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arXiv 2018年

作者： Ishida, E.E.O. Beck, R. González-Gaitán, S. De Souza, R.S. Krone-Martins, A. Barrett, J.W. Kennamer, N. Vilalta, R. Burgess, J.M. Quint, B. Vitorelli, A.Z. Mahabal, A. Gangler, E. Université Clermont Auvergne CNRS/IN2P3 LPC Clermont-FerrandF-63000 France Institute for Astronomy University of Hawaii 2680 Woodlawn Drive HonoluluHI96822 United States Department of Physics of Complex Systems Eötvös Loránd University Pf. 32 BudapestH-1518 Hungary CENTRA/COSTAR Instituto Superior Técnico Universidade de Lisboa Av. Rovisco Pais 1 Lisboa1049-001 Portugal Department of Physics & Astronomy University of North Carolina at Chapel Hill Chapel HillNC27599-3255 United States CENTRA/SIM Faculdade de Ciências Universidade de Lisboa Campo Grande Ed. C8 Lisboa1749-016 Portugal Institute of Gravitational-wave Astronomy School of Physics and Astronomy University of Birmingham EdgbastonBirminghamB15 2TT United Kingdom Department of Computer Science University of California Irvine Donald Bren Hall IrvineCA92617 United States Department of Computer Science University of Houston 3551 Cullen Blvd 501 PGH HoustonTX77204-3010 United States Max-Planck-Institut für Extraterrestrische Physik Giessenbachstrasse GarchingD-85748 Germany SOAR Telescope AURA-O Colina El Pino S/N Casila 603 La Serena Chile Instituto de Astronomia Geofísica e Ciências Atmosféricas Universidade de São Paulo São Paulo SP Brazil Center for Data-Driven Discovery California Institute of Technology PasadenaCA91125 United States

We report a framework for spectroscopic follow-up design for optimizing supernova photometric classification. The strategy accounts for the unavoidable mismatch be- tween spectroscopic and photometric samples, and can be used even in the beginning of a new survey - without any initial training set. The framework falls under the um- brella of active learning (AL), a class of algorithms that aims to minimize labelling costs by identifying a few, carefully chosen, objects which have high potential in im- proving the classifier predictions. As a proof of concept, we use the simulated data released after the Supernova Photometric Classification Challenge (SNPCC) and a random forest classifier. Our results show that, using only 12% the number of training objects in the SNPCC spectroscopic sample, this approach is able to double purity results. Moreover, in order to take into account multiple spectroscopic observations in the same night, we propose a semi-supervised batch-mode AL algorithm which selects a set of N = 5 most informative objects at each night. In comparison with the initial state using the traditional approach, our method achieves 2.3 times higher purity and comparable figure of merit results after only 180 days of observation, or 800 queries (73% of the SNPCC spectroscopic sample size). Such results were obtained using the same amount of spectroscopic time necessary to observe the original SNPCC spec- troscopic sample, showing that this type of strategy is feasible with current available spectroscopic resources. The code used in this work is available in the COINtoolbox. Copyright © 2018, The Authors. All rights reserved.

关键词： Photometry

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Author Correction: The FLUXNET2015 dataset and the ONEFlux processing pipeline for eddy covariance data

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Scientific data 2021年第1期8卷 72页

作者： Gilberto Pastorello Carlo Trotta Eleonora Canfora Housen Chu Danielle Christianson You-Wei Cheah Cristina Poindexter Jiquan Chen Abdelrahman Elbashandy Marty Humphrey Peter Isaac Diego Polidori Markus Reichstein Alessio Ribeca Catharine van Ingen Nicolas Vuichard Leiming Zhang Brian Amiro Christof Ammann M Altaf Arain Jonas Ardö Timothy Arkebauer Stefan K Arndt Nicola Arriga Marc Aubinet Mika Aurela Dennis Baldocchi Alan Barr Eric Beamesderfer Luca Belelli Marchesini Onil Bergeron Jason Beringer Christian Bernhofer Daniel Berveiller Dave Billesbach Thomas Andrew Black Peter D Blanken Gil Bohrer Julia Boike Paul V Bolstad Damien Bonal Jean-Marc Bonnefond David R Bowling Rosvel Bracho Jason Brodeur Christian Brümmer Nina Buchmann Benoit Burban Sean P Burns Pauline Buysse Peter Cale Mauro Cavagna Pierre Cellier Shiping Chen Isaac Chini Torben R Christensen James Cleverly Alessio Collalti Claudia Consalvo Bruce D Cook David Cook Carole Coursolle Edoardo Cremonese Peter S Curtis Ettore D'Andrea Humberto da Rocha Xiaoqin Dai Kenneth J Davis Bruno De Cinti Agnes de Grandcourt Anne De Ligne Raimundo C De Oliveira Nicolas Delpierre Ankur R Desai Carlos Marcelo Di Bella Paul di Tommasi Han Dolman Francisco Domingo Gang Dong Sabina Dore Pierpaolo Duce Eric Dufrêne Allison Dunn Jiří Dušek Derek Eamus Uwe Eichelmann Hatim Abdalla M ElKhidir Werner Eugster Cacilia M Ewenz Brent Ewers Daniela Famulari Silvano Fares Iris Feigenwinter Andrew Feitz Rasmus Fensholt Gianluca Filippa Marc Fischer John Frank Marta Galvagno Mana Gharun Damiano Gianelle Bert Gielen Beniamino Gioli Anatoly Gitelson Ignacio Goded Mathias Goeckede Allen H Goldstein Christopher M Gough Michael L Goulden Alexander Graf Anne Griebel Carsten Gruening Thomas Grünwald Albin Hammerle Shijie Han Xingguo Han Birger Ulf Hansen Chad Hanson Juha Hatakka Yongtao He Markus Hehn Bernard Heinesch Nina Hinko-Najera Lukas Hörtnagl Lindsay Hutley Andreas Ibrom Hiroki Ikawa Marcin Jackowicz-Korczynski Dalibor Janouš Wilma Jans Rachhpal Jassal Shicheng Jiang Tomomichi Kato Myr Computational Research Division Lawrence Berkeley National Laboratory Berkeley CA 94720 USA. gzpastorello@lbl.gov. DIBAF University of Tuscia Viterbo 01100 Italy. Euro-Mediterranean Centre on Climate Change Foundation (CMCC) Lecce 73100 Italy. Climate & Ecosystem Sciences Division Lawrence Berkeley National Laboratory Berkeley CA 94720 USA. Computational Research Division Lawrence Berkeley National Laboratory Berkeley CA 94720 USA. Department of Civil Engineering California State University Sacramento CA 95819 USA. Department of Geography Environment and Spatial Sciences Michigan State University East Lansing MI 48823 USA. Department of Computer Science University of Virginia Charlottesville VA 22904 USA. TERN Ecosystem Processes Menzies Creek VIC3159 Australia. Max Planck Institute for Biogeochemistry Jena 07701 Germany. Laboratoire des Sciences du Climat et de l'Environnement (LSCE) CEA CNRS UVSQ UPSACLAY Gif sur Yvette 91190 France. Key Laboratory of Ecosystem Network Observation and Modeling Institute of Geographic Sciences and Natural Resources Research Chinese Academy of Sciences Beijing 100101 China. Department of Soil Science University of Manitoba Winnipeg MB R3T2N2 Canada. Department of Agroecology and Environment Agroscope Research Institute Zürich 8046 Switzerland. School of Geography and Earth Sciences McMaster University L8S4K1 Hamilton ON Canada. Department of Physical Geography and Ecosystem Science Lund University Lund 22362 Sweden. Department of Agronomy and Horticulture University of Nebraska-Lincoln Lincoln NE 68583 USA. School of Ecosystem and Forest Sciences The University of Melbourne Richmond VIC3121 Australia. Department of Biology Research Group PLECO University of Antwerp Antwerp 2610 Belgium. Joint Research Centre European Commission Ispra 21027 Italy. TERRA Teaching and Research Center University of Liege Gembloux B-5030 Belgium. Finnish Meteorological Institute Helsinki 00560 Finland. ES

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PennyLane: Automatic differentiation of hybrid quantum-classical computations

arXiv

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arXiv 2018年

作者： Bergholm, Ville Izaac, Josh Schuld, Maria Gogolin, Christian Ahmed, Shahnawaz Ajith, Vishnu Alam, M. Sohaib Alonso-Linaje, Guillermo AkashNarayanan, B. Asadi, Ali Arrazola, Juan Miguel Azad, Utkarsh Banning, Sam Blank, Carsten Bromley, Thomas R. Cordier, Benjamin A. Ceroni, Jack Delgado, Alain Matteo, Olivia Di Dusko, Amintor Garg, Tanya Guala, Diego Hayes, Anthony Hill, Ryan Ijaz, Aroosa Isacsson, Theodor Ittah, David Jahangiri, Soran Jain, Prateek Jiang, Edward Khandelwal, Ankit Kottmann, Korbinian Lang, Robert A. Lee, Christina Loke, Thomas Lowe, Angus McKiernan, Keri Meyer, Johannes Jakob Montañez-Barrera, J.A. Moyard, Romain Niu, Zeyue O’Riordan, Lee James Oud, Steven Panigrahi, Ashish Park, Chae-Yeun Polatajko, Daniel Quesada, Nicolás Roberts, Chase Sá, Nahum Schoch, Isidor Shi, Borun Shu, Shuli Sim, Sukin Singh, Arshpreet Strandberg, Ingrid Soni, Jay Száva, Antal Thabet, Slimane Vargas-Hernández, Rodrigo A. Vincent, Trevor Vitucci, Nicola Weber, Maurice Wierichs, David Wiersema, Roeland Willmann, Moritz Wong, Vincent Zhang, Shaoming Killoran, Nathan Xanadu 777 Bay Street Toronto Canada Wallenberg Centre for Quantum Technology Department of Microtechnology and Nanoscience Chalmers University of Technology Gothenburg412 96 Sweden Indian Institute of Information Technology Kottayam India NASA Ames Research Center Moffett FieldCA94035 United States Mountain ViewCA94043 United States data cybernetics Martin-Kolmsperger-Str 26 Landsberg86899 Germany Department of Medical Informatics and Clinical Epidemiology Oregon Health and Science University PortlandOR97202 United States Dept. of Electrical and Computer Engineering The University of British Columbia VancouverBCV6T 1Z4 Canada Department of Physics Indian Institute of Technology Roorkee Uttarakhand Roorkee India qBraid 5235 South Harper Court ChicagoIL60615 United States Factal Analytics Level 2 Chimes Building Plot 61 Sector - 44 Haryana Gurgaon122003 India Centre for High Energy Physics Indian Institute of Science Karnataka Bengaluru560012 India ICFO - Institut de Ciencies Fotoniques The Barcelona Institute of Science and Technology Av. Carl Friedrich Gauss 3 Castelldefels Barcelona08860 Spain Chemical Physics Theory Group Department of Chemistry University of Toronto Canada DUG Technology 76 Kings Park Rd West PerthWA6005 Australia Rigetti Computing 2919 Seventh Street BerkeleyCA94710 United States Dahlem Center for Complex Quantum Systems Freie Universität Berlin Berlin14195 Germany Institute for Advanced Simulation Jülich Supercomputing Centre Forschungszentrum Jülich Jülich52425 Germany University of Amsterdam Netherlands School of Physical Sciences National Institute of Science Education and Research HBNI Odisha Jatni752 050 India Cervest United Kingdom Centro Brasileiro de Pesquisas Físicas Rua Dr. Xavier Sigaud 150 Rio de Janeiro22290-180 Brazil Zurich8092 Switzerland Neo4j UK Ltd United Kingdom Zapata Computing Inc. United States ITC Infotech Bangalore India Department of Microtechnology and Nanoscienc

PennyLane is a Python 3 software framework for differentiable programming of quantum computers. The library provides a unified architecture for near-term quantum computing devices, supporting both qubit and continuous-variable paradigms. PennyLane’s core feature is the ability to compute gradients of variational quantum circuits in a way that is compatible with classical techniques such as backpropagation. PennyLane thus extends the automatic differentiation algorithms common in optimization and machine learning to include quantum and hybrid computations. A plugin system makes the framework compatible with any gate-based quantum simulator or hardware. We provide plugins for hardware providers including the Xanadu Cloud, Amazon Braket, and IBM Quantum, allowing PennyLane optimizations to be run on publicly accessible quantum devices. On the classical front, PennyLane interfaces with accelerated machine learning libraries such as TensorFlow, PyTorch, JAX, and Autograd. PennyLane can be used for the optimization of variational quantum eigensolvers, quantum approximate optimization, quantum machine learning models, and many other applications. Copyright © 2018, The Authors. All rights reserved.

关键词： Machine learning

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Mathematical analysis of Carreau fluid model for blood flow in tapered constricted arteries

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AIP Conference Proceedings 2018年第1期2016卷

作者： D. S. Sankar Usik Lee Atulya K. Nagar Maziri Morsidi 1School of Applied Sciences and Mathematics Universiti Teknologi Brunei Gadong BE1410 Brunei Darussalam 2Department of Mechanical Engineering Inha University Nam-Gu Incheon 402-751 Republic of Korea 3Department of Mathematics and Computer Science Centre for Applicable Mathematics and Systems Science Liverpool Hope University Hope Park Liverpool L16 9JD UK 4Dept. of Mechanical Engineering Universiti Teknologi Brunei Gadong BE 1410 Brunei Darussalam

The pulsatile flow of blood through a tapered constricted narrow artery is investigated in this study by treating the blood as Carreau fluid model. The constriction in the artery is due to the formation of asymmetric stenosis in the lumen of the artery. The expressions obtained by Sankar (2016) for the various flow quantities, are used to analyze the flow with different arterial geometry. The influence of various flow parameters on the velocity distribution, wall shear stress and longitudinal impedance to flow is discussed. The velocity of blood increases with the increase of the power law index and stenosis shape parameter and it decreases considerably with the increase of the maximum depth of the stenosis. The wall shear stress and longitudinal impedance to flow decrease with the increase stenosis shape parameter, amplitude of the pulsatile pressure gradient, flow rate, power law index and Weissenberg number. The estimates of the percentage of increase in the wall shear stress and longitudinal impedance to flow increase with the increase of the angle tapering and these increase significantly with the increase of the maximum depth of the stenosis. The mean velocity of blood decreases considerably with the increase of the artery radius (except in arteriole), maximum depth of the stenosis and angle of tapering and it is considerably higher in pulsatile flow of blood than in the steady flow of blood.

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Plagiarism detection in Arabic documents: Approaches, architecture and systems

Journal of Digital Information Management

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Journal of Digital Information Management 2016年第2期14卷 124-135页

作者： Kahloula, Boubaker Berri, Jawad IGMO Computer Science Department University of Oran 2 Oran31000 Algeria Information Systems Department King Saud University P.O. Box 51178 Riyadh11543 Saudi Arabia

Plagiarism detection is a sensitive field of research which has gained lot of interest in the past few years. Although plagiarism detection systems are developed to check text in a variety of languages, they perform better when they are dedicated to check a specific language as they take into account the specificity of the language which leads to better quality results. Query optimization and document reduction constitute two major processing modules which play a major role in optimizing the response time and the results quality of these systems and hence determine their efficiency and effectiveness. This paper proposes an analysis of approaches, an architecture, and a system for detecting plagiarism in Arabic documents. This analysis is particularly focused on the methods and techniques used to detect plagiarism. The proposed web-based architecture exhibits the major processing modules of a plagiarism detection system which are articulated into four layers inside a processing component. The architecture has been used to develop a plagiarism detection system for the Arabic language proposing a set of functions to the user for checking a text and analyzing the results through a well-designed graphical user interface.

关键词： Efficiency

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Author Correction: Why rankings of biomedical image analysis competitions should be interpreted with care

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Nature communications 2019年第1期10卷 588页

作者： Lena Maier-Hein Matthias Eisenmann Annika Reinke Sinan Onogur Marko Stankovic Patrick Scholz Tal Arbel Hrvoje Bogunovic Andrew P Bradley Aaron Carass Carolin Feldmann Alejandro F Frangi Peter M Full Bram van Ginneken Allan Hanbury Katrin Honauer Michal Kozubek Bennett A Landman Keno März Oskar Maier Klaus Maier-Hein Bjoern H Menze Henning Müller Peter F Neher Wiro Niessen Nasir Rajpoot Gregory C Sharp Korsuk Sirinukunwattana Stefanie Speidel Christian Stock Danail Stoyanov Abdel Aziz Taha Fons van der Sommen Ching-Wei Wang Marc-André Weber Guoyan Zheng Pierre Jannin Annette Kopp-Schneider Division of Computer Assisted Medical Interventions (CAMI) German Cancer Research Center (DKFZ) 69120 Heidelberg Germany. l.maier-hein@dkfz.de. Division of Computer Assisted Medical Interventions (CAMI) German Cancer Research Center (DKFZ) 69120 Heidelberg Germany. Centre for Intelligent Machines McGill University Montreal QC H3A0G4 Canada. Christian Doppler Laboratory for Ophthalmic Image Analysis Department of Ophthalmology Medical University Vienna 1090 Vienna Austria. Science and Engineering Faculty Queensland University of Technology Brisbane QLD 4001 Australia. Department of Electrical and Computer Engineering Department of Computer Science Johns Hopkins University Baltimore MD 21218 USA. CISTIB - Center for Computational Imaging & Simulation Technologies in Biomedicine The University of Leeds Leeds Yorkshire LS2 9JT UK. Department of Radiology and Nuclear Medicine Medical Image Analysis Radboud University Center 6525 GA Nijmegen The Netherlands. Institute of Information Systems Engineering TU Wien 1040 Vienna Austria. Complexity Science Hub Vienna 1080 Vienna Austria. Heidelberg Collaboratory for Image Processing (HCI Heidelberg University 69120 Heidelberg Germany. Centre for Biomedical Image Analysis Masaryk University 60200 Brno Czech Republic. Electrical Engineering Vanderbilt University Nashville TN 37235-1679 USA. Institute of Medical Informatics Universität zu Lübeck 23562 Lübeck Germany. Division of Medical Image Computing (MIC) German Cancer Research Center (DKFZ) 69120 Heidelberg Germany. Institute for Advanced Studies Department of Informatics Technical University of Munich 80333 Munich Germany. Information System Institute HES-SO Sierre 3960 Switzerland. Departments of Radiology Nuclear Medicine and Medical Informatics Erasmus MC 3015 GD Rotterdam The Netherlands. Department of Computer Science University of Warwick Coventry CV4 7AL UK. Department of Radiation Oncology Massachusetts General Hospital Boston MA

In the original version of this Article the values in the rightmost column of Table 1 were inadvertently shifted relative to the other columns. This has now been corrected in the PDF and HTML versions of the Article.

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Quantum state and process tomography via adaptive measurements

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science China(Physics,Mechanics & Astronomy) 2016年第10期59卷 25-32页

作者： HengYan Wang WenQiang Zheng NengKun Yu KeRen Li DaWei Lu Tao Xin Carson Li ZhengFeng Ji David Kribs Bei Zeng XinHua Peng JiangFeng Du Hefei National Laboratory for Physical Sciences at Microscale and Department of Modern Physics University of Science and Technology of ChinaHefei 230026China Department of Applied Physics Zhejiang University of TechnologyHangzhou 310023China Institute for Quantum Computing University of WaterlooWaterloo N2L 3G1Canada 4Department of Mathematics&Statistics University of GuelphGuelph N1G 2W1Canada Centre for Quantum Computation&Intelligent Systems Faculty of Engineering and Information TechnologyUniversity of Technology SydneySydney NSW 2007Australia State Key Laboratory of Low-Dimensional Quantum Physics and Department of Physics Tsinghua UniversityBeijing 100084China Department of Physics University of GuelphGuelph N1G 2W1Canada State Key Laboratory of Computer Science Institute of SoftwareChinese Academy of SciencesBeijing 100190China Canadian Institute for Advanced Research Toronto M5G 1Z8Canada

We investigate quantum state tomography(QST) for pure states and quantum process tomography(QPT) for unitary channels via adaptive measurements. For a quantum system with a d-dimensional Hilbert space, we first propose an adaptive protocol where only 2d. 1 measurement outcomes are used to accomplish the QST for all pure states. This idea is then extended to study QPT for unitary channels, where an adaptive unitary process tomography(AUPT) protocol of d2+d.1measurement outcomes is constructed for any unitary channel. We experimentally implement the AUPT protocol in a 2-qubit nuclear magnetic resonance system. We examine the performance of the AUPT protocol when applied to Hadamard gate, T gate(/8 phase gate), and controlled-NOT gate,respectively, as these gates form the universal gate set for quantum information processing purpose. As a comparison, standard QPT is also implemented for each gate. Our experimental results show that the AUPT protocol that reconstructing unitary channels via adaptive measurements significantly reduce the number of experiments required by standard QPT without considerable loss of fidelity.

关键词： quantum tomography state reconstruction nuclear magnetic resonance

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Effect of doping on the characteristics of infrared photodetectors based on van der Waals heterostructures with multiple graphene layers

arXiv

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arXiv 2017年

作者： Ryzhii, V. Ryzhii, M. Leiman, V. Mitin, V. Shur, M.S. Otsuji, T. Research Institute of Electrical Communication Tohoku University Sendai980-8577 Japan Institute of Ultra High Frequency Semiconductor Electronics of RAS Moscow117105 Russia Center for Photonics and Infrared Engineering Bauman Moscow State Technical University Moscow111005 Russia Department of Computer Science and Engineering University of Aizu Aizu-Wakamatsu965-8580 Japan Laboratory of 2D Materials’ Optoelectronics Moscow Institute of Physics and Technology Dolgoprudny141700 Russia Department of Electrical Engineering University at Buffalo BuffaloNY1460-1920 United States Department of Electrical Computer and Systems Engineering Department of Physics Applied Physics and Astronomy Rensselaer Polytechnic Institute TroyNY12180 United States

We study the operation of infrared photodetectors based on van der Waals heterostructures with the multiple graphene layers (GLs) and n-type emitter and collector contacts. The operation of such GL infrared photodetectors (GLIPs) is associated with the photoassisted escape of electrons from the GLs into the continuum states in the conduction band of the barrier layers due to the interband photon absorption, the propagation of these electrons and the electrons injected from the emitter across the heterostructure and their collection by the collector contact. The space charge of the holes trapped in the GLs provides a relatively strong injection and large photoelectric gain. We calculate the GLIP responsivity and dark current detectivity as functions of the energy of incident infrared photons and the structural parameters. It is shown that both the periodic selective doping of the inter-GL barrier layers and the GL doping lead to a pronounced variation of the GLIP spectral characteristics, particularly near the interband absorption threshold, while the doping of GLs solely results in a substantial increase in the GLIP detectivity. The doping"engineering" opens wide opportunities for the optimization of GLIPs for operation in different parts of radiation spectrum from near infrared to terahertz. Copyright © 2017, The Authors. All rights reserved.

关键词： Van der Waals forces

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Detecting clinical progression from abnormal regional brain volumes at baseline in genetic frontotemporal dementia: A GENFI study

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Alzheimer's & Dementia 2021年第S1期17卷

作者： Martina Bocchetta Emily G Todd Jennifer M Nicholas Georgia Peakman David M Cash Rhian S Convery Lucy L Russell David L Thomas Juan Eugenio Iglesias John C van Swieten Lize C. Jiskoot Harro Seelaar Barbara Borroni Daniela Galimberti Raquel Sanchez-Valle Robert Laforce Jr. Fermin Moreno Matthis Synofzik Caroline Graff Mario Masellis Maria Carmela Tartaglia James B Rowe Rik Vandenberghe Elizabeth Finger Fabrizio Tagliavini Alexandre Mendonca Isabel Santana Christopher Butler Simon Ducharme Alexander Gerhard Adrian Danek Johannes Levin Markus Otto Sandro Sorbi Isabelle Le Ber Florence Pasquier Jonathan D Rohrer Genetic Frontotemporal dementia Initiative (GENFI) Dementia Research Centre UCL Queen Square Institute of Neurology University College London London United Kingdom Department of Medical Statistics London School of Hygiene and Tropical Medicine London United Kingdom Dementia Research Centre Department of Neurodegenerative Disease UCL Institute of Neurology Queen Square London United Kingdom Dementia Research Centre Queen Square Institute of Neurology University College London London United Kingdom Centre for Medical Image Computing Department of Medical Physics and Biomedical Engineering University College London London United Kingdom Neuroradiological Academic Unit UCL Queen Square Institute of Neurology UCL London United Kingdom Computer Science and Artificial Intelligence Laboratory Massachusetts Institute of Technology Cambridge MA USA Martinos Center for Biomedical Imaging Massachusetts General Hospital and Harvard Medical School Charlestown MA USA Alzheimer Center Erasmus Medical Center Rotterdam Netherlands Erasmus MC Rotterdam Netherlands Erasmus Medical Center Rotterdam Netherlands Centre for Neurodegenerative Disorders Neurology Unit Department of Clinical and Experimental Sciences University of Brescia Brescia Italy University of Milan Milan Italy Department of Biomedical Surgical and Dental Sciences University of Milan Milan Italy Neurology Department Hospital Clínic Institut d'Investigacions Biomediques Barcelona Spain Clinique Interdisciplinaire de Mémoire CHU de Québec/Université Laval/Hôpital de l’Enfant-Jésus Quebec City QC Canada Hospital Universitario Donostia San Sebastian Spain Centre for Neurology and Hertie-Institute for Clinical Brain Research Hoppe-Seyler-Str Tuebingen Germany Karolinska Institutet Department NVS Division of Neurogeriatrics Karolinska Institutet Stockholm Sweden Unit for Hereditary Dementia Theme Aging Karolinska University Hospital-Solna Stockholm Sweden LC Campbell Cognitive Neurology Research Unit Sunnybrook Research Institute Toronto ON Canada T

Background Genetic frontotemporal dementia is highly heterogeneous, with different progression patterns seen between individuals. Using in vivo MR images from the Genetic FTD Initiative (GENFI), we aimed to identify clinical progression in genetic mutation carriers from their brain volumes at baseline. Method Cortical and subcortical volumes of interest (VOIs) were generated using automated parcellation methods on volumetric 3T T1-weighted MRI scans for 480 carriers (198 GRN , 202 C9orf72 , 80 MAPT ). W-scores for 79 VOIs were computed from a linear regression model carried out on 298 non-carrier cognitively normal controls adjusting for the effect of age, sex, total intracranial volume and scanner type. Carriers were divided into three disease stages based on their global CDR® plus NACC FTLD score (CDR-GS): asymptomatic (0), possibly or mildly symptomatic (0.5) and fully symptomatic (1 or more). Cut-off points for each VOI were derived from Youden indices estimated with ROC curves to distinguish between CDR-GS=0 and CDR-GS≥1 within each gene. CDR-GS=0.5 carriers (30 GRN , 32 C9orf72 , 13 MAPT ) were classified as ‘normal’ or ‘abnormal’ based on these cut-off points. We compared the CDR® plus NACC FTLD sum-of-boxes scores (CDR-SOB) at one year follow-up in these two groups. Result Compared to those with normal baseline volumes, C9orf72 expansion carriers at CDR-GS=0.5 showed significantly higher CDR-SOB scores at follow-up if they had abnormal volumes in the total frontal (+5 points), orbitofrontal (+3), dorsolateral prefrontal (+6), or anterior cingulate (+4) cortices, the basal-paralaminar amygdala region (+2), CA1 region of the hippocampus (+4), total hippocampus (+6), cerebellar lobule VIIIb (+4), or lateral ventricles (+8). GRN mutation carriers showed significantly higher CDR-SOB scores if their volumes were abnormal in the frontal (+10), parietal (+14), insula (+8), orbitofrontal (+12), or medial parietal (+7) cortices, or the total hippocampus (+10). MAPT mu

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From brain volumes to subgroup classification in genetic mutation carriers for frontotemporal dementia: A cluster analysis in the GENFI study

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Alzheimer's & Dementia 2021年第S5期17卷

作者： Martina Bocchetta Emily G. Todd Jennifer M. Nicholas Carolin Heller Imogen J. Swift Georgia Peakman David M. Cash Rhian S. Convery Lucy L. Russell David L. Thomas Juan Eugenio Iglesias John C. van Swieten Lize C. Jiskoot Harro Seelaar Barbara Borroni Daniela Galimberti Raquel Sanchez-Valle Robert Laforce Jr. Fermin Moreno Matthis Synofzik Caroline Graff Mario Masellis Maria Carmela Tartaglia James B. Rowe Rik Vandenberghe Elizabeth Finger Fabrizio Tagliavini Alexandre Mendonca Isabel Santana Christopher Butler Simon Ducharme Alexander Gerhard Adrian Danek Johannes Levin Markus Otto Sandro Sorbi Isabelle Le Ber Florence Pasquier Jonathan D. Rohrer Genetic Frontotemporal dementia Initiative (GENFI) Dementia Research Centre UCL Queen Square Institute of Neurology University College London London United Kingdom Department of Medical Statistics London School of Hygiene and Tropical Medicine London United Kingdom Dementia Research Centre Queen Square Institute of Neurology University College London London United Kingdom Centre for Medical Image Computing Department of Medical Physics and Biomedical Engineering University College London London United Kingdom Dementia Research Centre Department of Neurodegenerative Disease UCL Institute of Neurology Queen Square London United Kingdom Neuroradiological Academic Unit UCL Queen Square Institute of Neurology UCL London United Kingdom Computer Science and Artificial Intelligence Laboratory Massachusetts Institute of Technology Cambridge MA USA Martinos Center for Biomedical Imaging Massachusetts General Hospital and Harvard Medical School Charlestown MA USA Alzheimer Center Erasmus Medical Center Rotterdam Netherlands Erasmus MC Rotterdam Netherlands Erasmus Medical Center Rotterdam Netherlands Centre for Neurodegenerative Disorders Neurology Unit Department of Clinical and Experimental Sciences University of Brescia Brescia Italy University of Milan Milan Italy Department of Biomedical Surgical and Dental Sciences University of Milan Milan Italy Neurology Department Hospital Clínic. Institut d'Investigacions Biomediques Barcelona Spain Clinique Interdisciplinaire de Mémoire CHU de Québec/Université Laval/Hôpital de l’Enfant-Jésus Quebec City QC Canada Hospital Universitario Donostia San Sebastian Spain Centre for Neurology and Hertie-Institute for Clinical Brain Research Hoppe-Seyler-Str Tuebingen Germany Karolinska Institutet Department NVS Division of Neurogeriatrics Karolinska Institutet Stockholm Sweden Unit for Hereditary Dementia Theme Aging Karolinska University Hospital-Solna Stockholm Sweden LC Campbell Cognitive Neurology Research Unit Sunnybrook Research Institute Toronto ON Canada T

Background Genetic frontotemporal dementia (FTD) is highly heterogeneous, with carriers of mutations in the same gene manifesting different phenotypes. Using in vivo MR images from the Genetic FTD Initiative (GENFI), we aimed to identify subgroups within the same genetic group whose brains were affected differently. Method Cortical and subcortical volumes of interest were generated using automated parcellation methods on volumetric 3T T1-weighted MRI scans for 479 carriers (198 GRN , 202 C9orf72 , and 79 MAPT mutation carriers). W-scores for 85 volumes of interest were computed from a linear regression model carried out on 298 non-carrier cognitively normal controls adjusting for the effect of age, sex, total intracranial volume and scanner type. Cluster analyses with the Ward agglomerating method were performed on all w-scores while considering the three genetic groups independently. The identified clusters were then compared for age, estimated years from onset, global and sum of boxes scores of the CDR® plus NACC FTLD (at baseline and after one year), neurofilament light chain (NfL) levels in the plasma and w-scores in brain regions typically showing early atrophy (Kruskal-Wallis test). Result We identified three clusters among the GRN mutation carriers and four in the MAPT and C9orf72 groups, which were all significantly different for the variables reported in the Table (p-value<0.003). For all three genetic groups, one cluster was formed by patients with a clinical diagnosis of FTD, with more extensive atrophy and increased disease severity. For the remaining clusters, there seemed to be an association with disease severity for MAPT and GRN mutation carriers but not so for C9orf72 expansion carriers where clinical scores were not clearly associated with a specific cluster. Conclusion By only looking at regional brain volumes, we were able to detect different clusters within carriers of mutations in the same gene, with C9orf72 expansion carriers being the most he

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