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We introduce a rare-event sampling scheme, named Markovian Weighted Ensemble Milestoning (M-WEM), which inlays a weighted ensemble framework within a Markovian milestoning theory to efficiently calculate thermodynamic and kinetic properties of long-time-scale biomolecular processes from short atomistic molecular dynamics simulations. M-WEM is tested on the Müller-Brown potential model, the conformational switching in alanine dipeptide, and the millisecond time-scale protein-ligand unbinding in a trypsin-benzamidine complex. Not only can M-WEM predict the kinetics of these processes with quantitative accuracy but it also allows for a scheme to reconstruct a multidimensional free-energy landscape along additional degrees of freedom, which are not part of the milestoning progress coordinate. For the ligand-receptor system, the experimental residence time, association and dissociation kinetics, and binding free energy could be reproduced using M-WEM within a simulation time of a few hundreds of nanoseconds, which is a fraction of the computational cost of other currently available methods, and close to 4 orders of magnitude less than the experimental residence time. Due to the high accuracy and low computational cost, the M-WEM approach can find potential applications in kinetics and free-energy-based computational drug design.
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http://dx.doi.org/10.1021/acs.jctc.1c00803 | DOI Listing |
Bidens macroptera symbolizes the change of a season, marking the transition from the rainy season to autumn, heralding the new year for Ethiopians. Despite a general understanding of its geographic regions, significant gaps remain in identifying the habitat distribution and key predictor variables of Bidens macroptera through species distribution modeling (SDM) in the context of climate change. We developed an ensemble species distribution model using 2 statistical and 3 machine learning algorithms.
View Article and Find Full Text PDFFront Public Health
September 2025
Institute of Physical Education, Sichuan University, Chengdu, China.
Objective: This study aimed to examine the relationship between physical activity volume and sleep duration in older adults, using objective monitoring data to investigate their non-linear association and threshold effects, thereby providing references for developing exercise programs to improve sleep duration.
Methods: The study used two consecutive waves of NHANES cross-sectional data (2011-2014) as the derivation cohort and NHANES 2005-2006 data as the validation cohort. Analysis of the derivation cohort included weighted univariate analysis, weighted multivariate logistic regression, and interpretable machine learning analysis.
Int Immunopharmacol
September 2025
The First Affiliated Hospital, Zhejiang University School of Medicine, 1367 West Wenyi Rd., Yuhang District, Hangzhou, Zhejiang Province, China. Electronic address:
Macrophages, pivotal orchestrators of the immune system, are integral to the initiation of specific immune responses and exert profound influence on the pathogenesis, progression, and therapeutic landscape of aortic dissection (AD). Leveraging the precision of single-cell RNA sequencing (scRNA-seq), this study aimed to dissect the heterogeneity of macrophages within the AD microenvironment. We identified a unique macrophage subpopulation, termed AD-associated macrophages (AD-mac), which is predominantly implicated in the early stages of AD pathogenesis.
View Article and Find Full Text PDFStud Health Technol Inform
September 2025
Junior Research Group (Bio-) Medical Data Science, Faculty of Medicine, Martin-Luther-University Halle-Wittenberg, Halle (Saale), Germany.
Introduction: The integration of Retrieval-Augmented Generation (RAG) into domain-specific systems enables context-aware and traceable information retrieval. This study explores chunking and embedding strategies for a RAG-based question-answering system tailored to administrative documents at University Hospital Halle, focusing on model selection, parameter tuning, and retrieval performance. The insights gained from this study should serve as the foundation for the future development of a Retrieval-Augmented Generation (RAG) based chatbot system that aims to facilitate access to document pool contents for hospital staff.
View Article and Find Full Text PDFJ Transl Med
September 2025
School of Data Sciences, Zhejiang University of Finance and Economics, Hangzhou, China.
Background: This study proposes a multi-task learning (MTL) model to predict the need for blood transfusion in patients with acute upper gastrointestinal bleeding (AUGIB), as well as to estimate the appropriate type and volume of transfusion. The proposed model demonstrates improved predictive performance over existing scoring systems and aims to support clinical decision-making in transfusion management.
Methods: Clinical data were retrospectively collected from 1256 emergency patients with AUGIB admitted to the First Hospital of Shanxi Medical University from January 1, 2022, to December 31, 2023.