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LimeSoDa: A dataset collection for benchmarking of machine learning regressors in digital soil mapping

Geoderma 2025年 459卷

作者： Schmidinger, Jonas Vogel, Sebastian Barkov, Viacheslav Pham, Anh-Duy Gebbers, Robin Tavakoli, Hamed Correa, Jose Tavares, Tiago R. Filippi, Patrick Jones, Edward J. Lukas, Vojtech Boenecke, Eric Ruehlmann, Joerg Schroeter, Ingmar Kramer, Eckart Paetzold, Stefan Kodaira, Masakazu Wadoux, Alexandre M.J.-C. Bragazza, Luca Metzger, Konrad Huang, Jingyi Valente, Domingos S.M. Safanelli, Jose L. Bottega, Eduardo L. Dalmolin, Ricardo S.D. Farkas, Csilla Steiger, Alexander Horst, Taciara Z. Ramirez-Lopez, Leonardo Scholten, Thomas Stumpf, Felix Rosso, Pablo Costa, Marcelo M. Zandonadi, Rodrigo S. Wetterlind, Johanna Atzmueller, Martin Osnabrück University Joint Lab Artificial Intelligence and Data Science Osnabrück Germany Department of Agromechatronics Potsdam Germany Piracicaba Brazil The University of Sydney Sydney Institute of Agriculture Sydney Australia Mendel University in Brno Department of Agrosystems and Bioclimatology Brno Czech Republic Leibniz Institute of Vegetable and Ornamental Crops Next Generation Horticultural Systems Grossbeeren Germany Eberswalde University for Sustainable Development Landscape Management and Nature Conservation Eberswalde Germany —Soil Science and Soil Ecology Bonn Germany Tokyo University of Agriculture and Technology Institute of Agriculture Tokyo Japan LISAH Univ. Montpellier AgroParisTech INRAE IRD L'Institut Agro Montpellier France Agroscope Field-Crop Systems and Plant Nutrition Nyon Switzerland University of Wisconsin-Madison Department of Soil Science Madison United States Federal University of Viçosa Department of Agricultural Engineering Viçosa Brazil Woodwell Climate Research Center Falmouth United States Academic Coordination Santa Maria Brazil Soil Department Santa Maria Brazil Division of Environment and Natural Resources Aas Norway University of Rostock Chair of Geodesy and Geoinformatics Rostock Germany Federal Technological University of Paraná Dois Vizinhos Brazil BÜCHI Labortechnik AG Data Science Department Flawil Switzerland Imperial College London Imperial College Business School London United Kingdom University of Tübingen Department of Geosciences Tübingen Germany University of Tübingen DFG Cluster of Excellence ‘Machine Learning for Science’ Germany Bern University of Applied Sciences Competence Center for Soils Zollikofen Switzerland Simulation and Data Science Müncheberg Germany Federal University of Jataí Institute of Agricultural Sciences Jatai Brazil Federal University of Mato Grosso Instute of Agricultural and Environmental Scinces Sinop Brazil Department of Soil and Environment Skara S

Digital soil mapping (DSM) relies on a broad pool of statistical methods, yet determining the optimal method for a given context remains challenging and contentious. Benchmarking studies on multiple datasets are needed to reveal strengths and limitations of commonly used methods. Existing DSM studies usually rely on a single dataset with restricted access, leading to incomplete and potentially misleading conclusions. To address these issues, we introduce an open-access dataset collection called Precision Liming Soil Datasets (LimeSoDa). LimeSoDa consists of 31 field- and farm-scale datasets from various countries. Each dataset has three target soil properties: (1) soil organic matter or soil organic carbon, (2) clay content and (3) pH, alongside a set of features. Features are dataset-specific and were obtained by optical spectroscopy, proximal- and remote soil sensing. All datasets were aligned to a tabular format and are ready-to-use for modeling. We demonstrated the use of LimeSoDa for benchmarking by comparing the predictive performance of four learning algorithms across all datasets. This comparison included multiple linear regression (MLR), support vector regression (SVR), categorical boosting (CatBoost) and random forest (RF). The results showed that although no single algorithm was universally superior, certain algorithms performed better in specific contexts. MLR and SVR performed better on high-dimensional spectral datasets, likely due to better compatibility with principal components. In contrast, CatBoost and RF exhibited considerably better performances when applied to datasets with a moderate number ( © 2025 The Author(s)

关键词： Random forests

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Colour vision defects in schizophrenia spectrum disorders: A systematic review

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Neuroscience Applied 2024年 3卷

作者： Gupta, Arnav Tran, Jason Fabiano, Nicholas Dhir, Vinita Larose, Katherine Lasker, Iris Wong, Stanley Mohammad, Ibrahim Y.Z. Le, Steven Shorr, Risa Bellato, Alessio Cortese, Samuele Zampieri, Andrea Solmi, Marco Department of Medicine University of Calgary Calgary AB Canada College of Public Health Kent State University Kent OH United States Department of Psychiatry University of Toronto Toronto ON Canada Department of Psychiatry University of Ottawa Ottawa ON Canada Sciences Lab Department of Psychiatry University of Ottawa Ottawa ON Canada Faculty of Science Carleton University Ottawa ON Canada Faculty of Medicine University of Ottawa Ottawa ON Canada Faculty of Social Science University of Ottawa Ottawa ON Canada Learning Services The Ottawa Hospital Ottawa ON Canada School of Psychology University of Southampton Southampton SO17 1BJ United Kingdom School of Psychology University of Nottingham Malaysia Semenyih 43500 Malaysia Mind and Neurodevelopment (MiND) Research Cluster University of Nottingham Malaysia Semenyih 43500 Malaysia Centre for Innovation in Mental Health University of Southampton Southampton SO17 1BJ United Kingdom Clinical and Experimental Sciences (CNS and Psychiatry) Faculty of Medicine University of Southampton Southampton United Kingdom Solent NHS Trust Southampton United Kingdom Hassenfeld Children's Hospital at NYU Langone New York University Child Study Center New York NY United States Division of Psychiatry and Applied Psychology School of Medicine University of Nottingham Nottingham United Kingdom Ophthalmology Unit Department of Neurological Neuropsychological Morphological and Movement Sciences University of Verona Verona Italy Department of Mental Health Ottawa Hospital Ottawa ON Canada Ottawa Hospital Research Institute (OHRI) Clinical Epidemiology Program University of Ottawa Ottawa ON Canada School of Epidemiology and Public Health Faculty of Medicine University of Ottawa Ottawa ON Canada Department of Child and Adolescent Psychiatry Charité Universitätsmedizin Berlin Germany

This systematic review synthesized the existing literature to summarize colour vision disturbances experienced by patients with schizophrenia. A comprehensive literature search compliant with PRISMA-2020 was conducted in Medline and Embase from inception to February 28, 2023. Studies were included if they: (1) included people diagnosed with schizophrenia, (2) investigated colour vision, (3) had a comparator with or without schizophrenia. Study quality appraisal was performed using the NIH Study Quality Assessment Tool. Seven studies of fair quality with 695 patients were included, of whom, 46.5% (n = 323) patients were diagnosed with a schizophrenia-spectrum disorder. Compared to healthy controls, patients with schizophrenia either made more mistakes in discriminating between colours, or were delayed in recognizing colours. One study found that Positive and Negative Syndrome Scale for Schizophrenia (PANSS) scores correlated weakly with error scores related to colour vision impairments. The most common shortcomings were lack of sample size justification (k = 7, 100%), and lack of blinding (k = 7, 100%). Our review indicates early evidence of colour vision deficits among patients with schizophrenia, and an unclear relationship between severity of schizophrenia with colour vision deficits. Possible mechanisms may include alterations in retinal dopamine transmission or schizophrenia-related cognitive deficits interacting with colour vision outcomes. Future studies may benefit from large registry analyses of patients with various schizophrenia spectrum disorders, analyzing ocular parameters (e.g., OCT), collecting data on cognitive impairment, and pursuing multivariate analyses to elucidate mechanisms for schizophrenia-related colour vision changes. © 2024

关键词： Colour blindness Colour vision Schizophrenia

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Belief of aesthetic intelligence and attribution of failure in science abilities predict Chinese students' learning engagement in drawing a future science world

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Thinking Skills and Creativity 2023年 47卷

作者： Qiao Mao Jon-Chao Hong Hoang Bao Ngoc Nguyen Department of Fine Arts National Taiwan Normal University No.162 Heping East Road Section 1 Taipei 106209 Taiwan Language and literature branch Shaoxing University Yuanpei College No. 2799 Qunxian Middle Road Shaoxing Zhejiang 312000 China Institute for Research Excellence in Learning Sciences National Taiwan Normal University No.162 Heping East Road Section 1 Taipei 106209 Taiwan Department of Industrial Education National Taiwan Normal University No.162 Heping East Road Section 1 Taipei 106209 Taiwan

Nowadays, aesthetic education plays an important role in the comprehensive development of students' competencies, but few studies have focused on incorporating science content into aesthetic education to generate students' imagination of a future world. To explore the effectiveness of this approach, “Future Science World” was implemented in arts learning, in which students were required to draw a picture including science content. Based on survey results, we examined the relationship between students' entity belief of aesthetic intelligence, attribution of failure in science abilities, and their engagement in art classes. Participants were 336 Grade 6 students from primary schools in Zhejiang, China. Structural equation modelling was used to test the hypotheses. The results showed that students' entity belief of aesthetic intelligence was positively related to their attribution of failure in science abilities. Besides, their attribution of failure was negatively related to all three engagement dimensions in drawing a “Future Science World.” Based on the findings, some recommendations are presented.

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Atlas of Gray Matter Volume Differences Across Psychiatric Conditions: A Systematic Review With a Novel Meta-Analysis That Considers Co-Occurring Disorders

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Biological Psychiatry 2025年

作者： Fortea, Lydia Ortuño, Maria De Prisco, Michele Oliva, Vincenzo Albajes-Eizagirre, Anton Fortea, Adriana Madero, Santiago Solanes, Aleix Vilajosana, Enric Yao, Yuanwei Del Fabro, Lorenzo Solé, Eduard Verdolini, Norma Farré-Colomés, Alvar Serra-Blasco, Maria Picó-Pérez, Maria Lukito, Steve Wise, Toby Carlisi, Christina Arnone, Danilo Kempton, Matthew J. Hauson, Alexander Omar Wollman, Scott Soriano-Mas, Carles Rubia, Katya Norman, Luke Fusar-Poli, Paolo Mataix-Cols, David Valentí, Marc Via, Esther Cardoner, Narcis Solmi, Marco Zhang, Jintao Pan, Pinglei Shin, Jae Il Fullana, Miquel A. Vieta, Eduard Radua, Joaquim Institut d'Investigacions Biomèdiques August Pi I Sunyer Barcelona Spain Department of Medicine University of Barcelona Institute of Neuroscience Barcelona Spain Centro de Investigación Biomédica en Red de Salud Mental Instituto de Salud Carlos III Madrid Spain Bipolar and Depressive Disorders Unit Hospital Clínic Barcelona Spain Department of Biomedical and Neuromotor Sciences University of Bologna Bologna Italy Psychiatric and Psychology Service Hospital Clínic Barcelona Spain Schizophrenia Unit Hospital Clínic Barcelona Spain Department of Psychology The University of Hong Kong Hong Kong Department of Systems Neuroscience University Medical Center Hamburg-Eppendorf Hamburg Germany Department of Pathophysiology and Transplantation University of Milan Milan Italy Local Health Unit Umbria 1 Department of Mental Health Mental Health Center of Perugia Perugia Italy Department of Addictive Behaviour and Addiction Medicine Central Institute of Mental Health Medical Faculty of Mannheim Heidelberg University Mannheim Germany eHealth ICOnnecta't Program and Psycho-Oncology Service Institut Català d'Oncologia L'Hospitalet de Llobregat Spain Psycho-oncology and Digital Health Group Health Services Research in Cancer Institut d'Investigació Biomèdica de Bellvitge L'Hospitalet del Llobregat Spain Live and Health Sciences Research Institute University of Minho Braga Portugal ICVS/3B's PT Government Associate Laboratory Guimarães Braga Portugal Departamento de Psicología Básica Universitat Jaume I Castelló de la Plana Spain Department of Child and Adolescent Psychiatry Institute of Psychology Psychiatry and Neuroscience King's College London London United Kingdom Department of Neuroimaging Institute of Psychiatry Psychology & Neurosciences King's College London London United Kingdom Division of Psychology and Language Sciences University College London London United Kingdom Centre for Affective Disorders Psychological Medicine King's College London Londo

Background: Regional gray matter volume (GMV) differences between individuals with mental disorders and comparison participants may be confounded by co-occurring disorders. To disentangle disorder-specific GMV correlates, we conducted a large-scale multidisorder meta-analysis using a novel approach that explicitly models co-occurring disorders. Methods: We systematically reviewed voxel-based morphometry studies indexed in PubMed and Scopus up to January 2023 that compared adults with major mental disorders (anorexia nervosa, schizophrenia spectrum, anxiety, bipolar, major depressive, obsessive-compulsive, and posttraumatic stress disorders plus attention-deficit/hyperactivity, autism spectrum, and borderline personality disorders) with comparison participants. Two authors independently extracted data and assessed quality using the Newcastle-Ottawa Scale. We derived GMV correlates for each disorder using: 1) a multidisorder meta-analysis that accounted for all co-occurring mental disorders simultaneously and 2) separate standard meta-analyses for each disorder in which co-occurring disorders were ignored. We assessed the alterations’ extent, intensity (effect size), and specificity (interdisorder correlations and transdiagnostic alterations) for both approaches. Results: We included 433 studies (499 datasets) involving 19,718 patients and 16,441 comparison participants (51% female, ages 20–67 years). We provide GMV correlate maps for each disorder using both approaches. The novel approach, which accounted for co-occurring disorders, produced GMV correlates that were more focal and disorder specific (less correlated across disorders and fewer transdiagnostic abnormalities). Conclusions: This work offers the most comprehensive atlas of GMV correlates across major mental disorders. Modeling co-occurring disorders yielded more specific correlates, supporting this approach's validity. The atlas NIfTI maps are available online. © 2024 Society of Biological Psychiatry

关键词： Anxiety disorders Comorbidity Gray matter volume Major depressive disorder Meta-analysis Psychiatric conditions

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Youth and family involvement in the development of a plain language trial results communication tool: CommuniKIDS

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research involvement and engagement 2023年第1期9卷 88页

作者： Ami Baba Dawn P Richards Maureen Smith Nicole Pallone Shelley Vanderhout Matthew Prebeg Ellen B M Elsman Beth K Potter Martin Offringa Nancy J Butcher Child Health Evaluative Sciences The Hospital for Sick Children Research Institute Toronto ON Canada. Clinical Trials Ontario Toronto ON Canada. Canadian Organization for Rare Disorders Ottawa ON Canada. CanPKU and Parent of a Child with an Inherited Metabolic Disease Sparwood BC Canada. Clinical Epidemiology Program Ottawa Hospital Research Institute Ottawa ON Canada. School of Epidemiology and Public Health University of Ottawa Ottawa ON Canada. Department of Psychiatry The Hospital for Sick Children Toronto ON Canada. Child Health Evaluative Sciences The Hospital for Sick Children Research Institute Toronto ON Canada. martin.offringa@sickkids.ca. Institute of Health Policy Management and Evaluation University of Toronto Toronto ON Canada. martin.offringa@sickkids.ca. Division of Neonatology The Hospital for Sick Children Toronto ON Canada. martin.offringa@sickkids.ca. Peter Gilgan Centre for Research and Learning The Hospital for Sick Children 686 Bay Street Toronto ON M5G 0A4 Canada. martin.offringa@sickkids.ca. Child Health Evaluative Sciences The Hospital for Sick Children Research Institute Toronto ON Canada. nancy.butcher@sickkids.ca. Department of Psychiatry University of Toronto Toronto ON Canada. nancy.butcher@sickkids.ca. Peter Gilgan Centre for Research and Learning The Hospital for Sick Children 686 Bay Street Toronto ON M5G 0A4 Canada. nancy.butcher@sickkids.ca.

BACKGROUND:Pediatric trials are possible through voluntary participation of children, youth (age ≤ 18 years), and their families. Despite important arguments for trialists to provide trial progress or results, and evidence that participants desire it, this information remains rarely shared with youth and their families. Little guidance exists on how trialists can best communicate trial results back to participants and their families. Guided by Liabo et al.'s framework, we describe how we developed a pediatric-specific, "plain language summary" clinical trial results template called CommuniKIDS with an adult patient partner, family partner (parent), youth advisors, and parent advisors, taking into account their unique knowledge needs and preferences. MAIN TEXT:Patient and Public Involvement (PPI) was integrated in the development of the CommuniKIDS template. In collaboration with Clinical Trials Ontario, we used a generic trial results template as a starting point. The core project leadership team included a patient partner and a family partner from project inception to completion. Five youth (ages 13-18 years) and eight parent advisors were consulted at each point of the development process through three virtual workshops conducted separately; youth workshops were led by a youth facilitator. During these workshops, advisors agreed on the importance and value of sharing trial results, and expressed their preferences on content, format, and timing of sharing trial results. PPI-led improvements included the addition of three new sections to the CommuniKIDS template: "at a glance," "side effects," and "next steps." We reflect on our PPI strategy in the context of five "values" and six "practicalities" identified as good PPI principles, and summarize lessons learned when collaborating with youth and families from this project. CONCLUSION:Involvement of a patient partner, a family partner, youth advisors, and parent advisors in the development of CommuniKIDS was critical

关键词： Clinical trials Family engagement Patient and public involvement (PPI) Patient engagement Plain language summary Trial results Youth engagement

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The SCUBA-2 Large eXtragalactic Survey: 850 μm map, catalogue and the bright-end number counts of the XMM-LSS field

arXiv

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arXiv 2023年

作者： Garratt, T.K. Geach, J.E. Tamura, Y. Coppin, K.E.K. Franco, M. Ao, Y. Chen, C.-C. Cheng, C. Clements, D.L. Dai, Y.S. Dannerbauer, H. Greve, T.R. Hatsukade, B. Hwang, H.S. Jiang, L. Kohno, K. Koprowski, M.P. Michalowski, M.J. Sawicki, M. Scott, D. Shim, H. Takeuchi, T.T. Wang, W.-H. Xue, Y.Q. Yang, C. Centre for Astrophysics Research University of Hertfordshire HatfieldAL10 9AB United Kingdom Division of Particle and Astrophysical Science Nagoya University Aichi Nagoya464-8602 Japan Purple Mountain Observatory Key Laboratory for Radio Astronomy Chinese Academy of Sciences 10 Yuanhua Road Nanjing210023 China School of Astronomy and Space Science University of Science and Technology of China Anhui Hefei230026 China Academia Sinica Institute of Astronomy and Astrophysics No. 1 Section 4 Roosevelt Road Taipei10617 Taiwan Chinese Academy of Sciences South America Center for Astronomy National Astronomical Observatories CAS Beijing100101 China Imperial College London Blackett Lab Prince Consort Road LondonSW7 2AZ United Kingdom National Astronomical Observatories Chinese Academy of Sciences 20A Datun RoadChaoyang District Beijing100101 China Tenerife La LagunaE-38205 Spain Universidad de La Laguna Dpto. Astrofísica Tenerife La LagunaE-38206 Spain National Space Institute DTU Space Technical University of Denmark Elektrovej 327 Kgs. LyngbyDK-2800 Denmark Department of Physics and Astronomy University College London Gower Street LondonWC1E 6BT United Kingdom Institute of Astronomy Graduate School of Science The University of Tokyo 2-21-1 Osawa Tokyo Mitaka181-0015 Japan Astronomy Program Department of Physics and Astronomy Seoul National University 1 Gwanak-ro Gwanak-gu Seoul08826 Korea Republic of SNU Astronomy Research Center Astronomy Program Seoul National University 1 Gwanak-ro Gwanak-gu Seoul08826 Korea Republic of Kavli Institute for Astronomy and Astrophysics Peking University No. 5 Yiheyuan Road Haidian District Beijing100871 China Institute of Astronomy Faculty of Physics Astronomy and Informatics Nicolaus Copernicus University Grudziadzka 5 Torun87-100 Poland Astronomical Observatory Institute Faculty of Physics Adam Mickiewicz University ul. Sloneczna 36 Poznań60-286 Poland Department of Astronomy and P

We present 850 μm imaging of the XMM-LSS field observed for 170 hours as part of the James Clerk Maxwell Telescope SCUBA-2 Large eXtragalactic Survey (S2LXS). S2LXS XMM-LSS maps an area of 9 deg2, reaching a moderate depth of 1σ 4 mJy beam−1. This is the largest contiguous area of extragalactic sky mapped by JCMT at 850 μm to date. The wide area of the S2LXS XMM-LSS survey allows us to probe the ultra-bright (S850μm & 15 mJy), yet rare submillimetre population. We present the S2LXS XMM-LSS catalogue, which comprises 40 sources detected at >5σ significance, with deboosted flux densities in the range of 7 mJy to 48 mJy. We robustly measure the bright-end of the 850 μm number counts at flux densities >7 mJy, reducing the Poisson errors compared to existing measurements. The S2LXS XMM-LSS observed number counts show the characteristic upturn at bright fluxes, expected to be motivated by local sources of submillimetre emission and high-redshift strongly lensed galaxies. We find that the observed 850 μm number counts are best reproduced by model predictions that include either strong lensing or source blending from a 15 arcsec beam, indicating that both may make an important contribution to the observed over-abundance of bright single dish 850 μm selected sources. We make the S2LXS XMM-LSS 850 μm map and >5σ catalogue presented here publicly available. Copyright © 2023, The Authors. All rights reserved.

关键词： Blending

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Lessons Learned from Assessing Trustworthy AI in Practice

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Digital Society 2023年第3期2卷 1-25页

作者： Vetter, Dennis Amann, Julia Bruneault, Frédérick Coffee, Megan Düdder, Boris Gallucci, Alessio Gilbert, Thomas Krendl Hagendorff, Thilo van Halem, Irmhild Hickman, Eleanore Hildt, Elisabeth Holm, Sune Kararigas, Georgios Kringen, Pedro Madai, Vince I. Wiinblad Mathez, Emilie Tithi, Jesmin Jahan Westerlund, Magnus Wurth, Renee Zicari, Roberto V. Computational Vision and Artificial Intelligence Lab Goethe University Frankfurt Frankfurt Am Main Germany Z-Inspection® Initiative Venice Italy Health Ethics and Policy Lab ETH Zurich Zurich Switzerland Strategy and Innovation Careum Foundation Zurich Switzerland Philosophie Departement Collège André-Laurendeau Montréal Canada École Des Médias Université du Québec À Montréal Montréal Canada Department of Medicine Division of Infectious Diseases and Immunology New York University Grossman School of Medicine New York City USA Department of Computer Science University of Copenhagen Copenhagen Denmark Digital Life Initiative Cornell Tech New York City USA Cluster of Excellence “Machine Learning: New Perspectives for Science” University of Tuebingen Tuebingen Germany School of Law University of Bristol Bristol UK Center for the Study of Ethics in the Professions Illinois Institute of Technology Chicago USA Department of Business Management and Analytics Arcada University of Applied Sciences Helsinki Finland Department of Food & Resource Economics University of Copenhagen Copenhagen Denmark Department of Physiology Faculty of Medicine University of Iceland Reykjavik Iceland QUEST Centre for Responsible Research Berlin Institute of Health Charité Universitätsmedizin Berlin Berlin Germany Faculty of Computing Engineering and the Built Environment School of Computing and Digital Technology Birmingham City University Birmingham UK Parallel Computing Labs Intel Santa Clara USA School of Economics Innovation and Technology Kristiania University College Oslo Norway Data Science Graduate School Seoul National University Seoul South Korea

Building artificial intelligence (AI) systems that adhere to ethical standards is a complex problem. Even though a multitude of guidelines for the design and development of such trustworthy AI systems exist, these guidelines focus on high-level and abstract requirements for AI systems, and it is often very difficult to assess if a specific system fulfills these requirements. The Z-Inspection® process provides a holistic and dynamic framework to evaluate the trustworthiness of specific AI systems at different stages of the AI lifecycle, including intended use, design, and development. It focuses, in particular, on the discussion and identification of ethical issues and tensions through the analysis of socio-technical scenarios and a requirement-based framework for ethical and trustworthy AI. This article is a methodological reflection on the Z-Inspection® process. We illustrate how high-level guidelines for ethical and trustworthy AI can be applied in practice and provide insights for both AI researchers and AI practitioners. We share the lessons learned from conducting a series of independent assessments to evaluate the trustworthiness of real-world AI systems, as well as key recommendations and practical suggestions on how to ensure a rigorous trustworthiness assessment throughout the lifecycle of an AI system. The results presented in this article are based on our assessments of AI systems in the healthcare sector and environmental monitoring, where we used the framework for trustworthy AI proposed in the Ethics Guidelines for Trustworthy AI by the European Commission’s High-Level Expert Group on AI. However, the assessment process and the lessons learned can be adapted to other domains and include additional frameworks.

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Correction to: European first-year university students accept evolution but lack substantial knowledge about it: a standardized European cross-country assessment

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Evolution: Education and Outreach 2022年第1期15卷 1-2页

作者： Paul Kuschmierz Dittmar Graf Anna Beniermann Alexander Bergmann Rianne Pinxten Tuomas Aivelo Justyna Berniak-Woźny Gustav Bohlin Anxela Bugallo-Rodriguez Lucia Vazquez-Ben Pedro Cardia Bento Filipe Barreiras Pinto Cavadas Umran Betul Cebesoy Lütfullah Türkmen Dragana D. Cvetković Emilie Demarsy Mirko S. Đorđević Uroš B. Savković Szymon M. Drobniak Liudmyla Dubchak Radka M. Dvořáková Jana Fančovičová Corinne Fortin Momir Futo Nicoleta Adriana Geamănă Niklas Gericke Donato A. Grasso Ádám Z. Lendvai Zoltán Németh Evangelia Mavrikaki Andra Meneganzin Telmo Pievani Athanasios Mogias Andrea Möller Paulo G. Mota Yamama Naciri Katarzyna Ożańska-Ponikwia Silvia Paolucci Péter László Pap Maria Petersson Barbara Pietrzak Alma Pobric Juris Porozovs Giulia Realdon Xana Sá-Pinto Mathieu Sicard Mircea T. Sofonea Andrej Sorgo Alexandru N. Stermin Ioan Tăușan Gregor Torkar Slavica Tutnjević Anna E. Uitto Máté Varga Mirna Varga Konstantinos Korfiatis Enrique Viguera Lisa Christine Virtbauer Albena Vutsova Inmaculada Yruela Jelle Zandveld Institute for Didactics of Biology Justus-Liebig-University Giessen Giessen Germany Institute for Biology Teaching and Learning Research in Biology Education Humboldt-Universität Zu Berlin Berlin Germany Faculty of Life Science Department of Biology University of Leipzig Leipzig Germany Didactica Research Unit Faculty of Social Sciences & Behavioural Ecology & Ecophysiology Group Department of Biology University of Antwerp Antwerp Belgium Organismal and Evolutionary Biology Research Program University of Helsinki Helsinki Finland University of Information Technology and Management in Rzeszów Rzeszów Poland Public & Science (VA) Stockholm Sweden Department of Pedagogy and Didactics University of A Coruña A Coruña Spain Escola Superior de Educação Politécnico Do Porto Porto Portugal Department of Science and Mathematics Polytechnic Institute of Santarém/School of Education Santarém Portugal Faculty of Education/Department of Mathematics and Science Education Usak University Uşak Turkey Chair of Genetics and Evolution Faculty of Biology University of Belgrade Belgrade Serbia Department of Botany and Plant Biology University of Geneva Geneva Switzerland Department of Evolutionary Biology Institute for Biological Research Siniša Stanković”—National Institute of Republic of Serbia University of Belgrade Belgrade Serbia Institute of Environmental Sciences Jagiellonian University Kraków Poland Department of Parliamentarism and Political Management National Academy for Public Administration Under the President of Ukraine Kiev Ukraine Department of Teaching and Didactics of Biology Charles University Prague Czech Republic Department of Biology Faculty of Education Trnava University Trnava Slovakia Laboratory of Didactic André Revuz University of Paris Paris France Institut Ruđer Bošković/Division of Molecular Biology Zagreb Croatia Department/Research Center in Systems Ecology and Sustainability University of Bucharest Bucharest Romania Department of Enviro

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Effects of aerobic exercise on cognitive function in adults with major depressive disorder: A systematic review and meta-analysis

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International journal of clinical and health psychology : IJCHP 2024年第2期24卷 100447页

作者： Fei-Fei Ren Charles H Hillman Wei-Guang Wang Ruei-Hong Li Wen-Sheng Zhou Wen-Ming Liang Yong Yang Feng-Tzu Chen Yu-Kai Chang Department of Physical Education Beijing Language and Culture University Beijing China. Department of Psychology Northeastern University Boston MA USA. Department of Physical Therapy Movement and Rehabilitation Northeastern University Boston MA USA. Department of Physical Education and Sport Sciences National Taiwan Normal University Taipei Taiwan. Department of Physical Education Jiangsu Second Normal University Jiangsu China. Life Sciences Center Vilnius University Vilnius Lithuania. Laboratory of Kinesiology and Rehabilitation School of Physical Education and Sport Chaohu University Anhui China. Department of Kinesiology National Tsing Hua University Hsinchu Taiwan. Social Emotional Education and Development Center National Taiwan Normal University Taipei Taiwan. Institute for Research Excellence in Learning Science National Taiwan Normal University Taipei Taiwan.

Background:Major Depressive Disorder (MDD) is a highly prevalent psychiatric disorder that impairs the cognitive function of individuals. Aerobic exercise stands out as a promising non-pharmacological intervention for enhancing cognitive function and promoting brain *** positive impacts of aerobic exercise on executive function in adults with depression have been documented, a comprehensive understanding of its benefits on overall cognitive function, including memory, attention, and processing speed, along with key moderating factors in adults with MDD, remains unexplored. The purpose of the systematic review and meta-analysis was to investigate the effects of aerobic exercise on overall cognitive function in adults with MDD, and to explore whether cognitive sub-domains, aerobic exercise characteristics, and study and sample variables modify the effects of aerobic exercise on cognition. Methods:Six English electronic databases (Embase, Cochrane Central, Scopus, APA PsycInfo, PubMed, Web of Science) were searched from inception to 2 April 2023. Randomized trials, including adults aged 18 years or above with a diagnosis of clinical depression, of the effects of aerobic exercise on cognitive function in adults with MDD compared to non-aerobic exercise groups were included. A three-level meta-analysis was conducted utilizing a random-effects model in R. The quality of the studies was evaluated using the Physiotherapy Evidence Database (PEDro) scale. The PROSPERO registration number is CRD42022367350. Results:Twelve randomized trials including 945 adults with MDD were included. Results indicated that aerobic exercise significantly improved overall cognitive function ( = 0.21; 95 % confidence intervals [CI] = 0.07, 0.34), and the sub-domains of memory ( = 0.25; 95 % CI = 0.06, 0.44) and executive function ( = 0.12; 95 % CI = 0.04, 0.20). Significant benefits in cognitive function were found from moderate-to-vigorous (mixed) intensity ( = 0.19; 95 % CI = 0.02, 0.3

关键词： Cognition Depression Executive function Memory Physical activity

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FUTURE-AI: International consensus guideline for trustworthy and deployable artificial intelligence in healthcare

arXiv

引用

arXiv 2023年

作者： Lekadir, Karim Feragen, Aasa Fofanah, Abdul Joseph Frangi, Alejandro F. Buyx, Alena Emelie, Anais Lara, Andrea Porras, Antonio R. Chan, An-Wen Navarro, Arcadi Glocker, Ben Botwe, Benard O. Khanal, Bishesh Beger, Brigit Wu, Carol C. Cintas, Celia Langlotz, Curtis P. Rueckert, Daniel Mzurikwao, Deogratias Fotiadis, Dimitrios I. Zhussupov, Doszhan Ferrante, Enzo Meijering, Erik Weicken, Eva González, Fabio A. Asselbergs, Folkert W. Prior, Fred Krestin, Gabriel P. Collins, Gary S. Tegenaw, Geletaw S. Kaissis, Georgios Misuraca, Gianluca Tsakou, Gianna Dwivedi, Girish Kondylakis, Haridimos Jayakody, Harsha Woodruf, Henry C. Mayer, Horst Joachim Aerts, Hugo JWL Walsh, Ian Chouvarda, Ioanna Buvat, Irène Tributsch, Isabell Rekik, Islem Duncan, James Kalpathy-Cramer, Jayashree Zahir, Jihad Park, Jinah Mongan, John Gichoya, Judy W. Schnabel, Julia A. Kushibar, Kaisar Riklund, Katrine Mori, Kensaku Marias, Kostas Amugongo, Lameck M. Fromont, Lauren A. Maier-Hein, Lena Alberich, Leonor Cerdá Rittner, Leticia Phiri, Lighton Marrakchi-Kacem, Linda Donoso-Bach, Lluís Martí-Bonmatí, Luis Cardoso, M. Jorge Bobowicz, Maciej Shabani, Mahsa Tsiknakis, Manolis Zuluaga, Maria A. Bielikova, Maria Fritzsche, Marie-Christine Camacho, Marina Linguraru, Marius George Wenzel, Markus De Bruijne, Marleen Tolsgaard, Martin G. Ghassemi, Marzyeh Ashrafuzzaman, Md Goisauf, Melanie Yaqub, Mohammad Abadía, Mónica Cano Mahmoud, Mukhtar M.E. Elattar, Mustafa Rieke, Nicola Papanikolaou, Nikolaos Lazrak, Noussair Díaz, Oliver Salvado, Olivier Pujol, Oriol Sall, Ousmane Guevara, Pamela Gordebeke, Peter Lambin, Philippe Brown, Pieta Abolmaesumi, Purang Dou, Qi Lu, Qinghua Osuala, Richard Nakasi, Rose Zhou, S. Kevin Napel, Sandy Colantonio, Sara Albarqouni, Shadi Joshi, Smriti Carter, Stacy Klein, Stefan Petersen, Steffen E. Aussó, Susanna Awate, Suyash Raviv, Tammy Riklin Cook, Tessa Mutsvangwa, Tinashe E.M. Rogers, Wendy A. Niessen, Wiro J. Puig-Bosch, Xènia Zeng, Yi Mohammed, Yunusa G. Aquino, Yves Saint James Salahuddin, Zohaib Starmans, Martijn P.A. Department de Matemàtiques i Informàtica Universitat de Barcelona Barcelona Spain Barcelona Spain DTU Compute Technical University of Denmark Kgs Lyngby Denmark Department of Mathematics and Computer Science Faculty of Science and Technology Milton Margai Technical University Freetown Sierra Leone Center for Computational Imaging & Simulation Technologies in Biomedicine Schools of Computing and Medicine University of Leeds Leeds United Kingdom Cardiovascular Science and Electronic Engineering Departments KU Leuven Leuven Belgium Institute of History and Ethics in Medicine Technical University of Munich Munich Germany Faculty of Engineering of Systems Informatics and Sciences of Computing Galileo University Guatemala City Guatemala Department of Biostatistics and Informatics Colorado School of Public Health University of Colorado Anschutz Medical Campus AuroraCO United States Department of Medicine Women’s College Research Institute University of Toronto Toronto Canada Universitat Pompeu Fabra BarcelonaBeta Brain Research Center Barcelona Spain Department of Computing Imperial College London London United Kingdom School of Biomedical & Allied Health Sciences University of Ghana Accra Ghana Department of Midwifery & Radiography School of Health & Psychological Sciences City University of London United Kingdom Kathmandu Nepal European Heart Network Brussels Belgium Department of Thoracic Imaging University of Texas MD Anderson Cancer Center Houston United States IBM Research Africa Nairobi Kenya Departments of Radiology Medicine and Biomedical Data Science Stanford University School of Medicine Stanford United States Institute for AI and Informatics in Medicine Klinikum rechts der Isar Technical University Munich Munich Germany Department of Computing Imperial College London London United Kingdom Muhimbili University of Health and Allied Sciences Dar es Salaam Tanzania United Republic of Ioannina Greece Almaty AI Lab Almaty Kazakhstan

Background: Despite major advances in artificial intelligence (AI) research for healthcare, the deployment and adoption of AI technologies remain limited in clinical practice. In recent years, concerns have been raised about the clinical, technical, ethical and legal risks associated with healthcare AI. To increase adoption in the real world, it is essential that AI technologies are trusted and accepted by patients, clinicians, health organisations and authorities. This paper describes the FUTURE-AI framework as the first international consensus guideline for trustworthy AI in healthcare. Methods: The FUTURE-AI consortium was founded in 2021 and now comprises 117 interdisciplinary experts from 50 countries representing all continents, including AI scientists, clinical researchers, biomedical ethicists, and social scientists. Over a two-year period, the consortium established guiding principles and best practices for trustworthy and deployable AI through an iterative process comprising an in-depth literature review, a modified Delphi survey, and online consensus meetings. Findings: The FUTURE-AI framework was established based on six guiding principles for trustworthy AI in healthcare, i.e. Fairness, Universality, Traceability, Usability, Robustness and Explainability. Through consensus, a set of 30 best practices were defined, addressing technical, clinical, socio-ethical and legal dimensions of trustworthy AI. The recommendations cover the entire lifecycle of healthcare AI, from design, development and validation to regulation, deployment, and monitoring. Interpretation: FUTURE-AI is a structured, risk-informed framework which provides guidance for constructing healthcare AI tools that will be trusted, deployed and adopted in real-world clinical practice. researchers are encouraged to take the recommendations into account in proof-of-concept stages to facilitate future translation towards clinical practice of healthcare AI. Copyright © 2023, The Authors. All rights

关键词： Clinical research

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