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Unlocking capacities of viral genomics for the COVID-19 pandemic response

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arXiv 2021年

作者： Knyazev, Sergey Chhugani, Karishma Sarwal, Varuni Ayyala, Ram Singh, Harman Karthikeyan, Smruthi Deshpande, Dhrithi Comarova, Zoia Lu, Angela Porozov, Yuri Wu, Aiping Abedalthagafi, Malak S. Nagaraj, Shivashankar H. Smith, Adam L. Skums, Pavel Ladner, Jason Lam, Tommy Tsan-Yuk Wu, Nicholas C. Zelikovsky, Alex Knight, Rob Crandall, Keith A. Mangul, Serghei Department of Computer Science College of Art and Science Georgia State University 1 Park Place AtlantaGA30303 United States Department of Pharmacology and Pharmaceutical Sciences School of Pharmacy University of Southern California 1985 Zonal Avenue Los AngelesCA90089 United States Department of Computer Science University of California Los Angeles 580 Portola Plaza Los AngelesCA90095 United States Department of Neuroscience College of Life Sciences University of California Los Angeles 580 Portola Plaza Los AngelesCA90095 United States Department of Electrical Engineering Indian Institute of Technology Hauz Khas New Delhi110016 India Department of Pediatrics University of California San Diego San DiegoCA United States Paradigm Environmental 3911 Old Lee Highway FairfaxVA22030 United States Department of Pharmacology and Pharmaceutical Sciences School of Pharmacy University of Southern California 1985 Zonal Avenue Los AngelesCA90089-9121 United States World-Class Research Center Digital Biodesign and Personalized Healthcare I.M. Sechenov First Moscow State Medical University Moscow Russia Department of Computational Biology Sirius University of Science and Technology Sochi Russia Center for Systems Medicine Institute of Basic Medical Sciences Chinese Academy of Medical Sciences Peking Union Medical College Beijing100005 China Suzhou Institute of Systems Medicine Suzhou215123 China Genomics Research Department Saudi Human Genome Project King Fahad Medical City King Abdulaziz City for Science and Technology Riyadh Saudi Arabia Centre for Genomics and Personalised Health Queensland University of Technology BrisbaneQLD4059 Australia Translational Research Institute Brisbane Australia Astani Department of Civil and Environmental Engineering University of Southern California 3620 South Vermont Avenue Los AngelesCA90089 United States Department of Computer Science College of Art and Science Georgia State University 1 Park Place Atlanta

More than any other infectious disease epidemic, the COVID-19 pandemic has been characterized by the generation of large volumes of viral genomic data at an incredible pace due to recent advances in high-throughput sequencing technologies, the rapid global spread of SARS-CoV-2, and its persistent threat to public health. However, distinguishing the most epidemiologically relevant information encoded in these vast amounts of data requires substantial effort across the research and public health communities. Studies of SARS-CoV-2 genomes have been critical in tracking the spread of variants and understanding its epidemic dynamics, and may prove crucial for controlling future epidemics and alleviating significant public health burdens. Together, genomic data and bioinformatics methods enable broad-scale investigations of the spread of SARS-CoV-2 at the local, national, and global scales and allow researchers the ability to efficiently track the emergence of novel variants, reconstruct epidemic dynamics, and provide important insights into drug and vaccine development and disease control. Here, we discuss the tremendous opportunities that genomics offers to unlock the effective use of SARS-CoV-2 genomic data for efficient public health surveillance and guiding timely responses to COVID-19. © 2021, CC BY-NC-ND.

关键词： SARS

Understanding deep neural network predictions for medical imaging applications

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arXiv 2019年

作者： Narayanan, Barath Narayanan de Silva, Manawaduge Supun Hardie, Russell C. Kueterman, Nathan K. Ali, Redha Department of Electrical and Computer Engineering University of Dayton 300 College Park DaytonOH45469 United States Sensors and Software Systems Division University of Dayton Research Institute 1700 South Patterson Blvd. DaytonOH45409 United States

computer-aided detection has been a research area attracting great interest in the past decade. Machine learning algorithms have been utilized extensively for this application as they provide a valuable second opinion to the doctors. Despite several machine learning models being available for medical imaging applications, not many have been implemented in the real-world due to the uninterpretable nature of the decisions made by the network. In this paper, we investigate the results provided by deep neural networks for the detection of malaria, diabetic retinopathy, brain tumor, and tuberculosis in different imaging modalities. We visualize the class activation mappings for all the applications in order to enhance the understanding of these networks. This type of visualization, along with the corresponding network performance metrics, would aid the data science experts in better understanding of their models as well as assisting doctors in their decision-making process. Copyright © 2019, The Authors. All rights reserved.

关键词： Deep neural networks

UAV-assisted Data Dissemination With Proactive Caching and File Sharing in V2X Networks

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UAV-assisted Data Dissemination With Proactive Caching and F...

IEEE Global Communications Conference

作者： Rui Lu Rongqing Zhang Xiang Cheng Liuqing Yang Department of Electronics State Key Laboratory of Advanced Optical Communication Systems and Networks School of EECS Peking University Beijing China Shenzhen Research Institute of Big Data (SRIBD) Shenzhen China School of Software Engineering Tongji University Shanghai China Department of Electrical and Computer Engineering Colorado State University CO USA

ISBN: (数字)9781728109626

ISBN: (纸本)9781728109633

Vehicle-to-Everything (V2X) communications refers to an intelligent and connected vehicular network where all vehicles and infrastructure systems are interconnected with each other. Data dissemination is playing an increasingly significant role in enhancing the network connectivity and data transmission performance. However, conventional scenarios and protocols cannot satisfy the growing pluralistic and superior quality of services (QoS) requirements of included vehicles. Therefore, in this paper, we propose a novel unmanned aerial vehicle (UAV)-assisted data dissemination protocol with proactive caching at the vehicles and an advanced file sharing strategy for revolutionizing communications. Specifically, in the proactive caching phase, we employ UAVs to act as flying base stations (BSs) for information interactions. Considering the time-variant network topology, we further propose a spatial scheduling (SS) algorithm for the trajectory optimization of each UAV, which can expedite the caching process and boost the system throughput. Then in the file sharing phase, based on the previous caching status, we provide a relay ordering algorithm to enhance the network transmission performance. Numerical results verify that our proposed UAV-assisted data transmission protocol can achieve a desirable system performance in terms of the downloading process, network throughput, and average data delivery delay.

关键词： Data dissemination Relays Protocols Quality of service Scheduling Throughput Wireless communication

Feasibility of winding faults detection on power transformers based on a new proposed reflection method

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Feasibility of winding faults detection on power transformer...

VDE-Fachtagung Hochspannungstechnik 2018 - VDE Conference on High Voltage Technology 2018

作者： Rahimbakhsh, M. Seifi, S. Werle, P. Gockenbach, E. Hartje, M. Leibniz Universität Hannover Institute of Electric Power Systems Division of High Voltage Engineering Asset Management Schering-Institute Callinstr. 25A Hannover30167 Germany High Voltage Institute Electrical and Computer Engineering Department University of Tehran Tehran Iran Labor Hochspannungstechnik Hochschule Bremen Germany

ISBN: (纸本)9783800748075

In this paper, the feasibility of winding faults detection is investigated by evaluating the effects of shorted turns fault, as one of the most significant winding faults,. The shorted turns faults are evaluated based on a new setup of winding reflection test. This novel measurement setup is proposed to perform an impedance measurement in frequency domain. The shorted turns could be detected and also evaluated using the proposed method. The results are capable of determining the number of the turns that have been shorted via a percentage value, and furthermore an estimation for the location of the shorted points is provided. The proposed method can be applied with the same measuring instrument for FRA and also the presented rules and pattern can increase the accuracy of fault detection compared FRA or simple turn ratio measurements. This estimation is presented with some general rules, which are determined based on empirical data obtained by performing measurements on windings of a distribution transformer. © VDE VERLAG GMBH · Berlin · Offenbach

关键词： Winding

Graph temporal logic inference for classification and identification

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arXiv 2019年

作者： Xu, Zhe Nettekoven, Alexander J. Agung Julius, A. Topcu, Ufuk University of Texas Austin AustinTX78712 United States Walker Department of Mechanical Engineering University of Texas at Austin AustinTX78712 United States Department of Electrical Computer and Systems Engineering Rensselaer Polytechnic Institute TroyNY12180 United States University of Texas Austin AustinTX78712 United States

Inferring spatial-temporal properties from data is important for many complex systems, such as additive manufacturing systems, swarm robotic systems and biological networks. Such systems can often be modeled as a labeled graph where labels on the nodes and edges represent relevant measurements such as temperatures and distances. We introduce graph temporal logic (GTL) which can express properties such as "whenever a node's label is above 10, for the next 3 time units there are always at least two neighboring nodes with an edge label of at most 2 where the node labels are above 5". This paper is a first attempt to infer spatial (graph) temporal logic formulas from data for classification and identification. For classification, we infer a GTL formula that classifies two sets of graph temporal trajectories with minimal misclassification rate. For identification, we infer a GTL formula that is informative and is satisfied by the graph temporal trajectories in the dataset with high probability. The informativeness of a GTL formula is measured by the information gain with respect to given prior knowledge represented by a prior probability distribution. We implement the proposed approach to classify the graph patterns of tensile specimens built from selective laser sintering (SLS) process with varying strengths, and to identify informative spatial-temporal patterns from experimental data of the SLS cooldown process and simulation data of a swarm of robots. Copyright © 2019, The Authors. All rights reserved.

关键词： Laser heating

Iterative Learning Control for Coupled Temperature and Humidity in Buildings

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IFAC-PapersOnLine 2017年第1期50卷 13420-13425页

作者： Minakais, Matt Okaeme, Charles C. Mishra, Sandipan Wen, John T. Electrical Computer and Systems Engineering Department United States Mechanical Aerospace and Nuclear Engineering Department United States Industrial and Systems Engineering Department Rensselaer Polytechnic Institute TroyNY12180 United States

This paper presents the design of a passivity-based iterative learning control (ILC) algorithm for coupled temperature and humidity in buildings. Since buildings are subjected to repeating diurnal patterns of disturbances, ILC algorithms can significantly improve performance. Moreover, since it is a feedforward control scheme, it can be used in conjunction with either model-free or model-based approaches such as the popular model predictive control techniques. However, model-based control is challenging for buildings because of the difficulty in identifying building thermohygrometric models. Furthermore, the control law should be designed in such a way as to address both temperature and humidity set points. We propose a model-free ILC design approach facilitated by the inherent passivity of building thermohygrometric dynamics. We first demonstrate that the building dynamics are strictly output-incremental passive. This property is then exploited to design ILC laws that guarantee convergence in the iteration domain, while being robust to model uncertainty. Since we wish to control both temperature and humidity using only one input - mass flow rate of supply air, convergence to a point is not guaranteed; instead convergence to an ellipse on the temperature-humidity plane is shown. The controller performance is demonstrated through simulation examples. © 2017

关键词： Two term control systems Architectural design Atmospheric humidity Feedforward control Humidity control Intelligent buildings Iterative methods Learning algorithms Model predictive control Structural dynamics Temperature Three term control systems Uncertainty analysis Building automation Controller performance Feed forward control scheme Human comforts Iterative learning control Iterative learning Control (ILC) Passivity based control Temperature and humidities

Correction: Gut microbiome in endometriosis: a cohort study on 1000 individuals

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BMC medicine 2024年第1期22卷 448页

作者： Inmaculada Pérez-Prieto Eva Vargas Eduardo Salas-Espejo Kreete Lüll Analuce Canha-Gouveia Laura Antequera Pérez Juan Fontes Andres Salumets Reidar Andreson Oliver Aasmets Katrine Whiteson Elin Org Signe Altmäe Department of Biochemistry and Molecular Biology I Faculty of Sciences University of Granada Granada Spain. inmapprieto@ugr.es. Instituto de Investigación Biosanitaria Ibs.GRANADA Granada Spain. inmapprieto@ugr.es. Department of Biochemistry and Molecular Biology I Faculty of Sciences University of Granada Granada Spain. Instituto de Investigación Biosanitaria Ibs.GRANADA Granada Spain. Systems Biology Unit Department of Experimental Biology Faculty of Experimental Sciences University of Jaen Jaen Spain. Institute of Genomics Estonian Genome Centre University of Tartu Tartu Estonia. Department of Physiology Faculty of Veterinary University of Murcia Campus Mare Nostrum Murcia Spain. Department of Computer Engineering Automation and Robotics University of Granada Granada Spain. U. Reproducción UGC Laboratorio Clínico y UGC Obstetricia y Ginecología. HU Virgen de Las Nieves Granada Spain. Competence Centre On Health Technologies Tartu Estonia. Division of Obstetrics and Gynaecology Department of Clinical Science Intervention and Technology Karolinska Institutet Huddinge Stockholm Sweden. Department of Gynaecology and Reproductive Medicine Karolinska University Hospital Huddinge Stockholm Sweden. Department of Obstetrics and Gynaecology Institute of Clinical Medicine University of Tartu Tartu Estonia. Institute of Molecular and Cell Biology University of Tartu Tartu Estonia. School of Biological Sciences University of California Irvine CA USA.

The learning of fuzzy cognitive maps with noisy data: A rapid and robust learning method with maximum entropy

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arXiv 2019年

作者： Feng, Guoliang Lu, Wei Pedrycz, Witold Yang, Jianhua Liu, Xiaodong School of Control Science and Engineering Dalian University of Technology Dalian116023 China School of Automation Engineering Northeast Electric Power University Jilin132012 China School of Control Science and Engineering Dalian University of Technology Dalian116023 China Department of Electrical and Computer Engineering University of Alberta EdmontonABT6R 2V4 Canada Systems Research Institute Polish Academy of Sciences Warsaw00-901 Poland Department of Electrical and Computer Engineering Faculty of Engineering King Abdulaziz University Jeddah21589 Saudi Arabia

Numerous learning methods for fuzzy cognitive maps (FCMs), such as the Hebbian-based and the population-based learning methods, have been developed for modeling and simulating dynamic systems. However, these methods are faced with several obvious limitations. Most of these models are extremely time consuming when learning the large-scale FCMs with hundreds of nodes. Furthermore, the FCMs learned by those algorithms lack robustness when the experimental data contain noise. In addition, reasonable distribution of the weights is rarely considered in these algorithms, which could result in the reduction of the performance of the resulting FCM. In this article, a straightforward, rapid, and robust learning method is proposed to learn FCMs from noisy data, especially, to learn large-scale FCMs. The crux of the proposed algorithm is to equivalently transform the learning problem of FCMs to a classic-constrained convex optimization problem in which the least-squares term ensures the robustness of the well-learned FCM and the maximum entropy term regularizes the distribution of the weights of the well-learned FCM. A series of experiments covering two frequently used activation functions (the sigmoid and hyperbolic tangent functions) are performed on both synthetic datasets with noise and real-world datasets. The experimental results show that the proposed method is rapid and robust against data containing noise and that the well-learned weights have better distribution. In addition, the FCMs learned by the proposed method also exhibit superior performance in comparison with the existing methods. Copyright © 2019, The Authors. All rights reserved.

关键词： Fuzzy rules

Heat Transfer Optimization of end Winding Rotor Generator by the Use of Modified Axial Spacer: A Case Study

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Journal of Physics: Conference Series 2021年第1期1845卷

作者： Y Baskoro I Jaya A Glowacz M Sulowicz W Caesarendra PT Indonesia Power Indonesia Department of Automatic Control and Robotics AGH University of Science and Technology 30-059 Kraków Poland Institute of Electromechanical Energy Conversion Faculty of Electrical and Computer Engineering Cracow University of Technology Warszawska 24 31-155 Cracow Poland Faculty of Integrated Technologies Universiti Brunei Darussalam Jalan Tungku Link Gadong BE1410 Brunei Darussalam

This paper presents a case study of a rotor fault occurring in the rotor generator of a steam power plant located in Lontar, Banten Province, Indonesia. The steam power plant has 3 x 315 MV capacity and uses hydrogen in the cooling system. The issue arising during the operation was caused by heat transfer exceeding the normal level. The grounding of the rotor occurred through rising vibration and temperature, tripping the generator unit. During repairs, the axial spacer was replaced with a new model, where it is longer than the original, adding a bar spacer at a previously unavailable position and creating a cooling track profile. This is a case study on the replacement of the axial spacer model aimed at preventing the expansion of the winding, so as not to touch with each other, through the installation of a longer axial spacer and the addition of the spacer bar, and lastly adding the track profile to the cooling hydrogen gas flow thereby maximizing the cooling process from the end winding rotor generator. Heat transfer is an analysed parameter related to the model change of the axial spacer. The addition of a track cooling gas flow has been able to maximize the heat transfer process, which is simulated in this paper. The performance of the rotor generator increased through several parameters after the replacement of the axial spacer model.

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