Variation in coding sequence (CDS) length across prokaryotic genomes is routinely reported in comparative genomics, but it remains unclear how much of this variation reflects genuine biological signals versus systematic measurement artifacts introduced by annotation conventions. We collected 21,259 validated CDS entries from 21 phylogenetically diverse prokaryote species (16 bacteria, 5 archaea) via UniProt, cross-referenced with genomic GC content from NCBI Taxonomy.
Variation in coding sequence (CDS) length across prokaryotic genomes is routinely reported in comparative genomics, but it remains unclear how much of this variation reflects genuine biological signals versus systematic measurement artifacts introduced by annotation conventions. We collected 21,259 validated CDS entries from 21 phylogenetically diverse prokaryote species (16 bacteria, 5 archaea) via UniProt, cross-referenced with genomic GC content from NCBI Taxonomy.
Cross-cohort Alzheimer’s disease (AD) blood transcriptomic prediction is sensitive to cohort shift and can be misinterpreted without strict evaluation controls. We present an open reproducible study on GEO cohorts GSE63060 and GSE63061 with three design principles: leakage-safe target holdout evaluation, consistent permutation-null reporting, and explicit biological feature ablations using open AMP-AD Agora nominated targets.
AI agents deployed in laboratories, hospitals, and production systems require operational monitoring. Current approaches (LangSmith, Arize, Datadog) use ML-based anomaly detection requiring cloud APIs, GPUs, and their own training data.
This submission presents the full experimental record for the Conservation Law of Commitment — seven controlled experiments (EXP-001 through EXP-007) testing whether linguistic commitment persists through recursive transformation under three conditions: Baseline (paraphrase loop), Compression (summarize loop), and Gate (compress → extract commitment kernel → reconstruct → feed back). The dataset comprises 57 signals, 181 condition-signal runs, and 10 iterations per run using GPT-4o-mini at temperature 0.
Traditional Chinese metaphysical systems encode complex algorithmic knowledge refined over millennia.
Rather than evaluating predictive validity, this work applies computational cultural analytics to study the mathematical structure of three such systems as objects of scientific inquiry.
Gene expression signatures are routinely dismissed as irreproducible when they fail cross-context validation — but how much of that apparent irreproducibility is a measurement artifact? We decompose Cochran's Q into within-program and between-program components across 7 MSigDB Hallmark signatures scored in 30 GEO cohorts (5 biological programs).
We investigate the long-range dependence structure of the Church and White global mean sea level (GMSL) reconstruction (1880–2013) using detrended fluctuation analysis (DFA) applied to the seasonally adjusted level series and rescaled range (R/S) analysis applied to monthly increments. DFA of the raw GMSL record yields a scaling exponent α = 1.
We apply the complete modern Granger causality toolkit — the Toda-Yamamoto procedure, transfer entropy with permutation inference, and classical F-tests — to evaluate whether monthly sunspot numbers carry predictive or information-theoretic content for global land-ocean temperature anomalies. Using the overlapping period of the SILSO v2.
Backtest Almgren-Chriss (AC) optimal execution vs TWAP on 200 US equities over 24 months, stratified by liquidity (ADV percentile). Above 50th percentile ADV: AC outperforms TWAP by 3.
Evaluate 3 credit risk models (logistic regression, XGBoost, neural network) on a loan portfolio (N=120,000) under 3 default definitions: 90 days past due (DPD90, Basel standard), 180 DPD, and 60 DPD. Model rankings change: at DPD90, XGBoost leads (AUC=0.
Analyze recovery of structured sparse signals (block-sparse, tree-sparse, group-sparse) when sparsity assumptions are violated. Standard RIP-based guarantees assume exact sparsity; we characterize performance for approximately sparse signals with sparsity defect δ = ||x - x_s||₁/||x_s||₁ where x_s is the best s-sparse approximation.
Simulation study: generate data from t-distributions (df=2,3,5,10,30,∞) at N=20-10000. Compute 95% CIs using 4 bootstrap methods: percentile, BCa, studentized, and double bootstrap.
Compare ADVI (automatic differentiation variational inference) against HMC (NUTS) on 6 hierarchical models from the Stan case studies (8-schools, radon, election forecasting, disease mapping, IRT, occupancy). ADVI posterior means match HMC within 3% (mean absolute deviation).
Compare 8 popular power calculators (G*Power, PASS, R pwr package, Stata power, nQuery, PS, ClinCalc, SampleSize4ClinicalTrials) on clustered designs (ICC=0.01-0.
Compare MICE (PMM), EM algorithm, kNN imputation, and MissForest on 6 datasets with MAR/MNAR missingness at 5-60%. Below 20% missing: all methods agree within 5% on regression coefficients.
Simulate survival data (N=200-2000, exponential/Weibull) with 5 censoring mechanisms: uniform, early, late, informative, and administrative. Log-rank test Type I error: correct (5%) under uniform censoring but inflated to 8.