{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "c000",
   "metadata": {},
   "source": [
    "# Plate artifacts and corrections\n",
    "\n",
    "Plates drift, well position matters, and cell count affects most features. This page looks for those artifacts\n",
    "on a real screen, `pki`: eight plates of kinase inhibitors, two in each of four cell lines. It\n",
    "removes them and measures whether removing them helped.\n",
    "\n",
    "Every correction here can make results worse on a given dataset. The last section shows a real case where\n",
    "sphering, a standard step of the pipeline, cuts mechanism retrieval by more than three quarters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "c001",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-11-11T02:25:49.041427Z",
     "iopub.status.busy": "2026-11-11T02:25:49.041106Z",
     "iopub.status.idle": "2026-11-11T02:26:32.448357Z",
     "shell.execute_reply": "2026-11-11T02:26:32.447158Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "UserWarning: 133 of 5839 features have no spread in at least one group of 'Metadata_Plate' among the rows selected by reference='negcon' (Cells_AreaShape_EulerNumber, Cells_Correlation_Manders_AGP_BFHigh, Cells_Correlation_Manders_AGP_BFLow, Cells_Correlation_Manders_AGP_Brightfield, Cells_Correlation_Manders_BFHigh_BFLow and 128 more). They cannot be scaled there, so their values in those groups are set to NaN. They are flagged in var['degenerate_scale'], and pp.feature_select drops them by default; to drop them now, adata = adata[:, ~adata.var['degenerate_scale']].copy(). A variance or outlier cut does not catch a feature that varies across a plate but is constant among its control wells; the flag does.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "UserWarning: 1 selected feature(s) hold values more than outlier_cutoff=500 robust standard deviations from their own median (Nuclei_ObjectSkeleton_NumberNonTrunkBranches_mito_skel). They can dominate distances and PCA. Add 'drop_outliers' to operations to drop them, or check where the extreme values come from; on single cells, before aggregation, since a median profile hides a few extreme cells.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "AnnData object with n_obs × n_vars = 3072 × 854\n",
       "    obs: 'Metadata_plate_map_name', 'Metadata_broad_sample', 'Metadata_mg_per_ml', 'Metadata_mmoles_per_liter', 'Metadata_solvent', 'Metadata_Plate', 'Metadata_Well', 'Metadata_Site_Count', 'Metadata_Count_Cells', 'Metadata_Count_CellsIncludingEdges', 'Metadata_Count_Cytoplasm', 'Metadata_Count_Nuclei', 'Metadata_Count_NucleiIncludingEdges', 'Metadata_Object_Count', 'Metadata_Barcode', 'Metadata_Supplier', 'Metadata_Supplier_Catalog', 'Metadata_pert_type', 'Metadata_control_type', 'Metadata_Row', 'Metadata_Col', 'Metadata_CellCount', 'Metadata_SiteCount', 'Metadata_Control_Type', 'Metadata_Control', 'Metadata_Compound', 'Metadata_Concentration', 'Metadata_ConcentrationUnit', 'Metadata_MOA', 'Metadata_Perturbation', 'Metadata_Perturbation_Type', 'Metadata_CellLine'\n",
       "    var: 'object', 'feature_group', 'feature', 'channel', 'scale', 'angle', 'gray_levels', 'radial_bin', 'params', 'is_feature', 'feature_kind', 'degenerate_scale', 'selected'\n",
       "    uns: 'mantispy'\n",
       "    layers: None (.X)"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import logging\n",
    "import warnings\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import scanpy as sc\n",
    "\n",
    "import mantispy as mt\n",
    "\n",
    "warnings.formatwarning = lambda message, category, *a, **k: f\"{category.__name__}: {message}\\n\"\n",
    "\n",
    "wells = mt.ds.pki()\n",
    "mt.pp.normalize(wells, method=\"mad_robustize\", by=\"Metadata_Plate\", reference=\"negcon\")\n",
    "mt.pp.feature_select(wells, na_cutoff=0.0)\n",
    "wells = mt.pp.subset_features(wells)\n",
    "wells"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c002",
   "metadata": {},
   "source": [
    "## Position effects\n",
    "\n",
    "Edge wells evaporate, corners sit at a different temperature, and dispensing drifts along a row.\n",
    "`pl.plate_effects` shows the row and column medians of each plate. A position artifact appears as a trend across\n",
    "rows or columns, while noise scatters around the plate median. Two of the eight plates:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "c003",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-11-11T02:26:32.452974Z",
     "iopub.status.busy": "2026-11-11T02:26:32.452740Z",
     "iopub.status.idle": "2026-11-11T02:26:33.483745Z",
     "shell.execute_reply": "2026-11-11T02:26:33.482444Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 900x600 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "two = wells[wells.obs[\"Metadata_Plate\"].isin(wells.obs[\"Metadata_Plate\"].cat.categories[:2]).to_numpy()].copy()\n",
    "mt.pl.plate_effects(two);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c004",
   "metadata": {},
   "source": [
    "Correcting is not free. A row and column effect fitted from a few dozen control wells can fit their own scatter, and subtracting it strips signal rather than an artifact. `detect_plate_position` cross-validates the same median polish on held-out controls and reports, per plate, whether the fit generalizes, so you can correct only the plates where the effect is real instead of every plate.\n",
    "\n",
    "`correct_plate_position` fits Tukey's median polish to the row and column effects of each plate, so a few extreme wells do not define the gradient. Its default, `method=\"b_score\"`, then divides the residual by the plate's MAD (median absolute deviation), which resists outliers and gives a position-corrected z-score per plate. The z-score is the screening standard for calling hits against a gradient, and the plate-position case study shows it on data. Here we use `method=\"median_polish\"`, which subtracts the effects and keeps each feature's units, so we can read how much of each feature position explains, before and after, as its correlation with the row and the column it sits in:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "c005",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-11-11T02:26:33.489332Z",
     "iopub.status.busy": "2026-11-11T02:26:33.488943Z",
     "iopub.status.idle": "2026-11-11T02:26:39.196621Z",
     "shell.execute_reply": "2026-11-11T02:26:39.195610Z"
    }
   },
   "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>before</th>\n",
       "      <th>after</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>row: median |r|</th>\n",
       "      <td>0.055</td>\n",
       "      <td>0.023</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>row: largest |r|</th>\n",
       "      <td>0.316</td>\n",
       "      <td>0.133</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>column: median |r|</th>\n",
       "      <td>0.070</td>\n",
       "      <td>0.087</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>column: largest |r|</th>\n",
       "      <td>0.692</td>\n",
       "      <td>0.346</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     before  after\n",
       "row: median |r|       0.055  0.023\n",
       "row: largest |r|      0.316  0.133\n",
       "column: median |r|    0.070  0.087\n",
       "column: largest |r|   0.692  0.346"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def position(adata):\n",
    "    \"\"\"Median and largest absolute correlation of a feature with its well's row and column.\"\"\"\n",
    "    values = np.asarray(adata.X, dtype=float)\n",
    "    result = {}\n",
    "    for name, col in ((\"row\", \"Metadata_Row\"), (\"column\", \"Metadata_Col\")):\n",
    "        axis = adata.obs[col].to_numpy()\n",
    "        r = np.abs([np.corrcoef(axis, values[:, j])[0, 1] for j in range(values.shape[1])])\n",
    "        result[f\"{name}: median |r|\"] = round(float(np.nanmedian(r)), 3)\n",
    "        result[f\"{name}: largest |r|\"] = round(float(np.nanmax(r)), 3)\n",
    "    return result\n",
    "\n",
    "\n",
    "corrected = mt.pp.correct_plate_position(wells, method=\"median_polish\", copy=True)\n",
    "pd.DataFrame({\"before\": position(wells), \"after\": position(corrected)})"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c006",
   "metadata": {},
   "source": [
    "Position explains little of most features and a lot of a few. Before correction the median feature correlates with\n",
    "its well's column at 0.07, and the most affected one at 0.69; median polish takes the largest column correlation to\n",
    "0.35 and the largest row correlation from 0.32 to 0.13, while the median column correlation does not fall (0.07 to 0.09)."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c007",
   "metadata": {},
   "source": [
    "If treatments are laid out by column, fit the effects on the controls only, with\n",
    "`reference=\"negcon\"`, so that treatment effects are not absorbed into a column effect and\n",
    "subtracted.\n",
    "\n",
    "This has a limit. A median polish can only fit a column effect for columns that contain\n",
    "reference rows, and most layouts put the controls in a few fixed columns, such as 1 and 2 or\n",
    "23 and 24. With controls in four of twenty-four columns there is no control-only column effect\n",
    "to fit. Row effects still work, because controls in an edge column span every row. For column\n",
    "effects on a column-wise layout, the design has confounded the effect you want to correct, and\n",
    "the fix is a randomized layout on the next plate.\n",
    "\n",
    "Whether to correct at all, and how to tell, turns on what you are measuring. The {doc}`plate-position case study </case_studies/plate_position>` works through it on real data. The correction helps hit-calling against a gradient, and it removes signal from multivariate profiles. `detect_plate_position` is how you decide per plate, or flag a plate for exclusion."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c008",
   "metadata": {},
   "source": [
    "## Confounders\n",
    "\n",
    "Cell count is the most common one: a sparse well looks different from a confluent one regardless of treatment.\n",
    "`regress_out` fits each feature against the confounder within each plate and keeps the residual.\n",
    "\n",
    "The fit has to be made on wells whose density varies for technical reasons only. In a compound screen the\n",
    "treatments change density too, and a compound that thins its wells usually has a phenotype of its own, so a fit\n",
    "over every well mistakes that phenotype for a density effect. `reference=\"negcon\"` fits on the DMSO wells, keeps\n",
    "them where normalization centred them, and corrects a well sparser or denser than any control as if it sat at the\n",
    "edge of their range rather than by extrapolating."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "c009",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-11-11T02:26:39.199936Z",
     "iopub.status.busy": "2026-11-11T02:26:39.199755Z",
     "iopub.status.idle": "2026-11-11T02:26:40.729345Z",
     "shell.execute_reply": "2026-11-11T02:26:40.727791Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "median cells per well, by plate: {'BR00122970': 1974.5, 'BR00122971': 1956.0, 'BR00122972': 607.5, 'BR00122973': 602.5, 'BR00122974': 1640.0, 'BR00122975': 1631.0, 'BR00122977': 1820.0, 'BR00122978': 1803.5}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "UserWarning: regress_out: the pooled mean of ['Metadata_CellCount'] lies outside the range Metadata_Plate=['BR00122972', 'BR00122973'] span, so those groups are corrected by extrapolating their own fit, which can leave the result more dependent on the covariate than it was. Pass reference='negcon' to anchor each group at its own controls.\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>before</th>\n",
       "      <th>fit on every well</th>\n",
       "      <th>fit on the DMSO wells</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>DMSO wells, within plates: median |r|</th>\n",
       "      <td>0.180</td>\n",
       "      <td>0.193</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>every well: median |r|</th>\n",
       "      <td>0.142</td>\n",
       "      <td>0.547</td>\n",
       "      <td>0.132</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DMSO wells off centre</th>\n",
       "      <td>0.000</td>\n",
       "      <td>0.410</td>\n",
       "      <td>0.014</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                       before  fit on every well  \\\n",
       "DMSO wells, within plates: median |r|   0.180              0.193   \n",
       "every well: median |r|                  0.142              0.547   \n",
       "DMSO wells off centre                   0.000              0.410   \n",
       "\n",
       "                                       fit on the DMSO wells  \n",
       "DMSO wells, within plates: median |r|                  0.000  \n",
       "every well: median |r|                                 0.132  \n",
       "DMSO wells off centre                                  0.014  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "counts = wells.obs[\"Metadata_CellCount\"].to_numpy(dtype=float)\n",
    "plates = wells.obs[\"Metadata_Plate\"].astype(str).to_numpy()\n",
    "control = wells.obs[\"Metadata_Control_Type\"].to_numpy() == \"negcon\"\n",
    "\n",
    "\n",
    "def density(adata):\n",
    "    \"\"\"How much of each feature follows the cell count, and whether the controls stay centred.\"\"\"\n",
    "    values = np.asarray(adata.X, dtype=float)\n",
    "    within = []\n",
    "    for plate in np.unique(plates):\n",
    "        rows = (plates == plate) & control\n",
    "        r = [np.corrcoef(counts[rows], values[rows, j])[0, 1] for j in range(values.shape[1])]\n",
    "        within.append(np.nanmedian(np.abs(r)))\n",
    "    every = [np.corrcoef(counts, values[:, j])[0, 1] for j in range(values.shape[1])]\n",
    "    return {\n",
    "        \"DMSO wells, within plates: median |r|\": round(float(np.median(within)), 3),\n",
    "        \"every well: median |r|\": round(float(np.nanmedian(np.abs(every))), 3),\n",
    "        \"DMSO wells off centre\": round(float(np.median(np.abs(np.median(values[control], axis=0)))), 3),\n",
    "    }\n",
    "\n",
    "\n",
    "print(\n",
    "    \"median cells per well, by plate:\",\n",
    "    wells.obs.groupby(\"Metadata_Plate\", observed=True)[\"Metadata_CellCount\"].median().to_dict(),\n",
    ")\n",
    "\n",
    "fit_on_everything = mt.pp.regress_out(\n",
    "    wells, keys=(\"Metadata_CellCount\",), by=\"Metadata_Plate\", reference=None, copy=True\n",
    ")\n",
    "fit_on_controls = mt.pp.regress_out(\n",
    "    wells, keys=(\"Metadata_CellCount\",), by=\"Metadata_Plate\", reference=\"negcon\", copy=True\n",
    ")\n",
    "pd.DataFrame(\n",
    "    {\n",
    "        \"before\": density(wells),\n",
    "        \"fit on every well\": density(fit_on_everything),\n",
    "        \"fit on the DMSO wells\": density(fit_on_controls),\n",
    "    }\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c010",
   "metadata": {},
   "source": [
    "Two of the eight plates, the MDA-MB-231 pair, hold about a third of the others' cells. With two plates per line\n",
    "the design cannot tell a cell-line difference from a plating one. Fitted on every well, each plate's fit is\n",
    "re-expressed at the density of the whole screen, which those two plates never reach, so their correction is an\n",
    "extrapolation, and `regress_out` warns about it. The result follows the cell count more closely than before across\n",
    "the screen, 0.55 against 0.14, and the DMSO wells end 0.41 off the centre normalization gave them.\n",
    "\n",
    "Fitted on the DMSO wells, the correction removes the count's pull on them, zero by construction since the fit was\n",
    "made on them, and leaves them centred. Across every well the correlation stays about where it was. What remains\n",
    "there is mostly the treatments' own effect: a compound that thins its wells also changes the cells that are left,\n",
    "and a correction for technical density should not remove that."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c011",
   "metadata": {},
   "source": [
    "Missing values stay missing. Imputing them for the regression and writing the fitted value\n",
    "back would make an unmeasured value look like a real measurement."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c012",
   "metadata": {},
   "source": [
    "## Plates as batches\n",
    "\n",
    "A batch effect moves the controls too. `pl.control_drift` projects the control wells onto components fitted on the\n",
    "controls alone, so if the controls of different plates land in different places, the reference itself shifts\n",
    "between them."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "c013",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-11-11T02:26:40.731921Z",
     "iopub.status.busy": "2026-11-11T02:26:40.731735Z",
     "iopub.status.idle": "2026-11-11T02:26:42.317964Z",
     "shell.execute_reply": "2026-11-11T02:26:42.316661Z"
    }
   },
   "outputs": [
    {
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       "        <script type=\"module\">import \"https://cdn.plot.ly/plotly-4.1.1.min\"</script>\n",
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   "source": [
    "mt.pl.control_drift(wells, groupby=\"Metadata_Plate\");"
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    "### Measuring the result\n",
    "\n",
    "`mt.metrics` scores a representation with several metrics. Batch-mixing metrics and biological-signal metrics\n",
    "trade off against each other, and a correction that improves one at the expense of the other has not helped.\n",
    "\n",
    "Normalizing each plate against its own controls is itself a batch correction. Here it is measured against one\n",
    "normalization pooled over the whole screen, on the same features:"
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   "id": "gen0915",
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    "`mt.metrics.evaluate_integration` runs the integration benchmark from\n",
    "[scib-metrics](https://scib-metrics.readthedocs.io), the field's implementation, which mantispy imports rather\n",
    "than reimplements. Install it with `pip install \"mantispy[integration,map]\"`. It reads scib-metrics' public\n",
    "results and draws its own heatmap, one row per representation, with the columns grouped into blocks: bio\n",
    "conservation, batch correction, retrieval, and the aggregate scores. Higher is better in every cell. cLISI is\n",
    "how label-pure a well's neighbourhood is (bio conservation); iLISI is how batch-mixed it is, and PCR comparison\n",
    "is how much less of the variance the batch explains after correction (batch correction).\n",
    "\n",
    "With [copairs](https://github.com/cytomining/copairs) installed the heatmap gains a retrieval block, the\n",
    "cross-replicate mean average precision (mAP), and a second aggregate column. `Total` is scib-metrics' own\n",
    "weighted score (0.4 batch correction, 0.6 bio conservation), left exactly as scib computes it; `Total+mAP` is\n",
    "mantispy's, which folds mAP into the bio group as one more bio signal. With only copairs it draws the mAP column\n",
    "alone, and with neither it raises and points you to `mt.metrics.batch_variance_explained` (stack several\n",
    "covariates) or `mt.metrics.pc_regression` (a single covariate) for a native batch-variance check.\n",
    "`pc_regression` is deliberately not in this panel: whether a covariate's share of the variance, such as the cell\n",
    "count's, should be small depends on the screen. The call returns the numeric results frame as well, so the\n",
    "values are always reachable."
   ]
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   "execution_count": 6,
   "id": "c015",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-11-11T02:26:42.319782Z",
     "iopub.status.busy": "2026-11-11T02:26:42.319623Z",
     "iopub.status.idle": "2026-11-11T02:27:54.900575Z",
     "shell.execute_reply": "2026-11-11T02:27:54.899265Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "UserWarning: 130 of 5839 features have no spread in at least one group of None among the rows selected by reference='negcon' (Cells_Correlation_Manders_AGP_BFHigh, Cells_Correlation_Manders_AGP_BFLow, Cells_Correlation_Manders_AGP_Brightfield, Cells_Correlation_Manders_BFHigh_BFLow, Cells_Correlation_Manders_BFHigh_Brightfield and 125 more). They cannot be scaled there, so their values in those groups are set to NaN. They are flagged in var['degenerate_scale'], and pp.feature_select drops them by default; to drop them now, adata = adata[:, ~adata.var['degenerate_scale']].copy(). A variance or outlier cut does not catch a feature that varies across a plate but is constant among its control wells; the flag does.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "FutureWarning: The argument key_added is deprecated and will be removed in the future. Use `out` instead.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "UserWarning: PCR comparison score is negative, meaning variance contribution increased after integration. Setting to 0.\n"
     ]
    },
    {
     "data": {
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8J2U9xmVdJwAMGDAAGzZswLRp0wAAI0aMAApXD0Ph3AdHR0c2OfIre/fuBQB4eHggOjoaOjo6cHBwgL+/P96/fw8rKysYGRkhNjYWycnJ0NbWRlhYGLe/kpJSmZd/rei5rVSpEgwMDBAXF4cpU6bAxMRErF1Z15eRkYFr167ByMgIJ0+eRHx8PNasWVPsNopeW9+7rV8dK+Z/QQEBAVi1ahVu3rwJkUiEXr16lbgaxPLly9G7d2/06tULTZs2xY4dO+Ds7Fzihy8ArFmzBt27d0fnzp0xcOBAqKmpcRNgf/S6vnXgwAEkJCTA0NAQVapUwevXr7mJpStXrkSvXr3QtWtXDBw4ECoqKsBXXwAlGTFiBIYPH47Q0FBMnToVysrKAICGDRvC398fo0ePRmhoqNgRi6KlJy9cuIDKlStzE2j/zWPG/LrOnTuHkJAQHDx4EAAwZMgQ2NvbQ1tbG/7+/pg5cya3ZGCRotfgli1b8OrVK4wdOxZz5szBokWL0Lp1a24y4LcTYMujSpUqpV6XiYkJpk6ditWrV6Nfv35QUlLCy5cv8fvvv8POzq7ct2FiYoLp06dj+fLlaN++PTw8PCAnJwcNDQ2MHj26zPtS0n3/9rorIuOvrnXr1nBzc4O/vz+2b98Ob29vXLlyBZMnT0ZQUBC0tbUBAJ06dYKFhQWUlZUREBCApUuXonHjxuW6jbKus1q1ahg5ciQ2bNiAV69eoWnTptyRlEaNGuH+/fvw8vJC5cqVERAQIMFHQnaEhYXh9u3bcHBwwNq1a7ntRkZG8PLywv79+zF//nzMmjULEydORI8ePeDo6IiLFy+KXc/3Li/J6tWrMXToULRr1w59+vSBkpISAGDcuHFlXp+6ujqcnJwQGBiICRMm4OnTp2I/Cove7ydPnoSioiJ69OhR5m19vQjFr4gV878QFxcXeHp6AoXrAzds2BCjRo1Cx44dgcJlFlVUVLgPzm7duuHVq1e4cOECkpKSsGfPHnTv3l2sN+1r7du3x7t373D27FlEREQgNTWVm8H+vev69rYbNmwIT09PNGzYEChcISY2NhZycnJo0aIFVqxYgZo1awIAunTpgqCgIJw7dw6fPn1CcnIyAEAoFBa73iK9e/fGmzdvQERiRcG5c+ewZ88efP78Gc7OznB2dgYAqKiooFmzZjh58iSePHmCmJgYZGVl/fBj9r37yfz39erVi1vDOzExEQMHDkTz5s25tZQfPHiAo0ePIiUlBV27dgUKC1UAmD59OoyMjPD+/XtER0dDKBRi4cKF6NSpE27duoWYmBgUzoUCCpdujIqK4n6s9+jRA05OTtxh72+VdV2rVq1Cly5d4O/vD5FIBF9fX7Rs2ZLb99vbKm3bsmXL0LVrV/j5+SEuLg5ExP1oLuv2S7rv317/j2b83uPxKyga4lK1alVu25o1a3D06FHk5eWhWrVqCA0NxYkTJxAWFsYNh8jKyoKuri6uX7+OK1euICEhAWlpaSUuQ/nttrKuEwBq1KiBJUuWIDExEZ06deKuZ+XKlahZsybevHkDkUjEfZ9VrVoV2trav+zyl9HR0Zg6dSrq1asntr1r166Ijo7mhpxOmDABtra28PPzQ25uLpycnPDixQuuA+x7l3fo0AFGRkawtLTkbmPAgAFo0KABLl26hMjISIhEIqCwR/1713fjxg3s2bMHMTExcHFxgYuLC3e9nTt3xuHDh/HixQtER0cjJyenzNv61QmIPQoMwzAMwzD/aUWdbUSEmJgY7NixA5qamnj06BGMjIy+e3lF3x5TcdjAQYZhGIZhmP+4nJwcREdHIzY2FlpaWti6dStev37NFdbfu7yib4+pOKxnnmEYhmEYhmFkFOuZZxiGYRiGYRgZxYp5hmEYhmEYhpFRrJhnGIZhGIZhGBnFinmGYRiGYRiGkVGsmGcYhmEYhmEYGcWKeYZhGIZhGIaRUayYZxiGYRiGYRgZxYp5hmEYhmEYhpFRrJhnGIZhGIZhGBnFinmGYRiGYRiGkVGsmC8BEfEdoVxIJBs5AUAkFPEdoVxk5bkHAKFIyHeEcvmZnLLyPIhk6D0oK58XsvKYCmXkMw0/kVVWXiv4yawy8/nCclY4Wcn6ozkFJCuv6v+z+LBE5GXn8x2jVCqaytAx0cKzK2+RlpjJd5wyGVTVgX1jSxyfew6xYQl8xymVXWMreIxtitjgeORm5fEdp0xq2irQNdfBuMNzERL7ge84pbIxsMCmvot+at+07FQISXp/sCjKKUJduRLunAxASnw633HKVMVGH7VbVcOTS2+QLsWfF/oWunBwtYTv/vtIjk7lO06pzByM0KBjTSzpvwrhbyL4jlOmqtXNMPvgtB/e7/m1d0hPkt7XCgBU0lFDLQ+7n9o3NPUZsoTS+77VUtSHWSV7HAh+hZisDL7jlKq6th46mFvjyqcXSMqV3pwAYK5eGY0NbXH98wsk5Ujvc6+jXAktq9T8oX0UJJZGxuVl5yNPigs6BZWCpy4tMROpcdL9BqqkowoAiA1LQFRQDN9xSlXZQhcAkJuVh9xM6X3uAUBRteD5D4n9gJeRQXzHkQghCSEUSe8PanmBPAAgJT4dSVHSW3gCgGZldQBAemImUuKk90us6LMiOToV8Z+S+Y5TKm1DDQBA+JsIBD8L5TuORKQnZSI1Xrq/W/6NLGE6MvOl932rIl/wno3JysCnTOl9zxqoqgEAknIzEJedxnecMukoFTymSTnpSMiR7qw/ig2zYRiGYRiGYRgZxYp5hmEYhmEYhpFRrJhnGIZhGIZhGBnFinmGYRiGYRiGkVGsmGcYhmEYhmEYGcWKeYZhGIZhGIaRUayYZxiGYRiGYRgZxYp5hmEYhmEYhpFRrJhnGIZhGIZhGBnFinmGYRiGYRiGkVGsmGcYhmEYhmEYGcWKeYZhGIZhGIaRUayYZxiGYRiGYRgZpcB3gP+ChKQE3PC/jkrqleDe1AMKCsUf1tj4WFz394W2pjZaNXGHoqIiAOBDRBjuPLwDVRUVuDdtDU0NTYlmffrmIeKTY1G7Wj0Y6BoVuzw3LwcPAu8gJycbdR0aQkdTDzm5Objgf0qsnUUVa9SxbyCxnLom2rCsZ46kzyl4/yi82OVGtvqwbmgJAHjrF4yEj0llbpeUnNwcXPf3QXZODlq6toKWplaxNlnZWbjmdxVCoRCt3FpBU0MLn6MjccH3vFg71/pucLBzlFjWGib2qGlqj+cRrxH4+V2xy+UEcnCv7goDjcp4EBaA4NgwAEATm/pwrGIHADj08AxSs9MllvFnBQcF497d+7CrZotGjRsVu/zxw8e4f+8B93fLVi3g4OTA/R0TE4PjR06gV5+eMDA0kFjO7NxsBLx7CBGJ4GzXAKrKasXapKQnISD4EVSUVOFcrSGUFJQAAJk5mXgadB9yAjnUqdYQKkqqEssJAEEfXuHD5/ewt3REVWOrYpeLRCI8ffsQCclxqGHrjCr6ptxlSakJeB78FEmpCbCv6ojqVjUkljM1MxmB4QFQU1ZHTYu6kJMr3kcVk/QZQZGvoKehj+rmNSEnkCv3vhWpfps60DfRw+NrAYiNiPuhNuXZl0+BIc/xKSYcjta1YGZUtdjlQpEQjwLvITktEbWr1YNR5SoAgH+uH4NQlM+1M9SrAjfn5v/X7N8T9DIUr58Fo3ptG9jXtCmxzcNbAfj0IQq1GlSHdXULbtu7wPdcm5adXFHF3FBiObNTUvHh3gPIKynB0s0FCkpKJbYjIrw8fQ6VbaxRpaYjslJS8Pr8FQCAgb0tzOo6SyxjkaD7z5DwKRq2DWpB37xKiRnf+D9GUlQc7BrVLtbm/dNXiHz3Hk36dJJozoTIGLy59ww6RpXh6FavxDZxHz/jzb1n0DMxhINrXQgEgjK3S8L/vWc+MTERbdu2RVRUFLft1KlTmDBhwv87SoVISk5CpwHtceveTew6tBNjZ/5erM3rd68xcFw/BAQ+w86DOzBgXD8AQODbQHQe1BEvXj/H5euX0L5/W2TnZEss687TG7HnzGa8CQvEtLW/IzI2QuzyjKx0TF83Dnef38KTtw8w5s+BiI7/DAIhKTWR+3fl7jnEJ8VKLKexnQFG7RoA8xom6DDNHS1GNC7WRkFZERp66qjbqQaMbAy+u11Shk8dgr9PHcB1fx/8NqILMrMyxS5Pz0hH37G9cOveDfjcvop2/VsjJTUFOXm5iEuI4/5t3rcRQpFIYjm71PLAnsErUdvUAXuHrEZbx+JflrsGLcfYZgNhb2SNnQOXoYlNfQCAhkolGGjoYXyLwdBV15ZYxp917859dGnXFU8eP8WEMZOwffOOYm1u3fDD9WvXERsTi9iYWGRnf3mfiUQizJ4+F3t27EFk5GeJ5RSRCEv2zYTfcx/cf+WHRXumI1+YL9Ym5NNbLNn3B959fI2rD89h/s6pEImESMlIxpK9M/HqfQDuBd7CrK0TkJMnuc+KK3fPYdX+hQiOeAvvrV4ICHpcrI331mm4dOcfBIW/xtTVoxAYEgAACP74FhNXDMODl/5ITk1EVk5mCbdQMVIzk/HnkT/wNuIlrj45iz3XNhZr8+DtbWy7tAYRsWE44X8Ae69tKve+FWnCX6MwatkQOLk6YPOD1ahibVzuNuXZl0+nrx/F2gN/IujDG8z8ayJev38pdjkRYfra8bh69zxehb7AuD8HI+jDawBAUtqX75Zbj30Q/vl9KbfCD99z/vAcsBBvAt7Ba9Bi+J71L9Zm1ohlOLDhBN4+D8aoTjNw88JdAMD1c/54/uA1EmKTkBCbhLzcPInlzM3MwpHh4xDqdxcvTp3DOa85pbZ9duQEXp27hI8PC97XonwhMhMSEf7gEd773ZVYxiIXNuzDiSWb8eHFW6wbMBWRb0OLtdnntRT+R84j/OVbrO49AWHP33CXJUXF4vKWg7i8+aBEc0aFfsTS3pMQ+uw1Tq3ahZMrdxZr8/LWQ2ydtBgRb97j1Kpd+HveX2Vul5T/e8+8rq4umjdvjtGjR+Ps2bP4/PkzJkyYgKtXr/6/o1SIkxdOwK1hE6yYtwpCoRAtuzfDu9Ag2FlX49pUUlPHqT1noK6mDpFIhOpudsjOycbj54/Qrd1vWDB9EQCgZffm+PT5E2wsS/7l/29kZKXD9+El7PI+BjUVdZzwOYjzficxusdkrg0RYdqgeTA1NAcALN/rjddhL9GyfhsM7zoOKOxZvPv8FprWaVXhGYs07lsPN3bdxYPjT6GmrYrJx0fCb98DCPOEXJtPgZ/xKfAzKumpi+1b2nZJePryCRKTE3HhwGUIBAJMmjcB56+dRa/Ofbg2BMLGJZthamwGAOg3tjfehLxGozoumDWx4MM2OOwdHgY8QA17yfVejm8xBGMPzcHDD8/RyMoZ8zpMwuVXN7nLFeTk0byaC2zmNINQJISLVR1MbjUct0Me4WLgDVwMvIHm1Vwklu/f2LR+M+b/OR+9+vRE+IdwtGvZHqPGjizWzriKMSytLNGgUX04On05ArJh7Ub07tcLER8jiu1TkV6GPIWivCKm918IAFhxcB6eBj1AAwdXro2aijrmD18NNRV1iEiECWsGISktEUqKSpjWbz50NHQBALO2jkd0QiSqGllLJOtxn78xd+RSVDW2woOX/jh1/TBqVxPvlRrQYQSqVS04ulFZ2wDPgh7DyaY29p7dgk7NeqCXx0CJZPva3dc3UMuqPvo2HwahSIi5+yciNjkaBtpfjjoa6Zpgdp/lkJeTR0pGEhYemlbufSuKhk4luA9ogd7mQ5GdkY3+s3qh67gO2Dx153fbHFh05Lv78omIcOzKAaz12g6jylVw4+FVnPQ5BIdRS8XajO4xEXZVqwMANNW1EBD0BNUsHDC0y+9f2izqjzaNO/J2X0qy76/jWLDZE3Vda+L5g9dY9cdWtOrsJtam35hucKpb8J1v42CJe9efonmHgk4oIzN9VLUxQR3XGjC3MpFYznfXrsOwuh3aLZoLIsKhQaMQ/eoNjByri7WLefsO6XHxsG7qCpGw4DtVXU8XTSb+jhenziEp/KPEMgJAXk4u/A6dxZzzu6Chpw2/Q2dx88Bp9F8yTaydx8g+MKlWcERQs7Iu3t17Bsta1SESCnFm9U50mzEaG4fNkGjWGwfPwn1wN7Qd2RtZ6RmY7TEU7cf0hWqlL/WFnokhZh3fAHkFeSRFx2FZnyllbpcUXsbMe3l5ITo6GgcOHMCwYcPg6ekJR8fvDy9wc3PDjRs30LNnTwwcOBDBwcHcZXFxcfD09ISHhwfc3Ny4HwddunSBm5sb2rVrh6VLlyI3N7dC70vohxDUqVkXACAvL49ajrURHBYs1sbctCoSEuOxdd9mTJ47Eb279oGKsgratmiLN8GvsXrLSsxeOguO1RxhbSGZL+fohM8w0jOBmkrBi7C6pRMiYsSHr1RS0+AK+YysdETGRqCmrfjhtluPr6G+owtUlCV3iL+yhR4iXkYCADKTs5CWmAEtQw2J3d7Peh8eilqOtblDZ3Vq1EHIhxCxNhrqGqiso48dB7dh0doFUFJSgrOj+GP694n96Nu1n0SzWumb4cnHQADA4w8vYGMgfhg8XyTEi09vsK3/n5jYcih+bzYAdoaWEs1UUYLfBaN+g4JCs6pFVcjJyyMhPkGsTf0G9aBXWQ8vAl7itw49cPjvIwCAh/cfIisrCx5t3CWe83N8BGxM7bm/bU2r43O8+A+IKpXNuPdoREwY9DT1oatZGRpqWlBSVMKFuyex58ImVNY2gKmBhURy5uTmIC0zlRtaY2/phE8xxYe6FRXyQmE+Xr9/gfqOLiAivPv4BnXsG+DSnTN4/OoeRBI84hSd9BlWxrYAAHk5eVQ1sEJ0UqRYm6oGVpCXkwcAvIl4iVqW9cq9b0WpYm2MiKBIZGcUHE15dfcNqlY3K1eb8uzLp9T0ZMjJyXPDZqpbOSEiWvz1IicnxxXyefl5ePvhNeo5iA+He/z6PkwNq6KyjuSPqP6IjyGRqFGvILtTvWr4+L74a6SokCciPL79HE3aNgQANGjmDAUFBbx4+AbD23ri3vUnEsuZGP4RRoVDBwUCAYwcqyPxg/jzkJuRiaeHjsF1zAiJ5fiepOg4aFbWgYZewVFeS2cHRL8v3pFiUs0K909fwYUN+xD88Dmc2zYFAFzZdhhufTpx+0tSdNgnWNUqeO5VK6lD39wY8RHRYm2q2FRFZNB7XN5xDAcXbIT74G5lbpcUXop5eXl57N27F2PHjkV2djamTCnfL5Y7d+5g1apVGDZsGKysrNCqVStkZ2cjJycHbm5uyMzMxPTp07Fs2TI4OxcUTLNnz8ayZcswefJkPHz4EGvXrq3Q+yIUCsXGWcrLy5f45ZWTl4vYhDikpachPiEeQqEQiclJyM3NRUJSAlLTUpCWniZ2+L8iiUQiyMl9Ga8lJydX6pdsakYKlu6ei5HdJqCytvgH68U7/6CNS2eJZPySTQASEfc3CUWQk5e+udpCkZAbe4vCgqCkx5RAiEuIQ0JSPDKzMpGanspdlpGZAZ/b19DJo4tEswogABEV5in4+1v9dk3C7ZCHUJRXwN67x0GgEq5J+ohKeA8KhUKxNk1bNMXCPxdg7cbVOHB0Hzav34KcnBxMnTANamqq2PjXJsREx+DEkROI+hxVwq1UQE4SQU5QvvdgWFQI9l/ahsm9Z3M/FkUiEVLSk5CelYasnExk52ZJJCeRSOx1LScoPWdefi5W/70ELRu0hb2FI0QkAhHhyOW9iIj+gEOX92DDkeUSyQnuMf2SVSCQg4hKzvo05AGehtxHn+bDf3jff0tOXg701WMoKuEzrbQ25dmXT0JR+V/X2bnZWLprHrq26AlrM1uxyy74nUY7N8l+t/wMkUiEorsnEAhABO6z9GtCoRB/Tt0A58Y14OZRMESxZSdXTFowHPM2TMGMleNwdPtZieUkoQiCrz4HBfLyEAnFnwe/vzZDRUMDAcdO4/OLQES9fIXIgBcSy1RyTqHY2HE5OTmQsOTXS3piCtLik5CbnYP0xBSEv3iLVzcfIPzlW9w+fA652TnwO3hGslm/ekxLe23nZOUgJS4ROZlZSIqJ/+52SeBtAmzRBFANDY0fmhSwadMmWFhYoF27dvDx8cHt27dBRFBXV8eWLVuKtU9MTMSePXsQHR2NxMTEYl/y/5aFmQVevQ0EOvcGCsfBD+sr/qv3U9Qn2FraYt5UbwBAu35t8Prdaxw5fQhd2/+GIb2HAgBGTB2G2w/80Lp5mwrNCACGesb4HPcJuXk5UFJURmjEO7EJa0Wi4z9jzd+LMaLbeNhVdRC77E3YSygpKMHSRDJHD4okfEqGcTVDRL2LhbK6EjQNNJASnVqOPf+/qppa4Mg/h7m/XwUFwrGak1ibmLgY6OnocUNq5q6YjSs3L2NA90EAgNOXTqJVE3eoqRafCFmRPiVFwbGKLV5GBsGxih3CE4v3LmXkZGLfvZMAgKnuI/Dy01uJZqoollaWePn8JapaVEVMTAwyMzOhb6BfantVNVXk5+cXDIvzaIH4wl78vLw8JCYmIi9PMuNaDXWr4GXoM+7vD1GhqF+9+HyQFyFPcNb/OCb1mg1N9YIJ1Qmp8dDR0EW/1gWfLRtPLsfz4MdoXKPiJwqqKKtCSVEJ0fGfYVS5CkI/vYNxCZ8V6ZlpWLlvAdq6doZLzYJeM3k5eejrGGFgx5GoamyF1PRkjFkqueE2+lpG+BgXhvp2riAiRMR9QMeGPYu1u/H8EkI+B2FUu6lQkFf4oX0rQtT7aJhVM4GikgLycvNhW8cakcGfy9WmPPvySVtDBzm52UhMSYCulh5CSvluSUlLwtJd89Ddox/qO4oP2YtJiMLH6A+oW73h/zF5+ZhYGOFd4Hs41qmGkFcfYGxmUKxmycrIxrwxq9CkbQN07te6xOtRUVWGML9i64+vaZuaIDboq9EKQcGo5t5CrE1lW2ukRcUgMyERuZlZgEhU8N//I20jA6TEJiArPQOqldTx6XUI9KuKT27Nzc5BblY23If3AgA8On8dtw+fRYvB3WFTv0ZhgZ8LEgmRlpAssaz65lUQ8SYEtvWckJeTi5gPkahsIj4MLyEyBrb1nGBbzwlEhMkNuqPT+AHITEkvcfvXQ3QqEi/FvFAoxKBBg7B69Wrs2LED+/fvx6BBg8q1r4GBgdj/p6SkQCgUokqV4rOh3759iyFDhmD58uWoWrUqHj9+jMuXL1fofenesQfa9W0DeXl5REZHQltTGzWq18Dn6M94+OwBurbrhhv+13Hz7g3UcqyNDx/DkJqWCmsLa9R2csbabauRlJyItPQ0PAp4CO9pCyo0XxFNdS3Ud2yM+Vunw97SCdfunYf37ysAAD73L6K+U2OIRCJ4rR2Deo4uCAx9jsDQ56hh4wxb84KhARdun0ZbV8n3nDw88RS9l3bhVrR5fukV8nLyYVzNEGpaKgh9GA4tQw04eVSHvoUeqIkIlfTU8eD401K3S0KD2g2Rn5+HSfMmQFdbFzfv3cDM8bMhFAqx+8hOjOw/Gp9jIjFhzli41ndDaloqLt+8hAG/fSlu/j51AGvnS3ZiDADsunMUm/stxtnn19C5VmtsurEPANDcrhFi0xLwOiqYW7XGSt8c7Z1aovf2sQCAmqbV0diqDnTUtNC3fme8inqHs899JJ65vIaPHo5pk7zw8nkgfK75YtjIoRAIBHh4/yGEQhFcXBvh2JHjiI2JRVpqGo4eOoaRY0ZATU0NC//88n57cO8hRo0dBfOq5hLJWdu2Hg5f241tZ9ZCUV4RYZ+DMaabJ3LysnHr2TW0btAJge+f4a9jS9DOpRtuFz7GbjVb4nNcBLacWgVHy5pIzUjB67AX6NWyfJ+ZP6Njk+74c/ccNKzhhpuPrmBol4LXwoOX/jAxNIeJvhlmrB8PIz1jRCd8xukbR2BhbA1n+/ro0rwnthxfgzrVG+JV6AvUtK0jsZwu1Zth2dFZICLEJkejsqYBquiaIj41Fh+iQ1DPrjF8Ay7g8uN/4O7cEdefXwIAeDh3LHVfSUiOS8Hjq8+w7OICvHkQhHbDPTC99TwAQPthHvD/536pbcraVxrIycmhfZNumL/FC/UcXeBz/xIm9PMCANx+eh025tVQWUsfU1aNhpWJDT5GheFjVBhszO1Ry67gtXHh9mm0dukg8dWEfkavEZ0xb8wqtO7WDD5nbqPfmILhEvdvPIVuZW3Y1bDCxN7zoKAoj9SkdPy96RTMrKqgWbtGOLnnIrIys5GSmIpzh30wdUnxuTwVxa51CzzcdxA3V29AVkoqRPn5qFK7BjISExF+7yEcOrRF7Z5fhno83PM3REIhLBs3hEgoxLMjJxH96jUyk5Lx5OAxOHRsA1Wt4quz/VvKaiqo274Fto6eA9sGtXD/1BUMW1vQ2fXonC9sG9SCsroqto2ZC5v6NSGvII/HF26g3ZgBMHeyg7lTwcpq6UkpeH7NHx0mDq7wjEWa9m6Pjb/PQ0p8Et4HvEGtlo2grq2ByHdhSIyKQ41mDXDz0DkkxyXA2MocYS/ewtjKHKqV1HFxy+ESt0sKL8X8smXLoK+vj1GjRsHV1RXu7u7w8PCAsfH3Z+ifOHECgwYNQnR0NO7cuYOVK1dCIBBgzJgxCA4Ohq3tl0N3oaGhsLOzw+DBg0FE2L9/f4XfF0N9I5zecwYXfS/AwswSPToV9Ozk5eUiKaVgWcSBPQfBqqo1Hjy9h7q16mGepzfUVNXQs3MvGOob4smLx6isWxmndp+BWRXJjYWc1HcGbjy+hoTkWCwYswpWpgWPVUpGMvKF+ZATCNC8fkGvQlJqIlB4WBSFYwEN9YzhVrulxPIV+fDsE/6eegq2LpZ4ePIZXlwuWPVASVURyurKAAAFJQVo6Kkj7EnBZJ2iCa+lbZcEOTk5HNx0BKcvn0J2Tg5O7vgHWppayM/PR1xCwdJxzk51sHjGUly5eQlVjKrg5I5/YG5SUCwmJCWgs0cXVLd1+M4t/Xv77p1ERFIUaps6YO6ZVbj57j4AQFO1ErfUpIZKJehr6CI49gPa/DUQkckFYwPVlFRgoKGH088uQ1FeAdqqFf8B/2+0adcaWlqauH3LHxMmj0fX7gVDljIyMpBf2BOWlJCE2JhYaGhoYMPW9WjSzK3Y9fTo1R2GElyWUkFeEd7DVuHOi+sgIvw2oh+UFJWRlZOJlPSC3iUlBWW0qtceuXk5yM3LAQrnM9SwdkYlNQ08C3oIQ11jLBq5DnpapR99+Ld6egyAqWFVhH8OxYS+M7m5M+lZacjNywERoV5hL2py4WdFpk5Bj1Xbxp1hqGuMtx8C0bROSzStI7n5CJU1DeDVYyGehT6EQVUjNKreDACQL8xHRk7B61pbXRcN7ZsiLevL0T0ClbqvpPw5aA1aD2yJyiZ6mN56HkJfFCz9qqWvBXlF+TLblLZdWgzpMhpWpjb4FPMRM4Z6w9GmFlB49CYvPw8EQuNaBUdvir5bsrK/rHKkoaaJVg3b8ZS+bN0GtYWRqT5ePQnC5EUj4OpeMIQmIy0TqmoqAIC6bjWQk5WLhNiC73xtvYIlppMSUpCRlgktHQ2sOTgPDs52EsupqqWF3js3ItjnJrTNTdFy+iQIBAKI8oXITil+ZNvIyeHL8C0CMhMSoWlkBE0jI2QmJEIkwaMIPeaMw5MLN5DwKRojN85H1RoFcw4yklMhzM+HaiV1jNq0EI/O+SAvJxeDls+AlbP4vEolFWW49ZHsZGmLGtUwcccSvLz1EC5d3dGoc8HCH7nZuchKzwAAdPcagadX/fHp7XvUad0Eddu4lbldUgRU0uAvCQoICEDHjh3x5MkTGBoWrLf6559/4v79+zh7tuzxZAKBAF27dsX79+8RHh6OCRMmYNGigpVgNm3ahFmzZsHW1hYqKiqYN28eXFxcUL9+fRARcnNzYWdnB6FQCB+f7/cqRr2JRV6W5JaR+rdUdVShb6kLv8NPkRqXwXecMlWxq4w6batj04C9iAqK4TtOqWq0qY7eizvj04so5GZK73MPAOp6qjC01UebvwbiZWQQ33FKVcOkGq5MOvBT+yZnJYmtPy1tlOSVoaGiiYvb/JEUJX3DwL5WtYYx3Lo749bBJ0iJk77zBBQxsdNH3fYOOLniKuI/Se7w+b9lU9cMrQa7YFSdSQh+VnxZPWli62yN7U9//OjfnWMBSI2X7u8WzcrqcO1V+6f2DUy6jcx86X3f6iobw0azDla9eIhPmdL7nq2jZ4BBtk448v4e4rLT+I5TJjtNI7QxrYmTYXeRkCO9WfWUNdDdsvgQzLL833vmNTQ04OfnxxXyADBjxgzcv38fubm5UCrlJAdFTp8+jZCQEKioqMDU9Msh0XHjxmHgwIEICgpCTk4OqlWrBg0NDTx//hxv3ryBqakpFBUVEREh2eXnGIZhGIZhGOb/5f9ezFtbF588KS8vD1dX1xLbl8TGpuR12DU1NVG/fn2xbcrKyqhd+8svdy0JjAFjGIZhGIZhGD7wtppNSeLi4tCtW/G1ODt16oQZM2bg9u3bvORiGIZhGIZhGGkkVcW8lpYWli1bVmy7kVHBxCo3N8lOIGAYhmEYhmEYWSJVxbySkhIr2BmGYRiGYRimnKRvUVeGYRiGYRiGYcqFFfMMwzAMwzAMI6NYMc8wDMMwDMMwMooV8wzDMAzDMAwjo1gxzzAMwzAMwzAyihXzDMMwDMMwDCOjWDHPMAzDMAzDMDKKFfMMwzAMwzAMI6NYMc8wDMMwDMMwMooV8wzDMAzDMAwjo1gxzzAMwzAMwzAySoHvANJKRVMZCirS+/AoqysCAAyq6qCSjirfccqkY6wJALBrbIXKFrp8xylV1ZqmAAA1bRUoqkrvcw8AKpWUAQAtqrnARr8q33FKZaZb5af3VZRThLxAvkLzVCQFuYLXSBUbfWhWVuc7Tpn0zXQK/muhK9WfFzpVtAAAZg5G0DbU4DtOqQwtKwMAGrarC3N7U77jlMnY0vCn9tM314G6tvS+VgBATVPlp/fVUtSHirz0vm8rKRS8Z6tr68FAVY3vOKWyrFTwnjVXrwwdJel9PAHASFUbAGBWSR/aUpxVQ+nHn28BEZFE0sgwIoJAIOA7xneRiCCQk/6cACASiiAnL/0HgmTluQcAoUgIeTnpLXaL/ExOWXm9yEpOABAKRZCXgawikQhyctKfUygUQl5e+t9/+ImssvIc4CezysrnvIgIcjKQU1YeT8hQ1h/NyYr5UmSlZ0MkFPEdo1TyivJQUVPGs6tBSE/K5DtOmfTNdWDvYoFNv+9EZHA033FKVaulI3rP7oastCyI8qX3uQcAeSV5qKirYNLx+QiJ/cB3nFLZGFjgr57zf2pfWXm9HJ97DrFhCXzHKZNdYyt4jG2KJf1XIfxNBN9xStWwXV0MXzII59ddR8KnZL7jlMrS2RRN+zeQ+scTAKpWN8Psg9N+eL9LW/yQ+DlFIpkqim4VLbQb0/Sn9s0VvoOIsio8U0WRF2hDUb4qdga9RnSm9H7HO+roopuFFZ4lPEd6Xjrfccqkr6IPe207vEt5hsx86c2qplAJdlrOP7SPdI8l4JFIKJLqYl6usEc+PSkTqXEZfMcpk3rhYf3I4Gh8ePGR7zilqmJrBAAQ5Uv3cw8AcsKCnqiQ2A94FfWO7zgSISuvl9iwBEQFxfAdp0xFw9vC30Qg+Fko33FKVTRkJeFTslT/QNI1KThcL+2P57+R+DkFceGJfMeQGBFlgSC9350iKvjejM7MxMcM6S08jQqHAKXnpSM1L5XvOGVSVygYWpOZn46MfOnO+qNk4zgawzAMwzAMwzDFsGKeYRiGYRiGYWQUK+YZhmEYhmEYRkaxYp5hGIZhGIZhZBQr5hmGYRiGYRhGRrFinmEYhmEYhmFkFCvmGYZhGIZhGEZGsWKeYRiGYRiGYWQUK+YZhmEYhmEYRkaxYp5hGIZhGIZhZBQr5hmGYRiGYRhGRrFinmEYhmEYhmFkFCvmGYZhGIZhGEZGKfAd4GclJCRAT0/v/7bf94hEIsjJff+3UVZWFhQUFKCoqIi8vDzk5OSIXa6mplau6/k3vpeViAAAAoGA25aVkynWRkFOAYqKShJMWXD7RVnK20ZeQR4Kyl9e1vk5+RDmCyWaE+V8/nNyckBEUFFRgVAoRFZWltjlysrKUFRUlGjO8jym8nLyEIqKP2ZyAjmISCTBdP9ORbxe8rLzIBLyex+VVAteAyKhCPm54s+DQE4AEpV9HyvSzzymP7JvhREAKOWmBHICKCjJAwCEeUKIhMUbyivKQ05OgLycfEknlZ3H9GeU8TzIK8pxn5FfP84KSgr46qsGIpEIwjzp+5wpz2d8WW0yMrKgoCAPZWXJfm+SSARBOWqI/JwcCOTkIP/Vd45IKIScvLxE833tZ2qRIsJ8IeQV/j9Zy/PcC4VCyJfy2GVnZkMgEEBZVVlCCX+gZz4vLw9JSUli24RCIRISEiSR67tMTEyQnZ39w/sZGhpWaA4iwoxZM2BmaQpreyucPH2y1LaPHj+CnYMttmzbDADYtGUjbB1suH8mFlXwKfJTheb72svgZxju3Qs9p7XGij3zkZsn/kNCJBLhxLWD6D+rE3p4emDNgSXIF+YjNSMFA2d15f71md4ehy7tkVhOZXVlTNk7BnvCN2DNg0Wwql213G1aDW6K7UFrsOXVSmx5tRLuQ5tJLCcAHDh4AJb2ljC3McfCJQtLbZeQkICGTRpi5JiRAIC79+/C1tGW+2dqbYpzF85JLKeJthFOj96Ot943cHLUNhhp6hdr08jSGbemHsebeddxc8ox1K9aCwDQ0akV7k8/g1dzfXBgyDroqmlLLOfPUNdWw4yjE7Hn40Ys9/OGqX2VcrfpNLGN2OvFtUdDiWZ16VsPs65NxGzfSWg6uPhtKasrYcbl8fjj6gT0WNiJ265SSRm/zWuPOTcmY9a1iWjct55Ec5rZmWD7079wOesUVvsugVZlzXK3Kc++FUUgJ4D7iMaYdGAwxuzsD5sGxT8rqlQzxNidAzB+7yA06Fpb7DI7F0uM3NwbE/YNwtB1PSSWEwBqN6+Bw2G7cDH9OGYd8ISiUvG+tNLalGdfPlU208HAP7tg/I4B6OrZCspqxQvWNqObYOSGXhi3oz/kFb+UHsPXdsfIDb0wckMvjN7cB62GNv4/py/bp4gYtGgyGroaLdHMdSQiPkaXu01ubh6mTVmHKvptYWHSCXP+2CzRrA+37sD+Dp1xoGNXvLt4udR2CSGhOPRbLwQcOAgAyM/Ohv+qtdjfrhP2d+yCV6dOSzRn0LO3GNl0BHpU+w1LRi1GdqZ4LUdEOLPrHwys2x/d7bph+dilyM3OBQD4HvdBf+e+6G7fDZM7TETUhyiJ5czJyoH37yvgYd0TPesPx/P7gcXaBL96jzGdvNDKojv6uf2O5w9ecZed2nMBXWsPQgfH/pjUc7bEcuJHivns7GzUqVMHt27d4rZ5eXlh+vTpksomEy5fvYw7d/0R+PwVzpw8C6+Z05CUnFSsXUpqCtatX4t+fftz2yZPnIKoj9GI+hiNs6fOoalbU5ibmUskp1AkxNq//8SYXlNxePkFZOVm4cpd8eIxISUOGVnp2DrnIPYsOokPn0NxN+AWNNW1cGL1Ve6fhYk13Bu1k0hOAGg3uhXy8/IxwmYyDi86jdHrh/xQm9NrLmCE9WQMs5iIKzuuSyxndHQ0vBd748q5K3j+6DnOXjiLu/fuFmtHRJg5dyZGDhvJbWvi2gRR4VGICo9C8KtgmJmaoX3b9hLLOrvtBNx4dw+OC1vBP+QhZrYZW6yNl8fvWO2zHXbzm2Hjzb2Y5jEacgI5/Nl1Bobtn4bqC1vi2pvbmNpqhMRy/owuk9sjITIJw60m4uJWHwxfPeCH2hz0PsG9Xm4fvSexnHpmOmgysAE2D9qL9b13of5vzjC0Ef9RlZORi0XN1uLIH2fEtlvUMUPYk49Y6rEeu34/hFa/N4G6rprEsk7aNAZX9vqifaUeCH0ehiHz+5W7TXn2rSi2DSxgaK2PLaMO4eSSy2g92g2KKuJHtyLfROOvAXtx7/gzse16ptpoN64prm71x7p+e7B9zBGJ5ZSTl8PMfVOwauQGdNHrBw1dDbQb5lGuNuXZl2+thrrg6ZXX2Dz6ENISM1G/o1OxNhc33sLmUYeQk5Ertn3buKPYPOoQNo86hI+Bn/H6dsj/Mfn3zZq5CS1a1UNcsg/atm+MWTM3lbvNmlUHcffOCzwKOIC4ZB+sXDNZYjk/PwtAuL8/eh36G502r8fDrduRmZBYrF1+djae7tkH+04duG3x74Jh4FgdA8//g86bN+DRtp3IzciQSE4iwjrPtRg8YwiOBh6HgoI8zu4W/7xLT0lH7KdYbLq2BQeeHERcVDxu/nMDALBt/lYsOrgEp4PPoGbjWji945REcgLAmQOXkZWehYtvDmPairFYMmkdhELxo6V3rj7EOO9huB5+Cr8N6YAN83YCAB7fDsDulQexdM8c+IadxNbzqySWEz9SzGtoaGDHjh0YPnw4MjMzcePGDZw5cwbr1q377r7x8fEAgPz8fIhEJR8+K6uXvbw98N8OWygiEomQny+Zw6d+t2+hT+++0NHWgZOjE+rXq49Hjx8VazfXew4Wzl8EFeWSD7Ps3L0DQ4cMk0hGAIiOj4SigiLqOTaCkqIy2rt1RUDQE7E2+jqGGNx5NDTVtaCprgUdDV1oa+qKtXkTFgh1lUowMZDMjw4AcHSzh+9eP+Tn5uPR+adQ1VCFtqFWudrk5+aj3Wh37AnfgEVXZ8GkmrHEct69fxdNXZvCztYOenp66NOzD2743SjWbtO2TejWuRuqmhfvNQSAI8ePoGP7jlBRUZFY1sZWdbH33gnki4TYfe8YXK3rF2sTkfQZeuo60FbVhI6aFsITPkFEIohEIuhr6EJbVRPaappws2kgsZw/w7GJPa7tuQlhnhB+h+/CtFqVYgVdaW3yc4XoPr0T9oRvgPf56TCoWlliOS3rmSPodiiSo1KRFp+OQJ+3sK5f8mviW2/9QvDsQiDyc4WI/5iE7LQc5H5TFFUUgUAAx8b2+GfzBQjzhTi94RzquNcuV5vy7FuRzGtUwaub75CbmYfYsATEfUiEkU35nkN7VysE3Q1D+IvIguEhEmRmZ4KstCw88QlAbnYuzm29iLrfPC6ltSnPvnxSUJKHrokWXvkFQyQU4bnPW5g5Fj869j0aeurQNtTApzfFe775dOvGE4wZ1wOKigoYN6EXbl5/Uu42x49ew7wFI2BkpFvCNVesz0+ewtq9FVR1tKFtbo4qdZ0R/eJFsXYPtmxH3RFDoaCqym0zqlkDNq09IMzPR05KKpS1NMWG31SkxJhEZKRlwq2DGxSVFdFhcEcE+AeItdHQ1sBI71HQ0tOCuqY69Az1oF254IiwgakhkuKSkJqYiozUDBhbSO47/on/c3Qe1BbKKkpo2KIulJQU8Tlc/PU5ZEofONWrDjk5OVhUM4OufkHOa6duoevg9qhWy0Zi+b72Q4Oz3d3d4e7ujvHjx2PYsGHYs2cPNDQ0vrufvr4+pk+fDgMDA+jo6GDv3r3cZYcOHeK216xZE8+fPy/XZV+7fPkyrK2tYWhoCCMjIxw58qWHZffu3dDW1oaBgQFmzpz5I3e3XBITE6Gn+2UMvp6uHhITxX8N792/Bw3rN4ShgSHy8vOQm5uLvLw87vKExAT43/FHx/YdKzxfkdSMVGiqfymINdW1kJaRUmr7MzeOQV/XCDVtncW2X/A7hbaunSWWEwAq6agjLTGd+zstMR2VdNTL1eb6gdsYXW0qhladgAdnHmPkmkESy5mYlAhd3S8f0np6xZ/7Z8+fITIyEk3dmiI7JxtCkbDYj9Nde3dh6KChEsspEAhQSVkNqdlpAICUrDRoqlQq1m7b7YMY22wQHs44hxGufbDd/xAAYMqJhZjZZhyuTTwIBTkF6KhJbtjEz6iko460hILXAhEhIykDGiW9Xkpoc37jFYyuNhXDLCfi9Z0gDFnWV2I51bRUkZH8Ze5JRnIm1LRUy9znW3Lycug+vwOubrwpsfHd6lpqyMnK5eaapMSnQktPo1xtyrNvRVLVUEZm6pf3U2ZaNlQrle9HcSVddcgpyGHsrv6YfHAo2oxtKrGiXlNPEynxqdzfKfGp0NTTLFeb8uzLJ5VKygW97YVj5bPSsqFa6cfHBtdsVQ2BN4MrPuC/lJiYhsqFhaSmpjrS07OKdUqW1uZzZDyuXr4PfW13WJh0xNHDVyWWMyc1FSpaX77jVbS0kJ2SKtYm1Oc6tKuaQcPQCKK8PIjy8iAsrEViAl/hYJfuODdhEmr17QN5JcmM7U9LToWm7pfXr6aOFtKS00ptf+XwZSipKKGBe8GwxIGeA/HnqMUY3GAgggKC4N5TckepUpPSoP1VVi09TaQklZw1+lMsdq04iImLCo7Ax0UnID4mEe2r94WHVQ9sXbK3xP0qyg/PtFy5ciX++ecftGnTBk2bNi33furq6khISMDNmzcxdepUfP78GRERERg7diwuXLiAzMxMDBo0CIMGFRRfZV32tZiYGEyZMgXHjx/H+/fvcebMGUyaNAnp6emIiIjAtGnTcPv2bSQkJEBJAi9OfX19xMbGcH9Hx8RAX1/80Pm+A/sxbeY02DrYYNuObVi2chk2bdnIXb7/wD70+K2HRPIV0a6kg6S0L4VmUmoCtCrplNj2wPkdiE6IwrjenmLbU9KSEBjyHC61yv+8/4zU+DRoGXx5A2kbaCI1Pu2H2oiEIvjsvSXRnnn9yvqIjY3l/o6OiS723B88fBB79++FraMtxk4ci0tXLmHU2FHc5f53/KGjrQM7WzuJ5SQipGSncWPdK6vrIDmz+A+5Hf2XY/TBmbCb3wwTj3lja98/AQA33t1Fu42DUH95J7yKeofIZOnqOUuNT4N24WtBTl4OlfQqITUh/YfaCPOE8N3nBxM7yb1eMhIzoaH35UeURuVKSE/KLHOfrympKqLfym54czMYzy+/llBKID05A8qqSlBSKfg80jPWRVJsSrnalGffipSZkg117S/DjSrpqCEzteQjtMX3zYKqhgq2jzmCzcP/hr65DqzqSOaIY3JsMnSMvnze6hnrIjk2uVxtyrMvn7LSsqFSSRkCuYJfQuraqmI/sMpDXkEO9o2t8No/VEIpf15lfW1ERxfMDUxISIGmZvFFKkpro2+gA1s7cySm3cCZC2swafyqYsM0KoqKjg6yvupMykxIhKqO+Pymt+fO4+HW7fi76294fugIXhw5hoD9fwMAjGvVxNBrl9D3+BG8PHYCie/DJJJTS08byfFfXr+JsYnQ1tMqse2xjUcR9CwIU9ZMBQCkJKRg06yN2OyzFf+EnkW7/u2wceYGieQEAO3KWkiI/TJsOiEmETqVi88ZC33zAQvGrMSsvybD1LLgqJSOnhaE+UKcDzyIYw92wvef2wgOfC+xrD9czPv4+EBNTQ0+Pj7I+IExVVOmTIFAIICzszNcXV3x8OFDPHjwAC4uLqhfvz4EAgGmTJmCoKAgJCcnl3nZ1+7evYuwsDC0bt0a9vb26NChA4RCIT5+/IgHDx6gWbNmqFWrFgQCATw9PUvN97PcW3pg34F9CA5+h+s3ruP5iwA0rN9QbLWSG9ducGPjx48Zj3mz52HyxClA4RCgPfv3YvCg4uPCK5JR5SqQl5OHz/2LiEuKwT83jqGeYyMAQHZudsFQJGE+Vu9fjNy8XAzqNBLZuVnIF37p/bty7zya1XOHooJkV1x5fv0VOoz1gK6xNtyHNkNSdDJS49MKVh4pnPhVWhtFZQUoqytDy0ATv03riLCAcInldGvsBv97/vC/44+3QW9x8PBBtHZvDQBITy8oFFctW8WNjd+9bTc6tuuI/bv3c9exc89ODB0suV75IreCH2BC8yEw0KiM8c2H4Ma7grHhSvKKkJcrmIGvrKgMfQ09aKlqQL+SHlSUVLg2akqqcDS2g5f7aBx+fFbieX/Ei+uB6DShLXSMtNFhXGu8f/oB+bn5kFeUh7yifJltFFUUoayuDG1DLXSd3A7vn0vu9RLyIAzVmljD1NEYRnYGqOFhj+B7BR/uXw8LUlRRhIKSAuTkBFBSVYRAToBKeuoYuqkPAi6+QpB/SMF2CQ4Neer7Av1n9YKesS76/dETDy4+LsimrAg5ebky25S2XRLCnkWgVmt7aBtpwrKOGXRNtBEVHCe2gg0AKKooQF5RDvKKclBUKfgMCX38EUoqilDVVIGalioUlBWQL6GjHZ+CP0NeXg4eA1rAwEwf3Sd3wf3Cx0VZVRkCgaDUNmXtKw2EeSLEvI9HvfaOUNdRQ70ONfDhRcFCDvKKctxKJHLyclAsXDlKUVkR8gpfyg/bBhaIfBuD7PScUm6FPx6tG2LF0n34/DkOy5bsQZu2LgCAnJxc5OXll9mmXfvGSExMRUJCCmJiEqGoqCCx1erMGtTHu8tXkBgWhqiA54gKeA5j59ogkQj5havmdfhrDYZevYShVy/BefBA1B7YH3WHD8Xr0//gg58/spNTkBmfAGFujsSOUuno60DPUBfn951DfFQ8Tm8/iXotCoZ95mTnFKxmJBRi4x8bEPc5DiO9RyEnKwd5uXkQiUTIyc5BcnwSUpNSkZqYipxsyb1mGraoi+PbzyL6UywuHfWFgoICqpgbIj9fiNycgiMaT/yfY/GENZi5ZiL0DHW5ybyNPRogMyMLyYmpSIxPRl5eHpRVJNdh+0OvqtjYWIwfPx7nzp1Dy5Ytf2jYytdDC7Kzs6GkpARFRUWxpRnz8gqeLEVFxTIv+5qCggLq1q2L+Ph4sX8ODg7FruNnVr/5nubNmqNfn374rddvmD1vFrZv2YFKlSrhwcP76Nmn+OoISsrKYks63rh1A9Xs7GBlaVXh2b4mEAgwfch8XLpzBtPWjIGliQ3cGxZMYp2xbhw+RochKu4T7j33wyX/M9zKNRdv/wMU9u7efHwNbRp3+s4t/XtXd91AfEQiFl75A67dG2Lz2N0AgBYD3dB33m9ltpm0azS2vFqJpTfmwsjKENsn75NYTj09PWxYswFTZ0xFr/69MGbUGNSpXQc5OTmwdbQt1l5eQR4qql+GAMTHx+Pp86fo3EGyw5YAYMmlDTDVMcb5sbtRRcsQy69uAQB4d5iM7s4Fr4NpJxdhQvMhuDH5KEY16Qevk4sBADPbjMXDGWexvtcCnAy4hAMPJDfh6Gec3XAFuVm5WHxtFmo2d8COqQU/ljqOa42uU9qX2WbmsUnY8mollvjMhrqOOvbOPCyxnCkxabi07jq6L+iAPku7wHerPxI+JkFdRw2TT3yZHD3x6HD85t0e1g0tMOPyeFjVM4d9UxsYWFdGt7ntMOPyeMy4PB5GtgYSy7pu7GZUb2CHbU/WQUlFCfsXFjwuM/ZMhluXRmW2KW27JLx/GoF398LQy7s9mvarj/PrrkOYJ4S5kzG6eBUcflfXUcPYnQNQr2MN1OtYA2N3DoCapgo+v4vFu/th6D2/A3p5t0fQ3ff4GPhZIjmJCAv7rEC38R2x8e5KBD0OxrUDBfNrNt1bBbNqJqW2KWtfaXFt112YO1VB3/kdkJ+Xj8cXClb06DC+OcydCo52Ne5eGyM39IJAXoBha7qjxaAvqzk5NLHGi+tBvOUvy5JlYxEZGQfXhsMRFvYZS5aPAwDM9NqA/XsvlNlm9rzhCA2OQL3aA+E9Zxv2H1pY4jKLFcHA0QFOPbrj6sw58F+9Fk2me0JFUxMJIaE4P6H4xFt5BQXIKRT8uLJq1RJhN2/hxKCh8Fu2EnWGDIaupaVEcgLA1LXT4HfmFqZ2ngJ9EwO0H1gwGXfegDkIehaEhOgE3Dx9AzdOXcfAuv0xsG5/nN5+Cjr6Ohj6xzCsmbIav7cYhZf3X2D4HMktyNCxrwdsHC0xppMXTu+7CO8tXhAIBLh28ibW/FHwHXpi1zl8ev8ZI9pMQZcaAzGoxXgAQItOrrCwM8Po9p6YPWwJBk/uA3MbU4llFdAPLFrbpUsXNGzYELNmzUJaWhpq1qyJffv2fXe4jUAgwNChQzF37lw8ePAA48ePR3BwMEQiEezs7LBu3To0adIEK1euxJs3b3D9+nUkJCSUehkAqKioIDk5GTk5OahevTomTZqEvn37Qk1NDQKBAHp6etx1bN68GQ0aNMD8+fNx8ODBck2GzUjJ5H296bIoKMpDVUMVt48+Q2qcZGadVxRju8qo09oes1otxocXH/mOU6rG3Rtg/NYRyEjKkOrnHoVrM6tqqqLDpiF4FfWO7zilcjS2w4VxPzdWUFZeL5sG7EVUUEw59uBPjTbV0XtxZ4yqMwnBz6RvKEORVn2bYc4hL+ybdgqxYfwse1we9m7W6DSlpdQ/ngBg62yN7U//+uH9Ds49h7jw4quhSBP9qrrov+jnOpiy85+DIL3fnXKoDGUFOyx+9hgfM9LLsQc/GlQ2wAh7B9yOvoPUvNRy7MEfY1Vj1KlcGwEJt5GRL71Z1RU0UVuvyQ/tU+6e+ZMnTyI9PR0zZswACle32b59O6ZPn47c3O+vquDq6opOnTph1apVOH78OHR0dKCnp4czZ85gx44daN68OeLi4nDgwAGgsNeztMsAoHLlyhAIBNDS0oKPjw/u37+PJk2awN7eHnXr1uWu49ixY1i5ciU6d+6M5s2bo3Jlya1WwTAMwzAMwzD/T+U+80T37t3RvXt3sW0eHh7w8CjfTOLhw4dj+PDhxba7ubnBz8+vxH3KuuzTpy8nV3JwcMDp0yWf5KBVq1Z4/PjL+MKhQyU/PplhGIZhGIZh/h8q5DRyIpGo2JJ8KBwKU6lSJejp6ZW4H8MwDMMwDMMwP69CivmYmBjUqFGj2PaBAwdi7dq13EmjGIZhGIZhGIapOBVSzBsbG7OCnWEYhmEYhmH+zySz4CnDMAzDMAzDMBLHinmGYRiGYRiGkVGsmGcYhmEYhmEYGcWKeYZhGIZhGIaRUayYZxiGYRiGYRgZxYp5hmEYhmEYhpFRrJhnGIZhGIZhGBnFinmGYRiGYRiGkVGsmGcYhmEYhmEYGcWKeYZhGIZhGIaRUayYZxiGYRiGYRgZxYp5hmEYhmEYhpFRCnwHkFZy8tL9O0dQmK+SjhrfUb5LTUMFAGBia8R3lDLpm+kBAOQUpPu5BwCBvAAAYGNgwXeUMv2bfLLyejGw1OM7ynfpVtEGAFStbsZ3lDIZWxoCAPRMtfmOUiYtg0qADDye+BcZdatoVXiWivZvMsoJVCGiCo1ToeQEygAAIzXp/o7XUyn4fq+kWInvKN+lpqBW+F/pzvoz+QREJMUvZ34QEQQCAd8xvotEBIGc9OcEAJFQJPU/kCBDzz0ACEVCyMvJ8x3ju34mp6y8XmQlJwAIhULIy0v/60UkEkFOTvofU1l5PAFAKBRB/gdep7LyHOAns8rK57yICHIykFNWHk/IUNYfzcmKeYZhGIZhGIaRUbLx05thGIZhGIZhmGJYMc8wDMMwDMMwMooV87+g/Px8viOUi6zkjI2N5TtCucnKY8pIh1u3bmH06NF8x2AY5j9Glr43ZQEr5n8xAQEBcHFxQVJSEt9Rvsvd3R3nzp3jO0aZwsLC4OLigtevX/Md5bsyMzNRu3ZtvHjxgu8oFS4lJQXLly/HuHHjkJmZyXecMt27dw9+fn58x/iuW7duoVevXujXrx/fURiG+Q/Jz8+Hh4cHTp48yXeUcgkMDMSRI0eQlZXFd5RSsWK+gh0/flyqez9r166NDh06wN3dXeoL+g0bNmDMmDFSXdBbWlpi9+7d6NSpk9QX9Gpqali+fDnatWv3nyrok5KSUK9ePcTHx2PixIlQVlbmO1KZiAh9+vSR6oL+1q1b6NChA5YtW4ZmzZrxHafc9uzZw3cEhmG+Q0FBARcuXMCcOXOkvqBftWoV3N3dsXHjRjRt2hR5eXl8RyoZMRVm0aJFVKNGDUpISOA7SjF79uwhf39/7m9vb2+qU6cOJSYm8prre168eEEmJiZ09uxZvqOU6ebNm2RlZUWvXr3iO0oxcXFxtGHDBtq/fz8REZ0/f56qVKlCz58/5ztahVi4cCH179+f+/vu3bvk5eVFly5d4jVXSYo+G+7cuUPGxsZ069YtviMVc/PmTTIwMCAvLy8yMzOjd+/e8R2p3Jo3b04BAQF8xyhTYGAgubi4kJub23/mPVgkMjKS2rZtS87OzuTj48N3nAqVkpJCvXv3JkdHRzp06BDfccq0a9cuqlatGg0fPpzS0tL4jlOqiIgIsre3pxMnTvAdpUSLFi0iBwcHioqKIiIic3NzevLkCd+xSsSK+Z+Qn59Pc+bMoczMTG5bUTEXGxtLt27dovHjx9ObN294zVkkJyeHdHR0yNjYWKoL+piYGOrQoQOpqKiQp6cnt50V9D/v7du3VKVKFZo7dy7dv3+f2/5fKujnz59Pv/32G0VHR9OkSZNozpw5dPbsWdLW1qagoCC+4xER0ePHj6l27drk5uZGjx49IpLSgj48PJzq1atHN2/eJCKivXv3ylRB//fff9OUKVP4jlGmBg0a0M6dO+nq1atkY2NDDx484DtShenTpw95e3vT/fv3ydHRkU6fPs13pAozc+ZMGjlyJD179oxcXFxo48aNfEcq0fv378nS0pLu3btHf/75J7m6ulJqairfsUolrQW9UCikrl27Uo8ePSg/P58uXLhApqamlJWVxXe0ErFi/id5e3tTy5YtuYJ+69at1LFjRxo1ahQNGjSI5s6dS0OGDOE7Jmfq1Kk0ffp0MjExKbGgz8jI4DVfZGQk2dnZ0dy5cykkJIRsbGzECs2igv7KlSu85vzWo0ePaODAgTR16lRKSEiQuoK+Q4cOtHr1au5vf39/WrduHUVHR3MF/du3b3nN+G+lpqbSsGHDqGfPnlyviVAoJFNTU7p37x7f8Sg1NZWMjY3pn3/+4bYJhUIiKSvoi3rkw8PDxbZLe0H/119/0T///EN5eXmUlZVF9vb2lJeXx3esYtLS0qhx48bUs2dPbtuLFy/IxsaGHj9+zGu2f0skElGrVq2oRYsW3LbIyEhydHSU+k6Y8ujduzfVrVuXcnJyiAp76V1cXGjz5s18RxNz8eJFatiwIW3atInbtn79enJ1deX9O/5riYmJtG3bNu7ojbQW9Lm5udS1a1dq1aoVGRgY0MOHD/mOVCpWzP8LXxf0OTk5tHHjRrp16xYlJydT7dq16ciRI7zm+/oL7ePHj+Tm5kb3798vVtBfu3aNp4RftGnThnr06EFERNnZ2WRjY0OzZ8+ms2fPUnp6OlHh4emiw13S4OnTp2RoaEh///03tW7dmjp27EhUWBRJyw+5Ro0a0d69eykhIYFGjRpF3t7e5OnpSQ4ODkSFxf3XR5hkQUhICNdzXJKcnBwaN24ctWzZ8v+aqzQXL16k2rVrExV+OcybN49cXV3JysqKgoOD6c6dOzR8+HBeMxYV8kWPa9GPjSJFBf23hT4fjh07xn0OnD9/nvz8/Gj48OFUrVo1mjVrFnXu3JnOnDnDd8wSHTt2jIyNjSkiIoLbJm2faz/rzp07pKenR0+fPuW2RUZG0uvXr3nNVRGCgoLI2NiYzp8/z21LSUmRuqMqQqGQBg4cSJ07dxZ7D/v6+pJIJOI1W5GgoCCytLSk0aNHU6NGjWj8+PEkEokoIiKC3N3dKTs7m++IYooK+rZt21J+fj7fcUrFivl/6dse+pSUFGrYsCFNnTqV11x79uyhypUr06xZsyg6OpqIiAYMGEC3bt0qsaDnW3h4OFlbW9PmzZupW7du1Lx5c/rrr7+oT58+ZGBgQNOnT+c7YjH9+/enw4cP09KlS8ne3p4iIyNp586dxQqh/7eHDx/Svn37iIjo/v37VLduXerduzf3pRoeHk5KSkqUm5vLa86f1blzZ2rcuDE1bNiQdu3aJXbYc8OGDWRra0sDBw6kpKQkXnN++vSJiIhiY2PJ2NiYpkyZQp06deJ64X///XeaNm0arxmJiPz8/EhfX58r5CMjI6lWrVrFCncfHx/eXjOZmZlc58Tff/9NTk5ONGHCBHJ2duZ+7GdmZtLBgwepdevW1K1bN15yliQmJob++usvOnToEAmFQjp27BjZ29tzrw9ZlpaWRlu2bKEdO3ZQVlYW3blzhywtLf8Tw/dyc3Np3759tHHjRkpKSqKgoCCytraWis6vbz18+JBWrFhBDx8+5Ar6wYMH8/5d9K2MjAyqWrUq7du3j65fv04GBgbk4uJCo0aNkoofG2vXrqVq1arR+vXrxYYmFRX0ffr0kdqCnhXzPyk5OZnrTfm6oM/IyJCKQ2++vr5kZGREXl5e5OLiQqNHj6aTJ09Shw4diAqLvGXLlvEdU0x4eDhZWlpSnTp1xIqGrKws3guzkgwYMIB69OjBfTEPHTqUWrRowXtPd1hYGFlaWtLu3buLXZaenk5du3aliRMn8pKtIhw/fpymTJlCCQkJtGbNGq5H9tOnT5SZmSk1PTtubm60ePFiIiKKjo4mHx8fLltOTg45OztLxUS6oKAgMjMzo6NHj3LD3VauXEn0zdE9Pv3111+0detW7u9u3bqRnJxcqcPZqlWrJhULEXz48IFMTU1pyJAh1Lp1a+rQoQNX0Hft2pXveP9KcnIyVa9enXr06EE9e/akunXrUmZmJt25c4caNWokdYXkj8jNzaWmTZuSh4cHDR06lKytrSkmJoaCgoKoZs2aUjWpdPfu3WRmZkYTJ04kGxsb2rNnD1fQ7927l9ds8fHx3Byh0NBQysjIoPfv31NMTAwZGRmRn58f9e/fnyZNmsR1OvJp8+bNNGXKFFq3bh01atSIJk2aRCEhIUSFr4lu3bpJ5cIKxIr5n7N+/XoyMDCgWrVq0ciRIykvL48r6IvG1PHFx8eHBg4cSEKhkHx9fcnU1JSePn1KV69epdatW5OBgQHvxSYVjpFbsGBBsd6poh76HTt28Jbta6tWrRIbU/78+XOaN28eHTt2jF6+fEkaGho0bdo0atasGXXs2FFqxiV+W9Dn5+fT9OnTydLSkn7//XeZ7ZWnwvvi7OxMWVlZtHv3bjI3N6dVq1aRjY0N/fnnn3zH48THx1PNmjW5gp4KezJ9fX2pcePG1K9fP6nojaKvCnozMzOukC/68tq1axff8UgkEnGPlVAoJB8fH9q0aRM5OTlRVFQUJSYm0po1a4gKJ31raWmJDWXhy9ChQ8Vek25ubnTu3DkiKfqhVB5+fn60Z88esW0LFiygUaNGcX/379+fNmzYQCRj9+3du3e0dOlSsW379u2j1q1bc3/Pnj2bW5SBz/s2Y8YMio2N5f7Oy8sjbW1truD8+PEjaWtrU35+PgmFQt4/X16+fEmWlpZ04MABMjU1pQsXLhAR0dGjR6lr1660bNkyqlOnDq8Zv5aRkUG1atUioVBIKSkpZGNjQxYWFtSxY0e6du0a749nWdg68z/o0aNH+Ouvv/D48WPY2toiJiYGeXl5mD9/PqZPnw4lJSVe8zVr1gyZmZkYNGgQmjdvjn379qFz586oXLkyrly5gvfv30NVVZXXjABw4sQJ+Pr6ol+/fujbty/u3r0LADA3N8f169exbNkyHDlyhO+YcHZ2Rvv27REUFISAgAC4u7sjPz8fe/bswfr16/HixQuYmJjA09MTZ8+ehZqaGt+RAQAWFha4fv06Fi1ahD179kBeXh5jxoxBQEAAtmzZAkVFRb4jlptIJMLr16+Rk5MDAJCXl0efPn0wYMAAzJ8/H76+vvD09MTr168xfvx4vuNy9PT0cP36dRw7dgxLliwBCk8yduTIEUybNg0HDx6EQCDgJdvLly/RvXt3DB8+HMnJybCzs4OPjw+ICObm5sjLy0Pv3r0BAAMHDuQl49cEAgEEAgH++ecf9OnTB82aNcPYsWPxxx9/oEmTJqhfvz4SEhIAAPr6+tizZw9MTU15yysUCgEAqampMDMz47arqqoiIyMDKFxrW1Y4ODjAyclJbNu3901NTU0m75uZmRkaNWoktk1a71vr1q2hpaXF/Z2Xl4f8/HwYGRkBAFRUVJCbmwuhUAg5OTnePl+KODk5YcWKFRg4cCAGDRqE9u3bAwDatWsHoVAIf39/nD59mteMX1NTU0Pr1q1x9OhRtG3bFm3btsX79+8xbtw4REZG8v54lonvXxOyZvXq1bRs2TLq0aMHde7cmWJjY8nFxUVs2T8+fH1IMy8vj7p37079+/cv1kMvLZKSkqh27dokEonoxYsX1LBhQ6pXrx7t37+fcnJyKDw8nCIjI/mOSVQ4ZMnKyoqGDRtGR48eJSp8vB0dHenFixd8x6Nnz55RfHx8iZeVNeRGFoSFhVGtWrVIT0+PHBwcuGVUExMTqVKlSlI1ua60YQUl9dDzqWiy6/r162nMmDE0ZswY7rKiHvpGjRpRt27dpOoIzsOHD0kkEtHvv/9OPXv25HpIAwMD6fbt23zH4zx58oRq1KhBmZmZdP/+fTI1NaVNmzbR6NGjqWbNmlK7tF15fPz4kQ4ePEhUuASiqakprVy5kmbOnEnm5uZivcayJiUlhRsim5iYSNbW1jRv3jxaunQpGRoaStWqX3v37uWG+U6fPp1atGhBu3btorp169K8efP4jicmKCiIlixZQpaWlnT37l0iIvrnn3+k5ij2t4rmlI0fP57vKD+EFfMlyM7OLnUoyt27d0lNTY06duxIoaGhVK9ePZo8efL/PePX9u/fT1paWtStWzfasWMHRUZGlljQS9sY+TFjxpCPjw8dP36cWylg+PDh5OHhIXXjLX19fUlVVZVbJSM/P5/s7e25Dyc+rVy5kmrXrl1mQT9y5EipPkRYktDQUKpatSp36L5nz550+PBh7vLff/9daoZjxcXFUc2aNenDhw8lXl5U0PO9wpW/vz/p6elxk12vX79OjRo1olGjRtHly5eJCr98R48eLVWFfNHEuaysrBILer7ExcWJ/f3hwwcyNjbmPidiYmLo8ePHNGLECJozZw6lpKTwlLRixMfHU+3atbn3XXBwMI0dO5amTJlCnz9/5jveD8nNzeWej+zsbMrKyqI2bdrQH3/8QVT43Hl6etKYMWN4P1/F16+z7OxsbhJ4UUG/e/duGjRoEB04cIDHlAXzKIq+uxMTE2nlypW0du1aSktLo2fPnpGlpSUNGDCALC0tS/2s5MODBw/EJvx3795dasfGl4YV8yU4cOCA2Ao1GzZsIC0tLTIxMaGjR4/S5s2bydLSkszNzemvv/7iOy49efKEjIyMaMGCBTRz5kyqVasW1a5dm7y8vMjS0pIr6KXNmzdvyMnJiYyNjaWih/t7fH19ydzcnBYvXkxt2rShTp06SU2BvHDhwmIFvVAopLlz58psb9m0adOoVq1alJOTQ5mZmeTo6EgeHh7k5eVFvr6+9PLlS+rcuTNv+UQikdi6yLt27SIrK6tiX1J5eXkUERFBCQkJvE/OLZrgunHjRoqMjCQbGxuaMmUKHT58mCpVqiTVSySOHTuWm9BXVNDzOYE4LS2NmjZtKvaD4o8//qA5c+bQkydPqHnz5mRvb8+deVmWhYeHk4eHB2VkZBQr6GXVuXPnqH79+hQWFkb16tUjHx+fYgW9NMjNzeXW61+7di23UtO3BT3f4uPjqVatWnTy5EmKj48na2trGjJkCA0dOpScnJwoLS2NwsPDad26dVL1nTRjxgyysLAgc3Nz7jXt5+dHDRs25DvaD2HFfCmKJrQWndQjJCSEPnz4QA4ODtyJA6SlkKPCgt7U1JSuX79OVDjBdNu2bdSlSxeaPXs23/FK1bZtW957K0vj6+tL1apVo2rVqtGxY8e4bbq6unTy5Empev7pm4JeKBTSoEGDyMPDQyomPP+MohUZOnToQB4eHtSzZ0+6c+cObdmyhdzd3alt27a8HqrNzc2lOXPmiA2b+Lagz8vLox49eohNFOTDnTt3uImXRQW9kZERN9k1IyODtLS06OPHj7zmHD16tFgv5IULF7gl4l6/fi11X7BFnwFFCx9cuXKFrK2tqVevXhQREUFDhgzhJubKim8XcUhISCAbGxvuCBl91UNftASurFq6dCkpKirShAkTuG1FBb00DVcJCwsjIyMjMjc3F5vYXVTQ871yU1EhP2fOHCIimjt3rtgwlaFDh9LatWt5TFi6mjVrUnp6OsXHx1OdOnVoy5YtRERSe66K0rBivgze3t5kZWUlNjxlwYIFUvUm/9q3Bb0suHTpErVr147vGNzyWUXOnDnDLZ01d+5cUlBQ4N7kDx48kJrxfsePHxdbiaGooO/Xr59MFvKpqal05MgRbsnBooLeyMhIqu/L16+fooI+ODiYm1vD9ypXU6ZM4VaSoG966HNzc6lLly68/+CgwvdWUYGcnp5Onp6eVLVqVRo3bhy9efOGWrVqJXVnSxUKhVS/fv1iZ/FdunQp1yMpK9LS0qhRo0ZiRxsWLVpEY8eOpU+fPlHPnj3JwsKCTp8+TfHx8fTmzRte8/5bZ86cIXd3d6pfvz63/HF4eDhlZWVJ1essMzOTOnbsSLa2ttwZdYuOkty5c4fXjqWiQl5bW5vrDJg+fTp5e3tzbYYMGcJ1HEiL+fPnk4eHB7Vv357blpCQQHXr1pWJkQLfYsX8d3h7e1PdunUpKSmJYmJiqHr16mKnZZc20lrQP3nypMS5BSKRiLp27cprsZOWlkY1a9YUmyQ6f/58evLkCW3fvp3s7Ozo8OHDpKurK1XP/Y0bN0ocorRw4UKZLOSDg4PJxsaG3N3dydDQkJtU/nUPPd9FcWm6du1KU6ZM4f7etWsXqaioSEUhT0Q0YsQI8vPzE9tWVNDXrl1b6ia7rl27llu2NjMzk3bt2kX16tUjQ0ND2rZtG9/xOO/evaOBAwdSYGAgWVpacgX9ixcvyMHBQWqGQPyIbz83jh8/TjY2NtS8eXN68uQJTZs2jRYuXMhbvoqQlJREs2bN4k4AtH79eqpXrx5t3bqVDAwMpOJMx0XS0tK4if9hYWFka2tL69evp6ZNm9KCBQt4zfZ1j/z69evJ2tqaPn78SBEREWRubk7e3t40fvx4sra25u6DNDh27Bi5uLjQxYsXqV69emJHDaTh8/pnsGK+BPfu3aMdO3Zw47q8vb1JVVWVWrZsSevWreM7Hqe0FXSePHkiVZNd3759S4aGhlJ92Co2NrZYQZ+VlUXGxsYUEhJC3bp1o1OnTklNjzwRkbOzs1hv67crGsmS4OBgMjc3p507dxIR0caNG2nOnDn04MEDbs3kgQMH8n5m5dLk5ORQp06dxAr6K1eu8PrFcP/+fa7o6tWrFz179qxYm8jISJo0aRLvhfzXxVNqamqpZ0kt7SRRfIiKiqIRI0ZwQ02+LehltSigws8Sd3d37vEumih65MgRMjMzk4oT/PysnJwcatSoUbGzLx89epS6desmVT3yW7ZsIX19fXJ0dOR6uiMjI6lfv360fPly3od6vnnzRuzI8LcF/fTp02nu3LmlLs7Ah8WLF1O9evXowYMHRIWTdhs2bCi1w4DKixXz31i8eDFZWVnRH3/8QSYmJtxyjt7e3twwC2nwxx9/UN26dXn/Ei6Pzp0709atWykoKIi6detGzs7OUrPs5Ne+LehzcnLIysqK6tatS+3bt5e6ScQmJibcYc38/Hzq379/sRO7yIqlS5eSra0tpaSkUEZGBjVo0IBbrrRnz55EhQVGcnIy31FLVVJBz6ekpCSqW7cuzZ07l9q1a0cnT56ku3fv0q1bt8jX15cuX74sFSuQZGdnk4ODA504cYJmzpxJffr0ISrsPSupoJcWvXv3Jh0dHbE5E4GBgdS2bVvei6yKcPPmTbKysuIK+levXlGVKlWk6gfVz1i7dq1MnH131apVZGdnR2FhYTRo0CDS0dERG9svrb4u6KVRQkICOTs7i53MLTk5WSrOxv1v/NLF/OjRo+nly5fc3zt27KAaNWpQdHQ09ezZk1q2bEn6+vpStT47FR72dHR0pMTERHr8+DG39JO0GjZsGLVo0YI8PDwoKCiIWrRoQVeuXOE7Vom+LegjIyPJ19eXOxwrTZYuXUqurq60Z88eatmyJbVv35731VL+jRkzZlDDhg2pRYsWNHToUMrPz6f379+TiooK39HKLScnh5YuXSo1R0aKCnodHR3q3Lkz/fbbb9SrVy/q168feXl5SU1nQEREBOno6JCtra3Y4fiigl4af8RlZmaSu7s7eXl58R2lwoSHh1OnTp24z5FvC/pvl+KURaNHjxYbqnXz5k2pmC/yrUOHDlFkZCTNnDmTWrRoQefOnSNra2upXTDia+vXr6cWLVrwHYN27NjBnR2XChcCSExMpISEBLHJrv8Fv3Qx7+fnR5aWllxBn5ycTHFxcbRkyRLy8PCgkJAQat26dbFTPfNt7NixNGPGDJowYQL16dOHunfvTqtXr+Y7lph3797RmDFjaOvWrZSXl8et0/vnn39SnTp1pKaIKElJQ26k1d9//01Dhw6lLVu2SN2Rg58xY8YM0tXV5SajjR8/nvr168d3LJn2dQ+9tEpLS6P+/fuTvb09t9xnYmIiRUVFSdVktE+fPtGMGTNo3bp1lJubyxX00vzYlldqaipVq1ZNbNUaKix2GzVqJPNHG6KjoykvL4+uX79Otra2dPXqVdq4cSPp6+tz512QBgcPHuROTpienk6GhoYUHh5ODRo04H2y64+Qhg7GJ0+eUM2aNSkkJIR8fX3JwMCAG14jy5NdS/JLF/NUQkFPhWORjx8/TnXr1pWqX8Hv37+n6OhoCg8Pp4kTJ9KFCxcoISGB7OzspGrCa2ZmJpmZmdH8+fOpZ8+eNGDAABIKhXTgwAGyt7eXiUlhsbGxNHbsWJn54PwvKeqhnzJlCjk7O8v8iXakgTQX9EuWLOHG80dERJC9vT0tWbKEatWqJVXLOgYEBJCxsTEtXLiQOnXqRG5ubpSamkqZmZk0cuRImX+drly5kgYNGiS2regIkzR3vnxPWloatWvXjmxsbMjOzo6uXr1KZ86coVatWlGPHj2kbtjQw4cPqVmzZkSFwyfr1q1Lenp65OnpyXc0mfTkyRNydHQkAwODYmeKluV5Ld/6pYr50nouvy3ojx49SrVq1aIVK1b8nxOWbvXq1aSnp0empqa0fft2osKeK2dnZ6n6gg4MDKTJkydzK9cUndylf//+lJKSIlUTpx48eEC9evWi0aNHS8XYYeaLGTNmkLOzs1StgCDrkpKS6I8//pCqIWMrV66kWrVqiU2Qi4mJod9//12qjozl5eWRvb09nTp1ii5cuEBGRkbUo0cPaty4MbcOvqybOXMmLV68mPv7yJEj5OHhwWumijB+/HgaPXo0vXnzhkxNTWnfvn1S30nTsGFD7kdGTk6OVH1vSqvExMQSvy+KeuSdnJzEhtz81/xSxfzMmTNLXY2mpB56aRESEkI1atSgmJgYSk5OJhcXF9q4cSMFBQXRkiVL+I4nJi0tjVxdXWnIkCHcNpFIRIsWLZKqGe0BAQFkaGhIR48epU6dOpG7uzvfkZhvSNPKQUzFi42NJV1dXZn5IZ2Wlkbx8fFkaGhIjx8/pgEDBtCyZctkJn9JsrOz6fr16xQSEkIhISFkYmJCK1asoEmTJpGpqanU9Vr/iKSkJMrKyqLq1avTgwcPuEL+5s2b1KRJE6kq6I8dO0Zubm505MgRysvLo/3794uddIn5viNHjlDdunXFCno/Pz+uR/7rITf/Rb9UMZ+WlkZubm7fLeilaaWVuLg4sra2pr59+3LbUlJSyMXFhfz9/XnN9q309HSirwp6aVoe81vDhg2jAwcO0NKlS8ne3p4iIyNp06ZNMn04mWFkyePHj6lu3brc30lJSeTh4SFVq2CEhITQ8OHDacSIERQcHEw3b94kFxcXmjp1KtWtW1eqCsIfFRcXR05OTtS+fXuqXr06rVmzht69e0dTp06luXPnUkxMDN8R/5XWrVvT9u3baciQIaSpqUn79u2jc+fOkYGBgVSNkd+3bx/17duXHj58SIMHDyYrKyuaPXs2GRoact+pTPls2rRJrKCPiIigu3fvcpc/efKE9u7dy2NCyfmlinkqR0Evjb/agoKCyMLCgq5du8Zt+3o5NL69fPmSqlevTjY2NtSvXz/KyMjgCvqiIUF8evr0KX348IGo8M0sEolo1KhR1KlTJ27pu6FDh1LLli1l7kRLDCNrEhISKCIignJzc6l27dq0YMECOnXqFDk6OhZb+5tPWVlZZG5uTosWLaKtW7eShYUFhYaG0qxZs2jMmDHceUhk1YgRI8jb25vu3r1LBgYGdPXqVW7iuSxLTk6mDRs2kKurK1Hh661NmzZkYWFBtWvXpidPnvAdkXPlyhVq2bIlhYWFcdvi4+NpxYoV5OXlJdM/FvnybUH/q/jlivndu3eTh4cHycvLS9UJoL519OhRGjBgAB0+fJiosKC3trYWK+ilQUBAABkZGdGRI0fo9OnTpKmpSc2aNeMKemkY63fu3Dmys7OjLVu2kJGREYWGhlJISAjp6enRuHHjqEmTJtSpUyc2rINhJGzNmjVkaGhIderUoaFDh1JkZCSNHTuWOnbsKFXrPAcFBdGwYcNo5MiR3LaVK1dK1Y+Nn/Xx40f6+PEjNWjQgE6cOMEV8keOHCF7e3upWVb1Z6SkpNCxY8fI3d2dmjdvLnaZtN0vkUhEvXv3JgMDA3ZEuIL9igX9L1XMX758maysrCgsLIxiYmKodu3aUlnQz5w5k5ycnOiff/4hPT097qyXQUFBtHz5cr7jibl79y6dPn2a7t69y40ltbOzo2HDhvEdTcyMGTMIAJ0+fZrbFhUVRVu2bKFLly6xHhCGkbAHDx6QjY0Nffr0ibp160bdunWT2iNh2dnZ1L59e2rfvj23bcSIEVK12MDPcnFxoV27dtHChQtJTk6OTp48SWvXrv1PnBBq7NixNGXKFG7J0Dlz5vAdqUT5+fmUk5ND+fn51KtXLxo+fDj7DvqXTpw4QYsXL+bOJv2rFfQCIiL8IubNmwd5eXl4e3sDAFasWIGNGzfiwIEDaNasGd/xAAD379/H+PHjceXKFYwaNQpycnIIDAxEu3btsGbNGr7jiUlLS4OGhgYAoE2bNhg/fjyePHkCoVCIP/74A2pqarxle/78Od6+fYvevXvj8uXLSE1NxYcPH7Br1y5cvXoV5ubm2L9/PwYNGgSBQMBbTob5VaxZswa5ubl4+PAhAGDbtm3o0KEDNmzYgIYNG/Idj0NEEAgEyMnJwW+//YbMzEyoqqoiLi4OV69ehY6ODt8Rf0pSUhJmzZqF0NBQXL16Ffn5+fD29sbp06dRs2ZNrFq1CqampnzH/Gm9evVCSEgIHj58CAUFBWRlZaFz585o2rQp5s6dy3c8zp49ezBz5kyoq6tj1KhR8PLyQr9+/aCtrY2tW7ey76MfJBKJMGLECLx69QrdunXDypUrcf78ebi4uGDz5s3Q1NTEgAED+I4peXz/mvh/un37Njk6OlJ0dDRFR0eTo6MjPX/+nO9YxeTm5tKWLVuoVatWFBERQR06dKCDBw/yHYsTGBhI1atXJ1tbW2rdujXFxcXR2LFjycrKimrWrCkVq9ZERUWRo6MjTZ48mYyMjLh1rM+cOUM2NjbUqlUrcnd3l6q5BwzzX1TU43j//n1SVVWlLl26UEhICDk7O0vVsJU3b96QjY0NVa5cmRveWNRDP3bsWL7jVQhPT0+ytbWV6TNFl+bOnTukqanJnRSICs95Ik1j5B8+fMjNvRg0aBB5eHhwPfQ7duxgvfM/Yfny5eTu7k4JCQnUuHFj6tu3L2lqaopNfP0V/OeL+W3btpGlpSXVqVOHbt++TTt27CA9PT1SVVUVW1OXbydPnqRBgwbRli1bSCQScWvjOjo6StUph4VCIdnZ2dGRI0fo+PHjZGxsTIGBgSQSiejly5dSNfbv0KFDBIAWLFggtv358+e0e/duqRtDyTD/NatXryZVVVVydXWlDx8+0Pbt28nKyoosLCxo06ZNfMcT07dvX9qzZw99/PiRateuTTt27CD6qqD38vLiO+JPSU5OpvXr19Pu3bspJyeHPD09qUOHDv+JE+bk5+fTyZMn6dChQ5SVlUV37twhS0tLqeykIyJauHAhrV+/nivk4+LiqG3bthQYGMh3NJlx+PBhysnJIaFQSH5+fpSenk6ZmZk0btw4GjJkCD1+/Jh69OghVeep+H/4Txfz586dI3t7e3r37h21b9+eevXqRVQ4EUaaPsi2bNlCDg4OdOrUKTIyMqJ169ZRdHQ0TZo0ifdJYREREWIr/Lx7946aNGnCFfIvXrygZcuWSdUPoyIJCQl05coVcnR0pIMHD5JQKKTZs2fL9LrQDCMrwsLCyM7OjsLDw+nMmTNUrVo1ev/+Pd+xigkNDaU///yTmjZtym2Lj4+n2rVri/XQz549W+Z6tLOzs8ne3p769OlDAwcOJBcXF8rOziZPT0+xc4HIotzcXGrRogW5ublRp06dyNnZmdLS0ujOnTvk5OQkVd/xJ0+epOzsbPrnn39IV1eXPDw8KCIigpo0aUIjR45kPfI/oOjH6MCBA6lNmzbcY2dnZ0d79+4lS0tLunHjBt8x/+/+08V837596fz589SjRw/q3LkzxcbG0sCBAyk5OZnXXN++catWrUohISHcpLDY2Fg6c+YMb/m+Vrt2bTI3N+cK+qysLKpatSoZGxtTQEAAeXt7k729PUVFRfEdlXPnzh1q1aoVtWnThh4/fkxRUVFUq1Ytql69OrVt25atWsMwEnb79m2ysbERm7B/9epVqlatmlQMwyuSlpZGpqamNHbsWHJwcKA1a9Zwl8XHx8v0D//w8HBavHgxDR8+nNs2fPhw2rx5M1HhUERZtmPHDrEJyr///jt3EkVpmli9fv16srGx4V5LXl5eZGNjQ1WqVKGFCxeyQv4HiUQicnBwIC0tLbFa7uzZs9SmTRs6duwYr/n4Isf3mP2Kcvz4cdy4cQMAsHPnTqSmpqJq1aqYPHkycnNzsWXLFnTs2BF6enrQ0tLiLeeePXvQunVrZGVlcdvk5eUxdepUAMDWrVvRvn173Llzh7eMX2vRogVcXV3h7u6O0NBQqKioYPfu3ahUqRK6deuGJ0+ewM/PD0ZGRnxHBQC8efMG3bt3x5QpU2BiYoJRo0bB0NAQ9+7dw44dO3D+/HleJ+YyzK/Azc0No0aNwpEjR5CWlgYA8PDwwJkzZ6Cnp8d3PABAYGAgZs2ahb59+2LTpk24c+cODh06hE2bNgEA9PT0YGxszHfMn5afn4/t27eLPd7KyspISEgAAKn5zP5ZsbGxcHBw4P42NDTk7puqqiqPyb549uwZVqxYgRs3biAiIgITJ05E3759ERAQgNDQUMydO5dNeP1BAoEAs2bNwtChQ9G/f3/k5uYiIiIC2dnZuHz5Mnr27Ml3RH7w/Wuiorx+/ZqsrKxo8ODBVKNGDYqNjaWUlBRq0KABeXh4kLGxsVQMBRk3bhzZ29uLnaBo06ZNpK6uTmvWrCFbW1upWk4rPDycmjRpQseOHSMLCwuuh14kElFqairf8Yrx8vKizZs3i53ZdcGCBVLVG8gw/1Vbt26l1q1b0549e4iIaMWKFdS4cWOp/KyIj4+nmjVr0sSJE7ltSUlJ1KdPH6nq2f03QkNDydramubOnUteXl5kbm4u0z3ySUlJtGvXLqLCIaCmpqa0YcMGWr9+PRkYGFBQUBDfEcUcOXKEWrRoQZMmTaIePXrQsmXLqGvXrnzHkilFS01S4Ty4sWPH0tmzZ4mIaPr06VS/fn0yMzOjjRs38piSf/+ZYp6IaPbs2QRA7DBLfn4+PXv2jOLi4njNViQ0NJR7c39d0F+/fp2WLFnC+wzsiIgI6tmzJ507d447/Ne/f3/y8/MrVtBLk+DgYCIiWrJkCTVu3Jjs7e0pIiKChgwZQi1btmRDaxhGwnx8fKh69ep09uxZ8vDw4NZkX7FihVRNds3JyaEXL15QZmYmNza+aLLrf1FoaCiZmZnR9OnTKSUlhe84Py0rK4vq168vtgJScHAwDR48mAYMGCBVk0ifPn1KV65coby8PNq6dStdvXqVMjIyyNXVlbZu3cp3PJmxaNEiatCgAYlEIpo5cybVrVuX1qxZI3aOoCtXrtD9+/f5jsq7/1Qx/+HDB7p16xZZWVmRr68vJScn0++//857L8u9e/fEMvTq1Yvu3btXrKCXBkeOHCE1NTX67bffqEGDBrR371569OgRdezYkYiIjh07Ru3ateM7ZjEtW7akZcuWUUpKCtWoUYN+++03ql+/Pv3222+skGcYCVu/fj21b9+em3hWtAKMtJ1k6c2bN2RtbU22trZkampK/v7+xSa7/heFhoaSjY0NXbhwge8oP23evHnUr18/vmN81759+8jY2JiaN29OHTp0oKysLMrMzKSWLVuKzV9gyrZo0SJycHCgmJgYSklJIQ0NDUpKSiIiok+fPpG2tjabb/CV/0Qxf/bsWfr999/p/PnzRIUf2La2tlSlShWaMWMGr9n8/PxIQUGBHB0dacGCBZSQkECPHj2i3377jYiIK+j5XtIxIiKC3r59S0REu3btIjs7OwoICKD58+eTs7MzaWpq0ocPH4ik8LTYVDiRzdXVlZYtW0ZZWVl07tw5unfvHt+xGOaXEBkZSba2trR+/XpuW3Z2NjckQlrUr1+fy3T37l0yNTWl/Px8mZ/sWh6vX7+mLl26kFAo5DvKT2nXrh1dvHiR+/vmzZtiE5algY+PD3Xs2JHCwsKICgtSDw8PSklJoY0bN7Lis5wWLVpE8vLyNG7cOKLC73dtbW1uwuvLly9JX1+f55TSRaaK+ZKKyAULFlC1atXo0KFDpKury60pnpqaSq9fv+Yh5RepqakkFApp6NCh1K5dO9q+fTs1aNCAJk6cSNWrV+fG9507d47XnEREQ4cOFTtUVVTQf/r0iTIzM8U+RKXFhQsX6PLly9zfKSkppK+vT8uWLeM1F8P8KgICAujo0aMUHx9PkZGRVL16dTp69CjfsUokFApJWVlZbHlJHR0dmR5DToXjyL29vcnLy4siIiJKbDNnzhwaOnTo/z1bRdqwYQO5u7tTSEgI7du3jwwMDOjmzZt8x+KIRCLq2rUrWVhYiP1gKlpbnimfoh75t2/fUt26dbnzOyxZsoScnZ1pzpw5ZG5uTgcOHOA7qlSRqWJ+9uzZ5O3tzf19+/ZtqlGjBsXExFCfPn2oU6dOZGVlJdaGL5cvX+aWACsq6Dt37kzZ2dl09uxZatu2Lb18+ZLvmJyuXbvSq1evxLZ9XdBLo+DgYLK2tuYK+tzcXGrfvj15enpK5dEDhvkvWbFiBZmamlLXrl3J3NycAgMDuYK+6IzL0uDNmzfcmZ579uxJU6ZMoc+fP9PMmTPJzc2N73j/Snp6OlWvXp2mTp1KEyZMIDMzs2Jr4c+ZM4cbriDLRCIRLVy4kGxsbKhly5YUEBDAd6RicnNzqWvXrjR+/Hi+o8is9evXc6/VpKQksYL+0qVL5O3tzY66l0CmivmMjAxq0aKFWLGemZlJ27Zto2bNmtGnT5+oY8eOtG3bNl5zEhH9+eeftHfvXu7vrwt6vofUFImIiOAmDbVq1arEXp1du3ZR586deUhXuqSkJLp79y6lp6dTcHAw2djY0OLFi6lz5840ZswYvuMxzH/ehw8fyMDAgGJjY4mI6ODBg+Th4UFUeMZRafHy5UsyMjLienBTUlJo0KBBZGBgQN27d5f5Anfr1q00YsQIunv3LhkYGNDVq1e5yZYkQ4V8QkIC3xF+2sGDB8nCwoKsrKzo+PHjXEE/YcIEvqP9J3xb0DMlk6l15tXU1HD+/Hn4+flh/vz5QOF6sk+ePIGzszM6deqENm3aYNSoUbxlFAqFAIDU1FRoaGhw2+Xk5LBz507o6emhR48eXDs+BQYGok2bNggMDCyWt8iwYcNw/PhxXvKVxNfXF/b29hg/fjycnJyQmZmJ27dvIy4uDnXr1sWaNWv4jsgw/3lRUVEwMzODvr4+AMDS0hJRUVEAwOt5PL6Wn5+P7t27Y+3atWjWrBkAQFNTE/v27UNMTAxOnDgBAwMDvmP+lNzcXABAVlYW0tPT0bVrV/z9999ISEjAkCFDEBkZCR8fH5w6dQo3btyQ+vvZv39/bn3/9PR0dOrUCbq6upg3bx6IiO94pfLz88OsWbNw7NgxnDx5EjNmzMDTp09x7NgxODo6SnV2WaGtrQ0fHx/cuHFDas6/I5X4/jXxM77tof/48SMNHz6cdu7cyWuuZ8+eUcOGDSkpKYnGjRtHV69eLdZGKBTSiRMneMlXkjNnzpCJiQnp6+uTqakpValShYyMjMjIyIhcXFykZklPKpwzYWhoSP7+/kSF2Z2dnfmOxTC/jEOHDlFubi7l5eVRgwYN6I8//qDz58+To6Oj1C259+zZM6pZs6bYtmPHjnFDbmRZly5duLkKJiYm1L9/f5o6dSpZWFiIDZdMS0vjNWd5JSUlUb169Wjjxo20YsUKGjlyJMXGxlL//v1p1KhRUjtxdNKkSbRy5Uru73nz5tHs2bN5zfRfJS0jGqSVgGTop+OTJ0/w7NkzuLu7w8DAAB07dkTTpk25XnppMGfOHFy9ehXGxsYwNTWFqakp8vPzkZeXh/z8fPTv3x+Ojo58xxRz9uxZjB49GhcvXoSzszPfcUoVGRmJevXqcT2ASUlJMDExQUZGBjuLHsNI2OLFi3HixAncuHEDOjo6SEpKwty5c/Hu3TsMGjQIAwYM4DuimMTERDg5OeHWrVuwsbHB4sWL8ffff+PWrVsye/bTqKgoDB48GEKhEC9evMCmTZvQvHlzbN++Haqqqhg6dCh0dXX5jvlTkpOT4eHhgbS0NNy6dQuGhoYQiUQYMmQIdHV1sW7dOr4jcjIzM6GmpoZ9+/bh4MGDOH/+PLKystC8eXPMmzcP3bp14zsi86vh+9dEea1fv57MzMxo0KBBZGJiQjdv3uR66L9e0UQazJ49mxQUFGju3Lm0fv162rx5M23fvl2qeuS/VdRDLw2Tch89eiT2d0pKCkVHRxMRUZMmTWj+/Pn09u1b6tGjB40YMYKnlAzz63j8+DGZmZlJ1ZG68jh9+jQZGBiQvr4+NW/eXOaXn+zVqxfX8/vp0ycyMDAQO0mirCrqeS8aH718+XLuMqFQWGxxBj6tWrWKO++KSCSi0aNHk5GREenq6tL06dP5jsf8omSimE9MTCQdHR2KjIwkKlxftkaNGkSFaxlLo9mzZ1P9+vW5kxzIgjNnzlCrVq14zZCWlkY1a9ak3bt3ExGRr68v6erqkqGhIc2dO5fi4uKoX79+ZG1tTZMnT/5PHDJnGGn3119/0cSJE7m/k5KSaNasWZSfn89rrpSUFFq+fDmtX7++1MPwOTk5XGeArKtXr55YZ0ebNm3I0dGRnj9/zmuufyMpKYlcXV25paS/HnIjbc6ePUtWVlZcLVIkIiJC5pc4ZWSbTEyATUpKgoaGBoyNjQEAhoaGSEhIAAAoKyvznK5kixf/r707jYq6fN8AfuFWKhlWoiKLGzAFJWqMWzoOIkiIohJYuSS5FK5pZiUqcTA9gEcQFc08mJBikZYbFqi50FFIcgszTWVRQdn0gCADc/9f/HVOlP1ywb4zcH3ezXKO1wvPzMUz9/M8YfDw8ICHhwdu3bqldJz7MmzYMHz//feK/fu5ubkwNzdHamoqoqKiEBcXh8jISOzYsQPZ2dk4c+YMPv30U3z55Zc4f/48li9fjieffFKxvEQNxYABA7Bz506cOXMGWVlZcHNzQ1VVFRo3bqxYpry8PPTu3RvHjh1DUlIS5syZc8/3NWvWDG3btv3P8z0OEyZMwNy5c5GVlYXo6GiUlpZixowZ+Oyzz5SO9lD0ej38/PzQq1cvPP/888CdDY8pKSlISkoyjFQai+joaERHR8PKygoAUF5ejvz8fFhbW5vs6BbVD0Zd5j/55BMsWbIEnTt3houLCwIDA5GUlISAgIB//OA2JmFhYQgNDUWLFi2UjnLflPpyvnz5MjQaDdLT09GmTRtDoS8sLETfvn3xxBNPYPPmzcjJycGaNWsUyUjUULm4uGDx4sXw9vaGl5cXAgMDERERoViey5cvQ6vVYtKkSdiyZQuWLVuGc+fOGcUpYXXp6tWrGDlyJFQqFVatWoWgoCD4+vritddew65du5CQkIAff/wRzz33nNJRH8rBgwdRUlKCyMhIw3M3b96EhYUF9u/fb1jAMxbm5uYoLS0F7uT09vbm9xEZB6V/Gvgn0dHR4uzsLNeuXROdTidlZWUSEhIiw4cPl40bNyodj+pQWVmZiIgcO3ZMOnXqJEePHhURkWvXromzs3OtU4p0Op3cunVLsaxE9U1FRUWtGytNQVFRkXTv3l0WLVokt2/fFk9PT2nbtq1YWVmJm5ubxMTEmPxoTXZ2tnTu3FkWL14sycnJYmZm9reztoODg8XZ2dnwGWrMqqqqJC0tTeTOKE1BQYH88MMPotFoDO/Zv3+/2NjYyI0bNxRM+s9Onz4tNjY24u/vL7a2tjJr1iyjPWmHGhajKfPbt2+X4uJiw+PWrVtLdna2xMbGilqtFpVKZbTz8fTwMjIyZNy4cYbH9yr0f56hJ6K6FRkZKePHjzcU+uXLl4utra2MHz/eKI82zMvLk+LiYkOhd3BwED8/P6msrJSamho5ePCgzJs3z6RnmG/fvi1xcXESExMjp0+fFktLS/n222/F3NxcQkNDRe5svgwODjb6C6HuunLlijg6OsqGDRvk5ZdflmXLlkl1dbUMHDhQ/P39ZerUqdK2bVvDBV/G6vr16/LVV18Z1S3HREZzNGVoaCi+++47pKamonXr1vD09ER+fj78/f3x9ttv48UXX8TFixdhbm6udFSqY9XV1WjSpAn0ej0aNWqEzMxM+Pn5ITExEWq1GtevX0dISAhWrlzJIyiJ6tjd4/8aNWqE999/H6+99hq2bNmC3bt3Y+fOnUhOTr7nhXL/tYKCAowdOxYlJSV49913ERgYiOLiYri7u2Po0KEIDQ1VOmKdKC8vh1qtxvbt29GlSxe4u7sjMDAQN27cwJUrVxAYGIhOnTopHfOhnDhxAt27d8fw4cOxbds2AIBOp0N8fDxKSkoQEBAAa2trpWMCAM6ePYtTp05Bo9EYLkcjMlpK/zXxZ4sWLZIePXoYVuhramqktLRU1Gq1YTWC6hedTidyZ7XN1dXVcPTdX1foiejxqampkbFjx0qPHj0kLi7O8PyKFSukX79+ip8apdfrpV+/fjJv3ry/jTX8eeTG1GVnZ0tERIRMnz7d8JxWq5Xhw4eLtbW15ObmKprvURUUFEhISIg4OjrKtm3bREQkLS3N6H5d+Pzzz6Vdu3YybNgwsba2lp9//lnpSET/k1GVeblHoX/77bdl1qxZSseiOpaVlSVubm7SrVs3yczMFBGR9evXS7du3WoV+sDAQIWTEjUMdwu9r69vrRn6Q4cOKZpLRCQ9PV3s7Oz+Ntuv1+uloKBAioqKJCAgwCjHgh7EoUOHpFWrVrJ69WrDc9evX5f4+Hi5du2aotkeRXV1tezZs8fwWZ+dnS0qlUqmTJkibdu2NczS/9d0Op1ERUXVOmK1uLhYnnnmGbl06ZKIiOzYsUPUarUi+Yjul9GVeflLoTeFjT30YMrKysTOzk42bNjwt9f+WuiJ6PGKi4uTESNGyJIlS6SyslLGjh1ba4beGOzcufOeheqPP/6QMWPGKJKprt3dE/bjjz+KnZ2dUV2U9ChKS0tl4MCBotVqpW/fvjJnzhzR6/Vy/fp1Wbp0qZw5c0axbHq9XtasWVPrjoKsrCzp2rWr4fFvv/0mHTp0UCgh0f0xyqMpQ0JC4OPjgw0bNqBly5ZKx6E6dvcotfHjx0Ov12P58uXo06cP1Go1vLy8MGPGDISEhCgdk6je27JlCyIiIvD666/jwoULGDNmDDZs2AC9Xo+kpCRFs92+fdtwtn2fPn1w9uxZHDp0qNZ7wsPD4eTkpFjGunD16lUMHDgQ9vb2GDhwILp06YIvvvgCPj4+yMrKUjreI6mpqYGXlxdcXFywbt065OXlITU1FdOmTcOzzz6LefPmQaVSKZbPzMwMU6ZMQdOmTbFjxw7U1NRApVLB2toac+bMQUpKCsaNG4egoCDFMhLdD6PZAEsNR25uLrp37445c+YgPT0dgYGB8PHxwbhx42Bra4uwsDCICDe7Ej1GkZGR+P777xEeHo7u3bsDABwcHLB792507twZjRopt9aTl5eHoUOHomnTpsjOzkZcXBxKS0sxdepUhISEwMnJCfHx8cjMzER6erpJ3eXxV+7u7hg0aBA0Gg18fX2xZcsWaLVaHDx4EM899xxeeOEFpSM+kAMHDsDMzAwDBgwAABw5cgQ9e/aEi4sLZs6cicuXLyMzMxPh4eGGi6KUUFNTA51OZ7h4cNKkSSgrK0NCQgJu3bqF4OBgnDhxAv7+/izzZPyU/mmAGqZz585JYmKi4Tzh6upq6dWr1z1Hb4io7h07dkxat25t2IhYUFAgzzzzjOKbLHNzc6Vr164SGRkpIiJr166VESNGiNw5h9zb21tefvllmTt3rpSWliqa9VGVl5dLp06dJC0tTSwtLeWHH36QxMREmTFjhtLRHlhVVZW88cYb4uDgIPb29rJ06VLDawcPHpQBAwbIrl27xMbGRvFjpvfs2SPt2rWT5s2bG+6t0ev1MnHiRBk9enStGXoiU8CVeVKUXq/HqVOn8OGHH6JJkyb47rvvFF0RJKrvysrKYGZmhpYtWyIzMxM+Pj7QarU4cuQIpkyZgrlz5yqab+/evQgICEBKSgqcnJzg4eGBUaNGYfr06Yrmqku5ubnIysqCp6cnnJycUFhYiISEBFy+fBnz589HSkqKSa3I63Q6BAQEAAA2b96Mq1ev4q233kJUVBTs7e0BAEOGDEFhYSHWr1+Pvn37KpZ127ZtCAoKQlJSEqysrODh4YFz584B/7+4icmTJxtW6JW6EZ3ogSn91wQ1bD/99JOMGDFC1q1bZ1Qb7ojqo4iICGnTpo2oVCqJiYkRubNCb2dnZxSn1tyVnJwsVlZWMmjQIPH39693t2weP35cevfuLXLnpB5HR0dxdnYWV1dXOXfunNLxHtiRI0ekefPmkpGRISIiCxculPbt28uQIUOkY8eOUlJSonREERG5cOGCNG3aVHbv3i0iIr/88ou0adNG7O3tZciQIVJeXi56vV7i4+Pr3f85qt9Y5omIGoD58+dLt27dJCcnR0aMGCEWFhYSFhYmYqT3OiQnJ0uLFi0kPT1d6SiPRf/+/Q1HNVZXV5v00ZNy58QhKysrmTVrljg4OBjGtV544QVJTU1VOp7BihUrxNHRUfbv3y/W1tYSGxsrOp1OevfuXeuOBSJTwnkGIqJ67vfff4ednR327t2LxYsXo6amBjExMVi/fj327t2LHj16ICkpCefPn1c6qsGQIUPwzTffwNfXF7/88ovScR5ZTk4OQkNDkZeXBwCYOnUqYmNjAQCNGzc2+VtGvb298dlnn2H16tWIioqCtbU19u7di5KSEvTq1UvpeAbTp0/H1KlTodVq8d577+Gdd95BRUUF8vPz4eDgoHQ8ooej9F8TRET0+GzatElcXV1Fr9fLlStXxM7OTnJzc8XZ2VlOnz6tdLx/lZycLG5ubkrHeGRFRUUSHh4uDg4O4ufnJ6mpqWJvb2/ym3j/6u4KfWxsrFhaWsrhw4eVjnRPd1fof//9d9FoNDJz5kylIxE9NG6AJSKqp6Kjo5GcnIyoqCioVCqUl5ejZ8+eKCwsxIwZM7Bw4UKlI94XvV5vshvjKyoqcODAATg6OqJTp07Q6/XYs2cPVq1ahZMnT+Lw4cOws7NTOmad2rVrF8aNG4etW7dCo9EoHecfxcTEYPbs2ZgwYQLWrl3L45DJZLHMExHVE1VVVTh+/Dh69uyJxo0bY9euXRg5ciQOHz4MV1dXAMCtW7dw8+ZNtGvXTum49V5OTg5effVVdOnSBRcvXsS0adMwefJkw+s6nQ5NmzZVNOPjcvPmTbRq1UrpGP9q37590Gq1LPJk0kxzqYOIiGrZuXMnOnfujBUrVuD06dPAnTnmrVu34s0338TFixcBAC1atGCR/w+UlJRAq9Vi8uTJ+Pjjj1FQUIBly5Zh1apVhvfU1yIPwCSKPAC4ubmxyJPJY5knIjJxhw8fxoQJE7Bjxw4kJCQAADQaDa5duwZvb28sX74cnp6eKCoqUjpqg9G6dWts3LgRo0ePxqhRo7Bx40b07t0bqampKC0tVToeEdUjHLMhIjJxnp6eGDlyJKZMmYLz58+jf//+GDx4MDIzM7Fv3z5YWloiKyvLpC4iqi/WrVuHjIwMqNVqrF27FhkZGUpHIqJ6hmWeiMjEde3aFfHx8ejTpw90Oh0yMjLQt29fBAUFoWvXrpg9e7bSERusS5cuwdPTE0899RQ2bdrE4w+JqM6xzBMRmbixY8fiqaeewurVqw3PFRUVoV+/fti+fTsLJBFRPdZE6QBERPRoPv30U/Tq1QvNmzdHUFAQLly4gGnTpmHixIks8kbi0qVL6Nixo9IxiKge4gZYIiITZ2Njg7S0NJw/fx7dunXD9OnT8dFHH2Hu3LlKR6v3rly5Ah8fH7zyyis4cuTIPd+zYMECBAQEgD+EE9HjwDEbIiKih+Tn5wcnJydoNBoEBQVh5cqVcHd3N7y+YMECbN26Ffv374elpaWiWYmofmKZJyIiekA1NTXw8vJCo0aNsGfPHgDAhQsX4OXlhdWrV2PQoEEs8kT0n2CZJyIieggpKSl4/fXXkZaWBkdHR+BOoa+srMTTTz8Nf39/bNu2jUWeiB4rlnkiIqL7VFlZiUOHDkGlUsHGxgYpKSl49913kZycDHt7e6XjEVEDxA2wRERE9yEnJweurq5YsWIFXn31VXzxxRcYPHgwYmNj8dZbb3GDKxEpgivzRERE/6KoqAhqtRozZ86EWq3G8OHD0apVK8ybNw8TJ05EVVUVmjVrpnRMImqAWOaJiIjuw4EDB6BSqdCzZ0+sX78emzZtQnFxMb788ku0atVK6XhE1EDx0igiIqJ/cPv2bfz000+wtbWFRqPBmjVrMGzYMOTl5eG3337D0aNHlY5IRA0cV+aJiIjuobCwEFqtFra2trh48SImTZoEHx8feHh4wMLCAomJibxhl4gUxzJPRER0D5MmTUKHDh3g6ekJX19fJCQkwNXVFRYWFkpHIyIy4Gk2RERE93Dy5Em8+OKLhiJfXFyMPn36oLq6WuloREQGnJknIiK6h6FDh8Lf3x9ff/01fv31V0RERCAlJQVNmvCrk4iMBz+RiIiIAFy9ehUffPABzM3NERYWho8++giVlZUIDg7GSy+9hKNHj8La2lrpmEREtXBmnoiICICXlxdcXFxgYWGBjRs3IjU1Fe3bt1c6FhHR/8SVeSIiatAqKipw9uxZPPnkk1iyZAkAwM7ODu7u7iz0RGT0WOaJiKjBunHjBtRqNRo3boz8/Hzk5OTA1tYWo0ePRpMmTVBSUsIyT0RGjWM2RETUIOXk5CApKQn5+fkIDw/HN998g/nz5yMlJQU2NjZKxyMiui9cmSciogYpMzMTwcHB2Lx5MwBg1KhRMDMzw5gxY3DgwAGl4xER3ReuzBMRUYOVmJiITz75BPv27TOM01RUVKB58+ZKRyMiui9cmSciogZr9OjRAFBrsyuLPBGZEpZ5IiJq0LjZlYhMGcdsiIiIiIhMVCOlAxARERER0cNhmSciIiIiMlEs80REREREJoplnoiIiIjIRLHMExERERGZKJZ5IiIiIiITxTJPRERERGSiWOaJiIiIiEwUyzwRERERkYlimSciIiIiMlEs80REREREJur/ABppADOTMlwfAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 875.5x242.5 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pooled = mt.ds.pki()\n",
    "mt.pp.normalize(pooled, method=\"mad_robustize\", by=None, reference=\"negcon\")\n",
    "pooled = pooled[:, wells.var_names].copy()\n",
    "\n",
    "sc.pp.pca(wells, n_comps=20)\n",
    "sc.pp.pca(pooled, n_comps=20)\n",
    "wells.obsm[\"X_pooled\"] = pooled.obsm[\"X_pca\"]\n",
    "\n",
    "mt.metrics.evaluate_integration(\n",
    "    wells, reps=(\"X_pca\", \"X_pooled\"), label_key=\"Metadata_Perturbation\", batch_key=\"Metadata_Plate\"\n",
    ");"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c017",
   "metadata": {},
   "source": [
    "Read the heatmap by column, where higher is better everywhere. The aggregate block on the right carries two\n",
    "totals: scib-metrics' `Total` weights bio conservation at 0.6 against batch correction at 0.4, and mantispy's\n",
    "`Total+mAP` folds the retrieval block into the bio group before the same weighting. `X_pca` is the per-plate\n",
    "normalization and `X_pooled` the pooled one; per-plate scores higher on batch correction, because scoring each\n",
    "plate against its own controls takes the plate's share of the variance out, and a well's neighbours then come\n",
    "from more plates. It gives a little bio conservation back in return, the trade-off these metrics are built to\n",
    "show, which is why the pages that follow judge a correction by retrieval, the question a screen asks. The\n",
    "retrieval block is that mAP, cross-replicate here. On `pki` the plate also carries the cell line, so a higher\n",
    "batch-correction score here partly means the lines were pulled together.\n",
    "\n",
    "`pl.batch_variance` shows the plate's share per component, which tells you where in the embedding it sits."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "c018",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-11-11T02:27:54.904561Z",
     "iopub.status.busy": "2026-11-11T02:27:54.904082Z",
     "iopub.status.idle": "2026-11-11T02:27:55.348688Z",
     "shell.execute_reply": "2026-11-11T02:27:55.347056Z"
    }
   },
   "outputs": [
    {
     "data": {
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     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mt.pl.batch_variance(wells, keys=[\"Metadata_Plate\", \"Metadata_Perturbation\"], use_rep=\"X_pca\");"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c019",
   "metadata": {},
   "source": [
    "Harmony, the last step of the JUMP recipe, corrects an embedding rather than the features.\n",
    "[Reproducing across laboratories](../../case_studies/cross_laboratory.ipynb) runs it on JUMP and measures the result.\n",
    "For a correction on the features instead of an embedding, `sc.pp.combat` is the usual choice.\n",
    "\n",
    "## Sphering, and a correction that makes things worse\n",
    "\n",
    "Sphering is typical variation normalization. The covariance of the negative controls describes variation that is\n",
    "not of interest, and whitening removes it, leaving the variation caused by the perturbations. `ZCA-cor` is the\n",
    "default because it rotates back into the original feature basis, so the columns still match `var`.\n",
    "\n",
    "Sphering needs more control wells than features. BBBC021 {cite:p}`Ljosa_2013` has few control wells for the\n",
    "features that survive selection, so its control covariance is singular. Score mechanism retrieval with and\n",
    "without sphering, with the not-same-compound rule from [Mechanism of action](../compounds/mechanism_of_action.ipynb)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "c020",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-11-11T02:27:55.351941Z",
     "iopub.status.busy": "2026-11-11T02:27:55.351523Z",
     "iopub.status.idle": "2026-11-11T02:27:55.797392Z",
     "shell.execute_reply": "2026-11-11T02:27:55.796606Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sphere: fitted on 330 reference rows for 343 features, so the covariance is singular\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'control wells': 330,\n",
       " 'features': 343,\n",
       " 'without sphering': 0.777,\n",
       " 'with sphering': 0.175}"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def not_same_compound(adata):\n",
    "    \"\"\"Not-same-compound mechanism retrieval on one signature per treatment.\"\"\"\n",
    "    treated = adata[~adata.obs[\"Metadata_Control\"].to_numpy()].copy()\n",
    "    signatures = mt.tl.consensus(treated, method=\"median\", min_replicates=1)\n",
    "    signatures = signatures[signatures.obs[\"Metadata_MOA\"].notna().to_numpy()].copy()\n",
    "    mt.tl.nn_moa_classify(signatures, scheme=\"nsc\")\n",
    "    return round(signatures.uns[\"mantispy\"][\"moa\"][\"accuracy\"], 3)\n",
    "\n",
    "\n",
    "bbbc = mt.ds.bbbc021()\n",
    "mt.pp.normalize(bbbc, method=\"mad_robustize\", by=\"Metadata_Plate\", reference=\"negcon\")\n",
    "mt.pp.feature_select(bbbc, na_cutoff=0.0)\n",
    "bbbc = mt.pp.subset_features(bbbc)\n",
    "\n",
    "sphered = bbbc.copy()\n",
    "records = []\n",
    "handler = logging.Handler()\n",
    "handler.emit = records.append\n",
    "logging.getLogger(\"scverse.mantispy\").addHandler(handler)\n",
    "mt.pp.sphere(sphered, method=\"ZCA-cor\", reference=\"negcon\")\n",
    "logging.getLogger(\"scverse.mantispy\").removeHandler(handler)\n",
    "print(records[0].getMessage())\n",
    "\n",
    "{\n",
    "    \"control wells\": int(bbbc.obs[\"Metadata_Control\"].sum()),\n",
    "    \"features\": bbbc.n_vars,\n",
    "    \"without sphering\": not_same_compound(bbbc),\n",
    "    \"with sphering\": not_same_compound(sphered),\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c021",
   "metadata": {},
   "source": [
    "Sphering cuts not-same-compound accuracy from 0.78 to 0.18, a loss of more than three quarters, on 330 control\n",
    "wells for 343 features. mantispy's sphering is tested against pycytominer's.\n",
    "\n",
    "Measure a correction on the question the screen asks before adopting it. For this dataset, either select fewer\n",
    "features so the control covariance is well determined, or skip sphering.\n",
    "\n",
    "Next: [Is the screen any good?](screen_quality.ipynb)"
   ]
  }
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