「pca」の検索結果

Running Suricata against PCAPs offline and validating results via eve.json

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Collects bounded packet captures from AKS nodes and Azure network configuration for wire-level evidence. WHEN: "capture packets on an AKS node", "take a pcap", "run tcpdump on AKS", "prove where packets drop". Use for explicit packet-capture intent after read-only diagnostics, not general AKS connectivity or ingress troubleshooting.

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Persist player data in Roblox with DataStoreService: GetDataStore, GetAsync/ SetAsync/UpdateAsync/IncrementAsync wrapped in pcall, load-on-join and save-on-leave plus BindToClose, retries, and OrderedDataStore leaderboards. Use when saving or loading persistent data in a Roblox experience — when the user mentions DataStore, DataStoreService, GetAsync, SetAsync, UpdateAsync, save player data, or leaderboards. For general Luau scripting use roblox-luau.

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Tests genotype-environment associations and identifies adaptive loci while correcting for the four-confound landscape (structure, demography, background selection, sampling design) using LFMM2 with mandatory K via sNMF cross-entropy elbow (LEA 3), BayPass Core/AUX/C2/IS with Omega covariance matrix, RDA / pRDA for polygenic adaptation (Forester 2018; requires imputed genotypes), OutFLANK with trimmed FST null, pcadapt, gradient forests (Ellis-Smith-Pitcher 2012, NOT mis-cited Ellis-Manel), Capblancq & Forester 2021 RDA Swiss-army-knife, genomic-offset prediction with Lind & Lotterhos 2025 three-regime caveat, Lotterhos-Whitlock sampling optima, Wang & Bradburd 2014 IBD vs IBE, and Circuitscape + ResistanceGA. Use when identifying adaptive loci across gradients, choosing K for LFMM2, deciding among GEA methods, predicting maladaptation with the novel-environment caveat, distinguishing IBD vs IBE, or optimizing sampling design.

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Designs pooled sgRNA libraries for CRISPR knockout, interference (CRISPRi), activation (CRISPRa), Cas12a multiplex, base-editor, and prime-editor screens. Covers on-target scoring (Rule Set 2, Azimuth, DeepSpCas9, CRISPRon), off-target scoring (CFD, MIT), TSS-relative positioning for CRISPRi/a (Horlbeck, Dolcetto, Calabrese), PAM-variant chemistries, control-guide composition, oligo cloning architecture, and library QC. Use when choosing a genome-wide library (GeCKOv2 vs Avana vs Brunello vs TKOv3 vs Inzolia), designing a focused or paralog-focused custom library, picking CRISPRi vs CRISPRa TSS windows, deciding control-guide proportions, or diagnosing library skew and dropout in a freshly cloned pool.

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Handles batch effects in bulk RNA-seq via design-matrix inclusion (the correct path for DE), ComBat/ComBat-seq for visualization, SVA for unknown latent factors, RUVSeq for negative-control-gene-anchored unwanted variation, and limma::removeBatchEffect for plotting only. Encodes the Nygaard 2016 cardinal sin against testing on a batch-corrected matrix, the choice between SVA/RUVg/RUVs/RUVr, the confounding non-identifiability problem, the single-cell boundary (Harmony/MNN are NOT for bulk), and the Goh 2017 harmonization critique. Use when designing a DE analysis with batch structure, troubleshooting batch-dominated PCA, choosing ComBat vs ComBat-seq, handling unknown batch via SVA, integrating across studies, or deciding when (rarely) to subtract batch.

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Batch effect correction for CRISPR screens covering ComBat empirical-Bayes, RUV, SVA, control-sgRNA normalization, and the model-based alternative of including batch as a covariate in MAGeCK MLE or Chronos. Covers screen-specific batch sources (passage cohort, library lot, infection day, sequencing run, Cas9 lot, FBS lot), PCA + variance-decomposition diagnostic to decide if correction is needed, when correction harms biology by over-correcting condition into batch, limma removeBatchEffect for visualization-only correction, and relationship to multi-condition design matrices. Use when combining screens for joint analysis, when passage cohort confounds biology, when DepMap-style panels need Chronos with batch covariates, when picking ComBat vs RUV, or when correction harms biology and should be replaced with explicit covariate modeling.

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Normalize read-depth copy-ratio profiles and segment them into copy-number regions using circular binary segmentation (CBS, DNAcopy), hidden Markov models, HaarSeg, and fused-lasso methods. Covers GC-content, mappability, and replication-timing (wave-artifact) bias correction, panel-of-normals/PCA denoising, diploid-baseline centering, and algorithm selection by sequencing depth and event size. Use when choosing a segmentation algorithm, correcting depth bias, diagnosing oversegmentation or a mis-centered baseline, tuning CBS or HMM parameters, or understanding why a downstream CNV caller produced fragmented or shifted segments.

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Read and write 3-D point cloud data using Lidar Toolbox file I/O. Covers PLY, PCD, LAS/LAZ, PCAP (Velodyne/Ouster/Hesai), E57, and IDC (Ibeo) formats. Use when loading point clouds from disk, saving to disk, choosing the correct reader or writer for a file format, extracting or preserving lidar point attributes, reading Ibeo IDC sensor recordings, or converting between formats.

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Expert SOX IT General Controls (ITGC) advisor for finance, internal audit and IT compliance teams. Covers the four ITGC domains external auditors test for SOX 404 — access to programs and data, program changes, computer operations, and program development — plus scoping in-scope systems from the financial statements, designing risk-and-control matrices (RCMs), writing control narratives and test scripts, evaluating deficiencies (control deficiency vs significant deficiency vs material weakness under AS 2201 and Reg S-X), drafting remediation plans, SOX 404(a)/404(b) applicability and filer status, IPO readiness timelines, and COSO 2013 alignment. Use this skill whenever a user mentions SOX, ITGC, IT general controls, 404(a)/404(b), ICFR, material weakness, user access reviews, segregation of duties in IT, change management controls for financial systems, SOX testing or walkthroughs, PCAOB audits, or quarterly compliance for financial reporting systems.

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Compliant, retrieval-grounded research over investor-State dispute settlement (ISDS) awards and decisions. Use when the user asks about ICSID / investment-treaty arbitration cases, awards, or doctrines (fair and equitable treatment, expropriation, jurisdiction, costs, annulment, etc.) and wants answers grounded in the actual document text with pinpoint citations. Identifies the correct document on the case page, confirms it against the PDF's own first pages, retrieves primary documents on demand from ICSID, PCA, etc., never scrapes or hosts a corpus in violation of applicable terms, and cites only retrieved text.

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用于磁盘镜像、内存转储、事件日志、PCAP、隐写分析、Windows 注册表、音频信号、Docker 镜像、coredump 以及已删除文件与凭据恢复等取证类 CTF 题;触发名:ctf-forensics

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SpatialFeatureExperiment (SFE) is a new S4 class for working with spatial single-cell genomics data. The voyager package implements basic exploratory spatial data analysis (ESDA) methods for SFE. Univariate methods include univariate global spatial ESDA methods such as Moran's I, permutation testing for Moran's I, and correlograms. Bivariate methods include Lee's L and cross variogram. Multivariate methods include MULTISPATI PCA and multivariate local Geary's C recently developed by Anselin. The

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Provides R wrappers of several on-target and off-target scoring methods for CRISPR guide RNAs (gRNAs). The following nucleases are supported: SpCas9, AsCas12a, enAsCas12a, and RfxCas13d (CasRx). The available on-target cutting efficiency scoring methods are RuleSet1, Azimuth, DeepHF, DeepCpf1, enPAM+GB, and CRISPRscan. Both the CFD and MIT scoring methods are available for off-target specificity prediction. The package also provides a Lindel-derived score to predict the probability of a gRNA to

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Expert knowledge for Azure Migrate development including troubleshooting, best practices, decision making, architecture & design patterns, limits & quotas, security, configuration, integrations & coding patterns, and deployment. Use when sizing and migrating VMs/SQL/PostgreSQL, configuring appliances/Arc, using AppCAT/CAST, or Private Link, and other Azure Migrate related development tasks. Not for Azure Database Migration Service (use azure-database-migration), Azure Site Recovery (use azure-site-recovery), Azure Virtual Machines (use azure-virtual-machines), SQL Server on Azure Virtual Machines (use azure-sql-virtual-machines).

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嵌入式 CAN / CAN-FD 调试工具，用于扫描接口、监控报文、发送测试帧、记录日志、数据库文件解码和总线统计。 当用户提到 CAN、CAN-FD、DBC 解码、总线抓包、USB-CAN 联调、报文发送、总线统计、 PCAN、Vector、slcan、CAN 接口扫描、CAN ID 过滤、ASC 日志、BLF 文件时自动触发， 也兼容 /can 显式调用。即使用户只是说"看看 CAN 报文"、"发一帧试试"或"解码一下 DBC"， 只要上下文明确提到 CAN 总线通信的操作或问题就应触发此 skill。

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Event Sourcing and CQRS as DDD implementation patterns — when to use, aggregate event streams, projections, snapshots, sagas, upcasting, conflict resolution

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在R中实现对数据框按主分组（如年龄）筛选后，对子分组（如学历）之间的数值变量（如PCA）进行独立样本t检验，并输出统计结果。

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Use for authorized digital forensics including memory dumps, disk timelines, PCAP investigation, artifact triage, and IR evidence preservation.

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Use for authorized reverse engineering of custom binary protocols, Protobuf/gRPC, WebSocket frames, and PCAP-driven protocol recovery.

日本語の概要は準備中です。原文の説明を表示しています。

NGS analysis toolkit. BAM to bigWig conversion, QC (correlation, PCA, fingerprints), heatmaps/profiles (TSS, peaks), for ChIP-seq, RNA-seq, ATAC-seq visualization.

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GWAS and population genetics tool. Processes PLINK (.bed/.bim/.fam), VCF, and BGEN; runs QC (MAF, HWE, missingness), IBD estimation, PCA, and linear/logistic regression GWAS. Outputs Manhattan-ready summary stats. Use regenie or SAIGE for biobanks (>100k samples) needing mixed models.

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NGS CLI for ChIP/RNA/ATAC-seq. BAM→bigWig with RPGC/CPM/RPKM, sample correlation/PCA, heatmaps/profiles around features, fingerprints. For alignment use STAR/BWA; for peak calling use MACS2.

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scRNA-seq with Scanpy: QC, normalization, HVG selection, PCA, neighborhood graph, UMAP/t-SNE, Leiden clustering, markers, cell annotation, trajectory inference. Standard scRNA-seq exploration.

日本語の概要は準備中です。原文の説明を表示しています。