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Towards a consensus atlas of human and mouse adipose tissue at single-cell resolution

Nature metabolism 2025年 2025 May 13页

作者： Anne Loft Margo P Emont Ada Weinstock Adeline Divoux Adhideb Ghosh Allon Wagner Ann V Hertzel Babukrishna Maniyadath Bart Deplancke Boxiang Liu Camilla Scheele Carey Lumeng Changhai Ding Chenkai Ma Christian Wolfrum Clarissa Strieder-Barboza Congru Li Danh D Truong David A Bernlohr Elisabet Stener-Victorin Erin E Kershaw Esti Yeger-Lotem Farnaz Shamsi Hannah X Hui Henrique Camara Jiawei Zhong Joanna Kalucka Joseph A Ludwig Julie A Semon Jutta Jalkanen Katie L Whytock Kyle D Dumont Lauren M Sparks Lindsey A Muir Lingzhao Fang Lucas Massier Luis R Saraiva Marc D Beyer Marc G Jeschke Marcelo A Mori Mariana Boroni Martin J Walsh Mary-Elizabeth Patti Matthew D Lynes Matthias Blüher Mikael Rydén Natnael Hamda Nicole L Solimini Niklas Mejhert Peng Gao Rana K Gupta Rinki Murphy Saeed Pirouzpanah Silvia Corvera Su'an Tang Swapan K Das Søren F Schmidt Tao Zhang Theodore M Nelson Timothy E O'Sullivan Vissarion Efthymiou Wenjing Wang Yihan Tong Yu-Hua Tseng Susanne Mandrup Evan D Rosen Center for Functional Genomics and Tissue Plasticity (ATLAS) Department of Biochemistry and Molecular Biology University of Southern Denmark (SDU) Odense Denmark. anlo@bmb.sdu.dk. Section of Endocrinology Diabetes and Metabolism University of Chicago Chicago IL USA. margo@uchicago.edu. Department of Medicine Section of Genetic Medicine University of Chicago Chicago IL USA. Translational Research Institute AdventHealth Orlando FL USA. Department of Health Sciences and Technology Institute of Food Nutrition and Health ETH Zurich Schwerzenbach Switzerland. Department of Electrical Engineering and Computer Sciences University of California Berkeley Berkeley CA USA. Department of Molecular and Cell Biology University of California Berkeley Berkeley CA USA. Center for Computational Biology University of California Berkeley Berkeley CA USA. Department of Biochemistry Molecular Biology and Biophysics Institute on the Biology of Aging and Metabolism The University of Minnesota-Twin Cities Minneapolis MN USA. Center for Functional Genomics and Tissue Plasticity (ATLAS) Department of Biochemistry and Molecular Biology University of Southern Denmark (SDU) Odense Denmark. Laboratory of Systems Biology and Genetics Institute of Bioengineering School of Life Sciences École Polytechnique Fédérale de Lausanne (EPFL) Lausanne Switzerland. Department of Pharmacy and Pharmaceutical Sciences Faculty of Science National University of Singapore Singapore Singapore. Department of Biomedical Informatics Yong Loo Lin School of Medicine National University of Singapore Singapore Singapore. Genome Institute of Singapore (GIS) Agency for Science Technology and Research (A*STAR) Singapore Singapore. Precision Medicine Translational Research Programme Yong Loo Lin School of Medicine National University of Singapore Singapore Singapore. Cardiovascular-Metabolic Disease Translational Research Programme Yong Loo Lin School of Medicine National University of Singapore Singapor

Adipose tissue (AT) is a complex connective tissue with a high relative proportion of adipocytes, which are specialized cells with the ability to store lipids in large droplets. AT is found in multiple discrete depots throughout the body, where it serves as the primary repository for excess calories. In addition, AT has an important role in functions as diverse as insulation, immunity and regulation of metabolic homeostasis. The Human Cell Atlas Adipose Bionetwork was established to support the generation of single-cell atlases of human AT as well as the development of unified approaches and consensus for cell annotation. Here, we provide a first roadmap from this bionetwork, including our suggested cell annotations for humans and mice, with the aim of describing the state of the field and providing guidelines for the production, analysis, interpretation and presentation of AT single-cell data.

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National identity predicts public health support during a global pandemic (vol 13, 517, 2022)

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NATURE COMMUNICATIONS 2022年第1期13卷 517-517页

作者： Van Bavel, Jay J. Cichocka, Aleksandra Capraro, Valerio Sjastad, Hallgeir Nezlek, John B. Pavlovic, Tomislav Alfano, Mark Gelfand, Michele J. Azevedo, Flavio Birtel, Michele D. Cislak, Aleksandra Lockwood, Patricia L. Ross, Robert Malcolm Abts, Koen Agadullina, Elena Aruta, John Jamir Benzon Besharati, Sahba Nomvula Bor, Alexander Choma, Becky L. Crabtree, Charles David Cunningham, William A. De, Koustav Ejaz, Waqas Elbaek, Christian T. Findor, Andrej Flichtentrei, Daniel Franc, Renata Gjoneska, Biljana Gruber, June Gualda, Estrella Horiuchi, Yusaku Huynh, Toan Luu Duc Ibanez, Agustin Imran, Mostak Ahamed Israelashvili, Jacob Jasko, Katarzyna Kantorowicz, Jaroslaw Kantorowicz-Reznichenko, Elena Krouwel, Andre Laakasuo, Michael Lamm, Claus Leygue, Caroline Lin, Ming-Jen Mansoor, Mohammad Sabbir Marie, Antoine Mayiwar, Lewend Mazepus, Honorata McHugh, Cillian Minda, John Paul Mitkidis, Panagiotis Olsson, Andreas Otterbring, Tobias Packer, Dominic J. Perry, Anat Petersen, Michael Bang Puthillam, Arathy Riano-Moreno, Julian C. Rothmund, Tobias Santamaria-Garcia, Hernando Schmid, Petra C. Stoyanov, Drozdstoy Tewari, Shruti Todosijevic, Bojan Tsakiris, Manos Tung, Hans H. Umbres, Radu G. Vanags, Edmunds Vlasceanu, Madalina Vonasch, Andrew Yucel, Meltem Zhang, Yucheng Abad, Mohcine Adler, Eli Akrawi, Narin Mdarhri, Hamza Alaoui Amara, Hanane Amodio, David M. Antazo, Benedict G. Apps, Matthew Ay, F. Ceren Ba, Mouhamadou Hady Barbosa, Sergio Bastian, Brock Berg, Anton Bernal-Zarate, Maria P. Bernstein, Michael Bialek, Michal Bilancini, Ennio Bogatyreva, Natalia Boncinelli, Leonardo Booth, Jonathan E. Borau, Sylvie Buchel, Ondrej Cameron, C. Daryl Carvalho, Chrissie F. Celadin, Tatiana Cerami, Chiara Chalise, Hom Nath Cheng, Xiaojun Cian, Luca Cockcroft, Kate Conway, Jane Cordoba-Delgado, Mateo Andres Crespi, Chiara Crouzevialle, Marie Cutler, Jo Cypryanska, Marzena Dabrowska, Justyna Daniels, Michael A. Davis, Victoria H. Dayley, Pamala N. Delouvee, Sylvain Denkovski, Ognjan Dezecache, Guillaume Dhaliwal, Nathan A. Diato, Department of Psychology and Neural Science New York University New York NY USA School of Psychology University of Kent Canterbury England Department of Economics Middlesex University London London England Department of Strategy and Management Norwegian School of Economics Bergen Norway SWPS University of Social Sciences and Humanities Poznań Poland Department of Psychological Sciences College of William and Mary Williamsburg VA USA Institute of Social Sciences Ivo Pilar Zagreb Croatia Department of Philosophy Macquarie University Sydney NSW Australia Stanford Graduate School of Business Stanford University Stanford CA USA Institute of Communication Science Friedrich-Schiller University Jena Jena Germany School of Human Sciences Institute for Lifecourse Development University of Greenwich London England Department of Experimental Psychology University of Oxford Oxford England Center for Human Brain Health School of Psychology University of Birmingham Birmingham England Department of Psychology Macquarie University Sydney NSW Australia KU Leuven Leuven Belgium National Research University Higher School of Economics (HSE) Moscow Russia De La Salle University Manila Philippines Department of Psychology University of the Witwatersrand Johannesburg South Africa Department of Political Science Aarhus University Aarhus Denmark X University Toronto Canada Department of Government Dartmouth College Hanover NH USA Department of Psychology University of Toronto Toronto ON Canada Gatton College of Business and Economics University of Kentucky Lexington KY USA Department of Mass Communication National University of Science and Technology (NUST) Islamabad Pakistan Department of Management Aarhus University Aarhus Denmark Faculty of Social and Economic Sciences Comenius University Bratislava Slovakia IntraMed Buenos Aires Argentina Macedonian Academy of Sciences and Arts North Macedonia Republic of North Macedonia University of Colorado Boulder Bould

Changing collective behaviour and supporting non-pharmaceutical interventions is an important component in mitigating virus transmission during a pandemic. In a large international collaboration (Study 1, N = 49,968 across 67 countries), we investigated self-reported factors associated with public health behaviours (e.g., spatial distancing and stricter hygiene) and endorsed public policy interventions (e.g., closing bars and restaurants) during the early stage of the COVID-19 pandemic (April-May 2020). Respondents who reported identifying more strongly with their nation consistently reported greater engagement in public health behaviours and support for public health policies. Results were similar for representative and non-representative national samples. Study 2 (N = 42 countries) conceptually replicated the central finding using aggregate indices of national identity (obtained using the World Values Survey) and a measure of actual behaviour change during the pandemic (obtained from Google mobility reports). Higher levels of national identification prior to the pandemic predicted lower mobility during the early stage of the pandemic (r = −0.40). We discuss the potential implications of links between national identity, leadership, and public health for managing COVID-19 and future pandemics. Understanding collective behaviour is an important aspect of managing the pandemic response. Here the authors show in a large global study that participants that reported identifying more strongly with their nation reported greater engagement in public health behaviours and support for public health policies in the context of the pandemic.

关键词： Viral infection Human behaviour

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A network approach to quantifying radiotherapy effect on cancer: radiosensitive gene group centrality

arXiv

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arXiv 2018年

作者： Yao, Yu-Xiang Bing, Zhi-Tong Huang, Liang Huang, Zi-Gang Lai, Ying-Cheng School of Physical Science and Technology Lanzhou University Lanzhou730000 China Evidence Based Medicine Center School of Basic Medical Science of Lanzhou University Lanzhou730000 China Key Laboratory of Evidence Based Medicine and Knowledge Translation of Gansu Province Lanzhou730000 China Department of Computational Physics Institute of Modern Physics Chinese Academy of Sciences Lanzhou730000 China Key Laboratory of Biomedical Information Engineering of Ministry of Education National Engineering Research Center of Health Care and Medical Devices Key Laboratory of Neuro-informatics & Rehabilitation Engineering of Ministry of Civil Affairs Institute of Health and Rehabilitation Science School of Life Science and Technology Xi’an Jiaotong University Xi’an710049 China School of Electrical Computer and Energy Engineering Arizona State University TempeAZ85287 United States Department of Physics Arizona State University TempeAZ85287 United States

Radiotherapy plays a vital role in cancer treatment, for which accurate prognosis is important for guiding sequential treatment and improving the curative effect for patients. An issue of great significance in radiotherapy is to assess tumor radiosensitivity for devising the optimal treatment strategy. Previous studies focused on gene expression in cells closely associated with radiosensitivity, but factors such as the response of a cancer patient to irradiation and the patient survival time are largely ignored. For clinical cancer treatment, a specific pre-treatment indicator taking into account cancer cell type and patient radiosensitivity is of great value but it has been missing. Here, we propose an effective indicator for radiosensitivity: radiosensitive gene group centrality (RSGGC), which characterizes the importance of the group of genes that are radiosensitive in the whole gene correlation network. We demonstrate, using both clinical patient data and experimental cancer cell lines, which RSGGC can provide a quantitative estimate of the effect of radiotherapy, with factors such as the patient survival time and the survived fraction of cancer cell lines under radiotherapy fully taken into account. Our main finding is that, for patients with a higher RSGGC score before radiotherapy, cancer treatment tends tobe more effective. The RSGGC can have significant applications in clinical prognosis, serving as a key measure to classifying radiosensitive and radioresistant patients. Copyright © 2018, The Authors. All rights reserved.

关键词： Radiotherapy

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A community-powered search of machine learning strategy space to find NMR property prediction models

arXiv

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arXiv 2020年

作者： Bratholm, Lars A. Gerrard, Will Anderson, Brandon Bai, Shaojie Choi, Sunghwan Dang, Lam Hanchar, Pavel Howard, Addison Huard, Guillaume Kim, Sanghoon Kolter, Zico Kondor, Risi Kornbluth, Mordechai Lee, Youhan Lee, Youngsoo Mailoa, Jonathan P. Nguyen, Thanh Tu Popovic, Milos Rakocevic, Goran Reade, Walter Song, Wonho Stojanovic, Luka Thiede, Erik H. Tijanic, Nebojsa Torrubia, Andres Willmott, Devin Butts, Craig P. Glowacki, David R. Participants, Kaggle School of Chemistry University of Bristol Cantock’s Close BristolBS8 1TS United Kingdom School of Mathematics University of Bristol Fry Building Woodland Road BristolBS1 1UG United Kingdom Dept. of Computer Science University of Bristol Merchant Venturer’s Building BristolBS8 1UB United Kingdom Intangible Realities Laboratory University of Bristol Cantock’s Close BristolBS8 1TS United Kingdom Bosch Center for Artificial Intelligence PittsburghPA15222 United States Carnegie Mellon University PittsburghPA15213 United States National Institute of Supercomputing and Network Korea Institute of Science and Technology Information 245 Daehak-ro Yuseong-gu Daejeon34141 Korea Republic of BNP Paribas Cardif Fyusion Inc. Kaggle Google Inc. Mountain View United States Ebay Korea 34F Gangnam Finance Center 152 Teheran Ro Gangnam Gu Seoul Korea Republic of Department of Computer Science University of Chicago 5730 S Ellis Ave ChicagoIL60637 United States Department of Statistics University of Chicago 5747 S Ellis Ave ChicagoIL60637 United States Center for Computational Mathematics Flatiron Institute 162 5th Ave. New YorkNY10010 United States Bosch Research and Technology Center CambridgeMA02139 United States Department of Chemical and Biomolecular Engineering Korea Advanced Institute of Science and Technology 291 Daehak-ro Yuseong-gu Daejeon34141 Korea Republic of 111 Daedeok-daero 989beon-gil Yuseong-gu Daejeon Korea Republic of MINDS AND COMPANY 2621 Nambusunhwan-ro Gangnam-gu Seoul06267 Korea Republic of Totient Inc Sindjeliceva 9 Belgrade11000 Serbia KAIST Web Security & Privacy Lab 291 Daehak-ro Yuseong-guDaejeon34141 Korea Republic of *** Alicante Spain

The rise of machine learning (ML) has created an explosion in the potential strategies for using data to make scientific predictions. For physical scientists wishing to apply ML strategies to a particular domain, it can be difficult to assess in advance what strategy to adopt within a vast space of possibilities. Here we outline the results of an online community-powered effort to swarm search the space of ML strategies and develop algorithms for predicting atomic-pairwise nuclear magnetic resonance (NMR) properties in molecules. Using an open-source dataset, we worked with Kaggle to design and host a 3-month competition which received 47,800 ML model predictions from 2,700 teams in 84 countries. Within 3 weeks, the Kaggle community produced models with comparable accuracy to our best previously published ‘in-house’ efforts. A meta-ensemble model constructed as a linear combination of the top predictions has a prediction accuracy which exceeds that of any individual model, 7-19x better than our previous state-of-the-art. The results highlight the potential of transformer architectures for predicting quantum mechanical (QM) molecular properties. Copyright © 2020, The Authors. All rights reserved.

关键词： Nuclear magnetic resonance

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Fundamental Physics Opportunities with the Next-Generation Event Horizon Telescope

arXiv

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arXiv 2023年

作者： Ayzenberg, Dimitry Blackburn, Lindy Brito, Richard Britzen, Silke Broderick, Avery E. Carballo-Rubio, Raúl Cardoso, Vitor Chael, Andrew Chatterjee, Koushik Chen, Yifan Cunha, Pedro V.P. Davoudiasl, Hooman Denton, Peter B. Doeleman, Sheperd S. Eichhorn, Astrid Eubanks, Marshall Fang, Yun Foschi, Arianna Fromm, Christian M. Galison, Peter Ghosh, Sushant G. Gold, Roman Gurvits, Leonid I. Hadar, Shahar Held, Aaron Houston, Janice Hu, Yichao Johnson, Michael D. Kocherlakota, Prashant Natarajan, Priyamvada Olivares, Héctor Palumbo, Daniel Pesce, Dominic W. Rajendran, Surjeet Roy, Rittick Saurabh Shao, Lijing Tahura, Shammi Tamar, Aditya Tiede, Paul Vincent, Frédéric H. Visinelli, Luca Wang, Zhiren Wielgus, Maciek Xue, Xiao Yakut, Kadri Yang, Huan Younsi, Ziri Theoretical Astrophysics Eberhard-Karls Universität Tübingen TübingenD-72076 Germany Center for Astrophysics Harvard & Smithsonian 60 Garden Street CambridgeMA02138 United States Harvard University 20 Garden Street CambridgeMA02138 United States CENTRA Departamento de Física Instituto Superior Técnico – IST Universidade de Lisboa – UL Avenida Rovisco Pais 1 Lisboa1049-001 Portugal Max-Planck-Institut für Radioastronomie Auf dem Hügel 69 BonnD-53121 Germany Perimeter Institute for Theoretical Physics 31 Caroline Street North WaterlooONN2L 2Y5 Canada Department of Physics and Astronomy University of Waterloo 200 University Avenue West WaterlooONN2L 3G1 Canada Waterloo Centre for Astrophysics University of Waterloo WaterlooONN2L 3G1 Canada CP3-Origins University of Southern Denmark Campusvej 55 Odense MDK-5230 Denmark Florida Space Institute University of Central Florida 12354 Research Parkway Partnership 1 OrlandoFL32826 United States Niels Bohr International Academy Niels Bohr Institute Blegdamsvej 17 Copenhagen2100 Denmark Princeton Gravity Initiative Jadwin Hall Princeton University PrincetonNJ08544 United States Department of Physics University of Maryland College ParkMD20742 United States Institute for Research in Electronics and Applied Physics University of Maryland College ParkMD20742 United States Harvard-Smithsonian Center for Astrophysics 60 Garden Street CambridgeMA02138 United States Campus de Santiago Aveiro3810-183 Portugal High Energy Theory Group Physics Department Brookhaven National Laboratory UptonNY11973 United States Asteroid Initiatives LLC United States Perimeter Institute for Theoretical Physics WaterlooONN2L2Y5 Canada Kavli Institute for Astronomy and Astrophysics Peking University Beijing100871 China Institut für Theoretische Physik und Astrophysik Universität Würzburg Emil-Fischer-Strasse 31 Würzburg97074 Germany Institut für Theoretische Physik Goethe Universität Frankfurt Max-von

The Event Horizon Telescope (EHT) Collaboration recently published the first images of the supermassive black holes in the cores of the Messier 87 and Milky Way galaxies. These observations have provided a new means to study supermassive black holes and probe physical processes occurring in the strong-field regime. We review the prospects of future observations and theoretical studies of supermassive black hole systems with the next-generation Event Horizon Telescope (ngEHT), which will greatly enhance the capabilities of the existing EHT array. These enhancements will open up several previously inaccessible avenues of investigation, thereby providing important new insights into the properties of supermassive black holes and their environments. This review describes the current state of knowledge for five key science cases, summarising the unique challenges and opportunities for fundamental physics investigations that the ngEHT will enable. Copyright © 2023, The Authors. All rights reserved.

关键词： Telescopes

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Electronic Excitations in Complex Molecular Environments: Many-Body Green’s Functions Theory in VOTCA-XTP

arXiv

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arXiv 2018年

作者： Wehner, Jens Brombacher, Lothar Brown, Joshua Junghans, Christoph Çaylak, Onur Khalak, Yuriy Madhikar, Pranav Tirimbò, Gianluca Baumeier, Björn Max Planck Institute for Polymer Research Ackermannweg 10 MainzD-55128 Germany Eindhoven University of Technology Department of Mathematics and Computer Science & Institute for Complex Molecular Systems P.O. Box 513 Eindhoven5600MB Netherlands Dept. of Electrical Computer and Energy Engineering University of Colorado Boulder 425 UCB BoulderCO80309 United States Renewable and Sustainable Energy Institute University of Colorado Boulder 4001 Discovery Dr BoulderCO80303 United States Computer Computational and Statistical Sciences Division Los Alamos National Laboratory Los AlamosNM87545 United States Eindhoven University of Technology Department of Mathematics and Computer Science & Institute for Complex Molecular Systems P.O. Box 513 Eindhoven5600MB Netherlands

Many-body Green’s functions theory within the GW approximation and the Bethe-Salpeter Equation (BSE) is implemented in the open-source VOTCA-XTP software, aiming at the calculation of electronically excited states in complex molecular environments. Based on Gaussian-type atomic orbitals and making use of resolution of identify techniques, the code is designed specifically for non-periodic systems. Application to the small molecule reference set successfully validates the methodology and its implementation for a variety of excitation types covering an energy range from 2-8 eV in single molecules. Further, embedding each GW -BSE calculation into an atomistically resolved surrounding, typically obtained from Molecular Dynamics, accounts for effects originating from local fields and polarization. Using aqueous DNA as a prototypical system, different levels of electrostatic coupling between the regions in this GW -BSE/MM setup are demonstrated. Particular attention is paid to charge-transfer (CT) excitations in adenine base pairs. It is found that their energy is extremely sensitive to the specific environment and to polarization effects. The calculated redshift of the CT excitation energy compared to a nucelobase dimer treated in vacuum is of the order of 1 eV, which matches expectations from experimental data. Predicted lowest CT energies are below that of a single nucleobase excitation, indicating the possibility of an inital (fast) decay of such an UV excited state into a bi-nucleobase CT exciton. The results show that VOTCA-XTP’s GW -BSE/MM is a powerful tool to study a wide range of types of electronic excitations in complex molecular environments. Copyright © 2018, The Authors. All rights reserved.

关键词： Quantum chemistry

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Cover, Volume 41, Issue 8

引用

Human Mutation 2020年第8期41卷

作者： Ceren Tunca Tuncay Şeker Fulya Akçimen Cemre Coşkun Elif Bayraktar Robin Palvadeau Seyit Zor Cemile Koçoğlu Ece Kartal Nesli Ece Şen Hamid Hamzeiy Aslıhan Özoğuz Erimiş Utku Norman Oğuzhan Karakahya Gülden Olgun Tahsin Akgün Hacer Durmuş Erdi Şahin Arman Çakar Esra Başar Gürsoy Gülsen Babacan Yıldız Barış İşak Kayıhan Uluç Haşmet Hanağası Başar Bilgiç Nilda Turgut Fikret Aysal Mustafa Ertaş Cavit Boz Dilcan Kotan Halil İdrisoğlu Aysun Soysal Nurten Uzun Adatepe Mehmet Ali Akalın Filiz Koç Ersin Tan Piraye Oflazer Feza Deymeer Öznur Taştan A. Ercüment Çiçek Erşen Kavak Yeşim Parman A. Nazlı Başak Suna and İnan Kıraç Foundation Neurodegeneration Research Laboratory (NDAL) Research Center for Translational Medicine (KUTTAM) Koç University School of Medicine Istanbul Turkey Suna and İnan Kıraç Foundation Neurodegeneration Research Laboratory (NDAL) Department of Molecular Biology and Genetics Boğaziçi University Istanbul Turkey Genomize Inc. Boğaziçi University Technology Development Region Istanbul Turkey Department of Computer Engineering Bilkent University Ankara Turkey Department of Anesthesiology and Reanimation American Hospital Istanbul Turkey Department of Neurology Istanbul Medical School Istanbul University Istanbul Turkey Department of Neurology Faculty of Medicine Bezmialem Vakıf University Istanbul Turkey Department of Neurology Marmara University School of Medicine Istanbul Turkey Department of Neurology Namık Kemal University School of Medicine Tekirdağ Turkey Department of Neurology Medipol University School of Medicine Istanbul Turkey Department of Neurology Karadeniz Technical University School of Medicine Trabzon Turkey Department of Neurology Faculty of Medicine Sakarya University Sakarya Turkey Department of Neurology Bakırköy Research and Training Hospital for Neurologic and Psychiatric Diseases Istanbul Turkey Department of Neurology Cerrahpaşa Medical School Istanbul University-Cerrahpaşa Istanbul Turkey Department of Neurology Çukurova University Medical School Adana Turkey Department of Neurology Hacettepe University Medical School Ankara Turkey Department of Computer Science and Engineering Sabancı University Istanbul Turkey Department of Computational Biology Carnegie Mellon University Pittsburgh Pennsylvania

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Allele-defined genome of the autopolyploid sugarcane Saccharum spontaneum L (vol 50, pg 1565, 2018)

引用

NATURE GENETICS 2018年第12期50卷 1754-1754页

作者： Zhang, Jisen Zhang, Xingtan Tang, Haibao Zhang, Qing Hua, Xiuting Ma, Xiaokai Zhu, Fan Jones, Tyler Zhu, Xinguang Bowers, John Wai, Ching Man Zheng, Chunfang Shi, Yan Chen, Shuai Xu, Xiuming Yue, Jingjing Nelson, David R. Huang, Lixian Li, Zhen Xu, Huimin Zhou, Dong Wang, Yongjun Hu, Weichang Lin, Jishan Deng, Youjin Pandey, Neha Mancini, Melina Zerpa, Dessiree Nguyen, Julie K. Wang, Liming Yu, Liang Xin, Yinghui Ge, Liangfa Arro, Jie Han, Jennifer O. Chakrabarty, Setu Pushko, Marija Zhang, Wenping Ma, Yanhong Ma, Panpan Lv, Mingju Chen, Faming Zheng, Guangyong Xu, Jingsheng Yang, Zhenhui Deng, Fang Chen, Xuequn Liao, Zhenyang Zhang, Xunxiao Lin, Zhicong Lin, Hai Yan, Hansong Kuang, Zheng Zhong, Weimin Liang, Pingping Wang, Guofeng Yuan, Yuan Shi, Jiaxian Hou, Jinxiang Lin, Jingxian Jin, Jingjing Cao, Peijian Shen, Qiaochu Jiang, Qing Zhou, Ping Ma, Yaying Zhang, Xiaodan Xu, Rongrong Liu, Juan Zhou, Yongmei Jia, Haifeng Ma, Qing Qi, Rui Zhang, Zhiliang Fang, Jingping Fang, Hongkun Song, Jinjin Wang, Mengjuan Dong, Guangrui Wang, Gang Chen, Zheng Ma, Teng Liu, Hong Dhungana, Singha R. Huss, Sarah E. Yang, Xiping Sharma, Anupma Trujillo, Jhon H. Martinez, Maria C. Hudson, Matthew Riascos, John J. Schuler, Mary Chen, Li-Qing Braun, David M. Li, Lei Yu, Qingyi Wang, Jianping Wang, Kai Schatz, Michael C. Heckerman, David Van Sluys, Marie-Anne Souza, Glaucia Mendes Moore, Paul H. Sankoff, David VanBuren, Robert Paterson, Andrew H. Nagai, Chifumi Ming, Ray Fujian Agriculture and Forestry University and University of Illinois at Urbana-Champaign School of Integrative Biology Joint Center for Genomics and Biotechnology National Sugarcane Engineering Technology Research Center Fujian Provincial Key Laboratory of Haixia Applied Plant Systems Biology Key Laboratory of Genetics Breeding and Multiple Utilization of Corps Ministry of Education Fujian Agriculture and Forestry University Fuzhou China Department of Plant Biology University of Illinois at Urbana-Champaign Urbana IL USA Hawaii Agriculture Research Center Kunia HI USA Institute for Plant Physiology and Ecology Chinese Academy of Sciences Shanghai China Department of Plant Biology University of Georgia Athens GA USA Department of Horticulture Michigan State University East Lansing MI USA Department of Mathematics and Statistics University of Ottawa Ottawa Ontario Canada Department of Microbiology Immunology and Biochemistry University of Tennessee HSC Memphis TN USA Chinese Academy of Sciences-Max-Planck-Gesellschaft Partner Institute for Computational Biology Chinese Academy of Sciences Shanghai China China Tobacco Gene Research Center Zhengzhou Tobacco Research Institute of CNTC Zhengzhou China Division of Biological Sciences University of Missouri Columbia MO USA Department of Agronomy University of Florida Gainesville FL USA Texas A&M AgriLife Research Texas A&M University System Dallas TX USA Centro de Investigación de la Caña de Azúcar de Colombia (Cenicaña) Cali Colombia Department of Crop Sciences University of Illinois at Urbana-Champaign Urbana IL USA Departments of Computer Science and Biology Johns Hopkins University Baltimore MD USA Microsoft Research Redmond WA USA Departamento de Botânica Instituto de Biociências Universidade de São Paulo São Paulo Brazil Departamento de Bioquímica Instituto de Química Universidade de São Paulo São Paulo Brazil

Modern sugarcanes are polyploid interspecific hybrids, combining high sugar content from Saccharum officinarum with hardiness, disease resistance and ratooning of Saccharum spontaneum. Sequencing of a haploid S. spontaneum, AP85-441, facilitated the assembly of 32 pseudo-chromosomes comprising 8 homologous groups of 4 members each, bearing 35,525 genes with alleles defined. The reduction of basic chromosome number from 10 to 8 in S. spontaneum was caused by fissions of 2 ancestral chromosomes followed by translocations to 4 chromosomes. Surprisingly, 80% of nucleotide binding site-encoding genes associated with disease resistance are located in 4 rearranged chromosomes and 51% of those in rearranged regions. Resequencing of 64 S. spontaneum genomes identified balancing selection in rearranged regions, maintaining their diversity. Introgressed S. spontaneum chromosomes in modern sugarcanes are randomly distributed in AP85-441 genome, indicating random recombination among homologs in different S. spontaneum accessions. The allele-defined Saccharum genome offers new knowledge and resources to accelerate sugarcane improvement.

关键词： Genomics Plant genetics

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Recent developments in the PySCF program package

arXiv

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arXiv 2020年

作者： Sun, Qiming Zhang, Xing Banerjee, Samragni Bao, Peng Barbry, Marc Blunt, Nick S. Bogdanov, Nikolay A. Booth, George H. Chen, Jia Cui, Zhi-Hao Eriksen, Janus Juul Gao, Yang Guo, Sheng Hermann, Jan Hermes, Matthew R. Koh, Kevin Koval, Peter Lehtola, Susi Li, Zhendong Liu, Junzi Mardirossian, Narbe McClain, James D. Motta, Mario Mussard, Bastien Pham, Hung Q. Pulkin, Artem Purwanto, Wirawan Robinson, Paul J. Ronca, Enrico Sayfutyarova, Elvira Scheurer, Maximilian Schurkus, Henry F. Smith, James E.T. Sun, Chong Sun, Shi-Ning Upadhyay, Shiv Wagner, Lucas K. Wang, Xiao White, Alec Whitfield, James Daniel Williamson, Mark J. Wouters, Sebastian Yang, Jun Yu, Jason M. Zhu, Tianyu Berkelbach, Timothy C. Sharma, Sandeep Sokolov, Alexander Yu. Chan, Garnet Kin-Lic AxiomQuant Investment Management LLC Shanghai200120 China Division of Chemistry and Chemical Engineering California Institute of Technology PasadenaCA91125 United States Department of Chemistry and Biochemistry The Ohio State University ColumbusOH43210 United States Beijing National Laboratory for Molecular Sciences State Key Laboratory for Structural Chemistry of Unstable and Stable Species Institute of Chemistry Chinese Academy of Sciences Beijing100190 China Simbeyond B.V. P.O. Box 513 EindhovenNL-5600 MB Netherlands Department of Chemistry Lensfield Road CambridgeCB2 1EW United Kingdom Max Planck Institute for Solid State Research Heisenbergstraße 1 Stuttgart70569 Germany Department of Physics King's College London Strand LondonWC2R 2LS United Kingdom Department of Physics University of Florida GainesvilleFL32611 United States Quantum Theory Project University of Florida GainesvilleFL32611 United States School of Chemistry University of Bristol Cantock's Close BristolBS8 1TS United Kingdom Division of Engineering and Applied Science California Institute of Technology PasadenaCA91125 United States Google Inc. Mountain ViewCA94043 United States FU Berlin Department of Mathematics and Computer Science Arnimallee 6 Berlin14195 Germany TU Berlin Machine Learning Group Marchstr. 23 Berlin10587 Germany Department of Chemistry Chemical Theory Center Supercomputing Institute University of Minnesota 207 Pleasant Street SE MinneapolisMN55455 United States Department of Chemistry and Biochemistry The University of Notre Dame du Lac 251 Nieuwland Science Hall Notre DameIN46556 United States Simune Atomistics S.L. Avenida Tolosa 76 Donostia-San Sebastian Spain HelsinkiFI-00014 Finland Key Laboratory of Theoretical and Computational Photochemistry Ministry of Education College of Chemistry Beijing Normal University Beijing100875 China Department of Chemistry The Johns Hopkins University BaltimoreMD21218 United States AMGEN

PYSCF is a Python-based general-purpose electronic structure platform that both supports first-principles simulations of molecules and solids, as well as accelerates the development of new methodology and complex computational workflows. The present paper explains the design and philosophy behind PYSCF that enables it to meet these twin objectives. With several case studies, we show how users can easily implement their own methods using PYSCF as a development environment. We then summarize the capabilities of PYSCF for molecular and solid-state simulations. Finally, we describe the growing ecosystem of projects that use PYSCF across the domains of quantum chemistry, materials science, machine learning and quantum information science. Copyright © 2020, The Authors. All rights reserved.

关键词： Python

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Author Correction: Opportunities and challenges of single-cell and spatially resolved genomics methods for neuroscience discovery

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Nature neuroscience 2025年第1期28卷 216页

作者： Boyan Bonev Gonçalo Castelo-Branco Fei Chen Simone Codeluppi M Ryan Corces Jean Fan Myriam Heiman Kenneth Harris Fumitaka Inoue Manolis Kellis Ariel Levine Mo Lotfollahi Chongyuan Luo Kristen R Maynard Mor Nitzan Vijay Ramani Rahul Satijia Lucas Schirmer Yin Shen Na Sun Gilad S Green Fabian Theis Xiao Wang Joshua D Welch Ozgun Gokce Genevieve Konopka Shane Liddelow Evan Macosko Omer Ali Bayraktar Naomi Habib Tomasz J Nowakowski Helmholtz Pioneer Campus Helmholtz Zentrum München Neuherberg Germany. Physiological Genomics Biomedical Center Ludwig-Maximilians-Universität München Munich Germany. Laboratory of Molecular Neurobiology Department of Medical Biochemistry and Biophysics Karolinska Institutet Stockholm Sweden. The Broad Institute of MIT and Harvard Cambridge MA USA. Moleculent Stockholm Sweden. Gladstone Institute of Neurological Disease San Francisco CA USA. Gladstone Institute of Data Science and Biotechnology San Francisco CA USA. Department of Neurology University of California San Francisco San Francisco CA USA. Department of Biomedical Engineering Johns Hopkins University Baltimore MD USA. Department of Brain and Cognitive Sciences MIT Cambridge MA USA. The Picower Institute for Learning and Memory MIT Cambridge MA USA. UCL Queen Square Institute of Neurology University College London London UK. Institute for the Advanced Study of Human Biology (WPI-ASHBi) Kyoto University Kyoto Japan. Computer Science and Artificial Intelligence Lab Massachusetts Institute of Technology Cambridge MA USA. Spinal Circuits and Plasticity Unit National Institute of Neurological Disorders and Stroke Bethesda MD USA. Institute of Computational Biology Helmholtz Center Munich - German Research Center for Environmental Health Neuherberg Germany. Wellcome Sanger Institute Wellcome Genome Campus Cambridge UK. Department of Human Genetics University of California Los Angeles Los Angeles CA USA. Lieber Institute for Brain Development Baltimore MD USA. Department of Psychiatry Johns Hopkins School of Medicine Baltimore MD USA. Department of Neuroscience Johns Hopkins School of Medicine Baltimore MD USA. School of Computer Science and Engineering The Hebrew University of Jerusalem Jerusalem Israel. Racah Institute of Physics The Hebrew University of Jerusalem Jerusalem Israel. Faculty of Medicine The Hebrew University of Jerusalem Jerusalem Israel. Department of Biochemis

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