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Decoding the elite soccer player's psychological profile.
Proc Natl Acad Sci U S A
January 2025
Center for Psychiatry Research and Center for Cognitive and Computational Neuropsychiatry, Department of Clinical Neuroscience, Karolinska Institutet, Stockholm 17177, Sweden.
Soccer is arguably the most widely followed sport worldwide, and many dream of becoming soccer players. However, only a few manage to achieve this dream, which has cast a significant spotlight on elite soccer players who possess exceptional skills to rise above the rest. Originally, such attention was focused on their great physical abilities.
View Article and Find Full Text PDFHIV OctaScanner: A Machine Learning Approach to Unveil Proteolytic Cleavage Dynamics in HIV-1 Protease Substrates.
J Chem Inf Model
January 2025
State Key Laboratory of Microbial Metabolism, Joint International Research Laboratory of Metabolic & Developmental Sciences and School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai 200030, P.R. China.
The rise of resistance to antiretroviral drugs due to mutations in human immunodeficiency virus-1 (HIV-1) protease is a major obstacle to effective treatment. These mutations alter the drug-binding pocket of the protease and reduce the drug efficacy by disrupting interactions with inhibitors. Traditional methods, such as biochemical assays and structural biology, are crucial for studying enzyme function but are time-consuming and labor-intensive.
View Article and Find Full Text PDFProtocol for the impact of machine learning-based clinician decision support algorithims in perioperative care (IMAGINATIVE) in Singapore general hospital : a large prospective randomised controlled trial.
BMJ Open
December 2024
Health Services and Systems Research, Duke-NUS Medical School, Singapore.
Article Synopsis
- The introduction describes the growing issue of postoperative complications and how a new risk stratification tool, CARES-ML, aims to reduce these risks by identifying high-risk patients early.
- The study design involves a randomized controlled trial with over 9,200 patients undergoing elective surgeries, comparing outcomes between those guided by the CARES tool and those who are not.
- The ethics section ensures patient consent and language support, with the study approved by the relevant review board and funding from Singapore’s National Medical Research Council, leading to findings that will be shared in peer-reviewed journals.
Environmental degradation due to the rapid increase in CO₂ emissions is a pressing global challenge, necessitating innovative solutions for accurate prediction and policy development. Machine learning (ML) techniques offer a robust approach to modeling complex relationships between various factors influencing emissions. Furthermore, ML models can learn and interpret the significance of each factor's contribution to the rise of CO.
View Article and Find Full Text PDFAn examination of daily CO emissions prediction through a comparative analysis of machine learning, deep learning, and statistical models.
Environ Sci Pollut Res Int
January 2025
Department of Geology and Mineral Science, Kwara State University, Malete, P.M.B. 1530, Ilorin, Kwara State, Nigeria.
Human-induced global warming, primarily attributed to the rise in atmospheric CO, poses a substantial risk to the survival of humanity. While most research focuses on predicting annual CO emissions, which are crucial for setting long-term emission mitigation targets, the precise prediction of daily CO emissions is equally vital for setting short-term targets. This study examines the performance of 14 models in predicting daily CO emissions data from 1/1/2022 to 30/9/2023 across the top four polluting regions (China, India, the USA, and the EU27&UK).
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