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arXiv 2018年

作者： Liu, Quan-Hui Lü, Feng-Mao Zhang, Qian Tang, Ming Zhou, Tao Web Sciences Center School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu611731 China Laboratory for the Modeling of Biological and Socio-technical Systems Northeastern University BostonMA02115 United States Big Data Research Center University of Electronic Science and Technology of China Chengdu611731 China School of Information Science Technology East China Normal University Shanghai200241 China Institute of Fundamental and Frontier Science University of Electronic Science and Technology of China Chengdu611731 China

Opinion leaders are ubiquitous in both online and offline social networks, but the impacts of opinion leaders on social behavior contagions are still not fully understood, especially by using a mathematical model. Here we generalize the classical Watts threshold model and address the influences of the opinion leaders, where an individual adopts a new behavior if one of his/her opinion leaders adopts the behavior. First, we choose the opinion leaders randomly from all individuals in the network and find the impacts of opinion leaders make other individuals adopt the behavior more easily. Specifically, the existence of opinion leaders reduces the lowest mean degree of the network required for the global behavior adoption, and increases the highest mean degree of the network that the global behavior adoption can occur. Besides, the introduction of opinion leaders accelerates the behavior adoption, but does not change the adoption order of individuals. The developed theoretical predictions agree with the simulation results. Second, we randomly choose the opinion leaders from the top h% of the highest degree individuals, and find an optimal h% for the network with the lowest mean degree that the global behavior adoption can occur. Meanwhile, the influences of opinion leaders on accelerating the adoption of behaviors become less significant and can even be ignored when reducing the value of h%. Copyright © 2018, The Authors. All rights reserved.

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50 Years of Quantum Chromodynamics

arXiv

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arXiv 2022年

作者： Gross, Franz Klempt, Eberhard Brodsky, Stanley J. Buras, Andrzej J. Burkert, Volker D. Heinrich, Gudrun Jakobs, Karl Meyer, Curtis A. Orginos, Kostas Strickland, Michael Stachel, Johanna Zanderighi, Giulia Brambilla, Nora Braun-Munzinger, Peter Britzger, Daniel Capstick, Simon Cohen, Tom Crede, Volker Constantinou, Martha Davies, Christine Debbio, Luigi Del Denig, Achim DeTar, Carleton Deur, Alexandre Dokshitzer, Yuri Dosch, Hans Günter Dudek, Jozef Dunford, Monica Epelbaum, Evgeny Escobedo, Miguel A. Fritzsch, Harald Fukushima, Kenji Gambino, Paolo Gillberg, Dag Gottlieb, Steven Grafstrom, Per Grazzini, Massimiliano Grube, Boris Guskov, Alexey Iijima, Toru Ji, Xiangdong Karsch, Frithjof Kluth, Stefan Kogut, John B. Krauss, Frank Kumano, Shunzo Leinweber, Derek Leutwyler, Heinrich Li, Hai-Bo Li, Yang Malaescu, Bogdan Mariotti, Chiara Maris, Pieter Marzani, Simone Melnitchouk, Wally Messchendorp, Johan Meyer, Harvey Mitchell, Ryan Edward Mondal, Chandan Nerling, Frank Neubert, Sebastian Pappagallo, Marco Pastore, Saori Peláez, José R. Puckett, Andrew Qiu, Jianwei Rabbertz, Klaus Ramos, Alberto Rossi, Patrizia Rustamov, Anar Schäfer, Andreas Scherer, Stefan Schindler, Matthias Schramm, Steven Shifman, Mikhail Shuryak, Edward Sjöstrand, Torbjörn Sterman, George Stewart, Iain W. Stroth, Joachim Swanson, Eric de Téramond, Guy F. Thoma, Ulrike Vairo, Antonio van Dyk, Danny Vary, James Virto, Javier Vos, Marcel Weiss, Christian Wobisch, Markus Wu, Sau Lan Young, Christopher Yuan, Feng Zhao, Xingbo Zhou, Xiaorong Thomas Jefferson National Accelerator Facility 12000 Jefferson Avenue Newport NewsVA23606 United States Department of Physics William and Mary WilliamsburgVA23187 United States Helmholtz-Institut für Strahlen- und Kernphysik Universität Bonn Nußallee 14-16 BonnD 53115 Germany Theoretical Physics SLAC National Accelerator Laboratory 2575 Sand Hill Road Menlo ParkCA94025 United States Institute for Advanced Study Technische Universität München Lichtenbergstraße 2a Garching b. MünchenD-85748 Germany KarlsruheD-76128 Germany Physikalisches Institut Universität Freiburg Freiburg79104 Germany Carnegie Mellon University PittsburghPA15213 United States Department of Physics Kent State University 800 E Summit St KentOH44240 United States Physikalisches Institut Universität Heidelberg Heidelberg69120 Germany Max-Planck-Institut für Physik Föhringer Ring 6 München80805 Germany Physik Department Technische Universität München James-Franck-Straße 1 Garching b. MünchenD-85748 Germany Munich Data Science Institute Technische Universität München Walther-von-Dyck-Straße-10 Garching b. MünchenD-85748 Germany Extreme Matter Institute EMMI GSI Darmstadt64291 Germany Department of Physics Florida State University TallahasseeFL32306 United States Department of Physics University of Maryland College ParkMD20742 United States Physics Department Temple University 1925 N. 12th Street PhiladelphiaPA19122 United States School of Physics and Astronomy University of Glasgow GlasgowG12 8QQ United Kingdom Higgs Centre for Theoretical Physics School of Physics and Astronomy The University of Edinburgh EdinburghEH9 3FD United Kingdom PRISMA+ Cluster of Excellence Institut für Kernphysik Helmholtz Institute Mainz Johannes Gutenberg University Mainz MainzD-55128 Germany Department of Physics and Astronomy University of Utah Salt Lake CityUT84112 United States Riga Technical University Center of High Energy Physics and Accelerator Technologies Riga

Quantum Chromodynamics, the theory of quarks and gluons, whose interactions can be described by a local SU(3) gauge symmetry with charges called "color quantum numbers", is reviewed;the goal of this review is to provide advanced Ph.D. students a comprehensive handbook, helpful for their research. When QCD was "discovered" 50 years ago, the idea that quarks could exist, but not be observed, left most physicists unconvinced. Then, with the discovery of charmonium in 1974 and the explanation of its excited states using the Cornell potential, consisting of the sum of a Coulomb-like attraction and a long range linear confining force, the theory was suddenly widely accepted. This paradigm shift is now referred to as the November revolution. It had been anticipated by the observation of scaling in deep inelastic scattering, and was followed by the discovery of gluons in three-jet events. The parameters of QCD include the running coupling constant, αs(Q2), that varies with the energy scale Q2 characterising the interaction, and six quark masses. QCD cannot be solved analytically, at least not yet, and the large value of αs at low momentum transfers limits perturbative calculations to the high-energy region where Q2 ΛQCD2 (250 MeV)2. Lattice QCD (LQCD), numerical calculations on a discretized spacetime lattice, is discussed in detail, the dynamics of the QCD vacuum is visualized, and the expected spectra of mesons and baryons are displayed. Progress in lattice calculations of the structure of nucleons and of quantities related to the phase diagram of dense and hot (or cold) hadronic matter are reviewed. Methods and examples of how to calculate hadronic corrections to weak matrix elements on a lattice are outlined. The wide variety of analytical approximations currently in use, and the accuracy of these approximations, are reviewed. These methods range from the Bethe-Salpeter, Dyson-Schwinger coupled relativistic equations, which are formulated in both Minkowski or Euclidean

关键词： Quantum theory

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Self-Evolutionary Neuron Model for Fast-Response Spiking Neural networks

TechRxiv

引用

TechRxiv 2019年

作者： Zhang, Anguo Han, Ying Hu, Jing Niu, Yuzhen Gao, Yueming Chen, Zhizhang Zhao, Kai College of Physics and Information Engineering Fuzhou University Fujian350108 China The Key Laboratory of Medical Instrumentation Pharmaceutical Technology of Fujian Province Fuzhou350116 China Research Institute of Ruijie Ruijie Networks Co. Ltd Fujian350002 China School of Public Health Xiamen University Xiamen361102 China College of Information and Intelligent Transportation Fujian Chuanzheng Communications College Fuzhou350007 China Fujian Key Laboratory of Network Computing and Intelligent Information Processing College of Mathematics and Computer Science Fuzhou University The Key Laboratory of Spatial Data Mining and Information Sharing Ministry of Education Fujian350108 China College of Physics and information Engineering Fuzhou University the Key Laboratory of Medical Instrumentation Pharmaceutical Technology of Fujian Province Fujian350108 China College of Physics and Information Engineering Fuzhou University 350108 China Faculty of Science and Technology University of Macau 999078 China

We propose two simple and effective spiking neuron models to improve the response time of the conventional spiking neural network. The proposed neuron models adaptively tune the presynaptic input current depending on the input received from its presynapses and subsequent neuron firing events. We analyze and derive the firing activity homeostatic convergence of the proposed models. We experimentally verify and compare the models on MNIST handwritten digits and FashionMNIST classification tasks. We show that the proposed neuron models significantly increase the response speed to the input signal. Experiment codes are available at https://***/anvien/Evol-SNN. © 2019, CC0.

关键词： Neural networks

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DESI Strong Lens Foundry I: HST Observations and Modeling with GIGA-Lens

arXiv

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arXiv 2025年

作者： Huang, X. Baltasar, S. Ratier-Werbin, N. Storfer, C. Sheu, W. Agarwal, S. Tamargo-Arizmendi, M. Schlegel, D.J. Aguilar, J. Ahlen, S. Aldering, G. Banka, S. BenZvi, S. Bianchi, D. Bolton, A. Brooks, D. Cikota, A. Claybaugh, T. de la Macorra, A. Dey, A. Doel, P. Edelstein, J. Filipp, A. Forero-Romero, J.E. Gaztañaga, E. Gontcho, S.A. Gontcho Gu, A. Gutierrez, G. Honscheid, K. Jullo, E. Juneau, S. Kehoe, R. Kirkby, D. Kisner, T. Kremin, A. Kwon, K.J. Lambert, A. Landriau, M. Lang, D. Le Guillou, L. Liu, J. Meisner, A. Miquel, R. Moustakas, J. Myers, A.D. Perlmutter, S. Pérez-Ràfols, I. Prada, F. Rossi, G. Rubin, D. Sanchez, E. Schubnell, M. Shu, Y. Silver, E. Sprayberry, D. Suzuki, N. Tarlé, G. Weaver, B.A. Zou, H. Department of Physics & Astronomy University of San Francisco San FranciscoCA94117 United States Physics Division Lawrence Berkeley National Laboratory 1 Cyclotron Road BerkeleyCA94720 United States Department of Physics Complutense University of Madrid Madrid28040 Spain Department of Mathematics Complutense University of Madrid Madrid28040 Spain Institute for Astronomy University of Hawai’i HonoluluHI96822-1897 United States Department of Physics & Astronomy University of California Los Angeles Los AngelesCA90095 United States University of Chicago Department of Astronomy ChicagoIL60615 United States Department of Physics & Astronomy University of Pittsburgh PittsburghPA15260 United States Physics Dept. Boston University 590 Commonwealth Avenue BostonMA02215 United States Department of Electrical Engineering & Computer Sciences University of California Berkeley BerkeleyCA94720 United States Department of Physics & Astronomy University of Rochester 206 Bausch and Lomb Hall P.O. Box 270171 RochesterNY14627-0171 United States Dipartimento di Fisica "Aldo Pontremoli" Università degli Studi di Milano Via Celoria 16 MilanoI-20133 Italy NSF’s National Optical-Infrared Astronomy Research Laboratory TucsonAZ85719 United States Department of Physics & Astronomy University College London Gower Street LondonWC1E 6BT United Kingdom Gemini Observatory NSF’s NOIRLab Casilla 603 La Serena Chile Instituto de Física Universidad Nacional Autónoma de México Circuito de la Investigación Científica Ciudad Universitaria Cd. de MéxicoC. P. 04510 Mexico Space Sciences Laboratory University of California Berkeley 7 Gauss Way BerkeleyCA94720 United States Université de Montréal Physics Department 1375 Av. Thérèse-Lavoie-Roux MontréalQCH2V 0B3 Canada Ciela – Montreal Institute for Astrophysical Data Analysis and Machine Learning 1375 Av. Thérèse-Lavoie-Roux MontréalQCH2V 0B3 Canada Technical University Munich TUM School of Natural Sciences

We present the Dark Energy Spectroscopic Instrument (DESI) Strong Lens Foundry. We discovered ∼ 3500 new strong gravitational lens candidates in the DESI Legacy Imaging Surveys using residual neural networks (ResNet). We observed a subset (51) of our candidates using the Hubble Space Telescope (HST). All of them were confirmed to be strong lenses. We also briefly describe spectroscopic follow-up observations by DESI and Keck NIRES programs. From this very rich dataset, a number of studies will be carried out, including evaluating the quality of the ResNet search candidates and lens modeling. In this paper, we present our initial effort in these directions. In particular, as a demonstration, we present the lens model for DESI-165.4754-06.0423, with imaging data from HST, and lens and source redshifts from DESI and Keck NIRES, respectively. In this effort, we have applied a fully forward-modeling Bayesian approach (GIGA-Lens), using multiple GPUs, for the first time in both regards, to a strong lens with HST data, or any high resolution imaging. © 2025, CC BY.

关键词： Dark energy

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Author Correction: Deep learning improves macromolecule identification in 3D cellular cryo-electron tomograms

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Nature methods 2022年第1期19卷 129页

作者： Emmanuel Moebel Antonio Martinez-Sanchez Lorenz Lamm Ricardo D Righetto Wojciech Wietrzynski Sahradha Albert Damien Larivière Eric Fourmentin Stefan Pfeffer Julio Ortiz Wolfgang Baumeister Tingying Peng Benjamin D Engel Charles Kervrann Serpico Project-Team Centre Inria Rennes-Bretagne Atlantique and CNRS-UMR 144 Inria CNRS Institut Curie PSL Research University Campus Universitaire de Beaulieu Rennes Cedex France. Department of Computer Science Faculty of Sciences University of Oviedo Oviedo Spain. Health Research Institute of Asturias (ISPA) Avenida Hospital Universitario s/n Oviedo Spain. Institute of Neuropathology Cluster of Excellence 'Multiscale Bioimaging: from Molecular Machines to Networks of Excitable Cells' University of Göttingen Göttingen Germany. Helmholtz Pioneer Campus Helmholtz Zentrum München Neuherberg Germany. Helmholtz AI Helmholtz Zentrum München Neuherberg Germany. Max Planck Institute of Biochemistry Martinsried Germany. Fourmentin-Guilbert Scientific Foundation Noisy-le-Grand France. Zentrum für Molekulare Biologie der Universität Heidelberg Heidelberg Germany. Ernst Ruska-Centre Wilhelm-Johnen-Straße Jülich Germany. Helmholtz Pioneer Campus Helmholtz Zentrum München Neuherberg Germany. ben.engel@helmholtz-muenchen.de. Department of Chemistry Technical University of Munich Garching Germany. ben.engel@helmholtz-muenchen.de. Serpico Project-Team Centre Inria Rennes-Bretagne Atlantique and CNRS-UMR 144 Inria CNRS Institut Curie PSL Research University Campus Universitaire de Beaulieu Rennes Cedex France. charles.kervrann@inria.fr.

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Global investments in pandemic preparedness and COVID-19: development assistance and domestic spending on health between 1990 and 2026

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The Lancet Global Health 2023年第3期11卷 e385-e413页

作者： Micah, Angela E. Bhangdia, Kayleigh Cogswell, Ian E. Lasher, Dylan Lidral-Porter, Brendan Maddison, Emilie R. Nguyen, Trang Nhu Ngoc Patel, Nishali Pedroza, Paola Solorio, Juan Stutzman, Hayley Tsakalos, Golsum Wang, Yifeng Warriner, Wesley Zhao, Yingxi Zlavog, Bianca S. Abbafati, Cristiana Abbas, Jaffar Abbasi-Kangevari, Mohsen Abbasi-Kangevari, Zeinab Abdelmasseh, Michael Abdulah, Deldar Morad Abedi, Aidin Abegaz, Kedir Hussein Abhilash, E.S. Aboagye, Richard Gyan Abolhassani, Hassan Abrigo, Michael R.M. Abubaker Ali, Hiwa Abu-Gharbieh, Eman Adem, Mohammed Hussien Afzal, Muhammad Sohail Ahmadi, Ali Ahmed, Haroon Ahmed Rashid, Tarik Aji, Budi Akbarialiabad, Hossein Akelew, Yibeltal Al Hamad, Hanadi Alam, Khurshid Alanezi, Fahad Mashhour Alanzi, Turki M. Al-Hanawi, Mohammed Khaled Alhassan, Robert Kaba Aljunid, Syed Mohamed Almustanyir, Sami Al-Raddadi, Rajaa M. Alvis-Guzman, Nelson Alvis-Zakzuk, Nelson J. Amare, Azmeraw T. Ameyaw, Edward Kwabena Amini-Rarani, Mostafa Amu, Hubert Ancuceanu, Robert Andrei, Tudorel Anwar, Sumadi Lukman Appiah, Francis Aqeel, Muhammad Arabloo, Jalal Arab-Zozani, Morteza Aravkin, Aleksandr Y. Aremu, Olatunde Aruleba, Raphael Taiwo Athari, Seyyed Shamsadin Avila-Burgos, Leticia Ayanore, Martin Amogre Azari, Samad Baig, Atif Amin Bantie, Abere Tilahun Barrow, Amadou Baskaran, Pritish Basu, Sanjay Batiha, Abdul-Monim Mohammad Baune, Bernhard T. Berezvai, Zombor Bhardwaj, Nikha Bhardwaj, Pankaj Bhaskar, Sonu Boachie, Micheal Kofi Bodolica, Virginia Botelho, João Silva Botelho Braithwaite, Dejana Breitborde, Nicholas J.K. Busse, Reinhard Cahuana-Hurtado, Lucero Catalá-López, Ferrán Chansa, Collins Charan, Jaykaran Chattu, Vijay Kumar Chen, Simiao Chukwu, Isaac Sunday Dadras, Omid Dandona, Lalit Dandona, Rakhi Dargahi, Abdollah Debela, Sisay Abebe Denova-Gutiérrez, Edgar Desye, Belay Dharmaratne, Samath Dhamminda Diao, Nancy Doan, Linh Phuong Dodangeh, Milad dos Santos, Wendel Mombaque Doshmangir, Leila Dube, John Eini, Ebrahim El Sayed Zaki, Maysaa El Tantawi, Maha Enyew, Daniel Berhanie Institute for Health Metrics and Evaluation University of Washington Seattle WA United States Department of Applied Mathematics University of Washington Seattle WA United States Department of Health Metrics Sciences School of Medicine University of Washington Seattle WA United States Nuffield Department of Medicine University of Oxford Oxford United Kingdom Department of Juridical and Economic Studies La Sapienza University Rome Italy Antai College of Economics Shanghai Jiao Tong University Shanghai China Non-communicable Diseases Research Center Tehran University of Medical Sciences Tehran Iran Research Center for Immunodeficiencies Tehran University of Medical Sciences Tehran Iran Multiple Sclerosis Research Center Tehran University of Medical Sciences Tehran Iran Children’s Medical Center Tehran University of Medical Sciences Tehran Iran Department of Epidemiology and Biostatistics Tehran University of Medical Sciences Tehran Iran Tehran Heart Center Tehran University of Medical Sciences Tehran Iran Faculty of Medicine Tehran University of Medical Sciences Tehran Iran National Institute for Health Research Tehran University of Medical Sciences Tehran Iran Department of Pharmacology Tehran University of Medical Sciences Tehran Iran Department of Cardiology Tehran University of Medical Sciences Tehran Iran Sina Trauma and Surgery Research Center Tehran University of Medical Sciences Tehran Iran Digestive Diseases Research Institute Tehran University of Medical Sciences Tehran Iran Department of Neurology Tehran University of Medical Sciences Tehran Iran Social Determinants of Health Research Center Shahid Beheshti University of Medical Sciences Tehran Iran Department of Epidemiology Shahid Beheshti University of Medical Sciences Tehran Iran Department of Health Economics and Statistics Shahid Beheshti University of Medical Sciences Tehran Iran Department of Neurosurgery Shahid Beheshti University of Medical Sciences Tehran Iran Ophthalmic R

Background: The COVID-19 pandemic highlighted gaps in health surveillance systems, disease prevention, and treatment globally. Among the many factors that might have led to these gaps is the issue of the financing of national health systems, especially in low-income and middle-income countries (LMICs), as well as a robust global system for pandemic preparedness. We aimed to provide a comparative assessment of global health spending at the onset of the pandemic;characterise the amount of development assistance for pandemic preparedness and response disbursed in the first 2 years of the COVID-19 pandemic;and examine expectations for future health spending and put into context the expected need for investment in pandemic preparedness. Methods: In this analysis of global health spending between 1990 and 2021, and prediction from 2021 to 2026, we estimated four sources of health spending: development assistance for health (DAH), government spending, out-of-pocket spending, and prepaid private spending across 204 countries and territories. We used the Organisation for Economic Co-operation and Development (OECD)'s Creditor Reporting System (CRS) and the WHO Global Health Expenditure database (GHED) to estimate spending. We estimated development assistance for general health, COVID-19 response, and pandemic preparedness and response using a keyword search. Health spending estimates were combined with estimates of resources needed for pandemic prevention and preparedness to analyse future health spending patterns, relative to need. Findings: In 2019, at the onset of the COVID-19 pandemic, US$9·2 trillion (95% uncertainty interval [UI] 9·1–9·3) was spent on health worldwide. We found great disparities in the amount of resources devoted to health, with high-income countries spending $7·3 trillion (95% UI 7·2–7·4) in 2019;293·7 times the $24·8 billion (95% UI 24·3–25·3) spent by low-income countries in 2019. That same year, $43·1 billion in development assistance was provided

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Label distribution learning forests 17

Label distribution learning forests

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Proceedings of the 31st International Conference on Neural Information Processing Systems

作者： Wei Shen Kai Zhao Yilu Guo Alan Yuille Key Laboratory of Specialty Fiber Optics and Optical Access Networks Shanghai Institute for Advanced Communication and Data Science School of Communication and Information Engineering Shanghai University and Department of Computer Science Johns Hopkins University Key Laboratory of Specialty Fiber Optics and Optical Access Networks Shanghai Institute for Advanced Communication and Data Science School of Communication and Information Engineering Shanghai University Department of Computer Science Johns Hopkins University

ISBN: (纸本)9781510860964

Label distribution learning (LDL) is a general learning framework, which assigns to an instance a distribution over a set of labels rather than a single label or multiple labels. Current LDL methods have either restricted assumptions on the expression form of the label distribution or limitations in representation learning, e.g., to learn deep features in an end-to-end manner. This paper presents label distribution learning forests (LDLFs) - a novel label distribution learning algorithm based on differentiable decision trees, which have several advantages: 1) Decision trees have the potential to model any general form of label distributions by a mixture of leaf node predictions. 2) The learning of differentiable decision trees can be combined with representation learning. We define a distribution-based loss function for a forest, enabling all the trees to be learned jointly, and show that an update function for leaf node predictions, which guarantees a strict decrease of the loss function, can be derived by variational bounding. The effectiveness of the proposed LDLFs is verified on several LDL tasks and a computer vision application, showing significant improvements to the state-of-the-art LDL methods.

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OTHR-04. DEVELOPMENT OF A FUNCTIONAL PLATFORM FOR REAL-TIME PERSONALIZED DRUG SENSITIVITY PROFILING OF PATIENT-DERIVED 3D FRESH TUMOR TISSUE CULTURES IN THE PEDIATRIC PRECISION ONCOLOGY PROGRAM INFORM

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Neuro-Oncology 2023年第SUPPLEMENT_1期25卷 i74–i75页

作者： Heike Peterziel Nora Jamaladdin Dina ElHarouni Xenia F Gerloff Sonja Herter Petra Fiesel Yannick Berker Mirjam Blattner-Johnson Kathrin Schramm Barbara C Jones David Reuss Laura Turunen Aileen Friedenauer Tim Holland-Letz Martin Sill Lena Weiser Christopher Previti Gnanaprakash Balasubramanian Nicolas U Gerber Johannes Gojo Caroline Hutter Ingrid Øra Olli Lohi Antonis Kattamis Bram de Wilde Frank Westermann Stephan Tippelt Norbert Graf Michaela Nathrath Monika Sparber-Sauer Astrid Sehested Christof M Kramm Uta Dirksen Olli Kallioniemi Stefan M Pfister Cornelis M van Tilburg David T W Jones Jani Saarela Vilja Pietiäinen Natalie Jäger Matthias Schlesner Annette Kopp-Schneider Sina Oppermann Till Milde Olaf Witt Ina Oehme Hopp Children’s Cancer Center Heidelberg (KiTZ) Heidelberg Germany Clinical Cooperation Unit Pediatric Oncology German Cancer Research Center (DKFZ) National Center for Tumor Diseases (NCT) Heidelberg and German Cancer Consortium (DKTK) Heidelberg Germany Bioinformatics and Omics Data Analytics German Cancer Research Center (DKFZ) Division of Pediatric Neurooncology German Cancer Research Center (DKFZ) and German Cancer Consortium (DKTK) Heidelberg Germany Clinical Cooperation Unit Neuropathology German Cancer Research Center (DKFZ) Heidelberg Germany Division of Pediatric Glioma Research German Cancer Research Center (DKFZ) and German Cancer Consortium (DKTK) Heidelberg Germany Division of Pediatric Glioma Research German Cancer Research Center (DKFZ) Department of Pediatric Oncology Hematology Immunology and Pulmonology Heidelberg University Hospital and German Cancer Consortium (DKTK) Heidelberg Germany Department Neuropathology at Institute of Pathology Heidelberg University Hospital Heidelberg Germany Institute for Molecular Medicine Finland (FIMM) Helsinki Institute of Life Science (HiLIFE) University of Helsinki Helsinki Finland Division of Biostatistics German Cancer Research Center (DKFZ) Heidelberg Heidelberg Germany Division of Pediatric Neurooncology German Cancer Research Center (DKFZ) and German Cancer Consortium (DKTK) Heidelberg Germany Core Facility Omics IT and Data Management (ODCF) German Cancer Research Center (DKFZ) Heidelberg Germany Department of Oncology University Children’s Hospital Zürich Zürich Switzerland Department of Pediatrics and Adolescent Medicine Comprehensive Cancer Center and Comprehensive Center for Pediatrics Medical University of Vienna Vienna Austria St. Anna Children’s Hospital Department of Pediatrics and Adolescent Medicine Medical University of Vienna and St. Anna Children’s Cancer Research Institute (CCRI) Vienna Austria Children’s Hospital Pediatric Oncology Skåne University Hospital Lund &amp Karolinska Univ

The international precision oncology program INFORM enrolls relapsed/refractory pediatric cancer patients for comprehensive molecular analysis. We report a two-year pilot study implementing ex vivo drug sensitivity profiling (DSP) using a library of 75-78 clinically relevant drugs. We included 132 viable tumor samples from 35 pediatric oncology centers in seven countries. DSP was conducted on multicellular fresh tumor tissue spheroid cultures in 384-well plates with an overall mean processing time of three weeks. In 89 cases (67%), sufficient viable tissue was received; 69 (78%) passed internal quality controls. The DSP results matched the identified molecular targets, including BRAF, ALK, MET and TP53 status. Drug vulnerabilities were identified in 80% of cases lacking actionable (very) high-evidence molecular events, adding value to the molecular data. Striking parallels between clinical courses and the DSP results were observed in selected patients. Overall, DSP in clinical real-time is feasible in international multicenter precision oncology programs.

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Label distribution learning forests

arXiv

引用

arXiv 2017年

作者： Shen, Wei Zhao, Kai Guo, Yilu Yuille, Alan Key Laboratory of Specialty Fiber Optics and Optical Access Networks Shanghai Institute for Advanced Communication and Data Science School of Communication and Information Engineering Shanghai University Department of Computer Science Johns Hopkins University

关键词： Decision trees

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Essential guidelines for computational method benchmarking

arXiv

引用

arXiv 2018年

作者： Weber, Lukas M. Saelens, Wouter Cannoodt, Robrecht Soneson, Charlotte Hapfelmeier, Alexander Gardner, Paul P. Boulesteix, Anne-Laure Saeys, Yvan Robinson, Mark D. Institute of Molecular Life Sciences University of Zurich Zurich Switzerland SIB Swiss Institute of Bioinformatics University of Zurich Zurich Switzerland Data Mining and Modelling for Biomedicine VIB Center for Inflammation Research Ghent Belgium Department of Applied Mathematics Computer Science and Statistics Ghent University Ghent Belgium Institute of Medical Informatics Statistics and Epidemiology Technical University of Munich Munich Germany Department of Biochemistry University of Otago Dunedin New Zealand Institute for Medical Information Processing Biometry and Epidemiology Ludwig-Maximilians-University Munich Germany Friedrich Miescher Institute for Biomedical Research SIB Swiss Institute of Bioinformatics Basel Switzerland

In computational biology and other sciences, researchers are frequently faced with a choice between several computational methods for performing data analyses. Benchmarking studies aim to rigorously compare the performance of different methods using well-characterized benchmark datasets, to determine the strengths of each method or to provide recommendations regarding suitable choices of methods for an analysis. However, benchmarking studies must be carefully designed and implemented to provide accurate, unbiased, and informative results. Here, we summarize key practical guidelines and recommendations for performing high-quality benchmarking analyses, based on our experiences in computational biology. Copyright © 2018, The Authors. All rights reserved.

关键词： Benchmarking

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