Exercise & Fitness

RADAR-WMH: Relaxometry And Diffusion Analysis beyond Radiologically defined WMH

Background: White matter hyperintensities (WMH) represent the most visible manifestation of cerebral small vessel disease and of white matter pathology more broadly, yet empirical evidence points to a brain tissue injury extending beyond radiologically detectable lesions on fluid-attenuated inversion recovery (FLAIR) MRI. We present RADAR-WMH (Relaxometry And Diffusion Analysis for Radiological WM...

MatCreatioNN: Machine learning-guided computational discovery of photocatalysts for environmental applications

The rational design of photocatalysts for environmental remediation and CO2 conversion remains limited by the high computational cost and sparse experimental data describing multi-parameter photocatalytic behavior. This work presents an integrated machine-learning framework that couples reinforcement learning-based metal-organic framework (MOF) generation with a multi-stage Crystal Graph Convoluti...

From Unsupervised Subgroups to Hypothetical State-Intervention Policies: An Evaluation of Selected Subgrouping Methods in Observational Health Data

Conventional subgroup analyses can yield unstable and difficult-to-interpret conclusions, especially in observational biomedical data where each indiv...

RAG-HAR+: Towards Cost-Efficient LLM-Based Human Activity Recognition for Edge Deployment

Human Activity Recognition (HAR) from wearable sensors supports applications in healthcare, rehabilitation, fitness tracking, and smart environments. ...

Deep Learning-Based Pretreatment Cardiovascular Risk Stratification in Women with Breast Cancer

Background: Cardiovascular disease is a leading non-cancer cause of morbidity and mortality among breast cancer (BC) survivors. Existing cardiovascula...

AI-Enabled Echocardiography Identifies an Adverse Epicardial Adiposity Phenotype Associated with Cardiometabolic Dysfunction

Background: Excess epicardial adipose tissue (EAT) is associated with cardiovascular-kidney-metabolic (CKM) dysfunction, but its assessment has tradit...

Risk stratification for the rapid pain progression phenotype in knee osteoarthritis using interpretable multimodal machine learning: Development in the Osteoarthritis Initiative and external evaluation in the Prospective Cohort of Osteoarthritis from A Coruna

Objective. To develop an interpretable multimodal machine-learning model for risk stratification of the rapid pain progression phenotype in knee osteo...

How does CBT work? Causal discovery modelling locates the mechanisms of change in cognitive behavioural treatment for eating disorders

Background: Cognitive behavioural therapy (CBT) is the most frequently used and recommended therapy program for mental health conditions, including fo...

Combining Stability-Centered Atomistic Design with Machine Learning for Targeted Enzyme Optimization

FuncLib and high-throughput FuncLib (htFuncLib) generate diverse, functional protein libraries using a stability-centered design; however, this substr...

Machine learning combining FIT with up to 1,025 clinical variables: limited referral reduction but potential for faster diagnosis

Background The faecal immunochemical test (FIT) is central to triaging symptomatic patients with suspected colorectal cancer (CRC) in UK primary care,...

Causal-Adversarial Probing of Clinical Covariates for Prostate MRI Grading

Deep learning models for prostate MRI-based cancer grading may encode clinical covariates that either reflect useful disease-related signal or non-gen...

EcoXAI: Autonomous Agentic Ecosystem for Explainable Artificial Intelligence and Biomedical Discovery

Motivation: As biomedical datasets and knowledge graphs continue to grow in size, complexity, and heterogeneity, navigating and extracting actionable ...

DeepPySR -- A Symbolic Regression Framework with Dynamic Pruning, Pareto Selection, and Hierarchical Composition for Real-World Scientific Discovery

Symbolic regression (SR) discovers analytical equations from data, yielding glass-box models with directly interpretable formulas, unlike black-box me...

Reward Valuation in Vision Language Models: Causal Mechanisms Underlying Anhedonia

Recent Vision-Language Models capture increasingly complex aspects of human cognition. Here we ask whether this alignment extends to reward valuation,...

Machine Learning for Depression Screening and Intervention: an Original Circadian Rhythm Score-based Methodology

Depression screening from large-scale behavioral data is challenged by fragmented circadian indicators, limited interpretability, and the lack of inte...

Adaptive Group-Based Counterfactual Explanations for Time-Series Rehabilitation Data

Counterfactual explanations (CEs) for multivariate time-series classifiers are often difficult to interpret in domains where experts reason in terms o...

EduArt: An educational-level benchmark for evaluating art history knowledge in large language models

Large language models now score near ceiling on general benchmarks, but these aggregate measures reveal little about how models behave within single d...

Evaluating Open-Source Wrist-Worn Accelerometer Models for Sedentary Time Detection Against Thigh-Worn Accelerometer Data

Abstract Objective Wrist-worn accelerometers are common in large-scale epidemiological studies, but their ability to measure sedentary behaviour in fr...

Epigenetic signatures of infection within and across generations in the endangered Loggerhead sea turtle

Infection can substantially reduce host fitness and influence population dynamics, yet it is often difficult to detect and quantify in wild animal pop...

Healthy-to-Stroke Translation of EEG-Based BMIs: EEG Characterization and Reinforcement Learning-Based Decoder Evaluation

Purpose: EEG-based brain-machine interfaces (BMIs) may support assistive technologies for individuals with stroke-related motor impairment by translat...