Soil is a major environmental sink for the emerging organic pollutants phthalates (PAEs), and the determination of key factors influencing PAEs accumulation in soil is crucial for agricultural sustainability and food security. Aiming at the time-consuming and inefficient characteristics of traditional batch experiments and statistical prediction models in comprehensively capturing PAEs dynamics in soil, an intelligent analysis framework based on machine learning was proposed and developed. In this study, thirty features were incorporated, including soil PAEs-concentrations, pollutant emissions, agricultural inputs, soil physicochemical properties, and climatic parameters. Six data-driven machine learning models were established: Random Forest Regression (RFR), Gradient Boosting Regression Tree (GBRT), Extreme Gradient Boosting (XGBoost), Multilayer Perceptron (MLP), Support Vector Regression (SVR), and k-Nearest Neighbors (KNN). Results showed that the MLP model exhibited optimal performance in predicting soil PAEs concentrations (R²=0.8637), followed by SVR (R²=0.8132) and XGBoost (R²=0.8096). Through feature importance analysis, it was determined that hydrometeorological factors, soil moisture conditions, and nutritional characteristics were the key factors controlling PAEs spatial distribution. Furthermore, non-linear effect analysis elucidated significant synergistic interactions among these environmental covariates. The spatiotemporal prediction model revealed continuous declining trends in PAEs pollution levels in eastern coastal regions over the next 5-10 years, while accumulation tendencies were observed in inland provinces particularly in Guizhou. This study demonstrates the effectiveness and advantages of machine learning in predicting soil PAEs-pollution, providing a new perspective for pollutant risk assessment and management in the era of environmental big data.
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http://dx.doi.org/10.1016/j.jhazmat.2024.136604 | DOI Listing |
J Dent Sci
January 2025
Institute of Statistics, National Yang Ming Chiao Tung University, Hsinchu City, Taiwan.
Background/purpose: In this study, we utilized magnetic resonance imaging data of the temporomandibular joint, collected from the Division of Oral and Maxillofacial Surgery at Taipei Veterans General Hospital. Our research focuses on the classification and severity analysis of temporomandibular joint disease using convolutional neural networks.
Materials And Methods: In gray-scale image series, the most critical features often lie within the articular disc cartilage, situated at the junction of the temporal bone and the condyles.
Front Cell Infect Microbiol
January 2025
Department of Clinical Laboratory Medicine Center, Inner Mongolia Autonomous Region People's Hospital, Hohhot, Inner Mongolia, China.
Introduction: This study aims to utilize proteomics, bioinformatics, and machine learning algorithms to identify diagnostic biomarkers in the serum of patients with acute and chronic brucellosis.
Methods: Proteomic analysis was conducted on serum samples from patients with acute and chronic brucellosis, as well as from healthy controls. Differential expression analysis was performed to identify proteins with altered expression, while Weighted Gene Co-expression Network Analysis (WGCNA) was applied to detect co-expression modules associated with clinical features of brucellosis.
Biomater Transl
November 2024
Applied Oral Sciences and Community Dental Care, Faculty of Dentistry, The University of Hong Kong, Hong Kong Special Administrative Region, China.
The convergence of organoid technology and artificial intelligence (AI) is poised to revolutionise oral healthcare. Organoids - three-dimensional structures derived from human tissues - offer invaluable insights into the complex biology of diseases, allowing researchers to effectively study disease mechanisms and test therapeutic interventions in environments that closely mimic in vivo conditions. In this review, we first present the historical development of organoids and delve into the current types of oral organoids, focusing on their use in disease models, regeneration and microbiome intervention.
View Article and Find Full Text PDFJ Appl Crystallogr
January 2024
NIST Center for Neutron Research, National Institute of Standards and Technology, Gaithersburg, Maryland, USA.
Neutron reflectometry (NR) is a powerful technique for interrogating the structure of thin films at interfaces. Because NR measurements are slow and instrument availability is limited, measurement efficiency is paramount. One approach to improving measurement efficiency is active learning (AL), in which the next measurement configurations are selected on the basis of information gained from the partial data collected so far.
View Article and Find Full Text PDFFront Cardiovasc Med
January 2025
Department of Cardiology, Zhongda Hospital, Medical School of Southeast University, Nanjing, China.
Background: Acute myocardial infarction (AMI), a subset of acute coronary syndrome, remains the major cause of mortality worldwide. Mitochondrial dysfunction is critically involved in AMI progression, and mitophagy plays a vital role in eliminating damaged mitochondria. This study aimed to explore mitophagy-related biomarkers and their potential molecular basis in AMI.
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