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First observation of isomeric states in Zr111, Nb113, and Mo115

Physical Review C 2022年第6期106卷 064328-064328页

作者： J. Wu S. Nishimura P.-A. Söderström A. Algora J. J. Liu V. H. Phong Y. Q. Wu F. R. Xu J. Agramunt D. S. Ahn T. A. Berry C. G. Bruno J. J. Bundgaard R. Caballero-Folch A. C. Dai T. Davinson I. Dillmann A. Estrade A. Fijałkowska N. Fukuda S. Go R. K. Grzywacz T. Isobe S. Kubono G. Lorusso K. Matsui A. I. Morales N. Nepal S. E. A. Orrigo B. C. Rasco K. P. Rykaczewski H. Sakurai Y. Shimizu D. W. Stracener T. Sumikama H. Suzuki J. L. Tain H. Takeda A. Tarifeño-Saldivia A. Tolosa-Delgado M. Wolińska-Cichocka R. Yokoyama National Nuclear Data Center Brookhaven National Laboratory Upton New York 11973 USA RIKEN Nishina Center 2-1 Hirosawa Wako-shi 351-0198 Saitama Japan Extreme Light Infrastructure – Nuclear Physics (ELI-NP) Bucharest-Magurele 077125 Romania Instituto de Física Corpuscular (CSIC-Universitat de Valencia) Apartado Correos 22085 E-46071 Valencia Spain Institute of Nuclear Research ATOMKI 4026 Debrecen Hungary Department of Physics University of Hong Kong Pokfulam Road Hong Kong School of Physics and State Key Laboratory of Nuclear Physics and Technology Peking University Beijing 100871 China Department of Physics University of Surrey Guildford GU2 7XH United Kingdom School of Physics and Astronomy University of Edinburgh Edinburgh EH9 3FD United Kingdom Department of Physics and Astronomy University of Tennessee Knoxville Tennessee 37996 USA TRIUMF Vancouver British Columbia Canada V6T 2A3 Department of Physics and Science of Advanced Materials Program Central Michigan University Mount Pleasant Michigan 48859 USA Department of Physics and Astronomy Rutgers University New Brunswick New Jersey 08854 USA Faculty of Physics University of Warsaw Warsaw PL-02-093 Poland Physics Division Oak Ridge National Laboratory Oak Ridge Tennessee 37830 USA Chemical Medical and Environmental Science Division National Physical Laboratory Teddington TW110LW United Kingdom Department of Physics University of Tokyo Hongo 7-3-1 Bunkyo-ku 113-0033 Tokyo Japan INTE-ETSEIB Universitat Politecnica de Catalunya (UPC) E-08028 Barcelona Spain Heavy Ion Laboratory University of Warsaw Ludwika Pasteura 5A 02-093 Warszawa Poland

Isomeric states in the neutron-rich nuclei Zr111 [T1/2=0.10(7)μs], Nb113 [T1/2=0.7(4)μs], Mo115 [T1/2=46(3)μs] were first identified at the Radioactive Ion Beam Factory (RIBF) of RIKEN by using in-flight fission and fragmentation of a U238 beam at an energy of 345 MeV/u. This is a brief report of the γ transitions de-exciting from isomeric states and half-lives measurements, which provides the first spectroscopy in the nuclear region of prolate-to-oblate shape-phase transition around mass A ≈110.

关键词： Isomer decays Nuclear structure & decays 90 ≤ A ≤ 149 Radioactive beams

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Ten quick tips for deep learning in biology

arXiv

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arXiv 2021年

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

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

关键词： Learning algorithms

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Author Correction: Single-cell transcriptomics uncovers EGFR signaling-mediated gastric progenitor cell differentiation in stomach homeostasis

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Nature communications 2023年第1期14卷 6540页

作者： Hitomi Takada Yohei Sasagawa Mika Yoshimura Kaori Tanaka Yoshimi Iwayama Tetsutaro Hayashi Ayako Isomura-Matoba Itoshi Nikaido Akira Kurisaki Laboratory of Stem Cell Technologies Graduate School of Science and Technology Nara Institute of Science and Technology Takayama-cho Ikoma Nara Japan. Laboratory for Bioinformatics Research RIKEN Center for Biosystems Dynamics Research Wako Saitama Japan. Department of Functional Genome Informatics Biological Data Science Medical Research Institute Tokyo Medical and Dental University Bunkyo Tokyo Japan. Laboratory for Bioinformatics Research RIKEN Center for Biosystems Dynamics Research Wako Saitama Japan. itoshi.nikaido@riken.jp. Department of Functional Genome Informatics Biological Data Science Medical Research Institute Tokyo Medical and Dental University Bunkyo Tokyo Japan. itoshi.nikaido@riken.jp. Master's/Doctoral Program in Life Science Innovation (Bioinformatics) Degree Programs in Systems and Information Engineering Graduate School of Science and Technology University of Tsukuba Tsukuba Ibaraki Japan. itoshi.nikaido@riken.jp. Laboratory of Stem Cell Technologies Graduate School of Science and Technology Nara Institute of Science and Technology Takayama-cho Ikoma Nara Japan. akikuri@bs.naist.jp.

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A consensus protocol for functional connectivity analysis in the rat brain (vol 26, pg 673, 2023)

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NATURE NEUROscience 2023年第6期26卷 1127-1128页

作者： Grandjean, Joanes Desrosiers-Gregoire, Gabriel Anckaerts, Cynthia Angeles-Valdez, Diego Ayad, Fadi Barriere, David A. Blockx, Ines Bortel, Aleksandra Broadwater, Margaret Cardoso, Beatriz M. Celestine, Marina Chavez-Negrete, Jorge E. Choi, Sangcheon Christiaen, Emma Clavijo, Perrin Colon-Perez, Luis Cramer, Samuel Daniele, Tolomeo Dempsey, Elaine Diao, Yujian Doelemeyer, Arno Dopfel, David Dvorakova, Lenka Falfan-Melgoza, Claudia Fernandes, Francisca F. Fowler, Caitlin F. Fuentes-Ibanez, Antonio Garin, Clement M. Gelderman, Eveline Golden, Carla E. M. Guo, Chao C. G. Henckens, Marloes J. A. G. Hennessy, Lauren A. Herman, Peter Hofwijks, Nita Horien, Corey Ionescu, Tudor M. Jones, Jolyon Kaesser, Johannes Kim, Eugene Lambers, Henriette Lazari, Alberto Lee, Sung-Ho Lillywhite, Amanda Liu, Yikang Liu, Yanyan Y. Lopez-Castro, Alejandra Lopez-Gil, Xavier Ma, Zilu MacNicol, Eilidh Madularu, Dan Mandino, Francesca Marciano, Sabina McAuslan, Matthew J. McCunn, Patrick McIntosh, Alison Meng, Xianzong Meyer-Baese, Lisa Missault, Stephan Moro, Federico Naessens, Daphne M. P. Nava-Gomez, Laura J. Nonaka, Hiroi Ortiz, Juan J. Paasonen, Jaakko Peeters, Lore M. Pereira, Mickael Perez, Pablo D. Pompilus, Marjory Prior, Malcolm Rakhmatullin, Rustam Reimann, Henning M. Reinwald, Jonathan Del Rio, Rodrigo Triana Rivera-Olvera, Alejandro Ruiz-Perez, Daniel Russo, Gabriele Rutten, Tobias J. Ryoke, Rie Sack, Markus Salvan, Piergiorgio Sanganahalli, Basavaraju G. Schroeter, Aileen Seewoo, Bhedita J. Selingue, Erwan Seuwen, Aline Shi, Bowen Sirmpilatze, Nikoloz Smith, Joanna A. B. Smith, Corrie Sobczak, Filip Stenroos, Petteri J. Straathof, Milou Strobelt, Sandra Sumiyoshi, Akira Takahashi, Kengo Torres-Garcia, Maria E. Tudela, Raul van den Berg, Monica van der Marel, Kajo van Hout, Aran T. B. Vertullo, Roberta Vidal, Benjamin Vrooman, Roel M. Wang, Victora X. Wank, Isabel Watson, David J. G. Yin, Ting Zhang, Yongzhi Zurbruegg, Stefan Achard, Sophie Alcauter, Sarael Auer, Dorothee P. Barbier, Emmanuel L. Baudewig, Juergen Beckmann, Chri Donders Institute for Brain Behaviour and Cognition Radboud University Nijmegen The Netherlands Department for Medical Imaging Radboud University Medical Center Nijmegen The Netherlands Department of Neuroscience and Pharmacology Rudolf Magnus Institute of Neuroscience University Medical Center Utrecht Utrecht The Netherlands Nuffield Department of Clinical Neurosciences Wellcome Centre for Integrative Neuroimaging FMRIB University of Oxford John Radcliffe Hospital Headington Oxford UK Erwin L. Hahn Institute for MR Imaging University of Duisburg-Essen Essen Germany Cerebral Imaging Centre Douglas Mental Health University Institute Verdun QC Canada Integrated Program in Neuroscience McGill University Montreal QC Canada Biological and Biomedical Engineering McGill University Montreal QC Canada Physical Sciences Platform Sunnybrook Research Institute Toronto QC Canada Department of Psychiatry McGill University Montreal QC Canada Bio-imaging Lab University of Antwerp Antwerp Belgium µNEURO Research Centre of Excellence University of Antwerp Antwerp Belgium Instituto de Neurobiología Universidad Nacional Autónoma de México Campus Juriquilla Querétaro Mexico McConnell Brain Imaging Centre McGill University Montreal QC Canada Montreal Neurological Institute McGill University Montreal QC Canada Center for Translational Neuroimaging Northeastern University Boston MA USA Department of Neurology and Neurosurgery McGill University Montreal QC Canada Department of Physiology McGill University Montreal QC Canada UMR INRAE/CNRS 7247 Physiologie des Comportements et de la Reproduction Physiologie de la reproduction et des comportements Centre de recherche INRAE de Nouzilly Tours France Center for Animal MRI University of North Carolina at Chapel Hill Chapel Hill NC USA Department of Neurology University of North Carolina at Chapel Hill Chapel Hill NC USA Biomedical Research Imaging Center University of North Carolina at Chapel Hill Chapel Hill

Task-free functional connectivity in animal models provides an experimental framework to examine connectivity phenomena under controlled conditions and allows for comparisons with data modalities collected under invasive or terminal procedures. Currently, animal acquisitions are performed with varying protocols and analyses that hamper result comparison and integration. Here we introduce StandardRat, a consensus rat functional magnetic resonance imaging acquisition protocol tested across 20 centers. To develop this protocol with optimized acquisition and processing parameters, we initially aggregated 65 functional imaging datasets acquired from rats across 46 centers. We developed a reproducible pipeline for analyzing rat data acquired with diverse protocols and determined experimental and processing parameters associated with the robust detection of functional connectivity across centers. We show that the standardized protocol enhances biologically plausible functional connectivity patterns relative to previous acquisitions. The protocol and processing pipeline described here is openly shared with the neuroimaging community to promote interoperability and cooperation toward tackling the most important challenges in neuroscience.

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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SUPPL 1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 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 University of Aberdeen Aberdeen Scotland UK Department of Integrativ

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Maternal human papillomavirus infection and the risk of congenital malformations: A nationwide population-based cohort study

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Journal of medical Virology 2024年第4期96卷 e29549页

作者： Hsieh, Tina Yi Jin Chen, Thomas Yen Ting Liao, Pei-Lun Huang, Jing-Yang Ma, Kevin Sheng-Kai Hung, Yao-Min Chang, Renin Wei, James Cheng-Chung Department of Obstetrics and Gynecology Beth Israel Deaconess Medical Center Boston MA United States Department of Bioinformatics Harvard Medical School Boston MA United States Department of Otolaryngology-Head and Neck Surgery Kaohsiung Medical University Hospital Kaohsiung Taiwan Institute of Medicine Chung Shan Medical University Taichung Taiwan Center for Health Data Science Department of Medical Research Chung Shan Medical University Hospital Taichung Taiwan Department of Epidemiology Harvard T.H. Chan School of Public Health Boston MA United States Department of Dermatology Massachusetts General Hospital Boston MA United States Division of Pharmacoepidemiology and Pharmacoeconomics Department of Medicine Brigham and Women's Hospital Harvard Medical School Boston MA United States Center for Global Health Perelman School of Medicine University of Pennsylvania Philadelphia PA United States Division of Nephrology Department of Internal Medicine Taipei Veterans General Hospital Taitung Branch Taipei Taiwan Master Program in Biomedicine College of Science and Engineering National Taitung University Taitung Taiwan College of Health and Nursing Meiho University Pingtung Taiwan Department of Medical Education and Research Kaohsiung Veterans General Hospital Kaohsiung Taiwan Department of Emergency Medicine Kaohsiung Veterans General Hospital Kaohsiung Taiwan Department of Recreation and Sports Management Tajen University Pingtung Taiwan Department of Allergy Immunology & Rheumatology Chung Shan Medical University Hospital Taichung Taiwan Department of Nursing Chung Shan Medical University Taichung Taiwan Graduate Institute of Integrated Medicine China Medical University Taichung Taiwan Department of Medical Research Taichung Veterans General Hospital Taichung Taiwan

Previous research has explored theories regarding the vertical transmission of human papillomavirus (HPV) infection and its association with adverse pregnancy and perinatal outcomes. However, the impact of maternal HPV infection on congenital anomalies (CAs) in offspring remains relatively understudied. We conducted a population-based cohort study linking the Taiwan Birth Registry, Taiwan Death Registry, and National Health Insurance research database, in which newborns born in Taiwan between 2009 and 2015 were included. We established a maternal HPV infection cohort comprising 37 807 newborns and matched them with a comparison group of 151 228 newborns at a 1:4 ratio based on index year, age, and sex. The study examined a composite outcome and subgroups of different types of congenital malformations. Differences in cumulative incidence of CAs were assessed using Kaplan–Meier curves and log-rank tests. Adjusted hazard ratios (aHRs) were estimated using Cox proportional hazard regressions. No significant association was found between HPV infection and the broad spectrum of CAs (aHR: 1.04, 95% confidence interval [CI]: 0.98–1.10;log-rank test p = 0.14). However, we observed a 19% increased risk of musculoskeletal CAs in the maternal HPV infection group (aHR: 1.19;95% CI: 1.05–1.34) compared to those without maternal HPV exposure. Other factors, including the type of HPV (aHR: 0.65;95% CI: 0.16–2.63), the timing of exposure (during or before pregnancy), and maternal age (aHR for <30 years: 1.02, 95% CI: 0.94–1.1;aHR for 30–39 years: 1.05, 95% CI: 0.99–1.11;aHR for ≥40 years: 0.88, 95% CI: 0.67–1.17), did not significantly affect the risk for any CA. In conclusion, gestation detection of HPV infection was associated with musculoskeletal CAs but not other major CAs. Prospective studies are warranted to elucidate the necessity of prenatal screening in populations at risk. © 2024 Wiley Periodicals LLC.

关键词： adverse pregnancy congenital malformations human papillomavirus infection outcomes

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

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

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

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

关键词：帕金森病 precise correction

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Unravelling the asphericities in the explosion and multi-faceted circumstellar matter of SN 2023ixf

arXiv

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arXiv 2024年

作者： Singh, Avinash Teja, Rishabh Singh Moriya, Takashi J. Maeda, Keiichi Kawabata, Koji S. Tanaka, Masaomi Imazawa, Ryo Nakaoka, Tatsuya Gangopadhyay, Anjasha Yamanaka, Masayuki Swain, Vishwajeet Sahu, D.K. Anupama, G.C. Kumar, Brajesh Anche, Ramya M. Sano, Yasuo Raj, A. Agnihotri, V.K. Bhalerao, Varun Bisht, D. Bisht, M.S. Belwal, K. Chakrabarti, S.K. Fujii, Mitsugu Nagayama, Takahiro Matsumoto, Katsura Hamada, Taisei Kawabata, Miho Kumar, Amit Kumar, Ravi Malkan, Brian K. Smith, Paul Sakagami, Yuta Taguchi, Kenta Tominaga, Nozomu Watanabe, Arata Hiroshima Astrophysical Science Centre Hiroshima University 1-3-1 Kagamiyama Higashi-Hiroshima Hiroshima739-8526 Japan Department of Astronomy The Oskar Klein Center Stockholm University AlbaNova University Center StockholmSE 106 91 Sweden Indian Institute of Astrophysics II Block Koramangala Karnataka Bengaluru560034 India Pondicherry University UT of Puducherry R.V. Nagar Kalapet Pondicherry605014 India National Astronomical Observatory of Japan National Institutes of Natural Sciences 2-21-1 Osawa Mitaka Tokyo181-8588 Japan Graduate Institute for Advanced Studies SOKENDAI 2-21-1 Osawa Mitaka Tokyo181-8588 Japan School of Physics and Astronomy Monash University ClaytonVIC3800 Australia Department of Astronomy Kyoto University Kitashirakawa-Oiwake-cho Sakyo-ku Kyoto606-8502 Japan Department of Physics Graduate School of Advanced Science and Engineering Hiroshima University 1-3-1 Kagamiyama Higashi-Hiroshima Hiroshima739-8526 Japan Astronomical Institute Tohoku University Aoba Sendai980-8578 Japan Department of Physics Graduate School of Advanced Science and Engineering Hiroshima Astrophysical Science Centre Hiroshima University 1-3-1 Kagamiyama Higashi-Hiroshima Hiroshima739-8526 Japan Graduate School of Science and Engineering Kagoshima University 1-21-35 Korimoto Kagoshima Kagoshima890-0065 Japan Department of Physics Indian Institute of Technology Bombay Powai Mumbai400076 India South-Western Institute for Astronomy Research Yunnan University Yunnan Kunming650504 China Steward Observatory University of Arizona 933N Cherry Avenue TucsonAZ85721 United States Observation and Data Center for Cosmosciences Faculty of Science Hokkaido University Kita-ku Sapporo-shi Hokkaido060-0810 Japan Nayoro Observatory 157-1 Nisshin Nayoro-shi Hokkaido096-0066 Japan Indian Centre for Space Physics 466 Barakhola Netai Nagar West Bengal Kolkata700099 India Cepheid Observatory India Fujii Kurosaki Observatory 4500 Kurosaki

We present a detailed investigation of photometric, spectroscopic, and polarimetric observations of the Type II SN 2023ixf. Earlier studies have provided compelling evidence for a delayed shock breakout from a confined dense circumstellar matter (CSM) enveloping the progenitor star. The temporal evolution of polarization in SN 2023ixf revealed three distinct peaks in polarization evolution at 1.4 d, 6.4 d, and 79.2 d, indicating an asymmetric dense CSM, an aspherical shock front and clumpiness in the low-density extended CSM, and an aspherical inner ejecta/He-core. SN 2023ixf displayed two dominant axes, one along the CSM-outer ejecta and the other along the inner ejecta/He-core, showcasing the independent origin of asymmetry in the early and late evolution. The argument for an aspherical shock front is further strengthened by the presence of a high-velocity broad absorption feature in the blue wing of the Balmer features in addition to the P-Cygni absorption post 16 d. Hydrodynamical light curve modeling indicated a progenitor mass of 10 M☉ with a radius of 470 R☉ and explosion energy of 2 × 1051 erg, along with 0.06 M☉ of 56 Ni, though these properties are not unique due to modeling degeneracies. The modeling also indicated a two-zone CSM: a confined dense CSM extending up to 5×1014 cm, with a mass-loss rate of 10−2 M☉ yr−1 and an extended CSM spanning from 5×1014 cm to at least 1016 cm with a mass-loss rate of 10−4 M☉ yr−1, both assuming a wind-velocity of 10 km s−1. The early nebular phase observations display an axisymmetric line profile of [O I], red-ward attenuation of the emission of Hα post 125 days and flattening in the Ks-band marking the onset of dust formation. Copyright © 2024, The Authors. All rights reserved.

关键词： Supernovae

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Metal ratio and bimetal nanoarchitectonics of ammonia-based fluoride complex induced nickel hydroxide and manganese oxide composites as active materials of an energy storage device

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Journal of Energy Storage 2024年 80卷

作者： Hsu, Chuan-Chih Yu, You Zher Wu, Chung-Hsien Lee, Pin-Yan Chen, Hung-Ming Husain, Sadang Kongvarhodom, Chutima Hsiao, Yu-Cheng Lin, Lu-Yin Division of Cardiovascular Surgery Department of Surgery School of Medicine College of Medicine Taipei Medical University Taiwan Division of Cardiovascular Surgery Department of Surgery Taipei Medical University Hospital Taiwan Department of Chemical Engineering and Biotechnology National Taipei University of Technology Taipei Taiwan Gingen Technology Co. LTD. Rm. 7 10F. No.189 Sec. 2 Keelung Rd. Xinyi Dist. Taipei11054 Taiwan Department of Physics Faculty of Mathematics and Natural Science Lambung Mangkurat University Banjarmasin70124 Indonesia Department of Chemical Engineering King Mongkut's University of Technology Thonburi 126 Pracha-u-thit Toong-kru Bangkok10140 Thailand Department of Chemical Engineering Ming Chi University of Technology New Taipei City24301 Taiwan Cell Physiology and Molecular Image Research Center Wan Fang Hospital Taipei Medical University Taipei11696 Taiwan International PhD Program in Biomedical Engineering College of Biomedical Engineering Taipei Medical University Taipei11031 Taiwan Stanford Byers Center for Biodesign Stanford University CA94305-5428 United States Graduate Institute of Biomedical Optomechatronics College of Biomedical Engineering Taipei Medical University Taipei11031 Taiwan

Nickel and manganese compounds are widely applied as efficient battery-type active materials for energy storage devices, due to high theoretical capacities and abundant redox states for generating redox reactions to store charges. Morphology plays significant roles on energy storage ability. Structure directing agent (SDA) is widely reported to have ability for modulating configurations of active materials and to achieve excellent surface properties. In this work, it is the first time to use NH4BF4 and NH4HF2 as structure directing agents for synthesizing nickel manganese compounds derived from ZIF67 as active materials of energy storage devices. Bimetallic compounds are composed of nickel hydroxide and manganese oxide, which presents the favorable two-dimensional flower-like structure with high surface area and abundant redox states. The largest specific capacitance (CF) of 1358.2 F/g corresponding to the capacity of 226.4 mAh/g are achieved for the bimetallic compound synthesized using the Ni to Mn precursor ratio of 1 to 1 (NiMn11). The energy storage device assembled using NiMn11 and reduced graphene oxide on Ni foams presents a maximum energy density of 11.45 Wh/kg at the power density of 0.35 kW/kg, and excellent long-term stability with the CF retention of 85.6% and Coulombic efficiency of 87.5% after 10,000 cycles. © 2023

关键词： Fluorine compounds

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Scientific discovery in the age of artificial intelligence

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NATURE 2023年第7978期621卷 E33-E33页

作者： Wang, Hanchen Fu, Tianfan Du, Yuanqi Gao, Wenhao Huang, Kexin Liu, Ziming Chandak, Payal Liu, Shengchao Van Katwyk, Peter Deac, Andreea Anandkumar, Anima Bergen, Karianne Gomes, Carla P. Ho, Shirley Kohli, Pushmeet Lasenby, Joan Leskovec, Jure Liu, Tie-Yan Manrai, Arjun Marks, Debora Ramsundar, Bharath Song, Le Sun, Jimeng Tang, Jian Velickovic, Petar Welling, Max Zhang, Linfeng Coley, Connor W. Bengio, Yoshua Zitnik, Marinka Department of Engineering University of Cambridge Cambridge UK Department of Computing and Mathematical Sciences California Institute of Technology Pasadena CA USA NVIDIA Santa Clara CA USA Department of Computational Science and Engineering Georgia Institute of Technology Atlanta GA USA Department of Computer Science Cornell University Ithaca NY USA Department of Chemical Engineering Massachusetts Institute of Technology Cambridge MA USA Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology Cambridge MA USA Department of Computer Science Stanford University Stanford CA USA Department of Physics Massachusetts Institute of Technology Cambridge MA USA Harvard-MIT Program in Health Sciences and Technology Cambridge MA USA Mila – Quebec AI Institute Montreal Quebec Canada Université de Montréal Montreal Quebec Canada HEC Montréal Montreal Quebec Canada CIFAR AI Chair Toronto Ontario Canada Department of Earth Environmental and Planetary Sciences Brown University Providence RI USA Data Science Institute Brown University Providence RI USA Center for Computational Astrophysics Flatiron Institute New York NY USA Department of Astrophysical Sciences Princeton University Princeton NJ USA Department of Physics Carnegie Mellon University Pittsburgh PA USA Department of Physics and Center for Data Science New York University New York NY USA Google DeepMind London UK Department of Computer Science and Technology University of Cambridge Cambridge UK Microsoft Research Beijing China Department of Biomedical Informatics Harvard Medical School Boston MA USA Broad Institute of MIT and Harvard Cambridge MA USA Harvard Data Science Initiative Cambridge MA USA Kempner Institute for the Study of Natural and Artificial Intelligence Harvard University Cambridge MA USA Department of Systems Biology Harvard Medical School Boston MA USA Deep Forest Sciences Palo Alto CA USA BioMap Beijing China Mo

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