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413 results on '"Gu Xiaoying"'

1. Exploration and Research on Chinese Culture in University English Translation Teaching in the Background of Internet+

Author

Gu Xiaoying

Subjects
bm word alignment model, tf-idf similarity, skeleton knowledge, probability estimation, english translation teaching, 68t05, Mathematics, QA1-939
Abstract

Under the background of Internet+, for the problems of high comprehension difficulty and inaccurate translation in university English translation teaching, this paper proposes the skeleton knowledge translation model to assist university English translation teaching that integrates Chinese culture. Starting from the objectives of university English translation teaching, Chinese culture is explored in university English translation materials to complete the design of university English translation teaching that integrates Chinese culture. Propose the application of Internet technology in the above teaching design, utilize the IBM word alignment model and TF-IDF similarity to preprocess the university English translation textbooks, and on the basis of preprocessing, estimate the probability and decode the generated phrase pairs, and then complete the construction of the English translation model based on skeleton knowledge. The English translation model’s performance analysis is carried out using skeleton knowledge after determining the data set and evaluation criteria. The results show that for the whole 400 000 batch training, the training time of the English translation model based on skeleton knowledge is about one-fifth less than that of the unsupervised neural machine translation model, indicating that with the increase of the number of rounds, the performance of the English translation model based on skeleton knowledge is also better. This study serves as a guide for exploring and researching Chinese culture in university English translation teaching and promotes the development of university English translation teaching in colleges and universities.

Published
2024
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2. Preparation of organic-inorganic hybrid nanoparticles by radiation induced grafting and their dispersion in a polymer matrix

Author

FU Zhiang, GU Xiaoying, GU Yu, LU Manli, LI Yongjin, and LI Jingye

Subjects
radiation grafting, barium titanate, nanoparticles, polyvinylidene fluoride (pvdf), dispersion, Nuclear and particle physics. Atomic energy. Radioactivity, QC770-798, Medical physics. Medical radiology. Nuclear medicine, R895-920
Abstract

Organic-inorganic hybrid nanoparticles were successfully prepared by grafting polyvinylidene fluoride (PVDF) onto barium titanate (BT) nanoparticles (F-BT) by the co-irradiation technique at room temperature under vacuum. The results of Fourier transform infrared (FTIR), thermogravimetric analysis (TGA), and transmission electron microscopy (TEM) demonstrated that PVDF was covalently grafted onto the BT surface. The crystallization behavior of F-BT was investigated by differential scanning calorimetry (DSC), which revealed that the crystalline phase of PVDF on the BT surface was the β phase, while there is no indication of the presence of an α phase. The prepared F-BT nanoparticles were then dispersed in the PVDF matrix to obtain the nanocomposites. Compared to pristine BT, F-BT showed better dispersion in the PVDF matrix and enhanced mechanical properties of the nanocomposites. This can be attributed to the grafted tails on the F-BT, which had specific interactions with PVDF molecular chains.

Published
2021
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5. The persistence of SARS-CoV-2 in tissues and its association with long COVID symptoms: a cross-sectional cohort study in China

Author

Zuo, Wenting, He, Di, Liang, Chaoyang, Du, Shiyu, Hua, Zhan, Nie, Qiangqiang, Zhou, Xiaofeng, Yang, Meng, Tan, Haidong, Xu, Jiuyang, Yu, Yanbing, Zhan, Yuliang, Zhang, Ying, Gu, Xiaoying, Zhu, Weijie, Zhang, Hui, Li, Hongyan, Sun, Weiliang, Sun, Mingzhi, Liu, Xiaolei, Liu, Liguo, Cao, Chuanzhen, Li, Rui, Li, Jing, Zhang, Yun, Zhang, Yuting, Guo, Jing, Zhao, Ling, Zhang, Chuan-Peng, Liu, Hongyu, Wang, Shiyao, Xiao, Fei, Wang, Yeming, Wang, Zai, Li, Haibo, and Cao, Bin

Published
2024
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19. 6-month consequences of COVID-19 in patients discharged from hospital: a cohort study

Author

Huang, Chaolin, Huang, Lixue, Wang, Yeming, Li, Xia, Ren, Lili, Gu, Xiaoying, Kang, Liang, Guo, Li, Liu, Min, Zhou, Xing, Luo, Jianfeng, Huang, Zhenghui, Tu, Shengjin, Zhao, Yue, Chen, Li, Xu, Decui, Li, Yanping, Li, Caihong, Peng, Lu, Li, Yong, Xie, Wuxiang, Cui, Dan, Shang, Lianhan, Fan, Guohui, Xu, Jiuyang, Wang, Geng, Wang, Ying, Zhong, Jingchuan, Wang, Chen, Wang, Jianwei, Zhang, Dingyu, and Cao, Bin

Published
2023
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21. Biologic and prognostic significance of HER2-low and ER-low expressions in de novo metastatic breast cancer.

Author

Ren, Chongxi, primary, Sun, Jianna, additional, Kong, Lingjun, additional, Yang, Yunmei, additional, Liu, Jiabin, additional, Zhou, Tong, additional, Yin, Ruisheng, additional, Li, Fengying, additional, Wang, Xin, additional, Wang, Hongqiao, additional, Gu, Xiaoying, additional, Li, Sheng, additional, Ren, Haixia, additional, Yu, Hongjun, additional, Di, Shuping, additional, and Zhao, Jing, additional

Published
2024
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24. Novel serum metabolites associate with cognition phenotypes among Bogalusa Heart Study participants

Author

Shi, Mengyao, Bazzano, Lydia A, He, Jiang, Gu, Xiaoying, Li, Changwei, Li, Shengxu, Yaffe, Kristine, Kinchen, Jason M, Stuchlik, Patrick, Mi, Xuenan, Nierenberg, Jovia L, Razavi, Alexander C, and Kelly, Tanika N

Subjects
Digestive Diseases, Good Health and Well Being, Cognition, Cross-Sectional Studies, Female, Humans, Longitudinal Studies, Male, Metabolome, Metabolomics, Middle Aged, Phenotype, metabolomics, metabolite network, dementia, Alzheimer's disease, cognition, Alzheimer’s disease, Biochemistry and Cell Biology, Physiology, Oncology and Carcinogenesis, Developmental Biology
Abstract

BackgroundMetabolomics study provides an opportunity to identify novel molecular determinants of altered cognitive function.MethodsDuring 2013 to 2016 Bogalusa Heart Study (BHS) visit, 1,177 participants underwent untargeted, ultrahigh performance liquid chromatography-tandem mass spectroscopy metabolomics profiling. Global cognition and five cognition domains were also assessed. The cross-sectional associations of single metabolites with cognition were tested using multiple linear regression models. Weighted correlation network analysis was used to examine the covariable-adjusted correlations of modules of co-abundant metabolites with cognition. Analyses were conducted in the overall sample and according to both ethnicity and sex.ResultsFive known metabolites and two metabolite modules robustly associated with cognition across overall and stratified analyses. Two metabolites were from lipid sub-pathways including fatty acid metabolism [9-hydroxystearate; minimum P-value (min-P)=1.11×10-5], and primary bile acid metabolism (glyco-alpha-muricholate; min-P=4.10×10-5). One metabolite from the glycogen metabolism sub-pathway (maltose; min-P=9.77×10-6), one from the polyamine metabolism sub-pathway (N-acetyl-isoputreanine; min-P=1.03×10-5), and one from the purine metabolism sub-pathway (7-methylguanine; min-P=1.19×10-5) were also identified. Two metabolite modules reflecting bile acid metabolism and androgenic steroids correlated with cognition (min-P=5.00×10-4 and 3.00×10-3, respectively).ConclusionThe novel associations of 5 known metabolites and 2 metabolite modules with cognition provide insights into the physiological mechanisms regulating cognitive function.

Published
2019

27. Associations of residential greenness with lung function and chronic obstructive pulmonary disease in China

Author

Xiao, Yalan, Gu, Xiaoying, Niu, Hongtao, Meng, Xia, Zhang, Lina, Xu, Jianying, Yang, Lan, Zhao, Jianping, Zhang, Xiangyan, Bai, Chunxue, Kang, Jian, Ran, Pixin, Shen, Huahao, Wen, Fuqiang, Huang, Kewu, Chen, Yahong, Sun, Tieying, Shan, Guangliang, Lin, Yingxiang, Wu, Sinan, Zhu, Jianguo, Wang, Ruiying, Shi, Zhihong, Xu, Yongjian, Ye, Xianwei, Song, Yuanlin, Wang, Qiuyue, Zhou, Yumin, Ding, Liren, Li, Diandian, Yao, Wanzhen, Guo, Yanfei, Xiao, Fei, Lu, Yong, Peng, Xiaoxia, Zhang, Biao, Xiao, Dan, Wang, Zuomin, Zhang, Hong, Bu, Xiaoning, Zhang, Xiaolei, An, Li, Zhang, Shu, Cao, Zhixin, Zhan, Qingyuan, Yang, Yuanhua, Liang, Lirong, Cao, Bin, Dai, Huaping, Wu, Tangchun, He, Jiang, Kan, Haidong, Chen, Renjie, Yang, Ting, and Wang, Chen

Published
2022
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32. Oral Simnotrelvir for Adult Patients with Mild-to-Moderate Covid-19

Author

Cao, Bin, primary, Wang, Yeming, additional, Lu, Hongzhou, additional, Huang, Chaolin, additional, Yang, Yumei, additional, Shang, Lianhan, additional, Chen, Zhu, additional, Jiang, Rongmeng, additional, Liu, Yihe, additional, Lin, Ling, additional, Peng, Ping, additional, Wang, Fuxiang, additional, Gong, Fengyun, additional, Hu, Honglin, additional, Cheng, Cong, additional, Yao, Xiangyang, additional, Ye, Xianwei, additional, Zhou, Hourong, additional, Shen, Yinzhong, additional, Liu, Chenfan, additional, Wang, Chunying, additional, Yi, Zhennan, additional, Hu, Bijie, additional, Xu, Jiuyang, additional, Gu, Xiaoying, additional, Shen, Jingshan, additional, Xu, Yechun, additional, Zhang, Leike, additional, Fan, Jia, additional, Tang, Renhong, additional, and Wang, Chen, additional

Published
2024
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41. Prevalence and risk factors of small airway dysfunction, and association with smoking, in China: findings from a national cross-sectional study

Author

Wang, Chen, He, Jiang, Xu, Jianying, Yang, Lan, Xu, Yongjian, Zhang, Xiangyan, Bai, Chenxue, Kang, Jian, Ran, Pixin, Shen, Huahao, Wen, Fuqiang, Huang, Kewu, Yao, Wanzhen, Shan, Guangliang, Yang, Ting, Lin, Yingxiang, Wu, Sinan, Zhu, Jianguo, Wang, Ruiying, Shi, Zhihong, Zhao, Jianping, Ye, Xianwei, Song, Yuanlin, Yang, Dong, Wang, Qiuyue, Hou, Gang, Zhou, Yumin, Li, Wen, Ding, Liren, Wan, Chun, Wang, Hao, Chen, Yahong, Guo, Yanfei, Xiao, Fei, Lu, Yong, Peng, Xiaoxia, Zhang, Biao, Xiao, Dan, Wang, Zuomin, Bu, Xiaoning, Zhang, Hong, Zhang, Xiaolei, An, Li, Zhang, Shu, Cao, Zhixin, Zhan, Qingyuan, Yang, Yuanhua, Cao, Bin, Dai, Huaping, Liang, Lirong, Wang, Chaolong, Tong, Xunliang, Wu, Tangchun, Kan, Haidong, Chen, Renjie, Cai, Hua, Xiong, Weining, Zhang, Pengjun, Li, Yong, Niu, Wenquan, Chen, Chung-Shiuan, Xu, Guodong, Zhang, Xinran, Gu, Xiaoying, Dong, Fen, Liu, Zhao, Cheng, Anqi, Pei, Zhengcun, Niu, Hongtao, Huang, Ke, Chen, Simiao, Chung, Kian Fan, Chen, Zhengming, Bai, Chunxue, and Sun, Tieying

Published
2020
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42. 3-year outcomes of discharged survivors of COVID-19 following the SARS-CoV-2 omicron (B.1.1.529) wave in 2022 in China: a longitudinal cohort study

Author

Zhang, Hui, primary, Huang, Chaolin, additional, Gu, Xiaoying, additional, Wang, Yeming, additional, Li, Xia, additional, Liu, Min, additional, Wang, Qiongya, additional, Xu, Jiuyang, additional, Wang, Yimin, additional, Dai, Huaping, additional, Zhang, Dingyu, additional, and Cao, Bin, additional

Published
2023
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49. Association of Monocyte Count With Lung Function and Exercise Capacity Among Hospitalized COVID‐19 Survivors: A 2‐Year Cohort Study.

Author

Gu, Xiaoying, Huang, Lixue, Li, Xia, Zhou, Yuting, Zhang, Hui, Wang, Yeming, Cui, Dan, Yu, Ting, Wang, Yimin, and Cao, Bin

Abstract

Background: Abnormal changes of monocytes have been observed in acute COVID‐19, whereas associations of monocyte count with long COVID were not sufficiently elucidated. Methods: A cohort study was conducted among COVID‐19 survivors discharged from hospital. The primary outcomes were core symptoms of long COVID, distance walked in 6 min, and lung function, and the secondary outcomes were health‐related quality of life and healthcare use after discharge. Latent variable mixture modeling was used to classify individuals into groups with similar trajectory of monocyte count from discharge to 2‐year after symptom onset. Multivariable adjusted generalized linear regression models and logistic regression models were used to estimate the associations of monocyte count trajectories and monocyte count at discharge with outcomes. Results: In total, 1389 study participants were included in this study. Two monocyte count trajectories including high to normal high and normal trajectory were identified. After multivariable adjustment, participants in high to normal high trajectory group had an odds ratio (OR) of 2.52 (95% CI, 1.44–4.42) for smell disorder, 2.27 (1.27–4.04) for 6‐min walking distance less than lower limit of normal range, 2.45 (1.08–5.57) for total lung capacity (TLC) < 80% of predicted, 3.37 (1.16–9.76) for personal care problem, and 1.70 (1.12–2.58) for rehospitalization after discharge at 2‐year follow‐up compared with those in normal trajectory group. Monocyte count at discharge showed similar results, which was associated with smell disorder, TLC < 80% of predicted, diffusion impairment, and rehospitalization. Conclusions: Monocyte count may serve as an easily accessible marker for long‐term management of people recovering from COVID‐19. [ABSTRACT FROM AUTHOR]

Published
2024
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50. Quality prediction of kiwifruit based on transfer learning.

Author

Zhou, Yancong, Ma, Yumei, Sun, Xiaochen, Peng, Aihuan, Zhang, Bo, Gu, Xiaoying, Wang, Yan, He, Xingxing, and Guo, Zhen

Subjects
KIWIFRUIT, MACHINE learning, BACK propagation, RANDOM noise theory, PREDICTION models, RANDOM forest algorithms
Abstract

Kiwifruit has a high decay rate, in part because quality changes during storage cannot be easily monitored in real time. In order to better monitor the shelf life of kiwifruit and understand the quality changing process during storage, internal quality indexes such as hardness, respiratory intensity and TSS(Total Soluble Solid) were considered into the prediction models. The prediction models were constructed based on BPNN (Back Propagation Neural Network), Random Forest (RF) and XGBoost (eXtreme Gradient Boosting) respectively. And transfer learning algorithm was used to construct the quality prediction models with BPNN, RF, and XGBoost algorithms as the base learner. In the experiments, sample data were augmented by adding Gaussian noise, which effectively prevented the model from over-fitting. The experimental results showed that the prediction accuracy of each index based on transfer learning was better than that of individual BPNN, RF and XGBoost. Moreover, the average prediction accuracy of the models based on transfer learning was 96.2%, and that of respiratory intensity was as high as 99.4%. Therefore transfer learning can be used to effectively analyze and predict changes of kiwifruit quality indexes during storage. [ABSTRACT FROM AUTHOR]

Published
2024
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