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JMIR human factors 2024年 11卷 e69628页

作者： Aimee-Rose Wrightson-Hester Georgia Anderson Joel Dunstan Peter M McEvoy Christopher J Sutton Bronwyn Myers Sarah Egan Sara Tai Melanie Johnston-Hollitt Wai Chen Tom Gedeon Warren Mansell Curtin enAble Institute Faculty of Health Sciences Curtin University Perth Australia. Discipline of Psychology School of Population Health Curtin University Perth Australia. School of Arts and Humanities Edith Cowan University Perth Australia. Mental Health Commission of Western Australia Perth Australia. Curtin Institute for Data Science Curtin University Perth Australia. Centre for Clinical Interventions North Metropolitan Health Service Nedlands Australia. Centre for Biostatistics School of Health Sciences The University of Manchester Manchester United Kingdom. Alcohol Tobacco and Other Drug Research Unit South African Medical Research Council Parow South Africa. Division of Addiction Psychiatry Department of Psychiatry and Mental Health University of Cape Town Cape Town South Africa. Department of Clinical Psychology School of Health Sciences The University of Manchester Manchester United Kingdom. Mental Health Service Fiona Stanley Hospital Perth Australia. Curtin Medical School Curtin University Perth Australia. Centre of Excellence in Medical Biotechnology Faculty of Medical Science Naresuan University Phitsanulok Thailand. Optus-Curtin Centre of Excellence in AI School of Electronic Engineering Computing and Mathematical Sciences Curtin University Perth Australia.

[This corrects the article DOI: 10.2196/46849.].

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The Wide-field Spectroscopic Telescope (WST) science White Paper

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arXiv 2024年

作者： Mainieri, Vincenzo Anderson, Richard I. Brinchmann, Jarle Cimatti, Andrea Ellis, Richard Hill, Vanessa Kneib, Jean-Paul McLeod, Anna Opitom, Cyrielle Roth, Martin M. Sáez, Paula Sánchez Smiljanic, Rodolfo Tolstoy, Eline Bacon, Roland Randich, Sofia Pasquini, Luca Barsanti, Stefania Bryant, Julia Bryson, Ian Casali, Mark Colless, Matthew Couch, Warrick Danielski, Camilla Dierickx, Philippe Driver, Simon Fontana, Adriano Garilli, Bianca de Jong, Roelof S. Lee, David Lehnert, Matt Magrini, Laura Montet, Ben Sanchez-Janssen, Ruben Pandey-Pommier, Mamta Sargent, Mark Schipani, Pietro Steinmetz, Matthias Travouillon, Tony Tresse, Laurence Vernet, Joël Yeche, Christophe Ziegler, Bodo Audard, Marc Arévalo, Patricia Bayo Aran, Amelia M. Bisero, Sofia Bisogni, Susanna Blondin, Stéphane Bonito, Rosaria Branchesi, Marica Catelan, Márcio D'Ammando, Filippo Dainotti, Maria G. De Cia, Annalisa Dhawan, Suhail Dupletsa, Ulyana Hazra, Nandini Ilić, Dragana Ivanov, Valentin D. Izzo, Luca Kovačević, Andjelka B. Kuncarayakti, Hanindyo Kwon, Yuna La Forgia, Fiorangela Lazzarin, Monica Lira, Paulina Loffredo, Eleonora Maguire, Kate Majidi, Fatemeh Z. Marconi, Marcella McGee, Sean Migliorini, Alessandra Minniti, Dante Nicholl, Matt Onori, Francesca Pala, Anna F. Panda, Swayamtrupta Pawlak, Michal Pessi, Priscila J. Popović, Luka Č. Smith, Graham P. Vergani, Susanna D. Vietri, Giustina Ye, Quanzhi Zafar, Tayyaba Matsuno, Tadafumi Kordopatis, Georges Annibali, Francesca Bayo, Amelia Bellazzini, Michele Beltran, Maria Teresa Berni, Leda Bianchi, Simone Biazzo, Katia Bland-Hawthorn, Joss Boffin, Henri M.J. Bono, Giuseppe Bowman, Dominic Braga, Vittorio F. Bragaglia, Angela Brucalassi, Anna Busà, Innocenza Casali, Giada Casasola, Viviana Castro, Norberto Cavallo, Lorenzo Chiappini, Cristina Colzi, Laura Damiani, Francesco da Silva, Ronaldo D'Orazi, Valentina Escorza, Ana Fabrizio, Michele Fiorentino, Giuliana Fontani, Francesco François, Patrick Galindo-Guil, Francisco J. Galli, Daniele Garcia-Rojas, Jorge Guarcello, Mario Giuseppe Helmi, Am European Southern Observatory Karl-Schwarzschild-Strasse 2 Garching bei München85748 Germany Switzerland Instituto de Astrofísica e Ciências do Espaço Universidade do Porto Portugal Dipartimento di Fisica e Astronomia "Augusto Righi" Alma Mater Studiorum Università di Bologna Italy Department of Physics and Astronomy University College London United Kingdom Observatoire de la Côte d'Azur Université Côte d'Azur CNRS Laboratoire Lagrange France Centre for Extragalactic Astronomy Department of Physics Durham University United Kingdom Institute for Computational Cosmology Department of Physics University of Durham United Kingdom Institute for Astronomy University of Edinburgh United Kingdom Germany Nicolaus Copernicus Astronomical Center Polish Academy of Sciences Poland Kapteyn Astronomical Institute University of Groningen Netherlands France INAF - Osservatorio Astrofisico di Arcetri Italy Stockholm University Sweden INAF - Osservatorio di Astrofisica e Scienza dello Spazio di Bologna Italy Universidad de Valparaíso Chile University of Geneva Switzerland Research School of Astronomy and Astrophysics Australian National University CanberraACT2611 Australia Australia Instituto de Astrofísica de Canarias Spain Universidad de La Laguna Spain SISSA - Scuola Internazionale Superiore di Studi Avanzati Italy University of Florence Italy INAF - Osservatorio Astronomico di Roma Italy GEPI Observatoire de Paris Universite PSL CNRS France INAF Istituto di Astrofisica Spaziale e Fisica cosmica Milano Italy Sydney Institute for Astronomy School of Physics The University of Sydney Australia Aix Marseille Univ CNRS CNES LAM France INAF - Osservatorio Astronomico di Palermo Italy University of Rome Tor Vergata Italy Newcastle University United Kingdom Italy UK Astronomy Technology Ctr. Royal Observatory Edinburgh United Kingdom INAF OACT Italy Dipartimento di Fisica Università degli Studi di Torino Italy Istituto Nazionale di Fisica Nucleare INFN Sezione di

The WST is proposed as a new facility dedicated to the efficient delivery of spectroscopic surveys and this white paper summarises the initial concept as well as the corresponding science cases. It will provide an end-to-end service from the preparation of surveys to the delivery of high-quality calibrated science data product. Uniquely, it will have the ability to simultaneously deliver fibre-fed multi-object spectroscopy over a large field of view (MOS) and integral field imaging spectroscopy of a central field of 3 × 3 arcmin2 (IFS). WST will fill a crucial gap in the astronomical landscape and will allow full exploitation of major current and upcoming ground and space-based facilities. WST will address outstanding scientific questions in the areas of cosmology;galaxy assembly, evolution, and enrichment, including our own Milky Way;origin of stars and planets;time domain and multi-messenger astrophysics. WST features a 12 meter aperture telescope that covers the UV to the H band and operates in seeing limited conditions. Its field of view (FoV) of 3.1 deg2 will be the largest of any telescope equipped with a MOS. In particular, its FoV is 2.5 times larger than PFS@Subaru and a remarkable 22 times larger than MOONS@VLT. In addition, the WST has a substantial aperture, twice as large (100 square metres) as its 8 meter class competitors. A central circular area with a diameter of 13 arcmin is available for simultaneous IFS observations. Within this area, the IFS with a FoV of 3 × 3 arcmin2 (9 times larger than MUSE@VLT) can be utilised simultaneously with the MOS. In addition, the IFS can be used to mosaic a larger area within the 13 arcmin circle (an area larger than the entire FoV of the ELT) while the MOS positioner can perform multiple configurations. The WST operational model will also implement target of opportunity (ToO) operations at both the telescope and MOS fibre levels to support the time domain science cases. The demand for a facility dedicated to spect

关键词： Telescopes

Changing life expectancy in European countries 1990–2021: a subanalysis of causes and risk factors from the Global Burden of Disease Study 2021

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The Lancet Public Health 2025年第3期10卷 e172-e188页

作者： Steel, Nicholas Bauer-Staeb, Clarissa Maria Mercedes Ford, John A. Abbafati, Cristiana Abdalla, Mohammed Altigani Abdelkader, Atef Abdi, Parsa Zuñiga, Roberto Ariel Abeldaño Abiodun, Olugbenga Olusola Abolhassani, Hassan Abu-Gharbieh, Eman Abukhadijah, Hana J. Abu-Zaid, Ahmed Addo, Isaac Yeboah Addolorato, Giovanni Adekanmbi, Victor Adetunji, Juliana Bunmi Adeyeoluwa, Temitayo Esther Agardh, Emilie E. Agyemang-Duah, Williams Ahmad, Danish Ahmed, Ayman Ahmed, Syed Anees Akinosoglou, Karolina Akkaif, Mohammed Ahmed Al Awaidy, Salah Al Hasan, Syed Mahfuz Al Zaabi, Omar Ali Mohammed Aldridge, Robert W. Algammal, Abdelazeem M. Al-Gheethi, Adel Ali Saeed Ali, Abid Ali, Mohammed Usman Ali, Syed Shujait Ali, Waad Alicandro, Gianfranco Alif, Sheikh Mohammad Al-Jumaily, Adel Allebeck, Peter Alrawashdeh, Ahmad Al-Rifai, Rami H. Alsabri, Mohammed A. Alshahrani, Najim Z. Aluh, Deborah Oyine Al-Wardat, Mohammad Al-Zyoud, Walid A. Amiri, Sohrab Anderlini, Deanna Andrei, Catalina Liliana Anil, Abhishek Anvari, Saeid Anyasodor, Anayochukwu Edward Appiah, Seth Christopher Yaw Aquilano, Michele Arabloo, Jalal Arafat, Mosab Areda, Demelash Aremu, Abdulfatai Armani, Keivan Armocida, Benedetta Ärnlöv, Johan Asaduzzaman, Muhammad Astell-Burt, Thomas Aujayeb, Avinash Ausloos, Marcel Azadnajafabad, Sina Aziz, Shahkaar Azzam, Ahmed Y. Babu, Giridhara Rathnaiah Badache, Andreea Corina Badiye, Ashish D. Bahramian, Saeed Baig, Atif Amin Baker, Jennifer L. Bansal, Hansi Bärnighausen, Till Winfried Barone, Mark Thomaz Ugliara Barrow, Amadou Barteit, Sandra Bashir, Shahid Bashiru, Hameed Akande Basso, João Diogo Bastan, Mohammad-Mahdi Basu, Sanjay Batra, Kavita Bauckneht, Matteo Baune, Bernhard T. Beghi, Massimiliano Beiranvand, Maryam Béjot, Yannick Bell, Michelle L. Bello, Olorunjuwon Omolaja Belo, Luis Beloukas, Apostolos Beneke, Alice A. Bettencourt, Paulo J.G. Bhagavathula, Akshaya Srikanth Bhala, Neeraj Bhaskar, Sonu Bisulli, Francesca Bjørge, Tone Bodunrin, Aadam Olalekan Carvajal, Alejandro Botero Bouaoud, Souad Brayne, Carol Brenner, H Department of Primary Care and Public Health University of East Anglia Norwich United Kingdom Office for Health Improvement and Disparities Department of Health and Social Care London United Kingdom Department of Legal and Economic Studies La Sapienza University Rome Italy Rome Italy Hull York Medical School University of Hull Hull United Kingdom Department of Mathematics and Sciences Ajman University Ajman United Arab Emirates Department of Medicine Memorial University St. John's NL Canada Postgraduate Department University of Sierra Sur Miahuatlan de Porfirio Diaz Mexico Yhteiskuntadatatieteen keskus (Centre for Social Data Science) Helsinki Finland Department of Internal Medicine Federal Medical Centre Abuja Nigeria Research Center for Immunodeficiencies Tehran University of Medical Sciences Tehran Iran Department of Medical Biochemistry and Biophysics Karolinska Institutet (Karolinska Institute) Stockholm Sweden Department of Clinical Sciences University of Sharjah Sharjah United Arab Emirates Department of Biopharmaceutics and Clinical Pharmacy University of Jordan Amman Jordan Medical Research Center Hamad Medical Corporation Doha Qatar Department of Biochemistry and Molecular Medicine Alfaisal University Riyadh Saudi Arabia College of Graduate Health Sciences University of Tennessee Memphis TN United States School of Medicine Sydney NSW Australia Centre for Social Research in Health University of New South Wales Sydney NSW Australia Internal Medicine and Alcohol Related Disease Unit Fondazione Policlinico Universitario A. Gemelli IRCCS Rome Italy Department of Medical and Surgical Sciences Università Cattolica di Roma (Catholic University of Rome) Rome Italy Department of Obstetrics and Gynecology University of Texas Medical Branch Galveston TX United States Department of Biochemistry Osun State University Osogbo Nigeria Department of Pharmacology and Therapeutics University of Medical Sciences Ondo Ondo Nigeria Department of Veteri

Background: Decades of steady improvements in life expectancy in Europe slowed down from around 2011, well before the COVID-19 pandemic, for reasons which remain disputed. We aimed to assess how changes in risk factors and cause-specific death rates in different European countries related to changes in life expectancy in those countries before and during the COVID-19 pandemic. Methods: We used data and methods from the Global Burden of Diseases, Injuries, and Risk Factors Study 2021 to compare changes in life expectancy at birth, causes of death, and population exposure to risk factors in 16 European Economic Area countries (Austria, Belgium, Denmark, Finland, France, Germany, Greece, Iceland, Ireland, Italy, Luxembourg, the Netherlands, Norway, Portugal, Spain, and Sweden) and the four UK nations (England, Northern Ireland, Scotland, and Wales) for three time periods: 1990–2011, 2011–19, and 2019–21. Changes in life expectancy and causes of death were estimated with an established life expectancy cause-specific decomposition method, and compared with summary exposure values of risk factors for the major causes of death influencing life expectancy. Findings: All countries showed mean annual improvements in life expectancy in both 1990–2011 (overall mean 0·23 years [95% uncertainty interval [UI] 0·23 to 0·24]) and 2011–19 (overall mean 0·15 years [0·13 to 0·16]). The rate of improvement was lower in 2011–19 than in 1990–2011 in all countries except for Norway, where the mean annual increase in life expectancy rose from 0·21 years (95% UI 0·20 to 0·22) in 1990–2011 to 0·23 years (0·21 to 0·26) in 2011–19 (difference of 0·03 years). In other countries, the difference in mean annual improvement between these periods ranged from –0·01 years in Iceland (0·19 years [95% UI 0·16 to 0·21] vs 0·18 years [0·09 to 0·26]), to –0·18 years in England (0·25 years [0·24 to 0·25] vs 0·07 years [0·06 to 0·08]). In 2019–21, there was an overall decrease in mean annual life expectancy a

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Roadmap on Electronic Structure Codes in the Exascale Era

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arXiv 2022年

作者： Gavini, Vikram Baroni, Stefano Blum, Volker Bowler, David R. Buccheri, Alexander Chelikowsky, James R. Das, Sambit Dawson, William Delugas, Pietro Dogan, Mehmet Draxl, Claudia Galli, Giulia Genovese, Luigi Giannozzi, Paolo Giantomassi, Matteo Gonze, Xavier Govoni, Marco Gulans, Andris Gygi, François Herbert, John M. Kokott, Sebastian Kühne, Thomas D. Liou, Kai-Hsin Miyazaki, Tsuyoshi Motamarri, Phani Nakata, Ayako Pask, John E. Plessl, Christian Ratcliff, Laura E. Richard, Ryan M. Rossi, Mariana Schade, Robert Scheffler, Matthias Schütt, Ole Suryanarayana, Phanish Torrent, Marc Truflandier, Lionel Windus, Theresa L. Xu, Qimen Yu, Victor W.-Z. Perez, Danny Department of Mechanical Engineering University of Michigan Ann ArborMI48109 United States SISSA ISAS Trieste Trieste34136 Italy IOM-CNR Trieste Trieste34136 Italy Duke University DurhamNC27708 United States London Centre for Nanotechnology LondonWC1H 0AH United Kingdom WPI-MANA National Institute for Materials Science Ibaraki305-0044 Japan Humboldt-Universität zu Berlin Berlin10117 Germany McKetta Department of Chemical Engineering University of Texas at Austin AustinTX78712 United States Center for Computational Materials Oden Institute University of Texas at Austin AustinTX78712 United States Department of Physics University of Texas at Austin AustinTX78712 United States RIKEN Center for Computational Science Hyogo Kobe650-0047 Japan Pritzker School of Molecular Engineering University of Chicago ChicagoIL60637 United States Materials Science Division Argonne National Laboratory LemontIL60439 United States Université Grenoble Alpes MEM L_Sim GrenobleF-38000 France University of Udine Udine33100 Italy IMCN Université catholique de Louvain Louvain-la-neuve Belgium University of Latvia RīgaLV-1586 Latvia University of California DavisCA95616 United States Department of Chemistry & Biochemistry The Ohio State University ColumbusOH43210 United States Berlin14195 Germany Dynamics of Condensed Matter Center for Sustainable Systems Design Chair of Theoretical Chemistry Paderborn University D-33098 Germany Paderborn Center for Parallel Computing Paderborn University D-33098 Germany Department of Computational and Data Sciences Indian Institute of Science Karnataka Bengaluru560012 India Lawrence Livermore National Laboratory LivermoreCA94550 United States Department of Computer Science Paderborn University D-33098 Germany Centre for Computational Chemistry School of Chemistry University of Bristol BristolBS8 1TS United Kingdom Ames National Laboratory AmesIA50011 United States Iowa State University AmesIA50

Electronic structure calculations have been instrumental in providing many important insights into a range of physical and chemical properties of various molecular and solid-state systems. Their importance to various fields, including materials science, chemical sciences, computational chemistry and device physics, is underscored by the large fraction of available public supercomputing resources devoted to these calculations. As we enter the exascale era, exciting new opportunities to increase simulation numbers, sizes, and accuracies present themselves. In order to realize these promises, the community of electronic structure software developers will however first have to tackle a number of challenges pertaining to the efficient use of new architectures that will rely heavily on massive parallelism and hardware accelerators. This roadmap provides a broad overview of the state-of-the-art in electronic structure calculations and of the various new directions being pursued by the community. It covers 14 electronic structure codes, presenting their current status, their development priorities over the next five years, and their plans towards tackling the challenges and leveraging the opportunities presented by the advent of exascale computing. Copyright © 2022, The Authors. All rights reserved.

关键词： Electronic structure

Learning from learning machines: A new generation of AI technology to meet the needs of science

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arXiv 2021年

作者： Pion-Tonachini, Luca Bouchard, Kristofer Martin, Hector Garcia Peisert, Sean Holtz, W. Bradley Aswani, Anil Dwivedi, Dipankar Wainwright, Haruko Pilania, Ghanshyam Nachman, Benjamin Marrone, Babetta L. Falco, Nicola Prabhat Arnold, Daniel Wolf-Yadlin, Alejandro Powers, Sarah Climer, Sharlee Jackson, Quinn Carlson, Ty Sohn, Michael Zwart, Petrus Kumar, Neeraj Justice, Amy Tomlin, Claire Jacobson, Daniel Micklem, Gos Gkoutos, Georgios V. Bickel, Peter J. Cazier, Jean-Baptiste Müller, Juliane Webb-Robertson, Bobbie-Jo Stevens, Rick Anderson, Mark Kreutz-Delgado, Ken Mahoney, Michael W. Brown, James B. Pattern Computer Inc. Friday HarborWA98250 United States Biosciences Area Lawrence Berkeley National Lab BerkeleyCA94803 United States Computational Research Division Lawrence Berkeley National Lab BerkeleyCA94720 United States Helen Wils Neuroscience Institute Redwood Center for Theoretical Neuroscience Uc Berkeley BerkeleyCA94720 United States Doe Agile BioFoundry Lawrence Berkeley National Laboratory BerkeleyCA94803 United States Joint BioEnergy Institute Lawrence Berkeley National Laboratory BerkeleyCA94803 United States Bcam Basque Center for Applied Mathematics Bilbao48009 Spain Computer Science University of California Davis DavisCA95616 United States Cenic La MiradaCA90638 United States Health Informatics University of California Davis School of Medicine SacramentoCA95817 United States Berkeley Institute for Data Science University of California Berkeley BerkeleyCA94720 United States Industrial Engineering and Operations Research University of California Berkeley BerkeleyCA94720 United States Environmental & Earth Sciences Area Lawrence Berkeley National Laboratory BerkeleyCA94803 United States Nuclear Engineering University of California Berkeley BerkeleyCA94720 United States Materials Science and Technology Division Los Alamos National Laboratory Los AlamosNM87545 United States Physics Division Lawrence Berkeley National Lab BerkeleyCA94720 United States Bioscience Division Los Alamos National Laboratory Los AlamosNM87545 United States Earth and Environmental Sciences Area Lawrence Berkeley National Lab BerkeleyCA94803 United States Energy Technologies Area Lawrence Berkeley National Lab BerkeleyCA94803 United States Computing and Computational Sciences Oak Ridge National Laboratory Oak RidgeTN37831 United States Industrial & Systems Engineering The University of Tennessee KnoxvilleTN37996 United States Department of Computer Science University of Missouri-Saint Louis St. LouisMO63121 United

We outline emerging opportunities and challenges to enhance the utility of AI for scientific discovery. The distinct goals of AI for industry versus the goals of AI for science create tension between identifying patterns in data versus discovering patterns in the world from data. If we address the fundamental challenges associated with "bridging the gap" between domain-driven scientific models and data-driven AI learning machines, then we expect that these AI models can transform hypothesis generation, scientific discovery, and the scientific process itself. © 2021, CC BY.

关键词： Learning systems

Presenting unbinned differential cross section results

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arXiv 2021年

作者： Arratia, Miguel Butter, Anja Campanelli, Mario Croft, Vincent Gillberg, Dag Ghosh, Aishik Lohwasser, Kristin Malaescu, Bogdan Mikuni, Vinicius Nachman, Benjamin Rojo, Juan Thaler, Jesse Winterhalder, Ramon Department of Physics and Astronomy University of California RiversideCA92521 United States Thomas Jefferson National Accelerator Facility Newport NewsVA23606 United States Institut für Theoretische Physik Universität Heidelberg Heidelberg Germany University College London LondonWC1E 6BT United Kingdom Department of Physics and Astronomy Tufts University BostonMA02155 United States Department of Physics Carleton University OttawaONK1S 5B6 Canada Department of Physics and Astronomy University of California IrvineCA92697 United States Physics Division Lawrence Berkeley National Laboratory BerkeleyCA94720 United States University of Sheffield SheffieldS10 2TN United Kingdom LPNHE Sorbonne Université Université de Paris CNRS/IN2P3 Paris France National Energy Research Scientific Computing Center BerkeleyCA94720 United States Berkeley Institute for Data Science University of California BerkeleyCA94720 United States Nikhef Theory Group Science Park 105 Amsterdam1098 XG Netherlands Department of Physics and Astronomy Vrije Universiteit Amsterdam AmsterdamNL-1081 HV Netherlands Center for Theoretical Physics Massachusetts Institute of Technology CambridgeMA02139 United States The NSF AI Institute for Artificial Intelligence and Fundamental Interactions Université Catholique de Louvain Louvain-la-Neuve1348 Belgium

Machine learning tools have empowered a qualitatively new way to perform differential cross section measurements whereby the data are unbinned, possibly in many dimensions. Unbinned measurements can enable, improve, or at least simplify comparisons between experiments and with theoretical predictions. Furthermore, many-dimensional measurements can be used to define observables after the measurement instead of before. There is currently no community standard for publishing unbinned data. While there are also essentially no measurements of this type public, unbinned measurements are expected in the near future given recent methodological advances. The purpose of this paper is to propose a scheme for presenting and using unbinned results, which can hopefully form the basis for a community standard to allow for integration into analysis workflows. This is foreseen to be the start of an evolving community dialogue, in order to accommodate future developments in this field that is rapidly evolving. © 2021, CC BY.

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Method for the calculation of the Hamaker constants of organic materials by the Lifshitz macroscopic approach with DFT

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arXiv 2019年

作者： Takagishi, Hideyuki Masuda, Takashi Shimoda, Tatsuya Maezono, Ryo Hongo, Kenta School of Materials Science JAIST Asahidai 1-1 Nomi Ishikawa923-1292 Japan School of Information Science JAIST Asahidai 1-1 Nomi Ishikawa923-1292 Japan Computational Engineering Applications Unit RIKEN 2-1 Hirosawa Wako Saitama351-0198 Research Center for Advanced Computing Infrastructure JAIST Asahidai 1-1 Nomi Ishikawa923-1292 Japan Center for Materials Research by Information Integration Research Services Division of Materials Data and Integrated System National Institute for Materials Science Tsukuba305-0047 PRESTO Japan Science and Technology Agency 4-1-8 Honcho Kawaguchi-shi Saitama322-0012 Japan

The Hamaker constants, which are coefficients providing quantitative information on intermolecular forces, were calculated for a number of different materials according to the Lifshitz theory via simple DFT calculations without any experimental measurements being performed. The physical properties (polarizability, dipole moment, molecular volume, and vibrational frequency) of organic molecules were calculated using the B3LYP density functional and the aug-cc-pVDZ basis set. Values for the Hamaker constants were obtained using the approximation of the Lorentz-Lorenz equation and Onsager's equation with these properties. It was found that, in the case of 'non-associative' materials, like hydrocarbons, ethers, ketones, aldehydes, carboxylic acids, esters, nitriles, and hydrosilanes, and halides, the calculated Hamaker constants were similar in value to their experimentally determined counterparts. Moreover, with this calculation method, it is easy to create the molecular model and the CPU time can be shortened. Copyright © 2019, The Authors. All rights reserved.

关键词： Ketones

Assessing the Global Impact of Brain Small Vessel Disease on Cognition: The Multi-Ethnic Study of Atherosclerosis

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Alzheimer's & Dementia 2025年第6期21卷

作者： Sokratis Charisis Tanweer Rashid Christina Dintica Mitzi Gonzales Hangfan Liu Jeffrey B. Ware Thomas R. Austin Paul N. Jensen Alison E. Fohner Jordan E. Tanley Jingzhong Ding José A. Luchsinger Bonnie Sachs Ilya M. Nasrallah R. Nick Bryan Kathleen M. Hayden David Wolk Katya Rascovsky Christos Davatzikos William T. Longstreth Jr Kristine Yaffe Sudha Seshadri Susan R. Heckbert Timothy Hughes Mohamad Habes Neuroimage Analytics Laboratory (NAL) and the Biggs Institute Neuroimaging Core (BINC) Glenn Biggs Institute for Alzheimer's and Neurodegenerative Diseases University of Texas Health Science Center San Antonio San Antonio Texas USA Department of Neurology University of Texas Health Science Center at San Antonio San Antonio Texas USA Department of Psychiatry and Behavioral Sciences University of California San Francisco San Francisco California USA Department of Neurology Cedars Sinai Medical Center Los Angeles California USA Center for Advanced Imaging Research (CAIR) University of Maryland School of Medicine Baltimore Maryland USA Department of Radiology Perelman School of Medicine University of Pennsylvania Philadelphia Pennsylvania USA Department of Epidemiology University of Washington Seattle Washington USA Cardiovascular Health Research Unit University of Washington Seattle Washington USA Department of Medicine University of Washington Seattle Washington USA Department of Internal Medicine Wake Forest School of Medicine Winston-Salem North Carolina USA Departments of Medicine and Epidemiology Columbia University Medical Center New York New York USA Department of Neurology Wake Forest University School of Medicine Winston-Salem North Carolina USA AI2D Center for AI and Data Science for Integrated Diagnostics and Center for Biomedical Image Computing and Analytics University of Pennsylvania Philadelphia Pennsylvania USA Department of Social Sciences and Health Policy Division of Public Health Sciences Wake Forest University School of Medicine Winston-Salem North Carolina USA Alzheimer's Disease Research Center University of Pennsylvania Philadelphia Pennsylvania USA Department of Neurology University of Pennsylvania Philadelphia Pennsylvania USA Department of Neurology University of Washington Seattle Washington USA

INTRODUCTION We aimed to examine the global impact of brain small vessel disease (SVD) on cognitive performance. METHODS In 892 participants from the Multi-Ethnic Study of Atherosclerosis (MESA), we derived perivascular spaces (PVS), white matter hyperintensities (WMH), microbleeds (MB), and white matter fractional anisotropy (FA) and trace (TR). Cognitive function was assessed with a comprehensive neuropsychological battery. RESULTS A composite SVD measure was constructed as a linear combination of basal ganglia PVS, thalamus PVS, periventricular WMH, subcortical WMH, and white matter FA and TR, and exhibited associations with worse global and domain-specific cognitive performance. Additionally, SVD mediated the effect of age and cardiovascular disease risk on global cognitive function, both directly and through smaller gray matter (GM) volume. DISCUSSION Integrating multiple individual SVD endophenotypes may more accurately reflect the neurobiology of SVD and capture its global impact on cognition. SVD mediates the effects of age and cardiovascular disease risk on cognition through both atrophy-related and non–atrophy-related pathways. Highlights Associations between individual magnetic resonance imaging (MRI) markers of brain small vessel disease and cognitive outcomes might not fully capture the global impact of small vessel disease on cognition. We modeled small vessel disease as a latent construct, integrating multiple MRI endophenotypes in strategic brain regions. The small vessel disease construct was associated with worse global and domain-specific cognitive performance. The small vessel disease construct exhibited mediating effects in the relationships of aging and cardiovascular disease risk with cognition through pathways that both involve and are independent of brain atrophy. Integrating information from multiple relevant imaging endophenotypes could open new avenues in small vessel disease research, broadening our understanding of its risk factors and

关键词： aging atrophy attention cardiovascular disease risk cerebral small vessel disease cognition cognitive performance deep learning delayed memory diffusion tensor imaging enlarged perivascular spaces executive function factor analysis fractional anisotropy Framingham risk score global cognition gray matter volume immediate memory language latent variable magnetic resonance imaging mediation microbleeds phonemic fluency processing speed semantic fluency structural equation modeling trace visuospatial functioning white matter hyperintensities

Expanding the horizon of automated metamaterials discovery via quantum annealing

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arXiv 2019年

作者： Kitai, Koki Guo, Jiang Ju, Shenghong Tanaka, Shu Tsuda, Koji Shiomi, Junichiro Tamura, Ryo Graduate School of Frontier Sciences University of Tokyo Chiba277-8568 Japan Department of Mechanical Engineering University of Tokyo Tokyo113-8654 Japan Research and Services Division of Materials Data and Integrated System National Institute for Materials Science Ibaraki305-0047 Japan Green Computing Systems Research Organization Waseda University Tokyo162-0042 Japan JST PRESTO Saitama332-0012 Japan RIKEN Center for Advanced Intelligence Project Tokyo103-0027 Japan International Center for Materials Nanoarchitectonics National Institute for Materials Science Ibaraki305-0044 Japan

Complexity of materials designed by machine learning is currently limited by the inefficiency of classical computers. We show how quantum annealing can be incorporated into automated materials discovery and conduct a proof-of-principle study on designing complex thermofunctional metamaterials consisting of SiO2, SiC, and Poly(methyl methacrylate). Empirical computing time of our quantum-classical hybrid algorithm involving a factorization machine, a rigorous coupled wave analysis, and a D-Wave 2000Q quantum annealer was insensitive to the problem size, while a classical counterpart experienced rapid increase. Our method was used to design complex structures of wavelength selective radiators showing much better concordance with the thermal atmospheric transparency window in comparison to existing human-designed alternatives. Our result shows that quantum annealing provides scientists gigantic computational power that may change how materials are designed. Copyright © 2019, The Authors. All rights reserved.

关键词： Metamaterials

Author Correction: The landscape of viral associations in human cancers

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Nature genetics 2023年第6期55卷 1077页

作者： Marc Zapatka Ivan Borozan Daniel S Brewer Murat Iskar Adam Grundhoff Malik Alawi Nikita Desai Holger Sültmann Holger Moch Colin S Cooper Roland Eils Vincent Ferretti Peter Lichter Division of Molecular Genetics German Cancer Research Center (DKFZ) Heidelberg Germany. Informatics and Bio-computing Program Ontario Institute for Cancer Research Toronto Ontario Canada. Norwich Medical School University of East Anglia Norwich UK. Earlham Institute Norwich UK. Heinrich-Pette-Institute Leibniz Institute for Experimental Virology Hamburg Germany. German Center for Infection Research (DZIF) Partner Site Hamburg-Borstel-Lübeck-Riems Hamburg Germany. Bioinformatics Core University Medical Center Hamburg-Eppendorf Hamburg Germany. Bioinformatics Group Department of Computer Science University College London London UK. Biomedical Data Science Laboratory Francis Crick Institute London UK. National Center for Tumor Diseases (NCT) Heidelberg Heidelberg Germany. Division of Cancer Genome Research German Cancer Research Center (DKFZ) Heidelberg Germany. German Cancer Consortium (DKTK) Heidelberg Germany. Department of Pathology and Molecular Pathology University and University Hospital Zürich Zurich Switzerland. Institute of Cancer Research London UK. University of East Anglia Norwich UK. Division of Theoretical Bioinformatics German Cancer Research Center (DKFZ) Heidelberg Germany. Department of Bioinformatics and Functional Genomics Institute of Pharmacy and Molecular Biotechnology Heidelberg University and BioQuant Center Heidelberg Germany. Center for Digital Health Berlin Institute of Health and Charité Universitätsmedizin Berlin Berlin Germany. Ontario Institute for Cancer Research MaRS Centre Toronto Ontario Canada. Department of Biochemistry and Molecular Medicine University of Montreal Montreal Québec Canada. Division of Molecular Genetics German Cancer Research Center (DKFZ) Heidelberg Germany. peter.lichter@dkfz-heidelberg.de. German Cancer Consortium (DKTK) Heidelberg Germany. peter.lichter@dkfz-heidelberg.de.