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Structured Information Extraction of Pathology Reports with Attention-based Graph Convolutional Network

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Structured Information Extraction of Pathology Reports with ...

IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Jialun Wu Kaiwen Tang Haichuan Zhang Chunbao Wang Chen Li School of Computer Science and Technology Xi’an Jiaotong University Xi’an Shaanxi China National Engineering Lab for Big Data Analytics Xi’an Jiaotong University Xi’an Shaanxi China Shaanxi Province Key Laboratory of Satellite and Terrestrial Network Tech.R&D Xi’an Jiaotong University Xi’an Shaanxi China School of Computer Science and Technology Qian Xuesen College Xi’an Jiaotong University Xi’an Shaanxi China the First Affiliated Hospital of Xi’an Jiaotong University Xi’an Shaanxi China

ISBN: (数字)9781728162157

ISBN: (纸本)9781728162164

Electronic medical data contains biochemical, imaging, pathological information during diagnosis and treatment. The pathology report is a kind of highly liberalized unstructured textual data, which is the basis and gold standard of cancer diagnosis and is very important for the prognosis and treatment of patients. The application of information extraction technology to pathological reports can obtain structured data that can be understood and analyzed by computers, helping pathologists make appropriate decisions. In this work, we proposed an attention-based graph convolutional network (GCN) for converting unstructured pathological reports into a structured form suitable for computer analysis to improve the current pathologist's workflow, collected medical data from different platforms, and provided more accurate assistance for diagnosis and treatment. We used pathology reports data from TCGA (The Cancer Genome Atlas) database with fine-grained annotations on 3632 pathology reports including four types of cancers. Our method performs better in our pathology report dataset with higher F1 score than traditional methods and deep learning methods. The results indicate that our method is robust, thus may work with other types of cancer pathology report.

关键词： Pathology Cancer Task analysis Annotations Information retrieval Medical diagnostic imaging data mining

Graph based Tensor Recovery for Accurate Internet Anomaly Detection

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Graph based Tensor Recovery for Accurate Internet Anomaly De...

2018 IEEE Conference on Computer Communications, INFOCOM 2018

作者： Xie, Kun Li, Xiaocan Wang, Xin Xie, Gaogang Wen, Jigang Zhang, Dafang College of Computer Science and Electronics Engineering Hunan University China CAS Key Lab of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Sciences China Department of Electrical and Computer Engineering State University of New York at Stony Brook United States Institute of Computing Technology Chinese Academy of Sciences China

ISBN: (纸本)9781538641286

Detecting anomalous traffic is a crucial task of managing networks. Many anomaly detection algorithms have been proposed recently. However, constrained by their matrix-based traffic data model, existing algorithms often suffer from low detection accuracy. To fully utilize the multi-dimensional information hidden in the traffic data, this paper takes an initiative to investigate the potential and methodologies of performing tensor factorization for more accurate Internet anomaly detection. Only considering the low-rank linearity features hidden in the data, current tensor factorization techniques would result in low anomaly detection accuracy. We propose a novel Graph-based Tensor Recovery model (Graph-TR) to well explore both low rank linearity features as well as the non-linear proximity information hidden in the traffic data for better anomaly detection. We encode the non-linear proximity information of the traffic data by constructing nearest neighbor graphs and incorporate this information into the tensor factorization using the graph Laplacian. Moreover, to facilitate the quick building of neighbor graph, we propose a nearest neighbor searching algorithm with the simple locality-sensitive hashing (LSH). We have conducted extensive experiments using Internet traffic trace data Abilene and GEANT. Compared with the state of art algorithms on matrix-based anomaly detection and tensor recovery approach, our Graph-Trcan achieve significantly lower False Positive Rate and higher True Positive Rate. © 2018 IEEE.

关键词： Anomaly detection

An overview on visualization of ontology alignment and ontology entity 5th

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An overview on visualization of ontology alignment and ontol...

5th Euro-China Conference on Intelligent data Analysis and Applications, ECC 2018

作者： Chen, Jie Xue, Xingsi Huang, Lili Ren, Aihong College of Information Science and Engineering Fujian University of Technology FuzhouFujian350118 China Intelligent Information Processing Research Center Fujian University of Technology FuzhouFujian350118 China Fujian Provincial Key Laboratory of Big Data Mining and Applications FuzhouFujian350118 China Fujian Key Lab for Automotive Electronics and Electric Drive Fujian University of Technology FuzhouFujian350118 China College of Humanities and Law Fujian Agriculture and Forestry University FuzhouFujian350002 China School of Mathematics and Information Science Baoji University of Arts and Sciences BaojiShaanxi721013 China

ISBN: (纸本)9783030037659

Visualization of ontology alignment and ontology entity is becoming a method to help Solve the problem of semantic heterogeneity. This paper first introduces the basic concepts of ontology and ontology matching, and then illustrates the importance of ontology visualization technique. The existing partial visualization tools are discussed respectively in two aspects of visualization of ontology alignment and visualization entity on ontology. Finally, four researching directions on ontology visualization technique are pointed out for the future work. © Springer Nature Switzerland AG 2019.

关键词： Ontology

Bicycle flow dynamics on wide roads: Experiment and modeling

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arXiv 2019年

作者： Guo, Ning Jiang, Rui Wong, S.C. Hao, Qing-Yi Xue, Shu-Qi Hu, Mao-Bin School of Automotive and Transportation Engineering Hefei University of Technology Hefei230009 China Key Lab. of Transport Industry of Big Data Application Technologies for Comprehensive Transport Ministry of Transport Beijing Jiaotong University Beijing100044 China Department of Civil Engineering University of Hong Kong Pokfulam Road Hong Kong School of Mathematics and Computational Science Anqing Normal University Anqing246133 China School of Engineering Science University of Science and Technology of China Hefei230026 China

Cycling is a green transportation mode, and is promoted by many governments to mitigate traffic congestion. However, studies concerning the traffic dynamics of bicycle flow are very limited. This study experimentally investigated bicycle flow dynamics on a wide road, modeled using a 3-m-wide track. The results showed that the bicycle flow rate remained nearly constant across a wide range of densities, in marked contrast to single-file bicycle flow, which exhibits a unimodal fundamental diagram. By studying the weight density of the radial locations of cyclists, we argue that this behavior arises from the formation of more lanes with the increase of global density. The extra lanes prevent the longitudinal density from increasing as quickly as in single-file bicycle flow. When the density is larger than 0.5 bicycles/m2, the flow rate begins to decrease, and stop-and-go traffic emerges. A cognitive-science-based model to reproduce bicycle dynamics is proposed, in which cyclists apply simple cognitive procedures to adapt their target directions and desired riding speeds. To incorporate differences in acceleration, deceleration, and turning, different relaxation times are used. The model can reproduce the experimental results acceptably well and may also provide guidance on infrastructure design. Copyright © 2019, The Authors. All rights reserved.

关键词： Traffic congestion

An Improved multi-objective genetic algorithm based on orthogonal design and adaptive clustering pruning strategy

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arXiv 2019年

作者： Yang, Xinwu You, Guizeng Zhao, Chong Dou, Mengfei Guo, Xinian Department of Faculty of Information Technology Beijing Municipal Key Lab of Multimedia and Intelligent Software Technology Beijing Key Laboratory on Integration and Analysis of Large Scale Stream Data Beijing University of Technology No.100 Pingleyuan Chaoyang District Beijing100124 China Beijing University of Technology College of Computer and Information Engineering Henan Normal University.

Two important characteristics of multi-objective evolutionary algorithms are distribution and convergency. As a classic multiobjective genetic algorithm, NSGA-II is widely used in multi-objective optimization fields. However, in NSGA-II, the random population initialization and the strategy of population maintenance based on distance cannot maintain the distribution or convergency of the population well. To dispose these two deficiencies, this paper proposes an improved algorithm, OTNSGA-II II, which has a better performance on distribution and convergency. The new algorithm adopts orthogonal experiment, which selects individuals in manner of a new discontinuing non-dominated sorting and crowding distance, to produce the initial population. And a new pruning strategy based on clustering is proposed to self-adaptively prunes individuals with similar features and poor performance in non-dominated sorting and crowding distance, or to individuals are far away from the Pareto Front according to the degree of intraclass aggregation of clustering results. The new pruning strategy makes population to converge to the Pareto Front more easily and maintain the distribution of population. OTNSGA-II and NSGA-II are compared on various types of test functions to verify the improvement of OTNSGA-II in terms of distribution and convergency. Copyright © 2019, The Authors. All rights reserved.

关键词： Clustering algorithms

Fog-assisted multi-user swipt networks: Local computing or offloading

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arXiv 2019年

作者： Zheng, Haina Xiong, Ke Fan, Pingyi Zhong, Zhangdui Letaief, Khaled Ben School of Computer and Information Technology Beijing Key Lab of Traffic Data Analysis and Mining Beijing Jiaotong University Beijing China Beijing Key Laboratory of Security and Privacy in Intelligent Transportation Beijing Jiaotong University Beijing100044 China National Laboratory for Information Science and Technology Department of Electronic Engineering Tsinghua University Beijing100084 China State Key Lab of Rail Traffic Control and Safety Beijing Engineering Research Center of High-speed Railway Broadband Mobile Communications Beijing Jiaotong University Beijing100044 China Clear Water Bay Hong Kong

This paper investigates a fog computing-assisted multi-user simultaneous wireless information and power transfer (SWIPT) network, where multiple sensors with power splitting (PS) receiver architectures receive information and harvest energy from a hybrid access point (HAP), and then process the received data by using local computing mode or fog offloading mode. For such a system, an optimization problem is formulated to minimize the sensors' required energy while guaranteeing their required information transmissions and processing rates by jointly optimizing the multi-user scheduling, the time assignment, the sensors' transmit powers and the PS ratios. Since the problem is a mixed integer programming (MIP) problem and cannot be solved with existing solution methods, we solve it by applying problem decomposition, variable substitutions and theoretical analysis. For a scheduled sensor, the closed-form and semi-closedform solutions to achieve its minimal required energy are derived, and then an efficient multi-user scheduling scheme is presented, which can achieve the suboptimal user scheduling with low computational complexity. Numerical results demonstrate our obtained theoretical results, which show that for each sensor, when it is located close to the HAP or the fog server (FS), the fog offloading mode is the better choice;otherwise, the local computing mode should be selected. The system performances in a frame-by-frame manner are also simulated, which show that using the energy stored in the batteries and that harvested from the signals transmitted by previous scheduled sensors can further decrease the total required energy of the sensors. Copyright © 2019, The Authors. All rights reserved.

关键词： Energy harvesting

Precise Epitope Organization with Self-adjuvant Framework Nucleic Acid for Efficient COVID-19 Peptide Vaccine Construction

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Angewandte Chemie 2023年第21期135卷

作者： Dr. Fengyun Shen Zijian Xiong Dr. Yumin Wu Prof. Dr. Rui Peng Yue Wang Dr. Lele Sun Prof. Dr. Chunhai Fan Prof. Dr. Zhuang Liu Institute of Functional Nano & Soft Materials (FUNSOM) Jiangsu Key Lab Carbon Based Functional Materials and Devices Soochow University 215123 Suzhou Jiangsu China School of Chemistry and Chemical Engineering Shanghai Jiao Tong University 201240 Shanghai China Contribution: Conceptualization (lead) Data curation (lead) Formal analysis (lead) Investigation (lead) Methodology (lead) Writing - original draft (lead) Contribution: Data curation (supporting) Investigation (equal) Contribution: Investigation (supporting) Methodology (supporting) Contribution: Supervision (supporting) Contribution: Methodology (supporting) School of Life Sciences Shanghai University 200444 Shanghai China Contribution: Conceptualization (lead) Funding acquisition (lead) Investigation (lead) Methodology (lead) Supervision (equal) Writing - review & editing (equal) Contribution: Supervision (equal) Writing - review & editing (equal) Contribution: Conceptualization (equal) Funding acquisition (lead) Supervision (equal) Writing - review & editing (lead)

Peptide vaccines have advantages in easy fabrication and high safety, but their effectiveness is hampered by the poor immunogenicity of the epitopes themselves. Herein, we constructed a series of framework nucleic acids (FNAs) with regulated rigidity and size to precisely organize epitopes in order to reveal the influence of epitope spacing and carrier rigidity on the efficiency of peptide vaccines. We found that assembling epitopes on rigid tetrahedral FNAs (tFNAs) with the appropriate size could efficiently enhance their immunogenicity. Further, by integrating epitopes from SARS-CoV-2 on preferred tFNAs, we constructed a COVID-19 peptide vaccine which could induce high titers of IgG against the receptor binding domain (RBD) of SARS-CoV-2 spike protein and increase the ratio of memory B and T cells in mice. Considering the good biocompatibility of tFNAs, our research provides a new idea for developing efficient peptide vaccines against viruses and possibly other diseases.

关键词： COVID-19 Epitope Organization Framework Nucleic Acids Peptide Vaccine

Design for intelligent control system of curtain based on arduino 1st

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Design for intelligent control system of curtain based on ar...

1st International Conference on Smart Vehicular Technology, Transportation, Communication and Applications, VTCA 2017

作者： Meng, Sheng-Hui Huang, An-Chi Lee, Chia-Jung Huang, Tian-Jiun Dal, Jian-Nan Fujian Provincial Key Lab of Big Data Mining and Applications Fujian University of Technology Fuzhou350118 China School of Information Science and Engineering Fujian University of Technology FuzhouFujian350118 China Department of Electrical Engineering National Sun Yat-Sen University Kaohsiung Taiwan Kaohsiung Municipal ChungShan Senior High School Kaohsiung Taiwan

In this research, a design for smart home curtains was proposed. The design is based on an Arduino UNO R3 development board equipped with an ESP8266-01 Wi-Fi module that communicates with an Android client application... 详细信息

ISBN: (纸本)9783319707297

关键词： HTTP

Remote health monitoring in the time of COVID-19

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arXiv 2020年

作者： Behar, Joachim A. Liu, Chengyu Tsutsui, Kenta Corino, Valentina D.A. Singh, Janmajay Pimentel, Marco A.F. Karlen, Walter Warrick, Philip Zaunseder, Sebastian Andreotti, Fernando Osipov1, Maxim McSharry, Patrick E. Kotzen, Kevin Karmakar, Chandan Clifford, Gari D. Faculty of Biomedical Engineering Technion-IIT Haifa Israel State Key Laboratory of Bioelectronics School of Instrument Science and Engineering Southeast University Nanjing Department of Cardiovascular Medicine Saitama International Medical Center Saitama Department of Electronics Information and Bioengineering Milano Italy Independent Researcher Department of Engineering Science University of Oxford Oxford United Kingdom Mobile Health Systems Lab Department of Health Sciences and Technology ETH Zurich Zurich Switzerland PeriGen CaryNC United States Faculty of Information Technology FH Dortmund Dortmund Germany Sensyne Health Oxford United Kingdom Babylon Health London United Kingdom Carnegie Mellon University Africa Kigali Rwanda African Centre of Excellence in Data Science University of Rwanda Kigali Rwanda Oxford Man Institute of Quantitative Finance Oxford University Oxford United Kingdom School of Information Technology Deakin University Geelong Australia Department of Electrical and Electronic Engineering University of Melbourne Melbourne Australia Department of Biomedical Informatics Emory University AtlantaGA United States Department of Biomedical Engineering Georgia Institute of Technology Emory University AtlantaGA United States

Coronavirus disease (COVID-19) is caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) that is rapidly spreading across the globe. The clinical spectrum of SARS-CoV-2 pneumonia ranges from mild to critically ill cases and requires early detection and monitoring, within a clinical environment for critical cases and remotely for mild cases. The fear of contamination in clinical environments has led to a dramatic reduction in on-site referrals for routine care. There has also been a perceived need to continuously monitor non-severe COVID-19 patients, either from their quarantine site at home, or dedicated quarantine locations (e.g., hotels). Thus, the pandemic has driven incentives to innovate and enhance or create new routes for providing healthcare services at distance. In particular, this has created a dramatic impetus to find innovative ways to remotely and effectively monitor patient health status. In this paper we present a short review of remote health monitoring initiatives taken in 19 states during the time of the pandemic. We emphasize in the discussion particular aspects that are common ground for the reviewed states, in particular the future impact of the pandemic on remote health monitoring and consideration on data privacy. Copyright © 2020, The Authors. All rights reserved.

关键词： Coronavirus