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IEEJ Transactions on Power and Energy 2020年第5期140卷 402-408页

作者： Fujita, Shogo Igarashi, Hajime Applied Electromagetics Lab. Research Group of Informatics for System Synthesis Division of Systems Science and Informatics Graduate School of Information Science and Technology Hokkaido University Kita 14 Nishi 9 Kita-ku Sapporo060-0814 Japan

In this paper, a homogenized finite element method for transient analysis of electric apparatus including a multi-turn coil with elliptic cross-section. The tensor-type complex permeability of the multi-turn coil is expanded into partial fractions, which can then be transformed into time-domain. The convolution integral is effectively evaluated using the RC method which does not need large memory storage. It is shown that the power computed by the proposed method is in good agreement with that computed by the conventional finite element method without homogenization. © 2020 The Institute of Electrical Engineers of Japan.

关键词： Finite element method

GRAS transcription factors regulate cell division planes in moss overriding the default rule (vol 120, e2210632120, 2023)

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PROCEEDINGS OF THE NATIONAL ACADEMY OF scienceS OF THE UNITED STATES OF AMERICA 2023年第10期120卷 e2210632120-e2210632120页

作者： Ishikawa, Masaki Fujiwara, Ayaka Kosetsu, Ken Horiuchi, Yuta Kamamoto, Naoya Umakawa, Naoyuki Tamada, Yosuke Zhang, Liechi Matsushita, Katsuyoshi Palfalvi, Gergo Nishiyama, Tomoaki Kitasaki, Sota Masuda, Yuri Shiroza, Yoshiki Kitagawa, Munenori Nakamura, Toru Cui, Hongchang Hiwatashi, Yuji Kabeya, Yukiko Shigenobu, Shuji Aoyama, Tsuyoshi Kato, Kagayaki Murata, Takashi Fujimoto, Koichi Benfey, Philip N. Hasebe, Mitsuyasu Kofuji, Rumiko Division of Evolutionary Biology National Institute for Basic Biology Okazaki 444-8585 Japan Department of Basic Biology The Graduate School for Advanced Studies (SOKENDAI) Okazaki 444-8585 Japan Graduate School of Natural Science and Technology Kanazawa University Kanazawa 920-1192 Japan Department of Biological Sciences Graduate School of Science Osaka University Toyonaka 560-0043 Japan School of Engineering Utsunomiya University Utsunomiya 321-8585 Japan Division of Integrated Omics Research Research Center for Experimental Modeling of Human Disease Kanazawa University Kanazawa 920-0934 Japan Department of Biology Kanazawa University Kanazawa 920-1192 Japan Present Address: National Key Laboratory for Germplasm Innovation and Utilization for Fruit and Vegetable Horticultural Crops Huazhong Agricultural University Wuhan 430070 China. Department of Biology Duke University Durham NC 27516 HHMI Duke University Durham NC 27516 Department of Biological Science Florida State University Tallahassee FL 32306-4295 School of Food Industrial Sciences Miyagi University Sendai 982-0215 Japan Present address: Live Imaging Center Institute of Transformative Bio-Molecules Nagoya University Nagoya 466-8601 Japan Bioimage Informatics Group Exploratory Research Center on Life and Living Systems (ExCELLS) National Institutes of Natural Sciences Okazaki 444-8585 Japan Interdisciplinary Research Unit National Institute for Basic Biology Okazaki 444-8585 Japan Department of Applied Bioscience Kanagawa Institute of Technology Atsugi 243-0292 Japan Faculty of Biological Science and Technology Institute of Science and Engineering Kanazawa University Kanazawa 920-1192 Japan

Plant cells are surrounded by a cell wall and do not migrate, which makes the regulation of cell division orientation crucial for development. Regulatory mechanisms controlling cell division orientation may have contributed to the evolution of body organization in land plants. The GRAS family of transcription factors was transferred horizontally from soil bacteria to an algal common ancestor of land plants. SHORTROOT (SHR) and SCARECROW (SCR) genes in this family regulate formative periclinal cell divisions in the roots of flowering plants, but their roles in nonflowering plants and their evolution have not been studied in relation to body organization. Here, we show that SHR cell autonomously inhibits formative periclinal cell divisions indispensable for leaf vein formation in the moss Physcomitrium patens, and SHR expression is positively and negatively regulated by SCR and the GRAS member LATERAL SUPPRESSOR, respectively. While precursor cells of a leaf vein lacking SHR usually follow the geometry rule of dividing along the division plane with the minimum surface area, SHR overrides this rule and forces cells to divide nonpericlinally. Together, these results imply that these bacterially derived GRAS transcription factors were involved in the establishment of the genetic regulatory networks modulating cell division orientation in the common ancestor of land plants and were later adapted to function in flowering plant and moss lineages for their specific body organizations.

关键词： GRAS TFs geometry rule cell division orientation horizontal gene transfer

NFDI4Energy Task Area 4: FAIR Data for Energy System Research

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NFDI4Energy Task Area 4: FAIR Data for Energy System Researc...

2023 Designing Futures: Zukunfte gestalten, INFORMATIK 2023 - 2023 Designing Futures: Shaping the Future, INFORMATIK 2023

作者： Wein, Amanda Reinkensmeier, Jan Weidlich, Anke Lilliestam, Johan Hagenmeyer, Veit Richter, Mascha Auer, Sören Nieße, Astrid Lehnhoff, Sebastian OFFIS E.V. RandD Division Energy Escherweg 2 Oldenburg26121 Germany Friedrichstr. 39 Freiburg79110 Germany Friedrich-Alexander-Universität Erlangen-Nörnberg Lehrstuhl for Sustainability Transition Policy Lange Gasse 20 Nörnberg90403 Germany Karlsruhe Institute of Technology Institute for Automation and Applied Informatics Kaiserstr. 89 Karlsruhe76131 Germany Reiner Lemoine Institut GGmbH Research Unit Transformation of Energy Systems Rudower Chaussee 12 Berlin12489 Germany Technische Informationsbibliothek Data Science and Digital Libraries Research Group Welfengarten 1 B Hannover30167 Germany Carl von Ossietzky Universität Oldenburg Department for Informatik Ammerländer Heerstr. 114-118 Oldenburg26129 Germany

ISBN: (纸本)9783885797319

The NFDI4Energy consortium will create a research data infrastructure for energy system research, emphasizing the openness and FAIRness of data and models in this research domain. Within the consortium, Task Area 4 focuses on the development of resources and services that will provide a semantic layer for the overall platform built by NFDI4Energy. The team of this Task Area will produce artifacts including a domain ontology, metadata standards, a knowledge graph, a Persistent Identifier service, and integration infrastructure to join these artifacts to the NFDI4Energy platform. © 2023 Gesellschaft fur Informatik (GI). All rights reserved.

关键词： Metadata

Upcycling Human Excrement: The Gut Microbiome to Soil Microbiome Axis

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arXiv 2024年

作者： Meilander, Jeff Herman, Chloe Manley, Andrew Augustine, Georgia Birdsell, Dawn Bolyen, Evan Celona, Kimberly R. Coffey, Hayden Cocking, Jill Donoghue, Teddy Draves, Alexis Erickson, Daryn Foley, Marissa Gehret, Liz Hagen, Johannah Hepp, Crystal Ingram, Parker John, David Kadar, Katarina Keim, Paul Lloyd, Victoria Osterink, Christina Queeney, Victoria Ramirez, Diego Romero, Antonio Ruby, Megan C. Sahl, Jason W. Soloway, Sydni Stone, Nathan E. Trottier, Shannon Van Orden, Kaleb Painter, Alexis Wallace, Sam Wilcox, Larissa Wood, Colin V. Yancey, Jaiden Gregory Caporaso, J. Center for Applied Microbiome Science Pathogen and Microbiome Institute Northern Arizona University FlagstaffAZ United States Department of Biological Sciences Northern Arizona University FlagstaffAZ United States School of Informatics Computing and Cyber Systems Northern Arizona University FlagstaffAZ United States Genetics Core Facility Office of the Vice President for Research Northern Arizona University FlagstaffAZ United States Pathogen and Microbiome Division Translational Genomics Research Institute FlagstaffAZ United States Pathogen and Microbiome Institute Northern Arizona University FlagstaffAZ United States

Human excrement composting (HEC) is a sustainable strategy for human excrement (HE) management that recycles nutrients and mitigates health risks while reducing reliance on freshwater, fossil fuels, and fertilizers. We present a comprehensive microbial time series analysis of HEC and show that the initial gut-like microbiome of HEC systems transitions to a microbiome similar to soil and traditional compost in fifteen biological replicates tracked weekly for one year. © 2024, CC BY.

关键词： Anthropogenic

SRBench++: Principled Benchmarking of Symbolic Regression With Domain-Expert Interpretation

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IEEE Transactions on Evolutionary Computation 2024年 1-1页

作者： de Franca, F.O. Virgolin, M. Kommenda, M. Majumder, M.S. Cranmer, M. Espada, G. Ingelse, L. Fonseca, A. Landajuela, M. Petersen, B. Glatt, R. Mundhenk, N. Lee, C.S. Hochhalter, J.D. Randall, D.L. Kamienny, P. Zhang, H. Dick, G. Simon, A. Burlacu, B. Kasak, Jaan Machado, Meera Wilstrup, Casper Cavaz, W.G.L. Federal University of ABC Santo Andre Brazil Evolutionary Intelligence group Centrum Wiskunde and Informatica Science Park Amsterdam Netherlands University of Applied Sciences Upper Austria Hagenberg Austria Computational Health Informatics Program Boston Children’s Hospital Harvard Medical School Boston USA Center for Computational Astrophysics Flatiron Institute and Department of Astrophysical Sciences of Princeton University USA LASIGE Faculdade de Ciências Universidade de Lisboa Lisboa Portugal Computational Engineering Division Lawrence Livermore National Laboratory Livermore USA Department of Mechanical Engineering University of Utah Utah USA Meta FAIR France Victoria University of Wellington School of Engineering and Computer Science New Zealand University of otago Department of Information Science New Zealand Institut für Angewandte Physik Universität TübingenMax Planck Institute for Intelligent Systems Tübingen Germany Abzu AI Orient Plads 1 Nordhavn Denmark

Symbolic regression searches for analytic expressions that accurately describe studied phenomena. The main promise of this approach is that it may return an interpretable model that can be insightful to users, while maintaining high accuracy. The current standard for benchmarking these algorithms is SRBench, which evaluates methods on hundreds of datasets that are a mix of real-world and simulated processes spanning multiple domains. At present, the ability of SRBench to evaluate interpretability is limited to measuring the size of expressions on real-world data, and the exactness of model forms on synthetic data. In practice, model size is only one of many factors used by subject experts to determine how interpretable a model truly is. Furthermore, SRBench does not characterize algorithm performance on specific, challenging sub-tasks of regression such as feature selection and evasion of local minima. In this work, we propose and evaluate an approach to benchmarking SR algorithms that addresses these limitations of SRBench by 1) incorporating expert evaluations of interpretability on a domain-specific task, and 2) evaluating algorithms over distinct properties of data science tasks. We evaluate 12 modern symbolic regression algorithms on these benchmarks and present an in-depth analysis of the results, discuss current challenges of symbolic regression algorithms and highlight possible improvements for the benchmark itself. Authors

关键词： Benchmarking

Molecular docking and molecular dynamics simulation studies of inhibitor candidates against Anopheles gambiae 3-hydroxykynurenine transaminase and implications on vector control

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Heliyon 2025年第1期11卷 e41633页

作者： Adedeji, Eunice O. Ogunlana, Olubanke O. Oduselu, Gbolahan O. Koenig, Rainer Adebiyi, Ezekiel Soremekun, Opeyemi S. Fatumo, Segun Covenant University Bioinformatics Research (CUBRe) Covenant University Ogun State Ota Nigeria Department of Biochemistry Covenant University Ogun State Ota Nigeria Department of Biology University of York York United Kingdom Covenant Applied Informatics and Communication Africa Centre of Excellence Covenant University Ota Nigeria Institute for Infectious Diseases and Infection Control (IIMK RG Systems Biology) Jena University Hospital Am Klinikum 1 Jena 07747 Germany African Center of Excellence in Bioinformatics & Data Intensive Science Makerere University Kampala 10218 Uganda Division of Applied Bioinformatics German Cancer Research Center (DKFZ) G200 Im Neuenheimer Feld 280 Heidelberg 69120 Germany The African Computational Genomics (TACG) Research Group MRC/UVRI and LSHTM Entebbe Uganda Precision Healthcare University Research Institute Queen Mary University of London United Kingdom Department of Non-Communicable Disease Epidemiology London School of Hygiene & Tropical Medicine United Kingdom

Isoxazole and oxadiazole derivatives inhibiting 3-hydroxykynurenine transaminase (3HKT) are potential larvicidal candidates. This study aims to identify more suited potential inhibitors of Anopheles gambiae 3HKT (Ag3HKT) through molecular docking and molecular dynamics simulation. A total of 958 compounds were docked against Anopheles gambiae 3HKT (PDB ID: 2CH2) using Autodock vina and Autodock4. The top three identified hits were subjected to 300 ns molecular dynamics simulation using AMBER 18 and ADMET analysis using SWISSADME predictor and ADMETSAR. Replacement of alkyl attachment on C5 of isoxazole or oxadiazole derivative with a cycloalkyl group yielded compounds with lower binding energy than their straight chain counterparts. The top three compounds were brominated compounds, 2‐[3‐(4‐bromophenyl)‐1,2‐oxazol‐5‐yl]cyclopentane‐1‐carboxylic acid, 2-[3-(4-bromophenyl)-1,2,4-oxadiazol-5-yl]cyclopentane-1-carboxylic acid, 3-[3-(4-bromo-2-methylphenyl)-1,2,4-oxadiazol-5-yl]cyclopentane-1-carboxylic acid, and they had binding energies of −8.58, −8.25, and −8.18 kcal/mol in virtual screening against 2CH2 protein target, respectively. These compounds were predicted to be less toxic than temephos, a standard larvicide and more biodegradable than previously reported inhibitors. The three compounds exhibited a greater stabilizing effect on 2CH2 protein target than 4-[3-(4-bromophenyl)-1,2,4-oxadiazol-5-yl]butanoic acid, a previously reported inhibitor candidate with good larvicidal activity on Aedes aegypti. Further thermodynamic calculations revealed that the top three compounds possessed total binding energies (ΔG_bind) of −26.64 kcal/mol, −24.26 kcal/mol and −14.11 kcal/mol, respectively, as compared to −12.02 kcal/mol for 4-[3-(4-bromophenyl)-1,2,4-oxadiazol-5-yl]butanoic acid. These compounds could be better larvicides than previously reported isoxazole or oxadiazole derivatives and safer than temephos. © 2025 The Authors

关键词： 1,2,4-Oxadiazole 3HKT Isoxazoles Larvicide Mosquito Vector control

Interpretable Symbolic Regression for Data science: Analysis of the 2022 Competition

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arXiv 2023年

作者： de Franca, F.O. Virgolin, M. Kommenda, M. Majumder, M.S. Cranmer, M. Espada, G. Ingelse, L. Fonseca, A. Landajuela, M. Petersen, B. Glatt, R. Mundhenk, N. Lee, C.S. Hochhalter, J.D. Randall, D.L. Kamienny, P. Zhang, H. Dick, G. Simon, A. Burlacu, B. Kasak, Jaan Machado, Meera Wilstrup, Casper La Cava, W.G. Federal University of ABC Santo Andre Brazil Evolutionary Intelligence Group Centrum Wiskunde & Informatica Science Park 123 Amsterdam Netherlands Computational Health Informatics Program Boston Children's Hospital Harvard Medical School Boston United States University of Applied Sciences Upper Austria Hagenberg Austria Center for Computational Astrophysics Flatiron Institute Department of Astrophysical Sciences Princeton University United States LASIGE Faculdade de Ciências Universidade de Lisboa Lisboa Portugal Computational Engineering Division Lawrence Livermore National Laboratory Livermore United States University of Utah Department of Mechanical Engineering Utah United States Meta FAIR France Victoria University of Wellington School of Engineering and Computer Science New Zealand University of otago Department of Information Science New Zealand Institut für Angewandte Physik Universität Tübingen Max Planck Institute for Intelligent Systems Tübingen Germany Abzu AI Orient Plads 1 Nordhavn 2150 Denmark

Symbolic regression searches for analytic expressions that accurately describe studied phenomena. The main attraction of this approach is that it returns an interpretable model that can be insightful to users. Historically, the majority of algorithms for symbolic regression have been based on evolutionary algorithms. However, there has been a recent surge of new proposals that instead utilize approaches such as enumeration algorithms, mixed linear integer programming, neural networks, and Bayesian optimization. In order to assess how well these new approaches behave on a set of common challenges often faced in real-world data, we hosted a competition at the 2022 Genetic and Evolutionary Computation Conference consisting of different synthetic and real-world datasets which were blind to entrants. For the real-world track, we assessed interpretability in a realistic way by using a domain expert to judge the trustworthiness of candidate models. We present an in-depth analysis of the results obtained in this competition, discuss current challenges of symbolic regression algorithms and highlight possible improvements for future competitions. Copyright © 2023, The Authors. All rights reserved.

关键词： Machine learning

Understanding metric-related pitfalls in image analysis validation

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arXiv 2023年

作者： Reinke, Annika Tizabi, Minu D. Baumgartner, Michael Eisenmann, Matthias Heckmann-Nötzel, Doreen Kavur, A. Emre Rädsch, Tim Sudre, Carole H. Acion, Laura Antonelli, Michela Arbel, Tal Bakas, Spyridon Benis, Arriel Blaschko, Matthew B. Buettner, Florian Cardoso, M. Jorge Cheplygina, Veronika Chen, Jianxu Christodoulou, Evangelia Cimini, Beth A. Collins, Gary S. Farahani, Keyvan Ferrer, Luciana Galdran, Adrian van Ginneken, Bram Glocker, Ben Godau, Patrick Haase, Robert Hashimoto, Daniel A. Hoffman, Michael M. Huisman, Merel Isensee, Fabian Jannin, Pierre Kahn, Charles E. Kainmueller, Dagmar Kainz, Bernhard Karargyris, Alexandros Karthikesalingam, Alan Kenngott, Hannes Kleesiek, Jens Kofler, Florian Kooi, Thijs Kopp-Schneider, Annette Kozubek, Michal Kreshuk, Anna Kurc, Tahsin Landman, Bennett A. Litjens, Geert Madani, Amin Maier-Hein, Klaus Martel, Anne L. Mattson, Peter Meijering, Erik Menze, Bjoern Moons, Karel G.M. Müller, Henning Nichyporuk, Brennan Nickel, Felix Petersen, Jens Rafelski, Susanne M. Rajpoot, Nasir Reyes, Mauricio Riegler, Michael A. Rieke, Nicola Saez-Rodriguez, Julio Sánchez, Clara I. Shetty, Shravya Summers, Ronald M. Taha, Abdel A. Tiulpin, Aleksei Tsaftaris, Sotirios A. van Calster, Ben Varoquaux, Gaël Yaniv, Ziv R. Jäger, Paul F. Maier-Hein, Lena Faculty of Mathematics and Computer Science Heidelberg University Heidelberg Germany Heidelberg Division of Intelligent Medical Systems Germany NCT Heidelberg A Partnership Between DKFZ University Medical Center Heidelberg Germany Heidelberg Division of Medical Image Computing Germany Heidelberg Division of Intelligent Medical Systems Germany MRC Unit for Lifelong Health and Ageing UCL Centre for Medical Image Computing Department of Computer Science University College London London United Kingdom School of Biomedical Engineering and Imaging Science King’s College London London United Kingdom Instituto de Cálculo CONICET – Universidad de Buenos Aires Buenos Aires Argentina Centre for Medical Image Computing University College London London United Kingdom McGill University Montreal Canada Division of Computational Pathology Dept of Pathology & Laboratory Medicine Indiana University School of Medicine IU Health Information and Translational Sciences Building Indianapolis United States University of Pennsylvania Richards Medical Research Laboratories FL7 PhiladelphiaPA United States Department of Digital Medical Technologies Holon Institute of Technology Holon Israel European Federation for Medical Informatics Le Mont-sur-Lausanne Switzerland Center for Processing Speech and Images Department of Electrical Engineering KU Leuven Leuven Belgium partner site Frankfurt/Mainz a partnership between DKFZ and UCT Frankfurt Marburg Germany Heidelberg Germany Goethe University Frankfurt Department of Medicine Germany Goethe University Frankfurt Department of Informatics Germany and Frankfurt Cancer Insititute Germany Department of Computer Science IT University of Copenhagen Copenhagen Denmark Leibniz-Institut für Analytische Wissenschaften – ISAS – e.V. Dortmund Germany Imaging Platform Broad Institute of MIT and Harvard CambridgeMA United States Centre for Statistics in Medicine University of Oxford Oxford United Kingdom Center for Biomedical In

Validation metrics are key for tracking scientific progress and bridging the current chasm between artificial intelligence (AI) research and its translation into practice. However, increasing evidence shows that particularly in image analysis, metrics are often chosen inadequately. While taking into account the individual strengths, weaknesses, and limitations of validation metrics is a critical prerequisite to making educated choices, the relevant knowledge is currently scattered and poorly accessible to individual researchers. Based on a multi-stage Delphi process conducted by a multidisciplinary expert consortium as well as extensive community feedback, the present work provides the first reliable and comprehensive common point of access to information on pitfalls related to validation metrics in image analysis. While focused on biomedical image analysis, the addressed pitfalls generalize across application domains and are categorized according to a newly created, domain-agnostic taxonomy. The work serves to enhance global comprehension of a key topic in image analysis validation. © 2023, CC BY.

关键词： Computer vision

Estimands and their Estimators for Clinical Trials Impacted by the COVID-19 Pandemic: A Report from the NISS Ingram Olkin Forum Series on Unplanned Clinical Trial Disruptions

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arXiv 2022年

作者： van Lancker, Kelly Tarima, Sergey Bartlett, Jonathan Bauer, Madeline Bharani-Dharan, Bharani Bretz, Frank Flournoy, Nancy Michiels, Hege Parra, Camila Olarte Rosenberger, James L. Cro, Suzie Department of Biostatistics Johns Hopkins Bloomberg School of Public Health Baltimore United States Department of Applied Mathematics Computer Science and Statistics Ghent University Ghent Belgium Division of Biostatistics Medical College of Wisconsin United States Department of Mathematical Sciences University of Bath United Kingdom Division of Infectious Diseases Keck School of Medicine University of Southern California Los Angeles United States Novartis Pharmaceuticals East Hanover United States Novartis Pharma AG Basel Switzerland Section for Medical Statistics Center for Medical Statistics Informatics and Intelligent Systems Medical University of Vienna Vienna Austria Department of Statistics University of Missouri Columbia United States National Institute of Statistical Sciences Department of Statistics Penn State University University ParkPA16802-2111 United States Imperial Clinical Trials Unit Imperial College London United Kingdom

The COVID-19 pandemic continues to affect the conduct of clinical trials globally. Complications may arise from pandemic-related operational challenges such as site closures, travel limitations and interruptions to the supply chain for the investigational product, or from health-related challenges such as COVID-19 infections. Some of these complications lead to unforeseen intercurrent events in the sense that they affect either the interpretation or the existence of the measurements associated with the clinical question of interest. In this article, we demonstrate how the ICH E9(R1) Addendum on estimands and sensitivity analyses provides a rigorous basis to discuss potential pandemic-related trial disruptions and to embed these disruptions in the context of study objectives and design elements. We introduce several hypothetical estimand strategies and review various causal inference and missing data methods, as well as a statistical method that combines unbiased and possibly biased estimators for estimation. To illustrate, we describe the features of a stylized trial, and how it may have been impacted by the pandemic. This stylized trial will then be re-visited by discussing the changes to the estimand and the estimator to account for pandemic disruptions. Finally, we outline considerations for designing future trials in the context of unforeseen disruptions. © 2022, CC BY.

关键词： Sensitivity analysis

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INTRODUCTION

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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