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QU-BraTS: MICCAI BraTS 2020 Challenge on Quantifying Uncertainty in Brain Tumor Segmentation - Analysis of Ranking Scores and Benchmarking Results

arXiv

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arXiv 2021年

作者： Mehta, Raghav Filos, Angelos Baid, Ujjwal Sako, Chiharu McKinley, Richard Rebsamen, Michael Dätwyler, Katrin Meier, Raphael Radojewski, Piotr Murugesan, Gowtham Krishnan Nalawade, Sahil Ganesh, Chandan Wagner, Ben Yu, Fang F. Fei, Baowei Madhuranthakam, Ananth J. Maldjian, Joseph A. Daza, Laura Gómez, Catalina Arbeláez, Pablo Dai, Chengliang Wang, Shuo Reynaud, Hadrien Mo, Yuanhan Angelini, Elsa Guo, Yike Bai, Wenjia Banerjee, Subhashis Pei, Linmin Murat, A.K. Rosas-González, Sarahi Zemmoura, Ilyess Tauber, Clovis Vu, Minh H. Nyholm, Tufve Löfstedt, Tommy Ballestar, Laura Mora Vilaplana, Veronica McHugh, Hugh Talou, Gonzalo Maso Wang, Alan Patel, Jay Chang, Ken Hoebel, Katharina Gidwani, Mishka Arun, Nishanth Gupta, Sharut Aggarwal, Mehak Singh, Praveer Gerstner, Elizabeth R. Kalpathy-Cramer, Jayashree Boutry, Nicolas Huard, Alexis Vidyaratne, Lasitha Rahman, Md Monibor Iftekharuddin, Khan M. Chazalon, Joseph Puybareau, Elodie Tochon, Guillaume Ma, Jun Cabezas, Mariano Llado, Xavier Oliver, Arnau Valencia, Liliana Valverde, Sergi Amian, Mehdi Soltaninejad, Mohammadreza Myronenko, Andriy Hatamizadeh, Ali Feng, Xue Dou, Quan Tustison, Nicholas Meyer, Craig Shah, Nisarg A. Talbar, Sanjay Weber, Marc-André Mahajan, Abhishek Jakab, Andras Wiest, Roland Fathallah-Shaykh, Hassan M. Nazeri, Arash Milchenko, Mikhail Marcus, Daniel Kotrotsou, Aikaterini Colen, Rivka Freymann, John Kirby, Justin Davatzikos, Christos Menze, Bjoern Bakas, Spyridon Gal, Yarin Arbel, Tal McGill University MontrealQC Canada Group University of Oxford Oxford United Kingdom University of Pennsylvania PhiladelphiaPA United States Department of Radiology Perelman School of Medicine The University of Pennsylvania PhiladelphiaPA United States Department of Pathology and Laboratory Medicine Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States University Institute of Diagnostic and Interventional Neuroradiology University of Bern Inselspital Bern University Hospital Bern Switzerland Department of Radiology University of Texas Southwestern Medical Center DallasTX United States Department of Bioengineering University of Texas DallasTX United States Advanced Imaging Research Center University of Texas Southwestern Medical Center DallasTX United States Universidad de los Andes Bogotá Colombia Data Science Institute Imperial College London London United Kingdom NIHR Imperial BRC ITMAT Data Science Group Imperial College London London United Kingdom Department of Brain Sciences Imperial College London London United Kingdom Machine Intelligence Unit Indian Statistical Institute Kolkata India Department of CSE University of Calcutta Kolkata India Department of Information Technology Uppsala University Uppsala Sweden Department of Diagnostic Radiology The University of Pittsburgh Medical Center PittsburghPA United States UMR U1253 iBrain Université de Tours Inserm Tours France Department of Radiation Sciences Umeå University Umeå Sweden Department of Computing Science Umeå University Umeå Sweden Signal Theory and Communications Department Universitat Politècnica de Catalunya Barcelona Tech Barcelona Spain Faculty of Medical and Health Sciences University of Auckland Auckland New Zealand Radiology Department Auckland City Hospital Auckland New Zealand Auckland Bioengineering Institute University of Auckland New Zealand Athinoula A. Martinos Center for Biomedical Imaging Department of Radiology

Deep learning (DL) models have provided state-of-the-art performance in various medical imaging benchmarking challenges, including the Brain Tumor Segmentation (BraTS) challenges. However, the task of focal pathology multi-compartment segmentation (e.g., tumor and lesion sub-regions) is particularly challenging, and potential errors hinder translating DL models into clinical workflows. Quantifying the reliability of DL model predictions in the form of uncertainties could enable clinical review of the most uncertain regions, thereby building trust and paving the way toward clinical translation. Several uncertainty estimation methods have recently been introduced for DL medical image segmentation tasks. Developing scores to evaluate and compare the performance of uncertainty measures will assist the end-user in making more informed decisions. In this study, we explore and evaluate a score developed during the BraTS 2019 and BraTS 2020 task on uncertainty quantification (QU-BraTS) and designed to assess and rank uncertainty estimates for brain tumor multi-compartment segmentation. This score (1) rewards uncertainty estimates that produce high confidence in correct assertions and those that assign low confidence levels at incorrect assertions, and (2) penalizes uncertainty measures that lead to a higher percentage of under-confident correct assertions. We further benchmark the segmentation uncertainties generated by 14 independent participating teams of QU-BraTS 2020, all of which also participated in the main BraTS segmentation task. Overall, our findings confirm the importance and complementary value that uncertainty estimates provide to segmentation algorithms, highlighting the need for uncertainty quantification in medical image analyses. Finally, in favor of transparency and reproducibility, our evaluation code is made publicly available at https://***/RagMeh11/QU-BraTS. © 2021, CC BY.

关键词： Benchmarking

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The Wide-field Spectroscopic Telescope (WST) science White Paper

arXiv

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arXiv 2024年

作者： Mainieri, Vincenzo Anderson, Richard I. Brinchmann, Jarle Cimatti, Andrea Ellis, Richard Hill, Vanessa Kneib, Jean-Paul McLeod, Anna Opitom, Cyrielle Roth, Martin M. Sáez, Paula Sánchez Smiljanic, Rodolfo Tolstoy, Eline Bacon, Roland Randich, Sofia Pasquini, Luca Barsanti, Stefania Bryant, Julia Bryson, Ian Casali, Mark Colless, Matthew Couch, Warrick Danielski, Camilla Dierickx, Philippe Driver, Simon Fontana, Adriano Garilli, Bianca de Jong, Roelof S. Lee, David Lehnert, Matt Magrini, Laura Montet, Ben Sanchez-Janssen, Ruben Pandey-Pommier, Mamta Sargent, Mark Schipani, Pietro Steinmetz, Matthias Travouillon, Tony Tresse, Laurence Vernet, Joël Yeche, Christophe Ziegler, Bodo Audard, Marc Arévalo, Patricia Bayo Aran, Amelia M. Bisero, Sofia Bisogni, Susanna Blondin, Stéphane Bonito, Rosaria Branchesi, Marica Catelan, Márcio D'Ammando, Filippo Dainotti, Maria G. De Cia, Annalisa Dhawan, Suhail Dupletsa, Ulyana Hazra, Nandini Ilić, Dragana Ivanov, Valentin D. Izzo, Luca Kovačević, Andjelka B. Kuncarayakti, Hanindyo Kwon, Yuna La Forgia, Fiorangela Lazzarin, Monica Lira, Paulina Loffredo, Eleonora Maguire, Kate Majidi, Fatemeh Z. Marconi, Marcella McGee, Sean Migliorini, Alessandra Minniti, Dante Nicholl, Matt Onori, Francesca Pala, Anna F. Panda, Swayamtrupta Pawlak, Michal Pessi, Priscila J. Popović, Luka Č. Smith, Graham P. Vergani, Susanna D. Vietri, Giustina Ye, Quanzhi Zafar, Tayyaba Matsuno, Tadafumi Kordopatis, Georges Annibali, Francesca Bayo, Amelia Bellazzini, Michele Beltran, Maria Teresa Berni, Leda Bianchi, Simone Biazzo, Katia Bland-Hawthorn, Joss Boffin, Henri M.J. Bono, Giuseppe Bowman, Dominic Braga, Vittorio F. Bragaglia, Angela Brucalassi, Anna Busà, Innocenza Casali, Giada Casasola, Viviana Castro, Norberto Cavallo, Lorenzo Chiappini, Cristina Colzi, Laura Damiani, Francesco da Silva, Ronaldo D'Orazi, Valentina Escorza, Ana Fabrizio, Michele Fiorentino, Giuliana Fontani, Francesco François, Patrick Galindo-Guil, Francisco J. Galli, Daniele Garcia-Rojas, Jorge Guarcello, Mario Giuseppe Helmi, Am European Southern Observatory Karl-Schwarzschild-Strasse 2 Garching bei München85748 Germany Switzerland Instituto de Astrofísica e Ciências do Espaço Universidade do Porto Portugal Dipartimento di Fisica e Astronomia "Augusto Righi" Alma Mater Studiorum Università di Bologna Italy Department of Physics and Astronomy University College London United Kingdom Observatoire de la Côte d'Azur Université Côte d'Azur CNRS Laboratoire Lagrange France Centre for Extragalactic Astronomy Department of Physics Durham University United Kingdom Institute for Computational Cosmology Department of Physics University of Durham United Kingdom Institute for Astronomy University of Edinburgh United Kingdom Germany Nicolaus Copernicus Astronomical Center Polish Academy of Sciences Poland Kapteyn Astronomical Institute University of Groningen Netherlands France INAF - Osservatorio Astrofisico di Arcetri Italy Stockholm University Sweden INAF - Osservatorio di Astrofisica e Scienza dello Spazio di Bologna Italy Universidad de Valparaíso Chile University of Geneva Switzerland Research School of Astronomy and Astrophysics Australian National University CanberraACT2611 Australia Australia Instituto de Astrofísica de Canarias Spain Universidad de La Laguna Spain SISSA - Scuola Internazionale Superiore di Studi Avanzati Italy University of Florence Italy INAF - Osservatorio Astronomico di Roma Italy GEPI Observatoire de Paris Universite PSL CNRS France INAF Istituto di Astrofisica Spaziale e Fisica cosmica Milano Italy Sydney Institute for Astronomy School of Physics The University of Sydney Australia Aix Marseille Univ CNRS CNES LAM France INAF - Osservatorio Astronomico di Palermo Italy University of Rome Tor Vergata Italy Newcastle University United Kingdom Italy UK Astronomy Technology Ctr. Royal Observatory Edinburgh United Kingdom INAF OACT Italy Dipartimento di Fisica Università degli Studi di Torino Italy Istituto Nazionale di Fisica Nucleare INFN Sezione di

The WST is proposed as a new facility dedicated to the efficient delivery of spectroscopic surveys and this white paper summarises the initial concept as well as the corresponding science cases. It will provide an end-to-end service from the preparation of surveys to the delivery of high-quality calibrated science data product. Uniquely, it will have the ability to simultaneously deliver fibre-fed multi-object spectroscopy over a large field of view (MOS) and integral field imaging spectroscopy of a central field of 3 × 3 arcmin2 (IFS). WST will fill a crucial gap in the astronomical landscape and will allow full exploitation of major current and upcoming ground and space-based facilities. WST will address outstanding scientific questions in the areas of cosmology;galaxy assembly, evolution, and enrichment, including our own Milky Way;origin of stars and planets;time domain and multi-messenger astrophysics. WST features a 12 meter aperture telescope that covers the UV to the H band and operates in seeing limited conditions. Its field of view (FoV) of 3.1 deg2 will be the largest of any telescope equipped with a MOS. In particular, its FoV is 2.5 times larger than PFS@Subaru and a remarkable 22 times larger than MOONS@VLT. In addition, the WST has a substantial aperture, twice as large (100 square metres) as its 8 meter class competitors. A central circular area with a diameter of 13 arcmin is available for simultaneous IFS observations. Within this area, the IFS with a FoV of 3 × 3 arcmin2 (9 times larger than MUSE@VLT) can be utilised simultaneously with the MOS. In addition, the IFS can be used to mosaic a larger area within the 13 arcmin circle (an area larger than the entire FoV of the ELT) while the MOS positioner can perform multiple configurations. The WST operational model will also implement target of opportunity (ToO) operations at both the telescope and MOS fibre levels to support the time domain science cases. The demand for a facility dedicated to spect

关键词： Telescopes

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11th German Conference on Chemoinformatics (GCC 2015) Abstracts

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JOURNAL OF CHEMINFORMATICS 2016年第1-Sup期8卷 1-27页

作者： [Anonymous] GDCh-CIC Division Associated Board Member Beilstein-Institut zur Förderung der Chemischen Wissenschaften Trakehner Str. 7–9 60487 Frankfurt Germany Division Medicinal Chemistry Amsterdam Institute for Molecules Medicines and Systems (AIMMS) VU University Amsterdam The Netherlands Centre for Bioinformatics Uni Hamburg Bundesstr. 43 20146 Hamburg Germany Sanofi-Aventis Deutschland GmbH 65926 Frankfurt am Main Germany GlaxoSmithKline Stevenage SG1 2NY UK Discngine Paris 75011 France Organisch-Chemisches Institut Westfälische Wilhelms-Universität Münster Germany Bundeskriminalamt Wiesbaden Central Analytics II 65173 Wiesbaden Germany Department of Chemistry and Biochemistry University of California Santa Barbara CA 93111 USA Center for Drug Research Ludwig-Maximilians University Munich Munich Germany Inorganic Chemistry and Center for Nanointegration University of Duisburg-Essen Essen Germany CAM-D Technologies Essen Germany Institute for Bioinformatics and Chemoinformatics Westphalian University of Applied Sciences Recklinghausen Germany Leiden University Leiden Netherlands Culgi BV Berlin Germany Physikalische Chemie III TU Dortmund 44227 Dortmund Germany Centre for Molecular Informatics Department of Chemistry University of Cambridge Lensfield Road Cambridge CB2 1EW United Kingdom Unilever Centre for Molecular Informatics Department of Chemistry Lensfield Road Cambridge CB2 1EW UK Centre for Molecular Science Informatics Department of Chemistry University of Cambridge Cambridge CB2 1EW UK Department of Chemistry Bar-Ilan University Ramat-Gan 5290002 Israel Department of Pharmaceutical Chemistry University of Marburg Marburg Germany Pharmaceutical and Medicinal Chemistry Institute of Pharmacy Freie Universität Berlin Königin-Luise Straße 2+4 14195 Berlin Germany Computer-Aided Drug Design Institute of Pharmacy Freie Universität Berlin 14195 Berlin Germany Center for Bioinformatics University of Hamburg 20146 Hamburg Germany Department of P

Information about several papers discussed at the 11th German Conference on Chemoinformatics (GCC) held November 8-10, 2015 sponsored by the Chemistry-Information-Computer (CIC) division of the German Chemical Society is presented. Topics include accurate description of protein-ligand binding and its functional activity, combination of chemoinformatics and bioinformatics using three dimensional structure of amino acids, and Human Genome Project leading to advancement in drug discovery.

关键词： CHEMINFORMATICS PREDICTION models MONTE Carlo method

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

arXiv

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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

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DP03 Melanoma detection in whole-slide images using a convolutional neural network for objective prognostic biomarker generation

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British Journal of Dermatology 2024年第SUPPLEMENT_1期191卷 i108–i108页

作者： Clarke, Emily L Magee, Derek Newton-Bishop, Julia Merchant, William Hall, Marlous Insall, Robert Maher, Nigel G Scolyer, Richard A Farnworth, Grace Ali, Anisah Saldanha, Gerald Bamford, Mark Sitcova, Eva Kujal, Petr O’Shea, Sally Treanor, Darren Department of Histopathology Leeds Teaching Hospitals NHS Trust Leeds UK Division of Pathology and Data Analytics University of Leeds Leeds UK School of Computing University of Leeds Leeds UK Leeds Institute for Medical Research University of Leeds Leeds UK Department of Clinical and Population Sciences Leeds Institute of Cardiovascular and Metabolic Medicine University of Leeds Leeds UK Department of Cell and Developmental Biology University College London London UK Melanoma Institute Australia The University of Sydney Sydney Australia Faculty of Medicine and Health The University of Sydney Sydney Australia Tissue Pathology and Diagnostic Oncology Royal Prince Alfred Hospital and NSW Health Pathology Sydney Australia Charles Perkins Centre The University of Sydney Sydney NSW Australia Leicester Cancer Research Centre University of Leicester Leicester UK Department of Histopathology University Hospitals of Leicester NHS Trust Leicester UK Department of Pathology Third Faculty of Medicine Charles University and Teaching Hospital Královské Vinohrady Prague Czechia Dermatology Department South Infirmary Victoria University Hospital Cork Ireland School of Medicine University College Cork Ireland Department of Clinical Pathology and Department of Clinical and Experimental Medicine Linköping University Linköping Sweden Center for Medical Image Science and Visualization (CMIV) Linköping University Linköping Sweden

The current melanoma staging system is predictive of 74% of the variance in survival, with prognostic biomarkers subject to high levels of interobserver and intraobserver variation. Melanoma morphology appears to be of greater significance than in other solid tumours, with Breslow thickness remaining the strongest prognostic indicator. The application of convolutional neural networks (CNNs) to whole-slide images (WSIs) may reveal new insights into tumour morphology and therefore patient prognosis. This work outlines the development and evaluation of a CNN for invasive cutaneous melanoma detection in WSIs, to enable the creation of objective prognostic biomarkers based on the tissue morphology. In total, 1157 WSIs containing cutaneous melanoma from five sources have been used in the initial development and evaluation of a custom-designed two-class tumour segmentation network with a fully convolutional architecture. The CNN detected and located invasive melanoma tissue of no specific type with an average per-pixel sensitivity and specificity of 97.6% and 99.9%, respectively across, the five test sets (three external). There were no statistical differences between tumour dimensions generated by the CNN compared with manual annotation. Similarly, there were no statistically significant differences between CNN-generated tumour dimensions across three scanning platforms. Furthermore, we have identified that this CNN can be used to calculate the ‘digital Breslow thickness’, which is a strong independent prognostic predictor of overall survival and melanoma-specific survival, across three test sets with follow-up data (hazard ratio 1.26, 95% confidence interval 1.19–1.34,P< 0.001). We have also shown that the ‘nodularity index’ determined by the tumour shape, independently of size, is predictive of survival; the rounder the tumour the worse the outcome (hazard ratio 0.71, 95% confidence interval 0.60–0.83,P< 0.001). We have developed and performed initial evaluation of a CN

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Search for Cosmic-Ray Electron and Positron Anisotropies with Seven Years of Fermi Large Area Telescope data

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Physical Review Letters 2017年第9期118卷 091103-091103页

作者： S. Abdollahi M. Ackermann M. Ajello A. Albert W. B. Atwood L. Baldini G. Barbiellini R. Bellazzini E. Bissaldi E. D. Bloom R. Bonino E. Bottacini T. J. Brandt P. Bruel S. Buson M. Caragiulo E. Cavazzuti A. Chekhtman S. Ciprini F. Costanza A. Cuoco S. Cutini F. D’Ammando F. de Palma R. Desiante S. W. Digel N. Di Lalla M. Di Mauro L. Di Venere B. Donaggio P. S. Drell C. Favuzzi W. B. Focke Y. Fukazawa S. Funk P. Fusco F. Gargano D. Gasparrini N. Giglietto F. Giordano M. Giroletti D. Green S. Guiriec A. K. Harding T. Jogler G. Jóhannesson T. Kamae M. Kuss S. Larsson L. Latronico J. Li F. Longo F. Loparco P. Lubrano J. D. Magill D. Malyshev A. Manfreda M. N. Mazziotta M. Meehan P. F. Michelson W. Mitthumsiri T. Mizuno A. A. Moiseev M. E. Monzani A. Morselli M. Negro E. Nuss T. Ohsugi N. Omodei D. Paneque J. S. Perkins M. Pesce-Rollins F. Piron G. Pivato G. Principe S. Rainò R. Rando M. Razzano A. Reimer O. Reimer C. Sgrò D. Simone E. J. Siskind F. Spada G. Spandre P. Spinelli A. W. Strong H. Tajima J. B. Thayer D. F. Torres E. Troja J. Vandenbroucke G. Zaharijas S. Zimmer Department of Physical Sciences Hiroshima University Higashi-Hiroshima Hiroshima 739-8526 Japan Deutsches Elektronen Synchrotron DESY D-15738 Zeuthen Germany Department of Physics and Astronomy Clemson University Kinard Lab of Physics Clemson South Carolina 29634-0978 USA Los Alamos National Laboratory Los Alamos New Mexico 87545 USA Santa Cruz Institute for Particle Physics Department of Physics and Department of Astronomy and Astrophysics University of California at Santa Cruz Santa Cruz California 95064 USA Dipartimento di Fisica “Enrico Fermi” dell’Università di Pisa I-56126 Pisa Italy Istituto Nazionale di Fisica Nucleare Sezione di Pisa I-56127 Pisa Italy Istituto Nazionale di Fisica Nucleare Sezione di Trieste I-34127 Trieste Italy Dipartimento di Fisica Università di Trieste I-34127 Trieste Italy Dipartimento di Fisica “M. Merlin” dell’Università e del Politecnico di Bari I-70126 Bari Italy Istituto Nazionale di Fisica Nucleare Sezione di Bari I-70126 Bari Italy W. W. Hansen Experimental Physics Laboratory Kavli Institute for Particle Astrophysics and Cosmology Department of Physics and SLAC National Accelerator Laboratory Stanford University Stanford California 94305 USA Istituto Nazionale di Fisica Nucleare Sezione di Torino I-10125 Torino Italy Dipartimento di Fisica Università degli Studi di Torino I-10125 Torino Italy NASA Goddard Space Flight Center Greenbelt Maryland 20771 USA Laboratoire Leprince-Ringuet École polytechnique CNRS/IN2P3 F-91128 Palaiseau France Agenzia Spaziale Italiana (ASI) Science Data Center I-00133 Roma Italy College of Science George Mason University Fairfax Virginia 22030 USA resident at Naval Research Laboratory Washington DC 20375 USA Istituto Nazionale di Fisica Nucleare Sezione di Perugia I-06123 Perugia Italy Institute for Theoretical Particle Physics and Cosmology (TTK) RWTH Aachen University D-52056 Aachen Germany INAF Istituto di Radioastronomia I-40129 Bologna Italy Dipartimento di Astronomia Un

The Large Area Telescope on board the Fermi Gamma-ray Space Telescope has collected the largest ever sample of high-energy cosmic-ray electron and positron events since the beginning of its operation. Potential anisotropies in the arrival directions of cosmic-ray electrons or positrons could be a signature of the presence of nearby sources. We use almost seven years of data with energies above 42 GeV processed with the Pass 8 reconstruction. The present data sample can probe dipole anisotropies down to a level of 10−3. We take into account systematic effects that could mimic true anisotropies at this level. We present a detailed study of the event selection optimization of the cosmic-ray electrons and positrons to be used for anisotropy searches. Since no significant anisotropies have been detected on any angular scale, we present upper limits on the dipole anisotropy. The present constraints are among the strongest to date probing the presence of nearby young and middle-aged sources.

关键词： Cosmic ray & astroparticle detectors Cosmic ray propagation Cosmic ray sources Cosmological parameters

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Inferred Cosmic-Ray Spectrum from Fermi Large Area Telescope γ-Ray Observations of Earth’s Limb

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Physical Review Letters 2014年第15期112卷 151103-151103页

作者： M. Ackermann M. Ajello A. Albert A. Allafort L. Baldini G. Barbiellini D. Bastieri K. Bechtol R. Bellazzini R. D. Blandford E. D. Bloom E. Bonamente E. Bottacini A. Bouvier T. J. Brandt M. Brigida P. Bruel R. Buehler S. Buson G. A. Caliandro R. A. Cameron P. A. Caraveo C. Cecchi E. Charles R. C. G. Chaves A. Chekhtman J. Chiang G. Chiaro S. Ciprini R. Claus J. Cohen-Tanugi J. Conrad S. Cutini M. Dalton F. D’Ammando A. de Angelis F. de Palma C. D. Dermer S. W. Digel L. Di Venere E. do Couto e Silva P. S. Drell A. Drlica-Wagner C. Favuzzi S. J. Fegan E. C. Ferrara W. B. Focke A. Franckowiak Y. Fukazawa S. Funk P. Fusco F. Gargano D. Gasparrini S. Germani N. Giglietto F. Giordano M. Giroletti T. Glanzman G. Godfrey G. A. Gomez-Vargas I. A. Grenier J. E. Grove S. Guiriec M. Gustafsson D. Hadasch Y. Hanabata A. K. Harding M. Hayashida K. Hayashi J. W. Hewitt D. Horan X. Hou R. E. Hughes Y. Inoue M. S. Jackson T. Jogler G. Jóhannesson A. S. Johnson T. Kamae T. Kawano J. Knödlseder M. Kuss J. Lande S. Larsson L. Latronico F. Longo F. Loparco M. N. Lovellette P. Lubrano M. Mayer M. N. Mazziotta J. E. McEnery J. Mehault P. F. Michelson W. Mitthumsiri T. Mizuno A. A. Moiseev C. Monte M. E. Monzani A. Morselli I. V. Moskalenko S. Murgia R. Nemmen E. Nuss T. Ohsugi A. Okumura M. Orienti E. Orlando J. F. Ormes D. Paneque J. H. Panetta J. S. Perkins M. Pesce-Rollins F. Piron G. Pivato T. A. Porter S. Rainò R. Rando M. Razzano S. Razzaque A. Reimer O. Reimer S. Ritz M. Roth M. Schaal A. Schulz C. Sgrò E. J. Siskind G. Spandre P. Spinelli A. W. Strong H. Takahashi Y. Takeuchi J. G. Thayer J. B. Thayer D. J. Thompson L. Tibaldo M. Tinivella D. F. Torres G. Tosti E. Troja V. Tronconi T. L. Usher J. Vandenbroucke V. Vasileiou G. Vianello V. Vitale M. Werner B. L. Winer K. S. Wood M. Wood Z. Yang Deutsches Elektronen Synchrotron DESY D-15738 Zeuthen Germany Space Sciences Laboratory 7 Gauss Way University of California Berkeley California 94720-7450 USA W. W. Hansen Experimental Physics Laboratory Kavli Institute for Particle Astrophysics and Cosmology Department of Physics and SLAC National Accelerator Laboratory Stanford University Stanford California 94305 USA Istituto Nazionale di Fisica Nucleare Sezione di Pisa I-56127 Pisa Italy Istituto Nazionale di Fisica Nucleare Sezione di Trieste I-34127 Trieste Italy Dipartimento di Fisica Università di Trieste I-34127 Trieste Italy Istituto Nazionale di Fisica Nucleare Sezione di Padova I-35131 Padova Italy Dipartimento di Fisica e Astronomia “G. Galilei” Università di Padova I-35131 Padova Italy Istituto Nazionale di Fisica Nucleare Sezione di Perugia I-06123 Perugia Italy Dipartimento di Fisica Università degli Studi di Perugia I-06123 Perugia Italy Santa Cruz Institute for Particle Physics Department of Physics and Department of Astronomy and Astrophysics University of California at Santa Cruz Santa Cruz California 95064 USA NASA Goddard Space Flight Center Greenbelt Maryland 20771 USA Dipartimento di Fisica “M. Merlin” dell’Università e del Politecnico di Bari I-70126 Bari Italy Istituto Nazionale di Fisica Nucleare Sezione di Bari 70126 Bari Italy Laboratoire Leprince-Ringuet École polytechnique CNRS/IN2P3 Palaiseau France Consorzio Interuniversitario per la Fisica Spaziale (CIFS) I-10133 Torino Italy INAF-Istituto di Astrofisica Spaziale e Fisica Cosmica I-20133 Milano Italy Laboratoire AIM CEA-IRFU/CNRS/Université Paris Diderot Service d’Astrophysique CEA Saclay 91191 Gif sur Yvette France Center for Earth Observing and Space Research College of Science George Mason University Fairfax Virginia 22030 USA Agenzia Spaziale Italiana (ASI) Science Data Center I-00044 Frascati (Roma) Italy Istituto Nazionale di Astrofisica - Osservatorio Astronomico di Roma I-00040 Monte Porzio Catone (Roma)

Recent accurate measurements of cosmic-ray (CR) species by ATIC-2, CREAM, and PAMELA reveal an unexpected hardening in the proton and He spectra above a few hundred GeV, a gradual softening of the spectra just below a few hundred GeV, and a harder spectrum of He compared to that of protons. These newly discovered features may offer a clue to the origin of high-energy CRs. We use the Fermi Large Area Telescope observations of the γ-ray emission from Earth’s limb for an indirect measurement of the local spectrum of CR protons in the energy range ∼90 GeV–6 TeV (derived from a photon energy range 15 GeV–1 TeV). Our analysis shows that single power law and broken power law spectra fit the data equally well and yield a proton spectrum with index 2.68±0.04 and 2.61±0.08 above ∼200 GeV, respectively.

关键词： COSMIC rays ASTROPHYSICAL radiation EARTH (Planet) PHOTONS POWER law (Mathematics)

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Correction: X-chromosome-linked miR548am-5p is a key regulator of sex disparity in the susceptibility to mitochondria-mediated apoptosis

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Cell death & disease 2019年第11期10卷 828页

作者： Paola Matarrese Paolo Tieri Simona Anticoli Barbara Ascione Maria Conte Claudio Franceschi Walter Malorni Stefano Salvioli Anna Ruggieri Center for Gender Specific Medicine Istituto Superiore di Sanità viale Regina Elena 299 Rome Italy. CNR National Research Council IAC Institute for Applied Computing Via dei Taurini 19 Rome Italy. Data Science Program La Sapienza University of Rome Rome Italy. Department of Experimental Diagnostic and Specialty Medicine (DIMES) University of Bologna Bologna Italy. Interdepartmental Centre "L. Galvani" (CIG) University of Bologna via San Giacomo 12 40126 Bologna Italy. IRCCS Istituto delle Scienze Neurologiche di Bologna Via Altura 3 40139 Bologna Italy. Lobachevsky State University of Nizhny Novgorod Nizhny Novgorod Russia. School of Mathematical Physical and Natural Sciences and Faculty of Medicine University of Tor Vergata Rome Italy. Department of Experimental Diagnostic and Specialty Medicine (DIMES) University of Bologna Bologna Italy. stefano.salvioli@unibo.it. Interdepartmental Centre "L. Galvani" (CIG) University of Bologna via San Giacomo 12 40126 Bologna Italy. stefano.salvioli@unibo.it. Center for Gender Specific Medicine Istituto Superiore di Sanità viale Regina Elena 299 Rome Italy. anna.ruggieri@iss.it.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

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Search for Cosmic-Ray Electron and Positron Anisotropies with Seven Years of Fermi Large Area Telescope data

arXiv

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arXiv 2017年

作者： Abdollahi, S. Ackermann, M. Ajello, M. Albert, A. Atwood, W.B. Baldini, L. Barbiellini, G. Bellazzini, R. Bissaldi, E. Bloom, E.D. Bonino, R. Bottacini, E. Brandt, T.J. Bruel, P. Buson, S. Caragiulo, M. Cavazzuti, E. Chekhtman, A. Ciprini, S. Costanza, F. Cuoco, A. Cutini, S. D’Ammando, F. de Palma, F. Desiante, R. Digel, S.W. Di Lalla, N. Di Mauro, M. Di Venere, L. Donaggio, B. Drell, P.S. Favuzzi, C. Focke, W.B. Fukazawa, Y. Funk, S. Fusco, P. Gargano, F. Gasparrini, D. Giglietto, N. Giordano, F. Giroletti, M. Green, D. Guiriec, S. Harding, A.K. Jogler, T. Jóhannesson, G. Kamae, T. Kuss, M. Larsson, S. Latronico, L. Li, J. Longo, F. Loparco, F. Lubrano, P. Magill, J.D. Malyshev, D. Manfreda, A. Mazziotta, M.N. Meehan, M. Michelson, P.F. Mitthumsiri, W. Mizuno, T. Moiseev, A.A. Monzani, M.E. Morselli, A. Negro, M. Nuss, E. Ohsugi, T. Omodei, N. Paneque, D. Perkins, J.S. Pesce-Rollins, M. Piron, F. Pivato, G. Principe, G. Rainò, S. Rando, R. Razzano, M. Reimer, A. Reimer, O. Sgrò, C. Simone, D. Siskind, E.J. Spada, F. Spandre, G. Spinelli, P. Strong, A.W. Tajima, H. Thayer, J.B. Torres, D.F. Troja, E. Vandenbroucke, J. Zaharijas, G. Zimmer, S. Department of Physical Sciences Hiroshima University Higashi-HiroshimaHiroshima739-8526 Japan Deutsches Elektronen Synchrotron DESY ZeuthenD-15738 Germany Department of Physics and Astronomy Clemson University Kinard Lab of Physics ClemsonSC29634-0978 United States Los Alamos National Laboratory Los AlamosNM87545 United States Santa Cruz Institute for Particle Physics Department of Physics Department of Astronomy and Astrophysics University of California at Santa Cruz Santa CruzCA95064 United States Dipartimento di Fisica "Enrico Fermi" dell’Università di Pisa PisaI-56126 Italy Istituto Nazionale di Fisica Nucleare Sezione di Pisa PisaI-56127 Italy Istituto Nazionale di Fisica Nucleare Sezione di Trieste TriesteI-34127 Italy Dipartimento di Fisica Università di Trieste TriesteI-34127 Italy Dipartimento di Fisica "M. Merlin" dell’Università e del Politecnico di Bari BariI-70126 Italy Istituto Nazionale di Fisica Nucleare Sezione di Bari BariI-70126 Italy W. W. Hansen Experimental Physics Laboratory Kavli Institute for Particle Astrophysics and Cosmology Department of Physics SLAC National Accelerator Laboratory Stanford University StanfordCA94305 United States Istituto Nazionale di Fisica Nucleare Sezione di Torino TorinoI-10125 Italy Dipartimento di Fisica Università degli Studi di Torino TorinoI-10125 Italy NASA Goddard Space Flight Center GreenbeltMD20771 United States Laboratoire Leprince-Ringuet École polytechnique CNRS/IN2P3 PalaiseauF-91128 France Science Data Center RomaI-00133 Italy College of Science George Mason University FairfaxVA22030 United States Istituto Nazionale di Fisica Nucleare Sezione di Perugia PerugiaI-06123 Italy RWTH Aachen University AachenD-52056 Germany INAF Istituto di Radioastronomia BolognaI-40129 Italy Dipartimento di Astronomia Università di Bologna BolognaI-40127 Italy Università Telematica Pegaso Piazza Trieste e Trento 48 NapoliI-80132 Italy Università di Udine UdineI-33100

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Resolving the Extragalactic γ-Ray Background above 50 GeV with the Fermi Large Area Telescope

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Physical Review Letters 2016年第15期116卷 151105-151105页

作者： M. Ackermann M. Ajello A. Albert W. B. Atwood L. Baldini J. Ballet G. Barbiellini D. Bastieri K. Bechtol R. Bellazzini E. Bissaldi R. D. Blandford E. D. Bloom R. Bonino J. Bregeon R. J. Britto P. Bruel R. Buehler G. A. Caliandro R. A. Cameron M. Caragiulo P. A. Caraveo E. Cavazzuti C. Cecchi E. Charles A. Chekhtman J. Chiang G. Chiaro S. Ciprini J. Cohen-Tanugi L. R. Cominsky F. Costanza S. Cutini F. D’Ammando A. de Angelis F. de Palma R. Desiante S. W. Digel M. Di Mauro L. Di Venere A. Domínguez P. S. Drell C. Favuzzi S. J. Fegan E. C. Ferrara A. Franckowiak Y. Fukazawa S. Funk P. Fusco F. Gargano D. Gasparrini N. Giglietto P. Giommi F. Giordano M. Giroletti G. Godfrey D. Green I. A. Grenier S. Guiriec E. Hays D. Horan G. Iafrate T. Jogler G. Jóhannesson M. Kuss G. La Mura S. Larsson L. Latronico J. Li L. Li F. Longo F. Loparco B. Lott M. N. Lovellette P. Lubrano G. M. Madejski J. Magill S. Maldera A. Manfreda M. Mayer M. N. Mazziotta P. F. Michelson W. Mitthumsiri T. Mizuno A. A. Moiseev M. E. Monzani A. Morselli I. V. Moskalenko S. Murgia M. Negro E. Nuss T. Ohsugi C. Okada N. Omodei E. Orlando J. F. Ormes D. Paneque J. S. Perkins M. Pesce-Rollins V. Petrosian F. Piron G. Pivato T. A. Porter S. Rainò R. Rando M. Razzano S. Razzaque A. Reimer O. Reimer T. Reposeur R. W. Romani M. Sánchez-Conde J. Schmid A. Schulz C. Sgrò D. Simone E. J. Siskind F. Spada G. Spandre P. Spinelli D. J. Suson H. Takahashi J. B. Thayer L. Tibaldo D. F. Torres E. Troja G. Vianello M. Yassine S. Zimmer Deutsches Elektronen Synchrotron DESY D-15738 Zeuthen Germany Department of Physics and Astronomy Clemson University Kinard Lab of Physics Clemson SC 29634-0978 USA W. W. Hansen Experimental Physics Laboratory Kavli Institute for Particle Astrophysics and Cosmology Department of Physics and SLAC National Accelerator Laboratory Stanford University Stanford CA 94305 USA Santa Cruz Institute for Particle Physics Department of Physics and Department of Astronomy and Astrophysics University of California at Santa Cruz Santa Cruz CA 95064 USA Università di Pisa and Istituto Nazionale di Fisica Nucleare Sezione di Pisa I-56127 Pisa Italy Laboratoire AIM CEA-IRFU/CNRS/Université Paris Diderot Service d’Astrophysique CEA Saclay F-91191 Gif sur Yvette France Istituto Nazionale di Fisica Nucleare Sezione di Trieste I-34127 Trieste Italy Dipartimento di Fisica Università di Trieste I-34127 Trieste Italy Istituto Nazionale di Fisica Nucleare Sezione di Padova I-35131 Padova Italy Dipartimento di Fisica e Astronomia “G. Galilei” Università di Padova I-35131 Padova Italy Dept. of Physics and Wisconsin IceCube Particle Astrophysics Center University of Wisconsin Madison WI 53706 USA Istituto Nazionale di Fisica Nucleare Sezione di Pisa I-56127 Pisa Italy Istituto Nazionale di Fisica Nucleare Sezione di Bari I-70126 Bari Italy Istituto Nazionale di Fisica Nucleare Sezione di Torino I-10125 Torino Italy Dipartimento di Fisica Generale “Amadeo Avogadro” Università degli Studi di Torino I-10125 Torino Italy Laboratoire Univers et Particules de Montpellier Université Montpellier CNRS/IN2P3 Montpellier France Department of Physics University of Johannesburg PO Box 524 Auckland Park 2006 South Africa Laboratoire Leprince-Ringuet École polytechnique CNRS/IN2P3 Palaiseau France Consorzio Interuniversitario per la Fisica Spaziale (CIFS) I-10133 Torino Italy Dipartimento di Fisica “M. Merlin” dell’Università e del Politecnico di Bari I-70126 Bari Italy INAF-Istituto di As

The Fermi Large Area Telescope (LAT) Collaboration has recently released a catalog of 360 sources detected above 50 GeV (2FHL). This catalog was obtained using 80 months of data re-processed with Pass 8, the newest event-level analysis, which significantly improves the acceptance and angular resolution of the instrument. Most of the 2FHL sources at high Galactic latitude are blazars. Using detailed Monte Carlo simulations, we measure, for the first time, the source count distribution, dN/dS, of extragalactic γ-ray sources at E>50 GeV and find that it is compatible with a Euclidean distribution down to the lowest measured source flux in the 2FHL (∼8×10−12 ph cm−2 s−1). We employ a one-point photon fluctuation analysis to constrain the behavior of dN/dS below the source detection threshold. Overall, the source count distribution is constrained over three decades in flux and found compatible with a broken power law with a break flux, Sb, in the range [8×10−12,1.5×10−11] ph cm−2 s−1 and power-law indices below and above the break of α2∈[1.60,1.75] and α1=2.49±0.12, respectively. Integration of dN/dS shows that point sources account for at least 86−14+16% of the total extragalactic γ-ray background. The simple form of the derived source count distribution is consistent with a single population (i.e., blazars) dominating the source counts to the minimum flux explored by this analysis. We estimate the density of sources detectable in blind surveys that will be performed in the coming years by the Cherenkov Telescope Array.

关键词： Gamma ray astronomy Galactic nuclei & quasars

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