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| 1 | Enhanced efficiency of generating induced pluripotent stem (iPS) cells from human somatic cells by a combination of six transcription factors显示文摘 | Jing Liao Zhao Wu Ying Wang Lu Cheng Chun Cui Yuan Gao Taotao Chen Lingjun Rao Siye Chen Nannan Jia Huiming Dai Shunmei Xin Jiuhong Kang Gang Pei Lei Xiao | 2008 | Cell Research2008，18，5： | 61 |
| 2 | Wheat straw burning and its associated impacts on Beijing air quality显示文摘Based on MODIS images, large-scale flow field charts and environmental monitoring data, we thor- oughly analyzed the spatial distribution of wheat straw burning in North China, with focus on its envi- ronmental impacts on the air quality of Beijing and pollution transport paths. And we anatomized changes of air quality in Beijing under the impacts of pollution generated by wheat straw burning around. The results indicate that: (1) The North China Plain, a winter-wheat growing area, is the main source of pollutants induced by wheat straw burning in Beijing. The direction of south-west is the dominant heavy pollution transport path. (2) Impacts of wheat straw burning on air quality are mainly manifested by significantly increasing CO concentration. (3) Precursors of O3 generated by wheat straw burning, combining with favorable meteorological conditions, can induce increasing O3 concentration greatly. NO concentration will be greatly increased due to decreasing O3 concentration at night. (4) Atmospheric particles, especially the fine ones, from wheat straw burning exert considerable influ- ence on Beijing air quality. (5) Different contributions of wheat straw burning to pollutants are identified. Ratios of PM10/SO2, CO/SO2, etc., can be applied to indicate pollution extent of wheat straw burning. High ratios of PM10/SO2 and CO/SO2 show that the air quality was heavily impacted by wheat straw burning and these ratios can be employed as indicators of contribution of wheat straw burning to the degradation of Beijing air quality. (6) Randomness of wheat straw burning activities renders random outbreak of air pollution of this type. Regional and extensive wheat straw burning activities can cause serious air pollution event. | LI LingJun WANG Ying ZHANG Qiang LI JinXiang YANG XiaoGuang JIN Jun | 2008 | Science China Earth Sciences2008，51，3： | 37 |
| 3 | 在体细胞重编程早期瞬时激活的线粒体炫对Nanog的调控显示文摘体细胞重编程的各种机理已经有很多的报道,但是线粒体信号的不同信号和功能还不清楚。 | Zhongfu Ying Keshi Chen Lingjun Zheng Yi Wu Linpeng Li Rui Wang Qi Long Liang Yang Jingyi Guo Deyang Yao Yong Li Feixiang Bao Ge Xiang Jinglei Liu Qiaoying Huang Zhiming Wu Andrew Paul Hutchins Duanqing Pei 刘兴国 | 2017 | 科学新闻2017，19，4： | 0 |
| 4 | Establishment and Evaluation of a Prediction Model of BLR for Severity in Coronavirus Disease 2019显示文摘Background:Coronavirus disease 2019(COVID-19)is an emerging infectious disease and has spread worldwide.Clinical risk factors associated with the severity in COVID-19 patients have not yet been well delineated.The aim of this study was to explore the risk factors related with the progression of severe COVID-19 and establish a prediction model for severity in COVID-19 patients.Methods:We retrospectively recruited patients with confirmed COVID-19 admitted in Enze Hospital,Taizhou Enze Medical Center(Group)and Nanjing Drum Tower Hospital between January 24 and March 12,2020.Take the Taizhou cohort as the training set and the Nanjing cohort as the validation set.Severe case was defined based on the World Health Organization Interim Guidance Report criteria for severe pneumonia.The patients were divided into severe and non-severe groups.Epidemiological,laboratory,clinical,and imaging data were recorded with data collection forms from the electronic medical record.The predictive model of severe COVID-19 was constructed,and the efficacy of the predictive model in predicting the risk of severe COVID-19 was analyzed by the receiver operating characteristic curve(ROC).Results:A total of 402 COVID-19 patients were included in the study,including 98 patients in the training set(Nanjing cohort)and 304 patients in the validation set(Nanjing cohort).There were 54 cases(13.43%)in severe group and 348 cases(86.57%)in nonsevere group.Logistic regression analysis showed that bodymassindex(BMI)and lymphocyte count wereindependent risk factors for severe COVID-19(all P<0.05).Logistic regression equation based on risk factors was established as follows:Logit(BL)=–5.552–5.473L+0.418BMI.The area under the ROC curve(AUC)of the training set and the validation set were 0.928 and 0.848,respectively(allP<0.001).The model was simplified to get a new model(BMI and lymphocyte count ratio,BLR)for predicting severe COVID-19 patients,and the AUC in the training set and validation set were 0.926 and 0.828,respectively(all P<0.001).Conclusions:Higher BMI and lower lymphocyte count are critical factors associated with severity of COVID-19 patients.The simplified BLR model has a good predictive value for the severe COVID-19 patients.Metabolic factors involved in the development of COVID-19 need to be further investigated. | Zebao He Fajuan Rui Hongli Yang Zhengming Ge Rui Huang Lingjun Ying Haihong Zhao Chao Wu Jie Li | 2022 | Infectious Diseases & Immunity2022，2，2： | 0 |
| 5 | Integrated data-model-knowledge representation for natural resource entities显示文摘The unified management and planning of national or provincial natural resources distributed both aboveground and underground have become increasingly important.Accurate depictions of natural resource elements and their interactions are key to achieving integrated and systematic management of natural resources.However,current spatiotemporal data models are based only on data descriptions,attribute records,and other model knowledge of a more general basis,without intuitively describing relationships between these elements and natural resources.This paper,therefore,proposes an integrated data-model-knowledge representation model to explicitly describe the time,space,and interaction of natural resource entities through an integrated knowledge graph.First,this study constructs a conceptual model using the aspects of semantics,scale,and data-model-knowledge,thereby explicitly describing the relationships of natural resources.Second,a logical model of natural resource representation is proposed,that is integrated with time,space,attributes,and relationships.Finally,taking the management of water resources as an example,this paper realizes the meticulous presentation of the levels of detail and rich semantic relations of natural resource entities.The findings of this study lay the foundation for a more efficient,precise,and lucid perception of the distribution laws and complicated interactional relationships of natural resources,both aboveground and underground. | Yulin Ding Zhaowen Xu Qing Zhu Hankan Li Yan Luo Ying Bao Lingjun Tang Sen Zeng | 2022 | International Journal of Digital Earth2022，15，1： | 0 |
| 6 | Inflammation as a Mediator of Microbiome Dysbiosis-Associated DNA Methylation Changes in Gastric Premalignant Lesions显示文摘Evidence for the influence of chronic inflammation induced by microbial dysbiosis on aberrant DNA methylation supports a plausible connexion between disordered microbiota and precancerous lesions of gastric cancer(PLGC).Here,a comprehensive study including multi-omics data was performed to estimate the relationships amongst the gastric microbiome,inflammatory proteins and DNA methylation alterations and their roles in PLGC development.The results demonstrated that gastric dysbacteriosis increased the risk of PLGC and DNA methylation alterations in related tumour suppressor genes.Seven inflammatory biomarkers were identified for antrum and corpus tissues,respectively,amongst which the expression levels of several biomarkers were significantly correlated with the microbial dysbiosis index(MDI)and methylation status of specific tumour suppressor genes.Notably,mediation analysis revealed that microbial dysbiosis partially contributed to DNA methylation changes in the stomach via the inflammatory cytokines C-C motif chemokine 20(CCL20)and tumour necrosis factor receptor superfamily member 9(TNFRSF9).Overall,these results may provide new insights into the mechanisms that might link the gastric microbiome to PLGC. | Lingjun Yan Wanxin Li Fenglin Chen Junzhuo Wang Jianshun Chen Ying Chen Weimin Ye | 2023 | Phenomics2023，3，5： | 0 |
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