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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

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

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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

Correction to: Gait variability as digital biomarker of disease severity in Huntington's disease

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Journal of neurology 2020年第6期267卷 1602页

作者： Heiko Gaßner Dennis Jensen Franz Marxreiter Anja Kletsch Stefan Bohlen Robin Schubert Lisa M Muratori Bjoern Eskofier Jochen Klucken Jürgen Winkler Ralf Reilmann Zacharias Kohl Department of Molecular Neurology University Hospital Erlangen Friedrich-Alexander University Erlangen-Nürnberg (FAU) Schwabachanlage 6 91054 Erlangen Germany. George-Huntington Institute (GHI) GmbH Münster Germany. Rehabilitation Research and Movement Performance Laboratory (RRAMP Lab) Stony Brook University Stony Brook NY USA. Machine Learning and Data Analytics Lab Friedrich-Alexander University Erlangen-Nürnberg (FAU) Erlangen Germany. Medical Valley-Digital Health Application Center GmbH Bamberg Germany. Fraunhofer Institute for Integrated Circuits IIS Erlangen Germany. Department of Radiology University of Muenster Muenster Germany. Department of Neurodegenerative Diseases and Hertie-Institute for Clinical Brain Research University of Tuebingen Tuebingen Germany. Department of Molecular Neurology University Hospital Erlangen Friedrich-Alexander University Erlangen-Nürnberg (FAU) Schwabachanlage 6 91054 Erlangen Germany. Zacharias.Kohl@klinik.uni-regensburg.de. Center for Rare Diseases Erlangen University Hospital Erlangen Erlangen Germany. Zacharias.Kohl@klinik.uni-regensburg.de. Department of Neurology University of Regensburg Regensburg Germany. Zacharias.Kohl@klinik.uni-regensburg.de.

The original version of this article unfortunately contained a mistake. The name of one author is not presented correctly in the author group.

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Euclid: Early Release Observations of diffuse stellar structures and globular clusters as probes of the mass assembly of galaxies in the Dorado group

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arXiv 2024年

作者： Urbano, M. Duc, P.-A. Saifollahi, T. Sola, E. Lançon, A. Voggel, K. Annibali, F. Baes, M. Bouy, H. Cantiello, M. Carollo, D. Cuillandre, J.-C. Dimauro, P. Erwin, P. Ferguson, A.M.N. Habas, R. Hilker, M. Hunt, L.K. Kluge, M. Larsen, S.S. Liu, Q. Marchal, O. Marleau, F.R. Massari, D. Müller, O. Peletier, R.F. Poulain, M. Rejkuba, M. Schirmer, M. Stone, C. Zöller, R. Altieri, B. Andreon, S. Auricchio, N. Baccigalupi, C. Baldi, M. Balestra, A. Bardelli, S. Basset, A. Battaglia, P. Branchini, E. Brescia, M. Camera, S. Capobianco, V. Carbone, C. Carretero, J. Casas, S. Castellano, M. Castignani, G. Cavuoti, S. Cimatti, A. Colodro-Conde, C. Congedo, G. Conselice, C.J. Conversi, L. Copin, Y. Courbin, F. Courtois, H.M. Degaudenzi, H. De Lucia, G. Dubath, F. Dupac, X. Dusini, S. Farina, M. Farrens, S. Faustini, F. Ferriol, S. Frailis, M. Franceschi, E. Fumana, M. Galeotta, S. George, K. Gillis, B. Giocoli, C. Gómez-Alvarez, P. Grazian, A. Grupp, F. Guzzo, L. Haugan, S.V.H. Hoar, J. Hoekstra, H. Holmes, W. Hormuth, F. Hornstrup, A. Hudelot, P. Jahnke, K. Jhabvala, M. Keihänen, E. Kermiche, S. Kubik, B. Kümmel, M. Kunz, M. Kurki-Suonio, H. Le Mignant, D. Ligori, S. Lilje, P.B. Lindholm, V. Lloro, I. Maiorano, E. Mansutti, O. Marcin, S. Marggraf, O. Markovic, K. Martinelli, M. Martinet, N. Marulli, F. Massey, R. Medinaceli, E. Mei, S. Melchior, M. Meneghetti, M. Merlin, E. Meylan, G. Moscardini, L. Nakajima, R. Neissner, C. Nichol, R.C. Niemi, S.-M. Padilla, C. Paltani, S. Pasian, F. Pedersen, K. Percival, W.J. Pettorino, V. Pires, S. Polenta, G. Poncet, M. Popa, L.A. Pozzetti, L. Raison, F. Renzi, A. Rhodes, J. Riccio, G. Romelli, E. Roncarelli, M. Rossetti, E. Saglia, R. Sapone, D. Sartoris, B. Scaramella, R. Schneider, P. Secroun, A. Seidel, G. Serrano, S. Sirignano, C. Stanco, L. Steinwagner, J. Tallada-Crespí, P. Taylor, A.N. Tereno, I. Toledo-Moreo, R. Torradeflot, F. Tutusaus, I. Vassallo, T. Verdoes Kleijn, G. Wang, Y. Weller, J. Williams, O.R. Zucca, E. Bolzonella, M. Burigana, C. Mora, A. Scottez, V. Université de Strasbourg CNRS Observatoire astronomique de Strasbourg UMR 7550 Strasbourg67000 France Institute of Astronomy University of Cambridge Madingley Road CambridgeCB3 0HA United Kingdom INAF-Osservatorio di Astrofisica e Scienza dello Spazio di Bologna Via Piero Gobetti 93/3 Bologna40129 Italy Sterrenkundig Observatorium Universiteit Gent Krijgslaan 281 S9 Gent9000 Belgium 1 rue Descartes Paris75231 Cedex 05 France Laboratoire d'Astrophysique de Bordeaux CNRS Université de Bordeaux Allée Geoffroy St. Hilaire Pessac33165 France INAF - Osservatorio Astronomico d'Abruzzo Via Maggini Teramo64100 Italy INAF-Osservatorio Astronomico di Trieste Via G. B. Tiepolo 11 Trieste34143 Italy Université Paris-Saclay Université Paris Cité CEA CNRS AIM Gif-sur-Yvette91191 France INAF-Osservatorio Astronomico di Roma Via Frascati 33 Monteporzio Catone00078 Italy Observatorio Nacional Rua General Jose Cristino 77-Bairro Imperial de Sao Cristovao Rio de Janeiro20921-400 Brazil Max Planck Institute for Extraterrestrial Physics Giessenbachstr. 1 Garching85748 Germany Institute for Astronomy University of Edinburgh Royal Observatory Blackford Hill EdinburghEH9 3HJ United Kingdom European Southern Observatory Karl-Schwarzschild-Str. 2 Garching85748 Germany INAF-Osservatorio Astrofisico di Arcetri Largo E. Fermi 5 Firenze50125 Italy Department of Astrophysics IMAPP Radboud University PO Box 9010 Nijmegen6500 GL Netherlands Leiden Observatory Leiden University Einsteinweg 55 Leiden2333 CC Netherlands Universität Innsbruck Institut für Astro- und Teilchenphysik Technikerstr. 25/8 Innsbruck6020 Austria Observatoire de Sauverny Versoix1290 Switzerland Clare Hall University of Cambridge Cambridge United Kingdom Kapteyn Astronomical Institute University of Groningen PO Box 800 Groningen9700 AV Netherlands Space physics and astronomy research unit University of Oulu Pentti Kaiteran katu 1 OuluFI-90014 Finland Max-Planck-Institut

Deep surveys have helped unveil the history of past and present galaxy mergers. In particular, they reveal their tidal debris and associated compact stellar systems. Euclid's unique combination of capabilities (spatial resolution, depth, and wide sky coverage) will make it a groundbreaking tool for galactic archaeology in the local Universe, bringing low surface brightness (LSB) science into the era of large-scale astronomical surveys. Euclid's Early Release Observations (ERO) demonstrate this potential with a field of view that includes several galaxies in the Dorado group. In this paper, we aim to derive from this image a mass assembly scenario for its main galaxies: NGC 1549, NGC 1553, and NGC 1546. We detect internal and external diffuse structures, and identify candidate globular clusters (GCs). By analysing the colours and distributions of the diffuse structures and candidate GCs, we can place constraints on the galaxies' mass assembly and merger histories. The results show that feature morphology, surface brightness, colours, and GC density profiles are consistent with galaxies that have undergone different merger scenarios. We classify NGC 1549 as a pure elliptical galaxy that has undergone a major merger. NGC 1553 appears to have recently transitioned from a late-type galaxy to early type, after a series of radial minor to intermediate mergers. NGC 1546 is a rare specimen of galaxy with an undisturbed disk and a prominent diffuse stellar halo, which we infer has been fed by minor mergers and then disturbed by the tidal effect from NGC 1553. Finally, we identify limitations specific to the observing conditions of this ERO, in particular stray light in the visible and persistence in the near-infrared bands. Once these issues are addressed and the extended emission from LSB objects is preserved by the data-processing pipeline, the Euclid Wide Survey will allow studies of the local Universe to be extended to statistical ensembles over a large part of the extrag

关键词： Stars

learning instrumental variables with structural and non-gaussianity assumptions

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The Journal of Machine Learning Research

The Journal of machine learning research 2017年第1期18卷

作者： Ricardo Silva Shohei Shimizu Department of Statistical Science and Centre for Computational Statistics and Machine Learning University College London UK The Center for Data Science Education and Research Shiga University Shiga Japan and The Institute of Scientific and Industrial Research Osaka University Japan

learning a causal effect from observational data requires strong assumptions. One possible method is to use instrumental variables, which are typically justified by background knowledge. It is possible, under further assumptions, to discover whether a variable is structurally instrumental to a target causal effect X → Y. However, the few existing approaches are lacking on how general these assumptions can be, and how to express possible equivalence classes of solutions. We present instrumental variable discovery methods that systematically characterize which set of causal effects can and cannot be discovered under local graphical criteria that define instrumental variables, without reconstructing full causal graphs. We also introduce the first methods to exploit non-Gaussianity assumptions, highlighting identifiability problems and solutions. Due to the difficulty of estimating such models from finite data, we investigate how to strengthen assumptions in order to make the statistical problem more manageable.

关键词： causal discovery causality instrumental variables

FRI0590 BIOMARKERS OF HEMODYNAMIC SEVERITY OF SYSTEMIC-SCLEROSIS ASSOCIATED PULMONARY ARTERIAL HYPERTENSION BY SERUM PROTEOME analysis

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Annals of the Rheumatic Diseases 2020年 79卷 900-900页

作者： S. Sanges L. Rice L. Tu J.L. Cracowski D. Montani J. Mantero C. Ternynck G. Marot E. Hachlla D. Launay M. Humbert C. Guignabert R. Lafyatis Boston University School of Medicine E5 Arthritis Center Boston United States of America Univ. Lille U1286 – INFINITE – Institute for Translational Research in Inflammation Lille France INSERM U1286 Lille France Université Paris Saclay School of Medicine Le Kremlin Bicetre France INSERM UMR_S 999 Hôpital Marie-Lannelongue Le Plessis Robinson France Univ. Grenoble Alpes INSERM HP2 Grenoble France AP-HP Department of Respiratory and Intensive Care Medicine Hôpital Bicêtre Le Kremlin Bicetre France Univ. Lille CHU Lille ULR 2694 - METRICS: Évaluation des technologies de santé et des pratiques médicales Lille France Inria Lille Nord-Europe - MODAL MOdels for Data Analysis and Learning Lille France Univ. Lille Institut Français de Bioinformatique Billille - plateforme de bioinformatique et bioanalyse de Lille Lille France CHU Lille Département de Médecine Interne et Immunologie Clinique Lille France Centre National de Référence Maladies Systémiques et Auto-immunes Rares (Sclérodermie Systémique) Lille France Health Care Provider of the European Reference Network on Rare Connective Tissue and Musculoskeletal Diseases Network (ReCONNET) Lille France Division of Rheumatology and Clinical Rheumatology Department of Medicine University of Pittsburgh Pittsburgh France

Objectives: To investigate alterations in the serum proteome of patients with systemic sclerosis (SSc)-associated pulmonary hypertension (PAH), to identify proteins that correlated with hemodynamic severity and to determine their possible pathogenic role. Methods: Patients were included if they fulfilled the following criteria: diagnosis of limited cutaneous SSc, no extensive interstitial lung disease (ILD), and no treatment with PAH-specific therapy. Patients were classified as cases if they had a definitive diagnosis of PAH confirmed by right heart catheterization (RHC) and a serum sample collected on the same day as RHC. They were classified as controls if they had no sign suggestive of PAH on echocardiography. Results: In a first exploratory step, serum expression of 1129 proteins was assessed in 15 cases and 16 controls by a high-throughput proteomic assay (SOMAscan). We identified 53 proteins differentially expressed between the 2 groups. Among these 53 candidates, only 2 correlated significantly with pulmonary vascular resistance (PVR): chemerin ( p= 0.01, ρ= 0.62) and SET nuclear proto-oncogen (SET) ( p= 0.01, ρ= 0.62). To validate these results, serum levels of chemerin and SET were measured by ELISA assay in 25 additional cases and 19 additional controls. Chemerin levels were confirmed to be significantly higher in cases ( p= 0.01) and correlated with PVR ( p= 0.01, ρ= 0.46). In a second step, to study the potential pathophysiological role of chemerin, we performed confocal immunofluorescence analyses on explanted lungs of healthy controls, SSc-ILD without PAH and SSc-PAH patients. Chemerin receptor, CMKLR1, was significantly increased on SSc-ILD and SSc-PAH pulmonary artery smooth muscle cells (PA-SMC). We then tested the effect of chemerin on PA-SMC proliferation by stimulating PA-SMCs from idiopathic pulmonary arterial hypertension (iPAH) patients with serum from SSc patients with and without PH, in the presence or absence of a CMKLR1 inhibitor. PA-SMCs

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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

Artificial intelligence research activities and sirections in the NTT Group

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NTT Technical Review 2016年第5期14卷

作者： Yamada, Takeshi Takahashi, Satoshi Naya, Futoshi Ikebe, Takashi Furukawa, Shigeto Research Planning Section Machine Learning and Data Science Center NTT Communication Science Laboratories Japan Speech and Language Media Project NTT Media Intelligence Laboratories Japan Innovative Communication Laboratory NTT Communication Science Laboratories Japan NTT Network Service Systems Laboratories Japan Sensory Resonance Research Group Human Information Science Laboratory NTT Communication Science Laboratories Japan

The research and development of artificial intelligence (AI) at NTT is advancing along four directions: (1) Agent-AI for analyzing information issued by people and understanding intentions and emotions in that information;(2) Heart-Touching-AI for analyzing unconscious and unnoticeable aspects of a person's mind and body and understanding deep psychological, intellectual, and instinctual states in that person;(3) Ambient-AI for analyzing and understanding just about anything in the world (objects, people, the environment) and instantaneously predicting and controlling those things;and (4) Network-AI for organically connecting and cultivating multiple types of AI and optimizing the entire social system. This article introduces the technologies supporting these four forms of AI and specific AI-related activities in the NTT Group. © 2016, Nippon Telegraph and Telephone Corp. All rights reserved.

关键词： Artificial intelligence

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