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Altered Static and Dynamic Functional Network Connectivity and Combined Machine Learning in Stroke.
Brain Topogr
January 2025
Department of Radiology, Jiangxi Provincial People's Hospital, The First Affiliated Hospital of Nanchang Medical College, No 152, Ai Guo Road, Dong Hu District, Nanchang, Jiangxi, 330006, China.
Stroke is a condition characterized by damage to the cerebral vasculature from various causes, resulting in focal or widespread brain tissue damage. Prior neuroimaging research has demonstrated that individuals with stroke present structural and functional brain abnormalities, evident through disruptions in motor, cognitive, and other vital functions. Nevertheless, there is a lack of studies on alterations in static and dynamic functional network connectivity in the brains of stroke patients.
View Article and Find Full Text PDFThe paper introduces a method for predicting damage intensity in masonry residential buildings situated in mining areas, focusing on the impact of large-scale continuous ground deformation. The research utilizes in situ data collected in a database, encompassing structural and material features, as well as information on maintenance quality and building durability. In addition to this information, the database collected data on the intensity of continuous deformation of the mining area at the location of the building, as well as the range and intensity of damage identified in buildings.
View Article and Find Full Text PDFUnderstanding system interdependencies in sustainable paper production from residue grass biomass: Insights from fuzzy cognitive mapping.
Sci Rep
January 2025
Innovations in Sociotechnical Systems, Department of Technology Assessment, Leibniz Institute for Agricultural Engineering and Bioeconomy (ATB), 14469, Potsdam, Germany.
This research investigates the pulp and paper industry's transition to sustainability by valorizing unused roadside and natural grasses for paper production. Large-scale production from residual grass poses multifaceted challenges, requiring collaboration across stakeholders, from biomass collection to manufacturing. To understand key drivers and barriers within this complex system, experts from various fields, including local farmers, researchers, policymakers, and industry executives were interviewed, leading to the development of a Fuzzy Cognitive Map (FCM).
View Article and Find Full Text PDFCoverage bias in small molecule machine learning.
Nat Commun
January 2025
Chair for Bioinformatics, Institute for Computer Science, Friedrich Schiller University Jena, Jena, Germany.
Small molecule machine learning aims to predict chemical, biochemical, or biological properties from molecular structures, with applications such as toxicity prediction, ligand binding, and pharmacokinetics. A recent trend is developing end-to-end models that avoid explicit domain knowledge. These models assume no coverage bias in training and evaluation data, meaning the data are representative of the true distribution.
View Article and Find Full Text PDFMulti-region infectious disease prediction modeling based on spatio-temporal graph neural network and the dynamic model.
PLoS Comput Biol
January 2025
Complex Systems Research Center, Shanxi University, Taiyuan, Shanxi, China.
Human mobility between different regions is a major factor in large-scale outbreaks of infectious diseases. Deep learning models incorporating infectious disease transmission dynamics for predicting the spread of multi-regional outbreaks due to human mobility have become a hot research topic. In this study, we incorporate the Graph Transformer Neural Network and graph learning mechanisms into a metapopulation SIR model to build a hybrid framework, Metapopulation Graph Transformer Neural Network (M-Graphormer), for high-dimensional parameter estimation and multi-regional epidemic prediction.
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