Results 151 to 160 of about 62,284 (197)

Aerobic fitness, phase angle, and bioelectrical impedance vector analysis in adolescents living with HIV: a cross-sectional study. [PDF]

Rev Paul Pediatr
Silva Junior MCPD, Silva DAS, Martins PC, Bueno NB, Moura FA, Lima LRA.   +5 more
europepmc   +1 more source

AS‐pHopt: An Optimal pH Prediction Model Enhanced by Active Site of Enzymes

Advanced Intelligent Discovery, EarlyView.
To address the low accuracy of enzyme optimal pH (pHopt) prediction, this study develops active site‐based pHopt (AS‐pHopt), a prediction model enhanced by active site information and pseudo‐label prediction. Integrating key structural and physicochemical features affecting enzyme pHopt, AS‐pHopt uses Evolutionary Scale Modeling (ESM)‐2 with active ...
Wenxiang Song, Xinmin Li, Hongbo Yu, Le Xiong, Guixia Liu, Weihua Li, Yun Tang   +6 more
wiley   +1 more source

Healthcare Development, Relaxed Natural Selection, and COVID-19 Infection Rates: An Evolutionary Population-Level Analysis. [PDF]

Health Sci Rep
You W, Coventry B, Saniotis A, Galassi FM, Henneberg R, Henneberg M.   +5 more
europepmc   +1 more source

Uncertainty‐Guided Selective Adaptation Enables Cross‐Platform Predictive Fluorescence Microscopy

Advanced Intelligent Discovery, EarlyView.
Deep learning models often fail when transferred to new microscopes. A novel framework overcomes this by selectively adapting the early layers governing low‐level image statistics, while freezing deep layers that encode morphology. This uncertainty‐guided approach enables robust, label‐free virtual staining across diverse systems, democratizing ...
Kai‐Wen K. Yang, Andrew Bai, Alexandra Bermudez, Yunqi Hong, Zoe Latham, Iris Sloan, Michael Liu, Vishrut Goyal, Cho‐Jui Hsieh, Neil Y. C. Lin   +9 more
wiley   +1 more source

VAE+DDPG: An Attention‐Enhanced Variational Autoencoder for Deep Reinforcement Learning‐Based Autonomous Navigation in Low‐Light Environments

Advanced Intelligent Systems, EarlyView.
Variational Autoencoder+Deep Deterministic Policy Gradient addresses low‐light failures of infrared depth sensing for indoor robot navigation. Stage 1 pretrains an attention‐enhanced Variational Autoencoder (Convolutional Block Attention Module+Feature Pyramid Network) to map dark depth frames to a well‐lit reconstruction, yielding a 128‐D latent code ...
Uiseok Lee, Chanhong Hwang, Hyunseok Kang, Yechan Park, Hangwoo Jin, Tuan Anh Nguyen, Eunmi Choi, Dugki Min   +7 more
wiley   +1 more source

A semi-automated quality assurance tool for cardiovascular magnetic resonance imaging: application to outlier detection, artificial intelligence evaluation and trainee feedback. [PDF]

BMC Med Inform Decis Mak
Hadler T, Grassow L, Kuhnt J, Hickstein R, Saad H, Fenski M, Gröschel J, Trauzeddel RF, Blaszczyk E, Ammann C, Viezzer D, Hennemuth A, Lange S, Schulz-Menger J.   +13 more
europepmc   +1 more source

Machine Learning‐Augmented Loop‐Mediated Isothermal Amplification‐Enabled Point‐of‐Care for Mpox‐Specific Detection

Advanced Intelligent Systems, EarlyView.
A low‐cost, portable point‐of‐care platform for rapid Mpox detection using loop‐mediated isothermal amplification is reported. The device integrates fluorescence readout and mobile monitoring. A machine‐learning model analyzes temperature data and correlates thermal changes with DNA concentration, enabling sensitive and reliable molecular diagnosis in ...
Nazente Atceken, Ahmed Choukri Abdullah, Olgac Ozarslan, Ziya Yıldız, Savas Tasoglu   +4 more
wiley   +1 more source

Spatiotemporal Co-occurrence Patterns of Periodontitis and Oral Cancer: A Global Risk Factor and Network Analysis. [PDF]

Int Dent J
Xia Y, Song J.
europepmc   +1 more source

Deep Learning Approaches for Classifying Crack States With Overload and Predicting Fatigue Parameters in a Titanium Alloy

Advanced Intelligent Systems, EarlyView.
This study proposes a deep learning approach to evaluate the fatigue crack behavior in metals under overload conditions. Using digital image correlation to capture the strain near crack tips, convolutional neural networks classify crack states as normal, overload, or recovery, and accurately predict fatigue parameters.
Seon Du Choi, Suman Timilsina, Youn Woo Shin, Yong Nam Kwon, Kee‐Sun Sohn, Ji Sik Kim   +5 more
wiley   +1 more source

Bayesian Optimisation for the Experimental Sciences: A Practical Guide to Data‐Efficient Optimisation of Laboratory Workflows

Advanced Intelligent Systems, EarlyView.
This study provides an introduction to Bayesian optimisation targeted for experimentalists. It explains core concepts, surrogate modelling, and acquisition strategies, and addresses common real‐world challenges such as noise, constraints, mixed variables, scalability, and automation.
Chuan He, Martin Singull, T. Jesper Jacobsson   +2 more
wiley   +1 more source