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arXiv 2022年

作者： Aubry, Jean-Francois Bates, Oscar Boehm, Christian Pauly, Kim Butts Christensen, Douglas Cueto, Carlos Gélat, Pierre Guasch, Lluis Jaros, Jiri Jing, Yun Jones, Rebecca Li, Ningrui Marty, Patrick Montanaro, Hazael Neufeld, Esra Pichardo, Samuel Pinton, Gianmarco Pulkkinen, Aki Stanziola, Antonio Thielscher, Axel Treeby, Bradley Van't Wout, Elwin Physics for Medicine Paris INSERM U1273 ESPCI Paris PSL University CNRS UMR 8063 France Department of Bioengineering Imperial College London Exhibition Road LondonSW7 2AZ United Kingdom Institute of Geophysics ETH Zürich Sonneggstrasse 5 Zürich8092 Switzerland Department of Radiology Stanford University StanfordCA United States Department of Biomedical Engineering Department of Electrical and Computer Engineering University of Utah United States Department of Surgical Biotechnology Division of Surgery and Interventional Science University College London LondonNW3 2PF United Kingdom Earth Science and Engineering Department Imperial College London London United Kingdom Centre of Excellence IT4Innovations Faculty of Information Technology Brno University of Technology Bozetechova 2 Brno612 00 Czech Republic Graduate Program in Acoustics The Pennsylvania State University University Park PA16802 United States Joint Department of Biomedical Engineering University of North Carolina at Chapel Hill North Carolina State University NC United States Department of Electrical Engineering Stanford University StanfordCA United States Zurich Switzerland Laboratory for Acoustics / Noise control Empa Swiss Federal Laboratories for Materials Science and Technology Dubendorf Switzerland Zurich Switzerland Radiology and Clinical Neurosciences Departments Cumming School of Medicine University of Calgary Canada Department of Applied Physics University of Eastern Finland Kuopio70211 Finland Department of Medical Physics and Biomedical Engineering University College London Gower Street LondonWC1E 6BT United Kingdom Technical University of Denmark Denmark Danish Research Center for Magnetic Resonance Copenhagen University Hospital Hvidovre Denmark Institute for Mathematical and Computational Engineering School of Engineering and Faculty of Mathematics Pontificia Universidad Catolica de Chile Santiago Chile

Computational models of acoustic wave propagation are frequently used in transcranial ultrasound therapy, for example, to calculate the intracranial pressure field or to calculate phase delays to correct for skull distortions. To allow intercomparison between the different modeling tools and techniques used by the community, an international working group was convened to formulate a set of numerical benchmarks. Here, these benchmarks are presented, along with intercomparison results. Nine different benchmarks of increasing geometric complexity are defined. These include a single-layer planar bone immersed in water, a multi-layer bone, and a whole skull. Two transducer configurations are considered (a focused bowl and a plane piston), giving a total of 18 permutations of the benchmarks. Eleven different modeling tools are used to compute the benchmark results. The models span a wide range of numerical techniques, including the finite-difference time-domain method, angular-spectrum method, pseudospectral method, boundary-element method, and spectral-element method. Good agreement is found between the models, particularly for the position, size, and magnitude of the acoustic focus within the skull. When comparing results for each model with every other model in a cross comparison, the median values for each benchmark for the difference in focal pressure and position are less than 10% and 1 mm, respectively. The benchmark definitions, model results, and intercomparison codes are freely available to facilitate further comparisons. Copyright © 2022, The Authors. All rights reserved.

关键词： Bone

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Low-temperature electron-phonon interaction of quantum emitters in hexagonal boron nitride

arXiv

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arXiv 2019年

作者： Grosso, Gabriele Moon, Hyowon Ciccarino, Christopher J. Flick, Johannes Mendelson, Noah Toth, Milos Aharonovich, Igor Narang, Prineha Englund, Dirk R. Advanced Science Research Center City University of New York Photonics Initiative New YorkNY United States Physics Program Graduate Center City University of New York New YorkNY United States Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology CambridgeMA United States John A. Paulson School of Engineering and Applied Sciences Harvard University CambridgeMA United States School of Mathematical and Physical Sciences University of Technology Sydney UltimoNSW2007 Australia Center for Computational Quantum Physics Flatiron Institute New YorkNY United States

关键词： Electron-phonon interactions

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Exciton Localization of High-Quality ZnO/Mg_xZn_1-xO Multiple Quantum Wells on Si (111) with a Y₂O₃ Buffer Layer (vol 1, pg 3829, 2018)

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ACS APPLIED NANO MATERIALS 2018年第10期1卷 5958-5958页

作者： Liu, Wei-Rein Huang, Wei-Lun Wu, Yung-Chi Lai, Liang-Hsun Hsu, Chia-Hung Hsieh, Wen-Feng Chiang, Tsung-Hung Wan, H. W. Hong, M. Kao, J. National Synchrotron Radiation Research Center Hsinchu 30076 Taiwan Program for Science and Technology of Accelerator Light Source National Chiao Tung University Hsinchu 30010 Taiwan Department of Photonics College of Electrical and Computer Engineering National Chiao Tung University Hsinchu 30010 Taiwan Department of Photonics College of Electrical and Computer Engineering National Chiao Tung University Hsinchu 30010 Taiwan Department of Materials Science & Engineering National Tsing Hua University Hsinchu 30013 Taiwan Graduate Institute of Applied Physics and Department of Physics National Taiwan University Taipei 10617 Taiwan Department of Physics National Tsing Hua University Hsinchu 30013 Taiwan

We report the structural and optical properties of ten-period ZnO/MgxZn1–xO multiple quantum wells (MQWs) prepared on the most widely used semiconductor material, Si. The introduction of a nanometer thick high-k Y2O3transition layer between Si (111) substrate and a ZnO buffer layer significantly improves the structural perfection of the MQWs grown on top of it. The high structural quality of the ZnO/MgxZn1–xO MQWs is evidenced by the appearance of pronounced high order satellite peaks in X-ray crystal truncation rods; high resolution cross-sectional transmission electron microscopy images also confirmed the regularly arranged well and barrier layers. When the well width is less than ∼2.7 nm, the quantum-confined Stark effect in MQWs can be negligible. Not only the increasing exciton-binding energy but also reducing exciton–phonon coupling determined in temperature-dependent photoluminescence spectra indicate quantum-size effect. Our results demonstrate that ZnO/MgxZn1–xO MQWs integrated on Si have great potential in UV optoelectronic device applications.

关键词： Excitons Phonons Oxides Binding energy Layers

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Quasielectrostatic Wave Propagation Beyond the Delay-Bandwidth Limit in Switched Networks

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Physical Review X 2019年第3期9卷 031015-031015页

作者： Mykhailo Tymchenko Dimitrios Sounas Aravind Nagulu Harish Krishnaswamy Andrea Alù Department of Electrical and Computer Engineering The University of Texas at Austin 1 University Station C0803 Austin Texas 78712 USA Department of Electrical and Computer Engineering Wayne State University Detroit Michigan 48202 USA Department of Electrical Engineering Columbia University 1300 South West Mudd 500 West 120th Street New York New York 10027 USA Photonics Initiative Advanced Science Research Center City University of New York New York New York 10031 USA Physics Program Graduate Center City University of New York New York New York 10026 USA Department of Electrical Engineering City College of New York New York New York 10031 USA

The delay-bandwidth limit implies a stringent trade-off between the time delay, bandwidth, and propagation distance of an electromagnetic signal. Here, we show that temporal modulation can overcome this constraint, enabling extremely broadband wave propagation with close-to-zero group velocity dispersion in switched multipath electronic networks. Contrary to time-invariant waveguides, in which wave propagation implies a delicate balance between electric and magnetic stored energies, in such modulated networks the stored energy is largely electrostatic in nature. We show that in this case the phase and group velocities become independent of the properties of their constituent elements, and they are controlled only by the modulation scheme. Based on these findings, we provide practical designs of deeply subwavelength CMOS-compatible reciprocal and nonreciprocal microwave components, such as delay lines, phase shifters, couplers, and circulators. The obtained results also explicitly show that temporally modulated systems are not bound by constraints of time-invariant systems and can achieve arbitrarily large delay-bandwidth products.

关键词： Metamaterials

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Polynomial Root-Finding and Algebraic Eigenvalue Problem

arXiv

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arXiv 2018年

作者： Pan, Victor Y. Department of Computer Science Lehman College of the City University of New York BronxNY10468 United States Ph.D. Program in Mathematics and Computer Science The Graduate Center of the City University of New York New YorkNY10036 United States

Univariate polynomial root-finding has been studied for four millennia and very intensively in the last decades. Our new near-optimal root-finders approximate all complex zeros of a polynomial almost as fast as one accesses its coefficients with the precision required for the solution within a prescribed error bound. The root-finders work even for a black box polynomial, defined by an oracle (black box subroutine) for its evaluation rather than by its coefficients. Hence they (i) support approximation of the eigenvalues of a matrix in a record Las Vegas expected bit operation time and (ii) are particularly fast for polynomials that can be evaluated fast such as the sum of a few shifted monomials of the form (x−c)d or a Mandelbrot-like polynomial defined by a recurrence. Our divide and conquer algorithm of ACM STOC 1995 is the only other known near-optimal polynomial root-finder, but it extensively uses the coefficients, is quite involved, and has never been implemented, while according to extensive numerical experiments with standard test polynomials already a slower initial implementation of our new root-finders competes with user’s choice package of root-finding subroutines MPSolve and supersedes it more and more significantly as the degree of a polynomial grows large. We elaborate upon design and analysis of our algorithms, comment on their potential heuristic acceleration, and briefly cover polynomial root-finding by means of functional iterations. Our techniques can be of independent interest. Copyright © 2018, The Authors. All rights reserved.

关键词： Subroutines

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The molecular epidemiology of multiple zoonotic origins of SARS-CoV-2

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四川生理科学杂志 2022年第7期44卷 1299-1299页

作者： Jonathan E Pekar Bioinformatics and Systems Biology Graduate Program University of California San Diego La Jolla CA 92093 USA. Department of Biomedical Informatics University of California San Diego La Jolla CA 92093 USA. Department of Human Genetics David Geffen School of Medicine University of California Los Angeles Los Angeles CA 90095 USA. Department of Immunology and Microbiology The Scripps Research Institute La Jolla CA 92037 USA. Department of Computer Science and Engineering University of California San Diego La Jolla CA 92093 USA. Department of Mathematics University of California San Diego La Jolla CA 92093 USA. Department of Ecology and Evolutionary Biology University of Arizona Tucson AZ 85721 USA. W. Harry Feinstone Department of Molecular Microbiology and Immunology Johns Hopkins Bloomberg School of Public Health Baltimore MD 21205 USA. New York City Public Health Laboratory New York City Department of Health and Mental Hygiene New York NY 11101 USA. Department of Microbiology Institute for Viral Diseases Biosafety Center College of Medicine Korea University Seoul South Korea. BK21 Graduate Program Department of Biomedical Sciences Korea University College of Medicine Seoul 02841 Republic of Korea. National Public Health Laboratory National Centre for Infectious Diseases Singapore. Malaysia Genome and Vaccine Institute Jalan Bangi 43000 Kajang Selangor Malaysia. Department of Medicine University of California San Diego La Jolla CA 92093 USA. Department of Microbiology and Immunology School of Medicine Tulane University New Orleans LA 70112 USA. Zalgen Labs Frederick MD 21703 USA. Global Virus Network (GVN) Baltimore MD 21201 USA. Sydney Institute for Infectious Diseases School of Life and Environmental Sciences and School of Medical Sciences The University of Sydney Sydney NSW 2006 Australia. Institute of Evolutionary Biology University of Edinburgh King’s Buildings Edinburgh EH9 3FL UK. Department of Biomathematics David Geffen Sch

Understanding the circumstances that lead to pandemics is important for their prevention. Here, we analyze the genomic diversity of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) early in the coronavirus disease 2019 (COVID-19) pandemic. We show that SARS-CoV-2 genomic diversity before February 2020 likely comprised only two distinct viral lineages, denoted A and B. Phylodynamic rooting methods, coupled with epidemic simulations, reveal that these lineages were the result of at least two separate cross-species transmission events into humans. The first zoonotic transmission likely involved lineage B viruses around 18 November 2019 (23 October-8 December), while the separate introduction of lineage A likely occurred within weeks of this event.

关键词： epidemiology likely acute

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Author Correction: BigNeuron: a resource to benchmark and predict performance of algorithms for automated tracing of neurons in light microscopy datasets

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Nature methods 2024年第10期21卷 1959页

作者： Linus Manubens-Gil Zhi Zhou Hanbo Chen Arvind Ramanathan Xiaoxiao Liu Yufeng Liu Alessandro Bria Todd Gillette Zongcai Ruan Jian Yang Miroslav Radojević Ting Zhao Li Cheng Lei Qu Siqi Liu Kristofer E Bouchard Lin Gu Weidong Cai Shuiwang Ji Badrinath Roysam Ching-Wei Wang Hongchuan Yu Amos Sironi Daniel Maxim Iascone Jie Zhou Erhan Bas Eduardo Conde-Sousa Paulo Aguiar Xiang Li Yujie Li Sumit Nanda Yuan Wang Leila Muresan Pascal Fua Bing Ye Hai-Yan He Jochen F Staiger Manuel Peter Daniel N Cox Michel Simonneau Marcel Oberlaender Gregory Jefferis Kei Ito Paloma Gonzalez-Bellido Jinhyun Kim Edwin Rubel Hollis T Cline Hongkui Zeng Aljoscha Nern Ann-Shyn Chiang Jianhua Yao Jane Roskams Rick Livesey Janine Stevens Tianming Liu Chinh Dang Yike Guo Ning Zhong Georgia Tourassi Sean Hill Michael Hawrylycz Christof Koch Erik Meijering Giorgio A Ascoli Hanchuan Peng Institute for Brain and Intelligence Southeast University Nanjing China. Microsoft Corporation Redmond WA USA. Tencent AI Lab Bellevue WA USA. Computing Environment and Life Sciences Directorate Argonne National Laboratory Lemont IL USA. Kaya Medical Seattle WA USA. University of Cassino and Southern Lazio Cassino Italy. Center for Neural Informatics Structures and Plasticity Krasnow Institute for Advanced Study George Mason University Fairfax VA USA. Faculty of Information Technology Beijing University of Technology Beijing China. Beijing International Collaboration Base on Brain Informatics and Wisdom Services Beijing China. Nuctech Netherlands Rotterdam the Netherlands. Janelia Research Campus Howard Hughes Medical Institute Ashburn VA USA. Department of Electrical and Computer Engineering University of Alberta Edmonton Alberta Canada. Ministry of Education Key Laboratory of Intelligent Computation and Signal Processing Anhui University Hefei China. Paige AI New York NY USA. Scientific Data Division and Biological Systems and Engineering Division Lawrence Berkeley National Lab Berkeley CA USA. Helen Wills Neuroscience Institute and Redwood Center for Theoretical Neuroscience UC Berkeley Berkeley CA USA. RIKEN AIP Tokyo Japan. Research Center for Advanced Science and Technology (RCAST) The University of Tokyo Tokyo Japan. School of Computer Science University of Sydney Sydney New South Wales Australia. Texas A&M University College Station TX USA. Cullen College of Engineering University of Houston Houston TX USA. Graduate Institute of Biomedical Engineering National Taiwan University of Science and Technology Taipei Taiwan. National Centre for Computer Animation Bournemouth University Poole UK. PROPHESEE Paris France. Department of Neuroscience Columbia University New York NY USA. Mortimer B. Zuckerman Mind Brain Behavior Institute Columbia University New York NY USA. Department of Computer Science Northern Illinois Universit

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Light-Based Analog Computing Using a Single Array of Polarizable Particles 12

Light-Based Analog Computing Using a Single Array of Polariz...

引用

12th International Congress on Artificial Materials for Novel Wave Phenomena, METAMATERIALS 2018

作者： Rardi, Y. Alu, A. Department of Electrical and Computer Engineering University of Texas at Austin AustinTX78712 United States Photonics Initiative Advanced Science Research Center City University of New York New YorkNY10031 United States Physics Program Graduate Center City University of New York New YorkNY10016 United States Department of Electrical Engineering City College of New York New YorkNY10031 United States

ISBN: (纸本)9781538647028

Light-based signal processing in the spatial domain enables parallel computation, resulting in faster operations in comparison to digital-based systems. We propose a basic platform to realize spatial differentiation and integration of the impinging wavefront using a basic array of electric/magnetic dipoles. We also leverage bianisotropic inclusions to realize odd operations on the impinging wavefront. Our results show that a single array of polarizable particles enables sufficient degrees of freedom to realize optical analog computing using metasurfaces. © 2018 IEEE.

关键词： Wavefronts

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Metagratings for Efficient Wavefront Manipulation 12

Metagratings for Efficient Wavefront Manipulation

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12th International Congress on Artificial Materials for Novel Wave Phenomena, METAMATERIALS 2018

ISBN: (纸本)9781538647028

Metagratings are two-dimensional periodic arrays of individual scatterers that enable highly-efficient extreme wave transformation which was impossible using conventional metasurfaces. We review our recent progress in this area and, notably, discuss metagrating designs based on asymmetric electric dipoles that are practically easier to realize compared to bianisotropic inclusions. In addition, we present the design of a tunable metagrating based on graphene strips. It will be shown that the proposed structure can provide multiple functionalities by tuning just two bias voltages. The efficiency of the proposed design is only limited by dissipation in graphene. © 2018 IEEE.

关键词： Graphene

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A large-scale ENIGMA multisite replication study of brain age in depression

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Neuroimage: Reports 2022年第4期2卷 100149-100149页

作者： Han, Laura K.M. Dinga, Richard Leenings, Ramona Hahn, Tim Cole, James H. Aftanas, Lyubomir I. Amod, Alyssa R. Besteher, Bianca Colle, Romain Corruble, Emmanuelle Couvy-Duchesne, Baptiste Danilenko, Konstantin V. Fuentes-Claramonte, Paola Gonul, Ali Saffet Gotlib, Ian H. Goya-Maldonado, Roberto Groenewold, Nynke A. Hamilton, Paul Ichikawa, Naho Ipser, Jonathan C. Itai, Eri Koopowitz, Sheri-Michelle Li, Meng Okada, Go Okamoto, Yasumasa Churikova, Olga S. Osipov, Evgeny A. Penninx, Brenda W.J.H. Pomarol-Clotet, Edith Rodríguez-Cano, Elena Sacchet, Matthew D. Shinzato, Hotaka Sim, Kang Stein, Dan J. Uyar-Demir, Aslihan Veltman, Dick J. Schmaal, Lianne Centre for Youth Mental Health The University of Melbourne Parkville VIC Australia Orygen Parkville VIC Australia Department of Cognitive Science & Artificial Intelligence Tilburg University Tilburg Netherlands Institute for Translational Psychiatry University of Münster Münster Germany Faculty of Mathematics and Computer Science University of Münster Münster Germany Centre for Medical Image Computing Department of Computer Science University College London United Kingdom Dementia Research Centre Queen Square Institute of Neurology University College London United Kingdom Scientific Research Institute of Neuroscience and Medicine Novosibirsk Russian Federation Novosibirsk State University Novosibirsk Russian Federation Department of Psychiatry & Mental Health Neuroscience Institute University of Cape Town South Africa Department of Psychiatry and Psychotherapy Jena University Hospital Jena Germany Service Hospitalo-Universitaire de Psychiatrie de Bicêtre Mood Center Paris Saclay Assistance Publique-Hôpitaux de Paris Hôpitaux Universitaires Paris-Saclay Hôpital de Bicêtre Le Kremlin Bicêtre F-94275 France MOODS Team INSERM 1018 CESP (Centre de Recherche en Epidémiologie et Santé des Populations) Université Paris-Saclay Faculté de Médecine Paris-Saclay Le Kremlin Bicêtre F-94275 France Sorbonne Université Institut du Cerveau - Paris Brain Institute - ICM CNRS Inria Inserm AP-HP Hôpital de La Pitié Salpêtrière Paris F-75013 France Institute for Molecular Bioscience The University of Queensland St Lucia 4072 QLD Australia FIDMAG Hermanas Hospitalarias Research Foundation Barcelona Spain CIBERSAM Barcelona Spain SoCAT Lab Department of Psychiatry School of Medicine Ege University Izmir Turkey Department of Psychology Stanford University Stanford CA United States Laboratory of Systems Neuroscience and Imaging in Psychiatry (SNIP-Lab) University Medical Center Göttingen Göttingen Germany Center for Social and Affective Neuroscience Department o

Background: Several studies have evaluated whether depressed persons have older appearing brains than their nondepressed peers. However, the estimated neuroimaging-derived “brain age gap” has varied from study to study, likely driven by differences in training and testing sample (size), age range, and used modality/features. To validate our previously developed ENIGMA brain age model and the identified brain age gap, we aim to replicate the presence and effect size estimate previously found in the largest study in depression to date (N = 2126 controls & N = 2675 cases;+1.08 years [SE 0.22], Cohen's d = 0.14, 95% CI: 0.08–0.20), in independent cohorts that were not part of the original study. Methods: A previously trained brain age model (***/enigma_brainage) based on 77 FreeSurfer brain regions of interest was used to obtain unbiased brain age predictions in 751 controls and 766 persons with depression (18–75 years) from 13 new cohorts collected from 20 different scanners. Meta-regressions were used to examine potential moderating effects of basic cohort characteristics (e.g., clinical and scan technical) on the brain age gap. Results: Our ENIGMA MDD brain age model generalized reasonably well to controls from the new cohorts (predicted age vs. age: r = 0.73, R² = 0.47, MAE = 7.50 years), although the performance varied from cohort to cohort. In these new cohorts, on average, depressed persons showed a significantly higher brain age gap of +1 year (SE 0.35) (Cohen's d = 0.15, 95% CI: 0.05–0.25) compared with controls, highly similar to our previous finding. Significant moderating effects of FreeSurfer version 6.0 (d = 0.41, p = 0.007) and Philips scanner vendor (d = 0.50, p < 0.0001) were found, leading to more positive effect size estimates. Conclusions: This study further validates our previously developed ENIGMA brain age algorithm. Importantly, we replicated the brain age gap in depression with a comparable effect size. Thus, two large-s

关键词： Biological aging Brain age Depression ENIGMA consortium Replication study

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