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arXiv 2018年

作者： Secunda, Amy Bellovary, Jillian Low, Mordecai-Mark Mac Ford, K.E. Saavik McKernan, Barry Leigh, Nathan Lyra, Wladimir Department of Astrophysics American Museum of Natural History Central Park West at 79th Street New YorkNY10024 United States Department of Physics Queensborough Community College BaysideNY11364 United States Department of Science Borough of Manhattan Community College City University of New York New YorkNY10007 United States Physics Program Graduate Center CUNY New YorkNY10016 United States Department of Physics and Astronomy Stony Brook University Stony BrookNY11794-3800 United States Center for Computational Astrophysics Flatiron Institute New YorkNY10010 United States Department of Physics and Astronomy California State University Northridge 18111 Nordhoff Street NorthridgeCA91330 United States Jet Propulsion Laboratory California Institute of Technology 4800 Oak Grove Drive PasadenaCA91109 United States

The merger rate of stellar mass black holes binaries (sBHBs) inferred by the Advanced Laser Interferometer Gravitational-Wave Observatory (LIGO) suggests the need for an efficient source of sBHB formation. Active galactic nucleus (AGN) disks are a promising location for the formation of these sBHBs, as well as binaries of other compact objects, because of powerful torques exerted by the gas disk. These gas torques cause orbiting compact objects to migrate towards regions in the disk where inward and outward torques cancel, known as migration traps. We simulate the migration of stellar mass black holes (sBHs) in an analytically modeled AGN disk using an augmented N-body code that includes migration torques, a stochastic gravitational force exerted by turbulent density fluctuations in the disk, and inclination and eccentricity dampening produced by passages through the gas disk, in addition to the standard gravitational forces between objects. We find that sBHBs form rapidly in the disk as sBHs migrate towards the migration trap. These sBHBs are likely to subsequently merge on short time-scales. The process continues, leading to the build up of a population of over-massive sBHs. sBHBs that form in AGN disks could contribute significantly to the sBHB merger rate inferred by LIGO. Copyright © 2018, The Authors. All rights reserved.

关键词： Merging

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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: Rare variant analyses in 51,256 type 2 diabetes cases and 370,487 controls reveal the pathogenicity spectrum of monogenic diabetes genes

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Nature genetics 2025年第4期57卷 1060页

作者： Alicia Huerta-Chagoya Philip Schroeder Ravi Mandla Jiang Li Lowri Morris Maheak Vora Ahmed Alkanaq Dorka Nagy Lukasz Szczerbinski Jesper G S Madsen Silvia Bonàs-Guarch Fanny Mollandin Joanne B Cole Bianca Porneala Kenneth Westerman Josephine H Li Toni I Pollin Jose C Florez Anna L Gloyn David J Carey Inês Cebola Uyenlinh L Mirshahi Alisa K Manning Aaron Leong Miriam Udler Josep M Mercader Programs in Metabolism and Medical & Population Genetics Broad Institute of Harvard and MIT Cambridge MA USA. Center for Genomic Medicine Massachusetts General Hospital Boston MA USA. Diabetes Unit Massachusetts General Hospital Boston MA USA. Graduate Program in Genomics and Computational Biology University of Pennsylvania Philadelphia PA USA. Department of Genomic Health Geisinger Danville PA USA. Section of Genetics and Genomics Department of Metabolism Digestion and Reproduction Imperial College London London UK. National Heart and Lung Institute Faculty of Medicine London UK. Department of Endocrinology Diabetology and Internal Medicine Medical University of Bialystok Bialystok Poland. Clinical Research Centre Medical University of Bialystok Bialystok Poland. Institute of Mathematics and Computer Science University of Southern Denmark Odense Denmark. The Novo Nordisk Foundation Center for Genomic Mechanisms of Disease Broad Institute of MIT and Harvard Cambridge MA USA. Department of Biochemistry and Molecular Biology University of Southern Denmark Odense Denmark. Centre for Genomic Regulation The Barcelona Institute of Science and Technology Barcelona Spain. Centro de Investigación Biomédica en Red de Diabetes y Enfermedades Metabólicas Asociadas Madrid Spain. Department of Metabolism Digestion and Reproduction Imperial College London London UK. Department of Medicine Harvard Medical School Boston MA USA. Division of Endocrinology Boston Children's Hospital Boston MA USA. Department of Biomedical Informatics University of Colorado School of Medicine Aurora CO USA. Division of General Internal Medicine Massachusetts General Hospital Boston MA USA. Department of Medicine Massachusetts General Hospital Boston MA USA. University of Maryland School of Medicine Baltimore MD USA. Endocrine Division Massachusetts General Hospital Boston MA USA. Department of Pediatrics Division of Endocrinology Stanford School of Medicine Stanford

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Homoharringtonine as a PHGDH inhibitor: Unraveling metabolic dependencies and developing a potent therapeutic strategy for high-risk neuroblastoma

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Biomedicine and Pharmacotherapy 2023年第1期166卷 115429-115429页

作者： Hsieh, Chiao-Hui Huang, Chen-Tsung Cheng, Yi-Sheng Hsu, Chun-Hua Hsu, Wen-Ming Chung, Yun-Hsien Liu, Yen-Lin Yang, Tsai-Shan Chien, Chia-Yu Lee, Yu-Hsuan Huang, Hsuan-Cheng Juan, Hsueh-Fen Department of Life Science National Taiwan University Taipei Taiwan Center for Computational and Systems Biology National Taiwan University Taipei Taiwan Graduate Institute of Biomedical Electronics and Bioinformatics National Taiwan University Taipei Taiwan Institute of Plant Biology National Taiwan University Taipei Taiwan Genome and Systems Biology Degree Program National Taiwan University and Academia Sinica Taipei Taiwan Department of Agricultural Chemistry National Taiwan University Taipei Taiwan Department of Surgery National Taiwan University Hospital and National Taiwan University College of Medicine Taipei Taiwan Department of Pediatrics School of Medicine College of Medicine Taipei Medical University Taipei Taiwan Institute of Biomedical Informatics National Yang Ming Chiao Tung University Taipei Taiwan Center for Advanced Computing and Imaging in Biomedicine Taipei Taiwan

Neuroblastoma, a childhood cancer affecting the sympathetic nervous system, continues to challenge the development of potent treatments due to the limited availability of druggable targets for this aggressive illness. Recent investigations have uncovered that phosphoglycerate dehydrogenase (PHGDH), an essential enzyme for de novo serine synthesis, serves as a non-oncogene dependency in high-risk neuroblastoma. In this study, we show that homoharringtonine (HHT) acts as a PHGDH inhibitor, inducing intricate alterations in cellular metabolism, and thus providing an efficient treatment for neuroblastoma. We have experimentally verified the reliance of neuroblastoma on PHGDH and employed molecular docking, thermodynamic evaluations, and X-ray crystallography techniques to determine the bond interactions between HHT and PHGDH. Administering HHT to treat neuroblastoma resulted in effective cell elimination in vitro and tumor reduction in vivo. Metabolite and functional assessments additionally disclosed that HHT treatment suppressed de novo serine synthesis, initiating intricate metabolic reconfiguration and oxidative stress in neuroblastoma. Collectively, these discoveries highlight the potential of targeting PHGDH using HHT as a potent approach for managing high-risk neuroblastoma. © 2023 The Authors

关键词： computational molecular docking Drug discovery High-risk neuroblastoma Metabolic reprogramming Molecular target therapy Phosphoglycerate dehydrogenase

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Erratum to: A combined treatment of hydration and dynamical effects for the modeling of host-guest binding thermodynamics: the SAMPL5 blinded challenge

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Journal of computer-aided molecular design 2017年第1期31卷 45页

作者： Rajat Kumar Pal Kamran Haider Divya Kaur William Flynn Junchao Xia Ronald M Levy Tetiana Taran Lauren Wickstrom Tom Kurtzman Emilio Gallicchio Department of Chemistry Brooklyn College 2900 Bedford Avenue Brooklyn NY 11210 USA. Ph.D. Program in Biochemistry The Graduate Center of the City University of New York New York NY 10016 USA. Department of Chemistry Lehman College The City University of New York 250 Bedford Park Blvd. West Bronx New York NY 10468 USA. Ph.D. Program in Chemistry The Graduate Center of the City University of New York New York NY 10016 USA. Center for Biophysics and Computational Biology Institute of Computational Molecular Science and Department of Chemistry Temple University Philadelphia PA USA. Department of Physics and Astronomy Rutgers University Piscataway NJ 08854 USA. Borough of Manhattan Community College Department of Science The City University of New York 199 Chambers Street New York NY 10007 USA. Department of Chemistry Brooklyn College 2900 Bedford Avenue Brooklyn NY 11210 USA. egallicchio@brooklyn.cuny.edu. Ph.D. Program in Biochemistry The Graduate Center of the City University of New York New York NY 10016 USA. egallicchio@brooklyn.cuny.edu. Ph.D. Program in Chemistry The Graduate Center of the City University of New York New York NY 10016 USA. egallicchio@brooklyn.cuny.edu.

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The bulk van der Waals layered magnet CrSBr is a quasi-1D material

arXiv

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arXiv 2022年

作者： Klein, Julian Pingault, Benjamin Florian, Matthias Heißenbüttel, Marie-Christin Steinhoff, Alexander Song, Zhigang Torres, Kierstin Dirnberger, Florian Curtis, Jonathan B. Weile, Mads Penn, Aubrey Deilmann, Thorsten Dana, Rami Bushati, Rezlind Quan, Jiamin Luxa, Jan Sofer, Zdenek Alù, Andrea Menon, Vinod M. Wurstbauer, Ursula Rohlfing, Michael Narang, Prineha Lončar, Marko Ross, Frances M. Department of Materials Science and Engineering Massachusetts Institute of Technology CambridgeMA02139 United States John A. Paulson School of Engineering and Applied Sciences Harvard University CambridgeMA02138 United States QuTech Delft University of Technology Delft2600 GA Netherlands Department of Electrical and Computer Engineering Department of Physics University of Michigan Ann ArborMI48109 United States Institut für Festkörpertheorie Westfälische Wilhelms-Universität Münster Münster48149 Germany Institut für Theoretische Physik Universität Bremen P.O. Box 330 440 Bremen28334 Germany Bremen Center for Computational Materials Science University of Bremen Bremen28359 Germany Department of Physics City College of New York New YorkNY10031 United States College of Letters and Science UCLA Los AngelesCA90095 United States Department of Physics Technical University of Denmark Kgs. LyngbyDK-2800 Denmark MIT.nano Massachusetts Institute of Technology CambridgeMA02139 United States Department of Physics The Graduate Center City University of New York New YorkNY10016 United States Photonics Initiative CUNY Advanced Science Research Center New YorkNY10031 United States Physics Program Graduate Center City University of New York New YorkNY10026 United States Department of Inorganic Chemistry University of Chemistry and Technology Prague Technická 5 Prague 6166 28 Czech Republic Institute of Physics Center for Nanotechnology University of Münster Münster48149 Germany

Correlated quantum phenomena in one-dimensional (1D) systems that exhibit competing electronic and magnetic order are of strong interest for studying fundamental interactions and excitations, such as Tomonaga-Luttinger liquids and topological orders and defects with properties completely different from the quasiparticles expected in their higher-dimensional counterparts. However, clean 1D electronic systems are difficult to realize experimentally, particularly magnetically ordered systems. Here, we show that the van der Waals layered magnetic semiconductor CrSBr behaves like a quasi-1D material embedded in a magnetically ordered environment. The strong 1D electronic character originates from the Cr-S chains and the combination of weak interlayer hybridization and anisotropy in effective mass and dielectric screening with an effective electron mass ratio of meX/meY ∼ 50. This extreme anisotropy experimentally manifests in strong electron-phonon and exciton-phonon interactions, a Peierls-like structural instability and a Fano resonance from a van Hove singularity of similar strength of metallic carbon nanotubes. Moreover, due to the reduced dimensionality and interlayer coupling, CrSBr hosts spectrally narrow (1 meV) excitons of high binding energy and oscillator strength that inherit the 1D character. Overall, CrSBr is best understood as a stack of weakly hybridized monolayers and appears to be an experimentally attractive candidate for the study of exotic exciton and 1D correlated many-body physics in the presence of magnetic order. Copyright © 2022, The Authors. All rights reserved.

关键词： Excitons

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Erratum to: The real cost of sequencing: scaling computation to keep pace with data generation

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Genome biology 2016年第1期17卷 78页

作者： Paul Muir Shantao Li Shaoke Lou Daifeng Wang Daniel J Spakowicz Leonidas Salichos Jing Zhang George M Weinstock Farren Isaacs Joel Rozowsky Mark Gerstein Department of Molecular Cellular and Developmental Biology Yale University New Haven CT 06520 USA. Systems Biology Institute Yale University West Haven CT 06516 USA. Integrated Graduate Program in Physical and Engineering Biology Yale University New Haven CT 06520 USA. Program in Computational Biology and Bioinformatics Yale University New Haven CT 06520 USA. Department of Molecular Biophysics and Biochemistry Yale University New Haven CT 06520 USA. The Jackson Laboratory for Genomic Medicine Farmington CT 06032 USA. Program in Computational Biology and Bioinformatics Yale University New Haven CT 06520 USA. mark@***. Department of Molecular Biophysics and Biochemistry Yale University New Haven CT 06520 USA. mark@***. Department of Computer Science Yale University New Haven CT 06520 USA. mark@***.

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Cover Image, Volume 86, Issue 5

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Proteins: Structure, Function, and Bioinformatics 2018年第5期86卷

作者： J. C. Gaines S. Acebes A. Virrueta M. Butler L. Regan C. S. O'Hern Program in Computational Biology and Bioinformatics Yale University New Haven Connecticut 06520 Integrated Graduate Program in Physical and Engineering Biology (IGPPEB) Yale University New Haven Connecticut 06520 Department of Mechanical Engineering and Materials Science Yale University New Haven Connecticut 06520 Department of Physics and Astronomy University of Southern California Los Angeles California 90007 Department of Molecular Biophysics & Biochemistry Yale University New Haven Connecticut 06520 Department of Chemistry Yale University New Haven Connecticut 06520 Department of Physics Yale University New Haven Connecticut 06520 Department of Applied Physics Yale University New Haven Connecticut 06520

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Senescence atlas reveals an aged-like inflamed niche that blunts muscle regeneration (vol 613, pg 169, 2023)

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NATURE 2023年第7949期614卷 E45-E45页

作者： Moiseeva, Victoria Cisneros, Andres Sica, Valentina Deryagin, Oleg Lai, Yiwei Jung, Sascha Andres, Eva An, Juan Segales, Jessica Ortet, Laura Lukesova, Vera Volpe, Giacomo Benguria, Alberto Dopazo, Ana Benitah, Salvador Aznar Urano, Yasuteru del Sol, Antonio Esteban, Miguel A. A. Ohkawa, Yasuyuki Serrano, Antonio L. L. Perdiguero, Eusebio Munoz-Canoves, Pura Department of Medicine and Life Sciences Pompeu Fabra University Barcelona Spain CIBERNED Barcelona Spain Altos labs Inc San Diego CA USA ICREA Barcelona Spain Cardiovascular Regeneration Program CNIC Centro Nacional de Investigaciones Cardiovasculares Madrid Spain Laboratory of Integrative Biology Guangzhou Institutes of Biomedicine and Health Chinese Academy of Sciences Guangzhou China Key Laboratory of Regenerative Biology Guangzhou Institutes of Biomedicine and Health Chinese Academy of Sciences Guangzhou China Guangdong Provincial Key Laboratory of Stem Cells and Regenerative Medicine Guangzhou Institutes of Biomedicine and Health Chinese Academy of Sciences Guangzhou China University of Science and Technology of China Hefei China Bioland Laboratory Guangzhou Regenerative Medicine and Health Guangdong Laboratory Guangzhou China CIC bioGUNE-BRTA (Basque Research and Technology Alliance) Bizkaia Technology Park Derio Spain Computational Biology Group Luxembourg Centre for Systems Biomedicine University of Luxembourg Esch-sur-Alzette Luxembourg IKERBASQUE Basque Foundation for Science Bilbao Spain Genomic Unit Centro Nacional de Investigaciones Cardiovasculares and CIBERCV Madrid Spain Institute for Research in Biomedicine and BIST Barcelona Spain Laboratory of Chemistry & Biology Graduate School of Pharmaceutical Sciences and School of Medicine The University of Tokyo Tokyo Japan Division of Transcriptomics. Medical Institute of Bioregulation Kyushu University Fukuoka Japan

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Galactic transient sources with the Cherenkov Telescope Array Observatory

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Monthly Notices of the Royal Astronomical Society 2025年第1期540卷 205–238页

作者： Abe, K Abe, S Abhir, J Abhishek, A Acero, F Acharyya, A Adam, R Aguasca-Cabot, A Agudo, I Aguirre-Santaella, A Alfaro, J Alfaro, R Alvarez-Crespo, N Alves Batista, R Amans, J-P Amato, E Ambrosi, G Ambrosino, F Angüner, E O Antonelli, L A Aramo, C Arcaro, C Arnesen, T T H Asano, K Ascasibar, Y Aschersleben, J Ashkar, H Augusto Stuani, L Baack, D Backes, M Balazs, C Balbo, M Baquero Larriva, A Barbosa Martins, V Barres de Almeida, U Barrio, J A Barrios-Jiménez, L Batković, I Batzofin, R Baxter, J Becerra González, J Becker Tjus, J Belmont, R Benbow, W Bernete, J Bernlöhr, K Berti, A Bertucci, B Beshley, V Bhattacharjee, P Bhattacharyya, S Bigongiari, C Bissaldi, E Blanch, O Blazek, J Bocchino, F Boisson, C Bolmont, J Bonnoli, G Bonollo, A Bordas, P Bosnjak, Z Bottacini, E Böttcher, M Bradascio, F Bronzini, E Brown, A M Brunelli, G Bulgarelli, A Burelli, I Burger-Scheidlin, C Burmistrov, L Burton, M Buscemi, M Cailleux, J Campoy-Ordaz, A Cantlay, B K Capasso, G Caproni, A Capuzzo-Dolcetta, R Caraveo, P Caroff, S Carosi, A Carosi, R Carquin, E Carrasco, M-S Cascone, E Cassol, F Castaldini, L Castrejon, N Castro-Tirado, A J Cerasole, D Ceribella, G Cerruti, M Chadwick, P M Chaty, S Chen, A W Chernyakova, M Chiavassa, A Chudoba, J Chytka, L Cicciari, G M Cifuentes, A Coimbra Araujo, C H Colapietro, M Conforti, V Contreras, J L Cortina, J Costa, A Costantini, H Cotter, G Cristofari, P Cuevas, O Curtis-Ginsberg, Z D’Aì, A D’Amico, G D’Ammando, F Dai, S Dazzi, F de Bony de Lavergne, M De Caprio, V De Cesare, G De Frondat Laadim, F de Gouveia Dal Pino, E M De Lotto, B De Lucia, M de Martino, D de Menezes, R de Naurois, M de Ona Wilhelmi, E de Souza, V del Peral, L del Valle, M V Delgado, C Dell’aiera, M Della Valle, M della Volpe, D Depaoli, D Devin, J Di Girolamo, T Di Piano, A Di Pierro, F Di Tria, R Di Venere, L Diebold, S Dignam, C Dima, R Dinesh, A Djuvsland, J Dominik, R M Dominis Prester, D Donini, A Dorner, D Dörner, J Doro, M Dubos, C Ducci, L Dwarkadas, V V Ebr, J Eckner, C Egberts, K Einecke, S Elsässer, D Emery Department of Physics Tokai University 4-1-1 Kita-Kaname Hiratsuka Kanagawa 259-1292 Japan Institute for Cosmic Ray Research University of Tokyo 5-1-5 Kashiwa-no-ha Kashiwa Chiba 277-8582 Japan ETH Zürich Institute for Particle Physics and Astrophysics Otto-Stern-Weg 5 CH-8093 Zürich Switzerland INFN and Università degli Studi di Siena Dipartimento di Scienze Fisiche della Terra e dell’Ambiente (DSFTA) Sezione di Fisica Via Roma 56 I-53100 Siena Italy Université Paris-Saclay Université Paris Cité CEA CNRS AIM F-91191 Gif-sur-Yvette Cedex France FSLAC IRL 2009 CNRS/IAC E-38205 La Laguna Tenerife Spain University of Alabama Tuscaloosa Department of Physics and Astronomy Gallalee Hall Box 870324 Tuscaloosa AL 35487-0324 USA Université Côte d’Azur Observatoire de la Côte d’Azur CNRS Laboratoire Lagrange UF 06304 Nice France Laboratoire Leprince-Ringuet CNRS/IN2P3 École Polytechnique Institut Polytechnique de Paris F-91120 Palaiseau France Departament de Física Quàntica i Astrofísica Institut de Ciències del Cosmos Universitat de Barcelona IEEC-UB Martí i Franquès 1 E-08028 Barcelona Spain Instituto de Astrofísica de Andalucía-CSIC Glorieta de la Astronomía s/n E-18008 Granada Spain Institute for Computational Cosmology and Department of Physics Durham University South Road Durham DH1 3LE UK Pontificia Universidad Católica de Chile Av. Libertador Bernardo O’Higgins 340 8331150 Santiago Chile Universidad Nacional Autónoma de México Delegación Coyoacán 04510 Ciudad de México Mexico IPARCOS-UCM Instituto de Física de Partículas y del Cosmos and EMFTEL Department Universidad Complutense de Madrid E-28040 Madrid Spain Instituto de Física Teórica UAM/CSIC and Departamento de Física Teórica Universidad Autónoma de Madrid c/ Nicolás Cabrera 13-15 Campus de Cantoblanco UAM E-28049 Madrid Spain LUTH GEPI and LERMA Observatoire de Paris Université PSL Université Paris Cité CNRS 5 place Jules Janssen F-92190 Meudon France INAF – Osservatorio

A wide variety of Galactic sources show transient emission at soft and hard X-ray energies: low- and high-mass X-ray binaries containing compact objects, isolated neutron stars exhibiting extreme variability as magnetars as well as pulsar-wind nebulae. Although most of them can show emission up to MeV and/or GeV energies, many have not yet been detected in the TeV domain by Imaging Atmospheric Cherenkov Telescopes. In this paper, we explore the feasibility of detecting new Galactic transients with the Cherenkov Telescope Array Observatory (CTAO) and the prospects for studying them with Target of Opportunity observations. We show that CTAO will likely detect new sources in the TeV regime, such as the massive microquasars in the Cygnus region, low-mass X-ray binaries with low-viewing angle, flaring emission from the Crab pulsar-wind nebula or other novae explosions, among others. Since some of these sources could also exhibit emission at larger time-scales, we additionally test their detectability at longer exposures. We finally discuss the multiwavelength synergies with other instruments and large astronomical facilities.

关键词： binaries: general stars: magnetars novae cataclysmic variables pulsars: general gamma-rays: general

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