Tissue microarrays, a high-throughout approach to quantifying biomarkers used in hundreds of cancer studies every year, are susceptible to batch effects that can alter results but that are readily addressable.

This quick reference guide for researchers addresses common misconceptions in the use of covariates in observational research and offers tips for preventing misstatements and mistakes.

In human neuroimaging, the intra-class effect decomposition (ICED) framework indexes overall reliability, and enables researchers to identify and quantify multi-source contributions to measurement error with the goal of developing more reliable measures.

When a common error drives parallel implicit and explicit learning systems, increases in the explicit response will suppress implicit adaptation.

A proposal for a complete cellular/network implementation of trial-and-error motor learning in the olivo-cerebellar system.

A combined experimental and modeling approach provides insight into potential biases when inferring transcription rates from static mRNA distributions, and shows that correcting for cell-cycle phase and post-transcriptional noise provides rates that agree with live-cell transcription measurements.

Midbrain dopamine neurons in rats signal discrepancies between predicted and actual rewards, regardless of whether the rewards are predicted on the basis of experience or inference.

Model based on Bayesian cue combination shows that procedural motor learning is driven by perceptual error in localizing one's effector.

In stau^-mutants, the Hunchback boundary shifts posteriorly by 10% EL but shows comparable variability to that of the wild type in short time windows in nuclear cycle 14.

A single human cell makes 10-100 transcriptional errors per second; these errors affect splicing and may influence the evolution of coding sequences.