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science Bulletin 2024年第5期69卷 583-588页

作者：盛斌管洲榆 Lee-Ling Lim 江泽铧 Nestoras Mathioudakis 李佳佳刘茹涵包玉倩 Yong Mong Bee 王亚星郑颖丰 Gavin Siew Wei Tan 纪宏伟 Josip Car 王海波 David C.Klonoff 李华婷覃宇宗黄天荫贾伟平 Shanghai International Joint Laboratory of Intelligent Prevention and Treatment for Metabolic Diseases Department of Computer Science and EngineeringSchool of ElectronicInformationand Electrical EngineeringShanghai Jiao Tong UniversityDepartment of Endocrinology and MetabolismShanghai Sixth People’s Hospital Affiliated to Shanghai Jiao Tong University School of MedicineShanghai Diabetes InstituteShanghai Clinical Center for DiabetesShanghai 200240China MOE Key Laboratory of Artificial Intelligence Artificial Intelligence InstituteShanghai Jiao Tong UniversityShanghai 200240China Department of Medicine Faculty of MedicineUniversity of MalayaKuala Lumpur 50603Malaysia Department of Medicine and Therapeutics The Chinese University of Hong KongHong KongChina Asia Diabetes Foundation Hong KongChina Tsinghua Medicine of Tsinghua University Beijing 100084China School of Clinical Medicine Beijing Tsinghua Changgung HospitalBeijing 102218China Division of Endocrinology Diabetes&MetabolismJohns Hopkins University School of MedicineBaltimoreMD 21211USA Department of Endocrinology Singapore General HospitalSingapore 169608Singapore Beijing Institute of Ophthalmology Beijing Tongren HospitalCapital University of Medical ScienceBeijing Ophthalmology and Visual Sciences Key LaboratoryBeijing 100730China State Key Laboratory of Ophthalmology Zhongshan Ophthalmic CenterSun Yat-sen UniversityGuangdong Provincial Key Laboratory of Ophthalmology and Visual ScienceGuangzhou 510060China Singapore Eye Research Institute Singapore National Eye CentreSingapore 168751Singapore Department of Cardiology the Affiliated Hospital of Qingdao UniversityQingdao 266011China Centre for Population Health Sciences Lee Kong Chian School of MedicineNanyang Technological University SingaporeSingapore 639798Singapore Department of Primary Care and Public Health School of Public HealthImperial College LondonLondon SW72BUUK Clinical Trial Unit Research Centre of Big Data and Artificial Intelligence in Medicine

The increasing prevalence of diabetes has become a global public health concern in the 21st *** 2021,it was estimated that 537 million people had diabetes,and this number is projected to reach 643 million by 2030,and 783 million by 2045[1].Such a huge burden of diabetes brings great challenges in its prevention and management,including early diagnosis,timely interventions,and regular monitoring of risk factor control and complications *** self-care support and patient empowerment can enhance clinical and psychobehavioural outcomes[2],although these require additional resources including manpower,infrastructure(hard and technology),and *** emergence of digital health technologies(DHTs),especially artificial intelligence(AI),may help address these obstacles and alleviate the burden of diabetes[3].Large language models(LLMs),a generative AI that can accept image and text inputs and produce text outputs,have shown promise in various aspects of medical care.

关键词： prevention diagnosis finance

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Penetrance of neurodevelopmental copy number variants is associated with variations in cortical morphology

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Biological Psychiatry: Cognitive Neuroscience and Neuroimaging 2025年

作者： Ana I. Silva Ida E. Sønderby George Kirov Abdel Abdellaoui Ingrid Agartz David Ames Nicola J. Armstrong Eric Artiges Tobias Banaschewski Anne S. Bassett Carrie E. Bearden John Blangero Rune Boen Dorret I. Boomsma Robin Bülow Nancy J. Butcher Vince Calhoun Linda E. Campbell Eva W.C. Chow Simone Ciufolini David E.J. Linden School for Mental Health and Neuroscience Department of Psychiatry and Neuropsychology Faculty of Health Medicine and Life Sciences Maastricht University Maastricht The Netherlands Neuroscience and Mental Health Innovation Institute Cardiff University Cardiff United Kingdom Center for Magnetic Resonance Research Department of Radiology University of Minnesota Minneapolis MN USA Centre for Precision Psychiatry Institute of Clinical Medicine University of Oslo Oslo Norway Department of Medical Genetics Oslo University Hospital Oslo Norway KG Jebsen Centre for Neurodevelopmental Disorders University of Oslo Oslo Norway Division of Psychological Medicine and Clinical Neurosciences Cardiff University Cardiff United Kingdom Centre for Neuropsychiatric Genetics and Genomics School of Medicine Cardiff University Cardiff United Kingdom Department of Psychiatry Amsterdam UMC University of Amsterdam Amsterdam the Netherlands Norwegian Centre for Mental Disorders Research (NORMENT) Institute of Clinical Medicine University of Oslo Oslo Norway Centre for Psychiatry Research Department of Clinical Neuroscience Karolinska Institutet & Stockholm Health Care Services Stockholm Region Stockholm Sweden Department of Psychiatric Research Diakonhjemmet Hospital Oslo Norway University of Melbourne Academic Unit for Psychiatry of Old Age Kew Victoria Australia Mathematics and Statistics Curtin University Perth WA Australia Institut National de la Santé et de la Recherche Médicale INSERM U1299 “Trajectoires développementales et psychiatrie” Université Paris-Saclay Ecole Normale supérieure Paris-Saclay CNRS Centre Borelli Gif-sur-Yvette France Psychiatry Department EPS Barthélémy Durand Etampes Île-de-France France Department of Child and Adolescent Psychiatry and Psychotherapy Central Institute of Mental Health Medical Faculty Mannheim University of Heidelberg Mannheim Germany Clinical Genetics Research Program Centre for Addiction and Mental Health Toronto Ontario

BACKGROUND Copy number variants (CNVs) increase risk for neurodevelopmental conditions. The neurobiological mechanisms linking these high-risk genetic variants to clinical phenotypes are largely unknown. An important question is whether brain abnormalities in individuals carrying CNVs are associated with their degree of penetrance. METHODS We investigated if increased CNV-penetrance for schizophrenia and other developmental disorders was associated with variations in cortical and subcortical morphology. We pooled T1-weighted brain magnetic resonance imaging and genetic data from 22 cohorts from the ENIGMA-CNV consortium. In the main analyses, we included 9,268 individuals (aged 7 to 90 years, 54% females), from which we identified 398 carriers of 36 neurodevelopmental CNVs at 20 distinct loci. A secondary analysis was performed including additional neuroimaging data from the ENIGMA-22q consortium, including 274 carriers of the 22q11.2 deletion and 291 non-carriers. CNV-penetrance was estimated through penetrance scores that were previously generated from large cohorts of patients and controls. These scores represent the probability risk to develop either schizophrenia or other developmental disorders (including developmental delay, autism spectrum disorder and congenital malformations). RESULTS For both schizophrenia and developmental disorders, increased penetrance scores were associated with lower surface area in the cerebral cortex and lower intracranial volume. For both conditions, associations between CNV-penetrance scores and cortical surface area were strongest in regions of the occipital lobes, specifically in the cuneus and lingual gyrus. CONCLUSIONS Our findings link global and regional cortical morphometric features with CNV-penetrance, providing new insights into neurobiological mechanisms of genetic risk for schizophrenia and other developmental disorders.

关键词： neuroimaging structural imaging autism schizophrenia psychiatric disorders ASD autism spectrum disorder BP breakpoint CM congenital malformations CNV Copy number variant DD developmental delay ICV intracranial volume MRI magnetic resonance imaging NDD-CNVs copy number variants with potential risk for neurodevelopmental disorders PC principal component PCA principal component analysis PenDD Penetrance scores for developmental disorders PenSZ Penetrance scores for schizophrenia

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Double-Cable Conjugated Polymers with Pendent Near-Infrared Electron Acceptors for Single-Component Organic Solar Cells

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Angewandte Chemie 2022年第35期134卷

作者： Shijie Liang Baiqiao Liu Dr. Safakath Karuthedath Jing Wang Dr. Yakun He Wen Liang Tan Hao Li Prof. Yunhua Xu Prof. Ning Li Prof. Jianhui Hou Prof. Zheng Tang Prof. Frédéric Laquai Prof. Christopher R. McNeill Prof. Christoph J. Brabec Prof. Weiwei Li Beijing Advanced Innovation Center for Soft Matter Science and Engineering & State Key Laboratory of Organic-Inorganic Composites Beijing University of Chemical Technology Beijing 100029 China Contribution: Data curation (lead) Formal analysis (lead) Investigation (equal) Writing - original draft (supporting) Writing - review & editing (supporting) Department of Chemistry School of Science Beijing Jiaotong University Beijing 100044 P. R. China KAUST Solar Center (KSC) Physical Sciences and Engineering Division (PSE) Material Science and Engineering Program (MSE) King Abdullah University of Science and Technology (KAUST) Thuwal 23955-6900 Kingdom of Saudi Arabia Contribution: Data curation (supporting) Formal analysis (supporting) Investigation (supporting) Writing - original draft (supporting) Writing - review & editing (supporting) Center for Advanced Low-dimension Materials College of Materials Science and Engineering Donghua University Shanghai 201620 P. R. China Contribution: Data curation (supporting) Formal analysis (supporting) Investigation (supporting) Methodology (supporting) Writing - review & editing (supporting) Institute of Materials for Electronics and Energy Technology (i-MEET) Friedrich-Alexander-Universität Erlangen-Nürnberg Martensstrasse 7 91058 Erlangen Germany Contribution: Data curation (supporting) Formal analysis (supporting) Investigation (supporting) Methodology (supporting) Writing - original draft (supporting) Writing - review & editing (supporting) Department of Materials Science and Engineering Monash University Wellington Road Clayton Victoria 3800 Australia State Key Laboratory of Polymer Physics and Chemistry Beijing National Laboratory for Molecular Sciences Institute of Chemistry Chinese Academy of Sciences Beijing 100190 P. R. China Contribution: Data curation (supporting) Formal analysis (supporting) Writing - review & editing (supporting) Contribution: Funding acquisition (supporting) Writing - review & editing (supportin

Double-cable conjugated polymers with near-infrared (NIR) electron acceptors are synthesized for use in single-component organic solar cells (SCOSCs). Through the development of a judicious synthetic pathway, the highly sensitive nature of the 2-(3-oxo-2,3-dihydroinden-1-ylidene)malononitrile (IC)-based electron acceptors in basic and protonic solvents is overcome. In addition, an asymmetric design motif is adopted to optimize the packing of donor and acceptor segments, enhancing charge separation efficiency. As such, the new double-cable polymers are successfully applied in SCOSCs, providing an efficiency of over 10 % with a broad photo response from 300 to 850 nm and exhibiting excellent thermal/light stability. These results demonstrate the powerful design of NIR-acceptor-based double-cable polymers and will enable SCOSCs to enter a new stage.

关键词： Double-Cable Conjugated Polymers Electron Acceptors Near Infrared Single Component Solar Cells

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The science of Precision Prevention: Research Opportunities and Clinical Applications to Reduce Cardiovascular Health Disparities

JACC: Advances

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JACC: Advances 2024年第1期3卷 100759-100759页

作者： Pearson, Thomas A. Vitalis, Debbie Pratt, Charlotte Campo, Rebecca Armoundas, Antonis A. Au, David Beech, Bettina Brazhnik, Olga Chute, Christopher G. Davidson, Karina W. Diez-Roux, Ana V. Fine, Lawrence J. Gabriel, Davera Groenveld, Peter Hall, Jaclyn Hamilton, Alison B. Hu, Hui Ji, Heng Kind, Amy Kraus, William E. Krumholz, Harlan Mensah, George A. Merchant, Raina M. Mozaffarian, Dariush Murray, David M. Neumark-Sztainer, Dianne Petersen, Maya Goff, David College of Medicine and College of Public Health and Health Professions University of Florida Health Science Center Gainesville FL United States Division of Cardiovascular Sciences National Heart Lung and Blood Institute National Institutes of Health Bethesda MD United States Cardiovascular Research Center Massachusetts General Hospital and Broad Institute Massachusetts Institute of Technology Cambridge MA United States Center of Innovation for Veteran-Centered and Value-Driven Care University of Washington Seattle WA United States UH Population Health University of Houston Houston TX United States Johns Hopkins Medicine Institute for Clinical and Translational Research Baltimore MD United States Institute of Health System Science Feinstein Institutes for Medical Research Northwell Health New Hyde Park NY United States Feinstein Institutes for Medical Research Northwell Health Manhasset NY United States Urban Health Collaborative Drexel Dornsife School of Public Health Philadelphia PA United States Biomedical Informatics and Data Science Section Johns Hopkins University School of Medicine Baltimore MD United States Center for Health Care Transformation and Innovation University of Pennsylvania Perelman School of Medicine Philadelphia PA United States Department of Health Outcomes and Biomedical Informatics Institute for Child Health Policy College of Medicine University of Florida Gainesville FL United States Center for the Study of Healthcare Innovation Implementation & Policy VA Greater Los Angeles Healthcare System Los Angeles CA United States Channing Division of Network Medicine Department of Medicine Brigham and Women's Hospital and Harvard Medical School Boston MA United States Department of Computer Science University of Illinois Urbana-Champaign Champaign IL United States Center for Health Disparities Research (CHDR) University of Wisconsin School of Medicine and Public Health Madison WI United States Duke Molecular Physiology Insti

Precision prevention embraces personalized prevention but includes broader factors such as social determinants of health to improve cardiovascular health. The quality, quantity, precision, and diversity of data relatable to individuals and communities continue to expand. New analytical methods can be applied to these data to create tools to attribute risk, which may allow a better understanding of cardiovascular health disparities. Interventions using these analytic tools should be evaluated to establish feasibility and efficacy for addressing cardiovascular disease disparities in diverse individuals and communities. Training in these approaches is important to create the next generation of scientists and practitioners in precision prevention. This state-of-the-art review is based on a workshop convened to identify current gaps in knowledge and methods used in precision prevention intervention research, discuss opportunities to expand trials of implementation science to close the health equity gaps, and expand the education and training of a diverse precision prevention workforce. © 2023 The Authors

关键词： data science health equity health promotion implementation science personalized medicine precision analytics

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INTRODUCTION

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Bulletin of the American Meteorological Society 2024年第8期105卷 S1-S11页

作者： Boyer, T. Blunden, J. Dunn, R.J.H. NOAA/NESDIS National Centers for Environmental Information Silver Spring Maryland NOAA/NESDIS National Centers for Environmental Information Asheville North Carolina Met Office Hadley Centre Exeter United Kingdom European Centre for Medium-Range Weather Forecasts Reading United Kingdom University of Maryland College Park Maryland Scripps Institution of Oceanography University of California San Diego La Jolla California NOAA/NWS National Centers for Environmental Prediction Climate Prediction Center College Park Maryland Brandenburg University of Technology (BTU) Cottbus-Senftenberg Germany Servicio Meteorológico Nacional Buenos Aires Argentina NOAA/OAR Physical Sciences Laboratory Boulder Colorado European Centre for Medium-Range Weather Forecasts Bonn Germany Center for Geophysical Research and School of Physics University of Costa Rica San José Costa Rica Department of Meteorology and National Centre for Earth Observation University of Reading Reading United Kingdom Centro Nacional de Monitoramento e Alertas de Desastres Naturais CEMADEN São Paulo Brazil Université Grenoble Alpes Institut des Géosciences de l’Environnement IRD CNRS Grenoble INP Grenoble France Hampton University Hampton Virginia Seychelles Meteorological Authority Mahe Seychelles Section for Glaciers Ice and Snow Norwegian Water Resources and Energy Directorate Oslo Norway National Research Institute for Agriculture Food and Environment (INRAE) CARRTEL Université Savoie Mont Blanc Thonon les Bains France Graduate School of Agriculture Osaka Metropolitan University Sakai Japan Science Faculty Universidad Nacional de Colombia Bogotá Colombia University of Bremen Bremen Germany NOAA Global Monitoring Laboratory Boulder Colorado Servicio Nacional de Meteorología e Hidrología del Perú Lima Perú Centro de Investigaciones sobre Desertificación – Spanish National Research Council (CSIC-UV-GVA) Valencia Spain Hydro-Climate Extremes Lab (H-CEL) Ghent University Ghent Belgium Department o

In 2023, La Niña conditions that generally prevailed in the eastern Pacific Ocean from mid-2020 into early 2023 gave way to a strong El Niño by October. Atmospheric concentrations of Earth’s major greenhouse gases—carbon dioxide, methane, and nitrous oxide—all increased to record-high levels. The annual global average carbon dioxide concentration in the atmosphere rose to 419.3±0.1 ppm, which is 50% greater than the pre-industrial level. The growth from 2022 to 2023 was 2.8 ppm, the fourth highest in the record since the 1960s. The combined short-term effects of El Niño and the long-term effects of increasing levels of heat-trapping gases in the atmosphere contributed to new records for many essential climate variables reported here. The annual global temperature across land and oceans was the highest in records dating as far back as 1850, with the last seven months (June–December) having each been record warm. Over land, the globally averaged temperature was also record high. Dozens of countries reported record or near-record warmth for the year, including China and continental Europe as a whole (warmest on record), India and Russia (second warmest), and Canada (third warmest). Intense and widespread heatwaves were reported around the world. In Vietnam, an all-time national maximum temperature record of 44.2°C was observed at Tuong Duong on 7 May, surpassing the previous record of 43.4°C at Huong Khe on 20 April 2019. In Brazil, the air temperature reached 44.8°C in Araçuaí in Minas Gerais on 20 November, potentially a new national record and 12.8°C above normal. The effect of rising temperatures was apparent in the cryosphere, where snow cover extent by June 2023 was the smallest in the 56-year record for North America and seventh smallest for the Northern Hemisphere overall. Heatwaves contributed to the greatest average mass balance loss for Alpine glaciers around the world since the start of the record in 1970. Due to rapid volume loss beginning in 2021, St. A

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Age patterns of HIV incidence in eastern and southern Africa: a modelling analysis of observational population-based cohort studies

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The Lancet HIV 2021年第7期8卷 e429-e439页

作者： Risher, Kathryn A Cori, Anne Reniers, Georges Marston, Milly Calvert, Clara Crampin, Amelia Dadirai, Tawanda Dube, Albert Gregson, Simon Herbst, Kobus Lutalo, Tom Moorhouse, Louisa Mtenga, Baltazar Nabukalu, Dorean Newton, Robert Price, Alison J Tlhajoane, Malebogo Todd, Jim Tomlin, Keith Urassa, Mark Vandormael, Alain Fraser, Christophe Slaymaker, Emma Eaton, Jeffrey W MRC Centre for Global Infectious Disease Analysis School of Public Health Imperial College London London UK United Kingdom Department of Population Health London School of Hygiene & Tropical Medicine London UK United Kingdom Medical Research Council/Wits University Rural Public Health and Health Transitions Research Unit (Agincourt) School of Public Health Faculty of Health Sciences University of the Witwatersrand Johannesburg South Africa Institute of Health and Wellbeing University of Glasgow Glasgow UK United Kingdom The Manicaland Centre for Public Health Research Harare Zimbabwe Malawi Epidemiology and Intervention Research Unit Karonga Malawi Biomedical Research and Training Institute Harare Zimbabwe Africa Health Research Institute KwaZulu-Natal Durban South Africa Department of Science and Innovation–Medical Research Council South African Population Research Infrastructure Network Durban South Africa Rakai Health Sciences Program Kalisizo Uganda National Institute for Medical Research Kisesa HDSS Mwanza Tanzania Medical Research Council/Uganda Virus Research Institute and London School of Hygiene & Tropical Medicine Uganda Research Unit Entebbe Uganda Department of Health Sciences University of York York United Kingdom KwaZulu-Natal Research Innovation and Sequencing Platform UKZN Durban South Africa Heidelberg Institute of Global Health Faculty of Medicine University of Heidelberg Heidelberg Germany Oxford Big Data Institute Li Ka Shing Centre for Health Information and Discovery Nuffield Department of Medicine University of Oxford Oxford UK United Kingdom

Background: As the HIV epidemic in sub-Saharan Africa matures, evidence about the age distribution of new HIV infections and how this distribution has changed over the epidemic is needed to guide HIV prevention. We aimed to assess trends in age-specific HIV incidence in six population-based cohort studies in eastern and southern Africa, reporting changes in mean age at infection, age distribution of new infections, and birth cohort cumulative incidence. Methods: We used a Bayesian model to reconstruct age-specific HIV incidence from repeated observations of individuals' HIV serostatus and survival collected among population HIV cohorts in rural Malawi, South Africa, Tanzania, Uganda, and Zimbabwe, in a collaborative analysis of the ALPHA network. We modelled HIV incidence rates by age, time, and sex using smoothing splines functions. We estimated incidence trends separately by sex and study. We used estimated incidence and prevalence results for 2000–17, standardised to study population distribution, to estimate mean age at infection and proportion of new infections by age. We also estimated cumulative incidence (lifetime risk of infection) by birth cohort. Findings: Age-specific incidence declined at all ages, although the timing and pattern of decline varied by study. The mean age at infection was higher in men (cohort mean 27·8–34·6 years) than in women (24·8–29·6 years). Between 2000 and 2017, the mean age at infection per cohort increased slightly: 0·5 to 2·8 years among men and −0·2 to 2·5 years among women. Across studies, between 38% and 63% (cohort medians) of the infections in women were among those aged 15–24 years and between 30% and 63% of infections in men were in those aged 20–29 years. Lifetime risk of HIV declined for successive birth cohorts. Interpretation: HIV incidence declined in all age groups and shifted slightly to older ages. Disproportionate new HIV infections occur among women aged 15–24 years and men aged 20–29 years, supporting focused

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Ten quick tips for deep learning in biology

arXiv

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arXiv 2021年

作者： Lee, Benjamin D. Gitter, Anthony Greene, Casey S. Raschka, Sebastian Maguire, Finlay Titus, Alexander J. Kessler, Michael D. Lee, Alexandra J. Chevrette, Marc G. Stewart, Paul Allen Britto-Borges, Thiago Cofer, Evan M. Yu, Kun-Hsing Carmona, Juan Jose Fertig, Elana J. Kalinin, Alexandr A. Signal, Beth Lengerich, Benjamin J. Triche, Timothy J. Boca, Simina M. In-Q-Tel Labs School of Engineering and Applied Sciences Harvard University Department of Genetics Harvard Medical School United States Department of Biostatistics and Medical Informatics University of Wisconsin-Madison MadisonWI United States Morgridge Institute for Research MadisonWI United States Department of Systems Pharmacology and Translational Therapeutics Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Department of Biochemistry and Molecular Genetics University of Colorado School of Medicine AuroraCO United States Center for Health AI University of Colorado School of Medicine AuroraCO United States Department of Statistics University of Wisconsin Madison United States Faculty of Computer Science Dalhousie University Canada University of New Hampshire Bioeconomy.XYZ United States Department of Oncology Johns Hopkins University United States Institute for Genome Sciences University of Maryland School of Medicine United States Genomics and Computational Biology Graduate Program University of Pennsylvania United States Department of Systems Pharmacology and Translational Therapeutics University of Pennsylvania United States Wisconsin Institute for Discovery Department of Plant Pathology University of Wisconsin-Madison United States Department of Biostatistics and Bioinformatics Moffitt Cancer Center TampaFL United States Section of Bioinformatics and Systems Cardiology Klaus Tschira Institute for Integrative Computational Cardiology University Hospital Heidelberg Germany University Hospital Heidelberg Germany Lewis-Sigler Institute for Integrative Genomics Princeton University PrincetonNJ United States Graduate Program in Quantitative and Computational Biology Princeton University PrincetonNJ United States Department of Biomedical Informatics Harvard Medical School United States Department of Pathology Brigham and Women's Hospital United States Philips Healthcare CambridgeMA United States Philips Research

Machine learning is a modern approach to problem-solving and task automation. In particular, machine learning is concerned with the development and applications of algorithms that can recognize patterns in data and use them for predictive modeling. Artificial neural networks are a particular class of machine learning algorithms and models that evolved into what is now described as deep learning. Given the computational advances made in the last decade, deep learning can now be applied to massive data sets and in innumerable contexts. Therefore, deep learning has become its own subfield of machine learning. In the context of biological research, it has been increasingly used to derive novel insights from high-dimensional biological data. To make the biological applications of deep learning more accessible to scientists who have some experience with machine learning, we solicited input from a community of researchers with varied biological and deep learning interests. These individuals collaboratively contributed to this manuscript's writing using the GitHub version control platform and the Manubot manuscript generation toolset. The goal was to articulate a practical, accessible, and concise set of guidelines and suggestions to follow when using deep learning. In the course of our discussions, several themes became clear: the importance of understanding and applying machine learning fundamentals as a baseline for utilizing deep learning, the necessity for extensive model comparisons with careful evaluation, and the need for critical thought in interpreting results generated by deep learning, among others. © 2021, CC BY.

关键词： Learning algorithms

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ANTARCTICA AND THE SOUTHERN OCEAN

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Bulletin of the American Meteorological Society 2024年第8期105卷 S331-S370页

作者： Clem, K.R. Raphael, M.N. Susheel, Adusumilli Charles, Amory Rebecca, Baiman Alison F, Banwell Sandra, Barreira Rebecca L., Beadling Deniz, Bozkurt Kyle R., Clem Steve, Colwell Lawrence, Coy Rajashree T, Datta Pranab, Deb Jos, De Laat Marcel, du Plessis Denise, Fernandez Ryan L., Fogt Helen A., Fricker Sarah T, Gille Bryan, Johnson Simon A., Josey Linda M., Keller Natalya A, Kramarova Jessica, Kromer Leslie, R., Lait Matthew A., Lazzara Jan L., Lieser Michael, MacFerrin Graeme M., MacGilchrist Michelle L., MacLennan Andreas, Marouchos Robert A., Massom Robert A., Massom Clive R., McMahon David E., Mikolajczyk Thomas L., Mote Paul A., Newman Taylor, Norton Irina, Petropavlovskikh Luciano P., Pezzi Michael, Pitts Marilyn N., Raphael Phillip, Reid Michelle L., Santee Theodore A., Scambos Cristina, Schulz Jia-Rui, Shi Everaldo, Souza Sharon, Stammerjohn Sandy, Thomalla Sarat Chandra, Tripathy Luke D., Trusel Katherine, Turner Ziqi, Yin Jessicca, Allen Amy V., Camper Bridgette O., Haley Gregory, Hammer S. Elizabeth, Love-Brotak Laura, Ohlmann Lukas, Noguchi Deborah B., Riddle Sara W., Veasey School of Geography Environment and Earth Sciences Victoria University of Wellington Wellington New Zealand Department of Geography University of California Los Angeles Los Angeles California Scripps Institution of Oceanography University of California San Diego La Jolla California Université Grenoble Alpes Institut des Géosciences de l’Environnement IRD CNRS Grenoble INP Grenoble France Department of Atmospheric and Oceanic Sciences University of Colorado Boulder Boulder Colorado Earth Science and Observation Center Cooperative Institute for Research in Environmental Sciences (ESOC/CIRES) University of Colorado Boulder Boulder Colorado Argentine Naval Hydrographic Service Buenos Aires Argentina Department of Earth and Environmental Science Temple University Philadelphia Pennsylvania Department of Meteorology University of Valparaíso Valparaíso Chile Center for Climate and Resilience Research (CR)2 Santiago Chile British Antarctic Survey Cambridge United Kingdom Science Systems and Applications Inc. Lanham Maryland NASA Goddard Space Flight Center Greenbelt Maryland Centre for Ocean River Atmosphere and Land Sciences (CORAL) Indian Institute of Technology Kharagpur Kharagpur India Royal Netherlands Meteorological Institute (KNMI) DeBilt The Netherlands Department of Marine Sciences University of Gothenburg Gothenburg Sweden National Institute of Water and Atmospheric Research Ltd. (NIWA) Auckland New Zealand Department of Geography Ohio University Athens Ohio NOAA/OAR Earth System Research Laboratory Global Monitoring Laboratory Boulder Colorado University of Colorado Boulder Boulder Colorado Marine Systems Modelling National Oceanography Center Southampton United Kingdom Department of Atmospheric and Oceanic Sciences University of Wisconsin-Madison Madison Wisconsin Antarctic Meteorological Research and Data Center Space Science and Engineering Center University of Wisconsin-Madison Madison Wisconsin Department of Geography Pennsylvania Sta

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Genome-wide analyses identify 30 loci associated with obsessive–compulsive disorder

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Nature Genetics 2025年 1-13页

作者： Strom, Nora I. Gerring, Zachary F. Galimberti, Marco Yu, Dongmei Halvorsen, Matthew W. Abdellaoui, Abdel Rodriguez-Fontenla, Cristina Sealock, Julia M. Bigdeli, Tim Coleman, Jonathan R. Mahjani, Behrang Thorp, Jackson G. Bey, Katharina Burton, Christie L. Luykx, Jurjen J. Zai, Gwyneth Alemany, Silvia Andre, Christine Askland, Kathleen D. Bäckman, Julia Banaj, Nerisa Barlassina, Cristina Nissen, Judith Becker Bienvenu, O. Joseph Black, Donald Bloch, Michael H. Børte, Sigrid Bosch, Rosa Breen, Michael Brennan, Brian P. Brentani, Helena Buxbaum, Joseph D. Bybjerg-Grauholm, Jonas Byrne, Enda M. Cabana-Dominguez, Judit Camarena, Beatriz Camarena, Adrian Cappi, Carolina Carracedo, Angel Casas, Miguel Cavallini, Maria Cristina Ciullo, Valentina Cook, Edwin H. Crosby, Jesse Cullen, Bernadette A. De Schipper, Elles J. Delorme, Richard Djurovic, Srdjan Elias, Jason A. Estivill, Xavier Falkenstein, Martha J. Fundin, Bengt T. Garner, Lauryn Gironda, Christina Goes, Fernando S. Grados, Marco A. Grove, Jakob Guo, Wei Haavik, Jan Hagen, Kristen Harrington, Kelly Havdahl, Alexandra Höffler, Kira D. Hounie, Ana G. Hucks, Donald Hultman, Christina Janecka, Magdalena Jenike, Eric Karlsson, Elinor K. Kelley, Kara Klawohn, Julia Krasnow, Janice E. Krebs, Kristi Lange, Christoph Lanzagorta, Nuria Levey, Daniel Lindblad-Toh, Kerstin Macciardi, Fabio Maher, Brion Mathes, Brittany McArthur, Evonne McGregor, Nathaniel McLaughlin, Nicole C. Meier, Sandra Miguel, Euripedes C. Mulhern, Maureen Nestadt, Paul S. Nurmi, Erika L. O’Connell, Kevin S. Osiecki, Lisa Ousdal, Olga Therese Palviainen, Teemu Pedersen, Nancy L. Piras, Federica Potluri, Sriramya Rabionet, Raquel Ramirez, Alfredo Rauch, Scott Reichenberg, Abraham Riddle, Mark A. Ripke, Stephan Rosário, Maria C. Sampaio, Aline S. Schiele, Miriam A. Skogholt, Anne Heidi Sloofman, Laura G. Smit, Jan Artigas, María Soler Thomas, Laurent F. Tifft, Eric Vallada, Homero van Kirk, Nathanial Veenstra-VanderWeele, Jeremy Vulink, Nienke N. Walker, Christopher P. Wang, Ying Wendland, Jens R. Winsvold Department of Psychology Humboldt-Universität zu Berlin Berlin Germany Department of Psychiatric Phenomics and Genomics (IPPG) Ludwig-Maximilians University Munich Munich Germany Centre for Psychiatry Research Department of Clinical Neuroscience Karolinska Institutet and Stockholm Health Services Region Stockholm Stockholm Sweden Department of Biomedicine Aarhus University Aarhus Denmark University Hospital of Psychiatry and Psychotherapy University of Bern Bern Switzerland Department of Mental Health and Neuroscience Translational Neurogenomics QIMR Berghofer Medical Research Institute Brisbane QLD Australia Department of Population Health and Immunity Healthy Development and Ageing Walter and Eliza Hall Institute of Medical Research Melbourne VIC Australia Department of Psychiatry Human Genetics Yale University New Haven CT United States VA Connecticut Healthcare System West Haven CT United States Department of Center for Genomic Medicine Psychiatric and Neurodevelopmental Genetics Unit Massachusetts General Hospital Boston MA United States Stanley Center for Psychiatric Research Broad Institute of MIT and Harvard Cambridge MA United States Department of Genetics University of North Carolina at Chapel Hill Chapel Hill NC United States Department of Psychiatry Amsterdam University Medical Centers University of Amsterdam Amsterdam Netherlands Center for Research in Molecular Medicine and Chronic Diseases (CiMUS) Genomics and Bioinformatics University of Santiago de Compostela Santiago de Compostela Spain Grupo de Medicina Xenómica Genetics Instituto de Investigación Sanitaria de Santiago de Compostela (FIDIS) Santiago de Compostela Spain Vanderbilt Genetics Institute Vanderbilt University Nashville TN United States Department of Psychiatry and Behavioral Sciences SUNY Downstate Health Sciences University Brooklyn NY United States VA NY Harbor Healthcare System Brooklyn NY United States Social Genetic and Developmental Psychiatry Centre Insti

Obsessive–compulsive disorder (OCD) affects ~1% of children and adults and is partly caused by genetic factors. We conducted a genome-wide association study (GWAS) meta-analysis combining 53,660 OCD cases and 2,044,417 controls and identified 30 independent genome-wide significant loci. Gene-based approaches identified 249 potential effector genes for OCD, with 25 of these classified as the most likely causal candidates, including WDR6, DALRD3 and CTNND1 and multiple genes in the major histocompatibility complex (MHC) region. We estimated that ~11,500 genetic variants explained 90% of OCD genetic heritability. OCD genetic risk was associated with excitatory neurons in the hippocampus and the cortex, along with D₁ and D₂ type dopamine receptor-containing medium spiny neurons. OCD genetic risk was shared with 65 of 112 additional phenotypes, including all the psychiatric disorders we examined. In particular, OCD shared genetic risk with anxiety, depression, anorexia nervosa and Tourette syndrome and was negatively associated with inflammatory bowel diseases, educational attainment and body mass index. © The Author(s) 2025.

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Deciphering seasonal depression variations and interplays between weather changes, physical activity, and depression severity in real-world settings: Learnings from RADAR-MDD longitudinal mobile health study

arXiv

引用

arXiv 2024年

作者： Zhang, Yuezhou Folarin, Amos A. Ranjan, Yatharth Cummins, Nicholas Rashid, Zulqarnain Conde, Pauline Stewart, Callum Sun, Shaoxiong Vairavan, Srinivasan Matcham, Faith Oetzmann, Carolin Siddi, Sara Lamers, Femke Simblett, Sara Wykes, Til Mohr, David C. Haro, Josep Maria Penninx, Brenda W.J.H. Narayan, Vaibhav A. Hotopf, Matthew Dobson, Richard J.B. Pratap, Abhishek Department of Biostatistics & Health Informatics Institute of Psychiatry Psychology and Neuroscience King's College London London United Kingdom Institute of Health Informatics University College London London United Kingdom NIHR Biomedical Research Centre at South London and Maudsley NHS Foundation Trust London United Kingdom Health Data Research UK London University College London London United Kingdom NIHR Biomedical Research Centre University College London Hospitals NHS Foundation Trust London United Kingdom Department of Computer Science University of Sheffield Sheffield United Kingdom Janssen Research and Development LLC TitusvilleNJ United States Department of Psychological Medicine Institute of Psychiatry Psychology and Neuroscience King's College London London United Kingdom School of Psychology University of Sussex Falmer United Kingdom Teaching Research and Innovation Unit Parc Sanitari Sant Joan de Déu Fundació Sant Joan de Déu Barcelona Spain Centro de Investigación Biomédica en Red de Salud Mental Madrid Spain Department of Psychiatry Amsterdam UMC Vrije Universiteit Amsterdam Netherlands Mental Health Program Amsterdam Public Health Research Institute Amsterdam Netherlands Department of Psychology Institute of Psychiatry Psychology and Neuroscience King's College London London United Kingdom South London and Maudsley NHS Foundation Trust London United Kingdom Center for Behavioral Intervention Technologies Department of Preventive Medicine Northwestern University ChicagoIL United States Boehringer Ingelheim Pharmaceuticals Inc. RidgefieldCT United States University of Washington Seattle United States

Prior research has shown that changes in seasons and weather can have a significant impact on depression severity. However, findings are inconsistent across populations, and the interplay between weather, behavior, and depression has not been fully quantified. This study analyzed real-world data from 428 participants (a subset;68.7% of the cohort) in the RADAR-MDD longitudinal mobile health study to investigate seasonal variations in depression (measured through a remote validated assessment - PHQ-8) and examine the potential interplay between dynamic weather changes, physical activity (monitored via wearables), and depression severity. The clustering of PHQ-8 scores identified four distinct seasonal variations in depression severity: one stable trend and three varying patterns where depression peaks in different seasons. Among these patterns, participants within the stable trend had the oldest average age (p=0.002) and the lowest baseline PHQ-8 score (p=0.003). Mediation analysis assessing the indirect effect of weather on physical activity and depression showed significant differences among participants with different affective responses to weather. Specifically, the temperature and day length significantly influenced depression severity, which in turn impacted physical activity levels (p Copyright © 2024, The Authors. All rights reserved.

关键词： Radar measurement

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