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MVCL-DTI: Predicting Drug-Target Interactions Using a Multiview Contrastive Learning Model on a Heterogeneous Graph.
J Chem Inf Model
January 2025
School of Computer Science and Technology, Soochow University, Jiangsu 215006, China.
Accurate prediction of drug-target interactions (DTIs) is pivotal for accelerating the processes of drug discovery and drug repurposing. MVCL-DTI, a novel model leveraging heterogeneous graphs for predicting DTIs, tackles the challenge of synthesizing information from varied biological subnetworks. It integrates neighbor view, meta-path view, and diffusion view to capture semantic features and employs an attention-based contrastive learning approach, along with a multiview attention-weighted fusion module, to effectively integrate and adaptively weight the information from the different views.
View Article and Find Full Text PDFAxonFinder: Automated segmentation of tumor innervating neuronal fibers.
Heliyon
January 2025
Cancer Early Detection Advanced Research Center (CEDAR), Knight Cancer Institute, Oregon Health and Science University, Portland, OR, USA.
Neurosignaling is increasingly recognized as a critical factor in cancer progression, where neuronal innervation of primary tumors contributes to the disease's advancement. This study focuses on segmenting individual axons within the prostate tumor microenvironment, which have been challenging to detect and analyze due to their irregular morphologies. We present a novel deep learning-based approach for the automated segmentation of axons, AxonFinder, leveraging a U-Net model with a ResNet-101 encoder, based on a multiplexed imaging approach.
View Article and Find Full Text PDFMechanochemical destruction of perfluorooctane sulfonate (PFOS) using boron carbide (BC).
J Hazard Mater
December 2024
State Key Joint Laboratory of Environment Simulation and Pollution Control (SKLESP), Beijing, China; Key Laboratory for Emerging Organic Contaminants Control (BKLEOC), Beijing, China; Laboratory for Environmental Frontier Technologies (BLEFT), School of Environment, Tsinghua University, Beijing 100084, China. Electronic address:
Widespread detection in soils and sediments underscores the potential threats posed by persistent, bioaccumulative and toxic perfluorooctane sulfonate (PFOS) to ecosystems and organisms. Nevertheless, the formidable energy of the C-F bond imparts stability and hampers degradation. This study investigates the potential of boron carbide (BC), a hard-ceramic material often utilized in armor and abrasion contexts, for degrading solid-phase PFOS through ball milling.
View Article and Find Full Text PDFLost circulation intensity characterization in drilling operations: Leveraging machine learning and well log data.
Heliyon
January 2025
Department of Petroleum Engineering, Amirkabir University of Technology, Tehran, Iran.
Article Synopsis
- Lost circulation is a critical issue in drilling that leads to financial losses and operational risks, primarily caused by various geological factors.
- This study introduces machine learning models to predict the severity of lost circulation incidents using well-log data from a gas field in northern Iran, categorizing them into six intensity classes.
- The best-performing models, Random Forest, Extra Trees, and Hard Voting, demonstrate strong predictive capabilities, suggesting that machine learning can significantly enhance lost circulation management and improve drilling efficiency and safety.
Impacts of City Life on Nutrition: Evidence From Resettlement Lotteries in China.
Health Econ
January 2025
Department of Agricultural and Applied Economics, University of Georgia, Athens, Georgia, USA.
Urban environments are thought to improve food security, by offering enhanced access to markets and income opportunities. Yet this idea is hard to test empirically due to an abundance of confounding factors and selection issues. This study leverages a resettlement program in China to provide the first quasi-experimental estimate of city life on food consumption and nutrition among low-income households.
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