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Physical Review Letters 2018年第24期121卷 248003-248003页

作者： Arman Boromand Alexandra Signoriello Fangfu Ye Corey S. O’Hern Mark D. Shattuck Beijing National Laboratory for Condensed Matter Physics and CAS Key Laboratory of Soft Matter Physics Institute of Physics Chinese Academy of Sciences Beijing China Department of Mechanical Engineering and Materials Science Yale University New Haven Connecticut 06520 USA Program in Computational Biology and Bioinformatics Yale University New Haven Connecticut 06520 USA School of Physical Sciences University of Chinese Academy of Sciences Beijing China Department of Physics Yale University New Haven Connecticut 06520 USA Department of Applied Physics Yale University New Haven Connecticut 06520 USA Benjamin Levich Institute and Physics Department The City College of the City University of New York New York New York 10031 USA

We introduce the deformable particle (DP) model for cells, foams, emulsions, and other soft particulate materials, which adds to the benefits and eliminates deficiencies of existing models. The DP model combines the ability to model individual soft particles with the shape-energy function of the vertex model, and adds arbitrary particle deformations. We focus on 2D deformable polygons with a shape-energy function that is minimized for area a0 and perimeter p0 and repulsive interparticle forces. We study the onset of jamming versus particle asphericity, A=p02/4πa0, and find that the packing fraction grows with A until reaching A*=1.16 of the underlying Voronoi cells at confluence. We find that DP packings above and below A* are solidlike, which helps explain the solid-to-fluid transition at A* in the vertex model as a transition from tension- to compression-dominated regimes.

关键词： Jamming Monolayer films Molecular dynamics

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A Bayesian mixture model for clustering on the stiefel manifold

arXiv

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arXiv 2017年

作者： Sengupta, Subhajit Pal, Subhadip Mitra, Riten Guo, Ying Banerjee, Arunava Ji, Yuan Program for Computational Genomics and Medicine NorthShore University HealthSystem Department of Bioinformatics and Biostatistics University of Louisville Department of Biostatistics and Bioinformatics Emory University Department of Computer and Information Science and Engineering University of Florida Department of Public Health Sciences University of Chicago

Analysis of a Bayesian mixture model for the Matrix Langevin distribution on the Stiefel manifold is presented. The model exploits a particular parametrization of the Matrix Langevin distribution, various aspects of which are elaborated on. A general, and novel, family of conjugate priors, and an efficient Markov chain Monte Carlo (MCMC) sampling scheme for the corresponding posteriors is then developed for the mixture model. Theoretical properties of the prior and posterior distributions, including posterior consistency, are explored in detail. Extensive simulation experiments are presented to validate the efficacy of the framework. Real-world examples, including a large scale neuroimaging dataset, are analyzed to demonstrate the computational tractability of the approach. Copyright © 2017, The Authors. All rights reserved.

关键词： Mixtures

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Data-driven brain network models predict individual variability in behavior

arXiv

引用

arXiv 2018年

作者： Bansal, Kanika Medaglia, John D. Bassett, Danielle S. Vettel, Jean M. Muldoon, Sarah F. Department of Mathematics University at Buffalo BuffaloNY United States U.S. Army Research Laboratory Aberdeen Proving GroundMD United States Department of Biomedical Engineering Columbia University New YorkNY United States Department of Psychology Drexel University PhiladelphiaPA United States Department of Neurology Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Department of Biomedical Engineering University of Pennsylvania PhiladelphiaPA United States Department of Electrical and Systems Engineering University of Pennsylvania PhiladelphiaPA United States Department of Physics and Astronomy University of Pennsylvania PhiladelphiaPA United States Department of Psychological and Brain Sciences University of California Santa BarbaraCA United States Computational and Data-Enabled Science and Engineering Program University at Buffalo-SUNY BuffaloNY United States

The relationship between brain structure and function has been probed using a variety of approaches, but how the underlying structural connectivity of the human brain drives behavior is far from understood. To investigate the effect of anatomical brain organization on human task performance, we use a data-driven computational modeling approach and explore the functional effects of naturally occurring structural differences in brain networks. We construct personalized brain network models by combining anatomical connectivity estimated from diffusion spectrum imaging of individual subjects with a nonlinear model of brain dynamics. By performing computational experiments in which we measure the excitability of the global brain network and spread of synchronization following a targeted computational stimulation, we quantify how individual variation in the underlying connectivity impacts both local and global brain dynamics. We further relate the computational results to individual variability in the subjects' performance of three language-demanding tasks both before and after transcranial magnetic stimulation to the left-inferior frontal gyrus. Our results show that task performance correlates with either local or global measures of functional activity, depending on the complexity of the task. By emphasizing differences in the underlying structural connectivity, our model serves as a powerful tool to predict individual differences in task performances, to dissociate the effect of targeted stimulation in tasks that differ in cognitive complexity, and to pave the way for the development of personalized therapeutics. Copyright © 2018, The Authors. All rights reserved.

关键词： Digital storage

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A multi-protease, multi-dissociation, bottom-up-to-top-down proteomic view of the Loxosceles intermedia venom

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Scientific Data 2017年第1期4卷 170090-170096页

作者： Trevisan-Silva, Dilza Bednaski, Aline V. Fischer, Juliana S.G. Veiga, Silvio S. Bandeira, Nuno Guthals, Adrian Marchini, Fabricio K. Leprevost, Felipe V. Barbosa, Valmir C. Senff-Ribeiro, Andrea Carvalho, Paulo C. Department of Cell Biology Federal University of Paraná Curitiba 81531-980 Brazil Computational Mass Spectrometry and Proteomics Group Carlos Chagas Institute Fiocruz Curitiba 81.350-010 Brazil Center for Computational Mass Spectrometry University of California San Diego 92093-0404 United States Functional Genomics Laboratory Carlos Chagas Institute Fiocruz Curitiba 81.350-010 Brazil Mass Spectrometry Facility RPT02H Carlos Chagas Institute Fiocruz Curitiba 81.350-010 Brazil Systems Engineering and Computer Science Program COPPE Federal University of Rio de Janeiro Rio de Janeiro 21941-914 Brazil Laboratory of Toxinology Fiocruz Rio de Janeiro 21040-900 Brazil

Venoms are a rich source for the discovery of molecules with biotechnological applications, but their analysis is challenging even for state-of-the-art proteomics. Here we report on a large-scale proteomic assessment of the venom of Loxosceles intermedia, the so-called brown spider. Venom was extracted from 200 spiders and fractioned into two aliquots relative to a 10 kDa cutoff mass. Each of these was further fractioned and digested with trypsin (4 h), trypsin (18 h), pepsin (18 h), and chymotrypsin (18 h), then analyzed by MudPIT on an LTQ-Orbitrap XL ETD mass spectrometer fragmenting precursors by CID, HCD, and ETD. Aliquots of undigested samples were also analyzed. Our experimental design allowed us to apply spectral networks, thus enabling us to obtain meta-contig assemblies, and consequently de novo sequencing of practically complete proteins, culminating in a deep proteome assessment of the venom. Data are available via ProteomeXchange, with identifier PXD005523.

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Search for Gravitational Waves Associated with Fast Radio Bursts Detected by CHIME/FRB during the LIGO-Virgo Observing Run O3a

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Astrophysical Journal 2023年第2期955卷 155-155页

作者： Abbott, R. Abbott, T.D. Acernese, F. Ackley, K. Adams, C. Adhikari, N. Adhikari, R.X. Adya, V.B. Affeldt, C. Agarwal, D. Agathos, M. Agatsuma, K. Aggarwal, N. Aguiar, O.D. Aiello, L. Ain, A. Ajith, P. Akutsu, T. Albanesi, S. Allocca, A. Altin, P.A. Amato, A. Anand, C. Anand, S. Ananyeva, A. Anderson, S.B. Anderson, W.G. Ando, M. Andrade, T. Andres, N. Andrić, T. Angelova, S.V. Ansoldi, S. Antelis, J.M. Antier, S. Appert, S. Arai, Koya Arai, Y. Araki, S. Araya, A. Araya, M.C. Areeda, J.S. Arène, M. Aritomi, N. Arnaud, N. Aronson, S.M. Arun, K.G. Asada, H. Asali, Y. Ashton, G. Aso, Y. Assiduo, M. Aston, S.M. Astone, P. Aubin, F. Austin, C. Babak, S. Badaracco, F. Bader, M.K.M. Badger, C. Bae, S. Bae, Y. Baer, A.M. Bagnasco, S. Bai, Y. Baiotti, L. Baird, J. Bajpai, R. Ball, M. Ballardin, G. Ballmer, S.W. Balsamo, A. Baltus, G. Banagiri, S. Bankar, D. Barayoga, J.C. Barbieri, C. Barish, B.C. Barker, D. Barneo, P. Barone, F. Barr, B. Barsotti, L. Barsuglia, M. Barta, D. Bartlett, J. Barton, M.A. Bartos, I. Bassiri, R. Basti, A. Bawaj, M. Bayley, J.C. Baylor, A.C. Bazzan, M. Bécsy, B. Bedakihale, V.M. Bejger, M. Belahcene, I. Benedetto, V. Beniwal, D. Bennett, T.F. Bentley, J.D. BenYaala, M. Bergamin, F. Berger, B.K. Bernuzzi, S. Berry, C.P.L. Bersanetti, D. Bertolini, A. Betzwieser, J. Beveridge, D. Bhandare, R. Bhardwaj, U. Bhattacharjee, D. Bhaumik, S. Bilenko, I.A. Billingsley, G. Bini, S. Birney, R. Birnholtz, O. Biscans, S. Bischi, M. Biscoveanu, S. Bisht, A. Biswas, B. Bitossi, M. Bizouard, M.-A. Blackburn, J.K. Blair, C.D. Blair, D.G. Blair, R.M. Bobba, F. Bode, N. Boer, M. Bogaert, G. Boldrini, M. Bonavena, L.D. Bondu, F. Bonilla, E. Bonnand, R. Booker, P. Boom, B.A. Bork, R. Boschi, V. Bose, N. Bose, S. Bossilkov, V. Boudart, V. Bouffanais, Y. Boumerdassi, A. Bozzi, A. Bradaschia, C. Brady, P.R. Bramley, A. Branch, A. Branchesi, M. Brau, J.E. Breschi, M. Briant, T. Briggs, J.H. Brillet, A. Brinkmann, M. Brockill, P. Brooks, A.F. Brooks, J. Brown, D.D. Brunett, S. Bruno, G. Bruntz, R. Bryant, J. Buchanan, J. B LIGO Laboratory California Institute of Technology Pasadena 91125 CA United States Louisiana State University Baton Rouge 70803 LA United States Dipartimento di Farmacia Università di Salerno Fisciano Salerno I-84084 Italy INFN Sezione di Napoli Complesso Universitario di Monte S. Angelo Napoli I-80126 Italy OzGrav School of Physics & Astronomy Monash University Clayton 3800 VIC Australia LIGO Livingston Observatory Livingston 70754 LA United States University of Wisconsin-Milwaukee Milwaukee 53201 WI United States OzGrav Australian National University Canberra 0200 ACT Australia Max Planck Institute for Gravitational Physics (Albert Einstein Institute) Hannover D-30167 Germany Leibniz Universität Hannover Hannover D-30167 Germany Inter-University Centre for Astronomy and Astrophysics Pune 411007 India University of Cambridge Cambridge CB2 1TN United Kingdom Theoretisch-Physikalisches Institut Friedrich-Schiller-Universität Jena Jena D-07743 Germany University of Birmingham Birmingham B15 2TT United Kingdom Center for Interdisciplinary Exploration & Research in Astrophysics (CIERA) Northwestern University Evanston 60208 IL United States Instituto Nacional de Pesquisas Espaciais São Paulo São José dos Campos 12227-010 Brazil Gravity Exploration Institute Cardiff University Cardiff CF24 3AA United Kingdom INFN Sezione di Pisa Pisa I-56127 Italy International Centre for Theoretical Sciences Tata Institute of Fundamental Research Bengaluru 560089 India Gravitational Wave Science Project National Astronomical Observatory of Japan (NAOJ) Mitaka City Tokyo 181-8588 Japan Advanced Technology Center National Astronomical Observatory of Japan (NAOJ) Mitaka City Tokyo 181-8588 Japan INFN Sezione di Torino Torino I-10125 Italy Università di Napoli “Federico II Complesso Universitario di Monte S. Angelo Napoli I-80126 Italy Université de Lyon Université Claude Bernard Lyon 1 CNRS Institut Lumière Matière Villeurbanne F-69622

We search for gravitational-wave (GW) transients associated with fast radio bursts (FRBs) detected by the Canadian Hydrogen Intensity Mapping Experiment Fast Radio Burst Project, during the first part of the third observing run of Advanced LIGO and Advanced Virgo (2019 April 1 15:00 UTC-2019 October 1 15:00 UTC). Triggers from 22 FRBs were analyzed with a search that targets both binary neutron star (BNS) and neutron star-black hole (NSBH) mergers. A targeted search for generic GW transients was conducted on 40 FRBs. We find no significant evidence for a GW association in either search. Given the large uncertainties in the distances of our FRB sample, we are unable to exclude the possibility of a GW association. Assessing the volumetric event rates of both FRB and binary mergers, an association is limited to 15% of the FRB population for BNS mergers or 1% for NSBH mergers. We report 90% confidence lower bounds on the distance to each FRB for a range of GW progenitor models and set upper limits on the energy emitted through GWs for a range of emission scenarios. We find values of order 10⁵¹-10⁵⁷ erg for models with central GW frequencies in the range 70-3560 Hz. At the sensitivity of this search, we find these limits to be above the predicted GW emissions for the models considered. We also find no significant coincident detection of GWs with the repeater, FRB 20200120E, which is the closest known extragalactic FRB. © 2023. The Author(s). Published by the American Astronomical Society.

关键词： Gravitational wave astronomy Radio transient sources

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Synthesis and characterization of titanium dioxide-polypyrrole nanocomposites for the photodegradation of bromophenol blue

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AIP Conference Proceedings 2018年第1期1958卷

作者： Salvador C. Buenviaje Jr. Ken Aldren S. Usman Leon M. Payawan Jr. Materials Science and Engineering Program Computational Science Research Center National Science Complex University of the Philippines Diliman Quezon City Philippines Natural Sciences Research Institute Miranda Hall P. Velasquez Street University of the Philippines Diliman Quezon City Philippines Institute of Chemistry National Science Complex University of the Philippines Diliman Quezon City Philippines

Titania (TiO2) nanoparticles were synthesized through sol-gel method and stabilized by polypyrrole through photopolymerization. The titania-polypyrrole (TP) nanocomposites were then characterized on the basis of particle size, stability, dispersity, band gap energy, and surface morphology. Using dynamic light scattering, TP products of varied mole ratios were found to be all nano in size with bare TiO2 having 66.6 nm and the rest with ∼90 nm. These materials were also found to be stable in water with Zeta potentials all above 25 mV. The band gap energies of TP products were precise and accurate relative to the 3.52 eV of bare TiO2 using ultraviolet-visible spectra. The particles were also monodisperse enough with ∼PDI values of 0.450. The TP material was also drop-casted on mica, and found to have cone-shaped peaks at 60 nm and almost uniform surface using atomic force microscopy. Photodegradation of bromophenol blue was also optimized and found to have 97.53% efficiency in the presence of TP as photocatalyst and at pH 7.

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Author Correction: The lactate receptor HCAR1 drives the recruitment of immunosuppressive PMN-MDSCs in colorectal cancer

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Nature immunology 2025年第4期26卷 635页

作者： Jiacheng He Xiaolei Chai Qiansen Zhang Yang Wang Yijie Wang Xinyu Yang Jingbo Wu Bo Feng Jing Sun Weiwei Rui Shuyin Ze Yuanyuan Fu Yumiao Zhao Ying Zhang Yao Zhang Meizhen Liu Chuang Liu Meifu She Xiangfei Hu Xueyun Ma Huaiyu Yang Dawei Li Senlin Zhao Guichao Li Zhen Zhang Zhonghui Tian Yanlin Ma Lingyan Cao Bo Yi Dali Li Ruth Nussinov Charis Eng Timothy A Chan Eytan Ruppin J Silvio Gutkind Feixiong Cheng Mingyao Liu Weiqiang Lu Shanghai Key Laboratory of Regulatory Biology Institute of Biomedical Sciences and School of Life Sciences East China Normal University Shanghai China. Department of Urology and Department of Pathology The Fifth People's Hospital of Shanghai Fudan University Shanghai China. Department of General Surgery and Department of Pathology Ruijin Hospital Shanghai Jiaotong University School of Medicine Shanghai China. Research Center for Complexity Sciences Hangzhou Normal University Hangzhou China. Department of Colorectal Surgery Department of Radiation Oncology Fudan University Shanghai Cancer Center Shanghai China. School of Life Sciences Fudan University Shanghai China. Hainan Provincial Key Laboratory for Human Reproductive Medicine and Genetic Research Hainan Provincial Clinical Research Center for Thalassemia Department of Reproductive Medicine Key Laboratory of Reproductive Health Diseases Research and Translation (Hainan Medical University) Ministry of Education The First Affiliated Hospital of Hainan Medical University Haikou China. Shanghai Ninth People's Hospital Shanghai Jiao Tong University School of Medicine College of Stomatology Shanghai Jiao Tong University Shanghai China. Department of Gastrointestinal Surgery The Third Xiangya Hospital Central South University Changsha China. Computational Structural Biology Section Basic Science Program Frederick National Laboratory for Cancer Research National Cancer Institute at Frederick Frederick MD USA. Department of Human Molecular Genetics and Biochemistry Sackler School of Medicine Tel Aviv University Tel Aviv Israel. Genomic Medicine Institute Lerner Research Institute Cleveland Clinic Cleveland OH USA. Department of Molecular Medicine Cleveland Clinic Lerner College of Medicine Case Western Reserve University Cleveland OH USA. Case Comprehensive Cancer Center Case Western Reserve University School of Medicine Cleveland OH USA. Center for Immunotherapy and Precision Immuno-Oncology Cleveland Clini

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Optimal Learning for Sequential Decision Making for Expensive Cost Functions with Stochastic Binary Feedbacks

arXiv

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arXiv 2017年

作者： Wang, Yingfei Wang, Chu Powell, Warren Department of Computer Science Princeton University PrincetonNJ08540 United States Program in Applied and Computational Mathematics Princeton University PrincetonNJ08544 United States Department of Operations Research and Financial Engineering Princeton University PrincetonNJ08544 United States

We consider the problem of sequentially making decisions that are rewarded by "successes" and "failures" which can be predicted through an unknown relationship that depends on a partially controllable vector of attributes for each instance. The learner takes an active role in selecting samples from the instance pool. The goal is to maximize the probability of success in either offline (training) or online (testing) phases. Our problem is motivated by real-world applications where observations are time consuming and/or expensive. We develop a knowledge gradient policy using an online Bayesian linear classifier to guide the experiment by maximizing the expected value of information of labeling each alternative. We provide a finite-time analysis of the estimated error and show that the maximum likelihood estimator based produced by the KG policy is consistent and asymptotically normal. We also show that the knowledge gradient policy is asymptotically optimal in an offline setting. This work further extends the knowledge gradient to the setting of contextual bandits. We report the results of a series of experiments that demonstrate its efficiency. Copyright © 2017, The Authors. All rights reserved.

关键词： Maximum likelihood estimation

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Frontier Orbitals of Dehydrogenated Tetrahydrocurcumin in Water Solvent: A Theoretical Study

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Journal of Physics: Conference Series 2018年第1期1090卷

作者： Lusia S. P. Boli Vera Khoirunisa Adhitya G. Saputro Mohammad K. Agusta Febdian Rusydi Heni Rachmawati Hermawan K. Dipojono Engineering Physics Research group Institut Teknologi Bandung Indonesia Engineering Physics Program Institut Teknologi Sumatera Indonesia Research Center for Nanoscience and Nanotechnology Institut Teknologi Bandung Indonesia Research Center for Computational Quantum Engineering Faculty of Science and Technology Universitas Airlangga Surabaya Indonesia School of Pharmacy Institut Teknologi Bandung Bandung Indonesia

We studied two frontier orbitals - the highest occupied molecular orbital (HOMO) and lowest unoccupied molecular orbital (LUMO)- of tetrahydrocurumin (THC) using density-functional theory (DFT) in water solvent. These orbitals were observed in THC molecule without one hydrogen atom (dehydrogenated THC). The loss of hydrogen atom is due to the transfer of the atom from THC molecule toward reactive oxygen species (ROS) (Hydrogen atom transfer –HAT- mechanism). We began our investigation by optimizing dehydrogenated THC at three X-H sites. Then, water solvent was added by using polarized continuum model (PCM) method. This study observed that dehydrogenated THC at two O-H sites has wider gap of HOMO-LUMO compare to C-H site.

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Toward the integration of speciation research

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Evolutionary Journal of the Linnean Society 2024年第1期3卷

作者： Stankowski, Sean Cutter, Asher D Satokangas, Ina Lerch, Brian A Rolland, Jonathan Smadja, Carole M Segami Marzal, J Carolina Cooney, Christopher R Feulner, Philine G D Domingos, Fabricius Maia Chaves Bicalho North, Henry L Yamaguchi, Ryo Butlin, Roger K Wolf, Jochen B W Coughlan, Jenn Heidbreder, Patrick Hernández-Gutiérrez, Rebeca Barnard-Kubow, Karen B Peede, David Rancilhac, Loïs Salvador, Rodrigo Brincalepe Thompson, Ken A Stacy, Elizabeth A Moyle, Leonie C Garlovsky, Martin D Maulana, Arif Kantelinen, Annina Cacho, N Ivalú Schneemann, Hilde Domínguez, Marisol Dopman, Erik B Lohse, Konrad Rometsch, Sina J Comeault, Aaron A Merrill, Richard M Scordato, Elizabeth S C Singhal, Sonal Pärssinen, Varpu Lackey, Alycia C R Kumar, Sanghamitra Meier, Joana I Barton, Nicholas Fraïsse, Christelle Ravinet, Mark Kulmuni, Jonna Institute of Science and Technology Austria (ISTA) Klosterneuburg Austria Department of Ecology and Evolution University of Sussex Brighton United Kingdom Ecology & Evolutionary Biology University of Toronto Toronto Canada Organismal and Evolutionary Biology Research Programme University of Helsinki Helsinki Finland Department of Biology University of North Carolina at Chapel Hill Chapel Hill NC United States Centre de Recherche sur la Biodiversité et l’Environnement (CRBE) Université de Toulouse CNRS IRD Toulouse INP Université Toulouse 3 – Paul Sabatier (UT3) Toulouse France Institut des Sciences de l’Evolution de Montpellier ISEM Univ. Montpellier CNRS IRD Montpellier France Department of Biology Duke University Durham NC United States Ecology and Evolutionary Biology School of Biosciences University of Sheffield Sheffield United Kingdom Department of Fish Ecology and Evolution Centre of Ecology Evolution and Biogeochemistry EAWAG Swiss Federal Institute of Aquatic Science and Technology Kastanienbaum Switzerland Division of Aquatic Ecology and Evolution Institute of Ecology and Evolution University of Bern Bern Switzerland Departamento de Zoologia Universidade Federal do Paraná Curitiba PR Brazil Department of Zoology University of Cambridge Cambridge United Kingdom Department of Advanced Transdisciplinary Sciences Hokkaido University Sapporo Japan Department of Zoology & Biodiversity Research Centre University of British Columbia Vancouver Canada Department of Marine Sciences University of Gothenburg Gothenburg Sweden Division of Evolutionary Biology LMU Munich Munich Germany Department of Ecology & Evolutionary Biology Yale University New Haven CT United States Department of Evolution Ecology and Organismal Biology University of California Riverside Riverside CA United States Department of Biology James Madison University Harrisonburg VA United States Department of Ecology Evolution and Organismal Biology Brown University Provi

Speciation research—the scientific field focused on understanding the origin and diversity of species—has a long and complex history. While relevant to one another, the specific goals and activities of speciation researchers are highly diverse, and scattered across a collection of different perspectives. Thus, our understanding of speciation will benefit from efforts to bridge scientific findings and the diverse people who do the work. In this paper, we outline two ways of integrating speciation research: (i) scientific integration, through the bringing together of ideas, data, and approaches; and (ii) social integration, by creating ways for a diversity of researchers to participate in the scientific process. We then discuss five challenges to integration: (i) the multidisciplinary nature of speciation research, (ii) the complex language of speciation; (iii) a bias toward certain study systems; (iv) the challenges of working across scales; and (v) inconsistent measures and reporting standards. We provide practical steps that individuals and groups can take to help overcome these challenges, and argue that integration is a team effort in which we all have a role to play.

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