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Graph contrastive learning of subcellular-resolution spatial transcriptomics improves cell type annotation and reveals critical molecular pathways.
Brief Bioinform
November 2024
School of Artificial Intelligence, Jilin University, Qianjin Street 2699, 130010 Changchun, China.
Imaging-based spatial transcriptomics (iST), such as MERFISH, CosMx SMI, and Xenium, quantify gene expression level across cells in space, but more importantly, they directly reveal the subcellular distribution of RNA transcripts at the single-molecule resolution. The subcellular localization of RNA molecules plays a crucial role in the compartmentalization-dependent regulation of genes within individual cells. Understanding the intracellular spatial distribution of RNA for a particular cell type thus not only improves the characterization of cell identity but also is of paramount importance in elucidating unique subcellular regulatory mechanisms specific to the cell type.
View Article and Find Full Text PDFEffects of acute exercise on cognitive function: A meta-review of 30 systematic reviews with meta-analyses.
Psychol Bull
January 2025
Department of Kinesiology, University of North Carolina at Greensboro.
This meta-review provides the first meta-analytic evidence from published meta-analyses examining the effectiveness of acute exercise interventions on cognitive function. A multilevel meta-analysis with a random-effects model and tests of moderators were performed in R. Thirty systematic reviews with meta-analyses (383 unique studies with 18,347 participants) were identified.
View Article and Find Full Text PDFDevelopment and validation of an explainable machine learning prediction model of hemorrhagic transformation after intravenous thrombolysis in stroke.
Front Neurol
January 2025
Department of Neurology, The First Affiliated Hospital of Dalian Medical University, Dalian, China.
Objective: To develop and validate an explainable machine learning (ML) model predicting the risk of hemorrhagic transformation (HT) after intravenous thrombolysis.
Methods: We retrospectively enrolled patients who received intravenous tissue plasminogen activator (IV-tPA) thrombolysis within 4.5 h after symptom onset to form the original modeling cohort.
Prediction of outpatient rehabilitation patient preferences and optimization of graded diagnosis and treatment based on XGBoost machine learning algorithm.
Front Artif Intell
January 2025
Department of Rehabilitation Medicine, The First Affiliated Hospital of Shenzhen University, The Second People's Hospital of Shenzhen, Shenzhen, Guangdong, China.
Background: The Department of Rehabilitation Medicine is key to improving patients' quality of life. Driven by chronic diseases and an aging population, there is a need to enhance the efficiency and resource allocation of outpatient facilities. This study aims to analyze the treatment preferences of outpatient rehabilitation patients by using data and a grading tool to establish predictive models.
View Article and Find Full Text PDFGlobal remapping emerges as the mechanism for renewal of context-dependent behavior in a reinforcement learning model.
Front Comput Neurosci
January 2025
Institute for Neural Computation, Faculty of Computer Science, Ruhr University Bochum, Bochum, Germany.
Introduction: The hippocampal formation exhibits complex and context-dependent activity patterns and dynamics, e.g., place cell activity during spatial navigation in rodents or remapping of place fields when the animal switches between contexts.
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