| \n", " | Endothelial | \n", "CAFs | \n", "PVL | \n", "B-cells | \n", "T-cells | \n", "Myeloid | \n", "Normal Epithelial | \n", "Plasmablasts | \n", "Cancer Epithelial | \n", "
|---|---|---|---|---|---|---|---|---|---|
| AAACAAGTATCTCCCA-1 | \n", "4.408002e-08 | \n", "1.344865e-01 | \n", "1.163032e-05 | \n", "1.227270e-08 | \n", "2.295571e-15 | \n", "7.177038e-02 | \n", "2.895089e-03 | \n", "0.710977 | \n", "7.985977e-02 | \n", "
| AAACACCAATAACTGC-1 | \n", "1.171519e-07 | \n", "1.374211e-03 | \n", "4.819937e-03 | \n", "1.112235e-10 | \n", "4.900738e-12 | \n", "1.146527e-06 | \n", "1.117701e-02 | \n", "0.052059 | \n", "9.305690e-01 | \n", "
| AAACAGAGCGACTCCT-1 | \n", "3.391671e-02 | \n", "1.936128e-01 | \n", "2.727795e-02 | \n", "4.451080e-04 | \n", "2.502903e-11 | \n", "4.681284e-02 | \n", "2.960148e-03 | \n", "0.407055 | \n", "2.879199e-01 | \n", "
| AAACAGGGTCTATATT-1 | \n", "2.218915e-15 | \n", "4.154082e-11 | \n", "2.590493e-15 | \n", "2.262676e-14 | \n", "1.393533e-24 | \n", "7.183441e-11 | \n", "1.015703e-14 | \n", "1.000000 | \n", "2.528053e-16 | \n", "
| AAACAGTGTTCCTGGG-1 | \n", "1.013191e-07 | \n", "1.529676e-01 | \n", "6.344583e-04 | \n", "3.170551e-12 | \n", "5.254014e-14 | \n", "3.290586e-04 | \n", "4.684718e-03 | \n", "0.076279 | \n", "7.651046e-01 | \n", "
| ... | \n", "... | \n", "... | \n", "... | \n", "... | \n", "... | \n", "... | \n", "... | \n", "... | \n", "... | \n", "
| TTGTTGTGTGTCAAGA-1 | \n", "2.754395e-08 | \n", "6.213241e-02 | \n", "1.627493e-03 | \n", "3.769111e-05 | \n", "2.289319e-11 | \n", "4.615246e-02 | \n", "8.118926e-03 | \n", "0.075952 | \n", "8.059793e-01 | \n", "
| TTGTTTCACATCCAGG-1 | \n", "5.099808e-05 | \n", "8.506021e-02 | \n", "9.885762e-03 | \n", "4.234758e-06 | \n", "2.160174e-10 | \n", "2.123389e-01 | \n", "9.587400e-03 | \n", "0.102382 | \n", "5.806904e-01 | \n", "
| TTGTTTCATTAGTCTA-1 | \n", "1.268465e-05 | \n", "4.958436e-02 | \n", "9.410104e-03 | \n", "1.736694e-09 | \n", "3.880088e-12 | \n", "1.701309e-04 | \n", "1.462710e-02 | \n", "0.067921 | \n", "8.582742e-01 | \n", "
| TTGTTTCCATACAACT-1 | \n", "4.913381e-05 | \n", "3.794668e-01 | \n", "6.773481e-04 | \n", "1.162436e-03 | \n", "1.674868e-07 | \n", "3.836030e-02 | \n", "5.926389e-04 | \n", "0.304768 | \n", "2.749229e-01 | \n", "
| TTGTTTGTGTAAATTC-1 | \n", "2.284275e-07 | \n", "5.668434e-03 | \n", "6.644045e-03 | \n", "6.492100e-06 | \n", "5.853031e-14 | \n", "3.053288e-02 | \n", "1.407399e-02 | \n", "0.125464 | \n", "8.176102e-01 | \n", "
3798 rows × 9 columns
\n", "| \n", " | Endothelial | \n", "CAFs | \n", "PVL | \n", "B-cells | \n", "T-cells | \n", "Myeloid | \n", "Normal Epithelial | \n", "Plasmablasts | \n", "Cancer Epithelial | \n", "
|---|---|---|---|---|---|---|---|---|---|
| AAACAAGTATCTCCCA-1 | \n", "4.408002e-08 | \n", "1.344865e-01 | \n", "1.163032e-05 | \n", "1.227270e-08 | \n", "2.295571e-15 | \n", "7.177038e-02 | \n", "2.895089e-03 | \n", "0.710977 | \n", "7.985977e-02 | \n", "
| AAACACCAATAACTGC-1 | \n", "1.171519e-07 | \n", "1.374211e-03 | \n", "4.819937e-03 | \n", "1.112235e-10 | \n", "4.900738e-12 | \n", "1.146527e-06 | \n", "1.117701e-02 | \n", "0.052059 | \n", "9.305690e-01 | \n", "
| AAACAGAGCGACTCCT-1 | \n", "3.391671e-02 | \n", "1.936128e-01 | \n", "2.727795e-02 | \n", "4.451080e-04 | \n", "2.502903e-11 | \n", "4.681284e-02 | \n", "2.960148e-03 | \n", "0.407055 | \n", "2.879199e-01 | \n", "
| AAACAGGGTCTATATT-1 | \n", "2.218915e-15 | \n", "4.154082e-11 | \n", "2.590493e-15 | \n", "2.262676e-14 | \n", "1.393533e-24 | \n", "7.183441e-11 | \n", "1.015703e-14 | \n", "1.000000 | \n", "2.528053e-16 | \n", "
| AAACAGTGTTCCTGGG-1 | \n", "1.013191e-07 | \n", "1.529676e-01 | \n", "6.344583e-04 | \n", "3.170551e-12 | \n", "5.254014e-14 | \n", "3.290586e-04 | \n", "4.684718e-03 | \n", "0.076279 | \n", "7.651046e-01 | \n", "
| ... | \n", "... | \n", "... | \n", "... | \n", "... | \n", "... | \n", "... | \n", "... | \n", "... | \n", "... | \n", "
| TTGTTGTGTGTCAAGA-1 | \n", "2.754395e-08 | \n", "6.213241e-02 | \n", "1.627493e-03 | \n", "3.769111e-05 | \n", "2.289319e-11 | \n", "4.615246e-02 | \n", "8.118926e-03 | \n", "0.075952 | \n", "8.059793e-01 | \n", "
| TTGTTTCACATCCAGG-1 | \n", "5.099808e-05 | \n", "8.506021e-02 | \n", "9.885762e-03 | \n", "4.234758e-06 | \n", "2.160174e-10 | \n", "2.123389e-01 | \n", "9.587400e-03 | \n", "0.102382 | \n", "5.806904e-01 | \n", "
| TTGTTTCATTAGTCTA-1 | \n", "1.268465e-05 | \n", "4.958436e-02 | \n", "9.410104e-03 | \n", "1.736694e-09 | \n", "3.880088e-12 | \n", "1.701309e-04 | \n", "1.462710e-02 | \n", "0.067921 | \n", "8.582742e-01 | \n", "
| TTGTTTCCATACAACT-1 | \n", "4.913381e-05 | \n", "3.794668e-01 | \n", "6.773481e-04 | \n", "1.162436e-03 | \n", "1.674868e-07 | \n", "3.836030e-02 | \n", "5.926389e-04 | \n", "0.304768 | \n", "2.749229e-01 | \n", "
| TTGTTTGTGTAAATTC-1 | \n", "2.284275e-07 | \n", "5.668434e-03 | \n", "6.644045e-03 | \n", "6.492100e-06 | \n", "5.853031e-14 | \n", "3.053288e-02 | \n", "1.407399e-02 | \n", "0.125464 | \n", "8.176102e-01 | \n", "
3798 rows × 9 columns
\n", "| \n", " | n_spots | \n", "n_spots_sig | \n", "n_spots_sig_pval | \n", "
|---|---|---|---|
| CXCL12_ITGB1 | \n", "3513 | \n", "6 | \n", "186 | \n", "
| NPNT_ITGB1 | \n", "3321 | \n", "5 | \n", "254 | \n", "
| FBN1_ITGAV | \n", "2986 | \n", "5 | \n", "191 | \n", "
| MFGE8_PDGFRB | \n", "2964 | \n", "5 | \n", "144 | \n", "
| ITGAV_THY1 | \n", "3312 | \n", "5 | \n", "214 | \n", "
| ... | \n", "... | \n", "... | \n", "... | \n", "
| FGF2_FGFR1 | \n", "568 | \n", "0 | \n", "173 | \n", "
| FGF22_FGFRL1 | \n", "120 | \n", "0 | \n", "106 | \n", "
| FGF22_FGFR2 | \n", "76 | \n", "0 | \n", "69 | \n", "
| FGF2_GPC4 | \n", "289 | \n", "0 | \n", "145 | \n", "
| ZP3_MERTK | \n", "388 | \n", "0 | \n", "180 | \n", "
1548 rows × 3 columns
\n", "" ], "text/plain": [ " n_spots n_spots_sig n_spots_sig_pval\n", "CXCL12_ITGB1 3513 6 186\n", "NPNT_ITGB1 3321 5 254\n", "FBN1_ITGAV 2986 5 191\n", "MFGE8_PDGFRB 2964 5 144\n", "ITGAV_THY1 3312 5 214\n", "... ... ... ...\n", "FGF2_FGFR1 568 0 173\n", "FGF22_FGFRL1 120 0 106\n", "FGF22_FGFR2 76 0 69\n", "FGF2_GPC4 289 0 145\n", "ZP3_MERTK 388 0 180\n", "\n", "[1548 rows x 3 columns]" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "lr_info = block1.uns['lr_summary'] # A dataframe detailing the LR pairs ranked by number of significant spots.\n", "lr_info" ] }, { "cell_type": "markdown", "id": "18647362", "metadata": { "papermill": { "duration": 0.025663, "end_time": "2025-07-07T03:47:32.076629", "exception": false, "start_time": "2025-07-07T03:47:32.050966", "status": "completed" }, "tags": [] }, "source": [ "### P-value adjustment\n", "Below can adjust the p-value using different approaches; the p-value has already been corrected by running st.tl.cci.run. This is what was used for the paper and is the default of the below function. The difference between correct_axis is adjusting by no. of LRs tested per spot (LR), no. of spots tested per LR (spot), or no adjustment (None). " ] }, { "cell_type": "code", "execution_count": 11, "id": "da435253", "metadata": { "execution": { "iopub.execute_input": "2025-07-07T03:47:32.127223Z", "iopub.status.busy": "2025-07-07T03:47:32.127066Z", "iopub.status.idle": "2025-07-07T03:47:32.331592Z", "shell.execute_reply": "2025-07-07T03:47:32.331269Z" }, "papermill": { "duration": 0.230919, "end_time": "2025-07-07T03:47:32.332444", "exception": false, "start_time": "2025-07-07T03:47:32.101525", "status": "completed" }, "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Updated adata.uns[lr_summary]\n", "Updated adata.obsm[lr_scores]\n", "Updated adata.obsm[lr_sig_scores]\n", "Updated adata.obsm[p_vals]\n", "Updated adata.obsm[p_adjs]\n", "Updated adata.obsm[-log10(p_adjs)]\n" ] } ], "source": [ "st.tl.cci.adj_pvals(block1, correct_axis='spot', pval_adj_cutoff=0.05, adj_method='fdr_bh')" ] }, { "cell_type": "markdown", "id": "9e87ad73", "metadata": { "papermill": { "duration": 0.024014, "end_time": "2025-07-07T03:47:32.380850", "exception": false, "start_time": "2025-07-07T03:47:32.356836", "status": "completed" }, "tags": [] }, "source": [ "### Visualise the overall ranking of LRs by significant spots" ] }, { "cell_type": "code", "execution_count": 12, "id": "057e76fb", "metadata": { "execution": { "iopub.execute_input": "2025-07-07T03:47:32.430102Z", "iopub.status.busy": "2025-07-07T03:47:32.429935Z", "iopub.status.idle": "2025-07-07T03:47:32.576210Z", "shell.execute_reply": "2025-07-07T03:47:32.575834Z" }, "papermill": { "duration": 0.172315, "end_time": "2025-07-07T03:47:32.577412", "exception": false, "start_time": "2025-07-07T03:47:32.405097", "status": "completed" }, "tags": [] }, "outputs": [ { "data": { "image/png": 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9/OUv73YVAAAAAABgVlU6GLjooou6XQUAAAAAAJhVO30wcPXVV2e3hx56KJYtWxbHHHNMnHzyyd2uFgAAAAAA9KSeCwY2bNgwpmzBggVRr49eDuGHP/xhnHfeefGLX/xizP6HHnpofPCDH4zTTz99RusKAAAAAAA7m55afPiLX/xiLF26dNRtl112ieuvv37UfmmEwJlnnpmFAs1mc8zt5ptvjmc84xnxla98pWttAQAAAACAXtRTwcD3vve9MZ38Rx55ZDzucY8btd/5558fGzduzLZrtVrH2/DwcLz0pS+NtWvXdqk1AAAAAADQe3oqGLj88suz+9SxX9w/73nPG7XPNddcE1ddddXIPp1GDBQefvjheM973jOrbQAAAAAAgF7WM8FA6tC/7rrrRjr8C6eddtqox5deeumo17SPFGg/5ic/+ckZrjkAAAAAAOw8eiYYWL169ZiFh1NH/1Oe8pRRZd///vfHvLY8WiC9prhPVq5cGffee+8M1x4AAAAAAHYOPRMM3HLLLWPKDjrooFi4cOHI49ThnxYeLo8MSAsUf+pTn4qf/vSn2YLD5VCgsGrVqhmuPQAAAAAA7Bx6Jhi47bbbRraLzv3DDz981D633nprbN68edQ+5513Xpx99tnx+Mc/Pj7+8Y/HwMDAmGPffPPNs9ACAAAAAADofT0TDKxZs2ZM2a677jrq8S9/+csx+5xyyikj23vssUccf/zxoxYgTtauXTutdQUAAAAAgJ1VzwQDGzduHFO2aNGiUY9/9atfjdnniCOOGPX4wAMPHLNP+9oFAAAAAABQVT0TDMyfP39MWfuV/jfddNOox2kqoUMOOWRUWX9//5jjdCoDAAAAAIAq6plgYMmSJaM6/NN0QO2LBl9zzTVjRgfMmzdvVNkjjzyy3ZEHAAAAAABQVT0TDOy///5jym688cb4whe+MBIKXHHFFSOhQbo/6qijxrzm4YcfHlO29957z1CtAQAAAABg59Izc+y0d/IXAcDZZ58dK1asyEKCRqORlReOO+64McdJ+5X3SQ4++OAZrDkAAAAAAOw8embEQFpEuHxlfzEqYGhoKH7+859nCwi3d/iffvrpY9YkuPPOO8cc+zGPecwM1hwAAAAAAHYePRMMpE7/s846KwsE2sOB4vmyffbZJ0455ZRRZZdffvmY46Z1CHbfffcZqzcAAAAAAOxMeiYYSN761reOLCZcDgLK20VY8PrXvz76+vpGvf7rX//6mP2e+MQnzkrdAQAAAABgZ9BTwcChhx4a733ve0dGDbSPEiiccMIJ8ed//uejytL6A5/+9KfHjDA47bTTZrzeAAAAAACws+ipYCA577zz4rOf/Ww8+tGPzgKC8i2NJnjta1+bjQzo7x+9bvJXvvKVuOOOO8a85rd/+7e71hYAAAAAAOg1tWZ5Uv8ec/3118fq1atj48aN2ZoCaaTAokWLOu47ODgYW7ZsGVO+ePHiWahp71uxYkWsWrUqli9fHitXrux2dQAAAAAA6JLRl933mGOOOSa7TUQaTVCsTwAAAAAAAOwkUwkBAAAAAAAVGTHwyle+ckzZu971rth9990nfIxO0wnNnz9/h+sGAAAAAABzQU+tMVCv16NWq2XbqVpp+5ZbbomDDjpoQq//9a9/nS1aXJaOMTQ0FFVnjQEAAAAAAHpuxEBhR7KKHso5AAAAAACg5/TkGgPFqIEdef2OHgMAAAAAAOaingwGAAAAAACAmTGngoE1a9Z0uwoAAAAAANDT5lQw8M1vfrPbVQAAAAAAgJ4264sPv/Od75zU/v/6r/8au+666zb32bx5c9xwww3xuc99bszaAvPnz59SPQEAAAAAYC6qNZvN5myesF6vj7swcKeqTGYR4eL1xWvS47333jvuvvvuqLoVK1bEqlWrYvny5bFy5cpuVwcAAAAAgKqMGChMNI+YTG6RAoFi/3SfHh9xxBFTriMAAAAAAMw1XQsGOo0E2NERA52cfPLJO/R6AAAAAACYS+bUiIH2ICFNW/TKV75y0nUDAAAAAIC5ak6OGCiO83d/93dx+OGHT7GGAAAAAAAw93QlGJjMKICprI185JFHxtvf/vZ48YtfPOnXAgAAAADAXDbrwUDqsB/P3/7t346MECgWD37DG94Qu+666zaP2dfXF0uWLIl99tknjj322DjqqKOmvd4AAAAAADAX1JpTuSR/hqQ1AdqDgVtuuSUOOuigbldtp7dixYpYtWpVLF++PFauXNnt6gAAAAAA0CX1bp0YAAAAAACo0OLD4ykPYOihwQwAAAAAADAn9FQw0Gg0ul0FAAAAAACY00wlBAAAAAAAFdJTIwbG89BDD8V3vvOduPzyy+Ouu+6K+++/P5tmaM8994z99tsvTj755Dj99NNjt91263ZVAQAAAACgp/V0MLB69er4u7/7u/jEJz4Rw8PD4+73nve8J+r1epxzzjnx13/91/GYxzxmVusJAAAAAAA7i56dSujiiy+OY445Jj72sY/F0NBQNkJgW7cUHHz84x+Pxz72sfGhD32o29UHAAAAAICe1JPBwD/8wz/Eq171qti8eXPW6V+r1SZ0S/tu2bIlzjvvvHjnO9/Z7WYAAAAAAEDP6blg4Mtf/nL8zd/8zahAINneiIGkHBCkYOBLX/pSl1sDAAAAAAC9paeCgTRC4JWvfOVIKJAUHf8TGS1QDggajUY26mDTpk1dbhUAAAAAAPSOngoGPvzhD8e9997bsaM/2d5ogWKfwn333RcXXXRRV9oCAAAAAAC9qD96yH/+53+Oelzu7O/r64sTTjghW5B49913z8offPDBuP766+OKK67IFh8uAoVC2v6P//iPeO1rXzvLLQEAAAAAgN7UM8HAmjVr4uc///lIGFDu5H/1q18d73jHO2Kfffbp+No0yiCtS/DBD35wzOuvu+667NjLli2bxdYAAAAAAEBv6pmphNJV/2ldgEKxrsDb3va2eP/73z9uKJDsvffe8YEPfCD++q//etSIgSQd88c//vGM1h0AAAAAAHYWPRMM3HXXXWPK9thjj2wkwESlfffcc88JHRsAAAAAAKqoZ4KBtF5A+2iBk046KVtbYKL6+/uz17SPGigfGwAAAAAAqqxngoHNmzePKVu0aNGkj9PpNVu2bJlyvQAAAAAAYC7pmWBgt912G/U4XfX/q1/9atLHufHGG7d7bAAAAAAAqKqeCQbSegKFNI1Q8tOf/jS7TdQ111wTV1999cjrOx0bAAAAAACqrGeCgcc+9rGjHqfO/TRq4Pd+7/fipptu2u7r0z5p306OOeaYaasnAAAAAADszPqjRxx55JGx9957x3333TcqHLj55pvjcY97XLz85S+P5z73uVkn/+677z6yqPB1110Xn//85+NjH/tYtk5B+2iBvfbaK4466qhZbw8AAAAAAPSingkGktTx/6EPfWhktEBh06ZN8cEPfjC7jae8f/H6dJ+OCQAAAAAA9NhUQsmf/dmfjVzxX74vOvq3dSv2K48YSNtvetObutYeAAAAAADoNT0VDKTphF7xileMuvq/2C53/He6lfctgoI//MM/jKOPPrpLrQEAAAAAgN7TU8FA8r73vS9WrFgxahTA9kYLlEcNFFIgcOGFF3a1LQAAAAAA0Gt6LhhYuHBhfO1rX4tjjz2242iBdp1GDTz+8Y+Pr371q9mxAAAAAACAHg4Gkv333z8uv/zyeO1rXxv9/f0jIwKS8aYQSre076tf/erstY961KO63AoAAAAAAOg9PRkMJOlq/wsuuCBuvPHGeOtb3xq/9Vu/1XFaoSSNLnjLW94SN9xwQ7z//e+PxYsXd7v6AAAAAADQk2rN8kq/PW7Dhg1xzz33xP3335893mOPPWLfffeNRYsWdbtqPS+t27Bq1apYvnx5rFy5stvVAQAAAACgS/pjJ5ICgEMPPTS7AQAAAAAAc2gqIQAAAAAAoOIjBtatW5dNJfTggw9mj3fffffYZ599YsmSJd2uGgAAAAAA7BR6Phi48sor48Mf/nBcdtll2eLC7UsipAWJjzzyyHjqU58ar3zlK+NJT3pS1+oKAAAAAAC9rmcXH04L5J533nnx4x//OHu8vWqmgCA58cQT49///d+zxXbZyuLDAAAAAAD07BoDqWP/+OOPz0KBFAgUoUDq/O90S4r9fvjDH8Zxxx0XH/zgB7vcCgAAAAAA6D09N5XQBRdcEG94wxtGhQFlnaYSat9ny5Yt8drXvjaGhobi/PPPn4VaAwAAAADAzqGnRgxce+218cY3vnFUh38xEqA8cqCs0/PF69KxrrvuullvBwAAAAAA9KqeCgbe9KY3ZVf5F7Y3hdB4UwoVBgcHs2MCAAAAAAA9Fgzceeed8Z3vfGdMB3/7GgLj3cr7lrfTMdOxAQAAAACAHgoGLr/88pEO/nRfnkpowYIF2ZoBX/rSl+LWW2+NNWvWZLdbbrklK3vNa16T7ZMUrymk7XRsAAAAAACghxYfvuOOO0a2i879dL98+fKs8//ggw8e85qlS5dm5c985jPjzW9+czzrWc+KG264YcxixOVjAwAAAABAlfXMiIFO6vV6fPrTn+4YCrQ75JBDsn3bQwEAAAAAAKAHg4FHPepRI9vFaIEnP/nJceSRR074GEcffXT2mvJUQskBBxwwrXUFAAAAAICdVc8EA7/1W781pmwiIwUm8prjjjtuyvUCAAAAAIC5pGeCgcMOOyxOOOGEbLuYDuiBBx6Y9HEefPDBke10nOOPPz47NgAAAAAA0EPBQPJ3f/d3I9tpOqDLL7881qxZM+HXp30vu+yykcWLk7/8y7+ckboCAAAAAMDOqKeCgTPOOCPe+MY3jqwxsGHDhjjvvPOi0Whs97Vpn3PPPTd7TREKvPrVr46zzjprFmoOAAAAAAA7h54KBpJ3vetd8ad/+qcjnfuXXnppnHjiifHFL34xBgcHx+yfyr7whS9kiw5/5jOfycpSqJBCggsvvHDW6w8AAAAAAL2sP3rI6aefPrI9MDAQW7ZsyQKCq666Krvyv7+/P1svYPfddx9ZT2D16tUxNDQ0EiQUr73ppptGHW9bUpDwrW99awZaBAAAAAAAvaXWLPeod1m9Xh9ZeDgpphRqr2KxT6fy4jUTVew/PDwcc9mKFSti1apVsXz58li5cmW3qwMAAAAAQJf01IiBQrnDv1NHf/H8eOUTzTomEyAAAAAAAMBc0JPBQPuIgPYgYLxAoP31AAAAAADAThAMjHfF/0RHAhgxAAAAAAAAO1EwsDN22N9///3xgx/8IK688sq47rrrskWR77zzzli3bl3MmzcvdttttzjmmGPi1FNPjZe97GVxwAEHdLvKMLOawxHNwYjavIhI282IWn9eln71NFpl2a+hoqwZ0WzkZUMRtXp+rPayWkRzKCLm5WXpd0a9VZbO1yzK+sbWIZVlr2mvQyrrG6cO6XzDpbLS+bKy4fHPNyfbXK5D6XzZsQAAAADodT23+HA3TMfiw895znPiS1/60oT2HRgYiL/4i7+It73tbbPWZosPMysG74zYdHXE4M0RW1ZFDN0bMfxgRGND3kmeOp/T93z+ayfdZTlg6oRutDqks19Jza3bY57PX5h1ShfHi9J2fl9+bVaWjlHrUIdSWfGa8jmK35Dl82X7NUvHqbWdryhLd53q1RxbVpxjwm0eqdgE2twcv17ba3P56zLq2Hl4UF8c0bd7RP8+EfOXR8x/dMSC4yL6953hbzYAAAAA5sSIge985zsxF+y5555x9NFHx8EHHxxLliyJDRs2xE033ZSNJhgaGorNmzfHO97xjrj55pvjIx/5SLerCztueE3Eui9EbLkpYujuiMGbIhqbIxopFFjfuqK8uTGika6sb+tIL3dclzupRzrHO5XFBI4z0f22d77S9qgO8/KxU+d5Wyf8Ns/X621uK+vU5hQM1Be1RgoM3xcxdF/r6z//MREbr4oYODJi8XMi+pbO9ncjAAAAADvTiIGd2bve9a5YunRpPP3pT4/DDz+84z733HNPvPGNb4z/+q//Gin71Kc+FS984QtnvH5GDDBjttwa8ch/RwxvjNjyy4jheyKam1ujBdK0M81NrcfZVDWDeU9zfjV7pu1q9u2WTeU1UynrdGX+RI9THskwXfWKWW7zeO97PlKgNj+/H4ioL2xNKZRGDaTy/v0i5h/RCg52eUnEvEdN4BsJAAAAgNkiGJhl6e0+44wz4tvf/nb2OG1/4xvfmPHzCgaYsamD1lzUGhGQQoE0WiBtD93V6mRubMxDgfRrZkspFGi0dTgXtle2I6+ZrtdO5Tg7UtZrba53CAcWRNQXtMr792+tZdB/QGv0QH0gYtmrIvr3bjsnAAAAAN3SnUn9KyytZ/BHf/RHI49/+tOfdrU+MGVpNMC6z+RBwL15KNCMGL4/X+A2jQ7YnO9cjBQogoFCoy0o2F5Z8frmJMrKt6mcbyp1mOr5er3NMXqf7Gsc+aiQtChxI//6NyOG7ogYfqA1pdQj6fuk/HUHAAAAoJsEA12w1157jWw/8sgjXa0LTNnGKyKG7m91+KY1BZLmutYIgZHO4nQ/3NapHG2dz4VyJ3VzG2XNCZa1H6ddYwbrMN75Gjtxmzsdu9SmtIZEdr+ptdh0suXGPCS4u7UoNQAAAAA9oacWH56Kq6++Ors99NBDsWzZsjjmmGPi5JNPjl6WpvQpHHLIIV2tC0xJGhmw6arWdrpCvJmmCUp9xHnQlS02nAKBbIfiRR06pctz55cfl7fHK6tNoCwmURazUIcdbfNE6jBTbS5PL1R8LdOCxEOtKYXS1zvb7o9orI2oL26FBMMPRvTtEbHpyoiFT+xwbgAAAACi6sHAhg35laYlCxYsiHp99OCGH/7wh3HeeefFL37xizH7H3roofHBD34wTj/99Og1d955Z7ZQcWE2Fh6GaTd8d8Tww/l2mjom8oWG84Agm1YmDxBGjRLYXkAwGc1pLosK1mFHz1sstFyEBIOtYCBbV2I4otbX+v5IwcDQfa2phdI2AAAAAF3VU1MJffGLX4ylS5eOuu2yyy5x/fXXj9ovjRA488wzs1AgLebbfrv55pvjGc94RnzlK1+JXgk70iiBd7/73XHsscdm4UBy9NFHx1vf+tZuVw8mb6j1PTx6lMCW8g7buBKdndu2RjAUo0NK3w/F90exWDUAAAAAXddTIwa+973vZR37ZUcddVQ87nGPG1V2/vnnx8aNG7OFfNOtk+Hh4XjpS18at9xySxYuzKbLL788nvrUp25zn2c961lxySWXZOHHVF1wwQVx4YUXTmjf1atXT/k8MMZwqbO3WFNgJAzICtvumds6rWsw3Pb9kZ5eO6u1AgAAAGAnCAZSh3qSOvtTQJDun/e8543a55prromrrrpqJBBoDxKK1ycPP/xwvOc974m//uu/jl6x2267ZZ35L37xi3f4WPfdd9+o9QqgOzqHczCW7xUAAACAXtAzwUDq4L/uuuvGjAA47bTTRj2+9NJLR72mff9yUJC2P/nJT856MLD//vvH6173upE6PPLII3HDDTdkoUZaJPkP/uAPsjUQPvCBD8QRRxwx5fPstddesXz58gmPGNi8uXTlLuyIvl23btcXRDTWt/06qedXj+sIroba2Nnp0loD2f3Czt83AAAAAHRNrdnpkvsuuOmmm7JO8vJIgLTgcOpUX7hwa8fSySefnC08XB5VUG5Ce3m6v+uuu2LvvfeObktrC/zVX/1VXHzxxSOjB7773e+OmSppJqxYsSIbXZCChJUrV874+Zjj0kKyD13Q2t7yy4ihu1uLzQ79plXW2JgvQJt+NotAqlGaaqZ9QWJ2vs7/+tbt2kDr+dqCVlCUzDuo9Xz/ARHzH9Mq2/2NEX3LZr/qAAAAAPTm4sNpLYB2Bx100KhQIHX0p4WHy6ME0hz9n/rUp+KnP/1ptuBwp1EEvTLdThpJcNFFF8Ub3vCG7HEaPZCmFErrIcBOpX+viP48bOvbq3Vf62t1DGfb8/L79LPYl7+o/HPZPpJgKiMLarNQ1s3z9UIdahN4rm/r9sjXfeHWPy/peyWZ9yihAAAAAECP6Jlg4LbbbhvZLjr3Dz/88FH73HrrrSPT4RT7nHfeeXH22WfH4x//+Pj4xz8eAwPpytXRbr755ugl//iP/ziyIPIvfvGL+MpXvtLtKsHkLTyhdV/ffet0MfVdtk4jk4KCTDkY6BQItAcG5Vv7Pu337a+Z7rLxzjdTddhZ2lzar/g6l7/mxfdBfUlEPZ8+aEH+/QIAAABA1/VMMLBmzZoxZbvuOno+6l/+8pdj9jnllFNGtvfYY484/vjjxyxIvHbt2uglixYtipNOOmnk8Q9+8IOu1gemZOC3IuYd2BoVMP/IVll90daQoJa20329FA7UtxES1LdTVp7KZlud1xN9TX2a6lD+NTob5xuvzTNdh1qH8/WVphMqvu6LI+ppO31f5GuozH90xMAxbecAAAAAIKoeDGzcuLFjB3rZr371qzH7tC/ee+CBB47ZZ8OGDdFr0voChQceeKCrdYEpSYHA0udH1Be3FpWdd3CrvG/PrVePjyw821/qWC7/2inmqW/vaC+X1Uod0OWy8uP2++I4xWvby7Z1vsnWoTaFOsxEm2uz0Oby8WpbFxhOQUD29Z4X0bd7q2zeoa2RA327RCw5K59WCgAAAIBekPfqdN/8+fPHlLVf6Z8WKC5LUwkdcsgho8r6+8c2qVNZt6UFkQu77553pMHOJnUCL3tFxNpLIuLQVmfx4K0RfftGDN+ztR+5uTGikX7GB/MXduokTiN9ptJ5nF7TbNueaNmOquULKdeneL6ptrm9DrPd5hQKpK9nLQ8FBlqP+/ZuhQUpFEiLD/ftEbHsJdYWAAAAAOgxPdNjvmTJklEd/mk6oPZFg6+55poxowPmzcsXu8w98sgj2x150G1phMCPfvSjkcdHH310V+sDOyQtLrvb+RHrv9G6ajzNKT+YRvf0RTQejmikgC+Vp3Agn1qo1sy3axHNRj7dULZ4SH5lealTO23Wi+3a1v1GylJB6Ti18n7lsrb9itd2Ol8qGzlOpzq07zfBOkypzcWxy23u1L7iueb4bS7XoRwSTKXN6WuaTRfVn48M2DWivrQ1fVAKjBY8MWLx6VsXJAYAAACgZ/RMMLD//vuPKbvxxhvjC1/4Qjz3uc/NQoErrrhiJDRI90cdddSY1zz88MNjyvbee++YSQ8++OCEr/pvNBrxx3/8xyOLKKfFkp/znOfMaP1gxqWrxZc8O2LRKRGbfhYxeFvEllURg3e0goFGEdjVI5pD+RQ0qXO5EdFM0w4NltYhSJ3Y/a39mqnzOZUNt55vpvvUwZ3vN1JWyzvah/Jfa3lZdsziOPn5sueH8udSWd7JnZXVO9ShfL68LKtDbXS9ssdFHeblz5XanF5ba+Qd7EWbS+9Dcb6Rem3nfFmb076DbW3O69AsnW9Mmzu9D+3ve7kO+bFTUNBsa3MxXVD/oyIGjo7oPzhiweNbU0wBAAAA0JN6Jhho7+QvAoCzzz47VqxYkYUEqVM9lReOO+64McdJ+5X3SQ4+OJ/7fIZ89KMfjUsuuSRe//rXx/Oe97zYZZddOu537bXXxpvf/Ob42te+NlL253/+59miyTAn1JdELDq527UAAAAAAHaGYCAtIpyu7L/vvvuyx8WogKGhofj5z3+elbV3+J9++ulj1iS48847x+z3mMc8Zsbr/5Of/CRe/vKXZ+sZpJDjyCOPzBYYTnVJUwelUKB9jYQUerz97W+f8boBAAAAAEDPBQOpA/2ss86K//iP/xjp2C/CgeK+bJ999olTTjllVNnll18+5rhpHYKZXtw3TQdUSEHG9ddfn93Gs3Tp0njHO94Rf/InfxJ9fcX0KQAAAAAAUKFgIHnrW98aF198cda5XgQCSTkUKEKCNG1Pe6f617/+9TH7PfGJT5zxer/2ta+Npz/96fHNb34zWwdh5cqVcdttt42sd5CmFtpvv/3iCU94Qpxxxhn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