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Time irreversibility and amplitude irreversibility measures for nonequilibrium processes in complex systems

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arXiv 2020年

作者： Yao, Wenpo Wang, Jun Perc, Matjaž Yao, Wenli Dai, Jiafei Guo, Daqing Yao, Dezhong Clinical Hospital of Chengdu Brain Science Institute MOE Key Lab for NeuroInformation Center for Information in Medicine School of Life Science and Technology University of Electronic Science and Technology of China Chengdu611731 China Smart Health Big Data Analysis and Location Services Engineering Lab of Jiangsu Province Nanjing University of Posts and Telecommunications NanjingJiangsu210023 China Faculty of Natural Sciences and Mathematics University of Maribor Koroška cesta 160 Maribor2000 Slovenia Complexity Science Hub Vienna Josefstädterstraße 39 Vienna1080 Austria School of Mines China University of Mining and Technology Xuzhou221116 China Department of Neurology Jinling Hospital Medical School of Nanjing University NanjingJiangsu210002 China Sichuan Institute for Brain Science and Brain-Inspired Intelligence University of Electronic Science and Technology of China Chengdu611731 China Research Unit of NeuroInformation Chinese Academy of Medical Sciences Chengdu2019RU035 China

Time irreversibility, which characterizes nonequilibrium processes, can be measured based on the probabilistic differences between symmetric vectors. To simplify the quantification of time irreversibility, symmetric permutations instead of symmetric vectors have been employed in some studies. However, although effective in practical applications, this approach is conceptually incorrect. Time irreversibility should be measured based on the permutations of symmetric vectors rather than symmetric permutations, whereas symmetric permutations can instead be employed to determine the quantitative amplitude irreversibility – a novel parameter proposed in this paper for nonequilibrium calculated by means of the probabilistic difference in amplitude fluctuations. Through theoretical and experimental analyses, we highlight the strong similarities and close associations between the time irreversibility and amplitude irreversibility measures. Our paper clarifies the connections of and the differences between the two types of permutation-based parameters for quantitative nonequilibrium, and by doing so, we bridge the concepts of amplitude irreversibility and time irreversibility and broaden the selection of quantitative tools for studying nonequilibrium processes in complex systems. Copyright © 2020, The Authors. All rights reserved.

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NEID Rossiter-McLaughlin Measurement of TOI-1268b: A Young Warm Saturn Aligned with Its Cool Host Star

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arXiv 2022年

作者： Dong, Jiayin Huang, Chelsea X. Zhou, George Dawson, Rebekah I. Stefánsson, Gudmundur K. Bender, Chad F. Blake, Cullen H. Ford, Eric B. Halverson, Samuel Kanodia, Shubham Mahadevan, Suvrath McElwain, Michael W. Ninan, Joe P. Robertson, Paul Arpita, R.O.Y. Schwab, Christian Stevens, Daniel J. Terrien, Ryan C. Vanderburg, Andrew Kraus, Adam L. Douglas, Stephanie Newton, Elisabeth Rampalli, Rayna Krolikowski, Daniel M. Collins, Karen A. Rodriguez, Joseph E. Feliz, Dax L. Srdoc, Gregor Ziegler, Carl Barkaoui, Khalid Pozuelos, Francisco J. Jehin, Emmanuel Michaël, C. Benkhaldoun, Zouhair Lewin, Pablo Forés-Toribio, Raquel Muñoz, Jose A. McLeod, Kim K. Özyurt, Fiona Powers Horta, Ferran Grau Murgas, Felipe Latham, David W. Quinn, Samuel N. Bieryla, Allyson Howell, Steve B. Gnilka, Crystal L. Ciardi, David R. Lund, Michael B. Dressing, Courtney D. Giacalone, Steven Savel, Arjun B. Strakhov, Ivan A. Belinski, Alexander A. Ricker, George R. Seager, S. Winn, Joshua N. Jenkins, Jon M. Torres, Guillermo Paegert, Martin Department of Astronomy & Astrophysics 525 Davey Laboratory The Pennsylvania State University University ParkPA16802 United States Center for Exoplanets and Habitable Worlds 525 Davey Laboratory The Pennsylvania State University University ParkPA16802 United States University of Southern Queensland Centre for Astrophysics West Street ToowoombaQLD4350 Australia Department of Physics Kavli Institute for Astrophysics and Space Research Massachusetts Institute of Technology CambridgeMA02139 United States Institute for Computational and Data Sciences The Pennsylvania State University University ParkPA16802 United States Center for Astrostatistics 525 Davey Laboratory The Pennsylvania State University University ParkPA16802 United States Department of Astrophysical Sciences Princeton University 4 Ivy Lane Princeton NJ08540 United States Steward Observatory The University of Arizona 933 N. Cherry Ave TucsonAZ85721 United States Department of Physics and Astronomy University of Pennsylvania 209 S 33rd St PhiladelphiaPA19104 United States Jet Propulsion Laboratory California Institute of Technology 4800 Oak Grove Drive PasadenaCA91109 United States Penn State Extraterrestrial Intelligence Center 525 Davey Laboratory The Pennsylvania State University University ParkPA16802 United States Exoplanets and Stellar Astrophysics Laboratory NASA Goddard Space Flight Center GreenbeltMD20771 United States Department of Physics & Astronomy The University of California Irvine IrvineCA92697 United States Space Telescope Science Institute 3700 San Martin Dr BaltimoreMD21218 United States Department of Physics and Astronomy Johns Hopkins University 3400 N Charles St BaltimoreMD21218 United States Department of Physics and Astronomy Macquarie University Balaclava Road North RydeNSW2109 Australia Carleton College One North College St. NorthfieldMN55057 United States Department of Astronomy The University of Texas at Austin AustinTX78712 U

Close-in gas giants present a surprising range of stellar obliquity, the angle between a planet's orbital axis and its host star's spin axis. It is unclear whether the obliquities reflect the planets' dynamical history (e.g., aligned for in situ formation or disk migration versus misaligned for high-eccentricity tidal migration) or whether other mechanisms (e.g., primordial misalignment or planet-star interactions) are more important in sculpting the obliquity distribution. Here we present the stellar obliquity measurement of TOI-1268 (TIC-142394656, Vmag∼10.9), a young K-type dwarf hosting an 8.2-day period, Saturn-sized planet. TOI-1268's lithium abundance and rotation period suggest the system age between the ages of Pleiades cluster (∼120 Myr) and Prasepe cluster (∼670 Myr). Using the newly commissioned NEID spectrograph, we constrain the stellar obliquity of TOI-1268 via the Rossiter-McLaughlin (RM) effect from both radial velocity (RV) and Doppler Tomography (DT) signals. The 3σ upper bounds of the projected stellar obliquity |λ| from both models are below 60◦. The large host star separation (a/R*∼17), combined with the system's young age, makes it unlikely that the planet has realigned its host star. The stellar obliquity measurement of TOI-1268 probes the architecture of a young gas giant beyond the reach of tidal realignment (a/R* 10) and reveals an aligned or slightly misaligned system. Copyright © 2022, The Authors. All rights reserved.

关键词： Extrasolar planets

Author Correction: Opportunities and challenges of single-cell and spatially resolved genomics methods for neuroscience discovery

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Nature neuroscience 2025年第1期28卷 216页

作者： Boyan Bonev Gonçalo Castelo-Branco Fei Chen Simone Codeluppi M Ryan Corces Jean Fan Myriam Heiman Kenneth Harris Fumitaka Inoue Manolis Kellis Ariel Levine Mo Lotfollahi Chongyuan Luo Kristen R Maynard Mor Nitzan Vijay Ramani Rahul Satijia Lucas Schirmer Yin Shen Na Sun Gilad S Green Fabian Theis Xiao Wang Joshua D Welch Ozgun Gokce Genevieve Konopka Shane Liddelow Evan Macosko Omer Ali Bayraktar Naomi Habib Tomasz J Nowakowski Helmholtz Pioneer Campus Helmholtz Zentrum München Neuherberg Germany. Physiological Genomics Biomedical Center Ludwig-Maximilians-Universität München Munich Germany. Laboratory of Molecular Neurobiology Department of Medical Biochemistry and Biophysics Karolinska Institutet Stockholm Sweden. The Broad Institute of MIT and Harvard Cambridge MA USA. Moleculent Stockholm Sweden. Gladstone Institute of Neurological Disease San Francisco CA USA. Gladstone Institute of Data Science and Biotechnology San Francisco CA USA. Department of Neurology University of California San Francisco San Francisco CA USA. Department of Biomedical Engineering Johns Hopkins University Baltimore MD USA. Department of Brain and Cognitive Sciences MIT Cambridge MA USA. The Picower Institute for Learning and Memory MIT Cambridge MA USA. UCL Queen Square Institute of Neurology University College London London UK. Institute for the Advanced Study of Human Biology (WPI-ASHBi) Kyoto University Kyoto Japan. Computer Science and Artificial Intelligence Lab Massachusetts Institute of Technology Cambridge MA USA. Spinal Circuits and Plasticity Unit National Institute of Neurological Disorders and Stroke Bethesda MD USA. Institute of Computational Biology Helmholtz Center Munich - German Research Center for Environmental Health Neuherberg Germany. Wellcome Sanger Institute Wellcome Genome Campus Cambridge UK. Department of Human Genetics University of California Los Angeles Los Angeles CA USA. Lieber Institute for Brain Development Baltimore MD USA. Department of Psychiatry Johns Hopkins School of Medicine Baltimore MD USA. Department of Neuroscience Johns Hopkins School of Medicine Baltimore MD USA. School of Computer Science and Engineering The Hebrew University of Jerusalem Jerusalem Israel. Racah Institute of Physics The Hebrew University of Jerusalem Jerusalem Israel. Faculty of Medicine The Hebrew University of Jerusalem Jerusalem Israel. Department of Biochemis

The Brain Tumor Sequence Registration (BraTS-Reg) Challenge: Establishing Correspondence Between Pre-Operative and Follow-up MRI Scans of Diffuse Glioma Patients

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arXiv 2021年

作者： Baheti, Bhakti Chakrabarty, Satrajit Akbari, Hamed Bilello, Michel Wiestler, Benedikt Schwarting, Julian Calabrese, Evan Rudie, Jeffrey Abidi, Syed Mousa, Mina Villanueva-Meyer, Javier Fields, Brandon K.K. Kofler, Florian Shinohara, Russell Takeshi Iglesias, Juan Eugenio Mok, Tony C.W. Chung, Albert C.S. Wodzinski, Marek Jurgas, Artur Marini, Niccolò Atzori, Manfredo Müller, Henning Grosbröhmer, Christoph Siebert, Hanna Hansen, Lasse Heinrich, Mattias P. Canalini, Luca Klein, Jan Gerken, Annika Heldmann, Stefan Hering, Alessa Hahn, Horst K. Meng, Mingyuan Bi, Lei Feng, Dagan Kim, Jinman Zeineldinal, Ramy A. Karar, Mohamed E. Mathis-Ullrich, Franziska Burgert, Oliver Abderezaei, Javid Pionteck, Aymeric Chopra, Agamdeep Kurt, Mehmet Yan, Kewei Yan, Yonghong Tang, Zhe Ma, Jianqiang Nasser, Sahar Almahfouz Kurian, Nikhil Cherian Meena, Mohit Shamsi, Saqib Sethi, Amit Tustison, Nicholas J. Avants, Brian B. Cook, Philip Gee, James C. Tian, Lin Greer, Hastings Niethammer, Marc Hoopesar, Andrew Hoffmannas, Malte Dalcaar, Adrian V. Christodoulidis, Stergios Estiene, Théo Vakalopoulou, Maria Paragios, Nikos Marcus, Daniel S. Davatzikos, Christos Sotiras, Aristeidis Menze, Bjoern Bakas, Spyridon Waldmannstetter, Diana Division of Computational Pathology Department of Pathology and Laboratory Medicine Indiana University School of Medicine IndianapolisIN United States Department of Electrical and Systems Engineering Washington University in St. Louis St. LouisMO United States GE HealthCare San RamonCA United States University of Pennsylvania PhiladelphiaPA United States Department of Radiology Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Mallinckrodt Institute of Radiology Washington University School of Medicine St. LouisMO United States Institute for Informatics Data Science & Biostatistics Washington University School of Medicine St. LouisMO United States Department of Bioengineering Santa Clara University Santa ClaraCA United States Department of Quantitative Biomedicine University of Zurich Zurich Switzerland Department of Computer Science TUM School of Computation Information and Technology Technical University of Munich Munich Germany Department of Diagnostic and Interventional Neuroradiology School of Medicine Klinikum rechts der Isar Technical University of Munich Munich Germany Department of Nuclear Medicine University Hospital Regensburg Regensburg Germany University Hospital Regensburg Regensburg Germany Department of Radiology & Biomedical Imaging University of California San FranciscoCA United States Department of Radiology Duke University Medical Center DurhamNC United States Department of Radiology University of California San DiegoCA United States Helmholtz AI Helmholtz Munich Neuherberg Germany Penn Statistics in Imaging and Visualization Center Department of Biostatistics Epidemiology and Informatics University of Pennsylvania Philadelphia United States Massachusetts General Hospital Harvard Medical School United States Centre for Medical Imaging Computing University College London United Kingdom The Hong Kong University of Science and Technology Hong Kong University of Applied Sciences West

Registration of longitudinal brain Magnetic Resonance Imaging (MRI) scans containing pathologies is challenging due to dramatic changes in tissue appearance. Although there has been considerable progress in developing general-purpose medical image registration techniques, they have not yet attained the requisite precision and reliability for this task, highlighting its inherent complexity. Here we describe the Brain Tumor Sequence Registration (BraTS-Reg) challenge, as the first public benchmark environment for deformable registration algorithms focusing on estimating correspondences between pre-operative and follow-up scans of the same patient diagnosed with a diffuse brain glioma. The challenge was conducted in conjunction with both the IEEE International Symposium on Biomedical Imaging (ISBI) 2022 and the International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI) 2022. The BraTS-Reg data comprise de-identified multi-institutional multi-parametric MRI (mpMRI) scans, curated for size and resolution according to a canonical anatomical template, and divided into training, validation, and testing sets. Clinical experts annotated ground truth (GT) landmark points of anatomical locations distinct across the temporal domain. The training data with their GT annotations, were publicly released to enable the development of registration algorithms. The validation data, without their GT annotations, were also released to allow for algorithmic evaluation prior to the testing phase, which only allowed submission of containerized algorithms for evaluation on hidden hold-out testing data. Quantitative evaluation and ranking was based on the Median Euclidean Error (MEE), Robustness, and the determinant of the Jacobian of the displacement field. The top-ranked methodologies yielded similar performance across all evaluation metrics and shared several methodological commonalities, including pre-alignment, deep neural networks, inverse consistency

关键词： Magnetic resonance imaging

Erratum: “The NANOGrav 15 yr data Set: Search for Signals from New Physics” (2023, ApJL 951 L11)

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Astrophysical Journal. Letters 2024年第1期971卷 L27页

作者： Afzal, Adeela Agazie, Gabriella Anumarlapudi, Akash Freedman, Gabriel E Islo, Kristina Jones, Megan L Kaplan, David L McEwen, Alexander Sardesai, Shashwat C Susobhanan, Abhimanyu Vigeland, Sarah J Archibald, Anne M Arzoumanian, Zaven Baker, Paul T Bécsy, Bence Burnette, Rand Case, Robin DeGan, Dallas Deng, Heling Hazboun, Jeffrey S Laal, Nima Sun, Jerry P Taylor, Jacob Jose Juan Blanco-Pillado Blecha, Laura Boddy, Kimberly K Nay, Jonathan Brazier, Adam Brook, Paul R Burke-Spolaor, Sarah Cheeseboro, Belinda D Fiore, William Fonseca, Emmanuel Garver-Daniels, Nate Gentile, Peter A Glaser, Joseph Jennings, Ross J Kaiser, Andrew R Liu, Tingting Lorimer, Duncan R McLaughlin, Maura A Turner, Jacob E Wahl, Haley M Charisi, Maria Gersbach, Kyle A Lamb, William G McMann, Natasha Petrov, Polina Pol, Nihan S Schult, Levi Taylor, Stephen R Wang, Qiaohong Chatterjee, Shami Cordes, James M Cromartie, H Thankful Stella Koch Ocker Chatziioannou, Katerina Hourihane, Sophie Lee, Vincent S H Meyers, Patrick M Chen, Siyuan Cohen, Tyler Cornish, Neil J Crawford, Fronefield Crowter, Kathryn Stairs, Ingrid H Cutler, Curt J Vallisneri, Michele DeCesar, Megan E Demorest, Paul B Stovall, Kevin Dolch, Timothy Drachler, Brendan Lam, Michael T Young, Olivia Richard von Eckardstein Schmitz, Kai Schröder, Tobias Stratmann, Peter Ferrara, Elizabeth C Good, Deborah C Guertin, Lydia Gültekin, Kayhan Johnson, Aaron D Kelley, Luke Zoltan Kerr, Matthew Ray, Paul S Key, Joey S Lazio, T Joseph W Lewandowska, Natalia Rafael R Lino dos Santos Littenberg, Tyson B Luo, Jing Lynch, Ryan S Chung-Pei, Ma Madison, Dustin R McKee, James W Meyers, Bradley W Mingarelli, Chiara M F Mitridate, Andrea Natarajan, Priyamvada Ng, Cherry Nice, David J Swiggum, Joseph K Olum, Ken D Pennucci, Timothy T Perera, Benetge B P Radovan, Henri A Ransom, Scott M Romano, Joseph D Schmiedekamp, Ann Schmiedekamp, Carl Shapiro-Albert, Brent J Siemens, Xavier Simon, Joseph Siwek, Magdalena S Stinebring, Daniel R Tanner Trickle Unal, Caner Verma, Sonali Witt, Caitlin A Wright, David Zu Institute for Theoretical Physics University of Münster D-48149 Münster Germany comments@*** Department of Physics Quaid-i-Azam University Islamabad 45320 Pakistan Center for Gravitation Cosmology and Astrophysics Department of Physics University of Wisconsin-Milwaukee P.O. Box 413 Milwaukee WI 53201 USA Newcastle University NE1 7RU UK X-Ray Astrophysics Laboratory NASA Goddard Space Flight Center Code 662 Greenbelt MD 20771 USA Department of Physics and Astronomy Widener University One University Place Chester PA 19013 USA Department of Physics Oregon State University Corvallis OR 97331 USA Department of Physics University of Basque Country UPV/EHU E-48080 Bilbao Spain EHU Quantum Center University of Basque Country UPV/EHU Bilbao Spain IKERBASQUE Basque Foundation for Science E-48011 Bilbao Spain Physics Department University of Florida Gainesville FL 32611 USA Department of Physics The University of Texas at Austin Austin TX 78712 USA Cornell Center for Astrophysics and Planetary Science and Department of Astronomy Cornell University Ithaca NY 14853 USA Cornell Center for Advanced Computing Cornell University Ithaca NY 14853 USA Institute for Gravitational Wave Astronomy and School of Physics and Astronomy University of Birmingham Edgbaston Birmingham B15 2TT UK Department of Physics and Astronomy West Virginia University P.O. Box 6315 Morgantown WV 26506 USA Center for Gravitational Waves and Cosmology West Virginia University Chestnut Ridge Research Building Morgantown WV 26505 USA Department of Physics and Astronomy Vanderbilt University 2301 Vanderbilt Place Nashville TN 37235 USA Cornell Center for Astrophysics and Planetary Science and Department of Astronomy Cornell University Ithaca NY 14853 USA Division of Physics Mathematics and Astronomy California Institute of Technology Pasadena CA 91125 USA Kavli Institute for Astronomy and Astrophysics Peking University Beijing 100871 People's Republi

Skipping Strategy (SS) for Initial Population of Job-Shop Scheduling Problem

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Journal of Physics: Conference Series 2018年第1期978卷

作者： M Abdolrazzagh-Nezhad E B Nababan H M Sarim Department of Computer Engineering Faculty of Engineering Bozorgmehr University of Qaenat Qaen Iran. Department of Information Technology Faculty of Computer Science and Information Technology Universitas Sumatera Utara Medan Indonesia Data Mining and Optimization Research Group Center for Artificial Intelligence Technology Universiti Kebangsaan Malaysia 43600 UKM Bangi Selangor Malaysia

Initial population in job-shop scheduling problem (JSSP) is an essential step to obtain near optimal solution. Techniques used to solve JSSP are computationally demanding. Skipping strategy (SS) is employed to acquire initial population after sequence of job on machine and sequence of operations (expressed in Plates-jobs and mPlates-jobs) are determined. The proposed technique is applied to benchmark datasets and the results are compared to that of other initialization techniques. It is shown that the initial population obtained from the SS approach could generate optimal solution.

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Author Correction: Federated learning enables big data for rare cancer boundary detection

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Nature communications 2023年第1期14卷 436页

作者： Sarthak Pati Ujjwal Baid Brandon Edwards Micah Sheller Shih-Han Wang G Anthony Reina Patrick Foley Alexey Gruzdev Deepthi Karkada Christos Davatzikos Chiharu Sako Satyam Ghodasara Michel Bilello Suyash Mohan Philipp Vollmuth Gianluca Brugnara Chandrakanth J Preetha Felix Sahm Klaus Maier-Hein Maximilian Zenk Martin Bendszus Wolfgang Wick Evan Calabrese Jeffrey Rudie Javier Villanueva-Meyer Soonmee Cha Madhura Ingalhalikar Manali Jadhav Umang Pandey Jitender Saini John Garrett Matthew Larson Robert Jeraj Stuart Currie Russell Frood Kavi Fatania Raymond Y Huang Ken Chang Carmen Balaña Jaume Capellades Josep Puig Johannes Trenkler Josef Pichler Georg Necker Andreas Haunschmidt Stephan Meckel Gaurav Shukla Spencer Liem Gregory S Alexander Joseph Lombardo Joshua D Palmer Adam E Flanders Adam P Dicker Haris I Sair Craig K Jones Archana Venkataraman Meirui Jiang Tiffany Y So Cheng Chen Pheng Ann Heng Qi Dou Michal Kozubek Filip Lux Jan Michálek Petr Matula Miloš Keřkovský Tereza Kopřivová Marek Dostál Václav Vybíhal Michael A Vogelbaum J Ross Mitchell Joaquim Farinhas Joseph A Maldjian Chandan Ganesh Bangalore Yogananda Marco C Pinho Divya Reddy James Holcomb Benjamin C Wagner Benjamin M Ellingson Timothy F Cloughesy Catalina Raymond Talia Oughourlian Akifumi Hagiwara Chencai Wang Minh-Son To Sargam Bhardwaj Chee Chong Marc Agzarian Alexandre Xavier Falcão Samuel B Martins Bernardo C A Teixeira Flávia Sprenger David Menotti Diego R Lucio Pamela LaMontagne Daniel Marcus Benedikt Wiestler Florian Kofler Ivan Ezhov Marie Metz Rajan Jain Matthew Lee Yvonne W Lui Richard McKinley Johannes Slotboom Piotr Radojewski Raphael Meier Roland Wiest Derrick Murcia Eric Fu Rourke Haas John Thompson David Ryan Ormond Chaitra Badve Andrew E Sloan Vachan Vadmal Kristin Waite Rivka R Colen Linmin Pei Murat Ak Ashok Srinivasan J Rajiv Bapuraj Arvind Rao Nicholas Wang Ota Yoshiaki Toshio Moritani Sevcan Turk Joonsang Lee Snehal Prabhudesai Fanny Morón Jacob Mandel Konstantinos Kamnitsas Ben Glocker Luke V M Dixon Matthew Williams Peter Zamp Center for Biomedical Image Computing and Analytics (CBICA) University of Pennsylvania Philadelphia PA USA. Department of Radiology Perelman School of Medicine University of Pennsylvania Philadelphia PA USA. Department of Pathology and Laboratory Medicine Perelman School of Medicine University of Pennsylvania Philadelphia PA USA. Department of Informatics Technical University of Munich Munich Bavaria Germany. Intel Corporation Santa Clara CA USA. Department of Neuroradiology Heidelberg University Hospital Heidelberg Germany. Clinical Cooperation Unit Neuropathology German Cancer Consortium (DKTK) within the German Cancer Research Center (DKFZ) Heidelberg Germany. Department of Neuropathology Heidelberg University Hospital Heidelberg Germany. Division of Medical Image Computing German Cancer Research Center Heidelberg Germany. Pattern Analysis and Learning Group Department of Radiation Oncology Heidelberg University Hospital Heidelberg Germany. Neurology Clinic Heidelberg University Hospital Heidelberg Germany. Department of Radiology & Biomedical Imaging University of California San Francisco San Francisco CA USA. Symbiosis Center for Medical Image Analysis Symbiosis International University Pune Maharashtra India. Department of Neuroimaging and Interventional Radiology National Institute of Mental Health and Neurosciences Bangalore Karnataka India. Department of Radiology School of Medicine and Public Health University of Wisconsin Madison WI USA. Department of Medical Physics School of Medicine and Public Health University of Wisconsin Madison WI USA. Leeds Teaching Hospitals Trust Department of Radiology Leeds UK. Department of Radiology Brigham and Women's Hospital Harvard Medical School Boston MA USA. Athinoula A. Martinos Center for Biomedical Imaging Massachusetts General Hospital Charlestown MA USA. Catalan Institute of Oncology Badalona Spain. Consorci MAR Parc de Salut de Barcelona Catalonia Spain. Department of Radiology (IDI

Identifying the best machine learning algorithms for brain tumor segmentation, progression assessment, and overall survival prediction in the BRATS challenge

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arXiv 2018年

作者： Bakas, Spyridon Reyes, Mauricio Jakab, Andras Bauer, Stefan Rempfler, Markus Crimi, Alessandro Shinohara, Russell Takeshi Berger, Christoph Ha, Sung Min Rozycki, Martin Prastawa, Marcel Alberts, Esther Lipkova, Jana Freymann, John Kirby, Justin Bilello, Michel Fathallah-Shaykh, Hassan M. Wiest, Roland Kirschke, Jan Wiestler, Benedikt Colen, Rivka Kotrotsou, Aikaterini Lamontagne, Pamela Marcus, Daniel Milchenko, Mikhail Nazeri, Arash Weber, Marc-Andr Mahajan, Abhishek Baid, Ujjwal Gerstner, Elizabeth Kwon, Dongjin Acharya, Gagan Agarwal, Manu Alam, Mahbubul Albiol, Alberto Albiol, Antonio Albiol, Francisco J. Alex, Varghese Allinson, Nigel Amorim, Pedro H.A. Amrutkar, Abhijit Anand, Ganesh Andermatt, Simon Arbel, Tal Arbelaez, Pablo Avery, Aaron Azmat, Muneeza Pranjal, B. Bai, Wenjia Banerjee, Subhashis Barth, Bill Batchelder, Thomas Batmanghelich, Kayhan Battistella, Enzo Beers, Andrew Belyaev, Mikhail Bendszus, Martin Benson, Eze Bernal, Jose Bharath, Halandur Nagaraja Biros, George Bisdas, Sotirios Brown, James Cabezas, Mariano Cao, Shilei Cardoso, Jorge M. Carver, Eric N. Casamitjana, Adri Castillo, Laura Silvana Cat, Marcel Cattin, Philippe Cérigues, Albert Chagas, Vinicius S. Chandra, Siddhartha Chang, Yi-Ju Chang, Shiyu Chang, Ken Chazalon, Joseph Chen, Shengcong Chen, Wei Chen, Jefferson W. Chen, Zhaolin Cheng, Kun Choudhury, Ahana Roy Chylla, Roger Clrigues, Albert Colleman, Steven Colmeiro, Ramiro German Rodriguez Combalia, Marc Costa, Anthony Cui, Xiaomeng Dai, Zhenzhen Dai, Lutao Daza, Laura Alexandra Deutsch, Eric Ding, Changxing Dong, Chao Dong, Shidu Dudzik, Wojciech Eaton-Rosen, Zach Egan, Gary Escudero, Guilherme Estienne, Tho Everson, Richard Fabrizio, Jonathan Fan, Yong Fang, Longwei Feng, Xue Ferrante, Enzo Fidon, Lucas Fischer, Martin French, Andrew P. Fridman, Naomi Fu, Huan Fuentes, David Gao, Yaozong Gates, Evan Gering, David Gholami, Amir Gierke, Willi Glocker, Ben Gong, Mingming Gonzlez-Vill, Sandra Grosges, T. Guan, Yuanfang Guo, Sheng Gupta, Sudeep Han, Woo-Sup Han, Il Song Harmuth, Ko Center for Biomedical Image Computing and Analytics University of Pennsylvania PhiladelphiaPA United States Department of Radiology Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Department of Pathology and Laboratory Medicine Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Institute for Surgical Technology and Biomechanics University of Bern Bern Switzerland Center for MR-Research University Children's Hospital Zurich Zurich Switzerland Support Centre for Advanced Neuroimaging Inselspital Institute for Diagnostic and Interventional Neuroradiology Bern University Hospital Bern Switzerland University Hospital of Zurich Zurich Switzerland Center for Clinical Epidemiology and Biostatistics University of Pennsylvania Philadelphia United States Image-Based Biomedical Modeling Group Technical University of Munich Munich Germany Icahn School of Medicine Mount Sinai Health System New YorkNY United States Leidos Biomedical Research Inc. Frederick National Laboratory for Cancer Research FrederickMD21701 United States Cancer Imaging Program National Cancer Institute National Institutes of Health BethesdaMD20814 United States Department of Neurology University of Alabama at Birmingham BirminghamAL United States Department of Diagnostic Radiology University of Texas MD Anderson Cancer Center HoustonTX United States Department of Psychology Washington University St. LouisMO United States Neuroimaging Informatics and Analysis Center Washington University St. LouisMO United States Department of Radiology Washington University St. LouisMO United States Institute of Diagnostic and Interventional Radiology Pediatric Radiology and Neuroradiology University Medical Center Rostock Ernst-Heydemann-Str. 6 Rostock18057 Germany Tata Memorial Centre Homi Bhabha National Institute Mumbai India Shri Guru Gobind Singhji Institute of Engineering and Technology Nanded India NVIDIA Santa Clara

Gliomas are the most common primary brain malignancies, with different degrees of aggressiveness, variable prognosis and various heterogeneous histologic sub-regions, i.e., peritumoral edematous/invaded tissue, necrotic core, active and non-enhancing core. This intrinsic heterogeneity is also portrayed in their radio-phenotype, as their sub-regions are depicted by varying intensity profiles disseminated across multi-parametric magnetic resonance imaging (mpMRI) scans, reflecting varying biological properties. Their heterogeneous shape, extent, and location are some of the factors that make these tumors difficult to resect, and in some cases inoperable. The amount of resected tumor is a factor also considered in longitudinal scans, when evaluating the apparent tumor for potential diagnosis of progression. Furthermore, there is mounting evidence that accurate segmentation of the various tumor sub-regions can offer the basis for quantitative image analysis towards prediction of patient overall survival. This study assesses the state-of-the-art machine learning (ML) methods used for brain tumor image analysis in mpMRI scans, during the last seven instances of the International Brain Tumor Segmentation (BraTS) challenge, i.e., 2012-2018. Specifically, we focus on i) evaluating segmentations of the various glioma sub-regions in pre-operative mpMRI scans, ii) assessing potential tumor progression by virtue of longitudinal growth of tumor sub-regions, beyond use of the RECIST/RANO criteria, and iii) predicting the overall survival from pre-operative mpMRI scans of patients that underwent gross total resection. Finally, we investigate the challenge of identifying the best ML algorithms for each of these tasks, considering that apart from being diverse on each instance of the challenge, the multi-institutional mpMRI BraTS dataset has also been a continuously evolving/growing dataset. Copyright © 2018, The Authors. All rights reserved.

关键词： Tumors

Quantum computing at the frontiers of biological sciences

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arXiv 2019年

作者： Emani, Prashant S. Warrell, Jonathan Anticevic, Alan Bekiranov, Stefan Gandal, Michael McConnell, Michael J. Sapiro, Guillermo Aspuru-Guzik, Alán Baker, Justin Bastiani, Matteo McClure, Patrick Murray, John Sotiropoulos, Stamatios N. Taylor, Jacob Senthil, Geetha Lehner, Thomas Gerstein, Mark B. Harrow, Aram W. Program in Computational Biology and Bioinformatics Yale University New HavenCT06520 United States Department of Molecular Biophysics and Biochemistry Yale University New HavenCT06520 United States Yale School of Medicine Department of Psychiatry New HavenCT06511 United States University of Virginia School of Medicine Department of Biochemistry and Molecular Genetics CharlottesvilleVA22903 United States Department of Psychiatry Semel Institute David Geffen School of Medicine University of California–Los Angeles 695 Charles E. Young Drive South Los AngelesCA90095 United States Univ. of Virginia School of Medicine Department of Neuroscience CharlottesvilleVA22903 United States Department of Electrical and Computer Engineering Duke University DurhamNC27708 United States Department of Chemistry University of Toronto 80 St. George Street TorontoONM5S 3H6 Canada TorontoONM5S 1M1 Canada CIFAR Artificial Intelligence Research Chair Vector Institute TorontoONM5S 1M1 Canada Department of Computer Science University of Toronto 40 St. George Street TorontoONM5S 2E4 Canada Schizophrenia and Bipolar Disorder Program McLean Hospital BelmontMA02478 United States Department of Psychiatry Harvard Medical School BostonMA02114 United States Sir Peter Mansfield Imaging Centre School of Medicine University of Nottingham NottinghamNG7 2RD United Kingdom Machine Learning Team National Institute of Mental Health BethesdaMD20892 United States Section on Functional Imaging Methods National Institute of Mental Health BethesdaMD20892 United States Department of Psychiatry Yale University School of Medicine New HavenCT06511 United States Department of Physics Yale University New HavenCT06511 United States Joint Center for Quantum Information and Computer Science University of Maryland College ParkMD20742 United States National Institute of Standards and Technology GaithersburgMD20899 United States Office of Genomics Research Coordination N

The search for meaningful structure in biological data has relied on cutting-edge advances in computational technology and data science methods. However, challenges arise as we push the limits of scale and complexity in biological problems. Innovation in massively parallel, classical computing hardware and algorithms continues to address many of these challenges, but there is a need to simultaneously consider new paradigms to circumvent current barriers to processing speed. Accordingly, we articulate a view towards quantum computation and quantum information science, where algorithms have demonstrated potential polynomial and exponential computational speedups in certain applications, such as machine learning. The maturation of the field of quantum computing, in hardware and algorithm development, also coincides with the growth of several collaborative efforts to address questions across length and time scales, and scientific disciplines. We use this coincidence to explore the potential for quantum computing to aid in one such endeavor: the merging of insights from genetics, genomics, neuroimaging and behavioral phenotyping. By examining joint opportunities for computational innovation across fields, we highlight the need for a common language between biological data analysis and quantum computing. Ultimately, we consider current and future prospects for the employment of quantum computing algorithms in the biological sciences. Copyright © 2019, The Authors. All rights reserved.

关键词： Machine learning