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Molecular and Serologic Diagnostic Technologies for SARS-CoV-2

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arXiv 2022年

作者： Rando, Halie M. Brueffer, Christian Lordan, Ronan Dattoli, Anna Ada Manheim, David Meyer, Jesse G. Mundo, Ariel I. Perrin, Dimitri Mai, David Wellhausen, Nils Gitter, Anthony Greene, Casey S. Department of SystemsPharmacology and Translational Therapeutics University of Pennsylvania PhiladelphiaPA United States Department of Biochemistry and MolecularGenetics University of Colorado School of Medicine AuroraCO United States Center for Health AI University of Colorado School of Medicine AuroraCO United States Department of Clinical Sciences Lund University Lund Sweden Institute for TranslationalMedicine and Therapeutics Perelman School of Medicine University of Pennsylvania PhiladelphiaPA19104-5158 United States Department of Medicine Perelman School of Medicine University of Pennsylvania PhiladelphiaPA19104 United States Department of Systems Pharmacology and Translational Therapeutics Perelman School of Medicine University of Pennsylvania PhiladelphiaPA19104 United States Department of Pathology Laboratory Medicine The Children’s Hospital of Philadelphia PhiladelphiaPA United States Department of Systems Pharmacology & Translational Therapeutics Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States 1DaySooner Delaware United States Communication Research Center School of Public Health University of Haifa Haifa Israel Technion Israel Institute of Technology Haifa Israel Department of Biochemistry Medical College of Wisconsin MilwaukeeWI United States Department of Biomedical Engineering University of Arkansas FayettevilleAR United States School of Computer Science Queensland University of Technology Brisbane Australia Centre for Data Science Queensland University of Technology Brisbane Australia Department of Bioengineering University of Pennsylvania PhiladelphiaPA United States Center for Cellular Immunotherapies Perelman School of Medicine Parker Institute for Cancer Immunotherapy University of Pennsylvania PhiladelphiaPA United States Department of Systems Pharmacology and Translational Therapeutics University of Pennsylvania PhiladelphiaPA United States Department of Biostatistics Medical

The COVID-19 pandemic has presented many challenges that have spurred biotechnological research to address specific problems. Diagnostics is one area where biotechnology has been critical. Diagnostic tests play a vital role in managing a viral threat by facilitating the detection of infected and/or recovered individuals. From the perspective of what information is provided, these tests fall into two major categories, molecular and serological. Molecular diagnostic techniques assay whether a virus is present in a biological sample, thus making it possible to identify individuals who are currently infected. Additionally, when the immune system is exposed to a virus, it responds by producing antibodies specific to the virus. Serological tests make it possible to identify individuals who have mounted an immune response to a virus of interest and therefore facilitate the identification of individuals who have previously encountered the virus. These two categories of tests provide different perspectives valuable to understanding the spread of SARS-CoV-2. Within these categories, different biotechnological approaches offer specific advantages and disadvantages. Here we review the categories of tests developed for the detection of the SARS-CoV-2 virus or antibodies against SARS-CoV-2 and discuss the role of diagnostics in the COVID-19 pandemic. © 2022, CC BY.

关键词： SARS

MedShapeNet - A Large-Scale dataset of 3D Medical Shapes for Computer Vision

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arXiv 2023年

作者： Li, Jianning Zhou, Zongwei Yang, Jiancheng Pepe, Antonio Gsaxner, Christina Luijten, Gijs Qu, Chongyu Zhang, Tiezheng Chen, Xiaoxi Li, Wenxuan Wodzinski, Marek Friedrich, Paul Xie, Kangxian Jin, Yuan Ambigapathy, Narmada Nasca, Enrico Solak, Naida Melito, Gian Marco Vu, Viet Duc Memon, Afaque R. Schlachta, Christopher De Ribaupierre, Sandrine Patel, Rajnikant Eagleson, Roy Chen, Xiaojun Mächler, Heinrich Kirschke, Jan Stefan de la Rosa, Ezequiel Christ, Patrick Ferdinand Li, Hongwei Bran Ellis, David G. Aizenberg, Michele R. Gatidis, Sergios Küstner, Thomas Shusharina, Nadya Heller, Nicholas Andrearczyk, Vincent Depeursinge, Adrien Hatt, Mathieu Sekuboyina, Anjany Löffler, Maximilian Liebl, Hans Dorent, Reuben Vercauteren, Tom Shapey, Jonathan Kujawa, Aaron Cornelissen, Stefan Langenhuizen, Patrick Ben-Hamadou, Achraf Rekik, Ahmed Pujades, Sergi Boyer, Edmond Bolelli, Federico Grana, Costantino Lumetti, Luca Salehi, Hamidreza Ma, Jun Zhang, Yao Gharleghi, Ramtin Beier, Susann Sowmya, Arcot Garza-Villarreal, Eduardo A. Balducci, Thania Angeles-Valdez, Diego Souza, Roberto Rittner, Leticia Frayne, Richard Ji, Yuanfeng Ferrari, Vincenzo Chatterjee, Soumick Dubost, Florian Schreiber, Stefanie Mattern, Hendrik Speck, Oliver Haehn, Daniel John, Christoph Nürnberger, Andreas Pedrosa, João Ferreira, Carlos Aresta, Guilherme Cunha, António Campilho, Aurélio Suter, Yannick Garcia, Jose Lalande, Alain Vandenbossche, Vicky Van Oevelen, Aline Duquesne, Kate Mekhzoum, Hamza Vandemeulebroucke, Jef Audenaert, Emmanuel Krebs, Claudia van Leeuwen, Timo Vereecke, Evie Heidemeyer, Hauke Röhrig, Rainer Hölzle, Frank Badeli, Vahid Krieger, Kathrin Gunzer, Matthias Chen, Jianxu van Meegdenburg, Timo Dada, Amin Balzer, Miriam Fragemann, Jana Jonske, Frederic Rempe, Moritz Malorodov, Stanislav Bahnsen, Fin H. Seibold, Constantin Jaus, Alexander Marinov, Zdravko Jaeger, Paul F. Stiefelhagen, Rainer Santos, Ana Sofia Lindo, Mariana Ferreira, André Alves, Victor Kamp, Michael Abourayya, Amr Nensa, Felix Hörst, Fabian Brehmer, Alexander Hein Girardetstraße 2 Essen45131 Germany Graz University of Technology Inffeldgasse 16c Graz8010 Austria Graz Austria The Department of Computer Science Johns Hopkins University Malone Hall 3400 N Charles St BaltimoreMD21218 United States Rte Cantonale Lausanne1015 Switzerland The Department of Measurement and Electronics AGH University of Science and Technology Krakow Poland Sierre Switzerland Department of Biomedical Engineering University of Basel Hegenheimermattweg Allschwil167C CH-4123 Switzerland The Boston College 140 Commonwealth Ave Chestnut HillMA02467 United States University Hospital B7-200 339 Windermere Road LondonN6A 5A5 Canada The Department of Radiology Renji Hospital School of Medicine Shanghai Jiao Tong University Shanghai200240 China The Research Center for Connected Healthcare Big Data ZhejiangLab Zhejiang Hangzhou311121 China The Faculty of Statistics Technical University Dortmund August-Schmidt-Straße 1 Dortmund44227 Germany The Institute of Mechanics Graz University of Technology Kopernikusgasse 24/IV Graz8010 Austria The Institute of Biomedical Manufacturing and Life Quality Engineering State Key Laboratory of Mechanical System and Vibration School of Mechanical Engineering Shanghai Jiao Tong University 800 Dongchuan Road Shanghai200240 China The Department of Cardiac Surgery Medical University Graz Auenbruggerplatz 29 Graz8036 Austria The Department of Diagnostic and Interventional Radiology University Hospital Giessen Justus-Liebig-University Giessen Klinikstraße 33 Giessen35392 Germany The Department of Mechanical Engineering Mehran University of Engineering and Technology Jamshoro Sindh76062 Pakistan The Institute of Medical Robotics Shanghai Jiao Tong University Shanghai China The Geschäftsführender Oberarzt Abteilung für Interventionelle und Diagnostische Neuroradiologie Universitätsklinikum der Technischen Universität München Ismaningerstr. 22 München81675 Germany icometrix Kolonel Be

Prior to the deep learning era, shape was commonly used to describe the objects. Nowadays, state-of-the-art (SOTA) algorithms in medical imaging are predominantly diverging from computer vision, where voxel grids, meshes, point clouds, and implicit surface models are used. This is seen from numerous shape-related publications in premier vision conferences as well as the growing popularity of ShapeNet (about 51,300 models) and Princeton ModelNet (127,915 models). For the medical domain, we present a large collection of anatomical shapes (e.g., bones, organs, vessels) and 3D models of surgical instrument, called MedShapeNet, created to facilitate the translation of data-driven vision algorithms to medical applications and to adapt SOTA vision algorithms to medical problems. As a unique feature, we directly model the majority of shapes on the imaging data of real patients. As of today, MedShapeNet includes 23 datasets with more than 100,000 shapes that are paired with annotations (ground truth). Our data is freely accessible via a web interface and a Python application programming interface (API) and can be used for discriminative, reconstructive, and variational benchmarks as well as various applications in virtual, augmented, or mixed reality, and 3D printing. Exemplary, we present use cases in the fields of classification of brain tumors, skull reconstructions, multi-class anatomy completion, education, and 3D printing. In future, we will extend the data and improve the interfaces. The project pages are: https://***/ and https://***/Jianningli/*** Codes 68T01 © 2023, CC BY.

关键词： Large dataset

Region-wise generative adversarial image inpainting for large missing areas

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arXiv 2019年

作者： Ma, Yuqing Liu, Xianglong Bai, Shihao Wang, Lei Liu, Aishan Tao, Dacheng Hancock, Edwin State Key Lab of Software Development Environment Beihang University Beijing100191 China Beijing Advanced Innovation Center for Big Data-Based Precision Medicine UBTECH Sydney Artificial Intelligence Centre School of Information Technologies Faculty of Engineering and Information Technologies University of Sydney DarlingtonNSW2008 Australia Department of Computer Science University of York York United Kingdom

Recently deep neutral networks have achieved promising performance for filling large missing regions in image inpainting tasks. They usually adopted the standard convolutional architecture over the corrupted image, leading to meaningless contents, such as color discrepancy, blur and artifacts. Moreover, most inpainting approaches cannot well handle the large contiguous missing area cases. To address these problems, we propose a generic inpainting framework capable of handling with incomplete images on both contiguous and discontiguous large missing areas, in an adversarial manner. From which, region-wise convolution is deployed in both generator and discriminator to separately handle with the different regions, namely existing regions and missing ones. Moreover, a correlation loss is introduced to capture the non-local correlations between different patches, and thus guides the generator to obtain more information during inference. With the help of our proposed framework, we can restore semantically reasonable and visually realistic images. Extensive experiments on three widely-used datasets for image inpainting tasks have been conducted, and both qualitative and quantitative experimental results demonstrate that the proposed model significantly outperforms the state-of-the-art approaches, both on the large contiguous and discontiguous missing areas. Copyright © 2019, The Authors. All rights reserved.

关键词： Generative adversarial networks

A survey on dialogue systems: Recent advances and new frontiers

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arXiv 2017年

作者： Chen, Hongshen Liu, Xiaorui Yin, Dawei Tang, Jiliang Data Science Lab *** Data Science and Engineering Lab Michigan State University

Dialogue systems have attracted more and more attention. Recent advances on dialogue systems are overwhelmingly contributed by deep learning techniques, which have been employed to enhance a wide range of big data applications such as computer vision, natural language processing, and recommender systems. For dialogue systems, deep learning can leverage a massive amount of data to learn meaningful feature representations and response generation strategies, while requiring a minimum amount of hand-crafting. In this article, we give an overview to these recent advances on dialogue systems from various perspectives and discuss some possible research directions. In particular, we generally divide existing dialogue systems into task-oriented and nontask- oriented models, then detail how deep learning techniques help them with representative algorithms and finally discuss some appealing research directions that can bring the dialogue system research into a new frontier. Copyright © 2017, The Authors. All rights reserved.

关键词： Speech processing

Correction: On the usage of average Hausdorff distance for segmentation performance assessment: hidden error when used for ranking

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European radiology experimental 2022年第1期6卷 56页

作者： Orhun Utku Aydin Abdel Aziz Taha Adam Hilbert Ahmed A Khalil Ivana Galinovic Jochen B Fiebach Dietmar Frey Vince Istvan Madai CLAIM - Charité Lab for Artificial Intelligence in Medicine Charité Universitätsmedizin Berlin Berlin Germany. orhun-utku.aydin@charite.de. Research Studio Data Science Research Studios Austria Salzburg Austria. CLAIM - Charité Lab for Artificial Intelligence in Medicine Charité Universitätsmedizin Berlin Berlin Germany. Centre for Stroke Research Berlin Charité Universitätsmedizin Berlin Berlin Germany. Department of Neurology Max Planck Institute for Human Cognitive and Brain Sciences Leipzig Germany. Mind Brain Body Institute Berlin School of Mind and Brain Humboldt-Universität Berlin Berlin Germany. School of Computing and Digital Technology Faculty of Computing Engineering and the Built Environment Birmingham City University Birmingham UK.

Age-Based Utility Maximization for Wireless Powered Networks: A Stackelberg Game Approach

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Age-Based Utility Maximization for Wireless Powered Networks...

IEEE Global Communications Conference

作者： Haina Zheng Ke Xiong Pingyi Fan Zhangdui Zhong Khaled Ben Letaief School of Computer and Information Technology Beijing Jiaotong University Beijing China Beijing Key Laboratory of Traffic Data Analysis and Mining Beijing Jiaotong University Beijing China Department of Electronic Engineering Tsinghua University Beijing China State Key Lab of Rail Traffic Control and Safety Beijing Jiaotong University Beijing China The Hong Kong University of Science and Technology Hong Kong

ISBN: (数字)9781728109626

ISBN: (纸本)9781728109633

This paper investigates the efficient cooperation in wireless-powered communication networks, where an access point (AP) and multiple helpers first together charge up a sensor via radio-frequency (RF)-based wireless power transfer (WPT), and then the sensor uses the harvested energy to transmit real-time status updates to the AP. Due to the selfishness of the helpers, payment is provided as an incentive to them by the sensor-AP communication pair. For such a system, a Stackelberg game approach is designed to establish efficient cooperation between the helpers and the sensor-AP communication pair. An Age of Information (AoI)-based utility and a profit-based utility are defined for the sensor-AP pair and the helpers, respectively. Optimization problems are formulated to maximize their utilities. An explicit expression of the optimal transmit power of the helper is derived, with which a Dinkelbach's Programming (DP)-based algorithm is designed to jointly find the optimal payment price and transmit power at the AP, and at the same time, the Stackelberg equilibrium (SE) is achieved. Moreover, a closed-form expression of the minimum AoI of the system is also presented. Numerical results show that the more helpers there exist, the higher payment price the AP should fix, and meanwhile, the lower AoI of the sensor-AP communication pair can be achieved. Besides, it is shown that by increasing the transmit power at the AP, the optimal average AoI could not be reduced evidently.

关键词： Games Real-time systems Energy harvesting Wireless communication Measurement Batteries Wireless sensor networks

Deep reinforcement learning based dynamic trajectory control for UAV-assisted mobile edge computing

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arXiv 2019年

作者： Wang, Liang Wang, Kezhi Pan, Cunhua Xu, Wei Aslam, Nauman Nallanathan, Arumugam The Department of Computer and In-formantion Science Northumbria University Newcastle upon TyneNE1 8ST United Kingdom School of Electronic Engineering and Computer Science Queen Mary University of London E1 4NS United Kingdom The National Mobile Communications Research Lab Southeast University Nanjing210096 China Henan Joint International Research Laboratory of Intelligent Networking and Data Analysis Zhengzhou University Zhengzhou450001 China

In this paper, we consider a platform of flying mobile edge computing (F-MEC), where unmanned aerial vehicles (UAVs) serve as equipment providing computation resource, and they enable task offloading from user equipment (UE). We aim to minimize energy consumption of all UEs via optimizing user association, resource allocation and the trajectory of UAVs. To this end, we first propose a Convex optimizAtion based Trajectory control algorithm (CAT), which solves the problem in an iterative way by using block coordinate descent (BCD) method. Then, to make the real-time decision while taking into account the dynamics of the environment (i.e., UAV may take off from different locations), we propose a deep Reinforcement leArning based Trajectory control algorithm (RAT). In RAT, we apply the Prioritized Experience Replay (PER) to improve the convergence of the training procedure. Different from the convex optimization based algorithm which may be susceptible to the initial points and requires iterations, RAT can be adapted to any taking off points of the UAVs and can obtain the solution more rapidly than CAT once training process has been completed. Simulation results show that the proposed CAT and RAT achieve the considerable performance and both outperform traditional algorithms. Copyright © 2019, The Authors. All rights reserved.

关键词： Unmanned aerial vehicles (UAV)

iSplit LBI: Individualized partial ranking with ties via split LBI

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arXiv 2019年

作者： Xu, Qianqian Sun, Xinwei Yang, Zhiyong Cao, Xiaochun Huang, Qingming Yao, Yuan Key Lab. of Intelligent Information Processing Institute of Computing Technology CAS Microsoft Research Asia State Key Laboratory of Information Security Institute of Information Engineering CAS School of Cyber Security University of Chinese Academy of Sciences School of Computer Science and Tech. University of Chinese Academy of Sciences Key Laboratory of Big Data Mining and Knowledge Management CAS Peng Cheng Laboratory Department of Mathematics Hong Kong University of Science and Technology Hong Kong

Due to the inherent uncertainty of data, the problem of predicting partial ranking from pairwise comparison data with ties has attracted increasing interest in recent years. However, in real-world scenarios, different individuals often hold distinct preferences. It might be misleading to merely look at a global partial ranking while ignoring personal diversity. In this paper, instead of learning a global ranking which is agreed with the consensus, we pursue the tie-aware partial ranking from an individualized perspective. Particularly, we formulate a unified framework which not only can be used for individualized partial ranking prediction, but also be helpful for abnormal user selection. This is realized by a variable splitting-based algorithm called iSplitLBI. Specifically, our algorithm generates a sequence of estimations with a regularization path, where both the hyperparameters and model parameters are updated. At each step of the path, the parameters can be decomposed into three orthogonal parts, namely, abnormal signals, personalized signals and random noise. The abnormal signals can serve the purpose of abnormal user selection, while the abnormal signals and personalized signals together are mainly responsible for individual partial ranking prediction. Extensive experiments on simulated and real-world datasets demonstrate that our new approach significantly outperforms state-of-the-art alternatives. The code is now availiable at https://***/qianqianxu010/ NeurIPS2019-iSplitLBI. Copyright © 2019, The Authors. All rights reserved.

关键词： Forecasting

Mobility-Aware and Migration-Enabled Online Edge User Allocation in Mobile Edge Computing

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Mobility-Aware and Migration-Enabled Online Edge User Alloca...

IEEE International Conference on Web Services (ICWS)

作者： Qinglan Peng Yunni Xia Zeng Feng Jia Lee Chunrong Wu Xin Luo Wanbo Zheng Shanchen Pang Hui Liu Yidan Qin Peng Chen Software Theory and Technology Chongqing Key Lab Chongqing University Chongqing China DISCOVERY TECHNOLOGY (shenzhen)limited Shenzhen China Chinese Academy of Sciences Chongqing Institute of Green and Intelligent Technology Chongqing China Data Science Research Center Kunming University of Science and Technology Kunming China China University of Petroleum Beijing Changping-qu Beijing CN School of Software Xinjiang University Urumqi China School of Computer and Software Engineering Xihua University Chengdu China

The rapid development of mobile communication technologies prompts the emergence of mobile edge computing (MEC). As the key technology toward 5th generation (5G) wireless networks, it allows mobile users to offload their computational tasks to nearby servers deployed in base stations to alleviate the shortage of mobile resource. Nevertheless, various challenges, especially the edge-user-allocation problem, are yet to be properly addressed. Traditional studies consider this problem as a static global optimization problem where user positions are considered to be time-invariant and user-mobility-related information is not fully exploited. In reality, however, edge users are usually with high mobility and time-varying positions, which usually result in users reallocations among different base stations and impact on user-perceived quality-of-service (QoS). To overcome the above limitations, we consider the edge user allocation problem as an online decision-making and evolvable process and develop a mobility-aware and migration-enabled approach, named MobMig, for allocating users at real-time. Experiments based on real-world MEC dataset clearly demonstrate that our approach achieves higher user coverage rate and lower reallocations than traditional ones.

关键词： Servers Resource management Base stations Cloud computing Edge computing Task analysis Optimization