「acl」の検索結果

Build enhancer-driven gene regulatory networks (eGRNs) by integrating single-cell RNA-seq and ATAC-seq using SCENIC+, CellOracle base GRNs, Pando, FigR, DIRECT-NET, TRIPOD, and scMEGA. Covers the accessibility-defines-enhancers principle, peak-to-gene linking and its cell-composition confound, the paired-vs-unpaired decision, and TF-region-gene eRegulon triplets. Use when analyzing 10x multiome or paired/unpaired scRNA+scATAC to infer cis-regulatory GRNs. For RNA-only regulons see scenic-regulons; for in silico TF perturbation see perturbation-simulation.

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Maps between gene identifier systems (Ensembl, Entrez, HGNC symbol, UniProt, RefSeq, MANE) using AnnotationDbi, biomaRt, mygene, pyensembl, and Ensembl REST. Encodes Ensembl version stripping with GENCODE _PAR_Y preservation, the Ziemann 2016 Excel autocorrect debacle and Bruford 2020 HGNC renames (SEPT*->SEPTIN*, MARCH*->MARCHF*, MARC*->MTARC*, DEC1->DELEC1), OCT4/POU5F1 alias resolution, biomaRt archive endpoints for release pinning, the `filters` (plural) gotcha, MANE Select for clinical reporting, cross-species orthology via Ensembl Compara / OMA / OrthoDB, and tx2gene construction for tximport. Use when converting gene IDs across systems, handling renamed symbols, building tx2gene, pinning to a specific Ensembl release for reproducibility, or mapping cross-species orthologs.

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Simulate transcription factor perturbation effects on cell state in silico with CellOracle and Dynamo, and predict transcriptional responses to genetic perturbations with GEARS, scGen, and CPA. Covers the direction-not-magnitude principle, local-linear validity, the GRN/velocity error it inherits, baseline discipline (mean and additive baselines), and the validation gap. Use when predicting TF knockout or overexpression effects, ranking driver TFs for fate transitions, or planning perturbation experiments. For GRN construction see multiomics-grn; for experimental Perturb-seq see single-cell/perturb-seq.

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Detect introgression and admixture between species or populations using Dsuite (Malinsky 2021 fast D-statistics), Patterson's D / ABBA-BABA test (Green 2010; Durand 2011), f4-ratio and f-branch statistic (Malinsky 2018), TreeMix (Pickrell & Pritchard 2012), HyDe (Blischak 2018), QuIBL (Edelman 2019), sprime (Browning 2018), Twisst (Martin 2017), PhyloNet (Than 2008) for explicit phylogenetic networks, and qpAdm / qpGraph (Patterson 2012). Distinguish introgression from incomplete lineage sorting (ILS), ancestral structure, ghost-lineage admixture, and rate variation. Use when testing inter-species gene flow, dating admixture events, identifying introgressed segments, building phylogenetic networks for reticulate evolution, or applying the ABBAclustering (Koppetsch-Malinsky-Matschiner 2024) framework for divergent-species gene flow.

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Call protein-RNA binding sites from CLIP-seq BAM with CLIPper, PureCLIP, Skipper, Piranha, omniCLIP, CTK, CLAM, or Paraclu. Use when choosing between coverage-based, HMM-based, beta-binomial window-based, and crosslink-site-based peak callers; applying ENCODE eCLIP thresholds (log2 IP/SMInput >= 3, -log10 p >= 3); deciding when SMInput is mandatory; or reconciling peak-set discordance between callers for the same RBP.

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Reads, validates, and prepares CDISC SDTM and ADaM clinical trial data for analysis. Covers SDTM domain joins (DM, AE, EX, VS, LB, DS), ADaM architecture (ADSL, BDS, OCCDS, ADTTE) with traceability, treatment-emergent AE conventions, baseline derivation, SUPPQUAL/NSV handling, Define-XML 2.1, and Pinnacle 21 / CORE validation. Use when working with clinical trial datasets in CDISC SDTM/ADaM format, preparing analysis-ready data, or validating for regulatory submission.

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Reads from, writes to, and manages relational databases using MATLAB Database Toolbox. Use when connecting to databases, reading data with sqlread or fetch, filtering with rowfilter, writing with sqlwrite, updating with sqlupdate, executing SQL statements, managing transactions with commit and rollback, mapping MATLAB classes to tables with ORM (Mappable, ormread, ormwrite, ormupdate), or performing any database operation from MATLAB. Triggers on: database, SQL, sqlread, sqlwrite, sqlupdate, fetch, execute, rowfilter, RowFilter, ORM, Mappable, ormread, ormwrite, ormupdate, orm2sql, transaction, commit, rollback, Database Toolbox, PostgreSQL, MySQL, SQLite, SQL Server, Oracle, database connection, database table, query database, insert data, update rows, delete rows, stored procedure, prepared statement, odbc, databaseConnectionOptions, datasource, data source, DSN, connection string, multithreaded, parallel.

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When the user wants to choose or execute third-party platform SEO (high-authority sites for rankings or backlinks). Also use when the user mentions "parasite SEO," "parasitic SEO," "barnacle SEO," "hosted content," "third-party publishing," "Medium SEO," "Reddit SEO," "GitHub parasite SEO," "LinkedIn Pulse SEO," "high-authority platforms," "distributed authority," "borrow domain authority," or "rank without own website." For GitHub-specific playbooks, use github. For Medium.com posts, use medium-posts. For Grokipedia, use grokipedia-recommendations. For AI answer-engine visibility (not platform selection), use generative-engine-optimization.

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Expert knowledge for Azure Virtual Machines development including troubleshooting, best practices, decision making, architecture & design patterns, limits & quotas, security, configuration, integrations & coding patterns, and deployment. Use when choosing VM/VMSS sizes, configuring disks/GPU/InfiniBand, using ADE/CMK, Trusted Launch, or Oracle/HPC workloads, and other Azure Virtual Machines related development tasks. Not for Azure Data Science Virtual Machines (use azure-data-science-vm), SQL Server on Azure Virtual Machines (use azure-sql-virtual-machines), Azure Virtual Machine Scale Sets (use azure-vm-scalesets), Azure Virtual Desktop (use azure-virtual-desktop).

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Expert knowledge for Azure NetApp Files development including troubleshooting, best practices, decision making, architecture & design patterns, limits & quotas, security, configuration, integrations & coding patterns, and deployment. Use when deploying ANF for SAP HANA/Oracle, AVS datastores, AzAcSnap, cross-region replication, or object REST API, and other Azure NetApp Files related development tasks. Not for Azure Files (use azure-files), Azure Blob Storage (use azure-blob-storage), Azure Elastic SAN (use azure-elastic-san), Azure Managed Lustre (use azure-managed-lustre).

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Expert knowledge for Azure Operator Nexus development including troubleshooting, best practices, decision making, architecture & design patterns, limits & quotas, security, configuration, and deployment. Use when configuring Nexus fabric ACLs/BGP, securing RBAC/SSH, planning cluster placement, or diagnosing fabric/K8s issues, and other Azure Operator Nexus related development tasks. Not for Azure Operator Insights (use azure-operator-insights), Azure Operator Service Manager (use azure-operator-service-manager), Azure Networking (use azure-networking), Azure Virtual Network (use azure-virtual-network).

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Expert knowledge for Azure Data Box development including troubleshooting, best practices, limits & quotas, security, configuration, and integrations & coding patterns. Use when using Data Box/Disk/Heavy for offline data copy, SMB/NFS uploads, REST APIs, CMKs/Key Vault, or NTFS ACL preservation, and other Azure Data Box related development tasks. Not for Azure Import Export (use azure-import-export), Azure Stack Edge (use azure-stack-edge), Azure Virtual Machines (use azure-virtual-machines).

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Expert knowledge for Azure Energy Data Services development including troubleshooting, decision making, architecture & design patterns, security, configuration, integrations & coding patterns, and deployment. Use when configuring ADME metrics/partitions, choosing tiers, securing auth/ACLs/legal tags, or integrating ACZ/DDMS with Databricks/Fabric, and other Azure Energy Data Services related development tasks. Not for Azure Data Explorer (use azure-data-explorer), Azure Synapse Analytics (use azure-synapse-analytics), Azure Data Factory (use azure-data-factory), Azure Data Manager for Agriculture (use azure-data-manager-for-agri).

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Expert knowledge for Azure Confidential Ledger development including troubleshooting, decision making, security, integrations & coding patterns, and deployment. Use when configuring ACL auth/attestation, integrating with Blob/Cosmos, using MST payloads, or deploying via ARM/Terraform, and other Azure Confidential Ledger related development tasks. Not for Azure Confidential Computing (use azure-confidential-computing), Azure Key Vault (use azure-key-vault), Azure Dedicated HSM (use azure-dedicated-hsm), Azure Payment Hsm (use azure-payment-hsm).

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Expert guidance for planning and executing cloud adoption using Azure Cloud Adoption Framework. Covers strategy, planning, readiness & landing zones, adoption patterns, governance, security, operations & management, organization & teams, and adoption scenarios. Use when planning AVD, SAP, Oracle, VMware, AI/ML workloads, or designing Azure landing zones and governance, and other Azure Cloud Adoption Framework related development tasks.

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Designs irresistible Grand Slam Offers using Alex Hormozi's value equation, obstacle-solution mapping, value-cost trim/stack, and offer-naming patterns. Use when fixing low conversion rates, packaging products, pricing services, designing high-converting offers, or when prospects browse but don't buy. Covers value stacking, guarantees, scarcity, urgency, and pricing psychology for validated markets.

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LlamaIndex 官方用户文档离线知识库，用于检索并回答 LlamaIndex Python 框架的安装、RAG、数据加载、索引、检索与查询、Agent、Workflow、模型、Embedding、向量库、评估、可观测性、部署、LlamaCloud 和 LlamaParse 等问题，也可生成有文档依据的示例代码与排障建议。当用户提到 LlamaIndex、llama-index、LlamaParse、VectorStoreIndex、QueryEngine、Retriever、AgentWorkflow 或相关集成用法时使用；纯源码贡献、内部实现审查或与 LlamaIndex 无关的通用 AI 问题不使用。

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Writes the character-performance layer of a video prompt as behavior under pressure, not displayed emotion — objective, obstacle, tactics, beats, subtext, listening, body/status/proxemics, and mandatory eye life. Produces a reusable 150–220-word acting master profile per character plus a per-scene rewrite of it, and a locked voice prompt. Use whenever a prompt needs acting, performance, or emotion direction; whenever characters read wooden, dead-eyed, or 'AI'; whenever a character must stay themselves across many shots; or when the user asks for character behavior, mannerisms, tics, a gait, or how someone reacts. Pairs with higgsfield-facs (muscle-level AU codes) and higgsfield-seedance (the prompt the paragraph goes into).

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Tests whether a scene is structurally worth generating before any shot is prompted — a five-element engine of Goal, Obstacle, Tactic, Reversal, and Value Shift. Use when the user has a scene, sequence, beat outline, or script and wants it audited or strengthened; when a sequence generates cleanly but lands flat and nobody can say why; when shots look good individually but the run of them does not build; or when the user asks 'is this scene working', 'what's weak here', or 'why doesn't this land'. Upstream of prompting — it decides WHICH shots deserve the credits, not how to write them. Pairs with higgsfield-character-design (who the characters are) and higgsfield-shotlist-director (turning the settled scene into shots).

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DDD-guided legacy refactoring patterns -- strangler fig, bubble context, ACL migration, 14 tactical/strategic/infrastructure patterns, and incremental monolith-to-microservices methodology

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Audits existing tests for risk coverage, reliable oracles and maintenance value; does not edit tests.

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Core Python 3.11+ language standards for typing, dataclasses, imports, pathlib, and stdlib-first code. Use for idiomatic language constructs in Python modules or stubs; defer pytest fixtures, database/client configuration, subprocess security, and other specialized concerns.

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End-to-end academic paper writing workflow. Takes a research repository and produces a publication-ready LaTeX manuscript for top ML/AI conferences (NeurIPS, ICML, ICLR, ACL, AAAI, COLM). Covers repo understanding, structure planning, section drafting, citation management, anti-AI polishing, and conference formatting. Triggers on "write paper", "draft paper", "scholar writing", "paper writing workflow".

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ALWAYS USE for test work that requires changes: write, add, generate, repair, or strengthen tests for existing code in xUnit, MSTest, NUnit, pytest, Vitest/Jest, Go, or another framework. Includes regression cases, failing or flaky tests, coverage-driven additions, and audit-then-fix requests. Focused work stays direct; broad or multi-stage work invokes test-engineer. DO NOT USE for only running tests, analysis-only audits, framework/platform migrations, a test blocked on a missing production seam (testability-obstacle), or MSTest API/configuration corrections that do not design new cases (writing-mstest-tests). Within an active test-engineer pipeline, reuse supplied guidance and do not re-enter this skill.

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