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Enhancing Activation Energy Predictions under Data Constraints Using Graph Neural Networks.
J Chem Inf Model
January 2025
Department of Chemical Engineering, National Taiwan University, No. 1, Section 4, Roosevelt Road, Taipei 10617, Taiwan.
Accurately predicting activation energies is crucial for understanding chemical reactions and modeling complex reaction systems. However, the high computational cost of quantum chemistry methods often limits the feasibility of large-scale studies, leading to a scarcity of high-quality activation energy data. In this work, we explore and compare three innovative approaches (transfer learning, delta learning, and feature engineering) to enhance the accuracy of activation energy predictions using graph neural networks, specifically focusing on methods that incorporate low-cost, low-level computational data.
View Article and Find Full Text PDFMachine Learning-Based predictive model for adolescent metabolic syndrome: Utilizing data from NHANES 2007-2016.
Sci Rep
January 2025
Department of Anesthesiology and Surgical Intensive Care Unit, Kunming Children's Hospital, Kunming, Yunnan, China.
Metabolic syndrome (Mets) in adolescents is a growing public health issue linked to obesity, hypertension, and insulin resistance, increasing risks of cardiovascular disease and mental health problems. Early detection and intervention are crucial but often hindered by complex diagnostic requirements. This study aims to develop a predictive model using NHANES data, excluding biochemical indicators, to provide a simple, cost-effective tool for large-scale, non-medical screening and early prevention of adolescent MetS.
View Article and Find Full Text PDFMulti-objective design of multi-material truss lattices utilizing graph neural networks.
Sci Rep
January 2025
Advanced Manufacturing Lab, ETH Zürich, Leonhardstrasse 21, 8092, Zurich, Switzerland.
The rapid advancements in additive manufacturing (AM) across different scales and material classes have enabled the creation of architected materials with highly tailored properties. Beyond geometric flexibility, multi-material AM further expands design possibilities by combining materials with distinct characteristics. While machine learning has recently shown great potential for the fast inverse design of lattice structures, its application has largely been limited to single-material systems.
View Article and Find Full Text PDFQuantitative and Visual Benefits of Data-Driven Motion Correction on Oncologic PET/CT: A Prospective Cross-sectional Study.
Acad Radiol
January 2025
Department of Radiology, Washington University School of Medicine, 510 S. Kingshighway Blvd, St. Louis, MO 63110 (S.I., M.A.T., M.I., C.S., R.L., A.H., R.L.W., T.J.F.). Electronic address:
Rationale And Objective: Conventional positron emission tomography (PET) respiratory gating utilizes a fraction of acquired PET counts (i.e., optimal gate [OG]), whereas elastic motion correction with deblurring (EMCD) utilizes all PET counts to reconstruct motion-corrected images without increasing image noise.
View Article and Find Full Text PDFAdherence to tuberculosis (TB) treatment in high compared to low TB burden countries: study protocol for a systematic review and meta-analysis with a qualitative meta-synthesis of themes.
BMJ Open
January 2025
Faculty of Health Sciences, Simon Fraser University, Burnaby, BC, Canada.
Introduction: Non-adherence to tuberculosis (TB) treatment poses a significant challenge to effective TB management globally and is a major contributor to the emergence of multidrug-resistant TB. Although adherence to TB treatment has been widely studied, a comprehensive evaluation of the comparative levels of adherence in high- versus low-TB burden settings remains lacking. The objective of this systematic review and meta-analysis is to assess the levels of adherence to TB treatment in high-TB burden countries compared to low-burden countries.
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