From 70f25ae9b24b369ba51440a8aeb0929f7a0f5d15 Mon Sep 17 00:00:00 2001 From: Stephan Breimann Date: Fri, 26 Jun 2026 01:04:20 +0200 Subject: [PATCH 1/2] doc(use-cases): add Use Cases guide + UC1 gamma-secretase reproduction Introduce "Use Cases" as a third subchapter under the GUIDES nav (alongside Tutorials and Protocols): a use case reproduces a published study end to end from bundled data only. First use case (use_case1_gamma_secretase) reproduces the key results of Breimann et al., "Charting gamma-secretase substrates by explainable AI", Nat. Commun. 16, 5428 (2025) on the bundled DOM_GSEC set (63 substrates + 63 non-substrates): the CPP signature (feature map + profile, cf. Fig. 2b/2c) and the CPP-vs-scale-based benchmark (~85% vs ~75% balanced accuracy, cf. Fig. 3a), with the cleavage-region / conformational feature breakdown the paper reports. Wire the new use_cases/ notebook folder into the docs: export_use_case_ notebooks_to_rst() (conf.py + create_notebooks_docs.py), a use_cases.rst toctree, the GUIDES caption, the landing-page routing line (was "coming soon"), and a release note. Co-Authored-By: Claude Opus 4.8 (1M context) --- docs/source/conf.py | 3 +- docs/source/create_notebooks_docs.py | 24 + docs/source/index.rst | 3 +- docs/source/index/release_notes.rst | 11 +- docs/source/use_cases.rst | 32 + use_cases/use_case1_gamma_secretase.ipynb | 969 ++++++++++++++++++++++ 6 files changed, 1037 insertions(+), 5 deletions(-) create mode 100644 docs/source/use_cases.rst create mode 100644 use_cases/use_case1_gamma_secretase.ipynb diff --git a/docs/source/conf.py b/docs/source/conf.py index 7b1b134a..bb2f8323 100755 --- a/docs/source/conf.py +++ b/docs/source/conf.py @@ -13,10 +13,11 @@ # Create notebooks rst and table rst first from create_tables_doc import generate_table_rst from create_notebooks_docs import (export_example_notebooks_to_rst, export_tutorial_notebooks_to_rst, - export_protocol_notebooks_to_rst) + export_protocol_notebooks_to_rst, export_use_case_notebooks_to_rst) export_tutorial_notebooks_to_rst() export_protocol_notebooks_to_rst() +export_use_case_notebooks_to_rst() export_example_notebooks_to_rst() generate_table_rst() diff --git a/docs/source/create_notebooks_docs.py b/docs/source/create_notebooks_docs.py index 0fb395b4..7a4c868d 100644 --- a/docs/source/create_notebooks_docs.py +++ b/docs/source/create_notebooks_docs.py @@ -35,6 +35,7 @@ FOLDER_SOURCE = os.path.dirname(os.path.abspath(__file__)) + SEP FOLDER_TUTORIALS = FOLDER_PROJECT + "tutorials" + SEP FOLDER_PROTOCOLS = FOLDER_PROJECT + "protocols" + SEP +FOLDER_USE_CASES = FOLDER_PROJECT + "use_cases" + SEP FOLDER_GENERATED_RST = FOLDER_SOURCE + "generated" + SEP # Saving .rst directly in 'generated' FOLDER_EXAMPLES = FOLDER_PROJECT + "examples" + SEP FOLDER_EXAMPLES_RST = FOLDER_GENERATED_RST + "examples" + SEP @@ -122,6 +123,29 @@ def export_protocol_notebooks_to_rst(): writer.write(output, resources, notebook_name=filename.replace('.ipynb', '')) +def export_use_case_notebooks_to_rst(): + """Export Jupyter use cases to RST without execution. + + Use-case notebooks live flat in the top-level ``use_cases/`` folder and are + written to the same ``generated/`` directory as the tutorials and protocols, + so the ``use_cases.rst`` toctree references them as ``generated/``. + """ + for filename in os.listdir(FOLDER_USE_CASES): + if filename.endswith('.ipynb') and filename not in LIST_EXCLUDE: + notebook_name = filename.replace('.ipynb', '') + full_path = os.path.join(FOLDER_USE_CASES, filename) + # Load the notebook + with open(full_path, 'r') as f: + notebook = nbformat.read(f, as_version=4) + # Convert to notebook to RST + custom_preprocessor = CustomPreprocessor(notebook_name=notebook_name) + rst_exporter = nbconvert.RSTExporter(preprocessors=[custom_preprocessor]) + output, resources = rst_exporter.from_notebook_node(notebook) + # Write the RST and any accompanying files (like images) + writer = FilesWriter(build_directory=FOLDER_GENERATED_RST) + writer.write(output, resources, notebook_name=filename.replace('.ipynb', '')) + + def export_example_notebooks_to_rst(): """Export Jupyter examples to RST without execution.""" for root, dirs, files in os.walk(FOLDER_EXAMPLES): diff --git a/docs/source/index.rst b/docs/source/index.rst index 56e79301..b2e36ca9 100755 --- a/docs/source/index.rst +++ b/docs/source/index.rst @@ -19,7 +19,7 @@ Pick a section by what you want to do: - **New here** and want a first result → :ref:`Getting Started `. - Learn what **one specific tool** does, with its parameters and outputs → :ref:`Tutorials `. - **Design a valid, end-to-end analysis** for a biological question → :ref:`Protocols `. - - **Adapt a full biological case study** to your own data → Use Cases *(coming soon)*. + - **Adapt a full biological case study** to your own data → :ref:`Use Cases `. - Look up the **exact signature, parameters, or return value** → :doc:`API `. Install @@ -85,6 +85,7 @@ classes grouped by capability, the prediction levels (residue / domain / protein tutorials.rst protocols.rst + use_cases.rst .. toctree:: :maxdepth: 2 diff --git a/docs/source/index/release_notes.rst b/docs/source/index/release_notes.rst index cd11c368..71d6ae2e 100644 --- a/docs/source/index/release_notes.rst +++ b/docs/source/index/release_notes.rst @@ -199,9 +199,14 @@ Added - **A minimal CPP analysis** tutorial (``tutorial0_minimal``): the shortest end-to-end loop — load a dataset, run CPP, read out the signature. - **Documentation navigation**: the sidebar is grouped into four sections — *Overview*, - *Guides* (Tutorials · Protocols), *Reference*, and *Project* — and the landing page gains a - "You want to… / Go to" routing table; the previously unwired **Comparison Harness** - tutorial (``tutorial6_comparison_harness``) is now reachable. + *Guides* (Tutorials · Protocols · Use Cases), *Reference*, and *Project* — and the landing + page gains a "You want to… / Go to" routing table; the previously unwired **Comparison + Harness** tutorial (``tutorial6_comparison_harness``) is now reachable. +- **Use Cases** guide (third *Guides* subchapter): each use case reproduces a published + study end to end from bundled data. The first, *UC1: Charting gamma-secretase substrates* + (``use_case1_gamma_secretase``), reproduces the key results of Breimann *et al.*, Nat. + Commun. 2025 — the CPP signature and the CPP-vs-scale-based benchmark (~85% balanced + accuracy) — on the bundled ``DOM_GSEC`` set. - **Standardized tutorial header box**: every tool tutorial now opens with a uniform green *You will learn* box (Tool · Input · Output · Best used for · Related protocol · Related API), giving a one-glance answer to *what tool, what goes in, what comes out, diff --git a/docs/source/use_cases.rst b/docs/source/use_cases.rst new file mode 100644 index 00000000..d9b0b890 --- /dev/null +++ b/docs/source/use_cases.rst @@ -0,0 +1,32 @@ +.. + Developer Notes: + Use-case notebooks live in the top-level ``use_cases/`` folder (flat) and are + auto-converted to ``generated/`` by ``create_notebooks_docs.py`` (called from + ``conf.py``). A use case reproduces a published study end to end from bundled + data; it must stay distinct from the per-function Tutorials and the + single-question Protocols (no content overlap). +.. + + +.. _use_cases: + +Use Cases +========= + +**Tutorials, protocols, and use cases are different things.** A +:ref:`tutorial ` teaches you *one function*. A :ref:`protocol ` +teaches you a *workflow* that answers one biological question. A **use case** goes +one level up: it **reproduces a published study** end to end with AAanalysis, so you +can see that a real result drops out of the standard pipeline — and use it as the +template to adapt to your own paper-style analysis. Where a use case calls a tool, +it links to that tool's tutorial for the mechanics instead of repeating them, so the +three stay distinct with no overlap. + +Each use case runs from **bundled data only** (no downloads) and is a simplified, +fast reproduction: it reproduces the *key results* and the *biology*, and points to +the Protocols for scaling up to the full study. + +.. toctree:: + :maxdepth: 1 + + generated/use_case1_gamma_secretase diff --git a/use_cases/use_case1_gamma_secretase.ipynb b/use_cases/use_case1_gamma_secretase.ipynb new file mode 100644 index 00000000..367a621c --- /dev/null +++ b/use_cases/use_case1_gamma_secretase.ipynb @@ -0,0 +1,969 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "cf8d6f0b", + "metadata": {}, + "source": [ + "# UC1: Charting gamma-secretase substrates\n", + "\n", + "**Reproduce the key result of a published study with AAanalysis.** This use case\n", + "walks the central finding of\n", + "\n", + "> Breimann *et al.*, *Charting gamma-secretase substrates by explainable AI*,\n", + "> **Nature Communications** 16, 5428 (2025). https://doi.org/10.1038/s41467-025-60638-z\n", + "\n", + "— the paper that AAanalysis was built to support — and reproduces it end-to-end\n", + "from **bundled data only** (no downloads). Where a *tutorial* teaches one tool and\n", + "a *protocol* teaches one workflow, a **use case** re-runs a real study: it shows\n", + "that a published result drops out of the standard AAanalysis pipeline, and it is\n", + "the template you adapt to your own paper-style analysis.\n", + "\n", + "**The question.** *gamma-secretase* is an intramembrane protease implicated in\n", + "Alzheimer's disease and cancer. It cleaves only a subset of single-span membrane\n", + "proteins, with **no consensus sequence motif** marking which ones. The paper asks:\n", + "*what physicochemically distinguishes a gamma-secretase substrate from a\n", + "non-substrate, and can we predict substrates from sequence alone?*\n", + "\n", + "**The answer, in one sentence.** *Comparative Physicochemical Profiling* (CPP)\n", + "finds an interpretable, position-resolved **signature** that separates substrates\n", + "from non-substrates and, fed to a tree model, predicts substrate status at\n", + "**~85% balanced accuracy** — far above a scale-average baseline that ignores\n", + "*where* in the sequence a property sits." + ] + }, + { + "cell_type": "markdown", + "id": "df77c458", + "metadata": {}, + "source": [ + "**What this reproduces (key results, simplified).**\n", + "\n", + "| Paper figure | Reproduced here |\n", + "| --- | --- |\n", + "| Fig. 2b/2c — CPP profile & feature map | the gamma-secretase **signature** over the TMD-JMD parts |\n", + "| Fig. 3a — CPP vs. scale-based benchmark | **CPP ~85%** vs. **scale-average baseline ~75%** balanced accuracy |\n", + "| Fig. 2d / text — conformational, cleavage-region features dominate | the signature's **part** and **category** breakdown |\n", + "\n", + "**Simplifications (so it runs in seconds on bundled data).** The paper trains on\n", + "the **imbalanced** human N-out proteome (63 expert substrates vs. 631 others, then\n", + "dPULearn-balanced) across **ten** model types with leave-one-out CV, and compares\n", + "**three** transmembrane annotations (UniProt / TMHMM / Phobius). Here we use the\n", + "bundled, **balanced** `DOM_GSEC` set (63 substrates + 63 non-substrates) with its\n", + "single embedded TMD/JMD annotation, one tree ensemble, and 5-fold cross-validation.\n", + "The headline number (~85% for CPP, matching the paper's 84%) and the biology\n", + "(cleavage-region, conformational features) reproduce; the absolute baseline gap is\n", + "narrower than the paper's because this set is balanced and proteome-free. Scaling\n", + "to the full study is exactly the *Protocols* (P1, P4, P8-P10)." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "ce67dfa9", + "metadata": { + "execution": { + "iopub.execute_input": "2026-06-25T22:56:54.749376Z", + "iopub.status.busy": "2026-06-25T22:56:54.749286Z", + "iopub.status.idle": "2026-06-25T22:56:56.155233Z", + "shell.execute_reply": "2026-06-25T22:56:56.154987Z" + } + }, + "outputs": [], + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "from sklearn.ensemble import RandomForestClassifier\n", + "from sklearn.model_selection import cross_val_predict, StratifiedKFold\n", + "from sklearn.metrics import balanced_accuracy_score\n", + "\n", + "import aaanalysis as aa\n", + "\n", + "aa.options[\"verbose\"] = False\n", + "aa.options[\"random_state\"] = 42" + ] + }, + { + "cell_type": "markdown", + "id": "4d886d93", + "metadata": {}, + "source": [ + "## 1. Load the gamma-secretase dataset\n", + "\n", + "`DOM_GSEC` is a **domain-level** (`DOM_`) benchmark: one row per protein, a binary\n", + "`label` (1 = substrate / **test group**, 0 = non-substrate / **reference group**),\n", + "and the transmembrane geometry (`tmd_start` / `tmd_stop`) plus the pre-cut\n", + "`jmd_n` / `tmd` / `jmd_c` parts. It is the bundled, balanced subset behind the\n", + "paper's Fig. 2-3 (63 substrates + 63 non-substrates)." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "e82221c6", + "metadata": { + "execution": { + "iopub.execute_input": "2026-06-25T22:56:56.156676Z", + "iopub.status.busy": "2026-06-25T22:56:56.156548Z", + "iopub.status.idle": "2026-06-25T22:56:56.225706Z", + "shell.execute_reply": "2026-06-25T22:56:56.225491Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "126 proteins: 63 substrates (label=1) + 63 non-substrates (label=0)\n", + "DataFrame shape: (126, 8)\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
 entrysequencelabeltmd_starttmd_stopjmd_ntmdjmd_c
1P05067MLPGLALLLLAAWTA...GYENPTYKFFEQMQN1701723FAEDVGSNKGAIIGLMVGGVVIATVIVITLVMLKKKQYTSIHH
2P14925MAGRARSGLLLLLLG...EEEYSAPLPKPAPSS1868890KLSTEPGSGVSVVLITTLLVIPVLVLLAIVMFIRWKKSRAFGD
3P70180MRSLLLFTFSACVLL...RELREDSIRSHFSVA1477499PCKSSGGLEESAVTGIVVGALLGAGLLMAFYFFRKKYRITIER
4Q03157MGPTSPAARGQGRRW...HGYENPTYRFLEERP1585607APSGTGVSREALSGLLIMGAGGGSLIVLSLLLLRKKKPYGTIS
5Q06481MAATGTAAAAATGRL...GYENPTYKYLEQMQI1694716LREDFSLSSSALIGLLVIAVAIATVIVISLVMLRKRQYGTISH
6P35613MAAALFVLLGFALLG...HQNDKGKNVRQRNSS1323345IITLRVRSHLAALWPFLGIVAEVLVLVTIIFIYEKRRKPEDVL
7P35070MDRAARCSGASSLPL...DITPINEDIEETNIA1119141LFYLRGDRGQILVICLIAVMVVFIILVIGVCTCCHPLRKRRKR
8P09803MGARCRSFSALLLLL...RFKKLADMYGGGEDD1711733GIVAAGLQVPAILGILGGILALLILILLLLLFLRRRTVVKEPL
9P19022MCRIAGALRTLLPLL...PRFKKLADMYGGGDD1724746RIVGAGLGTGAIIAILLCIIILLILVLMFVVWMKRRDKERQAK
10P16070MDKFWWHAAWGLCLV...DETRNLQNVDMKIGV1650672GPIRTPQIPEWLIILASLLALALILAVCIAVNSRRRCGQKKKL
\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "df_seq = aa.load_dataset(name=\"DOM_GSEC\")\n", + "labels = df_seq[\"label\"].to_list()\n", + "n_sub = sum(labels)\n", + "print(f\"{df_seq.shape[0]} proteins: {n_sub} substrates (label=1) + {len(labels) - n_sub} non-substrates (label=0)\")\n", + "aa.display_df(df=df_seq, n_rows=10, show_shape=True)" + ] + }, + { + "cell_type": "markdown", + "id": "373d23db", + "metadata": {}, + "source": [ + "## 2. CPP: find the physicochemical signature\n", + "\n", + "CPP splits each sequence into **parts** (`jmd_n` / `tmd` / `jmd_c`), applies\n", + "**splits** (position selectors) within each part, averages an amino-acid **scale**\n", + "over those positions, and keeps the `Part-Split-Scale` features that best separate\n", + "substrates from non-substrates. The result — the **signature** — is interpretable\n", + "biology, not a black box. (Mechanics: the `CPP` tutorial; workflow: *Protocol 1*.)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "c4e481fe", + "metadata": { + "execution": { + "iopub.execute_input": "2026-06-25T22:56:56.226786Z", + "iopub.status.busy": "2026-06-25T22:56:56.226724Z", + "iopub.status.idle": "2026-06-25T22:57:01.331963Z", + "shell.execute_reply": "2026-06-25T22:57:01.331719Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "signature: 100 features\n", + "DataFrame shape: (100, 13)\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
 featurecategorysubcategoryscale_namescale_descriptionabs_aucabs_mean_difmean_difstd_teststd_refp_val_mann_whitneyp_val_fdr_bhpositions
1TMD_C_JMD_C-Seg...2,3)-QIAN880106Conformationα-helixα-helix (middle)Weights for alp...ejnowski, 1988)0.3870000.1180000.1180000.0680000.0800000.0000000.00000027,28,29,30,31,32,33
2TMD_C_JMD_C-Pat...,14)-CRAJ730103Conformationβ-turnβ-turnNormalized freq...d et al., 1973)0.3770000.285000-0.2850000.1640000.1770000.0000000.00000027,31
3TMD_C_JMD_C-Seg...6,9)-FAUJ880104ShapeSide chain lengthSteric parameterSTERIMOL length...e et al., 1988)0.3670000.2630000.2630000.1610000.1680000.0000000.00000032,33
4TMD_C_JMD_C-Seg...6,9)-ONEK900101OthersUnclassified (Others)ΔG values in peptidesDelta G values ...-DeGrado, 1990)0.3660000.1110000.1110000.0700000.1140000.0000000.00000032,33
5TMD_C_JMD_C-Pat...,15)-QIAN880107Conformationα-helixα-helix (middle)Weights for alp...ejnowski, 1988)0.3630000.1620000.1620000.0910000.1180000.0000000.00000024,28,32,35
6TMD_C_JMD_C-Seg...3,4)-HUTJ700103EnergyEntropyEntropyEntropy of form...Hutchens, 1970)0.3600000.1870000.1870000.1150000.1280000.0000000.00000031,32,33,34,35
7TMD_C_JMD_C-Seg...2,3)-WOLS870103OthersPC 4Principal Component 3 (Wold)Principal prope...d et al., 1987)0.3590000.159000-0.1590000.0900000.1300000.0000000.00000027,28,29,30,31,32,33
8TMD_C_JMD_C-Pat...,12)-CRAJ730103Conformationβ-turnβ-turnNormalized freq...d et al., 1973)0.3520000.227000-0.2270000.1500000.1700000.0000000.00000024,28,32
\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "sf = aa.SequenceFeature()\n", + "df_parts = sf.get_df_parts(df_seq=df_seq)\n", + "\n", + "cpp = aa.CPP(df_parts=df_parts)\n", + "df_feat = cpp.run(labels=labels, n_filter=100, n_jobs=1)\n", + "print(f\"signature: {df_feat.shape[0]} features\")\n", + "aa.display_df(df=df_feat, n_rows=8, show_shape=True)" + ] + }, + { + "cell_type": "markdown", + "id": "e40851ec", + "metadata": {}, + "source": [ + "## 3. Rank the signature by importance\n", + "\n", + "Fit a `TreeModel` (a random-forest ensemble — the paper's family) on the CPP\n", + "feature matrix and attach a Monte-Carlo **feature importance** column. This is the\n", + "unsigned, group-level ranking that drives the figures below." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "0eb90b68", + "metadata": { + "execution": { + "iopub.execute_input": "2026-06-25T22:57:01.333094Z", + "iopub.status.busy": "2026-06-25T22:57:01.333026Z", + "iopub.status.idle": "2026-06-25T22:57:01.749277Z", + "shell.execute_reply": "2026-06-25T22:57:01.749037Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "DataFrame shape: (100, 6)\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
 featurecategorysubcategorymean_difabs_aucfeat_importance
1TMD_C_JMD_C-Seg...2,3)-QIAN880106Conformationα-helix0.1180000.3870004.453000
2TMD_C_JMD_C-Pat...,14)-CRAJ730103Conformationβ-turn-0.2850000.3770003.463000
3TMD_C_JMD_C-Seg...6,9)-FAUJ880104ShapeSide chain length0.2630000.3670003.273000
4TMD_C_JMD_C-Seg...6,9)-ONEK900101OthersUnclassified (Others)0.1110000.3660001.296000
5TMD_C_JMD_C-Pat...,15)-QIAN880107Conformationα-helix0.1620000.3630002.491000
6TMD_C_JMD_C-Seg...3,4)-HUTJ700103EnergyEntropy0.1870000.3600001.858000
7TMD_C_JMD_C-Seg...2,3)-WOLS870103OthersPC 4-0.1590000.3590001.786000
8TMD_C_JMD_C-Pat...,12)-CRAJ730103Conformationβ-turn-0.2270000.3520002.637000
\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "X_cpp = sf.feature_matrix(features=df_feat[\"feature\"], df_parts=df_parts)\n", + "\n", + "tm = aa.TreeModel()\n", + "tm = tm.fit(X_cpp, labels=labels)\n", + "df_feat = tm.add_feat_importance(df_feat=df_feat)\n", + "aa.display_df(\n", + " df=df_feat[[\"feature\", \"category\", \"subcategory\", \"mean_dif\", \"abs_auc\", \"feat_importance\"]],\n", + " n_rows=8, show_shape=True,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "d94aa44e", + "metadata": {}, + "source": [ + "## 4. The signature as a feature map (cf. Fig. 2c)\n", + "\n", + "Rows = scale **subcategories**, columns = **positions** along the parts, colour =\n", + "`mean_dif` (direction & strength of the substrate-vs-non-substrate difference), top\n", + "bars = cumulative feature importance. Read coherent **blocks** (one property family,\n", + "one region), not single cells." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "2af2d947", + "metadata": { + "execution": { + "iopub.execute_input": "2026-06-25T22:57:01.750354Z", + "iopub.status.busy": "2026-06-25T22:57:01.750286Z", + "iopub.status.idle": "2026-06-25T22:57:02.254234Z", + "shell.execute_reply": "2026-06-25T22:57:02.253991Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "cpp_plot = aa.CPPPlot()\n", + "aa.plot_settings(font_scale=0.65, weight_bold=False)\n", + "cpp_plot.feature_map(df_feat=df_feat, name_test=\"substrate\", name_ref=\"non-substrate\")\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "61cf9991", + "metadata": {}, + "source": [ + "## 5. The CPP profile (cf. Fig. 2b)\n", + "\n", + "The profile collapses the map onto the sequence axis: cumulative feature importance\n", + "per position. The signal concentrates around the **cleavage region** in the\n", + "C-terminal TMD / JMD-C — exactly where the paper localises the substrate signature." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "88e2c60f", + "metadata": { + "execution": { + "iopub.execute_input": "2026-06-25T22:57:02.255325Z", + "iopub.status.busy": "2026-06-25T22:57:02.255242Z", + "iopub.status.idle": "2026-06-25T22:57:02.447721Z", + "shell.execute_reply": "2026-06-25T22:57:02.447491Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "aa.plot_settings()\n", + "cpp_plot.profile(df_feat=df_feat, add_legend_cat=True)\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "b3d45ae8", + "metadata": {}, + "source": [ + "## 6. Benchmark: CPP vs. a scale-average baseline (cf. Fig. 3a)\n", + "\n", + "The paper's core claim is that **positional splitting matters**: averaging\n", + "physicochemical scales over the *whole* sequence (the \"scale-based\" baseline) throws\n", + "away *where* a property sits, while CPP keeps it. We compare, apples-to-apples with\n", + "the same tree model and 5-fold cross-validation:\n", + "\n", + "- **CPP** — the `Part-Split-Scale` signature from step 2.\n", + "- **Scale-based** — each amino-acid scale averaged across the whole TMD-JMD sequence\n", + " (no splits), the baseline described in the paper's Methods." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "9d4f77dc", + "metadata": { + "execution": { + "iopub.execute_input": "2026-06-25T22:57:02.448918Z", + "iopub.status.busy": "2026-06-25T22:57:02.448844Z", + "iopub.status.idle": "2026-06-25T22:57:03.433567Z", + "shell.execute_reply": "2026-06-25T22:57:03.433288Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "CPP (100 features): 84.9% balanced accuracy\n", + "Scale-based (586 features): 75.4% balanced accuracy\n" + ] + } + ], + "source": [ + "# Scale-based baseline: average every scale over the whole TMD-JMD sequence (no splits)\n", + "df_scales = aa.load_scales()\n", + "seqs = (df_seq[\"jmd_n\"] + df_seq[\"tmd\"] + df_seq[\"jmd_c\"]).to_list()\n", + "X_scale = np.array([df_scales.loc[[a for a in s if a in df_scales.index]].mean(axis=0).values for s in seqs])\n", + "\n", + "cv = StratifiedKFold(n_splits=5, shuffle=True, random_state=42)\n", + "def balanced_acc(X):\n", + " y_pred = cross_val_predict(RandomForestClassifier(n_estimators=200, random_state=42),\n", + " X, labels, cv=cv)\n", + " return balanced_accuracy_score(labels, y_pred) * 100\n", + "\n", + "bacc_cpp = balanced_acc(X_cpp)\n", + "bacc_scale = balanced_acc(X_scale)\n", + "print(f\"CPP ({X_cpp.shape[1]:>3d} features): {bacc_cpp:.1f}% balanced accuracy\")\n", + "print(f\"Scale-based ({X_scale.shape[1]:>3d} features): {bacc_scale:.1f}% balanced accuracy\")" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "7bbf654c", + "metadata": { + "execution": { + "iopub.execute_input": "2026-06-25T22:57:03.434682Z", + "iopub.status.busy": "2026-06-25T22:57:03.434590Z", + "iopub.status.idle": "2026-06-25T22:57:03.481901Z", + "shell.execute_reply": "2026-06-25T22:57:03.481671Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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2HwHwi82TJ4+xDT6Q6JBp33m4CDkGbAnvA+RtXcw++ahHwYIF5QX3tq2D3Zb+vqdod+1rjO3y5s0r++jAPdpt0JWghlDVgqBIkSJ2Ucqwrz/q6Oqehfec+PL+uDunv++duT1xHnN7mqOUWUXGCtR7cvr0acPHXLtq4XuMYCd4b7Xg9VRQX/TiG+DuHrg6D54dCHy9HwQ22hFBXfQ6+IzjWbRqfwpqDzh06JCtVq1admHm0MgQcrpH895779ntgwgz5cuXN7bXHxg81Hg4tN8cXlizD19kENS+XF94wPkfH19zTx4LHuimTZtKFCkrbty4Ifsh9KB5P3ww8GF29XHCegRzMX948NLDP3779u0R/gB5W5dHjx7Z3nzzTbvtT5065VNbB7stXeFtPZYsWSICzHxfEA0K7+PYsWPdCmocs0+fPkbkPSwQvIg45a86urtn4T0n3t5Td+cMxL3T7QHBA//hRIkSGceEzzIiqVkRqPdEByJp1aqVXWcHQh8jcQQUiYig9uYb4O4euDsP7itC0TqGtkUHxdVzGShBHQP/qEgGJvER+L9x48YSxN+dgQoMCjA/ApcBbYyA+TK4bsAwxGo+DXNgcMHAPjAsaNKkicx7aT++/PnzG+b3OBYsLPPmzevk2gCrQBg+6GQaVgY2mKeBb53ZkCYQxzRbfJ46dUosKNEe2k8Qhjh6Hsl8fZ6Cx+jMmTPqxo0bsi8C/2srzfD2w5yTTnCQI0cOw4XBHTAwgh8r2iNz5syGf6IjMAjBvCHm2135XvpSFzyLWJCcAO4cZkM7b9s62G3p7nje1APPH1x6YGSTKlUqWQdLbLQF3CNxvxyfdbzP8NpAm2HOF+dzlYzD1zpa3TNPnxNv76m758Sf9w5zpvhuLV26VBJO6PbEenfuRoF+T7TvMN5Z3Gd8V9EGSJiBBfYHZpcy/VzAgDCZg01ARL8B7u5BRM4DIzw80+7O4+7bDdBucDPD/dBGax5hi2RgjgC9dFQdvRMzCO+HeQiUHzx4UNRf5p6QeUFEI0fQ83e1D3prmDcjhJBQJbwRHYmcRCqrb/TgYHZvZfEH0CuDxR/KYaGJXj0ikWEkjAVp3jSO0XDQS2zYsKHdPuilz5o1S0YFiDyEdG+EEEJIMIk0gnrZsmWi6kWeVeSqtQIqGJ3jFKo2qDagCoabAIQ01DtaVYUczuYgCwiagNyjUKEgeAHy2sKR/e2331azZ882TPJdBWYghBBCoq2ghu9fjRo1xOG+adOm6vPPP3frP4g4rHqUjPlczC9AaNepU0dt3LhRYmjD12/VqlXGftq/GoIeIQrNICG51T6EEEJIoIkUghqBSgoWLKj++OMP9dNPP0n8X1cgQpIO7TZ//nwJwIBoPIiw9fvvv4tzOkLhaUd8x0DsuswRq30IISSUQFhVRGd7+eWXn3dVSHQLIdq6dWvVt29flxlYHIFVJoD6GjGBHdHWpHo78/9dhdWz2ocQQkIJWM+TqEekENTu3Aocgbr79u3bboOlazN8HerOm30IIYSQkBLU8M1zl5HFF+BT5q8ct+Y6ujI60+v1tt7sYwUMzVwZm8GafO3atTLCd5wDJ4QQ4l8ePHgg9kmwbwp0asxA47F0xMV6kovUGzCn4q/A5ghEgHlqOKm7Ss6thakOjODNPlYMGTJE9e/f3w9XQQghxB/8/PPPErQqMhMpVN8RBZF14HMNdbaOjGRGp240R6LxZh9HevfurT755BPLsk2bNklnBw9NgQIFvLouQgghngGPH3gJRSgCWFQR1LC8Rso5f1C7dm3xXfY38KeGFTfCteH/jmiDMHMqNG/2sVKPu1Kda9ULhDRSzBFCCAk8CaLAVGOEBTXipJpj9foCYr4GyvIRQhej2IoVKzqVYz0oXry4T/sQQgghIeNHjche8CUOL2B7RAjEMc0Jy6dOneo054yAJwiej4DpOhG5t/sQQgghISOoJ0+erNatWyfZVfxFII4JEIEM/tCHDx9W9erVE8s/ZLdZvny5atCggWzTrl07uw6CN/sQQgghkc6YDNbRcENCNDCMTJHWrly5cpIeLFjAMvuHH34Q460lS5YYadggeAGi9vTr18/nfQghhJBIFUL0t99+k/nratWqqbZt26pOnTqpRo0aiQHWRx995NL1KRAgexbmlV9//XXJmYqoZvBhRpxwJPiIHz++X/YhhBBCAkkM5Lr0x4Ewiq5UqZKMQKtXry5+0VATw4oaAhwBP1q1aiVJM3wF/s4IPIIgKZ4GSkG9IpqI3Zt9XIE5bmT/gi86rb4JISSw7IhC31y/qb7HjRsngu2zzz5TAwYMsCvr1q2bJNWAanno0KEqZcqUPp0LwUmwRARvBK6/hDQhhBDiLR5Lorlz56qwsDCX5WfPnpVfjKodyZkzp8qSJYuMhM+fP+9tXQkhhJBoh8eCGuEx8+bNK6NibWBlRmep+u6775ziYSPd5LFjx2SONypEiSGEEEJCTlB/8cUXEuHlgw8+kOAgiFBmpnv37ipRokRqzpw5KkOGDKpy5coyVw3Xq/r160uGKqjFYaRFCCGEEM/wWFDDz3jfvn0yYoZhGP5GBK/NmzcbuZ/xf1h5IxXkqlWrxAcZfsl58uRREydOVH369PH0dIQQQgiJqHsWLKzbtGkjPtIYYcOqrkyZMqphw4bqyJEjqlChQmrmzJmSXgyqbsTxvnTpkgjr1q1bs8EJIYSQCOKVWTNU3Aj+AWEMf+kFCxaIVTcid0EwQ6DDgKxIkSISG5wQQggh3uGT/xGE8Lfffisq8TfeeEPU4jAqgxDXaSEJIYQQ4j1+cRSGNTiMyJC8Aok24EcNgT1+/HgnC3BCCCGEBEhQww/6xx9/VG+++aYYj0FAV6hQQay5L168KPPViFC2cOFClSpVKvXxxx+LSnz27Nli9U0IIYSQAAlqJNt47bXX1Pvvvy+CGOpuGJBBMA8aNEgMyRCqDSBW9p49e8TS+/79+2IJXrp0abV69eoIVo8QQgiJ3ngsqL/55hv1999/S8ASGI+dOXNG3bhxQ+3evVs1a9ZMXb9+XTVt2tTYHiE+YekNYT5w4EB18OBByeW8a9euQF0LIYQQEn0F9dKlS+V31KhRYjiGLFnJkydXRYsWVdOmTZMgJxDGEMxmEiZMqD799FOxEO/QoQPjZxNCCCGBSMqh55it4n0/efLEMBpDakgr0qRJI6NyQgghhARAUNeoUUPmmLt06SLCGmnD4C+NNJajR4+WaGUFChQwYn4TQgix5u7du6KlPHnypGgjq1WrptKlS2e3Db6rN2/edNq3QYMGYsyLwRGSJSEhUqlSpcSw18yJEyfE+BdRJJF2mERibB4SFhZme/311zGstlzSp09v27lzp6eHi3Zs375d2gm/hJDoy549e2yZM2e2+34mS5bM9ueff9p9b2PHjm35rZ0xY4ZsU7VqVVuqVKlsLVu2lPXDhg2zO0+DBg3kuNeuXbNFR7ZHoW+uxyPqePHiibX3hg0b1IwZM2TO+d69e9ILLFeunHrvvfdkzpoQQohrEIYZo2Ck/oVHzMqVKyUcM76hWB8nThy1f/9+mVJEEqOuXbva7Y/RNIxyYdyLyJAIOoUkSSNGjFDdunWTbbZu3SqxLQYPHqxSpkzJ2xHJ8VhQa15++WVZCCGERAyoq3Uio+nTp6tKlSqJx0zq1KnV5cuX1aFDh0QQI08CQBwK5FVwRGcv1EIYcSvgMgvhjinJnj17ikq9U6dOvEXRSVCvWbNG3LHwYPlr5ByIYxJCSKgCIQpPmNu3bxvrzMGgkiRJIr9aUEMAT5o0Sb6TZcuWVeXLl5f1mTJlkl8Ief2bPn16Of6SJUske+H3338v5yJRAE915C+99JLo+7du3eo3vXsgjhmqRKX5EkKI9wwcOFC+BdmzZ7d1797dVqxYMfm7YcOGxjaYf7aan27cuLHt6dOndnPUrVq1MuaoUVa0aFFbnjx5bI8fP47Wt2l7FPrmRlj1TQghxHs++eQTmV+GRnHYsGHGCHno0KHGNnpEjXnsevXqSargP//8U+yDEKq5Y8eOMnKG1TeCT8Ejp2LFiqJOx74I24zR9fLly2U+G944iH8RM6Zf0juQIBNhQd23b1+VIkUKv5z8+PHjfjkOIYREBp49eyahmBF6GfPPtWvXFkOyFStWiADetm2bCGcIXMxXN27cWGJQgKpVq8p2EMIQ1DA6e+edd+zCPCPvAlyx4MKFQFNfffWVGK8hoyFCO8+cOfM5Xj0JmqBetmyZ1ycjhJDoDEbRENIY7eIXxmCYoy5ZsqTkSpgwYYJYeT948EDWw+pb88ILL4ig1vPSjowbN07iWkyZMkX+HjlypMS7wPpTp06pWbNmiWW4nt8mUVBQIxkHenSBANaJhBAS1bly5Yr8QghjdG3OTAgQ4OTOnTui7gYQqhgdA20tnj9/fqfj3rp1S1yxEDgF3+lr166JsDdbhQO4f1FQR2FBjZSVhBBCvOeVV16RmBRQUyP+BNTRmEPWyYqqV6+usmbNqipXriz+1S1atJCRN/yq169fL9tYuVwNGTJELMPxCyCgEyRIIAIb6F/kaCCRkOdtzRZdiEoWiIQQ75k5c6YtefLkdtbcsWLFsvXo0cPY5vTp07YiRYrYbYNIZcOHD3c63tmzZ20JEiSwNWrUyG593759bTFjxrS1a9fOsjyqsz0KfXNp9U0IIUEE0chq1aolVtuYO0awE/hH58mTx9gGBmUwMoOlN7ISwh8aRmg5c+Z0Oh5ievfo0UM1b97cbv2gQYPEEhyj9Z9++slQp5PIRwxI6+ddiegAXrqXXnpJDEZg4EEIIYTfXE+gUx0hhBASwlD1TQiJ1BzIX+B5V4EEiAIHD7BtOaImhBBCQhuqvgkhhJAQhoKaEEIIiaqCGi4GXbp0MZz1Q42TJ0+qDh06qOLFi4tbQ4UKFSSsXlhYmMt9EOEHMXK1uwSi/Pzwww92qegIIYSQSGFMhkTnS5cuVaNHj1ZFixaVMKNNmjRRadOmVc+bjRs3it+hOe8r/A0R5QeB6REzV+d+1SC83ssvvyzZaDRHjx6VbRcuXCiZamLFihXU6yCEEBK98WlEPWrUKPXee+9J4HikVkP6NsSRRVg8CLVHjx6p58Hjx4/Vu+++K0IamWT++usvdeDAAUnAjtB6W7duVd27d3fa7+233xYhXapUKUm8fuTIETV+/Hi5PghqjMYJIYSQSBfw5P79+2revHkS/QajTx1gHkIRadoQMQfZYYIFhCxi5SLiD4Rt8uTJjbI//vhD1alTRyVKlEhi4yJVHEB+WAS0R0o57JMsWTJjHwjpN998U4534cIFyXwTURjwhJDAQPesqIsv7lk7olCQKb8YkyG8XdOmTWXkihHp8OHDJSUb0rFhRIoRaqFChdTXX3+tzp8/rwINVNgAN8kspEGNGjUkefq9e/eMQPUAKeDABx98YCekARKu58uXT129elX9888/Aa8/IYQQEjCrb6SsRD5VGJjt3r1bMr1g1IrsLz179lTZsmVTb731llq3bp0KFMg+AxAj98mTJ3ZlmHNGejlksMEIWYOE7QAGZ1bo9Rs2bAhYvaMSMMrTWYGsFsQ41hw6dMhyG2g8NHfv3pV7h2kNR5CJCNtfvHgxaNdHCCGR2j0LgnD58uUymp4xY4bdxzVGjBiiJodVNfKsmo29/AWOXbhwYREGH330kQgNAJV2s2bN5P+tWrWyU2HDQhzkzp3b8pjZs2e32464B/cf1vauFnTaNGXKlLHcBtMU2hYC0yhIMAAbiNWrV9udCxocqLbOnTvH20IIiXL4NYTo4cOH1bRp09T06dPtPpoZM2YUozOolTGK/fXXXyVvKgzOMJ89f/58f1ZDVNuwRocV+tSpU8W9CipwjNBgtf3hhx/aGYZhmh4J20GKFCksj6lV6Ho74h60F6Y/zOBe7927V/6vO0mnT5+WNsU9K1KkiN32EM6YKkFmIBgootMHj4L27durffv2yTaYjsCUCgwBMdVBCCFRDZ8FNUbEmN+FgDarhePGjSsfVyQ+x7yw2a0JH1qsw7z1ggULRGWZPn165U/gOgb1thbEWo0KNTzWwwAOdQTmEb/ex5H48ePLrztLdqhgsVgB1W10Av7njv71X331lerdu7d01vr37y/r4C2gNRlW/vgw8sP0BcpxvzCtASGN+4C/kcoP9xK/hBASFfFJUGOkM27cOPXgwQNjXbFixUQ4w586VapULvfFhzdv3rwywoKw96egxnxzpUqVxGAMVudQf+P4+MB/+eWXauzYsdKpgE91ggQJ5IOPER1U9lZzoGYBra3ErYCWQAsgYg/sBb744gv5P7QcuXLlshPUeBauXLkiBoh4NnTHDq5x5vbHLzpTuA/wi//uu+9U69atXU5ZEEJItJ6jXrlypQhpqCg//vhjMYffuXOn6tixo1shbbYWr1KlivHR9hd9+vQRIY06Qc2OuU1YbdevX18ENDoTMNmfOHGisY8WCK5Gvnfu3JFfR4twMxgtYj7calmzZo2KzqBTB20D7BLq1q1rrNeCGu2TLl06lT9/fjFI/OWXX2Q91OeZM2eWOe8lS5aIQVnt2rXF1gER5KBC//zzz5/bdRFCSEgLavgqQ+0N32KMUmEAFBE2b94sqk1/R/vSKnh0GBzBaKxdu3ZGR8PRWMwclcyMXg+h4QocO2nSpJaL7ghE19H04sWLRWsxYMAAuzItqNERQtvCvx0jaxj94dmAxmPZsmUS+Q7aEEyboIMFNTnmrBHCFtoSjLQhxKEGJ4SQqIRPglob8ei53lAA85n6Yw0BaYVeDzWrRhsyIWqZFVu2bJFfRwMpEj5jxowROwEEmilQoIDTXParr74qghyGZVigBsf2I0aMkG2wDzQj69evl2PB4A9W49DkIMIcBDlG4TgO1k2YMIG3hRASZfCLe9aePXtk9IownWbw4YRBGaKVBQuoQvV85ezZsy23gXsYgJpVAyECYBSnI6uZR30Q1FpVTyIGIrsBhHV1BNb3sMqvWbOm/A1Bq1XjcKezAs8ThDOmODAV0bZtW+l8QciXLl1ade7c2c4HmxBCorWghuob4UGh+sa8sBmoM3///XcZNXXr1k0FCxiPAYy6vv/+e8PYDa486FBAgGOOE25aGnQooHqFUEbIU/2hx5w75rYxwoPvtWOkM+IeqKgxNYLpDcwtm0HHDtb0mHbQAWe0xb5263ME9wH3FdbfUIPDpuD48eOSHQ33FPYOyI4GV0FCCFHRXVAjohTmEmEkhFjY+FiaQRIMjG4wNwk15pQpU1QwwDkR9hMCGqMtjLpg3IY43uhQgKFDh9rFH8doGXOfGJHDkAn+utgegTSOHTsmc6QDBw4MSv2jEjoCHVzxHOfpodHQc/7oXMFmAFbcyG4GYHhm1TGEISCs62ETAEGPe2a2CgfR2SaAEBK18ElQQ/DCnQmqSgQtgdrSDIzLEFUKH18QLEGH0RvU2998843Mb6KOmI/GBx1RyxAMxSp7FtSvUKsi1SXU3xiBYwQN4zMIEVdz3sQ1UEcDx7lpgBEwniG4WmHkjWkFtDXuFyz127RpY7c91sPSG0IfAXQA7il88qH5gDocLnfa9Y8QQlR096OGOxaAStgdKIe6EiE98eHWsbgDCUbxHTp0kAXGZVDDwwgpPMM3xPSG0RLUp9jPsfNBIgaEMAzwHKOOaSCcIaTRoYMqHFbesBeAJsTxXqGDhZHy4MGD5f5qfvzxR/HRxigbHTH8352/OyGERBtBDWEGzMktrMBHFSpOhIrEEgxBbQZqbSwRASpVHY2MeA9cqrC4o2DBgjJNEh6wI8DiCKY19JQGIYRENXxSfSNBAtDxm12BeUOdLQlzv4QQQggJwogaAU9gQY3sRe+8847LOVwYkkH1jHlDf8f0jk7oEJwk6sF7SwgJyIgawhmCF/6uZcuWFaGNBBsIOgK3GQQPgUFQ3759jTCShBBCCAnSiBpuT3ClgfHP/v37VaNGjVxuixSXLVu29OV0hBBCSLTD54AncKNBkBBky7JSfcPaF9G+guVDTQghhEQlfM5HDXLkyKF+/vln8XOF+xUiS+ncwQgaQgghhJDnKKg18F1FCEd/p60khBBCoit+ScrhCZcuXZK0hK7SSBJCCCEkACNqJFwYNGiQWrVqlbp9+7ZYfJsTKGgLcB2DGZbgWbJk8fW0hBBCSLTAJ0GNpBcIuXn06FGPA6TAUpwQQgghQVB9T58+XYQ0kmB06tRJ/KhLlCghZcgVPHnyZLEKB126dFFnz55VefLk8eWUhBBCSLTCJ0ENdTdA/ubRo0erhg0bqlq1ask6pCCE3zSyUVWvXl2NGTNGbdy40T+1JoQQQqIJPgnq8+fPyy/SXGqQtxns2bNHfjHaRojRZ8+eMUwiIYQQEkxBDeHrmGhDu2YdO3bMLuhJxowZJaczDMsIIYQQEgRBrdNbXr9+3ViXM2dOJ0ENkNcZFuAIiEIIIYSQIAjqkiVLyu/ixYuNdQgjihE2XLWOHz8u6yCgYUgGEiVK5MspCSGEkGiFT4K6cePGKnbs2GrChAmqa9euksoSVK1aVX579uwpo+3BgwermzdvioFZ5syZ/VNzQgghJBrgk6CGmnvgwIHy/5EjR6pDhw7J/zt27KhixIih5syZo1KlSqX69esn61u1aiXGZYQQQggJUgjRXr16ieq7SpUqEusblC5dWty19N963YABA3w9HSGEEBKt8Cky2YEDB1TixIlV7dq1ZTGDUTXyU2/btk1G1aVKlVIxYwYttDghhBASJfBJcg4dOlRlz55djR071rI8Xbp0IsDLlClDIU0IIYQEW1DDBQu+1AUKFPDlMIQQQggJpB+1tvYmhBBCSAgJ6t69e6uECRNKAg7HACeEEEIIec7GZNeuXVMff/yxGjFihMqXL5/E+cYvgp7APcsKCPWsWbP6clpCCCEk2uCToP7ss8/U9u3bjb937twpizvgS01BTQghhARBUL/55puqWLFiEdonTZo0vpySEEIIiVb4JKg//fRT/9WEEEIIIU4wAgkhhBASwlBQE0IIIVFV9f3666+rf//9N0L7/P7776pIkSK+nJYQQgiJNvgkqM+fP69OnToVoX0ePnzoyykJIYSQaIVPgvrrr7+WfNNW3L9/X507d07Nnz9fbd26VVy52rVrZ0QzCxYLFy6UdJsnT56UfNiIO456ZMyY0XL7R48eqYkTJ6o1a9aoixcvqmzZsqkmTZqomjVrBrXehBBCiM+CunLlyh6lwWzRooXkrS5UqJBk1AoGGLm//fbbatGiRXbrV6xYocaPH6/++ecfJxU8Oh0VKlRQ+/btM9atW7dO/fLLL+rDDz9UEyZMCErdCSGEkKAZkyFC2bBhw1TcuHElKpnNZlPBoFOnTiKkkYZzyJAhMkKeNWuWjKhv3ryp6tevr54+fWq3z7vvvitCOm/evGrGjBlq7dq1oglA3THKxkIIIYREmhF1RIKc5MyZUx08eFCdOXMm4JHJ9u/fL0I1Tpw4avny5SKczUFaXnjhBanLhg0bVPny5WX9pk2b1F9//aWSJUsmo22k6ATlypWTumutACKrMa82IYSQKOWehVH0jRs3gpZp6+eff5Zztm/f3k5IA4yOIZAxb54/f35j/U8//SS/77//viGkNc2bN5e822fPnhXhTgghhEQpQY253UuXLskIF8ZZgWbVqlXy+95771mWY0RfokQJu3CmmzdvdjnvDvV9pUqV5P/r168PUK0JIYQQP6u+R44cqU6fPu2y/MGDB6KGhkEWeOONN2TOONBgnhnq6cKFC6sjR46o77//Xu3YsUPFihVLjMU6dOigUqRIYbfPiRMn5DdPnjyWx8yVK5f8Hj9+POD1J4QQQvwiqH/99Ve77FnuyJEjh6TDDDR3794V9XqqVKnENQxq67CwMDurbwhuqL+11bdZNY/9rNCC3ZU7GiGEEBJygrpWrVp287yOYASL3NQvvfSSuEolTJhQBUNQg3v37omQzpQpk7iI5c6dWwzIvvzyS5lrRlQ1jPYTJEigHj9+bFijx48f3/K42C68gC0oc1Wu60UIIYQETVAPGDBAhRra5QqjaAhn5MfW6nbMM7/11lviz40AKDA6a926tRiYQVX+7Nkz9eTJE8vjQpgDzLO7Am5g/fv3D8h1EUIIiZ5EuaQc5jlwjKQd58RhQNa2bVv5/+rVq431iRIlMkbi7kbESZIkcXnu3r17q1u3blku8OMmhBBCnoug3rNnj+rYsaM6cOCA3fpXX31VVMyYFw4WULVroYvAJVbky5dPfq9evWqsy5Ili/xCLW6FXu8q9ChAiFKc32oJhhEdIYSQqIfPghrRvkqWLKnGjh3rNBqFUReyZVWtWlV169ZNBQO4UsHa253QvXDhgvya3bP0PrAOt0IbzRUtWtTvdSaEEEICIqgPHTqkmjVrJgZUiPiFCF5mJk2apDp37izzv7D4njJligoGMHIDkydPdiqD0djcuXPl/2XLljXW66QbmLd25NixY2rjxo0yl12lSpUA1pwQQgjxo6CG4IWRVd26dcUVKmXKlHblxYsXV6NGjVLfffed/I0QnMGgZcuWMpe8cuVKyZSlo6HBwOzjjz8WoZs8eXL1zjvvGPsg9jcyeyGUaJcuXSSLFoCfeIMGDcTIDJ0S8yicEEIICWlBrdXEiH/tDpRDMCJ3tbsAKf4CLlmI9Q33MHQS0qdPrwoWLCi+0N9++62M8OFLbfaZxjzyN998I6rz0aNHyz7ws0agk127dokf+ODBgwNed0IIIcRvgloHEgkvxzQEY+bMmeX/yFwVDDBa/vvvv2X+HLmxYeiG+hYrVkwtWbLEMt1m48aNJXc1hDMCoOzdu1fcvTDaRljStGnTBqXuhBBCiF/8qDFyBRBoL7/8ssvtoEbGaBoEU9jBb3rLli3qypUr6uLFi5JsI7zzQyhjQX1hHIeRtA52QgghhESqEbVOYDF8+HB1+/Ztl9vBkAzzxHCXgko52GBeGWrsiHQSkDwE6nIKaUJIdOKfu3fVFReBnwLF1SdP1EFTqOedD+6rv+/cUQsWLDCWy5cv2+2DSJMIBa29eMwgCRQGaVb5J5YtW2a3DhEqkX45ygpqqJcheJH4AhbUs2fPlpErDK8QIASpJNu0aaP69u0r2/fo0cNf9SaEEBIAel28oHY/eBDUtu1z4YJ6YHv2/3W4cEFNu3FdTZs2zVjOnTtneO4goiQSLCEaJAaAZg8fJGWCu+3UqVOd3HRh3GwOdAUwvQlZhsiUUVL1nSxZMjVz5kxVp04d6ZVYzftqPvjgA7HGJoQQEnEOPwxTMVQMFS9GDHXpyRNVMH48lShmLPXEZlMnHz1S5x8/VslixVKF4sdXsWPEkH2OPHyobMqmMsSOo/aEhalEMWOqovHji9GsBoLv37Aw9dD2TL2YwDofA45/9vEjlSNuXJUpTlxjvT5+pjhx1K4HYSplrFiqQPz46tl/x3xqs6kXEiQw6mPFH7dvq9gxlCr+37nvPH2qzj5+rH7MklW9umCB0/awI1q0aJEE2sJ0JuyH4JILF1tMx0JoQ95AHiHPxMKFCyXwFgJcwVMJYaXNaKNhpGPWUSujlKAGFStWVP/++6/67LPPJLiJowocjdC1a1dJkEEIIcQ75t66pbbcv68ePrOpeDFjqGEZMiqbeqLanD2jUseOrZLGjKX2hD2Q/8/Nll3FixlTzb91S+14cF8Ee7Y4cdWBh2EiyKdkziLCGkLxw7Nn1OUnT1T62P/LY/Do2f8SFIEHz56pbhfOywg7d9x4at/DMNUgWTLVM206Kcfx0YG4+OSJShc7ttr54IFqkCy5OvzwoXqmbOrEo0cqd7x4aup/57Ni8vVrql2q/zdIRh0h8CHcYRCcI0cOI80w+O233yRnA4S0joCJ5FAQyHDHhTbXHJVSB+JCHgZE0ISHjyOQTxilf/jhh+ItFOUENUBDIlAIfKrhfoW5BAQHyZo1K/2OCSHET2CkuSxHTpUi9v8+3T/duK6apkihWqT8n6vphcePVbXjx9Sm+/dVxf/CFh94+FDNz5Zd5YwXT0bGdU4cF4H6YsKEasK1ayrMZlOLc+RUCWLGVD/fuK52Xv5/tffYq1dlnz9y5JTR+vGHD1XDUydVmYSJjONvf/BALcyeQ2WNG1f9euOGGnT5kuqfLr1qmDy5Ovf4kap2/Lg6+PChjLQd2RcWpk4/eqQq/Bf2Weob9lDdf/ZMvX/mtMo+ZIjavHmzeOTA5RbCHnPTji7BefLkMUJYQ3APGjTI8EoqXbq0Onr0qFq+fLnLVMswPNY5GbTtVZQT1BpklkLPx9z7IYQQ4h9yx41rCGnQLEVKOxV2mO2ZqLehxtYUihdfhDTIHjeuShM7trrwn7HYirt35BgQ0qBR8hRq1JUrxr7L795RzVOkECENcJzqSZKoJXduG4IaI3QIaZDnv/PU+C95EdTkqA9G7AUsrmfL/Xsy4sboXxM3RgwR8t3SpFVFVqwQG6gXX3xRvfLKK+r9998Xra1j7gT8rQNbQahjG2h4tdYXCZowoo4dO7basGGDpDPGMTUYRUNNjvnrUBTUUS4pByGERFXSmoQ0uPfsqQjWeidPqBJHDquPzp5VD22YNf5/UsS2V+XiL8wdg0v/qaw1cWLEUOn+U4FL+ePHKrNpThpgPvrcf2l/gRbiZoGS1GGduT5mLjyGyt3+mhqnSKF6pU1nzGvnyZNHQlQvXbpU/oa21jEdMbS52kMHo24YMetImJiahab37bffFhU3bKUwf/3JJ5/YHQOxPoIRkMsbolxSDkIIiarAmMzM5xcvqtV376pOqdOov3LmUsty5lLxHeaCXZtxKRnt3nWwdjZbX6eKHVvdfvrUrvz202cqYYyYHh0/PB7bbCqmQ3033Lsn6nDHzIRP/6sHBOr58+ftyvG3q8yGiDT59ddfq2vXrolnEryRECoakSi1elyPqpG3IhSJkkk5CCEkOrD5/n3VJEUKVSlxYjEi2xv2QN1+9kyZ7MHc8lKChGqdaYCFOWioqTUvxE+gVt+7a/yNkfg/9+6qogmc55u9AXW+6dARmHHzhhp99f/V7w8fPhSjsvLlyxuqbLMvNOaW4TONeWYroBZHGSJUYtSNvxFOGiNvrNPAKjxDhgwqFImSSTkIISQ6ANen6Teuq0W3bqlp16+rDufOqcQxY6pbz+yFnyvap06t1ty7q768dEnNvXVTdTp/zm5E3gHld++qzy9eUAtv3VJtzp4VNbZ5btwXCsePrw7Bxes/VTxomyq1WnX3rvrq8iU1ffp0VaNGDXEF/uijj6Qclt3Hjx+XuWj4V1evXl18qrUg12j1OKZl9UgcQa/69esnMT3ga22WWcjpUKJECRWKRMmkHIQQEtXIGzeeKuJgOf1V+gyqVpKkau29e+r60ydqUuYs4ur09D+5lydeXFU0vn0I5FcSJVIZ4sQxjL9mZM0mvtBw/cLc8FvJkovBGcgVL56akz27ShgzpoykSyRMoH7Llt2Yl3Y8fvJYsVRlB0OvCokw2rd2eSqTMKGov2EVrikYP76alz27qNSRl6FGjRpq3bp1YgCmI03CEhyjX0Qmw9wzfKsdQWwPkDt3bvmFZhfz3IhahlG6NjYDJ06cEPW5Tnccpay+I5qUAwk5sMBtixBCiOe8lTy507oksWKpdg7fX1hRa+olc96nf3p79S6E8afp0tsJcjM54sYTAW6F4/FxrHGZ/peASTPMxdwxgLX3O8mTiz+22X0L54SvdoGZM12GeB45cqRyR9GiRZ3WwSMJmRMd+eGHH8SiHCrxKDeiNiflcMfzSspBCCEktGmZMpWo368HOb64Bq5cv/zyixHqOhSJFkk5CCGEhCZQl/dJm04M454Ha9asUYMHDw5pTS+TchBCCHmuIHhKTYvQnsGgbt26bvNUhAJMyhFkHIPCYE4EIVgx36+NH8zo6DlwhXNM5wYDPbgbwH/dUaOBoACpUqWSjDAwnnAEUxDwG7x+/bqT72CSJEnEhQEp4WBTYAaRfWDMAazSy8FeARHqsB/2N5MoUSKJs4vz4byOdgw6di/q65jJBtaZ8KXEdTr666MN0BbwQICLhSPa5QJ5yR0DJeg2RHxgHdlIg/PhvPDfdEyxB1Bf1Bv+mZjeMYPrxPVatSHaR9t16DbUhpkA6VVhOAMDlxs3bjhdCxa0A8IiOta3UKFCRpAHx2uFRgv3FdmEHK8H9cGIAu4qCNFoBtdYrFgx+T+eUbPvKYBbJtoRmfMc/VuxHuVoH6spMhwXxz98+LDcAzOoD+qFe+pohIrrwPXgOdnvUB89T4sIV2cePVJ3HJ4lGEql+c8/GCE5zWAfPcd7ICzMKVAHklIgihcSYDi6FSE+dfo4cdS9Z8/UKYfnAaZU+f6bg0UiCxhQ2V1rnDgqcaxYkl7SMcVkUtj4xI2rHj57po45HFcbX4ETjx6qBw5+WRnjxJERK9TKiMdtBgZiiFQGlytYXjsCQzMEQIFPs6OvNQKvwLXq1tOndsFPAJKGYK4aWN2bnHHjqvgxY8p+2N9MqlixVDppw6fq1KPH6oHpvYgTJ47kjtBBtvC+29U3Tx75diHLFr4hjt/aSI3NDxw/ftzWpEkTW9KkSfGU2C1FihSxTZs2zRbd2b59u1PbYEG7gSNHjliWa8qUKeNUVq9ePVu/fv1sNWvWdCrLlSuXlPXq1cvyuN26dZPyvHnzOpVVr15dyho0aOBUlj59einDEitWLKfytm3bSlnx4sWdyl555RUpa968uVNZkiRJjOPi/47l2AdlOIZjGc6FMpzbsQx11MdF3R3LcY0owzU7lqFtUIa2smpDtC3K0daOZbgnKMM9cizLnDmzUSer4+JZAHg2HMuwD/jzzz8t77kmderUTuUbNmyQsi5dujiVtWvXzuVzivuhKViwoFP5woULpWzw4MGW7QvOnDljea1hYWFSXrFiRaeySZMmSRl+HcuwPcD+VsddmTOXbX++/LbqiZ2fpc6p00jZuEyZnNswblwpw5IoZkyn8jnZsktZ4+TJnZ/RFCmk7Nes2ZzKUsSKZRw3S5w4TuUTM2eWsnapUjmV1UmSVMqW5shpea36uC/Ej+9U9lX6DFL2adp0TmWvJEwkZVty57E87rpcuaX81USJncp6pEkrZSMzZnQqKxAvnlGnODFiOD8v2XNI2VvJkjmVtUqZUsqmZcniVJYpUybjOcT/HctXrVolZY7fPDzTkZ0Y+MdfQp9JOVyDERNiySJ5SYECBbwaUTtaOXJEHXVG1NpHFHBEHbER9fyczrkFOKKOGiPqHPPm+jSibtq0qdq+fbtdXO/IiF8FNQlfUPvy0HzxxRds4igK7633HMhvle6BRAUKHPRefe2Pb26o4JekHJ6AHs6MGTPUmTNngnVKQgghJNLjc5pLqO6Q+3PVqlVi4GJWLWKwjr+hVtSqQQREz5Ili6+nJYQQQqIFPglqzL8hxqqj9am7ACmI2UoIIYSQIKi+ETAdQhpuPp06dZIUYjqoeZ8+fdTkyZMl0wno0qWLuIVgwp8QQgghQRDUUHcDJONGzs+GDRuqWrVqGRazSNC9YsUKyW4yZswYtXHjRl9ORwghhEQ7fBLUOrgBIrs4BkKH+TzAaBshRuFGQctWQgghJIiCWkePMifaQHYScOzYMWMdfN8yZsyoVq5c6RR9iBBCCCEBEtQ6aIM5HCTCBToKaoDAEbAAZz5qQgghJEiCumTJkvK7ePFiu4hMGGHDVev48eOyDgIahmQA0ZoIIYQQEgRB3bhxY0nSMGHCBNW1a1cjBGPVqlXlt2fPnjLaRgoxhFGEgVnmzPZJxQkhhBASIEENNffAgQPl/4hDjXjUoGPHjipGjBhqzpw5ksGpX79+sr5Vq1ZiXEYIIYSQIIUQ7dWrl6i+q1SpIkHTQenSpcVdS/+t1w0YMMDX0xFCCCHRCp9DiILatWvLYgajaiTj3rZtm4yqS5UqJZmGCCGEEBJkQe0KpAF0FOCEEEII8ZxoNcSFiv61116TUKeuQCKRmTNnqrZt26p69eqpzp07qy1btgS1noQQQkhQRtShxOXLl9UHH3ygrly5oipVqmS5DYKxQJCvX7/ebv0333wjc/GwXieEEEKCSbQZUX/44YcipN3RokULEdLI8jV27Fg1f/58GVnDgn3IkCHq119/DVp9CSGEkGgzov7xxx/VwoULVdy4cY282I7s2rVL3MkSJkyo1q1bp7Jnzy7r33zzTZU/f37JDvbZZ5+J7zgENyGEEBIMovyI+syZMyJks2XLpt5//32X202bNk1+33vvPUNIa9q3by+jbERagxU7IYQQEiyitKCGYRjmpRHOFII4SZIkLrfdtGmT/FarVs2pDEFaKleuLP9fs2ZNAGtMCCGERCNBPX78ePX333/LiNqVAZlGJxHJmzevZbnOCnb06NEA1JQQQgiJZoL6yJEjEms8X758HllrX7t2zSllZ3iZwgghhJCQMSaDT/GePXt8PuGCBQtU4cKFVSB5+vSpat68uXr48KGaPn26SpAggdvtHz9+LGpyED9+fMtt9DFwTFegzFU583ATQggJqKCGUZZjjmlvCAsLU4Fm2LBhauPGjerTTz+V0KXhgQxgsOSGsIaQtwKpOoG7pCJw4erfv78PNSeEEEK8FNRff/21pdp37969kkELhloff/yxevXVV1WGDBlklHrw4EE1adIktWLFCvXWW29JFq08efKoQIJRP85TrFgx9fnnn3u0D4Q08mRj1Hvv3j2VIkUKp22wHiROnNjlcXr37q0++eQTl+5fFStW9Pg6CCGEkAgJam31bObChQuqXbt2Mn+7YcMGw+BK88ILL0hiDmzz3Xffyd/wRQ4kgwYNEl9pdBTq1q1rV6bTcP7www9q9erVktFLj4DhfoXyc+fOWebMPnv2rPxmzJjR5bmRbxuLFe4EPCGEEBKQgCdIZYloX/h1FNKOqmgEHcFv9+7dXc4D+wM9R7xv3z5ZrDh8+LAsUHlrChUqJIJ69+7dIsAdwXq9HSGEEBIpBLVOVlGyZEm320GtDEEOtfSJEydUgQIFVKCA2rtNmzaWZZMnT1Zz586VUKFvv/22nYV39erV1bx58yQhB8KNOo6m165dK/PTyLtNCCGERApBrdW8ly5dcrsdDLG06jhZsmQqkBQvXtxlGXyqta80km+YadCggerRo4datWqVqM/79u0rc9eYl3/33XdFnY7woe5U34QQQkhI+VFjzhmMGTPGpbW0zj5148YNlTVrVjE0C0VSpUqlhg4dKv/HPHrOnDnFMA7hRDGaTp8+vfrqq6+edzUJIYREM3wS1FARY54XYTVhuLV9+3bDHxnAMAuW0JiXBvgN5YQWUJlPnDhRhPbJkyfF4OzOnTtirQ3LdXQ0CCGEkEij+sa884QJE1SrVq3U0qVLZUH2qZQpU6oHDx4Y0b5AkyZNJLnF86R169aqatWqEq3M3TaYw4bxGFyycI2wCCeEEEIiZZpLJL2Ab3SvXr0kyMj9+/dl0eTIkUNCeWL0/bxH0xDQ7oS0BlqCl156KSh1IoQQQgKej7p8+fJq/fr1YlQGy264bCEACuZ3Ax0ulBBCCInK+EVQa9KlSycLIYQQQkIse9bNmzfVjBkzRM2NOWsYY4FFixYZ4TcJIYQQ8hwENdyzYBENf2PEBJ8yZYq6evWqlA0YMEDKYBlOCCGEkCAL6i+++EJ17txZ3Jhy586tkiZNaleePHlyCRpSs2ZNcd8ihBBCSJAE9f79+yVzFqykEXrzyJEjTtmxFi5cKC5RcNdCtC9CCCGEBElQIwvVs2fPJFAIsmS5ivONVJdwzUIIT7PrFiGEEEICKKiRYxk4ppN0BG5a8KdGmNFTp075ckpCCCEkWuGToNajY1c5mM0kSJBAft3FBCeEEEKIHwU1ElXouWp33Lp1S+avQagm5SCEEEKinKCGkRgYPXq0CgsLc7ndp59+KmkiixYtKgkvCCGEEBIEQd28eXNJWHH48GFJCbl8+XL1+PFjKYPg3rBhg6pTp44aN26crOvatasvpyOEEEKiHT6FEEWmrHnz5qnq1aurTZs2ya+mQoUKdikvkbzjvffe8622hBBCSDTD54AnpUqVUjt27BD3rLhx4xrrtZCGtTdSYSJaGSGEEEKeQ1KOnDlzSsATBDVB9iyED4XQhlsWopURQgghJASyZ8EFCyNsQgghhIRQUg6Mojt27KgOHDhgtx4GZq+//rpasWKFP05DCCGERDt8FtSzZs1SJUuWVGPHjnVKZ4lEHb///ru4cXXr1s3XUxFCCCHRDp8E9aFDh1SzZs3Uw4cP1Ztvvilz1WYQ4xuZtWLGjKlGjBhBgzJCCCEkmIIaltzwm0as7/nz56uUKVPalRcvXlyNGjVKfffdd/I3Mm0RQgghJEiCGm5ZoFWrVm63QznyUiMhx+nTp305JSGEEBKt8ElQ67ChqVOndn+SmDFV5syZ5f83b9705ZSEEEJItMInQY3woWDv3r1ut0Ocb53eMm3atL6ckhBCCIlW+CSoK1euLL/Dhw9Xt2/fdrkdDMlgAZ43b14j4xYhhBBCAiyo33nnHRG8SGFZtmxZNXv2bHXx4kX15MkTdffuXbV161bVpk0b1bdvX9m+R48evpyOEEIIiXb4FJksWbJkEjoUGbKQkxrxvl2BpBwtW7b05XSEEEJItMPngCcVK1ZU//77r2rSpIlKmjSpU3mRIkXUtGnT6ENNCCGEPK9Y38iQ9fPPP4tPNdyvLl++LEk5smbNqtKkSeOPUxBCCCHREr8m5YgTJ47KlSuXLIQQQggJkaQchBBCCAlRQX3t2jX1ySefqAIFCqhUqVKpJEmSqMSJE7tcdu7c6Z+aE0IIIdEAn1TfDx48UOXLl3dKb+mOp0+f+nJKQgghJFrhk6CePHmyIaThevXaa69JYg6EDHVFvnz5VLBAVq958+apLVu2qFu3bkmo00qVKqmaNWuqGDFiuNzv77//VmvWrBGf8GzZsonbWZ48eYJWb0IIIcQvghoCDbRr106NHz9ehRJIGFK/fn0jdKlm2LBhqkyZMmrBggUqXbp0ToK9YcOGkkPbTP/+/dXQoUNFxU8IIYREGkF95coV+W3QoIEKJa5fvy6jZriJwXWsefPmEkENMcmhBdi0aZPkz96wYYPdyLpt27YipFOkSKE6duwoiUSWLVumfvvtN9W1a1eVO3du9frrrz/XayOEEBK98ElQw3hMz1WHEt9++60I6RdffFGtX79exY8f3yhr0aKFhDuFsF66dKmqVauWrD948KD68ccfxf977dq1qlChQkaKTuTR/vzzz1Xv3r0pqAkhhEQeq2/M94K//vpLhRJ//vmn/EKwmoU0gPB+66235P8YUWumTp2qnj17JhHWtJDW9OrVS9TkCJO6a9euoFwDIYQQ4rOgbt26taS6/O6779SSJUtCpkW7deumfvrpJyO7lyM61SYiqWm00IZBnFUgF32s1atXB6jWhBBCiJ9V36tWrRKL6JEjR6ratWuLL3XRokXFX9oVn332mVhSBxLMP7sDqm0dh1xz9OhRt1bpmJ8GyBRGCCGERApBjbnb7du3G3/DVSs8n2qkvQy0oHbHjBkzxCIc8+tmwzBtGOcqX7YehevtCCGEkJAX1JjrLVGiRIT20QLveYAsX+goAGgBdLYvqMAxPw0c57Q1CRIkMFy4XIEyV+XIz00IIYQEVVDDWCuyACFdrVo1dfv2bdWpUyf13nvvGWWxY8cWNy2bzWYIbFcR1WLFiuXyHEOGDBGfa0IIIcRfRIukHJiThoU6XLbgHz1q1Ci7cghpPWJ25Wp2//59+U2UKJHbjgsioFktiHRGCCGEhKygvnTpkswPnzlzRgUTBCvBSPrmzZtq0KBBasyYMZbhQzNmzCi/58+ftzyOXu9qDhvEixdP1OlWizsDO0IIISRg+agvXLggAhAW4FArP3nyxCiDKhl/Y3720aNHsm7r1q0qS5YsKhiMGzdO1Nxwr/rll19U48aNXW5bsGBBsfzes2eP5bw71uvtCCGEkEiTPatChQqGa1N4wOc6WbJkKljRyTp06KCSJ0+uFi1aJFm+3FGlShXZbu7cuRK9zAwsvaG6xkjclW82IYQQEnKq7+nTp4uQhoEVRq6zZ882RqN9+vSRuNoVK1aUv7t06aLOnj0blCxU69atk7loqKIRpSw8IQ3efvttmaf+448/1Pfff283Nw3BjU7JG2+88VxdywghhEQ/fA54ApD0YvTo0fJ/JL7Ytm2bCEmkvnz//fclnjbmhpGZCnG2Aw2ENKy0kVRj7NixsrgaRevRM+aev/jiC9WzZ09JzoFOBhJ6IFY41PtI1PHVV18FvO6EEEKI3wS1NrCqW7eusQ6RycxzuhhtDx8+XNZDEAY6LjjcsHbu3Cn/R4pLxzSXZmDgZVZz9+jRQwT8gAEDJJCLDuaCCGYQ3MHMpU0IIYT4LKi1z7E5iEmuXLnk99ixY8Y6CDpYVa9cuVIMywJpAY26IM63J+TNm9fSxap9+/Zq48aN6t69e3I9qH/MmNHCk40QQkhUEtSpU6c28j9rcubM6SSoQcqUKWUEfvr06YBaTkOF3bRpU5+OAXeqGjVq+K1OhBBCiLf4NEwsWbKk/C5evNhOyGFUC1et48ePyzq4aMGQLLyAIYQQQgjxo6CGXzLCb06YMEF17dpV3blzR9ZXrVpVfmGYhdH24MGDJeAIDMxg4EUIIYSQIAhqqLmRQUsnuTh06JBhdQ2f4zlz5kiWqn79+sn6Vq1auY2VTQghhBB7fLaQ6tWrl6i+4eqECGCgdOnS4q6l/9brYE1NCCGEkCCGEAW1a9eWxQxG1Y0aNRKfaoyqS5UqRctpQggh5HkIalekS5fOSYATQgghxHPoHEwIIYREhRE1jMXgA+0rsA4PVvYsQgghJNoI6l9//dUIqekLCEZCQU0IIYT4WVAjsUb+/PmVr8CwjBBCCCF+FtR0rSKEEEKCD43JCCGEkBAmaIL60qVLasaMGerMmTPBOiUhhBAS6fHZj/rChQtq0KBBatWqVZKIAwk4NDabTf5GastHjx7Juq1bt9KYjBBCCAmGoH7w4IGqUKGCOnr0qEfbZ8qUSSVLlsyXUxJCCCHRCp9U39OnTxchjUQbnTp1UrNnz1YlSpSQsj59+qjJkyerihUryt9dunSRVJd58uTxT80JIYSQaIBPghrqbtC8eXNJwtGwYUNx4wJIadmyZUu1YsUKVb16dTVmzBi1ceNG/9SaEEIIiSb4JKjPnz8vv3Xr1jXWFS1aVH737NkjvxhtDx8+XD179kx98cUXvtWWEEIIiWb4JKghfEHatGmNdbly5ZLfY8eOGeuKFCmiMmbMqFauXCmGZYQQQggJgqBOnTq1/F6/ft1YlzNnTidBDVKmTCkW4P6IF04IIYREF3wS1CVLlpTfxYsXG+uSJk0qI2y4ah0/flzWQUDDkAwkSpTItxoTQggh0QifBHXjxo1V7Nix1YQJEyQr1p07d2R91apV5bdnz54y2h48eLC6efOmGJhlzpzZPzUnhBBCogE+CWqouQcOHGikwTx06JD8v2PHjipGjBhqzpw5koSjX79+sr5Vq1ZiXEYIIYSQIIUQ7dWrl6i+q1SpouLEiSPrSpcuLe5a+m+9jok9CCGEkCCHEAW1a9eWxQxG1Y0aNVLbtm2TUXWpUqVUzJjMAUIIIYQEXVC7Il26dE4CnBBCCCGeE7AhLnyszW5bhBBCCAmSoH78+LHav3+/2rFjh7p3755d2blz58QaPGHChKLyhq919+7dGeiEEEIICbSgRtrKESNGSJrKQoUKqZdeeknU23DDgvC+deuWqlatmpo5c6Z6+PCh7HPt2jUJIYrkHI5CnRBCCCF+nKNu3769+u677+zWQfh+/fXX6tSpU+qFF15QBw4cULlz51YffvihjKiXL18ughuj71GjRqlPP/00IqckhBBCojUej6g3b95sCOk2bdqo7du3S+QxCOEMGTKoWbNmqUGDBolv9datW0Xd/cEHH6gZM2ZIwBPw008/Be5KCCGEkOg8okbwEtCsWTO7UXWOHDkk4UaFChXU/fv3Vbt27VTy5Mnt9u3cubP4UB8+fFiilyVJksSf10AIIYREWTwW1DAeA2+++aZTWfny5cVo7OrVq6pw4cJO5QkSJJCRNo6B1Jj58uVToczOnTvVP//8oy5evKiyZcum6tWrJ3PxhBBCSMgKau1qlT59estyrIegTpYsmWU5hDXAqDtUefr0qWrdurWaNm2aGM5pPvnkE/Xtt9+q999//7nWjxBCSPTDY0ENq24QN25cy3Ik3JADxo4drjAMVZBY5IcffhDXshYtWkgCERjDIY92y5YtVdasWVXlypWfdzUJIYREIxjT8z9OnDihxo8fL0lDIJjHjRsnccxXrFghI2oEcOnRo8fzvVuEEEKiHRTU/zFlyhTJm4345EggYgbW7ClTphRLd7ifEUIIIcGCgvo/1q5dK7+1atWynF/XKu+///47aDeHEEIIiXBSjldeeUVyTTvy6NEjj8pDlSNHjshvwYIFLcu1pTpczAghhJCQFdThCdxQF8iuuHz5svwieIsV2j3rypUrQa0XIYSQ6I3HgrpPnz6GMPMF+CWHGrBo19bo2o3MEb3+wYMHLo+D+OY6xrkjcF0DvsxxX7hwwet9SWiDELvEO06EhbHpoigPfHgvDvz3rXX3zY5ygrp+/foqqgJLb43Zf9qMXh8zputp/SFDhqj+/fu7PVfTpk29rieJukycOPF5V4GQ0OOll3w+xMmTJ2VKNlqpvqMiEL4YMaPnhcUxBKo5UAt8rF3Ru3dvceVyNaKGwRoSlrgatZP/cffuXcm2tmbNGpU4cWI2CyF8LyIMvuUQ0jVq1FCRHQpqU2Q1+FJDvWw1T63Vzu5CiSLoiw784kjSpEkljCoJn9u3b8tvsWLFpN0IIXwvvCGyj6Q1dM/6D23trWOaO6LXFyhQIFj3hhBCCKGg1lSqVEl+FyxY4PRY3Lx5U61atcpuO0IIISQYcET9H4hIhjjm8+bNU7/99pvRQIhWhtSdmDfFXEeePHmCcmMIIYQQCmoTWbJkUd26dRPrbgjt1157TbVt21bSds6YMUMlSpRIffXVV3xqCCGEBBUak5kYOHCgCgsLU6NHj1Z//fWXne/35MmTxbiJEEIICSYU1A5uWiNGjFBdunQR16B79+6pXLlyqXLlyrm05ib+B23dr18/tjkhfC+IUiqGzVWED0IIIYQ8d2hMRgghhIQwFNSEEEJICENBTQghhIQwFNSEEEJICEOr70gE0nEiH3bs2LFV6tSp3WbyCjTPnj1Tx48flzoEMoY5bB2PHTumYsSIIRb4UZlbt27J/UVSGNxfEvXA83zp0iX5f9q0ad2+w0gEdP78eaf1eBfwDUBsh1SpUsnf/tyXhCCw+iahza+//mqrUKGCLXbs2LDQlyVu3Li2GjVq2BYtWvRc6nTlyhWpR6JEiQJ6njt37sh5YsWKZYvqjB07Vq61ffv2z7sqxI/cv3/fNmnSJFvZsmVt8eLFM97hJEmS2Bo0aGDbsWOH5X5Lly41tnW1JE2a1NaoUSPbgQMH/LYvCT04og7x3jfyV//666/yN3rCmTNnlqAsSJuJoCxYWrVqpSZNmvS8q0sIceDQoUOqQYMGau/evfI3RrDI1IeQxHfu3FFz5sxR8+fPV2PGjFHt27e3bL9YsWKp7Nmz2617+vSpZJm7fv26mjVrllq6dKnEfnAMyuTLviR04Bx1CPPDDz+IkI4fP76aNm2avNhnzpwR9ShetGHDhqk4ceJI1DQshJDQ4ezZs6ps2bIipPPnzy+hiKGSRspcvMtYX7duXRGcHTp0UL///rvlcSDYjx49arcgJe+1a9fUjh07VI4cOeR7gJwE/tyXhA4U1CHM9OnT5XfQoEGqefPmIrA1SZIkkdjkgwcPlr8R9tTdyPzixYsi4D0BiUjwMcEcF+bFvQUfIBzD0/N6CiLG4bg4fiCuB8fFRxb7YN9AXSs0IzgHYw5FTfDO3rhxQ5UoUUJt3LhRvfPOO3bvcKFChWQ0/cYbb8gz0KlTJ4+faU3x4sUNbRrOcfr06aDsS4ILBXUIc+7cOfktUqSIy22aNWumcufOLQLo0aNHTvu3bt1ahHqGDBnEeAU96AkTJlgea+3atapmzZqyfcaMGVWmTJlU0qRJ5UNy8OBBj+sN4dOyZUuVIkUKOQbOi3jp0ABE9EPkaGzVpEkTlSxZMjkuDK66du0qakR/XA9UgZhGQL2RpAX74FzY/t9///XbtW7atEnSpSZMmFDOkTVrVhltkajD9u3b1cqVK0XVDW0XDAStgGp61KhR8ovneN++fRE+V4UKFQyjMIyWg7UvCSLPe5KcuKZOnTpi9FGvXj3b48ePI9RUMBDJkCGDYTiSMWNGMR7Rf48YMcJu+1mzZtlixowpZTB4yZw5sy1FihTG9qlSpbJdvnw5XGOyY8eOyb7m/VKmTGn8jWt59uyZV8ZkMKjD/5MnT25LnTq1ccyXX37Z9ujRI5+uB/vr42O/dOnSyT7agC9x4sS2U6dO+Xyty5Yts8WPH1/K48SJI/vj2vD3q6++SmOyKMInn3wi9xLPlCecOXPGaZ02CMuUKZPbfS9evGg8c5s3b/Z5XxJ6UFCHMGvWrDE+4tmzZ7f16dPHtnr1atuDBw/C3bd8+fKyX+nSpW0HDx6UdRAaP/74owgIHPf8+fOyPiwszJY2bVrZfujQofK35siRI7YSJUpI2ciRI8MV1BCaWF+xYkU7a1JcS9asWaVs3LhxERbU2tJ96tSptqdPn0rZ4sWLjc7HsGHDjH28uZ7ffvtN1hUvXtzuo3n79m1b69atbbly5bKNHz/ep2t9+PChLUeOHLK+fv36tqtXrxofy1q1ahnXSavvyE+lSpXkXn7++edeH8NTYfvll1/KdgkTJjS+Db7sS0IPCuoQZ+7cuTJSM7tVQGCVK1fONmDAAEvXit27d8t2yZIlE4HqSK9evUTwzJgxQ/7etGmT/I0RoBVfffWVHK9NmzZuBfX69etlXZYsWWy3bt1yOg46GSjPmTOnV4L666+/dipHxwNlEIAab65nyJAhsq5Tp04e1cuba50/f76xztx50C482bJlo6COIuTNm1fu5ZQpU7w+hha26dOnlw6mecF7v2LFCnletdane/fuftmXhB50zwpx6tevr6pUqSLW3wsXLpR5V1iOrlu3Thakg2zRooUYk2EuFixfvlx+X3/9dcvAGUOGDJFFU7p0abEGBXD72rVrl8zhYr4M86m7d++WsocPH7qt67Jly+QXlq6XL1+WxQzmcFFHBEqB4QrmZj0Fc3gfffSR0/rGjRurtm3byvzaqVOnZH7Ym+vJkyeP/H777beyHta4mL9LnDix3651xYoVRp0d06YmSJBA7A1gOEgiPzB4BGbjMW+BIah+Pl3x2muvqQEDBvh1XxI6UFBHAmDQBGGEBQZj27ZtU6tWrRLBvXXrVjV16lQRCNp4RVtvFixY0ONzwO2rd+/eYtSEqGNm4saNK+d1XO/IkSNH5Hf27NmyuANCFcIL57XqAMAoC4ZfGviCmv/WwD0NxnQw9jp58qQIam+uB4K5VKlSasuWLer777+XBdu9+uqrqmHDhmKxi2hOvlyrvi9w1bGiaNGibo9DIg94fmHMCTcoX7Hyhcazie8CLMdh7Fi7dm3LKGe+7EtCBwrqEAUWyFgcw0niJXv55Zdl6du3r1q0aJFq1KiRWr16tfhhYhQNv0g9SvMEbF++fHkRKAAvMFw3IDiwfvPmzapz584ejyLSpEljKVTNaCEJAalHuGbgNgILbA0spF2hr1NbvXtzPWhXdH6++uor8V+HexaOp4PKfPHFFxIYonDhwl5fK3xnzfV1JLzjkMgDOow7d+5Uhw8f9mh7eA/gPUfH05UvtDf4si8JHSioQxSouhEE4a233pLoRa6AYIbKFIINvpD4W6vAb9686dG54K4FoYaXesmSJSLUzGCk7gla0EBFPXDgQI/2wUjTyr0KPX4z7q4F6m2QMmVKn64HnQGoAPv37y/uNRDQf//9t/rnn39EcMPVDW3s7bXqDperUZbuYJHIT/Xq1dWCBQvkGfIEqJ8hUL/++muXEcpI9IX6jhBFq0EhKPRIzBXaDxIhRs2qVajFrcAcNz4K6MUDjDBBmzZtnIQa0D7E4am+CxQoIL+YO3cF1NM4tx79QiPgGDkJC9TNZiAorQQc5uCg9oeKT5/fm+uBqlzXC+2JIBXQWGCU/eeffxrH1QFTvLlWjOzBhg0bLLd3db9I5AMdZryPmCIJb2oEzxCeSbyXL7zwQtDqSCIPFNQhClS0+fLlkyAfCKjhypALsYR1sIyKFSvKL4J8YM4JBk9WgTowaoSBCXrvZkGvBbcZjCznzZsn/3cMqOII1NgAangIYEcwkkXAFRhgRTQaF7YfOnSo03qMflFWrVo1Qz3uzfX06dNH2sQqZjrqrA2DdGfIm2utV6+e/OLDfeDAAbvtkVEJKncSNYAxITRi4OOPPzY6j46gAwr7B1CuXDlZCHHieZudE9esXLnScJ/InTu3bfjw4eIWtH//fnH/gV+19iOuWrWq3b7vvfeerIc/8cSJE2179+6Vfbt27WqLESOG+FJr164xY8bItjgXXJd27twpLk5w/0KgD+3LDV/f8Pyo3377bVmfIEEC26BBg+RY27dvl7rjWCiD72ZE3bPgkobfjz/+2LZ161YJzvD+++8bwVA2btxo7OPN9fz5559GcJSBAwfK9nv27BH/6sKFC0tZkyZNfL5WvQ/cZqZNm2bbt2+fuODlyZPHuEb6UUcN4Lannx08Vx988IHtjz/+kHcRzyueGe16iWA8hw8fttvfU19oK3zZl4QeFNQhzoIFC2xp0qRxm64OAufmzZt2+yFQh4605bhASENIaO7du2crUqSI5bYFChSwLVy40OmldyWo8XFCABBXdW3atKlXkckKFSpka9WqldPxIHTRETHjzfWAzp07u6x3wYIFnXzSvbnW69ev28qUKeO0LdoRHQkK6qjFjRs3jIhzrhb4XO/atctpXwpqoqExWYgD9wn4886dO1dU2ZhLhTochlOIAY653MqVKzvtB4MyqF+R2APGaJgvheq2ZMmSoorT1ssAKmP4Zw8fPlx8sDEnjljXb775pqjdoUbH3BmMvnAcuHtgTjhXrlx2LkvayArnnTlzppwXc7ToEObNm1e9++67YhwXEXBunAdGZxMnTpR6QNWPOiItH6y3X3zxRbt9vLkegJjLaGvUHVMKSMiBtKJ16tQRQzJH32dvrhUxwaEunzJliiRkwBw75q4xHw6jOFwrLMlJ1ABeG3hGYFSG+42pKCTqwLOE5wTPFrw2HJ8tgHcLzwPi9EcUX/YloUcMSOvnXQlCCCGEWENjMkIIISSEoaAmhBBCQhgKakIIISSEoaAmhBBCQhgKakIIISSEoaAmhBBCQhgKakIIISSEoaAOIMiGhKQQSJ3oT4YMGSKBOL755hsVSD777DM5D7JRkdDn559/ltzcOu91oEBWKMQ69zQ7WzBBWAjkZ/c041tkAUFSkGGrQYMGxjokiME65JOODm3gjzbzBuQEQGCpadOmqecFBXUA6dKli0S+QipEf4LIZEhKH+i0iHjQcZ7wsneR0AARrpCQ5IMPPgjYORBJDdHdkG8ZUbdCDUTdQ/2Qyz0qgaQ8Ou2q5unTp0a+9OjQBv5oM29ArnpkM2zXrp1kQ3seUFAHCOQtRjaknj17SthIQgJNnDhxJDPaihUr1KxZswJyjh49esgH8PPPP1ehCLKQRRcgQJYuXSo516NrGwSLTz75RMIyoxP0PKCgDhAQ0IkTJ2YSeBL0UXXOnDklbSdilfuTHTt2iHr9ww8/VGnTpvXrsUnEQcx6qHarV6/O5gswiJ3etWtXybeg89MHEwrqAICEEFjw0UQvzJxjGnO+u3btctrn5ZdflrKffvrJqQyqTJStX7/eqQy9506dOkmCjoIFC0pu2y1btris2549e0QVj6QUCNqPHNZIXoGk9RHhxIkTcl4kxMBxkMwCx3nw4EGEjmM+HqYKcDzkccYvEmFs2rTJr3VYtWqVzFkVKFBAlS5dWo0ZM0Y9e/ZMNWvWTNr4ypUrHtcZx4KKEeeHcESSEPy9detWu+169+4tx9b5vx3B1AKuGcdwnGb4559/ZH4MiTtwHuTfDgsLkw4gjumYbxwf7xYtWqjjx49LshB/MmjQIJn/xH0xg/pAYHTs2FH+RtIRtAPaGclGcJ9cgbZfvHix3EckMcGCRCtW6sp79+7JeTCqx//RrsjxjdHOhg0bpGzdunWy7Zdffil/IymLK9q0aSPbDBs2zDJPNPK6Yzl48KBH7YNc56gbkr/g2rt162Z5Hb60lxnHOWrUObK0gZmdO3fKPrj3+H4hOQ6m3Xx9XsJj27Ztqnv37qp+/fpyXrTB5cuXXW6P9wpaK7wHQcfIo0X8xrvvvivp65YvX263/qOPPpL1o0aNslt/4cIFI+Udciybefr0qS1lypS2hAkT2h48eCDrevbsKdsi5zTyGjumzUMe5sWLFzvV69tvvzXyW1ul2jtx4oTd9siLjLJhw4bZrUeaSORgdpVG8uzZsxFqryNHjsg1Wh0PubOnTp3qtI83dUBuaKvt0Y41atSQ/+NeeEKbNm1cpi2MGTOm5LHW4DnQqTKt+Pnnn6W8Tp06duuRS9vq+EiT+frrr8v/kZvbEeQ1RtnLL79s8xdoT6QUffHFF12mIn3ppZds33zzjeXz+OOPPzrtd+7cOVvx4sVdtiPeI8eUkViP9K3Vq1e3e0ZWrFhheYwffvjB5TXpNJLIFe34vDRv3lzKGjZs6FH7jB492uV11K1bV95jX9pLfyOSJUtmrMP3QKd6BcgvH1naADx69Mj24YcfWm6P79qhQ4d8el4uWLQZQD2Q197qGNgWOcNdUbNmTdnOKi1pIKGg9jNPnjyxJU2aVIRIWFiYXdn8+fPlJtevX99u/ezZs40HJV++fHZl27dvd/qIa0GtcyrjpcZD/eeff0reZiuhsHLlShEgKGvUqJFtw4YNtqNHj9p+/fVXW7Zs2WR90aJF5eVxJ6gPHjxoCEi8wKtWrZLj4OEuVaqUrC9XrlyEck7rfL1NmjSxbdy40Xb8+HHbzp07bf3795f1yMdtfsm9qcM///xjtFmHDh1s//77r7xs+kOROHFijwX1nDlzZFvc5++//962Z88eOf/q1attjRs3lrKcOXPaPRPp0qWT9fv373c6nha6v/zyi7FuzZo1Rn07duxo2717t9S5S5cuhnByJagB7im2uXz5ss0faIHSp08fpzIteFKlSiVCpkSJEpLvfObMmUYHCHXZsmWL3X6VKlWSshdeeME2btw46XyhDXC9EBwoW7JkiZOgRj51LAMHDpR3asKECba7d++K0MF9xzZ9+/aVv8PrNOKZw/boeGnwXOBdQefx4sWL4bYNnlUIy/jx40v7oJOGPPJDhw41OtJ4z3xpL08EdWRqA9C7d29ZHzduXLnn8+bNk06FzteeP39+2+PHj71+XlwJarSLPi++BWh3HOett96S9bgGvNNWYJCl2zaYUFD7GbxcuJEQGI7cvn1bPjAQPGbwoOkHBL9Xr141yiAksW78+PFOghrCCgLCzN69e40P/LVr14z1FStWtByxA7zI+Gig/KeffnIrqJs1aybrWrdu7XQcfDjy5Mkj5cuWLfOwxWxy3fgw3bt3z6ns/PnztjNnztgJam/qoHvCeDEdMfeuPRHUtWvXlm2tRvqoZ6JEiZzaH+fFugEDBthtf+vWLfnI4F7iA66pUqWKbN+9e3enc/Tr18+orytBrT865pG9L9SrV0+Ot2jRIqcyLXj0aPfhw4dGGTpL77zzjpS99tprTs8pOjB4LxzRH3Hz9WtBjQWdOH9c95UrV2ypU6eW91JrlKpVqybHgPD0BK356NWrl1MZOp0Qluhc+tJengjqyNQGOCcEJd57R+0f2kOPnLVWcq8Xz4tVm+F7gutEBwmdd0dQf+zz5ptvWl7n5s2b/a6t8gQKaj8zZswYuZEtWrSwLNcC0/zQ4qHEx12rzM0PrhYwZrW0FtT4eFqhha5WHUEY4GXGutOnT1vu89lnnxkjVFeCGh8RPPTmYzuCUQ7KO3XqZPMUaAV0m7nqyWq8qcP9+/eNHvepU6ectseoU08JeCKosQ06Dxgp678xqh85cqSo+LTmwtzW0BRorYUZdIyw/u233zbW4X7hY4J7Zhb2GoycMBXiTlB/8cUXUo4RuD/AhxzHw0fXEbPgwcjKEbQDPsj4MOvOCD6Y+HibVYjQ5uCDPGnSJEMzgukiK0Ht6jnxpoOipx5eeeUV+dDj/1Cte4oeZRUpUsRSY+KP9gqkoH4ebTB9+nTZHto0V9NheD6OHTvm9fNi1WYYiVtpNTXoBKA98f7pqUYzuB+4N67KAwUFtZ/RPTIIPiuGDBki5Xi4zA8GVF5avajVKlD7QCULFZAZLajxawXU5yjXD7Ue5WfJksVlvf/66y/ZpnDhwi4F9cmTJ40PDISr1ZIiRYoIv+SDBg0yjqt7zZgfg+rMPNrwtg4Q6PgbwsYVuG5PBbU+JlSGrubWHTtXIEeOHE4dDAh2rIMK17HX7njfzZQtW9atoMbzhXKo4n0FUzg4Fj5QjvOMZsGD++YKTAVgG0f197p162QEiWfWyn6iVatWloLaPEXjD02C7hBjQafZ8d65A50X3RHEgmuFwECH23H6y9v2CrSgDnYbdO3aVbb7/PPPI1THdRF4XqzaDIMBXT98c60WfR07duywrAM0oijHtyhY0Orbz2ir4aRJk1qWw8ISwCocwFoVgQsqV66sypUrZ6wDsN6+e/euqlWrluWx4P7lDnTEgA6MkipVKpfbpkyZUn5hTesKcyQqBEKxWrS1JoKyAAQcgHWy41KyZEnjWLDehYWqDqABS/Yff/xRLEezZMmi5syZ41Md9N/uAnRExN0IEZ8QAOGXX36RoBIJEiQQi2y4Lc2bN8+l3zwsS4G+HtwXuHskS5ZMLGs1V69eDbe++n65AscEEbFid4U+BlxUYFXuikyZMrksS5Mmjd21AViu45mHdfqhQ4fEnQyBVJo2bWq0lX6GzSRMmFCsb/2J2ZIX58+ePbvH+8Jif+7cuSpdunTyNyzuEc2vTp06KkOGDOqLL76Qd9zX9go0wWwDfV36Oj2hvZfPi5lr164Z9dPBYhwXxAkArizP9bc9mPcmdtDOFE3QNxkRoqyAW1T69OnF7cYssF999VVx70mdOrUIaDzUOgSg+SPuDRAkAELfFdotyF1wlvjx48tvvHjx1OHDh91+tLENgKsUhKc7cBz4/cLNBoLrjz/+kKAdx44dE3eJxo0bq/z586vChQt7VQfdoXHXCUGYQE+BSwvuc5kyZSSMK4R0rFixjPImTZpY7ofrQPhXCGpc78KFC+U47777rlFXoK/Rncucu2sxP3/6efQF/dwgwIY7rISR2SUJaHdF3ONvv/1W/g93L7QZwu3qjyBc2Vy5l7l6t3xh5MiRxv/RAUPnEULAU+AiderUKbku3Fc8x4jiho89XOrwHgwdOtTr9goGwWwD/d7CxcwT/vDheTGj31N0quHa5w58q63Q74G776m/oaD2M/rF0j03q49MjRo1ZMQIH1g8zHjY8LFHGUbWs2fPVmvWrFG//fabjGLgH+wL8M8F8M/EKFOPthx9GQF6v67ASBgPOj7+eEjhtx0e0CCcOXPGaX3s2M6PHgTU66+/LgvYt2+fjKqPHj0q8aUhqL2pA3r7ON+FCxdE8DuOntELRw/dE1AXhBHEhwYfD8eRLbQB2o/bsXcPX3dcA9oabaJH1hDgZvLkySO/OA+u0yzE9XH379/vtp66t+9KsxMR8AxqbQb8WF11jhAuF3VzFKQQSOh0AWhIAJ5t7Zs6ceJEp2Ppzl14IyR/gOheEEx4RvCuoAPYtm1bp4hf4YH7BJ9cLGDv3r2iKYIAwejSUVBHpL2iWhtobYK+zvD4zU/Piz4v2lhrNyOKDs0ankbTn1D17We0usidWkQ/IAhugOAYEMS6p1elShX57dChgwhy/B3eSCY8MIKHQMPDOX78eKdyCAP98KMT4e6DjVEkGDx4sOU2GBVDmGLkqIWvleobddIBWPB3tWrVnI6FIB96ve55e1MHqEp1Z0f3ys2g9++pilhrHvBx1ZoKM999953xf6vRghbKENLopKHTgM6ZGXycEZAFAt9qlIBOi7vADOaOYkTUl65A5w0fcAhpd4k40Al0jDut64tOVb58+YwRmq4/kog4guvGPiCi0dUiOtrG/dSx+BF0Y8aMGfIuQnDh/57Qq1cveacRuc0MOmUI3qHP43gtEWmvqNYGCPAEfv/9d0ttFoKa4HgjRozw6/OCoFMAU1RW7xDeG6jrsbgaMWtBHaxOlBC02fBoAnyZ0aww9nEFLHm1ZTAWWAubjTLMxhHw03VEG5PButmdMZnZonTKlCmyDgYYsNDUFoswGNG+mzCSgNuEO/cs7QuOBYZzN2/eNAxk4HoEgyOcA1abngArbhi54XgwMLl+/bpRBp9I+CqjDH7gvtRBG8vB8AZGe9oYCZakZoOw8IzJcA5tbAIfbO1ShnsKwxjzfbUyRtH3V1vmwzXMnTFYkiRJxG8bRlxoq99//90wlnNnTAZfeZRPnDjR5g/gl4/jwXrdnRUz7uW+ffuMsm3bttnSpk0rZWh3Tbt27WQdntVLly4Z69H+tWrVMo5n9kLQxmQwdArPJ9h8Lne0bdvWCJKjjZ569OhhvA9mV8nwXJPwzpuvBV4B8ClGmTlQjDftFRFjssjQBlifPXt2I+AQPDPMAXu0Md2sWbO8fl6s2gzXlzFjRlkPF0hznAG8w9ot7Y033rC8Tv3+ujNMDQQU1H5G+0rDtcKd+X7p0qVdumho62BX7kTeCGoAH2p9XAgbbb2IBdblEGaeRCaD25PeDwIRx9HuX1jQKYho50YLOAhZCDGzBSmsPB3xpg76g4EFfsvJkyc3Xn4IRE8EtdliVX8kUV8dgAQvunbBc2V1a77369evt9wGghlBbvR2qJ/utOCDlzVrVreWqbrz42mHydNIbMOHD3cq04IH9yFDhgzSJghaUbJkSeO+IliFdmcDeDa11S6ePbgFIVIX3h20JT64jh1eTwS19rqA+1rlypUtI/Rp4EeLc6GOCACkwXuLSH04DrwPwgP1yp07t/FeIYAJngG0Bdbh+OgQ+tJeERHUkaENAAIE6fccnU9cM1xVdRtgAKGDFu304nlxFfAEvtn6vIjhgGNhPx3HAoLclUW3dqeEZX0woaAOADogBh5EV+igFRjNObq8IJAHyhBlzApvBTUe+smTJ4uvoxYA+OihB47wg464EtQAUYbwMmoBhZcLAsgqIIYnYPSMIAJmlwtcB8ISmj9YvtYBUdx0QBTsB/co+GjqEa4nghr1gfud3ke7UiEojS7TI253vvaIHuYught6/ziWHmHhgwx/a/T8dWfO6r5pF7ZixYrZ/K0psvLd14IHHz/4N6MtdLvg44eAPlYuOojIp/2z9YJnEx1GjOK0ZkQHuPBEUOPadScFS+fOnS23MwsiK1/ztWvXGs+VYyhgK9Ah0pGzzAtGqeZoWd62V0QEdWRoA7O7lWNYUN0G5lG2N8+LK0EN0Ckxd5j1go6Nq/gM5g4r/M6DSQz8EzxFe/QA8yt169YVy0IYUFiBeRXMh2D+2dG4CXMjmAvE3KqVGw7cerDAKMzKKhQGTZgfhauEKzcWWBSjDu5ctlAH1AVuQq4MJzC/je1QF22t7AuYr4KVKIygrOaA/VUHbA/DF30OWNvjfsDgTM+fhwdeHdgi4Bjm9kHbYh4Lx7dyP8E1Yn4M+7lrfzM4HubntWEZ7gnmOHF+x2PAeOfTTz+V+XJYqPsD2Dcg8QnqDUtes5EanhE8h6gbrJUxl43EM7hO2Ea4M2jDc7p7926Zv8yaNaucQ4NkFThe2bJl5d5i/hHJFzBfXrVqVZfHxHkxV4rt8+bNa+l6B5sEJJAASExj9azBTRLvGZ4HeGR4At49GGKhvTCHab4eX9oLZfACwfus7ViwH+wcYNznmEEr1NvAERi6wrgO7y9cH7UBoy/PyyOLNrNKPAJXLbzLaCd3xrRoS9i+oA6nT592WceAENRuQTQBoyREoIKq0rFXSEKTiIyogwF6+wjeYp6/dEzygRjKVkAFiTKrUawvjB07Vs6L2NquRogkfNhekZP5/9nGIOpfsKHVdwCA1SX8ZNEThcsDIREFmhS4nCDIg057iF4/Ums2b95c/kagB0eQ0hAuZEgv6OjW5SutWrUSLc2kSZP8elxCIgOTJk0SzRk8coJO0LsG0YiqVavKXFEwY8KSqDGihpGZ2aAOhm/a2AUL5vUQ89sRzNkjHKpj6FV/oWM0I7yrhiPEiMH2inxs/C9W/4gRI57L+TmiDiCTJ0+W+dZx48YF8jQkCgI/U/jYN2jQQOYsMaeO+TfM+WH+ed26dU5zZAgKgWAqU6dO9dn33hXNmjWTCFTQGLmLrEVIVKJnz56qdOnSqnPnzs/l/DQmCzAwUILRR0Ri2pLgAyMyCJ6MGTO6DUv6vHA0frMCnUIY0Oh4y4ECBmzbtm1Tr7zyihhFod2gckeQDKvANcQetlfk4uHDhzLlBCM3fB+eBxTUhBBCSAgTekMHQgghhBhQUBNCCCEhDAU1IYQQEsJQUBNCCCEhDAU1IYQQEsJQUBNCCCEhDAU1IYQQEsJQUBNCCCEhDAU1IYQQEsJQUBNCCCEqdPk/eTdYvLb8uK8AAAAASUVORK5CYII=", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "aa.plot_settings()\n", + "fig, ax = plt.subplots(figsize=(5, 4))\n", + "names = [\"Scale-based\\n(whole-seq avg)\", \"CPP\\n(part x split x scale)\"]\n", + "vals = [bacc_scale, bacc_cpp]\n", + "bars = ax.bar(names, vals, color=[\"tab:gray\", \"tab:red\"], width=0.6)\n", + "ax.axhline(50, ls=\"--\", color=\"black\", lw=1)\n", + "ax.text(1.45, 51, \"random (50%)\", ha=\"right\", va=\"bottom\", fontsize=10)\n", + "for b, v in zip(bars, vals):\n", + " ax.text(b.get_x() + b.get_width() / 2, v + 1, f\"{v:.0f}%\", ha=\"center\", fontsize=12, weight=\"bold\")\n", + "ax.set_ylabel(\"Balanced accuracy [%]\")\n", + "ax.set_ylim(0, 100)\n", + "ax.set_title(\"gamma-secretase substrate prediction\")\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "ea8a944d", + "metadata": {}, + "source": [ + "## 7. Where the signature lives (cf. Fig. 2b/2d)\n", + "\n", + "The paper reports that substrate-defining features cluster in the **cleavage region**\n", + "(C-terminal TMD into JMD-C) and that **conformational** properties dominate. The same\n", + "falls out of our signature: most features map to the `TMD_C_JMD_C` part, and\n", + "*Conformation* is the leading scale category." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "e3918016", + "metadata": { + "execution": { + "iopub.execute_input": "2026-06-25T22:57:03.483013Z", + "iopub.status.busy": "2026-06-25T22:57:03.482945Z", + "iopub.status.idle": "2026-06-25T22:57:03.485672Z", + "shell.execute_reply": "2026-06-25T22:57:03.485482Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Features per part:\n", + " TMD_C_JMD_C : 85 (85%)\n", + " TMD : 15 (15%)\n", + "\n", + "Features per AAontology category:\n", + " Conformation : 31\n", + " Energy : 13\n", + " Polarity : 12\n", + " ASA/Volume : 11\n", + " Shape : 10\n", + " Others : 10\n", + " Composition : 10\n", + " Structure-Activity : 3\n" + ] + } + ], + "source": [ + "part = df_feat[\"feature\"].str.split(\"-\").str[0].value_counts()\n", + "print(\"Features per part:\")\n", + "for p, c in part.items():\n", + " print(f\" {p:<14s}: {c:>3d} ({c / len(df_feat) * 100:.0f}%)\")\n", + "print(\"\\nFeatures per AAontology category:\")\n", + "for cat, c in df_feat[\"category\"].value_counts().items():\n", + " print(f\" {cat:<20s}: {c:>3d}\")" + ] + }, + { + "cell_type": "markdown", + "id": "0dfaac54", + "metadata": {}, + "source": [ + "## Summary\n", + "\n", + "Reproduced from bundled data, in seconds:\n", + "\n", + "- **CPP signature** — an interpretable, position-resolved feature set separating\n", + " gamma-secretase substrates from non-substrates (steps 2-5).\n", + "- **~85% balanced accuracy** for CPP vs. **~75%** for the whole-sequence scale-average\n", + " baseline — the same direction as the paper's Fig. 3a (84% vs. ~50-57% on the full\n", + " imbalanced proteome): *positional splitting is what makes the difference*.\n", + "- **Biology matches** — the signature concentrates in the cleavage region\n", + " (`TMD_C_JMD_C`) and is dominated by conformational properties, as the paper reports.\n", + "\n", + "**Scale it up.** To approach the full study — the imbalanced proteome, dPULearn\n", + "non-substrate mining, multi-model leave-one-out CV, SHAP single-residue explanations,\n", + "and the proteome-wide substrate prediction — follow the **Protocols**: *P1 (CPP\n", + "signature)*, *P4 (engineer features)*, *P7-P8 (selection & prediction)*, and *P9-P10\n", + "(interpretability & validation)*.\n", + "\n", + "> Breimann *et al.*, *Charting gamma-secretase substrates by explainable AI*,\n", + "> Nat. Commun. 16, 5428 (2025). https://doi.org/10.1038/s41467-025-60638-z" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.13.11" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} From b1764dc4cd0b0eb7a5299abc9d877b728cfa566b Mon Sep 17 00:00:00 2001 From: Stephan Breimann Date: Fri, 26 Jun 2026 04:57:38 +0200 Subject: [PATCH 2/2] doc(use-cases): mention Use Cases pillar in guide-enumerating prose MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Review follow-up: three docs pages enumerated the guide pillars (Tutorials/Protocols) but omitted the new Use Cases subchapter — the Introduction guides overview, the Getting Started dev note, and the prediction-tasks "Where to go next". Add Use Cases to each for nav consistency. Co-Authored-By: Claude Opus 4.8 (1M context) --- docs/source/getting_started.rst | 2 +- docs/source/index/introduction.rst | 5 +++-- docs/source/index/usage_principles/prediction_tasks.rst | 2 ++ 3 files changed, 6 insertions(+), 3 deletions(-) diff --git a/docs/source/getting_started.rst b/docs/source/getting_started.rst index ad22e820..8462d5b5 100644 --- a/docs/source/getting_started.rst +++ b/docs/source/getting_started.rst @@ -3,7 +3,7 @@ The paths to notebooks are relative to ensure compatibility with the Sphinx referencing system used throughout the documentation. Getting Started is the onboarding pillar: only the first success belongs here (install + first result + publication-ready plots), no deep theory — that - is what the Tutorials and Protocols pillars are for. + is what the Tutorials, Protocols, and Use Cases pillars are for. .. diff --git a/docs/source/index/introduction.rst b/docs/source/index/introduction.rst index 99b07004..3eb3916c 100755 --- a/docs/source/index/introduction.rst +++ b/docs/source/index/introduction.rst @@ -50,8 +50,9 @@ mental models necessary for effective application, and see the :ref:`Evaluation strategies for a transparent, objective analysis of the algorithms´ outcomes. **Guides**: For hands-on experience, the :ref:`Tutorials ` teach each tool with its -parameters and outputs, while the :ref:`Protocols ` walk through complete, end-to-end -analyses for biological questions. +parameters and outputs, the :ref:`Protocols ` walk through complete, end-to-end +analyses for biological questions, and the :ref:`Use Cases ` reproduce published +studies end to end from bundled data. **Reference**: Look up the exact signatures in the :ref:`API ` documentation, browse the overview :ref:`Tables ` (including the **AAontology** scale classification and benchmark diff --git a/docs/source/index/usage_principles/prediction_tasks.rst b/docs/source/index/usage_principles/prediction_tasks.rst index ebb03656..88266781 100644 --- a/docs/source/index/usage_principles/prediction_tasks.rst +++ b/docs/source/index/usage_principles/prediction_tasks.rst @@ -165,5 +165,7 @@ Where to go next this table into a start-to-finish workflow. - **Mechanics:** the per-function :ref:`tutorials ` cover ``CPP``, ``SequenceFeature``, ``AAclust``, and the rest. + - **Full case studies:** the :ref:`use cases ` reproduce a + published study end to end, as a template to adapt to your own data. - **Vocabulary:** every term used here is defined in the :ref:`glossary `.