AlphaFold Y-Shape Studio 1.4.0 is a desktop application for screening AlphaFold 3 IgG1 antibody models and inspecting their structures with Matplotlib. It assigns chains and domains, measures hinge and Fab/Fc geometry, reports candidate disulfide bonds, and provides an interactive three-dimensional preview. Analysis runs locally. Changes in version 1.4.0 Accepts arbitrary, case-insensitive CIF filenames. Optional seed- and sample- metadata may be read from filenames or enclosing folders. Models are processed in numeric seed/sample order, and files without numbered metadata follow the numbered models. Reads the first coordinate model directly from mmCIF without an intermediate PDB file. Multi-character author chain IDs, insertion codes, and mmCIF coordinate precision are retained by the analysis reader. Reopens completed and partial runs in read-only mode. Version 1.4 previews are embedded in run_summary.json and remain available if the original CIF files are moved; version 1.3 tables also reopen, with previews rebuilt when their original CIF files remain accessible. The screening criteria and thresholds are unchanged from version 1.3.0. Downloads Windows: AlphaFold_Y_Shape_Studio_Windows_x86_64_v1.4.0.zip contains the ready-to-run executable and bundled runtime. Extract the entire ZIP, keep the _internal folder beside the executable, and double-click AlphaFold_Y_Shape_Studio.exe. Python is not required. The executable is unsigned; do not disable operating-system security protections. Linux and source: AlphaFold_Y_Shape_Studio_Source_v1.4.0.tar.gz contains the source, pinned requirements, setup and launch scripts, regression tests, build scripts, licenses, and sample. Python 3.10 with Tk, venv support, and a desktop display is recommended. Run ./scripts/setup_ui_env.sh once and then ./run_ui_from_source.sh. Later launches need only the second command. The first setup requires internet access. Both archives include the complete 15-model sample dataset with its AF3 JSON files, dependency notices, and the same README.md. The general README.md is also supplied separately. Validation and limitations Version 1.4.0 passed 86 regression tests on each tested platform: Windows 11 x64 with Python 3.10.4 and Ubuntu 22.04.2 under WSL2/WSLg with Python 3.10.12, using NumPy 2.2.6, Biopython 1.88, and Matplotlib 3.10.9. The bundled Windows executable was tested with Python absent from PATH. Default and AF3 JSON sample runs each analyzed all 15 structures with 3 passing and 0 failed inputs. Testing also covered numeric seed/sample ordering, arbitrary Unicode filenames, direct mmCIF reading, saved-run reopening, invalid inputs, cancellation, output preservation, UI interaction, and image export. This software is a geometric screening heuristic, not experimental validation of structure, function, or biological activity. Additional profiles are retained, but execution on the antibody sample does not establish their scientific classification accuracy. Other operating systems, Linux distributions, Python versions, and hardware configurations have not been exhaustively tested. No standalone Linux binary or tested container image is supplied.
Hongyun Zhao· Zenodo (CERN European Organi...· 0 citations
This v1.5 release accompanies the manuscript by Gerhard Rambold, “Full-sky CMB temperature topology as evidence for large-scale differential rotation in the early universe”. The analysis applies one frozen multiscale reconstructibility operator (AC*) separately to the Planck PR3 SMICA, NILC, Commander, and SEVEM temperature reconstructions. The protected analysis chain runs from the support-indexed AC* field through high-response morphology, reciprocal high-resolution lineages, a reconstructed spherical tangent field and blind topology to Focus A, its 3D-compatible persistence, differential-rotation profile, transverse physical realization, and a common regular-vector recombination state. A pre-specified sign-sensitive polarization operator then tests the corresponding second-channel relation. The opposite-sign relation is reproduced across all four temperature-derived physical source realizations and both Planck half missions. The four component-separation products are correlated reconstructions of the same CMB sky and are therefore used for cross-reconstruction robustness, not as four independent detections. The release does not claim an absolute material speed, physical angular velocity, vorticity amplitude, unique volumetric flow, unique Bianchi type, cosmological centre, or unique ultimate cosmological generator. The record contains exactly four public objects: the frozen 28-page manuscript; a portable interactive scientific viewer; an authority-bound Python analysis/replay package with explicit four-map branches; and a licences/manifests/checksums archive containing frozen SHA-256 bindings, observational-input and mask bindings, provenance, and freeze receipts. The canonical AC* implementation and response field remain frozen and reproducibly bound by SHA-256. The public package contains no raw Planck or WMAP FITS files. Original third-party observational products must be obtained from their public source archives under the providers’ applicable terms. Author-created scientific text, figures, documentation, manifests and derived presentation material are released under CC BY 4.0 to the extent rights are held; original author-created software is released under the MIT License.
Gerhard Rambold· Zenodo (CERN European Organi...· 0 citations
ملخص: تهدف هذه الدراسة إلى تحليل ومقارنة المتغيرات البيوميكانيكية المرتبطة بأداء مهارة القلبة الهوائية الأمامية المستقيمة مع لفتين على بساط الحركات الأرضية بين لاعبي المنتخبين العراقي والإيراني في الجمناستك الفني، حيث اعتمد الباحث على المنهج الوصفي بأسلوبه المسحي، وتكونت العينة من (6) لاعبين من المنتخب العراقي ولاعب واحد من المنتخب الإيراني تم اختياره عمديًا وفق تصنيفات الاتحاد المختص، حيث تم استخدام تقنيات التحليل الحركي من خلال برمجيات (Kinovea) و(Tracker)، إضافة إلى جهاز قياس القوة (Dynafoot)، بهدف استخراج مجموعة من المتغيرات الكينماتيكية والكينيتكية مثل زوايا المفاصل، السرعة الزاوية، ارتفاع مركز الثقل، زمن الطيران، وقوى الدفع والاصطدام. وتمت معالجة البيانات إحصائيًا باستخدام الوسط الحسابي والانحراف المعياري واختبار (t-test) لبيان الفروق بين المجموعتين. وقد أظهرت النتائج وجود فروق واضحة في عدد من المتغيرات البيوميكانيكية بين لاعبي المنتخبين، خاصة في قوة الدفع، وزوايا المفاصل، وارتفاع مركز الثقل، حيث أظهر اللاعب الإيراني كفاءة أعلى في التحكم الحركي وتوليد القوة. في المقابل، لم تظهر بعض المتغيرات فروقًا ذات دلالة إحصائية، مما يعكس تقارب الأداء في بعض مراحل المهارة، كما استنتجت الدراسة أن المتغيرات البيوميكانيكية، ولا سيما قوة الدفع، وزوايا المفاصل، وارتفاع مركز الثقل، تعد محددات أساسية لجودة الأداء الفني في هذه المهارة. وأوصت بضرورة اعتماد التحليل البيوميكانيكي في تصميم البرامج التدريبية، والعمل على تصحيح الأخطاء الفنية وتحسين الكفاءة الحركية للاعبين. الكلمات المفتاحية: الجمناستك الفني؛ التحليل البيوميكانيكي؛ القلبة الهوائية الأمامية؛ المتغيرات الكينماتيكية؛ الأداء الحركي. Abstract: This study aimed to analyze and compare the biomechanical variables associated with the performance of the straight front somersault with two twists on the floor exercise between Iraqi and Iranian artistic gymnastics national team athletes. The descriptive survey method was adopted, and the sample consisted of six Iraqi national team gymnasts and one Iranian gymnast who was intentionally selected based on federation rankings. Motion analysis techniques were applied using Kinovea and Tracker software, in addition to the Dynafoot force measurement system, to extract key kinematic and kinetic variables, including joint angles, angular velocity, center of mass height, flight time, take-off force, and impact force. Data were statistically analyzed using mean, standard deviation, and independent t-test to identify differences between the two groups. The results indicated clear differences between the two teams in several biomechanical variables, particularly take-off force, joint angles, and center of mass height, with the Iranian gymnast demonstrating superior biomechanical efficiency in force production and movement control. However, some variables showed no statistically significant differences, indicating similarity in certain phases of execution. The study concluded that biomechanical variables—especially take-off force, joint angles, and center of mass height—are critical determinants of performance quality in this skill. It recommends integrating biomechanical analysis into training programs to enhance performance efficiency and correct technical errors. Keywords: Artistic gymnastics; biomechanical analysis; front somersault; kinematic variables; motor performance.
This record contains the software, manuscript source, tests, and publication-ready derived artifacts for the study “Strict Trace Replay and Local Diagnosis for Distributed Accelerator Slack Recovery”. The archive includes the current manuscript, Python source code, unit tests, and the CSV, JSON, LaTeX-table, and figure artifacts used in the paper. Raw Alibaba and Microsoft Philly traces are not redistributed here; they should be obtained from the public sources cited in the manuscript.
Dorn Yuriy· Zenodo (CERN European Organi...· 0 citations
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This is a major release: three changes break a straight git pull upgrade from v1.1.0 and require the manual steps in the Upgrade Tutorial below: migrating dependency management from pip to uv, adding Node/npm as a required build-time dependency on the server, and moving scheduled maintenance jobs onto a persistent Dagster daemon process. Changelog Breaking Changes Dependency management: pip → uv. requirements.txt is gone; dependencies are now declared in pyproject.toml and installed via uv sync. Existing venvs built with pip need to be rebuilt. Node.js + npm now required on the server. The map dashboard's frontend is a Vite app that must be built during deploy (npm ci && npm run build). v1.1.0 had no frontend build step at all. Maintenance jobs now require a running dagster-daemon process. The old subprocess.Popen-based trigger from the admin is gone; jobs are submitted via dagster job launch and need the daemon (plus a DAGSTER_HOME) to actually run. Existing deployments need a new supervised process (systemd unit or equivalent) — see Upgrade Tutorial. Python 3.13+ now required. Permission system rewritten. Group/permission definitions changed; a one-time --reset of the predefined groups is recommended after upgrading (applied automatically otherwise via post_migrate, but --reset guarantees a clean rebuild — see Upgrade Tutorial). Security Fixes Fixed an IDOR in project re-parenting in the admin layer (58bb604). Fixed a bbox filter bypass and hardened import_landforms against malformed input (ec3eeee). FieldPhoto.file is no longer served as a raw media URL — downloads now go through a project-scoped, permission-checked view (5c7cb4d). Fixed a fallback in ReferenceAdmin.has_delete_permission that could grant unintended delete access (007758a). Fixed a 500 error path in RasterSceneAdmin for non-superusers that could leak state (fb74c61). Broader admin-layer security review: fixed several access-control issues found during an internal architecture/security audit (7a044a1, 84906f0). CI hardened with a full bandit, vulture, xenon, basedpyright and mypy sweep, plus pylint, to catch this class of issue earlier (d0959f6). New Features Raster data app: import, admin, and metadata recomputation for raster scenes; corpus_path/file precedence fixed during recompute (5f2b101). Geodata app: extended API and location import; GPS accuracy tracking added to location capture. Map: Vite-based rebuild; Google Satellite basemap. Analysis: cosmogenic nuclide dating model and admin support. Admin/UX: sample-admin now summarizes all sample-related measurements in one place; luminescence, grainsize, and location admin interfaces revised. Internal data_quality flag added to LuminescenceDating and RadiocarbonDating, with test coverage. Permission system rewrite, covering project-, group- and object-level access. deploy management command for scripted, repeatable production deploys. Dagster orchestration for scheduled database maintenance, including per-table DuckDB export failure tracking (MaintenanceRun.log now populated for daemon-triggered runs). Bug Fixes & Reliability Fixed inconsistent on_delete behavior across Sample-related foreign keys, preventing orphaned or unexpectedly cascaded records (fa3fb7c). Fixed Meta.ordering inheritance and duplicate ordering on M2M relations; fixed eager validator binding (47b4517). Fixed process/notes property casing mismatch in landform import (82aa5a2). Added error handling around three previously unguarded map data loaders (23a0d66). Smaller fixes across UI, dashboard, datetime handling, and admin resources (campaign, sample, researcher, manufacturer). Infrastructure & Developer Experience CI pipeline added for tests and linting, with a throwaway local_settings.py so Django settings actually load in CI (9a0db28). Pre-commit hardened: full basedpyright coverage across all 8 apps, plus bandit, vulture, xenon, pylint duplicate-code detection. Dead code and leftover Dagster boilerplate removed. Test coverage extended (analysis, field_data, geodata, laboratory, raster_data, orchestration), including direct reachability tests for permission fallbacks and coverage for CosmogenicNuclideDating. Routine dependency updates via Dependabot (Pillow, GitPython, and others). Documentation Sphinx-based documentation site added (docs/), with a GitHub Actions workflow that builds it on every push (publishing to GitHub Pages is not yet enabled). CONTRIBUTING.md, CODE_OF_CONDUCT.md, and a revised security policy/reporting process added. Pull request template added. README overhauled: deploy workflow, Vite frontend, Dagster daemon migration, and full test structure documented. Upgrade Tutorial Audience: operators upgrading an existing v1.1.0 (or earlier) CGDB deployment to v2.0.0 "Arica". Steps 1–3 are one-time migration steps specific to this release; step 4 onward is the normal deploy flow. Before you start: Take a full database backup yourself, independent of the automatic pre-migrate backup manage.py deploy takes — this upgrade touches more than a single migration. Schedule a maintenance window. Steps 1–3 involve service restarts and are not zero-downtime. Confirm shell access with the same privileges your normal deploy uses (the app-owning user for most steps, sudo only for manage.py deploy itself). 1. Install prerequisites on the server Python 3.13+ — confirm with python3 --version; upgrade the interpreter first if it's older. uv — install if not already present. Node.js + npm — required from this release on, as a one-shot build tool only (nothing Node-based runs persistently). Any reasonably current LTS Node works; there's no pinned minimum version. Confirm with node --version / npm --version. 2. Migrate dependency management from pip to uv Do this once, before the first uv sync on the server: # from the project root, with the OLD pip-based venv still in place deactivate 2>/dev/null || true rm -rf .venv # the old pip-managed venv — uv creates its own requirements.txt no longer exists in the new codebase; uv sync (run automatically by manage.py deploy, see step 4) reads pyproject.toml/uv.lock instead and creates a fresh .venv. If anything outside this repo activates the old venv by hard-coded path (a systemd unit, a cron job, a supervisor config), update those paths — uv sync still creates .venv in the same location, so most setups need no changes here, but double-check anything referencing .venv/bin/pip directly. 3. Migrate scheduled maintenance jobs to the Dagster daemon Skip this step if you never enabled the optional Dagster orchestration on this deployment. Older deployments triggered maintenance jobs via a detached subprocess.Popen from the admin, with no daemon and no dedicated Dagster run storage. This release requires a persistent dagster-daemon process instead: Deploy the new code first (step 4 below covers this) — uv sync pulls in dagster>=1.13.16, dagster-webserver>=1.13.11, dagster-postgres>=0.29.11. No Django migration is involved in this particular change; it's a config/behavior change, not a schema change. Make orchestration/dagster_home writable by the same user your web server process runs as (e.g. www-data on Debian/Ubuntu, apache on RHEL/CentOS — check whichever user your existing WSGIDaemonProcess, or equivalent, is configured with). Triggering a maintenance job from the admin runs dagster job launch synchronously in the web request (orchestration/admin.py, via subprocess.run) — not just the daemon. Both that in-request launch and the daemon itself need write access to DAGSTER_HOME (SQLite run storage creates a history/ subdirectory there on first use). Since the directory typically arrives owned by whoever ran git pull (your personal account) or root (from sudo manage.py deploy), fix ownership explicitly: sudo chown -R : /orchestration/dagster_home (Optional — only if you want PostgreSQL run storage instead of the SQLite default) create a dedicated Postgres database for Dagster's own run storage — reuse the same Postgres instance/host/credentials your app already runs on, just a separate database name (e.g. dagster): CREATE DATABASE dagster; GRANT ALL PRIVILEGES ON DATABASE dagster TO ; Then edit orchestration/dagster_home/dagster.yaml: comment out the sqlite storage block, uncomment the postgres block, and set: export DAGSTER_PG_USER=dagster export DAGSTER_PG_PASSWORD=... export DAGSTER_PG_HOST=localhost export DAGSTER_PG_DB=dagster The SQLite default needs none of this — just DAGSTER_HOME (next step). Add the daemon as its own supervised process. If served via Apache/mod_wsgi, add a new, separate systemd unit — Apache doesn't need to know about it. First check which user your existing WSGIDaemonProcess runs as (/etc/apache2/sites-available/*.conf, the user=/group= on that directive) so the daemon runs as the same user rather than a new one. Example /etc/systemd/system/cgdb-dagster-daemon.service: [Unit] Description=CGDB Dagster daemon After=network.target [Service] Type=simple User= Group= WorkingDirectory= Environment=DAGSTER_HOME= /orchestration/dagster_home # The four lines below are only needed if you switched dagster.yaml to # PostgreSQL storage (step 3 above) — omit them for the SQLite default. # Environment=DAGSTER_PG_USER=dagster # Environment=DAGSTER_PG_PASSWORD=... # Environment=DAGSTER_PG_HOST=localhost # Environment=DAGSTER_PG_DB=dagster ExecStart= /.venv/bin/dagster-daemon run Restart=on-failure RestartSec=5 [Install] WantedBy=multi-user.target sudo systemctl daemon-reload sudo systemctl enable --now cgdb-dagster-daemon 4. Deploy git pull --ff-only sudo python manage.py deploy This runs, in order: a clean-working-tree check, a pre-migrate database backup, uv sync (now against pyproject.toml, per step 2), np
Dennis Handy· Zenodo (CERN European Organi...· 0 citations
This dataset is the reproducibility and robustness evidence companion to the SCILLA v1.1.0 canonical research note (DOI: 10.5281/zenodo.22209746). It contains the synthetic evidence used to audit and reproduce the principal computational results of SCILLA, including matched run-level results, aggregate policy effects, paired-bootstrap confidence intervals, sign-flip tests, parameter sensitivity sweeps, prior-mismatch and other ablation experiments, independent trace-verifier results, canonical trace records, the frozen experimental protocol, assumptions, and seed registry. The evidence preserves both positive and negative results. In particular, the released experiments do not support universal adaptive superiority for raw discovery. The dataset instead supports more bounded findings concerning paired ascent/return reacquisition, the constructed priority-weighted Track Debt metric, and decision-weighted utility under the declared synthetic reference conditions. All performance data contained in this record are synthetic or recomputed from the released SCILLA research software. No physical SCILLA field measurement is included. The dataset does not establish operational detection range, field tracking performance, customer ROI, patent status, QPU execution, or quantum advantage. An independent trace verifier is included in the associated evidence architecture to recompute geometry, eligibility, scan constraints, observation identities, paired-state counts, and final metric identities without selecting the optimizer’s actions. This dataset supplements the canonical SCILLA v1.1.0 research publication: Michele Giletto, “SCILLA: Altitude-Coupled Transient Maritime Surface Observation — Geometry, Paired State Estimation, and Falsification of Adaptive Scan Allocation,” Giletto Systems Lab, v1.1.0, 2026. DOI: 10.5281/zenodo.22209746. The next validation gates are external historical sensor replay and a cooperative paired-observation field experiment using synchronized platform navigation and independent target ground truth.
Michele Giletto· Zenodo (CERN European Organi...· 2 citations
Version 0.5.0 of the open exchange and preservation standard for digital representations of musical instruments and other acoustic objects. This release adds identified cross-carrier distributions, carrier and descriptor fixity, release-inventory validation, and a two-carrier repository profile for bootstrap and preservation delivery. Specification text, schemas, semantic artifacts, fixtures, and knowledge documentation are CC BY 4.0; reference software, tests, and automation are Apache-2.0. The authoritative per-file mapping is in REUSE.toml.
Copilot CLI Local Memory v1.1.2 — Citation and provenance This release makes the project's authorship, design history, and public release chronology explicit and machine-readable. It does not change the plugin's runtime behaviour. What changed Added CITATION.cff so GitHub, Zenodo, reference managers, and researchers can identify and cite the software consistently. Added DESIGN-AND-PROVENANCE.md to record the implemented design, public release chronology, and relationship to earlier Copilot memory proposals. Added Shankar Balakrishna to the MIT copyright notice. Linked the citation and provenance records from the README. Prepared the release for independent preservation through Zenodo and Software Heritage. The provenance document deliberately acknowledges earlier public proposals for persistent and local Copilot memory. The contribution recorded here is this project's concrete implementation: explicit save, search, and delete commands over plain local modular-instruction files, with deterministic search, optional file scopes, duplicate and common-secret detection, and no database or server. Verification All 11 automated tests pass. The Agent Plugins v1.0.0 manifest checks pass. CITATION.cff validates against the Citation File Format 1.2.0 schema. Runtime behaviour is unchanged from v1.1.1. Install or update copilot plugin install shankarnarayanb/copilot-cli-local-memory copilot --experimental Verify the plugin with /plugin list and /extensions manage. Full Changelog: https://github.com/shankarnarayanb/copilot-cli-local-memory/compare/v1.1.1...v1.1.2
Shankar Balakrishna· Zenodo (CERN European Organi...· 0 citations
Multi-vendor enterprise systems operating under regulatory mandates face a structural software quality failure that existing governance frameworks do not address: interpretation divergence. When multiple vendors independently translate the same regulatory requirement into system behavior, each may produce an implementation that is internally correct yet incompatible with the others at system integration boundaries. In regulated healthcare exchange programs under the Affordable Care Act (ACA), this failure is compounded by geospatial risk—regulatory terms such as service area, county boundary, and rating region carry implicit spatial data model dependencies that different vendors resolve through incompatible geographic representations, producing divergent eligibility determinations for the same enrollees. This paper introduces AIRIA: the AI-Assisted Regulatory Interpretation Alignment framework. AIRIA is a Design Science Research artifact—a five-layer human-AI architecture that intercepts interpretation divergence before it propagates into acceptance criteria and implementation. The five layers are: (1) Regulatory Ingestion, (2) Ambiguity Detection, (3) Geospatial Interpretation Analysis, (4) Regulatory Knowledge Graph construction, and (5) Divergence Detection and Alerting. AI performs computationally intensive consistency work across these layers while human governance structures retain all binding decision authority. This paper proposes a Design Science Research framework to investigate three research questions: whether geospatial regulatory terms can be systematically classified by spatial data model divergence risk; whether a knowledge graph schema can represent regulatory interpretation lineage with explicit geospatial nodes; and whether AI-assisted governance can detect multi-vendor divergence before implementation. AIRIA is grounded in more than 13 years of practitioner observation across healthcare exchange and government-regulated enterprise implementations involving multiple independent vendor organizations, presented as a conceptual architecture with a five-phase empirical validation roadmap. Contributions include a regulatory geospatial term taxonomy, a Geospatial Ambiguity Risk Score formula, a nine-node Regulatory Knowledge Graph schema, and an Alignment Confidence Score model—all specified as testable artifacts for future empirical validation.
Sreedhar Ailu· International Journal of Glo...· 0 citations
Copilot CLI Local Memory v1.1.2 — Citation and provenance This release makes the project's authorship, design history, and public release chronology explicit and machine-readable. It does not change the plugin's runtime behaviour. What changed Added CITATION.cff so GitHub, Zenodo, reference managers, and researchers can identify and cite the software consistently. Added DESIGN-AND-PROVENANCE.md to record the implemented design, public release chronology, and relationship to earlier Copilot memory proposals. Added Shankar Balakrishna to the MIT copyright notice. Linked the citation and provenance records from the README. Prepared the release for independent preservation through Zenodo and Software Heritage. The provenance document deliberately acknowledges earlier public proposals for persistent and local Copilot memory. The contribution recorded here is this project's concrete implementation: explicit save, search, and delete commands over plain local modular-instruction files, with deterministic search, optional file scopes, duplicate and common-secret detection, and no database or server. Verification All 11 automated tests pass. The Agent Plugins v1.0.0 manifest checks pass. CITATION.cff validates against the Citation File Format 1.2.0 schema. Runtime behaviour is unchanged from v1.1.1. Install or update copilot plugin install shankarnarayanb/copilot-cli-local-memory copilot --experimental Verify the plugin with /plugin list and /extensions manage. Full Changelog: https://github.com/shankarnarayanb/copilot-cli-local-memory/compare/v1.1.1...v1.1.2
Shankar Balakrishna· Zenodo (CERN European Organi...· 0 citations
Analysis code, configuration, intermediate result tables and figure-generating scripts supporting the manuscript "African and European Haplotypes Carry Different Slick PRLR Alleles in Criollo and Senepol Cattle" (submitted to Animal Genetics). What the study does. The slick coat phenotype of tropically adapted cattle arises from independent truncating mutations of the prolactin receptor (PRLR). Allele counts locate such a variant but do not source it. This work phases the receptor gene without the minor-allele-frequency filter that phasing normally applies, so that each carrier haplotype can be read at the causal site itself, and then assigns every carrier haplotype to a reference panel separately by functional class of site, keeping N'Dama distinct from the sanga populations rather than pooling them as "African". Three truncating alleles segregating in Criollo cattle of the Caribbean and northern South America are resolved: SLICK1 (c.1382del, p.Ala461ValfsTer2) sits on a West African taurine (N'Dama) background in four of five carrier haplotypes, whereas SLICK2 (c.1489C>T, p.Arg497*) and SLICK3 (c.1394C>A, p.Ser465*) sit on European taurine backgrounds. A fourth truncating allele, SLICK5 (c.1396A>T, p.Lys466*), fails a panel-specificity test and is reported as excluded rather than assigned. Referred to its lineage-matched British control, the Senepol carries about six percentage points more West African ancestry, which keeps an African route into tropically adapted taurine cattle open. Files. The manuscript, the Supporting Information, the graphical abstract, the reference list and the README are provided as standalone files. The complete reproducible tree is in slick_ancestry_analysis_code_and_data.zip: scripts/lib/hap_ancestry/ — locus-agnostic engine for haplotype ancestry assignment by functional class (likelihood assignment, site resampling, haplotype network). Everything locus-specific lives in a single YAML. scripts/ — supervised ancestry estimation, titration calibration, lineage-paired controls, background-homozygosity control, panel-specificity filter, informativeness analysis, data acquisition, panel labelling and every figure script. config/slick.yaml — focal sites, reference panels, consequence classes. data/genotypes/ — genotypes at the three focal sites for all 1,880 phased samples, plus the panel labels; these are the inputs that regenerate Figure 1. tables/ and figures/ — the tables and figures of the article and of the Supporting Information. What this archive does not contain. The primary sequence data are public and are referenced by accession rather than redeposited: European Nucleotide Archive ERP150979, PRJEB39353 and PRJEB39924; SRA PRJNA604048; GEO GSE192471 (Senepol liver RNA-seq); Dryad doi:10.5061/dryad.th092 (high-density array genotypes); the WIDDE portal; and CLARITY doi:10.5281/zenodo.17909700 (physical-genetic map). Reference assembly ARS-UCD1.2. See README.md for the software environment, the data sources, and how to go from the public accessions to each reported number.
Luiz Dione Barbosa de Melo· Zenodo (CERN European Organi...· 0 citations
Analysis code, configuration, intermediate result tables and figure-generating scripts supporting the manuscript "African and European Haplotypes Carry Different Slick PRLR Alleles in Criollo and Senepol Cattle" (submitted to Animal Genetics). What the study does. The slick coat phenotype of tropically adapted cattle arises from independent truncating mutations of the prolactin receptor (PRLR). Allele counts locate such a variant but do not source it. This work phases the receptor gene without the minor-allele-frequency filter that phasing normally applies, so that each carrier haplotype can be read at the causal site itself, and then assigns every carrier haplotype to a reference panel separately by functional class of site, keeping N'Dama distinct from the sanga populations rather than pooling them as "African". Three truncating alleles segregating in Criollo cattle of the Caribbean and northern South America are resolved: SLICK1 (c.1382del, p.Ala461ValfsTer2) sits on a West African taurine (N'Dama) background in four of five carrier haplotypes, whereas SLICK2 (c.1489C>T, p.Arg497*) and SLICK3 (c.1394C>A, p.Ser465*) sit on European taurine backgrounds. A fourth truncating allele, SLICK5 (c.1396A>T, p.Lys466*), fails a panel-specificity test and is reported as excluded rather than assigned. Referred to its lineage-matched British control, the Senepol carries about six percentage points more West African ancestry, which keeps an African route into tropically adapted taurine cattle open. Files. The manuscript, the Supporting Information, the graphical abstract, the reference list and the README are provided as standalone files. The complete reproducible tree is in slick_ancestry_analysis_code_and_data.zip: scripts/lib/hap_ancestry/ — locus-agnostic engine for haplotype ancestry assignment by functional class (likelihood assignment, site resampling, haplotype network). Everything locus-specific lives in a single YAML. scripts/ — supervised ancestry estimation, titration calibration, lineage-paired controls, background-homozygosity control, panel-specificity filter, informativeness analysis, data acquisition, panel labelling and every figure script. config/slick.yaml — focal sites, reference panels, consequence classes. data/genotypes/ — genotypes at the three focal sites for all 1,880 phased samples, plus the panel labels; these are the inputs that regenerate Figure 1. tables/ and figures/ — the tables and figures of the article and of the Supporting Information. What this archive does not contain. The primary sequence data are public and are referenced by accession rather than redeposited: European Nucleotide Archive ERP150979, PRJEB39353 and PRJEB39924; SRA PRJNA604048; GEO GSE192471 (Senepol liver RNA-seq); Dryad doi:10.5061/dryad.th092 (high-density array genotypes); the WIDDE portal; and CLARITY doi:10.5281/zenodo.17909700 (physical-genetic map). Reference assembly ARS-UCD1.2. See README.md for the software environment, the data sources, and how to go from the public accessions to each reported number.
Luiz Dione Barbosa de Melo· Zenodo (CERN European Organi...· 0 citations
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