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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8247749 | PMC |
http://dx.doi.org/10.25259/SNI_526_2021 | DOI Listing |
J Clin Invest
September 2025
The University of Texas at Austin, Austin, United States of America.
Background: Following SARS-CoV-2 infection, ~10-35% of COVID-19 patients experience long COVID (LC), in which debilitating symptoms persist for at least three months. Elucidating biologic underpinnings of LC could identify therapeutic opportunities.
Methods: We utilized machine learning methods on biologic analytes provided over 12-months after hospital discharge from >500 COVID-19 patients in the IMPACC cohort to identify a multi-omics "recovery factor", trained on patient-reported physical function survey scores.
Cereb Cortex
August 2025
Department of Developmental Psychology, University of Amsterdam, Nieuwe Achtergracht 129b, 1018 WS Amsterdam, The Netherlands.
Social learning, a hallmark of human behavior, entails integrating other's actions or ideas with one's own. While it can accelerate the learning process by circumventing slow and costly individual trial-and-error learning, its effectiveness depends on knowing when and whose information to use. In this study, we explored how individuals use social information based on their own and others' levels of uncertainty.
View Article and Find Full Text PDFCereb Cortex
August 2025
Faculty of Psychology and Education Science, Department of Psychology, University of Geneva, Chemin des Mines 9, Geneva, 1202, Switzerland.
Language learning and use relies on domain-specific, domain-general cognitive and sensory-motor functions. Using fMRI during story listening and behavioral tests, we investigated brain-behavior associations between linguistic and non-linguistic measures in individuals with varied multilingual experience and reading skills, including typical reading participants (TRs) and dyslexic readers (DRs). Partial Least Square Correlation revealed a main component linking cognitive, linguistic, and phonological measures to amodal/associative brain areas.
View Article and Find Full Text PDFProc Natl Acad Sci U S A
September 2025
Max Planck Institute for Solar System Research, Göttingen 37077, Germany.
Turbulent convection governs heat transport in both natural and industrial settings, yet optimizing it under extreme conditions remains a significant challenge. Traditional control strategies, such as predefined temperature modulation, struggle to achieve substantial enhancement. Here, we introduce a deep reinforcement learning (DRL) framework that autonomously discovers optimal control policies to maximize heat transfer in turbulent Rayleigh-Bénard convection.
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