Machine learning in physical activity

Protein-ligand interaction prediction presents a significant challenge in drug design. Numerous machine learning and deep learning (DL) models have been developed to accurately identify docking poses of ligands and active compounds against specific targets. However, current models often suffer from inadequate accuracy or lack practical physical significance in their scoring systems. In this research paper, we introduce IGModel, a novel approach that utilizes the geometric information of protein-ligand complexes as input for predicting the root mean square deviation of docking poses and the binding strength (pKd, the negative value of the logarithm of binding affinity) within the same prediction framework...

BACKGROUND: Childhood obesity has emerged as a major health issue due to the rapid growth in the prevalence of obesity among young children worldwide. Establishing healthy eating habits and lifestyles in early childhood may help children gain appropriate weight and further improve their health outcomes later in life. OBJECTIVE: This study aims to classify clusters of young children according to their eating habits and identify the features of each cluster as they relate to childhood obesity...

Assessing the intensity characteristics of specific soccer drills (matches, small-side game, and match-based exercises) could help practitioners to plan training sessions by providing the optimal stimulus for every player. In this paper, we propose a data analytics framework to assess the neuromuscular or metabolic characteristics of a soccer-specific exercise in relation with the expected match intensity. GPS data describing the physical tasks' external intensity during an entire season of twenty-eight semi-professional soccer players competing at the fourth Italian division were used in this study...

Smart biosensors attract significant interest due to real-time monitoring of user health status, in which bioanalytical electronic devices designed to detect and track various activities and biomarkers in the human body have potential applications in physical sign monitoring and health care. Bioelectronics can also be well integrated by output signals with wireless communication modules for transferring data to portable devices used as smart biosensors in performing real-time diagnosis and analysis. In this review, the scientific keys of bio-sensing devices and the current trends in the field of smart biosensors, i...

The current investigation reports the usage of adaptive neuro-fuzzy inference system (ANFIS) and artificial neural network (ANN), the two recognized machine learning techniques in modelling tetracycline (TC) adsorption onto Cynometra ramiflora fruit biomass derived activated carbon (AC). Many characterization methods utilized, confirmed the porous structure of synthesized AC. ANN and ANFIS models utilized pH, dose, initial TC concentration, mixing speed, time duration, and temperature as input parameters, whereas TC removal percentage was designated as the output parameter...

Serving as a vital linkage between surface water and groundwater, the hyporheic zone (HZ) plays a fundamental role in improving water quality and maintaining ecological security. In arid or semi-arid areas, effluent discharge from wastewater treatment facilities could occupy a predominant proportion of the total base flow of receiving rivers. Nonetheless the relationship between microbial activity, abundance and environmental factors in the HZ of effluent-receiving rivers appear to be rarely addressed. In this study, a spatiotemporal field study was performed in two representative effluent-dominated receiving rivers in Xi'an, China...

Mitochondria within skeletal muscle cells are located either between the muscle contractile apparatus (interfibrillar mitochondria, IFM) or beneath the cell membrane (subsarcolemmal mitochondria, SSM), with several structural and functional differences reported between IFM and SSM. However, recent 3D imaging studies demonstrate that mitochondria are particularly concentrated in the proximity of capillaries embedded in sarcolemmal grooves rather than in proximity to the sarcolemma itself (paravascular mitochondria, PVM)...

INTRODUCTION: Hippocampal atrophy is an established biomarker for conversion from the normal ageing process to developing cognitive impairment and dementia. This study used a novel hypothesis-free machine-learning approach, to uncover potential risk factors of lower hippocampal volume using information from the world's largest brain imaging study. METHODS: A combination of machine learning and conventional statistical methods were used to identify predictors of low hippocampal volume...

Throughout evolution, bacteria and other microorganisms have learned efficient foraging strategies that exploit characteristic properties of their unknown environment. While much research has been devoted to the exploration of statistical models describing the dynamics of foraging bacteria and other (micro-) organisms, little is known, regarding the question of how good the learned strategies actually are. This knowledge gap is largely caused by the absence of methods allowing to systematically develop alternative foraging strategies to compare with...

IMPORTANCE: Toxic effects of concurrent chemoradiotherapy (CRT) can cause treatment interruptions and hospitalizations, reducing treatment efficacy and increasing health care costs. Physical activity monitoring may enable early identification of patients at high risk for hospitalization who may benefit from proactive intervention. OBJECTIVE: To develop and validate machine learning (ML) approaches based on daily step counts collected by wearable devices on prospective trials to predict hospitalizations during CRT...

Physical exercise affects many facets of life, including mental health, social interaction, physical fitness, and illness prevention, among many others. Therefore, several AI-driven techniques have been developed in the literature to recognize human physical activities. However, these techniques fail to adequately learn the temporal and spatial features of the data patterns. Additionally, these techniques are unable to fully comprehend complex activity patterns over different periods, emphasizing the need for enhanced architectures to further increase accuracy by learning spatiotemporal dependencies in the data individually...

This study investigates the roles of mucosal-associated invariant T (MAIT) cells and Vα7.2+ /CD161- T cells in skin diseases, focusing on atopic dermatitis. MAIT cells, crucial for bridging innate and adaptive immunity, were analyzed alongside Vα7.2+ /CD161- T cells in peripheral blood samples from 14 atopic dermatitis patients and 10 healthy controls. Flow cytometry and machine learning algorithms were employed for a comprehensive analysis. The results indicate a significant decrease in MAIT cells and CD69 subsets in atopic dermatitis, coupled with elevated CD38 and polyfunctional MAIT cells producing TNFα and Granzyme B (TNFα+ /GzB+ )...

BACKGROUND: In children, objective, quantitative tools that determine functional neurodevelopment are scarce and rarely scalable for clinical use. Direct recordings of cortical activity using routinely acquired electroencephalography (EEG) offer reliable measures of brain function. METHODS: We developed and validated a measure of functional brain age (FBA) using a residual neural network-based interpretation of the paediatric EEG. In this cross-sectional study, we included 1056 children with typical development ranging in age from 1 month to 18 years...

Occupational ergonomics aims to optimize the work environment and to enhance both productivity and worker well-being. Work-related exposure assessment, such as lifting loads, is a crucial aspect of this discipline, as it involves the evaluation of physical stressors and their impact on workers' health and safety, in order to prevent the development of musculoskeletal pathologies. In this study, we explore the feasibility of machine learning (ML) algorithms, fed with time- and frequency-domain features extracted from inertial signals (linear acceleration and angular velocity), to automatically and accurately discriminate safe and unsafe postures during weight lifting tasks...

This paper describes the analysis of electrodermal activity (EDA) in the context of students' scholastic activity. Taking a multidisciplinary, citizen science and maker-centric approach, low-cost, bespoken wearables, such as a mini weather station and biometric wristband, were built. To investigate both physical health as well as stress, the instruments were first validated against research grade devices. Following this, a research experiment was created and conducted in the context of students' scholastic activity...

Circadian rhythms are indispensable intrinsic programs that regulate the daily rhythmicity of physiological processes, such as feeding and sleep. The cricket has been employed as a model organism for understanding the neural mechanisms underlying circadian rhythms in insects. However, previous studies measuring rhythm-controlled behaviours only analysed locomotive activity using seesaw-type and infrared sensor-based actometers. Meanwhile, advances in deep learning techniques have made it possible to analyse animal behaviour and posture using software that is devoid of human bias and does not require physical tagging of individual animals...

Multi-functional actuation systems involve the mechanical integration of multiple actuation and sensor devices with external energy sources. The intricate combination makes it difficult to meet the requirements of lightweight. Hence, polypyrrole@graphene-bacterial cellulose (PPy@G-BC) films are proposed to construct multi-responsive and bilayer actuators integrated with multi-mode self-powered sensing function. The PPy@G-BC film not only exhibits good photo-thermoelectric (PTE) properties but also possesses good hydrophilicity and high Young's modulus...

BACKGROUND: Breathlessness is a troublesome and prevalent symptom in the population, but knowledge of related factors is scarce. The aim of this study was to identify the factors most strongly associated with breathlessness in the general population and to describe the shapes of the associations between the main factors and breathlessness. METHODS: A cross-sectional analysis was carried out of the multicentre population-based Swedish CArdioPulmonary bioImage Study (SCAPIS) of adults aged 50 to 64 years...

Electrochemical CO2 transformation to fuels and chemicals is an effective strategy for conversion of renewable electric energy into storable chemical energy in combination with reducing green-house gas emission. Metal-nitrogen-carbon (M-N-C) single atom catalysts (SAC) have shown great potential in the electrochemical CO2 reduction reaction (CO2 RR). However, exploring advanced SACs with simultaneously high catalytic activity and high product selectivity remains a great challenge. In this study, density functional theory (DFT) calculations are combined with machine learning (ML) for rapid and high-throughput screening of high performance CO reduction catalysts...

Hydrothermal carbonization was applied to taro peel wastes to produce hydrochars in a facile environmentally friendly process. Four different entities were prepared: hydrochar (TPh), phosphoric-activated hydrochar (P-TPh) and silver@hydrochars (Ag@TPh, Ag@P-TPh). The elemental compositions of the single and composite hydrochars were confirmed by EDX. Among the produced hydrochars, the morphology of the Ag@hydrochar composites demonstrated more wrinkled structure and Ag nanoparticles decorated the surface. The optimal experimental conditions for levofloxacin adsorption were determined to be contact time of 45 min, hydrochar dose of 0...