Exercise & Fitness

Intelligent Design and Simulation of High-Entropy Alloys via Machine Learning and Multiobjective Optimization Algorithms.

High-entropy alloys (HEAs) are innovative metallic materials with unique properties and wide potential applications. However, the compositional complexity of HEAs poses a great challenge to investigate the physical mechanisms controlling their performance. Herein, we propose a novel framework composed of high-entropy alloys design and simulations (HEADS) that combines machine learning (ML), molecu...

A supervised machine learning approach with feature selection for sex-specific biomarker prediction.

Biomarkers are crucial in aiding in disease diagnosis, prognosis, and treatment selection. Machine learning (ML) has emerged as an effective tool for identifying novel biomarkers and enhancing predictive modelling. However, sex-based bias in ML algorithms remains a concern. This study developed a supervised ML model to predict nine common clinical biomarkers, including triglycerides, BMI, waist ci...

A generalized platform for artificial intelligence-powered autonomous enzyme engineering.

Proteins are the molecular machines of life with numerous applications in energy, health, and sustainability. However, engineering proteins with desir...

Improved convolutional neural network for precise exercise posture recognition and intelligent health indicator prediction.

This paper presents a novel framework for accurate exercise posture recognition and health indicator prediction based on improved convolutional neural...

Nurses' perspectives on AI-Enabled wearable health technologies: opportunities and challenges in clinical practice.

BACKGROUND: Wearable health technologies, such as smartwatches, biosensor patches, and fitness trackers, have evolved from basic monitoring tools to a...

Machine learning for detection of diffusion abnormalities-related respiratory changes among normal, overweight, and obese individuals based on BMI and pulmonary ventilation parameters: an observational study.

BACKGROUND: The integration of machine learning (ML) algorithms enables the detection of diffusion abnormalities-related respiratory changes in indivi...

How does social support influence autonomous physical learning in adolescents? Evidence from a chain mediation and latent profile analysis.

PURPOSE: This study examines how social support influences adolescents' autonomous physical learning behavior, exploring the mediating roles of self-e...

White Box Modeling of Self-Determined Sequence Exercise Program Among Sarcopenic Older Adults: Uncovering a Novel Strategy Overcoming Decline of Skeletal Muscle Area.

BACKGROUND: Resistance exercise, Taichi exercise, and the hybrid exercise program consisting of the two aforementioned methods have been demonstrated ...

Investigating the benefits of artificial neural networks over linear approaches to BMI decoding.

Brain-machine interfaces (BMI) aim to restore function to persons living with spinal cord injuries by 'decoding' neural signals into behavior. Recentl...

Mapping the space of protein binding sites with sequence-based protein language models.

MOTIVATION: Binding sites are the key interfaces that determine a protein's biological activity, and therefore common targets for therapeutic interven...

Modelling Complex Spatial Distribution in Central Italy: A Random Forest Approach Revealing Underrepresented Lowland Populations Based on Spatially-Explicit Predictors.

Species distribution models are powerful tools to infer ecology and support management of conservation and socio-economic valuable taxa, such as brown...

A hybrid approach to heart disease prediction using a fractional-order mathematical model and machine learning algorithm.

Heart disease remains one of the leading causes of morbidity and mortality worldwide, necessitating the development of more accurate and efficient dia...

A Machine Learning Analysis of Physiological Monitoring Signals to Detect Small Airway Narrowing Due to Cold Air Exposure in Asthma.

Asthma is a chronic inflammatory disease of the small airways, affecting over 200 million people globally. Cold air exposure is a potential risk facto...

Prime editor-based high-throughput screening reveals functional synonymous mutations in human cells.

Synonymous mutations are generally considered neutral, while their roles in the human genome remain largely unexplored. Here we use the PEmax system t...

Lipidomic profiling of human adiposomes identifies specific lipid shifts linked to obesity and cardiometabolic risk.

BACKGROUNDObesity, a growing health concern, often leads to metabolic disturbances, systemic inflammation, and vascular dysfunction. Emerging evidence...

Evaluating a brief smartphone-based stress management intervention with heart rate biofeedback from built-in sensors in a three arm randomized controlled trial.

Perceived stress is prevalent in industrial societies, negatively impacting mental health. Smartphone-based stress management interventions provide ac...

Correction: Machine learning for cardio-oncology: predicting global longitudinal strain from conventional echocardiographic measurements in cancer patients.

Droplet microfluidics integrated with machine learning reveals how adipose-derived stem cells modulate endocrine response and tumor heterogeneity in ER breast cancer.

Approximately 70% of breast cancer (BC) diagnoses are estrogen receptor positive (ER) with ∼40% of ER BC patients presenting resistance to endocrine ...

Developing and comparing a new BMI inclusive energy expenditure algorithm on wrist-worn wearables.

Estimating energy expenditure (EE) in real-world settings is crucial for studying human behavior and energy balance. Despite advances in wrist-worn in...

Exploring the potential of artificial intelligence in individualized cognitive training: A systematic review.

To tackle the challenge of responders heterogeneity, Cognitive Training (CT) research currently leverages AI Techniques for providing individualized c...