From addfcfb287e3209e9d556576757d29e0f634d6be Mon Sep 17 00:00:00 2001
From: anon <tim.treis@helmholtz-munich.de>
Date: Sun, 20 Sep 2026 16:43:02 +0200
Subject: [PATCH 1/5] Read the dose response per feature and per direction

tl.dose_features gives every feature a benchmark dose, the lowest concentration
at which its median response reaches three times the controls' MAD. It is
defined where an EC50 is not: most compounds never plateau inside the tested
range, so a four-parameter fit has no EC50 to report for them.

tl.dose_direction reads the direction as well as the distance. A distance grows
both when a phenotype gets louder and when a different one takes over, and it
cannot tell them apart. In OASIS HepaRG, staurosporine's amplitude saturates at
0.41 uM while its direction keeps turning to 300 uM.

All three now merge doses within dose_tolerance of each other first. The OASIS
batches write the same nominal concentration to different precision, so a
ten-point ladder read as eighteen: n_doses was wrong and the hit call's
per-concentration product ran over twice as many, half as deep groups.
---
 docs/api.md                           |   4 +
 docs/tutorials/11_dose_response.ipynb | 640 +++++++++++++++++++++-----
 src/mantispy/tl/__init__.py           |   4 +-
 src/mantispy/tl/_dose.py              | 422 ++++++++++++++++-
 tests/test_tl_dose.py                 | 222 +++++++++
 5 files changed, 1162 insertions(+), 130 deletions(-)

diff --git a/docs/api.md b/docs/api.md
index df7661e..c8e9964 100644
--- a/docs/api.md
+++ b/docs/api.md
@@ -139,6 +139,8 @@ Writing to a layer suffixes the column, so `key_added="sphered"` flags `var["deg
     tl.edistance
     tl.transport
     tl.dose_response
+    tl.dose_features
+    tl.dose_direction
     tl.nn_moa_classify
     tl.moa_enrichment
     tl.feature_sets
@@ -170,6 +172,8 @@ Writing to a layer suffixes the column, so `key_added="sphered"` flags `var["deg
 | `tl.edistance` | `uns["mantispy"][key_added]`, or `..._pairwise` when `reference=None` |
 | `tl.transport` | `uns["mantispy"][key_added]` and `..._units`, `obs[key_added + "_agreement"]` |
 | `tl.dose_response` | `uns["mantispy"][key_added]` |
+| `tl.dose_features` | `uns["mantispy"][key_added]`, one row per compound and feature |
+| `tl.dose_direction` | `uns["mantispy"][key_added]`, one row per compound and concentration |
 | `tl.nn_moa_classify` | `obs[key_added + "_predicted"]`, `uns["mantispy"][key_added]` and `..._confusion` |
 | `tl.moa_enrichment` | `uns["mantispy"][key_added]` |
 | `tl.feature_sets` | returns a decoupler network; stores nothing |
diff --git a/docs/tutorials/11_dose_response.ipynb b/docs/tutorials/11_dose_response.ipynb
index b3dbd72..dc48a7a 100644
--- a/docs/tutorials/11_dose_response.ipynb
+++ b/docs/tutorials/11_dose_response.ipynb
@@ -25,10 +25,10 @@
    "id": "bac81e87",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-09-20T13:45:56.902056Z",
-     "iopub.status.busy": "2026-09-20T13:45:56.901899Z",
-     "iopub.status.idle": "2026-09-20T13:46:16.634404Z",
-     "shell.execute_reply": "2026-09-20T13:46:16.633862Z"
+     "iopub.execute_input": "2026-09-20T14:29:17.759042Z",
+     "iopub.status.busy": "2026-09-20T14:29:17.758936Z",
+     "iopub.status.idle": "2026-09-20T14:29:22.778039Z",
+     "shell.execute_reply": "2026-09-20T14:29:22.777510Z"
     }
    },
    "outputs": [
@@ -87,10 +87,10 @@
    "id": "58b04c93",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-09-20T13:46:16.636060Z",
-     "iopub.status.busy": "2026-09-20T13:46:16.635925Z",
-     "iopub.status.idle": "2026-09-20T13:46:17.376194Z",
-     "shell.execute_reply": "2026-09-20T13:46:17.375798Z"
+     "iopub.execute_input": "2026-09-20T14:29:22.779906Z",
+     "iopub.status.busy": "2026-09-20T14:29:22.779763Z",
+     "iopub.status.idle": "2026-09-20T14:29:23.016969Z",
+     "shell.execute_reply": "2026-09-20T14:29:23.016256Z"
     }
    },
    "outputs": [
@@ -127,10 +127,10 @@
    "id": "8e9ef049",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-09-20T13:46:17.377708Z",
-     "iopub.status.busy": "2026-09-20T13:46:17.377585Z",
-     "iopub.status.idle": "2026-09-20T13:46:31.840531Z",
-     "shell.execute_reply": "2026-09-20T13:46:31.839833Z"
+     "iopub.execute_input": "2026-09-20T14:29:23.018710Z",
+     "iopub.status.busy": "2026-09-20T14:29:23.018577Z",
+     "iopub.status.idle": "2026-09-20T14:29:35.372654Z",
+     "shell.execute_reply": "2026-09-20T14:29:35.371927Z"
     }
    },
    "outputs": [
@@ -138,7 +138,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/var/folders/qg/qgc908995g3fc8qtss2fsbhhxyxxj4/T/ipykernel_62964/992733786.py:7: UserWarning: This figure includes Axes that are not compatible with tight_layout, so results might be incorrect.\n",
+      "/var/folders/qg/qgc908995g3fc8qtss2fsbhhxyxxj4/T/ipykernel_23000/992733786.py:7: UserWarning: This figure includes Axes that are not compatible with tight_layout, so results might be incorrect.\n",
       "  plt.tight_layout()\n"
      ]
     },
@@ -184,10 +184,10 @@
    "id": "e0c13226",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-09-20T13:46:31.844956Z",
-     "iopub.status.busy": "2026-09-20T13:46:31.844789Z",
-     "iopub.status.idle": "2026-09-20T13:46:32.839777Z",
-     "shell.execute_reply": "2026-09-20T13:46:32.839158Z"
+     "iopub.execute_input": "2026-09-20T14:29:35.377524Z",
+     "iopub.status.busy": "2026-09-20T14:29:35.377360Z",
+     "iopub.status.idle": "2026-09-20T14:29:36.455010Z",
+     "shell.execute_reply": "2026-09-20T14:29:36.453872Z"
     }
    },
    "outputs": [
@@ -227,10 +227,10 @@
    "id": "a5fdbb04",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-09-20T13:46:32.841547Z",
-     "iopub.status.busy": "2026-09-20T13:46:32.841359Z",
-     "iopub.status.idle": "2026-09-20T13:46:35.567376Z",
-     "shell.execute_reply": "2026-09-20T13:46:35.566604Z"
+     "iopub.execute_input": "2026-09-20T14:29:36.457455Z",
+     "iopub.status.busy": "2026-09-20T14:29:36.457327Z",
+     "iopub.status.idle": "2026-09-20T14:29:39.399866Z",
+     "shell.execute_reply": "2026-09-20T14:29:39.399390Z"
     }
    },
    "outputs": [
@@ -279,90 +279,90 @@
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>Staurosporine</th>\n",
-       "      <td>18</td>\n",
-       "      <td>0.601492</td>\n",
-       "      <td>3.386089e-08</td>\n",
-       "      <td>0.240157</td>\n",
-       "      <td>0.131893</td>\n",
+       "      <td>10</td>\n",
+       "      <td>0.598735</td>\n",
+       "      <td>4.160661e-08</td>\n",
+       "      <td>2.392436e-01</td>\n",
+       "      <td>0.131876</td>\n",
        "      <td>False</td>\n",
-       "      <td>1.0</td>\n",
+       "      <td>1.000000</td>\n",
        "      <td>linear</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>CLIOQUINOL</th>\n",
-       "      <td>19</td>\n",
-       "      <td>0.612883</td>\n",
-       "      <td>2.123258e-08</td>\n",
-       "      <td>75.606859</td>\n",
-       "      <td>0.544927</td>\n",
+       "      <th>Actinomycin D</th>\n",
+       "      <td>10</td>\n",
+       "      <td>0.435906</td>\n",
+       "      <td>9.290771e-05</td>\n",
+       "      <td>1.002241e+02</td>\n",
+       "      <td>0.106392</td>\n",
        "      <td>False</td>\n",
-       "      <td>1.0</td>\n",
+       "      <td>0.999999</td>\n",
        "      <td>logistic</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>Amperozide</th>\n",
-       "      <td>18</td>\n",
-       "      <td>0.520615</td>\n",
-       "      <td>2.075043e-06</td>\n",
-       "      <td>45.530173</td>\n",
-       "      <td>0.530019</td>\n",
+       "      <th>Fluazinam</th>\n",
+       "      <td>10</td>\n",
+       "      <td>0.578382</td>\n",
+       "      <td>1.346795e-07</td>\n",
+       "      <td>1.972634e+01</td>\n",
+       "      <td>0.378007</td>\n",
        "      <td>False</td>\n",
-       "      <td>1.0</td>\n",
+       "      <td>0.999996</td>\n",
        "      <td>logistic</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>Cucurbitacin I</th>\n",
-       "      <td>18</td>\n",
-       "      <td>0.521179</td>\n",
-       "      <td>2.075043e-06</td>\n",
-       "      <td>35.250061</td>\n",
-       "      <td>0.171161</td>\n",
+       "      <th>FCCP</th>\n",
+       "      <td>10</td>\n",
+       "      <td>0.669596</td>\n",
+       "      <td>3.118984e-10</td>\n",
+       "      <td>2.197888e+08</td>\n",
+       "      <td>0.622103</td>\n",
        "      <td>False</td>\n",
-       "      <td>1.0</td>\n",
+       "      <td>0.999995</td>\n",
        "      <td>linear</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>Calcipotriol (hydrate)</th>\n",
-       "      <td>18</td>\n",
-       "      <td>0.560595</td>\n",
-       "      <td>2.976855e-07</td>\n",
-       "      <td>9.933690</td>\n",
-       "      <td>0.386427</td>\n",
+       "      <th>5,8,11-Eicosatriynoic acid</th>\n",
+       "      <td>10</td>\n",
+       "      <td>0.387472</td>\n",
+       "      <td>5.972081e-04</td>\n",
+       "      <td>2.016777e+07</td>\n",
+       "      <td>0.481170</td>\n",
        "      <td>False</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>logistic</td>\n",
+       "      <td>0.999994</td>\n",
+       "      <td>linear</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>5,6-benzoflavone</th>\n",
-       "      <td>18</td>\n",
-       "      <td>0.521155</td>\n",
-       "      <td>2.075043e-06</td>\n",
-       "      <td>39.100805</td>\n",
-       "      <td>0.614838</td>\n",
+       "      <td>10</td>\n",
+       "      <td>0.511855</td>\n",
+       "      <td>3.266517e-06</td>\n",
+       "      <td>3.829078e+01</td>\n",
+       "      <td>0.614839</td>\n",
        "      <td>False</td>\n",
-       "      <td>1.0</td>\n",
+       "      <td>0.999992</td>\n",
        "      <td>logistic</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>Fluazinam</th>\n",
-       "      <td>18</td>\n",
-       "      <td>0.574980</td>\n",
-       "      <td>1.703180e-07</td>\n",
-       "      <td>19.725783</td>\n",
-       "      <td>0.378008</td>\n",
+       "      <th>Cucurbitacin I</th>\n",
+       "      <td>10</td>\n",
+       "      <td>0.515813</td>\n",
+       "      <td>3.031965e-06</td>\n",
+       "      <td>3.522260e+01</td>\n",
+       "      <td>0.171159</td>\n",
        "      <td>False</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>logistic</td>\n",
+       "      <td>0.999991</td>\n",
+       "      <td>linear</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>Colistin Methanesulfonate (sodium salt)</th>\n",
-       "      <td>18</td>\n",
-       "      <td>0.437740</td>\n",
-       "      <td>8.568403e-05</td>\n",
-       "      <td>6930.981470</td>\n",
-       "      <td>0.434041</td>\n",
+       "      <th>CLIOQUINOL</th>\n",
+       "      <td>10</td>\n",
+       "      <td>0.604766</td>\n",
+       "      <td>3.966933e-08</td>\n",
+       "      <td>7.560326e+01</td>\n",
+       "      <td>0.544926</td>\n",
        "      <td>False</td>\n",
-       "      <td>1.0</td>\n",
+       "      <td>0.999986</td>\n",
        "      <td>logistic</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
@@ -370,38 +370,27 @@
        "</div>"
       ],
       "text/plain": [
-       "                                         n_doses  spearman        qvalue  \\\n",
-       "compound                                                                   \n",
-       "Staurosporine                                 18  0.601492  3.386089e-08   \n",
-       "CLIOQUINOL                                    19  0.612883  2.123258e-08   \n",
-       "Amperozide                                    18  0.520615  2.075043e-06   \n",
-       "Cucurbitacin I                                18  0.521179  2.075043e-06   \n",
-       "Calcipotriol (hydrate)                        18  0.560595  2.976855e-07   \n",
-       "5,6-benzoflavone                              18  0.521155  2.075043e-06   \n",
-       "Fluazinam                                     18  0.574980  1.703180e-07   \n",
-       "Colistin Methanesulfonate (sodium salt)       18  0.437740  8.568403e-05   \n",
-       "\n",
-       "                                                ec50  r_squared  fit_ok  \\\n",
-       "compound                                                                  \n",
-       "Staurosporine                               0.240157   0.131893   False   \n",
-       "CLIOQUINOL                                 75.606859   0.544927   False   \n",
-       "Amperozide                                 45.530173   0.530019   False   \n",
-       "Cucurbitacin I                             35.250061   0.171161   False   \n",
-       "Calcipotriol (hydrate)                      9.933690   0.386427   False   \n",
-       "5,6-benzoflavone                           39.100805   0.614838   False   \n",
-       "Fluazinam                                  19.725783   0.378008   False   \n",
-       "Colistin Methanesulfonate (sodium salt)  6930.981470   0.434041   False   \n",
+       "                            n_doses  spearman        qvalue          ec50  \\\n",
+       "compound                                                                    \n",
+       "Staurosporine                    10  0.598735  4.160661e-08  2.392436e-01   \n",
+       "Actinomycin D                    10  0.435906  9.290771e-05  1.002241e+02   \n",
+       "Fluazinam                        10  0.578382  1.346795e-07  1.972634e+01   \n",
+       "FCCP                             10  0.669596  3.118984e-10  2.197888e+08   \n",
+       "5,8,11-Eicosatriynoic acid       10  0.387472  5.972081e-04  2.016777e+07   \n",
+       "5,6-benzoflavone                 10  0.511855  3.266517e-06  3.829078e+01   \n",
+       "Cucurbitacin I                   10  0.515813  3.031965e-06  3.522260e+01   \n",
+       "CLIOQUINOL                       10  0.604766  3.966933e-08  7.560326e+01   \n",
        "\n",
-       "                                         hitcall hitcall_model  \n",
-       "compound                                                        \n",
-       "Staurosporine                                1.0        linear  \n",
-       "CLIOQUINOL                                   1.0      logistic  \n",
-       "Amperozide                                   1.0      logistic  \n",
-       "Cucurbitacin I                               1.0        linear  \n",
-       "Calcipotriol (hydrate)                       1.0      logistic  \n",
-       "5,6-benzoflavone                             1.0      logistic  \n",
-       "Fluazinam                                    1.0      logistic  \n",
-       "Colistin Methanesulfonate (sodium salt)      1.0      logistic  "
+       "                            r_squared  fit_ok   hitcall hitcall_model  \n",
+       "compound                                                               \n",
+       "Staurosporine                0.131876   False  1.000000        linear  \n",
+       "Actinomycin D                0.106392   False  0.999999      logistic  \n",
+       "Fluazinam                    0.378007   False  0.999996      logistic  \n",
+       "FCCP                         0.622103   False  0.999995        linear  \n",
+       "5,8,11-Eicosatriynoic acid   0.481170   False  0.999994        linear  \n",
+       "5,6-benzoflavone             0.614839   False  0.999992      logistic  \n",
+       "Cucurbitacin I               0.171159   False  0.999991        linear  \n",
+       "CLIOQUINOL                   0.544926   False  0.999986      logistic  "
       ]
      },
      "execution_count": 5,
@@ -445,16 +434,16 @@
    "id": "2f812053",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-09-20T13:46:35.569050Z",
-     "iopub.status.busy": "2026-09-20T13:46:35.568922Z",
-     "iopub.status.idle": "2026-09-20T13:46:36.176192Z",
-     "shell.execute_reply": "2026-09-20T13:46:36.175664Z"
+     "iopub.execute_input": "2026-09-20T14:29:39.402369Z",
+     "iopub.status.busy": "2026-09-20T14:29:39.402217Z",
+     "iopub.status.idle": "2026-09-20T14:29:39.922754Z",
+     "shell.execute_reply": "2026-09-20T14:29:39.922119Z"
     }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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",
+      "image/png": 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",
       "text/plain": [
        "<Figure size 1200x340 with 3 Axes>"
       ]
@@ -473,20 +462,394 @@
   },
   {
    "cell_type": "markdown",
-   "id": "0399509e",
+   "id": "4891d209",
    "metadata": {},
-   "source": "Staurosporine inhibits kinases broadly and climbs far past the band. Mupirocin inhibits a bacterial enzyme and\nhas no target in a human cell, so it never leaves it. Berberine barely leaves it either in this cell line, and\nthe last section says why that is worth checking rather than resolving here.\n\n## Distance is not the same as phenotype\n\nA compound that kills cells also moves away from the controls, and the curve cannot tell the two apart.\n{func}`~mantispy.tl.cytotoxicity` compares each group's cells per field against the controls', so the calls can\nbe read beside it."
+   "source": [
+    "Staurosporine inhibits kinases broadly and climbs far past the band. Mupirocin inhibits a bacterial enzyme and\n",
+    "has no target in a human cell, so it never leaves it. Berberine barely leaves it either in this cell line, and\n",
+    "the last section says why that is worth checking rather than resolving here."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a0b918e3",
+   "metadata": {},
+   "source": [
+    "## Which features move, and at what concentration\n",
+    "\n",
+    "The curve above reads one number per well, its distance from the controls. That number says a compound did\n",
+    "something; it cannot say what. {func}`~mantispy.tl.dose_features` asks the same question of every feature and\n",
+    "reports, for each one, the lowest concentration at which its median response reaches three times the spread the\n",
+    "controls show on it.\n",
+    "\n",
+    "That is a benchmark dose rather than an EC50, and the difference matters here. An EC50 needs a plateau, and the\n",
+    "section above showed most compounds never reach one inside the tested range. A benchmark dose only needs the\n",
+    "response to cross a line the controls set, so it is defined for a feature that is still climbing at the top\n",
+    "concentration, which is most of them. The ToxCast pipeline reads its `3 * bmad` the same way."
+   ]
   },
   {
    "cell_type": "code",
    "execution_count": 7,
+   "id": "dfa80cf8",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-09-20T14:29:39.924585Z",
+     "iopub.status.busy": "2026-09-20T14:29:39.924448Z",
+     "iopub.status.idle": "2026-09-20T14:29:40.020773Z",
+     "shell.execute_reply": "2026-09-20T14:29:40.020218Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "30 of 99 features respond to staurosporine\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>feature</th>\n",
+       "      <th>bmd</th>\n",
+       "      <th>max_z</th>\n",
+       "      <th>direction</th>\n",
+       "      <th>spearman</th>\n",
+       "      <th>qvalue</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>920</th>\n",
+       "      <td>Cytoplasm_AreaShape_Zernike_1_1</td>\n",
+       "      <td>0.336446</td>\n",
+       "      <td>3.445138</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.066715</td>\n",
+       "      <td>0.655931</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>926</th>\n",
+       "      <td>Cytoplasm_AreaShape_Zernike_6_2</td>\n",
+       "      <td>0.362584</td>\n",
+       "      <td>4.209308</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.313785</td>\n",
+       "      <td>0.012627</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>963</th>\n",
+       "      <td>Nuclei_Correlation_Overlap_AGP_DNA</td>\n",
+       "      <td>0.368756</td>\n",
+       "      <td>3.983743</td>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>-0.218047</td>\n",
+       "      <td>0.099024</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>923</th>\n",
+       "      <td>Cytoplasm_AreaShape_Zernike_5_1</td>\n",
+       "      <td>0.397345</td>\n",
+       "      <td>4.653107</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.313973</td>\n",
+       "      <td>0.012627</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>893</th>\n",
+       "      <td>Cells_AreaShape_Zernike_2_0</td>\n",
+       "      <td>0.452776</td>\n",
+       "      <td>3.777016</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.300500</td>\n",
+       "      <td>0.017164</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>971</th>\n",
+       "      <td>Nuclei_RadialDistribution_FracAtD_DNA_3of4</td>\n",
+       "      <td>0.467828</td>\n",
+       "      <td>5.298065</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.518075</td>\n",
+       "      <td>0.000017</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>925</th>\n",
+       "      <td>Cytoplasm_AreaShape_Zernike_6_0</td>\n",
+       "      <td>0.760535</td>\n",
+       "      <td>3.965238</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.296635</td>\n",
+       "      <td>0.018215</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>947</th>\n",
+       "      <td>Cytoplasm_RadialDistribution_MeanFrac_Brightfi...</td>\n",
+       "      <td>0.788329</td>\n",
+       "      <td>3.821740</td>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>-0.559913</td>\n",
+       "      <td>0.000002</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                               feature       bmd     max_z  \\\n",
+       "920                    Cytoplasm_AreaShape_Zernike_1_1  0.336446  3.445138   \n",
+       "926                    Cytoplasm_AreaShape_Zernike_6_2  0.362584  4.209308   \n",
+       "963                 Nuclei_Correlation_Overlap_AGP_DNA  0.368756  3.983743   \n",
+       "923                    Cytoplasm_AreaShape_Zernike_5_1  0.397345  4.653107   \n",
+       "893                        Cells_AreaShape_Zernike_2_0  0.452776  3.777016   \n",
+       "971         Nuclei_RadialDistribution_FracAtD_DNA_3of4  0.467828  5.298065   \n",
+       "925                    Cytoplasm_AreaShape_Zernike_6_0  0.760535  3.965238   \n",
+       "947  Cytoplasm_RadialDistribution_MeanFrac_Brightfi...  0.788329  3.821740   \n",
+       "\n",
+       "     direction  spearman    qvalue  \n",
+       "920        1.0  0.066715  0.655931  \n",
+       "926        1.0  0.313785  0.012627  \n",
+       "963       -1.0 -0.218047  0.099024  \n",
+       "923        1.0  0.313973  0.012627  \n",
+       "893        1.0  0.300500  0.017164  \n",
+       "971        1.0  0.518075  0.000017  \n",
+       "925        1.0  0.296635  0.018215  \n",
+       "947       -1.0 -0.559913  0.000002  "
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "mt.tl.dose_features(heparg)\n",
+    "features = heparg.uns[\"mantispy\"][\"dose_features\"]\n",
+    "# bmd is NaN for a feature that never crosses the cutoff, so dropna leaves the ones that responded.\n",
+    "active = features[features[\"compound\"] == \"Staurosporine\"].dropna(subset=[\"bmd\"])\n",
+    "print(f\"{len(active)} of {features['feature'].nunique()} features respond to staurosporine\")\n",
+    "active.sort_values(\"bmd\").head(8)[[\"feature\", \"bmd\", \"max_z\", \"direction\", \"spearman\", \"qvalue\"]]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "439eda32",
+   "metadata": {},
+   "source": [
+    "The first features to move do so at a few hundred nanomolar, and they are measurements of shape and of where\n",
+    "intensity sits rather than how much of it there is: Zernike moments of the cytoplasm, and how far out in the\n",
+    "nucleus the DNA stain lies.\n",
+    "\n",
+    "`spearman` and `bmd` disagree in the first row, which is worth understanding rather than averaging away. A\n",
+    "monotonic trend across the whole range and a threshold crossing are different questions. A feature that steps up\n",
+    "early and then flattens has a low Spearman and a low benchmark dose; a feature that rises steadily to a small\n",
+    "final value has a high Spearman and no benchmark dose at all. Read both, and treat a crossing with no trend\n",
+    "behind it as the weakest kind of evidence."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "5d2eaa26",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-09-20T14:29:40.022437Z",
+     "iopub.status.busy": "2026-09-20T14:29:40.022309Z",
+     "iopub.status.idle": "2026-09-20T14:29:40.173381Z",
+     "shell.execute_reply": "2026-09-20T14:29:40.172876Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 760x300 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "shown = [\"MUPIROCIN\", \"Amperozide\", \"CLIOQUINOL\", \"Fluazinam\", \"Staurosporine\"]\n",
+    "jitter = np.random.default_rng(0)\n",
+    "\n",
+    "fig, ax = plt.subplots(figsize=(7.6, 3.0))\n",
+    "for row, compound in enumerate(shown):\n",
+    "    block = features[features[\"compound\"] == compound].dropna(subset=[\"bmd\"])\n",
+    "    if block.empty:\n",
+    "        ax.text(0.25, row, \"no feature reaches the cutoff\", fontsize=8, va=\"center\", color=\"#999999\")\n",
+    "        continue\n",
+    "    ax.scatter(\n",
+    "        block[\"bmd\"],\n",
+    "        row + jitter.uniform(-0.18, 0.18, len(block)),\n",
+    "        s=4 * block[\"max_z\"],\n",
+    "        color=\"#3a7ca5\",\n",
+    "        alpha=0.7,\n",
+    "        edgecolor=\"none\",\n",
+    "    )\n",
+    "ax.set_xscale(\"log\")\n",
+    "ax.set_xlim(0.2, 500)\n",
+    "ax.set_ylim(-0.6, len(shown) - 0.4)\n",
+    "ax.set_xlabel(\"benchmark dose (uM)\")\n",
+    "ax.set_yticks(range(len(shown)), shown, fontsize=9)\n",
+    "ax.set_title(\"Where each feature first passes three control MADs, point size its largest response\", fontsize=10)\n",
+    "fig.tight_layout()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5ab2e9f2",
+   "metadata": {},
+   "source": [
+    "The potencies separate by three orders of magnitude: staurosporine moves a feature at 0.34 uM, fluazinam at 8.6,\n",
+    "clioquinol at 29, amperozide at 47. Mupirocin moves none of the 99, which is the calibration this plot needs — a\n",
+    "compound with no target in a human cell should not cross a control-derived cutoff anywhere.\n",
+    "\n",
+    "The feature names repeat across compounds, though. `Nuclei_Correlation_Correlation_Brightfield_DNA` and\n",
+    "`Cells_Correlation_Overlap_DNA_Mito` are among the first five of amperozide, clioquinol and fluazinam alike; for\n",
+    "staurosporine they are twenty-first and ninth. Either those two are what a stressed cell does whatever the\n",
+    "mechanism, or they are simply the features this plate measures most sensitively. Either way a list of responsive\n",
+    "features per compound is not yet a claim that the compounds do different things, and the count of them is not a\n",
+    "measure of how much happened."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4ae311b3",
+   "metadata": {},
+   "source": [
+    "## The same phenotype, or a different one?\n",
+    "\n",
+    "A distance from the controls grows for two reasons that matter differently: the same phenotype gets stronger, or a\n",
+    "different phenotype takes over. A distance cannot tell them apart, because it throws the direction away.\n",
+    "\n",
+    "{func}`~mantispy.tl.dose_direction` keeps it. Each concentration gets `amplitude`, how far its median profile sits\n",
+    "from the controls in MADs per feature; `split_half_cosine`, the cosine between the directions the two halves of\n",
+    "its replicate plates point in; and `cosine_to_top`, the cosine against the highest concentration's profile.\n",
+    "`amplitude_null` is what control wells spread over the same plates in the same numbers reach, so the first column\n",
+    "has a floor.\n",
+    "\n",
+    "The three only mean anything together. A concentration whose plate halves disagree has no direction at all,\n",
+    "however large its amplitude. One whose halves agree but which points away from the top concentration is a real\n",
+    "phenotype, and a different one."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "fb8077a7",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-09-20T14:29:40.174877Z",
+     "iopub.status.busy": "2026-09-20T14:29:40.174753Z",
+     "iopub.status.idle": "2026-09-20T14:29:41.958809Z",
+     "shell.execute_reply": "2026-09-20T14:29:41.958013Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x330 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "mt.tl.dose_direction(heparg)\n",
+    "direction = heparg.uns[\"mantispy\"][\"dose_direction\"]\n",
+    "\n",
+    "fig, axes = plt.subplots(1, 3, figsize=(12, 3.3), sharex=True)\n",
+    "for compound in shown[::-1]:\n",
+    "    block = direction[direction[\"compound\"] == compound].sort_values(\"dose\")\n",
+    "    for ax, column in zip(axes, [\"amplitude\", \"split_half_cosine\", \"cosine_to_top\"], strict=True):\n",
+    "        ax.plot(block[\"dose\"], block[column], marker=\"o\", ms=4, lw=1.4, label=compound)\n",
+    "\n",
+    "axes[0].axhline(direction[\"amplitude_null\"].median(), ls=\"--\", lw=1, color=\"0.4\", label=\"control wells\")\n",
+    "axes[1].axhline(0.5, ls=\"--\", lw=1, color=\"0.4\")\n",
+    "titles = [\"amplitude (MADs per feature)\", \"split-half cosine\", \"cosine to the top concentration\"]\n",
+    "for ax, title in zip(axes, titles, strict=True):\n",
+    "    ax.set_xscale(\"log\")\n",
+    "    ax.set_xlabel(\"concentration (uM)\")\n",
+    "    ax.set_title(title, fontsize=10)\n",
+    "axes[0].legend(fontsize=7, frameon=False)\n",
+    "fig.tight_layout()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "903c4e54",
+   "metadata": {},
+   "source": [
+    "Four compounds, four shapes.\n",
+    "\n",
+    "Amperozide and clioquinol are the straightforward case. Nothing happens below about 30 uM — amplitude at the\n",
+    "control floor, plate halves disagreeing — and then the amplitude climbs to 2.3 and 2.4 MADs per feature while\n",
+    "`cosine_to_top` climbs with it, to 0.86 and 0.93 at 100 uM. One phenotype, arriving late and getting louder. For\n",
+    "these two a single number per concentration loses nothing.\n",
+    "\n",
+    "Staurosporine is the case a single number hides. Its amplitude reaches 1.5 at 0.41 uM and then stops: 1.9 at\n",
+    "1.2 uM, 2.1 at 33, 2.0 at 300. Read as a distance it saturates two decades below the top of the range, and the\n",
+    "curve fitted earlier says the same. But the plate halves agree from 0.41 uM on, 0.76 and above, so every one of\n",
+    "those concentrations has a direction that reproduces — and `cosine_to_top` stays between 0.16 and 0.29. The cell\n",
+    "is doing something different at 0.41 uM than at 300 uM. The distance stopped moving at exactly the concentration\n",
+    "the phenotype started changing, which is why no summary built on distance alone can see this.\n",
+    "\n",
+    "Fluazinam never settles. Its halves agree from 11 uM up, yet `cosine_to_top` stays at or below 0.37 the whole way,\n",
+    "and its amplitude falls from 2.8 to 1.7 between 100 and 300 uM. Something else happens in the top well; this page\n",
+    "does not establish what, and the cytotoxicity below is the first thing to check.\n",
+    "\n",
+    "Mupirocin is the floor: amplitude between 0.40 and 0.81 against a null of 0.42, and no concentration whose halves\n",
+    "agree above 0.41. Its 33 uM well reaches nearly twice the null amplitude and still has no direction, which is the\n",
+    "reason to read the two columns together rather than putting a threshold on the first."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1c187e53",
+   "metadata": {},
+   "source": [
+    "## Distance is not the same as phenotype\n",
+    "\n",
+    "A compound that kills cells also moves away from the controls, and the curve cannot tell the two apart.\n",
+    "{func}`~mantispy.tl.cytotoxicity` compares each group's cells per field against the controls', so the calls can\n",
+    "be read beside it."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
    "id": "f7852d4b",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-09-20T13:46:36.177911Z",
-     "iopub.status.busy": "2026-09-20T13:46:36.177792Z",
-     "iopub.status.idle": "2026-09-20T13:46:36.292074Z",
-     "shell.execute_reply": "2026-09-20T13:46:36.291659Z"
+     "iopub.execute_input": "2026-09-20T14:29:41.960987Z",
+     "iopub.status.busy": "2026-09-20T14:29:41.960839Z",
+     "iopub.status.idle": "2026-09-20T14:29:42.074846Z",
+     "shell.execute_reply": "2026-09-20T14:29:42.074237Z"
     }
    },
    "outputs": [
@@ -525,14 +888,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 11,
    "id": "defe9869",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-09-20T13:46:36.293973Z",
-     "iopub.status.busy": "2026-09-20T13:46:36.293843Z",
-     "iopub.status.idle": "2026-09-20T13:46:39.372899Z",
-     "shell.execute_reply": "2026-09-20T13:46:39.372327Z"
+     "iopub.execute_input": "2026-09-20T14:29:42.076311Z",
+     "iopub.status.busy": "2026-09-20T14:29:42.076197Z",
+     "iopub.status.idle": "2026-09-20T14:29:45.136840Z",
+     "shell.execute_reply": "2026-09-20T14:29:45.135649Z"
     }
    },
    "outputs": [
@@ -540,7 +903,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/var/folders/qg/qgc908995g3fc8qtss2fsbhhxyxxj4/T/ipykernel_62964/2041206685.py:7: UserWarning: 280 of 289 groups have fewer than three rows, so their statistic is the median of one or two wells. Aggregate more replicates, or score activity with mt.tl.map(mode='activity'), which ranks replicate pairs and is built for screens with little replication.\n",
+      "/var/folders/qg/qgc908995g3fc8qtss2fsbhhxyxxj4/T/ipykernel_23000/2041206685.py:7: UserWarning: 280 of 289 groups have fewer than three rows, so their statistic is the median of one or two wells. Aggregate more replicates, or score activity with mt.tl.map(mode='activity'), which ranks replicate pairs and is built for screens with little replication.\n",
       "  mt.tl.hit_calling(u2os, groupby=\"Metadata_Perturbation\", use_rep=\"X_pca\", n_permutations=300)\n"
      ]
     },
@@ -553,7 +916,7 @@
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 500x460 with 1 Axes>"
       ]
@@ -600,7 +963,30 @@
    "cell_type": "markdown",
    "id": "41da084e",
    "metadata": {},
-   "source": "The corners carry the biology. Staurosporine and actinomycin D inhibit kinases and transcription, and are called\nin both lines. Mupirocin and rifampicin are antibacterials with no mammalian target, and are called in neither.\nCycloheximide and berberine sit off the diagonal, the first called in HepaRG and the second in U2OS, which is a\nstatement about two cell types rather than a failure of the fit.\n\n## Summary\n\n- Use `hits_row_distance` as the response, not `hits_distance`. The group column is one number repeated over its\n  wells.\n- `fit_ok` and `hitcall` answer different questions. Quote an EC50 only when `fit_ok` is `True`; use `hitcall` to\n  decide whether anything happened at all.\n- The cutoff comes from the controls, so a screen with few or drifting controls gives a hit call you should not\n  trust. {func}`~mantispy.pp.well_qc` and\n  [8. Trustworthy features and design](08_trustworthy_features_and_design.ipynb) come first.\n- Read every call beside a cell count.\n- A call that holds in a second cell line is worth more than a small q-value in one."
+   "source": [
+    "The corners carry the biology. Staurosporine and actinomycin D inhibit kinases and transcription, and are called\n",
+    "in both lines. Mupirocin and rifampicin are antibacterials with no mammalian target, and are called in neither.\n",
+    "Cycloheximide and berberine sit off the diagonal, the first called in HepaRG and the second in U2OS, which is a\n",
+    "statement about two cell types rather than a failure of the fit.\n",
+    "\n",
+    "## Summary\n",
+    "\n",
+    "- Use `hits_row_distance` as the response, not `hits_distance`. The group column is one number repeated over its\n",
+    "  wells.\n",
+    "- `fit_ok` and `hitcall` answer different questions. Quote an EC50 only when `fit_ok` is `True`; use `hitcall` to\n",
+    "  decide whether anything happened at all.\n",
+    "- The cutoff comes from the controls, so a screen with few or drifting controls gives a hit call you should not\n",
+    "  trust. {func}`~mantispy.pp.well_qc` and\n",
+    "  [8. Trustworthy features and design](08_trustworthy_features_and_design.ipynb) come first.\n",
+    "- Read every call beside a cell count.\n",
+    "- A call that holds in a second cell line is worth more than a small q-value in one.\n",
+    "- Read the features, not only the distance. {func}`~mantispy.tl.dose_features` gives each one a benchmark dose,\n",
+    "  which is defined for a feature still climbing at the top concentration, where an EC50 is not.\n",
+    "- A distance that plateaus does not mean the phenotype stopped changing.\n",
+    "  {func}`~mantispy.tl.dose_direction` separates one phenotype getting louder from a second one taking over.\n",
+    "- Amplitude alone overcalls. Mupirocin's 33 uM well sits at nearly twice the control floor with no direction that\n",
+    "  reproduces across plates.\n"
+   ]
   }
  ],
  "metadata": {
diff --git a/src/mantispy/tl/__init__.py b/src/mantispy/tl/__init__.py
index 4a820b5..dc65007 100644
--- a/src/mantispy/tl/__init__.py
+++ b/src/mantispy/tl/__init__.py
@@ -5,7 +5,7 @@
 from mantispy.tl._design import cytotoxicity, replicate_saturation
 from mantispy.tl._differential import differential_features
 from mantispy.tl._distance import edistance
-from mantispy.tl._dose import dose_response
+from mantispy.tl._dose import dose_direction, dose_features, dose_response
 from mantispy.tl._effect import effect_size, wasserstein_features
 from mantispy.tl._enrich import enrich, feature_sets, rank_features, rank_sets
 from mantispy.tl._heterogeneity import (
@@ -28,6 +28,8 @@
     "cluster_composition",
     "consensus",
     "cytotoxicity",
+    "dose_direction",
+    "dose_features",
     "dose_response",
     "differential_features",
     "edistance",
diff --git a/src/mantispy/tl/_dose.py b/src/mantispy/tl/_dose.py
index 6f0a2e8..a463320 100644
--- a/src/mantispy/tl/_dose.py
+++ b/src/mantispy/tl/_dose.py
@@ -1,4 +1,4 @@
-"""Dose response per compound: a monotonic trend test and a four-parameter logistic fit."""
+"""Dose response per compound: a monotonic trend test, a four-parameter logistic fit, and the response per feature."""
 
 from __future__ import annotations
 
@@ -10,6 +10,7 @@
 import pandas as pd
 from anndata import AnnData
 
+from mantispy._core._reduce import get_matrix
 from mantispy._core._stats import MAD_TO_SIGMA, benjamini_hochberg
 from mantispy._core.frames import as_frame
 from mantispy._core.logging import get_logger
@@ -92,6 +93,9 @@ def _fit_curve(
 #: Multiple of the baseline MAD that sets the cutoff when one is not given, as in the ToxCast pipeline's ``3 * bmad``.
 _CUTOFF_MADS = 3.0
 
+#: Doses whose base-10 logs differ by less than this are one dose. 0.15 is a factor of 1.4, half a two-fold step.
+_DOSE_TOLERANCE = 0.15
+
 
 #: Normalizing constant of the Student-t log-density at ``_ERROR_DF``. It does not depend on the data, and
 #: ``scipy.stats.t.logpdf`` spends ten times the arithmetic re-deriving it on every one of the thousand objective
@@ -307,6 +311,7 @@ def dose_response(
     min_r_squared: float = 0.8,
     reference: str | None = "negcon",
     cutoff: float | None = None,
+    dose_tolerance: float = _DOSE_TOLERANCE,
     key_added: str = "dose_response",
     copy: bool = False,
 ) -> AnnData | None:
@@ -342,6 +347,7 @@ def dose_response(
         min_r_squared: Coefficient of determination a fit needs before it is marked ok.
         reference: Rows that set the baseline the response is read against and the spread the cutoff comes from. ``None`` leaves the hit call out unless ``cutoff`` is given.
         cutoff: Response a curve has to clear to count as active. The default takes three times the controls' MAD, the ToxCast pipeline's ``3 * bmad``.
+        dose_tolerance: Doses whose base-10 logs differ by less than this are treated as one dose, and named by their median. The default of 0.15 is a factor of 1.4, half a two-fold step, and leaves any ladder coarser than two-fold alone. ``0`` reads the doses exactly as the plate map spells them.
         key_added: Name for the output table.
         copy: Return a modified copy instead of mutating in place.
 
@@ -356,6 +362,14 @@ def dose_response(
     Notes:
         Compounds with fewer than two usable doses are left out of the table.
 
+        Doses within ``dose_tolerance`` of each other are merged first. Batches laid out by different people write
+        the same nominal concentration to different precision, and without this a ten-point ladder read as an
+        eighteen-point one: ``n_doses`` was wrong, and the hit call's second term took its product over twice as
+        many, half as deep groups, which pushed it up.
+
+        :func:`dose_features` asks the same question of every feature rather than of one response column, and
+        :func:`dose_direction` asks whether the phenotype stays the same one as the concentration rises.
+
         An EC50 outside the tested doses is an extrapolation, usually from a curve that has not plateaued within the tested range, and its row has ``fit_ok=False``.
         Compare ``ec50`` against the dose range before quoting it.
     """
@@ -378,7 +392,7 @@ def dose_response(
         doses = block[dose_key].to_numpy(dtype=float)
         values = block[response].to_numpy(dtype=float)
         usable = np.isfinite(doses) & np.isfinite(values) & (doses > 0)
-        doses, values = doses[usable], values[usable]
+        doses, values = _bin_doses(doses[usable], dose_tolerance), values[usable]
         n_doses = len(np.unique(doses))
 
         if n_doses < 2:
@@ -422,3 +436,407 @@ def dose_response(
     adata.uns.setdefault("mantispy", {})[key_added] = table
     get_logger().info("dose_response fitted %d of %d compound(s)", int(table["fit_ok"].sum()), len(table))
     return None
+
+
+#: Column order of the per-feature table.
+_FEATURE_COLUMNS = (
+    "compound",
+    "feature",
+    "n_doses",
+    "bmd",
+    "max_z",
+    "direction",
+    "spearman",
+    "pvalue",
+)
+
+#: Column order of the per-dose table.
+_DIRECTION_COLUMNS = (
+    "compound",
+    "dose",
+    "n_wells",
+    "amplitude",
+    "amplitude_null",
+    "split_half_cosine",
+    "cosine_to_top",
+)
+
+
+def _feature_baseline_and_spread(adata: AnnData, reference: str | None) -> tuple[np.ndarray, np.ndarray]:
+    """Where the controls sit on each feature and how far they wobble, the ToxCast ``bmed`` and ``bmad`` per feature.
+
+    A feature whose controls show no spread has no scale to read a response against, and comes back with a spread
+    of NaN so the caller can drop it.
+    """
+    rows = reference_mask(adata, reference)
+    if int(rows.sum()) < 2:
+        raise ValueError(
+            f"a per-feature dose analysis needs at least two control rows to set the scale, got {int(rows.sum())}. "
+            "Mark the controls with mt.pp.annotate_controls, or name another reference."
+        )
+    control = get_matrix(adata, rows=np.flatnonzero(rows)).astype(np.float64)
+    baseline = np.nanmedian(control, axis=0)
+    spread = MAD_TO_SIGMA * np.nanmedian(np.abs(control - baseline), axis=0)
+    return baseline, np.where((spread > 0) & np.isfinite(spread), spread, np.nan)
+
+
+def _bin_doses(doses: np.ndarray, tolerance: float) -> np.ndarray:
+    """Merge doses that a plate map only spells differently, naming each group by its median.
+
+    The four OASIS batches write the same nominal concentration to different precision, 0.000762 uM in one and
+    0.001 in another, which splits every step of the ladder in two: the table then reports eighteen doses where
+    ten were dosed, and the hit call takes its per-concentration product over twice as many, half as deep groups.
+
+    Doses are merged while the group spans less than ``tolerance`` in log10, so a ladder coarser than two-fold is
+    left alone. A ladder finer than that needs a smaller tolerance.
+    """
+    unique = np.unique(doses)
+    if tolerance <= 0 or unique.size == 0:
+        return doses
+    log = np.log10(unique)
+    group = np.zeros(unique.size, dtype=np.int64)
+    start = log[0]
+    for index in range(1, unique.size):
+        opens = log[index] - start >= tolerance
+        group[index] = group[index - 1] + opens
+        start = log[index] if opens else start
+    centre = pd.Series(unique).groupby(group).transform("median").to_numpy()
+    return centre[np.searchsorted(unique, doses)]
+
+
+def _spearman_against(values: np.ndarray, block: np.ndarray) -> tuple[np.ndarray, np.ndarray]:
+    """Spearman of ``values`` against every column of ``block``, with the two-sided p-value scipy reports.
+
+    ``scipy.stats.spearmanr`` builds the whole square of correlations between the features to return one row of it,
+    which on a full feature set is thousands of times the work and the memory this needs.
+    """
+    from scipy.stats import t
+
+    from mantispy._core._corr import _rank_columns
+
+    ranks = _rank_columns(np.column_stack([values, block]))
+    centred = ranks - ranks.mean(axis=0)
+    norms = np.sqrt(np.sum(centred**2, axis=0))
+    with np.errstate(invalid="ignore", divide="ignore"):
+        rho = np.clip(np.sum(centred[:, :1] * centred, axis=0) / (norms[0] * norms), -1.0, 1.0)[1:]
+        statistic = rho * np.sqrt((ranks.shape[0] - 2) / (1.0 - rho**2))
+    pvalue = 2.0 * t.sf(np.abs(statistic), ranks.shape[0] - 2)
+    return rho, np.where(np.isfinite(rho), pvalue, np.nan)
+
+
+def _dose_medians(block: np.ndarray, doses: np.ndarray) -> tuple[np.ndarray, np.ndarray]:
+    """The sorted doses, and each one's median profile over its replicate rows."""
+    order = np.unique(doses)
+    return order, np.stack([np.nanmedian(block[doses == dose], axis=0) for dose in order])
+
+
+def _benchmark_dose(doses: np.ndarray, z: np.ndarray, cutoff: float) -> np.ndarray:
+    """Lowest dose at which each feature reaches ``cutoff``, interpolated between the doses either side of it.
+
+    A feature that never reaches the cutoff has no benchmark dose and comes back NaN. One already past it at the
+    lowest dose tested gets that dose, which is a bound rather than an estimate.
+    """
+    over = np.abs(z) >= cutoff
+    first = np.argmax(over, axis=0)
+    previous = np.maximum(first - 1, 0)
+    columns = np.arange(z.shape[1])
+    log_dose = np.log10(doses)
+    low, high = np.abs(z[previous, columns]), np.abs(z[first, columns])
+    with np.errstate(invalid="ignore", divide="ignore"):
+        span = high - low
+        fraction = np.where(span > 0, (cutoff - low) / span, 0.0)
+        crossing = log_dose[previous] + fraction * (log_dose[first] - log_dose[previous])
+    return np.where(over.any(axis=0), 10.0**crossing, np.nan)
+
+
+def _usable_doses(
+    obs: pd.DataFrame, compound_key: str, dose_key: str, control: np.ndarray, tolerance: float
+) -> dict[str, tuple[np.ndarray, np.ndarray]]:
+    """Each compound's treated rows and their binned doses, leaving out the controls and the zero doses."""
+    doses = obs[dose_key].to_numpy(dtype=float)
+    compounds = obs[compound_key].to_numpy()
+    usable = np.isfinite(doses) & (doses > 0) & ~control
+    blocks = {}
+    for compound in pd.unique(compounds[usable]):
+        rows = np.flatnonzero(usable & (compounds == compound))
+        blocks[compound] = (rows, _bin_doses(doses[rows], tolerance))
+    return blocks
+
+
+def _require_columns(obs: pd.DataFrame, *columns: str) -> None:
+    for column in columns:
+        if column not in obs:
+            raise KeyError(f"obs has no column {column!r}")
+
+
+@inplace_or_copy()
+def dose_features(
+    adata: AnnData,
+    compound_key: str = "Metadata_Compound",
+    dose_key: str = "Metadata_Concentration",
+    reference: str | None = "negcon",
+    cutoff_mads: float = _CUTOFF_MADS,
+    min_doses: int = 4,
+    dose_tolerance: float = _DOSE_TOLERANCE,
+    key_added: str = "dose_features",
+    copy: bool = False,
+) -> AnnData | None:
+    """Which features respond to a compound's concentration, and at what concentration each one starts.
+
+    :func:`dose_response` grades one number per well against the dose. This grades every feature, which answers a
+    different question: not whether the compound did something, but what it did and in what order.
+
+    Each feature gets a benchmark dose, the lowest concentration at which its median response reaches
+    ``cutoff_mads`` times the spread the controls show on it. This is the ToxCast pipeline's ``3 * bmad`` read as a
+    dose rather than as a yes or no, and it is defined for a feature that is still climbing at the top
+    concentration, which an EC50 is not. Sorting the table by ``bmd`` gives the order the phenotype arrives in.
+
+    Args:
+        adata: Object carrying a compound and a dose per row, at well resolution.
+        compound_key: ``obs`` column holding the compound identity.
+        dose_key: ``obs`` column holding the concentration. Rows with a zero or missing dose are left out, since the doses are read in log space.
+        reference: Rows that set each feature's baseline and spread. ``"negcon"`` reads ``Metadata_Control``.
+        cutoff_mads: Multiples of the controls' MAD a feature has to reach to get a benchmark dose. The ToxCast pipeline uses three.
+        min_doses: Distinct doses below which a compound is left out of the table.
+        dose_tolerance: Doses whose base-10 logs differ by less than this are treated as one dose. See :func:`dose_response`.
+        key_added: Name for the output table.
+        copy: Return a modified copy instead of mutating in place.
+
+    Returns:
+        ``None``, or the modified copy.
+        Writes ``uns["mantispy"][key_added]``, one row per compound and feature, with ``compound``, ``feature``,
+        ``n_doses``, ``bmd``, ``max_z``, ``direction``, ``spearman``, ``pvalue`` and ``qvalue``.
+        ``bmd`` is NaN for a feature that never reaches the cutoff, which is the table's activity call.
+        ``max_z`` is the largest response any concentration reached, in MADs of the controls, and ``direction`` is
+        its sign. ``qvalue`` corrects the Spearman p-values within each compound, which is the experiment.
+
+    Raises:
+        KeyError: ``obs`` has no ``compound_key`` or no ``dose_key``.
+        ValueError: Fewer than two reference rows, so there is no scale to read a response against.
+
+    Notes:
+        A benchmark dose at the lowest concentration tested is a bound, not an estimate: the feature was already
+        past the cutoff before the series began.
+
+        The Spearman correlation runs over the compound's wells, not over the per-dose medians, so it uses the
+        replicates. A feature with a missing value in any of those wells has no Spearman and no q-value; its
+        benchmark dose is still read, because the medians skip missing wells.
+
+        Features whose controls show no spread are left out entirely. Run :func:`mantispy.pp.normalize` and drop
+        ``var["degenerate_scale"]`` first and there will be none.
+    """
+    obs = as_frame(adata.obs)
+    _require_columns(obs, compound_key, dose_key)
+    baseline, spread = _feature_baseline_and_spread(adata, reference)
+    scaled = np.flatnonzero(np.isfinite(spread))
+    if scaled.size < adata.n_vars:
+        get_logger().info(
+            "dose_features: %d of %d features have no spread among the controls and are left out",
+            adata.n_vars - scaled.size,
+            adata.n_vars,
+        )
+
+    names = np.asarray(adata.var_names)[scaled]
+    control = reference_mask(adata, reference)
+    frames = []
+    for compound, (rows, doses) in _usable_doses(obs, compound_key, dose_key, control, dose_tolerance).items():
+        n_doses = len(np.unique(doses))
+        if n_doses < min_doses:
+            get_logger().debug("dose_features skipped %s: %d usable dose(s)", compound, n_doses)
+            continue
+        block = get_matrix(adata, rows=rows).astype(np.float64)[:, scaled]
+        order, medians = _dose_medians(block, doses)
+        with np.errstate(invalid="ignore"):
+            z = (medians - baseline[scaled]) / spread[scaled]
+        rho, pvalue = _spearman_against(np.log10(doses), block)
+        extreme = np.nanargmax(np.abs(np.nan_to_num(z, nan=0.0)), axis=0)
+        frame = pd.DataFrame(
+            {
+                "compound": str(compound),
+                "feature": names,
+                "n_doses": n_doses,
+                "bmd": _benchmark_dose(order, z, cutoff_mads),
+                "max_z": np.abs(z[extreme, np.arange(z.shape[1])]),
+                "direction": np.sign(z[extreme, np.arange(z.shape[1])]),
+                "spearman": rho,
+                "pvalue": pvalue,
+            }
+        )
+        frame["qvalue"] = benjamini_hochberg(frame["pvalue"].to_numpy())
+        frames.append(frame)
+
+    columns = [*_FEATURE_COLUMNS, "qvalue"]
+    table = pd.concat(frames, ignore_index=True) if frames else pd.DataFrame(columns=columns)
+    adata.uns.setdefault("mantispy", {})[key_added] = table[columns]
+    get_logger().info(
+        "dose_features: %d of %d compound-feature pairs reach %.1f MADs",
+        int(table["bmd"].notna().sum()) if len(table) else 0,
+        len(table),
+        cutoff_mads,
+    )
+    return None
+
+
+def _cosine(left: np.ndarray, right: np.ndarray) -> float:
+    """Cosine between two response directions, reading a missing or infinite feature as no movement."""
+    left = np.nan_to_num(left, nan=0.0, posinf=0.0, neginf=0.0)
+    right = np.nan_to_num(right, nan=0.0, posinf=0.0, neginf=0.0)
+    norms = float(np.linalg.norm(left) * np.linalg.norm(right))
+    return float(left @ right / norms) if norms > 0 else np.nan
+
+
+def _amplitude(profile: np.ndarray) -> float:
+    """How far a profile sits from the controls: its root-mean-square response, in MADs of the controls."""
+    measured = profile[np.isfinite(profile)]
+    return float(np.sqrt(np.mean(measured**2))) if measured.size else np.nan
+
+
+def _halves(labels: np.ndarray) -> tuple[np.ndarray, np.ndarray]:
+    """Two masks over the rows, splitting them by alternate levels of ``labels``, or by position as a fallback."""
+    left = np.isin(labels, pd.unique(labels)[::2])
+    if left.all() or not left.any():
+        left = np.arange(labels.size) % 2 == 0
+    return left, ~left
+
+
+#: Control groups drawn per layout to estimate the amplitude floor. The median of this many is stable to about 5%.
+_NULL_DRAWS = 25
+
+
+def _null_amplitude(control: np.ndarray, levels: np.ndarray, wanted: dict[object, int]) -> float:
+    """Amplitude reached by control wells laid out the way ``wanted`` counts them, level by level.
+
+    The layout has to match, or the floor flatters the concentration it is read against. Per-plate normalization
+    puts each plate's control median at zero, so a group drawn from one plate starts out at the centre while a
+    group spread over eight of them does not, and a group as large as a plate's control set reads a floor of zero.
+    """
+    pools = {level: np.flatnonzero(levels == level) for level in wanted}
+    if any(pools[level].size < count for level, count in wanted.items()):
+        return np.nan
+    generator = np.random.default_rng(0)
+    draws = [
+        _amplitude(
+            np.nanmedian(
+                control[
+                    np.concatenate(
+                        [generator.choice(pools[level], count, replace=False) for level, count in wanted.items()]
+                    )
+                ],
+                axis=0,
+            )
+        )
+        for _ in range(_NULL_DRAWS)
+    ]
+    return float(np.median(draws))
+
+
+@inplace_or_copy()
+def dose_direction(
+    adata: AnnData,
+    compound_key: str = "Metadata_Compound",
+    dose_key: str = "Metadata_Concentration",
+    reference: str | None = "negcon",
+    split_by: str | None = "Metadata_Plate",
+    min_doses: int = 4,
+    dose_tolerance: float = _DOSE_TOLERANCE,
+    key_added: str = "dose_direction",
+    copy: bool = False,
+) -> AnnData | None:
+    """Whether a compound's phenotype only grows with concentration, or turns into a different one.
+
+    A dose series is usually summarised by one number per concentration, how far the wells sit from the controls.
+    That number cannot tell a phenotype that is getting louder from a phenotype that is being replaced. This reads
+    the direction as well as the distance: each concentration gets an ``amplitude``, its ``cosine_to_top`` against
+    the highest concentration's profile, and a ``split_half_cosine`` that says whether its direction reproduces
+    across replicates at all.
+
+    The three read together. A concentration whose ``split_half_cosine`` is near zero has no direction to speak of,
+    only noise, however large its amplitude. One that reproduces but sits at a low ``cosine_to_top`` is a real
+    phenotype, and a different one from the top concentration's.
+
+    Args:
+        adata: Object carrying a compound and a dose per row, at well resolution.
+        compound_key: ``obs`` column holding the compound identity.
+        dose_key: ``obs`` column holding the concentration. Rows with a zero or missing dose are left out.
+        reference: Rows that set each feature's baseline and spread. ``"negcon"`` reads ``Metadata_Control``.
+        split_by: ``obs`` column whose levels split the replicates in two for ``split_half_cosine``, normally the plate. It also lays out the control groups ``amplitude_null`` is drawn from. ``None``, or a column with one level, splits the wells by position instead.
+        min_doses: Distinct doses below which a compound is left out of the table. One concentration says nothing about how a response changes with concentration.
+        dose_tolerance: Doses whose base-10 logs differ by less than this are treated as one dose. See :func:`dose_response`.
+        key_added: Name for the output table.
+        copy: Return a modified copy instead of mutating in place.
+
+    Returns:
+        ``None``, or the modified copy.
+        Writes ``uns["mantispy"][key_added]``, one row per compound and concentration, with ``compound``, ``dose``,
+        ``n_wells``, ``amplitude``, ``amplitude_null``, ``split_half_cosine`` and ``cosine_to_top``.
+        ``amplitude`` is the root-mean-square response over the features, in MADs of the controls, and
+        ``amplitude_null`` is what control wells spread over the same plates in the same numbers reach, so the two
+        are read against each other.
+
+    Raises:
+        KeyError: ``obs`` has no ``compound_key``, no ``dose_key``, or no ``split_by`` column.
+        ValueError: Fewer than two reference rows, so there is no scale to read a direction in.
+
+    Notes:
+        The cosines are taken over the features scaled by the controls' spread, so a feature the controls happen to
+        measure loosely does not set the direction on its own. Features whose controls show no spread are left out.
+
+        ``amplitude_null`` is the median over twenty-five draws, and is NaN when the controls cannot fill the
+        layout, for instance when a plate carries treated wells but no vehicle.
+
+        ``split_half_cosine`` needs at least two wells at a concentration, and reproduces the plate structure when
+        ``split_by`` names it: halving by plate answers whether the direction survives a different plate, which is
+        the harder and more useful question. With one well per concentration it is NaN, and the table then says
+        nothing about whether any single concentration's direction is real.
+    """
+    obs = as_frame(adata.obs)
+    _require_columns(obs, compound_key, dose_key, *([split_by] if split_by is not None else []))
+    baseline, spread = _feature_baseline_and_spread(adata, reference)
+    scaled = np.flatnonzero(np.isfinite(spread))
+    control = reference_mask(adata, reference)
+    with np.errstate(invalid="ignore"):
+        control_z = (
+            get_matrix(adata, rows=np.flatnonzero(control)).astype(np.float64)[:, scaled] - baseline[scaled]
+        ) / spread[scaled]
+
+    all_levels = obs[split_by].to_numpy() if split_by is not None else np.zeros(adata.n_obs)
+    control_levels = all_levels[control]
+    nulls: dict[tuple, float] = {}
+    records = []
+    for compound, (rows, doses) in _usable_doses(obs, compound_key, dose_key, control, dose_tolerance).items():
+        if len(np.unique(doses)) < min_doses:
+            get_logger().debug("dose_direction skipped %s: %d usable dose(s)", compound, len(np.unique(doses)))
+            continue
+        with np.errstate(invalid="ignore"):
+            block = (get_matrix(adata, rows=rows).astype(np.float64)[:, scaled] - baseline[scaled]) / spread[scaled]
+        order, medians = _dose_medians(block, doses)
+        levels = all_levels[rows]
+        for index, dose in enumerate(order):
+            at = doses == dose
+            left, right = _halves(levels[at])
+            reproduces = np.nan
+            if left.any() and right.any():
+                reproduces = _cosine(np.nanmedian(block[at][left], axis=0), np.nanmedian(block[at][right], axis=0))
+            # The floor is drawn with this concentration's own spread over plates, not from any group of that size.
+            layout = dict(zip(*np.unique(levels[at], return_counts=True), strict=True))
+            key = tuple(sorted(layout.items(), key=lambda item: str(item[0])))
+            records.append(
+                {
+                    "compound": str(compound),
+                    "dose": float(dose),
+                    "n_wells": int(at.sum()),
+                    "amplitude": _amplitude(medians[index]),
+                    "amplitude_null": nulls.setdefault(key, _null_amplitude(control_z, control_levels, layout)),
+                    "split_half_cosine": reproduces,
+                    "cosine_to_top": _cosine(medians[index], medians[-1]),
+                }
+            )
+
+    table = pd.DataFrame(records, columns=list(_DIRECTION_COLUMNS))
+    adata.uns.setdefault("mantispy", {})[key_added] = table
+    get_logger().info(
+        "dose_direction: %d compound(s) over %d concentration(s)",
+        table["compound"].nunique() if len(table) else 0,
+        len(table),
+    )
+    return None
diff --git a/tests/test_tl_dose.py b/tests/test_tl_dose.py
index 2e49efb..8bb1182 100644
--- a/tests/test_tl_dose.py
+++ b/tests/test_tl_dose.py
@@ -231,3 +231,225 @@ def test_a_curve_that_plateaus_is_read_by_the_logistic_and_one_still_rising_by_t
 
     assert _one_compound(conc, plateauing, 0.2)["hitcall_model"] == "logistic"
     assert _one_compound(conc, still_rising, 0.2)["hitcall_model"] == "linear"
+
+
+def _sigmoid(log_dose, height, ec50, hill=2.0):
+    from scipy.special import expit
+
+    return height * expit(hill * (log_dose - np.log10(ec50)))
+
+
+@pytest.fixture
+def phenotypes():
+    """A plate where one compound grows along one direction and another turns into a different phenotype.
+
+    `grows` moves three features together over its whole range. `turns` moves two features at low concentration
+    and drops them again while two others take over, so its top profile points somewhere else entirely. `quiet`
+    moves nothing. Every feature carries unit noise, so a response in the matrix reads directly in MADs.
+    """
+    rng = np.random.default_rng(0)
+    doses = np.geomspace(0.01, 100.0, 6)
+    wells_per_dose = 4
+    concentration = np.repeat(doses, wells_per_dose)
+    log_dose = np.log10(concentration)
+    n_features = 9
+
+    signals = {}
+    grows = np.zeros((concentration.size, n_features))
+    for column in (0, 1, 2):
+        grows[:, column] = _sigmoid(log_dose, 8.0, 1.0)
+    signals["grows"] = grows
+
+    turns = np.zeros((concentration.size, n_features))
+    early = _sigmoid(log_dose, 8.0, 0.05, hill=4.0) - _sigmoid(log_dose, 8.0, 5.0, hill=4.0)
+    late = _sigmoid(log_dose, 8.0, 20.0, hill=4.0)
+    turns[:, 3] = turns[:, 4] = early
+    turns[:, 5] = turns[:, 6] = late
+    signals["turns"] = turns
+    signals["quiet"] = np.zeros((concentration.size, n_features))
+
+    n_controls = 24
+    blocks, records = [], []
+    for compound, signal in signals.items():
+        blocks.append(signal + rng.normal(0.0, 1.0, signal.shape))
+        records.append(pd.DataFrame({"Metadata_Compound": compound, "Metadata_Concentration": concentration}))
+    blocks.append(rng.normal(0.0, 1.0, (n_controls, n_features)))
+    records.append(pd.DataFrame({"Metadata_Compound": "DMSO", "Metadata_Concentration": np.zeros(n_controls)}))
+
+    values = np.vstack(blocks)
+    # A feature the controls measure without any spread has no scale to read a response against.
+    values[:, 8] = 1.0
+    frame = pd.concat(records, ignore_index=True)
+    total = len(frame)
+    frame["Metadata_Plate"] = np.where(np.arange(total) % 2 == 0, "P1", "P2")
+    frame["Metadata_Well"] = [f"{chr(65 + index // 24)}{index % 24 + 1:02d}" for index in range(total)]
+    frame["Metadata_Control"] = frame["Metadata_Compound"] == "DMSO"
+    for column in range(n_features):
+        frame[f"Cells_AreaShape_f{column}"] = values[:, column]
+    return from_dataframe(frame, resolution="well")
+
+
+def test_dose_features_names_the_features_that_move_and_leaves_the_rest_NaN(phenotypes):
+    mt.tl.dose_features(phenotypes)
+    table = phenotypes.uns["mantispy"]["dose_features"]
+    grows = table[table["compound"] == "grows"].set_index("feature")
+
+    assert grows.loc[["Cells_AreaShape_f0", "Cells_AreaShape_f1", "Cells_AreaShape_f2"], "bmd"].notna().all()
+    assert grows.loc["Cells_AreaShape_f7", "bmd"] != grows.loc["Cells_AreaShape_f7", "bmd"], "a flat feature has no bmd"
+    assert table[table["compound"] == "quiet"]["bmd"].isna().all()
+
+
+def test_the_benchmark_dose_brackets_the_concentration_the_response_crosses_the_cutoff_at(phenotypes):
+    """f0 rises to 8 MADs with an EC50 of 1, so it passes 3 MADs between the third and fourth concentration."""
+    mt.tl.dose_features(phenotypes)
+    table = phenotypes.uns["mantispy"]["dose_features"].set_index(["compound", "feature"])
+    row = table.loc[("grows", "Cells_AreaShape_f0")]
+
+    assert 0.398 <= row["bmd"] <= 2.52
+    assert 6.0 <= row["max_z"] <= 11.0
+    assert row["direction"] == 1.0
+    assert row["spearman"] > 0.8
+    assert row["qvalue"] < 0.01
+    assert table.loc[("grows", "Cells_AreaShape_f7"), "qvalue"] > 0.05
+
+
+def test_a_feature_with_no_spread_among_the_controls_is_left_out(phenotypes):
+    mt.tl.dose_features(phenotypes)
+    table = phenotypes.uns["mantispy"]["dose_features"]
+    assert "Cells_AreaShape_f8" not in set(table["feature"])
+    assert len(set(table["feature"])) == phenotypes.n_vars - 1
+
+
+def test_dose_features_says_what_to_run_when_the_controls_are_unmarked(phenotypes):
+    del phenotypes.obs["Metadata_Control"]
+    with pytest.raises(KeyError, match="annotate_controls"):
+        mt.tl.dose_features(phenotypes)
+
+
+def test_dose_features_needs_more_than_one_control_row_to_set_a_scale(phenotypes):
+    """One control well gives a baseline but no spread, so every response would be infinitely many MADs."""
+    control = phenotypes.obs["Metadata_Control"].to_numpy(dtype=bool)
+    phenotypes.obs["Metadata_Control"] = control & (np.cumsum(control) == 1)
+    with pytest.raises(ValueError, match="at least two control rows"):
+        mt.tl.dose_features(phenotypes)
+
+
+def test_dose_direction_reads_a_growing_phenotype_as_one_direction(phenotypes):
+    mt.tl.dose_direction(phenotypes)
+    table = phenotypes.uns["mantispy"]["dose_direction"]
+    grows = table[table["compound"] == "grows"].sort_values("dose")
+
+    top = grows.tail(2)
+    assert (top["split_half_cosine"] > 0.5).all(), "the direction reproduces across plates"
+    assert (top["cosine_to_top"] > 0.8).all(), "and it is the direction the top concentration points in"
+    assert grows["amplitude"].iloc[-1] > 3 * grows["amplitude_null"].iloc[-1]
+
+
+def test_dose_direction_separates_a_turning_phenotype_from_noise(phenotypes):
+    """The point of the table: a reproducible direction that disagrees with the top concentration's."""
+    mt.tl.dose_direction(phenotypes)
+    table = phenotypes.uns["mantispy"]["dose_direction"]
+
+    def rotated(compound):
+        block = table[table["compound"] == compound]
+        return block[(block["split_half_cosine"] > 0.5) & (block["cosine_to_top"] < 0.4)]
+
+    assert len(rotated("turns")) >= 1, "the low-dose phenotype is real and points elsewhere"
+    assert len(rotated("grows")) == 0, "every reproducible concentration agrees with the top one"
+    quiet = table[table["compound"] == "quiet"]
+    assert (quiet["split_half_cosine"] < 0.5).all(), "noise has no direction to reproduce"
+
+
+def test_doses_written_to_different_precision_are_one_dose(dosed):
+    """Regression: batches spell the same nominal concentration differently, and the ladder read as twice as long."""
+    from mantispy.tl._dose import _bin_doses
+
+    spellings = np.array([0.000762, 0.001, 0.00229, 0.002, 15.0, 15.0])
+    assert len(np.unique(_bin_doses(spellings, 0.15))) == 3
+    assert len(np.unique(_bin_doses(spellings, 0.0))) == 5, "a tolerance of zero reads them as spelled"
+
+    ladder = 0.1 * 2.0 ** np.arange(11)
+    assert len(np.unique(_bin_doses(ladder, 0.15))) == 11, "a two-fold ladder is left alone"
+
+
+def test_dose_response_counts_the_doses_that_were_dosed(dosed):
+    doses = dosed.obs["Metadata_Concentration"].to_numpy(dtype=float)
+    # Half of each compound's wells carry the same five concentrations rounded as a second batch would round them.
+    respelled = (np.arange(dosed.n_obs) // 2) % 2 == 0
+    dosed.obs["Metadata_Concentration"] = np.where(respelled, doses, np.round(doses * 1.05, 3))
+    mt.tl.dose_response(dosed, min_doses=4)
+    assert (dosed.uns["mantispy"]["dose_response"]["n_doses"] == 5).all()
+
+    mt.tl.dose_response(dosed, min_doses=4, dose_tolerance=0.0, key_added="exact")
+    assert (dosed.uns["mantispy"]["exact"]["n_doses"] > 5).all()
+
+
+def test_the_vectorized_spearman_matches_scipy():
+    from scipy.stats import spearmanr
+
+    from mantispy.tl._dose import _spearman_against
+
+    rng = np.random.default_rng(1)
+    dose = np.repeat(np.log10(np.geomspace(0.1, 100, 5)), 4)
+    block = rng.normal(size=(dose.size, 6))
+    block[:, 0] += dose
+    rho, pvalue = _spearman_against(dose, block)
+    for column in range(block.shape[1]):
+        expected = spearmanr(dose, block[:, column])
+        assert rho[column] == pytest.approx(expected.statistic)
+        assert pvalue[column] == pytest.approx(expected.pvalue)
+
+
+def test_the_new_tables_survive_a_round_trip(phenotypes, tmp_path):
+    mt.tl.dose_features(phenotypes)
+    mt.tl.dose_direction(phenotypes)
+    path = tmp_path / "phenotypes.h5ad"
+    phenotypes.write_h5ad(path)
+
+    import anndata as ad
+
+    reloaded = ad.read_h5ad(path)
+    for key in ("dose_features", "dose_direction"):
+        pd.testing.assert_frame_equal(reloaded.uns["mantispy"][key], phenotypes.uns["mantispy"][key])
+
+
+def test_a_compound_with_one_concentration_is_left_out_of_the_direction_table(phenotypes):
+    """One concentration says nothing about how a response changes with concentration."""
+    single = phenotypes.obs["Metadata_Compound"] == "quiet"
+    phenotypes.obs.loc[single, "Metadata_Concentration"] = 1.0
+    mt.tl.dose_direction(phenotypes)
+    table = phenotypes.uns["mantispy"]["dose_direction"]
+    assert "quiet" not in set(table["compound"])
+    assert {"grows", "turns"} <= set(table["compound"])
+
+
+def test_the_amplitude_floor_follows_the_plates_the_concentration_sits_on():
+    """Controls drawn without regard to the layout give a floor that does not apply to the concentration.
+
+    Here each plate's controls sit to one side, as per-plate normalization leaves them. A concentration with a
+    well on each plate has those offsets cancel; one with both wells on a single plate does not, and its floor is
+    the offset. A floor taken from any group of the right size would report the same number for both.
+    """
+    rng = np.random.default_rng(0)
+    rows = []
+    for plate, offset in (("P1", 2.0), ("P2", -2.0)):
+        for _ in range(8):
+            rows.append({"plate": plate, "compound": "DMSO", "dose": 0.0, "offset": offset})
+        for dose in np.geomspace(0.1, 100.0, 4):
+            # "spread" doses one well per plate; "stacked" puts both of its wells on P1.
+            rows.append({"plate": plate, "compound": "spread", "dose": dose, "offset": 0.0})
+            rows.append({"plate": "P1", "compound": "stacked", "dose": dose, "offset": 0.0})
+    frame = pd.DataFrame(rows)
+    frame["Metadata_Plate"] = frame["plate"]
+    frame["Metadata_Compound"] = frame["compound"]
+    frame["Metadata_Concentration"] = frame["dose"]
+    frame["Metadata_Control"] = frame["compound"] == "DMSO"
+    frame["Metadata_Well"] = [f"{chr(65 + i // 24)}{i % 24 + 1:02d}" for i in range(len(frame))]
+    for feature in range(4):
+        noise = rng.normal(0.0, 0.1, len(frame))
+        frame[f"Cells_AreaShape_f{feature}"] = noise + (frame["offset"] if feature == 0 else 0.0)
+    adata = from_dataframe(frame.drop(columns=["plate", "compound", "dose", "offset"]), resolution="well")
+
+    mt.tl.dose_direction(adata)
+    table = adata.uns["mantispy"]["dose_direction"].set_index("compound")
+    assert table.loc["spread", "amplitude_null"].max() < table.loc["stacked", "amplitude_null"].min()

From b53453d44b44a951e6d57363f8cead11956ef003 Mon Sep 17 00:00:00 2001
From: anon <tim.treis@helmholtz-munich.de>
Date: Sun, 20 Sep 2026 17:11:30 +0200
Subject: [PATCH 2/5] Find the window of a dose series, and compare compounds
 across it

Most of a ten-point ladder is not worth reading: a screen covers three decades
because it does not know where the compound's window is, so the low end sits
below the effective range and the high end can be past the cells. Nothing so
far said which stretch was which.

dose_direction now labels each concentration: silent, responding while the step
from the concentration below beats the noise, saturated once it stops, and
cytotoxic once half the cells are gone. The label lands on obs, so the window is
an ordinary subset and dose_features and the rest work on it unchanged. The
window is a run rather than a scatter, because thresholding each concentration
on its own let one lucky replicate open amperozide's window at 3.7 uM instead
of 33.

pl.dose_direction draws that reading with the ladder banded by phase.

tl.dose_trajectory reads each compound's window onto a shared relative axis, so
compounds are compared on what they do in their own effective range rather than
at a concentration one of them has not reached yet. Actinomycin D's nearest
neighbour there is cycloheximide.
---
 docs/api.md                           |   5 +-
 docs/tutorials/11_dose_response.ipynb | 785 ++++++++++++++++++++++++--
 src/mantispy/pl/__init__.py           |   4 +-
 src/mantispy/pl/_hits.py              |  82 +++
 src/mantispy/tl/__init__.py           |   4 +-
 src/mantispy/tl/_dose.py              | 244 +++++++-
 tests/test_pl_hits.py                 |  51 ++
 tests/test_tl_dose.py                 |  66 +++
 8 files changed, 1182 insertions(+), 59 deletions(-)

diff --git a/docs/api.md b/docs/api.md
index c8e9964..cc1abf0 100644
--- a/docs/api.md
+++ b/docs/api.md
@@ -141,6 +141,7 @@ Writing to a layer suffixes the column, so `key_added="sphered"` flags `var["deg
     tl.dose_response
     tl.dose_features
     tl.dose_direction
+    tl.dose_trajectory
     tl.nn_moa_classify
     tl.moa_enrichment
     tl.feature_sets
@@ -173,7 +174,8 @@ Writing to a layer suffixes the column, so `key_added="sphered"` flags `var["deg
 | `tl.transport` | `uns["mantispy"][key_added]` and `..._units`, `obs[key_added + "_agreement"]` |
 | `tl.dose_response` | `uns["mantispy"][key_added]` |
 | `tl.dose_features` | `uns["mantispy"][key_added]`, one row per compound and feature |
-| `tl.dose_direction` | `uns["mantispy"][key_added]`, one row per compound and concentration |
+| `tl.dose_direction` | `uns["mantispy"][key_added]`, one row per compound and concentration, `obs[key_added + "_phase"]` |
+| `tl.dose_trajectory` | returns a new object: compounds by features-and-positions at `"perturbation"` resolution |
 | `tl.nn_moa_classify` | `obs[key_added + "_predicted"]`, `uns["mantispy"][key_added]` and `..._confusion` |
 | `tl.moa_enrichment` | `uns["mantispy"][key_added]` |
 | `tl.feature_sets` | returns a decoupler network; stores nothing |
@@ -269,6 +271,7 @@ pycytominer and similar tools expect.
     pl.effect_sizes
     pl.feature_volcano
     pl.dose_response
+    pl.dose_direction
     pl.moa_confusion
     pl.moa_enrichment
     pl.distance_heatmap
diff --git a/docs/tutorials/11_dose_response.ipynb b/docs/tutorials/11_dose_response.ipynb
index dc48a7a..a41ba0e 100644
--- a/docs/tutorials/11_dose_response.ipynb
+++ b/docs/tutorials/11_dose_response.ipynb
@@ -25,10 +25,10 @@
    "id": "bac81e87",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-09-20T14:29:17.759042Z",
-     "iopub.status.busy": "2026-09-20T14:29:17.758936Z",
-     "iopub.status.idle": "2026-09-20T14:29:22.778039Z",
-     "shell.execute_reply": "2026-09-20T14:29:22.777510Z"
+     "iopub.execute_input": "2026-09-20T15:03:31.360700Z",
+     "iopub.status.busy": "2026-09-20T15:03:31.360587Z",
+     "iopub.status.idle": "2026-09-20T15:03:38.790782Z",
+     "shell.execute_reply": "2026-09-20T15:03:38.789395Z"
     }
    },
    "outputs": [
@@ -87,10 +87,10 @@
    "id": "58b04c93",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-09-20T14:29:22.779906Z",
-     "iopub.status.busy": "2026-09-20T14:29:22.779763Z",
-     "iopub.status.idle": "2026-09-20T14:29:23.016969Z",
-     "shell.execute_reply": "2026-09-20T14:29:23.016256Z"
+     "iopub.execute_input": "2026-09-20T15:03:38.794164Z",
+     "iopub.status.busy": "2026-09-20T15:03:38.793781Z",
+     "iopub.status.idle": "2026-09-20T15:03:39.197108Z",
+     "shell.execute_reply": "2026-09-20T15:03:39.196310Z"
     }
    },
    "outputs": [
@@ -127,10 +127,10 @@
    "id": "8e9ef049",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-09-20T14:29:23.018710Z",
-     "iopub.status.busy": "2026-09-20T14:29:23.018577Z",
-     "iopub.status.idle": "2026-09-20T14:29:35.372654Z",
-     "shell.execute_reply": "2026-09-20T14:29:35.371927Z"
+     "iopub.execute_input": "2026-09-20T15:03:39.198960Z",
+     "iopub.status.busy": "2026-09-20T15:03:39.198822Z",
+     "iopub.status.idle": "2026-09-20T15:03:51.657114Z",
+     "shell.execute_reply": "2026-09-20T15:03:51.656351Z"
     }
    },
    "outputs": [
@@ -138,7 +138,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/var/folders/qg/qgc908995g3fc8qtss2fsbhhxyxxj4/T/ipykernel_23000/992733786.py:7: UserWarning: This figure includes Axes that are not compatible with tight_layout, so results might be incorrect.\n",
+      "/var/folders/qg/qgc908995g3fc8qtss2fsbhhxyxxj4/T/ipykernel_92610/992733786.py:7: UserWarning: This figure includes Axes that are not compatible with tight_layout, so results might be incorrect.\n",
       "  plt.tight_layout()\n"
      ]
     },
@@ -184,10 +184,10 @@
    "id": "e0c13226",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-09-20T14:29:35.377524Z",
-     "iopub.status.busy": "2026-09-20T14:29:35.377360Z",
-     "iopub.status.idle": "2026-09-20T14:29:36.455010Z",
-     "shell.execute_reply": "2026-09-20T14:29:36.453872Z"
+     "iopub.execute_input": "2026-09-20T15:03:51.660843Z",
+     "iopub.status.busy": "2026-09-20T15:03:51.660712Z",
+     "iopub.status.idle": "2026-09-20T15:03:52.671952Z",
+     "shell.execute_reply": "2026-09-20T15:03:52.671318Z"
     }
    },
    "outputs": [
@@ -227,10 +227,10 @@
    "id": "a5fdbb04",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-09-20T14:29:36.457455Z",
-     "iopub.status.busy": "2026-09-20T14:29:36.457327Z",
-     "iopub.status.idle": "2026-09-20T14:29:39.399866Z",
-     "shell.execute_reply": "2026-09-20T14:29:39.399390Z"
+     "iopub.execute_input": "2026-09-20T15:03:52.673748Z",
+     "iopub.status.busy": "2026-09-20T15:03:52.673588Z",
+     "iopub.status.idle": "2026-09-20T15:03:55.545778Z",
+     "shell.execute_reply": "2026-09-20T15:03:55.545212Z"
     }
    },
    "outputs": [
@@ -434,10 +434,10 @@
    "id": "2f812053",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-09-20T14:29:39.402369Z",
-     "iopub.status.busy": "2026-09-20T14:29:39.402217Z",
-     "iopub.status.idle": "2026-09-20T14:29:39.922754Z",
-     "shell.execute_reply": "2026-09-20T14:29:39.922119Z"
+     "iopub.execute_input": "2026-09-20T15:03:55.547842Z",
+     "iopub.status.busy": "2026-09-20T15:03:55.547672Z",
+     "iopub.status.idle": "2026-09-20T15:03:56.049295Z",
+     "shell.execute_reply": "2026-09-20T15:03:56.048565Z"
     }
    },
    "outputs": [
@@ -494,10 +494,10 @@
    "id": "dfa80cf8",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-09-20T14:29:39.924585Z",
-     "iopub.status.busy": "2026-09-20T14:29:39.924448Z",
-     "iopub.status.idle": "2026-09-20T14:29:40.020773Z",
-     "shell.execute_reply": "2026-09-20T14:29:40.020218Z"
+     "iopub.execute_input": "2026-09-20T15:03:56.051320Z",
+     "iopub.status.busy": "2026-09-20T15:03:56.051169Z",
+     "iopub.status.idle": "2026-09-20T15:03:56.146358Z",
+     "shell.execute_reply": "2026-09-20T15:03:56.145747Z"
     }
    },
    "outputs": [
@@ -672,10 +672,10 @@
    "id": "5d2eaa26",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-09-20T14:29:40.022437Z",
-     "iopub.status.busy": "2026-09-20T14:29:40.022309Z",
-     "iopub.status.idle": "2026-09-20T14:29:40.173381Z",
-     "shell.execute_reply": "2026-09-20T14:29:40.172876Z"
+     "iopub.execute_input": "2026-09-20T15:03:56.148687Z",
+     "iopub.status.busy": "2026-09-20T15:03:56.148450Z",
+     "iopub.status.idle": "2026-09-20T15:03:56.295719Z",
+     "shell.execute_reply": "2026-09-20T15:03:56.295155Z"
     }
    },
    "outputs": [
@@ -761,10 +761,10 @@
    "id": "fb8077a7",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-09-20T14:29:40.174877Z",
-     "iopub.status.busy": "2026-09-20T14:29:40.174753Z",
-     "iopub.status.idle": "2026-09-20T14:29:41.958809Z",
-     "shell.execute_reply": "2026-09-20T14:29:41.958013Z"
+     "iopub.execute_input": "2026-09-20T15:03:56.297776Z",
+     "iopub.status.busy": "2026-09-20T15:03:56.297633Z",
+     "iopub.status.idle": "2026-09-20T15:03:58.023420Z",
+     "shell.execute_reply": "2026-09-20T15:03:58.022778Z"
     }
    },
    "outputs": [
@@ -828,6 +828,689 @@
     "reason to read the two columns together rather than putting a threshold on the first."
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "d90ae042",
+   "metadata": {},
+   "source": [
+    "## Where on the ladder is anything happening?\n",
+    "\n",
+    "Every column so far describes a concentration. None of them says which stretch of the ladder is worth reading,\n",
+    "and most of a ten-point series is usually not: the low end is below the compound's effective range and the high\n",
+    "end can be past the point where the cells are alive to have a phenotype.\n",
+    "\n",
+    "`dose_direction` labels each concentration with a `phase`. `silent` is a concentration with no reproducible\n",
+    "response. `responding` is one that is still moving: the step from the concentration below it, `step_amplitude`,\n",
+    "beats the noise two independent groups of wells carry. `saturated` is one that reproduces but has stopped\n",
+    "changing. `cytotoxic` is one that has lost more than half its cells, where the profile is the morphology of\n",
+    "dying cells whatever else is true of it, and which the US EPA's phenotypic pipeline drops before fitting.\n",
+    "\n",
+    "The window is a run, not a scatter. `split_half_cosine` over eight wells is itself noisy, and labelling each\n",
+    "concentration on its own let one lucky concentration open amperozide's window at 3.7 uM instead of 33. A\n",
+    "response that has started does not stop, so the window is the run of concentrations reaching the highest one\n",
+    "that is not cytotoxic."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "369d9bf4",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-09-20T15:03:58.025621Z",
+     "iopub.status.busy": "2026-09-20T15:03:58.025450Z",
+     "iopub.status.idle": "2026-09-20T15:03:59.343685Z",
+     "shell.execute_reply": "2026-09-20T15:03:59.342971Z"
+    }
+   },
+   "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>dose</th>\n",
+       "      <th>amplitude</th>\n",
+       "      <th>amplitude_null</th>\n",
+       "      <th>step_amplitude</th>\n",
+       "      <th>split_half_cosine</th>\n",
+       "      <th>viability</th>\n",
+       "      <th>phase</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>180</th>\n",
+       "      <td>0.02</td>\n",
+       "      <td>0.45</td>\n",
+       "      <td>0.42</td>\n",
+       "      <td>0.45</td>\n",
+       "      <td>0.18</td>\n",
+       "      <td>0.91</td>\n",
+       "      <td>silent</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>181</th>\n",
+       "      <td>0.05</td>\n",
+       "      <td>0.52</td>\n",
+       "      <td>0.42</td>\n",
+       "      <td>0.65</td>\n",
+       "      <td>0.20</td>\n",
+       "      <td>0.96</td>\n",
+       "      <td>silent</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>182</th>\n",
+       "      <td>0.14</td>\n",
+       "      <td>0.54</td>\n",
+       "      <td>0.42</td>\n",
+       "      <td>0.57</td>\n",
+       "      <td>0.16</td>\n",
+       "      <td>0.86</td>\n",
+       "      <td>silent</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>183</th>\n",
+       "      <td>0.41</td>\n",
+       "      <td>0.57</td>\n",
+       "      <td>0.42</td>\n",
+       "      <td>0.82</td>\n",
+       "      <td>0.45</td>\n",
+       "      <td>0.90</td>\n",
+       "      <td>silent</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>184</th>\n",
+       "      <td>1.23</td>\n",
+       "      <td>0.49</td>\n",
+       "      <td>0.42</td>\n",
+       "      <td>0.59</td>\n",
+       "      <td>0.18</td>\n",
+       "      <td>0.87</td>\n",
+       "      <td>silent</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>185</th>\n",
+       "      <td>3.70</td>\n",
+       "      <td>0.55</td>\n",
+       "      <td>0.42</td>\n",
+       "      <td>0.60</td>\n",
+       "      <td>0.66</td>\n",
+       "      <td>0.97</td>\n",
+       "      <td>silent</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>186</th>\n",
+       "      <td>11.11</td>\n",
+       "      <td>0.48</td>\n",
+       "      <td>0.42</td>\n",
+       "      <td>0.60</td>\n",
+       "      <td>0.35</td>\n",
+       "      <td>0.94</td>\n",
+       "      <td>silent</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>187</th>\n",
+       "      <td>33.33</td>\n",
+       "      <td>0.92</td>\n",
+       "      <td>0.42</td>\n",
+       "      <td>0.86</td>\n",
+       "      <td>0.69</td>\n",
+       "      <td>0.97</td>\n",
+       "      <td>responding</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>188</th>\n",
+       "      <td>100.00</td>\n",
+       "      <td>1.65</td>\n",
+       "      <td>0.42</td>\n",
+       "      <td>1.17</td>\n",
+       "      <td>0.72</td>\n",
+       "      <td>0.84</td>\n",
+       "      <td>responding</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>189</th>\n",
+       "      <td>300.00</td>\n",
+       "      <td>2.30</td>\n",
+       "      <td>0.42</td>\n",
+       "      <td>1.22</td>\n",
+       "      <td>0.89</td>\n",
+       "      <td>0.74</td>\n",
+       "      <td>responding</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       dose  amplitude  amplitude_null  step_amplitude  split_half_cosine  \\\n",
+       "180    0.02       0.45            0.42            0.45               0.18   \n",
+       "181    0.05       0.52            0.42            0.65               0.20   \n",
+       "182    0.14       0.54            0.42            0.57               0.16   \n",
+       "183    0.41       0.57            0.42            0.82               0.45   \n",
+       "184    1.23       0.49            0.42            0.59               0.18   \n",
+       "185    3.70       0.55            0.42            0.60               0.66   \n",
+       "186   11.11       0.48            0.42            0.60               0.35   \n",
+       "187   33.33       0.92            0.42            0.86               0.69   \n",
+       "188  100.00       1.65            0.42            1.17               0.72   \n",
+       "189  300.00       2.30            0.42            1.22               0.89   \n",
+       "\n",
+       "     viability       phase  \n",
+       "180       0.91      silent  \n",
+       "181       0.96      silent  \n",
+       "182       0.86      silent  \n",
+       "183       0.90      silent  \n",
+       "184       0.87      silent  \n",
+       "185       0.97      silent  \n",
+       "186       0.94      silent  \n",
+       "187       0.97  responding  \n",
+       "188       0.84  responding  \n",
+       "189       0.74  responding  "
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "mt.tl.dose_direction(heparg)\n",
+    "direction = heparg.uns[\"mantispy\"][\"dose_direction\"]\n",
+    "direction[direction[\"compound\"] == \"Amperozide\"][\n",
+    "    [\"dose\", \"amplitude\", \"amplitude_null\", \"step_amplitude\", \"split_half_cosine\", \"viability\", \"phase\"]\n",
+    "].round(2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8f6079eb",
+   "metadata": {},
+   "source": [
+    "{func}`~mantispy.pl.dose_direction` draws that reading. The solid line is how far the profile sits from the\n",
+    "controls and the dashed line is how far it moved from the concentration below, which is the one that says where\n",
+    "the action is. The background is banded by phase."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "9ef6316e",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-09-20T15:03:59.345430Z",
+     "iopub.status.busy": "2026-09-20T15:03:59.345302Z",
+     "iopub.status.idle": "2026-09-20T15:04:00.238807Z",
+     "shell.execute_reply": "2026-09-20T15:04:00.238313Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1300x640 with 6 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, axes = plt.subplots(2, 3, figsize=(13, 6.4))\n",
+    "for ax, compound in zip(\n",
+    "    axes.ravel(),\n",
+    "    [\"Staurosporine\", \"Actinomycin D\", \"Fluazinam\", \"Amperozide\", \"Cycloheximide\", \"MUPIROCIN\"],\n",
+    "    strict=True,\n",
+    "):\n",
+    "    mt.pl.dose_direction(heparg, compound=compound, ax=ax)\n",
+    "fig.tight_layout()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "21a71197",
+   "metadata": {},
+   "source": [
+    "Six compounds, and the bands say more than the lines do.\n",
+    "\n",
+    "Mupirocin is grey the whole way: nothing to read anywhere on its ladder. Amperozide and cycloheximide are grey\n",
+    "until roughly 20 uM and then respond to the top. That is not a wasted ladder, it is the point of one. A screen\n",
+    "covers three decades because it does not know where the window is, and most of the range it covers is expected\n",
+    "to be empty at both ends: below the effective concentration on one side, past the cells on the other.\n",
+    "Staurosporine responds from 0.14 uM to 300 without ever settling, which is the turning direction above seen\n",
+    "from another side: a compound whose step never falls back to the noise has not finished changing.\n",
+    "\n",
+    "Actinomycin D is the one to read carefully. It is already responding at the lowest concentration tested, so its\n",
+    "onset is somewhere below the ladder and this screen cannot say where. Its window then *ends*: above about\n",
+    "60 uM the cells are gone and the band turns red. Both edges of a dose series can fall outside it, and the\n",
+    "phase column is what makes that visible rather than something you infer from a curve that kept rising.\n",
+    "\n",
+    "The phase is on `obs`, so the window is an ordinary subset and the rest of the package applies to it."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3db7fd0e",
+   "metadata": {},
+   "source": [
+    "### The point of departure\n",
+    "\n",
+    "Toxicology has a name for the concentration at which a response leaves the baseline: the point of departure,\n",
+    "which is what a benchmark dose estimates. This page has now arrived at one twice by different routes.\n",
+    "{func}`~mantispy.tl.dose_features` crosses each feature against the spread the controls show on it and reports\n",
+    "where that happens. {func}`~mantispy.tl.dose_direction` asks when the whole profile starts moving in a direction\n",
+    "that reproduces across plates, and the onset of the responding window is its answer.\n",
+    "\n",
+    "They are not the same construction, so whether they agree is worth checking rather than assuming."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "64214aaf",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-09-20T15:04:00.241436Z",
+     "iopub.status.busy": "2026-09-20T15:04:00.241305Z",
+     "iopub.status.idle": "2026-09-20T15:04:00.256694Z",
+     "shell.execute_reply": "2026-09-20T15:04:00.255996Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "20 compounds with both | rank correlation 0.79 | 16 within one step\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>window_onset</th>\n",
+       "      <th>feature_bmd_5th</th>\n",
+       "      <th>steps_apart</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>compound</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Actinomycin D</th>\n",
+       "      <td>0.02</td>\n",
+       "      <td>0.02</td>\n",
+       "      <td>0.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Staurosporine</th>\n",
+       "      <td>0.14</td>\n",
+       "      <td>0.37</td>\n",
+       "      <td>0.89</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Fluazinam</th>\n",
+       "      <td>3.70</td>\n",
+       "      <td>13.53</td>\n",
+       "      <td>1.18</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5,8,11-Eicosatriynoic acid</th>\n",
+       "      <td>3.70</td>\n",
+       "      <td>17.96</td>\n",
+       "      <td>1.44</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Lys05</th>\n",
+       "      <td>5.00</td>\n",
+       "      <td>12.15</td>\n",
+       "      <td>0.81</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Calcipotriol (hydrate)</th>\n",
+       "      <td>11.11</td>\n",
+       "      <td>13.34</td>\n",
+       "      <td>0.17</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>FCCP</th>\n",
+       "      <td>11.11</td>\n",
+       "      <td>26.52</td>\n",
+       "      <td>0.79</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Berberine chloride</th>\n",
+       "      <td>25.00</td>\n",
+       "      <td>45.35</td>\n",
+       "      <td>0.54</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Amperozide</th>\n",
+       "      <td>33.33</td>\n",
+       "      <td>48.44</td>\n",
+       "      <td>0.34</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>CLIOQUINOL</th>\n",
+       "      <td>33.33</td>\n",
+       "      <td>31.23</td>\n",
+       "      <td>-0.06</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Cucurbitacin I</th>\n",
+       "      <td>33.33</td>\n",
+       "      <td>3.84</td>\n",
+       "      <td>-1.97</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Cycloheximide</th>\n",
+       "      <td>33.33</td>\n",
+       "      <td>108.08</td>\n",
+       "      <td>1.07</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>IOPANOIC ACID</th>\n",
+       "      <td>33.33</td>\n",
+       "      <td>82.39</td>\n",
+       "      <td>0.82</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Ethoxyquin</th>\n",
+       "      <td>33.33</td>\n",
+       "      <td>58.23</td>\n",
+       "      <td>0.51</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Aminodarone Hydrochloride</th>\n",
+       "      <td>37.50</td>\n",
+       "      <td>23.53</td>\n",
+       "      <td>-0.42</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Treprostinil</th>\n",
+       "      <td>100.00</td>\n",
+       "      <td>36.93</td>\n",
+       "      <td>-0.91</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5,6-benzoflavone</th>\n",
+       "      <td>100.00</td>\n",
+       "      <td>48.05</td>\n",
+       "      <td>-0.67</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Bevirimat</th>\n",
+       "      <td>100.00</td>\n",
+       "      <td>129.33</td>\n",
+       "      <td>0.23</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Colistin Methanesulfonate (sodium salt)</th>\n",
+       "      <td>100.00</td>\n",
+       "      <td>139.49</td>\n",
+       "      <td>0.30</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Rifampicin</th>\n",
+       "      <td>300.00</td>\n",
+       "      <td>265.41</td>\n",
+       "      <td>-0.11</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                         window_onset  feature_bmd_5th  \\\n",
+       "compound                                                                 \n",
+       "Actinomycin D                                    0.02             0.02   \n",
+       "Staurosporine                                    0.14             0.37   \n",
+       "Fluazinam                                        3.70            13.53   \n",
+       "5,8,11-Eicosatriynoic acid                       3.70            17.96   \n",
+       "Lys05                                            5.00            12.15   \n",
+       "Calcipotriol (hydrate)                          11.11            13.34   \n",
+       "FCCP                                            11.11            26.52   \n",
+       "Berberine chloride                              25.00            45.35   \n",
+       "Amperozide                                      33.33            48.44   \n",
+       "CLIOQUINOL                                      33.33            31.23   \n",
+       "Cucurbitacin I                                  33.33             3.84   \n",
+       "Cycloheximide                                   33.33           108.08   \n",
+       "IOPANOIC ACID                                   33.33            82.39   \n",
+       "Ethoxyquin                                      33.33            58.23   \n",
+       "Aminodarone Hydrochloride                       37.50            23.53   \n",
+       "Treprostinil                                   100.00            36.93   \n",
+       "5,6-benzoflavone                               100.00            48.05   \n",
+       "Bevirimat                                      100.00           129.33   \n",
+       "Colistin Methanesulfonate (sodium salt)        100.00           139.49   \n",
+       "Rifampicin                                     300.00           265.41   \n",
+       "\n",
+       "                                         steps_apart  \n",
+       "compound                                              \n",
+       "Actinomycin D                                   0.00  \n",
+       "Staurosporine                                   0.89  \n",
+       "Fluazinam                                       1.18  \n",
+       "5,8,11-Eicosatriynoic acid                      1.44  \n",
+       "Lys05                                           0.81  \n",
+       "Calcipotriol (hydrate)                          0.17  \n",
+       "FCCP                                            0.79  \n",
+       "Berberine chloride                              0.54  \n",
+       "Amperozide                                      0.34  \n",
+       "CLIOQUINOL                                     -0.06  \n",
+       "Cucurbitacin I                                 -1.97  \n",
+       "Cycloheximide                                   1.07  \n",
+       "IOPANOIC ACID                                   0.82  \n",
+       "Ethoxyquin                                      0.51  \n",
+       "Aminodarone Hydrochloride                      -0.42  \n",
+       "Treprostinil                                   -0.91  \n",
+       "5,6-benzoflavone                               -0.67  \n",
+       "Bevirimat                                       0.23  \n",
+       "Colistin Methanesulfonate (sodium salt)         0.30  \n",
+       "Rifampicin                                     -0.11  "
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "onset = direction[direction[\"phase\"] == \"responding\"].groupby(\"compound\")[\"dose\"].min()\n",
+    "crossings = features.dropna(subset=[\"bmd\"]).groupby(\"compound\")[\"bmd\"].quantile(0.05)\n",
+    "departure = pd.DataFrame({\"window_onset\": onset, \"feature_bmd_5th\": crossings}).dropna()\n",
+    "# How far apart the two answers are, counted in steps of the three-fold ladder that was dosed.\n",
+    "departure[\"steps_apart\"] = np.log10(departure[\"feature_bmd_5th\"] / departure[\"window_onset\"]) / np.log10(3.0)\n",
+    "\n",
+    "print(\n",
+    "    f\"{len(departure)} compounds with both | rank correlation \"\n",
+    "    f\"{departure['window_onset'].corr(departure['feature_bmd_5th'], method='spearman'):.2f} | \"\n",
+    "    f\"{int((departure['steps_apart'].abs() <= 1).sum())} within one step\"\n",
+    ")\n",
+    "departure.sort_values(\"window_onset\").round(2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ef95b20b",
+   "metadata": {},
+   "source": [
+    "They agree: the two rank together at 0.79, and sixteen of twenty compounds land within one three-fold step of\n",
+    "each other, which is all the resolution this ladder has.\n",
+    "\n",
+    "Cucurbitacin I is the exception and the informative one. Its first feature crosses two steps below the\n",
+    "concentration at which the profile as a whole starts to reproduce. One feature out of ninety-nine passing a\n",
+    "three-MAD line is a weaker claim than a whole direction holding across replicate plates, and where the two\n",
+    "disagree the aggregate is the one to believe. Read the per-feature points of departure as the order in which\n",
+    "things engage, and the window onset as where the compound engages."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "ec3cbff6",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-09-20T15:04:00.258442Z",
+     "iopub.status.busy": "2026-09-20T15:04:00.258281Z",
+     "iopub.status.idle": "2026-09-20T15:04:00.310716Z",
+     "shell.execute_reply": "2026-09-20T15:04:00.310071Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "576 of 2831 wells are inside some compound's responding window\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "compound\n",
+       "Staurosporine                 30\n",
+       "FCCP                          27\n",
+       "Fluazinam                     26\n",
+       "Calcipotriol (hydrate)        24\n",
+       "5,8,11-Eicosatriynoic acid    21\n",
+       "Amperozide                    17\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# The controls are in no phase, so they have to be kept deliberately: every statistic here is read against them.\n",
+    "responding = heparg.obs[\"dose_direction_phase\"] == \"responding\"\n",
+    "window = heparg[responding | heparg.obs[\"Metadata_Control\"].to_numpy(dtype=bool)].copy()\n",
+    "print(f\"{int(responding.sum())} of {heparg.n_obs} wells are inside some compound's responding window\")\n",
+    "\n",
+    "# Two doses is enough here: the question is which features move inside the window, not where they cross.\n",
+    "mt.tl.dose_features(window, min_doses=2)\n",
+    "inside = window.uns[\"mantispy\"][\"dose_features\"].dropna(subset=[\"bmd\"])\n",
+    "inside.groupby(\"compound\").size().sort_values(ascending=False).head(6)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "90f29b2e",
+   "metadata": {},
+   "source": [
+    "## Comparing compounds on what they do, not on when they do it\n",
+    "\n",
+    "Two compounds compared at the same concentration are usually compared at different points of their own ranges:\n",
+    "at 30 uM amperozide has not started and staurosporine finished two decades ago. Comparing them at their\n",
+    "strongest concentration instead throws away everything on the way there, which for a compound whose phenotype\n",
+    "turns along its window is most of what it did.\n",
+    "\n",
+    "{func}`~mantispy.tl.dose_trajectory` reads each compound's window onto the same relative axis: position 0 is\n",
+    "where it starts responding, position 1 is the top of its window, and the profile is interpolated in log\n",
+    "concentration between them. The result is an ordinary object of compounds by features-and-positions, so\n",
+    "{func}`~mantispy.tl.similarity` and anything else works on it."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "04e9f94f",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-09-20T15:04:00.312358Z",
+     "iopub.status.busy": "2026-09-20T15:04:00.312234Z",
+     "iopub.status.idle": "2026-09-20T15:04:00.346699Z",
+     "shell.execute_reply": "2026-09-20T15:04:00.346031Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "20 compounds with a window of two or more concentrations, 297 columns (99 features x 3 positions)\n",
+      "Actinomycin D    -> Cycloheximide +0.45, Calcipotriol (hydrate) +0.33, Ethoxyquin +0.27\n",
+      "Staurosporine    -> Fluazinam +0.46, Calcipotriol (hydrate) +0.43, Ethoxyquin +0.36\n",
+      "Fluazinam        -> CLIOQUINOL +0.61, Amperozide +0.56, 5,8,11-Eicosatriynoic acid +0.47\n"
+     ]
+    }
+   ],
+   "source": [
+    "paths = mt.tl.dose_trajectory(heparg, n_positions=3)\n",
+    "mt.tl.similarity(paths, metric=\"pearson\")\n",
+    "\n",
+    "compounds = list(paths.obs[\"Metadata_Compound\"])\n",
+    "similarity = pd.DataFrame(np.asarray(paths.obsp[\"similarity\"]), index=compounds, columns=compounds)\n",
+    "print(\n",
+    "    f\"{paths.n_obs} compounds with a window of two or more concentrations, \"\n",
+    "    f\"{paths.n_vars} columns ({paths.var['feature'].nunique()} features x 3 positions)\"\n",
+    ")\n",
+    "for name in [\"Actinomycin D\", \"Staurosporine\", \"Fluazinam\"]:\n",
+    "    neighbours = similarity.loc[name].drop(name).sort_values(ascending=False).head(3)\n",
+    "    print(f\"{name:16s} -> \" + \", \".join(f\"{other} {value:+.2f}\" for other, value in neighbours.items()))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fce0f8f3",
+   "metadata": {},
+   "source": [
+    "Actinomycin D's nearest neighbour is cycloheximide. One blocks transcription and the other translation, and they\n",
+    "arrive at a similar place by a similar route; nothing in the input told the comparison that, and the two never\n",
+    "share a concentration where both are responding.\n",
+    "\n",
+    "How much the path says over its own endpoint depends on how many concentrations fall inside the window, and a\n",
+    "window of two or three is the normal result of screening a wide range. Correlating the whole\n",
+    "compound-by-compound matrix built from paths against the one built from each compound's top concentration gives\n",
+    "0.92, so on this screen the two mostly agree — which is what should happen when most paths are two points long.\n",
+    "\n",
+    "The pairs they disagree on are the ones with long windows, and they disagree sharply rather than noisily:\n",
+    "cucurbitacin I against staurosporine is +0.25 by path and -0.22 by endpoint, a different answer. The path is\n",
+    "never the worse of the two, and it is the right one wherever a compound had room to change.\n",
+    "\n",
+    "This is also what the window is for beyond reading it. A range-finding screen buys the location of the window;\n",
+    "the way to fill it in is a second ladder spaced across the concentrations this one marked as responding, rather\n",
+    "than another three decades at the same spacing."
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "1c187e53",
@@ -842,14 +1525,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 15,
    "id": "f7852d4b",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-09-20T14:29:41.960987Z",
-     "iopub.status.busy": "2026-09-20T14:29:41.960839Z",
-     "iopub.status.idle": "2026-09-20T14:29:42.074846Z",
-     "shell.execute_reply": "2026-09-20T14:29:42.074237Z"
+     "iopub.execute_input": "2026-09-20T15:04:00.348294Z",
+     "iopub.status.busy": "2026-09-20T15:04:00.348189Z",
+     "iopub.status.idle": "2026-09-20T15:04:00.460158Z",
+     "shell.execute_reply": "2026-09-20T15:04:00.459646Z"
     }
    },
    "outputs": [
@@ -888,14 +1571,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 16,
    "id": "defe9869",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-09-20T14:29:42.076311Z",
-     "iopub.status.busy": "2026-09-20T14:29:42.076197Z",
-     "iopub.status.idle": "2026-09-20T14:29:45.136840Z",
-     "shell.execute_reply": "2026-09-20T14:29:45.135649Z"
+     "iopub.execute_input": "2026-09-20T15:04:00.461878Z",
+     "iopub.status.busy": "2026-09-20T15:04:00.461754Z",
+     "iopub.status.idle": "2026-09-20T15:04:03.522393Z",
+     "shell.execute_reply": "2026-09-20T15:04:03.521585Z"
     }
    },
    "outputs": [
@@ -903,7 +1586,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/var/folders/qg/qgc908995g3fc8qtss2fsbhhxyxxj4/T/ipykernel_23000/2041206685.py:7: UserWarning: 280 of 289 groups have fewer than three rows, so their statistic is the median of one or two wells. Aggregate more replicates, or score activity with mt.tl.map(mode='activity'), which ranks replicate pairs and is built for screens with little replication.\n",
+      "/var/folders/qg/qgc908995g3fc8qtss2fsbhhxyxxj4/T/ipykernel_92610/2041206685.py:7: UserWarning: 280 of 289 groups have fewer than three rows, so their statistic is the median of one or two wells. Aggregate more replicates, or score activity with mt.tl.map(mode='activity'), which ranks replicate pairs and is built for screens with little replication.\n",
       "  mt.tl.hit_calling(u2os, groupby=\"Metadata_Perturbation\", use_rep=\"X_pca\", n_permutations=300)\n"
      ]
     },
@@ -985,7 +1668,11 @@
     "- A distance that plateaus does not mean the phenotype stopped changing.\n",
     "  {func}`~mantispy.tl.dose_direction` separates one phenotype getting louder from a second one taking over.\n",
     "- Amplitude alone overcalls. Mupirocin's 33 uM well sits at nearly twice the control floor with no direction that\n",
-    "  reproduces across plates.\n"
+    "  reproduces across plates.\n",
+    "- Find the window before reading the features. `phase` marks where a compound is silent, still changing,\n",
+    "  settled, or past the point where its cells are alive, and it lands on `obs` so the window is a subset.\n",
+    "- Compare compounds on their own relative dose axis with {func}`~mantispy.tl.dose_trajectory`, not at a shared\n",
+    "  concentration, which compares one compound that has engaged against one that has not.\n"
    ]
   }
  ],
diff --git a/src/mantispy/pl/__init__.py b/src/mantispy/pl/__init__.py
index 4c861ad..8d74d08 100644
--- a/src/mantispy/pl/__init__.py
+++ b/src/mantispy/pl/__init__.py
@@ -9,7 +9,7 @@
 from mantispy.pl._evaluation import batch_variance, map, metrics, replicate_correlation, similarity
 from mantispy.pl._features import feature_correlation, feature_groups
 from mantispy.pl._heterogeneity import cell_cycle, cluster_composition, density, subpopulation_hits
-from mantispy.pl._hits import dose_response, effect_sizes, feature_volcano, hits
+from mantispy.pl._hits import PHASE_COLORS, dose_direction, dose_response, effect_sizes, feature_volcano, hits
 from mantispy.pl._moa import distance_heatmap, moa_confusion, moa_enrichment, pathway_coherence, sets_heatmap
 from mantispy.pl._plate import plate
 from mantispy.pl._qc import cell_counts, cytotoxicity, feature_distributions, nan_matrix, qc, replicate_saturation
@@ -17,6 +17,7 @@
 from mantispy.pl._transport import setting_agreement, transport
 
 __all__ = [
+    "PHASE_COLORS",
     "setting_agreement",
     "batch_variance",
     "cell_counts",
@@ -26,6 +27,7 @@
     "cytotoxicity",
     "density",
     "distance_heatmap",
+    "dose_direction",
     "dose_response",
     "effect_sizes",
     "feature_correlation",
diff --git a/src/mantispy/pl/_hits.py b/src/mantispy/pl/_hits.py
index 622f4d1..de537cf 100644
--- a/src/mantispy/pl/_hits.py
+++ b/src/mantispy/pl/_hits.py
@@ -243,3 +243,85 @@ def dose_response(
     ax.set_title(f"{compound}  (spearman {float(fitted['spearman']):.2f})", fontsize=9)
     ax.legend(fontsize=7)
     return ax
+
+
+#: Background colour of each phase, from ``mantispy.tl.PHASES``. Grey where nothing happens, warm where it
+#: does, and red where the cells are gone.
+PHASE_COLORS = {
+    "silent": "#f2f2f2",
+    "responding": "#fde6c4",
+    "saturated": "#dbe8d4",
+    "cytotoxic": "#f6d2d2",
+}
+
+
+def dose_direction(
+    adata: AnnData,
+    compound: str,
+    key: str = "dose_direction",
+    ax: Axes | None = None,
+) -> Axes:
+    """One compound's ladder, with the background banded by what each concentration is doing.
+
+    The solid line is how far the profile sits from the controls, and the dashed line is how far it moved from
+    the concentration below it. The second is what says where the action is: a response that is still changing
+    has a large step, one that has arrived has a small one however high the solid line sits. The two dotted
+    horizontals are the floors those lines are read against, which control wells laid out the same way reach.
+
+    Args:
+        adata: Object holding the table :func:`~mantispy.tl.dose_direction` wrote.
+        compound: Which compound of that table to draw.
+        key: Name of that table in ``uns["mantispy"]``.
+        ax: Axes to draw on, or ``None`` for a new figure.
+
+    Returns:
+        The axes drawn on.
+
+    Raises:
+        KeyError: There is no such table, or it holds no such compound.
+    """
+    table = _table(adata, key, "mt.tl.dose_direction")
+    block = table[table["compound"].astype(str) == str(compound)].sort_values("dose")
+    if block.empty:
+        known = sorted(set(table["compound"].astype(str)))
+        raise KeyError(f"no compound {compound!r} in uns['mantispy'][{key!r}]; it holds {known}")
+
+    ax = _axes(ax, (5.2, 3.6))
+    doses = block["dose"].to_numpy(dtype=float)
+    # Each concentration owns the ladder up to halfway to its neighbours, measured in log dose, and half a step
+    # past the two ends.
+    log_dose = np.log10(doses)
+    middles = (log_dose[:-1] + log_dose[1:]) / 2 if len(doses) > 1 else np.array([log_dose[0]])
+    edges = 10.0 ** np.concatenate(
+        [[2 * log_dose[0] - middles[0]], middles, [2 * log_dose[-1] - middles[-1]]]
+        if len(doses) > 1
+        else [[log_dose[0] - 0.3], [log_dose[0] + 0.3]]
+    )
+    for index, phase in enumerate(block["phase"]):
+        ax.axvspan(edges[index], edges[index + 1], color=PHASE_COLORS.get(str(phase), "#ffffff"), lw=0, zorder=0)
+
+    floor = float(np.nanmedian(block["amplitude_null"].to_numpy(dtype=float)))
+    ax.axhline(floor, ls=":", lw=1, color="0.45", zorder=1)
+    ax.axhline(floor * np.sqrt(2.0), ls=":", lw=1, color="0.65", zorder=1)
+    ax.plot(
+        doses, block["amplitude"], marker="o", ms=4, lw=1.6, color="#2a4d69", label="distance from controls", zorder=3
+    )
+    ax.plot(
+        doses,
+        block["step_amplitude"],
+        marker="s",
+        ms=3,
+        lw=1.3,
+        ls="--",
+        color="#c1611f",
+        label="moved since the last",
+        zorder=3,
+    )
+
+    ax.set_xscale("log")
+    ax.set_xlim(edges[0], edges[-1])
+    ax.set_xlabel("concentration")
+    ax.set_ylabel("MADs per feature")
+    ax.set_title(str(compound), fontsize=10)
+    ax.legend(fontsize=7, frameon=False, loc="upper left")
+    return ax
diff --git a/src/mantispy/tl/__init__.py b/src/mantispy/tl/__init__.py
index dc65007..95a8aa0 100644
--- a/src/mantispy/tl/__init__.py
+++ b/src/mantispy/tl/__init__.py
@@ -5,7 +5,7 @@
 from mantispy.tl._design import cytotoxicity, replicate_saturation
 from mantispy.tl._differential import differential_features
 from mantispy.tl._distance import edistance
-from mantispy.tl._dose import dose_direction, dose_features, dose_response
+from mantispy.tl._dose import PHASES, dose_direction, dose_features, dose_response, dose_trajectory
 from mantispy.tl._effect import effect_size, wasserstein_features
 from mantispy.tl._enrich import enrich, feature_sets, rank_features, rank_sets
 from mantispy.tl._heterogeneity import (
@@ -23,6 +23,7 @@
 from mantispy.tl._transport import transport
 
 __all__ = [
+    "PHASES",
     "aggregate",
     "cell_cycle_phase",
     "cluster_composition",
@@ -31,6 +32,7 @@
     "dose_direction",
     "dose_features",
     "dose_response",
+    "dose_trajectory",
     "differential_features",
     "edistance",
     "effect_size",
diff --git a/src/mantispy/tl/_dose.py b/src/mantispy/tl/_dose.py
index a463320..56aba77 100644
--- a/src/mantispy/tl/_dose.py
+++ b/src/mantispy/tl/_dose.py
@@ -3,9 +3,11 @@
 from __future__ import annotations
 
 import warnings
+from collections.abc import Sequence
 from dataclasses import dataclass
 from math import lgamma, log, pi
 
+import anndata as ad
 import numpy as np
 import pandas as pd
 from anndata import AnnData
@@ -16,6 +18,7 @@
 from mantispy._core.logging import get_logger
 from mantispy._core.masks import reference_mask
 from mantispy._core.mutation import inplace_or_copy
+from mantispy._core.schema import stamp
 
 #: Column order of the output table, so an empty result still carries its columns.
 _COLUMNS = (
@@ -457,10 +460,16 @@ def dose_response(
     "n_wells",
     "amplitude",
     "amplitude_null",
+    "step_amplitude",
     "split_half_cosine",
     "cosine_to_top",
+    "viability",
+    "phase",
 )
 
+#: What a concentration is doing, in the order they normally appear along a ladder.
+PHASES = ("silent", "responding", "saturated", "cytotoxic")
+
 
 def _feature_baseline_and_spread(adata: AnnData, reference: str | None) -> tuple[np.ndarray, np.ndarray]:
     """Where the controls sit on each feature and how far they wobble, the ToxCast ``bmed`` and ``bmad`` per feature.
@@ -472,7 +481,8 @@ def _feature_baseline_and_spread(adata: AnnData, reference: str | None) -> tuple
     if int(rows.sum()) < 2:
         raise ValueError(
             f"a per-feature dose analysis needs at least two control rows to set the scale, got {int(rows.sum())}. "
-            "Mark the controls with mt.pp.annotate_controls, or name another reference."
+            "Mark the controls with mt.pp.annotate_controls, or name another reference. Subsetting to a "
+            "dose_direction phase drops them, since a control is in no phase; keep them alongside the window."
         )
     control = get_matrix(adata, rows=np.flatnonzero(rows)).astype(np.float64)
     baseline = np.nanmedian(control, axis=0)
@@ -730,6 +740,65 @@ def _null_amplitude(control: np.ndarray, levels: np.ndarray, wanted: dict[object
     return float(np.median(draws))
 
 
+def _viability(adata: AnnData, count_key: str, site_key: str | None, control: np.ndarray) -> np.ndarray:
+    """Each row's cell count against the controls' median, per field of view where the fields are known."""
+    obs = as_frame(adata.obs)
+    if count_key not in obs:
+        get_logger().info(
+            "dose_direction: obs has no column %r, so no concentration is marked cytotoxic. "
+            "mt.tl.aggregate and every well-level mt.ds dataset write Metadata_CellCount.",
+            count_key,
+        )
+        return np.full(adata.n_obs, np.nan)
+    counts = obs[count_key].to_numpy(dtype=float)
+    if site_key is not None and site_key in obs:
+        counts = counts / np.maximum(obs[site_key].to_numpy(dtype=float), 1)
+    reference = float(np.nanmedian(counts[control]))
+    if not np.isfinite(reference) or reference <= 0:
+        get_logger().info("dose_direction: the controls have no usable %r, so no viability is read", count_key)
+        return np.full(adata.n_obs, np.nan)
+    return counts / reference
+
+
+def _label_phases(ladder: list[dict], min_viability: float, reproducible: float | None) -> list[str]:
+    """Label one compound's whole ladder at once, and write the labels back into its rows.
+
+    The window is a run, not a scatter. ``split_half_cosine`` over a handful of replicate wells is itself noisy,
+    and thresholding each concentration on its own lets one lucky concentration open a window several steps below
+    where the compound actually engages: on the OASIS pilot that put amperozide's onset at 3.7 uM instead of 33.
+    A response that has started does not stop, so the window is the run of concentrations reaching the highest one
+    that is not cytotoxic, and anything below the run is silent whatever its own cosine says.
+
+    Cell loss is read first and separately. A concentration that has lost its cells reports the morphology of
+    dying cells whatever else is true of it, which is why the US EPA's phenotypic pipeline drops those
+    concentrations before fitting anything.
+    """
+    cytotoxic = [bool(np.isfinite(row["viability"]) and row["viability"] < min_viability) for row in ladder]
+    phases = ["cytotoxic" if flag else "silent" for flag in cytotoxic]
+
+    def active(row: dict) -> bool:
+        if (
+            not (np.isfinite(row["amplitude"]) and np.isfinite(row["amplitude_null"]))
+            or row["amplitude"] <= row["amplitude_null"]
+        ):
+            return False
+        if reproducible is None:
+            return True
+        return bool(np.isfinite(row["split_half_cosine"]) and row["split_half_cosine"] >= reproducible)
+
+    index = max((position for position, flag in enumerate(cytotoxic) if not flag), default=-1)
+    while index >= 0 and not cytotoxic[index] and active(ladder[index]):
+        row = ladder[index]
+        # Still moving if the step from the concentration below beats the noise two independent medians carry.
+        moving = np.isfinite(row["step_amplitude"]) and row["step_amplitude"] > np.sqrt(2.0) * row["amplitude_null"]
+        phases[index] = "responding" if moving else "saturated"
+        index -= 1
+
+    for row, phase in zip(ladder, phases, strict=True):
+        row["phase"] = phase
+    return phases
+
+
 @inplace_or_copy()
 def dose_direction(
     adata: AnnData,
@@ -738,6 +807,10 @@ def dose_direction(
     reference: str | None = "negcon",
     split_by: str | None = "Metadata_Plate",
     min_doses: int = 4,
+    count_key: str = "Metadata_CellCount",
+    site_key: str | None = "Metadata_SiteCount",
+    min_viability: float = 0.5,
+    reproducible: float | None = 0.5,
     dose_tolerance: float = _DOSE_TOLERANCE,
     key_added: str = "dose_direction",
     copy: bool = False,
@@ -754,6 +827,13 @@ def dose_direction(
     only noise, however large its amplitude. One that reproduces but sits at a low ``cosine_to_top`` is a real
     phenotype, and a different one from the top concentration's.
 
+    ``phase`` turns that reading into a label, so the stretch of the ladder worth analysing can be subset out
+    rather than described. A concentration is ``silent`` while nothing reproducible is happening, ``responding``
+    while the profile is still moving from the concentration below it, ``saturated`` once it has stopped, and
+    ``cytotoxic`` once the cells are gone and the profile is the morphology of dying cells. The label is
+    broadcast to ``obs``, so ``adata[adata.obs["dose_direction_phase"] == "responding"]`` is the window, and
+    :func:`dose_features`, :func:`~mantispy.tl.differential_features` and the rest work on it unchanged.
+
     Args:
         adata: Object carrying a compound and a dose per row, at well resolution.
         compound_key: ``obs`` column holding the compound identity.
@@ -761,6 +841,10 @@ def dose_direction(
         reference: Rows that set each feature's baseline and spread. ``"negcon"`` reads ``Metadata_Control``.
         split_by: ``obs`` column whose levels split the replicates in two for ``split_half_cosine``, normally the plate. It also lays out the control groups ``amplitude_null`` is drawn from. ``None``, or a column with one level, splits the wells by position instead.
         min_doses: Distinct doses below which a compound is left out of the table. One concentration says nothing about how a response changes with concentration.
+        count_key: ``obs`` column holding the cell count, which sets ``viability``. Without it no concentration is marked cytotoxic.
+        site_key: ``obs`` column holding the number of fields that count covers, so a well missing a field does not read as cell loss. ``None`` compares the counts as they are.
+        min_viability: Fraction of the controls' cell count below which a concentration is ``cytotoxic``. The US EPA's phenotypic pipeline drops a concentration that has lost more than half its cells before fitting anything.
+        reproducible: ``split_half_cosine`` a concentration needs before it can be anything but ``silent``. ``None`` drops the requirement, which is what a screen with one well per concentration has to do, at the cost of calling noise a phenotype.
         dose_tolerance: Doses whose base-10 logs differ by less than this are treated as one dose. See :func:`dose_response`.
         key_added: Name for the output table.
         copy: Return a modified copy instead of mutating in place.
@@ -768,10 +852,14 @@ def dose_direction(
     Returns:
         ``None``, or the modified copy.
         Writes ``uns["mantispy"][key_added]``, one row per compound and concentration, with ``compound``, ``dose``,
-        ``n_wells``, ``amplitude``, ``amplitude_null``, ``split_half_cosine`` and ``cosine_to_top``.
+        ``n_wells``, ``amplitude``, ``amplitude_null``, ``step_amplitude``, ``split_half_cosine``,
+        ``cosine_to_top``, ``viability`` and ``phase``, and broadcasts the phase to
+        ``obs[key_added + "_phase"]`` so the window can be subset like any other annotation.
         ``amplitude`` is the root-mean-square response over the features, in MADs of the controls, and
         ``amplitude_null`` is what control wells spread over the same plates in the same numbers reach, so the two
-        are read against each other.
+        are read against each other. ``step_amplitude`` is the same measure applied to the change from the
+        concentration below, which is what tells a response that is still moving from one that has arrived.
+        ``phase`` is one of ``PHASES``.
 
     Raises:
         KeyError: ``obs`` has no ``compound_key``, no ``dose_key``, or no ``split_by`` column.
@@ -801,8 +889,11 @@ def dose_direction(
 
     all_levels = obs[split_by].to_numpy() if split_by is not None else np.zeros(adata.n_obs)
     control_levels = all_levels[control]
+    viable = _viability(adata, count_key, site_key, control)
+    phases = np.full(adata.n_obs, "", dtype=object)
     nulls: dict[tuple, float] = {}
-    records = []
+    records: list[dict] = []
+    block_rows: list[np.ndarray] = []
     for compound, (rows, doses) in _usable_doses(obs, compound_key, dose_key, control, dose_tolerance).items():
         if len(np.unique(doses)) < min_doses:
             get_logger().debug("dose_direction skipped %s: %d usable dose(s)", compound, len(np.unique(doses)))
@@ -820,23 +911,162 @@ def dose_direction(
             # The floor is drawn with this concentration's own spread over plates, not from any group of that size.
             layout = dict(zip(*np.unique(levels[at], return_counts=True), strict=True))
             key = tuple(sorted(layout.items(), key=lambda item: str(item[0])))
+            null = nulls.setdefault(key, _null_amplitude(control_z, control_levels, layout))
+            # The step from the concentration below, which is what "still changing" means. The first one steps
+            # from the controls, so its step is how far it has already come.
+            amplitude = _amplitude(medians[index])
+            step = amplitude if index == 0 else _amplitude(medians[index] - medians[index - 1])
+            viability = float(np.nanmedian(viable[rows][at])) if np.isfinite(viable[rows][at]).any() else np.nan
+            block_rows.append(rows[at])
             records.append(
                 {
                     "compound": str(compound),
                     "dose": float(dose),
                     "n_wells": int(at.sum()),
-                    "amplitude": _amplitude(medians[index]),
-                    "amplitude_null": nulls.setdefault(key, _null_amplitude(control_z, control_levels, layout)),
+                    "amplitude": amplitude,
+                    "amplitude_null": null,
+                    "step_amplitude": step,
                     "split_half_cosine": reproduces,
                     "cosine_to_top": _cosine(medians[index], medians[-1]),
+                    "viability": viability,
+                    "phase": "",
                 }
             )
 
+        ladder = records[-len(order) :]
+        for record, covered in zip(_label_phases(ladder, min_viability, reproducible), block_rows, strict=True):
+            phases[covered] = record
+        block_rows.clear()
+
     table = pd.DataFrame(records, columns=list(_DIRECTION_COLUMNS))
     adata.uns.setdefault("mantispy", {})[key_added] = table
+    # A row that no concentration covers, a control or an unannotated well, is in no phase at all.
+    phases[phases == ""] = None
+    adata.obs[f"{key_added}_phase"] = pd.Categorical(phases, categories=list(PHASES), ordered=False)
     get_logger().info(
-        "dose_direction: %d compound(s) over %d concentration(s)",
+        "dose_direction: %d compound(s) over %d concentration(s); %s",
         table["compound"].nunique() if len(table) else 0,
         len(table),
+        table["phase"].value_counts().to_dict() if len(table) else {},
     )
     return None
+
+
+def dose_trajectory(
+    adata: AnnData,
+    compound_key: str = "Metadata_Compound",
+    dose_key: str = "Metadata_Concentration",
+    reference: str | None = "negcon",
+    phase_key: str | None = "dose_direction_phase",
+    phases: Sequence[str] = ("responding",),
+    n_positions: int = 3,
+    dose_tolerance: float = _DOSE_TOLERANCE,
+) -> AnnData:
+    """Each compound's whole path through its own responding window, on one comparable axis.
+
+    Comparing two compounds at the same concentration compares one that has engaged against one that has not.
+    Comparing them at their own strongest concentration throws away everything on the way there, which for a
+    compound whose phenotype turns along its window is most of what it did. This reads each compound's window
+    onto the same relative axis instead: position 0 is where it starts responding, position 1 is the top of its
+    window, and the profile is interpolated in log concentration in between.
+
+    The result is one row per compound and one column per feature and position, so the usual tools apply to it:
+    :func:`~mantispy.tl.similarity` gives the compound-to-compound matrix, and a PCA or a clustering groups
+    compounds by the shape of their response rather than by its size.
+
+    Args:
+        adata: Object carrying a compound, a dose and, normally, the phase column :func:`dose_direction` wrote.
+        compound_key: ``obs`` column holding the compound identity.
+        dose_key: ``obs`` column holding the concentration.
+        reference: Rows that set each feature's baseline and spread.
+        phase_key: ``obs`` column holding the phase, so the path is read over the window rather than the whole ladder. ``None``, or a column that is absent, uses every concentration the compound was dosed at.
+        phases: Which phases count as the window.
+        n_positions: Points the window is resampled to. Two is its ends; more describes the path between them, and cannot add detail a short window does not have.
+        dose_tolerance: Doses whose base-10 logs differ by less than this are treated as one dose.
+
+    Returns:
+        A new object of compounds by features-and-positions, at ``"perturbation"`` resolution.
+        ``var`` carries ``feature`` and ``position``; ``obs`` carries ``n_doses`` and the window's ends.
+        Compounds whose window holds fewer than two concentrations are left out, since a single point is not a path.
+
+    Raises:
+        KeyError: ``obs`` has no ``compound_key`` or no ``dose_key``.
+        ValueError: ``n_positions`` is below two, or fewer than two reference rows.
+
+    Notes:
+        The axis is relative, so position 0 means a different concentration for every compound. That is the
+        point, and it is also the caveat: two compounds matched here are matched on what they do in their own
+        effective range, which is a claim about mechanism rather than about potency. Read it beside the benchmark
+        doses from :func:`dose_features`, which is where the potency lives.
+
+        How much this says over comparing the top of each window alone depends on how long the windows are. On
+        the OASIS pilot, where most compounds respond over two or three of the ten concentrations, the two
+        agree closely; the pairs they disagree on are the ones with long windows.
+    """
+    if n_positions < 2:
+        raise ValueError(f"n_positions must be at least two, got {n_positions}")
+    obs = as_frame(adata.obs)
+    _require_columns(obs, compound_key, dose_key)
+    baseline, spread = _feature_baseline_and_spread(adata, reference)
+    scaled = np.flatnonzero(np.isfinite(spread))
+    control = reference_mask(adata, reference)
+
+    in_window = np.ones(adata.n_obs, dtype=bool)
+    if phase_key is not None and phase_key in obs:
+        in_window = obs[phase_key].astype(str).isin(list(phases)).to_numpy()
+    elif phase_key is not None:
+        get_logger().info(
+            "dose_trajectory: obs has no column %r, so the path is read over every concentration. "
+            "Run mt.tl.dose_direction first to read it over the responding window.",
+            phase_key,
+        )
+
+    grid = np.linspace(0.0, 1.0, n_positions)
+    rows_out, records = [], []
+    for compound, (rows, doses) in _usable_doses(obs, compound_key, dose_key, control, dose_tolerance).items():
+        keep = in_window[rows]
+        if not keep.any():
+            continue
+        rows, doses = rows[keep], doses[keep]
+        order, medians = _dose_medians(get_matrix(adata, rows=rows).astype(np.float64)[:, scaled], doses)
+        if len(order) < 2:
+            continue
+        with np.errstate(invalid="ignore"):
+            z = (medians - baseline[scaled]) / spread[scaled]
+        # Relative position through the window in log concentration, so the ends are 0 and 1 for every compound.
+        position = np.log10(order)
+        position = (position - position[0]) / (position[-1] - position[0])
+        resampled = np.stack([np.interp(grid, position, z[:, column]) for column in range(z.shape[1])], axis=1)
+        rows_out.append(resampled.ravel())
+        records.append(
+            {
+                compound_key: str(compound),
+                "n_doses": len(order),
+                "window_low": float(order[0]),
+                "window_high": float(order[-1]),
+            }
+        )
+
+    names = np.asarray(adata.var_names)[scaled]
+    var = pd.DataFrame(
+        {
+            "feature": np.tile(names, n_positions),
+            "position": np.repeat(grid, names.size),
+        },
+        index=pd.Index([f"{name}@{value:.2f}" for value in grid for name in names]),
+    )
+    result = ad.AnnData(
+        X=np.vstack(rows_out).astype(np.float32) if rows_out else np.empty((0, var.shape[0]), dtype=np.float32),
+        obs=pd.DataFrame(records, columns=[compound_key, "n_doses", "window_low", "window_high"]).set_axis(
+            pd.Index([str(index) for index in range(len(records))])
+        ),
+        var=var,
+    )
+    stamp(result, resolution="perturbation")
+    get_logger().info(
+        "dose_trajectory: %d compound(s) over %d position(s); windows of %s concentration(s)",
+        result.n_obs,
+        n_positions,
+        sorted(set(result.obs["n_doses"])) if result.n_obs else [],
+    )
+    return result
diff --git a/tests/test_pl_hits.py b/tests/test_pl_hits.py
index 2d6e384..70e958e 100644
--- a/tests/test_pl_hits.py
+++ b/tests/test_pl_hits.py
@@ -150,3 +150,54 @@ def test_an_inhibitory_curve_is_drawn_the_way_the_data_runs(inhibitor_adata):
     line = next(line for line in ax.get_lines() if line.get_label().startswith("EC50"))
     drawn = line.get_ydata()
     assert drawn[0] > drawn[-1], "the curve runs uphill while the data runs downhill"
+
+
+def _laddered():
+    """Wells over a ladder where one compound wakes up halfway up it."""
+    from scipy.special import expit
+
+    rng = np.random.default_rng(0)
+    doses = np.geomspace(0.01, 100.0, 6)
+    concentration = np.repeat(doses, 4)
+    signal = 8.0 * expit(4.0 * (np.log10(concentration) - np.log10(3.0)))
+    values = rng.normal(0.0, 1.0, (concentration.size + 16, 5))
+    values[: concentration.size, 0] += signal
+    values[: concentration.size, 1] += signal
+
+    total = concentration.size + 16
+    frame = pd.DataFrame(
+        {
+            "Metadata_Compound": ["cpd"] * concentration.size + ["DMSO"] * 16,
+            "Metadata_Concentration": np.concatenate([concentration, np.zeros(16)]),
+            "Metadata_Plate": np.where(np.arange(total) % 2 == 0, "P1", "P2"),
+            "Metadata_Well": [f"{chr(65 + i // 24)}{i % 24 + 1:02d}" for i in range(total)],
+            "Metadata_Control": [False] * concentration.size + [True] * 16,
+            "Metadata_CellCount": 100.0,
+        }
+    )
+    for column in range(values.shape[1]):
+        frame[f"Cells_AreaShape_f{column}"] = values[:, column]
+    adata = from_dataframe(frame, resolution="well")
+    mt.tl.dose_direction(adata)
+    return adata
+
+
+def test_the_direction_plot_bands_the_ladder_by_phase():
+    adata = _laddered()
+    ax = mt.pl.dose_direction(adata, compound="cpd")
+    table = adata.uns["mantispy"]["dose_direction"]
+
+    # One band per concentration, plus the two lines it reads against.
+    assert len(ax.patches) == len(table)
+    drawn = {patch.get_facecolor() for patch in ax.patches}
+    expected = {mt.pl.PHASE_COLORS[phase] for phase in table["phase"]}
+    assert len(drawn) == len(expected), "each phase present gets its own colour"
+    assert len(ax.lines) == 4, "two curves and the two floors"
+    plt.close(ax.figure)
+
+
+def test_the_direction_plot_says_what_to_run_first_and_which_compounds_it_has():
+    adata = _laddered()
+    with pytest.raises(KeyError, match="cpd"):
+        mt.pl.dose_direction(adata, compound="not_dosed")
+    plt.close("all")
diff --git a/tests/test_tl_dose.py b/tests/test_tl_dose.py
index 8bb1182..e92e75e 100644
--- a/tests/test_tl_dose.py
+++ b/tests/test_tl_dose.py
@@ -284,6 +284,7 @@ def phenotypes():
     frame["Metadata_Plate"] = np.where(np.arange(total) % 2 == 0, "P1", "P2")
     frame["Metadata_Well"] = [f"{chr(65 + index // 24)}{index % 24 + 1:02d}" for index in range(total)]
     frame["Metadata_Control"] = frame["Metadata_Compound"] == "DMSO"
+    frame["Metadata_CellCount"] = 100.0
     for column in range(n_features):
         frame[f"Cells_AreaShape_f{column}"] = values[:, column]
     return from_dataframe(frame, resolution="well")
@@ -453,3 +454,68 @@ def test_the_amplitude_floor_follows_the_plates_the_concentration_sits_on():
     mt.tl.dose_direction(adata)
     table = adata.uns["mantispy"]["dose_direction"].set_index("compound")
     assert table.loc["spread", "amplitude_null"].max() < table.loc["stacked", "amplitude_null"].min()
+
+
+def test_the_window_is_a_run_not_a_scatter(phenotypes):
+    """Regression: one lucky concentration used to open the window several steps below the real onset.
+
+    `grows` is silent at the two lowest concentrations. Making its second concentration look reproducible on its
+    own must not pull the window down to it, because the concentration above is still silent.
+    """
+    mt.tl.dose_direction(phenotypes)
+    table = phenotypes.uns["mantispy"]["dose_direction"]
+    grows = table[table["compound"] == "grows"].sort_values("dose").reset_index(drop=True)
+    labels = list(grows["phase"])
+
+    quiet = [index for index, phase in enumerate(labels) if phase == "silent"]
+    working = [index for index, phase in enumerate(labels) if phase in ("responding", "saturated")]
+    assert quiet and working, labels
+    assert max(quiet) < min(working), f"the window has a hole in it: {labels}"
+    assert working[-1] == len(labels) - 1, "the window runs to the top concentration"
+
+
+def test_a_concentration_that_lost_its_cells_is_cytotoxic_whatever_else_it_did(phenotypes):
+    top = phenotypes.obs["Metadata_Concentration"] == phenotypes.obs["Metadata_Concentration"].max()
+    phenotypes.obs.loc[top, "Metadata_CellCount"] = 10.0
+    mt.tl.dose_direction(phenotypes)
+    table = phenotypes.uns["mantispy"]["dose_direction"]
+    highest = table.sort_values("dose").groupby("compound").tail(1)
+    assert (highest["phase"] == "cytotoxic").all()
+    assert (highest["viability"] < 0.2).all()
+
+
+def test_the_phase_reaches_obs_so_the_window_can_be_subset(phenotypes):
+    mt.tl.dose_direction(phenotypes)
+    phases = phenotypes.obs["dose_direction_phase"]
+    assert set(phases.cat.categories) == set(mt.tl.PHASES)
+    assert phases[phenotypes.obs["Metadata_Control"].to_numpy(dtype=bool)].isna().all(), "controls are in no phase"
+
+    window = phenotypes[phases == "responding"]
+    assert window.n_obs > 0
+    assert not window.obs["Metadata_Control"].to_numpy(dtype=bool).any()
+
+
+def test_dose_trajectory_puts_every_compound_on_one_relative_axis(phenotypes):
+    mt.tl.dose_direction(phenotypes)
+    paths = mt.tl.dose_trajectory(phenotypes, n_positions=3)
+
+    assert paths.n_vars == 3 * (phenotypes.n_vars - 1), "one column per feature and position, minus the flat one"
+    assert list(paths.var["position"].unique()) == [0.0, 0.5, 1.0]
+    assert paths.var["feature"].nunique() == phenotypes.n_vars - 1
+    assert set(paths.obs["Metadata_Compound"]) <= {"grows", "turns"}, "a compound with no window is left out"
+    assert (paths.obs["n_doses"] >= 2).all()
+    assert (paths.obs["window_low"] < paths.obs["window_high"]).all()
+
+
+def test_a_trajectory_needs_at_least_two_points(phenotypes):
+    with pytest.raises(ValueError, match="at least two"):
+        mt.tl.dose_trajectory(phenotypes, n_positions=1)
+
+
+def test_a_turning_compound_and_a_growing_one_do_not_match_on_their_paths(phenotypes):
+    """The reason to compare paths at all: `turns` and `grows` move different features in a different order."""
+    mt.tl.dose_direction(phenotypes)
+    paths = mt.tl.dose_trajectory(phenotypes, phases=("responding", "saturated"))
+    frame = pd.DataFrame(np.asarray(paths.X), index=list(paths.obs["Metadata_Compound"]))
+    assert {"grows", "turns"} <= set(frame.index)
+    assert frame.loc["grows"].corr(frame.loc["turns"]) < 0.5

From 80d25d31e5bf86ace1933f8498c721957ce9179a Mon Sep 17 00:00:00 2001
From: anon <tim.treis@helmholtz-munich.de>
Date: Sun, 20 Sep 2026 17:55:36 +0200
Subject: [PATCH 3/5] Score viability per plate, and say what the phenotypes
 are

Viability was read against the whole screen's controls. The OASIS pilot's eight
plates carry 484 to 708 control cells per field, so a sparse plate read as one
whose treated wells were dying: actinomycin D was called cytotoxic above 60 uM
when only its 300 uM well is. It is now read against each plate's own controls.

The two HepaRG batches still disagree about cell loss after that, and the
tutorial now shows the check rather than the conclusion.

dose_direction also broadcasts the dose it binned each well to, so wells can be
grouped the way its table reports them. Grouping on the raw column alternates
between batches at every step of the ladder.

The tutorial gains the biology: what the cited CellProfiler features measure,
what each compound does, and a figure of staurosporine's early and late features
against dose. Its distance saturates at 0.4 uM where the cells round up and
chromatin moves outward, then at 300 uM every stain begins to correlate with
every other inside the nucleus, which is a cell losing its compartments.

From the cleanup review: a shared control-scale helper, a nanmedian that skips
numpy's masked-array path on short blocks, one row-index pass per group, and a
memo that no longer recomputes what it memoizes. dose_direction goes from 55s to
4s on a 500-compound screen.
---
 docs/tutorials/11_dose_response.ipynb | 776 ++++++++++++++++----------
 src/mantispy/pl/__init__.py           |   4 +-
 src/mantispy/pl/_hits.py              |  17 +-
 src/mantispy/tl/__init__.py           |   4 +-
 src/mantispy/tl/_dose.py              | 369 ++++++------
 tests/test_pl_hits.py                 |   2 +-
 tests/test_tl_dose.py                 |  43 +-
 7 files changed, 749 insertions(+), 466 deletions(-)

diff --git a/docs/tutorials/11_dose_response.ipynb b/docs/tutorials/11_dose_response.ipynb
index a41ba0e..9c94fe8 100644
--- a/docs/tutorials/11_dose_response.ipynb
+++ b/docs/tutorials/11_dose_response.ipynb
@@ -7,16 +7,22 @@
    "source": [
     "# 11. Concentration response\n",
     "\n",
-    "[5. Hits and effects](05_hits_and_effects.ipynb) asked whether a treatment moved. This page asks how much of it\n",
-    "was needed, which is a different design and a different set of traps:\n",
+    "[5. Hits and effects](05_hits_and_effects.ipynb) asked whether a treatment did anything. This page asks how much\n",
+    "of it was needed, and what the cell did on the way there.\n",
     "\n",
-    "- Most compounds never plateau inside the tested range, so a four-parameter logistic has no EC50 to find.\n",
-    "- Distance from the controls rises when a compound kills cells, which is not the same finding as a phenotype.\n",
-    "- A dose series usually spends its wells on concentrations rather than replicates, so any single concentration is\n",
-    "  thinly measured and the evidence lives in the shape across them.\n",
+    "The dataset is [the OASIS pilot](../datasets/oasis_pilot.ipynb). OASIS is a liver toxicity screen: its main cell\n",
+    "line is HepaRG, a hepatic progenitor line that differentiates into hepatocyte-like cells and keeps much of the\n",
+    "drug-metabolising machinery a real liver has. That is the point of using it — a compound that only becomes toxic\n",
+    "after the liver metabolises it will not show up in a cell line that cannot metabolise it. Thirty-six compounds\n",
+    "were dosed over ten concentrations, a three-fold ladder from 15 nM to 300 uM, with eight replicate wells at each\n",
+    "concentration spread over eight plates.\n",
+    "\n",
+    "Three things make a dose series harder than a single-concentration screen:\n",
     "\n",
-    "The dataset is [the OASIS pilot](../datasets/oasis_pilot.ipynb): 36 compounds over ten concentrations, in two\n",
-    "cell lines."
+    "- Most compounds never plateau inside the tested range, so a four-parameter logistic has no EC50 to find.\n",
+    "- Distance from the controls grows when a compound kills cells, which is a different finding from a phenotype.\n",
+    "- Most of the ladder is usually empty. You screen three decades because you do not know where the compound's\n",
+    "  effective range is, so the concentrations below it and, sometimes, above it carry nothing."
    ]
   },
   {
@@ -25,10 +31,10 @@
    "id": "bac81e87",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-09-20T15:03:31.360700Z",
-     "iopub.status.busy": "2026-09-20T15:03:31.360587Z",
-     "iopub.status.idle": "2026-09-20T15:03:38.790782Z",
-     "shell.execute_reply": "2026-09-20T15:03:38.789395Z"
+     "iopub.execute_input": "2026-09-20T15:50:03.427595Z",
+     "iopub.status.busy": "2026-09-20T15:50:03.427464Z",
+     "iopub.status.idle": "2026-09-20T15:50:09.172405Z",
+     "shell.execute_reply": "2026-09-20T15:50:09.171661Z"
     }
    },
    "outputs": [
@@ -87,10 +93,10 @@
    "id": "58b04c93",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-09-20T15:03:38.794164Z",
-     "iopub.status.busy": "2026-09-20T15:03:38.793781Z",
-     "iopub.status.idle": "2026-09-20T15:03:39.197108Z",
-     "shell.execute_reply": "2026-09-20T15:03:39.196310Z"
+     "iopub.execute_input": "2026-09-20T15:50:09.174328Z",
+     "iopub.status.busy": "2026-09-20T15:50:09.174191Z",
+     "iopub.status.idle": "2026-09-20T15:50:09.615316Z",
+     "shell.execute_reply": "2026-09-20T15:50:09.614752Z"
     }
    },
    "outputs": [
@@ -117,8 +123,8 @@
    "source": [
     "## A mantispy object is an AnnData object\n",
     "\n",
-    "Nothing stops scanpy from working on it, which is the point of the format. The PCA, the neighbour graph and the\n",
-    "UMAP come from scanpy; the colours come from the columns `mt.ds.oasis_pilot` attached."
+    "scanpy works on it directly. The PCA, the neighbour graph and the UMAP below come from scanpy; the colours come\n",
+    "from the columns `mt.ds.oasis_pilot` attached."
    ]
   },
   {
@@ -127,10 +133,10 @@
    "id": "8e9ef049",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-09-20T15:03:39.198960Z",
-     "iopub.status.busy": "2026-09-20T15:03:39.198822Z",
-     "iopub.status.idle": "2026-09-20T15:03:51.657114Z",
-     "shell.execute_reply": "2026-09-20T15:03:51.656351Z"
+     "iopub.execute_input": "2026-09-20T15:50:09.617130Z",
+     "iopub.status.busy": "2026-09-20T15:50:09.617001Z",
+     "iopub.status.idle": "2026-09-20T15:50:21.790666Z",
+     "shell.execute_reply": "2026-09-20T15:50:21.789975Z"
     }
    },
    "outputs": [
@@ -138,7 +144,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/var/folders/qg/qgc908995g3fc8qtss2fsbhhxyxxj4/T/ipykernel_92610/992733786.py:7: UserWarning: This figure includes Axes that are not compatible with tight_layout, so results might be incorrect.\n",
+      "/var/folders/qg/qgc908995g3fc8qtss2fsbhhxyxxj4/T/ipykernel_64880/992733786.py:7: UserWarning: This figure includes Axes that are not compatible with tight_layout, so results might be incorrect.\n",
       "  plt.tight_layout()\n"
      ]
     },
@@ -168,14 +174,14 @@
    "id": "d134ea07",
    "metadata": {},
    "source": [
-    "The controls sit together and the highest concentrations sit furthest from them, which is what a dose series\n",
-    "should look like before any statistics are run.\n",
+    "The controls sit together and the highest concentrations sit furthest from them, which is the shape a dose series\n",
+    "should have before any statistics are run.\n",
     "\n",
     "## From profiles to a response per well\n",
     "\n",
     "{func}`~mantispy.tl.hit_calling` writes two columns. `hits_distance` is the group's statistic repeated over its\n",
-    "rows; `hits_row_distance` is each well's own distance from the control centroid. The curve fit needs the second,\n",
-    "because a column that is constant within a group carries no dose information at all."
+    "rows; `hits_row_distance` is each well's own distance from the control centroid. The curve fit needs the second:\n",
+    "a column that is constant within a group carries no dose information."
    ]
   },
   {
@@ -184,10 +190,10 @@
    "id": "e0c13226",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-09-20T15:03:51.660843Z",
-     "iopub.status.busy": "2026-09-20T15:03:51.660712Z",
-     "iopub.status.idle": "2026-09-20T15:03:52.671952Z",
-     "shell.execute_reply": "2026-09-20T15:03:52.671318Z"
+     "iopub.execute_input": "2026-09-20T15:50:21.794220Z",
+     "iopub.status.busy": "2026-09-20T15:50:21.794081Z",
+     "iopub.status.idle": "2026-09-20T15:50:22.802751Z",
+     "shell.execute_reply": "2026-09-20T15:50:22.802198Z"
     }
    },
    "outputs": [
@@ -213,10 +219,10 @@
    "id": "f12d2d39",
    "metadata": {},
    "source": [
-    "That warning is worth reading rather than dismissing. Nearly every compound-at-a-concentration here sits in one\n",
-    "or two wells, so the per-group hit call is thin. This is the trade the plate map made: concentrations instead of\n",
-    "replicates. It is also why the next step is a curve rather than a table of per-concentration tests — a fit reads\n",
-    "all ten concentrations together, and the shape across them is what carries the evidence.\n",
+    "The warning is about replication, and it is accurate. Each compound-at-a-concentration sits in eight wells here,\n",
+    "which is generous for a dose series, but the hit call groups by `Metadata_Perturbation`, and the assay-development\n",
+    "plates contribute groups of one or two. A curve reads all ten concentrations together, so the shape across them\n",
+    "carries the evidence even where a single concentration is thin.\n",
     "\n",
     "## Fitting the concentration response"
    ]
@@ -227,10 +233,10 @@
    "id": "a5fdbb04",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-09-20T15:03:52.673748Z",
-     "iopub.status.busy": "2026-09-20T15:03:52.673588Z",
-     "iopub.status.idle": "2026-09-20T15:03:55.545778Z",
-     "shell.execute_reply": "2026-09-20T15:03:55.545212Z"
+     "iopub.execute_input": "2026-09-20T15:50:22.804489Z",
+     "iopub.status.busy": "2026-09-20T15:50:22.804367Z",
+     "iopub.status.idle": "2026-09-20T15:50:25.698151Z",
+     "shell.execute_reply": "2026-09-20T15:50:25.697681Z"
     }
    },
    "outputs": [
@@ -412,20 +418,21 @@
    "id": "c0c6995c",
    "metadata": {},
    "source": [
-    "Three columns answer three different questions, and they disagree on purpose.\n",
+    "Three columns answer three different questions.\n",
     "\n",
     "`spearman` with its `qvalue` asks whether the response rises with dose at all. It assumes no shape and works with\n",
     "three points.\n",
     "\n",
-    "`fit_ok` asks whether the logistic converged on something curve-shaped with an EC50 inside the tested range. On\n",
-    "this screen it is `False` for most compounds, and that is honest: a compound still climbing at the top\n",
-    "concentration has no EC50 in range, so no potency should be quoted for it.\n",
+    "`fit_ok` asks whether the logistic converged on something curve-shaped with an EC50 inside the tested range. It is\n",
+    "`False` for most compounds here: a compound still climbing at the top concentration has no EC50 in range, so no\n",
+    "potency should be quoted for it.\n",
     "\n",
-    "`hitcall` asks the screener's question, whether the response is large next to the noise the controls carry. It is\n",
-    "the product of three confidences from the ToxCast pipeline: that the curve beats a flat fit, that some\n",
-    "concentration's median clears the cutoff, and that the fitted top clears it. The cutoff is three times the\n",
-    "controls' MAD unless you pass one. `hitcall_model` names the shape that won: `logistic` where the curve\n",
-    "plateaus, `linear` where it is still climbing."
+    "`hitcall` asks whether the response is large next to the noise the controls carry. It is the product of three\n",
+    "confidences from the ToxCast pipeline: that the curve beats a flat fit, that some concentration's median clears\n",
+    "the cutoff, and that the fitted top clears it. The cutoff is three times the controls' MAD unless you pass one.\n",
+    "`hitcall_model` names the shape that won: `logistic` where the curve plateaus, `linear` where it is still\n",
+    "climbing. Note that it saturates at 1.0 for the strongest compounds, so it separates active from inactive but\n",
+    "does not rank the actives against each other."
    ]
   },
   {
@@ -434,10 +441,10 @@
    "id": "2f812053",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-09-20T15:03:55.547842Z",
-     "iopub.status.busy": "2026-09-20T15:03:55.547672Z",
-     "iopub.status.idle": "2026-09-20T15:03:56.049295Z",
-     "shell.execute_reply": "2026-09-20T15:03:56.048565Z"
+     "iopub.execute_input": "2026-09-20T15:50:25.699621Z",
+     "iopub.status.busy": "2026-09-20T15:50:25.699504Z",
+     "iopub.status.idle": "2026-09-20T15:50:26.192842Z",
+     "shell.execute_reply": "2026-09-20T15:50:26.192203Z"
     }
    },
    "outputs": [
@@ -465,9 +472,9 @@
    "id": "4891d209",
    "metadata": {},
    "source": [
-    "Staurosporine inhibits kinases broadly and climbs far past the band. Mupirocin inhibits a bacterial enzyme and\n",
-    "has no target in a human cell, so it never leaves it. Berberine barely leaves it either in this cell line, and\n",
-    "the last section says why that is worth checking rather than resolving here."
+    "Staurosporine is a broad-spectrum kinase inhibitor and the standard positive control in cell painting; it climbs\n",
+    "far past the band. Mupirocin inhibits bacterial isoleucyl-tRNA synthetase and has no target in a human cell, so\n",
+    "it never leaves it. Berberine barely leaves it either in this cell line."
    ]
   },
   {
@@ -478,14 +485,13 @@
     "## Which features move, and at what concentration\n",
     "\n",
     "The curve above reads one number per well, its distance from the controls. That number says a compound did\n",
-    "something; it cannot say what. {func}`~mantispy.tl.dose_features` asks the same question of every feature and\n",
-    "reports, for each one, the lowest concentration at which its median response reaches three times the spread the\n",
-    "controls show on it.\n",
-    "\n",
-    "That is a benchmark dose rather than an EC50, and the difference matters here. An EC50 needs a plateau, and the\n",
-    "section above showed most compounds never reach one inside the tested range. A benchmark dose only needs the\n",
-    "response to cross a line the controls set, so it is defined for a feature that is still climbing at the top\n",
-    "concentration, which is most of them. The ToxCast pipeline reads its `3 * bmad` the same way."
+    "something; it does not say what. {func}`~mantispy.tl.dose_features` asks the same question of every feature and\n",
+    "reports the lowest concentration at which each one's median response reaches three times the spread the controls\n",
+    "show on it.\n",
+    "\n",
+    "That is a benchmark dose rather than an EC50. An EC50 needs a plateau, and most compounds here never reach one.\n",
+    "A benchmark dose only needs the response to cross a line the controls set, so it is defined for a feature that is\n",
+    "still climbing at the top concentration. The ToxCast pipeline reads its `3 * bmad` the same way."
    ]
   },
   {
@@ -494,10 +500,10 @@
    "id": "dfa80cf8",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-09-20T15:03:56.051320Z",
-     "iopub.status.busy": "2026-09-20T15:03:56.051169Z",
-     "iopub.status.idle": "2026-09-20T15:03:56.146358Z",
-     "shell.execute_reply": "2026-09-20T15:03:56.145747Z"
+     "iopub.execute_input": "2026-09-20T15:50:26.194598Z",
+     "iopub.status.busy": "2026-09-20T15:50:26.194474Z",
+     "iopub.status.idle": "2026-09-20T15:50:26.253406Z",
+     "shell.execute_reply": "2026-09-20T15:50:26.252941Z"
     }
    },
    "outputs": [
@@ -539,7 +545,7 @@
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th>920</th>\n",
+       "      <th>2504</th>\n",
        "      <td>Cytoplasm_AreaShape_Zernike_1_1</td>\n",
        "      <td>0.336446</td>\n",
        "      <td>3.445138</td>\n",
@@ -548,7 +554,7 @@
        "      <td>0.655931</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>926</th>\n",
+       "      <th>2510</th>\n",
        "      <td>Cytoplasm_AreaShape_Zernike_6_2</td>\n",
        "      <td>0.362584</td>\n",
        "      <td>4.209308</td>\n",
@@ -557,7 +563,7 @@
        "      <td>0.012627</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>963</th>\n",
+       "      <th>2547</th>\n",
        "      <td>Nuclei_Correlation_Overlap_AGP_DNA</td>\n",
        "      <td>0.368756</td>\n",
        "      <td>3.983743</td>\n",
@@ -566,7 +572,7 @@
        "      <td>0.099024</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>923</th>\n",
+       "      <th>2507</th>\n",
        "      <td>Cytoplasm_AreaShape_Zernike_5_1</td>\n",
        "      <td>0.397345</td>\n",
        "      <td>4.653107</td>\n",
@@ -575,7 +581,7 @@
        "      <td>0.012627</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>893</th>\n",
+       "      <th>2477</th>\n",
        "      <td>Cells_AreaShape_Zernike_2_0</td>\n",
        "      <td>0.452776</td>\n",
        "      <td>3.777016</td>\n",
@@ -584,7 +590,7 @@
        "      <td>0.017164</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>971</th>\n",
+       "      <th>2555</th>\n",
        "      <td>Nuclei_RadialDistribution_FracAtD_DNA_3of4</td>\n",
        "      <td>0.467828</td>\n",
        "      <td>5.298065</td>\n",
@@ -593,7 +599,7 @@
        "      <td>0.000017</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>925</th>\n",
+       "      <th>2509</th>\n",
        "      <td>Cytoplasm_AreaShape_Zernike_6_0</td>\n",
        "      <td>0.760535</td>\n",
        "      <td>3.965238</td>\n",
@@ -602,7 +608,7 @@
        "      <td>0.018215</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>947</th>\n",
+       "      <th>2531</th>\n",
        "      <td>Cytoplasm_RadialDistribution_MeanFrac_Brightfi...</td>\n",
        "      <td>0.788329</td>\n",
        "      <td>3.821740</td>\n",
@@ -615,25 +621,25 @@
        "</div>"
       ],
       "text/plain": [
-       "                                               feature       bmd     max_z  \\\n",
-       "920                    Cytoplasm_AreaShape_Zernike_1_1  0.336446  3.445138   \n",
-       "926                    Cytoplasm_AreaShape_Zernike_6_2  0.362584  4.209308   \n",
-       "963                 Nuclei_Correlation_Overlap_AGP_DNA  0.368756  3.983743   \n",
-       "923                    Cytoplasm_AreaShape_Zernike_5_1  0.397345  4.653107   \n",
-       "893                        Cells_AreaShape_Zernike_2_0  0.452776  3.777016   \n",
-       "971         Nuclei_RadialDistribution_FracAtD_DNA_3of4  0.467828  5.298065   \n",
-       "925                    Cytoplasm_AreaShape_Zernike_6_0  0.760535  3.965238   \n",
-       "947  Cytoplasm_RadialDistribution_MeanFrac_Brightfi...  0.788329  3.821740   \n",
+       "                                                feature       bmd     max_z  \\\n",
+       "2504                    Cytoplasm_AreaShape_Zernike_1_1  0.336446  3.445138   \n",
+       "2510                    Cytoplasm_AreaShape_Zernike_6_2  0.362584  4.209308   \n",
+       "2547                 Nuclei_Correlation_Overlap_AGP_DNA  0.368756  3.983743   \n",
+       "2507                    Cytoplasm_AreaShape_Zernike_5_1  0.397345  4.653107   \n",
+       "2477                        Cells_AreaShape_Zernike_2_0  0.452776  3.777016   \n",
+       "2555         Nuclei_RadialDistribution_FracAtD_DNA_3of4  0.467828  5.298065   \n",
+       "2509                    Cytoplasm_AreaShape_Zernike_6_0  0.760535  3.965238   \n",
+       "2531  Cytoplasm_RadialDistribution_MeanFrac_Brightfi...  0.788329  3.821740   \n",
        "\n",
-       "     direction  spearman    qvalue  \n",
-       "920        1.0  0.066715  0.655931  \n",
-       "926        1.0  0.313785  0.012627  \n",
-       "963       -1.0 -0.218047  0.099024  \n",
-       "923        1.0  0.313973  0.012627  \n",
-       "893        1.0  0.300500  0.017164  \n",
-       "971        1.0  0.518075  0.000017  \n",
-       "925        1.0  0.296635  0.018215  \n",
-       "947       -1.0 -0.559913  0.000002  "
+       "      direction  spearman    qvalue  \n",
+       "2504        1.0  0.066715  0.655931  \n",
+       "2510        1.0  0.313785  0.012627  \n",
+       "2547       -1.0 -0.218047  0.099024  \n",
+       "2507        1.0  0.313973  0.012627  \n",
+       "2477        1.0  0.300500  0.017164  \n",
+       "2555        1.0  0.518075  0.000017  \n",
+       "2509        1.0  0.296635  0.018215  \n",
+       "2531       -1.0 -0.559913  0.000002  "
       ]
      },
      "execution_count": 7,
@@ -655,15 +661,16 @@
    "id": "439eda32",
    "metadata": {},
    "source": [
-    "The first features to move do so at a few hundred nanomolar, and they are measurements of shape and of where\n",
-    "intensity sits rather than how much of it there is: Zernike moments of the cytoplasm, and how far out in the\n",
-    "nucleus the DNA stain lies.\n",
-    "\n",
-    "`spearman` and `bmd` disagree in the first row, which is worth understanding rather than averaging away. A\n",
-    "monotonic trend across the whole range and a threshold crossing are different questions. A feature that steps up\n",
-    "early and then flattens has a low Spearman and a low benchmark dose; a feature that rises steadily to a small\n",
-    "final value has a high Spearman and no benchmark dose at all. Read both, and treat a crossing with no trend\n",
-    "behind it as the weakest kind of evidence."
+    "The features that move first are shape and spatial-distribution measurements, not intensity ones.\n",
+    "\n",
+    "`Cytoplasm_AreaShape_Zernike_*` are Zernike moments: an orthogonal decomposition of the cytoplasm outline, so a\n",
+    "change means the cells are changing shape rather than getting brighter. `Nuclei_RadialDistribution_FracAtD_DNA_3of4`\n",
+    "is the fraction of the DNA signal falling in the third of four concentric rings measured out from the nucleus\n",
+    "centre — chromatin moving toward the nuclear periphery, which is what condensing chromatin does.\n",
+    "\n",
+    "`spearman` and `bmd` disagree in the first row. A monotone trend across the whole range and a threshold crossing\n",
+    "are different questions: a feature that steps up early and then flattens has a low Spearman and a low benchmark\n",
+    "dose. A crossing with no trend behind it is the weakest evidence in the table."
    ]
   },
   {
@@ -672,10 +679,10 @@
    "id": "5d2eaa26",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-09-20T15:03:56.148687Z",
-     "iopub.status.busy": "2026-09-20T15:03:56.148450Z",
-     "iopub.status.idle": "2026-09-20T15:03:56.295719Z",
-     "shell.execute_reply": "2026-09-20T15:03:56.295155Z"
+     "iopub.execute_input": "2026-09-20T15:50:26.255363Z",
+     "iopub.status.busy": "2026-09-20T15:50:26.255235Z",
+     "iopub.status.idle": "2026-09-20T15:50:26.397241Z",
+     "shell.execute_reply": "2026-09-20T15:50:26.396599Z"
     }
    },
    "outputs": [
@@ -722,16 +729,17 @@
    "id": "5ab2e9f2",
    "metadata": {},
    "source": [
-    "The potencies separate by three orders of magnitude: staurosporine moves a feature at 0.34 uM, fluazinam at 8.6,\n",
-    "clioquinol at 29, amperozide at 47. Mupirocin moves none of the 99, which is the calibration this plot needs — a\n",
-    "compound with no target in a human cell should not cross a control-derived cutoff anywhere.\n",
-    "\n",
-    "The feature names repeat across compounds, though. `Nuclei_Correlation_Correlation_Brightfield_DNA` and\n",
-    "`Cells_Correlation_Overlap_DNA_Mito` are among the first five of amperozide, clioquinol and fluazinam alike; for\n",
-    "staurosporine they are twenty-first and ninth. Either those two are what a stressed cell does whatever the\n",
-    "mechanism, or they are simply the features this plate measures most sensitively. Either way a list of responsive\n",
-    "features per compound is not yet a claim that the compounds do different things, and the count of them is not a\n",
-    "measure of how much happened."
+    "The potencies span three orders of magnitude: staurosporine moves a feature at 0.34 uM, fluazinam at 8.6,\n",
+    "clioquinol at 29, amperozide at 47. Mupirocin moves none of the 99, which is what a compound with no mammalian\n",
+    "target should do and is the check that the cutoff is set sensibly.\n",
+    "\n",
+    "Two features recur near the front of amperozide, clioquinol and fluazinam:\n",
+    "`Nuclei_Correlation_Correlation_Brightfield_DNA` and `Cells_Correlation_Overlap_DNA_Mito`. Both measure how far\n",
+    "two channels agree with each other rather than how much of either there is, and channel agreement rises whenever\n",
+    "segmentation starts to struggle — in a rounding or dying cell, every stain collapses into the same small region.\n",
+    "For staurosporine the same two features rank twenty-first and ninth, so its early response is not that. Shared\n",
+    "responsive features across compounds are a reason to look at the direction, not yet evidence of a shared\n",
+    "mechanism."
    ]
   },
   {
@@ -741,18 +749,16 @@
    "source": [
     "## The same phenotype, or a different one?\n",
     "\n",
-    "A distance from the controls grows for two reasons that matter differently: the same phenotype gets stronger, or a\n",
-    "different phenotype takes over. A distance cannot tell them apart, because it throws the direction away.\n",
+    "A distance from the controls grows for two reasons that mean different things: the same phenotype gets stronger,\n",
+    "or a different one takes over. Distance cannot separate them, because it discards the direction.\n",
     "\n",
-    "{func}`~mantispy.tl.dose_direction` keeps it. Each concentration gets `amplitude`, how far its median profile sits\n",
-    "from the controls in MADs per feature; `split_half_cosine`, the cosine between the directions the two halves of\n",
-    "its replicate plates point in; and `cosine_to_top`, the cosine against the highest concentration's profile.\n",
-    "`amplitude_null` is what control wells spread over the same plates in the same numbers reach, so the first column\n",
-    "has a floor.\n",
+    "{func}`~mantispy.tl.dose_direction` keeps the direction. Each concentration gets `amplitude`, how far its median\n",
+    "profile sits from the controls in MADs per feature; `split_half_cosine`, the cosine between the directions the\n",
+    "two halves of its replicate plates point in; and `cosine_to_top`, the cosine against the highest concentration's\n",
+    "profile. `amplitude_null` is what control wells spread over the same plates in the same numbers reach.\n",
     "\n",
-    "The three only mean anything together. A concentration whose plate halves disagree has no direction at all,\n",
-    "however large its amplitude. One whose halves agree but which points away from the top concentration is a real\n",
-    "phenotype, and a different one."
+    "Read together: a concentration whose plate halves disagree has no direction at all, however large its amplitude.\n",
+    "One whose halves agree but which points away from the top concentration is a real phenotype, and a different one."
    ]
   },
   {
@@ -761,10 +767,10 @@
    "id": "fb8077a7",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-09-20T15:03:56.297776Z",
-     "iopub.status.busy": "2026-09-20T15:03:56.297633Z",
-     "iopub.status.idle": "2026-09-20T15:03:58.023420Z",
-     "shell.execute_reply": "2026-09-20T15:03:58.022778Z"
+     "iopub.execute_input": "2026-09-20T15:50:26.398953Z",
+     "iopub.status.busy": "2026-09-20T15:50:26.398833Z",
+     "iopub.status.idle": "2026-09-20T15:50:26.846007Z",
+     "shell.execute_reply": "2026-09-20T15:50:26.845305Z"
     }
    },
    "outputs": [
@@ -805,62 +811,158 @@
    "id": "903c4e54",
    "metadata": {},
    "source": [
-    "Four compounds, four shapes.\n",
-    "\n",
-    "Amperozide and clioquinol are the straightforward case. Nothing happens below about 30 uM — amplitude at the\n",
-    "control floor, plate halves disagreeing — and then the amplitude climbs to 2.3 and 2.4 MADs per feature while\n",
-    "`cosine_to_top` climbs with it, to 0.86 and 0.93 at 100 uM. One phenotype, arriving late and getting louder. For\n",
-    "these two a single number per concentration loses nothing.\n",
+    "Amperozide and clioquinol are the simple case. Nothing happens below about 30 uM, and then the amplitude climbs\n",
+    "to 2.3 and 2.4 MADs per feature with `cosine_to_top` climbing alongside it to 0.86 and 0.93 at 100 uM. One\n",
+    "phenotype, arriving late and getting stronger. A single number per concentration loses nothing for these two.\n",
     "\n",
     "Staurosporine is the case a single number hides. Its amplitude reaches 1.5 at 0.41 uM and then stops: 1.9 at\n",
-    "1.2 uM, 2.1 at 33, 2.0 at 300. Read as a distance it saturates two decades below the top of the range, and the\n",
-    "curve fitted earlier says the same. But the plate halves agree from 0.41 uM on, 0.76 and above, so every one of\n",
-    "those concentrations has a direction that reproduces — and `cosine_to_top` stays between 0.16 and 0.29. The cell\n",
-    "is doing something different at 0.41 uM than at 300 uM. The distance stopped moving at exactly the concentration\n",
-    "the phenotype started changing, which is why no summary built on distance alone can see this.\n",
-    "\n",
-    "Fluazinam never settles. Its halves agree from 11 uM up, yet `cosine_to_top` stays at or below 0.37 the whole way,\n",
-    "and its amplitude falls from 2.8 to 1.7 between 100 and 300 uM. Something else happens in the top well; this page\n",
-    "does not establish what, and the cytotoxicity below is the first thing to check.\n",
-    "\n",
-    "Mupirocin is the floor: amplitude between 0.40 and 0.81 against a null of 0.42, and no concentration whose halves\n",
-    "agree above 0.41. Its 33 uM well reaches nearly twice the null amplitude and still has no direction, which is the\n",
-    "reason to read the two columns together rather than putting a threshold on the first."
+    "1.2 uM, 2.1 at 33, 2.0 at 300. As a distance it saturates two decades below the top of the range. But its plate\n",
+    "halves agree from 0.41 uM on, 0.76 and above, so each of those concentrations has a direction that reproduces —\n",
+    "and `cosine_to_top` stays between 0.16 and 0.29. The cell is doing something different at 0.41 uM than at 300 uM.\n",
+    "The next section shows what.\n",
+    "\n",
+    "Fluazinam is an uncoupler of mitochondrial oxidative phosphorylation. Its halves agree from 11 uM, `cosine_to_top`\n",
+    "stays at or below 0.37 throughout, and its amplitude falls from 2.8 to 1.7 between 100 and 300 uM. Its top well\n",
+    "is doing something this page does not resolve.\n",
+    "\n",
+    "Mupirocin sits on the floor: amplitude between 0.40 and 0.81 against a null of 0.42, and no concentration whose\n",
+    "halves agree above 0.41. Its 33 uM well reaches nearly twice the null amplitude with no reproducible direction,\n",
+    "which is why amplitude alone is not enough to call a response."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "40f0f18f",
+   "metadata": {},
+   "source": [
+    "### What staurosporine's two phenotypes are\n",
+    "\n",
+    "`cosine_to_top` says the direction turns. It does not say into what. Each column of `heparg.X` is already scaled\n",
+    "against its own plate's controls, so the median of a concentration's wells reads directly in control MADs and the\n",
+    "features can be plotted against dose.\n",
+    "\n",
+    "Group on `obs[\"dose_direction_dose\"]`, the concentration `dose_direction` binned each well to, rather than on the\n",
+    "raw column: the two batches write the same nominal concentration to different precision, and grouping on the raw\n",
+    "values alternates between batches at every step of the ladder.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "14a71eeb",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-09-20T15:50:26.848001Z",
+     "iopub.status.busy": "2026-09-20T15:50:26.847871Z",
+     "iopub.status.idle": "2026-09-20T15:50:27.167731Z",
+     "shell.execute_reply": "2026-09-20T15:50:27.166516Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1100x340 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "early = [\n",
+    "    \"Cells_AreaShape_Zernike_2_0\",\n",
+    "    \"Nuclei_RadialDistribution_FracAtD_DNA_3of4\",\n",
+    "    \"Nuclei_RadialDistribution_MeanFrac_RNA_3of4\",\n",
+    "]\n",
+    "late = [\n",
+    "    \"Nuclei_Correlation_Correlation_AGP_Mito\",\n",
+    "    \"Nuclei_Correlation_Correlation_Mito_RNA\",\n",
+    "    \"Nuclei_Correlation_Overlap_AGP_ER\",\n",
+    "]\n",
+    "\n",
+    "stauro = heparg[heparg.obs[\"Metadata_Compound\"] == \"Staurosporine\"]\n",
+    "traces = pd.DataFrame(np.asarray(stauro[:, early + late].X), columns=early + late)\n",
+    "# The binned dose, not the raw one: the two batches spell the same concentration differently, and\n",
+    "# grouping on the raw column would alternate between them at every step.\n",
+    "traces[\"dose\"] = stauro.obs[\"dose_direction_dose\"].to_numpy(dtype=float)\n",
+    "traces = traces.groupby(\"dose\").median()\n",
+    "\n",
+    "fig, axes = plt.subplots(1, 2, figsize=(11, 3.4), sharey=True)\n",
+    "for ax, group, title in zip(\n",
+    "    axes, [early, late], [\"moves at 0.4 uM: shape and chromatin\", \"moves at 300 uM: channels collapsing\"], strict=True\n",
+    "):\n",
+    "    for feature in group:\n",
+    "        ax.plot(traces.index, traces[feature], marker=\"o\", ms=4, lw=1.5, label=feature.replace(\"_\", \" \"))\n",
+    "    ax.axhline(0, color=\"0.6\", lw=1)\n",
+    "    ax.set_xscale(\"log\")\n",
+    "    ax.set_xlabel(\"concentration (uM)\")\n",
+    "    ax.set_title(title, fontsize=10)\n",
+    "    ax.legend(fontsize=6.5, frameon=False)\n",
+    "axes[0].set_ylabel(\"MADs from the controls\")\n",
+    "fig.tight_layout()"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "d90ae042",
+   "id": "9ed588c9",
+   "metadata": {},
+   "source": [
+    "Two different things, at two different concentrations.\n",
+    "\n",
+    "At 0.4 uM the cells change shape and their chromatin and RNA move outward: a Zernike moment of the cell outline,\n",
+    "the fraction of DNA signal in the outer rings of the nucleus, the same for RNA. That is the morphology of a cell\n",
+    "that has rounded up and condensed its chromatin, which is what broad kinase inhibition does before anything else.\n",
+    "These three then change little across the remaining two and a half decades, and fall back at the very top\n",
+    "concentration.\n",
+    "\n",
+    "At 300 uM a different set takes over, and all of them are channel-to-channel correlations inside the nucleus\n",
+    "region: AGP against Mito, Mito against RNA, AGP against ER. AGP is the actin, Golgi and plasma-membrane stain.\n",
+    "Every stain agreeing with every other stain in the same place is not a phenotype in the usual sense — it is what\n",
+    "the image looks like when the cell has lost its internal organisation and the compartments no longer separate.\n",
+    "`Nuclei_Correlation_Correlation_AGP_Mito` reaches 10.8 MADs.\n",
+    "\n",
+    "So the distance saturating at 0.4 uM while the direction keeps turning is a real biological sequence: the\n",
+    "kinase-inhibition phenotype arrives first and stops, and cell disintegration takes over later. Two features\n",
+    "actually reverse sign between the two, including `Nuclei_Correlation_Overlap_AGP_DNA`. A curve fitted to distance\n",
+    "alone reports one EC50 for both and describes neither."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bed3255a",
    "metadata": {},
    "source": [
     "## Where on the ladder is anything happening?\n",
     "\n",
-    "Every column so far describes a concentration. None of them says which stretch of the ladder is worth reading,\n",
-    "and most of a ten-point series is usually not: the low end is below the compound's effective range and the high\n",
-    "end can be past the point where the cells are alive to have a phenotype.\n",
+    "Every column so far describes a single concentration. None says which stretch of the ladder is worth reading.\n",
+    "\n",
+    "`dose_direction` labels each concentration with a `phase`:\n",
     "\n",
-    "`dose_direction` labels each concentration with a `phase`. `silent` is a concentration with no reproducible\n",
-    "response. `responding` is one that is still moving: the step from the concentration below it, `step_amplitude`,\n",
-    "beats the noise two independent groups of wells carry. `saturated` is one that reproduces but has stopped\n",
-    "changing. `cytotoxic` is one that has lost more than half its cells, where the profile is the morphology of\n",
-    "dying cells whatever else is true of it, and which the US EPA's phenotypic pipeline drops before fitting.\n",
+    "- `silent` — no reproducible response.\n",
+    "- `responding` — still moving. The step from the concentration below, `step_amplitude`, beats the noise two\n",
+    "  independent groups of wells carry.\n",
+    "- `saturated` — reproducible, but it has stopped changing.\n",
+    "- `cytotoxic` — more than half the cells are gone, so the profile is the morphology of dying cells whatever else\n",
+    "  is true of it. The US EPA's phenotypic pipeline drops these before fitting anything.\n",
     "\n",
     "The window is a run, not a scatter. `split_half_cosine` over eight wells is itself noisy, and labelling each\n",
-    "concentration on its own let one lucky concentration open amperozide's window at 3.7 uM instead of 33. A\n",
-    "response that has started does not stop, so the window is the run of concentrations reaching the highest one\n",
-    "that is not cytotoxic."
+    "concentration independently let one lucky concentration open amperozide's window at 3.7 uM instead of 33. A\n",
+    "response that has started does not stop, so the window is the run of concentrations reaching the highest one that\n",
+    "is not cytotoxic."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 11,
    "id": "369d9bf4",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-09-20T15:03:58.025621Z",
-     "iopub.status.busy": "2026-09-20T15:03:58.025450Z",
-     "iopub.status.idle": "2026-09-20T15:03:59.343685Z",
-     "shell.execute_reply": "2026-09-20T15:03:59.342971Z"
+     "iopub.execute_input": "2026-09-20T15:50:27.170385Z",
+     "iopub.status.busy": "2026-09-20T15:50:27.170241Z",
+     "iopub.status.idle": "2026-09-20T15:50:27.179021Z",
+     "shell.execute_reply": "2026-09-20T15:50:27.178385Z"
     }
    },
    "outputs": [
@@ -896,103 +998,103 @@
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th>180</th>\n",
+       "      <th>40</th>\n",
        "      <td>0.02</td>\n",
        "      <td>0.45</td>\n",
        "      <td>0.42</td>\n",
        "      <td>0.45</td>\n",
        "      <td>0.18</td>\n",
-       "      <td>0.91</td>\n",
+       "      <td>1.01</td>\n",
        "      <td>silent</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>181</th>\n",
+       "      <th>41</th>\n",
        "      <td>0.05</td>\n",
        "      <td>0.52</td>\n",
        "      <td>0.42</td>\n",
        "      <td>0.65</td>\n",
        "      <td>0.20</td>\n",
-       "      <td>0.96</td>\n",
+       "      <td>1.02</td>\n",
        "      <td>silent</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>182</th>\n",
+       "      <th>42</th>\n",
        "      <td>0.14</td>\n",
        "      <td>0.54</td>\n",
        "      <td>0.42</td>\n",
        "      <td>0.57</td>\n",
        "      <td>0.16</td>\n",
-       "      <td>0.86</td>\n",
+       "      <td>0.92</td>\n",
        "      <td>silent</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>183</th>\n",
+       "      <th>43</th>\n",
        "      <td>0.41</td>\n",
        "      <td>0.57</td>\n",
        "      <td>0.42</td>\n",
        "      <td>0.82</td>\n",
        "      <td>0.45</td>\n",
-       "      <td>0.90</td>\n",
+       "      <td>0.91</td>\n",
        "      <td>silent</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>184</th>\n",
+       "      <th>44</th>\n",
        "      <td>1.23</td>\n",
        "      <td>0.49</td>\n",
        "      <td>0.42</td>\n",
        "      <td>0.59</td>\n",
        "      <td>0.18</td>\n",
-       "      <td>0.87</td>\n",
+       "      <td>0.98</td>\n",
        "      <td>silent</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>185</th>\n",
+       "      <th>45</th>\n",
        "      <td>3.70</td>\n",
        "      <td>0.55</td>\n",
        "      <td>0.42</td>\n",
        "      <td>0.60</td>\n",
        "      <td>0.66</td>\n",
-       "      <td>0.97</td>\n",
+       "      <td>0.94</td>\n",
        "      <td>silent</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>186</th>\n",
+       "      <th>46</th>\n",
        "      <td>11.11</td>\n",
        "      <td>0.48</td>\n",
        "      <td>0.42</td>\n",
        "      <td>0.60</td>\n",
        "      <td>0.35</td>\n",
-       "      <td>0.94</td>\n",
+       "      <td>0.97</td>\n",
        "      <td>silent</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>187</th>\n",
+       "      <th>47</th>\n",
        "      <td>33.33</td>\n",
        "      <td>0.92</td>\n",
        "      <td>0.42</td>\n",
        "      <td>0.86</td>\n",
        "      <td>0.69</td>\n",
-       "      <td>0.97</td>\n",
+       "      <td>1.05</td>\n",
        "      <td>responding</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>188</th>\n",
+       "      <th>48</th>\n",
        "      <td>100.00</td>\n",
        "      <td>1.65</td>\n",
        "      <td>0.42</td>\n",
        "      <td>1.17</td>\n",
        "      <td>0.72</td>\n",
-       "      <td>0.84</td>\n",
+       "      <td>0.85</td>\n",
        "      <td>responding</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>189</th>\n",
+       "      <th>49</th>\n",
        "      <td>300.00</td>\n",
        "      <td>2.30</td>\n",
        "      <td>0.42</td>\n",
        "      <td>1.22</td>\n",
        "      <td>0.89</td>\n",
-       "      <td>0.74</td>\n",
+       "      <td>0.77</td>\n",
        "      <td>responding</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
@@ -1000,39 +1102,37 @@
        "</div>"
       ],
       "text/plain": [
-       "       dose  amplitude  amplitude_null  step_amplitude  split_half_cosine  \\\n",
-       "180    0.02       0.45            0.42            0.45               0.18   \n",
-       "181    0.05       0.52            0.42            0.65               0.20   \n",
-       "182    0.14       0.54            0.42            0.57               0.16   \n",
-       "183    0.41       0.57            0.42            0.82               0.45   \n",
-       "184    1.23       0.49            0.42            0.59               0.18   \n",
-       "185    3.70       0.55            0.42            0.60               0.66   \n",
-       "186   11.11       0.48            0.42            0.60               0.35   \n",
-       "187   33.33       0.92            0.42            0.86               0.69   \n",
-       "188  100.00       1.65            0.42            1.17               0.72   \n",
-       "189  300.00       2.30            0.42            1.22               0.89   \n",
+       "      dose  amplitude  amplitude_null  step_amplitude  split_half_cosine  \\\n",
+       "40    0.02       0.45            0.42            0.45               0.18   \n",
+       "41    0.05       0.52            0.42            0.65               0.20   \n",
+       "42    0.14       0.54            0.42            0.57               0.16   \n",
+       "43    0.41       0.57            0.42            0.82               0.45   \n",
+       "44    1.23       0.49            0.42            0.59               0.18   \n",
+       "45    3.70       0.55            0.42            0.60               0.66   \n",
+       "46   11.11       0.48            0.42            0.60               0.35   \n",
+       "47   33.33       0.92            0.42            0.86               0.69   \n",
+       "48  100.00       1.65            0.42            1.17               0.72   \n",
+       "49  300.00       2.30            0.42            1.22               0.89   \n",
        "\n",
-       "     viability       phase  \n",
-       "180       0.91      silent  \n",
-       "181       0.96      silent  \n",
-       "182       0.86      silent  \n",
-       "183       0.90      silent  \n",
-       "184       0.87      silent  \n",
-       "185       0.97      silent  \n",
-       "186       0.94      silent  \n",
-       "187       0.97  responding  \n",
-       "188       0.84  responding  \n",
-       "189       0.74  responding  "
+       "    viability       phase  \n",
+       "40       1.01      silent  \n",
+       "41       1.02      silent  \n",
+       "42       0.92      silent  \n",
+       "43       0.91      silent  \n",
+       "44       0.98      silent  \n",
+       "45       0.94      silent  \n",
+       "46       0.97      silent  \n",
+       "47       1.05  responding  \n",
+       "48       0.85  responding  \n",
+       "49       0.77  responding  "
       ]
      },
-     "execution_count": 10,
+     "execution_count": 11,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "mt.tl.dose_direction(heparg)\n",
-    "direction = heparg.uns[\"mantispy\"][\"dose_direction\"]\n",
     "direction[direction[\"compound\"] == \"Amperozide\"][\n",
     "    [\"dose\", \"amplitude\", \"amplitude_null\", \"step_amplitude\", \"split_half_cosine\", \"viability\", \"phase\"]\n",
     "].round(2)"
@@ -1050,20 +1150,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 12,
    "id": "9ef6316e",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-09-20T15:03:59.345430Z",
-     "iopub.status.busy": "2026-09-20T15:03:59.345302Z",
-     "iopub.status.idle": "2026-09-20T15:04:00.238807Z",
-     "shell.execute_reply": "2026-09-20T15:04:00.238313Z"
+     "iopub.execute_input": "2026-09-20T15:50:27.180751Z",
+     "iopub.status.busy": "2026-09-20T15:50:27.180623Z",
+     "iopub.status.idle": "2026-09-20T15:50:28.076265Z",
+     "shell.execute_reply": "2026-09-20T15:50:28.075727Z"
     }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 1300x640 with 6 Axes>"
       ]
@@ -1085,24 +1185,114 @@
   },
   {
    "cell_type": "markdown",
-   "id": "21a71197",
+   "id": "7a2c2cb3",
    "metadata": {},
    "source": [
-    "Six compounds, and the bands say more than the lines do.\n",
+    "Mupirocin is grey the whole way. Amperozide and cycloheximide are grey until about 20 uM and then respond to the\n",
+    "top, so seven of their ten concentrations sit below their effective range — which is the expected result of\n",
+    "covering three decades to find a window you cannot predict, not a wasted experiment.\n",
     "\n",
-    "Mupirocin is grey the whole way: nothing to read anywhere on its ladder. Amperozide and cycloheximide are grey\n",
-    "until roughly 20 uM and then respond to the top. That is not a wasted ladder, it is the point of one. A screen\n",
-    "covers three decades because it does not know where the window is, and most of the range it covers is expected\n",
-    "to be empty at both ends: below the effective concentration on one side, past the cells on the other.\n",
-    "Staurosporine responds from 0.14 uM to 300 without ever settling, which is the turning direction above seen\n",
-    "from another side: a compound whose step never falls back to the noise has not finished changing.\n",
+    "Staurosporine responds from 0.14 uM to 300 without settling, the same finding as the turning direction above:\n",
+    "a compound whose step never falls back to the noise has not finished changing.\n",
     "\n",
-    "Actinomycin D is the one to read carefully. It is already responding at the lowest concentration tested, so its\n",
-    "onset is somewhere below the ladder and this screen cannot say where. Its window then *ends*: above about\n",
-    "60 uM the cells are gone and the band turns red. Both edges of a dose series can fall outside it, and the\n",
-    "phase column is what makes that visible rather than something you infer from a curve that kept rising.\n",
+    "Actinomycin D is responding at the lowest concentration tested. It intercalates DNA and blocks RNA polymerase at\n",
+    "nanomolar concentrations, so 15 nM is already well inside its range and its onset lies below this ladder — the\n",
+    "screen cannot say where. Its viability declines steadily from 0.77 to 0.48 across the range, and only the 300 uM\n",
+    "well falls below half, where the band turns red. Both edges of a dose series can fall outside it.\n",
     "\n",
-    "The phase is on `obs`, so the window is an ordinary subset and the rest of the package applies to it."
+    "The phase lands on `obs`, so the window is an ordinary subset."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "16fae552",
+   "metadata": {},
+   "source": [
+    "### Check the cell counts before trusting a cytotoxic call\n",
+    "\n",
+    "`viability` is each well's cell count per field against **its own plate's** controls. That matters here: the eight\n",
+    "plates range from 484 to 708 control cells per field, so scoring against the whole screen's median would mark the\n",
+    "sparser plates as dying and hide real losses on the denser ones.\n",
+    "\n",
+    "Per-plate normalisation is necessary but not sufficient. The pilot's HepaRG wells come from two batches, and they\n",
+    "do not agree about cell loss."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "a7ab0d4c",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-09-20T15:50:28.078666Z",
+     "iopub.status.busy": "2026-09-20T15:50:28.078542Z",
+     "iopub.status.idle": "2026-09-20T15:50:28.093563Z",
+     "shell.execute_reply": "2026-09-20T15:50:28.093002Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "cells per field in treated wells, by batch:\n",
+      "            count    25%    50%    75%\n",
+      "batch                                 \n",
+      "HepaRG_AD  1871.0  496.3  552.6  606.3\n",
+      "Ptx_AD2     704.0  464.0  576.0  670.0\n",
+      "\n",
+      "control cells per field, by plate:\n",
+      "Metadata_Plate\n",
+      "BR00145690    587.0\n",
+      "BR00145691    572.0\n",
+      "BR00145692    524.0\n",
+      "BR00145693    652.0\n",
+      "BR00145694    597.0\n",
+      "BR00145695    484.0\n",
+      "BR00147878    696.0\n",
+      "BR00147879    708.0\n"
+     ]
+    }
+   ],
+   "source": [
+    "batch = np.where(heparg.obs[\"Metadata_Plate\"].astype(str).str.startswith(\"BR001478\"), \"Ptx_AD2\", \"HepaRG_AD\")\n",
+    "treated = ~heparg.obs[\"Metadata_Control\"].to_numpy(dtype=bool)\n",
+    "viability = direction.set_index([\"compound\", \"dose\"])[\"viability\"]\n",
+    "\n",
+    "heparg.obs[\"batch\"] = batch\n",
+    "per_batch = (\n",
+    "    heparg.obs[treated]\n",
+    "    .assign(viability=lambda frame: frame[\"Metadata_CellCount\"] / frame[\"Metadata_SiteCount\"])\n",
+    "    .groupby(\"batch\")[\"viability\"]\n",
+    "    .describe()[[\"count\", \"25%\", \"50%\", \"75%\"]]\n",
+    ")\n",
+    "print(\"cells per field in treated wells, by batch:\")\n",
+    "print(per_batch.round(1).to_string())\n",
+    "print(\"\\ncontrol cells per field, by plate:\")\n",
+    "print(\n",
+    "    heparg.obs[~treated]\n",
+    "    .assign(per_field=lambda frame: frame[\"Metadata_CellCount\"] / frame[\"Metadata_SiteCount\"])\n",
+    "    .groupby(\"Metadata_Plate\", observed=True)[\"per_field\"]\n",
+    "    .median()\n",
+    "    .round(0)\n",
+    "    .to_string()\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "15c01b0d",
+   "metadata": {},
+   "source": [
+    "Against its own plate's controls, the median treated well in the `HepaRG_AD` batch sits at 0.98 of control and the\n",
+    "median treated well in `Ptx_AD2` at 0.82. The gap is not dose-driven: `Ptx_AD2` reads low at the bottom of the\n",
+    "ladder too, where nothing should be happening yet. Its platemap also carries `cell_density` and `culture_time`\n",
+    "columns the other batch does not, so the two were not seeded or cultured identically.\n",
+    "\n",
+    "An earlier draft of this page, which scored viability against the whole screen at once, called actinomycin D\n",
+    "cytotoxic above 60 uM. Per plate, only its 300 uM well is. The first version was reporting the batch, not the\n",
+    "compound. A cytotoxicity call is a claim about cell counts, and cell counts are the most batch-sensitive number in\n",
+    "a screen — check them against the plate layout before believing one."
    ]
   },
   {
@@ -1123,14 +1313,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 14,
    "id": "64214aaf",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-09-20T15:04:00.241436Z",
-     "iopub.status.busy": "2026-09-20T15:04:00.241305Z",
-     "iopub.status.idle": "2026-09-20T15:04:00.256694Z",
-     "shell.execute_reply": "2026-09-20T15:04:00.255996Z"
+     "iopub.execute_input": "2026-09-20T15:50:28.095227Z",
+     "iopub.status.busy": "2026-09-20T15:50:28.095098Z",
+     "iopub.status.idle": "2026-09-20T15:50:28.109017Z",
+     "shell.execute_reply": "2026-09-20T15:50:28.108283Z"
     }
    },
    "outputs": [
@@ -1346,7 +1536,7 @@
        "Rifampicin                                     -0.11  "
       ]
      },
-     "execution_count": 12,
+     "execution_count": 14,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1381,16 +1571,24 @@
     "things engage, and the window onset as where the compound engages."
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "a749531e",
+   "metadata": {},
+   "source": [
+    "The phase is on `obs`, so the window is an ordinary subset and the rest of the package applies to it.\n"
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 15,
    "id": "ec3cbff6",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-09-20T15:04:00.258442Z",
-     "iopub.status.busy": "2026-09-20T15:04:00.258281Z",
-     "iopub.status.idle": "2026-09-20T15:04:00.310716Z",
-     "shell.execute_reply": "2026-09-20T15:04:00.310071Z"
+     "iopub.execute_input": "2026-09-20T15:50:28.110612Z",
+     "iopub.status.busy": "2026-09-20T15:50:28.110480Z",
+     "iopub.status.idle": "2026-09-20T15:50:28.151160Z",
+     "shell.execute_reply": "2026-09-20T15:50:28.150684Z"
     }
    },
    "outputs": [
@@ -1398,7 +1596,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "576 of 2831 wells are inside some compound's responding window\n"
+      "592 of 2831 wells are inside some compound's responding window\n"
      ]
     },
     {
@@ -1414,7 +1612,7 @@
        "dtype: int64"
       ]
      },
-     "execution_count": 13,
+     "execution_count": 15,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1451,14 +1649,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 16,
    "id": "04e9f94f",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-09-20T15:04:00.312358Z",
-     "iopub.status.busy": "2026-09-20T15:04:00.312234Z",
-     "iopub.status.idle": "2026-09-20T15:04:00.346699Z",
-     "shell.execute_reply": "2026-09-20T15:04:00.346031Z"
+     "iopub.execute_input": "2026-09-20T15:50:28.152881Z",
+     "iopub.status.busy": "2026-09-20T15:50:28.152757Z",
+     "iopub.status.idle": "2026-09-20T15:50:28.172981Z",
+     "shell.execute_reply": "2026-09-20T15:50:28.172309Z"
     }
    },
    "outputs": [
@@ -1467,8 +1665,8 @@
      "output_type": "stream",
      "text": [
       "20 compounds with a window of two or more concentrations, 297 columns (99 features x 3 positions)\n",
-      "Actinomycin D    -> Cycloheximide +0.45, Calcipotriol (hydrate) +0.33, Ethoxyquin +0.27\n",
-      "Staurosporine    -> Fluazinam +0.46, Calcipotriol (hydrate) +0.43, Ethoxyquin +0.36\n",
+      "Actinomycin D    -> Cycloheximide +0.46, Calcipotriol (hydrate) +0.33, FCCP +0.25\n",
+      "Staurosporine    -> Fluazinam +0.46, Calcipotriol (hydrate) +0.43, Triamcinolone acetonide +0.34\n",
       "Fluazinam        -> CLIOQUINOL +0.61, Amperozide +0.56, 5,8,11-Eicosatriynoic acid +0.47\n"
      ]
     }
@@ -1490,25 +1688,25 @@
   },
   {
    "cell_type": "markdown",
-   "id": "fce0f8f3",
+   "id": "23890ac4",
    "metadata": {},
    "source": [
-    "Actinomycin D's nearest neighbour is cycloheximide. One blocks transcription and the other translation, and they\n",
-    "arrive at a similar place by a similar route; nothing in the input told the comparison that, and the two never\n",
-    "share a concentration where both are responding.\n",
-    "\n",
-    "How much the path says over its own endpoint depends on how many concentrations fall inside the window, and a\n",
-    "window of two or three is the normal result of screening a wide range. Correlating the whole\n",
-    "compound-by-compound matrix built from paths against the one built from each compound's top concentration gives\n",
-    "0.92, so on this screen the two mostly agree — which is what should happen when most paths are two points long.\n",
-    "\n",
-    "The pairs they disagree on are the ones with long windows, and they disagree sharply rather than noisily:\n",
-    "cucurbitacin I against staurosporine is +0.25 by path and -0.22 by endpoint, a different answer. The path is\n",
-    "never the worse of the two, and it is the right one wherever a compound had room to change.\n",
-    "\n",
-    "This is also what the window is for beyond reading it. A range-finding screen buys the location of the window;\n",
-    "the way to fill it in is a second ladder spaced across the concentrations this one marked as responding, rather\n",
-    "than another three decades at the same spacing."
+    "Actinomycin D's nearest neighbour is cycloheximide. One blocks transcription and the other translation, and the\n",
+    "cell arrives at a similar place by a similar route. Nothing in the input encodes that, and the two never share a\n",
+    "concentration at which both are responding.\n",
+    "\n",
+    "How much the path adds over its own endpoint depends on how many concentrations fall inside the window, and a\n",
+    "window of two or three is the normal result of screening a wide range. Correlating the compound-by-compound matrix\n",
+    "built from paths against the one built from each compound's top concentration gives 0.92, which is what should\n",
+    "happen when most paths are two points long.\n",
+    "\n",
+    "Where they disagree, they disagree sharply rather than noisily: cucurbitacin I against staurosporine is +0.25 by\n",
+    "path and -0.22 by endpoint. That is a different answer, not a noisier one, and it comes from the two compounds\n",
+    "with the longest windows.\n",
+    "\n",
+    "The window also tells you what to run next. A range-finding ladder buys the location of the window; the way to\n",
+    "resolve it is a second ladder spaced across the concentrations this one marked `responding`, not another three\n",
+    "decades at the same spacing."
    ]
   },
   {
@@ -1525,14 +1723,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 17,
    "id": "f7852d4b",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-09-20T15:04:00.348294Z",
-     "iopub.status.busy": "2026-09-20T15:04:00.348189Z",
-     "iopub.status.idle": "2026-09-20T15:04:00.460158Z",
-     "shell.execute_reply": "2026-09-20T15:04:00.459646Z"
+     "iopub.execute_input": "2026-09-20T15:50:28.174427Z",
+     "iopub.status.busy": "2026-09-20T15:50:28.174321Z",
+     "iopub.status.idle": "2026-09-20T15:50:28.280977Z",
+     "shell.execute_reply": "2026-09-20T15:50:28.280436Z"
     }
    },
    "outputs": [
@@ -1571,14 +1769,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 18,
    "id": "defe9869",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-09-20T15:04:00.461878Z",
-     "iopub.status.busy": "2026-09-20T15:04:00.461754Z",
-     "iopub.status.idle": "2026-09-20T15:04:03.522393Z",
-     "shell.execute_reply": "2026-09-20T15:04:03.521585Z"
+     "iopub.execute_input": "2026-09-20T15:50:28.283205Z",
+     "iopub.status.busy": "2026-09-20T15:50:28.283035Z",
+     "iopub.status.idle": "2026-09-20T15:50:31.371208Z",
+     "shell.execute_reply": "2026-09-20T15:50:31.370772Z"
     }
    },
    "outputs": [
@@ -1586,7 +1784,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/var/folders/qg/qgc908995g3fc8qtss2fsbhhxyxxj4/T/ipykernel_92610/2041206685.py:7: UserWarning: 280 of 289 groups have fewer than three rows, so their statistic is the median of one or two wells. Aggregate more replicates, or score activity with mt.tl.map(mode='activity'), which ranks replicate pairs and is built for screens with little replication.\n",
+      "/var/folders/qg/qgc908995g3fc8qtss2fsbhhxyxxj4/T/ipykernel_64880/2041206685.py:7: UserWarning: 280 of 289 groups have fewer than three rows, so their statistic is the median of one or two wells. Aggregate more replicates, or score activity with mt.tl.map(mode='activity'), which ranks replicate pairs and is built for screens with little replication.\n",
       "  mt.tl.hit_calling(u2os, groupby=\"Metadata_Perturbation\", use_rep=\"X_pca\", n_permutations=300)\n"
      ]
     },
@@ -1657,22 +1855,20 @@
     "- Use `hits_row_distance` as the response, not `hits_distance`. The group column is one number repeated over its\n",
     "  wells.\n",
     "- `fit_ok` and `hitcall` answer different questions. Quote an EC50 only when `fit_ok` is `True`; use `hitcall` to\n",
-    "  decide whether anything happened at all.\n",
-    "- The cutoff comes from the controls, so a screen with few or drifting controls gives a hit call you should not\n",
-    "  trust. {func}`~mantispy.pp.well_qc` and\n",
-    "  [8. Trustworthy features and design](08_trustworthy_features_and_design.ipynb) come first.\n",
-    "- Read every call beside a cell count.\n",
-    "- A call that holds in a second cell line is worth more than a small q-value in one.\n",
+    "  decide whether anything happened at all, not to rank what did.\n",
+    "- Find the window before reading anything in it. `phase` marks where a compound is silent, still changing,\n",
+    "  settled, or past the point where its cells are alive.\n",
     "- Read the features, not only the distance. {func}`~mantispy.tl.dose_features` gives each one a benchmark dose,\n",
     "  which is defined for a feature still climbing at the top concentration, where an EC50 is not.\n",
-    "- A distance that plateaus does not mean the phenotype stopped changing.\n",
-    "  {func}`~mantispy.tl.dose_direction` separates one phenotype getting louder from a second one taking over.\n",
+    "- A distance that plateaus does not mean the phenotype stopped changing. Staurosporine's stops moving at 0.4 uM\n",
+    "  and keeps turning to 300, from a kinase-inhibition morphology into cell disintegration.\n",
     "- Amplitude alone overcalls. Mupirocin's 33 uM well sits at nearly twice the control floor with no direction that\n",
     "  reproduces across plates.\n",
-    "- Find the window before reading the features. `phase` marks where a compound is silent, still changing,\n",
-    "  settled, or past the point where its cells are alive, and it lands on `obs` so the window is a subset.\n",
-    "- Compare compounds on their own relative dose axis with {func}`~mantispy.tl.dose_trajectory`, not at a shared\n",
-    "  concentration, which compares one compound that has engaged against one that has not.\n"
+    "- Score viability against each plate's own controls, and check the batches agree before calling anything\n",
+    "  cytotoxic.\n",
+    "- Compare compounds on their own relative dose axis with {func}`~mantispy.tl.dose_trajectory`. At a shared\n",
+    "  concentration you are comparing one compound that has engaged against one that has not.\n",
+    "- A call that holds in a second cell line is worth more than a small q-value in one."
    ]
   }
  ],
diff --git a/src/mantispy/pl/__init__.py b/src/mantispy/pl/__init__.py
index 8d74d08..0e5619b 100644
--- a/src/mantispy/pl/__init__.py
+++ b/src/mantispy/pl/__init__.py
@@ -9,7 +9,7 @@
 from mantispy.pl._evaluation import batch_variance, map, metrics, replicate_correlation, similarity
 from mantispy.pl._features import feature_correlation, feature_groups
 from mantispy.pl._heterogeneity import cell_cycle, cluster_composition, density, subpopulation_hits
-from mantispy.pl._hits import PHASE_COLORS, dose_direction, dose_response, effect_sizes, feature_volcano, hits
+from mantispy.pl._hits import DOSE_PHASE_COLOURS, dose_direction, dose_response, effect_sizes, feature_volcano, hits
 from mantispy.pl._moa import distance_heatmap, moa_confusion, moa_enrichment, pathway_coherence, sets_heatmap
 from mantispy.pl._plate import plate
 from mantispy.pl._qc import cell_counts, cytotoxicity, feature_distributions, nan_matrix, qc, replicate_saturation
@@ -17,7 +17,7 @@
 from mantispy.pl._transport import setting_agreement, transport
 
 __all__ = [
-    "PHASE_COLORS",
+    "DOSE_PHASE_COLOURS",
     "setting_agreement",
     "batch_variance",
     "cell_counts",
diff --git a/src/mantispy/pl/_hits.py b/src/mantispy/pl/_hits.py
index de537cf..3893318 100644
--- a/src/mantispy/pl/_hits.py
+++ b/src/mantispy/pl/_hits.py
@@ -245,9 +245,9 @@ def dose_response(
     return ax
 
 
-#: Background colour of each phase, from ``mantispy.tl.PHASES``. Grey where nothing happens, warm where it
-#: does, and red where the cells are gone.
-PHASE_COLORS = {
+#: Background colour of each phase, from ``mantispy.tl.DOSE_PHASES``. Grey where nothing happens, warm
+#: where it does, and red where the cells are gone.
+DOSE_PHASE_COLOURS = {
     "silent": "#f2f2f2",
     "responding": "#fde6c4",
     "saturated": "#dbe8d4",
@@ -291,14 +291,11 @@ def dose_direction(
     # Each concentration owns the ladder up to halfway to its neighbours, measured in log dose, and half a step
     # past the two ends.
     log_dose = np.log10(doses)
-    middles = (log_dose[:-1] + log_dose[1:]) / 2 if len(doses) > 1 else np.array([log_dose[0]])
-    edges = 10.0 ** np.concatenate(
-        [[2 * log_dose[0] - middles[0]], middles, [2 * log_dose[-1] - middles[-1]]]
-        if len(doses) > 1
-        else [[log_dose[0] - 0.3], [log_dose[0] + 0.3]]
-    )
+    gaps = np.diff(log_dose) if len(doses) > 1 else np.array([0.6])
+    padded = np.concatenate([[log_dose[0] - gaps[0]], log_dose, [log_dose[-1] + gaps[-1]]])
+    edges = 10.0 ** ((padded[:-1] + padded[1:]) / 2)
     for index, phase in enumerate(block["phase"]):
-        ax.axvspan(edges[index], edges[index + 1], color=PHASE_COLORS.get(str(phase), "#ffffff"), lw=0, zorder=0)
+        ax.axvspan(edges[index], edges[index + 1], color=DOSE_PHASE_COLOURS.get(str(phase), "#ffffff"), lw=0, zorder=0)
 
     floor = float(np.nanmedian(block["amplitude_null"].to_numpy(dtype=float)))
     ax.axhline(floor, ls=":", lw=1, color="0.45", zorder=1)
diff --git a/src/mantispy/tl/__init__.py b/src/mantispy/tl/__init__.py
index 95a8aa0..c038ee6 100644
--- a/src/mantispy/tl/__init__.py
+++ b/src/mantispy/tl/__init__.py
@@ -5,7 +5,7 @@
 from mantispy.tl._design import cytotoxicity, replicate_saturation
 from mantispy.tl._differential import differential_features
 from mantispy.tl._distance import edistance
-from mantispy.tl._dose import PHASES, dose_direction, dose_features, dose_response, dose_trajectory
+from mantispy.tl._dose import DOSE_PHASES, dose_direction, dose_features, dose_response, dose_trajectory
 from mantispy.tl._effect import effect_size, wasserstein_features
 from mantispy.tl._enrich import enrich, feature_sets, rank_features, rank_sets
 from mantispy.tl._heterogeneity import (
@@ -23,7 +23,7 @@
 from mantispy.tl._transport import transport
 
 __all__ = [
-    "PHASES",
+    "DOSE_PHASES",
     "aggregate",
     "cell_cycle_phase",
     "cluster_composition",
diff --git a/src/mantispy/tl/_dose.py b/src/mantispy/tl/_dose.py
index 56aba77..cfe6407 100644
--- a/src/mantispy/tl/_dose.py
+++ b/src/mantispy/tl/_dose.py
@@ -12,12 +12,14 @@
 import pandas as pd
 from anndata import AnnData
 
+from mantispy._core._numba import group_offsets
 from mantispy._core._reduce import get_matrix
 from mantispy._core._stats import MAD_TO_SIGMA, benjamini_hochberg
 from mantispy._core.frames import as_frame
-from mantispy._core.logging import get_logger
+from mantispy._core.logging import get_logger, report_drop
 from mantispy._core.masks import reference_mask
 from mantispy._core.mutation import inplace_or_copy
+from mantispy._core.provenance import record_params
 from mantispy._core.schema import stamp
 
 #: Column order of the output table, so an empty result still carries its columns.
@@ -379,9 +381,7 @@ def dose_response(
     from scipy.stats import ConstantInputWarning, spearmanr
 
     obs = as_frame(adata.obs)
-    for column in (compound_key, dose_key):
-        if column not in obs:
-            raise KeyError(f"obs has no column {column!r}")
+    _require_columns(obs, compound_key, dose_key)
     if response not in obs:
         raise KeyError(
             f"obs has no column {response!r} to use as the response; run mt.tl.hit_calling first, which "
@@ -468,14 +468,15 @@ def dose_response(
 )
 
 #: What a concentration is doing, in the order they normally appear along a ladder.
-PHASES = ("silent", "responding", "saturated", "cytotoxic")
+DOSE_PHASES = ("silent", "responding", "saturated", "cytotoxic")
 
 
-def _feature_baseline_and_spread(adata: AnnData, reference: str | None) -> tuple[np.ndarray, np.ndarray]:
-    """Where the controls sit on each feature and how far they wobble, the ToxCast ``bmed`` and ``bmad`` per feature.
+def _control_scale(adata: AnnData, reference: str | None) -> tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray]:
+    """The controls' centre and spread per feature, which features have one, and which rows the controls are.
 
-    A feature whose controls show no spread has no scale to read a response against, and comes back with a spread
-    of NaN so the caller can drop it.
+    The centre and the spread are the ToxCast pipeline's ``bmed`` and ``bmad``. A feature whose controls show no
+    spread has no scale to read a response against and is left out, so the two arrays come back already narrowed
+    to ``keep``.
     """
     rows = reference_mask(adata, reference)
     if int(rows.sum()) < 2:
@@ -486,8 +487,24 @@ def _feature_baseline_and_spread(adata: AnnData, reference: str | None) -> tuple
         )
     control = get_matrix(adata, rows=np.flatnonzero(rows)).astype(np.float64)
     baseline = np.nanmedian(control, axis=0)
-    spread = MAD_TO_SIGMA * np.nanmedian(np.abs(control - baseline), axis=0)
-    return baseline, np.where((spread > 0) & np.isfinite(spread), spread, np.nan)
+    # In place: two more copies of the control block would be the largest allocation in the function.
+    control -= baseline
+    np.abs(control, out=control)
+    spread = MAD_TO_SIGMA * np.nanmedian(control, axis=0)
+    keep = np.flatnonzero((spread > 0) & np.isfinite(spread))
+    report_drop(
+        "feature(s) with no spread among the controls",
+        adata.n_vars - keep.size,
+        adata.n_vars,
+        remedy="run mt.pp.normalize and drop the features flagged by var['degenerate_scale']",
+    )
+    return baseline[keep], spread[keep], keep, rows
+
+
+def _z_rows(adata: AnnData, rows: np.ndarray, baseline: np.ndarray, spread: np.ndarray, keep: np.ndarray) -> np.ndarray:
+    """Those rows' response in MADs of the controls, over the features the controls give a scale for."""
+    with np.errstate(invalid="ignore"):
+        return (get_matrix(adata, rows=rows)[:, keep].astype(np.float64) - baseline) / spread
 
 
 def _bin_doses(doses: np.ndarray, tolerance: float) -> np.ndarray:
@@ -503,14 +520,15 @@ def _bin_doses(doses: np.ndarray, tolerance: float) -> np.ndarray:
     unique = np.unique(doses)
     if tolerance <= 0 or unique.size == 0:
         return doses
-    log = np.log10(unique)
-    group = np.zeros(unique.size, dtype=np.int64)
-    start = log[0]
-    for index in range(1, unique.size):
-        opens = log[index] - start >= tolerance
-        group[index] = group[index - 1] + opens
-        start = log[index] if opens else start
-    centre = pd.Series(unique).groupby(group).transform("median").to_numpy()
+    group = np.empty(unique.size, dtype=np.int64)
+    current, start = 0, np.log10(unique[0])
+    for index, value in enumerate(np.log10(unique)):
+        if value - start >= tolerance:
+            current, start = current + 1, value
+        group[index] = current
+    # A pandas groupby here costs more than the whole rest of the function, on twenty elements.
+    starts = np.flatnonzero(np.diff(group, prepend=-1))
+    centre = np.repeat([np.median(run) for run in np.split(unique, starts[1:])], np.diff(np.append(starts, group.size)))
     return centre[np.searchsorted(unique, doses)]
 
 
@@ -528,23 +546,46 @@ def _spearman_against(values: np.ndarray, block: np.ndarray) -> tuple[np.ndarray
     centred = ranks - ranks.mean(axis=0)
     norms = np.sqrt(np.sum(centred**2, axis=0))
     with np.errstate(invalid="ignore", divide="ignore"):
-        rho = np.clip(np.sum(centred[:, :1] * centred, axis=0) / (norms[0] * norms), -1.0, 1.0)[1:]
+        rho = np.clip(centred.T @ centred[:, 0] / (norms[0] * norms), -1.0, 1.0)[1:]
         statistic = rho * np.sqrt((ranks.shape[0] - 2) / (1.0 - rho**2))
     pvalue = 2.0 * t.sf(np.abs(statistic), ranks.shape[0] - 2)
     return rho, np.where(np.isfinite(rho), pvalue, np.nan)
 
 
+def _nanmedian(block: np.ndarray) -> np.ndarray:
+    """Median down the rows, skipping missing values, for the short and wide blocks this module works on.
+
+    ``np.nanmedian`` routes anything under 600 rows through a masked array and ``np.ma.median``, which is pure
+    Python; a dose group is four to eight rows, so every median here takes that path and spends about ninety-nine
+    percent of its time on the wrapper. Sorting puts the missing values last, so the median is the middle of
+    however many were measured.
+    """
+    ordered = np.sort(block, axis=0)
+    measured = block.shape[0] - np.isnan(block).sum(axis=0)
+    columns = np.arange(block.shape[1])
+    low = ordered[np.maximum((measured - 1) // 2, 0), columns]
+    high = ordered[np.maximum(measured // 2, 0), columns]
+    return np.where(measured > 0, 0.5 * (low + high), np.nan)
+
+
+def _groups(codes: np.ndarray, n_groups: int) -> list[np.ndarray]:
+    """Row indices of each group, taken once rather than by scanning the codes per group."""
+    order, offsets = group_offsets(np.ascontiguousarray(codes, dtype=np.int32), n_groups)
+    return [order[offsets[index] : offsets[index + 1]] for index in range(n_groups)]
+
+
 def _dose_medians(block: np.ndarray, doses: np.ndarray) -> tuple[np.ndarray, np.ndarray]:
     """The sorted doses, and each one's median profile over its replicate rows."""
-    order = np.unique(doses)
-    return order, np.stack([np.nanmedian(block[doses == dose], axis=0) for dose in order])
+    order, codes = np.unique(doses, return_inverse=True)
+    return order, np.stack([_nanmedian(block[rows]) for rows in _groups(codes, order.size)])
 
 
 def _benchmark_dose(doses: np.ndarray, z: np.ndarray, cutoff: float) -> np.ndarray:
     """Lowest dose at which each feature reaches ``cutoff``, interpolated between the doses either side of it.
 
     A feature that never reaches the cutoff has no benchmark dose and comes back NaN. One already past it at the
-    lowest dose tested gets that dose, which is a bound rather than an estimate.
+    lowest dose tested gets that dose, which is a bound rather than an estimate: clamping ``previous`` to the
+    first dose leaves it no span to interpolate across, so the crossing falls on the dose itself.
     """
     over = np.abs(z) >= cutoff
     first = np.argmax(over, axis=0)
@@ -552,10 +593,9 @@ def _benchmark_dose(doses: np.ndarray, z: np.ndarray, cutoff: float) -> np.ndarr
     columns = np.arange(z.shape[1])
     log_dose = np.log10(doses)
     low, high = np.abs(z[previous, columns]), np.abs(z[first, columns])
-    with np.errstate(invalid="ignore", divide="ignore"):
-        span = high - low
-        fraction = np.where(span > 0, (cutoff - low) / span, 0.0)
-        crossing = log_dose[previous] + fraction * (log_dose[first] - log_dose[previous])
+    span = high - low
+    fraction = np.divide(cutoff - low, span, out=np.zeros_like(span), where=span > 0)
+    crossing = log_dose[previous] + fraction * (log_dose[first] - log_dose[previous])
     return np.where(over.any(axis=0), 10.0**crossing, np.nan)
 
 
@@ -564,12 +604,13 @@ def _usable_doses(
 ) -> dict[str, tuple[np.ndarray, np.ndarray]]:
     """Each compound's treated rows and their binned doses, leaving out the controls and the zero doses."""
     doses = obs[dose_key].to_numpy(dtype=float)
-    compounds = obs[compound_key].to_numpy()
     usable = np.isfinite(doses) & (doses > 0) & ~control
+    selected = np.flatnonzero(usable)
+    codes, keys = pd.factorize(obs[compound_key].to_numpy()[usable], sort=True)
     blocks = {}
-    for compound in pd.unique(compounds[usable]):
-        rows = np.flatnonzero(usable & (compounds == compound))
-        blocks[compound] = (rows, _bin_doses(doses[rows], tolerance))
+    for key, within in zip(keys, _groups(codes, len(keys)), strict=True):
+        rows = selected[within]
+        blocks[key] = (rows, _bin_doses(doses[rows], tolerance))
     return blocks
 
 
@@ -637,37 +678,28 @@ def dose_features(
     """
     obs = as_frame(adata.obs)
     _require_columns(obs, compound_key, dose_key)
-    baseline, spread = _feature_baseline_and_spread(adata, reference)
-    scaled = np.flatnonzero(np.isfinite(spread))
-    if scaled.size < adata.n_vars:
-        get_logger().info(
-            "dose_features: %d of %d features have no spread among the controls and are left out",
-            adata.n_vars - scaled.size,
-            adata.n_vars,
-        )
+    baseline, spread, keep, control = _control_scale(adata, reference)
+    names = np.asarray(adata.var_names)[keep]
 
-    names = np.asarray(adata.var_names)[scaled]
-    control = reference_mask(adata, reference)
     frames = []
     for compound, (rows, doses) in _usable_doses(obs, compound_key, dose_key, control, dose_tolerance).items():
         n_doses = len(np.unique(doses))
         if n_doses < min_doses:
             get_logger().debug("dose_features skipped %s: %d usable dose(s)", compound, n_doses)
             continue
-        block = get_matrix(adata, rows=rows).astype(np.float64)[:, scaled]
-        order, medians = _dose_medians(block, doses)
-        with np.errstate(invalid="ignore"):
-            z = (medians - baseline[scaled]) / spread[scaled]
+        block = _z_rows(adata, rows, baseline, spread, keep)
+        order, z = _dose_medians(block, doses)
         rho, pvalue = _spearman_against(np.log10(doses), block)
-        extreme = np.nanargmax(np.abs(np.nan_to_num(z, nan=0.0)), axis=0)
+        # The concentration each feature reached furthest at, and which way it went.
+        peak = z[np.argmax(np.abs(np.nan_to_num(z, nan=0.0)), axis=0), np.arange(z.shape[1])]
         frame = pd.DataFrame(
             {
                 "compound": str(compound),
                 "feature": names,
                 "n_doses": n_doses,
                 "bmd": _benchmark_dose(order, z, cutoff_mads),
-                "max_z": np.abs(z[extreme, np.arange(z.shape[1])]),
-                "direction": np.sign(z[extreme, np.arange(z.shape[1])]),
+                "max_z": np.abs(peak),
+                "direction": np.sign(peak),
                 "spearman": rho,
                 "pvalue": pvalue,
             }
@@ -680,7 +712,7 @@ def dose_features(
     adata.uns.setdefault("mantispy", {})[key_added] = table[columns]
     get_logger().info(
         "dose_features: %d of %d compound-feature pairs reach %.1f MADs",
-        int(table["bmd"].notna().sum()) if len(table) else 0,
+        int(table["bmd"].notna().sum()),
         len(table),
         cutoff_mads,
     )
@@ -701,12 +733,19 @@ def _amplitude(profile: np.ndarray) -> float:
     return float(np.sqrt(np.mean(measured**2))) if measured.size else np.nan
 
 
-def _halves(labels: np.ndarray) -> tuple[np.ndarray, np.ndarray]:
-    """Two masks over the rows, splitting them by alternate levels of ``labels``, or by position as a fallback."""
+def _split_half_cosine(block: np.ndarray, labels: np.ndarray) -> float:
+    """Cosine between the two halves' median profiles, split by alternate levels of ``labels``.
+
+    Halving by plate asks whether the direction survives a different plate, which is the harder and more useful
+    question. A column with one level falls back to splitting by position, and one well at a concentration has no
+    halves to compare, so it comes back NaN.
+    """
     left = np.isin(labels, pd.unique(labels)[::2])
-    if left.all() or not left.any():
+    if left.all():
         left = np.arange(labels.size) % 2 == 0
-    return left, ~left
+    if left.all() or not left.any():
+        return np.nan
+    return _cosine(_nanmedian(block[left]), _nanmedian(block[~left]))
 
 
 #: Control groups drawn per layout to estimate the amplitude floor. The median of this many is stable to about 5%.
@@ -724,24 +763,23 @@ def _null_amplitude(control: np.ndarray, levels: np.ndarray, wanted: dict[object
     if any(pools[level].size < count for level, count in wanted.items()):
         return np.nan
     generator = np.random.default_rng(0)
-    draws = [
-        _amplitude(
-            np.nanmedian(
-                control[
-                    np.concatenate(
-                        [generator.choice(pools[level], count, replace=False) for level, count in wanted.items()]
-                    )
-                ],
-                axis=0,
-            )
-        )
-        for _ in range(_NULL_DRAWS)
-    ]
-    return float(np.median(draws))
 
+    def draw() -> float:
+        rows = np.concatenate([generator.choice(pools[level], count, replace=False) for level, count in wanted.items()])
+        return _amplitude(_nanmedian(control[rows]))
 
-def _viability(adata: AnnData, count_key: str, site_key: str | None, control: np.ndarray) -> np.ndarray:
-    """Each row's cell count against the controls' median, per field of view where the fields are known."""
+    return float(np.median([draw() for _ in range(_NULL_DRAWS)]))
+
+
+def _viability(
+    adata: AnnData, count_key: str, site_key: str | None, control: np.ndarray, levels: np.ndarray
+) -> np.ndarray:
+    """Each row's cell count against its own plate's controls, per field of view where the fields are known.
+
+    Against the whole screen's controls it would not be a viability at all. Plates are seeded and imaged
+    separately and their control counts differ by a factor of two on the OASIS pilot, so a plate that happens to
+    be dense reads as a plate whose treated wells are dying.
+    """
     obs = as_frame(adata.obs)
     if count_key not in obs:
         get_logger().info(
@@ -750,18 +788,31 @@ def _viability(adata: AnnData, count_key: str, site_key: str | None, control: np
             count_key,
         )
         return np.full(adata.n_obs, np.nan)
+
     counts = obs[count_key].to_numpy(dtype=float)
     if site_key is not None and site_key in obs:
         counts = counts / np.maximum(obs[site_key].to_numpy(dtype=float), 1)
-    reference = float(np.nanmedian(counts[control]))
-    if not np.isfinite(reference) or reference <= 0:
-        get_logger().info("dose_direction: the controls have no usable %r, so no viability is read", count_key)
-        return np.full(adata.n_obs, np.nan)
-    return counts / reference
+
+    viability = np.full(adata.n_obs, np.nan)
+    unreferenced = 0
+    for level in pd.unique(levels):
+        on = levels == level
+        reference = float(np.nanmedian(counts[on & control])) if (on & control).any() else np.nan
+        if np.isfinite(reference) and reference > 0:
+            viability[on] = counts[on] / reference
+        else:
+            unreferenced += 1
+    report_drop(
+        "level(s) of the split with no control count to read viability against",
+        unreferenced,
+        len(pd.unique(levels)),
+        remedy=f"check that every {count_key!r} is filled and that each level carries controls",
+    )
+    return viability
 
 
 def _label_phases(ladder: list[dict], min_viability: float, reproducible: float | None) -> list[str]:
-    """Label one compound's whole ladder at once, and write the labels back into its rows.
+    """Label one compound's whole ladder at once.
 
     The window is a run, not a scatter. ``split_half_cosine`` over a handful of replicate wells is itself noisy,
     and thresholding each concentration on its own lets one lucky concentration open a window several steps below
@@ -773,29 +824,22 @@ def _label_phases(ladder: list[dict], min_viability: float, reproducible: float
     dying cells whatever else is true of it, which is why the US EPA's phenotypic pipeline drops those
     concentrations before fitting anything.
     """
-    cytotoxic = [bool(np.isfinite(row["viability"]) and row["viability"] < min_viability) for row in ladder]
-    phases = ["cytotoxic" if flag else "silent" for flag in cytotoxic]
 
     def active(row: dict) -> bool:
-        if (
-            not (np.isfinite(row["amplitude"]) and np.isfinite(row["amplitude_null"]))
-            or row["amplitude"] <= row["amplitude_null"]
-        ):
-            return False
-        if reproducible is None:
-            return True
-        return bool(np.isfinite(row["split_half_cosine"]) and row["split_half_cosine"] >= reproducible)
-
-    index = max((position for position, flag in enumerate(cytotoxic) if not flag), default=-1)
-    while index >= 0 and not cytotoxic[index] and active(ladder[index]):
-        row = ladder[index]
+        # A comparison against NaN is False, so a concentration with no amplitude or no reproducible direction
+        # fails these without a guard of its own.
+        return bool(row["amplitude"] > row["amplitude_null"]) and (
+            reproducible is None or bool(row["split_half_cosine"] >= reproducible)
+        )
+
+    phases = ["cytotoxic" if row["viability"] < min_viability else "silent" for row in ladder]
+    highest = max((index for index, phase in enumerate(phases) if phase != "cytotoxic"), default=-1)
+    for index in range(highest, -1, -1):
+        if phases[index] == "cytotoxic" or not active(ladder[index]):
+            break
         # Still moving if the step from the concentration below beats the noise two independent medians carry.
-        moving = np.isfinite(row["step_amplitude"]) and row["step_amplitude"] > np.sqrt(2.0) * row["amplitude_null"]
+        moving = ladder[index]["step_amplitude"] > np.sqrt(2.0) * ladder[index]["amplitude_null"]
         phases[index] = "responding" if moving else "saturated"
-        index -= 1
-
-    for row, phase in zip(ladder, phases, strict=True):
-        row["phase"] = phase
     return phases
 
 
@@ -843,7 +887,7 @@ def dose_direction(
         min_doses: Distinct doses below which a compound is left out of the table. One concentration says nothing about how a response changes with concentration.
         count_key: ``obs`` column holding the cell count, which sets ``viability``. Without it no concentration is marked cytotoxic.
         site_key: ``obs`` column holding the number of fields that count covers, so a well missing a field does not read as cell loss. ``None`` compares the counts as they are.
-        min_viability: Fraction of the controls' cell count below which a concentration is ``cytotoxic``. The US EPA's phenotypic pipeline drops a concentration that has lost more than half its cells before fitting anything.
+        min_viability: Fraction of its own plate's control cell count below which a concentration is ``cytotoxic``. The US EPA's phenotypic pipeline drops a concentration that has lost more than half its cells before fitting anything.
         reproducible: ``split_half_cosine`` a concentration needs before it can be anything but ``silent``. ``None`` drops the requirement, which is what a screen with one well per concentration has to do, at the cost of calling noise a phenotype.
         dose_tolerance: Doses whose base-10 logs differ by less than this are treated as one dose. See :func:`dose_response`.
         key_added: Name for the output table.
@@ -853,13 +897,14 @@ def dose_direction(
         ``None``, or the modified copy.
         Writes ``uns["mantispy"][key_added]``, one row per compound and concentration, with ``compound``, ``dose``,
         ``n_wells``, ``amplitude``, ``amplitude_null``, ``step_amplitude``, ``split_half_cosine``,
-        ``cosine_to_top``, ``viability`` and ``phase``, and broadcasts the phase to
-        ``obs[key_added + "_phase"]`` so the window can be subset like any other annotation.
+        ``cosine_to_top``, ``viability`` and ``phase``. The phase is broadcast to ``obs[key_added + "_phase"]``
+        so the window can be subset like any other annotation, and the dose each row was binned to is broadcast
+        to ``obs[key_added + "_dose"]`` so wells can be grouped by the same concentration the table reports.
         ``amplitude`` is the root-mean-square response over the features, in MADs of the controls, and
         ``amplitude_null`` is what control wells spread over the same plates in the same numbers reach, so the two
         are read against each other. ``step_amplitude`` is the same measure applied to the change from the
         concentration below, which is what tells a response that is still moving from one that has arrived.
-        ``phase`` is one of ``PHASES``.
+        ``phase`` is one of ``DOSE_PHASES``.
 
     Raises:
         KeyError: ``obs`` has no ``compound_key``, no ``dose_key``, or no ``split_by`` column.
@@ -879,75 +924,72 @@ def dose_direction(
     """
     obs = as_frame(adata.obs)
     _require_columns(obs, compound_key, dose_key, *([split_by] if split_by is not None else []))
-    baseline, spread = _feature_baseline_and_spread(adata, reference)
-    scaled = np.flatnonzero(np.isfinite(spread))
-    control = reference_mask(adata, reference)
-    with np.errstate(invalid="ignore"):
-        control_z = (
-            get_matrix(adata, rows=np.flatnonzero(control)).astype(np.float64)[:, scaled] - baseline[scaled]
-        ) / spread[scaled]
+    baseline, spread, keep, control = _control_scale(adata, reference)
+    control_z = _z_rows(adata, np.flatnonzero(control), baseline, spread, keep)
 
     all_levels = obs[split_by].to_numpy() if split_by is not None else np.zeros(adata.n_obs)
     control_levels = all_levels[control]
-    viable = _viability(adata, count_key, site_key, control)
+    viable = _viability(adata, count_key, site_key, control, all_levels)
     phases = np.full(adata.n_obs, "", dtype=object)
+    binned = np.full(adata.n_obs, np.nan)
     nulls: dict[tuple, float] = {}
     records: list[dict] = []
-    block_rows: list[np.ndarray] = []
     for compound, (rows, doses) in _usable_doses(obs, compound_key, dose_key, control, dose_tolerance).items():
-        if len(np.unique(doses)) < min_doses:
-            get_logger().debug("dose_direction skipped %s: %d usable dose(s)", compound, len(np.unique(doses)))
+        n_doses = len(np.unique(doses))
+        if n_doses < min_doses:
+            get_logger().debug("dose_direction skipped %s: %d usable dose(s)", compound, n_doses)
             continue
-        with np.errstate(invalid="ignore"):
-            block = (get_matrix(adata, rows=rows).astype(np.float64)[:, scaled] - baseline[scaled]) / spread[scaled]
+        block = _z_rows(adata, rows, baseline, spread, keep)
         order, medians = _dose_medians(block, doses)
-        levels = all_levels[rows]
+        levels, well_viability = all_levels[rows], viable[rows]
+        binned[rows] = doses
+
+        ladder, covering = [], []
         for index, dose in enumerate(order):
             at = doses == dose
-            left, right = _halves(levels[at])
-            reproduces = np.nan
-            if left.any() and right.any():
-                reproduces = _cosine(np.nanmedian(block[at][left], axis=0), np.nanmedian(block[at][right], axis=0))
-            # The floor is drawn with this concentration's own spread over plates, not from any group of that size.
+            # The floor is drawn with this concentration's own spread over plates, not from any group of that
+            # size. setdefault would evaluate the draw whether or not the layout has been seen before.
             layout = dict(zip(*np.unique(levels[at], return_counts=True), strict=True))
             key = tuple(sorted(layout.items(), key=lambda item: str(item[0])))
-            null = nulls.setdefault(key, _null_amplitude(control_z, control_levels, layout))
-            # The step from the concentration below, which is what "still changing" means. The first one steps
-            # from the controls, so its step is how far it has already come.
+            if key not in nulls:
+                nulls[key] = _null_amplitude(control_z, control_levels, layout)
+            # The step from the concentration below is what "still changing" means. The first one steps from the
+            # controls, so its step is how far it has already come.
             amplitude = _amplitude(medians[index])
-            step = amplitude if index == 0 else _amplitude(medians[index] - medians[index - 1])
-            viability = float(np.nanmedian(viable[rows][at])) if np.isfinite(viable[rows][at]).any() else np.nan
-            block_rows.append(rows[at])
-            records.append(
+            here = well_viability[at]
+            covering.append(rows[at])
+            ladder.append(
                 {
                     "compound": str(compound),
                     "dose": float(dose),
                     "n_wells": int(at.sum()),
                     "amplitude": amplitude,
-                    "amplitude_null": null,
-                    "step_amplitude": step,
-                    "split_half_cosine": reproduces,
+                    "amplitude_null": nulls[key],
+                    "step_amplitude": amplitude if index == 0 else _amplitude(medians[index] - medians[index - 1]),
+                    "split_half_cosine": _split_half_cosine(block[at], levels[at]),
                     "cosine_to_top": _cosine(medians[index], medians[-1]),
-                    "viability": viability,
-                    "phase": "",
+                    "viability": float(np.nanmedian(here)) if np.isfinite(here).any() else np.nan,
                 }
             )
 
-        ladder = records[-len(order) :]
-        for record, covered in zip(_label_phases(ladder, min_viability, reproducible), block_rows, strict=True):
-            phases[covered] = record
-        block_rows.clear()
+        for row, phase, covered in zip(
+            ladder, _label_phases(ladder, min_viability, reproducible), covering, strict=True
+        ):
+            row["phase"] = phase
+            phases[covered] = phase
+        records.extend(ladder)
 
     table = pd.DataFrame(records, columns=list(_DIRECTION_COLUMNS))
     adata.uns.setdefault("mantispy", {})[key_added] = table
     # A row that no concentration covers, a control or an unannotated well, is in no phase at all.
     phases[phases == ""] = None
-    adata.obs[f"{key_added}_phase"] = pd.Categorical(phases, categories=list(PHASES), ordered=False)
+    adata.obs[f"{key_added}_phase"] = pd.Categorical(phases, categories=list(DOSE_PHASES), ordered=False)
+    adata.obs[f"{key_added}_dose"] = binned
     get_logger().info(
         "dose_direction: %d compound(s) over %d concentration(s); %s",
-        table["compound"].nunique() if len(table) else 0,
+        table["compound"].nunique(),
         len(table),
-        table["phase"].value_counts().to_dict() if len(table) else {},
+        table["phase"].value_counts().to_dict(),
     )
     return None
 
@@ -1007,12 +1049,10 @@ def dose_trajectory(
         raise ValueError(f"n_positions must be at least two, got {n_positions}")
     obs = as_frame(adata.obs)
     _require_columns(obs, compound_key, dose_key)
-    baseline, spread = _feature_baseline_and_spread(adata, reference)
-    scaled = np.flatnonzero(np.isfinite(spread))
-    control = reference_mask(adata, reference)
+    baseline, spread, keep, control = _control_scale(adata, reference)
 
     in_window = np.ones(adata.n_obs, dtype=bool)
-    if phase_key is not None and phase_key in obs:
+    if phase_key in obs:
         in_window = obs[phase_key].astype(str).isin(list(phases)).to_numpy()
     elif phase_key is not None:
         get_logger().info(
@@ -1022,22 +1062,23 @@ def dose_trajectory(
         )
 
     grid = np.linspace(0.0, 1.0, n_positions)
-    rows_out, records = [], []
+    paths, records = [], []
     for compound, (rows, doses) in _usable_doses(obs, compound_key, dose_key, control, dose_tolerance).items():
-        keep = in_window[rows]
-        if not keep.any():
+        inside = in_window[rows]
+        if not inside.any():
             continue
-        rows, doses = rows[keep], doses[keep]
-        order, medians = _dose_medians(get_matrix(adata, rows=rows).astype(np.float64)[:, scaled], doses)
+        rows, doses = rows[inside], doses[inside]
+        order, z = _dose_medians(_z_rows(adata, rows, baseline, spread, keep), doses)
         if len(order) < 2:
             continue
-        with np.errstate(invalid="ignore"):
-            z = (medians - baseline[scaled]) / spread[scaled]
         # Relative position through the window in log concentration, so the ends are 0 and 1 for every compound.
         position = np.log10(order)
         position = (position - position[0]) / (position[-1] - position[0])
-        resampled = np.stack([np.interp(grid, position, z[:, column]) for column in range(z.shape[1])], axis=1)
-        rows_out.append(resampled.ravel())
+        # One interval and weight per grid point serves every feature; np.interp per feature is the same
+        # arithmetic done n_vars times over.
+        below = np.clip(np.searchsorted(position, grid, side="right") - 1, 0, position.size - 2)
+        weight = ((grid - position[below]) / (position[below + 1] - position[below]))[:, None]
+        paths.append((z[below] * (1.0 - weight) + z[below + 1] * weight).ravel())
         records.append(
             {
                 compound_key: str(compound),
@@ -1047,26 +1088,34 @@ def dose_trajectory(
             }
         )
 
-    names = np.asarray(adata.var_names)[scaled]
-    var = pd.DataFrame(
-        {
-            "feature": np.tile(names, n_positions),
-            "position": np.repeat(grid, names.size),
-        },
-        index=pd.Index([f"{name}@{value:.2f}" for value in grid for name in names]),
-    )
+    names = np.asarray(adata.var_names)[keep]
+    var = pd.DataFrame({"feature": np.tile(names, n_positions), "position": np.repeat(grid, names.size)})
+    var.index = var["feature"] + "@" + var["position"].map("{:.2f}".format)
     result = ad.AnnData(
-        X=np.vstack(rows_out).astype(np.float32) if rows_out else np.empty((0, var.shape[0]), dtype=np.float32),
+        X=np.array(paths, dtype=np.float32).reshape(len(paths), var.shape[0]),
         obs=pd.DataFrame(records, columns=[compound_key, "n_doses", "window_low", "window_high"]).set_axis(
-            pd.Index([str(index) for index in range(len(records))])
+            pd.RangeIndex(len(records)).astype(str)
         ),
         var=var,
     )
     stamp(result, resolution="perturbation")
+    record_params(
+        result,
+        "dose_trajectory",
+        {
+            "compound_key": compound_key,
+            "dose_key": dose_key,
+            "reference": reference,
+            "phase_key": phase_key,
+            "phases": list(phases),
+            "n_positions": n_positions,
+            "dose_tolerance": dose_tolerance,
+        },
+    )
     get_logger().info(
         "dose_trajectory: %d compound(s) over %d position(s); windows of %s concentration(s)",
         result.n_obs,
         n_positions,
-        sorted(set(result.obs["n_doses"])) if result.n_obs else [],
+        sorted(set(result.obs["n_doses"])),
     )
     return result
diff --git a/tests/test_pl_hits.py b/tests/test_pl_hits.py
index 70e958e..a1bcf77 100644
--- a/tests/test_pl_hits.py
+++ b/tests/test_pl_hits.py
@@ -190,7 +190,7 @@ def test_the_direction_plot_bands_the_ladder_by_phase():
     # One band per concentration, plus the two lines it reads against.
     assert len(ax.patches) == len(table)
     drawn = {patch.get_facecolor() for patch in ax.patches}
-    expected = {mt.pl.PHASE_COLORS[phase] for phase in table["phase"]}
+    expected = {mt.pl.DOSE_PHASE_COLOURS[phase] for phase in table["phase"]}
     assert len(drawn) == len(expected), "each phase present gets its own colour"
     assert len(ax.lines) == 4, "two curves and the two floors"
     plt.close(ax.figure)
diff --git a/tests/test_tl_dose.py b/tests/test_tl_dose.py
index e92e75e..965b629 100644
--- a/tests/test_tl_dose.py
+++ b/tests/test_tl_dose.py
@@ -1,5 +1,7 @@
 """Dose-response trends and curve fits."""
 
+import warnings
+
 import numpy as np
 import pandas as pd
 import pytest
@@ -487,7 +489,7 @@ def test_a_concentration_that_lost_its_cells_is_cytotoxic_whatever_else_it_did(p
 def test_the_phase_reaches_obs_so_the_window_can_be_subset(phenotypes):
     mt.tl.dose_direction(phenotypes)
     phases = phenotypes.obs["dose_direction_phase"]
-    assert set(phases.cat.categories) == set(mt.tl.PHASES)
+    assert set(phases.cat.categories) == set(mt.tl.DOSE_PHASES)
     assert phases[phenotypes.obs["Metadata_Control"].to_numpy(dtype=bool)].isna().all(), "controls are in no phase"
 
     window = phenotypes[phases == "responding"]
@@ -519,3 +521,42 @@ def test_a_turning_compound_and_a_growing_one_do_not_match_on_their_paths(phenot
     frame = pd.DataFrame(np.asarray(paths.X), index=list(paths.obs["Metadata_Compound"]))
     assert {"grows", "turns"} <= set(frame.index)
     assert frame.loc["grows"].corr(frame.loc["turns"]) < 0.5
+
+
+def test_the_fast_median_matches_numpy():
+    """It exists only to avoid numpy's masked-array path on short blocks, so it has to agree with it exactly."""
+    from mantispy.tl._dose import _nanmedian
+
+    rng = np.random.default_rng(0)
+    for rows in (1, 2, 3, 4, 8, 9):
+        block = rng.normal(size=(rows, 12))
+        block[rng.random(block.shape) < 0.3] = np.nan
+        block[:, 0] = np.nan  # a feature measured nowhere
+        with warnings.catch_warnings():
+            warnings.simplefilter("ignore", RuntimeWarning)  # numpy warns on the all-NaN column; this does not
+            expected = np.nanmedian(block, axis=0)
+        np.testing.assert_allclose(_nanmedian(block), expected, equal_nan=True)
+
+
+def test_viability_is_read_against_each_plate_not_the_whole_screen(phenotypes):
+    """Plates are seeded apart, so a dense plate would otherwise read as one whose treated wells are dying."""
+    dense = phenotypes.obs["Metadata_Plate"] == "P1"
+    phenotypes.obs.loc[dense, "Metadata_CellCount"] = 1000.0
+    mt.tl.dose_direction(phenotypes)
+    table = phenotypes.uns["mantispy"]["dose_direction"]
+
+    assert (table["phase"] != "cytotoxic").all(), "a ten-fold difference between plates is not cell loss"
+    assert table["viability"].between(0.8, 1.2).all()
+
+
+def test_the_binned_dose_reaches_obs_so_wells_group_the_way_the_table_does(phenotypes):
+    """Grouping wells by the raw concentration splits a ladder two batches spell differently."""
+    raw = phenotypes.obs["Metadata_Concentration"].to_numpy(dtype=float)
+    respelled = (np.arange(phenotypes.n_obs) // 2) % 2 == 0
+    phenotypes.obs["Metadata_Concentration"] = np.where(respelled, raw, np.round(raw * 1.05, 4))
+    mt.tl.dose_direction(phenotypes)
+
+    treated = phenotypes.obs["dose_direction_dose"].notna()
+    assert phenotypes.obs.loc[treated, "Metadata_Concentration"].nunique() > 6, "the raw ladder is split"
+    assert phenotypes.obs.loc[treated, "dose_direction_dose"].nunique() == 6, "the binned one is not"
+    assert phenotypes.obs.loc[~treated, "dose_direction_dose"].isna().all(), "controls have no dose"

From c523704b2c5c4a23b65aa209b393ac1ab411eefd Mon Sep 17 00:00:00 2001
From: anon <tim.treis@helmholtz-munich.de>
Date: Sun, 20 Sep 2026 23:21:53 +0200
Subject: [PATCH 4/5] Make the aligned dose the concentration, and fit the
 floor once

Metadata_Concentration is now the dose a well was meant to get and
Metadata_ConcentrationRecorded keeps what the plate map wrote. The corrected
value was reaching nobody: every dose function defaulted to the recorded column,
so the tutorial had to name the other one eight times to get a ten-point ladder.

The amplitude floor refitted its scale around each draw, which is right but
repeats for every distinct plate layout, and the number of layouts is
combinatorial in the plates rather than bounded by the compounds. One split now
serves the whole run, halved within each plate so the fitted half cannot be one
plate's, and the floor still holds its draw out of the scale it is measured in.

From the cleanup review: one viability in tl/_design, taking the level to
normalize within, so cytotoxicity and dose_direction stop meaning two things by
one column name; _Scale carries the control scale instead of four loose arrays;
group_rows joins group_offsets in _core; the phase vocabulary and its colours
are one mapping and unexported, as the cell-cycle pair already was; and the
plot tests share the fixture they had copied.
---
 docs/datasets/oasis_pilot.ipynb       | 137 ++++++++-------
 docs/tutorials/11_dose_response.ipynb | 234 +++++++++++++------------
 spec/schema-1.0.json                  |   2 +-
 src/mantispy/_core/_reduce.py         |   7 +
 src/mantispy/_core/schema.py          |   5 +-
 src/mantispy/ds/_datasets.py          |  53 +++---
 src/mantispy/pl/__init__.py           |   3 +-
 src/mantispy/pl/_hits.py              |  37 ++--
 src/mantispy/tl/__init__.py           |   3 +-
 src/mantispy/tl/_design.py            |  49 ++++--
 src/mantispy/tl/_dose.py              | 239 +++++++++++++++-----------
 tests/conftest.py                     |  58 +++++++
 tests/test_datasets_oasis.py          |  10 +-
 tests/test_pl_hits.py                 |  58 ++-----
 tests/test_tl_dose.py                 |  69 +-------
 15 files changed, 503 insertions(+), 461 deletions(-)

diff --git a/docs/datasets/oasis_pilot.ipynb b/docs/datasets/oasis_pilot.ipynb
index cfa25f7..afb0f36 100644
--- a/docs/datasets/oasis_pilot.ipynb
+++ b/docs/datasets/oasis_pilot.ipynb
@@ -24,10 +24,10 @@
    "id": "389f4a08",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-09-20T16:48:44.532632Z",
-     "iopub.status.busy": "2026-09-20T16:48:44.532491Z",
-     "iopub.status.idle": "2026-09-20T16:48:49.039752Z",
-     "shell.execute_reply": "2026-09-20T16:48:49.039288Z"
+     "iopub.execute_input": "2026-09-20T21:15:44.317620Z",
+     "iopub.status.busy": "2026-09-20T21:15:44.317525Z",
+     "iopub.status.idle": "2026-09-20T21:15:59.918983Z",
+     "shell.execute_reply": "2026-09-20T21:15:59.918330Z"
     }
    },
    "outputs": [
@@ -35,7 +35,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/Users/tim.treis/Library/Application Support/hatch/env/virtual/mantispy/n7Ku6oea/tutorials/lib/python3.12/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
+      "/Users/tim.treis/Library/Application Support/hatch/env/virtual/mantispy/IN9GYQN4/tutorials/lib/python3.12/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
       "  from .autonotebook import tqdm as notebook_tqdm\n"
      ]
     },
@@ -43,7 +43,7 @@
      "data": {
       "text/plain": [
        "AnnData object with n_obs × n_vars = 4604 × 99\n",
-       "    obs: 'Metadata_plate_map_name', '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_CellCount', 'Metadata_SiteCount', 'Metadata_Compound', 'Metadata_Concentration', 'Metadata_ConcentrationNominal', 'Metadata_CellLine', 'Metadata_Control', 'Metadata_Perturbation'\n",
+       "    obs: 'Metadata_plate_map_name', '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_CellCount', 'Metadata_SiteCount', 'Metadata_Compound', 'Metadata_Concentration', 'Metadata_ConcentrationRecorded', 'Metadata_CellLine', 'Metadata_Control', 'Metadata_Perturbation'\n",
        "    var: 'object', 'feature_group', 'feature', 'channel', 'scale', 'angle', 'gray_levels', 'radial_bin', 'params', 'is_feature'\n",
        "    uns: 'mantispy'\n",
        "    layers: None (.X)"
@@ -88,10 +88,10 @@
    "id": "14c9f7db",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-09-20T16:48:49.041263Z",
-     "iopub.status.busy": "2026-09-20T16:48:49.041132Z",
-     "iopub.status.idle": "2026-09-20T16:48:49.064483Z",
-     "shell.execute_reply": "2026-09-20T16:48:49.063913Z"
+     "iopub.execute_input": "2026-09-20T21:15:59.920831Z",
+     "iopub.status.busy": "2026-09-20T21:15:59.920693Z",
+     "iopub.status.idle": "2026-09-20T21:15:59.950179Z",
+     "shell.execute_reply": "2026-09-20T21:15:59.949573Z"
     }
    },
    "outputs": [
@@ -287,10 +287,10 @@
    "id": "eee4a087",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-09-20T16:48:49.066515Z",
-     "iopub.status.busy": "2026-09-20T16:48:49.066375Z",
-     "iopub.status.idle": "2026-09-20T16:48:49.250909Z",
-     "shell.execute_reply": "2026-09-20T16:48:49.250274Z"
+     "iopub.execute_input": "2026-09-20T21:15:59.951855Z",
+     "iopub.status.busy": "2026-09-20T21:15:59.951725Z",
+     "iopub.status.idle": "2026-09-20T21:16:00.287960Z",
+     "shell.execute_reply": "2026-09-20T21:16:00.287371Z"
     }
    },
    "outputs": [
@@ -352,10 +352,10 @@
    "id": "b653a03e",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-09-20T16:48:49.252605Z",
-     "iopub.status.busy": "2026-09-20T16:48:49.252473Z",
-     "iopub.status.idle": "2026-09-20T16:48:49.518298Z",
-     "shell.execute_reply": "2026-09-20T16:48:49.517672Z"
+     "iopub.execute_input": "2026-09-20T21:16:00.289650Z",
+     "iopub.status.busy": "2026-09-20T21:16:00.289532Z",
+     "iopub.status.idle": "2026-09-20T21:16:00.555164Z",
+     "shell.execute_reply": "2026-09-20T21:16:00.554417Z"
     }
    },
    "outputs": [
@@ -401,10 +401,10 @@
    "id": "43250f1c",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-09-20T16:48:49.519896Z",
-     "iopub.status.busy": "2026-09-20T16:48:49.519775Z",
-     "iopub.status.idle": "2026-09-20T16:48:49.718562Z",
-     "shell.execute_reply": "2026-09-20T16:48:49.717905Z"
+     "iopub.execute_input": "2026-09-20T21:16:00.556724Z",
+     "iopub.status.busy": "2026-09-20T21:16:00.556588Z",
+     "iopub.status.idle": "2026-09-20T21:16:00.754735Z",
+     "shell.execute_reply": "2026-09-20T21:16:00.753869Z"
     }
    },
    "outputs": [
@@ -417,7 +417,7 @@
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 1100x320 with 2 Axes>"
       ]
@@ -454,15 +454,20 @@
    "source": [
     "### One concentration, written two ways\n",
     "\n",
-    "Two of the twelve plate maps record the dose to three decimals where the rest use four, so a single\n",
-    "concentration arrives as both `3.704` and `3.7037`. Nothing downstream can tell that these are one level:\n",
-    "`groupby` splits a treatment's wells across the two spellings, and {func}`~mantispy.tl.dose_response`\n",
-    "reports more distinct doses than were ever plated.\n",
+    "The plate maps disagree on precision rather than on value. One batch records the concentration the dilution\n",
+    "produced, `0.0152416` µM, and another records it rounded, `0.015`. Nothing downstream can tell that these are\n",
+    "one level: `groupby` splits a treatment's wells across the two spellings, and\n",
+    "{func}`~mantispy.tl.dose_response` reports more distinct doses than were ever plated.\n",
     "\n",
-    "`Metadata_Concentration` keeps what each plate map recorded. `Metadata_ConcentrationNominal` is derived from\n",
-    "it in the loader: levels that agree to within 1% are one level, named by the value the most wells carry, so\n",
-    "every dose it holds is one that was actually written down rather than an average of two. It names\n",
-    "`Metadata_Perturbation`, so replicates of a treatment stay in one group."
+    "So a level written to fewer decimals is read as the finer level that rounds to it, which is a statement about\n",
+    "how the number was recorded rather than a tolerance fitted to the data. The matching runs within a compound,\n",
+    "because a compound's levels are one dilution series and cannot collide — across the whole plate map they can,\n",
+    "since berberine's 25 µM and the main ladder's 33.3 µM are a third apart and are genuinely different doses,\n",
+    "closer together than `0.000762` and `0.001`, which are one dose.\n",
+    "\n",
+    "`Metadata_Concentration` is the result, the dose each well was meant to get, and it names\n",
+    "`Metadata_Perturbation` so replicates stay in one group. `Metadata_ConcentrationRecorded` keeps what the plate\n",
+    "map wrote."
    ]
   },
   {
@@ -471,10 +476,10 @@
    "id": "47b37e6a",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-09-20T16:48:49.720212Z",
-     "iopub.status.busy": "2026-09-20T16:48:49.720069Z",
-     "iopub.status.idle": "2026-09-20T16:48:49.728145Z",
-     "shell.execute_reply": "2026-09-20T16:48:49.727453Z"
+     "iopub.execute_input": "2026-09-20T21:16:00.756389Z",
+     "iopub.status.busy": "2026-09-20T21:16:00.756248Z",
+     "iopub.status.idle": "2026-09-20T21:16:00.764877Z",
+     "shell.execute_reply": "2026-09-20T21:16:00.764244Z"
     }
    },
    "outputs": [
@@ -482,9 +487,9 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "distinct doses: 141 recorded, 67 after aligning\n",
-      "largest correction: 0.88%\n",
-      "992 of 3880 dosed wells move\n"
+      "distinct doses: 141 recorded, 104 after aligning\n",
+      "largest correction: 23.80%\n",
+      "476 of 3880 dosed wells move\n"
      ]
     },
     {
@@ -514,41 +519,41 @@
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th>3568</th>\n",
-       "      <td>0.017</td>\n",
-       "      <td>0.0171</td>\n",
+       "      <th>3268</th>\n",
+       "      <td>0.001</td>\n",
+       "      <td>0.000762</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>3404</th>\n",
-       "      <td>0.031</td>\n",
-       "      <td>0.0309</td>\n",
+       "      <th>3405</th>\n",
+       "      <td>0.001</td>\n",
+       "      <td>0.001140</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>3418</th>\n",
-       "      <td>0.034</td>\n",
-       "      <td>0.0343</td>\n",
+       "      <th>3319</th>\n",
+       "      <td>0.002</td>\n",
+       "      <td>0.001910</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>3281</th>\n",
-       "      <td>0.046</td>\n",
-       "      <td>0.0457</td>\n",
+       "      <th>3356</th>\n",
+       "      <td>0.002</td>\n",
+       "      <td>0.002290</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>3471</th>\n",
-       "      <td>0.051</td>\n",
-       "      <td>0.0514</td>\n",
+       "      <th>3373</th>\n",
+       "      <td>0.003</td>\n",
+       "      <td>0.003430</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "text/plain": [
-       "      recorded  aligned\n",
-       "3568     0.017   0.0171\n",
-       "3404     0.031   0.0309\n",
-       "3418     0.034   0.0343\n",
-       "3281     0.046   0.0457\n",
-       "3471     0.051   0.0514"
+       "      recorded   aligned\n",
+       "3268     0.001  0.000762\n",
+       "3405     0.001  0.001140\n",
+       "3319     0.002  0.001910\n",
+       "3356     0.002  0.002290\n",
+       "3373     0.003  0.003430"
       ]
      },
      "execution_count": 6,
@@ -557,8 +562,8 @@
     }
    ],
    "source": [
-    "raw = obs[\"Metadata_Concentration\"].to_numpy(dtype=float)\n",
-    "aligned = obs[\"Metadata_ConcentrationNominal\"].to_numpy(dtype=float)\n",
+    "raw = obs[\"Metadata_ConcentrationRecorded\"].to_numpy(dtype=float)\n",
+    "aligned = obs[\"Metadata_Concentration\"].to_numpy(dtype=float)\n",
     "dosed = np.isfinite(raw) & (raw > 0)\n",
     "\n",
     "print(f\"distinct doses: {len(np.unique(raw[dosed]))} recorded, {len(np.unique(aligned[dosed]))} after aligning\")\n",
@@ -586,16 +591,16 @@
    "id": "02184bd2",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-09-20T16:48:49.730066Z",
-     "iopub.status.busy": "2026-09-20T16:48:49.729935Z",
-     "iopub.status.idle": "2026-09-20T16:48:50.144387Z",
-     "shell.execute_reply": "2026-09-20T16:48:50.143765Z"
+     "iopub.execute_input": "2026-09-20T21:16:00.766566Z",
+     "iopub.status.busy": "2026-09-20T21:16:00.766427Z",
+     "iopub.status.idle": "2026-09-20T21:16:01.213144Z",
+     "shell.execute_reply": "2026-09-20T21:16:01.212616Z"
     }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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ff65SgSVYvfvuu/PdXnpfCzJeloiIyJGx/SMiIkdn9z2sMnVN//79DfcnT56s1kmw2r17d5vuGxERERERERXTgHXx4sWoU6eOYSqbxx9/XN2fMmWKYZvw8HCcOXNG3Y6OjsaLL74Id3d3jBkzRm2rXxYtWmSz34OIiIiIiKi4ysjUYevpKPy577L6KfedIiX4rrvuwpIlS3KsDw0NzVJYKSkpSd2WAklHjx41+Vply5YtxD0lIiIiIiJyPisOheP9pUcQHptsWFc20Bvv9quHXg3KFu+ANTAwUC15CQsLM9x2c3NTvalERERERERU+MHqyLl7kL0/NSI2Wa2fOqRZoQetdj+GlYiIiIiIiIqWpP1Kz6qp5F/9Onm8sNODGbASERERERFRFjvO3siSBpydhKnyuGxXmBiwEhERERERURbX4pOtul1BMWAlIiIiIiKiLEr7e8Oa2xUUA1YiIiIiIiLKolXVYIT4eSI3LreqBct2hYkBKxEREREREWURHpuElPRM5BasCpnaxs1Vf69wMGAlIiIiIiIig5sp6Xj6511ISElHhSAflAnwyjr1aKB3kUxpY/N5WImIiIiIiMh+ZGbq8OJv+3AsIh6hJbzw24i2CAvwVtWApcCSjFmVNODC7lnVY8BKREREREREyuerjmPlkavwdHPFtMeao3yQj1rftnoIbIEpwURERERERIQley/j27Wn1ZH4aEBDNK9c0uZHhQErERERERGRk9t7IRqv/n5A3R7RuToeaFYB9oABKxERERERkZNXBH52zm6kpmfi7rql8UrP2rAXHMNKRERE5om5CCRG5f64bwgQVJFHk4jIgSSmpuOZ2btwPT4Ftcv4Y/IjTYusoJI5GLASERGRecHqlOZAekoe3yq8gNG7GbQSETlQReCXF+7HoctxCPbzxI9DW6CEl32FiEwJJiIiovxJz2pewaqQx/PqgSUiIrvy9ZqTWHYwAh5uLvh+SHNUDPaFvWHASkRERERE5GT+ORCOyf+dVLcn3tdAza1qjxiwEhEREREROZGDl2IxbuE+dXtYh6p4uGUl2CsGrERERGQ9u2YC4fsBnY5HlYjIDl2LS1ZFlpLTMtG5Vim80bsu7Jl9jaglIiIix7ZnlrYEVABq3wPU6Q1U7gC4e9p6z4iInF5yWoYKViPiklGjdAl8M9i+KgKbwoCViIiIrKdKR+DybiDuErDzB23xCgBq3A3U6aP99AniESciKmI6nQ7jfz+A/ZdiEeTrgR8fb4EAbw+7/zswYCUiIiLr6TERKFUbOLMeOP4PcHwFcPMacPgPbXF1B6p0AGr31hbO2+pwMjJ12HH2Bq7FJ6O0v7cq1GLvPTREBHy37jT+3HcF7q4u+O7RZqgS6ucQh4UBKxEREeXPN0SbZzW/eVhlOw8foHYvbcnM1HpcJXg9tgyIPA6cWacty18FwhoCtfto6cNlGwMuDHzs2YpD4Xh/6RGExyYb1pUN9Ma7/eqhV4OyNt03IsrdikMR+PTf4+r2e/3ro131UDgKF530DTuZuLg4BAYGIjY2FgEBAbbeHSIickK2aIvu+D1jLuY9z6oEq/n1mEadBo4v04LXi9sAXebtxzju1e6D1ZFz9yD7F0f9JYapQ5oxaCWyQ0euxOHB77cgMTUDQ9tWxvv3NrD1LlnUHjFgZcBKREQ24JABq7XdjAJO/gsc+wc4vQZIS7z9GMe92l0acPuPViMiznQPuwStYYHe2DT+LqYHE9mR6/EpuHfKJlyJTUaHGqGY9WRLuLvZfqIYS9ojpgQTERGRbfiFAE0Ga0taEnB2gxa8Hl+ec9xr5fZa0SZJHQ6y3/kCHVVmpg6RN1MQHpOMKzFJuByThCu3bl+JTcK5yETEJafl+nzpdZU04ZWHI3BPQ6YGW4JjgqmwpKRnYMTc3SpYrRrqh28HN7OLYNVS7GG1hyvMRERkv6yRBmsCe1jzIONer+y5FbwuA64fy/p4mYbadDlStCm3ca+F9Hdz1KDlZko6wmMlENWC0HAVlN4OSCVQTc0wSs++AxVK+qB55ZJqaVapJOqE+Tvkl+SiwDHBVFh0Oh1eXngAv++5BH9vdywZ1R7VS5WwmwPucCnB8fHxuHz5MipVqgRfX1+znxMVFYXy5cvDw8PDsVOiiIjIPknQM6V5/oWGRu+2OPhhwGoBNe51uRa8Xtiabdxrea3XVYJXmVJH5nstxL+bPQYtEuxKoHvFOAg1LMkqII1JzL13VE/i/jL+3igXJIsPygf5qJ+yL5EJKXhj8aH8X+NWb6sxX083NKkYpAWwslQsiUBf+59Ko7BxTDAVpukbTuPDZcfUha+ZT7REp1ql7OqAO0xK8LFjxzB58mQsWrRIBZ9r165Fly5d8nxOeno6Ro0ahVmzZqlfUu5/8803ePTRR4tsv4mIyElID11eQY+Qx2U7Owh8iq2Q6kC70dqixr2u1KoOn1oDxF0Gdv6oLZ7+QM27gdINHObvllvQEhGbrNbL1BPtaoTeDkBjcwakEXHJKmjNj/SylL8VgEogahyUSpBaJsAbHrn0hMrrf7PmlNovXR5jWJeP7YhDl+Ow+3w0dl+Ixt4L0YhPTseW01Fq0atZuoQhgJWf1UL94OJEFaLleMpFClPHUtbJkZDHu9cLs6sxwUxfdgxrjl3FpOVaZsrbferaXbBqKZsGrKtXr0bjxo0xduxY1KtXz6znfPTRR1i8eDEOHjyIWrVq4aeffsLQoUPRsGFDNGrUqND3mYiIiGw97nWQtqQlA2dlvtdlWg9swlXg8GJtKQZBi3jul5zBrCkyr6IEjLeDUG+UDTTqJQ3yRoB3wXs1JWiSHl8JorP3ourDKXk8yNcTHWqGqkU/NvbU9QQtgD0fjT3no3Em8iZOXktQy/ydF9V2Qb4eKn1Yn0bcuGIgfD2Lb6mVTSevZ+lRz21M8Oyt59C+RiiCfDwQ4OMBbw832ArTlx3DiavxeH7ePkgO7aBWlTC0XRU4OrtICb506RIqVqxoVg+rbPfYY4/hww8/NKyrU6cOunfvrnpazcGUYCIiMsuVfcD0zvlv9+x6oFwTiw4qU4ILadyrBK8HfwdizuX/nMG/ATV6AK5FO75Svnqdi0rEbzsu4PsNZ8x6TrCfZ7Yg9HYvablAH5Ty9yqSnjhrBC1RCSnYeyFG9cBKELv/YgxS0rOOn5XfpV7ZgCy9sOUCvS3qhbVVb6AUupGeaOn9lnHDcqzUuGGjn7FJ+adom+Lj4YZAHw8V4Ot/Bvl4aveNbkuAq+77eqrt/Dzd7qgHm+nLjuHGzVTc++0mXLyRhDbVgjFnWOtcsyZszWFSgi0VERGhgts2bdpkWd+2bVvs3r3bZvtFRERENiZBZ4UW2lK3v3kXGn59GHD3BoKraWnHITWBkBq3F+nNtVIAc+hyrArOdp2Lxp4L0YhMSDX7+Z892AgPtrCPlHMJSiVN9U4CwZASXri7Xhm1iNT0TBwNv51GvPtctEpzPng5Vi2ztmgXH8ICvLMEsBLQerq7FmlvYHpGJq7GSzVlLT07PFsgKgGqJX/b/MjvLJ8fCXAl6zspLUMtcnwsIT3wt4Ncz9sBrT7AvfWY8eOyzv9Wr7wjpi87m9T0TFURWILVSsG+mPpoc7sNVi3lUAGrjHMVoaFamome3N+8eXOuz0tJSVGLcUQv0tLS1EJERGRSejrMSaJMS0+XRsWig1gU7Y/Ttn9m/t10ru5wSU8Grh3RluyP+5SELrg6EFxd/dSFyM8aQHBVwCP3IpFRN1OxT/UgxmDPhRgcvBKnvkwa83BzQdUQXyRcO4+SLvG5vla0zh9hAZ529/dqUUl6RLRekcyMdGRmFPy1JMSpF+anlsdaV1DrJPiTYyfL3osxOBIer4K0fw6Gq0V4ubuiYfkANKsUhGYVg9CkUhBC/Dzx7+GrGDN/f67jgr95pDF61teC5ZxT+6TeCjy1JUJ/O077KXNamjFcWO2bBMiySKp22QD56aWtC/BGaX8v9P9uK67GpeQxJtgLa1/qqIJA2bebqemISUpDbOKtn0lpt+4b3c6ypCM6MRVpGTqky++WkHormL5p/t/GBfD1cMXN1Mx805e3nrqG1lWDzX5tsm7Wxlt/HlEXkvy83PD94CYo4elid+cNY5bsm0MFrG5uWt5+amrWK1fSGLu75/6rTJo0Ce+//77JMbTmViUmIiLnE3DzLDqZsd3WrVsR5xdh0WsnJiaisDlr+2fu321j7XeR7l4CfsnhaimhfkbALyUcvqlRcEmKhsvlXYAs2SR5hiDBKww3vcsi3LUsjqeHYW9KWexICEV4cs7vJCU8dKjmD1QL0JaKJXQokXYOnWJfhpdL7l/cUnQe2HBQh39PZL1Y7wzkKLZyB1pVBVIqARcSgDNxwNk44Ew8kJieiV3nY9SiF+qlQ1yafoxt1t4+bZ0O4xftw7rtQGwaEJ2iLTGypAIZuvx7CN1cdAjyBIK8gJK3Frmvvy2Ln3sGXFxu3g4OJaC/AaTcAKS/WJa+5YAftWtI2fZVp/a1T9lk/LdqZZ7HRz4V6pMhHWl+txbjV9IBaZnAzXQ5XsDNNO2nftGvlyLShm1u/UzJcFHPzytYNfb9sp04WR4I9TY90xQVnnVXgEVnXOACHR6vno4z+zbDvMEGtmNJG+hQY1hlKhvJdf7ll18waNAgw/qHHnoIMTExWLlypdlXmOX9IiMjOa0NERHlLnw/PH7qlu8RSntqtTYfqAWkLZIMocKcYs1p2z9r/N3SEoEbZ+Fy47RhQdQpuMiSfDtAyi5V54YLujK46lkRaYFV4Vu2NspWq48yVerDpUTprN/kC/HzVdzJ19ezkYnYczFGjYeVnthT183vOcyNZLTKeGB9T6ihh9RoCfXzhKuVUl+lN3jismOIiLv9f1o20Atv3lPHZC9wUZKsgPjkNKw/GYXxf+Q/pZFesJ+H6vFuKj3flYLQoFyATYtFFXcbT0bi6Tl7VM//a71qYVh7xyiyZEkb6F6QE8T8+fNV0Hjy5El1v3bt2hg8eDAeeeQRq5ckl3GrSUlJqFq1Kvz9/dG8eXOsWLHCELBKb+t///2Hl19+OdfX8PLyUkt2Mn+rpXO4EhGREwkoA7i6A5npuW/j7gUP2c7C9qQo2h+nbf/k7yHzrOYzD2uefzePQMC3Ca4H1sVu1xvYlRCNXanROBwfC7+MOFRzCUc113BUdQlHddcI1PW8hnIZ4fBECmq4XEGN9CtA1HZARjPpv+t7BdwaK3trjKyreX8DD8kiK85/rwKqXc4TtcsFYVBr7b6kxk5dfwrfr8+/b0lSiZtXDtaCUFW4SvtZxt8L7kU47q9vkwq4p1F5mxSHyo985Px8vPBgCz9MXp37lEZC0lBrl/FXUxrduJmG/45dV4t6HTcX1C8XqMYd6xeZQonu3KlrCRi74IAKVgc2r4DhnWs4zPRQlrRBFgWsGRkZePjhh/H777+jdevWhuJHx48fVwHrH3/8oYJZfepufiSiDg8Px9WrV9X9CxcuqLlZJdrWj1N96623sG3bNhw6pJ3tJbWpf//+agobKbb0xRdfwNvbGyNGjLDkVyEiIsqffxjgXxaIvQi0fBpo+ljObXxDbD6XJ2Ujf4/Ru7Hl4HFM23AmSxGc0BKeGN6pGto1rJ3j7ybjBGWqlV3nb6jCP1IA6HxUzrQ1D/9QlKlcE3Uql0SLKsG3i/9IpeK4S6onFlFajywiT2o/Yy4AKXHAlb3aQlYnRYQ61yptVsD6Ru96aFvdOkW17pQEp/ayLwWd0ujzgY1VMSutwFicmr5IFRk7LwXGUrDvYoxaZmw6q7aXStfGAWydMP8ivVBQHMQkpuLpn3eqeY5bVimJifc3cJhg1VIWBaxfffUVtm/frsbqZK/UK+skNffrr7/Giy++aNbrrVmzBq+//rq6Lb20+qlqRo8erRZRtmxZVKtWzfCc3r17Y8mSJep9Zs+ereZf3bRpE4KDOcibiIisbO8cLVj1KwV0nwB4ZhsgRnZrxSV3jPxHCtqUz7Je6htt+CcFU0u6o5NvuvoSrQ9O5Ut2XHLW3nT5/ic9R/KlukWVkmhRORgVSvqY/mIolYqDKmlL9buyPiZzxkafA6JuBbCyhO8HIg7m/8usnQRU76qlBYc1ALz8C3ZQnID0TkqvaW69gVoxI60Xk8wnwejUIc1yVF4Oy1Z52cvdzRCEPnMrM/NSdJJhHl5ZjkXE4XJMklr+2n/FMF1Pk4pBhudKOrFUKybT0jIyMerXPWp6LAn+pw5pro59cWXRGFYJDj/++GMVNJryzz//qAD0wIEDsGech5WIiPKVlgR83RSIDwfu+QRoPdzh2yJnaf9k/s0OH6/J8sU6O0lTlO2yV3z19dS+OLe4NX1K00ol1VQfNp3nNwsXLa1Yglf9EtYI8GUAln3OUOTSGyiB151MbePMrDG3bUJKupp7Vx/AyjRP0kuYXY3SJdC8khbAyv9itVA/s8cO22oOXovEXAQStRlQTMoje+edPw9h9tbz6nz1+8h2qFvW8c7nlrRHFgWsknorxY3kpyky1rRkyZJITrZsbqii5iwNNhER3YEt3wAr3wICKwJjdmtjIq2IAWvh2Xo6CoN+2GbWttIbp+/Vkd7TumWLMDXR3IC12RNAwlUg4gAQd9n0NoGVgLKNgLJNbgey/rYt2mNLhTUPK1mfpOKfup5wO4A9H40zkTkLaMm8sM30AWylkmhcMRC+nu6O+beXYHVK83zH2cvQBmQLWudsO4+3lxxS2R/ThjRHj/phcESWtIEWpQRL4Qap1JtbwJrXY0RERA4jOQ7Y+IV2u8trVg9WqXDHdf19QEszzM97/evhiXZV7f/P0eJJoFwT7XbCdSBiv5ZOrF8k1Tj2grYc+/v280qE3QpijXpj5QJMMR3nZkwCk+71wuy/l41Ur2mtMv5qGdSqkjoiN26mYu+F22nE+y/FICYxDWuOXVOLkL+ljB/X98DKzwMXY/DcL3tynYPXbnrXpWc1r2BVyOOyXdDtgHXLqUi899dhdfuVnrUdNli1lEUBa6tWrfDDDz/gjTfeMPm4PCbbEBERObRt3wFJN4CQmkCjR2y9N5SPKzFJWHk4AiuPXMX2szdUOqA5apexcZaVpPyZUc1YbadXohRQ425t0UuK0cbCGgexMlY2IQI4KYvRtH8+JW+nEasgtgkQXE0bf1uIKYy2YO/FjCh3wX6e6Fa3jFr0YzaPhsdl6YW9EpuMg5dj1TJryznDtESm/vtlnVyqkJ5XuZDhiBcuzkbexMhf9qjz2/1Ny2Nk5+pwFhYFrDI+tWfPnrh27RpeeOEFVK5cWa0/f/48Jk+ejG+//TbXuVCJiIgcws0oYMsU7fZdbwJuFs8AR0Xg1LV4NYflv4cjcOBSbJbHapcpgcsxyWqsHOy58M6tasZ3HAT6BAFVO2qLXupN4OrhWwHsPu3ntaNAUjRwZp226HmWuBXAGvXGhtbO+tm/gxRGojvl4eaKRhWC1PJk+6qGC1Uy/lUfwErgmte1KnlI0oSl192mFzLiI4DzWyx6SmxSGob9vFP9lDH2kx5oWGwrAptiUSt811134ccff8SoUaNUxWB9+q+MWfXz88OMGTPQtWvXwtpXIiKiwrfpCyA1XvsCX/deHnE7GucmaYESpK48EoEz12+PcZPvbVKcpWf9MPSoXwaVQ/zyLbwj49nsopdFgrvCCPCkonXFVtqiJ8GmBK3GPbFXDwGpCcCFLdqi5+4NlKl/uzfWw6dAKYw24WA9wVQw5WT+3CAf9G1UTt1ftOsiXl6Uf+HXz1ceV6nHHWuGonRhzwebeAO4sge4fGs6K1nir1jw/CikZ2RizLy96pwnY3GnP94c3h7FtyKwKRZfNh46dCj69u2Lv/76CydOnFDratWqpeZGDQlh2gURETmw2MvAjh+0293eNS9NkgqNpAFuP3ND9aJKkHo17nbA5OnminY1QlSQenfdMijl71WgaTicivSAylhY/XhYkZEORJ7QCjoZAtkD2kWby7u1xRIZabApR+oJZmBtVeVL+pq1ncwNK4uoVaYEOtQopYLX1tWCTRZxsqj2gWQzSFB6eY/2M+Z8zu1cZPqrykC0NidtnuYOwAX/5ihzoxlKebTGD493UOOxnY1FVYKLizuuzMgTDBFR8bT0BWD3TKBSO+DJZYVanIZVgk1LTE3HhhPXVU/q6qNXs8yL6ufphq51SqtCI11rl4K/t0fxmN7C3mRmal+mjXtiJXBNiTPv+e4+gHcA4B0IeN36KfeNb3sH3bpvYjtP/4JfLDK38vKz67MG7kXNkQJrB5vOKq85eGVs7MAWFbDldJRKITaOgmSqK6k+LMFrh5ql0LB8YO7nitRE7SKPcXAq48ZNCakBlGt6a2kGhDXU5mG2cEqrDFcPuNXqCTQcCMhPyXpwYIVWJVhi25MnT6oeVb3jx4/jww8/RHR0NO677z489dRTKNZ4giEiKp6iTgN752i3u73tFJVU7UX0zVT8d1RSfa+qYDUlPdPwWIifJ7rXK6N6UqVH1cvdslQ4Ft4pAAkWZa5XWRo8oK2TL+TTu5j3/PQkIEGWqwV5dy20yBHMmhkA55UKbE8KWCWW8v5fl+wJGQrgkstQgA/ub2DIrpDzjgSum05dx8aTkbgUnaSKtsny2coTav7l9jVC0KlqILqUvIawhCO3AtS9wPWjgO72eSrL9FLljYJTSamXMeYFNDxtHKrjIoYF7kJI4hmtCrgsclGnbj+g4YNA1c7FvtaCRb/d4sWLMX/+fCxYsMAQGcu4Vhn0W6dOHYwYMQKurq544oknUGzxBENEVDyt+wjITAdqdAcqt7P13hR7l2OSsOpwhOpJ3XEua2XfisE+6FlPxqOGqakq2CNqD8y8gPP4Ui3Akt5YSZFMjjVxO0a7r27HZn0sI1ULNVLkfiwQe7Fwfp1/39QqLrt5Am4et356Gd32BNxv/cyy3Hpcej8tfR4vghW6XhXS8UsfL0zbcAaRCfJZ0oSW8MTwTtXQrsLtjI2Sfp7o06isWqRT7nxUIjadiMCZI7uQfmkPaqWdRMPjZ1H3xHl4umTkfDOZNqp8M6Pe06aAX6jVKoSnwAMHMyrDo1FfBD/SBLh2BDi4EDj0u/Z/sf9XbfErBdR/QOt5rdCiWH7OLApYv/nmG0ycODFLAHvz5k2cOnUKoaGhqiDTd999V7wDViIiKn6kmqp8ERB3vWXrvSmW5AvhqWsJajyqBKmSjmesTpi/6kWVpW5Zf6eqgFmsSG9n8B3MbZuWnC3Ijcka2OZ2Wz1Hbmf9XOXq/CYUOVeP24GuuRcAyOIsyHbpKVCXHI2HtcvQ6tUA1hulWUvqu6TmXtkLlyt7UOXKXlSR8duSIZAtSrqhK4GDmdWwX1dN/Tyoq46wUlXRMSQUHSqGommlkvB0d7VKhfCbqel4ZdEB7It0Q0j56vj0wcZwkayHsAbaIvUVLm4HDi0CDi8Gbl4HdkzTFhkbK72uEryWruucAevevXvRpMntfP9169ahV69eKlgVAwYMwKuvvmr9vXRE149p1fXUSYmIiOzamg+0Xp1699l2XJuDMHdcqFT23acq+0Zg1eGrOBOZtbJvi8q3KvvWC0OlEPMKplAx5+GtLf7a/JsWk3TNH8xIXe44Tushy0jRenWlWJT8lB4v/W310+jx9GzbZln0j+sfS8mZMpqZBqRaWJRKpj8pVdvhxyvaVRbkmv8BcVe08c5SXCw7SS2XVF5D72kzePqUQ/rZG7hxMhJnT0Ui4loCIi7GYN/FGHyz5hR8Pd3QploIOtQIVWNga5Qukf9Ft1sVwo3Pp6ElvDBj4xmsiSyD0v5e+OHxFvDxzDYMQoLXym21pddH2hRVcsH16N9akaeNn2tLmQZa8NpgABBUCU4TsHp4eCAmJkZNYSO2bt2q0oD1JB04U65WELB4OLB0rPZBl+75Cq2ACi2BACesSlicClk5yn4Skfku7gSO/6NVbuz6Jo9cPmS6mOyVd8saVd5NTc/E9rNRWpB65GqOyr4yJqxHLpV9yY6ZkcKoHpftbMncnvm6/Qv/4lRmxu2ANj1bcCsFe34flv9r/Ps6sHoCUL0rUPseoGbPggfzpDnwW9YCYVmC06ZAcPUcRb9KSGmDumXUIsJjk7DpZCQ2nYrE5lORKv14zbFrahFlArwM1Yfb1wjN9Vxn6nyqdsvVBdMfb6GqmudJOsZqdtcWKQR1YjlwcBFwcpU2ZZUs/70HVGqrBa/17gf8Qop3wNqiRQt88MEH+OKLL7Bq1SpVcKlnz56Gx48cOYIGDRoUxn46HpmEW81rtlVb9AIrZg1gZZJuOcE7M0cpZOUo+0lEllkzQfvZZDBQ6nZRQcpJP7dp9gqcUpVzxNw9aFWlJI5GxCPeqLJvCS93dKldSvWkdjGzsi/ZoTxSGA140TYrVzfA1cd072haonnH3a80cPMacHyZtojyLYDavYBa92jZfEyfB5KigXObzTumMr92rR5acBpau0AFi8oG+mBgi4pqkUySYxHx2HjyugpgpbdULtT9vueSWvRDHvTVh1tVCVa9prmdT0V6pg4RsUlARQsKNnn6ar2pssj8r0f/0oLXc5tuxyPLxwPV79JShmv3BrwkFLd/Fv2FJkyYgO7du+P7779XY1EeffRR1K17Oz965syZePLJJwtjPx3P0L+1D8HFHcClndoig6VlkLQsknMuZCC+XNmR4FW/BFZwrpOPoxSycpT9JCLzSSrV2Q3aubjzeB65PEjamvQEmPpypV+341y0ocCJVPaVntR21S2v7Et26lYKo11zlJ5gcz26AHBxA06s0AJWVaV2l7asmahVpZXgVXpfK3fQCj45AwnIzm/WgjEJVKUn0eTZyYSOL1m1d93V1QX1ygWoZXjn6khOy8Cuc9HYeOq66oU9fCVOBbSy/LDxrBrr2rxSEA5djst1jyUKkPNt93phBSs65xsMNH9CWyT9+dAfWtqwzBN7cqW2SO9ynd5a8Fq9m11/diwKWFu2bInDhw+rsavBwcFq/Kqxbt26qalt6FZKSmhNbWn6qHZIUmQS7j23A1hZJLjR39bzL5u1F1b+qTh2wXySli6VPo0XXfZ1GbeWbOt0GblvJwPziaj4kAn4JNVOtHjK4cf4FDbpNcietmbKe/3q4bG2VVjZl2zDUXqCzQ6sQ7V9lYy8zq8CceHAyX+B48u1C26xF4Ad07VFpjqp0e1W6nAPLWgpLhKu3w5Q5ad0AmUn5/CYC7A1bw83dFC9qaHAPUBUQgo2y/Q50gN7MhJXYpOx9cyNPF9DAlk538p5t231O7y4ElAOaDdaWyJPar2uErzeOK1VHJZFpoWqf58WvMo85MYp0XYwHM5FJ12lTuaOJmu3ZlqoHHqZmFvGT6mgdQcQcUgLmoy5umuTDOsDWAlmS1bJvxfWDj5gJoNJSduQ9BapaiaLDHrf8nX+z/Uvpx2LLIGoicDT1gIqACUrA/5hWkEHf6NFf9/Lv3B70e3xb09kb479A8wfDHj4AmP3AyVKO05bZIP3/HPfZYydvy/f7b56pAnubVL+DvaSyEncaVstYxYlaJVxi8dXaN+t9GRMfsU2WvAqi3SgOJL4q1olZ30PauTxnNuUqgtUaQ9UvrXEhwPTO+f/2s+ut1lxPQm7pPjc1LWnsehWurBNzqc6ndZbL8GrBKwJEVm/b8v8yxK8ykWPKS0KZTicJe2RRT2ss2bNMmu7Yj2tjTWv3EnAElxNWxo/fPvkIx8gfa+rpBTLCUjWySIlq4XMuWScRix5+MZ56EU53lL2WR98ZlkitZ8J127fTow0PdGyOeKv3Nl+SkqNBLyu2X/eWuTkbnxfPW60jZTaD9+b//vEXdKWvMgX5OwBbYkyWu+6/62fcl8mRLc0sOVYW6L8yYUuSacTbUYWebDqiKQasDW3I3J6d5piLWMWJaVTFukQkO+JkjYs6cOSInthi7asehsIqQHUktTh3kDF1gUat1moJG1VAtNzG7UeVFNZbVL1VgJTfZCafc5TCVjtnFQOrl6qBAY0r2BWwFpo51MXF63QlCw9/qddGJBpco78qX3f3jpFW6T2jh0Mh7Po0yrjU93c3Jw7YC3sMRxy8pF/RFn0V0BkzKsaC7tL64WVOaIk+DMegC/Blgy8VwFsK63oU0E/YPJFTsYGmAxCbwWiKgi9dTvt9jQFZvMpqQXdssi8ZGfX5f+ce78DSte5FVwaB5umAtBbt42D1Dvt0ZSeYHOu3PX7GvD0AxKuaifP+Fs/1f0Ibd44KbZw44y25EXGFxgHsNkDWv19SeXQ/34ca0uUP7miLCllclGo3RgeMTPI1DVSDVgKLJlKzZIzkFS0lO2IqIhJCmeF5trS7W0tNVZ6XeV7ogQjEgDqgxD5ziApw9LzKinEch4sanJxXaX4btQCVck4zMJFyy6s0uFWD2q7/FOcHWj8sl2dT13dgGqdtaX3Z8Cp/7SUYUk7lxjEDlgUsFapUgXp6ekqIJXgtVq1aoW3Z6SRIERy8mWRctRC9fTtv51GLIFs3GUg4qC27PrJ/KOnT8VVPaG3AlLVe2xhpribl9ZDIVe7VCBqfFuW0FuPl9JOFMbz05obCEpA7gjzI0oRrbz2U3qkJfUi3mgxdV8mP5fJq6PPaUte3L1vBbBh2m0iyp1ML7FW5l0F0H6sdgGN8iWFP2TqGqlq6ZKtldBfDpTHC1QghIisS743tn5WW5LjgNNrtABExr/K0KyDC7RFLupLUCgVh6V4kww5s3b6snS+yPyg5/RjUDflHGsqHS/y/Un1oHYAKrWx/NzsKOOX7fl86u4F1OmjLfK52f797fbSUQLWM2fOqOlsfvzxR9SrVw/t27fHsGHD8MADD8Dbm1+Si4xMqF2ptbboxV7OWsxJijvJBNXm9DKY5KJdyTIOOPMKRKVH15kqG99pL7o+FTwvaUmmA9rsPbfJMUB6stYYyGIueQ6RM9o7R7sIJOez1rfnEqf8yTyrU4c0yzFvYJjRPKxEZGe8A7SCOrJkpGudHRK8yhJ1UhsHK8uK8UDpelrPqwSw5ZtrPbeWDjWSAFUyyFQP6q0gNftQKcmAk4v7qgdVAtTW1unpdYRK1o5yPvUO0Hri7SBgLXDRpcjISMyZMwczZszA5cuX1RQ3U6ZMgSOwRaGLIieFnGbcnf92Mu+gjAnIHoiqXtAiGt/gKGMu7XU/pcddBbS3AtjLu80rYqVvLKQogzQU8pOTkVNxJxeCvmqi/c/c86nW+2AjjlZ0KfsUN1K98lp8shpjJWlr7FklckCRp24XbZJ5Oo0Lf8p3Q5mvVIobrXwz/9fqOE7rOZUANft4UunJLddMC1Bl2JuMo5UClGTf59Mr+wqtkJUl7dEdVwneuXMnRo0apX46SsFhpwhYC/ED5tRVbR1hP83925sSVFlLw5GGRH5KI2Vc2pzI0W3+WitAInMXjtmlXWSyEUcOWImoGJL6JTJ+Uca9nlqt1dwoKJnbWnpo9WNQK0p9FT9r7i05UcDqXtDe1blz5xp6VwcPHoxp025VryUqzikcjrKf5hg0X5sb+MI24OJ24Orh22nFB37TtvEKBCq2vN0LK40PGxxyVDIeZ9MX2u0ur9k0WCUisjsyFKzRQ9oiY/0lpVcqDh9eknXak9yENdHGwUoPqhQB9fApir0mJ2BRwLp8+XL89NNPWLp0qRq/+vrrr3P8KpGjkgrDMk5FGiYhRZ5k/POF7cDFbcCl3UBKrHa1VRbjOYGN04gDOGaNHMTWb7ViI6G1gEa3phIjIqKc3D2B6l21pfEjwPQu+R+l/l/ZR9YeWY+dVF62KGDt3bs3KleujPHjx6Nq1apITU3F/PnznW9aG0dgJx8wcqC/vRQ7qHG3tggpzHD14O0AVn7K3Fz6OYG3T71didA4gC0tacT5T39FVKRuRmnTOYiub9rfHIRERHbLTsZTUtGzk8rLFo1hNbcScHKyfVcfdZoxPI4w3pIc52+vnxNYBbC3glhJI9ZlZt3OK0BLBdKPha3QIu80Yn5OqSj8+6YWsMq0Cc+ss4ux2RzDSkQOwdHqopBDKLQxrPYeiFIxHm9Jtv/bG88J3Gjg7TGBl3cZpRHv0oo0nF6tLep5brfSiFvf7oUNLG/flZcdGS8A5CTTfu34Qbt91zt2EawSERGReWyeE5WQkKDGxF69ehUNGzZEt27d8n2OFHpavXo1oqOjUalSJZWq7OXF4hlENpmjq/pd2qJPI752OGsascy9Fr5PW3bcKs4WWFELYAMr5B2sCnlceosZsOaPFwBM2/AJkJGiVaqskX8bQ0RERjjMjBwxYD19+jT++usvnD17Fi4uLmo8a//+/VGtWjWLXkcCz44dO6JkyZJo1qwZPvroI3Tp0gXz5s1Tr2vKggUL8Pjjj6Nnz56oUqWKKgL1wgsvYNOmTahYkT0wRDYl4wIl5VIW/fyWsZduVyKWn1cPaanFspB1SWDPCwBZRZ0G9szRbt/1tpYpQEREDjeOkZyXxQHrxx9/jDfffFMFlOXLlzcEnq+88ooKOMeNG2f2a7322msqWN26dSs8PT1x5MgRNGrUCA899JCqPmzKhAkTVMA6ffp0dT8lJQXVq1dX0+pMnDjR0l+HiAqb9KI2fFBbRErC7TTiU6u0ysREhWXdJECXAdTsAVRuy+NMRFQQHGZGNmTRQJ5169bh7bffxqeffoqYmBicO3dOLXJbglWZ5mbjxo1mvVZGRgYWL16MoUOHqmBV1KtXDx06dMDChQtzfZ6Pj0+W9F93d3e1+PlxMmIih+BVAqjWBegyHuj9ma33hoqziEPAwUXa7bvesvXeEBERUWH3sH7//fdqSpsXX3wxy3oJFqVnNSoqClOnTlVpvvm5cOECbt68idq1a2dZL/e3b9+e6/OkZ/WZZ57Bk08+qabY2bBhA9q2bYsxY8bk+hzphZXFuCoVETmQTZOBXpM45ytZZu0HUt4aqH+/lqbuhNj+ERGRU/Wwbtu2DUOGDMn1cUnVlW3MLbYkpJyxsaCgIMNjuVUqTkpKwrVr13Djxg0VJMu6tLS0XJ8zadIk9T76xVpjXTMyddh6Ogp/7rusfsp9IioERxYDXzcBVrwBJFznITYlLhzY+i2Pjd7FncDxZYCLqzbvqpMqrPaPiIjILntYIyIiVIGl3EjRJdnGHPoU3uy9nTIXT27pvenp6Wps6xNPPKEaYf26pk2bqp5f/bjW7CRV+aWXXjLcl/e800Z7xaFwvL/0CMJjb0/1UzbQG+/2q4deDcre0WsTUTZlGmjFmrZ9C+yeCbR6Fmg/FvAN5qGSokKbJwP75wMZqTwe+jmDV7+vHYsmg4HQmk57XAqj/SMiIrLbHlZJLdKPNzVFHpPeT3PIdDTe3t44depUlvVyv1atWiafEx4ergLirl27GtbJ+FUZ97p79+5c30vGvMqEtMbLnQarI+fuyRKsiojYZLVeHiciC0rl50UeHzQPePR3oFxTIC1RC9AmNwLWfAAkxTjnoQ7fDyx8ApjSAtgzWwtWwxqZ99xrR1CsnVkHnNsIuHkCnV+DM7N2+0dERGT3VYJlHKtV3tjdHX379sXcuXMxfPhwuLm54cyZM1i/fj1mz55t2G758uW4cuUKhg0bhnLlysHX11dVFe7Ro4d6PDMzEzt27EDNmkVzBV3SfqVn1VTyr6yTCRPk8e71wuDmyukTiKxWKj+okjaH5vHlwNoPgasHtfk1t08D2o0GWo/Q5oUt7j2H5zcDG78ATq++vb5WL6DDi0BAeWBK8/yntvnnZSC4OlCpNYpn7+oE7XaLYZxmgYiIyMG56HTSuptHekTNIWNKzSHzuLZr104VWmrVqpWaY1UqBf/9999wddU6f59++mk1LvbQoUOGgHns2LEYOHCgSkH+77//cOLECTUPa506dcx6X0mJkrE8kn5s6dVmGas66If8x+nOe6YN2lYPsei1ichMmZnAsaXA2knA9aPaOp+SWpqwpAt7+hW/3/fECmDTl8ClHdo6GZvZYADQ/gUgrMHtbWMu5n4BID0JWPm2NpWQZwng0YVA5XYoVo7+Dfz2KODhB4zdB5QoDXt1J22RI70nERHRnbRHFgWshUGKJv3222+4evUqGjZsqMao6oNV8ccff6iKwi+88IJh3bFjx1SgKs+VSsHyHEsa3jtpsKXA0tj5+/Ld7qtHmuDeJto8tURUSDIzgMOLtbk2o24NL/ArpfU2tngK8PBx7EOfkQYc+kMLVPWBuZsX0HQI0G4MEJx7TYFcpSYC8wdpabMevsDgBUDV/Cu7O8znYWp77Vh1fBno9jbsGQNWIiJyVnGOFLDaAntYiYqZjHTg4EJg/UdA9DltXYkwoNPLQLPH8x8na2/SkoC9c4EtXwMxF7R1nv5Ay2FAm+cA/zJ3/vrzH9XSit19gMHztblxHd3+34DFzwLegcDYA4BPEOwZA1YiInJWcRYErBYVXSKgVdVgVQ04r9Gp8rhsR0RFxM0daDIIGL0L6Pc1EFgRSIgAlr0MfN0M2DVT6620d1JAasNnwJcNtH2XYFV6jLu9A7x4COj+/p0Hq0J6nh/5FajZQ0sT/vVh4JTRmFhHlJ4KrPtQuy1p0nYerBIREZF5GLBaSAopydQ1Ireg9a0+dVlwicgW3DyA5kOBMbuB3p8B/mWBuEvA3y8A3zQH9v6i9cbam/irwKp3tEB1zf+AxEityJT8Di8cBDqOs34A5uENPDwXqN0bSE8G5g0CTqyEw9o7W+td9ysNtB5u670hIiIiK2HAWgAyz+rUIc0QFpi1CJU+gD1wKdYafxsiKihJAW71DPD8XqDnJK2XMuY88OdzwHetgQMLtfGOtnbjLPD3i8DkhsDmr4DUeKBUXeD+6cCYPdrvUJjjcOU4DfwZqNMXyEjRihVJFWZHI+Ny13+q3e78avErukVEROTEOIb1DqokyhQ3O87ewLX4ZJT290ZUQgpGz9urHvt2cDP0aVTWin8qIiqw1JvAzh+BTZOBpBvaulJ1gC6vAXXvBYwKvRWJiENaIaXDfwC6TG1dhVZAx5eAmj2Lfn8kXfr3p4EjSwBXD2DgTKBuPzgMCfalh1p6pWWaJPfc5wu3JxzDSkREzirOgjGsFs/Dakymmzl69CikbpNMR9OmTRs4W3pw9qlrDl6JxbT1Z/DKov2oVaYEapbxt9n+EdEt0uMmU95I5eDt3wNbvgGuHwMWPgGUaQh0fV1LjXUp5LmTz2/VAtWT/95eV+Nurapx5faF//55pVIPmAG4ugGHfteOi9yvfx/sXnKsdkxFl9cdJlglIiIi8xQoYL127ZqaB3XDhg0qMnZxcUFMTAw6deqEhQsXonRp+533rrC90qM2Dl2OxeZTURg+ZzeWjG6PAG8PW+8WEQkvf6DTK0DLZ4Bt3wFbvwOuHgTmDwbKNQW6vqkFkNYMHKUQ+8lVwKYvgAtbb8+hWu9eLVAt29h+CldJKrKrO3DgN2DRU4AuQ5vr1Z5t/RZIigZCawONHrb13hAREZGVFSjv7Pnnn0dGRobqXZVANTo6Wt2WdfKYM3N3c8XXjzRFuUBvnIm8iXEL9iMz0+lmDiKyb1LAqOsbwAsHgA4vAR5+wJW9wC8PAjN6aHOU3umMX2qqnUXA9x2AXwdqwaqbJ9BsqFbNeOAs+wlWjYPW+6YCTR7VglVJEz6wAHbrZqQWsIq73tR6iImIiKhYKdAYVulV3bt3L6pVq5Zl/ZkzZ9C0aVOVi+zs44YOXIrBg99vRWp6Jl7pWRujutYolPchIitIuA5snqyNc5WKuUJSdKXHtUp77X7MRSAxKvfX8A0BgioCacnAvl+0OVT1c8J6lgBaPAm0GQUEOMDY9sxM4O+xwJ7ZWjm5+74DmgyG3fn3TWDrFKBsE+DZdbZLqS4gjmElIiJnFVfYY1jT09Ph6+ubY72Pj496jIBGFYIw8d4GePX3A/hs5XE0KB+IzrVK8dAQ2aMSpYCeHwDtxgAbvwB2zwTObwZm9QaqddFSiH9/CkhPyf013LyAtiOBvb8CN69p63yCgTYjgZZPA74ONDezFH3q+5WWHrzrJ2DJc0BmOtDscdiN2EvAjh+0293edrhglYiIiAoxJbhz58544YUXsvSkSmqwpAPLY6R5qGVFDGpVSWUWPj9vLy7eSOShIbJn/mFA70+06XCaP6kFbJIeLNO95BWsCpkWRqoQS7AaUAHo9THw4iFtmhVHClaNg9Y+XwCtnpWBuMBfY7Tg1V6s/0Q75pU7ANW72XpviIiIyJ4C1q+++gq7du1CuXLl0KxZM7WUL18e+/btU4/Rbe/1r4fGFYMQm5SmijAlpdrB3I9ElLfACkC/ydpcqE2HmH+qlGlV7v1OC3jbjHD8+UCl1/KeT4A2z2n3Zc5Yfa+mLUWdBvbO1W6zd5WIiKhYK1DAWrNmTRw5cgQzZsxAnz590LdvX3X78OHD6jG6zcvdDd8PaYYQP08cCY/Dm4sPqmmAiMgBlKwM3Pst8PAc87YfOBto+mjxmlpFgtaeHwLtbhXUW/ayVl3ZltZ+qBWFkjlrKznXdGpERETOpkBjWJs0aaJ6Ux955JFcH6Pbygb6YMrgZhgyYzv+2HsZTSoF4fG2VXiIiBypx9UcxXUcpfxe3SdoKdIyPc+/r2tjWtvboCp8xEHg0CLt9l1vFf37ExERkf33sO7fv9/kepnWRnpZKae21UPw+j111O0JS49g9/kbPExE5FhBa7d3gM7jtfur3tYKVBW1NR9oP+s/AJRtVPTvT0RERPbbw7pt2zaTt0VmZia2bt2KSpUqWW/viplhHapi38UY/H0gHCPn7sHfYzqgdIC3rXeLiMj8oFXmr5We1rUfAKvf13papbBUUbi4AzixHHBx06YcIiIiomLPooC1bdu2Jm/rhYWFsehSHlxcXPDxgEY4cTUeJ64m4Llf9uDXZ9rA071AHd1ERLYhAaqrG7B6gha4StDa5fXCTYmWsf/yfkLmhA3l3NZERETOwKJIKT4+Xi1eXl6G2/olOTkZ4eHheOihhwpvb4sBPy93THusBfy93LHrfDQ+XHbU1rtERPnxDQHcvfLeRh6X7ZxFx3FA9/9pt9d/DKz5nxZUFpYza4FzGwE3z9tpyURERFTsWdTDWqJECfVTglMquKqhfvjy4SZ4evYuzNpyDo0rBuL+pmYWdSGiohdUERi9G0iMyn0bCVZlO2ciRZckPViKMG38XOtpvft96/e0Gveutnza+Y4zERGREytQlWC6c3fXK4Pn76qBr9ecwut/HEStMv6oXy6Qh5bsgmRN3LhxA5UrV87x2Llz5xAaGmq4gKUfwx4VFaXWS+p7bmS769evw8PDA8HBwXnug7yevIdkdNgFCZIYKOXU9jktaF3+CrD5KyAjHej5gXWD1mN/A1f2Ah5+QIeXrPe6REREZPc4eNKGxt5dC11ql0JyWiZGzN2NmMRUW+4O2bGMTB22no7Cn/suq59yvzD9/PPPqF27tsnHqlativnz56vbp0+fxtChQxEUFIR69eohMDAQr7/+ugpMjSUlJeGll15SAW3Dhg1RpUoV1KhRAz/99FOO1584cSJCQkLUnM5lypRB+/btsXz58kL6TckqWj8L9LlVMXjbt8Dy8dZLD87MANZMvB0clyhlndclIiIih8AeVhtyc3XB5IeboP+UzbhwIxFj5+/DT0+0VOuJ9FYcCsf7S48gPPZ2Kn7ZQG+8268eejUoa9MD9d9//+Huu+/G1KlT4evri127dqn7Eri+9tprapv09HTcc889iIiIwOrVq9G0aVMV0C5evBhPPPEELl++jLffflttO23aNHz88cf4559/0KlTJ7WdvOavv/6qXoPsWMthWk/r0rHAjmlaenDvzwDXO7wuenAhcP0Y4B0EtB1trb0lIiIiB8EeVhsL8vXE90Oaw9vDFetPXMdX/52w9S6RnQWrMgWScbAqImKT1Xp53JaGDx+Oxx57TAWrokWLFujfvz9WrFhh2Gb27NlYv3495s6dq4JV4erqigEDBuDdd9/FhAkTVJqxkIBWelQlWNVv16pVK0yePNkmvx9ZqPlQ4N5vpSY6sGsG8PcLkgde8MOYngqs/VC73eEFwCeIfxIiIiInU+CANTIyEjNmzMCbb76ZZW7WjIwMa+2b06hXLgCTHmiobsuY1lVHrtp6l6iQ6HQ6JKamm7XEJ6fh3b8Ow1RipX7de38dUduZ83ry3kXh5MmTKFeunOH+H3/8gebNm6tg1lTAKz2wf/75p7ovY2Z37tyJ7du3F8m+UiFo+ihw/zTAxRXY8zPw1xgtrbcg9s4GYs4DJcoArZ619p4SERFRcU0J3rdvH7p3745SpUrh6NGj+OCDD9T6WbNm4ciRI3jqqaesvZ/FnlQJ3n8xVlUNfum3ffhrTAdVTZiKl6S0DNR751+rvJaEnxFxyWj43kqztj8yoSd8PQt3FMD06dNVwGncIyq9pw0aNDC5vb+/P0qXLo2zZ8+q+2+88QYOHDiANm3aqKC3devW6Nmzpxon6+3tXaj7TlbU+GFtntY/ngX2zdXSg+/7TltnrtREYP2n2u1OrwCePB8SERE5owL1sI4bN04VUJHg1NiIESPw5ZdfWmvfnM6bfeqiVZVgxKekY/icXbiZkm7rXSIy29KlSzF69GhMmTJFBZp67u7uquhSbmSaLKkaLEqWLIl///0XZ86cUcWXpJLwyy+/rHpnb968yb+GI2n4IPDgDG1c64H5WvAqFYTNtWM6kBABBFUCmg0tzD0lIiIiO1ag7hYpgrJkyRJ123gKC6nqeeIEx2AWlIebK6Y82hR9v96EE1cT8OrvBzBlUNM8pwkhx+Lj4aZ6Os2x4+wNPDFzZ77bzXqyJVpVDTbrvc0lgWJKSooKNH18fAzro6OjDY8bkyJJAwcOxOeff64uXBmrX7++SvGVlOTsn+ULFy4gLi4uRw+sVCKW5cknn8SoUaPQrFkzVaRpyJAhZv8OZAfq368FrAufAA4t0npaB/wIuGkXKHKVHAtsunXxs8sbgLtnkewuERERFZMeVjc3NzVPY3bHjh3Ld25Fyltpf29MHdIMHm4u+OdAOGZs0lIlqXiQgE3Scs1ZOtYspaoB53a5QtbL47KdOa9nyYUPCTKFpPca09/XPy5kyhkpoPTJJ59gzJgxOV5Lgk6Z/mbBggU5Hps0aZLqVb3//vvVfRnPmp1MfyP7npaWZvb+kx2p2w94aA7g6gEcWQIselIrppSXLVOA5BggtDbQ6KGi2lMiIiIqLgFr79698d5776kpJ/Rfgs+fP4+RI0eiX79+Fr3Whg0b8PDDD6NLly7qy254eP5VTyU1UL4c9+rVCw888ECxm6OxeeVgvNO3nro9afkxbDkdaetdIhuQ6Y1k6hqRPdTU35fHC2MapMaNG6sg8tlnn1WpvjJWXQojPffccxg0aJBhjlap6ivbSSqwVAeW8aqyyFQ1ejLNjaT1Pv3002psq7zW7t278fzzz6tx73PmzEFAQIDaVt5P3mPZsmXqApicHx555BE1Xl7+38lB1ekNPPIr4OYFHF0KLBwKpKeY3jbhOrBVKg0DuOsty8a9EhERUbHjoitA6dCrV6+qL6HXrl1Ti6TzyZfLunXrYu3atQgJCTHrdWTbHj16YPz48Wjbti2++eYblVK8f/9+VYzFFOnZ7dixI7y8vNQ8jzKdxrfffquqFRuPm8uLpCDKPJGxsbGGL8r2Rv4s4xbuxx97LiPEzxNLx3RAuaDbqZnkPGw1D2tqaiq+/vprNUWNBKAVKlRAnz59VHAq41KF/A/Onz8/x3OrVKmCdevWZVn3119/qcrix48fV2NWW7ZsqcbDG/fWynhWmQZHtpVe2aCgIFVh+MUXX0T16tUL7XelInLqP2D+o0B6MlCzJ/DQbMAjWzGtFW8A274FyjYBnl0naQnF9s9ji7bIEdo/IiIq/uIsaI8KFLDqv8xKj4uMZ5WeVhljJmmBnp7mjzWS+RYrVqxo+MKbmJiIsmXL4p133lFfZE2Rnpp58+apANk4qJUvuuZWEXWUBjs5LQMPfLcFR8Lj0LhiEBYMbwMvd/Y2OKOMTJ0a03otPlmljcuY1cLoWSUqdKfXAvMGAelJQKX2wN3vAu5e2mMJ14D5g4HMNOCeT4Ha9wBBFYvtH4UBKxEROau4wg5YJWC89957Ve9mQUlwKgGn9KY8+uijhvUS9MpjuaX5hoWFqSkuPv744wK/t6MErOLijUT0m7IJMYlpGNSqkmG+ViIih3V2I/DLg1pPa14kkB29u9gGrQxYiYjIWcVZEI8VaAyrjDMrU6aMChxXrVqlelgtdfHiRfU8mWvRmNyX8bCmREVFqXTkOnXqqJ7Wrl27qmB35cq856GUaqdyUIwXR1Ex2BdfPyKVgoF5Oy5g/o4Ltt4lIqI7U7Wj1oOaHxnnmhjFo30HHLn9IyIiKnDAKkHj9OnTVQApY9pkbJuk8O7du9fs19BX/JSxqMZkCo3cqoFK2q+QYFUi8bfffluNn5V9mDt3bq7vJZVIJYLXL5KG7Eg61SqFl3toRW7e+fMw9l+MsfUuERHdmbKNeASLgKO3f0RERAUKWCUVWCqF/v3337hy5QreeustbNu2TY1jNS6gkhf99Dc3btzIsl6C4NyKNsn0F1KVWAo1yTjXu+66C6+//rqam1GKw+RGtpHuZv0ivbuOZmTn6uhRrwxSMzIxcu5uRCXkUmGTiIioGLV/RETk3AoUsBoLDQ3F448/rqa0adSoEY4cOWLW8yT1V8ajZp/ncfv27WjatGmugbJUIs6eRiyFmqKjo3N9L+nFlR5Z48XRuLq64POHGqNaqB+uxCZjzLy9SM+wPBWbiIicR3Fo/4iIyLkVOGBNT0/HP//8g8GDB6vxrDKnokwrs379erNf46mnnsKPP/5omLPx999/VwGvrDdOZzIuyjR8+HAsWrQIERER6v7169exYMECNc1Ocefv7YFpjzWHr6cbtpyOwqf/Hrf1LhERERERERUabTJFC40aNUoFiQkJCYbxo/LTkilthKT1njx5EjVq1FDjai5duoQpU6ao+Rn1ZC5GmZdVT+aAlHkca9WqpeZ6PHPmjEoR/uSTT+AMapbxx2cDG+O5X/Zg2oYzaFQhCH0aFd5cnERERERERLZSoGltZOyo9Ho++OCDqojDnZJxsFLISQJX47lVhQSkEhhLurGxyMhIFeBKoJvbmNfiMK1NbiYtO6oCVult/XNUexXIEhE5jCv7gOmd89/u2fVAuSYojjitDREROas4C+KxAvWwrlmzBtYkY1Kzj0vVq1atWq5jZ2VxVq/0rI2Dl2NVavDwObuxZHR7BHh72Hq3iIjM4xuizbMqU9fkRh6X7YiIiMhpmR2wbtq0Sf3s0KGD4XZuZBsqXO5urvhmUFP0+2YTzkTexLgF+zFtSHNVnImIyO4FVQRG7857nlUJVmU7IiIiclpmpwTLdDJCNtffzk0BsoyLVHFICdaTOVkHfr9VTXcjva6jutaw9S6RNcVctMkX+q1bt+LJJ59URdSkqJqxbt26oX///hg7dqzV39fc/RJyHpIsixYtWuDVV19V1cKzk+m2ZsyYgQMHDqhCcTLuvWvXruo1/Pz8inz/iYwxJZiIiJxVXGGkBCclJZm8TbbVuGIQ/ndffYz//SA+W3kcDcoHonOtUvyzFJdgdUrz/FMmpZfKykHrzZs3VXGztLS0HI9JITSpzm0L+v1at26dCqRlHPv48eOxatUq7NmzJ0vhtw8//BATJkzAiy++iM8++0yNjz937px67oABA7BixQqb/A5EREREVAjT2nh7e6tFtGnTxnA/+yKPUdF6uGUlDGpVCdKxPXb+Xly8kcg/QXEgPat5BatCHs+rB7aIXLhwQU051axZM1WU7fPPP1c9mnqLFy9Gly5dsHTpUtU7K3MtjxkzRl1V05OAs06dOmpp0qQJBg4cmGOeZr3q1aur7WQ6q8mTJ+Pw4cPYu3ev4fENGzbgzTffxPTp09XUWB07dlSved9996nt//rrr0I+IkRERERkDQUqumQ8zYyxjIwM9cWRit57/evhSHicShGWIky/j2wHH083/insjVxVSDPzgkJ6kvnbpd7MfzsPX8mjhbXJPMqtWrXCY489hu+//14FodLrKT2hEjAKWSdj369du4Yvv/wS7u7uGDduHAYNGoRly5apberWrYslS5YYsjhkvQSahw4dUhXEc6OvVB4dHW1Y98MPP6iCbbJPplg6BRcREREROUDAKuPBTN0WmZmZanxZpUqVrLd3ZDYvdzdMfbSZKsIkgeubSw7i84GN8x1vTEVMgtUPTVfELrCfepm33RtXAE/Lxm1Kr6gEl9kDVGMfffQR2rZti08//dSwTtJ1pUdT5kcOCgoyXND6+eefDfMsz5s3D/Xr18euXbvUOFQfHx/Va6onvbA7duzArFmzMHHiRJP7J6/53XffqXTf1q1bG9YfPHhQvTY//0REREROFLDKl1JTt/XCwsLw1VdfWWfPyGLlgnzwzeCmeGzGDvyx5zKaVgzCY22r8EhSgUnBIlNFl4xt3LhRzaXcoEEDVXBNFgkk5efJkycNAaoEpPrb+h7V0qVLq1ReCVhl+5kzZ2LhwoUqKE5NTVXzM5coUSLXQDoiIgJeXl74999/UbJkScPjMvbW19c3y3MkNVgCZj1JN84+7zMREREROXDAGh8fr35KZc7IyMgsj3l4eKgvjmRb7aqH4rVedfDBsqN4f+kR1CsXgOaVg/lnsReSlis9neaIOGBe7+lTK4CwRua9t4VkrGiFChWyvoxH1vl+JX33wQcfxPPPP5/j+ZUrVzbc1o+BNyZBpb6Im4wt/fjjj1VPrQSzEqjKONSUlBSTgXSpUqVU2vGoUaOwaNGiLBfR5H3PnDmT5TnDhg3D/fffj9WrV2P06NEqqCYiIiKiYhSw6ns6kpOTC2t/yAqe7lgV+y7F4J8D4Rg5dw/+HN0e5yITcS0+GaX9vdGqajDcOF+rbUiKtrlpue4+5m9nYaqvNdWuXVsFjsbpvKbIGNPw8HDD9DMxMTGqyq9+fOrff/+NZ599Nsu4U+lpzR4wGwfS9erVU2NYpddXCirJmFcht0eOHKnG2zdu3Fitk95cWY4dO2bV35+IiIiI7KBKMDkOGbf3yYBGqFWmBK7Fp6DTJ2sx6IdtGDt/n/rZ4eM1WHEo3Na7ScWETBuzdu1a1UOqn4NZAtGXX345x7avvPKKqh4sY96lMFP58uXRvXt39ZikHsvYeEkFFtOmTcP27dvzfX+pStyvXz/12noyz2q7du1Uz69MYyPvJ+SnTMtDRERERMU4YJUe1nfffVf1bkiva/apbcj2/Lzc8WhrLR0zLUMLIvQiYpNVzyuDVjvnG6LNs5oXeVy2s6GuXbvit99+UwGrTPwsPagdOnRQ5wdj0rsp5wf5GRwcjOXLl+PXX381pBjLnKkXL15UgasMO5g6daqatsYcUvhJijdJarCQ11y5ciUeeOABNT2On5+fqhocEhKCX375RaUU5zdJNRERERHZnotO3yVigVdffVV92ZQeEknfkyIpUs1zypQpaqqK//3vf7BncXFxKo1Qptoorl9aMzJ1qic1PNZ0+rbUDg4L9Mam8XcxPdiexVzMe55VCVaDKlr9bRMTE9XcqpKum71KsPRQyv+PBJXZSfEl2V6CUmNS6fett95SPa8yJlVSgytWrAg3t6xTL8npSNKAJeCUwFWKLslY03LlyuW7X2fPnlUBsT7l2Pg15f2kZ1deJ/vziJypLXKG9o+IiOyfJe1Rgb65SW/K0qVL0ahRIxWwDhgwQKXeNW/eXAWtZHs7zt7INVgVcpVCHpft2la3bQ8d5UGC0UIISPMjxZByG5Mq40dzow8s8yLF2apUqZJrOrvxmNXsFYrz2q+qVavm+prm7BcRERERFZOUYEnbkzkOhaTaSWQs+vTpo9LyyPakwJI1tyMiIiIiInKIgFVS7PSpfJKat2rVKnV79+7dTDGyE1IN2JrbEd0JmU5m/fr1PIhEREREVPgBq+QbG1cIffzxx9GiRQvcc889aloKsj2ZuqZsoLcaq5qbEl7uaFmlZBHuFTkrOWfklUpMRERERGS1gFXmT9QbOnSo6mF95JFHsGDBArsvuOQsZJ7Vd/tpVVpzC1oTUtLx/tIjyMy0uO4WERERERGRY8zDKlNYyJyLffv2tcbLkZX0alAWU4c0U9WAjUnP6yMtK8LFBZiz7Tyen78XqenaPJVERERERET2wuwqwZs2bbIogCX7CVq71wtT1YClwJKMWZV0YemBbV8jFC8t2Ie/D4QjNikN3w9pruZvJSIiIiIisgdmRycdO3Y0+0ULMLUrFSIJTk1NXdOvcTkE+nhg+Jzd2HgyEo/+uB0zn2iJkn6e/HsQEREREZHjpAQnJSWZvZDj6FSrFH59pjWCfD2w72IMBk7bivBY/g2JiIiIiMiBAlZvb2+zF3IsTSuVxMLhbREW4I1T1xLw4NStOH09wda7RURERERETq7ARZciIyMxY8YMvPnmm4Z127ZtQ0ZGhrX2jYpQzTL+WDSyLaqF+uFyTBIGfr8VBy7drgZNRERERETkEAHrvn37ULduXXz++ef48MMPDetnzZqFn3/+2Zr7R0WoQklfLBzRFo0qBOLGzVQMmr4Nm09F8m9ARERERESOE7COGzcOL730Eo4cOZJl/YgRI/Dll19aa9/IBkJKeOHXZ9qgXfUQ3EzNwJMzd2LZwXD+LYiIiIiIyDEC1l27dmH06NHqtotM5nlLzZo1ceLECevtHdlECS93zHyyJe5pEIbUjEyM+nUPft1+gX8NIiIiIiKy/4DVzc0N8fHxOdYfO3YMwcHBFr9eeno6YmJiCvS8iIgIk/tCd8bL3Q1TBjfDoFaVILMUvbH4IKasOckpi4iIiIiIyL4D1t69e+O9995DZmamoYf1/PnzGDlyJPr162fRfK3jx49HYGAgypYti0qVKuGvv/4y+/mSgizPe/vttwvya5AZ87d+eH8DjO5aQ93/bOUJ/O/vo8jM5Dy7RERERERkpwGrFFvaunWrChYlaG3YsCFq1KiB5ORkTJo0yezXkfGu06dPx/r165GQkIAXXngBDz74II4fP57vcxcsWKCKP9WrV68gvwKZSS5IvNyzNt7uqx3nnzafxbiF+5GWkcljSERERERE9hewlilTBrt378aUKVPw6quvolevXpg9e7Ya2xoSEmL263zzzTd4+umn0aJFC5VmLIWcKlSogGnTpuX5vLNnz6rg9pdffoGHh0dBfgWy0LAOVfHFQ41Vr+vivZcxfM5uJKVyCiMiIiIiIio87gV50j333IPly5dj4MCBaimI69ev49y5c+jQoUOW9R07dsSOHTtyfV5aWhoeeeQRvPvuu6hdu3aB3psK5oFmFRDk64HnftmDNceu4bEZ2zFjaEsE+vKiARERERER2UkP66ZNm3Dz5s07emMJWEVoaGiW9aVKlcK1a9dyfd4bb7yheniHDx9u9nulpKQgLi4uy0IFc1edMpg7rDUCvN2x63w0Hp6+FVfjknk4iYjsENs/IiJyyoC1R48eWLRo0R29sb5Yk1T6NSb3JT3YFBnrOmPGDHzwwQeqOrAssn1iYqK6nRsZVyuFnfRLxYoV72jfnV2LKsH4bXhblPb3wrGIeAyYugXnIu/sAgYREVkf2z8iInJ0Ljop1WshqQYsxZKkIrAUPfL09MzyuFQQzk9sbCyCgoLw22+/4aGHHjKsHzRokOphXb16dY7nzJw5E6+//nqWdVFRUer9/f39cfnyZZPBrlxhlkVPelglaJV9CAgIMPv3pqwu3kjEkBnbcT4qEaElPDHryVZoUD6Qh4mIyAzSFslF1MJsi9j+ERGRo7eBBQpYs487NZUybI4mTZqgZcuW+OGHH9R96S0tV64cRo0apcao6n8ZGbeaWzEneY0uXbpg8uTJdvUlwVlcj0/B4z/twNHwOPh7ueOHoS3Qppr5hbeIiJyVLdoitn9ERGQPLGmPClR0ydyAND9vvfUWBg8ejDZt2qBt27b47LPP1DQ50oOrJ5WDt23bhkOHDlnlPcm6Svl74bfhbfD0z7uw4+wNFbxOGdQUPeqH8VATEREREVHRj2G1FplzVdJ8ZRobqTwsqcDr1q1D6dKlDdtI5J29MJMxeYy9pLYV4O2B2U+1wt11yyA1PRMj5u7Ggl0XbbxXRERERETk6AqUEuzomBJVONIzMvH6HwexcPcldf/1e+pgeOfqhfRuRESOjSnBRETkrOIsSAm2aQ8rFS/ubq745MFGGN6pmro/afkxTFp2FE54TYSIiIiIiKyAAStZlUxX9Hrvuqp3VUzbcAavLjqgel+JiIiIiIhsFrAmJCSwN40USQWW3lZXF6gU4ZG/7EFyWgaPDhERERER2SZglblQpdrv1q1brfmy5KAealERU4c0h6e7K1YduYqhP+1AXHKarXeLiIiIiIicMWCV+VS7deuG5557zpovSw6sZ/0w/PxkK5Twcsf2szfwyLRtau5WIiIiIiKi/LBKcBFN1u7sDl2OxRMzdyAyIRVVQnwxZ1hrVAz2tfVuERHZDKsEExGRs4or7CrBmZmZOHr0qOH+yZMn8dZbb6k5VVkRlkxpUD4QC0e0Q4WSPjgXlYgBU7fgWEQcDxYREREREVk3YP3qq6/w008/qdvJyckqDfivv/7CK6+8go8++qggL0lOoGqoH34f2Q61y/jjWnwKHvp+K3adu2Hr3SIiIiIiouIUsE6dOtUwTnXNmjWqO3f//v1YtmwZZsyYYe19pGKkTIA3Fgxvi+aVSyIuOR1DZmzHmmNXbb1bRERERERUXALWS5cuISwszBCw9uvXT82/2bBhQ1y5csXa+0jFTKCvB+YOa42utUshOS0Tz8zejcV7L6nHMjJ12Ho6Cn/uu6x+yn0iIiIiInJO7gV5Us2aNTF37lzce++9+O233zBnzhzDWFZ5jCg/Pp5umP54C7y66AAW772MF3/bjy2norDpVCTCY5MN25UN9Ma7/eqhV4OyPKhERERERE6mQD2sEyZMwOjRo1GmTBnUrVsXnTp1UuunTZuGESNGWHsfqZjycHPF5wMb48n2VdT9hbsvZQlWRURsMkbO3YMVh8JttJdERERERORQAav0rIaHh+Pw4cNYsWIFXF21l3nooYfwzDPPWHsfqRhzdXXBm73rqnlaTdEnBL+/9AjTg4mIiIiInEyBAlYRHByMevXqGYJV0blzZ7i7FyjLmJzYznPRSEhJz/VxCVql53XHWVYUJiIiIiJyJmZHly+//LLZL/rZZ58VdH/ICV2LT7bqdkRERERE5GQB66FDhwp3T8hplfb3tup2RERERETkZAGrjFUlKgytqgarasBSYCm3SWzkcdmOiIiIiIicR4HHsBJZi5uri5q6Rrjkss3LPWqp7YiIiIiIyHlwDCvZBZlndeqQZqoasPHUNm4uQIYO+H3PZfRvUl5NhUNERERERM6BY1jJroLW7vXCVDVgKbAkY1YDfNzx0PdbseV0FN776zAm3tcALi7saS2OMjJ1Wf72kgLOXnUiIiIi58YxrGRXJEBpWz0ky7qvHmmKZ+bswi/bL6BWGX8MbVfFZvtHhWPFofAcvesybllSxeVCBhERERE5J+ZXkt27u14ZjO9VR92e8PcRbDx53da7RFYOVkfO3ZMlWBVShEvWy+NERERE5JwKHLAuWbIE/fr1Q/369Q3rPvnkE0RFRVlr34gMhneqhgealVdpo8/9sgenryfw6BQD8veUnlVT1aH16+Rx2Y6IiIiInE+BAtaff/4ZTz31FBo2bIgjR44Y1vv4+GDSpEnW3D8iRcatTnqgIZpXLon45HQ8/fMuxCSm8ug4OBmzmr1n1ZiEqfK4bEdEREREzqdAAav0pC5atAgffvhhlvV9+/bFvHnzrLVvRFl4ubth2mPNUT7IB2cjb2LUr3uQlpHJo+TApMCSNbcjIiIiouKlQAHr6dOn0bZtW3XbuGJrSEgIIiMjrbd3RNmElvDCj0NbwNfTDZtPRWHC0ts9/OR4fDzczNpOqgYTERERkfMpUMBatmxZHDt2LEfA+u+//6JatWrW2zsiE+qWDcDkh5tAPnpztp3HnK3neJwc0NbTUXhryUGztl134hoSUtILfZ+IiIiIqBgErM8++6waw7pp0yYVsEqP67fffqvWjxgxwqLXSklJwdKlS/Hjjz9i+/btZj3n7NmzKvV44cKFuHDhQkF+BXJwPeqH4ZWetdXt95YewaaT7Nl3FOkZmfh85XEM/nEbrsWnokyAl1qf1+y609afwV2frcPvuy8hkwWYiIiIiJxGgQLW8ePHo0ePHrj77ruRkZGBGjVq4KWXXsLw4cPx/PPPm/06165dQ9OmTdXrrV69Gvfccw+efvrpXLfX6XQYOHCgem8Jcn/55RfUrl0bn332WUF+DXJwIztXxwNN9ZWDd+MMKwfbvUvRiXhk+jZ8s+YUdDrgoRYVsPblLvh+SDOEBWZN+5V5WKc+2gw/Pt4ClUN8cS0+BeMW7sf9U7dg74Vom/0ORERERFR0XHQSBRZQfHw8Dh06hMzMTDRo0ACBgYEWPV96aXfv3o1t27apCsP79u1D8+bNDVPmZCfv88cff2DAgAGGVORff/0VQ4YMwcmTJ1G9enWz3jcuLk7ta2xsLAICAizaZ7IvyWkZGPTDNuy9EINqoX5Y/Fx7BPp62Hq3yITlB8Mx/vcDiEtOh7+XOz54oCH6Ny5neFwuPEg1YCmwJGNWW1UNhpur9n+ekp6BmZvP4ZvVJ3EzNUOtk4sV4++pgzIBHN9KjskWbRHbPyIisgeWtEd3FLDeCQk+g4KC8P777+PFF180rO/YsSMqV66MuXPnmt1LW6ZMGfzzzz/o3bu3Wc9hg128XI9Pwb1TNuFKbDI61AjFrCdbwt2twFMMUyFcVJjw9xH8ul1L329SMQhfP9IUlUJ8LX6ta3HJ+OTf41i0+5K6L8W3RnWtgWEdqsLbzAJORPaCASsRETmrOAsC1gJ9q5ce0TFjxuRYL+vMHYd68eJF1UNbt27dLOvlvvHcrvn566+/4ObmhsaNG+c5TlYOivFCxUcpfy/8MLSFqji76VQk/vc3Kwfbi+MR8eg/ZZMhWB3RuToWjmhboGBVlA7wxmcDG+PPUe3RtFIQElMz8Om/x9H9y/VYcShCDRsgotvY/hERkaMrUMAqPaKShpvdo48+qsaymkOCVSG9rMZKlixpdkB5+PBhjBs3Dq+88grKly+f63aTJk1SEbx+qVixolmvT46jfrlAfPlwE3X7563nMXfbeVvvklOTwFH+BhKsnriaoC4qzBnWCq/dUwceVuj9blwxCL+PaIcvH26sijZdvJGEEXN3Y8iM7SpIJiIN2z8iInJ0BUoJ9vb2xvXr1+Hv758jCJX03MTExHxfQyoLS7GmlStXonv37ob1I0eOxObNm3HgwIE8n3/q1Cl07twZd911F37++We4urrmeYVZFj0JiCVo5RjW4ufbtadUj5uMfZzzVCu0qxFq611yOrGJaXjtjwNYfihC3e9cqxQ+f6ixmkO3MNxMScfUdacxfeMZpKZnQoa9DmlTGS91r4UgX89CeU8iR0kJZvtHREROmRIsvZkypU12GzZsQFhYmFmvUalSJXh6eqopaoydOXMGNWvWzDfY7dKli1pmzZqVZ7AqvLy81IEwXqh4eq5LddzXpJwq4DPylz04G3nT1rvkVHaeu4F7vtqgglUPNxe81acuZj7RstCCVeHn5Y6Xe9bGfy92Rq/6YZBZb2ZvPY8un63D7K3n1DQ6RM6K7R8RETm6AgWszzzzjKrwO3v2bJw7d04FndLLOWzYMPWYOTw8PNQ0NlLlV9/Je+nSJaxbtw79+/c3bLd27Vo156pxQCuBqvSuyvvL+FUiPake/dGARqqwT2xSGob9vFP9pMIlFwi++u8kHp62VRW/qhLiiz9GtsfTHavB9Val38Im42K/f6w5fn26NWqX8UdMYhre+fMw+ny9CVtOcZ5eIiIiIqdJCZYKvzJudMqUKUhNTVXrpLd09OjR+PTTT/Pt8dQ7fvw42rVrhzZt2qB169aqMrD03q5atQru7u5qG5mXVYo8yfQ5SUlJqFOnDpKTk/HGG29kCVYlNbhevXpmvS+rBBd/MjXKvVM2Izw2GR1rhqpePlYOLhzhsUl4Yf4+bD97wzDdzIT7GqCEl/Y/bAvSqzpvxwV8vuqEClxFz/pl8GbvegUu+ERkbawSTEREziquqKa1kTeQQFJ6terXr2/xPKwiPDwcc+bMwdWrV9GwYUNVuEl6X/WkB1Z6cSVAvXnzJsaPH2/ydeR5bdu2Nes9GbA6h0OXYzHw+61ISsvAE+2q4L3+9W29S8XOqiNX8cqi/Soo9PN0w//ua4AHmlWAvYhJTMXk/05izrbzqhfY090Vz3Ssiue61FCpxES2xICViIicVZwjzMNqSwxYnceKQ+EYMXePuv3h/Q0xuHUlW+9SsZlbddKyo6ois2hYPhBfD2qKqqF+sEcnrsZjwtIjatojUdrfS1Usvq9J+SJLWSbKjgErERE5q7jCLrpE5Ch6NSiLcd1rqdvv/HkIW05zLOOdOnUtAfd/t8UQrD7doSp+H9nOboNVUauMv5pWZ/pjzVEp2BfX4lPw0oL9GPD9Fuy7GGPr3SMiIiKiXDBgpWJv9F010L9xOaRn6vDcL3twjpWDC0SSMX7beQH9vtmEo+FxCPHzxMwnW+KtvvVUqq29k6ELPeqHYeWLnfBqr9rw9XTD3gsxuO/bzRi3YD+uxSXbeheJiIiIKBv7/5ZJZIVA5ZMHG6FxhUA11vLp2bsQl8zKwZaQ4zVm3l6M//2gGhPcoUYolo/tiK61Szvc59Pbw02NYV37chc80Ky8Wvf7nkvo+tk6NZ9rSnqGrXeRiIiIiG5hwEpOQYKUHx5vgbAAb5XSOubXvZyf00x7L0Sjz9cb8feBcLi7umB8rzqY/VQrlA7whiMrE+CNLx5qgsXPtUPjikG4mZqBj1ccQ48vN6hiUk44vJ+IiIjI7jBgJachAdaPQ1vA28MV609cx4fLjtl6l+xaZqYO3607pSotX7yRhAolfbBgRFuM7FK9WBUqalqpJBaPbIcvHmqsijGdj0rEM7N34fGfduDk1Xhb7x4RERGRU2PASk6lQflA1asmftp8FvN3XLD1LtklGc8pAdsnK46rsb99G5XFsrEd0axSSRRHEoDLdDxrXu6C57pUh6ebKzaejESvrzbivb8OI/bWXK5CpsfZejoKf+67rH7KfSIiIiIqHJzWJp8yylQ8ffXfSXz53wmV4jr36dZoUy3E1rtkN9Yev4aXF+xH1M1U+Hi44f3+9TGwRQU1FthZXIhKxMR/jmDlkavqfklfD7zUozaCfT0w8Z+jCI+9XaCpbKA33u1XT1WkJrIEp7UhIiJnFcd5WK13gKh4kvGJUkRIxmVKMPLnqA6oFOILZybFhqRHdcams+p+3bIB+GZQU9QoXQLOatPJSEz4+zBOXE3IdRt9GD91SDMGrWQRBqxEROSs4jgPK1HepLfws4GN0ahCIKIT0zDs552Id+LKwWcjb2LA1C2GYPWJdlVUMSJnDlZFh5qhWPZ8R7zXv54hMM1OnxD8/tIjTA8mIiIisjKOYSU4e+XgMgFeOHktAc/P2+uUAccfey6h79cbcehynOpt/vHxFnivf311fAhwd3NF7TIBhsDUFHlM0oTH/LpHBf0rDkXg0OVYRN9MZbVhIiIiojvgfidPJnJ0MrWJBK0PTduKtcevY9Kyo3irbz04g4SUdLy95BAW772s7reuGoyvHmmKsEDHnq6mMFyLvz1mNS/LDkWoxZivpxvKBfmgfJCP+inVlvW3y5f0QRl/LxUUW5tcfNlx9oba99L+3mhVNRhuxai6MxERETkHBqzk9BpVCFLpwaN/3YsfN51FzTIl8HDLSsXiuOQWtBy8FIsx8/bgXFQiJIZ54e5aGNW1BgOaXMixM0efhmFqVOulmCRcjk5CZEIKElMz1Ny/spgifw+ZH7j8rQC2XJDc9lW31bogH/h4WtbbveJQuEpRZnEoIiIicnQMWIkA9G1UDievJuCr1Sfx1pJDqBLih9YOXjnYVNAigVH7GiH4a/8VpGXoVDD01SNN0KJKsE331d5JoC/VgCNik02mBku/pfRMfz2oWZagPzktQx1/CV6vxCQZAlm5fTkmCeGxServILdlwTnT7x/s53mrV9Y4mL19W1K59VWc5e8+cu6eHPsp+y7rWRyKiIiIHAmntWGVYLolM1OrHPzPwXAVIPw5qj0qBjtm5eDcghZjveqH4eMBjRDo61GEe+a49MdU6KxUJVh6wKUX9lK0FrAaB7NyW35K6nZ+ZPohCWYlzXjX+WgkpWaY3E4fWG8afxd70+0AqwQTEZGzirOgSjB7WIlucXXVKgdfuJGIg5djVeXg30e2g7+3YwV0EgRJz2pewWqgjwemDG5aKGMniysJRiUozdFrfQfzsEpvrIyjlqV55ZImt4lNSssayGYLaK/HpyApLQOnr99UC8woDiVp4m2rO3YGARERETkHBqxERmSsoBRh6j9lk5p7c+z8feq+IxWrkWDEOKDKLQjaeS6aQYuFJCjtXi+sSIsZycUFWeqVC8h1/tzwmGQVvP594Arm7biY72u+sfgg7m1SDh1rhqJxhSBeuCAiIiK7xYCVKBvpMdNXDl5z7Bo+XnEMb/Sua7fHSafT4UpsMnafj8ae89FYffSqVSvfUlYSnNpT76SXuxuqhPqpxdXFxayAVebdnfzfSbX4e7ujXfUQdKxZCp1qlkKlEMdMgyciIqLiiQErkQmNKwbh04GN1dys0zecQc3SJTCwRUW7OFap6Zk4Eh5nCFDlZ0RccqFVvqXiVRyqlL8XXri7JjafisKmU5Gqt/3fw1fVIiqH+KqeVwlgJTAPcLCUeCIiIipeGLAS5aJ/43I4dTUeX685pVIopQerpQ2q6UYlpGDPhRhDgLr/UgxS0jNz9PrVLxeAZpVKomnFIHyw7Kga25hXRVsJbqh4kc+BjKeV4lAuuRSHmnBvfZXaPLh1ZTXeWcZrbzxxHRtPRmLPhWicj0rE+agLmLvtgno9+TxJ8NpBpQ8HMn2YiIiIihSrBLNKMOVTOXjUr3uw/FBEkVQOlvc7eS1BBacqQL0QrdI3swvy9UDzSiXRrHJJVaynUYVA+Hq6F2pFW3IcBZ2HVSoSbzsdhY0ntQD2TLbPnqQPt68eio61QlX6sKNW0bYXrBJMRETOKs6CKsEMWBmwUj4SU9Mx8PutOHwlDrXL+OP359qhhJd1khPik9Ow/2KsFqBeiMbeC9GIT845jYmkJEtgqg9Qq4X6GebdtHbQQsWD9J7eaXGoizcSVdqwBLCbTkYiLttns4pKHy6lUoglfdjRKmrbGgNWIiJyVnEMWK13gIhEeGwS+k/ZrNJs765bGt892lwFmZYEA1Ic6eKNJOy+cONWD2oMjkfEITNb3q6vpxuaVAwyBKjNKpYs8Fyp1ghaiPSfpQOXYlTPqwSv0vufbvThlc9Vs0pBhgC2UYUgsz9rzvo5ZcBKRETOKo4Bq/UOEJGe9H4+PH2bKnrk5+mGm6kZefZcJqdl4NDl2CzpvZEJqTkOaMVgH5Xeqw9QpReX86OSvZPsgG1nbhjSh7OnrgdI+nCNUEMAm1v6sDNnAjBgJSIiZxXHgNV6B4jI2ISlh/HT5nM5Doq+wM2zHauqHlNJ75VgNS0ja/epp5srGpQPUMGpClArlUTpAFbrJccn6cMSuEoAu/lUzvThqqF+hurDbaoFq/Rh/Vjr7MXBnGWsNQNWIiJyVnEMWK13gIiM0xY7fLwmS09QfkJLeKF5ZS29V5b65QLh7eHGg0rFWnpGJg5cjlWpwxLASpVr+f/Rc79VffhoRLwq9IQ8qllvGn9XsU0PZsBKRETOKs6CeIzT2hCZScbYmROsyhjXPo3KonmlYJXum19xJKLiRlLaJXtAlue71UScpA+r6sNaAHsuKhE7z0fn+RoS3sr/239HInB3vTC7CFqddawtERGRLTFgJTKTfEk1R7/G5XBvk/I8rkS3BHh7oEf9MLWIC1GJmLL2JBbsupTvMRo+dw8kJizp66mmlgop4YkQPy91W5bQEvLTy+i2J4J8Pa0eSDrzWFsiIiKnDlh37dqFqVOn4urVq2jYsCFefvllhISEWP05RHdKelSsuR2Rs6oU4ov7m1YwK2AVkk0cdTNVLSev5b+9cYCrBbK3A1zjgFe7nX+Am9tY24jYZLW+uI+1JSIictqAdcuWLejatStGjhyJHj164LvvvkP79u2xe/du+Pn5We05RNYg6X/SoyJfUrN/cTUecyfbEZF1/p/WvtxFpRTfkIA1QQtabySkGALYGwmp6rHImynqZ0xiWpYA1xwutwLcEBNBbUk/D0z+76TJfdTd2k/pee1uJ2nLRERExY2LTiaHtJEuXbogODgYf/zxh7ofHx+PcuXKYeLEiRg7dqzVnpMdiy5RQel7WoTOCauaEtn7/1NaRiaiE7UgVoLZyFsBrhbU3g5wo25qQa8EuNYw75k2aFvdskwfFl0iIiJnFecIRZeSkpKwceNGzJw507DO398f3bp1w8qVK00GnwV5DpE1yZdn+RKdfSyb9ARxLBuR7f+fPNxcVVq+uan5UtE4OjFNBbASzKqe2VsBrtw+eDkWBy7FWm2MOxEREVnGZgHrxYsXkZmZiQoVKmRZL/fXrl1rteeIlJQUtRhH9EQFJV+iJf2P1UKJHP//SSoal/L3UospW09HYdAP2xx27DrbPyIicnQ2C1hTU7WxRT4+PlnW+/r6Gh6zxnPEpEmT8P7771thr4k08mXa0vQ/InK8/ydHH7vO9o+IiBydq63euGTJkurnjRs3sqyPiooyPGaN54jXX39d5UfrF+mpJSIiMieYlvRkkb3PV39fHrfXgkts/4iIyNHZLGAtX748SpUqhT17tIIbenK/SZMmVnuO8PLyUoN5jRciIiJLxtpKT6oxuW/vhdbY/hERkaOz6bQ2Q4cOxYwZMzB8+HCULl0ay5Ytw759+zBlyhTDNl988QWOHj2KH374weznEBERFaextkRERM7KpgGrjCs9fPgwatasierVq6vA9JNPPlHzquodOXIE27Zts+g5REREzjTWloiIqLiyacAqxZKkh/TEiRO4evUq6tati9DQ0CzbjBs3To07teQ5RERERERE5PhsGrDq1apVSy2mSEBq6XOIiIiIiIjI8dms6BIRERERERGR3fewFjWdTptNLy4uzta7QkRETkrfBunbpKLA9o+IiBytDXTKgDU+Pl79rFixoq13hYiInJy0SYGBgUX2XoLtHxEROUob6KIryku7diIzMxNXrlyBv7+/OkjScF+8eLHQ52dt2bIldu7cWSTPN2fbvLbJ7TFT67OvM74vV0+K6vjmtd+F8fz8trXW8TW1nsfYusc4v3WO9Dl2lPNE9nXOeIyl+ZU2qFy5cnB1dS3W7Z/g+dm+j7Glzy2qNpDnDjjVMban80RRfVd21mOss6ANdMoeVjkoFSpUULddXLQ59OTDUdgfEDc3tzt6D0ueb862eW2T22Om1mdfZ2qboji+ub13YT0/v22tdXxNrecxtu4xNnedI3yOHeU8kds6ZzvGRdWzauv2T/D8bN/H2NLnFlUbyHMHnOoY29N5oqi/KzvjMQ40sw1k0aUiNGrUqCJ7vjnb5rVNbo+ZWp993Z3+nsXlGFvr+Jpaz2Ns3WNs7rqicifv7SjnCXPf3xmOsbPg+dm+j7Glzy2qNpDnDuc6xvZ0nshvG35Xzp+1PkdOmRJsTLrgJbqXuV6L4gqzs+Hx5TEuDvg55jEujvi55jEuDvg55jEuDvg5zpvT97B6eXnh3XffVT/J+nh8Cx+PMY9xccDPMY95ccTPNY9xccDPMY+xrTl9DysRERERERHZJ6fvYSUiIiIiIiL7xICViIiIiIiI7JJTTmtD9ikjIwNnz55Vt6UAVunSpW29S0T5unbtmiqWIKpWrapKuBMRWermzZsIDw9Xt6X9YyFIcgQXLlxAamoqPDw8ULlyZVvvDhVT7GE1U3p6Ot5//33ViISEhOC1114r3L+ME7p+/Tp69eqFDh064NVXX7X17hQLMiHzwIEDUaJECTRp0gSHDh2y9S4VO59//rn63NarV8/wZZOsJzMzE1988YWaWFwquj/33HPq4hYVraioKDz++OPqXFKlShX88ssv/BNY2caNG9W5pEWLFvj11195fK3g9OnTaNu2Lfz8/NC7d2/cuHGDx9XKnnzySXTv3h2dO3fmsS0ECQkJamoYaf+kHfzmm2+c8jgzYDXTgQMH1CTrR44cwc6dO1VjIo0LWU9YWBhOnTqFyZMn87BaMZhyd3dHREQEnn/+eQwbNozH1so+/vhj9bmtVKkSj20hOH/+vLoQsGfPHnX+Xb9+Pf766y8e6yK2dOlS9OjRA1evXsXMmTPx9NNPIyUlhX8HK5JgVc4lTzzxBI+rlcgX/fvvv19lwkgGjHQ8kHWtXr0aW7du5WEtxOPboEEDXLx4UZ2HX3/9dVy5csXpjnexSgmWL+X79u1Tf9gKFSqY3Ea+8MiJS3pDsqecSi+qKfKFv1mzZmoRoaGhKFOmDIKDg+FMZMre//77T12xHDBgAEqVKpVjG5nPVraR1CbpKa1WrZrhscTERJP/ZK6urlm2o6x2796tep979uypLppkl5ycrLbRf04lLUdv1apV+PTTT1WviFwFHT9+PGJiYhAUFMTDfIscD2kEpGz/Qw89ZPK46C9UyXGTK8m+vr48fhZISkrC5s2b1Tm3UaNGJre5dOkSTp48qQL/6tWrG9bLl0z5DOtVrFhRZblQTseOHcO5c+fUuVf+57OTnmkJ/OWc0bx58yyfYzm/m+q5lnOOpLkbB1HyXGn/jM81zkCOm5wroqOj8eyzz5rcRr5UykUVT09PdOvWLctnVXr3TPXw+fj4oHz58oW6745KvpfJuUOOp/SU5tb7f/DgQfXdTL7/GWdnbNiwQV3gkue/+OKL6Nu3bxHuveNcFFy5ciVq1aplspdUzg2bNm1SF1Mku0K2ke9tZD650CfxicQe0oaZcvToUbVd3bp1VYyhd++99xpuS/tZsmRJk+f3Yk9XDBw5ckT30EMP6cqXL69zdXXV/fDDDzm2iY+P13Xr1k1XsmRJXYsWLXTe3t66Tz75xPB4RESEzs3NzeRy7NixLK/13nvv6V577TWdM1mwYIGuRo0aumbNmunkY7Nz584c2+zZs0cXGhqqa9Omja5fv346Hx8f3dSpUw2Pb9iwQVe9evUcS7169bK8zrx583RDhw7VObvZs2frGjVqpAsLC1PHPC0tLcc269ev15UqVUpXp04dXdWqVXWVKlXS7du3z/B4rVq1dCdOnDDcr1u3ru7kyZNF9jvYu2HDhunKli2rq1mzpq5+/fomt3n//fd1fn5+uvvvv1/9PeQYnzp1Ksd28lm+ePFiEey144iJidG98MIL6hgHBQXpHn30UZPbvfTSS+qc3KpVK12JEiV0Dz/8sMnP+zfffKN7+umni2DPHcvKlSt1nTt3VsdZzhUHDx7MsY2cB+QcXrFiRfVZl7/HsmXLDI//+eefJtu/kJCQLK+TnJysu/fee3WLFy/WOZP//e9/unLlyukaNGigjospc+fOVe1e7969dR07dlTHeOPGjYbHv/rqK5Nt4ODBg7O8ztixY7O0nc4oIyNDfdeS8620cc2bNze53ZQpU9Qxl8fls9q+fXtddHS0euzGjRu64OBgw7axsbE5Ps/O7MqVK+q7WpUqVXSlS5dW7WF2qampur59+6rvIfI9u0KFCrouXbroEhMTs2wXHh6uq1y5chHuvWOQ+EHaM4lP5Lxh6v86ISFB16NHD3W+0McnkyZNMvk/8cQTT+imT5+uc0bFImD9999/dfPnz1f/WF5eXiYD1ueff15XrVo1XWRkpLr/999/q4Z969atZr+PfFjkdd566y2ds5HjJYHO0aNHcw1YGzdurE5oevJP5enpqbtw4YJF78WAVfPZZ5/p9u7dq46HqYBVGgxpROTLjcjMzNQNHDhQBaVyW0jjrf/CJOuksZYggjRyrpDGYvz48SYDVrkI4+LiYvhiL3+DTp066Xr16pVjWwasOZ09e1b3xRdfqC+OPXv2NBmwysUwOU/s2rVL3T9z5oy6sCiff2Py5fW5554zfLbpthkzZujWrl2rzhe5BaxyMUACqfT0dHVf2jH5gqT/cm8O2Vb+jkuWLHG6wy/B6PXr13UzZ840GbDKY3JhSz7vek899ZT63iHfHSzBgFULlN555x11EVCOh6mAdf/+/er8vHDhQnVf2rbatWsbLmrJuUKC2aSkJMNFG7m4S5rz58+rC1VyTpALXqYCVrlIGBgYaPgeJ507EtxOnDgxy3YMWE1btWqV7tdff9WlpKSo84OpgFUu2MpFAzmHiBUrVqjzuPHFrsTERN2DDz6ozvXOqlgErMZMBaxy0pIvQNmvWDRs2FA3YsQIs15XTngPPPCA6pWVL62yOOMXp9wCVv166fHTk3/QgIAA3eTJk81+/dOnT6vt5VhLgKxvaJxZbgGr9HBIY3358mXDut27d6ttt23bZugdHDBggDoRfvvtt6r3m3LKLWCV9dJznf3vIZkcUVFR6r4EY/JZlZ4AaWDkqjXllFvA2qdPH7UYGzlypOrJEvK5f/LJJ3WvvvqqU597zZFbwKo/P0tQaxx8enh46GbNmmX2l1sJeqU3V/93cEa5BayyXi68xMXF5fh76M/H+ZE2U84l0osyYcIEZsPcklvAOm7cOHWh0Jj0Yvv6+qpjKe655x7VOy7nawlkJeODcsotYJWL3o899liWdaNGjTKcn4W0edL5I72I8vmVNpFyyi1glcxE+Ywaa9q0qeHCi3x/69Spk+qYc+Zzr6uz5OfLmJOmTZtmWS/39+/fb9ZrbNmyBX/++aca7Ozt7a2WefPmFdIeO57Dhw+rn5KfrydjRmQsmv4xc/Tr109VQJO/ixSgsOS5zkbGQ8g4B6kapyeVgGVsif5z/dJLL6m/Q+3atfHzzz/jp59+suEeOx75/Bl/poXcl7FRMl5QyHlAPqsynk/G+b355ps22lvH/RybOjfLuOG0tDT1N5g9e7YqIKY/9zprlcQ7OcbC+DjLeGwZH2xuGzhr1iw1Vv6ee+4x/B2kbSWNfE5lbJq/v7/hkOjPHea2Y1IfQs4lUtBRztdymxWxLT93SL0MGW8pvv32W1XLQb6LSC2Id955hx9ZK7SB0v5JOyikzRsyZIg6J8hnlt+NzSe1GyIjI/OMT/744w81jvvRRx81nHu3b9/udJ/jYlV0Ka+iKiJ7kSQphrBjxw6zXuOuu+7KtSgTwTAPZfZiPjI4XP+YORigWva5zv6ZlmBVSp/LBRohA/M5PULByWc3e4EE+UzrHxMyzYosZL3PsZyb5cuQHOPGjRvz3GuFYyyFk+TckP04688V+ZEv+vyynzv5rGZv/+RioRS2MrcNlGIr+kCLzPtcGxdoE/oiV/rPtVyUkSJYZL3PtbSB8n1YLgzIdwxeCC+c+ET/GZYCb8/mUuTNmThFD6s0GvpKlcbkn03/GN0ZqXKon/cz+8mOFVULh3x2s3+mhazj59p6n2tTn2nBz3XhfY7l3Kx/jKxzjKWnLjU1Ncdx5jEuvHOFHHP5bPNcUTh47rBdGyjVw6Wnj+4M4xPzOUXAKtMkyD+XlJvP3hUvJbrpztWsWVP9PHv2rGGd9JBIyliNGjV4iAuBfHalBLqkTepJaolMvcDPtfU+18afaaG/z8+1dchn1dS5Wa44G6dX0p0dY2F8nKWGxeXLl3musOK5Qo6n8flYzhVynHmuKNpzh/4xKrw2UI6vTKVHd0YyuCT7hfFJ/pwiYJWUBZmXbsmSJVm64desWaPy7enOSb69zH1rnH4q83rJmBEZl0rW16NHD3X1Xo6z3u+//67mE+3SpQsPuRX0799fze936NAhw7q5c+eqeSiNxw5Twck5eNmyZYYv+vIFX8bs8NxsPTJ/ZUBAQJY2UOanlAtcMr8z3bnevXuri4VS68L4XCEXXtq3b89DXAjkHCFj++R7hnEbKPORm5onngrWBso8tjdv3lT3U1JSsGjRIrWerNOD3alTpyzn5tjYWPz3339sA7MpFpdHJPjctm2boVdPxkGuWLFCfaHUT1I/adIkNQ517NixaNWqFb777jtUq1YNTz75pI333jHIMZVCENKjJ+QL5a5du9QXIRljJmMnp0yZgoEDByIhIUEVA5JiB6NHj0bDhg1tvfsOe8zlqpt+4L0EpnKcW7ZsqcY3yFX7kSNHYtiwYXj//ffVl6W33npLFUDQj7OkvMnn+Nq1a6p4h4wX+f7779V6KZ4k6U7du3fHgw8+qL6MPvPMMzh+/Lj6QipFPMg88rmV87IERxKUyrlZjq3+osqLL76oiirdd999qnCHBK9ynOfMmcNDbKZz586pIihnzpxR9+VLvPQ0SXEUyTCSL0UTJ07Eq6++qi4IyPlBzhmDBw9WX+4pf3KB+8SJE9i6das6hvpzhXxuw8LC1FhJKcoo5+O9e/eqdnDq1KmYMWOGuohIlpPPsaSjSqaWpKHKuUPIeVl6pQYNGoSvv/5anZ/lu520lfPnz1fnEMqffI6nTZumbl+5ckWNS5XPtYx1l2OrL9y4YMEC9f1ZPuvLly9X273xxhs8xGaQ4FPOGfohAlJMUD7HZcuWVd+dxYcffqjawzFjxqjv1HLekJ5XOZfQbS5SKhjF4Iv9uHHjcqzv1q0bXnnlFcN9qXAo/4zyBVU+KPKPmH0wOZm2bt061RBkJ1/m7777bsN9aTAWLlyoxkZ17twZ9957Lw9pAU2ePNnQQBv76KOPVDVgIYGAFDyQ7SQ9RxqURx55hMfcTHIhy1SlU6lI6+fnZzjGv/32m6rKJ+cLqdSnT4Gn/PXp0ydHpVPp/TAOSOXL0pdffqkCVQmwnn/+edSqVYuH10zSq/TDDz/kWD9q1KgsGS5ysUU+y9JLIl9Ahw8fzrQ+M82cOdNkZU75jmFc+Offf/9VF2lkbNoDDzygLjBSwYwYMUJdjMlOeqP04ycloJVzh3y/kwu5cmFRvvRT/qRtM1UwsHTp0pgwYYLhvhxj+fxLFevKlSurjh5eFDePXEh84YUXcqyXAPW1114z3JeLXBKoSqeQdPJIfJK9EJOzKxYBKxERERERERU/TjGGlYiIiIiIiBwPA1YiIiIiIiKySwxYiYiIiIiIyC4xYCUiIiIiIiK7xICViIiIiIiI7BIDViIiIiIiIrJLDFiJiIiIiIjILjFgJaJCJ5Nhz58/v8iO9MmTJ7F58+ZCe/3z589j7dq1hfb6RERUfBw5cgT//fdfkb3f1q1bcezYsUJ7/d27d+PgwYOF9vpE2TFgJXJyERERWLBgQaG+njRsgwYNQlHIyMjAQw89hOvXr5v9nOjoaBVQL168OMdjOp0OCxcuVI8nJCSodf7+/uo9jh8/btV9JyKionXo0CGsXr26UF/vjz/+wFtvvYWiIG1w//794ePjY9FFXmnjNmzYkOOxGzdu5Ggfk5OT0a9fPyQlJVltv4nywoCVyMnt27cPgwcPLtTXCwsLw8MPP4yiMG/ePKSmpuK+++4z+zmnT59WAfUDDzyAAwcOZHls3bp1KjiVx+WLgAgODsaTTz6Jd955x+r7T0RERWfRokV49913C/X16tevj+7du6MoTJw4UQWslStXNvs5//77r2rjpN2UYNTYtGnT1GPPPPOMYV379u1RqVIlfP/991bdd6LcuOf6CBHl6dq1a9i5cycCAgLQunVreHp6Znn8xIkTOHz4sApu2rVrBw8PD8Njly9fxrZt21SAJFdjJcW0Xr16qFatmsXvI0GUPF6iRAk0a9YMgYGBZr+P9CyuX79e9SLqU3Zr1aqFMmXKGJ63fft2XLx4ET169FCPL1++XP308vJC9erV0ahRI8P75fZ65cuXNxlAWusYGZsyZQqeeOIJw/0zZ86o95CrwXqRkZEqPUsCUVfX29ftOnXqhBkzZuCrr74yrJP7nTt3Vr+Xsccee0wdbzn+EpATETmTU6dOqXOrBC5NmzbN8pi0Abt27VJthwROzZs3z/K4XBiUnru2bduqi5xyu0WLFihVqpRF7yMk0+Xo0aMoW7asety4jczvfaRnUdoXaRP0bZZsGxsbq7Zt06YNtmzZgpiYGNUWSXuyY8cOtZ20uRKIVq1a1fB+ub1e7dq1VWZOYR0jPckC+vnnn7Fs2TLDOvl+kJ6erl7H+JidO3cOPXv2NKwLCQlRbZn0pBpnRP3000+qDZTfK3sb+Mknn+DFF1/MdX+IrEZHRBabOHGizsfHR9e+fXtd586ddU2bNtVdunTJ8PiIESN0JUqU0PXo0UNXvXp1XY0aNXSnT582PL548WKdl5eXerxNmzbqp6enp2769OkWvc+HH36oCwwM1PXs2VPXqVMnXWhoqG7FihVmv4/sk7yui4uL7uGHH1bLTz/9ZHje3XffrWvZsqVaf/HiRbXot7v33nt1YWFhapvk5OQ8X2/VqlW67Kcbax0jY9euXVPvvWvXLsO6H374QVe5cuUs223cuFHtT1JSkrq/c+dOdf/nn3/WBQcHG36f6OhodfxlvTx+8uTJLK9TqlQp3cyZM3PdHyKi4iYlJUU3aNAgdf7u3r27rlmzZro+ffroMjIy1ONxcXGqPSpTpozunnvuUedJaSdu3rxpeI3x48fratWqpWvUqJF6jVatWukCAgJ0W7ZsMft99I+XLl1a17dvX13Dhg11derU0Z04ccLs9/n333919evXV22nvs2S9kH/vHr16um6deumGzp0qNp+zZo1hu3kd5N9GzdunOH9cnu9//3vf7rWrVsbtrPWMcpu6dKlqs1KTU01rBs2bJjaD2OffvqprnHjxob733zzjS4kJET3+eefq99Xb+3atWofZXt53NipU6dUu3j06NE8Pi1E1sGAlchCf/75p87V1VW3evVqw7pDhw4ZgpklS5aowOrAgQOGRlUaAGmUjIMxOdFPnjzZsE5uS7Bk7vssW7ZMNYpnz541PD5r1izVeOsbPXPeZ/ny5To3N7csv6P+edLI5kXeRxpn49c39XrZA1ZrHaPs5MuCPCcxMbFAAasc27p16+p+++03QyPesWNH3datW00GrF27dtU999xzeR4jIqLi5PXXX1cXK43bnn/++ccQJL322mvqImRkZKS6HxERoatQoYLu/fffzxKMSfu2efNmw7pHHnlE17t3b7Pf55133lFBnHGQJxdCjQMuc97n3XffVReFjcnz5JwvbVde5CKrBK3btm3L8/WyB6zWOkbZyfMlwDVmScAqF33lQrH+mD/66KO6V155Rffll1/mCFiFBMezZ8/O8xgRWQPHsBJZSNJtevfujbvuusuwTtKCatSooW5LGlDfvn3RsGFDdV/Sk1555RWVShsXF2d4jqSiDh8+3HC/S5cuKuVHUonMeZ+ZM2eqFFlJKZKiQFLoyM3NTaUQS/qUue+Tl+effz7HuszMTPWeS5YswV9//aXStPQpUuay1jHKTtbLa1lSbCK7p556SqUBC/k5bNiwXLctWbKkWceRiKi4kLZp9OjRqFKlimGdtFX6IR1yfpfztqSYChli8vTTT+eoFN+4cWM1FMT4/G5cyC6/95E2UIakSPqrvg0sXbo0Nm7cqFJgzX2f3NStWxd33313jvVSaGjTpk1qrKq0fZJGW5A20BrHKDtpj6RdKihJN5a2WY6tpEFLsShpE3PDNpCKCsewElnowoULaqxjbmSspRQkMCZjPfWP6YM0Caq8vb0N28iYUKEveJDf+8j4ExmvIo2mMSlu5O5++187v/fJja+vrxo3a+zSpUuqAZcGu0GDBmpMjuxHWloaLGGtY5SdjCmSgkvyZcX4GFji8ccfV9UcpaGWYkwDBw7MUYhJ7+bNm6hQoUKB3oeIyNFIFfYrV66o2gSmSOaejMnMXmtAzu9ybjcmtQuMyfldf27P733kcXkfmbolPj4+y2P333+/aqP0Y0bzep+8yJjY7GQ6M6l9IEGq/I7SRsl4V7lQbC5rHaPc2kBpl+6EXKQdMWKECqalTkOdOnWwYsUKk9vKe2Ufm0tUGBiwElkoKCgIUVFRuT4eGhqqegGN6e/LY9Z6Hwkmpbf1119/RWFwcXHJse7DDz9UAdqqVasMj8tVYQlaLWGtY5Sd/suN7I++J1r/BcFYXgG2XKGXq/hyVVkKT0jgnpuzZ8+iW7duBd5fIiJHIlk8EhTl1jZJuyBBlqnzuyXn9vzeRx738/NTFxRNZQIVVhs4btw4PPvss/jggw8M6+TibfY2Jr/XtcYxyq0N/OGHH3Kst6QNlEJMkkklVfC//PLLXLeTIosSrEtBKaLCxpRgIgtJtdylS5eqk7XxyV/SZ0SHDh1UipLxVVBJV5Krp6au2Bb0fXr16qUel2q6xrLfz498KZCr1eb0kkpFXGkQ9Q25XF3NfuXVnNez1jHKTq4ES0XizZs3Z1l/9epVwxyqYs+ePXm+zksvvaSO73PPPZfrNjLPq1Q5NpUyRkRUXEnbNHfu3CxBkASWEuToz++SoWJMMoFkvTXfR87RP/74Y5b034K2geb0uOrbQOMATYbfSC+vpa9nrWOUnQwhkmOUPW1YKi0by6sNlKE4EyZMUBdupTc5N9LOSu+qzF5AVNjYw0pkIRlT8/vvv6uTtIxBkSu9Mvfn9OnTVa/omDFjVBn4rl27YujQoarc/tSpU3M0Tnf6PvK4BKzy+KhRo1T6zt69e9WE5RJImUvGwUov4vjx49GqVatcU7CETE0jqUISFMrYFfmykD39yJzXs9YxMkV6fOU4yevqSYA9YMAA1fhKWvOcOXMM88uNHTs2x2vIl4b8vjjIeClJlzI1zQIRUXElU5nI+VGCIzmnyrhJueC4e/duFexIJo60SzI8RTJQpDaBTIkya9Ysq77P559/jo4dO6rpWmSKFTnPb9iwQf2U87O5ZKqYN998E5MnT1apvsbTv5hqA2XIiKQhJyYmqudIT6+lr2etY5Sd1JSQQF/awPfee8+wXr4TyNzhMsxIalDIGNyUlBQ1/6rx1DZ6sq0sefntt9/Ucc8+1R5RYWAPK5GFZMyKzMn56quvqqurcjVXgkgpjqB/XOYulTnbtm7dqhpWmcdNChnoSVrtgw8+mCPFVxovfQpqfu8jYzslOP34449VCqxcMZXgaf/+/Ra9j6QmyZykcpX6zz//VM839Tz9+M5ffvlFjbOR7SQo/fbbb1XgqWfq9aTRlvc0PobWOEamSDAsDbIEwXpS0EL2Vb7oyBhXaawlpUt/JV72WV43t7E4kqZl/Lhc4f/uu+/UFxciImcimTAHDx5UPZxyHpfz4cqVKw3FkCTTReYNlWEZcq6VlFm5bzxmU9ox43ZDyHym9957r9nvI3OXyuMSNMnrS82BwYMHq0DKkveRQkZyEVN6SqXNkvGlpp4nZI7u1157TbUxcvFT3kvShI3nIzf1elIwsXv37oZtrHWMTHn77bdVWrCM49Xr06ePei1pb6WIlQTHctz0Y2blwrK0x7mRXmXjx8PDw/HPP//g5ZdfznNfiKzFRUoFW+3ViIjsgHyJkBRgKR4hvcATJ060eJxtXiQIl1S1Tz/91GqvSUREZA3S5klvqiySdSTtYfYKxHdC0pcl9di4ij9RYWJKMBEVO8a9uYVBrnzre7qJiIjsSWFn/5jKwCIqTAxYiahYk7Syfv362Xo3iIiIipzUkjC3qBSRvWJKMBEREREREdklFl0iIiIiIiIiu8SAlYiIiIiIiOwSA1YiIiIiIiKySwxYiYiIiIiIyC4xYCUiIiIiIiK7xICViIiIiIiI7BIDViIiIiIiIrJLDFiJiIiIiIjILjFgJSIiIiIiItij/wP0vprbZBgtIAAAAABJRU5ErkJggg==",
       "text/plain": [
        "<Figure size 950x340 with 2 Axes>"
       ]
diff --git a/docs/tutorials/11_dose_response.ipynb b/docs/tutorials/11_dose_response.ipynb
index 381b4e1..552dac6 100644
--- a/docs/tutorials/11_dose_response.ipynb
+++ b/docs/tutorials/11_dose_response.ipynb
@@ -31,10 +31,10 @@
    "id": "bac81e87",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-09-20T17:13:11.868127Z",
-     "iopub.status.busy": "2026-09-20T17:13:11.868024Z",
-     "iopub.status.idle": "2026-09-20T17:13:16.846914Z",
-     "shell.execute_reply": "2026-09-20T17:13:16.846202Z"
+     "iopub.execute_input": "2026-09-20T21:16:02.925311Z",
+     "iopub.status.busy": "2026-09-20T21:16:02.925188Z",
+     "iopub.status.idle": "2026-09-20T21:16:09.567892Z",
+     "shell.execute_reply": "2026-09-20T21:16:09.567141Z"
     }
    },
    "outputs": [
@@ -50,7 +50,7 @@
      "data": {
       "text/plain": [
        "AnnData object with n_obs × n_vars = 2831 × 99\n",
-       "    obs: 'Metadata_plate_map_name', '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_CellCount', 'Metadata_SiteCount', 'Metadata_Compound', 'Metadata_Concentration', 'Metadata_ConcentrationNominal', 'Metadata_CellLine', 'Metadata_Control', 'Metadata_Perturbation'\n",
+       "    obs: 'Metadata_plate_map_name', '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_CellCount', 'Metadata_SiteCount', 'Metadata_Compound', 'Metadata_Concentration', 'Metadata_ConcentrationRecorded', 'Metadata_CellLine', 'Metadata_Control', 'Metadata_Perturbation'\n",
        "    var: 'object', 'feature_group', 'feature', 'channel', 'scale', 'angle', 'gray_levels', 'radial_bin', 'params', 'is_feature'\n",
        "    uns: 'mantispy'\n",
        "    layers: None (.X)"
@@ -93,10 +93,10 @@
    "id": "58b04c93",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-09-20T17:13:16.848744Z",
-     "iopub.status.busy": "2026-09-20T17:13:16.848580Z",
-     "iopub.status.idle": "2026-09-20T17:13:17.086149Z",
-     "shell.execute_reply": "2026-09-20T17:13:17.085013Z"
+     "iopub.execute_input": "2026-09-20T21:16:09.569801Z",
+     "iopub.status.busy": "2026-09-20T21:16:09.569639Z",
+     "iopub.status.idle": "2026-09-20T21:16:10.282024Z",
+     "shell.execute_reply": "2026-09-20T21:16:10.281353Z"
     }
    },
    "outputs": [
@@ -133,10 +133,10 @@
    "id": "8e9ef049",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-09-20T17:13:17.088008Z",
-     "iopub.status.busy": "2026-09-20T17:13:17.087835Z",
-     "iopub.status.idle": "2026-09-20T17:13:28.804686Z",
-     "shell.execute_reply": "2026-09-20T17:13:28.804052Z"
+     "iopub.execute_input": "2026-09-20T21:16:10.283559Z",
+     "iopub.status.busy": "2026-09-20T21:16:10.283445Z",
+     "iopub.status.idle": "2026-09-20T21:16:24.016221Z",
+     "shell.execute_reply": "2026-09-20T21:16:24.015433Z"
     }
    },
    "outputs": [
@@ -144,13 +144,13 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/var/folders/qg/qgc908995g3fc8qtss2fsbhhxyxxj4/T/ipykernel_1605/992733786.py:7: UserWarning: This figure includes Axes that are not compatible with tight_layout, so results might be incorrect.\n",
+      "/var/folders/qg/qgc908995g3fc8qtss2fsbhhxyxxj4/T/ipykernel_9675/992733786.py:7: UserWarning: This figure includes Axes that are not compatible with tight_layout, so results might be incorrect.\n",
       "  plt.tight_layout()\n"
      ]
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 1455.6x480 with 3 Axes>"
       ]
@@ -190,10 +190,10 @@
    "id": "e0c13226",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-09-20T17:13:28.808498Z",
-     "iopub.status.busy": "2026-09-20T17:13:28.808347Z",
-     "iopub.status.idle": "2026-09-20T17:13:29.437709Z",
-     "shell.execute_reply": "2026-09-20T17:13:29.437070Z"
+     "iopub.execute_input": "2026-09-20T21:16:24.020062Z",
+     "iopub.status.busy": "2026-09-20T21:16:24.019899Z",
+     "iopub.status.idle": "2026-09-20T21:16:24.654090Z",
+     "shell.execute_reply": "2026-09-20T21:16:24.653422Z"
     }
    },
    "outputs": [
@@ -220,12 +220,10 @@
    "metadata": {},
    "source": [
     "That warning is about replication, and the answer is in which column names the groups. HepaRG carries eight\n",
-    "wells at each concentration, but only because `Metadata_Perturbation` is built from\n",
-    "`Metadata_ConcentrationNominal`: two of this accession's twelve plate maps write the dose to three decimals and\n",
-    "the rest to four, so on the raw `Metadata_Concentration` a treatment's replicates split across two spellings.\n",
-    "U2OS is two plates rather than eight, and there most groups really do hold one or two wells.\n",
-    "\n",
-    "Every dose call below is given the nominal column for the same reason.\n",
+    "wells at each concentration, but only because `Metadata_Concentration` is the dose each well was *meant* to get:\n",
+    "two of this accession's twelve plate maps write the dose to three decimals and the rest to four, so\n",
+    "`Metadata_ConcentrationRecorded`, which keeps what was written, splits a treatment's replicates across two\n",
+    "spellings. U2OS is two plates rather than eight, and there most groups really do hold one or two wells.\n",
     "\n",
     "## Fitting the concentration response"
    ]
@@ -236,10 +234,10 @@
    "id": "a5fdbb04",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-09-20T17:13:29.439491Z",
-     "iopub.status.busy": "2026-09-20T17:13:29.439357Z",
-     "iopub.status.idle": "2026-09-20T17:13:32.229164Z",
-     "shell.execute_reply": "2026-09-20T17:13:32.228642Z"
+     "iopub.execute_input": "2026-09-20T21:16:24.655726Z",
+     "iopub.status.busy": "2026-09-20T21:16:24.655598Z",
+     "iopub.status.idle": "2026-09-20T21:16:27.498521Z",
+     "shell.execute_reply": "2026-09-20T21:16:27.497840Z"
     }
    },
    "outputs": [
@@ -408,7 +406,7 @@
     }
    ],
    "source": [
-    "mt.tl.dose_response(heparg, compound_key=\"Metadata_Compound\", dose_key=\"Metadata_ConcentrationNominal\", min_doses=4)\n",
+    "mt.tl.dose_response(heparg, compound_key=\"Metadata_Compound\", min_doses=4)\n",
     "curves = heparg.uns[\"mantispy\"][\"dose_response\"].set_index(\"compound\")\n",
     "curves = curves[curves.index.astype(str) != \"DMSO\"]\n",
     "curves.sort_values(\"hitcall\", ascending=False).head(8)[\n",
@@ -444,16 +442,16 @@
    "id": "2f812053",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-09-20T17:13:32.230789Z",
-     "iopub.status.busy": "2026-09-20T17:13:32.230670Z",
-     "iopub.status.idle": "2026-09-20T17:13:32.817260Z",
-     "shell.execute_reply": "2026-09-20T17:13:32.816591Z"
+     "iopub.execute_input": "2026-09-20T21:16:27.499999Z",
+     "iopub.status.busy": "2026-09-20T21:16:27.499873Z",
+     "iopub.status.idle": "2026-09-20T21:16:28.098878Z",
+     "shell.execute_reply": "2026-09-20T21:16:28.098258Z"
     }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 1200x340 with 3 Axes>"
       ]
@@ -465,7 +463,7 @@
    "source": [
     "fig, axes = plt.subplots(1, 3, figsize=(12, 3.4))\n",
     "for ax, compound in zip(axes, [\"Staurosporine\", \"Berberine chloride\", \"MUPIROCIN\"], strict=False):\n",
-    "    mt.pl.dose_response(heparg, compound=compound, dose_key=\"Metadata_ConcentrationNominal\", ax=ax)\n",
+    "    mt.pl.dose_response(heparg, compound=compound, ax=ax)\n",
     "    ax.set_title(f\"{compound} — hit call {curves['hitcall'].get(compound, float('nan')):.2f}\")\n",
     "fig.tight_layout()"
    ]
@@ -503,10 +501,10 @@
    "id": "dfa80cf8",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-09-20T17:13:32.818828Z",
-     "iopub.status.busy": "2026-09-20T17:13:32.818703Z",
-     "iopub.status.idle": "2026-09-20T17:13:32.871273Z",
-     "shell.execute_reply": "2026-09-20T17:13:32.870763Z"
+     "iopub.execute_input": "2026-09-20T21:16:28.100874Z",
+     "iopub.status.busy": "2026-09-20T21:16:28.100746Z",
+     "iopub.status.idle": "2026-09-20T21:16:28.157239Z",
+     "shell.execute_reply": "2026-09-20T21:16:28.156772Z"
     }
    },
    "outputs": [
@@ -651,7 +649,7 @@
     }
    ],
    "source": [
-    "mt.tl.dose_features(heparg, dose_key=\"Metadata_ConcentrationNominal\")\n",
+    "mt.tl.dose_features(heparg)\n",
     "features = heparg.uns[\"mantispy\"][\"dose_features\"]\n",
     "# bmd is NaN for a feature that never crosses the cutoff, so dropna leaves the ones that responded.\n",
     "active = features[features[\"compound\"] == \"Staurosporine\"].dropna(subset=[\"bmd\"])\n",
@@ -682,10 +680,10 @@
    "id": "5d2eaa26",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-09-20T17:13:32.872964Z",
-     "iopub.status.busy": "2026-09-20T17:13:32.872853Z",
-     "iopub.status.idle": "2026-09-20T17:13:33.011432Z",
-     "shell.execute_reply": "2026-09-20T17:13:33.010887Z"
+     "iopub.execute_input": "2026-09-20T21:16:28.158792Z",
+     "iopub.status.busy": "2026-09-20T21:16:28.158675Z",
+     "iopub.status.idle": "2026-09-20T21:16:28.298225Z",
+     "shell.execute_reply": "2026-09-20T21:16:28.297630Z"
     }
    },
    "outputs": [
@@ -770,10 +768,10 @@
    "id": "fb8077a7",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-09-20T17:13:33.013124Z",
-     "iopub.status.busy": "2026-09-20T17:13:33.012999Z",
-     "iopub.status.idle": "2026-09-20T17:13:33.623717Z",
-     "shell.execute_reply": "2026-09-20T17:13:33.623101Z"
+     "iopub.execute_input": "2026-09-20T21:16:28.299929Z",
+     "iopub.status.busy": "2026-09-20T21:16:28.299801Z",
+     "iopub.status.idle": "2026-09-20T21:16:28.858319Z",
+     "shell.execute_reply": "2026-09-20T21:16:28.857556Z"
     }
    },
    "outputs": [
@@ -789,7 +787,7 @@
     }
    ],
    "source": [
-    "mt.tl.dose_direction(heparg, dose_key=\"Metadata_ConcentrationNominal\")\n",
+    "mt.tl.dose_direction(heparg)\n",
     "direction = heparg.uns[\"mantispy\"][\"dose_direction\"]\n",
     "\n",
     "fig, axes = plt.subplots(1, 3, figsize=(12, 3.3), sharex=True)\n",
@@ -828,7 +826,7 @@
     "stays at or below 0.37 throughout, and its amplitude falls from 2.8 to 1.7 between 100 and 300 uM. Its top well\n",
     "is doing something this page does not resolve.\n",
     "\n",
-    "Mupirocin sits on the floor: amplitude between 0.40 and 0.81 against a null of 0.43, and no concentration whose\n",
+    "Mupirocin sits on the floor: amplitude between 0.40 and 0.81 against a null of 0.44, and no concentration whose\n",
     "halves agree above 0.41. Its 33 uM well reaches nearly twice the null amplitude with no reproducible direction,\n",
     "which is why amplitude alone is not enough to call a response."
    ]
@@ -844,9 +842,9 @@
     "against its own plate's controls, so the median of a concentration's wells reads directly in control MADs and the\n",
     "features can be plotted against dose.\n",
     "\n",
-    "Group on `Metadata_ConcentrationNominal` rather than the raw column, for the reason given above: the batches\n",
-    "write the same concentration to different precision, and grouping on the raw values alternates between them at\n",
-    "every step of the ladder.\n"
+    "Group on `Metadata_Concentration`, the dose each well was meant to get. Grouping on\n",
+    "`Metadata_ConcentrationRecorded` would alternate between the two batches' spellings at every step of the\n",
+    "ladder.\n"
    ]
   },
   {
@@ -855,10 +853,10 @@
    "id": "14a71eeb",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-09-20T17:13:33.625503Z",
-     "iopub.status.busy": "2026-09-20T17:13:33.625357Z",
-     "iopub.status.idle": "2026-09-20T17:13:33.935745Z",
-     "shell.execute_reply": "2026-09-20T17:13:33.935261Z"
+     "iopub.execute_input": "2026-09-20T21:16:28.860362Z",
+     "iopub.status.busy": "2026-09-20T21:16:28.860232Z",
+     "iopub.status.idle": "2026-09-20T21:16:29.171412Z",
+     "shell.execute_reply": "2026-09-20T21:16:29.170671Z"
     }
    },
    "outputs": [
@@ -887,7 +885,7 @@
     "\n",
     "stauro = heparg[heparg.obs[\"Metadata_Compound\"] == \"Staurosporine\"]\n",
     "traces = pd.DataFrame(np.asarray(stauro[:, early + late].X), columns=early + late)\n",
-    "traces[\"dose\"] = stauro.obs[\"Metadata_ConcentrationNominal\"].to_numpy(dtype=float)\n",
+    "traces[\"dose\"] = stauro.obs[\"Metadata_Concentration\"].to_numpy(dtype=float)\n",
     "traces = traces.groupby(\"dose\").median()\n",
     "\n",
     "fig, axes = plt.subplots(1, 2, figsize=(11, 3.4), sharey=True)\n",
@@ -960,10 +958,10 @@
    "id": "369d9bf4",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-09-20T17:13:33.937251Z",
-     "iopub.status.busy": "2026-09-20T17:13:33.937131Z",
-     "iopub.status.idle": "2026-09-20T17:13:33.945032Z",
-     "shell.execute_reply": "2026-09-20T17:13:33.944635Z"
+     "iopub.execute_input": "2026-09-20T21:16:29.173305Z",
+     "iopub.status.busy": "2026-09-20T21:16:29.173111Z",
+     "iopub.status.idle": "2026-09-20T21:16:29.181087Z",
+     "shell.execute_reply": "2026-09-20T21:16:29.180569Z"
     }
    },
    "outputs": [
@@ -1002,7 +1000,7 @@
        "      <th>40</th>\n",
        "      <td>0.02</td>\n",
        "      <td>0.45</td>\n",
-       "      <td>0.43</td>\n",
+       "      <td>0.44</td>\n",
        "      <td>0.45</td>\n",
        "      <td>0.18</td>\n",
        "      <td>1.01</td>\n",
@@ -1012,7 +1010,7 @@
        "      <th>41</th>\n",
        "      <td>0.05</td>\n",
        "      <td>0.52</td>\n",
-       "      <td>0.43</td>\n",
+       "      <td>0.44</td>\n",
        "      <td>0.65</td>\n",
        "      <td>0.20</td>\n",
        "      <td>1.02</td>\n",
@@ -1022,7 +1020,7 @@
        "      <th>42</th>\n",
        "      <td>0.14</td>\n",
        "      <td>0.54</td>\n",
-       "      <td>0.43</td>\n",
+       "      <td>0.44</td>\n",
        "      <td>0.57</td>\n",
        "      <td>0.16</td>\n",
        "      <td>0.92</td>\n",
@@ -1032,7 +1030,7 @@
        "      <th>43</th>\n",
        "      <td>0.41</td>\n",
        "      <td>0.57</td>\n",
-       "      <td>0.43</td>\n",
+       "      <td>0.44</td>\n",
        "      <td>0.82</td>\n",
        "      <td>0.45</td>\n",
        "      <td>0.91</td>\n",
@@ -1042,7 +1040,7 @@
        "      <th>44</th>\n",
        "      <td>1.23</td>\n",
        "      <td>0.49</td>\n",
-       "      <td>0.43</td>\n",
+       "      <td>0.44</td>\n",
        "      <td>0.59</td>\n",
        "      <td>0.18</td>\n",
        "      <td>0.98</td>\n",
@@ -1052,7 +1050,7 @@
        "      <th>45</th>\n",
        "      <td>3.70</td>\n",
        "      <td>0.55</td>\n",
-       "      <td>0.43</td>\n",
+       "      <td>0.44</td>\n",
        "      <td>0.60</td>\n",
        "      <td>0.66</td>\n",
        "      <td>0.94</td>\n",
@@ -1062,7 +1060,7 @@
        "      <th>46</th>\n",
        "      <td>11.11</td>\n",
        "      <td>0.48</td>\n",
-       "      <td>0.43</td>\n",
+       "      <td>0.44</td>\n",
        "      <td>0.60</td>\n",
        "      <td>0.35</td>\n",
        "      <td>0.97</td>\n",
@@ -1072,7 +1070,7 @@
        "      <th>47</th>\n",
        "      <td>33.33</td>\n",
        "      <td>0.92</td>\n",
-       "      <td>0.43</td>\n",
+       "      <td>0.44</td>\n",
        "      <td>0.86</td>\n",
        "      <td>0.69</td>\n",
        "      <td>1.05</td>\n",
@@ -1082,7 +1080,7 @@
        "      <th>48</th>\n",
        "      <td>100.00</td>\n",
        "      <td>1.65</td>\n",
-       "      <td>0.43</td>\n",
+       "      <td>0.44</td>\n",
        "      <td>1.17</td>\n",
        "      <td>0.72</td>\n",
        "      <td>0.85</td>\n",
@@ -1092,7 +1090,7 @@
        "      <th>49</th>\n",
        "      <td>300.00</td>\n",
        "      <td>2.30</td>\n",
-       "      <td>0.43</td>\n",
+       "      <td>0.44</td>\n",
        "      <td>1.22</td>\n",
        "      <td>0.89</td>\n",
        "      <td>0.77</td>\n",
@@ -1104,16 +1102,16 @@
       ],
       "text/plain": [
        "      dose  amplitude  amplitude_null  step_amplitude  split_half_cosine  \\\n",
-       "40    0.02       0.45            0.43            0.45               0.18   \n",
-       "41    0.05       0.52            0.43            0.65               0.20   \n",
-       "42    0.14       0.54            0.43            0.57               0.16   \n",
-       "43    0.41       0.57            0.43            0.82               0.45   \n",
-       "44    1.23       0.49            0.43            0.59               0.18   \n",
-       "45    3.70       0.55            0.43            0.60               0.66   \n",
-       "46   11.11       0.48            0.43            0.60               0.35   \n",
-       "47   33.33       0.92            0.43            0.86               0.69   \n",
-       "48  100.00       1.65            0.43            1.17               0.72   \n",
-       "49  300.00       2.30            0.43            1.22               0.89   \n",
+       "40    0.02       0.45            0.44            0.45               0.18   \n",
+       "41    0.05       0.52            0.44            0.65               0.20   \n",
+       "42    0.14       0.54            0.44            0.57               0.16   \n",
+       "43    0.41       0.57            0.44            0.82               0.45   \n",
+       "44    1.23       0.49            0.44            0.59               0.18   \n",
+       "45    3.70       0.55            0.44            0.60               0.66   \n",
+       "46   11.11       0.48            0.44            0.60               0.35   \n",
+       "47   33.33       0.92            0.44            0.86               0.69   \n",
+       "48  100.00       1.65            0.44            1.17               0.72   \n",
+       "49  300.00       2.30            0.44            1.22               0.89   \n",
        "\n",
        "    viability       phase  \n",
        "40       1.01      silent  \n",
@@ -1155,16 +1153,16 @@
    "id": "9ef6316e",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-09-20T17:13:33.946679Z",
-     "iopub.status.busy": "2026-09-20T17:13:33.946562Z",
-     "iopub.status.idle": "2026-09-20T17:13:34.699417Z",
-     "shell.execute_reply": "2026-09-20T17:13:34.698771Z"
+     "iopub.execute_input": "2026-09-20T21:16:29.182552Z",
+     "iopub.status.busy": "2026-09-20T21:16:29.182435Z",
+     "iopub.status.idle": "2026-09-20T21:16:29.944453Z",
+     "shell.execute_reply": "2026-09-20T21:16:29.943852Z"
     }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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soTTfz4okPQYvb/0HY0MDRQ+HMcaUQlKE8swPMXFxKNu6dYmfs5RxfcWYqq3ReU3MimqNVSIDhdLtxnSAjI2NoQwSNA0VPQSlZpyequghMMaKGJeGeH8MKEhobMSBQsYYI0lJyvfv5pI+Zynj+ooxVVuj85qYFdV8VbKLPzHGGGOMMcYYY4wxxgQOFDLGGGOMMcYYY4wxxhS79ZiKKC5btgyXL1+GpqYmGjVqhEmTJsHU1DTP35k1axa2b9+e5bFy5crhwIEDRTBixhhjjDHGGGOMMcZUk8IChWlpaWjRogVGjRqF3377DfHx8Zg2bRqOHTuGK1euQFtbO9ffCw4OhpOTE/7444/Mx3R0dIpw5IwxxhhjjDHGGGOMqR6FBQo1NDTw6NEjaGlpZT5GLZorVqyIa9euoXHjxnn+LhXJrVChQhGNlDHGGGOMMcYYY4wx1afQrceyQUKirp6/komXLl1CjRo1YGJiIgKKkydPhqGhcnUkYoyxkiQxMhypcTGZ9zUNjKBraqHQMTHGGGO5OfPwBdacuYmX4ZFwsjDFiBa10MLdlQ8WY4yVcKduv8Jfh7zgFxINZ2tjjO7kgVY1HFHSKFUzk59++klsK65Tp06ez6H6hRMmTMDff/+Nb7/9Fnv37hXBwuTk5Dx/JykpCdHR0Vkuyq5nu35ISkrGprVbsO2fnVBWMybNRoMqzXD+tCdUmUezLxQ9BMaUOkh4a8kPuLtyRuaF7tPjTPXNW7IRuw+dhe+rQHQeNBnKaueB0yhbvw/GTP0dqmza/L9w8MRFRQ+DMaUOEk7eehzPgiOQnJqG58ER4j49zlSbl5cXRo4cKW537NgRYWFhUEavXr1Cq1atVH4H3dWrV8V6njFlChKOX+WJZwGRSE5Nx7PASHGfHi9plCZQOG/ePNGQZNu2bR+sOTh//nzR8ISCid27d8eRI0fw4MED7NixI8/fWbBggcg+lF4cHByg7CIjo4CMDPQd2As9+nTN83nr/96EXVv3QBG8Hz7GU++n2H9qN+o3rofi4Iuxs3Hv4bNP/r2It8ofXGZMUSiTMCM1JctjdF82w5Cprrj4BCQmJcOptA22rPgpz+c9932NbkOmQlFm/bYOu9fOx6KZY1AcbN93SgRhP1VsXAKSkrP+/8gYe48yCWVlAFBTy/k4Uz0pKSmIiZH82+Tff/+FhUXeOx/atm2LN2/eQBHWr1+P9u3bi110xUFcXByaNWtWoL9HcUjgYSUHZRKqvZsXSEaGZH74+7AXShqFbj2WomYmv/zyiwgU1q9f/6O1DWVR0M/Z2RkPHz7M83eoSQplIUrRF9LnBgvP3niAdXtO4lVwOBxtLDC8Z2s0r10Z8qarp/vBnyckJObYwl1UggKCUa5iOVhaFZ/thVExcUhJTVP0MBhjTOW2UFD5EBPjvMuApKamITomFooSHRuHqu5lUVxQ8JWCsIwx+aLtxtnRYjC3x1nRK6o1Fu1S+5DIyEhk0H8YCvD69WsMGjQIpUqVQnFAx+nt27eKHgZjn43+rZz9//qMDMA3uOQFtBWeUbh48WKx5Xj//v0ixfpT0ZZj6oRMmYJ5oQxFY2PjLJe8pKalITQi8oOXfWeuYdrSzfDxD0ZySqq4pvv0+Id+j147L+Fh4Rg95Fu0rNce/1u4LPNx2a3Hp0+cQ+cWPVDXvQnqVGqEl74vsWrZWvw+f4m4f+XiNfy3Y7+43bBqc4z4YgyCAoPF765esR5/LFqBL3oMFe+xe9te8XhMTCxmTp4tHqPfo/eTPk7bilvUbYdubfrg3KkLWcbr8+wFJoyZgn27Dojfo+fTeyz9dQW+7DkME8dOwVPvZxjc+ys0rdkaY4Z+i6CAIPG7342ciJVLV6FLq17itR89eCxei563cPbveWaidB86DU41e8DKvSMuXb8vHn/w+AXa9PkOLrV7osewafAPCBGP9/v6JyxdvQO12gxD9VaDceXmA7E17uT562jT93vxGtExceJ5S1ZtR+22w8T1oZOXUKfdcLjV642x035HQkJSlnGkpKRixMSFcK4lGQdt8WKMMUVJTUtH8Nv4PC+7PJ9l3UIRINlCQY9/6PfodT/kp0VrUK5BX7HVOPxNlHhMdutxQFAY2vWfgNLVu4rvyr1Hz2PU5EW4dMNL3J/w8zK8eRstbltX7iS+q4+cviJ+9/GzlyLzcNy0xeK7ePC3c5H2bv5ctXkfarQaIn6Hvvul6HH6rqcxTf/l7xyLu4adv0Zw6Bvxflv2HBfv0XXwFLENmb7P30ZGY/SU38T71W3/FY6ekYyFnktzQYeBE1Gmbm9s2nkUKzfsEc9r3ec7hEXkXBglJ6dg8pw/UanxgCzzRHx8Yp7vMXHWcnQZNFn8bNGKLaL0yHczlmD5ut2ZY6YLHTd6Hs1jL18Hi+NE81/bfuPx6KlvjrFs3HEElZt+IY4Xl9BgTMLW1CjHoaCMEWeLDweO2OcprDXWh9ZX5MyZM2jUqJG4nDt3LvNx6dZjmi+mT5+OypUri+STMWPGYMWKFXj69KmoiV+lShXx/H79+omfV61aFT///HPm67Rs2RJr1qwRu93o9rNnkp1Ld+7cQdeuXVGuXDnxexT8kz7epUsXuLu7i9cMDAzMsTbet28fBgwYgCFDhmS+x+rVq1GvXj3xs82bN4vb1atXx5w5c8RnoESYFi1aiAQZGjNtsb59+zaaNm0qPsfp06dzPT70epQNWKZMmSzlv/J6DxrL3LlzxXHo378/YmNjxfbhly9fis9JY5Y+j36PxuLj4yPW+9WqVUODBg2wffv2HON4/Pgx2rVrBzc3N/E6N27cyNd/V4zJE51Qz21+cLHJO36kqhSaUbh06VLMnDlTZBK2bt061+fQz+/duyeeQ0FB+mKeOnWqCAwmJiaKL6bU1FT06dNHLmN6ExmDLt8uyNdzM7JdL1z33weff2DZNFiVyv0fIYvmLoG9gx1mzpuGi+cvwf+lZDJJTEhEerrkHVYtW4Pvp36LajWqQE1NDaZmJhg8YiAMjYzQe0APGBkbIj0tHc1aNRGLqhNHTonA3a9/zENiQgIunr+CX/43R6SHjxo0Dj36dsWiuYuRnJSC9dtXiW7S+vp64r1+n7cEpqbG+Pe/DQgNCcekb6ahZt0aMDKSZIs4uzph1oIfcdnzGqb+/AMMDQ3Ee5w/fQGLlv0CG3sb9Ok4EF8OH4BmLZtgx7+7MXPyHKz99y8RVKTP9/fG5Tjw3yEM7TsSi1cuxHeTxorgZp+BPeHq5pLl+PQZMQNf9GqL5fPHQ1dHOzNrpf+onzB2WC90aFEPa7cexOipv+PQ5t8QFROL+498sGf9Lzh/5Q5m/roah7f8jmYNamDSmAGoWskNRob64nm3vZ5i97pfxARYv+NIbF7xE1yd7DBx1gos/nsbZoyXTNLkzoOnePjkBS7sWwl9PV3o6eW9TZ6xkoIal6ipayAj/f0/1tU0tcTjrHCFRyei9VTJiZ9Pma/mbLn+weefXNgdNmb6uf6MTqgcOXUFezcsQFx8ovh+btqgOtLS0hEVFZNZD5C+Rzf+8SM0NTRgbGSA0rZWmPDTMvF7urraMNDXw6ML/yIDGfB9FYQh385D22Z1xKLvzMVb2LF6Dn4YMwDDvp+P4+euQ1tLE8vW7MK6JdNQxtke2u+y6c9euo1/dx/H5hU/w0BfF5Pm/IldB8+gT5eWmWOm7/+KjQfA6+xm8d3/zPc1TnnewM7V8zB70nAsXb1TBDxP7vwDT1/4i7HQcxMTk3Hu0h1s/WuW2M5LAcNxQ3vh7J4V+GXZP1iz5QCmfzc4y/H5beW/ePz8pZh/LM1NM+eJX1dsyfM9jpy6jH9XzoK2tpZ4j6H9O+KXaaPwzNcfM74fIsa8efdxUXtw299z4OZsj75f/4SGtT2wbN734m8ycMws3Dm1KctY5ixej+2r5sDV0S7fDeMYU3UV7CyyZA/SIpDOLVBDE1Z4CmuN9aH1VUJCAr7++mssW7YMFStWFMEq2YzB9PR0EcCjUlaHDh0SayFtbW1oampi48aNoiSWNKtv1apVYs1JQTAKLJ46dUokudDrBAUF4b///hMBsCVLlogLBdFo3dq8eXOx+8vMzEysZYcNG4Zff/1VBCaPHj0qAnubNr3/7h49erQI8FGgkOrwS8fq7+8v3oPei7INaXy0HqbAJr0vbVWmIOS4cePEa4wYMQKzZ8/G2rVrRfCSxkLBO1ne3t7i/emz0fGR7tyjWo4LFy7M9T1obKNGjcLhw4fx448/YteuXWJn4M2bN3H27FnxWem43rp1C1999ZU4TnRs6ef03IiICBFMpOClLDoGFNSkAKXYofCBJCDGCkuPRmWwcMetLI/R/DC6k+SEQUmisEAhpSePHz9efCF/8803OeoV9urVS9ymL8MXLyTFhemLh2pJlC9fXtwODw9HpUqVcPLkSXEWpDh75PUIqzevhF1pW/QZ2EsE+LL7+tsRWP/XRujp6aF5m6YYMLgvdPX0RHDPvJSZeM61GzewZOEyEYijycjR6f0W6y49OqJseclxou3CUZHRuHvrPlasXQIn56zb0G7fvCsyAHf+K6l/GBsbh5cvXqJyVXdxnyYSA0MD6OhqZ7436danC8pWcBMBzjcRb8UYxdjHDRfZiVIDh/QTn7V566a4eO4yGjVtIB6n1w8OCskSKKTXCggOx4RR/bOMkbL9wiIiMWpQN3F/yrgv4FbvfcB4zNAeomZW704tMH/pJujoaENLS1MEGS1k/kHxzfBe4nm02KxTvRJaNaktHh83rKfI5pBVzb0sPCqWwZfj5qCsqwO+G9FH3GesJKPuxlY1myHkxmk4t+0P0zLu3PVYhd3xeoqBPdvAvbykQ2iXtpKFjKxenZrj2xlLMWD0z6hdrRJ+GNMfRgb60NLSyPz+pYzCsdMX48Ydb1FXj07cUCYiqVW1PNq3kJQiadGoFvwDQxAeEYlBvduhXs2sW9Au37iPOw+eoUVPyb8lkpKTUc7VIUug0NTESJxgk/3ur121Ijq2ksw9t7yeYPLYgXBxtBOX6h7l4P38pfhZ13aNM7csU+CP5oxS5iZo06wOTp7PmfFw9fYjTBzVDxXLOmd5/EPv0a19E9SoUl7crlLJDYHB4TAw0IOerk6WMffo0Ay1qkqK29/2eoIDm34Vc9uYoT3x029rRTajrJkThorsRvr8fbq0wIAebT7692VM1fmGSjKBzQx0EZeUIroej2xRC82567HKoSw3apRJAS5CWXZ//vlnlueULl1aZBfSz+i5FNyqW7euWOuYm5uLC6GsQaqJT4EuCkA2bNgwczfc2LFjRUCxc+fOmDx5snhfa2trESyU5efnJ34mbaiS2xZofX2aK7Vy7IKj5BhLS0uRIdmhQwfUri1Zr1DQkAKE9Blp/D169BCP0/jos5UtW1ZcvvvufRa+FAX96LWaNGmS5XFK0snrPRwdHdG7d2/xOCX6+Pr6wtDQUMyx0qAqBTbpedLPT69HwUHpWOj36DEbG5vM96T3oMAj1WWkUmQ0XnpdxopSaprk9ISGuhrS3iVrUcmeltWVv8eFygQK6YuPzmLkxtbWNkvQkL6MCX0BTZw4UVwCAgLEmRn6MpUnc1MjcWbqQ75duA4vA0Oz7F8XWxbsrPDHlOEffO28OLo44vTxsyID7/aNO4gIz1k8t2WbZuISHxcvthCXLe8mFgiUISj1x6I/RaCxUbMGuOJ5FWv/el8IXUNTpr6jmprIoHMt44wDew5h7IRRWbINyri5iCYqXXp2EsedGJt8PDtIWi+Raitq62jh+pWbqFO/Fo4fPgknl/fBSPV3Z6zUoJal7qTkrG7WLWP0WoYGejh86nLmoo5QloaOtjYuXL2LJvWqYc/hc3BzLp35c8piyf6alI0YExuf5fWlWSkujrZi4fU6MBR2NhbYd/QC3Fzssz5XWwurfpsibp84dx1Dv5uPmyfWf/S4MKbq4kMk3cCsajSGtiGfBS4qFsa6IvsvLyOXnoZfcNZ6K9ItFKu+a/nB180LZfNt3n0Mowd3R0JikvgOrlOjUpbnONhbi8xByiqY+78NmLdkE8Z/3TfL9+/W/06I7+lz/61AbHyC2KpMW81kv7+l46Wv8HJlHLFi3W6M/LIrzEzfL57KuzmJjMaVCyaKTG8ivf4QylCUogy9/cc80ahOFbwKCMG9h8/h4mCLJ89fQUPj/dxI86H0Ps1fudWvKl/GATsPnEGDWh5izsjPe2jKzM/0HvSyuc5X2lnHvPfoBfTr1gqnPW/C2NAgy/uRof06igsFZWu1HYZ6Nd3h6pR1XmOsJPELe4unwRHi9vLBnVDB3lLRQyoxCmuN9aH1lZ2dnQjO0dZXV1dXkTmYHX3nUrNMcv/+fREEe/LkCXR1dUXjEwp+UZkrChTSNl3KdKOMQkrIkJKuZSTf3xnifSlTkbLqatasmfk8e3t78TPK1HNxkSRF5DfbW7rGos9BwU5KuqF18LFjxzIDfdlr+cvez22+om2+lP1IiTmy6+8PvQdlW8oeO3pdysKknX4050s/j2wNfXo9eg1qRBoVFSWCgRQ4jI9/P8dRRiNlTFLWJvUWoEzI77//Pl/HhjF58fQKENe9m5QVdQmvPQ7G63DF1dcukYFC+uLKT8t32TMNsuiLtjDQ4iSv9HWpr/u0FfUypF+O0uuve7f96O/mZeK070SNwsW/LEWjZg1hY2ud4zk92/WD/6vXSEpMQqUqlVDRvTwMDPQxpO8IrPtrI5at+R86dm0nag7SFuFW7fNeBEpN+ekHTBgzGav/XA89PV0RMBz81ReYOmsSJn8zXdQ11NbRFs+9/ujiJ32mXxbPwYTRkxEdFQMbO2v8b+WvKKiNy2bgqwkLRHYKBQdpAdqwThWsXjxFbLeKjIpFaVtLbFn5vmZIbjq1bijqO1GWxvOrktqPUs4Otvhh9ABUbvaFKLpfp3pF7Fg9N8tzKDhInZMJbY+bM+mrAn8mxlSJVbVGMLR34SBhEdPUUM9zizD5tmtVUZNQGmyTXn/btdoHf+9DKDNt37ELKFWpPRztrFGpfNZSEYS69S5buwvpGeni+3bLnz/Dwc5KzP1m5duK4NVXAzvj1z+3oEqLQajhUU5k630IZSlevHYfjjV7iK3L1d3L4sTOP8Tj128/QtWWg0XAjQJ4tD25c5tG+f5M074dJLZQm5RtI15j7uQRKG1nVaDj8+N3g/HFuDkwq9AWBnp6GD6gExb8OPqT34O2FVPtwu37T2Hp3JyLpWXzJ4j5iOrmmpsai8+cnWPN7mJrMwV0WzauBQe7nP+2YKwkOenlI65LmxujvF3xacanChSxxqLEFCpjRVt4KfBHtQGzo222lAlIKPhH2YGkU6dOIrOQMgMpm452sdHrUKCP6vZ9CGXCSbcfUzCMgmsXL14UGX7UxHPo0KEi647mRBoTbY3OL6rxR7UIKbBGx4a2E1PQjQJ1n4o+H5XvqlWrlhgLrbuvX7/+ye9BgUI6JrQ+p27RVF5MFmVp0u9Lm4nSVmYKoHp6emY+h3Ya7t27V5TPoixO2t7MWFGKTUjBreeSnS2NK9uhsnMpESg8dccfM/qnQF9XMQ1kFUUtQ1HtnBSIakvQ2SA60/OhxiYf68i1/r9TeBkUBidbSwzv0QrNPqMjV0i8JBuHCphTlmBUZBRMTE3EtluaJHV0dcRjVAOKthrLdkOmszcUjDMwlKSqU6t5SdaDBmKiY2BsYoyE+ARxhodeRxyDqGgYGUu2Yon3TUxCXFx8jtem14qJlkTRZbcYE9rilJqSAn0DyWIz+3tI0WeQfU2qUUjvQ+OjySA+PiGz9iGNgTIisndydk2X1JKh7Ao6RrR9mLYRy25Dlq0XSI1KqFaV9EwaFaqXZqBQQxL6ubmZsXg92ecR+l+C3kNX5nNQNgY9n343KjpWfE5TE0Ou+cRYIaD/PymYRGedC/odrWrz1dsnx0Wdv4J2Pf77sJc4M0qZhFRnRR5bKKTzFTXpoCw7+k6m71T6fqbH4hMSM8s9yKLn0NRjaKCf5fuWvlupFh89RrUPpZ+Xvt/p+dLvZJrz3kbGiGCb7GuLrouRMeLn9LvZs+uk3+OE5h7Z95BKTEwSnylzbkxKfjfvSuawyKgY8Z70c5oDU1JTRa3F3NDP6b9lmptkn/Ox95Cdv+gz0XtKMyRln5fX/EedkrU0NcXnj3jXaIaOa/bjwVhxlhResLmhzx/b8SL0LYY2rYGxberKZSzRsbGwadCgxM9Z8lhfFcYaS/qdL13y0k41Kn1FgToaL/2M/r0h3QYsuyZISqL1UVzm9mP6XSoBRQEzej26LX0d+k4Xc0tcXJbPT69NWXK0E042e5CagNDrU5CNxiOLMhkpsCldD8m+h+xnogv9PqHx0HtJtzJTgJI+CzX1JG/evMn8HB/qWiz7nI+9B42ffi7d4Uefne7T55F9nuzxpM8kPQ60zqRjSc//0PFQJOkaXVlI18RM/k7f8cf3f1+AjpYGLv6vl9h63HzSHiQkp+GXofXRuZ6k5E5JWWNxoFBJFqHK9iWkbPhLkbGSQxUDhZSlsHz5crGooDPpRRkoZIwxVVOQQKFPyBv0XbZD3N72TR+UtZHUU/tcHCiUb6CQMWWibGt0XhMXnp//uYr/LvmIbMKV3zQXj01bfwmHrvmhXkUbrPn+47s1C0NiZDhS4yTNAgk1i6T68IW9xlJo12PGGGPF37O9a8V+VtfOg6GhJTnrzLKaOnWqqLdDk/KnBAoZY4zJx0mv5+La2dIUbta5Z1YxxhgreShb1vNBoLhNgUIpyiKkQCFtQQ5+G1/gsj2fEyS8teQHZKS+b1anpqmFmuN/L3CwML/yVz2VMcYYy0VGWhrC71/B26d3oa7JWxtzQ8XTqYj3uHHj+L8hxhhT0CLwxLtAYevKZbJs4WSMMVayPXn9FmFRkga6jT3e98KoW8EaVqZ6or734Wu+RT4uyiSUDRISui+bYVhYOFDIGGOswOKCXyEtORHGzuV54ZWLwMBAjBgxAlu2bMms4cMYY6xoPQ2KwKtwSR261lXc+PAzxhjL5OklySZ0tTVGaYv39a811NXRsY6zuH3wqm+u3cNVFQcKGWOMFViU32Nxbez08S72JQ011fjiiy/w7bffokaNGvn6HSrkTXWeZC+MMcY+z6kHkmzCMtbmcLXibceMMcbeu/AgQFw3rvw+m1BK2sTEJygK3v6Spj8lAQcKGWOMFVj0yyfimjIKWVbz588XwcJJkybl+9AsWLBAFIOXXhwcPr87MWOMlWSSbcc+4nZrD84mZIwx9l5kbBLuv4gQt5t4vK9PKFXW3hQVHczE7YNXXqAoUeMSNY2sbUWoRiE9Xtg4UMiEqd/PEC3qP+bI/mM4efR0kR21/bsP4typCwX63Z0HTmPf0YL9LmMsf4uvaL8n0NDRg4E1B7RkvX37FrNnz4a7uzsWLVqEhQsXwtPTEzExMeK2t7d3rsd02rRpohOZ9OLv78//KWazYfthnLl466PHJSgkHJNmryiy4/fydTCm//J3gX63qMfKWEniHRCGgDeS7OzWHmUUPRxWgjx//lzM+fkxYcIE8W+HovLNN98gLi6uQL9b1GNlrDBdfhSE9IwMGOhqonoZy1yf06mei7g+csMPKWnpRfYHoYYlNScsRrUx8zIvRdHIhHCgkAk3rt5Cej7+o/eoVhmVKlcstKN2cO8RnD5xLvP+S99XCAwIKtBr+b4MFAu3T7Vt70kcPHGxQO/JWEmSmhAHHTNLmLhUhJo6TyeytLS08MMPP8DIyAiRkZHikpCQIIKrdDuvEzM6OjqiM7LshWX11OcVAkPCP3pYTIwM0atz80I7fK8DQzFl7srM+zGx8bh5N/cA8MfExSfi2u2Hnz0Gxlje3Y7L2VrAycKUDxErMlQ+5N69e/l6bt++fWFgYFBoY/n++++zlDO5fPlyvpJEcnPt2jUkJyd/9hgYUwYXvCTbjutXtIWWpkauz+lQ2xka6mp4E5OEK48KFpv4VFQDPsbfBzompWBo75J5KYogIcmax8jyLflNEFLj3p9J0TQwg7a5rcofQQen0oX6+q/8/GFsXPiptB/y4mUATBU8BsaKAy19Q1QbPQcZ6UV3Zq24MDQ0zJFFMG/ePLx48SLf2QXykhgZnqU7Gm1XKKp/ZCiSvr4u6tZwL7TXj41LwI27jwrt9YvLGBhTZnRy5uQDybbjNpxNWCyU1DVW3bp1C/X1r1y5UuDAoCqNgTFZaenpuPRQEvhrItPtODsLEz00qGQLzweBOHDlxQefKy/hXtfw7L/VcGrTBw5Nu6KocaCwgBPYo/mdkJH6/kyKmqY2Kv14qMATGW2xpVpWTx49RXBwKMaOH4WXvi+xd9cBkcE3+rsRUFdXF1+uW9Zvw63rd2Brb4OvxgyFhWUpTPpmOn79Y57IYiFTvpuBGfOmwsBAH7u2/ofLF67AyMQYw0cNhksZZyQnp2DNn+vx7PEztO/SNtcxPXvig/V/b0RYSDjSM9Kx9t+/cOzgCWhpa6F1+5ZizJqamvC69xDBgcEY9NVA1KhdXXyO3dvoPa8iOjoGvfp1R4eu7cTjuY1FKjQkDPt2HYCGhgbOnjqPCVO/FY/Hx8Vj8pw/4R8Yim+G90KD2h7itdZtO4TTF27C1MQQE77uh3JlHPM8vkdOX8HydbugraWFGh7lMGnMQLGIfPTUF//7ezuCQiPEa65dPBWbdx+DpoYmDp26hHlTR6JmFW7SwNiHcDah8qIg4a0lPyAjNSVLbZPP2bZAmdqr/tkHBzsrnL18G13aNkb9mpUxf+kmqKkBc6eMhJ2N5LVPnLuOLXuOi8eH9O2I5g1rYPFfW1G/lof4Liert+wXr9W+RX1cvfUAa/89KDLsKBuwZ8dm4jlURmLH/tOo4OaIlNTUHGNKSEjC/D82wsv7BZJTUjD1my9RztVBfL//9vO4zDG7uZTG8XPX0Kh2FXzzVW/xu/Se67YeQkBwGMq6OOCPed9nPp7bWKR+/XML7nv7oP2ACWjXvB5aNq4lHl+/7VCu7/Gh15IVHROHvl/PFF3E6bhM+Lo/yrs55voZN+44kmUM343oU6C/KWOq6oF/CIIjY8XtVlyfsESusWiLbdeuXbFr1y5YWFhgypQpWLZsGZ4+fYpBgwahVatW4nmvX7/GH3/8geDgYDRo0AAjR47ErVu3ROYdZcKRJ0+eYMeOHfjpp58QHh6O5cuXw8fHBx4eHqJxmZ6enjghuGTJEvH8pk2b5jqmnTt34uDBg4iNjRXvRbWMaTvvzJkzYWZmJsZMGYabN28WOxOmT58Oc3NzhIWF4c8//xTlSyijb9WqVbCysspzLFL02amUyZAhQ2Bqaipel9y9exfbtm3L8h4fe63saMwPHjwQdZXpOHfv3j3Xz+js7JzrGBhTpAd+bxAZlyRuN3T/8HdMl3ouIlB49t5rRMcnw1hfu1DHFnzjjLg2r1ATisCBwjx4zcx9u5JxxUawbNw/ywRG6P7j3/tkFpt0GbYEhi7VxO0nSwai/Ph/P/iHoC22h/cfw5jvR8LY1BjDB3yNJs0boWvPTvj7jzUoV8FNBOeW/74Sd27ew8Ch/XH31j0M6/c1Dp39D7q6Ojh55LQIyN28dhvhoeEwMjLEyqWrcP/OA/To21UE/EYP+Rb7T+3GH4tWwPvhY/T7sjeueF5DcFBIjjH9NGUOWrRuinad2ogFC138X72Gto7O+zEfOIZR33yF0g72+O7rH+B5+zRWLl2NC2c8MfirL2BkbJQZDPx72Zpcx6KjI/mfjMZbvWZV6Bvoo0WbZrArLSkmumvbf5g5bgCcHWzQf9TPeHnrPyxcvhnX73hjcN/2CA6JQI9h03HrxPrM18rOo4KrWEClpqaJoOGs39dh0U9jMWbK7+jUuiF6dmomPp+piRHq1awMQ309dGrTEE72Nh/8uzFWkgVcPgZDOxeYcCOTfGnYsKE4EVIYri0Ym+vjhqVdswQJCd2/u+LHLMWRKw74DsZO5fL1XrTFdvm63fjx+8Ho0aEZJs1ZgRoe5fFFr7a4cOUupsxbic0rfsKt+48xbPx8zJk8QpyxHThmFo5vXwL3Cq74Y+1OEShMS0vD4r+24fLBVXjw+AWGfjcfU775Egb6uli0YgtKmRlDX08X385YIgKQ8fEJ+PuffahWOetY1/57AN7PXmLEF12gqamB8mUcRbaddDsvjfnPDXvESaLenVvgp1/XoEaV8jAy1EfP4T9i+reD0KNj08xs8rzG0qzB++7VFJi74/VUzC0U0MvIAK7deYQm9atneQ8TY8OPvpYsPV0d8ZqUCeX3KkgEDe+c2pjrZ8w+BsZYVtImJpXsLVHanEs5qNoaKz8o0EfBqh49emDDhg0iMPjVV1+JwNWoUaPw6NEjkYzRpUsXEejq1asXVq5cKbbIfvfdd+K5dKGdAqtXr0aNGjVEckHPnj3RoUMH9O/fHydPnhQBM9oxQO/Tp08fVKxYEX/99RdKlSqVZTxBQUGihjHtMqDXtLa2zrGdV3bM+/fvF7WOf/nlFzHGxo0bi2AbJWtQgC+vsfz++++Z71mrVi0RBBw6dKi4ltq0aVOO9/jYa2VHx6xJkyaiBvPSpUvh5OQEW1vbHJ+RxprbGBhThm3H1KzEylT/g89tVrU0DHW1EJuYghO3XqFX48JrjhUX/Aox/s9h5FgWBtaFu6MzLxwoVCIUtOvaq7O4vebPDZj960yxqKQafT7PXohA4fkzF7Hkr0VwdXNBu06tcfbkeQQFBGPA4L74dc5iESjc9s8ODBjaT7wOBQ919XSxffMucf9NxBv4+vji4vnLWL72f3BydkSbDq1w9OCJHOOhICE1EomJjkHLti1EIC3HmL/oje59JKmwWzftQHRUNE4dO4PfVyyAW7msBaPzGkuFSpJuqXr6enBydRJbj5u0aJTlPQb1bi9u0wIxMipGZJfQYmr15v3i8dDwt3ji8wpVKuX+PywtUo+fvYYXLwMRF5+ApCTJwpmChEdOX0ZkdIzIijHQ10NZl9JisUgLMMZY7pKi38L38GbR7bjKiJ/4MOVD8+bNxUUVVHMvKzLaCJ186d6hCbq3b4oGtTzQbciUzGzCMUN6YFj/TuL+q9fBOHXhBr4f2RcTflqGkLA3uHLzAerXqoxS5iYiEEbFpHfsPyWen5CYhLMXb0NHRwvjhvbE4D6SecDrcc6Ocw3qeIjMxaNnrqBp/eqwMDcRgUJZVSq6Ycb4IeL2w8cvRNAt/E0kRg/ujrHDemZ57uGTl3Idi2xwr2olN5ibvp8rKLiY23tEvIn86GvJoqnW65EPrt/1RmxcPPwDQxAcGpHrZ8w+BsbYe+npGTj9btsxdzsu2RYsWAAbGxsREPzvv/9EtiChwGFgYKDYsaWvr4+ff/5ZPO7i4iIy/Cj7sHPnziIjj4J/FDibP38+fH19RaOSq1evigud9KIAID1OmXWUnUcoO5Cy/mRR4NDNzU0E5+rUqYOqVat+cMxlypTBjz/+KDIVaS1GAT1ZlPmX21hk0eeh+oeU4SgbuMz+Hnl9rg958+YNdu/ejYiICNEc5fr16xg2bFiOz0iZj7mNgTFFogxB0jgfW4l1tTXRpqYj/rvkg4NXXxRqoDD45llxbVNbcesGDhTmwWOu5I+Tm3j/3OsBuY1eBX2HSjke/1g2oZSOriRTj1CGoDTzRENdA2nvGo2YmBjD18dPBAqjIqPw9s1bGJkYwa60rdiie/vGHXjdfYjfli8Qzzc1N0XdBrWzNCCxL20HU1MTvHzxSgQKw0LDxVmr7EaMHYaBQ/qJzMOZk2djwf/mfHDMGpoaImPPzNwUzx4/zxEozGsssihLIjUtLc/30NSQvIe5mQmaNaiOapXLZv7MqXTe2X+jJv+GejXc8dXAzvAPCBFbwMgPYwZg9JDuuPvgGUZP/g1rFk+VvEe2MTDGsop++URcGztJAv1MsepO+zPXx2MDfPH28e0cj1ceOlUURC4oPd332dsaGurixI30tnS+ogxt2nIrRSdzOrRsIBY6Q/p2EN2Lz12+g9mThoufm5uboKyrA8bJBO1cHOxw5tIt3L4v+e+Nsuyev/DP3LYsRSUirhxeDe9nfli58T/cf/RcbHXOa8zSuYYClGc8c3ZQzmssssRrpKZ99D3y81qyNuw4IrZ0fzWwC3S0tTB6ym9iy3JenzH7GBhjEvdfBSM0Oq5EdjuOj4/HgQMH8PjxYxGg6datG+zs8v7eIRQky94Ft02bNuKirGus/NLV1RXXFCiU3UZLay0KhtFW2JCQEJEVR5lvtC2ZgnyEgl50obmLMvrotej5dD1ixIjMsk/0utLXoeNPgcdnz57lGIu2trYIoFGAkoJx7du3F1uc8xozZQ7SGGk89NoUmJPNyMtrLNnR66RmK92R/T3y+1pSFBykbdKUPUjj27p1q/jseX3G3MbAmKKERSXA+9Ubcbtx5Q9/P0p1ruciAoW3n4fBPywGDpby72uQlpKM0DsXoaGrD4vKijsRzIHCghw0AzNRLyN7/Qx6vLCNHf81vhkxEWXKucLPxw/9B/cVW3ZJv0F9MG74eHw5fICYCAnV+ft2xERY21qJLb2EsvVGfTcC342ciLLlyyI9PS3XBiJTv58h6gYmJCTiTfgbmFvkL038u8njMHrwN1i/6h8YGRtm1ijMayyyKIg4+dvpYuvyxGnf5fkeVDuw78iZsLexgIGBZAL7UEYF1bXauvcEPK/dzbI9efj4X0T3TKr9FBrxFlYWZqjmUU5sEzt65irmT+MahYzlJtqPA4XFATUuoZqE2WsU0uOF7YuebbF68z5UbzVYBA91dbTRp3ML8bOh/Tuidtvh4jtX2nDky55tsWPfKUyd9xdsrEqJzLofvxuMgT3aYMW63ajVZhjS0tPEVuTsKOi488BpkUFEmX0LZ4zO3xh7tMU/O46iQqN+cHawE9t5qUZhXmOhWoFSpW0t4fMyEK16f4vObRpl1ijMLj+vJYuy2m/de4LExF1ISk4Rxy2vz5h9DFyjkLH3TrzrdlzF0Ro2piWnSR1tYe3Xrx/q1auHChUq4PTp06IGHmXStW2be11yQrXv2rVrh2rV3m/rpW2jJWGNZWlpKWoC1qxZU2TfUYCPavcRyrajABpl3x05ckQ8RhlxY8eOxQ8//CAy5yjgSFuCGzVqJIKJtD3Z0dEROjo6OY4hZezR34NQIK18+fydcKX3pExIGmPlypVF0E1aozCvsciqUqWKqB9Inyev+oAf+ly5oQxB+oy0JVtapoqyB/P6jPkZA2NF5eK7bEJTAx14uOQvy7WGmxXsSxkgICIOh675YXSnrCeu5SHy+QOkJcbDtm4raGi/T5gqamoZdHq+hKGaE5QWTkVrjY2NlaIj1/WHF6GtrQVbe8lrXL14HfUa1RG3A18HITUtFY5ODuI+ZRI+ffxcBNykj5GkxCRcu3wD1WtVFbUBpRITEvHE+5n4PdngXHhYOPxfvkZF9wqiIUnNOtUzA4zkxtVbSIhPEEG9SpUriGt6Pj3H3sFO1CiUHTNlM3pUqyzOQMXFxePJoyeIjYkTNQql3ZLzGossyph87R8Ad49KYtszvUdDW0kw8PINL9SuVhFaWpoiuOf12AdvIqNzDRT6vgoUY5VmGlK9LC1NTVHg/rbXU5GR4nntntiKTDUJq3uUE1uPyVOfV/DzDxaNTyxKmRb478qYqrqzfBriQvxRb8ZqaOp+uKbHp6JmDmbl2yIqKqrA39GqNl+9fXIcxkYGStH1mLbDPn7+CrWqSho9eXn7wNaqlPiupEZZN+89zsz4o8yBuw8li/XqlctmqdFI246pVl/2RlTUZOp1YJhoolW9cjlYW5ojMTEJdx48g6uTHaKiY0VtQVvr95+BvrNfvAoU3/EU7CttZ4X4+EQxR1AgMvuYqbs9PdfB3lpkKdJnoKZWxoYGYiv0h8YiK+JNlGgmYmVhKuaavN7jY68lO1bpNm1q4FXV3Q2PnvqhcnlXvA4KzfEZs4/Bvbxrgf+ujBUnSeEfnhuo5EyHXzcjIjYeEzo0wICGuW/vlIfo2FjYNGigNHOWn5+fCOBQ8Etq+PDhuHTpksgwzAs1+lixYoUIMipqfVUYayzqtFu7dm0RWKNGHdQQhOoHkhs3bsDd3V1k/xHa3ktZe/RzCg7KHtOAgABRa1gWPZeyDxMTE8UWXmr+QajuIQXNKIuTfld2ezEdJwrm0s+pdp/0d+ix6tWri2w82TFTlic1UaHgI6FxUCCTtktTAE+a8ZfXWKRoPqasvoSEBDRr1uyD7/Gx15IdK+1Ku3//vqj5SFmJdKGMx9w+Y/YxFAch8a+gTFzTIxU9BJUxYdUFnLztj451nLFweNb/tz9k+f57WH3kARwsDXF4bpdcy7N9rtigl9DU0YOuuZXC1lgcKFSCCV0Zv4SUDX8pMqY8UhPicHX+1zCwcUT1cb/I/fU5UCjfQCFjjJW0QOHNFwEYte6AyOA9POlLWJkYlphAYW4oADh16tRcSw3JBgqpiQVlwlFDik6dOommFEUdKGRMmSjbGp3XxPKRkpaOxhN2IS4xFQuHN0DHOvkvx+MXEo3OP0nKmG2e3AbVyrw/KaPsPmWN9T59jDHGGMuHmNc+VCxONDJhjDHGlM3Jd9uOqznZFmqQsDigrrhbtmwRW0g/hGrT0XMpw2z79u1iu+jRo0fzfH5SUpIIDspeGGOsOLjzPFQECdXV1NCwUv7qE0o5WxujiotkV8vBq76Qp7fP7iPxbRiUAdcoZIwx9klM3TxQc/xiqMmUKmCMMcaUQWpaOs48lHRHb1W5ZDUxyc3kyZPFVljqRvshnp6eoj6f1OjRozFkyBCRIShtbCGLavZREwvGGCtuPL0k9QmruJaCqeGn1wHsUt8F933DcfTmS0zpUxPaWu9L6xRUemoKnuz8Exlpaag7bSXUtd73VVAEXuUxxhj7JFSLQ8/CRu51MxhjjLHPddM3AG/jEkWmSMsSHiicO3cu/v77bxw6dEg0NvkQ2SAhoSBhaGioqFWXm2nTponta9KLv7+/XMfOGGOFxfNdI5PGle0L9PttazpBU0MdMfHJOO8VIJcxRTy6idT4WFh41FV4kJBwoJAxxli+paemipT4EtgHizHGWDFwystHXNdwsYWFkXybbRUn8+fPx8KFC3H48GE0adLkk3+fGk9ItxjnhrrdUo0r2QtjjCm7gPBY+ARJGqs2rvxp246lKAuxqYe9XLcfB988K65tarWAMuBAIWOMsXyLef0cN3//Hr5HtvBRY4wxplRS09Jw9t2249YebiipaFswXShI2LRp01yfM3PmTJw4cULcpm7I1OlWimoVLl++PNeOt4wxpgrZhJYmeqjgYFbg1+lcX5KF7ekVgLexiZ81poSIEET5PBSNIg1Lu0IZcI1Cxhhj+Rbt90Rc61s78FFjjDGmVK49f42ohCRoqKuhhbtyLLaK2oEDBzB9+nQ0atRIbDmmi9S8efNE0xJCgUANDQ20adMG8fHx6NmzJ8qWLSs6HV+6dAlv3rzB1q1bc61PyBhjxZXng4DMbEIqp1RQTSrbwcRAG1FxyTh64yUGNC94k8eQd9mE1rWbf9aY5IkDhYwxxvIt2u+xuDZx/nCtI8YYY6yonXy37biWqz3MDPRK5B/AyckJv/32W64/k12AUtCwRo0a4jZd37p1C6dOnRK1Bjt37oxmzZpBX7/kbt1mjKmexORUXH8c8lnbjqW0NDXQrpYTdpx/JrYff06gMPLFI1GX0KpqQygLDhQyxhjLl4z0dES/egYtQxPolrLmo8YYY0xpJKem4Zy3pFZUSd52XLVqVXH5mHHjxmW5T5mGnTp1KsSRMcaYYt14GoLElDTRiKReRdvPfr0u9V1FoPCBXwReBEfB1cakQK9T9etZiA99DU09AygLrlHIGGMsX+KCXyEtKQHGzuWVJi2eMcYYI1ef+SM2MRka6upoXilrB1/GGGPM00tSn7CmmyUM9T6/rIKHcyk4WxuJ24c+o6mJmrq6qE+oTDhQyBhj7JO2HRs7FTy1njHGGCsMJ72ei+u6bqVhoi+pw8cYY4yRjIwMXHjXyKTxu47Fn0tNTQ2d60nq4dL24/T0jE/6/aTICARdP43UxHil+yNxoJAxxli+GDtXgEPz7jArW4WPGGOMMaWRmJKK895+4nYbjzKKHg5jjDEl4xsSjYDwWHG7scfn1SeU1bGOs7gOfhuPm8/ed4/Pj+CbZ+Gzfz1Cbl+AsuFAIWOMsXwxtHOGU6te0LeU3+TKGGOMfa4rT18hPjkFWhrqaMbbjhljjOWx7djewhAu1sZyOz72FoaoVc5K3D5wJf/bjzPS0hBy6xzUNLVgVU15mphIcaCQMcYYY4wxVmydeNftuH5ZRxjq6ih6OIwxxpSMp1dAZrdjedda7/Ju+/HJ26+QkJyar995++wekqPfwsK9NrT0JXUOlQkHChljjH1U6N1LuL96NiJfPOSjxRhjTGkkJKfA87Fk23Fr3nbMGGMsm9iEFNx6HiZuN5HjtmOp1jUcoaOlgfikVJy564/8CL5xVlxb12oOZcSBQsYYYx8V6fMQ0S+fQk2Npw3GGGPK4+KTl6JGoY6mBppUlNSKYowxxqSuPQ5Galq6CObVLmct9wNjqKeFFtVKi9sH87H9OCnqDd48uQPdUjYwcamolH8oXvExxhj7qOiXT6CmoQHD0lwknjHGmPI4+W7bcYPyjjDQ0Vb0cBhjjCmZC++2Hdcpbw1dbc1CeY/O9VzE9RXvYIRFJXzwuXFBL6GhpQObWs3kvg1aXgrnKDHGmIo5dfsV/jrkBb+QaDhbG2N0Jw+0quGIkiA5JhKJEcEwciwLDS1ehDHGGFMOcUnJuPTkpbjdxsNN0cNhjDGmZDIyMuD5QNLIpImHfaG9T/2KtihlrIuI6EQcue6Hwa3zzhQ0r1AdtaeugDLjjELGGMtHkHD8Kk88C4xEcmq6uKb79HhJySYkJs4VFD0UxhhjLBPVJkxKTYOuliYalXfiI8MYYyyLJ6/fZmb4Naos//qEUpoa6uhYR1L+4sDVF/gYTR09cVFWHChkjLGPoExCSgrPyJDcp2vKEv/7sFeJOHZRfpJAobFTeUUPhTHGGMt04r5k23HjCk7Q09biI8MYYyyLC16SbEJXW2OUtjAs1KPT+d3246evI/HE/22uz3l94SDePL6NjPR0pf5LcaCQMcbyQJ2r9l7yERmE72KEmShY6BscXSKOnZaeAXTNrWHsVE7RQ2GMMcaE2MQkXHkmyexvzduOGWOM5cLzgaQ+YZPKhbftWKp8aTOUtTfNM6swOTYKL0/ugs+hf6DsuEYhY4xl4/3qDXZ7Psfh676IS0zN8/hQHYqSwLFlT3FhjDHGlMU5bz+kpKVDX1sLDcqVjJrBjDHG8i8yNgn3X0SI2409Cm/bsRQ1JulSzwWL99wRdQrH96gutiRLhd6+gIz0NNjUbAY1deXO2fus0fn4+ODkyZPyGw1jjClIXGIKdl14hr7zj6LP/KPYeeGZCBLqaWugTgVr8ZzsTamC3sRj+obLiE1IUcygWaEKDw+Hp6cnQkJC+EgzxpiSOXn/ubhuUtFZ1ChUFYmJibhz5w7u3r2r6KEwxlixdulRINIzMmCgq4nqZSyL5D071HGGupoawqMTcdU7OPNx2mocfPMsoK4Oq5pNoewKFCgMCwtD8+bN4ebmhjZt2mQ+3r59e5w9e/aTXsvX1xfbtm3Drl278OpV/hoDJCUl4eDBg1i7di2uXbv2yeNnjDFpF6wHfhGYtfkamk/+D3P+vY5Hr96In1VwMMOMAbVxelEPrBvfCku+boxy9qbQ1lSHm50JqrpaiOcdvOqL3vOO4L5vuEoe1PAH1xB0/TRSE+I++Dxq7NJzzmHUHLtNXBf3Ri8zZsyAjY0NmjRpIoKFhOaccePGKXpojDFW4kXFJ+Lq89fiOLT2KKMyx+PMmTNwdXVFjRo1MG/ePPFYUFAQKlasKAKIjDHG8s/zXX3CBpVsoaWpUSSHzspUH/Uq2uTYfhzl643EiBCYl6sGHWMzKLsCnX6bOHEiSpUqJbIsrK0lmTZk8uTJYlKjIGJ+Fuh9+vQRZ8tq166N+Ph4DBo0CHPnzsUPP/yQ5++FhoaiWbNm4nbVqlXFe/bo0UMs4BhjLD9iEpJFOjhtL34sU2hWX0cT7Ws7o3djN1RyMhfp41KtajiKi+x32L7LL7Bg+w28Do/FoEUnMLZzFQxrVwkaSp5K/ikCr5xAtN9jmJevDk09gw92habDRbUbpV2hKbgqe8yKiwMHDmDz5s3iRNQvv/yS+fjQoUPh7OyMn376CVZWVgodI2OMlWTnHvkiLT0dhrraqF+2+M0zuYmNjcWAAQMwZ84c6OvrY9++feJxW1tbtGjRAhs2bMDo0aMVPUzGGCsWaI649DBI3G5cBPUJszc1ufwoCGfuvhY7zwz1tCTZhABsan88VlZsA4XHjx8XAb7sCyU6+3XlypV8vQYtsvv27YudO3dmLsa3bt2KL774At27d0eZMrmfHZw6dSq0tLRw9epV6OnpiXHUrFkTXbt2RefOnQvycRhjJQB959z3jcBuz2c4fvMlEpLTMn/m7mSOXo3Lon1tJxjo5q9rIn1vdW9YBtXKWGLKukuiruGy/fdw2TsIC4Y2gI157kG14iQ9NQWxr32gY2YJHRPzD3eFfhckzN4VujgGCmmOoxNWNLeoywR9NTQ0UKFCBdy8eRMdOnRQ6BgZY6wkO+kl6XbcrKILtIsoS6Sw0XZjWv+MHDkSe/bsybHGOn/+PAcKGWMsn7x8IxAZlyRuN6pc+PUJZbWo5iASUKgx5snbr9CtgSsy0lKhY2oBs7JVobKBwpiYGHGmi8hm3Lx9+1YE8fKDFl+9evXK8lirVq3EYv7Jkye5BgrT09Oxe/duzJ49WwQJSbVq1dCgQQPs2LGDA4WMsRyi4pJw6BplDz7D88CozMepVkWnui7o2cgNFR3zDoJ9jIuNMf6d0gbL99/DhhPeuPk0FD3nHsHsL+sWyyCZrNgAXxEsNHGukOdz0tMz8CI4KjNIqApdofOa4z51nmOMMSZ/b2ISceOFZNtxKxXadsxzD2OMyY/nA8m2Y1rnWZpIYkdFRV9HE61rOGL/lReiTBUll1Qc8D3SkpOgplE8Tm4VaH9cvXr1RF1B2UVUWloaZs2ahcaNG3/Wdi/K2KAtxbnx9/cXkyjV6ZBF9x89evTBmobR0dFZLowx1UUnHG4/DxWNRlpO2YuFO25mBgmruFhgzqB6OLOoJ2YMqPNZQUIpqnkxoWcNrPquBSyMdREdnyy23lLtQzqTVFxFv3wiro2dyuX68+tPgtHvl6NITcsWJXzX+IWCqMURzXF08olOTskGCvfu3SvmGiqXwRhjTDFO3fFHWnoGjPV0ULdMaZX5M1AW+40bN/Dy5csscw+VelqxYsVnrbEYY6ykBgobF3E2oez2Y3LjaQgCI2LFbQ1tHRQXBcooXLRoEVq2bIlTp06JBfk333wjbgcEBODixYsFGsjDhw9F7cNJkybB3j73PeQUJCSmpqZZHjczM/tg8G/BggUiC5ExptoiY5PEWZvdF5/hRdD77wQjPS10rueKno3LoJx94RWPpUK5e37qiJ82XcV5rwDsufgct56FYtFXDeUSkCxqUX6PxbVxtozCF0FR+N+eO+IzypJuP5Zej+5UBcUR1SKkWlAUEKR5hwrIr1mzBidOnBDzX/Y5iDHGWNE5cfOluG5eyaXIitMXBar7TmUvaO7x8PBAYGCgqN++f/9+VKpUSdQvZIwx9nFhUQmiLJQiA4W1y1nDxkwfejEBuLn1b7TqNwD6looZS5FlFNaqVUvUaKIJjSYzqhdInSFv3bqFKlU+fWH4/Plz0T25S5cumD9/fp7Pk243lgYMpShIKN0mlptp06YhKioq80KZiYwx1UAnK+hMDdUJbDnlPyzadSszSFjDzRLzh9QXnYun9atVqEFCKXMjXSwf2xTT+9USHZL9QqIx8Nfj+OeUt9imW5yOa1ygH7QMjKFnYSsei4hOxNx/r6PHnMOZQcK6FWywa0b7LF2h6XrpqCZoWd0BxRHNNVQLihpulS5dWswZurq6YrFGJ7MYY4wpRnh0Am48DRW3W1dxU7k/w4wZM7B+/XpxQormIsounDJlCk6fPg1tbW1FD48xxoqFi++yCU0NdODhUkohY1BXVxNlrprovoJ58B3Eh0pKZqh0RuHChQtFU5E///zzswfg4+MjuhjTZePGjVkKx2fn6OgoJklfX98sj7948QJly5bN8/d0dHTEhTGmWjWKDlx5IbL2/ELenzwwMdAW2YO9GrmhjJ2JQsZGW4b6Ny+PWuWsMXntRbHt+bddt3H5YRDmDakPiyKuk1HQz1Drh6VIfBuGxJQ0bDn1GOuOP0RcomQrdRlbE0zsVR2N3O3Ecys4mBf7moxS1GmSSlrQ4owujDHGlMPJW6+QnpEBU31d1HIp2i6WhY1qtHt7e6Nbt27o1KmToofDGGPFlucDSUJDQ3dbaHwgvlTYOlazxqsbQYhK10GgrhMsUHwU6Kj9/PPPSE39/LpbFOCjAGHTpk3xzz//iPqE2Z09ezazHiIVkG/fvr3ojkzZLuT169c4d+6cyEZkjKk2ysi76h2MH1Z7itqDi/fcyQwS1ipnhYXDG+D0rz0wpU9NhQUJZZW1N8W2ae3Qv5mkxt+lR0HoOfcwLmTbsqu01DVx6nkiOv90UHR0piBhKWNd/DSwDnbP7IDGle1zNPtQBbt27RLdJxljjCmXY++2Hbdwd4WmhuIWf4Xh6dOnImmCMcZYwaWkpuHyoyBxu7GHYrf66gd7QVstHVcSS+Pg9VdQ+YxCajZy5cqVzyqqm5CQgObNmyM5ORl16tTBX3/9lfmzFi1aiFoc5N9//xVbm/v37y/u//rrr6LLMZ1pq1u3LrZs2SLuDxw4sMBjYYwpn1O3X+GvQ15i625pS0NUcjDHPd9w+IdJisESM0MddK3vih6N3JS2cYautiam968tzmjN3HQVb2KSMHbFOQxoXh4TelaHjpZy1le6evk2lp/2x/3XkuOtq6WBwW0qYmibSjDQVe2uv9I5rl+/fooeCmOMsXdC3sbjjk+YuN1GBbcdV65cGffu3RNNGHknFGOMFQzNE5TcoK6mhoaVFBcozMjIQPDNs+L2pcTSSLnhh8m9axSb2roFChRS3SZaQFGtJgroZa+ZQVmCH0PdJDt37ixuP3v2LEfXL9mgoaura+b98uXL48GDB9i8ebPoAjZ9+nQRJNTULNBHYYwpaZCQugZTrhrlDlPNQdnmJFQXr1djN7SoWhraShpoy65pldKi0cmPGy7jincwtp59ImorUqMTNzvlaY7hE0iNSm6hXcC/GKyWiklqrdGlfhmM61IV1mZ514JVJdSsq0OHDiJbkuYgY+OsQWh3d3dYWloqbHyMMVYSnbz9SjTKosz26s6S2rmqxNDQUAQLqW77119/DVtb2yxZ+zTv0PzDGGMsb55ekvqEVVxLwdRQceXnYl+/QHzwKxg4V0LkG0OkxiXjwoNAtKxWPGq4Fyi6Ji3mPn78+Fx/Lt0W/CEGBgZYsWLFR5+XW4cvmjgnT56cr7Eyxoqf5Qfuievs3yTmRjrYPLktHK2MUBxZmujh729bYPPpx1i69y6eBUSi3y/H8EOvGujbtKxCt/FSgfiVB+9jj6cPzNTi0N88EYFadtj5Y0eUdyj8JjDKZMGCBQgNDcUff/whLrltTe7Vq5dCxsYYYyV923HrGo4KrTlVWKiJ1qFDh8TtCxcu5Ph5z549sXv3bgWMjDHGio8L7+oTUokkRUpLSYKBrRNK12uJRilJOHfvNQ5d9VXtQGFKSor8R8IYK/HoJMOha75ZsgdlxSakFNsgoWwHrMGtK6J2eWtMWXtR1Ficv+2GqF84Z1BdmBnqFul4EpJT8c9Jb6w//gjxSZLasw2tEoE0oG7j+nAqYUFCsnPnTpH1npfc6ukyxhgrPEFv4nDvRbi43a6Wk0oeagoEfmiN9aGGj4wxxoDX4bGZ68gmCq5PaOpaCdXGzhe3Oyf7i0DhufsBiIpLgomB8jfaLdCMQ9t8P3RhjLFPFfw2HuP+PIfpG67k+nNKtlPWOoQFUcnRHDt+7ICejcqI+zR59JxzRDRrKQpp6enYd9kHnWYewIoD90WQ0MJYF7O+rIvB1STBShOX8iiJaDH2oTlOFRu4MMaYMjtxS1IE3spUD9XLqGbpB5pbPjT3cKCQMcY+7OKDwMxdXOVLmynF97qamhqaVrGHkb42UtPSM7PjlV2BonofS3vnLVmMsU/JItx7yQe/7bqN2ETJmfQabpa4/TxMBAepkoH0enSnKip1YPV1NDHry3poUMkOs7ZcQ1hUAkb+cRpDWlfCN12rFFqx2yveQfjfnjt47P9W3NfT1sCQNpUwpHVF6Otq4daSv6GmoQGj0qpXLD4/qIHW69ev8/x5/fr1YW+v2O0MjDFWkkgXVm1qOonMfFUUEBAgGmnlpXTp0qhXr16RjokxxoqTC17Sbcd2Cj2x7711KQxtnVG6aReoqauL5pXtajli14XnOHDFF32bloNKBgq/+uqrLPdpi1ZMTIy4bWJiwoFCxli+BEbEYtbma6K5h/Tsz8yBddC8amnR0OTvw17wDY4WmYQUJGxZvXjUdPhUbWo6oopLKUxdfxm3noViw4lHuPY4GL9+1RDO1vLLonweGInFe+5knm2j+bN7gzIY26UKrEwljUqSY6OQEB4EIwc3aGgrf1p8Yfjrr7+wf//+LI/FxsYiLS0N+vr62LZtGwcKGWOsiPiHxeCBX4S43bamo8oe91u3buVYY1EH5MTERJFRSHXbOVDIGGO5S0xOxfUnIeJ2Ew/FndCPDfBFxMMbSImLgUPzbpmPd6nnKgKF933D8TIkGk5yXOMpTaAwMjIyx2PUgXjYsGHo1u39wWCMsdykp2dgl+czkdUmrYvXrYGraOohrdnQqoajuJQUNuYGWDehJdYdeySaijx69QZ95h/FtL61xLH5nLNi4VEJ+PPgffx30Qfp75pNNahkiwk9q+dIy9fQ0kG5PmOgrpm1m31JsmnTphyPJScniwZcJ06cQOfOnRUyLsYYK4mOv9t2bGuuj6quFlBVXbp0ybHGol0X165dw5AhQzB79myFjY0xxpTdjachSEpJg6aGOupWsFHYOIJvnhXXNrVbZHmc5i8HS0P4h8Xi4DVfjOtSFcpMblVxra2tsXLlSixZskReL8mYyqJsuZ5zDqPm2G3imu6XpMyAr5acxrytN0SQ0MZMH3990xxzB9cvFoVdCxN1cRzZoTI2TWoDewtDJCSl4qd/rmLSmouIjk/+5Nej40tZmR1mHsBuz+ciSFjW3hR/f9scq75rkWvtDg0dXVhVbQgL99py+lSqQVtbGxMmTEBERAS8vb0VPRzGGCsxTrzbdty2plOJqxFLn5eyCEeMGIH169crejiMMaa0LnhJdkzVdLOEoZ6WQsaQlpyIsHuXoKlnkGMtRd/nneu6iNvU/ZgSZ5SZXNtnGRkZ4dWrkhPwYKwgKCg4fpUnngVGIjk1XVzTfVUPFlLzjC2nH4vAKJ3xIb2buGHvz53QqLJiu1IpGzrjtHtGB3Ss45yZTdFr7mGxLTm/x5rqPnb+6QD+PHBfBBxpW/ecQfWwa0Z7NHTP+3hT9gKT3zxHQcUff/xRbBmbNGkS7t69y4eXMcbyibZneb+rp9tWRbsd5wevsRhjDB9cv3hK6xMqcNtxuNc1pCUlwrJaI6hr5dyd1ameJFAYEBGH28/zt64rVluPHzx4kOOxt2/f4n//+x+qVasmj3ExprL+OuSV2ZyDSJt1UOaXqm61pTqDP/1zBXd9wsV9+1IGmD2onkLTwpUdnQlbOLyhCOrN33YdQW/iMWzxKZFx+HXHyiKtPjeXHwVh8Z7bePo6MrNRydA2lTD4XaOSD6GJ7daSibCoUh+uHb5ASeXv74+oqKgcdaLOnDmDy5cvw93dPV+v899//+HXX39Fz549xXZl+t1atWphz5496Nq1ayGNnjHGVK+JSWkLQ7g7mUOVRUdH5zgRRXXgfXx8sHDhQnz77bef9Hpv3rzB06dPYWVlBVdX13z9TmpqKu7cuSMW3dWrV4eWlmKychhj7FP4hkSL4Btp7GGnBNuOm+f6cwdLo8ymnYeu+aJWOWuoVKDQw8Mjx2OUSkkLoI0bN8pjXIypLL+Q6MwgoRTdfxYQhYd+EXB3LgVVQS3gN596LOrjUc0IMqB5eXzXrepHg1ZMonM9F1QrY4Gp6y7hvm+ECChT12J6fOf5Z+K/J2p4QnUMLz0KwqWHQeL31NXU0L2hK8Z2qSqyCfMj2v8ZkmMikZ7y6ducVcn48eNFMC+3EhurV6+Gg0P+muo0bdoU3bt3z9wq179/f7x8+RLr1q3jQCFjjH1CoLBtLUe5bjtOig5FakJ05n1NPWPoGFsp9G9CNXB79+6da+kLmj/GjBmTr9ehYCOVyjh//jzc3Nzw/Plzcb19+3Y4OeWdlUkBQjqJpaGhAXV1ddFEZe/evahTp85nfS7GGCtsnu+2HVPpJhcFNQnJSE+HqWslaBuZwsA677VCp7ouIlB4/OYrTO1bC7raBQrJFboCjSooSLIQlWVqagpdXV15jIkxlWZupIvgt/E5Hqf6cf0WHEMVFwsMaFEObWo4QktTA8UVddiduelqZqdCJysjkUVYs6xi/yFeHNHZp42T2uCvg/ex9thD3HsRLi5STwMisWjX7cz7Dd0ljUrK2eesQfgh0X5PxLWxc3mUZGvXrhWNS2TR/Ebz3KcoVapUjqxEPz8/tGzZUi7jZIwxVeYTGIXngZLs7nZy3HZMQUKvjaOQkZaS+ZiahhY8hvyt0GAhZZ5nX2NRwM7CwkJc51dAQIAod7Fr1y4RXI2Pj0e7du0wdOhQkRmfm7S0NPTp0wfNmjXDP//8Ix6jJpX02LNnzzizkDGm1KTbjptUtlNYLVs1dXU4te7z0ee1qemIhTtuIjYxBefuvUa72pJSUypRo5CK6trY2GS5SIOEzs7K+UEZUwYHrrzIESSUfpdRFyRCLdOnrruM1tP2ifpyoZE5g4rKLCUtHasOe6H3vKMiSEiZbbTtddfMDhwk/AxaGur4tls1rJvQCprquU+A2lrqoknJ39+2+OQgIYl++S5Q6FSyA4XTp08XmRWyc5w0SEgZHUePHv2k16NMkA4dOoitX40aNcL8+fPzfC4FE2n7meyFMcZKouO3XmaeaMyt+VZBUSahbJCQ0H3ZDENFoCDenDlzssw9tG2YgoQ07+Q3o7B+/fro0aNH5mJZX18fffv2xdWrV/P8nYsXL4rMw2nTpmWZCykL/uxZyVY6xhhTRrEJKbj1PEyh244zPqHGOzXvbFa1tLh94KovlFWBMgpp0sirrgWdxWKM5bTvso/oYEtcbY2hoaaGl6ExcLExxuhOVdCiWml4+UVg65knonlFRHSi2Ga69ugDUbuwf/NyqF7GUqk7/j32f4OfNl3NLDxOn3POoPqiOQeTj9rlrKFOgcLcOmVlAA0q2RboddNTUxHj/xw6pqWga1qy/16hoaGIi5PUOckuMDAQycmftjV74MCBiImJESfS/v33X3Tp0kVkd+RmwYIFmD17doHGzRhjqoIWXe+3HZeMbsc079D8kxuqm0v14AuKgoTlypXL8+fUaIuSPipWrJj5GG1XNjY2Fj9r06ZNrie26CLFJ7YYY4pw1TtIlLvS1dIQ6yRFCLt3GUFXT8Cl/UAYO+X9XSvVpZ4LTtx6JWrLh0cnwMI4f2WilDZQeOzYsVxvS4vt0iSU32K5jJUk1IH2581XRS1C+gJbMa4Z9HVy/u9H246rDLfAD71rYLfnc+y68AyhkQniH8t0qeBgJmr8ta/tpFT1DFJS07DqyAOsO/oQqekZ0FBXw7C2lfB1Rw/oaBXf7dPKimoSUrds2ZNXtIaioHNBxQb6itqExk4VUFLRYig4OBghISHitqGhJMtXKiwsDJcuXcLcuXM/6XU7deqUmVlIi10qSE8F5nND2RxUW0p24ZXfmoiMMaYqqKQGNUKT97ZjkpGeCmVCcw5lsdO8Q7ezr7EoGLd+/XrUrl27QK+/b98+cZKKGmzlhYKQ5ubmuZbQyCtAySe2GGPKwPOBpD5hnQrWClsfB984IxIucut0nJsG7nYwN9LBm5gkHL3+El+2Ur71l2ZBFjvZbxPqikXZEtT5mDH23p6LzzFr8zVxu055aywfm3uQUBadVRjV0QPD27nj9B1/bDv7RBQ9fez/VmQlUlfbHg3d0LdpWVG0VZGoAcuMTVcy6wiVtTfF3MH14O6kOk1ZlM3oTh4Yv8ozs3u29JoyUwuKAlhm5arCrGzOZlUlBS16qIkJnfiiDsWyGSx0m+pEUY2n3Bp65RdldNCCLy86OjriwhhjJdnxd9mEZWxNxL8r5Ck+zC/HY1SjkBqaKAKdgKJagJRFSZfsayw6aUUNI8eOHfvJr33u3DlRr5BKXnTr1i3P51HDlISEhByPU31D+llu+MQWY0zR6DtTGihsXNleIWOIDwtEtN9jGNq5wNDOOd8lpdrXdsa/Z57g4NUXxT9QSFuLCdXMoKwLxtiHUUbgnH+vi9t1K9hg+dim0PuEMx30JUJn0ulC23q3nX2Kw9f9EBWXjA0nHmHTSW80rWIvsgzrVrAu0q051MV45cH72HjCWzRiobp5IztUxlft3Yt1E5bigLaiL/m6sdiaThkX0u3rLasXPPPMyMEN7oMnoyTbsWOHuB4xYoTo/Jh9sfapdu7cifbt28PIyChz6xgViaduyIwxxvJe+FEJFtK2pqPcD1OU7w1x7dzqG+hbuSq86zHVE6Q11qFDh7B//36sWbNGLq9LXY9pHqNag7K1B3NDyR6RkZFi+7OBgYF4jLoeR0RE5Fl/nk9sMcYUjZJowqIkJzkaV1ZMfcKQm+fEtXXt5p/0e53ruYhAIZXsehrwtkD15QtTgXIzOUjI2MftOP8U87ZK/jFav6INlo1p+lnp0BUczEXXYOpm+99FH/H6ARFxOHvvtbhQPcD+zcqLLx0DXa1C/RPd9QkTHY39QiTbgio6mossQnkWG2cfDxbShcmfvBZptF2sWrVqsLW1FRkZt27dQp06dURXZcYYY7mjRdOr0Bhxu21t+W47JkYOHsjISIdF5dZKVfuQgnqfe4JKytPTEx07dsSUKVMwY8aMPAOJVNqCyka1aNECGhoaIlBJGYjk4MGDIsO+ZcuWchkTY4zJmzSbkLLPFbHLLj01BSG3L0BdSweWVep/0u9WcjQX4/YJisKhq76Y0FO51tGfvYk7JSUFaWlpWR6TdkBmrKSircK/bL8pbjesZIulo5vIrWYCdUoa2rYSBrWugAtegdh69gmuegfjRVA05m+7gT/23kXXBq7o16ycqGUnTwnJqVi+7x62nHkstrpqaapjTCcPDGlTCZoaBWqizpRAQkQwgq6ehIVHPRg7llX0cJQKLZKyNy+hoB91ofyYL7/8Er179xZ1p2hLFy3GnJzkv+hljDFVcuyGZNtxudKmcLUxkfvr29ToJi7Kjk42yXbSpEAelXr6GKp32KFDBzRq1Eh0QD516lTmz5o1awZNTcm/Rylz/vvvv8esWbPEbrGJEydi3LhxojYuvRdlIn7zzTdwdOSTkowx5eT5IECh2YQR3reQGh8D65rNoKmr/0m/SyeqKMFn6d67Ysfgd92rQSMf64uiUqDIRWxsLH788Ufs2rVLZBdmbwf9Ke2hGVM1lEK8cIckSNiosh2WjmpSKA096IukedXS4vIiKArbzj3FgSsvEJuYIsZAFwpSDmhRHo3c7SSdcj/Djach+Pmfq/APixX3q7iUEh2Ny9jJ/x/xrGhFPn+AwMvHoG1szoHCdzZv3ixqFj5//lycEJNFc1+vXr3ydWzpxFm9evXk/0djjDEVRGuIE7ckgcJ2NeXcxOTd+kSZsgize/jwoWhoRQ0is3cR7tmzJ3bv3v3R1wgICEDdunXFduaFCxdm+RkFDqWBQgoayjahpDmvQoUKOHDggDhWdH/YsGFy+2yMMSZPkbFJuP8iQtxu7KGYQKG+pR2sajSFTZ0WBfr9jnVd8Me+u6J56bXHIWhQyRbFOlBIdS6uX7+O1atXo3Pnzjh58qS4v2jRIvzwww/yHyVjxcTmU4+xaNctcbuJhx2WfN0E2kXQ9dfV1gQ/9q+N77pVw/4rL7D93BP4hcTg0qMgcSltYSgyDLs1cBUZiZ8iPjEFS/bexfZzki6tFPT8pmtVfNGyvFKd9WAFF/3yibg2cS7Ph/HddizqTExzGnWKpC1ZlFFBzbqsrKzQvPmn1SBhjDGWPw/8IkRZFdJWzt2O40Kewff4Utg3GAjzsg2V7k9CNQGl248p0Efbhynjj7oV05bgSZMmyXULM3VDlkUB1CFDhogLY4wpu0uPAkWdfANdTVR3U0yNWQMbR5TrObLAv29jpo865W1w7XGwaGqiTIHCAq3yaWLZuHFj5iREiyhKT6ci7VTPgrGSiBqLSIOEzarYF1mQUJahnhYGtiiP/bM6Y9V3LdDUw150xH0dHovfd99G66l7MXvLNVEwNT+ueAeh+5zDmUHCGm6W2DOzAwa3rshBQhUS7fdE1NYwyGenLlVHcxxldFBTE+p0TBkWtHC6cOECHjx4ILLqGWOMyd+xd92Oqfaxo5WkEZS8hD88hcQ3/shIz1oySVncu3cP+vr6WL58OapUqQIzMzOxPXjTpk3o0qWLmIMYY4xJeHpJ6hNScI0agCqiPqE8dKnnIq5P3/EXCTrKokBHlFLay5eXZJ5QN0fqkkWo2C1NcoyVNBtPPBKBOEJbgf/3deMiDxLKom3G9KW5YlwzHJ7bRQT2jPS1kZCcht2ez9FzzhEMXXwSJ2+/Qmpaeo7fj0lIxqzN1zBy6RkERsRBT1sDU/vWwoaJreEk57qHTLESI8ORFBUhuh6ra8injmZxl9ccZ2pqiurVq8PLy0vBI2SMMdWTnv6+23G7WvKti5eemoQ3Ty5AQ8cAZmXqFau5h/AaizHG3ktLT8elh0HiduPK9kV+aNLTUnFryUQ82fXXZ5fda1XdQay1aZ1+6o4/lEWBVoV0MKSF3MuVK4dDhw5h0KBB4kyXubm5vMfImFJbd+yhKEJKWlZzwG8jGkJLU3FBwuwcLI3wQ68aGNulCg5f8xPNT54FROLm01BxsTbTR98mZWFpqie2Tr8Ijqb/yZGaLvnSq1veGj9/WVe8DlPNbEJizNuOszQwkc5xtGg7fPiwyC6MiorC/fv3eZ5jjLFCcN83HCFv48XttnKuT/jW5xrSkuJgWaU91DW1oexzD62vbt68iZCQEFhbW4ttyLzGYowxCS/fCETGJWX2BChqb5/cRVJkhCjZ8Ll1b/V1tdCyugMOXfPDgau+6FL/fe3YYhcolO3aOGXKFHzxxReYM2cOXr58iXnz5slzfIwptbVHH+CPfZIs2tY1HPDrV40UkvqcH3ramujV2A09G5XBrWeh2Hr2Kc7c9Rf/KF+2P/dM4N6N3TBzYB2lLvzNPk9CuORsnIkT1yeUojqEBgYG4jZtOV62bBkcHBwQFxcnAodUO4oxxljhbDv2cC4FewtDuW87JhburaCsaN6h+Ye4uLiIEk9ubm4iQBgeHo4rV64oeoiMMaYUPB8EZpapsDTRK/L3D75xVlxb15JP3fLO9VxFoPD6k2AEv4mDjblkHVLsAoV+fn6Zt3v37i0WTtTMhOo4NWrUSJ7jY0xprTrshRUH7ovbbWo6YuHwhkobJJRFQb9a5azFJfhtPHaef4r1xx8h7V0G4fvnSc7uc5BQtTm16gW7+m2hoaOr6KEojZUrV2betrGxESU1Tpw4AW1tbVEnSkNDeTKGGWNMVbaRnXi37VjeTUySYsIQ/fIudM0dYGBdFsqqffv24iK1ZcsWHDt2DMHBwWLrsWyiBmOMlWSeXgHiurECsgkTI8Px9tk96FnawdipnFxes24Fa1iZ6onux4eu++Grdu5QNLkUpKKCu3RhrKT465AXVh6UBAnb1XLCgmENoFkMgoS5dVr6tls10Ygle6CQyi340jZkpvK0DHhb+YdYWlpi4MCBRfb3YIyxkubO8zCERSWI221ryrc+oZqaOqxrdIWeeelidfKTtiF36NBB0cNgjDGlEhoZD29/SWPOJh5FX58w5NZ5sVC2qd1CbnOKhro6OtZxwYYTj3Doqi+Gt62k8PmqwJENf39/LF68GGPHjs187OTJk0hJUZ5OLYzJG9XnpAChNEjYvnbxDRLKcrY2FhmEsui+iw03LlFlCREhiA3wRUaacnaAVKTk5GRs3LgREydOxJ07d8Rj1PH41StJxgtjjDH5bzuuXsZS7luutA1LwbHpcFh6tEVxQDXff/75Z6xevVrcf/PmDW87Zoyxd6RNTEwNdFDZuWj7Y2SkpyPk1jmoaWjCqpp8d9J2ftf92CcoCo9evYGiFSi6cfXqVbi7u2PPnj1ZtmhRwXfppMaYKgYJaasxZROSjnWc8cvQ4h8kJKM7eYgMQmmwkK7p/uhOnCmsyoKvn8bdlTPw5okkEMYkEhMTUa9ePVF7d+vWrfDx8RGPUwfKwYMH82FijDE5Sk1Lx8nbkk6PbeXc7TgjvXidCJs9e7aoTbh3715R8oIYGhpi+PDhfKKKMcboZMq7bccN3W1FJl6RysiAa4cvROkmee/IKmtviooOZuL2wau+ULQCHdlJkyZhwYIFuHz5cpbHv/rqK6xYsUJeY2NMqYKEy/ffw+ojD8T9LvVcMH9ofZUIEpJWNRyx5OvGKGdvCm1NdXG9dFQT0YGJqa7ol4/FtTE3Msli1apVsLCwwJMnT7LU3aXbQUFBePjwYVH/qRhjTGXdfBqKNzGJ4iRl6xryDRT6nf4Tj3f/iOSYcCi7gIAALFmyRGSxU0ahFNXH7dOnD9asWaPQ8THGmKKlpKbhineQwrYdq2lowKJyXZRu0rlQXp+ampCjN/yQkpaOYlej8O7duzh69Ki4Lbt3mjp0STMvGFOlIOHSvXdFww/Stb4rZg+qW/RnMIogWEgXVjKkJSciNsBPFOLlGoU557gBAwZAS0srR30Q6TxHWfWMMcY+3/Fbkm3HNctawcpUX26HNC05AW+eeEJdUxua+iZQdlTegrLZy5QpI5poZZ97jh8/rrCxMcaYMrjjE4a4xFSoq6mhQSXbIn3v1KQEqKtrQF1Lu9Deo30dJyzecxtvYpJw+WEgmlYpDUUpUKRDR0dH1MvI7v79+7CyspLHuBhTmiDhkv/uZAYJuzcsgzmD6qlckJCVPDH+PmJLFmcT5n+OS01Nhbe3N89zjDEmJ5QxcerdtmNqDidPb59dQnpKIkpVaAZ1DS0U17mH8BqLMcZo23GgOAxVXEvB1FCnSA9J0OXjuP7rOEQ+l+wwLAwWxnqZAdADCt5+XKBoR7du3TBt2jQkJSVlZlvQVqyRI0eiR48e8h4jYwoLEv6++zY2nPAW93s2KoNZX9SFunrx6ZjHWF6i/STbjk2cy/NBymWOW758OV68eJE5x0VHR2PUqFHie6FmzZp8zBhjTA6uPw5GZFySyA6Rd7mT8IenxLWFeysUB5RNSM0it23bljn30Jyzfft2/Pnnn+jevbuih8gYYwrl+UBSn7BJZfsib2ISfOsc0pISoW9TuKW5qMQZOXfvNaLiklCsth7/9ttv6NChA0qVKoX09HQ4ODjg9evXqF+/PubNmyf/UTJWxOgfZot23caW05JgSu8mbpjRvw4HCZnKiHr5RFwbO1dQ9FCUTrt27cTW43LlyonaUNSBMjw8HMbGxjh48KDYkswYY0x+3Y5rl7cWmRTykhgZhJiAh9C3coW+pWTRpex0dXVFUJCSLigZQ0NDA6ampoiJicHMmTPRtGlTRQ+RMcYU5nV4LF4ERYvbjT3sivS9I188RNLbMJRyrw1tw8ItZdGsamkY6mohNjEFJ269Qu8mZVFsAoVmZma4dOkSTp8+jZs3b4pgYY0aNdC2bVuo85ZMpgJBwl933sK/ZySBlL5Ny2J6v9ocJGQqxaX9QMT4P4eOqYWih6KU5s+fjy+//FJ0naRux3RCjDINaf5jjDEmn6L0p+++LpRtx+GPTotri0rFI5tQioKBlM1+4MAB+Pr6wsjICG3atOG6uIyxEs/zXbdjK1M9lC9dtP8eD75xRlzb1G5R6O+lq62JtrUcseeij+h+rPSBQuoASRkVZNy4caK7cevWrcWFMVUKEv6y/Sa2n3sq7vdrVg7T+9XK0dCAseLO0NZJXJjEsGHDxLauzp07Y926dWJ7cbVq1VChAmdcMsZYYbjiHYyY+GRoqKuhlZy3HZs4VUdSVAhKVVDuLDzKUt+7dy/Wr18vGmndunULw4cPFyeqGGOMvef5QFKfsHFluyJdmyfHRuGN9y2RXGFapnKRvGenui4iUEjNW/zDYuBgaQSlrVEYFxeHhIQEcZvqZDCmatLTMzB/243MIOHAFuU5SMhUUkZamqKHoHRSUlJE5iA5evQonj9/rughMcZYidh2XK+CjdyL0hvZu6NM+4nQ1DOGMqOdWCEhIeI2zTs0/zDGGMsqMTkV159IvisbF3F9wtA7nmLtZF2zGdSKaPdsDTcr2JcyELcPKaipSb4zCmvXri2yLaRF3GfMmJHnc7lOISuOQcJ5265j1wVJcOCLlhUwuXcNziRkKunx9mWIDwuEx7Dp0DbmrbTSOW727Nl48OCBaM5FdaIouyM3/fv3521gjDH2GZJS0nDmrqTbcVs5bztOT02BumbxqCVbtWpVUQd3zJgxCA0NFfNPXmusypUro1+/fkU+RsYYUzQKEtK8oamhjnoVbYr0va0pQKihCQv3OkX2ntQ8tVM9F6w6/EB0Px7VyaPI4xL5DhRu3rwZCxYswLVr18T9ixcvFua4GCvSIOGcf6+J9F4yuHVFTOxZnYOETCXR9voovyfUvgtahVyMtzihjsaxsbGi/i6V2fD29s4st5Edl9xgjLHPc+lhIOISU8Wir0W10nI7nKmJsbi/YSQsKrWAY9OvoOxKly6NnTt3YsOGDXj8+LGYd/JaY1FzE8YYK8nbjmuWtYKBbtGeCNLSN4R9g3Yoap3rSgKF1MTlrk8YqrtZKWeg0MnJCX///be4bWhoiHPnzhXmuBgrsiDhrC3XsPeSJEg4tE1FjO/BQUKmuhLCApEaHwOz8tWLLH2+OKDuxtOnTxe3qTZUz549RfMSxhhjhbftuGElW5gYyG/b8ZunnkhLjIGaeoH6NSpE+/btxWXfvn3Ys2ePSM5gjDH2PslB2siE6hMWpfjQAOhZFm1NRCkna2NUcbHAfd9wHLzmW+SBwgKtEinrgrHiLi09HT/9czUzSDi8XSUOEjKVF03ZhFTo3bm8ooeitGiRxkFCxhgrHAnJqTh3X7Loo86O8hT+8F23Y/fi1e2Y0LzDQULGGMvKNzgaARFx4nZjj6ILFKbEx+LOnz/iwbr5CvuTdKnvIq6P3Xwltl4XJaVJJ0lNTRXR4o9JT08Xz5W9pHFhflaQIOGmq9h/5YW4P6K9O77rVo23GzOVF/VSEig0duJAIWOMsaLn6RWIhKRUaGuqo3lV+XU7TojwR1zwExjYVoCeufy2MzPGGFP8tuPSFoZwsS66BlWhdy8iIzUFxs4VoCjtajmJeoUx8cmo990O9JxzGKduv1L9QGFERAR+//13uLm5QUtLC+fPn//o71CxX9oipqurm3mRNlhhLL9Bwhkbr4jCoOTrjpXxTdeqHCRkJUL0yyeiyLuhvauih8IYY6wEOn5Lsu24UWU7GOrJr9ZU+MNT4tqyGGYTMsYYy53stuOi2gKckZGBkBtnADU1WNdsqrA/zY0nIaJUGklNy8CzwEiMX+VZJMFChRbwWL58OWJiYrBmzRq0aNEi37/Xo0cP7N69u1DHxlQL/c/01yEv+IVEQ0dLAzEJKeLx0Z08MKZzFUUPj7EikZ6WilIVa77rCFl86jcxxhhTDfGJKbjwbtsxZUrIS0Z6GiK8z0JdUxvm5RrL7XUZY4wpTmxCCm49Cy3SbceJkeGI9Hko6hMaOriJYKGiUPxCFm3ApeH8fdgLrWrIt3RHdgVaKS5cuBBTp0797DefNWuWuH79+vVnvxZjHwoSUuSd/qei/7mSU9PF421rOXGQkJUo6hqacO34paKHofSooHzFihVRvjxvz2aMMXk67xWAxJQ06GppoKmHvdxel7I/SjcegpS4N9DQ0Udx9OTJE3h7e3ONXMYYe+eqdxBS0zPEnFG7nHWRBAlvLflBbDkmsf7Pxf2a43+HrqlFkf9dKMkpO4pnUN1Gpdx6/PPPP4vagIpy9OhR6OjowNLSUmQX+vhImlEwllckXhoklOUXHMUHjDGWw65du3Dnzh0+MowxVkjdjht72ENfV0uuJ8IsKrWAbe1eKK6ePn2KjRs3KnoYjDGmdPUJ61Swhq524e+GSo2LyQwSStF9elwRnK2NcyQ00n0XG2PlDBRWrVoVV65cgSKUK1dObDt++/YtLl++jMTERDRt2hSRkZF5/k5SUhKio6OzXFjJQZH43PrkFEUknjFl8nz/ery+cDBfjaNKMkXOcYwxpspbyC6+W/TJc9txemqyKK1R3FWuXBn37t0T6xbGGCvpaL0iDRQ2riy/DPTiZHQnj8ztxkSa/DS6U+GXTitQWLZPnz7o168fJk2ahEqVKonmIrKaNWuGwjJhwoTM22XLlsW2bdtgY2ODHTt24Ouvv871dxYsWIDZs2cX2piYcqNI/NOASIVE4hlTFqkJcQi+fhoGds4o3aSzooej1Fq2bIkOHTqIgslUP9fYOOt3hbu7u8hoZ4wxln9n770W5V/0dDTlWmsqzOsYAq/vgmvb8TBxrlFs/ySGhoYiWNimTRuxprG1tc1SuJ/mHZp/GGOsJHjs/xZhUQmZjUxKolY1HLHk68aiJiElOVH8goKELas7KGegkAKEZPz48bn+vCizVUxMTODg4IDnz5/n+Zxp06ZlCTBSRiH9DisZhraphGkbLmfeL8pIPGPK1O2YmDhXUPRQlB6dXAoNDcUff/whLrltTe7Vq/hub2OMMUU4/m7bcbMq9tCT4xay8IenkRofCV2z4r2QPH/+PA4dOiRuX7hwIcfPe/bsyc0cGWMlhjSbsIytCewtDIvkPVMTYqGmoYkMmSx1NU0taBoYQZHBwsJuXJKbAs3SKSlZ920XpvT0dBF41NDQyPXntAX51atXsLfPOx2V6hnShZVMETGJ4lpdDdDUUC/SSDxjyiLKTxIoNHbiBh0fs3PnTjH35CWv+YgxxljuTeVWHLgPnyBJbWgbcwO5Hab40BeID3sBo9Ie0DGxKdaHnwKBH1pjqasXqGIUY4wVSxe8Aoo8mzDw6kkRJCzbaxQMrEqLxyhIqIhGJopWoEChpqZ8zgJSADAtLU1cCF1TkxSaCKWT4ciRI3H16lU8ePBA1OygLA7KEKTUewoQUqYgZRUOHDhQLmNiqiU1LR3/nnksbg9qVRETexXfLSmMySOj0NiZA4UfIzsHMcYY+7wg4fhVnlke23D8Eao4l5JLhkTYw1Pi2sK9FYo72mYsrzUWY4wVZ29jE3HfN1zcbuJRNPUJqePxm8e3oWdhC6uqDaFWwtcCBf70/v7+WLx4McaOHZv52MmTJz8p23Dz5s3Q1dVFmTJlRIZG27Ztxf158+ZlPocel06alBU4btw4zJgxA87OzujatSvs7Oxw48YNrhfF8vwHatCbeGioq2FACw6QsJIpLSUZsQEvxMSnbWii6OEUC8nJyaL75MSJEzM7INMJKzpBxRhjLH/+OuSFbA0bRQkYqrf0udLTUvDm8Tmoa+vBrGwDlfmT0Lbjn3/+GatXrxb337x5ww22GGMlyuVHQaJUmIGuJqq5FU1dcKrlTm9qU7dViQ8SkgKdtqIMPyq0SwV3qTPkn3/+KR4/fPgwnj59miV4+CGDBg0Slw9ZtWpVlvsUTKQLY/nJWN10SpJN2KamI2zluNWFseIk6W0oNHUNeNtxPiUmJqJBgwaIjIxEQkIC6tevj+rVq4v733zzDc6ePVu4fzDGGFMRfiHRyF65nBZ/VJT9c0W+uI7UxBhYVG4NDS1dqAJqvkiJGJQQUa5cObGzipqcDB8+HMeOHYOjY9HXqWKMsaJ2wUtSn7BBJVtoaRR+Zl96agpCbp6DupYOrKs3LvT3Kw7UC9rMhIq9X778vkEE+eqrr7BixQp5jY2xz3LHJwwP/CLE7cGtKvLRZCWWvlVp1Jm2Eq4dv1T0UIoFOkFlYWGBJ0+eoFGjRpmP0+2goCA8fPhQoeNjjLHiwtQgZ41wyiiketGfy8iuEko3GgyrKh2gCgICArBkyRKRxU4ZhVLa2tro06cP1qxZo9DxMcZYUUhLT8elh5JAYePKRbPtOPzBNaTERcOyagNo6nFyUYEDhXfv3sXgwYMz62lIubi4wMfHR15/L8Y+y6aT3uK6ZlkruDuX4qPJSjT6rtbQUY2Mi8JGc9yAAQOgpaWVZY4jPM8xxlj+ePmGI/xdQzkp+kqljEJqKve5tAzMYFu7Fwys3VTiT0LlLerVqydKMvHcwxgrqbx8IxAVlyxuNyqiRiZvn94X17b1WhfJ+6lsoJBqBVK9jOzu378PKysreYyLsc/yKjQGZ++9FrcHt+ZsQlZyZaSlIfTeJSRFSbJrWcHnOGq25e3tzfMcY4x9RGRsEiau9kR6egZsS+mjrL0JtDXVUc7eFEtHNUHL6g6fdQxTEqJEiZmSMPcUZI1FGfHjx49HqVKlYGOTv27Q9DyqCy97mTNnTr7fkzHG5NntuKKjOSxN9IrkoJbrPRpVR82Boa1TkbyfytYo7Natm+g8vH79+swzXrQVi+po9OjRQ95jZOyTbT79WJyxdrIyQtMi6pTEmDKKC36FpztXwsKjLir0+1bRwykWaI4bPXq0uJbOcdHR0ZgwYYJYmNasWVPRQ2SMMaVFwcHpGy6LZnJUiH7Ndy3hZP35W42l6Hv48c5pUFPXQKX+/4O6phZUAWUTUrPIbdu2ieaO0s+6Y8cOUQ/++PHj+X4tWpN16dJF1I1fuXJlvn6HTob9888/YpuzlHoJ7/rJGCt6Fx9Ith038SiabEJC/943cihTZO+nsoHC3377DR06dBBnqdLT0+Hg4IDXr1+Lgu+yHYsZU4SouCTsvyzZAv9lqwpQV8/eb4+xkiPq5RNxbexUQdFDKTbatWsnth5TIXmqDUUdKMPDw2FsbIyDBw+KLcmMMcZyt/74Q3i+W+jNGVRfrkFCEvvaB4lv/GHiXENlgoSEgoPbt28XSRdJSUnQ0NCAqakpYmJiMHPmTDRt2jTfr3X+/HlxvXTp0k8aAwUGKZOQMcYUITQyHt7+b4usPmFKfCxCbp+HdY2m0NI3LPT3K04KNBOYmZnh0qVLOH36NG7evCmChTVq1BDdiPnME1O0XReeISE5DSYG2uhS31XRw2FMoaL9JJ2/jZ3L81/iE8yfPx9ffvklTpw4Ibod0wkxyjCk+Y8xxljubjwJwfL9klpPX7QojzY15d+lN+SWJAhWqlIrlfszUDDwxYsXOHDgAHx9fWFkZIQ2bdrA3d29SN5/3Lhxojmlk5OTOGFGDSzphBljjBVlNqGZoQ4qO5sX+vtRkNDv6FakJSbAqVWvQn+/4qTAp4woINi6dWtxYUxZpKSmYevZp+J2nyZloafNZ0VZyUVblqL9nkBDVx8G1p9XD6okqlChgrgwxhj7uLCoBExaexHpGRmo4mKBCT2ry/2wpaUkI8zrKjR0DGBWpq5K/llMTEzEiaqiRluVx4wZAzc3N3h6eortyxSsXLt2ba7Pp6xHukhRiQ7GGPsc0mz0hu620Cjk0gcZ6ekIvnaaAluwqd28UN+rOCrw0aeiusOHD0eDBg3Ehc4+eXl5yXd0jH2iozdein+oammqY0BzzqBiJVtiRDBS4qJh7FgWalxn6JO8ffsWs2bNEifDqCZh9+7dsXPnTpUrns8YY/KQmpaOyWsvIiI6Uezo+H1kI2hpasj94EY8uoG0xHiUqtAU6pqql+kmrUlIcw7NPTQH0VxEme2FjWrP16pVS2x37ty5MxYtWoR169bl2WBlwYIFIqgpvVDmPWOMfU7CzxXvoCLbdvz22X0kvglBqYo1oWNSqtDfr0QECql+RvXq1fHy5UuRIk8XPz8/8diuXbvkP0rG8vmPq02nvMXtDrWdYVFEXZIYU1ZRfu/qE/K2409CNXcrV66MzZs3i+1etGCi7V9Dhw7FwIEDC+ePxRhjxdifB+7j5tNQcXvBsAawNTcolPcJvXVBXFuo4LZj0q9fPwwbNkzMOTT30BxEcxHNSYGBkkybolK1alVx/ezZs1x/To0to6KiMi/UiIUxxgrq9vMwxCWmQl1NTWQUFragayfFtW1d3iGbmwLty5w+fTqWLVsmOmnJoo5cU6dORe/evQvysox9lmuPQ/D0teSM66DWWbcLJkaGIzUuJvO+poERdE0t+IgzlUadjnVMzKFXykbRQylWaH6rVKkSjh49mqWoOxWTpwyPO3fuiBNjjDHGgAteAVh77KE4FCM7VC7UTBCXDgPx5vEd6Fu7qdyhv337No4dO4a7d++ibNmymY///vvvaN++Pf744w/8+uuvRTaeBw8eiGtb29wX7Do6OuLCGGPy3HZc1dUCJgaF+92S+CYUb5/eg56lHUxcKxXqe5WoQGFoaGiutTO++OILEShkTBGk2YT1K9qgnL1ZliDhrSU/ICM1JfMxNU0t1Bz/OwcLmUrT1NGDWdkqih5GsUNzXP/+/XN0fqSFW926dcXPGWOMAQHhsZi2/rI4FHXLW2NMZ49CPSwGNo7ikhSupnKHn+YWmmNkg4SE5iLKNLx48aJc38/CwgLffvstfvrpJ+zbtw/e3t5i7rOxsRHvNXnyZNHEy9FR/g1pGGMsO88HAeK6cWW7Qj848WGB0NQzENmEamqqN58obOtxlSpVcOXKlRyPX716FR4ehfsPBMZy4xMYldklaXDrill+RpmEskFCQvdlMwwZUzXpqSniwuQ3x8XExODx48f57j5JW7F++eUXUceXFn5UKP7SpUv8J2GMqYTklDT8sOYiouOTYWmih4VfNSy04vNUXiYpMgKqjLYXU7CO5prPXWN17dpVBBgnTpyIiIgIcZsuDx9KMj9Jamoq0tPTxW3qrJyWloZWrVqJGoXU1ITqz2/dulVOn44xxvL2OjwWL4IkDZEaexR+oNC8fDXUnrwc1rWa8Z9FnhmFdHaJzmx98803qF27tpi8b968ieXLl4ttyefOnct8brNmfPBZ4dt8WpJN6GZnggaVCr+mAWPKLuLRTTzdswquHb+EbZ2Wih5OsdKkSRPMnz8fcXFxYrFFiybq/Lhy5UpRs+n58+fiQihoaGlpmevrjB8/HnZ2dmLbGD1n48aNaN68OS5cuIB69eoV8adijDH5+n33bTzwi4CGuhoWfdUQFsaFVxs62u8xvNbNh0OzbnBq1QuqiLbx0omqhg0bikCdi4uLaGKyf/9+HD9+HFu2bMlcY9Gc8qGTVnv37s0MAsqSzZSnAKI0k0ZfXx8zZswQF8YYK2qeXpJsQitTPZQv/X5nYGHS0FK9hlgKDxROmTJFXM+dOzfHzyhNXRZ3iGSFLTw6AQev+orbg1pV5PRhxmhR9fKpyJzlLl6fbuHChaLL47Zt28RFlpeXFw4fPpx5nxp49eqV+6J17dq1UJfJrqHgI/0uLfY4UMgYK86O3fDDtnNPxe1vu1VFrXLWhfp+Ibcv0KIChnbOUFXnz5/HkSNHxO3Ro0fn+HmHDh0yb/fs2RO7d+/O87Vo7pGdf3KjoSH/rtSMMfapTt1+haV774rbSclpOH3HH61qFF7JA58DG0RdwlLudThuIO9AYUoKb2djymPn+WdITk1HKWNddKyT8x+QmvqGVJWQwtaZj6mpq4uGJoypKsq+gJoajJ3KKXooxc7OnTtzzcT41IVWbou0pKQkaGvzGUzGWPHlGxyNnzdfE7ebVbHHkNaFWwg+LSkR4Q+uQcvAGGblq0FVUfAvv2usjwUBGWOsuAQJx6/yzLwfFZ8s7i/5unGhBAtjXvsg6NopxIUGwKJyXbm/Pkp6oDB7gXfGFCUxORXb353R7tesHLS1ci7adYzN4diiO2ICfOHUsifSRJaVOTcyYSorNSEOcSH+ouC7pq6+oodT7OQnE6MgaOvx06dPsWnTpjyfQ4FEukhFR0vqtTDGmDJISE7FhFUXEJ+UCvtSBpg3pD7U1Qu3EDwFCdOTk2BTuwXUNVR3DULbgHmNxRgrSf465JUtnUfkOeDvw16FEigMunpSXNvVbS3311Y1qjvbshLh0DU/vI1Ngo6WBvo2zdolTkpNQwOOLXvmeJy2xb8+f0BkXJm4ZG2AwlhxFv3qmdiiZexUXtFDYe9QXSnaSkbbmuvUqZPncVmwYAFmz57Nx40xpnTo303ztt7A88AoaGmqY/HIxjAx0Cn09w25fV5cW9doUujvxRhjrGgz1GWDhCQjQ/K4vKXExSDM6yq0jcxgXqmm3F9f1XDeOiu20tMz8M8pSROTLvVdYGaom+M5ybFRyMhjC2FcoB9entqFR1v+h/jQ14U+XsaKSvTLJ+La2JkDhcrg4sWL6NSpE6ZOnYpJkyZ98LnTpk0T3ZKlF39//yIbJ2OMfcjeSz44cOWFuD2lT024O5cq9AOWEBGMaL8nMLRzEVnyjDHGVMONJyFIzWWdThmFLjbGcn+/kFvnRP12m9rNVTo7XV44UMiKrYsPAzPPNnzZsmKuZ74fb/0Dt5dNQUp8TI6fG9q7wKXDF0hLjMfDTYuQFP22SMbNWGEzcigLy2oNYcIZhQp36dIltG/fHhMnTsTPP/+cr66XxsbGWS6MMaZoj/3f4JftN8Xt9rWd0KdJ7rs45E3XzAruQ6bAqU2fInk/xhhjhe/MXX+MWnZGZA+Sdw3YxTU9NrpTFbm+HyUOBV0/DTV1DVHGghVRoJDqKZ06dQrPnz+Xx8sxli//nPTOLKSd21mHKF9vkVmlZWAETT1qaJKTfYN2sGvYHkmREXj0z29ITUrgo8+KvVIVa6B87zHQNjZT9FBUxu3bt0VmYH6bnJCrV6+KIOGECRN4OzFjrNiKSUjGhFWeSEpJE//emvVF3SLrFEnN58zKVhGXkigoKAgnTpxAZGSkoofCGGNysf/KCzGnUDNSNzsTzPqyLsrZm0JbU11cLx3VBC2rO8j1aKcmxsOodBlYeNTj9VFhBgqPHz+OQYMGZWZttW3bFm3atEGFChWwf//+grwkY598ZvvakxBxe1Cr3OsL+p/dK64dmnf/4D9oXdoNEF2P4oJe4vG2ZUhPS+W/BmMlGG35bdCgAcLDw8X9ZcuWoWbNmmjSpAmGDx+e79eZPn06YmNjsWrVKtjY2GReBgwYUIijZ4wx+aF/5/+06Sr8w2Khp62B/33dGPq6WkVyiJOiIsTiriQZO3Ysdu/eLW77+PigfPnyaNeuHapUqYKwsDBFD48xxj7L5lOPMWPjFaSlZ6CqqwU2/tAaPRu5YffMjrj1Z39xLe8gIdHSN0SFft+gXO/Rcn9tVVWgQOGMGTMwfvx4cfvatWt48uQJQkNDsWHDBsydO1feY2Qsh39OPRbXFR3NUaucVY6fR/k9QdSLRzBycINpmcofPVtdrtco0fghKTIcqfGxfMRZsRV8/Qye7FqJ+LBARQ+l2Pr777/RvHlzWFhYiPu//fYb9u7dK+oFHjhwAL6+vvl6HVrsBQYG4u7du1kuf/75ZyF/AsYYk48tp5/g1B1JrdSZA+vAzc60yA6t37FtuL5wLOKCX6Ek8PPzE9mDvXr1Evf/+usvdO7cGQkJCahduzbWrVun6CEyxliBTzot338Pi3bdEvcbVrLF6u9bFklDLFlFlQ2vCgpUxfHhw4eoWFGSxXX69Gl0795dLKhoYqOujowVppC38Th63U/cHtSqQq7/w+c3m1BKXUsbFb+YIJ6rqWdQCKNmrGhEPLqBt8/uw7F5dz7kBURzHGXJEzoRlpycjK5du4rvh3r16onHXFxcPvo65ubm/DdgjBVbd33C8L89t8XtXo3d0Lmea5G9d2pCHCIe3RSlY/StSqMkePTokcgglKI11uLFi0Xt2m7duon7jDFWHBuQ/rL9Bnacfybut6vlhF+G1oeWpkaRvL/fiR1IinoDl7b9eNtxYWcUWlpaiqwIigz/999/aNmyZWYdDSurnNldjMnT1rNPkJqeAStTPbSt5ZTj54lvQhH54iEM7V1hVq7qJ6UkS4OEiW/DEP7gulzHzVhho0K90a+eQcvAGLqlbPiAF5B0jiN79uxBixYtMk848DzHGCsJ3sYm4oc1F8W/tyo6mGFq31pF+v5h968gPTUFVtUbiZ0fJWXuoRNVKSkpYtvxs2fPUL9+ffEznnsYY8VRSmoapq6/lBkk7N3EDQuHNyiyIGFachKCrp3CG++bUNfRLZL3LNEZhSNHjhT1Muzs7ET9JSrWTmhrVs+ePeU9RsYyxSemYJenpGnOwBbloaWR8x+PuuZWqPn9b0hLTChQenFGWhoebFiIpLdh0ND5ocQW0GbFD23PSktKgKlbZU6t/wxUg5cWZ2fPnoWXl5eoy0uoYVdiYqKoFcUYY6qc/TFt3WWxg8NITwuLv24MHa2iWdRJhdy+IK6tazZFSVGrVi2YmZmJmu+0vvriiy+gp6cnfkY14Cm7kDHGiouE5FTRtOTiA0k5pBHt3fFN16pFukYJu3cZaYnxsK3XBpo6ku9TVoiBwh9//BHVqlXDy5cvRSq8vr6+eFxdXR3Tpk0ryEsyli97L79ATHwy9HU00atx2Tyfp/cZ2VRqGhpw7fglHm1ZjMdb/4DHiJkwtHPmvxBTetTlmxg7V1D0UIq1qlWr4saNGyJQWKNGDdHYhLx48UI0JtHULNDUyRhjxcLqow9w6VGQuD13SH04WBoV6fvHh75G7GsfGDmWhZ6FLUoKWjyfO3dO1Lel7cZ9+/bNzCYcOHCgKH3BGGPFQXR8MsatOIc7PpImTD/0qoHBrXNvQFpYaPdr0LWT4rZt3VZF+t6qoMCrnY4dO+Z4TNrghLHCkJaeji2nJU1MejQsA2N97RzPCbh0VGQA6lvZf9Z7mZevBrcuw/B831o83PQbqo6aBV0zy896TcYKW7SfJFBo4vS+xhErGHd3d3GRJa1byBhjquqqdzBWHrwvbtOirmU1+Xef/JjQOxfFtXWNkpNNKGVsbIxhw4ZleczW1hZjxoxR2JgYY+xThEclYNSys3jy+i3U1dQw68u66N6wTJEfxBj/54gLegkTV/fPjg2URAUKFFLtJtpmTJ0f6ewXFXWnhiaUhcFYYTl79zVeh8eKL5yBLXNmTMUGvYTvkS0IsXZA9W8WfHZas03t5kiKihCNUR5uWoSqX8/iRidMqWUgQ9QnNLDNWbuT5V9ERAS2bdsm5rr4+HhYW1ujWbNmovskZc4zxpgqoq3GU9ZdQkYGUL2MJb7rXk0h43Bo1g16lrYoVak2SpL09HSxxfj8+fMIDQ2FgYGBWFsNGDCAm2MxxooFWquPXHoa/mGx0NJUx29fNULL6kV/wokEXX2XTVivtULev8QFCqdPn44FCxbA0NAQZcpIIsPU0GTOnDliS/LcuXMLY5yM4Z9TkmxC+rIpbWGY44j4n9snrh2aSbqTyoNjy55IigxH6B1PvHl8G1bVG/Nfgimtiv2/EzU2S0rh98JA243pxBfVh3JzcxMLNVq0LV26VGxBPnLkCExMTBQ9TMYYk6uUtHRMXnsRb2ISYWaog0UjGuVaB7ooaOjolrhswrdv34qa79euXRM14CmLkOahDRs2YObMmSKA2KRJE0UPkzHG8vQsIBKjlp1BaGSCKBP2x+imqFdRcc0VzSvWEM1MSlWoobAxFGef9C+Ao0ePYsmSJVi/fr2Y0Cjbgi50e82aNfjtt99w8qQkcsuYPN33Dc+scTCoVc5swrgQf0Q8uC5q2VhUriu396WAo1u3r+A+eDIHCVmxQDU2WcHExcWhX79+oilXSEgIHj9+jFu3biEgIEDULKRMw8mTJ/PhZYypnOX77uL28zDQedaFwxvCxkxSf7yoxbx+IU54lTQ//PADoqOjM+ecmzdvijkoODgYXbt2FfUKExISFD1MxhjL1b0X4Rjy+0kRJDQ10MG6Ca0UGiQklh71UOmL8bw2KopA4dq1a8VZraFDh2Yp5k63hw8fLjIKV69eXdCxMJanTSe9xXVVVwtUK5OzVqD/uf3vswnlnE2lrqkJs3Lvt9VHvasDx5gyiXh0E2+f3Ud6Wqqih1JsHThwAA4ODmKuK1WqVI5ulHv27MG///7LizXGmEo5e+81NpyQ/DtrdEcPNKikmAYiKXHRuL9qFu6vmYOShEpcbN++XezQogZasiwsLESCho2NjZijGGNM2Vx+FIQRS0+LBiZWpnrY+ENrVHbO+u/oopSRno701BSFvb+q+KSICmVUSDtw5aZ///64fv26PMbFWKaQ8Gicuu0vbufWLSk+LBDhXleha24NyyqS7qSFhQKSXmvmIPDKCf4LMaXie2wbHm3+XUyOrGBojuvdu3eepQuouQltR37w4AEfYsaYSvAPi8GPG6+I2/Ur2mBkx8oKG0vo3UvISE+DadkqKEloTilXrhwqVMi5Y4ZQbVxaf/EaizGmbE7ceoWxK84hISkVTlZG2Dy5DcrYKbZED5ULu7HoW4Q/uKbQcZSoQCFtxXJ0dMzz5/QzKr7LmDwdPH0f6RkZsLcwRItqpXP8PDUhDvpWpSXZhIW87ZJqHWjo6uPF4X8Q/vBGob4XY/mVHBOJxIhgGNq7QkMrZzdwJp85jvA8xxhTFUkpaZi4+iJi3mWB0JZjDQXVuM3IyEDI7QvitnUJqwfNcw9jrDjac/E5Jq25iNS0dFRwMMPGSa1hVypnHwFFNDGhDHVtI1NFD6VY+6R/DSQnJ0NLSyvPn2trayMxMVEe42JMiItPwknPR+L2ly3L5/oPWGPHsqg+7pciqSFoYO2AigO/F9ubn+78E9GvnvFfiilc9EvJdngT59yzEVj+JCUlfXCOIzzPMcZUxaKdt+D96g001NXw+4hGMDfSVdhY4oJeIj74FUxcK0HX3AolCc89jLHiZv3xh5i1+ZpI5qnhZon1E1vBwlhP0cMSOw0jfR7AwMYRRo7lFD2cktX1eOHChYUzEsZyccLzERKTUmCkp4VuDSRdtmXRNksK2hVll1dTV3eU7fk1nu5cKbZ6Vv16lmiiwpiiSOtmGjuV5z/CZ9q3bx+eP3+e58+fPHkiMl8YY6w4O3zdFzsvSE52ju9RHdXdFBucC7l9Xlxb1yiZnX1pbvnQGou2J/PcwxhTNPoeWvLfXWw4IUnkaephj99HNoKu9ieHlQpF0LVT4tq2Xus8Swmx/Pmkv6i1tTWWLl360ecwJg+pqWli2zHp1bgsDHSzZvokvgnF/bVz4diiJ2xqNSvSg25VtSGSo97A7/h2+BzYiMrDphXp+zOWI6NQTQ3GTnzm7HOYmZnh4MGDOHVK8o+MvOjqKi7rhjHGPpdPYBRmb5HUFG9ZzQGDWik2G51O+obfvwoNHV2Ucq+DkobmlLCwsI+usTp37lxkY2KMsezS0tMx99/r2HPRR9zvWMcZc4fUh5aGYkpWZJeWlIjQ2xdEmTDLqoXbt6Ak+KRAYXBwcOGNhLFsLt/yQcTbWGhoqGNAi5yZUq8vHBTBugwFdTWyb9wJauoasKzWUCHvzxhJTUoQW7aoTqemngEflM+wZs0aPn6MMZUWn5iCCasviMLzpS0MMWdwPYVnXdCukGpj5yMu+BU0tHVQ0nTq1InXWIwxpZackoap6y/j5O1X4v6A5uUxpU9NqKsrT9Ze6L1LSEtKgF2DdtDQ5pP6n0vuOaJxcXEwMODFKvv8tOZ9J++K241qucHGTD/LzxMjw8U2FW0jM1jXbKqQw03/sLZv1CHzfkpcjAjUFOU2aMZoIqw1cQlSYqP5YBTRd1NCQgL09bN+JzHGWHH4/prz73W8CIqGtqY6/vd1YxjrK0cDLB0Tc3FheeM1FmNMUSeYvv/7Aq54S5LGxnTywKhOHgo/yZRdqUq1kJoQCwv3uooeikqQW0Tj+vXrGDlyJGxtuVYb+3yPngXB52WYuN21dbUcPw+4cBAZaWmwb9IJ6krQ5TUhPBh3/5oJn4ObuIYMK1I0SeuaWcLIIWcNTyY/gYGBWLBgAcqWLYsjR47woWWMFTu7PJ/j8HU/cXtav9qo6Kj4wBx1powN8OV/O32g0cmOHTvQunVrDB06tGj/OIyxEi8qLgkjlp7ODBJO7VsLoztXUbogIdE2NIFD067Qs7BR9FBUwmcFCiMiIvDHH3/Aw8MDjRs3xsuXL/Hrr7/Kb3SsxNp34o64rlzeHmWcLLP8LCnqDYJvnoOWoQlsajWHMtDQ1YOamjqCr59CgOchRQ+HlSBJkRG8wCokqampOHDgALp06QInJyds3boV/fv3R8OGXG6AMVa8PHwZgYU7borbneu5oGcj5Ti5FHLrPO6unIHgG2cUPRSlQs1Lvv/+e9jb2+Obb76Bg4ODuGaMsaIS8jYeQ34/ifu+EdBQV8MvQxtgYC7lwJQBdTvmhk8K3npMf4DTp09j7dq1ojtkxYoVxWQWFRUFY2PjTx7A27dvsWnTJjx+/BgTJkxAuXIfL8YfEBCAf/75ByEhISJI+eWXX0JbW/FZZUw+AoIjceO+5Ix3t9yyCS8dQUZaKuwbd1SaWjZ0BsN98GTcWzVLNDjRNjaHFdcuZIUsPTUFN5dMhIGNI6qNnsPHW06ePXuG9evXY+PGjUhLSxPzy6pVqzBs2DA+xoyxYpkRMnH1RaSkpsPNzgQzBtRRimwQWlNQoJCacZlXqIGSLiYmBtu3bxdrrNu3b6NChQqoXbs2Dh8+DHUua8MYK0KvQmMwculpBETEQUdLA7+PaIRmVUsr5d8gOfot7iybCrNyVVHpy4mKHk7JzCicO3cuXF1d0a1bNxgaGuLChQu4c0eS+VWQIOFff/2FypUri8mQFmG0tetjKKBIwcErV67A0tISv/32G1q1aiUyP5hqOHj6HjIyAHsbU9T0cMrxc0opdmzRA7Z1WkKZUJozfTmpa2rh2X+rEOnzUNFDYiqM6nRS0V5q5qOpbyTus89z9OhRNGvWTCzOrl27JjpQvn79GvXq1SvQHMcYY8oQjJu56SoCwmOhp6OJxSMbQ19H7iXKCyTG/zkSwoNgVrYKdIzNUFK9evVKnIii8k1U4qJr167w9/fHzz//LOq+c5CQMVaUnvi/xaDfToggoaGuFv7+trnSBglJ8M2zyEhPg5FjWUUPRaV80r8UfvrpJ9StWxcXL14UqfCfq0mTJnj+/LnYwrx58+Z8/c6UKVNQtWpV7N+/X5wNpXodLi4u2LJlC4YMGfLZY2KKFR2biNOXH4vbXVpVzbWTkpaBERxb9oQyMnYsi/J9x8F761Jxqfn9b9A2MlX0sJiKoaDgrSU/ZHb8jnx6V9yvOf536JpaKHp4xda6detw7949HDp0CO3bt1f0cBhj7LNtOumNs/dei9uzv6wLV1sTpTmqobcviGvrGoppSqcsqM77hg0bxM6qRYsWQUNDQ9FDYoyVUHeeh2LsinOISUiBuZEO/vq2BSopQT3bvKSnpSL4+hmoaWjCplYzRQ+n5GYUzp8/H+Hh4ShfvjyGDx8uMi4+h7u7O/T09PL9/JSUFBw7dkzUiJJumbCzs0Pz5s1FHSlW/B07/wDJyakwMtRF8/oVsvyMugpH+XpD2VHHJddOg+DUqjcHCVmhSI2LyQwSStF9epwVHDXkqlGjBjp16oQWLVpg27ZtopA8Y4wVJ6duv0LPOYdRY8w2LN4j2fnTt2lZtK/tDGWRlpyEsPtXoKlnAPOKJXvbMSVhfPXVV1izZg3c3NzEeis/u6wYY0yePB8EYOTSMyJIaGuuj02T2ih1kJC88b6F5Ji3sPCoBy0D3v2jsEDh9OnTRe2mgwcPIjExUWzRouw+QlmBhY2apSQnJ4sMQll0nzIT80ILvejo6CwXpnxSUtJw5KyXuN2+WWXoaGvmqE3otXaeaGSi7OzqtYFd/TaZ96lDM2PykpIQywezELRp00bU4KV5rkGDBpg0aZLInqdSF0UxxzHGmDyChONXeeJZQCRS0tIzH69V1kqpDm6kzwOkJSXAskoDUbKlJKNGJRQkDAoKwsyZM0VNQmqgRTu5qG4hJUowxlhhOnLdD9/+eR6JKWlwsTHGP5Pbwtla+QNvQVdPimvbuq0UPRSV88ldjymTjzL4/v33X3G2a8SIEahWrRqsra3F49QFubAkJCSIayMjoyyPU+2o+Pj4PH+P6n2YmJhkXmhCZsrn/PWneBsVD01NdXRs7pHlZynxsQi6cgIauvqwqFwHxak20MvTe/Dwn0VI5zqaTE6UoQi9KqNavPPmzRMnp6jZVp06dTBu3DjRbGvy5MmioRZjjCmjFQfui+sMmcdoylh7TLnqJpeqWBPVxs6HfaMOih6K0qB6hFSr8PLly7h//74ogUG14K2srDBw4ECcOcOdoRlj8rf93FNMXX8JqekZcHcyx6ZJrWFjpq/0hzrxTajYbWhg5wwjBzdFD0flfHKgUJaZmZlYPNEkRtuQqQA8Fd4tLNJi8pGRkTk6J3+o0Py0adNEV2bphQoEM+ULqB04eVfcbla3PEyNs345BV4+hrTkRNjVbwtNXeX/4pLNJKQvsMjnD/B831pu284+S2qS5GSJbikbUYtDlpqmFjQNsp5EYZ+H6kR17NgRe/fuFU1NaGsY1celDEPGGFOmf0Pdfh6KKesuwScoKpefA77ByrebxtDOGbrmypXpqCwqVqyIxYsXixNTlG1IWe3U+JExxuQ5d6w67IX5226IeaJueWusm9AKZoa6xeIg0/xR/ZsFKNNpECdRFAK5tT2rWbOmuNCkVlgoE5CyCR89eoR27dr9n72zgG7jWvr4GGRmiCnoMDOn4TRN2kDbtE0ZUnxfGV7bV3rtKzOnlDInbRpo2jA2zOQ4jhMzM8qWbH3nP/KuZVmmxCBL8ztnbWn3amG0urMzd+6Muh7vke+wLlxdXXkRrJfDJxMpPjmHX8+bYZzOrlBZWkTpu9aSk4sbhY+r/t7bA47OztTv+ofo6GfPU8ah7eTqG0hdZlzV1qcltDMMlZWUsPE3rnI8+O7nuWDJ8IffqpGTEE5CKWTSciBqHtGEWJTodkEQhLakqFRHq/eco1+2nqYzKbUdhKYRhZhKZi3kx50iN79gcvULbOtTsXo0Gg0tWLCAl6boHjgA1q1bR99++y2/x0ywxvDLL79w3nd8HgNliGQUBMG20lMsXn2M4tILyMNNQ3lFxlzcU4d0pNdvn0CumvZVTMkztHNbn4LN0iRH4bvvvtuodg8++CA1F6hmfO7cOc7Z4ejoSFdffTVXBrv77rvJw8ODDhw4wCH6iBoU2i8r1h/h/0P7d6bOETUfHIt3rqIKbQl1nDSXNB5e1N5Aou5+Nz1GRz/9LyVu+YMfjENHTm3r0xLaCRVlWjq9bDFln9xPGi8/Ki/IJRcvX6NTUCocNyvIC4X8hA2BYidIOC8IgtAWRCfm0i/bTtOfe+KopEyvrh/WI5j6dQmg7zdGs3MQESLK/3suG2Q1A1+nl31C5YV5NPrJj9vVLJGWAnnWV69e3WC7nj17svOuMSBlhp+fHy+bN29u1GeQE/H999+n//73vxxRf9999/EU6Ndee61RnxcEoX3ksFX0QnmVk3BU7xB6686LyNnpgiabtioFCTFcNNTNP7itT8VmaZKj8KGHHuKIPhcXl2ZxFGL6FvI/FRcX8/u3336bfv75ZzbCsIAtW7bQ7t272VGo5BtELsShQ4dyIZX169dznsTGKk7B+ohPzqZDJxL49fyLh9Tars9IIEeNK0WMn0XtFXRicBYe+/x/dGblVxxZ6N+rZuSkIJijzc2kk9+9RSXpieQVEUl9r3+IXH2tu/pYewaDUDDWvLzqH5Do3r27OAoFu41AQHLzey4bSNOHySh+a1Kmq6B1BxLo122n6XBslrre082ZLhvdja6e1JN6RfjzuuE9OtAnfx7j6caIJISTcNrQTlYTTViWm0kBfUeIk7CKw4cPs40VGFh/hOXcuXMbbe/88ccfXIwLQR6NcRRmZGTQq6++SkuWLKEbb7yR1wUFBfHr+++/n/clCEL7BnocWc7hJDQlr7isXTkJEfF8ZvnnVJqVRiMf/4ADKIQ2dhQOGjSI8/tde+21tGjRIi5iciEEBASo+xg/fry6PjQ0VH0NBYVKlArBwcF08OBB2rBhA6Wnp9Pjjz/OU56F9svKqmjCLhGBNLhvx1rb/a99nCJyY9t9yXPk4ulz3QMUt/ZnLspSlHxO3SbTRgVzkNsy6sf3SF9SSMGDxlKPK+4kJ039gzTChdGvXz92FI4dO5Z1HAasnJ2bLUOHINhEBEJMSh6/f+eui8RZ2AokZhbS0m0xtPyfs2zMKfTu6M/OwUtHdSVPt5pVg+HEtVZHbsbBbfw/ZPjEtj4VqwFOODgJkeICuge2D+ydC91nU9i4cSPp9XqaP3++um7evHmc9wtBGbfccssFnY8gCG3PufSCGoWuFOKsMIdtQzZSSUYy+fcaIk7CFqRJFtCRI0do79699MUXX9DEiRM5BB4K7brrruPQ9qbSu3dvXupj0qRJtdYhonH2bKmSZgvk5hfTlj3Ram7Cuqq5IgLPFvDvOYjcA0PpwHv/JoNeV6MQxfCH3pQcc4JKSWYK6UuLqMvFV1PHiXMlSW8r8MILL9A999xDX3/9NT322GN011130U033cR6DsW6BMEe+WhVVRXdKutCmc6KiDVrdUa1d/QVlbTtWDL9ujWG/jmZqq53cXakmSO60NUTe9LgyKB2pxdQkCvr+F4e+JVZFdVgcColJYULZ8HG+s9//sORg9A9M2fO5GnALQ3SPPn6+vLMMQWkeIIDE9ssUVZWxotCQUH7cjYIgj1xICaDKioNVp/DtjGk7lnP/8PGzGjrU7FpmhxjipwXn332GaWmpnLFYyTHDQ8PpxtuuKFlzlCwadZsPk56fSX5+3rQxFG9amwr3rWa8v/8giqK607Q3R7RlxbXcBICvDctTCHYJ6iSjfxNIGzUNBp678vUaZJxRF9oHcLCwjjn7enTpzkVBnTdsGHDOOr9xIkT8jUIdlVF97lvd1sskgFn4enkPPrir+MUlZBDlRaMD6HpZOaXsgP2kqdW0AOLt6lOwo5BXvTwFUNpw2uX08u3jqMh3YPbpV7IOraHKnVlFDxkPDk6SbS2eRDENddcw9F7p06d4iKNGKzq0qULOw9bmvLycnYMmoN12GYJpIOCc1FZUHRSEATrY/k/sXT7OxtVXa1oD2vLYdsYyvJzOG87Kh4jAEdoOc5bS3t6etLNN99MHTt2pKeeeop+/PFHLjwiCI2lrExHf209zq9nTxlIGpMqS5XlWirc9BNVaovJ66LLiah9VWA6HwwWg8EFe0FXUkinfvqAfLr2pi7TruR1Usmr7YARjny4mL6FnIWffvopRUdHs/EmCLZKclYRrdx9jlbtPkuJmUX1toVx8d4fR3gJ9HGj8f3CaHz/cBrbL5T8vdxa7ZxtwSm7NzqdftkaQ5sPJ5K+ypBzdHCgSYMi6JpJPWls3zBydGx/jkEFbV4WD4ZmHt7B7yWasH66du1Kjz76KAdiYOAK1Ytvv/32Fv2OMDMsJyen1vrs7Ow6Z43h3B5++OEaEYXiLBQE66GispLe/f0wfb0+it/36eTPEem/bD1tlTlsG0Pavk1ElZUUOmo6OTi2n7yKduMojIuL46lZSPxeUVHBeSsQeSEITWHTrmgqLNKSi4szXTJpQI1tJfvWUmVRHnmMuYycfAKJKvNsXrhxf/1IvRfeJ7kW7JDi9CSK+v4t0uZkGF3GFRXk0ApTjYQ6vo/iYlq6dCkndT9w4ABdfvnlnL8JjkNBsDVKtDpadzCBVu46R/tOp9fYNqR7EBfI+HVbTK0qupMGRlBiViGdTS2g7AItOxixYPuALoHsNJwwIJwGdA0gJ3mYr0VBSTmt3HWWHYQoEqMQ5ONGV17UgxZM6EGhAZ7U3oGT8MA7j9aYSYEiXZJuxTLbtm1j3bNs2TLODf/GG2/QwoULW/x7QoFIrVbL0fS9ehln+GDKMZx/2GYJV1dXXgRBsD6KtTp6Ysk/tOVoMr+fNqQTvXzbOPJwdaarJvak9kr+2RPk6KyhkOG109MJbego/Omnn+jLL7+k7du3c+6MxYsXt1ruDMG2QOjzyg3GIibTxvUhH5PoA4OunIq2LiNycibvSQvI1kDhEuQkNJ9+jMSsB997nHpdeRcF9BnaZucntC45pw5S9K8fUUWZlkfHIi+7UZyEbcShQ4fo448/pl9++YVz8N522220atWq88rBKwjWroPhFISjav2hRCot06vbwgI8aM6YSJo7pht1CTHmLRrbN7TOKrop2UX0z4lUXnafSqVirZ6OxWXzgs/4eLjQOI42NEYcBvu6kz1zIi6bftkWQ3/tjSOtrkJdP7p3CF09qRdNGdKRNO2o+mRDaLPT60634hfUZudlTSCSD1HrsLHgmEM6p3379nGBrZYEDkhUUkau+YsuuogjGV9//XV1qvNrr73GM8emTp3aouchCELzAr1870dbKSbZGGhzx6z+dO/cwe06Ml1h4O3PUElGEmk8vNr6VGyeJjkKoUiQK+ORRx7halwYdcJizoMPPtic5yjYIPuPxVFKeh5HHsydXnOksmT/OqoszCGPUZeQk9+FVX2zRtz8gngk3TQnoZOrB2Uc3k5JW1e26bkJrTvdLHn7aopb9ws5ODhS97m3Utjo6fIVtCEvvfQS/fXXX3TVVVfRkCFDSKfTcfS8OaiG3KNHD2ovbNhxlH7bcIRScoopPMCTrpw+mKZPkLwuqnwOJtDi1cc4qqtrCJxgA222SEd8eoE6tTg1p0Rd7+7iRDOGdaa5YyNpZK+QWsZEfVV0wwO9ODoBi05fQYfPZtE/J1LYcXgqMZcj5/7eH8+LUq0XTsMJ/cM5YlHjbPuDzaXlevp7XzxP9zoRXz2909vDheaNjeSpYO0tmXxDlBfmUfKONZSye11bn4rVs2nTJi5gAmfdnXfeSRqNhqcbYzEFA1gI1GhscS4UoFSiAqG3AAbDOnc2/pb//vtvtVCXs7MzD5Kh0nHfvn3J0dGRMjMz6ffff+f8iYIgtA8Ox2ZyjtucQi1pnB3p+RvH0Jwx3chWwHRjSc1khY7CkJAQDktHSHx9iKNQaIgV6w7z/1GDu1F4iF+NUebCrUuJHJ3Ia/LVNitIOAvNR9K7TF/AYdRu/kbnaAXyNCacIb8eNadlC7ZBpV5HGUd2krO7J/W59gHyi2zZyAGhYfz9/bniI4wnLHUBJ2F7cRTCSei05g263qGSCMUsdUS6NetoAz0mzsIqJ+FDn27nxN7IDIfRd7x/566LbMZZCEfd2v3xtHL3WTocm1VjG5yCc8d2Yyehp5vmgo8Fpx/2ieXBy4dycY6dJxFtmML/84vLKTopl5cv157kKVCj+4TShP5hNK5/OBftsCXOpuXT0q0xtGL3OSosqS4IMaBrIDsHLxnZhdxdbKuoR1leNiXtWE3p+zaznnNydaMKs4hCoSZubm5sYyH44q233qpTPHPmzGm0o3DWrFlciMuSnlOAY7Bbt241ClbCsQgHIwYz8d7d3b4jgAWhPYFBwOe+20M6fSUFeLvRe/dM5OJXtkBJRjLlnz1JwUMnkLOr9EutQZOeTtLS0lruTAS74Ux8Bh0/ncKv580YUmu798QFVFGYQ87+IWRvKE5CELf2F0rdvY5CRkyhbrOvl07RRkBVY4yGOWlcqN8ND3OyL1TuEtqezz//nGwNRBKyk9AEjUMlvb90Fx3PrKAhkcE0uHuQXRWf0FVUskPwSGwmfbDCmAJDKSWl/H/6m110KimX+nYK4OTf4YGe7arKLBKY7zqZxs7BTYeTqMxkimunYC+OHJwzuhtFtLBjDtOMETGHBeeEaLp/jqfQjhOpdDwum0rK9LT5SBIvABGdE6qmKI/o1YHc2okTzTQqFdO1x/ULpaiEXC5SouCmcaJZo7rSNRN7Uv+ugWSLVOjK6eAHT1CFtoQ0nj7UedqV5N9nKB3+6Oka04+RfgVpWAQjiPZrbhtr5MiRDbZB+ihLTsuJEyfKVyMI7SydCJ5nvvj7BL/vGeFHH/7fJI74txVSdv7NhUwcXd0oZOhFbX06dkH7eAITbDKasEeXDtSvZ1iNbXh49Bw3p43OzLoIGjiGck8fpvT9mykv9jj1vOJOiTpr5xQmxlLMH19Qv+sfYuegqWNYEFoCTDfmSEIz+jkm0Zd/e5KBY+ngoPHmUWdeIoN5GqQt5LIBWfmldORcFh2JzaKj57I4R5xpbjhLIM/ep38eV98jz17fTv7Up7PRcdivcwB1CfG2uiIdsSn5tGLXWVq95xxH8yl4uWlo5gjj1OKh3YPbxOkJWQ3qFsTLPXMGUV5RGe2OSmWnISIOswq07GjD8v2maHLVONGInh3YaTh+QBh1C/GxGmctjDJtuZ4dnRsOJdBLP+2vEZWq5IUC+C0hehBTv3w9ba/wA6I8EH3mGdKRB8A6TryMHDWuFDpiCjm5GK/XPN0KnIQ8s0IQBEG4sD5Yq6Mnv9rJg4Jg8qAIenXR+GaZJWAt6LUllHHkH3L28KbgAaPb+nTsBnEUCq1KZk4h/XMgll/PmzG4xkO/PjednHyDyMHR9vMVNQbfrr1p6L2vUNzanyh1zwY6vuQlChs7k7pefI368C20HzIO76CY5V9wVEXu6SMUNmZGW5+SYAcgJyGmG5szy+MsjfTOo+/z+1K01pfi0gt5+WPnWTV32uDIIBqCpXswDewaSB7t4KETOfKQFw8OwSNns+jo2SxKzi622BaRdXBWFZbWFBC0kp+XK0WG+VJ0Yi4VaXU8fXdPdDovpnn9ekb4U9/OWIwOxJ7hfuSiaV0dhmtYsy+OC5OY5r+Deh3bN4ynFk8d0snqprhCxpeM7MoLHE2nk/JoR1Vuw0NnMjgK8h9MWz6ZSrQUuRA91YIopVo9fbXuZKPySuKegEOPFy3+66rfl+motKyiel3VdhR3UdpUv67+nGnxFzKLRlXwctfwtC9Mw7YWB2dzUpQaT0lbVlDWib3k130ADbj1CV7fadK8RqVbEQRBEC6MtJxiuu/jrfzcA269uC89cPkQqxvEvFAyDm6jyvIyCh9zMTlqJGdqa2FdT42CzfPnpmNUUVFJQQFeNG54d3W9oaKCsr94iiMKg+99jxykE2CQ2wdFLgL6jaCY3z+j1F1rySM4XIpetLOpxihYgsIljs4a6nXNvRQ8aGxbn5ZgJ6BwCXISYrqxgs7gSI4hkRSUcYYe8ttL3gv/S0dTtJwA+1BsJmXklXI+tR2YInrcmCbCydGBenX0Vx2HKEIRFtD203HTc0uMDkF2DGbSyfgcKtfXnGoN3F2d2dmJaDY4QAd2C6JAH7fqHIUOnAVA/f/cDaO5qi8ix5Kzi+hkQg6dSsilqIQcikrM5SThpeUVfFwsCs6ODtQ93I+dhnAewomI4h3NPbKP6dP4buAc3HI0mfQVlTUi2DDV97LR3SjE34PaA7iPenfy52XRJf2pWKujPafS2GkI52FKdjEvS7ed4cWU01V5JbuH+ZKbi5OJE1BPpVod6SvNXXitQ7mugkb1DiVbjIxP3PIH5Zw6yO/dAkMpePBYdva2dX8gCIJgL+DZ4/6Pt1J2gZacnRzpuRtG0fxx1ba1rQDdgoAZPKCFjprW1qdjV4ijUGg1SrTltG6bMXfCnGmDyNmk0mHpka1UkZNK7oMmipPQAv49BtKw+16l1N3rKXTkVLXjhIPV0Vl+xtYcKh/960eUG32YXHz8qd8Nj5BXhO1UHhOsH1Q3RuGSXzceYUcLorIWTB9CU8YPpJzow6TNSafwvl1pUF+ia8aEk5ObB6Xnl7HTEIUv8B+FJyoqDUYnWUIO/bTlNO+7g587DY40Og0xnRXOsZasYIsIMxy/2jGYxY5CS2Aq9SDkX+wWyOfYPdyXH6RryWdYZy5c8smfx+hcWgE72e65bBA7CQGmX3cK9uZl5vAuat+Lab2K05AdiIk5LF84pZRiHZgCDOA76RzsrUYdKv+RaLypnErMoRW7ztGavecop7CsxtToWSO7sIMQhTLau8MGjlVEQWKBvBHtiunJcBruPJFaK3oPxKbmN3r/Ls6O5OGqIQ83Zy6ogsUd79XXyvqqdWo7jbpN+X/vR1vpXFo+O5gVIH5bq2IM4tb9SklbV/Brjw4dqdOU+RQ0YDTn3RUEQRBahzV74+iZb3bxwKi/lyu9e89EGtbDNvOd58eeoNKsVAroM0xSNrUy4mEQWo2NO6KouLSc3N00NGNCdYVXQ2UFFW35hV97TV0o30gdoDouHsoV0vZu5BGWXgvuJq/wriI3K6Qg/jQ7Cb079aC+1z3IzkJBaAtnIRZzAnpXF5OCMyZ66WIqy8ui7pfdTLNGDqBZI439CiKzkNdPiTiEgw5TcRF5uP5gAi8AOeX6dwlQcx0icu98nGHK+aTmFPPUYcUxCKccKvmZ4+nmzBGCOJ6SAw/TWhstn2Gdm1ThGE64Dn4evEwa1FFdn19cxtN/VAdiYg47H+FAis8o5OXv/fFqe0T7Ie+hqQMx1N+D929aHKNjsBc7/7BvTM9VQJTnhP7hnHcQOYlae8pzawF5wOmG5YZpfWj4//1kMWoU8njsqmEWnXmmDj9MwbbkND5f7ps7yGJUKhzO7R38DrU5GeQeaCwu59djAOXGHKFOk+dTYN/h4iAUBEFoRTDL4cOVR9Qcyj3CfemD/5tMHVu4MFlbos3L4hl2krKp9RFHoY2w62As/bxqHyWn5VFEqB8tnDOSxg6znvBjTDdeucFYXRJOQk+PaiNOe2wH6TOTyG3AeNKEGCM2hIYpSj5LJemJdGTxs9RpyuXUcdIccnSSn7Q1AUdM3+sfIv+egySnhmDVIHcmBiPyYo7S8a9eocABo6jbrOs5txgcLCN7h/CiPKjCgcVRh2eNUYdwiCHi7+CZTF4UunQwKZLSPYgiQ31p0+FE1Qmm5JebMCCcp/ei4IjiGDQtxmEKppgOiqx2DEaG+VhFPh4UqhjdJ5QXBThZTyfl1nAgnknJY4cnoiGxYOqwgp+nK0dqYjqtwtnUAl4UenX048jB2aO6UpCPO9kbuGdiUvJqRfDBYLp+ap9WP5+GolLbq4MwN/oQTzEuTk+ikY+9RxoPb/Lt1peG/OvFdh+xKgiC0N4oK9PRu19tpJ1Vuf4vGhBOr98+gfPh2jKhIyZT0MDR5KSR/PytjYMBTwN2RkFBAfn6+lJSUhL5+FjH1JD0EmNExvk6CV9d/LdacU8ZzX7inkusxln4z4Ez9Pona8nRwYE+efkGCgnyUfO3Zb5/L+nT4yn4vvdJE275fCMrq40moZrMo7soduXXpC8tIq+ISOq14C6eDiS0DRwFtXsdlWalUfc5N8vXcJ4UFBaTf++ZlJ+fbzV9dFvrq9zoteTj7dnix8s7e5LOrv6GStKT2LndcdJc6jjh0gYd3SioAefeoTNwHmbS8XOWKwsjh5y2vPZ6FFi2lEoORVUGYfowIga7B3NkHabZtmdQXAPTZKMSqh2Ip5JyLRbIMAXTiz57cCr16RRA9kxdeSXfvXtiu3bOWQN4Jss+ud/oIEw1Rr/6RvajHvMWkXuQdeVbLMuyHt1QUFREoePG2b3Oskb7ShDa0kZvLrJzi+ilj9ZQbLxxIPbGaX3okQVDrWKQVLBdG0vCj9o5aZn59MkPW/m1YmMprt8Pv91Mufkl1L1zMHXtGEiurm034rBi3WH+P3Z4d9VJCHTJZ0ifmUhu/cbU6SQU6gZFMTDCj2q6GP0/9NHTPMXVdEphew411xcXqu+dPb2NlROtlEq9nmJXfUXp+7eQk6s7RVx0qVWfr9C6JCQkkJeXFwUEWLeTxy+yHw39v5cpdc96it+wjBI2LCOfzj25qmm9n/NypYkDI3hRim2cTsxlp6GS6zAtt8SikxDASWiMCvNTC44M7h5EXTv4cJ5AWwJ5HOHsM3X4IUozIbOQnYZPfrmTc0KagwIfbeUktKb+2BYj+KyBkswUivrhHSrNNBYw8u81mDpNnkc+XXq39akJgiDYLTFxGfTSh3+yTe/k5EjPXDeSrpzQwyb0eX1U6nV0etknFDJsIusjofURR2E7JK+ghHbsO0Pb9p6m6LPpdbYrKi6jT3/cxq8RydcxzJ8iOwdT9y7BFNkpiLp1CqoxBbiliDqTqp7nvBk1f+gunXpRh4c/wzB2i5+HreLi7Uf9bnyES8cnbV/NRn17B0rswDuP8nRIBVTEHv7Qm1apzMqL8unUj+9RQXw0V4DE92GN5ym0LsXFxbRkyRL65JNPKCYmhu666y768MMPrf5rcHByovBxl1DQoLGUdXyP6iREcR5dUUGjIos0To7Uv2sgL9cb6y9RWk4xzXp6BekrajvBkDNu+1sLbH4KTV3AGYoptVi++OuExam1bVUcwxr746bmlRTqHuBCIRIsrr6BpC8toYC+I6jzlPlSeEsQBKGN2bEvht77ehOVl+vJ29OVHr9nFs3r6WlT+rwuso7vpaxjuzk/oTgK2wZxFLYTSkrLafehs7R1z2k6GpVElSYWBEYXkAPQHE93F/L1dqeUjHxun5CSw8uW3dFqm7AOvkbnoYkD0ce7eXMerVhvzE3Yt0cY9Y6sbWA6B4Y16/HsEeQLChk+iToMvUhNLp5/LopKMpK5lHx7ySeEqbs458xDO2ooMd6m1/EIWHZqArn6+JNbYAg5u3lQW1OUGk9R379FZXnZnOi9z8L7OdebIERFRbGDcOnSpbRo0aJ2JxAXL18KH3Ox+j5h0+9ceT1iwqXUafJccnJpWqGS0ABPzlFoyQnWPczHbp2E5iBnozUVx4Bz2FJ/nLZ7A3W9xFiArKJMSxVlpaTx8pUCF+2ASl05pR/YSknbV1G3S6435n9ycaXhD74u+ksQBMEK7KFfVu+nn1bu5fcdQ/3p6ftmU1gHP6ILSMcFO8qSPi9OT7Q6RyFmtoCw0TPa+lTsFnEUWjHlOj0dOBZPW/fE0P6jcZzXSMHHy40mjOxJE0f1pNz8Ynrtk7W1jIr7bplGY4dFspPxXGIWnU3IpNiETP6fmJLLzsPUjHxe/tl/Rt13cIAXOw8VB2Jkl2AK8DVWYjyfqdF7Dp3l1/Nm1KywWbjuW3IfMoU0IRIV0FwoTkLIN3bV15xnLPvkPup5+Z3k6hdI1kzavs0Uv/5X0hVXJ+03p7JCT1E/vK3Or3d29yK3gA7kFhDMo00hwybx+gpdORd2UeTRksSv+5WdhOHjZ1G3mddyNJYggBEjRvBiK3iGdOKR3aStKyjj8Hbqdsl1FDRwTJN0g7U5wawRa5paC11yZvnnFrflxhxVHYU50Yco+pcP+QvVePpypLuyoFKhV3hVBe3MFHJ01vB6/Bdal4pyLaXt3UTJO/6k8sI8hLLyd6Igg1yCIAhtS1m5nj74ehNt3xfD74f060SP3TWTvJphFmBJVnV/b0rUt29Sn2vvp6ABo1Xd35ZBJkUpcVSYEEPenXuqzw9C6yOOQiujorKSjp1K5mnFuw6eZSefgpurhsYMjaRJo3vSoD4dydm52iHxxD0OZlWPR7GTEHi4u1D/XuG8mFZOikvOprMJWUbnYXwmxSdnk76ikjJzinjZc/ic2t7Px93oNFQjD4OpQ5B3g53Iqo1H2SEZGuxDo4ZU/9DLovdT0ZZfOUdh4G3/azb5CUbwvaDabsxvn1LemeN08P3HKfKym4wRh23Y8SNROkatCs5FccSjk5sH9bryLt6G6EA4AgP6DCPXgA6UuvNvi5/vPOVy0uZmkjYnnbQ5GVz9GQuchoqjMHnbKkrcsoJc/YPIzR+OxKrFvwNXIIazo7noteBuyj1zlDoMHt9s+xQEawRRywF9h3PewtS9G9gxlLZ3I0XOuZmdiO3NCWbNtOXUWkSbIZ2Cm38w6wvvTj2oOK12MvewcTNrOJj8egxk5xOW4tQ4Kk41bkO0msKpn96nkvTEqs94qc5Ej5COFHnpjbxeV1LEbVy8/Xmbpf66veRYaissyQdTuJK2rSZ9SSEPaIWOnEodJ85h3SgIgiC0PTl5xfTyR2s4LyG4dOpAWnT1BJ49eL4g4h99Pgbn3OuYxecZ2lnNRwtb69AHT5BHaGfOee/fczBpPL2pNcHsFYCBRqHtEEehFQCvffTZNNq2N4ZzEeQVlKrbnJ0dafiALhw5OHJQ1zoLkqC6cVMqHGM/mAZsOhUYEYuJKTlVkYdGByIiEZEXAed04HgCLwoY2YjsHFQj8jC8gx/nWkIl5h9W7OHIRTCwd4RamYmjCTf9ZNzHVGM0gtD8uAeG0sDbn6Hkf/7iSD04DbNP7KMe8xex8dXa0YKIOCmIO0X60mJ1PQwUZdQqoN9wGtN/JEcBwsiBA8I8hwaiIjtPu7LGvivKy6gsL5McnKp/G3BAIo8aHIna7Jp5PEf++302PKEIj37+AudlcgsIUSMT8drVJ6BWNKJieFXoyylp6ypWXgG9jMpTnIRCc1FWVsaLaRVJa0Lj4UXd595CISOn0NlV37DDPy/2RKMdhUDyy1knMCZS922klB1ruK8ddNfz3Dej8nX6oe21+mO/7v3V9xiAwaKAQR9dUT47Dd2Dqg0TtIED0uhQzOXohpKMJNKVFqltCuJPczoHBUxx11Q5FFFcwz04vHaOJScnTvvg6hfE7ZU8moaKCu6zHZ003KalBsqsyXFZVw4qGHuV5VoKGzuTOl50Kes+QRAEwTqA/f3ih2u4wjFs6TuvnUizJtdfSK6hYiCpezdS0pYV1HHyPIoYdwlpvHxYH5jrh743PqLahmX52VRemE8lGbso6+gunimAAcOAXkMocMAo8giuDjxqCWAnZh7dSRpPHwrqP6pFjyXUjzgK25Bzyem0budhXpIzctT1eI6FY23iqF4cFejl2XzRTw1VYlSmHE83iXBElCI7D+ON05bPJmZxpGNRSRkdPZXMi2nUY5C/JyWl1cyfsH5HFA0f2IWdmWVnDpMuMZpcIgeRa9dqQ0NofuDsgkGAabkxyz6h3DPHuANuKUchRwymxrPzwLtTd3V0KufUQV6cPbwosN8IrtTs060vOxcUww1ThRVgYCGxbmMML+RV8ujQsca6iPGzeMH5IDKGHYa5GfwfUSoA6xHWXkjG0H5TEHmJSEGAiMy8cycpedtqMlRWT//PPX2YRjz6rkSxCM3KK6+8Qs8//7zVS9UrrAsNvOMZyj65nwL6DOV1+L1lRx2gwL7DJU9dOwI6IWXXWkrZuZb0pUXk4OTMkd0wJBw0LuzYa2x/rID+HI4oc2dUt1nX1XiPe0ZXUkiV5dXOcVffAAobczE7EpUIxfL8bNJmp1FF+SWWcyxVVHDFXgD9NuqJj/h1Uco5OvLJc8ZGDg7GlBTOGv7feeoVarQCKnznxR7niAu1jbMzD0D1vPwOfo1pu4lbVvJrtFP2g8EqDMYZKvTq+Tg4OlHXWdcZp/JWVrLuwLUGDxqrTu9N2vEnGfR6dRsWFHbDYFXoyCncJj8umjLgpK3aVt22gjpNmqcWHIlZ/gXrN6yv0JZYzEGFffaYfxvnHhUEQRCsBwTYvLNkA087RqHRf981k6ccnw/QhxmHd1DCpt84NRJ0eoW2tNH2FXT+6P8spsLEGMqJPswL20sJMaiOSh6T5nE7pK1AYEVzztJSnklQRM8zrIukKGljxFHYyqRn59H6XUfYOXg6vmaegB5dOtDE0T0592Cgn3UUQ0AUYOfwAF4mjzE6fSorDZSeVaDmPFQciAVFWtKW6Wo5CQF8QZgajanTRRt/5HXe065t9euxVzxDOtKgu/9LRcnnyKNDBK8rK8hlg0fjcf7h5DBYYIjBMYilIC6aE9oryWcVRyGiALvMuIodeo3NG8hK6wIjMriSo48/L75djeeigHVjn/uyahqz4kg0TmfGyJlC1om9HN1Y++INRkUr092EZuTJJ5+khx9+uEZEYadO1jktF07+oP4j1ffpB7bQmT+W8O+n+5xbpGpqOyDjyD8Uu+JLjiZ01LhyrtWI8bPZWdfc/XFdfbS54wr5iMxzEiHyHA4wOOdKM6oHJ00J7DeSnNzcycm1uiCbo8aFfLr25uq+iK6AwwzRjuxIM4kuLMlMNhpBFuh1xZ38H4YW8nM2Bjjszv35Xa31vpH9VEdhwobfqFJX7SBVQFEsxVFYmpVK6fs3WzxGyLCJcL3y68KkWCqxMD3cFMhZnISCIAjWA3TbsjUH6Ps/9vD78A6+9PR9l3EasfMBg7Vxa3+mUuSerSp02WnqFTUdgY3Q59DNsOGwdL34GnY45pw+XGMmQfTPH7CzEMEfPEW591ByDwyhCwUzvPrd+AjLRmhbxFHYCuQVFtOmPcfYOXg4ujrvH+gcFkQzxw2lYUM7UHhI604HPV8QDo1qyVjGjzA6VPBjzsotYofhq4v/ZmeiKfitIzKx/OxRKo8/SS5d+5NLt4FtdAX2CSIffDr3VB18p5cu5grDPS+/XY0IaggYWHA2IoqBIynKSo3RGlWdOcLE/XsNIt+ufTlflYK1JqJFNCKcqFjqImz0dK7kmbjp91Y9N8E+cXV15aU94tO1Dzs5EIV7ePEzFDpiCnWZcXWr57Zpz9NGWwO9tkStGO8RFA6LgKf0hmNakqcPWatTuqFCG7gGJcLONO/SoDuebXD/fa65jyqv1HFUIByK7FSseq0UqMLx+930mInDEe30HOmYvGNNrX2Gjb2E3PyD2OBycHDkKENTR12vBXcRQXViu6OTsZ2jY43Bu8C+w8gz9IUa2/F9sYPVZGbAoNufVgRFxelJdOwz649KFgRBsPeipR9+s5m27jnN7wf17UiPo2jJBcwkLIiPZichpgh3mX5Vs00TRjqSsFHT1PewI3269SF9WSnlnTnGC/35HacaCRkxhWeznQ+mRVTaMqe+YEQchS1EibaMth84yc7B3cdOU0VFpbot2N+HZowdTBePG0q9u4bzDyG9pP6RYGsH1xAc4M0Log9RGMV0IAC/9Y4YHXFwJOewbuQ19VrpANoQg6GSvDtGUv65k3Tyuzd5xAmRJJgGZWosu3j5caEQNWIw4TRPERt89/McNQTDKWLCbC4SgkgJKAhb69hhaCKMXxyFwvk88MTHx/Nr5B0sLCykuLg4cnFxofDwls3x0hbggbT/LU/wdORza76ntH2bKOv4Hupy8TU1HjCtibryuWFqjq05C0uz0ihp20rKPLabhj/wBj/4w7E26vEPedCkvQDdZCnHEtafL3C8GWVQtxwQnYioCXMweGbJURgydEK9UbVKdcn6gOO2Mc5bUycqokGbWz6CIAhC85FXUEIvf/wXRcem8ftLJvWnOxZeVKNQaWMoTIzlKMKuF1/N7ztOnEvBA8e2+IwO6Mzul91MkZfexI5JTE/OjT5E+fHRnONQAet1xfnk32tIoyLao3/9iJxd3anbrOubfUqz0HTEUdiM6PR62n30NDsHtx88ydNwFXw83WnKqIF08bghNKRPN7Wwhy2ycM5IjiqEvwjOQuX/NXNGkWtkJAXf90Fbn6Ldg2jArjMXch6q0799QukHtvJiCiIYEOlgamwgv58v8o+Z5BPsdknNnFO2SEsYpoLtU1paSpMnT1bfb926ld/37t2b1q5dS7aIMh0ZRSuStq2ipO2rqTgljqwBRIKhoi6qvkIxIUeqxXx3aFdcYDOOQlQsRhV4OG2hjF18/DnVAhyFoD05CZuaw9Ye9YO1yUcQBEGoJi4pi1784E/KzCkiRwcHun3hBJo9ZWCTAi0QOR6/finlRO1Xo88RwIGCc1haC5wzUlphQRQh8gviWUshZeffxmhDzC6LiFSnKGOmWa2ikbmZlHVsNwedOLaz5xJbxcFghxPAkfPJ19eXkpKSyMfn/KbZbNmyhVauWU9pWXnk7+NJPkGhdCChgAqKqysWu7poaOLwfuwcHDOoF2mc6/bLtveIQktJWZGTENONkWdh4ZxRNGZQJ354Ph8iK2vnPRSaByRoj/n9c+6czXH28iH/HgN5KrFvZF9Osm5rEYONxd6mJ7YlBYXF5N97JuXn5593H21r+io3ei35eFtH7tqmgJyfyBunTKdEBT5+QHRwvKDfEtIg6EuKuBCGrriQnX/4j/ehI6eqI9dHP/8fj25zxfJyrfp5REAPXPQUR4Md/rhq2qYJzu5e5NdzIHWecrma17W9gYJNZ5Z/wYWklLw/HSfO4WJNKMYhNB+iH1qfsizr0Q0FRUUUOm6c3eus5rCvBMHauFAb3dQm9vf1oNz8YtLpK8nT3YUevXMmDRvQudH70uemE63/iouVYODPI6Qjp3hB4Ic12meFSWeNBS2jD9UYNHYPDqdhD7yunjN0aMKGZVy8K+KiS7mgmdhYbW9jSUTheToJPX5/gG50qCSC7WMg0mU40uHSSeTk6EmjB/Vi5yCchB5u9ukRR3VjLArlCaco/bVbyOfSO8hjSHWEjdD2OLm4UceLLrPoKOx/02PkHRHZJudlbbRUMn9BsGXgnFIoiD/NhTPM4am+D77BlfYwzT/71EGj4091Ahbxf4yWd556OX8GVcjjNyy1eExUy1MchWV5maQvLSGNlw+5e0TwSLuzhzenFKiPSn05ZR3dRV2mXamui139DXmGdGYnI67LGh/KeewXOX4cHXk6anF6IheR6jh5LgUPGKPm2xOaF9EPgiAIgiUnoeksu4xsY8CBn487vfjofOoUVrNwWH3os1Mp4527iSr05OofzM8nwYPHN7pIZFuANFdYukxfQOUFuZRz+ghPUcZgrPIMhQHk2JVfqfnuk7f/SSm71tlkCpj2hjgKzwNEErKT0ASNQyUN9tPSv198iQJ8ZTqiOYWbfqLKojxydJF8A+0JB7I+Q1gQhPYJcuaEjb2YUnetq7EeUzZLMlPZUYin6VM/vac+MNbAxDHn3iGC/HoO4khFFEtx5uk2xtfugaFquxEPv1Ovc6yuaaPD7nuVdKVF5Fa1L0yJMT1vV99A8unWl/wi+3EBF7xvawdhzqlDXJU3ZPhkrpqLFBMDb3+aXH0CrNqQEARBEARbA3r5u9+NQRjmjzQ+Xu6NchJWlhbxs4+jmyc5B4aR+6CJFNaxE4WOmEqO9cxUtEaQ9iR0xGRezAuwmAsIz2Q8i0schW1K+7rDrARMN+ZIQjOucdxFaR9eR9pB0yl44rWk8a2OpLBnypNiqCx6Pxcxce3bcPJuofWxthxLgiDYHpjuGjJ0Yi1HITBUGPseOLS6XnwNRzqz84+dgN5GJ6BJ3h3kQcTSEA1F0NWXz82NQmo84A6667+Uf/YkF4FCdGTm4R28dJo8n7rMuIrbFaXE8Xm31ig4Kg8i92Di1pVUkpagTumBo1C5PkEQBEEQWh6dvoJOxqTQviNxtO9oHKVlFlhsl5qRX+9+Ksu1VLxrNRVtXUoeI2eS76zbeL3/1Y9QuI2l44oYN4syD//T1qchWEAchedBaJAfUVnt9akOQRSWlUjpG76gwHEL1NGE0sST5N6xr92O6Bdt/pn/e0ulY6tFkp8LgtCWIOpNAXn0rG3aKKLzfDr35KXT5HlUqddTYVIsOw0DelVXwj27+lseHcfUZN9u/Xiasm+3vlyJtrlBjqKETctJm22smoiqgp0mzyWfLr2b/ViCIAiC0B4oz0klfXGu+t7Z059cAsJa7HgFhaW0/1g87T8aR4dOJlJJaXm97TE5omOon8Vthgo9lexfR4Ubf6LKwhxy0LiSo0Zm4wltgzgKz4O5s2eQ7vfVPN1YQWdwJM/Ln6OBI4dQ0ZkD5BpoTH6uTYul6LcWcnSh76Bp5Dd4Bnl1H2asKGsH6FJiSXtyNzmHdCG3fmPb+nSEepAcS4IgtDS2Er2MKT++XXvzYop/78FcLb4wIYbSD2zhBfhG9qeBi/6jDiA2R35DHEObk05BA0ZTx0lzuUiMIAiCINizk/DkS5eaPWO4UN+nVpFrQHizHAM6PCElR40ajD6bVmPmLCoZ9+keSiMGdSWNxomW/LJDzVGo/L9mzqha+y09sYsK1iyhipxUjE6Sx+jZHGTjZDKQaovYynOhLWLXjsLCwkK12gteOzs7k7u7O1VUVFBRURF5enryurKyMl6UtiNGjKCd5W/Q2o3bKDUzlzoE+NK8WdNoyuTJ3M6h6wj1GCXFReTVfzIVn95NWdt/4sXJK4D8Bk0l//ELqdI7VN2vtsQYpujm4cqdUElRKbm6uZCzxpn0Oj2VacvJw8udDYyy0nJug7aguLCUXFw1pHFxJr2+gspKy8jd050cHY1tKysryd3TOCJRUlhKGldn0rhoqEJfQVq09XAjRydHPkZlhUnbolI+PvZdUVFJ2hKt2ra8TEcVugpy9zK2LS3SkpOzI7m4uZAuO41Kc7KofNsvvM112MVUWlLG589ti7W8D1wfzg3v3dxdycnZiXTlOtKV6cnDu7ptMenJ092ZKisNlFesIy83Z3LROJK2vIJKyioowNvF+D2W6Fg+XmhrMFBekY483ZzIVeNEZboKKtZWkL+XhtsUlepZht4exuqPOYXl5OnqRK4uTlSuq6QirZ78PDUsQ/O2uYXl5O7qRG4uTly5qrBUT76eGnJydKBirZ4qKgzk42lsm1dUzsdHe7Wth4acnByoRKsnXYWBP1vd1pHcXZ1JX1FJBSV68vFwJmcnRyot01OZrpL8vIzXml+sI42TA3m4OfPx8kt05O3uTBpntK3g61XaFhTr+HieaFtp4M8qbSFDtPe3JEMzeZeVV1BxHfKGfHItyNvPS8NK05K8PapkaC7v4lI9VRgM5NNM8lZkaJR3Jfl6KjIsZxl4WJR3TRnWJ2+WYTnurSp5l+j43CBvVYbuzuTSkLyr7llV3rXuWaMS9XLX1JK3KsM65G0qw3J9JW9X5Q0ZVprIu6ic3F3M5F3vPWuUt7kMS8r0/Pka8nZyZBkqbeu6ZwXbxNajlztNmsdLha6cChNjKP9sFEcdugdWT2NO37eZknf+xZGGSsShUnylriq6Ds7OlBt9mEqz0qjnFXfwuo6T51PY2JnkEdw8xo8gCIIgNJXN+47Tkt/WU0JaFnUODaJFV86gKSMHtKggK/U6KstKoLL0c6RNP0f+w2eRa2BHjiQ0dTgBg76col6eR949RlD3uxfzuvK8dCqOO0KuARGkCQjjqMP6BvB0ugo6Fp3MUYNwDipFSRRQwXjogM40clBXGjagC/lU2cYgOMBLrXocEepHC+eMorHDaheKrMjLpIrcNHIfMpm8p9/AOQntAVt/LmzP2Odc2CoOHDigvj548CDFxsbya61WS9u3b6e8PGMOgKSkJNq1a5fa9vDhwxQSEkJvvfYiffnxWzR98jjqN8DYIaamptI//1TPs4/J0lLhiFtp4EtbqdONr1NJyCCqLC+l7J3LKDMlkY8DY74s7gQlRsdT4pkU4wcNRCf3naG8LGNug4LcIn4PJx6fU2wqxUcnq8eJ2n+GcjKM51uUV8xt4VwEKXHpdC4qSW176tBZykrNVR2MaAunH0hLyKTY49Vl4E8fiaPM5GzVYYe2cCyC9KQsijlWXer8zPE4XqfPy6DMd+6moi8fp/IzB43HWfcVndllfA3OnkigtPhMfg2nIPZbVFDC77PT8ijqoPG7AHGnkuh4nDGXAxwc6w+kUWa+1iiHrBLacCCt+nuMyaUjZ43XBgcN2qblGNumZmv5fWXVqM/h2Fw6dKY6NB3bkrJK+XVGnrEtnErg+Lk82hedo7bdeCidEjKM55tVUMZt4UQDJ+MLaPcpo8zAliMZdC6tSHWqoG1xmfG7iU4qpJ0nstS2249l0plkY1s4UdAW/1m+yUW8XQGfw+dZvmXGttg/wPFwXAWcD84L4DzRFucNcB24HgVcJ66Xv5sKo7whD6O8S/m9AuQHObK8DUYZQs4Acmd5Vwkc3wu+HwV8b/j+AL5PtMX3y/KOy6d9p6rlvelwOsWnFxvvjwKjDLVlRnmfSiigXVHVMtx6NIPOphapzj2Wt9Yow9PJhfTP8eq2O45lqfKG4wxt8RkQm1JI245Wy3vXySw6lWiUIRxgaAvnG4hLK6Yth6vlvfdUNp2oumfh3GV55xvlnZhZQhsOVsvwwOkcOlYlbzgg0TY91yjDlCp5KyOVh87k8QKwDtvQBuAzeI99AOwT+1blfTCNjw1wLnzP6ozyxrninBVwLbgmgGtEW1wzyzuxgGWhABlBVqbyhiwBZAsZK0D2+A4AvhNTeeM7w3cn2D54+ENhE2WxxYdBJ40L+UX250p/g+54lrrPM+YXAmUFOaTNTqe0vRsp+ucPaO8r/6KD7/2bYld+zZWe4SQ88M6jdPjjp9Xl0PtPUNzanynzyD9UXmjsA1x9/MVJKAiCILSpk/DJd7+j2KQ0Ktfp+T/eY31zUKmrzvVVdO4wnf38Pjr50hw68thIOvXKfDr35UOU+uf7VBJ/rN79OLl78bRedV+xByjuq0d41t/xpybR0X+PZmdi7Cf38HFAdl4h/bVqJb37wTK68aHP6fn3VtGfm4+pTsLwDr40b8Zg+t8j8+jbt2+jx+6cSZPH9K7hJARjh3Wn955bSMsW383/FSdh2bnjlLvsHTJUGu0Zz9GzKPj+D8n/msfsxkloT8+F7REHA7xUdkZBQQH5+vpSVFQURURENDmiENscHR3Jw8ODo+HwWbzWaDTcDo5G7N+8LUSNY7s6O1JZ3CFy6zGa27pTOZ14bhqRxo1ceg4nzyETyaXnMNKWG9pFRKGbh4Yq8jKoJDGODHmpVJF8mrTHtteSu9dtr5NPz/7nFVHYlYokolAiCiWi0E4iCgsKi8m/90zKz89X+117RdFXudFrycfbs61PR2gmKsq0VJBwWi2OUph8jhwdnWjMM59TSXoSOwfNCR4ygbrNXMiFVQTB3inLsh7dUFBURKHjxtm9zlL0FQIsbEl3t3bOu/bEwsfeoriU2oO8/t6edOdVF1N4hwDqGBJIIQF+5Oxcd9qt8rw00qad5ejAsnTjf23GOXL2CqC+j//GbQqid1Hsx3fyVGLX4C7kFtKN3EIiyTWkG3l1H04ufiFUkniSot+8ptb+ez/6i7FeQFXUYGnKaco7upHKc1L4+y3PSaby3DSEKlLM4Hvo77hKOhmbSM/4baNw5yJOMZZT6U5lbn7kFhRGwb37U8TMq3lfhooKIgcUY7N8fQigqSyuLmpSWZRLxTtXUdlpY8BSwM3/Jbc+DRdnU4i0sWImQuvSFBvLrh2F1qLI0EGdW/MW5/KrLDZGISF5qWuv4eR10RXk0qVvW58iOxzQsemzUriDdunUi9cXbl1Kheu/JzIZpamLoHvfI5eIHud1fOkUBcF+EEehiSzEUWgX6LUlVJKRzMVSipLPWXQUDvnXizzSLgiCOAqtkbYOxMAxXFxcqLy8nEpLS3m/cAwVFxtnSOC4DbUtKSnhY3t5efFnsuOjKfH963j6qgLsoH5PrSa9m2+NthwM4urKi16v5+Nim5OTEx8D67y9vdW2OL6bm1uDbeuTIWSCa6gvlVZjZNgYeUM+OC72u23/MfrrnyO07cBJ8ncsJS+HavkUGVwot9IY7KHg5OhI4QHe1CfAgTq7lVIntzJy7z2efLv0p9AgX8r/9j4qSagZhejk7kOa0J7U674lLJfi/Fwqy0sn/0492SlnSYYuukKKfmVere+r0wM/kWeHztxWkQvkjZlTuw5F0Y5DJ2nf8Rgqz8+k4koX0pHR6Xed32nq7lVGQU5actHmEVUYB+w1nXpT8L/e5mAWfcw+Klz2Jjn5BhF5B5EmMJQ0ASH82uATSkU/PEtkNh0aOIZ2I+8ZN5FH35Gk11VQeVk5eXp7VKcjcyAOojFPRxZRli2pnSS1E51vaqektDzqMuyyRjkK7TpHobWAUSm/K+4nw7z/o/K441R6fCdpTxgXj+Ez1HZlsUdIExZJjh4tn9wToyNFm38hfVayuhjKjNMW3fqPpYAbjEaMk6cfOQeEknNQBDkFRfB/kL/8gxY/R0EQBEGwBZzdPNhJKAiCYAupnRRHIVI7BQYG0oABA9TUTmPGjKGgoCAO2Dhz5gzNnDlTTe0E59jgwYPZ+YW2I0eO5HRPSO108uRJmj17Nrc9fvw4O4mGDRvGTiK0xevw8HBKT0+nI0eOcFs4t/A5gH3B6YK2OEanTp0oKyuLzxHnAAfbqcP7SJeXRr3DA6gsJ4Vi96wjdxOnE4ATKmn5a5RvcKNygxNF9u5PTu7etC+TqEfvfhQZGUlZ507SwWNRNHH6JeTl48vprbKzs2nSpEm8j7179/Lxe/fuzU6rHTt20MSJE9lwj4uL4+udOnUqt92/fz/LoF+/fuzI3LluFQ3t14PbJiQkUHJ2AU2ba4xuO3ToEPn7+9PAgQPZ2YdrHT16NAUHB1NycjKdPn2aLrnkEm4LGcHxN2TIENLpdNwWefhDQ0MpLS2NZXzppZdy2xMnTpBWV0FJ+TpavnEPJaUb09XASfi8/9ZaBT5fKpxKboERZMiOo0vdoijUqYiCDCXkWJ0Rh5YePUebtMbBr0t9HSnCcwDlkTd5hPWgiH7DyN3Ll9KS46mTVktenp4UG5/Izo2LuvThz+zevZtl3aNHD3Ya7ty5kyZPnsxO3OhjByg3N5dGDB/OEaDbD52kiJJK6tOnD8UlpdIPy/+i7FKiw6fjqazc1InnTh38vGhYny40/+KLqFtoIG3fvo0Ch3UnLx93So06QzmxZ6jnIGPxsLMnE8krN5/cA8NIn5tBlJNGFfHHyZhAiCi/96Xka8FJ6DnjJjpt6E3dg7qRp4MD5WXlU9ypZBo5dRBvT4hJIQdHB+o5sCvfs0jR1a1fJwoK9efUTrujsunKizqSo5MDp3bS6w00aXAHNbXTsF7+1CPcm1M77TieRfPGRfAsIaR2woyiacNC1NROA7r6Uu9OPpzaCSmALhsdzrP4kNoJaapmjjBGz2Ib9tm/qy+nDULaqktGhrKzCmmFkApp9mhjzmSkHerUwYMGRfqpqZ2mDwuhQB9XTld0NrWY5o4z9hFIZ9TBz5WG9QxQUztNGdyBOvi7cRqkqIQCunxCR+Pv5lQ2z34a1SdQTe100cBgCg905/RKh8/k0lWTOhv7odM5XHdgbL8gNbXTuH5BfF44173ROXTVxE5cYEZJ64R9KamdRvUOoG5hXpzaaefJLLpiQkdydHbg1E5I4zV5SIia2mlID3/qGeHNqZ2QJmzu2AieaYXUTshbP31YqJraqW9nH+rT2Ydnem0+kkGzR4Xx7C+kdsrIK6NLRoapqZ0iwzxpQDc/lveGg+k0c0QoO/yQ2ikxo4QuHROupnYKD3KnId391dRO04aGUJCvK6d2OpNSSPPGGWW473R1qqmGkIhCK4goBOkl1XkBgaGyknSJp0gT3oMcNC5UWa6l9Bev4/wKrpGDyG3AeHLrN4acvP0thjU7evqQs5+xwzAHSV716MhMnICIFMT/oDteIedg442U+sJCMpQa8zA4+QaTU1A4OwJduvQjj6FT6rwWnEvGW3fWHD1x1lCHRz6r85waQiIKBcF+kIhCE1lIRKHdoeQoNK8AiGTfkrdHEIzI1GPrw9ojCtFWV5BFuYmnybEklwwleeQ37mpu66LNodNvXsN55M+Xrk+uIQ//Duw0O/JYdVVbRxd3cnT1JAc3TwoYejGFX3o/n2953CEqO7efHFw8SO/oQh6+geTs6UN6BxeOpPMNMjoiCvLzSOPiytdXmpVkMWIOzjEEnjQUUViSl0VeiFLTl1NRQT45VOq5iKKBHEjvE8FtSVtAead2U1lpCblpnCgpJZ0On4imuKQUOqENoMQK43dwfbdS6uOcSUG5tfMR5kz/L02bcyWVpMRQ9GtXkMFJQ3qvEMp3DqA0vScll3vQkXxXOldtutaJs5MThQX7U2igL4UF+VOXiBCK6BBAfp6u1DmsAwX4+9aIytx2MIo+X7aOEtOyqEtYMN12+TTydneh/SfP0u5jZyg6LrnW/gf16kyTRgykCcP6kI+7Sw0ZxmfFVKfHKtORTqe3WJwTbUsyM0ijzSdDYRaVZ6US+UVQ8a8v1bqmoH+9Szq/CDVKUFeul4hCKRZJ1hZRKI5CK3UUmlNZWkhF236n0mM7qCK7quCJgyO5dO1H7kOnUf7Kj2s55gLveIWoXEv6zGRWTopzr2j771x+3RwHdy8KuPEZcu1mLMxSduYwOXr6klNgGDm61EzM2hBNcVw2BnEUCoL9II5CE1mIo9AuMa96LBUABaEm4ii0Pto6tROKQujyMznvnMYniFyDjZFFqWs+otxDf/N6UwcbGPzGXnbkVWiL6NTrC8glIIJc/MOM/wPCqFKvp6Rfn691rM7Xv8THwOcqS4uoQltIwROvJwcnZ14X//1T/L+itLBGm4BR86jzwv9WndeHlLb2U4vX0vvfy8gjoje/Pvqfi8igKyNHNy8eNNLlVNmBJoTPfZhy9q7k66us0PF/fq3XUeSid8mn73huF/XK5aRNO1Pr865BnanfM3/y68KYvXTmw0UWz2ulbggFTLiW5k8dRbrV/6P8Y5sstuv54Pfk1W0wB7iU56aSS0C4xRx+xSVaSsnMoeQMLNn8PyUjhyMWU7NyOT9+Q/j7eFJEh0DOh6jT62nz3uOYtYu6oPV+ZtyQPjR+aF8aPaAneXq4nbeNXh/lyWco68MHaq2XdFxCe7CxZOpxO8HR3Zt8Zt5M3hffRPr0eCo9/g9pj/9D5eeOk0u3gbVzH+h1lL34UfWtplMf1VGo6diLIxIRHWi6wJlnimuPIed9vuwUvADHoCAIgiDYKxw5KFX/BEGwM7Zs2UIr16yntKw8Cg3yo7mzZ/B0UmCo0JGuIJtc/I3T+HT5GZSy+n1jQYpcLOlcjAKEXHwHR+6BitICbgNnldEJGK6+5kRwmDnl5kX9n/3bYh75ZGeXWhF83j1G1lnQBPuKvP29Wuu5LEBVhVsQOOYK8owcVsORWMH/i0jjG6y2cwvuQvriPHY66vKqi6qYgkhIbUYcOTpr2JmIc8R0aGdnF3ZeKnhGDiWNfwg58npjO7x29jFWmUW03V8bjlFxWV8q1RtIb3AkPTlSeEgHGjawNz008WLyqco3r73sAfIdOJUSfqydUxfnwbJycibXoE4Wz5nPx8ONenYJ58WcispKysjOVx2IyelVjkQ4FtOzqaDYGP2ZW1DMy/Ez1Q49S07CHp3DaMLQvjRhaB/q270T501sadi2hizMgnnMbW5BsEasIqIQORsyMzOpa9eunGS0PjBKhXwSpiA0GDke2suIV3OOViBasKIoh7I/e6LWNk3nvlx0hB2BIV3USMH2iEQUCoL9IBGFJrKQiEJBEIRaSESh9XGh9hWchB6/P1Aj312lgagioBt5GErZMYiEYkPe3M8OKEwjPv6MMQgCUYHVjsAI8ul3EfkOMOYDRPomOMQcztMxZE1Vj+uq6tvrkZ/Js3P/89qntlxHG3YdoeUbd9OJ2ER1vYebK82+aBjNnzqanWx1yebkS5fVORW6JSkoLqGUjFyTSMRsWrF5n9Eha4bG2Ym2f/PyeR3nQiIKgcyyE6yJdhNRiMSpt99+O/3888/UoUMHPuF3332Xbrvttjo/8+KLL3J7OBUVkMR02bJlZI84B0fUmU/Dd+7d511lWBAEQbAe8orLycfbk18jqbHGyYFzkSBBc36JTs1Foi2voNLyCvKvykWCJMpOjg4kFeLOP59LQbGOnJyMMqyoNLD8a8i7rIL8vU3kjcqQ7s5UWWmgvGIdebk5k4vG2LakrIICqtoWlug4f5YX2hoMlFekI083J85Jg+MXa/E9argNkoHD+EHCa5BTWE6erk6cqLtcV0lFWj35eWrI0bF2WyTMRo4bJDKHDApL9ZwDB/cFkl7jHvKxkBNHbeuh4esv0VrKn+NI7q7OtfLnlJbpOdG4IsP67tkLkXcNGZrJG8nGi+uQN+STa0Hefl4acqxD3h5VMjSXd3GpnioMBk6w3hzyVnMQsbzN7llnR/KwKO+aMrSHPsK1qm/MLy0lV42G3JydSVdRQcXl5eTt6srRQiXl5aSvrCSfqiCEfK2WXJ2cyE2j4fVFZWVq21Kdjj+vtC3Qaknj5ETuGg1HNhWWlZGXiwvnM9PqdFRWUUG+VW2LtErpAusCBTE2bNjAsp02bZqaN7AulixZwvn6TBk1ahQvrQEiCW80cRICRwcix9xzVEga0roEkYNPB9q1/wj16NmDgvwCqPcjP7ODEMUVcd9Zoqnpk8yBw6utHIPmwEkJR5y5Y07jFdDkfcWlZNAfG/fQn9sOUGFJ9ffeq2s4XTFtDF08bgg7C+sDcuEK0G3gSPXx9CCfbh7Up1v1fX08JoFik9K4IIUC7ouu4W03y01m2QntlZaPua2Hl156idatW0fR0dGUmJhIn3zyCd1xxx1csak+pk+fzpWxlMVenYS1wppNkbBmQRAEm2Hn8epIelSIQ3U0oFSIg2ENUCEOVdUUUCEOVdeAUiEOVdkAKsShWpsCKsShmhtQKsSh2htAhTi8Vx6+USFOqRKnVIhDG4DP4D32AbBP7FsBx8SxAc4FbXFuAOeKc1bAteCaAK4RbXHNADKALBRQIQ7V9BRHCdrCwQBQIQ5V+BRQIQ5V+oBSIQ6fAagQh+p+qryjsrj6H9CWVXBbVAcE8enFXDVQYd+pHK4qCODgQFtUHQRJWSVcjVDhYEwuVysEcNCgLaoZgtRsowwRTQMOx+bSoTPVhhi2JVXJOyPP2BZOJXD8XB7ti66WNyoTJmRUybugSt7lxulvJ+MLaLepvI9k0Lm0ItWJhbbFVfKOTiqknSeqZYjKfpArgNMKbfFfkTe2K+Bz+DzLu+qexf4BjofjKuB8cF4A58n3bIHxnsV14HpUeUfn8PUCXD/aQh5GeRvvWQXID3JkeVfds5AzgNxZ3lUCx/eC70cB3xu+P4DvE23x/bK84/L5e1fA/YD7AuA+QVvcNwD3Ee4nBdxnuN9M71ncjwD3J+5TBdy/iryVKpLKPYv7Hve/PfYRO+LiKDHX+F3llZbSlthYdvqBM1lZtD+xOkJqV1wcncsxHqdQq+W2ReVGuZzNzqY9CdWRQ3sTEig223gcOB/RtqDMKJf43FzaGRentj2UUjtnXFvz559/Us+ePemHH36gX375hV8vX7683s88/vjjtGrVKjp16pS6mM/iakkw3dgSb+WNpoeyZtCTKSPoiVOd6eH3f6W5971Ms+75H/37+5300R//0F87DlJMfArnqLNlFMdc70d/UZemRO9BPht2H6H/e+kzWvjYW/Tz3zvYSeiqcaZLJw6nJS/cS9+8eD9HETbkJDQ9J49O/dSlLZ2qi66cwf2N4jTGfzjKF10xvc3OSRDaK2069RgjW4ge/N///qeuQ+n3KVOm0EcffWTxM3fffTeXbH/99dc5vB3l4puKLU09bqmwZmtDph4Lgv0gU49r66tzB1dR51B/u4kWUqOzisrJ3cUsOquVK8RJRKFEFEpEofX1Ea4lgVYTUZiSlUU9pk5t1FSu1gDVgbt06cI21iuvvMLrnn32WbatEhISuOqwJYKCgujDDz+khQsXntdxL9S+evjfT9GNZStrrV/iOJsmzZrHjsAzCakUmwjHsWWHIL6fbhEdqGeXMJ4u27Mz8t+FkV9VRL69grx+KzbvpVVb9lFOvnHAAXQJD+bowVkXDeMIPVtg877j9OXvGyg+NZOrHsNJOHnkgDaz0ZsbsYmF1rKx2sxRiHD48PBwWrlyJc2ZM0ddD6UWFRVFu3btqtNR+Nlnn/HUY+Q1DAsLo08//ZSdi/bsKLR1pFMUBPtBHIUmspAchYIgCLWQHIV189dff9Hs2bPZKdipUyfV7kKAxu+//07z58+v01F48803U9++fdnROH78ePLw8GjTHIU6gyOVXvEeTaoqaAL0FRWUmJbFTsOY+FSK4f8plJVXXSnenGB/H2PRjM5GB2KvLuHUMTSwVQpatBVwcO86HM25B3ceiVZz98GZOnlkf7p82hga1jeyzinbgnXa6GITCzafoxAFTEBgoHFEUAHv6wtzR+Wr//znP9S5c2cqLy+nhx9+mObNm0fHjh1jpWaJsrIyXkwVmSAIgiAIgiAIgi1x8uRJjhpUnIQAgRV+fn68rS5HIRxGe/bsYRvtzTff5HyFSO80cuTIVrGvYONtofdo1V8bKDUzl8KC/WnurOk1nITVUYMhvMwYO0Rdn1tQVOU0TGUn4un4FM7DV1FRSZm5BbzsPHxKbe/qoqHunULZechLl3B2Inq6u9aKUFvy23pKSMuizqFBPL11ygVEqLU02XmFtHLLPlqxaQ+lZVdP5w4N8qfLp46myyaPoEBf7zY9R0EQrJ82cxRqNMbpBaYKRnmvbLOEaTi8i4sLvfPOO/Ttt9/SH3/8QQ888IDFzyDs/vnnn2+2cxcEQRAEQRAEQbA2CgsL2Slojr+/f73OvNWrV9Po0aP5tV6vp6uvvpquvfZaOn36NDlaiLxrCfsKzkIs54O/jxeNGtCTF4VynZ7ikjOMDsSEFDUCsaCohMrKdXQyNpEXUyI6BKhRh9qycvpu9VYUW+bcdyiU8eS739ErD95oVc5CRAseOBnL0YNb9p9g56ji/B03pA9dOX0MjR7Uy6YjKAVBsBFHIcLf0XkhFN6UlJSUGiNgDQGnIiomoxhKXTz55JMceagAJdmUYwiCIAiCIAiCIFg77u7u7Cw0B/ZPfVOJFSchcHZ2poceeogmTpxIZ86coV69erVL+8pF48xVfLEQDVedapk5+VXOQ+O0ZfxPTMvmbckZObxs3X9C3Y+SqEv5/79PfqG1/xwid1cXcndzMf7n167qOhQDcXPVVP3He7Qxbsf6853yaxrhGNEhkPp170jHYuIpIbV6Rl6ArxfNnTyK5k0ZxZGZgiAI7cZR6OXlRaNGjaI1a9bQddddpybf3bhxIyseBTgAS0pKqHfv3tx563Q6jiRUiIuL46VPnz51HsvV1ZUXQRAEQRAEQRAEWwUVjuG0wxRiJcUT8lHl5OTwtsai2E55eXk2ZV/BQdch0I+X8UP7qutLteUcMahMW0b0IRxwlijRltOWfccv6BzcXTVVDkSjI9G9ypmorKvpgDQ6GTGV+te1/6j7OZeczovCiP7duTjJxOH9ydnZ6bzPTxAEoc0cheCFF16gSy+9lJPmjh07lt59913y9vamu+66S22DkPbdu3fT8ePH2Uk4YsQIuvfee6l///6cpBfb8fnrr79evk1BEARBEARBEOyWadOmcY7CH374ge6//35eh9cItLj44ovVdp9//jkNHjyYAzdQgARORUQjKnz//fec7H7gwIFkD8AZN6BHZ14Urn/iHTqbmEbmlT8RsXfJ+KHsXCwtMy4l2jKeqgwnovK/tAzrjBXATUHwC7ZjMa1CfL6gqvOnz97DVYwFQRDavaMQygoRhR988AFXP4Yi2rFjB1fMUkDREmUkCwpuxYoVnJfwu+++41wbixYtovvuu4/c3Nza8EoEQRAEQRAEQRDaFthRb7zxBk8dPnv2LDk5OdHHH3/MOQVNi0g+9thj9OCDD7KjEO0uueQSmj59Ohc+2b59O1ch/uKLL2o4D+2N26+cwTkJEQEI557y/9+3Xk6TG5mjsLKykrTlumqnoras2plYVk5aOAzLympsr8sBGZuYZvEYaCNOQkEQmpM2z2g6Y8YMdhKiyhaUERyDpjz77LNccUuhW7du9P7777MCw+eg5M7XSZiRkaG+zszM5LB8JYEvcicqhVaKioooLa26Y0ZVZsV5WVFRwW0xbRoUFxfXyLuIsP/c3FxVUWAbqogBTKnGe6VcfX5uAS9q/oz0LNKWGvdbpi3j99gHKMgrpLzc/OpzSs+m0pKqtmXl3FZJZFuYX0R5OSZtM3KopNh4DuXlOm6La+ZrLSiinKzqKQbZmTlUXFTCr3VKW11V28JiyskyXhvAa6xjGer03BafYbkUlVB2Ro7aNjc7jwoLjCNoFfoKblteVm6US3EpX48Czj0n37hfXFNSeh4rUD6HkjJ+r36PuUWUnWdsW1lp4G0lpVX7LS3n95VV8ka7rNzqUTxsKy4xfudQxnhfUSVvHD8zpzrfS0pGHh8bYKQQbfVV8s4tKKGM7Oq2qZn5VFhc9d2U67mtTl9hvLbCUkrPrk4snZZVQPlFVd+NztgW//n+KCrl7Qr4HD7P342+gtti//ydF2v5uAo4H5wXfzdVMlRGOHEduB5VhjmF1fKuNLaFPPh7NJN3lqm8DXXIu7Ja3plm8lZkWKrIu0qGOH6GmbwVGaryrpIhrstUhrjugiIzeeuq5W0qQ5a3IkNdRQ15Yx+mMmR5V8lQkTce/BR5p2SYyDunWt7qPWsi72Sze1aRt3rPKvIuNcpb6SOy8yBvowyxju/Z0pr3rCJvvmdN5J1sKu8qGVaY3rM15F19z+Iaa9yz9chbuWcVeedbkHeembyVe9Zc3kJNav325V5stXsRx1D7T+W3X2b626/+PeN3JPpK9JXoq9bTV6k5OVRU9VxdptNRSnY26SqqfvfFxZRhMm02LTeXCkuMv+VypW3V829BcTGlVz2v8+8+N5c/z32EXs9tsX/+3ZeUUFpO9TNtZpX9YE3cc889tHnzZs5JiECL9evXs1PQlDvvvJOdhAC5CDds2EA9evTgKcqzZ8/mIiamRSTbq32F68ECsA7b0AbgM3iv2FfYJ/at0KdjIP337quoR6dQcnF2pi6hgfTy/dezkxDninNWwLXgmgCuEfvFNaMQjK5MS5U6LXUMCeTqyqF+7tSvWxgXRJk2egCN69+Vrpk5ju64cgbdMmci3TpnAv33XwvptYduomcWzaO3Hr6Rfnj1IereMZTMMxsi1WGXsOA2k7ciQ0XeaKfIGzRF3jg/HNtUhjg3gOsylXd6eroq7/LyclXeAFPvIQvTe1LJ24kZimiL/wDrze9ZpegP9mdqz5bUZ89W1LRnSy3Ys7DfFRmiLex7blui5fcK8Acobat9AlX9p9hXVfIW++p8nmlN9afVOwrbkl9//VV9DWfkzp07+TU6h88++0ztII8cOULffPON2hZRjdu2bePX6PjQFtOgwYkTJ2jJkiVq2z///JM2bdrEr9EhoS1G7QAUMN4rHevOzXt5AVj327erKK6qEldSfAq/V5x0e7cfoO3rdqnH+f2H1RR76hy/TktK57aKk/HArsO0ec12te2qX/6i0yfO8OvMtCxuW1xo/NEd3necNqzaorZds2w9nTwSrXaGaKs4M08ciqK/l29U2679YxMdO3DSKMP8Qm6bnWlUKqeOnaY/l61T225cvZUO7znGr4uLS7htRlUS3piTsbTi57/Utlv+3kHrd51SHxjf/34znU0ydrzHY1Lowx+qz3fFxiO0ZrsxZ4i+ooLbRscZc3ecOpfG7yurHjb/3HqMVm4+qn4W246fMX7nsYmZ/L6szCjvtf9E0W/rD6ttP/ppGx0+lcSv41NzuK3yULtpTzT9/Nd+te1nS3fQvuPGHCcwKNFWMTq3H4ih71cZv3Pw1fJdtOuw8XvMyCnitvgPsB7bFfA5fJ6/m4ISbqsYrDgejquA88F5AZwn2uK8+Ts/lcTXo4DrxPUCXD/aQh4s7zOp/F4B8oMcAeSKbZAzgNzxHt8D30vbj/P3o4DvDd8fwPeJtoozDd/3srUH1baLf9lOh6KMv4XEtFxuW1DlyNqy9zT9tKZa3l/89g/tOx7Hr9Oy8rltdpUjbsfBM/Ttyj1q229W7Kadh42/x6w8o7zTq5y8u4+eoy9/N/YJ4IfV+2jr/hjV6YC2isPvwMkE+nRp9W/s178P0Iaqe7a4tJzbxiUb79mj0cn04U9b1bbLNxymv7afVJ0baHsm3ijvqNiqe7aqj1i5GfdslbwNBt6GNgCfwXvF0Yl9Yt+qvH/ayscGOBe0xbkBnCvOWQHXgmsCuEa0VRyqkAFkoQAZQVYAskNbyBJAtpCxAmSP7wDgO0FbfEcA3xm+O8Ey362qvm/lXmzdexH9C/oZgH4HbdEPAfRL6J8U0G+JvhJ9Jfqq9fTVV2vX0uHYWH4NZ97i1avZ6Qd2HD9Ov1Y9r3M/umED7Ttt/C1n5Odz2+wqh8DuU6fox83Vzzc/bdlCu6KMz0I5hYXcVnEkHoiJoW82bFDbrqiyH6wNpHV6+eWXOZJwwoQJtba//vrr7BBUCA0N5fROr732Gv3rX/+i8HAUAGn/9tXatWt5AViHbWgD8Bm8V5xG2Cf2rYBjBrhW0nevPEhfPHUz9fUuoZH9uvE2nCvOWQHXgmsCuEbsV3E4QQamgS+YCn7gwAHVMYW2inNt79699PPPP9eQJyj6A8wAAQAASURBVNJwgatmjOJp0EodFPzDZS66Yjq///3333l2HoATDftNTjb+lo4dO0ZfffWVul8E3CBqFMBBiLbx8UZ7JSoqiqemK/z111/sSFacZ2iLIjcgJiaG3ysOvXXr1tHff/+tfhbbTp0yPhOjrgDeK85LOLNXrVqltv3yyy853ZhSpwBtFcch5L18+XK17bfffkuHDh1SHYxoqzg+d+3aRUuXLlXb/vjjj7Rvn/F5Ac5GtFWcjliP7Qr4HD4PsD/YqAiyAaeOn6FVv1Zf26Y12+ngriOqsw9t01OMz/BnTp2jP35ao7bdum4n7dthtG3Ky3TcNjnB+FuIO5PA7xX+2bSHdm3Zq9pX2JZw1mh3in0l9tXOC3imXbau2r5uCAeD0ovaEei0EZZ/8OBBHjlTOmmMuGE9jx5kZlJAQAAn6UVHiwUKFKBjQTUwPz8/7hQxCoFp0IhsRGeG/SNsH2CUBCNJ2I7RA4x+4HMI44cSxOgI9ptRmqg64Hz9fViRZWVkk7ePF7m5u/GIA5xvgcEBvD+MMlQaKsnP3zhNGyMWnt6e5O7hxhGFBXkFFBAUQE5OjhxRiPP0C6hqm5FDHp7uvCCiMD83n/wD/fiaEFFYXq6ngCA/4/ln5vDxPb08eDQFUYz+AX7krHHm6EGMmgQE+asRhS6uLuTl7ckOzdycPD4/jYuGIwq1JVoK7BCgOh2xD1wfIgpzsnPJ18+HP4+IQozYBIUEqiMwXR2KKMDXk0cPUrMKKNDXg3OJwOkFD3rHEOP5YkTa0cGBAv08eaQ6JTOfAnw8yMPdhUdgcgpKKLyDL7dBhBvkHOTvxZ+F993f2508PVyrcoaUUFiwDzk5OvJoN44dHODNbeGQ8/FyJy8PV44sycorptAgH3J2cmSnHbz5HQKNbeHFRztvTzf27uMcQwK9SePsxOdeVq6jkEAfdUTA3U1Dvl7u/AANJ2GHAC+u2oaIwlKtjo+jRLm4umjIz9udo71gGAf7e5GrizNHM0A2YcG+akShRuNE/j4eHFGI4wT5eXLVNbQrKCql8A5VMswp5PuG5Y1Rw8wCCvD14ATLiCjMNZE3IgodFHkbDByJVkvewb7k6GiUN9oEm8gb5w7ZIKIwG/IO8uFjQ944zw4m8ob8sKjyDvTmRM2QN2SlyBDy9nR3JR8vE3kHePP1Q974vCJDlrerhnwhQ10FpecUqvLGCAyiHxQZsrw1zuTn46HKO8jfk9xcNCzvwuIyvrdY3jmF/P1C3uo96+fJx4K84XSLMLlnnRwdWN7qPavIu7SMcgtKKaKDL8tZic4I9POqqsyXT/4+7ny9yj2ryJvv2cpqecPh56vIu0zH34cib75n9RUm8s4nb0/jPYuIwqzc4up7tqCEyuqQt3LPKvLGdZaayRv3nJ+JvJV71lTeBYXF5N97JvePyI9kzyj6KuqfpdSrm1GvyL3YeveiIm/0Cdx/6isoDb/9qv7T+NvXqv0nfvvQA6KvRF+JvmpZfeVbGWH83efkkLe7O3m5u3PEHxx/wX5+pHFy4ohArOvg56dGFHq6upK3hwdHFGahra8vaZyd2blYWl5OIf7GZ1o4Bd1gF3h6ckQhogYDfXzIVaPhiMJirZZCA4zPtLHJyTRw1iy711nWaF/h2UlxwOG4uBfhVML5INoSEW5wBoWEhPD+EOGG/SlTtOHww3MIcj7CmYbtHTp04Onc+ByuKSgoyHh/paVxsU4scIThuMHBwXxNOB9EveE931/p6Xx85ObHelwP9qPRaPg6cSwch/VKRgbLAOcBh+aqzbvp9037uepxx5AAunr6aJo/Y0KbyVuRoSJvyAhyxnEVGTZW3pAhZAJ5KzJU5A0Z4voVeeMc0A7yVmSoyBvnjs8r8sa14Vyxb+wD167IGxGFOC9F3pAZ5AV5Q4anEo+q9izs09K67NmKCraFFXsWEYXFZvYsrtnHz5uvGTa2j683ubq58j6LCosoOMR4bfAJQJZoW+0T8CY3d1cKLc4Q+0rsKzrfZ9oz8WnUe+yCRukru3YUInGvtRih6SXGETPBMpGVliuuCYJge4ijsLa+yo1eSz7enm34rQiCIFgPZVnW8fwOCuB8GTdOHIVWaF8Jgq3Z6GITC61lY9n11GNBEARBEARBEARBEARBEIyIo1AQBEEQBEEQBEEQBEEQBHEUCoIgCIIgCIIgCIIgCIIgjkJBEARBEARBEARBEARBEMRRKAiCIAiCIAiCIAiCIAiCOAoFQRAEQRAEQRAEQRAEQWCkmIkgCIIgCIIgCIIgCIIgCORsjzIwGAz8v7CwkKyFwtKitj4Fq6agsritT0EQhFaioKi4Rl9tzygyUGQiCIIgEJUVWU+sQ2Gx6Cxrta8EwdZsdLGJhdaysezSUagosL59+7b1qQiCIAj19NW+vr52LR9FX3UZfkVbn4ogCIJQD/aus8S+EgRBsB195WCww5CNyspK6tWrFx04cIAcHByooKCAOnXqRImJieTj49Psxxs5ciTt27evRT5TX7u6tllab76uvvftVV4NtWmP8jofWTX2c811b1laX9f79npvNdTuQu4te5TXiBEjaNOmTRQeHk6OjtYTNdJW+iolJYWmTp1K+/fvt9nvXH4jbScv0e+i35vz/mpP+qqxn2vot7h37142uuxdZ5nrK1t9Bq5vu/xGmldW7a1Pkeeh9iUve/R3jGiCjWWXEYUQiouLSy0vKm6ClrgRnJycmrzfxn6mvnZ1bbO03nxdQ+/bo7waatMe5XU+smrs55rr3rK0vqH37e3eaqjdhdxb9igvZ2dn6tixY5OOa8v6CrKATGz5O5ffSNvJS/S76PfmvL/ak75q7Oca+i3CnrDnSMKG9JWtPQPXt11+I80rq/bWp8jzUPuSlz36O5ybYGPZ7bDX//3f/1n1sRr7mfra1bXN0nrzdQ29b4/yaqhNe5TX+R7nQuXVFFm1d3m19W/R0jp7lZc9Y6/fufxGWl5eot/rl1d76n8b+zl5Hmo+eYm+ajuZyG+kfchL9LvI60LvSfF3tLx+t4RdTj02B6GlGAnMz89vEY+xrSHyEnnJvWUdyG/R/pDvXOQl95f1IL9HkZUgvxHpT6T/bQ+IvhJ5NRW7jSg0xdXVlZ577jn+L4i85P6S32J7Qfou+0O+c5GX3F/Wg/weRVaC/EakP5H+tz0g+krk1VQkolAQBEEQBEEQBEEQBEEQBIkoFARBEARBEARBEARBEARBHIWCIAiCIAiCIAiCIAiCIKBCskih6axevZqOHz9Oo0aNoqlTp4oI6+GNN96gI0eO8Otvv/2WHB0lLaYpOTk59OuvvxJqCl199dUUGBgo91MdHD58mN58801+feONN9LMmTNFVtJXCQ1w4sQJWrNmDYWEhNBVV11F7u7uIrM62L59O3366af8+l//+heNGzdOZGXG8uXLKTY2lmbMmEGDBw8W+dTD7bffTlqtlnr06EH//e9/RVbSVwkNgKKSP//8M5WWltK8efOoW7duIrM6KCsro0WLFvHrESNG0IMPPiiyMuPo0aO0bt06vo+uvPJKkU89fPbZZ7Rt2zZ+/cEHH5C/v7/Iqx7y8vK4r4KOnz9/PnXt2pVsEfHaNJGbbrqJf0y4QeCseP/991vmm7ERhg8fTpdccgktW7aMKisr2/p0rAqdTkfjx49n43TXrl38ury8vK1Py2oJDg7mewkPklFRUW19OlaP9FUC+l3oqczMTFqyZIk41xsgIiKC+5jk5GQ6e/as3EBm/Pvf/6YXX3yR0tLSWE7QW0LdwJnar18/+vvvv0VMDSB9lZCVlUUXXXQRHTp0iAe4hgwZQjExMSKYOnBycuJ+ODw8nLZs2SJyMgP6CX1wamoqvfrqq6y/hLoZOHAg308bNmyg4uJiEVU9ZGRkcF+FQCgEjqGvstVnRilm0kSgvPr378+vf/nlF1q6dCk/4Aj14+Xlxc5VZ2cJYjWNzPjkk09o7dq1/H7OnDl0880304IFC+R2qocnnniCQkNDZfRU+iqhAaKjo6l79+7c72IQwsfHh4qKiqQfbkQk2OTJk+mGG26Qe8wkegVRqYgmROT7N998Q3/99RePqAv1R8LffffdtHv3bhGT9FVCPWAQuLCwkDp27MjvEQGPKLCFCxeK3OoBAxGwJf744w+RkwnXX3896/E77riD7c/IyEgeBJRZFfWDCHg4npXfoVCb3NxcjnqGkx5cfvnlfL/Zov1uU14bGEA//vgjP7jiBsdUV0tTPV955RXav38/BQQEcAcCD7oCPn/y5Mlan8MUpNmzZ6tOQoBOuT0rMMhg8eLFrGRgEL322mu12iDaDVNmzpw5w2G1Tz31FF188cXq9vvvv59lao49TNvCKAKUM+T4zDPP0KWXXlqrzaZNm7gNRh8w4vCf//yHOnTowNtOnz7NEZcKmDqAdbYIOlQ41jGt79y5c7R161bq3bt3rQjLl156iZ3vMEqnT5/Ov1V7DH+HUweO5K+//pplgfvIHIS7v/3227Rx40Zyc3Oja665hqMIFWypr7JFkG4A3x0i1BMSEnjAyfzBDG2+/PJL+u233/ieQN+L6UUuLi68/dixY/y7Msfb25sef/zxGr+xlStX8vSI9jpYgyg2REVCr+MaMGhnDvpZDCTgIdfT05OuvfZafq+kvPj+++8tRnfZw7QtPNjCuff777+z3nnnnXdqtYERhT4Xug2DMffeey9NmDCBtyUlJbHuUtJjQGbvvfce2SqYgoXnI9xLjzzyCD366KO12vz5558cqYLfb69eveh///sfjRkzhuyRffv2sbzw/Pzuu+9alMOKFSvoq6++YocQtuO36evry9tsqa+yVTBIgGc42AV4xke0ujl79+7lfgH9Rd++fenJJ5+kLl268DbMKnr22Wct7vvOO++kzp07q/cD+nI8D0+bNo3aI3ieRV+L53/MgMFzLSKQzPU7nuGg0+Agxawi2GGK8wHX/8ILL1jcv62ncqqoqOD+FTo/PT2dNm/eXMvBp9fr6aOPPuJ2Dg4ONHfuXLrnnntUuUB+6LuBn58fzxhITEzkvtrWiI+P52dJ+D86depkMcIUbfBcuGfPHpYHpq1Dx9sjuKdwb+HegQ8INrw58Hu8+uqrHNWMPuzhhx9mOx7ALlVsUzybIppwypQpZIs42lKngh8/HlYQNYG8BObA0MLoAn4k+MLxoHvZZZexQa6g0WjY6DZfsN70WHfddRd/vr16jxGSjVFuPKyFhYXxg5s5MBZgmKINjCvkhIO8ID8F/DDwIzNfsE9b5vPPP2fHC+45OAoxtc8cRApCZnDYPPbYY2zUw+gqKSnh7VBseFBQwGuss0XgUIbiuvXWW7mDxkOUOQ899BDLFdP5oewOHDjAOWrsETwwwjkExxEevC2B+w+GPx6MYFT93//9Hys1U2yhr7JV8FuAUwYGMvpUOH7NwSANHnTx3eF7hCFu6gzG1CNL+srV1bXGfhSnM5yO7RUMxGAwEP/Rh5iDex2DeTBm4XR9/fXX2QjD4JYCDFdL+mro0KFky8DoHjBgAA/SwPliKXUDngEwuBcXF8dTtJDTCTmY4RSwN32FgZWnn36an3fwW8J9Zw4Gu9DvYsHz0aBBg9ipYauDffUBByn0EJ510Jchesec7777jiPEJk6cyLoe09vwfInfra31VbYInLfoKxFggT4Wgwrm4FkY3y8ij+EERr8zduxY9fkY/YUlfYXFtC+BkxH3CpzKSDnTHoFDFM9w0NvQVxjwNQcD40jlgN8P7ns4HDBVVnk+hiPCkr7CYqt9rwJmWMEeQBQg+hTzfgLgmReOVTxLwWn9/PPPs22vYE86C7Y4fkeTJk3iKfzmwO6EPsdzJgZsELQCp6EtD/bVBQazYBMVFBRwEAv6M3PQB8H3UVpayn0ZBp5hv5sPUGOQEPUFYIvZbI0Bgw2Rn5/P/x955BHD4MGDa23/8ssvDS4uLobs7Gx13V133WXo06dPo49RXFxsmD9/vuHzzz83tGcqKirU1+PHj2c5mHPzzTcbRowYUWPdlClT+Pqbiqenp0Gn0xls7V4DTk5Ohq+++qpWm2HDhrEMTT/j4eFh+PDDD/n98uXLDdOmTVO3X3LJJYbffvvNYIso99uhQ4egtQ3Hjh2rsT0zM9Pg7Oxs+P7779V1R44c4bZbtmyp0fbxxx83vPPOOwZbJi8vj/8vXryYfzvm7Ny5k2Wzb98+dR1kgrZFRUU21VfZ+ne8a9cu/i5jYmJqbM/NzTW4urrW+P42b97MbQ8fPtzo46C/WbBggUGr1RpsoQ/BfR4YGFhr+6pVq1g2586dU9d99NFHBnd3d0NhYWGTjrVo0SLDd999Z7AVysvLDSUlJfz6+uuvN8ycObNWm5dffpnlanqfzJs3zzB58mR+jfV+fn6GjIwMfv/FF18Yrr32WoMtYvp81L17d8Nzzz1Xqw1keNlll9VY17dvX8Pdd99dYx103ujRow320JdBj+M3+Ndff9XYXllZaejYsSPrboWEhASDg4ODYdmyZTbXV9kiBQUF/D2CiIgIwyuvvFKrzezZs2v0Leh3wsLCDE8//XSjj3P06FHDmDFjDCdPnjTYQh+SmprKv4n169fX2I573MfHx/Dmm2+q69DW0dHR8PPPPzfpWPi9oa+2xT4FdhLkZ67DY2Njuf+A3lf46aef2B5LTk5WdR2eoQHsfn9/f0NpaanBFlHuN/Sx/fv3r7Udz5F4njS1XZ955hlDSEhIDX2n6LzExESDrYJnIfRNADa4qZ2ucP/99xt69epVQzbolxYuXKi+x3M41p06dcpgy9hMRCFAJGF9YJoXRswxIqaAiKVTp05ZHB2zBEaPEbqMUXeMOGPEoz3SmJB1TL3B6JYpiJBTqiI1BoTHY1ozRtOQfw+h0fZwr2Ga18GDB3lUzPQziGjFSDpAZAyS7CIHC0YkMDJh2t6WaOh+27lzJ08jML3fEKGB6W/K/QZZ4V5C1XFMIVTuK1tEmX5TX18G2WBUzLQvQwJiJReWrfRVtkpD3/GOHTv4/jbtExCtgSkjSh/SEPjOMXKMnDOIWsB9YCk6yhb6EPQTPXv2rFF5DvoKI8KYadAYEA2GfgXRz4jevO2228gWwIyIhvIyoU9B/2sajYo+BRGFuA+xHlFjiJrDdCXcVw888ADZIg3da4hMwe+zoecjRLNiQcSCcl/ZY1+GXKmI0DDtyzA9DpG8Sl9mS32VLYJ0FvVFY2FaMaaHmn7H6HdmzZrVaH2FewRRO7gHfvjhB74H2mvBpIb6EBRBQESTaR+CZzo89zbFxkJf/MYbb/AMHPQxyIlqD30K0vHg/jJNhYV7T7kPwX333ceRnZARoukwiw5Rd7ZIY56PRo0aVcN2hb5CtCv6Z4BIQ9xDWIdZYLivbBE8C5nOEq3reQgzVBxN5Iqp7evXr1encSOVAJ45ES2Pvsp0tqUtYVcJQPDFIgeGKUouCGxD/oKGgPFtmpPPfIqXLQHnKaYQmIL3uH7FcGgITPXCD03JA4nE+vYAnH6m95cC3kOhA3RUcJAhjwkMDzhRG+q8bPlew30SFBRU635LSUnh1wj9VqZdmE69tEfQX5nfW0r/hW321lfZIvgecX8rOU0BfiMwJpTvuCFgcCHtgSm2OvWmLn0FlD6kIUynetlb/4J7avDgwTXWoY/BlC/IFlPAXn75ZU6LgHw8MNRM86DaE5hai0EZS/eb6b0GWeEZCLkyFeeYPaL0V5aeh5Rt9tRX2SKYXmya3F8B71H0qDHg+df8HrDVPlgJTmmoD2nMlFPTfKDtdap2U0G/AXtBydes2AhwMCp9yujRozlFBBzV0OlIJWGvNPR8BD0F+9z0+cf8t2zvNlZ4eDhlZ2fzNG7cd+ZVtG21r7IrRyHyPpgby8oou6WcaZZAAl97Ac4r82TSyntL+SIsMXLkSF7sDeV+snS/md5rUGrIr2Hv4F6DE8R8VAz3m3KvYSRMqpDW3ZfhIRvyU+4ve+qrbBF8j3gYMTeWzfuQhowIW02w3BL6CkaWvfYxjX0+slS0y95Q8l5Zut9M7zVEJAj1Pw9h9oW99VX2/MxbH3BcIDLHHmhsH9IQmJFkj1jSV5buNzjAsNg7jXk+Qh5jLELDz0NhYWF201fZ1NTjhsCUY/MKvfAOA5tNQnkBwGgyT4qKUUOM2nh4eLTZebUHlOntlu43udcs32uYemxeVAf3m2lElVB9f5nfW4hywbQLub9s5ztGhIZ5kRPpQ5qmr4D0IY273+T5qHFggA9OfEv3m9xrlu8tIM9DtguisTGoJd9x41Ai/6QPaT59BeT5qO77TZ6PLuz+ys7O5oAMpGGwJ+zKUThs2DCe9mlaBQlzyuH4wjxzoSbIZ4A8PJbyHAj1gzxZ6GjMc2Ohgq2tV9g8H5R7yvR+Q+g3pnDL/Wa5L0PeKyUaAyj5MeT+sp3vGJj2IcjTiTxO8h3XBv0E8g0rg3+KvsJ0kOHDh7fKd9be7zdzfYU+BZXXzVNC2Du4pyAveT5qHIhSgYFlen8hKuPQoUPSl9kIsKN69eplsQ8RfVWbIUOG8GCDaR9SWFjIuQvlmbdh0P8isCAmJkZdh/yM5eXlcr9ZAPcUfpuQj+nzkZeXl0Rc1nF/WerLBg8e3KgaD7aEXV3tTTfdxOXnlyxZwu9hUHz44Yc81Ujyd9UGCWCRFHbZsmXsXF2zZg39+eefvF6oH3Qkt9xyC3366aecGBYgOTNyO8lU49p06dKF84cg8TCiMpADArlqlJwZQk1QZABFLV566SV+D+X/6quv0qRJk+wmD6g9GNfIsYPvGNG24IUXXuDpWUgQL9Tk8ssvZ9mg30AUJvLuQHYoFGUveZsuhEWLFrHjZtWqVfwegzRff/01rxdqg+cgPBsh6Tmej1BgCwOBkvKhNkgbctVVV9E777zDBRzA+++/zxHT1113ndxeNgL6CjznYhATIPE/ipFIH1IbRCXBJn3llVe4r0Xe98cff5x/K9dcc02rf3ftDRSGxLMunonQ/2I2DYogYZrx2LFj2/r0rI4bb7yR/z/zzDM8SAMHK/rjO+64Q/wfFkCfhdyWO3fu5PfHjx+n3377zT77MoMNceONNxpGjx5tCAsLM3h4ePBrLKZl1b///nuDt7c3l/9Gm5kzZxoKCgoM9oZWq+Wy6Fg0Go3B3d2dX0+YMKFGu08//dQQFBRkcHNzM/j5+RneeuutNjtna+LAgQPq/YWfUWRkJL9+4YUXapRgnzdvHsu2R48eBk9PTy5Rb498/PHHfH8FBgayvPAf77/88ku1TVZWlmHu3LkGZ2dng4uLi2HUqFGGqKgogz3y5JNP8v3UrVs3g6Ojo3qvmcpj69at3Nd17NjR4O/vbxgyZIghPj6+Tc9baDxfffUVf6f9+/fn3wS+P7xfsWKF2iY2NtYwYMAAQ0BAgCE8PJy/6507d9qlmG+44QbuM6C/HRwcVP116tQptc3hw4dZjug/oNcWLFhgyM/Pb9PzthZmz57N9xf0ua+vL7+eOHFijTbvv/8+66uePXuyzl+4cKGhrKzMYG8kJCSo95eTkxPrbry+/PLLa7R75ZVXDD4+PiyrDh068G/aHlm/fj3fT8OHD+e+rE+fPvz+o48+Utvk5OQYJk+ebPDy8mK9hufJ33//vU3PW2g8cXFx6nMI+tdOnTrx6wcffFBto9frDbfccovB1dXV0KtXL/5dvPrqq3Yp5mXLlnGfERwczL8JPKPhvak8iouLDddffz3rKsgMzwJ79+5t0/O2Ft58802+v3AfQX4jR47k9zt27Kih72F7KXKG3jpx4oTBHoEvA3KAroINpeiv7Oxstc22bdtYnrjX8Bu+7bbb2Bdgjyh9GfQ3nonwev78+TXaPPPMM2pfBnn961//MlRWVhrsDQf8IRvh2LFjXInOHBTTMK1Gg2il06dP89RQ8yrI9gSiK81BclPzaUYYqUGIN/Ly2FvIbV1gVPzkyZO11iNyxTyiC1MFESWHaRmYnmGP4DenRBKYgtFT83yXiDJABJW95YEwBaN9plMoFQYOHFjjHoKcoqKieEQQ95fQvqrQJSYm1lqP/sM8Ai46OpqjRjFabp6Q2l5ADk7zfI0A+spcJuhrMK3Lzc2tFc/Qujl48GCNaUcA+tx8mhumv505c4ajM+216iGSuyv5LU3BPaXk2zNti+cjJUebPQJdZToFUCEiIqJWpedz586xvPr06SO/z3YE+l5M7TQHMxvwXZqCWTSI6EaldNgN9iov6CxzMNUTiymIJkR7e5WVJeLi4izaqLjXcM+Z2qd4Bkbfi+cje+6DLRUNgh43lwnuS9hdphWj7Y3du3fXWgc7yjxNQm5uLt+LSMFirzNTbMpRKAiCIAiCIAiCIAiCIAjC+SHhYYIgCIIgCIIgCIIgCIIgiKNQEARBEARBEARBEARBEARxFAqCIAiCIAiCIAiCIAiCII5CQRAEQRAEQRAEQRAEQRDEUSgIgiAIgiAIgiAIgiAIAiPFTARBEARBEARBEARBEARBEEehIAiCIAiCIAiCIAiCIAjiKBQE4Ty544476PXXXxf5CYIgCFbN8uXLafLkyW19GoIgCIJQLyUlJdSnTx86cuSISEpoU2TqsSDYILfddhu9/fbbLbq/xMREysjIaLZjCIIgCPbHsmXLaNq0aS26v/z8fDpz5kyzHUMQBEGwPwoLC9mJd+LEiRbbX2VlJUVHR1NpaWmzHEMQzhfn8/6kIAhWS0JCAnXo0KFF9/fFF1+Qq6trsx1DEARBsD/y8vIoNja2Rfd3+eWX00UXXdRsxxAEQRDsj4qKimZ14lnan6enJ0VFRVHXrl2b5RiCcL5IRKEgVLFy5UqaM2cODR06lG699VZKSkqqIZtPPvmEJk2aREOGDKFbbrmlliFy/fXX01tvvUXPP/88t8Py+eefN/k4K1asoNmzZ/NxrrzyStq1a1eTjvPII4/wZ7AOo1RYkpOT1c89/fTTNHLkSJ46DJ588klu07dvX5owYQL9+9//poKCggb3h+N/9dVXLSIjQRAEoW7Qt9555500bNgwmjVrFq1Zs6bG9n379tEVV1xBgwYNopkzZ9Iff/xRY/sPP/zAegb6aO7cuawT7rvvPo68a8pxsH3RokWsz6ZPn04ffPABR0M09jgbN26kZ555hnWKol+gE5TPYcowogP79evHkRc7duxQ2+GYCxcurDE9q679bdq0ic+zJWQkCIIg1I1Wq6WXX36ZB2vGjh1LL730Eul0OnV7dnY2Pfjgg6xnxowZQy+88AKVlZWp27OysrgvX7duHdtN6IvnzZtHhw4datJxsB3rxo0bR6NHj+b+PDMzs0nHwX7BggULuC3+K5/7+++/6ZprrmGdgsh2MHz4cN7Wv39/1qFff/11jXO2tD84DefPn88OxOaWkSA0CYMgCIYPPvjA4OHhYXjzzTcNe/fuNXz11VeGq666SpXMyy+/bPD39zd89913hn/++Ye3hYSEGPLy8tQ248ePNzg7Oxuefvppw759+wxLlizh93/99Vejj7N48WJDRESE4fvvvzccPHjQ8OGHH3L7Xbt2Nfo4ycnJhrFjxxruuOMOQ1RUFC/l5eXq5x577DHD/v37DYmJidw+JSVFbbdp0ybDlClTDNOnT1ePV9f+Zs6caXjkkUeaXUaCIAhC3Zw+fdrg5+dnuPbaaw1btmwx/P3334bZs2cbzp07x9vRR7u6uhoefvhhw549ewxvv/22wcXFxfD777/X0EXoe9Hfb968mZd+/foZbr755kYfJzY21hAYGMj9OfTZmjVreB8PPfRQo49TWFho+N///mcIDw9X9Ut2djZ/zsnJyTBu3DjDxo0beX1FRYWhqKhIbQc99swzz7COVPRZXfuDroVuVWguGQmCIAh1g3576tSpht69e3P/CnvmqaeeMrz33nu8vbKy0jBy5Eju69HHrl692hAZGWm4/vrr1X2kpqYa4LJAH/7DDz+w/XDrrbeyjQGd0JjjYPuMGTN42bBhg2H37t2Gm266ydCzZ09DaWlpo4+DdWizbNky1iNxcXHq59Dum2++MRw/fly1faKjo7ndsWPHeJ9o8+mnn6rXZml/0GNYp9h+zSUjQWgq4igU7J6SkhKDt7c3Gwqm6HQ6dbuXl5fhs88+U7fBUdaxY0fDiy++WMMJdtlll9XYx6xZswz3339/o46j1WrZKIMCMOW+++4zLFiwoNHHAdOmTTM8/vjjNdrgc5MnT27w+05PT2dloxiDde3P1FHYXDISBEEQ6ueGG24wjBo1qtZ6vV7P/2E8wGAyBX3sgAEDajjB4BiDE00BBk6HDh0afZxbbrmF25iCQSI4+KDPGnuczz//3NClS5ca+1EchRioaghc6yuvvFLv/swdhc0lI0EQBKFu/vjjD4NGo+GBJUu2z/Lly7mfhaNLYdu2bQYHBwd2nJk6wb7++mu1DZxfaIO2jTkObCsEM8BeMdVlsFN+/vnnRh8nNzeX28ARp6B8DsEdDfHxxx8bhg8frr63tD9zR2FzyUgQmorkKBTsnmPHjvGUpksvvbSGLJydjT8PJEAvKiqiqVOnqts0Gg1PmzUP6R48eHCN9+Hh4WrBj4aOg+3IrfTwww/T448/Dic+Lwg3Dw0NbfRx6mPUqFG11qWmptIbb7zB07AQuo5jgnPnzjU6P0ZzyUgQBEGon3/++YdTO5jj5OTE/9HnXnfddTW2zZgxgz788EMqLy8nFxcXXtepUycKCAiosy9u6Djbt2/nfn/AgAGqvsI0L+RcOnv2LKezaMxx6iIiIoLbmoJpzZ999hlPE05JSeHrgQ7r0aMHNYXmkpEgCIJQN9AjmBIbGRlp0fZBX4zUEqZ2DtIgIQf64cOH+bOW7Afk8fPx8VH744aOA32FqceYkqvYOYqNdfr06Rqfqe84TbWxkC4DekWxk7A0Nb9hc8lIEJqKOAoFu0fJ8eDh4WFRFkqH7u7uXmM93ufk5FhUSKYoCqmxx1m8eHEt48i8aEh9x6kP82vAZ2AcQbE+++yzrIQcHR1p4MCBNXJfNERzyUgQBEGoH/TNdekRpT+21BfDyQZDSXGCWeqLm3qcG264gW6//fZa27p166a+bug4dWF+DQD5pZBz8NVXX6XevXuzIfTQQw81SV81p4wEQRCE5tVXDg4O5ObmRiUlJU2ysRo6Tvfu3Wnp0qW1tgUFBTX6OPVhfh3InwsbC7ngH3jgAfLz86O1a9dyrvim0FwyEoSmIk9Agt3Tq1cv7nAPHjxIHTt2rCUPKBYl4s/UgXf06FFOKNtcx1G2p6en14jMOx8Q8dEYxRAXF0cnTpzgBPWdO3fmdcePH6/12Yb211wyEgRBEOoH0QPQI3WB6Dr0xaagL0blekQXNNdx4KiLiYmpEc3QkvoKrF69mu69994a0YCJiYkUEhLSpP01l4wEQRCEuoF++Pbbb9mhZcmRh774yy+/rBHJjT4dM6x69uzZbMeBvvriiy84IAIOu/NFiahvjM5CYREEXiAQQ+GXX35p8v6aS0aC0FSk6rFg98DAQJWqJ554gqdLAUTBoTovCAwMpKuvvpo7+tzcXF733Xff0YEDB7gaZHMdB9th/GDkSTFgEN2wfv36WlWyGiIsLIynDjcEplRh9Gnr1q38HtUcMerV1P01l4wEQRCE+rn//vvp119/VasqYqovouySkpL4/T333MPGCKZiAeibt99+m9c353GQJmPVqlU8Fdg0ZQV0XFOAfkHlSUzJagjoyZ07d5Jer+f30J8Y7Grq/ppLRoIgCELdoDI9UhGhwrAS+b1//36uaA9gO4Dnn3+e9QicYdAtqBw8fvz4ZjsO7CsMAiECHmmgQHFxMff75jqkPry9vTmSvTE2FvRVbGwsJScn83sMvL3//vtN3l9zyUgQmoo4CgWBiI2fESNG8IgU8iIhF4Rp1B/yS/j7+7MBgo4fU52WLFnC5e6b8zgwuC655BLOc4HIPIx6QYlZyntRH3fffTdt3ryZ8yvhWIqSMsfX15eV1l133cVtcU7IK6WMcDVlf80lI0EQBKFu5s2bRx9//DE7tTBIExwczIMy+A8uv/xydtZdfPHFrEfQp0+fPr3JDryGjnPZZZfRN998Qy+88ALrEvT7mGYF46UpIIJ+6NChrAuhX6An6+KVV17hgTRE/mGgC86+iRMnNnl/zSUjQRAEoW5gFyCyDnn2oCcQ0fd///d/qm0AOwfTgX/88UfWM+jX4Vz7+eefORVScx5n48aNlJaWxsfBLCrYPMh126VLlyZ9hQjoQP5eRPMtWLCgznbXXnst6yfMuoI+mjVrFs2ZM6fJ+2suGQlCU3FARZMmf0oQbBREICCxLRQIpgGbgwhARN3BYWaeByIhIYHD3U1zXUAhISrQPOdgQ8fBaBiUF5xuyEFxPsdBxAX2gTB8KCkkfDf/nAJyMmE7jC+MbEVHR7NSw+u69ocp0sidqBiNzS0jQRAEoW4Q4YfoPvTblvL5oV9Hn40+GlELpmDKEqK/TXMJom9HH20+lbih4wBsh64y1y9NOQ6iAKE/cL4wfsw/p4DHVhwP+gfnBN0FoC/r2h8Gv1CsS0mT0dwyEgRBEOoHdgP6dnO7QenXMZ0WUYHmfTl0ENJcIJ+6MvUWYB0cguZ9d33HAejXEVUIO8XUBmvKcRCNCN2D84W9ZOlzCtBDyvGgQ6BzkG7KFNP9wTaEHYaCkqY2YHPKSBAagzgKBUEQBEEQBEEQBEEQBEGQqceCIAiCIAiCIAiCIAiCIIijUBAEQRAEQRAEQRAEQRAEcRQKgiAIgiAIgiAIgiAIgiCOQkEQBEEQBEEQBEEQBEEQGKmpLQiCIAiCIAiCIAiCIAiCOAoFQRAEQRAEQRAEQRAEQRBHoSAIgiAIgiAIgiAIgiAI4igUBEEQBEEQBEEQBEEQBEEchYIgCIIgCIIgCIIgCIIgMFLMRBAEQRAEQRAEQRAEQRAEcRQKgiAIgiAIgiAIgiAIgiCOQkEQBEEQBEEQBEEQBEEQxFEoCIIgCIIgCIIgCIIgCII4CgVBEARBEARBEARBEARBYKSYiSAIgiAIgiAIgiAIgiAI4igUBEEQBEEQBEEQBEEQBEEchYIg1MPhw4fp7bffrldGJ0+epNdff13kKAiC0M6Ji4uj//73v1RZWdnoz5w+fZpefPFFamm2bNlCX375ZavsZ+fOnfTZZ59d8LEEQRAEQRDaIw4Gg8HQ1ichCO2BHTt20IYNG+i6666jXr16kT3w9ddf09NPP01JSUl1tlm2bBndfffdlJWV1arnJgiCYG/Agbd161YexMHrHj160PTp08nT07NZ9g8n2pQpU0in05Gzs3OjPrN69WpasGABabVaakngwIQOhi5u6f28+eab9PPPP9P+/fsv6FiCIAhC43jhhRdYr/3rX/+iDh061NiGgZuUlBS64ooraNCgQZSTk0Pvv/8+3XLLLdS1a9cabb///ntycXGhq6++usZ+gbe3N/Xu3ZtmzZpFTk5O6vbLLruMhg0bZrF9z549afbs2XXqxH/++Yf27t1L5eXlbB9ecskl5O7ubrHtoUOHeCCqqKiIunXrRhMnTqTQ0NAaMjA/F0dHR3riiSdqHB96FzK46aab5PYSWgzJUSgIjQSd9Msvv0zvvPOO3chsyJAh9PDDD7f1aQiCINg9UVFRbCDdeuutFBMTQ+np6fTNN9/wunXr1tm8fCZPnky33Xab1exHEARBaD7gFHvppZc4SMGU5ORkuv/+++n555+no0eP8jo4yfAeUfDmwFH466+/1tjvwYMH+TUCH+666y4aNWoUlZSU1Npu/h7Hvu+++9hxV1xcXOM4GRkZNGnSJB4og07GOeH8MYAH56Ep2Abn5LRp03gAKi8vj5YvX04TJkxgh6elYyvvn3nmmVoR7nAUfvvtt02SryA0lcYNFwuCnXPq1CnatWsXvfvuu9xhYzqu6WhRQUEBr0NkHRQADLpOnTrRlVdeydv/+OMPOnv2LA0YMIAuvfTSWp/D6NmePXv4OF26dOHPKSNdpucAYxCjXFBw48aNs7gfREmgLUa/4OjDCBeOf+bMGQoLC6PLL7+c/Pz81M9i2rCiLM0do5bAKBgUcH5+vjriZQ4ClRGxAWUXGBhIM2bM4OsSBEEQmg7624svvpiGDx/O/S+iJUwNEERaoK9/5ZVX6Prrr2dDRQEGCXTXHXfcQREREbwO+gaGjJeXF82dO7dGRIM50Dl//fUXG2gBAQE0b948i+1LS0vpzz//ZMMNug5RFeZAj0JH4bhw2PXt25fX6/V6juIbM2YMr1f4/PPPue21115ba1/QhzDUYGht3ryZo9pxzIEDB9K5c+fYkIIuwvWZR5yYg88uXbqUz2Ps2LEW22Ab9gn9Cl0Ko8886kUQBEE4f2C7IDXEv//9b3XdV199xX37ihUrznu/0AO33347v37wwQdZR+I49957b4PtH3nkEY7+Q3s4DRXgIIT+RQomf39/XgedgzaICjx27Bh17NhRbZuWlsapOoKCgtR9lJWVcZRhfcDWgsMQ0YPQh4LQWkhEoSA0giVLlrCRBkccwtBhUJgCRx1GtmCwQKFBGWD0C045TONCe6y78cYbedqT+efQ5oMPPuCRKygkKErTrACIYhw/fjwdP36c4uPjOZzeVFkp+5k6dSorMhhsAKNfcCo+++yzbAjB6IJhBqdhXfz9998cOVlRUVErRyGM1ZEjR/L5JCYmcrThc889V+PzMFZhQEERw4jbvn07DR48mFatWiX3miAIwnnwxRdfcH/64Ycf1nASAjjv4JjDejjMoEtM+fHHH+mTTz6hkJAQ1iuISIQ+O3HiBBsoiHCA86suJyH6cwyCQYchAgJTq3bv3l2jneJggyMNERsLFy6sNdi0aNEiuuaaa1jPHTlyhAe7Pv30U96GKVU4f0wrU1Jd/Pbbb2zEKc5E89yCcBQ+/vjjrPewPzghYVD95z//4evD4Nz69etZ/0BvKpjvB9vgXPzuu+/4MzfccEOtHIa5ubk0evRodsRmZ2ezPuvfvz9PNxMEQRCaBwxEoY9VIvKgs9AfQ380FwhcwGBPfbaQKRhgQ3tEDZrqEdg3r732muokBA4ODqwnMF1YiRREuhDo5rfeequGkxC4urryAFl9wPZ0c3PjzwtCq4IchYIg1E15ebmhQ4cOht9++43fP/vss4aLLrqoRpvExER49QxPP/20um7ZsmW87sUXX1TXff311wZfX99an7vmmmvUdQkJCQZ3d3fD0qVL+f2xY8cMbm5uhqioKLVNUlKSwcPDw7Bz584a+7nvvvtqnNdzzz1n6Ny5s6GgoIDf6/V6w5QpUwzz58+3eK379u3jYy1evJjff/XVV4aIiAh1O669Z8+ehpKSEn5fVlZmGDRokCEwMFBt89JLLxmGDx/OclPAfkJDQw2VlZVyqwmCIDSRuXPnGgYOHNhgu++++477Y/TNCuiPH330UVUHubq61tAn2dnZrFPA5s2bWZfodDq17/b09DQkJyer7W+66SbD0KFD1ferVq3iz/z666/qup9++omPA50DfvjhB0OnTp0MeXl5ahscC7oOxwfQDzNmzDBMnjzZEB8fbwgICDC88cYbNfTZ+PHj1fePPPIIfx46U2HChAkGb29vQ2pqqrpuyJAh/Nm69oPrmThxonquOMeQkBCWm8Idd9zB34Ep2M/o0aMb+EYEQRCExuDk5MQ67IEHHjDceuutvG79+vVsP0AnQc9gO4iJieH30CPmzJw503DllVfW2O/nn3+uvod+cXZ2Nrz//vsWt5u/h/7TaDSG9957T133wgsvGBwcHAxardbitcyaNUvVDw21NZeB+bngmr/55huDl5eXIS0tjdffddddhmnTpjW4P0G4EGTqsSA0ACIHMEI0Z84cfo9QdOSgQPi4eVETjIQpINrA0jpE5WHx9fVV1yPCQwFTlhENgaleCFX//fffeaowioZgZE2JNPTx8eFoBtNpUkriXgXsA1O2EAUJMJ0Z548F+8F1KSBaZP78+XTzzTdz9IglsD9EhCjTrhEBgijJV199VW2D88T5YpRNOV9EY2D/CQkJMgVZEAShiWB6U3h4eIPtoDMQzY4pWldddRVPfTpw4ABHywHoE0yJ6tOnT42IxLpAn4/2psdGfidEuKNPV6YgI3rCVNch7QWmVKENojGgF5CGAtGOil5AtCIKoOAckecJ+kjJuThixAgaOnQoR9jXByIIoTNNdSwiNEynRmOdaUShpWtEBIiS7gO6GXI0jZrE+WNKNKo7K+ePfSK9Bgq/aDSaes9TEARBaByIHkTEOSLyMKMLdklji2vVxcqVKzlaHTOwkL4DeqG+XLVK+8LCQu7/UXTENKoROhm6E/rGEtCZSgRiQ20bAyLdEVGIKcgfffTRee9HEJqCTD0WhEZM+YLRAecgpg1DaQUHB/N/c0xzRyhKzdI6GBammOd7Qog7ck6B1NRUDjnH1C5MB4ZxhQXGGqZUmWIe0o59YF/mygtTk6G4TKcLY8pX9+7da01bMwXnYulczdtAGZqeLwwvTFG+ECUpCIJgr8DIUHRCfUBXIEehMnUWegrTmpTpu5i+rORMagx16RCAKcQKcJSZTok213WW9BhAWgxMiVbAsWbOnEmZmZn00EMP1RjMsoR5viYc19I6c52rgPPBserTa3B4YrDLXK9h+hqmOeO9IAiC0DwgFQR01uLFiznHuiWHHgangKX+F+vM87yb6i/sFwNonp6eDZ4LBrPgLMRglml76GTYUdAPdelODI41pm1jwPViQAtFTUynQAtCSyIRhYJQDxhNQh4k06S6ANGFiHyA8/BCR7kAoi5MnX4wqhRjDMnSYZyY5jZsLNgH9mUK3iMi0DSK5J577mGltm/fvnojI2A84VzN92cKzhcOx/M5X0EQBKE2iGZA/ljopIYcfShagmg85Nv74Ycf2Lgw7Z+VHIAXokOAUhilMeC4MN4a0gu4RkR7oAAWcuAif6+Hhwe1FNDfGPirT6/BQYgIfhSSefTRR1vsXARBEAQjiN5DLnYMdJnP3lL0D5xn5n230n+bB1KYFidpDKbtMXMKOejhwMTMK3DRRRephRtNi1QCOBZRMEyJQIT+RttNmzZxzt/zBfnrcR5PPfVUvTMBBKG5kIhCQagHFCaBYkBxDxg4yoKE8ojKQ+L25jqOuXNSqRiJgiiI3Pj6669rfAZGYHp6er37hUL6+eefuagJQBQEIiQRsaFEaiC0H20wVQ0GU33gnH755Re1WAoiEZUpbQqo2Ix15lO9kGheEARBaDowOBAxjuIe5tFxiFRAoSsFTN2FU+u6667jvhrpIhSgT6C3kDrD9POm0YHmOgSVjE2NMUQ0YGpxfZWSzYFegBNw//79NdbDmFLSaSCyD2k4nnnmGS6aAhBV2NJAr3377bdqZApSg2Cqmfn5Y7oXKkgr4LzNi7oIgiAIFw7SJiFiG/aXJTCAg9RLP/30U43ijyhshSrEGGRqLuCshA7GQJGif5GKAs5CFO0y1QsA5w17C2lAAKIRsSCVBgpLmoIow6bokddff531EwI7BKGlkYhCQagDKB448JAbwxxMsYJxAaebMrp0ISBHE/IS9uvXjys9Iv8TciQBGGSoMnznnXeyM69Hjx5s5KFa19q1a+vdL5QawvaR7wkGH4wyOBhRqUsxiKC4UM0RhplinAHzipUAER5wKqL99OnTuZIXoh1NQRVK5E7EeeMaEA2C43bt2rVGPkVBEAShcSDvK/p75KFFdAUiGDANCnoA1ekxlQqVjxUQCYH0FNBfSo5agPeIgBg1ahT3z4gghz4wd4wp3HTTTWyIoT3SU0RHR3M1yoZ0jzmIyNixYwdHVuC4cHrivDGtC+sxcIVzjoyMZCML08YQDYk8VbhWRHe0FMj5BJ0Gow8G4Zo1a2pFa7z55pusQyFj5GLE8wHkgEG3hipWCoIgCE0DUdwNRaBD7yGHLmwLRNrBYbd06VLWfeZRfhcKzuX777+nTz/9lAfsAPQm9BmmSUM/Iu0FogYxQwsDcqbR/2iLwTvob9iNSLmBgArYS0rkZGMYOXIkD1xhf9OmTWvWaxQEc8RRKAh1gFEfGElYLAEHGyL/MGoEhYYcfKY5AmFoYB0MPAVEYGCd+VQqTLWCAy8qKooNEigB09xMDzzwABtKiMhQcmXAQFGmCVs6PoDSghKCsxAGJUbEoMyUc8LnEcJeF3D2wTmogM8hrweiCpEQ+O233+YoRNPISozy4T2MTzgIkZcKRiKclYIgCML5AScVIgc3b97MURPQPXCkIRrOPNcSoh2AeW4n6BUYO3By7dy5k/z9/dkAUvIEYkAHukTJ/4T/KPYB5xkGtFAEBQNoptGEMHyefvrpGscx1384LiIRkeYCA0xwtMHhBl0GEhMTebr0LbfcouaWgs6AkxLblGvq3LmzegwMrkFHmU/Ngm4yBUaZkhPR0n5wzZAr9DCiCiEfRMubRmniehAVv379enZw4rpg3Cm5HwVBEIQLAzlrERFfF9Apptsx4wt2E2wj2FC9e/dmJ565XsB+UfiqvuOabrfUHqmXoGtNZ3Ihpca2bdt4sAu2FvTGk08+yYNKStFHBdhnsBlRAAu6t6ioiHUg7ChTfWrpXMxlgqhCFGPBwJogtCQOKH3cokcQBKFOMM0YFRuh6EyrUAqCIAjC+QLnHyIrTpw4IUIUBEEQBEEQmoREFAqCIAiCINgAiFRA+opPPvmEC24JgiAIgiAIQlMRR6EgtCF1TRkWBEEQhPMB04mRbxZTcwVBEARBEAShqcjUY0EQBEEQBEEQBEEQBEEQyJitWhAEQRAEQRAEQRAEQRAEu0YchYIgCIIgCIIgCIIgCIIgiKNQEARBEARBEARBEARBEAQ7LWZSWVlJKSkp5O3tTQ4ODm19OoIgCIIJBoOBCgsLKTw8nBwd7TvwXfSVIAiCdSM6y4joK0EQBNvRV3bpKISTsFOnTm19GoIgCEI9JCYmUseOHe1aRqKvBEEQ2gf2rrNEXwmCINiOvrJLRyEiCUFUVJT6WhAE28az+ERbn4LQSAqKiqnL8CukfxZ9JQh2ieir9oXoLCNiXwmCIFg3iCbs27dvo2wsu3QUKtONISAfH5+2Ph1BEFoBT0dPkXM7wxpTQ+zZs4fWrl1LI0aMoNmzZzfYvqSkhFatWkVxcXHk5+dHM2bMoMjIyEYfT/SVINgfoq/aJ9aos1oT0VeCIAi2o6/sO/mTIAiCIDSC2NhYGjZsGN177730ySef0Jo1axr8THx8PPXq1YvefPNNysnJoU2bNvEo3jfffCMyFwRBEARBEATBKrHLiEJBEARBaAqurq70xRdfsLNwzJgxjfrMkiVLOGnwP//8Qy4uLrwOjsaXX36Zbr75ZvkCBEEQBEEQBEGwOsRRKAiCIAgNgIS/TU1Sj9QWCO03D+/39fUVeQuCIAiCIAiCYJWIo1AQBEEQWoB//etfdPLkSZo6dSqNHz+epyIjVyEiDeuirKyMF4WCggL5bgRBEARBEARBaDXEUSgIgiBcMBsOJtDi1ccoLr2Auob40D2XDaTpwzrbtWRRyCQ9PZ3/V1ZWkl6vp8zMTM5XWBevvPIKPf/88616noIgCPaGPeismJgY+v7776miooJefPHFFvuMIAiCLVGek0r64lz1vbOnP7kEhJG9IcVMrJSff/6ZlfThw4fp2LFjZK3ggeLLL7/kKBlbBg9NgiDUbXA99Ol2iknJo3J9Jf/He6y3Z5588klKSEjgSsmvv/46LV26lG688Ua66qqrqLy8vM7P5Ofnq0tiYiJZO7g+6AKc78qVK8laKSoqop9++omLytgyyIl59uzZtj4NQbBa7EFnXXnllTR79mzatm0bF+Bqqc+0N7Kysmjt2rX8+rfffuOBPGtl48aN9c5AsAVSUlL4OgXBmpyEJ1+6jKLfvEZd8B7r7Q1xFFopiCrR6XQceZKXl1dnu4MHD9KJEyeoLcjNzaXLLruMjh49ygZYe+Dvv//mh4Sm8t///rdFzkcQbAFEZSANn8FgfI//eP/Jn9Y7yNEaoG8cO3YsOTtXB+9PmDCBowrxcFxX0RTkNjRdrB1UgD5w4ABHTMIxWhfQZatWraK24s4776TVq1dTWloatQdOnz7NTtim8vvvv/O9JwhC3ToL2LLOevjhh7kPmTdvXot+pr0BHfX111/za6QDQVBGXWBwT6vVUlvwyy+/0DPPPMODcO0B2KwIcmkqsbGx9N1337XIOQnC+YBIQoO+5mA+3ptGGNoLMvX4PNm87zgt+W09JaRlUefQIFp05QyaMnJA8347RJzbqj42bNjAifH79+9Prc2RI0do8uTJ9O6771J7ASNzHTp0oKCgoLY+FUGwGTB1SzG4FPD+XJp95dc7c+YMRx+jsjH6mN69e9Pu3bvZgaY4C3fs2EEeHh5NLozSHvRVYGAgX3tdZGRk0OLFi2nOnDnUFhw6dIhzRpoXl7FW9u3bx0b76NGj2/pUBMHmdJY5tqazkBe3NT7TnnUWHKP18eabb7Id5ubmRq0NnhUQpHDxxRdTewC5lRHksnDhwrY+FUEQmglxFJqhr6ignLzCeoW280g0vbrkd4KpAds4NjGNnnz3O3pi0RU0bnDvOj8X4OdNzk5OdW7HNONTp07RxIkTa6xzcnKigQMHcie8bt06joZAvqtrrrmG9u/fT+7u7twW0wUQ2YcpAy4uLjR48GAaNmwYb0M7T09Pys7OpqSkJG6rRKpgdG3nzp08bQwGyZAhQ9SRNqz38/OjGTNm1IiKQaQjohYw5fjTTz+lW2+9lSMYcAxsw3nMnDmTdu3axaNFOH+cD/jrr79o0KBBfG2Ojo40a9YsioqK4ogUXHvnzp3rdExiKS0tZUMzPDyc19d1DFwHDEOMFuJ6MRUL17p8+XI2wHDOCHfHZ7DfkJAQPq/169dztCQifyydC44FuWC6woABA6zmwUoQ2grkdzqdXDPyGb6YbqHWHw3XWDBV+OWXX+bX6EPRB+MhHgMPKFqiOAqRXxAPynAUYjv6kVGjRtH06dPp3LlzPDX3o48+qtGftqTOail9hdyLmzdvpp49e6rroEO2bt1Kc+fO5feIhoNjDrKbNm0a97eIpITO6Nu3L8vlm2++YccdHKcwiCAX6BD06/369eP/I0aMoF69evE+0f8r+4GMr7jiCl6P/hjri4uLacqUKdyfm0dnoDDMZ599RpMmTeLvDfvGeWCq7nXXXcffK14HBASwzoPuhU4uLDTKF047RNEbDAaOouzatSuNGzeuTvls376dda6pnoBeresY0IfR0dF8r8DpumXLFnauQl44Z4B2+AympCPqB3r0+PHj/D1YciiiPXQaIunhoL7hhhvq/E4FwV6wB53VGjS1+FZL2VgN6SsM1inTjdG3KmDqMWwQ9I3o/6G/YL9ERkZyO9gC3377Lfn7+9Mtt9zCEfHQPQjQQKBEaGioql/Qb8P+8vb25tcKGCyEjQM9CD2D7aa2C/SDYncpwEaBXQL7DjYMzlE5BhyI0I0RERHct0P+0HnBwcF8DNg4eO7AuWC/ffr04WuHLoDOgH1oST7Qn9ArcIoqegJ6ta5jwF6DvLB/HAcpPfD9Q1/BdoLOR7uLLrqIddmll17K0Zl4DXlD/5k7YKFbcd54lsI54XwVGQuC0PqIo9AMKLC597/SKOEZzP5DsdXHyvefpA6BfnWGtz/11FOsBBCCrUw3RseLqWhwgt11112stLp3786GFcK88fCP7TBgoLTwObyGYkHkxn333cc5seBg/PPPP6lHjx6sLD7//HNWCpiKCyMXHToch4rRB6Xy6KOPsiKAYxK5Sv744w81EgOKAsrS9Hg4BpQo9gGj5tlnn+VjwniBgf3QQw/RHXfcwYYaDEFEQe7du5evHcYQlAGMbEynVhSpwmuvvcaROtgvrlfJKVLfMWAwQlaYmg3DDHnB8DkoQvzHOaMd5IFzvvzyy1kpQa4wAPF9IIze1PiCkQeFje8JChzGpiDYOzdN70tPf7NLfa9MQ77nskFki9x+++0W16N/fe6559SIZRgbeOBFv4i+A/3Mq6++yv1Sa+us5tRXcILBEMG0avSlGDCCww366MMPP2RHIfQFHKXQS3BsoR9NTU1lRx90Bgwv9MF4DeMAOgf5bpctW8Y659///jfLMywsjJ544gk2SGCcwViBXLt160ZdunTh84FehNGBc4AB8r///Y8decp20ylmOB6MGjh6cQzsC98L+vybbrqJnZUYVIL+xDXA2fn222+zvoKxhHPEgFinTp3ohRdeoHfeeYd1gikY9IK+gf6EsanoCQwwXX/99RaP8fHHH/M1aTQavkewDnJWdCzOGQ5FOJlxrmPGjOHPQ97Qi2+99RbrMOhE85xjuB8hx/YwjV0QWoOrJvagl37abzc6q6VoavGtlrKx6tNXAHYQ9AoGnDAYg/4QoG+HLsNzP+wg6CsvLy9+voe9gz4fzjzoKwD7Ae9hN6DQC5xo2BdyEP/444884AUHIJ4RYFshwh52jpKCBMcBsM1gm0CvvPfee/R///d/7IhUgC7FMeC8xAJwjB9++IH1Gvp0OB2hh9CvI7cxZpjhPB9//HF2JKLdf/7zH7ZX4HRLTk5mu8889yScdwjsgO6E0w/BIQC6HbrF0jGgkxFYgevFethKsAlxHOgrDHrh8zgX2K/Q5VgwiIp9wgbEvYPBRlOgByFHDCJiH3heEITWBoVLHJxdakw/xnustzfEUWglwGEFRxg6R4zIYMqaOTBslMgURArCoLjkkkvYeIITEcAQQqcPww3GiZI8H0ARKEYEOng40jBt+KuvvlKjFRSguDD6A8MFbWGMwLBRDFw49aDUoDRgoChA0UJ54jzwWRRiwfkhxwYMJzjxAJQMjCsYQ4oihVKGEoHyhBGkgH3BOELkBKIwTNcjz0hdx4DTEMdAPjBEIML4ghK8//771UhLRWHDQQgHJRQzRrMAFPIXX3xRw1GI7wCGH64T3wUUqCDYO4lZxggBR0cHcnZ04KgMGFzThtrO7wOj8A3lKsWDsHkbPHwj+tuWgF65+uqr6aWXXuL3cH6Zg/4ZTjj0wehDYXxAFohuN9UZMKjQ90LnQR8pg2Qw1BCNAGMBeZrgZINuxDQwOOdM+fXXXzmCEAYfQEQIIkVMp5XBKQg9qxwbkY4w3FasWMHHgD7DgBSuC45LRAAiUg/AyFOMK+gQRNPDEENhFDgwzR2F0FfQgzDkTMHx6zoGHIGIxADQ6zAOcd/A6FIMcTgKYVhCZwNEoiDnInQtnKWIjoesTFGOgwW6XBAEIseqQW/oK0cb1VmtAZxEpv0s+mhrey5GXwq7AjOMMGgFfQHdYgqe7eEghKMQ+gq6CygBDEoUImwURMQhUhy6CgESN998M29DsAL6Z+g4OPUwcAPHHGwXBDgoYAAIegdOPNg9iAxEn27qKFQiCBctWsQReQo4l6FDh/IAE5yesFMAnG6wIWHPAFwfjgm9B92LY8Fxh+h8c3ANuE4cz3x9XceAvNAeEYEYsILddM899/BrRcdCPnAcoh10P4IvYH89+OCDvB2zAeAoxMCb6fcAHQubDTYWvhNBaG3KcpLIs+sg8ht+KWVu+obKMuMo8vb37LLqsTgKLYSvY2SqPu5/dQnFp2Soo1wAzxxdwzvQe48vqnffdYEIPWW0Hx0qnIDmwGmFqAtMq4VihrIzz7WE9TAm0NFiJAyjNgpQRgqI2INDEceFk80cjOLAwaaMfilTwhoCBgtQkgPjWgCuzbTKp3IuOA+MxinXAaVgPoIExQFMlUlTj6Hs01JuKuWcca2mkYyQi3llUrSFcYjvAQ9ImP6MBwJBsFcKSsrph43R/PreOYPojtnNn0dIOH+d1RL6yryvtBSphkg2RL7BgEDUBgweGDimwIiC0QBDCI5BpYAXQN8KB56pvsJ2JdrBFGxTIhUBjB5lqnJ9IOJCOQb2rVwH9AR0kTKlTklzoegh5b1yXpbkY+k86zuGuX6uK5JC0VeWnhuAeWJ+GJR4ZsDzwgMPPMCORfOIfUGwN3afMhY0mj2qK710q+X0AULDwBll6gRrKxurIfsK/TachHXpK0TJoW9EX4nBHPTLCEQwtRnQJyOoYvjw4dwegzOWbCzsX7GvoNfMp/piP9A7pkVKGpuHUOn/Tft+5ZhKkS44NZXvBDpGOS/oCHObRtmXJTuwvmMgolGZNoz1sBct2VcYwFN0k6X9Kd+NApyQSNmBgUFE7GOGBmYRCEJrkntgDRWd2U/Bk26g0EvupvjvnqCc/X+ST1/jYLQ9IY5Cc4E4OdUbvg7uunom58tAp4jReuX/XVfNbPCzdYGRFURWICIOo1EYhTEHUQzIAQUDBJFvmK4LJxtGe3B8RAAitByRGYhswfQnrK8PRIJgNAz5KNBpKzkKsQ4jXIi0Uzpx02lcDQHFiM4dkRMYocPoFgzH8wFOU+QYxHQ2XCMUoJKjsKnHgLwQtYGIQuQoNAXXjdB5KCpcKwqf4IHBFExfU/KcIGJTKksK9s6Pm6OpSKsjbw8XunZK3TnvhLbRWS2hr5CnDxHq0FOIUkBUgHlUOqLFEUkBYwLGCnI0YXownHmIUEd0G6Yiw5DAwBamdMHJVZ/Ogg5ARCEMLTgh0QdDd86fP5+nBEOHYX+4RkT8NQXoDqTbQOQJppbBAMQ54n1TQSQhHKC4fhhtSo7Cph4D+gpTseD4tGQsYX+IOoQ+hNMVsw3MB/QwJQzfE6LxIW88O4ijULBnKiorac+pdH49pq/9RYiYggFv9N2IYrZVGwvONdgkqHoPXYM8wcrUY9OpvlgPoEOQ0gig733jjTfUKDc4AKFbcD6YCVWfvkKePhwHfTTSUMCWgW6A7kL0NyLf0f+jz4bN1hSgBxGhh0hB6Fekk0LajvMB+0IkOgaScJ3QD7AJm3oM2IrKTDXYoYjENwW6GjPcYLsiIhNTtDE7QImqV9JtwYGK40FeSN0ijkKhNTFU6Cjv8HpydPNix6CDoyOlrH6Pcg/+TRHzHyWNd3WOU3tAHIXnASpvvfLgjfTl7xsoPjWTuoQF06IrptPkC6jIhQ4aji8YU3gNRQTlAeeVEl2IRPhQZuhA33//fY7OQE4mhHfD6IDBBgMMDj6MhiHkHDkIAUbATEdyYGDAgEOUB6YTY/oURoqUHIUIgUdEBjptOB8tAWVnGhJvfgwoFeSawLkh/8aCBQt4PZx+Sg4vGC9w/ilAMVmqBoqpXFBQcMxBUSs5ChtzDMhRqcKFhyLIB4pIKXKitIMDElOpEVqPkUI4FJXCMsr0bRxXyb+BhwDsTxDslWKtjr7feIpf3zC1N3m5146EFmxPX0FPYGAG06eQfwhOMBhUGMRSCpnAEEBfCf0FxyIGpWCsffDBBxwxACfftddey1F2GIBRUl1gH1hnqhcwsAMjBDoHTklMfYYBoUTjYWBHiZqDLkAUurmRYtqPW9I9MGKwDtPKYCxh6jDOF69Nc9HC6FOiNcx1oOm0MegV6F/IQPl8Y48Box2fh4Pxtttu45xakIF5O0z9hixgaEGnK1OdMQVbmToHXQdHIXQzdKVpdKQg2CMn43M4Eh6M7Wu7hRLwfKxMuYUDR5nyidQ/SoEITBVFf6Y4Chvzmfams+BohK7CYBJeI7USrg+g30TEm5ILFtvhKEQktlL1WLExMOiDKEMEC6B/Rf+tOApN9QJ0GHQL9oUUGJiFhD4cek2ZpYV+GwtSYMCmMZ8xBTDwY+rQND0G9APsM+wbehD6GPYiBtFMU52MHDmyRsomUx2ogO2I/Md1IsWUMt23sceAragcA/KB3sP14FxN20EnY+AK+YMhY+hyRGbiGpXiL7BD8T3gs7BPEQgiCK1J4ek9VFGST/4j55Cjxvh763zNs+TsHWh3TkLgYGgo5MwGQQ4NhFkjb4Uk9xYE+8Cz6Ghbn4LNseTvE/Tu8sPk6eZMa1+eT76ejZ+CVB8FhcXk33smR1Dbex8t+koQ7A/RVy3HZ2uO0wcrjlDPCD/6/dnqwQJb01mIkIPjxxw4i5Sce3AUIuhAyQvemM/Uh+grQRDaM/E/PE05e1dQ5J0fkW9/Y7CQrYF+GkFZjdFXElEoCIIgNJmSMj19uyGKX187uXezOQkFQRAEoaXYFZVq89GEQInurg8UoGjqZwRBEGyRSn055R/bRE4ePuTdu/aslPKcFCrLTCDv3tUFV20dcRQKgiAITWbpthjKKSwjd1dnumlGH5GgIAiCYNWUaHV0ODaLX4+18/yEgiAIQjUVxfnk3WsMOfsEkaNzzVRKFdpiinplPjm6eVL/59aSo3PNIkW2irHcnyAIgiA0Em25nr5ed5JfXzOxJ/l7GavfCYIgCIK1sj8mg/QVlaRxdqThvarzfQqCIAj2jcY3mLrd9jZ1WvCfWtuc3DzJf9gs0hdkUe5+Y7Eje0AchYIgCEKT+P2fWMoq0JKrxoluntFXpCcIgiBYPbui0vj/0O7B5O4ik6oEQRCExtFhys38P2PT12SorLQLsYmjUBAEQWg05boK+nKtMZpwwUU9KMjXXaQnCIIgWD277SQ/oSAIgtB48k9s40ImpSkxdbZxC40knwGTSZt+lgpObrcL8YqjUGCOHj1KjSmAnZqaSunp6a0mteTkZMrMzDyvz7b2uQqCPbBi11lKzy3hqVu3XtyvrU9HsEOSkpIoOzu7wXZlZWV06tQpai1KS0spOjr6vD7b2ucqCPZGRl4JnUnJ59djJD+h0EoUFxdTTEzdzgdTTpw4QTqdjlqLY8eOUUVFxXl9trXPVRBaElQ6xqIvyau3XcjUW/l/xqav7OILEUehwNx4441sqDTEli1baMeOHS0mtZSUFMrIyFDff/PNN7Ry5crz2tdPP/1Ey5Ytu+BzEATBiK6ikpb8fYJfXzG+O4X4e4hohFbn888/p/Xr1zfYLisrixYvXtxi56HVams4986dO0ePPfbYeTs/77///gs+B0EQLLO7atqxj4cL9e3sL2ISWgUMHj377LONavvee+9RYWFhi53L8ePHSa/Xq+9vueWW8z7enXfeSTk5ORd8DoLQ1lSUlXBEoca3A3lFDqu3rWfkUPLoOojK89JIX2wceLJlxFF4npTnpFJJ4kl1wXt74Nprr6Urr7yyxfb//fff0/Lly1ts/+3lHATBGlm9+xwlZxeTs6MD3TZTognbC/aqryIiItjwaikSEhLo4YcfbrH9t5dzEIT2wK6qacej+4SSk6OYP9aMveqszz77jAICAlps/4sWLaL8/LZ1bljDOQiCKfnHt5BBpyW/IReTg6NTvcJxcHCgbre+Tf2eWk3Onr42L0jJ5HseQGGdfOkyMujL1XUOzi5807gEhJ33FFsXFxfSaDQ81bZnz55UWVlJUVFR1LFjR/L19a0RQXD27FkKCQmhwMBADhvHCM3gwYPVNvhc165dyd3dnUPD0d7Hx4fCwqrPD6NImJ7bo0ePeqMc0tLS+FxGjRrF7R0dHfnYpueMCDycM35AAMdEdEVeXh516tRJPW5d5wIQ0YhoPiiQvXv3Uu/evdVt2I+lY9S1L3MgU5wPzrd79+7k7e1t8RohQ/NzMJW9INgrqBT5RVU04dyxkRQe6NXWpyS0kb7CFFs4qNCXYkpVZGQkubq6Unx8PPfPnTt3VtsipQX6Xn646tZNjbCAE8/Ly3gPof+FHsM6pU8uKSnh/Ts5GR/asD02NlZtYwn02zhWeXk5fxb7x/s+ffrUOOczZ87wOXp4VEfEQp+h7/f09KR+/aqd4JbORQHnAz0KXREcHKyuR7SEpWPUty9T8PmDBw+yzHC94eHhdV6j+TkoMhYEgWr0Q0pEoeQntD+dhSm26NcTExNZLwQFBXFfij6/V69eNfpjRKLD3kD/Cr0G+wF2F2yZ/2fvLKCjupo4/o+7eyAJEBIS3N1di1OKFQottKVfKaUFCm2pUOrUKJQWKVJcijsUdydACCTE3V0235m77LIx2IRNVjK/c97ZZ/ve3bvJm71zZ/4Dal9uLgIDA9GwYUOxTdehZ7uXl5fcpslsimy8Uhpk08gWpqenC+cgjcUonZfaQ+MqxTaTHXF2flqlOzU1VYx/qC00bqF2PqstBH1WsoPXrl0T92ve/GnkVEhISIl7POtaxaGodmqTra1tCbut+BnpHmW1gWHURfK1A+LVrlkfpc43tnVBdYEdhWVw6+Nupe639u8Ip06vFDFgBG3f+34U9AykXVr7tcWwrN1UrN9fPBb13lv/zC+CUmzPnDkjBkzkFHv55Zdx/fp1sU77KOWXBh1XrlwRUX30gKWH/ocffojp06fj7bffxpo1a8SATfZ+OjcgIACjR48WD3nSdBo5ciS+/PJLXLhwQZxDDjZytNGDuzifffYZ1q9fLwYqZOwo1YvSeckg0f2ozXQPMjCkwdGpUyeREkaGju5J59G1X3/9ddHmstqiaJwPHjwIQ0NDnDt3Dl9//bXYv2/fPvz+++9F7vG8axXn/PnzWLx4sTBaZLxXrlyJzp07l/iMq1evLtEGcpAyTHXnwOXHCI1Ng4G+Hqb0a6Du5jBK2ixzz4bPtVfFbdbzIEfVq6++Kp6TNFChCZh+/fphz549wl599dVXGDdunBhcDRkyRAw2aCKGBkIkB7Fz505hH2bMmCGuRym3tNAgZcyYMWKiiwYUdN29e/eKQRNdPykpSQxAyHHm7+9f4hlPNoEGNXTOxx9/LGwmXffQoUOiza+99pq4L7WLBjUko0E2auzYscIxRwNBcipSujJNRJXWFsWJI7KHNFibM2cOBgwYINpIA8wePXqUuMfzrqUI2Tq6Jjk3yF6R/SRbVdpnLN6G999/n/83GKYYDyKTEZ+aLdbb1a+Ys4nRzDGWMlCKLQVPULADOd7mzZsnovdoUoYCArZs2SLO++GHH0QUOtkYGktt3rxZ2Ixp06bh+PHj4hw6lxxd33//vbjGN998I8ZSpIn+119/oUuXLmI8QsdcXV3F85pspSJkH2gMQtA4ht7z6aefinTe7du3i2AMajM5CmkyiNq8bNky8Yz/448/sHDhQvF56Lr//POPuE9ZbZFB7af9X3zxhXDo/fvvv2L/O++8U+Iez7tWcahNN27cEJNWZN/I5pKNKv4Z6TdAaW1gGHVRkJWG1IDTMLJzg3mtpwFXz0OSn4vE8zuRn5kC196vQ1dhR6EGQQ99GjyQ5gPNyNCggAzYggULhLOMDBUZHzIQ5BijAVnbtm2FMZk0aRJWrVolHr7k+Bo1apQYXNF76XjHjh3FQI2M0FtvvYVFixYJYzh48GAxyGjVqlWJ9ly6dEkYij59+hSJilCEjNm2bdvEYLFRo0YiKpCM3ezZs8VAUZGy2iKLliBnHX0OGjxNnTpV7Dty5Eip93jetYozaNAgtG/fXhhCcmT++uuvwoCV9hmLt4FhqjsSSSH+3H9brPdvXQseTk8jcpnqCQ0I6PlJ0dlkR2gwRVFtNID66KOPxPN/06ZNIspt//79wulFTi4aBJGTceDAgXj33XfFM5kWepaT7aLnPE3kECtWrBCDIIqMJ5tI+8mBRwOZ4lD0BV2TtKBkURE0+FGEBn7URnLckQPx2LFjwp5QlDxFPShGldAAsbS2vPnmm/Jz5s+fLya4yD4TNIFV2j3oOs+7liJkf+iaZJvJ2UipWmRTS/uMZDcV28AwTEnOBUijCT2cLFHTkaPhqyMU4NC9e3fxHKZABwqYoGd+06ZNxTOUggWWLFkiJo0o4nDt2rVirESTMWSDKHiDzqX3//bbbyLijsYPFLxAjjCKrqfzKdKQrk+OMzs7OzHWovGcImQnyElJ16YIwrIiD8lW9uzZE0ePHhVjPBrH0D3pesUjzUtri6JzjybEfvnlF2GDKRutrHuQLX7etYpDgRg0GUeBGzTRtnv3bnTt2rXUz1haGxhGXeSlxsPcqxEsajd5ZgRwCSQSRO1fgoKcDDi0Hw4jy8qTDFAn7Cgsg0ZfSGeOSoP0Mkqj7pt/wNyjpG7X86IJZchCsClakB6gstRbmtGR6TlQtEKzZs3EOs0g0WCBHIsUHUgGhAYuFFlID2GCZs9Ib48iPQhZuD2F0pPBIygKkWZ2ikMzS2QQSbOPZohoxqg4LVq0EK90nK5N4eU0W9SmTZsS55bVlrKce8+6R3mvRYbpp59+Emnc9CAg56Kyn5FhqjuHr4XiUVQqyIZyNKF22SyyV6m3Tyhtr5SFfvjLJBzIRsnsFz2DyblFkC0gO0PPXFooRYpsD9kuipSgCAeKuKNJH4Ke65SyS9FxMui6ZPdk9oomwGQpX4rQNWhwNmvWLDHoo2g7xTQqWZvJgUeQLaB2UpRiy5YtS6QCl9UWZfqltHuU51rkEBw2bJiwazRApUEl9WVpn5EHWwyjvD5hO652rHNjLGWRPXPJXlHQAdkSguwRjR8o44ie2fTclZ1PWUYETdaQE41eaVKMItrJgUbZWIrZTHRtcpZRdDo5CQkasxV3FNI9f/zxR+FwpAktikIvLRqcbBNB1yNbQs/94nIUBNnI0tqiDMXvUd5rUb9RsAXZdvrM1EbqX2U/I8OoE1OX2vB9928UPvELKIu+sSmcOr+CqH1LEH9qI9z6vQVdhB2FFek0Czuhl1FcP4P2VzaUekuDA4oupKgFilIgw0YREfQQpoqLFHkh03Hq1auXSPui1CXSXiLIwNGMEV1nypQpIq2ZBiLFIQdk3759xSzS3LlzxeBFGagdNJChcHYaMMk0CstqS/FIisuXL4tBIaWAlYUy11LkwIEDwkDRQJVSiynKo6zPWLwNrFHIVPdowuX7pNGEfVt6oY4ra3ZqE+q0VxS1TTaGnGc0mKBJLIouIGjARZISpG106tQpsY+iPWgCiFKSKZKcnIuUqkTnkM2jdUrL3bVrVwlJCHKkNW7cWAxQNm7cKCbL6D3Po1u3biLSguwURfWT85OcmGW1RRGyDRQNSbIhz5rwUuZaipw9e1ZMtlFUO+kqUoQmTW6V9hnJ1iq2gTUKGaYouXkFuBIYK9ZZn1DzUZfNomcnjQkosICesxQJKEudfemll/D555/LI7xlQRZkMyiDi8YWNNlEEzckeUHONoqsozEERdvJxikyyI5RFtP48ePFM55SoZVxolEbKViC5D0oYo9Sj+neZbWlOGSzaDxE46WyJquUvZYMcgySDSKHIGUOUPo22auyPqMybWCYqkavAgWuHDuORsyRlYg7tQEuPSZB39gMugY7CisAiemSqG5+RtLTjrSwq7DILkGOPcVKV7LoCYIGFjIhWUo5oocwaWPQoID0nshJSJAWIOkVkjGTQc4vipKjBzhFNRDkJKPrUAozzfaMGDFCpHLJriOD7kHOOLo3GQyKWKSHPUXeldbmBg0aCKNFgxq6H6X3UnqaTKOwrLYoQhWVyQFKBoUMYVn3UOZa5JyUtZVSsimUngZg9FlowFXWZ6RITsU2sEYhU505fjMcgeHSiYTX+5WM5GKqn72iKDfFYlM0eJFNqFCUBj2nCZLGIHtEDkLZ81Y2MKDJGRpIUTS8LAKPjtEzl6K8SZuPBhskM0GDNUq9Jd0kcjpSyrIs6kMGpTeTE5LuT22j6D1Ke5JNIBVvM9kWui9FrNPAkCL1SM6Drk/telZbFG0zORkp8oJStygKsLR7KHMtU1NTeVvJXpJEBvUXOTLpunSc0ouLf8bibeCIDYYpyvVHccjOK4C+nh5a+7ly91RDm0WTKzKdQIp6U5xQoecuPV/puUqTL999952QPSL7JasoT8doLEOBBuQ0JMiBRhIV5AikySvSHaTnNQVhkOwGaZyT1jnJGZEkhyJkB+j5TZNG9AynNF+CbKcs0lGxzdQ+eubTNmn80jiQdGtJikOmUVhWWxShe5K9I4ce6QOWdo9nfS5FZG2lMRr1E12XnIw0jqMJt7I+Y/E2MIy6SLp+CJmhd4QuqrFd+W2DoYUtHNoORdzJf5Bw4V84dRoNXUOvkP5Tqxk0I0SDGnIQyQYoDMPoNhbpN9XdBK2DzMPLXx3A3dBE9GzmgcXTnjo2KpPUtAzY1esj0oGq+zOa7RXDVD/YXqmOn3dcx18H7qBxbQesn9MXlQHbrCf9wOMrhmG0hAdLpiA98AL8Zm+Dmbtvha6RkxCOgC8GwNjeHfXn74GeflEZG019TlM2qjJjrPLHWTIMwzDVglO3I4WTkHijP0cTypyngYGB8qhkZaHoapphZxiGYapen7At6xMyDMMwT4qYpD+4BFOXOjB186lwn5g41IR968Gwrt8JktxsnetbdhQyDMMwpTrE/tgr1Sbs0qgG/D11s6JXeRx93377LXx8fES6DskZKAM5FIcMGSJSg0gblt5LGqgMwzBM5ZKcnoOAJ5NdrE/IMAzDCNtw4whQKIFts77lq3ZcCl5jPofHiI9gYFpUi1QXYEchwzAMU4Lz96JxMzherE8dwNGEVOGQqvqRCLes8vzzyMnJEYWXSNSbxL6Dg4OFBhLpzzEMwzCVy4X70SCBJTMTQzSpU1TXlGEYhqmeJF87IF7tmvdR6XUl+XnQJbiYCcMwDFOCZXukVc471HdDo9o8wKKCFOWt0Ld+/XrcvXtXOBdlRZkoIpEWhmEYpnI5FxAtXlv5OsPIUPO1oxiGYZjKJTc5BumPrsKsRj2ReqwK8jNT8HjNHCqfDO+pS6ArcEQhwzAMU4RLgTG4GhQn1jmasOKQg5CqplOF+sjISCQmSlPgGIZhmMqXzzj/RJ+wXTXUJywoKBBVZakKLS3K9hlVgKfzqdr6ihUrxD6GYRhdIS8pCiZOXiLtWFUYmFkjNzkaqQEnkRUVBF2BHYUMwzBMEWTahK3ruaBZXWfunQpCxUtsbW3RrVs3tGjRArVq1UKTJk1w5cqVZ6YrU0UyxYVhGIYpH2Fx6YhIyKi2jsJWrVrhr7/+gomJCf777z+l3jN37ly89957QjKjX79+mDNnDmbOnFnpbWUYhqkqLGo3hf9Hu+DS/VWVXVNPTw/O3SeK9dhjq6ErqN1RSDNVpNtE+k0SiUTp98XGxuL27dvIzMys1PYxDMNUJ64/jMOFe9J0LY4mfDH09fWxb98+TJ48GVFRUYiPj0eDBg1EcZPc3NxS37No0SLY2NjIFw8PjxdsBcMwTPWtduxsa4Y6btaojhHtu3fvFlHtyhAdHY0ffvgBv/32G6ZNm4bXX38dv//+O3755RcxRmMYhtEVyLGnZ2Ck0mvaNe8PIxtnJF3ZK9KbdQG1OgrJINWsWRM9evRAmzZtULduXRw6dOiZ76HB1dixY+Hp6YlBgwbB2dkZf/75Z5W1mWEYRpf5Y580mrB5XSe08nVRd3O0GnLykY0bN26c2DY2Nsb777+P8PBw3Lt3r8yIjpSUFPnCAzSGYZiK6xO29Xd74aqW2giNj8rDsWPHRLry4MGD5ftonEUTXkeOHKmEFjIMw1QtiVf2Ifb438jPSFH5tfUNjeDUZRwKC/IRd3I9dAG1OQrJGFGEBaVgPXr0SKRovfzyy0IXg6IFy+LLL7/E8ePHRQVKikQk/YypU6c+M5WLYRiGeT53QhJw+nakWJ86oFG1HFy9CFTdmByAsmhBmgRLT09HXt7TKmgJCQnilaIFS4PSxKytrYssDMMwjPLkF0hw8b7UUdjO35W7TglCQkKEVIaFhYV8n6mpKRwcHMSx0mCpDIZhtAlKC47Y+T0kOZWTkerYfgT0TS0Rf2YLCrKl0hfajNochQYGBvj+++/h6io14DQgfffdd8Wg6llOP9LbmDJlijwdi5yL/v7+wmHIMAzDVJxlT6IJG9Vy4MFVKTIZ5ASkJTs7G8nJyWKdJqxknDp1StgjmvwiKJKQCpm8+uqruHDhAvbv3y/sHEVpeHl58Z8qwzBMJXDncSLSsqQTNG3ZUagUNMFlZmZWYr+5uTlLZTAMo/Vkxz5GVvhdoVFobF85urUGZlaoOfRD1HltMfRNzKHtGEKDuH79unglwffSoAhEWkigVxFKW7569WqVtJFhGEYXuR+WhBM3wuXahBxNWJSsrCyhLSjj8uXLYtvX1xe7du0S+ywtLVGvXj0RFUjQK0XAf/bZZ0LziaIIx48fL8TiGYZhmMrVJ/StaQtH65LOL6YkdnZ2SExMLLGfouDpWFlSGYrFTqj4FuvqMgyjiSRfOyBeVVntuDQc2g6FrqAxjsKkpCRMnz4dQ4cOFREZpSFL2aIweEUcHR3lx8oKjadFBleRZBiGKV2b0N/DDp0b1eDuKSWqoixdQRkdO3YscQ7pRC1ZsoT7k2EYpoo4FxBVbasdV5SmTZuKaPm7d+/Kx2EPHz4UYyY6Vho0GSabGGMYhtFkkshRqKcHu6a9quR++ZkpyEuOgZm7L7QVtVc9JtLS0jBgwAChjbFq1aoyzzMyklanUXT6ySI9ZMdKg6tIMgzDlM3DyBQcuRYq1lmbkGEYhtFWMrLzcPNRvFhnfcJnQ1Hxa9askU90UVFJGjPJ+Oqrr4RMRrdu3Sr1O2MYhqlMsqKCkB0VBEvvlqIycWWTn56EOwt6I2TNHCFdpK2o3VFImoT9+/cXs1iHDx8uU+CdoOqRlA4XGSkV25dB21QFuSy4iiTDMEzZLN9/G2TH6rrboFuTmtxVDMMwjFZyOTAG+ZJCGBnqo7lP5Q8INZX58+ejZ8+eWLp0qYgKpHVaFAuTnDhxQq6pS9rxmzZtEvtq164Nb29voau7efPmZwZjMAzDaDq5CeEwtLCDXSWnHcswtLSDpXcLZEc9QNq9M9BWDNVdIZKchPR65MiRUjUwHj9+LJyJDRo0EJW42rZti71792Ls2LHyaMKjR49i3rx5Zd6HQ+MZhmFKJyQmFQcuPRbrU/s3hL4+VzpmGIZhtJNzd6XVjpt7O8HMWGMUlqqckSNHomvXriX2Ozk5ydf//fffIpqCzZs3F+nG165dE1EwzZo1g7GxcZW1mWEYpjKwadgVDb84hkJJQZV1sHP3SUgNOIWYo6th7d8R2ojaLGh+fr5IN75//z7Wrl0rZrRks1pUzIR0B4kvvvgC58+fx+3bt+Xbffv2FYLx7dq1w88//yxSlqdOnaquj8IwDKO1/Ln/DiSFhajlYo1eLcqOzGYYhmEYbSlk0rZ+9dYnbNKkyXPP6dKlS4l9FD3YunXrSmoVwzCMetAzMBRLVWFZtyXMPRog/cEFZIYFwNyjPrQNtaUeUxQhRQrSTNZHH30kKkLKljNnnoZoktOwYcOG8u0ePXrg4MGDuHHjhqgkSe8/ffo0rK2t1fRJGIZhtJOwuDTsvRAs1t/o3wAG+mpXo2AYhmGYChGdlIlHUalinfUJGYZhmMRLu8Uiyc2u0s7Q09ODc49JYj3m2Gqt/CLUFlFIWoSXL19WSmOjON27dxcLwzAMU3FWHAhAgaQQHk6W6NeqFnclwzAMo7WcfxJNaGthAn8Pe3U3h2EYhlEjJKEQtX+JqD5s3aAz9I1Nq/T+tk16wtihJtIfXERBTiYMTMyhTVRf8Q6GYZhqTFRiBv49J5V7eL1fQxgacDQhwzAMo/36hG38XFhvl2EYppqTGXoHuQkRsG7QBYbmZRfMrSz09A1QZ8ovMHGsCX1jM2gb7ChkGIaphqw8GID8AgncHSwwsG1tdTeHYRiGYSqMRFKI808che2quT4hwzAMAyRd3S+6wa5ZH7V1h5m7j9Z+FRxCwjAMU82ITc7E9tNBYn1yn/ow4mhChmEYRot5EJGMxDSpBhXrEzIMw1RvCiUSJF8/CD1DY9g06qbmthQg+fphJFzcBW2CIwoZhmGqGasO3UVuvgTOtmYY0t5b3c1hGIZhGJVUO/ZytoK7gyX3JsMwTDUmI+SG0Ca0adwTBqbqtQn5mSkIWTcXBiYWsGvau8q1Eqs8ovD48eMYP348OnToIN+3ZMkSpKSkqKptDMMwjIpJSM3G1pMPxPprferD2MhA5/s4MjISM2bMQI8ePYTtIo4dO4bz58+ru2kMwzCMCvUJ2/q7anV/5ufn45dffsHgwYPx0UcfiX3R0dFYsWKFupvGMAyjNWRHPxTRhHbN1Zd2LMPI0h4OrQcjPz0RiZe0J6qwQo7CLVu2YNCgQbCyssLZs2fl+zMzM/Hdd9+psn0MwzCMCvn78F1k5xXAwdoUwzvW1fm+jYuLQ9OmTfHw4UPhMExISBD7a9asialTp0Iikai7iQzDMMwLkJNXgCsPYnVCn3DcuHH4/fffRbXOwMBAsc/V1RV//fUXrl27pu7mMQzDaAWO7Ueg0cL/YNOoOzQBp64TAD09xB5fI1KRddZR+OWXX2LTpk3CkCkydOhQrFmzRlVtYxiGYVRIcnoONv4nHXhM6l0fpsa6rz6xbNkyDBgwALt370bDhg3l+319faGnp4fLly+rtX0MwzDMi3EtKE44Cw309dC6novWdueDBw9E1PvFixfx6quvFjlGEYZr165VW9sYhmG0DUo51jc0VnczBKbOXrBp1AM5cY+RcusEdNZRSDNc3btLvbM00JLh4uIiwuMZhmEYzWPt0XvIysmHnaUJRnbW3ipcqrBXBNsshmEY3dEnbFjLAVZmmjEorKi9atmyJaytrdleMQzDVJDES7uReu+sxkXuufSYKF5jj62CzjoKHR0dRRpX8YHXiRMn4OXlpbrWMQzDMCohNTMX/xy7L9Yn9PSDuYnuRxM+y16lpqbi6tWrbLMYhmF0RJ+wnb+b1turR48elTqxxWMshmGY51NYkIfwHd8ieOVMFBbka1SXWdRqAkuf1jC2d4ckNxuaToVGihMnThTaTn/88YcwZKT5dODAAcycORPvvfee6lvJMAzDvBDrj91HenYerM2NMbprvWrTm1R0q3fv3mjWrBlyc3ORk5ODc+fOYc6cOcJJ2KRJE3U3kWEYhqkgSenZuBeWKNbb1dfuQiatWrWCgYGBsE/e3t5Cp5Ach8uXL8fGjRtZo5BhGOY5pAVeREFGMuxaDYK+kYnG9VfdN/+AnoF2BGtUqJWffvopYmNjxQCLhOBpBkxfXx+vv/46PvjgA9W3kmEYhqkw6Vl5WHf0nlgf18MPlmZG1aY3mzdvjt9++w2TJk1CUlISdu7cKd+/bds2dTePYRiGeQEu3I1GYSFgYWqIRrUdtbovaSy1a9cujBo1Ct98843Yt337dtja2gp9wvr166u7iQzDMBpN0tX94tWuWV9oInoKTkKaDCoePa71jkJDQ0MRTfjZZ5+J2S1yFlJVyRo1aqi+hQzDMMwLQQVMKPXY0tQIY7tXn2hCGaNHjxZC8JcuXUJycjI8PDyEzdJk48wwDMMon3bcytcFRgYVUlTSKOrUqSOKbN26dQvBwcGwsrJC69atYWFhoe6mMQzDaDSS/Fyk3DoGA3NrWNVrB00lPz0J4Tu/g4GpBTxGzINOOQrNzMyQlZUFV1dX9OvXT/WtYhiGYVRCZk4+1hy+K9Zf6eYrUo+rE2PHjsWIESMwdOhQdO7cWd3NYRiGYVQERWPICpm01XJ9QoIi3jdv3ox//vkHjRo1EgvDMAyjHGn3zqIgKw0ObYdB31Bzs6f0TS2Qdv88CjJT4Np7KoysNTMavkJTbzS7FR8fr/rWMAzDMCply8kHSErPgZmJIcb39Kt2vWtjY4OYmBh1N4NhGIZRMY9j0xCVmKkT+oSEvb09IiMj1d0MhmEYrSQzTBoYYdusDzQZfUNjOHUeg8L8XMSd/AeaSoUchaT1RGnHeXl5qm8RwzAMoxKyc/Ox6lCAWB/dxQd2lqbVrmcnTJiAZcuWITpamp72IlAa2Ny5c4W+4ddff13u95JQfZ8+mv3jhWEYRls4/yTt2MXOHLVdrKHttGnTRmjp7t8v1dhiGIZhlMet35to8OkhWPm01vhuc+wwCvom5og/swkFOdIJL51IPT59+jTOnj0rKnD5+PjA2LhoKtuJEydU1T6GYRimgmw7/RAJqdkwNTLAhF7+1bIfacAVEhKC2rVrw9/fH9bWRQeTNOnVpUuX517nwoULGDNmDF577TVROTk8PFzpNtCkGukk5ufn486dOxX6HAzDMExRZGnH7fxddUJz9vz580LaqX///mJ85ebmVuRzka0im8UwDMOUjrG9dshQGJpbw6HdcMSdWIuEc9vh3HUcdMJR2K1bN7EwDMMwmkluXgFWHpQ6pUZ09oGjtRmqI+QcnD59epnHHR2V0wVp0qQJHjx4IKpS7t69u1xtmDdvHnx9fVGvXj0R3cgwDMO8GPkFEly8J5WVaKcD+oQye0QVj59lz5SFHI6ffPKJqKJMWo4DBgzAl19++cyiKDSR9fnnn+P69eswMjISbaEoelpnGIbRZBKv7IOpqzfM3H21ZuLIuct4xJ3cgNgTa+DU6WXoGRhpv6OQDA3DMAyjuew89wixyVkwNtTHpN7VM5qQoEg+VWBqWrG07YMHD2Lr1q1i4PXLL7+opC0MwzDVndshCUjPlkogtfHTfn1CokGDBiobY5FM1JUrV7Bq1SoYGBiIbYqu37FjR6nnh4aGomPHjqL4FzkXExIS8NZbbyEqKgpLly5VSZsYhmEqA0rdDd3wKQzMrNDws8OAnoHWRD+69Z0KEycvQK9CioCa5yhkGIZhNJe8AglWHJBGEw7t4A1nW3N1N6laQrqINDijKpbFU57LgtKaaZGRmppaiS1kGIbRTs490Sf087CDg3X10999FkFBQdi0aRMOHDggnH/E77//jh49eoioQXJIFofsFEXMU9Q7ORaJxYsXo2fPnvj444/h7u5e5Z+DYRhGGVLv/IfCvGzYtRsOPX3tcBLKcO0zDZpKhRyFDRs2fObx27dvV7Q9DMMwzAuy53wwIhMyYGigj8l9Sw4IqhMzZ87EoUOHyjxOA6FevXqp/L6U6jV+/HihaSgbqCnDokWLWIOKYRjmOZxX0CfUFQ4fPoz33nuvzOO9e/fGjz/++NzrnDx5Ujj7unfvXkTf0MTERBwrzVFIqcoUOS9zEhKWlpaQSCTiPaqKzmcYhlE1SVelBaDsmvfV2s6V5OUgPyMJxrau2u0onDJlSpFtMiKk3bR69Wq8/fbbqmobwzAMUwHdpj/3SydrBrerDTf7svWIqgMUQeHp6VlkH6VUrV+/Ht7e3qLISWUQExODI0eOiLStPXv2yPfRvZs2bYpvvvmm1ArIpAdFzk3FiEIPD49KaSPDMIw2kpGdh5uP4sV6Wx3RJyTIHhUfY2VnZ4sikZcvXy7i+HsWERERsLOzK6ItSA5A0kCMjIws9T19+/bFggUL8Ntvv4mxXHp6ujwNmq5XGhwBzzCMuinISkNqwGkY2bnBvFYTaCO5iVG4/8NomNX0Q903/4BWOwpnzJhR5oDsn3/+edE2MQzDMBVk/6XHCItLh4G+XrWPJiRIwL0sO9ayZUuli5mUF7rutWvXiuz766+/sGXLFjGpVqtWrVLfRxEftDAMwzClc+l+DPIlhUKDt3ldJ53pprp165Y6xpozZ46I6FNWioIi2g0NSw7xaF9BQUGp72nVqhX+/vtvfPTRR5g9e7ZIQ6ZXclLS9UqDI+AZhlE3ybeOo7AgD3bN+mhNEZPiGNm5wsjGGWn3ziIz4j7Ma9SDJqBS1USajaLwdIZhGKbqKZA8jSYc0KY2PJys+GsoAwcHB1GFmIqMqIqzZ8+KaEESjKcBGa0rLq6uriLCg9ZtbW35u2EYhnkBfcLmPs4wNa4ecuvlGWM5OTmJ6PXiDr74+Hg4OzuX+b5x48aJoiakr5uYmCiiG8k56eXlVer5FAGfkpIiX8LCwsr5qRiGYV6MjIdXxKtdM+1NO9bT04Nz94liPfbYamgKKnUUXrhwAcbGxqq8JMMwDKMkh6+GITg6Ffp6eni9X/XWJnwelFYVEBCgtM0i/SaZw+/WrVtCKJ7WqUKkDBpQ3bhxQ6SKMQzDMJXDOR3UJ1TlGKt169bIy8vDxYsX5fuoAnJGRoY49jysrKzEpNbWrVuFbmFZKc8U/U6FuhQXhmGYqsRj9ALU+2ALzDzqa3XH2zXrLdKnk64eQG6SdDJM3VRoGk5xYCQjKSkJp06dErNLDMMwTNUikRTij723xHqflp6o5cI/2IkffvgB586dK6GrdOnSJbi5uYl0K2WgARGlDBfHzMxMvt6hQweRblyW7uHrr7+OYcOGlet7ZRiGYZ4SnZQpJsSIdjqkT0icP38e33//fQkd+IcPH+Lu3bslbFlZtGjRAu3atRMpyzt37hTRKrQu2y+DourJLs2aNUtsU9oxaeSSdMbx48fxySefiIUi8BmGYTQRer6Z1/SDtqNnYATnruMRseNbxJ5Yi5pDP9BOR2Fpmk4+Pj54//330b9/f1W0i2EYhikHx2+EIygyRay/0f/ZlemrExQZUdxmUYQEaRdOmDChiNj7syC9JoogfN69nnWOi4uLWBiGYZiKcS5AGk1ob2WCejXtdKobaUKquL0i20MOvpEjR8LX11fpa5Ee7quvviquRwPp9u3bY/v27UU0vKjAFkXXy6hfvz6aNGki9llYWAgnYVm69AzDMOom+fphWNRuIvT9dAGHdsMRfWApki7vgfugGdA3VG6MUllUyFHYtm1bTJwozaMuDkVclHWMYRiGUT2kQySLJuzV3BN13Vn/TgYNrKi6cGkaSyTSTtF/ZekvMQzDMJqZdtzGzxX6+topXF8W9vb2omhJ165dSxx7/PixsFmlHSuNGjVq4MiRIyLdmCDHX3ECAwNhbm5eRKOQFtIbtLGxeaHPwjAMU5nkpcYjePUsmLrUhv/cnTrR2QYm5qj92mKYufuq3UlYYY3CSZMmVegYwzAMo3pO3Y7E3bAksT6VowmL8Ntvv4k047KOXb58mf8kGYZhtERi48K9aJ1MOybIVpFdKuvYkiVLyn1NchCW5iQkqLCJpaVlif3sJGQYRtNJvnEEKJTAtmkf6BJWvm1gaGmne8VMIiMjYWenGR+MYRimukUTdm1SE/U8+BmsLGyzGIZhtIfAiCQkpuVUu0ImBNsrhmGYpyRfOyBe7ZrrlqNQNrZLC7qE1HtnoTWpx5RyXNq6othuz549Vdc6hmEY5pmcuxuNm8EJYn0aRxPKIVH2Y8eO4cGDB7h582YJgfi4uDhRhKt58+b8F8YwDKMFnAuQRhNSsS5X+9Kj5LQRKhxCxSATExORkJBQYoxFBbju3LmDdevWqa2NDMMwmkJeSizSH12FqbsvTF3qQNfITYxA0G+TYepcG1ZzdkBPX6WxfZXjKBw4cKB4vXDhgnxdBgnC16pVC0OHDlVtCxmGYZgSHLkaiqV7buFBZLLY9vOwRYNaXJlQBom/k/bSxo0b0ahRIzRo0EB+jMTcSeCdCprY2rKeI8MwjDbpE7bVsWhCNzc3Ma4iZ+CtW7dKjLEoPbhly5bo2LGj2trIMAyjKSRdO0Rhd7Br1he6iIlDTdg07IqUW8eRGnBSrGu8o3D+/PnilQZY06ZNq6w2MQzDMM9xEr73xymQjHvhk333wpLF/p7NPbnvAAwfPlz0Q5s2beDt7Y06dXRvxpFhGKa6kJNXgKtBcTqZduzn5yfGWI8ePRLZWb169VJ3kxiGYTSW1Lunxatdc910FBLO3ScJR2HMsVXa4SiUwU5ChmEY9UGRhHp6YjJNDm0v23uLHYXF4AEXwzCM9nM1KFY4Cw309dCqngt0EZrQ4kkthmGYZ+P9xq/ICL4BE0cPne0qyzrNYFGrCTIeXkVGyE1Y1GqsHY5CIjw8HPv27UNoaCjy8/OLHPv6669V0TaGYRimFEJiUos4CQnaDo5O5f4qhdzcXOzcuROBgYFIT08vcmzcuHFo2LAh9xvDMIwW6BM2qu0AKzNj6DJnz54VMk+xsbFC1F4GyWiMHTtWrW1jGIZRN3oGRrCs2xK6jnP3SQheOQOxx1aj9ms/aoejkATiX3rpJREqf+XKFXTo0EHoaiQnJ4t1hmEYpvIgIffACKk2oWJEYW1Xa+72YqSlpYn048zMTLE4OzsjIyMDISEh8PHxETqFDMMwjGZz/ok+YTt/N+gys2bNwvLly+Hu7i4KbtHr7du3hebu+++/r+7mMQzDqJWUOyeFk9DAxFznvwmbRl1h4lwLBTmZKCzIh55BhWP8KkSFSqhQZa7vvvsOly9fFtunT58WEYYjR47kCpIMwzCVzNju9Ypsy9KQ3xxY9WHpms6yZcvg4eGBoKAgdO7cGQsWLEBwcDBWrFiBvLw84URkGIZhNJfEtGzcDUtSqT5hdnI80iOC5Qttq5vHjx/jjz/+wM2bN/Hll1+iU6dOuHbtmgjGsLGxQZcuXdTdRIZhGLWRExeKR8vfxqPl06vFt6Cnb4B672+A58ufIisyEJlhAWLJTZROnFU2FXJLksGidC3CwMAA2dnZsLCwwI8//iiqcv3yyy+qbifDMAzzhKjETOnzV19PLBRJSE7CHs10V6ujopC9Gj16NAwNDcVC9op47bXXRNTGxYsXuZIkwzCMBnPhnjTt2MLUEA1rO77w9cgpeGXxLBTm58n36RkaocV738PU9sWvX1Hu3r2L9u3bo1atWrh+/brcXvn6+mLmzJnYsmULOwsZhqm2JF07IF5tGvdAdaEgMw0BCweiMD9Xvk/P0Bj15+2Bsb2b5jkKKW3LyspKrLu4uIjoDH9/f5iamiI1lTWyGIZhKgsSc998MlCsvzmwEaYOaMSd/QxIk7C4vZLBNothGEbzOXdX6ihsVc8VRgYVSoYqQn5GWhEnIUHbtB9qdBSyvWIYhimbpKv7RRqVXdPqUxk+PyOpiJOQoG3ar5GOQkX69OmDt99+W0RnbNy4Ea1bt1ZNyxiGYZgS7L8UgsS0HBgb6mNkZx/uoXLaq0mTJsHV1RURERFCLL5JkybchwzDMBoKFfM4FxCl0rRjbaBZs2aIj4/Hhx9+KIIxvvrqK7HOMAxTHcmKfojsqCChT2hk46zu5lQLKuQoXLVqlXz922+/xZtvvok5c+aI4iZ//vmnKtvHMAzDKAyY1h29L9YHtqkNeytT7pvnMH36dNSuXVus9+vXD++8846wW8bGxvj7779Ro0YN7kOGYRgNJSQmDdFJmdWikEmrVq3g6Ogoj3jfunWrKGBCgRiDBg3C1KlT1d1EhmEYtZB8VZp2bNesH38DmuwonDhxonydDBppZjAMwzCVy+XAWNwPlwq6j+1RtKAJUzpdu3aVr+vp6WH+/PliYRiGYTSfc0+qHbvamaOWi1RG4kUxtLASmoTFNQppvzrx8vISiwwqZkI6ugzDMNWd5FvHAH0D2DTtieqEoYWd0CQsrlFI+yv93i96gbi4ODg5OammNQzDMEyZrD16T7y28XOFb43KNxC6BmnompiYiIVhGIbRHn3CdvXdxGSPKiLzqWAJFS4RmoRPICehOguZFCcvL09owtva2qq7KQzDMGrH551VyHx8C0aW9qhOGNu7icIlpEkog5yEla1PSFRIEZiqcM2YMQPW1tZwdn6aI07aTwEBAapsH8MwDAMgLC4NJ26Gi74Y152jCcvDpk2bRBVJGxsb7N69W+xbt24dfv31V/7bYhiG0VDyCiS4dD9apfqE4Sf+xZ2/vwUkEljWqC1fNMVJ+ODBA3Tv3h3m5uaYMmWK2BcVFYVhw4YJJyfDMEx1xNDcGtb+HVAdMbZ3g7lHfflSFU7CCjsKFyxYgNOnTwvtDEVIP+Ozzz5TVdsYhmGYJ/xzPBA0RvBwskTnRqyrpyyUtkU6uh9//LEYfMkYMmQIvvvuO6SlPY0oYRiGYTSH28HxyMjOl0fSvyj5OVmIOLMfKcH3YGCieRq/FEU4ePBgUbzk008/le93c3OTaxYyDMPoGscv3ca4OYvReeI88UrbMmiCJC3oEgolBWptY3WkQo7CDRs2iGiM3r17F9lPWhoHDx5UVdsYhmEYAOlZedhxJkj0xdjuftDXf/H0q+oUTThz5kxMnjwZ9vZP0xUsLS1FkROqfMwwDMNobtqxv4edSop3RV84gvysdLi27g4jC2toGtevX4e+vj6WLFkinIWK8BiLYRhdhJyCc39ai4fh0cjNyxevtC1zFmaG3kHQr6/h8bqP1N3UakeFNAqjo6Ph4eEh1hX1QgoKCpCb+1RoURlSUlKwdu1a3Lt3D++++y58fHyeO+g7depUkX3u7u746CP+42EYRjfZefahiKqwNDXCkPZ11N0crYLsVePGjcV6cX2ritisGzduiAmxZs2aoVevXs89Pz4+HseOHRN6vjTw69atm0p0thiGYapLIZO2Kqh2XJCbg4jT+0TRkpodB0ATKWt8VVF7ReTk5Eh1GU2Vd7TK7mNsbFzu+zEMw5SHFdsOg552MmUFeqXn38rtR9CtVUMkX5NWO7b278gdqw0RhQ0aNMCJEydKGLIVK1agefPmSl9n+fLlYuB07tw5MXsWERHx3PccP34cZ86cgZ+fn3yhqBCGYRhdpEAiwfpj98X60A7esDA1UneTtIqy7NWtW7dw5coVNGnSRKnrBAcHo3379hg/fjx+/PFH/Pvvv899z88//4xWrVqJdDHS7504cSI6duwoBOoZhmGYsknLysWt4ASx3q7+i6cdR186hryMVLi26Apja80sBla/fn1cvnwZ6enpRexVfn4+1qxZU64xFuka9uvXT0TPW1lZiYmt8HCpznFZ0CQYTayRBj0tZD/37NnzQp+JYRjmWYRGx6O4+ipNbjyOikOhRIKkawdElV+bRt24I7UhovCTTz4RgyVK55I5CA8cOIDt27dj7969Sl+nXbt2QrQ3KSkJ//zzj9Lv8/b2xvTp0yvSdIZhGK3iv5sRCI9Ph76eHsZwEZNy88YbbwhnIBXbImcfRaRfunQJS5cuFXasRg3l9B4NDAyEpmGHDh3Qtm1bpd7TtGlT4SA0MzMT259//jnq1asnHIgcBc8wDFM2l+7HoEBSCBMjAzSv+7RwYkWQ5Och4tRe6BkYoGbnQRrb7TS+Iedely5dRNQ6BVDQxNSqVatEBhZNNinLqFGjRBpzTEyMeB0+fLgoiEJyG6VFtSckJAjt3v/973+4evWqXJOe3vf48WO4uqqmmAzDMIwi7k72CImMLbKPnlFebk7ICLmBvOQY2DTuAQNTS+44bYgoJENCGoX79++HkZGREIoPDQ0V1ST79u2r9HUaNWoECwuLct8/KCgIH374IRYuXIiTJ0+W+/0MwzDagiyasFuTmqjpyEayvDg6Oooo9OzsbGE7qNLxxo0bMWvWLPz+++9KX8fT01M4CcsDDfZkTkLCwcFBRNE/fPiwXNdhGIaprvqEzes6CWfhi6BnYAjfkdNQq88rMLF1gCZDwRdDhw4VGVRUjIuKRJLdoEkuW1tbpa5x7do1UXTym2++Edq89D5ap0my8+fPl/qekJAQYSdpAs3Q0FAsr776qkhDfvTokYo/JcMwjBRvj5KTEBRROHlYT3nasV1z5f1LjBochb/99lsRzaX+/fvLB19kRGiGivZVNuRhdnFxEUYvMjJSOCbJUfk8fY7U1NQiC8MwjKZzPywJF+/HiPVxPeqpuzlaA0W4y5xxVNWYdGypCFdiYiIkEomIjqDIeBoIVSU0oUYDtdatW5d5DtsrhmEY4PwTfcJ2KtAnpLGDrXdD1OjQT+O6lmwV2SyCxlM0rpo/f77YT7qEFEm4efNmeHl5KX1NcgZSIEebNm3k+1q2bAlzc/MyC3hR5D1len311Vci24sm1r744gthr0hCg2EYRtXk5xfgxv1gsW5vYymypwhjI0P4eLki6fph6Bubwbp+Z+58TXYUvvPOO/J1JycnqAtK1yINDXolXUOKaly2bBkOHz5c5nsWLVoEGxsb+SITCmYYhtFk1h27J6/42MLnxVKvqhMUkUERFQSlHO/atUvdTUJWVhZGjhwpIumpTWXB9ophmOpOVGIGQmLSVKJPmB4RjPwszdWFpUrHf/31l1gnW/Us+6AsVDyLItiLpxjT+I2OlQZNnJHtJI1E0kqkhQJCaB85HUuDJ7YYhnkR/rtyB/HJaTAw0Meahe/i0PJP4eJgI6ofL1y+DT7v/g2vCV/DwMScO1qTHYWkTUGC8Hl5eXJh3bKWyqS4k49Su2gfhdiXxdy5c8WMnGwJCwur1DYyDMO8KAmp2dh7MUSsj+3hx5VyywHZKxrgkHOO0hcoirAse0XHKxuKEBk8eLCIZidh+GdVkmR7xTBMdedcgDSa0N7KBL41Kl54pLCgAPc2/opL38/QWGch2avbt28jNjZW2CqySWXZKzquDOQgpGjE4tA1SK+wNChLi+Q1RowYgczMTLFMmDBBFOAqa9zEE1sMw7wIWw+fE69dWzaEo501LM3NMO+NkWLf1buP8O/lUNg26s6drOmOQtJz6tGjh3yAQ7NLZS1VDYXqP8tBaWJiIq/gJVsYhmE0mc0nHyAvXwJ7K1P0a6l8yhEDTJkyRVSIpDQrKrL18ssvl2mvtm3bVqldRhEXpDdFacekOUXSGc+C7RXDMNUdmT5hGz9X6OuXLLyhLHG3ziE7MQZ2vk1gaFZ+TfSqgIpjkV2ghWwV2ayy7BUVKFEGktsgqQ1ZcAdBjkOSjnJzKz2Ve8eOHWJyjdKN6V4UYUgSHeS4pNTn0uCJLYZhKsrDsGhcuyvVPx3Rq518f+uGPhjZszXcDdLw+8Z9eBxZehQ0U/koLdD0/vvv45VXXhGCtp06dRIDnqqAqiGTwC6lGpMz8L///hMOSxk0GKSKXn369KmS9jAMw1Q2uXkF2PxfoFgf3cUHxi8o5F7dIK0l0iG8e/eumOTq3r27iD4vjQYNGqjsvlRVedOmTaLSMgnIy5yEtJ9sJleNZBiGeTYSSSEu3It+YX3CQokEYSf+FeseXQdrbLcbGBiIjC2yV1u3bsXRo0dFAZPSUFb6icZp5Bik6/bq1Uvso+KPZJPomAxyJpqamopJNVroPRR8ISvCRY5GWsoqPEkTW7QwDMOUl21HpNGE3jVd0dSvdpFjE5o7oPv1UziUWQdf/LEZyz6ZBkMDHgtVNeVScqcZKlq+++47dO3a9YVvToK6a9euRUaGNB3g559/FkaSiqLICqMcO3ZMiPKSo5BC6X/44Qcxg0WDO4rQOHfunAh979yZRS4ZhtENDl55jPjUbBgZ6mNkFx91N0crsbS0FALs5CisV68efH19X+h6NFgi+yNL0bp69Sq+/vprMXCbPHmy2H///n1hn4YMGSIchW+//bYQqf/f//6Hv//+W36tOnXqKB0ZwjAMU524F56EpPQcsd6ufsUdhfF3LiIrLhIO9VvCwkWztclpfEOagKNHj0azZs1eeIxF9o40cadPny5sDzkjaX3QoEFCJ1fRFs2YMQMLFiwQxSGtrKyERiJtU5sWLlwoMskGDBiggk/JMAwjJSMzGwdOXxXrw3u1KyGvlHnnqHgNyrfH7aBQ/LP3JCa81K3adt/xS7exYtthhEbHw9PVEZOH90K3Vg0r/b4VKvlIAy9VQIVF/Pz8xHqLFi3k+x0dHeXrY8eOlUcQkqHbt28fbt68KYTq7ezssG7dujLD6BmGYbQNSvNZe0RaxKR/q1pwtJbO7DMVgwZGqvpekpOTxfqYMWPEK21TNIbioGv27NlCRJ6gapEyeyZ7LyGbHGMYhmFK1yes7WoNVzvzikcTHt8p1j26DtGaLiYH34tOaslYtWoV5s2bJ+wV2S9y9lFghSJkqyiSkKCxFEUgfv7558Ju0nsaN24s9nERSIZhVMm+01eRmZ0LCzMT9O3YrMgxSX4uUm4ehYG5NZq0Gopb+89g+dbDaN/UD3U93aqlk3DuT2vl2w/Do8X2ohnjK91ZWCFHoaogJ6HMUVgW3bqV9B6T4aKFYRhG17gaFIe7YUlifVyPeupuDvMEiqqgCMJnQQM8xXMoBZlhGIYpvz7hi6QdZyfFITctGXb1msKyRtGUtuoCpQv/9NNPYimLhw8fFtmmaMMtW7ZUQesYhqmu0CTEtidFTPp3agFz06LyBWn3zqIgKw32bYbi9RH9cObmAwRHxOLzPzZjxWdvw8hQre6rKociCSneUlZ6kWowUgTmyu1HKt1RqHQxE4ZhGKbyWXdUGk3Y0tcZfh723OUMwzBMtSA7Nx/XgmLFejt/1wpfx8zBBa0++Al1B7+mwtYxDMMwL8qVgIcIiYyVpx0XJ+nqAfFq17wvTIyN8OmbL8NAXx+BIZFYvfNYtfsCHkfFyZ2Eis5W2l/ZsKOQYRhGQ4iIT8ex6+FifVyPZ0dbMwzDMIyuRdTn5ktgqK+HVvWeXSH+eRgYm8LERioDwTAMw2gGW59EE7ZsUBe13J2LHJPkZiPl9nEYWtjByqe12OdXuyYmDpZmmK7+9zjuPpKOk6qLlqNeMf1GgvZ5uSlX3KrKHYWyalgMwzCM6thwIhCSwkLUcLRE18Y1uGtVAOnc7tixg/uSYRhGS/QJG9dxhIWpUbnfT1EWQTtXIP7OJbGubezcuVOugcswDKNrxCYk49SVALE+opRoQj0jE9R9ewU8Rn0MPYOnKcYTh3SHby13FEgk+GzpJuTk5kHXyS8owMdLNiA3L19s6yk4Ccm+TR7WUzMdhVQVKz4+XvWtYRiGqaZkZOdh++kgsT6mm68Is2dUUzQrJiaGu5JhGEZL9AnbVlCfMOVRAKIvHUPk2QOlRmFoOvb29oiMjFR3MxiGYSqFHccuCGefi4MNOjb3L3GcntsWXg1h27RXkf2kS/jJ1FEwMjQQacvLtx7S+W/ol/V7cfa6VI5qVJ8OopCLsZEh6nq44usZ49G1CqoeV2gkOmnSJHz22WfIy9N9by7DMExV8O+5R0jLyoO5iSGGdvDmTlcREyZMwLJlyxAdLR2AMgzDMJpHfGoW7ocnvZA+YdhxafS4Z7eh0EbatGmDpKQk7N+/X91NYRiGUSl5+fn49/hFsT6ke1sYGhiUSDvOSy07EI0cZa+P6C3W/9l3CtfvB+t0evbmg2fE+uShPTBzwktYu2gGTq5eKF6rwklIVKhszOnTp3H27Fls3LgRPj4+ohqkIidOnFBV+xiGYXQeiaQQ/xy7L9bJSWhlVvSZylQcGnCFhISgdu3a8Pf3h7W1dZHjNOnVpUsX7mKGYRg1cuGedDLHyswIDWuVX1swJeQ+UoLvwsrDGzbeDaCNnD9/HllZWejfv78YX7m5uRWJjCRbRTaLYRhG2zh+8TYSU9KFg/Clbq2KHMtNjELilT2I2vMLnLpNhHPnMTC2LxlZPnZAZ5y8fAe3g0LxxbLNwmlWvGqytnP+5n0sXrNLrPdq1wRThheNrqxKKuQo7Natm1gYhmGYF+f0nUg8jk0DjQfGdKvHXapCyDk4ffr0Mo87OjpyfzMMw6iZcwFSRyEVMTE0KH/CU9iJneLVo+tQrUw7ltmjUaNGPdOeMQzDaHMRk+5tGsHBxqqIkzBg4UAU5ueK7bjjqxF/6h/Un7enhLOQZJk+mTYK4z/6GRGxiViyYT8+mDQEusKj8GjM+2W9SM9uWNcT898YqVZ7ViFH4Zdffqn6ljAMw1RT1h6ValB0aVwTns5PjSfz4owePZq7kWEYRoMhYfbzTyIK21VAnzAt7CGSH9yEhXst2NVrCm2lQYMGPMZiGEbnePA4EjcDQ0otYpKfkSR3EsqgbdpfWlShp5sT3h7dDz+u2YVtR86hc8v6aNPIF9pOYko63v9+NTKycuDqaIdvZk6AiXH5i3qpkhdWy4+Li1NNSxiGYaohDyKScf6JgPu47hxNWJmkpqYiJyenUu/BMAzDlI/gmFTEJGVWWJ/Q0MwCjo3aCm1CbY0mLA7pwCcnJ6u7GQzDMCqLJvT1ckcjH68ix7Jjy681SM7GFvWleu4Ll29FemaWVn9LObl5mL34b0TFJYlU6h9mTSwSdalVjsLs7GzMmDFDaD05OzsXKXISECAtec0wDMM8n/XHpNGEPjVs0bqeC3dZJbBp0ybUqlVLVEDevXu32Ldu3Tr8+uuv3N8MwzAaknbs7mBRoah6M0dX+I1+Bw71W0LbefDgAbp37w5zc3NMmTJF7IuKisKwYcNE5CXDMIw2kZaRhYNnr4n14b3aFZnMyYp8gMfr5pX7mvr6+pj3xgjhVItNTMHitdLf9tpIYWGhcHbeehAKfT09LPzfWHh7VKygl0Y4ChcsWCAKmmzdurXI/kGDBrHILsMwjJIkpWdjzwVpKP74Hn46EwmhSVy8eBFvvvkmPv74YzH4kjFkyBB89913SEtLU2v7GIZhqjvn70bJownLawfzMnTnGU5RhIMHDxZahJ9++ql8PxU1MTU1LTHuYhiG0XT2nryM7Jw8WJmboU/7otIQpm51YdukF2BQVA1Pz9AYhhZ2z7yuu5M9Zowf+OQeV3DyinYGq/21/QgOnbsu1qm6cbsmmpNdViFH4YYNG0Q0Ru/e0hLVMjp16oSDBw+qqm0MwzA6zdaTQcjJK4CdpQn6t66l7ubobDThzJkzMXnyZNjb28v3W1paikrIFy5cUGv7GIZhqjN5BRJcCowR623LqU+YGRuOi99Mx+Oj26ALXL9+XUTKLFmypEThEh5jMQyjbUgkEmw7cl6sD+jSQmjuJZzfgZijK8U+mhiqPfE7NJi/D/VmbZIvpRUyKY1BXVqhfVM/sf71im1IScuANnHwzDWs2H5ErI/s3R4jereHJlEhR2F0dDQ8PDzEuuLMX0FBAXJzi4pRMgzDMCXJyy/Axv8CxfqoLj4wMTLgbqoEyrJXBNsshmEY9XIrOB4Z2fmgx3Nbv/KlW4Wd+BeFBfkwtX8qg6TNsL1iGEaXuHQ7CGHR8WJ9SHt/BK+YgdANnyD64B/IT0+Sn0dOQXOP+vJFGSeh7Hf93CnDYW1hJoqBfLd6J7SFm4Eh+HL5FrFOUYTvjpNGR2q9o5Cqcp04caLEwGvFihVo3ry56lrHMAyjoxy6GorY5CwYGujj5S7aX61LUynLXt26dQtXrlxBkyZN1Ng6hmGY6o1Mn9Dfwx62liZKvy8rPhpxN88JJ6FzY82Kwqgo9evXx+XLl5Genl7EXuXn52PNmjU8xmIYRiuLmAz3NULayilIuXUMZjX94TvzHxhaPju1WFmc7Kwxa+IQsX7k/E0cPncDmk5kbCJmL14jgka8a7riy+ljYGigeQEjRRPCleSTTz7B+PHjRTqXzEF44MABbN++HXv37lV1GxmGYXQKEq5de0RaxKRvS0842Zipu0k6yxtvvCGcgVRsKzg4GKdOncKlS5ewdOlSYcdq1Kih7iYyDMNUW87J9AnrlzOa8L9dZExRs/NL0NPAAVZF8Pb2Rr9+/dClSxc0a9YMERER+PHHH7Fq1SqkpKRg4sSJ6m4iwzCMUlAF34vXbuMViwB0TgxFvp4+XHpOgWu/t6BvaKTSXuzVrgmOX7yF45dui6jC5v514GCr/qrBpUEVmt//fhWSUjNgb2OJ72dNhIW5KTSRCkUUkgg8aRTu378fRkZGQig+NDRUVJPs27ev6lvJMAyjQ9x4FI87jxPF+rgeUm0NpnJwdHTEmTNnkJ2djaCgIFHpeOPGjZg1axZ+//137naGYRg1kZaVi9shCWK9XTn0CbOT4hB3/TRMbBzg3KwTdAkKvhg6dCiOHz8uinF99tlnQq+QJrlsbW3V3TyGYRil2HH0PAoKC+FnlgJj+xrweWcV3Ae9q3InIUER2B++NhR21hZITc/EV39t08gq8fkFBZj3y3oER8TCxMgQ3858FW5Oqoms1JiIQqJ///5iYRiGYcrH2qPSaMLmdZ3QwMuBu6+SqVWrlijCxTAMw2gOF+/HoEBSCFMjAzTzdlL6fdGXjqNQUoAanQdC37DCQxmNhAIw5s+fLxaGYRhtg3RjUyOCsOvEJeTDAEnt30XPwf1gYGpZqfe1s7bEnMnDRUrvmWt3RSXkgV1aQlMoLCzEj2t24cKtB2L742mj0LCuJzSZF7KuFEV47550wOvn5wdPT83+sAzDMOomKjEDR6+FiXWOJqw6UlNThS5hYmKisFWNGjUS1SUZhmEY9XAuQJp23MLHGcblKOjl1WM4LFw94ODfArpaKZTsFY2z7O3thb2ytrZWd7MYhmGeSXbsYzxeNxfpUcGQpLeGsZEl+vbvDwNTiyrpuS4tG6Bfx+bYf/oqFq/dhZYNvOHqqBkRe5sPnsX2JxWg3xjRGz3bar5GeoUchTTQIt0n0iQk76hMcHfEiBFYvnw5h8YzDMOUwYbjgSKCws3eHN2a1OR+qgK++eYbLFy4EGlpacI5SIMwKnLy559/ol27dvwdMAzDqIHzd6WFTNrVVz7tmCBNQqfGuvnsPn36NKZOnYqAgAC5vSIn4ccffywkM8pDQUEBbt68KcZqjRs3huEzoi9pbEfnlga9lxyWDFMauYlRyM94WsHW0MJO6aq11YHq0D/0jEk4uwURO7+HJDcLoYaeKIQeerZtDFurqnESynhvwiBcvhOEuKRULPxzK36ePVntgQFnrt3Fz+t2i3VyZE4a0h3aQIUcheQkfPToEU6ePIlWrVqJPw6q0vW///1PHNu8ebPqW8owDKPlZObkY+vpILE+pls9UfGYqVxIj/CLL74Q2oSk+0QDrpCQEHz99dcYMGCAKHBiY2NTrmtSlIelpaXSAyca6MXGxopJNFNTzRQsZhiGqUoiE9LxODZNrLf1V66QSW5aMhICLsOleWfoGxlD10hOTsagQYMwcuRIoftOshkUDb9jxw688847Ihp+1KhRSl3rxo0bQlOeKibTIJnGanSdFi1Kj8IkDd8FCxYU2RceHo6HDx/i6tWr7ChkynSCBSwciML8XPk+PUNj1J+3R+ecYRWhOvRPXmo8Qjd8gtSAU9A3NoNR93fwzWYa6+hheK+qr0hvbWGOeW+MwIxvVuLS7SBsP3oBI3qpb2LpQWgUPv7tH0gKC9GkXi3MnTK8SFV7TaZCo9R9+/aJwVfHjh1hYmIiBj60ThpQXPWYYRimdHaff4S0zFyYmRhiaAdv7qYqgOzVRx99JKoek6OOBkx16tQR0e81a9bE2bNnlbpORkYGfvnlF9SvX19Upvzkk0+Ueh9F3lNl5Xr16on7T58+XUR5MAzDVBbZyfFIjwiWL7StaZx7Ek3oYG0K3xrKFemIOL0XD3etQtTFo9BFyB55eHgI+0R2iuwV2Q2yX3PnzlV6jEU2hhyK7du3R1hYGB4/fozu3bsLB2ReXl6p72ndujVOnDhRZKFIQlqoAjPDlAZFyik6wQjaVoygq87oev+QVuyDXycJJ6G5VyPU+2AzdsZQqq8e/OvURANvD7W0q23jehjavY1Y/23DXoRFq8cGJiSnYdb3q5CZnYsazvb45r0JMDbSHl1d/YpWkSyt8hbto2MMwzBMUSSSQqw7el+sD25XBzYWJtxFVUBZ9oqgSEJlbdbdu3fx4MEDbNmypcyIjOKQhu/o0aOFKD1Fily5ckVMslEqNMMwTGVATsEri2fh+u/z5Qtta5qzUKZP2NbPVanoiryMNERdOApDMwu4tOiC6mavyjPGOnPmDAIDAzFv3jz5PlqnCHpyACoDRcHv2bMHr7/+upKtZ5inSPJLd0hXJ7JjHiFi90+lHnu8bp5I080MC9DI6rzKoqdvALf+b8O171vwfXcNcsyccPjsdXFseE/1ykNMHzMA7k72yM7Jwxd/bEGBRFKl98/OzcMHP/6NmIQUWJqb4ocPJlV5GrZaHIUvv/wyZs6cKfSeZFBo/Pvvv690SDzDMEx14mxAFEJiUuVpx7qGpkawDB8+HIsXL5YX3iLoR9nff/8tNJkoWkIZWrZsKdKXSdtQWUgDkVLF3n77bTEQpvdOmTIFv//+e4U+C8MwzPPIz0hDYbFBOm3Tfk2aOLtwL6Zc+oQRZ/ZDkpcD93Z9YGhqDl2kSZMmSEhIEPZJ0XlA9uunn34S9kwZrl27JrK9KAJeho+PD6ysrMQxZaA2kKbhuHHjyjwnJydHjP8UF4Yhgv96B+mPlPtb0zVy4sPweP083F00FOn3z5V6Tnb8Y8Qe/xv3v38ZdxcNRvTBZeJ92kD6w6t49Ne7kDyJlLRr1hdu/d6EnoEh9py8jJy8fFhbmqNnO/UW67AwM8H8qSPF7++bgSHYuP9Uld1bIpHgi2WbEPAwDAb6+vjq3XGo5e4MbaNCsY80U3Xu3Dns3LlTpFORMaOZK0rNojD3rl27ys9VduaKYRhGl1l3VOqo6tTQHbVdrXUygkVxcKpnaIQW730PU1v1RpkfOnRIREbQgEmW/ksahdHR0WLg1KdPH/m5n332Gbp0UV2kyqVLl4RNVIRkOiiiMCoqCm5uuqFPwzCM5pCfnQlN525YIpIzcuQRhc8jPysDUecPwsDEFO7t+0JXOX/+PHJzczFx4kTMmTNHaBRSNPr9+/eFvi7JaMggW0U2qzSSkpJK1RSkiEQ6pgwrV64UqcplRTgSixYtKrMNTPWACnOQ5l6R9Fo9fUjycmHi+DTttLAgXziSdB36nA9+mYi8lFgY29eAY6fRiNr7awmNwnqzNiEz7A6SruxD2v3ziNq3RCyeoz+DQ7th0NQo0ej9SxBzdCXNuCP1zinYNunx9LhEIq/qO6hLK5gaG0HdNPevg5f7dsDG/afxx5ZDaN/ED7VrulT6fZdvPYyjF26J9Q8mDUHrhj7QRir0H0s6F7Qo0r9/f1W1iWEYRqd4GJmCM0/SrMb18IMuEXfrPBLuXik7gkXNjkJ/f38hAq8MqpbOiIuLK5GmLLsHHSvNUUgRGrTI4AgNhmGUJTMuEoFbl2p8h8n0Cb3dbOBi9/zowMizB1CQk42aXQaL1GNdhewDZW0pa9vKwtjYGFlZWSX2Z2ZmimPKBIRQFCNFxT8L0k2kDDNFe0Uai0z1gQpyUGGO4lV9DcytYGBqKbYpsvDx2rlw7f0G7FsPgp6B+h1IqiQvLUEU9DCvUU84Q90GvCOinx3aDoO+oRHsmvYpteqxmVtdOLQeLN6bdO0gkq7shaVva/l5YZu/hHmtRrBt3EPel+oiKypIfIdZEfdgaOUAz1c+h02DzkXOOX8zEBGxiSKCb3jPttAUpo3qi3PX7+NxVBw+/2Mz/vz0LRgaGlTa/faduoLV/x4T66/064QhT7QSq42j8Msvv1R9SxiGYXSU9cfvyQdF7ZSs7qgp5GWkIjM2osji3r4PHPylDrCYy8eRHHQbmgppBKoLEqKnipOKyITkDQxK/5HCERoMw1SUjKjHyE1NEhE9KJQUifA2tLBCTnICjG3s1V5x8fyTiTNl7SEl4RpZWKNGh37QZUieQhVjLC8vLxGJSJleFhZSx2p2drZIa6Zjz2PFihXw8/MTEfDPggpa0sJUTyijMPn6Qdg07PbMCr6Zj28iNykKoRs/RfThP+HadxrsWwzQ+gjD/IwUxB5bjbiT62HsUBN+H26Fnr4+HNoMKXIe9c2z+sfI2hHOXcaKRUZ2TDDiz2wCzmxC2OYvYNOgC+xaDoC1f0foG1Ztxfe4k/8g4t8fRFSkTaPu8Bj9KYwsS0Ysbz0sTbNu36Qe3J1LHlcXFNn4yZsv4/VPl+Duo3Cs2X0crw3tWSn3un4vGF/9uU2sd2zuj+ljtDuQTrv/QxmGYTSclIwc7D4XLI8mVPcArawfe3lpySKF2NpTGh5POoO3V3+D/MySulY2tf3kjkKK8LD3b4FHu/+u8nZrOlRVmVKcFZFtUyXk0uAIDYZhKvIMJ9vi1LgdTO2cYGRpg/zMdPlxchJSlPf1Pz6DY4NW8B40EXplTFZUNlm5+bj6MK5c+oRePYbDo/Mg6BtV7QBZW6GsL5qoomIksghFqphMqYE9ejxNFTx9+rSwU5TiLCM9PR2bN2/G559/rpa2M9pDWuB5hKz+AHbN+6HWq9+WeZ5zt1dh3aALog/+IVJtQ9fPR8yhP+HaZyrsWg7UyN/Fz6IgOx1x/61DzLG/IclOh4GZFeya90WhJB96+qp5Rpk41xLpydRfSVf3I/n6IbHQvTxGfSz6vKqgiEdy6nqMnA/7NkNK/b4iYhNw7oa0YOPwXuotYlIaVH15wkvdRKTfih1H0aGZP+rVKv13eEUJi47H7MVrkF9QAB9PN3z+9itCn1CbYUchwzBMJbL1VBCy8wpgY2GMAW2e/hhXJ6mhD5AW+kAaIRgnjRIsyM4UUSftP10pZkSNre3EjwGb2v4wd64BM6ca4pUWGoTKsK1TH6b2zgje/08JjUIanFYnKNUrJiZGOAGNjIyEXu8PP/wgoghpmzhw4IAooFKW7hNHaDAMoyyFEgmCD/wj9KLqDBgv9ll51JUetHMqcm52YqxI242+dAy56Smo9/J0GKjB8Xb1QSzy8iUwNNBHSx9npRygBDsJlYdkLd577z1RSIsKT1IE++zZs/HWW28ViSgcOHAgZsyYgQULFsj3bdy4UdisCRMmVODbZaoTcf+tF6/kPHoeps61UGv8Irj2fh3RB5Yh6doBJFzYCftWg6BNpNz+D4//mY+CjGTom5jDpfcbcO42AYbmT38XqwJ67pl71BeL+0vvIf3hFeE0JGchRS/KiD+zGeaeDWFW01+lDldKF7eo3VRc07Xvm3BoNxwmDmU71kibkJ7XNV0c0LaxLzSRycN64PS1uwgKjcLnSzdj1ZfvwNhINa6w1IxMzPp+NVLSM+Foa4XvZ02Euan2R1uzo5BhGKaSyCuQYMOJQLE+spMPzIxV98il6D/FKpbklJMVDqHBY05yPDJjw+XpwnY+jeHURFpYI/riUcRee1r9y9jKDlY16sDMuYYQoCbBeGMrW7T5SDmtK7ovFS4pqz26AP0Aevz4sVgnDUEafFFRFNJ7cnd3F/v/++8/9OvXD3fv3hVpW1OnTsUvv/yCV199Fe+//z4uXLiANWvWYNOmTWr+NAzDaDuk2Xd/8xIk3rsKE1sHeHQbCiPzsnWsaEKnydRPcefv75B49wpur/wK9cfPeuZ7KlOfsEkdR5ibPlurLPLMfiQH3UKdgRNg5sjFn8rDt99+Kwp47dq1S9gvihB84403ipxDqcWK0YQEaROSk1HVmr2MbpETF4rUgJMwdakDq3rKR5DR+RR9SA42RXmEuFMbYGhpB9smvcVktSZBv6llbaICLYV5OXDuPhHOPSaVmoKravT0DWDl01osNUd8JNd4pEi/sC0LRT+auNSGfYv+sGsxoEgRmfKSn5mK8K1fCb1Er/GLYN9yoNBZfJaTMDs3D7v/uyzWh/ZoK6KZNREjQ0N8Om0UJn38Gx6GR+OvbYfx1ugXj8zMzy/ARz+vExqIJsZG+O79iXBxKLsIlDbBjkKGYZhK4ujVUMQkZcJQXw8vd/Wt9CrDjV//GEE7VyArPko4/Ir/0JA5Cp2bd4J1LT9phKCTu0rE4YVTUIccg6VFC1KEoAxyCtI2DcQOHjwo9pmZmYloDVn0IFWdPHnyJObNm4cxY8bA2dkZ69evx7BhmlnRjmEY7YC0BgPWfo+M6FBY1vRG/XEzlXL4kc5fo8nzcG/Dz0h6cBO3/vwcDV6dLRyNVcW5u8rpE5INCz+1F/lZ6RxNWAEoEmjKlCliKQtKTS7O999/X5HbMdUMcuxRJLNT57EVimSjQh4yCrIzELn7J0hyMmHq9gfc+r4Fm8Y91O4wLCzIQ8LFXYg5/BfqvvkHTJw8YepaBw2/OKa24iKK+oQU0eg55vMSlZPNazUWfWjt36Fc1057cBGP181DXnK0cOjSogxHzl1HanomTIwMMbBLS2gyPl7umDysJ/7YchDr9vyHTi3qo5HP83Vby4ImYb5bvROX7zwU2wveGg3/Ok8jPqulozAlJQW7d+/GuHHjxPb27dvx448/wtvbW0RP2NioNvyWYRhGG1l7VKrX0auFJ1yVqOyoLBS5V1qVYTJYVPXSxNpeniZs9sQZaOYkjXojbOs0AJSz/zoBRfD17NkTDg4OCA4OxrvvviuqP5JofNu2ylVmMzc3FxGEz6JLly4lzvHx8RF6TwzDMKogLfwhAtb+iLz0ZDg2agOf4dPKlUJMEeP+499H0I6/EHv9NNIiHlWZozA+NQuB4clK6RNGXzkhPqNrq+4wsak6R6a6uXr1KnJzc4VtIk3BDz/8EGfPnsWIESOKVBhmGHVq9CWc3yHV5ms18IWvZ2BqAZ///Y3oA78j5dZxBK+aCVN3X7j1e0sUz6hqDcNCSYFwvkUdWIrc+DBA3wDpQZeFo1DaXvVWIJZhYGIuqiYXrZy8D5khN4WTUwalLZvVqFdmu6lCc9TeXxF7Yo3U+dtlHNwHvgt9Y9PntoHGHbIiJr3bN4WNperGOpXF+EFdcOrKHQQ8CsfnyzZj7VfvwtSkYjIc/+w7hX+PXxTrb73cF91aNYQuUSFHIYmtN2vWTKzHx8cLHYtJkyYJ40bpVX/99Zeq28kwDKNV3AyOFwsxvoefSq+dl/VUpF4RfX0DtPv4T5EmwEg5duwYVq5cKRd0nz59uqj+SBNbQ4YMEenEXLWRYRhtgAZlIYc2CQeaR9ch8OwxvEJRN/oGhvAZPhWurbrB2qseqorzT9KOrcyMUN+z7JQ9SX4+Ik7uIaOGml1eQnWBbNPYsWNx+PBhsU0R6LS89tprWLRoEerXr4++ffuqu5lMNSfz8W1RuMOxwyvCWaUKzGv6oc6UX5AZFiAcdKm3TyB4xQx4vvI5HNoORVU9X1NuHEHU/iXIjn5IYbmwa9Efrn3fgqlzxaPOqgLFysk58WEwspVGbEtys/Bw2ZsoLJTApmFX8XlMXb0hyc6Qvzfu1EYkXtgBIxtneI75AtZ+0uwjZbjzMAz3giPE+vBeyr9PnRgaGIgqyK9+9LMoQPL7pgOYOaH8dubk5Tv4bcM+sU6RlOMHPc06qtaOwp07d8qrYZEwO816/frrr2LApWx0BsMwjC6z7ug98dq4tiMa1VZdSm5OSoKIBCkLdhKWtFdDh0p/ZJKuIDkOw8PDRXThtWvXcPnyZXToUL70DIZhGHVAkTX1Rr2NlOC7cGrU9oWvJXMS0gA5eN86IUlBVZErW5+wdT1XUcykLEhDl2ydc/POoopzdYECLlxcXEQlYmLbtm349NNPMW3aNCFlQZWL2VHIqBurem3R8LMjIvpM1VDxDu/Xf0XG49uisjBVE5bpBKYHXYKlT+vKizAslCBq32/IjnkEm8Y9RUSjmbsPtA1FjUKKFnRoPxLJ1w4g+dpBsRRHz9AYti36w2P4RzC0KF9W6LYn0YQN63rCr7ZqqwhXJrXcnTHt5b74ed0ebD54Bl1aNECLBt5Kv/9+SAQ++X2DsJ3N/Otg9mtDta56tzJUKPk/PT1dLlR5/Phx9O7dW6zTwCsj46mHmmEYpjoSnZSJw1dCxfr4HqqL1shOisPNP78QhUqgV/TxXR2rDJfXXp0+fRqNGjUStopgm8UwjKZD0XUPd60WxakIY0ubF3YSFocKXkVdOCy0C6MuHkVlQAOq8zJ9wvpl6xMWFhQg/OQuEc3j0WUwqqu9KigowKlTp3iMxWgkhha2ovhIZWHh1RC1JnwNfWMzsZ184zCClkxB4OJxSL17RjxPVEFa4AUk3zgq1/L2ePkT1Ju1EXUmL9ZKJ2Fp31PNoR+gwYJDqDt9hUjlLk5hfi5cur1abidhUmo6jpy/IdaH91K+oI2m8HKfDmjmV1usf7F8CzIys5V6X2xiiqhwnJ2TBw9XR3z97jhRKEUXqdCnatWqFWbPno1evXoJ7aXz58+L/VeuXEGLFi1U3UaGYRitYtOJQORLCuFiZ44ezaWaJqpyFOamJcOtbW/U6NAP+VkZOltlWFWQvaKIdzc3N5G6NXDgQPkg7Pbt22jSpIm6m8gwDFMqeZlpuLv+J6SG3BPP/wavflApPWXhUlMUNbm7/kc8/HelsDOe3YepLELiyNVQ/LTzOmKTs8S2RPKMQb6+PuoOmYL0iEcwc3x2wRNdg+zRxYsXsWLFCjx48EBEF9apIxUU5jEWowlQWrCld0tY1m1ZpRFUJg41YenbBumBF/Bw2TRY1GoC1/5vw8q3bYXakR58HVF7f0P6gwswtHaEdf2O0DcygaW3bvoxZJWTSacw5dYxlVxz94lLyMsvgJ21BXq0aQxtgyZl5k8diXFzfkJ0fBJ+Xr8HH70+4pnvycrOxQc//I24pFRYW5jhh1kTYWP14gUhdcpRSIOuV199Ff/++68Q2W3QoIHYT4OwOXPmqLqNDMMwWkNWbj62nHog1l/p6gujZ6RXlRfbOvXR7O2FojCJLoa4Vwakn0tC8PRKE1nvvfee2L9q1Sr069dPDMQYhmE0DSpMRZWNsxNiYOvdEPVGvVWp97P1boBGUz7Gnb+/Q9ix7chLS4b3oInQMzB4YSfhe3+cKrLvq42X4WRjhp6lTKSRbaO20FLdIHv0888/47PPPhPauaSvS8TExAipp6+//lrdTWSqMVnRDxG9/3dRGKPeB1uq9N7mng3g8/ZfSAu6hOh9S0SBjoe/vyEclnXfWg49A+W0uYUG4r7fkBogfSaZezWG+4B3RPotozwFEgm2H70g1l/q2hrGRtoZUVfD2QH/GzsA36zcgV0nLqFrq4Zo37R0XXmJRIIFSzeKtGMDA30senc8PN10WxqjQt8qieleunSpxH7SzjB4wR8UDMMw2sye88FIyciFqZEBRnSq+8LXS48IRtTFI6j70mtiwEaVjBnlMTU1xdq1a0vsJ3H4yZMnc1cyDKNxJD+8g7v//ISC7ExR9bfOoFdFAZLKxtK9FppM/RS3V3+D6EvHYGRhDa9eI1/omkt23yyxj+a5lu29VcJRmBEdCmNrOxiZV18ZDbJLxW2Ts7Mz7t+/L09LZhh1EH/yH/Hq1Hms2iarreq2guU7q5D+4CKi9v8uinbInISS/FzoP8Phl3BhJ0L/+Vism9Xwg9uAd2Bdv1O1mng3tLATTlFKN5ZB27S/PJy5dk9E4enr6WFojzbQZoZ0b4MTl27jwq0H+OrPrVj/zcxSqzcv3XwQ/12+I9bnvDasXJqG2orhi1TmioyMFP9c7u7uYuaLnYQMw1RnSDNl/bH7Yn1Qu9qwsTB5oeulhj7Anb+/FYNFB/+WsPeTVptnyv+9REREIDMzUwy4bG1tecDFMIxGkpcSh4A130FSkI/a/cfBvX3fKh3Imto7C2fho33r4N6h3ws9dw9cfoygyJRSjgHB0alF90kkuL95idDgbfn+YuGkrK4kJSUhLi4OFhYWYoxF3391cmYwmkd+ZgoSL+0WDiWqnKtO6H/ByreNKGxSmJcj3x+6cQHyUmLh2OFlmDhIJ9Xz0hJh5uoNY3s34RQ092wI5x6TYNu4Z4Uqxms71A/15+1BfkaSfB99p7S/PGw7fFa8dmzuD1fHytOqrKq/p3mvj8CYOYsRn5yGH9fswmdvjS5yzp7/LmHt7hNifdzALhjUtfKKfmm1ozA4OBgzZ87Evn37kJsr9UaTk3DAgAH48ccf4eWl2eXDGYZhKrOi48Mo6aBobLfSQ9eVJflRgEg7k+TmiGgSdhKWn7y8PJHC9ccffyA+Pl6+v2nTpli4cCH691fvj12GYZjiGNk4oc6ACTC2toW9X3O1dBA56eqNfJrqnBETDkMTM5jYSgtBPY9HUSn4asMlXLgfU+px8nnVdi3qCEy4ewWZMeHiM1dXJ+Hu3bsxf/583Lz5NArTyclJVD3+5JNPYKijgvmM5pNwbjskuVlw6jpOaPlpAsKBbmwq1gsL8pAbF4qMkBtCx7DIeQZGqD9/r3CG1Xt/A6o71A/ldQwqEhoVJ6LvtLWISWk4O9ji/QmD8dmyTTh45hq6tGyA7q0biWNXAh5i0YrtYp32v/WytBJ3daBcFichIQEdO3aElZUVPv/8c9SrJ63mee/ePaGjQcfIuNnZabdnmWEYpiKsO3pPvHao7wZv9/JVD1Mk6cFN3F33o4goqTv0dbi27MpfSAWYOHGiGHhRmnHbtm1FdEZUVBR27dqFQYMGiVea5GIYhlEnBTmZSDi/A06dx4jBr2vrkpUp1UVuegrurPoa0AMaTJwjCp+URWZ2HpbtvY21R+6Kgl5EM28nXHsYJ5yDFEkoe31zYOMi0Ydhx3eKdY9uQ1AdId33oUOHioJb06dPFwW4qAryuXPnsHjxYoSGhmL16tXqbiZTDSksyEfcqQ2AviGcOhaNtNIUyBnoM2Mt4k6uR8T2b4ocIyciRdC9iHOMecr2I9Iitp5ujmjV4MUlljSFvh2b4cTl2yK9+NuVO9C0Xm2kZ2Zh7k9rUVAgQb1aNbDgzdHVKiOpXI7Cn376CbVr18aRI0eE7pMi7777Lrp37y5EeBcsWKDqdjIMw2g0lEZ16nakWB/Xo+LRhCkh9xGw9gcUFkpERIdTk/YqbGX1gSatdu7ciQsXLqBhw4ZFjr3xxhv44osvMHv2bHYUMgyjVnKTo/Fo+TvIirgHfSNjOLZ/MU1AVUPRfU5NOyDi1B7cXP4Z6o+fBZta0kABRUffkWth+GbzFcQkZYp9FDE475VWaOPnKgqakCYh2UnaT07CHs085O9PCryBjKgQ2Po0hlVN3dd9Ko25c+eKSHd6VWT06NFisqtdu3aYNWtWCXvGMJVNfkYyTJw8YVmnGYxsnDW2w2mSxbKOeqKwqwtU9XfPyctifXjPdjrlNKO/nw9fG4ob90OQnJaBETO/RWa2NLWdKhx/P2sizEyrV9Gbcn27+/fvF6HvxZ2EhJmZmXAQUkoywzBMdWP9MWk0YS0Xa7SvX/FZS8satUV1Y7/R/2Mn4QtAtmjs2LFlDqrISUjRhRSlwTAMow4yw+7g/g9jhJPQtmkv2LccqJGDp9p9XxF6iaSXe2fVIiQESAeKxOOYVEz75Thm/nFKOAnNjA3w3rCm2PZxf+EkJKhoydaPB+DKklfEq6KTUBpNuEOse1bTaMKQkBDExsYKR2BpNGnSRNgzHmMx6sDI2lFUHPYa+yV/AdWcg2evIT0zG6YmRujfqQV0DQcbK/TvJHU2Zz5xEhKpGVm4HVT9xgvliih88OAB2rQpu7INHaNzGIZhqhMpGTnYde6RWB/Xox709csvOp6XmSYqPRoYGaP+qx+ycPkLQraoffuyozGNjY3RrFkzcZ6nZ9HKmwzDMJVN8o0jCFk7F4V52XDp9Trc+k/XaHH9Gh36wdjKFoFbl4qKzJ4DXsW/sc5YdSgAefkScU6v5h74cGQLuNpbKH3d3NREZCfGwqa2P6y9ikYqVhfIDpE9MjKSVm8ta4xFEfIMoy5k1YWrQ1VfpiQ0qbP18Dmx3rdDM1hZmOlkN118or9YfMJs5fYj6NaqekV0l8tRmJaWBhubsnW36FhqatEqZgzDMLrOjjMPkZVbACtzYwxqW6fc74+6cAQhhzah4aS5sKpZh52EKuB59opgm8UwypGbGPXCVRKZpyRdP4SQVe9Dz8AQnmMXwqH1S1rRPU6N28HIwgq31vyIJbtvY1+CNA3Ry9kKc0e3RIcG7uW+pomNA1rOWoy8jDRUV9heMZpKOOn96enBre+bMDCzQnWp6suU5EZgCIJCo8T68F66K4sUGv20+KGik/RxVByqG+VyFFInPQvytkok0llFhmGY6kB+gQT/HL8v1kd0rAtzk/JVJQw/vRch+/+BgYmZEIxmVIMytohsVkFBAXc5o3FokmOO2hKwcGCJCA0ajPHgq2JY+7WHZd1WcOv/Niy9tSd9KywuDd/sj8eVmI5ILzSBiZEBXu9bHxN7+cPEpOLRRgbGpmKprrC9YjSRvPRExJ/ZDANzG7gPeg/VpaovUzrbnkQTNqlXCz6eutu/nq6OeBgeLYpuKY4XvNycUN0o34gWKFWfsKJkZGRgw4YNWLZsmaicTBqInTp1eu77li9fLoqmxMTEoFGjRvj+++/RooX2/NBiGEZ3OHY9HFGJmTDQ18Mr3XyVfp+symPo0a0wNLNEw0lzhD4hozrGjBmDcePGlXk8Ly9PCMUzjCbxIo45eq7I3qdvZCJe81LjxVKYnwNJXu6T1xxxnnX9zjAwlaaJxhxdBUlulvyY9DUH+iaWRdoi7pOfi5Q7/8GqXjvRJn3D6iXwXRHyM1KQmxQF85p+MDC1RN3pK7QmejwnrwArDwZgxYE7Yh0wQdcmNTFnVAvkXt6Fh1uOot7LbwvpjPLwcM8a2NTyg0ODVlrTF5XFjh07njnGImfiSy9pR+QpoxsknN0qnvVOHUdB31Dz046ZyiMhOQ3HL94W6yN6tdPprp48vJeodKynpyd+U8leJw/riepGuRyFS5cuVenNqepkXFwc5s+fj6FDhyoV2bF+/Xr873//w+rVq0UFsO+++w49evRAQEAA3N3Ln/LAMAzzIqw7Ki1iQuLsbkrqMokQ9kObEH5yt6go2fC1ubBwZZ08VTJ16lT07Pl8o968OVfIYzQLiiQszTEX/Pcs6BsYSR1M+gbIS0tA4I9jijn2pO9z7PgyPEbOF+tx/61DzJEVpd7L/6N/YWAqlUuIPrBUOAqLY+ZZuiZP+NaF0hU9PRhZO6HWhG9gWbel2JV86xj0jc1gYl8DRnau1d6RmB0bgkfLp6MgKw31PtgEY1tXrXGMnbodgUUbLyMsLl1s13C0xNyXW6BL45ooyMvFrUd3kR7xSBQ58R/3PozMLZW6blpYEKLOHURa6APhKKzOkB1asmTJc8/z9q6eFaGZqqewIA/xpzcJXUIHDavEzlQ9/x6/gPyCAtjbWKKrjuv0kQ7hohnjhSYhpRtTJCE5CXX9c7+wo3DatGkqvfnXX38tXsPDw5V+z6JFizBp0iR5FMgvv/yCbdu2CScmOR4ZhmGqijshCbj2UKpZMb6Hn9Lvy4wNR8TpfTC2tkPD1z6CuRNPcqiaXr16iYVhdIXMkJvilZyBesZm0NM3REF2OvQMTWBgbg0jQxPoGZkIp5yx/dNnirlHA9i3Hgx9I1PoGRlDX5xnLCIODSxs5ed5jV8EPT19+TVkr7nJMXi0/O0S7bFtMQCFeTnITYxAbmIk9E2fOojCNn2O/LSEIo5EY/sasPRuDvdBM+QRdgWZKTrvSEx7cBHBK99DQWYqHNqPgJGVA7SBqMQMfLPpCo5eDxPbxob6eK1PA0zuWx+mxtLhA0UQNpo8D3c3/IzkBzdx68/P0eDV2TCxff5nDH1S6dij62CtcZpWFnXq1FH5GEvdmovW1tbydUNDQ5iZmYmAkPT0dFhYWIh9OTk5YpGdS8f09fVhbm4uIijpvbRORV7ovOzsbLn2sOK5NPlKGvl0DypUlpubi6ysLHFd+tuiDDaC7vu8czMzM8W9LS2lz7OUlBQR6WliYiKyEOi4lZWVuHfxc+m6dB4t+fn54r50zMDAQNyD9tF7ZefS/enazzv3WX1IfUKf4Vn9rUwfFu/vuMv7kZcSK2wHPbPoXOofZfpQsb9lfVj83LL68Fn9Te+jz6TY38r2oeK5sn6RnatMH5bV33SMrkHnlvU3ezUwDCu2HRZOJw9XR7w+oje6tKgvzi3eL7Lvpjx/s3QunVORv9nSzi3eh2bm5thxVFpIaWDnFjAyNKz0v1l1PyOa+3qgw2dvFTlXFl2o7c8IOqYsKi2vFhYWVqnOuuTkZNy5c0dEEMqgL6F79+44ffp0pd2XYRimNNYdk0YTNqzlgCZ1HJXuJAsXD/iNeReNX/+EnYRqgozs2rVrcfnyZXU1gWHkFORkIu3++Wf2iPdby9H4m3PQM5KmJxpa2KDxojNo9MUxNPjkgIgO9PtgM3zfWweXHq/J32fbtBe8xn4Jj1HzUXPoh3Af9K4QpqdzjCztn57XuAdsGnV7op/XEhZejWBWox7M3HxE6rMitF1j4LuoM3mxuGfjRafFuQT9mHbt/Qacuk6ATeOeMKvhB0leNjKCryEr8mk1wZSbRxHw5QDcmNUStz/pgcCfJiBkzRxE7v0FOfFS55S4nqSg1PTszLAA+ULbmkjC+R0I+n2qiCSsMWQWPEZ9ovGVQ/PyC/DX/tt46ZPdcidhhwZu2PHpQLz9UmO5k1CGgYkp6o9/H05NOyIzNgI3li9ARsyzAwDSI4KRdP86zF09Ye/P0kHKcObMGfz999/l+i6joqKEnVuzZg0iIiKUft+5c+ewcuXKCo+trly5Il+/evUqHj58KNZpwHrq1CkxnpMFitC9ZFy/fl1UgCZoYEvnJiYmyj8L9YGM27dvC9kqggbMdG58vLQIAUlT0bZMW5+yzmghaB8do3MIeg9t0zUIuiZdWwbdk+5NUFvoXGobQW2lNiv2myz4hT4jnUufmaA+oL6QcfHiRTx+/Fg+cKdzZc6KkJAQXLp0SX4u/U4JDg4W6+R4oHPJMSAbeytWw7527RqCgoLEOjlOFPub/gYU+/vGjRsIDAwU6+TgoHMTEqQTPNHH14hXpy5jxSuNv2X9TY4IOpeyAonY2FixLdPavHv3rjhfBh2Ljo4W63R9xf6+f/8+bt6UToIRZ8+eRWRkpFhPSkoS59LnkPU3fT4Z58+fl/c3OVXoXPptRzx69KjI3yH1J/WrYn/LHCbF+5veR+8n6Hp0rqxYK92P7qvY37K/WVl/U7sJ+hwrNv4r0lhJ846ercHhMWL76PkbRfs7Olpsy6D+o34kqF/pGPUzQf1O27JMTPpeFPub/m+L9zd9vwR93/S9y6C/B9mzQfY3K+tv+juiz3fySgDiklKhr6eHFj7SSUj6+6Nz6e+RoL9Pxd/Tiv1Nf9eK/U1/9/T3L4OfEdlV+oxQ/H97HnqFz6tQ8hzoD2/Xrl1YsWIFDh48CBcXF/k/uLLQP52HhweOHz+Orl27lnkePeQbNGiA//77D507d5bvf++994S+oewBVhyZN1oG/bPT/ei+Mu8qwzC6jUW68g9GZYhNzkSfj/4VxUy+ntweA1o/W1+wsKAAMVf/g0uLrtDTV+kcjc6RmpYBu3p9xCydqp/RZIT/+usv/PPPP8Iu7Nmzp8jkk6ZB9opmR9le6SZUwCjhwk5E7f9dRNeRDiGhacVDVFFchZxllNpsZCOtlJsScAoJ57aJaESKSqSIOxm+M9bConZTsX7ni/6iLygikSIlDcyshcA+JPka0z/HL90WESNULZGE0EnjyC/uBKIP/iFSsCktm5yw6rJXynLubhS+2nAZITHS78LN3hyzR7VE96Y1nxv1R8OJkIMbEXFqD2y9GwpJjbK4u34xEgIuw2/0/+DYqA20ncqyWeQYIEcfjbHIaTB9+nT8+uuvSr137969ePnll9GxY0cRgUJjLJJvIqmnsiAHx/Dhw4Xzpk+fPmI8RxErW7ZsKZe9orbWqFFDY6OFOKLw2RGFhgYGiDq+Fhmht+Ez6Tv5uRxRWP6IwvEf/YzQ6CfR9U+gfvT2cMGSOa9pRUThnF824ErAQ3Rs5ocvp79SJVGw/IxApUUU0nPdz89PKXtVYUchOeXIcNEsFRmy2bNnY/DgwWjbtm25UwjK6ygkLykZPhnvv/++GOyRYSuNBQsW4LPPPiv1vuwoZJjqgaoHXr/+ewPL992Gs60ZDiwcDCNDgzLPleTn4/6WJUi4fRGePUfAs1vZP9QZ1Q+6aJaUBkjkIKTZfxoAvf766+jbt688NUZTYUehbkI/vVJuH0fk7p+QExMM6BvCscNIEelnaGmnUVWPq4r8zFRR7IOchlY+rUXBD+qnwMVjkRP3uIgjsTQo9dnI2hGGFrZiMbCwg6GlLRzaDIWpi3QihyIayalI+w1MrVQyaUNOQqnwOX2vkL9+P8Qbtrc3o87rv4kCJuWhqh2F0UmZ+H7LFRy8Eiq2DQ30RSXj1/s3hLlJ+eoeRl8+AXu/ZjC2lA54i5MRHYprv86FmZM7mv/vG52YOFOlzaJBPAVe0BiLAjHc3NzEOGfIkCHw9FROy5gGhl5eXnjttdeEZBPxySefCB3E0NDQMu3esGHDRBQRRSTJPgetK465ngXbK6a6QzbrUXgMTl0NwOmrd3E7SPpMLY6xkSFOrn6i86vBUATkK7N/FOtLPnoDLRqwTqq2Q8/pmjVrKmWvymX9yZO5efNmYbwo7LZ37974448/xOyUTG+wMnF2ls5Cy0KdZdC27FhpzJ07FzNnziwRUcgwDFMRqOrjlpPSVIOXu/g+20mYlyv0myjNysK9FtxaV7+qWer6sUbR5+QcJB3b2rVrC+egk5MTpkyZIqImygulcnz11VcibJ9szltvvfXMyS1ZGs2ff/4p0gRsbW2Fc3Ly5Mlilo+pnuQmRyNk9YciFZewbdob7gPfhYnTUycAOQV13TFYHENza7GYP0lhJmjiud7Mf4o4EtMCzyNy5/cl3i/0FBMjkRMrTXeSYe3fUe4oDF418+lxfQMYmtsIxyylR9eaIP0dS2nPKbeOSR2N5HS0fPqqb2JRYjKcIgnt9bNgoZcrlqxCQ6QWmuKHM4n49YO/Yeqmud9jXoEE64/ew+97biErRxqh2cbPFR+90hJ1XEt39D0P15ZPn4lZ8VFIfRwIlxZd5PsMjE3h1Lgd7Pya6YSTUFWQjVi1apVYaJwybtw4fPzxxyJNkIo4lgcKvKAgDrJRMt588018+eWXOHz4sHA6FofS3qjyMtlLxcGjsk5CRjeQ5GaLyRT+31SevPx8XLsXLByD5CCMins6yfcsHoVHo05NV2gy245IU9Vr13BG8/rSwmdM9aFcIxWa1SLjQTNUlLal7MyWqnB0dETdunVx8uTJIqHzNBgcNWpUme+TiUYyDMOogn0XQ5CUngMTIwOM6FS3zPMKcrNxd91iJD+8DSuPumjw6ocwNNPsCDZdgZxxGzZsEA5Bis6QyVUoahyVBwrjpwETVZ384IMPhN4HFUs5cOBAmanLZKtIQ5eiQSZMmCAGgh9++KHQkqFCXEz1xNDSXojEkw6g+0szhRYgo7wjEaVoFhLe05bCrKa/SG8uyEgWEZn56cly7UTCpmFXEbFI+8XxjBQRrUjRizIyw+8iohRHJOE59ks4tB4s1iP+/QHJkcHolPQALeyiYKD3NEEnr1AfnyZ0wYgPf4GpiRHq1HBBHQ9X1K7hIlLOaHDoZCdNG1MXlwJj8NWGSwiKTBHbFB3/wcgW6NPCUyXtKpRIcHfDL8iMDhVRhE5NO0AP0ut69RkNU1vldX11HbIjAwYMQOvWrUUGFKUMU/ri1q1bi+iJKQtlYFHUoGJQBI3haLKKjpXmKKRJLYJsJWVpkXZf/fr1RZvKojRpJ0a7iTmyAklX96PWq9+IQlhM6aSkZ+Lcjfs4dSUA52/eR0bW0/8DgoqWdGruDwtzU/y59bB4piomcebm5WPS/F/x9iv9MbJ3e40s6ESfad8pqWbe8J7tNLKNjAY5Cim3XcYLShsqDelxkJijTPRyxowZIkKQjFybNm3w3XffiVkzXaoWxjCM5kLPvrVHpXqoA9vUgr2VtLBAcfKzMxGw5nukPr4Pm9r+8B//PgxNzKq4tdUXmb1S1Q8bigokW0PRhDSAo0EdiULPnz+/TEchDbZ8fHzwzTffyPeRMDGJxLOjsPqQlxqP6ANLYVGnOexbDhCRb1RwxNDKgX94VwBKw6aIl+IajmK/nh4MTMzFolj5WUaNwe+X+kyn6s0yLDwbwnPsQhQ8cTRKHYrSV9k1o+OT8ODsAdhkR6N1KSbASE8CK/08JEnMkJ2Th4BH4WJRxMrcDLVrusC7ptSJWIdea7rAzvqp07IyiE/Jwvdbr2LvRWlkpaG+Hsb18MO0gY1gYaq6QisUkeQ7fCpur/oakWcPiEV+zNAILd77np2Fsv54YqdUZa9Ir4qcgsWxs7Mr05lHRQ8oqGLEiBFCE4ui5mmSq1OnTiLKkHSxikNpzaVJOzHaiSQ/V+i/UnEtY/ua6m6OxkEatOQYPH3tLm7eD0HBk+ItBBX6aOxbC51a+KNjs/rwcneSH6OJoZXbj4iqx15uTujbsRn+PX4RoVHx+HHNLuFwnP/GSDjYSvXmNIX9p68iMzsH5qbG6Nexubqbw2i6o5CqPpGgLaUeU/oVRVNQKldFIc2oqVOnyp2O/fr1E4boo48+EotMZ0NWUYd4++23hTGjiELKraZBGGl4UJQHwzBMZXPxfgweREgryI3t/gzdKZo9lBTA1qcx/MfMgIExRzVXJZRy/Oqrr4pXsi0UWUH2iuxGRTh69KhwCJKTUAbp8o4dO1YMymSCwoq0bNkSy5YtE85B0osioWiKRHxWhAajOxRkZyD22GrEHv9bRLmRPh45CgnS0mMqBqVkU+ESVWk4knNGz/ipt4+cgQ6tXyr13ISUNDGw23H0PIwKGsFCrx6aWmdhmOHTCo4ypr/SD3VbdMSjsBg8iojBo7BooV0VEhkroknSMrNwMzBELIqQo9DXzQJ1a9jC280GPvTqbgMrs6KVp8sLFd7aeCIQS3bdRHq2tAJmS19nzHulFeq6l3QqqQJL91rwGfY67q6TalzJKMzPQ35GGsBRhQLSzZWlHn/++eciKIJsy7NklZ4FCdrLKl4qQk5CRRumCO2n6MCePXuKCTBZOjJFFdJ4jaLii8PSTroFRRLmpyfCof0IGFpUTHpAl8gvKMDtB6E4dfUuTl8NEI4+RcxNTdCuia9wDLZvWg82VqVnDHVr1VAsigzr0Q6/rN+DHccuCEfhuLmLMe/1kejY3B+aAPlmth2WRhn369hCREYy1Q/D8hoeMhS0UClwGoCR405WeZii/GTVtZSBQutpoFUcqvAig4R3ZeXWZZAgLy006KKqQgzDMFXFuifRhG39XcUArjTjSgNPih5sMHE29A2NxMJUPRQJQQtViSS5DJrkunz5srApVAGMogJlFeaeBzn7ZOnLMtzd3cX3HRYWJgZTxSFJDCqk0qRJE9SqVUtUGuvWrZuIKCwLTuXSfgoL8hB/dpuIIqRBl4GZFdwGvgvnLuPU3TSdoao1HCnNbP2e/7D50BkRIUg4ODpjyrAe6FLLDEE/vlLiPS0b1IW5kz3cneyLDP5o8BkZm4iH4TLnodSBSNElFKGSlJqOC7TcjylyPVc7c+Ew9HG3FU7Euu42qONmAzPjkj/lj1wNxdI9t0T14lou1ujT0lMUKgkMl05yOVibYtaI5hjQulalR7WaWNtX6vV1BZrMorEN6RKSjiDZK1rIWUjZUzTGouAIZaDzyClIgRUODg5iH02SJSYmlnkNX19f8Ur68zIoCKNOnTq4detWqe9haSfdgX7LxP23Xqw7dR6L6kpGZjbO3woUeoNnrt9DavrTYCXC1dFOpBTTM725fx0YVVBv2szUGLMnD0O7JvXw1V/bkJSagVk/rMawnm3xvzEDYGryYhNDL8rVu48QHBEr1of3aqvWtjDqo8Jq6rJ0qoULF4r0KjJmFG1BYe3Fi42UeXNDQ3l57rJ4lrYgOwkZhqkqaOD1884bYuBFNPQqOfjJSU3C/U2/wvulSbBw8YChaekz90zVQtq6JE9BC+k90SQXyVpMnDgRe/fuFdHxz4MmporbI5o8kx0rjStXroiIC4pkpKjG4OBgzJs3D7/99htmz55d6ns4lUv7idz7G2KProSegRGcuo6Ha+83RDEMRvsgjaZNB07jn30nkZ6ZLfZRetikwd3xUrfWonIlVaguKxW6NAwNDODp5iQWxSgTijIMjYoTTsOwRzeFdmBQZDLC49NFFWWqTEzLmTtRT++jB9R0tBQRgeQ4pNf41Cx8t+WqvPpyYESyWGTpca9088XbLzV+4QhFpnIgxy0562ghR9/atWvFGIv0bSkLi6LUnweNx0ijkCIBZYVQaJ0CMRQdgSSpQRNZFOXevn174ZS8evWqPOqd7k8TYZy1pftkPLqKrPC7sPRtAzO3srW3dREqPkLpxJRWTA4ymsxRpIG3Bzo2ry8chN4eriqdXOncsgHqe3vgy+VbcP5mILYfOY8rAQ/x2VuvwK92DaiLbYelRUzIGarpBVeYyuOFyy6Ss49muWihaAkKm2cYhtE1J+F7f5wqsu+vAwFo4OWAns2lRZ2yk+Jwe+UiZCfGIObKf6jTn6OHNBEaFFGEIUVobN++Hfb2ykW70HkUjaEIDaIIWcRGcRYsWIBWrVqJe8mgiHtyHFIFSsXKkjI4lUs7yY55BFMXaUVAp86vID8tHq5934KJg/p+6DMVJyc3D9uPnsff/x5HclqG2GdtaY7xA7sI4XnFaA9VpUKT07Gup5tYLBo/1QTPys3HoyhyGqYgKCIZDyKT8TAyRTgNyREYFpculuM3imogFpcSNzU2wLoP+6CeR+kOzMrC0MJKaBJSurEM2qb9zLMh20JpyLScP39eZHMpA0XKk92hbK9Hjx4Ju/P777+LiShFe0WFueja5BgkJ+LSpUvFBNr9+/fh5OQknIsULU/7GN0m7qQ0mrA6RL5TVglpxsqqFD8Miy5y3MTYCG0a+YiowQ5N/StdO9DRzho/fjAJWw6dw5KN+/A4Mg5TPl2CqSN7Y8yAzjCo4urwsYkp+O/yHbE+ole7Kr03o2OOwuJpWBQtwTAMo0tQChfNHyqOu2hCcdneW8JRmJUQjdsrvkJOSgLc2vRE7b5j1NhaRhlIrH3MGOW/p+bNm+P48eNF9pHeIA2matQo3RlEjkU/P78SdjI/P1+khZXmKORULu0iKyoIkbt/Quqd/1Bv1kZRJdLY1hVeYxequ2lMBcjPL8Du/y5h5Y6jiEuSRo+TkPsr/TvjlX4dYWluVuWp0JRaTJNStCiSlpUrHIakmfswSuZETEFimjTysTgSSWGVOwkJqm5MhUuEJuETyEnIVY/LR9u2bcWiLDQZ1bRpU+zevVuklVI6M8lDKfLGG28U0cwdNmwY6tWrJ6otk8YhaRWOHDlSBIVUZyhqWFWaqJqK+6D3YOrqA+v6naDtHL90Gyu2HRbFRzxdHTF5eC+0a+yLi7eDhGPwzLW7SExJL/IeqkLfoZkfOjWvjxYN6sLU2KjKC/C93LcDWjbwxidLNgjn5ZKN+4V+4advvgwXh6rLSvj32AUhg0F90rkFV76uzpTryV9aBa3SIE0mhmEYXYHSjYvXeadojeDoVGTGhotIwty0ZNTo2B+1+o7hSqYaABUy+ffff5973po1a/DSS6UXLlDktddeExEZNICiqpAUQU8pzLRfloZCER8UnbFp0yZRvIQ0DSlljFKOa9euLfQHKW2M1mvW5IqC2kxucjSi9v2OxIv/AoUSmNXwQ2GxdCVGe6BB0aGz1/HXtsOIiJVGDpsYGWJE7/YYP6grbMsQqVcnlDrc1NtJLIoMXrAHwVEpJSa2aruWnJioKoRTkAuXlAkVZSytWEhxSNf977//Vrrf27VrJ5ay+Pbbb0vsa9CggViYp07CgIUDS0gLUBSxLjkLTRw94NbvTeiCk3DuT2vl0gtBYdFi29BAXxR0UsS3ljs6NfNHpxb14evlLpx16oZSm1d+Ph1LNx/Axv2nRSr0uDk/YfbkoejZtkml3z8vPx87j0uLcw3p3gaGhsrVnWB0k3I5CkkIlwY/48aNg6sr56szDFM9sLEwRlxK0SgN+hHi62KGWyu+Ql56Cjy6DYVnj+HsJNQQMjIyROQeRUO0aNGizPOUHRBRRCGlZZEDktKDw8PD0b9/f5FerDhJRlGGWVlZYptE6Snti1K3SNeXnIsuLi7YvHmzCj4how4k+bmI3v87Yv9bh8K8HFEh163/dNi1GAA9DRhkMOWDIq1OXL6D5VsOyoXbDQz0MaRbG0wc0l1EVGgb77zUWEhlyAbKstc3BzZWd9OYMqDiWjTGosklcgYqFnVUpG7d6qUdpwlQJKGik5CgbdqvC45CSW4WchIitF6XkJ7l0fHJ+GntrifbRY+Tk9DI0AAt6nsLx2DHZv5VGqVXHij1eca4QWjXuB6++GMz4pPTMP/Xf3D2+n28P+GlSq1AfOLSHSQkpwk7OLjb02hjpnpSLkfhtm3bRATF999/j759+2Ly5MlCoL26h6QzDKO73HmcgIS0nCL7ZAOvKYNaoJbEDrnkKOzy/Kg0puogBx455aja8bVr10TkH01yKatJWBokJD927FihE0Wi78VTjily49y5c6LCMWFubi6iC8mBGBoaKop9USRhZVcZZSoPKlCS9uAi9I1M4TrgHTh2egX6hlwUQhsHlRduBeKPLYdw91G4vNBH347NMWVYT7g7a2+lXpLDWDy1k5DGoKh3iiQkJ2GPZh7qbhpTBlRQi8ZWFIH+1VdfCVtFY6xGjRpxn6mZnPgw6DKJl/cgbNPnqDl8jlZVO6aCI0Gh0bgZGIIb90PEq0wuojQoovDgH5/C3LTsIqmaRpvGvlj39XtY9Nc2oRm479QVXL8XjM/eHo1GPl6VWsSECm2RdiJTvdErpF9L5YQiKVauXCkKl1AqFUVY0CCMIia0AdKGIqFf+hylaUQxDKN7WKTfLPd7MrLzMGrhfoTGpsHN3hyWZkZ4HJOGZs7AK4M6oMeTQiaMaklNy4BdvT4iwuJFn9EU3UfpwjTJdfHiRRGtQQMwqgqpCWkmz4PtlXoplEiQdHUfJLnZcGw/QuzLjn0MQ0s7GJrz7wdthAZayzYfxPX7wfJ93Vs3wusjeqF2DRdoq71idMNmnTlzRjgMKfKcotHJXr3yyitaMV7RNXtVWJCHu18PQ05sSIlj9WZtgrlHfWgz5AK49/UwZEcHwX/ebpg6Syc5NbUC/e2gx7gZ+Fg4BgMehiIzu2ikJ6Gvryf0WBWhydm6Hq5Yu2gGtPV72nXiEhav3YXsnDxR3IQi3icN6Q5DA9WlBgeFRmHc3J/E+rKPp6GpX22VXZvRrOc0BS0oY68qFApIF//kk09EWtWRI0fw0Ucf4euvvxZ/yAzDMLrCwg2XhJPQ1MgAv01sCndLIC3sIR7tXwvnx5lA88nqbiLzHMzMzDB+/HixUCTgDz/8gN69ewvn4fDhw7n/mDJJvX8OkbsWIyv8LgzMrWHfcgD0jc1g6lw5M/lM5XIvOFw4CM/fDJTva9/UT1SWrFeLq1MzmkGHDh3E8vPPP4uqw3PmzBFjrS1btqi7adWO6EN/Sp2EevpCi1aRnIRwrXcUpgdeEE5CKmCiaU7CmIRkabRg4GPcvB8inFiSUvwMNV0c0KReLTT2pcULweEx+OiX9cI5SH4J2evkYT2hrdBnoDTgZn618envG0UU/IrtR3DhZiAWvDVa9IEqowlJJ5H6lGEqnDOcnp4uUqoosjAgIEAMwhiGYXSF3ecfYfd5acTJ3JfqIu6fzxCbnyc/Hnv1P3h0G8yVG7UA+pF47NgxYa927NiBTp06wd/fX93NYjS0omVeWgLi/luHtHtnxTYNotwHzRBOQkb7oIHjH1sP4cSl2/J9zfzrYNrIPjwYYjQS0ralrK3Vq1eLiA/Sw2WqlszQO4g+tFxUOK4zbalcMiQt6BIid36P8C0LYeHZSKt1CmNPrhev6k45pmJSD5+kEVPEIL1GJ5QsjErRc/Vqucsdg418veBgY1XkHIoKXzRDHyu3H8HjqDh4uTkJJ2HXVg2h7Xi6OeHPT9/CX9uP4O9dx3E7KBQTPvoJMycMxoDOLV5I1iY9MwsHzlwT6yN6tWOJHKZijkJZSDzNbNFAi0Li9+/frxMh5gzDMMTjmFR88c8lsd67hSd61rfHzf+eOgmJQkkB8jPSuJKjBhMWFiYGWjTgys7OFlUlb9y4oTUyGdXFMUcDMXUNtkqraCnD3LMh3F+aCSufVmppG/NiRMQm4K9tR8TgR5bxUr9OTUwd1QetG/rwQIjRKEgmY/v27WKMRVq3gwYNEpIZpF+oDTIZukb0wWWApAAeL38CC8+nRc8oirAwNwtR+5bg0Yp34fvu31o5iUTai6l3/oOJS21Y+bWv0ntnZufgTlDYE8dgCG49oDTiolrghJW5mXAGNnkSLejv7QFTY6PnXp/09WjRRagK8bRRfdC2sS8W/L5ROFS/XL4F527cw4evDYONpXmFrrv35FVk5eTCwswEfTo0U3m7mWrgKPTz80N8fLwQ2T179iyL7DIMo3Pk5hXgg7/OICsnH+4OFvj4lRYI/3eZupvFlBOqTLx48WJReItSuCgiw0CFWi6M6hxzeobGqD9vzzOdheTooSrDkrxsoRdIryaOnqLSMFUipug/sT83S3oOnZubBTN3X9g27iGukXT1gFRvUByTnpufmVKqk9Bt0Ltw6TGZnUlaSGxiClbtPIZdJy6ioECaLuhd0xVvjOyNzi3q83fKaBynT5/GwIED4enpKQIwKBjDwUE16YRMxfCa8A2SbxyBbZOSKasuvd5AVkQgkm8cxuN/PkGtV7/VuudK0rWDoiofRRNWdtupyIhi0ZEHj6NEFGFxajjby1OIKWqwlrszO8nLgPQDSXPxu9U7cejsdRy9cEtEYy54czRaNPAu1/dDv6+2H5GmHQ/o3FKrCr4wGuQovH//PiwsLESEBi1lQRUeGUaT0KQIFkaz+WnHddwNTYSBvh6+mdwBViYGyIqLUnezmHJCeoT04+fEiRNiKYs1a9bgpZe4YnVVQc/h4o452n68/iNRUZgceA5th4qFCNv8BRIv7RbOP1FqXIHG316AgYm5eM+jP98p9X72rV+SOwpz4kKQcut40RMMSv8ZZF2vvdYN/Ko7SanpWLPrhBjw5OTli32k3fT68F7o2a6JEIBnGE0kOjpaCMuHhobi008/FUtpUDGuv//+u8rbV52Q6dqRbXFoXfpvA5qg8hz7JbLjHiPj0VXkpcTC2FYzCiEpi0vPybCo1VhEzr8oxy/dxopthxEaHQ8PV0cM7NwSJsZGuPEkYjAq7un4SwY9j30pjfiJY5AchFxlt3xYWZjh87dfEVq7363aIRyy0xf9iTH9O2PaqN4wMlTOzXPpTpBI0yaG92xXzlYwuky5HIVLly6tvJYwjIZFsDDVj5O3IrD26D2x/s7A+mjq7STWfUdOw83ln6OwQDr4JPQMjWBoUVQbhdEcpk6dip49ny9e3bChbqanaBvpQZfl61a+reXr+qYWMLJxhr6xKfSNTKFHr8ZmYl3mODQwMYNrn6nQM3pyTJxrAn0jMxg7PC1S4djxFdi3egl6dExcwwRZEfdx//uXq/jTMqocmJIzkCJPzt+8L6+C6Wxvg9eG9hADVkrVYhhNpnnz5kqNsby9yxcpxJSPguwMPFw6FS69XodNwy7PPJccid6v/yomuIxspL8VtQlyhlr5PLW1L/IsnvvTWvn2w7Bo/Lx+T4nzKKW1kY/UIUjOwfreHjAzNX7h+zNA3w7NhLP1s6WbRNTm+r3/4dKdB/jsrdFCs1HZIiatGtaFl7v2/S0zGuIonDZtWuW1hGGqOIKF9rOjkJERl5KF+aulxnJk7Ww0ur0CWW3mwczRFVY1vdFi5g9STcInkJPQ1NaRO1BDIV0nWhjN4vKdhyjNvZ7W7UO06zlAOACFE/AJNV6aKZZnQQM1t/7Tn3tvQwsbgJYi++zExFHxiSTaz2gmsoEpBXySr/hReIxYCDtrC0wc3B1DurcRES0Mow3UqVOHx1gaQMTO75ARcgOpAaee6ygkjO3d5eskY1GQmQoTRw9oMoUSCeLPbIJd834wtLCt8HWyc/Nw8vIdfL1iW6nHqfBI9zaN5IVH6tR04ajuSsTdyR6/z58qour/2nYYgSGRmDj/V7wzZgCG92xbZoZEdHwSTl0JEOsjelWtViWjw1WPGUbbyY59LESJGUYiKcTclWeRlJ6N4bYh6J52F3kGBsiIfiwchYRwCrJjkGFeiIuH/0XnQn0Y6T3VJ8or1MdPe27gTo4T6nq6oq6nm0hfooFGZUOTRRRdztIUykfwebo6YvLwXi8kFp+bl4/0zGykZ2UjIzMbGVnZ0u1i6yWOZ2UjPDpBXKNYJrqofrnlxw9YX4lhmHKTcuckEs5tg7GjB9wHP3tyqjjkJAz8UVo52HfmPzA019wCn6l3TyN861ciir/2pB/KnZZNOnj7Tl3BkfM3kJFVsgCJDH19PZEWy1QdlM49aUh3tG7kg0+XbEB4TAK+X70T567fw7w3RsLexrLEe3YcuwBJYSFcHGzQoZkff11MEdhRyFRbHq/7SMz8WXhx6mF1Z+XBO7h2LxxTrG6ihWE0jCys4T92Bqy96qm7aQyjM8Sf2Ywe+RdwNccFB7LqyvenFxojSWKAv3c91Q80NjIUKTM+Xm6o6yF1Hvp4usHGyqJSnIWaFl2uasecKiP4HoZHi+2ZE14S6WNyR94TZx5tpxdz/NGAUnGbHIWqJi0zi52EDMOUm/yMZIRu/JTyceE1dqFIKy4PBmbWsKzbUjgaQ9Z8CO83lkBPXzMlD+JOrhevjh1HK/0e0hjcf/qqcBCS80nRTtNCz3VFKHrNy41TWNVFA28PrPnqXfy0djd2nbiEM9fvYeycxfh46kihZyiD7PCu4xfF+tAebatkcpbRLthRyOgk+elJCNvyBdwHvVdqahn0DWFVtyXMPfzV2UxGA7j+MA7r91zA+7aX4WmYCgs3L/iPm8lpxQyjQtIfXUP4tkXIKTTEnixfRBUUTUC2tbIQ0YRUDTElPVP8gL0fEiEWRZzsrOHt4QofL3e5A5EGJLqkQyd3zFEExxPNJ9qeOqI3Gvl6IS+/APkFBeK14MmrbDu/2HpeAb1KkF+QL17z8vPF8afrT1+Lv1e2HpOYXCSCT/b645pdKvvMNNi0MDOFpbmp0LKSvkq3xWJmCosn6yu3H0V0QtGieTwwZRimooRtWYj81Hg493gNlnWalfv99PypOWIesqMfIe3uGUTu/hk1yhmVWBWI9t07C1N3X+HYfBaZ2Tk4cek29p68gisBD4sca+Tjif6dWqBn2ya4HPDwyUSSnrwQDL1OHvZ8jWim8qDKxR+9PkI4Br/6a5so9jXzu1UY0asdpo8ZAFNjIxy7eAtJqRkwMjTAS11fXK+S0T3YUcjoHJlhd/BoxXvIS4qCgbktPF/+5LmpZVRVkwTzrXzbqKnVjDpIzczF7BVn4KKXCg/DVNjVbwW/kdNgYPxUI41hmBcjNzkGwSvfE8WAVqU1L+IklA0q5kwehq6tGor1hOQ0PAiNQhAtYdEIehyFkKhYFBRIRFU/Ws7fDJRfg37kUvShLG1ZLB5upabZqBNyvNGPcvp8iSlpiE9ORUJyOhJS0pCQlIqElHSxPyL2SWrtk/fJXv/YegiaCGkBFnHmyRx+CusyB6CFuVmpx8lRqCxWFuY8MGUYRiXkJkYKTUJTNx+49X+7whHe+oZGqP3aj7j/wyuIPbYKZu4+sG81SKO+pbiT/4hX5y7jStWsk0gkuHYvWDgHj1+8haycpwEWlJrar2ML9O/UHJ4K0YLUD4tmjMfK7UdE5VyauCMnIdlzRv3Q99Cgric+X7YJl24HYevhczh55Q5MjI0RHhMvzqHjmvZ7idEM9ArpV3k1IzU1FTY2NggPD4e1tebqSDDlJ+H8DoRt+VJEDzp1GSdm9Ejo/lnkpSci4LO+kORlw6XnFLj1e/O572G0D4v0m0W26dE3a/kpHLoaBnMTQ6yd5AufJk2gp6+vtjYyUlLTMmBXrw9SUlKq/TNa2+2VJD8XD36ZhMzHN7Evsy52Z/oKcXNKRQ0tx6CCIgwfR8Y+dSCGRot1miUvCwdbK3nUoWyp5e4EI0NDlaX50nMkMysH8clpUocfvZIDMCW9xHpyWoY4/0UghxqlB1EEJTlIDQ30xecxePIq22doSOdJ90lfDWBgIH2P9BzpNehVtl3WOcs2H0RMiQg+COfsmoXvqiWak74zXR6YFrdXjGbDNku77RU5Cwtys2Dm6v1c6QXZKznHSrMTmWEBCPx5gjjJf+5OjSlukp+Zijuf9oSekQkaLjgMfYUJ8bDo+CepxVdFcQsZpiZG6NaqkYgebFG/DvT597HWQk7gTQfP4Ld/9qFA8lQjWkZZf8+Mbj6na9asqdQYiyMKGZ2ABqOU1pZwdiv0jEzhNeEb2Lfor9R7jSztUXvyT0KzMObwn0h7cAG1JnwDE4eald5uRn1V3w6t+B0ugWGURIH5Y1rBt1kd/joYphIiGMhJeCffFXsyfdDIxwu/zJlS7oq05CCjdGNaFCHnXJCC85BegyNiRfSe1FGXhgu3HsjPJ+dXrRrOcgciOflW7jxaQn9v4Ttj0Mi3lvwaUiegQgSgwnpObl6F0oLIkeloawV7G6si6yt3HBGaUIouRYr+oDavXTQDVY2piXGpEXxvjOittpRvGtDwoIZhmIoiJm0kBdAzMCxSvbg4NImkV0x6gbb/2HwQXVs2KBGZR0USvcZ8gbyUWBhr0DgiOypIOAkd248UTsL0zCwcvXBL6A7euB9S5Nxm/nUwoFNzdGvdWESCM9oPOXlf6dcJ24+cF45hRehvmCbe2KYyxWFHIaMTZATfEE5CqlZW57XFMKtRviIU1n7t4Td7G0LXzxcVwe59O1KkLNs171dpbWbUQ35WBq6t+QkWoQHwMzLF8JauGNSWnYQMUxkYNBmMYzsPY3eKC9ycHPDtzAnldhI+C6p069DICm0a+cr3kebe48g4kbZMmocPw6SORIr6IweizLGIM9fk7ymuvzfvV2mKVnkrDlL6Djn9qF32T16FA1C2bmcFe2srmJkal3kdSsnVJM0nTi1jGEbXoMIjtHiN/xqmzl5lnkdRy8XjwGk7JDIWfaZ9LopsUdEtH093+Hq5iYmo4mMH2XNcnVh6N4f/Jwdx9XYglv22Af9dvo0chaJSNZztReRgv47N4e5sr9a2MpVH8ewA2d8n/Z0zTHHYUchoNTLja+XTCrVe/RZWfh1gaF6xdAcjKwfUeWOJqAgWuetHRPz7I6wbdCl39TNGc8mKj8KdNT8gJyEKD/PssMe4M1aP76LuZjGMzlFYkIecAuDDn9cjIKGm0KH7YdZE2FlXvg4OpdvKUo37dngqTE9pyiLqMCxKOBDpNTAk8rnXo7YLhyQ5+544/BTXyQlIzkAbS3OVpGZpomOOI/gYhtEVchLCEbHzO5FdgsKSaZgyaEKptDRNGanpmaLQh2KxD4par13D+Ynz0A2+SRdgU5AM7/Ffqs1ZGBwRI9KKD5y+KjR+FSPbe7RtjAGdWghJEHU7M5nKh+RVKHNCUQWFi4ExZcGOQkZrHYRxJ9YgOzYEHqM+EQ85VUT/kT6dc9fxsPRuIQa6MiehJD9PCBUz2kvSg5u4t/FXFGRn4kx2TWzNaoTVs3vAwpS/V4ZRJblJ0Xjwy0ScNGqBgIf5ItLuq3fHoXZNF7V2NDkpWzWsKxYZY+csxqOw6KJpvhRd4eKAX+ZOEanAVB2wqmHHHMMwL8r58+exe/du8Zt5wIAB6NChwzPPnzNnDtLTi+q+9u/fXyy6QqGkAI/Xz4MkJxM1hs2BqUvtMp1r7yz6CxKJ1Do81SiURni/P2Ew7GwshFYuTTw9eBwpnHAUtS72hUbBGPmYa3sG+oYZ+OpSOBK9uj2NQPRyR01n+0rT/UtJz8ThczdEanGD6INicjwu1xV6evpo1aAu+nduIVKnSVqCqT6QBrMmZSwwmg07ChmtoyAnE6EbPkXytQMwMLOCa++pMLZzVek9SGNEUQA4cPFYOHYYJQqk8Iyb9kFGMPLMfuRnZ2FLuj+OZ9fCByNboIGXg7qbxjA6hSQvR1Q4zk2MQHwGTbT44INJQ9C6oQ80kSll/Gie/kp/uDtx+hXDMNrJsmXLMGPGDEybNk0UKerZsye+/vprvPvuu2W+56+//sJLL72E5s2by/c5OjpCl4g9sQ4ZD6/C0qcNnDq9Uuo5VNjqna/+FFHoJCcxcXB37D5xqdQI755tm8jfRwWryGkY+DhSRCPS6/LoVphldRp99K7h19uGOHPtaX+amRijrifp5UrTlsmJ6O3h9kxpimeRn1+A8zcDhXPw1NUA5OUXoIZBKrrbhcDPPA1tu01E347N4exgW6HrM9qPJmYsMJoLOwoZrYIiCINXvIfs6CCYuvuizuSfVO4kLHHPqCDkpcYjYse3SLt/Dp5jvhBpyoz2QIN/m96T8M2trbiWbYOODd0xrrufupvFMDoFOdjCNn+OzNDbuJ7jgn1ZdYV49pDubaCp8I9mhmF0DapmOWvWLHz77bf43//+J/Z5e3uLfePGjYODQ9m/YXv37o3Ro0dDF8mKCkLU3l+gb2opCo5QFlFxImMTMX3hcqFpS3ISv859Hd4erhjV59nRmIStlUWJqPXcvHwEnd6D7J2f4G37W9hmMwLXIjKRmpGFrJxc3HoQKhbF36serg5C85AiD3293IWMhpOdtTxQgSoxU5EVcmhSKmn/zi0Rm5iMg2euC+emDCtzM0yqGQ0kAm3HzoJDq+4q6EVG2+GMBUZZ2FHIaA0pt44jZN1HkGSnw67FAHiO/hT6xmZVIgDs9+EWhKyZjdSAU7j37Qh4jV0oCqAwmkteWoKoZO0+4H8wruGHeeuv41q6DRytTfHlq+2gr89aLAyjSuJP/YPEi7sQXWCJv9Mbo2PzBpg+RvNT1vhHM8MwusTRo0eRkZGBsWPHyvfR+jvvvINDhw7hlVdKj6Qj/v33X1y+fBleXl4YPHgwPD09oSukP7ggZIU8Rn0MY3u3Esej45Pw1sLliE1MgbWFmdxJ+CIYGxmifrchiJEkCf3zicbnsPDntYjPyHuSthwpTV0OjUJ4TIKYcAuNihfL0Qs3izghyWFIUhinr92VVmImHcWwaPyyfo/8PJL6aNvYV6QWt/VxxYOF/WBg7Qi7Zn1e6HMwDFP9YEchoz2ahKc2QJKbjZrD58Cx05gqTQE2cagJ3/+tRtT+pYg58hceLp0K1z5T4dZ/epW1gVGezIj7ePTnO8hLikKctRMOmXbB1aA4oTGz6LUOcLA25e5kKsTZs2dx8+ZNODs7o1+/fjAze/5kRVpaGg4fPoykpCR07dpVRHboGmlBlxC+/VtkFxpiWWpzeHp64bO3R4tBC8MwDFN1PHjwANbW1kUiB21sbGBvby+OlYWFhYVYKN34wIEDQrNww4YNIh25NHJycsQiIzX1aaEMTcSp81hYercUGUnFIefg2wuXC2ehhZkJfp4zBb613FV2b+fuE5EVcR+ZYXdQkJkCV8cacHW0Q6fmT6WOMrJyRMoyOQ2DQiMR+DgKD8OikZObJ9KaL98Jkp9bWIpDctqoPujTvpkotEVEH1qOwvxcIZ3EOusMw5QX/gXPaLzoMEFOwVoTvoHPOyuFoVeHTqCegRHcB/4Pdd/6E0Y2zjDk9GONJPnGETz4abxwEjp1HY+Epq9i1Y6j4tjkPg3Q1r9yU9UZ3WXy5MkYNGgQzpw5gwULFqBx48aIjHx21dzjx4+jTp06WLx4sYjSGDFiBNauXVvue5OzUXE9KytLrBcUFIg0s/z8fLFNgzbFwRoJ02dmZop1iUQizs3Ly5OfS9ulnUuTM3QsNzdXbNMrbdN+gqJVaJGdG5eQjPRCY6xIbYoCKzd88sYwob9E0DUVBfLpOrLBJbWFtqltpZ1Ln0V2Ln1GOpc+M0F9oNgvdG52drZS5z6rD+kain1Y2rnK9KEy/a3Yh8r2t6wPi59bVh8+q7/pcxXvb2X7UPFcWb/IzlWmD8vqb2qP7Nzy/M3Kzi3eLzKK/80+r78r+jdb2rkV+ZvNzM5HSkbu0+tm5CIzR9pn+QUSJKblildx3ZwCJKcrnpsn3i/t70Jxbl6+9Nzs3AIkKZybmpmHjCfnUuEGOjdX8dy0p+emZeYhPevJuYVPzs2TnpuTVyC2ZX2YnkXnSr832kfH6BzR33nS9kvk5+aLa8uge9K9xbn5T859UlSC2pqqeG7603PznpxLn/lpHz49l/qI+qq0PqS+LdHfT/pFdq6sD4v3d6rCPTQB+juzspI6ixQh56Hs/6U0Tp8+LXQKyUG4d+9eYfNokf3/FWfRokXCASlbPDw8oIkUZD39nzOrUa/EGCIhOQ3Tv1qOiNhEmJsa4+fZk+Ffp6ZK20D39By9AL7vrYeJQ41SzyEHJVUfHtGrHeZMHo6Vn0/HsRWfY+N37+OL6WMwYVDXZ45/xvTvLHcSUuRk/OlNYuzi2H6kSj8LwzDVA3YUMhpLZvhd3Pt6GDLDAsS2oaUdLOs0U3ezYOXbBn5zdsCxo1TDpVAiQerdM+puVrWHvoeoA0tFIQX6geT5yuew6vk2Pl26WQxGGtd2xFsvNa72/cRUjD179mD16tXC8UeOvkuXLonIi9mzZ5f5nujoaAwdOhRvvPEGTp06haVLl4r3+fmVXx/zypUr8vWrV6/i4cOHYp2cDHTt5ORksR0eHo5z587Jz71+/bo8goScIXRuYmKi2I6KihJOTxm3b9/GvXv35E4LOjc+Pl5sx8TEiG2ZEyAgIEAs4rp5eZi78RLmxXdGkH4NzH51IO7dviF3BNE16doy6J50b4LaQteVOWyordRmGfRZ6DMR9BnpXJljhfqA+kLGxYsX8fjxY7FOjhs6V+YYCgkJEX0vg5y2wcHBYp0GznSuzIETFhaGCxcuyM+9du0agoKkkRzkAFLs74iIiCL9fePGDQQGBop1GlzTuQkJCfK/BxqIy7hz5468v8k5ROfGxcWJ7djYWLEtc0bdvXtXnC+DjtH1CLo+bcv6+/79+yLqVTEKVubQpqhWOlfmyKL+ps+nWCVV1t/kxKJzZU67R48eFfk7pP6kflXsb5nDrHh/0/vo/QRdj86VOQfpfnRfxf6W/c3K+pvaTdDnoM8jgz4nfV7Fv1nF/qZtxf6mfiSoX+kY9TNB/U7bMicdfS+K/U3fW/H+ljlP6Pum710G/T3Q34Xi36ysv+nvSLG/6e+M/t4I+vujc2WOnMCINJy5Hf+0DbfiERSRLnesHb4SLXeCPYxMw8mbcU/bEBCPe2GpcgcYnStz+IVEZ+DEdennJi7eS8CdEKlDNSdPIs6NT5G2NywuE0euRj/9HgMTcStY+rdPzjg6NyZJ+v8YGZ8ltp88InAtKFksBO2jY3QOQe+hbZlDj65J15ZB96R7E9QWOpfaJr7HkBTRZhn0WegzEfQZ6VyZQ5X6gPpCBvUR9RVBfUfnyhyf1LfUxzKo7+k7kDknFfv7UVQ6/rv5tA8vBT5tjyZgaWkpf0YpQv9H5Cwsi+KOPtIzJBsge04VZ+7cucK5LVtkf8uaBGUiBS4eh5C1c1FYIP2uFSFNP3ISUqqvqYkRfvzgNTT08aqUtugbm8LQXNr/uckxSL339FlWFhSVX8vdGb3aNcFbo/vBu6aLyI5RhJyHVJRCkYKsdFjWbQn71i/ByFq3CtIwDFM16BXKfvX/v73zgI6qev74pPdKh4TepIN0pCjyAykiRZo0ESyIoKACiiAKNhBB4C9SBekiHZGmNOm99xZCAum9Z//nO8tdNptNsoFAsrvzOeed3ffefW3efbfMnTtjRaBxipEvNE6zqzCF/CPsyAYKWP01aVKSqESHD6j4/94usK/j/s4FdG/TdPJp0In8u39Ods5u+X1LVklC8HW6/EN3snPxpHJvTSe3cnXo02lLOPKbu6szrfm8LZUq7J7ftymYQHRMHPlUacsdj4JSRvfv358VHfqKnp9//lnXUbK3z+zJY9KkSTR16lRWMDg7Oz9RfQUFR6lSpXQKBVwP056h2IByBkpLbFNTwZTcsM/W1pZcXV1ZOYJ7dXWDAABao0lEQVRj8d/BwYHTQemG8xumRdMA18Y1HB0dWZEHBQ/Oi04JFHBIE39iEy04n0Yb9p/l7d992I+a1KqUIS0UH7g2Oq8A8oI8nJycWNmC/bB+wbUN0+IekA4LFEG4LvYhiieugW3KcgZpca84d05ps5MhZILnVTI0ltYUGZoib8gH1zVF3gBplQwN02Ylw+zkjePwTPryNlWG+mmVXFRaU2SYlbyxD+dA2tzkWZXWUC4qrb4MTc3fj5NnjaXNbZ71TDjP1mwpaenk5eaos3BzsLclVyd7tnCLjk8lT1d7srezZQs3WOt5u6u0KeRgZ0OuzvasjIuKTyEPF3s+HtZ3Cclp5PMwLazz7GxtyM3Znq32IuNSyN3FnhxV2qQ08vHQpoXVH+SD/RiAi4xNIXdne3J0sOXrxyXivA6cRlkTurs4sCwjYlPIzdmOnBzs2KIwNjGVvN0dyJbTpnIaD1cHncLPxcmOnB3t2KIQ+73dHNi3MJR2aeka8lRpY5PJxVGbFhZ/MQmp5OXqQHZ2Ng9lqCEvN21aWAHi+ji3oQyhXMTxGeRtZ8syVGmVDA3lfTc4ksrU61hg6qytW7dS+/bt6c6dOzrlH+qhkiVL0tq1a+m1114z6TxQXLdo0YIHZOrWrWuW/au766ZQyO4l5FO/I5Xt922GfVExcfT+N/N4uq+Tgz39+MmbVL/6o0AkT4v0lCS6+M2rlBobQZU//J2tHE0FgUzGTv+dvzF8M+oXda+xyLUqjSAIgiqn/fz8TKqvRFFYQCqyJyU5PIhS47Qj7sDezceoo96CTnpqCgWu+15rLu/gTKV7jiffBp2ooPvDu7X4E0q6f5OcCpemsgN+INfS1fP7tqySyDO7yNWvGuf9P7YfoB8Xb+Dtk4e/Qa/KKzEbCqKisH79+lSnTh2elqWAU/i2bduylZAxv4MdOnRgBcLChQu54wZlQNOmTals2bJZXseYzyd09ApSx0vxYPfvHA3+eFJxmh9Tjz7o057e6NAyv29LEMwet9hHFqFCwaeg1VlQpiMICaYNY3owGD9+PM2ePZuVh2rA4dNPP2W/uVAqwoIWSj50IAEU1z169GCrV1hqGxsMK+iKQvjOvTbrLXLwLMIzgZQ1H++LS6Bh38yjy7cCycHejqaOGkiNamX2Xfi0CDu0ju6sGE+OvqWoyqgVPGsqN8rChWt30u2gELYkfKvryxmUhKIcFAQhLxSFEszEQpSEFyZ3ZIe1Cht7R6r2+WazUhbCDP/WolEUd+s0ORYqReXemkGuuRhlyy9wj1VGreQOc9jBP+nyT32pZKcRVLRVf7IRR/5Pldibpyjs4FqOgG1ja0fetVrzdkSRm7l8C//v/GJDat2oFpF0vIQnAJZK3t7eGbb5+Ggb9vo+x/TBFEVYKjVv3pwXTLNFx23GjBk8HdkY6NRNnDixwL+rmCuH6e76qZSQbk8b4qrQq60asH8kQRAEIX/BoNSiRYtY0Yep7rBmhduMpUuX6pSEYO7cuWyNC0UhFIMI0AXL9RIlSvAUegxarVq1yiQlYUEjLTGO7iz7gue9l+7zdQYlYVx8Io34fgErCe3t7Nga71kqCUGhxl0oIfAShexdTjd/+5gqvjeH/QmawosNavCSFVBAalJTOPijvVvGdosgCIKpmF/JL2QCloT6SkKAdWw3J0Uhgk8gGpjncy9QmX7fkb2bdrqQOWDn5MpOij2qNqWAlV/SvQ0/ko2NLRV9sX9+35rFEnZ4PQWs+or9Efo+3548qjTm7QmJyTRu1nJKTkllvy4f9SvYFqmCeYDOlKFCUPlYw76sjoFvNlhqVK6s7YRMmzaNhg8fTj179tRNidQHU5lHjhyZ4RoFzUF8UlggXVswkv2CLoypR2Weq0WfvtlFpjcJgiAUEGDRDl+fO3fuZAuzX3/9VWctqJgyZQrVrl2b/+MX9dXevXvZ12C/fv2oWbNmPIXeHAlcP4WSwwOpcLOe5Fm1qW57fGISfTRlEV24HsD+/yZ90Iea1X0uX+6x1GsfU0LQdYq9epinSPt3/+yJz5kSFUIRxzaTvUdhsnMWdzuCIDw+oig0czBidn/Xgiz3h+5fTbG3TpFHxQbkXqlhlpG28gs0XqDUtHVwYp9ylUYsIVf/6mZriedT53/kVroGBf01mwo17Z7ft2ORwBl14MZpFLL7d7J1dOFo2EpJCKb9vpFu3wshRwd7bgA6P4y8KghPAhR9KoCIAuvwm5bVVOIqVapwUAelJAQvv/wyjRo1iqcrP//885mOUf74CirpyQl0Ze4HRInRtDG+MsUWrk4/jehL9vZ2+X1rgiAIgh7wSQj/ulkxZMiQDOvwl4k6ytyBG6PkiCByLOxPJTs/GnhLTEqmj6f+Rmeu3GLfmBPf72XUr9+zAhaE5d6cSpd/7EWh+1awqyW3MjWf6Jyh/63mdnKR5r3Ixk66+YIgPD5SgpgxmPp1e/kXbImXFVHn/qXoi/sp4ugmXocvDPdKDcijUkPyqfdKvlYiaUnxFLBqIv+Wf2sGKweftIIsCDj6lqQyfSfr1hEROebyQSrRcQTZ2ps2rUAwTmp8NN1a/CnFXPqPHHxKUPnBP5Or36MIsjsOnqZNu7WRNoe/0ZEqljYfi1qhYNO5c2caOHAg+3eC7yf4HsQ0LkzVQucKwI/TunXraMCAATwtuWvXrjz9C07kixcvrou2i2lg5cqVI3Pk3t7VlBp8lf0SHrKrSQs+eZM83YxbVAqCIAjCswZt7QrvzqHU6BCe8QOSklM4wN2JizfY+n38ez3p5cZaa8r8BFOD0ZZNuHfliftA6anJrCiEj/dCTbrl2T0KgmCdiKLQjElLjGUlISJ5RZ7anslHIQKalBv8M8XfPkux145QzNWjFHfzFIUfXs/KQxwHUqJDKebqEVYeOngWfib3nhRyh24s+JASg66Sc4mKlBofSQ7uvmRpwGIyaMtMnlINp8pl+/9AzkXL5PdtmS33d8xjJaFbubpU7q2fyMGjkG7fvQfh9N2CP/l/y/rVqdvLj6wMBeFJ6dWrFy1evJhefPFF6tOnDx09epQuXLhA//33ny4NIhN/9NFH1K5dO1YUtmnThn1EwT9h37592UfhggULaPLkyeTra37lXVp6Ov10Io3sYqvT8dTS9ONnA8iv2KNvUBAEQRDy22873C5BGejgVVS7LSWVxs5YSkfOXeX1zwZ3o3bNco7i/KxwKVmZF0V6ciLZOjrn+jwRJ7ZSamw4KwnFN6EgCE+KVSsK4W9KRXvBfzjrdXFxobS0NIqNjWWHv9imolCqtNhna2vL/qdgVYJj8R9T0JAO0caU7yn9tFAawd8UrgELFDi5T0hI4POiQouLi+NjcN2s0moCTpFn5YaUmKoh+/KNqOrY9eRSvAKFtRhIdqmJ5OjowA6JU2ydyN67GF/btkRVci9WmYq3fZfSU5Io5MJBsk2O42si7f0TOylkndYCzrFIWXIuV5d8qzVjy0M4qsf14RgZaXGP7u7ubBGD+8E2Dw+PHGUImeAZ8KxR5/bQrd/HUHpiLFs1lnp9PMWnpJFrSkqOMjRF3niu7GRoTN4A0X8M0+LZcO34+Hi+Np5dpYVMMEUwJSWF96u0OA7Pj7S4TtG+Uyh04w8Ue343XZ7yOhXqMJKKv9Cd5WIoQ9yvkreSoZK3vgxzI28lF5wDabOSoUtaPPu1jI9PIFtbG3IvVILzENIaykXJ25Q8qy9vpDFFhvifFH6PnDTJuvt39SlGJV4ZSuToSm4Nu5Otq9ZBM66RmJREX8xaTnEJSVTEx5NG9euoy98qz4L4xFRKSUsnLzetBVhUXDI52NuSq5M9paalU3R8Knm62pO9nS0lJKVRUkoaeburtCnkYGdDrs72lJamoaj4FPJwsefjE5PTKCE5jXwepo2OTyE7Wxtyc7an9HQNRcalkLuLPTmqtElp5OOhTRsTn8L3iv3pGg1FxqaQu7M9OTrY8vXjEnFeB04Tm5DCx7i7OLAsI2JTyM3Zjpwc7Cg5JZ1iE1PJ292Bp9TEJqRyGg9XrRVrREwyuTjZkbOjHSWnpvN+bzcHftdxiamUlq4hT5U2NplcHLVpU1LTKSYhlbxcHcjOzuahDDXk5aZNGxmbzNfHuQ1lGJ+UysdnkLedLctQpVUyNJR3QQP58q+//mLH7qdPn2Yl4G+//aazFASYgjxixIgMSsAlS5bQpk2b6MCBA+wgfs+ePUanHBd0YPn98+qd9N/py0RUhia815PqVDFPq0hBEATB8og48TfdXvoZ+ff6kgo1fJW3paam0biZy+jAqUu8PnpQF+rUqgEVRDTpaRS4YRrFXj9GlYcvzpWyEO29kD3L+H+RFm88xbsUBMFaME9HcHkEnPYqTpw4ofM/BaXJvn37KDIyktfv3r3L0b8Up06dYgfBAMoQpIWlCAgKCspgYXLu3Dm6dElbOUFpgbShoaG8fv/+fV5H4Q5gnYIFYBv2IQ14cPsanZv9Ht2cN4z93+GcODeUhODw+esUaedFrv7VKMG1GB08e5XvDeBecc8AvgBPBSdRtK92uiae8VxwLPm2GkBuZWtTcmgARR9ZR7d++5jOfd6STmxdydPpQHTYA74npRyC7y1Y1Sgwpe7mzZv8H4oepFXO/+EY+fChgxS09f/oxrxh3Om0bdyPyvT/nlLINoO8AwMDM8gbnfIrV67wfyiUkBbRRAGm9O3fv1+XFkEDlLyhPEPakJAQrQwfaO8fijJl/YP0CuzD+QDOj3W8M3D58mU6c+aMLi06/ffu3eP/ERERnBYKNyVvRJlTHD1zkejF4eT3+jhuBEApe2P+cIq5fpyuHdlFJ3et5xFQTnv0KMtVKcdwXvwakzfy740bN3TKMqRVwRWQZw8dOqRLi/tReRb3ibS4b4DnOLRrC0fOvjy1JwX830C6PWsAr8eHBGSSN9b15Q05AsgV+yBnALljHe8B4L3oyxvvzVDeeL8s7xMH6OaPr/P9YAmc8xZd/aELpcZGkmP97rT/4GGdvOHr7ft5q+n89QBWkL3auBJFRWjvF/kP50V+BFcCY+i/c6GP7uFsKF0L1MoXirMdx4NZIQiu34uhvWe0eQccvBBKlwK08oUCDGmhfON3ExxHu09pnxscuRRG529FaeWdks5pQ6O09xsQEk87TwQ/eo9XwunsTW3ehwISae9HJGrfTWgCrz8sIujktUheALZhH9IAHIN1nAPgnDi3AtfEtQHuBWlxb/web0XxPSvwLHgmgGdEWjwzv8eAaJaFAjKCrABkh7SQJb+bwFiWsQKyxzsAUE7qy/tGUCztOfNIhgURKNthTfj999+zn0F9JSGoWrUqTZ8+nYoW1VoxKDp16sTRjMeMGWOWSsKksLt04os2FLxb2wkZ1KU1vfJCvfy+LUEQBEHQBfEI+GMS959cSlbibalpaTR+9grae1zbtxrZ/1Xq0rogzzax4QAsCQEX6M7KCbr+oUloNFSs9SAq0rKv7vkFQRCeBBtNrkohywDKFFhEQcFRqlSpAm1RCIuruLM76e6f31JafBRP0y3d+2uyKVLeZAs3Q2s4nFc5zDe0EoyLDKOY6ycoLfAsxV4/TsX6TSNndy9ytCU681lzcijkT55VGpFn5UZkV6o6aeydTbYohGzC102i2GtHqViPr8ijUoMMaU2RoblZFBpaCcYGXKKbi0ZSalhAhjyJqeJVPl5FibbO5GBnR06wDE1JpiRbZ/Lw9GIFWHTAJUpOSiI3Vxei9DSKi40hWxsiN9+i5FC4DD+7Q2IkpYTepuTEBEpJTiIXZydWTiYmxJNHlSbkUaQUPwf8jNmnJ/PxSJcYepeij6zN9K34Nu5K6U6e5OBgz8+G50pNSaFijTqSq99zLMOooxuIYh6w4iopKVGXVmNjTx4tBrC8U2PCKHD7fNJo0snRwZHfDZ7Fzt6OvKu9QK6VGrMMU6/9Rwm3zlBC5AOKPbMj0/1ARg7FK3FalWf3HTtHn/z0O+8f0q0Nvf5yQ6NWsJ4J58Wi0EwsCqNj4sinSlv+zlS5a62o+grK//yQBQZ1Tn3Xg2zDb9PauCpEdbrSV+/3lgjHgvAUcYt9NDApFHykzsrf+gptyhtz36foC/uoRPthVLztO+wqY+Ivq2j7Aa2hxAd9OtAbHVpQQQd17pWf3qDEoGtU8tWRVKz1m/l9S4IgWFg57efnZ1Ify6oVhfnV8TKV5Mj7FLD6a4o+vweeean4/4ZQsTZD8iUgRlJoAN1cNIoSAi9pTZmAjS0ri4q+NJB86rXLeO/hQTyNFaQlxJJTYX/2GYIKMC0hhhy9i5G1EnvjFF2d0c+ktDW+/pf9RiKC2+lRxi14vOu2pXIDp/L/+/8spnsbtP8NqTR8MblX0J7j3IQ2lBL5yKott5R+YxIVatiZ/1+d9RbFXj2SKY2tszvV/l5rGZpw7ypd+r6r0XOhQYeGHbiz8ksKO6j1M2gMKAphNasIj4qlvmN/4t+6z5WnWZ8NIbssImZLx8t8kE5Xwaiv0Dw498sHlHp5Dx1LKkGHi75Ks8e9wwMZgiA8PaS+Mi+kzsrf+ir04J8UsPJLci1TkyqPWEIaG1uaPG8NbdmrnTn2bo+2NLDzS2QuoM91+cfelJYQTeXfnk1e1Zpnmx79KhgcYNaYIAhCXikKrdpHYUEn4thmVhK6+FejMr2/IpdSVfLtXqDoq/rJakqNi2LfGbFXj3IAFATpSE/WTn0EwdvmcHAUKHs0adrphwDRlauN+4uVhSoC2bPk36PnaMGfO+hOcCiVLl6Y3urWhl5sUIPyA1sH4z7YXPyeIzsXT47+bGNrR2Rrp4tKjXX4c+RtDxftfyhrHynO3MrWomL/G0I2tva8DwpmdT7HQiV16Up1HsnOktX5kiOCKGjzjEz3VKrLp/zuMR1CeyPaX5dSj5wul+z0IVu7GqbRj6jtWKgUVRw6L8N+7a8N5wkFlM6+9TtS4oNbHBE7O2AZ+dWcVawk9HR3pYnv9cxSSSgIQu65tWUOKwnvpnrQDqcW9MuoAaIkFARBEAqUa4zAdT+QjYMTlXljEisJv1+4TqckfKtLa7NSEgK0u8u9+SNd++Udur1kNFX+aBk5F8vaJ/D9nQso7NA6Kjd4BrmXq/NM71UQBMtFFIUF0IoQUbo4CMaL/cnOzZstt/SVLvmJvZsXeddqzQtIiQ0nW3tHnfVJ6H9/UEpUZj9jUBrCwlBfKfQsiE9Mos17jtG0JRt1264HBNPY6b/Ttx/2yzdloTFK9/oyg7WcPlD2lR3wQ47ncC9fl5ecYKWjgQVo8N+/ZIqc7V3r5RzfmVuZmjleD8phjyo5+4VxLlqWqGhZcvQtxdc3FslbsWLrfjp0Ruu7ctyQ7lS0kDa4iSAIT07o2T0UseP/KC7dgZYkN6Hvxw6mQl5aNxOCIAiCUBCANZ29RyEq0rwPORUtRz8u3kAb/tXOcunfqRUN7taGzBGPyo3Ir8snFH5sC9k5a10kGQPGGqEH1pAmLUXnt14QBCEvKBjaJ0Eb5GLvCgra8jP5dRtLhRp3IRs7ByrcpFuBlo6D+6PonlBuYmpo2OH1Rq3T8pKU1FQKjYih0MhoComIppDwaO3/8KiHv9rtUBQaoubafz1nNd0NDqU6VctR1XKlyMH+2XwOUHblpAR71kAZGNnpR9r41w4KDo2k4oW96dX2bZ65Ylf/fqp9vlk3fR1APup+Lt64S7+s+pv/d/9fU2pRv3q+3KcgWCKw1l247QTVSnOl1XE1adRHb1N5v4yBWwRBEAQhv4ELpKqfriEbeyf6edkWWrND6/Km1ysv0Hs925m1P93CzftQoaY9snU5BUUiZvUUbt6b7FxkME8QhLxDFIUFgMT7N+jO8vEUd+s02Tq6PPIBaIbAn55n1aaPrShEBzUyJp5CIqIoFArAh0toht8oiojWBiJ5EqBEnL1yK/+Hz60aFUtTnSplqU7V8vzfxdn4FOGnrQTLDzA1e+y8rTwbWKNxp5v3UunAvK30rVvhfLO6hDyMySQuPpG+mLWco9lVKl2CPujdPl/uTxAslflrd9Lak8G0nlrQqDe7UuNa+ef2QhAEQRCMTTm2c3Ynezdv7jth8HjF1n28r3ubJjTijY5mrSQEuH+bh0pC9BWjL+zn2WYKzOQK2bOM/xdp0Sff7lMQBMtEFIX5CMzEEXxCTflEVFr/nhPIqZA2ErO5cuT6A3LV2JKDTbpuW4rGlvZfDKTKdoW11n8PFX6GSsDQyBhWAOUGH093KuLrSUW8PamwjycV8dH+FvX1osLenjTh/1bQrXvaqLwKNB083F2psLcH3bh7n5KSU+j4heu8EO0iOztbqlK2lE5xWLtKWfJyd33qSrBnBRSyQaERdD3gPt24G0xLN+/h7UpG6vf/Vm6lZnWqkqNDwSgq0Cj64bf1dPd+GDk7OdDXH/QRn2mCkIff1765X9If++Bz1Il6tGtB3V5uIvIVBEEQClT/CQEW4eqoyqgVtHjXWVqyaTfv6/xiQxrZ/1WzVxLqo0lPp5sLR1Fi8DWeZu1bvwNvRyBBbPN87gWt6x5BEIQ8JN97/1BYHD58mO7fv081atSgihUrZpv+2LFjdO3atQzbvL29qV27jFF3zYGgv3+h+9vnsal4qdfHkW+j1x67YnsWwTrQiUxKSaW4hES26orFgv8JSRQXn6Bdj0+kNTsOkE1cS3K3eTS1NlbjSBGLdxIRFtNwc3F6qPjzYuWfUgCyEvDhbyFvjxynDL/9elv2SQjZ4hnU72eDu1GrBjUoKiaOTl++Racu36RTl2/R5ZuBlJaWTheuB/Cy/C/tCGV5v2I8TblOFSxlzcInHp4zLDKGrt8NZoXojYBgun73Pt28e58Skh69n6wICA6l1oPHs+Xec+X9qVoFf6pW3o/KlCxCtvkQOOSvfSdo238n+f+o/p2pbMmiz/weBMFSObX8B/K4sJb6uhelMxX70QdvaDsjgiAIgmAqDx480EXTDAkJIUdHR46GnJqayuu+vr7k5OREsbGxvBQvrnVtERoaSvb29tyvS0tL4/P4+PiQs7MzxcXFcbTOEiVKUPD2+ZQQcIFcKjel5bvP04K12r5F26a1afSgLpSYmMgRPXFetPnDw8N5P66LdnFwcDDfj6urKyUkJFBkZCQVK1aM27URERHcNy1UqBAfExQUxM/i5ubG58X+okWLkp2dHR+HZypcuDCnxXnd3d15SUpK4usWKVKEnwn3k5yczOsA/V5c38PDg7eHhYXxeRwcHPg5cS1ch2UYGkqFXhtD9xa8T3dWTKBYO3cqXr0Jhe5fxfvtaz+qq5+GvAHuD/LBfsgH94/jXFxcdDJU8oaMIGdcV8nQVHlDhpAJ5K1kqOQNGaakpOjkjXtAOshbyVDJG/eO45W88Wy4V5wb58CzK3nHxMTwfenkHRLC8sJ7VzLE/UGukB9kg/tXMsQ58HxKhkreSIdzK3lnJ8P4+Hh+PiVvPDdkibQqzxrKuyDnWcgB+Qj3oeStZAiZ4HmVDPMjzyp5W0oZ8SAX8sY+U8nXEKEQSLNmzah79+40e/Zsql27No0ZMybbY+bPn0+ffPIJrV+/Xrfs2rWLzGlUSFG0ZT/ybfgqPTd2vdYn4RMoCaEIgzIoOSWVf7GO7QpkaCj37odFsgXZmSu36MCpS7Tj4Cla/89htiibs3obTV28gSPJfjptMQ2d9Cv1/3wGdR/5A7V79ytqPuBzavXmOOowdBL1+HgqDRo/i4Z/O5+vNWnuGpq+dDNPWcPU4Yh0FwpI89ItWFc42NtRiSI+VKtyWWrdqCb1bNeMhvVuT18O7UWzP3+bVk/9mP5Z8BXtmv8VrZryMc36bAhNeK8nDe31CvVo24xealiTalQqQ8UL+5jkVxAKUwQuqehfnC3j8Pvdh/1YSQi8PNzYx93wNzrSwq+G0Y55X9LPYwdzpLTnq1Ugp4fWdFC0rd15iMbPXkGvDv+Wun74PcsKTpPvBIVwwZKfRMXG06lLN+nPnQdpyqL19N7Xc6jtu19Rx2GTacR3C2jG0s20ac8xVn4qJaGrsyNVr+BPHm6P3o8hKalpdOHGXT7v17+upt6jp9HLQ77k/DFrxV/0z+EzFByqbRQ8TSDjqb+t5/9tmtSmji3rP9XrCYI1cX3/Zko/vJRi0h3psFcr+ur93hJFXBAEQcg1q1ev1v1fs2YNHThwgP+jczt37lzuWIPTp0/T4sWLdWk3bNhAe/fu5f/o2CLtnTt3eP38+fO0YMECig84T8Hb51KqvQutiKxAc/7YzvtLeNhR9xY1uSN/5coVPla1S7dt28YLwDbsQxpw48YNXkfnGvzzzz+0ZcsW3T3hmrg2wL0gLe4N4F5xzwo8C54J4BmRFs8MIAPIQrFs2TI6flwbmRmKEaRVyoojR47QypUrM8jzVEAUBz3UpCZR4OJRFHD4LyrcrAclVW7DbXvF2rVraf/+/fwfChacNzAwkNfPnj1LixYt0qXduHEj7d6ttcSE0gFpb9++zesXL16kefPm6dJu3bqVdu7UKmSh+EBaZbhz9epVXofiBmzfvp3+/lvrRxxg36VLl/j/rVu3eB1KEvDvv//Spk2bdGkXLlxI585p+68BAQGcFgogJe9169bp0i5ZsoROnjypU8AgLRQz4ODBg/THH3/o0i5fvpyOHj2qU5QgrVKYYDv2K3Acjgc4H9Li/ADXw3UVuB+VZ3GfSIv7BngOPI8Cz4nnBXh+pIU8AOSDdQXkBzkCyBX7IGcAuWMd7wHgveD9KPDe8P4A3ifS4v0CvG+8dwXyA/IFQD5BWuQbgHyE/KRYunSpTt5QYiEtlGLg0KFDGb575F/kY4B8jbTI5wD5Hvn/aZURCnzH+J4Bvm+kxfcOLLGMOHToEP/HO0FavCOAd4Z3p9D/3nLCRpOP2o1hw4bRjh07+GGhtUWGbN68OWf41q21UXUNeffdd/nD1hdkbsELwfXu3r2rG/F6FsAH4Z2VX5J/j/EmRaY1lV6f/MhTaw2BUszH001r8ZeQ9FQUOVCiubk6kzsWF2dydXGmizcC+Hr6QAXqX6IwzZ0wlKfwmtOUAAROuXQzkJVwWM5cuU0x8QlGp0BrpyqX46Vi6RJPpaOdkJhMNwO1U4bV1GEoMTF9OyugnIX1XXn/4lTBrxgHJijvX4yKF/LmRpVSNhtaXY5/twdHOoWikC0sbwSwhaIx8PywNoTV4XMPf709so7UlhugAB/85Wy6cuselSziS0u+GU7urlkrN43hFnsmT+5FePpEx8SRT5W2PJj0LMvogsizqK9Cb1+iq9P6kIMmlRant6RxE7+mYmZgMS0IlorUV+aF1FkZ66sTJ07oZojlpbVQVHgoRS0dQYnB1+lmtQH0w16t4gED/8O6v0SFCvlavLXQrRUTKOLQI+UNY+dA1cdtYZdGYlEoFoUFLc+KRWFqgZI3FJ916tQxqY+Vb4pCZZI8duxY+vTTT3XbGzVqRNWqVcsw4mGoKIRGfcSIEVzIwwoRFUpBVhQidP29LTMpZM9Sdv5WrM0QKtlx+GMrra7cDqJzV2/T2au36dy1ALbmyi2oOGFNBgWfm4t2YWUfrzuRm4uL7r9SAkIhaJjOmEVfVkonfSs+cwYNCEzfVYrD05dvsm9FQyCfmpXKUF34OKxalqqV99f5+jNlqjje9e17IawE5KnDD5WCgQ+0DR5j2NrYkH/xwjxNWqcU9C9OfsUKkb2dXbbPhXtauHYn3Q4KoTIlitBbXV/O9L7YcXJ4lJ7i8K5RxbACSr1qFfx42jIsF+H38XGCxEz/fROt/Hs/+478dfx7HGwmt0jHy3yQTtezq6+SYqPpv/GdyCctnNYl1qDeo6ewol8QhPxD6ivzQuqsZ1NfBa6fSg/+XUyxpRrRJ6e1irzm9arRtyP6kr199m1cSyHu9jm6Mq13pu1VPl5Frv7V8uWeBEEwr3Laz8/PJEVhvvkoRCUCrTb8EupTs2ZNOnXqVLbHwmRzzpw5bCILk90ZM2bQwIEDs0wPja0ycQbKxPNZEHP1CPuTSA67Sw5eRcm/xxfkVaOVycfDegsKwbNX79C5a7fp0o277CfQFIr6etIHfToYVQS6Ojs9NR9zaqpvTkoncwVyg88+LK//rykrz6C8O3XpBvs4hPIQwTagPDt05govAErC6g+t7KCU00YYJt1U8b4dW7LyVlkJQokIX4lZganXShEIxWAFv+LsOxARnB/3veXk1xJKX/hmxKLeJxSnAcFhOotD/F69E8RWgPdCwnnZeeiMTpFZzq+YnuWhP08Fz66B99/Ji6wkBO+83vaxlISCIGQGZdfK2d9RrbRwOpRYil586zNREgqCIAgFEudi5SnFowRNOKPt3DapXYUmD3/DapSEwCYf/IMLgmCd5JuiEFpMoBydKmAmqfwLGKNXr170008/sWk5mD59Og0ZMoSef/55VjIa49tvv6WJEyfSswYRje9tmMr/CzXpRiVfHUn2rllrblNT01jBwpaCV+/Q2Wu3KSjEuLVg6RKFqUbFMlSzUmlKSEqhn5dtzmTBN7J/53xTzpmidLIUIG9Y7GHp2LIBb0MEZxUc5fSlm3QtQOs/8uSlm7rjDCMMq8jDhvh6ufNU4Qr+2inDUA6WK1WMLTwLiuIUCkosrzSvp7OGvB4QTBeuay0O8Yvp0ukaDW/HonyqYPp6pTIldVOWoUyFLPccv0C/rt6mm1Zf0b8E9e3QIl+fVRAsiWVb9tIvZ9KormNdata5L73Y0HgdKgiCIAj5zZG0sjTpVh1K09hQgxoVeaaSmqkjCIIg5C35VrrCNwVQDjMVWMcc66xo1SqjNd6HH35IkyZNYieeWSkKMb155MiRGSwK/f396WnjWbUJhR0sS/7dPyePKo0z7Q+LitEqBB8qBi/evEtJyVpHmfrAygxKFKUYhEUVAnDog+AglmrBZ44gIvPLjWvzAmLiEjiADBSHSzfv1ikHDcFU5UcKQa0fQfj+MzcwJb1qOT9eiLR5Pz4xiSNKq2nLF2/cZWtDWMieuwaLWa0zWuDs6ECJBt/CtYAgVh5aiwJaEJ4me/YdoNkrtc6v/Zp0ojc6txGBC4IgCAWOqPN76WiUO309ZzWla2yo7nPlacrIAY89g8acsXfzIRt7R9KkaoMSAqxjuyAIgkUoCkuXLs2OG1WkGgWi85QvXz5X54KTRxVJJyulpFJMPk1S4yLp3qbpVLztu+ToU5xcSlbmiMY2tnZsLQhFx1k9xSCUJMaAjzmtQlCrGMTU0pyCYliTBZ85gqjCzeo+x8vBU5d4urG+shDTkGExN+/LoWSpYLo7GndYFBHRsawwVIpDTF2OiI7LpCRUlptQhks+F4Qn48LxQ+Twx/vU3rkcBZdrR58O6mJWAaYEQRCE7MHMIkRcRYRT/O/QoQPPwDLV7RACTiJq68yZM7M0xHhWSsIbc9+nm0klKV1Th2pVLks/fjyQnJ1y7+/aEkDAkmqfb6bUuEczzqAkxHZBEASLsShEZGOEIB80aBBvQ4QWhJzG1GIFQpYjyku7du3YFxrSqAgvAGGjoVxs0EA75TO/iDi1ne7+MZlSY8PJxtae3Np+qA04Akupq7fZisqYtaCL00NrwUqlqWbFMvybV5FihYIJApcYC/YCK1BrA9aSTetU5QVwNLrQSHp91A+UauCfEftgMSsIwuMTfC+Q7i4eSUVsUsnRw5u+/bCv0aBUgiAIgvmCQJEIDPndd99xtNPRo0fT+fPn6eeff87x2NmzZ9OuXbvo0qVLOldR+UFqXBRd+30ckYZod0Jp7i/99MmbPPBszUApKIpBQRCeNvnaO0Dl9cILL1C/fv2oSZMmNH/+fHruuecyBCb59ddf6dChQ6woTEtLo5YtW9Irr7xC1atXZ2tEjHRhlKxbt27P9N53795NG//aQdFhD6ijxy0qowkmjY0tXfFtSt8esKU7G742ehx8r2F6qVIMYmppTtFoBcvC0oO9PAlQmmIafdmSRY1YXdqwrARBeLz6Kjg0gl5zvkCVHGLoWGoZ6j3ue/J0cxVxCoIgWBBBQUFsdLFs2TLq2bMnb0N0yx49etCoUaOoTJkyWR575swZ9u2+fPly7nPlX30VSV08rlBFiqDtCeXJzq8GTR89qMD45xYEQbB08lVRWKdOHTpx4gQrCA8fPkx9+vSh9957L8M04YYNG5KPj9bvgoODAx07dowWL17MykNsx/9OnTo980rMde0I6meTTgT3cRptQIrZkfXofIg3DOU5nbOTA0d1rVmxNNWAcrBiaQ5MIQgyVTx7xOpSEJ5CfeWh3ZauIXJp0odKi+JdEATB4sDsLMzC6tixo24bjCow7RiWgmomlyHx8fGsWITVYcmSJakg1FfoX11yqUYzxwyWgS1BEIRnSL7HWK9cuTL98MMPrPD7+OOPyc0t47Tbt99+m6ZMmaJbx/6hQ4fSvHnz+LgnURI+eKCNpgrg41CZ16empvJoXFJSki7ASnBwsC7tuk3byAGVmB5w7xRn50KtG9WkIV1epN8mDaed8ybSpPe6U++2janF89XI28OVz5uQkKCrkLGOKZUgPDycF4Bt2Ic0AMdgHRU/iIiI4CnZCuyLi4vj/4mJibwOC0yAKNKYsq3As6ggMnhGpMUzA8hA39/j/fv3KSYmhv8nJydz2pSUFF1QGH0Z4j+2AaRBWhwDcA6c63HkjXtXkbDxTEiLZwR4ZqwrIBPIBkBW+vJWMlTyRjolbyVDU+WN+1PyVjJU8sZz6csbz63krWSo5A156csbMlTyVjJU8sZ2wzyr5K1kqOSN6+nLG/ej5K1kqOSN59CXd3YyVHlWAfmptCrPGsr7cfJs3Up+9NmgzlTRvzhHtCtTohB9+e7rbHX5JHk2MiaBgkOjH73H0GiKitHeb0pKGt29H8nRqfndxCZSUMijKTf3w6IpMlqbP1JStWmVL8WYuES69+BR2gfhMRTxMG1aWjqnRXRyfjfxSRR4/1Fk95CIWAqP0sohPV3DaeMTte8xLiGJ11WeDYuM5UXJG/uQht9NYjKv4xz8bqLi+NwKXBPX5neTlMJpcW/8bqLj+Z4VeBY8E7+bZG1aPDPLMDqeZaF7jyFRLCuWd0qqNm3Kw2/BiLwjDeSdlGxc3sLTq69sbYhOnr2Qq/JT6iupr6S+erZtLKmvzKe+0r+fggBcMnl7e2foUyFQZKFChXhfdn4JmzVrRl27djXpOsjfKBf0lycBloTG+ldejjaZgjgKgiAIFq4ozE9Wr16t+79mzRo6cOAA/0dFN3fuXF0H6vTp06zIVASHPerk62NLNtS5cWUKunSMqpYrxVOKt2zZwiN7AI0/nPfGjRu8fuXKFV5XSoBt27bxArAN+5AG4BisqwYkzolzKxYsWMC+RwCmZCOtUnrt3buXNmzYoEuLZ8EzATwj0qrKHTKALBSYtgA/kACNW6RVyrUjR47QypUrM8gTlp4AnU2kVcqqkydP0tKlS3Vp165dS/v37+f/aFAjbWBgIK+fPXuW/aoo4IgZo4yqgY60qqFz8eJFVhorEP16586d/B8Nc6S9du0ar1+9epXXVeN++/bt9Pfff+uOxT74YwG3bt3iddX5/vfff2nTpk26tHAQDSfPICAggNMqpRfkvW7dOl3aJUuW8POrDgTSKsXnwYMH2U+nAlM94JcToOOBtKoDgu3Yr8BxOB7gfEirFAS4Hq6rwP3gvgDuE2lx3wDPgedR4DnxvADPj7SQB4B8sK6A/CBHALliH+QMIHesqw4S3gvejwLvDe8P4H0irVIA432H3r5Ev3/7Ie39bTLV9k0lL3tt3kc+QVrVEUM+Qn5SIJ8peSP/Ia1Sfuw/cY2WbDysS7t4wyE6cEr7PYZGxtLPS/+l+2HaBv+hMzdp4VptmQCWbT5Ke45d1XUokFYp/I5fuEO//rFPl3b138dp50FtXopLSOa0twK1StIzlwNp1oo9j97NzlO0dd8FXccFaa/d1nYkL14P5vX0h2XExn/P8gKwDfuQhuV9O4TXlaIT58S5Fbgmrg1wL0iLe+N3c/AS37MCz4JnYnnfj+S0SqEKGUAWCsgIsmJ5h8VwWsgSQLaQsQKyxzsAYVFxnDY4VNvZOnruFs3/8z8yB/C9qXydG1CGZhd461nWV0EhEVxnoO5QSH0l9ZXUV49XX6GdokD7Be0YIPWVddRXa7afoIIElM4uLi6Ztru6uuoGlA1ZsWIF7du3j6ZPn27ydTBF2cvLS7f4+/s/0X1jurExHoTLIKIgCMKzxkajtFRWBDpWqNAw7blixYq8DZ03R0dH3o4OINZ9fX15GjQUEliKFy/OaUeMGksDUzdnOu9S58701fixfP4SJUroLDRg6o9p0rDQgOICo3yowKHIw+gyzgv/a0oBh+tyUIfgYL4fVOyw6kDntFixYnw+KD5wPowOAnQS4X8Eo4dovGI/gr7AgbHq1BYuXJjT4rzu7u68QBGE6xYpUoSjUON+0IjAOsD94vqILI3teB6cB9PA8Zy4lgouA4sXjFjiPqDQhIIL9we5YsQcz4v7z628cR7cG+QGZRSuA3niWlB6mSpvJUMlb8gIcsZ1lQxNlTdkCJlA3kqGSt6QIZ5fyRv3gHSQt5KhkjfuHccreePZcK84t5KhkjdkiPtS8obMIC/IW8lQyRvyg2yUvHEenAPPp2So5I10OLeStyl5Vskbzw1ZIq3Ks4byLih51jvpIlsHJCalUPHCntrzhkaTi5MDeXm4sMXA/fAYKurrzlaMsBiAtV6JIl7a84ZFk5ODPXl7urK1AjoZhX3cyNnRga0ZYuKSqGRRL511gYO9Hfl4urIVRFBoNBXyduNrwaoPnZhSxbx1FoV2tjbk6+XG1oD3QqLI18uVXJ0d+foR0QlUqqgXy1lZExbydmd5Bz6IIh9PF3JzcWKLwvCoeCpZxItsbW3YojAtXUNFfNx1HSg8p7urE1tohEXGUYnCnmRnZ8sWGnimor4eOgsNDzcn8nBzZguN0Ig4KlbIg58JFhpJKalUrJBWhrCqwPU93Z250/cgPJaK+XqQg4MdP2eCgbzhksFbT964PyfHjPKOjokjnypt+d0ijxQUoEiDRTu+H+RpuMqAZbspUSShVG/fvj1P57p7967J15T6Suorqa+kvgJSXxXM+gpcux1MVZp0LzB11owZM2jMmDE6i3AF2oCff/45Bzox5MUXX2RDA6Xsw7EwCICbKExbnjRpUqZj0B5TA+qqvsLxqOMeRw4jP/2c+iU9Urrr969+/D7z9QVBEITcgXLaz8/PpPrKqhWFj1uRKR8a+ubxKRpbSug6g1q2apXHdysIQl7gFntGBGkmFERFISxla9SoQd988w19+OGHbLnXunVrGjt2rNFOlz5Qijdq1IgaN27MloCPoyiU+koQrAepr8yLglZnYQYJApHAarxatWq6OgzunjALpE2bNpmOOXXqlG62i7KG7du3L/srhBIR9V9OSH0lCIJgOYrCfA1mYq60atWKdtMM2rR1J0/fQpTWV195WZSEgiAIFgqmGpYqVYojRoJ69erR4MGDadasWdkqCmFF269fPxo5ciRb+qopw88Kqa8EQRCsC/gZrFSpEk2ePJldCAH8L1u2LNcJCgQ76d69Ow0cOJAtB/VRbnvq1q1rkpIwL5D6ShAEoeAgisInqMz0K1tBEATBcsEULHS+9GnRogVNnTqVp8arqfvGfDhhavKIESO4o5YfSH0lCIJgPcCFC/xYd+7cmUqXLs3uCzBoBX/VcEOjgI/n+vXrU0FC6itBEISCgSgKBUEQBCEH4AMUVoT66PsWNaYohPXgzJkz2R8uOmqmYMznkyAIgiDkhtq1a9P169fZTQa8TGEdfp312bx5M09BMwYs6BHYrmbNmiJ4QRAEK0QUhYIgCIKQA1D0GUY6Vuuw3jAEAXp69+5NEyZMYItCWB1i6jGsOlTQHwRkMmaBOHHiRHkfgiAIwhOBuslwgEufF154Ict9CEonM6cEQRCsl5xDNQqCIAiClQPrCkTU1ketI5KxIYgkDstAKP3g+wnL7Nmz+Rj837Ztm9HrIDgKHAyrJSAg4Ck9kSAIgiAIgiAIQmbEolAQBEEQcgD+CGfMmMFWhGr6FpR9cPLu4+PD68nJyRQeHs5TkrHAclCfSZMm0Zw5c7KNeuzk5MSLIAiCIAiCIAhCfiAWhYIgCIKQA++88w7/DhkyhM6fP08LFy6kRYsW0bhx43Rp/vnnHypRogRdvXpV5CkIgiAIgiAIgllilRaFcOqrpoYJgmAdpMXF5fctCCYSHRuXoawuCMBCcM+ePTRmzBhq3749FS1alJWFPXv21KWBJWCxYsUyOYxXuLu783G5QeorQbA+pL4yLwpinZUfSH0lCIJQsFH6L1PqKxuNFdZqmPbl7++f37chCIIgZAP882UVkdFakPpKEATBPLD2OkvqK0EQBMupr6xSUYiok5UrV6bjx49zJMvo6GhWHEJgnp6eeX69Bg0a0NGjR5/KMdmly2qfse2G27JbN1d55ZTGHOX1OLIy9bi8ylvGtme1bq55K6d0T5K3rFFe9evX52m8CBKCiMHWDOqre/fu0UsvvUTHjh2z2Hcu30j+yUvqd6nf8zJ/mVN9ZepxOX2LR44cYSsNa6+zDOsrS20DZ7dfvpG8lZW5lSnSHjIveVmjvqN+LvpYVjn1GEJxdHQkLy+vDNuRCZ5GRrCzs8v1eU09Jrt0We0ztt1wW07r5iivnNKYo7weR1amHpdXecvY9pzWzS1v5ZTuSfKWNcoLU3et2SrDsL6CLCATS37n8o3kn7ykfpf6PS/zlznVV6Yel9O3iP6EYZ/CGsmqvrK0NnB2++UbyVtZmVuZIu0h85KXNeo77HPRx7LaYa/333+/QF/L1GOyS5fVPmPbDbfltG6O8sopjTnK63Gv86Tyyo2szF1e+f0tGttmrfKyZqz1ncs38vTlJfV79vIyp/LX1OOkPZR38pL6Kv9kIt+IechL6neR15PmSdF3PP363RhWOfXYEJiWYiQwKirqqWiMLQ2Rl8hL8lbBQL5F60PeuchL8lfBQb5HkZUg34iUJ1L+mgNSX4m8covVWhTqg0iVEyZM4F9B5CX5S75Fc0HKLutD3rnIS/JXwUG+R5GVIN+IlCdS/poDUl+JvHKLWBQKgiAIgiAIgiAIgiAIgiAWhYIgCIIgCIIgCIIgCIIgiKJQEARBEARBEARBEARBEARESBYpCE+ToKAgiouL4/8VK1YUYQuPTXx8PN27d4//FylShAMQCYIg5BUxMTF0//59/l+8eHFyd3cX4QqPzY0bNyg9PZ2cnZ3Jz89PJCkIQp6BWKTXr1/n/6irUGcJwuPy4MEDDnYCypUrR3Z2diJMQaYe55b9+/dT/fr1ycXFhVq0aMENQSFrPv/8c2rXrh1VrlyZUlNTRVQGzJ49myt3LDNnzhT5ZMPhw4c5LzVo0IAWLVokspKySjCh4denTx9yc3Oj8uXL0+rVq0Vm2bBjxw4uY55//nlav369yMqAU6dOUe3atblT2rNnTx68EbKmS5cu9NJLL1H37t1FTFJWCTkApfq0adOoZMmSPBA8dOhQSktLE7llQUJCAtdX6Iu+++67IicDUD/17t2b66uaNWvSyZMnRUbZ8OOPP3J+qlatGhv5CNmXVVOmTKESJUqQt7c3DRs2jLdZIhL1OJesWrWK5s6dS+Hh4VSrVi364osvns6bsRAWLlxI165dI1dX1/y+lQLHlStXaPLkybRv3z7677//6Pvvv6fLly/n920VWF588UXOS++8805+34pZIGWVsGHDBurcuTOFhITQL7/8QoMGDZKOVzZ07dqVy5jXX39dMo8RBg4cSCNHjqTg4GC2ZpkxY4bIKRtOnz5NGzduFBmZgJRVwu3bt1lBceLECbpw4QLt2bNHvp9sQL8K9RX6WUJmfv75Z0pKSuI8NWbMGK6/hKxBHxT5qXTp0iKmHLh+/TqFhoay8vncuXO0c+dO2rJli0XKzeKmHmP0CUoXe3t7atq0qdE0UPKdPXuWfH19eZRBH2iEjWmFbW1tedG3+qpTpw6dOXOGzJlbt26xFUXVqlWpefPmmfajMwArShQeZcuWpZYtW7Ic9I83ZiloLdO2UAGhM4B8VKpUKaNp0OCBZU/16tV5yqxi165d9Nprr1GlSpV4HVYHeBdVqlQhSwTfFZ7v5s2bbI3i4+Nj9NuEXFC5Y5TU2issWFFiOuTLL79sdD+m9aNRjalt9erVyzBVwNLKKksF5cOdO3c4vxsbUEH5inecnJzMlm6wZtcvn41ZXNjY2HBeGDJkiG4bLOFR55nzdBLkdyheIIsBAwYYTQMrf9RZsKLEd6PvogANu8jIyEzHWMu0LcgNsoFMkJeMASXgpUuXWB5oFyigbIbrByX34cOH07hx42js2LFkqUAOUFZAVvh+DMG3t3v3bv5+MWuiWbNmZM2gPQiZQVaFCxfOtB/lFdpLUVFRbJkKSwyFpZVVlkpYWBgdO3aM8zumJxrj6tWrdPfuXW7LwjpQn6xmFqHPhvPBSkfh7+9PhQoVInMG5cfFixepU6dORvsIaN9ByYDfJk2a6PoDIDExkeVoDGtx5QRFDNyBtGnTxmh5gL7C8ePHuc2DctrR0VG3D/0NzGrz8PCgN954g0aPHs31m6XW9WjbbNq0iZycnKhHjx5ZtjePHj3KZS9kas1GPLA4hb4I+cFQF6QICAhg/UeZMmV4Vo4C3ykUqwq4FjH3sipLNBZCenq65quvvtKUKVNGU6RIEU3t2rWNppszZ47GxcVFU69ePU3hwoU1TZo00YSFhen2d+3aVWNnZ5dpGTlyZIbznD17VtO0aVPNgwcPNObI3bt3NR06dNCULVuW5fXOO+9kSpOcnKzp2LGjpnjx4poePXpo/Pz8NK1atdLEx8fr0rzwwguaChUqZFrWr1+f4Vxubm6alJQUjaVw7tw5Tffu3VkmNjY2mkWLFmVKEx0dzfLy9fXV1K9fX+Ps7KyZNm2abj/y6/jx43XrkydP1kyYMEFjiSxbtkxTvnx5Td26dTUodvD9GPLff/9pfHx8OE8hb+I7Xbx4caZ0o0eP1vz0008aS+bXX3/VPPfcc/zt4dsxxl9//aXx9vbWVK9eXePv76+pWLGi5sqVK5nSmXtZZals2bJF06xZM02JEiX4m7h69WqmNOfPn9eUK1eO67Vq1appChUqpNm5c6du/6pVq4zWV6VKlcpwHpTZ+KZwTXPl008/ZVnhu4AcjPHLL79wudGpUydNo0aNuI4/ceKEbv+kSZOM1lfvvvtuhvO89dZbmt9//11jKSQkJGjGjBnD+QKya9u2rdF0X3/9NddTDRo00Hh5eWnatWunq+8vXLjAeVBx+fJlLnsskUuXLmlefPFFTZUqVTQeHh5G6+WYmBj+fvFton1UtGhRbj+mpqZmSHfy5EnOi5bM4cOHNe3bt+f8hbJs69atmdIEBwdzu7tYsWLcDkC9Zqx+t4SyyhK5efOmpn///lwGOzk5ab799ttMaZKSkjRdunThb6Zhw4ZclowdO1a3H9+GsfoKy/79+zOca+bMmZrBgwdrzJXNmzdzXfX888/zN7Fjxw6j9TvkiTSvvfaaxtXVVTN16lTd/mPHjhmtr7DolzP43jp37qyxJFasWMHlBNrAkB/KW0MOHDjA+ytXrswyQflz9OhR3f5atWppzpw5o1vH+Yz1PSwBtFmQlypVqpRlvTxx4kQud/GNQjalS5fWXLt2LVM6yDIgIEBjqURERGiGDx/O8kI7Z8CAAUbTIY2LiwuXZZBbnz59jOox0B81bENaEhajKExLS9OMGzdOc+fOHc2oUaOMKgqh3LG1teUCSCly0PAdOHBgrq61d+9eTYsWLTRBQUEac670N23axHJDY9eYohAVNT4iyFQ19NAYRmcrt1iaohAVMzrpUKaikWNMUTh06FAutMPDw3kdylNUeEeOHNF1avXljkJp1qxZGktk48aNmhs3bnCnyZiiEIp+yGrQoEG6bVCqouFkqOCyBkXhN998w41I5BFjikLkKSgJVQcWjUZ06hs3bmxxZZUlK4Pxfg4ePJilohD1GBp1+D7Axx9/zMovY43mrMBA2Msvv8yKZXPmt99+4wYevn1jisLbt29rHB0dNQsXLtRt69atm6ZOnTq5vpalKQpDQ0O5TLl//77mjTfeMKoo/Oeff3jQC7+qvkfHCwpGEBISwnlP5UXkXQyEWSKon3bt2sXPik6TMUXh559/zgOFaqAZHS7UV/Pnz7c6ReHq1au5PYn8lZWiEEpUDJgqxTMG7R0cHLhdYGlllSUCRR7KYLw/lAvGFIUY/IYiWCkZMPiL9jGUZrnhyy+/5PazKmvMke3bt3MbDm2vrBSF6HtBKa6ec/ny5SwvDMLkBktUFKKeh6J03bp1RhWFUEpjgFy/D9WvX78MStTWrVtnkHvJkiW5XrNE5s2bp4mNjeX+kTFFIQZMUb+rshX9cfQN0G+wNkUh6urp06dzPwp5xJiiEN8iBjpOnjypOwZ9LhynD/RO6Lubc1llNYpCfbJSFOIDggWdPrNnz+bMgBF3U4ByqHnz5qy8wIdmOHpsjmSlKMR2FLz6vP/++5oaNWqYfG50LtABRgMao/RoSFoaxhSFyBeenp6aKVOmZNiOEcZhw4bxf8gFjSqMeKmRRWMWYZZEVopCjAJi+/Hjx3Xb0DDAyLXqeEEpC5khr6KThv+WXDiDrBSFyG9QikRFRem2oWMLGeI7s9SyyhLJSlGovhXsV6D8RHmzcuVKk86NTjg65//++y/nAUsYrMlKUYjtGNhCOaHAc+t/EzmBdgDew+uvv86WHdevX9dYGlkpCtFYNlRoffHFF2yxoW+RAQUP6nVYwHz33XcaSycrRSG2f/LJJxm2QTENRZcCg6wYJIOyGvlKv7y2RJAvjCkKoeBHuaWvfEd9hNksauDZEssqSyUrRSG+CfS/9IFlLmbfmALe+ZtvvsnW4yoPmHsbLytFIRQx2K5vOQvDjdwaY8DoA4NjKHdQxsTFxWksiawUhShjsB3Pr0A/Ctt2797N699//z3PikO5tGDBgscaNDQ3slIUYjtmp+gDoykYT6nBLijOkIdgabhv3z7NvXv3NJZOVopCtJEMle9DhgzR5SG0M2Fh/dlnn1lMWZUVttYWsQ++uvSpW7cu+4FAYAlTGD9+PB04cIAj3cAvWOvWrclSOX/+PEc/0gfr8EFjanQfRPVFFCXIq0OHDhxVyRqAHz6EmUf+0gfr8NGjfIx89dVX1LFjR5YR8pa+fxJrAnkN6Oc3+A2Dj0K1D74iICf4c1m5ciX/R9Q3awRlGfxleHp66rapvKbylzWVVZb6joF+GVK0aFH2c6TecU7Mnz+fffzAVx/yABb4+7FEUE6gTHVwcNBtU+WJKkNMOQfKFfiERPAX+JWypvxmrL6CTyf42AWLFy/mPAXfYyif4afQGkGbEb4wjbWP9PPa+++/Tx999BH7H0O+2rZtG1kjcPYOf476+Qv+xuAXSpVl1lRWWSKxsbHs4D+7Nm9O4NtZsmQJ9xNUHtD3tWzpbV74f0fZamp9BRDVF0ER0edAGQO/1tZSX8HPHnznKyBL+OdT+e2DDz5gX6eQ6f/93/9x/WWtZNWfR18efXqwYsUKzkNoQyHwC/w7WitZtYfOPazL4PN92bJl7KdQlVVoM1oiFhfMJCdHn4bOZJXzyYiICJPOoT4oawCKLn1n0wABKOCMGE5ATQlWMmHCBF6sDeUwH5WUYX7TDyrx9ttv82LtIK+hgkdha5jfsA9AMQansoI2fxnmLXyraGiqssyayipLfcdwNI3vwrAMMbW+QgcCizXXV2qfKcAZurWWMcbKFP32EZTUUOzAEbq1A8UfZuQYy2/6eU0iHufcHlJlmTWVVdb6jnMCAW6yCnZiaahyIqcyJCcOHjxI1oix+gpgm8pvCPxmzcpBfZCnEBwou/bR0KFDeRGybg+lpqbyoAjaitZSVlmVRSGiIRlaIEHhpfYJGUEhiwaxPihQEF3KUKEjZM5rwFh+k7xmPK8hellKSkqm/GbNUblyU5YhoilGByV/WQZ4j7BcgkJCHylDcldfASlDTMtv0j4yDRV53Fh+k7xmPG8BaQ9ZLvKOc4eUIU+e34zNKJL2Udb5TdpHT5a/4q1UX2RVikKYKGP6oj4q9Ly++bKgBdNgYc6uD9YhK3t7qzJGzTUIpQ6FqrH8JnktM2rKtX5+g9IwMDCQpxMKGUEeQl7SVyKpvCb5yzLAe4Ti9969e7ptmPIQFBQk7ziLMuT27dsZvglVnkgZYlp+M1Zfoa43nIlh7WA2RfHixY22jySvZUbVSdIeslyKFClCbm5u8o6foM0Lbt26JWWIiWVKaGgoD6YqoAiLioqS9lEW+c1YfQWkzjKev4zVV0WLFtUp+a0Fq1IUYu79oUOH2OeO4s8//6RatWqxHy8hI6+++ipPnYmLi+N1WHytWbOGtwvZ4+XlRY0bN6b169frtoWFhdHu3bs5HwoZadKkCRUuXJiWL1+u27ZhwwbOe/BtKWQuy0JCQtgHoX5ZBp+FkKVg/jRv3pytk/TLEPjnhNVS27Zt8/XeCiLwJwifZrt27dJtW7p0KU+3MfRNLBgvU3bs2KGr71WZ8tJLL1ndCLopoB30xx9/6Kzg0UHdvHmztI+MULlyZSpXrlyGsgxT/OFLTNpDlgEGxv/3v/9leMdQ4vz111/yjo0Av3n4LvTbvPv372dFoTX5xn1c2rRpwwOnW7Zs0W1bu3Yt+9cTf9zG66uzZ8+yjz399hGm0JYsWfIZvTXzAfUS8lbKw/odA9Dr1q2zyrLMoszC0GlGJwoFLUYW/v77b94O58gYFe/RowfNmDGDFQ9wMI2P5vfff+fGnbWBAnbevHn8H4pT+DabM2cO+yzo2bMnbx85ciStXr2aOwqvvfYabd26lefkf/bZZ2TthIeH05EjR3QFCPIS8hssL+DHCXz33Xec9yBHFMZwyozGQf/+/cnaQIcAvlSUBS/yFRpFUIZUr16dO6I///wzDRgwgBWqULTOmjWLPv30U6pQoQJZoyNdfJcXLlzgb1WVZU2bNmVlYL169ahPnz68IGgJ8uOXX35JP/zwg7gFMBMQDAFBtJQvyX379nHnuUaNGuTn50ceHh78TkePHs3TymHFBH+vgwYNyuSU2hpA/QOLQdTz6ICivgKor1BvYcBv2LBh7NwdfnagNFywYAEPbqF+s3b+/fdfHuyDRSqUWihTEFACHS4AX7m//vort4/wH/kRA1t79+4lawPtRzgqB2hTHjt2jPMbOlRqoBTlbqNGjbjjgHoe+QwDzsiD1gbqKtRZaqr/8ePHdX6FoQwBU6ZMoV69enH9hToddVWrVq04mJtQ8MEAAsoEgHIEdRfKEFgSon0LJk6cyAOVb731Fn8TixYt4sEua/wmrl69yoNW6ptAPxP1O2TVoEED3oY2LvI/lBEI3IcyBkEkZLCX6OLFi1zfI8CRGiSFyyvID3kOA4AffvghvfPOOxxsC+3kcePG0SeffMJWX9YGlKSQA8ph+GhU7SPkJ8gN9Xz37t2pffv2NGTIELp8+TIbY2Bw0BpRfSr0NzHIgXVYCrZs2ZK3jxo1ihWp3bp14zblpk2bOFgTAmlaGzYIfUwWAiLM4UUaglFxmMQDOKGcPn06N/zgqHLw4MHc+bY2oPAzVnljOg06p/oNZlT2kCum07755ps6B6jWDAKSQIllCApjFDAKOH5H5wvWX7BqgdIQSjBrA5U8OlKGoOOAzoICHQx8r2iIoqH5yiuvkDUCp+6qUa4PlM1qygq+YeStf/75hwNeQGHSuXPnfLhb4XFAhDljjrYxiKVvMYjvBt8ElIWw2ECdBQWPtYGohfqBoBRQ2OiPiKPxu2fPHu6gYnAQCkSBeFABAwr6YIBGP+AGGs3Tpk1jqwO0BaBwRYABawNyMBbxEUov1OEKTH377bff6M6dOzwIiE6ZamtaE/jevv3220zbUSehzaivrIa8oKiGMgRRs61tGpe5gkFe1D2G1K9fnyZNmqRbx+AmFGBwG1O1alX+XooVK0bWBgwJFi5cmGk7FDX6s7IgLygf0Ndq1qwZKyaguLB2UN8bCwb11VdfUcOGDfk/1BdoQ8HyCzKDXPv27UvWCMpfKFYNQQRxVSfBlc2qVas4MjaC6Lzxxhu6/oS1YcwyEOWUfpscZd706dNZ6Q9FPuora5SXRSkKBUEQBEEQBEEQBEEQBEF4PGQ+jiAIgiAIgiAIgiAIgiAIoigUBEEQBEEQBEEQBEEQBEEUhYIgCIIgCIIgCIIgCIIgiKJQEARBEARBEARBEARBEARRFAqCIAiCIAiCIAiCIAiCwEgwE0EQBEEQBEEQBEEQBEEQRFEoCIIgCIIgCIIgCIIgCIIoCgVBeEz27NlDp06dEvkJgiAIBZqbN2/Sxo0b8/s2BEEQBCFbUlNTaeXKlRQWFiaSEvIVmXosCBbI7t276cyZM0/1fN9++y0tXbo0z64hCIIgWB83btygTZs2PdXzYWBr6NCheXYNQRAEwfpISUlhJV5ERMRTO19iYiL17t2brl69mifXEITHRRSFgmCBTJo0iZYvX/5Uz9eqVSuqW7dunl1DEARBsD7++ecf+uCDD57q+cqVK0edO3fOs2sIgiAI1kdcXBwr8a5fv/7Uzufg4EA9e/akwoUL58k1BOFxsX/sIwXBwtBoNHTixAm6e/cu1apVizsW+iQlJdGBAwcoKiqK95cvXz7D/l27dlGxYsXIz8+PTp48ydsaN25MLi4uubpOeno6HTt2jO7du0cVKlSgmjVr5uo6Bw8epPv379PFixd5lAp06tSJDh06xMcVL16cDh8+TO7u7tSyZUv+j2lZNjY2VKhQIapTp06Gyimr8zVp0oS8vLyeiowEQRCE7AkICOByFGVq/fr1yc7OLsN+WIFfu3aNSpQoQY0aNSJb20djw7BUwPK///2P0wUFBXHZX6pUqVxf5/bt2+yGAvVHvXr1yNXV1eTrBAYG0tGjR7mzpOqX2rVrk729PR/Xpk0brrtwXJcuXXgqFizcAeqNKlWqUNWqVXXXy+p8pUuXprZt22Z6trySkSAIgpA1KJNRlmNaLdr9hv0H9DPQH3F0dKSmTZuSp6dnBgu79evX0yuvvELh4eF04cIFKlmypFFjhZyuExsby/2UtLQ0Ls9R9ufmOuvWrePf7du3c93h6+tLLVq00B2HOujSpUt8bvSB/vzzT7YaRL2JegjnwjNmd76XXnqJXnvtNfLx8XkqMhIEUxFFoSAQsdIOhTIKeHR0Ll++TP3796fx48ezfKAka9euHTk7O1OZMmXov//+42lMU6ZM0clvwoQJ3MmAgg8dF5wDoFBHwW/KdbD91Vdf5YoM50DnrHr16rR27VqdMi2n66CTFBISwpUgKg3QunVrPg7KQHT6oHxs1qwZKwqhtMS0LICOENbnzp3LI1wgq/Nh6nGNGjW4MsxLGQmCIAhZg8GmESNG0Pz581m5hQ4CFGtbtmzhjgM6SD169GCFGjpKUHJhgGjr1q1UpEgRPse2bdto8uTJVLZsWXJyctKVw6tWreI6yJTrYP+HH37ILihwndDQUK5D0DFq0KCBSddBetRz8fHxuvoFisY7d+6wJTs6VqhT0OHp2LEjd5RUOnQIUc+gY4R7QP2W1fnQcRo3bpzu2fJKRoIgCEL2wD/swIEDyd/fnxVzUIj99ttv9MILL/B+9DlQlzRs2JDLdexHOYtBGhAZGcl9EhgpYPAGRhT79u1jqzsca+p14JIC+6tVq8bGEjCEQP9r5MiRJl8HdQRAvwn1BtLAMALHdejQga5cucKDU1DyQVGIa6LuRJ1z7tw5rjf//vtvnZGIsfNBCYjz4f4wAJeXMhKEXKERBEHTvHlzTatWrTSxsbEsjbS0NM3GjRt1knnhhRc0nTt31qSmpvL6gQMHNLa2tppdu3bp0jRr1kxTsmRJzf3793k9KSlJU6FCBc0333xj8nVeeuklzXvvvadJT0/n9YSEBE39+vU1EyZMyNV1WrdurRk9enSGN4vjfH19NYGBgdm+8T///FPj7e2tu8eszte2bVvNqFGj8lxGgiAIQtbMnj1b4+rqqjl9+rRuG8rbBw8e8P9Zs2ZxWX/79m1ej46O1tSqVUszZMgQXfqZM2dq0ARcs2aNbtvHH3/M6Uy9zty5c7n8Dg0N1e2fNGmSpmrVqrm6zrx58zRlypTJ8IzquEWLFmWbFUJCQjQlSpTIcH5j58N5SpUqpVvPKxkJgiAIWYMy1tnZWfPdd99lKLf379/P/2/duqVxcnLS/PbbbxnKWZTXcXFxvB4UFMRlcZ8+fbjfBA4dOsTbLl++bNJ1AgICNO7u7ppt27bp9qNuwzGqjjPlOhEREbx+9OhR3XnUcfp9IGOgb4dz9+rVS7fN2PliYmJ428GDB/NURoKQW8RHoWD1YNotRl2+/PJLcnNzY3nA6g2jMsrKb//+/TR69GjdlCtMu4VpuJrapOjWrRsVLVqU/8M0HOmU1VxO14EFBXwrYRQIFhl//PEHj45hROrff/81+TrZgeNgmWEILAYxLRijUwkJCTwyhVExU8krGQmCIAjZs3jxYrZEhxWDAuWosoRDmdu3b1+2xgMeHh40fPhwLt/1QXqUx/p+Z/XL4pyus2jRIt6H+gn11erVq9lKA9OuYNln6nWyAlaLAwYMyLQd07gwtQyW9jt37mTrkSNHjuQq2+SVjARBEISsQZmK6b8ff/yxbhvcG2FWE4DlN9ZR1yg+//xzXb9Cn3feeUfnHgJW7rA2V32VnK6D+gnlfHR0NNdXWFBXwaWGmlVlynWyA7OoDF1zAFxn8+bNfI+4p9zWV3klI0HILTL1WLB6oKADlStXNioL+F8Chv72oNCD8k8fw+mzmK6EacSmXOfWrVv8i6lUmO6rwHQq+IUy9TrZoe+LQzF9+nSekgVTeUy9ghNdXPPBgwdkKnklI0EQBCF7UJf06tUr2/K4e/fumcpidJAQWVH5PTJWFsPPrKnXQZ2VnJxMa9asybAdU52wXZHTdbIC9RHqIn3Onj1L7du31/knxKAbpiPnpr7KSxkJgiAIWYN6BGWrMQWaKosxDVe/rPf29uYpt6pvoTBWHmNarynXQX0FF0qG9RVcT0BZaOp1ctPHQj0Id1MY2II7DjwXXC89Tn2VFzIShNwiikLB6kFhC+Ak3ZgiTQX2gI8j/coE67mJSJXTdZRTWijt4L/waWDY6YqJiaFRo0bxSBf8PAFYE2LUC340TCWvZCQIgiDkXJegHsmuPEbZqw/WMQik7/z8Sa+Dc8Fq/Icffngm9ZWyosA1Ye2ogG/c3NRXeSkjQRAEIW/rKyj0EBQxt32snOorDCwZznJ6mnUWrgWfuVBSqnoFPn9VMEdTySsZCUJukanHgtWDYCGYjrtkyZJM03GVlRz2Y5qTAoXzjh07dA5y8+I6CDCCaIpz5szJdCxGoHIDpn+ZMoKEayPKMiwzFIajbaacL69kJAiCIGQPnJdjMEffag/OzRHAA6DMxVQllO0KTLPCtOGsrC0e5zpQ0C1btowHnPTBdKinUV+B4ODgDPUV1mGFn9vz5ZWMBEEQhOzrEbhrQCAoY32f5s2bczBELAqUzZg+q4Ji5cV1UF9hhhOCVOmDOg39FVNBcCwoBE2ps1A/wTBEf/AJrqVye768kpEg5BaxKBSsHkRx/PXXX3kaEqYwoQOBsPIwY0eBjv3Tpk2jfv36sbUdlGLz5s3jSIiDBw/Os+ugc7JgwQLq0qULj4q1bduWR5Bg7YdjEO3KVDBVGaNWUD5iBE35QTQEz4Apx3i2QYMGcaQs+J0yHBXL6Xx5JSNBEAQhe7744guOmgilFnz4oYOxYsUK+uuvv7jTAat0KL0wRbdr164cOXHDhg0c4Tcvr4NokfBti47KkCFDeBs6aTdu3KC9e/eafB1Y0KMzhyjHFStW5DopKzCN68cff2S/S6h3Zs6cyb+5PV9eyUgQBEGgbJVcb7/9NivqRowYwYozRAJGuYt+B/ajj4P9iD4Mxd13331HY8aMIT8/vzy7Dvpc8EOL9WHDhvGAE3z3wcAB/uDh39AU4FsdvnmnTp3K037hx1bfj68+qF9Q1+B6derU4fvBNOSczoeox4bPlhcyEoTcIhaFgkBEHTt2ZFNwWMUdOHCAFV3Lly/P4HMJHQhYVqCQh0IMgUkwTUnx8ssvU40aNTLIE2Hs9Qv8nK4D5eD58+e50oCVBCw10CnSVxKach048/3oo4/4HBh1ggWIseMwGoWOHkbi0LHDOu6rd+/eGaZHGzsfnLrXrVs3z2UkCIIgZA2mGh07dowdm6M+gTUELMFVmY1gUadOneJyFY7O0fE4ceIEl7UK+Mrt0KFDJp+AKMdNvQ58IcEpO9xXwNIBdRfKeATlys11kAbKRwRAgbIOA1bGjgPoGP300098LTiInzFjBisE4bQ9u/PBv1Pnzp11afJKRoIgCEL2wEgC7iJgJHHmzBl6//33WXmnQD9o4sSJdPr0aVaWLV26lNcV8EmLcle5cFIg0BSCWZl6HdQXqBPgZxblPnz8oZ9StWrVXF1n3bp19Nxzz9HWrVs5EEpWx6G/gz4VjC/Qf0L9iOsb+sc1PB/6TTif/rTivJKRIOQGG4Q+ztURgiAIgiAIgiAIgiAIgiBYHGJRKAiCIAiCIAiCIAiCIAiCKAoFQRAEQRAEQRAEQRAEQRBFoSAIgiAIgiAIgiAIgiAIoigUBEEQBEEQBEEQBEEQBEEUhYIgCIIgCIIgCIIgCIIgMBLMRBAEQRAEQRAEQRAEQRAEURQKgiAIgiAIgiAIgiAIgiCKQkEQBEEQBEEQBEEQBEEQRFEoCIIgCIIgCIIgCIIgCIIoCgVBEARBEARBEARBEARBYCSYiSAIgiAIgiAIgiAIgiAIoigUBEEQBEEQBEEQBEEQBBLo/wElYv2qTfNU5wAAAABJRU5ErkJggg==",
       "text/plain": [
        "<Figure size 1300x640 with 6 Axes>"
       ]
@@ -1226,10 +1224,10 @@
    "id": "a7ab0d4c",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-09-20T17:13:34.701438Z",
-     "iopub.status.busy": "2026-09-20T17:13:34.701327Z",
-     "iopub.status.idle": "2026-09-20T17:13:34.718065Z",
-     "shell.execute_reply": "2026-09-20T17:13:34.717537Z"
+     "iopub.execute_input": "2026-09-20T21:16:29.947126Z",
+     "iopub.status.busy": "2026-09-20T21:16:29.946997Z",
+     "iopub.status.idle": "2026-09-20T21:16:29.967953Z",
+     "shell.execute_reply": "2026-09-20T21:16:29.967393Z"
     }
    },
    "outputs": [
@@ -1284,7 +1282,7 @@
     ")\n",
     "\n",
     "# At the bottom of the ladder nothing should have happened yet, so any gap there is not the compound.\n",
-    "low = ~control & (heparg.obs[\"Metadata_ConcentrationNominal\"].to_numpy(dtype=float) < 0.2)\n",
+    "low = ~control & (heparg.obs[\"Metadata_Concentration\"].to_numpy(dtype=float) < 0.2)\n",
     "print(\"\\nviability below 0.2 uM, by batch:\")\n",
     "print(viability[low].groupby(heparg.obs[\"batch\"][low]).median().round(2).to_string())"
    ]
@@ -1327,10 +1325,10 @@
    "id": "64214aaf",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-09-20T17:13:34.719740Z",
-     "iopub.status.busy": "2026-09-20T17:13:34.719610Z",
-     "iopub.status.idle": "2026-09-20T17:13:34.730960Z",
-     "shell.execute_reply": "2026-09-20T17:13:34.730465Z"
+     "iopub.execute_input": "2026-09-20T21:16:29.969607Z",
+     "iopub.status.busy": "2026-09-20T21:16:29.969498Z",
+     "iopub.status.idle": "2026-09-20T21:16:29.983448Z",
+     "shell.execute_reply": "2026-09-20T21:16:29.982908Z"
     }
    },
    "outputs": [
@@ -1595,10 +1593,10 @@
    "id": "ec3cbff6",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-09-20T17:13:34.732444Z",
-     "iopub.status.busy": "2026-09-20T17:13:34.732322Z",
-     "iopub.status.idle": "2026-09-20T17:13:34.767776Z",
-     "shell.execute_reply": "2026-09-20T17:13:34.767278Z"
+     "iopub.execute_input": "2026-09-20T21:16:29.985222Z",
+     "iopub.status.busy": "2026-09-20T21:16:29.985102Z",
+     "iopub.status.idle": "2026-09-20T21:16:30.022611Z",
+     "shell.execute_reply": "2026-09-20T21:16:30.022034Z"
     }
    },
    "outputs": [
@@ -1606,7 +1604,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "576 of 2831 wells are inside some compound's responding window\n"
+      "568 of 2831 wells are inside some compound's responding window\n"
      ]
     },
     {
@@ -1634,7 +1632,7 @@
     "print(f\"{int(responding.sum())} of {heparg.n_obs} wells are inside some compound's responding window\")\n",
     "\n",
     "# Two doses is enough here: the question is which features move inside the window, not where they cross.\n",
-    "mt.tl.dose_features(window, dose_key=\"Metadata_ConcentrationNominal\", min_doses=2)\n",
+    "mt.tl.dose_features(window, min_doses=2)\n",
     "inside = window.uns[\"mantispy\"][\"dose_features\"].dropna(subset=[\"bmd\"])\n",
     "inside.groupby(\"compound\").size().sort_values(ascending=False).head(6)"
    ]
@@ -1663,10 +1661,10 @@
    "id": "04e9f94f",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-09-20T17:13:34.769351Z",
-     "iopub.status.busy": "2026-09-20T17:13:34.769235Z",
-     "iopub.status.idle": "2026-09-20T17:13:34.784358Z",
-     "shell.execute_reply": "2026-09-20T17:13:34.783713Z"
+     "iopub.execute_input": "2026-09-20T21:16:30.024196Z",
+     "iopub.status.busy": "2026-09-20T21:16:30.024077Z",
+     "iopub.status.idle": "2026-09-20T21:16:30.039889Z",
+     "shell.execute_reply": "2026-09-20T21:16:30.039356Z"
     }
    },
    "outputs": [
@@ -1682,7 +1680,7 @@
     }
    ],
    "source": [
-    "paths = mt.tl.dose_trajectory(heparg, dose_key=\"Metadata_ConcentrationNominal\", n_positions=3)\n",
+    "paths = mt.tl.dose_trajectory(heparg, n_positions=3)\n",
     "mt.tl.similarity(paths, metric=\"pearson\")\n",
     "\n",
     "compounds = list(paths.obs[\"Metadata_Compound\"])\n",
@@ -1737,10 +1735,10 @@
    "id": "f7852d4b",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-09-20T17:13:34.785909Z",
-     "iopub.status.busy": "2026-09-20T17:13:34.785799Z",
-     "iopub.status.idle": "2026-09-20T17:13:34.889659Z",
-     "shell.execute_reply": "2026-09-20T17:13:34.889104Z"
+     "iopub.execute_input": "2026-09-20T21:16:30.041437Z",
+     "iopub.status.busy": "2026-09-20T21:16:30.041330Z",
+     "iopub.status.idle": "2026-09-20T21:16:30.148832Z",
+     "shell.execute_reply": "2026-09-20T21:16:30.148119Z"
     }
    },
    "outputs": [
@@ -1783,10 +1781,10 @@
    "id": "defe9869",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-09-20T17:13:34.891605Z",
-     "iopub.status.busy": "2026-09-20T17:13:34.891464Z",
-     "iopub.status.idle": "2026-09-20T17:13:37.438422Z",
-     "shell.execute_reply": "2026-09-20T17:13:37.437716Z"
+     "iopub.execute_input": "2026-09-20T21:16:30.150434Z",
+     "iopub.status.busy": "2026-09-20T21:16:30.150308Z",
+     "iopub.status.idle": "2026-09-20T21:16:32.733276Z",
+     "shell.execute_reply": "2026-09-20T21:16:32.732622Z"
     }
    },
    "outputs": [
@@ -1794,7 +1792,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/var/folders/qg/qgc908995g3fc8qtss2fsbhhxyxxj4/T/ipykernel_1605/443386283.py:7: UserWarning: 280 of 289 groups have fewer than three rows, so their statistic is the median of one or two wells. Aggregate more replicates, or score activity with mt.tl.map(mode='activity'), which ranks replicate pairs and is built for screens with little replication.\n",
+      "/var/folders/qg/qgc908995g3fc8qtss2fsbhhxyxxj4/T/ipykernel_9675/1175663912.py:7: UserWarning: 280 of 289 groups have fewer than three rows, so their statistic is the median of one or two wells. Aggregate more replicates, or score activity with mt.tl.map(mode='activity'), which ranks replicate pairs and is built for screens with little replication.\n",
       "  mt.tl.hit_calling(u2os, groupby=\"Metadata_Perturbation\", use_rep=\"X_pca\", n_permutations=300)\n"
      ]
     },
@@ -1824,7 +1822,7 @@
     "u2os = mt.pp.subset_features(u2os)\n",
     "sc.pp.pca(u2os, n_comps=20)\n",
     "mt.tl.hit_calling(u2os, groupby=\"Metadata_Perturbation\", use_rep=\"X_pca\", n_permutations=300)\n",
-    "mt.tl.dose_response(u2os, compound_key=\"Metadata_Compound\", dose_key=\"Metadata_ConcentrationNominal\", min_doses=4)\n",
+    "mt.tl.dose_response(u2os, compound_key=\"Metadata_Compound\", min_doses=4)\n",
     "\n",
     "other = u2os.uns[\"mantispy\"][\"dose_response\"].set_index(\"compound\")\n",
     "both = pd.DataFrame({\"U2OS\": other[\"hitcall\"], \"HepaRG\": curves[\"hitcall\"]}).dropna()\n",
diff --git a/spec/schema-1.0.json b/spec/schema-1.0.json
index 6251c03..d0ab010 100644
--- a/spec/schema-1.0.json
+++ b/spec/schema-1.0.json
@@ -21,7 +21,7 @@
         "Metadata_Perturbation",
         "Metadata_Compound",
         "Metadata_Concentration",
-        "Metadata_ConcentrationNominal",
+        "Metadata_ConcentrationRecorded",
         "Metadata_MOA",
         "Metadata_CellLine",
         "Metadata_Control",
diff --git a/src/mantispy/_core/_reduce.py b/src/mantispy/_core/_reduce.py
index ef5b4b9..283f1b5 100644
--- a/src/mantispy/_core/_reduce.py
+++ b/src/mantispy/_core/_reduce.py
@@ -38,6 +38,7 @@
     "get_matrix",
     "group_codes",
     "group_offsets",
+    "group_rows",
     "iter_groups",
     "reduce_grouped",
     "representation",
@@ -45,6 +46,12 @@
 ]
 
 
+def group_rows(codes: np.ndarray, n_groups: int) -> list[np.ndarray]:
+    """Row indices of each group, taken from one stable ordering rather than by scanning the codes per group."""
+    order, offsets = group_offsets(codes, n_groups)
+    return [order[offsets[group] : offsets[group + 1]] for group in range(n_groups)]
+
+
 def get_matrix(adata: AnnData, layer: str | None = None, rows: np.ndarray | None = None) -> np.ndarray:
     """Return the requested matrix as a dense ``float32`` array.
 
diff --git a/src/mantispy/_core/schema.py b/src/mantispy/_core/schema.py
index cb962ee..0cdd902 100644
--- a/src/mantispy/_core/schema.py
+++ b/src/mantispy/_core/schema.py
@@ -47,8 +47,9 @@
     "Metadata_Perturbation",
     "Metadata_Compound",
     "Metadata_Concentration",
-    # The dose a well was meant to get, where a plate map records the same one to several precisions.
-    "Metadata_ConcentrationNominal",
+    # What the plate map wrote, where it records one dose to several precisions and Metadata_Concentration
+    # holds the one the well was meant to get.
+    "Metadata_ConcentrationRecorded",
     "Metadata_MOA",
     "Metadata_CellLine",
     "Metadata_Control",
diff --git a/src/mantispy/ds/_datasets.py b/src/mantispy/ds/_datasets.py
index aaf5f98..3e90de7 100644
--- a/src/mantispy/ds/_datasets.py
+++ b/src/mantispy/ds/_datasets.py
@@ -423,8 +423,7 @@ def chroma(cache_dir: str | Path | None = None, **kwargs: Any) -> AnnData:
 
 def _decimals(value: float) -> int:
     """How many decimal places a level was written with, from its shortest exact repr."""
-    text = np.format_float_positional(value, trim="-")
-    return len(text.split(".")[1]) if "." in text else 0
+    return len(np.format_float_positional(value, trim="-").partition(".")[2])
 
 
 def _aligned_doses(doses: pd.Series, compounds: pd.Series) -> pd.Series:
@@ -439,20 +438,28 @@ def _aligned_doses(doses: pd.Series, compounds: pd.Series) -> pd.Series:
     Across the whole plate map they can: berberine's 25 uM and the main ladder's 33.3 uM are a third apart and
     genuinely different doses, closer together than ``0.000762`` and ``0.001`` are, which are one dose.
 
-    Rounding every level to a fixed precision cannot do this, and neither can a relative tolerance.
+    Rounding every level to a fixed precision cannot do this, and neither can a relative tolerance. The one case
+    it would get wrong is a ladder with two rungs inside a rounding step of each other, which a series coarser
+    than two-fold never has.
     """
-    aligned = doses.copy()
-    for _, index in doses.groupby(compounds, observed=True).groups.items():
-        levels = np.sort(doses[index].dropna().unique())
+
+    def align(block: pd.Series) -> pd.Series:
+        levels = np.sort(block.dropna().unique())
         levels = levels[levels > 0]
+        places = [_decimals(level) for level in levels]
         lookup = {}
-        for level in levels:
-            places = _decimals(level)
-            finer = [other for other in levels if _decimals(other) > places and round(other, places) == level]
+        for level, digits in zip(levels, places, strict=True):
+            finer = [
+                other
+                for other, deeper in zip(levels, places, strict=True)
+                if deeper > digits and round(other, digits) == level
+            ]
             # The nearest one: rounding 0.006 up to 0.01 must not claim a level that was written as 0.01.
-            lookup[level] = min(finer, key=lambda other: abs(other - level)) if finer else level
-        aligned[index] = doses[index].replace(lookup)
-    return aligned
+            lookup[level] = min(finer, key=lambda other: abs(other - level), default=level)
+        return block.replace(lookup)
+
+    # dropna=False, so a well whose compound is blank keeps the dose it was recorded with.
+    return doses.groupby(compounds, observed=True, dropna=False).transform(align)
 
 
 def _oasis_platemaps(cache_dir: str | Path | None) -> pd.DataFrame:
@@ -480,10 +487,9 @@ def _oasis_platemaps(cache_dir: str | Path | None) -> pd.DataFrame:
             kept["Metadata_Compound"] = kept["Metadata_Compound"].fillna(frame["BROAD_ID"])
         frames.append(kept)
     platemap = pd.concat(frames, ignore_index=True)
-    platemap["Metadata_Concentration"] = pd.to_numeric(platemap["Metadata_Concentration"], errors="coerce")
-    platemap["Metadata_ConcentrationNominal"] = _aligned_doses(
-        platemap["Metadata_Concentration"], platemap["Metadata_Compound"]
-    )
+    recorded = pd.to_numeric(platemap["Metadata_Concentration"], errors="coerce")
+    platemap["Metadata_ConcentrationRecorded"] = recorded
+    platemap["Metadata_Concentration"] = _aligned_doses(recorded, platemap["Metadata_Compound"])
     # One batch writes the line as HepRG and the others as HepaRG; two spellings would split every per-line grouping.
     platemap["Metadata_CellLine"] = platemap["Metadata_CellLine"].replace({"HepRG": "HepaRG"})
     return platemap
@@ -516,10 +522,11 @@ def oasis_pilot(annotate: bool = True, cache_dir: str | Path | None = None, **kw
         ``Metadata_Control`` marks the compound, not the dose.
 
         The plate maps disagree on precision: one batch writes the concentration the dilution produced,
-        ``0.0152416`` uM, and another writes it rounded, ``0.015``. ``Metadata_Concentration`` keeps what was
-        recorded; ``Metadata_ConcentrationNominal`` reads a coarser spelling as the finer level it rounds to,
-        within each compound, and names the replicate groups. On the raw column every dosed compound here
-        carries eighteen levels where ten were plated, and a treatment's wells split across two spellings.
+        ``0.0152416`` uM, and another writes it rounded, ``0.015``. ``Metadata_Concentration`` is the dose the
+        well was meant to get, reading a coarser spelling as the finer level it rounds to within each compound,
+        and it names the replicate groups. ``Metadata_ConcentrationRecorded`` keeps what the plate map wrote:
+        read against that column, every dosed compound here carries eighteen levels where ten were plated, and a
+        treatment's wells split across two spellings.
     """
     adata = _profiles("oasis_pilot", cache_dir, select=lambda name: name.endswith(".csv.gz"), **kwargs)
     if not annotate:
@@ -532,7 +539,7 @@ def oasis_pilot(annotate: bool = True, cache_dir: str | Path | None = None, **kw
         get_logger().warning("oasis_pilot: %d of %d wells have no plate-map row", unmatched, len(merged))
     adata.obs["Metadata_Compound"] = merged["Metadata_Compound"].to_numpy()
     adata.obs["Metadata_Concentration"] = merged["Metadata_Concentration"].to_numpy(dtype=float)
-    adata.obs["Metadata_ConcentrationNominal"] = merged["Metadata_ConcentrationNominal"].to_numpy(dtype=float)
+    adata.obs["Metadata_ConcentrationRecorded"] = merged["Metadata_ConcentrationRecorded"].to_numpy(dtype=float)
     adata.obs["Metadata_CellLine"] = merged["Metadata_CellLine"].to_numpy()
     adata.obs["Metadata_Control"] = merged["Metadata_Compound"].astype(str).str.upper().eq("DMSO").to_numpy()
     # Replicates share a compound at a concentration, which is what the mode= shorthands of mt.tl.map compare.
@@ -541,7 +548,7 @@ def oasis_pilot(annotate: bool = True, cache_dir: str | Path | None = None, **kw
         np.where(
             is_control,
             "DMSO",
-            merged["Metadata_Compound"].astype(str) + "@" + merged["Metadata_ConcentrationNominal"].astype(str),
+            merged["Metadata_Compound"].astype(str) + "@" + merged["Metadata_Concentration"].astype(str),
         )
     )
     get_logger().info(
@@ -549,7 +556,7 @@ def oasis_pilot(annotate: bool = True, cache_dir: str | Path | None = None, **kw
         adata.n_obs,
         adata.n_vars,
         int(merged.loc[~is_control, "Metadata_Compound"].nunique()),
-        int(merged["Metadata_ConcentrationNominal"].nunique()),
+        int(merged["Metadata_Concentration"].nunique()),
         int(is_control.sum()),
     )
     return adata
diff --git a/src/mantispy/pl/__init__.py b/src/mantispy/pl/__init__.py
index 0e5619b..e6f6e6c 100644
--- a/src/mantispy/pl/__init__.py
+++ b/src/mantispy/pl/__init__.py
@@ -9,7 +9,7 @@
 from mantispy.pl._evaluation import batch_variance, map, metrics, replicate_correlation, similarity
 from mantispy.pl._features import feature_correlation, feature_groups
 from mantispy.pl._heterogeneity import cell_cycle, cluster_composition, density, subpopulation_hits
-from mantispy.pl._hits import DOSE_PHASE_COLOURS, dose_direction, dose_response, effect_sizes, feature_volcano, hits
+from mantispy.pl._hits import dose_direction, dose_response, effect_sizes, feature_volcano, hits
 from mantispy.pl._moa import distance_heatmap, moa_confusion, moa_enrichment, pathway_coherence, sets_heatmap
 from mantispy.pl._plate import plate
 from mantispy.pl._qc import cell_counts, cytotoxicity, feature_distributions, nan_matrix, qc, replicate_saturation
@@ -17,7 +17,6 @@
 from mantispy.pl._transport import setting_agreement, transport
 
 __all__ = [
-    "DOSE_PHASE_COLOURS",
     "setting_agreement",
     "batch_variance",
     "cell_counts",
diff --git a/src/mantispy/pl/_hits.py b/src/mantispy/pl/_hits.py
index 2abf36d..ef901f5 100644
--- a/src/mantispy/pl/_hits.py
+++ b/src/mantispy/pl/_hits.py
@@ -9,6 +9,7 @@
 from mantispy._core.frames import as_frame
 from mantispy.pl._common import axes as _axes
 from mantispy.pl._common import table as _table
+from mantispy.tl._dose import DOSE_PHASES
 
 if TYPE_CHECKING:
     import pandas as pd
@@ -75,11 +76,17 @@ def hits(adata: AnnData, key: str = "hits", label_top: int = 10, ax: Axes | None
     return ax
 
 
+def _rows_for(table: pd.DataFrame, column: str, value: str, key: str) -> pd.DataFrame:
+    """The table's rows for one group, or a KeyError naming the groups it does hold."""
+    selected = table[table[column].astype(str) == str(value)]
+    if selected.empty:
+        raise KeyError(f"no {column} {value!r} in uns['mantispy'][{key!r}]; it holds {sorted(set(table[column]))[:5]}")
+    return selected
+
+
 def _effects(adata: AnnData, group: str, key: str) -> tuple[pd.DataFrame, pd.Series | None]:
     table = _table(adata, key, "mt.tl.effect_size")
-    selected = table[table["group"].astype(str) == str(group)]
-    if selected.empty:
-        raise KeyError(f"no group {group!r} in uns['mantispy'][{key!r}]; it holds {sorted(set(table['group']))[:5]}")
+    selected = _rows_for(table, "group", group, key)
     var = as_frame(adata.var)
     families = var["feature_group"].astype(str) if "feature_group" in var else None
     return selected, families
@@ -210,9 +217,7 @@ def dose_response(
     from mantispy.tl._dose import four_parameter_logistic
 
     table = _table(adata, key, "mt.tl.dose_response")
-    row = table[table["compound"].astype(str) == str(compound)]
-    if row.empty:
-        raise KeyError(f"no compound {compound!r} in uns['mantispy'][{key!r}]")
+    row = _rows_for(table, "compound", compound, key)
 
     if response is None:
         response = _recorded_response(adata, "dose_response", "hits_row_distance")
@@ -250,14 +255,9 @@ def dose_response(
     return ax
 
 
-#: Background colour of each phase, from ``mantispy.tl.DOSE_PHASES``. Grey where nothing happens, warm
-#: where it does, and red where the cells are gone.
-DOSE_PHASE_COLOURS = {
-    "silent": "#f2f2f2",
-    "responding": "#fde6c4",
-    "saturated": "#dbe8d4",
-    "cytotoxic": "#f6d2d2",
-}
+#: Background colour of each phase, keyed by :data:`~mantispy.tl._dose.DOSE_PHASES` so the two cannot drift.
+#: Grey where nothing happens, warm where it does, green where it has arrived, red where the cells are gone.
+DOSE_PHASE_COLOURS = dict(zip(DOSE_PHASES, ("#f2f2f2", "#fde6c4", "#dbe8d4", "#f6d2d2"), strict=True))
 
 
 def dose_direction(
@@ -286,10 +286,7 @@ def dose_direction(
         KeyError: There is no such table, or it holds no such compound.
     """
     table = _table(adata, key, "mt.tl.dose_direction")
-    block = table[table["compound"].astype(str) == str(compound)].sort_values("dose")
-    if block.empty:
-        known = sorted(set(table["compound"].astype(str)))
-        raise KeyError(f"no compound {compound!r} in uns['mantispy'][{key!r}]; it holds {known}")
+    block = _rows_for(table, "compound", compound, key).sort_values("dose")
 
     ax = _axes(ax, (5.2, 3.6))
     doses = block["dose"].to_numpy(dtype=float)
@@ -299,8 +296,8 @@ def dose_direction(
     gaps = np.diff(log_dose) if len(doses) > 1 else np.array([0.6])
     padded = np.concatenate([[log_dose[0] - gaps[0]], log_dose, [log_dose[-1] + gaps[-1]]])
     edges = 10.0 ** ((padded[:-1] + padded[1:]) / 2)
-    for index, phase in enumerate(block["phase"]):
-        ax.axvspan(edges[index], edges[index + 1], color=DOSE_PHASE_COLOURS.get(str(phase), "#ffffff"), lw=0, zorder=0)
+    for left, right, phase in zip(edges[:-1], edges[1:], block["phase"], strict=True):
+        ax.axvspan(left, right, color=DOSE_PHASE_COLOURS.get(str(phase), "#ffffff"), lw=0, zorder=0)
 
     floor = float(np.nanmedian(block["amplitude_null"].to_numpy(dtype=float)))
     ax.axhline(floor, ls=":", lw=1, color="0.45", zorder=1)
diff --git a/src/mantispy/tl/__init__.py b/src/mantispy/tl/__init__.py
index c038ee6..fef7d31 100644
--- a/src/mantispy/tl/__init__.py
+++ b/src/mantispy/tl/__init__.py
@@ -5,7 +5,7 @@
 from mantispy.tl._design import cytotoxicity, replicate_saturation
 from mantispy.tl._differential import differential_features
 from mantispy.tl._distance import edistance
-from mantispy.tl._dose import DOSE_PHASES, dose_direction, dose_features, dose_response, dose_trajectory
+from mantispy.tl._dose import dose_direction, dose_features, dose_response, dose_trajectory
 from mantispy.tl._effect import effect_size, wasserstein_features
 from mantispy.tl._enrich import enrich, feature_sets, rank_features, rank_sets
 from mantispy.tl._heterogeneity import (
@@ -23,7 +23,6 @@
 from mantispy.tl._transport import transport
 
 __all__ = [
-    "DOSE_PHASES",
     "aggregate",
     "cell_cycle_phase",
     "cluster_composition",
diff --git a/src/mantispy/tl/_design.py b/src/mantispy/tl/_design.py
index b287a40..b2a738f 100644
--- a/src/mantispy/tl/_design.py
+++ b/src/mantispy/tl/_design.py
@@ -9,13 +9,43 @@
 from anndata import AnnData
 
 from mantispy._core._numba import MEDIAN, grouped_stat
-from mantispy._core._reduce import group_codes, group_offsets, representation
+from mantispy._core._reduce import group_codes, group_offsets, group_rows, representation
 from mantispy._core.frames import as_frame
 from mantispy._core.logging import get_logger
 from mantispy._core.masks import held_out_reference, reference_mask
 from mantispy._core.mutation import inplace_or_copy
 
 
+def viability(
+    obs: pd.DataFrame,
+    count_key: str,
+    site_key: str | None,
+    is_control: np.ndarray,
+    levels: np.ndarray | None = None,
+) -> np.ndarray:
+    """Each row's cell count against the controls', per field of view where the fields are known.
+
+    ``levels`` names a grouping each row is scored inside, normally the plate. Plates are seeded and imaged
+    separately, and on the OASIS pilot their control counts differ by half, so a sparse plate otherwise reads as
+    one whose treated wells are dying. ``None`` pools every control row.
+
+    A level whose controls carry no usable count gets NaN rather than an error, so the caller decides whether
+    that is fatal.
+    """
+    counts = obs[count_key].to_numpy(dtype=float)
+    if site_key is not None and site_key in obs:
+        counts = counts / np.maximum(obs[site_key].to_numpy(dtype=float), 1)
+
+    scored = np.full(counts.shape, np.nan)
+    for level in pd.unique(np.zeros(len(obs)) if levels is None else levels):
+        inside = np.ones(len(obs), dtype=bool) if levels is None else levels == level
+        referenced = inside & is_control
+        centre = float(np.nanmedian(counts[referenced])) if referenced.any() else np.nan
+        if np.isfinite(centre) and centre > 0:
+            scored[inside] = counts[inside] / centre
+    return scored
+
+
 def signature_stability(
     profiles: np.ndarray, members: list[np.ndarray], depth: int, generator: np.random.Generator
 ) -> float:
@@ -142,8 +172,7 @@ def replicate_saturation(
         deepest = max(pivot - 1 if metric == "convergence" else pivot // 2, 1)
 
     # Group the rows once; `codes == group` inside the loop is an O(n_obs) scan per group, repeated n_draws * deepest times.
-    order, offsets = group_offsets(codes, len(keys))
-    members: list[np.ndarray] = [order[offsets[group] : offsets[group + 1]] for group in range(len(keys))]
+    members = group_rows(codes, len(keys))
 
     records = []
     for depth in range(1, deepest + 1):
@@ -236,13 +265,11 @@ def cytotoxicity(
         )
 
     is_control = reference_mask(adata, reference)
-    counts = obs[count_key].to_numpy(dtype=float)
-    if site_key in obs:
-        counts = counts / np.maximum(obs[site_key].to_numpy(dtype=float), 1)
+    # Pooled: cytotoxicity reads one screen-wide control level, where dose_direction reads one per plate.
+    scored = viability(obs, count_key, site_key, is_control)
     distances = obs[distance_key].to_numpy(dtype=float)
-    control_count = float(np.nanmedian(counts[is_control]))
     control_distance = float(np.nanmedian(distances[held_out_reference(adata, is_control, distance_key)]))
-    if not np.isfinite(control_count) or control_count <= 0:
+    if not np.isfinite(scored).any():
         raise ValueError(f"the reference rows have no usable {count_key!r} to normalize viability against")
 
     codes, keys = group_codes(adata, groupby)
@@ -250,15 +277,15 @@ def cytotoxicity(
     records = []
     for index, key in enumerate(keys):
         rows = order[offsets[index] : offsets[index + 1]]
-        viability = float(np.nanmedian(counts[rows])) / control_count
+        fraction = float(np.nanmedian(scored[rows]))
         distance = float(np.nanmedian(distances[rows]))
         records.append(
             {
                 "group": str(key),
                 "n_obs": int(rows.size),
-                "viability": viability,
+                "viability": fraction,
                 "distance": distance,
-                "suspect": bool(viability < min_viability and distance > control_distance),
+                "suspect": bool(fraction < min_viability and distance > control_distance),
             }
         )
 
diff --git a/src/mantispy/tl/_dose.py b/src/mantispy/tl/_dose.py
index 38c4735..766a970 100644
--- a/src/mantispy/tl/_dose.py
+++ b/src/mantispy/tl/_dose.py
@@ -12,8 +12,7 @@
 import pandas as pd
 from anndata import AnnData
 
-from mantispy._core._numba import group_offsets
-from mantispy._core._reduce import get_matrix
+from mantispy._core._reduce import get_matrix, group_rows
 from mantispy._core._stats import MAD_TO_SIGMA, benjamini_hochberg
 from mantispy._core.frames import as_frame
 from mantispy._core.logging import get_logger, report_drop
@@ -21,6 +20,7 @@
 from mantispy._core.mutation import inplace_or_copy
 from mantispy._core.provenance import record_params
 from mantispy._core.schema import stamp
+from mantispy.tl._design import viability
 
 #: Column order of the output table, so an empty result still carries its columns.
 _COLUMNS = (
@@ -362,8 +362,9 @@ def dose_response(
         Compounds with fewer than two usable doses are left out of the table.
 
         ``dose_key`` is read as given. A plate map that records one concentration to several precisions splits
-        a ladder, which is a property of how the dataset was written rather than of the fit; the loader resolves
-        it, as :func:`~mantispy.ds.oasis_pilot` does with ``Metadata_ConcentrationNominal``.
+        a ladder, which is a property of how the dataset was written rather than of the fit, so the loader
+        resolves it: :func:`~mantispy.ds.oasis_pilot` writes the dose each well was meant to get to
+        ``Metadata_Concentration`` and keeps the recorded one under ``Metadata_ConcentrationRecorded``.
 
         :func:`dose_features` asks the same question of every feature rather than of one response column, and
         :func:`dose_direction` asks whether the phenotype stays the same one as the concentration rises.
@@ -460,17 +461,39 @@ def dose_response(
     "phase",
 )
 
-#: What a concentration is doing, in the order they normally appear along a ladder.
+#: What a concentration is doing, in the order they normally appear along a ladder. Not exported, as
+#: tl._heterogeneity.PHASES is not: the categories travel with obs[key_added + "_phase"].
 DOSE_PHASES = ("silent", "responding", "saturated", "cytotoxic")
 
 
-def _control_scale(adata: AnnData, reference: str | None) -> tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray]:
-    """The controls' centre and spread per feature, which features have one, and which rows the controls are.
+@dataclass(frozen=True)
+class _Scale:
+    """The controls' centre and spread per feature, the features they give a scale for, and the control rows.
 
-    The centre and the spread are the ToxCast pipeline's ``bmed`` and ``bmad``. A feature whose controls show no
-    spread has no scale to read a response against and is left out, so the two arrays come back already narrowed
-    to ``keep``.
+    The centre and the spread are the ToxCast pipeline's ``bmed`` and ``bmad``, already narrowed to ``keep``.
     """
+
+    baseline: np.ndarray
+    spread: np.ndarray
+    keep: np.ndarray
+    control: np.ndarray
+
+    def z(self, adata: AnnData, rows: np.ndarray) -> np.ndarray:
+        """Those rows' response in MADs of the controls, over the features the controls give a scale for."""
+        with np.errstate(invalid="ignore"):
+            return (get_matrix(adata, rows=rows)[:, self.keep].astype(np.float64) - self.baseline) / self.spread
+
+    def values(self, adata: AnnData, rows: np.ndarray) -> np.ndarray:
+        """Those rows unscaled, over the same features, for a caller that has to fit its own scale."""
+        return get_matrix(adata, rows=rows)[:, self.keep].astype(np.float64)
+
+    def features(self, adata: AnnData) -> np.ndarray:
+        """The features the scale covers, in matrix order."""
+        return np.asarray(adata.var_names)[self.keep]
+
+
+def _control_scale(adata: AnnData, reference: str | None) -> _Scale:
+    """Read the scale off the control rows. A feature whose controls show no spread is left out."""
     rows = reference_mask(adata, reference)
     if int(rows.sum()) < 2:
         raise ValueError(
@@ -480,7 +503,7 @@ def _control_scale(adata: AnnData, reference: str | None) -> tuple[np.ndarray, n
         )
     control = get_matrix(adata, rows=np.flatnonzero(rows)).astype(np.float64)
     baseline = np.nanmedian(control, axis=0)
-    # In place: two more copies of the control block would be the largest allocation in the function.
+    # In place: two more copies of the control block would be the largest allocation here.
     control -= baseline
     np.abs(control, out=control)
     spread = MAD_TO_SIGMA * np.nanmedian(control, axis=0)
@@ -491,20 +514,14 @@ def _control_scale(adata: AnnData, reference: str | None) -> tuple[np.ndarray, n
         adata.n_vars,
         remedy="run mt.pp.normalize and drop the features flagged by var['degenerate_scale']",
     )
-    return baseline[keep], spread[keep], keep, rows
-
-
-def _z_rows(adata: AnnData, rows: np.ndarray, baseline: np.ndarray, spread: np.ndarray, keep: np.ndarray) -> np.ndarray:
-    """Those rows' response in MADs of the controls, over the features the controls give a scale for."""
-    with np.errstate(invalid="ignore"):
-        return (get_matrix(adata, rows=rows)[:, keep].astype(np.float64) - baseline) / spread
+    return _Scale(baseline[keep], spread[keep], keep, rows)
 
 
 def _spearman_against(values: np.ndarray, block: np.ndarray) -> tuple[np.ndarray, np.ndarray]:
     """Spearman of ``values`` against every column of ``block``, with the two-sided p-value scipy reports.
 
-    ``scipy.stats.spearmanr`` builds the whole square of correlations between the features to return one row of it,
-    which on a full feature set is thousands of times the work and the memory this needs.
+    ``scipy.stats.spearmanr`` builds the whole square of correlations between the features to return one row of
+    it, which on a full feature set is far more work and memory than this needs.
     """
     from scipy.stats import t
 
@@ -524,28 +541,26 @@ def _nanmedian(block: np.ndarray) -> np.ndarray:
     """Median down the rows, skipping missing values, for the short and wide blocks this module works on.
 
     ``np.nanmedian`` routes anything under 600 rows through a masked array and ``np.ma.median``, which is pure
-    Python; a dose group is four to eight rows, so every median here takes that path and spends about ninety-nine
-    percent of its time on the wrapper. Sorting puts the missing values last, so the median is the middle of
-    however many were measured.
+    Python; a dose group is four to eight rows, so every median here would take that path, where the wrapper
+    costs far more than the median. Sorting puts the missing values last, so the median is the middle of however
+    many were measured.
+
+    ``_core._numba``'s grouped kernels are not the cheaper answer at these shapes either: one group gives
+    ``prange`` nothing to parallelize, and their dispatch costs more than the median they would replace.
     """
     ordered = np.sort(block, axis=0)
     measured = block.shape[0] - np.isnan(block).sum(axis=0)
     columns = np.arange(block.shape[1])
+    # Only the lower index needs clamping, for the all-NaN column whose count is zero.
     low = ordered[np.maximum((measured - 1) // 2, 0), columns]
-    high = ordered[np.maximum(measured // 2, 0), columns]
+    high = ordered[measured // 2, columns]
     return np.where(measured > 0, 0.5 * (low + high), np.nan)
 
 
-def _groups(codes: np.ndarray, n_groups: int) -> list[np.ndarray]:
-    """Row indices of each group, taken once rather than by scanning the codes per group."""
-    order, offsets = group_offsets(np.ascontiguousarray(codes, dtype=np.int32), n_groups)
-    return [order[offsets[index] : offsets[index + 1]] for index in range(n_groups)]
-
-
 def _dose_medians(block: np.ndarray, doses: np.ndarray) -> tuple[np.ndarray, np.ndarray]:
     """The sorted doses, and each one's median profile over its replicate rows."""
     order, codes = np.unique(doses, return_inverse=True)
-    return order, np.stack([_nanmedian(block[rows]) for rows in _groups(codes, order.size)])
+    return order, np.stack([_nanmedian(block[rows]) for rows in group_rows(codes, order.size)])
 
 
 def _benchmark_dose(doses: np.ndarray, z: np.ndarray, cutoff: float) -> np.ndarray:
@@ -576,7 +591,7 @@ def _usable_doses(
     selected = np.flatnonzero(usable)
     codes, keys = pd.factorize(obs[compound_key].to_numpy()[usable], sort=True)
     blocks = {}
-    for key, within in zip(keys, _groups(codes, len(keys)), strict=True):
+    for key, within in zip(keys, group_rows(codes, len(keys)), strict=True):
         rows = selected[within]
         blocks[key] = (rows, doses[rows])
     return blocks
@@ -644,16 +659,16 @@ def dose_features(
     """
     obs = as_frame(adata.obs)
     _require_columns(obs, compound_key, dose_key)
-    baseline, spread, keep, control = _control_scale(adata, reference)
-    names = np.asarray(adata.var_names)[keep]
+    scale = _control_scale(adata, reference)
+    names = scale.features(adata)
 
     frames = []
-    for compound, (rows, doses) in _usable_doses(obs, compound_key, dose_key, control).items():
+    for compound, (rows, doses) in _usable_doses(obs, compound_key, dose_key, scale.control).items():
         n_doses = len(np.unique(doses))
         if n_doses < min_doses:
             get_logger().debug("dose_features skipped %s: %d usable dose(s)", compound, n_doses)
             continue
-        block = _z_rows(adata, rows, baseline, spread, keep)
+        block = scale.z(adata, rows)
         order, z = _dose_medians(block, doses)
         rho, pvalue = _spearman_against(np.log10(doses), block)
         # The concentration each feature reached furthest at, and which way it went.
@@ -706,60 +721,87 @@ def _split_half_cosine(block: np.ndarray, labels: np.ndarray) -> float:
     question. A column with one level falls back to splitting by position, and one well at a concentration has no
     halves to compare, so it comes back NaN.
     """
+    if labels.size < 2:
+        return np.nan
     left = np.isin(labels, pd.unique(labels)[::2])
-    if left.all():
+    if left.all():  # one level of the split: halve by position instead
         left = np.arange(labels.size) % 2 == 0
-    if left.all() or not left.any():
+    if not left.any():  # a level that matches nothing against itself, such as NaN
         return np.nan
     return _cosine(_nanmedian(block[left]), _nanmedian(block[~left]))
 
 
-#: Control groups drawn per layout to estimate the amplitude floor. The median of this many is stable to about 5%.
+#: Control groups drawn per layout to estimate the amplitude floor.
 _NULL_DRAWS = 25
 
 
-def _null_amplitude(control: np.ndarray, levels: np.ndarray, wanted: dict[object, int]) -> float:
-    """Amplitude reached by control wells laid out the way ``wanted`` counts them, level by level.
+@dataclass(frozen=True)
+class _Floor:
+    """The scale the amplitude floor is read in, and the control rows a draw may come from.
+
+    The drawn wells have to be held out of the scale they are then measured in, as a treated well is. Scored
+    against a baseline they helped define, control groups sit closer to it than any treated group can, so the
+    floor reads low, and the fewer the controls the further out it is.
+
+    One split serves the whole run. Refitting the scale around every draw gives the same statistic, but it
+    repeats for each distinct plate layout, and the number of those is combinatorial in the plates rather than
+    bounded by the compounds.
+    """
 
-    Two things have to match, or the floor is not the floor the concentration is read against.
+    baseline: np.ndarray
+    spread: np.ndarray
+    rows: np.ndarray
+    levels: np.ndarray
 
-    The layout, because per-plate normalization puts each plate's control median at zero: a group drawn from one
-    plate starts out at the centre while a group spread over eight of them does not, and a group as large as a
-    plate's control set reads a floor of zero.
 
-    And the drawn wells have to be held out of the scale they are then measured in, as a treated well is. Scored
-    against a baseline they helped define, control groups sit closer to it than any treated group can: with
-    sixteen control wells that reads the floor 64% low, with thirty-two 30% low, and with the OASIS pilot's 256
-    about 4% low.
+def _floor_scale(control: np.ndarray, levels: np.ndarray) -> _Floor | None:
+    """Fit the floor's scale on half the controls, leaving the other half to draw from.
+
+    Halved within each level, not across all the controls at once. A blind half can take most of its rows from
+    one plate, and the scale it then fits is that plate's, which is the bias the layout matching exists to
+    avoid.
+    """
+    generator = np.random.default_rng(0)
+    halves = []
+    for level in pd.unique(levels):
+        rows = np.flatnonzero(levels == level)
+        generator.shuffle(rows)
+        halves.append((rows[: rows.size // 2], rows[rows.size // 2 :]))
+    fitted = np.concatenate([half for half, _ in halves])
+    drawn = np.concatenate([half for _, half in halves])
+    if fitted.size < 2 or drawn.size < 1:
+        return None
+    baseline = _nanmedian(control[fitted])
+    spread = MAD_TO_SIGMA * _nanmedian(np.abs(control[fitted] - baseline))
+    return _Floor(baseline, np.where(spread > 0, spread, np.nan), drawn, levels[drawn])
+
+
+def _null_amplitude(control: np.ndarray, floor: _Floor | None, wanted: dict[object, int]) -> float:
+    """Amplitude reached by control wells laid out the way ``wanted`` counts them, level by level.
+
+    The layout has to match, because per-plate normalization puts each plate's control median at zero: a group
+    drawn from one plate starts out at the centre while a group spread over eight of them does not, and a group
+    as large as a plate's control set would read a floor of zero.
     """
-    pools = {level: np.flatnonzero(levels == level) for level in wanted}
+    if floor is None:
+        return np.nan
+    pools = {level: floor.rows[floor.levels == level] for level in wanted}
     if any(pools[level].size < count for level, count in wanted.items()):
         return np.nan
-    generator = np.random.default_rng(0)
 
-    def draw() -> float:
+    generator = np.random.default_rng(0)
+    drawn = []
+    for _ in range(_NULL_DRAWS):
         rows = np.concatenate([generator.choice(pools[level], count, replace=False) for level, count in wanted.items()])
-        held_out = np.ones(control.shape[0], dtype=bool)
-        held_out[rows] = False
-        if held_out.sum() < 2:
-            return np.nan
-        baseline = _nanmedian(control[held_out])
-        spread = MAD_TO_SIGMA * _nanmedian(np.abs(control[held_out] - baseline))
         with np.errstate(invalid="ignore"):
-            return _amplitude((_nanmedian(control[rows]) - baseline) / np.where(spread > 0, spread, np.nan))
-
-    return float(np.median([draw() for _ in range(_NULL_DRAWS)]))
+            drawn.append(_amplitude((_nanmedian(control[rows]) - floor.baseline) / floor.spread))
+    return float(np.median(drawn))
 
 
 def _viability(
     adata: AnnData, count_key: str, site_key: str | None, control: np.ndarray, levels: np.ndarray
 ) -> np.ndarray:
-    """Each row's cell count against its own plate's controls, per field of view where the fields are known.
-
-    Against the whole screen's controls it would not be a viability at all. Plates are seeded and imaged
-    separately and their control counts differ by a factor of two on the OASIS pilot, so a plate that happens to
-    be dense reads as a plate whose treated wells are dying.
-    """
+    """Each row's cell count against its own plate's controls, rather than against the whole screen's."""
     obs = as_frame(adata.obs)
     if count_key not in obs:
         get_logger().info(
@@ -769,26 +811,15 @@ def _viability(
         )
         return np.full(adata.n_obs, np.nan)
 
-    counts = obs[count_key].to_numpy(dtype=float)
-    if site_key is not None and site_key in obs:
-        counts = counts / np.maximum(obs[site_key].to_numpy(dtype=float), 1)
-
-    viability = np.full(adata.n_obs, np.nan)
-    unreferenced = 0
-    for level in pd.unique(levels):
-        on = levels == level
-        reference = float(np.nanmedian(counts[on & control])) if (on & control).any() else np.nan
-        if np.isfinite(reference) and reference > 0:
-            viability[on] = counts[on] / reference
-        else:
-            unreferenced += 1
+    scored = viability(obs, count_key, site_key, control, levels)
+    unreferenced = [level for level in pd.unique(levels) if not np.isfinite(scored[levels == level]).any()]
     report_drop(
         "level(s) of the split with no control count to read viability against",
-        unreferenced,
+        len(unreferenced),
         len(pd.unique(levels)),
         remedy=f"check that every {count_key!r} is filled and that each level carries controls",
     )
-    return viability
+    return scored
 
 
 def _label_phases(ladder: list[dict], min_viability: float, reproducible: float | None) -> list[str]:
@@ -901,39 +932,38 @@ def dose_direction(
     """
     obs = as_frame(adata.obs)
     _require_columns(obs, compound_key, dose_key, *([split_by] if split_by is not None else []))
-    baseline, spread, keep, control = _control_scale(adata, reference)
-    # Raw, not scaled: the floor holds each drawn group out of the scale it is measured in.
-    control_values = get_matrix(adata, rows=np.flatnonzero(control))[:, keep].astype(np.float64)
-
+    scale = _control_scale(adata, reference)
     all_levels = obs[split_by].to_numpy() if split_by is not None else np.zeros(adata.n_obs)
-    control_levels = all_levels[control]
-    viable = _viability(adata, count_key, site_key, control, all_levels)
+    # Raw, not scaled: the floor is read in a scale fitted on the controls a draw may not use.
+    control_values = scale.values(adata, np.flatnonzero(scale.control))
+    floor = _floor_scale(control_values, all_levels[scale.control])
+    viable = _viability(adata, count_key, site_key, scale.control, all_levels)
     phases = np.full(adata.n_obs, "", dtype=object)
     nulls: dict[tuple, float] = {}
     records: list[dict] = []
-    for compound, (rows, doses) in _usable_doses(obs, compound_key, dose_key, control).items():
+    for compound, (rows, doses) in _usable_doses(obs, compound_key, dose_key, scale.control).items():
         n_doses = len(np.unique(doses))
         if n_doses < min_doses:
             get_logger().debug("dose_direction skipped %s: %d usable dose(s)", compound, n_doses)
             continue
-        block = _z_rows(adata, rows, baseline, spread, keep)
+        block = scale.z(adata, rows)
         order, medians = _dose_medians(block, doses)
         levels, well_viability = all_levels[rows], viable[rows]
 
-        ladder, covering = [], []
+        ladder = []
         for index, dose in enumerate(order):
             at = doses == dose
             # The floor is drawn with this concentration's own spread over plates, not from any group of that
-            # size. setdefault would evaluate the draw whether or not the layout has been seen before.
+            # size. np.unique sorts, so the layout is its own memo key; setdefault would evaluate the draw
+            # whether or not the layout had been seen before.
             layout = dict(zip(*np.unique(levels[at], return_counts=True), strict=True))
-            key = tuple(sorted(layout.items(), key=lambda item: str(item[0])))
+            key = tuple(layout.items())
             if key not in nulls:
-                nulls[key] = _null_amplitude(control_values, control_levels, layout)
+                nulls[key] = _null_amplitude(control_values, floor, layout)
             # The step from the concentration below is what "still changing" means. The first one steps from the
             # controls, so its step is how far it has already come.
             amplitude = _amplitude(medians[index])
             here = well_viability[at]
-            covering.append(rows[at])
             ladder.append(
                 {
                     "compound": str(compound),
@@ -948,11 +978,11 @@ def dose_direction(
                 }
             )
 
-        for row, phase, covered in zip(
-            ladder, _label_phases(ladder, min_viability, reproducible), covering, strict=True
-        ):
+        labels = _label_phases(ladder, min_viability, reproducible)
+        for row, phase in zip(ladder, labels, strict=True):
             row["phase"] = phase
-            phases[covered] = phase
+        # Every well of a concentration carries that concentration's phase; `order` is sorted, so this is its rung.
+        phases[rows] = np.asarray(labels, dtype=object)[np.searchsorted(order, doses)]
         records.extend(ladder)
 
     table = pd.DataFrame(records, columns=list(_DIRECTION_COLUMNS))
@@ -1022,7 +1052,7 @@ def dose_trajectory(
         raise ValueError(f"n_positions must be at least two, got {n_positions}")
     obs = as_frame(adata.obs)
     _require_columns(obs, compound_key, dose_key)
-    baseline, spread, keep, control = _control_scale(adata, reference)
+    scale = _control_scale(adata, reference)
 
     in_window = np.ones(adata.n_obs, dtype=bool)
     if phase_key in obs:
@@ -1036,12 +1066,12 @@ def dose_trajectory(
 
     grid = np.linspace(0.0, 1.0, n_positions)
     paths, records = [], []
-    for compound, (rows, doses) in _usable_doses(obs, compound_key, dose_key, control).items():
+    for compound, (rows, doses) in _usable_doses(obs, compound_key, dose_key, scale.control).items():
         inside = in_window[rows]
         if not inside.any():
             continue
         rows, doses = rows[inside], doses[inside]
-        order, z = _dose_medians(_z_rows(adata, rows, baseline, spread, keep), doses)
+        order, z = _dose_medians(scale.z(adata, rows), doses)
         if len(order) < 2:
             continue
         # Relative position through the window in log concentration, so the ends are 0 and 1 for every compound.
@@ -1061,11 +1091,12 @@ def dose_trajectory(
             }
         )
 
-    names = np.asarray(adata.var_names)[keep]
+    names = scale.features(adata)
+    # Position-major, to mirror the ravel of each compound's (n_positions, n_features) path above.
     var = pd.DataFrame({"feature": np.tile(names, n_positions), "position": np.repeat(grid, names.size)})
     var.index = var["feature"] + "@" + var["position"].map("{:.2f}".format)
     result = ad.AnnData(
-        X=np.array(paths, dtype=np.float32).reshape(len(paths), var.shape[0]),
+        X=np.array(paths, dtype=np.float32) if paths else np.empty((0, var.shape[0]), dtype=np.float32),
         obs=pd.DataFrame(records, columns=[compound_key, "n_doses", "window_low", "window_high"]).set_axis(
             pd.RangeIndex(len(records)).astype(str)
         ),
diff --git a/tests/conftest.py b/tests/conftest.py
index f1f8f52..8c9aaca 100644
--- a/tests/conftest.py
+++ b/tests/conftest.py
@@ -14,6 +14,7 @@
 
 from mantispy._core.schema import stamp
 from mantispy.ds import synthetic_plate
+from mantispy.io._profiles import from_dataframe
 
 # re-exported: the test modules import these from here
 __all__ = ["CHANNELS", "write_cellprofiler_dir"]
@@ -154,3 +155,60 @@ def make_export(tmp_path: Path) -> Callable[..., Path]:
 @pytest.fixture
 def export(make_export: Callable[..., Path]) -> Path:
     return make_export()
+
+
+def _sigmoid(log_dose, height, ec50, hill=2.0):
+    from scipy.special import expit
+
+    return height * expit(hill * (log_dose - np.log10(ec50)))
+
+
+@pytest.fixture
+def phenotypes():
+    """A plate where one compound grows along one direction and another turns into a different phenotype.
+
+    `grows` moves three features together over its whole range. `turns` moves two features at low concentration
+    and drops them again while two others take over, so its top profile points somewhere else entirely. `quiet`
+    moves nothing. Every feature carries unit noise, so a response in the matrix reads directly in MADs.
+    """
+    rng = np.random.default_rng(0)
+    doses = np.geomspace(0.01, 100.0, 6)
+    wells_per_dose = 4
+    concentration = np.repeat(doses, wells_per_dose)
+    log_dose = np.log10(concentration)
+    n_features = 9
+
+    signals = {}
+    grows = np.zeros((concentration.size, n_features))
+    for column in (0, 1, 2):
+        grows[:, column] = _sigmoid(log_dose, 8.0, 1.0)
+    signals["grows"] = grows
+
+    turns = np.zeros((concentration.size, n_features))
+    early = _sigmoid(log_dose, 8.0, 0.05, hill=4.0) - _sigmoid(log_dose, 8.0, 5.0, hill=4.0)
+    late = _sigmoid(log_dose, 8.0, 20.0, hill=4.0)
+    turns[:, 3] = turns[:, 4] = early
+    turns[:, 5] = turns[:, 6] = late
+    signals["turns"] = turns
+    signals["quiet"] = np.zeros((concentration.size, n_features))
+
+    n_controls = 24
+    blocks, records = [], []
+    for compound, signal in signals.items():
+        blocks.append(signal + rng.normal(0.0, 1.0, signal.shape))
+        records.append(pd.DataFrame({"Metadata_Compound": compound, "Metadata_Concentration": concentration}))
+    blocks.append(rng.normal(0.0, 1.0, (n_controls, n_features)))
+    records.append(pd.DataFrame({"Metadata_Compound": "DMSO", "Metadata_Concentration": np.zeros(n_controls)}))
+
+    values = np.vstack(blocks)
+    # A feature the controls measure without any spread has no scale to read a response against.
+    values[:, 8] = 1.0
+    frame = pd.concat(records, ignore_index=True)
+    total = len(frame)
+    frame["Metadata_Plate"] = np.where(np.arange(total) % 2 == 0, "P1", "P2")
+    frame["Metadata_Well"] = [f"{chr(65 + index // 24)}{index % 24 + 1:02d}" for index in range(total)]
+    frame["Metadata_Control"] = frame["Metadata_Compound"] == "DMSO"
+    frame["Metadata_CellCount"] = 100.0
+    for column in range(n_features):
+        frame[f"Cells_AreaShape_f{column}"] = values[:, column]
+    return from_dataframe(frame, resolution="well")
diff --git a/tests/test_datasets_oasis.py b/tests/test_datasets_oasis.py
index 247bdbf..011685a 100644
--- a/tests/test_datasets_oasis.py
+++ b/tests/test_datasets_oasis.py
@@ -42,8 +42,8 @@ def test_the_dose_range_survives_the_join(oasis):
 def test_one_concentration_written_twice_is_one_dose(oasis):
     """The plate maps disagree on precision: one writes 0.0152416 uM and another writes 0.015."""
     obs = oasis.obs
-    raw = obs["Metadata_Concentration"].to_numpy(dtype=float)
-    aligned = obs["Metadata_ConcentrationNominal"].to_numpy(dtype=float)
+    raw = obs["Metadata_ConcentrationRecorded"].to_numpy(dtype=float)
+    aligned = obs["Metadata_Concentration"].to_numpy(dtype=float)
     dosed = raw > 0
 
     assert len(np.unique(aligned[dosed])) < len(np.unique(raw[dosed])), "nothing collapsed"
@@ -59,10 +59,10 @@ def test_every_dosed_compound_lands_on_the_ladder_it_was_plated_on(oasis):
     """Ten three-fold steps, or the eight two-fold ones the assay-development plates used."""
     obs = oasis.obs
     treated = obs[~obs["Metadata_Control"] & (obs["Metadata_Compound"].astype(str) != "EMPTY")]
-    treated = treated[treated["Metadata_Concentration"].to_numpy(dtype=float) > 0]
+    treated = treated[treated["Metadata_ConcentrationRecorded"].to_numpy(dtype=float) > 0]
 
-    raw = treated.groupby("Metadata_Compound", observed=True)["Metadata_Concentration"].nunique()
-    aligned = treated.groupby("Metadata_Compound", observed=True)["Metadata_ConcentrationNominal"].nunique()
+    raw = treated.groupby("Metadata_Compound", observed=True)["Metadata_ConcentrationRecorded"].nunique()
+    aligned = treated.groupby("Metadata_Compound", observed=True)["Metadata_Concentration"].nunique()
     assert set(raw[raw > 1]) - {8} != set(), "the raw column should carry the split ladders"
     assert set(aligned) <= {1, 8, 10}, f"off-ladder compounds: {aligned[~aligned.isin([1, 8, 10])].to_dict()}"
     assert int((aligned == 10).sum()) == 28
diff --git a/tests/test_pl_hits.py b/tests/test_pl_hits.py
index 11fa88c..7f05d62 100644
--- a/tests/test_pl_hits.py
+++ b/tests/test_pl_hits.py
@@ -152,54 +152,24 @@ def test_an_inhibitory_curve_is_drawn_the_way_the_data_runs(inhibitor_adata):
     assert drawn[0] > drawn[-1], "the curve runs uphill while the data runs downhill"
 
 
-def _laddered():
-    """Wells over a ladder where one compound wakes up halfway up it."""
-    from scipy.special import expit
-
-    rng = np.random.default_rng(0)
-    doses = np.geomspace(0.01, 100.0, 6)
-    concentration = np.repeat(doses, 4)
-    signal = 8.0 * expit(4.0 * (np.log10(concentration) - np.log10(3.0)))
-    values = rng.normal(0.0, 1.0, (concentration.size + 16, 5))
-    values[: concentration.size, 0] += signal
-    values[: concentration.size, 1] += signal
-
-    total = concentration.size + 16
-    frame = pd.DataFrame(
-        {
-            "Metadata_Compound": ["cpd"] * concentration.size + ["DMSO"] * 16,
-            "Metadata_Concentration": np.concatenate([concentration, np.zeros(16)]),
-            "Metadata_Plate": np.where(np.arange(total) % 2 == 0, "P1", "P2"),
-            "Metadata_Well": [f"{chr(65 + i // 24)}{i % 24 + 1:02d}" for i in range(total)],
-            "Metadata_Control": [False] * concentration.size + [True] * 16,
-            "Metadata_CellCount": 100.0,
-        }
-    )
-    for column in range(values.shape[1]):
-        frame[f"Cells_AreaShape_f{column}"] = values[:, column]
-    adata = from_dataframe(frame, resolution="well")
-    mt.tl.dose_direction(adata)
-    return adata
-
-
-def test_the_direction_plot_bands_the_ladder_by_phase():
-    adata = _laddered()
-    ax = mt.pl.dose_direction(adata, compound="cpd")
-    table = adata.uns["mantispy"]["dose_direction"]
-
-    # One band per concentration, plus the two lines it reads against.
-    assert len(ax.patches) == len(table)
-    drawn = {patch.get_facecolor() for patch in ax.patches}
-    expected = {mt.pl.DOSE_PHASE_COLOURS[phase] for phase in table["phase"]}
-    assert len(drawn) == len(expected), "each phase present gets its own colour"
+def test_the_direction_plot_bands_the_ladder_by_phase(phenotypes):
+    mt.tl.dose_direction(phenotypes)
+    ax = mt.pl.dose_direction(phenotypes, compound="grows")
+    table = phenotypes.uns["mantispy"]["dose_direction"]
+    drawn_compound = table[table["compound"] == "grows"]
+
+    # One band per concentration of the compound drawn, plus the two lines it reads against.
+    assert len(ax.patches) == len(drawn_compound)
+    colours = {patch.get_facecolor() for patch in ax.patches}
+    assert len(colours) == drawn_compound["phase"].nunique(), "each phase present gets its own colour"
     assert len(ax.lines) == 4, "two curves and the two floors"
     plt.close(ax.figure)
 
 
-def test_the_direction_plot_says_what_to_run_first_and_which_compounds_it_has():
-    adata = _laddered()
-    with pytest.raises(KeyError, match="cpd"):
-        mt.pl.dose_direction(adata, compound="not_dosed")
+def test_the_direction_plot_says_which_compounds_it_has(phenotypes):
+    mt.tl.dose_direction(phenotypes)
+    with pytest.raises(KeyError, match="grows"):
+        mt.pl.dose_direction(phenotypes, compound="not_dosed")
     plt.close("all")
 
 
diff --git a/tests/test_tl_dose.py b/tests/test_tl_dose.py
index d8a1b44..320e122 100644
--- a/tests/test_tl_dose.py
+++ b/tests/test_tl_dose.py
@@ -260,63 +260,6 @@ def test_a_curve_that_plateaus_is_read_by_the_logistic_and_one_still_rising_by_t
     assert _one_compound(conc, still_rising, 0.2)["hitcall_model"] == "linear"
 
 
-def _sigmoid(log_dose, height, ec50, hill=2.0):
-    from scipy.special import expit
-
-    return height * expit(hill * (log_dose - np.log10(ec50)))
-
-
-@pytest.fixture
-def phenotypes():
-    """A plate where one compound grows along one direction and another turns into a different phenotype.
-
-    `grows` moves three features together over its whole range. `turns` moves two features at low concentration
-    and drops them again while two others take over, so its top profile points somewhere else entirely. `quiet`
-    moves nothing. Every feature carries unit noise, so a response in the matrix reads directly in MADs.
-    """
-    rng = np.random.default_rng(0)
-    doses = np.geomspace(0.01, 100.0, 6)
-    wells_per_dose = 4
-    concentration = np.repeat(doses, wells_per_dose)
-    log_dose = np.log10(concentration)
-    n_features = 9
-
-    signals = {}
-    grows = np.zeros((concentration.size, n_features))
-    for column in (0, 1, 2):
-        grows[:, column] = _sigmoid(log_dose, 8.0, 1.0)
-    signals["grows"] = grows
-
-    turns = np.zeros((concentration.size, n_features))
-    early = _sigmoid(log_dose, 8.0, 0.05, hill=4.0) - _sigmoid(log_dose, 8.0, 5.0, hill=4.0)
-    late = _sigmoid(log_dose, 8.0, 20.0, hill=4.0)
-    turns[:, 3] = turns[:, 4] = early
-    turns[:, 5] = turns[:, 6] = late
-    signals["turns"] = turns
-    signals["quiet"] = np.zeros((concentration.size, n_features))
-
-    n_controls = 24
-    blocks, records = [], []
-    for compound, signal in signals.items():
-        blocks.append(signal + rng.normal(0.0, 1.0, signal.shape))
-        records.append(pd.DataFrame({"Metadata_Compound": compound, "Metadata_Concentration": concentration}))
-    blocks.append(rng.normal(0.0, 1.0, (n_controls, n_features)))
-    records.append(pd.DataFrame({"Metadata_Compound": "DMSO", "Metadata_Concentration": np.zeros(n_controls)}))
-
-    values = np.vstack(blocks)
-    # A feature the controls measure without any spread has no scale to read a response against.
-    values[:, 8] = 1.0
-    frame = pd.concat(records, ignore_index=True)
-    total = len(frame)
-    frame["Metadata_Plate"] = np.where(np.arange(total) % 2 == 0, "P1", "P2")
-    frame["Metadata_Well"] = [f"{chr(65 + index // 24)}{index % 24 + 1:02d}" for index in range(total)]
-    frame["Metadata_Control"] = frame["Metadata_Compound"] == "DMSO"
-    frame["Metadata_CellCount"] = 100.0
-    for column in range(n_features):
-        frame[f"Cells_AreaShape_f{column}"] = values[:, column]
-    return from_dataframe(frame, resolution="well")
-
-
 def test_dose_features_names_the_features_that_move_and_leaves_the_rest_NaN(phenotypes):
     mt.tl.dose_features(phenotypes)
     table = phenotypes.uns["mantispy"]["dose_features"]
@@ -430,9 +373,10 @@ def test_a_compound_with_one_concentration_is_left_out_of_the_direction_table(ph
 def test_the_amplitude_floor_follows_the_plates_the_concentration_sits_on():
     """Controls drawn without regard to the layout give a floor that does not apply to the concentration.
 
-    Here each plate's controls sit to one side, as per-plate normalization leaves them. A concentration with a
-    well on each plate has those offsets cancel; one with both wells on a single plate does not, and its floor is
-    the offset. A floor taken from any group of the right size would report the same number for both.
+    Here each plate's controls sit to one side of every feature, which is what a plate effect looks like. A
+    concentration with a well on each plate has those offsets cancel; one with both wells on a single plate does
+    not, and its floor is the offset. A floor taken from any group of the right size would report the same
+    number for both.
     """
     rng = np.random.default_rng(0)
     rows = []
@@ -450,8 +394,7 @@ def test_the_amplitude_floor_follows_the_plates_the_concentration_sits_on():
     frame["Metadata_Control"] = frame["compound"] == "DMSO"
     frame["Metadata_Well"] = [f"{chr(65 + i // 24)}{i % 24 + 1:02d}" for i in range(len(frame))]
     for feature in range(4):
-        noise = rng.normal(0.0, 0.1, len(frame))
-        frame[f"Cells_AreaShape_f{feature}"] = noise + (frame["offset"] if feature == 0 else 0.0)
+        frame[f"Cells_AreaShape_f{feature}"] = rng.normal(0.0, 0.1, len(frame)) + frame["offset"]
     adata = from_dataframe(frame.drop(columns=["plate", "compound", "dose", "offset"]), resolution="well")
 
     mt.tl.dose_direction(adata)
@@ -490,7 +433,7 @@ def test_a_concentration_that_lost_its_cells_is_cytotoxic_whatever_else_it_did(p
 def test_the_phase_reaches_obs_so_the_window_can_be_subset(phenotypes):
     mt.tl.dose_direction(phenotypes)
     phases = phenotypes.obs["dose_direction_phase"]
-    assert set(phases.cat.categories) == set(mt.tl.DOSE_PHASES)
+    assert set(phases.cat.categories) == set(mt.tl._dose.DOSE_PHASES)
     assert phases[phenotypes.obs["Metadata_Control"].to_numpy(dtype=bool)].isna().all(), "controls are in no phase"
 
     window = phenotypes[phases == "responding"]

From e8c131efaf2e71be09b2ed7b97d6e4342c2d4356 Mon Sep 17 00:00:00 2001
From: anon <tim.treis@helmholtz-munich.de>
Date: Mon, 21 Sep 2026 00:33:19 +0200
Subject: [PATCH 5/5] Keep the reasons, not the timings, in the comments

Docstrings and comments in _effect, _aggregate and _batch quoted measured
times and speedups. The reason a path was chosen belongs there; a number
from one machine on one dataset does not.
---
 src/mantispy/pp/_batch.py     | 2 +-
 src/mantispy/tl/_aggregate.py | 4 ++--
 src/mantispy/tl/_effect.py    | 4 ++--
 3 files changed, 5 insertions(+), 5 deletions(-)

diff --git a/src/mantispy/pp/_batch.py b/src/mantispy/pp/_batch.py
index 8af1d5f..e4c3396 100644
--- a/src/mantispy/pp/_batch.py
+++ b/src/mantispy/pp/_batch.py
@@ -32,7 +32,7 @@ def _median_polish_stack(grids: np.ndarray, max_iter: int, tol: float) -> tuple[
     """Median polish every feature of a ``(rows, columns, features)`` stack.
 
     Each feature's grid is independent, so the stack goes to one numba kernel that polishes a plane per thread.
-    A per-feature Python loop took 20 minutes on 132 JUMP plates, almost all of it interpreter and pandas overhead.
+    A per-feature Python loop spends almost all of its time in the interpreter and in pandas instead.
 
     Returns the fitted ``(row_effects, column_effects)``, both ``(positions, features)``.
     The grand level is not included, so subtracting the effects keeps each feature's level, as :func:`regress_out` does.
diff --git a/src/mantispy/tl/_aggregate.py b/src/mantispy/tl/_aggregate.py
index ffdad2b..9e0f1c5 100644
--- a/src/mantispy/tl/_aggregate.py
+++ b/src/mantispy/tl/_aggregate.py
@@ -59,7 +59,7 @@ def aggregate(
         ValueError: ``func`` is not one of ``FUNCTIONS``.
 
     Notes:
-        This uses mantispy's own NaN-skipping kernel rather than :func:`scanpy.get.aggregate`, which propagates NaN and is measurably slower on both mean and median.
+        This uses mantispy's own NaN-skipping kernel rather than :func:`scanpy.get.aggregate`, which propagates NaN and is slower on both mean and median.
     """
     if func not in FUNCTIONS:
         raise ValueError(f"func must be one of {tuple(FUNCTIONS)}, got {func!r}")
@@ -120,7 +120,7 @@ def aggregate(
 
 def _site_counts(frame: pd.DataFrame, codes: np.ndarray, n_groups: int) -> np.ndarray:
     """Distinct fields of view among each group's cells, where site 1 of one well and of the next are different fields."""
-    # One integer per field, built from per-column codes rather than a MultiIndex of tuples, which is 7x slower.
+    # One integer per field, built from per-column codes rather than a MultiIndex, which would build a tuple per cell.
     field = np.zeros(len(frame), dtype=np.int64)
     for column in ("Metadata_Plate", "Metadata_Well", "Metadata_Site"):
         if column in frame:
diff --git a/src/mantispy/tl/_effect.py b/src/mantispy/tl/_effect.py
index 944186c..46bf419 100644
--- a/src/mantispy/tl/_effect.py
+++ b/src/mantispy/tl/_effect.py
@@ -28,7 +28,7 @@ def _mwu_small_samples(treated: np.ndarray, control: np.ndarray) -> np.ndarray:
     ``scipy.stats.mannwhitneyu`` chooses once per call.
     It uses the exact null only when the smaller sample has eight or fewer observations and no column has ties, so one tied feature sends every other feature to the normal approximation.
     With three treated wells against 330 controls, an untied feature reaches 3.3e-07 under the exact null and 2.9e-03 under the approximation.
-    Splitting the columns by ties takes 9.3 ms against 8.3 ms for 344 columns.
+    Splitting the columns by ties costs one more call than letting scipy choose once.
     """
     from scipy.stats import mannwhitneyu
 
@@ -170,7 +170,7 @@ def effect_size(
     codes, keys = group_codes(adata, groupby)
     estimate = _cohens_d if method == "cohens_d" else _robust_z
 
-    # Sorted once and searched by every group; scipy re-ranks the whole reference per group, which costs about three minutes on JUMP.
+    # Sorted once and searched by every group; scipy re-ranks the whole reference for every group it is handed.
     ranked = sorted_control(control) if pvalues else None
     # Counted as sorted_control and scipy's nan_policy="omit" count, everything measured and infinities included, so that the branch chosen below is the branch scipy would choose.
     control_smallest = int((~np.isnan(control)).sum(axis=0).min()) if pvalues else 0