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

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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The Brain Tumor Segmentation in Pediatrics (BraTS-PEDs) Challenge: Focus on Pediatrics (CBTN-CONNECT-DIPGR-ASNR-MICCAI BraTS-PEDs)

arXiv

引用

arXiv 2024年

作者： Kazerooni, Anahita Fathi Khalili, Nastaran Liu, Xinyang Gandhi, Deep Jiang, Zhifan Anwar, Syed Muhammed Albrecht, Jake Adewole, Maruf Anazodo, Udunna Anderson, Hannah Baid, Ujjwal Bergquist, Timothy Borja, Austin J. Calabrese, Evan Chung, Verena Conte, Gian-Marco Dako, Farouk Eddy, James Ezhov, Ivan Familiar, Ariana Farahani, Keyvan Franson, Andrea Gottipati, Anurag Haldar, Shuvanjan Iglesias, Juan Eugenio Janas, Anastasia Johansen, Elaine Jones, Blaise V. Khalili, Neda Kofler, Florian LaBella, Dominic Lai, Hollie Anne Van Leemput, Koen Li, Hongwei Bran Maleki, Nazanin McAllister, Aaron S. Meier, Zeke Menze, Bjoern Moawad, Ahmed W. Nandolia, Khanak K. Pavaine, Julija Piraud, Marie Poussaint, Tina Prabhu, Sanjay P. Reitman, Zachary Rudie, Jeffrey D. Sanchez-Montano, Mariana Shaikh, Ibraheem Salman Sheth, Nakul Tu, Wenxin Wang, Chunhao Ware, Jeffrey B. Wiestler, Benedikt Zapaishchykova, Anna Bornhorst, Miriam Deutsch, Michelle Fouladi, Maryam Lazow, Margot Mikael, Leonie Hummel, Trent Kann, Benjamin de Blank, Peter Hoffman, Lindsey Aboian, Mariam Nabavizadeh, Ali Packer, Roger Bakas, Spyridon Resnick, Adam Rood, Brian Vossough, Arastoo Linguraru, Marius George Children’s Hospital of Philadelphia PhiladelphiaPA United States Department of Neurosurgery University of Pennsylvania PhiladelphiaPA United States University of Pennsylvania PhiladelphiaPA United States Sheikh Zayed Institute for Pediatric Surgical Innovation Children’s National Hospital WashingtonDC United States Sage Bionetworks United States Lab Crestview Radiology Lagos Nigeria McGill University MontrealQC Canada Department of Radiology Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Division of Computational Pathology Department of Pathology and Laboratory Medicine Indiana University School of Medicine IndianapolisIN United States Department of Neurosurgery The University of Southern California CA United States Department of Radiology Duke University Medical Center DurhamNC United States Mayo Clinic MN United States Center for Global Health Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Department of Informatics Technical University Munich Germany TranslaTUM - Central Institute for Translational Cancer Research Technical University of Munich Germany Cancer Imaging Program National Cancer Institute National Institutes of Health BethesdaMD United States C. S. Mott Children’s Hospital University of Michigan MI United States Biomedical Engineering Rutgers University New BrunswickNJ United States Athinoula A Martinos Center for Biomedical Imaging Massachusetts General Hospital BostonMA United States Yale University New HavenCT United States PrecisionFDA U.S. Food and Drug Administration Silver SpringMD United States Cincinnati Children’s Hospital Medical Center OH United States Helmholtz AI Helmholtz Munich Germany Department of Radiation Oncology Duke University Medical Center DurhamNC United States Department of Radiology Children’s Health Orange County CA United States Department of Applied Mathematics and Computer Science Technical University of De

Pediatric tumors of the central nervous system are the most common cause of cancer-related death in children. The five-year survival rate for high-grade gliomas in children is less than 20%. Due to their rarity, the diagnosis of these entities is often delayed, their treatment is mainly based on historic treatment concepts, and clinical trials require multi-institutional collaborations. Here we present the CBTN-CONNECT-DIPGR-ASNR-MICCAI BraTS-PEDs challenge, focused on pediatric brain tumors with data acquired across multiple international consortia dedicated to pediatric neuro-oncology and clinical trials. The CBTN-CONNECT-DIPGR-ASNR-MICCAI BraTS-PEDs challenge brings together clinicians and AI/imaging scientists to lead to faster development of automated segmentation techniques that could benefit clinical trials, and ultimately the care of children with brain tumors. © 2024, CC BY.

关键词： Deep learning

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End-to-end symmetry preserving inter-atomic potential energy model for finite and extended systems 32

End-to-end symmetry preserving inter-atomic potential energy...

引用

32nd Conference on Neural Information Processing Systems, NeurIPS 2018

作者： Zhang, Linfeng Han, Jiequn Wang, Han Saidi, Wissam A. Car, Roberto Weinan, E. Program in Applied and Computational Mathematics Princeton University United States Institute of Applied Physics and Computational Mathematics China CAEP Software Center for High Performance Numerical Simulation China Department of Mechanical Engineering and Materials Science University of Pittsburgh United States Department of Chemistry Department of Physics Princeton University United States Princeton Institute for the Science and Technology of Materials Princeton University United States Department of Mathematics Princeton University United States Beijing Institute of Big Data Research China

Machine learning models are changing the paradigm of molecular modeling, which is a fundamental tool for material science, chemistry, and computational biology. Of particular interest is the inter-atomic potential energy surface (PES). Here we develop Deep Potential - Smooth Edition (DeepPot-SE), an end-to-end machine learning-based PES model, which is able to efficiently represent the PES of a wide variety of systems with the accuracy of ab initio quantum mechanics models. By construction, DeepPot-SE is extensive and continuously differentiable, scales linearly with system size, and preserves all the natural symmetries of the system. Further, we show that DeepPot-SE describes finite and extended systems including organic molecules, metals, semiconductors, and insulators with high fidelity. © 2018 Curran Associates Inc..All rights reserved.

关键词： Potential energy

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Spintronic superconductor in a bulk layered material with natural spin-valve structure

arXiv

引用

arXiv 2020年

作者： Sakuragi, Shunsuke Sasaki, S. Akashi, R. Sakagami, R. Kuroda, K. Bareille, C. Hashimoto, T. Nagashima, T. Kinoshita, Y. Hirata, Y. Shimozawa, M. Asai, S. Yajima, T. Doi, S. Tsujimoto, N. Kunisada, S. Noguchi, R. Kurokawa, K. Azuma, N. Hirata, K. Yamasaki, Y. Nakao, H. Kim, T.K. Cacho, C. Masuda, T. Tokunaga, M. Wadati, H. Okazaki, K. Shin, S. Kamihara, Y. Yamashita, Minoru Kondo, Takeshi Institute for Solid State Physics University of Tokyo Kashiwa Chiba Japan Department of Physics University of Tokyo Tokyo Japan Department of Applied Physics and Phisico-Informatics Keio University Yokohama Japan Department of Applied Physics National Defense Academy of Japan Yokosuka Japan Division of Materials Physics Graduate School of Engineering Science Osaka University Toyonaka Japan Research Center for Computational Design of Advanced Functional Materials National Institute of Advanced Industrial Science and Technology Tsukuba Japan Research and Services Division of Materials Data and Integrated System National Institute for Materials Science Tsukuba Japan RIKEN Center for Emergent Matter Science Wako Japan PRESTO Japan Science and Technology Agency Kawaguchi Japan Condensed Matter Research Center and Photon Factory Institute of Materials Structure Science High Energy Accelerator Research Organization Tsukuba Japan Diamond Light Source Harwell Campus Didcot United Kingdom Graduate School of Material Science University of Hyogo Hyogo Japan TheUniversity of Tokyo Kashiwa Chiba277-8581 Japan Center for Spintronics Research Network Keio University Yokohama Japan

Multi-layered materials provide fascinating platforms to realize various functional properties, possibly leading to future electronic devices controlled by external fields. In particular, layered magnets coupled with conducting layers have been extensively studied recently for possible control of their transport properties via the spin structure. Successful control of quantum-transport properties in the materials with antiferromagnetic (AFM) layers, so-called natural spin-valve structure, has been reported for the Dirac Fermion and topological/axion materials [1-11]. However, a bulk crystal in which magnetic and superconducting layers are alternately stacked has not been realized until now, and the search for functional properties in it is an interesting yet unexplored field in material science. Here, we discover superconductivity providing such an ideal platform in EuSn2As2 with the van der Waals stacking of magnetic Eu layers and superconducting Sn-As layers, and present the first demonstration of a natural spin-valve effect on the superconducting current. Below the superconducting transition temperature (Tc), the electrical resistivity becomes zero in the in-plane direction. In contrast, it, surprisingly, remains finite down to the lowest temperature in the out-of-plane direction, mostly due to the structure of intrinsic magnetic Josephson junctions in EuSn2As2. The magnetic order of the Eu layers (or natural spin-valve) is observed to be extremely soft, allowing one to easy control of the out-of-plane to in-plane resistivities ratio from 1 to ∞ by weak external magnetic fields. The concept of multi-functional materials with stacked magnetic-superconducting layers will open a new pathway to develop novel spintronic devices with magnetically controllable superconductivity. Copyright © 2020, The Authors. All rights reserved.

关键词： Europium compounds

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Synergy of Binary Substitutions for Improving the Cycle Performance in LiNiO2 Revealed by Ab Initio Materials Informatics

arXiv

引用

arXiv 2020年

作者： Yoshida, Tomohiro Maezono, Ryo Hongo, Kenta Department of Computer-Aided Engineering and Development Sumitomo Metal Mining Co. Ltd. 3-5 Sobiraki-cho NiihamaEhime792-0001 Japan School of Information Science JAIST Asahidai 1-1 IshikawaNomi923-1292 Japan Computational Engineering Applications Unit RIKEN 2-1 Hirosawa SaitamaWako351-0198 Japan Research Center for Advanced Computing Infrastructure JAIST Asahidai 1-1 NomiIshikawa923-1292 Japan Center for Materials Research by Information Integration Research and Services Division of Materials Data and Integrated System National Institute for Materials Science 1-2-1 Sengen TsukubaIbaraki305-0047 Japan PRESTO Japan Science and Technology Agency 4-1-8 Honcho SaitamaKawaguchi-shi322-0012 Japan

We explore LiNiO2-based cathode materials with two-element substitutions by an ab initio simulation based materials informatics (AIMI) approach. According to our previous study, a higher cycle performance strongly correlates with less structural change during charge–discharge cycles;the latter can be used for evaluating the former. However, if we target the full substitution space, full simulations are infeasible even for all binary combinations. To circumvent such an exhaustive search, we rely on Bayesian optimization. Actually, by searching only 4% of all the combinations, our AIMI approach discovered two promising combinations, Cr-Mg and Cr-Re, whereas each atom itself never improved the performance. We conclude that the synergy never emerges from a common strategy restricted to combinations of "good" elements that individually improve the performance. In addition, we propose a guideline for the binary substitutions by elucidating the mechanism of crystal structure change. Copyright © 2020, The Authors. All rights reserved.

关键词： Binary alloys

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Advances of machine learning in molecular modeling and simulation

arXiv

引用

arXiv 2019年

作者： Haghighatlari, Mojtaba Hachmann, Johannes Department of Chemical and Biological Engineering University at Buffalo State University of New York BuffaloNY14260 United States Computational and Data-Enabled Science and Engineering Graduate Program University at Buffalo State University of New York BuffaloNY14260 United States New York State Center of Excellence in Materials Informatics BuffaloNY14203 United States

In this review, we highlight recent developments in the application of machine learning for molecular modeling and simulation. After giving a brief overview of the foundations, components, and workflow of a typical supervised learning approach for chemical problems, we showcase areas and state-of-the-art examples of their deployment. In this context, we discuss how machine learning relates to, supports, and augments more traditional physics-based approaches in computational research. We conclude by outlining challenges and future research directions that need to be addressed in order to make machine learning a mainstream chemical engineering tool. Copyright © 2019, The Authors. All rights reserved.

关键词： Machine learning

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Here There Be (Dusty) Monsters: High-redshift Active Galactic Nuclei Are Dustier than Their Hosts

引用

The Astrophysical Journal 2025年第2期986卷 177-177页

作者： Madisyn Brooks Raymond C. Simons Jonathan R. Trump Anthony J. Taylor Micaela B. Bagley Bren Backhaus Kelcey Davis Véronique Buat Nikko J. Cleri Alexander de la Vega Steven L. Finkelstein Michaela Hirschmann Benne W. Holwerda Dale D. Kocevski Anton M. Koekemoer Ray A. Lucas Fabio Pacucci Lise-Marie Seillé Department of Physics 196A Auditorium Road Unit 3046 University of Connecticut Storrs CT 06269 USA Department of Engineering and Physics Providence College 1 Cunningham Square Providence RI 02918 USA Department of Astronomy The University of Texas at Austin Austin TX USA Astrophysics Science Division NASA Goddard Space Flight Center 8800 Greenbelt Road Greenbelt MD 20771 USA Department of Physics and Astronomy University of Kansas Lawrence KS 66045 USA Aix Marseille Univ CNRS CNES LAM Marseille France Institut Universitaire de France (IUF) Paris France Department of Astronomy and Astrophysics The Pennsylvania State University University Park PA 16802 USA Institute for Computational and Data Sciences The Pennsylvania State University University Park PA 16802 USA Institute for Gravitation and the Cosmos The Pennsylvania State University University Park PA 16802 USA Department of Physics and Astronomy University of California 900 University Avenue Riverside CA 92521 USA Cosmic Frontier Center The University of Texas at Austin Austin TX USA Institute of Physics Laboratory of Galaxy Evolution Ecole Polytechnique Fédérale de Lausanne (EPFL) Observatoire de Sauverny 1290 Versoix Switzerland Department of Physics University of Louisville Natural Science Building 102 Louisville KY 40292 USA Department of Physics and Astronomy Colby College Waterville ME 04901 USA Space Telescope Science Institute 3700 San Martin Drive Baltimore MD 21218 USA Center for Astrophysics ∣ Harvard & Smithsonian 60 Garden Street Cambridge MA 02138 USA Black Hole Initiative Harvard University 20 Garden Street Cambridge MA 02138 USA

JWST spectroscopy has discovered a population of z ≳ 3.5 galaxies with broad Balmer emission lines and narrow forbidden lines that are consistent with hosting active galactic nuclei (AGN). Many of these systems, now known as "little red dots," are compact and have unique colors that are very red in the optical/near-infrared and blue in the ultraviolet. The relative contribution of galaxy starlight and AGN to these systems remains uncertain, especially for the galaxies with unusual blue+red spectral energy distributions. In this work, we use Balmer decrements to measure the independent dust attenuation of the broad and narrow emission-line components of a sample of 29 broad-line AGN identified from three public JWST spectroscopy surveys: CEERS, JADES, and RUBIES. Stacking the narrow components from the spectra of 25 sources with broad Hα and no broad Hβ results in a median narrow Hα/Hβ = $2.4{7}_{-0.05}^{+0.05}$

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Critical mass effect in evolutionary games triggered by zealots

arXiv

引用

arXiv 2019年

作者： Cardillo, Alessio Masuda, Naoki Department of Engineering Mathematics University of Bristol BristolBS8 1UB United Kingdom Department of Computer Science and Mathematics University Rovira i Virgili TarragonaE-43007 Spain University of Zaragoza ZaragozaE-50018 Spain Department of Mathematics University at Buffalo BuffaloNY14260-2900 United States Computational and Data-Enabled Science and Engineering Program University at Buffalo State University of New York BuffaloNY14260-5030 United States

Tiny perturbations may trigger large responses in systems near criticality, shifting them across equilibria. Committed minorities are suggested to be responsible for the emergence of collective behaviors in many physical, social, and biological systems. Using evolutionary game theory, we address the question whether a finite fraction of zealots can drive the system to large-scale coordination. We find that a tipping point exists in coordination games, whereas the same phenomenon depends on the selection pressure, update rule, and network structure in other types of games. Our study paves the way to understand social systems driven by the individuals’ benefit in presence of zealots, such as human vaccination behavior or cooperative transports in animal groups. Copyright © 2019, The Authors. All rights reserved.

关键词： Game theory

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Modeling state-transition dynamics in resting-state brain signals by the hidden Markov and Gaussian mixture models

arXiv

引用

arXiv 2020年

作者： Ezaki, Takahiro Himeno, Yu Watanabe, Takamitsu Masuda, Naoki Research Center for Advanced Science and Technology The University of Tokyo 4-6-1 Komaba Meguro-ku Tokyo153-8904 Japan PRESTO JST 4-1-8 Honcho Kawaguchi Saitama332-0012 Japan Department of Aeronautics and Astronautics The University of Tokyo 7-3-1 Hongo Bunkyo-ku Tokyo113-8656 Japan Laboratory for Cognition Circuit Dynamics RIKEN Centre for Brain Science Saitama351-0198 Japan International Research Center for Neurointelligence The University of Tokyo 7-3-1 Hongo Bunkyo-ku Tokyo113-0033 Japan Department of Mathematics State University of New York at Buffalo BuffaloNY14260-2900 United States Computational and Data-Enabled Science and Engineering Program State University of New York at Buffalo BuffaloNY14260-5030 United States

Recent studies have proposed that one can summarize brain activity into dynamics among a relatively small number of hidden states and that such an approach is a promising tool for revealing brain function. Hidden Markov models (HMMs) are a prevalent approach to inferring such neural dynamics among discrete brain states. However, the impact of assuming Markovian structure in neural time series data has not been sufficiently examined. Here, to address this situation and examine the performance of the HMM, we compare the model with the Gaussian mixture model (GMM), which is with no temporal regularization and thus a statistically simpler model than the HMM, by applying both models to synthetic time series generated from empirical resting-state functional magnetic resonance imaging (fMRI) data. We compared the GMM and HMM for various sampling frequencies, lengths of recording per participant, numbers of participants, and numbers of independent component signals. We find that the HMM attains a better accuracy of estimating the hidden state than the GMM in a majority of cases. However, we also find that the accuracy of the GMM is comparable to that of the HMM under the condition that the sampling frequency is reasonably low (e.g., TR = 2.88 or 3.60 s) or the data is relatively short. These results suggest that the GMM can be a viable alternative to the HMM for investigating hidden-state dynamics under this condition. Copyright © 2020, The Authors. All rights reserved.

关键词： Hidden Markov models

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A systematic theoretical study on FeOx-supported single-atom catalysts： M1/FeOx for CO oxidation

引用

Nano Research 2018年第3期11卷 1599-1611页

作者： Jinxia Liang Qi Yu Xiaofeng Yang Tao Zhang Jun Li Guizhou Provincial Key Laboratory of Computational Nano-Material Science Guizhou Synergetic Innovation Center of Scientific Big Data for Advanced Manufacturing Technology Guizhou Education University Guiyang 550018 China Shaanxi Key Laboratory of Catalysis Shaanxi University of Technology Hanzhong 723000 China Department of Chemistry and Key Laboratory of Organic Optoelectronics & Molecular Engineering of Ministry of Education Tsinghua University Beijing 100084 China State Key Laboratory of Catalysis Dalian Institute of Chemical Physics Chinese Academy of Sciences Dalian 116023 China

A single-atom catalyst （SAC） that was first proposed by us in 2011 has aroused significant recent interest. Among the various SACs, FeOx-based ones including Pt1/FeOx, Ir1/FeOx, Au1/FeOx, Ni1/FeOx, and Fe1/FeOx have been investigated either experimentally or theoretically for CO oxidation. However, a systematic study of FeO,-based SACs has not been conducted. For a comprehensive understanding of FeOx-supported single-metal-atom catalysts, extensive density functional theory calculations were carried out on the activities and catalytic mechanisms of SACs with the 3d, 4d, and 5d metals of group VIII to IB, i.e., M1/FeOx （M = Fe, Co, Ni, Cu; Ru, Rh, Pd, Ag; Os, Ir, Pt, Au） for CO oxidation. Remarkably, a new noble metal SAC, Pd1/FeOx, with high activity in CO oxidation was found and is predicted to be even better than the previously reported Pt1/FeOx and Ni1/FeOx. In comparison, other M1/FeOx SACs （M = Fe, Co, Cu; Ru, Rh, Ag; Os, Ir, Au） showed only low activities in CO oxidation. Moreover, the adsorption strength of CO on the single-atom active sites was found to be the key in determining the catalytic activity of these SACs for CO oxidation, because it governs the recoverability of oxygen vacancies on their surfaces in the formation of a second CO2 during CO oxidation. Our systematic studies of FeOx-supported SACs will help in understanding the fundamental mechanisms of the interactions between singly dispersed surface metal atoms and FeOx substrate and in designing highly active FeOx-supported SACs.

关键词： single-atom catalyst M1/FeOx density functional theory heterogeneous catalysis

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