diff --git a/01-Python_essentials.ipynb b/01-Python_essentials.ipynb
index d097f8345f7313046836af07427fba5ddf4885ff..bdfa22ec6c784a32272c4dc6e008004cfe2e8bbc 100644
--- a/01-Python_essentials.ipynb
+++ b/01-Python_essentials.ipynb
@@ -1,6 +1,7 @@
 {
  "cells": [
   {
+   "attachments": {},
    "cell_type": "markdown",
    "id": "66ce7a2d-ac19-4dbf-9690-e6ccfe41b4cf",
    "metadata": {},
@@ -11,6 +12,7 @@
    ]
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+   "attachments": {},
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    "id": "7546afa1-eac9-4f3d-985f-2416fc82eff3",
    "metadata": {},
@@ -22,7 +24,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": null,
    "id": "a5f45def-9bf7-4a6a-a000-f068cc84d802",
    "metadata": {},
    "outputs": [],
@@ -31,6 +33,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "id": "6a062da5-000e-4ec2-b2c7-4cc9e541b5c7",
    "metadata": {},
@@ -61,6 +64,7 @@
    ]
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+   "attachments": {},
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    "id": "febfd6ec-ef2e-4546-be70-f392634dd171",
    "metadata": {},
@@ -92,6 +96,7 @@
    ]
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+   "attachments": {},
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    "id": "2ea635b0-bb5f-4ffb-a956-6df964776c3b",
    "metadata": {},
@@ -113,6 +118,7 @@
    ]
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+   "attachments": {},
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    "id": "481a39ad-ba13-4622-b7f1-6d9fec4debac",
    "metadata": {},
@@ -142,6 +148,7 @@
    ]
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+   "attachments": {},
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    "id": "30bd6648-6a78-4439-b01c-2c5e75a7e1b7",
    "metadata": {},
@@ -181,6 +188,7 @@
    ]
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+   "attachments": {},
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    "id": "09779ac4-e719-4a7d-b101-b23c68c60e70",
    "metadata": {},
@@ -199,6 +207,7 @@
    ]
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+   "attachments": {},
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    "id": "039d2d0d-7e53-4ddb-82ca-f46c6c27bfbf",
    "metadata": {},
@@ -228,6 +237,7 @@
    ]
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+   "attachments": {},
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    "id": "af99b1fd-33ff-43f8-a0fb-c1433bba7c99",
    "metadata": {},
@@ -258,6 +268,7 @@
    ]
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   {
+   "attachments": {},
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    "id": "d7c10065-ad58-4f8b-827d-c77408c516d6",
    "metadata": {},
@@ -288,6 +299,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "id": "55f65a6f-bf8f-4ae4-a4af-095978a34440",
    "metadata": {},
@@ -318,6 +330,7 @@
    ]
   },
   {
+   "attachments": {},
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    "id": "fba2dafb-f50d-4e8c-9da0-88e6424a487d",
    "metadata": {},
@@ -326,6 +339,7 @@
    ]
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+   "attachments": {},
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    "id": "c6f0bbef-e9fc-4406-9bd4-c7d65a563bd2",
    "metadata": {},
@@ -348,6 +362,7 @@
    ]
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+   "attachments": {},
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    "id": "293df537-995e-459d-b58a-1e6d5ee917cc",
    "metadata": {},
@@ -385,6 +400,7 @@
    ]
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+   "attachments": {},
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    "id": "8415f62d-f481-471b-80a7-2e4ce688c41d",
    "metadata": {},
@@ -403,6 +419,7 @@
    ]
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+   "attachments": {},
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    "id": "e4db45f7-ad99-4d09-bbeb-37e4e5ea4cdc",
    "metadata": {},
@@ -432,6 +449,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "id": "440ade9f-138a-44ba-8915-e32c354cfd8a",
    "metadata": {},
@@ -440,6 +458,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "id": "7c3c510c-f931-42c3-9dd3-11c81f791b6c",
    "metadata": {},
@@ -496,6 +515,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "id": "7dcb5ca3-33c8-4558-a9e6-297095e8e285",
    "metadata": {},
@@ -514,6 +534,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "id": "9f4a5d9c-6a96-4cea-bb65-aa2333e30433",
    "metadata": {},
@@ -543,6 +564,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "id": "e9432ded-adee-4d63-a955-f533c3ccfdc4",
    "metadata": {},
@@ -551,6 +573,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "id": "2868cd34-275f-459f-a351-dcf5e5da8729",
    "metadata": {},
@@ -593,6 +616,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "id": "54598c4a-3832-4eab-af05-edf8501e94a6",
    "metadata": {},
@@ -611,6 +635,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "id": "8647423d-6129-4a29-a7ff-129f76b9ce97",
    "metadata": {},
@@ -627,6 +652,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "id": "e28bad31-26fc-4c87-a8e3-d08ab8238f66",
    "metadata": {},
@@ -647,6 +673,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "id": "8cb74c60-b4f1-4ea8-8b45-35de0a539699",
    "metadata": {},
@@ -659,6 +686,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "id": "ede758ba-c412-4525-8a8d-c12ac275503c",
    "metadata": {},
@@ -677,6 +705,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "id": "989b8eff-049f-4e43-8d3c-2220fb9688ad",
    "metadata": {},
@@ -689,6 +718,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "id": "27ed60ff-5fa2-4df6-a5e5-7c085b99e144",
    "metadata": {},
@@ -707,6 +737,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "id": "72bebe55-7343-417f-b473-404e632a073a",
    "metadata": {},
@@ -719,6 +750,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "id": "6c214d83-b39a-4515-85ee-9331fbc8eb00",
    "metadata": {},
@@ -741,6 +773,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "id": "5778b708-7733-41f1-a1c9-689d5b66022f",
    "metadata": {},
@@ -759,6 +792,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "id": "3210c9fb-27d0-452a-b180-b010f821951a",
    "metadata": {},
@@ -777,6 +811,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "id": "f0a1e843-9bad-4528-81f7-dad4a5fe0ad8",
    "metadata": {},
@@ -807,6 +842,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "id": "8bc9886f-626a-40e8-9444-dccf8f31cd03",
    "metadata": {},
@@ -835,6 +871,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "id": "2682bb17-0a31-4084-854c-3aaf68f87553",
    "metadata": {},
@@ -912,6 +949,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "id": "e8c2d72b-44a4-47ac-b2af-ec8b477017e5",
    "metadata": {},
@@ -920,6 +958,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "id": "c1359344-b830-4c68-b7fc-c5df418f5f1b",
    "metadata": {},
@@ -940,6 +979,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "id": "dd8787ba-8fec-420d-9ec9-e3e532d2d7f4",
    "metadata": {},
@@ -967,7 +1007,7 @@
    "outputs": [
     {
      "data": {
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dKTwdjLo9427tZJYcORxIRFC1YxUhUo+nSWnUI4TGiTWxUphiUQgcOKAjtk/grb0X71iPWUTOiMhvisiTIvJNEfkvmsf/axG5IiJfbf7/2KHX/M1mpOXTIvJ7X837BODFAFklaIhoBaXw0PJC5o1Z90AQEu/YUph0lPUI0ljp1oG6hi83PQpZAmdio/ymr+K9S0yHte2gE0HH2S189lW+/s22w7l9EpnTSICzDv5SC/7LFrx7EXpt+AMeTnQcs4WUG7rMlh7n590qX3ExTyaO+BSs9OB0xzpI78YEMtewLgJiqWTkGq8XmtRSQAQVQBzBRQZk4RApiQwuxGLId3Iyeu/Y64lAauCvquqXRWQB+JKI/Grzu/9BVf+7w08WkfdgKuzvBU4CvyYij6m+siKjYs1MDsiiQKGwERy7iXK6hqkaDL0dHC4LhEJIiCiBkQtshoC2gArW28rCxJzBebWKzoyXvxVdYpyANrAoJkxbFNZns4HNo7jXbuOXAFAHjwu8TyBLhLBgvRznS1hqKNkLsTLZD/QlZ7sT05E2Tkv2NOepSU3nWGBpCkMFP7No7NXu+W3MPxxma4aXzlZsSbvO53IY8vESSBOZ9qdFHgawqnoCNRGK4PHoUQJzD9gdOxBVvYbpkaCqIxF5Eps893L2k8DPqWoBvCgiz2GjLj/7nd7LqzLDs1I7BgIOk4XrKTyhcAI4S2DF94iij3K58wFG/ZRkGNiofw2pvsE+FfXMWqSrGL4n2MFfbpqTSm4uHopALzGEOwsG3WlpO/ouB7vym+lAHPb+rwRutoATEbx3AT6hwmmFz1bWDpB7IdTKjji2ghIH4YZ3OGKu77dI3AJpPyaflrh8CEtT4hXPQgEtD7uVqXwf5krcCjjOk4uAAeDzKEhwFAhB3AGAGuavnqcpzRWoG5ejAFHzJgGlwIDW2jCc13tCj+x1213BQETkHPBh4PPAJ4C/KCJ/EvgiFqXsYc7lc4dedpmXcTiHR1uCRQlDlNUQWHCG0U8Uvumgp+Ad9Giz3/7fcrXz/WwtZ1SZIsU3udr5AVaLs3R3PkWhe1xKhH5L0cIijE4Bx5ru3YqDVuugMJhag1IbOJ6ZGnxd289zoPHNtBaWsr1c5JMAPxDBf7UA/Szi+TzhS6FkRYURgnohVbgSCUUILAdH1U9pzzJ6tSdPu3h3nNZqRRhdI8vO45jRC5AswwM1nJ/Cfm5RyVzUaZ6Dz51HjDEfRS3Vs5KwEomwo1ASQMBrxEs1H8G+fykgPZSQNV+EEprI4yj6uDfsdTsQEekB/xL4K6o6FJG/B/xt7Br/beD/BvwZXuVYS7h5tKWIqMcw9+dFydTAwONYJFCIMg1C4h5G+P3caC2QF79GcGdw0Qhi5Ubn46z13svJyz/DfrjEVtGIELXt60INcWXh9lpsjFa0aZpScxyhhMXQ0JuxxZxhvQRvhs3Lqlft0Eg4KLcCnBD4Ew7+XEv4aub4rWnKdt0lZsxDDkYzqFGeI7aSt4KPhd444ZoI4kDqAW7cZ809gMv6dOoEWfkms7MeBMJ1G1xU1VA2/Rc9getN13OEAc1Z43TnHa4V9t6LzvOgBHZqIzjtAaH5FKKHqzGH3WN46TPOcZh3YhXtXrXX5UBEJMGcxz9R1X8FoKqbh37/94F/2/z4msdaHrYK29FigX2FEuEBVa46oYOjHW8wbrVJkoxp8GjWRRd+nDrZpt7e4Mp6TdAFjl3/f1GXF1FVKIWkZTvausALDqLU3sfP7OTM0/JhgAHmPHJsAb+ZNYCEptyqhisuYcvs0QROR/Cn1+A9IeIL05jfrlL21DGjz3XneKHOecQrF5zyAil1nLHkF8hCxnb3LAWn6GanGM2eJ3ZDJv48aRTRr9/D8QsXOXVywKiljBYNnD3jYFXBF8Z4vDEyZxFjKWI3slkkxxupBa+m3XI8Bu+UDzmIauEzwfMkBylZzU2yOi9FNPOU6F5oZDuym+2OHYiICPAPgCdV9b8/9PiJBh8B+IPAE833/xr4WRH57zEQ9VHgd1/Le84jkVRtp7uEIwrwQJQyZJ2B79GWFI1+jGlYp87bhCRDFhPEX2Gz816qtT/Exo1/wrjcIlN4BGEoynbDar1eGnBaYws1FSsl0+Akc37EXL0hptGY1Df25q4xGnI/gpUYPtaCc2KzWr87hW0f8Qtxn/9AyoWgpKkj5IGcZbI04ndqAWa0Y8eNqEctC2Txg1TyA0yWf5jZ4hbR9BtQtJnJGSTdpa6vUelnkBv79FatnWBWwjiySO1iDudn1sSYNCXiRGAgRvJbwMrA0woWFJ7ycCaDTg2tjvLXSvg3Hq6LpYp5bY5m0JzPRu2PCa+N1Ha/EMHeDvZ6IpBPAH8C+IaIfLV57G8Bf0xEPoRdw/PAXwBQ1W+KyD8HvoWth//9d6rAHDal4SU2C3mCso5SIewGx9Pi2Cu+guydJnGnqXUF10uQ/VW8G1JKgqsvsRut0V34PXTH/4JeXTJJAsEbJThEkJfGV5oITJzduEEN73MKSSMZMPWmfpaoLey7ecMuRRZpVMGk6cZYBNJz8O6WlZM/HNtiDAvwDybCt3zK5tpjqEvQTaV2Z9hPIk4kH+Z6NGOse1T1KlH7OudafcbtZxhqysg/Qtn/DVCP6z5F1EpZnAZm7iQheZF2NWbSsmbGvUaLdrYHxKbB2QLenZrzmLUsnVnwsJXDs2LDwEYOcoSOKPseLiQQR8YY7pc2La2uIena125pqeVArOVc9cBZv5xwj2DAds69Vxl7O9vrqcL8NrfHNV52bKWq/h3g79zpe86tQiiw8u45hY4LPMxz1NriurYo2xEkSh7OUbsW2hWY9QidEd59nGvlQ3zQfZW++xYfqIQnI+VKMCHcVQ/faFkPjafB9JpBPCoGYqaW/VABa9jztrg7VQGHidV4bAf/XuCbjTPrp/DJFrw3gaGDT7dgFDsuR44X1o6TTU9wfFywj7DvlTTqMY53uJ7VFNExfL5MyFJ2ok+wXD2OH+/ik4iQvhuyXaLek2i6wN74GMe2nyce/zbIiDiCegTj6zAawLWm89MBqwl8uGsiN9sZrI8acZtYqCIhRlnGBInbBZyuhCpScoVry7C0CWULegEmmW0QlVqkU5XQ9+BqiwoPO4fbOYmaI2rZm233PBP1sCl2k+QNEh8DTyGc1sCxcI2JPkiSbFBEMa0ihWJA0enhixhNa7QHWnjGHvL8k0xnz/Avq4oHgmknTCr4QGQzNoisf6aURjYRizRy4PG+SQZsYVoMnaZcebfKihW2QArga2LalWsZvLsDZxW+UsDzEeyJw0eO3bWIZPMsC+EjPBdFTNZzWvUOUeS4VHUpqwjfe4hpdweNNtE0YzJ8lOzkEuloBV+1CPWYKAxI21vM8ouEaUW38zQa1YQpbI/h0sAA1DlW0RZ4vG0T4DtBWJ+2GUQnmUbHCK1FfPwwu0zoy1UekWeZ7lznq35GLsYmLnehQMgjyEtlsGEVtcjb+dUbhp+0YhPRjhTi0s5L0RzDHKeaP3akAfPm2n3lQODAiQB4hAhhgvJiGLIQHD6fMGl9gDoZQn2MxD2HK4f46gW0HBKKHM1WuL7xAZYuPso+36JSiypO9GGUQ5lb+TFRm6PhgVoOGAvTqc0tnQW7qY0reXdsXs2Yt5MFLHU5kcFubHXxsg2bI1jbj9iZQraZodky11rH2V76CLiCWq8wWtzBb7WIZo4ynEbjDF25isrzeF2hcKfQ0CHtRpTxs8j6Pq3tDRhFkPep3Q2yWpnuwd7YPi/NcfUFfjCFT2QRk+oUX0i/lyc3PsQeD1PHjhBPkeR9uMrheilZuU3Xvci5vZ/jRv0pvFa4QrjmYDxSzgiEUvEtqDKQHJJGAyZJYSM2jGSrNhIcHDiyeX/PEcj65tt950DgAA8J8/+qLNKmpS2icoH1wSq7q5uMoq9DOEbiHiDJvoSOzqPRBJd+iKGusZl9CFc9TZHUtDzsdQ20S3IbAqRiOqxFg4EI5mjOKDwbLNRedU2ezusD7+avjbFhzttV4yylKZeqYS8pMG5DsidMulD2HLLr2A/L7PfOEZYWQE+Tx+v46LNo+yTUHbQHzmWE8TZ6zEF4P1rWVAvnScho7Z+lSo6js03O5k8yiH6bKrdJ8oPcnGlXLCJoR/DDLeETHON323+KXzv+I4xbEX5pnRBasP8EUi8hCymuMySua3J3lnw5QbP/Ap142P8UXpSygq3KEWXKSuGpphAv2uySpAM9D6MuDCobEn06gZMKm7Wd98ABh+fw5nJkb47dlw4EXurRpAImJJRsMNUhZ+vfZVs7LOz3iFZqBnlO1Vsmyf8Tot43iKrfQOVFQiuG5BzLklKoZylSdGr6kssxXMe4DGtVo8SN3ZxD4FP1nMMA18LdCZnnUYzHxnYqFv2IAx9b52zw4Jdhd2TC0oPUU46URV9wLb1MWKiQekYIECSHpI8uVMjsAlFxlTRZJ1TvIy/XUH8Zkg2Iv0k9eILe6M/SGV+k5a+xkPwDyuxrZHs1uzObllZF0ImhrfCYxjwQPcbP9X+MF898krF+GF+PCW6Mdh1sHQeWYZigC19E0jaMI6rWiMlkAPoTdHkS4SqzthBHMTk12yKsVgpjcF3Il63LeiGFx6f2/YWWgcsPzixKmardA0815/HIgby5dt86EJjv9kKN4GVKEa5QiWctCoz0wzA7Rqt3gpwxvu7gOscJ+Rhf7uHSR/BZiUsi+qkpWz8g0OrBoAQpbQ5HFsGi2vCdCYaJ7OiBA5tL4bweJyIC/dha6zdre58Eq/rU3qapn83gsTZMSniuB52hsh0J0QJslp7gItL9FpOTOygTKHJEjiH7e4g8iUxr1qJr7Kc/Qr63C/nPIv1P4JwDp1Sdb3Ju99c5G/0TVsIOq97z3MxU3VBII9PgXEsTqpN/gH+x+J8yDYvMpMJ3JgRfQzFDqhI613DVC9D6QeLiQbQYobGSyCJx3Cd2Y4I+wpZcR72phqcREAnjlrI4hLrVTBuMYK0ZLFavwYkcdM+GOGXOeCingO/GlOieAa5wlMq8WXZfOxCY3yglqjtIKBlLSV2tk0yO0Y8z6p0tWH8fvk5xm6eo2z+MrP4G7kaNq2OOBZg6m3c6LkFnpvh+XKAorVw7UVvczsFYD3a5uQPRQ8dyJzeuanMhbnmxABsK31NDawSfS+FUgFYujAqYZpCOlFnHs1ZvcT0fAH3C+GFcp4J8CP4ytC9QV1fYyv44ZbuPk+vg12DpW7jZSaS1BgsDju0vsp5tM8sDL87gQg1pF/rBKkKrccQs+gNcWP+zDOpHKKd9wrmr+L6gz6ckI0h2hLrXJSyeAhykj1POrhHYp6ohi1d4zLe4Uj1GT36HOgq0tEa6it+FTstxPA5sTm3yYNqBqxEs9MB34EaAM8vw3n0bEr08gwupXYiPjOF7KviywhfUiH9HjuSNtfvegVhvREDUxPc8KdPwRaL6BST/ID76EC2eY9b9BnXxHiRbhfwPkoQvQLRJz8GOV05PHLtOGWZKyGG1sDb4C5HR2SsxHCRtOAlzif/56MBFLAoZvIYjf9CZGPQUAygfFNihaUjFyHJtgaedYQHvG1gVInGQdWBlQXGxUm8JVf48lX8K2X8v0olI8j28nMenis6WkUefo/SfJwy2cJUH7+G5MXF5kWjtBMtVwcn27zJBuVaZolV3EVZj05mtg7CdfZTtlb/KKG8RiJHFPjpbhkGO89/AjZQQn8GvlKjLkd0BYXEK3YJ08AxxvkKlq5yXPqthi7yGOlPGXUVmJtkwrRyXE4VEkWDl26ht6Vs+hDC2dGVJ4LjCdgKPxfCVDnxjZtILJ2r48Rq+js1RmQDLWNp5tyUa3+l2nzsQPfRdAC0R3UPdGMIIVwrtpIUMtykXHqbuVUh9HvFTBGUxSVDncV64qCCpkKhQ1oHNBNbVFk/kGkakHjBQ5xT3OfI/4bWdzMOU7QQrET/rD1IjxZzUrho/5fHaWKhVDJEIoxTiPLAfCT6Fwk2IO58m7PwgmtS0wyqTzm/CQgs3HKG7beiPYPol6B0nmlRoVqFRYKFc4fiNNmX1eUZBeXJkkUe7C52ZLbxWWGNr7Y8zXBtT94DpCC0WCXuC8z0Ip6iPZ+jM4arLSHiWOPoA1KfJBpvE0qaMlaooGNZXiN03GTmlrgVfKC1g38VIDce9Z20o5A8oPrdzPHHW+LhcwEMLzYygRkDq0hj6EyO2nRfwYizirsI5rJepwIhm8/ku82tw8110ZK/V7nMHAgeX3zffKWjAI0zLF4l8wqj14+higsTX0PJJom5N3z1AvPs0T9UVPadEPaVQSGbyUtfrWCFLIa2bgTxYunEYqDvsSF4LD0SBq8Ea8haB/WBRx2FcxWPDlEcRfMbBtoelPkSFIL5LVY/JxopWULVqOsXnoP9riD5M1BGCfwA3WESOefTGDN2voK9oDyLpQOnpcJKT4wEP668wqfa4FCDJjLzV2oE9hNolzHp/gHzxLH61gx9vwtI2bH0dmS2j8gCkHikjJJ4Qu3XcjVNE9TnSlS3KfJdpdo7InSWuB6zm2wTdYs3BjSD4Wml7JfVKnllKeVng2BjK1Jz4QoCVxDghLyTWSBVnsLEF5xdgbwq9CB6ObCr9dUymIceixMNKqkdU97tnbwMHMjeDMRVAlYBnFmpit81qPmR3dAKVRULaQ8ouiyjt6kXWqClqSEoD81YV1iOjtb+QQD2zSkvMAQO11gNNDMF6UlYdXAkmm/hqLMFAyVxMY2Me1SQC686qQRVWth1EpsBdericw7tzpRoHLmWwsA4yUHwCVbVPX/8JYfcPUy29n6jfAgaweRpkhAwvI6cSZDchzZfo1MLy9CwfGP0mA//vGNSBYYQJMCkMxkI/cSz1f4ILJ36KwiX4/RqKVXQ0QOIZ2j4L2RbkFa7qEemIaCJ0WCTOAtPrZ8jbV3HJSdLJLv38Ehvl7zIIBdsZTHJYqoFaCOI5XSu1UwYtocyhHWC/gHYHfAo3HIQ9eGYRWgGWEnMs7WbafFk0EQgWsR0LJh41l2eOsWikbq7BItZRfeRQ7szeJg7kMIxpDBHVgKow9NuQf5213eOMoseoWx9mefQUVE/y3PgZupnyYAl5YbBA3lV2SmuiO+cNwOvFMG62MtED0tj8XVO1hZ9EvOo6Yk2jrypwogObMyOnBTU5gTmZbCLGzGxX8ILAaYGVODDojkkUFifCLAOJICSQtJ6hv/vPGY4jpFomilpI9DjS6kC2io4npPkai5N1TrvrnM5/nSG/wKVizHUHeWLCSaqOMnMUrfeye/qvk6+OqPfHqO7BuMa195DyCmHyfUi6QuqHEJ6mNUroRB8kikp2XAd6jrh+ADf5BsnO1wnhX7BfnGfiarwqq7ESEuV6CXlQtoEzKfRFSWcQTYzCX2ewGlkEEhTIoBzC3iocr0zaYV7Wjyd2LuvIZqU4PYjq5ud13hg54s11Hm83puzbxIHMbY4gzIPUKRoCw+qrQMXq/piN2QJL/gKXZs8yrXYhEkKqHO9Yl+jlNjyTmYaqyyFTWCpgvz6QPwy3vOMLgPTAZyCbr+6GVCysdsG4DHGTkM+7TufsSm1a4qclLHXgzIJ1DC914CLQHitVmpAcrwg70PEGqEb13yMbfBLtfQKcox60UT9Dq5RlXePh2RUeK36ea3yWF2cztgNwykaGsgNSKWm8ynjtbzNZPkbJZ9E2SPQRov0U2byMLPTphQllnbJYxvj6OFUUESV7TPw60VZOq7OLTDcpqp8jmn2OWDYh8qxVyjLKlhNmEyPsKYpHmDgjBxKbItypGZwbWUVMGqGRfB+oYTqCr01tiNKSwHBqwPSsoajO/Xmr+S/2spe6fAOHOq7fBHs7OQ+4D8Y6iMgdHqC89F9IcdJmzS3SihdZTDZx3GC6FPhYpJQNeHm2hi2xtvXfHMC2s/GAccN61GDpy7xXJcXinTVgpQvXHAxGr72hSzAspCsHQK1gIXaM3eDdyPggH3Iw6wjhpLJQg07h6pJQtJT4qpCuwua+I64DgyQmnZ2hdp/EhzWCdEjiLR7Ndvn47u/worvGea+8WEHqrZkt7kM1A91PSJf/OnuP/TW8zPDpp2F2kvTCgHj4NWKd0umdgWiLatZHWIbOe0iTDiFYyJbnn2O5/sfU9TViruKlIFOTCMpQMhzDJDAolb0GW8qccLwbkAwWZlYB26jhWArXloAW6NCisjOJaZJMxjBKYCg2RFow5uzIW4oYsOs2r56lmJ5KJSZp+XIdvu8ke9uOdbhzm9c4BCXHa8G236cdoEwCDwqsty3cvZBZe/k4QHvPcui+h6K2maLBWzk1lwan0IMybsBKr1uT1zdX1XOwC85ZqfMr6tV26KUAsxR8ouzVcDqHnRo2OsqVGSwtK6kDtx6YDZR4XLOUvcik/yKujImKGAk1J6RmZUH5bASjfchjwxA6Cn4KVRCido9p+0fQyXVc+TtI60O0h08Tlb9J3soIRZ/Z6BpxZ0StxxC3zdLkRXBL4E7Qn/00Zf3PWGCPXQKzqGHyeiF1kAehVsUHpXaOZVVKVRZTpa+CVkpVw1LLgOurDoZ9cBMbZbo4g14FF7AB0kVm12ocIPMGul4SS3fmMgA1lr70gI/E8LkmVTzSVr1zexs7kLkd4CMem17uJ3A5hpUdA+dOesBDtWBzY07dMOX2VmQg29DDbri5B4fm+wijvI+58/B0XrL1HHSXzpOwedv84wlMariuFhG1tmFWwUoEw+sQrcBDOexmMCuVUYCFLkwSqFMlqypWFiqCh09m8MzIKPKT3AhZ9aqR1fIc2plA54OU0WliAtXSGDcZ4fMtpuUCadomy1rk+Zfw4RgJQ1RTQn2FHmMiuYHnGXrRkEkd6AFUjpmDTAK9ANOk6VL0glclipQTAouq7ESOSJSFPnR8I6Pgob8JVWIdu6PEHGvdMoU6WtCfwczBuIAoHMz1GR+6bhVwA/gNb5Pc2lgqeWR3Zu8AB3Kzeaw5brOCfM/m7Z7owQcKaG0aqHa1DWkAH6ycWMqBkLiDRhD4QPOzi5V8X6/NIxh36Geaxy43GhxthRPegNsqggeAcWo5/1cXYWMCV0eOdFGJMkiGSjYDWjZT+NESXhzb0Kj2DZMjONk1DKaYQrft2I0dRf09VAspVa9G5JNEyQqxPIlGKa32x6g6p4gHBUy+wEJ4gUpalOEjlE5IdZOIbzKtanxQOiJ0Y1iRwATYBDJtWHk59J3SjxXXgmEl1LXigtAJSj+CvQQ0gtXKsCBRGLWgzqGsjTHsMCwpV+uQHjbnrg3sHzrH83O73VTSjpzH67N3nAOBJifG8AbZAYZwbcEqKR0PxxS2PFyaWYl1PtHcqU06HwGhboBQvZuT2759Zi3N397xEDtzICsFXEshCfBEG/ZLUwnrrNn4hbRQzg6V4gFT95pOIM7hZA2LbWAR4i24MIVOx3gf+a5pbnTrQCZCGtqUxRC/v0mcXsEnD5PLj+CWppRLfco0J13839B58Sw+eh6ZPkVVfoGJnMS7z9JzW4xRUhGuYhq2LWf09KxWWgTWnKVcg8iA0yRXCEriwCWOfZQ6MrW4bmVNdQ/lFl1ca4Btnxj7dFhadNHzByBpid3gc2kEx0Eqc7h58cju3F6XjIWInBeRbzQT6L7YPLYiIr8qIs82X5cPPf81T6Z7oyxgN1jhISrgY/vwniG0ZvCFEXy54RJ0A8SNoJAT27GLxnmsC3ysbW32d4RAvcxxHXYe87RpLqCjAZ5u1NIuKjxfGtCrMwi7kE9gxSllAu0tw3Z2SmitGzPz5B5E5w0/2FdrkR/GUMRWEZpF5ihzCZR+hyi6RDH7DEX+byg+1KU4d5bx2pRcvkioL1O1lhlOzzGp+0y4TAi/Q6GX2Ko9qcKSU5xA2wdmpX2+rGHM+VqYpFAHx0IESWq3Y1BBmzFUZWTRh1+GYQ5fdfC5DowzS2faORwvjXG61lRd9jjQUd3nIBWcn9N5hWsRY6ce2Z3b3YhAfkhVD083+Cng11X174rITzU//407nUz3RtkcdxgqfE7hcgmnBB4BPqrwu2P7nao5joe6cGMGg0N08zmzcSEDX5tzeb3mmg5gX988m76PRRwFRiwrCuM4JN6e206tGuHVAOAbNWxOYWli2Ea/htMxLE3h6gyulValSAPU2xC3rNdHc5i2PIvFV5iG72eSnEQ7jyLJMpoO8BvbhCvfAv8IZbVGEn+D1dYik/EyJW1C2KHFlNybbGGuQi2CF7FSrYdCoFBhGBvH5mw7sFMqJ1G0D3VhWivtDFqVkfq6EUxasFbDfmTXoS5gq9FrmRxi8s5v6jlLOGl+Lpv/8wjvtQg1H9nt7Y1IYX4S+MHm+58BPgX8DV7HZLo30uaRyIvACwq/jdHYM28hvW8qLpcqA2DnSH4E7Cl8YQZZbLv23TAXQb8Fw/HNClszDjQ/ywDbDROqG9vPaCN4XDcplrP8Pp/AQx6+Z2hCSf8/NTGkHMN2hsH+qAuwlJn+yGIQ8vibiEygBEeNX1tDmRIGX4cpuPYDRKOK0HoXk9FTxOwTB0+k+3R8oGxSO4IjiH3fc+AjOB2sRJ5XETuFo7tQsLIMWQk+hyRSFgDfM6fcU6Pzuy68KNZQd3ZidHsU9ptS7ag5V93mfM3xjTl4Onf485EcR+nL67fX60AU+PcNV+P/3QyEOjYf66Cq10Rko3nuHU+mO2y3Aox3yw5zWXfmx6EHOfR+s111sJ1rfjOWGJB3t8zXpj86r8LM+SCRHGAxvuGKZM14hau1RRMZcKm234fcnMFM4YqHfxjgWgKVg7iw16dqjm+xgGEEkkKCUDvHWnGdxf0nuJx8gO3sDJV/gDCrYXsTwnGywZQlLpNNSjbHX0Xrp0jdU6S+4LqCIk2kAS1RuiI4p0yxBR8FqMSRpZ5hpcwiaxzsVSbUHMeg23DKWeqVB9juQFbD8cKimCsJtMoDVTjHzY42bb4/PL1+HnneE/Y2aMp5vQ7kE6p6tXESvyoiT73Cc28HE9z29N06mW7++HoGH+jD1SE8VzTlzoZFqC/3x16HzW+2gI1SyJrxA7eyUe+WRcB6YozWeYWhIVQyaao+cxCwI1B5uFSaw9AmvZmEpiTchPS72KKawksrZz4iwWElzp7aKImlCKoC9mJwWrGY/30e2P87DPsPUe0uEhYrXHyGxfFZlv0Nyv0pdfhlkvDLTP0msRa4WClqRyw20zhyajqyXul4oUYZiaCx0s8KoljZV1gdQS8zjRMH1MvGSSlG9lmWY3gwgt0hRM1xqoPnY3Me0nA+5tdrfu3m5fHD0dw9s2bvmQO5c3tdDkRVrzZfb4jIz2MpyeZ8uJSInMDK7vA6J9MJ8KEF+PgZ+Po1eP4aPBLBqWV4egqXpzd3Wh4GIl/vYvdYuH8stlkl9RuwhfVSiz7izNTJdsPB+Mq505gfi2KRRcD6d1JsRz+NDQyfNs+NMVwjv+VGnadtYOdoBzhWWnrRdzARJRZ4rr7Kwvj/wvuu/GWGC+uM3Ab96+/Hz55hPBqTh5+lkm9Q+j0yCSyieJTjEiha9nmkMiqfB9QJvRjWnaOoA65U6Bgu08Ka5dZTI+5Fu6DLpoXqEtgq4YtjA7ST3KIRPGhhYzDmlbX559ND/1Oa4eh38XodmdkdOxAR6QJOVUfN978H+D9jE+j+FPB3m6+/2LzkX3MHk+ki7AYogW6O8QaiZuaqM5r3mrOFM7dbKxlwc4RyJzoQhbeFuSBvzDzcGEhjkFYTfjdyfTeaXfUwucxxiJfSfC2xFGU1WOVoIFZluBpe2YEq4GJ4JDWSWtRWqkrYihWJAlo9S7HzX7O+9+s8uPU4X5s9z7X8y2Qyo9PeZ1JUpJGQeGOXOhViB3llBxkBYxUCSqXQc0JPPXUbeqkSO5j1bDZxFKBYNOGgFFgrLA2bjCDNDeNwAQbBrkccjKE7T1nmzjXFnK9gZLL9u3eZjuwWez0RyDHg523CJTHws6r6v4jIF4B/LiJ/Fuv3+iPAHU+mE4yfUSh8bgo3LsH1wv7A9QB7exbKx9wceRymlUccAGbKnY8AuOFvljC8W+YwrdNTC1ZtoQLfhdn4ACCdX6h5pSGlmZqHnYtW8/1yBLkznsoUS1FeyTJsFs52z/pDZAlas8CpiQ0f35wog8k2uF+kXzjjY+Bt2Hfl6GpEJoEqUaJS6IuVXo+h7IoNz85rG7/hBHre3FkRQbcFOoZWF8RDu4ROaQ2MY2dkt87UPkcSQ9ebbkrRkPzm4kBzBxk15+G9TY9LgpHWJrwxKeeR3QfNdE7kpS6fhIPSHGLsybnTiLAcuMYeuNWZHGZ5HnYw98Knn5OaFlrw2ALcGFpbvc8tRVlYhNjDtYlJ/KXYwp+XJQVYxc7PscjywsobH+I7lSoT4H0xuB7EC/B4Dt/qQz6FsAbFEMo9kESsEa60jt++wlSEllf2OzCaCH0fSBKYiVDWkLSULjCohOUaShGWYv9Se0B7EXZL0NQIfCsCi1ETYRSmvp7HJuh0vWGb1sG6becl7sCBU5+X17tYWnbUJPfq7W3dTJc1X+fA39xJiNhjL2EdDeXcc/tqzUsVDUymcBrujTEAih3zoLAyZRVMk2NW2/G2nInm5MWBUnsZDsL2GnMaC5i2aqm2a5cv+45mEXAisZKqc7DRhustKyX7SoivwpmOUizBVqXENTyWCS8WwjAEHpbAcxWMS8GJcDZVtlOjlqNCtxQcEGUwVaUnyjQVUhU20kDPW3oxK0ErmMVwPjfMJkmAFLoz00cZe5uR42/ZHFrN55hywAge3eazHtkbY/e8AxGg1bBCF4BYbWG8hLDrAdIuDSM0OsQMDbdsQTVWUYmw0PZeMlXYmd0cGQWgHJkORrtlU9rqkXXhtjEAkeb5Q4z8dqvg0ctZS4yANq4M7OyMYbdjE+AeP6dc24G6D/m2neNWB57fU14sPMupEejyGHqV8lDqSVOIaqGfCm3Rl0DcngNxSlCIKqi6yiSBk/twqnEii5k5nj3MuRcFTKamwk44EAQq9YAs5jko2x5OZY7szbP7woGkIrRUQIRaHF1VZgQ8indqg6/nHInmdU6sqWqevswByAgLlVsc8D3uJbt1EdSYbsVMgRkkC5bO9PdNPvF2i+bVLqRa4eIEEGtxLzNbzP22je+MWrDXh1kO+XXrw9HYAN4HAmy3Ye0MdLZhd8dIaQuNtuliam33W0DegZMZFDswS+DG2Ko9Vx2EyipOu21YacFoCqNgzYFRUz4JWNo210qZR2zzjeMoTXnr7J7HQFou1hPJCcZRGy+BkpQOjl49IWJCCDllKMilptRgZCu1KCRuOCJzApZg1ZsWtiCHr/C+95LNQ/U1AYktpdurbcDV67l6wsFu3nbwQArag9UOtJaM2JV78AuGffRy+ADwpEJ/CmUPtvfEBpOXSqjgocjm6PbEIsVpC07G8MjI+nYuZvYZ0h7oCOqpiQN5gd3InFDpLZr0DcgRmqhjrmU6T9vgKOq4W/b2xUDiDtnSx9nXY8zIiYKnSGpKt8+av0ExCYRqj6S+QZAxKh6nluo4LG+enxnXOJaeszD9frEm+OCqWqqxgFVXXq/rd5hDPd3gQbve5BuTJeO6RKs2ZCvfN71VMrg8MR2SdAPaM/hgpIQ+PDGBhQiOzWCQ2ev7NYSZOYhLNYwy6KZQx9anI1NzUIUaKzao9cCgjdYHBzT0hnF/FHHcY3bPOxDVNjnnKKIPoFlE6O/i632i/EX2/UmWEmHXXyYZLhFXzxLVAxQlNF4joxGyVcvHi2BdqPdjI9U8PRtydxZRgvX9zMvkYS6s1IEyB78NbBgXIy3geAtu7IFUwkaldApYreEzM3ttVprw82JuDuRyBMeaHqIthe0AoxLcDKS0FLPWg5ELC2pqbzMOStTzdOWw85jzO2oOSHNH9tbYPe9AQkiI9r9CknjiaomQPoLOSpx6qv67iCWFUBPUEe+NSN2EQGXapWKcgliNSVqqhcWjJpfuOysL3m834d1oAhMM90AO5qdkwZidWQnRSRgPIFowvdRODmsJDI9BdkMZb1m15OuLMNyzVvrd2rCRXgnPeUu5rnrjdKBwo7DUpKvG48gPNSvGGKg9Vw+bO4p5yhIOHTfY8R41w731du87EEm5Fh+n8s+j5S5h0kHWNvB8DOW72NqfUjOmbiV00glxfpmWVIxju/Fbtd2UDsvzQ+NEFHikQf6/6d+ZYXFIDJh09UFVy3mrjugKdDsw3LJFvqCwl5v+xnQAF6fmAMoZIJBnpi1yuYIbsemT+tIU65PGG3hvJeleZG34pRq242jwkua4lAMOx2FW8fwaCQfNjEf21to970A0alG1PkiQa6iviFHofZIwewr8mFnrQXr1dWZlSdnaZbXK6EQTyBSX26zayjcdqli1JsEIS2UMGz0YjmwW7K0l37e1CVTtJp04xK+ogM1dc66jNXvAxXaj5G2IrsFeCTtNOpgEY8+O64NhTVVl3+fOUpRILRLsNL8f+AOOymEuy5zjM49ADpP95mXcw48f2Vtv97wDIYph5YeRyXnEvRuNPgLDE2hcEaJttJgCAW23qapjRKT4ugFMg0UdMzVy1pxPENT0M1tqPRWPdq0dfu+e6fN+403VqiBRUy2Zpwg5cK0Gtw9nYjiVQm8E/RzSPXhhaAB03ZTOS7VRGAnN/Jrm71RAJxyMvlCx388Okfcc9vy5xOAc7wC+jQAYcwCoHjmPe8fueQeiJGj7PejyWVx8A2bHEN0npH2oPb67y9R/F95toPFnWdeIcSrEQYmcMRgDEILdwPOddhygnMK4hAe68FgLvhns+e8Um6mR6tQdKKApjej0FKrrNqzpUmWsWJrKyH4wRzNPM+Yg9dwUO8+70uinNA57Tviavy7igKfj+PbIQjjo/XlbO4/7WBfknncgqKCFQ6JLoM9C6xKEhGh6GpIxkn0K6b0HV52hU8FStMq2Xsd1A+0SFirTDM2c5eBBGlCuSWmmwYhRD65Aqw2f2WpKiW9zm7NVpx6yxDgz83NSKvgKJpVpMMwXbw9zKPPmxHnkMO9Bmosv6Zy3oQegpxx6zRwUnb9+DpbOsY6ziXXzpqnhJxeqt3lfy338we59ByIjJNpC/WmQY7hqDK0nidwuUa3ErY+Q+AskoWYhiyii4xTF0yzXntIrnaZ7rgb2naH/BVa6FAeP9qzv5Kl9q87MKdJvZ4LSvHlv3hvU8s28HA4+9+HFDQdqX7OmanKrJELEwQCnw4/PNUjn2ia3wy8On2sFrlTNCIvKMJTv1NNzZG+d3QcOZIz2JrB3Ak0gpGuQ9tCwj5YvUN94kqLryPoP0E9WKKMZj3ixuSnAZsvSkom3gUw1ls7Mbdwoes2qJn9/nezO+8HmC7mDOYVxuDkCmD/nsJOBmwdD66H/h//mrVosc2fjME7OnH9zq+TCYRrknDj2TogE73e7DxzIMjpYQfY/BXoNXfykJdSdLnVnF6oITT6KT5R6epJ9iajayrIoCxVUtbWHB2fjKUus6jBTaAeYzOCGGKfhsGr3290CBzyMw583dqDhYOpeF7tJSgAxndg56Dp3IPMUft5zNE+PDkc0h9vv5zbXNRGxxr7BdxA/envYvJ709gDb7n0HoglEgoZfRHgWae+DX0brH4b6A0j3GVxVE+19Bvw66/WQAZ7MwfOZCdT0BfKyEROaSwCozRJ5SOE39Gah5Le7RVhnrZZWnaqbVavYz/MhTII1JSLmfNtirQFlfdBfNH8d3Nxbcyvl/PByOYyL1Bgv5GQLdNKMqXzbmuNA1Sbn7eAu730HIjUkKbL4hxH5MpJ+ANVt0F+EznF09j5cC5LqDPHVL7FdnSdxnuXEJpdFwcq1s9AodWHt4hLgeYXnuLky8Ha2ufBOCiSFcTjmHa3zczBf+DEWFYjYnGCJoCtWFtdgDNN5xAEHKc88zZlHHLc7p/OoZZ7yLCfw4CLsFTCs3g7L6naWcFCMnp/h+x/dufcdCCWiEbL0QYQYdv8tkj6MdH8Uxts49WQh5djeNp39T7PkRmwlitTNtHu15q1cjFAWqYXlFcZQLXm7BJPf2eYLs8Dm3szFhucEsNiZUNG7Inh3Ys6127PqVZHY8CkdmZhtPj3oBp5HHnMG6dypxHIQqdzqSA7/vF3BN3bhRnWggVvc5jX3r83P0LxgDQdx2v19970eUeXHgX926KGHgP8jJq35n2NSEAB/S1V/qXnN3wT+LHbW/rKq/sp3fCNVItmF6pto8hWIFpHkLJIlBD0HtdAaXCXa/hyhepIqqvkuD/upUdnHarm1YDd0S02YNxGjVZf39/V7TXYY8Jw7z0gM3KzUyq+pwGoEDzXyb3EG52emGr/WgXpmeiqnFX67gIv+IPqAg6gkwkSht/nO0d2oNibrg6uwMbbrs1fDM/qdl9fhStF3ilzminZzpxdLgwW9Sk81/1zzyOnV2xwtSl7zK+91u2MHoqpPAx8CEJEIuAL8PPCngf9BVf+7w8+/09GWTjMSyfCdHmG6AK2TyOoZZDgmvf7LtJI23e3PU1W/yiPMCA6+0IHtBOKRkaQSZ5WXOLYhzaXAcmY7nvP3+x5wZ+ax6CNSc6R5w8xNBIa1yRwWDs5PLP2TMSzUwjRAEsGsp/xYH35zDy4VkEbN5D5tyF9qbQSvlnbeAcIInqnhYQcbYpMCX+7adAUeW4Iqh+dmBw5s9jLvFwErbWugjCu7/g8uw40BfGZouPwrmTTHuOTs+53QTN57hednHADFvcgxQCmDUOqcBXP/33l3K4X5EeB5Vb3QqLTfzn6SOxhtmVQz+gNHSD9InjwGLqC7KbL3Ar29T7OUfJ1UX2QlmpJmdnOoKp3a5PG2gt3YidgEswpIE5gW1j36DmKv32SHy7DjZlMsgccc/IEEvpAI29qmnB0jkT4urtkZbVEne0S9mmRiPTGfTOFrzvpbBlg0Me87mkcjL4eFzB8XTPO1H9nUujGw4+GMM+d15Tav7Qr80LpFKlsX7L16mGTARL+92tNNYbVnzZUSLOIqK1jvwINTOF+bE3m54+xhg60+sgirGXx6G75a3Xz/HH5tF3hPAq1g3cjracXGsZrZjvJrw/C2AYvvlgP5z4B/eujnvygifxL4IvBXVXWPOxxtmZHxrq3/lm33ELNkDS0DIXuWVv6/EIUv0qpnnBTlIQ9fU8idsqagU2NZSmzt6bFAL9gO+UxuTuVtcg1fl916Dr4nhTrJuC4fper/BMPqYabJKbJ4iXh2jaXilxjv/1uWqmeYdT3H+8oPefjyCJ7yzXgJNZGiZ4rbi1YfTjvmP297U2pfWrCxENvB5oZ0HUT1t+/VY+DqFGaRLeJz7QOx7XJqTuzw5wsedsbQjqCoIC5hWtqUu0zgTATX65tlAubH2QKONwCybxjNZxLY8jYJsIUdw5wG4DG92s1gn6n0sKeBhxQePGFzmL80eE2X6Z611y1pKCIpJgr+XlXdFJFjWOqrwN8GTqjqnxGR/wn4rKr+f5vX/QPgl1T1X77S318Q0Y9LzEBikijBhZooqhiLMms5sk7N6j7sopDCcm19HTewnWimVjWIMU3Py+Fg6PKRmQlWoj0bwZ9ZjPjNhf8D31j4o+wsRtSTFJWI2D9M2J2S8ht0WOMDk39M4n+GRSq6mSLAV2dwLbfqzk+m8DOzm/U95u81jzwOs1hbmMyCzdKFD8fQymyE52cmljIcPt5+Ckst2JyYSv1qBMcTU1XbPhQWuEPvNW/qEz2Qd6Bp8ktosJpgx5FIg3mo6cm0nKUiLWDZWY/nxWCY0aw+xOTVAzrAS5FYg7c92oKHWvCFATxf31toyFspafj7gS+r6mZzIJvzX4jI3wf+bfPjHY22LIArEthIco5FJcPKbrKFIPg8ZWGmZOp5KDOhmvNteHLaVFya/g7X3LEXwjs3ZXklWwLOpfCjMVyMzvCV3h9ke/UF1FUEPYOLj1NvPgW9M5TpEhoe5sn0r/OBzYqy+scsRZ79yBb+sppI8j+a2W48X8C3YiG3OpTM2W4umLzhEvBEac5gdsseFwGrsQlG1Q2Iu+3Necx/P//b6swBzPtzArwEmNSYzkmNHWuS2PDuTrPw57hIInYP5c4YzVtqQ64CzVDyJiqJmr8twagCIZizQuxYz89shEXfwcMRbPqDkZutyCpgxX0WFt8NB/LHOJS+zOfiNj/+QeCJ5vt/zR2MtlRg6pQrIpyvlSUiNpxnO4DTGYVXrjvwuRDHaiMNG6JTrbAUWyi8X90bM2Du3A5vEHrLY7dyPF/bXxXgmIIExxPZu9hbaxHaH0HrCFrPQxgTVp5F8iWCW0VdTFkvc6n/Vzm++zm6k6d5f0u56A2MjaThhOjBYp4f9e2ONGBK7IJhB2PgazUcE5g0i/bW51+bQTW9OZKZYy6eprGvOT2uYbqWNBq5zUGkiUUH0fy91aKSbjDnsYFhMrUYvhM3TqN0II2zkdqiml4TgXSAQg6m4UU04zfVQNcb3po3+w4eDxYVdxJYWYLt2vqArs4s9bkfINbX5UBEpAP8KPAXDj38fxWRD2HX9fz8d3c62hIgBKHyyqLChMCuF1pZA4LRMCadCd+EAvYbFpOogWVPl9Yod58591vsVtRgvgzdocde+yeck7/ep/BClFDqBsE/ZhJjbhsZ/BbS+xBu8V34cB3CM4g7htJjED1C1P1rpNO/SjYZ2KTA2BZVrN+uYzqPRuZks8NYiGJzh2OxnXgstouPb+MXlZt36jlrVg/9HHEwN1jEftfFFndQa21Im/cisZJ/rDZUKwk25Gqthn5ohJCCRREhmCOZRZYqHwN8AlVmZeuOmsbMTvN4VsNAGx6SWpQ1wjCeDEuLOh6O78J6C348g3wZPj+CJ4b2/HvZXpcDUdUpNlXx8GN/4hWe/3eAv/Oa3kMAjBhWCGQoWwr9QphFShnsXq9bajuS2oVGrfN2t3g7kJLmHNL5UgRwCK75XPNlerjd7Ttb1vz/SQfv7gnXfMS2O4YfD9HkWeiuIek5GGzA7oOw+luQv4CX36Ja/25aiycYj/4wcvVX2Y3/Oe2gL5VRq/KANnVrcu0OfT//VIsCH27bk786tUV/sT74VIcjjVuvZcBu5DnPEzn0OjlozEscdCMblTnBHElLzVERzImccMayfbgFO4VFH3hYbsN2A8b321btI4IdLyyJsuAsPelFsBtbn9UuFmmsKdyILLVx/qAfq8YoBaNgw+HjCVyo4bEKHkpgFMELTX/QXeGs3tke84p2zzNRHTBS5XhkoeCuwHsioQVc9nAshj2U7fIgRK3UAKxS3w79LQcO49ZeV/22ntjXZgnwkIPv68BvieOCJuy03w+tMXJSke0pREKIFokGGdHCQ+j4DL5epOxtMCkdoR+xO/txrt34Zc7GQ75ZGS4x3/3nru9wNHKrM5gv9ouFYQFDPcA2ejG8fx3Ge/B80ajH33J2Dq+LBNtEVBowNDoYGZFHhnloCus1aA0amTOYukYlvjSnslNYtOGdRQztEs60gRpcAf3EGjWXYiWN7I1PlHDWQRps4S9EUKSw3Ehq7gMTgbiJckrMeSVYyuTVnOa+t5Ge6wrf1YbFHvz6AJ4r77003H3np7y1FqtNpvMqnABOC2Sq9CLluMLYKZ223RjOg87z1PvWeUQcJBYO+TblDTkU+h8O2h2v5dP2sKrLYynsRML1XNgTJeUZUneBVI8TVVMk/wFE12EZosunkMGDhL2KevTLVN2LTH3CNf1xXO+H6WXCRwQeSeaR40uH/NLRzvkhhx1JT+AHOgZSfqO8WWJxvQ0/+C740AmrFCVywCid/5+fsfnfF+y+iYP9R+11SQTTyErMtbPXrAbDPI73LMrQ2M5NVtgCbivUThgrXMyt1LsTYNg4MxRGOdQ5bKtw3RvAuiyQxrAUmn4sDFNZSQwAXo6tNLzQOLpYGqEmD5MaBjVcV2t6XFuBv3AWfmjx26O512RvwGK45yOQvsBHHWyhLDeg3HHgulOmiVBVpvcxLi339M2dpPcl5nEYz4AIh0qwz9MsD2n+6aHnHdjhvfjlI5M28GEH3baxM68p7JawXea4q/+SVvlu6v1HCUsnqdwJRBUZ7hD8CJ/9LHANz4hCH6fSPXx7la3qJzg2+be8l5pf1gOFM5XbYx6HS7m5wpdncOM21+zKBH7pazComnRCD17Pob8p2M2szYNBzeHMORoaW4QRdWwW8GowADXHvm/vYGkIFnEEgcIJa3FzXzUcl7QFralwVQGndFtQOdgfg0uV7QCLQD+xg9108IiHFxJoN5hJEtlAre3ooK1i3qM7l32ca/c+OYL9CE5G8N4ufHEEe/cQLnLPO5BthfNBWVW7MI5mEnsFe86WVTUyacKaJs/U14IE3Jtmt5+auxDF6wFUqEIzC/hWSPI721wZ7BmFYzWci+DaVBh4QZwSlVdo13tM65LQOU1de6KdkmRvRB7/a9BfgChFW38Yn55EeyVVNOWbo49wKjrOWnKZU6XV7KvmkOpDjuFwPNUScx4VVmK/nZUBvrbblHflkMwAB+72cBgtTYQyxxgA2rGlMVkG0QSWI3h/DNe97f4X46ZCUkOvMqd3Q4VMFK/CRgxXBfCCK2E/FgqvtCKhF5SpM1BVgJ43fEVrpaVwIrJJiA8mTTpVGu4xxlKcK4mBsom3x1oY6DoOFkXHAR70MIlhGXhfBp+b3Tt0hHvegXhstsh5gUtqvQipWtRRe8sdZxy0j1fhfpzUfjj9UGKkcQvzaMPjxKHqb0pdDgDUw+Dpy5d0M+CPOPhCgE2Fh4LNuL1YxtSqZDX02jNa+f+Hbf0AO9sPQtsj/ZQy3iCUyzB+DyQfQfRPAH20vEKIuwyTR7ix/X4yvcyDzsL8F9WqX3AzD0SxBX6qDVv5q99R5302B5+9GaLenIEUizLASripM03V4xEs1Q3WIQbY0oaFETwbjA59ozbZgtpD6YRcYRyERYF1hDMi5LFwtQaVjH6ySIhaXJEeQ4nRxQ5pOaHOBsTlgJHOmGjJUgi8oKAllKVSNnwXWsaMPiGGl/jGySWhicycOcBxBFccfESMjHa6BWu5RY33gt3zDgQMPAXb1XZr20UztfCzPBTSzkV77z9tj5uWFgEhQokJKI7gYlywunSkgqqneglMvR0keXurgN9R4zQsRdBrC1+vDbjrxuC9sCg1j9Rf5enq/84gPotzJyEe4btPQn8dXf1bwKNQXkN7A2R8DKkT8k7F1dYGJzCtkRMBRmK9Mbce0dyBXJ0a3nE7AaKXO0uHP3XFgetsOYscIoyE1hOLBtLUJuRlMZz0cMnBt9pwOYbvT2y0Z1HDGQ9tdcyArzZpSHBCnClpCTsuZeiOwer34fl+rnTezTSrKPsJWg2gDdFuTORfQKrzRFkfDWNa9ZOs7XyLfn4FHyqm2ji7LtQ1XJoaD0RSmFYgkXFRSm/0+/0Kzg+g7MMnYiPWDe6hm/u+cCBz5xCwMC9WC33nN1Lg5lmq91P0cSBEfJDgW3iuOHEgEQFPJQ0JQQKZA4JQIYdSme9sAbigxodIBK6nsK0xxxKPClyM4ENB+a5KeTH6d/Qnv5d8/c9Rux5KDNUHYHwBJjW64ZAwgigjsEw1WWEzPcEsF7Y6io5AqwMF98MuzjXfzLBF/65FGJa2MCbFzZq1HHrd/DPMMY951+88n4kTi0KCGMYQR7azbwU45mDPwYdrOFHBVxN4EqAyzGPioCQQJ8JSEIpKyJwQV6tcXfwhrruPM2mdoDym1NomSKBaifHVKSTtIteuwdIGGs0IexHafRj6y+T715h0haXRL/HQ1Z8nq7cZqDIqIUvtM9SpVQ7rDNoFrAoUcXPeaptXVE/AR1YlOjxH5622+8KBzKspDuN2BOzkZhw4j7mDud/swHmI9WUIxOrJxB6rBQSHEyFIwOFoqxIhDG8CU18db7EF/HBz1cPIMa4DaSyoeNbUhkZdEKv1uOKzrA3+ENeW2gjraB4h1T+E7lUY/zFwD0H4HESPQPf3EPZTfAX9GF5whtMkcnN3Ltw8R0YVro3hXcdAKmtwu53NMY/DiZpg3A5psBENULSsA3a1hFEKmhlR68YS3Jga2/ORBD42gCfV0uOJWsNeN7EScluE1XSRyfHfz7dO/QTT/rsor4GGEh99DtjF9daR4nGiehutn4OwhXRbhPoRgqvBn4SiJExLSumw3fmjDE4/yPLm/8SJ/CqxD8zU+n2cM9wkSqFfgc+aqKy2Zr+gMM3hvDNS2ymxNbGrb/1c5/vCgdxu93EYAFbr/es85javqkTqyFDaToicEBRKDYhzzW4bETQw1UAVO6jDSyzLV7sdeYE4tQrFvkLilH6kXEPAG014rEpdQRV/CZ9toX2H1vsQHodRBeUWmj8A7uPQX0VmPbQfE40X2Nl1nBFPFR0wN2+tvtxqwxouDO3r4ehjPlTKA53YcI38kORhaK5/y1k/iiiklZVor7ah1YM6gXwID41gkJljuzoSlp3hD0soFLAowlotVK2IJ7LHeHH1DzOL/gi+PaKsh9QbyzgXwX4HFUXLvuFSwaOTZYTE9A0GT6DpPmSK6ASJIlw9hMUNqt7D7FZ/FH/973PCD3l0DPtd2F+3qX8hN8xjFdhoW2UMjJQ3t5nabJ7UwbHUOs6fLt66+/++cCCHbZ4Hz8uDr5z13x+mDbcjFqXtoOMEEcGLI3ZKCDVliMkjQdQRGrDHAaJKQPGv8izUCp/P4X/Xhc9KYFGM50BT2amCckMFNKKbneByXKPbvwNRhqy8B93/SyCfhpOnoDdsEvcFRGu6xXWGIZAXBgjOHcBhpzG/XuHQzx64Ovr2Y50D44KRtg7/PHeaQZr+lpiX2hc6DtrrVnFZia08ezUGN7WytY9gu1TTk/DW8RtEGNDiq72PMVn/JLONVXydECXA6ASu3SWMr6H+o7iri+hD5wndJfCC7+xB9jzSejc6Swn1EBmWSLwK6wXMnoWsj0u6pOGDZMXHWNr7NfIo0K2hHJpQdXfZgF1VeCAH2jBsA7sQKvvMZW3A9KNt+L5zsDiBv33BlPfeCrvvHMjcfJNDH0b370+zuz4Wo1kvRkISQRQ8NQqpI4TAoCypHLTUUauzrdoZld2FgCqEW9KY2+XJ69gOtuhBEiVqZAqHhSNS4yVsOuVUkrCw9n0U5wSm74HBe5BS0JUhLJ/AVb8I7mPI0rtxWhNfyTlRPI3GyjjAQlNNeKkjmgPHcTNkfHCsK1gPyeFjnj9nHmXOiWNzDMTJAT/EOQgt42yMApTtpjQboJfApoO4ABLlRG39VeMgDBPYDwl+5YfYOfFdhO4xAg+j+iJV+STkq7hqDSkc7FzCD78Czwxx7Y8jy++DziZaXkHr96H9EyApIl9Hsh+A4sPURQy6h/PHKNubTNY+yfniy3TzbWbeyrkaLAJZS6Ac2Qf+UAeeXLZIsRqars2+GIHtSg1fuAxF+Z3V1N5Iu28dyOGc+v42IRLoRELPCccyyNNAVim5eGIED+zFShQ5kgpCbIu9FkWCNovToV7R5qzM1TdvRUZ2MBzpy5XwwRS+oLDjjCAlYiSzoMIwOBbcOlo+gQ4zotkiTHdh5XGYxlBuI+lvI1UfV2QsjK+S5l9jx1m7e6LWWDYXXr41UrzVuQWsd+R2a2F+redOAw6ciFMjaHW8YQd1ZZjG2dxo6qM2DLrGAVkIsB1Dmlmz3Gwf+qIcj4Xr8feyt/SnqNN9tLMIYZOwdwPVa0i5S5BFZPAfcHv/BhgSKiWMv4Xwk2j8ALRbQAblVeg9DaMryKiH9j5OWOyj+Q5h+jmUE4TeaeL0o/Snv0w7thSqLO1YdclKyUvOwNSPX4ZnxRiwiRodv3Zwo4TN4qDr962y+9aBvD1McAhxENpZYFWhX8fUayA7wmkC+67mSgwuUZLYkw4NafVEOKckVSBNwCVCNdaXBG2S5v+Qm0dRZk3+/GWUpIBYhA2F3DuGQZDI41TQeEo+vky0/x/Z4s9yGP17ZFaY5oV0EX8CaXdoTa7y4Oyf4bgOwaoKA9/odejLl2hvZazMgfLD1TVu+f1hENXTdGE7WA42CrMQC+evB1jSZnhYDJsdWKlMEHrqoEohbwkXC2VJM6T7vyYkHwZKtMrR8psgX0eqFO2uofll3PhThGQPVImXAn7/Crr3aej/J4i8C00egOQCUtfIRAitz4NchOIkQoG0xoTiOoEHkN7vYzb8NAljljGHNiqAgfFV6pbhOBnW5PfBFlwLRrPfzW3A+ZYesIDWnPFp3myC2ZEDeYtNgAWntBAeb8E0CpyslCoTchFSwK2CG0LSFroBsrGnqgMSK71IkMihzjpB42BEpX0sxTs8ZgEOUggnpqj1IZTNFEKhPBWUugZ1wqSsqQe/wLHN72On8xhl/3k0KpBakTgg7VPE11pk8hXODT/Fiv8XSFKx4iCLbPffl5t7X251Foc5HdDwI2IraYbbFJUWUziRwaWxPWfuRFRh1xmWcQw4m1k5eJTCbGKLbt73UjXCytJVbuSGmYw7i0xXfoCQbMPKJYgqJNtGB5dh0oZTXWQYQxWQKkY7ClMlqhyhHhLdmODbM1i5hDhBpIKFbdwkR3YvwtKj6PHThiNdSaDImWQpae8B9sffYpjD6Rj6wAMzuJZBEUHehjyHjdTo91UPzog5jn4E0Qz2FBZi+LFV+PQuPPEme5AjB/Km27yGBILSQWkBvSA8HpRP1cpxgX1RipZJ91WlIr4ZQ2HIKb1MSQSiVMm9MquEgJJE4IOQGnODcfOu8wU7VZh5GAYTazmWwdIiZHHNtaGj11F2PUjlEPc8ve2/TDv5j5jNHmSg30tdLMDGFdzlBRbLq2wU/4qN1r+H6YgsM4DvcgNYHO6+PexIDtvhnyOs8azQ20csGz14aA22XrRURTHsQGiGhUVGLhwAJzvwSSCfQd2Gb8awOYViCEkO5xJYUfMPdUgJYUJYvgg3riPJCTTdgWgN+glSVLAU0MFHYOdTEM8IZYz4HvTfC2uniPrvg/waUj6C1ClaPoprPUldCPjjyOirEK/DwgrIGHYzkskZgv+WiXynUJWOQgIdBWI40YCnz7cgxNYB3AE+Etn1eyJumNkCT46tZ6dL05V+m/ObiREv7yZkcuRA3nSbB+mBFqa6JTE8LsqeF6ZtmM6UugULpTLtCESOuoTutjINELUcqy5QKAwqRUol9xGpj3BiS7bB4W5rgpUDJ8DnpvCDkS38hyJlOyhJ5Mk0NLv8JZj9U1YnPTrxCkE2cKM1tNwnmz7HRucG/eWaQmBvZM2MWWU6oWO1lAK9uQP3dpaJ9Yx0sb6m29mLu3Bp72DodoSlSFOsGuODjXkoausl2W7DBwQ2vEk2DnL4UgUvergSg/TBbUOWzOhMnmd05TL0K0SHMNlC0vehZYCyi1YZrD4G+ZQov0ToxWj/e4kWPoZ0d3C1ImEM3Wepixkxa+jw+2DhYVzyFGHvGVjcQeoK7WwQdIEFdSw6wWVKVgidFLY65kB7FbSz5rNW8HgOx1rG8r0MnA1wsQX7sW0Sl8Z2jk81IOsQOy819ngK/FgHvjyFi3fRiRw5kDfVmtpzCMQCqyK0EiVaFFaC8kyhFF7xDgallVcnTpGW0Gkb3XxUCicUOkGYqLCdB7SZ1zICTCT7lW+PiIPBimNgNzKArueUS1gre+KULWDBe3qxZxjvcczvQfy80dMVXGbRUz2DHxjBl4Ll55QW5XidN/3d3nkIhl8cOw2twkZtXB28PChYc/NM3sABmFoGe78MCDXEU4tEhjEcD3BqAidSeH9q3bFX2nC1dAwiJaoHrJTPs736AeplCOV1ZKGPW1zBXX0XOnGoXER2PgcLH8VFP0rKPqV8BNEezicQX0OL95Imjrgf0NaIkghdLKG6gbYD6GVUHiZKHFF/BDvbTIJFGe1UKftKxxsVf1ia9s251KLO6RR2GgnG0pmI1uIM6sLOcdExjsx8RumCs7+zFwwc72FOqatGJnylmTavxdx3eoKI/EMRuSEiTxx6bEVEflVEnm2+Lh/63d8UkedE5GkR+b2HHv+IiHyj+d3/KK8wQOZtbcEa4tYR3h07okQ4M1GutODqWcVnsJsY6Wm7sBunGAf6Xhl1oF6x3STFMaoEVzkGKk3/z8EpvZV/4TAtioUGl6iwEutU4bNDi0YqhRM1kNhru83fyCKh1VGmqSKJ0u8oi6nS61vz4iTAV9R6ljoC4i0aEIUPxPA90e1dWhrZxLtWDusxfOCUYTOv9saYc0gUa6Ir1Jiku96ikN4Ujo/t71cKmyMIU6PMRxM46wMrtUJSs1B/kaWZI67WibSCvauE7RtovYgrEuIY4t5Hids/gWbvQuOCjlwhmmV09jzZ/jfozL7KQj1gbWuD/vAYricw9PjplDAs0cm70HqDMHFksxlX/GV2vDn+GxlcLWEygfWpVcOyzMDScxmc7phE4jTAe6aQ78OoBWVqsgzLMZwJsJRYujPB+mtakTmMnsDGaSsW3c2pBK8mAvmfgf8n8I8OPfZTwK+r6t8VkZ9qfv4b32H63N/DZr18Dvgl4PcBv3y3Psj9YHPieRflBxyExNNysFUAI2UAdArYcsYNKVagLoVOI5F3fQjroqyVsNdTikoIHiIaRFHndYzbs2MiZ9To2aGyiCiMvOEDJ5egNWtEhhPTBJUISlHqHEICUQuOT4EU2ktwNoUrkckAnhzDsyVc1YNGtygcRCG3WhWsKbIfw+VdeGYbXutwAaXJ+fVmta6hwrUKtpw10S0E67Z9VOETXXgmhmdmcDrAcKqM5Xd52L+XZ/k9lH0l+Ck+CEFrkBdwwxcgOYdLvgIorhPR1W3O7T2BTrcZ+YRKrpBudxhXI8q1Lm6vTTJyuPo4kiq6pISFDt0rXVr7/4pJvAOV8VZ6wEoJC4AvTN0+2TFwVcbwMYVkZOXbVgXTllWUamfaK88msBNZ6thyNpVgJ7eozGNjTi5esEjtTcVAVPXTInLulod/EvjB5vufAT4F/A1eZvqciJwH+qr6WQAR+UfAf8w7zIHMW85/3Cnf21F+NRUmImwHC10X1cp5ZQF1JMRT0LbSH8MNlG4XHtuHulauK2Sp41I4PPbgcE3jZqZFwCIasJA2FfvtDCN67XhzDO0WrGTWRdvah+uJiVUTQ1QKsSh7KTyewGQIL0yMHr9W2c090EY8uHnfL4WXJ/sFNWWvCwOYFbBxEtwEdvZe23m9HSeogQ7YBDotUza7OjTqd5XADwEPFHDFC1ccPB8mbE5+Cbf/MeIT30ux9zhuu0DPfBXtOsL2g7hlj1z9MqkmtMICa7NVFgfPs1J9nqqaMtEZF/UEbfdhQrSOjHv0/CK+ewxfr1Fc36e1vUP72r9k4n+DqA50VFgRpb0Lo4419dUdSy1TaejsCj/XhRNj6395zsEDAqvjJk1rBMZFrOSbNZ9/INaNvODg2blY7d30Htw5BnJsPrpBVa+JyEbz+MtNn6ua7299/B1jcxm+73Xw52P46bZwpamGhB60c9uJ6rZDEyUthDQBP1N2SiGJ4UyhuCX4ZmndptFYydVZX9BLtY7Iwga1BopbN/R5Dcio85ZTZ4AEeKGEzjq8Z2i6nzcWmz+n0OqCK5Q0haWxyUc+mNiO96waEe36pJnHw0F68Wpsb9LMXalhdFfUgw+sDPD8GC7PGvwkwFf24Hcj+P4uHENZTuD7cuHJ8CJLu/8V9df+HLv9P8r2wlXY/mU49m7qdITfzKC1SD1xtOIN8tHzvFD8IlF9hY/XEy65gpl3jKPfYnFng6w+ySB9D/uTLi0GtMpv0i6foAoD0lhAHZ1I6Yo56QUH6uFSYrhNy9k52U+Ek6OmxB4Z5+N6gOvOOD0PeHjOQ7+0EZ5FbXT9sy3TD1nowPZ5aDW9M1vVvQui3i4A1Vd4/PZ/5NBoy7eTnY3gjybClUj4RlB8DWWk1CWcUNhWRxhAp3YstGE/9kbfnsF6X6nb8NTEuo/7wOXCcI8ggM77VTsIGcIAJ7nptush3Xaxi55iIX3ZSP3VAvs19PZs51Jnmp11BXvr5mhoQb1tKdCFCiYVXA3QCeDHsFMb7XoObL4WCwo3rt+1U32T1Ri7c25FMGzh8gx+KIb/VQW5BqIE+tV5zhf/Dae2P8vJvR9kp/sw2/kaZVgk1GN65Udxg6epw2c4Uf8qN+qLbDvPJYFNlGkMA7apy22q8CQt/5uQO0pXI84xVtsYolg5FtvYiCoIezVopZA0A+DbDbu2bS0H10v40DLszOBUCQ/ENhoiBZ4JsIVwFmXkDQta69k1e35qeq0zhUl+QCp8qx3I5nyAlIicwFIsePnpc5eb7299/Lamqj8N/DSAiNzloOutsRh4dyL0E+GXYof3gQlQoJBCfwyX1LGMMFUY95Uygf4O6IKNp0gTA9LWCpgNYFY7RJtWVCdo0EbLLEKJcGK8gZUUxk1p09s9ijSU6ExtF5thAOtkAhedhfybM6Oj67VG9Dey3WxYQx1sdkuJpSvvL6y7da6UNQdx79WL57Gd+EsZzDrw+AS+X+EHeoEnZ2NuyL9ju/oNhmWXFzcfZKvjKMUjswnOX2PGmG9Fno4LpMCXaxNVrjDnsNa04cdRTV/FBqGFwE7HEWMD0KZtz40KXA26DPFQyKbGJnYtiDNY8dAfQp4YCHw5wJMVHO+Cz41M1sOu+FWFa80GsVjDlcJGUdRqgHk7sujkblLf79SB/GvgTwF/t/n6i4ce/7bpc6rqRWQkIh8DPg/8SeD/8bqO/D4yAR6Phd+XCp8VeKZW+gFy58AHWgg7mZCWwo3CkSfC6UlB1oa6FpyDLBaqUulF1pm67x2iEeJSK92qyfEqwcBaF1MHIYjNzok4ACjF2PAUDfkqaTgUpJZSxRV0S5tC71smA5g542ls1VZGjLwNS3K5KWhNG1zlsIDQveo85lYGuDSzz/gbU/i5AJ/I4ccVOmnAuSk7OqPHHoM8QhVGdWBBAknkkbopIQMjhFxAVcDDKFPWnZIreK9UidKLhX4BuRPKBMJQcE7RSAkDkEhInGEh1zxcn8JDiwasDxPDo9YKeARYzuHF2poGhwrriV2XQW0R6pMjE90CeCCCd3VgOYXP7JoOyt2y7zhcW0T+KQaYrmGY1P8J+AXgnwMPABeBP6Kqu83z/0vgz2CR419R1V9uHv9urKLTxsDTv6SvYrL32yECORHBX+k5fCT8Ug1bTimCkGlKueBxleeUh+dwTAR0pqz3AstdGA6g0KassaEsHIeli8ITm46iivEIKoGggSCNOKB6hAjBE4tphuAOogJxJmhciZX5ltSYkH2xm06D5eIbqcn+PTOxG7Vo5pmE2EYwVDH4GWTe2sw3sZkmcxHl+/HCOeAc8MczWMxg5B3bVcY1KdFKuIHQjaATai62lF2E/UpwlRBFShVBp47IO57lutEcqZWtSBg6YVw7ahSRQJooEkOvaYHsphDlzXlrG17RW7Dq1lIO54OlPM/EJrm4GaCaWTdvHJnOrdKU0Gsr+baA96UwdLDnrTJV3OZz3+lw7e/oQN5qu98dSBf4Q5Gw0Yp4PoPdSLiQC91ICRXkkfKuWthNA1dV8LVQa2CBQNRzjGIhFB5XQNZVVhfADxwX9x15M+/XCXgRVLuoKOgYRInQl+QRJW5GBWARQru5XVqJIfU+NWDUxaBTiHsQjS2tuRxDNDIHsw6stGwEZATs5XZjDrz1aNRqO3vQ+7dbWjAF9B9M4Ed6sJM7xhoYl8JMhRcj2wDqvrJfwyAXqhoyp6SRkAfHQqRUWUA9LAfhRh0TqEkiIY4E9YEoDrgArRhaLTgdCR/MlVxg1oN0aFWWLTVQdCvYoK3pyDg0V8WIZJUzBrBveDjb3mQZUkxlbdBEKq8Eat+pAzlior6BFgHvSeCRtvDLQei6wNJi4NkkZmFfiVYDrVmELgTioEQCcanUDnoLwl7l0AXFbSpZLCxEQl3C1tjwDhAiF3CihBAbg8slQBvnFIm8JcpNeuGahS0Codm1nJrjiCqr7PSccTfy3H6vx0zRfFbZn54UEAprXltW06PYCXajF1jFpsFt70ubR2lj4FdreHoG7wmB60AdK12vzeQ7K7MvteCEUyYti+hmtdJzgSxWLpZCT5SpKj6CLib8UdSBvghdJ+SiLHVgJUCiyoUWoNDNIW6Zg3DOuDpJbVFGSCwSmY/PFDWQuOdsM8gm9jfaMZztw9f3mxT1DbAjB3KXLeVAs+Kcg/80ivlVn3GDFmnV5l27EWuJQlKyHwVOh4qdQcFOVJK2A3kUcUqEXhW4MYyJs9xmucQ2e7XygssCdaGGNTQ8C6TGMcWFBO8EkRUiV+CoCHiCs+PqArMANKCa1gaktksD2lzV+KHUSreryzaqMXMQl8ZTmQxtjslWDL5jJeAsNKph3o7pDfUgc6DlDbB5mds1bzHwVsG4mhrxTZ1VwB6tAycriDViqLCpyo5TdnAUAm2n9IJSexN2brmKrgqREV9JFgOJtz6dZWAht76W7Qg0h0Fi75+XpiI5i2ClhrVgWEflD6j7TiwiKSvYEGOjXqhMQ2S288aquB85kLts86liH4qFH4gW+ZXewzy9/BEG7UfwnccY6KME3SJsXSZMFxj1LlLWOVV0iVnnG7QXnqa+uEc9Fdpp4GzPdC6uV8psVVjaNmQ9am6enDmvQ4kkIOJJXUYt0ggdKqK2S81b31tpk/bUNsv1RAvOTyw9yTHnkeUwrGA0MPC1lcD+1MLluG2hcl2BK+HMAuzNTC1/2kQgd7vr8ya7Q+cxjy6WMfB4+5X+jlialgvsJXCqB897K7HuTeA3A6x3oMo9LRVOJqAJtAtnEoyF4lwgSa2astZwOEo1FmlbFBesVJsPLTJcqOGMg/OFzQne9nbdnJiTVzGS3n7RzOuVBrNpm+P+fGENjalYOT1gg9neSDtyIHfZBOhJTNk5xuf7P8SXV36I6cY5pFBYbrO/vAGXFWGf1B9jv3yAPHLklUfrRapii2H/c7QG/5iV3tfZnJns0ARlZQSDma3OGBMazsVSD+tpT/Aak6rQoqbGemRcbDdgwFSuprHJGlLDu2tLQZZiUwXvzmz8Yxg1IxubUXat3G5gdZb+TIC8maVR56aEfiw2DsJ1PWikuxdSGQFOCqQ9eNjBZAx5bOxb5WAw1Vw7ZQ40RxEksQkSPVTC+ZaVvo9XcL2C5QwuVXCpVIoa1gP0oppUhbUU4o6lgjdKuC6QiBLXNu92I7eIZijw/szm5l4ewtcVvop1RzckVJxahLfjLNrrNvhGJZC07HNdz037JMCbetKPHMhdNAGOpTEfjNfZP/Z+Lqx+kGn8Lnx6gnhtG90ZU4dLuPqzoH2q1VNMRx3ypEOZDnC1UusS6v9zZov/MTr4b6H6n4lDDivKToB2rrRyq3ZIBhsRbE3BN4SyCI/H0SKwWMcMXEqQnBW1EQipWsl1NwZtW/fs86Xl/KEp3W54q7RkEXRro7dXzvL0UQPopdJUY2qYePubU8xx1OHgxpprmb6V5oAHFw2/GTa7d7886ErtYG3/31Q7D4mYvMBCYgr2VQSk1qy22TEHmk3hyszOR9pEMxOFDVFOZkrZh51mTOZSBFkCy85K57kzItQghY3KUsKfDfB8czy3rn9PM841WPXl+9smzXhtZtfzqaFN+HsrzvORA7lL1gGyyHGqs0q3+zBXk3PkvWMGbo530eoU9EAlh9EmOvkWoTumWH6QctYmFB30WIyMd9B6Fd917Cz8DdJLjiV+hpUwgX5gfQcu1Hajucr6ZoIYAJepp3QppUTUrZpenNDOO5RVjboan1qPS78Nk9Im1U+6hvZTmU7zVO3xLTU2fEegN2lEgjrQK20XXO9CpwfPT8yZFQ0vpPZWEXDBJALm9lY6EQ98fQTvVmirjdwsOWhFH2E7f4FtAh1n1Yu1NgxiI9FpDetiTmihgG4XLu6b4FOnskhiN0Acw8mu/c1pISSREnmI2lDkENfwbgfvDfDFAfxKsPJsfftDv8kUizqiDJYC5MumfzKdNOMy3wI7ciB3wU45+LG28JVsmZPdU5RhkSvaN03+BcHVDkkc6j0kEbr+Y1B8Dhk+T51fR9PjUD2I7rdRXUD5LBSBWj6MnPjT5MV1Kv4N6fWChdqEkduYfOGMJlVQSFVZwBPhKacgnZrjcYkPnvMpnEyAlkkNpr5hpI4NoU8jA0s7wUScJYVqqgxaFrm8p4QbCewXQqeApwrlTAfWYnMUu7UtyjkCGXwzPEpv1jnNxEq9t1Peu7WOeLcicYdhBM8279HHFMnOJPBbU3Mwo+Z5CTATi7q6Yg5zr2OiT0seZglEiUkLsgJX9wxoDbF1FS+chqKwqXN5odYB3YH9HB6orZu5O4b/f3tvGmRZfpZ3/t7/We6ee1bWXt3Vi6RWa+kWaAEhNrPaGIOBkG1sGNuBYcyEZ8JMDAzYgYcv45lhJuwYb2AIHGY3IHYxCCEkAVpoSb2pW1XdtXTXmnvm3c/2f+fDe64yu1TdXZXV3VUpnSfixr157pJnfc+7Ps8vD23Q8EYMxwv2SbnvgjZc2oDV1G5etwuVAblFhMDbpqAT1kmiuyG8h0tBSJLNEPjnYXaKILoHx1myvAGDeejdQzBdQO0YPu9C8BTqFkEuwejjMLWFbP0NdHYF7wYkw7/H5sUtZkZ/xuUiIysolevAIxRidIaFCCPnaEQ5U3HG3Zoz7Qq2FY4pxCObnI2nyztWbif+fAItcTR9RD9okrgOR8OIKC64UKS0yIgl4b5xSupztnPlrppnNVfCurVHN8rmtG1s1kRhp3mtHOnfHc5cr819Ur1awFz27kt89uUw+Y5grd7Kjorb25twogUXei+cXQ7LdYiw0CzM4HV1U7NrLFpIUvTMg5sGDoxgrQ5ThYU+4xxYhsYcJFtGbNRsmDdWeDP8SQafTuDCHj2GIfA7WxBtQyOyfX071ekqA3ILcJgncHYIz9ZqLDemOdaZZrl1iGAgpHo3DE8Sbg8o5DIcfB26egCJVnGdgkJytOXxfYV4EaYPQfLH0N+G8KNI7QFkCPlUSjr1ZWTDz9DVdZzCQAQ8RuJLRCAFNTwz5IS5Mog8j9aUKYUDap2JrQ4cSuFqYaHJTBdqOJ6IGmw0F+m6+zidHyObOowUS8zOFDTiK1zoJqTDLRbqmxwI1+n0V5Fsk9msy8FhQqTKRmil3X5ZUlQpx+y9JSRTbxWF7CUsQV7uz6+ow7kUnvC2jxcFpsWGxm7kjh1hOYYt7OJ6KDKSoWe8GYpnUqs25f6FBsRhFY9WaPoyqxEcTKyBrtkH17LE58XIJmfTDF43D4McPtm3Zq1xD2pTcOw4tNbK3pCh9Wg4LOl68RaJjzNsP76YDOhricqA7BEB8JViicyWCM9nAbPDMW72IC5/E+NI8LU2oevigh755hH03ApkZ9B4DCcfQmc82lAYvRuSYxCOoZaDPgRbPRiegE4d8nOES23YmGXU3yTH4xUCpyhC6DxRbN2QffHUnWd+CHEOgybgTCbAY+LNxwdwJgsYhHWG4QKXZk6S1k+ijYcZRjnJeBY697A96FBzI8JDFxmln2O9FrAi87Tzy9S7SnvwGNL/JHclK9xHRj0vNUtqRtM3mLRWi12YXl94wU6eJ55HbqvKp8bmrcTYCaoCobPqQ8FOR23GF3KtKmZw7ndQC+Hp3DyQtbKZrlATZToeWBl88lsTOAdRzQxfWoNzIRxwdhiuhKALNntyKTSNlkEd3jIDR8fwS6kZyuc34WgDpG16LxLCeNuqKeu3W8z2FUZlQPYIBZ5V6BTmcs+7iGPSYCsP6AZj8tbdONcgTrbwtUX8tkLwfsj/Ah0skK8dROcCZOUeXPgWNAvQ4UdKwd8HYGEB0sP4aA2pH6OfbzPPLEXhUHGEDoQcFyhRXDBfV9YLTzCGJW+Vkkytcaw+hHtrcCWFZwJYSkM2GsdZqb+ZXmsKFywwWjiJ8AY0z9F6jSIBZruM0jcQ1go0TmBwko2ox5Y7TC3s05qOWJ1ucuXKU6g8w0MMeLdTPqSWRxi6ko1Mgcnrcud9PryoGd3eZgKzgSVhS8IzygoyQ6zUXBMzCrViR2hKsfmdTEoCYYVU4Em1nEUm8FdFKYtZdtl6tYniusJba/BMZt4DQBRAPbVkqI9hM4BmZPwo3ZGFDI3UckheYKML5xW+5gB81VX4UA69PnzqPMw0rYIT92BBeUmi6/2KyoDcAtYALQkWklA4FLc4F3tG8+CCES7LYP4genUJkk+XyYGSW6n/EbT33cABWO0imULtKhouQRQg4QJCCFvgk00Wty6CzxjicGFuJ2IBDvPLN3NjeG+ojYKPsbi78EbOmw6FkToOOOGp5hs4f/ivM4g6pLTB9SwzWFdEBQ08rncFTd6Mb4zI4xy6Q4Lpq+hyj7R5P3lcoycruORu5ucWmE/neCT5Sx7UEbPOulGPRbCiVqFxZTNbTtk9WxqR1Nu6zoXw1XNweWjsWZlaXsVhRrBWTiHXQxvik7KR7mAI5DbuvprBMLehP1cmmInsJM+ysp3bAR4uFNa1eSCERrEzK+K8NZnN1KBX/k6i1piVAGk5Rr+Sm/dSn4HNITyVwPfW4JkcrmJyC3VvdAj1jvWRnB5a6fgOiDxeMVQGZI+YuN4J0EU5QsqRRsafTnui2FHkGUwV+O4cflCHYhXxZ5GogNzjk1MUq6uw9BzuwHF0ywFXgEVoH4HBLNIa4II28dVFFrdzLnobtEqLAhG7Q4dAvbCLYKukH2y1Tb7xSAaX+rAZQmukZCJ8Tg9zeeo7GU49iM+u4iOPbl5F8gg5uokUAbrxejQaw6gL+afRmSsICcX280hrCsdDaK2NNur4/AJdt8LIL9Bu5zx/4WPgUmqxNa9tehv976qFIYEvG9LKrkqfmQE5WC/5RTzgrfJzOLbW7bsCaDn4lMCm8HlJy7xl2x3FxqnxcAT9pslp9rC+iSCy50jNfmdiSd9uYUbh2cELK0I+tAaxOLAu21qZTO20IelDrwW1UZkYLaeTJbS8yOk2/MQq/McBDCPbnkBsey6HZuSuJ5i1n1EZkD2gJfDOJjyX2nj0LDBXDMg31jgQjTl/8iiuOAkbBTKYRXrjUvJwG5UMlQIJO0hYg8ZpZPUxNDmE1KZQUkgakGXIuI9cfZ7WQOnpkDAZEpNTiB04L9ahGHvzPOoCo9hIfd65aRfB6YF1VIYhtDRmdfGvk80/jI/rFFEf8SFMvxGd9mjokc1D0GyirSaa/wXMdpHeIai9E6Y+i6YZkvaRKMS11lF3gkTuJ3UX0ZU2eXGe5fpzvAEjCVYH/TrEY0tk5lhfxaQiMzEGFxNYWbH27Omatc4fcUaMczUw1rOjoZEjXU6FTS9oqqy0IB04VjKhiWOpKFic8RwfKFcK69KN6hjTfG5ewRUHy+WFPMmjhGKeRxBB1oQjBay1QQewUbOOUhGjc2wHMBDrxE2AqAP9EXy8BqsL8D0hvC+zJrOoPEaD3Ko4KSa3kFMyp3P7G+1uBZUB2QMebMIPPQCPrMAvPG+eyGUyouIyvtimsTViEHtk+oCFBGmOC+9H66cg3zJ69Po9aKdAyFEB130IvzBAko+hrRy8R/ubBNEMYfs0WXaF2lafkXrCqEwaFmVcrRYSNBTu9dZz0BvCct16E/oB3OPhT4Ml1jqvw7d6uDjA04HUoXoMwnnEr6D6BAQftq4nzsI4Q6fego4fRGop6GcheBxZiJFuio8UNg+jtcOMZrfZ3riHuLjAWuCJC2ubH+WQxCaKFBYl/2lZESlKb6SDddWKt5DleGifeSyyzwQ5SCRMj4Wmh4FTwjKXsVYIWQFp6EjwjDaVmjiaqowdNFCaHjadzesseMvRjBQWxHImeOPPCMpQxpfl2VTA9SEbC51A2QB6JQ+KmzGj1OzYzNBGAb5ms0TvasATYwiGEDeNNCePbPtPSMm7AjwcwIcL8z33IyoDsgecLeCDq3BpqxxQc9AIC86Ol/myjaf5ZLRK/8ADaD4iODBA6gLBu2B1AZUPWOZ16ZtgbgvSJtBGF2NogchbjVk4vUAYhkSjDuLquHRA5BNysaGWqDzxQyxUmHbm5mc1eEsfliO4mgpBqEQOMh/QnL+PfGZAMeijXUEab0DbHcRNQ++AzZCPlqEYo8VBkCPAOvi7obaGbg8hfR6CBH9+YPyKsweQxKHuLXi3zHrtHdx19S9YiRLGsRm2u+qwVMCpELZSy4nkZeerlu2gU5j2bd3B/WKh2GfGkGBEPYWAd8plL7RUmHFKb9Y6RAMXsF0UdCQnLTxaE+qxsq4wH5oBKpown5nR6qoNDm4X0FHjR92mHDgsoDUN0RAO9uDKlBkGl1jJe6DgRNGGkvbg6BTUr1iYM+jY/q9vw7ERvAm4Ogtbdej07P/FYsn3aYGvKg3p2h7Pw4OY99sHVnll9V5uFJUB2QNWx/Bzz9kcQojNgaQBdLKcfu/THJE/4FJzAXUtoEDGisxk6IGT6IUfQeYVbWzg+iEaOehsmhuRzEMzRAKHdC8Q0WA89nTGT8L2WTZ93+gEJ+VQZ7mCsdgJtFi3BGVNbIZjKlfWnCMeOp4LG6w3jlEUNQo6EF+A5DkknobmLKpNyK6ivWXkwNch7a9ExxGwAn4T1j4EvR40L8PCMXRUzu72l1BxEC/jxjFFMM9yLAxywSmoKJsCvi6seDgpSu5hEMDZsqqRY+3l/QS+tmmERY8W0EBoakgaNFkt6mRZTu66FBSMi4BgxbGuOXmjQSvIibIxsQPnlZW+MIiFoOZo5AWHCmWYwXOB4J1JcHlvrF6pgnfwZaGFpScu2aDiaQ/ZAGadsJ7HdIMlDg5myGWby/mAhCGME7ZSz2ysjFNL9F5p25zRkSEcDmBu21T3fA4rzlreh2ryDGt76EaFnaniOSwxm3N7vJjKgOwRuZZi0Bgl4EExHdhngxHB+HdonFklOPwNDJvfgrZivPYI14dQ66KhwsaKeSDuAlI8DHoEP9eHzStIZ5qwWYPGcXx4nsal88T5FQaSWmnT22h4FOxwb0gIl4Zwf91IkdMRzDk7cdXB1bxJT6dKrdfT+PAQTM8j4UOQH0Xql9HxaWS6gGAaVhaQpkPjZeh9AAmX4VCGjg/ChaOw9QdGF3/yq8DFaC9AoqMQfIYozpjCSHRytXLoWqHIdkDgPHFbWVDTev3LxMhyZgJ4kzMSnD9JhF7WYlz7Bnrh30M7dyPtBk3tMrv6Kca9n2HoTxGr0HEF/bTPsBCCsoltnAvbwDg03eATheAboIFyqPRmMrUeDXq2f2YjOFX2rmQBuFDwCbTTkLPyNiT4PoroTSyHY4pwmnF2nij9NEX+QdzoKeJswNJI6c6B75g+8OVpkIvG/zHCvMbpCIYZ9AubL+qU59BNt7QDZ7HffZCdrt3XGi9rQETk54G/Aayo6oPlsv8T+DYsJ3QG+O9UdasUoHoaOFV+/eOq+oPld97GDifqHwL/7EY4Ue9UTHoQCmceyOXM+hxahdDVhLvHHyY//zxFrcWlpdcTUEfmjpNmfYb9dVRqkJwG10CCCM3qiGZosoy0HVHrIHm+SuDPcrj7BKv5BiPVzzdSBVjydCIS1QhNmGqcw6huLdMbmcN5QVD6NMh1CV1ewc0+jm6FaOc9aO2QTXoNW4h/M+oipHcK4rei7ZbdmmlDpw+jAJFDaG8ZoieRcQFnTsKR70YOTxF0V9DeR9nKlbtU2Egg68BapAQjWJgq8IuWcNxsW9j11UN4esXupjMN4XIhDJN72W79SzaPPEwedvB6ERbm2axNsy6LHOu8hZnNn2UreT+10TadAiSCosiRGEa54FCmcyUNYb1wFImizhOkitSgmIV4G1qJHchs0iWrkKXwjECsNZbD/57V5v+En50lqjncYEzdRdB4iHz01zh/5QFm3R+RFH9MO16nNfREfZOdnNow2oBxDM95yLbLqWe1XE9f7cKfiCq9qEzBiyABzgOX2JsX80rAvfxH+AVMhnI3PgA8qKpvBk4DP7brvTOq+tby8YO7lk+kLe8rH9f+5r7C7lmLUQijGoycEqnyJoWGK+gmZ5ge/s/MPPfDHF75De49/5cE648QDRLCcYRwBHEPweo6kj6PSISbfROyMU/YTaB7mcXTH2J680me9yl9yju6GumxKhBYX0Ti4GoIvbaRAh8ohFqo5DVlSj0uOkYWH0bdNH7lrcj0vUhjw0In+nBXAS5EGt9gE3KNC9BfQfQKciAHV0fmOjCzBQdmQE5AYxaJ7kaibYL+ecLLH0DHH0ELoeesfyJ0wnjo8CIwY/yqGw3TKOllNvk6Mwv3tWAlFU4lB7g8++/pv+5BsumP4fM/wNdOoeMcHZ5hNPcXnDl+gHNz/xfD6KcI3Qk6Nc+MFoQo7ZHSVHMPwwAaqafnPJdzz4Yqmw0biKuP4VAI8x2o1yzBSWAG+VIB/SGM5bu4PPfjpI2DpPWY4bxnvHgRH3QJx4560SJofi394EcYB9/JSt6h3xdyD6sDu7NeCW1sIE6ApoUbjdA0eSYaPf9E4Du5sYvxesi4fbwrL+uBXE/aUlX/eNefHwe+66V+o9SO+aKStpwYD4/dCeIADkTCoiqhg81M6cRKkY+ZltNkozMM+/8VCR9mrv1e/OKb2AruJe83EDeN+Cn8eoqySNy/TPvShzg8+C3eknyGJ/2IBCt3BthJLuX/l9wYxo7kRn6zlsMnxjCvsBTB2liJAseBoM4l6cP9U2jvGHIoQvptZFWQaIhefhKGIzjwFmgsovoRm1HnLEIE2w7aCcQrwAw0vgsJOri4BR7qz6T49Z8n82M6TtgOhDBW8rGihVCIadPUFLZ74BvWnbm8AgdqsBTD6aEja38vw7veQTF1Hs2X0CMpuHvR1TkYn4PpExTDKZJgHq19L+m4Q67/ipaeZS4yBvznRsLYQzNSul5IAkeoBdNzIHXIB3B4Cy61y25YbwnpgTONnEMCY05weuFfMJjxaG2IdBowHpGFS3SnI0IPnW5IPZ+n55rM6z2Mg+MM/SnmNWM2UK6OTazrSgsWayXxU804VsiNltADTwhcoJxe3md4JXIg/xD4tV1/3y0in8G8s59Q1Y9iMpYXd33mIi8hbbkflOkmHdoFFscFCu9UpVcTnkiV3Clh4oixlsnNQUFDNpktPki88TEY3kdn++vZqn8taXoMZEAYpiykf06t+yssJJ/ioXybU6GnF9pJN2JnYlS0JDoOjNg4KWc27s9hfRbWM2XUA0kFH8EgaCP1Ns73IO2imzkSgZ68gK56go13wVwL1RQdDSC9Aq3DBP2vwQ0zivYQxufRzhK6oLAVI8wQJBu4i9vU039BGp7BjYVYzFXPCghDIWgqhbewqhgYp2e7BifX4C8SeHsd/iqEMD5Bsvj95C7FF/cBdeifRaIL6NICbDwA2+egGONnlDRrkMrXstC/SkP+EzXOcFALemWeoRcKrYZSo0BCZXvbWOaX1FTtC6z0vU2ZS/I2OPd27/iz+vez3r4LyRU3C4V0QVM0nyVrB/jNK/S7AZLOEQYNkjwnyju4oo1nkziH14mwgrIxhIXAuEMGBeRpSdiUWSn598tE6qtI9fqq4ZYMSKkBkwO/VC66AhxX1fUy5/HbIvJGvpDqAV7C69oPynSecrCrbNFuRXB4BH8iyihxFGqJvHvLhOYqSqgBWijDcEjXPcbB9ce53/87LussqWvTdAMWZI3tIKUmypuAP02NeTsOLE/QK3k2AqzxCbG/EzV19jy2hO7R2DohN7yyknk0XCHI30geJEj7U8j6FXRpDbe2AUkdf+JB4AzS7iLJBbTbRdzXIvJG5PJHCZbAH5yCtIm0jxAm9xKuXEJXnyEa/+8EcpaGKl5ggNCsQ5yGTI08FxGiWkEbq7ostuHEGKYym09pxdBwQtR8G+ODoK0MRqD1BcieQuJT5nUFA1g8jOZ9/FoGtZikXcfzThhdYpT9ARfyZ2m1C+a9MA6FRgYLc0rkgYEluhslh4ZXY5f3dZDEPLx6DFrM0Wv+LVyRETZjiq6HcYpGdfAheun38cMPkdEg1x8kzmt03BV6DoZBi264zXCjYLEN82PraE0aMF3q8641IUhNkydXMyK7m8kmF8sdeeJfgz0bEBH5Piy5+vWTZKiqJpS6Nar6KRE5A9zPTUpb7hdMwpiGQieF86Fd1K1QGXgYeOGZwnFvqCyJ0MRzwQutHIq60gyVcT5mSVZYbVxl1FdWw4DCC7O58GjgWSvMOPgy6ZLx+XEO8GbAnLN2cUntgrw8gvnIyrlx3U7H4fg0jf45koU5kAg3O4UnQ6IptD+LxNtocQ7dvISsXSIYJ7CQI7qFLl1E2odx3bcSr+V0gpTm6A/obf0JQf4YXscUKuQaEImn8JCKkgfQHQeoFgRDyBo26XoyBe3DoyOTL9xUaGUBz9XuIi/Oo8PT6EZmPfmNt0PvXUiYIlkX3XZI1EM66zh/iG4+Re/wm5nbGnFg1SH132eW08yOFR8oUnckQ6UbwvFpSMsJ2c2xtb+3piDdssllLfsyUnc/Uf0YTeqkNUfuc4r+PMF0QCEj2LqIymVUXk/gejjxbI8TRkFBRINhEqBhwXSiCMJarIRi7GFt4KizcCYrW/x3n0+T2Z8Yu3Hc6UZkTwZERL4Z+F+Ar1bV4a7li8BGKWV5EkuWnlXVjS9WaUuHySBsJuYBxAEcWVRW1x3kShslzaEWC5vAdqFMR56judDxsN2C5rYwShztumm8vNkpS4Fab8SuPoFJjFx621AaFlfeweLUvKEDIUQR9B1IIRzIhKTYpL/+n8hmv59hu46mC7jEodET6GIHNp5Fo4umKRAdQeYWCIttao0eOh8QPNNnfv1R2umfQfYJ+mxROBtsmXJKXAhTeFZwDJ0yzh1JoYQRBElIbdajLWUms4t0SeCR1F5/wxjCOGCj8QZ87SGY+SRceRZtHUWGLfTqBvj3ocVnwAVI/QfgoMNPFfg4QHJlM3oHLn4dhwbvZmv939APPkxbjfC5FzpaDc9WXwmykg923gxH3oN6UianS4/gSZ2j0wjw4Ygkq+NafdxcCx8BFz8F/DkqAaJvpl3M4+UxCr+KJ0GCESJC5IUhSjtQwlSInNI7ABupsex31HJZV8S6YyfhsGDHO8OMSAcjIrpTDcmNlHE/L20pIhcxacsfw4YhPyDGjDUp174H+N9EJMf2xw9OJC+BH+KF0pb7NoE6QVQ+CmdDU4PQhq3aISzOeKYKSLatL0FbytEE6m2hmzq2CiVDOJAbx2WYQB4oU05pD5TzgWXxh9jJ04xM6lBKX3cyzeoLu3NSDqIlmGu8NIITwIcDZSYXmgE8PfpzDj5zlCsHv5NxAzRooHoRhudg6goyWEJbb7D+EzegGPTI0z71pw9wZO0PmCr+lDfIOj014xaHQhPYKid4L3roBh4CwY/soolrEEiB6wthQ+nGcE8KjS2jZNzy8EQfHpoKUY0sw7m1CIffgQRvQZc3ofsrED8PeQ+JB6j/IDr8Vqg3kHEXFyvpYpuV1pN0R0vc5f85i9lTdLNVxplwPFfGCWgLBjHkAehRyPpWzfKR9YLkiQ3dbYUdltdi0o5AXcnX29ZZ1hih4RqkDufvxi++g0JXmN1+mpxlck0ZJkPEO2o+ZIgn854EmMqFY92yN6ZhXaMLarmtYWGGY9LenlKKkjXh/gb8zvr15SjvBNxIFebvXGfxz73IZ38T+M0Xee8RrOfliwYeK6WGQBxaN+iSmGpYOoA1BCd2woSZsi2wgJDkQhrDxihkOBCm6wXTNU8rh6VIeWsN/ig1vssUs8SbqQ181Zx5GUMt8y/lOhTlVOsghLXM5BsepZxFaXhqIcwMoDf+bxxbVy7d9Y/pF4dQ2UL0uMmguYdwmx5xC8TbV1gYeWa3/j+y9NdJ+ByXg4IoVc4r5KKMMvDOXKAaptGaAkPnqDUKnDo0hcjlhLNKMAsPpRCvwykRUqeot27McVYwk8DVgzXy7CSMZ0ynIPsghI+hhLggg7CFTj8I8QDmz6LZJdQ/AeN78J03o9rg3MJBwtWv40j+qzwpwnpHeRAlHwufDZR2Bo1z1sXbw/ZjPYDtwJoCgzhmvePIpkKSGjA1QuYdsh0gB94C+b244SJB2qYY/wxXi88wJduEbkisKSPNaaBEgdAXqzwVHpYzm+6dq0E/tnLyjC87U/3OcOGkundlBGvJnT3+X3Wi3gQCbBLTY3cNB1CGEFuZVRaSoWX0JbZx74UCInEMHIwzz7gQCnGkuTBUxVHQ9EpflCxXlguTANiqwXZmbdZQMnKrxc0TCObmFuwQ6Wx44zvtY8p4jVjY9rCeCWkizNUGFINf4eSzl9iY+j42Oq8ji+8heE5x2qGTdmkMTzGf/jHLo09wKX+EIB6QRYrUheeGwnIqTOUeF4gNn+VCF6EZFNRi0MJTjz3DVHEI9aYQzinfdAnWBf58ZHmiCePYkSbM1DPuSx7h7DNvIjsxS1FE6HQHeCdoF5ERRIfR1gE49jAkZ6B9APU12BhCXEf6iuYj0lqTjdZXc7D/36irUgyFAcLhQFgawaBmDWYyLiUaFLYTGGeWN+qnQzIdobU2BQ7faOAKQSNFFqaRYoogF8LNLqP+xyjGz+Ikoely+vWcWlYmR72y6GEcCkXd00qNvsBvwhtDMyLqrR+mXx5TV55nKbBxzfG+E1EZkBuEAMcb8DcX4eoIPrBm2fMC45uQctIzK6siWRNm2kqUC1mmDBpKI4dRIfTE0x4HFE6IIpgJhW7oCWI4pPAXuTUijV9mzjvHSrsTSkDUhsFGHi46Y19fGHlmmhAUjrzhWWjBei9lI/sTwvWPcqh3CM0OMnQdBnkCrDBI12m5HklNSMMMzQUZK4uJ0s2FulPGBDTEMxOYePNQhXUXoUVBRz2LmXlQbfUcSYR7rwgXh8qfZ2ZsG8DDahfKlIcZLZhL/pLDyd/m/OAEOvaoPolGW3D8HyOjGLIe9P8K1kZI443oMEZGB2H1GDK9iZAi+QGoZeThPQzcDG3dhERZrsPAKwtjteOTQq1mAk93NeAzmQ3N9R1EOiQah/S9IG2FNY+vBch0jGQ1pHiU+vAuOuMnSMLn8AzYUEWKgqQPuZigeQ7UQ2G+UFp9Kx1fxcb414CDDuYKGz0IC8t7TGgOHHZx5ty5+Q+oDMgNI8DG0i95WM1fWHrTAHCWFEqddaUeL2BzC/qitAOIM6WvjoYqWeiJWsq9ibDslMALwwRO1IxrMxiaF3EjN5/JCTctlr3vTVqyCwtpVr1pwTRDz5EIxjUYbxvVVnMqIc/OM0zPMy8xkdh4vAOuemiXyb7IK8MQWpnggdQLTafMAN4LHmEQCJrFzEvCiahgsYAlUY7UYDnz/HbP2Mkm27QNfEjMSwvHsBTAgc7nuLf4EOvDI3RnZ/Ebj+GTVfzMV8IoxPVa6IH7YHYD+suQL6HZMbuw4xmkKMd5hzGNYI5RDEVaUI/BZ0rWtuM4XYeVgdELzNRgqQUPOzifQhoCeglf5MiWlW8dBSQBGhWIbxM03k48TPHJKTTNyVCUnMzlxEVBEUELpZNA5kIG5Gx5qEXKgRzy3Fjdt8ru4W5mx26SPA0w47Li72zjAZUBuWFEWJfnH12BtOwgDLB2ZEfJPB6Yy3myAUcLuFIIjVRoOCUbwTx2J0SU+lDZFOG+EEiVubr1JlzpW4lveE1vwEtBAQTurxnj+vOpLcuxiVNfikFd8jC9BV/ulK1AKcZC5OEsjnXnQDLiQGh7oSUCOA5429JhprTwLKqQBXDWB6SFEnplxkGI4kj4cl9Qyz1P5nDfDDxXXpi7jccEo3IlLwDPjyGs9Whv/zqHlu5lVLwDnX0bsM14lECwjixu4Uab6NUzaH4ScedQDiNTEdKIgAQZjZCxUBRnafkeSUupl+3/eQRbIwj6ln843oO8bapya7GVw6cyYSq4yPZ4GVr3I22HLq6jvQLtOWjVmE4uEW/PMO2/kW3/ixQMyPHEoVrjYKDUCiFCmVZP3xnHYzNRpmpm45KhSYDOhsYJ60qOkKw8bsk+MB5QGZAbRoKdhINiRygJLIGZl+VUL8aidXIE7/NQD5V5Nc/C12DhoHLhgrKQObYzzyCAQaC8LoZ+CKeG5i1kpfdwMyfQloe/GlvHI7KTXPVYH0melbolYxg6z3xkVIhF4VhAeJaUWi4sCjSCnGdq8LwPGBWOk3judcp06FgTz3YKXyc5RwPlXC6cqylzwELqeSqHp8uYfi2B7RSu5C/tTSnwVAZbW8qRxjO8/sov0YoKzs7MM2q8AbexSqGfgc4QtiI0PoE2p5Fmh2CY4S45pJlSBOfQdI1A72Vh/AQLzYx+bt5cGlhTV2sa/NBCl20HYdd4SjZHcNhBKMq6dun4jzNo309+xKFzLTS6DOtL1FYHDNZHFJoyG27TSqepIRQoNWyMoT5SExn3UIgn0YCi7khESUSZEnA1E9JO5sCvW7nZpzsJ1NWbPP63C5UBuUF8Plxh58AWpRUJvFVBQoWpEfyuQN6EzixEG8pYhLsQNrbt9RFR1hrm5q4HRlbTT6DrLTlb6PWV214OIzXSmkRtQney3h77vbHahT0h0llACcQzKzArO3H4tzWVb1P4nAdxntflnovAKTEtlA2FNVUul3wWo7GJRz+SW2gyids/exMSBltYiJjlCdn6hzkaDtguvoqLMTTCnFH7IFqfJmivQ9ahyLcgVmTzOaT+IEE9IgwSinRMa/Q4s1O/ha4URAFEmRE0jwtoeIhjqDUh2zL6x9rYFOgyZ0neosgg+iPU/V3cGlAUaJSB2yCPQ3TxOFGQ0VsdUM9ifBEwJw4vBdse5lVw3rOOleEdyjATGs4RaEGtbl24KzVL3A6xalDOjj7NZMT/TjcilQF5CQhGBFz4FyqqTaBlK6oAFNZQdr68u2RqMgBvDOCqKG9vmZu+1AB1ahylmR2Ap0LolxwRmS89mj2sb4F1dUI5ZLtrnSePHGtM2vLWHixYl2Qrh5kyAXyqD18RwGLT80wGv1fAOYwuYCm0ytCXR9ZP8ZGk5Dl1O+73XpECayMYp33G8ggPdJWZaSVfWOR8/HqC7UP00jnScIS4PoQDqK0Ca8jUMZpyF0F2iJMb/4aof4puanmpQc28renEjNRcBNGGtf0nKfQSkMyqIo0adFOlk/wl272rZPkhGHp0bh7NlWxpCseYUS8gu3yBml4laihxoPhU8YERNicCbbX/38dTV0ekIQOUNPUENTPy6YjPnzsx1gYwOYYDKgOyrxEHcKwN3RTWR1/ohn8+lHHmjYyAVTHS5a7CvX2r+7c8pNuw7KzP46kcig5MJdaNeF6tc5SsVLfn1k8cvc7r3Yakia3vROWswDyYAWYYRwX0B5YfmGz3gsLbGhZivaMOz5YcehlmZF+JdR5hbflbYZ9O/gnu37zE2wcP8tj0Gh/qnGfkDiGjKfLwKE6WqW9sU8yk1C9fJEgG3Ot/jXn9zxxKUrbE+GDrM8Cqref0ls2irNVgoQ/3xNCswZOZhTmzOaQRdN1lgo0PUl/6HsatiKLowEKCXxuivQxfF5xv4kIlyJQRMBahnilbAIFjAwhUadeUiIJm7hk1HEkBszXPWgwHt4xpvi3wjhi+O4JPxPCLW5YQv9NRGZCXQFZYi0Hudy6866Gg1B5RCx8GmeVDxgV8Tk3W8HRsv3e1dPMbazZJuxzAcGyfTf3OxVw6N5QFnleE82F3+DVd/vYmO5QENSZ3TFgrexB2h1JPK4xGtq7P5HA632ly2ovHdD2k7AhJDXTM85zl+fEyX5M/zrf3D5OFxzgXLtANFojXCpbGQ57feJbh0lUOyue4d+sx7nZjBhkcbEOxaHIXzznI5sFlsNSHsGGNZNsCF6ZsNiYH7gktnPlclNNKf5ex/1tcCqbggCCSQngZ6RxFqeNaryfsv4N6/mHqaUoReGqBUsuVLFa2vRJ6Ry+ByNVYK1KODjxbbaVVJpZHU3BgA857eGMMBzN4Swy//Artz1cblQF5CXisZyEWI4AZFDsXNpSt5Ox0hA7Li7/uoZna3XygsKo2p5JEZiQ0tzvd82rNYr4oY2DdkWrcjcmY9yvVU+SxCyeAz4teT2gJJifE5nW+lwKnS4vx/KvU4DQh1Sl0UpXwpPQY+z6d9AINPs0UAUdjx5YLyHPlaJAyN0oI8oLlVDnXsNGCUCHaBDeG+ZElkdNpOwYbQ2NPj1PQ2Mb4nxjBR0J4cwsafYizP2U0Os3l9bcR9gRpFhTjeeJWl8INkOYhmrV/xWzwbxkUv0ii24gIDQpiKShiRcaABHhZIJV1knRMOIKsBVFqyfOJat/pHL4uhMe7lmfaD5A7nVXwdo7zTzyAuHQx169x0wUrv9XEPpM5m0uJvTFPLYmxi7vy1i8OlrGaf+ShG5Yl4dyU0SZizxOqgElrM9iF1QktT5K/AntkEn7tNliTi/eV8ib2ggOYbu2aN8MbRTsr1cQ8pI6auPVsYMNmg9CMc+FNpiFvwsIQLnuT1/Q1m4Sd8tAIzDANCshrMO1NprKrcHpsBvSgg4dD+Lg4huHf58I9/478cIhf99TURhOSsEDSJvWrcCT9HHn3J9jK/pzOOEPijEGWkIceRKwpLA0IBWYpqNcK3ti26tDy0MrctRwebEMwhg8lJo71WkJVX8zBfklUHsgNoMBaxK+XSJ1cfX6S+CyTiW0ppysVtsvEqDjzUvKyY7XwFu8n5fvFzs99wXMrgG8+Bp+4AudeAf7+6xmJ22k4JtjAunFTZ8a1KGMolZ0J1e3S46u5UlQrh3Zh2jGjGiwm1rODL3M8WEjZVasYDbDcUziC50t1uW1XCnBlcL6AdwKtwJMX72Nq5W/Tk28kKQKKcEi+PY04TBS5lbJem+MI38/6esHAPYqyjY9zNFXqAdTaSi3MyXIP3hHlkCQwVYfHAwtbv60BB3P4JTWu1P2CyoC8BMqGTlxZaRHdySPsNiJFudzvup0nYl2NA182TGFt5qGaqFEmdrJOyHwLfWGpeDciAA+PbcLKXuq7+wg58Hi5IwLgwRBWCrsjT4tpoVzAaA6SsmEuwyZ722qC2ttjG4qb3FMlt/0bRkBsOrmDtBSNiu17QcnPIVhI+tkc3h3BH0qfxsaPkWf3kSyeIGkESJAh7YjAK1qLKGiQHztOrf9uMr+Cz/uENTWXLvEoSicEBOo1TzwNxRbMJ1AbWM7syQw+jclb3MnDc9eiMiA3An1xujlhx9BM+DpELKbdLpOpqZShwoTwQSxsycp+j4n3sjvJuRuN8nFma3+dXHvF7qrRkQD+ZmyTrLMRfGBoSeiUnX0PVloeYmGPK0uiDSxcnMWS2/3MxvWhLDn7snzryqY6Nb3cw2IJ4zcArVhppE/THPwIfvanGcsRvKaQCC4P8a4g1BrbF6bppH2mdMTlMCNKhABBAiHKIEbJYliMYTq1m8qnR1byLYBPe/OM7gQv8GawVyLoLymEmBdwbZA4yVfoNY8Jc1hf7cQuCnPHJydt7s3ATCou1xqP3a+FnSTnPvJsXxFkwPsTeP8I2jE8F8Nj7BjR6+2PiTEfYeHQCvC1LXhDuSP7iXkfk5AxK8xDmXGwVJbjL4sZqXMJ3CfKuO2J3B+y1PsODjz7GzQvbhJfTgkGBVFvQLr5LI3xL9Nz7yNz54kYE7qcqaDgYFOpxUotAMmFzRosprCZwUYCW4VVxN4RmcHbb6g8kJeBYneL3Xe7a99/wesynBmXb0xy1JM7i8NGuCfLVK8xPrsg2AGaF7tobpf2x+1ECvyVwmcHVqm6WSOaAH/Yg3cFsISVqDe8hUOJmqGZVQhCI4bKyyHGqEx4v6ewxq5npxSGTxONf4Cp0b2k0dvINmdQ9cTJY8TuGebCLgmQZcZIJgonRClKdri4gLAHRQsoYHtkIwuJwrnsziUNeilUVZibxKQfZLeH4HYtm/w9QcBOnL77u5OH5/rGaXdlpIWRCW3eUXti/2Aa+PrA9mHbwZ+k8HdrpoD2nMJ6aro6A7U8l3N2TCIHD8fwrhk4HVhYk4xAExgjZC6glTmyTk6QBtR8gURKPQyQoccrzNc8YQa9huVzilLI6lngqS1Yv6bX5nZhr1WYlw1hROTnRWRFRJ7ctewnReSSiDxaPr5113s/JiLPisgpEfmmXcvfJiJPlO/9Wym5EPcTrjUeE1wbyuwuw/rrvLf7wTW/t9sgUX5mgFUeKuwN28DvFPCB3ES1/34Mv5fCcB6+ecnCh6+vw/0O3hrY5HLmTcfmVA5P1q26M9+CQx2YmoZ6W5Egpx2nxKknKzJyPHMjpZEUJj2aK8OxJdLvDuwG8EQCnx3D5aGV/h+QXXwu+xB7VaYD+H92KdD9IYCIPAC8F3hj+Z1/LyKT/bPvlenqWHLu5SzfboPSkJ2dvHv57s/u/tvxhQdlt7GpsDcUWHPc72WWuMwcNMcwXLP9PZPDV7Zh5CyUmOS7DxWwMoILIUw3YGoO/IwxQN4dWaXNA1M1Y6GTCIaFUh97TqiRSA9TOL0Mj6/B1TFcTqGWmCH7inB/5xH2pEz3Evh24FdLeYdzIvIs8HYROc8+V6YTzHhMXl+vGrI7PKF8HlxTnv2CnMkuuPJ/BIHNonwp5jxebTytcKaAA4GpxsUFXPSQzdhA4P19eIKyfE/JCta3cYPtBvQiCOfgWA2KCNKuTfYGY5iP4aTCoA+rBfylt3b1XO241gpT4fu+JmQjOBHAsyWNYrJPPcxbMX4/LCL/AHgE+OequompzX1812cmCnQZN6FMdydCMam9gJ2d9mJJ1etVU7hmOZQnqJQMYpgBeuM0fPNBeHQFPrJVhS6vNBQz/u+pWQXm44VVQoKu9X18Snc8RRwM6qApXO3D1jYcbVmlxs/CVgu6TcjPmKyHV6OSTDMjC9p9kxljF8HxHPwY3pdAJ4HPqiWH9yv2akD+A/BT2L7+KeCnMYnL63n3+hLLr4s7VdpSKVm+rrP8ehso13xmd+I1AE5EMB3DY4OdnMnGGD65ZW3V3cp4vGp4egjN2KoxRwVOFjDMX5ionuRCrqot76iR/kxtw3jdhvQuDGCc7BzTUKxqd21idHLufLaAZ0fWs/LFgD0ZEFVdnrwWkZ8Ffr/88yJwbNdHJwp0N6VMd6dLW+6eUdltJOoln0aZaP88rt2ACJvruCuAp9Od9z1wJoEzy1R4FaFY1yepnYhvj+CPM/MYJhQHYGGHG72w72Qlhbww8qer3j4/IQAKsEa0l6qqFHzxGA/YYyOZiBza9ed3AJMKze8C7xWRmojcjSVLP6mqV4CeiLyzrL78A+B3bmG97wjsNiKhwIkpOBq9sIoyeX/33xnWjzCqYzIIr83qVrgOLgO/mFl7/DIWpk4QYonzyeiRx8KNZW+DeruXl2yRd0RJ9rXEXpXpvkZE3oqd++eBfwKgqp8VkV8HnsL26T9V1UkH+BeVMp1gI+ONwGZgCmA8No4PsrJhbNdjwusxqcLkwGM9a3WvDMjtw4QL5XrIsYTqxANpYjrIm2oXzu7vfakew6qRbA8IKHVLa/DgPJzbNA2UCR1ho3y/y045sIaddJNcx7VhToU7D9f2/Mxhx3Gd6+fC9jNetUayCi+EAFOBlfwSD8MEyMwLmVRTZhw0r/EsUszFDSLTJal2/J2Paw38JhbmpHxxGY9bQeWB7AG7L/5QrP05ZeeEm3TOFewk1yaxcVjyToy/1ILlCnc09uqBVAbkVcakNf1VYgCsUOEVQRXC3KFQKuNR4YsXlQGpUKHCnlEZkAoVKuwZlQGpUKHCnlEZkAoVKuwZlQGpUKHCnlEZkAoVKuwZlQGpUKHCnlEZkAoVKuwZlQGpUKHCnlEZkAoVKuwZlQGpUKHCnlEZkAoVKuwZlQGpUKHCnlEZkAp3KAT5AnbZCnca9ipt+Wu7ZC3Pi8ij5fK7RGS0673/uOs7+17assJrBTs19At0/CrccVDVl3wA7wEeBp58kfd/GviX5eu7XuJznwTehZ0d7we+5eX+d/k9rR7Vo3q8uo8buRav93hZD0RVPwJsXO+90ov4HuBXXuo3ShmIKVX9mJpVmEhbVqhQYR/jVnMgXwUsq+ozu5bdLSKfEZEPi8hXlcuOcBPSliLyAyLyiIg8covrV6FChVcRtyoM/nd4ofdxBTiuqusi8jbgt0XkjdyktOWdrkxXoUIFw54NiIiEwHcCb5ssU9WEUm9HVT8lImeA+7lJacsKFSrsD9xKCPPXgM+p6udDExFZFJGgfH0Sk7Y8+8UqbVmhwpc6bqSM+yvAx4DXichFEflH5Vvv5QuTp+8BHheRx4DfAH5QVScJ2B8C/jPwLHCGfS5tWaFChUoXpkKFClDpwlSoUOG1R2VAKlSosGdUBqRChQp7RmVAKlSosGdUBqRChQp7RmVAKlSosGdUBqRChQp7RmVAKlSosGdUBqRChQp7RmVAKlSosGdUBqRChQp7RmVAKlSosGdUBqRChS9x3Aqr2K0yklWoUGEfYxYIbuH7lQdSocKXKALgHgfFLQisVAakQoUvUcwI3BeCvwXGncqAVKjwJYquwsc8pJUHUqFChZtBDJwU+AZ3a+KhN8KJekxEPiQiT4vIZ0Xkn5XL50TkAyLyTPk8u+s7P1ZKWJ4SkW/atbySt6xQ4TZjzsF7Y3hA4DEP/lZ+7AakJQ8BD5evO8Bp4AHg/wB+tFz+o8C/Ll8/ADwG1IC7MQLlYK/yltwBsn/Vo3rs14cDlWuWxaBvCdA3O/Tr6mjLvbrSlldU9dPl6x7wNKYq9+3Afyk/9l/Ykar8duBXVTVR1XMYC/vbK3nLChVeWzSA14fwegfHgHoAoUAErHjoRzC8Rf3ym8qBiMhdwEPAJ4ClUu+F8vlA+bEjwIVdX5vIWN6wvGUlbVmhwq0hwno8phzMODjYgcNfDosLMAVkYuHEycatJUJvuJFMRNrAbwL/o6p2XyJ98WIyljcsb1lJW1aocGs4iOUQsgI2YpARFI9DkZt0ZKzQz+FszC1ZkBv6qohEmPH4JVX9rXLxchmWUD6vlMsvYh7TBBMZy0reskKF1wg97OIe1MF1YFrg0AjaKTQCmK1DowYDXuVGsrJS8nPA06r6f+9663eB7ytffx87UpW/C7xXRGoicjcmb/nJSt6yQoXXDh5YEBiHEDjwTaAOrQAOBXC8Ds0GNJy9v1e8rDKdiLwb+CjwBDsVn/8Vy4P8OnAceB747omMpYj8OPAPgRwLed5fLv8y4Bew/M77gf9BX2YFqhCmQoWbhwOWAKnDiQiaBUgAwzG0YyAA7UCtBR+9AFvDvSnT7Qdpyx5w6navxyuMBWDtdq/EK4xqm/YHrrdNJ1R1cS8/th+mcU+p6pfd7pV4JSEij1TbdOej2qaXR9XKXqFChT2jMiAVKlTYM/aDAfmZ270CrwKqbdofqLbpZXDHJ1ErVKhw52I/eCAVKlS4Q1EZkAoVKuwZd6wBEZFvLvlEnhWRH73d63MzEJHzJe/Jo5OBwL3wp9xOiMjPi8iKiDy5a9m+5oB5kW36SRG5VB6rR0XkW3e9tx+26fby9eyVB+DVfGB8r2eAkxh50mPAA7d7vW5i/c8DC9csu2n+lNu8De8BHgaevJVtYA8cMK/xNv0k8CPX+ex+2abbytdzp3ogbweeVdWzqpoCv4rxjOxn3BR/ymu/ei+Eqn4E2Lhm8b7mgHmRbXox7Jdtuq18PXeqAXkxTpH9AgX+WEQ+JSI/UC67Wf6UOxGvGgfMbcYPi8jjZYgzcfX33Ta9Vnw9u3GnGpAb5g65Q/GVqvow8C3APxWR97zEZ/f7tsIrwAFzG/EfgHuAtwJXgJ8ul++rbbqWr+elPnqdZXverjvVgLwYp8i+gKpeLp9XgPdhIcnN8qfcifii44BR1WVVLVTVAz/LTvi4b7bpdvL13KkG5K+A+0TkbhGJgfdiPCN3PESkJSKdyWvgG4EnuUn+lNd2rW8YX3QcMJOLrMR3YMcK9sk23Xa+ntuVPb6B7PK3YhnlM8CP3+71uYn1PolluR8DPjtZd2Ae+CDwTPk8t+s7P15u5yluY0b/mu34Fcylz7C70z/ayzYAX4ZdlGeA/5ey+/kO2qb/inHdPF5eXIf22Ta9Gws1HgceLR/f+lodq6qVvUKFCnvGnRrCVKhQYR+gMiAVKlTYMyoDUqFChT2jMiAVKlTYMyoDUqFChT2jMiAVKlTYMyoDUqFChT3j/wednv4y8WRxTgAAAABJRU5ErkJggg==\n",
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",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -983,6 +1023,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "id": "24c944a9-2601-40fd-ba27-f43567120cff",
    "metadata": {},
diff --git a/README.md b/README.md
index 040c4dbc1a81687b49831b2bf713605285b36007..18b552bf941a68b66daef07bcf80521093a5eb99 100644
--- a/README.md
+++ b/README.md
@@ -4,22 +4,39 @@
 [![DOI](https://zenodo.org/badge/173477371.svg)](https://zenodo.org/badge/latestdoi/173477371)
 
 
-# Image processing for beginners
+# Overview
+
+## Schedule
+
+- Presentation: Course intro
+- Presentation: How to work with python and jupyter notebooks
+- Presentation: Python installation and environments
+- Hands-on: Installation
+- Hands-on: Python essentials + exercise
+- Presentation: Numpy and image processing in Python
+- Hands-on work with notebooks:
+  - Guided walk-through
+  - Exercises
+
+Slides available [here]().
+
+
+## This repository
 
 This repository contains a set of Jupyter notebooks to learn how to do basic image processing using Python and the scientific packages Numpy, scikit-image, Matplotlib and Pandas.
 
 The material assumes no pre-existing knowledge in programming but some familiarity with concepts of image processing. The goal of the course is not to learn all the details of Python, but to reach as fast as possible the ability to write short image processing scripts. The course therefore focuses only on essential knowledge and is not at all *exhaustive*.
 
-## Installation / Running the notebooks
-
+## Installation instructions for practical
 
+Follow the instructions provided [here](https://iah-public.pages.pasteur.fr/bioimage_analysis_with_python_course/README.html) to [download](book/download_repo.md) the course material, [set up](book/setup_environment.md) a python environment and [launch](book/launch_notebooks.md) the notebooks provided in this repository.
 
 ### Alternative: Run the notebooks in the cloud
 
 The notebooks in this repository can be run interactively using different cloud services. You can use the badges at the top of this Readme to run the notebooks either
 - in the classical Jupyter environment via
   - MyBinder
-  - Renkulab
+  - Renkulab (with graphical support to run `napari`)
 - as Google Colab notebooks
 
 The Mybinder and Renkulab sessions are only temporary, i.e. changes you make to notebooks or new notebooks are *erased* between sessions. When using Colab, to keep your changes, you need to *save a copy* of the notebook you are modifying. The saving is done in your Google Drive.
@@ -30,6 +47,7 @@ The material for this course and this repository has been copied from [this cour
 
 Installation instructions have been taken from the [Open Image Data Handbook](https://kevinyamauchi.github.io/open-image-data/intro.html) by Kevin Yamauchi.
 
+
 ## Data
 
 In this course, we are trying to reproduce a workflow used in other contexts, in particular Fiji, so that you can compare different approaches. For example you can check the excellent introduction to Fiji Macro programming by Anna Klemm [here](https://github.com/ahklemm/ImageJMacro_Introduction). We use images from the [Cell Atlas](https://www.proteinatlas.org/humanproteome/cell) of the Human Protein Atlas (HPA) project where a large collection of proteins have been tagged and imaged to determine their cellular location. Specifically, we downloaded a series of [images](images) from the Atlas, with some cells showing nucleoplasm localization and some nuclear membrane localization. The idea of the workflow is to compare the signal within the nucleus with that on its edge to determine for each image whether the protein is membrane bound or not.
@@ -39,5 +57,3 @@ The images in the [images](images) folder all come from the [Cell Atlas](https:/
 All images were downloaded directly from the HPA website using the same link construction and saved as tif files. For example the image [8346_22_C1_1.tif](images/8346_22_C1_1.tif) was downloaded using the link
 https://images.proteinatlas.org/8346/22_C1_1_blue_red_green.jpg.
 
-## Re-using the material and fixing errors
-Feel free to re-use this material to learn or to teach image processing. If you think some changes are needed, please use the issue tracker to discuss them, as the classical pull request system is not yet ideal for Jupyter Notebooks.
diff --git a/_config.yml b/_config.yml
index 30caaad338eadd7ce9ab7c4050c7d79d267e6ff9..0530ad5ddfc8e7d7c63c9231cfa6aaf043bb9daf 100644
--- a/_config.yml
+++ b/_config.yml
@@ -1,5 +1,5 @@
 # Book settings
-title: Image processing with Python for beginners
+title: "Course: Image processing with Python for beginners"
 author: Guillaume Witz
 #logo: 'logo.png'
 execute:
diff --git a/_toc.yml b/_toc.yml
index 66bc5e946fc8b0e71fef2703106c054879e0d7c3..863a78a76f6b552f4785a680e0c5745f8d68be1e 100644
--- a/_toc.yml
+++ b/_toc.yml
@@ -1,7 +1,9 @@
 format: jb-book
 root: README
 chapters:
+- file: book/download_repo.md
 - file: book/setup_environment.md
+- file: book/launch_notebooks.md
 - file: 01-Python_essentials
 - file: 02-Images_as_arrays
 - file: 03-More_on_images
diff --git a/book/LICENSE b/book/LICENSE
new file mode 100755
index 0000000000000000000000000000000000000000..e65b1e0ef6320d3e90bdf2f846c11e0c79c6fb67
--- /dev/null
+++ b/book/LICENSE
@@ -0,0 +1,32 @@
+
+
+BSD License
+
+Copyright (c) 2022, Kevin Yamauchi
+All rights reserved.
+
+Redistribution and use in source and binary forms, with or without modification,
+are permitted provided that the following conditions are met:
+
+* Redistributions of source code must retain the above copyright notice, this
+  list of conditions and the following disclaimer.
+
+* Redistributions in binary form must reproduce the above copyright notice, this
+  list of conditions and the following disclaimer in the documentation and/or
+  other materials provided with the distribution.
+
+* Neither the name of the copyright holder nor the names of its
+  contributors may be used to endorse or promote products derived from this
+  software without specific prior written permission.
+
+THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
+ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
+WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED.
+IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT,
+INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,
+BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
+DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY
+OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE
+OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED
+OF THE POSSIBILITY OF SUCH DAMAGE.
+
diff --git a/book/download_guide.png b/book/download_guide.png
new file mode 100644
index 0000000000000000000000000000000000000000..d294bb53ea6d2af7761965efd26102d033064233
Binary files /dev/null and b/book/download_guide.png differ
diff --git a/book/download_repo.md b/book/download_repo.md
index 772f910861e45726823839589aaf6e57b53c80cb..460d928ece8455bc90c00801c96c4251e1c1a11e 100644
--- a/book/download_repo.md
+++ b/book/download_repo.md
@@ -6,9 +6,9 @@ During this tutorial, we will be working through a set of notebooks. On this pag
 ### Downloading .zip
 You can download the notebooks as a .zip file. To do so, please do the following:
 
-1. Navigate your web browser to: [https://gitlab.pasteur.fr/iah-public](https://github.com/kevinyamauchi/neubias-napari-workshop)
+1. Navigate your web browser to: [https://gitlab.pasteur.fr/iah-public/bioimage_analysis_with_python_course](https://gitlab.pasteur.fr/iah-public/bioimage_analysis_with_python_course)
 2. Click the green "Code" button to open the download menu and then "Download ZIP"
-    ![download code](./resources/download_code.png)
+    ![download code](download_guide.png)
 3. Choose the location you would like to download the .zip.
 4. Open your file browser and double click on the .zip file to uncompress it.
 5. You have downloaded the notebooks! Proceed to the "Launching jupyter notebook" section.
@@ -24,31 +24,5 @@ cd ~/Documents
 and then clone the repository. This will download all of the files necessary for this tutorial.
 
 ```bash
-git clone https://github.com/kevinyamauchi/neubias-napari-workshop.git
+git clone https://gitlab.pasteur.fr/iah-public/bioimage_analysis_with_python_course
 ```
-
-## Launch jupyter notebook
-
-Open your terminal and navigate to the `notebooks` subdirectory of the `embl-napari-workshop` directory you just downloaded.
-
-```
-cd <path to neubias-napari-tutorial>/notebooks
-```
-
-Now activate your `napari-tutorial` conda environment you created in the installation step.
-
-```
-conda activate napari-tutorial
-```
-
-We will perform the analysis using Jupyter Notebook. To start Jupyter Notebook, enter
-
-```bash
-jupyter notebook
-```
-
-Jupyter Notebook will open in a browser window and you will see the following notebooks:
-
-- `part_0_spot_detection_tutorial_<beginner, advanced, solution>.ipynb`: in this notebook you will write a custom function to perform spot detection.
-
-![jupyter notebook](./resources/jupyter_notebook.png)
diff --git a/book/launch_notebooks.md b/book/launch_notebooks.md
index dfb207f4524a6b879e92c7d31d73415691a7c400..0780eb3a0bf57e893b15cc4b53e3576de01e4b09 100644
--- a/book/launch_notebooks.md
+++ b/book/launch_notebooks.md
@@ -1,54 +1,23 @@
-# Downloading and launching the notebooks
-
-## Downloading the notebooks.
-During this tutorial, we will be working through a set of notebooks. On this page, we will download the notebooks and launch jupyter notebook. There are two ways to download the notebooks: download as a .zip from github. Follow the instructions below for either "downloading zip" (recommended for beginners) or "cloning via git".
-
-### Downloading .zip
-You can download the notebooks as a .zip file. To do so, please do the following:
-
-1. Navigate your web browser to: [https://github.com/kevinyamauchi/neubias-napari-workshop](https://github.com/kevinyamauchi/neubias-napari-workshop)
-2. Click the green "Code" button to open the download menu and then "Download ZIP"
-    ![download code](./resources/download_code.png)
-3. Choose the location you would like to download the .zip.
-4. Open your file browser and double click on the .zip file to uncompress it.
-5. You have downloaded the notebooks! Proceed to the "Launching jupyter notebook" section.
-
-
-### Cloning via git
-You can use git to clone the repository containing the tutorial materials to your computer. We recommend cloning the materials into your Documents folder, but you can choose another suitable location. First, open your Terminal navigate to you the folder you will download the course materials into
-
-```bash
-cd ~/Documents
-```
-
-and then clone the repository. This will download all of the files necessary for this tutorial.
-
-```bash
-git clone https://github.com/kevinyamauchi/neubias-napari-workshop.git
-```
+# Launching the notebooks
 
 ## Launch jupyter notebook
 
-Open your terminal and navigate to the `notebooks` subdirectory of the `embl-napari-workshop` directory you just downloaded.
+Open your terminal and navigate to the `bioimage_analysis_with_python_course` directory you downloaded.
 
 ```
-cd <path to neubias-napari-tutorial>/notebooks
+cd <path to bioimage_analysis_with_python_course>
 ```
 
-Now activate your `napari-tutorial` conda environment you created in the installation step.
+Now activate your `pyimagecourse` conda environment you created in the installation step.
 
 ```
-conda activate napari-tutorial
+conda activate pyimagecourse
 ```
 
-We will perform the analysis using Jupyter Notebook. To start Jupyter Notebook, enter
+To start the Jupyter Notebook server, enter
 
 ```bash
 jupyter notebook
 ```
 
-Jupyter Notebook will open in a browser window and you will see the following notebooks:
-
-- `part_0_spot_detection_tutorial_<beginner, advanced, solution>.ipynb`: in this notebook you will write a custom function to perform spot detection.
-
-![jupyter notebook](./resources/jupyter_notebook.png)
+Jupyter Notebook will open in a browser window and you will see the course notebooks.
\ No newline at end of file
diff --git a/book/setup_environment.md b/book/setup_environment.md
index 9d9a404edc17d1c0fa4839801175f8cb16427830..1b5c885b320c567502054beb3e060553fb57d996 100644
--- a/book/setup_environment.md
+++ b/book/setup_environment.md
@@ -44,7 +44,7 @@ To install python via mambaforge, follow the instructions [here](install_python.
 ## Setting up your environment
 
 ```{admonition} Using conda instead of mamba?
-The following assumes that you have installed python using Mambaforge as [described above](content:references:napari_python_installation). If you are using a pre-existing installation of python via anaconda, miniconda, or miniforge, you can simply replace the `mamba` commands with `conda`.
+The following assumes that you have installed python using Mambaforge as [described above](content:references:napari_python_installation). If you are using a pre-existing installation of python via anaconda, miniconda, or miniforge, consider installing `mamba` into your existing installation using the command `conda install -c conda-forge mamba`. Alternatively, you can simply replace the `mamba` commands below with `conda`.
 
 ```
 
@@ -57,7 +57,7 @@ The following assumes that you have installed python using Mambaforge as [descri
 2. We use an environment to encapsulate the python tools used for this workshop. This ensures that the requirements for this workshop do not interfere with your other python projects. To create the environment (named `pyimagecourse`), enter the following command.
 
 	```bash
-	mamba create -n pyimagecourse python=3.9
+	mamba create -n pyimagecourse python=3.10
 	```
 
 3. Once the environment setup has finished, activate the environment. If you successfully activated the environment, you should now see `(pyimagecourse)` to the left of your command prompt.
@@ -66,10 +66,6 @@ The following assumes that you have installed python using Mambaforge as [descri
 	mamba activate pyimagecourse
 	```
 
-
-4. 
-
-
 4. Install the dependencies with the commands below
 
 	```bash
@@ -86,4 +82,4 @@ The following assumes that you have installed python using Mambaforge as [descri
 	
 	```bash
 	napari
-	```
\ No newline at end of file
+	```