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27 results on '"Guo, Yuntong"'

3. Fine‐Tuning Alkyl Chains on Quinoxaline Nonfullerene Acceptors Enables High‐Efficiency Ternary Organic Solar Cells with Optimizing Molecular Stacking and Reducing Energy Loss

Author

Guo, Yuntong, primary, Chen, Zhenyu, additional, Ge, Jinfeng, additional, Zhu, Jintao, additional, Zhang, Jinna, additional, Meng, Yuanyuan, additional, Ye, Qinrui, additional, Wang, Shijie, additional, Chen, Fei, additional, Ma, Wei, additional, and Ge, Ziyi, additional

Published
2023
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5. Replenishment Impacts on Hydrogeochemistry and Water Quality in the Hutuo River Plain.

Author

Zhang, Ruolin, Zhang, Baoyun, Guo, Yuntong, Kong, Xiangke, Li, Yasong, Liu, Yaci, Chen, Lining, and Gong, Qiuli

Subjects
WATER quality, ALLUVIAL plains, WATER chemistry, WATER diversion, GROUNDWATER quality, GROUNDWATER analysis, GEOCHEMICAL modeling
Abstract

To investigate the influence of the Hutuo River (North China) ecological water replenishment project on the hydrogeochemical processes of groundwater, 64 groundwater samples collected at different time intervals after four replenishment events, and four samples from the Middle Route of the South-to-North Water Diversion Project water, were analyzed for water chemistry. Hydrogeochemical methods such as the Piper diagram, chloride-alkalinity index, and ion correlation were employed to analyze the characteristics of groundwater chemical evolution through replenishment. The results demonstrated that the hydrochemical types of groundwater in the study area underwent significant changes during continuous replenishment in the Hutuo River region. During the initial replenishment period (October 2019), the dominant hydrochemical type of groundwater in the study area was Mg-Na-HCO3-SO4, whereas the dominant type in the Middle Route of the South-to-North Water Diversion Project water was Ca-Na-SO4-HCO3. As the replenishment continued, the hydrochemical types of groundwater in the study area evolved into Ca-Na-Mg-HCO3-SO4, Na-Ca-Mg-HCO3-SO4, and Ca-Na-Mg-SO4-HCO3. The groundwater experienced a dissolution of calcite, gypsum, nitratine, carbonate rocks, and gypsum, accompanied by dilution effects, resulting in reduced ion exchange as replenishment progressed. The input of the high quality Middle Route of South-to-North Water Diversion Project water effectively promoted groundwater quality improvement, leading to an overall decrease or stabilization of components other than Ca2+ in the groundwater. Water quality was assessed using the entropy water quality index, with indicators including Na+, SO42−, Cl−, pH, total dissolved solids, NO3−-N, NO2−-N, F−, Al, As, and Zn. The evaluation results showed that, except for one medium-quality water sample, the water quality of the other samples was suitable for drinking and domestic purposes during the early replenishment period. The Middle Route of the South-to-North Water Diversion Project exhibited excellent quality (Rank 1), and as replenishment progressed, all water samples demonstrated good quality by October 2020, with a gradual improvement. [ABSTRACT FROM AUTHOR]

Published
2023
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17. Eutrophication dangers the ecological status of coastal wetlands: A quantitative assessment by composite microbial index of biotic integrity

Author

Chao Wang, Wenlong Zhang, Linqiong Wang, Lihua Niu, Guanhua Zou, Qing Hu, Yi Li, and Guo Yuntong

Subjects
geography, Environmental Engineering, geography.geographical_feature_category, Ecology, Microbiota, Reproducibility of Results, Intertidal zone, Ecological assessment, Wetland, Eutrophication, Pollution, Index of biological integrity, Wetlands, Humans, Environmental Chemistry, Environmental science, Ecosystem, Waste Management and Disposal, Environmental degradation, Intertidal wetland, Environmental Monitoring
Abstract

The intertidal wetland ecosystem is vulnerable to environmental and anthropogenic stressors. Understanding how the ecological statuses of intertidal wetlands respond to influencing factors is crucial for the management and protection of intertidal wetland ecosystems. In this study, the community characteristics of bacteria, archaea and microeukaryote from Jiangsu coast areas (JCA), the longest muddy intertidal wetlands in the world, were detected to develop a composite microbial index of biotic integrity (CM-IBI) and to explore the influence mechanisms of stresses on the intertidal wetland ecological status. A total of 12 bacterial, archaea and microeukaryotic metrics were determined by range, responsiveness and redundancy tests for the development of ba-IBI, ar-IBI and eu-IBI. The CM-IBI was further developed via three sub-IBIs with weight coefficients 0.40, 0.33 and 0.27, respectively. The CM-IBI (R2 = 0.58) exhibited the highest goodness of fit with the CEI, followed by ba-IBI (R2 = 0.36), ar-IBI (R2 = 0.25) and eu-IBI (R2 = 0.21). Redundancy and random forest analyses revealed inorganic nitrogen (inorgN), total phosphorus (TP) and total organic carbon (TOC) to be key environmental variables influencing community compositions. A conditional reasoning tree model indicated the close associating between the ecological status and eutrophication conditions. The majority of sites with water inorgN 0.67 mg/L and TP > 0.11 mg/L. Microbial networks demonstrated the interactions of microbial taxonomic units among three kingdoms decreases with the ecological degradation, suggesting a reduced reliability and stability of microbial communities. Multi-level path analysis revealed fishery aquaculture and industrial development as the dominant anthropogenic activities effecting the eutrophication and ecological degradation of the JCA tidal wetlands. This study developed an efficient ecological assessment method of tidal wetlands based on microbial communities, and determined the influence of human activities and eutrophication on ecological status, providing guidance for management standards and coastal development.

Published
2022
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20. BRAF mutation in Chinese biliary tract cancer patients.

Author

Li, Wendong, primary, Cui, Yanzhi, additional, Yin, Fei, additional, Peng, Li, additional, Liu, Xiaogang, additional, Shen, Yunzhi, additional, Guo, Yuntong, additional, Wen, Shiwang, additional, Shi, Junping, additional, Lei, Mengping, additional, Javle, Milind M., additional, Wang, Kai, additional, and Jiang, Da, additional

Published
2020
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21. Degradation of river ecological quality in Tibet plateau with overgrazing: A quantitative assessment using biotic integrity index improved by random forest

Author

Yi Li, Luhuan Fan, Chao Wang, Nan Yang, Guo Yuntong, Lihua Niu, Linqiong Wang, and Qing Hu

Subjects
0106 biological sciences, River ecosystem, Ecological health, Fragile region, Resistance, General Decision Sciences, Yellow River, 010501 environmental sciences, 010603 evolutionary biology, 01 natural sciences, Index of biological integrity, Grazing, Ecosystem, Overgrazing, QH540-549.5, Ecology, Evolution, Behavior and Systematics, 0105 earth and related environmental sciences, Oxalobacteraceae, Yangtze River, Ecology, biology, Ecological assessment, biology.organism_classification, Bacterial metrics, Environmental science, Stability
Abstract

The river ecosystem in fragile regions is especially vulnerable to environmental or anthropogenic stressors. The ecological quality and stability of rivers in fragile regions urgently need to be evaluated before the reasonable protection and restoration. In this study, a random-forest-modified bacterial index of biological integrity (RF-BaIBI) was developed for ecological assessment of fragile rivers in the sources of Yangtze River and Yellow River (abbreviated as SYY) in Tibet Plateau, China. Three key bacterial metrics including Enterobacteriale, Fluviicola, and Oxalobacteraceae, were selected for the development of RF-BaIBI using range, responsiveness, redundancy tests, as well as random forest. The weight coefficients of metrics were calculated based on their relative importance to RF-BaIBI: Enterobacteriale (0.41), Fluviicola (0.34) and Oxalobacteraceae (0.25). The feasibility of RF-BaIBI was confirmed by its significant correlation with comprehensive environmental index (R2 = 0.71). Among 45 river sections, those with high or good status accounted for 26%, with moderate 51% and poor 13%. Redundancy and path analysis revealed that livestock grazing density was the most dominant factor influencing ecological conditions of the SYY. Under the pressure of grazing, rivers in the source of Yangtze River displayed higher stability than that of Yellow River via the resistance and co-occurrence network analyses. Compared to that of Yellow River, bacterial communities from the source of Yangtze River exhibited stronger competitive interaction which is very beneficial to the stability of the ecosystem. This study highlighted the great effect of overgrazing on the degradation of river ecological health status in Tibet Plateau based on the quantitative assessment of biotic integrity index, which provides a theoretical basis for the management of grazing in fragile plateau regions.

Published
2021
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26. “Fatty” or “steatotic”: Position statement from a linguistic perspective by the Chinese-speaking community

Author

Miao, Lei, Ye, Shu-Mian, Fan, Jian-Gao, Seto, Wai-Kay, Yu, Hon Ho, Yu, Ming-Lung, Kao, Jia-Horng, Boon-Bee Goh, George, Young, Dan Yock, Wong, Yu Jun, Chan, Wah-Kheong, Yang, Wah, Jia, Jidong, Lau, George, Wei, Lai, Shi, Junping, Zhang, Huijie, Bi, Yan, Pik-Shan Kong, Alice, Pan, Calvin Q., Zheng, Ming-Hua, Liang, Huiqing, Yang, Ling, Li, Xinhua, Zeng, Qing-Lei, Gao, Rong, Hu, Songhao, Yan, Bi, Jin, Xiaozhi, Li, Gang, Chen, En-Qiang, Hu, Dandan, Fan, Xiaotang, Hu, Peng, Chang, Xiangrong, Jin, Yihui, Cai, Yijing, Chen, Liangmiao, Wen, Qianjun, Sun, Jian, Xu, Hexiang, Li, Junfeng, Yang, Yongping, Huang, Ang, Zhang, Dongmei, Tan, Lin, Li, Dongdong, Zhu, Yueyong, Cai, Chenxi, Gu, Xuemei, Shen, Jilong, Zhong, Jianhong, Li, Lu, Li, Zhenzhen, Ma, Chiye, Liu, Yaming, Zhang, Yimin, Zhao, Lei, Han, Juqiang, Chen, Tao, Zhang, Qiang, Yang, Song, Zhang, Le, Chen, Lanlan, Feng, Gong, Wang, Qixia, Hao, Kunyan, Lu, Qinghua, Mao, Yimin, Zhong, Yandan, Wang, Ningjian, Xin, Yongning, Yu, Yongtao, Qi, Xingshun, Wang, Ke, He, Yingli, Du, Mulong, Zou, Zhengsheng, Xia, Mingfeng, Zhao, Suxian, Zhao, Jingjie, Xie, Wen, Zhang, Yao, Ji, Mao, Richeng, Du, Qingwei, Chen, Haitao, Song, Yongfeng, Wang, Cunchuan, Lu, Yan, Song, Yu, Zhang, Chi, Shi, Li, Mak, Lungyi, Chen, Li, Xu, Liang, Yuan, Hai-Yang, Hong, Liang, Hai, Li, Wu, Xiaoning, Yang, Naibin, Li, Jing-Wei, Jiejin, Zou, Zhuolin, Zheng, Wen, Zhao, Jian, Zhang, Xiang, Huang, Chen-Xiao, Yao, Ying, Yuan, Bao-Hong, Huang, Shanshan, Min, Lian, Chai, Jin, Hong, Wandong, Miao, Kai-Wen, Xiao, Tie, Chen, Shun-Ping, Ye, Feng, Song, Yuhu, Zhang, Jinshun, Zhou, Xiao-Dong, Wang, Mingwei, Dai, Kai, Lou, Jianjun, Duan, Xu, Yu, Hongyan, Jin, Xi, Fu, Liyun, Zhang, Yanliang, Ye, Junzhao, Liu, Feng, Chen, Qin-Fen, Zhou, Yong-Hai, Duan, Xiaohua, Zhang, Qun, Zhang, Faming, Cao, Zhujun, Li, Yingxu, Sun, Dan-Qin, Hu, Ai-Rong, Liu, Fenghua, Chen, Yuanwen, Zhang, Dianbao, Gao, Feng, Ye, Hua, Rao, Huiying, Luo, Kaizhong, Dai, Zhijuan, Wang, Chia-Chi, Tang, Shanhong, Hua, Jing, Deng, Cunliang, Zhou, Ling, Fan, Yu-Chen, Wu, Mingyue, Lu, Hongyan, Zhang, Xiaoxun, Zhang, Huai, Ni, Yan, Kei Ng, Stephen Ka, Li, Chunming, Liu, Chang, Zhang, Xia, Shi, Yu, Yan, Hongmei, Xu, Jinghang, Zhou, Yu-Jie, Cheng, Yuan, Bai, Honglian, Hu, Xiang, Gao, Yufeng, Lin, Biaoyang, Gu, Guangxiang, Chen, Jin, Hu, Xiaoli, Yuan, Xiwei, Wang, Jie, Chen, Qiang, Yiling, Li, Zhu, Xiao Jia, Chen, Xu, Zhu, Yongfen, Liu, Xiaolin, Wang, Bing, Cai, Mingyan, Chen, Enguang, Chen, Jun, Chen, Jingshe, Deng, Hong, Chen, Xiaoxin, Chen, Yingxiao, Cheng, Xinran, Chen, Fei, Ding, Yang, Dong, Zhixia, Ding, Yanhua, Qingxian, Cai, Deng, Zerun, Cai, Tingchen, Chen, Yaxi, Chen, Zhongwei, Chen, Xing, Huang, Jiaofeng, Huang, Mingxing, Fu, Lei, Jin, Jianhong, Geng, Bin, Chen, Yu, Chen, Ruicong, Jin, Weimin, Li, Dongliang, Jin, Xianghong, Li, Jian-Jun, Zhang, Jie, Matsiyit, Alimjan, Wang, Guiqi, Gao, Tian, Zhang, Shu, Yan, Wenmao, Liu, Jie, Chen, Peng, Hu, Hao, Li, Ming, Yuan, Ping Ge, Chen, Yi, Dong, Zhiyong, Li, Xiaopeng, Lin, Su, Li, Jie, Li Ang, Xujing, Liu, Xin, Liu, Shousheng, Li, Min-Dian, Qian, Hui, Qi, Minghua, Peng, Liang, Luo, Fei, Dang, Shuangsuo, Mao, Xianhua, Sheng, Qiyue, Lyu, Jiaojian, Liu, Chenghai, Qi, Kemin, Ma, Honglei, Lu, Zhonghua, Pan, Qiong, Miao, Qing, Li, Xiaosong, Lin, Huapeng, Shui, Guanghou, Qu, Shen, Fei, Wang, Liu, Chang-Hai, Xia, Fan, Wang, Dan, Pan, Ziyan, Hu, Fangzheng, Xu, Long, Xiong, Qing-Fang, Yang, Rui-Xu, Wang, Qi, Chen, Ligang, W Ang, Danny, Ren, Wanhua, Tong, Xiaofei, You, Ningning, Xing, Yanqing, Sun, Chao, Yu, Zhuo, Shuangxu, Xu, Honghai, Sun, Yi, Zhang, Taotao, Wu, Wei, Zhang, Yingmei, Ye, Qing, Zhang, Zhongheng, Yan, Jie, Zhou, Bengjie, Liu, Weiqiang, Li, Yongguo, Zhao, Lili, Lei, Siyi, Zhu, Guangqi, Ouyang, Huang, Zhou, Yaoyao, Yin, Jianhui, Xia, Yongsheng, He, Qiancheng, Zhang, Xiaoyong, Yang, Qiao, Yao, Libin, Pan, Xiazhen, Wang, Xiaodong, Li, Yangyang, Zhu, Shenghao, Zhao, Xinyan, Chen, Sui-Dan, Zhu, Jiansheng, Zeng, Jing, Tang, Liangjie, Hu, Kunpeng, Yang, Wanshui, Huang, Bingyuan, Zhuang, Chengle, Xun, Yunhao, Zhou, Jianghua, Xu, Wenjing, Wu, Bian, Zhang, Xuewu, He, Yong, Mei, Zubing, Xia, Zefeng, Lu, Bin Feng, Li, Qiang, Li, Jia, Yan, Xuebing, Wen, Zhengrong, Liu, Wenyue, Xu, Dongsheng, Chen, Huiting, Wang, Jing, Song, Juan, Peng, Jie, Chen, Jionghuang, Li, Shuchen, Zheng, Yongping, Zhi-Zhi, Xing, Tang, Jieting, Liu, Chuan, Chen, Chao, Guicheng, Wu, Ye, Quanzhong, Ka, Li, Zhou, Yuping, Jia, Xiaoli, Zou, Ziyuan, Zu, Fuqiang, Cai, Yongqian, Chen, Yunzhi, Chu, Jinguo, Yan, Bing, Wang, Tie, Pan, Qiuwei, Xie, Lingling, Zeng, Xufen, Liu, Bingrong, Su, Minghua, Mu, Yibing, Zeng, Menghua, Guo, Yuntong, Yang, Yongfeng, Zhang, Xiaoguan, Wu, Shike, Pan, Jin-Shui, Cao, Li, Feng, Wenhuan, Yubin, Yang, Wang, Na, Lu, Xiaolan, Lu, Guanhua, Xiong, Jianbo, Zhuang, Jianbin, Shi, Guojun, Zhu, Yanfei, Ying, Xing, Qiao, Zengpei, Zhang, Rui, Li, Yuting, Lei, Yuanli, Xixi, Wu, Tian, Na, Lian, Liyou, Zhang, Binbin, Xiaozhu, Huang, Yan, Chen, Wenying, Liu, Kun, Zhang, Ruinan, Lai, Qintao, Wang, Fudi, Wen, Caiyun, Zhang, Xinlei, Wu, Lili, Liang, Yaqin, Jie, You, Xinzhejin, Zeng, Qiqiang, Zhu, Qiang, Chao, Zheng, Shou, Lan, Jin, Wei-Lin, Ye, Chenhui, Han, Yu, Xie, Gangqiao, Zhao, Jing, Ye, Chunyan, Wang, Hua, Song, Lintao, Feng, Juan, Huang, Yubei, Su, Wen, Bai, Juli, Wong, Vincent, Wang, Huifeng, Ming, Wai-Kit, Yu, Yue-Cheng, Jin, Yan, Zhao, Yan, Gao, Lilian, Liangwang, Chen, Hanbin, Ruifangwang, Tang, Yuhan, Chen, Gang, Liu, Dabin, Cai, Xiaobo, Xue, Feng, Yang, Qinhe, Sun, Guangyong, Zhu, Chunxia, Huang, Zhifeng, Zhou, Hongwen, Xiao, Xiao, Hou, Xin, He, Jie, Ji, Dong, Xiao, Huanming, Chi, Xiaoling, Zou, Huaibin, Shi, Yiwen, Fan, Xingliang, Hu, Xiaoyu, Huang, Zhouqin, Cao, Haixia, Jiang, Jingjing, Zhao, Qiang, Chen, Wei, Li, Shi Bo, Zhang, Fan, Chen, Zhiyun, Liu, Jinfeng, Li, Shibo, Liu, Jing, Li, Li, Li, Ruyu, Kun, Ya, Xiao, ErHui, Wang, Tingyao, Wang, Chunjiong, Aili, Aikebaier, Liu, Xiaoxia, Ding, Ran, Zhu, Chonggui, Zeng, Xin, Wu, Miao, Li, Zhen, Yang, Tao, Qin, Yunfei, Sun, Lihua, Xu, Ying, Fu, Xianghui, Li, Yongyin, and Ye, Shumian

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27. Bioinformatics analysis of the association between obesity and gastric cancer.

Author

Ma X, Cui M, and Guo Y

Abstract

Background: Obesity and gastric cancer (GC) are prevalent diseases worldwide. In particular, the number of patients with obesity is increasing annually, while the incidence and mortality rates of GC are ranked high. Consequently, these conditions seriously affect the quality of life of individuals. While evidence suggests a strong association between these two conditions, the underlying mechanisms of this comorbidity remain unclear., Methods: We obtained the gene expression profiles of GSE94752 and GSE54129 from the Gene Expression Omnibus database. To investigate the associated biological processes, pathway enrichment analyses were conducted using Gene Ontology and Kyoto Encyclopedia of Genes and Genomes for the shared differentially expressed genes in obesity and GC. A protein-protein interaction (PPI) network was subsequently established based on the Search Tool for the Retrieval of Interacting Genes (STRING) database, followed by the screening of the core modules and central genes in this network using Cytoscape plug-in MCODE. Furthermore, we scrutinized the co-expression network and the interplay network of transcription factors (TFs), miRNAs, and mRNAs linked to these central genes. Finally, we conducted further analyses using different datasets to validate the significance of the hub genes., Results: A total of 246 shared differentially expressed genes (209 upregulated and 37 downregulated) were selected for ensuing analyses. Functional analysis emphasized the pivotal role of inflammation and immune-associated pathways in these two diseases. Using the Cytoscape plug-in CytoHubba, nine hub genes were identified, namely, CXCR4 , CXCL8 , CXCL10 , IL6 , TNF , CCL4 , CXCL2 , CD4 , and CCL2 . IL6 and CCL4 were confirmed as the final hub genes through validation using different datasets. The TF-miRNA-mRNA regulatory network showed that the TFs primarily associated with the hub genes included RELA and NFKB1, while the predominantly associated miRNAs included has-miR-195-5p and has-miR-106a-5p., Conclusion: Using bioinformatics methods, we identified two hub genes from the Gene Expression Omnibus datasets for obesity and GC. In addition, we constructed a network of hub genes, TFs, and miRNAs, and identified the major related TFs and miRNAs. These factors may be involved in the common molecular mechanisms of obesity and GC., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Ma, Cui and Guo.)

Published
2024
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