{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "c000",
   "metadata": {},
   "source": [
    "# Hits, effects and cell loss\n",
    "\n",
    "This page asks which treatments moved away from the controls, which measurements changed, and whether a hit is\n",
    "a phenotype or cells that died. Hit calls come with a permutation p-value and feature effects with\n",
    "Mann-Whitney q-values.\n",
    "\n",
    "Potency has its own page, [Concentration response](dose_response.ipynb). The same questions asked of single\n",
    "cells are on [What the well median hides](../single_cells/heterogeneity.ipynb)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "c001",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-11-11T01:19:07.160674Z",
     "iopub.status.busy": "2026-11-11T01:19:07.160265Z",
     "iopub.status.idle": "2026-11-11T01:20:02.791459Z",
     "shell.execute_reply": "2026-11-11T01:20:02.790341Z"
    }
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import scanpy as sc\n",
    "\n",
    "import mantispy as mt\n",
    "\n",
    "# Palette shared with the mantispy plotters (mt.pl.hits, mt.pl.cytotoxicity):\n",
    "# crimson = significant / flagged, blue = ordinary / ok, grey = background.\n",
    "ACCENT = \"crimson\"  # significant / flagged / highlighted (mt.pl.hits \"hit\")\n",
    "ACCENT_DARK = \"#7a1b1f\"  # a second flagged compound, darker crimson\n",
    "NEUTRAL = \"tab:blue\"  # ordinary / not-flagged (mt.pl.cytotoxicity \"ok\")\n",
    "NULL = \"lightgrey\"  # not-significant / background (mt.pl.hits \"not called\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "1534cf71",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-09-27T04:02:24.395530Z",
     "iopub.status.busy": "2026-09-27T04:02:24.394351Z",
     "iopub.status.idle": "2026-09-27T04:02:24.715964Z",
     "shell.execute_reply": "2026-09-27T04:02:24.714537Z"
    }
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "c002",
   "metadata": {},
   "source": [
    "## A screen with replicates\n",
    "\n",
    "`pki` is a kinase inhibitor set from the Broad Institute and the Gray lab: fifteen compounds, eleven of them at three\n",
    "doses, in four cell lines (PATU-8988T, MDA-MB-231, U2OS and A549, two plates each). A treatment has 32 to 64\n",
    "replicate wells across the four lines, 8 to 16 within one. That much replication is unusual, and it makes this\n",
    "a good place to see what a hit call does when the design is generous.\n",
    "\n",
    "This page pools the four lines, so a hit here is a treatment that moved in the pooled wells. To call hits in one\n",
    "line, subset by `Metadata_CellLine` first.\n",
    "\n",
    "The preparation is the one from [Normalize, select, aggregate](../profiles/normalize_and_select.ipynb)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "c003",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-11-11T01:20:02.794902Z",
     "iopub.status.busy": "2026-11-11T01:20:02.794546Z",
     "iopub.status.idle": "2026-11-11T01:20:21.880573Z",
     "shell.execute_reply": "2026-11-11T01:20:21.879308Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<path>:2: 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",
      "  mt.pp.normalize(wells, by=\"Metadata_Plate\", reference=\"negcon\")\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<path>:96: 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",
      "  func(target, **params, **extra)\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', 'pca'\n",
       "    obsm: 'X_pca'\n",
       "    varm: 'PCs'\n",
       "    layers: None (.X)"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wells = mt.ds.pki()\n",
    "mt.pp.normalize(wells, by=\"Metadata_Plate\", reference=\"negcon\")\n",
    "mt.pp.feature_select(wells, na_cutoff=0.0)\n",
    "wells = mt.pp.subset_features(wells)\n",
    "sc.pp.pca(wells, n_comps=20)\n",
    "wells"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c004",
   "metadata": {},
   "source": [
    "## Calling hits\n",
    "\n",
    "{func}`~mantispy.tl.hit_calling` scores each group's median distance from the control centroid, measured in the\n",
    "controls' own covariance so that directions the controls already vary in count for less. The null is the other\n",
    "ways to draw a group of that size from the group and the held-out controls pooled.\n",
    "[Hit callers on well profiles](hit_calling_methods.ipynb) compares this default with `method=\"mp_value\"`, which also\n",
    "counts the spread of the treatment's own wells.\n",
    "\n",
    "With 854 features and 832 control wells, half of them held out, the covariance would be singular, so the distance is\n",
    "read on a PCA representation. The function says so itself if you forget."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "c005",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-11-11T01:20:21.883193Z",
     "iopub.status.busy": "2026-11-11T01:20:21.882571Z",
     "iopub.status.idle": "2026-11-11T01:20:24.337700Z",
     "shell.execute_reply": "2026-11-11T01:20:24.336606Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "        <script>\n",
       "        window.PlotlyConfig = {MathJaxConfig: 'local'};\n",
       "        if (window.MathJax && window.MathJax.Hub && window.MathJax.Hub.Config) {window.MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}\n",
       "        </script>\n",
       "        <script type=\"module\">import \"https://cdn.plot.ly/plotly-4.1.1.min\"</script>\n",
       "        "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div style=\"height:525px; width:100%;\">            <script src=\"https://cdnjs.cloudflare.com/ajax/libs/mathjax/3.2.2/es5/tex-svg.min.js\"></script><script>if (window.MathJax && window.MathJax.Hub && window.MathJax.Hub.Config) {window.MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}</script>                <script>window.PlotlyConfig = {MathJaxConfig: 'local'};</script>\n",
       "        <script charset=\"utf-8\" src=\"https://cdn.plot.ly/plotly-4.1.1.min.js\" integrity=\"sha256-O24V1F27f8pb0glCkelh3cVHLNiHAJ5gCaVtq2aNch8=\" crossorigin=\"anonymous\"></script>                <div id=\"c3f9bc7d-0a71-4d9c-a293-a6e40011f2a3\" class=\"plotly-graph-div\" style=\"height:100%; width:100%;\"></div>            <script>                window.PLOTLYENV=window.PLOTLYENV || {};                                if (document.getElementById(\"c3f9bc7d-0a71-4d9c-a293-a6e40011f2a3\")) {                    Plotly.newPlot(                        \"c3f9bc7d-0a71-4d9c-a293-a6e40011f2a3\",                        [{\"customdata\":[[\"BRD-K15179513-001-03-4@2.0\"],[\"BRD-K15819326-001-01-6@2.0\"],[\"BRD-K40109029-001-06-9@2.0\"],[\"BRD-K95785537-001-26-9@2.0\"],[\"BRD-U00086672-001-01-9@0.4\"],[\"BRD-U00086672-001-01-9@1.0\"],[\"BRD-U00086673-001-01-9@0.2\"],[\"BRD-U00086673-001-01-9@0.4\"],[\"BRD-U00086673-001-01-9@1.0\"],[\"BRD-U00086674-001-01-9@0.04\"],[\"BRD-U00086674-001-01-9@0.2\"],[\"BRD-U00086674-001-01-9@0.4\"],[\"BRD-U00086675-001-01-9@0.04\"],[\"BRD-U00086675-001-01-9@0.2\"],[\"BRD-U00086675-001-01-9@0.4\"],[\"BRD-U00086676-001-01-9@0.004\"],[\"BRD-U00086676-001-01-9@0.01\"],[\"BRD-U00086676-001-01-9@0.04\"],[\"BRD-U00086677-001-01-9@0.04\"],[\"BRD-U00086678-001-01-9@0.004\"],[\"BRD-U00086678-001-01-9@0.01\"],[\"BRD-U00086678-001-01-9@0.04\"],[\"BRD-U00086679-001-01-9@0.2\"],[\"BRD-U00086679-001-01-9@0.4\"],[\"BRD-U00086679-001-01-9@1.0\"],[\"BRD-U00086680-001-01-9@0.2\"],[\"BRD-U00086680-001-01-9@0.4\"],[\"BRD-U00086681-001-01-9@0.2\"],[\"BRD-U00086681-001-01-9@0.4\"],[\"BRD-U00086682-001-01-9@0.04\"],[\"BRD-U00086682-001-01-9@0.2\"],[\"BRD-U00086682-001-01-9@0.4\"]],\"hovertemplate\":\"called=hit\\u003cbr\\u003edistance from the controls=%{x}\\u003cbr\\u003e-log10 q=%{y}\\u003cbr\\u003egroup=%{customdata[0]}\\u003cextra\\u003e\\u003c\\u002fextra\\u003e\",\"legendgroup\":\"hit\",\"marker\":{\"color\":\"#636efa\",\"symbol\":\"circle\"},\"mode\":\"markers\",\"name\":\"hit\",\"orientation\":\"v\",\"showlegend\":true,\"x\":{\"dtype\":\"f8\",\"bdata\":\"uZGPWmi8M0BS3T5swpQxQLS6lQy5pi1AkKwCgn6XNkC063AKDckSQKTyv0wY0hZAm\\u002fOx4FFxE0AqQvKEEOUTQEYMS78y5BZA1gEKicCJGUDKEPHd3wwzQKzHIIdlY0pAFB\\u002fee09KG0C3rmLjbck0QHHVgYWAU0NAmxjFJnwHJED28KshhqkmQGR+SDoFzypALLITLgeZEkC+DYTOdzMTQPLvD76FrBJAeijJwHc3FUB5GT1M3BMSQECr4H7LThNA0\\u002fFpR+TWE0BGa2e683gZQNYH2KCFvhxAolLU8GlOEkC5+9Y6WW4TQPdf07LSABpA9rsQ+iegIEAgmNzgtqUwQA==\"},\"xaxis\":\"x\",\"y\":{\"dtype\":\"f8\",\"bdata\":\"Zns2EgpoB0BmezYSCmgHQGZ7NhIKaAdAZns2EgpoB0BmezYSCmgHQGZ7NhIKaAdAZns2EgpoB0BmezYSCmgHQGZ7NhIKaAdAZns2EgpoB0BmezYSCmgHQGZ7NhIKaAdAZns2EgpoB0BmezYSCmgHQGZ7NhIKaAdAZns2EgpoB0BmezYSCmgHQGZ7NhIKaAdAZns2EgpoB0BmezYSCmgHQGZ7NhIKaAdAZns2EgpoB0BmezYSCmgHQGZ7NhIKaAdAZns2EgpoB0BmezYSCmgHQGZ7NhIKaAdAZns2EgpoB0BmezYSCmgHQGZ7NhIKaAdAZns2EgpoB0BmezYSCmgHQA==\"},\"yaxis\":\"y\",\"type\":\"scatter\"},{\"customdata\":[[\"BRD-U00086672-001-01-9@0.2\"],[\"BRD-U00086677-001-01-9@0.004\"],[\"BRD-U00086677-001-01-9@0.01\"],[\"BRD-U00086680-001-01-9@0.04\"],[\"BRD-U00086681-001-01-9@0.04\"],[\"DMSO\"]],\"hovertemplate\":\"called=not called\\u003cbr\\u003edistance from the controls=%{x}\\u003cbr\\u003e-log10 q=%{y}\\u003cbr\\u003egroup=%{customdata[0]}\\u003cextra\\u003e\\u003c\\u002fextra\\u003e\",\"legendgroup\":\"not called\",\"marker\":{\"color\":\"#EF553B\",\"symbol\":\"circle\"},\"mode\":\"markers\",\"name\":\"not called\",\"orientation\":\"v\",\"showlegend\":true,\"x\":{\"dtype\":\"f8\",\"bdata\":\"ZAWysBFiEEAEbI\\u002fm6qEQQPrVkPcxzxBA6l5QJlGeEECDBzT5pMQQQI7j2dth1A9A\"},\"xaxis\":\"x\",\"y\":{\"dtype\":\"f8\",\"bdata\":\"HCJ28qAw4z+dAoRTIJTnPxxF2+N7Jes\\u002fud75qrHw5j+bE0MMx8DrP58O00bLSdk\\u002f\"},\"yaxis\":\"y\",\"type\":\"scatter\"}],                        {\"template\":{\"data\":{\"histogram2dcontour\":[{\"type\":\"histogram2dcontour\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]}],\"choropleth\":[{\"type\":\"choropleth\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}],\"histogram2d\":[{\"type\":\"histogram2d\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]}],\"heatmap\":[{\"type\":\"heatmap\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]}],\"contourcarpet\":[{\"type\":\"contourcarpet\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}],\"contour\":[{\"type\":\"contour\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]}],\"surface\":[{\"type\":\"surface\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]}],\"mesh3d\":[{\"type\":\"mesh3d\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}],\"scatter\":[{\"fillpattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2},\"type\":\"scatter\"}],\"parcoords\":[{\"type\":\"parcoords\",\"line\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"scatterpolargl\":[{\"type\":\"scatterpolargl\",\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"bar\":[{\"error_x\":{\"color\":\"#2a3f5f\"},\"error_y\":{\"color\":\"#2a3f5f\"},\"marker\":{\"line\":{\"color\":\"#E5ECF6\",\"width\":0.5},\"pattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2}},\"type\":\"bar\"}],\"scattergeo\":[{\"type\":\"scattergeo\",\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"scatterpolar\":[{\"type\":\"scatterpolar\",\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"histogram\":[{\"marker\":{\"pattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2}},\"type\":\"histogram\"}],\"scattergl\":[{\"type\":\"scattergl\",\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"scatter3d\":[{\"type\":\"scatter3d\",\"line\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"scattermap\":[{\"type\":\"scattermap\",\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"scatterternary\":[{\"type\":\"scatterternary\",\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"scattercarpet\":[{\"type\":\"scattercarpet\",\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"carpet\":[{\"aaxis\":{\"endlinecolor\":\"#2a3f5f\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"minorgridcolor\":\"white\",\"startlinecolor\":\"#2a3f5f\"},\"baxis\":{\"endlinecolor\":\"#2a3f5f\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"minorgridcolor\":\"white\",\"startlinecolor\":\"#2a3f5f\"},\"type\":\"carpet\"}],\"table\":[{\"cells\":{\"fill\":{\"color\":\"#EBF0F8\"},\"line\":{\"color\":\"white\"}},\"header\":{\"fill\":{\"color\":\"#C8D4E3\"},\"line\":{\"color\":\"white\"}},\"type\":\"table\"}],\"barpolar\":[{\"marker\":{\"line\":{\"color\":\"#E5ECF6\",\"width\":0.5},\"pattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2}},\"type\":\"barpolar\"}],\"pie\":[{\"automargin\":true,\"type\":\"pie\"}]},\"layout\":{\"autotypenumbers\":\"strict\",\"colorway\":[\"#636efa\",\"#EF553B\",\"#00cc96\",\"#ab63fa\",\"#FFA15A\",\"#19d3f3\",\"#FF6692\",\"#B6E880\",\"#FF97FF\",\"#FECB52\"],\"font\":{\"color\":\"#2a3f5f\"},\"hovermode\":\"closest\",\"hoverlabel\":{\"align\":\"left\"},\"paper_bgcolor\":\"white\",\"plot_bgcolor\":\"#E5ECF6\",\"polar\":{\"bgcolor\":\"#E5ECF6\",\"angularaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"},\"radialaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"}},\"ternary\":{\"bgcolor\":\"#E5ECF6\",\"aaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"},\"baxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"},\"caxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"}},\"coloraxis\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"colorscale\":{\"sequential\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"sequentialminus\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"diverging\":[[0,\"#8e0152\"],[0.1,\"#c51b7d\"],[0.2,\"#de77ae\"],[0.3,\"#f1b6da\"],[0.4,\"#fde0ef\"],[0.5,\"#f7f7f7\"],[0.6,\"#e6f5d0\"],[0.7,\"#b8e186\"],[0.8,\"#7fbc41\"],[0.9,\"#4d9221\"],[1,\"#276419\"]]},\"xaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\",\"title\":{\"standoff\":15},\"zerolinecolor\":\"white\",\"automargin\":true,\"zerolinewidth\":2},\"yaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\",\"title\":{\"standoff\":15},\"zerolinecolor\":\"white\",\"automargin\":true,\"zerolinewidth\":2},\"scene\":{\"xaxis\":{\"backgroundcolor\":\"#E5ECF6\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"white\",\"gridwidth\":2},\"yaxis\":{\"backgroundcolor\":\"#E5ECF6\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"white\",\"gridwidth\":2},\"zaxis\":{\"backgroundcolor\":\"#E5ECF6\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"white\",\"gridwidth\":2}},\"shapedefaults\":{\"line\":{\"color\":\"#2a3f5f\"}},\"annotationdefaults\":{\"arrowcolor\":\"#2a3f5f\",\"arrowhead\":0,\"arrowwidth\":1},\"geo\":{\"bgcolor\":\"white\",\"landcolor\":\"#E5ECF6\",\"subunitcolor\":\"white\",\"showland\":true,\"showlakes\":true,\"lakecolor\":\"white\"},\"title\":{\"x\":0.05}}},\"xaxis\":{\"anchor\":\"y\",\"domain\":[0.0,1.0],\"title\":{\"text\":\"distance from the controls\"}},\"yaxis\":{\"anchor\":\"x\",\"domain\":[0.0,1.0],\"title\":{\"text\":\"-log10 q\"}},\"legend\":{\"title\":{\"text\":\"called\"},\"tracegroupgap\":0},\"title\":{\"text\":\"hits\"}},                        {\"responsive\": true}                    ).then(function(){\n",
       "                            \n",
       "var gd = document.getElementById('c3f9bc7d-0a71-4d9c-a293-a6e40011f2a3');\n",
       "var x = new MutationObserver(function (mutations, observer) {{\n",
       "        var display = window.getComputedStyle(gd).display;\n",
       "        if (!display || display === 'none') {{\n",
       "            console.log([gd, 'removed!']);\n",
       "            Plotly.purge(gd);\n",
       "            observer.disconnect();\n",
       "        }}\n",
       "}});\n",
       "\n",
       "// Listen for the removal of the full notebook cells\n",
       "var notebookContainer = gd.closest('#notebook-container');\n",
       "if (notebookContainer) {{\n",
       "    x.observe(notebookContainer, {childList: true});\n",
       "}}\n",
       "\n",
       "// Listen for the clearing of the current output cell\n",
       "var outputEl = gd.closest('.output');\n",
       "if (outputEl) {{\n",
       "    x.observe(outputEl, {childList: true});\n",
       "}}\n",
       "\n",
       "                        })                };            </script>        </div>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mt.tl.hit_calling(wells, groupby=\"Metadata_Perturbation\", use_rep=\"X_pca\", n_permutations=1000)\n",
    "mt.pl.hits(wells, label_top=6);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "c006",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-11-11T01:20:24.341263Z",
     "iopub.status.busy": "2026-11-11T01:20:24.341059Z",
     "iopub.status.idle": "2026-11-11T01:20:24.359386Z",
     "shell.execute_reply": "2026-11-11T01:20:24.357851Z"
    }
   },
   "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>n_obs</th>\n",
       "      <th>distance</th>\n",
       "      <th>pvalue</th>\n",
       "      <th>qvalue</th>\n",
       "      <th>is_hit</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>group</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>BRD-U00086674-001-01-9@0.4</th>\n",
       "      <td>64</td>\n",
       "      <td>52.776536</td>\n",
       "      <td>0.000999</td>\n",
       "      <td>0.001186</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BRD-U00086675-001-01-9@0.4</th>\n",
       "      <td>64</td>\n",
       "      <td>38.652360</td>\n",
       "      <td>0.000999</td>\n",
       "      <td>0.001186</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BRD-K95785537-001-26-9@2.0</th>\n",
       "      <td>32</td>\n",
       "      <td>22.591774</td>\n",
       "      <td>0.000999</td>\n",
       "      <td>0.001186</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BRD-U00086675-001-01-9@0.2</th>\n",
       "      <td>64</td>\n",
       "      <td>20.786833</td>\n",
       "      <td>0.000999</td>\n",
       "      <td>0.001186</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BRD-K15179513-001-03-4@2.0</th>\n",
       "      <td>32</td>\n",
       "      <td>19.735967</td>\n",
       "      <td>0.000999</td>\n",
       "      <td>0.001186</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BRD-U00086674-001-01-9@0.2</th>\n",
       "      <td>64</td>\n",
       "      <td>19.050291</td>\n",
       "      <td>0.000999</td>\n",
       "      <td>0.001186</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                            n_obs   distance    pvalue    qvalue  is_hit\n",
       "group                                                                   \n",
       "BRD-U00086674-001-01-9@0.4     64  52.776536  0.000999  0.001186    True\n",
       "BRD-U00086675-001-01-9@0.4     64  38.652360  0.000999  0.001186    True\n",
       "BRD-K95785537-001-26-9@2.0     32  22.591774  0.000999  0.001186    True\n",
       "BRD-U00086675-001-01-9@0.2     64  20.786833  0.000999  0.001186    True\n",
       "BRD-K15179513-001-03-4@2.0     32  19.735967  0.000999  0.001186    True\n",
       "BRD-U00086674-001-01-9@0.2     64  19.050291  0.000999  0.001186    True"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hits = wells.uns[\"mantispy\"][\"hits\"].set_index(\"group\")\n",
    "hits.sort_values(\"distance\", ascending=False).head(6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "85105ebd",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-11-11T01:20:24.362173Z",
     "iopub.status.busy": "2026-11-11T01:20:24.361841Z",
     "iopub.status.idle": "2026-11-11T01:20:24.501747Z",
     "shell.execute_reply": "2026-11-11T01:20:24.500932Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAArIAAAGGCAYAAACHemKmAAAAOnRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjExLjIsIGh0dHBzOi8vbWF0cGxvdGxpYi5vcmcvgI3uAAAAAAlwSFlzAAAPYQAAD2EBqD+naQAASlJJREFUeJzt3XlYVdX+x/HPYXRgEFBwwllzHsoCTdTUTEvNyhIzzSktSy2zm1Y3K+vStbpaWtpwTcvKsryZmlqaOQWpaaU4Yk44o8gog7B+f/z0PB6ZDseDcOz9eh6eh7P23mt/94bu/bhYe22LMcYIAAAAcDFupV0AAAAA4AiCLAAAAFwSQRYAAAAuiSALAAAAl0SQBQAAgEsiyAIAAMAlEWQBAADgkgiyAAAAcEkEWQAAALgkgizgQnr06KG2bduWSN8jRoxQ5cqVr9n5cH1LSEiQxWLR9OnTC20DgKtBkAVKSWRkpCwWiy5cuJDv9po1ayo8PLzIfjp37mzXftdSWawJriM+Pl4Wi0UzZ84s1Tpef/11WSwWnTt3rlTrKA5XrBm4Gh6lXQAA+61YseK6Ph8AAMXBiCwAAABcEkEWcCFXzlmtU6eO1q5dq19//VUWi0UWi0WVKlUqsp+33npL9erVU/ny5RUWFqZffvnFrvNJUnR0tO644w5VqVJFvr6+uvnmm/Xxxx8rNzfXrpqGDBlibXdzc1PlypXVp08f/fnnnzbnCQ8PV+fOnXXo0CHdeeedqlixoqpWrapJkyZZz3W51atX64477lBAQID8/PzUqVMnrVq1ymafVatWqVu3bvL391f58uV18803a/HixcW6voLYe1321Hrp2vfv36+ePXvK19dXkZGRkqRz585p3LhxqlWrlry8vBQaGqoxY8bk+VPy22+/rRYtWsjHx0fVq1dX3759tW3btmLvkx8PDw/rtXp5ealevXp65plnlJaWVuSxRdmwYYNCQ0MlSWPGjLGe54knnpAkxcXFyWKx6KOPPtIXX3yhZs2aydPTU0uXLpUkHThwQIMHD1bVqlXl5eWl+vXra8qUKcrJybGeY8GCBdZ+LRaLfHx8FB4eri+//NK6z6OPPqpJkyZJkgICAqz7xsTESJJeeuklWSwWJSYm6tFHH1VgYKAqV66sF154QcYYpaena9SoUapcubL8/f01atQoZWVl5blee+q9dK7k5GQ9+eSTqly5snx8fHTvvffq5MmTdtd84sQJjRgxQqGhoSpfvrwaN26siRMnMg0Brs0AKBX9+/c3kkx2dna+22vUqGHCwsJs2u644w5z00032bR16tQpz36FefHFF427u7uZPn26OXPmjNm5c6e58847Tbdu3UxQUFCh5zt+/Ljx9fU1w4YNM/Hx8SY9Pd1s3brVDB8+3GzdurXYNWVlZZnY2FjTp08fExISYk6fPm3dFhYWZtq2bWvuueces2nTJpOUlGQ++OADI8nMmjXLpp85c+YYi8ViRo0aZfbs2WOSk5PNunXrTO/eva37fPLJJ8ZisZinnnrKHDp0yJw9e9b85z//Me7u7ubzzz8v1vVdzXXZU2tYWJi56aabTI8ePUxMTIw5deqU+fzzz01GRoZp06aNqVatmlm5cqVJSkoyq1atMjVq1DAtW7Y06enpxhhjZs+ebTw9Pc2CBQtMcnKySUhIMEuWLDEPPvig9Rz27GOP5ORk8/3335tq1aqZhx9+2Np++vRpI8lMmzat0Lb8HDlyxEgyM2bMyLNt3759RpLp27eveeyxx8yhQ4fMtm3bzC+//GL27NljAgMDTefOnc22bdtMSkqKWb58uQkJCbGp7XK5ubnm6NGj5qWXXjIWi8WsXLnSui0qKspIMomJiXmOmzx5spFkhg8fbv73v/+ZpKQks2jRIuPp6WlmzJhhBg0aZL7++muTlJRkFi9ebLy8vMyrr75q04e99V4618iRI82XX35pkpKSzMaNG01ISIi5++67bfosrOYOHTqY1q1bmz/++MOcP3/e7Nu3z7zxxhtm5syZhf48gLKMIAuUkktBtrAvZwfZM2fOGG9vbzN8+HCb9lOnTpkKFSoUGWSXLl1qJJnffvut0PMUN1wnJiYaSeajjz6ytoWFhRk3Nzeza9cum30jIiJMmzZtrJ+Tk5ONn5+fufPOOwvsPzU11QQEBOT5P31jjHnooYdMzZo1TW5urt3X5+h12VOruXjtkszvv/9u034pyH/33Xc27cuXL7cJfpGRkaZJkyaFnsOefYpj5syZxs3NzRqmSzrItmjRIs+23r17m+DgYHPu3Dmb9vnz5xtJZvv27YWeNzw83PTr18/62Z4ge2UI7NOnj6lYsaKZPn26Tfs999xj6tat61C9l871zjvv2Oz32muvGUnmxIkTRdaclZVlLBaLee211wq9B4CrYWoBUMqys7N18R+VNl81atRw+rk2btyozMxM9enTx6a9SpUqat++fZHHN2nSRJ6ennriiSe0ZMkSpaSkFLuGY8eOaeTIkapdu7a8vLxksVgUEBAgXfyz8eXq16+vxo0b27Q1b95cf/31l/Xzhg0blJycrAcffLDAc27YsEGJiYm6//7782zr1q2b4uPj9ddff13V9dlzXfbUeknt2rXVqlUrm7bVq1fLy8tLd911l017jx495OPjo9WrV0uSWrVqpV27dunJJ5/Ub7/9ZvNn6kvs2acgP/zwg7p3766goCC5ublZ//Sfm5tr87MpSVf+DmdlZWnlypXq3r27/P39bbZ169ZNkrR27Vrp4n9z//rXv9SyZUtVqFDB5k/wV/4OFqVnz542nxs3bqy0tLQ87U2aNNHhw4etq5QUp95Lrvy5N2/eXLo4PaEonp6eatq0qWbMmKEPPvhAR48eLdZ1AmUVQRb4Gzlz5owkKSQkJM+2/NquVK9ePS1dulQWi0X33HOPAgICFB4ero8++kjGmCKPz8rKUufOnbVhwwbNnz9fZ86cUW5urrKysmSxWJSdnW2zf7Vq1fL04efnp6SkJOvnU6dOSVKhwf/EiROSpMGDB8vDw0Pu7u5yc3OTm5ubhgwZIl28N45en73XZU+tl+S3z5kzZxQcHCw3t7z/0x0SEqKEhARJ0tNPP62XXnpJixcvVtu2bRUUFKR+/fpp69at1v3t2Sc/69atU8+ePVWnTh3FxMTo/PnzMsZo7ty50sWQeC1ceX8SExOVlZWlzz77zPozvvRzrlq1qnTZ7/+YMWM0ZcoUPfvsszp06JAuXLggY4y6detW7Pqv/B319fUtsD0nJ0epqanFrregc/n5+UkX503b47vvvlNERITGjx+vmjVrqmHDhpo4caLNf0+AqyHIAn8jQUFBkmTzgMgl+bXlp3v37tq4caPOnj2rZcuWqXbt2nrkkUf07rvvFnlsTEyM9u3bp1dffVURERHy9fWVxWLRoUOH8g2KFoulyD6rVKkiSYWOMF160cOiRYt04cIF5eTkKDc3V7m5udYR8FtuucXh67P3uuyp9RJPT888bYGBgTp16lS+9+rkyZPW6/T09NTkyZN14MABHThwQNOmTdOOHTvUsWNHxcfH271PfubPn6/y5ctr9uzZatiwoby9vSU7RwWd6cr74+/vLw8PD40aNcr6M770c770M37xxRclSZ9++qkGDRqkgQMHqkqVKnJ3d3f4Ggr6HS3qd7c49drbZ1Hq1aunr776SomJidq0aZMGDBigadOmaeDAgVfVL1CaCLKAi6tYsaIyMzPt2rd9+/by9vbWkiVLbNoTEhIKXLmgIH5+frrjjju0YMECBQQEaN26dXbXdCn8XPLJJ58U69yXi4iIkJ+fnz7//PMC9+nYsaP8/PxsnkovSmHXV5CirsueWgvTtWtXZWVl6fvvv7dp/+GHH5SamqquXbvmOaZOnToaOnSo3nzzTaWlpeU74mrPPpe7NHXikpycHIevKT8VK1aUJLt/ryWpXLlyuv322/X9999bRz3zY4yRxWLJ87OKjo7W/v37r7oOZ9dbXPbU7OnpqZtvvlmvvPKK7rnnHrt+t4GyiiALuLjmzZtr79692rVrV5F/3g8KCtIzzzyjuXPnasaMGUpMTNTu3bs1dOhQu+bIfvLJJxo9erRiYmKUlJSk5ORkzZkzR4mJibrtttuKrOmmm25S9erVNWXKFMXFxens2bOaPXu29uzZYx0VKy5fX19Nnz5dy5cv16OPPqp9+/YpNTVVGzZssM6j9PX11cyZM/Xll19q7Nix2rt3r86fP6+4uDh9/PHH6t27d7Gu70r2Xpc9tRZm8ODBatWqlUaNGqVVq1YpJSVFa9as0YgRI9S8eXMNGzZMkvTwww9rxowZ2rNnjzIzM3Xo0CHNnTtXPj4+1uXU7NknP3369FFiYqL++c9/KikpSXFxcRowYECe+bxXIyAgQNWrV9dPP/1UrD97T5s2Tenp6erVq5eio6OVmpqq48ePa8WKFerTp4/27t0ri8WiXr166dNPP9WaNWuUlpamn376SU899VSe/wYuzUFdunRpiUyZsKfe4iqo5kOHDqlnz55atmyZjh8/royMDG3YsEHr1q0r9HcbKPNK+2kz4O/KWctvJSQkmF69ehk/Pz8jyfj7+xd63tzcXDN16lRTu3Zt4+3tbdq2bWvWr19vhg8fXuSqBenp6eb999837du3N/7+/qZSpUomLCzMfPzxx3bX9Pvvv5vbbrvN+Pj4mCpVqpjHHnvMpKWlGXd3d/P0009b9wsLCzOdOnXKU/+zzz5r8vufrpUrV5ouXboYX19f4+fnZzp16mR+/PFHm33Wr19vevfubYKCgoy3t7dp1KiRGTFihNmxY0exri8/9l6XPbUWdO3GGHP27Fnz+OOPmxo1ahgPDw9TvXp189hjj5kzZ85Y9/nrr7/MU089ZRo3bmzKlStnqlevbu6//36zbdu2Yu1TkPfff980atTIlCtXzjRp0sR88skn5osvvjCSrMdfzaoFxhizYsUK07x5c+Pl5WUkmccff9yYy1Yt+PDDD/M97siRI2bkyJGmVq1axtPT09SoUcPcddddZunSpSY3N9d6D4cOHWqCg4ONj4+P6d69u9mzZ4+56667TLNmzWz6mzBhgqlatapxc3Mzkkx0dLQxl60kcP78eZv9p0yZYiSZlJQUm/aCVhOwp96CzrVmzRojySxfvtyumlesWGH69OljqlWrZipUqGBuuOEG8/zzz5vk5OQifx5AWWUx9jyhAQAAAJQxTC0AAACASyLIAgAAwCURZAEAAOCSCLIAAABwSQRZAAAAuCSCLAAAAFySR2kXcC3k5ubq2LFj1tdGAgAAoGwyxiglJUXVq1eXm1vhY65/iyB77NgxhYaGlnYZAAAAsNORI0dUs2bNQvf5WwRZX19f6eIN8fPzK+1yAAAAUIDk5GSFhoZa81th/hZB9tJ0Aj8/P4IsAACAC7BnOigPewEAAMAlEWQBAADgkgiyAAAAcEkEWQAAALgkgiwAAABcEkEWAAAALokgCwAAAJdEkAUAAHCixMREPfPMM7r//vuVkpJS2uVoxowZ2r1791X3069fv2Ifc+HCBT355JPKzc296vPnhyALAACuiez4k8r8Y4/Tv7LjT15VXQ8++KCys7Od1sc///lPnT59Wv3799f58+c1atSoq+q7MP379y80JB45ckRz587VDTfckKfO4vrmm28K3HZ5v5fX5OHhITc3N82fP9+hcxblb/FmLwAAULqy40/qSPiDMplZTu/b4u2l0JjP5VkzxKHj77//frm7u19VDZf38ccff2jatGlq27atMjMzdffdd19V34X55ptvlJubKze3/McmP/jgA0VGRlrfkuWMa83P5f1eWdOQIUM0YsQIDR482OnnZUQWAACUuNwz50okxEqSycxS7plzRe63aNEiDRs2TP369dPo0aOt7QsXLlROTo4k6ejRo5owYYLGjBmjP/74Q/3797fu169fP+3YsUNjx47VuHHjFB8fn6ePyZMnKzY2VpMmTVK/fv00cOBALV682LpfWlqa3njjDQ0aNEj9+vXT+vXrJUmPPPKI+vXrp6FDh+rjjz+2GWUt6Lyvv/66cnNz1b9/f/Xr108ZGRl5rnnp0qXq0qVLvtda0P0oTFHXn19NLVu21KFDh3TixAm7zlEcBFkAAHDd27RpkyZMmKBbb71VkZGR6t27t3XbokWLlJOTo6ysLHXs2FHnz59XmzZt9OSTT9r8Of2bb77RP/7xD9144406f/68Tci91EeXLl0UEBCgO+64Q5GRkerRo4eWLVsmScrJyVHnzp0VHR2t7t27KzIyUrVr15Yk3X333YqMjFTnzp01b948vffee0WeNyIiQhaLRf3791dkZKQ8PT3zXHdsbKwaNmyYp87C7kdhirr+gmpq1KiR/vzzTzt/WvZjagEAALjupaSkyNfXV/Xr11dYWJjKly+fZ5+ff/5ZwcHBevfddyVJ7dq1U4sWLWz2+eijj1S9enUNGjRIvr6+ef6s36lTJwUEBKhbt25q3bq1Tpw4oZdeekmStG7dOqWnp+ubb76x/qn/kpCQEM2fP18nTpxQTk6OVq1apSeeeKLQ8956662yWCy699575eGRN9JlZWUpOztbFSpUcOh+5Keo6y+opgoVKigtLc2ucxQHQbaE7K8S4fCx9U+vd2otAAD83XXt2lWjRo3SP//5T+3YsUMPPfSQ3nnnHZtAmZCQoFq1alk/16lTJ08/1atXlyS5u7vLzc1NWVlZKleunF01nDx5UvXr188TYg8cOKC77rpLTz/9tNq1a6fY2FhFR0df9Xm9vLzk7++vM2fOKCTEdv6wPfcjP45ef341OANTCwAAwN/C6NGjtX79eh0+fFgLFizQgQMHbLY3adJEMTExSk9PlyT9+OOPTj1/q1atFB0drZMnbVdZiI2NVatWrfTss88qMjKyWCOX5cqVU2ZmZoHbw8LC9Mcff+S7raj74agra8rOzlZcXJzatGnjlP4vx4gsAAC47v344496//33JUmHDx9WnTp1bEZfJalNmzbq2LGjmjZtqhtuuEEXLlyQl5eX02po0qSJRo8erWbNmik8PFzlypXTuHHj1K5dO40cOVKdOnVSRkaGjDHy8/Ozq8/27durS5cuCg0N1fz58/OMjg4YMEDfffedunfvbtNuz/1w1JU1rVq1Sj169LB7+kJxEGQBAECJcwuqJIu3V4ktv+UWVKnQferXr6/IyEi5ubmpatWqCg8Pt87t/OKLL6yB9dNPP9XmzZuVlZWl9PR0TZgwwdrHwoULbfqcP3++9bjL+5g6darq1q0rSQoICNAHH3xgPebll1/WkCFDtGPHDmVmZqp27doKCgrSzp07FRMTo9DQUAUHB9u8wKCw83733XfasGGDzp07l+/DXgMGDND06dOVlJQkf39/a52F3Y+C2Hv9V9Y0e/Zsvfzyy4X27SiLMcaUSM9lSHJysvz9/ZWUlGT3v3CuFnNkAQCwlR1/0q5lsorLLaiSw2vIXumtt95SdHS0kpOTtXnzZn388cfq27evU/ouLdu3b1dgYKBq1Khxzc+dk5OjNWvWqFu3bnYfU5zcxogsAAC4JjxrhkhOCpwl5dZbb1Xt2rXl4+Oj1q1bq2rVqqVd0lW7cuWFgpw9e1YjR47M096tWzc9+uijDp3b3d29WCG2uAiyAAAAF4WHh5d2CaWmfPnyioyMzNN+aZpEWUSQBQAAgMqXL69+/fqVdhnFwvJbAAAAcEkEWQAAALgkgiwAAABcUqnOkd29e7fi4+Nt2nx8fPKdaL1v3z6dPHlSjRs3VuXKla9hlQAAAGVDdna2Tp06pWrVqhW57uvfQakG2enTp+t///ufzbIQdevWtQmy6enp6tevnzZu3Kh69epp9+7dmjJlis0CxQAAoOzLyspSTk6O0/t1d3d36hu4rnT06FGH1mB19LiC+vjhhx/0wAMPyMfHR7///rsuXLhQYsuDOaP2a6HUVy2IiIjQ119/XeD2yZMnKzY2Vvv27VNwcLC+//573XXXXWrfvr3at29/TWsFAACOycrK0r59+1QS72GyWCxq2LBhiYXZ2rVrKyMjQx4exYtNnTp1UmxsrLy9vR0+9+V9zJw5U5988on69Omj06dPq3fv3tq8ebPDfRfG0Wu+1kp9TDo9PV2//PKLYmNjlZWV97V18+bN04gRIxQcHCxJuvPOO9W6dWvNnTu3FKoFAACOyMnJKZEQK0nGGLtGei9NZ8zIyFBmZma+/SQlJdm0nTlzRro4QhkfH1/gNSQnJys+Pl4nTpywtq1du9YmxGZnZysjI8Panz11Xerj9OnTOnz4sLy9vRUfH6/MzEwtWbIkTx1nzpxRfHy80tPTJUnHjx9XfHx8nusq7Lz2XnNZUOpBds2aNXryySfVo0cP1a5dW99++61129GjR3X69GndeOONNsfceOON+v333wvsMzMzU8nJyTZfAADg7y00NFRPP/20atWqpYCAAH300UfWbR9//LECAwNVq1YtNWrUSL/99psk6emnn1ZOTo5uvfVWhYeH6/z58zZ9pqamqlOnTqpZs6bCw8N1zz33WLfVr1/fGlxnzpypSpUqqWbNmnryySdVu3Ztu+q61Mfo0aO1c+dODR48WOHh4brlllvUtm1b637ffvutatSooUaNGik8PFwrV66UJN11110KDw9X3bp11apVKx08eLDI8xZ1zWVJqQbZXr166dixY9q0aZMOHz6sESNGaMCAAdq7d68kKTExUZIUGBhoc1xQUJB1W36ioqLk7+9v/QoNDS3hKwEAAK6gQYMGOnXqlDZu3Kjnn39eknTo0CE99dRTWrt2rZKSkjRmzBgNHjxYkjR37ly5u7vr4MGDio+PV4UKFWz6W716tSpWrKikpCTFx8crOjo6zzkPHjyoyZMna9u2bUpISFCVKlXsqutyCxcuVMuWLbVy5UrFx8dr69at1m1Hjx7V0KFD9dlnn1lHZC8F6q1bt+rAgQPasWOH+vTpo6lTpxZ53qKuuSwp9SAbEBAgXZzf8vLLL6tcuXLWofJLc12u/JdAenp6ofNgJk2apKSkJOvXkSNHSvQ6AACAaxg+fLgkqU2bNkpNTVVWVpY2bdqk9u3bq2XLlpKkxx9/XPv379e5c+eK7K9Nmzb6448/dM8992j69Ok2Uwsu2bRpkzp37qxGjRpJksaMGWNXXfaKiYlRWFiYOnfubNOem5ur4cOHy8/PTzfeeKNmz56t/fv3O+28ZUGpTy24nJubmwIDA3X8+HHp4pC3m5tbniW64uPjVadOnQL78fb2lp+fn80XAADA5QNhFotFubm5KleunHVOqS5OUczJybHua7FYCuyvVq1a2r17twYNGqS9e/eqTZs2SklJsdnH29vbpv/Lvy+sLnuVK1dOqampedrXrl2rrVu36vTp0zpx4oRmz56dZy5xQect7JrLklILsrm5uXl+0Lt27dLBgwfVqlUr6eI7fyMiImzmzaakpGj16tXq3r37Na8ZAABcfzp06KDY2Fh9+OGH2r17t8aOHavbbrvN+if1oKAgrVu3Lt8Hn9LS0pSUlKSwsDA99NBDysrKypNvOnTooE2bNumzzz7Trl27NGHCBKcGxYiICB04cEBvvPGG4uLirA97eXp6Kjk5Wfv27VN0dLReeeUVu/ss7JrLklILsjk5Obrlllv0r3/9S4sXL9aMGTPUvXt3tWvXTv3797fu969//UvLli3TU089pa+++kq9evVSjRo1NGLEiNIqHQAAuKAr10WtUaOGLBaLAgICtGzZMi1cuFB9+/ZVZmamPv30U+t+UVFRGjt2bL4PPv3vf/9TeHi42rdvryeffFL/+c9/VL16dUlSzZo1ZbFYFBQUpK+//lrvvfeeBg8erIiICOvUysLqurwPSQoJCbGOoLq7u6tatWqSJD8/P61atUq//PKL7rjjDuvDXh06dNADDzygAQMG6MUXX9SQIUNs5ucWdt7CrrkssZhSjNmnT5/WrFmztG3bNgUEBCgiIkKDBw+Wu7u7zX5btmzRe++9p5MnT6pFixaaMGFCsd7ulZycLH9/fyUlJV2zaQb7q0Q4fGz90+udWgsAAKXNldeRdYazZ88qPT1dycnJeuutt5Samqovv/yytMsqk4qT20o1yF4rBFkAAEqfq77ZyxlGjRqlZcuWycfHR+Hh4Zo6dap1jXzYKk5uK9uvawAAANeNsh42S9L7779f2iVcl8rUqgUAAACAvQiyAAAAcEkEWQAAALgkgiwAAABcEkEWAAAALokgCwAAAJdEkAUAAIBLIsgCAADAJRFkAQAA4JIIsgAAAHBJBFkAAAC4JIIsAAAAXBJBFgAAAC6JIAsAAACXRJAFAACASyLIAgAAwCURZAEAAOCSCLIAAABwSQRZAAAAuCSCLAAAAFwSQRYAAAAuiSALAAAAl0SQBQAAgEsiyAIAAMAlEWQBAADgkgiyAAAAcEkEWQAAALgkgiwAAABcEkEWAAAALokgCwAAAJdEkAUAAIBLIsgCAADAJRFkAQAA4JIIsgAAAHBJBFkAAAC4JIIsAAAAXBJBFgAAAC6JIAsAAACXRJAFAACASyLIAgAAwCURZAEAAOCSCLIAAABwSQRZAAAAuCSCLAAAAFwSQRYAAAAuyWlBNjMz01ldAQAAAEVyKMju3btXL7zwgvXzs88+qwoVKqhOnTrasWOHM+sDAAAA8uVQkB0/frwiIiIkSXFxcZo5c6a++uor9erVS88884xDhbz22muqVKmSJk2alGfbnDlz1KpVK1WtWlW33367tm3b5tA5AAAAcP1wKMhu2LBBt956qyRp5cqV6tOnj+677z5NmTJFv/76a7H7W79+vT7++GMFBQXp/PnzNtu++OILPfbYY3rmmWe0ceNG1a9fX126dNHx48cdKR0AAADXCYeCrKenp06fPi1JWr58ubp06SJJunDhgjw8PIrV19mzZzV48GDNnTtXvr6+ebZHRUVp6NCheuihh1S/fn29++678vb21qxZsxwpHQAAANcJh4Jsz5499cADD2j06NH6+eef1bt3b0nSmjVrrKHWXsOGDdPAgQPVoUOHPNvOnTun7du3q2vXrtY2d3d3denSRRs2bHCkdAAAAFwnHAqy7777rjp37qyEhAQtWrRIVatWlST9+OOPeumll+zuZ8aMGTp27FiBxxw7dkySFBISYtMeHBxs3ZafzMxMJScn23wBAADg+lK8eQAX+fr66o033sjT/uGHH9rdx59//qmXX35Z0dHRRU5HcHOzzdseHh4yxhS4f1RUlF5++WW7awEAAIDrsTvIpqam2t2pj49PkfusW7dOSUlJuvnmm61tKSkp2rVrl+bOnaszZ84oODhYkpSQkGBz7KlTp6zb8jNp0iSNHz/e+jk5OVmhoaF21w8AAICyz+4gm9+DWAUpbLT0kpEjR+qhhx6yaYuIiFCHDh0UFRUld3d3Va5cWfXq1dP69evVt29f637r1q1Tv379Cuzb29tb3t7edtcLAAAA12N3kI2Ojnbqib28vOTl5WXT5u7uLm9vb1WqVMnaNm7cOP3zn//Uvffeq5tvvllvvfWWTpw4oVGjRjm1HgAAALgWu4NseHh4yVZSgDFjxighIUF33nmn0tLSVKdOHX377bdq2LBhqdQDAACAssFi7JkHUICUlBTFxcXJGKOGDRsWa/pBQf15eHiofPnyebYZY5SRkZHvtqIkJyfL399fSUlJ8vPzu6oa7bW/SoTDx9Y/vd6ptQAAALiK4uQ2h5bfyszM1Lhx4xQUFKQbb7xRN910k4KCgjRu3DhlZmY6Wrd8fX0LDKoWi8WhEAsAAIDrk0PLbz377LP67rvv9Mknnyg8PFwWi0XR0dGaOHGiLBaLpk+f7vxKAQAAgMs4NLUgJCREy5YtU9u2bW3aN2/erF69eunkyZPOrPGqudrUgqvBtAQAAODKSnxqwblz51S/fv087Q0aNFBSUpIjXQIAAADF4lCQbd26taZNm5an/a233lKrVq2cURcAAABQKIfmyL7xxhvq2bOnFi1apFtuuUWStGnTJh04cEDLly93do0AAABAHg6NyHbs2FF79uxRr169dPr0aSUkJKhXr17as2ePOnbs6PwqAQAAgCs4NCI7ceJEvf7663r99dedXxEAAABgB4dGZKdPn67s7GznVwMAAADYyaEge/PNN2vt2rXOrwYAAACwk0NTC+644w5FRkbqiSeeUNOmTeXl5WWzvW/fvs6qDwAAAMiXQy9E8PHxKXR7amrq1dTkdLwQAQAAwDUUJ7c5NCJb1oIqAAAA/n4cmiNbqVIlh7YBAAAAzuJQkC3oNbTZ2dk6f/781dYEAAAAFKlYUwsWLFiQ7/eSlJubq19//VUNGjRwXnUAAABAAYoVZJ944ol8v5ckT09P1alTR++++67zqgMAAAAKUKwgm5CQIElq3Lixdu/eXVI1AQAAAEVyaI4sIRYAAAClzaHltyRp+/btio6O1tmzZ/Nsmzhx4tXWBQAAABTKoSD77rvvasyYMapbt64CAgLybCfIAgAAoKQ5FGT//e9/67PPPtOAAQOcXxEAAABgB4fXkb377rudXw0AAABgJ4eC7M0336xNmzY5vxoAAADATg5NLbjtttsUGRmpp59+Wg0aNJDFYrHZ3rdvX2fVBwAAAOTLYowxxT3Ix8en0O2pqalXU5PTJScny9/fX0lJSfLz87sm59xfJeKanOdK9U+vL5XzAgAAOENxcptDI7JlLagCAADg78ehObIAAABAaXP4hQhZWVlatGiRdu3aJWOMmjZtqvvuu0+enp7OrRAAAADIh0MjsnFxcWratKmGDRumxYsXa8mSJRo2bJiaNm2quLg451cJAAAAXMGhIDt27Fi1atVK8fHx+v3337Vt2zYdPXpULVu21Lhx45xfJQAAAHAFh6YWrF27VnFxcQoMDLS2BQQEaObMmWrQoIEz6wMAAADy5dCIrIeHhzIyMvK0nz9/Xh4eDk+7BQAAAOzmUJDt2bOnhg4dajMfdt++fRoyZIh69uzpzPoAAACAfDkUZN9++20ZY9SwYUMFBgYqMDBQjRo1ksVi0dtvv+38KgEAAIArODQPICQkRGvXrlVMTIxiY2NlsVjUtGlThYeHO79CAAAAIB9XNaE1PDyc8AoAAIBS4dDUgoMHD+q1117L0/7aa6/p4MGDzqgLAAAAKJRDQfaJJ55QmzZt8rS3bt1aY8eOdUZdAAAAQKEcCrI///yzIiIi8rRHRETo559/dkZdAAAAQKEcCrJ+fn7as2dPnvbdu3erQoUKzqgLAAAAKJRDQfa+++7TiBEj9Oeff1rb/vjjD40YMUL9+vVzZn0AAABAvhwKslFRUQoKClKrVq1UqVIl+fv7q3Xr1goODtbrr7/u/CoBAACAKzi0/JaPj49Wr16t6Ohobd26VRaLRW3atFG7du2cXyEAAACQj6taR7Zdu3aEVwAAAJQKh6YWAAAAAKWNIAsAAACXRJAFAACAS7qqObLO8Msvv2jlypVKTU1Vs2bNNGDAAJUvX95mn8OHD2vevHk6efKkWrRooSFDhsjb27vUagYAAEDpsxhjjCMHZmZmKi4uTomJiXm2dejQwa4+nnjiCe3evVtdu3aVh4eHPvvsM2VkZCg6OloBAQGSpJ07d+rWW29V586dFR4erk8++USVKlXSzz//LE9PT7vOk5ycLH9/fyUlJcnPz6+YV+qY/VXyvvnsWqh/en2pnBcAAMAZipPbHAqyq1ev1sCBA3Xy5Ml8t9vb5cGDB1WnTh3r53Pnzqly5cqaN2+eBg4cKEnq3bu30tPTtWrVKlksFh0/flx169bVe++9p2HDhtl1HoIsAACAayhObnNojuzo0aM1dOhQHT9+XOfPn8/zZa/LQ6wknTlzRjk5OQoJCZEkZWVlaeXKlYqMjJTFYpEkVatWTbfddpuWLFniSOkAAAC4Tjg0R/bQoUN64YUXVLFixasuIDY2VtOmTVNycrK2bNmid955R926dZMuzo3Nzs7OE3jr1q2r9esLHnnMzMxUZmam9XNycvJV1wkAAICyxaER2datW2v79u1OKcDf31/h4eG68cYbVbFiRX3zzTdKSkqSJOvorq+vr80xvr6+hY78RkVFyd/f3/oVGhrqlFoBAABQdjg0Ivv2229r+PDhevTRR1W/fn3rn/0v6dGjh9191axZUyNGjJAuPvzVqFEjTZs2TS+99JJ1XsSVD5SdPXu20DkTkyZN0vjx462fk5OTCbMAAADXGYeC7Pbt27V7926NGTNG7u7uebZfuHDBoWJ8fHzUsGFDxcXFSZJCQ0Pl5+ennTt3qmfPntb9YmNj1bx58wL78fb2ZnkuAACA65xDUwsmT56sl156SSkpKbpw4UKeL3tkZmZq5cqVNm27du3Sb7/9pvDw8P8vzs1N/fv315w5c5SWliZJ2rx5s2JiYjRgwABHSgcAAMB1wqHlt/z8/HT8+PGretjrwoULuv/++3XgwAE1bdpU586d09q1a9W/f3+9//771jViExIS1LVrV6Wlpally5ZavXq1HnroIb377rt2n4vltwAAAFxDia8j26FDB0VFRSki4urD2t69e7Vt2zZVrFhRLVu2VK1atfLsk52drTVr1ljf7NW6detinYMgCwAA4BqKk9scmiMbERGh+++/X08//bQaNGiQ52Gvvn372t1Xo0aN1KhRo0L38fT0VPfu3R0pFQAAANcph0ZkfXx8Ct2empp6NTU5HSOyAAAArqHER2TLWlAFAADA349DqxYAAAAApc2hEVlJysrK0qJFi7Rr1y4ZY9S0aVPdd9991tUGAAAAgJLk0IhsXFycmjZtqmHDhmnx4sVasmSJhg0bpqZNm1pfZgAAAACUJIeC7NixY9WqVSvFx8fr999/17Zt23T06FG1bNlS48aNc36VAAAAwBUcmlqwdu1axcXFKTAw0NoWEBCgmTNnqkGDBs6sDwAAAMiXQyOyHh4eysjIyNN+/vx5eXg4PO0WAAAAsJtDQbZnz54aOnSozXzYffv2aciQIerZs6cz6wMAAADy5VCQffvtt2WMUcOGDRUYGKjAwEA1atRIFotFb7/9tvOrBAAAAK7g0DyAkJAQrV27VjExMYqNjZXFYlHTpk0VHh7u/AoBAACAfDgUZJs0aaJdu3YpPDyc8AoAAIBS4dDUghMnTigpKcn51QAAAAB2cijI3nfffZo1a5bzqwEAAADs5NDUgsTERE2aNEkLFixQ06ZN5eXlZbN97ty5zqoPAAAAyJdDQbZ8+fIaOHCg9fOFCxecWRMAAABQJLuD7NKlS9WrVy9J0vz580uyJgAAAKBIds+R7d27t/V7Hx+fkqoHAAAAsIvdQTYoKEh79+6VJKWlpZVkTQAAAECR7J5a8PDDD6t58+aqUqWKJKlmzZoF7hsfH++c6gAAAIAC2B1k33rrLd1///2Ki4vToEGD9Oqrr5ZsZQAAAEAhirVqwaU3ecXExGjIkCElVxUAAABQBIdeiDBz5kznVwIAAAAUg0NBFgAAAChtBFkAAAC4JIIsAAAAXBJBFgAAAC7JoSCbm5ur6dOnq0WLFqpYsaK1fcKECTp8+LAz6wMAAADy5VCQfeutt/TOO+9ozJgxSk9Pt7a3aNFCU6ZMcWZ9AAAAQL4cCrLvv/++vvrqK40cOdKmvVu3bvr222+dVRsAAABQIIeC7JEjR9SsWTNJksVisbaXL19eKSkpzqsOAAAAKIBDQbZu3brasmWLdEWQXbhwoZo0aeK86gAAAIACFOsVtZc8/fTTGjRokF599VVJ0urVq7VixQrNmDFD//3vf51dIwAAAJCHQ0H2kUce0YULF/Tss88qNzdX3bp1U0hIiKZPn66BAwc6v0oAAADgChZjjLmaDo4fP67c3FxVr17dZppBWZKcnCx/f38lJSXJz8/vmpxzf5WIa3KeK9U/vb5UzgsAAOAMxcltDo3IXq5atWpX2wUAAABQbA497LVs2TINGTIkT/uQIUP0/fffO6MuAAAAoFAOTS1o2bKlPv/8czVv3tymffv27Ro8eLC2bdvmzBqv2t9pasHVYFoCAAAobcXJbQ6NyO7du1e1atXK016rVi3t3r3bkS4BAACAYnEoyNavX1/Lli3L07506VLVqVPHGXUBAAAAhXLoYa+nnnpKI0eO1L59+9SxY0cZY7Ru3Tq98cYbmjZtmvOrBAAAAK7gUJAdMWKEzp8/r9dee02TJ0+WJIWEhCgqKkqPPPKIs2sEAAAA8riqdWSNMTp27JgsFouqVavGOrKX4WEvAACA4rtm68haLBbVqFHjaroAAAAAHOJQkE1LS9P06dO1ceNGnT17Ns/2mJgYZ9QGAAAAFMihIPvYY49pzZo1euCBBxQQEOD8qgAAAIAiOBRklyxZoo0bN6pp06bOrwgAAACwg0NBtkKFCqpZs6ZTCjh//rx27NghDw8PNW7cWOXLl893v127dunkyZNq0qSJQkJCnHJuAAAAuC6HgmyfPn00Z84cPfnkkw6f2Bij5557TnPmzFHt2rWVnp6ukydP6r333tP9999v3S8tLU333nuvNm3apAYNGmjHjh2aPHmyJk6c6PC5kT9HV1pgtQMAAFAaHAqyp0+f1uzZs7Vw4UI1aNAgz7Jbc+fOLbIPY4z8/f21f/9++fj4SJKmTp2qQYMGqX379tbVEF588UXt3btX+/btU+XKlbVy5Ur16NFDHTp0UIcOHRwpHwAAANcBh15RW65cOQ0cOFB169ZVTk6OLly4YPNl14nd3DRx4kRriJWkhx9+WJmZmfrjjz+sbZ988omGDx+uypUrS5LuuOMOtWnTRvPmzXOkdAAAAFwnHBqRnT9/vvMrkfTLL79Ikho1aiRJio+PV0JCgtq0aWOzX5s2bWzC7pUyMzOVmZlp/ZycnFwi9QIAAKD0ODQiWxKOHz+uMWPG6OGHH1aDBg0kSefOnZMkBQYG2uwbFBSkxMTEAvuKioqSv7+/9Ss0NLSEqwcAAMC15vCbvdLS0rRx40YdPnw4z3SCRx99tFh9JSQkqHv37mrYsKFmzZplbffy8pIurmxwufT0dOu2/EyaNEnjx4+3fk5OTibMAgAAXGccCrLbtm1Tr169lJWVpYSEBNWoUUPHjh2TMUZ169YtVpA9c+aMunXrpsDAQC1dutRm+a3Q0FC5u7vryJEjNsfEx8erTp06Bfbp7e0tb29vRy4NDnB0tQOx4gEAALgKDk0tGD9+vIYNG6bTp09LF4Pl4cOH1alTJz388MN293P27Fl169ZN/v7++v7771WxYkWb7eXLl1fHjh317bffWtuSkpK0atUq9ejRw5HSAQAAcJ2wGGNMcQ/y9/fXgQMHFBgYKDc3N2VkZMjLy0v79u1T165ddfjw4SL7yMrKUrt27RQfH6/33nvPJsQ2b97c+sKFmJgYde7cWY888ojatWunWbNmKSEhQb/99psqVKhgV73Jycny9/dXUlKS/Pz8inu5DrmaUcq/E0ZkAQDA5YqT2xyaWpCcnGx9ACs4OFjx8fGqV6+eQkJCdOrUKbv6yMjIUJUqVVSlShV9+OGHNtueeuopa5ANDw9XdHS0Zs2apS+//FKdOnXS+PHj7Q6xAAAAuD45NCJrsVh06bB+/frJ09NTjz/+uObNm6dt27Zpy5YtJVGrwxiRvT4xmgsAwPWnOLnNoTmykydPtn4/depUxcXFqWPHjlqzZo3ee+89R7oEAAAAisWhINu5c2fr9/Xq1dPmzZuVmZmpuLg4paenO7M+AAAAIF8OBdnbbrstT5unp2eB2wAAAABnc+qbvRITE+Xr6+vMLgEAAIB8FWvVgsjIyHy/l6Tc3FzFxsaqffv2zqsOAAAAKECxgqyPj0++3+vi1ILBgwdr+PDhzqsOAAAAKECxguxHH30kSapcubJef/31kqoJAAAAKJJDc2RffvllrVq1yvp548aNeuihhzR58mRlZWU5sz4AAAAgXw692WvKlCny9fVVt27dlJKSoj59+qh9+/b64osvlJaWpjfffNP5lQJXuJqXTvAyBQAAXJ9DI7Lz58/XoEGDJEk//PCDGjZsqCVLlmjx4sX66quvnF0jAAAAkIdDQTYhIcG6zNaaNWt05513SpJq166tM2fOOLdCAAAAIB8OBdnmzZvrzTff1KZNm7RgwQL16NFDkrRjxw41b97c2TUCAAAAeTgUZN98803Nnj1bYWFh6tu3r2655RZJ0rRp0/TUU085u0YAAAAgD4sxxjhyoDFG6enpqlixorXtr7/+Ut26dWWxWJxZ41VLTk6Wv7+/kpKS5Ofnd03OeTUPIqFs40ExAABKTnFym0OrFkiSxWKxCbGSVK9ePUe7AwAAAIrF7iDbr18/SdLXX39t/b4gX3/99dVXBgAAABTC7iBbuXLlfL8HAAAASoPDc2RdCXNk4UzMkQUAoOQUJ7c5tGoBAAAAUNrsnlrQq1cvuztdunSpo/UAAAAAdrF7RLZx48bWrypVqmjZsmWKj49XtWrVVK1aNcXHx2vZsmWqUqVKyVYMAAAAFGdE9s0337R+HxkZqSlTpuiFF16w2efVV19VbGyscysEAAAA8uHQOrI//fSTZs+enaf9iSee0A033OCMugAAAIBCOfSwV3Z2tnbs2JGnffv27crOznZGXQAAAEChHBqRHTp0qO6//35NmjRJN998s4wx2rJli6KiojRs2DDnVwmUIVeztBpLdwEA4DwOBdmpU6cqODhYr732mk6dOiVJCg4O1vjx4zVhwgRn1wgAAADkcdUvRDhz5owkKSgoyFk1OR0vREBZwYgsAACFK05uc2hE9nJlOcACAADg+sWbvQAAAOCSCLIAAABwSQRZAAAAuCSCLAAAAFwSQRYAAAAu6apXLQBgP16mAACA8zAiCwAAAJdEkAUAAIBLIsgCAADAJRFkAQAA4JIIsgAAAHBJBFkAAAC4JIIsAAAAXBJBFgAAAC6JIAsAAACXRJAFAACASyLIAgAAwCV5lHYBAOyzv0qEw8fWP73eqbUAAFAWMCILAAAAl0SQBQAAgEsq1SB7/vx5zZ07V+Hh4apUqZI2bNiQ735z5sxRq1atVLVqVd1+++3atm3bNa8VAAAAZUupBtmXXnpJa9as0TPPPKOkpCRduHAhzz5ffPGFHnvsMT3zzDPauHGj6tevry5duuj48eOlUjMAAADKhlINsq+//rrmzZunsLCwAveJiorS0KFD9dBDD6l+/fp699135e3trVmzZl3TWgEAAFC2lGqQtVgshW4/d+6ctm/frq5du1rb3N3d1aVLlwKnIQAAAODvoUwvv3Xs2DFJUkhIiE17cHCwtm7dWuBxmZmZyszMtH5OTk4uwSoBAABQGsp0kL3Ezc124NjDw0PGmAL3j4qK0ssvv3wNKgNcA2vQAgCuR2V6+a3g4GBJUkJCgk37qVOnrNvyM2nSJCUlJVm/jhw5UuK1AgAA4Noq00G2cuXKqlevntavtx0RWrduXaEPiHl7e8vPz8/mCwAAANeXMh1kJWncuHH66KOPtHHjRmVlZSkqKkonTpzQqFGjSrs0AAAAlKJSnSP7+eefa/To0db5rr169ZKHh4cmTpyoiRMnSpLGjBmjhIQE3XnnnUpLS1OdOnX07bffqmHDhqVZOgAAAEqZxRT21FQJy8rKUnp6ep72cuXKqVy5cjZtxhhlZGSofPnyxT5PcnKy/P39lZSUdM2mGVzNwzVAWcLDXgCAa6k4ua1UR2S9vLzk5eVl174Wi8WhEAsAAIDrU5mfIwsAAADkhyALAAAAl0SQBQAAgEtyiTd7ASg9jj64yENiAICSxogsAAAAXBJBFgAAAC6JIAsAAACXRJAFAACASyLIAgAAwCWxagGAEnE1r2lmxQMAgD0YkQUAAIBLIsgCAADAJTG1AECZw7QEAIA9GJEFAACAS2JEFsB15WpGc0sLo8gA4BhGZAEAAOCSCLIAAABwSQRZAAAAuCTmyAJAKSuteb3MzQXg6hiRBQAAgEsiyAIAAMAlMbUAAP6mePEEAFfHiCwAAABcEiOyAIBic8UXT5QGRq6BkkWQBQCghLhi4Cd8w5UQZAEAgJWrhW+C998bc2QBAADgkhiRBQAALmvHjh2lXcI11bx589IuoUwhyAIAALiIqwnu12MIJsgCAAD8DVyPIZg5sgAAAHBJBFkAAAC4JIIsAAAAXBJBFgAAAC6JIAsAAACXRJAFAACASyLIAgAAwCURZAEAAOCSCLIAAABwSQRZAAAAuCSCLAAAAFwSQRYAAAAuiSALAAAAl0SQBQAAgEsiyAIAAMAlEWQBAADgkgiyAAAAcEkepV2APQ4fPqx58+bp5MmTatGihYYMGSJvb+/SLgsAAAClqMyPyO7cuVOtWrXS1q1bFRoaqnfeeUddunRRdnZ2aZcGAACAUlTmR2SfffZZ3XjjjVq0aJEsFosGDx6sunXr6tNPP9WwYcNKuzwAAACUkjI9IpuVlaWVK1cqMjJSFotFklStWjXddtttWrJkSWmXBwAAgFJUpkdkDx8+rOzsbNWpU8emvW7dulq/fn2Bx2VmZiozM9P6OSkpSZKUnJxcgtXaSsm9cM3OBQDA31VGamppl/C3cC0z1KVzGWOK3LdMB9nz589Lknx9fW3afX19rdvyExUVpZdffjlPe2hoaAlUCQAASk27dqVdAUpISkqK/P39C92nTAdZPz8/SVJiYqJN+9mzZ63b8jNp0iSNHz/e+jk3N1dnz55VUFCQdYoCAAAAyh5jjFJSUlS9evUi9y3TQTY0NFR+fn7auXOnevbsaW2PjY1V8+bNCzzO29s7z/JclSpVKtFaAQAA4BxFjcReUqYf9nJzc1P//v01Z84cpaWlSZI2b96smJgYDRgwoLTLAwAAQCmyGHtm0paihIQEde3aVWlpaWrZsqVWr16thx56SO+++25plwYAAIBSVKZHZCWpcuXK2rJli9577z3dc889Wrt2LSEWwN/aihUrtHfvXqf2mZCQoAULFhT4lHB8fLwWLVrk1HPaU8eJEyf01Vdflfh5AbimMh9kJcnT01Pdu3fXoEGD1Lp169IuB/hb2Llzp1atWnVNz/nDDz9o165d1/ScRSmLNU2YMEHff/+9U/vcvXu3BgwYoJycnHy3x8TEXJOX0FxZx++//64HH3zQrmOPHTumhQsXlnCFAMoSlwiyAK69RYsW6YUXXrim53zuuee0ePHia3rOopTFmv5OqlWrpv79+9u179atWzVo0KASrwlA2VGmVy0AUDr279+v7du368yZM1qwYIEkKSwsTKmpqTp9+rTat2+vX375RWfPnlW/fv0kSTk5Odq0aZNOnTqlRo0aqUmTJjZ9btiwQfHx8bJYLAoODlbr1q0VEBBg3b527VolJibqzz//tJ7zvvvu0+rVq1WvXj0FBgZq27Ztcnd3V0REhDw9PXX8+HFt3rxZVapUUXh4eJ7l9YqqacWKFapXr56CgoK0bds2eXp6Kjw83LrqSUE1eXp6Fnr/LvXr7++vTZs2qXLlymrXrl2R98CemvJz4cIFLV68WLVq1dLNN98sSTp+/Li2bNkiX19f3XjjjfkuWbhlyxadPHlSzZo1K/R6rryn27dv16FDh9SmTRvVqlVLurju9+LFi3X77bcrKCjIun9aWpqWLFmi7t27KzAwMN8+C6sjJCREd999t01bcnKyNm/erNzcXN10000KDAzUmTNntH79euXm5lp/VjfccIPq1q2rFStWSBdXtGnQoIFatGhh0198fLw2bdqke+65R9u3b9fhw4fVrFkz1a1bN089J0+e1JYtW+Tv76+wsLA8vwv23HcAzkOQBZDHgQMHtGvXLiUmJurbb7+VLo6M/fjjj/ryyy9Vrlw5BQcHq0aNGurXr5/++usv9e7dW5JUv359bdmyRbfeequ++OILeXj8///M/Prrr9q8ebMk6ciRI4qNjdWnn35qPS46OlqJiYmKjY1Vbm6uJKlPnz6aMGGC/Pz8dPToUbVs2VKbN29WjRo1NHToUL311ltq3ry5YmJi1LVrV2uAkWRXTRMmTFBISIgOHjyopk2bavv27fLz81N0dLQqVqxYYE1FBdkJEyYoODhYf/31l1q1aqWuXbuqXbt2Rd4De2q6UkZGhh544AEdO3ZMy5cvlyRNmTJFb731lsLDw5Wenq7du3fr888/V7du3aSLazQ++OCDWr58udq1a6edO3eqadOmRf5eZGdnWx++rVixomJiYjRr1iwNHTpU5cuX18svv6y//vpLzz33nPWYBQsW6B//+IeOHTuWpz976rg0teCBBx6QJK1fv1533323mjRpIn9/f+3evVtTp05V69atFR0drZycHOvv7J133qnAwEDr54yMDMXExKh169b67rvv5OXlJV2cNvHwww+rffv2SktLk4+Pj9auXavZs2dr6NCh1lpeeeUVvf7667rpppvk5uZmDenVqlWz674DKAEGAPIxZcoUExYWZtP2/PPPG0lm+fLlNu033XSTmThxovVzSkqKady4sZk2bVqB/f/3v/81VatWNRcuXLDpJyoqyma/Zs2amYYNG5qkpCRjjDEHDx407u7upnnz5iYlJcUYY8yuXbuMJPPHH38Uq6ZmzZqZBg0amMTERGOMMampqaZq1armvffeK7SmojRr1szUrFnTnD59utD98rsH9tTUrFkzM23aNHPu3DnTsWNHc9ttt5nk5GRjjDHfffedCQ4ONocOHbLu/9FHH5mqVaua8+fPG2OM+fLLL03FihXNvn37rOdo1aqVkWSys7PzrXXhwoVGkhk/fry1bdasWcbHx8ecOnXKGGPMm2++aRo0aGBzXPv27c2YMWPy7dOeOpYvX27c3d2tx/Tq1cuMHTvW+jk1NdWsXLnSGGPMkiVLjLe3d6H3PDU11TRp0sTMnDkzz7Vd3vbmm2+a4OBg6+dvvvnGuLu7m59//tna9ueff5r9+/cbY+d9B+B8zJEFUCwNGzZUjx49rJ///PNP/fbbb6pdu7a+/vprLVy4UN9//70aNGigNWvW2Bx74sQJrVq1Sl9++aVycnJ04sQJHT58uMhzDhgwwPon2tq1a6tatWp68MEH5ePjI0lq3LixKlWqZH2Svzg1DRgwwPrClIoVK6pt27bas2fPVd+nAQMGqHLlynna7bkH9tR08uRJde7cWYGBgVq+fLn1Vd4ff/yxmjVrpk2bNmnhwoX66quv5OHhoRMnTlgfWvvqq690zz33qEGDBtZzPP7443Zd1z/+8Q/r94888og8PT2tD54NHjxYhw8f1rp16yRJe/bs0S+//FLgQ2KO1FG+fHkdOHBAKSkp1mO6d+9e6DG5ubnasmWL/ve//2nJkiUKDQ3Vpk2bbPZxd3fXyJEjrZ87d+6sU6dO6dy5c5KkefPmqXfv3urUqZN1nxYtWqhevXqSnfcdgPMxtQBAsVz6M+olBw8elCStWbPGZo5qxYoV1bBhQ+vnV155Rf/+97910003KTg42Np+6tSpfOciXu7KeaTe3t75tmVkZBSrJkl55m1e3s/VuPI+qRj3wJ6a3n77bZUrV04//fSTzfzZgwcPKjMzU19//bXN/v3795eb2/+PXRw+fFh33HGHzfaifgaSVK5cOYWEhFg/u7u7q1atWjp06JAkqUqVKurTp4/mzJmjjh07as6cOWrTpk2Bq804UkdUVJRGjBihkJAQhYWF6c4779To0aPznXYhSYcOHdLtt9+urKwsNWvWTL6+vjp06JD1Xlzi4+NjM2Xk0j29dN8PHz5caGC2574DcD6CLIBiufKBqksjpVFRUdbRqSsdPXpUkydP1i+//KJ27dpJF/+P/5tvvilw3dKrYU9NJe3K++Tse/Dcc89pxYoV6tWrl1asWGEdkfXz81OtWrX0ySefFHhsUFCQEhMTbdqu/JyfjIwMnT9/XuXLl7c57vKR50ceeUT33nuvpk2bpk8//dRmvqwz6qhfv77WrFmjhIQE/fzzz3rttde0fPly/fTTT/nu/9prr6levXpavny59WcyZMgQnThxosjrvVylSpV05syZArfbc98BOB//TASQLx8fH7tGJm+55RYFBARo9uzZNu3GGB0/fly6+GdwXXyK/JIrR66Kc05n1GSv/Gras2dPsRfpt/ceFKeuSw939ezZU6mpqZKkHj16aPHixXmu8+jRo9bvO3TooGXLlik7O9vaZu/LDi5fiuy3337TkSNH1L59e2vb7bffripVqmjYsGE6e/asBg4cWGBfjtRx6ToqV66sfv366bnnntOvv/4qXbwn2dnZ1gfzdHEqR6NGjawhNjU1VStXrrTrWi/XvXt3LV68WElJSda2rKws62d77jsA52NEFkC+2rZtq2eeeUb/+c9/VL16dYWFheW7X4UKFfThhx/qwQcf1NGjR3Xbbbfp5MmTWrx4sR577DENHTpUzZo1U4MGDfTAAw/owQcf1I4dO/Tpp5/me84FCxaoTp06KleunO677z6HarenpuLchytr+vbbb/XKK69Yn6S3h733oDh8fX21YsUK9ejRQz179tTy5cs1btw4LV26VGFhYRo9erQCAwO1detWrVu3Tjt37pQkjRkzRu+//766d++uAQMG6Ndff7Xr5Reenp6aMGGC9u/fr4oVK+qNN97Qgw8+aDN1wGKxaNiwYXrxxRcVGRmZZwrI5RypY+TIkfLz81NERISMMZo5c6Z1CbhmzZrJy8tLzz77rG666SbdcMMN6tu3r8aOHauqVauqUqVK+uCDDxz6x9K4ceO0aNEihYWFaeTIkbJYLPriiy80Z84c+fv723XfATgfI7IA8tWhQwd98cUX2rt3rxYvXqzDhw+rZcuW6tKlS55977vvPv3555+qV6+e1q9fr4yMDH3wwQfWwOjt7a3169crPDxcP//8s3x8fBQdHa3+/fvb/Fn6pZde0vDhw7V27Vp9++23ys7OVs+ePW1GMSWpV69e1geELunbt6/q1Kljd026OJJ5Zd8dOnSwrsVaUE0xMTEaO3Zsgfcuv37tvQf21HT5PpfCbJMmTTRv3jyVL19ea9as0b/+9S8dOHBAW7duVdu2bbV161br8X5+ftq0aZPatWunTZs2qWXLlvrxxx8Lnc8ZGhqqQYMGad26dcrKytLvv/+u559/Xh9//HGeffv27StJRb4JzJ46rnwhwpIlS9S3b1/FxsZq586deuGFF/Tf//5XujhHd9WqVcrMzNTixYu1fft2DRs2TPPmzdPBgwf1xx9/6Pnnn9eMGTPUuXNnm2u78h9NlSpVUv/+/a3TKCpUqKD169dr/Pjx2rFjh44fP67//ve/at68uXTxIbSi7jsA57OYkpigBgDXsccee0xTp061zkuFralTp2r27NmKi4vjQScAJYogCwBwip07d2r9+vV67rnn9O9//1sjRowo7ZIAXOf4pzIAwCn27Nmj9evXa8qUKYRYANcEI7IAAABwSYzIAgAAwCURZAEAAOCSCLIAAABwSQRZAAAAuCSCLAAAAFwSQRYAAAAuiSALAAAAl0SQBQAAgEsiyAIAAMAl/R/CTI5MPI2ATQAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 700x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Every treatment's hit distance, reusing the table hit_calling already wrote (no recompute).\n",
    "ranked = hits.sort_values(\"distance\", ascending=False)\n",
    "colours = [ACCENT if is_hit else NULL for is_hit in ranked[\"is_hit\"]]\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(7, 4))\n",
    "ax.bar(range(len(ranked)), ranked[\"distance\"], color=colours, width=1.0)\n",
    "ax.set_xticks([])\n",
    "ax.set_xlabel(\"treatment, ranked by distance\")\n",
    "ax.set_ylabel(\"distance from controls\")\n",
    "ax.set_title(\"Hit distances across all treatments\")\n",
    "handles = [\n",
    "    plt.Rectangle((0, 0), 1, 1, color=ACCENT),\n",
    "    plt.Rectangle((0, 0), 1, 1, color=NULL),\n",
    "]\n",
    "ax.legend(handles, [\"significant (is_hit)\", \"not significant\"], frameon=False, fontsize=8)\n",
    "fig.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d8d6658d",
   "metadata": {},
   "source": [
    "A handful of treatments tower over the rest, and the coloured bars are the ones whose permutation q-value clears significance; everything in grey below them is within reach of a control-sized draw."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c007",
   "metadata": {},
   "source": [
    "## Which features moved\n",
    "\n",
    "A hit call says a treatment moved. It does not say what changed. {func}`~mantispy.tl.effect_size` gives a\n",
    "per-feature effect against the controls with Mann-Whitney p-values, and one BH correction over the whole table."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "c008",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-11-11T01:20:24.505502Z",
     "iopub.status.busy": "2026-11-11T01:20:24.505311Z",
     "iopub.status.idle": "2026-11-11T01:20:26.491816Z",
     "shell.execute_reply": "2026-11-11T01:20:26.490490Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"height:525px; width:100%;\">            <script src=\"https://cdnjs.cloudflare.com/ajax/libs/mathjax/3.2.2/es5/tex-svg.min.js\"></script><script>if (window.MathJax && window.MathJax.Hub && window.MathJax.Hub.Config) {window.MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}</script>                <script>window.PlotlyConfig = {MathJaxConfig: 'local'};</script>\n",
       "        <script charset=\"utf-8\" src=\"https://cdn.plot.ly/plotly-4.1.1.min.js\" integrity=\"sha256-O24V1F27f8pb0glCkelh3cVHLNiHAJ5gCaVtq2aNch8=\" crossorigin=\"anonymous\"></script>                <div id=\"0872bfa0-8db0-4a83-9afb-cfead34f211e\" class=\"plotly-graph-div\" style=\"height:100%; width:100%;\"></div>            <script>                window.PLOTLYENV=window.PLOTLYENV || {};                                if (document.getElementById(\"0872bfa0-8db0-4a83-9afb-cfead34f211e\")) {                    Plotly.newPlot(                        \"0872bfa0-8db0-4a83-9afb-cfead34f211e\",                        [{\"customdata\":[[\"Cells_AreaShape_Extent\"],[\"Cells_AreaShape_FormFactor\"],[\"Cells_AreaShape_Solidity\"],[\"Cells_AreaShape_Zernike_1_1\"],[\"Cells_AreaShape_Zernike_2_0\"],[\"Cells_AreaShape_Zernike_2_2\"],[\"Cells_AreaShape_Zernike_3_1\"],[\"Cells_AreaShape_Zernike_4_0\"],[\"Cells_AreaShape_Zernike_4_2\"],[\"Cells_AreaShape_Zernike_5_1\"],[\"Cells_AreaShape_Zernike_5_3\"],[\"Cells_AreaShape_Zernike_6_0\"],[\"Cells_AreaShape_Zernike_6_2\"],[\"Cells_AreaShape_Zernike_7_1\"],[\"Cells_AreaShape_Zernike_7_3\"],[\"Cells_AreaShape_Zernike_8_0\"],[\"Cells_AreaShape_Zernike_8_2\"],[\"Cells_AreaShape_Zernike_8_4\"],[\"Cells_AreaShape_Zernike_9_1\"],[\"Cells_AreaShape_Zernike_9_3\"],[\"Cells_AreaShape_Zernike_9_5\"],[\"Cytoplasm_AreaShape_Compactness\"],[\"Cytoplasm_AreaShape_EulerNumber\"],[\"Cytoplasm_AreaShape_FormFactor\"],[\"Cytoplasm_AreaShape_Orientation\"],[\"Cytoplasm_AreaShape_Zernike_0_0\"],[\"Cytoplasm_AreaShape_Zernike_1_1\"],[\"Cytoplasm_AreaShape_Zernike_2_2\"],[\"Cytoplasm_AreaShape_Zernike_3_1\"],[\"Cytoplasm_AreaShape_Zernike_3_3\"],[\"Cytoplasm_AreaShape_Zernike_4_2\"],[\"Cytoplasm_AreaShape_Zernike_4_4\"],[\"Cytoplasm_AreaShape_Zernike_5_1\"],[\"Cytoplasm_AreaShape_Zernike_5_3\"],[\"Cytoplasm_AreaShape_Zernike_5_5\"],[\"Cytoplasm_AreaShape_Zernike_6_0\"],[\"Cytoplasm_AreaShape_Zernike_6_2\"],[\"Cytoplasm_AreaShape_Zernike_6_4\"],[\"Cytoplasm_AreaShape_Zernike_6_6\"],[\"Cytoplasm_AreaShape_Zernike_7_1\"],[\"Cytoplasm_AreaShape_Zernike_7_3\"],[\"Cytoplasm_AreaShape_Zernike_7_5\"],[\"Cytoplasm_AreaShape_Zernike_7_7\"],[\"Cytoplasm_AreaShape_Zernike_8_0\"],[\"Cytoplasm_AreaShape_Zernike_8_2\"],[\"Cytoplasm_AreaShape_Zernike_8_4\"],[\"Cytoplasm_AreaShape_Zernike_8_6\"],[\"Cytoplasm_AreaShape_Zernike_8_8\"],[\"Cytoplasm_AreaShape_Zernike_9_1\"],[\"Cytoplasm_AreaShape_Zernike_9_3\"],[\"Cytoplasm_AreaShape_Zernike_9_5\"],[\"Cytoplasm_AreaShape_Zernike_9_7\"],[\"Cytoplasm_AreaShape_Zernike_9_9\"],[\"Nuclei_AreaShape_Compactness\"],[\"Nuclei_AreaShape_Eccentricity\"],[\"Nuclei_AreaShape_Extent\"],[\"Nuclei_AreaShape_FormFactor\"],[\"Nuclei_AreaShape_Orientation\"],[\"Nuclei_AreaShape_Solidity\"],[\"Nuclei_AreaShape_Zernike_2_0\"],[\"Nuclei_AreaShape_Zernike_3_1\"],[\"Nuclei_AreaShape_Zernike_4_2\"],[\"Nuclei_AreaShape_Zernike_5_1\"],[\"Nuclei_AreaShape_Zernike_6_0\"],[\"Nuclei_AreaShape_Zernike_6_2\"],[\"Nuclei_AreaShape_Zernike_6_4\"],[\"Nuclei_AreaShape_Zernike_6_6\"],[\"Nuclei_AreaShape_Zernike_7_1\"],[\"Nuclei_AreaShape_Zernike_7_3\"],[\"Nuclei_AreaShape_Zernike_7_5\"],[\"Nuclei_AreaShape_Zernike_7_7\"],[\"Nuclei_AreaShape_Zernike_8_0\"],[\"Nuclei_AreaShape_Zernike_8_2\"],[\"Nuclei_AreaShape_Zernike_8_4\"],[\"Nuclei_AreaShape_Zernike_8_6\"],[\"Nuclei_AreaShape_Zernike_9_1\"],[\"Nuclei_AreaShape_Zernike_9_3\"],[\"Nuclei_AreaShape_Zernike_9_5\"]],\"hovertemplate\":\"family=AreaShape\\u003cbr\\u003eeffect size=%{x}\\u003cbr\\u003e-log10 q=%{y}\\u003cbr\\u003efeature=%{customdata[0]}\\u003cextra\\u003e\\u003c\\u002fextra\\u003e\",\"legendgroup\":\"AreaShape\",\"marker\":{\"color\":\"#636efa\",\"symbol\":\"circle\"},\"mode\":\"markers\",\"name\":\"AreaShape\",\"orientation\":\"v\",\"showlegend\":true,\"x\":{\"dtype\":\"f8\",\"bdata\":\"AAAAwB1F6L8AAADAhQcUwAAAACBtzQTAAAAAgNds7j8AAACgzW\\u002f8vwAAAAA2yw7AAAAAQBWR8j8AAACA6pb2PwAAAOC7i\\u002fO\\u002fAAAAQPxZAEAAAACgbWfQvwAAAMAkQfQ\\u002fAAAA4P1\\u002f8j8AAADgxsXzPwAAAEC1Ot4\\u002fAAAAwF9X8D8AAAAgXFPWPwAAAOA068I\\u002fAAAAwH028T8AAACA1ZrYPwAAAMB3Pcw\\u002fAAAA4C+O5j8AAACAFe\\u002fQvwAAAKDht8g\\u002fAAAAABhfyz8AAADgS+TwPwAAAOC3qOo\\u002fAAAAQL8tC8AAAADAj1MDwAAAACCRCAPAAAAAgHVc+L8AAACAzoANwAAAAOBFOwjAAAAAgAns778AAACgz9UEwAAAAOCyuda\\u002fAAAAAEcu+78AAABgoDr1vwAAAECnpwvAAAAAIIx58j8AAAAgdRDqvwAAAKB4PNy\\u002fAAAAABELBcAAAAAg9Rz9PwAAAMCKTe0\\u002fAAAAwDSr3L8AAABgne\\u002fjvwAAAMC9SgrAAAAAYB0U\\u002fz8AAACgsbDFvwAAACCF2ac\\u002fAAAAoCI60b8AAADgMyEHwAAAAAAGKdo\\u002fAAAAAPSc8L8AAADAjAP4PwAAAAAUWtG\\u002fAAAA4JjexT8AAACAHKUBQAAAAABXmdG\\u002fAAAAQDZYAsAAAACAJU3lPwAAAOCArgLAAAAAYCqp\\u002fT8AAACAjbTqPwAAAACKbNI\\u002fAAAAYIqv\\u002fb8AAABggDcDwAAAAGCd\\u002fgTAAAAAAPGFBMAAAAAAobwGwAAAAEBIHfw\\u002fAAAAALqaCUAAAACALR3zvwAAACBUQQXAAAAA4BrxAcAAAACgML0AwAAAAEBdugTA\"},\"xaxis\":\"x\",\"y\":{\"dtype\":\"f8\",\"bdata\":\"dGnIk7PFG0AAAAAAAAAoQAC0YY9vSiRAGU\\u002fCo6M\\u002fDECW\\u002fq9i5H4JQAAAAAAAAChA4+KYdj2kGEBtWeW+YhYEQHK8Izmzme4\\u002fAAAAAAAAKEBUSMd4QwT3PxW5PuXWHRpA\\u002fhvEy1uTD0BkEblcpkwhQNqRm2z\\u002fBfM\\u002fwF9yAbGhEUDTl7WCOr\\u002fjP0vXNvUTKN0\\u002fVFkLZY7KF0CivIqzbgLeP11\\u002fpVgCs88\\u002fVH9VnjT91z9KZuoviYrxP9zPvr1FBsc\\u002f5HVs9CKb8z+pfdSNmvLQPz4JxKeFXAJAAAAAAAAAKECmK2CTP+YnQAAAAAAAAChA5\\u002fppisn8+T8AAAAAAAAoQAAAAAAAAChAWWGeWRM44z8AAAAAAAAoQC0QskcBGQlAAAAAAAAAKEAR7xZ0QELmPwAAAAAAAChAFEGOR3xxEUAsUFM3nqYBQOxiPOQeALI\\u002fAAAAAAAAKEAftjhXUGohQBN4TQlo2RFAe177e9ck6T+CPG0HRvqRPwAAAAAAAChA\\u002fZcuhfMCHUBHfE7ldZLvPwAAAAAAAACA1R0yWffV3T8AAAAAAAAoQKOoKJfkcRtAPq+DesAKxD+ETxSzdnTjP9TechWfuxxAIFg1eyPAxz9XV7v37rEeQF+cas8ztAlAQk+YcWb3J0BL7upSL0r\\u002fP4LmW5IGmyVAdHBiskivEkD8An7IGLoKQCAu8mjPQNI\\u002fCM2IqywtDUAAAAAAAAAoQAAAAAAAAChAAAAAAAAAKEAAAAAAAAAoQGsJpB70IBJAAAAAAAAAKEAqfFDA4fQAQAAAAAAAAChAAAAAAAAAKEAjj9GRQpwiQEimBygFpyZA\"},\"yaxis\":\"y\",\"type\":\"scatter\"},{\"customdata\":[[\"Cells_Correlation_Correlation_AGP_BFHigh\"],[\"Cells_Correlation_Correlation_AGP_BFLow\"],[\"Cells_Correlation_Correlation_AGP_DNA\"],[\"Cells_Correlation_Correlation_AGP_Mito\"],[\"Cells_Correlation_Correlation_AGP_RNA\"],[\"Cells_Correlation_Correlation_BFHigh_BFLow\"],[\"Cells_Correlation_Correlation_BFHigh_Brightfield\"],[\"Cells_Correlation_Correlation_BFHigh_DNA\"],[\"Cells_Correlation_Correlation_BFHigh_ER\"],[\"Cells_Correlation_Correlation_BFHigh_Mito\"],[\"Cells_Correlation_Correlation_BFLow_ER\"],[\"Cells_Correlation_Correlation_BFLow_Mito\"],[\"Cells_Correlation_Correlation_Brightfield_Mito\"],[\"Cells_Correlation_Correlation_DNA_RNA\"],[\"Cells_Correlation_Correlation_ER_RNA\"],[\"Cells_Correlation_Correlation_Mito_RNA\"],[\"Cells_Correlation_K_AGP_DNA\"],[\"Cells_Correlation_K_BFHigh_BFLow\"],[\"Cells_Correlation_K_BFHigh_Brightfield\"],[\"Cells_Correlation_K_BFLow_Brightfield\"],[\"Cells_Correlation_K_Brightfield_BFHigh\"],[\"Cells_Correlation_K_Brightfield_BFLow\"],[\"Cells_Correlation_K_DNA_AGP\"],[\"Cells_Correlation_K_DNA_Mito\"],[\"Cells_Correlation_K_ER_RNA\"],[\"Cells_Correlation_K_Mito_DNA\"],[\"Cells_Correlation_K_RNA_ER\"],[\"Cells_Correlation_Manders_AGP_RNA\"],[\"Cells_Correlation_Manders_BFHigh_AGP\"],[\"Cells_Correlation_Manders_BFHigh_RNA\"],[\"Cells_Correlation_Manders_DNA_AGP\"],[\"Cells_Correlation_Manders_DNA_Mito\"],[\"Cells_Correlation_Manders_DNA_RNA\"],[\"Cells_Correlation_Manders_ER_RNA\"],[\"Cells_Correlation_Manders_RNA_AGP\"],[\"Cells_Correlation_Manders_RNA_ER\"],[\"Cells_Correlation_Manders_RNA_Mito\"],[\"Cells_Correlation_Overlap_AGP_BFHigh\"],[\"Cells_Correlation_Overlap_AGP_DNA\"],[\"Cells_Correlation_Overlap_AGP_ER\"],[\"Cells_Correlation_Overlap_AGP_Mito\"],[\"Cells_Correlation_Overlap_BFHigh_RNA\"],[\"Cells_Correlation_Overlap_BFLow_DNA\"],[\"Cells_Correlation_Overlap_DNA_Mito\"],[\"Cells_Correlation_Overlap_DNA_RNA\"],[\"Cells_Correlation_Overlap_ER_RNA\"],[\"Cells_Correlation_Overlap_Mito_RNA\"],[\"Cells_Correlation_RWC_AGP_Brightfield\"],[\"Cells_Correlation_RWC_AGP_DNA\"],[\"Cells_Correlation_RWC_AGP_Mito\"],[\"Cells_Correlation_RWC_AGP_RNA\"],[\"Cells_Correlation_RWC_BFLow_BFHigh\"],[\"Cells_Correlation_RWC_Brightfield_DNA\"],[\"Cells_Correlation_RWC_DNA_AGP\"],[\"Cells_Correlation_RWC_DNA_BFHigh\"],[\"Cells_Correlation_RWC_DNA_Brightfield\"],[\"Cells_Correlation_RWC_DNA_ER\"],[\"Cells_Correlation_RWC_DNA_Mito\"],[\"Cells_Correlation_RWC_ER_AGP\"],[\"Cells_Correlation_RWC_ER_Brightfield\"],[\"Cells_Correlation_RWC_ER_Mito\"],[\"Cells_Correlation_RWC_ER_RNA\"],[\"Cells_Correlation_RWC_Mito_AGP\"],[\"Cells_Correlation_RWC_Mito_BFHigh\"],[\"Cells_Correlation_RWC_Mito_Brightfield\"],[\"Cells_Correlation_RWC_Mito_DNA\"],[\"Cells_Correlation_RWC_Mito_RNA\"],[\"Cells_Correlation_RWC_RNA_DNA\"],[\"Cells_Correlation_RWC_RNA_ER\"],[\"Cytoplasm_Correlation_Correlation_AGP_BFHigh\"],[\"Cytoplasm_Correlation_Correlation_AGP_BFLow\"],[\"Cytoplasm_Correlation_Correlation_AGP_Brightfield\"],[\"Cytoplasm_Correlation_Correlation_AGP_DNA\"],[\"Cytoplasm_Correlation_Correlation_AGP_RNA\"],[\"Cytoplasm_Correlation_Correlation_BFHigh_BFLow\"],[\"Cytoplasm_Correlation_Correlation_BFHigh_Brightfield\"],[\"Cytoplasm_Correlation_Correlation_BFHigh_DNA\"],[\"Cytoplasm_Correlation_Correlation_BFHigh_ER\"],[\"Cytoplasm_Correlation_Correlation_BFHigh_Mito\"],[\"Cytoplasm_Correlation_Correlation_BFHigh_RNA\"],[\"Cytoplasm_Correlation_Correlation_BFLow_Brightfield\"],[\"Cytoplasm_Correlation_Correlation_BFLow_DNA\"],[\"Cytoplasm_Correlation_Correlation_BFLow_ER\"],[\"Cytoplasm_Correlation_Correlation_BFLow_Mito\"],[\"Cytoplasm_Correlation_Correlation_Brightfield_DNA\"],[\"Cytoplasm_Correlation_Correlation_Brightfield_Mito\"],[\"Cytoplasm_Correlation_Correlation_Brightfield_RNA\"],[\"Cytoplasm_Correlation_Correlation_DNA_Mito\"],[\"Cytoplasm_Correlation_Correlation_DNA_RNA\"],[\"Cytoplasm_Correlation_Correlation_ER_RNA\"],[\"Cytoplasm_Correlation_Correlation_Mito_RNA\"],[\"Cytoplasm_Correlation_K_AGP_BFLow\"],[\"Cytoplasm_Correlation_K_AGP_DNA\"],[\"Cytoplasm_Correlation_K_AGP_Mito\"],[\"Cytoplasm_Correlation_K_BFHigh_Mito\"],[\"Cytoplasm_Correlation_K_BFHigh_RNA\"],[\"Cytoplasm_Correlation_K_BFLow_AGP\"],[\"Cytoplasm_Correlation_K_BFLow_BFHigh\"],[\"Cytoplasm_Correlation_K_Brightfield_BFLow\"],[\"Cytoplasm_Correlation_K_DNA_AGP\"],[\"Cytoplasm_Correlation_K_DNA_ER\"],[\"Cytoplasm_Correlation_K_DNA_Mito\"],[\"Cytoplasm_Correlation_K_ER_DNA\"],[\"Cytoplasm_Correlation_K_ER_RNA\"],[\"Cytoplasm_Correlation_K_Mito_AGP\"],[\"Cytoplasm_Correlation_K_Mito_DNA\"],[\"Cytoplasm_Correlation_K_RNA_ER\"],[\"Cytoplasm_Correlation_Manders_AGP_ER\"],[\"Cytoplasm_Correlation_Manders_Brightfield_RNA\"],[\"Cytoplasm_Correlation_Manders_DNA_AGP\"],[\"Cytoplasm_Correlation_Manders_DNA_RNA\"],[\"Cytoplasm_Correlation_Manders_ER_RNA\"],[\"Cytoplasm_Correlation_Manders_Mito_AGP\"],[\"Cytoplasm_Correlation_Manders_Mito_DNA\"],[\"Cytoplasm_Correlation_Manders_Mito_ER\"],[\"Cytoplasm_Correlation_Manders_RNA_ER\"],[\"Cytoplasm_Correlation_Overlap_AGP_Brightfield\"],[\"Cytoplasm_Correlation_Overlap_AGP_DNA\"],[\"Cytoplasm_Correlation_Overlap_AGP_ER\"],[\"Cytoplasm_Correlation_Overlap_AGP_RNA\"],[\"Cytoplasm_Correlation_Overlap_BFHigh_DNA\"],[\"Cytoplasm_Correlation_Overlap_BFHigh_Mito\"],[\"Cytoplasm_Correlation_Overlap_BFHigh_RNA\"],[\"Cytoplasm_Correlation_Overlap_BFLow_Brightfield\"],[\"Cytoplasm_Correlation_Overlap_DNA_ER\"],[\"Cytoplasm_Correlation_Overlap_DNA_Mito\"],[\"Cytoplasm_Correlation_Overlap_ER_RNA\"],[\"Cytoplasm_Correlation_Overlap_Mito_RNA\"],[\"Cytoplasm_Correlation_RWC_AGP_BFLow\"],[\"Cytoplasm_Correlation_RWC_AGP_DNA\"],[\"Cytoplasm_Correlation_RWC_AGP_ER\"],[\"Cytoplasm_Correlation_RWC_AGP_Mito\"],[\"Cytoplasm_Correlation_RWC_AGP_RNA\"],[\"Cytoplasm_Correlation_RWC_BFHigh_Mito\"],[\"Cytoplasm_Correlation_RWC_BFLow_DNA\"],[\"Cytoplasm_Correlation_RWC_Brightfield_BFHigh\"],[\"Cytoplasm_Correlation_RWC_Brightfield_BFLow\"],[\"Cytoplasm_Correlation_RWC_DNA_AGP\"],[\"Cytoplasm_Correlation_RWC_DNA_BFHigh\"],[\"Cytoplasm_Correlation_RWC_DNA_BFLow\"],[\"Cytoplasm_Correlation_RWC_DNA_ER\"],[\"Cytoplasm_Correlation_RWC_DNA_Mito\"],[\"Cytoplasm_Correlation_RWC_ER_Brightfield\"],[\"Cytoplasm_Correlation_RWC_ER_RNA\"],[\"Cytoplasm_Correlation_RWC_Mito_BFHigh\"],[\"Cytoplasm_Correlation_RWC_Mito_BFLow\"],[\"Cytoplasm_Correlation_RWC_Mito_ER\"],[\"Cytoplasm_Correlation_RWC_RNA_AGP\"],[\"Cytoplasm_Correlation_RWC_RNA_ER\"],[\"Cytoplasm_Correlation_RWC_RNA_Mito\"],[\"Nuclei_Correlation_Correlation_AGP_BFHigh\"],[\"Nuclei_Correlation_Correlation_AGP_BFLow\"],[\"Nuclei_Correlation_Correlation_AGP_ER\"],[\"Nuclei_Correlation_Correlation_AGP_Mito\"],[\"Nuclei_Correlation_Correlation_AGP_RNA\"],[\"Nuclei_Correlation_Correlation_BFHigh_Brightfield\"],[\"Nuclei_Correlation_Correlation_BFHigh_DNA\"],[\"Nuclei_Correlation_Correlation_BFHigh_Mito\"],[\"Nuclei_Correlation_Correlation_BFHigh_RNA\"],[\"Nuclei_Correlation_Correlation_BFLow_DNA\"],[\"Nuclei_Correlation_Correlation_BFLow_Mito\"],[\"Nuclei_Correlation_Correlation_BFLow_RNA\"],[\"Nuclei_Correlation_Correlation_DNA_RNA\"],[\"Nuclei_Correlation_Correlation_ER_RNA\"],[\"Nuclei_Correlation_Correlation_Mito_RNA\"],[\"Nuclei_Correlation_K_AGP_ER\"],[\"Nuclei_Correlation_K_BFLow_Brightfield\"],[\"Nuclei_Correlation_K_BFLow_Mito\"],[\"Nuclei_Correlation_K_Brightfield_BFLow\"],[\"Nuclei_Correlation_K_ER_AGP\"],[\"Nuclei_Correlation_K_ER_BFLow\"],[\"Nuclei_Correlation_K_ER_Mito\"],[\"Nuclei_Correlation_K_Mito_BFLow\"],[\"Nuclei_Correlation_K_Mito_ER\"],[\"Nuclei_Correlation_K_RNA_ER\"],[\"Nuclei_Correlation_Manders_BFHigh_AGP\"],[\"Nuclei_Correlation_Manders_BFHigh_RNA\"],[\"Nuclei_Correlation_Manders_BFLow_DNA\"],[\"Nuclei_Correlation_Manders_Brightfield_Mito\"],[\"Nuclei_Correlation_Overlap_AGP_Brightfield\"],[\"Nuclei_Correlation_Overlap_AGP_DNA\"],[\"Nuclei_Correlation_Overlap_AGP_Mito\"],[\"Nuclei_Correlation_Overlap_BFHigh_BFLow\"],[\"Nuclei_Correlation_Overlap_BFHigh_RNA\"],[\"Nuclei_Correlation_Overlap_BFLow_Brightfield\"],[\"Nuclei_Correlation_Overlap_BFLow_DNA\"],[\"Nuclei_Correlation_Overlap_BFLow_Mito\"],[\"Nuclei_Correlation_Overlap_DNA_Mito\"],[\"Nuclei_Correlation_Overlap_DNA_RNA\"],[\"Nuclei_Correlation_Overlap_ER_RNA\"],[\"Nuclei_Correlation_RWC_AGP_BFHigh\"],[\"Nuclei_Correlation_RWC_BFHigh_AGP\"],[\"Nuclei_Correlation_RWC_BFHigh_BFLow\"],[\"Nuclei_Correlation_RWC_BFHigh_Brightfield\"],[\"Nuclei_Correlation_RWC_BFHigh_DNA\"],[\"Nuclei_Correlation_RWC_BFHigh_ER\"],[\"Nuclei_Correlation_RWC_BFHigh_Mito\"],[\"Nuclei_Correlation_RWC_BFLow_Brightfield\"],[\"Nuclei_Correlation_RWC_BFLow_DNA\"],[\"Nuclei_Correlation_RWC_Brightfield_AGP\"],[\"Nuclei_Correlation_RWC_Brightfield_DNA\"],[\"Nuclei_Correlation_RWC_Brightfield_ER\"],[\"Nuclei_Correlation_RWC_Brightfield_Mito\"],[\"Nuclei_Correlation_RWC_DNA_BFHigh\"],[\"Nuclei_Correlation_RWC_DNA_Brightfield\"],[\"Nuclei_Correlation_RWC_ER_BFHigh\"],[\"Nuclei_Correlation_RWC_Mito_BFHigh\"],[\"Nuclei_Correlation_RWC_Mito_BFLow\"],[\"Nuclei_Correlation_RWC_Mito_Brightfield\"],[\"Nuclei_Correlation_RWC_RNA_BFLow\"]],\"hovertemplate\":\"family=Correlation\\u003cbr\\u003eeffect size=%{x}\\u003cbr\\u003e-log10 q=%{y}\\u003cbr\\u003efeature=%{customdata[0]}\\u003cextra\\u003e\\u003c\\u002fextra\\u003e\",\"legendgroup\":\"Correlation\",\"marker\":{\"color\":\"#EF553B\",\"symbol\":\"circle\"},\"mode\":\"markers\",\"name\":\"Correlation\",\"orientation\":\"v\",\"showlegend\":true,\"x\":{\"dtype\":\"f8\",\"bdata\":\"AAAAQMlW+b8AAABgr0UMwAAAAKBtPvC\\u002fAAAAgBU+0D8AAAAA7an1PwAAAECrlOK\\u002fAAAAAC3o6b8AAACgDKQCQAAAAKC1E+k\\u002fAAAAQOHb6r8AAACgQqMSwAAAAODOewjAAAAAIPj7A8AAAAAgq4PsvwAAAEBqNO8\\u002fAAAAYC147T8AAABgesBqPwAAAGDUweI\\u002fAAAAQHJdrT8AAABgOu\\u002fbPwAAAKBlH9o\\u002fAAAAYGHD0D8AAACgBrvXPwAAAEDvYco\\u002fAAAAwGou4D8AAABASyHGvwAAAOCJZ+K\\u002fAAAAIBA1EMAAAADA+sPdvwAAAGCkwgnAAAAAIG2e7L8AAABAKqYHwAAAAGDaafK\\u002fAAAA4K2h+L8AAAAA2GbtPwAAAKABdgrAAAAAAD3pB8AAAACA1IziPwAAAOAwl+A\\u002fAAAAAJy3x78AAACgHtbmvwAAAKA6rNO\\u002fAAAAIFDT6j8AAACAx3PVvwAAACCu+uy\\u002fAAAAgHkZ+b8AAAAgWGkAwAAAAKBLDAHAAAAAINUHD8AAAABgDBPevwAAAIDEWAnAAAAAAE57vz8AAACAibf8vwAAAGAnqLq\\u002fAAAAAD6Z6L8AAABAgQEBwAAAAIA6svI\\u002fAAAAICPo+T8AAADACHr8PwAAACD4wg7AAAAAAKiS8j8AAABgnqXSvwAAAOB9Be0\\u002fAAAAYPFeC8AAAADgDZ4RwAAAAOBOvvK\\u002fAAAAgGShA8AAAABgwGXyvwAAAEAvs\\u002fO\\u002fAAAAAO9lBsAAAACA9y+YPwAAAKDxpfG\\u002fAAAAYA+P6T8AAADgxHAFQAAAAMCEKeI\\u002fAAAAIBaR4T8AAADA3HAEwAAAAGA6wKC\\u002fAAAAIBqXwD8AAABgTAmnvwAAAMBhmuC\\u002fAAAA4EJz+T8AAACgDePyvwAAAIBRdOq\\u002fAAAAQKVF4b8AAABgYsC\\u002fvwAAAGDCI+K\\u002fAAAAAAro+D8AAABALHf7PwAAAEB1Be0\\u002fAAAAgCD+5T8AAABgo23SvwAAACDf\\u002f\\u002f+\\u002fAAAA4B\\u002fOuL8AAACgk9XxPwAAAGDR9MG\\u002fAAAAwHPc7D8AAABAya4UwAAAAECgbRNAAAAAwD7HBUAAAACATg3gPwAAAIDin\\u002f4\\u002fAAAA4F9O478AAADAXcj4PwAAAMCRsNk\\u002fAAAAYL70\\u002fr8AAADgG8r3vwAAAIBXOgnAAAAA4GvxBsAAAABAbnjbvwAAACADvfy\\u002fAAAAQIPPBsAAAABAFvfqPwAAACAjid0\\u002fAAAA4K2t9L8AAABAWyv4vwAAAIAbEPc\\u002fAAAAoKZj7D8AAACgTTj3PwAAAAA5FQBAAAAAwIou9D8AAADgFOgFwAAAAOBAN\\u002f+\\u002fAAAAQGG\\u002f8z8AAACgUnL2PwAAAGCOcdQ\\u002fAAAAgI\\u002fu8b8AAACA+7EAwAAAACCYUty\\u002fAAAAoNCV5r8AAADgxlXiPwAAACDVpPm\\u002fAAAAAJRd2L8AAABAOKwNwAAAAOBpCOO\\u002fAAAAoInz1j8AAABA74\\u002fDvwAAAOA8NtY\\u002fAAAAAN87+L8AAACAyCzFPwAAAGCFK\\u002fU\\u002fAAAA4D\\u002f13z8AAADAxcH8vwAAAIDh3wLAAAAAgM9dDcAAAADgZYwCwAAAAOAHTOg\\u002fAAAAINP28T8AAABgCajzPwAAAADIseO\\u002fAAAAIFbcBsAAAAAgBsHlvwAAACACWOY\\u002fAAAAIE787b8AAADgsljuPwAAAECU+P+\\u002fAAAAQEE1+T8AAADg\\u002f+0OwAAAAABdqNu\\u002fAAAAYNa9EsAAAACg7WbBPwAAAICswQPAAAAA4Bhd7r8AAADg8hzmPwAAAIDSfci\\u002fAAAAQK2i2b8AAACA90X3PwAAAOAIIvI\\u002fAAAAAPbBBsAAAACgXbriPwAAAMCP8cS\\u002fAAAAAHAW5D8AAADgcRjjvwAAAOA1U+O\\u002fAAAAQOAwsj8AAAAAU+DwvwAAAAB2VwTAAAAAAMYH+L8AAACAnmH+vwAAAAAzx\\u002fA\\u002fAAAAIOxNyT8AAADAanDkvwAAAMCODfi\\u002fAAAAoI70A8AAAADA\\u002fPbxvwAAAEBgF9U\\u002fAAAAgC9v\\u002fr8AAABgaQv2vwAAAIB9owLAAAAAQMF17r8AAABgji0GwAAAAAAGbQDAAAAAAMXI8L8AAAAgMfD1vwAAAICCQeQ\\u002fAAAAoDqM4r8AAACA0YYNwAAAAOCFIPS\\u002fAAAAwE4\\u002fEMAAAADg1aEEwAAAAEDc4QTAAAAAQH9lCMAAAACAZ2YOwAAAACCw4OM\\u002fAAAA4LVkCMAAAABgvZT7vwAAACCs5g\\u002fAAAAAQBjB\\u002f78AAACgpRgMwAAAAGDBmw3A\"},\"xaxis\":\"x\",\"y\":{\"dtype\":\"f8\",\"bdata\":\"z68+77VAIUAAAAAAAAAoQP9ZIZV7Otc\\u002fiSd11TBQFkBAPIXhcywTQJNgnWBMs60\\u002fDjzapfD9E0AAAAAAAAAoQJ1FfNFXVhVACXxkjMGIFkAAAAAAAAAoQFxtXjlRZCVAAAAAAAAAKECxqJyQ6fLqPx5cYZNFhxNAiHIJIPkR\\u002fT9NcT7yJhr0PxTLjLkCrwJAkLdN2yKFxz9oxcHMkl39P1he+A\\u002fk7O4\\u002fIHFmZWK99D\\u002flcswoYlYNQL3Ulu+91RZAEnR+lrmfBUA\\u002f5wEnLU4EQFbGfz2vDQ5AAAAAAAAAKEASoGH80QfnPwAAAAAAAChAQK9eRJDB8z8AAAAAAAAoQAAAAAAAAChA1GR+KE\\u002fRH0AGGlDgTk0TQAAAAAAAAChAAAAAAAAAKEAFgXfz\\u002fI3sP4dYQYPsz8Y\\u002f2giwEVId8z+pyNPPB3PxPz0WTG5zicM\\u002fAAAAAAAAKEBAkXyvrGMFQAzwwyZq2hRAAAAAAAAAKEAAAAAAAAAoQJ2ZTBS4HiBAAAAAAAAAKEDHZjRFqa33PwAAAAAAAChAGINcoBoYzT8AAAAAAAAoQJ6qbrZmAOQ\\u002fiQ23yl15FEAAAAAAAAAoQDix12C2KBZAwzn3ruZQIEDZJNjWdhMgQAAAAAAAAChAe\\u002fH6iaYI3z95WjZMEIbjP+p0iCwxTQBAAAAAAAAAKEAAAAAAAAAoQLhD0QmIMSFAAAAAAAAAKEB31dQmAnb1P4VZVZp+ZKU\\u002fAAAAAAAAKEBY49TG2WAYQCCEKl2rtgxAQa+AAFay8T8AAAAAAAAoQOeWcqaqWQpA6T\\u002f7LAzWBkAAAAAAAAAoQMSPmNjXtQdAMCB+smikiT\\u002fZQ0o8z8v4P\\u002fzaMpv1CeI\\u002fqWrCGEDRIEAL2LGtKbTwP4+81hDCYgNA8snyjYWU9z9r2L+1imbsPwrLZ9TRbdM\\u002f2IBisButCECjW3JwaJYYQGJEtfvNHCRA592TiE22xT8VvNTkmMACQAAAAAAAAChAtOG\\u002fxEmI0j8AAAAAAAAoQFV8UVV13OI\\u002fQkYSyjtlF0AAAAAAAAAoQAAAAAAAAChAAAAAAAAAKEAAAAAAAAAoQAAAAAAAAChA\\u002fPLiUSNuHUAAAAAAAAAoQGNnmtd6u90\\u002fAAAAAAAAKEAAAAAAAAAoQAAAAAAAAChApepJ3vTyJ0CfAyM1mO76Pzy\\u002fojrDSx5AAAAAAAAAKEDzv3w24l3jP0fwecG7cxJASnHk4SbTFUA5IOyl\\u002fgoiQK3DcgpVAQVAEfy22pijHEC4vi7hn\\u002fsdQO+YUC7aOiNAAAAAAAAAKEAAAAAAAAAoQL6cweRuZh1AvEiL1hUzI0AAAAAAAAAoQCksiuO9xuU\\u002fk4x3NDHmI0AAAAAAAAAoQMSSfIq5lbo\\u002fagoQHJ\\u002fp4T8649dBnOjGP4WibLfAViRAtDQCNaKB1T8AAAAAAAAoQByzmTMZwBFAdiZEgoS1HEAZ+4yHg6q9P4a46iceDOQ\\u002fAAAAAAAAKEAlFMRABh4CQHoyNvxn+CRAX0fc6uRsrz\\u002fl0l3AGSYdQAAAAAAAAChAAAAAAAAAKEAAAAAAAAAoQN3cxqBYBwBAM7LdUZD9EkCknw2Jw3wdQCKJA\\u002f2rlRBAAAAAAAAAKEBOmTfSsurmP1izkCfCHPs\\u002fB8cczxi55T9yuM1ttUj6P+6NaxqFtidAHfJgJxssCUAAAAAAAAAoQIcQrod7Ed4\\u002fAAAAAAAAKEA6LaPO\\u002fqQZQAAAAAAAAChApwzyXdnG5D+QyHEu2DQQQIwJwWlA8tU\\u002frdWsnZSdBUDkWxe\\u002f+0AhQCkm12t+jyFAAAAAAAAAKEBSHIou+58EQAo9NW0iQtY\\u002fQEzqhamN\\u002fT9tCE1ifFMbQK9xW7oCbghASdzeW45o2z8A89Q35kuqPwuMCHhqfyFA6ajXvB3hGkAAAAAAAAAoQFlKa4GHeuw\\u002fUlsAf+x86D8VB3Zgh7X+PxAe42csixVAAAAAAAAAKEBLXWMwzN8UQHqi2foXmI4\\u002fAAAAAAAAKECB1qQmxdggQAAAAAAAAChA4qkHJU+\\u002fHEAAAAAAAAAoQAAAAAAAAChAAAAAAAAAKEAAAAAAAAAoQKvi39IhhQ9ALSHL1a1XAUAAAAAAAAAoQJBQVBHTKBBAAAAAAAAAKEAAAAAAAAAoQAAAAAAAAChAAAAAAAAAKEAAAAAAAAAoQNed0WU28sI\\u002fAAAAAAAAKEAAAAAAAAAoQAAAAAAAAChAIz+W9Q71E0AAAAAAAAAoQAAAAAAAAChA\"},\"yaxis\":\"y\",\"type\":\"scatter\"},{\"customdata\":[[\"Cells_Granularity_10_AGP\"],[\"Cells_Granularity_10_Mito\"],[\"Cells_Granularity_10_RNA\"],[\"Cells_Granularity_11_AGP\"],[\"Cells_Granularity_11_Mito\"],[\"Cells_Granularity_11_RNA\"],[\"Cells_Granularity_12_AGP\"],[\"Cells_Granularity_13_AGP\"],[\"Cells_Granularity_14_DNA\"],[\"Cells_Granularity_15_DNA\"],[\"Cells_Granularity_1_DNA\"],[\"Cells_Granularity_1_Mito\"],[\"Cells_Granularity_2_AGP\"],[\"Cells_Granularity_2_ER\"],[\"Cells_Granularity_2_Mito\"],[\"Cells_Granularity_2_RNA\"],[\"Cells_Granularity_3_AGP\"],[\"Cells_Granularity_3_BFHigh\"],[\"Cells_Granularity_3_BFLow\"],[\"Cells_Granularity_3_Brightfield\"],[\"Cells_Granularity_3_ER\"],[\"Cells_Granularity_3_RNA\"],[\"Cells_Granularity_4_AGP\"],[\"Cells_Granularity_4_BFHigh\"],[\"Cells_Granularity_4_BFLow\"],[\"Cells_Granularity_4_ER\"],[\"Cells_Granularity_5_BFHigh\"],[\"Cells_Granularity_5_Mito\"],[\"Cells_Granularity_6_BFHigh\"],[\"Cells_Granularity_7_BFHigh\"],[\"Cells_Granularity_8_DNA\"],[\"Cells_Granularity_8_Mito\"],[\"Cells_Granularity_9_AGP\"],[\"Cells_Granularity_9_Mito\"],[\"Cytoplasm_Granularity_10_DNA\"],[\"Cytoplasm_Granularity_11_AGP\"],[\"Cytoplasm_Granularity_11_DNA\"],[\"Cytoplasm_Granularity_12_AGP\"],[\"Cytoplasm_Granularity_12_DNA\"],[\"Cytoplasm_Granularity_12_Mito\"],[\"Cytoplasm_Granularity_12_RNA\"],[\"Cytoplasm_Granularity_13_AGP\"],[\"Cytoplasm_Granularity_13_DNA\"],[\"Cytoplasm_Granularity_13_ER\"],[\"Cytoplasm_Granularity_13_Mito\"],[\"Cytoplasm_Granularity_14_AGP\"],[\"Cytoplasm_Granularity_14_DNA\"],[\"Cytoplasm_Granularity_14_Mito\"],[\"Cytoplasm_Granularity_14_RNA\"],[\"Cytoplasm_Granularity_15_AGP\"],[\"Cytoplasm_Granularity_15_DNA\"],[\"Cytoplasm_Granularity_15_ER\"],[\"Cytoplasm_Granularity_15_Mito\"],[\"Cytoplasm_Granularity_16_AGP\"],[\"Cytoplasm_Granularity_16_DNA\"],[\"Cytoplasm_Granularity_16_Mito\"],[\"Cytoplasm_Granularity_16_RNA\"],[\"Cytoplasm_Granularity_1_AGP\"],[\"Cytoplasm_Granularity_1_BFHigh\"],[\"Cytoplasm_Granularity_1_BFLow\"],[\"Cytoplasm_Granularity_1_Brightfield\"],[\"Cytoplasm_Granularity_1_Mito\"],[\"Cytoplasm_Granularity_1_RNA\"],[\"Cytoplasm_Granularity_2_AGP\"],[\"Cytoplasm_Granularity_2_BFHigh\"],[\"Cytoplasm_Granularity_2_BFLow\"],[\"Cytoplasm_Granularity_2_Brightfield\"],[\"Cytoplasm_Granularity_2_DNA\"],[\"Cytoplasm_Granularity_2_ER\"],[\"Cytoplasm_Granularity_2_Mito\"],[\"Cytoplasm_Granularity_2_RNA\"],[\"Cytoplasm_Granularity_3_AGP\"],[\"Cytoplasm_Granularity_3_BFHigh\"],[\"Cytoplasm_Granularity_3_BFLow\"],[\"Cytoplasm_Granularity_3_Brightfield\"],[\"Cytoplasm_Granularity_3_DNA\"],[\"Cytoplasm_Granularity_3_ER\"],[\"Cytoplasm_Granularity_3_RNA\"],[\"Cytoplasm_Granularity_4_AGP\"],[\"Cytoplasm_Granularity_4_BFHigh\"],[\"Cytoplasm_Granularity_4_BFLow\"],[\"Cytoplasm_Granularity_4_Brightfield\"],[\"Cytoplasm_Granularity_4_DNA\"],[\"Cytoplasm_Granularity_4_Mito\"],[\"Cytoplasm_Granularity_4_RNA\"],[\"Cytoplasm_Granularity_5_AGP\"],[\"Cytoplasm_Granularity_5_BFLow\"],[\"Cytoplasm_Granularity_5_Brightfield\"],[\"Cytoplasm_Granularity_5_DNA\"],[\"Cytoplasm_Granularity_5_ER\"],[\"Cytoplasm_Granularity_6_AGP\"],[\"Cytoplasm_Granularity_6_BFLow\"],[\"Cytoplasm_Granularity_6_Brightfield\"],[\"Cytoplasm_Granularity_6_ER\"],[\"Cytoplasm_Granularity_6_Mito\"],[\"Cytoplasm_Granularity_8_DNA\"],[\"Cytoplasm_Granularity_9_DNA\"],[\"Nuclei_Granularity_10_AGP\"],[\"Nuclei_Granularity_10_BFHigh\"],[\"Nuclei_Granularity_10_BFLow\"],[\"Nuclei_Granularity_10_Brightfield\"],[\"Nuclei_Granularity_10_Mito\"],[\"Nuclei_Granularity_10_RNA\"],[\"Nuclei_Granularity_11_AGP\"],[\"Nuclei_Granularity_11_BFHigh\"],[\"Nuclei_Granularity_11_BFLow\"],[\"Nuclei_Granularity_11_Brightfield\"],[\"Nuclei_Granularity_11_Mito\"],[\"Nuclei_Granularity_11_RNA\"],[\"Nuclei_Granularity_12_AGP\"],[\"Nuclei_Granularity_12_BFHigh\"],[\"Nuclei_Granularity_12_BFLow\"],[\"Nuclei_Granularity_12_Brightfield\"],[\"Nuclei_Granularity_13_AGP\"],[\"Nuclei_Granularity_13_BFHigh\"],[\"Nuclei_Granularity_13_BFLow\"],[\"Nuclei_Granularity_13_Brightfield\"],[\"Nuclei_Granularity_14_BFHigh\"],[\"Nuclei_Granularity_14_BFLow\"],[\"Nuclei_Granularity_14_Brightfield\"],[\"Nuclei_Granularity_15_BFHigh\"],[\"Nuclei_Granularity_15_BFLow\"],[\"Nuclei_Granularity_15_Brightfield\"],[\"Nuclei_Granularity_16_BFHigh\"],[\"Nuclei_Granularity_16_BFLow\"],[\"Nuclei_Granularity_16_Brightfield\"],[\"Nuclei_Granularity_1_AGP\"],[\"Nuclei_Granularity_1_BFHigh\"],[\"Nuclei_Granularity_1_BFLow\"],[\"Nuclei_Granularity_1_Brightfield\"],[\"Nuclei_Granularity_1_DNA\"],[\"Nuclei_Granularity_1_ER\"],[\"Nuclei_Granularity_1_Mito\"],[\"Nuclei_Granularity_2_AGP\"],[\"Nuclei_Granularity_2_BFHigh\"],[\"Nuclei_Granularity_2_BFLow\"],[\"Nuclei_Granularity_2_DNA\"],[\"Nuclei_Granularity_2_ER\"],[\"Nuclei_Granularity_2_Mito\"],[\"Nuclei_Granularity_3_AGP\"],[\"Nuclei_Granularity_3_BFLow\"],[\"Nuclei_Granularity_3_Brightfield\"],[\"Nuclei_Granularity_3_ER\"],[\"Nuclei_Granularity_4_AGP\"],[\"Nuclei_Granularity_4_BFHigh\"],[\"Nuclei_Granularity_4_BFLow\"],[\"Nuclei_Granularity_4_Brightfield\"],[\"Nuclei_Granularity_4_ER\"],[\"Nuclei_Granularity_5_AGP\"],[\"Nuclei_Granularity_5_BFHigh\"],[\"Nuclei_Granularity_5_BFLow\"],[\"Nuclei_Granularity_5_Brightfield\"],[\"Nuclei_Granularity_5_DNA\"],[\"Nuclei_Granularity_5_Mito\"],[\"Nuclei_Granularity_6_BFHigh\"],[\"Nuclei_Granularity_6_BFLow\"],[\"Nuclei_Granularity_6_Brightfield\"],[\"Nuclei_Granularity_6_DNA\"],[\"Nuclei_Granularity_7_AGP\"],[\"Nuclei_Granularity_7_BFHigh\"],[\"Nuclei_Granularity_7_BFLow\"],[\"Nuclei_Granularity_7_Brightfield\"],[\"Nuclei_Granularity_7_DNA\"],[\"Nuclei_Granularity_7_Mito\"],[\"Nuclei_Granularity_7_RNA\"],[\"Nuclei_Granularity_8_AGP\"],[\"Nuclei_Granularity_8_BFHigh\"],[\"Nuclei_Granularity_8_BFLow\"],[\"Nuclei_Granularity_8_Brightfield\"],[\"Nuclei_Granularity_8_Mito\"],[\"Nuclei_Granularity_8_RNA\"],[\"Nuclei_Granularity_9_AGP\"],[\"Nuclei_Granularity_9_BFHigh\"],[\"Nuclei_Granularity_9_BFLow\"],[\"Nuclei_Granularity_9_Brightfield\"],[\"Nuclei_Granularity_9_Mito\"],[\"Nuclei_Granularity_9_RNA\"]],\"hovertemplate\":\"family=Granularity\\u003cbr\\u003eeffect size=%{x}\\u003cbr\\u003e-log10 q=%{y}\\u003cbr\\u003efeature=%{customdata[0]}\\u003cextra\\u003e\\u003c\\u002fextra\\u003e\",\"legendgroup\":\"Granularity\",\"marker\":{\"color\":\"#00cc96\",\"symbol\":\"circle\"},\"mode\":\"markers\",\"name\":\"Granularity\",\"orientation\":\"v\",\"showlegend\":true,\"x\":{\"dtype\":\"f8\",\"bdata\":\"AAAAwOjBd78AAAAAV6ICQAAAAADbo\\u002fQ\\u002fAAAAwJsf\\u002fj8AAACgjLsJQAAAAEBiBgRAAAAAwDPiB0AAAAAAzPgMQAAAACDjmg5AAAAAAJyxC0AAAADA4v\\u002f0PwAAAGDbn+q\\u002fAAAAYPVuBMAAAACAHXnyvwAAAOBtX\\u002fi\\u002fAAAAAHC9478AAACgGZEMwAAAAOAhAQJAAAAAYAvD4L8AAADggGu3PwAAAMAcn\\u002fO\\u002fAAAAwJSjwz8AAACgJdsFwAAAACD91wNAAAAA4HRuu78AAACAO+j8vwAAACCjOP8\\u002fAAAAQCxy878AAAAgT8X3PwAAAEAXyvA\\u002fAAAA4MRREEAAAADgJLm+PwAAAOC2D\\u002f6\\u002fAAAAQN0r9T8AAAAgtJ71PwAAAECZat8\\u002fAAAAYFsLAkAAAADggbr5PwAAAKDM7gRAAAAAoCdJB0AAAABAZM0AQAAAACDmNQNAAAAAoFQ6B0AAAACAQbwGQAAAACAavQpAAAAAoJd4BUAAAAAgoqgGQAAAAEAWDAxAAAAAoClfCkAAAACgul0IQAAAAICefAZAAAAAYBeEC0AAAABAky8MQAAAAMCT+QdAAAAAYCJ8BUAAAABALSQMQAAAAOADAApAAAAAYIX01D8AAAAAc6AMQAAAAKCflQtAAAAAAKimBEAAAACACCj1vwAAAOA3JeI\\u002fAAAAgCfP978AAACgPAAIwAAAAKBYWALAAAAAAI0P878AAABgljAHQAAAAKAgZOk\\u002fAAAAgAZW3r8AAAAguJTyPwAAAICrQPq\\u002fAAAAILg\\u002f6r8AAAAgzwXRvwAAAOCxL5g\\u002fAAAAYOJlxT8AAADAhofxPwAAAADiOfY\\u002fAAAAIGqa0L8AAADARsrlPwAAAOAEt+s\\u002fAAAAoGpV0j8AAACAbYICwAAAAKBR6+C\\u002fAAAAIOTr278AAADgoEztvwAAAIDkktk\\u002fAAAAIPJkx78AAAAg3ecMwAAAAACaFQLAAAAAYNEP\\u002fb8AAACgsNCuPwAAAODnira\\u002fAAAAgJkUBsAAAAAgXKLqvwAAAODHpAvAAAAAILpT7r8AAACA4FIDQAAAAICmzu0\\u002fAAAA4Km2sb8AAACgJMC8vwAAAECesA9AAAAAwKYQCUAAAABAWsEQQAAAAGCdItc\\u002fAAAAABqu2L8AAACA9f2yvwAAAKBazRJAAAAA4BpPEEAAAAAALLoTQAAAAEBXlLs\\u002fAAAAIHIr6L8AAADA5PPivwAAAKB2fxRAAAAAwNMTyb8AAACAxPzlvwAAACBCruG\\u002fAAAAYEeJ3b8AAADgRMHsvwAAAIAsD+K\\u002fAAAAANHa278AAACAgUTqvwAAAEBKO+W\\u002fAAAAQOw0478AAAAAYuDnvwAAAGCV0N2\\u002fAAAAIGdz3z8AAABAboX4PwAAAMDRn\\u002f8\\u002fAAAAwHpk\\u002fD8AAACAMvrbvwAAAMBiQ9e\\u002fAAAAQHoV+b8AAAAAzUAGwAAAAOCx1wdAAAAAQOjd9D8AAABAfyz2vwAAAAD6AKy\\u002fAAAAQOF2+b8AAAAAHBMNwAAAAEDpSfE\\u002fAAAA4Dto8j8AAABgDxitPwAAAMCwxwnAAAAAoEa5EEAAAADAxfnwPwAAAIDbZeo\\u002fAAAAoEhU8L8AAAAgTw4GwAAAAEAVXw9AAAAAYOzF4T8AAAAAh9bLPwAAAGCiDxXAAAAAQC0Q8r8AAADAZEQNQAAAAKD3qNc\\u002fAAAAIPmotD8AAAAAYhcGwAAAAGCYWgvAAAAA4BbtB0AAAADgvPr3PwAAACCTxeY\\u002fAAAAoMSTor8AAADAy6DgvwAAAABl3f6\\u002fAAAAYAX8CcAAAAAAyM8CQAAAAIAcm\\u002fI\\u002fAAAAgCsv5z8AAAAAXyDwPwAAAICnjeO\\u002fAAAAICTf6L8AAAAAhY\\u002f6PwAAAKBCwOQ\\u002fAAAAIJux3T8AAABgBPAFQAAAAGC3APk\\u002f\"},\"xaxis\":\"x\",\"y\":{\"dtype\":\"f8\",\"bdata\":\"h3NOhujPuj8AAAAAAAAoQFlRmSUoohFA2Ugvw5coIkAAAAAAAAAoQAAAAAAAAChAAAAAAAAAKEAAAAAAAAAoQAAAAAAAAChAAAAAAAAAKEAAAAAAAAAoQG05UQwE3OA\\u002fAAAAAAAAKEDo3fMptRwUQKSTJewssx1AeKrX1DJt6D8AAAAAAAAoQPr4+MAccBZA3MN+w33xCUDZTPsdU8u6PwAAAAAAAChAAFT\\u002fYMDQvj8AAAAAAAAoQAAAAAAAAChAdyUwjNvg0T8AAAAAAAAoQAAAAAAAAChAPhrG5aPdIUAMUO8G7VsYQJuXn7VUJQ9AAAAAAAAAKEBmCFHFLCPdPwAAAAAAAChA+Bw0FaDPFkCVknzQLAsOQNXcd0STWvQ\\u002f8O07SbZBHkBPf8ibLp8TQJDUpQM6midAAAAAAAAAKEAAAAAAAAAoQCcEwjkZvh1AAAAAAAAAKEAAAAAAAAAoQAAAAAAAAChAAAAAAAAAKEAAAAAAAAAoQAAAAAAAAChAAAAAAAAAKEAAAAAAAAAoQAAAAAAAAChAAAAAAAAAKEAAAAAAAAAoQAAAAAAAAChAAAAAAAAAKEAAAAAAAAAoQAAAAAAAAChAaD+dE1RN8T8AAAAAAAAoQAAAAAAAAChAAAAAAAAAKEDMT\\u002fs9FVUHQNTxUjJAovY\\u002foH+2w\\u002fIOGEAAAAAAAAAoQAAAAAAAAChAAAAAAAAAKEAAAAAAAAAoQPvyjVGFBApAEDsRtMXx5j\\u002f0qIGQvTwXQIIkrGcbIiVAvK48IpcEHkDepxMGhhPyP4J\\u002f6Otlzdg\\u002f+moprL1c1D+XHAMvRfnxPzkZM+TvwxRA4GG69qNLC0CXCTsEM065P0bqjcknSuk\\u002fEGtWUfB80z8AAAAAAAAoQFLVfrgkC\\u002fQ\\u002fzwkrlSe62T\\u002fv2bezomQjQLbhQqL9SoA\\u002f\\u002fmr17mHb6j8AAAAAAAAoQAAAAAAAAChAAAAAAAAAKEAzl\\u002fIIrVXkP2B0S0Lxp9Y\\u002fAAAAAAAAKECOOpPgEywhQAAAAAAAAChAk7D9a5B0IEAAAAAAAAAoQNRDWjWu9xRAIGd7vB+X4j9P2pSCfPfYPwAAAAAAAChAAAAAAAAAKEAAAAAAAAAoQOZn\\u002fu0qO\\u002fI\\u002fYC2C7+8PAkA\\u002f05cGyXC1PwAAAAAAAChAAAAAAAAAKEAAAAAAAAAoQJlzASLL3sQ\\u002fDIwT5WYvFUB85oh9b0INQAAAAAAAAChANCqp+hOZ4D\\u002fMpPQU3KYSQA4SnZHv+QdAezmptVRRCkCxMz3gcMohQD\\u002f0E9NpKgxAoOrugB7bBEDjXkfaC\\u002fQeQAmuD8OtVBdAOzxcbmrOFEDY7MxhqGccQGM89EvMsQlAv0Cx7xvklz\\u002fixWldDdQjQOYMV7VBJCNAQs+1EIFzHECpuokaO7vyP3Dldv9l4PA\\u002fCiWA\\u002fUY2FkAAAAAAAAAoQAAAAAAAAChAzelP17bLwT9nxuAHQCgWQPvKab4vV8E\\u002fpdiJD1cbFUAAAAAAAAAoQJ3\\u002fQKvRbZI\\u002fu5UjvWjn+T9XwJQEoESPPwAAAAAAAChAAAAAAAAAKEBzWjehTAXxPywPg8j\\u002fRPA\\u002fezlRlFNkE0AAAAAAAAAoQAAAAAAAAChA1ZQMjLdXsj\\u002fUWn6nCxeIPwAAAAAAAChArDFjBuazAkAAAAAAAAAoQLCnUEHC8uw\\u002fJxzU2Rsihj+7rMeScdgiQAAAAAAAAChAAAAAAAAAKEAAAAAAAAAoQFVsLobF1BRAiSWkjGMCG0BtHJSCjunCP3vn7tO09BRAAAAAAAAAKEAAAAAAAAAoQAUnImMeohNA0OGXdtu3EkCe8VpLLzkbQG+SmFyq38k\\u002fpIzxruZOAkAAAAAAAAAoQHNrZvKfXgNA5pMLuSKY+D8AAAAAAAAoQCiNT0B+XSFA\"},\"yaxis\":\"y\",\"type\":\"scatter\"},{\"customdata\":[[\"Cells_Intensity_IntegratedIntensityEdge_AGP\"],[\"Cells_Intensity_IntegratedIntensityEdge_DNA\"],[\"Cells_Intensity_IntegratedIntensityEdge_ER\"],[\"Cells_Intensity_MaxIntensityEdge_Mito\"],[\"Cells_Intensity_MinIntensityEdge_DNA\"],[\"Cells_Intensity_MinIntensityEdge_ER\"],[\"Cells_Intensity_MinIntensityEdge_RNA\"],[\"Cytoplasm_Intensity_LowerQuartileIntensity_AGP\"],[\"Cytoplasm_Intensity_MassDisplacement_AGP\"],[\"Cytoplasm_Intensity_MassDisplacement_BFLow\"],[\"Cytoplasm_Intensity_MassDisplacement_DNA\"],[\"Cytoplasm_Intensity_MassDisplacement_ER\"],[\"Cytoplasm_Intensity_MassDisplacement_Mito\"],[\"Cytoplasm_Intensity_MinIntensityEdge_AGP\"],[\"Cytoplasm_Intensity_MinIntensityEdge_Mito\"],[\"Nuclei_Intensity_IntegratedIntensityEdge_AGP\"],[\"Nuclei_Intensity_IntegratedIntensityEdge_BFHigh\"],[\"Nuclei_Intensity_IntegratedIntensityEdge_DNA\"],[\"Nuclei_Intensity_IntegratedIntensityEdge_Mito\"],[\"Nuclei_Intensity_IntegratedIntensity_DNA\"],[\"Nuclei_Intensity_MassDisplacement_AGP\"],[\"Nuclei_Intensity_MassDisplacement_BFHigh\"],[\"Nuclei_Intensity_MassDisplacement_BFLow\"],[\"Nuclei_Intensity_MassDisplacement_Brightfield\"],[\"Nuclei_Intensity_MassDisplacement_DNA\"],[\"Nuclei_Intensity_MassDisplacement_Mito\"],[\"Nuclei_Intensity_MassDisplacement_RNA\"],[\"Nuclei_Intensity_MinIntensity_AGP\"],[\"Nuclei_Intensity_MinIntensity_Brightfield\"],[\"Nuclei_Intensity_MinIntensity_Mito\"],[\"Nuclei_Intensity_StdIntensityEdge_AGP\"],[\"Nuclei_Intensity_StdIntensityEdge_Mito\"]],\"hovertemplate\":\"family=Intensity\\u003cbr\\u003eeffect size=%{x}\\u003cbr\\u003e-log10 q=%{y}\\u003cbr\\u003efeature=%{customdata[0]}\\u003cextra\\u003e\\u003c\\u002fextra\\u003e\",\"legendgroup\":\"Intensity\",\"marker\":{\"color\":\"#ab63fa\",\"symbol\":\"circle\"},\"mode\":\"markers\",\"name\":\"Intensity\",\"orientation\":\"v\",\"showlegend\":true,\"x\":{\"dtype\":\"f8\",\"bdata\":\"AAAAYGjoEEAAAAAgBWwEQAAAAGCxiN0\\u002fAAAA4Cy67b8AAACgUtXxvwAAAMA\\u002f1\\u002fC\\u002fAAAAgBYL9b8AAACgsIzZPwAAAODuxQ1AAAAAQLdb8T8AAAAAdqH1PwAAAIDTJhNAAAAAIGZjEkAAAACA59bdvwAAAEDeFO6\\u002fAAAAwOBjDEAAAABAPBX2PwAAAGBvffQ\\u002fAAAAACXeDkAAAAAgwAwNQAAAAAC1uPs\\u002fAAAAQHJM0D8AAACAmdzxvwAAAKBgZMm\\u002fAAAAwKoDB0AAAABAIgEQQAAAAKCnUAtAAAAAIMio\\u002fT8AAADgAGPMvwAAACDlL+M\\u002fAAAAoP8F5j8AAADA2ssCQA==\"},\"xaxis\":\"x\",\"y\":{\"dtype\":\"f8\",\"bdata\":\"AAAAAAAAKEAAAAAAAAAoQFOIGnXtANM\\u002fSHcRMIydCkChIUyetDAUQLB8MFfuMhdAPKliiIbDIUBLlGKAtpH5PwAAAAAAAChA2U6GmOizE0BHNBbUE1cfQAAAAAAAAChAAAAAAAAAKEBzC3MfGDXwPz102Br5DiNAAAAAAAAAKEAAAAAAAAAoQAAAAAAAAChAAAAAAAAAKEAAAAAAAAAoQDiLEw+02yNArs1rSARi7D8owQjsGn4HQGb6Iy3pz+A\\u002fmz4woVKwJkAAAAAAAAAoQAAAAAAAAChAAAAAAAAAKEDvYz0umOb0P6jRlPqhcBZA+EWIrjQvCUAAAAAAAAAoQA==\"},\"yaxis\":\"y\",\"type\":\"scatter\"},{\"customdata\":[[\"Cells_Neighbors_AngleBetweenNeighbors_5\"],[\"Cells_Neighbors_FirstClosestObjectNumber_5\"],[\"Cells_Neighbors_NumberOfNeighbors_5\"],[\"Nuclei_Neighbors_AngleBetweenNeighbors_1\"],[\"Nuclei_Neighbors_PercentTouching_1\"]],\"hovertemplate\":\"family=Neighbors\\u003cbr\\u003eeffect size=%{x}\\u003cbr\\u003e-log10 q=%{y}\\u003cbr\\u003efeature=%{customdata[0]}\\u003cextra\\u003e\\u003c\\u002fextra\\u003e\",\"legendgroup\":\"Neighbors\",\"marker\":{\"color\":\"#FFA15A\",\"symbol\":\"circle\"},\"mode\":\"markers\",\"name\":\"Neighbors\",\"orientation\":\"v\",\"showlegend\":true,\"x\":{\"dtype\":\"f8\",\"bdata\":\"AAAAoCuTDMAAAADg+aMSwAAAAABqqgjAAAAAQPYWC8AAAAAAw+0IwA==\"},\"xaxis\":\"x\",\"y\":{\"dtype\":\"f8\",\"bdata\":\"AAAAAAAAKEAAAAAAAAAoQAAAAAAAAChAAAAAAAAAKEAAAAAAAAAoQA==\"},\"yaxis\":\"y\",\"type\":\"scatter\"},{\"customdata\":[[\"Cells_RadialDistribution_FracAtD_AGP_2of4\"],[\"Cells_RadialDistribution_FracAtD_AGP_3of4\"],[\"Cells_RadialDistribution_FracAtD_AGP_4of4\"],[\"Cells_RadialDistribution_FracAtD_BFHigh_4of4\"],[\"Cells_RadialDistribution_FracAtD_DNA_2of4\"],[\"Cells_RadialDistribution_FracAtD_DNA_3of4\"],[\"Cells_RadialDistribution_FracAtD_Mito_3of4\"],[\"Cells_RadialDistribution_FracAtD_Mito_4of4\"],[\"Cells_RadialDistribution_FracAtD_RNA_3of4\"],[\"Cells_RadialDistribution_FracAtD_mito_tubeness_10of20\"],[\"Cells_RadialDistribution_FracAtD_mito_tubeness_12of20\"],[\"Cells_RadialDistribution_FracAtD_mito_tubeness_9of16\"],[\"Cells_RadialDistribution_MeanFrac_AGP_3of4\"],[\"Cells_RadialDistribution_MeanFrac_BFHigh_1of4\"],[\"Cells_RadialDistribution_MeanFrac_BFHigh_2of4\"],[\"Cells_RadialDistribution_MeanFrac_BFHigh_3of4\"],[\"Cells_RadialDistribution_MeanFrac_BFHigh_4of4\"],[\"Cells_RadialDistribution_MeanFrac_BFLow_3of4\"],[\"Cells_RadialDistribution_MeanFrac_BFLow_4of4\"],[\"Cells_RadialDistribution_MeanFrac_Brightfield_1of4\"],[\"Cells_RadialDistribution_MeanFrac_Brightfield_2of4\"],[\"Cells_RadialDistribution_MeanFrac_Brightfield_3of4\"],[\"Cells_RadialDistribution_MeanFrac_Brightfield_4of4\"],[\"Cells_RadialDistribution_MeanFrac_DNA_2of4\"],[\"Cells_RadialDistribution_MeanFrac_DNA_3of4\"],[\"Cells_RadialDistribution_MeanFrac_DNA_4of4\"],[\"Cells_RadialDistribution_MeanFrac_ER_3of4\"],[\"Cells_RadialDistribution_MeanFrac_Mito_3of4\"],[\"Cells_RadialDistribution_MeanFrac_mito_tubeness_15of16\"],[\"Cells_RadialDistribution_MeanFrac_mito_tubeness_1of20\"],[\"Cells_RadialDistribution_MeanFrac_mito_tubeness_5of20\"],[\"Cells_RadialDistribution_RadialCV_AGP_1of4\"],[\"Cells_RadialDistribution_RadialCV_AGP_2of4\"],[\"Cells_RadialDistribution_RadialCV_AGP_3of4\"],[\"Cells_RadialDistribution_RadialCV_AGP_4of4\"],[\"Cells_RadialDistribution_RadialCV_BFHigh_1of4\"],[\"Cells_RadialDistribution_RadialCV_BFLow_1of4\"],[\"Cells_RadialDistribution_RadialCV_Brightfield_4of4\"],[\"Cells_RadialDistribution_RadialCV_DNA_1of4\"],[\"Cells_RadialDistribution_RadialCV_DNA_3of4\"],[\"Cells_RadialDistribution_RadialCV_DNA_4of4\"],[\"Cells_RadialDistribution_RadialCV_ER_3of4\"],[\"Cells_RadialDistribution_RadialCV_Mito_1of4\"],[\"Cells_RadialDistribution_RadialCV_Mito_2of4\"],[\"Cells_RadialDistribution_RadialCV_Mito_3of4\"],[\"Cells_RadialDistribution_RadialCV_Mito_4of4\"],[\"Cells_RadialDistribution_RadialCV_RNA_1of4\"],[\"Cells_RadialDistribution_RadialCV_RNA_2of4\"],[\"Cells_RadialDistribution_RadialCV_RNA_4of4\"],[\"Cells_RadialDistribution_RadialCV_mito_tubeness_14of16\"],[\"Cells_RadialDistribution_RadialCV_mito_tubeness_2of16\"],[\"Cells_RadialDistribution_RadialCV_mito_tubeness_2of20\"],[\"Cells_RadialDistribution_RadialCV_mito_tubeness_3of16\"],[\"Cells_RadialDistribution_RadialCV_mito_tubeness_3of20\"],[\"Cells_RadialDistribution_RadialCV_mito_tubeness_6of16\"],[\"Cytoplasm_RadialDistribution_FracAtD_AGP_1of4\"],[\"Cytoplasm_RadialDistribution_FracAtD_AGP_4of4\"],[\"Cytoplasm_RadialDistribution_FracAtD_BFHigh_3of4\"],[\"Cytoplasm_RadialDistribution_FracAtD_BFHigh_4of4\"],[\"Cytoplasm_RadialDistribution_FracAtD_DNA_1of4\"],[\"Cytoplasm_RadialDistribution_FracAtD_DNA_4of4\"],[\"Cytoplasm_RadialDistribution_FracAtD_Mito_3of4\"],[\"Cytoplasm_RadialDistribution_FracAtD_RNA_1of4\"],[\"Cytoplasm_RadialDistribution_FracAtD_RNA_3of4\"],[\"Cytoplasm_RadialDistribution_FracAtD_RNA_4of4\"],[\"Cytoplasm_RadialDistribution_FracAtD_mito_tubeness_12of20\"],[\"Cytoplasm_RadialDistribution_FracAtD_mito_tubeness_13of16\"],[\"Cytoplasm_RadialDistribution_FracAtD_mito_tubeness_15of20\"],[\"Cytoplasm_RadialDistribution_FracAtD_mito_tubeness_16of16\"],[\"Cytoplasm_RadialDistribution_FracAtD_mito_tubeness_17of20\"],[\"Cytoplasm_RadialDistribution_FracAtD_mito_tubeness_19of20\"],[\"Cytoplasm_RadialDistribution_MeanFrac_AGP_1of4\"],[\"Cytoplasm_RadialDistribution_MeanFrac_AGP_4of4\"],[\"Cytoplasm_RadialDistribution_MeanFrac_BFHigh_1of4\"],[\"Cytoplasm_RadialDistribution_MeanFrac_BFHigh_2of4\"],[\"Cytoplasm_RadialDistribution_MeanFrac_BFHigh_3of4\"],[\"Cytoplasm_RadialDistribution_MeanFrac_BFLow_1of4\"],[\"Cytoplasm_RadialDistribution_MeanFrac_BFLow_3of4\"],[\"Cytoplasm_RadialDistribution_MeanFrac_Brightfield_1of4\"],[\"Cytoplasm_RadialDistribution_MeanFrac_Brightfield_2of4\"],[\"Cytoplasm_RadialDistribution_MeanFrac_Brightfield_3of4\"],[\"Cytoplasm_RadialDistribution_MeanFrac_DNA_1of4\"],[\"Cytoplasm_RadialDistribution_MeanFrac_DNA_4of4\"],[\"Cytoplasm_RadialDistribution_MeanFrac_Mito_1of4\"],[\"Cytoplasm_RadialDistribution_MeanFrac_Mito_4of4\"],[\"Cytoplasm_RadialDistribution_MeanFrac_RNA_1of4\"],[\"Cytoplasm_RadialDistribution_MeanFrac_RNA_4of4\"],[\"Cytoplasm_RadialDistribution_MeanFrac_mito_tubeness_10of16\"],[\"Cytoplasm_RadialDistribution_MeanFrac_mito_tubeness_12of20\"],[\"Cytoplasm_RadialDistribution_MeanFrac_mito_tubeness_13of16\"],[\"Cytoplasm_RadialDistribution_MeanFrac_mito_tubeness_14of20\"],[\"Cytoplasm_RadialDistribution_MeanFrac_mito_tubeness_15of20\"],[\"Cytoplasm_RadialDistribution_MeanFrac_mito_tubeness_17of20\"],[\"Cytoplasm_RadialDistribution_MeanFrac_mito_tubeness_18of20\"],[\"Cytoplasm_RadialDistribution_MeanFrac_mito_tubeness_19of20\"],[\"Cytoplasm_RadialDistribution_MeanFrac_mito_tubeness_20of20\"],[\"Cytoplasm_RadialDistribution_RadialCV_AGP_3of4\"],[\"Cytoplasm_RadialDistribution_RadialCV_AGP_4of4\"],[\"Cytoplasm_RadialDistribution_RadialCV_BFHigh_1of4\"],[\"Cytoplasm_RadialDistribution_RadialCV_BFLow_1of4\"],[\"Cytoplasm_RadialDistribution_RadialCV_Brightfield_1of4\"],[\"Cytoplasm_RadialDistribution_RadialCV_Brightfield_3of4\"],[\"Cytoplasm_RadialDistribution_RadialCV_DNA_1of4\"],[\"Cytoplasm_RadialDistribution_RadialCV_Mito_3of4\"],[\"Cytoplasm_RadialDistribution_RadialCV_Mito_4of4\"],[\"Cytoplasm_RadialDistribution_RadialCV_RNA_3of4\"],[\"Cytoplasm_RadialDistribution_RadialCV_RNA_4of4\"],[\"Cytoplasm_RadialDistribution_RadialCV_mito_tubeness_10of16\"],[\"Cytoplasm_RadialDistribution_RadialCV_mito_tubeness_12of20\"],[\"Cytoplasm_RadialDistribution_RadialCV_mito_tubeness_14of20\"],[\"Cytoplasm_RadialDistribution_RadialCV_mito_tubeness_15of20\"],[\"Cytoplasm_RadialDistribution_RadialCV_mito_tubeness_16of20\"],[\"Cytoplasm_RadialDistribution_RadialCV_mito_tubeness_20of20\"],[\"Nuclei_RadialDistribution_FracAtD_AGP_1of4\"],[\"Nuclei_RadialDistribution_FracAtD_BFHigh_3of4\"],[\"Nuclei_RadialDistribution_FracAtD_Brightfield_3of4\"],[\"Nuclei_RadialDistribution_FracAtD_DNA_1of4\"],[\"Nuclei_RadialDistribution_FracAtD_DNA_4of4\"],[\"Nuclei_RadialDistribution_FracAtD_Mito_2of4\"],[\"Nuclei_RadialDistribution_FracAtD_RNA_2of4\"],[\"Nuclei_RadialDistribution_FracAtD_RNA_4of4\"],[\"Nuclei_RadialDistribution_MeanFrac_AGP_4of4\"],[\"Nuclei_RadialDistribution_MeanFrac_BFHigh_1of4\"],[\"Nuclei_RadialDistribution_MeanFrac_BFHigh_2of4\"],[\"Nuclei_RadialDistribution_MeanFrac_BFHigh_3of4\"],[\"Nuclei_RadialDistribution_MeanFrac_BFHigh_4of4\"],[\"Nuclei_RadialDistribution_MeanFrac_BFLow_3of4\"],[\"Nuclei_RadialDistribution_MeanFrac_BFLow_4of4\"],[\"Nuclei_RadialDistribution_MeanFrac_Brightfield_1of4\"],[\"Nuclei_RadialDistribution_MeanFrac_Brightfield_2of4\"],[\"Nuclei_RadialDistribution_MeanFrac_Brightfield_3of4\"],[\"Nuclei_RadialDistribution_MeanFrac_Brightfield_4of4\"],[\"Nuclei_RadialDistribution_MeanFrac_DNA_1of4\"],[\"Nuclei_RadialDistribution_MeanFrac_DNA_3of4\"],[\"Nuclei_RadialDistribution_MeanFrac_Mito_1of4\"],[\"Nuclei_RadialDistribution_MeanFrac_Mito_3of4\"],[\"Nuclei_RadialDistribution_MeanFrac_Mito_4of4\"],[\"Nuclei_RadialDistribution_MeanFrac_RNA_3of4\"],[\"Nuclei_RadialDistribution_RadialCV_AGP_1of4\"],[\"Nuclei_RadialDistribution_RadialCV_AGP_2of4\"],[\"Nuclei_RadialDistribution_RadialCV_AGP_3of4\"],[\"Nuclei_RadialDistribution_RadialCV_AGP_4of4\"],[\"Nuclei_RadialDistribution_RadialCV_BFHigh_1of4\"],[\"Nuclei_RadialDistribution_RadialCV_BFHigh_2of4\"],[\"Nuclei_RadialDistribution_RadialCV_BFHigh_3of4\"],[\"Nuclei_RadialDistribution_RadialCV_BFLow_1of4\"],[\"Nuclei_RadialDistribution_RadialCV_BFLow_2of4\"],[\"Nuclei_RadialDistribution_RadialCV_Brightfield_1of4\"],[\"Nuclei_RadialDistribution_RadialCV_DNA_1of4\"],[\"Nuclei_RadialDistribution_RadialCV_DNA_2of4\"],[\"Nuclei_RadialDistribution_RadialCV_DNA_3of4\"],[\"Nuclei_RadialDistribution_RadialCV_DNA_4of4\"],[\"Nuclei_RadialDistribution_RadialCV_Mito_1of4\"],[\"Nuclei_RadialDistribution_RadialCV_Mito_3of4\"],[\"Nuclei_RadialDistribution_RadialCV_Mito_4of4\"],[\"Nuclei_RadialDistribution_RadialCV_RNA_2of4\"],[\"Nuclei_RadialDistribution_RadialCV_RNA_3of4\"],[\"Nuclei_RadialDistribution_RadialCV_RNA_4of4\"]],\"hovertemplate\":\"family=RadialDistribution\\u003cbr\\u003eeffect size=%{x}\\u003cbr\\u003e-log10 q=%{y}\\u003cbr\\u003efeature=%{customdata[0]}\\u003cextra\\u003e\\u003c\\u002fextra\\u003e\",\"legendgroup\":\"RadialDistribution\",\"marker\":{\"color\":\"#19d3f3\",\"symbol\":\"circle\"},\"mode\":\"markers\",\"name\":\"RadialDistribution\",\"orientation\":\"v\",\"showlegend\":true,\"x\":{\"dtype\":\"f8\",\"bdata\":\"AAAA4H2u3j8AAABA8n7hvwAAAKDNC8G\\u002fAAAAAIIV\\u002fD8AAACgE3XjPwAAAEAQB\\u002f+\\u002fAAAAIG5a0b8AAABAmH\\u002fwvwAAAIBzPPw\\u002fAAAAgMmB\\u002fD8AAABAgHzXPwAAAOBeffc\\u002fAAAAIB\\u002fYBkAAAACAn\\u002fDlPwAAAOBVSPo\\u002fAAAAwHAR7z8AAAAgOJv9vwAAACBYk\\u002fi\\u002fAAAAgGNCE0AAAABAVm8CwAAAAKCThQjAAAAAgF0J7L8AAAAgVkMMQAAAAABNDvk\\u002fAAAAQCIazr8AAADgQJnYvwAAAKBd5xFAAAAAoFPwCEAAAABgIVTXvwAAAKB1kPg\\u002fAAAAoFrM+D8AAADgfZy+PwAAAGAkL+m\\u002fAAAAoP9O678AAACgHhoDwAAAAECO2wDAAAAAQKQm9r8AAACgemQCwAAAAKBK9AdAAAAAIKlQ5z8AAABgjTEAwAAAAECr0ec\\u002fAAAAAGL+AUAAAAAgbS3wPwAAAGA7veg\\u002fAAAAAHk77L8AAADAM3UCQAAAAIA32fI\\u002fAAAAgL76tL8AAADAlhf8vwAAAOCRu\\u002fc\\u002fAAAAQPv+CkAAAAAAB88BwAAAACBB6Nm\\u002fAAAAQE22\\u002fb8AAADAdYsGQAAAACDOdAfAAAAA4CcC3b8AAABgYqfoPwAAACAAtgtAAAAAgDXwC8AAAACAm4L4PwAAAODjqfc\\u002fAAAAIFjWAUAAAADg2kcBwAAAAMBAggNAAAAA4NV7678AAACATP8AQAAAAKCEUu8\\u002fAAAAAO7sBMAAAABAGLf2vwAAAKCh8QxAAAAA4OHTCsAAAAAAIe3nPwAAAEBOEPw\\u002fAAAAgF7NDEAAAABAyD8EwAAAACCLWxHAAAAAYC+l+L8AAACAYFL\\u002fvwAAACANWPG\\u002fAAAAQF3DEUAAAAAgvvAPwAAAACB9ZQBAAAAAYKQrBMAAAABAE5QEQAAAAOCgUwbAAAAAgN\\u002fBDUAAAADAHJ0HQAAAAADDIt8\\u002fAAAAgDOr9T8AAADAUYHuPwAAAECEG+2\\u002fAAAA4Lgx8L8AAACgnMvqPwAAAMAacgdAAAAAoOfl8j8AAADg+lXqvwAAAECN\\u002fPy\\u002fAAAAQLU0AcAAAACgNDP9vwAAAOAC8\\u002f6\\u002fAAAAACOL8T8AAACgBwEMQAAAAGC\\u002fHQBAAAAAQHpYBkAAAACA5tT7PwAAAGCOMBBAAAAAAOjjCUAAAADglzIAQAAAAGDk0fC\\u002fAAAAYNmb578AAABg6Rb7vwAAAGCPeNC\\u002fAAAA4NEmB8AAAACg6MQTwAAAAOBrrMC\\u002fAAAAoJok8z8AAACgZx\\u002fjvwAAAKBcXuU\\u002fAAAAIMmB2z8AAADgdVgBQAAAAECsWwTAAAAAgC\\u002fwkr8AAACgmSIQQAAAAKC9NgPAAAAAYKQqD8AAAAAAK4wLQAAAAECA39g\\u002fAAAA4GVb878AAABg2QMDwAAAAAD\\u002fmvg\\u002fAAAAgFe96r8AAABgjdrSPwAAAMBcQ9a\\u002fAAAAQFhvCcAAAAAgxiUBQAAAAOBIdQjAAAAAYBr4+T8AAABgsBPFPwAAAGBrufO\\u002fAAAAIK2v\\u002fb8AAAAgTlv\\u002fPwAAAKBhOYO\\u002fAAAAYK6q9r8AAAAAeqMGQAAAAKCqwOY\\u002fAAAAYEjX\\u002fT8AAABgdSAJQAAAAADWcvs\\u002fAAAAgOqf0r8AAADg4y30vwAAAKAQSu0\\u002fAAAAYMFk2z8AAAAgf+TyPwAAAGDDvwdAAAAAgFHo7z8AAABgtDHwPw==\"},\"xaxis\":\"x\",\"y\":{\"dtype\":\"f8\",\"bdata\":\"tg\\u002fmBNdJDkAjyrs5UmMRQO2JkigTPBJA\\u002fGWtBhkeI0Adj3mIvm7pPw3n\\u002fi5xyxhAoGLfFcRY1D\\u002f0Mzd7fcr0PzZ4K0V5QR1A5Gm\\u002fdyhBE0CaAvClNeX6PwWpEngGvhNAAAAAAAAAKEAjyrs5UmMRQAAAAAAAAChAZLE5Pae9FEAAAAAAAAAoQF35eLLXHxJAAAAAAAAAKEAAAAAAAAAoQAAAAAAAAChApd7gGtbT\\u002fD8AAAAAAAAoQOdQx7P7shtA96Hgiffd+j9hX84rEmzHPwAAAAAAAChAAAAAAAAAKECJ+EMmXSb4P0uzTgZCPgVAJ334bJ9uGUAOy5etUJIDQH+R0yow1Lw\\u002fIQeSqxcq4j8AAAAAAAAoQAAAAAAAAChAIlOyjriMJUAAAAAAAAAoQAAAAAAAAChA\\u002fOlJEmVDEEAAAAAAAAAoQLgnzOUQD+s\\u002fAAAAAAAAKEAWgPIpbBsgQBU\\u002f8kBLaxNAEYNdKEyYEEAAAAAAAAAoQJEzorZi2QFA4CAdR6zCyz+\\u002fxZCfn5EjQLeskKIy3SBAAAAAAAAAKEBPE1z+rWIcQHIjKkp95NM\\u002fbpjeVatcFUAAAAAAAAAoQAAAAAAAAChAZKmm36ga1z\\u002fQiytuh87qPwAAAAAAAChAAAAAAAAAKEACnvXdQQEhQOg1VKPuYiFAAAAAAAAAKEAAAAAAAAAoQAAAAAAAAChAAAAAAAAAKEAAAAAAAAAoQAAAAAAAAChAAAAAAAAAKEBDojF6EdYgQAAAAAAAAChAAAAAAAAAKEBOsPXZgzwRQAAAAAAAAChAAAAAAAAAKEAAAAAAAAAoQAAAAAAAAChAZQ0I3EteH0AAAAAAAAAoQOovkq4tVhBAAAAAAAAAKEAAAAAAAAAoQEsPwaTPt\\u002fU\\u002fQoBb9vAmJEDkNk6kjDYhQJD3weRRkyRAAAAAAAAAKEAAAAAAAAAoQBlDGAUsEOY\\u002f08Npdv47JUBP+jvpiPYaQJRWOJOfSCJA969r30lQIEBnvfTrNC8lQAAAAAAAAChAXtx5ZPmVCED8An7IGLoKQAAAAAAAAChAAAAAAAAAKEAAAAAAAAAoQAAAAAAAAChACFxnl0AF9z8AAAAAAAAoQAAAAAAAAChAqt1\\u002fHCPwJ0D3qMemSdQbQAAAAAAAAChAAAAAAAAAKEAAAAAAAAAoQOUm\\u002fku6ZxtARsGBJTbwEkAAAAAAAAAoQBi\\u002fetJFTdY\\u002fAAAAAAAAKEAAAAAAAAAoQHyZ7deeGfI\\u002fBlmJCS7YEEAYNxrWbQTLP0KiYzWFEdo\\u002fbjWm+ARO5j\\u002fD4qI3M8QjQAAAAAAAAChA+rIyA\\u002fED0D8AAAAAAAAoQAAAAAAAAChAAAAAAAAAKEAAAAAAAAAoQHBzqPHk0AFA97q6o9cpCkAAAAAAAAAoQLJX7dUteRBA6q8p9vNY0T9WkecTh2\\u002fGP4r4CCr8Qv0\\u002fAAAAAAAAKECFdwsQEgMTQAAAAAAAAChAXTAACLv6IUDS2Y7tX6S7P5JVPG\\u002fIu\\u002fY\\u002fvIkCwiPjIkAAAAAAAAAoQORtWULALro\\u002flv5dH1zrEUAAAAAAAAAoQAAAAAAAAACASoTTgMnjE0AAAAAAAAAoQAAAAAAAAChAmtbeuqNpuT9qPfTBisAlQG4ce5jmrRRA6XKh2MHxB0CWKFN3LUYeQAAAAAAAAChAj8rVz2mEFkDrjVQQznUBQA==\"},\"yaxis\":\"y\",\"type\":\"scatter\"},{\"customdata\":[[\"Cells_Texture_AngularSecondMoment_AGP_10_03_256\"],[\"Cells_Texture_AngularSecondMoment_Mito_10_03_256\"],[\"Cells_Texture_Correlation_AGP_3_00_256\"],[\"Cells_Texture_Correlation_BFHigh_10_00_256\"],[\"Cells_Texture_Correlation_BFHigh_10_01_256\"],[\"Cells_Texture_Correlation_BFHigh_10_02_256\"],[\"Cells_Texture_Correlation_BFHigh_10_03_256\"],[\"Cells_Texture_Correlation_BFHigh_3_00_256\"],[\"Cells_Texture_Correlation_BFHigh_3_01_256\"],[\"Cells_Texture_Correlation_BFHigh_3_03_256\"],[\"Cells_Texture_Correlation_BFHigh_5_00_256\"],[\"Cells_Texture_Correlation_BFHigh_5_01_256\"],[\"Cells_Texture_Correlation_BFHigh_5_02_256\"],[\"Cells_Texture_Correlation_BFHigh_5_03_256\"],[\"Cells_Texture_Correlation_BFLow_10_00_256\"],[\"Cells_Texture_Correlation_BFLow_10_01_256\"],[\"Cells_Texture_Correlation_BFLow_10_02_256\"],[\"Cells_Texture_Correlation_BFLow_3_03_256\"],[\"Cells_Texture_Correlation_BFLow_5_00_256\"],[\"Cells_Texture_Correlation_BFLow_5_01_256\"],[\"Cells_Texture_Correlation_BFLow_5_02_256\"],[\"Cells_Texture_Correlation_BFLow_5_03_256\"],[\"Cells_Texture_Correlation_Brightfield_3_00_256\"],[\"Cells_Texture_Correlation_Brightfield_3_01_256\"],[\"Cells_Texture_Correlation_Brightfield_3_02_256\"],[\"Cells_Texture_Correlation_Brightfield_3_03_256\"],[\"Cells_Texture_Correlation_Brightfield_5_00_256\"],[\"Cells_Texture_Correlation_Brightfield_5_01_256\"],[\"Cells_Texture_Correlation_Brightfield_5_03_256\"],[\"Cells_Texture_Correlation_Mito_3_00_256\"],[\"Cells_Texture_InfoMeas1_AGP_10_02_256\"],[\"Cells_Texture_InfoMeas1_AGP_10_03_256\"],[\"Cells_Texture_InfoMeas1_AGP_3_01_256\"],[\"Cells_Texture_InfoMeas1_AGP_5_01_256\"],[\"Cells_Texture_InfoMeas1_BFHigh_3_02_256\"],[\"Cells_Texture_InfoMeas1_Brightfield_3_00_256\"],[\"Cells_Texture_InfoMeas1_DNA_10_00_256\"],[\"Cells_Texture_InfoMeas1_DNA_10_03_256\"],[\"Cells_Texture_InfoMeas1_DNA_5_03_256\"],[\"Cells_Texture_InfoMeas1_ER_10_03_256\"],[\"Cells_Texture_InfoMeas1_Mito_10_01_256\"],[\"Cells_Texture_InfoMeas1_Mito_10_02_256\"],[\"Cells_Texture_InfoMeas1_Mito_3_00_256\"],[\"Cells_Texture_InfoMeas1_Mito_5_01_256\"],[\"Cells_Texture_InfoMeas1_RNA_10_00_256\"],[\"Cells_Texture_InfoMeas1_RNA_5_03_256\"],[\"Cells_Texture_InfoMeas2_AGP_3_02_256\"],[\"Cells_Texture_InfoMeas2_Brightfield_5_00_256\"],[\"Cells_Texture_InfoMeas2_Mito_10_01_256\"],[\"Cells_Texture_InfoMeas2_Mito_10_02_256\"],[\"Cells_Texture_InfoMeas2_RNA_3_03_256\"],[\"Cytoplasm_Texture_AngularSecondMoment_BFHigh_3_00_256\"],[\"Cytoplasm_Texture_AngularSecondMoment_Mito_10_03_256\"],[\"Cytoplasm_Texture_AngularSecondMoment_RNA_10_03_256\"],[\"Cytoplasm_Texture_Contrast_Brightfield_5_00_256\"],[\"Cytoplasm_Texture_Contrast_Mito_3_02_256\"],[\"Cytoplasm_Texture_Correlation_AGP_3_00_256\"],[\"Cytoplasm_Texture_Correlation_BFHigh_10_01_256\"],[\"Cytoplasm_Texture_Correlation_BFHigh_10_03_256\"],[\"Cytoplasm_Texture_Correlation_BFHigh_3_00_256\"],[\"Cytoplasm_Texture_Correlation_BFHigh_3_01_256\"],[\"Cytoplasm_Texture_Correlation_BFHigh_3_02_256\"],[\"Cytoplasm_Texture_Correlation_BFHigh_3_03_256\"],[\"Cytoplasm_Texture_Correlation_BFHigh_5_00_256\"],[\"Cytoplasm_Texture_Correlation_BFHigh_5_01_256\"],[\"Cytoplasm_Texture_Correlation_BFHigh_5_02_256\"],[\"Cytoplasm_Texture_Correlation_BFHigh_5_03_256\"],[\"Cytoplasm_Texture_Correlation_BFLow_10_00_256\"],[\"Cytoplasm_Texture_Correlation_BFLow_10_01_256\"],[\"Cytoplasm_Texture_Correlation_BFLow_10_02_256\"],[\"Cytoplasm_Texture_Correlation_BFLow_3_00_256\"],[\"Cytoplasm_Texture_Correlation_BFLow_3_01_256\"],[\"Cytoplasm_Texture_Correlation_BFLow_3_02_256\"],[\"Cytoplasm_Texture_Correlation_BFLow_3_03_256\"],[\"Cytoplasm_Texture_Correlation_BFLow_5_00_256\"],[\"Cytoplasm_Texture_Correlation_BFLow_5_01_256\"],[\"Cytoplasm_Texture_Correlation_BFLow_5_02_256\"],[\"Cytoplasm_Texture_Correlation_BFLow_5_03_256\"],[\"Cytoplasm_Texture_Correlation_Brightfield_10_03_256\"],[\"Cytoplasm_Texture_Correlation_Brightfield_3_00_256\"],[\"Cytoplasm_Texture_Correlation_Brightfield_3_03_256\"],[\"Cytoplasm_Texture_Correlation_Brightfield_5_03_256\"],[\"Cytoplasm_Texture_Correlation_DNA_3_00_256\"],[\"Cytoplasm_Texture_Correlation_Mito_3_00_256\"],[\"Cytoplasm_Texture_DifferenceVariance_AGP_3_02_256\"],[\"Cytoplasm_Texture_DifferenceVariance_Brightfield_3_02_256\"],[\"Cytoplasm_Texture_DifferenceVariance_Mito_10_03_256\"],[\"Cytoplasm_Texture_Entropy_Mito_10_03_256\"],[\"Cytoplasm_Texture_Entropy_RNA_10_03_256\"],[\"Cytoplasm_Texture_InfoMeas1_AGP_10_02_256\"],[\"Cytoplasm_Texture_InfoMeas1_AGP_5_00_256\"],[\"Cytoplasm_Texture_InfoMeas1_AGP_5_01_256\"],[\"Cytoplasm_Texture_InfoMeas1_Brightfield_3_00_256\"],[\"Cytoplasm_Texture_InfoMeas1_DNA_10_00_256\"],[\"Cytoplasm_Texture_InfoMeas1_DNA_10_01_256\"],[\"Cytoplasm_Texture_InfoMeas1_DNA_5_03_256\"],[\"Cytoplasm_Texture_InfoMeas1_ER_10_00_256\"],[\"Cytoplasm_Texture_InfoMeas1_ER_10_03_256\"],[\"Cytoplasm_Texture_InfoMeas1_ER_5_00_256\"],[\"Cytoplasm_Texture_InfoMeas1_Mito_10_02_256\"],[\"Cytoplasm_Texture_InfoMeas1_Mito_10_03_256\"],[\"Cytoplasm_Texture_InfoMeas1_Mito_5_00_256\"],[\"Cytoplasm_Texture_InfoMeas1_Mito_5_01_256\"],[\"Cytoplasm_Texture_InfoMeas1_RNA_5_03_256\"],[\"Cytoplasm_Texture_InfoMeas2_AGP_3_00_256\"],[\"Cytoplasm_Texture_InfoMeas2_Brightfield_3_01_256\"],[\"Cytoplasm_Texture_InfoMeas2_DNA_10_02_256\"],[\"Cytoplasm_Texture_InfoMeas2_Mito_10_03_256\"],[\"Cytoplasm_Texture_InfoMeas2_Mito_5_01_256\"],[\"Cytoplasm_Texture_InfoMeas2_RNA_5_03_256\"],[\"Cytoplasm_Texture_SumVariance_Brightfield_3_00_256\"],[\"Cytoplasm_Texture_Variance_AGP_5_02_256\"],[\"Cytoplasm_Texture_Variance_Mito_5_02_256\"],[\"Nuclei_Texture_AngularSecondMoment_AGP_10_01_256\"],[\"Nuclei_Texture_AngularSecondMoment_BFHigh_10_01_256\"],[\"Nuclei_Texture_AngularSecondMoment_BFLow_10_01_256\"],[\"Nuclei_Texture_AngularSecondMoment_Brightfield_10_01_256\"],[\"Nuclei_Texture_AngularSecondMoment_DNA_10_03_256\"],[\"Nuclei_Texture_AngularSecondMoment_ER_10_01_256\"],[\"Nuclei_Texture_AngularSecondMoment_ER_10_03_256\"],[\"Nuclei_Texture_AngularSecondMoment_Mito_10_01_256\"],[\"Nuclei_Texture_Contrast_BFHigh_3_03_256\"],[\"Nuclei_Texture_Contrast_RNA_3_00_256\"],[\"Nuclei_Texture_Correlation_AGP_3_00_256\"],[\"Nuclei_Texture_Correlation_AGP_5_01_256\"],[\"Nuclei_Texture_Correlation_BFHigh_10_00_256\"],[\"Nuclei_Texture_Correlation_BFHigh_10_01_256\"],[\"Nuclei_Texture_Correlation_BFHigh_10_02_256\"],[\"Nuclei_Texture_Correlation_BFHigh_10_03_256\"],[\"Nuclei_Texture_Correlation_BFHigh_3_00_256\"],[\"Nuclei_Texture_Correlation_BFHigh_3_01_256\"],[\"Nuclei_Texture_Correlation_BFHigh_3_02_256\"],[\"Nuclei_Texture_Correlation_BFHigh_3_03_256\"],[\"Nuclei_Texture_Correlation_BFHigh_5_00_256\"],[\"Nuclei_Texture_Correlation_BFHigh_5_01_256\"],[\"Nuclei_Texture_Correlation_BFHigh_5_02_256\"],[\"Nuclei_Texture_Correlation_BFHigh_5_03_256\"],[\"Nuclei_Texture_Correlation_BFLow_10_00_256\"],[\"Nuclei_Texture_Correlation_BFLow_10_01_256\"],[\"Nuclei_Texture_Correlation_BFLow_10_02_256\"],[\"Nuclei_Texture_Correlation_BFLow_10_03_256\"],[\"Nuclei_Texture_Correlation_BFLow_3_00_256\"],[\"Nuclei_Texture_Correlation_BFLow_3_01_256\"],[\"Nuclei_Texture_Correlation_BFLow_3_02_256\"],[\"Nuclei_Texture_Correlation_BFLow_5_00_256\"],[\"Nuclei_Texture_Correlation_BFLow_5_01_256\"],[\"Nuclei_Texture_Correlation_BFLow_5_02_256\"],[\"Nuclei_Texture_Correlation_BFLow_5_03_256\"],[\"Nuclei_Texture_Correlation_Brightfield_10_00_256\"],[\"Nuclei_Texture_Correlation_Brightfield_10_01_256\"],[\"Nuclei_Texture_Correlation_Brightfield_10_02_256\"],[\"Nuclei_Texture_Correlation_Brightfield_10_03_256\"],[\"Nuclei_Texture_Correlation_Brightfield_3_00_256\"],[\"Nuclei_Texture_Correlation_Brightfield_3_01_256\"],[\"Nuclei_Texture_Correlation_Brightfield_3_03_256\"],[\"Nuclei_Texture_Correlation_Brightfield_5_00_256\"],[\"Nuclei_Texture_Correlation_Brightfield_5_01_256\"],[\"Nuclei_Texture_Correlation_Brightfield_5_02_256\"],[\"Nuclei_Texture_Correlation_Brightfield_5_03_256\"],[\"Nuclei_Texture_Correlation_DNA_3_02_256\"],[\"Nuclei_Texture_Correlation_ER_3_00_256\"],[\"Nuclei_Texture_Correlation_Mito_10_00_256\"],[\"Nuclei_Texture_Correlation_Mito_10_02_256\"],[\"Nuclei_Texture_Correlation_Mito_3_02_256\"],[\"Nuclei_Texture_Correlation_Mito_5_01_256\"],[\"Nuclei_Texture_Correlation_Mito_5_03_256\"],[\"Nuclei_Texture_Correlation_RNA_10_00_256\"],[\"Nuclei_Texture_Correlation_RNA_10_01_256\"],[\"Nuclei_Texture_Correlation_RNA_10_02_256\"],[\"Nuclei_Texture_Correlation_RNA_10_03_256\"],[\"Nuclei_Texture_DifferenceVariance_BFHigh_10_00_256\"],[\"Nuclei_Texture_DifferenceVariance_BFLow_10_01_256\"],[\"Nuclei_Texture_DifferenceVariance_ER_10_03_256\"],[\"Nuclei_Texture_Entropy_AGP_10_01_256\"],[\"Nuclei_Texture_Entropy_BFHigh_10_01_256\"],[\"Nuclei_Texture_Entropy_BFLow_10_01_256\"],[\"Nuclei_Texture_Entropy_Brightfield_10_01_256\"],[\"Nuclei_Texture_Entropy_ER_10_01_256\"],[\"Nuclei_Texture_InfoMeas1_AGP_3_02_256\"],[\"Nuclei_Texture_InfoMeas1_BFHigh_3_02_256\"],[\"Nuclei_Texture_InfoMeas1_BFLow_3_00_256\"],[\"Nuclei_Texture_InfoMeas1_DNA_3_00_256\"],[\"Nuclei_Texture_InfoMeas1_ER_3_00_256\"],[\"Nuclei_Texture_InfoMeas1_Mito_3_02_256\"],[\"Nuclei_Texture_InfoMeas1_RNA_3_00_256\"],[\"Nuclei_Texture_InfoMeas2_AGP_3_02_256\"],[\"Nuclei_Texture_InfoMeas2_BFHigh_3_02_256\"],[\"Nuclei_Texture_InfoMeas2_Mito_3_00_256\"],[\"Nuclei_Texture_SumEntropy_DNA_10_01_256\"],[\"Nuclei_Texture_SumVariance_AGP_10_03_256\"],[\"Nuclei_Texture_SumVariance_Brightfield_3_02_256\"],[\"Nuclei_Texture_SumVariance_DNA_10_03_256\"],[\"Nuclei_Texture_SumVariance_Mito_10_01_256\"]],\"hovertemplate\":\"family=Texture\\u003cbr\\u003eeffect size=%{x}\\u003cbr\\u003e-log10 q=%{y}\\u003cbr\\u003efeature=%{customdata[0]}\\u003cextra\\u003e\\u003c\\u002fextra\\u003e\",\"legendgroup\":\"Texture\",\"marker\":{\"color\":\"#FF6692\",\"symbol\":\"circle\"},\"mode\":\"markers\",\"name\":\"Texture\",\"orientation\":\"v\",\"showlegend\":true,\"x\":{\"dtype\":\"f8\",\"bdata\":\"AAAAQDMiwj8AAACAhrDiPwAAAADPlPc\\u002fAAAAwNvc\\u002fz8AAAAAwGYGQAAAAADf1\\u002f0\\u002fAAAAIEO3AUAAAACAT7jjPwAAAIAVjOQ\\u002fAAAA4DWJ2z8AAABAO2nmPwAAAODbp9a\\u002fAAAA4KNv2D8AAAAgkGDvvwAAAOAdoANAAAAA4KpYEUAAAABAfSD4PwAAAMCTHQjAAAAAAJp2C8AAAACAU\\u002fAFwAAAAOBOeQbAAAAAAM2dBcAAAACgp1v1vwAAAIB5SfC\\u002fAAAAwPjn6r8AAABA\\u002fAjzvwAAAAB1Nu6\\u002fAAAAwLhl7T8AAABAU7HqPwAAAAD37QtAAAAAwPS0tj8AAADAHa3sPwAAAKAZ+\\u002fE\\u002fAAAAIAHEzD8AAACAsBy4vwAAAOAk+MS\\u002fAAAAgKOy\\u002f78AAAAgztHZvwAAAIDMhgnAAAAAgM0l0T8AAABgZUT1vwAAAKAiPwvAAAAAgDfZCsAAAAAAazMMwAAAAMCDvvC\\u002fAAAA4PSY\\u002fr8AAAAA4lfovwAAAOA+mdm\\u002fAAAAwF+G8z8AAABgXzIDQAAAAMB+s\\u002fA\\u002fAAAAoHMABEAAAABAXnegvwAAAEA8o9Y\\u002fAAAAAOyT4b8AAAAA4SPiPwAAAEDqFwRAAAAAQBOmBUAAAADAh4UDQAAAAKAumvM\\u002fAAAAgH3P+T8AAAAAD67pPwAAAABsqvw\\u002fAAAAIMvCA0AAAACgdW4DQAAAACCpUQBAAAAAQBRLAkAAAAAg+1QEQAAAAAAlFQZAAAAAQKDYAkAAAADA2uK7vwAAACBOR+E\\u002fAAAAINe62r8AAACAjmrqPwAAAOAkLP8\\u002fAAAA4Dqv\\u002fz8AAADgKaDzPwAAAGB9Nv8\\u002fAAAAoKVfBEAAAADgdEHvPwAAAOBx1\\u002fQ\\u002fAAAAINYuAUAAAADAXfEHQAAAAMCWGxFAAAAAAO9A4b8AAAAg1iDtPwAAAEC5T+G\\u002fAAAAYIUb+D8AAACA7NXaPwAAAEA96u8\\u002fAAAAQKSosb8AAAAAq3XMPwAAAIB6Uuc\\u002fAAAAAIz4AMAAAABALyzjPwAAAEAghAvAAAAAQJxHub8AAACgU1IAQAAAAKCAyQbAAAAAIA+J9b8AAAAAZ7T4PwAAAAASChHAAAAAIDqzDsAAAABActn\\u002fvwAAAEAiP\\u002fA\\u002fAAAAgNDR8b8AAABAFzq1PwAAAGAjsdS\\u002fAAAA4As0B0AAAAAASJvyPwAAAIA5\\u002fOG\\u002fAAAA4NIn+j8AAAAggY8AQAAAAEDs+dy\\u002fAAAAwEa09L8AAABgH\\u002fDqvwAAACCBU9O\\u002fAAAAAG0j3r8AAACgAy\\u002fivwAAACCSbN+\\u002fAAAA4GKr6L8AAAAAwpvlPwAAAGAhWtE\\u002fAAAAoA\\u002fP8j8AAACgEZQQQAAAAOCWBPM\\u002fAAAAILNGA0AAAABAcALjPwAAAGBmWwFAAAAAgLgI6j8AAACAVNHsPwAAAIB3xcg\\u002fAAAAYGbC4z8AAABgWjzNPwAAAIBbNvm\\u002fAAAAAN5q2b8AAACAdnX\\u002fvwAAAOB+yQZAAAAAwG2yEkAAAAAg6jsEQAAAAMCWnRFAAAAA4AFi8r8AAAAgpP72vwAAAID5ie6\\u002fAAAA4JlUBcAAAADgZK38vwAAAIBPuAPAAAAAwBpaBMAAAADgol0MQAAAAKCqRhFAAAAAwBI4BkAAAABglLoQQAAAAOB0yAfAAAAAgLD8BsAAAACgP\\u002fcHwAAAAABiSAvAAAAAQFqg778AAADA1ScBwAAAAEAqUPa\\u002fAAAAwHul\\u002fD8AAAAAomsBQAAAAOAQvBJAAAAAAECJFEAAAABgg4kEQAAAACCHyRBAAAAA4DdhEEAAAAAgfGkRQAAAACBNQxJAAAAA4JbZEEAAAAAAiwoRQAAAAMAQAdW\\u002fAAAAoLvQw78AAACAFmilPwAAAOCWFPE\\u002fAAAAgBS1AEAAAADA1Nf5PwAAAKBqmOc\\u002fAAAAICZ4+T8AAAAgf4j+PwAAACAVqeA\\u002fAAAAIBQe6z8AAADg2eL0PwAAAMBmNfC\\u002fAAAA4A+r9L8AAAAAxZbjvwAAAAAQoe6\\u002fAAAA4BNY4L8AAACA3\\u002f7sPwAAAABF4vA\\u002fAAAAoM2y8z8AAAAgE1nqPwAAAIACefA\\u002fAAAAIPl8BUA=\"},\"xaxis\":\"x\",\"y\":{\"dtype\":\"f8\",\"bdata\":\"+opRBsb10D9paRbkoUsDQAv34Bx7JxFA\\u002fgmjnnjCJ0AAAAAAAAAoQAAAAAAAAChAAAAAAAAAKEB\\u002fltNNzq\\u002fRP3gCRzH4GO0\\u002f5To3LArd2D\\u002foZly\\u002fHT\\u002f8P1JbAH\\u002fsfOg\\u002f5cqp4aTxxz8txIXcZd0ZQAAAAAAAAChAAAAAAAAAKEAAAAAAAAAoQAAAAAAAAChAAAAAAAAAKEAAAAAAAAAoQAAAAAAAAChAAAAAAAAAKECs3P1SbHYGQO+MXb+epQZA14AGPYasAkASheQYwIgPQFnJOZ466BRAxN1EshxWHkC0E1Z3mJ4cQAAAAAAAAChAhCfUey1z1D\\u002fmWW+L86EVQHxwktFyEN0\\u002fXU04Ere07j\\u002fm2+3BZKMBQLBTSRT8U\\u002fw\\u002fAAAAAAAAKECQJXTKOSYHQAAAAAAAAChAHpnpeXyj6j\\u002fNMPIiluojQAAAAAAAAChAAAAAAAAAKEAAAAAAAAAoQBWtgr8xHwJAMXBX1eCXH0Bx+lyNLHTmP5eBsHV\\u002fHvk\\u002fik3Ba9VoIkAAAAAAAAAoQLtll7ojsBNAAAAAAAAAKEDL3uCEVlHAPw6BFoYqCPA\\u002fMJnDCLx4GUAIhFeLaBT9PwAAAAAAAChAAAAAAAAAKEAAAAAAAAAoQGAiiZsIiSBA9Z5uZf1qJUDd8YhzlnkXQK6CbMKXHh9AAAAAAAAAKEAAAAAAAAAoQAAAAAAAAChAAAAAAAAAKEAAAAAAAAAoQAAAAAAAAChAAAAAAAAAKEDdqWZkxljCP7B9p92Ghuk\\u002frdugl+cQ8D9bS23u8if2P7Dq\\u002fNIYuyNA5R3\\u002f4TjpJkAOTwqlNDr8P7FYSYS+MidAAAAAAAAAKEB8bTLUWSTxP7XpEJp1fhBAAAAAAAAAKEAAAAAAAAAoQAAAAAAAAChANjyOwkJjD0AAAAAAAAAoQPnz2gnoQgFAoodppuQDJEAIGhywLdcHQA8nLDnQZBNANSjktz\\u002f\\u002f9z+iTolGSqfGP0Ljwe+FWBRAAAAAAAAAKECEqLL74QDOPwAAAAAAAChAEyVesaOzoz8AAAAAAAAoQAAAAAAAAChAAAAAAAAAKEAsswbRsFUkQAAAAAAAAChAAAAAAAAAKEAAAAAAAAAoQP8uBVMPzSJAxplhdgaKI0DST9SMc44BQA6SiTuW9fA\\u002fAAAAAAAAKEBaXAaCaIUiQE2A+MdjDRZAWGFHHlOpJ0AAAAAAAAAoQLULV4RMRwVAQ+FQx49pJUDhr\\u002f+YT8UNQMROtH7reOI\\u002fAAAAAAAAKEAyqOVBVIsLQOyqgGvPbgRAA1LDeP28HUAKtSk9IqMSQOR1bPQim\\u002fM\\u002fQiGUDYhx\\u002fD8AAAAAAAAoQANSw3j9vB1AAAAAAAAAKECjjDPqeoQJQAAAAAAAAChAFFzpYIWGoj9geriF00f5P6k7eD8w\\u002fKo\\u002fHZkGmY2XuT+\\u002fQLHvG+SXP2yNeNj6ghlAvHGL0aqTAUC9ruGgh84gQAAAAAAAAChAAAAAAAAAKEAAAAAAAAAoQAAAAAAAAChAVevfVcK\\u002fFEDPrz7vtUAhQAIc\\u002f0VjfA9AAAAAAAAAKEAVUlGoGMQlQAAAAAAAAChAAAAAAAAAKEAAAAAAAAAoQAAAAAAAAChAAAAAAAAAKEAAAAAAAAAoQAAAAAAAAChAAAAAAAAAKEAAAAAAAAAoQAAAAAAAAChAC4Ru3kQs+j8AAAAAAAAoQK5LBTAhvBxA\\u002fYd5rhPfFUAAAAAAAAAoQAAAAAAAAChAAAAAAAAAKEAAAAAAAAAoQAAAAAAAAChAAAAAAAAAKEAAAAAAAAAoQAAAAAAAAChAAAAAAAAAKEAAAAAAAAAoQATHko8RcPA\\u002fnznScyg\\u002f4j\\u002fUWn6nCxeIPyFcuUwwORFAAAAAAAAAKEAoTYe4Q2ojQLPYtoFopPQ\\u002fAAAAAAAAKEAfeLoX5XoQQOn1X\\u002fa8L+Q\\u002fv6HjlgJh5D8AAAAAAAAoQAf2wKCrBwpAFSJri6c8HkA9RZ7+KFjoP2PTBOb1luY\\u002fSMm\\u002flCDn2z9b08P1LeIaQAAAAAAAAChAjGATI5fo8D8N6lImdyrCPzTMdKMtcCJAAAAAAAAAKEA=\"},\"yaxis\":\"y\",\"type\":\"scatter\"},{\"customdata\":[[\"Nuclei_ObjectSkeleton_NumberNonTrunkBranches_mito_skel\"]],\"hovertemplate\":\"family=ObjectSkeleton\\u003cbr\\u003eeffect size=%{x}\\u003cbr\\u003e-log10 q=%{y}\\u003cbr\\u003efeature=%{customdata[0]}\\u003cextra\\u003e\\u003c\\u002fextra\\u003e\",\"legendgroup\":\"ObjectSkeleton\",\"marker\":{\"color\":\"#B6E880\",\"symbol\":\"circle\"},\"mode\":\"markers\",\"name\":\"ObjectSkeleton\",\"orientation\":\"v\",\"showlegend\":true,\"x\":{\"dtype\":\"f8\",\"bdata\":\"AAAAQMs89j8=\"},\"xaxis\":\"x\",\"y\":{\"dtype\":\"f8\",\"bdata\":\"AAAAAAAAKEA=\"},\"yaxis\":\"y\",\"type\":\"scatter\"}],                        {\"template\":{\"data\":{\"histogram2dcontour\":[{\"type\":\"histogram2dcontour\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]}],\"choropleth\":[{\"type\":\"choropleth\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}],\"histogram2d\":[{\"type\":\"histogram2d\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]}],\"heatmap\":[{\"type\":\"heatmap\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]}],\"contourcarpet\":[{\"type\":\"contourcarpet\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}],\"contour\":[{\"type\":\"contour\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]}],\"surface\":[{\"type\":\"surface\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]}],\"mesh3d\":[{\"type\":\"mesh3d\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}],\"scatter\":[{\"fillpattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2},\"type\":\"scatter\"}],\"parcoords\":[{\"type\":\"parcoords\",\"line\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"scatterpolargl\":[{\"type\":\"scatterpolargl\",\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"bar\":[{\"error_x\":{\"color\":\"#2a3f5f\"},\"error_y\":{\"color\":\"#2a3f5f\"},\"marker\":{\"line\":{\"color\":\"#E5ECF6\",\"width\":0.5},\"pattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2}},\"type\":\"bar\"}],\"scattergeo\":[{\"type\":\"scattergeo\",\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"scatterpolar\":[{\"type\":\"scatterpolar\",\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"histogram\":[{\"marker\":{\"pattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2}},\"type\":\"histogram\"}],\"scattergl\":[{\"type\":\"scattergl\",\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"scatter3d\":[{\"type\":\"scatter3d\",\"line\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"scattermap\":[{\"type\":\"scattermap\",\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"scatterternary\":[{\"type\":\"scatterternary\",\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"scattercarpet\":[{\"type\":\"scattercarpet\",\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"carpet\":[{\"aaxis\":{\"endlinecolor\":\"#2a3f5f\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"minorgridcolor\":\"white\",\"startlinecolor\":\"#2a3f5f\"},\"baxis\":{\"endlinecolor\":\"#2a3f5f\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"minorgridcolor\":\"white\",\"startlinecolor\":\"#2a3f5f\"},\"type\":\"carpet\"}],\"table\":[{\"cells\":{\"fill\":{\"color\":\"#EBF0F8\"},\"line\":{\"color\":\"white\"}},\"header\":{\"fill\":{\"color\":\"#C8D4E3\"},\"line\":{\"color\":\"white\"}},\"type\":\"table\"}],\"barpolar\":[{\"marker\":{\"line\":{\"color\":\"#E5ECF6\",\"width\":0.5},\"pattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2}},\"type\":\"barpolar\"}],\"pie\":[{\"automargin\":true,\"type\":\"pie\"}]},\"layout\":{\"autotypenumbers\":\"strict\",\"colorway\":[\"#636efa\",\"#EF553B\",\"#00cc96\",\"#ab63fa\",\"#FFA15A\",\"#19d3f3\",\"#FF6692\",\"#B6E880\",\"#FF97FF\",\"#FECB52\"],\"font\":{\"color\":\"#2a3f5f\"},\"hovermode\":\"closest\",\"hoverlabel\":{\"align\":\"left\"},\"paper_bgcolor\":\"white\",\"plot_bgcolor\":\"#E5ECF6\",\"polar\":{\"bgcolor\":\"#E5ECF6\",\"angularaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"},\"radialaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"}},\"ternary\":{\"bgcolor\":\"#E5ECF6\",\"aaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"},\"baxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"},\"caxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"}},\"coloraxis\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"colorscale\":{\"sequential\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"sequentialminus\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"diverging\":[[0,\"#8e0152\"],[0.1,\"#c51b7d\"],[0.2,\"#de77ae\"],[0.3,\"#f1b6da\"],[0.4,\"#fde0ef\"],[0.5,\"#f7f7f7\"],[0.6,\"#e6f5d0\"],[0.7,\"#b8e186\"],[0.8,\"#7fbc41\"],[0.9,\"#4d9221\"],[1,\"#276419\"]]},\"xaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\",\"title\":{\"standoff\":15},\"zerolinecolor\":\"white\",\"automargin\":true,\"zerolinewidth\":2},\"yaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\",\"title\":{\"standoff\":15},\"zerolinecolor\":\"white\",\"automargin\":true,\"zerolinewidth\":2},\"scene\":{\"xaxis\":{\"backgroundcolor\":\"#E5ECF6\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"white\",\"gridwidth\":2},\"yaxis\":{\"backgroundcolor\":\"#E5ECF6\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"white\",\"gridwidth\":2},\"zaxis\":{\"backgroundcolor\":\"#E5ECF6\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"white\",\"gridwidth\":2}},\"shapedefaults\":{\"line\":{\"color\":\"#2a3f5f\"}},\"annotationdefaults\":{\"arrowcolor\":\"#2a3f5f\",\"arrowhead\":0,\"arrowwidth\":1},\"geo\":{\"bgcolor\":\"white\",\"landcolor\":\"#E5ECF6\",\"subunitcolor\":\"white\",\"showland\":true,\"showlakes\":true,\"lakecolor\":\"white\"},\"title\":{\"x\":0.05}}},\"xaxis\":{\"anchor\":\"y\",\"domain\":[0.0,1.0],\"title\":{\"text\":\"effect size\"}},\"yaxis\":{\"anchor\":\"x\",\"domain\":[0.0,1.0],\"title\":{\"text\":\"-log10 q\"}},\"legend\":{\"title\":{\"text\":\"family\"},\"tracegroupgap\":0},\"title\":{\"text\":\"BRD-U00086674-001-01-9@0.4\"}},                        {\"responsive\": true}                    ).then(function(){\n",
       "                            \n",
       "var gd = document.getElementById('0872bfa0-8db0-4a83-9afb-cfead34f211e');\n",
       "var x = new MutationObserver(function (mutations, observer) {{\n",
       "        var display = window.getComputedStyle(gd).display;\n",
       "        if (!display || display === 'none') {{\n",
       "            console.log([gd, 'removed!']);\n",
       "            Plotly.purge(gd);\n",
       "            observer.disconnect();\n",
       "        }}\n",
       "}});\n",
       "\n",
       "// Listen for the removal of the full notebook cells\n",
       "var notebookContainer = gd.closest('#notebook-container');\n",
       "if (notebookContainer) {{\n",
       "    x.observe(notebookContainer, {childList: true});\n",
       "}}\n",
       "\n",
       "// Listen for the clearing of the current output cell\n",
       "var outputEl = gd.closest('.output');\n",
       "if (outputEl) {{\n",
       "    x.observe(outputEl, {childList: true});\n",
       "}}\n",
       "\n",
       "                        })                };            </script>        </div>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mt.tl.effect_size(wells, groupby=\"Metadata_Perturbation\", reference=\"negcon\")\n",
    "strongest = hits.drop(index=\"DMSO\")[\"distance\"].idxmax()\n",
    "\n",
    "mt.pl.feature_volcano(wells, group=strongest);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "406055f3",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-11-11T01:20:26.494876Z",
     "iopub.status.busy": "2026-11-11T01:20:26.494524Z",
     "iopub.status.idle": "2026-11-11T01:20:26.784618Z",
     "shell.execute_reply": "2026-11-11T01:20:26.783868Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAArMAAAIcCAYAAADlrSYgAAAAOnRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjExLjIsIGh0dHBzOi8vbWF0cGxvdGxpYi5vcmcvgI3uAAAAAAlwSFlzAAAPYQAAD2EBqD+naQABAABJREFUeJzs3Xd8Tff/wPHXvZmyJZLYCUGM2CuIEXtvtUcpRVGqVGhrF6292lpFbUUVtVpirxi1tyARhGyRfX5/fOX8XBlyJSSR9/PxOI+HnPs57/P+nJvIO5/7OZ+jURRFQQghhBBCiGxIm9kJCCGEEEII8a6kmBVCCCGEENmWFLNCCCGEECLbkmJWCCGEEEJkW1LMCiGEEEKIbEuKWSGEEEIIkW1JMSuEEEIIIbItKWaFEEIIIUS2JcWsEEIIIYTItqSYFSKL8vb2RqPRsGfPnsxOJU3mzp2LRqPh8ePHb2174MABqlSpgpmZGRqNhuvXr3+QHIXIqdq2bUvJkiUzO40PQp++9unTh4IFC773nMT7JcWsEB+Yr68vn3/+OSVKlMDMzIzChQvTsmVLtm7dSlxcXGan996Fh4fTsWNHKleuzPPnz1EU5b38kh0yZAgWFhYZHleIrKpLly44Ozvn+BzS62PoQ04jxawQH9Dt27epWLEiZ86cYfny5QQGBnLs2DHc3Nz45JNPss0obHpcvHiR4OBgOnbsSK5cuTI7HSFEDrZy5Ur8/PwyOw2RToaZnYAQOcnixYsJCQlh9erVuLm5AWBubs706dNp0KABhoYf/4/ks2fPAKSQFUIIkSFkZFaIDygoKAiAAgUKJHmtUaNGeHp6JnvckiVLKFq0KKamplSvXp1Tp07pvP748WM0Go26mZiYULJkSSZPnkxsbKza7vV5uAsXLsTFxQUDAwMuXLgAwKVLl+jQoQN58uTBxMSEUqVKsXDhwiT5bN68mbJly2JqakqpUqXYvHlzmvrv4eFB27ZtAahduzYajYZ69eqpr6fl/DNnztTpq7W1NfXq1WPv3r1qm5YtW7Jo0SJevHih0zZxBKZp06ZUqFAhSX6JsRMLboDPPvuMPHnyEBwcTK9evbC1taVs2bLq6ytXrqRq1aqYmZlhaWlJ48aN8fHxeeu1MDU1Zfjw4Rw+fBh3d3dMTU2ZPn06AMHBwYwYMQJnZ2eMjY3Jnz8/Q4YMISwsTD3+5cuXfPPNN7i4uJArVy6KFClCv379ePjwodrmjz/+QKPRcObMGSZNmkT+/PkxMzOjfv366nv+unv37tGtWzfs7e0xMTHB1dWVKVOm6Ex/SYzp4+PDzJkzKVSoELly5aJu3bpcvnxZJ15acszI/ibn8uXLOt8DuXLlomzZssyePRtFUZLkMXLkSIoVK4apqSnFihVj3LhxREREJLme06ZNw8nJCa1Wq35f/fnnn9SoUQMzMzMsLCyoW7cu//zzj845Ll26RNu2bcmbNy/m5uZUqFCBefPmERMTo1ebN7m7u7Nx40bu37+v09+oqCidds+ePaNTp05YWlqSJ08eBg8eTHR0dJJ4p06domXLltja2mJqakq5cuVYtWpVqtc6LTmk5Rolznn19/enVatWWFhY4ODgwJAhQ3jx4kWqOejb1zfnzL6tD+/y3ogPQBFCfDBLly5VAGXYsGHKy5cvU2178OBBBVC6dOmiTJw4UXn8+LFy9+5dpWbNmkrevHmVyMjIFI8NCgpSNmzYoFhZWSnjxo1LErN9+/bKd999pwQEBCgHDx5Url27phw/flzJlSuX0q5dO+XatWtKWFiYsnHjRsXS0lInxsaNGxVAGT58uPLo0SPl/v37Sp8+fZTWrVsrgBIQEJBqv7Zt26YAypEjR3T2p/X8r4uPj1d8fX2VIUOGKMbGxsqFCxfU17744gvF3Nw82eOaNGmilC9fPsn+n376SQGUwMBAdV+/fv0UW1tbpVOnTsquXbuUoKAgZcWKFYqiKMrXX3+tmJiYKPPmzVOePHmi+Pv7K4MHD1ZMTU2Vc+fOpXodTExMlGbNmilt27ZVrl+/rty/f1/ZsWOHEhISopQsWVIpWbKkcuDAASU8PFw5c+aM4ubmpri7uyuxsbGKoijKwIEDFUdHR+Xw4cNKZGSk8vDhQ+W3335TRo8erZ5j8+bNCqB06NBBmTVrlvLs2TPl+vXriqenp2JlZaXcvn1bbfvw4UPF3t5eqVixonL27FklJCRE+f333xUzMzOlU6dOSWJ2795dmT17thqzbNmySvHixZW4uDi1bVpyzMj+psWzZ8+UZcuWKaampsqcOXPU/UFBQUqJEiWUokWLKn///bcSEhKi3LlzR5k2bZqyatUqnb537NhR+emnn5SnT58q27dvVx49eqQsX75cAZSvvvpK8ff3V+7fv6/069dP0Wq1ytatWxVFUZQXL14ojo6OSrt27ZS7d+8qL1++VC5duqSMGDFC2bdvX5rbpKRz586Kk5NTsq+1adNGcXFxUT755BPF29tbCQsLUzZt2qQYGRkp48eP12m7e/duxcjISOndu7dy+/ZtJSQkRFm+fLliYmKic830zSEt1ygx16JFiyotW7ZUvL29ldDQUOWvv/5SbG1tlSZNmigJCQmp5qBPX3v37q0UKFAgTX1Iz3sj3i8pZoX4gOLj45XPP/9cMTAwUCwsLJTGjRsro0ePVvbt26f+0k70euH5umPHjimAsnHjxreeb8yYMUqePHmSxGzWrFmSthUrVlRKliypxMTE6OyfPn26YmxsrDx58kRRFEUpUqSIUr16dZ02cXFxiouLS7qK2bSePyUFCxZUhg8frn6dkcUsoKxdu1an7eXLlxWNRpPkl2NCQoJSrlw5pUWLFqnma2JiolhZWSlhYWE6+728vBQDAwPl2rVrOvv/++8/BVDWr1+vKIqilCxZUunatWuq50gsvnr37q2z/9mzZ4q5ubnSq1cvdd/gwYMVQ0ND5c6dOzptp02bpvN+JcYcMGCATrvt27crgPLvv/+q+9KSY0b2Vx+fffaZUrJkSfXrr7/+WtFqtcqlS5dSPCax7z179tTZHx0dreTJk0epU6eOzv6EhASlbNmyirOzs6IoiuLj46MAyq5du1I8R1rapORtxSygHD58WGd/p06dlHz58qlfx8fHK05OToq7u3uSovHrr79WrKyslBcvXuidQ1qv0eu57t+/X6ftsmXLFOCthWNa+6roWcym570R75dMMxDiA9Jqtfzyyy88ePCARYsWUbZsWQ4ePEjjxo2pXLkyt2/fTnJMixYtdL5OnGt79+5dnf2bNm2iTp062NjYoNVq0Wg0TJ8+nWfPnhEaGqrTtnXr1jpf+/v7c/78edq2bYuRkZHOaw0bNiQmJoYTJ05w79497t27l+R4AwMDWrVq9Y5XJe3nB4iIiMDLy4uSJUtiamqqM4UgueuXUd7s886dO1EUhU6dOuns12g01K9fn0OHDr01pqenJ5aWljr7duzYQdmyZZOs8FCuXDny5Mmjxi1fvjx//vkn06dP5+bNm3rlbmdnR+3atTlw4IC6799//6VChQoULVpUp23Hjh3V11+Xlu/LtOT4Pvr7phUrVuDu7o6VlZX6/bJs2TKd75e///6bcuXKqf1IzZvX8+LFizx79oz27dvr7NdoNLRv3x5fX1/u3LlD0aJFsbS0ZMyYMWzevJng4OAksdPS5l1ZWlpSu3ZtnX1ubm4EBATw8uVLePUx+v379+nYsSMajUanbcOGDQkLC+P8+fN6nzut1yiRmZkZDRs21GmbOEXp9e/b9PRVX+/zvRHpI8WsEJkgf/789OrVi5kzZ3L69Gn27NnD1atX6devX5K2+fLl0/naysoKgJCQEHXfhg0b6Ny5M7Vr1+bChQtER0ejKApTpkwB0Jk3SzJzdhPXhv3xxx8xNDTEwMAAAwMDtFotVapUAeD58+c8f/4cAEdHxyR5JrcvrdJ6fl4tm7NkyRJ+/PFHHj16RHx8vLq815v91NebcygTWVlZJVnmKzHn8uXLqzlrtVq0Wi1z584lIiLirfPokps7/fjxY/777z8MDQ114ibO5U28DosXL6ZPnz7MmjULV1dX8ufPz4ABA5KdQ5rS+/X63ODnz5+TN2/eJO0S973eljR+X6Ylx/fR39ctXLiQfv360a5dO65cuUJsbCyKojB8+HCducBPnz5N9v1IzpvtEnN82/XLnTs3e/fuxcHBge7du2NnZ0elSpWYM2eO+r2bljbv6s33jNfet8Q/eBO/r0ePHp3kZ7Fp06Y6/dVHWq9RIgcHhyTt7OzsMDQ0TPK9mJy09FVf7/O9EekjxawQWUCTJk1wd3fnxIkTxMfH67z25uhIclavXk3x4sWZOnUqzs7O6ujmvXv3km3/5uhnnjx5AJg0aRJxcXHEx8cTHx9PQkICr6Yj0bdvX+zs7AB48uRJkpjJ7UurtJ7/+fPn7Nq1iy+//JLWrVtja2uLVqslISGB+/fvp/l81tbWhIeHJ9nv7++fbPs3r9frOd+5c0fNOSEhQSdnY2PjVPNIKW6tWrWIi4vTiZsYc9OmTQDY2tqyePFinjx5wpUrVxg1ahRbt26lXr16JCQk6MRM6f1KfD8T46X2vib2N1Favi/TkuP76O/rVq9eTY0aNfjmm28oVKiQumLImz8b9vb2Kb7/b3rzfbO1tdW5Vq978/rVqFGDf/75h5CQEP7991+qVKnCV199xcSJE9Vj0tLmXaTlPUvMc/HixSn+LL45Mp0W+lwjXv1x8abnz58TFxen832bkrT09V28r/dGpI8Us0J8QDNnzky26EpISCAgIAALCwsMDAzeKbaJiYnO1xEREWzbti1Nxzo5OVG2bNm3PrihSJEiODs7s2PHjiT579y5853y1uf8ib+g3uzrH3/8keSjQ3Nz8xRHRl1cXPDz89MZRYyPj9drnd+WLVsCsHHjxjQfkxatWrXizJkzKf4h8iatVkvp0qUZMWIEw4cP5+7du0nWzXzz/QoKCuLo0aM0aNBA3degQQPOnz+f5Ptzy5Yt6uvvKrUc30d/3/Tm98uTJ0/Yt2+fzr6WLVty8eLFJCsypEX58uWxs7NL8vOmKArbtm3D2dkZFxcXndfMzMzw9PRkyZIluLq6cvjw4SRx09Lmdebm5smuTKCPChUqULBgQfWPCH2llIO+1ygyMjLJ1Jbt27dDOr8X0yIt11Hf90a8X1LMCvEBXb58mXLlyjFjxgwePHhAVFQU165do0+fPty5c4dRo0a9U9zWrVtz+fJlFi1aREREBFeuXKFdu3bUrVs3zTF+/fVXrl+/TqdOnTh//jyRkZE8fPiQbdu24enpqS6JM23aNE6ePMnXX3/N48ePefjwIf3796d06dLvlLs+57e1taVWrVosWrSIM2fOEB4ezvbt21m4cCGlSpXSiefm5kZsbCy7d+9OMtr96aefEh8fz4gRI3j27Bm+vr70799fZ8mttylfvjyjRo3i+++/56effuLBgwe8fPmSK1euMHPmTIYMGfJO12Hs2LG4uLjQokULdu/eTXBwMM+fP+fo0aP07dtXLUzr16/PunXr8PX1JTo6mitXrrBt2zaKFy+e5GPw8PBw5s6dS1BQEDdv3qRz585oNBq+/fZbtY2Xlxc2NjZ06NCBCxcuEBYWxrp165g8eTLt27fHw8ND776kJcf30d/XtW7dmsOHD7NmzRpevHjB+fPn6dixY5L5mGPGjMHFxYU2bdqwZ88ewsLCuHfvHtOnT2f16tWp9tPY2Jhp06bh7e3N6NGjCQgIwM/Pj4EDB3Lx4kVmzpwJr+bl9u7dm8OHDxMUFMSLFy/4448/uHfvnrosX1rapMTNzY2nT59y8uTJVEerU2NgYMCSJUs4evQoPXv25MqVK7x8+RJfX182bNjw1v9TUsohrdcoUdGiRZk3bx5HjhwhPDycXbt28c0339CwYcMk711GS6kP6XlvxHuW2XegCZGTPH/+XFm0aJFSv359JX/+/IqhoaFiZ2enNGjQQNmwYYNO28SVB3bv3p0kDqB888036tcJCQnK1KlTFWdnZ8XU1FSpWLGisnPnziR356cWU1EU5dq1a0qPHj2U/PnzK0ZGRoqTk5PSsWNHxdvbW6fdhg0blDJlyijGxsZKiRIllPXr1ytz5sxJ12oGaT2/n5+f0qFDByV37tyKlZWV0r59e+XRo0dK+fLllSZNmqjtYmNjlU8//VSxs7NTNBqNAigPHz5UX9+0aZPi6uqqGBsbK25ubsquXbtSXM3Azs4uxf5s2LBBqVOnjmJpaamYmZkpbm5uypgxYxQ/P79Ur4OJiYny5ZdfJvtaaGio4uXlpeZnb2+v1K1bV1m5cqUSHR2tKK/urO7Vq5f6njs5OSkDBw7UOW/i3fenTp1SvvvuO8XR0VExNTVV6tWrp5w9ezbJeW/fvq107txZsbOzU4yMjJRixYopEydO1FlhIjHmmTNndI4NDAxUAOWnn35S96Ulx4zsb3JiYmIULy8vpWDBgkquXLmU6tWrK97e3so333yjvPkr8Pnz58qwYcMUJycnxdjYWClWrJgybtw4JTw8PNW+J/rjjz+U6tWrK7ly5VLMzMyU2rVrK3v27NHJ5ffff1fq1aun2NraKlZWVkrFihWV+fPnK/Hx8Wluk5IXL14onTt3VnLnzq0ACqAuAdimTRvF1dU1yTEp/dyeO3dO6dSpk+Lg4KAYGxsrRYsWVbp3766cPn36nXNIyzV6PdcHDx4ozZs3V8zMzJQ8efIogwYNUt+L1OjT1+RWM0ipD+l5b8T7pVFSuuNBCCFEtvbHH3/QqVMnzpw5o95IJ0RW17ZtW65fv87169czOxWRTcg0AyGEEEIIkW1JMSuEEEIIIbItKWaFEEIIIUS2JXNmhRBCCCFEtiUjs0IIIYQQItuSYlYIIYQQQmRbhpmdgBDif0/QevToEZaWlu/tMYxCCCHenaIohIeHkz9/frTajB8LjIqKSvGphfoyNjbG1NQ0Q2JlB1LMCpEFPHr0iEKFCmV2GkIIId7i4cOHFCxYMENjRkVFYZfLgkji09D67fLmzcu9e/dyTEErxawQWYClpSUA9w9vx8rCPLPTSbPYR3czOwW95fc6lNkpvJOAn97vIzzfh73WNTM7Bb01fXkus1N4JxtI+6OYswoXW7PMTkEvLyLC6ehRXv3/OiPFxMQQSTzdKYBxOmeAxpDA2sf+xMTESDErhPhwEqcWWFmYY2WZjYpZ81yZnYLeNAbGmZ3CO7Eyz16/+AHMLDL+l/77ZmWQ/a4zQC6y37U2t8ye1/p9TgXLhRZjTfqKWYMcuEaVFLNCCCGEEFmAgUaDQTqLZQM0kMMKWlnNQAghhBBCZFsyMiuEEEIIkQVoNWCQzlkMWshxI7NSzIr3KiEhgfj4eIyMjDI7lQyhKAqxsbEYG2fPeZdCCCGyrgybZpDDyDSDLCYqKirZTR/R0dHvLT99zjF//nysrKwYPHhwqu2ioqJISEhIEj8uLi7deb55nqioKGJjY985l1u3btGyZUt4rbAVQgghROaRYjYLef78OTY2NtjY2GBpaYmFhYX69ZMnT9Icx8nJ6b3m6efnh6ur61vbzZkzB19fX5YuXZpqrFy5cjFq1Cidfebm5nz99dcZljOAjY0N1tbWNGvW7J1zKVGiBPv27QPgypUrKcYSQggh9GWgyZgtp5FiNguxs7NTRw/Hjx/Pt99+q37t6OiIovz/JJj4+HidUcH4+Hji4uLUEcTXnyTy+ihqQkKC2iZxf0JCgk7s1//9Nq+PTr5+XExMDFFRUVhYWBAfH6/T/k0uLi78+++/arvff/8dd3f3FM/3utSemBIXF6cz0hoVFcW///6ban/elouiKOr5YmNjSUhIICoqSmcUWZ/rJ4QQQiRKnGaQ3i2nkWI2Gzh06BBubm6YmppSvnx5Ll68yLNnz6hUqRI3b94kPDyc6tWrc+7cOYYOHcqTJ0+wsbGhdu3aREVF6Yyibtq0SR1lLFy4MCNGjMDOzo4TJ04wduxYbG1tsbS0pE+fPjpFaEr+++8/PD09admyJWZmZjRq1IiYmBgqVqzI48ePsbGxYd26dSQkJDB48GCsra2xsbFh9OjRagxDQ0M8PT3VEc+tW7fSvn17nfMkl1tiPy0tLXF0dOT3338H4Nq1a5QqVQozMzNsbGzYvn17mq/123K5ceMGjRs3BqBfv34cOnQIGxsbRowYkWof3xQdHU1YWJjOJoQQQgj9STGbxcXHx/Ppp5/y66+/EhISwrRp0xg2bBiOjo6sXLmSzp070717d4YOHUq1atX4+eefcXR0JCoqilOnTqUaOzQ0lJo1axIUFMTjx485f/48V69exc/Pj/j4eNavX5+mHK9du8aMGTMIDQ3FyMiIvXv3cuXKFTWPnj17snHjRq5cuYKvry83b97k33//Zc+ePWqMXr16sWrVKk6dOkWZMmUwM/v/xbS3bt2abG6J8aOiojh27BiTJ08mPj6eHTt20KxZM3V0uF27dnpd89Ryed3q1avx9PQkKiqKBQsWvLWPr5s2bRrW1tbqJo+yFUIIIdMM3o0Us1lcQEAAvr6+NGjQgNy5c9O+fXvu3v3fI0QrV66Mk5MTt27domfPnske/+aTSl4fbbWwsKBTp05oNBouXrzIP//8g7OzM3nz5mXz5s3cunUrTTlWrlyZMmXKYGxsTLVq1Xj69GmSNtevX6dNmzbY2tri4OBA8+bNuXbtmvp6xYoVuXfvHvPnz6dXr146x6aU2/3793F3d8fU1BQ3Nzdu375NQEAAffv2JTw8nNq1a9OjRw98fX3T1I+05JKat/XxdV5eXoSGhqrbw4cP9cpRCCHEx0emGbwbKWazuPz581OiRAl27dpFYGAgISEh3LlzB4ClS5eSK1cuWrduzbfffqseY2BgQEBAADExMZiYmBAdHc25c+d49OgRa9euVdsZGv7/ymyVK1emYcOGXLt2jeDgYEJCQhg/fnyactRqdb+NkpszWq5cOTZt2oSvry+3bt1i27ZtlCtXTqdN586dOXXqFJ6enjr7U8pty5YtFC9enEePHnHixAkcHByIi4vDxsaGBQsWsGfPHpycnJgzZw68mscbExOjznN9c9WCtOTyOjMzM549e0Z4eDhxcXFp6mMiExMTrKysdDYhhBBC6E+K2SzK0NAQQ0NDtFota9euZfLkyRQuXBgbGxu10Fq5ciXLly9n2rRpXLp0iW3btgHw2Wef4ebmRu3atQH44YcfaNmyJc2bN6dChQrqmq+mpqbq+Vq1akWjRo1o3LgxdnZ22NjYsGvXrmRz02g0mJiYwKtC9vU1VxPzfjN++/btqVu3LrVq1aJ+/fp07tyZBg0a6MT66quvuH37NhqNBkNDQzXPlHJr164d169fp1SpUsyaNQsXFxc0Gg3fffcdNjY25M2bl3/++YfPPvsMXo24Nm/enOPHj2NjY8OWLVtS7FdKubzeX2dnZ5ycnChYsCAjRoxIsY9CCCFEWmheFWbp2XLeuCxoFLn1WqRAUZRk15N9vUgVGSMsLAxra2uCz/2DlaV5ZqeTZrF+tzM7Bb1ZjjiQ2Sm8k4gFTTM7Bb3tsqmd2SnoreXLM5mdwjv5nQqZnYLeitslfz9CVvUiPJxmFYoSGhqa4Z+mJf4O+Na0KKaa9I0zRikJTIm6+17yzKrkCWAiRZcuXaJatWpJ9uv7EAchhBBCiPdFilmRonLlyknhKoQQQnwgGbEagUFGJZONSDErhBBCCJEF/K+YTV81mxOLWbkBTAghhBBCZFsyMiuEEEIIkQXININ3I8WsEEIIIUQWkBEPPTDIgYtzSTErRBay+IkdphGWmZ1Gmt0KzH7LtH124ds0tMp6ftZkv7y/0FzP7BT0tsc86Qou2UFvgzuZnYLe4uxKZnYKegkzynlFYnYhxawQQgghRBagzYBpBjnxZigpZoUQQgghsgCZZvBucmIBL4QQQgghPhIyMiuEEEIIkQXIagbvRopZIYQQQogsQIrZdyPTDDJZ+/bt37lNWo4V+rl9+zajR49Osv/BgwcMHz48U3ISQgghRMpkZDYDderUiQoVKjBu3DgADh06xIkTJxgzZkyKx3z99ddvjXv8+HG99me0Tp06ERgYiKmpKWXLlsXLywtbW9sPcu5Ey5cvJ2/evLRo0YJ169axZMmSJG1q1KjBtGnT0nWeiIgILl68mGR/njx56NOnT7piCyGEEKmRG8DejRSzGejEiROcOXOGTz75hOLFixMYGMj166mvs1izZs0Plt+7OnHiBPPnz8fCwoJff/2V4cOHs3r16g+aw71791AUBQAPDw/y588PQJcuXViwYAH29vbY2dm9t/ObmZlRoUKF9xZfCCGEMCADphkoGZVN9iHTDDLYd999l+xI7M2bNxkwYAAtW7ZkxowZxMXFwRtTBY4fP06nTp3o378/GzduZOHChepru3bton379nz11VdERkaq+//880/at2/PqFGjCA8PB+DChQv06tWLNm3asGTJErUIbN++PTt37qR9+/ZcvHiRn376iWbNmlGvXj1+/vnnVPtVs2ZNGjduTLdu3bh//z4Af/zxB506daJTp07s378fgKNHj/LTTz8xadIkunfvTmxsLHPnzqVNmzbUq1ePP/74A4DY2FjmzJlD69at+fTTT9Wif8mSJaxfv57BgwfTrl07jh07xr179/j999/56aefqFevHkFBQdSrV4969ephampKjRo1qFevHnfv3qVjx4507tyZffv2qddi0qRJaj8WLlzIgQMHADh48CDdu3fH09OTwYMH6/R34cKFtG7dmlmzZsEb0wwSpyK82QZgzZo1tGnTBi8vL+bNm6ee603R0dGEhYXpbEIIIYTQnxSzGaxDhw48fvyYY8eOqftCQ0Np2bIllSpVYujQoVy4cEEtgBKnCjx79ox27drRsGFDmjdvzvTp07l9+za8+uh737599O/fn1u3bvHLL7+o+/fu3ctnn32Gv78/I0aMICgoiKZNm1K1alX69u3L4sWLWblyJQD79u1j9+7dDBo0iICAADZu3MiwYcOYMGECjRs3TrVf7du3p2bNmvTt25fPP/+c7du3M2vWLLp160bnzp0ZPnw4t2/f5tmzZ0yfPh1HR0eGDBnCuHHj2LlzJ/369WPChAm4u7vDq6L/zJkzDBo0iJo1a9KhQwdiY2O5e/cu06ZNo379+jRt2pQePXrg4OBAnTp1aN26NRMmTMDZ2TlJfsePH2fIkCF06NCBFi1a0Lt3by5dukRISAhXr15V292+fZunT59y5swZevToQf369fn+++/57LPP1DZHjx4lOjqaAQMGsGzZMo4fP05kZCQXLlxQr/vixYuTtDl06BDfffcdPXr0oESJEvzwww88ffo02es5bdo0rK2t1a1QoUJp/h4TQgjxcdK+mmaQnk2bzmkK2ZFMM3gPZs6cyciRI/nqq68AOH36NEFBQWzYsAFeFUMhISF888036jE+Pj7Uq1ePzz//HICQkBD+++8/AIyNjZk7dy4ajYaEhAT++usvALRaLQsXLsTAwAAPDw/Kly/PmTNnqFmzJkOHDgVAo9Gwfv16Pv30UwwNDZk/fz4GBgYEBQVhaWnJypUrqVmzJuXKlUu1T19//TX58uWjRIkS2NnZ0a9fP0JDQ5k3bx4A4eHhHDt2DGtra5o1a6b2o1+/fvz9999JCtAdO3ZgaWnJjBkzAPD39+fu3bsADB48mI4dO6rXUqvV4uTkhLOzM/Xq1Us2v/379zN06FC6du0KwPXr1zlw4ADly5dPtv3u3bsZOnQo/fr1S/JalSpVGDlyJACnTp3ixo0b1KhR461t7t+/z7Bhw+jUqRMAJ0+eTPF6enl5qd8fAGFhYVLQCiFEDpchqxnkvFpWitn3oUaNGhQoUIDNmzeTK1cuHBwcsLOz47vvvsPA4H+LZtjY2OgckydPHm7evElcXByGhoZcuXJFfc3Y2BjNq7+0DAwMiI+Ph1cf1d+8eZNSpUpx5coV8uTJg729PTdv3iQmJgZjY2P+++8/7O3tATA1NVXPb2try8GDBwkICGD79u188skn/PPPPyn2qWbNmuTNm1f92sHBAQ8PD7p166buK1GiBKdPn8bCwkLd5+joyNmzZ5MUsw4ODrRp00ZnHmqBAgXUPBMl9tfQ0JDY2NgU87O3t1eLR0VRuHTpEm5ublhaWvLo0SMAEhIS+O+//3B3d8fR0ZEDBw6gKIp6bRMld/43JdcmT5486uhtQkIC165dw9PTM9l8TUxMMDExSbE/QgghhEgbKWbfk2nTplGmTBm6du1K+fLladWqFX369MHZ2RkDAwO6deumU8hVqVIFFxcXihYtSu7cubG1taVatWqpnsPc3JyhQ4cSHh7O7du3+f3336lUqRKVKlWiaNGi2NvbExwczMGDB5Mce+jQIcaPH4+iKNy7d08d0UyrkSNH0rRpU06cOKHeeLVs2bJkr0O7du344YcfsLS0ZMiQIXTs2JEff/yRTz75BHt7e8zMzDA2NlbnuSanatWqfPrpp2zcuJG5c+cmuRmrV69eLF++nFKlShEbG0u+fPno0KEDWq2WsLAwypYti0ajwcrKSm2/bt06nJ2dcXZ2pkyZMixevFiva/Cmnj17snjxYsqUKQOApaWlFKxCCCHSLENWM8iB0ww0SuLdQSLdTpw4QdWqVTE0/N/fCOfPnydXrlyULFkSgMePH3Pnzh1iY2MpXLgwRYsW5fjx4+qKBoqicPXqVaytrZk8eTJVq1bls88+02kTFBREYGAgrq6uHD9+nOrVq3P58mXy58+vjsAC3Llzh9DQUMqUKaMWVK/HCQwM5MqVKxgYGFCoUKFk56G+3q/KlStjbGyss19RFG7cuMGTJ09QFIUqVaoQFRVFUFAQJUqUUNtFRkZy9epVIiIiKFasGAULFoRXN0Fdv36d4OBgtFotderU4e7du5iZmamjwKdPn6Zy5coYGBhw7949Hj58SLly5dSR7ZMnT1KxYkVMTEyIj4/n6tWraLVaSpcurY64RkdHc/nyZYoXL87Tp0+xsrLCwcEBgBs3bvD48WMsLS2pVKkSERER3L17V5124evri4mJCdbW1ty8eZMKFSqk2CZfvnzExMRw5coVChQoQIsWLfjtt99wc3N76/dOWFgY1tbWTN19HlNzy7e2zypuPQ7P7BT0pvmkbWan8E5cjyZ/M2FW9oXN/cxOQW97NKUyO4V30szgTmanoLe4vCUzOwW9hIWF41C4CKGhoerASMbF/t/vgDX2rphp0/fYg8iEeHoE3tA7z3v37nH06FEsLS1p3LgxZmZm6crjQ5JiNguZNGkSBw4cICAgAHNzc44cOYK5ufkHO3/jxo2JiYnR2bd48WJKly79wXLI7rp06UJAQAC3bt2iUaNGrFq1Kk3HSTH74Ugx++FIMfvhSDH7/n3Mxey4ceOYN28eTZs2xdTUlCtXrrBz5051+l9WJ9MMspCOHTtSp04d7OzsKFWqlDrC+6F8++23JCQk6OxLHEUVaTN8+HCio6MpUKAAxYoVy+x0hBBCZCOZMc1gxYoVzJw5k+PHj1O5cmV49YljdhrrlGI2CyldunSmjoLWqVMn0879sUhcekwIIYTQlzYDltbS9/iZM2fSvXt3tZAFUp16mBVJMSuEEEII8ZF582E8ya2iExwczLVr1xg3bhw+Pj78999/FChQAE9Pz2x1A7MUs0IIIYQQWYDGQINGm76R2cSbn99cu3z8+PFMmDBBZ9/z588BWLVqFY8fP6ZKlSqcOnWKqKgo/vnnH4oUKZKuXD4UKWaFEEIIIbIArYEGbTqL2cRpBg8fPtS5ASy5kdbEFQtevHjBhQsX0Gq1xMXF4e7uzjfffMOmTZvSlcuHIsWsEFnIQKdYrCxj0tAyi3BK3123mcG2nn5rKmcVQQWz38oRawJdMjsFvXUvGJHZKbyTA2HFMzsFvUX6ZaP/64DIiOyVr5WV1VtXM8iXLx+WlpY0aNAArVYLgKGhIZ6enuzcufMDZZp+UswKIYQQQmQFBlo0r4rKd6ZJ+yoEGo2GNm3acO7cOZ3958+fx8Ul+/wxKsWsEEIIIUQWoNFq0Bikc84s+h3/ww8/UKNGDTp06ED16tU5duwYPj4+HD58OF15fEjpLP+FEEIIIUR2VahQIS5evEidOnV4+vQpDRs25Pbt2+pTLrMDGZkVQgghhMgCtAYatOkcmdXqOTILYGtry5dffpmu82YmKWaFEEIIIbIAjTb9c2Y12ejJXRlFphmI92bLli08fPgws9PIMMuWLePFixcA/PLLL0RHR2d2SkIIIUSOJyOzOczff/9NaGgoXbtm3PJECQkJ7Nmzhxs3blChQgU8PT0BWL9+PY6OjkkWbn5fFEVh6tSpSfa7uLhkSH+jo6PVZ1XPnTuXLl26ZKsnpAghhMjaMmuaQXYnxWwOkpCQwOjRo0lISKBFixZvXX8urQYOHMjhw4dp0aIFe/bsYfPmzSxevDhDYutDURSioqJ09q1bt44vvvgiQ+JnVBwhhBAiORqDD7+awcdAitkcZM+ePXh6euLk5MTatWsZNGgQACdPngQgKCiIkJAQunXrxpEjRzh79ixOTk60bdsWjUbD3r17OXPmDObm5tSpU4fKlSsDsH//fvbt20fx4v9btPvChQvqOaOioli7di0vX76ke/fu5MqVK8U4W7Zswc3NjdOnTxMXF0fXrl0xNTUFSDafN2m1WqZMmaJ+vWrVKho1asTIkSPVfcnF2bJlC+XKlePMmTPky5ePGzdu0KpVK/7++2+srKzo1KkTWq2WZcuW0bVrV8zNzXXO+88//xAbG0uzZs0ICgrizz//JDIykpYtW+Ls7Jyh76EQQgghdMmc2Rxk2bJlDBkyhH79+rF27Vp1/9GjR+nduzebN2/m5cuXTJ48ma+++gp/f39+/fVX+vfvD0BsbCxRUVH4+/vTtWtXDhw4AMCgQYPw8vJi/fr13Lp1iwoVKqixx40bx+nTp9mwYcNb46xfv56uXbty5swZNm3aRLNmzQBSzCc1//77L1u2bGHRokXqvpTirF+/nm7dunHq1CliY2OZO3cuffv25fr160yfPp0ff/wRgIULFxIe/v9PYVIUhR9++IE1a9bg6enJ48ePqVq1KidOnODmzZvUrVuX27dvJ5tfdHQ0YWFhOpsQQoic7X8js9p0bjlvZFaK2RwiICCAU6dOsXnzZhYtWkRgYCBnzpxRX2/atCm//fYb/fr1Y+7cuTRt2hRra2s8PDzYunUr8fHxeHh4ULx4cezs7KhevTp//vknAKNHj+aXX37BxsaGX375hQ4dOpCQkKC+Nm/ePHbu3MmxY8cAUowD8PXXXzN//nx2797N8+fPuXPnTor5pOTy5ctMnjyZtWvXYmj4/x8+pBbnq6++Yt68eTRu3BiA+fPnM2vWLJYtW8bRo0eTPU/Xrl0xNjZm5cqVmJqasnbtWgoWLIiTkxMODg6ULl2aLVu2JHvstGnTsLa2VrcPNa9YCCFE1pU4Zza9W04jxWwOsWLFCqpXr05UVBRRUVHUqlWLpUuXqq8nFlOKoqAoCnFxcWrbwYMHEx8fT8OGDfn3338JCwsjNjaWoKAgAC5evEiePHlo1qwZs2bN4uzZs1y/fh0AR0dHAExNTYmNjQVIMQ6AgYGBzr8TEhJSzCc5AQEBDBw4kHXr1mFpaanuT61fr/c/UWLe5ubmSebhJqpcuTInT57k5cuXAMTHx6PRaNT4lStXpmLFiske6+XlRWhoqLp9TKs+CCGEEB+SzJnNARRFYcWKFRw5coT8+fPDq4+5XVxciIiI0Gmr0WgYPHgwBw8epH79+piYmODg4ICxsTHh4eFYWlqi0Wjw9/fHyckJgG+//RZFUShfvjznz58nISEh1bmiKcUB+PHHHzl37hy3bt3CzMyMYsWKpZjPm6KiomjZsiUlSpRgxYoV6n4HBwcGDBiQ5jhpNWrUKPbs2UPz5s3ZvHkzXbt2ZcmSJQQHB5M3b140Gk2K18HExERWQhBCCKFDo9Gg0abzBrCEnDcyK8VsDvD06VOGDh2qFrK8KqZ++OEH/P39qVGjhk77KVOm4O3tzcmTJ4mIiFDXU927dy+bNm3C3t6ebt26qaOv27dvZ/fu3Vy4cIH27duzevVqzMzM6NixI4ULF1bjJj5dJKU4AFOnTuXBgwc4OTnRu3dvNBpNivm8KSoqSp1n+/poamL7lOK8meegQYPUG8/y5MlDjx49AOjfvz8WFhY6bbp06UKhQoX4+++/6dWrF6dOneKPP/7A39+fhISEVKdDCCGEEK/TGmjRGqTvQ3OtkvM+dNcoSg58VITIkjp27Mjw4cPx8PB4a9stW7Zw7do1nX21a9embt267zHD9ycsLAxra2ueXz2NlaVFZqeTdglxmZ2B3mx7r8vsFN5J0MoumZ2C3tYE5s7sFPTWvWBMZqfwTrzDMmapxQ8pMjZ7/bEfGRFON3dXQkNDM2xpy0SJvwP2VnfH3DB944wv4uJocurke8kzq5KRWZFlvDlCmprEFRHe3CeEEEJkVxmyzqwi0wyEyDRduqR95EmftkIIIUR2IMXsu8l5EyuEEEIIIcRHQ0ZmhRBCCCGyALkB7N1IMSuEEEIIkRVkwDQDcuA0AylmhchCXlgXwiAb3X1qduNgZqegt+DFzTI7hXcS//BGZqegt+rL12R2CnpL6Nkxs1N4J/lXbsrsFPT25x/X09Aq64hSEjI7BZECKWaFEEIIIbIArUaDNp0PTdBqZGRWCCGEEEJkAo2BFk0658xqEnLenNmc12MhhBBCCPHRkJFZIYQQQogsQGugQZvOG8C0CTLNQAghhBBCZIIMeWhCDixmZZqB+OD++++/D3KMEEIIIT5+UswKAgMDuXXrFvHx8R/kfE2aNNH7mG+++Ub9d0YUtgEBAVy5ciXF12NjY/Hx8SE2NjbJa4qi4Ofnh6+vb7LH+Pj4cOPGjWSPFUIIIVKSeANYerecJuf1WKguXLhApUqVKFOmDK1atSJfvnysWrUqs9NK1p49e9R/v0sx/LqwsDA6dOhAhw4dUmyzZcsWGjRowNatW3X27969G2dnZ2rUqEGDBg0oWrQoBw4cAODJkyfUqlWLgQMH0qpVK4oUKcKNG9lvbVAhhBCZQ2vw//Nm333L7F58eFLM5lBRUVG0b9+eAQMG8PTpU65fv86tW7cICQlR21y8eBGA27dv8/LlS/777z/Onj3LgwcPdGIltrtz5w7h4eEAxMXF6Yx8Pnv2jEePHiXJ420xXz83wN27d4mLi1NHPx88eMDz58/V40JDQ7l7926qfR85ciReXl6ptlm2bBkbN25k2bJl6r4HDx7Qo0cPVqxYwcOHD7lz5w6HDh3SyT1fvnz4+Phw8+ZNOnTowG+//ZbqeYQQQgiRPnIDWA519uxZLC0tGThwILwq1J4+fUqtWrXgVTHaoEEDKlSoQGBgIJs2bWLkyJGEhITw9OlTqlevzubNm9V2lSpVws/Pj8ePH3P48GEcHR3p3Lkzly9fBuDPP//E19eXKVOm6OSRWszXz92kSRMeP37M8uXLCQ0NZeDAgVSoUIHWrVvzxx9/sHr1agAmTJhAuXLlKFq0aLL9XrFiBfXq1aNUqVIpXpvbt2+TK1cumjZtysKFC7lz5w4uLi7s3r2bpk2b0qBBAwBu3LhBeHg4VatWVY+NiYnBx8eH8PBwTp8+TZcuXZI9R3R0NNHR0erXYWFhaX7vhBBCfJw0Wg2adD40Ib3HZ0cyMptDBQYG4uDgoH69ceNGBg4cSLVq1dR9wcHBLFy4kAsXLlCiRAn27dvHhg0b2Lp1K35+fupoaUhICD///DNXrlzBy8uLTZvS/ljFlGK+ee5EU6dOxc7ODh8fH5YtW0arVq3477//ePz4MWFhYezevZvu3bsne67r16+zbds2XF1duXz5MlFRUVy6dClJu6VLl1K7dm18fHyoU6cOS5cuTfaazZ49m08//ZTOnTur+54/f87AgQOZOHEiDRo0YNCgQcnmMm3aNKytrdWtUKFCab5mQgghPk5arRatQTo3bc4r7WRkNocqVaoU//33HxEREVhYWDBq1ChGjRqFqamp2sbR0RFXV1cAnj59ioeHBwCWlpY8ePAAf39/ypQpg6OjozoS6uzszNGjR9FqtSQk/P9zrF8fhUz0tpiJ506NRqNhyJAh/Pzzz9jY2NCnTx+MjY2TbXv+/HkCAgIYOHAg0dHRPHnyhHHjxvHXX3+pbWJjY9m0aRN2dnZqUf78+XMmT55M6dKlmTFjBoqioNFo+PXXX7l9+zZt27ZVj0+cZvA2Xl5efPXVV+rXYWFhUtAKIYQQ70CK2RzK1dWVxo0b06xZM4YPH06+fPm4d++eTiGoee35zv/99x92dnbMmDGD4OBgvv76a51i9U25c+cmODiYjRs3YmVlxdKlS2nZsqVOm9Riat7ybOmDBw+SP39+XF1d6dWrl/pR/+HDh1M8pmvXrnTt2hVeTSVo2bKlTiELsH37durWrcvKlSvVfX369OGvv/6iTZs2TJs2jc6dO9O7d29sbGw4evQo5ubmqeaaHBMTE0xMTPQ+TgghxMcrQ9aZTefx2ZEUsznYqlWrWLZsGWvXriU4OJiyZcuqH7trNBrKly+vtvX09KRWrVp89913VKlSha5du2JjY5Okna2tLYULF0aj0fDbb78xc+ZMChYsyNChQ4mJiQGgQoUKesV8/RiAyZMnM378eEqUKMGyZcswMTGhefPmREdHY2Njk6a+m5qa4ubmlmT/qVOn6Nmzp86+3r178/fff9OhQwcOHTrEnDlzWLx4MbGxsVSrVo29e/cCYGxsTLly5dJ0fiGEEOJNGbG0Vk5cmkujKIqS2UkI8a5iYmI4efIkgwcPZseOHRQpUgQg2Y/63dzcdKZRZCVhYWFYW1vzwD8AKyurzE4nzcxuHMzsFPSmNbPM7BTeSfzzx5mdgt7uLF+T2SnorWjPjpmdwju5vTLt9ypkFX/+cT2zU9BLlJLAlKh7hIaGZvj/04m/A870bI6FsVG6YkXExFL197/fS55ZlYzMimzt+fPnjB49ms8//1wtZOPj49VVGl63ceNGXFxcMiFLIYQQ4u00Wi2adN7Ald7jsyMpZkW2li9fPk6ePKmzz8DAIE03YQkhhBBZSeKKBOmNkdPkvB4LIYQQQoiPhozMCiGEEEJkBRlwAxg5cGRWilkhhBBCiCxAo82A1QxkzqwQIjMZajUYZqNHEQY4183sFPRWrutPmZ3CO3myeVhmp6C32KlNMjsFvWmMQjM7hXdiM699Zqegt8GLs1fRFRYWxpQC+TI7DZEMKWaFEEIIIbKAzF7NICgoiIsXL1K4cGH1yZ7ZQfb6s0gIIYQQ4iP1v4cmGKRze7fSLiEhgY4dO9KgQQOWLFmS4X17n6SYFUIIIYTI4aZOnUru3LkpXrx4ZqeiN5lmIIQQQgiRBWTW42yPHTvG0qVLOX/+PLVq1UrX+VMTFBTEmjVr2Lt3L1evXiU0NBQ7OzsqVqxIixYt+OSTT8iVK5fecaWYFUIIIYTIArRaLdp0zplNPD4sLExnv4mJCSYmJknaBwcH0717d5YvX46dnV26zp2S8PBwpkyZwoIFCyhSpAi1a9embt26WFpaEhISwtWrV/n+++/56quvGDduHEOHDsXIKO2P9ZViVgghhBDiI1OoUCGdr8ePH8+ECROStOvbty/t27enUaNG7y2X8ePHExwczMmTJylXrlyybRRF4ciRI/z444/kypWLQYMGpTm+FLMiU61du5axY8fSokULFi9enNnpCCGEEJkmI6cZPHz4ECsrK3V/cqOyGzZs4MiRIwwcOBBvb28AIiMjefDgAYcOHaJu3YxZfvGHH37A1NQ09bw1GurUqUOdOnWIiorSK77cAJbNBAUFYWNjk+z29OnTNMdxdXV9r3k+evQoxb++XjdhwgT27NnDzJkzU42VO3duJk6cqO57+vQpefLkwcvLK8NyBtRr2bZt2xRzefO6T58+XWd/wYIF+eSTTwgJCcnQ3IQQQnzcEovZ9G4AVlZWOltyxaxGo8HNzY1p06YxYcIEJkyYwLNnzzh69CiTJk3KsH69rZBNb3sZmc1mcufOja+vLwAzZ84kLi6OMWPGAGBtbZ3mOKGh73dh8ISEhCTzdZITGRlJqVKl3hrLxsaGbdu28f3336PRaFi3bh1OTk68fPkyA7MGX19fTp48mWJxnZCQgJWVFRcvXlT3mZqa8uzZM3V/REQEgwYNYv78+Xz//fcZmp8QQgiRUTp37kznzp119pUsWZK2bdsyffr0DDvPlStXuHfvXpraurm54ezsrFd8GZnNZjQajToCaGpqiqmpKTY2Nvj6+tKoUSMKFChAkyZNuHv3Lo8ePaJ69er4+/sTExNDgwYNOHbsGMOGDePp06fY2NhQu3ZtoqOjdUZRt27dqo54urq6MmPGDIoWLcrJkyeZP38+pUuXxtnZmTFjxqAoyltzvnTpEq1bt+azzz6jcOHC9OzZk/j4eCpVqkRAQAA2NjasW7cOgEmTJlGsWDFcXV2ZPXu2GsPExAR3d3f1Y5CNGzfyySef6JwnudwS+2lra0v58uXZtWsXAPfv38fT0xN7e3tsbGz466+/4NXIrIWFRar90Wq1OiOziX9BJu7Ply8ftra2aDQpP8krOjqasLAwnU0IIUTOptFo1QcnvPOmyXql3YoVK+jSpYu6tWvXjlatWtGqVSudf3fo0IF9+/bpHT/r9VjoLSEhgS5dujBixAjOnTtH7969+eKLL8ifPz/Tpk2jU6dODBgwgBYtWlCrVi1mzJiBvb09vr6+/P333yiKolNMxcTEqCOeDx48wNzcnFOnThEUFMTWrVvZtm0bhw8f5ubNm2zduvWt+cXHx+Pt7U3Xrl05fvw4Dx48YP/+/Rw6dEjNo2PHjmzfvp0///yTHTt2sHnzZpYsWcKhQ4fUOL169WL16tVcvHgRJycnnZHovXv3JptbYvw7d+6wfPlyRo4cSUJCAhs2bKBs2bJcu3YNX19fmjVrlubr/eDBA51iNvEaJO43MzPj9u3bjBgxIsUY06ZNw9raWt3enKgvhBAi58nIaQbvqlq1ahn+9K9Zs2YRERFBREQEd+/epUCBAixcuJDg4GBiY2MJCAhg7NixlC1blt69e+sdX6YZfAQePXrErVu36N69u7ovcZ22+vXrkz9/fk6dOsXKlSvV1xJHeHk1Svi610dbzczMGDJkCACnT5/m9OnTVK9eHV4VvWXKlKFDhw5vzbF69eo0aNAAgHr16uHv74+lpaVOHufOnaNHjx7qtINOnTpx7tw5XFxcAKhZsyZDhw7l559/pmfPnjx8+FCNf+LEiWRzq127NgMGDMDHx0f9QXr8+DHdunVj2LBhdOjQgSpVqjBmzBjs7e3TdL0LFiyoM83A3NycJ0+eqPtv3bpFz549uXLliprPm7y8vPjqq6/Ur8PCwqSgFUIIkelWr179XuNv3LiRNm3a8MUXX6j78ubNy9SpU/Hx8eHIkSM0bNhQr5gyMvsRyJs3L4ULF2bXrl34+vri6+vLjRs3ANiyZQsvXrygZs2azJo1Sz1Gq9Wqo68mJiZERkbi5+dHdHQ027dvV9u9vs5bqVKlaNiwIZcvX8bX15dHjx4xbty4NOVoYGCg/luj0SQ7PaF48eL8/fffvHjxgpCQEPbu3ZvkSSRt27blzz//pEmTJjr7U8pt7dq1WFpacvr0aY4fP469vT0xMTEUKlSIbdu24e3tTa5cufjhhx/S1A+SmWaQeI0S91etWpXZs2czevToFGOYmJgkmZwvhBAiZ8sKI7Pv2/3791P8nWdlZcX9+/f1jpm1eyzSxNDQkGXLltGvXz8KFiyIs7MzAwYM4OLFi0yZMoV169bx888/s337dnUuStu2bbG1taV27doAjB49muLFi1O8eHF1pPRNn3zyCSVLlsTNzQ0nJyecnZ35999/M6wfXbt2pUCBAjg4OFCgQAGqVq1Ky5Ytddp4eXlx+/ZtDA11P1RIKbfGjRvj7e1NiRIl+PLLL3F0dATgu+++w8bGBisrK9asWUOnTp0AqFSpEs2aNePQoUM6Uwhe9+Y0g+SK1ubNmxMdHc3+/fsz7PoIIYT4uGkNtBmyZWVVqlThl19+4eTJkzr7t23bxo4dO6hcubLeMTVKWu7gEVlSdHQ0iqLoLGERFRVFVFQURkZG6pNEEpfjiI2NJSYmBnNzc3j1UXx0dDSWlpYAxMXFYWhoSGxsLHFxceTKlYuwsLBk/4IKCwsjISEBc3PzZJ/SoSgK4eHhWFlZER8fT1RUlHreqKgotFotxsbGycaPj49Ho9GoTzFRFIWIiAg1z0QxMTHEx8cnefRdcrnFxsZiZGREREQEZmZmxMTEEBUVhbGxMWZmZuqx4eHhxMfHq1+/2T9FUZKsBGFiYoKpqana39ffC9K4xEhYWBjW1tY8CnicrUZpn7+MT0OrrKVc158yO4V38mTzsMxOQW83XmS/mWwljd7vSi/vS6ChbWanoDczo6xddL0pLCwM5wL5CA0NzfD/pxN/B9yaOABLU+N0xQqPiqH4+CXvJc+MoCgKn3/+OcuWLcPJyYk8efLg7+/P8+fPmTJlCqNGjdI7Zvb7n0aoklszLnGFg+QYGRnpFGbGxsYYG///D03iaOfr7VL7KIBXKxUkju6+LiQkRG1jYGCgFrK8UdwlF//1KQm8mpbwZiGbmH9qub0usT+JKxWkdJ2SO8+buaQ0cv3mefVdJ08IIUTOptFq0KTzcbYabcor6WQFGo2GJUuWMGzYMA4ePMjTp08pWLAgzZo1o3Dhwu8UU4pZkS6lS5dW170VQgghxLvLyCeAZXVubm64ubllSCwpZkW6GBgYpDhSKYQQQgjxvkkxK4QQQgiRBeSkkdmMJMWsEEIIIUQWkPgEsPTGyGlyXo+FEEIIIUSWcfv2bU6dOqWzmpA+ZGRWiCzkYXgMFsRkdhpp5mCW/f4L0WoN0tAqC1ISMjsDvT17kX2+lxMpeZKuEpMdHHuQ/ZYUy2OWviWoPrQXEZHv/RwaAwO0Bun7P0qTzuPft9jYWFq0aMGvv/5KkSJF2Lp1K506dSIhIYHWrVvrPLgprWRkVgghhBAiC8gJTwDbuHEjBQoUoEiRIgBMnz6d2bNn8+jRI86fP8/p06f1jpm1eyyEEEIIIT4aly5dokyZMgCEhoZy6dIl+vfvT758+WjUqBFXr17VO2b2+4xQCCGEEOIjlBNWM7C1teXSpUsAbN++napVq6pP4nzy5Al58uTRO6YUs0IIIYQQWYBGmwGrGaTz+Petc+fOuLm5cePGDa5cucLixYsBCAoK4tq1a9SpU0fvmFLMCiGEEEKID8LZ2ZmzZ8+ya9cuSpQoQcuWLQG4evUqs2fPTvaR9G8jxax4by5fvsyCBQuoUKECgwYNyux0MsSjR49YtmwZ33//fWanIoQQ4iOTE6YZALi6uuLq6qqzz8PD453jZf0e5yDh4eF06dIl2S00NO3LrgwcOPC95hkUFMTQoUPf2q5Pnz6ULFmSSpUqpRqrS5cu/Pnnn+q+6OhoevTowerVqzMsZ0C9llOnTn3nXCwtLdUfOD8/vxRjCSGEEPrSaDXpX81Aq8nsbiTrwYMH+Pj4qFtgYGCGxZZiNgsxMTGhbdu2tG3bFkVRiIuLU782MUn72oevF2PvQ2RkJDt27Hhru8ePHzNixAiqV6+eaqwDBw4wa9Ysdd/27ds5ePAg586dy7CcAdq2bUupUqU4ePDgO+diaWlJ/fr1AQgJCUkxlhBCCCH+35w5c/Dw8FC3LVu2ZFhsKWazEGNjY3X0sGzZsri5udGlSxdatWrFwoUL6devH3PmzCE6OppHjx4xdOhQYmNj4dU6bSdOnGDJkiWEhobSpUsXxowZQ2xsrM4o6okTJ9RRxoEDB3LixAkGDx7MjRs3uHjxIl999RUDBw7kwIEDacr5/v37TJ8+nc2bNzNgwADWrl0LwPDhw3n+/DldunTh0KFDABw/fpwhQ4bw5Zdf6hSqtra25M6dm5s3bwKwevVqunfvrnOe5HJL7Ge3bt345ptvePjwIbwaTZ0xYwY9e/akS5cunDp1Cl6NzDZo0CDV/rwtl0ePHjFp0iQA5s+fz+XLl+nSpQvLly9PtY9vio6OJiwsTGcTQgiRsyXeAJbeLSuaM2cOUVFR6paRnyJnzR4LHa1bt+b+/fvUqVOHixcvMnz4cPLnz0+BAgUYMmQIy5cv5+zZs7i7u1OxYkV1hNfT05P4+HidUdT79++rRdbatWtZtGgRNWrU4Pnz57Rv3x4nJycqVarE0KFDOXPmzFtzCw4OZvr06Zw5c4Zq1aoxduxYzp07R6NGjdQ8ChcuzIULF2jXrh0uLi4UKFCApk2bcvv2bTVOr169WL16NU+ePOHFixcULVpUfe3atWvJ5pYYv1WrVlhaWtKuXTsAfv31Vw4fPkyTJk1o27YtBQoU0Ot6p5ZLWFiYWkxXrVoVe3t72rZtS/ny5d/ax9dNmzYNa2trdStUqJBeOQohhPj4aLQGGbLlNHIDWBb39OlTTp48ib29Pbt37yYhIUH9aHvMmDE0b94cb29vzp07h0ajoWrVqpiamtKlSxcAoqKiUoyt0WhYsWIFxsbG/PDDD5iZmXHixAkAcuXKxd69e6latepbc3Rzc+PHH38E4N69e1y6dInevXvr5DF58mS+/PJLRowYAUBgYCB79uyhbdu2ALRq1YqpU6diY2NDt27ddJ7PvG3btmRzq1y5MiEhIfj4+BAREcG9e/d49uwZFSpUYPXq1Vy9ehVPT0+9i9nUcnldjRo12Lhx41v7OGTIkCTHenl58dVXX6lfh4WFSUErhBAiR4iLi+PYsWM8fPiQuLg4ndfc3d0pWbKkXvGkmM3izMzMMDExoUWLFhgZGQHQtWtXAG7cuIG/vz+5c+fm7Nmzya7NZmxszMuXL4mLi8PQ0JBbt27pxDY2/t+zsS0tLSlcuLBaXAKULl06TTlaWFio/zYyMlKnPrzO0tJS59x+fn7qE0B4NV+4evXqzJw5kxs3brB+/XqdY5PLbcWKFWzevJnu3buTK1cuDh06REREBHXq1OHIkSOcPXuWJUuWcPr0acaNG5emvrwtl9cZGBiQkJCQ5j6+eQ595kELIYTIAbQG/9vSGyMLCwkJoVatWty4cQN7e3sMDHTznTJlihSzHxsLCwtGjhzJpEmTKFeuHEZGRlSqVInQ0FC6du3KypUryZMnD82bN2fXrl0ULlyYokWL0qRJEypWrMj06dOpWbMm1atXx87OjsjISKpUqZLkPL1792bVqlX8/PPP5M2bF41GQ7FixTKsHz179qRq1arUq1eP2NhYQkJCWLp0KSEhIWqbsWPH0rFjR6ytrdOUW968ebl+/To7duwgODhYLczXrl3Ljh07iI+P58yZM9StWxdezeO9fPmyOs/1yy+/pEaNGsnmm1IurytUqBCXLl2iQ4cONG/ePMU+CiGEEGmi1f5vS2+MLOy3337D0tKSJ0+eYGdnlyExpZjNotq3b6+O+o0bN46uXbty6dIlXr58SYECBQgICODXX3+lYsWKAGzcuJFnz55RuHBh/v77b44cOaIWd5s2beLgwYM4ODhga2vL8+fP4dXc0kRWVlacOnWKkydP4u/vT0JCAvny5Us2Nzs7OxYsWACvFj/28vJSX+vYsaP6WLrX49vZ2XH58mWOHTuGVqvFw8MDExMTDAwMmD17NgCFCxemcOHCADRq1EhdBSGl3KpUqcLevXt58OAB1apV47///sPe3p4yZcpgYGCAkZERkydPVv/Ca9SoEe7u7mpOb04/sLOze2suBQoUUNeYtbCw4OzZs5w9e5ZChQql2EchhBAiLTQGBmgM0jeymt7j37enT5/Svn37DCtkATSKoigZFk18VO7fv88333yTZP+GDRsyJZ+PWVhYGNbW1py6+QALS/2ffpJZHMyy39/DJT6ZlYZWWU/Ahuz34JFjGbeM5AdTK09CGlplPdsfxKWhVdaSx8w4s1PQy4uIcFpXciE0NPSdnlKVmsTfAY/X/oCVmWn6YkVGkbf72PeSZ0bYsGEDf//9d4auJZ/9fhOJD8ba2lpnnqoQQggh3qMcMGe2atWqjBs3jjFjxtC4cWP109xERYoUwdHRUa+YUsyKFNnY2Kh36gshhBDiPdNqM6CYzdpzZufMmcPdu3eZMWMGM2bMSPL6ggULkl0FKDVSzAohhBBCiA9i9uzZTJ8+PcXX3+VeEylmhRBCCCGygIx4gldWfQJYImNjY/UG9YwixawQQgghRFagyYA5s5qsPWcWQFEUtm/fzrlz52jcuDEeHh7cvXsXjUZDkSJF9I4nxawQWUjxkMtYxZlndhpplvAwJA2tspbSnh6ZncI7MXp2J7NT0FuXUcczOwW9+S3tmNkpvJN1px9ldgp6exYUmdkp6CUu6kVmp/BRUBSFli1b4uPjg5GREQ4ODnh4ePDy5Ut69eqFj48PGo1Gr5hZeyxaCCGEECKnSFzNIL1bFrZlyxb8/Py4c+cO7dq1U/eXKVMGc3NzvL299Y4pxawQQgghRBaQOGc2vVtWdvbsWbp06YKFhUWSEVgXFxedx8KnVdbusRBCCCGE+GgYGxsTGhqa7GtXr14lT548eseUYlYIIYQQIivIAdMMWrVqxcqVK7lx44Y6MhsbG8v48eO5du0a9evX1zum3AAmhBBCCJEV5ICHJlSpUoURI0bg5uaGmZkZlpaWeHl5ERcXx7p167CxsdE7phSz2dju3bvx8PDA0tLyvR4j/l9oaCgnTpygadOmmZ2KEEIIkS198803tG3bll27dhEYGEi+fPlo3749BQsWfKd4Usy+J7du3eLy5cvY29vj7u6OoWHGX+qpU6eycuXKVAvTXbt2UbduXSwsLAA4f/48lStXzrBidv/+/fj7+2NoaEiRIkWoVasWAP/88w+lSpWiQIECAOzZs4dChQpRpkwZAE6dOoW1tTUlS5YE4PHjx5w7dw4DAwNq1qyZJL/IyEg2bdqERqPB1NQUFxcXKleurH5Ekfh6/fr1KVy4MAA+Pj5YWlri6uoKwMOHDzl06BA9evRIti+JMapXr06pUqXU/WvWrKFEiRJUq1aNyMhILly4QNOmTQkJCeH06dM0btw4Q66lEEKInE1jYIDGIH0js+k9/kNxdXVVfz+nlxSzGUxRFD7//HN27NhBjRo1ePbsGfHx8ezZsydTRkMnT55MmTJl1GJ27NixGRp/1qxZKIqCg4MDp06dokaNGqxatYp9+/Zx/vx5Ro0ahaIoDB48mNq1a7Nq1SoARo0axdSpUylZsiSLFi1i/PjxVKtWDYARI0awefNmtfAFCAoKYsiQIXTs2JHw8HAuXLiAqakp+/fvJ3/+/AQFBfHZZ5/RrFkzduzYAcCGDRsoVqyY+sMyffp0du7cibOzMx4eSdcaDQoK4osvvqBp06Zs2bIFgGPHjvH555/Tv39/qlWrRr58+RgzZgwAfn5+/Pjjj1LMCiGEyBhabfqnCbzj8TExMRgaGqL9ANMU4uLiOHbsGA8fPiQuLk7nNXd3d3WgK62kmM1gq1at4vz589y6dUstIH18fIiNjQXg0aNHnDhxAnt7e2rXro1Go+HGjRu8fPkSIyMjHjx4gK2tLXnz5iUgIID4+Hhq1arFpUuXuH79OqVLl9Yp8hL9+++/PHz4ECsrK2rXro29vT1Xr14lMDCQP/74AwcHB3r16pVkmsHp06d58OABlStXVp+6sXPnTmrVqsXp06cxNTWlbt26qfZ51KhRNGzYkIcPH1K2bFkAGjRowJw5cxg1ahQXLlzA09OTS5cuwasR0MuXL+Pu7s6VK1eYMGECZ86cUc//6NEjgoKCkpwnT548rFy5EoCEhASGDRvGsGHD+OOPPwAoVqwY4eHheHt7U69ePZ1jIyMjOXr0KKtXr2bJkiXJFrMAhQoVIiAggKCgIGxtbVm1apXOOnivTzM4dOgQAQEBrFy5EldXV2rUqEFERATe3t4YGBhQr149cuXKleq1E0IIITKLoihs2rSJWbNmcfXqVeLj46lZsyZz585Vf59ntJCQEGrVqsWNGzewt7fH4I2R5ClTpkgxm9n27dvH559/rhayvJrsDHD8+HHatGlDnTp1uH37Ni4uLmzdupWDBw/yyy+/YGdnR506dQgMDOT06dPkzZuX1q1bs3v3brZt20a5cuUYN24cX3/9NQMGDNA576VLl7hw4QIREREMHz6cY8eO4efnR1hYGCdPnsTa2ppevXrpTE0YNmwYe/bsoXz58gwePJglS5bQtm1bJkyYgLW1NXZ2dpw7d46BAwfy9ddfp9rn+/fv888//9CgQQMAateuTdeuXYmNjeXAgQN4enry8uVLbt68yb1796hevTpGRkb8+++/tGnTRufxdfnz5yd//vypXmetVsvIkSOpXr26zv6ZM2cyePBgTp06pbN/48aNdO/enbp16+Ll5UVwcDC5c+dONnbnzp3ZsGEDffv25ezZs/Tv35/r168DEBAQwPTp02natClXr14lODgYb29vNBoNxYoVo2rVqri6uhIbG8uIESM4c+ZMsiPy0dHRREdHq1+HhYWl2l8hhBA5QEasRqDH8S9fvmTbtm0sXryYChUq8PLlSwYMGEDTpk25ceOGTi2TUX777TcsLS158uQJdnZ2GRJTitkM9vLlyxRH4+bNm8fcuXPp3r078fHxlClThmvXrgFQsmRJNmzYAMCQIUNo3bo13377LREREeTPn5/Zs2djaGhIjRo1mDt3bpJidsCAARw6dIjAwECMjIzYtm0bw4YNw8XFhZkzZ+Ls7KzTPiwsjPXr1+Pr64u5uTmHDh1i7NixtG3bFoAJEyZQu3ZtLl26xJdffplqMfvff/9x+/Ztjh8/zl9//QWAmZkZZcuW5dSpUxw8eJBff/2VyMhIDhw4wL1799SiN7Xr9Ta5cuXi5cuXOvuqVKlCsWLFWLdunc7+pUuX0rFjR1auXEnBggVZs2YNQ4cOTTZut27daN++Pba2trRq1SrF8w8aNIgbN26oo8Vz5syhVatWLFiwAICePXvyxx9/8OmnnyY5dtq0aUycOPGd+i2EEOLjpNEaoElnMavP8WZmZmrtAWBpacmUKVMoVqwY586do06dOunKJTlPnz6lffv2GVbIIuvMZrwqVaqoczYThYWFERkZyYsXL3B0dATAwMAAOzs7IiP/92zqN4fUE79OfP3o0aN4e3tz7tw5tRBMlJCQQOXKlZk/fz7//PMP9+/fJzAwECDF5xtHRkZiYWGBubk5AA4ODuq5AHWkNHfu3Dr7kzNq1Ci2bt3K1q1b+fTTT4mPj4dXUw327dvH48ePKVCgAJ6enhw4cIADBw6ofahSpQr79u0jJiZGjRcfH8/Tp09TPSevpkOUK1cuyf4ffviBSZMmqSOfly9fJjAwkIsXL+Lt7Y2ZmRkrVqxIMa69vT25c+fmhx9+oFevXim2e/Pavv7+ksw1fZ2XlxehoaHq9vDhw7f2VwghhHjf7t+/D69+F74P5cuX5/LlyxkaU0ZmM9jw4cNxd3enZcuWNG7cmOfPn7N9+3b2799P+/btGTJkCEOGDOHatWsEBgZStmxZzpw5k2I8BwcHatasiZGREdWqVcPIyIhChQrptHn58iVBQUH0798fAwMDbty4gaIoADg6OrJw4ULKlSunU5jlzZuX/Pnz06tXL9zd3VmxYgXt27dPV99r1qxJyZIlWbNmDb1796ZBgwa0adOGjh07AlC8eHGuXbvG06dPKV++PLwqeCtVqkT16tXp1q0bAH/++SdTpkzBwcFBJ35ERAQrV64kIiKC8+fPs2XLFvbs2ZMkD2dnZ1q3bs2yZcuYNm0aS5YsYdSoUTqj2Q0aNODEiRPUqFEj2b6MHDmSffv2UbRo0RT7mzgveenSpbi5udG6dWsaNmyIoaEhsbGxrFq1itOnTyd7rImJCSYmJmm6rkIIIXIITQbcAKb53/FvTl9Ly++dyMhIhg8fTsOGDXVW9UkvX19fHj9+DK/uTTl27BhjxoyhcePGmJmZ6bQtUqSIzsBQWsjIbAYzNzfHx8eHtm3bcvv2bYyMjNixYwf29vb07duX6dOnc+PGDRwcHDhy5AjGxsa4urpSoUIFNYa7u7vOtIDt27dTuXJlzp07h7e3t3ojVfPmzbGyssLc3Jz169dz584dYmJimDhxIpUqVQJgxowZxMfHc+jQIZ1jAP7++2/KlCnDlStX+PLLL/n222/h1dM5Ekdszc3NadGiRYr9bdSokc781smTJ6t/1VWrVo3WrVurRSpA//79GT16tM7dkuvXr2f8+PE8ffqUoKAg5s+fj6enZ5Lr2rJlSw4dOsTly5cpWbIkFy5cwN3dXX399Ru1xo0bR7t27XB1dcXQ0FDnNV4Vqw8ePEhyjsR2devWZerUqfBq+ZDElRasra3VNWYdHByYOXMmPj4+3Lx5k3Llyqlr5oWFhXHw4MFUi2EhhBDidYnTDNK78apotLa2Vrdp06aleu6YmBg6duxIVFQUa9asydB+zZw5kxo1alCjRg08PDy4e/cuM2bMoEGDBur+xG3z5s16x9coiUN4QqRi586dPHv2TGdfrVq1KF68eKbl9DEJCwvD2tqaoNN/Y2VhntnppFlCREhmp6A3zwMZf0PDh3C4g1kaWmUteb8+ntkp6M1vacfMTuGdfPLno8xOQW/PglKfwpbVxEW94NSE1oSGhqqDQhkl8XfAc+9NWFmk72c9LCISu3qfqCscJUptZDaxkL169Sre3t7v/PCClMTExOhMJ0yNiYkJRkZGesWXaQYiTc6ePcu9e/d09hUtWlSKWSGEECKjZODjbK2srNJUdMfGxtKpU6f3VsgCGBsbY2xsnOFxE0kxK9Jk/PjxmZ2CEEII8XH7wA9NiI+Pp0uXLvj4+LBr1y5MTU3VT2EtLS3f670d9+/f59ixYzx+/JhChQrh6elJnjx53imWFLNCCCGEEDnQ06dP1XtqGjZsqPPaL7/8ot7AndEmT57MlClTMDIywt7enoCAAExMTFi4cCE9e/bUO54Us0IIIYQQWYDGwACNQTrXmdXj+Hz58iW5H+Z9O3ToENOnT2fNmjV07NgRjUZDTEwMP//8M/3796devXpJVm16G1nNQAghhBAiK0h8Alh6tyxs7969DB48mE6dOqnrtRsbG/Pll1/SqFEjDh48qHdMGZkVIgtJyJ2fhGQef5tVhResktkp6G1d0YTMTuGdBGTDoQfbwk8yOwW9nQrPfqtGAMQnZL+FiW6dupTZKeglITYqs1P4KCQkJKS4WoGhoSEJCfr/H50N/3sUQgghhPgI5YCRWU9PT37++Wd1ri6AoiisWbOG3bt3v9MjdGVkVgghhBAiC9BotWjSuZpBeo9/35o0aULfvn2pX78++fLlw97eHj8/PyIiIpg5c+Y7PWxIilkhhBBCCPHBzJo1i759++Lt7a0uzdW0aVMKFy78TvGkmBVCCCGEyAo0GTBNQJO1pxkkKlOmDGXKlMmQWFl7LFoIIYQQIqfQaECjTeemyexepMjLy4srV64k+9qePXtYvHjxO8WVYjYDhISEEBWl312O73JMVhEQEPBBjhFCCCHEx+HKlSvs2bOH0qVLJ/u6h4cHP/30E6GhoXrHzhHFbGRkJNHR0e8t/pgxY9izZ0+qbYKDg3WK1xkzZujcyZeRQkJC3kvcRBUrVnxrmzeL1xYtWmTY+RMSEvDz80tx+Y7o6GgiIyN19sXHx+Pn54efn1+KPyiJbfz9/Xn+/HmKr8fExKj7QkNDefHihc65pXAXQgjxTtI9Kvtqy4IOHDhA3bp11bVl32RhYUGJEiU4c+aM3rGzZo8zyNGjR6lQoQL29vY4ODjQrVu3917opWTUqFH8888/6tfTpk2jSZMmGRY/Pj6eyZMnY2trS5EiRXB0dGTFihUZFl9fbxa8586dy7DYO3fupFixYuzatUtn/8mTJ6lWrRq5c+cmX758uLu7q+cNCAjA2dkZd3d3nJycKF++PA8fPtQ5PrFN9erVKVq0KLlz52bYsGHqH0IBAQEUKlQILy8v9ZjJkyfz+++/q1/PmjWLokWLcvfu3QzrrxBCiJxB0WgzZMuK7t+//9YbvAoXLsz9+/f1jp01e5wB/Pz8aNu2LePHjyciIoJnz57Rpk0bnbkaL1680BmxjYmJISgoCIDnz5/z4sULwsLCANT9vBr5TGlUMCwsDD8/P52RvcSRwufPn+Pn50dcXFyy0wyCg4OTfB0dHU1cXFySkcY3zZs3j507d3Lu3DmCg4O5evVqkoIqpfjx8fGEhoby9OlT4uPjiYqKUkcbExISUv0DICAgAH9/f52+BAUFqaOngYGBarvXxcbGEh4errPv8ePHAISHh6e6aPLSpUtZsWIFS5cuVfc9efKEVq1a8eWXXxIREUFISAgLFy7k8OHDapuCBQvi5+dHcHAwVapU4ZdffkkSO7FNaGgoPj4+nD9/nvHjx6uvFylShF27dnHv3r0kxyqKwqZNm5g7d65ObkIIIUROZ2lpib+/f6pt/P39sbKy0jv2R1vMbtmyhZYtW9KuXTs0Gg1GRkZ07tyZWrVqERUVRevWrcmbNy+2trYMGTIEgNOnT9OmTRuqVq1K1apV+f333+nTpw/FihWjZ8+enD9/nlKlSlG8eHHs7e3ZsGFDkvMuWrQId3d3SpYsSenSpbl//z5nzpxh+/btjBw5End3d+7fv68zNeH48eMULlwYFxcX8uXLp47gjhgxgsGDB+Pk5ESePHlYuHBhiv397bffmDVrFs7OzgDY2dkxZcqUt8YfOHAghQsXZubMmTRv3pxBgwZRtGhRtm3bxowZM7C1taVYsWJUrlw52Y/PmzRpQvXq1bG3t2fo0KEAfPfddzx//hx3d3c+//xzeGOkduzYsdjY2FCgQAEaNmxIeHg4cXFxVKhQgc6dO1O0aFGcnJySLRj9/Px4+fIl3bp148WLF/j5+cGr0dq6devSvXt3tFotz549I2/evHTt2lU9NnGawPXr13nw4MFbf2BcXFxYsGABGzduVPcZGxszfvx4ndHZRPv376d27dr07duXvXv3Ehsbm2Ls6OhowsLCdDYhhBA53Ec8zaB27dqsX78+xal+t27dwtvbm5o1a+odO2v2OAM8evQIJyenZF9bvnw5RkZGBAUFERAQwIkTJ/j3338BuHPnDjt37lRHNQMCArh48SK7du1i4MCBfP/995w/f569e/fyzTffoCi6jxD08vLi3r17XLp0ib59+zJ//nw8PDzo2rUrK1euxM/PDxcXF51jvvjiCxYsWEBQUBDr169nwIAB6mvm5ub4+flx9epVZs+e/U79TS2+iYkJDx8+ZPLkyQC4ubnx6NEjypUrx+rVqzlx4gQXLlygTZs2/PTTT0liX7x4kevXr3Px4kV8fHy4du0aixYtUhdB3rp1q077s2fPsnXrVh4+fEhwcDAFCxZk/vz5ADx79ozRo0cTGBhI3759WbNmTbLvXfv27fHz86N9+/bqVIo3+z948GCqVq1KgwYN1H0BAQG4u7vTrl07nJyc1D9iUlO4cOEkRXyXLl24d+8ep0+f1tm/dOlS2rZty5MnT/Dw8GDHjh0pxp02bRrW1tbqVqhQobfmIoQQ4iOn0WTMlgV5enpSpEgRatasyY4dO9Si9tmzZ6xatYp69erRvXt3ChQooHfsj3ad2eLFi/Pnn38m2Z+QkMDdu3dp3LgxRkZGGBkZUadOHe7du0fJkiVxd3fH0dFRbd+oUSPMzP73rO6bN28yatQonXivTz8A+OGHH5g+fTrm5uYkJCTg4eHx1lx9fX3VG6Tq1avH8+fPiY+PB6BZs2ZoNBqcnZ11bjxKrr9nz57VKYoSEhLQarWpxm/dujXa154W0rZtW7Wv9+/fp1GjRuprjRs31jlnSEgILVq04NKlS1haWhIaGoqvry+lSpVKMc+7d+9So0YNbG1t1f7t378fAEdHRypXrgxA2bJlOXbsmM6xCQkJrF69mujoaH744Qd4VYx/++23FCtWjCVLlqhtN2/ezO3bt9X+8GoKga+vb4q5JefcuXNJnkai0Wj46aef+Prrr6lWrRoAT58+Zf/+/Zw4cUJtd+vWLdq3b59sXC8vL7766iv167CwMClohRBCfLQ0Gg1bt27lk08+oXXr1vDqd3jidM9u3bqxYMGCd4r90RazXbt2ZerUqXzzzTd069aN2NhY1q5dS9OmTalWrRozZ86kevXqhIWFsWXLFnr37k1ERIROYQfofO3h4UH9+vVp1aoVpqamGBoaYmdnp9P+119/5Y8//qBIkSL8/PPP6givhYUFV65coUKFCuTNm1fnmCpVqjB58mR69OjBtm3bcHV1xcDgf4sep3TX35u8vLz44osviI6OpmLFity+fZtffvmFv/76K9X4KfW3SpUqODk5sXDhQlxcXNBqtVhbW+u0PX36NIqi4OPjQ3BwMP369VOLZEVRuHbtGnny5MHe3l49plKlSnz55ZccOHAAe3t7Fi1aRI8ePZLt65uj3rt376Zq1ao60zu6dOnCnj17aNeuHRMmTGDo0KH06dMHGxsbDh06hKGhft/iiVMRIiIiOH/+PGPHjuXbb79N0q5OnTrkzp2bnTt3Mnz4cH777Te+/vprnbbly5fH19dXnfrxOhMTE0xMTPTKTQghxEdOq/3flt4YWZSjoyOHDh3i+PHjHD9+nNDQUOzt7alfvz5ubm7vHPejLWbNzc05ceIE48ePp3fv3piYmNCjRw91dPHmzZv07NkTExMTJk2aRIUKFThz5oxOcWphYaEzr3LFihWMHTuWJUuWEB4eTqFChThx4gS5c+cmV65c8Oru9hEjRpA7d27atWunzoXs1asXAwcOZOHChXh7e+scs2zZMr766itatGiBq6ur+vG6ra0tpqam6vnz5cuXYn/btGlDrly5mDt3LuPHj6dEiRJMnDhRr/iOjo5q8efs7Mz8+fOZMmUKd+7cIS4ujtGjRzNs2DDy588Prwq6okWL0qpVK6pUqULt2rXVPn355Ze0adMGNzc3tm7dqh7j4uLC3LlzGTNmDC9evKBNmzb069cvSf/MzMywsbHR6eOOHTt05sACdO/enR07dtC8eXOOHj3KpEmTGDBgALGxsVSrVo2//voLAENDw1SvX2IbRVGoUaMGJiYmuLi4MGXKFLp3766+/vofIj/++CONGjXCwsKCjRs38vPPP+vE69mzJ3/++SfDhw9P9bxCCCEEr61mkN4YWV3NmjXfaW5sSjTKm8NfIksLDg7WWdeUV3cIvjlqKrKXsLAwrK2teXbrAlaWlpmdTpqFm+VNQ6usJSIm5ZUysjKDrP/7KYkG4//N7BT0tnR4rcxO4Z38uP9WZqegt9N7T6ehVdaREBvFs51jCA0Nfac77lOT+Dvg+dWTWFlapC9WeAR2pd3fS57vavz48VhaWjJw4EAsLFLu3+PHj5k9ezaurq7qQFdafLQjsx+ryZMns2nTJp19n332GRMmTMi0nIQQQgiRATJiNYIsODLbu3dvhg8fzoQJE2jdujUeHh4ULVoUCwsLQkJCuHr1Kt7e3vz777/06tWLNm3a6BVfitlsZvbs2amuaiCEEEKIbOojLWaLFi3KX3/9xdmzZ/n111/56aef1BuyNRoNpUqVonnz5ixYsCDJik9pIcWsEEIIIYR47ypXrqyuPBQVFUVYWBi5c+fGyMgoXXGlmBVCCCGEyAo+0pHZ5JiamurchJ4eUswKIYQQQmQBikaTAasZZM2HJrxP2aN8F0IIIYQQIhkyMitEFqLc+w/F3Cyz00gzzaWzmZ2C3nKbGmd2Cu9Ea5y+OWWZ4cvVv2d2Cnqr1v67zE7hnSz2987sFPRmWCV7PTgmPCoG153v+SQ5aJpBRpJiVgghhBAiK9Bo/relN0YOI8WsEEIIIYT4YBRFYfv27Zw7d47GjRvj4eHB3bt30Wg0FClSRO94UswKIYQQQmQFOWCagaIotGzZEh8fH4yMjHBwcMDDw4OXL1/Sq1cvfHx80Og5upy1eyyEEEIIkUMoGm2GbFnZli1b8PPz486dO7Rr107dX6ZMGczNzfH21n/+d9busRBCCCGE+GicPXuWLl26YGFhkWQE1sXFhVu3bukdU4rZ17Ru3ZqXL1+ycuVKVq5cmWrbtLR5l7ZvO37JkiWsX78+Q2IJ/d27d49PP/00s9MQQgjxMdJoQZvOLYuPzBobGxMaGprsa1evXiVPnjx6x8zaPdbDjRs3cHNzw83NjZo1azJu3Dji4+P1inHz5k3i4+Np1aoVrVq1SrXts2fPePbsGQCbNm3Czc2NcuXK4eHhwZAhQ7hx44ba9m3xli9fzpo1a1J8/fXjnz59yvPnz9Pcpzdjp6Vv+kjs+6VLl9R9u3btws3NjZMnT2bYeV4/V+LWvXv3DI3/5MkT3NzcWLhwobovNDSUChUqMGPGDAAKFCjA999/D8Dt27fp379/huYghBAiB0ucM5veLQtr1aoVK1eu5MaNG+rIbGxsLOPHj+fatWvUr19f75gfzQ1gL1++BGDDhg2EhIQwbNgwihUr9k6jaHZ2dnq1DwoKoly5cowdO5YnT56wZ88eatasyYULFyhUqNBb4wUGBqb6SDd980ktdnpiJScoKIi4uDhWrVrFzJkz4dXocVxcHBERERl+rsTrDGBmlrHrscbGxhIaGsrKlSsZMmQIABs3biQ6OpqAgAB49Rdl4p2WUVFR3Lt3L0NzEEIIIT5mVapUYcSIEbi5uWFmZoalpSVeXl7ExcWxbt06bGxs9I6Ztct3PZmamuLm5oaHhwclS5ZUh7G3bt2Km5sblSpVol+/fuqIanh4OIMGDcLDw4N58+apcV7/KD6lY99ka2uLm5sbDRo04KeffuKTTz5h8eLFSeIdP36cpk2bUq5cOdzc3PD19WXu3LnMmDEDNzc3Dh8+zJIlS5g3bx7du3dn8ODBSaYGPHnyhM6dO1OnTh02b94MwKxZs9i58/9Xc27evDl37txJEvv1WC9evGDUqFG4u7vTsWNHrly5Aq+K0fnz59O9e3c8PDzYtm1bqte9adOm/PPPP8THxxMYGEhoaCjFihVTX4+OjmbixInUqlWL5s2bc+zYsVSvbWrnT7zObm5uFC1alBs3bjBgwADGjx9PtWrVePr0qTpK3qJFC06dOqUeu2XLFpo0aULZsmVTHNU1NzendOnSnD59GoB169bRtWtX9fXXpxmMGjWKkydP4ubmxuTJkwFYsWIF9erVo0GDBmzcuDHFaxYdHU1YWJjOJoQQIofLASOzAN988w2XL19m/Pjx9OzZk6lTp3Lr1i2dG8L08dGMzAJcuXIFNzc3QkJCKF68OH379gXA09OTDRs2EBcXx86dOxk7dixLlixh3LhxhIaGMmvWLA4cOMDt27fh1RSCRCkd+zbu7u7s2LEjSbwxY8bw6aefUqVKFTQaDfnz56dr166YmJjQo0cPnJycOHz4MOvWrWP+/PkUL15cLVgTbd68mYULFxIVFcXAgQOpUKECAQEBODo6qm1u3ryJra1tktiJRRrAt99+y8OHD5k3bx5nzpyhRYsW3L59m6dPn7Jp0ybmzJlDZGQk/fv3p02bNmi1yf+AGBoaUq9ePfbv38/169fp2rWr2neAiRMn8vjxY+bMmcPTp0/p27cvPj4+KV7blM4PsH79evVOxxEjRlC5cmV+//13Fi9ezPLly7G1tWXDhg0oisKdO3fo27cvly9f5ujRo4waNYo5c+ZQtGhRzM3NU3zvevXqxerVq7G1tcXKygoHBweCgoLgVRGaOBr75ZdfEhISwtKlS7G1teXff/9l2rRpLF68mNjYWAYOHIiLiwtVqlRJco5p06YxceLEt34fCSGEyEFywNJc3t7e3Lt3j969e/PVV18leS137tyUL19er5gfVTHr4uLChg0buHTpklqoWllZ4efnx8SJE7l79y5RUVHqx9PHjh1j06ZNuLi4UL16dRYtWpQkZkrHvs2bxWWiAQMGsHjxYry9vWndujVubm44Ojqqo8qJPv30Uxo2bJhs7Ndf69Spk87o4+uMjIySjZ3oyJEjrFmzhpIlS1K9enWWLVuGr68vAH369KFBgwYA5M+fn2fPnuHg4JBif3v16sXMmTPx9fXl77//1ilm9+/fT2hoqFpIBwQEcP36dUxNTVO8tsmdH6BJkybqNIO8efPi5+dHtWrV1NHSiIgIfvnlF3x8fIiIiMDX15cnT55w4MABBg0apBbFqalfvz5ff/01FhYW9OzZM8U5ygULFsTc3Fy9tkuWLOHzzz+nUaNG6jU5evRossWsl5eXzg9xWFgYhQoVemtuQgghRHb2xx9/sGjRIjZv3sz69euxtrbWea1kyZJ6F7NZu3zXU2LR1rVrV0aMGMH48eMBGDlyJLVr12b58uWMHTuWqKgoeHUzz/Hjx+HVSGZgYGCSmCkdm5rz588zb968ZAunHj16cPz4cSZPnsxPP/3E4cOHMTY2Jjo6Wqddrly5Uox/8uRJEhISiImJwcfHhwIFCmBjY8Pdu3cBuHPnDn5+fvBqjuebsRMVKFCAo0ePAvDw4UP8/PzUgtXY+P+fX6/VaklISEi1z5UqVeL69esULFgwyXyXwoUL079/fzZs2MCGDRs4fvw4ZcqUSfXapnT+16cZJN7x+Pq1WrFiBc+ePWP+/PmsWLECKysroqKiKFy4MIcOHSImJibVfiSer2nTpqxYsYLWrVun2O7Na1ugQAFOnDhBQkICsbGxnDhxgoIFCyZ7rImJCVZWVjqbEEKInE3RaDJgndms/zjbkSNH4u/vT/Xq1bl582a6431UI7OvGzBgAK6urty8eZP69evz/fff4+joqFPtT5w4kebNmzN+/HicnJySHUlN6dg3JX78HRgYiKIojB07lsaNGydp17p1a+7evcvLly8xNDTE1dUVY2NjmjVrxurVq/n555/f2re4uDiKFi1KREQEDRo0wNPTk4IFC+Lh4cGqVasoXry4WujVrFkzxdiTJ0+mVatW/PDDDzx//pzJkyenq6jauXMnJiYmSfb/+OOPtG/fnkWLFmFhYQHAf//9l+Zrq4/q1aszYcIEzp49S+7cudXCukePHuzYsQMHBwcKFChAhQoVWLt2bYpxxo0bx+eff55sfxI5OTnx6NEjXF1d6dGjB6NGjWLjxo3ky5eP+Ph4atWq9c7zf4QQQuRAOWCaAYCzszPHjx+nd+/eVK9enfXr19O0adN3jqdRFEXJ0AwzSVRUFP7+/ri4uKj7/P39MTU1xc7OjidPnmBoaEju3Lm5e/eueoNSTEwMT548oVChQty6dQsXFxeCg4PhtTv/kzs28aNnOzs7goOD8ff3R6PRYGlpSaFChXQWAn697a1bt4iOjsbc3BwnJyd1HmpoaCj+/v4UKlSIqKgoDAwMsLW1TXJ8YGAgBgYG5MqVi+DgYPLnz69zDQIDA3X6otVqdWInjkwm9i0+Pp4HDx7g4OCgziNNPEfi+e/cuYOTkxOGhkn/9gkODiY6Opq8efPq7H/w4AF2dnY6c1P9/f3Va5v40Xxy1zal84eHhxMVFUW+fPl0+vzkyROcnJzUfREREQQFBVG4cGHu3btHwYIFMTIyUq/l48ePyZUrF0WLFtXJOS4ujnv37lG8ePEU+xgTE4O/v7+6okFMTAz379/H3NxcfS8CAgLQarXJ/nGUkrCwMKytrQnctwor84xdpeF9enHpbGanoDdDU+M0tMp6tMZGmZ2C3lYP+j2zU9Bb3z+/y+wU3snTA/o/NSmzGZqmPFiQFYVHxeA6eZk6hTEjJf4OeOp3P92xw8LCcCjo9F7yzAhDhgyhZMmSDBkyBEVRmDx5MpMnT2batGn4+vqqr+njoylmxfuVuMLA6+rWrZvsPGOhPylmPxwpZj8cKWY/HClm378PUsz6P8iYYrZA4WxRzCb6888/6dmzJxqNhh9++EHvYvajnWYgMla7du2oWbOmzr6s+EMihBBCZFuZNM1g+fLlLFq0iKdPn1KhQgV+/PFHSpcunb48UtCwYUPs7e119rVt25aTJ0/y7bffqp986kOKWZEm9vb2Sb75hBBCCJG9rV69mi+++ILly5dTpUoVfvjhB+rVq8e1a9cy/EFLvCpck1OmTJm3rmufEilmhRBCCCGygMQVCdIbQx9Tp07l888/Vx8mtHTpUhwdHVmyZAleXl7pyiXRjRs3ePjwISVLluTFixc8fPgwxbYlS5ZMcSWglEgxK4QQQgiRFXzgaQZPnjzh5s2b6uPoebXsZL169Thy5EiGFbMLFixg0aJFLFiwgOvXr6d6v82CBQtkzqwQQgghRE735mPSTUxMkiw3+ejRI4Akq+84Ojqm+ECmd7Fw4UIWLlyo83VGkmJWiCxEU6g0GkuLzE4jzcxLuGd2CnorNGBLZqfwTh4u7ZTZKejNZH+vzE5BfyUtMzuDd3Iit2dmp6A3C5PsVYJERoTD5GXv9Rz/e2hC+h56kHj8m0+VHD9+PBMmTNBt+2pBKwMDA539hoaGb31YUka6ffs2z58/p0qVKklySYusv7KuEEIIIUQOoCgZs/HqyZ6hoaHqltyUgcQbu998AmpgYOB7u+k7NjaWxo0bc+/ePQC2bt2Kq6sr7u7utG/f/p1iSjErhBBCCPGRefOR6ck90bJQoULkz59ffbQ9r0Zrjx49SvXq1d9LXhs3bqRAgQLqElzTp09n9uzZPHr0iPPnz3P69Gm9Y0oxK4QQQgiRBSQoSoZs+hg6dCg///wz586dIzY2lilTphAUFMRnn332Xvp46dIlypQpA6+efnrp0iX69+9Pvnz5aNSoEVevXtU7ZvaasCKEEEII8ZFSXm3pjaGP0aNH8+zZM+rUqUNMTAyFChVi+/bt7/TwgrSwtbXl0qVLAGzfvp2qVatiZva/J18+efKEPHny6B1TilkhhBBCiBxKq9Uyc+ZMZsyYQWRkJJaW7/cmyM6dO+Pm5saNGze4cuUKixcvBiAoKIhr165Rp04dvWNKMSveq7CwMBYtWoSJiQlfffVVZqcjhBBCZFkJyv+29MZ4FwYGBu+9kAVwdnbm7Nmz7Nq1ixIlStCyZUsArl69yuzZs7GystI7phSzWdD333+fZH04gMGDB1OiRIk0xZg7dy69evXC1tb2PWT4P15eXkycOBFjY+MU24wePZrIyEiaNWv21lhubm7qE0h4NSnc2tqaQYMGZVjOM2bMICAgACsrKyZNmpRiu4CAAGbMmME333xDvnz5dF47fPgwx48fJy4ujnr16uHh4QGv5v6MHz8eAEtLS+rUqUOjRo0yLHchhBAfN0VR1OWy0hMjq3N1dcXV1VVnX+Lv0nchN4BlQYUKFcLZ2Znw8HC8vb1xdnbG2dmZXLlypTnGhg0bki2IM9LPP/9MTExMqm3OnTvHjBkz6Nq161tjTZ8+nfj4eADu3r3L7Nmz2bIlY9cELVCggPqYvtQsX76cU6dOsWLFCp39AwYMoG/fvoSGhhIbG8u3337L5MmTAQgPD2f16tU4OztjZGRE7969+eOPPzI0fyGEEELokpHZLKh///4A/Pnnn4SGhjJ8+HAAdu3axa+//kqBAgX49NNP4dUzlb/55hssLCzYvXs3MTEx2NjYcP/+fSZNmkTu3LmZNWsWU6dOZdSoURgbG/P48WO2bdvGoEGDmD17Ns2aNWPnzp3UqlWLMmXKsHr1ap48eUKDBg3w9Hz7QtwJCQlMmjSJDh06sHXrVkqUKEHXrl1Zvnw5d+/eZeLEiTRv3pzWrVvz9OlTVq1axYsXL2jdujWVKlVS49SrV499+/bRrFkzVq9eTZcuXXTuagwNDU02t2+//ZYXL15gb29P9+7dcXJyAmDnzp0cPXqUqKgomjZtStOmTenRowchISHMmzcv1f5s2bKFvXv30qRJE8aOHYtGo+HQoUPs37+fK1euqJPVAW7duqX+28rKSn2/EhISOHnyJB07dkxyjujoaKKjo9Wv3/cfHkIIIbK+zJxmkJ3JyGw2MX78eH788UcsLS05ffo0HTp0wNTUFBcXF3r27MmFCxfw8vKibt266npyBQsWVAu73377TR1FffbsmTriuW7dOr744gvi4+MxNDSkZs2a3Lx5EwsLC4YPH87WrVvfmltCQgI//fQTkyZNwtjYmIkTJ7J582YcHBwwMjKiUKFC5M6dm7CwMKpWrcqdO3dISEigWbNmOmvb9erVi9WrV6MoCjt27KB169bqa5GRkSnmVqhQIZycnAgPD6dx48ZERERw7Ngxvv76a3Lnzo2zszM2NjZpvtb79u2jfv36ODg44Onpyb59+wA4deoULVq0UAvZhQsXJrlGQUFBDB8+nH79+jF37lyaNGmS7DmmTZuGtbW1ur35pBYhhBA5k5LOLSeSYjabWLp0KaVLlyYgIABra2uOHTvGixcv6NOnD/ny5aNhw4asW7cOGxsbKlasSN68eenbty/Dhg17a+yffvqJMWPG8OzZM2JiYjAwMODx48cUL148zR+TGxoasn79ery8vPjuu+84c+YMrVq1ws7Ojs8//5zatWvj7e1NlSpV+OWXX5g0aRJTpkxh/fr1aoyKFSty9+5ddu3aReXKlTE1NVVf8/b2TjG3MmXKEBISQlRUFAYGBpw/fx57e3v1+CpVqui1+POSJUt48OABw4cP58GDB+qUBCMjI2JjY9V2efPmxcLCgt9//13nOjg7O1OjRg1OnDiR4pxZLy8vnSezPHz4MM35CSGEENnVlClTWLt2rd6vpUaK2WzC0NCQggULqvNnJ0yYgEaj4eXLl5w/f56iRYty/fp1tb1Wq9WZBJ4rVy7Cw8MB8PPz04nt6OgIr+5kNDU1Vc/h4eFBly5d0pSfpaUlhob/m7ViYmKS7FxaIyMjIiMj1a8jIiIwMjLSadOxY0c+++wzevXSfaZ7SrlduHCBPn36oNVqKVy4MKampoSGhlKiRAkOHTpEmTJlWLNmDQMHDkxTP548ecKVK1eoVauWep7Lly/z5MkT6tSpw/bt23ny5Imaa58+fXSOT5xm8Nlnn1GqVKkUz2NiYpLk6SxCCCFytsRpBundsrLHjx8THByc7Gt+fn5EREToHVPmzGYTY8eOZd68edStWxdTU1NsbW0xMzOja9eufPrpp3Tq1AlPT0+KFy9O2bJlcXFxYejQobi6ujJr1iw8PT1p06YN5cuX58aNG8muQFC/fn1sbGzYv38/Li4uaLVa9SkdGaF+/fqMHj2a5s2bY29vz65duzhw4IBOmz59+mBmZoaHh4fOFISUctNqtbx8+RI/Pz9CQkLUuaenT59m3bp1KIrCjRs3KFq0KLy6scvHx4fw8HCGDx9O48aNad68uXqeFStW0LlzZ3XeK0BwcDC//fYbY8aMYciQIbi5udGwYUNsbGy4ePEijRs3zrBrJIQQIuf6mFczuHXrFv7+/vj7+2NgYIC3t7fO6yEhIfzzzz+0a9dO79hSzGZhZcuWVW/0GjhwIHXr1uX48eOEh4djZWWFv78/LVu2VJez2rhxI9evX6ds2bIsWLCA7du3ExoaCsCcOXPYtm0bABMnTuTkyZMAjBw5Up1PamRkhLe3N/v27ePOnTvExcVhbW2dYn7Tpk3DxMQEAwMDJk6cqO6vUKEC9vb2AIwZMwYLCwt4NRp5+vRpduzYwYsXL5gyZYo6V3TatGkYGBhgb2/PF198AUDRokUZPHhwqrmVK1eOv/76i3PnzlG5cmVCQkIoUqQIL1++xNnZGQMDAxo2bEjTpk0BcHBwwNXVlalTpwKQO3dunT4VL16catWq6ez77LPPOHXqFLy62axr166cPn2a2NhYhg0bpo7A2tjY8P3337/Tey2EEEJ8zObNm8eiRYvUr+fPn6/zuqWlJa1atXqnJS01SlYt4UWWkJCQkOzDDsaMGUPevHkzJaePUVhYGNbW1jy7dgYrS4vMTifNFJPsk2uiQgMydrm3D+Xh0k6ZnYLe1tyNy+wU9Na95PtfNP592H43Mg2tshYLk+w1nhYZEU6nqsUJDQ3N8Klhib8Dbj54hGU6Y4eHhVGicP73kmd6JCQkkJCQwJgxYyhevDj9+vVTX9NoNBgYGLxz7Oz1nSQ+OI1Gg7Ozc5L9b851FUIIIUT6KMr/tvTGyIq0Wq366NyMJsWsSJVGo9GZPyqEEEIIkR7Lli2jUaNG6vKhAD4+Pjx69EhnWc60ktUMhBBCCCGygJywmsGRI0f4/fffdQpZXi2zOXr06CQrLqWFFLNCCCGEEFlA4moG6d2ysj179iS7ClCuXLmoWrUqhw4d0jumFLNCCCGEEOKDMDExSfFBQQ8ePHinG8FkzqwQWYjifwPF3Cyz00gzrXnWuVM2rTTpuGM2M2l8z2d2CnqrmK9WZqegN+31w5mdwjup5FQvs1PQm41p9vpZDA9LeO/nSHi1pTdGVtaiRQtq1apF06ZNadu2LQDx8fHMnz8fHx8f6tevr3dMKWaFEEIIIbIAJQNWI8jakwygcuXKfP/997Rv3x4HBwfy5s3LgwcPiIyMZOXKlTg4OOgdU4pZIYQQQgjxwYwdO5a2bdvy999/8/z5c/Lnz0/btm3VBynpS4pZIYQQQogsIEFRSEjn0Gx6j/9QSpcuTenSpTMklhSzQgghhBBZgJIB0wSyQymrKArbt2/n3LlzNG7cGA8PD+7evYtGo6FIkSJ6x5NiVgghhBBCfBCKotCyZUt8fHwwMjLCwcEBDw8PXr58Sa9evfDx8UGj0egVU5bmEpkqPj6eEydOcOnSpcxORQghhMhUOeGhCVu2bMHPz487d+7Qrl07dX+ZMmUwNzfH29tb75gyMpvNxMbG8u+//yb7Wv369TE2Nk5TnIMHD+Lp6ZnB2f2/6OhoTp48Sd26dVNt179/fy5cuED37t0pW7ZsirEOHjxImTJldCaH//PPPxQsWJCSJUtmWN579uwBIE+ePFSpUiXFXF5XrFgxChUqpO63sLCgbNmyWFtbZ1heQgghcgAl/asZZPV5BmfPnqVLly5YWFgkGYF1cXHh1q1betcnUsxmMy9fvmTu3LkA3L17l4SEBIoVKwZA9erV01zMdu3alcePH7+3PAMDA+nduze+vr6pttu/fz++vr6pLpIcGBhImzZt6NixI2vXrgXg3LlztGjRgkGDBqnXIyPMnTuXkJAQLCws+Oeff5LNpV27djpFeo8ePTA1NVX3R0REcOXKFfbt20fVqlUzLDchhBAiuzM2NiY0NDTZ165evUqrVq30jinFbDZjZWWljh5OmTKFuLg4JkyYAEBAQABHjhyhePHi5M2bl8DAQO7evUv16tUBuH79OkZGRkRFRRETE8OePXuwsrLC3d2dI0eOqAXa48ePCQkJoWTJkhw8eBAPDw/Onz9PiRIlsLa25tKlS0RGRlKxYkVMTEzemnN4eDg3b96kRIkSXLp0iVKlSpE7d26OHDnCixcv2L9/P2XLlqVAgQLExcVx4cIFDAwMKFeunFrkFilShJs3bxIeHo6lpSWrVq2iZcuWOudRFCVJbjExMRw4cACtVkuhQoUoVaqU2v7Ro0dcu3aN2NhYKlasiKOjI3v27OHo0aPqNU1OYrvX+fn56ez38vJi06ZNUswKIYRIswQUEtI5tJre49+3Vq1a0bJlSz799FN1ZDY2NpYpU6Zw7do1eWhCTjZv3jxmzpxJsWLFuHXrFnPmzKFt27aMGTOGkSNH4urqSqdOnfjnn3/YsWMHL168YO7cubi4uFCpUiWdUVRvb29OnjzJ3Llz6dixI5UrVyY+Pp4ff/yRL7/8khcvXpArVy5CQkI4duwYuXPnTjW3O3fu0LNnTywsLDA2Nub27dtcunSJ3377Tc1j5MiRWFpaUrt2bQDi4uKwsrLS+Ui/Q4cO/PHHH/To0YPDhw/Tv39/bt68Ca9GrBs1apQkN16NtiqKwr1792jSpAkLFixg37599OjRg3LlymFoaMi3336Lo6Njmq71y5cvdYrZChUq6OyPiIhg9+7djBkzJsUY0dHRREdHq1+HhYWl6dxCCCE+XkoGTDPI6itzValShREjRuDm5oaZmRmWlpZ4eXkRFxfHunXrsLGx0TumFLMfgZcvXzJx4kSWLVuGmZkZT548YfLkyXTq1IlNmzZRv359tFotv/32G46OjgwfPpzp06erBVnU/7F331FRXH8fx9+A9GZB6YoVCyoqKpaoqDFq7D2CDTXGRE1iii3WGHs01iR2DdbYu8bE3huKXQTpHaTXZZ4/frAPK7sIiu6C93XOnrizM3c+M0z0y907d9LSVLadkpLCmjVrqFGjBn/++Sempqb89NNPAGzdupW//vqLCRMmvDZjWload+7cQV9fny+++IL//vuPjRs3cuzYMXmOVatW0bhxYzZt2gRA79692bNnD+3a/e8xjR4eHgwfPpzy5cvzySefoK39//cvbt26VWW2nTt3cv/+fRITExk9ejSLFi3C19eXrl27MnXqVGrVqlWk852QkKAwtGHKlClUr15dvtzX1xdbW1sGDBigso358+cze/bsIu1XEARBEEqDSZMm0atXL44ePUpUVBTW1tb06dMHOzu7N2pPFLOlwMuXL8nIyGDt2rXyZblfp5ctW5Zy5coRExMjH1v7Kh0dHWQymfx9amqq/M/m5uby7SIjI/H391co5ExNTQuVsVatWvIhCVZWViQmJuZbJzY2VuFmLkdHR2JjY+Xv7ezs0NHRYcGCBWzYsIHz5///Geqqsl25coVevXrh6OiIkZERSUlJREZGMmbMGMzNzVm0aBEPHz5k7ty5hf5qozDDDPr168fatWv54osvlLYxZcoUJk6cKH+fkJDwxk8+EQRBEEqH4piNQBNnM9i1axeWlpa0a9eOJ0+eYGxsjKOjI46OjsXSvihmSwFra2ucnZ1p2rQpzZo1k8/bBjBu3Dh69OhB5cqVGTx4MEeOHEFbWxtDQ0N27NhBlSpVaNmyJYaGhixduhQrKytWrFihdBaCbt26sXPnTgYOHIiVlRVaWlo0bty42I6jU6dO9OvXD0tLSzIzM9m0aRP//fefwjrjx4/nwIED1K1bV6GYVZVt586duLq64unpiZ+fH9evX0eSJHx9falQoQK9e/cmIyODY8eO0b59ey5cuMD169eJiYnhxIkTODs7Y2VlpZDh1WEGVapUyVfUL1q0iPbt2zN8+HAMDAzyHau+vn6hxhsLgiAIH47SOszg8uXLVK9enXbt2rFy5Upq167NuHHjiq19UcyWYNWrV5f3qB4+fJjly5ezceNGUlNTad68OZmZmZiZmfH9998DEBgYyJEjR+jRowfLly9ny5YtWFlZ0bJlS7Zv387SpUupVKkSM2fOJCIiAnKm+8rVqFEjNm7cyPr16wkJCSE7O5uZM2fKC+e8DAwM5AWxmZmZwjRXNWrUoGLFivnad3V1Ze3atWzbtg1tbW127txJvXr1iI6Olo+l7datm/zGr8qVK8sHj6vKNnLkSIKCgti2bRudOnXC09MTQ0NDTp8+zZ49e9DV1aVu3bpMmTIFgE2bNhEaGoqlpSW//fYbU6ZMUShmDQwMaNSokUIPcI8ePRgwYIDCLwDVqlVj5MiRXLhwgY8//vgtf9KCIAiCUHI5ODhw6NAhmjdvTnR0NCEhIXh7eytd187ODgsLiyK1ryVJmljDCyVFYmKi/EarvDp37qyWPCVVQkIC5ubmRJ32wszYSN1xCk3b2EzdEYrMbs4jdUd4I0GTi/6IR3W7b9lK3RGKzCnsfCHW0jwBVdqpO0KRlTVQPSWjJkpMSKCavQ3x8fGYmRXv3325/wZcfhKIienbtZ2UmEBLx8rvJOebiouLo0ePHly6dInXlZ0rV64scq+t6JkV3kpUVJTSeV5FMSsIgiAIRVNahxnkTseZlpbGuHHjqFmzJiNHjlS6romJSZHbF8Ws8FaqVauW72YoQRAEQRCEXNOnT6d69ery+0iMjY2LPJSgIKKYFQRBEARB0ADZkkT2W3atvu3270JaWhrR0dHvrH1RzAqCIAiCIGgAWfb/Xm/bhqZp3LgxkyZNIj4+nps3bxIQEMDLly+Vrtu5c2eFm8YLQxSzgiAIgiAIwjszcOBAAgMD+ffff3nx4gURERFERUUpXbd27dqimBWEkiwr1J8so/zz0moq3RY91B2hyJ51K5l3q2PdSd0JiszbyVXdEYrM6Z9f1R3hjcQN763uCEWWbmGo7ghFkpSR+c73UVqHGWhrazNp0iQmTZrE119/jaOjI19++WWxtS+KWUEQBEEQBA2QLUnISmExm9fy5cuLvU1RzAqCIAiCIAjvjSRJHDx4kNu3b9OpUydat26Nn58fWlpaVK1a9Dm1RTErCIIgCIKgAbKlt+9ZzX7HHbNxcXGsW7eOq1evoqenR9u2bRk5ciR6enqF2l6SJLp168bNmzfR1dWlUqVKtG7dmtTUVIYOHcrNmzflT/csLO03PBZBEARBEAShGOXOZvC2r3clNTWVRo0aERsbi4eHB127dmXJkiX06NGD7OzC7Xjv3r0EBwfz/Plzevf+/7He9erVw9jYmLNnzxY5l+iZFQRBEARBEF5LX18fHx8fTE1N5ctq1qxJy5YtuXfvHs7Ozq9t49atWwwaNAgTE5N8PbDVq1fn2bNnuLm5FSmX6JkVBEEQBEHQALmzGbzt613R1tZWKGQBypYtC0BKSkqh2tDT0yM+Pl7pZw8fPnyjJ4OJYraIZs2axfr16wkODn7tPGiFWedN1n3d9r6+vrRu3bpY2hIEQRAE4f2Q5cxm8LYvgISEBIVXenr6O8m8aNEibGxsaNKkSaHW7969O5s3b+bJkyfyntnMzExmzpzJo0ePaN++fZEzfHDFbO3atdHS0kJPT48GDRpw8eLFN2rHzs6OmzdvFnr9ly9foqWlhZaWFgYGBtStW5eff/6ZrKysQrX34sULXF1Vz9lY1DwFtf02bSmT99hzXy9evCiWtkNDQ9HX12fFihUKy62srBTely1bVv5zr169Ot9//z1JSUkq2/3333/R0dHhv//+U1j+7Nkz+vTpg5WVFRYWFvTr109+LMHBwfLjMzU15dNPPyU2NrZYjlMQBEEQisLe3h5zc3P5a/78+UrXmzNnDu3atSvwpeoBBytWrGDbtm14eXmhr69fqFwuLi58++23ODk5sXnzZhYsWED58uVZtGgRW7Zskff0FsUHOWb22bNnVKlShd9//50ff/yRy5cvv5f9VqhQgejoaJKSkrh79y4TJkwgIyODn3/++b3sX51yj724bdiwgdmzZ/PXX38xYcKEAtdNTEykTJkyPH78mB9//JGxY8fy119/KV137dq1/Prrr6xdu1b+W2JCQgLt27dn3Lhx/P777+jp6XHu3Dk2btzInDlzAKhSpQovXrwgISGBESNGsHLlSmbOnFnsxy0IgiCUPtnFMBtB7m1YQUFBmJmZyZerKjb79etHmzZtCmwzbzu51q5dyw8//MDu3buLPMZ10qRJ9OrVi6NHjxIVFYW1tTV9+vTBzs6uSO3k+uB6ZnPldm0bGRkBkJSUJO9Vs7OzY8uWLfJ1v/76a8qWLYuLiwv+/v7wylfxBW2rjImJCa1atWLTpk1s2LAhX3uBgYE0a9YMPT09tLS02Lx5M4MGDeLatWtoaWnh4eGBr68vTZs2ZfDgwRgbG/PgwQOFoQHp6ekMGDAAc3NzunTpQmxsLDdv3qRbt27ydX766SfWr1+fr+1XhxlMmzaNSpUqUbFiRX744QfIGcrQrFkzPDw8MDMzo1OnTqSmphbpZ1C7dm3mzJmDlZUVXl5etGvXDi0tLcqWLcuoUaPkd0aeOXOGRo0ayc9Hbk9odnY2Bw4c4Ouvv8bJyUn+S8mgQYOIiIhAS0sr33ALAwMDnJ2d2bVrF7t27VKaOSoqipCQEL755huCg4PlRfiBAwdo0KABkyZNwtLSknLlytGrVy95IZuXlpYW2tqq//dKT0/P9xWQIAiC8GGTZUvF8iKnAM37UlXM1q1b97U9s69uu379esaPH8+OHTvo1avXGx2ro6MjEydOZP78+UyYMOGNC1k+1GK2Zs2a6OrqMmfOHNauXQs5BaYkSUiSxMWLF1m0aBEpKSkcPXqUq1ev8ujRI/bs2cONGzfytadq29epXbs2YWFhZGRkKCw/cuQIzs7OpKSkIEkSw4cPZ+fOnTRv3hxJkvDy8gLg/v37fPnllyQmJmJubq7Qxv379xk/fjwhISFUq1ZN5dcLgNK2cx07dozjx49z69Yt7t69y/nz59m/fz/kDNT+6quvCAsLw8jIiEOHDqncR0xMjLzgz3u3Y5kyZQgICMDDw4OzZ88iSRKBgYGkpaVx9OhRUlNTGTx4MPPmzSM5ORlJknBwcADg1KlTdOjQAUNDQ8aPH8+6devkx2NpaSn/eShTrlw5LCwsCA4OzvfZli1b+PzzzwH4/PPP5b+cvHjxgtq1a8vXyy2+nZyc5MsCAgLQ0tLC2tqaxMRExo0bp3T/8+fPV/j6x97eXuW5EwRBEARNsXHjRr766it27NhBnz591B0HPtRi9tmzZ0RFRdG1a1d5oRIZGUmnTp0wNTWlatWqPHz4kMDAQB48eEDPnj2xtrbGwcGBTz/9NF97qrZ9nSdPnmBtbZ1vouFBgwaRlZWFm5sbw4YNw8/PT+n2DRo0oHXr1kp7AOvUqcNHH32EiYkJw4YN48GDB/mmwCjMnHD379+nV69e2NvbY2NjQ58+fbh//758/y1atMDY2JjWrVsTHh6usp0KFSrIC35vb2/58tGjR8t/45s4cSKVKlWibNmybNu2jSdPnvDixQtsbW3p0qULurq6Cm2uXbuWxYsXo6WlRZMmTdi9e7fKOyRf9fLlS6Kjo5X+Jrh+/XqGDRuGlpYWw4YNkxfJDg4OPH78WL7e2bNnefbsmcK2VapUQZIkkpKSOHHiBBUqVFC6/ylTphAfHy9/BQUFFSq3IAiCUHpJxTCTgfQOZzOIjIxk9OjRmJmZsWLFCoXe23/++eed7fd1PshiFsDCwoKVK1eyYcMGIiIiOHDgAKamprx48YKnT5/i4OBAVlYW9erV49ChQ4SHhxMYGMixY8fytaVqW1WSk5O5cuUKnp6ejBo1Kt/n5cuXZ+PGjZw7d446deowb948DA0NiYuLU7gb8dXiLq9Hjx5x6dIlkpOT+euvv6hXrx6WlpY8ePCAiIgIAgMDOXr0KIDStnM5OTlx8OBBQkJCCA0NZd++fdSrVw9ypujI603+B8o9htDQUHbt2sXly5eJiopi4MCBZGVl4eDgQEhICCdOnCAzM1O+XXh4OA8fPpQXyJIk8fXXX8t7lrOzs0lMTMy3v7S0NO7evcugQYMYNGgQhoaGCp+fPXsWR0dHhXZr1arFuXPn6NmzJ3fv3mXhwoVERESQnp6ucDdmUejr6+f7CkgQBEH4sMmk4nm9K+bm5vz777/8/fffzJo1S+HVoEGDd7fj1/hgi1lyfihjxoxh3rx5dO7cGT8/PxwcHJg4cSK2trYAfPrppzRv3pzatWvTp08fpVNWqdr2VblftZcvXx5PT0+6d+/OjBkz8q03a9YstLS00NfXx8vLi5EjR2JpaUmdOnUwMzPDw8Pjtcfm5OTEb7/9hrW1Nc+ePWPKlCnY2dnRp08fatasycCBA+Vf9xfUdteuXfn4449p2LAhDRo0oFWrVgpP7Cgu1tbWuLm50bBhQ9q3by8fy2xoaIiXlxc//vgjRkZG8jGzmzZtomfPngptDBw4UN6LOnDgQCwtLRXGzJqammJqakrv3r2pV68ea9asyZdj3bp19OvXL1+7a9euxdzcnDNnznD16lUaNGiAtbU1K1eufO0YaUEQBEEoDfT19VWOq7W0tFRbLi3pXfZHC4JQKAkJCZibmxO29WfMjAzUHafQdFv0UHeEIkv7Z5u6I7wRg4/d1R2hyLycSt714fHPr+qO8Ea8py9Vd4QiM7IwLMRamiMpIxPXXaeIj48v9m/Tcv8N2HHtKUYmpoXYQrWUpEQ+a17rneR8Uw8ePJDfQP86Tk5O8ntjCuuDnJpLeDd8fX2pWbNmvuWJiYmYmJioJZMgCIIglBR5ZyN4mzY0zcaNG/nzzz/l79PT0+XDMcuUKSP/s56eHitXrpTfhF1YH/QwA6F41ahRQ2Gsae5LFLKCIAiC8OH69ddfSUpKIikpCT8/P2xtbVm1ahVxcXFkZmYSFhbG1KlTqV+/PsOGDSty+6KYFQRBEARB0ABvO5NB7kuT7dq1i549e/LVV1/Jn/ZlZWXFL7/8QoUKFbhw4UKR2xTDDARBEARBEDRAccxG8C5nMygOAQEBKsfympmZERAQUOQ2Rc+sIAiCIAiC8F64uLjwxx9/cPXqVYXl+/fv5/DhwzRp0qTIbYqeWUEQBEEQBA1QHMMENH2YwcCBA/nvv/9o2bIlVapUwcLCgpCQEGJiYpg7d67CU0ILS0zNJQgaIHdallX/3cfwLadleZ+MdHXUHaHoPv5Y3QneSKXL59Qdocja6uZ/XLSmizDPPyNLSRCdKlN3hCJLTFf9cCFNlJyYyCcNq77TqbnWnX9YLFNzjW5TV6Om5lLm/v37nDlzhsjISOzs7OjSpQuVK1d+o7ZEz6wgCIIgCILwXjk5OeHk5FQsbYliVhAEQRAEQQNkF8MNYBo4zWw+kiRx8OBBbt++TadOnWjdujV+fn5oaWlRtWrVIrcnillBEARBEAQN8CGMmZUkiW7dunHz5k10dXWpVKkSrVu3JjU1laFDh3Lz5k20tLSK1KaYzUAQBEEQBEF4L/bu3UtwcDDPnz+nd+/e8uX16tXD2NiYs2fPFrlNUcwKgiAIgiBoAJkkFctLk926dYtBgwZhYmKSrwe2evXqPHv2rMhtimL2FZ6enqSlpfH333/z999/F7huYdZ5k3Vft/22bds4ePBgsbQlCIIgCIJmyM6WiuWlyfT09IiPj1f62cOHD7GwsChym6WqmH3+/DkdO3akY8eO9OnTh5UrV1LUmccuX75MVlYWzZs3p3nz5gWuGxAQIH9SxZEjR+jYsSOdOnWiX79+zJ07l/DwcPm6r2tv586d7Nu3T+Xnebf39/cnKCio0Mf0atuFObaiyD32vL9NnTt3jo4dO3Lnzp1i20/efeW+JkyYUKztR0dHy9vu1asXixYtIisrS+GzQ4cOyddfu3YtR44ckb8/efIknTt3JiUlpVhzCYIgCEJp0L17dzZv3syTJ0/kPbOZmZnMnDmTR48e0b59+yK3WapuAEtMTCQoKIjVq1fz8uVLpk2bhoWFBZ999lmR2yrqXGfBwcGYmJgwbtw4IiIiOHHiBI0bN+bevXtYWFi8tr0XL15gYGBQbHkKavtt2lImd+zLli1bmDt3LgBr1qzh+fPnxMTEFPu+cs8zgLm5ebG2n5aWxv379/Hy8iIpKYk5c+ZgaGjI+PHjSUtL486dO/z444988skn6Ovr8/TpU7Kzs+XbL126FHNzc3bt2sWIESOKNZsgCIJQusmK4XG0mj7jsIuLC99++y1OTk4YGRlhamrKlClTyMrKYvv27ZQtW7bIbZaqYhbA1NSUjh07AnDixAmCg/83affx48f59ddf0dPTo1GjRkyZMgUTExPS09NZsGABPj4+9OvXT95O7tfw/fv3V7ntq+zs7OT7dnd3Z8iQIaxcuZLZs2crtPfgwQMWL15MaGgo2dnZbN68mQ0bNqCtrc2RI0f45Zdf8PX1RZIk7ty5g4GBgfyJGP3794ecCZa//vprwsLCGDt2LG5ubqxdu5bq1avToUMHAIYNG8acOXPytR0YGChvKzMzkxUrVnDhwgXs7OyYNGkS9vb2bNu2DYDbt28TEBDAuHHjaNeuncrz3q1bN44ePcrPP/9MfHw8wcHB1KtXT/55dnY269at49SpU5ibm/Pdd99Rr149lee2oP3nPc/k9MivX7+eypUrc/z4cTZt2sTAgQPR1tbG3t6eH374gdq1awNw/vx5NmzYQGhoKHXq1GHFihX5jsXAwEDe/tWrV+XXEEDFihXp2rUrq1at4rvvvlPYzt/fH4Dly5czaNCgAovZ9PR00tPT5e8TEhJUrisIgiB8GD6E2QwAJk2aRK9evTh69ChRUVFYW1vTp08f7Ozs3qi9UlfMPn36lI4dO/Ly5Ut0dXWZP38+AA0bNmTy5MlkZWVx/Phxpk2bxvLly5k1axZ37txh7NixnD17lhcvXkDOEIJcqrZ9nfbt23P8+PF87Y0fP56uXbvi7u6OlpYW5cuXp23btujp6dGnTx+qV6/OP//8w+bNm5k1axZOTk6cPn1aoe0NGzYwe/Zs0tLS+Oyzz7h+/TpPnz7FyMhIvs6lS5cwNjbO1/aFCxfk68yePZvLly/z9ddfc+PGDT7++GMePnyIv78/Xl5eTJ8+ndq1a+Pu7k5wcLDK6TL09fVp1qwZ586d48mTJ/Tv318h84IFC7h27RojR44kMjKSvn37cufOHZXnVtX+AQ4ePMjjx48BGDVqFLVr12bFihX89NNPjB8/HhMTEyZPnowkSTx//pyBAwdy9+5d+aDzmTNnUq1aNZW//UVERNCxY0eSkpJIS0vLNz55+vTpuLi44OnpqbB8/fr1fPXVV1hbW2NjY8P9+/dVTgg9f/58Zs+erfQzQRAEQSjtHB0dcXR0LJa2Sl0xa21tzeTJk/Hx8WHNmjXy5RkZGRw4cAA/Pz+Sk5NJTU0F4PTp02zbto1atWrRs2dPdu3ala9NVdu+TlxcHOXKlcu3vFevXuzZs4eoqCi6d++OkZERNWrUUOgRJKdQ8/DwkOfMa+TIkfLP7t27x8WLF5VmMDAwUNp2rpMnT7J582bq1atHz549OXjwoLygHzVqFO7u7pAzbCA6OpqKFSuqPN6hQ4eyYcMGnj9/zt9//62Q+dChQ2hpacl7QqOjo3n48CEVKlRQeW6V7R+gSZMm8mEGNWvWJC4uTt6rS87P6+rVq9y8eZOkpCRevHhBZGQkx44dY8KECYwZM0blMZAzdGHy5Mk8e/aMJUuW5Bt3Xa5cOb788kt+/vlntLX/N+w8KyuLTZs2ceXKFVasWEFoaChr165V2vMLMGXKFCZOnCh/n5CQgL29fYG5BEEQhNKtOGYj0PTZDMj5N/PSpUsEBQXJ70vJ5erqKv82tbBKXTGbO8ygY8eOpKam8vPPP7NixQq+/PJLmjVrxqeffkpQUJC8yKhQoQKPHz+mVq1ahIWFERcXl69NVdsWJDAwkFWrVrFkyZJ8n02YMIHRo0fj4+PDl19+yerVqylTpky+H6iyoQy5cnsmJUniyZMndO3aFVNTU/lNZ5GRkYSFhQEobTtXhQoVePToEfXq1SMuLo7w8HB5AZ53nK2Ojg4yWcEjcVq1asXYsWOpUqUKlSpVUvjMwsKCli1b0qxZM/myGjVq8Nlnn6k8t6r2/+owg7i4OIVztX79eq5fv86oUaPQ09NjxIgRpKSkULFiRc6dO4ckSQVOyJxb+Hfs2BEdHR2mTp3K9u3bFdYZN24cDRs2pG7dutSoUYPDhw/j6urKl19+KV9nzJgxLFy4EENDw3z70NfXR19fv8DzKQiCIHxYsrMlZG85G4Gmz2bw8uVLWrVqxZMnT6hYsSI6OjoKn8+dO1cUs3lNmDCBOnXq8MMPP+Dk5MSmTZv4999/MTMzk68zdepU+vTpQ61ateRf+b9K1bavyv36OyoqCn9/f7788kv69OmTbz1PT08CAwNJTU0lLCwMGxsbGjduzMCBAzl+/Djz5s177bEFBgbSsGFD0tLSsLW15eOPP8ba2ho3Nzf279+Pnp4epqamAAW2PX36dHr37s3SpUvx9fXliy++UNqbXFg7d+5UGOqQ65dffqFPnz4cOnRIXnieOnWq0Oe2KGrXrs2MGTOIi4sjMzMTY2NjyOk59vLyokaNGjg4OFCvXr3X/mLi6enJokWLuH//vsKwBH19fWbNmsXgwYPp1KkT69atY8KECQpFdtu2bdmzZw9DhgwpluMSBEEQhJJu06ZNmJqaEhERQYUKFYqlTS2pqHNXabCkpCQePnyo0Pv34MEDzM3NsbOz4969e+jo6FCzZk1u376Nq6srADExMfj5+dGgQQNu375Ns2bNCAkJgTx3/ivbNvdGqsqVKxMSEsKjR4/Q0tLC1NSUunXrKvQW5l33ypUrJCcnY2xsjLOzs7znzt/fH39/fxo0aEBSUhJlypSRD4bOu/2LFy8oU6YMWlpaRERE0LBhQ/lvNpGRkQQEBNCwYUNu3rxJ8+bN0dHRUWg7d9qo3GNLSEjgwYMH2NjYUKVKFciZASHv/q9fv46zszN6enr5zntISAgpKSnUrFlTYfndu3ext7eX/4KQnp7O/fv35b3fHTp0QEtLS+m5VbX/qKgokpKSFMbZJCUl4evrK79JjpxZD0JDQ6lfvz4PHjzAyckJAwMDJEni4cOHhIeHY2ZmRtOmTRUyp6enc/PmTVq1aiVf9vTpU3R1dbGxseH27du0aNECcnrFz5w5g6OjI0+fPqV169bo6uoqnJeEhATq1KlTwFWL/Gdgbm7Oqv/uY2hi+tr1NYWRrk4h1tIwH3+s7gRvpNLlc+qOUGRtdYMLsZZmiTCvWYi1NE90qqbfw55fYrrybww1VXJiIp80rEp8fHyxdb7kyv034JfjdzAwfrt/A9KSE5nWpdE7yVkcpkyZQrly5fjxxx+Lrc1SVcwK79a2bdvYtGmTwjJXV1f5dFzCmxPF7Hskitn3RhSz748oZt+991HMzjl6u1iK2RmfNtbYYnbnzp0cO3aMrVu3FlubpXqYgVC8WrVqhaWlpcKygm4IEwRBEARByKtp06ZMmzaNyZMn06lTp3xDE6tWrZqv1ngdUcwKhebg4ICDg4O6YwiCIAhCqSTL5q1vAJNlF2IlNVq2bBl+fn4sXLiQhQsX5vt85cqV8hmLCksUs4IgCIIgCBpAVgyzGbzt9u/a0qVLWbBggcrP32SmH1HMCoIgCIIgCO+Fnp6e0pvJ34YoZgVBEARBEDTAh9Az++DBA/nj31+lra2NhYUFDRo0UJhr/nVEMSsIGiQ2NQMD7Qx1xyi0q+GJ6o5QZGNuX1J3hDdy8GGEuiMUmefuJ+qOUGQzviieeS/fN/0y2uqOUGRPIpPUHaFI0pPffd4P4aEJGzduZMWKFfKHOenq6pKZmanw53LlyrF+/Xqlc/UrU/KufkEQBEEQBKFEmjRpEnZ2dvz555+8fPmSjIwMwsPDmT59Og0aNCAyMpIff/yRESNGEBFRuF/iRTErCIIgCIKgAWSSJB9q8MYvDX98wK5du+jVqxeff/455ubmAFhaWjJnzhwqVKjA3bt3mTx5Mo0aNeLq1auFalMMMxAEQRAEQdAAH8KY2YCAAExNlT8YwszMjICAAACqVKlCUlLhhnaInllBEARBEAThvWjcuDF//PEH169fV1i+f/9+Dh8+TKNGjcjOzub69evyx8e/juiZFQRBEARB0AAfQs/soEGDOH36NK6urjg4OGBhYUFwcDDR0dHMmDGDxo0bc+vWLb755huqVatWqDZFMZvHpk2b8PDw4O7duwC4uLioXPfmzZuvXedN1n3d9levXsXAwABnZ+e3bksoutjYWP755x8GDhyo7iiCIAhCKZOVLaHzlsVoloYXs9ra2mzcuJFvvvmGc+fOERkZia2tLZ07d5Y/ZbRJkyY0adKk0G2WmmI2KiqKLVu2QM6YizZt2lC7du0itbFw4UL69+9PcnLya9c9e/Ys5BSFd+/e5Z9//kFbWxszMzOcnZ0VisXXtXf9+nXKlClD48aNlX6ed/vTp09TtmzZQhezr7ZdmGMritxjHzVqFGXLlgXA39+fvXv3MmDAACpXrlzs+8plY2PD4MGDi639pKQk/vjjD9zc3OT/E0mSxPLly2nYsCFubm7IZDJiY2MBiImJ4b///qN///7FlkEQBEEQPgQNGjSgQYMGxdJWqSlmQ0JCWLJkCR4eHjx+/JgffviBEydOFHq8RV5t27Yt0vpXrlxh27ZtdOjQgTt37jB79mycnJw4cuQIOjo6r23vv//+w8DAQGUxW9Q8BbX9Nm0pc+XKFebMmYOxsTFjx46FnOcqb9iwAWdn52ItZvOeZ97wkXcFefnyJXPmzOHs2bMcOXIEgDNnzjBz5kxGjBiBm5sbFStWlB9nWFgYf/75pyhmBUEQhGJRWocZPHnyhKCgIGrXrk1ycjJBQUEq161duzZ2dnZFar/UFLMAdnZ2LFmyBABDQ0POnj1LixYt8PHx4eTJk+jp6dGoUSM++ugj+TZXr17l/v378gKJV76KL2jbvFq1aiXfd3p6Om3btmXt2rWMHTtWob3U1FQOHjxIaGgo2dnZjBw5kgsXLlCmTBmysrLo06cPkZGR6OvrEx0dTVJSEvb29vLtcx0/fpywsDB69epF+fLlOXfuHJUqVaJOnToAbNiwgV69euVrO7dXMbete/fucfnyZezs7OjatSva2tpcvXpVvv+AgAB69+5NhQqqJxLv1asXO3bsYOzYsWRlZXHmzBlatmypsM6zZ884e/Ys5ubm9OzZE319fZXntqD95z3P5PTInz9/njp16nD27FmGDRvG77//jra2Nvb29vTs2VP+2LyEhASOHj1KaGgo1tbWSnt1bWxsSExMJCIiAktLS7Zs2UK/fv3kn+cdZnDkyBECAwNZsmQJzs7OdOzYkcjISI4dO4aOjg7dunWjXLlyKs+bIAiCIORVWh+asHLlSlavXs3KlSt5/Pgxq1evLnDdcePGFan9UjWbQUREBEuWLOGnn37i8OHDdO7cGYDU1FTCw8Px8/Pj66+/lp/Ebdu20a9fP27dusXo0aOJioqCnCEEucMIVG1bEH19fYYOHSqfHy1veyNGjOCvv/4iJCSE8PBwsrKySExMJDExkfDwcNLT0zl9+jQeHh5s2bKFmJgYhe0BVq9ezbZt2zh27BjNmjUjOTmZw4cPc+vWLfk68+fPJyUlJV/bedvatWsXnTt35u7du8ydO5cBAwZAzlCGoUOHsnXrVo4fP65Q6CtTqVIlzM3NefbsGSdOnODjjz9GR0dH/vnRo0f55JNPuHPnDrt376Zjx45kZ2erPLcF7d/b25slS5awZMkSrl+/TkhICN9++y3fffcdz58/Jysri/DwcEJCQti2bRu9evWSXxv169dn9+7dhIaGEhMTo/J4PvvsM7Zv305ycjKPHj2iadOm8s8iIyP5/fffAYiPjyc9PZ3w8HDi4+MJDg6mYcOG/PPPPxw+fJiGDRuq3E96ejoJCQkKL0EQBEEoSS5cuMC4ceM4dOhQgeutWrUKSZIYN26c/M+qXkUtZCltPbOZmZmEh4fj4+NDrVq1qF+/PgCNGjUiMjISPz8/TExM2L17N1999RV//vknf//9Ny1atCAlJQVra+t8bara9nXKlCmDTCbLt1xXV5e6devStWtXWrRogYGBAV27dsXAwIBvvvlGvl6XLl3kPZB5eyIBunbtyq+//grA4MGDOXnypNIM5cqVy9f20aNH5Z+vWbOGnTt30qZNG7KysqhatSphYWEAdO/enXnz5kFOl398fLx8cmNlcovPx48fM3PmTCZPniz/bOnSpXTp0oWqVatSrVo1fv/9dx4/flzguVW2f3LG/IaHh8v/rKenh7GxMceOHUNLSwty7pS8efMm1tbWLF26lMTERLy8vOjbty9Lly4t8OcGMHDgQLp27Ur58uULfJSeu7s7N27ckP98Fi9ezJAhQ1i0aBEAo0aNYt++fYwePTrftvPnz2f27NmvzSIIgiB8OGTS2z/04H09NCEyMpIhQ4bw8uVLTExM6NGjxxu35evrS0xMDC4uLgqdYYVVqnpmc4cZnDx5EktLS/744w8Axo0bx9KlS3n+/DnR0dHyr9qzsrIwMDCAnCKzTJn8tb2qbQsiSRJ79uxReife5s2b6dChAxcuXKB58+b4+/vLi7C8cu/oUyY3MznDKWQyGXp6eqSlpQEgk8nkxZ+ytnPlPX4dHR309PTkBbiNjY18PRMTE1JTUws85h49enDgwAFCQkJwcnJS+Cw9PZ2kpCTCw8MJDw+nd+/eGBkZFXhuVe0/d5jBkiVLcHNzg5yJlXOP8/DhwwwePJh79+4RHh4uv2ErKysLQ0PDAo8hV7ly5bC3t+eXX37Bw8ND5Xqvntu855M8PxtlpkyZQnx8vPxV0PghQRAE4cPw1k//KoZhCoUhSRJDhw5lwoQJWFlZFWnbzMxMOnXqhL+/PwD79u3D0dERV1fXAjuQClKqembz+vnnn2nbti0jR44kKioKW1tbKlWqhLe3N1LOby3u7u64u7szaNAg7ty5Q1ZWVr52VG37qtyvv6OiouTjQL/88st8623dupWYmBi0tLRISEggJiYGOzs7Vq1aRWZmJn379n3tse3atYuMjAzS09M5efIkS5YsQU9Pj++++46wsDDu3r1Leno65BT4qtr28PCQv27cuIG9vX2RB13n0tfX5/vvv6dSpUr5Phs7diwLFy6kT58+mJiYQE4BWthzWxRRUVGUL18eW1tbAgICyM7ORpIkBg4cSPPmzYmLi6Nq1arY2toWOBPCDz/8wIULFwo8H1ZWVty/f5/58+fTtGlT+vbtS6tWrUhJSSEzM5Ndu3Zx+/Ztpdvq6+sX+w1sgiAIgvA+LF68GJlMxrfffsvatWuLtO2uXbuwtbWlatWqACxYsIClS5cyYMAAmjdvzvXr12nWrFmR2iw1xWylSpUYMmSI/L2DgwPTp0/n6dOnrF27lo0bN6Kjo8Mvv/zC4cOHAfjqq6+ws7Pj3r17TJ8+nRYtWqCnp6cwRlLVtnnXcXZ2xtfXl4iICExNTZk7dy6ffPIJurq6+daNiooiMjISY2Njtm7diouLCw0bNiQxMRE/Pz/S09Plww9y5d2+RYsWtGnThufPnxMWFsalS5coV64cPXv2JCsri4cPHzJr1ixcXFzQ09NjwIABCm3nbWvs2LFUr16dS5cu0b17d/n5e3X/Q4YMwdjYWOl5d3Z2lhd8w4YNky/v06cPVapUgZxfGhwdHTl58qR8iIAkSSrPrar9Ozs75/sNsFKlSgo3aA0fPhwdHR0CAgIYPnw4DRs2xNzcnHLlynHr1i127dpFeHi4Qvu5TE1N8fT0lJ/z3HPl7Ows7ymuUKGCfI7ZChUqsHnzZi5cuEB8fDy1atXi0qVL7N27F21tbW7cuPHGvxwIgiAIH57inM3g1XsxiqsT5fr16yxbtoxbt24V+O2vKj4+PtSrVw9y7j3x8fFh9OjRGBkZ8fHHH/Pw4cMiF7NaUnF0hwml3tWrV7l48aLCsurVq9O7d2+1ZSpNEhISMDc3Z87R2xgYK39mtSZ6Fp6o7ghFNqal6iE8muzgwwh1Ryiyrbu91R2hyGZ80bIQa2ke/TIlb9Tgk8gkdUcokvTkJBb1bUp8fDxmZmbF2nbuvwGfrTuHnpHJW7WVkZLEjtH5p+GcOXMms2bNyrd806ZNCjeQKzN37lzKli1LQkICzs7OLF68WP5tb+3atenVqxcLFiwoVL6FCxfy8OFDtmzZwtatW1m/fj3nz58HoFu3bnzxxRd069atkEf7P6WmZ1Z4t/LeeJWrfPnyassjCIIgCIJqQUFBCkW3ql5ZW1vb1z5QKfeb5k2bNpGQkMCZM2c4c+YM5MwW9M8//5Camsry5ctfm2vgwIE4OTnx5MkTHjx4wJo1ayBn6stHjx7Rpk2bIh0nopgVCqtDhw6vnaJLEARBEIQ3J5OykWVnv3Ub5DwNtTA9yJ06daJTp06Fartt27b5ZhvQ19enQoUKODo6FqoNBwcHbt26xdGjR6lVq5a8F/bhw4csXbr0jXq9RTErCIIgCIKgATT9oQnOzs44OzsrLFu1ahWNGzdWetO7Ko6OjvmK39atW79xrpI3yEYQBEEQBEEQcoieWUEQBEEQBA0gy5bQLqbZDN6X6dOnU61atfe6z1eJYlYQNEiH6haYmBbvXbLvUjfH/PMKa7pHDVuoO8IbGXb/qrojFNlUrYuFWEuzJDlWUHeEN2J6c6+6IxSZjp29uiMUSUJSMove8T6yskHrLYvRrLcbcltk7u7u73eHSohhBoIgCIIgCEKJJXpmBUEQBEEQNEBJHGagCUQxKwiCIAiCoAFEMftmxDADQRAEQRAEocQSPbOCIAiCIAgaQPTMvpn32jP74sULUlJSSExMJDAwsMB1C7POm6z7uu3j4+MJDg4ulraED4O/v7/4mQuCIAhvLfehCW/zepcPTdBURSpmvb298fb25unTp6Snpxd5Z99//z337t3Dx8eH9evXF7jupUuXmDFjBgDJycl4e3tz9+5dnj17RmZmpsK6r2svISGBoKAglZ/n3f7MmTPMnTu30Mf0atuFObaiyD32pKQk+TKZTIa3tzdRUVHFtp+8+8p9+fj4FGv7efn4+Mj3ER8fr/Sz3Ffusfv4+JD9lo/5K4yIiAjCwsIKte53331H27Zt8/3MAwICePHixTtKKAiCIJRGb1vI5r4+NIUeZpCVlUXjxo1p0KABqampxMbGcuDAAVq1alXknbZs2ZKWLVsWev07d+7QuXNnatSoQVxcHLGxsQwbNowVK1agra392vbOnz/PgQMHVBaZRc1TUNtv05Yyd+7coV27dsyaNYuffvoJgFOnTtG9e3eWLFnCN998U6z7yj3PAEZGRly+fLnY2s/ro48+onLlykiSRGBgIHPmzOHrr79W+Exb+3+/a61fvx4XFxc++ugjgoODMTExeSeZMjIyGDNmDKdOnSIjI4MWLVqwd+9edHV1VW7z999/4+vri56ennzZ8+fP+eSTT3B2dmbPnj3vJKsgCIIgCP9TpJ5ZPT09vL29efLkCd999x1r164FICUlRd7LlpCQoLCNTCbD398fmUwmX5b3q/iCts3LxcUFb29vAgICuH//PlevXmXp0qX52iNnqEBu715qaioBAQHExsbi7e1NZGSkfChBVlYWz549Uzo0IDMzE39/f3lPYGxsrEJvnb+/PykpKfnafrUtSZJ48eIF0dHRCvmCg4PJzMzEz89P4dwo06JFC/bv3y9/v2XLFjp16pRvvZCQEEJCQuTvVZ3bgvafe569vb3lhezDhw8hZ5hIamoq9+/f5+7du4SGhubLkJCQgI+PT6F6dS9fvoyPjw8XLlzgt99+y/dZbg4XF5fXthUcHKzw8wkICCA5ORmA0NBQedakpCSVQwLi4+Pp3LkzISEhhIWFERgYyLVr11Tu49GjR6SmpvLw4UNiY2Mh57qZNGkSU6dOfW1mQRAEQchLkiSk7Ld8SR9ez2yRillJkvD29ubKlStcvHhR3oP3/Plzhg8fjru7O9WqVWPTpk0AhIWFUbduXdq3b0/Dhg3lRUTeIQSqti1IlSpVmD17NocOHcrX3t9//021atVwd3dn+PDh+Pv78/vvv3Pu3DmGDx/O0aNHOXPmDO7u7jg5OTFu3DiF7ckpUurWrUvbtm1p2rQpiYmJHDp0iOXLl8vX+fbbb7l3716+tvO2FRUVRaNGjWjXrh21atXiyy+/hJyhDB4eHjRs2BA3NzdatWpVYEFrbGyMk5MTV69eJT4+noCAABo0aCD/PD4+ntatW9OyZUtcXV3p1asX2dnZKs9tQftPSkqSF5GBgYFkZWXRrl07PvnkE7p160ZgYCDjx49n2LBhuLi48Nlnn8lz/Pjjj1SuXBl3d3fGjBnz2p+jj48Pt27d4uDBg9SvXz/fZ4UZ6pCWlkaHDh1o1qwZzs7O9OjRg6ysLLZt28aWLVsA8PT0ZOTIkQBs3LiRbdu2KW2rYsWK9OvXD29vb06cOEFaWho1atRQuY9Jkybx8uVLhg8fzsWL/3vS0cyZM/nhhx8oX758gbnT09NJSEhQeAmCIAgftuycMa9v+/rQFGk2g8zMTIYPH05wcDBWVlby3sL69etz9epVAgICCA8PZ/To0YwYMYIVK1bQq1cvFi5cyMuXLxUKsFyqtn0dKysrIiMj8y2/dOkS3333HV9//TXGxsYALFiwQGEowIEDB4iLi8Pb2xsDAwNOnDih0EZsbCz37t3D0NCQUaNGsWnTJszMlD9i9NW287a1bNky3NzcWLZsGSkpKTRt2pQbN25ATm/ynTt30NPTo127dty5c6fAHshhw4axdetWnJ2dGTBgABEREQr7qVWrFqtWrYKccZynTp2ic+fOKs+tsv0DPH78mOHDhwPw6aefMnv2bGJiYvjtt9+oU6cOAP/99x8BAQG8fPmSsWPHcu/ePdLS0jh69CiBgYEqz9WrxowZg5aWFhEREcyaNSvfZ9ra2piamnLhwgWVbWzdupVy5coRHBxMdnY2Xbp0Yf/+/XTp0oXp06czbNgwMjIyyM7OJjk5mePHj7NgwQKV7SUmJjJ8+HDCw8Pp1q0b5cuXZ/PmzUr3cejQIaysrPD29oacn31sbCz6+vr4+/sTHx+Pv78/VatWzbef+fPnM3v27EKdJ0EQBEEQVCtSMZs7zECSJNzd3Vm5ciUTJ07k5MmTeHh4YGFhgb6+PgEBAZDTM9u7d28AypYtS926dfO1qWrb17ly5QqOjo75ls+aNYsVK1bQv39/AFauXKl0+xYtWmBgYKD0M2dnZwwNDQFwdXXF19eXsmXLKtx8lJGR8dqMoaGhdOvWDXLGnzZq1Eg+DKBp06bo6+sDULVqVV6+fFlgW25ubnz33Xd4e3uzf/9+li1bJv8sICCAc+fOcfv2bfmy5OTkAs+tsv0bGBjg4uLC2bNn5etlZWVhZWUlL2SjoqJo06YN6enpmJqaEhISQnBwMKmpqTRp0qTQhSw5QwlMTExISUmhZs2adOrUiWrVqil89jqhoaG4urqira2NtrY2zZs3JyQkhH79+vH48WOOHDlChw4dkMlkHD16lGfPntGwYUOlbWVmZlK2bFn5NT5gwAC2bdumch+vun79OlevXpX3oMfFxbFs2TJWrFiRb90pU6YwceJE+fuEhATs7UvWc8oFQRCE4iVJbz9M4EMcZvBG88xqaWmxZMkSmjdvzujRozl27Bju7u4MHjwYb29vxo8fD0Dr1q1ZtGgR5cqVw8/PT/5VbF6qtn1V7tffL1++5MqVKyxatIhjx47lW+/ly5f06NGDLl26sGDBAo4cOULjxo15/Pgxt27dkhcMWlpaKo/v2LFj/P3331SoUIGVK1cyb948ypYty+LFi+nevbvCsZibm+drO1ebNm1YtGgRFhYWhIWFcerUKRYuXCjvnS0KLS0tPDw8ePLkCZaWlgqfderUiZCQECZNmkSFChUAcHR0ZPLkyYU6t4XZdy5vb2/Kli3Lr7/+SlxcHN9++y3Z2dm4urry1VdfsXHjRpydndHV1c03dOBVPj4+GBgY8PjxYxISEhRuoipom9xfNCwsLGjTpg1jxozBycmJzMxMNm/ezIEDB9DS0qJ169ZMnz6dXbt2IZPJGDx4MO3atVPZ9smTJzl37hw9e/bk5cuX+Pj4MHLkSKpUqaJ0H6+aMWOGfIjJgQMH8PLyUlrIAujr68t/mRAEQRAEQD7u9W3b+NAUupjV0tJS6NGysbFh1KhRHDx4kG+++YbvvvuOyZMnM2DAAHnB4OnpSVhYGD/99BOtWrXi888/x9jYmOzsbCpXrgygclszMzP5OiYmJmRlZTFixAgMDQ2pU6cOx48fx9XVNd+6q1ev5p9//sHAwIAmTZrg6emJoaEhtWvXZsyYMXz11Vc4ODgoFJ55tzc3N+err77i/Pnz3Lt3D09PTz799FMA+vbty9SpU+nQoQOenp4YGxtTp04dhbYdHR3lbQ0fPpzY2FhmzpyJiYkJO3fuxNbWlqdPnyrsv3Llypiamio97yYmJlSvXh1yhg/kPf+5hetnn31GRkYGixcvJiIiAkmS2LVrl8pza25urnT/+vr68nHQeX/u9erVk793c3OjefPm/PDDD7i4uNCvXz/Mzc2xtbVl//79LF68mFWrVmFgYFDgTAj169dn7NixaGtrY2Vlxfbt27Gzs5N/pqOjo3KbXP3792fatGlMnTqVxYsXo62tzdKlS+XDNfr168eLFy9wdnaGnKEp/fr1U5mpW7duBAcHM23aNIyNjZk2bRqdO3cGULkPJycnpW2Zm5srHV4gCIIgCELx0pI+xP5oDZQ7vjIvPT09pUMzSpLc8aR51a5dW+UQj/chNTWVJ0+e5FueW/SqQ0JCAubm5lx6HIiJaeGHaqibrrbqbzg01aOGLdQd4Y00vH9V3RGKzPbOLnVHKLIk188KsZbmMb25V90RikzHsmQNrUpISqaCa3fi4+OLNKSuUG3n/BvgOucwZQyM36qtrLRkrs54Nzk1lXicrYa4ffs23377rcIyGxsbpUMpSgqZTCa/mSyvvXv3ynub1SEwMFBpLmWFtyAIgiC8L1L2/15v28aHRhSzGsLNza3UFVM6OjoaeUyOjo4amUsQBEEQhKITxawgCIIgCIIGELMZvBlRzAqCIAiCIGiA4njowYf40IQiPQFMEARBEARBEDSJ6JkVBA2ilfMqKezNdNUdochqnV+r7ghvJNW45P11fXLoskKspVm6POym7ghv5NHv29UdociSI5PVHaFIkrOy3vk+xDyzb6bk/e0oCIIgCIJQGhVDMcsHWMyKYQaCIAiCIAhCiSV6ZgVBEARBEDRAtiSh9ZazEWSL2QwEQRAEQRAEdZCkYhgz+wEWs2KYgSAIgiAIglBiiZ5ZQRAEQRAEDSBmM3gzomdWeO8aNGjwXrYRBEEQhJIkO/v/H5zw5i91H8X7J4rZD1hycjLTp0+nefPmODo64u7uzvPnz9/5fiMjI4u8zb///iv/89sUtrt376Zp06Y4OTkxb948letdvXoVa2trrl69qrA8IiKCL7/8EmdnZ5ycnBg3bhwxMTEAhIWFYWVlhZWVFTVr1mTkyJEkJ5eseRQFQRAEoaQRxewHrF+/fjx48IAVK1Zw9OhRevTowdSpU9UdS6mKFSvK//wmxTCAn58f+/fv5/fff2fDhg2sW7eO48ePK133zz//ZMSIEaxd+/8T7KempvLRRx+hr6/Pli1b+PvvvxWKYplMhq6uLt7e3hw9epSgoCBWrlz5RlkFQRCED48kScXy+tCIMbMfqHv37uHj44Ofnx96enoA1KhRgwEDBkBOYda0aVMGDBjA5s2b2bt3LwMGDCA2NpaKFSvy9ddfM3LkSPl6AwcOZPPmzdSqVYstW7aQnZ1Nnz59OHv2LADbt28nKCiISZMmKeSoV6+eyjbz7vuzzz7j3r17fPHFF0RFRWFlZUXz5s3p1asX4eHhTJkyBYA//viD5ORkvvvuu3zHXLVqVXbs2CF/37BhQ7S08j9vKz4+ngcPHrBx40aaN29OQkICZmZm7N+/Hzs7O5Yt+/+nGtWpU0fhLw4dHR2srKyoVKkSdnZ2JCUlKT3/6enppKeny98nJCQU4acnCIIglEZS9v9eb9vGh0b0zH6gnj9/Tr169eSF7NSpU7GyssLa2hpyfjv09vbGzMyM06dP4+joyJkzZ7hz5w5//fUXq1evJigoSL5e+fLlOXbsGGZmZmzcuJHs7Gyio6Pl+0tJSSExMTFfjoLazLvv3N7YhQsXUqFCBby9vdm0aRP9+/dny5YtpKenk52dzapVq/Dw8FB6zHkL17179wLwySef5FvPy8sLT09PtLS0GDFiBF5eXvJz1qhRI/l6ffr0wcrKCjc3N/myoKAgrKysMDc35/bt23zxxRdKs8yfPx9zc3P5y97e/rU/M0EQBEEQ8hM9sx8oGxsbAgIC5O+nTJnChAkTcHBwkC+rUKECX375JQCZmZn88ssvnDx5kvj4eOLj43n06BHW1tZUqlSJ0aNHA9CjRw8uX76cr8dT2dceBbWZd995mZubo62tjZWVlXxZr1692LFjB+XKlaNFixZYWloWeOzr16/n1KlT7N69W2nP7Lp16wgODmbWrFlkZWVhb2/Pl19+iY2NDd7e3grtPH36lFGjRimc1xs3bmBiYoKJiYnKDFOmTGHixIny9wkJCaKgFQRB+MBlZ0toveVsBNliNgPhQ9G4cWP09PSYPn06qampmJqaoq+vr1B06uvry/987tw5bty4waFDh7h06RJOTk5kZGQAUKbM//9OpKWlhSRJmJmZERERQWxsLBkZGZw4cSJfhoLazLvvV2VnZyOTyeTvJ0yYwO+//87y5cuVDi/Ia8aMGVy6dIkdO3bIe6Xzun79OhYWFty/fx9vb2/u379PhQoVuHHjBt27d+fChQts2bKFrKwsypcvn68Yzh1mUFAhm3t8ZmZmCi9BEAThw5Y7Ndfbvj40opj9QOnq6nLw4EG8vb2xsLCgQoUKNG3alN9//13p+s7OzsTFxeHi4kLPnj1fW3zp6uoyevRobG1tqVmzptLirqht5nJzc6N8+fL07NkTcnpD69Wrh6mpKbVr11a53b179/j55585duwYtra2WFlZsW7dOoV11q5dy8CBA+WzElhZWTFo0CDWrl1LpUqVOHr0KH/88QempqZYWFjwxRdfKIyhFQRBEATh/dKSPsTb3gQF2dnZpKamYmxsrLA8KipKYRYBgMTERExNTUlISEBfXx99fX2F9dLT08nIyMDU1BRyZgAwMDAgLS0NmUyGiYlJvnZf16ayLAkJCfLeUXKGGkycOJE2bdqoPM6srCyFcbwAZmZmGBkZyd/HxMRgZmaGrq6uwnbx8fFUqFBBviwzMxOZTIaBgYHCeYyJicl3zgojISEBc3NzLj8OxMS05PTSVi2bv3db0xkG3VZ3hDeSat9Y3RGK7N9qLuqOUGRdHv5biLU0z6Mxw9UdociSI0vW1IXJWVl0unyV+Pj4Yv82LfffAMcvd6Kjb1SILVSTpafwZM2gd5JTU4kxswLa2tr5CllemQ4rV26Rmvd/kLzr5RajuQwNDRX+q6zd17Wp7H3uukFBQTg5OdGqVSt5ISuTybC1tc2X/dy5czg6Oio5A/8vb8Gaq0yZMvmW6+rqKhS85JzHNylkBUEQBAEgW5LQess+xuwPsI9SFLNCiWZjY8Pz58+xsLCQL9PR0VG4UStX3nUEQRAEQSgdRDErlGg6OjpKi9S8sx0IgiAIQklQHDdwva8bwPz9/bl48SKmpqZ06tRJYcje+yZuABMEQRAEQdAAklQMsxm8h2EG06ZNo379+hw8eJA9e/bQqlUrQkJC3vl+VRE9s4IgCIIgCEKhbNy4kSVLlnD58mWaNGkCwIsXL9T6GF1RzAqCIAiCIGgAKVt664cevOthBkuWLMHd3V1eyAIKD1xSB1HMCoIGuRz0EkNjWSHW1Ax6OuXVHaHIArVqqjvCG7FLyFR3hCL7pvssdUcossyQkvlg+9Wtpqo7QpGlJqWrO0KRZKUlw+Xe73QfkvT2wwRyt09ISFBY/upsQ28iLi6OR48eMW3aNG7evMndu3extbXFzc3trdt+G6KYFQRBEARBKGVefUT6zJkzmTUr/y+Yp06d4unTpwW2NWLECIyNjYmJiQFgy5YthIeH4+LiwrVr10hLS+P06dNUrVq1mI+icEQxKwiCIAiCoAGKczaDoKAghfnbVfWchoSE8Pjx4wLbzMz83zdDuTMWJCcn4+3tjba2NllZWbi6ujJp0iR27979VtnflChmBUEQBEEQNEB2tgRvWczmjrk1MzMr1BPARowYwYgRIwrVtrW1NaampnTo0AFt7f9NiFWmTBnc3Nw4cuTIW+V+G2JqLkEQBEEQBOG1tLS06NmzJ7dvKz4W/M6dO1SvXl1tuUTPrCAIgiAIggaQsmVI2W93E/Dbbv868+bNo0WLFvTt25fmzZtz6dIlbt68yfnz59/pfgsiemaLaN++fZw9e5a4uDhmzJhR4LqFWedN1n3d9hEREfz888/F0pYgCIIgCO9HbjH7tq93yd7ennv37tGmTRsiIyPp2LEjvr6+NGjQ4J3utyAfXM/sjz/+SGhoKHp6etSuXZsxY8Zgbm5e6O3v3buHnZ0dzZo1o0WLFgWum5yczLFjx5gzZw4pKSl8/vnnaGlpoa+vT7Vq1ejXrx+1atWCnIHZBbUXHR3NqlWrlN6J+Or2iYmJnDx5kunTpxfqmF5t+3VZiir32PP67bfflD6GtqgyMjL4/PPPGT16NK1atZIvHzduHKtWrZK/HzNmDMnJyejp6WFvb0/Pnj1p3LixynYDAgKYNm0av/zyC1WqVFH4bN++fVy+fJmsrCzatWtHr169IOeXgPHjxwNgampKmzZt+Oyzz976GAVBEARBk5QvX56vv/5a3THkPrie2UOHDtGoUSM++ugjzp49y9ixY9+oHSMjI7p06VLo9TMyMjh48CCffPIJLi4uBAUF0bRpU06fPl2o9pKSkjhx4kSx5Smo7bdpS5ncY+/cubP8ZWhoWCxt79u3j9DQUJYsWaKwfM+ePQrvd+3ahZubGy1btiQ5OZmOHTuyefNmle2uW7eOiIgI1q9fr7C8b9++/PLLL1hbW1O9enW8vLyYNGkS5PzycvLkSTp37kz9+vX56aef+Ouvv4rlOAVBEITST8rOLoae2ZI5V/Lb+OB6ZgF69uxJjRo1qF69OhMmTAAgPT2dkSNHoqWlha2tLWPHjpX3yF2+fJnt27dTo0YNZLL/dd/HxcWxbNky5syZU+C2eenr6+Ph4SF/365dOyZOnMi9e/cU2pPJZKxevRpvb28yMjIYM2YMhw4d4vnz53h4eNCqVSv69OnD2rVradSoEYcPH2bGjBn8+eefzJkzR97+wYMHOXXqFC1btsTd3R1/f3/27dvHd999BzkFn4WFBUePHlVoe9CgQfIs5Azs/uuvv5AkCXd3d1xcXIiIiGDt2rU0btyYo0eP4urqytChQ1We81ePnZxe8kGDBrFjxw569+7NmTNnePToEWXLlqV379506NABcortP//8kwcPHpCRkaHQq7thwwbWrl3L0KFDCQsLw9ramtWrVxMfH4+HhweOjo7yHuqBAwdiYmICQP/+/fnkk08YNmwYWlpaCrmysrI4cuQI586do23btsyaNQsdHR1OnTrFgwcPuHfvHnp6egCMHz+esLAw+bbGxsby4wwLC+PevXtKz0d6ejrp6f8/Yfirk1sLgiAIHx5JJkOSveWY2bfcviT64HpmAb7//nsGDRrE6NGj5b1qOjo6dO7cmU8++QQTExN69OhBdnY2T548oVevXlSpUoWkpCQ2bNgAeYYQFLTt63Tr1o3Hjx+TlZWl0N7WrVs5cOAAbdq0oXPnztja2tKwYUPMzc3p3LkzDRs2JDExkSVLlnDs2DFatWpFRkaGfHsAHx8fjh8/Tv369Zk/fz4bN24kJiaGM2fOyNfx9vbG19c3X9t5szx9+pROnTpRsWJFrK2t6dq1Kw8ePJDv/8SJEzRs2JDZs2dz8eJFlcealJSEh4cHHh4eTJs2DXJ6yWfOnEnt2rWxtramWbNmdO7cmbp16zJhwgR8fHyQJImOHTty584dPvroI4VeXV9fX4yNjalatSpjx45l48aNADRs2BB9fX06d+5M8+bNleZp3rw5urq6BAQE5PvsyJEjfPrpp5ibm9O1a1f5dCO3b9+mU6dO8kL2l19+wcPDgxUrVsi3jY6OxsPDg549e7JixQr5EIRXzZ8/H3Nzc/nr1cmtBUEQBEEonA+yZ/ajjz6iTJky+Pj4ULFiRciZJy07O5srV66QlJREREQEQUFBHDt2jDFjxvDDDz8AEB4enq89Vdvq6OgUmCMlJQU9Pb1869WtW5f4+Hh8fX1p27YtVatWRVtbGwsLC3mvn6+vL7a2tqxZswaA4OBghTZsbGz4448/AKhTpw7Lli1TOTi7devWCm3nbevYsWOMGjWKKVOmyDMfPnyYfv364eDgwMqVKwGIjY3l7t27tG7dWuk+dHV16dy5M+SMtcm1fPlyqlWrBjk3rh0/fpzo6GgMDAw4f/48RkZGJCYm4uXlla/NtWvXEhYWhoeHB5mZmdy4cYOpU6fSunVrDAwM8vUE5yVJEqmpqRgbGyttVyaT4eHhQVRUFHfv3qVnz54YGRmRnJwsX69Zs2YYGBiwadMm5s+fD4ChoSGdO3fGxMSE33//HRsbG6X7nzJlChMnTpS/T0hIEAWtIAjCB06SimE2A+nD65n9IIvZ3GEGuTfoPHjwgD179vDnn38ydOhQjI2NuXjxIomJiRgbG/Po0SP5tsqK2V27dindtmzZsiozSJLEkiVLaNOmTb6vuZs3b87Fixe5ceMGW7Zs4ezZs3zxxRf5envzFoWvio+PJz09HX19fSIiIjA2NsbY2Ji4uDj5Os+fP8fBwQEdHR2VPcnGxsYKX6OHhYXh5OQEOTc55dLV1ZU/IUQZZcMM8h5DXFwcffv25YcffsDZ2ZnExEQSExMxMTEhLi6O5ORkhcIzMzOTvXv3Mnv2bPmy2NhY/vnnHzp16qQyR641a9ZQuXJl+S8zuYKCgggMDGTy5MnyZQsWLCAoKIj27dvz888/8/z5c6pXr87HH39M1apV2bRpk8L5KqiIzns+1Pkca0EQBEHzlISpuTTRB1nM5mrUqBH169dny5YtODg44O/vzz///ENsbKy8wBwwYAALFiygY8eOZGVlER8fn6+dihUrKt32VblftScnJ3P//n10dHQ4efJkvvX27dvHvn37yM7O5ubNm4wdOxZLS0sCAwPp378/7du35+OPPy7w2HR1dXFzc8PS0pKLFy9y+PBhatasSUxMDB9//DEymYz4+Hg6dOiQr+3u3bvL2xkwYACLFy/Gzc0NLS0tXrx4wfz584mNjX2DM66anp4eWlpanD59GkmSCA8Pp3nz5lhaWjJw4EDq1q2Lq6srurq6/Pbbb5w5c4amTZsqFI5mZmasXbtWPiyiZ8+euLi4yMfMjho1iqysLJ48eUJcXByHDh3Kl2P9+vUMGjRIod3AwEA2bNjArFmzmD17Ns2aNaNx48aULVuWJ0+eMGDAgGI9F4IgCIIgFN4HV8wuXrwYKysr+ftff/2VZ8+e0b59e06dOsXz589p1qwZjx49wt7eHnNzc7y9vTl//jzVqlUjKysLY2NjypcvL5/LVdW2urq68nWMjY3ld8bnTs3VuHFjeeGbt70aNWrQuXNndHV1+fHHH3F2dgbg+vXrXLt2DVtbW6ysrBTmgs27vZWVFevWraN+/frcuHGD5cuXU7lyZQCuXbvGuXPnqF27NqmpqZiYmKCnp6fQdt62zM3NuXv3LufOnYOcIRrGxsbo6ekp7P/TTz9VWcQbGxvLhzy8+rPI7W01Njbm9u3bXLp0iRo1aqCvry/v6V22bBkjRozg0aNHZGZmYmhoSOXKlRV6ZQE++eQT0tLSAPjvv/+4cOGC/IavtWvXkpGRga6uLvb29jRr1owyZfJf/i1btqRhw4YKy0aNGoW3tzcAX375Jf3798fb25vMzEwaNWqEtbW1/Gfw22+/KT0HgiAIgvA6omf2zWhJkvR2DwEWhBwRERHymRLy2rBhg/hK/TUSEhIwNzdnyT/3MDQ2LcQWmqFDNdVDXTRVYHyauiO8ETszA3VHKLKu0/N/86TpFk9oVYi1NM/qf33VHaHIUpPSC7GW5shKS+bWL72Jj4/HzMysWNvO/TegUs9FaOu+3dSV2ZmpRB788Z3k1FQfXM+s8O4YGxvLb/LK63U3wgmCIAiCILwpUcwKxcbExKRQNz8JgiAIgpBf7kMT3raND40oZgVBEARBEDRAdrYM3rKYzf4Ax8x+kA9NEARBEARBEEoH0TMrCIIgCIKgAcRsBm9GFLOCoEFqVTDG2CT/U8k01QnfaHVHKLLlW++oO8Ib2fhjW3VHKLKjP7/+ASaaJruEzu+zZUhjdUcoMv0yyqdz1FSJCQnU/OXd7kMUs29GDDMQBEEQBEEQSizRMysIgiAIgqAJZDIk7bfsWZV9eD2zopgVBEEQBEHQAJL09rMZSNKHV8yKYQaCIAiCIAhCiSV6ZgVBEARBEDSAlJ399j2z4qEJglC80tLS+Oeff7CwsKBFixbqjiMIgiAIGksqhocmfIizGYhiVsOkp6fz999/K/2sX79+GBgYFKqdvXv30rdv32JO9/9SUlI4deoUvXr1KnC9oUOHEhcXR//+/VUWsykpKezbt49mzZpRq1Yt+fKdO3dSs2ZNmjRpUmy5vby8ALC2tqZDhw4qs5DzeF4XFxfs7OwUPmvbti329vYA3L59G1NTU2rWrAlAcHAwFy9eZNCgQcWWWRAEQRAE1UQxq2EyMzM5ceIEAPfv3yc7O5sGDRoA0L1790IXs1999dU7LWZjY2P55ptvXlvMXr58maCgILS0VM8nGBsby+eff86nn34qL+QvX76Mp6cnn3/+ebEWsydOnCAqKgqZTKa0mI2NjeWLL76gV69eJCUlMXToUA4fPkzbtm2JjY1l+PDhfPrppxw8eBCA7du3U6NGDXkxu2DBAg4dOoS9vT2tWrUqttyCIAhC6fe/YQZvN0zgQxxmIG4A0zAmJiZ4eXnh5eVFv3796NOnj/x9aGgo+/fv5/HjxwCEh4dz/Phx+bbXrl3j4cOHXL16lbS0NLy8vDh27BjZ2dkcOHBAvl5AQAC3bt2CnB7cxMREjh07RlRUFBkZGfz3338cOXKE+Pj4QmWOi4vjv//+IywsjEOHDhEcHAzAwYMHSUpKYtu2bfj5+QGQnJzMiRMn+Oeff0hLS5O3UblyZYKDg4mLiwNg69at+QplZdlyj3P79u1cuHABSfr/Gc8fPHjArl278PLyIjAwEHJ6ZqdPn17g8VhYWODl5cWBAwf45ptvOHz4sPyzGjVqEBcXx/nz5/Ntl5qayoULF9i6dSvr1q0r1LkTBEEQhFy5D01429eHRvTMlhCzZs1i586d1K9fnzt37jB58mQ8PT1Zs2YNsbGxODk58cUXX3DmzBmuXLlCeno6J06coEqVKrRv316hF/XKlStcvXqVJk2aMHr0aBo3bkyFChWwsrKiQ4cOWFpaYmhoyNdff82lS5ewsrIqMFtAQABjxozBysqKChUq4Onpyd27d7lw4YJCDnNzc5o2bUrVqlXJzMwkOjqa69evy9sZOHAgu3btYsSIEdy4cYNRo0bx5MkTABISEmjdunW+bIaGhpw4cQJJknj+/Dk1atSQF6ITJkygZcuWlClTBkdHRypXrlyoc52cnIyXlxdJSUns3r2bxYsXK3y+ZMkSxo8fz9WrVxWW7969m8GDB9OuXTumTp3Ky5cvKVu2rNJ9pKenk56eLn+fkJBQqGyCIAiCICgSxWwJkJyczLJly/jtt9/Q1dWlXbt2LF26lFGjRrFt2zbatm1LRkYGe/bsoWzZsowcOZJp06bJx4fm7QF9VVpaGlu2bMHW1pZVq1ZhZWXF0KFDIadndfv27UycOPG1GXV0dDh79iw6OjpMmDCB8+fPs2TJEnmvMsCyZcvo0qULq1evBsDd3Z09e/bQsWNHAAYPHkz//v0pX7483bp1UxiasHXrVpXZ5s2bx+3bt0lKSmLSpEkkJycTExND8+bNmTBhAi4uLujp6RX6fKempnLixAmePXtGxYoV6dKli8LnzZo1o2rVquzcuVNh+bp16+jfvz9eXl5UrlwZLy8vxo0bp3Qf8+fPZ/bs2YXOJAiCIJR+4gawNyOGGZQASUlJSJLEv//+y4kTJ7hy5Qpt2/7vOe1lypRBX18fgPLlyyvdvkyZMmRlZcnfJyYmyv9sZmaGra0tAPHx8URHR3PixAlOnDiBvr6+/LPXcXBwQEdHR54jOTk53zrJycnY2NjI31tbW5OUlCR/X6lSJczMzPjll18YMmSIwraqsv3333+0aNGCrVu3cuLECdLT04mOjmbkyJGMGjWKQ4cO0aZNGw4dOlSo4yDPMINr165Ro0YNli9fnm+d3GI0t3f14cOHhIWFcePGDU6cOEGZMmXYuHGjyn1MmTKF+Ph4+SsoKKjQ+QRBEITSKTtbViyvD43omS0BLC0tcXV1xdTUlGbNmqGrqyu/w37EiBGMGjUKe3t7BgwYwD///IOenh4mJiasXLmS6tWr07VrV8qWLcvUqVOxsrJi/fr1tG/fPt9++vTpw5YtW2jYsCFWVlZoaWkV601M3bp145NPPkFfX5/MzEw2b96c76v6iRMncuLECWrUqMHp06dfm+3QoUPUrVuXTz/9FH9/fyRJQpIkbt++TVRUFHXq1OHp06dcuXKFHj16cPDgQW7dukV4eDheXl589NFHVKlSRWXmuXPn4urqyueff66wvGrVqnz66ads2rSJefPmsXbtWn788UfGjBkjX8fNzY1r167RvHnzfO3q6+vLfwkRBEEQBOHNiWJWg9WvXx9ZzjOWDx48yIYNG7h8+TKpqak0atQIIyMjGjduzKhRowAICQnh9OnTdO3alQ0bNrB9+3bCw8Pp2rUre/fuZfXq1SQkJPDbb78RGhoKOdN95apTpw6HDx9m8+bN+Pj4kJ2dTZ06deSFc17Gxsb07t0bcnpi884M0KBBA3kvcd72nZ2dOXjwIDt27EBbW5vTp0/Lb6jq0aMH5BSAbm5uANSqVQsTE5MCs40aNYrExERu3bpFp06d+OabbzAxMeHKlSucOHECXV1dWrRowVdffQXAhQsXCA8Px9nZWV405y1m8x4XgK2tLZMmTeLmzZs4OzvLcwL89NNPREVFUatWLR49eqSwHcB3332Hn5+f0mJWEARBEF4lybJB6y2HGcg+vNkMtKS8t38Lwivi4uI4evRovuUeHh5qyVNaJSQkYG5uzqHbzzE2MVV3nELziUwsxFqaZfnWO+qO8EY2/thW3RGKrJKxrrojFFl2Cf0X0UxPR90Riky/jOopGzVRYkICNSvbEB8fj5mZWbG2nftvgH6TUWjpFP4eD2UkWQbpt9a/k5yaSvTMCgVKTEyUz3ublyhmBUEQBEHQBKKYFQqUe1e+IAiCIAjvlpQte/thBuIGMEEQBEEQBEEdRDH7ZsTUXIIgCIIgCEKJJXpmBUED5N6HmZJUsm6oSksuWXkBsjNS1B3hjSQnlrxznZRd8v6JKak3gGmVwBvAMnRK2A1gOf8Pvsv75qXMtLfvWZVlFlecEkPMZiAIGiA4OBh7e3t1xxAEQRBeIygoSOmUlW8jLS2NqlWrEh4eXiztWVlZ4e/vj4GBQbG0p+lEMSsIGiA7O5vQ0FBMTU0VHuP7thISErC3tycoKKhETdFSEnOLzO9PScwtMr8/7yq3JEkkJiZiY2ODtnbxj9JMS0sjIyOjWNrS09P7YApZxDADQdAM2traxf6bfl5mZmYl6h+jXCUxt8j8/pTE3CLz+/Mucpubmxdre3kZGBh8UAVocRI3gAmCIAiCIAgllihmBUEQBEEQhBJLFLOCUIrp6+szc+ZM9PX11R2lSEpibpH5/SmJuUXm96ek5hbenLgBTBAEQRAEQSixRM+sIAiCIAiCUGKJYlYQBEEQBEEosUQxKwiCIAiCIJRYopgVhFLs+fPnDBo0SN0xiqxfv34EBASoO8ZrBQcHM3v2bNzd3XF3d2fOnDmEhISoO1aBMjIy+PfffxXOb3x8PEeOHFFrrte5cOECX3/9Nf3792fUqFFs3LiR9PR0dccqUGRkJKdPnyYpKUm+7P79+9y9e1etuQqSnp7Oxo0bGTVqFP369WPChAlcuHBB3bGKxN3dnadPn6o7hvAeiRvABKEU8ff3Z8iQIfL3KSkpPHnyhEaNGlGtWjW2bt2q1nyquLu7KxRXt2/fpk6dOhgaGrJ9+3YqV66s1nzKBAQE0Lx5c4YNG0bTpk3R0tLi+vXr/PXXX1y5coUqVaqoO6JSPXv25Pnz5wB8++23jBw5kvv37zNo0CDu37+v7nhKbdq0iTlz5vDNN99QtWpV4uPj2bFjB5Ikcfz4cXXHUyoyMhInJyfq1atHTEwMO3bsoF69esydO5e0tDTmzp2r7ohKffrpp8hkMtzd3TE3N+fFixcsX76cqVOnMnLkSHXHU2rEiBE8e/ZM/t7b25uaNWtibGzM5s2bqVGjhlrzCe+eeAKYIJQihoaGJCYmMmXKFBwcHPDz82P69OksWbIEIyMjdcdTSSaT8dFHH9GzZ08ABg0axI8//kiVKlWoVKmSuuMpdejQIfr06cPChQvly/r27UtiYiKHDh1i/Pjxas2nTGRkJOfPnycsLIwyZcrg6emJoaEhDRo0UHe0Am3evJkNGzbQvn17+TJ3d3dsbGwICwvD2tparfmUOXDgAO3bt2fnzp0EBwfj7u7Opk2b1B2rQFFRUVy7do3IyEiFx7U6OzszZcoUjS1mJUnC1dWVfv36ATB06FAmTpxIzZo1sbGxUXc84T0QwwwEoRSxsrLi33//5ejRo0RGRtKgQQMMDQ1xdXXV6IJlx44dlC1bloMHD+Li4oKBgQENGzbE1dVVYx/vWL58eR4/foxMJpMvk8lkPH78mAoVKqg1myra2tro6upiYGBAmTJl2LhxIzt27ODw4cPqjlag8uXL5+s1Dg4OJiUl5Z0+XvRt6OjoYGpqCoCdnR0bN25k8ODBhIaGqjuaSqampqSnp+cb4nP//n2NvabJ6bm3sbFhz549NG7cGCMjI+rXr4+rq6tG/xIvFB8xzEAQSiFJkli+fDnXr1/n3r17Gvv18auuXr3KypUrOXPmDP/99x+1a9dWdySVMjIy6N+/P7dv36ZRo0YA3LlzBxcXF3bv3o2urq66Iyr12Wef0bt3bwYMGABAUlIS7dq1Iy0tTWOvk4cPH9K1a1cqVKiAg4MDCQkJ3Llzh59//pmxY8eqO55ScXFxfPTRR5w+fRorKysAbty4gZubG998843GDjP4448/mDZtGo0aNcLc3JyAgACio6M5evQo9erVU3e8At28eZOlS5dy6dIlDh48iLOzs7ojCe+JKGYFoRS7ffs2ly5d0sivvFWJj49n1apVjBkzBgsLC3XHea1nz57x9OlTtLS0qFWrlsaPz0tMTCQoKIi6devKl8XExPDs2TNcXV3Vmq0gMpmMmzdvEhYWhpmZGc7OzpQvX17dsQoUEBCAvr6+vJgFePToEXp6elSvXl2t2QoSFxeHt7c38fHxWFtb4+Ligo6OjrpjFUpiYiKrVq1ixIgRCuddKN1EMSsIH4CsrCzi4+M1+qtCZaKjoylXrlyJ+YeUnJ5OABMTE3VHKbSSen3ExMRgbm5OmTIl5/aP5ORkJEkqUdeHTCYjLi6uRPxymVdJvD6ENyPGzArCB+DZs2d0795d3TGKrGvXrvj5+ak7RpGsWrWKVatWqTtGkZTU66N79+4Kd7GXBL///jvLly9Xd4wi8fPzo2vXruqOUWS9evXi0aNH6o4hvAeiZ1YQBEEQBEEosUTfuyCUMmlpaWzbto0rV64QHx+PjY0N/fv3p3Xr1uqOVqBz586xd+9eQkNDKVu2LK1bt2bw4MHo6empO5pKERER+Pj44OrqKv/a+N69ewAaO3tESb0+7t27R3JyMi1atJAvO3LkCB999JHGzmgQEhLChg0bePLkCZIkUatWLUaNGoWdnZ26o6mUmZnJhQsXqFatGg4ODgAkJCRw7tw5je69v3TpErt37yY0NBQzMzNatGiBh4eHxs6GIhQvMcxAEEqZnj17snfvXtq2bcuQIUOoVq0aQ4YMYcuWLeqOptK6devw9PSkRo0aDB06lLZt27Jz50769u2r7mgqhYeH4+TkxC+//ELLli15/PgxAPv27WPfvn3qjqdSSbw+tm/fjpubG5MnT6ZPnz5kZGQAMHnyZIKCgtQdT6mgoCBcXFxITEykd+/e9O3bl/T0dJo2bYq/v7+646k0aNAgxo0bR7du3Vi7di0AoaGh/PDDD+qOptJff/2Fu7s7VatWZciQIbi5uXHgwAGNLr6FYiYJglBqhIaGSpaWlpJMJlNY/u+//0pt2rRRW67XadmypXTu3DmFZTKZTKpQoYIUGRmptlwFWb16teTu7i5JkiQFBARIbdq0kfz9/aWZM2dKM2fOVHc8pUrq9eHm5ibt379fkiRJ+vvvv6WBAwdKWVlZUr169SQfHx91x1NqzZo10ujRo/MtHz9+vLR06VK1ZHqdmJgYyczMTEpOTpaysrKk4cOHS1u3bpUePXokOTo6qjueSu3bt5dOnjypsCw7O1uysbGRAgMD1ZZLeH9Ez6wglCJmZmYkJycTHByssFzTJz1XNil+YGAg6enp8onnNU3eSfErV67M+vXr+eyzzwgPD1d3NJVK6vWR91z369ePDh06MGrUKDT5lo/ch2pkZWXJl2VnZ/Po0SONPddaWlro6upiaGiIjo4O69evZ8+ePRw4cEDd0Qqk6qEaiYmJlC1bVm25hPdHjJkVhFLE2NiY+fPn06hRIxo3boyZmRkvXrwgLi6OY8eOqTueSvPmzaNbt25s3LiRKlWqEB8fj7e3N7/++qvGjnnr168fK1euJDIykkqVKlGzZk2WLVtGhw4dNPYr2ZJ6fYwbN47Vq1fToUMHAEaPHk1QUBCbN29WdzSV+vTpw65du3BwcKBRo0ZoaWnh7e2Ns7MzgwYNUnc8pcqVK0eXLl3YsWMHgwcPRkdHRz7EQ5P9/PPPdO3aFS8vLxwcHEhMTOTOnTvMmzdPY38ZFoqXmM1AEEqh2NhYvL29SUhIwMbGhiZNmmj8XK1ZWVncunWLsLAwzM3NcXZ2ply5cuqOVaAXL15gaGiIpaWlfNnDhw8xMDCgWrVqas1WkJJ4fdy+fZv69esrPFnt3LlzuLi4YGxsrNZsBfH19eXp06fyG8Bq1qyp7kgFSkpKIjAwUOGhGrGxsTx58kTh5jtNI5PJuHXrlvwGsIYNG2psD7hQ/EQxKwillJ+fHyEhITg7O5eY3om4uDh8fHyoWrUq9vb26o5TKFlZWXh7e6OtrS3vgSsJSuL18fLlS+7du4eDgwOVK1dWd5xCkclkeHt7o6WlhbOzM9raJWN0n7+/P8HBwSXq+oiPj+fu3btUqVKFKlWqqDuO8B6VjP+rBEEotMzMTIYMGcLcuXO5fPkyHTt2xNfXV92xXmvjxo3079+f69evM3r0aDZt2qTuSK/15MkT2rVrx5EjR/jrr7/o1auXRo/jpARfH1u2bKFv375cu3aNL774gnXr1qk70ms9e/aMdu3acejQIbZt20bPnj3Jzs5Wd6wCZWVlMXz4cGbPns2VK1fo2LEjT548UXes1/Ly8qJ3795cu3aNL7/8kj/++EPdkYT3Sd13oAmCULwmTZokTZw4UcrKypKioqKkrVu3SiNGjJDS0tKkvn37auTsANeuXZNq164tpaWlSXFxcVJQUJBkZ2cnyWQyafHixdLff/+t7oj5ZGRkSHXq1JEuXrwoJScnS1FRUVL//v2lo0ePSjdu3JDGjRun7ohKlcTr4+bNm1LNmjWl1NRUKS4uTgoJCZHs7OykzMxMadmyZdKOHTvUHTGfzMxMycnJSTp37pyUkpIiRUVFSYMGDZIOHjwo3bp1Sxo7dqy6Iyo1bdo0acKECfLrY/v27dKQIUOk9PR0qV+/flJ4eLi6I+Zz584dqUaNGlJycrIUFxcnhYaGSvb29lJ6erq0YsUKycvLS90RhXdM9MwKQimSmZnJH3/8wYwZMwgPD6dbt2788ssvWFlZoa+vj6WlJYsXL1Z3zHxWrVrFDz/8gL6+PjNmzKBjx47Y29uTkJBAkyZNmDRpkroj5vPPP/9gZ2dHq1atOHXqFG3atOHBgwfo6OhQt25djhw5wvXr19UdU0FJvT5Wr17N999/j4GBAbNnz6Z9+/bY2toSHx9PkyZNmDx5ssb1iP/3339UrFiRNm3acPr0adq2bcu9e/coU6YMdevW5eTJk1y+fFndMRXIZDLWrFnDjBkziIyMpHv37syePRsrKyv09PSwsbFh4cKF6o6Zz5o1a/j2228xMjLil19+wc3NDWtra+Lj43FxcWHy5Mka3yMuvB1RzApCKRIYGIiZmRnm5uZUrFiRlJQUNm/eTFRUFADu7u6cP39e3THz8fHxwcnJCXKeNrR582ZSU1MxNjbGzc2N1NTUfNNJqVvezAYGBnTq1IkuXboAYGRkRO/evTXuXJeG6yMxMZH169eTkZGBqakpH330EdnZ2QQEBKg7poK8mQ0NDXFzc6N79+5IkoSBgQF9+/bVuHMdHByMgYEBFSpUwMLCgqSkJLy8vErc9fHHH38gk8kwNTWlRYsW6Orq4ufnp+6YwjskillBKEUMDAzIzMwEICwsjJiYGLKzs3nw4AEAycnJGnnXet7cDx48QJIkIiIi5P+IpqSkaFxuZZk1/VyXpusjOjpaPqevuD6KR97MkZGRREZGanxmVJzr2NhYQkNDQYNzC8VHFLOCUIrY2tpiamoqv6O3UaNGDB06lC+++AJyvvps27atumPm065dO06cOAHAmDFjGDJkCK6urtjY2HDr1i0qVaqEtbW1umMqaNeuHadOnUKSJPr06cOpU6c4fvw4ffv2RSaTcf78eY0716Xl+hgxYgSNGjWicuXK3L17FzMzM42b/aJt27acPn2a7OxsevfuzZkzZzh8+DD9+/dHJpNx7tw5jTvXlpaWWFhYcOvWLWxtbWnRogWDBw9m7NixUEKuj88//5zPP/+cOnXqUK1aNR48eICBgQEODg7qjim8S+oetCsIQvHav3+/1KpVKykpKUmScm5EkSRJunz5suTg4KCRN/hERkZKtWrVku7cuSNJeTLHxsZKTZs2lfbt26fmhMp5eHhIP//8syTlPH439zGx06ZNkwYOHKjmdMqVxOsjOjpaqlWrlnTz5k1JypM5Li5Oat68ubR79241J1Ru+PDh8kcb570+Zs6cKfXt21fN6ZQ7fPiw1KJFCykxMVGS8pzra9euSVWqVNHIG8BiY2MlR0dH6dq1a5KUJ/PLly+lli1bStu3b1dzQuFdE/PMCkIptH37dlauXEmPHj2wsbHh5s2b3Lx5k02bNlG7dm11x1Pq6dOnfP755zRs2JDGjRsTFhbGzp07+f777/Hw8FB3PKXS09OZOHEigYGBdOvWDS0tLfbs2YODgwMrVqzQ2KeXlcTrw9fXl9GjR+Pk5ESTJk2IiIhg586dfPPNNwwbNkzd8ZTKyMjg+++/5/nz53Tv3h1tbW327duHra0tq1atwtDQUN0Rldq1axe//fYb3bt3x87Ojps3b3L9+nU2btyo8DAFTeLn58eoUaOoW7cuLi4uREZGsmPHDsaPH4+np6e64wnvmChmBaGUSk9P5+rVq8TGxlK9enUaNGig7kiF4uPjg6+vL+XKlcPV1VVjC8K8IiIiuHHjBgAuLi5YWVmpO9JrldTr4/79+zx79oxy5crRvHlzjS0I84qMjOTGjRtIkkTjxo2xsbFRd6TXSk9P59q1a8TExFCtWjUaNmyo7kiF8uDBA54+fUrZsmVxdXUtEdeH8PbEmFlBKKX09fWxs7Nj165dJaZQIWf6qMDAQF6+fIm+vr6646h05swZ+ZhZS0tLunXrRrdu3UpEIQugq6tLbGwsgYGByGQydcd5rbNnz3LixAmcnJyQyWRcvHiRhIQEdccqlEqVKhEREUFQUJBGF7IpKSlcuXIFcv7+MDIyYufOnWzatInExER1xyuQj48Ply9fpl69evTu3Rs3NzfOnDlDXFycuqMJ74HomRWEUsTf358hQ4bI36ekpPDkyRMaNWpEtWrV2Lp1q1rzqdKkSRN27NjBgwcPGDNmDB4eHpw/fx5nZ2fWr1+v7nhKzZo1iw0bNmBkZMSIESPw9PSkUqVK6o5VoEePHjFs2DCuX7/OkCFDePz4MR999BF//fUXmzdv5tNPP1V3RKXWrFnDkiVLqFmzJpUqVcLPzw8nJyfu3bsnL740zdatW1m7dq38fVBQEJIkUblyZYYNG8bo0aPVmk+Zx48f06tXLx4/fkxWVhaVK1dm6tSpXLlyhXLlyrFq1Sp1R1Rq9+7dfPHFFzRo0ABzc3N2796Nvr4+zs7ObN68GWdnZ3VHFN6xMuoOIAhC8TE0NCQxMZEpU6bg4OCAn58f06dPZ8mSJRgZGak7nkrJyclkZ2ezatUq1qxZQ79+/YiPj8fW1pbly5djbGys7ohKjRw5ktGjR/Pnn3/StGlTXF1dGTNmDG5ubmhpaak7Xj4ymYyUlBRiY2M5cOAAoaGhmJqa0rRpU9asWaOxxezOnTv5888/6dixI1ZWVuzevZs2bdpQqVIlIiMjNfKXCBMTE7S1tZk7dy56enps3ryZ9PR0xowZg52dnbrjKWVqasrLly8BCA0NRU9Pj3HjxtGqVSuNLL5zrV+/nrVr19KvXz/279+Ph4cHO3bsUHcs4T0SwwwEoRSxsrLi33//5ejRo0RGRtKgQQMMDQ1xdXUtEUMNEhISqFevHgDm5ubY2toSEhKi7lgFsrW1Zc6cOfj6+tKnTx9mzZrFggUL1B2rQImJiVSuXBlTU1MAnJycCAoKUncslczMzJAkCS0tLaytrbGwsEBLSwsDAwONHSLRp08fFi5cyMqVK7G0tMTOzg5bW1tcXV01tpjNnbpt2bJlVKxYkXLlyhETE8OTJ0+oWrWquuOppKOjI7+We/fuTdeuXfH09NS4p8IJ744oZgWhlLGwsGDr1q34+fkxb948dccplHLlytGuXTsCAgLQ1dWVL9fS0ioxj6HU1dVl4MCBnD9/nm+++UbdcZQqU6YMERERNG/enPLly8uXa/p5nj59Or/++isymQxvb2/q1avH+fPnadCggcbNP5xXixYtWLduHfPnz+fs2bPqjlMou3bt4o8//sDBwYGsrCyqV6/O4sWLNfIxx7m++uor1qxZI38/YsQIatSowb1799SaS3h/xJhZQSjFbt++zaVLlxg/fry6o7yR27dvU6dOHY28IzkyMhJybu4p6fKOrdZUMplM4SlODx48wM7ODnNzc7XmKqxt27ZhaWlJx44d1R3ltbKysnj48CGRkZHY2Nho7HRced25c4d69eqhp6cnX3b+/HkaN26MiYmJWrMJ754oZgWhFImMjOTgwYMMGjRI/rVbSbBu3Trc3NyoUaOGuqMUyfPnz7l58yZdunQhKCiIXbt20blzZ1q2bKnuaEqV1OtDFS8vL7p06UKFChXUHSWfc+fOkZaWRqdOnTRy/LQqmZmZXLhwgWrVqsmfmpWQkMC5c+fo3r27uuMpJZPJ2LJlC5mZmXh6erJu3Tru3LnDF198QZMmTdQdT3gPxDADQShFIiMjmThxIg0aNGDMmDHcvn1b3ZEK5ddff+WTTz7h448/Zs+ePfLnrGsyf39/mjZtyoEDBxgwYAAeHh6UKVOGHj164O/vr+54SpXU60OVBQsWEBYWpu4YSl24cIFRo0ZRu3Zt5s+fT0REhLojFcqgQYMYN24c3bp1k8/GEBoayg8//KDuaCrNnj2bP//8kzt37tCtWzeOHTtGrVq16N69e4n4u0R4e6KYFYRSpkqVKjx69IiWLVsyduxYmjZtyvr160lOTlZ3tAIdPXqUmTNnsm/fPqpXr87UqVM1tigEOHz4MP3792fHjh1Uq1aN1q1bM2PGDPr27cvx48fVHU+lknh99O7dGycnp3wvX19fdUcr0LBhwzhz5gzp6ek0b96c/v37c/r0aY29MSk2NpbTp09z8+ZN7t69y5UrV/jrr7/UHeu1/vvvP5YsWcIff/yBr68vU6dO5YcffsDY2JgXL16oO57wHohiVhBKIQMDA4YNG8a1a9f4/fffuXTpEp988om6Y71W69at2b59Ozdu3MDIyAg3NzeeP3+u7lhK5d5hT84sEhUrVgTAyMhIY++wz1XSro9nz56xePFiDhw4oPCqXLmyuqO9lo2NDbNmzcLX15cBAwYwd+5c5s6dq+5YSmlpaaGrq4uhoSE6OjqsX7+ePXv2cODAAXVHK5CJiQlhYWGkpqaSmJhIdHQ0MpmM+Ph4jZ3WTyhmkiAIpYaPj49Ur149pZ+lpKS89zyF5ejoKD169Cjf8qysLCkjI0MtmV4nJSVFcnV1lZ49eyZflpCQIDVt2lQKDg5WazZVSur10bZtW+nx48f5lg8ZMkTy8/NTS6bX+fnnn6Vp06Yp/UyTz7WHh4e0bds2+fukpCSpadOmkqOjo1pzFeTYsWOSmZmZZGtrK/3+++9S1apVpSZNmkgdOnRQdzThPRE3gAlCKZKenk5QUFCJu5Hq2bNnVKlSReFO5JIgOzsbLS0t+Q0+4eHhJCcnU716dXVHU6qkXh8lUVRUFJIklbjZLpKSkggMDFSYwSA2NpYnT57QokULtWYrSEJCAllZWZQvX54XL17g6+tL69atMTAwUHc04T0QwwwEoRTJysoiOjpa/v7atWsMGDCAiRMnkpSUpNZsBYmOjiYrKwuAuLg4xo8fz6BBg7h79666oxVIW1sbLS0toqOjWbduHYcPH9boqaJK6vWRl7e3N8uXL2f//v0aPTducnIyiYmJ8vd//vknvXv3VnjErSbS0tLi0aNHPHz4kJSUFBYuXMi+fftwdXVVdzSVrl27hra2NuXLlychIYHffvuNdevWcf/+fXVHE94TUcwKQini7+/PqFGjIGeKnZ49e+Lm5kZYWBg//fSTuuOpNGLECAIDAwH48ccfiYuLo3Xr1vTs2VNjx5/OmzePefPmERsbi7OzM2fPnuXs2bM4OzsTHx+v7nhKldTro1mzZjx69IhDhw7x8ccfExAQwLx58/D09FR3NJW8vLzYtGkT5EzTNX/+fNzd3Vm0aJFGP0Che/furF69ms8++4xhw4bx4sUL1q9fz9KlS9UdTaUxY8bIbwacNm0aoaGhtG/fnl69eonZDD4QopgVhFIk77PVg4KCMDExYezYsUycOJGrV6+qO55KeXPfunWL77//nnHjxiFJksZOvZSRkUFGRgZ79+7F1dWVbdu2sW3bNpo3b86+ffvUHU+pknp9pKSkIJPJWL16NStXrmTp0qX8999/7N27l4SEBHXHU+rVa7pfv37069ePgQMHauy5joyM5O7du/zzzz+sXLmSc+fO8fvvvzN//nwOHjyo7ngqvXquJ06cyJgxYzA2NiYgIEDd8YT3QBSzglCK5I47XbFiBVZWVpiamhIXF6fxz1Zv164dv/zyC3FxcbRt25bHjx8TExNDSkoKlpaW6o5XoISEBOrVqyd/7+TkRFBQkFozqVJSr49cec+1qakplStXJjg4WN2xlGrTpg27d+/mxo0btGjRAj8/PwCNPte5sxhoaWlhaWkpH++r6TN0tGvXjvnz5xMTEyP/+yM+Pp64uDhsbW3VHU94D0QxKwilzO7du1m5ciXVqlUjMzOTqlWrsnz5chYuXKjuaCrNnj0bbW1tbG1tuXTpEp6enjRt2pQ1a9agq6ur7nhKGRkZsWbNGhYtWqQw/Y+WlpZGj+UsiddHuXLl6NixI8+fP1e4HjT5XDdq1IgZM2bg5uaGp6cn165dw/m3DFsAACANSURBVNLSEjMzM/r376/ueEqZmpoycuRIVq1ahaOjo3zM6bZt2+TDUzTRtGnTMDU1xc7OjtOnT/PVV1/h7OzMsmXLNPJR2ELxE7MZCEIplPts9aioKGxsbKhTp466IxVKQEAAz58/x8zMjLp162JkZKTuSEWWO/ZXk+dALanXx6vu3LmDo6OjRl8n8fHxPHjwgKysLGrWrIm1tbW6I72WTCZDR0dH/uebN2/SvHlzdcd6rcDAQHx9fTE1NaVu3bpijtkPiChmBaEUSU5OxsfHR37n8bVr1/j111+xs7Njzpw5mJiYqDuiUleuXKFhw4YYGRkRFxfHjBkziIqKYsqUKTRs2FDd8V4rOjqa/fv3Q87TqiwsLNQdSamSen0A3L17l7///puQkBAsLS3p0aMHLVu2VHcslV68eIFMJpNP0/bnn39y4sQJunTpwueff67ueCrJZDIOHDjAxYsXiY+Pp0aNGnh6emJlZaXuaCpdu3aNevXqYWJiQkJCAjNmzCAsLIwffvgBFxcXdccT3gMxzEAQSpGSere6mM3g/Sip18eePXsYMWIE/v7+REdHk5aWRvfu3fn111/VHU2lkjqbgbu7O3v27OHKlSuUL1+eW7duUadOHY2e5krMZiCIYlYQSpGSere6mM3g/Sip18fvv//O8uXL2bZtGz4+Pvz0009cuXKFGTNmkJGRoe54SpXE2Qzi4uI4e/Ys27dvZ9myZdy9e5e9e/cyZcoUZs2ape54KonZDARRzApCKVJS71YXsxm8HyX1+pDJZKSlpZGdnU1KSgoZGRnUqlULe3t7+SwBmqYkzmYgk8nIzMwkOzubtLQ0kpOTAejatSuPHz9WdzyVxGwGgihmBaGUKYl3q4vZDN6fknh9eHp6MmrUKNq1a0eDBg2wsbEBwMrKSv4oYU1TEmczsLCwoHXr1rRt25YRI0YwdOhQAAwMDChXrpy646kkZjMQxA1gglAKldS71cVsBu9HSbw+bty4QVhYGJ06dcLAwEDdcQqtpM1mkJGRwcmTJ6lYsaJGP8JWGTGbwYdL9MwKQikTGBjIsmXL5DcmrVmzhqlTp5KUlKTuaCplZmaybt06Tp06RZs2bbh06RJff/019+7dU3e0Ap09e5aFCxcSEhLC5cuXGTFiBGfOnNHoQrYkXh/r16+nbNmy9OjRo0QVsufPn+ePP/7A3t4ePT09pk6dKr8pTFPp6enRvXt3hUI2Li6OrVu3qjVXQWQyGRs3buT48eN89NFH3Lhxg2+++YZbt26pO5rwnoieWUEoRbKzs3F0dMTNzY1Hjx6hp6dHp06duHTpEtWqVeO3335Td0SlfvzxRy5dukSdOnUICgrCyMiIZs2a8ccff+Dv74+2tub93n3z5k06d+7M559/zrVr13j58iXjx4/np59+YufOnbRu3VrdEfMpqdeHk5MTqampODg4MGbMGHr37q2xw09yeXt706FDB8aMGcOVK1dITExk3LhxzJgxg61bt9KuXTt1Ryy0x48f06tXL40dNztjxgxOnjxJo0aN8Pf3R1dXl7Zt27Js2TICAgI0/loRioEkCEKp4e/vL1WuXFmSJEm6fPmyVLVqVUmSJOnq1atSs2bN1JxOtSZNmkjXr1+XJEmSbGxspFu3bkmSJEl2dnZSYGCgmtMpt3jxYmnixImSJEnSxIkTpR9++EGSJEn6/vvvpUWLFqk5nXIl9fqoV6+e5OPjI126dEkaPHiwZG9vL02ePFl6/vy5uqOptGzZMmnChAmSJEnSjz/+KH377beSJEnS5MmTpXnz5qk5nXJ+fn5SvXr18r1q1qwpOTo6qjueSq1atZLOnz8vSZIkVatWTbp06ZIkSZJUo0YN6enTp2pOJ7wPZdRdTAuCUHyMjY1JSEggOTmZ0NBQkpKSyMrKIiYmRqPHj5mYmBAWFkZSUhIpKSlER0eTmZlJYmKixo6bzc0MEBERgb6+PgAxMTEae7d6Sb0+crVs2ZKWLVsSGRnJ+vXr6dChAydOnMDR0VHd0fJ59frI/RI0JiZGY8fNpqenk5qaysmTJxWW+/n5MWHCBLXlep3cc52amkpiYiLR0dHIZDLi4+NLxHUtvD0xzEAQShlPT0+OHDmCpaUlAwcOZP/+/YSGhjJv3jxGjBih7nhKHTx4kKFDh2Jqasr06dNZuHAhZcuWxcbGhiNHjqg7nlIxMTE0bdqUrKwsunfvzsOHD9HR0cHb2xtvb2/s7OzUHVGpknh9ODk5sXPnTpycnBSWy2QyZDIZenp6asumSlxcHE2bNiUjI4MuXbrg5+eHJEncuXOH27dvU6VKFXVHzMfX15cRI0Zw4cIFheWBgYFMmTKFbdu2qS1bQY4fP86gQYMwNTXlp59+YtGiRZQvX56yZcty+vRpdccT3gNRzApCKRQaGoqFhQV6enpcvHgRExMTnJ2d1R2rQPHx8WRnZ1OuXDn8/Pzw8/OjTZs2Glmo5MrKyiI8PBw7OzuSk5O5cOECDRs21Niet1wl7frw9fXF3t5e3vtdUuS9PlJSUjh//rzC1GJC8UlISCArK4vy5cvz4sULfH19ad26dYm6YVB4c2KYgSCUMmlpaRw/fpwrV64QHx+PjY2Nxs5rmZe3tzd79+4lNDSUsmXLauQNVHmdOXOGzMxMPv74Y8j5Cr9z587qjvVaJfH6qFGjhtLlXl5edOnShQoVKrz3TIURERHBxo0befLkCZIkUatWrXy9y5omODiYHTt20KRJE5ydnZk1axZGRkbyuVw1lY+PD7t37yY0NBQzMzNatGih8X+HCMVH824RFgThrfTs2ZO9e/fStm1bhgwZQrVq1RgyZAhbtmxRdzSV1q1bh6enJzVq1GDo0KG0bduWnTt30rdvX3VHU+ncuXOMHDmS2rVrs2DBAiIjI9UdqVBK4vWhyoIFCzT2ccdBQUG4uLiQmJhI79696du3L+np6TRt2hR/f391x1MqOzubjh078uTJE2bNmsWAAQOwtrbmyZMnTJkyRd3xVPrrr79wd3enatWqDBkyBDc3Nw4cOED37t3VHU14X9R9B5ogCMUnNDRUsrS0lGQymcLyf//9V2rTpo3acr1Oy5YtpXPnziksk8lkUoUKFaTIyEi15SrIzJkzpZkzZ0rBwcHS9OnTpcqVK0sDBgyQ/v33Xyk7O1vd8ZQqqddHr169lN5lr6+vL/n4+Kg7nlJr1qyRRo8enW/5+PHjpaVLl6ol0+sEBQVJNjY2kiRJ0vXr1yV7e3tJkiTp5s2bUuPGjdWcTrX27dtLJ0+eVFiWnZ0t2djYaOxsKELxEj2zglCKmJmZkZycTHBwsMLy+/fva+xXsQDly5fn/v37CssCAwNJT0/X6K82AWxtbZkzZw6+vr706dOHWbNmsWDBAnXHUqqkXh/Pnj1j8eLFHDhwQOGlyQ+nKF++PI8fPyYrK0u+LDs7m0ePHmnsuTY2NiYpKYmkpCRCQ0NJSUkhMzNT42e7UPb3R3BwMImJiZQtW1ZtuYT3R4yZFYRSxNjYmPnz59OoUSMaN26MmZkZL168IC4ujmPHjqk7nkrz5s2jW7dubNy4kSpVqhAfH4+3tze//vpribmBQ1dXl4EDBzJw4EBSU1PVHUepknp9WFhYUK1atXxjZ11dXTW2yOrTpw+7du3CwcGBRo0aoaWlhbe3N87OzgwaNEjd8ZQqV64cAwcOpFq1alSsWJGJEyfSvHlzwsPDmT17trrjqfTzzz/TtWtXvLy8cHBwIDExkTt37jBv3jyN/2VYKB5iNgNBKIViY2Px9vYmISEBGxsbmjRpgo6OjrpjFSgrK4tbt24RFhaGubk5zs7OlCtXTt2xVModI1upUiV1Rymyknh9lFS+vr48ffpUfgNYzZo11R3ptfLOdnHp0iWMjIxo1KiRumMVSCaTcevWLfkNYA0bNtTYHnCh+ImeWUEoZdLS0ti/f7/C3eoZGRkaf2fvpUuXFGYzCAgIYPDgwRo7NVfeIjY6Opr9+/cD0Lt3bywsLNSYrGAl9frI5e3tzblz56hcuTI9e/bUyEcd5woJCWH79u0KsxmMGjVKY+cgzmVjY4MkSRw4cICAgAA++ugjdUd6ratXryrMZuDn54eHh0eJ+WZHeDua+7eAIAhvpCTerV4SZzOYN28e8+bNIzY2FmdnZ86ePcvZs2dxdnYmPj5e3fFUKonXR7NmzXj06BGHDh3i448/JiAggHnz5uHp6anuaCqVxNkMnj59SpMmTSDn4Rpz584lICCAzp07c/DgQXXHU0nMZiCI2QwEoRQpqXerl+TZDNauXSv17dtXvrxPnz7Sxo0b1ZpNlZJ6fdSrV0/y8fGROnXqJO3YsUOSJElKSEiQTExMpPj4eHXHU6okzmbw6NEjydHRUYqPj5eMjIzk53b37t1Sp06d1B1PJTGbgSB6ZgWhFCmpd6uX5NkMEhISqFevnvy9k5MTQUFBas2kSkm9PnLlPdempqZUrlw537FoipI4m0GuxMRE7OzsMDMzAw2/phGzGQhizKwglC4l9W71kjibgZGREUuWLEFLS4vvvvtOvlxLS4vs7Gy1ZlOlpF4f5cqVo2PHjmRnZ6OrqytfrsnnuiTOZlCmTBliYmJo0qQJVatWlS/X5POMmM1AELMZCELpVBLvVi9psxmoEhgYCKDRc6CWxOtDmTt37uDo6IiRkZG6o6hUEmczeFVqaiqPHj2icePG6o6ikpjN4MMmemYFoZQpqXerl7TZDABiYmLYsmULT548oUyZMjRr1gx3d3fKlNHcv1pL6vWhzIMHD6hcubLGFrMldTaDV6WlpXH//n2NLmbFbAYfNjFmVhBKmZJ4t3pJnM0gLCyMVq1aERgYyJUrV6hUqRJLliyhffv2CuMkNU1JvD5UWbBgAWFhYeqOoVRJnM1AlYiICObNm6fuGCqJ2QwEze0+EAShyMLCwrh79y6hoaEK82/Wr1+f2bNnM2zYMLXmU2Xz5s1s2rSJNm3ayJe5u7tTqVIloqKiqFixolrzKXPgwAE++eQTfvvtN77++mvs7Oy4ffs2rVu35sCBA/Tr10/dEfMpqddH7969efbsWb7lvr6+aslTGEeOHKF79+4sXrxYvqxv374kJydz4MABvv32W7XmU8bf319pAZiRkaHR8/lu3ryZtWvX0qlTJ/kyd3d37OzsCAoKwt7eXq35hHdPFLOCUIrkvVs975hNTb9bPfdu5LzFrKbPZiCTyUhLS4Ocr2GTk5PR1dXl448/5vHjx+qOp1RJvT6ePXvG4sWL84037dq1q9oyvU7e2Qxyh53kzmbg4uKi7nhKpaenk5qaysmTJxWW+/n5MWHChP9r795jqq7/P4A/D1cPILcON0GaGBoyuQiizXkpFYSUeUkn2hgKaGrqxMJZLCtoY83EIWWYiVPBS9TKlqJkWFmIbmwupQGRIIIiHA6IAnI5r+8fPz3zxDkQ/ei8P5/T67G5fT5v/nn26QnnzYf35/0RlmsoT35+PD2Z5d0M/lt4MsuYGZHr0+py3M1g6dKlSE9PR1NTEy5duoS3334bACT94SnXfqhUKvj5+eG5557TG58+fTrs7e2F5RqMXHczGDNmzIDrbGNjo3uZghTxbgaMdzNgzAzJ8Wl1Oe5m0NDQgMuXLyMiIkJWf8qUYz/kyhx2M5AD3s3gv43vzDJmRtRqNfLy8nTnCoUCbW1t8PDwwLPPPis022Dy8vKgVqt153Z2drCxscGMGTOE5hrMrVu34OvrO+AhtUePHqG0tBRz5swRls0YufbjybX+q4qKCowePVqSv0hcvnwZly5d0p1bWlqiq6sLPj4+UCqVQrMZo9FoYGVlZfBuZlFRERYsWCAk11COHDmCe/fu6c6VSiWsra0xc+ZMobmY6Uh3RTdjbNh6e3tx9+5d3b87d+7gwoULCAsLw8WLF0XHM6q5uVkv9/Xr15GQkIA333xTdDSjCgoKsH//fr2x3t5eLF++XLIPJsm1H8nJyQPWIVdXVyM2NlayDyY9fPhQ71rfunULn332GYKDg9HR0SE6nkFtbW1YtmwZenp69Mbz8vLw0UcfCcs1FLVarXetb9y4geTkZGzevFl0NGYqot+nyxj79x09epQWLVokOsawtLa2kr29PXV2doqOYtDDhw/phRdeoMLCQiIi6uvro1deeYW2bt0qOtqwSb0fV69epUmTJtHt27eJiOjmzZvk7+9P58+fFx1t2JYtW0aff/656BhGpaenU1xcHGm1WiIiKigooKCgIGpubhYdbVju379Pjo6O1NbWJjoKMwFp/krLGBtREyZMQFNTk+gYw+Li4gJXV1e0tLSIjmKQnZ0dTp8+jffffx8lJSVISEiAq6sr9u7dKzrasEm9H+Hh4dizZw9iY2Nx/fp1LFiwAFlZWZg/f77oaMMm9WudlpYGV1dXpKSk4KuvvkJGRgbOnTsHlUolOtqwjB49Gh4eHnrLD5j54sksY2ZOq9Xi5MmTCAkJER1lWH766Sc8evQI3t7eoqMYpVKp8M033yA+Ph4WFhb49NNPRUcaNrn0IyoqCtu2bcPUqVPxwQcf4OWXXxYdadja2tpw9uxZyV/r7Oxs1NfXY8eOHTh37hw8PT1FRxq2srIyaDQaSa8FZyOHdzNgzIxUV1cjOjpab6ylpQWTJ0/GF198IdkPpaioKNTU1OjOOzs7oVAocPDgwQH/PVKRkZGBjIwM4PFODJaWllAoFMDju1tpaWmCEw4k136EhYXhxo0bwON1v9bW1rqvlZeXY9KkSQLTGfbJJ59gz549uvP+/n40NzcjKSlJsnfvq6qqEBQUBPzfEkRotVrdLhcTJ07EtWvXBCc0LDY2FhUVFbrzrq4uaLVa5ObmIjY2Vmg2Zho8mWXMjPT29qKhoUF3rlAo8Mwzz8DBwUForqE0NDSgt7dXd65UKuHh4SE001C0Wi20Wq3Br1lYWEjywSS59qO/vx/GPqqevJBAatrb26HRaHTnlpaW8PT01JuIS5GxVzErFArJbt/W2Nio99CaHH5+sJHFk1nGzFhvby+am5sxZswY0VGGpaGhAe7u7pL/4H9aW1sbIPGXJvyVXPvR2NgINzc3WfWjvb0dRCS7fty7d0/SS30MaWxshEqlgo2NjegozESkd+uAMTZiKisr9V7xKBdz587VW3YgB3v37pXsn4+NkWs/IiMjUVlZKTrGsOzbtw+7d+8WHWNYampqMHfuXNExhi0mJkZv2QEzfzyZZYwxxhhjsiXNxUaMsRHh7u6O9evXi44xbBs2bJDdVkCzZs0SHWHY5NqP9evXw93dXXSMYZk5c6beunA5UKlU2LBhg+gYw5acnCzZhxnZv4PXzDJmprRaLe7fvy+rNXp4vPbU0dFRkg9QGdPV1QU8fvBELrgfpsP9MB059oP9//H/bcbMjFqtxooVK2Bvbw8PDw+4u7sjOztbdKxBERF2794NlUoFDw8PODg4YPXq1XpPg0tRaWkpgoKC4OTkBCcnJ4SEhKCsrEx0rEHJsR8AkJWVBTc3N3h4eMDe3h4rV65Ea2ur6FiDKisrQ0hIiK4fQUFBKC0tFR1rUK2trVi5cqWuH25ubsjKyhIda0jZ2dlwd3fX9WPFihVQq9WiYzET4cksY2YmMTER1tbWqK+vR3d3N86ePYusrCwUFhaKjmZUfn4+cnNzUVxcjO7ubtTV1aGnp0fSf+JsbW1FTEwMUlJS0NHRgY6ODmzZsgXR0dG6nQ2kSI79OHHiBHJycnDu3Dl0d3ejvr4eALBu3TrR0Yxqa2tDdHQ0Nm/erOvH9u3bERMTI+lJ1pNr+qQf58+fR05ODk6cOCE6mlGFhYXIysrCmTNn0N3djdu3b8Pa2hqJiYmiozFTEfw6XcbYCOrs7CSlUjngfeQHDx6kpUuXCss1lEWLFtGRI0f0xtRqNSmVSnr06JGwXIPJz8+nmJiYAeMLFiyggoICIZmGItd+LFmyhA4dOqQ3ptFoSKlUUmdnp7Bcgzl58iRFRkYOGF+4cOGArktFd3c3KZVKam1t1Rs/fPgwxcbGCss1lOXLl9OBAwf0xtrb28nOzo4ePHggLBczHb4zy5gZqa+vh6enJ5ycnPTGJ0+ejLq6OmG5hlJbW4vAwEC9MVdXV6hUKjQ2NgrLNRhDmSHxa21O/XB2doanp6feSyCkRI79aGhogEqlgouLi964lDPDyLV2dHSEt7e37i4+M288mWXMjPT09MDOzm7AuJ2dHbq7u4Vk+jvkmJszm44cc3Nm05FrbjZyeGsuxsxMRUUFRo0apTdGRPD39xeW6e8IDg6GQqHQG3v6FZVSlJGRgczMTL2xvr4+pKWlCcs0FLn2IywsTHb9yMzMHPCihP7+fuzYsUNYpqFUVVUZ7Me4ceOEZfo7pk2bJrt+sJHDW3MxZmbk+G71/v5+GPtRZGUlzd+5tVottFqtwa9ZWFhIdmsg7odpEBH6+/sNfo37MbLk2A82sqT53cQY+8esrKwG/Ovu7kZBQYHoaEZZWloazP3tt9/i7t27ouMZZGFhYTBzTU0NSkpKRMczypz68d1330l2TbVCoTCY+ebNm/jhhx9ExzPKWD/y8/NFRzPKWD/Onj3La2b/I3gyy5iZKS0tRUJCAt577z309PQgKysLAQEBqK6uFh3NqNbWVmzbtg1JSUmoq6vDjz/+iPDwcOTk5MDW1lZ0PKP279+P1atX6ybda9asQXR0tNG7RFIgx35oNBps374dSUlJqK2txc8//4yIiAjs3btX0v3Izc3F6tWr8fXXX+PevXtITExEZGSk0Tv6UlBWVoaEhATs2rULPT09yM7ORkBAACorK0VHM6qtrQ1vvPEGEhMT8eeff+KXX37B9OnT8dFHHw1YMsHMEy8zYMyM9Pb2wsfHB1u3bkVpaSnq6+sxceJE7NmzB97e3qLjGZWUlAS1Wo3Q0FCcOnUKAPDxxx9j9uzZoqMZ9f3332Pt2rVIS0vDhx9+CAcHByQkJGDjxo2wsbERHc8gufbjtddew507dzB16lQcP34cAJCTk4MXX3xRdDSjLl68iFdffRXvvPOOrh/x8fHYtGmTZCfgfX19GDt2LDZt2oSrV6+irq4O/v7+yMrKgo+Pj+h4Rr3++uuora3FjBkzcOzYMRAR9u3bh7lz54qOxkxF9N5gjLGR88cff5Cfnx8REV25coWcnZ2pv79fdKwhhYSEUHl5ORER+fr60vnz50VHGlJmZialpqYSEVFqaiqtXbtWdKQhybUf4eHhdOXKFSIi8vPzozNnzoiONKTdu3dTSkoKERG99dZbFB8fLzrSkGpra8nX15eIiMrLy8nR0ZH6+vpExxrS9OnT6ddffyUiogkTJtDp06dFR2ImxssMGDMjXV1duve/K5VKeHt7S/ZBk6f9NffYsWNFRxqSOWTmfvx7zCGzl5eXZB/6epocrzUbYaJn04yxkfPbb7+RQqEgW1tbsrGx0R3b2trSlClTRMczauLEiWRjY0O2trakUCh0x7a2tlRZWSk6nkG7du0iS0tLsrW1JUtLS92xra0tpaeni45nkFz7ERgYaLQfN27cEB3PoPT0dF0nrKys9Prx7rvvio5n0O+//260H0FBQaLjGRUcHGy0H9euXRMdj5kAr5llzMzw1jqmwVtzmY4c+8Fbc5mOHPvBRpY0v5sYY//YX7en6ejoQFdXl2Q/iGBgax2tVouWlhZJfxAZ2pqrqakJCoVCshMVcD9MxtDWXE1NTcDj7kiVoX50dnbKqh9EhObmZkn3g40s6X5HMcb+kaNHjyIiIgIJCQk4deoU/Pz84OHhgeLiYtHRjKqsrMT8+fMxZ84cXLx4EQEBARg/fjy2bNkiOppRvb292LBhA6ZMmYKDBw9izZo1CAgIQEBAANrb20XHM0qO/aiurkZkZCRmz56NkpISTJo0CePHj8fGjRtFRzOqr68PmzZtwpQpU5Cbm4vk5GQEBATg+eefh0ajER3PqPz8fERERCA+Ph6FhYUYP348PD09UVRUJDqaUTU1NYiKisKsWbNQUlKCwMBA+Pv7Y926daKjMVMRvc6BMTZyHjx4QI6OjlRcXEypqank6elJGo2GDh8+TDExMaLjGbVkyRLauXMnFRUVkZOTEx0/fpyam5vJwcGBOjo6RMcz6OTJkxQeHk7l5eU0a9YsWrx4MRERLVy4kA4dOiQ6nkFy7cfy5cspNTWViouLycXFhY4ePUpqtZpGjx5NGo1GdDyDvvzySwoNDaXy8nKaM2cOxcbGEhHR4sWL6cCBA6LjGdTZ2UmOjo5UVFREO3fuJDc3N1Kr1XTs2DGKjIwUHc+ouLg4SklJoQsXLpCrqyvl5eWRRqMhJycnamlpER2PmQDfmWXMjNTX18PLywvz5s1DXFwcXFxc4OzsjNDQUNy+fVt0PKOqqqqwatUqREVFQaVSISgoCCqVCl5eXrhz547oeAZVVVUhKioKoaGhmDlzJiZPngwAkr7Wcu5HXFwc5s2bB3d3dwQFBcHV1RU+Pj6S7kdkZCRCQ0Mxe/ZsBAYGAhLvR0NDA9zc3BAVFYVVq1bB2dkZrq6uks6Mp/rx0ksvwcvLC0FBQXB2doavr69k3xDHRhZPZhkzI319fbp1YlZWVrq1eRYWFkYfRpECOebmzKYjx9yc2XTkmpuNHF4dzZiZqaiowKhRo0BE6O3t1R37+/uLjjao4OBgKBQK9PT06B1LWUZGBjIzM3VPgD85TktLEx3NKLn2IywsTNeJp4+lLDMzE7t379Y9bf/keMeOHaKjGVVVVWWwH+PGjRMdbVDTpk3TdeLpY/bfwFtzMWZmeGsd0+CtuUxHjv3grblMR479YCOLJ7OMMcYYY0y2pPmrIWOMMcYYY38DT2YZY4wxxphs8WSWMcYYY4zJFk9mGWOMMcaYbPFkljHGGGOMyRZPZhljjDHGmGzxZJYxxhhjjMkWT2YZY4wxxphs8WSWMcYYY4zJ1v8A/G2rzx7IK0gAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 700x550 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "# Reuse the effect-size table tl.effect_size already wrote; do not recompute.\n",
    "effects = wells.uns[\"mantispy\"][\"effect\"]\n",
    "top_hits = hits.drop(index=\"DMSO\").sort_values(\"distance\", ascending=False).head(8).index.tolist()\n",
    "\n",
    "grid = effects[effects[\"group\"].isin(top_hits)].pivot(index=\"feature\", columns=\"group\", values=\"effect\")[top_hits]\n",
    "top_features = grid.abs().max(axis=1).sort_values(ascending=False).head(18).index\n",
    "grid = grid.loc[top_features]\n",
    "\n",
    "_COMPARTMENTS = (\"Cytoplasm_\", \"Nuclei_\", \"Cells_\")\n",
    "_CHANNELS = (\"DNA\", \"ER\", \"RNA\", \"AGP\", \"Mito\", \"Brightfield\")\n",
    "\n",
    "\n",
    "def short(name):\n",
    "    \"\"\"Shorten a CellProfiler feature name to its family and channel, dropping the compartment prefix and trailing indices.\"\"\"\n",
    "    label = name\n",
    "    for prefix in _COMPARTMENTS:\n",
    "        if label.startswith(prefix):\n",
    "            label = label[len(prefix) :]\n",
    "            break\n",
    "    parts = label.split(\"_\")\n",
    "    channels = [p for p in parts if p in _CHANNELS]\n",
    "    if channels:\n",
    "        family = parts[: parts.index(channels[0])]\n",
    "        return \"_\".join(family) + \" \" + \" \".join(channels)\n",
    "    while parts and parts[-1].isdigit():\n",
    "        parts.pop()\n",
    "    return \"_\".join(parts)\n",
    "\n",
    "\n",
    "def tick(group):\n",
    "    \"\"\"Shorten a treatment label to its first two compound tokens plus the dose.\"\"\"\n",
    "    head, dose = group.split(\"@\")\n",
    "    return \"-\".join(head.split(\"-\")[:2]) + \"@\" + dose\n",
    "\n",
    "\n",
    "vmax = float(np.nanmax(np.abs(grid.to_numpy())))\n",
    "fig, ax = plt.subplots(figsize=(7, 5.5))\n",
    "im = ax.imshow(grid.to_numpy(), aspect=\"auto\", cmap=\"RdBu_r\", vmin=-vmax, vmax=vmax)\n",
    "ax.set_xticks(range(grid.shape[1]))\n",
    "ax.set_xticklabels([tick(g) for g in grid.columns], rotation=90, fontsize=7)\n",
    "ax.set_yticks(range(grid.shape[0]))\n",
    "ax.set_yticklabels([short(f) for f in grid.index], fontsize=7)\n",
    "fig.colorbar(im, ax=ax, label=\"signed effect size (Cohen's d)\")\n",
    "ax.set_title(\"Shared feature responses across the top hits\")\n",
    "fig.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4bf16abe",
   "metadata": {},
   "source": [
    "Columns that share a red-and-blue pattern move the same features in the same direction, which is the raw material for asking whether two compounds share a mechanism in [Mechanism of action](mechanism_of_action.ipynb)."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c009",
   "metadata": {},
   "source": [
    "Each point is one CellProfiler measurement. The named ones are where this compound's effect lives, and they are\n",
    "the handle for the next question, whether two compounds move the same features, which is\n",
    "[Mechanism of action](mechanism_of_action.ipynb).\n",
    "\n",
    "## One profile per perturbation\n",
    "\n",
    "Replicate wells of a treatment are combined into a signature with {func}`~mantispy.tl.consensus`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "c010",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-11-11T01:20:26.789672Z",
     "iopub.status.busy": "2026-11-11T01:20:26.789481Z",
     "iopub.status.idle": "2026-11-11T01:20:28.704034Z",
     "shell.execute_reply": "2026-11-11T01:20:28.703416Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3072 wells -> 38 signatures\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "AnnData object with n_obs × n_vars = 38 × 854\n",
       "    obs: 'Metadata_Perturbation', 'Metadata_ReplicateCount', 'Metadata_plate_map_name', 'Metadata_broad_sample', 'Metadata_mg_per_ml', 'Metadata_mmoles_per_liter', 'Metadata_solvent', 'Metadata_Site_Count', 'Metadata_Barcode', 'Metadata_Supplier', 'Metadata_Supplier_Catalog', 'Metadata_pert_type', 'Metadata_control_type', 'Metadata_Control_Type', 'Metadata_Control', 'Metadata_Compound', 'Metadata_Concentration', 'Metadata_ConcentrationUnit', 'Metadata_MOA', 'Metadata_Perturbation_Type', 'hits_distance', 'hits_qvalue'\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": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "signatures = mt.tl.consensus(wells, by=\"Metadata_Perturbation\", method=\"median\")\n",
    "print(f\"{wells.n_obs} wells -> {signatures.n_obs} signatures\")\n",
    "signatures"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "4deec3a9",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-11-11T01:20:28.705953Z",
     "iopub.status.busy": "2026-11-11T01:20:28.705781Z",
     "iopub.status.idle": "2026-11-11T01:20:50.750755Z",
     "shell.execute_reply": "2026-11-11T01:20:50.749894Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# How consistent are a treatment's replicate wells? percent_replicating takes the median pairwise\n",
    "# correlation within each perturbation's replicates and compares it to a non-replicate null.\n",
    "treated = wells[wells.obs[\"Metadata_Control_Type\"].to_numpy() != \"negcon\"].copy()\n",
    "mt.tl.percent_replicating(treated, groupby=\"Metadata_Perturbation\", metric=\"pearson\")\n",
    "agreement = treated.uns[\"mantispy\"][\"percent_replicating\"][\"median_replicate_correlation\"]\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(6, 4))\n",
    "ax.hist(agreement, bins=20, color=ACCENT)\n",
    "ax.set_xlabel(\"median replicate correlation\")\n",
    "ax.set_ylabel(\"treatments\")\n",
    "ax.set_title(\"How consistent are replicate wells\")\n",
    "fig.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "98ca2977",
   "metadata": {},
   "source": [
    "Most treatments' replicates agree closely, so a plain median loses little; the low-correlation tail is where replicates disagree and where `modz`, by down-weighting off-consensus wells, earns its keep."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c011",
   "metadata": {},
   "source": [
    "`median` is the default and is what pycytominer does by default. `modz` weights each replicate by how well it\n",
    "agrees with the others, which helps when replicates are few and noisy."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c012",
   "metadata": {},
   "source": [
    "## Is it a hit, or did the cells die?\n",
    "\n",
    "A compound that kills four fifths of the cells leaves a well median computed from a fifth as\n",
    "many cells, and that median moves away from the controls for reasons unrelated to the biology\n",
    "being screened. It looks like a strong hit.\n",
    "\n",
    "The cell count is also a baseline to beat. {cite:t}`Seal_2025` found that across three\n",
    "bioactivity benchmarks a model given only the cell count often matched one given the whole\n",
    "Cell Painting profile, and {cite:t}`Ewald_2026` found that profiles predicted LDH release no\n",
    "better than cell count, plate and well position.\n",
    "\n",
    "{func}`~mantispy.tl.cytotoxicity` reads the two together: each group's viability, its cells per field\n",
    "against the controls', beside its median distance from the controls."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "c013",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-11-11T01:20:50.754133Z",
     "iopub.status.busy": "2026-11-11T01:20:50.753917Z",
     "iopub.status.idle": "2026-11-11T01:20:50.771425Z",
     "shell.execute_reply": "2026-11-11T01:20:50.770509Z"
    }
   },
   "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>group</th>\n",
       "      <th>n_obs</th>\n",
       "      <th>viability</th>\n",
       "      <th>distance</th>\n",
       "      <th>suspect</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>BRD-K15179513-001-03-4@2.0</td>\n",
       "      <td>32</td>\n",
       "      <td>0.783</td>\n",
       "      <td>19.736</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>BRD-K15819326-001-01-6@2.0</td>\n",
       "      <td>32</td>\n",
       "      <td>0.840</td>\n",
       "      <td>17.581</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>BRD-K40109029-001-06-9@2.0</td>\n",
       "      <td>32</td>\n",
       "      <td>0.831</td>\n",
       "      <td>14.826</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>BRD-K95785537-001-26-9@2.0</td>\n",
       "      <td>32</td>\n",
       "      <td>0.763</td>\n",
       "      <td>22.592</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>BRD-U00086672-001-01-9@0.2</td>\n",
       "      <td>64</td>\n",
       "      <td>1.030</td>\n",
       "      <td>4.096</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>BRD-U00086672-001-01-9@0.4</td>\n",
       "      <td>64</td>\n",
       "      <td>1.012</td>\n",
       "      <td>4.696</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>BRD-U00086672-001-01-9@1.0</td>\n",
       "      <td>64</td>\n",
       "      <td>0.983</td>\n",
       "      <td>5.705</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>BRD-U00086673-001-01-9@0.2</td>\n",
       "      <td>64</td>\n",
       "      <td>0.998</td>\n",
       "      <td>4.861</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>BRD-U00086673-001-01-9@0.4</td>\n",
       "      <td>64</td>\n",
       "      <td>0.988</td>\n",
       "      <td>4.974</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>BRD-U00086673-001-01-9@1.0</td>\n",
       "      <td>64</td>\n",
       "      <td>0.972</td>\n",
       "      <td>5.723</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>BRD-U00086674-001-01-9@0.04</td>\n",
       "      <td>64</td>\n",
       "      <td>0.973</td>\n",
       "      <td>6.385</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>BRD-U00086674-001-01-9@0.2</td>\n",
       "      <td>64</td>\n",
       "      <td>0.676</td>\n",
       "      <td>19.050</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>BRD-U00086674-001-01-9@0.4</td>\n",
       "      <td>64</td>\n",
       "      <td>0.266</td>\n",
       "      <td>52.777</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>BRD-U00086675-001-01-9@0.04</td>\n",
       "      <td>64</td>\n",
       "      <td>0.935</td>\n",
       "      <td>6.823</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>BRD-U00086675-001-01-9@0.2</td>\n",
       "      <td>64</td>\n",
       "      <td>0.497</td>\n",
       "      <td>20.787</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>BRD-U00086675-001-01-9@0.4</td>\n",
       "      <td>64</td>\n",
       "      <td>0.331</td>\n",
       "      <td>38.652</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>BRD-U00086676-001-01-9@0.004</td>\n",
       "      <td>64</td>\n",
       "      <td>0.968</td>\n",
       "      <td>10.015</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>BRD-U00086676-001-01-9@0.01</td>\n",
       "      <td>64</td>\n",
       "      <td>0.973</td>\n",
       "      <td>11.331</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>BRD-U00086676-001-01-9@0.04</td>\n",
       "      <td>64</td>\n",
       "      <td>0.931</td>\n",
       "      <td>13.404</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>BRD-U00086677-001-01-9@0.004</td>\n",
       "      <td>64</td>\n",
       "      <td>1.013</td>\n",
       "      <td>4.158</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>BRD-U00086677-001-01-9@0.01</td>\n",
       "      <td>64</td>\n",
       "      <td>1.008</td>\n",
       "      <td>4.202</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>BRD-U00086677-001-01-9@0.04</td>\n",
       "      <td>64</td>\n",
       "      <td>0.997</td>\n",
       "      <td>4.649</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>BRD-U00086678-001-01-9@0.004</td>\n",
       "      <td>64</td>\n",
       "      <td>0.979</td>\n",
       "      <td>4.800</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>BRD-U00086678-001-01-9@0.01</td>\n",
       "      <td>64</td>\n",
       "      <td>0.996</td>\n",
       "      <td>4.668</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>BRD-U00086678-001-01-9@0.04</td>\n",
       "      <td>64</td>\n",
       "      <td>1.005</td>\n",
       "      <td>5.304</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>BRD-U00086679-001-01-9@0.2</td>\n",
       "      <td>64</td>\n",
       "      <td>0.990</td>\n",
       "      <td>4.519</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>BRD-U00086679-001-01-9@0.4</td>\n",
       "      <td>64</td>\n",
       "      <td>0.991</td>\n",
       "      <td>4.827</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>BRD-U00086679-001-01-9@1.0</td>\n",
       "      <td>64</td>\n",
       "      <td>1.044</td>\n",
       "      <td>4.960</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>BRD-U00086680-001-01-9@0.04</td>\n",
       "      <td>64</td>\n",
       "      <td>1.000</td>\n",
       "      <td>4.155</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>BRD-U00086680-001-01-9@0.2</td>\n",
       "      <td>64</td>\n",
       "      <td>1.000</td>\n",
       "      <td>6.368</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>BRD-U00086680-001-01-9@0.4</td>\n",
       "      <td>64</td>\n",
       "      <td>1.005</td>\n",
       "      <td>7.186</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>BRD-U00086681-001-01-9@0.04</td>\n",
       "      <td>64</td>\n",
       "      <td>1.005</td>\n",
       "      <td>4.192</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>BRD-U00086681-001-01-9@0.2</td>\n",
       "      <td>64</td>\n",
       "      <td>0.994</td>\n",
       "      <td>4.577</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>BRD-U00086681-001-01-9@0.4</td>\n",
       "      <td>64</td>\n",
       "      <td>1.049</td>\n",
       "      <td>4.858</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>BRD-U00086682-001-01-9@0.04</td>\n",
       "      <td>64</td>\n",
       "      <td>1.030</td>\n",
       "      <td>6.501</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>BRD-U00086682-001-01-9@0.2</td>\n",
       "      <td>64</td>\n",
       "      <td>0.983</td>\n",
       "      <td>8.313</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>BRD-U00086682-001-01-9@0.4</td>\n",
       "      <td>64</td>\n",
       "      <td>0.941</td>\n",
       "      <td>16.647</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>DMSO</td>\n",
       "      <td>832</td>\n",
       "      <td>1.000</td>\n",
       "      <td>3.915</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           group  n_obs  viability  distance  suspect\n",
       "0     BRD-K15179513-001-03-4@2.0     32      0.783    19.736    False\n",
       "1     BRD-K15819326-001-01-6@2.0     32      0.840    17.581    False\n",
       "2     BRD-K40109029-001-06-9@2.0     32      0.831    14.826    False\n",
       "3     BRD-K95785537-001-26-9@2.0     32      0.763    22.592    False\n",
       "4     BRD-U00086672-001-01-9@0.2     64      1.030     4.096    False\n",
       "5     BRD-U00086672-001-01-9@0.4     64      1.012     4.696    False\n",
       "6     BRD-U00086672-001-01-9@1.0     64      0.983     5.705    False\n",
       "7     BRD-U00086673-001-01-9@0.2     64      0.998     4.861    False\n",
       "8     BRD-U00086673-001-01-9@0.4     64      0.988     4.974    False\n",
       "9     BRD-U00086673-001-01-9@1.0     64      0.972     5.723    False\n",
       "10   BRD-U00086674-001-01-9@0.04     64      0.973     6.385    False\n",
       "11    BRD-U00086674-001-01-9@0.2     64      0.676    19.050     True\n",
       "12    BRD-U00086674-001-01-9@0.4     64      0.266    52.777     True\n",
       "13   BRD-U00086675-001-01-9@0.04     64      0.935     6.823    False\n",
       "14    BRD-U00086675-001-01-9@0.2     64      0.497    20.787     True\n",
       "15    BRD-U00086675-001-01-9@0.4     64      0.331    38.652     True\n",
       "16  BRD-U00086676-001-01-9@0.004     64      0.968    10.015    False\n",
       "17   BRD-U00086676-001-01-9@0.01     64      0.973    11.331    False\n",
       "18   BRD-U00086676-001-01-9@0.04     64      0.931    13.404    False\n",
       "19  BRD-U00086677-001-01-9@0.004     64      1.013     4.158    False\n",
       "20   BRD-U00086677-001-01-9@0.01     64      1.008     4.202    False\n",
       "21   BRD-U00086677-001-01-9@0.04     64      0.997     4.649    False\n",
       "22  BRD-U00086678-001-01-9@0.004     64      0.979     4.800    False\n",
       "23   BRD-U00086678-001-01-9@0.01     64      0.996     4.668    False\n",
       "24   BRD-U00086678-001-01-9@0.04     64      1.005     5.304    False\n",
       "25    BRD-U00086679-001-01-9@0.2     64      0.990     4.519    False\n",
       "26    BRD-U00086679-001-01-9@0.4     64      0.991     4.827    False\n",
       "27    BRD-U00086679-001-01-9@1.0     64      1.044     4.960    False\n",
       "28   BRD-U00086680-001-01-9@0.04     64      1.000     4.155    False\n",
       "29    BRD-U00086680-001-01-9@0.2     64      1.000     6.368    False\n",
       "30    BRD-U00086680-001-01-9@0.4     64      1.005     7.186    False\n",
       "31   BRD-U00086681-001-01-9@0.04     64      1.005     4.192    False\n",
       "32    BRD-U00086681-001-01-9@0.2     64      0.994     4.577    False\n",
       "33    BRD-U00086681-001-01-9@0.4     64      1.049     4.858    False\n",
       "34   BRD-U00086682-001-01-9@0.04     64      1.030     6.501    False\n",
       "35    BRD-U00086682-001-01-9@0.2     64      0.983     8.313    False\n",
       "36    BRD-U00086682-001-01-9@0.4     64      0.941    16.647    False\n",
       "37                          DMSO    832      1.000     3.915    False"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mt.tl.cytotoxicity(wells)\n",
    "wells.uns[\"mantispy\"][\"cytotoxicity\"].round(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "c014",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-11-11T01:20:50.774578Z",
     "iopub.status.busy": "2026-11-11T01:20:50.774405Z",
     "iopub.status.idle": "2026-11-11T01:20:50.843959Z",
     "shell.execute_reply": "2026-11-11T01:20:50.843100Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"height:525px; width:100%;\">            <script src=\"https://cdnjs.cloudflare.com/ajax/libs/mathjax/3.2.2/es5/tex-svg.min.js\"></script><script>if (window.MathJax && window.MathJax.Hub && window.MathJax.Hub.Config) {window.MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}</script>                <script>window.PlotlyConfig = {MathJaxConfig: 'local'};</script>\n",
       "        <script charset=\"utf-8\" src=\"https://cdn.plot.ly/plotly-4.1.1.min.js\" integrity=\"sha256-O24V1F27f8pb0glCkelh3cVHLNiHAJ5gCaVtq2aNch8=\" crossorigin=\"anonymous\"></script>                <div id=\"14c37d1a-55c6-49de-9cd6-1bcc8e115ebd\" class=\"plotly-graph-div\" style=\"height:100%; width:100%;\"></div>            <script>                window.PLOTLYENV=window.PLOTLYENV || {};                                if (document.getElementById(\"14c37d1a-55c6-49de-9cd6-1bcc8e115ebd\")) {                    Plotly.newPlot(                        \"14c37d1a-55c6-49de-9cd6-1bcc8e115ebd\",                        [{\"customdata\":[[\"BRD-K15179513-001-03-4@2.0\"],[\"BRD-K15819326-001-01-6@2.0\"],[\"BRD-K40109029-001-06-9@2.0\"],[\"BRD-K95785537-001-26-9@2.0\"],[\"BRD-U00086672-001-01-9@0.2\"],[\"BRD-U00086672-001-01-9@0.4\"],[\"BRD-U00086672-001-01-9@1.0\"],[\"BRD-U00086673-001-01-9@0.2\"],[\"BRD-U00086673-001-01-9@0.4\"],[\"BRD-U00086673-001-01-9@1.0\"],[\"BRD-U00086674-001-01-9@0.04\"],[\"BRD-U00086675-001-01-9@0.04\"],[\"BRD-U00086676-001-01-9@0.004\"],[\"BRD-U00086676-001-01-9@0.01\"],[\"BRD-U00086676-001-01-9@0.04\"],[\"BRD-U00086677-001-01-9@0.004\"],[\"BRD-U00086677-001-01-9@0.01\"],[\"BRD-U00086677-001-01-9@0.04\"],[\"BRD-U00086678-001-01-9@0.004\"],[\"BRD-U00086678-001-01-9@0.01\"],[\"BRD-U00086678-001-01-9@0.04\"],[\"BRD-U00086679-001-01-9@0.2\"],[\"BRD-U00086679-001-01-9@0.4\"],[\"BRD-U00086679-001-01-9@1.0\"],[\"BRD-U00086680-001-01-9@0.04\"],[\"BRD-U00086680-001-01-9@0.2\"],[\"BRD-U00086680-001-01-9@0.4\"],[\"BRD-U00086681-001-01-9@0.04\"],[\"BRD-U00086681-001-01-9@0.2\"],[\"BRD-U00086681-001-01-9@0.4\"],[\"BRD-U00086682-001-01-9@0.04\"],[\"BRD-U00086682-001-01-9@0.2\"],[\"BRD-U00086682-001-01-9@0.4\"],[\"DMSO\"]],\"hovertemplate\":\"status=ok\\u003cbr\\u003eviability, relative to the controls=%{x}\\u003cbr\\u003edistance from the controls=%{y}\\u003cbr\\u003egroup=%{customdata[0]}\\u003cextra\\u003e\\u003c\\u002fextra\\u003e\",\"legendgroup\":\"ok\",\"marker\":{\"color\":\"#636efa\",\"symbol\":\"circle\"},\"mode\":\"markers\",\"name\":\"ok\",\"orientation\":\"v\",\"showlegend\":true,\"x\":{\"dtype\":\"f8\",\"bdata\":\"+xIAK2wR6T9sONOLyuTqPzLc3uoglOo\\u002fRjRr4vpo6D\\u002fDQf3IXXvwP19UJtdyL\\u002fA\\u002fvHGqKWZ27z+oRIpDBe3vPwIxB0v8me8\\u002f1Dd7Kj8c7z8WXicxXSPvP0SCH2OT7O0\\u002fY+c7uGf97j+BFbaIvCXvP\\u002f3CwkH9yO0\\u002foHrS3ZA28D9xUD9y1x7wPzyN++ul6u8\\u002f4GncXy9V7z+6QKPeadzvP1q7dbz6EvA\\u002fxqMLX1av7z9ygUa907jvPxkYl1Ies\\u002fA\\u002fAAAAAAAA8D+WSHGooP3vPw8XPWgqFPA\\u002fDxc9aCoU8D+iq9kojdDvP5PmYhKoyfA\\u002feJ3EdI188D9QuhvSBnTvP6LWRToGHO4\\u002fAAAAAAAA8D8=\"},\"xaxis\":\"x\",\"y\":{\"dtype\":\"f8\",\"bdata\":\"uZGPWmi8M0BS3T5swpQxQLS6lQy5pi1AkKwCgn6XNkBkBbKwEWIQQLTrcAoNyRJApPK\\u002fTBjSFkCb87HgUXETQCpC8oQQ5RNARgxLvzLkFkDWAQqJwIkZQBQf3ntPShtAmxjFJnwHJED28KshhqkmQGR+SDoFzypABGyP5uqhEED61ZD3Mc8QQCyyEy4HmRJAvg2EznczE0Dy7w++hawSQHooycB3NxVAeRk9TNwTEkBAq+B+y04TQNPxaUfk1hNA6l5QJlGeEEBGa2e683gZQNYH2KCFvhxAgwc0+aTEEECiUtTwaU4SQLn71jpZbhNA91\\u002fTstIAGkD2uxD6J6AgQCCY3OC2pTBAGJvLEEFRD0A=\"},\"yaxis\":\"y\",\"type\":\"scatter\"},{\"customdata\":[[\"BRD-U00086674-001-01-9@0.2\"],[\"BRD-U00086674-001-01-9@0.4\"],[\"BRD-U00086675-001-01-9@0.2\"],[\"BRD-U00086675-001-01-9@0.4\"]],\"hovertemplate\":\"status=suspect\\u003cbr\\u003eviability, relative to the controls=%{x}\\u003cbr\\u003edistance from the controls=%{y}\\u003cbr\\u003egroup=%{customdata[0]}\\u003cextra\\u003e\\u003c\\u002fextra\\u003e\",\"legendgroup\":\"suspect\",\"marker\":{\"color\":\"#EF553B\",\"symbol\":\"circle\"},\"mode\":\"markers\",\"name\":\"suspect\",\"orientation\":\"v\",\"showlegend\":true,\"x\":{\"dtype\":\"f8\",\"bdata\":\"ukIsR0Ch5T9TdIvzxwPRPzf0StEtzt8\\u002fpN5p3F8v1T8=\"},\"xaxis\":\"x\",\"y\":{\"dtype\":\"f8\",\"bdata\":\"yhDx3d8MM0CsxyCHZWNKQLeuYuNtyTRAcdWBhYBTQ0A=\"},\"yaxis\":\"y\",\"type\":\"scatter\"}],                        {\"template\":{\"data\":{\"histogram2dcontour\":[{\"type\":\"histogram2dcontour\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]}],\"choropleth\":[{\"type\":\"choropleth\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}],\"histogram2d\":[{\"type\":\"histogram2d\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]}],\"heatmap\":[{\"type\":\"heatmap\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]}],\"contourcarpet\":[{\"type\":\"contourcarpet\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}],\"contour\":[{\"type\":\"contour\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]}],\"surface\":[{\"type\":\"surface\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]}],\"mesh3d\":[{\"type\":\"mesh3d\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}],\"scatter\":[{\"fillpattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2},\"type\":\"scatter\"}],\"parcoords\":[{\"type\":\"parcoords\",\"line\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"scatterpolargl\":[{\"type\":\"scatterpolargl\",\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"bar\":[{\"error_x\":{\"color\":\"#2a3f5f\"},\"error_y\":{\"color\":\"#2a3f5f\"},\"marker\":{\"line\":{\"color\":\"#E5ECF6\",\"width\":0.5},\"pattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2}},\"type\":\"bar\"}],\"scattergeo\":[{\"type\":\"scattergeo\",\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"scatterpolar\":[{\"type\":\"scatterpolar\",\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"histogram\":[{\"marker\":{\"pattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2}},\"type\":\"histogram\"}],\"scattergl\":[{\"type\":\"scattergl\",\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"scatter3d\":[{\"type\":\"scatter3d\",\"line\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"scattermap\":[{\"type\":\"scattermap\",\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"scatterternary\":[{\"type\":\"scatterternary\",\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"scattercarpet\":[{\"type\":\"scattercarpet\",\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"carpet\":[{\"aaxis\":{\"endlinecolor\":\"#2a3f5f\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"minorgridcolor\":\"white\",\"startlinecolor\":\"#2a3f5f\"},\"baxis\":{\"endlinecolor\":\"#2a3f5f\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"minorgridcolor\":\"white\",\"startlinecolor\":\"#2a3f5f\"},\"type\":\"carpet\"}],\"table\":[{\"cells\":{\"fill\":{\"color\":\"#EBF0F8\"},\"line\":{\"color\":\"white\"}},\"header\":{\"fill\":{\"color\":\"#C8D4E3\"},\"line\":{\"color\":\"white\"}},\"type\":\"table\"}],\"barpolar\":[{\"marker\":{\"line\":{\"color\":\"#E5ECF6\",\"width\":0.5},\"pattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2}},\"type\":\"barpolar\"}],\"pie\":[{\"automargin\":true,\"type\":\"pie\"}]},\"layout\":{\"autotypenumbers\":\"strict\",\"colorway\":[\"#636efa\",\"#EF553B\",\"#00cc96\",\"#ab63fa\",\"#FFA15A\",\"#19d3f3\",\"#FF6692\",\"#B6E880\",\"#FF97FF\",\"#FECB52\"],\"font\":{\"color\":\"#2a3f5f\"},\"hovermode\":\"closest\",\"hoverlabel\":{\"align\":\"left\"},\"paper_bgcolor\":\"white\",\"plot_bgcolor\":\"#E5ECF6\",\"polar\":{\"bgcolor\":\"#E5ECF6\",\"angularaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"},\"radialaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"}},\"ternary\":{\"bgcolor\":\"#E5ECF6\",\"aaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"},\"baxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"},\"caxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"}},\"coloraxis\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"colorscale\":{\"sequential\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"sequentialminus\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"diverging\":[[0,\"#8e0152\"],[0.1,\"#c51b7d\"],[0.2,\"#de77ae\"],[0.3,\"#f1b6da\"],[0.4,\"#fde0ef\"],[0.5,\"#f7f7f7\"],[0.6,\"#e6f5d0\"],[0.7,\"#b8e186\"],[0.8,\"#7fbc41\"],[0.9,\"#4d9221\"],[1,\"#276419\"]]},\"xaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\",\"title\":{\"standoff\":15},\"zerolinecolor\":\"white\",\"automargin\":true,\"zerolinewidth\":2},\"yaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\",\"title\":{\"standoff\":15},\"zerolinecolor\":\"white\",\"automargin\":true,\"zerolinewidth\":2},\"scene\":{\"xaxis\":{\"backgroundcolor\":\"#E5ECF6\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"white\",\"gridwidth\":2},\"yaxis\":{\"backgroundcolor\":\"#E5ECF6\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"white\",\"gridwidth\":2},\"zaxis\":{\"backgroundcolor\":\"#E5ECF6\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"white\",\"gridwidth\":2}},\"shapedefaults\":{\"line\":{\"color\":\"#2a3f5f\"}},\"annotationdefaults\":{\"arrowcolor\":\"#2a3f5f\",\"arrowhead\":0,\"arrowwidth\":1},\"geo\":{\"bgcolor\":\"white\",\"landcolor\":\"#E5ECF6\",\"subunitcolor\":\"white\",\"showland\":true,\"showlakes\":true,\"lakecolor\":\"white\"},\"title\":{\"x\":0.05}}},\"xaxis\":{\"anchor\":\"y\",\"domain\":[0.0,1.0],\"title\":{\"text\":\"viability, relative to the controls\"}},\"yaxis\":{\"anchor\":\"x\",\"domain\":[0.0,1.0],\"title\":{\"text\":\"distance from the controls\"}},\"legend\":{\"title\":{\"text\":\"status\"},\"tracegroupgap\":0},\"title\":{\"text\":\"cytotoxicity\"}},                        {\"responsive\": true}                    ).then(function(){\n",
       "                            \n",
       "var gd = document.getElementById('14c37d1a-55c6-49de-9cd6-1bcc8e115ebd');\n",
       "var x = new MutationObserver(function (mutations, observer) {{\n",
       "        var display = window.getComputedStyle(gd).display;\n",
       "        if (!display || display === 'none') {{\n",
       "            console.log([gd, 'removed!']);\n",
       "            Plotly.purge(gd);\n",
       "            observer.disconnect();\n",
       "        }}\n",
       "}});\n",
       "\n",
       "// Listen for the removal of the full notebook cells\n",
       "var notebookContainer = gd.closest('#notebook-container');\n",
       "if (notebookContainer) {{\n",
       "    x.observe(notebookContainer, {childList: true});\n",
       "}}\n",
       "\n",
       "// Listen for the clearing of the current output cell\n",
       "var outputEl = gd.closest('.output');\n",
       "if (outputEl) {{\n",
       "    x.observe(outputEl, {childList: true});\n",
       "}}\n",
       "\n",
       "                        })                };            </script>        </div>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mt.pl.cytotoxicity(wells);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "50ba81b9",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-11-11T01:20:50.847244Z",
     "iopub.status.busy": "2026-11-11T01:20:50.847050Z",
     "iopub.status.idle": "2026-11-11T01:20:51.184279Z",
     "shell.execute_reply": "2026-11-11T01:20:51.183434Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Join the cytotoxicity table to each treatment's compound and dose, then follow the flagged compounds down the ladder.\n",
    "cyto = wells.uns[\"mantispy\"][\"cytotoxicity\"]\n",
    "meta = wells.obs.groupby(\"Metadata_Perturbation\", observed=True)[\n",
    "    [\"Metadata_Compound\", \"Metadata_Concentration\"]\n",
    "].first()\n",
    "viability_table = cyto.join(meta, on=\"group\")\n",
    "flagged = viability_table.loc[viability_table[\"suspect\"], \"Metadata_Compound\"].unique()\n",
    "\n",
    "# These are the same two compounds mt.pl.cytotoxicity draws as crimson \"suspect\" points above.\n",
    "# Colour them in crimson too, the more-toxic one (lowest surviving fraction) in ACCENT and the\n",
    "# second in a darker crimson, so the reader connects the two figures.\n",
    "toxicity = viability_table[viability_table[\"Metadata_Compound\"].isin(flagged)]\n",
    "order = toxicity.groupby(\"Metadata_Compound\", observed=True)[\"viability\"].min().sort_values().index\n",
    "palette = dict(zip(order, [ACCENT, ACCENT_DARK], strict=False))\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(6, 4))\n",
    "for compound in flagged:\n",
    "    block = viability_table[viability_table[\"Metadata_Compound\"] == compound].sort_values(\n",
    "        \"Metadata_Concentration\", key=lambda s: s.astype(float)\n",
    "    )\n",
    "    dose = block[\"Metadata_Concentration\"].astype(float)\n",
    "    label = \"-\".join(str(compound).split(\"-\")[:2])\n",
    "    ax.plot(dose, block[\"viability\"], marker=\"o\", color=palette[compound], label=label)\n",
    "ax.axhline(1.0, color=\"0.7\", lw=1, ls=\"--\")\n",
    "ax.set_xscale(\"log\")\n",
    "ax.set_xlabel(\"dose (Metadata_Concentration)\")\n",
    "ax.set_ylabel(\"viability (cells per field vs controls)\")\n",
    "ax.set_title(\"Viability falls at the top doses\")\n",
    "ax.legend(frameon=False, fontsize=8)\n",
    "fig.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d749a378",
   "metadata": {},
   "source": [
    "Both flagged compounds keep a control-sized complement of cells at their lowest dose and lose a growing share of it at the two highest, which is the confound `tl.cytotoxicity` marks: a large distance at high dose is partly cells dying rather than phenotype."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c015",
   "metadata": {},
   "source": [
    "Two compounds are flagged, each at its two highest doses, which keep between 27% and 68% of the\n",
    "controls' cells. The strongest, `BRD-U00086674` at 0.4, keeps 27% of its cells and sits further from the controls\n",
    "than any other treatment. At its lowest dose, 0.04, its cells survive and it sits close to the controls.\n",
    "\n",
    "Both conditions have to hold. Cell loss alone is a phenotype (a compound that arrests growth is a real finding),\n",
    "and a large distance alone is a hit. Only the combination is suspect. `tl.cytotoxicity` is a diagnostic and does\n",
    "not correct anything."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "hits-count-md",
   "metadata": {},
   "source": [
    "## Fewer cells, a noisier profile\n",
    "\n",
    "Cell loss has a second, quieter effect. A well median built from few cells is noisier than one built from many, so\n",
    "a treatment with few cells can sit away from the controls through sampling noise alone, even where no cell\n",
    "changed. The first check is whether distance follows cell count across the treatments."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "hits-count-code",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-11-11T01:20:51.188672Z",
     "iopub.status.busy": "2026-11-11T01:20:51.188472Z",
     "iopub.status.idle": "2026-11-11T01:20:51.385838Z",
     "shell.execute_reply": "2026-11-11T01:20:51.385046Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "-0.79"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cell_count = wells.obs.groupby(\"Metadata_Perturbation\", observed=True)[\"Metadata_CellCount\"].median()\n",
    "by_count = hits.drop(index=\"DMSO\").join(cell_count)\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(6, 4))\n",
    "ax.scatter(\n",
    "    by_count[\"Metadata_CellCount\"],\n",
    "    by_count[\"distance\"],\n",
    "    c=[ACCENT if is_hit else NULL for is_hit in by_count[\"is_hit\"]],\n",
    "    edgecolors=\"black\",\n",
    "    linewidths=0.3,\n",
    ")\n",
    "ax.set_xlabel(\"median cells per well\")\n",
    "ax.set_ylabel(\"distance from controls\")\n",
    "ax.set_title(\"Hit distance against cell count\")\n",
    "plt.show()\n",
    "round(float(by_count[\"Metadata_CellCount\"].corr(by_count[\"distance\"], method=\"spearman\")), 2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "hits-floor-md",
   "metadata": {},
   "source": [
    "Distance and cell count move together. The rank correlation across treatments is -0.8, and the treatments with the\n",
    "fewest cells, a few hundred per well against about 1,700 in a control well, sit furthest out. They include the\n",
    "cytotoxic doses flagged above, so part of their distance is phenotype and part may be noise from small samples.\n",
    "\n",
    "`pki` ships only well profiles, so the next cell measures that noise on the single cells of another plate,\n",
    "{func}`~mantispy.ds.jump_cells`. It draws a given number of cells from the DMSO wells, takes their median profile, and\n",
    "records how far it sits from the median of all DMSO cells, as the median shift across features in units of the\n",
    "controls' standard deviation. No treatment is involved, so any distance is sampling noise."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "hits-floor-code",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-11-11T01:20:51.389965Z",
     "iopub.status.busy": "2026-11-11T01:20:51.389783Z",
     "iopub.status.idle": "2026-11-11T01:21:28.307580Z",
     "shell.execute_reply": "2026-11-11T01:21:28.306509Z"
    }
   },
   "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>cells</th>\n",
       "      <th>median drift</th>\n",
       "      <th>95th percentile</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10</td>\n",
       "      <td>0.154</td>\n",
       "      <td>0.222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>25</td>\n",
       "      <td>0.100</td>\n",
       "      <td>0.147</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>50</td>\n",
       "      <td>0.070</td>\n",
       "      <td>0.098</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>100</td>\n",
       "      <td>0.051</td>\n",
       "      <td>0.073</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>250</td>\n",
       "      <td>0.031</td>\n",
       "      <td>0.042</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>500</td>\n",
       "      <td>0.021</td>\n",
       "      <td>0.027</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1000</td>\n",
       "      <td>0.014</td>\n",
       "      <td>0.017</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   cells  median drift  95th percentile\n",
       "0     10         0.154            0.222\n",
       "1     25         0.100            0.147\n",
       "2     50         0.070            0.098\n",
       "3    100         0.051            0.073\n",
       "4    250         0.031            0.042\n",
       "5    500         0.021            0.027\n",
       "6   1000         0.014            0.017"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "cells = mt.ds.jump_cells()\n",
    "dmso = np.asarray(cells[cells.obs[\"Metadata_Control_Type\"].to_numpy() == \"negcon\"].X, dtype=float)\n",
    "spread = np.nanstd(dmso, axis=0)\n",
    "usable = np.isfinite(spread) & (spread > 0)\n",
    "z = (dmso[:, usable] - np.nanmean(dmso[:, usable], axis=0)) / spread[usable]\n",
    "centre = np.nanmedian(z, axis=0)\n",
    "\n",
    "rng = np.random.default_rng(0)\n",
    "floor = []\n",
    "for n_cells in (10, 25, 50, 100, 250, 500, 1000):\n",
    "    drift = [\n",
    "        np.nanmedian(np.abs(np.nanmedian(z[rng.choice(len(z), n_cells, replace=False)], axis=0) - centre))\n",
    "        for _ in range(50)\n",
    "    ]\n",
    "    floor.append(\n",
    "        {\n",
    "            \"cells\": n_cells,\n",
    "            \"median drift\": round(float(np.median(drift)), 3),\n",
    "            \"95th percentile\": round(float(np.quantile(drift, 0.95)), 3),\n",
    "        }\n",
    "    )\n",
    "pd.DataFrame(floor)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "hits-floor-read",
   "metadata": {},
   "source": [
    "The drift falls with the square root of the cell count. A median of 10 DMSO cells sits 0.154 control standard\n",
    "deviations from the centre in a typical feature, 100 cells 0.051 and 1,000 cells 0.014, about a factor of three for\n",
    "every tenfold change. A profile built from few cells therefore starts out some distance from the controls before any\n",
    "phenotype. Read a low-count hit against the noise floor at its own cell count, computed on your own data and in the\n",
    "same units as the hit distance, and treat a strong hit with few cells with care. Regressing the cell count out of the\n",
    "features, as in [Plate artifacts](../profiles/artifacts.ipynb), is another option."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c016",
   "metadata": {},
   "source": [
    "## Summary\n",
    "\n",
    "- A hit call says a treatment moved away from the controls. {func}`~mantispy.tl.effect_size` says which\n",
    "  features moved, and {func}`~mantispy.tl.consensus` gives one signature per treatment.\n",
    "- Cell loss and phenotype are confounded by construction. Flag the combination with\n",
    "  {func}`~mantispy.tl.cytotoxicity` instead of correcting it away.\n",
    "- A profile from few cells is noisier, so distance follows cell count. Compare a low-count hit with the noise floor\n",
    "  at the same count.\n",
    "\n",
    "Next: [Mechanism of action](mechanism_of_action.ipynb), which asks whether two treatments moved the same way."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8795159a",
   "metadata": {},
   "source": [
    "{doc}`../genetics/hit_calling_methods` runs the three `hit_calling` statistics on single cells and on guide profiles of\n",
    "one screen and compares their calls with genes whose answer is known."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.13.15"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}