Exercise & Fitness

Decoding Diabetes: Harnessing AI to Accurately Predict Real-Time and Future Blood Glucose Levels for Diabetes Management Using Diet, Exercise, Insulin Intake, and Heart Rate Variability

Continuous glucose monitoring (CGM) systems play a crucial role in diabetes care. Yet, they focus solely on blood glucose levels (BGL), neglect diet, exercise, and medication, and lack predictive capabilities, leaving patients and clinicians with reactive rather than proactive solutions. This study introduces Dual Temporal Recurrent Ensemble (DTRE), a novel AI model that bridges these gaps by enab...

Machine Learning Driven Simulations of the SARS-CoV-2 Fitness Landscape from Deep Mutational Scanning Experiments

Predicting protein variant effects is a key challenge in preparing for pathogenic viral strains, understanding mutation-linked diseases, and designing new proteins. Protein sequence-structure-function relationships are difficult to model due to complex allosteric and epistatic effects. To investigate efficient modeling strategies, we trained supervised machine learning (ML) models with deep mutati...

Machine learning informs mitigation strategies for nitrous oxide emissions from wastewater operations

This study focused on the development of machine-learning- (ML) based strategies for mitigating nitrous oxide (N2O) emissions from various wastewater ...

Predicting Future Development of Stress-Induced Anhedonia From Cortical Dynamics and Facial Expression

The current state of mental health treatment for individuals diagnosed with major depressive disorder leaves billions of individuals with first-line t...

Fitness Landscape for Antibodies 2: Benchmarking Reveals That Protein AI Models Cannot Yet Consistently Predict Developability Properties

A prominent application of machine learning in therapeutic antibody design is the development of models that can generate or screen antibody candidate...

Social isolation upregulates takeout expression in female Drosophila melanogaster to promote sucrose feeding

Drosophila melanogaster provides a model system to examine how environmental stress interacts with sex to induce changes in brain function and behavio...

Closing the Sim-to-Real Gap: An End-to-End Robotic Ultrasound System Leveraging In Vivo Reinforcement Learning and 3D-Prior Guided Hybrid Control

Abdominal ultrasound is a crucial first-line diagnostic tool, yet its efficacy is inherently constrained by a strong dependency on operator skill, lea...

Successful Predictive Modeling of Pollen Fitness Phenotypes Is Enabled by Measures of Expression Specificity

The ability to predict phenotypes from genotypes in multicellular organisms remains limited despite rapid advances in genotyping and phenotyping metho...

Discovery of Electron Hole-hopping Redox Mutations in Myoglobin by Deep Mutational Learning

In addition to storing molecular oxygen, myoglobin catalyzes peroxidase-like reactions involving high valency iron(IV)-oxo species that support oxidat...

Can a history of crop rotations improve the prediction of soil organic carbon in the Andes? integrating machine learning multi-annual crop classification as a proxy of soil management

Soil organic carbon (SOC) is a crucial component related to various processes that ensure soil health and function. Its modeling is vital for assessin...

RegEvol: detection of directional selection in regulatory sequences through phenotypic predictions and phenotype-to-fitness functions

Regulatory DNA controls when and where genes are expressed, making it a key driver of phenotypic evolution. Yet detecting selection in non-coding regi...

Machine learning prediction algorithms for 2- , 5- and 10-year risk of Alzheimer’s, Parkinson’s and dementia at age 65: a study using medical records from France and the UK General Practitioners

Leveraging machine learning on electronic health records offers a promising method for early identification of individuals at risk for dementia and ne...

Study Research Protocol for Phenome India-CSIR Health Cohort Knowledgebase (PI-CHeCK): A Prospective multi-modal follow-up study on a nationwide employee cohort

Predicting individual health trajectories based on risk scores can help formulate effective preventive strategies for diseases and their complications...

Characterisation of 3000 patient reported outcomes with predictive machine learning to develop a scientific platform to study fatigue in Inflammatory Bowel Disease

Fatigue is commonly identified by IBD patients as major issue that affects their wellbeing. This presentation, however, is complex, multifactorial and...

Brain Age Gap Reduction Following Physical Exercise Mirrors Negative Symptom Improvement in Schizophrenia Spectrum Disorders

Schizophrenia spectrum disorders (SSD) are associated with accelerated brain aging, reflected in an increased brain age gap. This gap serves as a biom...

Predicting Hypertension Among HIV Patients on Antiretroviral Therapy in Rural Eastern Cape, South Africa Using Machine Learning

Hypertension continues to be a major challenge in developing countries like South Africa, as it significantly contributes to the cardiovascular diseas...

Automated IntraVascular UltraSound Image Processing and Quantification of Coronary Artery Anomalies: The AIVUS-CAA software

Coronary artery anomalies (CAA) with an intramural course are associated with elevated risks of ischemia and sudden cardiac death under stress. Intrav...

24-hour Physical Activity, Sedentary, and Sleep Profiles in Individuals with Cancer: A UK Biobank Cohort Study

The 24h behaviour profile, including physical activity, sedentary time, and sleep, is disrupted following a cancer diagnosis and contributes to cancer...

Automatic time in bed detection from hip-worn accelerometers for large epidemiological studies: The Tromsø Study

Accelerometers are frequently used to assess physical activity in large epidemiological studies. They can monitor movement patterns and cycles over se...

Cross-platform metabolomics imputation using importance-weighted autoencoders

Metabolomics data are often generated through different analytical platforms and different methods of identification and quantification which makes th...