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Constructing public health evidence knowledge graph for decision-making support from COVID-19 literature of modelling study

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Journal of Safety Science and Resilience 2021年第3期2卷 146-156页

作者： Yunrong Yang Zhidong Cao Pengfei Zhao Dajun Daniel Zeng Qingpeng Zhang Yin Luo The State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of SciencesBeijing 100190China School of Artificial Intelligence University of Chinese Academy of SciencesBeijing 100190China Shenzhen Artificial Intelligence and Data Science Institute(Longhua) ShenzhenChina School of Data Science City University of Hong Kong-Hong Kong SARChina

The needs of mitigating COVID-19 epidemic prompt policymakers to make public health-related decision under the guidelines of science.Tremendous unstructured COVID-19 publications make it challenging for policymakers to obtain relevant evidence.Knowledge graphs(KGs)can formalize unstructured knowledge into structured form and have been used in supporting decision-making recently.Here,we introduce a novel framework that can ex-tract the COVID-19 public health evidence knowledge graph(CPHE-KG)from papers relating to a modelling study.We screen out a corpus of 3096 COVID-19 modelling study papers by performing a literature assessment process.We define a novel annotation schema to construct the COVID-19 modelling study-related IE dataset(CPHIE).We also propose a novel multi-tasks document-level information extraction model SS-DYGIE++based on the dataset.Leveraging the model on the new corpus,we construct CPHE-KG containing 60,967 entities and 51,140 rela-tions.Finally,we seek to apply our KG to support evidence querying and evidence mapping visualization.Our SS-DYGIE++(SpanBERT)model has achieved a F1 score of 0.77 and 0.55 respectively in document-level entity recognition and coreference resolution tasks.It has also shown high performance in the relation identification task.With evidence querying,our KG can present the dynamic transmissions of COVID-19 pandemic in different countries and regions.The evidence mapping of our KG can show the impacts of variable non-pharmacological interventions to COVID-19 pandemic.Analysis demonstrates the quality of our KG and shows that it has the potential to support COVID-19 policy making in public health.

关键词： Evidence-based public health Knowledge graph COVID-19 Decision-making support

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Adaptively temporal graph convolution model for epidemic prediction of multiple age groups

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Fundamental Research 2022年第2期2卷 311-320页

作者： Yuejiao Wang Dajun Daniel Zeng Qingpeng Zhang Pengfei Zhao Xiaoli Wang Quanyi Wang Yin Luo Zhidong Cao The State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of SciencesBeijingChina School of Artificial Intelligence University of Chinese Academy of SciencesBeijingChina Shenzhen Artificial Intelligence and Data Science Institute(Longhua) ShenzhenChina School of Data Science City University of Hong Kong-Hong Kong SARChina Institute for Infectious Disease and Endemic Disease Control Beijing Center for Disease Prevention and Control BeijingChina

Introduction:Multivariate time series prediction of infectious diseases is significant to public health,and the deep learning method has attracted increasing attention in this research field.Material and methods:An adaptively temporal graph convolution(ATGCN)model,which leams the contact patterns of multiple age groups in a graph-based approach,was proposed for COVID-19 and influenza prediction.We compared ATGCN with autoregressive models,deep sequence learning models,and experience-based ATGCN models in short-term and long-term prediction tasks.Results:Results showed that the ATGCN model performed better than the autoregressive models and the deep sequence learning models on two datasets in both short-term(12.5%and 10%improvements on RMSE)and longterm(12.4%and 5%improvements on RMSE)prediction tasks.And the RMSE of ATGCN predictions fluctuated least in different age groups of COVID-19(0.029±0.003)and influenza(0.059±0.008).Compared with the Ones-ATGCN model or the Pre-ATGCN model,the ATGCN model was more robust in performance,with RMSE of 0.0293 and 0.06 on two datasets when horizon is one.Discussion:Our research indicates a broad application prospect of deep learning in the field of infectious disease prediction.Transmission characteristics and domain knowledge of infectious diseases should be further applied to the design of deep learning models and feature selection.Conclusion:The ATGCN model addressed the multivariate time series forecasting in a graph-based deep learning approach and achieved robust prediction on the confirmed cases of multiple age groups,indicating its great potentials for exploring the implicit interactions of multivariate variables.

关键词： Graph convolution model Infectious disease prediction Multiple age group Multivariate time series Public health

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A social computing method for energy safety

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Journal of Safety Science and Resilience 2024年第1期5卷 64-82页

作者： Pengfei Zhao Shuangqi Li Zhidong Cao Paul Jen-Hwa Hu Daniel Dajun Zeng Da Xie Yichen Shen Jiangfeng Li Tianyi Luo Institute of Automation Chinese Academy of SciencesUniversity of Chinese Academy of SciencesBeijing 100090China Systems Engineering Cornell UniversityIthacaNY 14853USA David Eccles School of Business the University of UtahUtahUSA Department of Electrical Engineering Shanghai Jiao Tong UniversityShanghai 200240China China University of Mining and Technology China

Information and communication technologies enable the transformation of traditional energy systems into cyber-physical energy systems(CPESs),but such systems have also become popular targets of cyberattacks.Currently,available methods for evaluating the impacts of cyberattacks suffer from limited resilience,efficacy,and practical value.To mitigate their potentially disastrous consequences,this study suggests a two-stage,discrepancy-based optimization approach that considers both preparatory actions and response measures,integrating concepts from social computing.The proposed Kullback-Leibler divergence-based,distributionally robust optimization(KDR)method has a hierarchical,two-stage objective function that incorporates the operating costs of both system infrastructures(e.g.,energy resources,reserve capacity)and real-time response measures(e.g.,load shedding,demand-side management,electric vehicle charging station management).By incorporating social computing principles,the optimization framework can also capture the social behavior and interactions of energy consumers in response to cyberattacks.The preparatory stage entails day-ahead operational decisions,leveraging insights from social computing to model and predict the behaviors of individuals and communities affected by potential cyberattacks.The mitigation stage generates responses designed to contain the consequences of the attack by directing and optimizing energy use from the demand side,taking into account the social context and preferences of energy consumers,to ensure resilient,economically efficient CPES operations.Our method can determine optimal schemes in both stages,accounting for the social dimensions of the problem.An original disaster mitigation model uses an abstract formulation to develop a risk-neutral model that characterizes cyberattacks through KDR,incorporating social computing techniques to enhance the understanding and response to cyber threats.This approach can mitigate the impacts more effectively than several existing methods,even

关键词： Cyberattacks Cyber-physical energy systems Distributionally robust optimization False data injections Two-stage modeling Social computing

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Facile Synthesis of Carbon-Supported Ultrasmall Ag@Pd Core-Shell Nanocrystals with Superior Electrocatalytic Activity for Direct Formic Acid Fuel Cell Application

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Chinese Journal of Chemistry 2016年第11期34卷 1086-1092页

作者： Jianyu cao Zhentao Zhu Weifang Zhao Juan Xu zhidong Chen Jiangsu Key Laboratory of Advanced Catalytic Materials and Technology School of Petrochemical Engineering Changzhou University Changzhou Jiangsu 213164 China Jiangsu Collaborative Innovation Center of Photovoltaic Science and Engineering School of Materials Science and Engineering Changzhou University Changzhou Jiangsu 213164 China

To design electrocatalysts with excellent performance, morphology, composition and structure is a decisive influential factor. In this work, ultrasmall Ag@Pd core-shell nanocrystals supported on Vulcan XC72R carbon with different Ag/Pd atomic ratios are synthesized via a facile successive reduction approach with formaldehyde and eth- ylene glycol as reducing agents, respectively. The Ag-core/Pd-shell nanostructures are revealed by high-resolution transmission electron microscopy （HRTEM）. Ag@Pd core-shell nanocrystals possess a narrow size distribution with an average size of ca. 4.3 nm. In comparison to monometallic Pd/C and commercial Pd black catalysts, such Ag@Pd core-shell nanocrystals display excellent electrocatalytic activities for formic acid oxidation, which may be due to high Pd utilization derived from the formation of Ag@Pd core-shell nanostructure and the strong interaction between Ag and Pd.

关键词： core-shell nanoparticles electrocatalyst formic acid electrooxidation direct formic acid fuel cell

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Control strategies and their effects on the COVID-19 pandemic in 2020 in representative countries

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Journal of Biosafety and Biosecurity 2021年第2期3卷 76-81页

作者： Rongzhang Hao Yewu Zhang Zhidong Cao Jing Li Qing Xu Lingling Ye Xudong Guo Tao Zheng Hongbin Song Chinese PLA Center for Disease Control and Prevention BeijingChina Department of Toxicology and Sanitary Chemistry School of Public HealthCapital Medical UniversityBeijingChina National Center for Public Health Surveillance and Information Service Chinese Center for Disease Control and PreventionBeijingChina State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of SciencesBeijingChina State Key Laboratory of Pathogen and Biosecurity Beijing Institute of Microbiology and EpidemiologyBeijingChina Division of Biological Safety Service Academy of Military Medical SciencesAcademy of Military Science of Chinese PLABeijingChina

COVID-19 is the most severe pandemic globally since the 1918 influenza pandemic.Effectively responding to this once-in-a-century global pandemic is a worldwide challenge that the international community needs to jointly face and solve.This study reviews and discusses the key measures taken by major countries in 2020 to fight against COVID-19,such as lockdowns,social distancing,wearing masks,hand hygiene,using Fangcang shelter hospitals,large-scale nucleic acid testing,close-contacts tracking,and pandemic information monitoring,as well as their prevention and control effects.We hope it can help improve the efficiency and effectiveness of pandemic prevention and control in future.

关键词： COVID-19 Control strategies Effect comparison

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Low temperature hydrothermal synthesis of SrTiO3 nanoparticles without alkali and their effective photocatalytic activity

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Journal of Advanced Ceramics 2016年第4期5卷 298-307页

作者： Hongfang SHEN Youjun LU Yanmin WANG zhidong PAN Guozhong cao Xianghui YAN Guoli FANG School of Materials Science and Engineering South China University of Technology Guangzhou 510640 ChinaSchool of Materials Science and Engineering Beifang University of Nationalities Yinchuan 750021 China School of Materials Science and Engineering Beifang University of Nationalities Yinchuan 750021 China School of Materials Science and Engineering South China University of Technology Guangzhou 510640 China Department of Materials Science and Engineering University of Washington Seattle WA 98195 USA

SrTiO3 nanoparticle (NP) photocatalyst was synthesized with a facile and environmentalfriendly hydrothermal method using tetrabutyltitanate, strontium oxide, and ethanolamine as precursors at low temperature without alkali as mineralizer for the first time. The SrTiO3 nanoparticles were characterized by X-ray diffraction (XRD), transmission electron microscopy (TEM), field emission scanning electron microscopy (FESEM), and N2 Brunauer–Emmett–Teller (BET) method. The SrTiO3 catalyst synthesized at 120 °C (STO-120) exhibited the highest photocurrent intensity among the samples synthesized at different hydrothermal temperatures. The high photocatalytic performance of STO-120 was mainly attributed to the more homogeneous and minimum nanoparticle size, the highest surface area, and the maximum light absorption property among the four different samples. This work presented an applicable and facile method to fabricate a highly active and stable SrTiO3 photocatalyst for organic pollutant degradation.

关键词： polyhedral SrTiO 3 hydrothermal synthesis low temperature photocurrent property photocatalytic activity stability

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纤支镜在AECOPD合并严重呼吸衰竭治疗中的价值探讨

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数理医药学杂志 2018年第11期31卷 1595-1597页

作者：曹志东茂名市中医院茂名525000

目的:探析临床治疗慢性阻塞性肺疾病急性加重(AECOPD)合并严重呼吸衰竭应用纤维支气管镜的疗效。方法:选择93例AECOPD合并严重呼吸衰竭患者,患者为某院2015年6月～2017年1月收治,根据不同治疗方案分组,即治疗组(n=53)和对照组(n=40),两... 详细信息

目的:探析临床治疗慢性阻塞性肺疾病急性加重(AECOPD)合并严重呼吸衰竭应用纤维支气管镜的疗效。方法:选择93例AECOPD合并严重呼吸衰竭患者,患者为某院2015年6月～2017年1月收治,根据不同治疗方案分组,即治疗组(n=53)和对照组(n=40),两组患者均首先接受抗感染治疗,治疗组在纤支镜的引导下进行气管插管、吸痰、支气管肺泡关系后进行有创和无创的序贯机械通气治疗,对照组在喉镜引导下进行气管插管、吸痰、有创无创序贯机械通气治疗,比较两组患者治疗效果。结果:治疗组一次插管成功率和痰培养阳性率明显高于对照组,而病死率和并发症发生率明显低于对照组,两组比较差异有统计学意义(P<0.05);治疗组平均插管时间、呼吸衰竭纠正时间、有创通气时间、PIC出现时间均显著短于对照组,两组比较,差异有统计学意义(P<0.05)。结论:临床治疗AECOPD合并严重呼吸衰竭可在纤支镜的引导下接受气管插管、吸痰、支气管肺泡灌洗、有创/无创及系统器治疗,能够迅速改善患者呼吸功能,提高抢救成功率。

关键词：纤支镜 AECOPD 呼吸衰竭疗效

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Advances in Tongue Diagnosis Objectification of Traditional Chinese Medicine

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Instrumentation 2023年第1期10卷 1-16页

作者： ZHANG zhidong ZHU Xiaolong cao Xiyuan LI Bo ZANG Junbin GUO Dong MEN Jiuzhang XUE Chenyang Key Laboratory of Instrumentation Science and Dynamic Measurement of Ministry of Education North University of ChinaTaiyuan 030051 School of Software North University of ChinaTaiyuan 030051 College of Basic Medical Science Shanxi University of Chinese MedicineTaiyuan 030051

Tongue diagnosis is a non-invasive,efficient,and accurate method for determining a person’s physical condition,and plays an essential role in disease diagnosis and health management.However,tongue diagnosis is easily influenced by the subjective experience of the practitioner and the light environment.In addition,tongue diagnosis lacks clear quantitative indicators and objective records.This all limits the transmission and development of tongue diagnosis.Therefore,the acquisition and analysis of tongue information using image equipment,image processing and computer vision have become a hot research topic for the objectification of tongue diagnosis.This paper reviews the research progress of tongue diagnosis objectification in Traditional Chinese medicine.The tongue image acquisition,color correction,segmentation,feature extraction and analysis,and disease prediction included in the study of tongue diagnosis objectification are reviewed.The shortcomings of current automated tongue diagnosis systems and future research ideas are also summarized to provide a reference for further development of tongue diagnosis objectification.

关键词： Tongue Diagnosis Tongue Diagnosis Objectification Image Processing Automated Tongue Diagnosis Systems

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医科院校优化试剂耗材管理模式探讨

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实验技术与管理 2019年第5期36卷 287-289页

作者：曹蓓禤智东南方医科大学设备与实验室管理处

试剂耗材管理是实验室管理工作的重要组成部分,该文对南方医科大学试剂耗材管理模式进行了分析,并进行了管理模式的探索,组建了管理机构,建立和完善了各项规章制度,建立了试剂耗材管理信息系统,完善了管理流程,为进一步优化试剂耗材管... 详细信息

试剂耗材管理是实验室管理工作的重要组成部分,该文对南方医科大学试剂耗材管理模式进行了分析,并进行了管理模式的探索,组建了管理机构,建立和完善了各项规章制度,建立了试剂耗材管理信息系统,完善了管理流程,为进一步优化试剂耗材管理模式提出了借鉴方案。

关键词：医科院校试剂耗材管理创新管理模式

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Mobility in China, 2020: a tale of four phases

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National Science Review 2021年第11期8卷 110-120页

作者： Suoyi Tan Shengjie Lai Fan Fang Ziqiang cao Bin Sai Bing Song Bitao Dai Shuhui Guo Chuchu Liu Mengsi Cai Tong Wang Mengning Wang Jiaxu Li Saran Chen Shuo Qin Jessica R.Floyd Zhidong Cao Jing Tan Xin Sun Tao Zhou Wei Zhang Andrew J.Tatem Petter Holme Xiaohong Chen Xin Lu School of Business Central South University Institute of Big Data and Internet Innovations Hunan University of Technology and Business College of Systems Engineering National University of Defense Technology WorldPop School of Geography and Environmental ScienceUniversity of Southampton School of Mathematics and Big Data Foshan University State Key Laboratory on Blind Signal Processing State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of Sciences Chinese Evidence-Based Medicine Center National Clinical Research Center for Geriatrics West China Hospital Sichuan University Big Data Research Center University of Electronic Science and Technology of China West China Biomedical Big Data Center West China Hospital Sichuan University Tokyo Tech World Hub Research Initiative Institute of Innovative Research Tokyo Institute of Technology

2020 was an unprecedented year,with rapid and drastic changes in human mobility due to the COVID-19 pandemic.To understand the variation in commuting patterns among the Chinese population across stable and unstable periods,we used nationwide mobility data from 318 million mobile phone users in China to examine the extreme fluctuations of population movements in 2020,ranging from the Lunar New Year travel season(chunyun), to the exceptional calm of COVID-19 lockdown,and then to the recovery period.We observed that cross-city movements,which increased substantially in chunyun and then dropped sharply during the lockdown,are primarily dependent on travel distance and the socio-economic development of cities.Following the Lunar New Year holiday,national mobility remained low until mid-February,and COVID-19 interventions delayed more than 72.89 million people returning to large cities.Mobility network analysis revealed clusters of highly connected cities,conforming to the social-economic division of urban agglomerations in China.While the mass migration back to large cities was delayed,smaller cities connected more densely to form new clusters.During the recovery period after travel restrictions were lifted,the netflows of over 55% city pairs reversed in direction compared to before the lockdown.These findings offer the most comprehensive picture of Chinese mobility at fine resolution across various scenarios in China and are of critical importance for decision making regarding future public-health-emergency response,transportation planning and regional economic development,among others.

关键词： human mobility travel restrictions COVID-19 mobile phone data behavioral response

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