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arXiv 2020年

作者： Sánchez-Sáez, Paula Reyes, I. Valenzuela, C. Förster, F. Eyheramendy, S. Elorrieta, F. Bauer, F.E. Cabrera-Vives, G. Estévez, P.A. Catelan, M. Pignata, G. Huijse, P. de Cicco, D. Arévalo, P. Carrasco-Davis, R. Abril, J. Kurtev, R. Borissova, J. Arredondo, J. Castillo-Navarrete, E. Rodriguez, D. Ruz-Mieres, D. Moya, A. Sabatini-Gacitúa, L. Sepúlveda-Cobo, C. Camacho-Iñiguez, E. Nuncio Monseñor Sótero Sanz 100 Providencia Santiago Chile Instituto de Astrofísica Facultad de Física Pontificia Universidad Católica de Chile Casilla 306 Santiago 22 Chile Faculty of Engineering and Sciences Universidad Adolfo Ibañez Diagonal Las Torres 2700 Peñalolén Santiago Chile Center for Mathematical Modeling Universidad de Chile Beauchef 851 North building 7th floor Santiago8320000 Chile Department of Electrical Engineering Universidad de Chile Av. Tupper 2007 Santiago8320000 Chile Data Observatory Diagonal Las Torres 2640 Peñalolén Santiago Chile Departmento de Matemáticas Facultad de Ciencia Universidad de Santiago de Chile Estación Central Av. Libertador Bernardo O'Higgins 3663 Santiago Chile Departamento de Astronomía Universidad de Chile Casilla 36D Santiago Chile Centro de Astroingeniería Pontificia Universidad Católica de Chile Av. Vicuña Mackenna 4860 Macul Santiago7820436 Chile Space Science Institute 4750 Walnut Street Suite 205 BoulderCO80301 United States Department of Computer Science University of Concepcón Edmundo Larenas 219 Concepción Chile Departamento de Ciencias Fisícas Universidad Andres Bello Avda. Republica 252 Santiago Chile Informatics Institute Universidad Austral de Chile General Lagos 2086 Valdivia Chile Instituto de Física y Astronomía Facultad de Ciencias Universidad de Valparaíso Gran Bretana No. 1111 Playa Ancha Valparaíso Chile Alonso de Córdova 3107 Vitacura Santiago Chile - Unidad Asociada al CSIC Plaza San Juan 1 TeruelE-44001 Spain

We present the first version of the ALeRCE (Automatic Learning for the Rapid Classification of Events) broker light curve classifier. ALeRCE is currently processing the Zwicky Transient Facility (ZTF) alert stream, in preparation for the Vera C. Rubin Observatory. The ALeRCE light curve classifier uses variability features computed from the ZTF alert stream, and colors obtained from AllWISE and ZTF photometry. We apply a Balanced Random Forest algorithm with a two-level scheme, where the top level classifies each source as periodic, stochastic, or transient, and the bottom level further resolves each of these hierarchical classes, amongst 15 total classes. This classifier corresponds to the first attempt to classify multiple classes of stochastic variables (including core- and host-dominated active galactic nuclei, blazars, young stellar objects, and cataclysmic variables) in addition to different classes of periodic and transient sources, using real data. We created a labeled set using various public catalogs (such as the Catalina Surveys and Gaia DR2 variable stars catalogs, and the Million Quasars catalog), and we classify all objects with ≥ 6 g-band or ≥ 6 r-band detections in ZTF (868,371 sources as of 2020/06/09), providing updated classifications for sources with new alerts every day. For the top level we obtain macro-averaged precision and recall scores of 0.96 and 0.99, respectively, and for the bottom level we obtain macro-averaged precision and recall scores of 0.57 and 0.76, respectively. Updated classifications from the light curve classifier can be found at the ALeRCE Explorer website. Copyright © 2020, The Authors. All rights reserved.

关键词： Surveys

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A human immune dysregulation syndrome characterized by severe hyperinflammation with a homozygous nonsense Roquin-1 mutation (vol 10, 4779 , 2019)

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NATURE COMMUNICATIONS 2019年第1期10卷 1-16页

作者： Tavernier, S. J. Athanasopoulos, V. Verloo, P. Behrens, G. Staal, J. Bogaert, D. J. Naesens, L. De Bruyne, M. Van Gassen, S. Parthoens, E. Ellyard, J. Cappello, J. Morris, L. X. Van Gorp, H. Van Isterdael, G. Saeys, Y. Lamkanfi, M. Schelstraete, P. Dehoorne, J. Bordon, V. Van Coster, R. Lambrecht, B. N. Menten, B. Beyaert, R. Vinuesa, C. G. Heissmeyer, V. Dullaers, M. Haerynck, F. Primary Immune Deficiency Research Lab Department of Internal Medicine and Pediatrics Centre for Primary Immunodeficiency Ghent Jeffrey Modell Diagnosis and Research Centre Ghent University Hospital Ghent Belgium VIB Center for Inflammation Research Unit of Molecular Signal Transduction in Inflammation Ghent Belgium Department of Biomedical Molecular Biology Ghent University Ghent Belgium Department of Immunology and Infectious Disease and Center for Personalised Immunology (NHMRC Centre for Research Excellence) John Curtin School of Medical Research Australian National University Canberra Australia Centre for Personalised Immunology (CACPI) Shanghai Renji Hospital Shanghai Jiao Tong University Shanghai China Department of Internal Medicine and Pediatrics Division of Pediatric Neurology and Metabolism Ghent University Hospital Ghent Belgium Institute for Immunology Biomedical Center Ludwig-Maximilians-Universität München Planegg-Martinsried Germany Research Unit Molecular Immune Regulation Helmholtz Zentrum München Munich Germany Department of Internal Medicine and Pediatrics Division of Pediatric Immunology and Pulmonology Ghent University Hospital Ghent Belgium Department of Internal Medicine and Pediatrics Ghent University Hospital Ghent Belgium Center for Medical Genetics Ghent University Hospital Ghent Belgium VIB Center for Inflammation Research Unit of Data Mining and Modeling for Biomedicine Ghent Belgium Department of Applied Mathematics Computer Science and Statistics Ghent University Gent Belgium VIB Bioimaging Core VIB Center for Inflammation Research Ghent Belgium The Australian Phenomics Facility John Curtin School of Medical Research Australian National University Canberra Australia VIB Center for Inflammation Research Ghent Belgium VIB Flow Core VIB Center for Inflammation Research Ghent Belgium Department of Internal Medicine and Pediatrics Division of Pediatric Rheumatology Ghent University Hospital Ghent Belgium Department of Inter

Hyperinflammatory syndromes are life-threatening disorders caused by overzealous immune cell activation and cytokine release, often resulting from defects in negative feedback mechanisms. In the quintessential hyperinflammatory syndrome familial hemophagocytic lymphohistiocytosis (HLH), inborn errors of cytotoxicity result in effector cell accumulation, immune dysregulation and, if untreated, tissue damage and death. Here, we describe a human case with a homozygous nonsense R688* RC3H1 mutation suffering from hyperinflammation, presenting as relapsing HLH. RC3H1 encodes Roquin-1, a posttranscriptional repressor of immune-regulatory proteins such as ICOS, OX40 and TNF. Comparing the R688* variant with the murine M199R variant reveals a phenotypic resemblance, both in immune cell activation, hypercytokinemia and disease development. Mechanistically, R688* Roquin-1 fails to localize to P-bodies and interact with the CCR4-NOT deadenylation complex, impeding mRNA decay and dysregulating cytokine production. The results from this unique case suggest that impaired Roquin-1 function provokes hyperinflammation by a failure to quench immune activation.

关键词： Disease genetics Inflammation Primary immunodeficiency disorders RNA decay

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Randomized reference models for temporal networks

arXiv

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arXiv 2018年

作者： Gauvin, Laetitia Génois, Mathieu Karsai, Márton Kivelä, Mikko Takaguchi, Taro Valdano, Eugenio Vestergaard, Christian L. Data Science Lab ISI Foundation Turin Italy Aix Marseille Univ. Univ. Toulon CNRS CPT Marseille France Department of Network and Data Science Central European University Vienna1100 Austria Université de Lyon ENS de Lyon INRIA CNRS LIP UMR 5668 IXXI Lyon69364 Cedex 07 France Alfréd Rényi Insititute of Mathematics BudapestH-1053 Hungary Department of Computer Science Aalto University School of Science P.O. Box 12200 AaltoFI-00076 Finland National Institute of Information and Communications Technology 4-2-1 Nukui-Kitamachi Koganei Tokyo184-8795 Japan Center for Biomedical Modeling The Semel Institute for Neuroscience and Human Behavior David Geffen School of Medicine University of California Los Angeles 760 Westwood Plaza Los AngelesCA90024 United States Decision and Bayesian Computation Department of Computational Biology Department of Neuroscience CNRS USR 3756 UMR 3571 Institut Pasteur 25 rue du Docteur Roux Paris75015 France

Many dynamical systems can be successfully analyzed by representing them as networks. Empirically measured networks and dynamic processes that take place in these situations show heterogeneous, non-Markovian, and intrinsically correlated topologies and dynamics. This makes their analysis particularly challenging. Randomized reference models (RRMs) have emerged as a general and versatile toolbox for studying such systems. Defined as random networks with given features constrained to match those of an input (empirical) network, they may for example be used to identify important features of empirical networks and their effects on dynamical processes unfolding in the network. RRMs are typically implemented as procedures that reshuffle an empirical network, making them very generally applicable. However, the effects of most shuffling procedures on network features remain poorly understood, rendering their use non-trivial and susceptible to misinterpretation. Here we propose a unified framework for classifying and understanding microcanonical RRMs (MRRMs) that sample networks with uniform probability. Focusing on temporal networks, we survey applications of MRRMs found in literature, and we use this framework to build a taxonomy of MRRMs that proposes a canonical naming convention, classifies them, and deduces their effects on a range of important network features. We furthermore show that certain classes of MRRMs may be applied in sequential composition to generate new MRRMs from the existing ones surveyed in this article. We finally provide a tutorial showing how to apply a series of MRRMs to analyze how different network features affect a dynamic process in an empirical temporal network. Our taxonomy provides a reference for the use of MRRMs, and the theoretical foundations laid here may further serve as a base for the development of a principled and automatized way to generate and apply randomized reference models for the study of networked systems. Copyright © 2018,

关键词： Taxonomies

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Spectral Structure of Elastic Neumann–Poincaré Operators

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Journal of Physics: Conference Series 2018年第1期965卷

作者： Yoshihisa Miyanishi Kazunori Ando Hyeonbae Kang Center for Mathematical Modeling and Data Science Osaka University Osaka 560-8631 Japan Department of Electrical and Electronic Engineering and Computer Science Ehime University Ehime 790-8577 Japan Department of Mathematics and Institute of Applied Mathematics Inha University Incheon 22212 S. Korea

In [1, 2], it is proved that the elastic Neumann–Poincaré operator defined on the smooth boundary of a bounded domain, which is known to be non-compact, is in fact polynomially compact. As a consequence, it is shown that the spectrum of the elastic Neumann-Poincaré operator consists of non-empty sets of eigenvalues accumulating to certain numbers determined by Lamé parameters. The purpose of this paper is to review these results and their proofs, and to discuss about some questions related to these results.

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Effectiveness of dispatcher instructions-dependent or independent bystander cardiopulmonary resuscitation on neurological survival among patients with out-of-hospital cardiac arrest

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Resuscitation 2019年 142卷 e103-e104页

作者： Hatakeyama, Toshihiro Kiguchi, Takeyuki Kobayashi, Daisuke Nakamura, Naotoshi Nishiyama, Chika Hayashida, Sumito Kiyohara, Kosuke Kitamura, Tetsuhisa Kawamura, Takashi Iwami, Taku Department of Emergency and Critical Care Medicine Emergency and Critical Care Center Dokkyo Medical University Saitama Medical Center Koshigaya City Japan Kyoto University Health Service Kyoto City Japan Center for Mathematical Modeling and Data Science Osaka University Toyonaka City Japan Department of Critical Care Nursing Kyoto University Graduate School of Human Health Science Kyoto City Japan Osaka Municipal Fire Department Osaka City Japan Department of Food Science Otsuma Women's University Chiyoda-ku Tokyo Japan Division of Environmental Medicine and Population Sciences Department of Social and Environmental Medicine Graduate School of Medicine Osaka University Suita City Japan

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Weak doping dependence of the antiferromagnetic coupling between nearest-neighbor Mn2+ spins in (Ba1−xKx)(Zn1−yMny)2As2

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Physical Review B 2018年第10期97卷 104418-104418页

作者： M. A. Surmach B. J. Chen Z. Deng C. Q. Jin J. K. Glasbrenner I. I. Mazin A. Ivanov D. S. Inosov Institut für Festkörper- und Materialphysik TU Dresden D-01069 Dresden Germany Institute of Physics Chinese Academy of Sciences 100190 Beijing China Center for High Pressure Science and Technology Advanced Research Beijing 100094 China School of Physics University of Chinese Academy of Sciences Beijing 100049 China Collaborative Innovation Center of Quantum Matter Beijing 100049 China Department of Computational and Data Sciences/Center for Simulation and Modeling George Mason University 4400 University Drive Fairfax Virginia 22030 USA Code 6393 Naval Research Laboratory Washington DC 20375 USA Institut Laue-Langevin 71 avenue des Martyrs CS 20156 F-38042 Grenoble Cedex 9 France

Dilute magnetic semiconductors (DMS) are nonmagnetic semiconductors doped with magnetic transition metals. The recently discovered DMS material (Ba1−xKx)(Zn1−yMny)2As2 offers a unique and versatile control of the Curie temperature TC by decoupling the spin (Mn2+, S=5/2) and charge (K+) doping in different crystallographic layers. In an attempt to describe from first-principles calculations the role of hole doping in stabilizing ferromagnetic order, it was recently suggested that the antiferromagnetic exchange coupling J between the nearest-neighbor Mn ions would experience a nearly twofold suppression upon doping 20% of holes by potassium substitution. At the same time, further-neighbor interactions become increasingly ferromagnetic upon doping, leading to a rapid increase of TC. Using inelastic neutron scattering, we have observed a localized magnetic excitation at about 13 meV associated with the destruction of the nearest-neighbor Mn-Mn singlet ground state. Hole doping results in a notable broadening of this peak, evidencing significant particle-hole damping, but with only a minor change in the peak position. We argue that this unexpected result can be explained by a combined effect of superexchange and double-exchange interactions.

关键词： Ferromagnetism Magnetic semiconductors Inelastic neutron scattering

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Portfolio Optimization under Cardinality Constraints: A Comparative Study

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Open Journal of Statistics 2017年第4期7卷 731-742页

作者： Henri Claver Jimbo Isidore Seraphin Ngongo Nicolas Gabriel Andjiga Takeru Suzuki Charles Awona Onana Department of Applied Mathematics and Statistics Laboratory of Big Data and Stochasticity Waseda University Tokyo Japan Department of Applied Mathematics Laboratory SAMM (Statistics Analysis and Multidisciplinary Modeling) University of Paris 1 Pantheon Sorbonne Paris France Department of Mathematics Faculty of Science University of Yaounde 1-ENS-Higher Training College Yaounde Cameroon

The Cardinality Constraint-Based Optimization problem is investigated in this note. In portfolio optimization problem, the cardinality constraint allows one to invest in assets out of a universe of N assets for a prespecified value of K. It is generally agreed that choosing a “small” value of K forces the implementation of diversification in small portfolios. However, the question of how small must be K has remained unanswered. In the present work, using a comparative approach we show computationally that optimal portfolio selection with a relatively small or large number of assets, K, may produce similar results with differentiated reliabilities.

关键词： Cardinality Constraints Diversity Portfolio Selection Portfolio Reliability Parametric Statistics

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Stochastic Newton and Quasi-Newton methods for large linear least-squares problems

arXiv

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arXiv 2017年

作者： Chung, Julianne Chung, Matthias Slagel, J. Tanner Tenorio, Luis Department of Mathematics Computation Modeling and Data Analytics Division Academy of Integrated Science Virginia Tech BlacksburgVA United States Department of Mathematics Virginia Tech BlacksburgVA United States Department of Applied Mathematics and Statistics Colorado School of Mines GoldenCO United States

We describe stochastic Newton and stochastic quasi-Newton approaches to efficiently solve large linear least-squares problems where the very large data sets present a significant computational burden (e.g., the size may exceed computer memory or data are collected in real-time). In our proposed framework, stochasticity is introduced in two different frameworks as a means to overcome these computational limitations, and probability distributions that can exploit structure and/or sparsity are considered. Theoretical results on consistency of the approximations for both the stochastic Newton and the stochastic quasi-Newton methods are provided. The results show, in particular, that stochastic Newton iterates, in contrast to stochastic quasi-Newton iterates, may not converge to the desired least-squares solution. Numerical examples, including an example from extreme learning machines, demonstrate the potential applications of these methods. Copyright © 2017, The Authors. All rights reserved.

关键词： Stochastic systems

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Efficient generalized Golub-Kahan based methods for dynamic inverse problems

arXiv

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arXiv 2017年

作者： Chung, Julianne Saibaba, Arvind K. Brown, Matthew Westman, Erik Department of Mathematics Computational Modeling and Data Analytics Division Academy of Integrated Science Virginia Tech BlacksburgVA United States Department of Mathematics North Carolina State University RaleighNC United States Department of Mining and Materials Engineering Virginia Tech BlacksburgVA United States

We consider efficient methods for computing solutions to and estimating uncertainties in dynamic inverse problems, where the parameters of interest may change during the measurement procedure. Compared to static inverse problems, incorporating prior information in both space and time in a Bayesian framework can become computationally intensive, in part, due to the large number of unknown parameters. In these problems, explicit computation of the square root and/or inverse of the prior covariance matrix is not possible. In this work, we develop efficient, iterative, matrix-free methods based on the generalized Golub-Kahan bidiagonalization that allow automatic regularization parameter and variance estimation. We demonstrate that these methods can be more flexible than standard methods and develop efficient implementations that can exploit structure in the prior, as well as possible structure in the forward model. Numerical examples from photoacoustic tomography, deblurring, and passive seismic tomography demonstrate the range of applicability and effectiveness of the described approaches. Specifically, in passive seismic tomography, we demonstrate our approach on both synthetic and real data. To demonstrate the scalability of our algorithm, we solve a dynamic inverse problem with approximately 43, 000 measurements and 7.8 million unknowns in under 40 seconds on a standard desktop. Copyright © 2017, The Authors. All rights reserved.

关键词： Seismology

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The need to implement FAIR principles in biomolecular simulations

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NATURE METHODS 2025年第4期22卷 1-5页

作者： Amaro, Rommie E. Aqvist, Johan Bahar, Ivet Battistini, Federica Bellaiche, Adam Beltran, Daniel Biggin, Philip C. Bonomi, Massimiliano Bowman, Gregory R. Bryce, Richard A. Bussi, Giovanni Carloni, Paolo Case, David A. Cavalli, Andrea Chang, Chia-En A. Cheatham, Thomas E. Cheung, Margaret S. Chipot, Christophe Chong, Lillian T. Choudhary, Preeti Cisneros, G. Andres Clementi, Cecilia Collepardo-Guevara, Rosana Coveney, Peter Covino, Roberto Crawford, T. Daniel Dal Peraro, Matteo de Groot, Bert L. Delemotte, Lucie De Vivo, Marco Essex, Jonathan W. Fraternali, Franca Gao, Jiali Gelpi, Josep Ll. Gervasio, Francesco L. Gonzalez-Nilo, Fernando D. Grubmueller, Helmut Guenza, Marina G. Guzman, Horacio V. Harris, Sarah Head-Gordon, Teresa Hernandez, Rigoberto Hospital, Adam Huang, Niu Huang, Xuhui Hummer, Gerhard Iglesias-Fernandez, Javier Jensen, Jan H. Jha, Shantenu Jiao, Wanting Jorgensen, William L. Kamerlin, Shina C. L. Khalid, Syma Laughton, Charles Levitt, Michael Limongelli, Vittorio Lindahl, Erik Lindorff-Larsen, Kresten Loverde, Sharon Lundborg, Magnus Luo, Yun L. Luque, F. Javier Lynch, Charlotte I. Mackerell, Alexander D. Magistrato, Alessandra Marrink, Siewert J. Martin, Hugh Mccammon, J. Andrew Merz, Kenneth Moliner, Vicent Mulholland, Adrian J. Murad, Sohail Naganathan, Athi N. Nangia, Shikha Noe, Frank Noy, Agnes Olah, Julianna O'Mara, Megan L. Ondrechen, Mary Jo Onuchic, Jose N. Onufriev, Alexey Osuna, Silvia Palermo, Giulia Panchenko, Anna R. Pantano, Sergio Parish, Carol Parrinello, Michele Perez, Alberto Perez-Acle, Tomas Perilla, Juan R. Pettitt, B. Montgomery Pietropaolo, Adriana Piquemal, Jean-Philip Poma, Adolfo B. Praprotnik, Matej Ramos, Maria J. Ren, Pengyu Reuter, Nathalie Roitberg, Adrian Rosta, Edina Rovira, Carme Roux, Benoit Rothlisberger, Ursula Sanbonmatsu, Karissa Y. Schlick, Tamar Shaytan, Alexey K. Simmerling, Carlos Smith, Jeremy C. Sugita, Yuji Swiderek, Katarzyna Taiji, Makoto Tao, Peng Tieleman, D. Peter Tikhonova, Irina G. Tirado-Rives, Julian Tunon, Inaki van der Kamp, Marc W. van Univ Calif San Diego Dept Mol Biol La Jolla CA 92093 USA Uppsala Univ Dept Cell & Mol Biol Uppsala Sweden SUNY Stony Brook Laufer Ctr Phys & Quantitat Biol Stony Brook NY 11794 USA Stony Brook Univ Hosp Renaissance Sch Med Dept Pediat Stony Brook NY USA Univ Barcelona Dept Biochem & Mol Biomed Barcelona Spain European Bioinformat Inst European Mol Biol Lab Hinxton England Inst Res Biomed IRB Barcelona Barcelona Spain Univ Oxford Dept Biochem Struct Bioinformat & Computat Biochem Oxford England Univ Paris Cite Inst Pasteur Computat Struct Biol Unit CNRS UMR 3528 Paris France Univ Penn Dept Biochem & Biophys Philadelphia PA 19104 USA Univ Manchester Div Pharm & Optometry Manchester Lancashire England Scuola Int Super Studi Avanzati SISSA Trieste Italy Forschungszentrum Julich Inst Adv Simulat IAS 5 Inst Neurosci & Med INM 9 Computat Biomed Julich Germany Univ Hosp RWTH Aachen Dept Cardiol Aachen Germany Rutgers State Univ Dept Chem & Chem Biol Piscataway NJ USA Ist Italiano Tecnol Drug Discovery & Dev Genoa Italy Ecole Polytech Fed Lausanne EPFL CECAM Ctr Europeen Calcul At & Mol Lausanne Switzerland Univ Calif Riverside Dept Chem Riverside CA 92521 USA Univ Utah Coll Pharm Dept Med Chem Salt Lake City UT 84112 USA Univ Washington Dept Phys Seattle WA 98185 USA Pacific Northwest Natl Lab Richland WA USA Univ Lorraine UMR 7019 LPCT F-54000 Nancy France Univ Illinois Dept Phys Urbana IL USA Univ Chicago Dept Biochem & Mol Biol Chicago IL USA Univ Pittsburgh Dept Chem Pittsburgh PA USA Univ Texas Dallas Dept Chem & Biochem Richardson TX 75080 USA Univ Texas Dallas Dept Phys Richardson TX USA Freie Univ Dept Phys Genet Biophys Berlin Germany Univ Cambridge Maxwell Ctr Dept Phys Cavendish Lab Cambridge England Univ Cambridge Yusuf Hamied Dept Chem Cambridge Cambridgeshire England Univ Cambridge Dept Psychol Cambridge Cambridgeshire England Univ London Univ Coll Dept Chem Ctr Computat Sci London WC1H 0AJ England U

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