TraitTrainR: accelerating large-scale simulation under models of continuous trait evolution.

Motivation: The scale and scope of comparative trait data are expanding at unprecedented rates, and recent advances in evolutionary modeling and simulation sometimes struggle to match this pace. Well-organized and flexible applications for conducting large-scale simulations of evolution hold promise in this context for understanding models and more so our ability to confidently estimate them with real trait data sampled from nature.

Results: We introduce , an R package designed to facilitate efficient, large-scale simulations under complex models of continuous trait evolution.

The effect of anti-pronation and anti-supination taping on neuromuscular control in recreational athletes with Achilles Tendinopathy: A randomized controlled cross-over trial.

Objectives: To investigate the effects of different directions of athletic taping on neuromuscular control of the lower limb in recreational athletes with Achilles tendinopathy (AT).

Design: Crossover Study.

Setting: Functional assessment laboratory.

International consensus to define outcomes for trials of chemoradiotherapy for anal cancer (CORMAC-2): defining the outcomes from the CORMAC core outcome set.

Variation in outcomes definitions and reporting limit the utility of clinical trial results. The Core Outcome Research Measures in Anal Cancer (CORMAC) project developed a core outcome set (COS) for chemoradiotherapy trials for anal squamous cell carcinoma (ASCC) through an international healthcare professional and patient consensus process. The CORMAC-COS comprises 19 outcomes across 4 domains (disease activity, survival, toxicity, life impact).

UltimateSynth: MRI Physics for Pan-Contrast AI.

Magnetic resonance imaging (MRI) is commonly used in healthcare for its ability to generate diverse tissue contrasts without ionizing radiation. However, this flexibility complicates downstream analysis, as computational tools are often tailored to specific MRI types and lack generalizability across the full spectrum of scans used in healthcare. Here, we introduce a versatile framework for the development and validation of pan-contrast AI models that can exhaustively cater to the full spectrum of scans achievable with MRI, enabling model deployment across scanner models, scan types, and age groups.