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Effect of adoption of electric vehicles on public health and air pollution in China: a modelling study

The Lancet Planetary health 2021年 5卷 S8-S8页

作者： Daniel E Horton Jordan L Schnell Daniel R Peters David C Wong Xi Lu Hao Gao Hongliang Zhang Patrick L Kinney Department of Earth and Planetary Sciences and Institute for Sustainability and Energy Northwestern University Evanston IL USA Cooperative Institute for Research in Environmental Sciences at the University of Colorado Boulder National Oceanic and Atmospheric Administration Global Systems Laboratory Boulder CO USA Program in Environmental Sciences Northwestern University Evanston IL USA Environmental Defense Fund Austin TX USA US Environmental Protection Agency Research Triangle Park NC USA School of Environment State Key Joint Laboratory of Environment Simulation and Pollution Control Tsinghua University Beijing China Beijing China Department of Earth System Science University of California Irvine Irvine CA USA Department of Environmental Science and Engineering Fudan University Shanghai China Department of Environmental Health Boston University School of Public Health Boston MA USA

Background Adoption of electric vehicles has the potential to reduce air pollutant and greenhouse gas emissions, hence China has implemented policies incentivising use of electric vehicles. However, much is unknown about the potential air quality and public health benefits of electric vehicles, including optimal vehicle type prioritisation and the vehicles' ability to reduce acute health impacts due to extreme air quality events. We aimed to assess the potential climate and acute public health benefits of use of electric vehicles during an extreme winter air pollution event. Methods In this modelling study, we used the Weather Research Forecast and Community Multiscale Air Quality Modeling system air quality model to simulate the interplay between weather and atmospheric chemistry. We used this model to examine potential co-benefits of electric vehicle adoption during an extreme pollution episode in China. We simulated heavy-duty and light-duty electric vehicle adoption scenarios, in which 40% of the population adopted a heavy-duty or light-duty passenger electric vehicle, re-mapped battery power needs to energy generation facilities, and characterised differences in public health outcomes using China-specific concentration-response functions. Findings We found that widespread adoption of heavy-duty electric vehicles would reduces nitric oxide and fine particulate matter resulting in 562 (95% CI 410–723) fewer premature acute deaths than the non-electrified baseline scenario. However, widespread adoption of heavy-duty electric vehicles does not reduce carbon dioxide emissions without the addition of emission-free electricity generation. By contrast, widespread adoption of light-duty electric vehicles robustly reduces greenhouse gas emissions, but results in lesser air quality improvements and fewer premature deaths avoided (145 [95% CI 38–333]) than the heavy-duty scenario. Economic effects of human health endpoints and carbon dioxide reductions for adoption of ligh

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Causes of blindness and vision impairment in 2020 and trends over 30 years, and prevalence of avoidable blindness in relation to VISION 2020: the Right to Sight: an analysis for the Global Burden of Disease Study (vol 2, pg 144, 2021)

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LANCET GLOBAL health 2021年第4期9卷 E408-E408页

作者： Steinmetz, Jaimie D. Vision and Eye Research Institute Anglia Ruskin University Cambridge United Kingdom Biomedical Informatics Seoul National University Hospital Seoul South Korea Institute for Health Metrics and Evaluation University of Washington Seattle WA United States Department of Health Metrics Sciences School of Medicine University of Washington Seattle WA United States Institute of Health Services Research Yonsei University Seoul South Korea Department of Ophthalmology University of Washington Seattle WA United States Department of Mathematics Imperial College London London United Kingdom WHO Collaborating Centre for Public Health Education and Training Imperial College London London United Kingdom Department of Primary Care and Public Health Imperial College London London United Kingdom School of Population and Global Health University of Melbourne Carlton VIC Australia School of Optometry and Vision Science UNSW Sydney NSW Australia Brien Holden Vision Institute Sydney Australia Department of Ophthalmology University of Adelaide Adelaide SA Australia Department of Parasitology and Mycology Jahrom University of Medical Sciences Jahrom Iran Department of Clinical Sciences University of Sharjah Sharjah United Arab Emirates Ophthalmic Research Center Shahid Beheshti University of Medical Sciences Tehran Iran Department of Ophthalmology Shahid Beheshti University of Medical Sciences Tehran Iran Research Institute for Endocrine Sciences Shahid Beheshti University of Medical Sciences Tehran Iran Ophthalmic Epidemiology Research Center Shahid Beheshti University of Medical Sciences Tehran Iran Health Services Management Department Qazvin University of Medical Sciences Qazvin Iran Department of Systems Populations and Leadership University of Michigan Ann Arbor MI United States Department of Medicine Sofia Ismail Memorial Medical Centre Bogura Bangladesh The Fred Hollows Foundation Sydney NSW Australia Berhan Public Health & Eye Care Consultancy Addis A

Hospital El Salvador: a novel paradigm of intensive care in response to COVID-19 in central America. Lancet Glob health 2021;9: e241?42?In this Comment, the conflict of interest statement should have included the following: ?By virtue of their roles within a public hospital or the Ministry of health, MB, LC, WH, and XS are government employees. The findings and conclusions in the Comment are only those of the authors.? This correction has been made as of Feb 26, 2021.

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Ten quick tips for deep learning in biology

arXiv

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arXiv 2021年

作者： Lee, Benjamin D. Gitter, Anthony Greene, Casey S. Raschka, Sebastian Maguire, Finlay Titus, Alexander J. Kessler, Michael D. Lee, Alexandra J. Chevrette, Marc G. Stewart, Paul Allen Britto-Borges, Thiago Cofer, Evan M. Yu, Kun-Hsing Carmona, Juan Jose Fertig, Elana J. Kalinin, Alexandr A. Signal, Beth Lengerich, Benjamin J. Triche, Timothy J. Boca, Simina M. In-Q-Tel Labs School of Engineering and Applied Sciences Harvard University Department of Genetics Harvard Medical School United States Department of Biostatistics and Medical Informatics University of Wisconsin-Madison MadisonWI United States Morgridge Institute for Research MadisonWI United States Department of Systems Pharmacology and Translational Therapeutics Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Department of Biochemistry and Molecular Genetics University of Colorado School of Medicine AuroraCO United States Center for Health AI University of Colorado School of Medicine AuroraCO United States Department of Statistics University of Wisconsin Madison United States Faculty of Computer Science Dalhousie University Canada University of New Hampshire Bioeconomy.XYZ United States Department of Oncology Johns Hopkins University United States Institute for Genome Sciences University of Maryland School of Medicine United States Genomics and Computational Biology Graduate Program University of Pennsylvania United States Department of Systems Pharmacology and Translational Therapeutics University of Pennsylvania United States Wisconsin Institute for Discovery Department of Plant Pathology University of Wisconsin-Madison United States Department of Biostatistics and Bioinformatics Moffitt Cancer Center TampaFL United States Section of Bioinformatics and Systems Cardiology Klaus Tschira Institute for Integrative Computational Cardiology University Hospital Heidelberg Germany University Hospital Heidelberg Germany Lewis-Sigler Institute for Integrative Genomics Princeton University PrincetonNJ United States Graduate Program in Quantitative and Computational Biology Princeton University PrincetonNJ United States Department of Biomedical Informatics Harvard Medical School United States Department of Pathology Brigham and Women's Hospital United States Philips Healthcare CambridgeMA United States Philips Research

Machine learning is a modern approach to problem-solving and task automation. In particular, machine learning is concerned with the development and applications of algorithms that can recognize patterns in data and use them for predictive modeling. Artificial neural networks are a particular class of machine learning algorithms and models that evolved into what is now described as deep learning. Given the computational advances made in the last decade, deep learning can now be applied to massive data sets and in innumerable contexts. Therefore, deep learning has become its own subfield of machine learning. In the context of biological research, it has been increasingly used to derive novel insights from high-dimensional biological data. To make the biological applications of deep learning more accessible to scientists who have some experience with machine learning, we solicited input from a community of researchers with varied biological and deep learning interests. These individuals collaboratively contributed to this manuscript's writing using the GitHub version control platform and the Manubot manuscript generation toolset. The goal was to articulate a practical, accessible, and concise set of guidelines and suggestions to follow when using deep learning. In the course of our discussions, several themes became clear: the importance of understanding and applying machine learning fundamentals as a baseline for utilizing deep learning, the necessity for extensive model comparisons with careful evaluation, and the need for critical thought in interpreting results generated by deep learning, among others. © 2021, CC BY.

关键词： Learning algorithms

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TDP-43 loss and ALS-risk SNPs drive mis-splicing and depletion of UNC13A (vol 603, pg 131, 2022)

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NATURE 2024年第8020期631卷 E7-E7页

作者： Brown, Anna-Leigh Wilkins, Oscar G. Keuss, Matthew J. Hill, Sarah E. Zanovello, Matteo Lee, Weaverly Colleen Bampton, Alexander Lee, Flora C. Y. Masino, Laura Qi, Yue A. Bryce-Smith, Sam Gatt, Ariana Hallegger, Martina Fagegaltier, Delphine Phatnani, Hemali Phatnani, Hemali Newcombe, Jia Gustavsson, Emil K. Seddighi, Sahba Reyes, Joel F. Coon, Steven L. Ramos, Daniel Schiavo, Giampietro Fisher, Elizabeth M. C. Raj, Towfique Secrier, Maria Lashley, Tammaryn Ule, Jernej Buratti, Emanuele Humphrey, Jack Ward, Michael E. Fratta, Pietro UCL Queen Square Motor Neuron Disease Centre Department of Neuromuscular Diseases UCL Queen Square Institute of Neurology UCL London UK The Francis Crick Institute London UK UK Dementia Research Institute University College London London UK Department of Molecular Biology and Nanobiotechnology National Institute of Chemistry Ljubljana Slovenia National Institute of Neurological Disorders and Stroke NIH Bethesda MD USA Medical Scientist Training Program Johns Hopkins University School of Medicine Baltimore MD USA Center for Alzheimer’s and Related Dementias National Institutes of Health Bethesda MD USA Queen Square Brain Bank UCL Queen Square Institute of Neurology University College London London UK Department of Neurodegenerative Disease UCL Queen Square Institute of Neurology University College London London UK Great Ormond Street Institute of Child Health Genetics and Genomic Medicine University College London London UK Center for Genomics of Neurodegenerative Disease New York Genome Center (NYGC) New York NY USA NeuroResource Department of Neuroinflammation UCL Queen Square Institute of Neurology London UK Molecular Genomics Core Eunice Kennedy Shriver National Institute of Child Health and Human Development NIH Bethesda MD USA Nash Family Department of Neuroscience and Friedman Brain Institute Icahn School of Medicine at Mount Sinai New York NY USA Ronald M. Loeb Center for Alzheimer’s Disease Icahn School of Medicine at Mount Sinai New York NY USA Department of Pathology Fishberg Department of Neuroscience Icahn School of Medicine at Mount Sinai New York NY USA Department of Genetics and Genomic Sciences and Icahn Institute for Data Science and Genomic Technology Icahn School of Medicine at Mount Sinai New York NY USA Estelle and Daniel Maggin Department of Neurology Icahn School of Medicine at Mount Sinai New York NY USA Department of Genetics Evolution and Environment UCL Genetics Institute University College London London UK Mole

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Small P values may not yield robust findings:an example using REST-meta-PD

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science Bulletin 2021年第21期66卷 2148-2152,M0003页

作者： Xi-Ze Jia Na Zhao Hao-Ming Dong Jia-Wei Sun Marek Barton Roxana Burciu Nicolas Carrière Antonio Cerasa Bo-Yu Chen Jun Chen Stephen Coombes Luc Defebvre Christine Delmaire Kathy Dujardin Fabrizio Esposito Guo-Guang Fan Federica Di Nardo Yi-Xuan Feng Brett W.Fling Saurabh Garg Moran Gilat Martin Gorges Shu-Leong Ho Fay BHorak Xiao Hu Xiao-Fei Hu Biao Huang Pei-Yu Huang Ze-Juan Jia Christina Jones Jan Kassubek Lenka Krajcovicova Ajay Kurani Jing Li Qing Li Ai-Ping Liu Bo Liu Hu Liu Wei-Guo Liu Renaud Lopes Yu-Ting Lou Wei Luo Tara Madhyastha Ni-Ni Mao Grainne McAlonan Martin J.McKeown Shirley Pang Andrea Quattrone Irena Rektorova Alessia Sarica Hui-Fang Shang James M.Shine Priyank Shukla Tomas Slavicek Xiao-Peng Song Gioacchino Tedeschi Alessandro Tessitore David Vaillancourt Jian Wang Jue Wang Z.Jane Wang Lu-Qing Wei Xia Wu Xiao-Jun Xu Lei Yan Jing Yang Wan-Qun Yang Nai-Lin Yao De-Long Zhang Jiu-Quan Zhang Min-Ming Zhang Yan-Ling Zhang Cai-Hong Zhou Chao-Gan Yan Xi-Nian Zuo Mark Hallett Tao Wu Yu-Feng Zang Center for Cognition and Brain Disorders the Affiliated HospitalHangzhou Normal UniversityHangzhou 310015China Zhejiang Key Laboratory for Research in Assessment of Cognitive Impairments Hangzhou 311121China National Basic Science Data Center Beijing 100190China State Key Laboratory of Cognitive Neuroscience and Learning&McGovern Institute for Brain Research Beijing Normal UniversityBeijing 100875China School of Information and Electronics Technology Jiamusi UniversityJiamusi 154007China Neuroscience Program Central European Institute of TechnologyCEITECMasaryk UniversityBrno 62500Czech Republic Department of Applied Physiology and Kinesiology University of FloridaGainesville FL 32611USA Univ.Lille InsermCHU LilleU1172-LilNCog-Lille Neuroscience&CognitionLille F-59000France The Institute for Biomedical Research and Innovation National Research CouncilMangone CS 87050Italy Department of Radiology The First Affiliated Hospital of China Medical UniversityShenyang 110001China Department of Radiology Guangdong Provincial Hospital of Chinese MedicineGuangzhou 510120China Department of Medicine Surgery and DentistryScuola Medica SalernitanaUniversity of SalernoFiscianoSA 132-84084Italy Department of Advanced Medical and Surgery Sciences University of Campania“Luigi Vanvitelli”Caserta 81100Italy Eye Center of the 2nd Affiliated Hospital Medical College of Zhejiang UniversityHangzhou 310020China Zhejiang Provincial Key Lab of Ophthalmology Hangzhou 310020China Department of Health and Exercise Science Colorado State UniversityFort Collins CO 80523USA Pacific Parkinson’s Research Centre University of British ColumbiaVancouver BC V6E 2M6Canada Department of Medicine(Neurology)University of British Columbia Vancouver BC V6T 1B7Canada Brain and Mind Center The University of SydneySydney NSW 2006Australia Department of Neurology Ulm UniversityUlm 89081Germany Division of Neurology Department of MedicineQueen Mary HospitalUniversity of Hong KongHong Kong 999077China Department of

Thousands of resting state functional magnetic resonance imaging(RS-f MRI)articles have been published on brain *** precise localization of abnormal brain activity,a voxel-level comparison is *** of the large number of voxels in the brain,multiple comparison correction(MCC)must be performed to reduce false positive rates,and a smaller P value(usually including either liberal or stringent MCC)is widely recommended[1].

关键词：帕金森病 precise correction

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Genomic basis for RNA alterations in cancer (vol 578, pg 129, 2020)

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NATURE 2023年第7948期614卷 E37-E37页

作者： Calabrese, Claudia Davidson, Natalie R. Demircioglu, Deniz Fonseca, Nuno A. He, Yao Lehmann, Kjong-Van Liu, Fenglin Shiraishi, Yuichi Soulette, Cameron M. Urban, Lara Greger, Liliana Li, Siliang Liu, Dongbing Perry, Marc D. Xiang, Qian Zhang, Fan Zhang, Junjun Bailey, Peter Erkek, Serap Hoadley, Katherine A. Hou, Yong Huska, Matthew R. Kilpinen, Helena Korbel, Jan O. Marin, Maximillian G. Markowski, Julia Nandi, Tannistha Pan-Hammarstrom, Qiang Pedamallu, Chandra Sekhar Siebert, Reiner Stark, Stefan G. Su, Hong Tan, Patrick Waszak, Sebastian M. Yung, Christina Zhu, Shida Awadalla, Philip Creighton, Chad J. Meyerson, Matthew Ouellette, B. F. Francis Wu, Kui Yang, Huanming Brazma, Alvis Brooks, Angela N. Goke, Jonathan Ratsch, Gunnar Schwarz, Roland F. Stegle, Oliver Zhang, Zemin European Molecular Biology Laboratory European Bioinformatics Institute Hinxton UK European Molecular Biology Laboratory European Bioinformatics Institute (EMBL-EBI) Cambridge UK Genome Biology Unit European Molecular Biology Laboratory (EMBL) Heidelberg Germany CIBIO/InBIO - Research Center in Biodiversity and Genetic Resources Universidade do Porto Vairão Portugal Berlin Institute for Medical Systems Biology Max Delbruck Center for Molecular Medicine Berlin Germany German Cancer Consortium (DKTK) partner site Berlin Germany German Cancer Research Center (DKFZ) Heidelberg Germany Berlin Institute for Medical Systems Biology Max Delbrück Center for Molecular Medicine Berlin Germany German Cancer Consortium (DKTK) Partner site Berlin Berlin Germany European Molecular Biology Laboratory Genome Biology Unit Heidelberg Germany Division of Computational Genomics and Systems Genetics German Cancer Research Center (DKFZ) Heidelberg Germany ETH Zurich Zurich Switzerland Memorial Sloan Kettering Cancer Center New York NY USA Weill Cornell Medical College New York NY USA SIB Swiss Institute of Bioinformatics Lausanne Switzerland University Hospital Zurich Zurich Switzerland Computational Biology Center Memorial Sloan Kettering Cancer Center New York NY USA Department of Biology ETH Zurich Zürich Switzerland Department of Computer Science ETH Zurich Zurich Switzerland Korea University Seoul South Korea Computational and Systems Biology Program Memorial Sloan Kettering Cancer Center New York NY USA Department of Physiology and Biophysics Weill Cornell Medicine New York NY USA Institute for Computational Biomedicine Weill Cornell Medicine New York NY USA Controlled Department and Institution New York NY USA Englander Institute for Precision Medicine Weill Cornell Medicine New York NY USA National University of Singapore Singapore Singapore Genome Institute of Singapore Singapore Singapore Computational and Systems Biology Genome Institute of Singap

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Obesity alters pathology and treatment response in inflammatory disease

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四川生理科学杂志 2022年第3期44卷 446-446页

作者： Sagar P Bapat NOMIS Center for Immunobiology and Microbial Pathogenesis The Salk Institute for Biological Studies La Jolla CA USA Gene Expression Laboratory The Salk Institute for Biological Studies La Jolla CA USA Medical Scientist Training Program University of California San Diego La Jolla CA USA Department of Laboratory Medicine University of California San Francisco San Francisco CA USA Department of Microbiology and Immunology University of California San Francisco San Francisco CA USA Diabetes Center University of California San Francisco San Francisco CA USA Medical Scientist Training Program University of California San Francisco CA USA Division of Pulmonary Critical Care Allergy and Sleep Department of Medicine University of California San Francisco San Francisco CA USA School of Pharmaceutical Sciences Xiamen University Xiamen China Department of Dermatology University of California San Diego La Jolla CA USA Department of Surgery University of California San Francisco San Francisco CA USA Department of Medicine University of California San Francisco San Francisco CA USA Sandler Asthma Basic Research Center University of California San Francisco San Francisco CA USA School of Medicine Wake Forest University Winston–Salem NC USA Department of Dermatology Icahn School of Medicine at Mount Sinai New York NY USA Lung Biology Center Department of Medicine University of California San Francisco San Francisco CA USA Department of Biomedical Science Graduate School Kyungpook National University Kyungpook National University Hospital Daegu South Korea Department of Dermatology University of California San Francisco San Francisco CA USA Department of Pathology University of California San Francisco San Francisco CA USA Department of Internal Medicine Division of Pulmonary Critical Care and Sleep Medicine University of Kentucky Lexington KY USA Pulmonary Critical Care Section Department of Medicine Veterans Affairs San Diego Hea

Decades of work have elucidated cytokine signalling and transcriptional pathways that control T cell differentiation and have led the way to targeted biologic therapies that are effective in a range of autoimmune,allergic and inflammatory *** evidence indicates that obesity and metabolic disease can also influence the immune system1-7,although the mechanisms and effects on immunotherapy outcomes remain largely ***,using two models of atopic dermatitis,we show that lean and obese mice mount markedly different immune *** converted the classical type 2 T helper(TH2)-predominant disease associated with atopic dermatitis to a more severe disease with prominent TH17 *** also observed divergent responses to biologic therapies targeting TH2 cytokines,which robustly protected lean mice but exacerbated disease in obese ***-cell RNA sequencing coupled with genome-wide binding analyses revealed decreased activity of nuclear receptor peroxisome proliferator-activated receptor-γ(PPARγ)in TH2 cells from obese mice relative to lean mice.

关键词： dermatitis inflammatory allergic

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Evaluating the microbial environment aboard the ISS to enable an optimized microbiome for deep space human exploration 69

Evaluating the microbial environment aboard the ISS to enabl...

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69th International Astronautical Congress: #InvolvingEveryone, IAC 2018

作者： Dannemiller, Karen C. Horack, John M. Meyer, Marit Stoll, Norbert Stoll, Regina Fleischer, Heidi Schwartz, Mariah Newton, Elizabeth K. Haines, Sarah R. Bope, Ashleigh Ohio State University College of Engineering ColumbusOH43210 United States Ohio State University College of Public Health ColumbusOH43210 United States Battelle Center for Science Engineering and Public Policy Ohio State University John Glenn Collegeof Public Affairs ColumbusOH43210 United States NASA/Glenn Research Center ClevelandOH43210 United States UniversitÓ"t Rostock Center for the Automation of the Life Sciences Rostock18055 Germany Ohio State University Environmental Science Graduate Program ColumbusOH43210 United States

The recent advent of high-throughput DNA sequencing technologies has allowed for unprecedented insight into various microbiomes, including those on occupied spacecraft. However, no standards currently exist to characterize a â€œhealthyâ€¥ or â€œunhealthyâ€¥ microbial community in this environment. This paper describes methods that can be used in the future to characterize and identify microbial communities and functions in dust found aboard the International Space Station. Chamber studies can be used to consider the impact of moisture on population dynamics that could occur in long-duration spaceflight. This can be coupled with DNA sequence-based approaches to characterize microbial communities under different environmental conditions. In addition to the biological information derived, we can also elucidate valuable engineering-design information which can lead to the future development of microbial standards for spacecraft, to avoid microbial-induced hardware corrosion as was seen on the space station Mir. In addition to mitigating exposure to unhealthy organisms, this knowledge will lead to the determination of criteria for curating a healthy spacecraft microbiome in future long-duration human spaceflight. This may be more sustainable than relying on sterilization to avoid pathogens or other unwanted microbes. Eventually, operating as a long-term, completely automated â€œMicrobial Observatory,â€¥ these results will provide a long-baseline approach to learning how these microbial communities in low-gravity, higher radiation, and closed-loop environments may influence crew health, with applicability to terrestrial environments such as submarines, aircraft and ships. Finally, we assess how to develop improved microbial clearance protocols that are more relevant to astronaut health, planetary protection, and spacecraft integrity. Copyright © 2018 by the International Astronautical Federation (IAF). All rights reserved.

关键词： Microorganisms

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I see sick people: Beliefs about sensory detection of infectious disease are largely consistent across cultures

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Brain, Behavior, and Immunity 2025年 128卷 737-750页

作者： Ackerman, Joshua M. Samore, Theodore Fessler, Daniel M.T. Kupfer, Tom R. Choi, Soyeon Merrell, Wilson N. Aarøe, Lene Aavik, Toivo Acabado, Stephen Akello, Grace Alfian, Ilham N. Al-Shawaf, Laith Alvarez, Marinés M. Ammann, Jeanine Arikan, Gizem Asha, Saiyeda A. Astobiza, Anibal M. Baeza-Ugarte, Carmen G. Barclay, Pat Barlow, Fiona Kate Bizarro, Lisiane Bressan, Paola Castellanos-Chacón, Andres Choy, Bryan K.C. Chusairi, Achmad Contreras-Garduño, Jorge Chávez Cosamalón, Brenda L. Costa-Neves, Bernardo De, Mallika de Lima, Tiago J.S. de Zoysa, Piyanjali Dryžaitė, Ieva Elbæk, Christian T. Fedor, Peter Fernández, Ana M. Fernandez-Morales, Regina Fülöp, Márta Gamsakhurdia, Vladimer Lado Garcia-Gómez, Leonor Garcia-Marques, Leonel Garduño-Franco, Jimena Pilar Grazioso, María del Habacht, Fanny Hasan, Youssef Haugestad, Camila P. Haugestad, Christian A.P. Havlíček, Jan Hernandez, Earl J. Hoang, Vu M. Hong, Minsung Hromatko, Ivana Iliško, Dzintra Imada, Hirotaka Jakšić, Ivana Jarmakowski, Tomasz Hjördísar Jónsdóttir, Harpa L. Kajokaite, Kotrina Kaňková, Šárka Kervyn, Nicolas Kim, Jinseok P. Kunst, Jonas R. Laakasuo, Michael Leongómez, Juan David Li, Norman P. Lu, Junsong Lynch, Nathan Maegli, María A. Manley, Harry Marcu, Gabriela McAfee, Thea Mitkidis, Panagiotis Molnár, Nándor B. Morvinski, Coby Muhamad, Haslina Nejat, Pegah Huy, Hoang Nguyen Oliveira, Angelica N. Olsson, Mats J. Onyishi, Charity N. Onyishi, Ike E. Orozco, Reegan Otterbring, Tobias Ottersen, Ida S. Pacheco-López, Gustavo Panagiotopoulou, Penny Paniagua, Walter Parvin, Roksana Pavlović, Zoran Prokop, Pavol Raffman, Emma Rizwan, Muhammad Rojas, Sheila Różycka-Tran, Joanna Sánchez, Oscar R. Selim, Heyla Sevi, Barış Shani, Yaniv Shastry, Madhulika S. Stieger, Stefan Suh, Eunkook M. Sumari, Melati Takemura, Kosuke Tognetti, Arnaud Trombetta, Roberta Z.R. Tybur, Joshua M. Ucak, Eylul B. Uchida, Yukiko Valentova, Jaroslava V. Viciana, Hugo Visine, Amandine Wang, Jin Wang, XT Yahiiaiev, Illia I. Zein, Rizqy A. Žeželj, Iris Department of Psychology University of Michigan Ann Arbor MI United States Religion Programme University of Otago Dunedin New Zealand Department of Anthropology University of California Los Angeles Los Angeles CA United States Center for Behavior Evolution & Culture University of California Los Angeles Los Angeles CA United States Bedari Kindness Institute University of California Los Angeles Los Angeles CA United States Department of Psychology Nottingham Trent University England Nottingham United Kingdom Department of Psychology University of Wisconsin-Madison Madison WI United States Center for the Experimental-Philosophical Study of Discrimination Aarhus University Aarhus Denmark Department of Political Science Aarhus University Aarhus Denmark Institute of Psychology University of Tartu Tartu Estonia Department of Mental Health Faculty of Medicine Gulu University Gulu Uganda Department of Psychology Universitas Airlangga East Java Indonesia Department of Psychology University of Colorado Colorado Springs Colorado Springs CO United States Department of Psychology Universidad del Valle Guatemala Guatemala City Guatemala Agroscope Research group Economic Modelling and Policy Analysis Ettenhausen Switzerland Department of Political Science Trinity College Dublin Dublin Ireland Department of Psychology International University of Business Agriculture and Technology Dhaka Bangladesh Department of Philosophy University of Granada Granada Spain Department of Psychology University of Guelph Guelph ON Canada School of Psychology University of Queensland Brisbane QLD Australia Graduate Program in Psychology Universidade Federal do Rio Grande Do Sul RS Porto Alegre Brazil Department of General Psychology University of Padova Padova Italy Faculty of Psychology Universidad El Bosque Bogotá Colombia Ministry of Defence Singapore Sustainability and Education Policy Network Saskatoon SK Canada Escuela Nacional de Estudios Superiores unid

Identifying cues to contagious disease is critical for effectively tracking and defending against interpersonal infection threats. People hold lay beliefs about the types of sensory information most relevant for identifying whether others are sick with transmissible illnesses. Are these beliefs universal, or do they vary along cultural and ecological dimensions? Participants in 58 countries (N = 19,217) judged how effective, and how likely they were to use, cues involving each of the five major sensory modalities in an imagined social interaction during a flu outbreak. Belief patterns were strongly consistent across countries (sight > audition > touch > smell > taste), suggesting a largely universal conceptualization of the role of sensory information for interpersonal respiratory disease detection. Results also support a safe senses hypothesis, with perceivers reporting that they would use senses that function at a distance—and thus reduce pathogen transmission risk—more than would be expected given participants’ beliefs as to the efficacy of these senses for disease detection. Where societal variation did emerge, it was captured by a cohesive set of socio-ecological factors, including human development, latitude, pathogen prevalence, and population density. Together, these findings reveal a shared lens through which contagious respiratory disease is assessed, one that prioritizes minimizing risk to perceivers, and may offer leverage for designing interventions to improve public health. © 2025 Elsevier Inc.

关键词： Behavioral immune system Culture Iterpersonal perception Pathogen threat Sensation Threat detection

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The definition of sng: The expert consensus by the sng taxonomy Task force of the Taiwan sng Society

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Journal of the Formosan Medical Association 2025年

作者： Han, Der-Sheng Lin, Jiann-Her Hung, Chih-Hsien Huang, Shuping Chen, Jen-Yin Chiang, Yung-Hsiao Chiu, Hsiao-Yean Chou, Li-Wei Hsieh, Cheng-Ta Huang, Ming-Chyi Huang, Wei-Lieh Kang, Jiunn-Horng Lee, Hsun-Hua Lin, Chia-Shu Lin, Chien-Min Lin, Chih-Peng Lin, Ying-Tung Liu, Yen-Chin Ro, Long-Sun Shen, Chiung-Chyi Su, Lily I-Wen Su, Yu-Jih Sun, Jui-Ming Sung, Jia-Ying Tsai, Rong-Kung Wang, Man-Ling Wen, Yeong-Ray Weng, Hao-Jui Wu, Ming-Shun Yu, Yu-Wen Hsieh, Sung-Tsang Wang, Shuu-Jiun Sun, Wei-Zen Chen, Chih-Cheng Department of Physical Medicine and Rehabilitation National Taiwan University Hospital Beihu Branch Taipei Taiwan Department of Physical Medicine and Rehabilitation National Taiwan University College of Medicine Taipei Taiwan Division of Neurosurgery Department of Surgery Taipei Medical University Hospital Taipei Taiwan Department of Surgery School of Medicine College of Medicine Taipei Medical University Taipei Taiwan Taipei Neuroscience Institute Taipei Medical University Taipei Taiwan TMU Research Center of Neuroscience Taipei Medical University Taipei Taiwan Department of Neurology Kaohsiung Medical University Hospital Kaohsiung Taiwan School of Medicine College of Medicine Kaohsiung Medical University Kaohsiung Taiwan Department of Foreign Languages and Literature National Sun Yat-sen University Kaohsiung Taiwan Department of Anesthesiology Chi Mei Medical Center Tainan Taiwan School of Medicine College of Medicine National Sun Yat-sen University Kaohsiung Taiwan School of Nursing College of Nursing Taipei Medical University Taipei Taiwan Department of Nursing Taipei Medical University Hospital Taipei Taiwan Department of Physical Medicine and Rehabilitation China Medical University Hospital Taichung Taiwan Department of Physical Medicine and Rehabilitation Asia University Hospital Asia University Taichung Taiwan Department of Physical Therapy and Graduate Institute of Rehabilitation Science China Medical University Taichung Taiwan Division of Neurosurgery Department of Surgery Cathay General Hospital Taipei Taiwan School of Medicine National Tsing-Hua University Hsinchu Taiwan Department of Medicine School of Medicine Fu Jen Catholic University New Taipei City Taiwan Department of Psychiatry Taipei City Psychiatric Center Taipei City Hospital Taipei Taiwan Department of Psychiatry National Taiwan University Hospital Yunlin Branch Yunlin Taiwan Department of Physical Medicine and Rehabilitation Taipei Medical University Hospital Taip

“Sng” is a Taiwanese term describing a distinct state of soreness. Accumulating evidence indicates that sng is a specific somatosensory modality, independent of pain, with distinct receptors and transduction pathways. In recent years, variations in its presentation across different medical specialties have highlighted the need for a standardized definition of sng-related terminology. To address this, the Taiwan Sng Society established a multidisciplinary Sng Taxonomy Task Force, comprising 32 experts in both clinical and basic sciences related to sng. Here we summarize the discussions and expert consensus from the Task Force. A total of 16 sng-related terminology was defined. The first edition of sng terminology aims to establish a clear, standardized framework for the clinical practice and research of sng. © 2025 Formosan Medical Association

关键词： Definition sng Sngception Taxonomy Terminology

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