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IEEE International Conference on Image Processing

作者： Mohammad Shehata Mohammed Ghazal Garth Beache Mohamed Abou EI-Ghar Amy Dwyer Hassan Hajjdiab Ashraf Khalil Ayman EI-Baz Bioengineering Department University of Louisville Louisville KY USA Radiology Department University of Louisville Louisville KY USA Radiolozy Department Mansoura University Mansoura Egypt Kidney Disease Program University of Louisville Louisville KY USA Electrical and Computer Engineering Department Abu Dhabi University Abu Dhabi UAE Computer Science and Information Technology Department Abu Dhabi University Abu Dhabi UAE

Recently, diffusion-weighted magnetic resonance imaging (DW-MRI) has been explored for non-invasive assessment of renal transplant functions. In this paper, a computer-aided diagnostic (CAD) system is developed to assess renal transplant functionality, which integrates both clinical and diffusion MRI -derived markers extracted from 4D DW-MRI (i.e. 3D + b-value). To extract the DW-MR image-markers, our framework performs multiple image processing steps, including kidney segmentation using a level-set approach and estimation of image-markers. To extract these image-markers, apparent diffusion coefficients (ADCs) are estimated from the segmented DW-MRIs and cumulative distribution functions (CDFs) of the ADCs are constructed at different b-values (i.e. gradient field strengths and duration). Finally, these markers (i.e. CDFs) are integrated with clinical biomarkers (e.g., creatinine clearance and serum plasma creatinine) to assess transplant status using stacked auto-encoders with non-negativity constraints based on deep learning classification approach. Our CAD system consists of two consecutive classification stages. The first stage classifier achieved a 96% accuracy, a 95% sensitivity, and a 100% specificity in distinguishing non-rejection (NR) from dysfunctional (DF) transplanted kidneys. Additionally, an overall accuracy of 94% has been obtained in the second stage in separating DF to acute rejection (AR) and different renal disease (DRD) transplants. Our preliminary results hold strong promise that the presented CAD system is of a high reliability to non-invasively diagnose renal transplant status.

关键词： Kidney Diseases Three-dimensional displays Image segmentation Magnetic resonance imaging Machine learning Radio frequency

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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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Publisher Correction: Development of an optogenetic toolkit for neural circuit dissection in squirrel monkeys

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Scientific reports 2019年第1期9卷 18775页

作者： Daniel J O'Shea Paul Kalanithi Emily A Ferenczi Brian Hsueh Chandramouli Chandrasekaran Werapong Goo Ilka Diester Charu Ramakrishnan Matthew T Kaufman Stephen I Ryu Kristen W Yeom Karl Deisseroth Krishna V Shenoy Neurosciences Program Stanford University Stanford CA USA. djoshea@stanford.edu. Department of Electrical Engineering Stanford University Stanford CA USA. djoshea@stanford.edu. Department of Electrical Engineering Stanford University Stanford CA USA. Department of Neurosurgery Stanford University Stanford CA USA. Neurosciences Program Stanford University Stanford CA USA. Department of Bioengineering Stanford University Stanford CA USA. Department of Otophysiologie Albert Ludwig University of Freiburg Freiburg im Breisgau Germany. BrainLinks-BrainTools Albert Ludwig University of Freiburg Freiburg im Breisgau Germany. Cold Spring Harbor Laboratory Cold Spring Harbor NY USA. Palo Alto Medical Foundation Palo Alto CA USA. Department of Radiology Stanford University Stanford CA USA. Department of Psychiatry and Behavioral Science Stanford University Stanford CA USA. Howard Hughes Medical Institute Stanford University Stanford CA USA. Department of Neurobiology Stanford University Stanford CA USA.

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

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Publisher Correction: Schools of skyrmions with electrically tunable elastic interactions

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Nature communications 2020年第1期11卷 668页

作者： Hayley R O Sohn Changda D Liu Ivan I Smalyukh Department of Physics and Materials Science and Engineering Program University of Colorado Boulder CO 80309 USA. Department of Physics and Materials Science and Engineering Program University of Colorado Boulder CO 80309 USA. ivan.smalyukh@colorado.edu. Department of Electrical Computer and Energy Engineering and Soft Materials Research Center University of Colorado Boulder CO 80309 USA. ivan.smalyukh@colorado.edu. Renewable and Sustainable Energy Institute National Renewable Energy Laboratory and University of Colorado Boulder CO 80309 USA. ivan.smalyukh@colorado.edu.

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

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Static Hopf Solitons and Knotted Emergent Fields in Solid-State Noncentrosymmetric Magnetic Nanostructures

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Physical Review Letters 2018年第18期121卷 187201-187201页

作者： Jung-Shen B. Tai (戴榮身) Ivan I. Smalyukh Department of Physics University of Colorado Boulder Colorado 80309 USA Materials Science and Engineering Program Soft Materials Research Center and Department of Electrical Computer and Energy Engineering University of Colorado Boulder Colorado 80309 USA Renewable and Sustainable Energy Institute National Renewable Energy Laboratory and University of Colorado Boulder Colorado 80309 USA

Two-dimensional topological solitons, commonly called Skyrmions, are extensively studied in solid-state magnetic nanostructures and promise many spintronics applications. However, three-dimensional topological solitons dubbed hopfions have not been demonstrated as stable spatially localized structures in solid-state magnetic materials. Here we model the existence of such static solitons with different Hopf index values in noncentrosymmetric solid magnetic nanostructures with a perpendicular interfacial magnetic anisotropy. We show how this surface anisotropy, along with the Dzyaloshinskii-Moriya interactions and the geometry of nanostructures, stabilize hopfions. We demonstrate knots in emergent field lines and computer simulate Lorentz transmission electron microscopy images of such solitonic configurations to guide their experimental discovery in magnetic solids.

关键词： Magnetism Chiral magnets Solitons Topology

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Noninvasive assessment of jugular venous pressure via force-coupled single crystal ultrasound

Noninvasive assessment of jugular venous pressure via force-...

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作者： Singh, Robin Jaffe, Alex Frydman, Galit H. Najia, Mohamad Ali Toufic Wei, Zijun Yang, Jason Majmudar, Maulik D. Department of Mechanical Engineering Medical Electronic Device Realization Centre Institute of Medical Engineering and Science Massachusetts Institute of Technology United States Research Laboratory of Electronics Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology United States Division of Comparative Medicine Department of Biological Engineering Massachusetts Institute of Technology United States Center for Surgery Innovation and Bioengineering Department of Surgery Massachusetts General Hospital United States Harvard-MIT Health Sciences and Technology United States Broad Institute of MIT and Harvard United States Department of Hematology/Oncology Boston Children's Hospital United States Department of Media Arts and Sciences Massachusetts Institute of Technology United States Healthcare Transformation Lab Division of Cardiology Department of Medicine Massachusetts General Hospital BostonMA02114-2696 United States

Objective: we have developed a handheld device for noninvasive quantitative assessment of jugular venous pressure (JVP). Methods: we used a single crystal ultrasound coupled to a force-sensing load cell to measure JVP based on the force necessary to collapse the internal jugular vein (IJV) walls. We used a gelatin-based model system of the IJV to test the ability of single crystal ultrasound to identify the IJV and verified the cross-sectional position and diameter of the vessels with conventional imaging ultrasound. We also tested our prototype device on healthy human volunteers. Results: experiments on model system demonstrated that vessel diameters determined with single crystal ultrasound were in close agreement with the diameters derived from conventional 2-D ultrasound. Proof-of-concept human experiments demonstrate that single crystal ultrasound can detect the IJV in basal and collapsed states, as compared to gold-standard sonography (insert stats). Assessment of JVP in human volunteers was physiologically consistent with and sensitive to postural changes (supine JVP 6.6 ± 2.4 mmHg;standing JVP 4.2 ± 1.9 mmHg (p © 1964-2012 IEEE.

关键词： Single crystals

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computer-aided diagnosis of label-free 3-D optical coherence microscopy images of human cervical tissue

arXiv

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arXiv 2018年

作者： Ma, Yutao Xu, Tao Huang, Xiaolei Wang, Xiaofang Li, Canyu Jerwick, Jason Ning, Yuan Zeng, Xianxu Wang, Baojin Wang, Yihong Zhang, Zhan Zhang, Xiaoan Zhou, Chao School of Computer Science Wuhan University China Third Affiliated Hospital of Zhengzhou University China Department of Electrical and Computer Engineering Lehigh University United States College of Information Sciences and Technology Penn State University United States Department of Pathology and Laboratory Medicine Rhode Island Hospital/Warren Alpert Medical School of Brown University United States Department of Bioengineering Department of Electrical and Computer Engineering Lehigh University United States

Objective: Ultrahigh-resolution optical coherence microscopy (OCM) has recently demonstrated its potential for accurate diagnosis of human cervical diseases. One major challenge for clinical adoption, however, is the steep learning curve clinicians need to overcome to interpret OCM images. Developing an intelligent technique for computer-aided diagnosis (CADx) to accurately interpret OCM images will facilitate clinical adoption of the technology and improve patient care. Methods: 497 high-resolution 3-D OCM volumes (600 cross-sectional images each) were collected from 159 ex vivo specimens of 92 female patients. OCM image features were extracted using a convolutional neural network (CNN) model, concatenated with patient information (e.g., age, HPV results), and classified using a support vector machine classifier. Ten-fold cross-validations were utilized to test the performance of the CADx method in a five-class classification task and a binary classification task. Results: An 88.3±4.9% classification accuracy was achieved for five fine-grained classes of cervical tissue, namely normal, ectropion, low-grade and high-grade squamous intraepithelial lesions (LSIL and HSIL), and cancer. In the binary classification task (low-risk [normal, ectropion and LSIL] vs. high-risk [HSIL and cancer]), the CADx method achieved an area-under-the-curve (AUC) value of 0.959 with an 86.7±11.4% sensitivity and 93.5±3.8% specificity. Conclusion: The proposed deep-learning based CADx method outperformed three human experts. It was also able to identify morphological characteristics in OCM images that were consistent with histopathological interpretations. Significance: Label-free OCM imaging, combined with deep-learning based CADx methods, hold a great promise to be used in clinical settings for the effective screening and diagnosis of cervical diseases. Copyright © 2018, The Authors. All rights reserved.

关键词： computer aided diagnosis

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QIIME 2 Enables Comprehensive End-to-End Analysis of Diverse Microbiome Data and Comparative Studies with Publicly Available Data

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Current Protocols in Bioinformatics 2020年第1期70卷 e100页

作者： Estaki, Mehrbod Jiang, Lingjing Bokulich, Nicholas A. McDonald, Daniel González, Antonio Kosciolek, Tomasz Martino, Cameron Zhu, Qiyun Birmingham, Amanda Vázquez-Baeza, Yoshiki Dillon, Matthew R. Bolyen, Evan Caporaso, J. Gregory Knight, Rob Department of Pediatrics University of California San Diego La Jolla CA United States Division of Biostatistics University of California San Diego La Jolla CA United States Center for Applied Microbiome Science Pathogen and Microbiome Institute Northern Arizona University Flagstaff AZ United States Department of Biological Sciences Northern Arizona University Flagstaff AZ United States Małopolska Centre of Biotechnology Jagiellonian University Kraków Poland Bioinformatics and Systems Biology Program University of California San Diego La Jolla CA United States Center for Microbiome Innovation University of California San Diego La Jolla CA United States Center for Computational Biology and Bioinformatics University of California San Diego La Jolla CA United States Jacobs School of Engineering University of California San Diego La Jolla CA United States Department of Computer Science and Engineering University of California San Diego La Jolla CA United States Department of Bioengineering University of California San Diego La Jolla CA United States

QIIME 2 is a completely re-engineered microbiome bioinformatics platform based on the popular QIIME platform, which it has replaced. QIIME 2 facilitates comprehensive and fully reproducible microbiome data science, improving accessibility to diverse users by adding multiple user interfaces. QIIME 2 can be combined with Qiita, an open-source web-based platform, to re-use available data for meta-analysis. The following basic protocol describes how to install QIIME 2 on a single computer and analyze microbiome sequence data, from processing of raw DNA sequence reads through generating publishable interactive figures. These interactive figures allow readers of a study to interact with data with the same ease as its authors, advancing microbiome science transparency and reproducibility. We also show how plug-ins developed by the community to add analysis capabilities can be installed and used with QIIME 2, enhancing various aspects of microbiome analyses—e.g., improving taxonomic classification accuracy. Finally, we illustrate how users can perform meta-analyses combining different datasets using readily available public data through Qiita. In this tutorial, we analyze a subset of the Early Childhood Antibiotics and the Microbiome (ECAM) study, which tracked the microbiome composition and development of 43 infants in the United States from birth to 2 years of age, identifying microbiome associations with antibiotic exposure, delivery mode, and diet. For more information about QIIME 2, see https://***. To troubleshoot or ask questions about QIIME 2 and microbiome analysis, join the active community at https://***. © 2020 The Authors. Basic Protocol: Using QIIME 2 with microbiome data. Support Protocol: Further microbiome analyses. © 2020 The Authors.

关键词： bioinformatics metagenomics microbiome QIIME 2 Qiita

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Electroenzymatic Sensors: A Mediator‐Free Electroenzymatic Sensing Methodology to Mitigate Ionic and Electroactive Interferents' Effects for Reliable Wearable Metabolite and Nutrient Monitoring (Adv. Funct. Mater. 10/2020)

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Advanced Functional Materials 2020年第10期30卷

作者： Xuanbing Cheng Bo Wang Yichao Zhao Hannaneh Hojaiji Shuyu Lin Ryan Shih Haisong Lin Stephanie Tamayosa Brittany Ham Phoenix Stout Kamyar Salahi Zhaoqing Wang Chuanzhen Zhao Jiawei Tan Sam Emaminejad Interconnected & Integrated Bioelectronics Lab (I2BL) Department of Electrical and Computer Engineering University of California Los Angeles CA 90095 USA Department of Materials Science and Engineering University of California Los Angeles CA 90095 USA David Geffen School of Medicine University of California Los Angeles CA 90095 USA Department of Bioengineering University of California Los Angeles CA 90095 USA Department of Chemical and Biomolecular Engineering University of California Los Angeles CA 90095 USA Department of Chemistry and Biochemistry University of California Los Angeles CA 90095 USA

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Hybrid Cryptography on Wireless Sensor Network: A Systematic Literature Review

Hybrid Cryptography on Wireless Sensor Network: A Systematic...

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International Conference on Information Management and Technology (ICIMTech)

作者： R. Benny Gandara Gunawan Wang Ditdit Nugeraha Utama Master Program of Electrical Engineering Mereu Buana University Jakarta Indonesia Information Systems Management Department Bina Nusantara University Jakarta Indonesia Computer Science Department Bina Nusantara University Jakarta Indonesia

Wireless technology gradual growth in the latest years has facilitated it to bridge its applications operated extensively in many types of industry. Wireless sensor network (WSN) technology brings several benefits over traditional wired technology by reason of the lower cost, immense flexibility escalation, and scalability which is able to be obtained from the old-style one. However, in its application WSN still experience some constraints on resource limitations in the form: processor, memory and energy, and any more challenges that related to availability, reliability and security. Based on its benefits, limitation, and challenges, and also three systematic stages, the review study was performed. The result of study was portrayed in this paper. It is regarding investigation result of hybrid cryptography (HC, as one of defense methods) realized for WSN to methodically increase the security level and decrease the resources usage. The result concluded that HC was proven in escalating the security defense level and downgrading the resources use on WSN compared to symmetric and asymmetric algorithms.

关键词： Wireless sensor networks Cryptography Libraries Protocols Systematics Bibliographies

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