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26th Annual computational Neuroscience Meeting (CNS*2017): Part 3 Antwerp, Belgium. 15-20 July 2017 Abstracts

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BMC NEUROSCIENCE 2017年第SUPPL 1期18卷 95-176页

作者： [Anonymous] Department of Neuroscience Yale University New Haven CT 06520 USA Department Physiology & Pharmacology SUNY Downstate Brooklyn NY 11203 USA NYU School of Engineering 6 MetroTech Center Brooklyn NY 11201 USA Departament de Matemàtica Aplicada Universitat Politècnica de Catalunya Barcelona 08028 Spain Institut de Neurobiologie de la Méditerrannée (INMED) INSERM UMR901 Aix-Marseille Univ Marseille France Center of Neural Science New York University New York NY USA Aix-Marseille Univ INSERM INS Inst Neurosci Syst Marseille France Laboratoire de Physique Théorique et Modélisation CNRS UMR 8089 Université de Cergy-Pontoise 95300 Cergy-Pontoise Cedex France Department of Mathematics and Computer Science ENSAT Abdelmalek Essaadi’s University Tangier Morocco Laboratory of Natural Computation Department of Information and Electrical Engineering and Applied Mathematics University of Salerno 84084 Fisciano SA Italy Department of Medicine University of Salerno 84083 Lancusi SA Italy Dipartimento di Fisica Università degli Studi Aldo Moro Bari and INFN Sezione Di Bari Italy Data Analysis Department Ghent University Ghent Belgium Coma Science Group University of Liège Liège Belgium Cruces Hospital and Ikerbasque Research Center Bilbao Spain BIOtech Department of Industrial Engineering University of Trento and IRCS-PAT FBK 38010 Trento Italy Department of Data Analysis Ghent University Ghent 9000 Belgium The Wellcome Trust Centre for Neuroimaging University College London London WC1N 3BG UK Department of Electronic Engineering NED University of Engineering and Technology Karachi Pakistan Blue Brain Project École Polytechnique Fédérale de Lausanne Lausanne Switzerland Departement of Mathematics Swansea University Swansea Wales UK Laboratory for Topology and Neuroscience at the Brain Mind Institute École polytechnique fédérale de Lausanne Lausanne Switzerland Institute of Mathematics University of Aberdeen Aberdeen Scotland UK Department of Integrativ

Redefining pandemic preparedness:Multidisciplinary insights from the CERP modelling workshop in infectious diseases,workshop report

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Infectious Disease Modelling 2024年第2期9卷 501-518页

作者： Marta C.Nunes Edward Thommes Holger Fröhlich Antoine Flahault Julien Arino Marc Baguelin Matthew Biggerstaff Gaston Bizel-Bizellot Rebecca Borchering Giacomo Cacciapaglia Simon Cauchemez Alex Barbier-Chebbah Carsten Claussen Christine Choirat Monica Cojocaru Catherine Commaille-Chapus Chitin Hon Jude Kong Nicolas Lambert Katharina B.Lauer Thorsten Lehr Cédric Mahe Vincent Marechal Adel Mebarki Seyed Moghadas Rene Niehus Lulla Opatowski Francesco Parino Gery Pruvost Andreas Schuppert Rodolphe Thiébaut Andrea Thomas-Bachli Cecile Viboud Jianhong Wu Pascal Crépey Laurent Coudeville Center of Excellence in Respiratory Pathogens(CERP) Hospices Civils de Lyon(HCL)and Centre International de Recherche en Infectiologie(CIRI)Équipe SantéPubliqueÉpidémiologie etÉcologieÉvolutive des Maladies Infectieuses(PHE3ID)Inserm U1111CNRS UMR5308ENS de LyonUniversitéClaude Bernard Lyon 1LyonFrance South African Medical Research Council Vaccines&Infectious Diseases Analytics Research UnitFaculty of Health SciencesUniversity of the WitwatersrandJohannesburgSouth Africa New Products and Innovation(NPI) Sanofi Vaccines(Global)TorontoOntarioCanada Department of Mathematics and Statistics University of GuelphGuelphOntarioCanada Fraunhofer Institute for Algorithms and Scientific Computing(SCAI) Department of BioinformaticsSchloss BirlinghovenSankt AugustinGermany University of Bonn Bonn-Aachen International Center for IT(b-it)BonnGermany Institute of Global Health Faculty of MedicineUniversity of GenevaGenevaSwitzerland and Swiss School of Public HealthZürichSwitzerland Department of Mathematics University of ManitobaWinnipegManitobaCanada MRC Centre for Global Infectious Disease Analysis School of Public HealthImperial College LondonLondonUK Centre for Mathematical Modelling of Infectious Diseases Department of Infectious Disease EpidemiologyLondon School of Hygiene&Tropical MedicineLondonUK National Center for Immunization and Respiratory Diseases(NCIRD) US Centers for Disease Control and Prevention(CDC)AtlantaGAUSA Departement of Computational Biology Departement of Global HealthInstitut PasteurParisFrance Institut de Physique des Deux Infinis de Lyon(IP2I) UMR5822IN2P3/CNRSUniversitéClaude Bernard Lyon 1VilleurbanneFrance Mathematical Modelling of Infectious Diseases Unit Institut PasteurUniversitéParis CitéUMR2000 CNRSParisFrance Decision and Bayesian Computation Institut PasteurUniversitéParis CitéCNRS UMR 3571France Fraunhofer-Institute for Translational Medicine and Pharmacology HamburgGermany Institute of Global Health Faculty of MedicineUniversity of

In July 2023,the center of Excellence in Respiratory Pathogens organized a two-day workshop on infectious diseases modelling and the lessons learnt from the Covid-19 *** report summarizes the rich discussions that occurred during the *** workshop participants discussed multisource data integration and highlighted the benefits of combining traditional surveillance with more novel data sources like mobility data,social media,and wastewater *** advancements were noted in the development of predictive models,with examples from various countries showcasing the use of machine learning and artificial intelligence in detecting and monitoring disease *** role of open collaboration between various stakeholders in modelling was stressed,advocating for the continuation of such partnerships beyond the pandemic.A major gap identified was the absence of a common international framework for data sharing,which is crucial for global pandemic ***,the workshop underscored the need for robust,adaptable modelling frameworks and the integration of different data sources and collaboration across sectors,as key elements in enhancing future pandemic response and preparedness.

关键词： Modelling Covid-19 Infectious diseases Pandemic preparedness Workshop

Tunable coupling of a quantum phononic resonator to a transmon qubit with flip-chip architecture

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arXiv 2024年

作者： Ruan, Xinhui Li, Li Liang, Guihan Zhao, Silu Wang, Jia-Heng Bu, Yizhou Chen, Bingjie Song, Xiaohui Li, Xiang Zhang, He Wang, Jinzhe Zhao, Qianchuan Xu, Kai Fan, Heng Liu, Yu-Xi Zhang, Jing Peng, Zhihui Xiang, Zhongcheng Zheng, Dongning Beijing National Laboratory for Condensed Matter Physics Institute of Physics Chinese Academy of Sciences Beijing100190 China Department of Automation Tsinghua University Beijing100084 China Key Laboratory of Low-Dimensional Quantum Structures and Quantum Control of Ministry of Education Key Laboratory for Matter Microstructure and Function of Hunan Province Department of Physics Synergetic Innovation Center for Quantum Effects and Applications Hunan Normal University Changsha410081 China School of Physical Sciences University of Chinese Academy of Sciences Beijing100049 China School of Integrated Circuits Tsinghua University Beijing100084 China Hefei National Laboratory Hefei230088 China School of Automation Science and Engineering Xi'an Jiaotong University Xi’an710049 China MOE Key Lab for Intelligent Networks and Network Security Xi'an Jiaotong University Xi’an710049 China CAS Center for Excellence in Topological Quantum Computation School of Physical Sciences University of Chinese Academy of Sciences Beijing100049 China

A hybrid system with tunable coupling between phonons and qubits shows great potential for advancing quantum information processing. In this work, we demonstrate strong and tunable coupling between a surface acoustic wave (SAW) resonator and a transmon qubit based on galvanic-contact flip-chip technique. The coupling strength varies from 2π×7.0 MHz to -2π×20.6 MHz, which is extracted from different vacuum Rabi oscillation frequencies. The phonon-induced ac Stark shift of the qubit at different coupling strengths is also shown. Our approach offers a good experimental platform for exploring quantum acoustics and hybrid systems. Copyright © 2024, The Authors. All rights reserved.

关键词： Hybrid systems

Correction: PDMATLAB2D: A Peridynamics MATLAB Two-dimensional Code

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Journal of Peridynamics and Nonlocal Modeling 2024年第3期6卷 530-530页

作者： Seleson, Pablo Pasetto, Marco John, Yohan Trageser, Jeremy Reeve, Samuel Temple Computer Science and Mathematics Division Oak Ridge National Laboratory Oak Ridge USA Department of Mechanical and Aerospace Engineering University of California San Diego La Jolla USA George W. Woodruff School of Mechanical Engineering Georgia Institute of Technology Atlanta USA Center for Control Dynamical Systems and Computation University of California at Santa Barbara Harold Frank Hall Room 5119 Santa Barbara USA Engineering Sciences Center Sandia National Laboratories Albuquerque USA Computational Sciences and Engineering Division Oak Ridge National Laboratory Oak Ridge USA

26th Annual computational Neuroscience Meeting (CNS*2017) of the Organization for computational Neuroscience Antwerp, Belgium, July 15-20, 2017

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BMC NEUROSCIENCE 2017年第SUPPL 1期18卷 59-59页

作者： [Anonymous] Indiana University Purdue University Indianapolis Indianapolis IN 46032 USA Stark Neurosciences Research Institute Indiana University School of Medicine Indianapolis IN 46032 USA Department of Mathematics East Carolina University Greenville NC 27858 USA Jülich Supercomputing Centre Forschungszentrum Jülich 52425 Jülich Germany Future Systems Swiss National Supercomputing Centre 8092 Zurich Switzerland User Engagement and Support Swiss National Supercomputing Centre 6900 Lugano Switzerland Institut de Neurosciences des Systèmes Aix Marseille Univ 13005 Marseille France Simulation Lab Neuroscience Forschungszentrum Jülich Jülich Germany Department of Experimental Psychology Ghent University 9000 Ghent Belgium Donders Center for Cognitive Neuroimaging Radboud University 6525HR Nijmegen The Netherlands Department of Electrical Computer and Energy Engineering University of Colorado Boulder CO 80309 USA Department of Neurosurgery Johns Hopkins School of Medicine Baltimore MD 21287 USA Department of Neurology Johns Hopkins School of Medicine Baltimore MD 21287 USA Department of Otolaryngology Johns Hopkins School of Medicine Baltimore MD 21287 USA INSERM U968 Paris France Sorbonne Universités UPMC University Paris 06 UMR_S 968 Institut de la Vision Paris France CNRS UMR_7210 Paris France Department of Computer Architecture and Technology University of Granada (CITIC) Granada Spain Sorbonne Universités UPMC Univ Paris 06 INSERM CNRS Institut de la Vision Paris France Department of Adaptive Machine Systems Osaka University Osaka Japan Department of Computer Science University of Cergy-Pontoise Cergy-Pontoise France Department of Physics and Astronomy College of Charleston Charleston SC 29424 USA School of Physics Faculty of Science University of Sydney Sydney NSW 2006 Australia Center of Excellence for Integrative Brain Function Australian Research Council Sydney Australia Max Planck Institute for Human Cognitive and Brain Sciences Saxony Lei

FULLY TRANSFORMER-BASED BIOMARKER PREDICTION FROM COLORECTAL CANCER HISTOLOGY: A LARGE-SCALE MULTICENTRIC STUDY

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arXiv 2023年

作者： Wagner, Sophia J. Reisenbüchler, Daniel West, Nicholas P. Niehues, Jan Moritz Veldhuizen, Gregory Patrick Quirke, Philip Grabsch, Heike I. van den Brandt, Piet A. Hutchins, Gordon G.A. Richman, Susan D. Yuan, Tanwei Langer, Rupert Jenniskens, Josien Christina Anna Offermans, Kelly Mueller, Wolfram Gray, Richard Gruber, Stephen B. Greenson, Joel K. Rennert, Gad Bonner, Joseph D. Schmolze, Daniel James, Jacqueline A. Loughrey, Maurice B. Salto-Tellez, Manuel Brenner, Hermann Hoffmeister, Michael Truhn, Daniel Schnabel, Julia A. Boxberg, Melanie Peng, Tingying Kather, Jakob Nikolas Helmholtz Munich German Research Center for Environment and Health Munich Germany School of Computation Information and Technology Technical University of Munich Munich Germany Technical University Dresden Dresden Germany Division of Pathology and Data Analytics Leeds Institute of Medical Research at St James’s University of Leeds Leeds United Kingdom Department of Pathology GROW School for Oncology and Developmental Biology Maastricht University Medical Center+ Maastricht Netherlands Department of Epidemiology Maastricht University Medical Center+ Maastricht Netherlands Heidelberg Germany Institute of Pathology und Molecular Pathology Johannes Kepler University Hospital Linz Linz Austria Gemeinschaftspraxis Pathologie Starnberg Germany Nuffield Department of Population Health University of Oxford Oxford United Kingdom Center for Precision Medicine Department of Medical Oncology City of Hope National Medical Center DuarteCA United States Department of Pathology City of Hope Comprehensive Cancer Center DuarteCA United States Department of Community Medicine & Epidemiology Lady Davis Carmel Medical Center Ruth & Bruce Rappaport Faculty of Medicine Technion-Israel Institute of Technology Israel Steve and Cindy Rasmussen Institute for Genomic Medicine Lady Davis Carmel Medical Center Technion Faculty of Medicine Clalit National Cancer Control Center Haifa Israel Precision Medicine Centre of Excellence Health Sciences Building The Patrick G Johnston Centre for Cancer Research Queen’s University Belfast Belfast United Kingdom Regional Molecular Diagnostic Service Belfast Health and Social Care Trust Belfast United Kingdom The Patrick G Johnston Centre for Cancer Research Queen’s University Belfast Belfast United Kingdom Department of Cellular Pathology Belfast Health and Social Care Trust Belfast United Kingdom Centre for Public Health Queen’s University Belfast Belfast United Kingdom Integrated Pathology Unit Institute for Cancer Research Royal Mars

Background: Deep learning (DL) can extract predictive and prognostic biomarkers from routine pathology slides in colorectal cancer. For example, a DL test for the diagnosis of microsatellite instability (MSI) in CRC has been approved in 2022. Current approaches rely on convolutional neural networks (CNNs). Transformer networks are outperforming CNNs and are replacing them in many applications, but have not been used for biomarker prediction in cancer at a large scale. In addition, most DL approaches have been trained on small patient cohorts, which limits their clinical utility. Methods: In this study, we developed a new fully transformer-based pipeline for end-to-end biomarker prediction from pathology slides. We combine a pre-trained transformer encoder and a transformer network for patch aggregation, capable of yielding single and multi-target prediction at patient level. We train our pipeline on over 9,000 patients from 10 colorectal cancer cohorts. Results: A fully transformer-based approach massively improves the performance, generalizability, data efficiency, and interpretability as compared with current state-of-the-art algorithms. After training on a large multicenter cohort, we achieve a sensitivity of 0.97 with a negative predictive value of 0.99 for MSI prediction on surgical resection specimens. We demonstrate for the first time that resection specimen-only training reaches clinical-grade performance on endoscopic biopsy tissue, solving a long-standing diagnostic problem. Interpretation: A fully transformer-based end-to-end pipeline trained on thousands of pathology slides yields clinical-grade performance for biomarker prediction on surgical resections and biopsies. Our new methods are freely available under an open source license. © 2023, CC BY.

关键词： Forecasting

Can we represent the depreobesity genetically?

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Obesity Medicine 2020年 19卷 100273-100273页

作者： Mediouni, Mohamed Bergantin, Leandro Bueno Madiouni, Riadh Kaczor-Urbanowicz, Karolina Elżbieta Urbanowicz, Andrzej 1420 Rue Laterrière Sherbrooke J1K 2R2 QC Canada Department of Pharmacology Universidade Federal de São Paulo Brazil University of Paris-Est Créteil Vitry sur Seine France Center for Oral and Head/Neck Oncology Research UCLA Section of Oral Biology Division of Oral Biology & Medicine Center for the Health Sciences UCLA School of Dentistry University of California at Los Angeles 10833 Le Conte Ave Box 951668 Los Angeles 90095-1668 CA United States UCLA Institute for Quantitative and Computational Biosciences University of California at Los Angeles 611 Charles E. Young Drive Boyer Hall 570 Box 951570 Los Angeles 90095-1606 CA United States Institute of Control and Computation Engineering Warsaw University of Technology Ul. Nowowiejska 15/19 Warsaw 00-665 Poland

Obesity and depression are serious public health problems and cause death in the world. In this article, we will study the concept of depreobesity that explains the depression in the context of obesity and vice versa. We will review whether animals can be used to understand the interrelationship between obesity and depression. We will propose saliva as a translational paradigm to predict the depreobesity. Policymakers need to identify new strategies for depreobesity by understanding the complexity of shared biology (depression-obesity). Perspectives for the future are promising. © 2020 Elsevier Ltd

关键词： Depreobesity Depression Genes Obesity Saliva

Effects of drug resistance in the tumour-immune system with chemotherapy treatment

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arXiv 2020年

作者： Trobia, José Gabrick, Enrique C. Seifert, Evandro G. Borges, Fernando S. Protachevicz, Paulo R. Szezech, José D. Iarosz, Kelly C. Santos, Moises S. Caldas, Iberê L. Tian, Kun Ren, Hai-Peng Grebogi, Celso Batista, Antonio M. Graduate Program in Science - Physics State University of Ponta Grossa Ponta GrossaPR84030-900 Brazil Department of Mathematics and Statistics State University of Ponta Grossa Ponta GrossaPR84030-900 Brazil Department of Physics State University of Ponta Grossa Ponta GrossaPR84030-900 Brazil Center for Mathematics Computation and Cognition Federal University of ABC São Bernardo do CampoSP09606-045 Brazil Institute of Physics University of São Paulo São PauloSP05508-900 Brazil Faculty of Telêmaco Borba FATEB Telêmaco BorbaPR84266-010 Brazil Graduate Program in Chemical Engineering Federal Technological University of Paraná Ponta Grossa Paraná84016-210 Brazil Shaanxi Key Lab of Complex System Control and Intelligent Information Processing Xi’an University of Technology Xi’an710048 China Xi’an Technological University Xi’an710021 China Institute for Complex Systems and Mathematical Biology University of Aberdeen AberdeenAB24 3UE United Kingdom

Cancer is a term used to refer to a large set of diseases. The cancerous cells grow and divide and, as a result, they form tumours that grow in size. The immune system recognise the cancerous cells and attack them, though, it can be weakened by the cancer. One type of cancer treatment is chemotherapy, which uses drugs to kill cancer cells. Clinical, experimental, and theoretical research has been developed to understand the dynamics of cancerous cells with chemotherapy treatment, as well as the interaction between tumour growth and immune system. We study a mathematical model that describes the cancer growth, immune system response, and chemotherapeutic agents. The immune system is composed of resting cells that are converted to hunting cells to combat the cancer. In this work, we consider drug sensitive and resistant cancer cells. We show that the tumour growth can be controlled not only by means of different chemotherapy protocols, but also by the immune system that attacks both sensitive and resistant cancer cells. Furthermore, for all considered protocols, we demonstrate that the time delay from resting to hunting cells plays a crucial role in the combat against cancer cells. Copyright © 2020, The Authors. All rights reserved.

关键词： Chemotherapy

Space QUEST mission proposal: Experimentally testing decoherence due to gravity

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arXiv 2017年

作者： Joshi, Siddarth Koduru Pienaar, Jacques Ralph, Timothy C. Cacciapuoti, Luigi McCutcheon, Will Rarity, John Giggenbach, Dirk Lim, Jin Gyu Makarov, Vadim Fuentes, Ivette Scheidl, Thomas Beckert, Erik Bourennane, Mohamed Bruschi, David Edward Cabello, Adan Capmany, Jose Carrasco-Casado, Alberto Diamanti, Eleni Dusek, Miloslav Elser, Dominique Gulinatti, Angelo Hadfield, Robert H. Jennewein, Thomas Kaltenbaek, Rainer Krainak, Michael A. Lo, Hoi-Kwong Marquardt, Christoph Milburn, Gerard Peev, Momtchil Poppe, Andreas Pruneri, Valerio Renner, Renato Salomon, Christophe Skaar, Johannes Solomos, Nikolaos Stipčević, Mario Torres, Juan P. Toyoshima, Morio Villoresi, Paolo Walmsley, Ian Weihs, Gregor Weinfurter, Harald Zeilinger, Anton Zukowski, Marek Ursin, Rupert Institute for Quantum Optics and Quantum Information Austrian Academy of Sciences Boltzmanngasse 3 ViennaA-1090 Austria Centre for Quantum Computation & Communication Technology Department of Physics University of Queensland St LuciaQLD4072 Australia European Space Agency Keplerlaan 1 - P.O. Box 299 AG Noordwijk ZH2200 Netherlands Department of Electrical and Electronic Engineering University of Bristol Merchant Venturers Building Woodland Road BristolBS8 1UB United Kingdom German Aerospace Center Institute of Communications and Navigation Muenchner Strasse 20 Wessling82234 Germany Institute for Quantum Computing Department of Electrical and Computer Engineering University of Waterloo WaterlooONN2L 3G1 Canada Institute for Quantum Computing Department of Physics and Astronomy University of Waterloo WaterlooONN2L 3G1 Canada Precision Engineering Department Fraunhofer IOF Albert-Einstein-Straße 7 Jena07745 Germany Physics department Stockholm University Albanova universitetscentrum Universitetsvägen 10 Stockholm114 18 Sweden York Centre for Quantum Technologies Department of Physics University of York HeslingtonYO10 5DD United Kingdom University of Seville SevillaE-41012 Spain iTEAM Research Institute Universitat Politcnica de Valencia Valencia46022 Spain 4-2-1 Nukui-Kitamachi Koganei Tokyo184-8795 Japan CNRS Universite Pierre et Marie Curie Paris75005 France Department of Optics Faculty of Science Palacky University 17. Iistopadu 12 Olomouc772 00 Czech Republic Max Planck Institute for the Science of Light Bldg. 24 Günther-Scharowsky Str. 1 ErlangenD-91058 Germany Piazza Leonardo da Vinci 32 Milano20133 Italy School of Engineering University of Glasgow GlasgowG12 8LT United Kingdom Vienna Center of Quantum Science and Technology Faculty of Physics University of Vienna Boltzmanngasse 5 ViennaA-1090 Austria NASA Goddard Space Flight Center GreenbeltMD20771 United States Center for Quantum Information and Quantum Con

Models of quantum systems on curved space-times lack sufficient experimental verification. Some speculative theories suggest that quantum properties, such as entanglement, may exhibit entirely different behavior to purely classical systems. By measuring this effect or lack thereof, we can test the hypotheses behind several such models. For instance, as predicted by Ralph and coworkers [T C Ralph, G J Milburn, and T Downes, Phys. Rev. A, 79(2):22121, 2009;T C Ralph and J Pienaar, New Journal of Physics, 16(8):85008, 2014], a bipartite entangled system could decohere if each particle traversed through a different gravitational field gradient. We propose to study this effect in a ground to space uplink scenario. We extend the above theoretical predictions of Ralph and coworkers and discuss the scientific consequences of detecting/failing to detect the predicted gravitational decoherence. We present a detailed mission design of the European Space Agency's (ESA) Space QUEST (Space - Quantum Entanglement Space Test) mission, and study the feasibility of the mission scheme. Copyright © 2017, The Authors. All rights reserved.

关键词： Quantum entanglement