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37th International Cosmic Ray Conference, ICRC 2021

作者： Abe, H. Aguasca, A. Agudo, I. Antonelli, L.A. Aramo, C. Armstrong, T. Artero, M. Asano, K. Ashkar, H. Aubert, P. Baktash, A. Bamba, A. Baquero Larriva, A. Baroncelli, L. Barres de Almeida, U. Barrio, J.A. Batkovic, I. Becerra González, J. Bernardos, M.I. Berti, A. Biederbeck, N. Bigongiari, C. Blanch, O. Bonnoli, G. Bordas, P. Bose, D. Bulgarelli, A. Burelli, I. Buscemi, M. Cardillo, M. Caroff, S. Carosi, A. Cassol, F. Cerruti, M. Chai, Y. Cheng, K. Chikawa, M. Chytka, L. Contreras, J.L. Cortina, J. Costantini, H. Dalchenko, M. De Angelis, A. de Bony de Lavergne, M. Deleglise, G. Delgado, C. Delgado Mengual, J. della Volpe, D. Depaoli, D. Di Pierro, F. Di Venere, L. Díaz, C. Dominik, R.M. Dominis Prester, D. Donini, A. Dorner, D. Doro, M. Elsässer, D. Emery, G. Escudero, J. Fiasson, A. Foffano, L. Fonseca, M.V. Freixas Coromina, L. Fukami, S. Fukazawa, Y. Garcia, E. Garcia López, R. Giglietto, N. Giordano, F. Gliwny, P. Godinovic, N. Green, D. Grespan, P. Gunji, S. Hackfeld, J. Hadasch, D. Hahn, A. Hassan, T. Hayashi, K. Heckmann, L. Heller, M. Herrera Llorente, J. Hirotani, K. Hoffmann, D. Horns, D. Houles, J. Hrabovsky, M. Hrupec, D. Hui, D. Hütten, M. Inada, T. Inome, Y. Iori, M. Ishio, K. Iwamura, Y. Jacquemont, M. Jimenez Martinez, I. Jouvin, L. Jurysek, J. Kagaya, M. Karas, V. Katagiri, H. Kataoka, J. Kerszberg, D. Kobayashi, Y. Kong, A. Kubo, H. Kushida, J. Lamanna, G. Lamastra, A. Le Flour, T. Longo, F. López-Coto, R. López-Moya, M. López-Oramas, A. Luque-Escamilla, P.L. Majumdar, P. Makariev, M. Mandat, D. Manganaro, M. Mannheim, K. Mariotti, M. Marquez, P. Marsella, G. Martí, J. Martinez, O. Martínez, G. Martínez, M. Marusevec, P. Mas, A. Maurin, G. Mazin, D. Mestre Guillen, E. Micanovic, S. Miceli, D. Miener, T. Miranda, J.M. Miranda, L.D.M. Mirzoyan, R. Mizuno, T. Molina, E. Montaruli, T. Monteiro, I. Moralejo, A. Morcuende, D. Moretti, E. Morselli, A. Mrakovcic, K. Murase, K. Nagai, A. Nakamori, T. Nickel, L. Nieto, D. Institute for Cosmic Ray Research University of Tokyo 5-1-5 Kashiwa-no-ha Kashiwa Chiba277-8582 Japan Departament de Física Quàntica i Astrofísica Institut de Ciències del Cosmos Universitat de Barcelona IEEC-UB Spain Instituto de Astrofísica de Andalucía-CSIC Granada18080 Spain INAF Osservatorio Astronomico di Roma Italy INFN Sezione di Napoli Italy Aix Marseille Univ CNRS IN2P3 CPPM France 08193 Spain IRFU CEA Université Paris-Saclay France LAPP Univ. Grenoble Alpes Univ. Savoie Mont Blanc CNRS-IN2P3 9 Chemin de Bellevue - BP 110 AnnecyCedex 74941 France Universität Hamburg Institut für Experimentalphysik Germany Graduate School of Science University of Tokyo Japan EMFTEL Department IPARCOS Universidad Complutense de Madrid MadridE-28040 Spain INAF Osservatorio di Astrofisica e Scienza Spazio di Bologna Italy Centro Brasileiro de Pesquisas Físicas Brazil INFN Sezione di Padova PadovaI-35131 Italy Università degli Studi di Padova Via Marzolo 8 Padova35131 Italy Instituto de Astrofísica de Canarias Departamento de Astrofísica Universidad de La Laguna Spain Max-Planck-Institut für Physik München80805 Germany Department of Physics TU Dortmund University Otto-Hahn-Str. 4 Dortmund44221 Germany Saha Institute of Nuclear Physics INFN Sezione di Trieste Università degli Studi di Udine Italy INFN Sezione di Catania Italy Italy University of Geneva Département de Physique Nucléaire et Corpusculaire Switzerland Palacky University Olomouc Faculty of Science Czech Republic CIEMAT Port d'Informació Científica Spain INFN Sezione di Torino via P. Giuria 1 Torino10125 Italy Dipartimento di Fisica Universitá degli Studi di Torino Italy INFN Sezione di Bari Università di Bari Italy University of Rijeka Department of Physics Croatia Institute for Theoretical Physics and Astrophysics Universität Würzburg Germany Physics Program Graduate School of Advanced Science and Engineering Hiroshima University Japan INFN Sezione di Bari Politecnico di B

The Large-Sized Telescope (LST) prototype of the future Cherenkov Telescope Array (CTA) is located at the Northern site of CTA, on the Canary Island of La Palma. It is designed to provide optimal performance in the lowest part of the energy range covered by CTA, observing gamma rays down to energies of tens of GeV. The LST prototype started performing astronomical observations in November 2019 during the commissioning of the telescope and it has been taking data since then. In this contribution, we will present the tuning of the characteristics of the telescope in the Monte Carlo (MC) simulations to describe the data obtained, the estimation of its angular and energy resolution, and an evaluation of its sensitivity, both with simulations and with observations of the Crab Nebula. © Copyright owned by the author(s) under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC BY-NC-ND 4.0)

关键词： Telescopes

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Splice2Deep: An ensemble of deep convolutional neural networks for improved splice site prediction in genomic DNA

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Gene: X 2020年 5卷 100035页

作者： Albaradei, Somayah Magana-Mora, Arturo Thafar, Maha Uludag, Mahmut Bajic, Vladimir B. Gojobori, Takashi Essack, Magbubah Jankovic, Boris R. Computer Electrical and Mathematical Sciences and Engineering Division (CEMSE) Computational Bioscience Research Center Computer (CBRC) King Abdullah University of Science and Technology (KAUST) Thuwal 23955-6900 Saudi Arabia Faculty of Computing and Information Technology King Abdulaziz University Saudi Arabia Saudi Aramco EXPEC-ARC Drilling Technology Team Dhahran 31311 Saudi Arabia Faculty of Computers and Information Systems Taif University Saudi Arabia Biological and Environmental Sciences and Engineering Division (BESE) King Abdullah University of Science and Technology (KAUST) Thuwal 23955-6900 Saudi Arabia

Background: The accurate identification of the exon/intron boundaries is critical for the correct annotation of genes with multiple exons. Donor and acceptor splice sites (SS) demarcate these boundaries. Therefore, deriving accurate computational models to predict the SS are useful for functional annotation of genes and genomes, and for finding alternative SS associated with different diseases. Although various models have been proposed for the in silico prediction of SS, improving their accuracy is required for reliable annotation. Moreover, models are often derived and tested using the same genome, providing no evidence of broad application, i.e. to other poorly studied genomes. Results: With this in mind, we developed the Splice2Deep models for SS detection. Each model is an ensemble of deep convolutional neural networks. We evaluated the performance of the models based on the ability to detect SS in Homo sapiens, Oryza sativa japonica, Arabidopsis thaliana, Drosophila melanogaster, and Caenorhabditis elegans. Results demonstrate that the models efficiently detect SS in other organisms not considered during the training of the models. Compared to the state-of-the-art tools, Splice2Deep models achieved significantly reduced average error rates of 41.97% and 28.51% for acceptor and donor SS, respectively. Moreover, the Splice2Deep cross-organism validation demonstrates that models correctly identify conserved genomic elements enabling annotation of SS in new genomes by choosing the taxonomically closest model. Conclusions: The results of our study demonstrated that Splice2Deep both achieved a considerably reduced error rate compared to other state-of-the-art models and the ability to accurately recognize SS in other organisms for which the model was not trained, enabling annotation of poorly studied or newly sequenced genomes. Splice2Deep models are implemented in Python using Keras API;the models and the data are available at https://***/SomayahAlbaradei/Sp

关键词： Bioinformatics Deep-learning Prediction Splice sites Splicing

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Increased sedentary behavior is associated with neurodegeneration and worse cognition in older adults over a 7-year period despite high levels of physical activity

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Alzheimer's and Dementia 2025年第5期21卷 e70157页

作者： Gogniat, Marissa A. Khan, Omair A. Li, Judy Park, Chorong Hudson Robb, W. Zhang, Panpan Sun, Yunyi Moore, Elizabeth E. Houston, Michelle L. Pechman, Kimberly R. Shashikumar, Niranjana Taylor Davis, L. Liu, Dandan Landman, Bennett A. Cole, Keith R. Bolton, Corey J. Gifford, Katherine A. Hohman, Timothy J. Full, Kelsie Jefferson, Angela L. Vanderbilt Memory and Alzheimer's Center Nashville TN United States Department of Neurology Vanderbilt University Medical Center Nashville TN United States Department of Neurology University of Pittsburgh School of Medicine University of Pittsburgh Medical Center Pittsburgh PA United States Department of Biostatistics Vanderbilt University Medical Center Nashville TN United States Vanderbilt Brain Institute Vanderbilt University Medical Research Building III Nashville TN United States College of Nursing Seoul National University Seoul Jongno District South Korea Department of Neurology Brigham and Women's Hospital Boston MA United States Center for Biomedical Ethics and Society Section of Surgical Sciences Vanderbilt University Medical Nashville TN United States Department of Radiology and Radiological Sciences Vanderbilt University Medical Center Nashville TN United States Department of Electrical Engineering and Computer Science Vanderbilt University Nashville TN United States Department of Biomedical Engineering Vanderbilt University Center Nashville TN United States Department of Orthopaedic Surgery Vanderbilt University Medical Center Nashville TN United States Division of Geriatrics Department of Medicine Vanderbilt University Medical Center Nashville TN United States Department of Anatomy and Neurobiology Boston University Boston MA United States Division of Epidemiology Department of Medicine Vanderbilt University Medical Center Nashville TN United States Division of Cardiovascular Medicine Department of Medicine Vanderbilt University Medical Center Nashville TN United States

INTRODUCTION: Sedentary behavior may be a modifiable risk factor for Alzheimer's disease (AD). We examined how sedentary behavior relates to longitudinal brain structure and cognitive changes in older adults. METHODS: Vanderbilt Memory and Aging Project participants (n = 404) completed actigraphy (7 days), neuropsychological assessment, and 3T brain MRI over a 7-year period. Cross-sectional and longitudinal linear regressions examined sedentary time in relation to brain structure and cognition. Models were repeated testing for effect modification by apolipoprotein E (APOE) ε4 status. RESULTS: In cross-sectional models, greater sedentary time related to a smaller AD-neuroimaging signature (β = -0.0001, p = 0.01) and worse episodic memory (β = -0.001, p = 0.003). Associations differed by APOE-ε4 status. In longitudinal models, greater sedentary time related to faster hippocampal volume reductions (β = -0.1, p = 0.008) and declines in naming (β = -0.001, p = 0.03) and processing speed (β = -0.003, p = 0.02;β = 0.01, p = 0.01). DISCUSSION: Results support the importance of reducing sedentary time, particularly among aging adults at genetic risk for AD. Highlights: Greater sedentary behavior is related to neurodegeneration and worse cognition. Associations differed by APOE-ε4 carrier status in cross-sectional models. Sedentary behavior is an independent risk factor for Alzheimer's disease. © 2025 The Author(s). Alzheimer's & Dementia published by Wiley Periodicals LLC on behalf of Alzheimer's Association.

关键词： Alzheimer's disease brain health cognition sedentary

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Presolar stardust in asteroid Ryugu

arXiv

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arXiv 2022年

作者： Barosch, Jens Nittler, Larry R. Wang, Jianhua O'D. Alexander, Conel M. De Gregorio, Bradley T. Engrand, Cécile Kebukawa, Yoko Nagashima, Kazuhide Stroud, Rhonda M. Yabuta, Hikaru Abe, Yoshinari Aléon, Jérôme Amari, Sachiko Amelin, Yuri Bajo, Ken-Ichi Bejach, Laure Bizzarro, Martin Bonal, Lydie Bouvier, Audrey Carlson, Richard W. Chaussidon, Marc Choi, Byeon-Gak Cody, George D. Dartois, Emmanuel Dauphas, Nicolas Davis, Andrew M. Dazzi, Alexandre Deniset-Besseau, Ariane Rocco, Tommaso Di Duprat, Jean Fujiya, Wataru Fukai, Ryota Gautam, Ikshu Haba, Makiko K. Hashiguchi, Minako Hibiya, Yuki Hidaka, Hiroshi Homma, Hisashi Hoppe, Peter Huss, Gary R. Ichida, Kiyohiro Iizuka, Tsuyoshi Ireland, Trevor R. Ishikawa, Akira Ito, Motoo Itoh, Shoichi Kamide, Kanami Kawasaki, Noriyuki Kilcoyne, David A.L. Kita, Noriko T. Kitajima, Kouki Kleine, Thorsten Komatani, Shintaro Komatsu, Mutsumi Krot, Alexander N. Liu, Ming-Chang Martins, Zita Masuda, Yuki Mathurin, Jérémie McKeegan, Kevin D. Montagnac, Gilles Morita, Mayu Mostefaoui, Smail Motomura, Kazuko Moynier, Frédéric Nakai, Izumi Nguyen, Ann N. Ohigashi, Takuji Okumura, Taiga Onose, Morihiko Pack, Andreas Park, Changkun Piani, Laurette Qin, Liping Quirico, Eric Remusat, Laurent Russell, Sara S. Sakamoto, Naoya Sandford, Scott A. Schönbächler, Maria Shigenaka, Miho Suga, Hiroki Tafla, Lauren Takahashi, Yoshio Takeichi, Yasuo Tamenori, Yusuke Tang, Haolan Terada, Kentaro Terada, Yasuko Usui, Tomohiro Verdier-Paoletti, Maximilien Wada, Sohei Wadhwa, Meenakshi Wakabayashi, Daisuke Walker, Richard J. Yamashita, Katsuyuki Yamashita, Shohei Yin, Qing-Zhu Yokoyama, Tetsuya Yoneda, Shigekazu Young, Edward D. Yui, Hiroharu Zhang, Ai-Cheng Abe, Masanao Miyazaki, Akiko Nakato, Aiko Nakazawa, Satoru Nishimura, Masahiro Okada, Tatsuaki Saiki, Takanao Tanaka, Satoshi Terui, Fuyuto Tsuda, Yuichi Watanabe, Seiichiro Yada, Toru Yogata, Kasumi Yoshikawa, Makoto Nakamura, Tomoki Naraoka, Hiroshi Noguchi, Takaaki Okazaki, Ryuji Sakamoto, Kanako Tachibana, Shogo Yurimoto, Hisayoshi Earth and Planets Laboratory Carnegie Institution of Washington 5241 Broad Branch Rd. NW WashingtonDC20015 United States Materials Science and Technology Division US Naval Research Laboratory WashingtonDC20375 United States IJCLab UMR 9012 Université Paris-Saclay CNRS Orsay91405 France Faculty of Engineering Yokohama National University Kanagawa Yokohama240-8501 Japan Hawai‘i Institute of Geophysics and Planetology University of Hawai‘i at Mānoa HonoluluHI96822 United States Department of Earth and Planetary Systems Science Hiroshima University Higashi-Hiroshima Hiroshima739-8526 Japan Graduate School of Engineering Materials Science and Engineering Tokyo Denki University Tokyo120-8551 Japan Institut de Mineralogie Physique des Materiaux et de Cosmochimie Museum National d'Histoire Naturelle CNRS Sorbonne Université Paris75231 France McDonnell Center for the Space Sciences and Physics Department Washington University St. LouisMO63130 United States Geochemical Research Center The University of Tokyo Bunkyo-ku Tokyo113-0033 Japan Guangzhou Institute of Geochemistry Chinese Academy of Sciences GD Guangzhou510640 China Department of Natural History Sciences Hokkaido University Sapporo060-0810 Japan Centre for Star and Planet Formation Globe Institute University of Copenhagen Copenhagen1350 Denmark Institut de Planétologie et d'Astrophysique Université Grenoble Alpes Grenoble38000 France Bayerisches Geoinstitut Universität Bayreuth Bayreuth95447 Germany Institut de Physique du Globe de Paris Université de Paris CNRS Paris75005 France Department of Physics and Astronomy Seoul National University Seoul08826 Korea Republic of UMR 8214 Université Paris-Saclay CNRS Orsay91405 France Department of the Geophysical Sciences Enrico Fermi Institute The University of Chicago ChicagoIL60637 United States UMR 8000 Université Paris-Saclay CNRS Orsay91405 France Faculty of Geosciences and Geography University of Göttingen Götti

We have conducted a NanoSIMS-based search for presolar material in samples recently returned from C-type asteroid Ryugu as part of JAXA’s Hayabusa2 mission. We report the detection of all major presolar grain types with O- and C-anomalous isotopic compositions typically identified in carbonaceous chondrite meteorites: 1 silicate, 1 oxide, 1 O-anomalous supernova grain of ambiguous phase, 38 SiC, and 16 carbonaceous grains. At least two of the carbonaceous grains are presolar graphites, whereas several grains with moderate C isotopic anomalies are probably organics. The presolar silicate was located in a clast with a less altered lithology than the typical extensively aqueously altered Ryugu matrix. The matrix-normalized presolar grain abundances in Ryugu are 4.8 !".$%&.' ppm for O-anomalous grains, 25 !(%$ ppm for SiC grains and 11 !)%(ppm for carbonaceous grains. Ryugu is isotopically and petrologically similar to carbonaceous Ivuna-type (CI) chondrites. To compare the in situ presolar grain abundances of Ryugu with CI chondrites, we also mapped Ivuna and Orgueil samples and found a total of 15 SiC grains and 6 carbonaceous grains. No O-anomalous grains were detected. The matrix-normalized presolar grain abundances in the CI chondrites are similar to those in Ryugu: 23 !$%' ppm SiC and 9.0 !).$%(.& ppm carbonaceous grains. Thus, our results provide further evidence in support of the Ryugu-CI connection. They also reveal intriguing hints of small-scale heterogeneities in the Ryugu samples, such as locally distinct degrees of alteration that allowed the preservation of delicate presolar material. Copyright © 2022, The Authors. All rights reserved.

关键词： Silicon carbide

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Coupling between structural relaxation and diffusion in glass-forming liquids under pressure variation

arXiv

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arXiv 2020年

作者： Phan, Anh D. Koperwas, Kajetan Paluch, Marian Wakabayashi, Katsunori Faculty of Materials Science and Engineering Phenikaa Institute for Advanced Study Phenikaa University Hanoi12116 Viet Nam Faculty of Computer Science Artificial Intelligence Laboratory Phenikaa University Hanoi12116 Viet Nam Department of Nanotechnology for Sustainable Energy School of Science and Technology Kwansei Gakuin University Sanda Hyogo669-1337 Japan University of Silesia in Katowice Institute of Physics 75 Pulku Piechoty 1 Chorzow41-500 Poland Silesian Center for Education and Interdisciplinary Research SMCEBI 75 Pulku Piechoty 1a Chorzow41-500 Poland

We theoretically investigate structural relaxation and activated diffusion of glass-forming liquids at different pressures using both the Elastically Collective Nonlinear Langevin Equation (ECNLE) theory and molecular dynamics (MD) simulation. An external pressure restricts local motions of a single molecule within its cage and triggers slowing down of cooperative mobility. While the ECNLE theory and simulation generally predict a monotonic increase of the glass transition temperature and dynamic fragility with pressure, simulation indicates a decrease of fragility as pressure above 1000 bar. The structural relaxation time is found to be linearly coupled with the inverse diffusion constant. Remarkably, this coupling is independent of compression. Theoretical calculations agree quantitatively well with simulations and are also consistent with prior works. Copyright © 2020, The Authors. All rights reserved.

关键词： Liquids

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Whole-disk sampling of molecular clouds in M83

arXiv

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arXiv 2024年

作者： Hirota, Akihiko Koda, Jin Egusa, Fumi Sawada, Tsuyoshi Sakamoto, Kazushi Heyer, Mark Lee, Amanda M. Maeda, Fumiya Boissier, Samuel Calzetti, Daniela Elmegreen, Bruce G. Harada, Nanase Ho, Luis C. Kobayashi, Masato I.N. Kuno, Nario Madore, Barry F. Martín, Sergio Meyer, Jennifer Donovan Muraoka, Kazuyuki Watanabe, Yoshimasa Joint ALMA Observatory Alonso de Córdova 3107 Vitacura Santiago763-0355 Chile National Astronomical Observatory of Japan 2-21-1 Osawa Mitaka Tokyo181-8588 Japan Department of Physics and Astronomy Stony Brook University Stony BrookNY11794-3800 United States Institute of Astronomy Graduate School of Science The University of Tokyo 2-21-1 Osawa Mitaka Tokyo181-0015 Japan Academia Sinica Institute of Astronomy and Astrophysics Taipei10617 Taiwan Department of Astronomy University of Massachusetts Amherst 710 North Pleasant Street AmherstMA01003 United States Research Center for Physics and Mathematics Osaka Electro-Communication University 18-8 Hatsucho Osaka Neyagawa572-8530 Japan Aix Marseille Univ. CNRS CNES Laboratoire d'Astrophysique de Marseille Marseille France Katonah NY10536 United States 2-21-1 Osawa Mitaka Tokyo181-1855 Japan Kavli Institute for Astronomy and Astrophysics Peking University Beijing100871 China Department of Astronomy School of Physics Peking University Beijing100871 China Universität zu Köln I. Physikalisches Institut Zülpicher Str. 77 KölnD-50937 Germany Division of Physics Faculty of Pure and Applied Sciences University of Tsukuba 1-1-1 Tennodai Ibaraki Tsukuba305-8571 Japan Tomonaga Center for the History of the Universe University of Tsukuba 1-1-1 Tennodai Ibaraki Tsukuba305-8571 Japan The Observatories Carnegie Institution for Science 813 Santa Barbara Street PasadenaCA91101 United States Department of Astronomy and Astrophysics University of Chicago 5640 S. Ellis Ave. ChicagoIL60637 United States European Southern Observatory Alonso de Córdova 3107 Vitacura Santiago763-0355 Chile National Radio Astronomy Observatory 520 Edgemont Road CharlottesvilleVA22903 United States Department of Physics Graduate School of Science Osaka Metropolitan University 1-1 Gakuen-cho Naka-ku Osaka Sakai599-8531 Japan Materials Science and Engineering College of Engineering Shibaura Institute of T

We present a catalog of clouds identified from the 12CO (1-0) data of M83, which was observed using Atacama Large Millimeter/submillimeter Array (ALMA) with a spatial resolution of ∼46 pc and a mass sensitivity of ∼104 M☉ (3 σ). The almost full-disk coverage and high sensitivity of the data allowed us to sample 5724 molecular clouds with a median mass of ∼ 1.9 × 105 M☉, which is comparable to the most frequently sampled mass of Giant Molecular Clouds (GMCs) by surveys in the Milky Way. About 60 percent of the total CO luminosity in M83's disk arises from clouds more massive than 106 M☉. Such massive clouds comprise 16 percent of the total clouds in number and tend to concentrate toward the arm, bar, and center, while smaller clouds are more prevalent in inter-arm regions. Most > 106 M☉ clouds have peak brightness temperatures Tpeak above 2 K with the current resolution. Comparing the observed cloud properties with the scaling relations determined by Solomon et al. 1987 (S87), Tpeak> 2 K clouds follow the relations, but Tpeakvir ∼ 2.7) and low surface mass densities (median Σ ∼22 M☉ pc−2) for the entire cloud samples, which are similar to values found for the Milky Way clouds by Rice et al. (2016) and Miville-Deschenes et al. (2017). However, once beam dilution is taken into account, the observed αvir and Σ for a majority of the clouds (mostly Tpeak ☉ pc−2 and αvir of ∼1, which are similar to the clouds of S87. © 2024, CC BY.

关键词： Luminance

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Predicting Spring Phenology in Deciduous Broadleaf Forests: Neon Phenology Forecasting Community Challenge

SSRN

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SSRN 2023年

作者： Wheeler, Kathryn I. Dietze, Michael C. LeBauer, David Peters, Jody Richardson, Andrew D. Ross, Arun A. Thomas, R. Quinn Zhu, Kai Bhat, Uttam Munch, Stephan Buzbee, Raphaela Floreani Chen, Min Goldstein, Benjamin Guo, Jessica Hao, Dalei Jones, Chris Kelly-Fair, Mira Liu, Haoran Malmborg, Charlotte Neupane, Naresh Pal, Debasmita Shirey, Vaughn Song, Yiluan Steen, McKalee Vance, Eric A. Woelmer, Whitney M. Wynne, Jacob Zachmann, Luke Department of Earth and Environment Boston University 685 Commonwealth Avenue BostonMA02215 United States Cooperative Programs for the Advancement of Earth System Science University Corporation for Atmospheric Research Foothills Laboratory Bldg 4 3300 Mitchell Lane BoulderCO80301 United States National Oceanic and Atmospheric Administration Foothills Laboratory Bldg 4 3300 Mitchell Lane BoulderCO80301 United States Department of Civil and Environmental Engineering Massachusetts Institute of Technology 15 Vassar St CambridgeMA02142 United States Arizona Experiment Station University of Arizona 1230 N Cherry Ave TucsonAZ85721 United States Department of Biological Sciences University of Notre Dame 100 Galvin Notre DameIN46556 United States School of Informatics Computing and Cyber Systems Northern Arizona University 1295 Knoles Dr. FlagstaffAZ86011 United States Center for Ecosystem Science and Society Northern Arizona University PO Box 5620 FlagstaffAZ86011 United States Department of Computer Science and Engineering Michigan State University 428 South Shaw Lane East LansingMI48824 United States Department of Forest Resources and Environmental Conservation Virginia Polytechnic Instit 310 West Campus Drive BlacksburgVA24061 United States Department of Biological Sciences Virginia Tech 926 West Campus Drive BlacksburgVA24061 United States Institute for Global Change Biology University of Michigan 440 Church Street Ann ArborMI48109 United States School for Environment and Sustainability University of Michigan 440 Church Street Ann ArborMI48109 United States Institute of Marine Sciences University of California Santa Cruz 1156 High Street Santa CruzCA95060 United States Department of Applied Mathematics University of California Santa Cruz 1156 High Street Santa CruzCA95060 United States Department of Environmental Science Policy and Management University of California Berkeley 4004 Valley Life Sciences Building Universi

Accurate models are important to predict how global climate change will continue to alter plant phenology and near-term ecological forecasts can be used to iteratively improve models and evaluate predictions that are made a priori. The Ecological Forecasting Initiative’s National Ecological Observatory Network (NEON) Forecasting Challenge, is an open challenge to the community to forecast daily greenness values, measured through digital images collected by the PhenoCam Network at NEON sites before the data are collected. For the first round of the challenge, which is presented here, we forecasted canopy greenness throughout the spring at eight deciduous broadleaf sites to investigate what factors impact current predictive skill of phenology forecasts. A total of 192,536 predictions were submitted, representing eighteen models, including a persistence and a day of year mean null models. We found that overall forecast skill was highest when forecasting earlier in the greenup curve compared to the end, for shorter lead times, for sites that greened up earlier, and when submitting forecasts during times other than near budburst. The models based on day of year historical mean had the highest predictive skill across the challenge period. In this first round of the challenge, by synthesizing across forecasts, we started to elucidate what factors affect the predictive skill of near-term phenology forecasts. © 2023, The Authors. All rights reserved.

关键词： Forecasting

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MODL-41. LOSS OF DISTAL CHROMOSOME 6Q AND ASSOCIATEDPDE10AINDUCES AN AGGRESSIVE GLIOMA PHENOTYPE BY FACILITATING PRONEURAL TO MESENCHYMAL TRANSITION

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Neuro-Oncology 2023年第SUPPLEMENT_5期25卷 v307–v308页

作者： Nuechterlein, Nicholas Shelbourn, Allison Alvinez, Neriah Szulzewsky, Frank Arora, Sonali Paddison, Patrick Merino-Galan, Leyre Pattwell, Siobhan Sulman, Erik Jung, Mi-Yeon Brown, Desmond Banasavadi-Siddegowda, Yeshavanth Kumar Tang, Kayen Jackson, Sadhana Stoica, Stefan Chittaboina, Prashant Patel, Anoop Holland, Eric Cimino, Patrick Shapiro, Linda Surgical Neurology Branch National Institutes of Neurological Disorders and Stroke NIH Bethesda MD USA Division of Human Biology Fred Hutchinson Cancer Center Seattle WA USA Ben Towne Center for Childhood Cancer Research Seattle Children's Research Institute Seattle WA USA New York University Grossman School of Medicine Laura and Isaac Perlmutter Cancer Center at NYU Langone Health New York NY USA Neurosurgical Oncology Unit Surgical Neurology Branch NINDS NIH Bethesda MD USA Department of Neurosurgery Duke University Durham NC USA Department of Computer Science and Engineering University of Washington Seattle WA USA

Somatic copy number alterations (SCNAs) are common in glioblastoma, with some SCNAs having prognostic implications. Identifying and characterizing SCNA-related genes acting as oncogenes or tumor suppressors may potentially provide new therapeutic avenues for gliomas. In this study, we sought to uncover novel SCNA-related genes by employing an unbiased log rank analysis screen of genomic regions associated with glioblastoma survival. We identified 6q27 loss as an independent poorly-prognostic SCNA across three separate glioblastoma cohorts. Anin vitroCRISPR screen using human neural stem cells identified five genes in the 6q27 region that caused increased cellular proliferation. One of these genes, phosphodiesterase 10A (PDE10A), had a gene dosage effect on expression and decreased expression in human and mouse glioma tumor tissue compared to normal brain, which are both common tumor suppressor phenotypes. Knockdown of Pde10a in mouse neural stem cellsin vitrovalidated the human cell CRISPR screen phenotype. Suppression of Pde10a in RCAS/Tva mouse gliomain vivocaused decreased survival with increased tumor grade, mitotic activity, necrosis, Vegfa expression, and AKT/pS6 signaling. Single cell RNA-sequencing of mouse gliomas revealed that Pde10a suppression results in a shift from proneural to mesenchymal-like state. In human glioma tissue, PDE10A loss is also associated with an increased mesenchymal signature. Top activated pathways in Pde10a suppressed gliomas involved cell-cell interaction, including upregulation of cell adhesion molecules and downregulation of focal adhesion complex disassembly genes. Functional analysisin vitroconfirmed that Pde10a suppression results in increased extracellular matrix adhesion and decreased migration. Relevant to standard glioblastoma treatment, PDE10A loss was associated with decreasedMGMTpromoter methylation, decreased DNA methyltransferase gene expression, and it potentiated radiation resistancein vitro. In summary, we leverag

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Towards Neural Charged Particle Tracking in Digital Tracking Calorimeters with Reinforcement Learning

TechRxiv

引用

TechRxiv 2022年

作者： Kortus, Tobias Keidel, Ralf Gauger, Nicolas R. Aehle, Max Alme, Johan Barnaföldi, Gergely Gábor Bodova, Tea Borshchov, Vyacheslav van den Brink, Anthony Chaar, Mamdouh Eikeland, Viljar Feofilov, Gregory Garth, Christoph Genov, Georgi Grøttvik, Ola Helstrup, Håvard Igolkin, Sergey Keidel, Ralf Kobdaj, Chinorat Leonhardt, Viktor Mehendale, Shruti Mulawade, Raju Ningappa Odland, Odd Harald O’Neill, George Papp, Gábor Peitzmann, Thomas Pettersen, Helge Egil Seime Piersimoni, Pierluigi Protsenko, Maksym Rauch, Max Rehman, Attiq Ur Richter, Matthias Röhrich, Dieter Santana, Joshua Schilling, Alexander Seco, Joao Songmoolnak, Arnon Sudár, Ákos Sølie, Jarle Rambo Tambave, Ganesh Tymchuk, Ihor Ullaland, Kjetil Varga-Kofarago, Monika Volz, Lennart Wagner, Boris Wendzel, Steffen Wiebel, Alexander Xiao, RenZheng Yang, Shiming Yokoyama, Hiroki Zillien, Sebastian University of Applied Sciences Worms Worms67549 Germany Chair for Scientific Computing TU Kaiserslautern Kaiserslautern67663 Germany Department of Physics and Technology University of Bergen Bergen5007 Norway Wigner Research Centre for Physics Budapest Hungary Kharkiv Ukraine Institute for Subatomic Physics Utrecht University/Nikhef Utrecht Netherlands St. Petersburg University St. Petersburg Russia Scientific Visualization Lab TU Kaiserslautern Kaiserslautern67663 Germany Department of Computer Science Electrical Engineering and Mathematical Sciences Western Norway University of Applied Sciences Bergen5020 Norway Institute of Science Suranaree University of Technology Nakhon Ratchasima Thailand Department of Oncology and Medical Physics Haukeland University Hospital Bergen5021 Norway Institute for Physics Eötvös Loránd University 1/A Pázmány P. Sétány BudapestH-1117 Hungary UniCamillus Saint Camillus International University of Health Sciences Rome Italy Department of Physics University of Oslo Oslo0371 Norway Department of Biomedical Physics in Radiation Oncology DKFZ—German Cancer Research Center Heidelberg Germany Department of Physics and Astronomy Heidelberg University Heidelberg Germany Budapest University of Technology and Economics Budapest Hungary Department of Diagnostic Physics Division of Radiology and Nuclear Medicine Oslo University Hospital Oslo Norway Bhubaneswar India Biophysics GSI Helmholtz Center for Heavy Ion Research GmbH Darmstadt Germany Department of Medical Physics and Biomedical Engineering University College London London United Kingdom College of Mechanical & Power Engineering China Three Gorges University Yichang China

We propose a novel technique for reconstructing charged particles in digital tracking calorimeters using reinforcement learning aiming to benefit from the rapid progress and success of neural network architectures without the dependency on simulated or manually-labeled data. Here we optimize by trial-and-error a behavior policy acting as an approximation to the full combinatorial optimization problem, maximizing the physical plausibility of sampled trajectories. In modern processing pipelines used in high energy physics and related applications, tracking plays an essential role allowing to identify and follow charged particle trajectories traversing particle detectors. Due to the high multiplicity of charged particles and their physical interactions, randomly deflecting the particles, the reconstruction is a challenging undertaking, requiring fast, accurate and robust algorithms. Our approach works on graph-structured data, capturing track hypotheses through edge connections between particles in the detector layers. We demonstrate in a comprehensive study on simulated data for a particle detector used for proton computed tomography, the high potential as well as the competitiveness of our approach compared to a heuristic search algorithm and a model trained on ground truth. Finally, we point out limitations of our approach, guiding towards a robust foundation for further development of reinforcement learning based tracking. © 2022, CC BY.

关键词： Reinforcement learning

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JWST and ALMA Multiple-Line Study in and around a Galaxy at z = 8.496: Optical to FIR Line Ratios and the Onset of an Outflow Promoting Ionizing Photon Escape

arXiv

引用

arXiv 2022年

作者： Fujimoto, Seiji Ouchi, Masami Nakajima, Kimihiko Harikane, Yuichi Isobe, Yuki Brammer, Gabriel Oguri, Masamune Giménez-Arteaga, Clara Heintz, Kasper E. Kokorev, Vasily Bauer, Franz E. Ferrara, Andrea Kojima, Takashi Lagos, Claudia del P. Laura, Sommovigo Schaerer, Daniel Shimasaku, Kazuhiro Hatsukade, Bunyo Kohno, Kotaro Sun, Fengwu Valentino, Francesco Watson, Darach Fudamoto, Yoshinobu Inoue, Akio K. González-López, Jorge Koekemoer, Anton M. Knudsen, Kirsten Lee, Minju M. Magdis, Georgios E. Richard, Johan Strait, Victoria B. Sugahara, Yuma Tamura, Yoichi Toft, Sune Umehata, Hideki Walth, Gregory Department of Astronomy The University of Texas at Austin AustinTX78712 United States Denmark Niels Bohr Institute University of Copenhagen Lyngbyvej 2 Copenhagen ØDK2100 Denmark National Astronomical Observatory of Japan 2-21-1 Osawa Mitaka Tokyo181-8588 Japan Institute for Cosmic Ray Research The University of Tokyo 5-1-5 Kashiwanoha Chiba Kashiwa277-8582 Japan University of Tokyo Chiba Kashiwa277-8583 Japan Department of Physics Graduate School of Science The University of Tokyo 7-3-1 Hongo Bunkyo Tokyo113-0033 Japan Center for Frontier Science Chiba University 1-33 Yayoi-cho Inage-ku Chiba263-8522 Japan Department of Physics Graduate School of Science Chiba University 1-33 Yayoi-Cho Inage-Ku Chiba263-8522 Japan Niels Bohr Institute University of Copenhagen Blegdamsvej 17 Copenhagen ØDK2100 Denmark Niels Bohr Institute University of Copenhagen Jagtvej 128 Copenhagen NDK-2200 Denmark Instituto de Astrofísica Facultad de Física Pontificia Universidad Católica de Chile Campus San Joaquín Av. Vicuña Mackenna 4860 Macul Santiago7820436 Chile Centro de Astroingeniería Facultad de Física Pontificia Universidad Católica de Chile Campus San Joaquín Av. Vicuña Mackenna 4860 Macul Santiago7820436 Chile Millennium Institute of Astrophysics Nuncio Monseñor Sótero Sanz 100 Of 104 Providencia Santiago Chile Scuola Normale Superiore Piazza dei Cavalieri 7 Pisa50126 Italy M468 University of Western Australia 35 Stirling Hwy CrawleyWA6009 Australia Australia Observatoire de Genève Université de Genève Chemin Pegasi 51 Versoix1290 Switzerland CNRS IRAP 14 Avenue E. Belin Toulouse31400 France Department of Astronomy School of Science The University of Tokyo 7-3-1 Hongo Bunkyo-ku Tokyo113-0033 Japan Research Center for the Early Universe The University of Tokyo 7-3-1 Hongo Bunkyo-ku Tokyo113-0033 Japan Institute of Astronomy Graduate School of Science The University of Tokyo 2-21-1 Osawa Mitaka Tokyo181-0015

We present ALMA deep spectroscopy for a lensed galaxy at zspec = 8.496 with log(Mstar/M) ∼ 7.8 whose optical nebular lines and stellar continuum are detected by JWST/NIRSpec and NIRCam Early Release Observations in SMACS0723. Our ALMA spectrum shows [O iii]88 µm and [C ii]158 µm line detections at 4.0σ and 4.5σ, respectively. The redshift and position of the [O iii] line coincide with those of the JWST source, while the [C ii] line is blue-shifted by 90 km s−1 with a spatial offset of 0′′ . 5 (≈ 0.5 kpc in source plane) from the centroid of the JWST source. The NIRCam F444W image, including [O iii]λ5007 and Hβ line emission, spatially extends beyond the stellar components by a factor of > 8. This indicates that the z = 8.5 galaxy has already experienced strong outflows as traced by extended [O iii]lambda5007 and offset [C ii] emission, which would promote ionizing photon escape and facilitate reionization. With careful slit-loss corrections and removals of emission spatially outside the galaxy, we evaluate the [O iii]88µm/λ5007 line ratio, and derive the electron density ne by photoionization modeling to be 220+230−130 cm−3, which is comparable with those of z ∼ 2–3 galaxies. We estimate an [O iii]88µm/[C ii]158µm line ratio in the galaxy of > 4, as high as those of known z ∼6–9 galaxies. This high [O iii]88 µm/[C ii]158 µm line ratio is generally explained by the high ne as well as the low metallicity (Zgas/Z = 0.04+0.02−0.02), high ionization parameter (log U > −2.27), and low carbon-to-oxygen abundance ratio (log(C/O) = [−0.52: −0.24]) obtained from the JWST/NIRSpec data;further [C ii] follow-up observations will constrain the covering fraction of photodissociation regions. © 2022, CC BY.

关键词： Galaxies

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