Results for “sample”

Think and work like an expert Human–Computer Interaction Researcher. Use when a task calls for Human–Computer Interaction Researcher judgment. Reasons from situated context, Fitts/GOMS/KLM, and CHI contribution types; runs contextual inquiry through LMM/CLMM analysis with SUS/NASA-TLX triangulation; uses Prolific/OSF and treats demand characteristics, novelty effects, ordinal misuse, and WEIRD samples as first-class failure modes.

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Think and work like an expert Geneticist. Use when a task calls for Geneticist judgment. Reasons from particulate inheritance, segregation, recombination, allele frequency, and genotype-phenotype evidence through ACMG/AMP-ClinGen classification, gnomAD/ClinVar/OMIM, HPO phenotyping, and PLINK/GATK/VEP QC while treating sample swaps, cryptic relatedness, population stratification, LD tagging, phenocopies, winner's curse, and build/transcript mismatch as first-class failure modes.

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Think and work like an expert Geotechnical Scientist. Use when a task calls for Geotechnical Scientist judgment. Reasons from Terzaghi effective stress, Mohr–Coulomb/CSSM, and consolidation/seepage through SPT/CPTU (Robertson SBT), triaxial/oedometer (ASTM D-series), Boulanger–Idriss liquefaction, Hoek–Brown/GSI rock mass, EC7 characteristic values, and PLAXIS/Slide2/RS2/GeoStudio workflows while treating sample disturbance, N-value correction chains, spatial variability, and LEM-vs-FEM mismatch as first-class failure modes.

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Think and work like an expert Hydrologist. Use when a task calls for Hydrologist judgment. Reasons from hydrologic-cycle closure (P = ET + Q + ΔS + I), runoff-generation mechanisms, and channel routing through HEC-HMS, HEC-RAS, the National Water Model, and split-sample KGE/NSE/PBIAS calibration, while treating post-flood rating-curve shifts, radar QPE bias, snow/rain misclassification, and non-stationarity in extremes as first-class failure modes.

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Think and work like an expert Conservation Scientist. Use when a task calls for Conservation Scientist judgment. Reasons from measurable biodiversity change, counterfactual impact, and effective population size through IUCN Red List/Green Status criteria, occupancy and PVA models (unmarked, Vortex), Marxan/prioritizr planning, and BACI/matching designs while treating detection heterogeneity, spatial pseudoreplication, REDD+ leakage, and Ne sample bias as first-class failure modes.

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Think and work like an expert Cosmochemist. Use when a task calls for Cosmochemist judgment. Reasons from oxygen three-isotope taxonomy (Δ17O), chondrite–achondrite classification, and presolar grain NanoSIMS through Meteoritical Bulletin curation, Al–Mg and Pb–Pb isochrons, CRE vs formation-age separation, and clean-lab sample prep while treating terrestrial weathering, mount contamination, and breccia mixing as first-class failure modes.

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Think and work like an expert Crop Scientist. Use when a task calls for Crop Scientist judgment. Reasons from genotype-by-environment-by-management interaction, yield-component partitioning, and phenology-gated critical periods through MET stability analysis (AMMI, Finlay-Wilkinson, GGE biplots), mixed models (ASReml-R, lme4), N-response curves (quadratic-plateau, MRTN), and crop models (APSIM, DSSAT) while treating pseudo-replicated subsamples, single-site yield champions, uncorrected harvest moisture, and weather-during-anthesis confounding as first-class failure modes.

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Think and work like an expert Cognitive Neuroscientist. Use when a task calls for Cognitive Neuroscientist judgment. Reasons from latent constructs through converging behavior, fMRI/M/EEG, TMS, and lesion evidence; designs factorial and dissociation contrasts, fMRIPrep/GLMsingle/MNE pipelines, MVPA/RSA, and COBIDAS reporting while treating pure insertion, reverse inference, motion confounds, and in-sample decoding as first-class failure modes.

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Think and work like an expert Cognitive Scientist. Use when a task calls for Cognitive Scientist judgment. Reasons from Marr's levels of analysis, latent processes behind RT and accuracy, and strong inference through PsychoPy paradigms, signal-detection d-prime/criterion, sequential-sampling and ACT-R models, and crossed mixed-effects designs while treating speed-accuracy tradeoffs, criterion shifts, item confounds, and underpowered WEIRD samples as first-class failure modes.

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Think and work like an expert Astroparticle Physicist. Use when a task calls for Astroparticle Physicist judgment. Reasons from flux times cross section times acceptance, Poisson counting over structured backgrounds, and Cherenkov photoelectron budgets through SkyLLH unbinned likelihoods, Geant4/CORSIKA chains validated on through-going-muon and calibration samples, and Feldman-Cousins/CLs limits, while treating atmospheric-neutrino and downgoing-muon contamination, ER/NR leakage and the neutrino fog, and look-elsewhere trials as first-class failure modes.

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Use when user asks to clone a voice, train a custom voice model, or synthesize speech with a cloned voice. iFlytek Voice Clone tts(声音复刻) — train a custom voice model from audio samples and synthesize speech with the cloned voice. Supports the full workflow: get training text → create task → upload audio → submit training → poll results → synthesize with cloned voice. Pure Python stdlib, no pip dependencies.

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Resolves GWAS associations to candidate causal variants and credible sets via SuSiE, susie_rss, FINEMAP, CAVIAR, DAP-G, PAINTOR, PolyFun, SuSiEx, MultiSuSiE, and FOCUS. Use when narrowing a GWAS lead SNP to a 95 percent credible set, choosing between in-sample and reference LD, calibrating non-sparse loci with SuSiE-inf or FINEMAP-inf, integrating functional priors via PolyFun, fine-mapping across ancestries with SuSiEx, diagnosing LD mismatch via estimate_s_rss and kriging_rss, handling HLA or long-range LD, or feeding credible sets into coloc.susie for colocalization.

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Processes environmental DNA metabarcoding data from raw amplicon reads to species occurrence tables using OBITools3, DADA2, and taxonomic assignment against BOLD, MIDORI2, or MitoFish databases. Handles COI, 12S, rbcL, and ITS barcode regions with primer removal, denoising, chimera detection, and contamination filtering via decontam. Includes occupancy modeling (occumb) for detection probability correction. Use when analyzing eDNA from water, soil, or bulk samples for biodiversity monitoring. Not for 16S human microbiome (see microbiome/amplicon-processing).

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End-to-end eDNA metabarcoding from raw amplicons to community ecology. Covers QC, primer removal, denoising with OBITools3 or DADA2, contamination filtering, taxonomy assignment, Hill number diversity, and constrained ordination. Use when processing environmental DNA samples for biodiversity assessment or ecological surveys.

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Build a differential-ready consensus peakset from per-replicate ATAC-seq peaks using iterative overlap removal, fixed-width re-centering, and majority-rule overlap. Use when generating a stable peak coordinate system for downstream differential accessibility, ML feature engineering, cross-sample comparison, or fixed-width peak counts; covers Corces 2018 iterative overlap (501 bp), DiffBind summit re-centering, and ENCODE consistency rules.

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Generate consensus FASTA sequences by applying VCF variants to a reference using bcftools consensus. Use when creating sample-specific reference sequences or reconstructing haplotypes.

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Comprehensive quality control for flow cytometry and CyTOF data. Covers flow rate stability, signal drift, margin events, dead cell exclusion, and batch QC. Use when assessing acquisition quality or identifying problematic samples before analysis.

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Quality control and exploration of RNA-seq count matrices before differential expression. Check for outliers, batch effects, and sample relationships. Use when assessing count matrix quality before DE analysis.

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Plot per-group distributions of continuous data using boxplots, violins, beeswarms, quasirandom jitter, and raincloud plots with sample-size honesty (Weissgerber 2015), KDE-bandwidth awareness, and N-aware encoding choices. Use when comparing distributions across a small number of groups — expression per cluster, biomarker per arm, scores per condition — and the bar-of-mean default is misleading.

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Visualize differential expression results using DESeq2/edgeR built-in functions. Covers plotMA, plotDispEsts, plotCounts, plotBCV, sample distance heatmaps, and p-value histograms. Use when visualizing differential expression results.

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Visualizes ChIP-seq data using deepTools (computeMatrix, plotHeatmap, plotProfile, bamCoverage, bamCompare), pyGenomeTracks (modern INI-driven track plots), Gviz (R browser-style), EnrichedHeatmap (ComplexHeatmap-based), ChIPseeker tag heatmaps, and IGV batch screenshots. Handles bigWig normalization choices (CPM, BPM, RPGC, spike-in scaled), bamCompare operations (log2 ratio, subtract, SES), k-means clustering of heatmaps for biological subgrouping, and spike-in-scaled tracks for global-shift experiments. Use when generating publication-quality ChIP-seq signal heatmaps, profile plots, genome-browser tracks, or comparing samples visually.

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Designs experiments to minimize and account for batch effects using balanced layouts and blocking strategies. Use when planning multi-batch experiments, assigning samples to sequencing lanes, or designing studies where technical variation could confound biological signals.

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Detect antimicrobial resistance genes using AMRFinderPlus, ResFinder, and CARD. Screen isolates and metagenomes for resistance determinants. Use when characterizing resistance profiles in clinical isolates, surveillance samples, or metagenomic data.

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Generates standardized quality control reports by aggregating metrics from FastQC, alignment, and other tools using MultiQC. Use when summarizing QC metrics across samples, creating shareable quality reports, or building automated QC pipelines.

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