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2018 Workshop on Multidisciplinary and Its Applications: Applied Mathematics, Computer Science, Information systems, and Information Technology, WMA-Mathcomtech 2018

作者： Mustakim Herianda, Della Maulina Ilham, Ahmad Daeng Gs, Achmad Laumal, Folkes E. Kurniasih, Nuning Iskandar, Akbar Manulangga, Gloria Indra Iswara, Ida Bagus Ary Rahim, Robbi Puzzle Research Data Technology UIN Sultan Syarif Kasim Riau Indonesia Department of Information System UIN Sultan Syarif Kasim Riau Indonesia Informatics Department Universitas Muhammadiyah Semarang Semarang Indonesia Department of Management University of 45 Surabaya Surabaya Indonesia Department of Electrical Engineering Politeknik Negeri Kupang Kupang Indonesia Faculty of Communication Sciences Library and Information Science Program Universitas Padjadjaran Bandung Indonesia Informatics Engineering STMIK AKBA Makassar Indonesia Informatics Department STMIK STIKOM Indonesia Denpasar Indonesia School of Computer and Communication Engineering Universiti Malaysia Perlis Kubang Gajah Malaysia

Berkah Mart is one of the new minimarkets in Pekanbaru city which began to develop as a retail business to meet the needs of the community. In his operation, Berkah Mart still uses the traditional store management perception that the placement of product layout on sale shelves has not been through sales analysis. Existing products have not been reviewed from the consumer's point of view, whereas the marketing economics concept provides a variety of product and consumer management methods for business management in Berkah Mart, including market bucket analysis techniques and various itemset search algorithms. In this research, Market Basket Basket Analysis with FP-Growth algorithm is proposed to determine the layout and planning of goods availability. The application of FP-Growth algorithm proved to be useful in generating many and informative association rules to find out the consumer spending pattern at Berkah Mart in Pekanbaru. Moreover, from the rules of the customer association can be segmented separately to meet the specific needs of customers with cost-effective by using some special promotions for the general group. The experimental results show that FP-Growth algorithm can analyze quickly and efficiently informing consumer shopping pattern at Berkah Mart and can increase Berkah Mart revenue. © Published under licence by IOP Publishing Ltd.

关键词： Sales

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A joint learning and communications framework for federated learning over wireless networks

arXiv

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arXiv 2019年

作者： Chen, Mingzhe Yang, Zhaohui Saad, Walid Yin, Changchuan Vincent Poor, H. Cui, Shuguang Chinese University of Hong Kong Shenzhen518172 China Department of Electrical Engineering Princeton University PrincetonNJ08544 United States Centre for Telecommunications Research Department of Informatics King's College London WC2B 4BG United Kingdom Bradley Department of Electrical and Computer Engineering Virginia Tech BlacksburgVA24060 United States Beijing Key Laboratory of Network System Architecture and Convergence Beijing University of Posts and Telecommunications Beijing100876 China Department of Electrical Engineering Princeton University PrincetonNJ08544 United States Shenzhen Research Institute of Big Data School of Science and Engineering Chinese University of Hong Kong Shenzhen518172 China

In this paper, the problem of training federated learning (FL) algorithms over a realistic wireless network is studied. In particular, in the considered model, wireless users execute an FL algorithm while training their local FL models using their own data and transmitting the trained local FL models to a base station (BS) that will generate a global FL model and send it back to the users. Since all training parameters are transmitted over wireless links, the quality of the training will be affected by wireless factors such as packet errors and the availability of wireless resources. Meanwhile, due to the limited wireless bandwidth, the BS must select an appropriate subset of users to execute the FL algorithm so as to build a global FL model accurately. This joint learning, wireless resource allocation, and user selection problem is formulated as an optimization problem whose goal is to minimize an FL loss function that captures the performance of the FL algorithm. To address this problem, a closed-form expression for the expected convergence rate of the FL algorithm is first derived to quantify the impact of wireless factors on FL. Then, based on the expected convergence rate of the FL algorithm, the optimal transmit power for each user is derived, under a given user selection and uplink resource block (RB) allocation scheme. Finally, the user selection and uplink RB allocation is optimized so as to minimize the FL loss function. Simulation results show that the proposed joint federated learning and communication framework can reduce the FL loss function value by up to 10% and 16%, respectively, compared to: 1) An optimal user selection algorithm with random resource allocation and 2) a standard FL algorithm with random user selection and resource allocation. Copyright © 2019, The Authors. All rights reserved.

关键词： Learning algorithms

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A rich ranking model based on the matthew effect optimization 7th

A rich ranking model based on the matthew effect optimizatio...

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7th International Conference on Computational data and Social Networks, CSoNet 2018

作者： Li, Jinzhong Liu, Guanjun Department of Computer Science and Technology College of Electronic and Information Engineering Jinggangshan University Ji’an343009 China Network and Data Security Key Laboratory of Sichuan Province University of Electronic Science and Technology of China Chengdu610054 China Department of Computer Science and Technology College of Electronic and Information Engineering Tongji University Shanghai201804 China Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University Shanghai201804 China

ISBN: (纸本)9783030046477

Most existing approaches of learning to rank treat the effectiveness of each query equally which results in a relatively lower ratio of queries with high effectiveness (i.e. rich queries) in the produced ranking model. Such ranking models need to be further optimized to increase the number of rich queries. In this paper, queries with different effectiveness are distinguished, and the queries with higher effectiveness are given higher weights. We modify the gradient in the LambdaMART algorithm based on a new perspective of Matthew effect to highlight the optimization of the rich queries and to produce the rich ranking model, and we present a consistency theorem for the modified optimization objective. Based on the effectiveness evaluation criteria for information retrieval, we introduce the Gini coefficient, mean-variance and quantity statistics to measure the performances of the ranking models. Experimental results show that the ranking models produced by the gradient-modified LambdaMART algorithm based on Matthew effect exhibit a stronger Matthew effect compared to the original LambdaMART algorithm. © Springer Nature Switzerland AG 2018.

关键词： Artificial intelligence

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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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Mapping essential urban land use categories in China(EULUC-China):preliminary results for 2018

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Science Bulletin 2020年第3期65卷 182-187页

作者： Peng Gong Bin Chen Xuecao Li Han Liu Jie Wang Yuqi Bai Jingming Chen Xi Chen Lei Fang Shuailong Feng Yongjiu Feng Yali Gong Hao Gu Huabing Huang Xiaochun Huang Hongzan Jiao Yingdong Kang Guangbin Lei Ainong Li Xiaoting Li Xun Li Yuechen Li Zhilin Li Zhongde Li Chong Liu Chunxia Liu Maochou Liu Shuguang Liu Wanliu Mao Changhong Miao Hao Ni Qisheng Pan Shuhua Qi Zhehao Ren Zhuoran Shan Shaoqing Shen Minjun Shi Yimeng Song Mo Su Hoi Ping Suen Bo Sun Fangdi Sun Jian Sun Lin Sun Wenyao Sun Tian Tian Xiaohua Tong Yihsing Tseng Ying Tu Hong Wang Lan Wang Xi Wan Zongming Wang Tinghai Wu Yaowen Xie Jian Yang Jun Yang Man Yuan Wenze Yue Hongda Zeng Kuo Zhang Neng Zhang Tao Zhang Yu Zhang Feng Zhao Yichen Zheng Qiming Zhou Nicholas Clinton Zhiliang Zhu Bing Xu Ministry of Education Key Laboratory for Earth System Modeling Department of Earth System ScienceTsinghua UniversityBeijing 100084China Tsinghua Urban Institute Tsinghua UniversityBeijing 100084China Center for Healthy Cities Institute for China Sustainable UrbanizationTsinghua UniversityBeijing 100084China Department of Land Air and Water ResourcesUniversity of CaliforniaDavisCA 95616-8627USA Department of Geological and Atmospheric Sciences Iowa State UniversityAmesIA 50011USA State Key Laboratory of Remote Sensing Science Institute of Remote Sensing and Digital EarthChinese Academy of SciencesBeijing 100101China AI for Earth Laboratory Cross-Strait InstituteTsinghua UniversityBeijing 100084China International Institute for Earth System Sciences Nanjing UniversityNanjing 210093China Research Center for Ecology and Environment of Central Asia Chinese Academy of SciencesUrumchi 830011China Key Laboratory of Forest Ecology and Management Institute of Applied EcologyChinese Academy of SciencesShenyang 110016China National Engineering Laboratory for Applied Technology in Forestry&Ecology in South China College of Life Science and TechnologyCentral South University of Forestry and TechnologyChangsha 410004China College of Surveying&Geo-Informatics Tongji UniversityShanghai 200092China Department of Urban Planning College of Architecture and Urban PlanningTongji UniversityShanghai 200092China Beijing Institute of Urban Planning Beijing 100045China Department of Urban Planning School of Urban DesignWuhan UniversityWuhan 430072China College of Earth Sciences Jilin UniversityChangchun 130061China Research Center for Digital Mountain and Remote Sensing Application Institute of Mountain Hazards and EnvironmentChinese Academy of SciencesChengdu 610041China Institute of Remote Sensing and Geographic Information System School of Earth and Space SciencesPeking UniversityBeijing 100871China Department of Urban and Regional Planning School of Geography and PlanningSun Yat-sen Univ

Land use reflects human activities on *** land use is the highest level human alteration on Earth,and it is rapidly changing due to population increase and *** areas have widespread effects on local hydrology,climate,biodiversity,and food production[1,2].However,maps,that contain knowledge on the distribution,pattern and composition of various land use types in urban areas,are limited to city *** mapping standard on data sources,methods,land use classification schemes varies from city to city,due to differences in financial input and skills of mapping *** address various national and global environmental challenges caused by urbanization,it is important to have urban land uses at the national and global scales that are derived from the same or consistent data sources with the same or compatible classification systems and mapping *** is because,only with urban land use maps produced with similar criteria,consistent environmental policies can be made,and action efforts can be compared and assessed for large scale environmental ***,despite of the fact that a number of urban-extent maps exist at global scales[3,4],more detailed urban land use maps do not exist at the same *** at big country or regional levels such as for the United States,China and European Union,consistent land use mapping efforts are rare[5,6](e.g.,https://***/open_land_use/).

关键词： EULUC-China 土地利用类型

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Synergy of Binary Substitutions for Improving the Cycle Performance in LiNiO2 Revealed by Ab Initio Materials Informatics

arXiv

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arXiv 2020年

作者： Yoshida, Tomohiro Maezono, Ryo Hongo, Kenta Department of Computer-Aided Engineering and Development Sumitomo Metal Mining Co. Ltd. 3-5 Sobiraki-cho NiihamaEhime792-0001 Japan School of Information Science JAIST Asahidai 1-1 IshikawaNomi923-1292 Japan Computational Engineering Applications Unit RIKEN 2-1 Hirosawa SaitamaWako351-0198 Japan Research Center for Advanced Computing Infrastructure JAIST Asahidai 1-1 NomiIshikawa923-1292 Japan Center for Materials Research by Information Integration Research and Services Division of Materials Data and Integrated System National Institute for Materials Science 1-2-1 Sengen TsukubaIbaraki305-0047 Japan PRESTO Japan Science and Technology Agency 4-1-8 Honcho SaitamaKawaguchi-shi322-0012 Japan

We explore LiNiO2-based cathode materials with two-element substitutions by an ab initio simulation based materials informatics (AIMI) approach. According to our previous study, a higher cycle performance strongly correlates with less structural change during charge–discharge cycles;the latter can be used for evaluating the former. However, if we target the full substitution space, full simulations are infeasible even for all binary combinations. To circumvent such an exhaustive search, we rely on Bayesian optimization. Actually, by searching only 4% of all the combinations, our AIMI approach discovered two promising combinations, Cr-Mg and Cr-Re, whereas each atom itself never improved the performance. We conclude that the synergy never emerges from a common strategy restricted to combinations of "good" elements that individually improve the performance. In addition, we propose a guideline for the binary substitutions by elucidating the mechanism of crystal structure change. Copyright © 2020, The Authors. All rights reserved.

关键词： Binary alloys

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A computational approach to measure the linguistic characteristics of psychotherapy timing, responsiveness, and consistency

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Npj mental health research 2022年第1期1卷 19页

作者： Adam S Miner Scott L Fleming Albert Haque Jason A Fries Tim Althoff Denise E Wilfley W Stewart Agras Arnold Milstein Jeff Hancock Steven M Asch Shannon Wiltsey Stirman Bruce A Arnow Nigam H Shah Department of Psychiatry and Behavioral Sciences Stanford University Stanford CA USA. aminer@stanford.edu. Center for Biomedical Informatics Research Stanford University Stanford CA USA. aminer@stanford.edu. Department of Biomedical Data Science Stanford University Stanford CA USA. Department of Computer Science Stanford University Stanford CA USA. Center for Biomedical Informatics Research Stanford University Stanford CA USA. Allen School of Computer Science & Engineering University of Washington Seattle WA USA. Departments of Psychiatry Medicine Pediatrics and Psychological & Brain Sciences Washington University in St. Louis St. Louis MO USA. Department of Psychiatry and Behavioral Sciences Stanford University Stanford CA USA. Clinical Excellence Research Center Stanford University Stanford CA USA. Department of Communication Stanford University Stanford CA USA. VA Palo Alto Health Care System Palo Alto CA USA. Division of Primary Care and Population Health Stanford University School of Medicine Stanford CA USA. National Center for Posttraumatic Stress Disorders Dissemination and Training Division VA Palo Alto Healthcare System Menlo Park CA USA. Technology and Digital Solutions Stanford Healthcare Stanford CA USA.

Although individual psychotherapy is generally effective for a range of mental health conditions, little is known about the moment-to-moment language use of effective therapists. Increased access to computational power, coupled with a rise in computer-mediated communication (telehealth), makes feasible the large-scale analyses of language use during psychotherapy. Transparent methodological approaches are lacking, however. Here we present novel methods to increase the efficiency of efforts to examine language use in psychotherapy. We evaluate three important aspects of therapist language use - timing, responsiveness, and consistency - across five clinically relevant language domains: pronouns, time orientation, emotional polarity, therapist tactics, and paralinguistic style. We find therapist language is dynamic within sessions, responds to patient language, and relates to patient symptom diagnosis but not symptom severity. Our results demonstrate that analyzing therapist language at scale is feasible and may help answer longstanding questions about specific behaviors of effective therapists.

关键词： Translational research Human behaviour

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Brain age acceleration as biomarker of Alzheimer’s disease progression: Functional network connectivity analysis

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Alzheimer's & Dementia 2021年第S1期17卷

作者： Mohammad S.E. Sendi David H Salat Vince D Calhoun School of Electrical and Computer Engineering (ECE) at the Georgia Institute of Technology Atlanta GA USA Tri-Institutional Georgia State University/Georgia Institute of Technology/Emory University Center for Translational Research in Neuroimaging and Data Science Atlanta GA USA Wallace H. Coulter Department of Biomedical Engineering at Emory University and Georgia Tech Atlanta GA USA Neuroimaging Research for Veterans Center VA Boston Healthcare System Boston MA USA Massachusetts General Hospital Charlestown MA USA Harvard Medical School Boston MA USA Georgia State University Atlanta GA USA

Background The brain age gap, the difference between an individual’s brain predicted age and chronological age, is used as a brain disease biomarker and aging. To date, although previous studies utilized mostly structural magnetic resonance imaging (MRI) data to predict brain age, less work has used functional network connectivity (FNC) from resting-state functional MRI (rs-fMRI) to indicate brain age and its link with Alzheimer’s disease (AD) progression. This study aims to estimate brain age from FNC and introduce it as a biomarker of AD progression. Method This study used 1091 rs-fMRI data and the chronological age at the time of scanning, ranging from 42-95, from the Open Access Series of Imaging Studies (OASIS)-3 cohort. This dataset includes 1021 healthy subjects (HC) and 70 AD patients. We used group independent component analysis to estimate 53 components for the whole-brain. Then, FNC feature, in total 1378, of each subject was calculated by applying the Pearson correlation. A support vector regression (SVR) was trained on 951 HC subjects in which FNC features and the chronological age was served as predictor and prediction output, respectively. We then tested the trained model on 70 HC (age mean± sd: 73.66± 7.47) and 70 AD (age mean± sd: 73.34± 7.51) subjects to predict their brain age. Result The correlation between predicted brain age and the chronological age of HC test data was R=0.73, (p=5.3e -13 , N=70), while the correlation between predicted brain age and AD patients' chronological age was R=0.33 (p=0.004, N=70). This result shows that the model based on HC could better predict the HC age than the AD patients. The brain age gap mean and standard deviation for the test HC and AD group was -2.2581± 5.0879 and 2.0814± 7.4520. This result shows that the brain age gap was increased in AD (Cohen’s d=0.68, p<0.001). Conclusion We proved that FNC, estimated from rs-fMRI, could predict the brain age in HC. Also, we found an acceleration in the brain predict

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The PLATO mission

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Experimental Astronomy 2025年第3期59卷 1-111页

作者： Rauer, Heike Aerts, Conny Cabrera, Juan Deleuil, Magali Erikson, Anders Gizon, Laurent Goupil, Mariejo Heras, Ana Walloschek, Thomas Lorenzo-Alvarez, Jose Marliani, Filippo Martin-Garcia, César Mas-Hesse, J. Miguel O’Rourke, Laurence Osborn, Hugh Pagano, Isabella Piotto, Giampaolo Pollacco, Don Ragazzoni, Roberto Ramsay, Gavin Udry, Stéphane Appourchaux, Thierry Benz, Willy Brandeker, Alexis Güdel, Manuel Janot-Pacheco, Eduardo Kabath, Petr Kjeldsen, Hans Min, Michiel Santos, Nuno Smith, Alexis Suarez, Juan-Carlos Werner, Stephanie C. Aboudan, Alessio Abreu, Manuel Acuña, Lorena Adams, Moritz Adibekyan, Vardan Affer, Laura Agneray, François Agnor, Craig Aguirre Børsen-Koch, Victor Ahmed, Saad Aigrain, Suzanne Al-Bahlawan, Ashraf Alcacera Gil, Ma de los Angeles Alei, Eleonora Alencar, Silvia Alexander, Richard Alfonso-Garzón, Julia Alibert, Yann Allende Prieto, Carlos Almeida, Leonardo Alonso Sobrino, Roi Altavilla, Giuseppe Althaus, Christian Alvarez Trujillo, Luis Alonso Amarsi, Anish Ammler-von Eiff, Matthias Amôres, Eduardo Andrade, Laerte Antoniadis-Karnavas, Alexandros António, Carlos Aparicio del Moral, Beatriz Appolloni, Matteo Arena, Claudio Armstrong, David Aroca Aliaga, Jose Asplund, Martin Audenaert, Jeroen Auricchio, Natalia Avelino, Pedro Baeke, Ann Baillié, Kevin Balado, Ana Ballber Balagueró, Pau Balestra, Andrea Ball, Warrick Ballans, Herve Ballot, Jerome Barban, Caroline Barbary, Gaële Barbieri, Mauro Barceló Forteza, Sebastià Barker, Adrian Barklem, Paul Barnes, Sydney Barrado Navascues, David Barragan, Oscar Baruteau, Clément Basu, Sarbani Baudin, Frederic Baumeister, Philipp Bayliss, Daniel Bazot, Michael Beck, Paul G. Belkacem, Kevin Bellinger, Earl Benatti, Serena Benomar, Othman Bérard, Diane Bergemann, Maria Bergomi, Maria Bernardo, Pierre Biazzo, Katia Bignamini, Andrea Bigot, Lionel Billot, Nicolas Binet, Martin Biondi, David Biondi, Federico Birch, Aaron C. Bitsch, Bertram Bluhm Ceballos, Paz Victoria Bódi, Attila Bognár, Zsófia Boisse, Isabelle Bolmont, Emeline Bonanno, Alfio Bonavita, Institut für Planetenforschung Deutsches Zentrum für Luft- und Raumfahrt DLR Rutherfordstrasse 2 Berlin 12489 Germany Fachbereich Geowissenschaften Institut für Geologische Freie Universität Berlin Malteserstr. 74-100 Berlin 12249 Germany Institute of Astronomy KU Leuven Leuven Belgium CNRS CNES LAM Aix Marseille University Marseille France Max Planck Institute for Solar System Research Göttingen Germany LESIA Paris Observatory PSL University Sorbonne University Paris Cité University CNRS Meudon France ESTEC European Space Agency Noordwijk Netherlands ESAC European Space Agency Madrid Spain Centro de Astrobiología CSIC-INTA Campus ESAC Madrid Spain Center for Space and Habitability University of Bern Bern Switzerland INAF - Osservatorio Astrofisico di Catania Catania Italy Department of Physics and Astronomy University of Padua Padua Italy Department of Physics University of Warwick Coventry United Kingdom Astronomical Observatory of Padova INAF - Italian National Institute for Astrophysics Padua Italy Armagh Observatory and Planetarium College Hill Armagh United Kingdom Geneve Observatory University of Geneve Geneve Switzerland IAS University of Paris-Saclay Orsay France AlbaNove University Centre Stockholm University Stockholm Sweden Department of Astrophysics University of Vienna Vienna Austria Department of Astronomy University of Sao Paulo Sao Paulo Brazil Astronomical Institute of the Czech Academy of Sciences Ondřejov Czech Republic Department of Physics and Astronomy Aarhus University Aarhus Denmark Institute for Space Research SRON Leiden Netherlands Instituto de Astrofísica e Ciências do Espaço CAUP Universidade do Porto Porto Portugal Departamento de Física e Astronomia Faculdade da Ci ncias Universidade do Porto Porto Portugal Mullard Space Science Laboratory University College London Holmbury Saint Mary United Kingdom Dept. Theoretical Physics and the Cosmos University of Granada Granada Spain Centre for Plan

PLATO (PLAnetary Transits and Oscillations of stars) is ESA’s M3 mission designed to detect and characterise extrasolar planets and perform asteroseismic monitoring of a large number of stars. PLATO will detect small planets (down to <2REarth) around bright stars (<11 mag), including terrestrial planets in the habitable zone of solar-like stars. With the complement of radial velocity observations from the ground, planets will be characterised for their radius, mass, and age with high accuracy (5%, 10%, 10% for an Earth-Sun combination respectively). PLATO will provide us with a large-scale catalogue of well-characterised small planets up to intermediate orbital periods, relevant for a meaningful comparison to planet formation theories and to better understand planet evolution. It will make possible comparative exoplanetology to place our Solar system planets in a broader context. In parallel, PLATO will study (host) stars using asteroseismology, allowing us to determine the stellar properties with high accuracy, substantially enhancing our knowledge of stellar structure and evolution. The payload instrument consists of 26 cameras with 12cm aperture each. For at least four years, the mission will perform high-precision photometric measurements. Here we review the science objectives, present PLATO‘s target samples and fields, provide an overview of expected core science performance as well as a description of the instrument and the mission profile towards the end of the serial production of the flight cameras. PLATO is scheduled for a launch date end 2026. This overview therefore provides a summary of the mission to the community in preparation of the upcoming operational phases. © The Author(s) 2025.

关键词： Asteroseismology Exoplanets PLATO mission

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Genome-wide association study of long COVID

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Nature Genetics 2025年 1-16页

作者： Lammi, Vilma Nakanishi, Tomoko Jones, Samuel E. Andrews, Shea J. Karjalainen, Juha Cortés, Beatriz O’Brien, Heath E. Ochoa-Guzman, Ana Fulton-Howard, Brian E. Broberg, Martin Haapaniemi, Hele H. Kanai, Masahiro Pirinen, Matti Schmidt, Axel Mitchell, Ruth E. Mousas, Abdou Mangino, Massimo Huerta-Chagoya, Alicia Sinnott-Armstrong, Nasa Cirulli, Elizabeth T. Vaudel, Marc Kwong, Alex S. F. Maiti, Amit K. Marttila, Minttu M. Posner, Daniel C. Rodriguez, Alexis A. Batini, Chiara Minnai, Francesca Dearman, Anna R. Warmerdam, C. A. Robert Sequeros, Celia B. Winkler, Thomas W. Jordan, Daniel M. Rešcenko, Raimonds Miano, Lorenzo Lane, Jacqueline M. Chung, Ryan K. Guillen-Guio, Beatriz Leavy, Olivia C. Carvajal-Silva, Laura Aguilar-Valdés, Kevin Frangione, Erika Guare, Lindsay Vergasova, Ekaterina Marouli, Eirini Striano, Pasquale Zainulabid, Ummu Afeera Kumar, Ashutosh Ahmad, Hajar Fauzan Edahiro, Ryuya Azekawa, Shuhei Maj, Carlo Pensel, Max C. Miller, Abigail Nöthen, Markus M. Behzad, Pari Schultz, Sonja Heggemann, Julia Balla, Daniella Morrison, David R. Mooser, Vincent Perley, Danielle Ragoussis, Jiannis Rousseau, Simon Shi, Fangyi Lathrop, G. Mark Yanishevsky, Solomia Laurent, Laetitia Auld, Daniel Bertrand, Mylene Boisclair, Ariane Bourque, Guillaume Bujold, David Durand, Madeleine Adra, Darin St-Cyr, Janick Niemi, Mari E. K. Iyengar, Sudha K. Cai, Tianxi Cho, Kelly Sarvadhavabhatla, Sannidhi Donaire, Maria Sophia Valenzuela-Jorquera, Héctor Lamoza, Eduardo Arévalo, Tamara V. Retamales-Ortega, Rocío Sanhueza, Sergio Selman, Carolina S. Silva, Andrea X. Alarcon, Teresa A. Martínez, Matías F. Quiroga, Romina Zuñiga-Pacheco, Paula Zapata-Contreras, Daniela Yáñez, Cristian E. Quiñones, Luis A. Tobar-Calfucoy, Eduardo A. Monardes-Ramírez, Virginia A. Signore, Iskra A. Guajardo, Lissette G. Figueroa, Alvaro Oróstica, Karen Y. Muñoz, Christian A. Fuentes-Guajardo, Macarena Bocchieri, Pamela Cabrera, Camilo Echeverria, Cesar A. Cerpa, Leslie C. Donoso, Gerardo Gutiérrez-Richards, Scarlett Nova-Lamperti, Estefania Saez Hidalgo Institute for Molecular Medicine Finland (FIMM) Helsinki Institute of Life Science (HiLIFE) University of Helsinki Helsinki Finland Department of Human Genetics McGill University Montreal QC Canada Centre for Clinical Epidemiology Department of Medicine Lady Davis Institute Jewish General Hospital McGill University Montreal QC 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 Department of Psychiatry and Behavioral Sciences University of California San Francisco San Francisco CA United States Program in Medical and Population Genetics Broad Institute of Harvard and MIT Cambridge MA United States Stanley Center for Psychiatric Research Broad Institute of Harvard and MIT Cambridge MA United States Analytic and Translational Genetics Unit Massachusetts General Hospital Boston MA United States Genomes for Life-GCAT Lab CORE Program Germans Trias i Pujol Research Institute (IGTP) Badalona Spain Grup de REcerca en Impacte de les Malalties Cròniques i les seves Trajectòries (GRIMTra) Barcelona Spain Sano Genetics Limited London United Kingdom Unidad de Biología Molecular y Medicina Genómica Instituto Nacional de Ciencias Médicas y Nutrición Salvador Zubirán Mexico City Mexico Genetics and Genomic Sciences Icahn School of Medicine at Mount Sinai New York City NY United States Broad Institute Cambridge MA United States Analytical and Translational Genetics Unit Massachusetts General Hospital Harvard Medical School Boston MA United States Department of Mathematics and Statistics University of Helsinki Helsinki Finland Department of Public Health University of Helsinki Helsinki Finland Institute of Human Genetics University of Bonn School of Medicine and University Hospital Bonn Bonn Germany MRC Integrat

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 of FOXP4 with 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. © The Author(s) 2025.

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