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| 1 | An epidemiological analysis of the Beijing 2008 Hand-Foot-Mouth epidemic显示文摘This paper presents an empirical analysis of the epidemiological data concerning the 18445 HFMD-infected cases in Beijing in 2008.The main findings are as follows.(i) Seasonal variations in incidence were observed,with a peak observed during the summer season,especially in May.Male patients outnumber female patients by 1.57:1.(ii) Most cases occurred in children 4 years old or younger.Outperforming Weibull distribution and Gamma distribution as to model fitness when analyzing patient ages,log-normal distribution indicates that the estimated mean age is 3.4 years.(iii) The age distribution seems to indicate cyclic peaks with roughly one-year intervals.(iv) Correlation analyses (ρ=0.9864) show that time of birth in different months has an impact on the chance of being infected by HFMD.Birth month seems to present a high risk factor on infants and young children.(v) The morbidity rate is 132.7/100000 during the HFMD epidemic in Beijing in 2008.The morbidity map shows that the risks of HFMD infection in areas close to the city center and suburbans are much lower than those in the urban-rural transition zones.Spatial risks inferred from the morbidity map demonstrate a clear circular pattern.(vi) The prevention and control measures taken by the public health departments seem to be effective during the summer season,resulting in the early ending of the epidemic (one month earlier than the natural season) and reduced outbreak size. | CAO ZhiDong ZENG DaJun WANG QuanYi ZHENG XiaoLong WANG FeiYue | 2010 | Chinese Science Bulletin2010，55，12： | 12 |
| 2 | Seafloor geodetic network establishment and key technologies显示文摘Seafloor geodetic network construction involves the development of geodetic station shelter, network configuration design, location selection and layout, surveying strategy, observation model establishment and optimization, data processing strategy and so on. This paper tries to present main technological problems involved in the seafloor geodetic network construction, and seek the technically feasible solutions. Basic conceptions of developing seafloor geodetic station shelters for shallow sea and deep-sea are described respectively. The overall criteria of seafloor geodetic network construction for submarine navigation and those of network design for crustal motion monitoring are both proposed. In order to enhance application performances of the seafloor geodetic network, the seafloor network configuration should prefer a symmetrical network structure. The sea surface tracking line measurements for determining the seafloor geodetic station position should also adopt an approximately symmetrical configuration, and we recommend circle tracking line observations combined with cross-shaped line(or double cross-shape line) observations for the seafloor positioning mode. As to the offset correction between the Global Navigation Satellite System antenna phase center and the acoustic transducer, it is recommended to combine the calibration through external measurements and model parameter estimation. Besides, it is suggested to correct the sound speed error with a combination of observation value correction and parameterized model correction, and to mainly use the model correction to reduce the influence of acoustic ray error on the seafloor positioning. Following the proposed basic designs, experiments are performed in shallow sea area and deep-sea area respectively. Based on the developed seafloor geodetic shelter and sufficient verification in the shallow sea experiment, a long-term seafloor geodetic station in the deep-sea area of 3000 m depth was established for the first time, and the preliminary positioning result shows that the internal precision of this station is better than 5 cm. | Yuanxi YANG Yanxiong LIU Dajun SUN Tianhe XU Shuqiang XUE Yunfeng HAN Anmin ZENG | 2020 | Science China Earth Sciences2020，63，8： | 11 |
| 3 | Spatio-temporal evolution of Beijing 2003 SARS epidemic显示文摘Studying spatio-temporal evolution of epidemics can uncover important aspects of interaction among people, infectious diseases, and the environment, providing useful insights and modeling support to facilitate public health response and possibly prevention measures. This paper presents an empirical spatio-temporal analysis of epidemiological data concerning 2321 SARS-infected patients in Beijing in 2003. We mapped the SARS morbidity data with the spatial data resolution at the level of street and township. Two smoothing methods, Bayesian adjustment and spatial smoothing, were applied to identify the spatial risks and spatial transmission trends. Furthermore, we explored various spatial patterns and spatio-temporal evolution of Beijing 2003 SARS epidemic using spatial statistics such as Moran’s I and LISA. Part of this study is targeted at evaluating the effectiveness of public health control measures implemented during the SARS epidemic. The main findings are as follows. (1) The diffusion speed of SARS in the northwest-southeast direction is weaker than that in northeast-southwest direction. (2) SARS’s spread risk is positively spatially associated and the strength of this spatial association has experienced changes from weak to strong and then back to weak during the lifetime of the Beijing SARS epidemic. (3) Two spatial clusters of disease cases are identified: one in the city center and the other in the eastern suburban area. These two clusters followed different evolutionary paths but interacted with each other as well. (4) Although the government missed the opportunity to contain the early outbreak of SARS in March 2003, the response strategies implemented after the mid of April were effective. These response measures not only controlled the growth of the disease cases, but also mitigated the spatial diffusion. | CAO ZhiDong ZENG DaJun ZHENG XiaoLong WANG QuanYi WANG FeiYue WANG JinFeng WANG XiaoLi | 2010 | Science China Earth Sciences2010，53，7： | 5 |
| 4 | Apogossypolone targets mitochondria and light enhances its anticancer activity by stimulating generation of singlet oxygen and reactive oxygen species显示文摘Apogossypolone (ApoG2), a novel derivative of gossypol, has been shown to be a potent inhibitor of antiapoptotic Bcl-2 family proteins and to have antitumor activity in multiple types of cancer cells. Recent reports suggest that gossypol stimulates the generation of cellular reactive oxygen species (ROS) in leukemia and colorectal carcinoma cells; however, gossypol-mediated cell death in leukemia cells was reported to be ROS-independent. This study was conducted to clarify the effect of ApoG2-induced ROS on mitochondria and cell viability, and to further evaluate its utility as a treatment for nasopharyngeal carcinoma (NPC). We tested the photocytotoxicity of ApoG2 to the poorly differentiated NPC cell line CNE-2 using the ROS-generating TL/10 illumination system. The rapid ApoG2-induced cell death was partially reversed by the antioxidant N-acetyl-L-cysteine (NAC), but the ApoG2-induced reduction of mitochondrial membrane potential (MMP) was not reversed by NAC. In the presence of TL/10 illumination, ApoG2 generated massive amounts of singlet oxygen and was more effective in inhibiting cell growth than in the absence of illumination. We also determined the influence of light on the anti-proliferative activity of ApoG2 using a CNE-2-xenograft mouse model. ApoG2 under TL/10 illumination healed tumor wounds and suppressed tumor growth more effectively than ApoG2 treatment alone. These results indicate that the ApoG2-induced CNE-2 cell death is partly ROS-dependent. ApoG2 may be used with photodynamic therapy (PDT) to treat NPC. | Zhe-Yu Hu Jing Wang Gang Cheng Xiao-Feng Zhu Peng Huang Dajun Yang Yi-Xin Zeng | 2011 | Chinese Journal of Cancer2011，30，1： | 2 |
| 5 | Constructing public health evidence knowledge graph for decision-making support from COVID-19 literature of modelling study显示文摘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. | Yunrong Yang Zhidong Cao Pengfei Zhao Dajun Daniel Zeng Qingpeng Zhang Yin Luo | 2021 | Journal of Safety Science and Resilience2021，2，3： | 2 |
| 6 | Bayesian learning in negotiation显示文摘 | Zeng Dajun Syeara K | 1998 | Int J Human-Computer Studies1998，48，： | 1 |
| 7 | The promotion of endothelial progenitor cells recruitment by nerve growth factors in tissue-engineered blood vessels显示文摘 | Wen Zeng Wei Yuan Li Li Jianhong Mi Shangcheng Xu Can Wen Zhenhua Zhou Jiaqiang xiong Jiansen Sun Dajun Ying Mingcan Yang Xiaosong Li Chuhong Zhu | 2009 | Biomaterials2009，，7： | 1 |
| 8 | Bayesian Learning in Negotiation显示文摘 | Dajun Zeng Katia Sycara | 1998 | International Journal of Human-Computer Studies1998，，48： | 1 |
| 9 | Latent subject-centered modeling of collaborative tagging An application in social search 显示文摘 | Peng Jing Zeng Daniel Dajun Huang Zan | 2011 | ACM Transactions on Management Information Systems2011，2，3： | 1 |
| 10 | The Epidemiological Investigation and Intelligent Analytical System for foodborne disease显示文摘 | Ligui Wang Yuanyong Xu Yong Wang Shicun Dong Zhidong Cao Wen Zhou Hailong Sun Donghui Huo Hui Zhang Yansong Sun Liuyu Huang Zhengquan Yuan Dajun Zeng Hongbin Song | 2010 | Food Control2010，，11： | 1 |
| 11 | Bayesian learning in negotiation显示文摘 | Zeng Dajun Katia S | 1998 | International Journal of Human-Computer Studies1998，48，1： | 1 |
| 12 | A social computing method for energy safety显示文摘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 with limited data availability.To extend this risk-neutral model,we incorporate conditional value at risk as an essential risk measure,capturing the uncertainty and diverse impact scenarios arising from social computing factors.The empirical results affirm that the KDR method,which is enriched with social computing considerations,produces resilient,economically efficient solutions for managing the impacts of cyberattacks on a CPES.By integrating social computing principles into the optimization framework,it becomes possible to better anticipate and address the social and behavioral aspects associated with cyberattacks on CPESs,ultimately improving the overall resilience and effectiveness of the system’s response measures. | Pengfei Zhao Shuangqi Li Zhidong Cao Paul Jen-Hwa Hu Daniel Dajun Zeng Da Xie Yichen Shen Jiangfeng Li Tianyi Luo | 2024 | Journal of Safety Science and Resilience2024，5，1： | 0 |
| 13 | Adaptively temporal graph convolution model for epidemic prediction of multiple age groups显示文摘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. | Yuejiao Wang Dajun Daniel Zeng Qingpeng Zhang Pengfei Zhao Xiaoli Wang Quanyi Wang Yin Luo Zhidong Cao | 2022 | Fundamental Research2022，2，2： | 0 |
| 14 | Recognition for avian influenza virus proteins based on support vector machine and linear discriminant analysis显示文摘Total 200 properties related to structural characteristics were employed to represent structures of 400 HA coded proteins of influenza virus as training samples. Some recognition models for HA proteins of avian influenza virus (AIV) were developed using support vector machine (SVM) and linear discriminant analysis (LDA). The results obtained from LDA are as follows: the identification accuracy (Ria) for training samples is 99.8% and Ria by leave one out cross validation is 99.5%. Both Ria of 99.8% for training samples and Ria of 99.3% by leave one out cross validation are obtained using SVM model, respectively. External 200 HA proteins of influenza virus were used to validate the external predictive power of the resulting model. The external Ria for them is 95.5% by LDA and 96.5% by SVM, respectively, which shows that HA proteins of AIVs are preferably recognized by SVM and LDA, and the performances by SVM are superior to those by LDA. | LIANG GuiZhao CHEN ZeCong YANG ShanBin MEI Hu ZHOU Yuan YANG Li ZHOU Peng YANG ShengXi SHU Mao LIAO ChunYang WU ShiRong LI GenRong HE Liu GAO JianKun Gan MengYu LI DeJing CHEN GuoPing WANG GuiXue LONG Sha JING JuHua ZHENG XiaoLin ZENG Hui ZHANG QiaoXia ZHANG MengJun YANG Qi TIAN FeiFei TONG JianBo WANG JiaoNa LIU YongHong LI Bo QIU LiangJia CAI ShaoXi ZHAO Na YANG Yan SU XiaLi SONG Jian CHEN MeiXia ZHANG XueJiao SUN JiaYing LI JingWei CHEN GuoHua CHEN Gang DENG Jie PENG ChuanYou ZHU WanPing XU LuoNan WU YuQuan LIAO LiMin LI Zhi LI Jun LU DaJun SU QinLiang HUANG ZhengHu ZHOU Ping LI ZhiLiang | 2008 | Science China Chemistry2008，51，2： | 0 |
| 15 | Preparation and antibacterial properties of polycaprolactone/quaternized chitosan blends显示文摘This article is a preliminary study on antibacterial blends of polycaprolactone,chitosan and quaternized chitosan by melt processing.Blends were characterized,mechanical test and antibacterial evaluation against Escherichia coli and Staphylococcus aureus,were conducted.Results showed that the antibacterial potential of chitosan was limited in blends and polycaprolactone/chitosan did not show significant antibacterial effect compared with neat polycaprolactone(PCL).Inhibition rates of polycaprolactone/quaternized chitosan were 39.2%99.9%against Escherichia coli,while inhibition rate was 40.9%99.9%against Staphylococcus aureus.When quaternized chitosan(QCTS)content was up to 20%,blends exhibited 99.9%inhibition rates against both two types of bacteria. | Anrong Zeng Yangtao Wang Dajun Li Juedong Guo Qiaowen Chen | 2021 | Chinese Journal of Chemical Engineering2021，34，4： | 0 |
| 16 | Platform governance in the era of AI and the digital economy显示文摘1 Introduction Companies that supply online services such as Twitter,Weibo,and Taobao are known as platform companies(Gorwa,2019).Many top companies offer their services through platforms and use Internet technology to facilitate economic transactions,transmit information,connect people,and make predictions(Fenwick et al.,2019).Such platforms cover all areas of society,including politics(Gillespie,2017),labor relations,cultural production(Scholz,2016;van Doorn,2017),and consumption(Nieborg and Poell,2018).The rapid development of Internet technology has increased the influence of network platforms.Such platforms have changed the rules of global business operations and influenced the global political landscape.Following some high-profile negative events,calls for strengthening the regulation of online platforms and holding them accountable have been growing(Suzor,2019). | Xiaolong ZHENG Gang ZHOU Daniel Dajun ZENG | 2023 | Frontiers of Engineering Management2023，10，1： | 0 |
| 17 | 放疗敏感性不同宫颈癌的蛋白质组学比较研究(英文)显示文摘Objective:To investigate the proteomic differences between the high-sensitivity(HS) group and low-sensitivity(LS) group of cervical cancer treated by radiotherapy and confirm the radiotherapy sensitivity associated proteins in early cer-vical cancer.Methods:The fresh carcinoma tissues were collected from 10 untreated cervical cancer patients and preserved in the-80 ℃ refrigeratory.The tissues were classified into two groups:high sensitivity group(HS) and low sensitivity group(LS),according to their response to radiotherapy.In the first part of our experiment,protein separating was performed by using two-dimensional gel electrophoresis(2-DE) with Amersham 18 cm linear pH 3-10 immobilized pH gradient(IPG) strips.The images of the gels were acquired by the scanner and then analyzed by using PD-quest7.3 software to find the differentially expression protein-spots in each group.Then the differentially expressed protein-spots was incised from the gels and digested by trypsin.The peptide mass fingerprintings(PMF) was acquired by matrix assisted laser desorption/ionization time-of-flight mass spectrometry(MALDI-TOF-MS) and the proteins were identified by data searching in the Mascot-database.Part of dif-ferentially expression proteins were assayed by Western Blot.Results:Most of the gels were clear and successfully analyzed by PD-quest7.3 software.Most of the protein-spots concentrated on the area of 20-100Kda(Mw) and pH4-8.The average number of the protein-spots was 754 ± 64 in HS group and 777 ± 48个in LS group.The match rate was 87.6% between two groups.Five high expression proteins were found in HS group which were low expression in LS group,3 high expres-sion protein were found in LS group which were low expression in HS group.Reselts of Western Blot were in coincidence to proteomic result.Conclusion:The 2-DE gels image of HS group and LS group with early cervical cancer tissues treated by radiotherapy are successfully acquired.Some differentially expression proteins between the two groups are further confirmed by immunohistochemical assay. | Liang Zeng Hong Zhu Dajun Li Haiping Pei Yaping Deng Jun Yuan | 2009 | The Chinese-German Journal of Clinical Oncology2009，8，4： | 0 |
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