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A Radial Basis Function Spike Model for Indirect Learning via Integrate-and-Fire Sampling and Reconstruction Techniques

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Advances in Artificial Neural systems 2012年第1期2012卷

作者： X. Zhang G. Foderaro C. Henriquez A. M. J. VanDongen S. Ferrari Laboratory for Intelligent Systems and Control (LISC) Department of Mechanical Engineering and Materials Science Duke University Durham NC 27708 USAduke.edu Department of Biomedical Engineering and Department of Computer Science Duke University Durham NC 27708 USAduke.edu Program in Neuroscience & Behavioral Disorders Duke-NUS Graduate Medical School Singapore Singapore

This paper presents a deterministic and adaptive spike model derived from radial basis functions and a leaky integrate-and-fire sampler developed for training spiking neural networks without direct weight manipulation. Several algorithms have been proposed for training spiking neural networks through biologically-plausible learning mechanisms, such as spike-timing-dependent synaptic plasticity and Hebbian plasticity. These algorithms typically rely on the ability to update the synaptic strengths, or weights, directly, through a weight update rule in which the weight increment can be decided and implemented based on the training equations. However, in several potential applications of adaptive spiking neural networks, including neuroprosthetic devices and CMOS/memristor nanoscale neuromorphic chips, the weights cannot be manipulated directly and, instead, tend to change over time by virtue of the pre- and postsynaptic neural activity. This paper presents an indirect learning method that induces changes in the synaptic weights by modulating spike-timing-dependent plasticity by means of controlled input spike trains. In place of the weights, the algorithm manipulates the input spike trains used to stimulate the input neurons by determining a sequence of spike timings that minimize a desired objective function and, indirectly, induce the desired synaptic plasticity in the network.

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An Open-Source Knowledge Graph Ecosystem for the Life sciences

arXiv

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arXiv 2023年

作者： Callahan, Tiffany J. Tripodi, Ignacio J. Stefanski, Adrianne L. Cappelletti, Luca Taneja, Sanya B. Wyrwa, Jordan M. Casiraghi, Elena Matentzoglu, Nicolas A. Reese, Justin Silverstein, Jonathan C. Hoyt, Charles Tapley Boyce, Richard D. Malec, Scott A. Unni, Deepak R. Joachimiak, Marcin P. Robinson, Peter N. Mungall, Christopher J. Cavalleri, Emanuele Fontana, Tommaso Valentini, Giorgio Mesiti, Marco Gillenwater, Lucas A. Santangelo, Brook Vasilevsky, Nicole A. Hoehndorf, Robert Bennett, Tellen D. Ryan, Patrick B. Hripcsak, George Kahn, Michael G. Bada, Michael Baumgartner, William A. Hunter, Lawrence E. Computational Bioscience Program University of Colorado Anschutz Medical Campus AuroraCO80045 United States Department of Biomedical Informatics Columbia University Irving Medical Center New YorkNY10032 United States Computer Science Department Interdisciplinary Quantitative Biology University of Colorado Boulder BoulderCO80301 United States AnacletoLab Computer Science Department University of Milan 20122 Italy Intelligent Systems Program University of Pittsburgh PittsburghPA15260 United States Department of Physical Medicine and Rehabilitation School of Medicine University of Colorado Anschutz Medical Campus AuroraCO80045 United States Division of Environmental Genomics and Systems Biology Lawrence Berkeley National Laboratory BerkeleyCA94720 United States Semanticly Ltd Athens Greece Department of Biomedical Informatics University of Pittsburgh School of Medicine PittsburghPA15206 United States Laboratory of Systems Pharmacology Harvard Medical School BostonMA02115 United States Division of Translational Informatics University of New Mexico School of Medicine AlbuquerqueNM87131 United States SIB Swiss Institute of Bioinformatics Basel Switzerland Berlin Institute of Health at Charité-Universitatsmedizin Berlin10117 Germany ELLIS European Laboratory for Learning and Intelligent Systems Germany Department of Biomedical Informatics University of Colorado School of Medicine AuroraCO80045 United States Data Collaboration Center Critical Path Institute 1840 E River Rd. Suite 100 TucsonAZ85718 United States Computer Electrical and Mathematical Sciences & Engineering Division Computational Bioscience Research Center King Abdullah University of Science and Technology Thuwal23955-6900 Saudi Arabia Department of Pediatrics University of Colorado School of Medicine AuroraCO80045 United States Janssen Research and Development RaritanNJ08869 United States Division of General Internal Medicine University of Colorado School of Medicine A

Translational research requires data at multiple scales of biological organization. Advancements in sequencing and multi-omics technologies have increased the availability of these data, but researchers face significant integration challenges. Knowledge graphs (KGs) are used to model complex phenomena, and methods exist to construct them automatically. However, tackling complex biomedical integration problems requires flexibility in the way knowledge is modeled. Moreover, existing KG construction methods provide robust tooling at the cost of fixed or limited choices among knowledge representation models. PheKnowLator (Phenotype Knowledge Translator) is a semantic ecosystem for automating the FAIR (Findable, Accessible, Interoperable, and Reusable) construction of ontologically grounded KGs with fully customizable knowledge representation. The ecosystem includes KG construction resources (e.g., data preparation APIs), analysis tools (e.g., SPARQL endpoints and abstraction algorithms), and benchmarks (e.g., prebuilt KGs and embeddings). We evaluated the ecosystem by systematically comparing it to existing open-source KG construction methods and by analyzing its computational performance when used to construct 12 large-scale KGs. With flexible knowledge representation, PheKnowLator enables fully customizable KGs without compromising performance or usability. © 2023, CC BY.

关键词： Ecosystems

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The association for the advancement of artificial intelligence 2020 workshop program

The association for the advancement of artificial intelligen...

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作者： Bang, Grace Barash, Guy Bea, Ryan Cali, Jacques Castillo-Effen, Mauricio Chen, Xin Cynthia Chhaya, Niyati Dhoopar, Rohan Dumancic, Sebastijan Espinoza, Huáscar Farchi, Eitan Fioretto, Ferdinando Fuentetaja, Raquel Galley, Michel Geib, Christopher Hernández-Orallo, José Huang, Xiaowei Keren, Sarah Kim, Seokhwan Jaidka, Kokil Liu, Xiaomo Lu, Tyler Ma, Zhiqiang Mallah, Richard McDermid, John Michalowski, Martin Mirsky, Reuth Héigeartaigh, Seán Ó. Ramachandran, Deepak Segovia-Aguas, Javier Shaban-Nejad, Arash Shehory, Onn Shwartz, Vered Srivastava, Siddharth Talamadupula, Kartik Tang, Jian Zhang, Dell Zhang, Jian S&P Global. Western Digital University of Southampton United Kingdom Blue Prism Group PLC Lockheed Martin's Advanced Technology Laboratories ArlingtonVA United States University of Hong Kong Hong Kong Adobe Research Google Research Katholieke Universiteit Leuven Belgium Commissariat à ĺÉnergie Atomique France IBM Research Department of Electrical Engineering and Computer Science Syracuse University United States Universidad Carlos III de Madrid Spain Microsoft Research AI SIFT LLC Universitat Politècnica de València Spain Leverhulme Centre for the Future of Intelligence United Kingdom Department of Computer Science University of Liverpool United Kingdom National University of Singapore Singapore Harvard School of Engineering and Applied Sciences in the of Fields of Multiagent Environment Design and Goal Recognition Design Amazon Alexa AI Future of Life Institute United States University of York Assuring Autonomy International Program United Kingdom University of Minnesota School of Nursing MinneapolisMN United States University of Texas at Austin AustinTX United States Institut de Robòtica i Informàtica Industrial Spanish Council for Scientific Research Technical University of Catalonia Spain Oak Ridge National Laboratory Center for Biomedical Informatics Department of Pediatrics University of Tennessee Health Science Center United States Bar Ilan University Israel University of Washington SeattleWA United States Autonomous Agents and Intelligent Robots lab of the School of Computing Informatics Decision Systems Arizona State University United States DiDi AI Labs School of Industrial and System Engineering Georgia Institute of Technology United States University of London United Kingdom Microsoft Azure

The Association for the Advancement of Artificial Intelligence 2020 Workshop program included twenty-three workshops covering a wide range of topics in artificial intelligence. This report contains the required reports, which were submitted by most, but not all, of the workshop chairs. Copyright © 2020, Association for the Advancement of Artificial Intelligence. All rights reserved.

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Interpretable Symbolic Regression for Data science: Analysis of the 2022 Competition

arXiv

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arXiv 2023年

作者： de Franca, F.O. Virgolin, M. Kommenda, M. Majumder, M.S. Cranmer, M. Espada, G. Ingelse, L. Fonseca, A. Landajuela, M. Petersen, B. Glatt, R. Mundhenk, N. Lee, C.S. Hochhalter, J.D. Randall, D.L. Kamienny, P. Zhang, H. Dick, G. Simon, A. Burlacu, B. Kasak, Jaan Machado, Meera Wilstrup, Casper La Cava, W.G. Federal University of ABC Santo Andre Brazil Evolutionary Intelligence Group Centrum Wiskunde & Informatica Science Park 123 Amsterdam Netherlands Computational Health Informatics Program Boston Children's Hospital Harvard Medical School Boston United States University of Applied Sciences Upper Austria Hagenberg Austria Center for Computational Astrophysics Flatiron Institute Department of Astrophysical Sciences Princeton University United States LASIGE Faculdade de Ciências Universidade de Lisboa Lisboa Portugal Computational Engineering Division Lawrence Livermore National Laboratory Livermore United States University of Utah Department of Mechanical Engineering Utah United States Meta FAIR France Victoria University of Wellington School of Engineering and Computer Science New Zealand University of otago Department of Information Science New Zealand Institut für Angewandte Physik Universität Tübingen Max Planck Institute for Intelligent Systems Tübingen Germany Abzu AI Orient Plads 1 Nordhavn 2150 Denmark

Symbolic regression searches for analytic expressions that accurately describe studied phenomena. The main attraction of this approach is that it returns an interpretable model that can be insightful to users. Historically, the majority of algorithms for symbolic regression have been based on evolutionary algorithms. However, there has been a recent surge of new proposals that instead utilize approaches such as enumeration algorithms, mixed linear integer programming, neural networks, and Bayesian optimization. In order to assess how well these new approaches behave on a set of common challenges often faced in real-world data, we hosted a competition at the 2022 Genetic and Evolutionary Computation Conference consisting of different synthetic and real-world datasets which were blind to entrants. For the real-world track, we assessed interpretability in a realistic way by using a domain expert to judge the trustworthiness of candidate models. We present an in-depth analysis of the results obtained in this competition, discuss current challenges of symbolic regression algorithms and highlight possible improvements for future competitions. Copyright © 2023, The Authors. All rights reserved.

关键词： Machine learning

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Ontologizing Health systems Data at Scale: Making Translational Discovery a Reality

arXiv

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arXiv 2022年

作者： Callahan, Tiffany J. Stefanski, Adrianne L. Wyrwa, Jordan M. Zeng, Chenjie Ostropolets, Anna Banda, Juan M. Baumgartner, William A. Boyce, Richard D. Casiraghi, Elena Coleman, Ben D. Collins, Janine H. Deakyne-Davies, Sara J. Feinstein, James A. Haendel, Melissa A. Lin, Asiyah Y. Martin, Blake Matentzoglu, Nicolas A. Meeker, Daniella Reese, Justin Sinclair, Jessica Taneja, Sanya B. Trinkley, Katy E. Vasilevsky, Nicole A. Williams, Andrew Zhang, Xingman A. Denny, Joshua C. Robinson, Peter N. Ryan, Patrick Hripcsak, George Bennett, Tellen D. Hunter, Lawrence E. Kahn, Michael G. Computational Bioscience Program University of Colorado Anschutz Medical Campus AuroraCO80045 United States Department of Biomedical Informatics Columbia University Irving Medical Center New YorkNY10032 United States Department of Physical Medicine and Rehabilitation School of Medicine University of Colorado Anschutz Medical Campus AuroraCO80045 United States National Human Genome Research Institute National Institutes of Health BethesdaMD20892 United States Department of Computer Science Georgia State University AtlantaGA30303 United States Department of Biomedical Informatics University of Pittsburgh School of Medicine PittsburghPA15260 United States Computer Science Università degli Studi di Milano Milan20122 Italy Division of Environmental Genomics and Systems Biology Lawrence Berkeley National Laboratory BerkeleyCA94720 United States The Jackson Laboratory for Genomic Medicine FarmingtonCT06032 United States Department of Haematology University of Cambridge Cambridge United Kingdom Department of Research Informatics & Data Science Analytics Resource Center Children’s Hospital Colorado AuroraCO80045 United States University of Colorado Anschutz School of Medicine AuroraCO80045 United States Center for Health AI University of Colorado Anschutz Medical Campus AuroraCO80045 United States Departments of Biomedical Informatics and Pediatrics University of Colorado School of Medicine AuroraCO80045 United States Semanticly Athens Greece Yale School of Medicine New HavenCT06510 United States HealthLinc ValparaisoIN46383 United States Intelligent Systems Program University of Pittsburgh PittsburghPA15260 United States Department of Family Medicine University of Colorado Anschutz School of Medicine AuroraCO80045 United States Translational and Integrative Sciences Lab University of Colorado Anschutz Medical Campus AuroraCO80045 United States Tufts Institute for Clinical Research and Health Policy Studies Tufts University Bos

Background: Common data models solve many challenges of standardizing electronic health record (EHR) data, but are unable to semantically integrate all the resources needed for deep phenotyping. Open Biological and Biomedical Ontology (OBO) Foundry ontologies provide computable representations of biological knowledge and enable the integration of heterogeneous data. However, mapping EHR data to OBO ontologies requires significant manual curation and domain expertise. Objective: We introduce OMOP2OBO, an algorithm for mapping Observational Medical Outcomes Partnership (OMOP) vocabularies to OBO ontologies. Results: Using OMOP2OBO, we produced mappings for 92,367 conditions, 8611 drug ingredients, and 10,673 measurement results, which covered 68-99% of concepts used in clinical practice when examined across 24 hospitals. When used to phenotype rare disease patients, the mappings helped systematically identify undiagnosed patients who might benefit from genetic testing. Conclusions: By aligning OMOP vocabularies to OBO ontologies our algorithm presents new opportunities to advance EHR-based deep phenotyping. © 2022, CC BY.

关键词： Ontology

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Author Correction: π-HuB: the proteomic navigator of the human body

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Nature 2025年第8046期637卷 E22页

作者： Fuchu He Ruedi Aebersold Mark S Baker Xiuwu Bian Xiaochen Bo Daniel W Chan Cheng Chang Luonan Chen Xiangmei Chen Yu-Ju Chen Heping Cheng Ben C Collins Fernando Corrales Jürgen Cox Weinan E Jennifer E Van Eyk Jia Fan Pouya Faridi Daniel Figeys George Fu Gao Wen Gao Zu-Hua Gao Keisuke Goda Wilson Wen Bin Goh Dongfeng Gu Changjiang Guo Tiannan Guo Yuezhong He Albert J R Heck Henning Hermjakob Tony Hunter Narayanan Gopalakrishna Iyer Ying Jiang Connie R Jimenez Lokesh Joshi Neil L Kelleher Ming Li Yang Li Qingsong Lin Cui Hua Liu Fan Liu Guang-Hui Liu Yansheng Liu Zhihua Liu Teck Yew Low Ben Lu Matthias Mann Anming Meng Robert L Moritz Edouard Nice Guang Ning Gilbert S Omenn Christopher M Overall Giuseppe Palmisano Yaojin Peng Charles Pineau Terence Chuen Wai Poon Anthony W Purcell Jie Qiao Roger R Reddel Phillip J Robinson Paola Roncada Chris Sander Jiahao Sha Erwei Song Sanjeeva Srivastava Aihua Sun Siu Kwan Sze Chao Tang Liujun Tang Ruijun Tian Juan Antonio Vizcaíno Chanjuan Wang Chen Wang Xiaowen Wang Xinxing Wang Yan Wang Tobias Weiss Mathias Wilhelm Robert Winkler Bernd Wollscheid Limsoon Wong Linhai Xie Wei Xie Tao Xu Tianhao Xu Liying Yan Jing Yang Xiao Yang John Yates Tao Yun Qiwei Zhai Bing Zhang Hui Zhang Lihua Zhang Lingqiang Zhang Pingwen Zhang Yukui Zhang Yu Zi Zheng Qing Zhong Yunping Zhu State Key Laboratory of Medical Proteomics Beijing Proteome Research Center National Center for Protein Sciences (Beijing) Beijing Institute of Lifeomics Beijing China. hefc@***. International Academy of Phronesis Medicine (Guangdong) Guangdong China. hefc@***. Department of Biology Institute of Molecular Systems Biology ETH Zurich Zurich Switzerland. aebersold@imsb.biol.ethz.ch. Macquarie Medical School Macquarie University Sydney New South Wales Australia. Institute of Pathology and Southwest Cancer Center Southwest Hospital Third Military Medical University (Army Medical University) and Key Laboratory of Tumor Immunopathology Ministry of Education of China Chongqing China. Institute of Health Service and Transfusion Medicine Beijing China. Department of Pathology and The Sidney Kimmel Comprehensive Cancer Center Johns Hopkins University Baltimore MD USA. State Key Laboratory of Medical Proteomics Beijing Proteome Research Center National Center for Protein Sciences (Beijing) Beijing Institute of Lifeomics Beijing China. Key Laboratory of Systems Biology Center for Excellence in Molecular Cell Science Shanghai Institute of Biochemistry and Cell Biology Chinese Academy of Sciences Shanghai China. Department of Nephrology First Medical Center of Chinese PLA General Hospital Nephrology Institute of the Chinese People's Liberation Army State Key Laboratory of Kidney Diseases National Clinical Research Center for Kidney Diseases Beijing Key Laboratory of Kidney Disease Research Beijing China. Institute of Chemistry Academia Sinica Taipei China. National Biomedical Imaging Center State Key Laboratory of Membrane Biology Institute of Molecular Medicine Peking-Tsinghua Center for Life Sciences College of Future Technology Peking University Beijing China. School of Biological Sciences Queen's University of Belfast Belfast UK. Functional Proteomics Laboratory Centro Nacional de Biotecnología-CSIC Madrid Spain. Computational Systems Biochemistry Research Group Ma

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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 2023年第6期20卷 824-835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

BigNeuron is an open community bench-testing platform with the goal of setting open standards for accurate and fast automatic neuron tracing. We gathered a diverse set of image volumes across several species that is representative of the data obtained in many neuroscience laboratories interested in neuron tracing. Here, we report generated gold standard manual annotations for a subset of the available imaging datasets and quantified tracing quality for 35 automatic tracing algorithms. The goal of generating such a hand-curated diverse dataset is to advance the development of tracing algorithms and enable generalizable benchmarking. Together with image quality features, we pooled the data in an interactive web application that enables users and developers to perform principal component analysis, t-distributed stochastic neighbor embedding, correlation and clustering, visualization of imaging and tracing data, and benchmarking of automatic tracing algorithms in user-defined data subsets. The image quality metrics explain most of the variance in the data, followed by neuromorphological features related to neuron size. We observed that diverse algorithms can provide complementary information to obtain accurate results and developed a method to iteratively combine methods and generate consensus reconstructions. The consensus trees obtained provide estimates of the neuron structure ground truth that typically outperform single algorithms in noisy datasets. However, specific algorithms may outperform the consensus tree strategy in specific imaging conditions. Finally, to aid users in predicting the most accurate automatic tracing results without manual annotations for comparison, we used support vector machine regression to predict reconstruction quality given an image volume and a set of automatic tracings.

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Molybdenum Disulfide: Scalable Fabrication of Molybdenum Disulfide Nanostructures and their Assembly (Adv. Mater. 43/2020)

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Advanced Materials 2020年第43期32卷

作者： Yun Huang Kang Yu Huaizhi Li Kai Xu Zexi Liang Debora Walker Paulo Ferreira Peer Fischer Donglei (Emma) Fan Materials Science and Engineering Program Texas Materials Institute The University of Texas at Austin Austin TX 78712 USA The Department of Advanced Electron Microscopy Imaging and Spectroscopy International Iberian Nanotechnology Laboratory Braga 4715‐330 Portugal Electrical and Computer Engineering University of Illinois at Urbana‐Champaign Urbana IL 61801 USA School of Engineering and Applied Sciences Harvard University Cambridge MA 02138 USA Mechanical Engineering Department and IDMEC Instituto Superior Técnico University of Lisbon Av. Rovisco Pais Lisboa 1049‐001 Portugal Max‐Planck‐Institute for Intelligent Systems Heisenbergstr. 3 Stuttgart 70569 Germany Institute of Physical Chemistry University of Stuttgart Pfaffenwaldring 55 Stuttgart 70569 Germany Walker Department of Mechanical Engineering The University of Texas at Austin Austin TX 78712 USA

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PERFORMANCE OF A PRIORITIZED ON-BOARD BASEBAND SWITCH FOR A MULTISERVICE MULTIBEAM SATELLITE

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INTERNATIONAL JOURNAL OF SATELLITE COMMUNICATIONS 1994年第3期12卷 283-297页

作者： BOHM, S ELHAKEEM, AK HACHICHA, M Department of Electrical and Computer Engineering Concordia University 1455 De Maisonneuve Blvd. West Montreal H3A 1M8 Canada Was born in Montreal Canada on 14 September 1966. He received the B. Eng. degree in electrical engineering from Concordia University Montreal Canada in 1989. He is at present completing the M.A.Sc. degree in electrical engineering at Concordia University. (S'75–S'79–M'79–SM'86) received the Ph.D. degree from Southern Methodist University Dallas TX in 1979. He spent the next two years working as a Visiting Professor in Egypt after which he moved to Ottawa Canada in 1982. He assumed teaching and research positions in Carleton and Manitoba Univerities and later moved to Concordia University Montreal Canada in 1983 where he is now a Professor in the Electrical and Computer Engineering Department. He has published numerous papers in IEEE and international journals in the areas of spread spectrum and networking. He is a well-known expert in these areas and serves as a consultant to many companies. His current research interests include wide-band metropolitan networks switching architectures and performance of on-board multibeam satellites acquisitionless CDMA networks code distribution and orthogonalization of CDMA signals responsive congestion control for ATM-based networks ARQ techniques and investigation of the novel SUGAR CDMA systems in fading channels. Dr. Elhakeem is a Senior Member of the Canadian Electrical Engineering Society and Armed Forces Association. He has chaired numerous technical sessions in IEEE Conferences was the Technical Program Chairman for IEEE Montech 1986 Montreal Canada. Dr. Elhakeem is the key guest editor of theIEEE Journal of Selected Areas in Communicationsfor the May June issues 1993 covering CDMA networks. Advanced Technology & Networks VISTAR Telecommunications Inc. Ottawa Ontario K1G 3J4 Canada An Associate Director of Advanced Technology & Networks Group VISTAR Telecommunications Inc. Ottawa Canada. He is also an Adjunct Pr

In this paper, we study the performance of a prioritized on-board baseband switch in conjunction with a multibeam satellite handling integrated services. The services considered for the analysis include voice, video, file transfer and interactive data. The prioritized switch uses both input and output buffering, switch speed-up as well as a two-phase head-of-line resolution algorithm, in order to reduce the buffer loss while maintaining acceptable user delays. The minimum required buffer capacity and switch speed-up for each service in a prioritized environment are found under uniform traffic conditions. It is shown that under uniform traffic conditions, only minimal buffering and switch speed-up are needed even for the lowest priority users. The performance dependence on the switch size is also substantially reduced with head of line resolution and buffering even in a prioritized environment.

关键词： SWITCH ON-BOARD BASEBAND MULTIBEAM PRIORITIZED HEAD-OF-LINE UNIFORM LOAD SPEED-UP

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The application of an extracellular vesicle-based biosensor in early diagnosis and prediction of chemoresponsiveness in ovarian cancer

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Cancer Letters 2024年 581卷 216533-216533页

作者： Tsang, Benjamin K. Asare-Werehene, Meshach Hunter, Rob Gerber, Emma Reunov, Arkadiy Brine, Isaiah Chang, Chia-Yu Chang, Chia-Ching Shieh, Dar-Bin Burger, Dylan Anis, Hanan Departments of Obstetrics & Gynecology and Cellular & Molecular Medicine Centre for Infection Immunity and Inflammation Interdisciplinary School of Health Sciences University of Ottawa Ottawa ON K1N 6N5 Canada Chronic Disease Program Ottawa Hospital Research Institute The Ottawa Hospital Ottawa ON K1Y 4E9 Canada Department of Biology St. Francis Xavier University 2320 Notre Dame Avenue Antigonish NS B2G 2W5 Canada School of Electrical Engineering and Computer Science Ottawa-Carleton Institute for Biomedical Engineering University of Ottawa Ottawa ON K1N 6N5 Canada Department of Biological Science and Department of Electrophysics Technology Center for Intelligent Drug Systems and Smart Bio-devices (IDS2B) National Yang Ming Chiao Tung University Hsinchu 30068 Taiwan Institute of Basic Medical Science Institute of Oral Medicine and Department of Stomatology Advanced Optoelectronic Technology Center and Center for Micro/Nano Science and Technology National Cheng Kung University Hospital National Cheng Kung University Tainan 704 Taiwan Institute of Physics Academia Sinica Taipei 10529 Taiwan

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