expert-profile plugins

by K-Dense-AI

Reference-build discipline (GRCh38/GENCODE/Ensembl, MANE), batch-as-covariate DE (DESeq2/edgeR), index hopping/UDI, GATK/BQSR/PLINK GWAS multiplicity, nf-core reproducibility, and scRNA-seq ambient-RNA/doublet artifacts.

by K-Dense-AI

Builds production genomics DAGs in Nextflow/nf-core, Snakemake, and WDL/Cromwell with digest-pinned containers, GIAB regression CI (nf-test, pytest), QC-gated MultiQC runbooks, and CLIA-grade provenance—distinct from analyst-focused bioinformatician DE/GWAS reasoning.

by K-Dense-AI

Reasons from light-nutrient-grazing coupling, the microbial loop, and size-structured export through CTD/MOCNESS sampling, 14C and O2/Ar production with 234Th export flux, imaging and flow cytometry enumeration, and SILVA/PR2 metabarcoding while treating spatial patchiness, diel-migration tow aliasing, CDOM-biased chlorophyll algorithms, and eDNA-detection-as-abundance as first-class failure modes.

by K-Dense-AI

Reasons from material-biology interfaces, degradation-product toxicity, and mechanical mismatch to host tissue through ISO 10993 biological evaluation plans, ISO 10993-12 extract conditions, GPC/ICP/SEM characterization, and sterilization validation while treating endotoxin contamination, pH shift from acidic degradation, extract-ratio artifacts, and non-final-form test articles as first-class failure modes.

by K-Dense-AI

Reasons from ISO 14971 risk management, ISO 10993 biocompatibility matrices, ASTM F/ISO 14242 mechanical and wear testing, and FDA 510(k) substantial equivalence; treats stress shielding, UHMWPE osteolysis, F2129 corrosion artifacts, and predicate/material mismatches as first-class failure modes.

by K-Dense-AI

Reasons from contrast mechanisms, the resolution-SNR-scan-time triangle, and measurement reliability through DICOM/BIDS pipelines, QIBA profiles, phantom QC (ACR, NEMA IQ, Catphan), and blinded central reads (RECIST, RANO, PERCIST) while treating motion, partial volume effects, and cross-scanner harmonization drift as first-class failure modes.

by K-Dense-AI

Reasons from free energy landscapes, binding equilibria (K_d, ΔG = ΔH − TΔS), and probe–system coupling through ITC, SPR/BLI, smFRET, AUC, and global fitting (KinTek, SEDFIT) while treating probe perturbation, mass-transport-limited kon, aggregation-driven avidity, and two-state melting violations as first-class failure modes.

by K-Dense-AI

Reasons from energy landscapes, kT-scale thermodynamics, conformational ensembles, and the equilibrium-versus-kinetics distinction through smFRET, optical/magnetic tweezers, patch clamp, cryo-EM, and MD with force-field validation while treating photobleaching and blinking, FRET crosstalk, tether and series-resistance artifacts, and sampling or force-field bias as first-class failure modes.

by K-Dense-AI

Reasons from QTPP–CPP–CQA QbD, CHO fed-batch/perfusion scale-up (P/V, kLa, mixing), platform mAb DSP (Protein A, low-pH viral hold, IEX polish, UF/DF), tech transfer and PPQ lifecycle; treats transport-limited scale-up, harvest fouling, on-column aggregation, SUB leachables, and arbitrary three-batch PPQ as first-class failure modes.

by K-Dense-AI

Reasons from kLa/OTR–OUR balance, fed-batch μ control, off-gas RQ, van't Riet scale-up, and contamination (phage, bioburden, adventitious agents); treats antifoam kLa penalty, exponential-feed open-loop risk, and SUB vs stainless transfer as first-class failure modes.

by K-Dense-AI

Reasons from estimands, SAPs, and error budgets; aligns ICH E9(R1), CONSORT/STROBE, multiplicity, MMRM, Cox survival, causal DAGs, and GWAS FDR while treating immortal time, ICEs, and batch confounding as first-class failure modes.

by K-Dense-AI

Reasons from coupled heat and mass transfer, moisture sorption (EMC/ERH on D245.7 isotherms), respiration, and closed unit-operation balances through ASABE standards (S352.2 oven moisture, S448 Page thin-layer kinetics, D272.3 airflow resistance), psychrometric aeration analysis, and SuperPro/Aspen bioprocess simulation while treating wet- vs dry-basis mixing, aeration that wets cool grain, case-hardened wet cores, and grain engulfment and dust explosion as first-class failure modes.

by K-Dense-AI

Reasons from morphology, floras (FNA/Jepson/eFlora), voucher herbarium specimens (Darwin Core, Index Herbariorum), nomenclature (IPNI/POWO/Madrid Code), APG IV phylogeny, DNA barcoding (rbcL/matK/ITS2), community ecology (vegan adonis2/betadisper, TRY traits), and CITES/ABS ethics; treats vegetative mis-ID, pseudoreplication, PERMDISP confounds, and unvouchered GBIF records as first-class failure modes.

by K-Dense-AI

Reasons from modality–paradigm fit (EEG, ECoG, Utah arrays), CSP/Riemannian decoding (pyriemann, MOABB), BCI2000/OpenBCI pipelines, and charge-density stimulation safety; validates within- vs cross-session claims and treats muscle ICA, impedance drift, and IDE/IRB gates as first-class failure modes.

by K-Dense-AI

Reasons from load paths, limit states, redundancy, and durability through AASHTO LRFD rating (LRFR/inventory vs. operating), FEM grillage and OpenSees models, HEC-18 scour analysis, and NDE such as half-cell potential and phased-array UT while treating fracture-critical fatigue details, lost composite action, scour, and bearing seizure as first-class failure modes.

by K-Dense-AI

Reasons from coupled heat-air-moisture transport through ASHRAE 160 moisture-design analysis, WUFI transient simulation, ACH50 leakage mapping, and ISO 10211 psi-values while treating exfiltration condensation, reservoir claddings, and mold-index sensitivity as first-class failure modes.

by K-Dense-AI

Reasons from hallmark capabilities, clonal evolution, and TME context; separates driver from passenger, cell-autonomous from stromal mechanisms, and 2D artifacts from PDO/PDX-validated dependencies using TCGA, DepMap, OncoKB, and REMARK-grade biomarker logic.

by K-Dense-AI

Reasons from carbon mass balance across reservoirs and timescales, sign conventions, and budget closure through the Global Carbon Budget protocol, ONEFlux/REddyProc eddy-covariance processing, atmospheric inversions (CarbonTracker, CAMS, GEOS-Chem), Δ¹⁴C/δ¹³C isotopic partitioning, and ILAMB model evaluation while treating NEE sign mismatches, gap-filled GPP read as measured, fixed-depth SOC comparisons, and uncounted lateral aquatic carbon export as first-class failure modes.

by K-Dense-AI

Reasons from oxygen supply-demand mismatch, forward flow, filling pressures, and pre-test probability through 12-lead ECG, TTE/TEE with ASE grading, cath hemodynamics, CMR, and ACC/AHA/ESC guidelines with risk scores like HEART, GRACE, and CHA2DS2-VASc, while treating context-free troponin elevation, low-flow low-gradient AS discordance, undiagnosed wide-complex tachycardia, and premature diagnostic closure as first-class failure modes.

by K-Dense-AI

Reasons from catalyst lifecycle margin, space velocity (WHSV/GHSV/LHSV), and deactivation economics through plant historian trends, MAT/pilot trickle-bed activity tests, Aspen HYSYS activity factors, and ASTM crush/attrition plus TPO/ICP spent-catalyst profiling while treating coke, poisoning, sintering, attrition, and channeling/maldistribution as first-class failure modes.

by K-Dense-AI

Reasons from active-site structure, turnover frequency, selectivity, and the Sabatier principle through CO/H2 chemisorption site-counting, Weisz-Prater and Mears transport checks, Langmuir-Hinshelwood/Mars-van Krevelen kinetics, and operando DRIFTS/XAS while treating diffusion-limited apparent rates, DRIFTS spectator species, mercury-test false positives for leached Pd, sintering, coking, and selectivity collapse at high conversion as first-class failure modes.

by K-Dense-AI

Reasons from structural causal models, potential outcomes, and identification logic through DAGs and do-calculus, doubly-robust estimators (AIPW/TMLE, IPTW, g-formula), and design-based methods (IV, RD, Callaway-Sant'Anna DiD, synthetic control) while treating colliders and M-bias, positivity/overlap failure, and unmeasured confounding (Rosenbaum bounds, E-values) as first-class failure modes.

by K-Dense-AI

Reasons from compartment thermodynamics, membrane electrophysics, and necessity-plus-sufficiency logic through STR authentication, confocal/TIRF imaging, CRISPR and siRNA perturbation with rescue, and Western blot, treating mycoplasma, passage and serum-lot drift, edge effects, antibody cross-reactivity, siRNA seed off-targets, and well-as-n pseudoreplication as first-class failure modes.

by K-Dense-AI

Reasons from RTK–RAS–MAPK and PI3K–Akt–mTOR phosphorylation networks, pathway crosstalk and feedback; validates with phospho-Western, phospho-flow, and PhosphoSitePlus while treating serum-starvation artifacts, inhibitor off-targets, and RNA footprint vs PTM mismatch as first-class failure modes.