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Fluctuating risk of acute kidney injury-related mortality for four weeks after exposure to air pollution: A multi-country time-series study in 6 countries

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Environment International 2024年 183卷 108367页

作者： Min, Jieun Kang, Duk-Hee Kang, Cinoo Bell, Michelle L. Kim, Ho Yang, Juyeon Gasparrini, Antonio Lavigne, Eric Hashizume, Masahiro Kim, Yoonhee Fook Sheng Ng, Chris Honda, Yasushi das Neves Pereira da Silva, Susana Madureira, Joana Leon Guo, Yue Pan, Shih-Chun Armstrong, Ben Sera, Francesco Masselot, Pierre Schwartz, Joel Maria Vicedo-Cabrera, Ana Pyo Lee, Jung Al-Aly, Ziyad Won Lee, Jung Kwag, Youngrin Ha, Eunhee Lee, Whanhee Department of Environmental Medicine College of Medicine Ewha Womans University Seoul Korea Republic of Graduate Program in System Health Science and Engineering College of Medicine Ewha Womans University Seoul Korea Republic of Division of Nephrology Department of Internal Medicine Ewha Womans University School of Medicine Ewha Medical Research Center Seoul Korea Republic of Department of Public Health Sciences Graduate School of Public Health Seoul National University Seoul Korea Republic of Yale School of the Environment Yale University New HavenCT United States Biostatistics Collaboration Unit Yonsei University College of Medicine Seoul Korea Republic of Lab Department of Public Health Environments and Society London School of Hygiene & Tropical Medicine London United Kingdom School of Epidemiology & Public Health Faculty of Medicine University of Ottawa Ottawa Canada Department of Global Health Policy Graduate School of Medicine The University of Tokyo Tokyo Japan Department of Global Environmental Health Graduate School of Medicine University of Tokyo Tokyo Japan School of Tropical Medicine and Global Health Nagasaki University Nagasaki Japan Center for Climate Change Adaptation National Institute for Environmental Studies Tsukuba Japan Department of Epidemiology Instituto Nacional de Saúde Dr. Ricardo Jorge Lisbon Portugal EPIUnit-Institute of Public Health University of Porto Porto Portugal Porto Portugal College of Medicine and NTU Hospital Taipei Taiwan National Institute of Environmental Health Science National Health Research Institutes Zhunan Taiwan Department of Public Health Environments and Society London School of Hygiene & Tropical Medicine London United Kingdom Department of Statistics Computer Science and Applications "G. Parenti" University of Florence Florence Italy Department of Environmental Health Harvard T.H. Chan School of Public Health BostonMA United States Institute of Social and Preventive Medicine Universi

Background: Recent studies have reported that air pollution is related to kidney diseases. However, the global evidence on the risk of death from acute kidney injury (AKI) owing to air pollution is limited. Therefore, we investigated the association between short-term exposure to air pollution—particulate matter ≤ 2.5 μm (PM2.5), ozone (O3), and nitrogen dioxide (NO2)—and AKI-related mortality using a multi-country dataset. Methods: This study included 41,379 AKI-related deaths in 136 locations in six countries during 1987–2018. A novel case time-series design was applied to each air pollutant during 0–28 lag days to estimate the association between air pollution and AKI-related deaths. Moreover, we calculated AKI deaths attributable to non-compliance with the World Health Organization (WHO) air quality guidelines. Results: The relative risks (95% confidence interval) of AKI-related deaths are 1.052 (1.003, 1.103), 1.022 (0.994, 1.050), and 1.022 (0.982, 1.063) for 5, 10, and 10 µg/m3 increase in lag 0–28 days of PM2.5, warm-season O3, and NO2, respectively. The lag-distributed association showed that the risk appeared immediately on the day of exposure to air pollution, gradually decreased, and then increased again reaching the peak approximately 20 days after exposure to PM2.5 and O3. We also found that 1.9%, 6.3%, and 5.2% of AKI deaths were attributed to PM2.5, warm-season O3, and NO2 concentrations above the WHO guidelines. Conclusions: This study provides evidence that public health policies to reduce air pollution may alleviate the burden of death from AKI and suggests the need to investigate the several pathways between air pollution and AKI death. © 2023 The Author(s)

关键词： Air quality

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Elemental Characterization of Ambient Particulate Matter for a Globally Distributed Monitoring Network: Methodology and Implications

ACS ES&T air

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ACS ES&T air 2024年第4期1卷 283-293页

作者： Xuan Liu Jay R Turner Christopher R Oxford Jacob McNeill Brenna Walsh Emmie Le Roy Crystal L Weagle Emily Stone Haihui Zhu Wenyu Liu Zilin Wei Nicole P Hyslop Jason Giacomo Ann M Dillner Abdus Salam Al-Amin Hossen Zubayer Islam Ihab Abboud Clement Akoshile Omar Amador-Muñoz Nguyen Xuan Anh Araya Asfaw Rajasekhar Balasubramanian Rachel Ying-Wen Chang Craig Coburn Sagnik Dey David J Diner Jinlu Dong Tareq Farrah Paterne Gahungu Rebecca M Garland Michel Grutter de la Mora Sina Hasheminassab Juanette John Jhoon Kim Jong Sung Kim Kristy Langerman Pei-Chen Lee Puji Lestari Yang Liu Tesfaye Mamo Mathieu Martins Olga L Mayol-Bracero Mogesh Naidoo Sang Seo Park Yoav Schechner Robyn Schofield Sachchida N Tripathi Eli Windwer Ming-Tsang Wu Qiang Zhang Michael Brauer Yinon Rudich Randall V Martin Department of Energy Environmental & Chemical Engineering Washington University in St. Louis St. Louis Missouri 63130 United States. Air Quality Research Center University of California Davis Davis California 95616 United States. Department of Chemistry University of Dhaka Dhaka 1000 Bangladesh. Air Quality Research Division Environment and Climate Change Canada Toronto Ontario M3H 5T4 Canada. Department of Physics University of Ilorin Ilorin 240003 Nigeria. Instituto de Ciencias de la Atmósfera y Cambio Climático Universidad Nacional Autónoma de México Mexico City 04510 Mexico. Institute of Geophysics Vietnam Academy of Science and Technology Hanoi 11307 Vietnam. Institute of Geophysics and Space Science Addis Ababa University Addis Ababa 1176 Ethiopia. Department of Civil and Environmental Engineering National University of Singapore Singapore 117576 Singapore. Department of Physics and Atmospheric Science Dalhousie University Halifax Nova Scotia B3H 4R2 Canada. Department of Geography and Environment University of Lethbridge Lethbridge Alberta T1K 3M4 Canada. Centre for Atmospheric Sciences Indian Institute of Technology Delhi New Delhi 110016 India. Jet Propulsion Laboratory California Institute of Technology Pasadena California 91109 United States. School of Environment Tsinghua University Beijing 100084 People's Republic of China. Research Laboratories Khalifa University Abu Dhabi 127788 United Arab Emirates. Institute of Applied Statistics University of Burundi Bujumbura BP1550 Burundi. Council for Scientific and Industrial Research Pretoria 0001 South Africa. Unit for Environmental Sciences and Management North-West University Potchefstroom 2531 South Africa. Department of Geography Geo-Informatics and Meteorology University of Pretoria Pretoria 0002 South Africa. Department of Atmospheric Sciences Yonsei University Seoul 03722 Republic of Korea. Department of Community Health and Epidemiology Dalhousie University Halifax Nova Scotia B3

Global ground-level measurements of elements in ambient particulate matter (PM) can provide valuable information to understand the distribution of dust and trace elements, assess health impacts, and investigate emission sources. We use X-ray fluorescence spectroscopy to characterize the elemental composition of PM samples collected from 27 globally distributed sites in the Surface PARTiculate mAtter Network (SPARTAN) over 2019-2023. Consistent protocols are applied to collect all samples and analyze them at one central laboratory, which facilitates comparison across different sites. Multiple quality assurance measures are performed, including applying reference materials that resemble typical PM samples, acceptance testing, and routine quality control. Method detection limits and uncertainties are estimated. Concentrations of dust and trace element oxides (TEO) are determined from the elemental dataset. In addition to sites in arid regions, a moderately high mean dust concentration (6 μg/m) in PM is also found in Dhaka (Bangladesh) along with a high average TEO level (6 μg/m). High carcinogenic risk (>1 cancer case per 100000 adults) from airborne arsenic is observed in Dhaka (Bangladesh), Kanpur (India), and Hanoi (Vietnam). Industries of informal lead-acid battery and e-waste recycling as well as coal-fired brick kilns likely contribute to the elevated trace element concentrations found in Dhaka.

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Euclid: Early Release Observations – A preview of the Euclid era through a galaxy cluster magnifying lens

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Astronomy and Astrophysics 2025年 697卷

作者： Atek, H. Gavazzi, R. Weaver, J.R. Diego, J.M. Schrabback, T. Hatch, N.A. Aghanim, N. Dole, H. Hartley, W.G. Taamoli, S. Congedo, G. Jimenez-Teja, Y. Cuillandre, J.-C. Bañados, E. Belladitta, S. Bowler, R.A.A. Franco, M. Jauzac, M. Mahler, G. Richard, J. Rocci, P.-F. Serjeant, S. Toft, S. Abriola, D. Bergamini, P. Biviano, A. Dimauro, P. Ezziati, M. Golden-Marx, J.B. Grillo, C. Hughes, A.C.N. Kang, Y. Kneib, J.-P. Lombardi, M. Mamon, G.A. McPartland, C.J.R. Meneghetti, M. Miyatake, H. Montes, M. Mortlock, D.J. Oesch, P.A. Okabe, N. Rosati, P. Taylor, A.N. Tarsitano, F. Weller, J. Kluge, M. Laureijs, R. Paltani, S. Saifollahi, T. Schirmer, M. Stone, C. Mora, A. Altieri, B. Amara, A. Andreon, S. Auricchio, N. Baldi, M. Balestra, A. Bardelli, S. Basset, A. Bender, R. Bodendorf, C. Bonino, D. Branchini, E. Brescia, M. Brinchmann, J. Camera, S. Candini, G.P. Capobianco, V. Carbone, C. Cardone, V.F. Carretero, J. Casas, S. Castander, F.J. Castellano, M. Cavuoti, S. Cimatti, A. Conselice, C.J. Conversi, L. Copin, Y. Corcione, L. Courbin, F. Courtois, H.M. Da Silva, A. Degaudenzi, H. Di Giorgio, A.M. Dinis, J. Douspis, M. Dubath, F. Dupac, X. Dusini, S. Ealet, A. Farina, M. Farrens, S. Ferriol, S. Fotopoulou, S. Frailis, M. Franceschi, E. Galeotta, S. Gillard, W. Gillis, B. Giocoli, C. Gómez-Alvarez, P. Grazian, A. Grupp, F. Guzzo, L. Hailey, M. Haugan, S.V.H. Hoar, J. Hoekstra, H. Holliman, M.S. Institut d’Astrophysique de Paris UMR 7095 CNRS Sorbonne Université 98 bis boulevard Arago Paris75014 France Aix-Marseille Université CNRS CNES LAM Marseille France Department of Astronomy University of Massachusetts AmherstMA01003 United States Instituto de Física de Cantabria Edificio Juan Jordá Avenida de los Castros Santander39005 Spain Universität Innsbruck Institut für Astro- und Teilchenphysik Technikerstr. 25/8 Innsbruck6020 Austria School of Physics and Astronomy University of Nottingham University Park NottinghamNG7 2RD United Kingdom Université Paris-Saclay CNRS Institut d’astrophysique spatiale Orsay91405 France Department of Astronomy University of Geneva ch. d’Ecogia 16 Versoix1290 Switzerland Physics and Astronomy Department University of California 900 University Ave. RiversideCA92521 United States Institute for Astronomy University of Edinburgh Royal Observatory Blackford Hill EdinburghEH9 3HJ United Kingdom Instituto de Astrofísica de Andalucía CSIC Glorieta de la Astronomía Granada18080 Spain Observatorio Nacional Rua General Jose Cristino 77-Bairro Imperial de Sao Cristovao Rio de Janeiro20921-400 Brazil Université Paris-Saclay Université Paris Cité CEA CNRS AIM Gif-sur-Yvette91191 France Max-Planck-Institut für Astronomie Königstuhl 17 Heidelberg69117 Germany INAF-Osservatorio di Astrofisica e Scienza dello Spazio di Bologna Via Piero Gobetti 93/3 Bologna40129 Italy Jodrell Bank Centre for Astrophysics Department of Physics and Astronomy University of Manchester Oxford Road ManchesterM13 9PL United Kingdom The University of Texas at Austin AustinTX78712 United States Department of Physics Centre for Extragalactic Astronomy Durham University South Road DH1 3LE United Kingdom Department of Physics Institute for Computational Cosmology Durham University South Road DH1 3LE United Kingdom Astrophysics Research Centre University of KwaZulu-Natal Westville Campus Durban4041 South Africa School of

We present the first analysis of the Euclid Early Release Observations (ERO) program that targets fields around two lensing clusters, Abell 2390 and Abell 2764. We use imaging data from the Visible instrument (VIS) and the Near-Infrared Spectrometer and Photometer (NISP) to produce photometric catalogs for a total of ∼500 000 objects. The imaging data reach a typical depth of 5 σ in the range 25.1–25.4 AB in the NISP bands and 27.1–27.3 AB in the VIS band. Using the Lyman-break method in combination with photometric redshifts, we searched for high-redshift galaxies. We identified 30 Lyman-break galaxy (LBG) candidates at z > 6 and 139 extremely red sources (ERSs), most of which likely lie at lower redshift. The VIS imaging is deeper than the NISP imaging, which means that we can routinely identify high-redshift Lyman-break galaxies at about a magnitude of 3, which reduces contamination by brown dwarf stars and low-redshift galaxies. The difficulty of spatially resolving most of these sources in 0′′ . 3 pix−1 imaging means that it is difficult to distinguish between galaxies and quasars. Spectroscopic follow-up campaigns of these bright sources will help us to constrain the bright end of the ultraviolet galaxy luminosity function and the quasar luminosity function at z > 6, and it will constrain the physical nature of these objects. Additionally, we performed a combined strong- and weak-lensing analysis of A2390, and we show that Euclid will contribute to constraining the virial mass of galaxy clusters better. We also identify optical and near-infrared counterparts of known z > 0.6 clusters in these data. These counterparts exhibit strong-lensing features. This establishes that Euclid can characterize high-redshift clusters. Finally, we provide a glimpse of the ability of Euclid to map the intracluster light out to larger radii than current facilities, which enables us to understand the cluster assembly history better and to map the dark matter distribution. This ini

关键词： Moon

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

arXiv

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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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Proceedings of the 16th Annual UT-KBRIN Bioinformatics Summit 2016: bioinformatics

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BMC Bioinformatics 2017年第9期18卷 1-9页

作者： Eric C. Rouchka Juw Won Park Julia H. Chariker David A. Tieri Shreedharkumar Rajurkar Vikas Singh Nishchal K. Verma Yan Cui Mark Farman Bradford Condon Jolanta Jaromczyk Neil Moore Jerzy Jaromczyk Daniel Harris Patrick Calie Eun Kyong Shin Robert L. Davis Arash Shaban-Nejad Joshua M. Mitchell Hunter N. B. Moseley Robert M. Flight Qing Jun Wang Richard M. Higashi Teresa W-M Fan Andrew N. Lane Liangqun Lu Bernie J Daigle Jr. Xi Chen Andrey Smelter Jerzy W. Jaromczyk Bailey K. Phan Nathaniel J. Serpico Ethan G. Toney Li Chen Caroline E. Melton Jennifer R. Mandel Bernie J. Daigle Jr. Hao Chen Kazi I. Zaman Ramin Homayouni Patrick J. Trainor Andrew P. DeFilippis Samantha M. Carlisle Shesh N. Rai Department of Computer Engineering and Computer Science University of Louisville Duthie Center for Engineering Louisville KY 40292 USA Kentucky Biomedical Research Infrastructure (KBRIN) Bioinformatics Core 522 East Gray Street Louisville KY 40292 USA Department of Psychological and Brain Sciences University of Louisville Louisville KY 40292 USA Department of Anatomical Sciences and Neurobiology University of Louisville Louisville KY 40292 USA Department of Electrical Engineering IIT Kanpur India Department of Microbiology Immunology and Biochemistry University of Tennessee Health Science Center Memphis TN 38103 USA Center for Integrative and Translational Genomics University of Tennessee Health Science Center Memphis TN 38103 USA Department of Plant Pathology University of Kentucky Lexington KY 40546 USA UK Healthcare Genomics University of Kentucky Lexington KY 40536 USA Department of Entomology & Plant Pathology University of Tennessee Knoxville TN 37996 USA Department of Forestry University of Kentucky Lexington KY 40506 USA Department of Computer Science University of Kentucky Lexington KY 40506 USA Department of Computer Science University of Kentucky Lexington KY 40508 USA Department of Biological Sciences Eastern Kentucky University Richmond KY 40475 USA University of Tennessee Health Science Center – Oak-Ridge National Lab (UTHSC-ORNL) Center for Biomedical Informatics Department of Pediatrics Memphis TN 38103 USA Department of Molecular & Cellular Biochemistry University of Kentucky Lexington KY 40536 USA Markey Cancer Center University of Kentucky Lexington KY 40536 USA Center for Environmental and Systems Biochemistry University of Kentucky Lexington KY 40536 USA Institute for Biomedical Informatics University of Kentucky Lexington KY 40536 USA Department of Molecular and Cellular Biochemistry University of Kentucky Lexington KY 40536 USA Department of Statistics University of Kentucky Louisville KY

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The ABCs of Experimental Evolution

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International Scholarly Research Notices 2013年第1期2013卷

作者： Zaid Abdo Mathew Stein Andrzej Wojtowicz Kim M. Pepin Departments of Statistics University of Idaho Moscow ID 83844 USAuidaho.edu Department of Mathematics University of Idaho Moscow ID 83844 USAuidaho.edu Program of Bioinformatics and Computational Biology (BCB) University of Idaho Moscow ID 83844 USAuidaho.edu USDA-ARS South Atlantic Area 950 College Station Road Athens GA 30605-2720 USA Department of Computer Engineering University of Idaho Moscow ID 83844 USAuidaho.edu Micron Technology Inc. Boise ID 83716 *** Department of Animal Sciences Washington State University Pullman WA 99164 USAwsu.edu Fogarty International Center National Institutes of Health 31 Center Drive MSC 2220 Bethesda MD 20892-2220 USAfic.nih.gov Department of Biology Colorado State University Campus Delivery 1878 Fort Collins CO 80523 USAcolostate.edu

Microbial evolution is complex and is influenced by many sources of variation. Experimental evolution is no exception, although it is more controlled, easily replicated, and typically devoid of interactions between species. Mathematical modeling of the evolutionary process can help in understanding the underlying mechanisms that drive outcome of such experiments. These models can be complex and parameter rich, limiting their feasibility for statistical inference. In this paper, we introduce the use of Approximate Bayesian Computation (ABC) as a tool for statistical inference in the study of experimental evolution. ABC is a fast and simple method for fitting complex models to data. We utilize this method, coupled with a mechanistic model of experimental evolution, to study the evolution process of bacteriophage ϕ X174 under benign selection pressure. Our results highlight three mutation-selection scenarios that could explain this process: high mutation/low selection pressure, low mutation/high selection pressure, and low mutation/low selection pressure, with posterior support of 19%, 9.5%, and 71.5% for each of these scenarios, respectively. Sequence data support the first candidate. Though surprising, this scenario was not improbable based on our analysis.

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Methods for Evaluating Medical Tests and Biomarkers

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Diagnostic and Prognostic Research 2017年第1期1卷 1-34页

作者： Gowri Gopalakrishna Miranda Langendam Patrick Bossuyt Mariska Leeflang Rob Scholten Anna Noel-Storr James Thomas Iain Marshall Byron Wallace Penny Whiting Clare Davenport Gowri GopalaKrishna Isabel de Salis Sue Mallett Robert Wolff Marie Westwood Jos Kleinen Richard Riley Gary Collins Hans Reitsma Karel Moons Antonia Zapf Katharina Kramer Annika Hoyer Oliver Kuss J. Ensor R. D. Riley J. J. Deeks E. C. Martin Gerta Rücker Martin Schumacher Susanne Steinhauser Joie Ensor Kym Snell Brian Willis Jon Deeks Thomas Debray Lavinia Ferrante di Ruffano Sian Taylor-Phillips Chris Hyde Stuart A. Taylor Gauraang Batnagar Lavinia Ferrante Di Ruffano Farah Seedat Aileen Clarke Sarah Byron Frances Nixon Rebecca Albrow Thomas Walker Carla Deakin Zhivko Zhelev Harriet Hunt Yaling Yang Lucy Abel James Buchanan Thomas Fanshawe Bethany Shinkins Laure Wynants Sabine Van Huffel Jan Verbakel Dirk Timmerman Ben Van Calster Aeliko Zwinderman Jason Oke Jack O’Sullivan Rafael Perera Brian Nicholson Hannah L. Bromley Tracy E. Roberts Adele Francis Denniis Petrie G. Bruce Mann Kinga Malottki Holly Smith Lucinda Billingham Alice Sitch Oke Gerke Mie Holm-Vilstrup Eivind Antonsen Segtnan Ulrich Halekoh Poul Flemming Høilund-Carlsen Bernard G. Francq Jac Dinnes Julie Parkes Walter Gregory Jenny Hewison Peter Selby Doug Altman William Rosenberg Julien Asselineau Paul Perez Aïssatou Paye Emilie Bessede Cécile Proust-Lima Christiana Naaktgeboren Joris de Groot Johannes Reitsma Anne Rutjes Emmanuel Ogundimu Jonathan Cook Yannick Le Manach Yvonne Vergouwe Romin Pajouheshnia Rolf Groenwold Karen Moons Linda Peelen Bavo De Cock Daan Nieboer Ewout W. Steyerberg Micael J. Pencina Jennifer Cooper Nick Parsons Chris Stinton Steve Smith Andy Dickens Rachel Jordan Alexandra Enocson David Fitzmaurice Peymane Adab Charles Boachie Gaj Vidmar Karoline Freeman Martin Connock Rachel Court Carl Moons Jessica Harris Zoe Plummer Barnaby Reeves Chris Rogers Andrew Mumford Kurtis Lee Veerle Verheyden Gianni D. Angelini Gavin J. Murphy Jeremy Huddy Melody Ni George Hanna Department of Clinical Epidemiology Biostatistics & Bioinformatics Academic Medical Center University of Amsterdam Amsterdam The Netherlands Department of Clinical Epidemiology and Biostatistics Academic Medical Center University of Amsterdam Amsterdam Netherlands Cochrane Netherlands Julius Center for Health Sciences and Primary Care University Medical Center Utrecht Utrecht Netherlands Cochrane Dementia and Cognitive Improvement Group University of Oxford Oxford UK EPPI-Centre Department of Social Science University College London London UK Division of Health and Social Care Research King’s College London London UK College of Computer and Information Science Northeastern University Boston USA University Hospitals Bristol NHS Foundation Trust School of Social and Community Medicine Bristol UK Institute of Applied Health Research University of Birmingham Birmingham UK School of Social and Community Medicine University of Bristol Bristol UK Kleijnen Systematic Reviews Ltd York UK Research Institute for Primary Care and Health Sciences Keele University Keele UK Centre for Statistics in Medicine Nuffield Department of Orthopaedics Rheumatology and Musculoskeletal Sciences University of Oxford Oxford UK Julius Center for Health Sciences and Primary Care University Medical Center Utrecht Utrecht The Netherlands Cochrane Netherlands University Medical Center Utrecht Utrecht The Netherlands Julius Center for Health Sciences and Primary Care University Medical Center Utrecht 3584 CG Utrecht Netherlands Department of Medical Statistics University Medical Center Göttingen Göttingen Germany Institute for Biometry and Epidemiology German Diabetes Center Leibniz Institute for Diabetes Research at Heinrich Heine University Düsseldorf Germany Manchester Pharmacy School University of Manchester Manchester UK Institute for Medical Biometry and Statistics Faculty of Medicine and Medical Center – University of Freiburg Stefan-Meier-Str. 26 79104 Freiburg Germany

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Correction to: Culture of mesenchymal stem cells derived from equine synovial membrane in alginate hydrogel microcapsules

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Stem cell research & therapy 2018年第1期9卷 259页

作者： Vitor Hugo Santos João Pedro Hübbe Pfeifer Jaqueline Brandão de Souza Betsabéia Heloisa Gentilha Milani Rogério Antonio de Oliveira Marjorie Golim Assis Elenice Deffune Andrei Moroz Ana Liz Garcia Alves Department of Veterinary Surgery and Anesthesiology University of Veterinary Medicine and Animal Science UNESP District of Rubião Júnior s / n Botucatu São Paulo Brazil. Departament of Statistics Institute of Biosciences UNESP District of Rubião Júnior s / n Botucatu SP Brazil. Departament of Graduate Program in Research and Development Medical Biotechnology UNESP Blood Centre Division District of Rubião Júnior s / n Botucatu SP Brazil. Departament of Urology Blood Centre Division - Laboratory of Cellular Engineering University of Medicine UNESP District of Rubião Junior s / n Botucatu SP Brazil. Departament of Bioprocesses and Biotechnology FCFAR - UNESP Rodovia Araraquara Jaú KM 01 São Paulo Brazil. Department of Veterinary Surgery and Anesthesiology University of Veterinary Medicine and Animal Science UNESP District of Rubião Júnior s / n Botucatu São Paulo Brazil. anaalves@fmvz.unesp.br.

The original article [1] contained a minor error regarding the mean diameter of the alginate microcapsules described in relation to Fig. 4 in the Results section. The microcapsules had an actual mean diameter of ...

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26th Annual Computational Neuroscience Meeting (CNS*2017) of the Organization for Computational Neuroscience Antwerp, Belgium, July 15-20, 2017

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BMC NEUROSCIENCE 2017年第SUPPL 1期18卷 59-59页

作者： [Anonymous] Indiana University Purdue University Indianapolis Indianapolis IN 46032 USA Stark Neurosciences Research Institute Indiana University School of Medicine Indianapolis IN 46032 USA Department of Mathematics East Carolina University Greenville NC 27858 USA Jülich Supercomputing Centre Forschungszentrum Jülich 52425 Jülich Germany Future Systems Swiss National Supercomputing Centre 8092 Zurich Switzerland User Engagement and Support Swiss National Supercomputing Centre 6900 Lugano Switzerland Institut de Neurosciences des Systèmes Aix Marseille Univ 13005 Marseille France Simulation Lab Neuroscience Forschungszentrum Jülich Jülich Germany Department of Experimental Psychology Ghent University 9000 Ghent Belgium Donders Center for Cognitive Neuroimaging Radboud University 6525HR Nijmegen The Netherlands Department of Electrical Computer and Energy Engineering University of Colorado Boulder CO 80309 USA Department of Neurosurgery Johns Hopkins School of Medicine Baltimore MD 21287 USA Department of Neurology Johns Hopkins School of Medicine Baltimore MD 21287 USA Department of Otolaryngology Johns Hopkins School of Medicine Baltimore MD 21287 USA INSERM U968 Paris France Sorbonne Universités UPMC University Paris 06 UMR_S 968 Institut de la Vision Paris France CNRS UMR_7210 Paris France Department of Computer Architecture and Technology University of Granada (CITIC) Granada Spain Sorbonne Universités UPMC Univ Paris 06 INSERM CNRS Institut de la Vision Paris France Department of Adaptive Machine Systems Osaka University Osaka Japan Department of Computer Science University of Cergy-Pontoise Cergy-Pontoise France Department of Physics and Astronomy College of Charleston Charleston SC 29424 USA School of Physics Faculty of Science University of Sydney Sydney NSW 2006 Australia Center of Excellence for Integrative Brain Function Australian Research Council Sydney Australia Max Planck Institute for Human Cognitive and Brain Sciences Saxony Lei

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Quantum computing at the frontiers of biological sciences

arXiv

引用

arXiv 2019年

作者： Emani, Prashant S. Warrell, Jonathan Anticevic, Alan Bekiranov, Stefan Gandal, Michael McConnell, Michael J. Sapiro, Guillermo Aspuru-Guzik, Alán Baker, Justin Bastiani, Matteo McClure, Patrick Murray, John Sotiropoulos, Stamatios N. Taylor, Jacob Senthil, Geetha Lehner, Thomas Gerstein, Mark B. Harrow, Aram W. Program in Computational Biology and Bioinformatics Yale University New HavenCT06520 United States Department of Molecular Biophysics and Biochemistry Yale University New HavenCT06520 United States Yale School of Medicine Department of Psychiatry New HavenCT06511 United States University of Virginia School of Medicine Department of Biochemistry and Molecular Genetics CharlottesvilleVA22903 United States Department of Psychiatry Semel Institute David Geffen School of Medicine University of California–Los Angeles 695 Charles E. Young Drive South Los AngelesCA90095 United States Univ. of Virginia School of Medicine Department of Neuroscience CharlottesvilleVA22903 United States Department of Electrical and Computer Engineering Duke University DurhamNC27708 United States Department of Chemistry University of Toronto 80 St. George Street TorontoONM5S 3H6 Canada TorontoONM5S 1M1 Canada CIFAR Artificial Intelligence Research Chair Vector Institute TorontoONM5S 1M1 Canada Department of Computer Science University of Toronto 40 St. George Street TorontoONM5S 2E4 Canada Schizophrenia and Bipolar Disorder Program McLean Hospital BelmontMA02478 United States Department of Psychiatry Harvard Medical School BostonMA02114 United States Sir Peter Mansfield Imaging Centre School of Medicine University of Nottingham NottinghamNG7 2RD United Kingdom Machine Learning Team National Institute of Mental Health BethesdaMD20892 United States Section on Functional Imaging Methods National Institute of Mental Health BethesdaMD20892 United States Department of Psychiatry Yale University School of Medicine New HavenCT06511 United States Department of Physics Yale University New HavenCT06511 United States Joint Center for Quantum Information and Computer Science University of Maryland College ParkMD20742 United States National Institute of Standards and Technology GaithersburgMD20899 United States Office of Genomics Research Coordination N

The search for meaningful structure in biological data has relied on cutting-edge advances in computational technology and data science methods. However, challenges arise as we push the limits of scale and complexity in biological problems. Innovation in massively parallel, classical computing hardware and algorithms continues to address many of these challenges, but there is a need to simultaneously consider new paradigms to circumvent current barriers to processing speed. Accordingly, we articulate a view towards quantum computation and quantum information science, where algorithms have demonstrated potential polynomial and exponential computational speedups in certain applications, such as machine learning. The maturation of the field of quantum computing, in hardware and algorithm development, also coincides with the growth of several collaborative efforts to address questions across length and time scales, and scientific disciplines. We use this coincidence to explore the potential for quantum computing to aid in one such endeavor: the merging of insights from genetics, genomics, neuroimaging and behavioral phenotyping. By examining joint opportunities for computational innovation across fields, we highlight the need for a common language between biological data analysis and quantum computing. Ultimately, we consider current and future prospects for the employment of quantum computing algorithms in the biological sciences. Copyright © 2019, The Authors. All rights reserved.

关键词： Machine learning

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