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DPHL:A DIA Pan-human Protein Mass Spectrometry Library for Robust Biomarker Discovery

Genomics, Proteomics & Bioinformatics 2020年第2期18卷 104-119页

作者： Tiansheng Zhu Yi Zhu Yue Xuan Huanhuan Gao Xue Cai Sander R.Piersma Thang V.Pham Tim Schelfhorst Richard R.G.D.Haas Irene V.Bijnsdorp Rui Sun Liang Yue Guan Ruan Qiushi Zhang Mo Hu Yue Zhou Winan J.Van Houdt Tessa Y.S.Le Large Jacqueline Cloos Anna Wojtuszkiewicz Danijela Koppers-Lalic Franziska Bottger Chantal Scheepbouwer Ruud H.Brakenhoff Geert J.L.H.van Leenders Jan N.M.Ijzermans John W.M.Martens Renske D.M.Steenbergen Nicole C.Grieken Sathiyamoorthy Selvarajan Sangeeta Mantoo Sze S.Lee Serene J.Y.Yeow Syed M.F.Alkaff Nan Xiang Yaoting Sun Xiao Yi Shaozheng Dai Wei Liu Tian Lu Zhicheng Wu Xiao Liang Man Wang Yingkuan Shao Xi Zheng Kailun Xu Qin Yang Yifan Meng Cong Lu Jiang Zhu Jin'e Zheng Bo Wang Sai Lou Yibei Dai Chao Xu Chenhuan Yu Huazhong Ying Tony K.Lim Jianmin Wu Xiaofei Gao Zhongzhi Luan Xiaodong Teng Peng Wu Shi'ang Huang Zhihua Tao Narayanan G.Iyer Shuigeng Zhou Wenguang Shao Henry Lam Ding Ma Jiafu Ji Oi L.Kon Shu Zheng Ruedi Aebersold Connie R.Jimenez Tiannan Guo Zhejiang Provincial Laboratory of Life Sciences and Biomedicine Westlake UniversityHangzhou 310024China Key Laboratory of Structural Biology of Zhejiang Province School of Life SciencesWestlake UniversityHangzhou 310024China Institute of Basic Medical Sciences Westlake Institute for Advanced StudyHangzhou 310024China School of Computer Science Shanghai Key Laboratory of Intelligent Information ProcessingFudan UniversityShanghai 200433China Thermo Fisher Scientific(BREMEN)GmbH Bremen 28195Germany OncoProteomics Laboratory Department of Medical OncologyVU University Medical CenterVU UniversityAmsterdam 1011The Netherlands Amsterdam UMC Vrije Universiteit AmsterdamUrologyCancer Center AmsterdamAmsterdam 1011The Netherlands Thermo Fisher Scientific Shanghai 201206China The Netherlands Cancer Institute Surgical OncologyAmsterdam 1011The Netherlands Amsterdam UMC Vrije Universiteit AmsterdamSurgeryCancer Center AmsterdamAmsterdam 1011The Netherlands Amsterdam UMC Vrije Universiteit AmsterdamPediatric Oncology/HematologyCancer Center AmsterdamAmsterdam 1011The Netherlands Amsterdam UMC Vrije Universiteit AmsterdamHematologyCancer Center AmsterdamAmsterdam 1011The Netherlands Amsterdam UMC Vrije Universiteit AmsterdamMedical OncologyCancer Center AmsterdamAmsterdam 1011The Netherlands Amsterdam UMC Vrije Universiteit AmsterdamNeurosurgeryCancer Center AmsterdamAmsterdam 1011The Netherlands Amsterdam UMC Vrije Universiteit AmsterdamPathologyCancer Center AmsterdamAmsterdam 1011The Netherlands Amsterdam UMC Vrije Universiteit AmsterdamOtolaryngology/Head and Neck SurgeryCancer Center AmsterdamAmsterdam 1011The Netherlands Erasmus MC University Medical Center PathologyRotterdam 1016LVThe Netherlands Erasmus MC University Medical Center SurgeryRotterdam 1016LVThe Netherlands Erasmus MC University Medical Center Medical OncologyRotterdam 1016LVThe Netherlands Department of Anatomical Pathology Singapore General HospitalSingapore 169608Singapore Division of Medical Scie

To address the increasing need for detecting and validating protein biomarkers in clinical specimens,mass spectrometry(MS)-based targeted proteomic techniques,including the selected reaction monitoring(SRM),parallel reaction monitoring(PRM),and massively parallel dataindependent acquisition(DIA),have been *** optimal performance,they require the fragment ion spectra of targeted peptides as prior *** this report,we describe a MS pipeline and spectral resource to support targeted proteomics studies for human tissue *** build the spectral resource,we integrated common open-source MS computational tools to assemble a freely accessible computational workflow based on *** then applied the workflow to generate DPHL,a comprehensive DIA pan-human library,from 1096 data-dependent acquisition(DDA)MS raw files for 16 types of cancer *** extensive spectral resource was then applied to a proteomic study of 17 prostate cancer(PCa)***,PRM validation was applied to a larger study of 57 PCa patients and the differential expression of three proteins in prostate tumor was *** a second application,the DPHL spectral resource was applied to a study consisting of plasma samples from 19 diffuse large B cell lymphoma(DLBCL)patients and 18 healthy control *** expressed proteins between DLBCL patients and healthy control subjects were detected by DIA-MS and confirmed by *** data demonstrate that the DPHL supports DIA and PRM MS pipelines for robust protein biomarker *** is freely accessible at https://***/page/***?id=IPX0001400000.

关键词： data-independent acquisition Parallel reaction monitoring Spectral library Prostate cancer Diffuse large B cell lymphoma

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X-ray and multiwavelength polarization of Mrk 501 from 2022 to 2023

arXiv

引用

arXiv 2024年

作者： Chen, Chien-Ting J. Liodakis, Ioannis Middei, Riccardo Kim, Dawoon E. Di Gesu, Laura Di Marco, Alessandro Ehlert, Steven R. Errando, Manel Negro, Michela Jorstad, Svetlana G. Marscher, Alan P. Wu, Kinwah Agudo, Iván Poutanen, Juri Mizuno, Tsunefumi Kouch, Pouya M. Lindfors, Elina Borman, George A. Grishina, Tatiana S. Kopatskaya, Evgenia N. Larionova, Elena G. Morozova, Daria A. Savchenko, Sergey S. Troitsky, Ivan S. Troitskaya, Yulia V. Vasilyev, Andrey A. Zhovtan, Alexey V. Aceituno, Francisco José Bonnoli, Giacomo Casanova, Víctor Escudero, Juan Agís-González, Beatriz Husillos, César Santos, Jorge Otero Sota, Alfredo Piirola, Vilppu Myserlis, Ioannis Angelakis, Emmanouil Kraus, Alexander Gurwell, Mark Keating, Garrett Rao, Ramprasad Kang, Sincheol Lee, Sang-Sung Kim, Sang-Hyun Cheong, Whee Yeon Jeong, Hyeon-Woo Song, Chanwoo Berdyugin, Andrei V. Kagitani, Masato Kravtsov, Vadim Nitindala, Anagha P. Sakanoi, Takeshi Imazawa, Ryo Sasada, Mahito Fukazawa, Yasushi Kawabata, Koji S. Uemura, Makoto Nakaoka, Tatsuya Akitaya, Hiroshi Casadio, Carolina Sievers, Albrecht Antonelli, Lucio Angelo Bachetti, Matteo Baldini, Luca Baumgartner, Wayne H. Bellazzini, Ronaldo Bianchi, Stefano Bongiorno, Stephen D. Bonino, Raffaella Brez, Alessandro Bucciantini, Niccoló Capitanio, Fiamma Castellano, Simone Cavazzuti, Elisabetta Ciprini, Stefano Costa, Enrico De Rosa, Alessandra Monte, Ettore Del Lalla, Niccoló Di Donnarumma, Immacolata Doroshenko, Victor Dovčiak, Michal Enoto, Teruaki Evangelista, Yuri Fabiani, Sergio Ferrazzoli, Riccardo Garcia, Javier A. Gunji, Shuichi Hayashida, Kiyoshi Heyl, Jeremy Iwakiri, Wataru Kaaret, Philip Karas, Vladimir Kislat, Fabian Kitaguchi, Takao Kolodziejczak, Jeffery J. Krawczynski, Henric Monaca, Fabio La Latronico, Luca Maldera, Simone Manfreda, Alberto Marin, Frédéric Marinucci, Andrea Marshall, Herman L. Massaro, Francesco Matt, Giorgio Mitsuishi, Ikuyuki Muleri, Fabio Ng, C.-Y. O’Dell, Stephen L. Omodei, Nicola Oppedisano, Chiara Papitto, Alessandro Pavlov, George G. Peirson, Abel Lawrence Science and Technology Institute Universities Space Research Association HuntsvilleAL35805 United States Astrophysics Office NASA Marshall Space Flight Center ST12 HuntsvilleAL35812 United States NASA Marshall Space Flight Center HuntsvilleAL35812 United States Institute of Astrophysics Foundation for Research and Technology-Hellas HeraklionGR-70013 Greece Space Science Data Center Agenzia Spaziale Italiana Via del Politecnico snc Roma00133 Italy Monte Porzio Catone00040 Italy INAF Istituto di Astrofisica e Planetologia Spaziali Via del Fosso del Cavaliere 100 Roma00133 Italy Dipartimento di Fisica Università degli Studi di Roma "La Sapienza" Piazzale Aldo Moro 5 Roma00185 Italy Dipartimento di Fisica Università degli Studi di Roma "Tor Vergata" Via della Ricerca Scientifica 1 Roma00133 Italy ASI - Agenzia Spaziale Italiana Via del Politecnico snc Roma00133 Italy Physics Department McDonnell Center for the Space Sciences Washington University in St. Louis St. LouisMO63130 United States Department of Physics and Astronomy Louisiana State University Baton RougeLA70803 United States Institute for Astrophysical Research Boston University 725 Commonwealth Avenue BostonMA02215 United States Saint Petersburg State University 7/9 Universitetskaya nab. St. Petersburg199034 Russia Mullard Space Science Laboratory University College London Holmbury St Mary Dorking SurreyRH5 6NT United Kingdom Instituto de Astrofísica de Andalucía—CSIC Glorieta de la Astronomía s/n Granada18008 Spain Department of Physics and Astronomy University of Turku 20014 Finland Hiroshima Astrophysical Science Center Hiroshima University 1-3-1 Kagamiyama Higashi-Hiroshima Hiroshima739-8526 Japan Finnish Centre for Astronomy with ESO University of Turku 20014 Finland Aalto University Metsähovi Radio Observatory Metsähovintie 114 KylmäläFI-02540 Finland Crimean Astrophysical Observatory RAS P/O Nauchny 298409 Pulkovo Observatory St.Petersburg196140 Russia I

We present multiwavelength polarization measurements of the luminous blazar Mrk 501 over a 14-month period. The 2–8 keV X-ray polarization was measured with the Imaging X-ray Polarimetry Explorer (IXPE) with six 100-ks observations spanning from 2022 March to 2023 April. Each IXPE observation was accompanied by simultaneous X-ray data from NuSTAR, Swift/XRT, and/or XMM-Newton. Complementary optical-infrared polarization measurements were also available in the B, V, R, I, and J bands, as were radio polarization measurements from 4.85 GHz to 225.5 GHz. Among the first five IXPE observations, we did not find significant variability in the X-ray polarization degree and angle with IXPE. However, the most recent sixth observation found an elevated polarization degree at > 3σ above the average of the other five observations. The optical and radio measurements show no apparent correlations with the X-ray polarization properties. Throughout the six IXPE observations, the X-ray polarization degree remained higher than, or similar to, the R-band optical polarization degree, which remained higher than the radio value. This is consistent with the energy-stratified shock scenario proposed to explain the first two IXPE observations, in which the polarized X-ray, optical, and radio emission arises from different regions. © 2024, CC BY-SA.

关键词： Polarization

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Differential Evolution Based High-order Peak Filter Design with Application to Compensation of Contact-induced Vibration in HDD Servo systems

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International Journal of Automation and computing 2017年第1期14卷 45-56页

作者： Deqing Huang Jian-Xin Xu Xin Deng Venkatakrishnan Venkataramanan The Cat Tuong Huynh Institute of Systems Science and Technology School of Electrical Engineering Southwest Jiaotong University Department of Electrical and Computer Engineering National University of Singapore Chongqing Key Laboratory of Computational Intelligence Chongqing University of Posts and Telecommunications Data Storage Institute Agency for Science Technology and Research

Sensitivity loop shaping using add-on peak filters is a simple and effective method to reject narrow-band disturbances in hard disk drive （HDD） servo systems. The parallel peak filter is introduced to provide high-gain magnitude in the concerned frequency range of open-loop transfer function. Different from almost all the known peak filters that possess second-order structures, we explore in this paper bow high-order peak filters can be designed to improve the loop shaping performance. The main idea is to replace some of the constant coefficients of common second-order peak filter by frequency-related transfer functions, and then differential evolution （DE） algorithm is adopted to perform optimal design. We creatively introduce chromosome coding and fitness function design, which are original and the key steps that lead to the success of DE applications in control system design. In other words, DE is modified to achieve a novel design for hard disk drive control. Owing to the remarkable searching ability of DE, the expected shape of sensitivity function can be achieved by incorporating the resultant high-order peak filter in parallel with baseline feedback controller. As a result, a seventh-order peak filter is designed to compensate for contact-induced vibration in a high-density HDD servo system, where the benefits of high-order filter are clearly demonstrated.

关键词： Differential evolution （DE） contact-induced vibration peak filter optimization sensitivity loop shaping hard disk drive（HDD）.

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Characterization of Markarian 421 during its most violent year: Multiwavelength variability and correlations

arXiv

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arXiv 2025年

作者： Abe, K. Abe, S. Abhir, J. Abhishek, A. Acciari, V.A. Aguasca-Cabot, A. Agudo, I. Aniello, T. Ansoldi, S. Antonelli, L.A. Engels, A. Arbet Arcaro, C. Asano, K. Baack, D. Babić, A. de Almeida, U. Barres Barrio, J.A. Batković, I. Bautista, A. Baxter, J. González, J. Becerra Bednarek, W. Bernardini, E. Bernete, J. Berti, A. Besenrieder, J. Bigongiari, C. Biland, A. Blanch, O. Bonnoli, G. Bošnjak, Ž. Bronzini, E. Burelli, I. Campoy-Ordaz, A. Carosi, R. Carretero-Castrillo, M. Castro-Tirado, A.J. Cerasole, D. Ceribella, G. Chai, Y. Cifuentes, A. Colombo, E. Contreras, J.L. Cortina, J. Covino, S. D’Amico, G. D’Ammando, F. D’Elia, V. Da Vela, P. Dazzi, F. De Angelis, A. De Lotto, B. de Menezes, R. Delfino, M. Delgado, J. Mendez, C. Delgado Di Pierro, F. Di Tria, R. Di Venere, L. Prester, D. Dominis Donini, A. Dorner, D. Doro, M. Eisenberger, L. Elsaesser, D. Escudero, J. Fariña, L. Fattorini, A. Foffano, L. Font, L. Fröse, S. Fukami, S. Fukazawa, Y. López, R.J. García Garczarczyk, M. Gasparyan, S. Gaug, M. Paiva, J.G. Giesbrecht Giglietto, N. Giordano, F. Gliwny, P. Godinović, N. Gradetzke, T. Grau, R. Green, D. Green, J.G. Günther, P. Hadasch, D. Hahn, A. Hassan, T. Heckmann, L. Llorente, J. Herrera Hrupec, D. Imazawa, R. Ishio, K. Martínez, I. Jiménez Jormanainen, J. Kankkunen, S. Kayanoki, T. Kerszberg, D. Kluge, G.W. Kouch, P.M. Kubo, H. Kushida, J. Láinez, M. Lamastra, A. Leone, F. Lindfors, E. Lombardi, S. Longo, F. López-Coto, R. López-Moya, M. López-Oramas, A. Loporchio, S. Lorini, A. Majumdar, P. Makariev, M. Maneva, G. Manganaro, M. Mangano, S. Mannheim, K. Mariotti, M. Martínez, M. Martínez-Chicharro, M. Mas-Aguilar, A. Mazin, D. Menchiari, S. Mender, S. Miceli, D. Miener, T. Miranda, J.M. Mirzoyan, R. González, M. Molero Molina, E. Mondal, H.A. Moralejo, A. Morcuende, D. Nakamori, T. Nanci, C. Neustroev, V. Nickel, L. Nigro, C. Nikolić, L. Nilsson, K. Nishijima, K. Ekoume, T. Njoh Noda, K. Nozaki, S. Okumura, A. Otero-Santos, J. Paiano, S. Japanese MAGIC Group Department of Physics Tokai University Kanagawa Hiratsuka259-1292 Japan The University of Tokyo Chiba Kashiwa277-8582 Japan ETH Zürich ZürichCH-8093 Switzerland Università di Siena INFN Pisa SienaI-53100 Italy Bellaterra BarcelonaE-08193 Spain Universitat de Barcelona ICCUB IEEC-UB BarcelonaE-08028 Spain Instituto de Astrofísica de Andalucía-CSIC Glorieta de la Astronomía s/n Granada18008 Spain RomeI-00136 Italy Università di Udine INFN Trieste UdineI-33100 Italy Max-Planck-Institut für Physik GarchingD-85748 Germany Università di Padova INFN PadovaI-35131 Italy Technische Universität Dortmund DortmundD-44221 Germany Zagreb10000 Croatia URCA RJ Rio de Janeiro22290-180 Brazil IPARCOS Institute EMFTEL Department Universidad Com-plutense de Madrid MadridE-28040 Spain Instituto de Astrofísica de Canarias Dpto. de Astrofísica Universidad de La Laguna La Laguna TenerifeE-38200 Spain University of Lodz Faculty of Physics and Applied Informatics Department of Astrophysics Lodz90-236 Poland Centro de Investigaciones Energéticas Medioambientales y Tecnológicas MadridE-28040 Spain Departament de Física CERES-IEEC Universitat Autònoma de Barcelona BellaterraE-08193 Spain Università di Pisa INFN Pisa PisaI-56126 Italy INFN MAGIC Group INFN Sezione di Bari Dipartimento Interateneo di Fisica dell’Università e del Politecnico di Bari BariI-70125 Italy Department for Physics and Technology University of Bergen Norway INFN MAGIC Group INFN Sezione di Torino Università degli Studi di Torino TorinoI-10125 Italy Croatian MAGIC Group University of Rijeka Faculty of Physics Rijeka51000 Croatia Universität Würzburg WürzburgD-97074 Germany Japanese MAGIC Group: Physics Program Graduate School of Advanced Science and Engineering Hiroshima University Hiroshima739-8526 Japan ZeuthenD-15738 Germany Armenian MAGIC Group: ICRANet-Armenia Yerevan0019 Armenia Split21000 Croatia Croatian MAGIC Group J

Aims. Mrk 421 was in its most active state around early 2010, which led to the highest TeV gamma-ray flux ever recorded from any active galactic nuclei (AGN). We aim to characterize the multiwavelength behavior during this exceptional year for Mrk 421, and evaluate whether it is consistent with the picture derived with data from other less exceptional years. Methods. We investigated the period from November 5, 2009, (MJD 55140) until July 3, 2010, (MJD 55380) with extensive coverage from very-high-energy (VHE;E > 100 GeV) gamma rays to radio with MAGIC, VERITAS, Fermi-LAT, RXTE, Swift, GASP-WEBT, VLBA, and a variety of additional optical and radio telescopes. We characterized the variability by deriving fractional variabilities as well as power spectral densities (PSDs). In addition, we investigated images of the jet taken with VLBA and the correlation behavior among different energy bands. Results. Mrk 421 was in widely different states of activity throughout the campaign, ranging from a low-emission state to its highest VHE flux ever recorded. We find the strongest variability in X-rays and VHE gamma rays, and PSDs compatible with power-law functions with indices around 1.5. We observe strong correlations between X-rays and VHE gamma rays at zero time lag with varying characteristics depending on the exact energy band. We also report a marginally significant (∼ 3σ) positive correlation between high-energy (HE;E > 100 MeV) gamma rays and the ultraviolet band. We detected marginally significant (∼ 3σ) correlations between the HE and VHE gamma rays, and between HE gamma rays and the X-ray, that disappear when the large flare in February 2010 is excluded from the correlation study, hence indicating the exceptionality of this flaring event in comparison with the rest of the campaign. The 2010 violent activity of Mrk421 also yielded the first ejection of features in the VLBA images of the jet of Mrk 421. Yet the large uncertainties in the ejection times of these unprece

关键词： Active Galactic Nuclei

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Nonconvex robust low-rank matrix recovery

arXiv

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arXiv 2018年

作者： Li, Xiao Zhu, Zhihui So, Anthony Man-Cho Vidal, René Department of Electronic Engineering Chinese University of Hong Kong Center for Imaging Science Mathematical Institute for Data Science Johns Hopkins University Department of Systems Engineering and Engineering Management Chinese University of Hong Kong

In this paper we study the problem of recovering a low-rank matrix from a number of random linear measurements that are corrupted by outliers taking arbitrary values. We consider a nonsmooth nonconvex formulation of the problem, in which we explicitly enforce the low-rank property of the solution by using a factored representation of the matrix variable and employ an `1-loss function to robustify the solution against outliers. We show that even when a constant fraction (which can be up to almost half) of the measurements are arbitrarily corrupted, as long as certain measurement operators arising from the measurement model satisfy the so-called `1/`2-restricted isometry property, the ground-truth matrix can be exactly recovered from any global minimum of the resulting optimization problem. Furthermore, we show that the objective function of the optimization problem is sharp and weakly convex. Consequently, a subgradient Method (SubGM) with geometrically diminishing step sizes will converge linearly to the ground-truth matrix when suitably initialized. We demonstrate the efficacy of the SubGM for the nonconvex robust low-rank matrix recovery problem with various numerical experiments. Copyright © 2018, The Authors. All rights reserved.

关键词： Matrix algebra

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

arXiv

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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

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Detection of X-ray Polarization from the Blazar 1ES 1959+650 with the Imaging X-ray Polarimetry Explorer

arXiv

引用

arXiv 2024年

作者： Errando, Manel Liodakis, Ioannis Marscher, Alan P. Marshall, Herman L. Middei, Riccardo Negro, Michela Peirson, Abel Lawrence Perri, Matteo Puccetti, Simonetta Rabinowitz, Pazit L. Agudo, Iván Jorstad, Svetlana G. Savchenko, Sergey S. Blinov, Dmitry Bourbah, Ioakeim G. Kiehlmann, Sebastian Kontopodis, Evangelos Mandarakas, Nikos Romanopoulos, Stylianos Skalidis, Raphael Vervelaki, Anna Aceituno, Francisco José Bernardos, Maria I. Bonnoli, Giacomo Casanova, Víctor Agís-González, Beatriz Husillos, César Marchini, Alessandro Sota, Alfredo Kouch, Pouya M. Lindfors, Elina Casadio, Carolina Escudero, Juan Myserlis, Ioannis Imazawa, Ryo Sasada, Mahito Fukazawa, Yasushi Kawabata, Koji S. Uemura, Makoto Mizuno, Tsunefumi Nakaoka, Tatsuya Akitaya, Hiroshi Gurwell, Mark Keating, Garrett K. Rao, Ramprasad Ingram, Adam Massaro, Francesco Antonelli, Lucio Angelo Bonino, Raffaella Cavazzuti, Elisabetta Chen, Chien-Ting Cibrario, Nicolò Ciprini, Stefano De Rosa, Alessandra Di Gesu, Laura Di Pierro, Federico Donnarumma, Immacolata Ehlert, Steven R. Fenu, Francesco Gau, Ephraim Karas, Vladimir Kim, Dawoon E. Krawczynski, Henric Laurenti, Marco Lisalda, Lindsey López-Coto, Rubén Madejski, Grzegorz Marin, Frédéric Marinucci, Andrea Mitsuishi, Ikuyuki Muleri, Fabio Pacciani, Luigi Paggi, Alessandro Petrucci, Pierre-Olivier Cavero, Nicole Rodriguez Romani, Roger W. Tavecchio, Fabrizio Tugliani, Stefano Wu, Kinwah Bachetti, Matteo Baldini, Luca Baumgartner, Wayne H. Bellazzini, Ronaldo Bianchi, Stefano Bongiorno, Stephen D. Brez, Alessandro Bucciantini, Niccolò Capitanio, Fiamma Castellano, Simone Costa, Enrico Del Monte, Ettore Di Lalla, Niccolò Di Marco, Alessandro Doroshenko, Victor Dovčiak, Michal Enoto, Teruaki Evangelista, Yuri Fabiani, Sergio Ferrazzoli, Riccardo Garcia, Javier A. Gunji, Shuichi Hayashida, Kiyoshi Heyl, Jeremy Iwakiri, Wataru Kaaret, Philip Kislat, Fabian Kitaguchi, Takao Kolodziejczak, Jeffery J. Monaca, Fabio La Latronico, Luca Maldera, Simone Manfreda, Alberto Matt, Giorgio Ng, C.-Y. O’Dell, Stephen L. Omodei Physics Department McDonnell Center for the Space Sciences Washington University in St. Louis St. LouisMO63130 United States Finnish Centre for Astronomy with ESO University of Turku 20014 Finland NASA Marshall Space Flight Center HuntsvilleAL35812 United States Institute for Astrophysical Research Boston University 725 Commonwealth Avenue BostonMA02215 United States MIT Kavli Institute for Astrophysics and Space Research Massachusetts Institute of Technology 77 Massachusetts Avenue CambridgeMA02139 United States Space Science Data Center Agenzia Spaziale Italiana Via del Politecnico snc Roma00133 Italy 00078 Italy Department of Physics and Astronomy Louisiana State University Baton RougeLA70803 United States Department of Physics Kavli Institute for Particle Astrophysics and Cosmology Stanford University StanfordCA94305 United States Instituto de Astrofísica de Andalucía - CSIC Glorieta de la Astronomía s/n Granada18008 Spain Saint Petersburg State University 7/9 Universitetskaya nab. St. Petersburg199034 Russia Pulkovo Observatory St.Petersburg196140 Russia Institute of Astrophysics Foundation for Research and Technology-Hellas HeraklionGR-70013 Greece Department of Physics University of Crete Heraklion70013 Greece Owens Valley Radio Observatory California Institute of Technology MC 249-17 PasadenaCA91125 United States Merate23807 Italy Calle Ríos Rosas 23 MadridE-28003 Spain University of Siena Astronomical Observatory Via Roma 56 Siena53100 Italy Department of Physics and Astronomy University of Turku 20014 Finland Instituto de Astrofísica de Andalucía-CSIC Glorieta de la Astronomía s/n Granada18008 Spain Institut de Radioastronomie Millimétrique Avenida Divina Pastora 7 Local 20 GranadaE–18012 Spain Max-Planck-Institut für Radioastronomie Auf dem Hügel 69 BonnD-53121 Germany Department of Physics Graduate School of Advanced Science and Engineering Hiroshima University Kagamiyama 1-3-1 Higashi-Hiroshima Hir

Observations of linear polarization in the 2-8 keV energy range with the Imaging X-ray Polarimetry Explorer (IXPE) explore the magnetic field geometry and dynamics of the regions generating non-thermal radiation in relativistic jets of blazars. These jets, particularly in blazars whose spectral energy distribution peaks at X-ray energies, emit X-rays via synchrotron radiation from high-energy particles within the jet. IXPE observations of the X-ray selected BL Lac-type blazar 1ES 1959+650 in 2022 May 3-4 showed a significant linear polarization degree of Πx = 8.0% ±2.3% at an electric-vector position angle ψx = 123◦ ± 8◦. However, in 2022 June 9-12, only an upper limit of Πx ≤ 5.1% could be derived (at the 99% confidence level). The degree of optical polarization at that time ΠO ∼ 5% is comparable to the X-ray measurement. We investigate possible scenarios for these findings, including temporal and geometrical depolarization effects. Unlike some other X-ray selected BL Lac objects, there is no significant chromatic dependence of the measured polarization in 1ES 1959+650, and its low X-ray polarization may be attributed to turbulence in the jet flow with dynamical timescales shorter than 1 day. © 2024, CC BY-NC-SA.

关键词： Synchrotron radiation

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Deep learning for detection and segmentation of artefact and disease instances in gastrointestinal endoscopy

arXiv

引用

arXiv 2020年

作者： Ali, Sharib Dmitrieva, Mariia Ghatwary, Noha Bano, Sophia Polat, Gorkem Temizel, Alptekin Krenzer, Adrian Hekalo, Amar Guo, Yun Bo Matuszewski, Bogdan Gridach, Mourad Voiculescu, Irina Yoganand, Vishnusai Chavan, Arnav Raj, Aryan Nguyen, Nhan T. Tran, Dat Q. Huynh, Le Duy Boutry, Nicolas Rezvy, Shahadate Chen, Haijian Choi, Yoon Ho Subramanian, Anand Balasubramanian, Velmurugan Gao, Xiaohong W. Hu, Hongyu Liao, Yusheng Stoyanov, Danail Daul, Christian Realdon, Stefano Cannizzaro, Renato Lamarque, Dominique Tran-Nguyen, Terry Bailey, Adam Braden, Barbara East, James Rittscher, Jens Institute of Biomedical Engineering and Big Data Institute Old Road Campus University of Oxford Oxford United Kingdom Translational Gastroenterology Unit Experimental Medicine Div. John Radcliffe Hospital University of Oxford Oxford United Kingdom Oxford NIHR Biomedical Research Centre Oxford United Kingdom Université de Versailles St-Quentin en Yvelines Hôpital Ambroise Paré France Instituto Onclologico Veneto IOV-IRCCS Padova Italy CRO Centro Riferimento Oncologico IRCCS Aviano Italy Computer Engineering Department Arab Academy for Science and Technology Alexandria Egypt Department of Computer Science University College London London United Kingdom CRAN UMR 7039 University of Lorraine CNRS Nancy France Graduate School of Informatics Middle East Technical University Ankara Turkey Department of Artificial Intelligence and Knowledge Systems University of Würzburg Germany School of Engineering University of Central Lancashire United Kingdom Department of Computer Science University of Oxford United Kingdom Mimyk Medical Simulations Pvt Ltd Indian Institute of Science Bengaluru India Dhanbad India Hanoi Viet Nam Le Kremlin-BicêtreF-94270 France School of Science and Technology Middlesex University London United Kingdom Department of Computer Science School of Informatics Xiamen University China Sungkyunkwan University Seoul Korea Republic of Claritrics India Pvt Ltd Chennai India School of Medical Science and Technology Indian Institute of Technology KharagpurWest Bengal India Shanghai Jiaotong University Shanghai China Ibn Zohr University Computer Science HIT Agadir Morocco

The Endoscopy Computer Vision Challenge (EndoCV) is a crowd-sourcing initiative to address eminent problems in developing reliable computer aided detection and diagnosis endoscopy systems and suggest a pathway for clinical translation of technologies. Whilst endoscopy is a widely used diagnostic and treatment tool for hollow-organs, there are several core challenges often faced by endoscopists, mainly: 1) presence of multi-class artefacts that hinder their visual interpretation, and 2) difficulty in identifying subtle precancerous precursors and cancer abnormalities. Artefacts often affect the robustness of deep learning methods applied to the gastrointestinal tract organs as they can be confused with tissue of interest. EndoCV2020 challenges are designed to address research questions in these remits. In this paper, we present a summary of methods developed by the top 17 teams and provide an objective comparison of state-of-the-art methods and methods designed by the participants for two sub-challenges: i) artefact detection and segmentation (EAD2020), and ii) disease detection and segmentation (EDD2020). Multi-center, multi-organ, multi-class, and multi-modal clinical endoscopy datasets were compiled for both EAD2020 and EDD2020 sub-challenges. The out-of-sample generalization ability of detection algorithms was also evaluated. Whilst most teams focused on accuracy improvements, only a few methods hold credibility for clinical usability. The best performing teams provided solutions to tackle class imbalance, and variabilities in size, origin, modality and occurrences by exploring data augmentation, data fusion, and optimal class thresholding techniques. © 2020, CC BY-NC-SA.

关键词： Endoscopy

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Metadata Concepts for Advancing the Use of Digital Health Technologies in Clinical Research

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Digital Biomarkers 2019年第3期3卷 116-132页

作者： Badawy, Reham Hameed, Farhan Bataille, Lauren Little, Max A. Claes, Kasper Saria, Suchi Cedarbaum, Jesse M. Stephenson, Diane Neville, Jon Maetzler, Walter Espay, Alberto J. Bloem, Bastiaan R. Simuni, Tanya Karlin, Daniel R. School of Computer Science University of Birmingham Birmingham United Kingdom Digital Medicine and Pfizer Innovation Research Lab Early Clinical Development Pfizer Inc. CambridgeMA United States College of Computer and Information Science Northeastern University BostonMA United States Analytics Informatics and Business Intelligence Chief Digital Office Pfizer Inc. New YorkNY United States Michael J. Fox Foundation for Parkinson's Research New YorkNY United States Media Lab Massachusetts Institute of Technology CambridgeMA United States UCB Biopharma Brussels Belgium Machine Learning and Healthcare Laboratory Departments of Computer Science Statistics and Health Policy Malone Center for Engineering in Healthcare Armstrong Institute for Patient Safety and Quality Johns Hopkins University BaltimoreMD United States Biogen CambridgeMA United States Critical Path Institute TucsonAZ United States Clinical Data Interchange Standards Consortium AustinTX United States Department of Neurology Christian Albrecht University Kiel Germany James J. and Joan A. Gardner Family Center for Parkinson's Disease and Movement Disorders University of Cincinnati CincinnatiOH United States Department of Neurology Donders Institute for Brain Cognition and Behavior Radboud University Medical Center Nijmegen Netherlands Department of Neurology Gardner Center for Parkinson's Disease and Movement Disorders UC Gardner Neuroscience Institute University of Cincinnati CincinnatiOH United States Tufts University School of Medicine BostonMA United States HealthMode New YorkNY United States School of Engineering and Applied Science Aston University BirminghamB47ET United Kingdom

Digital health technologies (smartphones, smartwatches, and other body-worn sensors) can act as novel tools to aid in the diagnosis and remote objective monitoring of an individual's disease symptoms, both in clinical care and in research. Nonetheless, such digital health technologies have yet to widely demonstrate value in clinical research due to insufficient data interpretability and lack of regulatory acceptance. Metadata, i.e., data that accompany and describe the primary data, can be utilized to better understand the context of the sensor data and can assist in data management, data sharing, and subsequent data analysis. The need for data and metadata standards for digital health technologies has been raised in academic and industry research communities and has also been noted by regulatory authorities. Therefore, to address this unmet need, we here propose a metadata set that reflects regulatory guidelines and that can serve as a conceptual map to (1) inform researchers on the metadata they should collect in digital health studies, aiming to increase the interpretability and exchangeability of their data, and (2) direct standard development organizations on how to extend their existing standards to incorporate digital health technologies. The proposed metadata set is informed by existing standards pertaining to clinical trials and medical devices, in addition to existing schemas that have supported digital health technology studies. We illustrate this specifically in the context of Parkinson's disease, as a model for a wide range of other chronic conditions for which remote monitoring would be useful in both care and science. We invite the scientific and clinical research communities to apply the proposed metadata set to ongoing and planned research. Where the proposed metadata fall short, we ask users to contribute to its ongoing revision so that an adequate degree of consensus can be maintained in a rapidly evolving technology landscape. © 2019 The Author(s

关键词： Metadata

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Beyond Low Earth Orbit: Biomonitoring, Artificial Intelligence, and Precision Space Health

arXiv

引用

arXiv 2021年

作者： Scott, Ryan T. Antonsen, Erik L. Sanders, Lauren M. Hastings, Jaden J.A. Park, Seung-Min Mackintosh, Graham Reynolds, Robert J. Hoarfrost, Adrienne L. Sawyer, Aenor Greene, Casey S. Glicksberg, Benjamin S. Theriot, Corey A. Berrios, Daniel C. Miller, Jack Babdor, Joel Barker, Richard Baranzini, Sergio E. Beheshti, Afshin Chalk, Stuart Delgado-Aparicio, Guillermo M. Haendel, Melissa Hamid, Arif A. Heller, Philip Jamieson, Daniel Jarvis, Katelyn J. Kalantari, John Khezeli, Kia Komarova, Svetlana V. Komorowski, Matthieu Kothiyal, Prachi Mahabal, Ashish Manor, Uri Martin, Hector Garcia Mason, Christopher E. Matar, Mona Mias, George I. Myers, Jerry G. Nelson, Charlotte Oribello, Jonathan Parsons-Wingerter, Patricia Prabhu, R.K. Qutub, Amina Ann Rask, Jon Saravia-Butler, Amanda Saria, Suchi Singh, Nitin Kumar Soboczenski, Frank Snyder, Michael Soman, Karthik van Valen, David Venkateswaran, Kasthuri Warren, Liz Worthey, Liz Yang, Jason H. Zitnik, Marinka Costes, Sylvain V. KBR Space Biosciences Division NASA Ames Research Center Moffett FieldCA94035 United States Department of Emergency Medicine Center for Space Medicine Baylor College of Medicine HoustonTX77030 United States Blue Marble Space Institute of Science Space Biosciences Division NASA Ames Research Center Moffett FieldCA94035 United States Department of Physiology and Biophysics Weill Cornell Medicine New YorkNY10065 United States Department of Urology Department of Radiology Stanford University School of Medicine StanfordCA94305 United States Bay Area Environmental Research Institute NASA Ames Research Center Moffett FieldCA94035 United States Mortality Research & Consulting Inc. HoustonTX77058 United States Space Biosciences Division NASA Ames Research Center Moffett FieldCA94035 United States UC Space Health Department of Orthopaedic Surgery University of California San Francisco San FranciscoCA94143 United States Center for Health AI Department of Biochemistry and Molecular Genetics University of Colorado School of Medicine Anschutz Medical Campus AuroraCO80045 United States Hasso Plattner Institute for Digital Health at Mount Sinai Department of Genetics and Genomic Sciences Icahn School of Medicine at Mount Sinai New YorkNY10065 United States Department of Preventive Medicine and Community Health UTMB GalvestonTX77551 United States Human Health and Performance Directorate NASA Johnson Space Center HoustonTX77058 United States Department of Microbiology and Immunology Department of Otolaryngology Head and Neck Surgery University of California San Francisco San FranciscoCA94143 United States The Gilroy AstroBiology Research Group The University of Wisconsin Madison MadisonWI53706 United States Weill Institute for Neurosciences Department of Neurology University of California San Francisco San FranciscoCA94158 United States Department of Chemistry University of North Florida JacksonvilleFL32224 United States Data Scienc

Human space exploration beyond low Earth orbit will involve missions of significant distance and duration. To effectively mitigate myriad space health hazards, paradigm shifts in data and space health systems are necessary to enable Earth-independence, rather than Earth-reliance. Promising developments in the fields of artificial intelligence and machine learning for biology and health can address these needs. We propose an appropriately autonomous and intelligent Precision Space Health system that will monitor, aggregate, and assess biomedical statuses;analyze and predict personalized adverse health outcomes;adapt and respond to newly accumulated data;and provide preventive, actionable, and timely insights to individual deep space crew members and iterative decision support to their crew medical officer. Here we present a summary of recommendations from a workshop organized by the National Aeronautics and Space Administration, on future applications of artificial intelligence in space biology and health. In the next decade, biomonitoring technology, biomarker science, spacecraft hardware, intelligent software, and streamlined data management must mature and be woven together into a Precision Space Health system to enable humanity to thrive in deep space. © 2021, CC BY.

关键词： Earth (planet)

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