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Global cross-cultural validation of a brief measure for identifying potential suicide risk in 42 countries

Public Health 2024年 229卷 13-23页

作者： Gewirtz-Meydan, A. Koós, M. Nagy, L. Kraus, S.W. Demetrovics, Z. Potenza, M.N. Ballester-Arnal, R. Batthyány, D. Bergeron, S. Billieux, J. Burkauskas, J. Cárdenas-López, G. Carvalho, J. Castro-Calvo, J. Chen, L. Ciocca, G. Corazza, O. Csako, R. Fernandez, D.P. Fujiwara, H. Fernandez, E.F. Fuss, J. Gabrhelík, R. Gjoneska, B. Gola, M. Grubbs, J.B. Hashim, H.T. Islam, M.S. Ismail, M. Jiménez-Martínez, M.C. Jurin, T. Kalina, O. Klein, V. Költő, A. Lee, S.-K. Lewczuk, K. Lin, C.-Y. Lochner, C. López-Alvarado, S. Lukavská, K. Mayta-Tristán, P. Miller, D.J. Orosová, O. Orosz, G. Ponce, F.P. Quintana, G.R. Quintero Garzola, G.C. Ramos-Diaz, J. Rigaud, K. Rousseau, A. De Tubino Scanavino, M. Schulmeyer, M.K. Sharan, P. Shibata, M. Shoib, S. Sigre-Leirós, V. Sniewski, L. Spasovski, O. Steibliene, V. Stein, D.J. Strong, C. Ünsal, B.C. Vaillancourt-Morel, M.-P. Van Hout, M.C. Bőthe, B. School of Social Work Faculty of Social Welfare and Health Sciences University of Haifa Israel Institute of Psychology ELTE Eötvös Loránd University Budapest Hungary Institute of Forensic Psychiatry and Sex Research University of Duisburg-Essen Essen Germany Doctoral School of Psychology ELTE Eötvös Loránd University Budapest Hungary Department of Psychology University of Nevada Las Vegas Las Vegas NV United States Centre of Excellence in Responsible Gaming University of Gibraltar Gibraltar Gibraltar Yale University School of Medicine New Haven CT United States Connecticut Council on Problem Gambling Wethersfield CT United States Departmento de Psicología Básica Clínica y Psicobiología University Jaume I of Castellón Spain Institute for Behavioural Addictions Sigmund Freud University Vienna Austria Département de Psychologie Université de Montréal Montréal Canada Institute of Psychology University of Lausanne Lausanne Switzerland Center for Excessive Gambling Addiction Medicine Lausanne University Hospitals (CHUV) Lausanne Switzerland Laboratory of Behavioral Medicine Neuroscience Institute Lithuanian University of Health Sciences Lithuania Virtual Teaching and Cyberpsychology Laboratory School of Psychology National Autonomous University of Mexico Mexico William James Center for Research Departamento de Educação e Psicologia Universidade de Aveiro Aveiro Portugal Department of Personality Assessment and Psychological Treatments University of Valencia Spain Department of Psychology College of Humanity and Social Science Fuzhou University China Section of Sexual Psychopathology Department of Dynamic and Clinical Psychology and Health Studies Sapienza University of Rome Rome Italy Department of Clinical Pharmaceutical and Biological Sciences University of Hertfordshire United Kingdom Department of Psychology and Cognitive Science University of Trento Italy Department of Psychology and Neuroscience Auckland University of Technology Auckland New Z

Objectives: This study aimed to examine the psychometric properties of the P4 suicide screener in a multinational sample. The primary goal was to evaluate the reliability and validity of the scale and investigate its convergent validity by analyzing its correlation with depression, anxiety, and substance use. Study design: The study design is a cross-sectional self-report study conducted across 42 countries. Methods: A cross-sectional, self-report study was conducted in 42 countries, with a total of 82,243 participants included in the final data set. Results: The study provides an overview of suicide ideation rates across 42 countries and confirms the structural validity of the P4 screener. The findings indicated that sexual and gender minority individuals exhibited higher rates of suicidal ideation. The P4 screener showed adequate reliability, convergence, and discriminant validity, and a cutoff score of 1 is recommended to identify individuals at risk of suicidal behavior. Conclusions: The study supports the reliability and validity of the P4 suicide screener across 42 diverse countries, highlighting the importance of using a cross-cultural suicide risk assessment to standardize the identification of high-risk individuals and tailoring culturally sensitive suicide prevention strategies. © 2024 The Royal Society for Public Health

关键词： Assessment Cross-cultural Suicide Validation

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The eleven-item Alcohol, Smoking and Substance Involvement Screening Test (ASSIST-11): Cross-cultural psychometric evaluation across 42 countries

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Journal of Psychiatric Research 2023年第1期165卷 16-27页

作者： Lee, Chih-Ting Lin, Chung-Ying Koós, Mónika Nagy, Léna Kraus, Shane W. Demetrovics, Zsolt Potenza, Marc N. Ballester-Arnal, Rafael Batthyány, Dominik Bergeron, Sophie Billieux, Joël Burkauskas, Julius Cárdenas-López, Georgina Carvalho, Joana Castro-Calvo, Jesús Chen, Lijun Ciocca, Giacomo Corazza, Ornella Csako, Rita I. Fernandez, David P. Fernandez, Elaine F. Fujiwara, Hironobu Fuss, Johannes Gabrhelík, Roman Gewirtz-Meydan, Ateret Gjoneska, Biljana Gola, Mateusz Grubbs, Joshua B. Hashim, Hashim T. Islam, Md. Saiful Ismail, Mustafa Jiménez-Martínez, Martha Jurin, Tanja Kalina, Ondrej Klein, Verena Költő, András Lee, Sang-Kyu Lewczuk, Karol Lochner, Christine López-Alvarado, Silvia Lukavská, Kateřina Mayta-Tristán, Percy Milea, Ionut Miller, Dan J. Orosová, Oľga Orosz, Gábor Ponce, Fernando P. Quintana, Gonzalo R. Garzola, Gabriel C.Quintero Ramos-Diaz, Jano Rigaud, Kévin Rousseau, Ann Scanavino, Marco De Tubino Schulmeyer, Marion K. Sharan, Pratap Shibata, Mami Shoib, Sheikh Sigre-Leirós, Vera Sniewski, Luke Spasovski, Ognen Steibliene, Vesta Stein, Dan J. Strizek, Julian Ünsal, Berk C. Vaillancourt-Morel, Marie-Pier Van Hout, Marie Claire Bőthe, Beáta Department of Family Medicine National Cheng Kung University Hospital College of Medicine National Cheng Kung University Tainan Taiwan Institute of Allied Health Sciences College of Medicine National Cheng Kung University Tainan Taiwan Biostatistics Consulting Center National Cheng Kung University Hospital College of Medicine National Cheng Kung University Tainan Taiwan Doctoral School of Psychology ELTE Eötvös Loránd University Budapest Hungary Institute of Psychology ELTE Eötvös Loránd University Budapest Hungary Department of Psychology University of Nevada Las Vegas Las Vegas NV United States Centre of Excellence in Responsible Gaming University of Gibraltar Gibraltar Gibraltar Yale University School of Medicine New Haven CT United States Connecticut Council on Problem Gambling Wethersfield CT United States Departmento de Psicología Básica Clínica y Psicobiología University Jaume I of Castellón Spain Institute for Behavioural Addictions Sigmund Freud University Vienna Austria Département de Psychologie Université de Montréal Montréal Canada Institute of Psychology University of Lausanne Lausanne Switzerland Center for Excessive Gambling Addiction Medicine Lausanne University Hospitals (CHUV) Lausanne Switzerland Laboratory of Behavioral Medicine Neuroscience Institute Lithuanian University of Health Sciences Lithuania Virtual Teaching and Cyberpsychology Laboratory School of Psychology National Autonomous University of Mexico Mexico William James Center for Research Departamento de Educação e Psicologia Universidade de Aveiro CPUP: Center for Psychology at Porto University Aveiro Portugal Department of Personality Assessment and Psychological Treatments University of Valencia Spain Department of Psychology College of Humanity and Social Science Fuzhou University China Section of Sexual Psychopathology Department of Dynamic and Clinical Psychology and Health Studies Sapienza University of Rome Rome Italy Department of Clinical Pharmaceuti

The Alcohol, Smoking and Substance Involvement Screening Test (ASSIST) is an instrument to screen substance-use-related health risks. However, little is known whether the ASSIST could be further shortened while remaining psychometrically sound across different countries, languages, gender identities, and sexual-orientation-based groups. The study aimed to validate a shortened 11-item ASSIST (ASSIST-11). Using the International Sex Survey data, 82,243 participants (M _age = 32.39 years) across 42 countries and 26 languages completed questions from the ASSIST-11 regarding gender identity, sexual orientation, and other information. Confirmatory factor analysis (CFA) and multigroup CFA (MGCFA) evaluated the ASSIST-11's structure and tested measurement invariance across groups. Cronbach's α and McDonald's ω were used to examine the internal consistency. Cohen's d and independent t-tests were used to examine known-group validity. The ASSIST-11 was unidimensional across countries, languages, age groups, gender identities (i.e., men, women, and gender-diverse individuals), and sexual orientations (i.e., heterosexual and sexual minority individuals). Cronbach's α was 0.63 and McDonald's ω was 0.68 for the ASSIST-11. Known-group validity was supported by Cohen's d (range between 0.23 and 0.40) with significant differences (p-values<0.001). The ASSIST-11 is a modified instrument with a unidimensional factor structure across different languages, age groups, countries, gender identities, and sexual orientations. The low internal consistency of the ASSIST-11 might be acceptable as it assesses a broad concept (i.e., use of several different substances). Healthcare providers and researchers may use the ASSIST-11 to quickly assess substance-use information from general populations and evaluate the need to follow up with more detailed questions about substance use. © 2023 Elsevier Ltd

关键词： Addictive behaviors Cross-cultural study Factor structure Measurement invariance Psychometrics

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Generative Artificial Intelligence in Healthcare: Ethical Considerations and Assessment Checklist

arXiv

引用

arXiv 2023年

作者： Ning, Yilin Teixayavong, Salinelat Shang, Yuqing Savulescu, Julian Nagaraj, Vaishaanth Miao, Di Mertens, Mayli Wei Ting, Daniel Shu Ling Ong, Jasmine Chiat Liu, Mingxuan Cao, Jiuwen Dunn, Michael Vaughan, Roger Hock Ong, Marcus Eng Sung, Joseph Jao-Yiu Topol, Eric J. Liu, Nan Centre for Quantitative Medicine Duke-NUS Medical School Singapore Singapore Centre for Biomedical Ethics National University of Singapore Singapore Singapore Wellcome Centre for Ethics and Humanities University of Oxford Oxford United Kingdom School of Medicine Imperial College London London United Kingdom Centre for Ethics Department of Philosophy University of Antwerp Antwerp Belgium Antwerp Center on Responsible AI University of Antwerp Antwerp Belgium Singapore Eye Research Institute Singapore National Eye Centre Singapore Singapore SingHealth AI Office Singapore Health Services Singapore Singapore Division of Pharmacy Singapore General Hospital Singapore Singapore Machine Learning and I-Health International Cooperation Base of Zhejiang Province Hangzhou Dianzi University Zhejiang China Artificial Intelligence Institute Hangzhou Dianzi University Zhejiang China Programme in Health Services and Systems Research Duke-NUS Medical School Singapore Singapore Department of Emergency Medicine Singapore General Hospital Singapore Singapore Lee Kong Chian School of Medicine Nanyang Technological University Singapore Singapore State Key Laboratory of Digestive Disease The Chinese University of Hong Kong Hong Kong Scripps Research Translational Institute Scripps Research La Jolla CA United States Institute of Data Science National University of Singapore Singapore Singapore Centre for Quantitative Medicine Duke-NUS Medical School 8 College Road Singapore169857 Singapore

The widespread use of ChatGPT and other emerging technology powered by generative artificial intelligence (GenAI) has drawn much attention to potential ethical issues, especially in high-stakes applications such as healthcare, but ethical discussions are yet to translate into operationalisable solutions. Furthermore, ongoing ethical discussions often neglect other types of GenAI that have been used to synthesise data (e.g., images) for research and practical purposes, which resolved some ethical issues and exposed others. We conduct a scoping review of ethical discussions on GenAI in healthcare to comprehensively analyse gaps in the current research, and further propose to reduce the gaps by developing a checklist for comprehensive assessment and transparent documentation of ethical discussions in GenAI research. The checklist can be readily integrated into the current peer review and publication system to enhance GenAI research, and may be used for ethics-related disclosures for GenAI-powered products, healthcare applications of such products and beyond. © 2023, CC BY.

关键词： Artificial intelligence

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MultiplEYE: Creating a multilingual eye-tracking-while-reading corpus 25

MultiplEYE: Creating a multilingual eye-tracking-while-readi...

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Proceedings of the 2025 Symposium on Eye Tracking Research and Applications

作者： Deborah Noemie Jakobi Maja Stegenwallner-Schütz Nora Hollenstein Cui Ding Ramune Kaspere Ana Matić Škorić Eva Pavlinusic Vilus Stefan Frank Marie-Luise Müller Kristine M Jensen de López Nik Kharlamov Hanne B. Søndergaard Knudsen Yevgeni Berzak Ella Lion Irina A. Sekerina Cengiz Acarturk Mohd Faizan Ansari Katarzyna Harezlak Pawel Kasprowski Ana Bautista Lisa Beinborn Anna Bondar Antonia Boznou Leah Bradshaw Jana Mara Hofmann Thyra Krosness Not Battesta Soliva Anila Çepani Kristina Cergol Ana Došen Marijan Palmovic Adelina Çerpja Dalí Chirino Jan Chromý Vera Demberg Iza Škrjanec Nazik Dinçtopal Deniz Dr. Inmaculada Fajardo Mariola Giménez-Salvador Xavier Mínguez-López Maroš Filip Zigmunds Freibergs Jéssica Gomes Andreia Janeiro Paula Luegi João Veríssimo Sasho Gramatikov Jana Hasenäcker Alba Haveriku Nelda Kote Muhammad M. Kamal Hanna Kędzierska Dorota Klimek-Jankowska Sara Kosutar Daniel G. Krakowczyk Izabela Krejtz Marta Łockiewicz Kaidi Lõo Jurgita Motiejūnienė Jamal A. Nasir Johanne Sofie Krog Nedergård Ayşegül Özkan Mikuláš Preininger Loredana Pungă David Robert Reich Chiara Tschirner Špela Rot Andreas Säuberli Jordi Solé-Casals Ekaterina Strati Igor Svoboda Evis Trandafili Spyridoula Varlokosta Mila Vulchanova Lena A. Jäger Department of Computational Linguistics University of Zurich Zurich Switzerland Humboldt-University of Berlin Berlin Germany and University of Koblenz Koblenz Germany Kaunas University of Technology Kaunas Lithuania Faculty of Education and Rehabilitation Sciences Dept of Speech and Language pathology Laboratory for Psycholinguistic Research University of Zagreb Zagreb Croatia Centre for Language Studies Radboud University Nijmegen Netherlands ZPID Leibnitz Institute for Psychology Trier Germany Aalborg University Aalborg Denmark Data and Decision Sciences Technion Haifa Israel Department of Psychology College of Staten Island Staten Island New York USA Cognitive Science Department Jagiellonian University Krakow Poland Silesian University of Technology Gliwice Poland Institute of Informatics Silesian University of Technology Gliwice Poland Basque Center on Cognition Brain & Language Donostia Spain Human-Centered Data Science University of Göttingen Göttingen Germany Department of Computational Linguistics University of Zurich Zurich Switzerland and Digital Society Initiative University of Zurich Zurich Switzerland Lab of Psycholinguistics & Neurolinguistics National and Kapodistrian University of Athens Athens Greece Department of Computational Linguistics University of Zurich University of Zurich Switzerland University of Zurich Zurich Switzerland University of Tirana Tirana Albania Faculty of Teacher Education University of Zagreb Zagreb Croatia Hrvatska Croatia and Laboratory for Psycholinguistic Research University of Zagreb Zagreb Croatia Hrvatska Croatia University of Zagreb Zagreb Croatia Dept. of Speech & Lang. Path. Laboratory for Psycholinguistic Research University of Zagreb Zagreb Croatia Hrvatska Croatia Academy of Sciences of Albania Tirana Albania Radboud University Nijmegen Netherlands Institute of Czech Language and Theory of Communication Faculty of Arts Charles University Prague Czech Republic Saarland Universit

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Impact of sharing Alzheimer’s disease biomarker results with individuals without dementia: a systematic review and meta-analysis of empirical data

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Alzheimer's & Dementia 2023年第S20期19卷

作者： Jetske van der Schaar Leonie N.C. Visser Johannes C.F. Ket Colin Groot Yolande A.L. Pijnenburg Philip Scheltens Annelien L. Bredenoord van den Mariette A. Hoven Wiesje M. van der Flier Alzheimer Center Amsterdam Neurology Vrije Universiteit Amsterdam Amsterdam UMC location VUmc Amsterdam Netherlands Amsterdam Neuroscience Neurodegeneration Amsterdam Netherlands Amsterdam Public Health Quality of Care Personalized Medicine Amsterdam Netherlands Department of Medical Psychology Amsterdam Public Health research Institute Amsterdam UMC location AMC Amsterdam Netherlands Neurobiology Care Sciences and Society Division of Clinical Geriatrics Center for Alzheimer Research Karolinska Institutet Stockholm/Solna Sweden Medical Library Vrije Universiteit Amsterdam Amsterdam Netherlands Alzheimer Center Amsterdam Neurology Amsterdam UMC Amsterdam Netherlands EQT Life Sciences (formerly LSP) Amsterdam Netherlands Erasmus School of Philosophy Erasmus University Rotterdam Rotterdam Netherlands Department of Ethics Law and Humanities Amsterdam University Medical Centers Amsterdam Netherlands Amsterdam Neuroscience Neurodegeneration Amsterdam Noord-Holland Netherlands Alzheimer Center Amsterdam Neurology Vrije Universiteit Amsterdam Amsterdam UMC location VUmc Amsterdam Noord-Holland Netherlands Department of Epidemiology & Data Science Amsterdam UMC location Vrije Universiteit Amsterdam Amsterdam Netherlands

Background A biological definition of Alzheimer’s disease (AD) is increasingly used in research, but little is known about the consequences of early biomarker testing from the perspective of professional (healthcare professionals) and personal (citizens, patients, and their families) stakeholders. We aim to provide a systematic literature review and meta-analysis of empirical evidence on expected and experienced implications of sharing AD biomarker results with individuals without dementia. Method We searched PubMed, Embase, APA PsycINFO, and Web of science Core Collection on 10 November 2021 for literature on sharing AD biomarker results with individuals without dementia. Two reviewers independently screened 8034 titles and abstracts, and 192 full-text records, for eligibility. Results from included studies were synthesized and quantitative data on psychosocial impact (anxiety, depression, stress, and suicidal ideation) were meta-analyzed using a random-effects model. Result We included 33 publications (20 quantitative and 13 qualitative data). Of these, eight reported on professional stakeholders’ attitudes and practices, 13 on personal stakeholders’ expectations, and 12 on their actual experiences. Most professional stakeholders valued biomarker testing, believing the benefits outweigh the risks, although attitudes and practices varied considerably. Most personal stakeholders expressed interest in (hypothetical) biomarker assessment, expecting to gain insight, reduce dementia risk and prepare for the future. Studies evaluating actual experiences found that sharing negative biomarker results led to relief and sometimes frustration for not having an explanation for concerns, while positive biomarkers prompted anxiety but also clarity. Those tested positive intended to adapt their lifestyle and plans, yet some worried about stigmatization. The majority did not regret being informed. Meta-analysis of 5 studies including 2012 participants (elevated amyloid = 1324, asy

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Author Correction: A shifting role of thalamocortical connectivity in the emergence of cortical functional organization

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Nature neuroscience 2024年第8期27卷 1632页

作者： Shinwon Park Koen V Haak Stuart Oldham Hanbyul Cho Kyoungseob Byeon Bo-Yong Park Phoebe Thomson Haitao Chen Wei Gao Ting Xu Sofie Valk Michael P Milham Boris Bernhardt Adriana Di Martino Seok-Jun Hong Center for Neuroscience Imaging Research Institute for Basic Science (IBS) Suwon Korea. Autism Center Child Mind Institute New York NY USA. Department of Cognitive Science and Artificial Intelligence Tilburg School of Humanities and Digital Sciences Tilburg University Tilburg The Netherlands. Donders Centre for Cognitive Neuroimaging Donders Institute Radboud University Radboud The Netherlands. Developmental Imaging Murdoch Children's Research Institute Parkville Victoria Australia. The Turner Institute for Brain and Mental Health School of Psychological Sciences and Monash Biomedical Imaging Monash University Clayton Victoria Australia. Center for Integrative Developing Brain Child Mind Institute New York NY USA. Department of Data Science Inha University Incheon South Korea. Department of Biomedical Sciences and Biomedical Imaging Research Institute Cedars-Sinai Medical Center Los Angeles CA USA. Department of Bioengineering University of California at Los Angeles Los Angeles CA USA. Department of Medicine University of California at Los Angeles Los Angeles CA USA. Max Planck Institute for Human Cognitive and Brain Sciences Leipzig Germany. Institute of Neuroscience and Medicine (INM-7) Brain and Behavior Forschungszentrum Juelich Germany. Center for the Developing Brain Child Mind Institute New York NY USA. Nathan S. Kline Institute for Psychiatric Research Orangeburg NY USA. McConnell Brain Imaging Centre Montreal Neurological Institute and Hospital McGill University Montreal Quebec Canada. Center for Neuroscience Imaging Research Institute for Basic Science (IBS) Suwon Korea. hongseokjun@skku.edu. Center for the Developing Brain Child Mind Institute New York NY USA. hongseokjun@skku.edu. Department of Biomedical Engineering Sungkyunkwan University Suwon South Korea. hongseokjun@skku.edu. Department of Intelligent Precision Healthcare Convergence Sungkyunkwan University Suwon South Korea. hongseokjun@skku.edu. Department of MetaBioHealt

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Pandemic drugs at pandemic speed: Infrastructure for accelerating COVID-19 drug discovery with hybrid machine learning- And physics-based simulations on high performance computers

arXiv

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arXiv 2021年

作者： Bhati, Agastya P. Wan, Shunzhou Alfè, Dario Clyde, Austin R. Bode, Mathis Tan, Li Titov, Mikhail Merzky, Andre Turilli, Matteo Jha, Shantenu Highfield, Roger R. Rocchia, Walter Scafuri, Nicola Succi, Sauro Kranzlmüller, Dieter Mathias, Gerald Wifling, David Donon, Yann Meglio, Alberto Di Vallecorsa, Sofia Ma, Heng Trifan, Anda Ramanathan, Arvind Brettin, Tom Partin, Alexander Xia, Fangfang Duan, Xiaotan Stevens, Rick Coveney, Peter V. Centre for Computational Science University College London Gordon Street LondonWC1H 0AJ United Kingdom Department of Earth Sciences London Centre for Nanotechnology and Thomas Young Centre University College London University College London Gower Street LondonWC1E 6BT United Kingdom Dipartimento di Fisica Ettore Pancini Università di Napoli Federico II Monte Sant’Angelo NapoliI-80126 Italy Department of Computer Science University of Chicago Institute for Combustion Technology RWTH Aachen University Aachen52056 Germany Brookhaven National Laboratory UptonNY11973 United States Rutgers State University of New Jersey PiscatawayNJ08854 United States Science Museum Exhibition Road LondonSW7 2DD United Kingdom Concept Lab Italian Institute of Technology Via Melen Genova Italy Center for Life Nanosciences at La Sapienza Italian Institute of Technology viale Regina Elena Roma Italy of the Bavarian Academy of Sciences and Humanities Boltzmannstraße 1 Garching bei München85748 Germany OpenLab CERN Geneva Switzerland Data Science and Learning Division Argonne National Laboratory LemontIL60439 United States Computing Environment and Life Sciences Directorate Argonne National Laboratory LemontIL60439 United States Institute for Informatics Science Park 904 University of Amsterdam Amsterdam1098 XH Netherlands

The race to meet the challenges of the global pandemic has served as a reminder that the existing drug discovery process is expensive, inefficient and slow. There is a major bottleneck screening the vast number of potential small molecules to shortlist lead compounds for antiviral drug development. New opportunities to accelerate drug discovery lie at the interface between machine learning methods, in this case developed for linear accelerators, and physics-based methods. The two in silico methods, each have their own advantages and limitations which, interestingly, complement each other. Here, we present an innovative infrastructural development that combines both approaches to accelerate drug discovery. The scale of the potential resulting workflow is such that it is dependent on supercomputing to achieve extremely high throughput. We have demonstrated the viability of this workflow for the study of inhibitors for four COVID-19 target proteins and our ability to perform the required large-scale calculations to identify lead antiviral compounds through repurposing on a variety of supercomputers. © 2021, CC BY.

关键词： Molecular dynamics

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Automatic Extraction of Medication Mentions from Tweets-Overview of the BioCreative VII Shared Task 3 Competition

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database : the journal of biological databases and curation 2023年第2023期2023卷 baac108页

作者： Davy Weissenbacher Karen O'Connor Siddharth Rawal Yu Zhang Richard Tzong-Han Tsai Timothy Miller Dongfang Xu Carol Anderson Bo Liu Qing Han Jinfeng Zhang Igor Kulev Berkay Köprü Raul Rodriguez-Esteban Elif Ozkirimli Ammer Ayach Roland Roller Stephen Piccolo Peijin Han V G Vinod Vydiswaran Ramya Tekumalla Juan M Banda Parsa Bagherzadeh Sabine Bergler João F Silva Tiago Almeida Paloma Martinez Renzo Rivera-Zavala Chen-Kai Wang Hong-Jie Dai Luis Alberto Robles Hernandez Graciela Gonzalez-Hernandez Department of Computational Biomedicine Cedars-Sinai Medical Center Los Angeles CA USA. DBEI The Perelman School of Medicine University of Pennsylvania Philadelphia PA USA. Department of Computer Science and Information Engineering National Central University No. 300 Zhongda Rd Zhongli District Taoyuan 320 Taiwan. IoX Center National Taiwan University Da'an District Section 4 Roosevelt Rd No. 1 Barry Lam Hall Taipei 106 Taiwan. Research Center for Humanities and Social Sciences Academia Sinica No. 128 Section 2 Academia Rd Nangang District Taipei 115 Taiwan. Computational Health Informatics Program Boston Children's Hospital Boston MA USA. Department of Pediatrics Harvard Medical School Boston MA USA. NVIDIA Santa Clara CA USA. Department of Statistics Florida State University Tallahassee FL USA. Data and Analytics Chapter F. Hoffmann-La Roche Ltd Switzerland. Pharmaceutical Research and Early Development Roche Innovation Center Basel Switzerland. Speech and Language Technology Lab DFKI Berlin Germany. Department of Biology Brigham Young University Provo UT USA. Department of Computational Medicine and Bioinformatics Medical School University of Michigan Ann Arbor MI USA. Department of Learning Health Sciences Medical School University of Michigan Ann Arbor MI USA. School of Information University of Michigan Ann Arbor MI USA. Department of Computer Science Georgia State University Atlanta GA USA. CLaC Labs Concordia University Montreal Canada. DETI Institute of Electronics and Informatics Engineering of Aveiro University of Aveiro Portugal. Department of Computation University of A Coruña Spain. Computer Science and Engineering Department Universidad Carlos III de Madrid Madrid Spain. Big Data Laboratory Chunghwa Telecom Laboratories Taoyuan Taiwan. Department of Computer Science National Yang Ming Chiao Tung University Hsinchu Taiwan. Department of Electrical Engineering College of Electrical Engineering and Computer Sci

This study presents the outcomes of the shared task competition BioCreative VII (Task 3) focusing on the extraction of medication names from a Twitter user's publicly available tweets (the user's 'timeline'). In general, detecting health-related tweets is notoriously challenging for natural language processing tools. The main challenge, aside from the informality of the language used, is that people tweet about any and all topics, and most of their tweets are not related to health. Thus, finding those tweets in a user's timeline that mention specific health-related concepts such as medications requires addressing extreme imbalance. Task 3 called for detecting tweets in a user's timeline that mentions a medication name and, for each detected mention, extracting its span. The organizers made available a corpus consisting of 182 049 tweets publicly posted by 212 Twitter users with all medication mentions manually annotated. The corpus exhibits the natural distribution of positive tweets, with only 442 tweets (0.2%) mentioning a medication. This task was an opportunity for participants to evaluate methods that are robust to class imbalance beyond the simple lexical match. A total of 65 teams registered, and 16 teams submitted a system run. This study summarizes the corpus created by the organizers and the approaches taken by the participating teams for this challenge. The corpus is freely available at https://***/tasks/biocreative-vii/track-3/. The methods and the results of the competing systems are analyzed with a focus on the approaches taken for learning from class-imbalanced data.

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Why Do People Watch Pornography? Cross-Cultural Validation of the Pornography Use Motivations Scale (PUMS) and Its Short Form (PUMS-8)

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Journal of sex research 2024年 1-17页

作者： Mónika Koós Léna Nagy Shane W Kraus Zsolt Demetrovics Marc N Potenza Émilie Gaudet Rafael Ballester-Arnal Dominik Batthyány Sophie Bergeron Joël Billieux Peer Briken Julius Burkauskas Georgina Cárdenas-López Joana Carvalho Jesús Castro-Calvo Yun-Hsuan Chang Lijun Chen Giacomo Ciocca Ornella Corazza Rita I Csako David P Fernandez Hironobu Fujiwara Elaine F Fernandez Johannes Fuss Roman Gabrhelík Ateret Gewirtz-Meydan Biljana Gjoneska Mateusz Gola Joshua B Grubbs Hashim T Hashim Yi-Ping Hsieh Md Saiful Islam Mustafa Ismail Martha C Jiménez-Martínez Tanja Jurin Ondrej Kalina Verena Klein András Költő Sang-Kyu Lee Karol Lewczuk Christine Lochner Silvia López-Alvarado Kateřina Lukavská Percy Mayta-Tristán Dan J Miller Oľga Orosová Gábor Orosz Fernando P Ponce Gonzalo R Quintana Gabriel C Quintero Garzola Jano Ramos-Diaz Kévin Rigaud Ann Rousseau Marco De Tubino Scanavino Marion K Schulmeyer Pratap Sharan Mami Shibata Sheikh Shoib Vera Sigre-Leirós Luke Sniewski Ognen Spasovski Vesta Steibliene Dan J Stein Julian Strizek Alexandar Štulhofer Berk C Ünsal Marie-Pier Vaillancourt-Morel Marie Claire Van Hout Beáta Bőthe Institute of Forensic Psychiatry and Sex Research University of Duisburg-Essen. Institute of Psychology ELTE Eötvös Loránd University. Department of Psychology University of Nevada. Centre of Excellence in Responsible Gaming University of Gibraltar. College of Education Psychology and Social Work Flinders University. Yale University School of Medicine. Connecticut Council on Problem Gambling. Département de Psychologie Université de Montréal. Departmento de Psicología Básica Clínica y Psicobiología University Jaume I of Castellón. Institute for Behavioural Addictions Sigmund Freud University Vienna. Institute of Psychology University of Lausanne. Center for Excessive Gambling Addiction Medicine Lausanne University Hospitals (CHUV). Institute for Sex Research Sexual Medicine and Forensic Psychiatry University Medical Centre Hamburg-Eppendorf. Laboratory of Behavioral Medicine Neuroscience Institute Lithuanian University of Health Sciences. Virtual Teaching and Cyberpsychology Laboratory School of Psychology National Autonomous University of Mexico. William James Center for Research Departamento de Educação e Psicologia Universidade de Aveiro. Department of Personality Assessment and Psychological Treatments University of Valencia. Institute of Gerontology College of Medicine National Cheng Kung University. Institute of Behavioral Sciences College of Medicine National Cheng Kung University. Department of Psychology College of Humanity and Social Science Fuzhou University. Section of Sexual Psychopathology Department of Dynamic and Clinical Psychology and Health Studies Sapienza University of Rome. Department of Clinical Pharmaceutical and Biological Sciences University of Hertfordshire. Department of Psychology and Cognitive Science University of Trento. Department of Psychology and Neuroscience Auckland University of Technology. Nottingham Trent University. Department of Neuropsychiatry Graduate School of Medicine Kyoto University. Decentralized Big Data Team RIKEN Center f

Motivations for pornography use may vary across gender identities, sexual orientations, and geographical regions, warranting examination to promote individual and public health. The aims of this study were to validate the Pornography Use Motivations Scale (PUMS) in a diverse, multicultural sample, and develop a short form (PUMS-8) that can assess a wide range of pornography use motivations. Using data from 42 countries ( = 75,117; M = 32.07; SD = 12.37), enabled us to thoroughly evaluate the dimensionality, validity, and reliability of the Pornography Use Motivations Scale (PUMS), leading to the development of the more concise PUMS-8 short scale. Additionally, language-, nationality-, gender-, and sexual-orientation-based measurement invariance tests were conducted to test the comparability across groups. Both the PUMS and the PUMS-8 assess eight pornography use motivations, and both demonstrated excellent psychometric properties. Sexual Pleasure emerged as the most frequent motivation for pornography use across countries, genders, and sexual orientations, while differences were observed concerning other motivations (e.g. self-exploration was more prevalent among gender-diverse individuals than men or women). The motivational background of pornography use showed high similarity in the examined countries. Both the PUMS and the PUMS-8 are reliable and valid measurement tools to assess different types of motivations for pornography use across countries, genders, and sexual orientations. Both scales are recommended for use in research and clinical settings.

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Inequality is rising where social network segregation interacts with urban topology

arXiv

引用

arXiv 2019年

作者： Tóth, Gergo Wachs, Johannes Di Clemente, Riccardo Jakobi, Ákos Ságvári, Bence Kertész, János Lengyel, Balázs Agglomeration and Social Networks Lendület Research Group Hungarian Academy of Sciences Hungary Spatial Dynamics Lab University College Dublin Ireland Computational Social Sciences and Humanities RWTH Aachen University Germany Centre for Advanced Spatial Analysis University College London United Kingdom Department of Computer Science University of Exeter United Kingdom Department of Regional Science Eötvös Loránd University Hungary Center for Social Sciences Hungarian Academy of Sciences Hungary Department of Network and Data Science Central European University Hungary International Business School Budapest Hungary

Social networks amplify inequalities due to fundamental mechanisms of social tie formation such as homophily and triadic closure. These forces sharpen social segregation reflected in network fragmentation. Yet, little is known about what structural factors facilitate fragmentation. In this paper we use big data from a widely-used online social network to demonstrate that there is a significant relationship between social network fragmentation and income inequality in cities and towns. We find that the organization of the physical urban space has a stronger relationship with fragmentation than unequal access to education, political segregation, or the presence of ethnic and religious minorities. Fragmentation of social networks is significantly higher in towns in which residential neighborhoods are divided by physical barriers such as rivers and railroads and are relatively distant from the center of town. Towns in which amenities are spatially concentrated are also typically more socially segregated. These relationships suggest how urban planning may be a useful point of intervention to mitigate inequalities in the long run. Copyright © 2019, The Authors. All rights reserved.

关键词： Urban planning

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