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arXiv 2023年

作者： Tu, Wei-Lin Vanderstraeten, Laurens Schuch, Norbert Lee, Hyun-Yong Kawashima, Naoki Chen, Ji-Yao Faculty of Science and Technology Keio University 3-14-1 Hiyoshi Kohoku-ku Yokohama223-8522 Japan Center for Nonlinear Phenomena and Complex Systems Université Libre de Bruxelles Belgium University of Vienna Faculty of Physics Boltzmanngasse 5 Wien1090 Austria University of Vienna Faculty of Mathematics Oskar-Morgenstern-Platz 1 Wien1090 Austria Division of Display and Semiconductor Physics Korea University Sejong30019 Korea Republic of Department of Applied Physics Graduate School Korea University Sejong30019 Korea Republic of Interdisciplinary Program in E·ICT-Culture-Sports Convergence Korea University Sejong30019 Korea Republic of Institute for Solid State Physics The University of Tokyo Chiba Kashiwa277-8581 Japan Trans-scale Quantum Science Institute The University of Tokyo Bunkyo Tokyo113-0033 Japan Center for Neutron Science and Technology Guangdong Provincial Key Laboratory of Magnetoelectric Physics and Devices School of Physics Sun Yat-sen University Guangzhou510275 China

Diagrammatic summation is a common bottleneck in modern applications of projected entangled-pair states, especially in computing low-energy excitations of a two-dimensional quantum many-body system. To solve this problem, here we extend the generating function approach for tensor network diagrammatic summation, a scheme previously proposed in the context of matrix product states. Taking the form of a one-particle excitation, we show that the excited state can be computed efficiently in the generating function formalism, which can further be used in evaluating the dynamical structure factor of the system. Our benchmark results for the spin-1/2 transverse-field Ising model and Heisenberg model on the square lattice provide a desirable accuracy, showing good agreement with known results. We then study the spin-1/2 J1-J2 model on the same lattice and investigate the dynamical properties of the putative gapless spin liquid phase. We conclude with a discussion on generalizations to multi-particle excitations. © 2023, CC BY.

关键词： Excited states

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Advancing electrochemical impedance analysis through innovations in the distribution of relaxation times method

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Joule 2024年第7期8卷 1958-1981页

作者： Maradesa, Adeleke Py, Baptiste Huang, Jake Lu, Yang Iurilli, Pietro Mrozinski, Aleksander Law, Ho Mei Wang, Yuhao Wang, Zilong Li, Jingwei Xu, Shengjun Meyer, Quentin Liu, Jiapeng Brivio, Claudio Gavrilyuk, Alexander Kobayashi, Kiyoshi Bertei, Antonio Williams, Nicholas J. Zhao, Chuan Danzer, Michael Zic, Mark Wu, Phillip Yrjänä, Ville Pereverzyev, Sergei Chen, Yuhui Weber, André Kalinin, Sergei V. Schmidt, Jan Philipp Tsur, Yoed Boukamp, Bernard A. Zhang, Qiang Gaberšček, Miran O'Hayre, Ryan Ciucci, Francesco Department of Mechanical and Aerospace Engineering The Hong Kong University of Science and Technology Hong Kong Metallurgical and Materials Engineering Colorado School of Mines Golden80401 United States Beijing Key Laboratory of Green Chemical Reaction Engineering and Technology Department of Chemical Engineering Tsinghua University Beijing China Green Energy Storage Trento38123 Italy Department of Manufacturing and Production Engineering Faculty of Mechanical Engineering and Ship Technology Institute of Machine and Materials Technology Gdańsk University of Technology Gdańsk Poland Electrode Design for Electrochemical Energy Systems University of Bayreuth Bayreuth Germany School of Chemistry University of New South Wales Sydney Australia School of Advanced Energy Sun Yat-Sen University Shenzhen China Sustainable Energy Center CSEM Neuchâtel2002 Switzerland Interdisciplinary Faculty of Science and Engineering Shimane University Matsue Japan Centre for Electronic and Optical Materials National Institute for Materials Science Tsukuba Japan Department of Civil and Industrial Engineering University of Pisa Pisa Italy Department of Materials Imperial College London Exhibition Road LondonSW7 2AZ United Kingdom Department of Chemical Engineering Massachusetts Institute of Technology Cambridge02139 United States Electrical Energy Systems University of Bayreuth Bayreuth Germany Ruder Boskovic Institute Zagreb Croatia Department of Materials and Minerals Resources Engineering National Taipei University Taipei Taiwan Finland Johann Radon Institute for Computational and Applied Mathematics Linz Austria School of Science and Engineering Nanjing Tech. University Nanjing China Karlsruhe Germany Department of Materials Science and Engineering University of Tennessee KnoxvilleTN37996 United States Physical Science Division Pacific Northwest National Laboratory RichlandWA99354 United States Systems Engineering for Electrical Energy Storage University of B

Electrochemical impedance spectroscopy (EIS) is widely used in electrochemistry, energy sciences, biology, and beyond. Analyzing EIS data is crucial, but it often poses challenges because of the numerous possible equivalent circuit models, the need for accurate analytical models, the difficulties of nonlinear regression, and the necessity of managing large datasets within a unified framework. To overcome these challenges, non-parametric models, such as the distribution of relaxation times (DRT, also known as the distribution function of relaxation times, DFRT), have emerged as promising tools for EIS analysis. For example, the DRT can be used to generate equivalent circuit models, initialize regression parameters, provide a time-domain representation of EIS spectra, and identify electrochemical processes. However, mastering the DRT method poses challenges as it requires mathematical and programming proficiency, which may extend beyond experimentalists’ usual expertise. Post-inversion analysis of DRT data can be difficult, especially in accurately identifying electrochemical processes, leading to results that may not always meet expectations. This article examines non-parametric EIS analysis methods, outlining their strengths and limitations from theoretical, computational, and end-user perspectives, and provides guidelines for their future development. Moreover, insights from survey data emphasize the need to develop a large impedance database, akin to an impedance genome. In turn, software development should target one-click, fully automated DRT analysis for multidimensional EIS spectra interpretation, software validation, and reliability. Particularly, creating a collaborative ecosystem hinged on free software could promote innovation and catalyze the adoption of the DRT method throughout all fields that use impedance data. © 2024 Elsevier Inc.

关键词： Electrochemical impedance spectroscopy

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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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DeePMD-kit v2: A software package for Deep Potential models

arXiv

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arXiv 2023年

作者： Zeng, Jinzhe Zhang, Duo Lu, Denghui Mo, Pinghui Li, Zeyu Chen, Yixiao Rynik, Marián Huang, Li'ang Li, Ziyao Shi, Shaochen Wang, Yingze Ye, Haotian Tuo, Ping Yang, Jiabin Ding, Ye Li, Yifan Tisi, Davide Zeng, Qiyu Bao, Han Xia, Yu Huang, Jiameng Muraoka, Koki Wang, Yibo Chang, Junhan Yuan, Fengbo Bore, Sigbjørn Løland Cai, Chun Lin, Yinnian Wang, Bo Xu, Jiayan Zhu, Jia-Xin Luo, Chenxing Zhang, Yuzhi Goodall, Rhys E.A. Liang, Wenshuo Singh, Anurag Kumar Yao, Sikai Zhang, Jingchao Wentzcovitch, Renata Han, Jiequn Liu, Jie Jia, Weile York, Darrin M. Weinan, E. Car, Roberto Zhang, Linfeng Wang, Han Laboratory for Biomolecular Simulation Research Institute for Quantitative Biomedicine Department of Chemistry and Chemical Biology Rutgers University PiscatawayNJ08854 United States AI for Science Institute Beijing100080 China DP Technology Beijing100080 China Academy for Advanced Interdisciplinary Studies Peking University Beijing100871 China HEDPS CAPT College of Engineering Peking University Beijing100871 China College of Electrical and Information Engineering Hunan University Changsha China Yuanpei College Peking University Beijing100871 China Program in Applied and Computational Mathematics Princeton University PrincetonNJ08540 United States Department of Experimental Physics Comenius University Mlynská Dolina F2 Bratislava842 48 Slovakia Center for Quantum Information Institute for Interdisciplinary Information Sciences Tsinghua University Beijing100084 China Center for Data Science Peking University Beijing100871 China ByteDance Research Zhonghang Plaza No. 43 North 3rd Ring West Road Haidian District Beijing China College of Chemistry and Molecular Engineering Peking University Beijing100871 China Baidu Inc. Beijing China Key Laboratory of Structural Biology of Zhejiang Province School of Life Sciences Westlake University Zhejiang Hangzhou China Westlake AI Therapeutics Lab Westlake Laboratory of Life Sciences and Biomedicine Zhejiang Hangzhou China Department of Chemistry Princeton University PrincetonNJ08544 United States SISSA Scuola Internazionale Superiore di Studi Avanzati Trieste34136 Italy Laboratory of Computational Science and Modeling Institute of Materials École Polytechnique Fédérale de Lausanne Lausanne1015 Switzerland Department of Physics National University of Defense Technology Hunan Changsha410073 China State Key Lab of Processors Institute of Computing Technology Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China School of Electronics Engineerin

DeePMD-kit is a powerful open-source software package that facilitates molecular dynamics simulations using machine learning potentials (MLP) known as Deep Potential (DP) models. This package, which was released in 2017, has been widely used in the fields of physics, chemistry, biology, and material science for studying atomistic systems. The current version of DeePMD-kit offers numerous advanced features such as DeepPot-SE, attention-based and hybrid descriptors, the ability to fit tensile properties, type embedding, model deviation, Deep Potential - Range Correction (DPRc), Deep Potential Long Range (DPLR), GPU support for customized operators, model compression, non-von Neumann molecular dynamics (NVNMD), and improved usability, including documentation, compiled binary packages, graphical user interfaces (GUI), and application programming interfaces (API). This article presents an overview of the current major version of the DeePMD-kit package, highlighting its features and technical details. Additionally, the article benchmarks the accuracy and efficiency of different models and discusses ongoing developments. © 2023, CC BY-NC-ND.

关键词： Molecular dynamics

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Building Ramps and Pathways to Data Science in K-12, Minority-Serving Institutions, and Two-Year Colleges

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Scatterplot 2024年第1期1卷

作者： Rachel Saidi Helen Burn Ben Baumer Jason Black Debzani Deb Zarek Drozda a Math Statistics and Data Science Department Montgomery College – Rockville Campus Rockville MD USA b Mathematics Department Highline College Des Moines Washington Des Moines WA USA c Statistical and Data Sciences Department Smith College Northampton MA USA d School of Business and Interdisciplinary Center for Creativity and Innovation (ICCI) Florida A&M University Tallahassee FL USA e Computer Science Department Center for Applied Data Science (CADS) Winston-Salem State University Winston-Salem NC USA f Data Science 4 Everyone (DS4E) Program University of Chicago Chicago IL USA

Global demand for data scientists and analysts is on a trajectory of sustained growth, underscoring the need to expand enrollment of students in data science and data analytics courses and programs. This article explores diverse pathways to data science for various groups, including high school students, those in two-year colleges, and students in small colleges and minority-serving institutions. This article identifies opportunities and challenges associated with pursuing data science for students in these sectors, including factors such as building partnerships and awareness of the field and difficulties in establishing articulation agreements between institutions. The authors, representing various organizations participating in a panel discussion at the 2023 Data Science Leadership Summit of the Academic Data Science Alliance (ADSA), share insights into efforts to develop nontraditional pathways, serving as a resource for schools, colleges, and organizations contemplating creation of data science programs. The panelists’ combined experience spans work with a broad range of organizations including ADSA, American mathematics Association of Two-Year Colleges, American Statistical Association, Data Science for Everyone, the Department of Education, HBCU Data Science Consortium, Mathematical Association of America, National Academies of Science, Engineering, and Medicine, HBCU Data Science Consortium, National Science Foundation, and StatPREP.

关键词： Data science pathways access K-12 minority-serving institution two-year college

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Correction to: binomialRF: interpretable combinatoric efficiency of random forests to identify biomarker interactions

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BMC bioinformatics 2020年第1期21卷 495页

作者： Samir Rachid Zaim Colleen Kenost Joanne Berghout Wesley Chiu Liam Wilson Hao Helen Zhang Yves A Lussier Center for Biomedical Informatics and Biostatistics University of Arizona Health Sciences 1230 N. Cherry Ave Tucson AZ 85721 USA. The Graduate Interdisciplinary Program in Statistics The University of Arizona 617 N. Santa Rita Ave Tucson AZ 85721 USA. College of Medicine Tucson 1501 N. Campbell Ave Tucson AZ 85721 USA. Center for Biomedical Informatics and Biostatistics University of Arizona Health Sciences 1230 N. Cherry Ave Tucson AZ 85721 USA. hzhang@math.arizona.edu. The Graduate Interdisciplinary Program in Statistics The University of Arizona 617 N. Santa Rita Ave Tucson AZ 85721 USA. hzhang@math.arizona.edu. Department of Mathematics College of Sciences The University of Arizona 617 N. Santa Rita Ave Tucson AZ 85721 USA. hzhang@math.arizona.edu. Center for Biomedical Informatics and Biostatistics University of Arizona Health Sciences 1230 N. Cherry Ave Tucson AZ 85721 USA. Lussier.Y@***. The Graduate Interdisciplinary Program in Statistics The University of Arizona 617 N. Santa Rita Ave Tucson AZ 85721 USA. Lussier.Y@***. College of Medicine Tucson 1501 N. Campbell Ave Tucson AZ 85721 USA. Lussier.Y@***. The Center for Applied Genetic and Genomic Medicine 1295 N. Martin Tucson AZ 85721 USA. Lussier.Y@***. The University of Arizona Cancer Center 3838 N. Campbell Ave Tucson AZ 85721 USA. Lussier.Y@***. The University of Arizona BIO5 Institute 1657 E. Helen Street Tucson AZ 85721 USA. Lussier.Y@***.

An amendment to this paper has been published and can be accessed via the original article.

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26th Annual Computational Neuroscience Meeting (CNS*2017): Part 3 Antwerp, Belgium. 15-20 July 2017 Abstracts

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BMC NEUROSCIENCE 2017年第Sup1期18卷 95-176页

作者： [Anonymous] Department of Neuroscience Yale University New Haven CT 06520 USA Department Physiology & Pharmacology SUNY Downstate Brooklyn NY 11203 USA NYU School of Engineering 6 MetroTech Center Brooklyn NY 11201 USA Kings County Hospital Center Brooklyn NY 11203 USA Departament de Matemàtica Aplicada Universitat Politècnica de Catalunya Barcelona 08028 Spain Institut de Neurobiologie de la Méditerrannée (INMED) INSERM UMR901 Aix-Marseille Univ Marseille France Center of Neural Science New York University New York NY USA Aix-Marseille Univ INSERM INS Inst Neurosci Syst Marseille France Laboratoire de Physique Théorique et Modélisation CNRS UMR 8089 Université de Cergy-Pontoise 95300 Cergy-Pontoise Cedex France Department of Mathematics and Computer Science ENSAT Abdelmalek Essaadi’s University Tangier Morocco Laboratory of Natural Computation Department of Information and Electrical Engineering and Applied Mathematics University of Salerno 84084 Fisciano SA Italy Department of Medicine University of Salerno 84083 Lancusi SA Italy Dipartimento di Fisica Università degli Studi Aldo Moro Bari and INFN Sezione Di Bari Italy Data Analysis Department Ghent University Ghent Belgium Coma Science Group University of Liège Liège Belgium Cruces Hospital and Ikerbasque Research Center Bilbao Spain BIOtech Department of Industrial Engineering University of Trento and IRCS-PAT FBK 38010 Trento Italy Department of Data Analysis Ghent University Ghent 9000 Belgium The Wellcome Trust Centre for Neuroimaging University College London London WC1N 3BG UK Department of Electronic Engineering NED University of Engineering and Technology Karachi Pakistan Blue Brain Project École Polytechnique Fédérale de Lausanne Lausanne Switzerland Departement of Mathematics Swansea University Swansea Wales UK Laboratory for Topology and Neuroscience at the Brain Mind Institute École polytechnique fédérale de Lausanne Lausanne Switzerland Institute of Mathematics

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Climate, food and humans predict communities of mammals in the United States

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Diversity and Distributions 2024年第9期30卷 e13900-e13900页

作者： Kays, Roland Snider, Matthew H. Hess, George Cove, Michael V. Jensen, Alex Shamon, Hila McShea, William J. Rooney, Brigit Allen, Maximilian L. Pekins, Charles E. Wilmers, Christopher C. Pendergast, Mary E. Green, Austin M. Suraci, Justin Leslie, Matthew S. Nasrallah, Sophie Farkas, Dan Jordan, Mark Grigione, Melissa LaScaleia, Michael C. Davis, Miranda L. Hansen, Chris Millspaugh, Josh Lewis, Jesse S. Havrda, Michael Long, Robert Remine, Kathryn R. Jaspers, Kodi J. Lafferty, Diana J. R. Hubbard, Tru Studds, Colin E. Barthelmess, Erika L. Andy, Katherine Romero, Andrea Hawkins, Melissa T. R. Lombardi, Jason V. Sergeyev, Maksim Fisher-Reid, M. Caitlin Rentz, Michael S. Nagy, Christopher Davenport, Jon M. Rega-Brodsky, Christine C. Appel, Cara L. Lesmeister, Damon B. Giery, Sean T. Whittier, Christopher A. Alston, Jesse M. Sutherland, Chris Rota, Christopher Murphy, Thomas Lee, Thomas E. Mortelliti, Alessio Bergman, Dylan L. Compton, Justin A. Gerber, Brian D. Burr, Jess Rezendes, Kylie DeGregorio, Brett A. Wehr, Nathaniel H. Benson, John F. Jachowski, David S. Gray, Morgan Beyer, Dean E. Belant, Jerrold L. Horan, Robert V. Lonsinger, Robert C. Kuhn, Kellie M. Hasstedt, Steven C. M. Zimova, Marketa Moore, Sophie M. Herrera, Daniel J. Fritts, Sarah Edelman, Andrew J. Flaherty, Elizabeth A. Petroelje, Tyler R. Neiswenter, Sean A. Risch, Derek R. Iannarilli, Fabiola van der Merwe, Marius Maher, Sean P. Farris, Zach J. Webb, Stephen L. Mason, David S. Lashley, Marcus A. Wilson, Andrew M. Vanek, John P. Wehr, Samuel R. Conner, L. Mike Beasley, James C. Bontrager, Helen L. Baruzzi, Carolina Ellis-Felege, Susan N. Proctor, Mike D. Schipper, Jan Weiss, Katherine C. B. Darracq, Andrea K. Barr, Evan G. Alexander, Peter D. Şekercioğlu, Çağan H. Bogan, Daniel A. Schalk, Christopher M. Fantle-Lepczyk, Jean E. Lepczyk, Christopher A. LaPoint, Scott Whipple, Laura S. Rowe, Helen Ivy Mullen, Kayleigh Bird, Tori Zorn, Adam Brandt, LaRoy Lathrop, Richard G. McCain, Craig Crupi, Anthony P. Clark, James Parsons, Arielle Department of Forestry and Environmental Resources North Carolina State University Raleigh NC United States North Carolina Museum of Natural Sciences Raleigh NC United States Department of Forestry and Environmental Conservation Clemson University Clemson SC United States Smithsonian's National Zoo and Conservation Biology Institute Front Royal VA United States Illinois Natural History Survey Prairie Research Institute University of Illinois Champaign IL United States Fort Hood Natural Resources Management Branch United States Army Garrison Fort Hood TX United States Environmental Studies Department Center for Integrated Spatial Research University of California Santa Cruz Santa Cruz CA United States Science Research Initiative University of Utah Salt Lake City UT United States Sageland Collaborative Salt Lake City UT United States Conservation Science Partners Inc. Truckee CA United States National Climate Adaptation Science Center United States Geological Survey Reston VA United States Biology Department Swarthmore College Swarthmore PA United States Entomology Department Cornell University Ithaca NY United States Information Technology Department Pace University Pleasantville NY United States Seattle University Department of Biology Seattle WA United States Department of Biology Pace University Pleasantville NY United States Department of Ecology and Evolutionary Biology Storrs CT United States Wildlife Biology Program Department of Ecosystem and Conservation Sciences University of Montana Missoula MT United States Department of Forest and Environmental Biology SUNY-ESF Syracuse NY United States Arizona State University College of Integrative Sciences and Arts Mesa AZ United States University of Washington School of Environmental and Forest Sciences Seattle WA United States Woodland Park Zoo Seattle WA United States Department of Biology Wildlife Ecology and Conservation Science Lab Northern Michigan University Marquette M

Aim: The assembly of species into communities and ecoregions is the result of interacting factors that affect plant and animal distribution and abundance at biogeographic scales. Here, we empirically derive ecoregions for mammals to test whether human disturbance has become more important than climate and habitat resources in structuring communities. Location: Conterminous United States. Time Period: 2010–2021. Major Taxa Studied: Twenty-five species of mammals. Methods: We analysed data from 25 mammal species recorded by camera traps at 6645 locations across the conterminous United States in a joint modelling framework to estimate relative abundance of each species. We then used a clustering analysis to describe 8 broad and 16 narrow mammal communities. Results: Climate was the most important predictor of mammal abundance overall, while human population density and agriculture were less important, with mixed effects across species. Seed production by forests also predicted mammal abundance, especially hard-mast tree species. The mammal community maps are similar to those of plants, with an east–west split driven by different dominant species of deer and squirrels. Communities vary along gradients of temperature in the east and precipitation in the west. Most fine-scale mammal community boundaries aligned with established plant ecoregions and were distinguished by the presence of regional specialists or shifts in relative abundance of widespread species. Maps of potential ecosystem services provided by these communities suggest high herbivory in the Rocky Mountains and eastern forests, high invertebrate predation in the subtropical south and greater predation pressure on large vertebrates in the west. Main Conclusions: Our results highlight the importance of climate to modern mammals and suggest that climate change will have strong impacts on these communities. Our new empirical approach to recognizing ecoregions has potential to be applied to expanded communities o

关键词： climate macroecology mammal communities masting species distribution models

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Patterns of somatic structural variation in human cancer genomes (vol 578, pg 112, 2020)

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NATURE 2023年第7948期614卷 E38-E38页

作者： Li, Yilong Roberts, Nicola D. Wala, Jeremiah A. Shapira, Ofer Schumacher, Steven E. Kumar, Kiran Khurana, Ekta Waszak, Sebastian Korbel, Jan O. Haber, James E. Imielinski, Marcin Weischenfeldt, Joachim Beroukhim, Rameen Campbell, Peter J. Cancer Genome Project Wellcome Trust Sanger Institute Hinxton UK Totient Inc Cambridge MA USA Wellcome Sanger Institute Wellcome Genome Campus Hinxton UK Department of Haematology University of Cambridge Cambridge UK Cambridge University Hospitals NHS Foundation Trust Cambridge UK Korea Advanced Institute of Science and Technology Daejeon South Korea Department of Zoology Genetics and Physical Anthropology University of Santiago de Compostela Santiago de Compostela Spain Centre for Research in Molecular Medicine and Chronic Diseases (CIMUS) University of Santiago de Compostela Santiago de Compostela Spain The Biomedical Research Centre (CINBIO) University of Vigo Vigo Spain Centre for Research in Molecular Medicine and Chronic Diseases (CiMUS) Universidade de Santiago de Compostela Santiago de Compostela Spain Department of Zoology Genetics and Physical Anthropology (CiMUS) Universidade de Santiago de Compostela Santiago de Compostela Spain The Biomedical Research Centre (CINBIO) Universidade de Vigo Vigo Spain The Broad Institute of Harvard and MIT Cambridge MA USA Bioinformatics and Integrative Genomics Harvard University Cambridge MA USA Department of Cancer Biology Dana-Farber Cancer Institute Boston MA USA Broad Institute of MIT and Harvard Cambridge MA USA Department of Medical Oncology Dana-Farber Cancer Institute Boston MA USA Harvard Medical School Boston MA USA Massachusetts General Hospital Center for Cancer Research Charlestown MA USA Dana-Farber Cancer Institute Boston MA USA Department of Biostatistics and Computational Biology Dana-Farber Cancer Institute and Harvard Medical School Boston MA USA Weill Cornell Medical College 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 Ne

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Search for Continuous Gravitational Waves from Known Pulsars in the First Part of the Fourth LIGO-Virgo-KAGRA Observing Run

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Astrophysical Journal 2025年第2期983卷 99-99页

作者： Abac, A.G. Abbott, R. Abouelfettouh, I. Acernese, F. Ackley, K. Adhicary, S. Adhikari, N. Adhikari, R.X. Adkins, V.K. Agarwal, D. Agathos, M. Aghaei Abchouyeh, M. Aguiar, O.D. Aguilar, I. Aiello, L. Ain, A. Ajith, P. Akutsu, T. Albanesi, S. Alfaidi, R.A. Al-Jodah, A. Alléné, C. Allocca, A. Al-Shammari, S. Altin, P.A. Alvarez-Lopez, S. Amato, A. Amez-Droz, L. Amorosi, A. Amra, C. Ananyeva, A. Anderson, S.B. Anderson, W.G. Andia, M. Ando, M. Andrade, T. Andres, N. Andrés-Carcasona, M. Andrić, T. Anglin, J. Ansoldi, S. Antelis, J.M. Antier, S. Aoumi, M. Appavuravther, E.Z. Appert, S. Apple, S.K. Arai, K. Araya, A. Araya, M.C. Areeda, J.S. Argianas, L. Aritomi, N. Armato, F. Arnaud, N. Arogeti, M. Aronson, S.M. Ashton, G. Aso, Y. Assiduo, M. Assis de Souza Melo, S. Aston, S.M. Astone, P. Attadio, F. Aubin, F. AultONeal, K. Avallone, G. Babak, S. Badaracco, F. Badger, C. Bae, S. Bagnasco, S. Bagui, E. Baier, J.G. Baiotti, L. Bajpai, R. Baka, T. Ball, M. Ballardin, G. Ballmer, S.W. Banagiri, S. Banerjee, B. Bankar, D. Baral, P. Barayoga, J.C. Barish, B.C. Barker, D. Barneo, P. Barone, F. Barr, B. Barsotti, L. Barsuglia, M. Barta, D. Bartoletti, A.M. Barton, M.A. Bartos, I. Basak, S. Basalaev, A. Bassiri, R. Basti, A. Bates, D.E. Bawaj, M. Baxi, P. Bayley, J.C. Baylor, A.C. Baynard, P.A. Bazzan, M. Bedakihale, V.M. Beirnaert, F. Bejger, M. Belardinelli, D. Bell, A.S. Benedetto, V. Benoit, W. Bentley, J.D. Ben Yaala, M. Bera, S. Berbel, M. Bergamin, F. Berger, B.K. Bernuzzi, S. Beroiz, M. Bersanetti, D. Bertolini, A. Betzwieser, J. Beveridge, D. Bevins, N. Bhandare, R. Bhardwaj, U. Bhatt, R. Bhattacharjee, D. Bhaumik, S. Bhowmick, S. Bianchi, A. Bilenko, I.A. Billingsley, G. Binetti, A. Bini, S. Birnholtz, O. Biscoveanu, S. Bisht, A. Bitossi, M. Bizouard, M.-A. Blackburn, J.K. Blagg, L.A. Blair, C.D. Blair, D.G. Bobba, F. Bode, N. Boileau, G. Boldrini, M. Bolingbroke, G.N. Bolliand, A. Bonavena, L.D. Bondarescu, R. Bondu, F. Bonilla, E. Bonilla, M.S. Bonino, A. Bonnand, R. Booker, P. Borchers, A. Boschi, V. Bose, S. Boss Max Planck Institute for Gravitational Physics (Albert Einstein Institute) Potsdam D-14476 Germany LIGO Laboratory California Institute of Technology Pasadena 91125 CA United States LIGO Hanford Observatory Richland 99352 WA United States Dipartimento di Farmacia Università di Salerno Fisciano Salerno I-84084 Italy INFN Sezione di Napoli Napoli I-80126 Italy University of Warwick Coventry CV 47AL United Kingdom The Pennsylvania State University University Park 16802 PA United States University of Wisconsin-Milwaukee Milwaukee 53201 WI United States Louisiana State University Baton Rouge 70803 LA United States Université Catholique de Louvain Louvain-la-Neuve B-1348 Belgium Inter-University Centre for Astronomy and Astrophysics Pune 411007 India Queen Mary University of London London E1 4NS United Kingdom Department of Physics and Astronomy Sejong University 209 Neungdong-ro Gwangjin-gu Seoul 143-747 South Korea Instituto Nacional de Pesquisas Espaciais São José dos Campos São Paulo 12227-010 Brazil Stanford University Stanford 94305 CA United States Università di Roma Tor Vergata Roma I-00133 Italy INFN Sezione di Roma Tor Vergata Roma I-00133 Italy Cardiff University Cardiff CF24 3AA United Kingdom Universiteit Antwerpen Antwerpen 2000 Belgium International Centre for Theoretical Sciences Tata Institute of Fundamental Research Bengaluru 560089 India Gravitational Wave Science Project National Astronomical Observatory of Japan 2-21-1 Osawa Mitaka City Tokyo 181-8588 Japan Advanced Technology Center National Astronomical Observatory of Japan 2-21-1 Osawa Mitaka City Tokyo 181-8588 Japan INFN Sezione di Torino Torino I-10125 Italy Theoretisch-Physikalisches Institut Friedrich-Schiller-Universität Jena Jena D-07743 Germany Dipartimento di Fisica Università degli Studi di Torino Torino I-10125 Italy SUPA University of Glasgow Glasgow G12 8QQ United Kingdom OzGrav University of Western Australia Crawley 6009 WA

Continuous gravitational waves (CWs) emission from neutron stars carries information about their internal structure and equation of state, and it can provide tests of general relativity. We present a search for CWs from a set of 45 known pulsars in the first part of the fourth LIGO-Virgo-KAGRA observing run, known as O4a. We conducted a targeted search for each pulsar using three independent analysis methods considering single-harmonic and dual-harmonic emission models. We find no evidence of a CW signal in O4a data for both models and set upper limits on the signal amplitude and on the ellipticity, which quantifies the asymmetry in the neutron star mass distribution. For the single-harmonic emission model, 29 targets have the upper limit on the amplitude below the theoretical spin-down limit. The lowest upper limit on the amplitude is 6.4 × 10^-27 for the young energetic pulsar J0537-6910, while the lowest constraint on the ellipticity is 8.8 × 10^-9 for the bright nearby millisecond pulsar J0437-4715. Additionally, for a subset of 16 targets, we performed a narrowband search that is more robust regarding the emission model, with no evidence of a signal. We also found no evidence of nonstandard polarizations as predicted by the Brans-Dicke theory. © 2025. The Author(s). Published by the American Astronomical Society.

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