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Look into my eyes:What can eye-based measures tell us about the relationship between physical activity and cognitive performance?

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Journal of Sport and Health Science 2023年第5期12卷 568-591页

作者： Liye Zou Fabian Herold Sebastian Ludyga Keita Kamijo Notger G.Muller Matthew B.Pontifex Matthew Heath Ryuta Kuwamizu Hideaki Soya Charles H.Hillman Soichi Ando Brandon L.Alderman Boris Cheval Arthur F.Kramer Body-Brain-Mind Laboratory School of PsychologyShenzhen UniversityShenzhen 518060China Research Group Degenerative and Chronic Diseases MovementFaculty of Health Sciences BrandenburgUniversity of PotsdamPotsdam 14476Germany Department of Sport Exerciseand HealthUniversity of BaselBasel 4052Switzerland Faculty of Liberal Arts and Sciences Chukyo UniversityNagoya 466-8666Japan Department of Kinesiology Michigan State UniversityEast LansingMI 48824USA School of Kinesiology Faculty of Health SciencesUniversity of Western OntarioLondon ON N6A 3K7Canada Canadian Centre for Activity and Aging University of Western OntarioLondon ONN6A 3K7Canada Graduate Program in Neuroscience University of Western OntarioLondon ONN6A 3K7Canada Laboratory of Exercise Biochemistry and Neuroendocrinology Faculty of Health and Sport SciencesUniversity of TsukubaTsukuba 305-0006Japan Sport Neuroscience Division Advanced Research Initiative for Human High Performance(ARIHHP)Faculty of Health and Sport SciencesUniversity of TsukubaTsukuba 305-0006Japan Center for Cognitive and Brain Health Department of PsychologyDepartment of Physical TherapyMovementand Rehabilitation SciencesNortheastern UniversityBostonMA 02115USA Graduate School of Informatics and Engineering The University of Electro-CommunicationsTokyo 182-8585Japan Department of Kinesiology and Health Rutgers UniversityNew BrunswickNew BrunswickNJ 08854USA Swiss Center for Affective Sciences University of GenevaGeneva 1205Switzerland Laboratory for the Study of Emotion Elicitation and Expression(E3Lab) Department of PsychologyUniversity of GenevaGeneva 1205Switzerland Department of Psychology Center for Cognitive and Brain HealthNortheastern UniversityBostonMA 02115USA Beckman Institute University of Illinois at Urbana-ChampaignChampaignIL 61820USA

Background:There is a growing interest to understand the neurobiological mechanisms that drive the positive associations of physical activity and fitness with measures of cognitive *** better understand those mechanisms,several studies have employed eye-based measures(e.g., eye movement measures such as saccades,pupillary measures such as pupil dilation,and vascular measures such as retinal vessel diameter)deemed to be proxies for specific neurobiological ***,there is currently no systematic review providing a comprehensive overview of these studies in the field of exercise-cognition ***,this review aimed to address that gap in the ***:To identify eligible studies,we searched 5 electronic databases on October 23,*** researchers independently extracted data and assessed the risk of bias using a modified version of the Tool for the assEssment of study qualiTy and reporting in EXercise(TESTEX scale,for interventional studies) and the critical appraisal tool from the Joanna Briggs Institute(for cross-sectional studies).Results:Our systematic review(n=35 studies) offers the following main findings:(a) there is insufficient evidence available to draw solid conclusions concerning gaze-fixation-based measures;(b) the evidence that pupillometric measures,which are a proxy for the noradrenergic system,can explain the positive effect of acute exercise and cardiorespiratory fitness on cognitive performance is mixed;(c) physical training-or fitness-related changes of the cerebrovascular system(operationalized via changes in retinal vasculature) are,in general,positively associated with cognitive performance improvements;(d) acute and chronic physical exercises show a positive effect based on an oculomotor-based measure function(operationalized via antisaccade tasks);and(e) the positive association between cardiorespiratory fitness and cognitive performance is partly mediated by the dopaminergic system(operationalized via spo

关键词： Cognition Exercise Fitness Pupil size RetinaTagedAPTARAEnd

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Digital Storytelling of Physics (DiS-Phy): Learning Physics from Home Through Stories

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Journal of Physics: Conference Series 2021年第1期2019卷

作者： L A Sanjaya E A Putri F C Wibowo D K Robby R W D Puspa W A Pertiwi L K Sianipar W Qoiriyah H Sitompul Department of Physics Education Universitas Negeri Jakarta Jakarta 13220 Indonesia Department of Management Education Universitas Negeri Jakarta Jakarta 13220 Indonesia Kartika VIII-1 Senior High School Jakarta Indonesia Management of Islamic Education STAI AHSANTA Jelutung Kota Jambi 36125 Jambi Indonesia Department of Physics Education Universitas Pelita Harapan Tangerang 15811 Indonesia Informatics Engineering study program Faculty of Engineering Sheikh Yusuf Islamic University Tangerang Indonesia

This study aims to develop a product such as "Digital storytelling of Physics (DiS-Phy)" that can be used as a physics' educational media for distance learning (PJJ) on magnetic field material. DiS-Phy developed as a digital storytelling media which is able to combine several learning media such as writing, pictures and learning videos in one medium. The developed DiS-Phy also equipped with scientific literacy questions. DiS-Phy can be accessed on all types of distance learning devices such as smartphones, laptops and tablets that are connected to the internet. DiS-Phy including the type of research and development (Research & Development) which refers to the ASSURE model. The result of validation test shows the percentages of 91,07%, according to subject-matter expert, 93,16%, according to media expert and 87,50% according to learning expert. DiS-Phy also tested by 4 physics teachers at senior high school in Jakarta that achieved 92,57% points that make this product has been qualified as a worthy physics' distance learning device. Also of 35 as respondents in this study shows the percentages of 87,48% points that make this product worthy to use as an educational media. Based on the results, it can be concluded that the DiS-Phy has been qualified as a worthy physics' distance learning device for teachers and students.

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In silico analysis of tetrahydroxycurcumin compound in tumeric (Curcuma longa) as inhibitor of SARS-CoV-2 virus replication

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AIP Conference Proceedings 2023年第1期2748卷

作者： Mohamad Amin Nanda Hilda Khikmawati Suryadi Suryadi Rifcha Fachrunnisa Ihya Fakhrurizal Amin Dina Maulina Ahya Zhilalikbar Amin Nik Ahmad Nizam Nik Malek Restu Fitriani Department of Biology Faculty of Mathematics and Natural Sciences Universitas Negeri Malang Jl. Semarang No.5 Malang 65145 Indonesia Special Education Department Faculty of Education Universitas Negeri Malang Jl. Semarang No.5 Malang 65145 Indonesia Faculty of Medicine Universitas Indonesia Jakarta Indonesia Biology Education Study Program Faculty of Teacher Training and Education Universitas Lampung Lampung Indonesia Department of Biosciences Faculty of Science Universiti Teknologi Malaysia Johor Malaysia Study Program of Informatics Engineering Faculty of Computer Sciencies Universitas Brawijaya Malang Indonesia Biotechnology Program Faculty of Mathematics and Natural Sciences Universitas Negeri Malang Jl. Semarang No.5 Malang 65145 Indonesia

There are many uses of natural ingredients to find out their potential in overcoming Coronavirus Disease 2019. One of the plants that is often used to treat various diseases, including SARS-Cov2 infection is turmeric. Turmeric contains Tetrahydroxycurcumin compounds. The aim of this research was to study the potential of Tetrahydroxycurcumin in preventing infection with the SARS-CoV-2 virus. The results showed that Tetrahydroxicurcumin has the potential to inhibit Tyrosine protein kinase ABL1, which is a protein that plays a role in the preparation of the cytoskeleton during viral infection. The binding affinity of Tetrahydroxycurcumin for this protein is -8.4 kcal/mol.

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Grasping and Attached Mode in Human-Computer Interaction in the study of Mouse Substitution

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Journal of Physics: Conference Series 2019年第1期1196卷

作者： R B Widodo R M Quita S Amrizal R S Gunawan C Wada A Ardiansyah I Sutrisno M B Rahmat A Z Arfianto C R Handoko A D Santoso A W B Santosa V Y P Ardhana E Setiawan Informatics Engineering Study Program Ma Chung University Malang 65151 Indonesia Department of Mathematics Faculty of Science National Central University Taoyuan City 32001 Taiwan Graduate School of Life Science and Systems Engineering Kyushu Institute of Technology Wakamatsu Fukuoka 808-0196 Japan Politeknik Pelayaran Surabaya Gunung Anyar 60294 Surabaya Indonesia Department of Marine Electrical Engineering Politeknik Perkapalan Negeri Surabaya Sukolilo Surabaya 60111 Indonesia Departemen Teknik Perkapalan Fakultas Teknik Universitas Diponegoro Semarang 50275 Indonesia Universitas Qamarul Huda Badaruddin Bagu Central Lombok Indonesia

Some disabilities caused by sensory malfunction, accident, or congenital disorder could cause people difficulty in grasping the computer mouse. A well-known substitute uses an inertial sensor to monitor the body's orientation. This paper evaluates the efficiency and effectiveness of different methods of holding the sensor. There are two modes of holding the sensor, the grasping mode and the attached mode; we also evaluate a mouse as a baseline. The attached mode works by placing the sensor on the back of user's hand. The quantitative and qualitative evaluation procedure is based on ISO/TS 9241 part 411: Ergonomics of human-system interaction standard. The test consists of four levels of difficulty and indicates that the throughput and task completion times were not statistically different between grasping and attached mode. We also found that grasping mode and attached mode did not show significantly statistical differences in comfort and fatigue based on the questionnaires. The results also suggest that the orientation of the hand used in grasping and attached modes is suitable only for lower-impact computer use.

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GIS modeling using fuzzy logic approach in mineral prospecting based on geophysical data

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AIP Conference Proceedings 2016年第1期1711卷

作者： Harman Setyadi Lilik Eko Widodo Sudarto Notosiswoyo Putri Saptawati Arief Ismanto Iip Hardjana 1Doctorate Student Mine Engineering Study Program – Faculty of Mine and Petroleum Engineering Institute Technology Bandung Jl. Ganesa 10 Bandung 40132 - INDONESIA 2Earth Resources Exploration Group - Faculty of Mine and Petroleum Engineering Institute Technology Bandung Jl. Ganesa 10 Bandung 40132 – INDONESIA 3Informatics– STIE Institute Technology Bandung Jl. Ganesa 10 Bandung 40132 - INDONESIA 4J. Resources Nusantara Equity Tower 48th floor SCBD. Jl. Jendral Sudirman Kav 52-53 Jakarta 12190 - INDONESIA

The geophysical exploration method is the superior over the project area due to the dense of vegetation and thick soil so very limited geological outcrops. Contrast of physical properties of every different rock type should be able to be distinguished by the geophysical data. Fuzzy logic approach and weight of evidence were used for geophysical data modeling. Posterior probability was used to calculate the weight of evidence (WofE) of every fuzzy map memberships. By combining each rock type model, the model provides better result compared from the model from mixed rock type on the data training. This method is able to eliminate the potential interference of different geophysical signature. So that, the understanding the geological feature of the area is key success for the mineral prosperity modeling. We verified the model by site visiting and drilling and it is estimated about 90% confident.

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Molecular docking study of fatty acids from Pliek U Oil in the inhibition of SARS-CoV-2 protein and enzymes

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IOP Conference Series: Materials Science and engineering 2021年第1期1087卷

作者： K Khairan R Idroes S G Tumilaar T E Tallei G M Idroes F Rahmadhany M U Futri N M Dinura S Mauliza M Diana C P Maisarah A Maulana T R Noviandy R Suhendra Muslem N Earlia Department of Pharmacy Universitas Syiah Kuala Banda Aceh 23111 Indonesia Department of Chemistry Universitas Syiah Kuala Banda Aceh 23111 Indonesia Pharmacy Study Program Universitas Sam Ratulangi Manado 95115 Indonesia Department of Biology Universitas Sam Ratulangi Manado 95115 Indonesia Department of Chemical Engineering UniversitasSyiah Kuala Banda Aceh 23111 Indonesia Department of Informatics Universitas Syiah Kuala Banda Aceh 23111 Indonesia Department of Chemistry Universitas Islam Negeri Ar-Raniry Banda Aceh 23111 Indonesia General Hospital ZainoelAbidin Aceh Province Indonesia 24415

This study aimed to analyze the fatty acid inPliek U oil andevaluate its inhibitor potential based on the interaction to several proteins and enzymes in SARS-CoV-2 using the in silico approach. Pliek U oil containing capric acid, caprylic acid, lauric acid, linoleic acid, myristic acid, oleic acid, and palmitic acids, with oleic acid as a dominant substance. Molecular docking analysis showed that linoleic acid has the best interaction to the receptors with the lowest binding affinity to 3CLpro (6LU7), Spike protein (6VXX), PLpro (6WX4), RdRp (6M71), E protein (5X29), and Spike Ectodomain Structure (6VYB) of -4.9, -5.8, -4.7, -4.3, -5.3 and -5.5 kcal/mol, respectively. The finding suggests that the binding of linoleic acid to the SARS-CoV-2 protein and enzyme may cause impairment of viral attachments to host cells, thus reducing infectivity in COVID-19 patients.

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25th Annual Computational Neuroscience Meeting CNS-2016, Seogwipo City, South Korea, July 2-7, 2016 Abstracts

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BMC NEUROSCIENCE 2016年第1期17卷 1-112页

作者： [Anonymous] Computational Neurobiology Laboratory The Salk Institute for Biological Studies San Diego USA UNIC CNRS Gif sur Yvette France The European Institute for Theoretical Neuroscience (EITN) Paris France ATR Computational Neuroscience Laboratories Kyoto Japan Krembil Research Institute University Health Network Toronto Canada Department of Physiology University of Toronto Toronto Canada Department of Medicine (Neurology) University of Toronto Toronto Canada Department of Physics University of New Hampshire Durham USA Department of Neurophysiology Nencki Institute of Experimental Biology Warsaw Poland Department of Theory Wigner Research Centre for Physics of the Hungarian Academy of Sciences Budapest Hungary Department of Mathematical Sciences KAIST Daejoen Republic of Korea Department of Mathematics University of Houston Houston USA Department of Biochemistry & Cell Biology and Institute of Biosciences and Bioengineering Rice University Houston USA Department of Biology and Biochemistry University of Houston Houston USA Grupo de Neurocomputación Biológica Dpto. de Ingeniería Informática Escuela Politécnica Superior Universidad Autónoma de Madrid Madrid Spain Department of Biological Sciences University of Southern California Los Angeles USA Center for Neuroscience Korea Institute of Science and Technology Seoul South Korea Department of Neurology Albert Einstein College of Medicine Bronx USA Center for Neuroscience KIST Seoul South Korea Department of Neuroscience University of Science and Technology Daejon South Korea Systems Neuroscience Group QIMR Berghofer Medical Research Institute Herston Australia Department of Psychology Yonsei University Seoul South Korea Department of Psychiatry Kyung Hee University Hospital at Gangdong Seoul South Korea Department of Psychiatry Veterans Administration Boston Healthcare System and Harvard Medical School Brockton USA Department of Electrical and Electronic Engineering The University of Melbourne Parkvil

A1 Functional advantages of cell-type heterogeneity in neural circuits Tatyana O. Sharpee A2 Mesoscopic modeling of propagating waves in visual cortex Alain Destexhe A3 Dynamics and biomarkers of mental disorders Mitsuo Kawato F1 Precise recruitment of spiking output at theta frequencies requires dendritic h-channels in multi-compartment models of oriens-lacunosum/moleculare hippocampal interneurons Vladislav Sekulić, Frances K. Skinner F2 Kernel methods in reconstruction of current sources from extracellular potentials for single cells and the whole brains Daniel K. Wójcik, Chaitanya Chintaluri, Dorottya Cserpán, Zoltán Somogyvári F3 The synchronized periods depend on intracellular transcriptional repression mechanisms in circadian clocks. Jae Kyoung Kim, Zachary P. Kilpatrick, Matthew R. Bennett, Kresimir Josić O1 Assessing irregularity and coordination of spiking-bursting rhythms in central pattern generators Irene Elices, David Arroyo, Rafael Levi, Francisco B. Rodriguez, Pablo Varona O2 Regulation of top-down processing by cortically-projecting parvalbumin positive neurons in basal forebrain Eunjin Hwang, Bowon Kim, Hio-Been Han, Tae Kim, James T. McKenna, Ritchie E. Brown, Robert W. McCarley, Jee Hyun Choi O3 Modeling auditory stream segregation, build-up and bistability James Rankin, Pamela Osborn Popp, John Rinzel O4 Strong competition between tonotopic neural ensembles explains pitch-related dynamics of auditory cortex evoked fields Alejandro Tabas, André Rupp, Emili Balaguer-Ballester O5 A simple model of retinal response to multi-electrode stimulation Matias I. Maturana, David B. Grayden, Shaun L. Cloherty, Tatiana Kameneva, Michael R. Ibbotson, Hamish Meffin O6 Noise correlations in V4 area correlate with behavioral performance in visual discrimination task Veronika Koren, Timm Lochmann, Valentin Dragoi, Klaus Obermayer O7 Input-location dependent gain modulation in cerebellar nucleus neurons Maria Psarrou, Maria Schilstra, Neil Davey, Benjamin Torben-Ni

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Scientific discovery in the age of artificial intelligence

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NATURE 2023年第7978期621卷 E33-E33页

作者： Wang, Hanchen Fu, Tianfan Du, Yuanqi Gao, Wenhao Huang, Kexin Liu, Ziming Chandak, Payal Liu, Shengchao Van Katwyk, Peter Deac, Andreea Anandkumar, Anima Bergen, Karianne Gomes, Carla P. Ho, Shirley Kohli, Pushmeet Lasenby, Joan Leskovec, Jure Liu, Tie-Yan Manrai, Arjun Marks, Debora Ramsundar, Bharath Song, Le Sun, Jimeng Tang, Jian Velickovic, Petar Welling, Max Zhang, Linfeng Coley, Connor W. Bengio, Yoshua Zitnik, Marinka Department of Engineering University of Cambridge Cambridge UK Department of Computing and Mathematical Sciences California Institute of Technology Pasadena CA USA NVIDIA Santa Clara CA USA Department of Computational Science and Engineering Georgia Institute of Technology Atlanta GA USA Department of Computer Science Cornell University Ithaca NY USA Department of Chemical Engineering Massachusetts Institute of Technology Cambridge MA USA Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology Cambridge MA USA Department of Computer Science Stanford University Stanford CA USA Department of Physics Massachusetts Institute of Technology Cambridge MA USA Harvard-MIT Program in Health Sciences and Technology Cambridge MA USA Mila – Quebec AI Institute Montreal Quebec Canada Université de Montréal Montreal Quebec Canada HEC Montréal Montreal Quebec Canada CIFAR AI Chair Toronto Ontario Canada Department of Earth Environmental and Planetary Sciences Brown University Providence RI USA Data Science Institute Brown University Providence RI USA Center for Computational Astrophysics Flatiron Institute New York NY USA Department of Astrophysical Sciences Princeton University Princeton NJ USA Department of Physics Carnegie Mellon University Pittsburgh PA USA Department of Physics and Center for Data Science New York University New York NY USA Google DeepMind London UK Department of Computer Science and Technology University of Cambridge Cambridge UK Microsoft Research Beijing China Department of Biomedical Informatics Harvard Medical School Boston MA USA Broad Institute of MIT and Harvard Cambridge MA USA Harvard Data Science Initiative Cambridge MA USA Kempner Institute for the Study of Natural and Artificial Intelligence Harvard University Cambridge MA USA Department of Systems Biology Harvard Medical School Boston MA USA Deep Forest Sciences Palo Alto CA USA BioMap Beijing China Mo

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BigNeuron: a resource to benchmark and predict performance of algorithms for automated tracing of neurons in light microscopy datasets

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Nature methods 2023年第6期20卷 824-835页

作者： Linus Manubens-Gil Zhi Zhou Hanbo Chen Arvind Ramanathan Xiaoxiao Liu Yufeng Liu Alessandro Bria Todd Gillette Zongcai Ruan Jian Yang Miroslav Radojević Ting Zhao Li Cheng Lei Qu Siqi Liu Kristofer E Bouchard Lin Gu Weidong Cai Shuiwang Ji Badrinath Roysam Ching-Wei Wang Hongchuan Yu Amos Sironi Daniel Maxim Iascone Jie Zhou Erhan Bas Eduardo Conde-Sousa Paulo Aguiar Xiang Li Yujie Li Sumit Nanda Yuan Wang Leila Muresan Pascal Fua Bing Ye Hai-Yan He Jochen F Staiger Manuel Peter Daniel N Cox Michel Simonneau Marcel Oberlaender Gregory Jefferis Kei Ito Paloma Gonzalez-Bellido Jinhyun Kim Edwin Rubel Hollis T Cline Hongkui Zeng Aljoscha Nern Ann-Shyn Chiang Jianhua Yao Jane Roskams Rick Livesey Janine Stevens Tianming Liu Chinh Dang Yike Guo Ning Zhong Georgia Tourassi Sean Hill Michael Hawrylycz Christof Koch Erik Meijering Giorgio A Ascoli Hanchuan Peng Institute for Brain and Intelligence Southeast University Nanjing China. Microsoft Corporation Redmond WA USA. Tencent AI Lab Bellevue WA USA. Computing Environment and Life Sciences Directorate Argonne National Laboratory Lemont IL USA. Kaya Medical Seattle WA USA. University of Cassino and Southern Lazio Cassino Italy. Center for Neural Informatics Structures and Plasticity Krasnow Institute for Advanced Study George Mason University Fairfax VA USA. Faculty of Information Technology Beijing University of Technology Beijing China. Beijing International Collaboration Base on Brain Informatics and Wisdom Services Beijing China. Nuctech Netherlands Rotterdam the Netherlands. Janelia Research Campus Howard Hughes Medical Institute Ashburn VA USA. Department of Electrical and Computer Engineering University of Alberta Edmonton Alberta Canada. Ministry of Education Key Laboratory of Intelligent Computation and Signal Processing Anhui University Hefei China. Paige AI New York NY USA. Scientific Data Division and Biological Systems and Engineering Division Lawrence Berkeley National Lab Berkeley CA USA. Helen Wills Neuroscience Institute and Redwood Center for Theoretical Neuroscience UC Berkeley Berkeley CA USA. RIKEN AIP Tokyo Japan. Research Center for Advanced Science and Technology (RCAST) The University of Tokyo Tokyo Japan. School of Computer Science University of Sydney Sydney New South Wales Australia. Texas A&M University College Station TX USA. Cullen College of Engineering University of Houston Houston TX USA. Graduate Institute of Biomedical Engineering National Taiwan University of Science and Technology Taipei Taiwan. National Centre for Computer Animation Bournemouth University Poole UK. PROPHESEE Paris France. Department of Neuroscience Columbia University New York NY USA. Mortimer B. Zuckerman Mind Brain Behavior Institute Columbia University New York NY USA. Department of Computer Science Northern Illinois Universit

BigNeuron is an open community bench-testing platform with the goal of setting open standards for accurate and fast automatic neuron tracing. We gathered a diverse set of image volumes across several species that is representative of the data obtained in many neuroscience laboratories interested in neuron tracing. Here, we report generated gold standard manual annotations for a subset of the available imaging datasets and quantified tracing quality for 35 automatic tracing algorithms. The goal of generating such a hand-curated diverse dataset is to advance the development of tracing algorithms and enable generalizable benchmarking. Together with image quality features, we pooled the data in an interactive web application that enables users and developers to perform principal component analysis, t-distributed stochastic neighbor embedding, correlation and clustering, visualization of imaging and tracing data, and benchmarking of automatic tracing algorithms in user-defined data subsets. The image quality metrics explain most of the variance in the data, followed by neuromorphological features related to neuron size. We observed that diverse algorithms can provide complementary information to obtain accurate results and developed a method to iteratively combine methods and generate consensus reconstructions. The consensus trees obtained provide estimates of the neuron structure ground truth that typically outperform single algorithms in noisy datasets. However, specific algorithms may outperform the consensus tree strategy in specific imaging conditions. Finally, to aid users in predicting the most accurate automatic tracing results without manual annotations for comparison, we used support vector machine regression to predict reconstruction quality given an image volume and a set of automatic tracings.

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Genome-wide association study of long COVID

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Nature Genetics 2025年 1-16页

作者： Vilma Lammi Tomoko Nakanishi Samuel E. Jones Juha Karjalainen Martin Broberg Hele H. Haapaniemi Matti Pirinen Mari E. K. Niemi Mattia Cordioli Mark J. Daly Andrea Ganna Hanna M. Ollila J. Brent Richards Yukinori Okada Shea J. Andrews Beatriz Cortés Rafael de Cid Susana Iraola-Guzmán Natalia Blay Xavier Farré Heath E. O’Brien Thompson Hannah Patrick J. Short Ana Ochoa-Guzman Michelle Duran-Gomez Brian E. Fulton-Howard Masahiro Kanai Axel Schmidt Daniella Balla Julia Heggemann Sonja Schultz Pari Behzad Markus M. Nöthen Abigail Miller Kerstin U. Ludwig Ruth E. Mitchell George Davey Smith Nicholas J. Timpson Abdou Mousas Evangelos Evangelou Evangelia Ntzani Konstantinos K. Tsilidis Massimo Mangino Alicia Huerta-Chagoya Nasa Sinnott-Armstrong Elizabeth T. Cirulli Francisco Tanudjaja Efren Sandoval Nicole L. Washington Simon White Alexandre Bolze Kelly M. Schiabor Barrett Marc Vaudel Alex S. F. Kwong Amit K. Maiti Minttu M. Marttila Daniel C. Posner Alexis A. Rodriguez Chiara Batini Beatriz Guillen-Guio Olivia C. Leavy Anna L. Guyatt Catherine John Louise V. Wain Francesca Minnai Anna R. Dearman Benedict Hignell Meena Kumari C. A. Robert Warmerdam Lude H. Franke Celia B. Sequeros Søren Brunak David Westergaard Karina Banasik Thomas W. Winkler Iris M. Heid Daniel M. Jordan Ryan C. Thompson Alexander W. Charney Laura G. Sloofman Nicole W. Simons Noam D. Beckmann Raimonds Rešcenko Vita Rovite Peculis Raitis Monta Briviba Janis Klovinš Laura Ansone Lorenzo Miano Flora Peyvandi Francesco Malvestiti Nicola Montano Alessandra Bandera Francesco Bruno Arturo Blasi Jacqueline M. Lane Jakob M. Cherry Ryan K. Chung Laura Carvajal-Silva Kevin Aguilar-Valdés Leslie C. Cerpa Tamara V. Arévalo Eduardo Lamoza Alicia Colombo Ricardo A. Verdugo Erika Frangione Xu Xinyi Jennifer Taher Jordan Lerner-Ellis Lindsay Guare Shefali S. Verma Ekaterina Vergasova Michil Trofimov Layal Shaheen Nikolay Plotnikov Anna Kim Dmitrii Kharitonov Alexei Kamelin Alexander Rakitko Eirini Marouli Pasquale Striano Ummu Afeera Zainulabid Ashutosh Kumar Hajar Fa Institute for Molecular Medicine Finland (FIMM) Helsinki Institute of Life Science (HiLIFE) University of Helsinki Helsinki Finland Department of Human Genetics McGill University Montreal Quebec Canada Centre for Clinical Epidemiology Department of Medicine Lady Davis Institute Jewish General Hospital McGill University Montreal Quebec Canada Kyoto-McGill International Collaborative Program in Genomic Medicine Graduate School of Medicine Kyoto University Kyoto Japan Department of Genome Informatics Graduate School of Medicine the University of Tokyo Tokyo Japan Research Fellow Japan Society for the Promotion of Science Tokyo Japan Program in Medical and Population Genetics Broad Institute of Harvard and MIT Cambridge MA USA Stanley Center for Psychiatric Research Broad Institute of Harvard and MIT Cambridge MA USA Analytic and Translational Genetics Unit Massachusetts General Hospital Boston MA USA Department of Mathematics and Statistics University of Helsinki Helsinki Finland Department of Public Health University of Helsinki Helsinki Finland Broad Institute Cambridge MA USA Analytical and Translational Genetics Unit Massachusetts General Hospital Harvard Medical School Boston MA USA Center for Genomic Medicine Massachusetts General Hospital Boston MA USA Anesthesia Critical Care and Pain Medicine Massachusetts General Hospital and Harvard Medical School Boston MA USA Broad Institute of MIT and Harvard Cambridge MA USA Department of Twin Research King’s College London London UK 5 Prime Sciences Inc Montreal Quebec Canada Department of Epidemiology Biostatistics and Occupational Health McGill University Montreal Quebec Canada Department of Statistical Genetics Osaka University Graduate School of Medicine Suita Japan Laboratory for Systems Genetics RIKEN Center for Integrative Medical Sciences Yokohama Japan Laboratory of Statistical Immunology Immunology Frontier Research Center (WPI-IFReC) Osaka University Suita Japan Departmen

Infections can lead to persistent symptoms and diseases such as shingles after varicella zoster or rheumatic fever after streptococcal infections. Similarly, severe acute respiratory syndrome coronavirus 2 (SARS‑CoV‑2) infection can result in long coronavirus disease (COVID), typically manifesting as fatigue, pulmonary symptoms and cognitive dysfunction. The biological mechanisms behind long COVID remain unclear. We performed a genome-wide association study for long COVID including up to 6,450 long COVID cases and 1,093,995 population controls from 24 studies across 16 countries. We discovered an association ofFOXP4with long COVID, independent of its previously identified association with severe COVID-19. The signal was replicated in 9,500 long COVID cases and 798,835 population controls. Given the transcription factor FOXP4’s role in lung physiology and pathology, our findings highlight the importance of lung function in the pathophysiology of long COVID.

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