjk5ORQbG0sqKip0+/ZtIb1t27aRpqZmqQuwIhcvXiQAdPjwYWHekydPSFNTk1atWkX5+fnk5ORE77//Pp0+fZpOnDhBrq6u1KlTpwrzQkT04YcfkpeXF8XFxdGVK1dowoQJZGBgQBkZGUJa2dnZpKysTAcPHlT84DHG6gzXoS9xHfoS16GF3pU6lBuZjJWhZ8+e5OPjU2r+nj17yMjISLiDGBISIvzoi6atrKyE6ZJ3ZmfNmkVdu3YVxXnjxg0CQElJSURUWFk4OztXmsfNmzeTgYGBMN2/f/9Sd9MGDx4sqiC7dOlCc+fOFYX5/fffycTEpNx0Dh8+TGZmZqSmpkZubm40duxYOnr0aKlt3Lt3b5nrT506lWxtbamgoECY9/PPP5OWlhbl5+eXu82K7CsiooSEBAJAaWlp5W5DTSmrXBRVTvHx8TR27FgaNmwYERENGzaMxo0bR/Hx8ZX2Jxk1ahT17t1bmC5ZQTZu3JjmzJkjWqd169bk7+8vykPJu7nFK8iy4i3i4OBACxYsEKa9vLzI29u73PwSEbVt25aGDh0qTK9Zs4Y0NDTo8ePHtGfPHlJRUaHr168Ly4sq1aI+WGXlJSYmhnR0dEpd7FhZWdHKlStF8/T09Cp9OsAYqx9ch77EdehLXIe+9C7Uodwnk7EyPH36FOrq6qXmh4aGon///lBVVQUADBw4ELGxsUhOTgZQOFJbWloaTpw4AQBYu3YtXFxcYGdnBwBISEjAwYMHoaWlJfwVLSuKAwBcXV1Lpb1v3z506dIFpqam0NbWxpdffomMjAzk5OQAAJKSktCmTRvROiWnExISMHPmTFH6I0aMQHp6uhBPSR07dkRKSgr279+PPn364OLFi3B3d8esWbMAAHK5HCoqKujUqVOZ6ycmJqJdu3ZQUlIS5nXo0AFZWVm4efNmudus6L6SSqUAUG7+a1J55aKIj48PNm/ejDt37mDz5s3w8fEpM9zPP/8MV1dXGBkZQUtLC6tWrcL169fLDPv48WPcvn0bHTp0EM3v0KEDEhMTRfPc3NyquEWFhg8fjrCwMADA3bt3ER0dXW7ei/j4+GDLli148uQJgMLfRt++faGtrY3ExESYm5vD3NxcCO/g4ACZTFYqz8UlJCQgKysLBgYGouOempoqOuZA4XGvi2POGKs6rkNf4jr0Ja5DX3oX6lDVWo2dsTeUoaEhHj16JJr38OFDbN++HXl5efj111+F+fn5+QgNDcWcOXNgbGwMDw8PrFu3Dm3btsW6devg5+cnhM3KyoKXlxcWLFhQKk0TExPh/5qamqJlaWlp+OSTT+Dn54c5c+ZAX18fR48eha+vL54/fw4NDQ2FtisrKwtBQUH4/PPPSy2r6MSvpqYGd3d3uLu7Y/LkyZg9ezZmzpyJyZMnCxXUqyq5zYruq4cPHwIAjIyMaiQfFSmrXBTXsmVL2NnZYeDAgbC3t0eLFi0gl8tFYTZs2ICAgAAEBwejXbt20NbWxo8//oiTJ0++cv5K7kNFDRkyBFOmTMHx48dx7NgxNG3aFO7u7hWuM2DAAIwbNw6bNm1Cx44dERsbi3nz5lUr/SJZWVkwMTHBoUOHSi0rOYz8w4cP6+SYM8aqjutQMa5DC3Ed+tK7UIdyI5OxMjg7O5cabW3t2rUwMzNDVFSUaP6ePXsQHByMmTNnQkVFBYMHD8akSZMwcOBApKSkYMCAAUJYFxcXbN26FRYWFsKdXEWcOXMGBQUFCA4OhrJy4QsImzZtEoWxtbVFXFycaF7JaRcXFyQlJcHa2lrhtMvi4OCAFy9eIDc3Fy1btkRBQQEOHz6MDz/8sFRYe3t7bN26FUQk3ImNjY2FtrY2zMzMyk1D0X114cIFmJmZwdDQ8JW2SRFllYuSfHx84O/vL7qIKi42Nhbt27eHv7+/MK/kHcbidHR00LhxY8TGxorudMfGxpa6y14ZiUSC/Pz8UvMNDAzw2WefISwsDMePH8ewYcMqjUtbWxt9+/ZFaGgokpOT0axZM6FStbe3x40bN3Djxg3hTuylS5eQmZkJBweHcvPi4uKCO3fuQFVVFRYWFuWmnZycjNzcXDg7Oyu66YyxOsR1aMW4Di0f16FvUR1aqy/jMvaGOnfuHKmqqtLDhw+FeY6OjjR58uRSYTMzM0kikQgDGTx+/JikUik5OjpSly5dRGFv3bpFRkZG1KdPHzp16hRdvXqVdu/eTd7e3vTixQsiKuxbMWbMGNF6crlc6C+QnJxMkZGRZGpqSgDo0aNHRPRy0ILg4GC6cuUKrVixggwMDEgmkwnx7N69m1RVVSkwMJAuXLhAly5dovXr19P3339f7r7o1KkTrVixgk6fPk2pqam0a9cusrW1JQ8PDyGMt7c3mZub0/bt2yklJYUOHjwodMIvGrRg1KhRlJiYSFFRUWUOWlBymxXZV0SFo7GV1fenNpRVLor3JyEiysvLo/v37wt9jkr2J1myZAnp6OjQ7t27KSkpiaZNm0Y6OjqivhUl+1qEhISQjo4ObdiwgS5fvkyTJ08uc9CCojwUKdmfZO3ataSpqUnx8fF0//59Ub+NPXv2kEQiIRUVFbp165ZC+yMmJoYAkJ6eHs2fP1+YX1BQQE5OTuTu7k5nzpyhkydPigYtKC8vBQUF9P7775OjoyP9888/lJqaSrGxsTR16lSKi4sTbVfx0SYZY68XrkNf4jr0Ja5Dxd72OpQbmYyVo02bNrRixQoiIjp9+rSow3VJH330EfXq1UuY7tevHwGg0NDQUmGvXLlCvXr1IplMRlKplOzs7Gjs2LFCp/6yKguiwtHRTExMSCqVUrdu3SgyMlJUQRIVDr9uamoqDL8+e/ZsMjY2FsWze/duat++PUmlUtLR0aE2bdrQqlWryt0Pc+fOpXbt2pG+vj6pq6uTpaUlffvtt6IPHT99+pTGjRtHJiYmJJFIyNraWrTtigy/XtY2V7avnj59Srq6unT8+PFy81/TipcLovIrpyIlK8jc3Fzy9vYmXV1dkslk5OfnR1OmTKmwgszPz6fAwEAyNTUlNTW1codfr6yCzM3Npd69e5NMJhMNeU70ctTHHj16VGl/2NralhpZj6ji4dcrysvjx49p9OjR1LhxY1JTUyNzc3MaPHiwaACErl270rx586qUT8ZY3eI6tBDXoWJch4q9zXUoNzIZK8fOnTvJ3t5eGL3tTTR8+PA6+/5Vffjll1/I09OzTtOsi3IxZcoU6tChQ63FX5YnT56Qjo4Obd26tU7TraoLFy5Qw4YNKTMzs76zwhirANehrz+uQ2sO16GlcZ9Mxsrx8ccf499//8WtW7dEI3y9zhYtWgRPT09oamoiOjoaERER+OWXX+o7W7VGTU0Ny5Ytq9M0a7NcEJEwCmFd9TcsKCjAgwcPEBwcDJlMhk8//bRO0q2u9PR0REZGQldXt76zwhirANehrz+uQ18d16HlUyIiqvVUGGN1ol+/fjh06BCePHkCS0tLjB49GiNHjqzvbDEFZWZmolGjRmjdujXWrl2LJk2a1HqaaWlpaNq0KczMzBAeHo4uXbrUepqMMfY64jr0zcZ16OuFG5mMMcYYY4wxxmqMcn1ngDHGGGOMMcbY24MbmYwxxhhjjDHGagw3MhljjDHGGGOM1RhuZDLGGGOMMcYYqzHcyGSMMcYYY4wxVmO4kckYY4wxxhhjrMZwI5MxxhhjjDHGWI3hRiZjjDHGGGOMsRrDjUzGGGOMMcYYYzWGG5mMMcYYY4wxxmoMNzIZY4wxxhhjjNUYbmQyxhhjjDHGGKsx3MhkjDHGGGOMMVZjuJHJGGOMMcYYY6zGcCOTMcYYY4wxxliN4UYmY4wxxhhjjLEaw41MxhhjjDHGGGM1hhuZjDHGGGOMMcZqDDcyGWOMMcYYY4zVGG5kMsYYY4wxxhirMdzIZIwxxhhjjDFWY7iRyRhjjDHGGGOsxnAjkzHGGGOMMcZYjVGt7wwwVhX5+fnIy8ur72wwxhhjjLFqUFNTg4qKSn1ng9UybmSyNwIR4c6dO8jMzKzvrDDGGGOMsVcgk8lgbGwMJSWl+s4KqyXcyGRvhKIGZsOGDaGhocEnJcYYY4yxNwwRIScnB/fu3QMAmJiY1HOOWG3hRiZ77eXn5wsNTAMDg/rODmOMMcYYqyapVAoAuHfvHho2bMivzr6leOAf9tor6oOpoaFRzzlhjDHGGGOvquiajsfZeHtxI5O9MfgVWcYYY4yxNx9f0739uJHJGGOMMcYYY6zGcCOTsXdIWloalJSUIJfL36i4izt06BCUlJSEkYbDw8Mhk8lqNU32dgkMDISTk5Mw7e3tjc8++6ze8vM2UlJSQlRU1CvFUfK4dO7cGWPHjn2lOIHSx/91Y2FhgZ9++kmYrol9ydirqup5smRdzd49PPAPe6Mt25tZZ2mN9pRVeZ379+9j+vTp2LVrF+7evQs9PT04Ojpi+vTp6NChA4DCC4jt27e/Exe5qamp+P7773Ho0CE8fPgQhoaGcHV1xYIFC2BnZ1etOPv3748ePXoI04GBgYiKiqr1xm557q3zrdP0Gg5ao3DYyl5PmjFjBgIDA18xR9Wj6O/g8OHDCAoKglwuR25uLkxNTdG+fXusXr0aEomkWmkvWbIERCRMd+7cGU5OTqIL/bq0Ovm7Ok1vhNW8KoVX5LyWnp4OPT29V8pXyeNSUwICAjB69Ghh2tvbG5mZma/ckMvPz8ePP/6I8PBwXLt2DVKpFDY2NhgxYgSGDx9e7XiL78u0tDQ0bdoU8fHx9dZQzvwps07Tk42VVSm8t7c3IiIiAACqqqrQ19dHq1atMHDgQHh7e0NZmZ+vMFYXuJHJWC3q3bs3nj9/joiICFhaWuLu3bvYv38/MjIy6jtr1fb8+fNqXczn5eXB09MTtra22LZtG0xMTHDz5k1ER0e/0p1OqVQqjFTHKpaeni78f+PGjZg+fTqSkpKEeVpaWlWKr7plobouXbqE7t27Y/To0Vi6dCmkUin+/fdfbN26Ffn5+dWOV1dXtwZz+fZT5LxmbGz8yunU9HEhIuTn50NLS6vKZV0RQUFBWLlyJZYvXw43Nzc8fvwYp0+fxqNHj14p3prYl++a7t27IywsDPn5+bh79y52796NMWPGYMuWLdixYwdUVfnyl7HaxrdzGKslmZmZiImJwYIFC/DBBx+gSZMmaNOmDb777jt8+umnAApfiwKAXr16QUlJSZhOTk5Gz5490ahRI2hpaaF169bYt2+fKH4LCwvMnTsXPj4+0NbWxv/+9z+sWrVKFObUqVNwdnaGuro63NzcEB8fL1qen58PX19fNG3aFFKpFLa2tliyZIkoTNErMnPmzEHjxo1ha2urUNwlXbx4EcnJyfjll1/Qtm1bNGnSBB06dMDs2bPRtm1bAC9fud2wYQPat28PdXV1tGjRAocPHy433uKvy4aHhyMoKAgJCQlQUlKCkpISwsPDK8zXu8TY2Fj409XVhZKSkjCdnZ2NwYMHV1rmZs2ahSFDhkBHRwdfffUVAGD16tUwNzeHhoYGevXqhcWLF5d6hfnPP/+Ei4sL1NXVYWlpiaCgILx48UKIFyj9Oyhpz549MDY2xsKFC9GiRQtYWVmhe/fuWL16tXCjoag8REVFwcbGBurq6ujWrRtu3LhR7n4p/hqYt7c3Dh8+jCVLlghlKC0trWo7+i2myHkNEL/iWfS73rRpE9zd3SGVStG6dWtcuXIFcXFxcHNzg5aWFj766CPcv39fiKOy1/N+//13uLm5QVtbG8bGxhg0aJDw7T3g5et60dHRcHV1RYMGDXD06FHR67KBgYGIiIjAn3/+KRzvQ4cOwcPDA998840ovfv370MikWD//v1l5mfHjh3w9/dH37590bRpUzg6OsLX1xcBAQFCmM6dO+Obb77BN998A11dXRgaGuKHH36o8Ilt8X3ZtGlTAICzszOUlJTQuXPnctd7lzVo0ADGxsYwNTWFi4sLpk6dij///BPR0dFCnVBWF4/MzEyhDAAvy9A///wDZ2dnSKVSeHh44N69e4iOjoa9vT10dHQwaNAg5OTkCPF07twZo0ePxtixY6Gnp4dGjRph9erVyM7OxrBhw6CtrQ1ra2tER0cDKLwBYm1tjUWLFom2Qy6XQ0lJCVevXi1zO4t+I3PnzkWjRo0gk8kwc+ZMvHjxAhMnToS+vj7MzMwQFhYmWu/8+fPw8PCAVCqFgYEBvvrqK2RlZQnL8/PzMX78eMhkMhgYGGDSpEmlymhBQQHmzZsnXD84Ojpiy5YtVTpO7O3GjUzGaknR3fKoqCg8e/aszDBxcXEAgLCwMKSnpwvTWVlZ6NGjB/bv34/4+Hh0794dXl5euH79umj94OBgoYHn7+8PPz8/4clUVlYWPvnkEzg4OODMmTMIDAwUXewAhZWEmZkZNm/ejEuXLmH69OmYOnUqNm3aJAq3f/9+JCUlYe/evdi5c6dCcZdkZGQEZWVlbNmypdKnThMnTsSECRMQHx+Pdu3awcvLS6Gnv/3798eECRPQvHlzpKenIz09Hf379690PaZ4mVu0aBEcHR0RHx+PH374AbGxsRg5ciTGjBkDuVwOT09PzJkzR7ROTEwMhgwZgjFjxuDSpUtYuXIlwsPDhXDl/Q5KMjY2Rnp6Oo4cOVLhtuTk5GDOnDmIjIxEbGwsMjMzMWDAAIX2w5IlS9CuXTuMGDFCKEPm5uYKrfsuUOS8Vp4ZM2Zg2rRpOHv2LFRVVTFo0CBMmjQJS5YsQUxMDK5evYrp06crHF9eXh5mzZqFhIQEREVFIS0tDd7e3qXCTZkyBfPnz0diYiJatWolWhYQEIB+/fqhe/fuwvFu3749hg8fjnXr1om28Y8//oCpqSk8PDzKzI+xsTEOHDggaiiXJSIiAqqqqjh16hSWLFmCxYsX47ffflNom0+dOgUA2LdvH9LT07Ft2zaF1mOAh4cHHB0dq7XPAgMDsXz5chw7dgw3btxAv3798NNPP2HdunXYtWsX9uzZg2XLlonWiYiIgKGhIU6dOoXRo0fDz88Pffv2Rfv27XH27Fl07doVX375JXJycqCkpAQfH59SjcGwsDB07NgR1tbW5ebtwIEDuH37No4cOYLFixdjxowZ+OSTT6Cnp4eTJ09i5MiR+Prrr3Hz5k0AQHZ2Nrp16wY9PT3ExcVh8+bN2Ldvn+imSnBwMMLDwxEaGoqjR4/i4cOH2L59uyjdefPmITIyEitWrMDFixcxbtw4fPHFFxXeFGbvFm5kMlZLVFVVER4ejoiICMhkMnTo0AFTp07FuXPnhDBGRkYAAJlMBmNjY2Ha0dERX3/9NVq0aAEbGxvMmjULVlZW2LFjhyiNHj16wN/fH9bW1pg8eTIMDQ1x8OBBAMC6detQUFCANWvWoHnz5vjkk08wceJE0fpqamoICgqCm5sbmjZtisGDB2PYsGGlGpmampr47bff0Lx5czRv3lyhuEsyNTXF0qVLMX36dOjp6cHDwwOzZs1CSkpKqbDffPMNevfuDXt7e/z666/Q1dXFmjWV9z2USqXQ0tKCqqqq8ISOX6VVjKJlzsPDAxMmTICVlRWsrKywbNkyfPTRRwgICECzZs3g7++Pjz76SLROUFAQpkyZgqFDh8LS0hKenp6YNWsWVq5cCaD830FJffv2xcCBA9GpUyeYmJigV69eWL58OR4/fiwKl5eXh+XLl6Ndu3ZwdXVFREQEjh07JlygV0RXVxcSiQQaGhpCGeIPhb+kyHmtPAEBAejWrRvs7e0xZswYnDlzBj/88AM6dOgAZ2dn+Pr6CucvRfj4+OCjjz6CpaUl2rZti6VLlyI6Olr0RAYAZs6cCU9PT1hZWUFfX1+0TEtLC1KpVHjyZWxsDIlEgs8//xxA4RP4IuHh4fD29i63b/PixYtx//59GBsbo1WrVhg5cqTwpKo4c3NzhISEwNbWFoMHD8bo0aMREhKi0DYX/TYMDAxgbGxcantYxezs7Kr1ZsLs2bNF5fTw4cP49ddf4ezsDHd3d/Tp06dU2XV0dMS0adNgY2OD7777Durq6jA0NMSIESNgY2OD6dOnIyMjQ/jteHt7IykpSThP5eXlYd26dfDx8akwb/r6+li6dClsbW3h4+MDW1tb5OTkYOrUqULaEokER48eBVB4bZCbm4vIyEi0aNECHh4eWL58OX7//XfcvXsXAPDTTz/hu+++w+effw57e3usWLFC9Pr6s2fPMHfuXISGhqJbt26wtLSEt7c3vvjiC+G8zhg3MhmrRb1798bt27exY8cOdO/eHYcOHYKLi0ulr3BmZWUhICAA9vb2kMlk0NLSQmJiYqmnSsXvyhe9+lj0uljRXXt1dXUhTLt27Uql9fPPP8PV1RVGRkbQ0tLCqlWrSqXTsmVLUd87ReMuadSoUbhz5w7Wrl2Ldu3aYfPmzWjevDn27t0rClc8LlVVVbi5uSExMbHS+Fn1KVrm3NzcRNNJSUlo06aNaF7J6YSEBMycOVN4CqalpSU8KSz+illlVFRUEBYWhps3b2LhwoUwNTXF3LlzhSfXRVRVVdG6dWth2s7ODjKZjMtQDanuea34+apRo0YACs8txecVf921MmfOnIGXlxf+97//QVtbG506dQKASsusItTV1fHll18iNDQUAHD27FlcuHChzCelRRwcHHDhwgWcOHECPj4+uHfvHry8vEoN+tO2bVtRQ7Vdu3b4999/X6lfMVMMEVXr+4wly66GhgYsLS1F80qW3eLrqKiowMDAoFR5ByCs17hxY3z88cdCmfvrr7/w7Nkz9O3bt8K8NW/eXDSYUaNGjUTpFKVd/NrA0dERmpqaQpgOHTqgoKAASUlJ+O+//5Ceno733ntPWF5UDxe5evUqcnJy4OnpKTqvR0ZGIjk5ucL8sncHNzIZq2Xq6urw9PTEDz/8gGPHjsHb2xszZsyocJ2AgABs374dc+fORUxMDORyOVq2bInnz5+LwqmpqYmmlZSUUFBQoHDeNmzYgICAAPj6+mLPnj2Qy+UYNmxYqXSKV0avSltbG15eXpgzZw4SEhLg7u6O2bNn11j8rHoULXPVKQtZWVnCiLBFf+fPn8e///4rulGhKFNTU3z55ZdYvnw5Ll68iNzcXKxYsaLK8bDqq855rfj5quhCv+Q8Rc9fRa/86ejoYO3atYiLixNe56up89fw4cOxd+9e3Lx5E2FhYfDw8ECTJk0qXEdZWRmtW7fG2LFjsW3bNoSHh2PNmjVITU2tVh5YzUpMTBT6tRY1zIr3NczLyytzvZLlVJG6t6wwZf0Giq83fPhwbNiwAU+fPkVYWBj69+8PDQ2NCrepsnTKy9+rKHpbYNeuXaLz+qVLl7hfJhNwI5OxOubg4IDs7GxhWk1NrdQd7NjYWHh7e6NXr15o2bIljI2Nq/yKj729Pc6dO4fc3Fxh3okTJ0ql0759e/j7+8PZ2RnW1tYK3YVUJG5FKCkpwc7OTrQ/Ssb14sULnDlzBvb29grFKZFI+IlANVS3zNna2pbqQ1ly2sXFBUlJSbC2ti71V3ShV9bvQBF6enowMTERlaEXL17g9OnTwnRSUhIyMzO5DNWikue12nb58mVkZGRg/vz5cHd3h52dXZWeghZX3vFu2bIl3NzcsHr1aoVeWyyLg4MDAIj2zcmTJ0VhTpw4ARsbG4Veyy56o4TLZ9UdOHAA58+fR+/evQG8fPW4+FsQ9fXpqyI9evSApqYmfv31V+zevbtaZa4y9vb2SEhIEJXJ2NhYKCsrw9bWFrq6ujAxMRGV06J6uIiDgwMaNGiA69evlzqncx92VoQbmYzVkoyMDHh4eOCPP/7AuXPnkJqais2bN2PhwoXo2bOnEM7CwgL79+/HnTt3hKHubWxssG3bNsjlciQkJGDQoEFVvgs5aNAgKCkpYcSIEbh06RL+/vvvUiPX2djY4PTp0/jnn39w5coV/PDDD+UOulLVuEuSy+Xo2bMntmzZgkuXLuHq1atYs2YNQkNDRfsDKHyFd/v27bh8+TJGjRqFR48eKVzZWlhYIDU1FXK5HA8ePKjy4CTvquqWudGjR+Pvv//G4sWL8e+//2LlypWIjo4WvZI2ffp0REZGIigoCBcvXkRiYiI2bNiAadOmCWHK+h2UtHLlSvj5+WHPnj1ITk7GxYsXMXnyZFy8eBFeXl5CODU1NYwePRonT57EmTNn4O3tjbZt25Z6jbc8FhYWOHnyJNLS0vDgwYMafQLwplP0vFbb/ve//0EikWDZsmVISUnBjh07MGvWrGrFZWFhgXPnziEpKQkPHjwQPc0aPnw45s+fDyJCr169KoynT58+CAkJwcmTJ3Ht2jUcOnQIo0aNQrNmzUTfAb5+/TrGjx+PpKQkrF+/HsuWLcOYMWMUymvDhg0hlUqxe/du3L17F//991+1tvlt9+zZM9y5cwe3bt3C2bNnMXfuXPTs2ROffPIJhgwZAqCwD3/btm2FQaEOHz4sOifVBxUVFXh7e+O7776DjY2NQt1Qqmrw4MFQV1fH0KFDceHCBRw8eBCjR4/Gl19+KbzCO2bMGMyfPx9RUVG4fPky/P39RZ8a09bWRkBAAMaNG4eIiAgkJyfj7NmzWLZsmfCNUsb4Q0HsjTbaU1bfWSiXlpYW3nvvPYSEhCA5ORl5eXkwNzfHiBEjMHXqVCFccHAwxo8fj9WrV8PU1BRpaWlYvHgxfHx80L59exgaGmLy5MmlBjdRJP2//voLI0eOhLOzMxwcHLBgwQLhLi4AfP3114iPj0f//v2hpKSEgQMHwt/fv8zBKqoad0lmZmawsLBAUFCQMHR80fS4ceNEYefPn4/58+dDLpfD2toaO3bsgKGhoULb3bt3b2zbtg0ffPABMjMzERYWVmE/qprWcFDlAxS9jqpb5jp06IAVK1YgKCgI06ZNQ7du3TBu3DgsX75cCNOtWzfs3LkTM2fOxIIFC6CmpgY7OztRX7WyfgcltWnTBkePHsXIkSNx+/ZtaGlpoXnz5oiKihL64wGAhoYGJk+ejEGDBuHWrVtwd3dXaOCoIgEBARg6dCgcHBzw9OlTpKamlvtZldowwmpenaVVVYqe12qbkZERwsPDMXXqVCxduhQuLi5YtGiR6DMqihoxYgQOHToENzc3ZGVl4eDBg8KnQQYOHIixY8di4MCBlb7a3a1bN6xfvx7z5s3Df//9B2NjY3h4eCAwMFD0XcYhQ4bg6dOnaNOmDVRUVDBmzBjhc0CVUVVVxdKlSzFz5kxMnz4d7u7uwuc26opsrKxO06uO3bt3w8TEBKqqqtDT04OjoyOWLl2KoUOHivovhoaGwtfXF66urrC1tcXChQvRtWvXesw54Ovri7lz52LYsGG1Er+Ghgb++ecfjBkzBq1bt4aGhgZ69+6NxYsXC2EmTJiA9PR0YX/5+PigV69eopsas2bNgpGREebNm4eUlBTIZDLhczGMAYASVfRxJsZeA7m5uUhNTUXTpk2r1X+LvTnS0tLQtGlTxMfHC9+xY2+eESNG4PLly4iJianztMPDwzF27FjRXXfGqistLQ1WVlaIi4uDi4vLK8fXuXNnODk54aeffnr1zLG3UkxMDLp06YIbN24ITxbfRnxt9/bjJ5mMMcZeyaJFi+Dp6QlNTU1ER0cjIiICv/zyS31ni7Fqy8vLQ0ZGBqZNm4a2bdvWSAOTsYo8e/YM9+/fR2BgIPr27ftWNzDZu4H7ZDLGGHslp06dgqenJ1q2bIkVK1Zg6dKlpT7bwNibJDY2FiYmJoiLi+ORi1mdWL9+PZo0aYLMzEwsXLiwvrPD2Cvj12XZa49fqWCMMcYYe3vwtd3bj59kMsYYY4wxxhirMdzIZG8MfujOGGOMMfbm42u6tx83MtlrT01NDQCQk5NTzzlhjDHGGGOvquiarugaj719eHRZ9tpTUVGBTCbDvXv3ABR+46n4h94ZY4wxxtjrj4iQk5ODe/fuQSaTQUVFpb6zxGoJD/zD3ghEhDt37vC37xhjjDHG3nAymQzGxsb80OAtxo1M9kbJz89HXl5efWeDMcYYY4xVg5qaGj/BfAdwI5MxxhhjjDHGWI3hgX8YY4wxxhhjjNUYbmQyxhhjjDHGGKsx3MhkjDHGGGOMMVZjuJHJGGOMMcYYY6zGcCOTMcYYY4wxxliN4UYmY4wxxhhjjLEaw41MxhhjjDHGGGM15v/uMq2vORagPgAAAABJRU5ErkJggg==",
+      "text/plain": [
+       "<Figure size 1100x500 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "print('Pytorch performances:')\n",
+    "plot_performance_metrics(\n",
+    "    df_cv=reports['cv_train'],\n",
+    "    df_test=reports['test'],\n",
+    "    df_test_majority=reports['majority_vote'][reports['majority_vote']['cv_models'].isna()],\n",
+    "    title=f'pytorch_performance',\n",
+    "    show_plot=False,\n",
+    "    metrics_to_plot = {\n",
+    "        'val_acc': 'Validation Accuracy',\n",
+    "        'val_roc_auc': 'Validation ROC AUC',\n",
+    "        'val_f1_score': 'Validation F1 Score',\n",
+    "        'val_precision': 'Validation Precision',\n",
+    "        'val_recall': 'Validation Recall',\n",
+    "        'test_acc': 'Test Accuracy',\n",
+    "        'test_roc_auc': 'Test ROC AUC',\n",
+    "        'test_f1_score': 'Test F1 Score',\n",
+    "        'test_precision': 'Test Precision',\n",
+    "        'test_recall': 'Test Recall',\n",
+    "    },\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### XGBoost Plots"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "XGBoost performances:\n",
+      "Metrics: ['Recall', 'ROC AUC', 'Precision', 'F1 Score', 'Accuracy']\n",
+      "Metric: Recall\n",
+      "Metric: ROC AUC\n",
+      "Metric: Precision\n",
+      "Metric: F1 Score\n",
+      "Metric: Accuracy\n",
+      "Plotting performance for main part of the paper...\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1100x500 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "print('XGBoost performances:')\n",
+    "plot_performance_metrics(\n",
+    "    df_cv=reports['xgboost_cv_train'],\n",
+    "    df_test=reports['xgboost_test'],\n",
+    "    df_test_majority=reports['xgboost_majority_vote'],\n",
+    "    title=f'xgboost_performance',\n",
+    "    show_plot=False,\n",
+    "    metrics_to_plot = {\n",
+    "        'val_acc': 'Validation Accuracy',\n",
+    "        'val_roc_auc': 'Validation ROC AUC',\n",
+    "        'val_f1_score': 'Validation F1 Score',\n",
+    "        'val_precision': 'Validation Precision',\n",
+    "        'val_recall': 'Validation Recall',\n",
+    "        'test_acc': 'Test Accuracy',\n",
+    "        'test_roc_auc': 'Test ROC AUC',\n",
+    "        'test_f1_score': 'Test F1 Score',\n",
+    "        'test_precision': 'Test Precision',\n",
+    "        'test_recall': 'Test Recall',\n",
+    "    },\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Cells as One-Hot Encoded"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Cells as one-hot performances:\n",
+      "Metrics: ['Recall', 'ROC AUC', 'Precision', 'F1 Score', 'Accuracy']\n",
+      "Metric: Recall\n",
+      "Metric: ROC AUC\n",
+      "Metric: Precision\n",
+      "Metric: F1 Score\n",
+      "Metric: Accuracy\n",
+      "Plotting performance for main part of the paper...\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1100x500 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "print('Cells as one-hot performances:')\n",
+    "plot_performance_metrics(\n",
+    "    df_cv=reports['cellsonehot_cv_train'],\n",
+    "    df_test=reports['cellsonehot_test'],\n",
+    "    df_test_majority=reports['cellsonehot_majority_vote'],\n",
+    "    title=f'cellsonehot_performance',\n",
+    "    show_plot=False,\n",
+    "    metrics_to_plot = {\n",
+    "        'val_acc': 'Validation Accuracy',\n",
+    "        'val_roc_auc': 'Validation ROC AUC',\n",
+    "        'val_f1_score': 'Validation F1 Score',\n",
+    "        'val_precision': 'Validation Precision',\n",
+    "        'val_recall': 'Validation Recall',\n",
+    "        'test_acc': 'Test Accuracy',\n",
+    "        'test_roc_auc': 'Test ROC AUC',\n",
+    "        'test_f1_score': 'Test F1 Score',\n",
+    "        'test_precision': 'Test Precision',\n",
+    "        'test_recall': 'Test Recall',\n",
+    "    },\n",
     ")"
    ]
   },
@@ -4137,441 +1173,1937 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### PyTorch Plots"
+    "### Proteins as Amino-Acid Counts"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 140,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Pytorch performances:\n",
-      "Metrics: ['ROC AUC', 'Recall', 'Precision', 'F1 Score', 'Accuracy']\n"
+      "Proteins as amino-acid counts performances:\n",
+      "Metrics: ['Recall', 'ROC AUC', 'Precision', 'F1 Score', 'Accuracy']\n",
+      "Metric: Recall\n",
+      "Metric: ROC AUC\n",
+      "Metric: Precision\n",
+      "Metric: F1 Score\n",
+      "Metric: Accuracy\n",
+      "Plotting performance for main part of the paper...\n"
      ]
     },
     {
      "data": {
-      "image/png": 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",
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yiIio5N63q3InTpzArFmzsHjxYjRs2BB37tzB559/jpkzZ+Lbb78t8Dll0YaqojhtaGqq8g9ROzu7Io8um5HxvDjVKrGqVauqpRx1KI8xoIrixECmtvLAPxnGCRCK2HWSMVB85TEGVFFeY6CobajoSeb48ePx1VdfoW/fvgCAOnXq4OHDh5g9ezYGDRoEa2trAEBsbCxsbGwUz4uNjUXdunULPObx48dx/fp1rFy5EuPHj0eHDh0gk8nQu3dv/PbbbwU+R0dHBzo6OuK+uPcM5xIjIipfzM3NoaGhgdjYWKX1sbGxivb1Td9++y0++ugjfPzxxwDy2mW5XI4RI0bgm2++gVSa/84ZtqFERKQK0e/JTEtLy9dgaWhoIDc3FwDg5OQEa2trHD16VLE9OTkZ586dg5+fX77jvXz5EkFBQVi2bJmi+21WVl73g6ysLOTk5Ij9Euhfr+YSe/V4s4sRVX6CICA1NVXxEAShrKtE9F7T1taGj4+PUhuam5uLo0ePFtiGAoW3ywD4N01ERKVC9CuZnTt3xvfffw8HBwd4eHggLCwM8+fPx9ChQwHk3Qg/duxYfPfdd6hZs6ZiChNbW1t069Yt3/FmzpyJDh06wNvbGwDg7++P8ePHY8iQIfjtt9/g7+8v9ksgon9x0nKi8ic4OBiDBg2Cr68vGjRogAULFkAulytGmx04cCDs7Owwe3bePHudO3fG/Pnz4e3tregu++2336Jz586KZJOIiEhMoieZCxcuxLfffotPP/0UcXFxsLW1xSeffIIpU6Yo9pkwYYKiq05iYiKaNGmCAwcO5JsU+tq1a9iyZQvCw8MV63r27IkTJ06gadOmqFWrFjZu3Cj2Sygxzu9ERESlrU+fPnj27BmmTJmCmJgY1K1bFwcOHFAMBhQVFaV05XLy5MmQSCSYPHkyoqOjYWFhoTghTESlh9PY0PtM9CTT0NAQCxYswIIFCwrdRyKRYMaMGZgxY8Zbj1W7dm3cvn1baZ1UKsXixYuxePFiMaorKs7vRERE6jB69GiMHj26wG0nTpxQWtbU1MTUqVMxdepUNdSMiF55n+dIJBL9nkwiIiIiIiJ6fzHJJCIiIiIiItHwGv57ZsXdSUXeNzNNeX6ntfenF7nbx3Dn2cWqFxERERERVQ68kklERERERESiYZJJREREREREomGSSURERERERKJhkklERERERESiYZJJREREREREouHoskQiEgQBcrlcsSyTySCRSMqwRkRERERE6sUkk0hEcrkcXbt2VSzv3LkTBgYGZVgjIiIiIiL1YpJJhdLS00D3BfWUlomIiIiIiN6GSSYVSiKRQFufIVLZrLg7qcj7ZqZlKy2vvT+9yDEx3Hl2sepFRERERJUDB/4hIiIiIiIi0fAyFdE7LDycWOR9M1/KlZaXHU+Ctm52IXsr+6yNSTFqRaQeHMyKiIiIiotJJhERFYqDWREREVFxsbssERERERERiYZJJhEREREREYmGSSYRERERERGJhvdkEolIS0cfHcauU1omIiIiInqfMMkkEpFEIoG2rqysq0H0VpwrlYiIiEoTu8sSERERERGRaJhkEhERERERkWiYZBIREREREZFomGQSERERERGRaDjwDxERFUpLTwPdF9RTWiYiIiJ6GyaZRERUKIlEUuTRZImIiIgAJplE9Ba8ikVERERExcUkk4gKxatYRERERFRcHPiHiIiIiIiIRMMkk4iIiIiIiETDfnBERO+w8HCiWsr5rI2JWsohqmziNg4r8r7yjByl5WdbRyNNp4j3mze0LE61iIjeW7ySSURERERERKJhkklERERERESiYZJJREREREREomGSSURERERERKJhkklERERERESiYZJJREREREREomGSSURERERERKJhkklERERERESiYZJJREREREREomGSSURERERERKJhkklERERERESiYZJJREREREREomGSSURERERERKLRLOsKUPEJggC5XK5YlslkkEgkZVgjIiIiIiKiPEwyKyC5XI6uXbsqlnfu3AkDA4MyrBEREREREVEedpclIiIiIiIi0TDJJCIiIiIiItEwySQiIiIiIiLRMMkkIiIiIiIi0TDJJCIiIiIiItEwySQiIiIiIiLRMMkkIiIiIiIi0XCeTCKiCkgQBMjlcsWyTCaDRCIpwxoRERER5WGSSURUAcnlcnTt2lWxvHPnThgYGJRhjYiIiIjysLssERERERERiYZJJhEREREREYmGSSYRERERERGJhvdkElVAHPSFiIiIiMorJplEFRAHfSEiIiKi8ordZYmIiIiIiEg0TDKJiIiIiIhINEwyiYiIiIiISDRMMomIiIiIiEg0TDKJiIiIiIhINEwyiYiIiIiISDSlkmRGR0djwIABqFKlCvT09FCnTh1cvHhRsV0QBEyZMgU2NjbQ09NDQEAAbt++rdiekZGBjz76CEZGRnBxccGRI0eUjj9v3jx89tlnpVF1IiKicm/RokVwdHSErq4uGjZsiPPnz791/8TERAQFBcHGxgY6OjpwcXHBvn371FRbIiJ634g+T+aLFy/g7++Pli1bYv/+/bCwsMDt27dhamqq2Gfu3Ln49ddfsXbtWjg5OeHbb79FYGAgbty4AV1dXSxfvhyhoaEICQnB/v378eGHHyI2NhYSiQT379/HihUrlJJWIiKi98XmzZsRHByMpUuXomHDhliwYAECAwNx69YtWFpa5ts/MzMTbdq0gaWlJf73v//Bzs4ODx8+hImJiforT0RE7wXRk8w5c+bA3t4ea9asUaxzcnJS/F8QBCxYsACTJ09WTCa/bt06WFlZYceOHejbty8iIiLQpUsXeHh4oHr16hg/fjzi4+NhYWGBUaNGYc6cOTAyMhK76kREROXe/PnzMXz4cAwZMgQAsHTpUuzduxerV6/GV199lW//1atXIyEhAWfOnIGWlhYAwNHRUZ1VJiKi94zo3WV37doFX19f9OrVC5aWlvD29saKFSsU2+/fv4+YmBgEBAQo1hkbG6Nhw4YICQkBAHh5eeH06dNIT0/HwYMHYWNjA3Nzc2zYsAG6urr44IMP3lmPjIwMJCcnKz2IiIgqsszMTISGhiq1oVKpFAEBAYo29E27du2Cn58fgoKCYGVlhdq1a2PWrFnIyckptBy2oUREpArRr2Teu3cPS5YsQXBwML7++mtcuHABY8aMgba2NgYNGoSYmBgAgJWVldLzrKysFNuGDh2KK1euwN3dHebm5tiyZQtevHiBKVOm4MSJE5g8eTI2bdoEZ2dnrF69GnZ2dvnqMXv2bEyfPj3f+ujo6GI1lvrZacV5+SX2+HFqkfeVy+VKy9HR0ZDJZEV6rk5ilWLVq6QeP36slnLUgTFQMoyB4mMMqEdKSkpZV6HE4uPjkZOTU2AbevPmzQKfc+/ePRw7dgz9+/fHvn37cOfOHXz66afIysrC1KlTC3xORWtD4zXy/w4oTJpGNoBHiuXnGrZI1yjazyGdRPX0oiqvfzslUR6/P1Xxvn9/lgRjoGTKawwUtQ0VPcnMzc2Fr68vZs2aBQDw9vbGtWvXsHTpUgwaNKhIx9DS0sKiRYuU1g0ZMgRjxoxBWFgYduzYgcuXL2Pu3LkYM2YMtm3blu8YkyZNQnBwsGI5OTkZ9vb2sLOzK1ZX27SIxCLvqwrtk+OKvG9WhvLZZ+0zs6Gto1Gk52Y0zH+/TmmoWrWqWspRB3XFQNWqJkXeNzVV+YvUzs4OBgYGRXpuRsbz4lSrxBgDxccYUI/37apcbm4uLC0tsXz5cmhoaMDHxwfR0dGYN29eoUlmRWtDzXOii7yv/I0ruFVynkCWU8Q21CSrWPUqqfL6t1MS5fH7UxXv+/dnSTAGSqa8xkBR21DRu8va2NjA3d1daZ2bmxuioqIAANbW1gCA2NhYpX1iY2MV2950/PhxXL9+HaNHj8aJEyfQoUMHyGQy9O7dGydOnCjwOTo6OjAyMlJ6EBERVWTm5ubQ0NAoVhtqY2MDFxcXaGj8l0i5ubkhJiYGmZmZBT6HbSgREalC9CTT398ft27dUloXGRmJatWqAcgbBMja2hpHjx5VbE9OTsa5c+fg5+eX73gvX75EUFAQli1bBg0NDeTk5CArK+9MYlZW1lvvKSEiIqpMtLW14ePjo9SG5ubm4ujRowW2oUBeu3znzh3k5uYq1kVGRsLGxgba2tqlXufyRl9bipUD7BUPfW1OGU5EJDbRv1m/+OILnD17FrNmzcKdO3ewceNGLF++HEFBQQAAiUSCsWPH4rvvvsOuXbtw9epVDBw4ELa2tujWrVu+482cORMdOnSAt7c3gLzGcvv27bhy5Qp+++03+Pv7i/0SiIiIyq3g4GCsWLECa9euRUREBEaNGgW5XK4YbXbgwIGYNGmSYv9Ro0YhISEBn3/+OSIjI7F3717MmjVL0S6/byQSCWQ6GoqHRCIp6yoREVU6ot+TWb9+ffz111+YNGkSZsyYAScnJyxYsAD9+/dX7DNhwgTI5XKMGDECiYmJaNKkCQ4cOABdXV2lY127dg1btmxBeHi4Yl3Pnj1x4sQJNG3aFLVq1cLGjRvFfglERETlVp8+ffDs2TNMmTIFMTExqFu3Lg4cOKAYDCgqKgpS6X/nkO3t7XHw4EF88cUX8PT0hJ2dHT7//HNMnDixrF4CERFVcqInmQDQqVMndOrUqdDtEokEM2bMwIwZM956nNq1a+P27dtK66RSKRYvXozFixeLUlciIqKKZvTo0Rg9enSB2woaq8DPzw9nz54t5VoRERHl4Y0IREREREREJJpSuZJJRMUXt3FYkfeVvzGNzbOto5FWxGlsoKZpbIiIiIjo/cQrmURERERERCQaJplEREREREQkGiaZREREREREJBommURERERERCQaJplEREREREQkGiaZREREREREJBpOYUJERERE7w1BECCXyxXLMpkMEomkDGtEVPkwySQiIiKi94ZcLkfXrl0Vyzt37oSBgUEZ1oio8mGSSURljmeViTFARERUeTDJJKIyx7PKxBggIiJ14YnN0sckk4iIiIiI3hs8sVn6OLosERERERERiYZJJhEREREREYmGSSYRERERERGJhvdkElGpSFyQWOR95VlypeWkJUnI1sou0nNNxpoUo1akTowBIlKXuI3DiryvPCNHafnZ1tFI09Eo2pMbWhanWkTvLV7JJCIiIiIiItEwySQiIiIiIiLRsLssERERERFVaOwyXb7wSiYRERERERGJhlcyKyB9bSlWDrBXWiYiIiIiIioPmGRWQBKJBLKiXtKnSqmynWjQ19THunbrlJbp/cIYICIiqjyYZBJVQJXtRINEIoFMS1bW1aAyxBggIiKqPJhkEhGVExy0gIio9FW23kBE5RGTTCIiIiJ6b1S23kBE5RFP3RAREREREZFoeCWTiIiIiIjeG+wyXfqYZFKZEwQBcrlcsSyTySCRSMqwRkRERERUWbHLdOljkkllTi6Xo2vXrorlnTt3wsDAoAxrREREREREJcVrw0RERERERCQaXsmkUpG4ILHI+8qz5ErLSUuSkK2V/c7nmYw1KWatiIiIiIiotDHJJCIiIipHinOitqR4orZ8U0cMAIyD8qyixwCTTCpz+pr6WNdundIyERERERFVTEwyqcxJJBLItGRlXQ0iIiIiIhIBB/4hIiIiIiIi0TDJJCIiIiIiItEwySQiIiIiIiLRMMkkIiIiIiIi0TDJJCIiIiIiItEwySQiIiIiIiLRMMkkIiIiIiIi0TDJJCIiIiIiItEwySQiIiIiIiLRMMkkIiIiIiIi0TDJJCIiIiIiItEwySQiIiIiIiLRMMkkIiIiIiIi0TDJJCIiIiIiItEwySQiIiIiIiLRMMkkIiIiIiIi0TDJJCIiIiIiItEwySQiIiIiIiLRMMkkIiIiIiIi0TDJJCIiIiIiItEwySQiIiIiIiLRMMkkIiIiIiIi0TDJJCIiIiIiItEwySQiIiIiIiLRMMkkIiIiIiIi0TDJJCIiIiIiItEwySQiIiIiIiLRMMkkIiIiIiIi0TDJJCIiIiIiItEwySQiIiIiIiLRMMkkIiIiIiIi0ZR6kvnDDz9AIpFg7NixinUvX75EUFAQqlSpAgMDA/To0QOxsbGK7QkJCejcuTMMDAzg7e2NsLAwpWMGBQXhp59+Ku2qExERlUuLFi2Co6MjdHV10bBhQ5w/f75Iz9u0aRMkEgm6detWuhUkIqL3WqkmmRcuXMCyZcvg6emptP6LL77A7t27sXXrVvz999948uQJunfvrtj+/fffIyUlBZcuXUKLFi0wfPhwxbazZ8/i3LlzSkkrERHR+2Lz5s0IDg7G1KlTcenSJXh5eSEwMBBxcXFvfd6DBw/w5ZdfomnTpmqqKRERva9KLclMTU1F//79sWLFCpiamirWJyUlYdWqVZg/fz5atWoFHx8frFmzBmfOnMHZs2cBABEREejbty9cXFwwYsQIREREAACysrIwcuRILF26FBoaGqVVdSIionJr/vz5GD58OIYMGQJ3d3csXboU+vr6WL16daHPycnJQf/+/TF9+nRUr15djbUlIqL3UaklmUFBQejYsSMCAgKU1oeGhiIrK0tpvaurKxwcHBASEgIA8PLywrFjx5CdnY2DBw8qroTOnTsXLVq0gK+v7zvLz8jIQHJystKDiIioIsvMzERoaKhSGyqVShEQEKBoQwsyY8YMWFpaYtiwYUUqh20oERGpQrM0Drpp0yZcunQJFy5cyLctJiYG2traMDExUVpvZWWFmJgYAMBXX32FUaNGwdnZGY6Ojli1ahVu376NtWvXIiQkBCNHjsShQ4fg6+uLFStWwNjYOF85s2fPxvTp0/Otj46OLlZjqZ+dVuR9VRGvYaeWcnQSjdRSTpzs7d22xJD6OLXUywAYAyWljhgA1BMH5TEG0jSyATxSLD/XsEW6RtG+0t/3GEhJSSmlmpS++Ph45OTkwMrKSmm9lZUVbt68WeBzTp8+jVWrViE8PLzI5bANLVhl+tthG1oylSkGgPe3DVXF+x4DRW1DRU8yHz16hM8//xyHDx+Grq5uiY5hbGyMjRs3Kq1r1aoV5s2bhw0bNuDevXu4desWhg8fjhkzZhQ4CNCkSZMQHBysWE5OToa9vT3s7OxgZFT04EiLSCzRaygu85xotZSTYZKllnIs5ZalXoZJVZNSLwNgDJSUOmIAUE8clMcYkOfkKC1XyXkCWU7RbiN432Pgfboql5KSgo8++ggrVqyAubl5kZ/HNrRglelvh21oyVSmGADe3zZUFe97DBS1DRU9yQwNDUVcXBzq1aunWJeTk4OTJ0/it99+w8GDB5GZmYnExESlq5mxsbGwtrYu8Jhr1qyBiYkJunbtiu7du6Nbt27Q0tJCr169MGXKlAKfo6OjAx0dHVFfGxERUVkyNzeHhoaG0ojsQOFt6N27d/HgwQN07txZsS43NxcAoKmpiVu3bsHZ2Tnf89iGEhGRKkRPMlu3bo2rV68qrRsyZAhcXV0xceJE2NvbQ0tLC0ePHkWPHj0AALdu3UJUVBT8/PzyHe/Zs2eYMWMGTp8+DSAvYc3KyjuDkJWVhZw3zuYTEb0P9LWlWDnAXmmZKj9tbW34+Pjg6NGjimlIcnNzcfToUYwePTrf/q6urvna5MmTJyMlJQW//PIL7O3t8z2HiIhIVaInmYaGhqhdu7bSOplMhipVqijWDxs2DMHBwTAzM4ORkRE+++wz+Pn5oVGjRvmON3bsWIwbNw52dnn9rP39/bF+/Xq0bdsWy5cvh7+/v9gvgYio3JNIJJDpcJTt91FwcDAGDRoEX19fNGjQAAsWLIBcLseQIUMAAAMHDoSdnR1mz54NXV3dfG3yq15Eb64nIiISS6kM/PMuP//8M6RSKXr06IGMjAwEBgZi8eLF+fY7ePAg7ty5g/Xr1yvWjR49GhcvXkTDhg3RoEEDTJ06VZ1VJyIiKlN9+vTBs2fPMGXKFMTExKBu3bo4cOCAYjCgqKgoSKW8sk1ERGVHLUnmiRMnlJZ1dXWxaNEiLFq06K3PCwwMRGBgoNI6fX19bNmyRewqEhERVRijR48usHsskL/NfdPvv/8ufoWIiIhew1OdREREREREJBommURERERERCQaJplEREREREQkGiaZREREREREJBommURERERERCQaJplEREREREQkGiaZREREREREJBommURERERERCQaJplEREREREQkGiaZREREREREJBommURERERERCQaJplEREREREQkGs2yrkBlk5OTjYTYRxCEXJhZ2kNTS7usq0RERERERKQ2TDJF9OjOFexZPQO5OTnIzc2BVKqB9gO/QnWPhqKXlZ2Ti8fxqcgVBFQ1N4C2poboZVDJZWakQ8jNhY6erFTLSc/MRm6uAJmuVqmWoy7pmenIFXIh0ynd943KL8YAERFRxcckUwW5ubmQSv/rcXz8f7+h4+DJcHCpCwC4fHo3jmxegBEz/hS13KsPnmPW5ovIzhWQkytAQyrBhB7eqO9iJWo5KQlpOLTyHB5ei4GQK6CqqyXaftwQptaGopbzPPU5fj3+Ky4/uoxcIRe1bWtjdMvRsDWxFbUcdYh/+gD71s1G3KPbgEQCc+tqaPfRRFg71BK1nAdxyZj3vzDceZoEAKhmaYgvu3vDxc5E1HLUFQMP4h9g9oHZuB13GxKJBNWqVMPEwImoZSXu+6YuqUnPcXTrr3h0+zKE3FzYVa+NVr1Gw8RcvJh+nvISi/dcxeX78cgVAA8HM3zasTZszMRNzhgDREREVFxMMlWw4cdP0bZvMKwcXAAAuTnZMDKzVGw3NLVEdnaWyuXk5gqQSiWK5SX7ruGr3j7wcjIHAOy98AC/7r6C9ePaqFzW6/b+9g9sXSzQrK83crJzcHHfTeyY/zeGzO0kajlzD82Fm7UbhvgNQVZuFnaE78B3+77D4g8Xi1qOOhz6cz68m3WDq09L5GRnIfTY/7Bv3Q8YOnmNqOX8svMKujRyQvPatsjOEbD9zF3M23YJK8a0ErUcdcXA/CPz0a1uN7Ss1RJZOVn436X/4Yf9P2DNYHHfN3U58Mdc2Di6wb/jEORkZyHs7x3Ys+Y7DBgvXkz/tD0crvYm+Kh1LWRnC9h17j5mbwnFryObiVYGwBgg9Vs+9UP0DJoDM0v7UisjOycXMS/SYGqgU+o9Qf749gA6fdYEJpYGoh0zQZ6AiKcRSEhLAACY6ZvBzcYNZjIz0cpQpzdP2r++PjXxGYzMxD2J/qb0zGzcjk6E57+/q1SRnZWDyHNRiL71DKmJ6QAAAxM92LlawqWBPTS1xOt5Vtni4HWbf/kC7QZMhHEV67KuiihK43sAKN8xwCRTBa17jcHBjT+iag0vNOk8FH4dBmHdnE9gZmmP3JxsJMRGoXXvMSqXM2bZSXze1Qs1bU0A5DWOlsZ6iu2WxnrIys5VuZxDK8+hxYB60P63wU14moweE1tCSycvTBp0cse6b/arXM7C4wvxsf/H0NPOew3RidGY0WUGdDR1AAA9vHvg8y2fq1yOOvy1bDICen8OQ1MLAEB6ahJqeDaGlrYutLR14eTREGGndqpcztQ/zmF0Z09Y/Pu5J8sz4edqDV3tvM+mvosVdp27r3I56oqByTsn4/NWn8PCMO99S0pPQmPnxtDV0oWuli4aOjXEznDV3zd1Obp1IZp2+RjaOnmfT+KzaHQdPgNa2nkx7dOyB/5coFpML957FUPauEHv38/8SUIqpn5YHzr//mD5wK86gleeVqkMgDFA6hN6fFuB61MS4nDt7AHIDPN+JPm07KFSOVtO3UbXRtWho6WBnNxcrDwYgZ1n7yEnV4BUIkFA3ar4vKsXNDVUGwsx8nxUgeujbsTizoVHMLLI62Xg0sChxGWkZ6Zj/pH5OH7rOCQSCQx18noUpGSkQBAEtHJtheCAYOhq6Za4DHXKSJfj4MZ5uHs1BNq6Mng16YTG7QdBqpH3vZaemojlUz/ElwuPlmo9njyXY/zqMzg4s4tKx0l4kow/ZxxGakIabF0sIDPO+xxi7ifg0sFbMKyij77ftoGZrZFK5VSmOLhz5Z8C1z++cxV3r4XAyDTv4k0NT3+VysnOycWawxE4feMpjPS10bG+I9r5/Pe3mJDyEv3mHlI5BtTxPQBUjBhgkqkCWyd3DBi/BOePbML6OSPRvNsIDPt2HZ4+uAFBEGDtUEuRfKgiqJMnft5xGZ6OVTAowBUDWtbCp4v/hr25AbJzBTx6loKgTp4ql2NYRR+rx+1Bq0E+cGngAPcmTlgzYS9q+NghJzsXt85GoXbz6iqXY2FggZEbRmJEsxHwd/ZHy1otMWrjKDRybITs3GycunMKAa4BKpejDu71A7Bl4TjUbdoV9Vp0h3fzbljz/TDY1/BEbk42HkaGoX6r3iqX06puVUxccwadGzqhWyMndGnkiBELj6OOYxXk5AgIu/cMPZvUULkcdcVAgGsAxv1vHLp6dUV37+7oVrcbhq0bBs+qnsjOyUbYozD09lH9fVMXQxMLxXdADU9/1PJpiQ3zRsHJoxFyc7Jx+/IpuNdXLabNjXQxevFJfBzoDj83a7SoY4fPlp5EAxcrZOfm4p8bT9Haq6rqr4UxQGpyfNsiGJiYQypVvrIjCLm4ce4QpBqagESicpK56lAE2ng7QEdLA9v/uYeDl6IwposXXO1NcPdJEpbtv47tZ+6id9OaKpWzdfYxSCQSCIKQb9vBlecAABKJBF9vH1TiMn478RtuxtzErA9mwcfBBxr/vnc5uTm4FHUJC48vxG/Hf8OXbb8scRnqdHrPajyLvocOg75GRloqQg6sR+yj2+g2fAY0NP+9wlzA+1leHVh2FpYOJvh4fmfo6CsP/JiRloldC07hwPKz+HBaW5XKqUxxsGP5t4BEUuDnfGzrwrz/SCQqn2jY+HckjoQ/Rk9/Z8gzsrBs/zXcfPwCY7t6qXTcN6njewCoGDHAJFNFUg0NNArsj1r1WuDwpp+hc+4gWvUaA0MT1btcvOJmb4qFI5tiy6k7ih+Zq8e2xs1HL5ArCHCxM1Fc4VKF3wd14OrniIPLz+LKsTtoO7whbGuaK+7FajXIF26Nq6lcTt/6fdGsZjP8cuwXHLh+AGNajoGrtavinsxPmn6C5i7NVS5HHWrVawFHN1/8vWM5NvwYhDZ9v0CvoLl4dDscubk5aNCmH2wc3VQup3ltO/jWsMTKgzfw+bJTGNPFC7MHmePy/Xjk5Aro3awGXKuaqlyOumKgRa0W8HX0xfJTyxH0ZxC+CPgCc7vPRfjjcOTk5qBf/X5ws1H9fVOXBm36wsW7GY5s/gXXzh5A615jYFPNNe+eTCEXzbt9Ahdv1WK6d9OaaOphi4W7r+JQWBQ+7VgHtexMcflBPIRcAR8HuqOZh+r3fDIGSF08/Tvh6YMIdBo8GVVs/oupn8a0Qc/R82Bu4yh6mcevRmNYWzfFFQxHy7wrSptO3lE5yXT2toNEKkGn0f6QmfzXJs/usQ4f/9wFFg4mKh0fAE7ePolZ3Wahjl0dpfUaUg3Ud6yP8W3H4+sdX1eI5AIA7lw5jfYfTVKMZVHDqwm2L5mE7Uu/xgeffJ+3k0RS+AGKqPv3b+99UVBCUBKPbsZhyNyO+RJMANDR10bzD+thzcS9KpdTmeLA0a0+JFIp2vWfAJnRf79jfhrTBoMmrRDte+DY5cf4opsXGrnmdb9t422Pb9adxY/bwjCue11RygDU8z0AVIwYYJKpomdP7uNF7COY2zqi92c/4trZA9j08+fwbd0L3s26iVaOhlSKfs1d0Ly2HX7ddRmHw7TwaafaMDdSPbl8nam1IfpOaYOrf9/F+m8OoEFndwQMqQ+JCF/yr7M1scWc7nNwOOIwxm4Zix71emBU81Gil6MOOnoGaNsvGI/vXsX+dbNRzdUXTToPhZa2uF0UZLpa+LyrF649eI652y6hXg0LDG7tqugyKxZ1xYCBjgGCA4JxNfoqZh+YDV8HXwz1H1ohuvcUxMTcFj2D5uDG+cPYtGAsfFr2QIvu4sa0jZkMswY1wtHwR/hy1T/4wK86PmnnIfpnwxggdWjbLxiR4afwv8UTUD+gL+o1/6DUynoVunGJafBwUL5Xyd3BDDEv5CqX0XdKG5zbdR2rx+9BuxGNULO++PeUCoIALY3C7yHVlGqKljCpQ3pqktJYFvoGxug1eh7+t3giti35CoEfivMDOSs7B50aOMHJquABy2IT0/HH8Vsql6Orr4WkuFRYViv4pG9iXAp09VW/B7gyxUHPoDm4eGwr/pg3EgG9P4dzncalUs7z5JdwtPqvm7JdFQPMG+qPiWvOYM7/LuHjQHdRylHH9wBQMWJAtRsQ3nMXjm7BhnmjcP7IJmz86TNcPr0HtRu1Q//xi/H0QQQ2/BiEZ9H3RCnrQWwyTl1/gpxcAT8MaYxGrlYYt/IfUe7De1NaykvUae6MofM6Ifbec/w+cS9iHySIXk5SehLauLXBkv5LcCfuDoL+DMLdZ3dFL6e0pcuTEBN1Cxa2Tvjoq+XQ1tXHuh9G4N71s6KWk5yWicjoRDhZG2Hxp82hr6OJTxf/jfO3YkUtB1BPDCSlJ+FW7C04mTthef/l0NfWx4g/RuDsPXHfN3VKlyfBvUEbDJiwBLGP72DDj0GIixY3ppPTMtG6rj1+G9kMd58m4fNlp3AvJknUMgDGAKmHS92m+DD4N9y+fAr/WzQR8mTx2xoA2HfhIf4KuQdNDSmS0zKVtqVnZkNLpGnAGnbxQK9JrXBsXSj2LT6DrIxsUY77SqPqjTD/yHzcjrudb9vtuNtYcHQB/Kr7iVpmaTI0tUJCrPI9bDp6MvQKmovsrEzsXDFFlHKcbYxhYayLtvUcCnw0dhNncJm6bVyw65dTOLfrOmIfJCD1RTpSX6Qj9kECzu26jj0L/4F3oOojZle2OPBt1QvdRnyHv3euwKE/f0JW5kvRyzA10MWTBOWTSRbGepg7tDEioxPx47Yw0coq7e8BoGLEAK9kquDCkc3oPmo2HFy8kfQ8Bv9bNBFeTTpB38AYHQZOwoOIi9i1ajqGTVmrUjn/++cu1h6JgJOVEaIT5BjWxg0d6juiYS0rLNt/HUfDH2NsVy84Wat2I/n9y0+wY/5JpCW/hKGZPrqPb4FOnzXBg6tPseOnk6jhWxXN+tZVDABSUqEPQ/H9vu+RmJ6IKgZVMLXTVEwInICwqDB8t/c7NKreCIMbD1YMBFSe3bhwBIc2/ghtXRmyszLQYeAk+HccDFefVji8ab6i66TMSLVRvo5dfoyfd4RDX0cLmdk5mNCjHga2ckWLOnb4ddcVHAyLQlDHOjAzVO0KkLpi4EjEEfx4+EfItGXIyM7ApPaTMLjxYLRybYX5R+bndaNuNaZcjI5WFA9vhmLP798jPTURBsZV0HnYVLQfMAFRkWHYs+Y7OHs0QuOOgxUDAZXEpbvP8MOWUCSlZaKKoS6+6euLcd29EX4vHrO3hKKBixUGtnZVDARUUowBUjdDUwv0/uwnnDu0EWt/GCH6PXgWxnrYH/oQAKClKcXtJ0lKo4iG34uHvbl4Iz5aV6+CoT92wpHVF7Dyi10QIN7rGdNqDL7f9z0++eMTGOoawkTPBACQmJ6I1IxU1K9WH2NaqT7goLo4uvrgash+VPdopLReW1cfPT+dg62/jRelnAYuVkh9Wfho/4b62mhTV/UrTs0/9IaWribO/nUNR9ZcUPT+EAQBBiZ68OteG34f1HnHUd6tssUBAFjZ18RHE5bi+LZFWDt7uOjfA3Wrm+PElWjUc1YeK8XcSA9zhjTG+NUFD0BUUqX5PQBUjBhgkqmCvMvQeV8gkgKG3nZ088XAr5arXM7WU3cw86NGqFvdHLEv0vD12rPoUN8RxjIdTOhZD6F34vDdpotYNVa16SsOLj8Hvw9qw6e9K+6FRePw6vMYMrcTHOvYYNhPnXF6y2WsDN6FUYu6q1TOr8d+RZ/6fdCtbjdceHABi08sxuIPF8PbwRvLBizDurPrMHz9cKwbsk6lctTh1K4VCOw/AW6+rRATdQsH/piLGp7+qGLtgL5jF+Dy6T3Y8NNojJi+UaVyVh2KwLgPvNHC0w6R0Yn46a8w+LlZw8HCED8O88e+Cw8wdvlprBun2uAy6oqBFadXYELbCWjl2gq3Ym9h7sG58Hf2h4OZAxb0XoA9V/Zg9KbR2DhMtfdNXY5s+RUNAvqgbrNueBBxAce3LcaA8Yvh4OKNgROXIWT/Oqz7YTiGTSl5TC/afRW9m9ZA54ZOuHg7Dsv2XcOvI5uhbnVzLPq0OTYcj8SoRSewemxrlV4LY4DKgkQiQaPA/nB080X03asqn5h73R9fvn16L9eqpvB0FG8cBQDQ0tFE+1F+iDwfhYdXY6BvJM5JUyNdI8zpPgcPnz/E9afX8UL+AgBgKjNFbdvacDBTbcRKdWvccTBSk54XuE1HT4Zeo+ch9lH+KzXF9WELl7dutzTWw5c9vFUuBwAad6+Dxt3r4EVMCuT/TmEiM9ETdW7hyhYHr2hp66Btv2DcufIPoiLDoSczFu3Y/Vu4ICo+tcBtFsZ6+HGYPy7deSZaeUDpfQ8AFSMGmGSqoH5Ab2xf8hUsqtbAi7jHaNp5WL59VLly8YogCIr7SSTS/PdE+dSwxOIg1QfKSX2Rhhq+VaGlo4nq3nY4suaCYpumtgZaDKgH96ZOKpfzXP4cftX9oKOpg/qO9bH47//mDtTW1MbHTT5Ga1fVfiirS1bGS5hZ5Z39NDG3RXZmhtJ2ryadVB52GwBeZmWj6r9n2m3NZMjIylHa3qG+I/zcbFQuR10x8DLrJezN8t43W2NbZGQrv2+dPDvB31n1901d5MnPUb22H7S0deDoVh/Ht/8X05pa2mja5WO4+qoW0wmpL9GglhV0tDTgW9MSy/ZfV2zT1tTAkDZuaOlpp1IZAGOAypa1Qy1YO6jenbA43B1K72q5SwMH1KxvL/r9zNWqVEO1KqoPwFXW9GRG0JMV3gtLR0+mGBSoojG1NhQ1sSxIZYmDN9Xw9Bflt9PrrEz1YWWqX+h2cyM9tK1XOomZSwMHlacsKUx5jgEmmSpoENAXjm71/x34pzqqWJdOAPVqUgOT151FdRtjRMenYkib/CMuqtpFDgBqNrDH9rknULO+PR5FxMHZJ/90CIXdzF4cjZ0bY9ruafBz9sO16Gto6Ngw3z5O5qr/iFUHj4aB2LZkEhxqeiEmKhLuDfKfMX99tLSSauNtj8nrz8LTqQpuRyehtVf+bj2mBqqf0FBXDAS6B2LSX5PgVdULkbGRaOOW/30zlalejro412mMXaumwbmOH6LvXkN1j/wxbWGrWkw3crXGzD8vws/VCtceJqCBi2W+fV4f1KCkGAOkTpf+/gsxD2/Cyb0h3Hxb4fr5Qzh3cCMEQUDNuk3QpONQxZyJqsjKzsWZiKeIePQCCal593uZGejCzcEUjV1toKVZekNU/NBzPYYv6AJzexNRjpeVk4XTd07jxtMbSJD/OwG7zAweNh7wr+H/1sFAyqPnTx/iyYPrsHWqjSrWDngeE4XQ4/9DTk423OsHoFqteqVW9sCfjmDWoEaKk7ilLTlejpN/hqHTZ01UPlZligN1xUByWibuxSSjurURjPS1kSTPwIHQKGRm56JZbVtUsxTnpEBWRjZi7j6HroFOvtFkszKyEXHmATxbqj7tXHmPASaZKrK0c4alnXOpltGraQ341LTAo/hUOFkZwcGidM6MdQzyR9ihSDx/nITazaujboBqw7kXZnzb8dh9ZTeiEqIQ4BaADrU7lEo56tCyx6ewr+mFhNhH8GjUDk5u9UulnJHta8PL0RyP4lPQ1tsBvjXzJxhiUFcMfNriU3jZe+FRwiO082iH+o6l876pS7v+43H59G48j42Ce/0A1PETP6aDu9XF3gsP8Cg+Fa3rVlWaRFpMjAFSl5D963H+yCY4uvni+PbFSE6IxYWjm+HTsickEglCj/0PGlJN+HcaolI50c9T8fXas3ie/BKu9qYwkeWdkLvzNAl7zj+AuXEEvh/YCHZVVEs0Dq8+X+B6QRBwZttV6P3bVa7N0AYlLuPxi8eYuH0i4lPj4WbjBlP9vBMxt+NuY9flXbAwtMAPH/yAqqaqz5mrDveun8OO5d9CW0cPWZkv0XX4DOxf9wMsqjpDEHLxv98moOfouSonGX+FFDwIY1xSGg5dioLpv+MZfOCn+hzAb5OekoErx++qnGRWpjhQVwzcfPwCk34PQVpGNmS6WvhhsB++23QRGlIJcgUBW07dxvzhTVDT1kSlcp5HJ+HPaYeQFC+HRCKBvZsluo1rDkOzvKuoGWlZ2LPwH5WTzIoQA0wyVbB9ydeo5dMCLnWbi9It9m2qWxujurV4fdMLoqmlgfodS39eOi0NLXT3Vu1+rvJE7C4dhfFzs4YfxBkBrzDqigEAeV0hS/f8jNpoaGqhXovSjWktTSm6lfIPIIAxQOpz7ewBtB8wES7ezRD3+A7WzxmJ9h9NVPQIMbNywMkdy1ROMhfuugJHS0Ms/rQ5ZLrKZ/blL7Mw93+X8Nvuq5g9WLWRGM/vvgErJ7N8cyQKgoDn0UnQjNdUecrHBUcX5I3GPGA5ZDoypW3yDDlmH5iNX479gnk95qlWkJqEHFiP+gF90LTzMERcPIa9v3+Puk27oGmXjwEAJ3cux7lDG1VOMJbuuwZzI11I37jlSBCAI+GPoaEhgQQSlZPMyPNRb93+IiZFpeO/UpniQF0x8PvhCDT1sMUn7T2w78JDTN94Hj41LBH8QV0AwE/bw7DheCSm9S/5SSAAOLYuFBbVTDH0p854mZqJw6vPY92kfRjwXTsYW4h3xbwixACTTBXcu34W9yMu4OiWX+Hq0wqe/h1L7V6SnWfv4dbjRDRwsUILTzscCX+ETX/fhiAA/u7WGBTgCo0CBh8qjrM7rsG1sSNMLNXTbSQuJQ4GOgbQ11buI5+dk43rT67Dy95LLfVQ1d2rZ/D04U04udWHnXMdPLx1CRePboEg5KKmV1N4NeksSjkZWTk4fiUa1x8+x/OUDEgkefdn+rlZ5xstTRVpKS8R9+AFLB1NoW+oi7Tklwg/chs5mTlw83cUrcvX2yTIE7D7ym4M8htU6mWVBkEQ8Oh2OF7ERcPA2AyO7g2goaH6121Zdvlb9Mn/0HdqG1SxLd2TXa9U9Bigd5MnP4d1tbw207JqDUAigUXV/87uW9nXRGpSvMrlXI9KwMKRzfIlmEDe/MODA9wwZtlJlctpOaAewg5FImBwfTh6/neP/Owe69DpsyaiTMJ+7ck1LPlwSb4flQAg05FhaOOh+PTPT1UuR12eP32ADh99BQCoVa8F9q2bDRfv/8aYcKvfBtfOHlS5nA6+1XDz8Qt81ctHqUtk+ym7MWtwIzhaqn6rAQBsnX0MEonkrfMTinF/bmWKA3XFQOSTJIzqWAcyXS180Lg6Vh66gQ71/7uXsUsjJ0z9o+DeCMXx+GYc+k8PhL6RLvSNdNH7m9Y4sPQs1n29HwNmtlN5ZPZXKkIMMMlU0aBJK/Ag4gKuhezHlTN7YWHrhDqNO8LNt/Vbb2Yvjg0nIrH11B341LDA0v3XEJuYhq2n76B7Y2dIJMD2M3lzfw1s7apSOUfXXsSx9aFwrG0Dr4CaqNXIAZoi3Ov5puepzzF552Tcir0FqUSK1q6t8XnrzxXJZvLLZAT/LxhHvzgqetliu3x6N45s+RUWdtVx6cR2tO49Bkc2/wLXei0hkUpxfNsiZGdlwKdlT5XKiX6eiolrQpCZlQMtTQ3EJ6ejvoslbj1+gd3n78Pf3QZf9/ZR+UTDk8hn2DjtEDLSs6Ar08aH09pi+7wTkGpIkZsr4Mxf1zBwVnvYOFdRqZx3SZAnYN3ZdRUmwdi2+Ct0GjIZOnoGSJcnYdviSYh5eBN6MmOkpyXD1KIq+n3xC/QNTUpchrq6/J3fc6PA9Unxclw5egcyUz0AQINO4kxcXZiKFgNUfPpGZoh/+gBGZlZIiHsEQchFQsxDxf3Lz2MeQN9Q9ftyZbpaiHmRVug9yzEv0gpMQIurcQ9PONaxwc5fTqGmrz1afuQDDZFP/BjoGOBp0tNCxy14mvwUBjrqOVEsmn+TLqlUCk1NLejo/fejWVtHDxnpBY8IWhyfd/XC6etP8fXas+jd1BldG5VOrxADU320+6QRajUs+HaGmHvPsfrLPaqXU9niQA0xkJ2Tqxi/RFNDCl0tDRi/1uvAWF873xy6JSonMwdSjf9OJEgkErQf5YcDy89i/eQD6PZFU5XLACpGDDDJVJGezBi+rXrBt1UvPH0Qgash+3B69yqc3LEMNTz9UadxR5Uv8R+6FIVx3euiqYct7j5NQtCSvzG+uzda/zunk725IVYeuq5ykgkAHT9tjMhzUdi14BR0ZFqo3aw66rZxEWWgj1eWn1oOiUSCxR8uhjxDjuWnliN4azDm9pgLI928HwFvOwtYnoSe2I6A3p/Dq0knREWGYdvir9Ci+yh4N+sGALBxdMOFI5tVTjIX772G+jUtMaaLJyQSCTafvI0rD57j15HN8Dg+FZPWhmDDiUgMbKVaDJzYEAY3f0cEDKmPSwcjsfWH43D2tkXHoLwuwXsWnsbpLZfRa5Jq0+XcfXb3rdsfvXik0vHV7f6N88jOyoKOHnB692pkZqTj42l/wMTcFskv4rBj+bc4vWc12vYLLnEZ6uryd3jVeRhWkeXvVpYr4OqJu5BqSCGRqJ5kVrYYoOJz822N/et+QA1PfzyMvIQGAX1w4q8lSJcnQyKR4OzBP+BSV/WR09v7VMPcbWHo38IF3s7mihM0ifIMhN2Nx8a/I9G1kTiDzdm6WGDoj51xcNlZrP5yN7p+0UyU477SoXYH/HDgB3zU6CPUc6inuA/rRdoLXIq6hD/O/YEP6n4gapmlycjMCi/iHsPUIm9k7A+/XAQjUyvF9uQXcZAZi3NSs4mHDWpVNcG8bWE4dysOX3avK8pxX2fjXAUxd58XmmS+6ypnUVWmOFBXDFgY6eFpghzW/44w+3UfH6W5xZ+nZMBYpl3Y04vMvKoxnt59nq/XV7sReXPBbpl1TOUygIoRA0wyRWTj6AYbRze07BGEm6HHcTVkP7b+Nh5fLlTtilxCykvUsjMBADjbGEMCCZxt/uuyVtPWGM+TX6pUxis1fKrCq3VNyBPTceX4HVw+cgcX992EjXMV1G1TE+5NnPLdb1JcoVGhmNllJlyt8xKihXYLMX3PdIzbOg4/9vwRgDjdSdQh+flTOLnnDVji4OKN3NxcVK3xXzdfB5e6OLrlF5XLuXI/HkuCWijel+6NnfH7kZtITstEVXMDjOpQG0v2XVM5yXx6Nx5tP24AHX1tNOjsjuPrQ+Hd5r/5xXw6uGHrLNWvMA9fP7zQxvbV+ooSA2+KigxH824jYGJuCwAwMrVE864jcPDPn1Q6rrq6/NVrWwvRt5+h2xfNlBrJ2T3Wod/UtqJ0+QMqdwxQ0fh3HAJNLR08vX8Dno07omHbD2FhVwMndy5DVmYGnGv7qXw/JgAMCnCFrrYGtp6+g+UHrittMzXQQZ+mNdC7qXgDXOnoaaHL2Ka4fuo+Nk49JOpJ06H+Q6GnpYfNFzdjyd9LFH8jgiDATGaGfvX7oW/9vqKVV9rqNu0KQchVLL85Cvf9G+fgUFOc+SuBvPkQ5wzxw6aTt/Hp4r8hQNwT2o26eSArI7vQ7aY2hhgws53K5VSmOFBXDDT3tEWS/L8rlQ1rKY9xcfZmDGrZqX5BxaWhA66fuoc6LfIPONBuRCMIuQIuHbylcjkVIQaYZJYCLW1d1PFrjzp+7ZEQ+/abwIvC1EAXD+NSYGmij8fxqcgVBDx8lqLo+vMwLgUmIkxf8TqZiR78PqgDvw/qIOp6DMKP3Mbh1RdwePUFTNg0QKVjyzPkMND97xK+tqY2ZnSZgWm7pyF4azC+af+NqtVXG12ZEZISYmFkZoWUxHgIuTlIeRGr+JJMeh4LXRG6TRvoaiE987+GKyMrB7mCAI1/rzY5WRkhISWjsKcXWU52LjS187qTaGhKoaWjAT2j/8706RvpIF2Ecox0jTCi2QjUsy/4Kv+D5w/wzc6KEwfAfydGXqalwPjfBPMVEws7le8tU1eXv/aj/HAz5CH+nHEYft1qw7eUBgGqjDFAxSOVSuHXTrk9cfNtBTdf1XpKFKRPs5ro06wmnibIkZCa9x1mZqADG7P89zOJxaOpE+zdLPH07nMYWYhXTr8G/dCvQT88SXyChLR/py3QN4Otie07nln+1G3a5a3bm3UZLnqZEokE/Zq7wKeGJa49fI4qBrrvflIROXi8fXA+bV0tVKstzgB+lSUO1BUD7zoJ3695zXw9eErCv6fnW7e3H+mH9iNV63H0SnmPASaZKqhawxMamm9/C82sVJ9moJWXHeZuC4OfqzXC78Wjd9MaWL7/BlLSsiCRABtP3EbT2jbvPtA7FHbVwMHDGg4e1ggc3hA3Tt9XuRwbExvcj78Pe9P/5nrUkGpgWudpmLZ7GibtmKRyGepSw9MfBzfMg0fDQNy9egbuDdri+LYlACSQSCT4+6+lcHRVfWqGejUssGz/dYzp4gktDSlWH46As42xIqmIS0pXdAFThVEVGRJjU2FilTcwQrdxzWHw7z14AJD6Il0p6SypmlY18Tz1OayNC25sUzNSK0yX6Vf2r/8BGppayM3JRvLzp0pnY+XJCdDVU23qIXV2+XP1qwZbF3Ps/uU07oQ+FmVOtzdVxhgg8ZTWlWwbM1mpJpZvMjKXwchcViqxbGtiW25+TJaG0u7N4GJnApd/e4mpQ2m9nsocB+rs0aKrXfop0fsWA0wyVdB37AK1lDOwlSu0NTUQ8egF2vs6oG+zmnC2NsaKgzeQkZWNRrWsMUiE+zHf1Qjq6GvDu63qo+c2cmyE3Vd2o1lN5XtVXiWaU3dPRXyq6iMKqkOzriOQk52FW6HHYFvdA617jUHoiW3Ysfxb5OZko2oNT8Uw3Kr4ONAD0zacx/BfjwPI6/Iz7cP/htlOkmeiVxPV54Jwb+oEedJ/Xa9r+torbb99/hFsa5qrXE4Xzy5Iz0ovdLulkSUmBE5QuRx18WgYqPh/DU9/ZGUqX+29HX4KllVV+3zU3eXPqIoMH05vizPbrmJV8C7Ru5VVthig4svOysSp3asQ8/Amqns0QsO2/RCyfz3OHdoASCSoUacx2vQNVhoEpCS+XX8OzevYoqmHrWLgj9KQnZmDExsu4cnteNTwrYrG3evg9JbLOLP9KgCgZn17dBjlp/ItJ6+LT43H7iu7EZ0YjSqyKuhYpyMczEpnDt3SUGgMHN4IAKLFwJvik9Ox78JDRD+Xw8xQF+19HUSZg7wsYgCo2HHAGBBHeYwBJpkVgFQqwYctXJTWtfC0QwtPO1HL+eavwaIerzDDmgzDy6yC7yHVkGpgeufpeJbyTC11UZW2jh4CP/xSaV2DgL7wbvYBcnOyRftSNDXQwS+fNMXj+FRk5+TC3sJAaSTZZrXFOYPVrG/dt2737+Upylm4pjXfPrqaka4R2nmoft+KurT/aOJbt/t1GAiJRPVRJtXd5U8ikcC/pyeq17XFo4g4pavaqqpsMUDFd2rXStwMPQZX39a4fu4gkl/E4t61ELTtNw4SqRT/7FmD07tXoXXvMSqVcz4yFhdvx2HRnqtoUccO7X2rlcoVrON/hOLG6QfwaOqEK8fuIPmZHLcvPkL7UX6QSiT4+88wnNgQhsDhDUtcRrtf22HTx5tgom+CB/EPMHrTaJjom6CGRQ2cvXcWOy/vxKJ+i+BsUTEmoC00BvoGixoDnafvwfov28BEpoMHccn4YvlpGMu04WxjjPO3YrHn/H388klTlecjV0cMAJUrDhgDJVMRYoBJZik6uWsF5Mkv0H4Az8a/TkOqUeC8Pq9vL6wLXUWhpa0DQNz7ZAGgqnnZDkct1vxO7xttHfGSM0D9Xf5sapjDpobqV7CJXhcZ/jc6DJyEaq4+8G7WFSunf4Suw2egpmfeaNZ6MmMc3Pijyj8uAWDJ6OYIvf0MBy9FYd/Fh3CyMkJ7Xwe08qwKQ5GuKNwMeYgunzeBk5ctfNq7Ysmn29FjYkvFSKN6RrrYt+gflX5cZmZnKnodrfxnJbyqemFGlxnQkGogNzcX3+//HqtOr8KsD2aJ8ppKm7piIDM7F686a605FIE6jlUw9cP60JDmTc/1w/9CsebwTcz8SLUf/uqIgbzXU3nigDFQ0tdT/mOg9GbuJqQmxiP5+dNSL2fXuftYf0z1kare5da5KFw5fqfUy/nnzj84eF31iXfLg9tX/sH1c6X/Ws5EPMXhsNKf8oExUDLqiAPGAFU06alJMLWsCgAwMbeFRCJVjMoMAKaWdkhPTRSlLGN9HfTwd8byz1ril0+awtXeFL8fuYkP5x3CrC0XEXZX9d4zackZMLPNG5jL1NoQEokEptb/db8zszGEPFn1gdNeuR13G318+0BDmtcFWCqVom/9voiMixStjNKmzhh45c7TZPRqUkPRG0gqlaB3k5q4/UT1ctQdA0DFjwPGgOrKawwwySxFHQZOQp/Pfy71ck5fV8+Py+PrQrFn4T+lXs7yU8sx99DcUi9HHU7uWI79f5T+a1l1MAI/bg8r9XIYAyWjjjhgDFBFY2hqheh7efcXP30QAYlEgpiHEYrtT+5HwMDEQvRyXauaYmxXL/w5oS1Gd/bEs6SX+Or3EJWPa2wuw+Obecnqk8hnkEiAJ7f/G18gOjIeRlX0VSpDIpEoblmQQJKvV5BMW4aUlykqlaFO6oyBV3d6SCSATFe5V45MVxOp6Vkql6GOGAAqVxwwBkqmIsQA+75VAnOHNlZLOSMXqWdS17VD1qqlHHUYNkU9r2XVWPGH/C8IY6Bk1BEHjAGqaLyadMKBP+bgasg+xEZFosUHI3Fq92okxD6GRAKEn9oF39a9S618XW1NBNZzQGA9Bzx6lqry8bwDXbBn4WlcPnIbT+/Go/WQ+vh7wyUkPEkCIMGlAzfRsGttlcoQBAEfrfkIEkiQnpWOu8/uKt1z9STpCcxkZiq+EvVRZwwM+fkoJBIJ0jOycS8mWeneuycJcpgaqn6LizpiAKhcccAYKJmKEANMMkuRPDkBl0/vRuMOg8q6KkREROWKb6te0Dc0xdP7N1DHrz3cfFvD3LY6/tm7BlmZL+HTsicaBao2LzMA1HGsAi2Nt3fcsrdQ/X73hl08IDPWQ3TkM3i1rgGPZtVhWc0Uf28MQ1ZGNhp08YB/r7fPofcub464bGeiPADgjac30LTG2wfVKk/UFQPjPqirtGz7xj3tEY9ewN9d9ang1BEDQOWKA8ZAyVSEGGCSWYrkyQk4s3+daElm2N1nuPYwAQkpLyGVSmBjqo9GrtaiDQZzM+QBnOtVVdvgLhFPI3D96XUkyP+dQFZmBg8bD7jZlM7k7+q0+Zcv0G7ARBhXEWcAo1PXn6B+TUu1zOP0phcxKXgRkwIDUz1YVjMt1bK+2PIFJgZOrLADPz19EIEn969DnpwX0zIjM9g6ecDGUbyYzs0VCpwwOjdXQHxyOixNVO+GIwgCEmNTYWwhg1RDiuysHNw6G4Wc7BzU8KkKfRHmSi1MRY8BKh73+gFwrx+gWHZwqQsHl19ELePHYf6iHu9tajevjtrNqyuWq9W2xsBZ7UU7/rtGXB7YaKBoZamLOmKgbb23T+UwoKXq07O9UtoxAFS+OGAMFF9FiAEmmSqIi7771u0JseLcJ/kiNQNT/ziHW9GJkEokyBUE1LAxxj83nmLloRvo0dgZw9t5qFzOtrknoKOnBbcmTqgbUBN2LuLfCwMAL+QvMHX3VFx7cg2WhpaKy/kJ8gQsTlmM2ra1Mb3zdJjKSjehEcOdKwXfm/b4zlXcvRYCI1NLAHlzJ6riu00Xoa+jiWa1bdHOpxrc7Evnvdm/NAStBvlCR08LWRnZ2PXLKdw6G6WYQNjBwwq9vm4NHT0tlcr5527B79vV6KsIuRcCS6O8983fWX0/DlUhT36BnSun4sm9azA0tYTMyOzf9QlIebEYttVro+vH0yEzKvnnJn+Zhfk7wnHuZiz0dTTRsb4jBrRyUQxckCjPwEc/HcHBmV1Uei3Po5Pw57RDSH6eBhMrA/Sb2hbb553A8+gkCAKgpaOBQT90QBVb1YZ5r2wxQERERP9hkqmCdbOH5909LBQwSfmr9SLMKbh471WYGepi+zftoaUpxYoDNyB/mYVFnzZH2N1n+H7zRVQx0kX3xqrPhdOwa21EnovC74cjYWFvAq+AmqjT0hn6huJduVhwbAFyhVz8Pvj3fBPFRiVEYd6heVhwbAGmd54uWpmlZcfybwuNgWNbF+b9RyLBlwuPqlxWD39nnImIwYHQU3CwMEQ7Hwe08baHkYgT+oYdikSzvnWho6eF01suIzoyHh9Obws7FwvE3HuOXb+cxj9br6DVQB+Vyvl257eQSCSK4bdft/B43vsmkUhw9AvV3zd1OLJlAQQhF0O//R1mVsoxnRAbhQN/zMORLQvQ9eOSx/TaozdxPyYZE3rWQ+rLLGw8EYnbTxMxtV8DaGmKN4bbsbUXYeVkht7fBODy0dvY/N0RVLE1wuA5HSHkCtg+7wROb7mMrmObqVROZYsBEp+6pgFbfegGXqRmYFx371It5/j6UMgT09HpsyalVsaK0yvwQv4iX1e6iooxUDKVKQ4YAyVTHmKASaYKdGVGaN5tBBxc6hW4/XnMA2xf+o3K5Vy8HYefhzeBTDfv6tHQtm7o/t1+BHWqA29nC4zsUBt/nrgtSpJZL9AFTft44emdeIQfuY1Tmy/j+PpLcKlvj7ptXVC9ru27D/IOFx5cwC99fsmXYAKAg5kDRrccjS+2fKFyOerg6FYfEqkU7fpPULpK9dOYNhg0aQXMbRxFK6tjfUcMaFkLkdGJOBD6EH8cv4XVhyPQyNUKHXyrwaeGpcplvP6D//bFx2g90AeOdfLuUbB3s0KbofVxdO1FlZPM+o71IZVIMaHtBKUr1m0WtMGKASvgaO6o0vHV7cGNC+j7xS/5EkwAMLNyQKteo7F5gWox/c+NGEzo6Q0vp7z5Kv3drPHt+nOY8sc5TB/QQKVjv+7xrWfoN7UNLB1N0by/N87vuYGOnzaGxr+JrF/32tgx/6TK5VS2GCDxpSbGI+VFXKmXE5/8Es+S0ku9nJTnaUh+Li/VMuJT4hGXUvrvmbowBkqmMsUBY6BkykMMMMlUgZV9TaQmPi/0vruM9NSCr3IWk6aGFHjtguirLrM5uXnH9nAwQ0ximsrlvO7V5OsBQ+rjZshDhB+5jU0zDsPIXIbRy3uqdGxtDW3IMwr/A0vPTIe2hnhX50pTz6A5uHhsK/6YNxIBvT+Hc53SH+nXxc4ELnYm+KR9bZy6/gQHQqPw9dqzsDTRw/pxbVQv4N+r76kv0mDxxj2Ylo5mSI5XPdbmdJ+DraFbMXLjSHze6nM0dlbPCMmlRUNLGxnphcd05st0aGipFtPJaRmwNNZTLBvLdDB7sB++XncWk9edwxfdvFQ6/iuZL7Oh9+8Ie9q6WtDW1YSB6WvlWhhAnvhS5XIqWwyQ+DoMnKSWcib0LPhEsdi6jC39QTgmtVfPe6YujIGSqUxxwBgomfIQA0wyVeDVpAuyMgs/62Foaol2Ilzer13NDOuP3sKXPbyhpSHFmsMRsDHTV3STTJRnwlDFe+QAKObbeZ2WjibqtHBGnRbOSHiSjCvHVJ+EvUWtFvjhwA8IahGEeg71FHP7yDPkuBR1CYv/XoxWruqZjkEMvq16wb5mXexdOwt3r4WgZY8gtZSro6WBgLr2CKhrj+jnqTh0SZx7gE9uDIOmjgYkEglSX6QrDfaTnpIBLR0NUcrp5dMLde3rYta+WQi5F4KgFup530pDrXotsH/9D2jZIwjVatWDjl5eTGeky/Hw1iWc2L4Ybj6qxbSlsT4ePUuFzWsj4sl0tTB7kB8mrQ3B9I0XVDr+Kwamekh+Jofxv6NtthroC33j/7rLy5NeQs9AnJNAlSkGqGTSUpNwLWR/gQNm1W7UDvqGJqKUkyTPwMFLUbgR9QIvUvMmQjc10IG7gyna1nOAiUz1qQsAIC35JS4fvY3HN59Bnpj3+0BmooeqrhbwbFUTMmPVbz1JSk/C/mv7cf3JdSSk/Ttwnr4ZPGw90M6jHUz0TVQuQ50YAyVTmeKAMVAy5T0GmGSqwKXu289G6MmMULvR20d/KooR7Tzw1e8h6P7dfgCArrYGJvf1VWyPepaCNt72KpdT0L1RrzOzNUKLAaqf6fm0+afIFXIxc+9M5Ag50JTmhWF2bjY0JBpoX7s9RjYbqXI56mRlXxMfTViK49sWYe3s4aJcwS4OuyoGGNJG9RFMHTysER+dBAAwtzdB0htzx90NfQwLB/EGHappWRNLByzFohOLMHz98HfGYHnVsvunEHJzsWfNTOTm5kBDIy+mc3KyIZVqoI5fezT/QLWYrlfDAgcvRaFBLSul9fo6mpg1sBEmiTCZPABU97JFfHQS7N3zyvFp76q0/X54NKyqizf3VmWJASq+pw8i8L9FE6GlrQOHWj4wtcxrx+TJCbj093acP/wnegTNgU0113cc6e1uPn6Br9eeha6WBrydzRUjsr9IfYmdZ+9j86k7mDWwEWpVVe277UnkM/w54zC0dDTh6GmDKnZGAIDUF+m4sPcmQrZfQ98pbWBb07zEZUQ8jcDE7ROho6UDHwcf2JvmvWcJaQnYHrYdf174E3O6z4GrtWrvmbowBkqmMsUBY6BkKkIMMMmsAGzMZFg2ugWuRyUgKzsXbvamMH7tbEvgO4ZlLqqgZT2VrliUFm1NbQQHBOOTpp8gMjZS6eyLi5WL4spmRaOlrYO2/YJx58o/iIoMh55MtdE3X7cuOAAmstLvQvzRd28/KeLRrDo8W9UQtUwdTR0EBwTjn7v/IPxROIz1xHvf1EVTSxtt+wWjebdPEBN1C2kpLwDknYm1sndRXNlUxcBWtfA8peBuqjJdLcwe7Ic7T5NULqf9KL+3bnfzd0KdlowBUt3RrQtRq15ztOkbnK8njSAIOLxpPo5tXYj+Xy5SqZzFe66iWW1bfN7Fs8Byftl1BYv3XsMvn6jWje3gynNwa+yI9qP8Cixn/5IQHFp5DoPndCxxGQuPL0Rzl+YIDij4PZt/ZD4WHl+IRf1Ue8/UhTFQMpUpDhgDJVMRYkC8IQnfU5f+/gv71s1GxMVjAIDr5w9h9czBWDVjEE7uWoHcnBxRytHV1oRPDUs0crWGtpYGDoRGYc3hCOw8ew/JaZmilHFu53U8uhEryrGKQqYjg7eDNxpXb4yM7AyERoXi0I1DSEpX/Yeyuhzd8ise3bmitK6Gpz9a9QxSabqKN1mZ6it9iaRnZpdKDBxccQ5R12MK3W5qbQgjc/FPAqRnpiMpPQk6mjo4EXmiQsUA8F8c6OjJUK1WPbj5toabb2s4uHiLkmACgKG+NhytjBTLb8ZATq6gGBRIFUWJAUMz1efifFNFjwEqvmfRd+HTsleBt2pIJBL4tOyFuMeq36JxLyYZ3RtXL7Sc7o2r464IJ2jiHrxAgy7uhZbToIs7Yu8nqFTG3Wd30cun8Pesl08v3IlT/T1TF8ZAyVSmOGAMlExFiAFeyVRByP71OH9kExzdfHF8+2IkJ8TiwtHN8GnZExKJBKHH/gcNqSb8Ow1RqZyPfzmG+cObwEhfG3FJ6QhecRryl1moam6AJwlybDgeiV8+aap0r1ZJXNwXgdD9N2FqbQivgJrwbFlDacAPsQz+fTB+6fMLjPWMEZcSh883f46UlymwN7XHk6QnWH92PX7r9xtsTVQfyba0hZ3cgbBTO2Fibos6fh1Qu1GgYo5EMb0ZA+NWnkZqeuWJgTGbxiA1I7VCxgCgnjhgDFBlIzMyQ8zDCFSxLrg3TszDCOgbqn6yztRQB7ceJ8LBwrDA7bceJ8LEQPV7sWQmenhyOx7mVU0K3P7kdrzK92KZycwQ8TSiwNHZgbwudKb65X+O6VcYAyVTmeKAMVAyFSEGmGSq4NrZA2g/YCJcvJsh7vEdrJ8zEu0/mgj3BnkjfJpZOeDkjmUqJ5mP4lMVI8muPnQD5ka6WDa6BWS6WkjLyMaMjeex5kgEvu7t+44jvVu/qW1w+8IjnN1xDX9vCEMNHzvUbeMCZ5+qkEpVn/MTyJsLMzc3FwCw4tQKmBuYY8VHK2CgY4C0zDRM2TUFq/5ZhW87fitKeaWtV9Bc3L0WggtHN+P0ntWo7tEQno07wMmjEaRScToLvBkDVQx1sTSo8sSAhaEFVg5cWWFjACj9OGAMUGXj27o3Dv75E2KiIlGtVj3FD8m0lBd4eOsSrp7Zq/K9zADQ098ZC3Zexu0nifCubqH4IZmYmoGwe8+w/+JDDG/noXI5jbp6YN/iEMTcfQ5HTxvITPJOzsgT0/HgylOEHb6N1oNU+/vs7dMbPx35CZFxkajnUE/xI/JF2gtcirqEvVf3VqgxDRgDJVOZ4oAxUDIVIQaYZKpAnvwc1tVqAQAsq9YAJBJYVP3vXiUr+5pITYoXtcyIRy8wpounYs5MfR1NfNSqFmZtuSTK8S2rmcLJyxatB9fHrbMPcfnobWz94RhkxnrwalUDnq1qwMzW6N0HKqIbT2/gi4AvYKCTdwO2vrY+BvkNwnf7vhOtjNJmblsd1Vx90PyDkbgdfgrXzu7HjuVToG9ogtqN2qF2o3YwtawqWnmMgfJJnXHAGKDKoF7zD6AnM0bo8f8h/NQuCLl5t5dIpBqwsq+JdgMmwtWnpcrldG1UHcb6Oth+5i52n3uA3H8Hl5JKJKhpa4wvu3ujeR07lcvx7egGPSNdnN99HaH7bylOokilUlg7m6HzZ/5wb+KkUhkfeH8AYz1j/O/S/7Dr8i7k/PueaUg1UNOyJiYGTkTLWqq/Z+rCGCiZyhQHjIGSqQgxwCRTBfpGZoh/+gBGZlZIiHsEQchFQsxDWNjmBc/zmAeiXOIHFFMXIjMrB1UMlS+zmxvpIUmeIUo5r2hoSuHexAnuTZyQ9CwVl4/ewZWjt3Fm+1V8vX2Qysd/1Yc8IzsDVWRVlLZZGFggMS1R5TLUTUNDE64+LeHq0xLJCbG4GrIf184ewLnDf+LLhUdVPj5joGIozThgDFBl4+bbCm6+rZCTk4301Lz7ofQMjBUjNIulhacdWnjaITsnF0n/3sNurK+dNw+1iDyaOsGjqRNysnORlpw3UJe+kS40NMUrp5VrK7RybYXsnGzFvcvGesbQFPk9UxfGQMlUpjhgDJRMeY+B8lGLCsrNtzX2r/sBNTz98TDyEhoE9MGJv5YgXZ4MiUSCswf/gEvd5qKUNWH1GWhqSJGWkY1H8alKA4DEJqYp5swsDcYWBmjWty6a9vHC/ctPRTlm8P+CoSnVRFpmGh69eAQn8//O6sQmx8JIT7yrJGXByMwK/h0Ho3GHQXh4M1SUYzIGKh6x44AxQJWVhoYmDIyrvHtHFWlqSPOdoCkNGprSUhkg63WaGpqoYlD675m6MAZKpjLFAWOgZMprDDDJVIF/xyHQ1NLB0/s34Nm4Ixq2/RAWdjVwcucyZGVmwLm2n8r3YwJA/5Yury1ZQVdb+WM7eysWtR1VH2TE2MIAEo3C77eSSCSoXlf1QTgGNhqotKyrpfyHHnIvBJ52niqXow5GZlaQvuUMmEQigaOb6n3vGQPlmzrigDFAREREFQWTTBVIpVL4tRugtO7VJX8xDWz19olUR4hwozIAjF7eU5TjvMvgxoPfun1k84pxszoAjJjxp1rKYQyUb+qIA8YAERERVRScJ5OIiIiIiIhEwySTiIiIiIiIRMMkk4iIiIiIiETDJJOIiIiIiIhEwySTiIiIiIiIRMMkk4iIiIiIiETDJJOIiIiIiIhEwySTiIiIiIiIRMMkk4iIiIiIiETDJJOIiIiIiIhEI3qSOXv2bNSvXx+GhoawtLREt27dcOvWLaV9Xr58iaCgIFSpUgUGBgbo0aMHYmNjFdsTEhLQuXNnGBgYwNvbG2FhYUrPDwoKwk8//SR21YmIiCqERYsWwdHREbq6umjYsCHOnz9f6L4rVqxA06ZNYWpqClNTUwQEBLx1fyIiIlWJnmT+/fffCAoKwtmzZ3H48GFkZWWhbdu2kMvlin2++OIL7N69G1u3bsXff/+NJ0+eoHv37ort33//PVJSUnDp0iW0aNECw4cPV2w7e/Yszp07h7Fjx4pddSIionJv8+bNCA4OxtSpU3Hp0iV4eXkhMDAQcXFxBe5/4sQJ9OvXD8ePH0dISAjs7e3Rtm1bREdHq7nmRET0vhA9yTxw4AAGDx4MDw8PeHl54ffff0dUVBRCQ0MBAElJSVi1ahXmz5+PVq1awcfHB2vWrMGZM2dw9uxZAEBERAT69u0LFxcXjBgxAhEREQCArKwsjBw5EkuXLoWGhobYVSciIir35s+fj+HDh2PIkCFwd3fH0qVLoa+vj9WrVxe4/4YNG/Dpp5+ibt26cHV1xcqVK5Gbm4ujR4+queZERPS+KPV7MpOSkgAAZmZmAIDQ0FBkZWUhICBAsY+rqyscHBwQEhICAPDy8sKxY8eQnZ2NgwcPwtPTEwAwd+5ctGjRAr6+vu8sNyMjA8nJyUoPIiKiiiwzMxOhoaFKbahUKkVAQICiDX2XtLQ0ZGVlKdrlgrANJSIiVWiW5sFzc3MxduxY+Pv7o3bt2gCAmJgYaGtrw8TERGlfKysrxMTEAAC++uorjBo1Cs7OznB0dMSqVatw+/ZtrF27FiEhIRg5ciQOHToEX19frFixAsbGxvnKnj17NqZPn55vfXR0dLEaS/3stGK84pKL17BTSzk6iUZqKSdOVnC3LTGlPk4t9TIAxkBJqSMGAPXEAWOgZMprDKSkpJRSTUpffHw8cnJyYGVlpbTeysoKN2/eLNIxJk6cCFtbW6VE9U1sQwtWmf522IaWTGWKAYBtaEm87zFQ1Da0VJPMoKAgXLt2DadPny7W84yNjbFx40alda1atcK8efOwYcMG3Lt3D7du3cLw4cMxY8aMAgcBmjRpEoKDgxXLycnJsLe3h52dHYyMih4caRGJxap7SZnnqOfemAyTLLWUYym3LPUyTKqalHoZAGOgpNQRA4B64oAxUDLlNQbe56tyP/zwAzZt2oQTJ05AV1e30P3YhhasMv3tsA0tmcoUAwDb0JJ432OgqG1oqSWZo0ePxp49e3Dy5ElUrVpVsd7a2hqZmZlITExUupoZGxsLa2vrAo+1Zs0amJiYoGvXrujevTu6desGLS0t9OrVC1OmTCnwOTo6OtDR0RH1NREREZUlc3NzaGhoKI3IDry9DX3lxx9/xA8//IAjR44obkMpDNtQIiJShej3ZAqCgNGjR+Ovv/7CsWPH4OTkpLTdx8cHWlpaSgMO3Lp1C1FRUfDz88t3vGfPnmHGjBlYuHAhACAnJwdZWXlnELKyspCTkyP2SyAiIiqXtLW14ePjo9SGvhrEp6A29JW5c+di5syZOHDgQJHGNSAiIlKF6Fcyg4KCsHHjRuzcuROGhoaK+yyNjY2hp6cHY2NjDBs2DMHBwTAzM4ORkRE+++wz+Pn5oVGjRvmON3bsWIwbNw52dnn9rP39/bF+/Xq0bdsWy5cvh7+/v9gvgYiIqNwKDg7GoEGD4OvriwYNGmDBggWQy+UYMmQIAGDgwIGws7PD7NmzAQBz5szBlClTsHHjRjg6OiraZQMDAxgYGJTZ6yAiospL9CRzyZIlAIAWLVoorV+zZg0GDx4MAPj5558hlUrRo0cPZGRkIDAwEIsXL853rIMHD+LOnTtYv369Yt3o0aNx8eJFNGzYEA0aNMDUqVPFfglERETlVp8+ffDs2TNMmTIFMTExqFu3Lg4cOKAYDCgqKgpS6X8dlZYsWYLMzEz07NlT6ThTp07FtGnT1Fl1IiJ6T4ieZAqC8M59dHV1sWjRIixatOit+wUGBiIwMFBpnb6+PrZs2aJSHYmIiCqy0aNHY/To0QVuO3HihNLygwcPSr9CREREryn1eTKJiIiIiIjo/cEkk4iIiIiIiETDJJOIiIiIiIhEwySTiIiIiIiIRMMkk4iIiIiIiETDJJOIiIiIiIhEwySTiIiIiIiIRMMkk4iIiIiIiETDJJOIiIiIiIhEwySTiIiIiIiIRMMkk4iIiIiIiETDJJOIiIiIiIhEwySTiIiIiIiIRMMkk4iIiIiIiETDJJOIiIiIiIhEwySTiIiIiIiIRMMkk4iIiIiIiETDJJOIiIiIiIhEwySTiIiIiIiIRMMkk4iIiIiIiETDJJOIiIiIiIhEwySTiIiIiIiIRMMkk4iIiIiIiETDJJOIiIiIiIhEwySTiIiIiIiIRMMkk4iIiIiIiETDJJOIiIiIiIhEwySTiIiIiIiIRMMkk4iIiIiIiETDJJOIiIiIiIhEwySTiIiIiIiIRMMkk4iIiIiIiETDJJOIiIiIiIhEwySTiIiIiIiIRMMkk4iIiIiIiETDJJOIiIiIiIhEwySTiIiIiIiIRMMkk4iIiIiIiETDJJOIiIiIiIhEwySTiIiIiIiIRMMkk4iIiIiIiETDJJOIiIiIiIhEwySTiIiIiIiIRMMkk4iIiIiIiETDJJOIiIiIiIhEwySTiIiIiIiIRMMkk4iIiIiIiETDJJOIiIiIiIhEwySTiIiIiIiIRMMkk4iIiIiIiETDJJOIiIiIiIhEwySTiIiIiIiIRMMkk4iIiIiIiETDJJOIiIiIiIhEwySTiIiIiIiIRMMkk4iIiIiIiETDJJOIiIiIiIhEwySTiIiIiIiIRMMkk4iIiIiIiETDJJOIiIiIiIhEwySTiIiIiIiIRMMkk4iIiIiIiETDJJOIiIiIiIhEwySTiIiIiIiIRMMkk4iIiIiIiETDJJOIiIiIiIhEU2ZJ5qJFi+Do6AhdXV00bNgQ58+fV2wLDg6GmZkZ7O3tsWHDBqXnbd26FZ07d1Z3dYmIiMqNt7WhBdm6dStcXV2hq6uLOnXqYN++fWqqKRERvY/KJMncvHkzgoODMXXqVFy6dAleXl4IDAxEXFwcdu/ejY0bN+LQoUOYO3cuPv74Y8THxwMAkpKS8M0332DRokVlUW0iIqIy97Y2tCBnzpxBv379MGzYMISFhaFbt27o1q0brl27puaaExHR+6JMksz58+dj+PDhGDJkCNzd3bF06VLo6+tj9erViIiIQIsWLeDr64t+/frByMgI9+/fBwBMmDABo0aNgoODQ1lUm4iIqMy9rQ0tyC+//IJ27dph/PjxcHNzw8yZM1GvXj389ttvaq45ERG9LzTVXWBmZiZCQ0MxadIkxTqpVIqAgACEhITg008/xfLly/HixQvcu3cP6enpqFGjBk6fPo1Lly5h8eLFRSonIyMDGRkZiuWkpCQAQHJycrHqmy4v3v4llZKWqZZy0lMy3r2TCJJflv77Jk1WzzkSxkDJqCMGAPXEAWOgZMprDLxqBwRBKI3qlKp3taEFCQkJQXBwsNK6wMBA7Nixo9By2IYWrDL97bANLZnKFAMA29CSeN9joMhtqKBm0dHRAgDhzJkzSuvHjx8vNGjQQBAEQZg6darg7Ows1K5dW9i+fbuQkZEh1K5dW7h48aKwcOFCwcXFRWjcuLFw7dq1QsuZOnWqAIAPPvjggw8+Cnw8evSoVNu70lCUNvRNWlpawsaNG5XWLVq0SLC0tCy0HLahfPDBBx98vO3xrjZU7Vcyi2LatGmYNm2aYnn69OkICAiAlpYWvvvuO1y9ehV79uzBwIEDERoaWuAxJk2apHTmNjc3FwkJCahSpQokEklpv4RyKTk5Gfb29nj06BGMjIzKujpUBhgDxBgABEFASkoKbG1ty7oq5Rbb0Pz4t0OMAWIMFL0NVXuSaW5uDg0NDcTGxiqtj42NhbW1db79b968iT/++ANhYWFYvXo1mjVrBgsLC/Tu3RtDhw5FSkoKDA0N8z1PR0cHOjo6SutMTExEfS0VlZGR0Xv7h0F5GAP0vseAsbFxWVehRIrbhgKAtbV1sfYH2Ia+zfv+t0OMAWIMFKUNVfvAP9ra2vDx8cHRo0cV63Jzc3H06FH4+fkp7SsIAj755BPMnz8fBgYGyMnJQVZWFgAo/s3JyVFf5YmIiMpQcdrQV/z8/JT2B4DDhw8Xuj8REZGqyqS7bHBwMAYNGgRfX180aNAACxYsgFwux5AhQ5T2W7lyJSwsLBTzYvr7+2PatGk4e/Ys9u/fD3d3d55ZJSKi98q72tCBAwfCzs4Os2fPBgB8/vnnaN68OX766Sd07NgRmzZtwsWLF7F8+fKyfBlERFSJlUmS2adPHzx79gxTpkxBTEwM6tatiwMHDsDKykqxT2xsLL7//nucOXNGsa5BgwYYN24cOnbsCEtLS6xdu7Ysql9h6ejoYOrUqfm6QNH7gzFAjIGK711taFRUFKTS/zoqNW7cGBs3bsTkyZPx9ddfo2bNmtixYwdq165dVi+hQuLfDjEGiDFQdBJBqIBjuBMREREREVG5pPZ7MomIiIiIiKjyYpJJREREREREomGSSURERERERKJhklkELVq0wNixYxXLjo6OWLBgwVufI5FIsGPHDpXLFus4REREZYFtKBHR+6dSJ5mdO3dGu3btCtx26tQpSCQSXLlypdjHvXDhAkaMGKFq9ZRMmzYNdevWzbf+6dOnaN++vahlFSY9PR1mZmYwNzdHRkaGWsqsiCQSyVsf06ZNU+nYxflB9Mknn0BDQwNbt24tcZlUMowDquzYhhbN77//rvi7l0qlsLGxQZ8+fRAVFZVv3+vXr6N3796wsLCAjo4OXFxcMGXKFKSlpeXbNywsDL169YKVlRV0dXVRs2ZNDB8+HJGRke+s059//gkNDQ0EBQUVWN/Cpn8r6Ltn27ZtaNGiBYyNjWFgYABPT0/MmDEDCQkJ76xHYWWU9Xfn6+UZGRmhfv362LlzZ7790tPTMXXqVLi4uEBHRwfm5ubo1asXrl+/nm/f5ORkfPPNN3B1dYWuri6sra0REBCA7du3411jbL7r91dhr2vw4MHo1q2b0ro7d+5gyJAhqFq1KnR0dODk5IR+/frh4sWLb39T1IgxkF9ljIFKnWQOGzYMhw8fxuPHj/NtW7NmDXx9feHp6Vns41pYWEBfX1+MKr6TtbW12oZJ3rZtGzw8PODq6lrmZ34FQUB2dnaZ1qEwT58+VTwWLFgAIyMjpXVffvmlWuqRlpaGTZs2YcKECVi9erVaynybzMzMsq6CWjEOCva+xUFlxja06F79/UdHR2Pbtm24desWevXqpbTP2bNn0bBhQ2RmZmLv3r2IjIzE999/j99//x1t2rRR+tvZs2cPGjVqhIyMDGzYsAERERH4448/YGxsjG+//fad9Vm1ahUmTJiAP//8Ey9fvizx6/rmm2/Qp08f1K9fH/v378e1a9fw008/4fLly1i/fn2JjllevjvXrFmDp0+f4uLFi/D390fPnj1x9epVxfaMjAwEBARg9erV+O677xAZGYl9+/YhOzsbDRs2xNmzZxX7JiYmonHjxli3bh0mTZqES5cu4eTJk+jTpw8mTJiApKSkt9ZFrN9fFy9ehI+PDyIjI7Fs2TLcuHEDf/31F1xdXTFu3LgSH1dsjIH8KmUMCJVYVlaWYGVlJcycOVNpfUpKimBgYCAsWbJEiI+PF/r27SvY2toKenp6Qu3atYWNGzcq7d+8eXPh888/VyxXq1ZN+PnnnxXLkZGRQtOmTQUdHR3Bzc1NOHTokABA+OuvvxT7TJgwQahZs6agp6cnODk5CZMnTxYyMzMFQRCENWvWCACUHmvWrBEEQch3nCtXrggtW7YUdHV1BTMzM2H48OFCSkqKYvugQYOErl27CvPmzROsra0FMzMz4dNPP1WU9TYtWrQQli5dKixZskRo06ZNvu3Xrl0TOnbsKBgaGgoGBgZCkyZNhDt37ii2r1q1SnB3dxe0tbUFa2trISgoSBAEQbh//74AQAgLC1Ps++LFCwGAcPz4cUEQBOH48eMCAGHfvn1CvXr1BC0tLeH48ePCnTt3hC5dugiWlpaCTCYTfH19hcOHDyvV6+XLl8KECROEqlWrCtra2oKzs7OwcuVKITc3V3B2dhbmzZuntH9YWJgAQLh9+/Y735N3WbNmjWBsbKy0bsWKFYKrq6ugo6Mj1KpVS1i0aJFiW0ZGhhAUFCRYW1sLOjo6goODgzBr1ixBEPLi6vUYqFat2lvL/v3334VGjRoJiYmJgr6+vhAVFaW0vbD35ZW3fZ5vxrwgCELXrl2FQYMGKZarVasmzJgxQ/joo48EQ0NDxba3xforu3btEnx9fQUdHR2hSpUqQrdu3QRBEITp06cLHh4e+V6rl5eXMHny5Le+H2WJccA4qIzYhhatDS3o7//XX38VAAhJSUmCIAhCbm6u4O7uLvj6+go5OTlK+4aHhwsSiUT44YcfBEEQBLlcLpibmyv+Ht704sWLQusiCIJw7949QU9PT0hMTBQaNmwobNiw4Z31feX19+vcuXMCAGHBggUlqkdRlNV355txkZycLAAQfvnlF8W6H374QZBIJEJ4eLjSc3NycgRfX1/B3d1dyM3NFQRBEEaNGiXIZDIhOjo6X1kpKSlCVlbWW9+Hd/3+erO+r7yKV0HIizEPDw/Bx8cnX4wJgjifV2lgDOSpjDFQqZNMQRCE8ePHC87OzoogEARBWL16teIL+PHjx8K8efOEsLAw4e7du8Kvv/4qaGhoCOfOnVPs/7YGMicnR6hdu7bQunVrITw8XPj7778Fb2/vfMEwc+ZM4Z9//hHu378v7Nq1S7CyshLmzJkjCIIgpKWlCePGjRM8PDyEp0+fCk+fPhXS0tIEQVAOqtTUVMHGxkbo3r27cPXqVeHo0aOCk5OT0g++QYMGCUZGRsLIkSOFiIgIYffu3YK+vr6wfPnyt75Pd+7cEXR0dISEhATh+fPngq6urvDgwQPF9sePHwtmZmZC9+7dhQsXLgi3bt0SVq9eLdy8eVMQBEFYvHixoKurKyxYsEC4deuWcP78ecV7VJwk09PTUzh06JBw584d4fnz50J4eLiwdOlS4erVq0JkZKQwefJkQVdXV3j48KHiWL179xbs7e2F7du3C3fv3hWOHDkibNq0SRAEQfj+++8Fd3d3pdc6ZswYoVmzZm99P4rqzS/HP/74Q7CxsRG2bdsm3Lt3T9i2bZtgZmYm/P7774IgCMK8efMEe3t74eTJk8KDBw+EU6dOKX6QxcXFKX4cPX36VIiLi3tr2U2bNhV+++03QRAEoUePHsKMGTOUtr/tfXnX51nU5MLIyEj48ccfhTt37igSk7fFuiAIwp49ewQNDQ1hypQpwo0bN4Tw8HBFA/Ho0SNBKpUK58+fV+x/6dIlQSKRCHfv3n3r+1GWGAeMg8qKbei729A3//5jY2OFli1bChoaGkJqaqogCHnxCyBfAv5KmzZtBC8vL0EQBGH79u0CAOHMmTOFlvk23377rdCzZ09BEARh4cKFQqtWrd5a39e9/n6NGTNGMDAwKNJJ6pIqq+/O119nVlaW8PPPPwsAhCVLlij28fT0FNq2bVvg8zds2KD4XZOTkyOYmpoKI0aMKNF78K7fX2/W93WvJxjvirHyijFQeWOg0ieZERERSsmMIOT9KBswYEChz+nYsaMwbtw4xfLbGsiDBw8KmpqaSmcu9u/fX2gwvDJv3jzBx8dHsTx16lRFA/O614+zfPlywdTUVNFoCYIg7N27V5BKpUJMTIwgCHnBVq1aNSE7O1uxT69evYQ+ffoUWhdBEISvv/5a6axp165dhalTpyqWJ02aJDg5ORXa2Nja2grffPNNgduKk2Tu2LHjrfUUBEHw8PAQFi5cKAiCINy6dUsAkO/q5ivR0dFKP3gyMzMFc3NzxZeVqt78cnR2ds73xz1z5kzBz89PEARB+Oyzz4RWrVop/WB73bvi5pXIyEhBS0tLePbsmSAIgvDXX38JTk5OiuO+63151+dZ1OSisDPtr3sz1v38/IT+/fsXun/79u2FUaNGKZY/++wzoUWLFu8spywxDhgHlRXb0He3oa+upMpkMkFfX19xFWXMmDGKfTZt2pSvHXzdmDFjBD09PUEQBGHOnDkCACEhIaHQMguTk5Mj2NvbK9rSZ8+eCdra2sK9e/eU6luUJLN9+/aCp6dnsetQHGX13QlA0NXVFWQymSCVSgUAgqOjo/D8+XPFPrq6uvm+/1559WN+8+bNQmxsrABAmD9//jvLLci7fn+9qu+7EozNmzcLAIRLly6VqB5lhTFQeWOgUt+TCQCurq5o3Lix4l6lO3fu4NSpUxg2bBgAICcnBzNnzkSdOnVgZmYGAwMDHDx4sMAb9gsSEREBe3t72NraKtb5+fnl22/z5s3w9/eHtbU1DAwMMHny5CKX8XpZXl5ekMlkinX+/v7Izc3FrVu3FOs8PDygoaGhWLaxsUFcXFyhx83JycHatWsxYMAAxboBAwbg999/R25uLgAgPDwcTZs2hZaWVr7nx8XF4cmTJ2jdunWxXk9BfH19lZZTU1Px5Zdfws3NDSYmJjAwMEBERITivQsPD4eGhgaaN29e4PFsbW3RsWNHxee/e/duZGRk5LtXRgxyuRx3797FsGHDYGBgoHh89913uHv3LoC8G7TDw8NRq1YtjBkzBocOHSpRWatXr0ZgYCDMzc0BAB06dEBSUhKOHTsG4N3vy9s+z+J48/MC3h3r4eHhb42V4cOHK+4jyszMxMaNGzF06FCV6qlOjIM873scVBZsQ9/dhgKAoaEhwsPDcfHiRfz000+oV68evv/++3z7Ce8Y/KOo+xTm8OHDkMvl6NChAwDA3Nwcbdq0KdH92qrUoyTU+d0JAD///DPCw8Oxf/9+uLu7Y+XKlTAzM1Pap7Q/r6L8/ioqdX9epYExkKeyxEClTzKBvMELtm3bhpSUFKxZswbOzs6KH13z5s3DL7/8gokTJ+L48eMIDw9HYGCgqINXhISEoH///ujQoQP27NmDsLAwfPPNN6U2QMabPxglEslbA/XgwYOIjo5Gnz59oKmpCU1NTfTt2xcPHz7E0aNHAQB6enqFPv9t2wBAKs0Ls9eDPysrq8B9X2/8AeDLL7/EX3/9hVmzZuHUqVMIDw9HnTp1FO/du8oGgI8//hibNm1Ceno61qxZgz59+pTKoBOpqakAgBUrViA8PFzxuHbtmuLm8Hr16uH+/fuYOXMm0tPT0bt3b/Ts2bNY5bz6Qtq7d6/i89LX10dCQoLih8S73peifGZvflkV9Jm9+XkVJdbfVXbnzp2ho6ODv/76C7t370ZWVlax36OyxDhgHFQ2bEPf3oYCeX8rNWrUgJubG4KDg9GoUSOMGjVKsd3FxQVAXqJbkIiICMU+r/69efNmseu+atUqJCQkQE9PT/G9sG/fPqxdu1bxGoyMjCCXy/O9psTERACAsbGxoh737t0rtL0Wm7q+O1+xtrZGjRo10LZtW8Vvg9dPJri4uLz183q1j4WFBUxMTEr0eRXl9xeQdxKjoIFjEhMTlT4voGRxU14wBipXDLwXSWbv3r0hlUqxceNGrFu3DkOHDoVEIgEA/PPPP+jatSsGDBgALy8vVK9evUjDg7/i5uaGR48e4enTp4p1r482BQBnzpxBtWrV8M0338DX1xc1a9bEw4cPlfbR1tZGTk7OO8u6fPky5HK5Yt0///wDqVSKWrVqFbnOb1q1ahX69u2r9AcdHh6Ovn37YtWqVQAAT09PnDp1qsDGxtDQEI6Ojkp/DK+zsLAAAKX3KDw8vEh1++effzB48GB88MEHqFOnDqytrfHgwQPF9jp16iA3Nxd///13ocfo0KEDZDIZlixZggMHDpTa1RArKyvY2tri3r17qFGjhtLDyclJsZ+RkRH69OmDFStWYPPmzdi2bZtiKHgtLa13xsG+ffuQkpKCsLAwpc/rzz//xPbt25GYmPjO9+VtnyeQ95m9/nnl5OTg2rVr73wPihLrnp6ehcYKAGhqamLQoEFYs2YN1qxZg759+xbpZEJ5wThgHFQ2bEOL76uvvsLmzZtx6dIlAEDdunXh6uqKn3/+OV9yd/nyZRw5cgT9+vUDALRt2xbm5uaYO3dugcd+lQy+6fnz59i5cyc2bdqk9J0QFhaGFy9eKK741KpVC9nZ2fna4Vd1ffVD9cMPP0RqaioWL15crHqUlLq+OwvSoEED+Pj4KF197tu3L44cOYLLly8r7Zubm4uff/4Z7u7u8PLyglQqRd++fbFhwwY8efIk37FTU1MLHS2/KL+/gLzPLDQ0VOm5OTk5uHz5suLzqlu3Ltzd3fHTTz8VeFJE7M+rNDAGKlkMlE0vXfUbNmyYYGpqKmhoaCjd+/HFF18I9vb2wj///CPcuHFD+PjjjwUjIyNF/2ZBePegBe7u7kKbNm2E8PBw4eTJk4KPj49S3+mdO3cKmpqawp9//in8v737DovieOMA/kXg5KhHFQQiUqSoAQSNJVgw2BJiDJaoiUHQRFCCBWssYNeIPQlqpCWWWIm/JBhLLIgNFbAhKELUiA0liogivL8/eFhZjnJSLe/neXgedm92ZnZvbmdnd2b2ypUrtGLFCtLT0xP1Qd+wYQNpaGhQYmIi3b17l/Lz84lI3Af78ePHZGJiQl5eXnTu3Dn6+++/ydLSUm7SgtJ5JyIKDAykLl26lHtc7ty5Q6qqqhQbGyv32Z9//kmNGzem7OxsunfvHunr6wsThKSlpVF0dLQwQUhkZCSpqanRihUrKC0tjU6fPk0rV64U4mrfvj25ubnRxYsX6eDBg9SuXbtyx2SWnfmqX79+5OTkRImJiZSUlESenp6kpaUl+j68vb3J3Nycdu7cSVevXqUDBw7Qr7/+Kopn2rRpJJFIyN7evtzjUF1lxxKsW7eOpFIprVixglJTU+ns2bMUHh5OoaGhREQUGhpKGzdupJSUFEpNTSVfX18yNjYWZgGzsbEhPz8/ysrKqnA8Tt++fcsdH1RYWEjGxsbCJDCVHZeqvs+wsDBSV1en33//nVJSUmjkyJGkra0tNxav9AyRRIqV9QMHDlCjRo2ECV/Onj0rzKpYIi0tjZSVlUlZWZmOHz9e9RfRwLgcLBPl4W0tB28yrkO7VHhsKhrjOHDgQPrwww+F5fj4eFJXV6dPPvmETpw4Qf/88w9t2bKFzM3NqWPHjkKeiYhiYmJIVVWVPD09ae/evZSRkUEJCQk0ceLECseHLlu2jExMTModqzZw4EBhMiAioh49epCjoyPt27ePrl69SrGxsWRraysX96RJk0hZWZkmTpxIR48epczMTNq3bx/179+/wllnX0ZDnDuJyh/fVnLNc+PGDSIievLkCb333ntkbm5OW7ZsoX/++YdOnjxJn3zyCWloaNCxY8eEbbOzs8nOzo7MzMwoKiqKLly4QGlpabR+/XqytrYud1ZPRa+/iIg2btxIUqmUvv/+e0pLS6PExETy8fEhHR0dYTwxUfGMwFpaWtSxY0f6448/KD09nZKTk2nu3Lm1NuFhbeMy8OaWgbemkXn06FECQH369BGtz87Opr59+5KmpiYZGRnR9OnTadiwYQpXkETFk2u8//77JJFIqEWLFrR79265wjtx4kTS19cnTU1NGjRoEC1btkz0o8rPzycvLy+SyWS1Mv16aZVVkEuWLCGZTFbuxB9Pnz4lmUwmTOecnJxMPXr0IHV1ddLS0iI3NzfRTI9hYWFka2tLqqqqZGJiQgEBAcJnFy9epA4dOpBUKiUnJydhivqqGpkZGRnUrVs3kkqlZG5uTqtXr5b7Pp48eULjxo0jExMTkkgkZG1tTeHh4aJ40tPTCQAtXry43ONQXeVdXGzYsIGcnJxIIpGQrq4ude7cmXbs2EFExRNPODk5kYaGBmlra1P37t1FA7R37dpF1tbWpKKiUu7U27du3SIVFRXasmVLufnx8/MjZ2dnIqr6uFT2fT579oz8/PxIT0+PjIyMaMGCBeVO+FK2cUFUdVknItq+fbtwjAwMDOjTTz+Vi8fNza3c11i8irgcLJPLw9tYDt5kXId2qfDYVNTIPHbsGAEQzbR79uxZ8vLyIj09PVJVVSUrKyuaPn06PX78WG77hIQE+vTTT8nQ0JAaN25M1tbW9NVXX1X4+q3WrVuTv79/uZ/9+uuvJJFIhEnCHjx4QN988w1ZWVmRVColGxsbmjRpkuhYlN62c+fOpKWlRRoaGvTuu+/S7Nmz6+wVJnV57ixRXgOjqKiI7OzsRBOOPX78mL799luytrYmVVVV0tPTE25SlJWTk0NTpkwhGxsbkkgk1KRJE/rggw9o586d5Tb8X+b6q+S4uLi4kJaWFjVp0oT69OlDycnJctumpqbSsGHDqGnTpiSRSKhZs2Y0ePDgV2YymLK4DLy5ZUCJ6BUbJcpYHYiLi0P37t1x/fp1NGnSpKGzw6pARLCxsYG/vz/Gjx/f0NlhDYTLAWOMMfZ6UmnoDDBWl54+fYq7d+8iODgYAwYM4Abma+Du3bvYvHkzbt26heHDhzd0dlgD4XLAGGOMvb64kcneaJs2bYKvry+cnJwQHR3d0NlhCjAyMoKBgQHWrl0LXV3dhs4OayBcDhhjjLHXF3eXZYwxxhhjjDFWa96KV5gwxhhjjDHGGKsf3MhkrALZ2dkwMjISvZeTvVo+++wzhIaG1mua9VEugoOD4eTkVON4IiMjIZPJahzPqyQsLAyenp4NnQ3GWBW4Dn31cR1aOa5Da6he57Jl7DUybtw4GjFiRLmf9ejRgxo1akQnT56s51zVv8ePH9OUKVPI0tKSGjduTAYGBtS5c2eKiYlp6KzRuXPnSFdXl3JycuotzbLlIiMjgwBQo0aNhHdrlbh58yYpKysTAMrIyFA4jUePHtG9e/dqnNe8vDy6ffu2sDxr1ixydHSsUZzbtm0rd19LWFtb07hx46qMp7p5efr0KTVt2pQOHz780tsyxuoP16HFuA4V4zr07alD+UkmY+XIy8vD+vXr4evrK/fZtWvXcPToUYwZMwbh4eF1npdnz57VeRqVGTVqFHbs2IFVq1bh0qVL2L17N/r374/s7Ow6S1PRfW7VqhWsrKzwyy+/1FleSqusXJiamspNLhUVFQVTU9OXTkdTUxP6+vrVzicAFBQUQCqVwsjIqEbxlPXxxx9DX18fUVFRcp8dPnwYV65cKff41BaJRIIhQ4Zg5cqVdZYGY6xmuA59gevQF7gOfcvq0DpvxjL2Gtq6dSsZGhqW+1lwcDB99tlnlJKSQjo6OpSXl0dExS+/BUApKSmi8EuXLiVLS0th+dy5c9SrVy/S0NAgIyMj+vzzz4UXZBMVv7h89OjRFBgYSPr6+tS1a1ciIgoNDaVWrVqRuro6mZmZkZ+fn9yLs9euXUtmZmYklUrpk08+odDQULmXHMfExJCzszM1btyYmjdvTsHBwVRQUFDhsdDR0aHIyMhKj1d+fj5NmjSJzMzMSCKRkJWVFf3000/C5wcPHqS2bduSRCIhY2Njmjx5sijNiva5qmNFRBQSEkLvv/9+pfmrLeWVi5K7sNOnTycbGxvRZy1atKAZM2aI7sI+f/6cfHx8yMLCgtTU1KhFixa0fPly0XZl71AWFhZSSEgImZqakkQiIUdHR4qNjZXLw+bNm6lz587UuHFjioiIEL3kOiIiggCI/iIiImj48OH04YcfitJ/9uwZGRoair7D0saPHy+3r0TFL7J/7733iIjon3/+oY8//pg0NDRIS0uLBgwYQLdu3ao0L0TFL4n39fUlAwMD0tLSom7dulFSUpIonUOHDpFEIhF+e4yxVwvXoS9wHfoC16HF3pY6lBuZjJXjm2++oV69esmtLyoqombNmtHvv/9OREQuLi4UHR0tfO7q6krTp08XbePi4iKse/DgARkaGtLUqVMpJSWFzpw5Qx4eHtStWzchfJcuXUhTU5MmTpxIly5dokuXLhER0bJly+jvv/+mjIwM2r9/P9na2pKfn5+w3ZEjR6hRo0b03XffUWpqKn3//fekp6cnqiAPHz5M2traFBkZSenp6bRnzx6ysLCg4ODgCo+Fra0tDRw4kB4+fFhhmIEDB5K5uTnt2LGD0tPTad++fbR582YiIrpx4wapq6uTv78/paSk0M6dO8nAwIBmzZpV6T4rcqyIiGJjY0kikVB+fn6F+ast5ZWLksrp5MmTZGBgQHFxcUREFBcXR4aGhnTy5ElRBfns2TOaOXMmJSQk0NWrV+mXX34hdXV1+vXXX4U4y1aQS5cuJW1tbdq0aRNdunSJJk2aRKqqqpSWlibKg4WFBW3fvp2uXr1KN2/eFFWQeXl5NGHCBGrZsiVlZWVRVlYW5eXlUXx8PCkrK9PNmzeF9Hbs2EEaGhpyF2AlLly4QADo0KFDwrpHjx6RhoYGrV27lgoLC8nJyYnef/99OnXqFB0/fpxcXFyoS5culeaFiOiDDz4gT09PSkhIoLS0NJowYQLp6+tTdna2kNbjx4+pUaNGdODAAcW/PMZYveE69AWuQ1/gOrTY21KHciOTsXL07duXfHx85Nbv2bOHDA0NhTuIy5YtE370JctWVlbCctk7s3PmzKEePXqI4rx+/ToBoNTUVCIqriycnZ2rzOPWrVtJX19fWB40aJDc3bShQ4eKKsju3bvT/PnzRWF+/vlnMjExqTCdQ4cOkZmZGamqqpKrqyuNHTuWjhw5IrePe/fuLXf7adOmka2tLRUVFQnrvv/+e9LU1KTCwsIK91mRY0VElJycTAAoMzOzwn2oLeWVi5LKKTExkcaOHUvDhw8nIqLhw4fTuHHjKDExscrxJKNHjyYvLy9huWwF2bRpU5o3b55om7Zt25K/v78oD2Xv5pauIMuLt4SDgwMtWrRIWPb09CRvb+8K80tE1L59e/ryyy+F5fXr15O6ujo9fPiQ9uzZQ8rKynTt2jXh85JKtWQMVnl5iYuLI21tbbmLHSsrK1qzZo1ona6ubpVPBxhjDYPr0Be4Dn2B69AX3oY6lMdkMlaOJ0+eQE1NTW59eHg4Bg0aBBUVFQDA4MGDER8fj/T0dADFM7VlZmbi+PHjAIANGzagTZs2sLOzAwAkJyfjwIED0NTUFP5KPiuJAwBcXFzk0t63bx+6d+8OU1NTaGlp4YsvvkB2djby8vIAAKmpqWjXrp1om7LLycnJmD17tij9kSNHIisrS4inrM6dO+Pq1avYv38/+vfvjwsXLsDNzQ1z5swBACQlJUFZWRldunQpd/uUlBR06NABSkpKwrpOnTohNzcXN27cqHCfFT1WUqkUACrMf22qqFyU8PHxwdatW3Hr1i1s3boVPj4+5Yb7/vvv4eLiAkNDQ2hqamLt2rW4du1auWEfPnyImzdvolOnTqL1nTp1QkpKimidq6vrS+5RsREjRiAiIgIAcPv2bcTGxlaY9xI+Pj7Ytm0bHj16BKD4tzFgwABoaWkhJSUF5ubmMDc3F8I7ODhAJpPJ5bm05ORk5ObmQl9fX/S9Z2RkiL5zoPh7r4/vnDH28rgOfYHr0Be4Dn3hbahDVeo0dsZeUwYGBnjw4IFo3f3797Fz504UFBTgxx9/FNYXFhYiPDwc8+bNg7GxMdzd3bFx40a0b98eGzduhJ+fnxA2NzcXnp6eWLRokVyaJiYmwv8aGhqizzIzM/HRRx/Bz88P8+bNg56eHo4cOQJfX188e/YM6urqCu1Xbm4uQkJC8Omnn8p9VtmJX1VVFW5ubnBzc8PkyZMxd+5czJ49G5MnTxYqqJoqu8+KHqv79+8DAAwNDWslH5Upr1yU1rp1a9jZ2WHw4MGwt7dHq1atkJSUJAqzefNmBAUFITQ0FB06dICWlha+++47nDhxosb5K3sMFTVs2DBMmTIFx44dw9GjR9G8eXO4ublVus1nn32GcePGYcuWLejcuTPi4+OxYMGCaqVfIjc3FyYmJjh48KDcZ2Wnkb9//369fOeMsZfHdagY16HFuA594W2oQ7mRyVg5nJ2d5WZb27BhA8zMzBATEyNav2fPHoSGhmL27NlQVlbG0KFDMWnSJAwePBhXr17FZ599JoRt06YNtm/fDgsLC+FOriJOnz6NoqIihIaGolGj4g4IW7ZsEYWxtbVFQkKCaF3Z5TZt2iA1NRXW1tYKp10eBwcHPH/+HPn5+WjdujWKiopw6NAhfPDBB3Jh7e3tsX37dhCRcCc2Pj4eWlpaMDMzqzANRY/V+fPnYWZmBgMDgxrtkyLKKxdl+fj4wN/fX3QRVVp8fDw6duwIf39/YV3ZO4ylaWtro2nTpoiPjxfd6Y6Pj5e7y14ViUSCwsJCufX6+vr45JNPEBERgWPHjmH48OFVxqWlpYUBAwYgPDwc6enpaNGihVCp2tvb4/r167h+/bpwJ/bixYvIycmBg4NDhXlp06YNbt26BRUVFVhYWFSYdnp6OvLz8+Hs7KzorjPG6hHXoZXjOrRiXIe+QXVonXbGZew1dfbsWVJRUaH79+8L6xwdHWny5MlyYXNyckgikQgTGTx8+JCkUik5OjpS9+7dRWH//fdfMjQ0pP79+9PJkyfpypUrtHv3bvL29qbnz58TUfHYisDAQNF2SUlJwniB9PR0io6OJlNTUwJADx48IKIXkxaEhoZSWloahYWFkb6+PslkMiGe3bt3k4qKCgUHB9P58+fp4sWLtGnTJvr2228rPBZdunShsLAwOnXqFGVkZNAff/xBtra25O7uLoTx9vYmc3Nz2rlzJ129epUOHDggDMIvmbRg9OjRlJKSQjExMeVOWlB2nxU5VkTFs7GVN/anLpRXLkqPJyEiKigooLt37wpjjsqOJ1mxYgVpa2vT7t27KTU1laZPn07a2tqisRVlx1osW7aMtLW1afPmzXTp0iWaPHlyuZMWlOShRNnxJBs2bCANDQ1KTEyku3fvisZt7NmzhyQSCSkrK9O///6r0PGIi4sjAKSrq0sLFy4U1hcVFZGTkxO5ubnR6dOn6cSJE6JJCyrKS1FREb3//vvk6OhIf/31F2VkZFB8fDxNmzaNEhISRPtVerZJxtirhevQF7gOfYHrULE3vQ7lRiZjFWjXrh2FhYUREdGpU6dEA67L6t27N/Xr109YHjhwIAGg8PBwubBpaWnUr18/kslkJJVKyc7OjsaOHSsM6i+vsiAqnh3NxMSEpFIp9ezZk6Kjo0UVJFHx9OumpqbC9Otz584lY2NjUTy7d++mjh07klQqJW1tbWrXrh2tXbu2wuMwf/586tChA+np6ZGamhpZWlrSN998I3rR8ZMnT2jcuHFkYmJCEomErK2tRfuuyPTr5e1zVcfqyZMnpKOjQ8eOHasw/7WtdLkgqrhyKlG2gszPzydvb2/S0dEhmUxGfn5+NGXKlEoryMLCQgoODiZTU1NSVVWtcPr1qirI/Px88vLyIplMJprynOjFrI99+vR5qeNha2srN7MeUeXTr1eWl4cPH1JAQAA1bdqUVFVVydzcnIYOHSqaAKFHjx60YMGCl8onY6x+cR1ajOtQMa5Dxd7kOpQbmYxV4Pfffyd7e3th9rbX0YgRI+rt/VcN4YcffiAPD496TbM+ysWUKVOoU6dOdRZ/eR49ekTa2tq0ffv2ek33ZZ0/f56MjIwoJyenobPCGKsE16GvPq5Daw/XofJ4TCZjFfjwww9x+fJl/Pvvv6IZvl5lS5YsgYeHBzQ0NBAbG4uoqCj88MMPDZ2tOqOqqopVq1bVa5p1WS6ISJiFsL7GGxYVFeHevXsIDQ2FTCbDxx9/XC/pVldWVhaio6Oho6PT0FlhjFWC69BXH9ehNcd1aMWUiIjqPBXGWL0YOHAgDh48iEePHsHS0hIBAQEYNWpUQ2eLKSgnJwdNmjRB27ZtsWHDBjRr1qzO08zMzETz5s1hZmaGyMhIdO/evc7TZIyxVxHXoa83rkNfLdzIZIwxxhhjjDFWaxo1dAYYY4wxxhhjjL05uJHJGGOMMcYYY6zWcCOTMcYYY4wxxlit4UYmY4wxxhhjjLFaw41MxhhjjDHGGGO1hhuZjDHGGGOMMcZqDTcyGWOMMcYYY4zVGm5kMsYYY4wxxhirNdzIZIwxxhhjjDFWa7iRyRhjjDHGGGOs1nAjkzHGGGOMMcZYreFGJmOMMcYYY4yxWsONTMYYY4wxxhhjtYYbmYwxxhhjjDHGag03MhljjDHGGGOM1RpuZDLGGGOMMcYYqzXcyGSMMcYYY4wxVmu4kckYY4wxxhhjrNZwI5MxxhhjjDHGWK3hRiZjjDHGGGOMsVrDjUzGGGOMMcYYY7WGG5mMMcYYY4wxxmoNNzIZY4wxxhhjjNUalYbOAGMvo7CwEAUFBQ2dDcYYY4wxVg2qqqpQVlZu6GywOsaNTPZaICLcunULOTk5DZ0VxhhjjDFWAzKZDMbGxlBSUmrorLA6wo1M9looaWAaGRlBXV2dT0qMMcYYY68ZIkJeXh7u3LkDADAxMWngHLG6wo1M9sorLCwUGpj6+voNnR3GGGOMMVZNUqkUAHDnzh0YGRlx19k3FE/8w155JWMw1dXVGzgnjDHGGGOspkqu6XiejTcXNzLZa4O7yDLGGGOMvf74mu7Nx41MxhhjjDHGGGO1hhuZjL1FMjMzoaSkhKSkpNcq7tIOHjwIJSUlYabhyMhIyGSyOk2TvVmCg4Ph5OQkLHt7e+OTTz5psPy8iZSUlBATE1OjOMp+L127dsXYsWNrFCcg//2/aiwsLLB8+XJhuTaOJWM19bLnybJ1NXv78MQ/7LW2am9OvaUV4CF76W3u3r2LmTNn4o8//sDt27ehq6sLR0dHzJw5E506dQJQfAGxc+fOt+IiNyMjA99++y0OHjyI+/fvw8DAAC4uLli0aBHs7OyqFeegQYPQp08fYTk4OBgxMTF13tityJ2NvvWantGQ9QqHrap70qxZsxAcHFzDHFWPor+DQ4cOISQkBElJScjPz4epqSk6duyIdevWQSKRVCvtFStWgIiE5a5du8LJyUl0oV+f1qVPrdf0RloteKnwipzXsrKyoKurW6N8lf1eaktQUBACAgKEZW9vb+Tk5NS4IVdYWIjvvvsOkZGR+OeffyCVSmFjY4ORI0dixIgR1Y639LHMzMxE8+bNkZiY2GAN5ZzlOfWanmys7KXCe3t7IyoqCgCgoqICPT09vPvuuxg8eDC8vb3RqBE/X2GsPnAjk7E65OXlhWfPniEqKgqWlpa4ffs29u/fj+zs7IbOWrU9e/asWhfzBQUF8PDwgK2tLXbs2AETExPcuHEDsbGxNbrTKZVKhZnqWOWysrKE/3/99VfMnDkTqampwjpNTc2Xiq+6ZaG6Ll68iF69eiEgIAArV66EVCrF5cuXsX37dhQWFlY7Xh0dnVrM5ZtPkfOasbFxjdOp7e+FiFBYWAhNTc2XLuuKCAkJwZo1a7B69Wq4urri4cOHOHXqFB48eFCjeGvjWL5tevXqhYiICBQWFuL27dvYvXs3AgMDsW3bNuzatQsqKnz5y1hd49s5jNWRnJwcxMXFYdGiRejWrRuaNWuGdu3aYerUqfj4448BFHeLAoB+/fpBSUlJWE5PT0ffvn3RpEkTaGpqom3btti3b58ofgsLC8yfPx8+Pj7Q0tLCO++8g7Vr14rCnDx5Es7OzlBTU4OrqysSExNFnxcWFsLX1xfNmzeHVCqFra0tVqxYIQpT0kVm3rx5aNq0KWxtbRWKu6wLFy4gPT0dP/zwA9q3b49mzZqhU6dOmDt3Ltq3bw/gRZfbzZs3o2PHjlBTU0OrVq1w6NChCuMt3V02MjISISEhSE5OhpKSEpSUlBAZGVlpvt4mxsbGwp+Ojg6UlJSE5cePH2Po0KFVlrk5c+Zg2LBh0NbWxldffQUAWLduHczNzaGuro5+/fph6dKlcl2Yf/vtN7Rp0wZqamqwtLRESEgInj9/LsQLyP8OytqzZw+MjY2xePFitGrVClZWVujVqxfWrVsn3GgoKQ8xMTGwsbGBmpoaevbsievXr1d4XEp3A/P29sahQ4ewYsUKoQxlZma+3IF+gylyXgPEXTxLftdbtmyBm5sbpFIp2rZti7S0NCQkJMDV1RWampro3bs37t69K8RRVfe8n3/+Ga6urtDS0oKxsTGGDBkivHsPeNFdLzY2Fi4uLmjcuDGOHDki6i4bHByMqKgo/Pbbb8L3ffDgQbi7u2PMmDGi9O7evQuJRIL9+/eXm59du3bB398fAwYMQPPmzeHo6AhfX18EBQUJYbp27YoxY8ZgzJgx0NHRgYGBAWbMmFHpE9vSx7J58+YAAGdnZygpKaFr164Vbvc2a9y4MYyNjWFqaoo2bdpg2rRp+O233xAbGyvUCeUN8cjJyRHKAPCiDP31119wdnaGVCqFu7s77ty5g9jYWNjb20NbWxtDhgxBXl6eEE/Xrl0REBCAsWPHQldXF02aNMG6devw+PFjDB8+HFpaWrC2tkZsbCyA4hsg1tbWWLJkiWg/kpKSoKSkhCtXrpS7nyW/kfnz56NJkyaQyWSYPXs2nj9/jokTJ0JPTw9mZmaIiIgQbXfu3Dm4u7tDKpVCX18fX331FXJzc4XPCwsLMX78eMhkMujr62PSpElyZbSoqAgLFiwQrh8cHR2xbdu2l/qe2JuNG5mM1ZGSu+UxMTF4+vRpuWESEhIAABEREcjKyhKWc3Nz0adPH+zfvx+JiYno1asXPD09ce3aNdH2oaGhQgPP398ffn5+wpOp3NxcfPTRR3BwcMDp06cRHBwsutgBiisJMzMzbN26FRcvXsTMmTMxbdo0bNmyRRRu//79SE1Nxd69e/H7778rFHdZhoaGaNSoEbZt21blU6eJEydiwoQJSExMRIcOHeDp6anQ099BgwZhwoQJaNmyJbKyspCVlYVBgwZVuR1TvMwtWbIEjo6OSExMxIwZMxAfH49Ro0YhMDAQSUlJ8PDwwLx580TbxMXFYdiwYQgMDMTFixexZs0aREZGCuEq+h2UZWxsjKysLBw+fLjSfcnLy8O8efMQHR2N+Ph45OTk4LPPPlPoOKxYsQIdOnTAyJEjhTJkbm6u0LZvA0XOaxWZNWsWpk+fjjNnzkBFRQVDhgzBpEmTsGLFCsTFxeHKlSuYOXOmwvEVFBRgzpw5SE5ORkxMDDIzM+Ht7S0XbsqUKVi4cCFSUlLw7rvvij4LCgrCwIED0atXL+H77tixI0aMGIGNGzeK9vGXX36Bqakp3N3dy82PsbEx/v77b1FDuTxRUVFQUVHByZMnsWLFCixduhQ//fSTQvt88uRJAMC+ffuQlZWFHTt2KLQdA9zd3eHo6FitYxYcHIzVq1fj6NGjuH79OgYOHIjly5dj48aN+OOPP7Bnzx6sWrVKtE1UVBQMDAxw8uRJBAQEwM/PDwMGDEDHjh1x5swZ9OjRA1988QXy8vKgpKQEHx8fucZgREQEOnfuDGtr6wrz9vfff+PmzZs4fPgwli5dilmzZuGjjz6Crq4uTpw4gVGjRuHrr7/GjRs3AACPHz9Gz549oauri4SEBGzduhX79u0T3VQJDQ1FZGQkwsPDceTIEdy/fx87d+4UpbtgwQJER0cjLCwMFy5cwLhx4/D5559XelOYvV24kclYHVFRUUFkZCSioqIgk8nQqVMnTJs2DWfPnhXCGBoaAgBkMhmMjY2FZUdHR3z99ddo1aoVbGxsMGfOHFhZWWHXrl2iNPr06QN/f39YW1tj8uTJMDAwwIEDBwAAGzduRFFREdavX4+WLVvio48+wsSJE0Xbq6qqIiQkBK6urmjevDmGDh2K4cOHyzUyNTQ08NNPP6Fly5Zo2bKlQnGXZWpqipUrV2LmzJnQ1dWFu7s75syZg6tXr8qFHTNmDLy8vGBvb48ff/wROjo6WL++6rGHUqkUmpqaUFFREZ7QcVdaxSha5tzd3TFhwgRYWVnBysoKq1atQu/evREUFIQWLVrA398fvXv3Fm0TEhKCKVOm4Msvv4SlpSU8PDwwZ84crFmzBkDFv4OyBgwYgMGDB6NLly4wMTFBv379sHr1ajx8+FAUrqCgAKtXr0aHDh3g4uKCqKgoHD16VLhAr4yOjg4kEgnU1dWFMsQvCn9BkfNaRYKCgtCzZ0/Y29sjMDAQp0+fxowZM9CpUyc4OzvD19dXOH8pwsfHB71794alpSXat2+PlStXIjY2VvREBgBmz54NDw8PWFlZQU9PT/SZpqYmpFKp8OTL2NgYEokEn376KYDiJ/AlIiMj4e3tXeHY5qVLl+Lu3bswNjbGu+++i1GjRglPqkozNzfHsmXLYGtri6FDhyIgIADLli1TaJ9Lfhv6+vowNjaW2x9WOTs7u2r1TJg7d66onB46dAg//vgjnJ2d4ebmhv79+8uVXUdHR0yfPh02NjaYOnUq1NTUYGBggJEjR8LGxgYzZ85Edna28Nvx9vZGamqqcJ4qKCjAxo0b4ePjU2ne9PT0sHLlStja2sLHxwe2trbIy8vDtGnThLQlEgmOHDkCoPjaID8/H9HR0WjVqhXc3d2xevVq/Pzzz7h9+zYAYPny5Zg6dSo+/fRT2NvbIywsTNR9/enTp5g/fz7Cw8PRs2dPWFpawtvbG59//rlwXmeMG5mM1SEvLy/cvHkTu3btQq9evXDw4EG0adOmyi6cubm5CAoKgr29PWQyGTQ1NZGSkiL3VKn0XfmSro8l3cVK7tqrqakJYTp06CCX1vfffw8XFxcYGhpCU1MTa9eulUundevWorF3isZd1ujRo3Hr1i1s2LABHTp0wNatW9GyZUvs3btXFK50XCoqKnB1dUVKSkqV8bPqU7TMubq6ipZTU1PRrl070bqyy8nJyZg9e7bwFExTU1N4Uli6i1lVlJWVERERgRs3bmDx4sUwNTXF/PnzhSfXJVRUVNC2bVth2c7ODjKZjMtQLanuea30+apJkyYAis8tpdeV7u5aldOnT8PT0xPvvPMOtLS00KVLFwCosswqQk1NDV988QXCw8MBAGfOnMH58+fLfVJawsHBAefPn8fx48fh4+ODO3fuwNPTU27Sn/bt24saqh06dMDly5drNK6YKYaIqvV+xrJlV11dHZaWlqJ1Zctu6W2UlZWhr68vV94BCNs1bdoUH374oVDm/ve//+Hp06cYMGBApXlr2bKlaDKjJk2aiNIpSbv0tYGjoyM0NDSEMJ06dUJRURFSU1Px33//ISsrC++9957weUk9XOLKlSvIy8uDh4eH6LweHR2N9PT0SvPL3h7cyGSsjqmpqcHDwwMzZszA0aNH4e3tjVmzZlW6TVBQEHbu3In58+cjLi4OSUlJaN26NZ49eyYKp6qqKlpWUlJCUVGRwnnbvHkzgoKC4Ovriz179iApKQnDhw+XS6d0ZVRTWlpa8PT0xLx585CcnAw3NzfMnTu31uJn1aNomatOWcjNzRVmhC35O3fuHC5fviy6UaEoU1NTfPHFF1i9ejUuXLiA/Px8hIWFvXQ8rPqqc14rfb4qudAvu07R81dJlz9tbW1s2LABCQkJQne+2jp/jRgxAnv37sWNGzcQEREBd3d3NGvWrNJtGjVqhLZt22Ls2LHYsWMHIiMjsX79emRkZFQrD6x2paSkCONaSxpmpccaFhQUlLtd2XKqSN1bXpjyfgOltxsxYgQ2b96MJ0+eICIiAoMGDYK6unql+1RVOhXlryZKegv88ccfovP6xYsXeVwmE3Ajk7F65uDggMePHwvLqqqqcnew4+Pj4e3tjX79+qF169YwNjZ+6S4+9vb2OHv2LPLz84V1x48fl0unY8eO8Pf3h7OzM6ytrRW6C6lI3IpQUlKCnZ2d6HiUjev58+c4ffo07O3tFYpTIpHwE4FqqG6Zs7W1lRtDWXa5TZs2SE1NhbW1tdxfyYVeeb8DRejq6sLExERUhp4/f45Tp04Jy6mpqcjJyeEyVIfKntfq2qVLl5CdnY2FCxfCzc0NdnZ2L/UUtLSKvu/WrVvD1dUV69atU6jbYnkcHBwAQHRsTpw4IQpz/Phx2NjYKNQtu6RHCZfPl/f333/j3Llz8PLyAvCi63HpXhAN9eqrEn369IGGhgZ+/PFH7N69u1plrir29vZITk4Wlcn4+Hg0atQItra20NHRgYmJiaicltTDJRwcHNC4cWNcu3ZN7pzOY9hZCW5kMlZHsrOz4e7ujl9++QVnz55FRkYGtm7disWLF6Nv375COAsLC+zfvx+3bt0Sprq3sbHBjh07kJSUhOTkZAwZMuSl70IOGTIESkpKGDlyJC5evIg///xTbuY6GxsbnDp1Cn/99RfS0tIwY8aMCiddedm4y0pKSkLfvn2xbds2XLx4EVeuXMH69esRHh4uOh5AcRfenTt34tKlSxg9ejQePHigcGVrYWGBjIwMJCUl4d69ey89OcnbqrplLiAgAH/++SeWLl2Ky5cvY82aNYiNjRV1SZs5cyaio6MREhKCCxcuICUlBZs3b8b06dOFMOX9Dspas2YN/Pz8sGfPHqSnp+PChQuYPHkyLly4AE9PTyGcqqoqAgICcOLECZw+fRre3t5o3769XDfeilhYWODEiRPIzMzEvXv3avUJwOtO0fNaXXvnnXcgkUiwatUqXL16Fbt27cKcOXOqFZeFhQXOnj2L1NRU3Lt3T/Q0a8SIEVi4cCGICP369as0nv79+2PZsmU4ceIE/vnnHxw8eBCjR49GixYtRO8BvnbtGsaPH4/U1FRs2rQJq1atQmBgoEJ5NTIyglQqxe7du3H79m38999/1drnN93Tp09x69Yt/Pvvvzhz5gzmz5+Pvn374qOPPsKwYcMAFI/hb9++vTAp1KFDh0TnpIagrKwMb29vTJ06FTY2NgoNQ3lZQ4cOhZqaGr788kucP38eBw4cQEBAAL744guhC29gYCAWLlyImJgYXLp0Cf7+/qJXjWlpaSEoKAjjxo1DVFQU0tPTcebMGaxatUp4Rylj/KIg9loL8JA1dBYqpKmpiffeew/Lli1Deno6CgoKYG5ujpEjR2LatGlCuNDQUIwfPx7r1q2DqakpMjMzsXTpUvj4+KBjx44wMDDA5MmT5SY3UST9//3vfxg1ahScnZ3h4OCARYsWCXdxAeDrr79GYmIiBg0aBCUlJQwePBj+/v7lTlbxsnGXZWZmBgsLC4SEhAhTx5csjxs3ThR24cKFWLhwIZKSkmBtbY1du3bBwMBAof328vLCjh070K1bN+Tk5CAiIqLScVS1zWhI1RMUvYqqW+Y6deqEsLAwhISEYPr06ejZsyfGjRuH1atXC2F69uyJ33//HbNnz8aiRYugqqoKOzs70Vi18n4HZbVr1w5HjhzBqFGjcPPmTWhqaqJly5aIiYkRxuMBgLq6OiZPnowhQ4bg33//hZubm0ITR5UICgrCl19+CQcHBzx58gQZGRkVvlalLoy0WlBvab0sRc9rdc3Q0BCRkZGYNm0aVq5ciTZt2mDJkiWi16goauTIkTh48CBcXV2Rm5uLAwcOCK8GGTx4MMaOHYvBgwdX2bW7Z8+e2LRpExYsWID//vsPxsbGcHd3R3BwsOi9jMOGDcOTJ0/Qrl07KCsrIzAwUHgdUFVUVFSwcuVKzJ49GzNnzoSbm5vwuo36Ihsrq9f0qmP37t0wMTGBiooKdHV14ejoiJUrV+LLL78UjV8MDw+Hr68vXFxcYGtri8WLF6NHjx4NmHPA19cX8+fPx/Dhw+skfnV1dfz1118IDAxE27Ztoa6uDi8vLyxdulQIM2HCBGRlZQnHy8fHB/369RPd1JgzZw4MDQ2xYMECXL16FTKZTHhdDGMAoESVvZyJsVdAfn4+MjIy0Lx582qN32Kvj8zMTDRv3hyJiYnCe+zY62fkyJG4dOkS4uLi6j3tyMhIjB07VnTXnbHqyszMhJWVFRISEtCmTZsax9e1a1c4OTlh+fLlNc8ceyPFxcWhe/fuuH79uvBk8U3E13ZvPn6SyRhjrEaWLFkCDw8PaGhoIDY2FlFRUfjhhx8aOluMVVtBQQGys7Mxffp0tG/fvlYamIxV5unTp7h79y6Cg4MxYMCAN7qByd4OPCaTMcZYjZw8eRIeHh5o3bo1wsLCsHLlSrnXNjD2OomPj4eJiQkSEhJ45mJWLzZt2oRmzZohJycHixcvbujsMFZj3F2WvfK4SwVjjDHG2JuDr+3efPwkkzHGGGOMMcZYreFGJntt8EN3xhhjjLHXH1/Tvfm4kcleeaqqqgCAvLy8Bs4JY4wxxhirqZJrupJrPPbm4dll2StPWVkZMpkMd+7cAVD8jqfSL3pnjDHGGGOvPiJCXl4e7ty5A5lMBmVl5YbOEqsjPPEPey0QEW7dusXvvmOMMcYYe83JZDIYGxvzQ4M3GDcy2WulsLAQBQUFDZ0NxhhjjDFWDaqqqvwE8y3AjUzGGGOMMcYYY7WGJ/5hjDHGGGOMMVZruJHJGGOMMcYYY6zWcCOTMcYYY4wxxlit4UYmY4wxxhhjjLFaw41MxhhjjDHGGGO1hhuZjDHGGGOMMcZqDTcyGWOMMcYYY4zVmv8D2VyFpJsver8AAAAASUVORK5CYII=",
       "text/plain": [
-       "<Figure size 3000x400 with 1 Axes>"
+       "<Figure size 1100x500 with 2 Axes>"
       ]
      },
      "metadata": {},
      "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "print('Proteins as amino-acid counts performances:')\n",
+    "plot_performance_metrics(\n",
+    "    df_cv=reports['aminoacidcnt_cv_train'],\n",
+    "    df_test=reports['aminoacidcnt_test'],\n",
+    "    df_test_majority=reports['aminoacidcnt_majority_vote'],\n",
+    "    title=f'aminoacidcnt_performance',\n",
+    "    show_plot=False,\n",
+    "    metrics_to_plot = {\n",
+    "        'val_acc': 'Validation Accuracy',\n",
+    "        'val_roc_auc': 'Validation ROC AUC',\n",
+    "        'val_f1_score': 'Validation F1 Score',\n",
+    "        'val_precision': 'Validation Precision',\n",
+    "        'val_recall': 'Validation Recall',\n",
+    "        'test_acc': 'Test Accuracy',\n",
+    "        'test_roc_auc': 'Test ROC AUC',\n",
+    "        'test_f1_score': 'Test F1 Score',\n",
+    "        'test_precision': 'Test Precision',\n",
+    "        'test_recall': 'Test Recall',\n",
+    "    },\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Compare Performance"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/tmp/ipykernel_1899217/2462072186.py:5: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
+      "Try using .loc[row_indexer,col_indexer] = value instead\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  tmp['Experiment'] = r\n",
+      "/tmp/ipykernel_1899217/2462072186.py:5: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
+      "Try using .loc[row_indexer,col_indexer] = value instead\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  tmp['Experiment'] = r\n",
+      "/tmp/ipykernel_1899217/2462072186.py:5: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
+      "Try using .loc[row_indexer,col_indexer] = value instead\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  tmp['Experiment'] = r\n"
+     ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Metric: ROC AUC\n"
+      "| Experiment                    | Study      |   Test Accuracy |   Test ROC AUC |\n",
+      "|:------------------------------|:-----------|----------------:|---------------:|\n",
+      "| Baseline                      | Standard   |        0.782051 |       0.845847 |\n",
+      "| Baseline                      | Target     |        0.526316 |       0.595819 |\n",
+      "| Baseline                      | Similarity |        0.779221 |       0.854336 |\n",
+      "| Cells as one-hot              | Standard   |        0.820513 |       0.875083 |\n",
+      "| Cells as one-hot              | Target     |        0.618421 |       0.61324  |\n",
+      "| Cells as one-hot              | Similarity |        0.74026  |       0.842141 |\n",
+      "| Proteins as amino-acid counts | Standard   |        0.705128 |       0.828571 |\n",
+      "| Proteins as amino-acid counts | Target     |        0.605263 |       0.543554 |\n",
+      "| Proteins as amino-acid counts | Similarity |        0.74026  |       0.815718 |\n",
+      "--------------------------------------------------------------------------------\n",
+      "Comparison of the best models majority vote:\n"
      ]
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
-       "<Figure size 600x400 with 1 Axes>"
+       "<Figure size 800x500 with 2 Axes>"
       ]
      },
      "metadata": {},
      "output_type": "display_data"
-    },
+    }
+   ],
+   "source": [
+    "df = []\n",
+    "for r in ['majority_vote', 'cellsonehot_majority_vote', 'aminoacidcnt_majority_vote']:\n",
+    "    tmp = reports[r]\n",
+    "    tmp = tmp[tmp['cv_models'].isna()]\n",
+    "    tmp['Experiment'] = r\n",
+    "    df.append(tmp)\n",
+    "df = pd.concat(df)\n",
+    "# Rename split_type to paper names\n",
+    "df['split_type'] = df['split_type'].replace({\n",
+    "    'random': 'Standard',\n",
+    "    'uniprot': 'Target',\n",
+    "    'tanimoto': 'Similarity',\n",
+    "    'standard': 'Standard',\n",
+    "    'target': 'Target',\n",
+    "    'similarity': 'Similarity',\n",
+    "})\n",
+    "# Rename columns to paper names\n",
+    "df.rename(columns={\n",
+    "    'split_type': 'Study',\n",
+    "    'test_acc': 'Test Accuracy',\n",
+    "    'test_roc_auc': 'Test ROC AUC',\n",
+    "}, inplace=True)\n",
+    "# Rename experiment names to paper names\n",
+    "df['Experiment'] = df['Experiment'].replace({\n",
+    "    'majority_vote': 'Baseline',\n",
+    "    'cellsonehot_majority_vote': 'Cells as one-hot',\n",
+    "    'aminoacidcnt_majority_vote': 'Proteins as amino-acid counts',\n",
+    "})\n",
+    "print(df[['Experiment', 'Study', 'Test Accuracy', 'Test ROC AUC']].to_markdown(index=False))\n",
+    "df['Experiment'] = df['Experiment'] = df['Experiment'].replace({\n",
+    "    'Cells as one-hot': 'Cells as\\none-hot',\n",
+    "    'Proteins as amino-acid counts': 'Proteins as\\nAA counts',\n",
+    "})\n",
+    "\n",
+    "def plot_comparison_df(df, filename=None):\n",
+    "    # Plot the test accuracy and ROC AUC in two bar-plots side by side, with Study as hue\n",
+    "    _, axes = plt.subplots(1, 2, figsize=(8, 5))\n",
+    "    sns.barplot(\n",
+    "        data=df,\n",
+    "        x='Experiment',\n",
+    "        y='Test Accuracy',\n",
+    "        hue='Study',\n",
+    "        errorbar=('sd', 1),\n",
+    "        palette=palette[:3],\n",
+    "        ax=axes[0])\n",
+    "    # Set ax[0] y-axis to percentage\n",
+    "    axes[0].yaxis.set_major_formatter(plt.matplotlib.ticker.PercentFormatter(1, decimals=0))\n",
+    "    # Set ax[0] y-axis limit from 0 to 100\n",
+    "    axes[0].set_ylim(0, 1.0)\n",
+    "    # Remove the x-axis label\n",
+    "    axes[0].set_xlabel('')\n",
+    "    axes[0].grid(axis='y', alpha=0.5, linewidth=0.5)\n",
+    "\n",
+    "    sns.barplot(\n",
+    "        data=df,\n",
+    "        x='Experiment',\n",
+    "        y='Test ROC AUC',\n",
+    "        hue='Study',\n",
+    "        errorbar=('sd', 1),\n",
+    "        palette=palette[:3],\n",
+    "        ax=axes[1])\n",
+    "    axes[1].set_ylim(0, 1.0)\n",
+    "    # Remove the legend from the first plot\n",
+    "    axes[0].legend().remove()\n",
+    "    # Set the legend outside the plot in the middle of the two subplots (3 columns)\n",
+    "    axes[1].legend(loc='upper center', bbox_to_anchor=(-0.15, -0.12), ncol=3)\n",
+    "    # Remove the x-axis label\n",
+    "    axes[1].set_xlabel('')\n",
+    "    axes[1].grid(axis='y', alpha=0.5, linewidth=0.5)\n",
+    "\n",
+    "    # Add values to the bar plots rotated 90 degrees at 0.5 height\n",
+    "    for i, ax in enumerate(axes):\n",
+    "        for p in ax.patches:\n",
+    "            if p.get_height() < 0.01:\n",
+    "                continue\n",
+    "            if i % 2 == 0:\n",
+    "                value = f'{p.get_height():.1%}'\n",
+    "            else:\n",
+    "                value = f'{p.get_height():.3f}'\n",
+    "            \n",
+    "            x = p.get_x() + p.get_width() / 2\n",
+    "            y = 0.3\n",
+    "            ax.annotate(value, (x, y), ha='center', va='center', color='black', fontsize=10, rotation=90, alpha=0.8)\n",
+    "\n",
+    "    if filename is not None:\n",
+    "        plt.savefig(f'plots/{filename}.pdf', bbox_inches='tight')\n",
+    "        # plt.savefig(f'plots/{filename}.png', bbox_inches='tight')\n",
+    "    plt.show()\n",
+    "\n",
+    "print('-' * 80)\n",
+    "print('Comparison of the best models majority vote:')\n",
+    "plot_comparison_df(df, 'embedding_comparison_majority_vote')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Metric: Recall\n"
+      "| Experiment                    | Study      |   Test Accuracy |   Test ROC AUC |\n",
+      "|:------------------------------|:-----------|----------------:|---------------:|\n",
+      "| Baseline                      | Similarity |        0.800866 |       0.857498 |\n",
+      "| Baseline                      | Standard   |        0.786325 |       0.851163 |\n",
+      "| Baseline                      | Target     |        0.618421 |       0.588386 |\n",
+      "| Cells as one-hot              | Similarity |        0.748918 |       0.826107 |\n",
+      "| Cells as one-hot              | Standard   |        0.807692 |       0.864895 |\n",
+      "| Cells as one-hot              | Target     |        0.622807 |       0.604413 |\n",
+      "| Proteins as amino-acid counts | Similarity |        0.753247 |       0.810298 |\n",
+      "| Proteins as amino-acid counts | Standard   |        0.747863 |       0.831672 |\n",
+      "| Proteins as amino-acid counts | Target     |        0.578947 |       0.540999 |\n",
+      "--------------------------------------------------------------------------------\n",
+      "Comparison of the best models mean values:\n"
      ]
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
-       "<Figure size 600x400 with 1 Axes>"
+       "<Figure size 800x500 with 2 Axes>"
       ]
      },
      "metadata": {},
      "output_type": "display_data"
-    },
+    }
+   ],
+   "source": [
+    "df = []\n",
+    "for r in ['test', 'cellsonehot_test', 'aminoacidcnt_test']:\n",
+    "    tmp = reports[r]\n",
+    "    tmp['Experiment'] = r\n",
+    "    df.append(tmp)\n",
+    "df = pd.concat(df)\n",
+    "# Rename split_type to paper names\n",
+    "df['split_type'] = df['split_type'].replace({\n",
+    "    'random': 'Standard',\n",
+    "    'uniprot': 'Target',\n",
+    "    'tanimoto': 'Similarity',\n",
+    "    'standard': 'Standard',\n",
+    "    'target': 'Target',\n",
+    "    'similarity': 'Similarity',\n",
+    "})\n",
+    "# Rename columns to paper names\n",
+    "df.rename(columns={\n",
+    "    'split_type': 'Study',\n",
+    "    'test_acc': 'Test Accuracy',\n",
+    "    'test_roc_auc': 'Test ROC AUC',\n",
+    "}, inplace=True)\n",
+    "# Group by experiment and split type then get the mean\n",
+    "df = df.groupby(['Experiment', 'Study']).mean(['Test Accuracy', 'Test ROC AUC']).reset_index()\n",
+    "# Rename experiment names to paper names\n",
+    "df['Experiment'] = df['Experiment'].replace({\n",
+    "    'test': 'Baseline',\n",
+    "    'cellsonehot_test': 'Cells as one-hot',\n",
+    "    'aminoacidcnt_test': 'Proteins as amino-acid counts',\n",
+    "})\n",
+    "# Order df by Experiment\n",
+    "df = df.sort_values(['Experiment'])\n",
+    "# Order Study by ['Standard', 'Target', 'Similarity']\n",
+    "df['Study'] = pd.Categorical(df['Study'], ['Standard', 'Target', 'Similarity'])\n",
+    "\n",
+    "print(df[['Experiment', 'Study', 'Test Accuracy', 'Test ROC AUC']].to_markdown(index=False))\n",
+    "df['Experiment'] = df['Experiment'] = df['Experiment'].replace({\n",
+    "    'Cells as one-hot': 'Cells as\\none-hot',\n",
+    "    'Proteins as amino-acid counts': 'Proteins as\\nAA counts',\n",
+    "})\n",
+    "\n",
+    "print('-' * 80)\n",
+    "print('Comparison of the best models mean values:')\n",
+    "plot_comparison_df(df, 'embedding_comparison_mean')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Ablation Studies"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Metric: Precision\n"
+      "--------------------------------------------------------------------------------\n",
+      "Plotting ablation study for standard CV split\n",
+      "--------------------------------------------------------------------------------\n"
      ]
     },
     {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 600x400 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
+     "name": "stderr",
      "output_type": "stream",
      "text": [
-      "Metric: F1 Score\n"
+      "/tmp/ipykernel_1899217/2296657062.py:75: UserWarning: The palette list has more values (4) than needed (1), which may not be intended.\n",
+      "  sns.barplot(data=final_df,\n"
      ]
     },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 600x400 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Metric: Accuracy\n"
+      "--------------------------------------------------------------------------------\n",
+      "Plotting ablation study for target CV split\n",
+      "--------------------------------------------------------------------------------\n"
      ]
     },
     {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 600x400 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
+     "name": "stderr",
      "output_type": "stream",
      "text": [
-      "Plotting performance for main part of the paper...\n"
+      "/tmp/ipykernel_1899217/2296657062.py:75: UserWarning: The palette list has more values (4) than needed (1), which may not be intended.\n",
+      "  sns.barplot(data=final_df,\n"
      ]
     },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1100x500 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "print('Pytorch performances:')\n",
-    "plot_performance_metrics(\n",
-    "    df_cv=reports['cv_train'],\n",
-    "    df_test=reports['test'],\n",
-    "    df_test_majority=reports['majority_vote'][reports['majority_vote']['cv_models'].isna()],\n",
-    "    title=f'pytorch_performance',\n",
-    "    show_plot=True,\n",
-    "    metrics_to_plot = {\n",
-    "        'val_acc': 'Validation Accuracy',\n",
-    "        'val_roc_auc': 'Validation ROC AUC',\n",
-    "        'val_f1_score': 'Validation F1 Score',\n",
-    "        'val_precision': 'Validation Precision',\n",
-    "        'val_recall': 'Validation Recall',\n",
-    "        'test_acc': 'Test Accuracy',\n",
-    "        'test_roc_auc': 'Test ROC AUC',\n",
-    "        'test_f1_score': 'Test F1 Score',\n",
-    "        'test_precision': 'Test Precision',\n",
-    "        'test_recall': 'Test Recall',\n",
-    "    },\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### XGBoost Plots"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 141,
-   "metadata": {},
-   "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "XGBoost performances:\n",
-      "Metrics: ['ROC AUC', 'Recall', 'Precision', 'F1 Score', 'Accuracy']\n"
+      "--------------------------------------------------------------------------------\n",
+      "Plotting ablation study for similarity CV split\n",
+      "--------------------------------------------------------------------------------\n"
      ]
     },
     {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 3000x400 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/tmp/ipykernel_1899217/2296657062.py:75: UserWarning: The palette list has more values (4) than needed (1), which may not be intended.\n",
+      "  sns.barplot(data=final_df,\n"
+     ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Metric: ROC AUC\n"
+      "--------------------------------------------------------------------------------\n",
+      "Plotting ablation study for standard CV split\n",
+      "--------------------------------------------------------------------------------\n"
      ]
     },
     {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 600x400 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/tmp/ipykernel_1899217/2296657062.py:75: UserWarning: The palette list has more values (4) than needed (1), which may not be intended.\n",
+      "  sns.barplot(data=final_df,\n"
+     ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Metric: Recall\n"
+      "--------------------------------------------------------------------------------\n",
+      "Plotting ablation study for target CV split\n",
+      "--------------------------------------------------------------------------------\n"
      ]
     },
     {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 600x400 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/tmp/ipykernel_1899217/2296657062.py:75: UserWarning: The palette list has more values (4) than needed (1), which may not be intended.\n",
+      "  sns.barplot(data=final_df,\n"
+     ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Metric: Precision\n"
+      "--------------------------------------------------------------------------------\n",
+      "Plotting ablation study for similarity CV split\n",
+      "--------------------------------------------------------------------------------\n"
      ]
     },
     {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 600x400 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/tmp/ipykernel_1899217/2296657062.py:75: UserWarning: The palette list has more values (4) than needed (1), which may not be intended.\n",
+      "  sns.barplot(data=final_df,\n"
+     ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Metric: F1 Score\n"
+      "--------------------------------------------------------------------------------\n",
+      "Plotting ablation study for standard CV split\n",
+      "--------------------------------------------------------------------------------\n"
      ]
     },
     {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 600x400 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/tmp/ipykernel_1899217/2296657062.py:75: UserWarning: The palette list has more values (4) than needed (1), which may not be intended.\n",
+      "  sns.barplot(data=final_df,\n"
+     ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Metric: Accuracy\n"
+      "--------------------------------------------------------------------------------\n",
+      "Plotting ablation study for target CV split\n",
+      "--------------------------------------------------------------------------------\n"
      ]
     },
     {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 600x400 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/tmp/ipykernel_1899217/2296657062.py:75: UserWarning: The palette list has more values (4) than needed (1), which may not be intended.\n",
+      "  sns.barplot(data=final_df,\n"
+     ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Plotting performance for main part of the paper...\n"
+      "--------------------------------------------------------------------------------\n",
+      "Plotting ablation study for similarity CV split\n",
+      "--------------------------------------------------------------------------------\n"
      ]
     },
     {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1100x500 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/tmp/ipykernel_1899217/2296657062.py:75: UserWarning: The palette list has more values (4) than needed (1), which may not be intended.\n",
+      "  sns.barplot(data=final_df,\n"
+     ]
     }
    ],
    "source": [
-    "print('XGBoost performances:')\n",
-    "plot_performance_metrics(\n",
-    "    df_cv=reports['xgboost_cv_train'],\n",
-    "    df_test=reports['xgboost_test'],\n",
-    "    df_test_majority=reports['xgboost_majority_vote'],\n",
-    "    title=f'xgboost_performance',\n",
-    "    show_plot=True,\n",
-    "    metrics_to_plot = {\n",
-    "        'val_acc': 'Validation Accuracy',\n",
-    "        'val_roc_auc': 'Validation ROC AUC',\n",
-    "        'val_f1_score': 'Validation F1 Score',\n",
-    "        'val_precision': 'Validation Precision',\n",
-    "        'val_recall': 'Validation Recall',\n",
-    "        'test_acc': 'Test Accuracy',\n",
-    "        'test_roc_auc': 'Test ROC AUC',\n",
-    "        'test_f1_score': 'Test F1 Score',\n",
-    "        'test_precision': 'Test Precision',\n",
-    "        'test_recall': 'Test Recall',\n",
-    "    },\n",
-    ")"
+    "def plot_ablation_study(report, title=''):\n",
+    "    # Define the ablation study combinations\n",
+    "    ablation_study_combinations = [\n",
+    "        'disabled smiles',\n",
+    "        'disabled poi',\n",
+    "        'disabled e3',\n",
+    "        'disabled cell',\n",
+    "        'disabled poi e3',\n",
+    "        'disabled poi e3 smiles',\n",
+    "        'disabled poi e3 cell',\n",
+    "    ]\n",
+    "\n",
+    "    for group in report['split_type'].unique():\n",
+    "        print('-' * 80)\n",
+    "        print(f'Plotting ablation study for {group} CV split')\n",
+    "        print('-' * 80)\n",
+    "        baseline = report[report['disabled_embeddings'].isna()].copy()\n",
+    "        baseline = baseline[baseline['split_type'] == group]\n",
+    "        baseline['disabled_embeddings'] = 'all embeddings enabled'\n",
+    "        # metrics_to_show = ['val_acc', 'test_acc']\n",
+    "        metrics_to_show = ['test_acc']\n",
+    "        # baseline = baseline.melt(id_vars=['fold', 'disabled_embeddings'], value_vars=metrics_to_show, var_name='metric', value_name='score')\n",
+    "        baseline = baseline.melt(id_vars=['disabled_embeddings'], value_vars=metrics_to_show, var_name='metric', value_name='score')\n",
+    "\n",
+    "        ablation_dfs = []\n",
+    "        for disabled_embeddings in ablation_study_combinations:\n",
+    "            tmp = report[report['disabled_embeddings'] == disabled_embeddings].copy()\n",
+    "            tmp = tmp[tmp['split_type'] == group]\n",
+    "            # tmp = tmp.melt(id_vars=['fold', 'disabled_embeddings'], value_vars=metrics_to_show, var_name='metric', value_name='score')\n",
+    "            tmp = tmp.melt(id_vars=['disabled_embeddings'], value_vars=metrics_to_show, var_name='metric', value_name='score')\n",
+    "            ablation_dfs.append(tmp)\n",
+    "        ablation_df = pd.concat(ablation_dfs)\n",
+    "\n",
+    "        dummy_test_df = pd.DataFrame()\n",
+    "        tmp = report[report['split_type'] == group]\n",
+    "        dummy_test_df['score'] = tmp[['test_active_perc', 'test_inactive_perc']].max(axis=1)\n",
+    "        dummy_test_df['metric'] = 'test_acc'\n",
+    "        dummy_test_df['disabled_embeddings'] = 'dummy'\n",
+    "\n",
+    "        # dummy_df = pd.concat([dummy_val_df, dummy_test_df])\n",
+    "        dummy_df = dummy_test_df\n",
+    "\n",
+    "        final_df = pd.concat([dummy_df, baseline, ablation_df])\n",
+    "\n",
+    "        final_df['metric'] = final_df['metric'].map({\n",
+    "            'val_acc': 'Validation Accuracy',\n",
+    "            'test_acc': 'Test Accuracy',\n",
+    "            'val_roc_auc': 'Val ROC-AUC',\n",
+    "            'test_roc_auc': 'Test ROC-AUC',\n",
+    "        })\n",
+    "\n",
+    "        final_df['disabled_embeddings'] = final_df['disabled_embeddings'].map({\n",
+    "            'all embeddings enabled': 'All embeddings enabled',\n",
+    "            'dummy': 'Dummy model',\n",
+    "            'disabled smiles': 'Disabled PROTAC information',\n",
+    "            'disabled e3': 'Disabled E3 information',\n",
+    "            'disabled poi': 'Disabled POI information',\n",
+    "            'disabled cell': 'Disabled cell information',\n",
+    "            'disabled poi e3': 'Disabled E3 and POI info',\n",
+    "            'disabled poi e3 smiles': 'Disabled compound, E3, and POI info\\n(only cell information left)',\n",
+    "            'disabled poi e3 cell': 'Disabled cell, E3, and POI info\\n(only PROTAC information left)',\n",
+    "        })\n",
+    "\n",
+    "        # Print final_df to latex\n",
+    "        tmp  = final_df.groupby(['disabled_embeddings', 'metric']).mean().round(3)\n",
+    "        # Remove fold column to tmp\n",
+    "        tmp = tmp.reset_index() #.drop('fold', axis=1)\n",
+    "\n",
+    "        # print('DF to plot:\\n', tmp.to_markdown(index=False))\n",
+    "\n",
+    "        fig, ax = plt.subplots(figsize=(3, 5))\n",
+    "        \n",
+    "        # fig, ax = plt.subplots()\n",
+    "\n",
+    "        sns.barplot(data=final_df,\n",
+    "            y='disabled_embeddings',\n",
+    "            x='score',\n",
+    "            hue='metric',\n",
+    "            ax=ax,\n",
+    "            errorbar=('sd', 1),\n",
+    "            palette=sns.color_palette(palette, len(palette)),\n",
+    "            saturation=1,\n",
+    "        )\n",
+    "\n",
+    "        # ax.set_title(f'{group.replace(\"random\", \"standard\")} CV split')\n",
+    "        ax.grid(axis='x', alpha=0.5)\n",
+    "        ax.tick_params(axis='y', rotation=0)\n",
+    "        ax.set_xlim(0, 1.0)\n",
+    "        ax.xaxis.set_major_formatter(plt.matplotlib.ticker.PercentFormatter(1, decimals=0))\n",
+    "        ax.set_ylabel('')\n",
+    "        ax.set_xlabel('')\n",
+    "\n",
+    "        # Plot the legend below the x-axis, outside the plot\n",
+    "        ax.legend(loc='upper center', bbox_to_anchor=(0.02, -0.1))\n",
+    "\n",
+    "        # For each bar, add the rotated value (as percentage), inside the bar\n",
+    "        for i, p in enumerate(plt.gca().patches):\n",
+    "            # TODO: For some reasons, there is an additional bar being added at\n",
+    "            # the end of the plot... it's not in the dataframe\n",
+    "            if i == len(plt.gca().patches) - 1:\n",
+    "                continue\n",
+    "            value = '{:.1f}%'.format(100 * p.get_width())\n",
+    "            y = p.get_y() + p.get_height() / 2\n",
+    "            x = 0.2 # p.get_height() - p.get_height() / 2\n",
+    "            plt.annotate(value, (x, y), ha='center', va='center', color='black', fontsize=10, alpha=0.8)\n",
+    "\n",
+    "        plt.savefig(f'plots/{title}{group}.pdf', bbox_inches='tight')\n",
+    "        plt.close()\n",
+    "\n",
+    "for experiment in ['', 'cellsonehot_', 'aminoacidcnt_']:\n",
+    "    reports[f'{experiment}test']['disabled_embeddings'] = pd.NA\n",
+    "    experiment_name = 'pytorch_' if experiment == '' else experiment\n",
+    "    plot_ablation_study(\n",
+    "        pd.concat([\n",
+    "            reports[f'{experiment}ablation'],\n",
+    "            reports[f'{experiment}test'],\n",
+    "        ]),\n",
+    "        title=f'{experiment_name}ablation_study_'\n",
+    "    )"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Cells as One-Hot Encoded"
+    "## Others"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 142,
+   "execution_count": 166,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Cells as one-hot performances:\n",
-      "Metrics: ['ROC AUC', 'Recall', 'Precision', 'F1 Score', 'Accuracy']\n"
-     ]
-    },
-    {
-     "data": {
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",
-      "text/plain": [
-       "<Figure size 3000x400 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Metric: ROC AUC\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 600x400 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Metric: Recall\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 600x400 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Metric: Precision\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 600x400 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Metric: F1 Score\n"
+      "/cephyr/users/ribes/Alvis/PROTAC-Degradation-Predictor\n"
      ]
-    },
+    }
+   ],
+   "source": [
+    "!pwd"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 95,
+   "metadata": {},
+   "outputs": [
     {
      "data": {
-      "image/png": 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",
+      "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>Compound ID</th>\n",
+       "      <th>Uniprot</th>\n",
+       "      <th>Smiles</th>\n",
+       "      <th>E3 Ligase</th>\n",
+       "      <th>InChI</th>\n",
+       "      <th>InChI Key</th>\n",
+       "      <th>Molecular Weight</th>\n",
+       "      <th>Heavy Atom Count</th>\n",
+       "      <th>Ring Count</th>\n",
+       "      <th>Rotatable Bond Count</th>\n",
+       "      <th>...</th>\n",
+       "      <th>Name</th>\n",
+       "      <th>Assay (DC50/Dmax)</th>\n",
+       "      <th>Exact Mass</th>\n",
+       "      <th>XLogP3</th>\n",
+       "      <th>Target (Parsed)</th>\n",
+       "      <th>POI Sequence</th>\n",
+       "      <th>E3 Ligase Uniprot</th>\n",
+       "      <th>E3 Ligase Sequence</th>\n",
+       "      <th>Cell Line Identifier</th>\n",
+       "      <th>Active - OR</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>Q07817</td>\n",
+       "      <td>Cc1ncsc1-c1ccc([C@H](C)NC(=O)[C@@H]2C[C@@H](O)...</td>\n",
+       "      <td>VHL</td>\n",
+       "      <td>InChI=1S/C73H88ClF3N10O10S4/c1-47(49-13-15-51(...</td>\n",
+       "      <td>SXPDUCVNMGMWBJ-FMZBIETASA-N</td>\n",
+       "      <td>1486.282</td>\n",
+       "      <td>101</td>\n",
+       "      <td>10</td>\n",
+       "      <td>24</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>MSQSNRELVVDFLSYKLSQKGYSWSQFSDVEENRTEAPEGTESEME...</td>\n",
+       "      <td>P40337</td>\n",
+       "      <td>MPRRAENWDEAEVGAEEAGVEEYGPEEDGGEESGAEESGPEESGPE...</td>\n",
+       "      <td>MOLT-4</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2</td>\n",
+       "      <td>Q07817</td>\n",
+       "      <td>Cc1ncsc1-c1ccc([C@H](C)NC(=O)[C@@H]2C[C@@H](O)...</td>\n",
+       "      <td>VHL</td>\n",
+       "      <td>InChI=1S/C74H90ClF3N10O10S4/c1-48(50-13-15-52(...</td>\n",
+       "      <td>HQKUMELJMUNTTF-NMKDNUEVSA-N</td>\n",
+       "      <td>1500.309</td>\n",
+       "      <td>102</td>\n",
+       "      <td>10</td>\n",
+       "      <td>25</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>MSQSNRELVVDFLSYKLSQKGYSWSQFSDVEENRTEAPEGTESEME...</td>\n",
+       "      <td>P40337</td>\n",
+       "      <td>MPRRAENWDEAEVGAEEAGVEEYGPEEDGGEESGAEESGPEESGPE...</td>\n",
+       "      <td>MOLT-4</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>3</td>\n",
+       "      <td>Q07817</td>\n",
+       "      <td>Cc1ncsc1-c1ccc([C@H](C)NC(=O)[C@@H]2C[C@@H](O)...</td>\n",
+       "      <td>VHL</td>\n",
+       "      <td>InChI=1S/C75H92ClF3N10O10S4/c1-49(51-16-18-53(...</td>\n",
+       "      <td>ATQCEJKUPSBDMA-QARNUTPLSA-N</td>\n",
+       "      <td>1514.336</td>\n",
+       "      <td>103</td>\n",
+       "      <td>10</td>\n",
+       "      <td>26</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>MSQSNRELVVDFLSYKLSQKGYSWSQFSDVEENRTEAPEGTESEME...</td>\n",
+       "      <td>P40337</td>\n",
+       "      <td>MPRRAENWDEAEVGAEEAGVEEYGPEEDGGEESGAEESGPEESGPE...</td>\n",
+       "      <td>MOLT-4</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>4</td>\n",
+       "      <td>Q07817</td>\n",
+       "      <td>Cc1ncsc1-c1ccc([C@H](C)NC(=O)[C@@H]2C[C@@H](O)...</td>\n",
+       "      <td>VHL</td>\n",
+       "      <td>InChI=1S/C76H94ClF3N10O10S4/c1-50(52-17-19-54(...</td>\n",
+       "      <td>FNKQAGMHNFFSEI-DTTPTBRMSA-N</td>\n",
+       "      <td>1528.363</td>\n",
+       "      <td>104</td>\n",
+       "      <td>10</td>\n",
+       "      <td>27</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>MSQSNRELVVDFLSYKLSQKGYSWSQFSDVEENRTEAPEGTESEME...</td>\n",
+       "      <td>P40337</td>\n",
+       "      <td>MPRRAENWDEAEVGAEEAGVEEYGPEEDGGEESGAEESGPEESGPE...</td>\n",
+       "      <td>MOLT-4</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>5</td>\n",
+       "      <td>Q07817</td>\n",
+       "      <td>Cc1ncsc1-c1ccc([C@H](C)NC(=O)[C@@H]2C[C@@H](O)...</td>\n",
+       "      <td>VHL</td>\n",
+       "      <td>InChI=1S/C77H96ClF3N10O10S4/c1-51(53-18-20-55(...</td>\n",
+       "      <td>PXVFFBGSTYQHRO-REQIQPEASA-N</td>\n",
+       "      <td>1542.390</td>\n",
+       "      <td>105</td>\n",
+       "      <td>10</td>\n",
+       "      <td>28</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>MSQSNRELVVDFLSYKLSQKGYSWSQFSDVEENRTEAPEGTESEME...</td>\n",
+       "      <td>P40337</td>\n",
+       "      <td>MPRRAENWDEAEVGAEEAGVEEYGPEEDGGEESGAEESGPEESGPE...</td>\n",
+       "      <td>MOLT-4</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2136</th>\n",
+       "      <td>2342</td>\n",
+       "      <td>O60885</td>\n",
+       "      <td>Cc1ncsc1-c1ccc(CNC(=O)[C@@H]2C[C@@H](O)CN2C(=O...</td>\n",
+       "      <td>VHL</td>\n",
+       "      <td>InChI=1S/C50H61ClN8O8S2/c1-29-31(3)69-49-42(29...</td>\n",
+       "      <td>VRVWHAZIBGEPEK-DPSJZEHMSA-N</td>\n",
+       "      <td>1001.673</td>\n",
+       "      <td>69</td>\n",
+       "      <td>7</td>\n",
+       "      <td>20</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Degradation of BRD4 long in HEK293 cells after...</td>\n",
+       "      <td>1000.374231</td>\n",
+       "      <td>6.76</td>\n",
+       "      <td>BRD4 long</td>\n",
+       "      <td>MSAESGPGTRLRNLPVMGDGLETSQMSTTQAQAQPQPANAASTNPP...</td>\n",
+       "      <td>P40337</td>\n",
+       "      <td>MPRRAENWDEAEVGAEEAGVEEYGPEEDGGEESGAEESGPEESGPE...</td>\n",
+       "      <td>HEK293</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2137</th>\n",
+       "      <td>2887</td>\n",
+       "      <td>Q05397</td>\n",
+       "      <td>CNC(=O)c1ccccc1Nc1cc(Nc2ccc(N3CCN(CCOCCOCCOCCO...</td>\n",
+       "      <td>VHL</td>\n",
+       "      <td>InChI=1S/C58H75F3N10O10S/c1-37(39-12-14-40(15-...</td>\n",
+       "      <td>FOOHAGZPIHCYKX-ZSFXBAAMSA-N</td>\n",
+       "      <td>1161.359</td>\n",
+       "      <td>82</td>\n",
+       "      <td>7</td>\n",
+       "      <td>27</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Degradation of FAK in A549 cells after 24 h tr...</td>\n",
+       "      <td>1160.534044</td>\n",
+       "      <td>6.81</td>\n",
+       "      <td>FAK</td>\n",
+       "      <td>MAAAYLDPNLNHTPNSSTKTHLGTGMERSPGAMERVLKVFHYFESN...</td>\n",
+       "      <td>P40337</td>\n",
+       "      <td>MPRRAENWDEAEVGAEEAGVEEYGPEEDGGEESGAEESGPEESGPE...</td>\n",
+       "      <td>A549 Cas9</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2138</th>\n",
+       "      <td>2889</td>\n",
+       "      <td>Q05397</td>\n",
+       "      <td>CNC(=O)c1ccccc1Nc1cc(Nc2ccc(N3CCN(CCOCCOCC(=O)...</td>\n",
+       "      <td>VHL</td>\n",
+       "      <td>InChI=1S/C54H67F3N10O8S/c1-33(35-12-14-36(15-1...</td>\n",
+       "      <td>RDCVMTUYWQXPEC-FSHOLZCKSA-N</td>\n",
+       "      <td>1073.253</td>\n",
+       "      <td>76</td>\n",
+       "      <td>7</td>\n",
+       "      <td>21</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Degradation of FAK in A549 cells after 24 h tr...</td>\n",
+       "      <td>1072.481615</td>\n",
+       "      <td>7.11</td>\n",
+       "      <td>FAK</td>\n",
+       "      <td>MAAAYLDPNLNHTPNSSTKTHLGTGMERSPGAMERVLKVFHYFESN...</td>\n",
+       "      <td>P40337</td>\n",
+       "      <td>MPRRAENWDEAEVGAEEAGVEEYGPEEDGGEESGAEESGPEESGPE...</td>\n",
+       "      <td>A549 Cas9</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2139</th>\n",
+       "      <td>2890</td>\n",
+       "      <td>Q05397</td>\n",
+       "      <td>CNC(=O)c1ccccc1Nc1cc(Nc2ccc(N3CCN(CCOCC(=O)N[C...</td>\n",
+       "      <td>VHL</td>\n",
+       "      <td>InChI=1S/C52H63F3N10O7S/c1-31(33-12-14-34(15-1...</td>\n",
+       "      <td>SLSLLSIRBMAERC-MGVZSLQJSA-N</td>\n",
+       "      <td>1029.200</td>\n",
+       "      <td>73</td>\n",
+       "      <td>7</td>\n",
+       "      <td>18</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Degradation of FAK in A549 cells after 24 h tr...</td>\n",
+       "      <td>1028.455400</td>\n",
+       "      <td>7.26</td>\n",
+       "      <td>FAK</td>\n",
+       "      <td>MAAAYLDPNLNHTPNSSTKTHLGTGMERSPGAMERVLKVFHYFESN...</td>\n",
+       "      <td>P40337</td>\n",
+       "      <td>MPRRAENWDEAEVGAEEAGVEEYGPEEDGGEESGAEESGPEESGPE...</td>\n",
+       "      <td>A549 Cas9</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2140</th>\n",
+       "      <td>2891</td>\n",
+       "      <td>Q05397</td>\n",
+       "      <td>CNC(=O)c1ccccc1Nc1cc(Nc2ccc(N3CCN(CCC(=O)N[C@H...</td>\n",
+       "      <td>VHL</td>\n",
+       "      <td>InChI=1S/C51H61F3N10O6S/c1-30(32-12-14-33(15-1...</td>\n",
+       "      <td>ASRIXACKPXMNKY-FCFVTTBASA-N</td>\n",
+       "      <td>999.174</td>\n",
+       "      <td>71</td>\n",
+       "      <td>7</td>\n",
+       "      <td>16</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Degradation of FAK in A549 cells after 24 h tr...</td>\n",
+       "      <td>998.444835</td>\n",
+       "      <td>7.31</td>\n",
+       "      <td>FAK</td>\n",
+       "      <td>MAAAYLDPNLNHTPNSSTKTHLGTGMERSPGAMERVLKVFHYFESN...</td>\n",
+       "      <td>P40337</td>\n",
+       "      <td>MPRRAENWDEAEVGAEEAGVEEYGPEEDGGEESGAEESGPEESGPE...</td>\n",
+       "      <td>A549 Cas9</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>2141 rows × 35 columns</p>\n",
+       "</div>"
+      ],
       "text/plain": [
-       "<Figure size 600x400 with 1 Axes>"
-      ]
-     },
+       "      Compound ID Uniprot                                             Smiles  \\\n",
+       "0               1  Q07817  Cc1ncsc1-c1ccc([C@H](C)NC(=O)[C@@H]2C[C@@H](O)...   \n",
+       "1               2  Q07817  Cc1ncsc1-c1ccc([C@H](C)NC(=O)[C@@H]2C[C@@H](O)...   \n",
+       "2               3  Q07817  Cc1ncsc1-c1ccc([C@H](C)NC(=O)[C@@H]2C[C@@H](O)...   \n",
+       "3               4  Q07817  Cc1ncsc1-c1ccc([C@H](C)NC(=O)[C@@H]2C[C@@H](O)...   \n",
+       "4               5  Q07817  Cc1ncsc1-c1ccc([C@H](C)NC(=O)[C@@H]2C[C@@H](O)...   \n",
+       "...           ...     ...                                                ...   \n",
+       "2136         2342  O60885  Cc1ncsc1-c1ccc(CNC(=O)[C@@H]2C[C@@H](O)CN2C(=O...   \n",
+       "2137         2887  Q05397  CNC(=O)c1ccccc1Nc1cc(Nc2ccc(N3CCN(CCOCCOCCOCCO...   \n",
+       "2138         2889  Q05397  CNC(=O)c1ccccc1Nc1cc(Nc2ccc(N3CCN(CCOCCOCC(=O)...   \n",
+       "2139         2890  Q05397  CNC(=O)c1ccccc1Nc1cc(Nc2ccc(N3CCN(CCOCC(=O)N[C...   \n",
+       "2140         2891  Q05397  CNC(=O)c1ccccc1Nc1cc(Nc2ccc(N3CCN(CCC(=O)N[C@H...   \n",
+       "\n",
+       "     E3 Ligase                                              InChI  \\\n",
+       "0          VHL  InChI=1S/C73H88ClF3N10O10S4/c1-47(49-13-15-51(...   \n",
+       "1          VHL  InChI=1S/C74H90ClF3N10O10S4/c1-48(50-13-15-52(...   \n",
+       "2          VHL  InChI=1S/C75H92ClF3N10O10S4/c1-49(51-16-18-53(...   \n",
+       "3          VHL  InChI=1S/C76H94ClF3N10O10S4/c1-50(52-17-19-54(...   \n",
+       "4          VHL  InChI=1S/C77H96ClF3N10O10S4/c1-51(53-18-20-55(...   \n",
+       "...        ...                                                ...   \n",
+       "2136       VHL  InChI=1S/C50H61ClN8O8S2/c1-29-31(3)69-49-42(29...   \n",
+       "2137       VHL  InChI=1S/C58H75F3N10O10S/c1-37(39-12-14-40(15-...   \n",
+       "2138       VHL  InChI=1S/C54H67F3N10O8S/c1-33(35-12-14-36(15-1...   \n",
+       "2139       VHL  InChI=1S/C52H63F3N10O7S/c1-31(33-12-14-34(15-1...   \n",
+       "2140       VHL  InChI=1S/C51H61F3N10O6S/c1-30(32-12-14-33(15-1...   \n",
+       "\n",
+       "                        InChI Key  Molecular Weight  Heavy Atom Count  \\\n",
+       "0     SXPDUCVNMGMWBJ-FMZBIETASA-N          1486.282               101   \n",
+       "1     HQKUMELJMUNTTF-NMKDNUEVSA-N          1500.309               102   \n",
+       "2     ATQCEJKUPSBDMA-QARNUTPLSA-N          1514.336               103   \n",
+       "3     FNKQAGMHNFFSEI-DTTPTBRMSA-N          1528.363               104   \n",
+       "4     PXVFFBGSTYQHRO-REQIQPEASA-N          1542.390               105   \n",
+       "...                           ...               ...               ...   \n",
+       "2136  VRVWHAZIBGEPEK-DPSJZEHMSA-N          1001.673                69   \n",
+       "2137  FOOHAGZPIHCYKX-ZSFXBAAMSA-N          1161.359                82   \n",
+       "2138  RDCVMTUYWQXPEC-FSHOLZCKSA-N          1073.253                76   \n",
+       "2139  SLSLLSIRBMAERC-MGVZSLQJSA-N          1029.200                73   \n",
+       "2140  ASRIXACKPXMNKY-FCFVTTBASA-N           999.174                71   \n",
+       "\n",
+       "      Ring Count  Rotatable Bond Count  ...  Name  \\\n",
+       "0             10                    24  ...   NaN   \n",
+       "1             10                    25  ...   NaN   \n",
+       "2             10                    26  ...   NaN   \n",
+       "3             10                    27  ...   NaN   \n",
+       "4             10                    28  ...   NaN   \n",
+       "...          ...                   ...  ...   ...   \n",
+       "2136           7                    20  ...   NaN   \n",
+       "2137           7                    27  ...   NaN   \n",
+       "2138           7                    21  ...   NaN   \n",
+       "2139           7                    18  ...   NaN   \n",
+       "2140           7                    16  ...   NaN   \n",
+       "\n",
+       "                                      Assay (DC50/Dmax)   Exact Mass XLogP3  \\\n",
+       "0                                                   NaN          NaN    NaN   \n",
+       "1                                                   NaN          NaN    NaN   \n",
+       "2                                                   NaN          NaN    NaN   \n",
+       "3                                                   NaN          NaN    NaN   \n",
+       "4                                                   NaN          NaN    NaN   \n",
+       "...                                                 ...          ...    ...   \n",
+       "2136  Degradation of BRD4 long in HEK293 cells after...  1000.374231   6.76   \n",
+       "2137  Degradation of FAK in A549 cells after 24 h tr...  1160.534044   6.81   \n",
+       "2138  Degradation of FAK in A549 cells after 24 h tr...  1072.481615   7.11   \n",
+       "2139  Degradation of FAK in A549 cells after 24 h tr...  1028.455400   7.26   \n",
+       "2140  Degradation of FAK in A549 cells after 24 h tr...   998.444835   7.31   \n",
+       "\n",
+       "      Target (Parsed)                                       POI Sequence  \\\n",
+       "0                 NaN  MSQSNRELVVDFLSYKLSQKGYSWSQFSDVEENRTEAPEGTESEME...   \n",
+       "1                 NaN  MSQSNRELVVDFLSYKLSQKGYSWSQFSDVEENRTEAPEGTESEME...   \n",
+       "2                 NaN  MSQSNRELVVDFLSYKLSQKGYSWSQFSDVEENRTEAPEGTESEME...   \n",
+       "3                 NaN  MSQSNRELVVDFLSYKLSQKGYSWSQFSDVEENRTEAPEGTESEME...   \n",
+       "4                 NaN  MSQSNRELVVDFLSYKLSQKGYSWSQFSDVEENRTEAPEGTESEME...   \n",
+       "...               ...                                                ...   \n",
+       "2136        BRD4 long  MSAESGPGTRLRNLPVMGDGLETSQMSTTQAQAQPQPANAASTNPP...   \n",
+       "2137              FAK  MAAAYLDPNLNHTPNSSTKTHLGTGMERSPGAMERVLKVFHYFESN...   \n",
+       "2138              FAK  MAAAYLDPNLNHTPNSSTKTHLGTGMERSPGAMERVLKVFHYFESN...   \n",
+       "2139              FAK  MAAAYLDPNLNHTPNSSTKTHLGTGMERSPGAMERVLKVFHYFESN...   \n",
+       "2140              FAK  MAAAYLDPNLNHTPNSSTKTHLGTGMERSPGAMERVLKVFHYFESN...   \n",
+       "\n",
+       "      E3 Ligase Uniprot                                 E3 Ligase Sequence  \\\n",
+       "0                P40337  MPRRAENWDEAEVGAEEAGVEEYGPEEDGGEESGAEESGPEESGPE...   \n",
+       "1                P40337  MPRRAENWDEAEVGAEEAGVEEYGPEEDGGEESGAEESGPEESGPE...   \n",
+       "2                P40337  MPRRAENWDEAEVGAEEAGVEEYGPEEDGGEESGAEESGPEESGPE...   \n",
+       "3                P40337  MPRRAENWDEAEVGAEEAGVEEYGPEEDGGEESGAEESGPEESGPE...   \n",
+       "4                P40337  MPRRAENWDEAEVGAEEAGVEEYGPEEDGGEESGAEESGPEESGPE...   \n",
+       "...                 ...                                                ...   \n",
+       "2136             P40337  MPRRAENWDEAEVGAEEAGVEEYGPEEDGGEESGAEESGPEESGPE...   \n",
+       "2137             P40337  MPRRAENWDEAEVGAEEAGVEEYGPEEDGGEESGAEESGPEESGPE...   \n",
+       "2138             P40337  MPRRAENWDEAEVGAEEAGVEEYGPEEDGGEESGAEESGPEESGPE...   \n",
+       "2139             P40337  MPRRAENWDEAEVGAEEAGVEEYGPEEDGGEESGAEESGPEESGPE...   \n",
+       "2140             P40337  MPRRAENWDEAEVGAEEAGVEEYGPEEDGGEESGAEESGPEESGPE...   \n",
+       "\n",
+       "     Cell Line Identifier Active - OR  \n",
+       "0                  MOLT-4         NaN  \n",
+       "1                  MOLT-4         NaN  \n",
+       "2                  MOLT-4         NaN  \n",
+       "3                  MOLT-4         NaN  \n",
+       "4                  MOLT-4        True  \n",
+       "...                   ...         ...  \n",
+       "2136               HEK293        True  \n",
+       "2137            A549 Cas9       False  \n",
+       "2138            A549 Cas9       False  \n",
+       "2139            A549 Cas9        True  \n",
+       "2140            A549 Cas9        True  \n",
+       "\n",
+       "[2141 rows x 35 columns]"
+      ]
+     },
+     "execution_count": 95,
      "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Metric: Accuracy\n"
-     ]
-    },
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import sys\n",
+    "import protac_degradation_predictor as pdp\n",
+    "\n",
+    "protac_df = pdp.load_curated_dataset()\n",
+    "protac_df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "metadata": {},
+   "outputs": [
     {
      "data": {
-      "image/png": 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",
       "text/plain": [
-       "<Figure size 600x400 with 1 Axes>"
+       "True"
       ]
      },
+     "execution_count": 56,
      "metadata": {},
-     "output_type": "display_data"
-    },
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from rdkit import Chem\n",
+    "\n",
+    "def canon_smiles(smi):\n",
+    "    mol = Chem.MolFromSmiles(smi)\n",
+    "    if mol is None:\n",
+    "        return None\n",
+    "    return Chem.MolToSmiles(mol)\n",
+    "\n",
+    "# Canonicalize SMILES\n",
+    "protac_df['canon_smiles'] = protac_df['Smiles'].apply(lambda x: canon_smiles(x))\n",
+    "# Check that all canon_smiles is equal to the Smiles column\n",
+    "protac_df['canon_smiles'].equals(protac_df['Smiles'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 97,
+   "metadata": {},
+   "outputs": [
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Plotting performance for main part of the paper...\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>Compound ID</th>\n",
+       "      <th>Uniprot</th>\n",
+       "      <th>Smiles</th>\n",
+       "      <th>E3 Ligase</th>\n",
+       "      <th>InChI</th>\n",
+       "      <th>InChI Key</th>\n",
+       "      <th>Molecular Weight</th>\n",
+       "      <th>Heavy Atom Count</th>\n",
+       "      <th>Ring Count</th>\n",
+       "      <th>Rotatable Bond Count</th>\n",
+       "      <th>...</th>\n",
+       "      <th>Name</th>\n",
+       "      <th>Assay (DC50/Dmax)</th>\n",
+       "      <th>Exact Mass</th>\n",
+       "      <th>XLogP3</th>\n",
+       "      <th>Target (Parsed)</th>\n",
+       "      <th>POI Sequence</th>\n",
+       "      <th>E3 Ligase Uniprot</th>\n",
+       "      <th>E3 Ligase Sequence</th>\n",
+       "      <th>Cell Line Identifier</th>\n",
+       "      <th>Active - OR</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>Q07817</td>\n",
+       "      <td>Cc1ncsc1-c1ccc([C@H](C)NC(=O)[C@@H]2C[C@@H](O)...</td>\n",
+       "      <td>VHL</td>\n",
+       "      <td>InChI=1S/C73H88ClF3N10O10S4/c1-47(49-13-15-51(...</td>\n",
+       "      <td>SXPDUCVNMGMWBJ-FMZBIETASA-N</td>\n",
+       "      <td>1486.282</td>\n",
+       "      <td>101</td>\n",
+       "      <td>10</td>\n",
+       "      <td>24</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>MSQSNRELVVDFLSYKLSQKGYSWSQFSDVEENRTEAPEGTESEME...</td>\n",
+       "      <td>P40337</td>\n",
+       "      <td>MPRRAENWDEAEVGAEEAGVEEYGPEEDGGEESGAEESGPEESGPE...</td>\n",
+       "      <td>MOLT-4</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2</td>\n",
+       "      <td>Q07817</td>\n",
+       "      <td>Cc1ncsc1-c1ccc([C@H](C)NC(=O)[C@@H]2C[C@@H](O)...</td>\n",
+       "      <td>VHL</td>\n",
+       "      <td>InChI=1S/C74H90ClF3N10O10S4/c1-48(50-13-15-52(...</td>\n",
+       "      <td>HQKUMELJMUNTTF-NMKDNUEVSA-N</td>\n",
+       "      <td>1500.309</td>\n",
+       "      <td>102</td>\n",
+       "      <td>10</td>\n",
+       "      <td>25</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>MSQSNRELVVDFLSYKLSQKGYSWSQFSDVEENRTEAPEGTESEME...</td>\n",
+       "      <td>P40337</td>\n",
+       "      <td>MPRRAENWDEAEVGAEEAGVEEYGPEEDGGEESGAEESGPEESGPE...</td>\n",
+       "      <td>MOLT-4</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>3</td>\n",
+       "      <td>Q07817</td>\n",
+       "      <td>Cc1ncsc1-c1ccc([C@H](C)NC(=O)[C@@H]2C[C@@H](O)...</td>\n",
+       "      <td>VHL</td>\n",
+       "      <td>InChI=1S/C75H92ClF3N10O10S4/c1-49(51-16-18-53(...</td>\n",
+       "      <td>ATQCEJKUPSBDMA-QARNUTPLSA-N</td>\n",
+       "      <td>1514.336</td>\n",
+       "      <td>103</td>\n",
+       "      <td>10</td>\n",
+       "      <td>26</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>MSQSNRELVVDFLSYKLSQKGYSWSQFSDVEENRTEAPEGTESEME...</td>\n",
+       "      <td>P40337</td>\n",
+       "      <td>MPRRAENWDEAEVGAEEAGVEEYGPEEDGGEESGAEESGPEESGPE...</td>\n",
+       "      <td>MOLT-4</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>4</td>\n",
+       "      <td>Q07817</td>\n",
+       "      <td>Cc1ncsc1-c1ccc([C@H](C)NC(=O)[C@@H]2C[C@@H](O)...</td>\n",
+       "      <td>VHL</td>\n",
+       "      <td>InChI=1S/C76H94ClF3N10O10S4/c1-50(52-17-19-54(...</td>\n",
+       "      <td>FNKQAGMHNFFSEI-DTTPTBRMSA-N</td>\n",
+       "      <td>1528.363</td>\n",
+       "      <td>104</td>\n",
+       "      <td>10</td>\n",
+       "      <td>27</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>MSQSNRELVVDFLSYKLSQKGYSWSQFSDVEENRTEAPEGTESEME...</td>\n",
+       "      <td>P40337</td>\n",
+       "      <td>MPRRAENWDEAEVGAEEAGVEEYGPEEDGGEESGAEESGPEESGPE...</td>\n",
+       "      <td>MOLT-4</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>5</td>\n",
+       "      <td>Q07817</td>\n",
+       "      <td>Cc1ncsc1-c1ccc([C@H](C)NC(=O)[C@@H]2C[C@@H](O)...</td>\n",
+       "      <td>VHL</td>\n",
+       "      <td>InChI=1S/C77H96ClF3N10O10S4/c1-51(53-18-20-55(...</td>\n",
+       "      <td>PXVFFBGSTYQHRO-REQIQPEASA-N</td>\n",
+       "      <td>1542.390</td>\n",
+       "      <td>105</td>\n",
+       "      <td>10</td>\n",
+       "      <td>28</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>MSQSNRELVVDFLSYKLSQKGYSWSQFSDVEENRTEAPEGTESEME...</td>\n",
+       "      <td>P40337</td>\n",
+       "      <td>MPRRAENWDEAEVGAEEAGVEEYGPEEDGGEESGAEESGPEESGPE...</td>\n",
+       "      <td>MOLT-4</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2136</th>\n",
+       "      <td>2342</td>\n",
+       "      <td>O60885</td>\n",
+       "      <td>Cc1ncsc1-c1ccc(CNC(=O)[C@@H]2C[C@@H](O)CN2C(=O...</td>\n",
+       "      <td>VHL</td>\n",
+       "      <td>InChI=1S/C50H61ClN8O8S2/c1-29-31(3)69-49-42(29...</td>\n",
+       "      <td>VRVWHAZIBGEPEK-DPSJZEHMSA-N</td>\n",
+       "      <td>1001.673</td>\n",
+       "      <td>69</td>\n",
+       "      <td>7</td>\n",
+       "      <td>20</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Degradation of BRD4 long in HEK293 cells after...</td>\n",
+       "      <td>1000.374231</td>\n",
+       "      <td>6.76</td>\n",
+       "      <td>BRD4 long</td>\n",
+       "      <td>MSAESGPGTRLRNLPVMGDGLETSQMSTTQAQAQPQPANAASTNPP...</td>\n",
+       "      <td>P40337</td>\n",
+       "      <td>MPRRAENWDEAEVGAEEAGVEEYGPEEDGGEESGAEESGPEESGPE...</td>\n",
+       "      <td>HEK293</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2137</th>\n",
+       "      <td>2887</td>\n",
+       "      <td>Q05397</td>\n",
+       "      <td>CNC(=O)c1ccccc1Nc1cc(Nc2ccc(N3CCN(CCOCCOCCOCCO...</td>\n",
+       "      <td>VHL</td>\n",
+       "      <td>InChI=1S/C58H75F3N10O10S/c1-37(39-12-14-40(15-...</td>\n",
+       "      <td>FOOHAGZPIHCYKX-ZSFXBAAMSA-N</td>\n",
+       "      <td>1161.359</td>\n",
+       "      <td>82</td>\n",
+       "      <td>7</td>\n",
+       "      <td>27</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Degradation of FAK in A549 cells after 24 h tr...</td>\n",
+       "      <td>1160.534044</td>\n",
+       "      <td>6.81</td>\n",
+       "      <td>FAK</td>\n",
+       "      <td>MAAAYLDPNLNHTPNSSTKTHLGTGMERSPGAMERVLKVFHYFESN...</td>\n",
+       "      <td>P40337</td>\n",
+       "      <td>MPRRAENWDEAEVGAEEAGVEEYGPEEDGGEESGAEESGPEESGPE...</td>\n",
+       "      <td>A549 Cas9</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2138</th>\n",
+       "      <td>2889</td>\n",
+       "      <td>Q05397</td>\n",
+       "      <td>CNC(=O)c1ccccc1Nc1cc(Nc2ccc(N3CCN(CCOCCOCC(=O)...</td>\n",
+       "      <td>VHL</td>\n",
+       "      <td>InChI=1S/C54H67F3N10O8S/c1-33(35-12-14-36(15-1...</td>\n",
+       "      <td>RDCVMTUYWQXPEC-FSHOLZCKSA-N</td>\n",
+       "      <td>1073.253</td>\n",
+       "      <td>76</td>\n",
+       "      <td>7</td>\n",
+       "      <td>21</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Degradation of FAK in A549 cells after 24 h tr...</td>\n",
+       "      <td>1072.481615</td>\n",
+       "      <td>7.11</td>\n",
+       "      <td>FAK</td>\n",
+       "      <td>MAAAYLDPNLNHTPNSSTKTHLGTGMERSPGAMERVLKVFHYFESN...</td>\n",
+       "      <td>P40337</td>\n",
+       "      <td>MPRRAENWDEAEVGAEEAGVEEYGPEEDGGEESGAEESGPEESGPE...</td>\n",
+       "      <td>A549 Cas9</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2139</th>\n",
+       "      <td>2890</td>\n",
+       "      <td>Q05397</td>\n",
+       "      <td>CNC(=O)c1ccccc1Nc1cc(Nc2ccc(N3CCN(CCOCC(=O)N[C...</td>\n",
+       "      <td>VHL</td>\n",
+       "      <td>InChI=1S/C52H63F3N10O7S/c1-31(33-12-14-34(15-1...</td>\n",
+       "      <td>SLSLLSIRBMAERC-MGVZSLQJSA-N</td>\n",
+       "      <td>1029.200</td>\n",
+       "      <td>73</td>\n",
+       "      <td>7</td>\n",
+       "      <td>18</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Degradation of FAK in A549 cells after 24 h tr...</td>\n",
+       "      <td>1028.455400</td>\n",
+       "      <td>7.26</td>\n",
+       "      <td>FAK</td>\n",
+       "      <td>MAAAYLDPNLNHTPNSSTKTHLGTGMERSPGAMERVLKVFHYFESN...</td>\n",
+       "      <td>P40337</td>\n",
+       "      <td>MPRRAENWDEAEVGAEEAGVEEYGPEEDGGEESGAEESGPEESGPE...</td>\n",
+       "      <td>A549 Cas9</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2140</th>\n",
+       "      <td>2891</td>\n",
+       "      <td>Q05397</td>\n",
+       "      <td>CNC(=O)c1ccccc1Nc1cc(Nc2ccc(N3CCN(CCC(=O)N[C@H...</td>\n",
+       "      <td>VHL</td>\n",
+       "      <td>InChI=1S/C51H61F3N10O6S/c1-30(32-12-14-33(15-1...</td>\n",
+       "      <td>ASRIXACKPXMNKY-FCFVTTBASA-N</td>\n",
+       "      <td>999.174</td>\n",
+       "      <td>71</td>\n",
+       "      <td>7</td>\n",
+       "      <td>16</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Degradation of FAK in A549 cells after 24 h tr...</td>\n",
+       "      <td>998.444835</td>\n",
+       "      <td>7.31</td>\n",
+       "      <td>FAK</td>\n",
+       "      <td>MAAAYLDPNLNHTPNSSTKTHLGTGMERSPGAMERVLKVFHYFESN...</td>\n",
+       "      <td>P40337</td>\n",
+       "      <td>MPRRAENWDEAEVGAEEAGVEEYGPEEDGGEESGAEESGPEESGPE...</td>\n",
+       "      <td>A549 Cas9</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>2141 rows × 35 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      Compound ID Uniprot                                             Smiles  \\\n",
+       "0               1  Q07817  Cc1ncsc1-c1ccc([C@H](C)NC(=O)[C@@H]2C[C@@H](O)...   \n",
+       "1               2  Q07817  Cc1ncsc1-c1ccc([C@H](C)NC(=O)[C@@H]2C[C@@H](O)...   \n",
+       "2               3  Q07817  Cc1ncsc1-c1ccc([C@H](C)NC(=O)[C@@H]2C[C@@H](O)...   \n",
+       "3               4  Q07817  Cc1ncsc1-c1ccc([C@H](C)NC(=O)[C@@H]2C[C@@H](O)...   \n",
+       "4               5  Q07817  Cc1ncsc1-c1ccc([C@H](C)NC(=O)[C@@H]2C[C@@H](O)...   \n",
+       "...           ...     ...                                                ...   \n",
+       "2136         2342  O60885  Cc1ncsc1-c1ccc(CNC(=O)[C@@H]2C[C@@H](O)CN2C(=O...   \n",
+       "2137         2887  Q05397  CNC(=O)c1ccccc1Nc1cc(Nc2ccc(N3CCN(CCOCCOCCOCCO...   \n",
+       "2138         2889  Q05397  CNC(=O)c1ccccc1Nc1cc(Nc2ccc(N3CCN(CCOCCOCC(=O)...   \n",
+       "2139         2890  Q05397  CNC(=O)c1ccccc1Nc1cc(Nc2ccc(N3CCN(CCOCC(=O)N[C...   \n",
+       "2140         2891  Q05397  CNC(=O)c1ccccc1Nc1cc(Nc2ccc(N3CCN(CCC(=O)N[C@H...   \n",
+       "\n",
+       "     E3 Ligase                                              InChI  \\\n",
+       "0          VHL  InChI=1S/C73H88ClF3N10O10S4/c1-47(49-13-15-51(...   \n",
+       "1          VHL  InChI=1S/C74H90ClF3N10O10S4/c1-48(50-13-15-52(...   \n",
+       "2          VHL  InChI=1S/C75H92ClF3N10O10S4/c1-49(51-16-18-53(...   \n",
+       "3          VHL  InChI=1S/C76H94ClF3N10O10S4/c1-50(52-17-19-54(...   \n",
+       "4          VHL  InChI=1S/C77H96ClF3N10O10S4/c1-51(53-18-20-55(...   \n",
+       "...        ...                                                ...   \n",
+       "2136       VHL  InChI=1S/C50H61ClN8O8S2/c1-29-31(3)69-49-42(29...   \n",
+       "2137       VHL  InChI=1S/C58H75F3N10O10S/c1-37(39-12-14-40(15-...   \n",
+       "2138       VHL  InChI=1S/C54H67F3N10O8S/c1-33(35-12-14-36(15-1...   \n",
+       "2139       VHL  InChI=1S/C52H63F3N10O7S/c1-31(33-12-14-34(15-1...   \n",
+       "2140       VHL  InChI=1S/C51H61F3N10O6S/c1-30(32-12-14-33(15-1...   \n",
+       "\n",
+       "                        InChI Key  Molecular Weight  Heavy Atom Count  \\\n",
+       "0     SXPDUCVNMGMWBJ-FMZBIETASA-N          1486.282               101   \n",
+       "1     HQKUMELJMUNTTF-NMKDNUEVSA-N          1500.309               102   \n",
+       "2     ATQCEJKUPSBDMA-QARNUTPLSA-N          1514.336               103   \n",
+       "3     FNKQAGMHNFFSEI-DTTPTBRMSA-N          1528.363               104   \n",
+       "4     PXVFFBGSTYQHRO-REQIQPEASA-N          1542.390               105   \n",
+       "...                           ...               ...               ...   \n",
+       "2136  VRVWHAZIBGEPEK-DPSJZEHMSA-N          1001.673                69   \n",
+       "2137  FOOHAGZPIHCYKX-ZSFXBAAMSA-N          1161.359                82   \n",
+       "2138  RDCVMTUYWQXPEC-FSHOLZCKSA-N          1073.253                76   \n",
+       "2139  SLSLLSIRBMAERC-MGVZSLQJSA-N          1029.200                73   \n",
+       "2140  ASRIXACKPXMNKY-FCFVTTBASA-N           999.174                71   \n",
+       "\n",
+       "      Ring Count  Rotatable Bond Count  ...  Name  \\\n",
+       "0             10                    24  ...   NaN   \n",
+       "1             10                    25  ...   NaN   \n",
+       "2             10                    26  ...   NaN   \n",
+       "3             10                    27  ...   NaN   \n",
+       "4             10                    28  ...   NaN   \n",
+       "...          ...                   ...  ...   ...   \n",
+       "2136           7                    20  ...   NaN   \n",
+       "2137           7                    27  ...   NaN   \n",
+       "2138           7                    21  ...   NaN   \n",
+       "2139           7                    18  ...   NaN   \n",
+       "2140           7                    16  ...   NaN   \n",
+       "\n",
+       "                                      Assay (DC50/Dmax)   Exact Mass XLogP3  \\\n",
+       "0                                                   NaN          NaN    NaN   \n",
+       "1                                                   NaN          NaN    NaN   \n",
+       "2                                                   NaN          NaN    NaN   \n",
+       "3                                                   NaN          NaN    NaN   \n",
+       "4                                                   NaN          NaN    NaN   \n",
+       "...                                                 ...          ...    ...   \n",
+       "2136  Degradation of BRD4 long in HEK293 cells after...  1000.374231   6.76   \n",
+       "2137  Degradation of FAK in A549 cells after 24 h tr...  1160.534044   6.81   \n",
+       "2138  Degradation of FAK in A549 cells after 24 h tr...  1072.481615   7.11   \n",
+       "2139  Degradation of FAK in A549 cells after 24 h tr...  1028.455400   7.26   \n",
+       "2140  Degradation of FAK in A549 cells after 24 h tr...   998.444835   7.31   \n",
+       "\n",
+       "      Target (Parsed)                                       POI Sequence  \\\n",
+       "0                 NaN  MSQSNRELVVDFLSYKLSQKGYSWSQFSDVEENRTEAPEGTESEME...   \n",
+       "1                 NaN  MSQSNRELVVDFLSYKLSQKGYSWSQFSDVEENRTEAPEGTESEME...   \n",
+       "2                 NaN  MSQSNRELVVDFLSYKLSQKGYSWSQFSDVEENRTEAPEGTESEME...   \n",
+       "3                 NaN  MSQSNRELVVDFLSYKLSQKGYSWSQFSDVEENRTEAPEGTESEME...   \n",
+       "4                 NaN  MSQSNRELVVDFLSYKLSQKGYSWSQFSDVEENRTEAPEGTESEME...   \n",
+       "...               ...                                                ...   \n",
+       "2136        BRD4 long  MSAESGPGTRLRNLPVMGDGLETSQMSTTQAQAQPQPANAASTNPP...   \n",
+       "2137              FAK  MAAAYLDPNLNHTPNSSTKTHLGTGMERSPGAMERVLKVFHYFESN...   \n",
+       "2138              FAK  MAAAYLDPNLNHTPNSSTKTHLGTGMERSPGAMERVLKVFHYFESN...   \n",
+       "2139              FAK  MAAAYLDPNLNHTPNSSTKTHLGTGMERSPGAMERVLKVFHYFESN...   \n",
+       "2140              FAK  MAAAYLDPNLNHTPNSSTKTHLGTGMERSPGAMERVLKVFHYFESN...   \n",
+       "\n",
+       "      E3 Ligase Uniprot                                 E3 Ligase Sequence  \\\n",
+       "0                P40337  MPRRAENWDEAEVGAEEAGVEEYGPEEDGGEESGAEESGPEESGPE...   \n",
+       "1                P40337  MPRRAENWDEAEVGAEEAGVEEYGPEEDGGEESGAEESGPEESGPE...   \n",
+       "2                P40337  MPRRAENWDEAEVGAEEAGVEEYGPEEDGGEESGAEESGPEESGPE...   \n",
+       "3                P40337  MPRRAENWDEAEVGAEEAGVEEYGPEEDGGEESGAEESGPEESGPE...   \n",
+       "4                P40337  MPRRAENWDEAEVGAEEAGVEEYGPEEDGGEESGAEESGPEESGPE...   \n",
+       "...                 ...                                                ...   \n",
+       "2136             P40337  MPRRAENWDEAEVGAEEAGVEEYGPEEDGGEESGAEESGPEESGPE...   \n",
+       "2137             P40337  MPRRAENWDEAEVGAEEAGVEEYGPEEDGGEESGAEESGPEESGPE...   \n",
+       "2138             P40337  MPRRAENWDEAEVGAEEAGVEEYGPEEDGGEESGAEESGPEESGPE...   \n",
+       "2139             P40337  MPRRAENWDEAEVGAEEAGVEEYGPEEDGGEESGAEESGPEESGPE...   \n",
+       "2140             P40337  MPRRAENWDEAEVGAEEAGVEEYGPEEDGGEESGAEESGPEESGPE...   \n",
+       "\n",
+       "     Cell Line Identifier Active - OR  \n",
+       "0                  MOLT-4         NaN  \n",
+       "1                  MOLT-4         NaN  \n",
+       "2                  MOLT-4         NaN  \n",
+       "3                  MOLT-4         NaN  \n",
+       "4                  MOLT-4        True  \n",
+       "...                   ...         ...  \n",
+       "2136               HEK293        True  \n",
+       "2137            A549 Cas9       False  \n",
+       "2138            A549 Cas9       False  \n",
+       "2139            A549 Cas9        True  \n",
+       "2140            A549 Cas9        True  \n",
+       "\n",
+       "[2141 rows x 35 columns]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
     },
     {
      "data": {
-      "image/png": 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",
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
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+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Compound ID</th>\n",
+       "      <th>Uniprot</th>\n",
+       "      <th>Smiles</th>\n",
+       "      <th>E3 Ligase</th>\n",
+       "      <th>InChI</th>\n",
+       "      <th>InChI Key</th>\n",
+       "      <th>Molecular Weight</th>\n",
+       "      <th>Heavy Atom Count</th>\n",
+       "      <th>Ring Count</th>\n",
+       "      <th>Rotatable Bond Count</th>\n",
+       "      <th>...</th>\n",
+       "      <th>Name</th>\n",
+       "      <th>Assay (DC50/Dmax)</th>\n",
+       "      <th>Exact Mass</th>\n",
+       "      <th>XLogP3</th>\n",
+       "      <th>Target (Parsed)</th>\n",
+       "      <th>POI Sequence</th>\n",
+       "      <th>E3 Ligase Uniprot</th>\n",
+       "      <th>E3 Ligase Sequence</th>\n",
+       "      <th>Cell Line Identifier</th>\n",
+       "      <th>Active - OR</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>Q07817</td>\n",
+       "      <td>Cc1ncsc1-c1ccc([C@H](C)NC(=O)[C@@H]2C[C@@H](O)...</td>\n",
+       "      <td>VHL</td>\n",
+       "      <td>InChI=1S/C73H88ClF3N10O10S4/c1-47(49-13-15-51(...</td>\n",
+       "      <td>SXPDUCVNMGMWBJ-FMZBIETASA-N</td>\n",
+       "      <td>1486.282</td>\n",
+       "      <td>101.0</td>\n",
+       "      <td>10.0</td>\n",
+       "      <td>24.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>MSQSNRELVVDFLSYKLSQKGYSWSQFSDVEENRTEAPEGTESEME...</td>\n",
+       "      <td>P40337</td>\n",
+       "      <td>MPRRAENWDEAEVGAEEAGVEEYGPEEDGGEESGAEESGPEESGPE...</td>\n",
+       "      <td>MOLT-4</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2</td>\n",
+       "      <td>Q07817</td>\n",
+       "      <td>Cc1ncsc1-c1ccc([C@H](C)NC(=O)[C@@H]2C[C@@H](O)...</td>\n",
+       "      <td>VHL</td>\n",
+       "      <td>InChI=1S/C74H90ClF3N10O10S4/c1-48(50-13-15-52(...</td>\n",
+       "      <td>HQKUMELJMUNTTF-NMKDNUEVSA-N</td>\n",
+       "      <td>1500.309</td>\n",
+       "      <td>102.0</td>\n",
+       "      <td>10.0</td>\n",
+       "      <td>25.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>MSQSNRELVVDFLSYKLSQKGYSWSQFSDVEENRTEAPEGTESEME...</td>\n",
+       "      <td>P40337</td>\n",
+       "      <td>MPRRAENWDEAEVGAEEAGVEEYGPEEDGGEESGAEESGPEESGPE...</td>\n",
+       "      <td>MOLT-4</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>3</td>\n",
+       "      <td>Q07817</td>\n",
+       "      <td>Cc1ncsc1-c1ccc([C@H](C)NC(=O)[C@@H]2C[C@@H](O)...</td>\n",
+       "      <td>VHL</td>\n",
+       "      <td>InChI=1S/C75H92ClF3N10O10S4/c1-49(51-16-18-53(...</td>\n",
+       "      <td>ATQCEJKUPSBDMA-QARNUTPLSA-N</td>\n",
+       "      <td>1514.336</td>\n",
+       "      <td>103.0</td>\n",
+       "      <td>10.0</td>\n",
+       "      <td>26.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>MSQSNRELVVDFLSYKLSQKGYSWSQFSDVEENRTEAPEGTESEME...</td>\n",
+       "      <td>P40337</td>\n",
+       "      <td>MPRRAENWDEAEVGAEEAGVEEYGPEEDGGEESGAEESGPEESGPE...</td>\n",
+       "      <td>MOLT-4</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>4</td>\n",
+       "      <td>Q07817</td>\n",
+       "      <td>Cc1ncsc1-c1ccc([C@H](C)NC(=O)[C@@H]2C[C@@H](O)...</td>\n",
+       "      <td>VHL</td>\n",
+       "      <td>InChI=1S/C76H94ClF3N10O10S4/c1-50(52-17-19-54(...</td>\n",
+       "      <td>FNKQAGMHNFFSEI-DTTPTBRMSA-N</td>\n",
+       "      <td>1528.363</td>\n",
+       "      <td>104.0</td>\n",
+       "      <td>10.0</td>\n",
+       "      <td>27.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>MSQSNRELVVDFLSYKLSQKGYSWSQFSDVEENRTEAPEGTESEME...</td>\n",
+       "      <td>P40337</td>\n",
+       "      <td>MPRRAENWDEAEVGAEEAGVEEYGPEEDGGEESGAEESGPEESGPE...</td>\n",
+       "      <td>MOLT-4</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>6</td>\n",
+       "      <td>Q07817</td>\n",
+       "      <td>Cc1ncsc1-c1ccc([C@H](C)NC(=O)[C@@H]2C[C@@H](O)...</td>\n",
+       "      <td>VHL</td>\n",
+       "      <td>InChI=1S/C78H98ClF3N10O10S4/c1-52(54-19-21-56(...</td>\n",
+       "      <td>DKBAKHBUQPFQDO-PXKQGBTKSA-N</td>\n",
+       "      <td>1556.417</td>\n",
+       "      <td>106.0</td>\n",
+       "      <td>10.0</td>\n",
+       "      <td>29.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>MSQSNRELVVDFLSYKLSQKGYSWSQFSDVEENRTEAPEGTESEME...</td>\n",
+       "      <td>P40337</td>\n",
+       "      <td>MPRRAENWDEAEVGAEEAGVEEYGPEEDGGEESGAEESGPEESGPE...</td>\n",
+       "      <td>MOLT-4</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1896</th>\n",
+       "      <td>384</td>\n",
+       "      <td>Q07820</td>\n",
+       "      <td>O=C(CCCCC(=O)NCCN1C(=O)c2cccc3c(Sc4ccc(Br)cc4)...</td>\n",
+       "      <td>CRBN</td>\n",
+       "      <td>InChI=1S/C45H45BrN6O8S/c46-27-15-17-28(18-16-2...</td>\n",
+       "      <td>BORXNUWYWZOREQ-UHFFFAOYSA-N</td>\n",
+       "      <td>909.860</td>\n",
+       "      <td>61.0</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>19.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>908.220296</td>\n",
+       "      <td>5.98</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>MFGLKRNAVIGLNLYCGGAGLGAGSGGATRPGGRLLATEKEASARR...</td>\n",
+       "      <td>Q96SW2</td>\n",
+       "      <td>MAGEGDQQDAAHNMGNHLPLLPAESEEEDEMEVEDQDSKEAKKPNI...</td>\n",
+       "      <td>HeLa</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1897</th>\n",
+       "      <td>910</td>\n",
+       "      <td>Q9UBN7</td>\n",
+       "      <td>O=C(CCCCCCC(=O)N/N=C/c1ccc(OCCOCCOCCn2cc(CNc3c...</td>\n",
+       "      <td>CRBN</td>\n",
+       "      <td>InChI=1S/C37H45N9O10/c47-31-15-14-30(35(50)40-...</td>\n",
+       "      <td>MHILTYZXXFOWJH-WVKHYPTHSA-N</td>\n",
+       "      <td>775.820</td>\n",
+       "      <td>56.0</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>23.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>775.328939</td>\n",
+       "      <td>1.00</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>MTSTGQDSTTTRQRRSRQNPQSPPQDSSVTSKRNIKKGAVPRSIPN...</td>\n",
+       "      <td>Q96SW2</td>\n",
+       "      <td>MAGEGDQQDAAHNMGNHLPLLPAESEEEDEMEVEDQDSKEAKKPNI...</td>\n",
+       "      <td>MM1.S</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1898</th>\n",
+       "      <td>2544</td>\n",
+       "      <td>O60760</td>\n",
+       "      <td>O=C1CCC(N2C(=O)c3cccc(N4CCN(C(=O)C5CCN(c6ccc(N...</td>\n",
+       "      <td>CRBN</td>\n",
+       "      <td>InChI=1S/C40H38N8O7/c49-33-14-13-32(36(51)44-3...</td>\n",
+       "      <td>KQNXUQJGOJWQGL-UHFFFAOYSA-N</td>\n",
+       "      <td>742.793</td>\n",
+       "      <td>55.0</td>\n",
+       "      <td>8.0</td>\n",
+       "      <td>8.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>PROTAC(H-PGDS)-7</td>\n",
+       "      <td>Degradation of HPGDS in KU812 cells after 6/24...</td>\n",
+       "      <td>742.286346</td>\n",
+       "      <td>2.77</td>\n",
+       "      <td>HPGDS</td>\n",
+       "      <td>MPNYKLTYFNMRGRAEIIRYIFAYLDIQYEDHRIEQADWPEIKSTL...</td>\n",
+       "      <td>Q96SW2</td>\n",
+       "      <td>MAGEGDQQDAAHNMGNHLPLLPAESEEEDEMEVEDQDSKEAKKPNI...</td>\n",
+       "      <td>Ku812</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1899</th>\n",
+       "      <td>1214</td>\n",
+       "      <td>P14174</td>\n",
+       "      <td>O=C1CCC(N2C(=O)c3cccc(NCCCCCCCC(=O)Nc4ccc(N5Cc...</td>\n",
+       "      <td>CRBN</td>\n",
+       "      <td>InChI=1S/C35H35N5O8/c41-24-15-10-21-20-39(35(4...</td>\n",
+       "      <td>HAHDZDUOFHMMEA-UHFFFAOYSA-N</td>\n",
+       "      <td>653.692</td>\n",
+       "      <td>48.0</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>12.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>653.248563</td>\n",
+       "      <td>4.16</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>MPMFIVNTNVPRASVPDGFLSELTQQLAQATGKPPQYIAVHVVPDQ...</td>\n",
+       "      <td>Q96SW2</td>\n",
+       "      <td>MAGEGDQQDAAHNMGNHLPLLPAESEEEDEMEVEDQDSKEAKKPNI...</td>\n",
+       "      <td>A549 Cas9</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1900</th>\n",
+       "      <td>136</td>\n",
+       "      <td>P00533</td>\n",
+       "      <td>O=C1CCC(N2Cc3c(NC(=O)CCCCCCCN4CCN(c5ccc(Nc6cc7...</td>\n",
+       "      <td>CRBN</td>\n",
+       "      <td>InChI=1S/C49H57FN12O5/c50-33-10-12-34(13-11-33...</td>\n",
+       "      <td>ZWLPWTDAYPOQGR-UHFFFAOYSA-N</td>\n",
+       "      <td>913.072</td>\n",
+       "      <td>67.0</td>\n",
+       "      <td>9.0</td>\n",
+       "      <td>17.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>912.455891</td>\n",
+       "      <td>5.26</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>MRPSGTAGAALLALLAALCPASRALEEKKVCQGTSNKLTQLGTFED...</td>\n",
+       "      <td>Q96SW2</td>\n",
+       "      <td>MAGEGDQQDAAHNMGNHLPLLPAESEEEDEMEVEDQDSKEAKKPNI...</td>\n",
+       "      <td>HCC827</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>1901 rows × 35 columns</p>\n",
+       "</div>"
+      ],
       "text/plain": [
-       "<Figure size 1100x500 with 2 Axes>"
+       "      Compound ID Uniprot                                             Smiles  \\\n",
+       "0               1  Q07817  Cc1ncsc1-c1ccc([C@H](C)NC(=O)[C@@H]2C[C@@H](O)...   \n",
+       "1               2  Q07817  Cc1ncsc1-c1ccc([C@H](C)NC(=O)[C@@H]2C[C@@H](O)...   \n",
+       "2               3  Q07817  Cc1ncsc1-c1ccc([C@H](C)NC(=O)[C@@H]2C[C@@H](O)...   \n",
+       "3               4  Q07817  Cc1ncsc1-c1ccc([C@H](C)NC(=O)[C@@H]2C[C@@H](O)...   \n",
+       "4               6  Q07817  Cc1ncsc1-c1ccc([C@H](C)NC(=O)[C@@H]2C[C@@H](O)...   \n",
+       "...           ...     ...                                                ...   \n",
+       "1896          384  Q07820  O=C(CCCCC(=O)NCCN1C(=O)c2cccc3c(Sc4ccc(Br)cc4)...   \n",
+       "1897          910  Q9UBN7  O=C(CCCCCCC(=O)N/N=C/c1ccc(OCCOCCOCCn2cc(CNc3c...   \n",
+       "1898         2544  O60760  O=C1CCC(N2C(=O)c3cccc(N4CCN(C(=O)C5CCN(c6ccc(N...   \n",
+       "1899         1214  P14174  O=C1CCC(N2C(=O)c3cccc(NCCCCCCCC(=O)Nc4ccc(N5Cc...   \n",
+       "1900          136  P00533  O=C1CCC(N2Cc3c(NC(=O)CCCCCCCN4CCN(c5ccc(Nc6cc7...   \n",
+       "\n",
+       "     E3 Ligase                                              InChI  \\\n",
+       "0          VHL  InChI=1S/C73H88ClF3N10O10S4/c1-47(49-13-15-51(...   \n",
+       "1          VHL  InChI=1S/C74H90ClF3N10O10S4/c1-48(50-13-15-52(...   \n",
+       "2          VHL  InChI=1S/C75H92ClF3N10O10S4/c1-49(51-16-18-53(...   \n",
+       "3          VHL  InChI=1S/C76H94ClF3N10O10S4/c1-50(52-17-19-54(...   \n",
+       "4          VHL  InChI=1S/C78H98ClF3N10O10S4/c1-52(54-19-21-56(...   \n",
+       "...        ...                                                ...   \n",
+       "1896      CRBN  InChI=1S/C45H45BrN6O8S/c46-27-15-17-28(18-16-2...   \n",
+       "1897      CRBN  InChI=1S/C37H45N9O10/c47-31-15-14-30(35(50)40-...   \n",
+       "1898      CRBN  InChI=1S/C40H38N8O7/c49-33-14-13-32(36(51)44-3...   \n",
+       "1899      CRBN  InChI=1S/C35H35N5O8/c41-24-15-10-21-20-39(35(4...   \n",
+       "1900      CRBN  InChI=1S/C49H57FN12O5/c50-33-10-12-34(13-11-33...   \n",
+       "\n",
+       "                        InChI Key  Molecular Weight  Heavy Atom Count  \\\n",
+       "0     SXPDUCVNMGMWBJ-FMZBIETASA-N          1486.282             101.0   \n",
+       "1     HQKUMELJMUNTTF-NMKDNUEVSA-N          1500.309             102.0   \n",
+       "2     ATQCEJKUPSBDMA-QARNUTPLSA-N          1514.336             103.0   \n",
+       "3     FNKQAGMHNFFSEI-DTTPTBRMSA-N          1528.363             104.0   \n",
+       "4     DKBAKHBUQPFQDO-PXKQGBTKSA-N          1556.417             106.0   \n",
+       "...                           ...               ...               ...   \n",
+       "1896  BORXNUWYWZOREQ-UHFFFAOYSA-N           909.860              61.0   \n",
+       "1897  MHILTYZXXFOWJH-WVKHYPTHSA-N           775.820              56.0   \n",
+       "1898  KQNXUQJGOJWQGL-UHFFFAOYSA-N           742.793              55.0   \n",
+       "1899  HAHDZDUOFHMMEA-UHFFFAOYSA-N           653.692              48.0   \n",
+       "1900  ZWLPWTDAYPOQGR-UHFFFAOYSA-N           913.072              67.0   \n",
+       "\n",
+       "      Ring Count  Rotatable Bond Count  ...              Name  \\\n",
+       "0           10.0                  24.0  ...               NaN   \n",
+       "1           10.0                  25.0  ...               NaN   \n",
+       "2           10.0                  26.0  ...               NaN   \n",
+       "3           10.0                  27.0  ...               NaN   \n",
+       "4           10.0                  29.0  ...               NaN   \n",
+       "...          ...                   ...  ...               ...   \n",
+       "1896         7.0                  19.0  ...               NaN   \n",
+       "1897         5.0                  23.0  ...               NaN   \n",
+       "1898         8.0                   8.0  ...  PROTAC(H-PGDS)-7   \n",
+       "1899         6.0                  12.0  ...               NaN   \n",
+       "1900         9.0                  17.0  ...               NaN   \n",
+       "\n",
+       "                                      Assay (DC50/Dmax)  Exact Mass XLogP3  \\\n",
+       "0                                                   NaN         NaN    NaN   \n",
+       "1                                                   NaN         NaN    NaN   \n",
+       "2                                                   NaN         NaN    NaN   \n",
+       "3                                                   NaN         NaN    NaN   \n",
+       "4                                                   NaN         NaN    NaN   \n",
+       "...                                                 ...         ...    ...   \n",
+       "1896                                                NaN  908.220296   5.98   \n",
+       "1897                                                NaN  775.328939   1.00   \n",
+       "1898  Degradation of HPGDS in KU812 cells after 6/24...  742.286346   2.77   \n",
+       "1899                                                NaN  653.248563   4.16   \n",
+       "1900                                                NaN  912.455891   5.26   \n",
+       "\n",
+       "      Target (Parsed)                                       POI Sequence  \\\n",
+       "0                 NaN  MSQSNRELVVDFLSYKLSQKGYSWSQFSDVEENRTEAPEGTESEME...   \n",
+       "1                 NaN  MSQSNRELVVDFLSYKLSQKGYSWSQFSDVEENRTEAPEGTESEME...   \n",
+       "2                 NaN  MSQSNRELVVDFLSYKLSQKGYSWSQFSDVEENRTEAPEGTESEME...   \n",
+       "3                 NaN  MSQSNRELVVDFLSYKLSQKGYSWSQFSDVEENRTEAPEGTESEME...   \n",
+       "4                 NaN  MSQSNRELVVDFLSYKLSQKGYSWSQFSDVEENRTEAPEGTESEME...   \n",
+       "...               ...                                                ...   \n",
+       "1896              NaN  MFGLKRNAVIGLNLYCGGAGLGAGSGGATRPGGRLLATEKEASARR...   \n",
+       "1897              NaN  MTSTGQDSTTTRQRRSRQNPQSPPQDSSVTSKRNIKKGAVPRSIPN...   \n",
+       "1898            HPGDS  MPNYKLTYFNMRGRAEIIRYIFAYLDIQYEDHRIEQADWPEIKSTL...   \n",
+       "1899              NaN  MPMFIVNTNVPRASVPDGFLSELTQQLAQATGKPPQYIAVHVVPDQ...   \n",
+       "1900              NaN  MRPSGTAGAALLALLAALCPASRALEEKKVCQGTSNKLTQLGTFED...   \n",
+       "\n",
+       "      E3 Ligase Uniprot                                 E3 Ligase Sequence  \\\n",
+       "0                P40337  MPRRAENWDEAEVGAEEAGVEEYGPEEDGGEESGAEESGPEESGPE...   \n",
+       "1                P40337  MPRRAENWDEAEVGAEEAGVEEYGPEEDGGEESGAEESGPEESGPE...   \n",
+       "2                P40337  MPRRAENWDEAEVGAEEAGVEEYGPEEDGGEESGAEESGPEESGPE...   \n",
+       "3                P40337  MPRRAENWDEAEVGAEEAGVEEYGPEEDGGEESGAEESGPEESGPE...   \n",
+       "4                P40337  MPRRAENWDEAEVGAEEAGVEEYGPEEDGGEESGAEESGPEESGPE...   \n",
+       "...                 ...                                                ...   \n",
+       "1896             Q96SW2  MAGEGDQQDAAHNMGNHLPLLPAESEEEDEMEVEDQDSKEAKKPNI...   \n",
+       "1897             Q96SW2  MAGEGDQQDAAHNMGNHLPLLPAESEEEDEMEVEDQDSKEAKKPNI...   \n",
+       "1898             Q96SW2  MAGEGDQQDAAHNMGNHLPLLPAESEEEDEMEVEDQDSKEAKKPNI...   \n",
+       "1899             Q96SW2  MAGEGDQQDAAHNMGNHLPLLPAESEEEDEMEVEDQDSKEAKKPNI...   \n",
+       "1900             Q96SW2  MAGEGDQQDAAHNMGNHLPLLPAESEEEDEMEVEDQDSKEAKKPNI...   \n",
+       "\n",
+       "     Cell Line Identifier Active - OR  \n",
+       "0                  MOLT-4         NaN  \n",
+       "1                  MOLT-4         NaN  \n",
+       "2                  MOLT-4         NaN  \n",
+       "3                  MOLT-4         NaN  \n",
+       "4                  MOLT-4         NaN  \n",
+       "...                   ...         ...  \n",
+       "1896                 HeLa         NaN  \n",
+       "1897                MM1.S         NaN  \n",
+       "1898                Ku812        True  \n",
+       "1899            A549 Cas9       False  \n",
+       "1900               HCC827        True  \n",
+       "\n",
+       "[1901 rows x 35 columns]"
       ]
      },
      "metadata": {},
@@ -4579,121 +3111,416 @@
     }
    ],
    "source": [
-    "print('Cells as one-hot performances:')\n",
-    "plot_performance_metrics(\n",
-    "    df_cv=reports['cellsonehot_cv_train'],\n",
-    "    df_test=reports['cellsonehot_test'],\n",
-    "    df_test_majority=reports['cellsonehot_majority_vote'],\n",
-    "    title=f'cellsonehot_performance',\n",
-    "    show_plot=True,\n",
-    "    metrics_to_plot = {\n",
-    "        'val_acc': 'Validation Accuracy',\n",
-    "        'val_roc_auc': 'Validation ROC AUC',\n",
-    "        'val_f1_score': 'Validation F1 Score',\n",
-    "        'val_precision': 'Validation Precision',\n",
-    "        'val_recall': 'Validation Recall',\n",
-    "        'test_acc': 'Test Accuracy',\n",
-    "        'test_roc_auc': 'Test ROC AUC',\n",
-    "        'test_f1_score': 'Test F1 Score',\n",
-    "        'test_precision': 'Test Precision',\n",
-    "        'test_recall': 'Test Recall',\n",
-    "    },\n",
-    ")"
+    "import numpy as np\n",
+    "\n",
+    "# Remove duplicates with a custom function\n",
+    "def merge_numerical_cols(group):\n",
+    "    key_cols = [\n",
+    "        'Smiles',\n",
+    "        'Uniprot',\n",
+    "        'E3 Ligase Uniprot',\n",
+    "        'Cell Line Identifier',\n",
+    "    ]\n",
+    "    class_cols = ['DC50 (nM)', 'Dmax (%)']\n",
+    "    # Loop over all numerical columns\n",
+    "    for col in group.select_dtypes(include=[np.number]).columns:\n",
+    "        if col == 'Compound ID':\n",
+    "            continue\n",
+    "        # Compute the geometric mean for the column\n",
+    "        values = group[col].dropna()\n",
+    "        if not values.empty:\n",
+    "            group[col] = np.prod(values) ** (1 / len(values))\n",
+    "\n",
+    "    row = group.drop_duplicates(subset=key_cols + class_cols).reset_index(drop=True)\n",
+    "\n",
+    "    assert len(row) == 1\n",
+    "\n",
+    "    return row\n",
+    "\n",
+    "\n",
+    "def remove_duplicates(df):\n",
+    "    key_cols = [\n",
+    "        'Smiles',\n",
+    "        'Uniprot',\n",
+    "        'E3 Ligase Uniprot',\n",
+    "        'Cell Line Identifier',\n",
+    "    ]\n",
+    "    class_cols = ['DC50 (nM)', 'Dmax (%)']\n",
+    "    # Check if there are any duplicated entries having the same key columns, if\n",
+    "    # so, merge them by applying a geometric mean to their DC50 and Dmax columns\n",
+    "    duplicated = df[df.duplicated(subset=key_cols, keep=False)]\n",
+    "\n",
+    "    # NOTE: Reset index to remove the multi-index\n",
+    "    merged = duplicated.groupby(key_cols).apply(lambda x: merge_numerical_cols(x))\n",
+    "    merged = merged.reset_index(drop=True)\n",
+    "\n",
+    "    # Remove the duplicated entries from the original dataframe df\n",
+    "    df = df[~df.duplicated(subset=key_cols, keep=False)]\n",
+    "    # Concatenate the merged dataframe with the original dataframe\n",
+    "    return pd.concat([df, merged], ignore_index=True)\n",
+    "\n",
+    "\n",
+    "display(protac_df)\n",
+    "display(remove_duplicates(protac_df))"
    ]
   },
   {
-   "cell_type": "markdown",
+   "cell_type": "code",
+   "execution_count": 93,
    "metadata": {},
+   "outputs": [],
    "source": [
-    "### Proteins as Amino-Acid Counts"
+    "pDC50_threshold = 6.0\n",
+    "Dmax_threshold = 0.6\n",
+    "protac_df['Active'] = protac_df.apply(\n",
+    "    lambda x: pdp.is_active(x['DC50 (nM)'], x['Dmax (%)'], pDC50_threshold=pDC50_threshold, Dmax_threshold=Dmax_threshold), axis=1\n",
+    ")\n",
+    "protac_df['E3 Ligase'] = protac_df['E3 Ligase'].str.replace('Iap', 'IAP')"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 143,
+   "execution_count": 90,
    "metadata": {},
    "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Proteins as amino-acid counts performances:\n",
-      "Metrics: ['ROC AUC', 'Recall', 'Precision', 'F1 Score', 'Accuracy']\n"
-     ]
-    },
-    {
-     "data": {
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",
-      "text/plain": [
-       "<Figure size 3000x400 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Metric: ROC AUC\n"
-     ]
-    },
     {
      "data": {
-      "image/png": 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",
+      "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>Smiles</th>\n",
+       "      <th>Uniprot</th>\n",
+       "      <th>Cell Line Identifier</th>\n",
+       "      <th>E3 Ligase Uniprot</th>\n",
+       "      <th>Active</th>\n",
+       "      <th>Database</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
       "text/plain": [
-       "<Figure size 600x400 with 1 Axes>"
+       "Empty DataFrame\n",
+       "Columns: [Smiles, Uniprot, Cell Line Identifier, E3 Ligase Uniprot, Active, Database]\n",
+       "Index: []"
       ]
      },
+     "execution_count": 90,
      "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Metric: Recall\n"
-     ]
-    },
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Get all entries with same ['Smiles', 'Uniprot', 'Cell Line Identifier', 'E3 Ligase Uniprot'] columns\n",
+    "tmp = protac_df.dropna(subset=['Smiles', 'Uniprot', 'Cell Line Identifier', 'E3 Ligase Uniprot', 'Active'])[['Smiles', 'Uniprot', 'Cell Line Identifier', 'E3 Ligase Uniprot', 'Active', 'Database']]\n",
+    "\n",
+    "# Get entries with duplicates\n",
+    "duplicates = tmp[tmp.duplicated(subset=['Smiles', 'Uniprot', 'Cell Line Identifier', 'E3 Ligase Uniprot', 'Active'], keep=False)]\n",
+    "# Sort duplicates, so that they appear close to each other\n",
+    "duplicates = duplicates.sort_values(['Smiles', 'Uniprot', 'Cell Line Identifier', 'E3 Ligase Uniprot', 'Active'])\n",
+    "duplicates.to_csv('duplicates.csv', index=False)\n",
+    "duplicates"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
     {
      "data": {
-      "image/png": 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",
+      "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>Smiles</th>\n",
+       "      <th>Uniprot</th>\n",
+       "      <th>Cell Line Identifier</th>\n",
+       "      <th>E3 Ligase</th>\n",
+       "      <th>Active</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Cc1ncsc1-c1ccc([C@H](C)NC(=O)[C@@H]2C[C@@H](O)...</td>\n",
+       "      <td>Q07817</td>\n",
+       "      <td>MOLT-4</td>\n",
+       "      <td>VHL</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>129</th>\n",
+       "      <td>O=C1CCC(N2Cc3c(NC(=O)CCCCCCCN4CCN(c5ccc(Nc6cc7...</td>\n",
+       "      <td>P00533</td>\n",
+       "      <td>HCC827</td>\n",
+       "      <td>CRBN</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>137</th>\n",
+       "      <td>Cc1ncsc1-c1ccc(CNC(=O)[C@@H]2C[C@@H](O)CN2C(=O...</td>\n",
+       "      <td>P00533</td>\n",
+       "      <td>HCC827</td>\n",
+       "      <td>VHL</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>175</th>\n",
+       "      <td>Cc1ncsc1-c1ccc(CNC(=O)[C@@H]2C[C@@H](O)CN2C(=O...</td>\n",
+       "      <td>O43353</td>\n",
+       "      <td>THP-1</td>\n",
+       "      <td>VHL</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>178</th>\n",
+       "      <td>Nc1ncnc2c1c(-c1ccc(Oc3ccccc3)cc1)nn2C1CCN(CCCC...</td>\n",
+       "      <td>Q06187</td>\n",
+       "      <td>Namalwa</td>\n",
+       "      <td>CRBN</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1578</th>\n",
+       "      <td>C=C(F)C(=O)N1CCN(c2nc(OC[C@@H]3CCCN3CCC(=O)NCC...</td>\n",
+       "      <td>P01116</td>\n",
+       "      <td>NCI-H2030</td>\n",
+       "      <td>VHL</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1641</th>\n",
+       "      <td>CCn1c(=O)n(CC(=O)NCCOCCNC(=O)COc2cccc3c2C(=O)N...</td>\n",
+       "      <td>O00418</td>\n",
+       "      <td>MDA-MB-231</td>\n",
+       "      <td>CRBN</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2034</th>\n",
+       "      <td>O=C1CCC(N2C(=O)c3cccc(NCCCCCCCC(=O)Nc4ccc(N5Cc...</td>\n",
+       "      <td>P14174</td>\n",
+       "      <td>A549 Cas9</td>\n",
+       "      <td>CRBN</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2120</th>\n",
+       "      <td>Cc1sc2c(c1C)C(c1ccc(Cl)cc1)=N[C@@H](CC(=O)NCCO...</td>\n",
+       "      <td>O60885</td>\n",
+       "      <td>HEK293T</td>\n",
+       "      <td>FEM1B</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2121</th>\n",
+       "      <td>Cc1sc2c(c1C)C(c1ccc(Cl)cc1)=N[C@@H](CC(=O)NCCO...</td>\n",
+       "      <td>O60885</td>\n",
+       "      <td>HEK293T</td>\n",
+       "      <td>FEM1B</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>112 rows × 5 columns</p>\n",
+       "</div>"
+      ],
       "text/plain": [
-       "<Figure size 600x400 with 1 Axes>"
+       "                                                 Smiles Uniprot  \\\n",
+       "4     Cc1ncsc1-c1ccc([C@H](C)NC(=O)[C@@H]2C[C@@H](O)...  Q07817   \n",
+       "129   O=C1CCC(N2Cc3c(NC(=O)CCCCCCCN4CCN(c5ccc(Nc6cc7...  P00533   \n",
+       "137   Cc1ncsc1-c1ccc(CNC(=O)[C@@H]2C[C@@H](O)CN2C(=O...  P00533   \n",
+       "175   Cc1ncsc1-c1ccc(CNC(=O)[C@@H]2C[C@@H](O)CN2C(=O...  O43353   \n",
+       "178   Nc1ncnc2c1c(-c1ccc(Oc3ccccc3)cc1)nn2C1CCN(CCCC...  Q06187   \n",
+       "...                                                 ...     ...   \n",
+       "1578  C=C(F)C(=O)N1CCN(c2nc(OC[C@@H]3CCCN3CCC(=O)NCC...  P01116   \n",
+       "1641  CCn1c(=O)n(CC(=O)NCCOCCNC(=O)COc2cccc3c2C(=O)N...  O00418   \n",
+       "2034  O=C1CCC(N2C(=O)c3cccc(NCCCCCCCC(=O)Nc4ccc(N5Cc...  P14174   \n",
+       "2120  Cc1sc2c(c1C)C(c1ccc(Cl)cc1)=N[C@@H](CC(=O)NCCO...  O60885   \n",
+       "2121  Cc1sc2c(c1C)C(c1ccc(Cl)cc1)=N[C@@H](CC(=O)NCCO...  O60885   \n",
+       "\n",
+       "     Cell Line Identifier E3 Ligase Active  \n",
+       "4                  MOLT-4       VHL   True  \n",
+       "129                HCC827      CRBN   True  \n",
+       "137                HCC827       VHL   True  \n",
+       "175                 THP-1       VHL   True  \n",
+       "178               Namalwa      CRBN   True  \n",
+       "...                   ...       ...    ...  \n",
+       "1578            NCI-H2030       VHL  False  \n",
+       "1641           MDA-MB-231      CRBN  False  \n",
+       "2034            A549 Cas9      CRBN   True  \n",
+       "2120              HEK293T     FEM1B   True  \n",
+       "2121              HEK293T     FEM1B   True  \n",
+       "\n",
+       "[112 rows x 5 columns]"
       ]
      },
      "metadata": {},
      "output_type": "display_data"
     },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Metric: Precision\n"
-     ]
-    },
     {
      "data": {
-      "image/png": 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",
       "text/plain": [
-       "<Figure size 600x400 with 1 Axes>"
+       "count    55.000000\n",
+       "mean      2.036364\n",
+       "std       0.188919\n",
+       "min       2.000000\n",
+       "25%       2.000000\n",
+       "50%       2.000000\n",
+       "75%       2.000000\n",
+       "max       3.000000\n",
+       "Name: Count, dtype: float64"
       ]
      },
+     "execution_count": 24,
      "metadata": {},
-     "output_type": "display_data"
-    },
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Get all entries with same ['Smiles', 'Uniprot', 'Cell Line Identifier', 'E3 Ligase'] columns\n",
+    "tmp = protac_df.dropna(subset=['Smiles', 'Uniprot', 'Cell Line Identifier', 'E3 Ligase', 'Active'])\n",
+    "tmp = tmp.groupby(['Smiles', 'Uniprot', 'Cell Line Identifier', 'E3 Ligase', 'Active']).size()\n",
+    "tmp = tmp[tmp > 1]\n",
+    "tmp = protac_df[\n",
+    "    protac_df.apply(lambda x: (x['Smiles'], x['Uniprot'], x['Cell Line Identifier'], x['E3 Ligase'], x['Active']) in\n",
+    "        tmp.index, axis=1)\n",
+    "]\n",
+    "display(tmp[['Smiles', 'Uniprot', 'Cell Line Identifier', 'E3 Ligase', 'Active']])\n",
+    "\n",
+    "# Display the tmp entries ['Smiles', 'Uniprot', 'Cell Line Identifier', 'E3 Ligase'] with an additional column with their repetition count\n",
+    "tmp = tmp[['Smiles', 'Uniprot', 'Cell Line Identifier', 'E3 Ligase', 'Active']]\n",
+    "tmp['Count'] = tmp.groupby(['Smiles', 'Uniprot', 'Cell Line Identifier', 'E3 Ligase', 'Active'])['Smiles'].transform('count')\n",
+    "tmp = tmp.drop_duplicates()\n",
+    "tmp['Count'].describe()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def print_duplicates(df, active_col = 'Active (Dmax 0.6, pDC50 6.0)'):\n",
+    "    tmp = df.dropna(subset=['Smiles', 'Uniprot', 'Cell Line Identifier', 'E3 Ligase', active_col])\n",
+    "    tmp = tmp.groupby(['Smiles', 'Uniprot', 'Cell Line Identifier', 'E3 Ligase', active_col]).size()\n",
+    "    tmp = tmp[tmp > 1]\n",
+    "    tmp = df[\n",
+    "        df.apply(lambda x: (x['Smiles'], x['Uniprot'], x['Cell Line Identifier'], x['E3 Ligase'], x[active_col]) in\n",
+    "            tmp.index, axis=1)\n",
+    "    ]\n",
+    "    # display(tmp[['Smiles', 'Uniprot', 'Cell Line Identifier', 'E3 Ligase', active_col]])\n",
+    "\n",
+    "    # Get entries with duplicates\n",
+    "    duplicates = tmp[tmp.duplicated(subset=['Smiles', 'Uniprot', 'Cell Line Identifier', 'E3 Ligase Uniprot', active_col], keep=False)]\n",
+    "    # Sort duplicates, so that they appear close to each other\n",
+    "    duplicates = duplicates.sort_values(['Smiles', 'Uniprot', 'Cell Line Identifier', 'E3 Ligase Uniprot', active_col])\n",
+    "    print(f'Duplicated entries (len {len(duplicates)} ({len(duplicates) / len(df):.4%})):')\n",
+    "    display(duplicates[['Smiles', 'Uniprot', 'Cell Line Identifier', 'E3 Ligase', active_col]])\n",
+    "\n",
+    "    # Display the tmp entries ['Smiles', 'Uniprot', 'Cell Line Identifier', 'E3 Ligase'] with an additional column with their repetition count\n",
+    "    tmp = tmp[['Smiles', 'Uniprot', 'Cell Line Identifier', 'E3 Ligase', active_col]]\n",
+    "    tmp['Count'] = tmp.groupby(['Smiles', 'Uniprot', 'Cell Line Identifier', 'E3 Ligase', active_col])['Smiles'].transform('count')\n",
+    "    tmp = tmp.drop_duplicates()\n",
+    "    print(tmp['Count'].describe())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Metric: F1 Score\n"
+      "--------------------------------------------------------------------------------\n",
+      "--------------------------------------------------------------------------------\n",
+      "Study: standard\n",
+      "--------------------------------------------------------------------------------\n",
+      "Test:\n",
+      "Duplicated entries (len 0 (0.0000%)):\n"
      ]
     },
     {
      "data": {
-      "image/png": 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",
+      "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>Smiles</th>\n",
+       "      <th>Uniprot</th>\n",
+       "      <th>Cell Line Identifier</th>\n",
+       "      <th>E3 Ligase</th>\n",
+       "      <th>Active (Dmax 0.6, pDC50 6.0)</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
       "text/plain": [
-       "<Figure size 600x400 with 1 Axes>"
+       "Empty DataFrame\n",
+       "Columns: [Smiles, Uniprot, Cell Line Identifier, E3 Ligase, Active (Dmax 0.6, pDC50 6.0)]\n",
+       "Index: []"
       ]
      },
      "metadata": {},
@@ -4703,14 +3530,57 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Metric: Accuracy\n"
+      "count    0.0\n",
+      "mean     NaN\n",
+      "std      NaN\n",
+      "min      NaN\n",
+      "25%      NaN\n",
+      "50%      NaN\n",
+      "75%      NaN\n",
+      "max      NaN\n",
+      "Name: Count, dtype: float64\n",
+      "--------------------------------------------------------------------------------\n",
+      "Train/Val:\n",
+      "Duplicated entries (len 0 (0.0000%)):\n"
      ]
     },
     {
      "data": {
-      "image/png": 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",
+      "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>Smiles</th>\n",
+       "      <th>Uniprot</th>\n",
+       "      <th>Cell Line Identifier</th>\n",
+       "      <th>E3 Ligase</th>\n",
+       "      <th>Active (Dmax 0.6, pDC50 6.0)</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
       "text/plain": [
-       "<Figure size 600x400 with 1 Axes>"
+       "Empty DataFrame\n",
+       "Columns: [Smiles, Uniprot, Cell Line Identifier, E3 Ligase, Active (Dmax 0.6, pDC50 6.0)]\n",
+       "Index: []"
       ]
      },
      "metadata": {},
@@ -4720,450 +3590,328 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Plotting performance for main part of the paper...\n"
+      "count    0.0\n",
+      "mean     NaN\n",
+      "std      NaN\n",
+      "min      NaN\n",
+      "25%      NaN\n",
+      "50%      NaN\n",
+      "75%      NaN\n",
+      "max      NaN\n",
+      "Name: Count, dtype: float64\n",
+      "--------------------------------------------------------------------------------\n",
+      "--------------------------------------------------------------------------------\n",
+      "Study: similarity\n",
+      "--------------------------------------------------------------------------------\n",
+      "Test:\n",
+      "Duplicated entries (len 0 (0.0000%)):\n"
      ]
     },
     {
      "data": {
-      "image/png": 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",
+      "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>Smiles</th>\n",
+       "      <th>Uniprot</th>\n",
+       "      <th>Cell Line Identifier</th>\n",
+       "      <th>E3 Ligase</th>\n",
+       "      <th>Active (Dmax 0.6, pDC50 6.0)</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
       "text/plain": [
-       "<Figure size 1100x500 with 2 Axes>"
+       "Empty DataFrame\n",
+       "Columns: [Smiles, Uniprot, Cell Line Identifier, E3 Ligase, Active (Dmax 0.6, pDC50 6.0)]\n",
+       "Index: []"
       ]
      },
      "metadata": {},
      "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "print('Proteins as amino-acid counts performances:')\n",
-    "plot_performance_metrics(\n",
-    "    df_cv=reports['aminoacidcnt_cv_train'],\n",
-    "    df_test=reports['aminoacidcnt_test'],\n",
-    "    df_test_majority=reports['aminoacidcnt_majority_vote'],\n",
-    "    title=f'aminoacidcnt_performance',\n",
-    "    show_plot=True,\n",
-    "    metrics_to_plot = {\n",
-    "        'val_acc': 'Validation Accuracy',\n",
-    "        'val_roc_auc': 'Validation ROC AUC',\n",
-    "        'val_f1_score': 'Validation F1 Score',\n",
-    "        'val_precision': 'Validation Precision',\n",
-    "        'val_recall': 'Validation Recall',\n",
-    "        'test_acc': 'Test Accuracy',\n",
-    "        'test_roc_auc': 'Test ROC AUC',\n",
-    "        'test_f1_score': 'Test F1 Score',\n",
-    "        'test_precision': 'Test Precision',\n",
-    "        'test_recall': 'Test Recall',\n",
-    "    },\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Compare Performance"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 146,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/tmp/ipykernel_2173070/2061096750.py:5: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  tmp['Experiment'] = r\n",
-      "/tmp/ipykernel_2173070/2061096750.py:5: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  tmp['Experiment'] = r\n",
-      "/tmp/ipykernel_2173070/2061096750.py:5: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  tmp['Experiment'] = r\n"
-     ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "| Experiment                    | Study      |   Test Accuracy |   Test ROC AUC |\n",
-      "|:------------------------------|:-----------|----------------:|---------------:|\n",
-      "| Baseline                      | Standard   |        0.825581 |       0.847826 |\n",
-      "| Baseline                      | Target     |        0.611765 |       0.614827 |\n",
-      "| Baseline                      | Similarity |        0.705882 |       0.823761 |\n",
-      "| Cells as one-hot              | Standard   |        0.651163 |       0.722826 |\n",
-      "| Cells as one-hot              | Target     |        0.458824 |       0.619844 |\n",
-      "| Cells as one-hot              | Similarity |        0.658824 |       0.715653 |\n",
-      "| Proteins as amino-acid counts | Standard   |        0.802326 |       0.842391 |\n",
-      "| Proteins as amino-acid counts | Target     |        0.505882 |       0.530658 |\n",
-      "| Proteins as amino-acid counts | Similarity |        0.670588 |       0.719032 |\n",
+      "count    0.0\n",
+      "mean     NaN\n",
+      "std      NaN\n",
+      "min      NaN\n",
+      "25%      NaN\n",
+      "50%      NaN\n",
+      "75%      NaN\n",
+      "max      NaN\n",
+      "Name: Count, dtype: float64\n",
       "--------------------------------------------------------------------------------\n",
-      "Comparison of the best models majority vote:\n"
+      "Train/Val:\n",
+      "Duplicated entries (len 0 (0.0000%)):\n"
      ]
     },
     {
      "data": {
-      "image/png": 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",
+      "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>Smiles</th>\n",
+       "      <th>Uniprot</th>\n",
+       "      <th>Cell Line Identifier</th>\n",
+       "      <th>E3 Ligase</th>\n",
+       "      <th>Active (Dmax 0.6, pDC50 6.0)</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
       "text/plain": [
-       "<Figure size 1100x500 with 2 Axes>"
+       "Empty DataFrame\n",
+       "Columns: [Smiles, Uniprot, Cell Line Identifier, E3 Ligase, Active (Dmax 0.6, pDC50 6.0)]\n",
+       "Index: []"
       ]
      },
      "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "df = []\n",
-    "for r in ['majority_vote', 'cellsonehot_majority_vote', 'aminoacidcnt_majority_vote']:\n",
-    "    tmp = reports[r]\n",
-    "    tmp = tmp[tmp['cv_models'].isna()]\n",
-    "    tmp['Experiment'] = r\n",
-    "    df.append(tmp)\n",
-    "df = pd.concat(df)\n",
-    "# Rename split_type to paper names\n",
-    "df['split_type'] = df['split_type'].replace({\n",
-    "    'random': 'Standard',\n",
-    "    'uniprot': 'Target',\n",
-    "    'tanimoto': 'Similarity',\n",
-    "    'standard': 'Standard',\n",
-    "    'target': 'Target',\n",
-    "    'similarity': 'Similarity',\n",
-    "})\n",
-    "# Rename columns to paper names\n",
-    "df.rename(columns={\n",
-    "    'split_type': 'Study',\n",
-    "    'test_acc': 'Test Accuracy',\n",
-    "    'test_roc_auc': 'Test ROC AUC',\n",
-    "}, inplace=True)\n",
-    "# Rename experiment names to paper names\n",
-    "df['Experiment'] = df['Experiment'].replace({\n",
-    "    'majority_vote': 'Baseline',\n",
-    "    'cellsonehot_majority_vote': 'Cells as one-hot',\n",
-    "    'aminoacidcnt_majority_vote': 'Proteins as amino-acid counts',\n",
-    "})\n",
-    "print(df[['Experiment', 'Study', 'Test Accuracy', 'Test ROC AUC']].to_markdown(index=False))\n",
-    "df['Experiment'] = df['Experiment'] = df['Experiment'].replace({\n",
-    "    'Proteins as amino-acid counts': 'Proteins as\\namino-acid counts',\n",
-    "})\n",
-    "\n",
-    "def plot_comparison_df(df, filename=None):\n",
-    "    # Plot the test accuracy and ROC AUC in two bar-plots side by side, with Study as hue\n",
-    "    fig, axes = plt.subplots(1, 2, figsize=(11, 5))\n",
-    "    sns.barplot(\n",
-    "        data=df,\n",
-    "        x='Experiment',\n",
-    "        y='Test Accuracy',\n",
-    "        hue='Study',\n",
-    "        errorbar=('sd', 1),\n",
-    "        palette=palette[:3],\n",
-    "        ax=axes[0])\n",
-    "    # Set ax[0] y-axis to percentage\n",
-    "    axes[0].yaxis.set_major_formatter(plt.matplotlib.ticker.PercentFormatter(1, decimals=0))\n",
-    "    # Set ax[0] y-axis limit from 0 to 100\n",
-    "    axes[0].set_ylim(0, 1.0)\n",
-    "    # Remove the x-axis label\n",
-    "    axes[0].set_xlabel('')\n",
-    "    axes[0].grid(axis='y', alpha=0.5, linewidth=0.5)\n",
-    "\n",
-    "    sns.barplot(\n",
-    "        data=df,\n",
-    "        x='Experiment',\n",
-    "        y='Test ROC AUC',\n",
-    "        hue='Study',\n",
-    "        errorbar=('sd', 1),\n",
-    "        palette=palette[:3],\n",
-    "        ax=axes[1])\n",
-    "    axes[1].set_ylim(0, 1.0)\n",
-    "    # Remove the legend from the first plot\n",
-    "    axes[0].legend().remove()\n",
-    "    # Set the legend outside the plot in the middle of the two subplots (3 columns)\n",
-    "    axes[1].legend(loc='upper center', bbox_to_anchor=(-0.15, -0.12), ncol=3)\n",
-    "    # Remove the x-axis label\n",
-    "    axes[1].set_xlabel('')\n",
-    "    axes[1].grid(axis='y', alpha=0.5, linewidth=0.5)\n",
-    "\n",
-    "    # Add values to the bar plots rotated 90 degrees at 0.5 height\n",
-    "    for i, ax in enumerate(axes):\n",
-    "        for p in ax.patches:\n",
-    "            if p.get_height() < 0.01:\n",
-    "                continue\n",
-    "            if i % 2 == 0:\n",
-    "                value = f'{p.get_height():.1%}'\n",
-    "            else:\n",
-    "                value = f'{p.get_height():.3f}'\n",
-    "            \n",
-    "            x = p.get_x() + p.get_width() / 2\n",
-    "            y = 0.3\n",
-    "            ax.annotate(value, (x, y), ha='center', va='center', color='black', fontsize=10, rotation=90, alpha=0.8)\n",
-    "\n",
-    "    if filename is not None:\n",
-    "        plt.savefig(f'plots/{filename}.pdf', bbox_inches='tight')\n",
-    "        plt.savefig(f'plots/{filename}.png', bbox_inches='tight')\n",
-    "    plt.show()\n",
-    "\n",
-    "print('-' * 80)\n",
-    "print('Comparison of the best models majority vote:')\n",
-    "plot_comparison_df(df, 'embedding_comparison_majority_vote')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 147,
-   "metadata": {},
-   "outputs": [
+     "output_type": "display_data"
+    },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "| Experiment                    | Study      |   Test Accuracy |   Test ROC AUC |\n",
-      "|:------------------------------|:-----------|----------------:|---------------:|\n",
-      "| Baseline                      | Similarity |        0.74902  |       0.822072 |\n",
-      "| Baseline                      | Standard   |        0.790698 |       0.84058  |\n",
-      "| Baseline                      | Target     |        0.588235 |       0.604051 |\n",
-      "| Cells as one-hot              | Similarity |        0.705882 |       0.716592 |\n",
-      "| Cells as one-hot              | Standard   |        0.658915 |       0.736594 |\n",
-      "| Cells as one-hot              | Target     |        0.556863 |       0.589929 |\n",
-      "| Proteins as amino-acid counts | Similarity |        0.694118 |       0.746434 |\n",
-      "| Proteins as amino-acid counts | Standard   |        0.775194 |       0.842935 |\n",
-      "| Proteins as amino-acid counts | Target     |        0.482353 |       0.516165 |\n",
+      "count    0.0\n",
+      "mean     NaN\n",
+      "std      NaN\n",
+      "min      NaN\n",
+      "25%      NaN\n",
+      "50%      NaN\n",
+      "75%      NaN\n",
+      "max      NaN\n",
+      "Name: Count, dtype: float64\n",
       "--------------------------------------------------------------------------------\n",
-      "Comparison of the best models mean values:\n"
+      "--------------------------------------------------------------------------------\n",
+      "Study: target\n",
+      "--------------------------------------------------------------------------------\n",
+      "Test:\n",
+      "Duplicated entries (len 0 (0.0000%)):\n"
      ]
     },
     {
      "data": {
-      "image/png": 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",
+      "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>Smiles</th>\n",
+       "      <th>Uniprot</th>\n",
+       "      <th>Cell Line Identifier</th>\n",
+       "      <th>E3 Ligase</th>\n",
+       "      <th>Active (Dmax 0.6, pDC50 6.0)</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
       "text/plain": [
-       "<Figure size 1100x500 with 2 Axes>"
+       "Empty DataFrame\n",
+       "Columns: [Smiles, Uniprot, Cell Line Identifier, E3 Ligase, Active (Dmax 0.6, pDC50 6.0)]\n",
+       "Index: []"
       ]
      },
      "metadata": {},
      "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "df = []\n",
-    "for r in ['test', 'cellsonehot_test', 'aminoacidcnt_test']:\n",
-    "    tmp = reports[r]\n",
-    "    tmp['Experiment'] = r\n",
-    "    df.append(tmp)\n",
-    "df = pd.concat(df)\n",
-    "# Rename split_type to paper names\n",
-    "df['split_type'] = df['split_type'].replace({\n",
-    "    'random': 'Standard',\n",
-    "    'uniprot': 'Target',\n",
-    "    'tanimoto': 'Similarity',\n",
-    "    'standard': 'Standard',\n",
-    "    'target': 'Target',\n",
-    "    'similarity': 'Similarity',\n",
-    "})\n",
-    "# Rename columns to paper names\n",
-    "df.rename(columns={\n",
-    "    'split_type': 'Study',\n",
-    "    'test_acc': 'Test Accuracy',\n",
-    "    'test_roc_auc': 'Test ROC AUC',\n",
-    "}, inplace=True)\n",
-    "# Group by experiment and split type then get the mean\n",
-    "df = df.groupby(['Experiment', 'Study']).mean(['Test Accuracy', 'Test ROC AUC']).reset_index()\n",
-    "# Rename experiment names to paper names\n",
-    "df['Experiment'] = df['Experiment'].replace({\n",
-    "    'test': 'Baseline',\n",
-    "    'cellsonehot_test': 'Cells as one-hot',\n",
-    "    'aminoacidcnt_test': 'Proteins as amino-acid counts',\n",
-    "})\n",
-    "# Order df by Experiment\n",
-    "df = df.sort_values(['Experiment'])\n",
-    "# Order Study by ['Standard', 'Target', 'Similarity']\n",
-    "df['Study'] = pd.Categorical(df['Study'], ['Standard', 'Target', 'Similarity'])\n",
-    "\n",
-    "print(df[['Experiment', 'Study', 'Test Accuracy', 'Test ROC AUC']].to_markdown(index=False))\n",
-    "df['Experiment'] = df['Experiment'] = df['Experiment'].replace({\n",
-    "    'Proteins as amino-acid counts': 'Proteins as\\namino-acid counts',\n",
-    "})\n",
-    "\n",
-    "print('-' * 80)\n",
-    "print('Comparison of the best models mean values:')\n",
-    "plot_comparison_df(df, 'embedding_comparison_mean')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Ablation Studies"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {},
-   "outputs": [
+    },
     {
-     "name": "stderr",
+     "name": "stdout",
      "output_type": "stream",
      "text": [
-      "/tmp/ipykernel_2173070/464303105.py:72: UserWarning: The palette list has more values (4) than needed (1), which may not be intended.\n",
-      "  sns.barplot(data=final_df,\n",
-      "/tmp/ipykernel_2173070/464303105.py:72: UserWarning: The palette list has more values (4) than needed (1), which may not be intended.\n",
-      "  sns.barplot(data=final_df,\n",
-      "/tmp/ipykernel_2173070/464303105.py:72: UserWarning: The palette list has more values (4) than needed (1), which may not be intended.\n",
-      "  sns.barplot(data=final_df,\n"
+      "count    0.0\n",
+      "mean     NaN\n",
+      "std      NaN\n",
+      "min      NaN\n",
+      "25%      NaN\n",
+      "50%      NaN\n",
+      "75%      NaN\n",
+      "max      NaN\n",
+      "Name: Count, dtype: float64\n",
+      "--------------------------------------------------------------------------------\n",
+      "Train/Val:\n",
+      "Duplicated entries (len 0 (0.0000%)):\n"
      ]
     },
     {
      "data": {
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IoSUkaQshhBBaQpK2EEIIoSUkaQshhBBaQr7lS4iX3BfTO/HP7eRi6+u07EGroEmk3fyTI9sWkZz4KwX5uVSv3ZQWPSdiYmZdap1XLpxj/09LSLkaT1b63/gP/RgX7zYaZWL3riX2x7UA1G8/kPrt+qu33Uj6jZ83zKf7+Gh0dHUrpqNCvABe+pH2gQMHUCgUpKamAhAdHY2lpeVTjyMpKQmFQkFsbGyF192mTRvGjBnzwDLOzs4sWrRI/VqhULBt27YKj+V5U5bjfv/vyKO6/xg/L7qPX82gObvVS2DoJwDU9GlPXs6/fPtJKKCg06jP6DpuBQX5eexcMpbCwsJS68zLy8GmqistgyaWuP3WXwmc2LmU9sFzaB88m+PffsataxcAKCwo4Kev5tG6d5gkbCHu81Ik7SNHjqCrq8sbb7zxrEPRGsnJyXTs2PFZhyGeAmMzK0zMbdTL5d8OYl6pGg6uDbh+8Vf+uZ2M74AZ2Di4YuPgiu+Amfx9JY5rCSdLrbOmpw+NA4ZTw7ttidvv3EjCxsGNau6NqOb+KjZV3Ui9ngRA7I9rcHD1oYpT7SfRXSG02kuRtFesWMHIkSP5+eefuXbt2rMORyvY2dlhaGj4rMMQT1lBfh7nT+zCo0kgCoWCgvxcAHR09dVldPUMQKEgOTH2kduxcXAl9eYV/rl9nX9uJZOWcgVrh5qk3fyTP45+y6sBIx63K0K8kF74pJ2RkcGGDRsYPnw4b7zxBtHR0Y9d59WrVwkKCsLS0hJra2s6d+5MUlKSentwcDBdunRh7ty52NraYmlpyaxZs8jPz2fChAlYW1tTrVo1Vq1aVazuP/74g2bNmmFkZETdunX56aefNLb/9ttvdOzYEaVSia2tLQMGDODvv/9Wb8/MzGTgwIEolUrs7e0JDw8v1kZKSgqBgYEYGxvj4uLCunXripW5d3r87hTyli1baNu2LSYmJnh7e3PkyBGNfZYtW4ajoyMmJiZ07dqViIgIjUsNv/76K23btsXMzAxzc3NeeeUVTp4sfbSWmprK0KFDqVy5Mubm5vj6+vLrr7+qt8+YMYP69euzdu1anJ2dsbCwoHfv3vzzzz/qMrt376ZFixZYWlpiY2NDQEAAiYmJ5T7u9zt06BAtW7bE2NgYR0dHRo0aRWZmZrmO8fPo0pkD5GT9g0fjQABsnb3QNzTm2I7F5OVmk5fzL0e2LUKlKiQr/e+H1FY6KzsXGgeO4Juod/jmk1Aad3oHKzsXft4wl6adR3I17ghfzenFxg/7cS3hl4rqnngKVCoVedkZD10yMoqWrKws9c8lLSqV6ll36bnywt+ItnHjRjw8PHB3d6d///6MGTOGsLAwFArFI9WXl5eHn58fTZs25eDBg+jp6TF79mz8/f05c+YMBgYGAOzbt49q1arx888/ExMTw5AhQzh8+DCtWrXi2LFjbNiwgbfeeosOHTpQrVo1df0TJkxg0aJF1K5dm4iICAIDA7l06RI2Njakpqbi6+vL0KFDWbhwIf/++y+TJk0iKCiIffv2qff/6aef2L59O1WqVGHy5Mn88ssv1K9fX91GcHAw165dY//+/ejr6zNq1ChSUlIe2vcpU6awYMEC3NzcmDJlCn369OHChQvo6ekRExPD22+/zfz58+nUqRN79+7l/fff19i/X79++Pj48Nlnn6Grq0tsbCz6+vqltAY9e/bE2NiYXbt2YWFhwdKlS2nXrh3nz5/H2rroJqjExES2bdvGt99+y507dwgKCuLDDz9kzpw5QNFJzLhx46hXrx4ZGRlMmzaNrl27Ehsbi47Of+esDzru90tMTMTf35/Zs2ezcuVKbt68SWhoKKGhoeoTsfIe45ycHHJyctSv09PTH/p+PAl/HNlO9TrNMLWsDBRNnb82+EN+3vAhZw9sAIUCt4Z+VKrmATza/6G76rToTp0W3dWv4499i76hCbY16vHlrO50n7CazNQUfoieQr8Z29HTN3is9sTTkZ+TyaHILg8tdyiybPVt374dpVL5eEG9QF74pL1ixQr69y+6K9Xf35+0tDR++ukn2rRp80j1bdiwgcLCQpYvX65O/KtWrcLS0pIDBw7w2muvAWBtbU1kZCQ6Ojq4u7vz0UcfkZWVxeTJkwEICwvjww8/5NChQ/Tu3Vtdf2hoKN27F/0h++yzz9i9ezcrVqxg4sSJREVF4ePjw9y5c9XlV65ciaOjI+fPn8fBwYEVK1bwxRdf0K5dOwBWr16tcVJw/vx5du3axfHjx2nUqJH6GHl6ej607+PHj1ffFzBz5kzq1KnDhQsX8PDwYPHixXTs2JHx48cDUKtWLQ4fPsy3336r3v/KlStMmDABDw8PANzc3Ept69ChQxw/fpyUlBT1NP2CBQvYtm0bmzZtYtiwYQAUFhYSHR2NmZkZAAMGDODHH39UJ+27x/Le41W5cmXOnTtH3bp1y3Tc7zdv3jz69eunvrnPzc2NyMhIWrduzWeffcaVK1fKfYznzZvHzJkzS93+NPxzK5k/40/gN/QjjfWOnk3oN2Mb/2akoqOji6GJGdGT/TCv1KHC2v43I5UTO5fRZewyUpJ+w6JKdSz/fyksyCft5hVsHFwrrD0htNULPT0eHx/P8ePH6dOnDwB6enr06tWLFStWPHKdv/76KxcuXMDMzAylUolSqcTa2prs7GyNadc6depojORsbW3x8vJSv9bV1cXGxqbY6Ktp06bqn/X09GjYsCFxcXHqtvfv369uV6lUqhNgYmIiiYmJ5Obm0rhxY3Ud1tbWuLu7q1/HxcWhp6fHK6+8ol7n4eFRpjvm69Wrp/7Z3t4eQB1/fHw8r776qkb5+1+PGzeOoUOH0r59ez788MMSp6nv+vXXX8nIyMDGxkajv5cuXdLYz9nZWZ2w78Z17zFNSEigT58+1KhRA3Nzc5ydnYGiE4h7Pei4lxRbdHS0Rlx+fn4UFhZy6dKlRzrGYWFhpKWlqZerV6+WWvZJ+ePYNxgrrXCq06LE7cZKSwxNzPgz/gTZ/9zBxat1hbV9eEsE3r59UVpWobCwgMKCfPU2VWEBqgfcqS7Ey+SFHmmvWLGC/Px8HBwc1OtUKhWGhoZERUVhYWFR7jozMjJ45ZVXSrxGWblyZfXP90/7KhSKEtc96LGZktoODAxk/vz5xbbZ29tz4cKFMtf1KO6N/+4sQ3ninzFjBn379uW7775j165dTJ8+na+++oquXbsWK5uRkYG9vT0HDhwotu3e5PewYxoYGIiTkxPLli3DwcGBwsJC6tatS25ubpnjLim2t956i1GjRhXbVr16dc6fP1/uOg0NDZ/pjX+FhYX8cfQb3Bu/Uewxq7gjO7Cyc8FYacWNS2c4tDmcem37YGnrpC6zY/FwXLzb4tUqCIDc7H/5O/U8KkVRXem3r/H3n+cxNDHHzNpOo/6rfxwjNeUKbfvPAKCKUx1SbyRx5dxh/rl9HYWODpZVnBDaQc/QlBajtj203IR2CgoKCkhMTKRmzZrolvJ4n6mpaQVHqN1e2KSdn5/PmjVrCA8PV09Z39WlSxe+/PJL3n777XLX26BBAzZs2ECVKlUwNzevqHDVjh49SqtWrYCiPpw6dYrQ0FB125s3b8bZ2Rk9veJvXc2aNdHX1+fYsWNUr14dgDt37nD+/Hlaty4aFXl4eKjrvTt1Gx8f/9jPILu7u3PixAmNdfe/hqJp81q1ajF27Fj69OnDqlWrSkzaDRo04Pr16+jp6alHx+V169Yt4uPjWbZsGS1btgSKpt1L8qDjXlJs586dw9W15OnaJ3WMn6S/4o+Tcec6Hk07F9uWlnKZY998Qk5WOmbWDrziN5h6bftqlEn/+y+yM1LVr5P/vMjGT+dx97r34S0LAXB/9Q18B8xQl8vPzeHgxo94bfA89cyU0rIKLXpOYN8XM9HVM8C3/wz0DORJBm2hUCjQN3r4NWilsihpm5iYoFQqS03aQtMLm7Tv3pg0ZMiQYiPq7t27s2LFikdK2v369ePjjz+mc+fOzJo1i2rVqnH58mW2bNnCxIkTNa4fP4pPPvkENzc3PD09WbhwIXfu3GHw4MEAvPPOOyxbtow+ffowceJErK2tuXDhAl999RXLly9HqVQyZMgQJkyYgI2NDVWqVGHKlCka0/Tu7u74+/vz1ltv8dlnn6Gnp8eYMWMwNjZ+rLhHjhxJq1at1Ddx7du3j127dqlH5P/++y8TJkygR48euLi48Oeff3LixIli15zvat++PU2bNqVLly589NFH1KpVi2vXrvHdd9/RtWtXGjZs+NCYrKyssLGx4fPPP8fe3p4rV67w3nvvlVj2Qcf9fpMmTaJJkyaEhoYydOhQTE1NOXfuHD/88ANRUVFP7Bg/SY6eTRi+uPhJFkCTziNp0nnkA/fvP3OHxmsn1zqMiDymHmmXRs/AkL7TNhdbX7tZF2o36/LgoIV4Cb2w17RXrFhB+/btS5wC7969OydPnuTMmTPlrtfExISff/6Z6tWr061bNzw9PRkyZAjZ2dkVMvL+8MMP+fDDD/H29ubQoUPs2LGDSpUqAeDg4EBMTAwFBQW89tpreHl5MWbMGCwtLdWJ+eOPP6Zly5YEBgbSvn17WrRooXFtFYpunHNwcKB169Z069aNYcOGUaVKlceKu3nz5ixZsoSIiAi8vb3ZvXs3Y8eOxcjICCi6hn/r1i0GDhxIrVq1CAoKomPHjqXefKVQKNi5cyetWrXizTffpFatWvTu3ZvLly9ja2tbpph0dHT46quvOHXqFHXr1mXs2LF8/PHHJZZ90HG/X7169fjpp584f/48LVu2xMfHh2nTpmlchnkSx1gIIRQqeQhOPCEhISH88ccfHDx48FmHonXS09OxsLBg6qZbGCqtnnU45aJQFVA5P4Gbem4PHWk/T7Q1btDO2Kf6FU2PJyQk4ObmpnXT43fu3MHa2pq0tLQncqm0NC/s9Lh4+hYsWECHDh0wNTVl165drF69mk8//fRZhyWEEC8MSdqiwhw/fpyPPvqIf/75hxo1ahAZGcnQoUOfdVhCCPHCkKQtKszGjRufdQhCCPFCe2FvRBNCCCFeNJK0hRBCCC0hSVsIIYTQEpK0hRBCCC0hSVsIIYTQEpK0hRBCCC0hSVsIIYTQEpK0hRBCCC0hSVsIIYTQEvKJaEI8x8b5KrCyUjzrMMqloEBBQoICNzcFurraE7u2xg3aHbsoHxlpCyGEEFpCkrYQQgihJSRpCyGEEFpCkrYQQgihJSRpCyGEEFpCkrYQQgihJSRpCyGEEFpCkrYQQgihJSRpCyGEEFpCPhFNiOdYxD4VhkrVsw6jXBQqFZXzVWxOUqFSaE/szzruqX7ySWbi4WSkLYQQQmgJSdpCCCGElpCkLYQQQmgJSdpCCCGElpCkLYQQQmgJSdpCCCGElpCkLYQQQmgJSdpCCCGElpCkLYQQQmgJSdpCCCGElpCkLYQQQmgJSdpCCCGElpAvDBHiJXdi5+ec3LVMY51lFSf6vL8JgJ++msuffxwnM+0m+oYm2Ll40aTzKKzsnEusr6Agn3071vJ7/AXS/76GgbGSau6NaNJpJKaWlQHIz8vlpy9nc+nMT5iYV6JVr0lUc39VXcfpvWvIuHOdlj0nPplOC6GlnslIW6FQsG3btgqpKzo6GktLyweWmTFjBvXr13+sdpKSklAoFMTGxj5WPS+KAwcOoFAoSE1NLdd+M2bMwNbWtkJ/B54GbYu3vKzsajBozm710mXscvW2StU8aNtvGr2nfk3AO4sB+PaTUAoLC0usKz83m+t/XqKh32B6TvoCv6EfkZpyhV2fv6suE3d4Kzev/EG3d1dRu1kX9kZPRaUq+mat9FvXiDu8ncaB7zzBHguhnSosaQcHB6NQKFAoFOjr62Nra0uHDh1YuXJlsf/cycnJdOzYsaKafm60adNGfQyMjIyoXbs2n376qXp7dHS0eruOjg729vb06tWLK1euFKvr999/JygoiMqVK2NoaEitWrWYNm0aWVlZwH9J80HLgQMHAPjzzz8xMDCgbt26JcatUqn4/PPPady4MUqlEktLSxo2bMiiRYvU7d2vWbNmJCcnY2FhUebjExcXx8yZM1m6dOlz+ztQ2gne8xpvRdHR1cPE3Ea9GCst1dvqtOiGg1sDzG0cqOzowasBw8lIvcE/t66VWJehsZK+I6bh2qA9lrZO2Ll40bLnRG5ejeOf29cBuHP9Es5eLbG2r0HdVkH8m3GH7IxUAH7eMI8mnUMxMDJ90t0WQutU6Ejb39+f5ORkkpKS2LVrF23btmX06NEEBASQn5+vLmdnZ4ehoWFFNv3cCAkJITk5mXPnzhEUFMQ777zDl19+qd5ubm5OcnIyf/31F5s3byY+Pp6ePXtq1HH06FEaN25Mbm4u3333HefPn2fOnDlER0fToUMHcnNz1Unz7hIUFKQ+/neXZs2aAUUnC0FBQaSnp3Ps2LFiMQ8YMIAxY8bQuXNn9u/fT2xsLO+//z7bt2/n+++/L7GfBgYG2NnZoVCU/TuAExMTAejcufNj/Q7k5eU90n6P40X+nQVIS7nC6ikd+WJ6Z/ZGT1Un1/vl5fzLH0e/wczaAaWVbZnrz83OQIECQxMzAGyq1iI5MZb83Byuxh3BxLwSRkpLzp/Yha6eATW821ZIv4R40VRo0jY0NMTOzo6qVavSoEEDJk+ezPbt29m1axfR0dHqcvdONebm5hIaGoq9vT1GRkY4OTkxb948ddmIiAi8vLwwNTXF0dGRESNGkJGRUaztbdu24ebmhpGREX5+fly9evWBsS5fvhxPT0+MjIzw8PDQGBEDHD9+HB8fH4yMjGjYsCGnT58u0zEwMTHBzs6OGjVqMGPGDNzc3NixY4dG3+3s7LC3t6dZs2YMGTKE48ePk56eDhSNeocMGYKnpydbtmzh1VdfxcnJiZ49e/LNN99w5MgRFi5cqE6adxdjY2P18b+7GBgYoFKpWLVqFQMGDKBv376sWLFCI96NGzeybt06vvzySyZPnkyjRo1wdnamc+fO7Nu3j7ZtS/7jef/0+N3LFHv27MHT0xOlUqk+iYCiEWxgYCAAOjo66mRfWFjIrFmzqFatGoaGhtSvX5/du3er27l7WWLDhg20bt0aIyMj1q1bR3BwMF26dGHu3LnY2tpiaWnJrFmzyM/PZ8KECVhbW1OtWjVWrVqlEfekSZOoVasWJiYm1KhRg/fff199EhAdHc3MmTP59ddf1bMVd39v758eP3v2LL6+vhgbG2NjY8OwYcM0fi/vxrdgwQLs7e2xsbHhnXfeeSYnHA9j61wX3/7TCRgRSate7/HP7WtsWxRCbvZ/syy//fw1y99txfLxrbhy7jCBoZ+gq6dfpvrz83I5sm0xrq+8ph49ezTthE21Wnw1J4hT36/itcHzyMn6hxPfLaVlz4kc++ZT1s3syrefhJKRmvJE+v2kqFQq8rIzyr1kZDzacveygng5PPEb0Xx9ffH29mbLli0MHTq02PbIyEh27NjBxo0bqV69OlevXtVIuDo6OkRGRuLi4sLFixcZMWIEEydO1EiyWVlZzJkzhzVr1mBgYMCIESPo3bs3MTExJca0bt06pk2bRlRUFD4+Ppw+fZqQkBBMTU0ZNGgQGRkZBAQE0KFDB7744gsuXbrE6NGjH6n/xsbG5ObmlrgtJSWFrVu3oquri66uLgCxsbGcO3eO9evXo6OjeU7l7e1N+/bt+fLLL5k0aVKZ2t+/fz9ZWVm0b9+eqlWr0qxZMxYuXIipqan6WLi7u9O5c+di+yoUinJNf2dlZbFgwQLWrl2Ljo4O/fv3Z/z48axbt47x48fj7OzMm2++qU7kAP/73/8IDw9n6dKl+Pj4sHLlSjp16sTvv/+Om5ubutx7771HeHi4+kTqwIED7Nu3j2rVqvHzzz8TExPDkCFDOHz4MK1ateLYsWNs2LCBt956iw4dOlCtWjUAzMzMiI6OxsHBgbNnzxISEoKZmRkTJ06kV69e/Pbbb+zevZu9e/cClNj/zMxM/Pz8aNq0KSdOnCAlJYWhQ4cSGhqqcXK6f/9+7O3t2b9/PxcuXKBXr17Ur1+fkJCQYnXm5OSQk5Ojfn33JO5pqF67mfpnm6pu2DrX5YvpgSSe/gHPpkW/F26NOlLNozFZaX/z674v+H5lGF3HrUBP3+CBdRcU5PPDyvcAFa16h6nX6+rq0SpI83d43xcz8Wrdi7//jOfSmZ8Iem89sXvXELNpAX5DP6q4Dj9h+TmZHIrsUu79DkU+Wnvbt2/H2Nj40XYWWuep3Ijm4eFBUlJSiduuXLmCm5sbLVq0wMnJiRYtWtCnTx/19jFjxtC2bVucnZ3x9fVl9uzZbNy4UaOOvLw8oqKiaNq0Ka+88gqrV6/m8OHDHD9+vMQ2p0+fTnh4ON26dcPFxYVu3boxduxYli5dCsD69espLCxkxYoV1KlTh4CAACZMmFCuPhcUFPDFF19w5swZfH191evT0tJQKpWYmppia2vL/v37eeedd9RJ9Pz58wB4enqWWK+np6e6TFmsWLGC3r17o6urS926dalRowZff/21entCQgLu7u7l6ltp8vLyWLJkCQ0bNqRBgwaEhoby448/AqivlQPqmQCABQsWMGnSJHr37o27uzvz58+nfv36LFq0SKPuMWPGqN8ve3t7AKytrYmMjMTd3Z3Bgwfj7u5OVlYWkydPxs3NjbCwMAwMDDh06JC6nqlTp9KsWTOcnZ0JDAxk/Pjx6t8nY2NjlEolenp6GjMY91u/fj3Z2dmsWbOGunXr4uvrS1RUFGvXruXGjRvqclZWVkRFReHh4UFAQABvvPGG+njcb968eVhYWKgXR0fHR3sTKoChiRkWVZxIu/nfybOhsRLLKtVxcGvAa0Pmk3ojiUu/7n9gPXcT9j+3rxMY+skDr1H/df4kt5MvUrd1L/46fxKnOs3RNzSmZoMO/JVwqsL6JoS2eypJW6VSlXrtMzg4mNjYWNzd3Rk1alSxa6h79+6lXbt2VK1aFTMzMwYMGMCtW7c0bpDS09OjUaNG6tceHh5YWloSFxdXrL3MzEwSExMZMmQISqVSvcyePVt9zTUuLo569ephZGSk3q9p06Zl6uunn36KUqnE2NiYkJAQxo4dy/Dhw9XbzczMiI2N5eTJk4SHh9OgQQPmzJlT4jF7XKmpqWzZsoX+/fur1/Xv319jirwip9ZMTEyoWbOm+rW9vT0pKaVPbaanp3Pt2jWaN2+usb558+bF3ruGDRsW279OnToasxG2trZ4eXmpX+vq6mJjY6MRw4YNG2jevDl2dnYolUqmTp1a4o2ADxIXF4e3t7f6ROtuzIWFhcTHx2vEd3cGBR58PMLCwkhLS1MvD7u88yTlZmeR/vefmJhXKnF70e+MioL8kmeQoChhf79yMqkpVwkM/QQj09JnbPLzcjm48SNa956Mjo4OKlUhhQVF98AUFuSjKuUudSFeRk/lOe24uDhcXFxK3NagQQMuXbrErl272Lt3L0FBQbRv355NmzaRlJREQEAAw4cPZ86cOVhbW3Po0CGGDBlCbm4uJiYm5Y7l7nXHZcuW0bhxY41t9/6BfVT9+vVjypQpGBsbY29vX2yKW0dHB1dXV6Bo1JyYmMjw4cNZu3YtALVq1QKKjpmPj0+x+uPi4tRlHubuiPDefqpUKgoLCzl//jy1atWiVq1a/PHHH4/U1/vp62te41QoFBV2UnBvgnxQeyWtu/v0wpEjR+jXrx8zZ87Ez88PCwsLvvrqK8LDwyskxrLEV9pjUoaGhs/sRrfDWxfhXLcVSms7stL+5sTOJSgUOri94kf6339x4ZfvqebRBGOlFZmpKZz+IRpdfUOq1/nvZOvLD3rQuNM71PBuS0FBPptXhXPzrxt0fHsRKlUhWem3ivppYl7sWvip3cupXqcZlR2LZnzsanhzZFskHk0COfvTBuxq1Ht6B6MC6Bma0mLUtnLvN6Fd2W/qvJepqWmpv1fixfPEk/a+ffs4e/YsY8eOLbWMubk5vXr1olevXvTo0QN/f39u377NqVOnKCwsJDw8XJ387p8aB8jPz+fkyZO8+mrRhzPEx8eTmppa4hSzra0tDg4OXLx4kX79+pUYj6enJ2vXriU7O1s92j569GiZ+mthYaFOymXx3nvvUbNmTcaOHUuDBg2oX78+Hh4eLFy4kN69e2sk/V9//ZW9e/dq3Kj3ICtWrODdd98lODhYY/2IESNYuXIlH374IX379qV3795s37692HVtlUpFenp6ua5rl4e5uTkODg7ExMTQunVr9fqYmBj1e1mRDh8+jJOTE1OmTFGvu3z5skYZAwMDCgoKHliPp6cn0dHRZGZmqk8mYmJi0NHRqbBLDU9TZmoKP0RPISczDSMzK+xreNPt3VUYm1lRWJBP8oXTnDnwFTlZ6RibWeNQ04du41ZiYmatriM15TK5/2ao60v4/ST5CkO+nq/5f6zTqCVUdXtF/fr2tUQST++l56R16nU16rfjWsIpti0KwaKKEx2CZz/hI1CxFAoF+kbKcu+nVD5a0hYvlwpN2jk5OVy/fp2CggJu3LjB7t27mTdvHgEBAQwcOLDEfSIiIrC3t8fHxwcdHR2+/vpr7OzssLS0xNXVlby8PBYvXkxgYCAxMTEsWbKkWB36+vqMHDmSyMhI9PT0CA0NpUmTJqX+4Z85cyajRo3CwsICf39/cnJyOHnyJHfu3GHcuHH07duXKVOmEBISQlhYGElJSSxYsKAiD5Wao6MjXbt2Zdq0aXz77bcoFApWrFhBhw4d6N69O2FhYdjZ2XHs2DHeffddmjZtypgxYx5ab2xsLL/88gvr1q3Dw8NDY1ufPn2YNWsWs2fPJigoiK1bt9KnTx+mTp3Ka6+9RuXKlTl79iwLFy5k5MiRdOnS5Yn0HWDChAlMnz6dmjVrUr9+fVatWkVsbCzr1q17+M7l5ObmxpUrV/jqq69o1KgR3333HVu3btUo4+zszKVLl4iNjaVatWqYmZkVGwH369eP6dOnM2jQIGbMmMHNmzcZOXIkAwYMwNa27I9BPS86vDm31G2mlpV5Y8TD75AavviE+mdzGwemLPyam3puqBQPnr2ydqhJ32lbNNbp6OjQqtd7tOr13kPbFeJlU6HXtHfv3o29vT3Ozs74+/uzf/9+IiMj2b59e6lTz2ZmZnz00Uc0bNiQRo0akZSUxM6dO9HR0cHb25uIiAjmz59P3bp1WbduXYmjTBMTEyZNmkTfvn1p3rw5SqWSDRs2lBrn0KFDWb58OatWrcLLy4vWrVsTHR2tnsJXKpV88803nD17Fh8fH6ZMmcL8+fMr5iCVYOzYsXz33XfqG+eaNWvG0aNH0dXVpWPHjri6uhIWFsagQYP44YcfyjSNumLFCmrXrl0sYQN07dqVlJQUdu7ciUKhYP369URERLBt2zZat25NvXr1mDFjBp07d8bPz6/C+3uvUaNGMW7cON599128vLzYvXs3O3bs0LhzvKJ06tSJsWPHEhoaSv369Tl8+DDvv/++Rpnu3bvj7+9P27ZtqVy5ssYz9neZmJiwZ88ebt++TaNGjejRowft2rUjKiqqwmMWQoh7KVTykJ8Qz527lyWmbrqFodLqWYdTLgpVAZXzE8o00n6ePOu4p/o9+vR4QUEBCQkJuLm5Vci9OU+LtsYNcOfOHaytrUlLS8Pc3PyptSvf8iWEEEJoCUnaQgghhJaQpC2EEEJoCUnaQgghhJaQpC2EEEJoCUnaQgghhJaQpC2EEEJoCUnaQgghhJaQpC2EEEJoCUnaQgghhJaQpC2EEEJoCUnaQgghhJZ44t+nLYR4dON8FVhZadf3LBcUKEhIUODmpkBXV3ti19a4xctFRtpCCCGElpCkLYQQQmgJSdpCCCGElpCkLYQQQmgJSdpCCCGElpCkLYQQQmgJSdpCCCGElpCkLYQQQmgJSdpCCCGElpBPRBPiORaxT4WhUvWswygXhUpF5XwVm5NUqBTaE7u2xg1FsXd3ftZRiKdBRtpCCCGElpCkLYQQQmgJSdpCCCGElpCkLYQQQmgJSdpCCCGElpCkLYQQQmgJSdpCCCGElpCkLYQQQmgJSdpCCCGElpCkLYQQQmgJSdpCCCGElpCkLYQQQmgJ+cIQIQQZqSkc3b6YK+cOk5+bjUVlR9r2m0YVp9oAqFQqTuxcSlzMNnL+/Qf7mt60DHoPyyrVS63z1PfRXDzzM6k3ktDTN8TWxYumnUdhaeukLhOzOYL449+hZ2BEk06h1GrUUb0t8Ze9xJ/4jtffWvjkOi6ElnkpR9oKhYJt27ZVSF3R0dFYWlo+sMyMGTOoX7/+Y7WTlJSEQqEgNjb2seqpKAcOHEChUJCamlqu/WbMmIGtrW2FvgdPg7bFWx7ZmelsWzgUHV093hgeSe+pX9Os6xgMTczVZWL3ruHsgQ206h1G9/HR6Okb8e0nI8nPyy213uQLp6nbsgfd3l1FwDtRFBbk880noeTl/AtA0tmfSTi1h4ARi2naeRQH1s/m34xUAHL+zeDYt5/RsuekJ9p3IbTNC5O0g4ODUSgUKBQK9PX1sbW1pUOHDqxcuZLCwkKNssnJyXTs2LGUmrRXmzZt1MfAyMiI2rVr8+mnn2qU+ffff5k+fTq1atXC0NCQSpUq0bNnT37//XeNcg870WjWrBnJyclYWFiUOb64uDhmzpzJ0qVLn9v3oLR+P6/xVoTYvatRWlbBt/90bJ3rYG7jgKNnEywqVwOKRtln9n/JK36DcanXGpuqbvgOnEVW+t8knTlQar0BI/6HR5NArO1rUKlaLXz7zyDjznVuXokD4M6NJKq6vUIVp9q4NfTDwFjJP7euAXB0eyR1WnbHzNruifdfCG3ywiRtAH9/f5KTk0lKSmLXrl20bduW0aNHExAQQH5+vrqcnZ0dhoaGzzDSJyckJITk5GTOnTtHUFAQ77zzDl9++SUAOTk5tG/fnpUrVzJ79mzOnz/Pzp07yc/Pp3Hjxhw9erTM7RgYGGBnZ4dCoSjzPomJiQB07tz5sd6DvLy8R9rvcbzIvzNJZw9SuXpt9qyYRHTYa2z8sB/nYraqt/9z6xpZ/9yimkdj9TpDYyW2znW4fulMmdvJzc4AwMi06ETPxsGNlCtxZGemk3IljvzcbMwrVSM5MZabV+Pxat27gnooxIvjhUrahoaG2NnZUbVqVRo0aMDkyZPZvn07u3btIjo6Wl3u3qnO3NxcQkNDsbe3x8jICCcnJ+bNm6cuGxERgZeXF6ampjg6OjJixAgyMjKKtb1t2zbc3NwwMjLCz8+Pq1evPjDW5cuX4+npiZGRER4eHsVGxMePH8fHxwcjIyMaNmzI6dOny3QMTExMsLOzo0aNGsyYMQM3Nzd27NgBwKJFizhy5AjffvstQUFBODk58eqrr7J582Y8PT0ZMmQIKpWqTO3cPz1+9zLBnj178PT0RKlUqk+ioGgEGxgYCICOjo462RcWFjJr1iyqVauGoaEh9evXZ/fu3ep27l4W2LBhA61bt8bIyIh169YRHBxMly5dmDt3Lra2tlhaWjJr1izy8/OZMGEC1tbWVKtWjVWrVmnEPWnSJGrVqoWJiQk1atTg/fffV58EREdHM3PmTH799Vf1jMXd35v7p8fPnj2Lr68vxsbG2NjYMGzYMI3fi7vxLViwAHt7e2xsbHjnnXeeyQnHw6Tf+ovfD23GsnJ13hixmDotunNo0wLij30LQFb6LQCMzaw19jM2s1Zve5jCwkJiNodj51IPa4eaAFSv3ZRajTqyecEg9n8xE98BM9A3NObnDR/Sqtd7/H5oE+tndWdrxGBuJ1+swB6/GFQqFXnZGeolKyuLjIwMjaWs/5+F9njhb0Tz9fXF29ubLVu2MHTo0GLbIyMj2bFjBxs3bqR69epcvXpVI+Hq6OgQGRmJi4sLFy9eZMSIEUycOFEjyWZlZTFnzhzWrFmDgYEBI0aMoHfv3sTExJQY07p165g2bRpRUVH4+Phw+vRpQkJCMDU1ZdCgQWRkZBAQEECHDh344osvuHTpEqNHj36k/hsbG5ObW3Tdcf369XTo0AFvb2+NMjo6OowdO5Z+/frx66+/PvL196ysLBYsWMDatWvR0dGhf//+jB8/nnXr1jF+/HicnZ1588031Ykc4H//+x/h4eEsXboUHx8fVq5cSadOnfj9999xc3NTl3vvvfcIDw9Xn8gcOHCAffv2Ua1aNX7++WdiYmIYMmQIhw8fplWrVhw7dowNGzbw1ltv0aFDB6pVK5rqNTMzIzo6GgcHB86ePUtISAhmZmZMnDiRXr168dtvv7F792727t0LUOL0f2ZmJn5+fjRt2pQTJ06QkpLC0KFDCQ0N1Tg53L9/P/b29uzfv58LFy7Qq1cv6tevT0hISLE6c3JyyMnJUb9OT09/pPfgUahUhVSp7knjTu8AUNnRnTvJifx+aAvujQMqpI2DG+dzO/kiXcYu11jf6PVhNHp9mPr1iZ2fU839VXR19Ti1ewW9wr4i6beD/LhmOj0nra2QWF4U+TmZHIrson59sIQy27dvR6lUPrWYxJP3Qo20S+Ph4UFSUlKJ265cuYKbmxstWrTAycmJFi1a0KdPH/X2MWPG0LZtW5ydnfH19WX27Nls3LhRo468vDyioqJo2rQpr7zyCqtXr+bw4cMcP368xDanT59OeHg43bp1w8XFhW7dujF27FiWLl0KFCXXwsJCVqxYQZ06dQgICGDChAnl6nNBQQFffPEFZ86cwdfXF4Dz58/j6elZYvm768+fP1+udu6Vl5fHkiVLaNiwIQ0aNCA0NJQff/wRAKVSqb5hz87ODju7omuVCxYsYNKkSfTu3Rt3d3fmz59P/fr1WbRokUbdY8aMUR8ve3t7AKytrYmMjMTd3Z3Bgwfj7u5OVlYWkydPxs3NjbCwMAwMDDh06JC6nqlTp9KsWTOcnZ0JDAxk/Pjx6vfT2NgYpVKJnp6eOkZjY+Ni/Vy/fj3Z2dmsWbOGunXr4uvrS1RUFGvXruXGjRvqclZWVkRFReHh4UFAQABvvPGG+njcb968eVhYWKgXR0fHR3sTHoGJeSUsbV001lnauZBx5/r/b7cB4N9/bmuU+fef2+ptD3Lw64+4/PshOo1agtKySqnl7lxPIuHkbhq98TZ/JZzCwdUHYzMrXBt04O8//yA3O6u8XRPihfNSJG2VSlXqtdfg4GBiY2Nxd3dn1KhRfP/99xrb9+7dS7t27ahatSpmZmYMGDCAW7dukZX13x8QPT09GjVqpH7t4eGBpaUlcXFxxdrLzMwkMTGRIUOGoFQq1cvs2bPV13zj4uKoV68eRkZG6v2aNm1apr5++umnKJVKjI2NCQkJYezYsQwfPlzjWDwpJiYm1KxZU/3a3t6elJSUUsunp6dz7do1mjdvrrG+efPmxY5dw4YNi+1fp04ddHT++xW2tbXFy8tL/VpXVxcbGxuNGDZs2EDz5s2xs7NDqVQydepUrly5UvZOUvT+eHt7Y2pqqhFzYWEh8fHxGvHp6uqqXz/oeISFhZGWlqZeHnZ5pSLZ1ahHWspljXVpKVfUN4GZ2ThgYmbDn/H/nYTmZmdyI+l37FzqlVqvSqXi4NcfcenXA3Qa+RnmNg4PLPvzhrk06zoGAyMTVKpCCguK7kO5+6+qsOCR+yjEi+KFnx6Hoj+yLi4uJW5r0KABly5dYteuXezdu5egoCDat2/Ppk2bSEpKIiAggOHDhzNnzhysra05dOgQQ4YMITc3FxMTk3LHcve657Jly2jcuLHGtnv/wD+qfv36MWXKFIyNjbG3t9dIarVq1SrxRAJQr69Vq9Yjt62vr6/xWqFQVNhJwr0J8kHtlbTu7tMDR44coV+/fsycORM/Pz8sLCz46quvCA8Pr5AYyxLf/U8y3GVoaPjMbnTzbtuPrRGDObVnJa4NOpBy+XfOxWyldZ/JQFHc9dr24Zc9K7GsUh0zGweOf/sZJuaVcK7XRl3PjsXDcfFuS72W3QH4+euPSTj5PR2HhWNgZKq+/m1gpETPQLOvcYe3YWRqibNXKwDsXLw5ufNzrl86y9Vzh7Gyq4GhidlTOBraQ8/QlBajtgGgUBXQqfpFatasqfF3pKT/N0K7vfBJe9++fZw9e5axY8eWWsbc3JxevXrRq1cvevTogb+/P7dv3+bUqVMUFhYSHh6uTn73T40D5Ofnc/LkSV599VUA4uPjSU1NLXEq2tbWFgcHBy5evEi/fv1KjMfT05O1a9eSnZ2tHm2X9c5uCwsLXF1dS9zWu3dvpkyZwq+//qpxXbuwsJCFCxdSu3btYte7nyRzc3McHByIiYmhdevW6vUxMTHqY1mRDh8+jJOTE1OmTFGvu3xZc4RpYGBAQcGDR3Senp5ER0eTmZmp/qMYExODjo4O7u7uFR73k1bFqTZ+IR9zbMcnnNq9AjMbB5p3H6fxQSf12w8kL/dfDnw5h9x/M7Cv6U3AO4vR0zdQl0n/+y+y//85a4DfD20GFGyPfEujvbb9puHRJFD9Ouuf2/zy/Sq6jl2hXmfrXAdv337sXDIGYzNrfPvPqPB+azuFQoG+UdH1aoWqABMTE5RKZYWc/Ivn1wuVtHNycrh+/ToFBQXcuHGD3bt3M2/ePAICAhg4cGCJ+0RERGBvb4+Pjw86Ojp8/fXX2NnZYWlpiaurK3l5eSxevJjAwEBiYmJYsmRJsTr09fUZOXIkkZGR6OnpERoaSpMmTUpNPDNnzmTUqFFYWFjg7+9PTk4OJ0+e5M6dO4wbN46+ffsyZcoUQkJCCAsLIykpiQULFjz28Rk7dizbt28nMDCQ8PBwGjduzI0bN5g7dy5xcXHs3bu3XI9wVYQJEyYwffp0atasSf369Vm1ahWxsbGsW7euwttyc3PjypUrfPXVVzRq1IjvvvuOrVu3apRxdnbm0qVLxMbGUq1aNczMzIqNgPv168f06dMZNGgQM2bM4ObNm4wcOZIBAwZga2tb4XE/Dc51W+Jct2Wp2xUKBa++8TavvvF2qWX6zyx6SgFV0UnPiMhjqBQPTyAmZtb/7XuPhh1DaNix+E17QrzMXqhr2rt378be3h5nZ2f8/f3Zv38/kZGRbN++vdSzTzMzMz766CMaNmxIo0aNSEpKYufOnejo6ODt7U1ERATz58+nbt26rFu3TuNxsLtMTEyYNGkSffv2pXnz5iiVSjZs2FBqnEOHDmX58uWsWrUKLy8vWrduTXR0tHoKX6lU8s0333D27Fl8fHyYMmUK8+fPf+zjY2RkxL59+xg4cCCTJ0/G1dUVf39/dHV1OXr0KE2aNHnsNspr1KhRjBs3jnfffRcvLy92797Njh07NO4cryidOnVi7NixhIaGUr9+fQ4fPsz777+vUaZ79+74+/vTtm1bKleurH7G/V4mJibs2bOH27dv06hRI3r06EG7du2Iioqq8JiFEOJeCpU8yCfEcyc9PR0LCwumbrqFodLqWYdTLgpVAZXzE7ip51amkfbzQlvjhqLYuztfwM3NTaumxwsKCkhISNC6uAHu3LmDtbU1aWlpmJubP3yHCvJCjbSFEEKIF5kkbSGEEEJLSNIWQgghtIQkbSGEEEJLSNIWQgghtIQkbSGEEEJLSNIWQgghtIQkbSGEEEJLSNIWQgghtIQkbSGEEEJLSNIWQgghtIQkbSGEEEJLvFBfzSnEi2acrwIrq6f7damPq6BAQUKCAjc3Bbq62hO7tsYNd2N/1lGIp0FG2kIIIYSWkKQthBBCaAlJ2kIIIYSWkKQthBBCaAlJ2kIIIYSWkKQthBBCaAlJ2kIIIYSWkKQthBBCaAlJ2kIIIYSWkE9EE+I5FrFPhaFS9azDKBeFSkXlfBWbk1SoFNoTu7bGDSXHPtVPuz7VTZSNjLSFEEIILSFJWwghhNASkrSFEEIILSFJWwghhNASkrSFEEIILSFJWwghhNASkrSFEEIILSFJWwghhNASkrSFEEIILSFJWwghhNASkrSFEEIILSFJWwghhNASkrSFEEIILSFJ+/8pFAq2bdtWIXVFR0djaWn5wDIzZsygfv36j9VOUlISCoWC2NjYx6rnUTzK8dq2bRuurq7o6uoyZsyYJxLXk9CmTRutivdRZKSmsHf1+6yc1I7PxzZnw9zepFw+p95+MXYf30S9w8pJ7fhsZCP+/vP8Q+ssKMjn5K5lrJvRhc/HNmfjvL5cOXdYo8z5E7tY8/4brJzoS8zmCI1t6beusX5Wd3KzMyumk0K8AF7opB0cHIxCoUChUKCvr4+trS0dOnRg5cqVFBYWapRNTk6mY8eOzyjSJ6dNmzbqY3Dv8vbbb6vLdOrUierVq2NkZIS9vT0DBgzg2rVrD6z3UY7XW2+9RY8ePbh69SoffPDBI/XnSTpw4AAKhYLU1FSN9Vu2bHku460o2ZnpbFs4FB1dPd4YHknvqV/TrOsYDE3M1WXyc7Oxr1mfJp1Hlrne499+xrmYrbToOYHeUzZSu0U3di+bwM2r8QD8m5HKgfWzadZlNAHvRJFwcjdJvx1U739ww4c06fQOBkamFddZIbTcC/992v7+/qxatYqCggJu3LjB7t27GT16NJs2bWLHjh3o6RUdAjs7u2cc6ZMTEhLCrFmzNNaZmJiof27bti2TJ0/G3t6ev/76i/Hjx9OjRw8OHz58f1Vq5T1eGRkZpKSk4Ofnh4ODQ/k6cI/c3FwMDAweef9HYW1t/VTbe9pi965GaVkF3/7T1evMbTTfo1qvvg4UjX7L6vyJ3fj4DcapTnMA6rbswV/xx/l13xe0H/QB6X//hYGxEtdXXgPAwe0V7ly/hHPdliSc3IOOrh416vs+bveEeKG80CNtAENDQ+zs7KhatSoNGjRg8uTJbN++nV27dhEdHa0ud+90b25uLqGhodjb22NkZISTkxPz5s1Tl42IiMDLywtTU1McHR0ZMWIEGRkZxdretm0bbm5uGBkZ4efnx9WrVx8Y6/Lly/H09MTIyAgPDw8+/fRTje3Hjx/Hx8cHIyMjGjZsyOnTp8t0DExMTLCzs9NYzM3/G0WNHTuWJk2a4OTkRLNmzXjvvfc4evQoeXl5pdZ57/G6O02/ZcsW2rZti4mJCd7e3hw5cgQoGsGamZkB4Ovri0Kh4MCBAwBs3ryZOnXqYGhoiLOzM+Hh4RrtODs788EHHzBw4EDMzc0ZNmyY+vLDt99+i7u7OyYmJvTo0YOsrCxWr16Ns7MzVlZWjBo1ioKCAnVda9eupWHDhpiZmWFnZ0ffvn1JSUlR96Ft27YAWFlZoVAoCA4OBopPj9+5c4eBAwdiZWWFiYkJHTt2JCEhQb39bnx79uzB09MTpVKJv78/ycnJZXq/nrakswepXL02e1ZMIjrsNTZ+2I9zMVsfu978/Fz09A011unqG3L94q8AWFR2JD83m5tX48nOTCfl8jlsHNzIzkzn+HdLaBk06bFjeFmoVCpys7PIy85QLxkZJS8qlepZhysewws/0i6Jr68v3t7ebNmyhaFDhxbbHhkZyY4dO9i4cSPVq1fn6tWrGglXR0eHyMhIXFxcuHjxIiNGjGDixIkaSTYrK4s5c+awZs0aDAwMGDFiBL179yYmJqbEmNatW8e0adOIiorCx8eH06dPExISgqmpKYMGDSIjI4OAgAA6dOjAF198waVLlxg9enSFH5vbt2+zbt06mjVrhr6+frn2nTJlCgsWLMDNzY0pU6bQp08fLly4QLNmzYiPj8fd3Z3NmzfTrFkzrK2tOXXqFEFBQcyYMYNevXpx+PBhRowYgY2NjTphAixYsIBp06YxfXrRSPDgwYNkZWURGRnJV199xT///EO3bt3o2rUrlpaW7Ny5k4sXL9K9e3eaN29Or169AMjLy+ODDz7A3d2dlJQUxo0bR3BwMDt37sTR0ZHNmzfTvXt34uPjMTc3x9jYuMR+BgcHk5CQwI4dOzA3N2fSpEm8/vrrnDt3Tn3MsrKyWLBgAWvXrkVHR4f+/fszfvx41q1bV2KdOTk55OTkqF+np6eX69g/jvRbf/H7oc14t+1Lg9cGk3L5dw5tWoCunj7ujQMeud7qHk34dd967Gv6YF6pGn+dP8GlX/ej+v9LU0am5vgOmMG+tdPJz8vBvfEbVK/dlP3rZlG3VU/S//6LnUvHUViQT6PXh1HTp11FdfmFk5+TydbPJmqsOxRZctnt27ejVCqfQlTiSXgpkzaAh4cHZ86cKXHblStXcHNzo0WLFigUCpycnDS23zvqcnZ2Zvbs2bz99tsaSTsvL4+oqCgaN24MwOrVq/H09OT48eO8+uqrxdqcPn064eHhdOvWDQAXFxfOnTvH0qVLGTRoEOvXr6ewsJAVK1ZgZGREnTp1+PPPPxk+fPhD+/rpp5+yfPlyjXVLly6lX79+6teTJk0iKiqKrKwsmjRpwrfffvvQeu83fvx43njjDQBmzpxJnTp1uHDhAh4eHlSpUgUommq+O7UeERFBu3bteP/99wGoVasW586d4+OPP9ZI2r6+vrz77rvq1wcPHiQvL4/PPvuMmjVrAtCjRw/Wrl3LjRs3UCqV1K5dm7Zt27J//3510h48eLC6jho1ahAZGUmjRo3IyMhAqVSqp8GrVKlS6o2Ed5N1TEwMzZo1A4pOuBwdHdm2bRs9e/YEit7/JUuWqOMLDQ0tdoniXvPmzWPmzJllOMoVT6UqpEp1Txp3egeAyo7u3ElO5PdDWx4rabfoPo79X83jq9lFx8S8UjXcmwTyx5Ed6jI1vNtSw7ut+vW1hF+4dS2RFj0nsn5mVzoEz8HE3IbNCwZh7+qDidmLfalCiId54afHS6NSqVAoFCVuCw4OJjY2Fnd3d0aNGsX333+vsX3v3r20a9eOqlWrYmZmxoABA7h16xZZWVnqMnp6ejRq1Ej92sPDA0tLS+Li4oq1l5mZSWJiIkOGDEGpVKqX2bNnk5iYCEBcXBz16tXDyMhIvV/Tpk3L1Nd+/foRGxursXTq1EmjzIQJEzh9+jTff/89urq6DBw4sNzTaPXq1VP/bG9vD6Cefi5JXFwczZs311jXvHlzEhISNKa1GzZsWGxfExMTdUIEsLW1xdnZWWMEYWtrq9H+qVOnCAwMpHr16piZmdG6dWug6CStrOLi4tDT01OfjAHY2Njg7u6u8d7eH5+9vf0Dj0VYWBhpaWnq5WGXUiqSiXklLG1dNNZZ2rmQcef6Y9VrbGZFx2HhDA0/SP9Z39Dn/U3oGxhjXqlaieXz83L5eeN8WvcOI/3mVVSFBTi4NcDS1gmLKk6kJP32WPEI8SJ4aUfacXFxuLi4lLitQYMGXLp0iV27drF3716CgoJo3749mzZtIikpiYCAAIYPH86cOXOwtrbm0KFDDBkyhNzcXI0bvMrq7vXwZcuWaSQDAF1d3fJ37j4WFha4uro+sEylSpWoVKkStWrVwtPTE0dHR44ePVrmEwNAYzr97gnR/XfpPwpT0+J3D98/dX/3CYH7191tPzMzEz8/P/z8/Fi3bh2VK1fmypUr+Pn5kZub+9gxliW+B50EGRoaYmhoWOr2J8muRj3SUi5rrEtLuYKZdcXcnKmnb4DSsgoFBflc/HU/rj7tSyz3y54VVPdsQmVHD25ejaew8L8Tt8KCfPW0uihOz9CUrsM/4m+9mqgURX8zJrQreVBS0v8noT1eyqS9b98+zp49y9ixY0stY25uTq9evejVqxc9evTA39+f27dvc+rUKQoLCwkPD0dHp2iiYuPGjcX2z8/P5+TJk+qp8Pj4eFJTU/H09CxW1tbWFgcHBy5evKgxZX0vT09P1q5dS3Z2tnq0ffTo0XL3vSzuJrp7r7E+CZ6ensWu8cfExFCrVq0KOVm51x9//MGtW7f48MMPcXR0BODkyZMaZe7elX7vKL+kmPPz8zl27Jh6evzWrVvEx8dTu3btCo35afFu24+tEYM5tWclrg06kHL5d87FbKV1n8nqMtmZ6WTcuU5m2k0AUm8kAWBiboOJuQ0AP66ZhqllFZoGFl2yuZH0Gxlpt6hUzZ2M1BRO7vocVWEh9dsPLBbD7eSLXPhlLz0nfQGAla0zCoWCuCPbMTGzIfVGEpWdtPP4Pg0KhQIDIxP09ZTqpK1Ulpy0hXZ74ZN2Tk4O169f13jka968eQQEBDBwYPE/HlB0rdXe3h4fHx90dHT4+uuvsbOzw9LSEldXV/Ly8li8eDGBgYHExMSwZMmSYnXo6+szcuRIIiMj0dPTIzQ0lCZNmpR4PRuKrgGPGjUKCwsL/P39ycnJ4eTJk9y5c4dx48bRt29fpkyZQkhICGFhYSQlJbFgwYIyHYOsrCyuX9ec6jQ0NMTKyopjx45x4sQJWrRogZWVFYmJibz//vvUrFmzXKPsR/Huu+/SqFEjPvjgA3r16sWRI0eIiooqdtd8RahevToGBgYsXryYt99+m99++63Ys9dOTk4oFAq+/fZbXn/9dYyNjYvdsOPm5kbnzp0JCQlh6dKlmJmZ8d5771G1alU6d+5c4XE/DVWcauMX8jHHdnzCqd0rMLNxoHn3cdRq9N9z+Elnf2L/uv+uyf8QPQWAhh1DaPT6MAAy7lxHofjviltBfi7Hv11C+q2/0Dc0pnrtZrQbMAtDEzON9lUqFT99OYdm3cagb1h085+egSFt+0/n4MaPKMjPpUXPCSgtqzyxYyCEtnjhk/bu3buxt7dHT08PKysrvL29iYyMZNCgQeqR8v3MzMz46KOPSEhIQFdXl0aNGrFz5050dHTw9vYmIiKC+fPnExYWRqtWrZg3b16xEwATExMmTZpE3759+euvv2jZsiUrVqwoNc6hQ4diYmLCxx9/zIQJEzA1NcXLy0t905tSqeSbb77h7bffxsfHh9q1azN//ny6d+/+0GOwbNkyli1bprHOz8+P3bt3Y2JiwpYtW5g+fTqZmZnY29vj7+/P1KlTn/h0bYMGDdi4cSPTpk3jgw8+wN7enlmzZmnchFZRKleuTHR0NJMnTyYyMpIGDRqwYMECjWv7VatWZebMmbz33nu8+eabDBw4UOOxwLtWrVrF6NGjCQgIIDc3l1atWrFz585y323/PHGu2xLnui1L3e7RJBCPJoEPrKPz6M+LflAVzVQ4uDag99Tis1D3UygUdB1X/P/Gw2IS4mWkUMlDe0I8d9LT07GwsGDqplsYKq2edTjlolAVUDk/gZt6buqpWm2grXFDybFP9Xv+p8cLCgpISEjAzc2twi+JPWl37tzB2tqatLQ0jc+9eNJe2rvHhRBCCG0jSVsIIYTQEpK0hRBCCC0hSVsIIYTQEpK0hRBCCC0hSVsIIYTQEpK0hRBCCC0hSVsIIYTQEpK0hRBCCC0hSVsIIYTQEpK0hRBCCC0hSVsIIYTQEi/8t3wJoc3G+Sqwsnr+v/jhXgUFChISFLi5KdDV1Z7YtTVu0O7YRfnISFsIIYTQEpK0hRBCCC0hSVsIIYTQEpK0hRBCCC0hSVsIIYTQEpK0hRBCCC0hSVsIIYTQEpK0hRBCCC0hSVsIIYTQEvKJaEI8xyL2qTBUqp51GOWiUKmonK9ic5IKlUJ7YtfWuOHhsU/1k09Je1HISFsIIYTQEpK0hRBCCC0hSVsIIYTQEpK0hRBCCC0hSVsIIYTQEpK0hRBCCC0hSVsIIYTQEpK0hRBCCC0hSVsIIYTQEpK0hRBCCC0hSVsIIYTQEpK0hRBCCC0hSVsIIYTQEi9d0lYoFGzbtq1C6oqOjsbS0vKBZWbMmEH9+vUfq52kpCQUCgWxsbGPVc+DtGnThjFjxqhfOzs7s2jRolLLBwcH06VLl3K1cf36dTp06ICpqelDj9vzpCzvs7bLSE1h7+r3WTmpHZ+Pbc6Gub1JuXxOvf3Ezs/58oMeLBvXkpUTffkmagQ3kn57YJ3XLvzCzqVjWT2lI5+NbMSlXw8UKxO7dy3RYa8RHfYasT9+obHtRtJvfD1/AIUFBRXSRyFeBC9E0g4ODkahUKBQKNDX18fW1pYOHTqwcuVKCgsLNcomJyfTsWPHZxTpi+N///sf0dHR5dpn4cKFJCcnExsby/nz559MYI+ppJOVXr16PbfxVoTszHS2LRyKjq4ebwyPpPfUr2nWdQyGJubqMpZVqtMyaCK9Jn9Fl7HLUVrZ8+0nofz7z51S683PzcbGwY2WQRNL3H7rrwRO7FxK++A5tA+ezfFvP+PWtQsAFBYU8NNX82jdOwwdXd2K7bAQWuyF+T5tf39/Vq1aRUFBATdu3GD37t2MHj2aTZs2sWPHDvT0irpqZ2f3jCN9MVhYWJR7n8TERF555RXc3Nweud3c3FwMDAweef9HYWxsjLGx8VNt82mK3bsapWUVfPtPV68zt3HQKOPW0F/jdfPu4/jj6A5uXUugmvurJdZbvXYzHOu0LLXdOzeSsHFwo5p7IwBsqrqRej0JGwdXYn9cg4OrD1Wcaj9qt4R4Ib0QI20AQ0ND7OzsqFq1Kg0aNGDy5Mls376dXbt2aYwI750ez83NJTQ0FHt7e4yMjHBycmLevHnqshEREXh5eWFqaoqjoyMjRowgIyOjWNvbtm3Dzc0NIyMj/Pz8uHr16gNjXb58OZ6enhgZGeHh4cGnn36qsf348eP4+PhgZGREw4YNOX369EP7n5OTw6RJk3B0dMTQ0BBXV1dWrFih3v7bb7/RsWNHlEoltra2DBgwgL///vuh9Zbm/unxNm3aMGrUKCZOnIi1tTV2dnbMmDFDvd3Z2ZnNmzezZs0aFAoFwcHBAFy5coXOnTujVCoxNzcnKCiIGzduqPe7e3lh+fLluLi4YGRkBBS9j0uXLiUgIAATExM8PT05cuQIFy5coE2bNpiamtKsWTMSExPVdSUmJtK5c2dsbW1RKpU0atSIvXv3avTh8uXLjB07Vj1zAyVPj3/22WfUrFkTAwMD3N3dWbt2rcZ2hULB8uXL6dq1KyYmJri5ubFjx45HPt5PUtLZg1SuXps9KyYRHfYaGz/sx7mYraWWL8jP49yhLRgYKbGpWuuR27VxcCX15hX+uX2df24lk5ZyBWuHmqTd/JM/jn7LqwEjHrnul5lKpSIvO0NjycgovqhUqmcdqngEL8xIuyS+vr54e3uzZcsWhg4dWmx7ZGQkO3bsYOPGjVSvXp2rV69qJFwdHR0iIyNxcXHh4sWLjBgxgokTJ2ok2aysLObMmcOaNWswMDBgxIgR9O7dm5iYmBJjWrduHdOmTSMqKgofHx9Onz5NSEgIpqamDBo0iIyMDAICAujQoQNffPEFly5dYvTo0Q/t68CBAzly5AiRkZF4e3tz6dIldVJOTU3F19eXoUOHsnDhQv79918mTZpEUFAQ+/btK+9hLdXq1asZN24cx44d48iRIwQHB9O8eXM6dOjAiRMnGDhwIObm5vzvf//D2NiYwsJCdcL+6aefyM/P55133qFXr14cOHBAXe+FCxfYvHkzW7ZsQfeeqdIPPviAiIgIIiIimDRpEn379qVGjRqEhYVRvXp1Bg8eTGhoKLt27QIgIyOD119/nTlz5mBoaMiaNWsIDAwkPj6e6tWrs2XLFry9vRk2bBghISGl9nPr1q2MHj2aRYsW0b59e7799lvefPNNqlWrRtu2bdXlZs6cyUcffcTHH3/M4sWL6devH5cvX8ba2rpYnTk5OeTk5Khfp6enP85bUS7pt/7i90Ob8W7blwavDSbl8u8c2rQAXT193BsHqMsl/XaQvaumkJ+bjYlFJQJDP8FYafnI7VrZudA4cATfRL0DQONO72Bl58I3USNo2nkkV+OOcGLn5+jo6tGi+7s4uDV43K6+FPJzMjkU2UVj3aHI4uW2b9+OUql8OkGJCvNCJ20ADw8Pzpw5U+K2K1eu4ObmRosWLVAoFDg5OWlsv//GrNmzZ/P2229rJO28vDyioqJo3LgxUJS4PD09OX78OK++WnzacPr06YSHh9OtWzcAXFxcOHfuHEuXLmXQoEGsX7+ewsJCVqxYgZGREXXq1OHPP/9k+PDhpfbx/PnzbNy4kR9++IH27dsDUKNGDfX2uycIc+fOVa9buXIljo6OnD9/nlq1Hn20dK969eoxfXrRFKubmxtRUVH8+OOPdOjQgcqVK2NoaIixsbH6EsUPP/zA2bNnuXTpEo6OjgCsWbOGOnXqcOLECRo1Kpo2zc3NZc2aNVSuXFmjvTfffJOgoCAAJk2aRNOmTXn//ffx8/MDYPTo0bz55pvq8t7e3nh7e6tff/DBB2zdupUdO3YQGhqKtbU1urq6mJmZPfAyyoIFCwgODmbEiKKR4Lhx4zh69CgLFizQSNrBwcH06dMHgLlz5xIZGcnx48fx9/cvVue8efOYOXNmWQ5zhVOpCqlS3ZPGnYqSZ2VHd+4kJ/L7oS0aSbuqW0N6vreOfzNSiTu8le9XhtFt/CpMzIqfhJRVnRbdqdOiu/p1/LFv0Tc0wbZGPb6c1Z3uE1aTmZrCD9FT6DdjO3r6T/fSiBDPmxdmerw0KpVKPc15v+DgYGJjY3F3d2fUqFF8//33Gtv37t1Lu3btqFq1KmZmZgwYMIBbt26RlZWlLqOnp6dOLlB0kmBpaUlcXFyx9jIzM0lMTGTIkCEolUr1Mnv2bPU0blxcHPXq1VNPAwM0bdr0gX2MjY1FV1eX1q1bl7j9119/Zf/+/Rptenh4AGhMHz+uevXqaby2t7cnJSWl1PJxcXE4OjqqEzZA7dq1ix0/JyenYgn7/vZsbW0B8PLy0liXnZ2tHrVmZGQwfvx4PD09sbS0RKlUEhcXx5UrV8rVz7i4OJo3b66xrnnz5sXe83vjMzU1xdzcvNTjERYWRlpamnp52CWWimRiXglLWxeNdZZ2LmTcua6xTt/QGIvKjti5eNG23zR0dHX548j2Covj34xUTuxcRoueE0lJ+g2LKtWxrFKdqrUaUliQT9rN8r1PQryIXviRdlxcHC4uLiVua9CgAZcuXWLXrl3s3buXoKAg2rdvz6ZNm0hKSiIgIIDhw4czZ84crK2tOXToEEOGDCE3NxcTE5Nyx3L3eviyZcvUI/O7dB/jDtmH3SSVkZFBYGAg8+fPL7bN3t7+kdu9n76+vsZrhUJR7O79R2FqavrQ9u6emJW07m4M48eP54cffmDBggW4urpibGxMjx49yM3NfewYHxbf3XhKOx6GhoYYGho+kTgexq5GPdJSLmusS0u5gpn1g2/aVBUWUpCfV2FxHN4SgbdvX5SWVUi5/DuFBfn3tFWAqgJ+l14GeoamtBi1TWPdhHbFBy6l/b8Sz7cXeqS9b98+zp49S/fu3UstY25uTq9evVi2bBkbNmxg8+bN3L59m1OnTlFYWEh4eDhNmjShVq1aXLt2rdj++fn5nDx5Uv06Pj6e1NRUPD09i5W1tbXFwcGBixcv4urqqrHcPbHw9PTkzJkzZGdnq/c7evToA/vp5eVFYWEhP/30U4nbGzRowO+//46zs3Oxdp/lf1xPT89i9xGcO3eO1NRUateu+LuGY2JiCA4OpmvXrnh5eWFnZ0dSUpJGGQMDAwoe8lywp6dnsXsWYmJinkjMT4N3237cSPqNU3tWknbzKgknd3MuZit1WvYEIC/nX47t+ITrl87yz61kUq7EsX/dLDLTblLTp726nh2Lh3P2543q17nZWfz953n+/rPocbn029f4+8/z/HNbcwQPcPWPY6SmXFG3WcWpDqk3krhy7jC/H9qCQkcHyypOxfYTxSkUCvSNlBrLvbNsd5fSZiDF8+2FGWnn5ORw/fp1jUe+5s2bR0BAAAMHDixxn4iICOzt7fHx8UFHR4evv/4aOzs7LC0tcXV1JS8vj8WLFxMYGEhMTAxLliwpVoe+vj4jR44kMjISPT09QkNDadKkSYnXs6Ho5qRRo0ZhYWGBv78/OTk5nDx5kjt37jBu3Dj69u3LlClTCAkJISwsjKSkJBYsWPDAvjs7OzNo0CAGDx6svhHt8uXLpKSkEBQUxDvvvMOyZcvo06eP+u7uCxcu8NVXX7F8+fLHGuU/jvbt2+Pl5UW/fv1YtGgR+fn5jBgxgtatW9OwYcMKb8/NzY0tW7YQGBiIQqHg/fffLzbydXZ25ueff6Z3794YGhpSqVKlYvVMmDCBoKAgfHx8aN++Pd988w1btmzRuBNdm1Rxqo1fyMcc2/EJp3avwMzGgebdx1GrUdHnGSh0dLlzI4n449+RnZGKoakFVap70mXMMqzt/7t3Iv3vv8jOSFW/vnk1ju2L31G/PrxlIQDur76B74AZ6vX5uTkc3PgRrw2eh45O0ThCaVmFFj0nsO+LmejqGeDbfwZ6Bs9mJkKI58kLk7R3796Nvb09enp6WFlZ4e3tTWRkJIMGDVL/IbifmZkZH330EQkJCejq6tKoUSN27tyJjo4O3t7eREREMH/+fMLCwmjVqhXz5s0rdgJgYmKivnP5r7/+omXLlhqPWt1v6NChmJiY8PHHHzNhwgRMTU3x8vJS3/SmVCr55ptvePvtt/Hx8aF27drMnz//gbMFUPQI0uTJkxkxYgS3bt2ievXqTJ48GQAHBwdiYmKYNGkSr732Gjk5OTg5OeHv71/qsXkaFAoF27dvZ+TIkbRq1QodHR38/f1ZvHjxE2kvIiKCwYMH06xZMypVqsSkSZOK3aU9a9Ys3nrrLWrWrElOTk6Jj8V06dKF//3vfyxYsIDRo0fj4uLCqlWraNOmzROJ+2lwrtsS57olP1Otp2+Af8jHD62j/8z/f6RNVTRTUdXtFYYvPvHQ/fQMDOk7bXOx9bWbdaF2sy4P3V+Il4lCJQ/rCfHcSU9Px8LCgqmbbmGotHrW4ZSLQlVA5fwEbuq5oVJoz6eZaWvc8PDYp/o9n1PhBQUFJCQk4Obm9sxm/B7VnTt3sLa2Ji0tDXNz84fvUEFe6GvaQgghxItEkrYQQgihJSRpCyGEEFpCkrYQQgihJSRpCyGEEFpCkrYQQgihJSRpCyGEEFpCkrYQQgihJSRpCyGEEFpCkrYQQgihJSRpCyGEEFpCkrYQQgihJV6Yb/kS4kU0zleBldXz+WUPpSkoUJCQoMDNTYGurvbErq1xg3bHLspHRtpCCCGElpCkLYQQQmgJSdpCCCGElpCkLYQQQmgJSdpCCCGElpCkLYQQQmgJSdpCCCGElpCkLYQQQmgJSdpCCCGElpBPRBPiORaxT4WhUvWswygXhUpF5XwVm5NUqBTaE7u2xg0lxz7VTz4Z7UUkI20hhBBCS0jSFkIIIbSEJG0hhBBCS0jSFkIIIbSEJG0hhBBCS0jSFkIIIbSEJG0hhBBCS0jSFkIIIbSEJG0hhBBCS0jSFkIIIbSEJG0hhBBCS0jSFkIIIbSEJG0hhBBCS7y0SVuhULBt27YKqSs6OhpLS8sHlpkxYwb169d/rHaSkpJQKBTExsY+Vj3Pk+DgYLp06fKsw1Br06YNY8aMKdc+f/zxB02aNMHIyOix3+NnJSM1hb2r32flpHZ8PrY5G+b2JuXyOfX2i7H7+CbqHVZOasdnIxvx95/ny1RvTtY//LxxPqsn+/P5mGasn9Wdy7/HqLefP7GLNe+/wcqJvsRsjtDYN/3WNdbP6k5udmbFdFKIF8ALlbSDg4NRKBQoFAr09fWxtbWlQ4cOrFy5ksLCQo2yycnJdOzY8RlF+uS0adNGfQzuXd5++211mU6dOlG9enWMjIywt7dnwIABXLt27RlGXboDBw5o9MPW1pbu3btz8eJFjXKHDx/m9ddfx8rKCiMjI7y8vIiIiKCgoECj3MNO1rZs2cIHH3xQrhinT5+Oqakp8fHx/Pjjj+Xa93mQnZnOtoVD0dHV443hkfSe+jXNuo7B0MRcXSY/Nxv7mvVp0nlkmestyM/jm09C+efWNV4bOp/e72+iTZ8pmFpUBuDfjFQOrJ9Nsy6jCXgnioSTu0n67aB6/4MbPqRJp3cwMDKtuM4KoeVeuO/T9vf3Z9WqVRQUFHDjxg12797N6NGj2bRpEzt27EBPr6jLdnZ2zzjSJyckJIRZs2ZprDMxMVH/3LZtWyZPnoy9vT1//fUX48ePp0ePHhw+fPhph1pm8fHxmJmZkZCQwLBhwwgMDOTMmTPo6uqydetWgoKCePPNN9m/fz+Wlpbs3buXiRMncuTIETZu3IhCUbbvFra2ti53bImJibzxxhs4OTmVe9/nQeze1Sgtq+Dbf7p6nbmNg0aZWq++DhSNfsvqj6PfkJOZRtdxK9DV1StWb/rff2FgrMT1ldcAcHB7hTvXL+FctyUJJ/ego6tHjfq+j9wvIV5EL9RIG8DQ0BA7OzuqVq1KgwYNmDx5Mtu3b2fXrl1ER0ery9074srNzSU0NBR7e3uMjIxwcnJi3rx56rIRERF4eXlhamqKo6MjI0aMICMjo1jb27Ztw83NDSMjI/z8/Lh69eoDY12+fDmenp4YGRnh4eHBp59+qrH9+PHj+Pj4YGRkRMOGDTl9+nSZjoGJiQl2dnYai7n5f6OmsWPH0qRJE5ycnGjWrBnvvfceR48eJS8vr9Q6H3YM7l4i2LNnD56eniiVSvz9/UlOTlaXKSgoYNy4cVhaWmJjY8PEiRNRqVRl6lOVKlWwt7enVatWTJs2jXPnznHhwgUyMzMJCQmhU6dOfP7559SvXx9nZ2eGDh3K6tWr2bRpExs3bixTG1B8etzZ2Zm5c+cyePBgzMzMqF69Op9//rl6u0Kh4NSpU8yaNQuFQsGMGTMAOHv2LL6+vhgbG2NjY8OwYcNK/J15HiSdPUjl6rXZs2IS0WGvsfHDfpyL2frY9V46+zO2Ll4c3Dif6Ml+fDWnF6f2/DfrZVHZkfzcbG5ejSc7M52Uy+ewcXAjOzOd498toWXQpMeO4UWnUqnIy84gLzuD3Ows9c952RlkZJS8lPX/nHg+vXAj7ZL4+vri7e3Nli1bGDp0aLHtkZGR7Nixg40bN1K9enWuXr2qkXB1dHSIjIzExcWFixcvMmLECCZOnKiRZLOyspgzZw5r1qzBwMCAESNG0Lt3b2JiYoq1B7Bu3TqmTZtGVFQUPj4+nD59mpCQEExNTRk0aBAZGRkEBATQoUMHvvjiCy5dusTo0aMr/Njcvn2bdevW0axZM/T19UstV9ZjsGDBAtauXYuOjg79+/dn/PjxrFu3DoDw8HCio6NZuXIlnp6ehIeHs3XrVnx9yzeaMjY2BopOtr7//ntu3brF+PHji5ULDAykVq1afPnll/Tq1atcbdwrPDycDz74gMmTJ7Np0yaGDx9O69atcXd3Jzk5mfbt2+Pv78/48eNRKpVkZmbi5+dH06ZNOXHiBCkpKQwdOpTQ0FCNE8d75eTkkJOTo36dnp7+yPGWV/qtv/j90Ga82/alwWuDSbn8O4c2LUBXTx/3xgGPUe81/kn4BbeGfrz+9iLSb17l543zKSzIp9HrwzAyNcd3wAz2rZ1Ofl4O7o3foHrtpuxfN4u6rXqS/vdf7Fw6Tl2+pk+7Cuz1iyE/J5NDkV1K3HYosuR9tm/fjlKpfHJBiSfqpUjaAB4eHpw5c6bEbVeuXMHNzY0WLVqgUCiKTXPeP/KaPXs2b7/9tkbCysvLIyoqisaNGwOwevVqPD09OX78OK+++mqxNqdPn054eDjdunUDwMXFhXPnzrF06VIGDRrE+vXrKSwsZMWKFRgZGVGnTh3+/PNPhg8f/tC+fvrppyxfvlxj3dKlS+nXr5/69aRJk4iKiiIrK4smTZrw7bffPrDOsh6DJUuWULNmTQBCQ0M1pukXLVpEWFiYus9Llixhz549D+3PvZKTk1mwYAFVq1bF3d2dnTt3AuDp6VlieQ8PD86fL9tNU6V5/fXXGTFiBFB03BYuXMj+/ftxd3fHzs4OPT09lEql+pLLsmXLyM7OZs2aNZiaFl2PjYqKIjAwkPnz52Nra1usjXnz5jFz5szHivNRqVSFVKnuSeNO7wBQ2dGdO8mJ/H5oy2MlbVQqjJVWtO4zFR0dHapU9yQz7Saxe9fS6PVhANTwbksN77bqXa4l/MKta4m06DmR9TO70iF4DibmNmxeMAh7Vx9MzMp/+UKIF8kLNz1eGpVKVep1zeDgYGJjY3F3d2fUqFF8//33Gtv37t1Lu3btqFq1KmZmZgwYMIBbt26RlZWlLqOnp0ejRo3Urz08PLC0tCQuLq5Ye5mZmSQmJjJkyBCUSqV6mT17NomJiQDExcVRr149jIyM1Ps1bdq0TH3t168fsbGxGkunTp00ykyYMIHTp0/z/fffo6ury8CBAx84bVaWY2BiYqJO2AD29vakpKQAkJaWRnJysvqk5u4xa9iwYZn6VK1aNUxNTXFwcCAzM5PNmzdjYGCg3v4kp/zq1aun/lmhUGBnZ6fuV0ni4uLw9vZWJ2yA5s2bU1hYSHx8fIn7hIWFkZaWpl4edmmlIpmYV8LS1kVjnaWdCxl3rj9mvTZY2lZHR+e/PzNWdi5k/XOLgvzil2Ly83L5eeN8WvcOI/3mVVSFBTi4NcDS1gmLKk6kJP32WPEI8SJ4aUbacXFxuLi4lLitQYMGXLp0iV27drF3716CgoJo3749mzZtIikpiYCAAIYPH86cOXOwtrbm0KFDDBkyhNzcXI0bvMrq7rXNZcuWaSQxAF1d3fJ37j4WFha4uro+sEylSpWoVKkStWrVwtPTE0dHR44ePVriiUFZj8H90+sKhaLCkunBgwcxNzenSpUqmJmZqdfXqlULKHp/mzVrVmy/uLg4ateu/Vhtl9Sv+59GeFyGhoYYGhpWaJ1lZVejHmkplzXWpaVcwcz68W7WtKtRj4RTP1BYWKhO3Kk3LmNiXgldveKXYn7Zs4Lqnk2o7OjBzavxFBb+d+d/YUE+qgo+5i8CPUNTWozahkJVQKX8RP7Wq4lKUfQ3ZEK7kgcp955MCu3zUoy09+3bx9mzZ+nevXupZczNzenVqxfLli1jw4YNbN68mdu3b3Pq1CkKCwsJDw+nSZMm1KpVq8THo/Lz8zl58qT6dXx8PKmpqSVO29ra2uLg4MDFixdxdXXVWO6eWHh6enLmzBmys7PV+x09evRxDkOp7iage6+p3qusx+BBLCwssLe359ixY+p1+fn5nDp1qkz7u7i4ULNmTY2EDfDaa69hbW1NeHh4sX127NhBQkICffr0KVesj8vT05Nff/2VzMz/ni+OiYlBR0cHd3f3pxpLWXi37ceNpN84tWclaTevknByN+ditlKnZU91mezMdP7+8zx3rl8CIPVGEn//eZ6s9FvqMj+umcbRHVHq13VbdCc7M42YzQtITbnC5d8O8csP0dRt9V+9d91OvsiFX/bS6I2iRxOtbJ1RKBTEHdnO5d8OkXojicpOj3fy9SJSKBToGynRN1JiYGSi/lnfSKkxi3fvUtYnKcTz6YUbaefk5HD9+nWNR77mzZtHQEAAAwcOLHGfiIgI7O3t8fHxQUdHh6+//ho7OzssLS1xdXUlLy+PxYsXExgYSExMDEuWLClWh76+PiNHjiQyMhI9PT1CQ0Np0qRJidezAWbOnMmoUaOwsLDA39+fnJwcTp48yZ07dxg3bhx9+/ZlypQphISEEBYWRlJSEgsWLCjTMcjKyuL6dc2pTUNDQ6ysrDh27BgnTpygRYsWWFlZkZiYyPvvv0/NmjVLnX4v6zF4mNGjR/Phhx/i5uaGh4cHERERpKamlruee5mamrJ06VJ69+7NsGHDCA0NxdzcnB9//JEJEybQo0cPgoKCHquN8urXrx/Tp09n0KBBzJgxg5s3bzJy5EgGDBhQ4vXsZ62KU238Qj7m2I5POLV7BWY2DjTvPo5ajf77HIOksz+xf91/9yf8ED0FgIYdQ9TXpzPuXEeh+G8coLSyJWDEYg5vWcjGeX0wtaiMV+te+HQI1mhfpVLx05dzaNZtDPqGRTcZ6hkY0rb/dA5u/IiC/Fxa9JyA0rLKkzoEQmiNFy5p7969G3t7e/T09LCyssLb25vIyEgGDRqkcW3tXmZmZnz00UckJCSgq6tLo0aN2LlzJzo6Onh7exMREcH8+fMJCwujVatWzJs3r9gJgImJCZMmTaJv37789ddftGzZkhUrVpQa59ChQzExMeHjjz9mwoQJmJqa4uXlpb7hS6lU8s033/D222/j4+ND7dq1mT9//gNnC+5atmwZy5Yt01jn5+fH7t27MTExYcuWLUyfPp3MzEzs7e3x9/dn6tSppU7PlvUYPMy7775LcnKy+r0YPHgwXbt2JS0trVz13K9Hjx7s37+fOXPm0LJlS7Kzs3Fzc2PKlCmMGTPmqY8sTExM2LNnD6NHj6ZRo0aYmJjQvXt3IiIiHr7zM+JctyXOdVuWut2jSSAeTQIfWEfn0f//KJzqv2ltuxr16DZ+1QP3UygUdB1X/P/Kw2IS4mWkUMlDe0I8d9LT07GwsGDqplsYKq2edTjlolAVUDk/gZt6burrq9pAW+OGkmOf6vf8T4MXFBSQkJCAm5tbhdzP8zTduXMHa2tr0tLSND4H40l7Ka5pCyGEEC8CSdpCCCGElpCkLYQQQmgJSdpCCCGElpCkLYQQQmgJSdpCCCGElpCkLYQQQmgJSdpCCCGElpCkLYQQQmgJSdpCCCGElpCkLYQQQmgJSdpCCCGElpCkLYQQQmiJF+6rOYV4kYzzVWBl9fx/W9O9CgoUJCQocHNToKurPbFra9yg3bGL8pGRthBCCKElJGkLIYQQWkKSthBCCKElJGkLIYQQWkKSthBCCKElJGkLIYQQWkKSthBCCKElJGkLIYQQWkI+XEWI51jEPhWGStWzDqNcFCoVlfNVbE5SoVJoT+zaGjcUj32qn3zAyotKRtpCCCGElpCkLYQQQmgJSdpCCCGElpCkLYQQQmgJSdpCCCGElpCkLYQQQmiJp5a0FQoF27Ztq5C6oqOjsbS0fGCZGTNmUL9+/cdqJykpCYVCQWxs7GPV8yBt2rRhzJgx6tfOzs4sWrToibX3PAoODqZLly7POgy1+9+Tsvjjjz9o0qQJRkZGj/17J4QQpXmspB0cHIxCoUChUKCvr4+trS0dOnRg5cqVFBYWapRNTk6mY8eOjxWsKO7uiUVJy9GjRwE4dOgQzZs3x8bGBmNjYzw8PFi4cOEzjrzsDhw4oNEvW1tbunfvzsWLFzXKHT58mNdffx0rKyuMjIzw8vIiIiKCgoICjXIPO4HcsmULH3zwQblinD59OqampsTHx/Pjjz+Wa18hhCirx/5wFX9/f1atWkVBQQE3btxg9+7djB49mk2bNrFjxw709IqasLOze+xgRen27t1LnTp1NNbZ2NgAYGpqSmhoKPXq1cPU1JRDhw7x1ltvYWpqyrBhw55FuI8kPj4eMzMzEhISGDZsGIGBgZw5cwZdXV22bt1KUFAQb775Jvv378fS0pK9e/cyceJEjhw5wsaNG1EoyvaBE9bW1uWOLTExkTfeeAMnJ6dy7yuEEGX12NPjhoaG2NnZUbVqVRo0aMDkyZPZvn07u3btIjo6Wl3u3tFNbm4uoaGh2NvbY2RkhJOTE/PmzVOXjYiIwMvLC1NTUxwdHRkxYgQZGRnF2t62bRtubm4YGRnh5+fH1atXHxjr8uXL8fT0xMjICA8PDz799FON7cePH8fHxwcjIyMaNmzI6dOnH9r/nJwcJk2ahKOjI4aGhri6urJixQr19t9++42OHTuiVCqxtbVlwIAB/P333w+tt7xsbGyws7PTWPT19QHw8fGhT58+1KlTB2dnZ/r374+fnx8HDx4sVxsPe1/uXrbYs2cPnp6eKJVK/P39SU5OVpcpKChg3LhxWFpaYmNjw8SJE1GpyvbpU1WqVMHe3p5WrVoxbdo0zp07x4ULF8jMzCQkJIROnTrx+eefU79+fZydnRk6dCirV69m06ZNbNy4scz9LOmSxdy5cxk8eDBmZmZUr16dzz//XL1doVBw6tQpZs2ahUKhYMaMGQCcPXsWX19fjI2NsbGxYdiwYSX+HgshRFk9kWvavr6+eHt7s2XLlhK3R0ZGsmPHDjZu3Eh8fDzr1q3D2dn5v6B0dIiMjOT3339n9erV7Nu3j4kTJ2rUkZWVxZw5c1izZg0xMTGkpqbSu3fvUmNat24d06ZNY86cOcTFxTF37lzef/99Vq9eDUBGRgYBAQHUrl2bU6dOMWPGDMaPH//Qvg4cOJAvv/ySyMhI4uLiWLp0KUqlEoDU1FR8fX3x8fHh5MmT7N69mxs3bhAUFPTQep+k06dPc/jwYVq3bl2u/cr6vixYsIC1a9fy888/c+XKFY3jGB4eTnR0NCtXruTQoUPcvn2brVu3lrsPxsbGQNEJ4Pfff8+tW7dKfL8CAwOpVasWX375ZbnbuFd4eLj6RG7EiBEMHz6c+Ph4oOjST506dXj33XdJTk5m/PjxZGZm4ufnh5WVFSdOnODrr79m7969hIaGPlYcQoiX2xP77HEPDw/OnDlT4rYrV67g5uZGixYtUCgUxaYU7x/lzJ49m7fffltjZJyXl0dUVBSNGzcGYPXq1Xh6enL8+HFeffXVYm1Onz6d8PBwunXrBoCLiwvnzp1j6dKlDBo0iPXr11NYWMiKFSswMjKiTp06/PnnnwwfPrzUPp4/f56NGzfyww8/0L59ewBq1Kih3h4VFYWPjw9z585Vr1u5ciWOjo6cP3+eWrVqlVp3eTVr1gwdHc1zsPtHddWqVePmzZvk5+czY8YMhg4dWq42yvq+LFmyhJo1awIQGhrKrFmz1NsXLVpEWFiY+n1YsmQJe/bsKVccycnJLFiwgKpVq+Lu7s7OnTsB8PT0LLG8h4cH58+fL1cb93v99dcZMWIEAJMmTWLhwoXs378fd3d37Ozs0NPTQ6lUqi8DLVu2jOzsbNasWYOpqSlQ9PsQGBjI/PnzsbW1fax4hBAvpyeWtFUqVanXEIODg+nQoQPu7u74+/sTEBDAa6+9pt6+d+9e5s2bxx9//EF6ejr5+flkZ2eTlZWFiYlJUeB6ejRq1Ei9j4eHB5aWlsTFxRVL2pmZmSQmJjJkyBBCQkLU6/Pz87GwsAAgLi6OevXqYWRkpN7etGnTB/YxNjYWXV3dUkesv/76K/v371ePvO+VmJhYoUl7w4YNpSatuw4ePEhGRgZHjx7lvffew9XVlT59+pS5jbK8LyYmJuqEDWBvb09KSgoAaWlpJCcnq0+0oOh9bNiwYZmmyKtVq4ZKpSIrKwtvb282b96MgYGBentZp9kfRb169dQ/KxQK7Ozs1P0qSVxcHN7e3uqEDdC8eXMKCwuJj4+XpC2EeCRPLGnHxcXh4uJS4rYGDRpw6dIldu3axd69ewkKCqJ9+/Zs2rSJpKQkAgICGD58OHPmzMHa2ppDhw4xZMgQcnNz1cmhPO6OOJctW6aRMAB0dXXL37n/d3eK9kHt3h1Z3c/e3v6R2y2Jo6Mjrq6uDyxz9/3w8vLixo0bzJgxo8xJu6zvy93r6HcpFIoKS6YHDx7E3NycKlWqYGZmpl5/9+QnLi6OZs2aFdsvLi6O2rVrP1bbJfXr/icktFlGagpHty/myrnD5OdmY1HZkbb9plHFqei4XYzdx++HNnPz6h/kZKXTc9I6KlV78ElnYux+fvlhDWk3r1JYkI9FlerU9+1HrVdfV5eJ3buW2B/XAlC//UDqt+uv3nYj6Td+3jCf7uOj0XmM/6dCvEieSNLet28fZ8+eZezYsaWWMTc3p1evXvTq1YsePXrg7+/P7du3OXXqFIWFhYSHh6une0u6iSg/P5+TJ0+qR9Xx8fGkpqaWONq0tbXFwcGBixcv0q9fvxLj8fT0ZO3atWRnZ6tH23cfmSqNl5cXhYWF/PTTT+rp8Xs1aNCAzZs34+zsrL6L/nlRWFhITk5OmcuX9X15EAsLC+zt7Tl27BitWrUCit7HU6dO0aBBg4fu7+LiUuLz+a+99hrW1taEh4cXS9o7duwgISGh3I9wPS5PT0+io6PJzMxUj7ZjYmLQ0dHB3d39qcbyMNmZ6WxbOBQHt1d4Y3gkxmZWpKVcwdDEXF0mPzcb+5r1qdmgAz99OadM9RqZmtPA702sbJ3R0dXn8m8H2ffFTIyUVlSv3ZRbfyVwYudSOr61EFCxc8lYHD2bYOPgSmFBAT99NY82faZIwhbiHo+dSXJycrh+/brGI1/z5s0jICCAgQMHlrhPREQE9vb2+Pj4oKOjw9dff42dnR2Wlpa4urqSl5fH4sWLCQwMJCYmhiVLlhSrQ19fn5EjRxIZGYmenh6hoaE0adKkxOvZADNnzmTUqFFYWFjg7+9PTk4OJ0+e5M6dO4wbN46+ffsyZcoUQkJCCAsLIykpiQULFjyw787OzgwaNIjBgwcTGRmJt7c3ly9fJiUlhaCgIN555x2WLVtGnz59mDhxItbW1ly4cIGvvvqK5cuXP9Yo/363bt3i+vXrGussLS0xMjLik08+oXr16nh4eADw888/s2DBAkaNGlXm+sv6vjzM6NGj+fDDD3Fzc8PDw4OIiAhSU1PLXc+9TE1NWbp0Kb1792bYsGGEhoZibm7Ojz/+yIQJE+jRo8dTv/mvX79+TJ8+nUGDBjFjxgxu3rzJyJEjGTBgwHM3NR67dzVKyyr49p+uXmdu46BR5u7oOP3WtTLXW9XtFVSK/37H67XtQ/zx77h+MZbqtZty50YSNg5uVHMvusxlU9WN1OtJ2Di4EvvjGhxcfdQjfSFEkce+e3z37t3Y29vj7OyMv78/+/fvJzIyku3bt5ealMzMzPjoo49o2LAhjRo1IikpiZ07d6Kjo4O3tzcRERHMnz+funXrsm7dOo3Hwe4yMTFh0qRJ9O3bl+bNm6NUKtmwYUOpcQ4dOpTly5ezatUqvLy8aN26NdHR0eopY6VSyTfffMPZs2fx8fFhypQpJU5r3++zzz6jR48ejBgxAg8PD0JCQsjMzATAwcGBmJgYCgoKeO211/Dy8mLMmDFYWloWu2msNMHBwbRp0+ah5dq3b4+9vb3GcvcRu8LCQsLCwqhfvz4NGzbkk08+Yf78+Ro3iEVHRz/wOeayvi8P8+677zJgwAAGDRpE06ZNMTMzo2vXruWu5349evRg//79XLlyhZYtW+Lu7s7ChQuZMmUKX331VZmf0a4oJiYm7Nmzh9u3b9OoUSN69OhBu3btiIqKeqpxlEXS2YNUrl6bPSsmER32Ghs/7Me5mPLf0f8gKpWKP+OPk3rjMvauRbMqNg6upN68wj+3r/PPrWTSUq5g7VCTtJt/8sfRb3k1YESFxvCiUqlU5GVnkJudRV52BnnZGWRkFF+e5D0f4ulRqOSdfK61bt2atm3bqp/9fVKmT5/OTz/9xIEDB55oO6JkOTk5Gpcr0tP/r737Doviav8G/l3aAsIuSkepAhGDIoIYewIkKITYAopdjLHGKPlZEntBjT5qLClqKD6vINGosWsUy2NBBAQiRSBERQ2IgmABKcv9/sHFxBVUUJBdc3+ua6+4Z87M3LO74Z45c+acBzA3N8e8Xwsg1mnZpPveMqMHAMDpg2GwcfZE/o1UnN+9Bn2Gfo13un4sV/dBwd+IWNT/hfe0RSSDYWUW7qrZ4cmTUvy/ed6QVZZDpKKKXv6z4dDtE6Fu6rndSD4ZWb1/92F4t+dgHNg0GY69/FFVJUPc4S1QUVVDz8Ffwczu5bdQXsfTcT/dQqDoKp48wrkNA15ab9++fXV2im1OMpkMWVlZsLOza9SWxzfh/v37aNWqFYqLiyGRSF6+QiNRrButTE5xcTGys7Nx6NChJt/XkSNHFPIq8N9ixYoVWLx4cbPsm6gKRhYO6PrJFACAofk7uJ+bjdRze2ol7YZSF2vDb04EKspKcSvjEi7sWQeJQWu0tnMBALzbczDe7TlYqJ8RexDqYm0Y23TEjiWDMXjmNjwuysfx8LkYvmgf1NQ1nrcrxv4VeJYvBSaVSnHr1q03cnb8vOfb2Zvx9ddfo7i4WHi9bHS/xqQtMYCesfyTHnom1nh0P+85a9SfiooKpIbmMGhjj04eI2DTyR2Jv4fVWbf0URHiDm9FT79ZyL+eAqmRBfSMLNDa3hVVskoU38157XgYU3Z8pc2YAhCLxRCLxc2ybxObjijOvyFXVpyfA91WTTFfAEFWWV7nkgt71sLJfRh09IyQfyMVVbLKf9aqkoHeokfsGpOauAV6fbEbBpXZuKfWFiRSxUyP2n04nh4zgCkvTtqM/cs5fTAce9cGIuFYKGw7f4j8G6lIO78XfQK+Eeo8efwAj+7n4XHxXQBA0Z3rAABtiT60JdUT00T/dwFa6Bmhm2/1KIIJv4fD0NIRUoM2kFWW40bqeWReOoxeQ+bUiuHm1VgU5efggxGLAABGlu+i6M515KRdwMPCPIhUVKBnxJOx1EUkEkFdUwcaldpQV9MBiVSho/NmO16yN4eTNmP/ckaW7eE1fjVi93+PhKMh0NU3Q4/BQbDv8s9UutevnMGpiH+eNjgePhcA4NpvPLp4V88U9+h+HkSif+64VZaX4uzOb/Ho/h2oaWiipbElPEYtga3LP6MfVtcrw9mdq/BR4ArhqQodPSP09JuJk9sXQ1VNA+4jFkFNo3laIhhTJA3uPV5QUCCM8f30JB+vHYhIhL1792LAgAGNts23mZWVFaZPny43HvjLnD9/HhMnTsTVq1fh4+PzwjmlFcmYMWNQVFTUpPFev34d1tbWSExMRKdOneq1DhFhwoQJ+PXXX3H//v3nrvvTTz/h0KFDOHDgQL3jefDgAaRS6RvpPd7YlLUXtrLGDdSOfZ6Xclxpc+/xhmtwR7Tg4GD079+/URN2Y6h5zlgkEkFFRQVt2rTB2LFj5caHrlkuEokgkUjQpUsX7Nu3r9a2SktLsXDhQtjb20MsFsPAwAB+fn5ITU0V6lhZWclt79nXmDFjhLpeXl5QVVVFXFxcnbEnJibCz88PxsbG0NTUhJ2dHcaPH//CSS7i4uIaPBd2UFAQOnXqhGvXrslNm6oorl+/DpFIhKSkJLny9evXK2S8R48eRXh4OA4ePIjc3Fw4OjrKTUFbIzAwEJcvX27wVKiMMfasBiXtkpIShISEYNy4cU0Vz2uRSCTIzc3FrVu3sHXrVhw5cgQjR46UqxMWFobc3FzEx8ejR48e+PTTT3HlyhVheVlZGTw9PREaGoply5YhMzMThw8fRmVlJbp27SoMbRoXF4fc3Fzk5uZi9+7dAKqHUq0pW79+PYDqGc0uXLiAqVOnIjQ0tFbMBw8exHvvvYeysjJEREQgPT0d27dvh1Qqxfz58597rIaGhg0ehz07Oxvu7u5o06ZNncOB1kd5ed2diJqSVCp95XibUnZ2NkxNTdG9e3dhpq+6aGhoYNiwYdiwYcMbjpAx9rZpUNI+fPgwxGIx3nvvPbnyM2fOwM3NDWKxGKamppgzZw4qK//p+fn+++9j2rRpwlCeJiYmLxwsxN3dvda8w3fv3oWGhgaio6Ofu17N7EtmZmbo168fpk2bhhMnTqC0tFSoo6enBxMTE9jb22Pp0qWorKzEqVOnhOXfffcdYmJicPDgQfj7+8PS0hJubm7YvXs3HBwcMG7cOBARDA0NYWJiAhMTE7Rq1QoAYGRkJJTVzB4WFhYmTLSxY8cOuVhKSkowduxYeHt7Y//+/fD09IS1tTW6du2K//znP9i8efNzj9XKygrfffed3LH//PPPGDhwILS1tWFnZ4f9+/cD+OcKtqCgAIGBgRCJRMKVa32+u6lTp2L69OkwMDCAl5cXTp8+DZFIhGPHjsHZ2RlaWlpwd3dHfn4+jhw5AgcHB0gkEgwbNgwlJSXCto4ePYqePXtCT08P+vr6+Pjjj5GdnS0srxmdztnZGSKRSBgJbsyYMXK3TcrKyjBt2jQYGRlBU1MTPXv2lGvFqIkvOjoarq6u0NbWRvfu3YX5r+srJSUF/fr1g46ODoyNjTFy5Ejcu3dPiOmLL75ATk4ORCIRrKyshNangQMHCmU1fH19sX//frnvnzHGGqpBSfvs2bNwcXGRK7t9+za8vb3RpUsXJCcn48cff0RISAiWLVsmV2/btm1o0aIFYmNjsWrVKixZsgTHjx+vcz+fffYZIiMj5UaI2r59O1q3bg13d/d6x6ulpYWqqiq5JFSjsrISISEhACA3vWNkZCQ+/PBDODk5ydVXUVHBjBkzkJaWhuTk5Hrtn4gQFhaGESNGoF27drC1tcWvv/4qLD927Bju3buHWbNm1bl+Q68uFy9eDH9/f/zxxx/w9vbG8OHDUVhYCHNzc+Tm5kIikeC7775Dbm4uhgwZ0qDvTkNDo9Z444sWLcKmTZtw4cIF3Lx5E/7+/vjuu+8QGRmJQ4cO4ffff8fGjRuF+o8fP0ZQUBDi4+MRHR0NFRUVDBw4UJgt69KlSwCqpwDNzc3Fnj176jzOWbNmYffu3di2bRsuX74MW1tbeHl5obCwUK7e3LlzsWbNGsTHx0NNTQ2BgYH1/iyLiorg7u4OZ2dnxMfH4+jRo7hz544whvn69euxZMkStGnTBrm5uYiLixNOHGpac54+kXB1dUVlZSViY2PrHQNjjD2rQb3Hb9y4ATMz+YkEfvjhB5ibm2PTpk0QiURo164d/v77b8yePRsLFiwQeoN27NgRCxdWT0hgZ2eHTZs2ITo6Gh9++GGt/QwaNAhTp07Fvn37hD+S4eHhGDNmTL3HkM7KysJPP/0EV1dXuWkcAwICoKqqitLSUlRVVcHKykpuMonMzEx88MEHdW6zZgaxzMzMenVWOnHiBEpKSuDl5QUAGDFiBEJCQoQm+6ysLAAQJvJ4XWPGjBGm2ly+fDk2bNiAS5cuoW/fvjAxMYFIJIJUKoWJSfXzt/X97uzs7LBq1SphP7m5uQCAZcuWoUeP6iEwx40bh6+//hrZ2dmwsbEB8M944LNnzwYADB78z8hXABAaGgpDQ0OkpaXB0dERhoaGAAB9fX0hxmc9fvwYP/74I8LDw9GvX3Xv5q1bt+L48eMICQnBzJkzhbrBwcHCXOdz5syBj4+P3CxuL7Jp0yY4Oztj+fLlcvGam5sjMzMT9vb20NXVhaqqaq1Ya1pznqatrQ2pVIobN+Sfh2aMsYZo0JV2aWlprT946enp6Natm1wy7dGjBx49eoRbt24JZR07dpRbz9TUVK6T2NM0NTUxcuRI4R7w5cuXkZKSIte5qy7FxcXQ0dGBtrY23nnnHRgbGyMiIkKuzrp165CUlIQjR46gffv2+Pnnn4Xm7RqNNRx7aGgohgwZItzrDAgIwPnz54Um4cYe9v3pz7hFixaQSCTP/YyB+n93z7au1LU/Y2NjaGtrCwm7puzp/WdlZSEgIAA2NjaQSCRC83FOTv1HusrOzkZFRYVwsgBUz/jm5uaG9PT058ZXM3/5iz6PpyUnJ+PUqVPQ0dERXjUnV0836TeElpaW3O0CxhhrqAZdaRsYGOD+/fuvtCN1dXW59yKRSGgWrctnn32GTp064datWwgLC4O7uzssLV88uIKuri4uX74MFRUVmJqaQktLq1YdExMT2NrawtbWFmFhYfD29kZaWhqMjIwAAPb29rX++NeoKbe3r3uihKcVFhZi7969qKiowI8//iiUy2QyhIaGIjg4WNjO1atX0a1bt5du82Ua+hnX1/NGUnp6fyKR6KX79/X1haWlJbZu3QozMzNUVVXB0dGxyTq3PRsfgHp/Ho8ePYKvr2+dM73VnAA0VGFhodCawBhjr6JBV9rOzs5IS0uTK3NwcEBMTIzcVeP58+ehq6uLNm3avHJgHTp0gKurK7Zu3YrIyMh63Y9UUVGBra0tbGxs6kzYz3Jzc4OLiwuCg4OFsqFDh+LEiRO17ltXVVVh3bp1aN++fa373XWJiIhAmzZtkJycjKSkJOG1Zs0ahIeHC9N1GhgYyDU9P+1155l+mab67upSUFCAjIwMzJs3Dx4eHnBwcKh1AljTt0Amkz13O23bthXur9eoqKhAXFwc2rdvvLmXO3fujNTUVFhZWQkneTWvFw0Hqa6uXmf82dnZePLkCZydnRstRsbYv0+DkraXlxdSU1Pl/thOnjwZN2/exBdffIGrV69i3759WLhwIYKCguo9Z/TzfPbZZ1i5ciWIqFHmXK7L9OnTsXnzZty+fRsAMGPGDLi5ucHX1xe7du1CTk4O4uLiMHjwYKSnpyMkJKRe99VDQkLw6aefwtHRUe41btw43Lt3D0ePHkWLFi3w888/49ChQ/jkk09w4sQJXL9+HfHx8Zg1axYmTpzYJMdcoym/u2e1bNkS+vr62LJlC/7880+cPHkSQUFBcnWMjIygpaUldPoqLi6utZ0WLVpg0qRJmDlzJo4ePYq0tDSMHz8eJSUljfoo4pQpU1BYWIiAgADExcUhOzsbx44dw9ixY194UmFlZYXo6Gjk5eXJ/X9y9uxZ2NjYoG3bto0WI2Ps36dBf5k7dOiAzp07Y+fOnUJZ69atcfjwYVy6dAlOTk6YOHEixo0bh3nz5r12cAEBAVBTU0NAQEC9Og+9ir59+8La2lq42tbU1MTJkycxatQofPPNN7C1tUXfvn2hqqqKixcv1nrcrS4JCQlITk6u1fEKqH7m2MPDQ+i53r9/f1y4cAHq6uoYNmwY2rVrh4CAABQXF9fqxd3YmvK7e5aKigqioqKQkJAAR0dHzJgxA6tXr5aro6amhg0bNmDz5s0wMzND//7969zWypUrMXjwYIwcORKdO3fGn3/+iWPHjqFly8YbOczMzAznz58XWkQ6dOiA6dOnQ09P74UnNGvWrMHx48dhbm4ud1W9Y8cOjB8/vtHiY4z9OzV4GNNDhw5h5syZSElJafSrsWddv34dbdu2RVxcHDp37tyk+2KsqaSmpsLd3R2ZmZnC8/svw8OYvnnKGjfAw5g2h+YaxrTBE4b4+PggKysLt2/fhrm5eVPEhIqKChQUFGDevHl47733OGEzpZabm4v//ve/9U7YjDH2PK80y1dDJql4FefPn8cHH3wAe3t7ucFIGFNGnp6ezR0CY+wtoZBTc77//vuN/gwzY4wxpuwUMmkzxqoFuYvQsqVy3J+sIZOJkJUlgp2dCKqqyhO7ssYNKHfsrGGaticZY4wxxhoNJ23GGGNMSXDzOGMKqKZPx4MHD5TuURiZTIZHjx4pXezKGjegvLEra9xA9f+bQOPPIfEynLQZU0AFBQUAIDcnN2NM8RQUFLzRxzk5aTOmgGpmnsvJyVG657sfPHgAc3Nz3Lx5840OOvG6lDVuQHljV9a4gepZJS0sLGrNEtnUOGkzpoBqRhuUSqVK98eshkQiUcrYlTVuQHljV9a4ATT5yKC19vdG98YYY4yxV8ZJmzHGGFMSnLQZU0BisRgLFy6EWCxu7lAaTFljV9a4AeWNXVnjBpov9gbP8sUYY4yx5sFX2owxxpiS4KTNGGOMKQlO2owxxpiS4KTNGGOMKQlO2owxxpiS4KTNWDP5/vvvYWVlBU1NTXTt2hWXLl0SlgUFBaFVq1YwNzdHRESE3Hq7du2Cr6/vG4lxxYoV6NKlC3R1dWFkZIQBAwYgIyNDrs6TJ08wZcoU6OvrQ0dHB4MHD8adO3eE5YWFhfD19YWOjg6cnZ2RmJgot/6UKVOwZs2aJj2OlStXQiQSYfr06Qof9+3btzFixAjo6+tDS0sLHTp0QHx8vLCciLBgwQKYmppCS0sLnp6eyMrKEpaXlZVh5MiRkEgksLe3x4kTJ+S2v3r1anzxxReNHrdMJsP8+fNhbW0NLS0ttG3bFkuXLpWbUENRYv/f//4HX19fmJmZQSQS4bfffpNb/rI4gerfx/DhwyGRSKCnp4dx48bh0aNHwvLr16+jd+/eaNGiBXr37o3r16/Lrf/xxx9j9+7dDQ+eGGNvXFRUFGloaFBoaCilpqbS+PHjSU9Pj+7cuUP79+8nY2NjiouLo8jISNLU1KS7d+8SEVFRURHZ2dnRjRs33kicXl5eFBYWRikpKZSUlETe3t5kYWFBjx49EupMnDiRzM3NKTo6muLj4+m9996j7t27C8uDgoKoT58+lJGRQdOnTycXFxdhWUxMDLm4uFBlZWWTHcOlS5fIysqKOnbsSF9++aVCx11YWEiWlpY0ZswYio2Npb/++ouOHTtGf/75p1Bn5cqVJJVK6bfffqPk5GT65JNPyNramkpLS4mIaMOGDeTg4EApKSm0evVqMjQ0pKqqKiIi+uuvv8jOzo6Ki4sbNW4iouDgYNLX16eDBw/StWvXaNeuXaSjo0Pr169XuNgPHz5Mc+fOpT179hAA2rt3r9zyl8VJRNS3b19ycnKiixcv0tmzZ8nW1pYCAgKE5YMGDaKhQ4dSZmYm+fv70+DBg4VlUVFR5Ovr+0qxc9JmrBm4ubnRlClThPcymYzMzMxoxYoV9O2339KQIUOEZUZGRnTp0iUiIvr8889p7dq1bzzeGvn5+QSAzpw5Q0TVJxHq6uq0a9cuoU56ejoBoJiYGCIi6tevH/34449ERJSWlkba2tpERFReXk5OTk4UFxfXZPE+fPiQ7Ozs6Pjx49SnTx8haStq3LNnz6aePXs+d3lVVRWZmJjQ6tWrhbKioiISi8W0Y8cOIiKaNGkSzZ49m4iISkpKCADl5+cTUfVJ2J49exo9biIiHx8fCgwMlCsbNGgQDR8+XKFjfzZp1yfOtLQ0AiD3Gzhy5AiJRCK6ffs2ERE5ODjQkSNHiKj6JKF9+/ZERHT//n2ytbWlnJycV4qXm8cZe8PKy8uRkJAAT09PoUxFRQWenp6IiYmBk5MT4uPjcf/+fSQkJKC0tBS2trY4d+4cLl++jGnTpjVb7MXFxQD+mYUsISEBFRUVcsfSrl07WFhYICYmBgDg5OSEkydPorKyEseOHUPHjh0BAKtWrcL7778PV1fXJot3ypQp8PHxkYtPkePev38/XF1d4efnByMjIzg7O2Pr1q3C8mvXriEvL08ubqlUiq5du8rFfe7cOZSWluLYsWMwNTWFgYEBIiIioKmpiYEDBzZ63ADQvXt3REdHIzMzEwCQnJyMc+fOoV+/fgof+9PqE2dMTAz09PTkfgOenp5QUVFBbGyscCwnTpxAVVUVfv/9d+H3M3PmTEyZMgXm5uavFuArpXrG2Cu7ffs2AaALFy7Ilc+cOZPc3NyIiGjhwoXUtm1bcnR0pD179lBZWRk5OjpSfHw8bdy4kezt7al79+6UkpLyxuKWyWTk4+NDPXr0EMoiIiJIQ0OjVt0uXbrQrFmziKj6KiUgIIAsLCyod+/elJqaSpmZmWRnZ0f37t2jCRMmkLW1Nfn5+VFRUVGjxbtjxw5ydHQUmjSfvtJW1LjFYjGJxWL6+uuv6fLly7R582bS1NSk8PBwIiI6f/48AaC///5bbj0/Pz/y9/cnouqWgMmTJ5OVlRW5urrS2bNnqaCggGxsbCgnJ4fmzp1Lbdu2pY8++ohu3brVKHETVf8+Zs+eTSKRiNTU1EgkEtHy5cuF5YoaO5650q5PnMHBwWRvb19rW4aGhvTDDz8QEdGtW7fIx8eHzM3NycfHh27dukVnzpwhV1dXKigoID8/P7K2tqYJEyZQWVlZvePlqTkZU0CLFi3CokWLhPeLFy+Gp6cn1NXVsWzZMly5cgUHDx7EqFGjkJCQ8EZimjJlClJSUnDu3LkGrSeVShEZGSlX5u7ujtWrVyMiIgJ//fUXMjIyMH78eCxZsqRROnfdvHkTX375JY4fPw5NTc1X2kZzxF1VVQVXV1csX74cAODs7IyUlBT89NNPGD16dL22oa6uju+//16ubOzYsZg2bRoSExPx22+/ITk5GatWrcK0adNerTNUHXbu3ImIiAhERkbi3XffRVJSEqZPnw4zMzOFj70ptG7dGgcPHhTel5WVwcvLC9u2bcOyZcugq6uLjIwM9O3bF5s3b653BztuHmfsDTMwMICqqqpcT2UAuHPnDkxMTGrVv3r1KrZv346lS5fi9OnT6N27NwwNDeHv74/Lly/j4cOHTR7z1KlTcfDgQZw6dQpt2rQRyk1MTFBeXo6ioiK5+s87FgAICwuDnp4e+vfvj9OnT2PAgAFQV1eHn58fTp8+3SjxJiQkID8/H507d4aamhrU1NRw5swZbNiwAWpqajA2NlbIuE1NTdG+fXu5MgcHB+Tk5ACAEFt9fzsAcOrUKaSmpmLq1Kk4ffo0vL290aJFC/j7+zda3EB1s++cOXMwdOhQdOjQASNHjsSMGTOwYsUKhY/9afWJ08TEBPn5+XLLKysrUVhY+NxjWb58OT766CO4uLjg9OnTGDx4MNTV1TFo0KAGHQsnbcbeMA0NDbi4uCA6Olooq6qqQnR0NLp16yZXl4gwYcIErF27Fjo6OpDJZKioqAAA4b8ymazJYiUiTJ06FXv37sXJkydhbW0tt9zFxQXq6upyx5KRkYGcnJxaxwIAd+/exZIlS7Bx40Yh9qePp7GOxcPDA1euXEFSUpLwcnV1xfDhw4V/K2LcPXr0qPVIXWZmJiwtLQEA1tbWMDExkYv7wYMHiI2NrTPumsfaNm/eDFVV1SaLGwBKSkqgoiKfUlRVVVFVVaXwsT+tPnF269YNRUVFcq1cJ0+eRFVVFbp27Vprm+np6YiMjMTSpUsBvObvp8E3ABhjry0qKorEYjGFh4dTWloaff7556Snp0d5eXly9bZs2SL3qEhsbCxJJBKKiYmhBQsWCD1Sm8qkSZNIKpXS6dOnKTc3V3iVlJQIdSZOnEgWFhZ08uRJio+Pp27dulG3bt3q3N6wYcNo48aNwvtvv/2WXFxcKC0tjfr160eTJ09usmN5+p62osZ96dIlUlNTo+DgYMrKyqKIiAjS1tam7du3C3VWrlxJenp6tG/fPvrjjz+of//+tR5HqvHNN9/QV199Jbz/5ZdfyMLCgpKTk2ncuHHk7e3dKHETEY0ePZpat24tPPK1Z88eMjAwEPoIKFLsDx8+pMTEREpMTCQAtHbtWkpMTBQepaxPnH379iVnZ2eKjY2lc+fOkZ2dndwjXzWqqqqoZ8+edODAAaFs0qRJ5OPjQ2lpaeTs7EyrVq2qd+yctBlrJhs3biQLCwvS0NAgNzc3unjxotzyvLw8srS0FB4hqbF48WJq1aoVtWvXjmJjY5s0RgB1vsLCwoQ6paWlNHnyZGrZsiVpa2vTwIEDKTc3t9a2jh49Sm5ubiSTyYSyx48fk5+fH+nq6pKHhwfduXOnyY7l2aStqHEfOHCAHB0dSSwWU7t27WjLli1yy6uqqmj+/PlkbGxMYrGYPDw8KCMjo9Z2rly5Qra2tnLP1MtkMpo0aRJJJBLq0qULZWVlNVrcDx48oC+//JIsLCxIU1OTbGxsaO7cuXKdrBQl9lOnTtX5ux49enS94ywoKKCAgADS0dEhiURCY8eOpYcPH9ba108//SR34k1EdOfOHfLw8CBdXV3y8/Ojx48f1zt2nk+bMcYYUxJ8T5sxxhhTEpy0GWOMMSXBSZsxxhhTEpy0GWOMMSXBSZsxxhhTEpy0GWOMMSXBSZsxxhhTEpy0GWOMMSXBSZsxxhhTEpy0GWOMMSXBSZsxxhhTEpy0GWOMMSXBSZsxxhhTEpy0GWOMMSXBSZsxxhhTEpy0GWOMMSXBSZsxxhhTEpy0GWOMMSXBSZsxxhhTEpy0GWOMMSXBSZsxxhhTEpy0GWOMMSWh1twBMPYsmUyGioqK5g6DKRl1dXWoqqo2dxiMNSlO2kxhEBHy8vJQVFTU3KEwJaWnpwcTExOIRKLmDoWxJsFJmymMmoRtZGQEbW1t/sPL6o2IUFJSgvz8fACAqalpM0fEWNPgpM0UgkwmExK2vr5+c4fDlJCWlhYAID8/H0ZGRtxUzt5K3BGNKYSae9ja2trNHAlTZjW/H+4Twd5WnLSZQuEmcfY6+PfD3nactBljjDElwUmbMcYYUxLcEY0pvGXH6I3ub55X/ZtYX9Ycu3DhQixatOiV4hCJRNi7dy8GDBhQr/oTJkzAzz//jKioKPj5+b3SPhljio2TNmOvITc3V/j3L7/8ggULFiAjI0Mo09HReSNxlJSUICoqCrNmzUJoaGizJ+3y8nJoaGg0awyMvY24eZyx12BiYiK8pFIpRCKRXFlUVBQcHBygqamJdu3a4YcffhDWLS8vx9SpU2FqagpNTU1YWlpixYoVAAArKysAwMCBAyESiYT3z7Nr1y60b98ec+bMwf/+9z/cvHlTbnlZWRlmz54Nc3NziMVi2NraIiQkRFiempqKjz/+GBKJBLq6uujVqxeys7MBAO+//z6mT58ut70BAwZgzJgxwnsrKyssXboUo0aNgkQiweeffw4AmD17Nuzt7aGtrQ0bGxvMnz+/Vs/uAwcOoEuXLtDU1ISBgQEGDhwIAFiyZAkcHR1rHWunTp0wf/78F34ejL2tOGkz1kQiIiKwYMECBAcHIz09HcuXL8f8+fOxbds2AMCGDRuwf/9+7Ny5ExkZGYiIiBCSc1xcHAAgLCwMubm5wvvnCQkJwYgRIyCVStGvXz+Eh4fLLR81ahR27NiBDRs2ID09HZs3bxZaAW7fvo3evXtDLBbj5MmTSEhIQGBgICorKxt0vP/5z3/g5OSExMREIanq6uoiPDwcaWlpWL9+PbZu3Yp169YJ6xw6dAgDBw6Et7c3EhMTER0dDTc3NwBAYGAg0tPT5Y49MTERf/zxB8aOHdug2Bh7W3DzOGNNZOHChVizZg0GDRoEALC2tkZaWho2b96M0aNHIycnB3Z2dujZsydEIhEsLS2FdQ0NDQH8Myzni2RlZeHixYvYs2cPAGDEiBEICgrCvHnzIBKJkJmZiZ07d+L48ePw9PQEANjY2Ajrf//995BKpYiKioK6ujoAwN7evsHH6+7ujq+++kqubN68ecK/rays8H//939CMz4ABAcHY+jQoVi8eLFQz8nJCQDQpk0beHl5ISwsDF26dAFQfRLTp08fufgZ+zfhK23GmsDjx4+RnZ2NcePGQUdHR3gtW7ZMaHYeM2YMkpKS8M4772DatGn4/fffX2lfoaGh8PLygoGBAQDA29sbxcXFOHnyJAAgKSkJqqqq6NOnT53rJyUloVevXkLCflWurq61yn755Rf06NEDJiYm0NHRwbx585CTkyO3bw8Pj+duc/z48dixYweePHmC8vJyREZGIjAw8LXiZEyZ8ZU2Y03g0aNHAICtW7eia9eucstqhtfs3Lkzrl27hiNHjuDEiRPw9/eHp6cnfv3113rvRyaTYdu2bcjLy4OamppceWhoKDw8PIThPZ/nZctVVFRAJN+Dv64Rx1q0aCH3PiYmBsOHD8fixYvh5eUlXM2vWbOm3vv29fWFWCzG3r17oaGhgYqKCnz66acvXIextxknbcaagLGxMczMzPDXX39h+PDhz60nkUgwZMgQDBkyBJ9++in69u2LwsJCtGrVCurq6pDJZC/cz+HDh/Hw4UMkJibKjbWdkpKCsWPHoqioCB06dEBVVRXOnDkjNI8/rWPHjti2bRsqKirqvNo2NDSU6yUvk8mQkpKCDz744IWxXbhwAZaWlpg7d65QduPGjVr7jo6Ofu49ajU1NYwePRphYWHQ0NDA0KFDX5roGXubcdJmrIksXrwY06ZNg1QqRd++fVFWVob4+Hjcv38fQUFBWLt2LUxNTeHs7AwVFRXs2rULJiYm0NPTA1B9Dzg6Oho9evSAWCxGy5Yta+0jJCQEPj4+wn3gGu3bt8eMGTMQERGBKVOmYPTo0QgMDMSGDRvg5OSEGzduID8/H/7+/pg6dSo2btyIoUOH4uuvv4ZUKsXFixfh5uaGd955B+7u7ggKCsKhQ4fQtm1brF27tl7Tp9rZ2SEnJwdRUVHo0qULDh06hL1798rVWbhwITw8PNC2bVsMHToUlZWVOHz4MGbPni3U+eyzz+Dg4AAAOH/+fAO/BcbeMsSYAigtLaW0tDQqLS1t7lBeWVhYGEmlUrmyiIgI6tSpE2loaFDLli2pd+/etGfPHiIi2rJlC3Xq1IlatGhBEomEPDw86PLly8K6+/fvJ1tbW1JTUyNLS8ta+8vLyyM1NTXauXNnnfFMmjSJnJ2diaj6850xYwaZmpqShoYG2draUmhoqFA3OTmZPvroI9LW1iZdXV3q1asXZWdnExFReXk5TZo0iVq1akVGRka0YsUK6t+/P40ePVpY39LSktatW1crhpkzZ5K+vj7p6OjQkCFDaN26dbU+o927dwufkYGBAQ0aNKjWdnr16kXvvvtuncf5tLfhd8TYi4iI6M0ON8VYHZ48eYJr167B2toampqazR0OUyBEBDs7O0yePBlBQUEvrMu/I/a24+ZxxpjCunv3LqKiopCXl8fPZjMGTtqMMQVmZGQEAwMDbNmypc57+oz923DSZowpLL57x5g8HlyFMcYYUxKctJlC4Ssr9jr498Pedpy0mUKoGdSjpKSkmSNhyqzm9/O6Q7Iypqj4njZTCKqqqtDT00N+fj4AQFtbGyKRqJmjYsqCiFBSUoL8/Hzo6enJjQ7H2NuEn9NmCoOIkJeXV6/RthirS82saHzCx95WnLSZwpHJZHVOSMHYi6irq/MVNnvrcdJmjDHGlAR3RGOMMcaUBCdtxhhjTElw0maMMcaUBCdtxhhjTElw0maMMcaUBCdtxhhjTElw0maMMcaUxP8HLr6nxRw++PcAAAAASUVORK5CYII=",
+      "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>Smiles</th>\n",
+       "      <th>Uniprot</th>\n",
+       "      <th>Cell Line Identifier</th>\n",
+       "      <th>E3 Ligase</th>\n",
+       "      <th>Active (Dmax 0.6, pDC50 6.0)</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
       "text/plain": [
-       "<Figure size 300x500 with 1 Axes>"
+       "Empty DataFrame\n",
+       "Columns: [Smiles, Uniprot, Cell Line Identifier, E3 Ligase, Active (Dmax 0.6, pDC50 6.0)]\n",
+       "Index: []"
       ]
      },
      "metadata": {},
      "output_type": "display_data"
     },
     {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 300x500 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "count    0.0\n",
+      "mean     NaN\n",
+      "std      NaN\n",
+      "min      NaN\n",
+      "25%      NaN\n",
+      "50%      NaN\n",
+      "75%      NaN\n",
+      "max      NaN\n",
+      "Name: Count, dtype: float64\n"
+     ]
+    }
+   ],
+   "source": [
+    "active_col = 'Active (Dmax 0.6, pDC50 6.0)'\n",
+    "studies_dir = 'data/studies'\n",
+    "train_val_perc = f'{int((1 - test_split) * 100)}'\n",
+    "test_perc = f'{int(test_split * 100)}'\n",
+    "active_name = active_col.replace(' ', '_').replace('(', '').replace(')', '').replace(',', '')\n",
+    "\n",
+    "experiments = ['standard', 'similarity', 'target']\n",
+    "\n",
+    "for split_type in experiments:\n",
+    "\n",
+    "    train_val_filename = f'{split_type}_train_val_{train_val_perc}split_{active_name}.csv'\n",
+    "    test_filename = f'{split_type}_test_{test_perc}split_{active_name}.csv'\n",
+    "    \n",
+    "    train_val_df = pd.read_csv(os.path.join(studies_dir, train_val_filename))\n",
+    "    test_df = pd.read_csv(os.path.join(studies_dir, test_filename))\n",
+    "\n",
+    "    print('-' * 80)\n",
+    "    print('-' * 80)\n",
+    "    print(f'Study: {split_type}')\n",
+    "    print('-' * 80)\n",
+    "    print('Test:')\n",
+    "    print_duplicates(test_df)\n",
+    "    print('-' * 80)\n",
+    "    print('Train/Val:')\n",
+    "    print_duplicates(train_val_df)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 168,
+   "metadata": {},
+   "outputs": [
     {
      "data": {
-      "image/png": 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",
       "text/plain": [
-       "<Figure size 300x500 with 1 Axes>"
+       "(1138, 1138)"
       ]
      },
+     "execution_count": 168,
      "metadata": {},
-     "output_type": "display_data"
+     "output_type": "execute_result"
     }
    ],
    "source": [
-    "def plot_ablation_study(report):\n",
-    "    # Define the ablation study combinations\n",
-    "    ablation_study_combinations = [\n",
-    "        'disabled smiles',\n",
-    "        'disabled poi',\n",
-    "        'disabled e3',\n",
-    "        'disabled cell',\n",
-    "        'disabled poi e3',\n",
-    "        'disabled poi e3 smiles',\n",
-    "        'disabled poi e3 cell',\n",
-    "    ]\n",
-    "\n",
-    "    for group in report['split_type'].unique():    \n",
-    "        baseline = report[report['disabled_embeddings'].isna()].copy()\n",
-    "        baseline = baseline[baseline['split_type'] == group]\n",
-    "        baseline['disabled_embeddings'] = 'all embeddings enabled'\n",
-    "        # metrics_to_show = ['val_acc', 'test_acc']\n",
-    "        metrics_to_show = ['test_acc']\n",
-    "        # baseline = baseline.melt(id_vars=['fold', 'disabled_embeddings'], value_vars=metrics_to_show, var_name='metric', value_name='score')\n",
-    "        baseline = baseline.melt(id_vars=['disabled_embeddings'], value_vars=metrics_to_show, var_name='metric', value_name='score')\n",
-    "\n",
-    "        ablation_dfs = []\n",
-    "        for disabled_embeddings in ablation_study_combinations:\n",
-    "            tmp = report[report['disabled_embeddings'] == disabled_embeddings].copy()\n",
-    "            tmp = tmp[tmp['split_type'] == group]\n",
-    "            # tmp = tmp.melt(id_vars=['fold', 'disabled_embeddings'], value_vars=metrics_to_show, var_name='metric', value_name='score')\n",
-    "            tmp = tmp.melt(id_vars=['disabled_embeddings'], value_vars=metrics_to_show, var_name='metric', value_name='score')\n",
-    "            ablation_dfs.append(tmp)\n",
-    "        ablation_df = pd.concat(ablation_dfs)\n",
-    "\n",
-    "        dummy_test_df = pd.DataFrame()\n",
-    "        tmp = report[report['split_type'] == group]\n",
-    "        dummy_test_df['score'] = tmp[['test_active_perc', 'test_inactive_perc']].max(axis=1)\n",
-    "        dummy_test_df['metric'] = 'test_acc'\n",
-    "        dummy_test_df['disabled_embeddings'] = 'dummy'\n",
-    "\n",
-    "        # dummy_df = pd.concat([dummy_val_df, dummy_test_df])\n",
-    "        dummy_df = dummy_test_df\n",
-    "\n",
-    "        final_df = pd.concat([dummy_df, baseline, ablation_df])\n",
-    "\n",
-    "        final_df['metric'] = final_df['metric'].map({\n",
-    "            'val_acc': 'Validation Accuracy',\n",
-    "            'test_acc': 'Test Accuracy',\n",
-    "            'val_roc_auc': 'Val ROC-AUC',\n",
-    "            'test_roc_auc': 'Test ROC-AUC',\n",
-    "        })\n",
-    "\n",
-    "        final_df['disabled_embeddings'] = final_df['disabled_embeddings'].map({\n",
-    "            'all embeddings enabled': 'All embeddings enabled',\n",
-    "            'dummy': 'Dummy model',\n",
-    "            'disabled smiles': 'Disabled PROTAC information',\n",
-    "            'disabled e3': 'Disabled E3 information',\n",
-    "            'disabled poi': 'Disabled POI information',\n",
-    "            'disabled cell': 'Disabled cell information',\n",
-    "            'disabled poi e3': 'Disabled E3 and POI info',\n",
-    "            'disabled poi e3 smiles': 'Disabled compound, E3, and POI info\\n(only cell information left)',\n",
-    "            'disabled poi e3 cell': 'Disabled cell, E3, and POI info\\n(only PROTAC information left)',\n",
-    "        })\n",
-    "\n",
-    "        # Print final_df to latex\n",
-    "        tmp  = final_df.groupby(['disabled_embeddings', 'metric']).mean().round(3)\n",
-    "        # Remove fold column to tmp\n",
-    "        tmp = tmp.reset_index() #.drop('fold', axis=1)\n",
-    "\n",
-    "        # print('DF to plot:\\n', tmp.to_markdown(index=False))\n",
-    "\n",
-    "        fig, ax = plt.subplots(figsize=(3, 5))\n",
-    "        \n",
-    "        # fig, ax = plt.subplots()\n",
-    "\n",
-    "        sns.barplot(data=final_df,\n",
-    "            y='disabled_embeddings',\n",
-    "            x='score',\n",
-    "            hue='metric',\n",
-    "            ax=ax,\n",
-    "            errorbar=('sd', 1),\n",
-    "            palette=sns.color_palette(palette, len(palette)),\n",
-    "            saturation=1,\n",
-    "        )\n",
-    "\n",
-    "        # ax.set_title(f'{group.replace(\"random\", \"standard\")} CV split')\n",
-    "        ax.grid(axis='x', alpha=0.5)\n",
-    "        ax.tick_params(axis='y', rotation=0)\n",
-    "        ax.set_xlim(0, 1.0)\n",
-    "        ax.xaxis.set_major_formatter(plt.matplotlib.ticker.PercentFormatter(1, decimals=0))\n",
-    "        ax.set_ylabel('')\n",
-    "        ax.set_xlabel('')\n",
-    "\n",
-    "        # Plot the legend below the x-axis, outside the plot\n",
-    "        ax.legend(loc='upper center', bbox_to_anchor=(0.02, -0.1))\n",
+    "from sklearn.preprocessing import OneHotEncoder\n",
     "\n",
-    "        # For each bar, add the rotated value (as percentage), inside the bar\n",
-    "        for i, p in enumerate(plt.gca().patches):\n",
-    "            # TODO: For some reasons, there is an additional bar being added at\n",
-    "            # the end of the plot... it's not in the dataframe\n",
-    "            if i == len(plt.gca().patches) - 1:\n",
-    "                continue\n",
-    "            value = '{:.1f}%'.format(100 * p.get_width())\n",
-    "            y = p.get_y() + p.get_height() / 2\n",
-    "            x = 0.2 # p.get_height() - p.get_height() / 2\n",
-    "            plt.annotate(value, (x, y), ha='center', va='center', color='black', fontsize=10, alpha=0.8)\n",
+    "cell2embedding = pdp.load_cell2embedding('../data/cell2embedding.pkl')\n",
     "\n",
-    "        plt.savefig(f'plots/ablation_study_{group}.pdf', bbox_inches='tight')\n",
-    "\n",
-    "reports['test']['disabled_embeddings'] = pd.NA\n",
-    "plot_ablation_study(pd.concat([\n",
-    "    reports['ablation'],\n",
-    "    reports['test'],\n",
-    "]))"
+    "# Get one-hot encoded embeddings for cell lines\n",
+    "onehotenc = OneHotEncoder(sparse_output=False)\n",
+    "cell_embeddings = onehotenc.fit_transform(\n",
+    "    np.array(list(cell2embedding.keys())).reshape(-1, 1)\n",
+    ")\n",
+    "cell_embeddings.shape\n",
+    "# cell2embedding = {k: v for k, v in zip(cell2embedding.keys(), cell_embeddings)}"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {