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Learning Bone Suppression from Dual Energy Chest X-rays using Adversarial Networks

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arXiv 2018年

作者： Oh, Dong Yul Yun, Il Dong Interdisciplinary Program in Bioengineering Seoul National University Korea Republic of Division of Computer and Electronic System Engineering Hankuk University of Foreign Studies Korea Republic of

Suppressing bones on chest X-rays such as ribs and clavicle is often expected to improve pathologies classification. These bones can interfere with a broad range of diagnostic tasks on pulmonary disease except for musculoskeletal system. Current conventional method for acquisition of bone suppressed X-rays is dual energy imaging, which captures two radiographs at a very short interval with different energy levels;however, the patient is exposed to radiation twice and the artifacts arise due to heartbeats between two shots. In this paper, we introduce a deep generative model trained to predict bone suppressed images on single energy chest X-rays, analyzing a finite set of previously acquired dual energy chest X-rays. Since the relatively small amount of data is available, such approach relies on the methodology maximizing the data utilization. Here we integrate the following two approaches. First, we use a conditional generative adversarial network that complements the traditional regression method minimizing the pairwise image difference. Second, we use Haar 2D wavelet decomposition to offer a perceptual guideline in frequency details to allow the model to converge quickly and efficiently. As a result, we achieve state-of-the-art performance on bone suppression as compared to the existing approaches with dual energy chest X-rays. Copyright © 2018, The Authors. All rights reserved.

关键词： Diagnosis

Leaf-based energy harvesting and storage utilizing hygroscopic iron hydrogel for continuous power generation

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Nature communications 2025年第1期16卷 5267页

作者： Shuai Guo Yaoxin Zhang Zhen Yu Ming Dai Xuanchen Liu Hongbo Wang Siqi Liu J Justin Koh Wanxin Sun Yuanping Feng Yuanzheng Chen Lin Yang Peng Sun Geyu Lu Cunjiang Yu Wenshuai Chen Stefaan De Wolf Zuankai Wang Swee Ching Tan Department of Materials Science and Engineering 9 Engineering Drive 1 Singapore 117575 Singapore. China-UK Low Carbon College Shanghai Jiao Tong University Shanghai 201306 P.R. China. Key Laboratory of Bio-based Material Science and Technology Ministry of Education Northeast Forestry University Harbin 150040 P.R. China. Department of Mechanical Engineering The Hong Kong Polytechnic University Hong Kong P.R. China. Institute of Materials Research and Engineering (IMRE) Agency for Science Technology and Research (A*STAR) 2 Fusionopolis Way Innovis #08-03 Singapore 138634 Singapore. Division of Nano Surfaces Bruker Corporation 11 Biopolis Way Singapore 138667 Singapore. Department of Physics National University of Singapore 2 Science Drive 3 Singapore 117551 Singapore. School of Physical Science and Technology Key Laboratory of Advanced Technologies of Materials Southwest Jiaotong University Chengdu 610031 P.R. China. Key Laboratory of Optoelectronic Technology and System of Ministry of Education College of Optoelectronic Engineering Chongqing University Chongqing 400044 P. R. China. State Key Laboratory on Integrated Optoelectronics College of Electronic Science and Engineering Jilin University Changchun China. Department of Electrical and Computer Engineering University of Illinois Urbana-Champaign Urbana IL 61801 USA. Department of Materials Science and Engineering University of Illinois Urbana-Champaign Urbana IL USA. Department of Mechanical Science and Engineering University of Illinois Urbana-Champaign Urbana IL USA. Department of Bioengineering Materials Research Laboratory Beckman Institute for Advanced Science and Technology Nick Holonyak Micro and Nanotechnology Laboratory University of Illinois Urbana-Champaign Urbana IL USA. Key Laboratory of Bio-based Material Science and Technology Ministry of Education Northeast Forestry University Harbin 150040 P.R. China. chenwenshuai@nefu.edu.cn. Physical and Engineering Division (PSE) Kin

In the era of big data, developing next-generation self-powered continuous energy harvesting systems is of great importance. Taking advantage of fallen leaves' specific structural advantage gifted by nature, we propose a facile approach to convert fallen leaves into energy harvesters from ubiquitous moisture, based on surface treatments and asymmetric coating of hygroscopic iron hydrogels. Upon moisture absorption, a water gradient is established between areas with/without hydrogel coating, and maintained due to gel-like behaviors and leaf veins for water retention and diffusion restriction, thus forming electrical double layers over the leaf surface and showing capacitance-like behavior for energy charging and discharging. Besides, the specific leaf cell structures with small grooves enabled uniform carbon coatings instead of aggregations, and high electrical conductivity, resulting in 49 μA/cm and 497 μW/cm electrical output, achieving competitive performance with the state-of-art and potential for lower environmental impact compared to other types of energy harvesters.

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Closed-loop sparse channel estimation for wideband millimeter-wave full-dimensional MIMO systems

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arXiv 2019年

作者： Liao, Anwen Gao, Zhen Wang, Hua Chen, Sheng Alouini, Mohamed-Slim Yin, Hao School of Information and Electronics Beijing Institute of Technology Beijing100081 China School of Electronics and Computer Science University of Southampton SouthamptonSO17 1BJ United Kingdom King Abdulaziz University Jeddah21589 Saudi Arabia Electrical Engineering Program Division of Physical Sciences and Engineering King Abdullah University of Science and Technology Thuwal Makkah Province Saudi Arabia Institute of China Electronic System Engineering Corporation Beijing100141 China

This paper proposes a closed-loop sparse channel estimation (CE) scheme for wideband millimeter-wave hybrid full-dimensional multiple-input multiple-output and time division duplexing based systems, which exploits the channel sparsity in both angle and delay domains. At the downlink CE stage, random transmit precoding matrix is designed at base station (BS) for channel sounding, and receive combining matrices at user devices (UDs) are designed whereby the hybrid array is visualized as a low-dimensional digital array for facilitating the multi-dimensional unitary ESPRIT (MDU-ESPRIT) algorithm to estimate respective angle-of-arrivals (AoAs). At the uplink CE stage, the estimated downlink AoAs, namely, uplink angle-of-departures (AoDs), are exploited to design multi-beam transmit precoding matrices at UDs to enable BS to estimate the uplink AoAs, i.e., the downlink AoDs, and delays of different UDs, whereby the MDU-ESPRIT algorithm is used based on the designed receive combining matrix at BS. Furthermore, a maximum likelihood approach is proposed to pair the channel parameters acquired at the two stages, and the path gains are then obtained using least squares estimator. According to spectrum estimation theory, our solution can acquire the super-resolution estimations of the AoAs/AoDs and delays of sparse multipath components with low training overhead. Simulation results verify the better CE performance and lower computational complexity of our solution over state-of-the-art approaches. Copyright © 2019, The Authors. All rights reserved.

关键词： Millimeter waves

Deep learning for waveform estimation and imaging in passive radar

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arXiv 2018年

作者： Yonel, Bariscan Mason, Eric Yazici, Birsen Department of Electrical Computer and System Engineering Rensselaer Polytechnic Institute TroyNY12180 United States Tactical Electronic Warfare Division Naval Research Laboratory WashingtonDC20375 United States

We consider a bistatic configuration with a stationary transmitter transmitting unknown waveforms of opportunity and a moving receiver and present a Deep Learning (DL) framework for passive synthetic aperture radar (SAR) imaging. Existing passive radar methods require two or more antennas that are either spatially separated or colocated with sufficient directivity to estimate the underlying waveform prior to imaging. Our approach to passive radar only requires a single receiver, hence reducing cost and increasing versatility. We approach DL from an optimization perspective and formulate image reconstruction as a machine learning task. By unfolding the iterations of a proximal gradient descent algorithm, we construct a deep recurrent neural network (RNN) that is parameterized by transmitted waveforms. We cascade the RNN structure with a decoder stage to form a recurrent-auto encoder architecture. We then use backpropagation to learn transmitted waveforms by training the network in an unsupervised manner using SAR measurements. The highly non-convex problem of backpropagation is guided to a feasible solution over the parameter space by initializing the network with the known components of the SAR forward model. Moreover, prior information regarding the waveform structure is incorporated during initialization and backpropagation. We demonstrate the effectiveness of the DL-based approach through extensive numerical simulations that show focused, high contrast imagery using a single receiver antenna at realistic SNR levels. Copyright © 2018, The Authors. All rights reserved.

关键词： Backpropagation

The glycolytic reaction PGAM restrains Th17 pathogenicity and Th17-dependent autoimmunity

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Cell reports 2025年第6期44卷 115799页

作者： Chao Wang Allon Wagner Johannes Fessler David DeTomaso Sarah Zaghouani Yulin Zhou Kerry Pierce Raymond A Sobel Clary Clish Nir Yosef Vijay K Kuchroo Biological Sciences Platform Sunnybrook Research Institute Toronto ON M4N 3M5 Canada Department of Immunology University of Toronto Toronto ON M5S 1A8 Canada. Electronic address: chao.wang@sri.utoronto.ca. Department of Electrical Engineering and Computer Sciences University of California Berkeley Berkeley CA 94720 USA Department of Molecular and Cell Biology University of California Berkeley Berkeley CA 94720 USA Center for Computational Biology University of California Berkeley Berkeley CA 94720 USA. Division of Immunology Otto Loewi Research Center Medical University of Graz Graz Austria. The Gene Lay Institute of Immunology and Inflammation Brigham and Women's Hospital Mass General Hospital and Harvard Medical School Boston MA 02115 USA. Department of Immunology University of Toronto Toronto ON M5S 1A8 Canada. Broad Institute of MIT and Harvard Cambridge MA 02142 USA. Palo Alto Veteran's Administration Health Care System and Department of Pathology Stanford University School of Medicine Stanford CA 94305 USA. Department of Systems Immunology Weizmann Institute of Science Rehovot Israel. Electronic address: nir.yosef@weizmann.ac.il. The Gene Lay Institute of Immunology and Inflammation Brigham and Women's Hospital Mass General Hospital and Harvard Medical School Boston MA 02115 USA Broad Institute of MIT and Harvard Cambridge MA 02142 USA. Electronic address: vkuchroo@bwh.harvard.edu.

Glucose metabolism is a critical regulator of T cell function, largely thought to support their activation and effector differentiation. Here, we investigate how individual glycolytic reactions determine the pathogenicity of T helper 17 (Th17) cells using Compass, an algorithm we previously developed for inferring metabolic states from single-cell RNA sequencing. Surprisingly, Compass predicted that the metabolic shunt between 3-phosphoglycerate (3PG) and 2-phosphoglycerate (2PG) is inversely correlated with pathogenicity in Th17 cells. Indeed, perturbation of phosphoglycerate mutase (PGAM), the enzyme catalyzing 3PG to 2PG conversion, induces a pathogenic gene expression program by suppressing a gene module associated with the least pathogenic state of Th17 cells. Finally, PGAM inhibition in Th17 cells exacerbates neuroinflammation in the adoptive transfer model of experimental autoimmune encephalomyelitis, consistently with PGAM promoting the non-pathogenic phenotype of Th17 cells. Overall, our study identifies PGAM, contrary to other glycolytic enzymes, as a negative regulator of pathogenic Th17 cell differentiation.

关键词： CP: Immunology CP: Metabolism EGCG PGAM PGM T helper 17 central carbon metabolism epigallocatechin-3-gallate glycolysis immune metabolism immunometabolism phosphoglycerate mutase

Local surface potential on hydrogenated microcrystalline silicon films

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Local surface potential on hydrogenated microcrystalline sil...

作者： Itoh, Takashi Sakai, Takashi Ito, Takanori Kuriyama, Hirishi Nonomura, Shuichi Department of Electrical Electronic and Computer Engineering Gifu University Gifu501-1193 Japan Environment Renewable Energy System Division Gifu University Gifu501-1193 Japan

The local surface potential on hydrogenated microcrystalline silicon films was investigated using a Kelvin force microscope. In the surface morphology image of the films deposited on Asahi U-type substrates, large and small convex grains were observed. In i-type films, the surface potentials on the large and small convex grains were higher than those in the concave regions between large convex grains and between small convex grains, respectively. The potential difference between a large grain and the region between large grains was larger than that between a small grain and the region between small grains. The surface potential distribution on p-type films showed the same tendency as that on the i-type films. On the other hand, the surface potential distribution on n-type films showed a reverse tendency to that on the i-type films. The potential difference between a large convex grain and the region between large convex grains depended on the doping ratio and that in the p-type films was smaller than that in the n-type films. © 2016 The Japan Society of Applied Physics.

关键词： Semiconductor doping

Suppression of electrical tree initiation by antioxidant and ultraviolet absorber, using a density-functional study

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Suppression of electrical tree initiation by antioxidant and...

Conference on Electrical Insulation and Dielectric Phenomena, (CEIDP)

作者： H. Uehara S. Iwata Y. Sekii T. Takada Y. Cao Dept. of Electrical and Electronic Engineering Kanto Gakuin University Yokohama Kanagawa Japan Sekii PE Laboratory Chiba Chiba Japan Research Division of Product Reliability Osaka Research Institute of Industrial Science and Technology Izumi Osaka Japan Dept. of Mechanical System Engineering Tokyo City University Setagaya Tokyo Japan Dept. of Electrical and Computer Engineering University of Connecticut Storrs CT USA

ISBN: (纸本)9781538611951

Antioxidants, ultraviolet (UV) absorbers, and light stabilizers play important role of suppressing the chain reaction of radical production in cross-linked polyethylene (XLPE). In this paper, we studied such suppression of electrical tree initiation by additives added in XLPE by using density-functional approach. The result shows that large electric dipole moments of additives facilitate charge trapping and lead to suppression of electrical tree initiation. The result also shows that molecular orbitals of additives are distributed in the vicinity of the conjugated π bonds of benzene rings, hydroxyl groups, and carbonyl groups which can form chemical defects.

关键词： Additives Orbits Two dimensional displays Chemicals Degradation Polymers Electric potential

Author Correction: Federated learning enables big data for rare cancer boundary detection

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Nature communications 2023年第1期14卷 436页

作者： Sarthak Pati Ujjwal Baid Brandon Edwards Micah Sheller Shih-Han Wang G Anthony Reina Patrick Foley Alexey Gruzdev Deepthi Karkada Christos Davatzikos Chiharu Sako Satyam Ghodasara Michel Bilello Suyash Mohan Philipp Vollmuth Gianluca Brugnara Chandrakanth J Preetha Felix Sahm Klaus Maier-Hein Maximilian Zenk Martin Bendszus Wolfgang Wick Evan Calabrese Jeffrey Rudie Javier Villanueva-Meyer Soonmee Cha Madhura Ingalhalikar Manali Jadhav Umang Pandey Jitender Saini John Garrett Matthew Larson Robert Jeraj Stuart Currie Russell Frood Kavi Fatania Raymond Y Huang Ken Chang Carmen Balaña Jaume Capellades Josep Puig Johannes Trenkler Josef Pichler Georg Necker Andreas Haunschmidt Stephan Meckel Gaurav Shukla Spencer Liem Gregory S Alexander Joseph Lombardo Joshua D Palmer Adam E Flanders Adam P Dicker Haris I Sair Craig K Jones Archana Venkataraman Meirui Jiang Tiffany Y So Cheng Chen Pheng Ann Heng Qi Dou Michal Kozubek Filip Lux Jan Michálek Petr Matula Miloš Keřkovský Tereza Kopřivová Marek Dostál Václav Vybíhal Michael A Vogelbaum J Ross Mitchell Joaquim Farinhas Joseph A Maldjian Chandan Ganesh Bangalore Yogananda Marco C Pinho Divya Reddy James Holcomb Benjamin C Wagner Benjamin M Ellingson Timothy F Cloughesy Catalina Raymond Talia Oughourlian Akifumi Hagiwara Chencai Wang Minh-Son To Sargam Bhardwaj Chee Chong Marc Agzarian Alexandre Xavier Falcão Samuel B Martins Bernardo C A Teixeira Flávia Sprenger David Menotti Diego R Lucio Pamela LaMontagne Daniel Marcus Benedikt Wiestler Florian Kofler Ivan Ezhov Marie Metz Rajan Jain Matthew Lee Yvonne W Lui Richard McKinley Johannes Slotboom Piotr Radojewski Raphael Meier Roland Wiest Derrick Murcia Eric Fu Rourke Haas John Thompson David Ryan Ormond Chaitra Badve Andrew E Sloan Vachan Vadmal Kristin Waite Rivka R Colen Linmin Pei Murat Ak Ashok Srinivasan J Rajiv Bapuraj Arvind Rao Nicholas Wang Ota Yoshiaki Toshio Moritani Sevcan Turk Joonsang Lee Snehal Prabhudesai Fanny Morón Jacob Mandel Konstantinos Kamnitsas Ben Glocker Luke V M Dixon Matthew Williams Peter Zamp Center for Biomedical Image Computing and Analytics (CBICA) University of Pennsylvania Philadelphia PA USA. Department of Radiology Perelman School of Medicine University of Pennsylvania Philadelphia PA USA. Department of Pathology and Laboratory Medicine Perelman School of Medicine University of Pennsylvania Philadelphia PA USA. Department of Informatics Technical University of Munich Munich Bavaria Germany. Intel Corporation Santa Clara CA USA. Department of Neuroradiology Heidelberg University Hospital Heidelberg Germany. Clinical Cooperation Unit Neuropathology German Cancer Consortium (DKTK) within the German Cancer Research Center (DKFZ) Heidelberg Germany. Department of Neuropathology Heidelberg University Hospital Heidelberg Germany. Division of Medical Image Computing German Cancer Research Center Heidelberg Germany. Pattern Analysis and Learning Group Department of Radiation Oncology Heidelberg University Hospital Heidelberg Germany. Neurology Clinic Heidelberg University Hospital Heidelberg Germany. Department of Radiology & Biomedical Imaging University of California San Francisco San Francisco CA USA. Symbiosis Center for Medical Image Analysis Symbiosis International University Pune Maharashtra India. Department of Neuroimaging and Interventional Radiology National Institute of Mental Health and Neurosciences Bangalore Karnataka India. Department of Radiology School of Medicine and Public Health University of Wisconsin Madison WI USA. Department of Medical Physics School of Medicine and Public Health University of Wisconsin Madison WI USA. Leeds Teaching Hospitals Trust Department of Radiology Leeds UK. Department of Radiology Brigham and Women's Hospital Harvard Medical School Boston MA USA. Athinoula A. Martinos Center for Biomedical Imaging Massachusetts General Hospital Charlestown MA USA. Catalan Institute of Oncology Badalona Spain. Consorci MAR Parc de Salut de Barcelona Catalonia Spain. Department of Radiology (IDI

Anomalous behavior of membrane fluidity caused by copper-copper bond coupled phospholipids

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arXiv 2017年

作者： Jiang, Xiankai Zhang, Jinjin Zhou, Bo Hu, Xiaojuan Zhu, Zhi Chang, Chao Lü, Junhong Song, Bo Terahertz Technology Innovation Research Institute Shanghai Key Lab of Modern Optical System Terahertz Science Cooperative Innovation Center School of Optical-Electrical Computer Engineering University of Shanghai for Science and Technology Shanghai200093 China Division of Physical Biology CAS Key Laboratory of Interfacial Physics and Technology Shanghai Institute of Applied Physics Chinese Academy of Science Shanghai201800 China School of Electronic Engineering Chengdu Technological University Chengdu611730 China Key Laboratory for Physical Electronics and Devices of the Ministry of Education Xi'an Jiaotong University Xi'an710049 China

Membrane fluidity, well-known to be essential for cell functions, is obviously affected by copper. However, the underlying mechanism is still far from being understood, especially on the atomic level. Here, we unexpectedly observed that a decrease in phospholipid (PL) bilayer fluidity caused by Cu2+ was much more significant than those induced by Zn2+ and Ca2+, while a comparable reduction occurred in the last two ions. This finding clearly disagrees with the placement in the periodic table of Cu just next to Zn and far from Ca. The physical nature was revealed to be a special attraction between Cu+ cations, which can induce a motif forming of two phospholipids coupled by Cu-Cu bond (PL-diCu-PL). Namely, upon Cu2+ ion binding to a negatively charged phosphate group of lipid, Cu2+ was reduced to Cu+. The special attraction of the cations then caused one Cu+ ion simultaneously binding to two lipids and another Cu+, resulting in the formation of PL-diCu-PL structure. In contrast, this attraction cannot occur in the cases of Zn and Ca ions due to their electron structure. Remarkably, besides lipids, the phosphate group widely exists in other biological molecules, including DNA, RNA, ADP and ATP, which would also induce the similar structure of Cu ions with the molecules. Our findings thus provide a new view for understanding the biological functions of copper and the mechanism underlying copper-related diseases. Copyright © 2017, The Authors. All rights reserved.

关键词： Heavy ions