300 results on '"Cai, Yunfei"'
Search Results
2. Comprehensive transcriptome analysis of Asparagus officinalis in response to varying levels of salt stress
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Wen, Shuangshuang, Ying, Jiali, Ye, Youju, Cai, Yunfei, and Qian, Renjuan
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- 2024
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3. RETRACTED ARTICLE: Is green finance a motivator for energy transformation investments in the OECD?
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Xu, Yuecheng and Cai, Yunfei
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- 2024
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4. Analysis of surface water area dynamics and driving forces in the Bosten Lake basin based on GEE and SEM for the period 2000 to 2021
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Li, Xingyou, Zhang, Fei, Shi, Jingchao, Chan, Ngai Weng, Cai, Yunfei, Cheng, Chunyan, An, Changjiang, Wang, Weiwei, and Liu, Changjiang
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- 2024
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5. Associations of exposure to phthalate with serum uric acid and hyperuricemia risk, and the mediating role of systemic immune inflammation
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Niu, Zhiping, Chen, Tianyi, Duan, Zhizhou, Han, Shichao, Shi, Yifan, Yu, Wenyuan, Du, Shuang, Tang, Hao, Shao, Wenpu, Sun, Jin, Chen, Han, Cai, Yunfei, Xu, Yanyi, and Zhao, Zhuohui
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- 2024
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6. Decoding anthocyanin biosynthesis regulation in Asparagus officinalis peel coloration: Insights from integrated metabolomic and transcriptomic analyses
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Ying, Jiali, Wen, Shuangshuang, Cai, Yunfei, Ye, Youju, Li, Lebin, and Qian, Renjuan
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- 2024
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7. RsOBP2a, a member of OBF BINDING PROTEIN transcription factors, inhibits two chlorophyll degradation genes in green radish
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Ying, Jiali, Hu, Jinbin, M'mbone Muleke, Everlyne, Shen, Feng, Wen, Shuangshuang, Ye, Youju, Cai, Yunfei, and Qian, Renjuan
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- 2024
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8. Postharvest disease, latent infection, and preharvest control of ‘Shine-Muscat’ grapes
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Ren, Weiheng, Sun, Chenxu, Wang, Lei, Zhu, Chuanxi, Ren, Dandan, Wang, Tan, Wang, Liping, Cai, Yunfei, Wang, Yiwen, Zhu, Pinkuan, and Xu, Ling
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- 2024
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9. Control of arbuscule development by a transcriptional negative feedback loop in Medicago
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Zhang, Qiang, Wang, Shuangshuang, Xie, Qiujin, Xia, Yuanjun, Lu, Lei, Wang, Mingxing, Wang, Gang, Long, Siyu, Cai, Yunfei, Xu, Ling, Wang, Ertao, and Jiang, Yina
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- 2023
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10. Poly(heptazine imide) ligand exchange enables remarkable low catalyst loadings in heterogeneous metallaphotocatalysis
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Xing, Liuzhuang, Yang, Qian, Zhu, Chen, Bai, Yilian, Tang, Yurong, Rueping, Magnus, and Cai, Yunfei
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- 2023
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11. Spatio-temporal changes in fractional vegetation cover and the driving forces during 2001–2020 in the northern slopes of the Tianshan Mountains, China
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He, Xin, Zhang, Fei, Cai, Yunfei, Tan, Mou Leong, and Chan, Ngai Weng
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- 2023
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12. Mineral resource management in Chinese rural areas: Policy assessment for green economic growth
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Xu, Jin, Liu, Chengjun, Dou, Gang, and Cai, Yunfei
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- 2024
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13. Spatio-temporal variability and trend of blue-green water resources in the Kaidu River Basin, an arid region of China
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Cai, Yunfei, Zhang, Fei, Gao, Guang, Jim, Chi Yung, Tan, Mou Leong, Shi, Jingchao, Wang, Weiwei, and Zhao, Qi
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- 2024
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14. Particle size dependence of the microsegregation and microstructure in the atomized Ni-based superalloy powders: Theoretical and experimental study
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Li, Jikang, Yang, Mingsheng, Cai, Yunfei, Zhang, Yuanyuan, Feng, Qingshuai, Liu, Jiantao, and Liu, Tong
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- 2024
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15. Recent trends in metal-doped carbon nitride-catalyzed heterogeneous light-driven organic transformations
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Zhou, Pengfei, Yang, Qian, Tang, Yurong, and Cai, Yunfei
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- 2023
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16. Effects of pre-precipitation treatment temperature on the microstructure and mechanical properties of nickel-based powder superalloy
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Cai, Yunfei, Zhang, Yuanyuan, Li, Jikang, Yang, Mingsheng, Liu, Jiantao, and Liu, Tong
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- 2023
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17. Remote sensing for chromophoric dissolved organic matter (CDOM) monitoring research 2003–2022: A bibliometric analysis based on the web of science core database
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Li, Zhihui, Zhang, Fei, Shi, Jingchao, Chan, Ngai Weng, Tan, Mou Leong, Kung, Hsiang-te, Liu, Changjiang, Cheng, Chunyan, Cai, Yunfei, Wang, Weiwei, and Li, Xingyou
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- 2023
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18. Opportunistic maintenance strategy optimization considering imperfect maintenance under hybrid unit-level maintenance strategy
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Li, Xinlong, Ran, Yan, Chen, Baojia, Chen, Fafa, Cai, Yunfei, and Zhang, Genbao
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- 2023
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19. Influences of adding Y2Ti2O7 and HfH1.98 nanoparticles on the microstructure and mechanical properties of oxide dispersion strengthen steels
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Cai, Yunfei, Wang, Linbo, Li, Jikang, Yang, Mingsheng, Feng, Qingshuai, and Liu, Tong
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- 2023
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20. Enhancing SWAT model with modified method to improve Eco-hydrological simulation in arid region
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Cai, Yunfei, Zhang, Fei, Shi, Jingchao, Carl Johnson, Verner, Ahmed, Zia, Wang, Jianguo, and Wang, Weiwei
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- 2023
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21. Vegetation cover changes in China induced by ecological restoration-protection projects and land-use changes from 2000 to 2020
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Cai, Yunfei, Zhang, Fei, Duan, Pan, Yung Jim, Chi, Weng Chan, Ngai, Shi, Jingchao, Liu, Changjiang, Wang, Jianguo, Bahtebay, Jupar, and Ma, Xu
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- 2022
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22. Stereoselective Access to Spiro-isoindolinone Scaffolds via Catalytic Asymmetric aza-Piancatelli Rearrangement.
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Yu, Ling, Tang, Yurong, Nie, Xukun, and Cai, Yunfei
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- 2024
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23. Adaptive robust dual-loop control scheme of ship-mounted Stewart platforms for wave compensation
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Cai, Yunfei, Zheng, Shutao, Liu, Weitian, Qu, Zhiyong, Zhu, Jiyue, and Han, Junwei
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- 2021
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24. Sliding-mode control of ship-mounted Stewart platforms for wave compensation using velocity feedforward
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Cai, Yunfei, Zheng, Shutao, Liu, Weitian, Qu, Zhiyong, Zhu, Jiyue, and Han, Junwei
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- 2021
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25. New understanding of miniaturized VOCs monitoring device: PID-type sensors performance evaluations in ambient air
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Xu, Wei, Cai, Yunfei, Gao, Song, Hou, Shuang, Yang, Yong, Duan, Yusen, Fu, Qingyan, Chen, Fei, and Wu, Jie
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- 2021
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26. Multi-Scenario Validation and Assessment of a Particulate Matter Sensor Monitor Optimized by Machine Learning Methods.
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Tang, Hao, Cai, Yunfei, Gao, Song, Sun, Jin, Ning, Zhukai, Yu, Zhenghao, Pan, Jun, and Zhao, Zhuohui
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ARTIFICIAL neural networks , *MACHINE learning , *PARTICULATE matter , *K-nearest neighbor classification , *COMPUTER monitors , *SUPPORT vector machines , *LENGTH measurement - Abstract
Objective: The aim was to evaluate and optimize the performance of sensor monitors in measuring PM2.5 and PM10 under typical emission scenarios both indoors and outdoors. Method: Parallel measurements and comparisons of PM2.5 and PM10 were carried out between sensor monitors and standard instruments in typical indoor (2 months) and outdoor environments (1 year) in Shanghai, respectively. The optimized validation model was determined by comparing six machining learning models, adjusting for meteorological and related factors. The intra- and inter-device variation, measurement accuracy, and stability of sensor monitors were calculated and compared before and after validation. Results: Indoor particles were measured in a range of 0.8–370.7 μg/m3 and 1.9–465.2 μg/m3 for PM2.5 and PM10, respectively, while the outdoor ones were in the ranges of 1.0–211.0 μg/m3 and 0.0–493.0 μg/m3, correspondingly. Compared to machine learning models including multivariate linear model (ML), K-nearest neighbor model (KNN), support vector machine model (SVM), decision tree model (DT), and neural network model (MLP), the random forest (RF) model showed the best validation after adjusting for temperature, relative humidity (RH), PM2.5/PM10 ratios, and measurement time lengths (months) for both PM2.5 and PM10, in indoor (R2: 0.97 and 0.91, root-mean-square error (RMSE) of 1.91 μg/m3 and 4.56 μg/m3, respectively) and outdoor environments (R2: 0.90 and 0.80, RMSE of 5.61 μg/m3 and 17.54 μg/m3, respectively), respectively. Conclusions: Sensor monitors could provide reliable measurements of PM2.5 and PM10 with high accuracy and acceptable inter and intra-device consistency under typical indoor and outdoor scenarios after validation by RF model. Adjusting for both climate factors and the ratio of PM2.5/PM10 could improve the validation performance. [ABSTRACT FROM AUTHOR]
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- 2024
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27. Is green finance a motivator for energy transformation investments in the OECD?
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Xu, Yuecheng and Cai, Yunfei
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This study examines the impact of green financial policies, specifically green bonds and green credit, on investments in green energy across 23 OECD (The Organization for Economic Co-operation and Development) member countries from 2010 to 2021. Analyzing panel data using CUP-FM analysis, it finds that both green bonds and green credit significantly foster investments in green resource projects, with green bonds showing greater sensitivity. Conversely, green fiscal policies, particularly green taxes, prove ineffective and sometimes counterproductive. The research highlights a positive correlation between increased investments in green resources, growth in green economies, and sustainable utilities trade, while emphasizing the detrimental effect of public health costs on green project development. The study underscores the importance of appropriate policies to promote investments in renewable resources, spanning green finance, green fiscal policies, green economic growth, and sustainable utilities trade. [ABSTRACT FROM AUTHOR]
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- 2024
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28. Noise reduction with adaptive filtering scheme on interferometric fiber optic hydrophone
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Cai, Yunfei, Yu, Zhihua, Mo, Daili, Liu, Rui, Chen, Ao, Dai, Bei, and Li, Yutong
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- 2020
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29. Remote Radical Azidation of Unactivated C(sp3)–H Bonds in Sulfamoyl Azides.
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Li, Jiawei, Zhong, Sishi, Zhou, Pan, Li, Xu, Xie, Danyang, Cai, Yunfei, and Xia, Yong
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- 2024
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30. Improved ambient air quality is associated with decreased prevalence of childhood asthma and infancy shortly after weaning is a sensitive exposure window.
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Chen, Tianyi, Shi, Su, Li, Xinyue, Zhou, Lu, Yu, Yongfu, Cai, Yunfei, Wang, Jing, Kan, Haidong, Xu, Yanyi, Huang, Chen, Tan, Yongqiang, Meng, Xia, and Zhao, Zhuohui
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ASTHMA in children ,AIR quality ,INFANTS ,PRESCHOOL children ,PARTICULATE matter - Abstract
Background: The urban ambient air quality has been largely improved in the past decade. It is unknown whether childhood asthma prevalence is still increasing in ever top‐ranking city of Shanghai, whether the improved air quality is beneficial for children's asthma and what time window of exposure plays critical roles. Methods: Using a repeat cross‐sectional design, we analyzed the association between early life exposure to particles and wheezing/asthma in each individual and combined surveys in 2011 and 2019, respectively, in 11,825 preschool children in Shanghai. Results: A significantly lower prevalence of doctor‐diagnosed asthma (DDA) (6.6% vs. 10.5%, p < 0.001) and wheezing (10.5% vs. 23.2%, p < 0.001) was observed in 2019 compared to 2011. Exposure to fine particulate matter (PM2.5), coarse particles (PM2.5–10) and inhalable particles (PM10) was decreased in 2019 by 6.3%, 35.4%, and 44.7% in uterus and 24.3%, 20.2%, and 31.8% in infancy, respectively. Multilevel log‐binomial regression analysis showed exposure in infancy had independent association with wheezing/DDA adjusting for exposure in uterus. For each interquartile range (IQR) increase of infancy PM2.5, PM2.5–10 and PM10 exposure, the odds ratios were 1.39 (95% confidence interval (CI): 1.24–1.56), 1.51 (95% CI:1.15–1.98) and 1.53 (95% CI:1.27–1.85) for DDA, respectively. The distributed lag non‐linear model showed the sensitive exposure window (SEW) was 5.5–11 months after birth. Stratified analysis showed the SEWs were at or shortly after weaning, but only in those with <6 months of exclusive breastfeeding. Conclusions: Improved ambient PM benefits in decreasing childhood asthma prevalence. We firstly reported the finding of SEW to PM at or closely after weaning on childhood asthma. [ABSTRACT FROM AUTHOR]
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- 2024
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31. Screening and Preliminary Identification of Asparagus officinalis Varieties under Low-Temperature Stress.
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Ye, Youju, Wen, Shuangshuang, Ying, Jiali, Cai, Yunfei, and Qian, Renjuan
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LOW temperatures ,TRANSCRIPTOMES - Abstract
To meet the large demand for Asparagus officinalis in the spring market and improve the economic benefits of cultivating asparagus, we explored the molecular mechanism underlying the response of A. officinalis to low temperature. First, "Fengdao No. 1" was screened out under low-temperature treatment. Then, the transcriptome sequencing and hormone detection of "Fengdao No. 1" and "Grande" (control) were performed. Transcriptome sequencing resulted in screening out key candidate genes, while hormone analysis indicated that ABA was important for the response to low temperature. The combined analysis indicated that the AoMYB56 gene may regulate ABA in A. officinalis under low temperature. And the phylogenetic tree was constructed, and subcellular localisation was performed. From these results, we speculated that the AoMYB56 gene may regulate ABA in A. officinalis. The results of this research provide a theoretical basis for the further exploration of low-temperature response in A. officinalis. [ABSTRACT FROM AUTHOR]
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- 2024
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32. Undergraduate Medical Academic Performance is Improved by Scientific Training
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Zhang, Lili, Zhang, Wei, Wu, Chong, Liu, Zhongming, Cai, Yunfei, Cao, Xingguo, He, Yushan, Liu, Guoxiang, and Miao, Hongming
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The effect of scientific training on course learning in undergraduates is still controversial. In this study, we investigated the academic performance of undergraduate students with and without scientific training. The results show that scientific training improves students' test scores in general medical courses, such as biochemistry and molecular biology, cell biology, physiology, and even English. We classified scientific training into four levels. We found that literature reading could significantly improve students' test scores in general courses. Students who received scientific training carried out experiments more effectively and published articles performed better than their untrained counterparts in biochemistry and molecular biology examinations. The questionnaire survey demonstrated that the trained students were more confident of their course learning, and displayed more interest, motivation and capability in course learning. In summary, undergraduate academic performance is improved by scientific training. Our findings shed light on the novel strategies in the management of undergraduate education in the medical school.
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- 2017
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33. Novel delay-partitioning stabilization approach for networked control system via Wirtinger-based inequalities
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Li, Zhichen, Bai, Yan, Huang, Congzhi, and Cai, Yunfei
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- 2016
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34. Determination of manganese content in cottonseed meal using near-infrared spectrometry and multivariate calibration
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YU, En, ZHAO, Rubing, CAI, Yunfei, HUANG, Jieqiong, LI, Cheng, LI, Cong, MEI, Lei, BAO, Lisheng, CHEN, Jinhong, and ZHU, Shuijin
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- 2019
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35. Evaluation of HuoXueHuaYu therapy for nonalcoholic fatty liver disease: a systematic review and meta-analysis of randomized controlled trial
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Cai, Yunfei, Liang, Qiuer, Chen, Weihao, Chen, Minghao, Chen, Ruixue, Zhang, Yun, Xiao, Ya, and Chen, Liguo
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- 2019
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36. Critical care admission following elective surgery was not associated with survival benefit: prospective analysis of data from 27 countries
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Kahan, Brennan C., Koulenti, Desponia, Arvaniti, Kostoula, Beavis, Vanessa, Campbell, Douglas, Chan, Matthew, Moreno, Rui, Pearse, Rupert M., Pearse, Rupert M., Beattie, Scott, Clavien, Pierre-Alain, Demartines, Nicolas, Fleisher, Lee A., Grocott, Mike, Haddow, James, Hoeft, Andreas, Holt, Peter, Moreno, Rui, Pritchard, Naomi, Rhodes, Andrew, Wijeysundera, Duminda, Wilson, Matt, Ahmed, Tahania, Everingham, Kirsty, Hewson, Russell, Januszewska, Marta, Pearse, Rupert M., Phull, Mandeep-Kaur, Halliwell, Richard, Shulman, Mark, Myles, Paul, Schmid, Werner, Hiesmayr, Michael, Wouters, Patrick, de Hert, Stefan, Lobo, Suzana, Beattie, Scott, Wijeysundera, Duminda, Fang, Xiangming, Rasmussen, Lars, Futier, Emmanuel, Biais, Matthieu, Venara, Aurélien, Slim, Karem, Sander, Michael, Koulenti, Despoina, Arvaniti, Kostoula, Chan, Mathew, Kulkarni, Atul, Chandra, Susilo, Tantri, Aida, Geddoa, Emad, Abbas, Muntadhar, Della Rocca, Giorgio, Sivasakthi, Datin, Mansor, Marzida, Luna, Pastor, Bouwman, Arthur, Buhre, Wolfgang, Beavis, Vanessa, Campbell, Douglas, Short, Tim, Osinaike, Tunde, Matos, Ricardo, Grigoras, Ioana, Kirov, Mikhail, Protsenko, Denis, Biccard, Bruce, Aldecoa, Cesar, Chew, Michelle, Hofer, Christoph, Hubner, Martin, Ditai, James, Szakmany, Tamas, Fleisher, Lee, Ferguson, Marissa, MacMahon, Michael, Shulman, Mark, Cherian, Ritchie, Currow, Helen, Kanathiban, Kathirgamanathan, Gillespie, David, Pathmanathan, Edward, Phillips, Katherine, Reynolds, Jenifer, Rowley, Joanne, Douglas, Jeanene, Kerridge, Ross, Currow, Helen, Garg, Sameer, Bennett, Michael, Jain, Megha, Alcock, David, Terblanche, Nico, Cotter, Rochelle, Leslie, Kate, Stewart, Marcelle, Zingerle, Nicolette, Clyde, Antony, Hambidge, Oliver, Rehak, Adam, Cotterell, Sharon, Binh QuanHuynh, Wilson, McCulloch, Timothy, Ben-Menachem, Erez, Egan, Thomas, Cope, Jennifer, Halliwell, Richard, Fellinger, Paul, Haselberger, Simone, Holaubek, Caroline, Lichtenegger, Paul, Scherz, Florian, Schmid, Werner, Hoffer, Franz, Cakova, Veronika, Eichwalder, Andreas, Fischbach, Norbert, Klug, Reinhold, Schneider, Elisabeth, Vesely, Martin, Wickenhauser, Reinhart, Grubmueller, Karl Gernot, Leitgeb, Marion, Lang, Friedrich, Toro, Nancy, Bauer, Marlene, Laengle, Friedrich, Mayrhofer, Thomas, Buerkle, Christian, Forstner, Karin, Germann, Reinhard, Rinoesl, Harald, Schindler, Elke, Trampitsch, Ernst, Fritsch, Gerhard, Szabo, Christian, Bidgoli, Jawad, Verdoodt, Hans, Forget, Patrice, Kahn, David, Lois, Fernande, Momeni, Mona, Prégardien, Caroline, Pospiech, Audrey, Steyaert, Arnaud, Veevaete, Laurent, De Kegel, Dirk, De Jongh, Karen, Foubert, Luc, Smitz, Carine, Vercauteren, Marcel, Poelaert, Jan, Van Mossevelde, Veerle, Abeloos, Jacques, Bouchez, Stefaan, Coppens, Marc, De Baerdemaeker, Luc, Deblaere, Isabel, De Bruyne, Ann, De Hert, Stefan, Fonck, Kristine, Heyse, Bjorn, Jacobs, Tom, Lapage, Koen, Moerman, Anneliese, Neckebroek, Martine, Parashchanka, Aliaksandra, Roels, Nathalie, Van Den Eynde, Nancy, Vandenheuvel, Michael, Van Limmen, Jurgen, Vanluchene, Ann, Vanpeteghem, Caroline, Wouters, Patrick, Wyffels, Piet, Huygens, Christel, Vandenbempt, Punitha, Van de Velde, Marc, Dylst, Dimitri, Janssen, Bruno, Schreurs, Evelien, Aleixo, Fábia Berganton, Candido, Keulle, Dias Batista, Hugo, Guimarães, Mario, Guizeline, Jaqueline, Hoffmann, João, Lobo, Suzana M., Lobo, Francisco Ricardo, Nascimento, Vinícius, Nishiyama, Katia, Pazetto, Lucas, Souza, Daniela, Souza Rodrigues, Rodrigo, Vilela dos Santos, Ana Maria, Jardim, Jaquelline, Silva, Joao, do NascimentoJunior, Paulo, Baio, Thalissa Hermínia, Pereira de Castro, Gabriel Isaac, Watanabe Oliveira, Henri Roger, Amendola, Cristina Prata, Cardoso, Gutemberg, Ortega, Daniela, Brotto, Ana Flavia, De Oliveira, Mirella Cristine, Réa-Neto, Álvaro, Dias, Fernando, Azambuja, Pedro, Knibel, Marcos Freitas, Martins, Antonio, Almeida, William, Neto, Calim Neder, Tardelli, Maria Angela, Caser, Eliana, Machado, Marcio, Aguzzoli, Crisitiano, Baldisserotto, Sérgio, Beck Tabajara, Fernanda, Bettega, Fernanda, Rodrigues Júnior, La Hore Correa, de Gasperi, Julia, Faina, Lais, Nolasco, Marcos Farias, da Costa Fischer, Bruna Ferreira, de Campos Ferreira, Mariana Fosch, Hartmann, Cristina, Kliemann, Marta, Ribeiro, Gustavo LuisHubert, Fraga, Julia Merladete, Netto, Thiago Motta, Pozza, Laura Valduga, Wendling, Paulo Rafael, Azevedo, Caroline, Garcia, Juliana, Lopes, Marcel, Maia, Bernardo, Maselli, Paula, Melo, Ralph, Mendes, Weslley, Neves, Matheus, Ney, Jacqueline, Piras, Claudio, Applewhaite, Christopher, Carr, Adrienne, Chow, Lorraine, Duttchen, Kaylene, Foglia, Julena, Greene, Michael, Hinther, Ashley, Houston, Kendra, McCormick, Thomas Jared, Mikhayel, Jennifer, Montasser, Sam, Ragan, Alex, Suen, Andrew, Woolsey, Adrianna, Yu, Hai Chuan, Funk, Duane, Kowalski, Stephen, Legaspi, Regina, McDonald, Heather, Siddiqui, Faisal, Pridham, Jeremy, Rowe, Bernadette, Sampson, Sonia, Thiessen, Barton, Zbitnew, Geoff, Bernard, Andre, George, Ronald, Jones, Philip, Moor, Rita, Siddiqui, Naveed, Wolfer, Alexandra, Tran, Diem, Winch, Denyse, Dobson, Gary, Hinther, Ashley, McCormick, Thomas, Montasser, Osama, Suen, Andrew, Woolsey, Adrianna, Bernard, Andre, George, Ronald, Hall, Richard, Bernard, Andre, George, Ronald, Hall, Richard, Applewhaite, Christopher, Baghirzada, Leyla, McCormick, Thomas Jared, Suen, Andrew, Dai, Si Yuan, Hare, Gregory, Lee, Esther, Shastri, Uma, Tsui, Albert, Yagnik, Anmol, Alvares, Danielle, Choi, Stephen, Dwyer, Heather, Flores, Kathrina, McCartney, Colin, Somascanthan, Priya, Beattie, Scott, Carroll, Jo, Pazmino-Canizares, Janneth, Wijeysundera, Duminda, Wolfer, Alexandra, Ami, Noam, Chan, Vincent, Perlas, Anahi, Argue, Ruth, Lavis, Katie, Mayson, Kelly, Cao, Ying, Gao, Hong, hu, tingju, Lv, Jie, Yang, Jian, Yang, Yang, Zhong, Yi, Zhou, Jing, Zou, Xiaohua, He, Miao, Li, Xiaoying, Luo, Dihuan, Wang, Haiying, Yu, Tian, Chen, Liyong, Wang, Lijun, Cai, Yunfei, Cao, Zhongming, Li, Yanling, Lian, Jiaxin, Sun, Haiyun, Wang, Sheng, Wang, Zhipeng, Wang, Kenru, Zhu, Yi, Du, Xindan, Fan, Hao, Fu, Yunbin, Huang, Lixia, Huang, Yanming, Hwan, Haifang, Luo, Hong, Qu, Pi-Sheng, Tao, Fan, Wang, Zhen, Wang, Guoxiang, Wang, Shun, Zhang, Yan, Zhang, Xiaolin, Chen, Chao, Wang, Weixing, Liu, Zhengyuan, Fan, Lihua, Tang, Jing, Chen, Yijun, Chen, Yongjie, Han, Yangyang, Huang, Changshun, Liang, Guojin, Shen, Jing, Wang, Jun, Yang, Qiuhong, Zhen, Jungang, Zhou, Haidong, Chen, Junping, Chen, Zhang, Li, Xiaoyu, Meng, Bo, Ye, Haiwang, Zhang, Xiaoyan, Bi, Yanbing, Cao, Jianqiao, Guo, Fengying, Lin, Hong, Liu, Yang, Lv, Meng, Shi, Pengcai, Song, Xiumei, Sun, Chuanyu, Sun, Yongtao, Wang, Yuelan, Wang, Shenhui, Zhang, Min, Chen, Rong, Hou, Jiabao, Leng, Yan, Meng, Qing-tao, Qian, Li, Shen, Zi-ying, Xia, Zhong-yuan, Xue, Rui, Zhang, Yuan, Zhao, Bo, Zhou, Xian-jin, Chen, Qiang, Guo, Huinan, Guo, Yongqing, Qi, Yuehong, Wang, Zhi, Wei, Jianfeng, Zhang, Weiwei, Zheng, Lina, Bao, Qi, Chen, Yaqiu, Chen, Yijiao, Fei, Yue, Hu, Nianqiang, Hu, Xuming, Lei, Min, Li, Xiaoqin, Lv, Xiaocui, Lv, Jie, Miao, Fangfang, Ouyang, Lingling, Qian, Lu, Shen, Conyu, Sun, Yu, Wang, Yuting, Wang, Dong, Wu, Chao, Xu, Liyuan, Yuan, Jiaqi, Zhang, Lina, Zhang, Huan, Zhang, Yapping, Zhao, Jinning, Zhao, Chong, Zhao, Lei, Zheng, Tianzhao, Zhou, Dachun, Zhou, Haiyan, Zhou, Ce, Lu, Kaizhi, Zhao, Ting, He, Changlin, Chen, Hong, Chen, Shasha, Cheng, Baoli, He, Jie, Jin, Lin, Li, Caixia, Li, Hui, Pan, Yuanming, Shi, Yugang, Wen, Xiao Hong, Wu, Shuijing, Xie, Guohao, Zhang, Kai, Zhao, Bing, Lu, Xianfu, Chen, Feifei, Liang, Qisheng, Lin, Xuewu, Ling, Yunzhi, Liu, Gang, Tao, Jing, Yang, Lu, Zhou, Jialong, Chen, Fumei, Feng, Yunlin, Hou, Benchao, Lin, Jiamei, Liu, Mei, Luo, Foquan, Shi, Xiaoyun, Xiong, Yingfen, Xu, Lin, Yang, Shuangjia, Zhang, Qin, Zhang, Huaigen, Zhao, Weihong, Zhao, Weilu, Bai, Yun, Chen, Linbi, Chen, Sijia, Dai, Qinxue, Geng, Wujun, Han, Kunyuan, He, Xin, Huang, Luping, Ji, Binbin, Jia, Danyun, Jin, 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Ramona–Mihaela, Sandu, Gigel, Bruma, Dana, Corneci, Dan, Dutu, Madalina, Krepil, Adriana, Copaciu, Elena, Dumitrascu, Clementina Oana, Jemna, Ramona, Mihaescu, Florentina, Petre, Raluca, Tudor, Cristina, Ursache, Elena, Kulikov, Alexander, Lubnin, Andrey, Grigoryev, Evgeny, Pugachev, Stanislav, Protsenko, Denis, Tolmasov, Alexander, Hussain, Ayyaz, Ilyina, Yana, Kirov, Mikhail, Roshchina, Anna, Iurin, Aleksandr, Chazova, Elena, Dunay, Artem, Karelov, Alexey, Khvedelidze, Irina, Voldaeva, Olga, Belskiy, Vladislav, Dzhamullaev, Parvin, Grishkowez, Elena, Kretov, Vladimir, Levin, Valeriy, Molkov, Aleksandr, Puzanov, Sergey, Samoilenko, Aleksandr, Tchekulaev, Aleksandr, Tulupova, Valentina, Utkin, Ivan, Allorto, Nikki Leigh, Bishop, David Gray, Builu, Pierre Monji, Cairns, Carel, Dasrath, Ashish, de Wet, Jacques, denHoedt, Marielle, Grey, Ben, Hayes, Morgan Philip, Küsel, Belinda Senta, Shangase, Nomcebo, Wise, Robert, Cacala, Sharon, Farina, Zane, Govindasamy, Vishendran, Kruse, Carl-Heinz, Lee, Carolyn, Marais, Leonard, Naidoo, Thinagrin Dhasarthun, Rajah, Chantal, Rodseth, Reitze Nils, Ryan, Lisa, von Rhaden, Richard, Adam, Suwayba, Alphonsus, Christella, Ameer, Yusuf, Anderson, Frank, Basanth, Sujith, Bechan, Sudha, Bhula, Chettan, Biccard, Bruce M., Biyase, Thuli, Buccimazza, Ines, Cardosa, Jorge, Chen, James, Daya, Bhavika, Drummond, Leanne, Elabib, Ali, Goad, Ehab Helmy Abdel, Goga, Ismail E., Goga, Riaz, Harrichandparsad, R., Hodgson, Richard E., Jordaan, J., Kalafatis, Nicky, Kampik, Christian, Landers, AT, Loots, Emil, Madansein, Rajhmum, Madaree, Anil, Madiba, Thandinkosi E., Manzini, Vukani T., Mbuyisa, Mbali, Moodley, Rajan, Msomi, Mduduzi, Mukama, Innocent, Naidoo, Desigan, Naidoo, Rubeshan, Naidu, Tesuven K., Ntloko, Sindiswa, Padayachee, Eneshia, Padayachee, Lucelle, Phaff, Martijn, Pillay, Bala, Pillay, Desigan, Pillay, Lutchmee, Ramnarain, Anupa, Ramphal, Suren R., Ryan, Paul, Saloojee, Ahmed, Sebitloane, Motshedisi, Sigcu, Noluyolo, Taylor, Jenna L., Torborg, Alexandra, Visser, Linda, Anderson, Philip, Conradie, Alae, de Swardt, Mathew, de Villiers, Martin, Eikman, Johan, Liebenberg, Riaan, Mouton, Johan, Paton, Abbey, van der Merwe, Louwrence, Wilscott-Davids, Candice, Barrett, Wendy Joan, Bester, Marlet, de Beer, Johan, Geldenhuys, Jacques, Gouws, Hanni, Potgieter, Jan-Hendrik, Strydom, Magdel, Wilberforce-Turton, Edwin, Chetty, Rubendraj R., Chirkut, Subash, Cronje, Larissa, de Vasconcellos, Kim, Dube, Nokukhanya Z., SibusisoGama, N., Green, Garyth M., Green-Thompson, Randolph, Kinoo, Suman Mewa, Kistnasami, Prenolin, Maharaj, Kapil, Moodley, Manogaran S., Mothae, Sibongile J., Naidoo, Ruvashni, Aslam, M., Noorbhai, F., Rughubar, Vivesh, Reddy, Jenendhiran, Singh, Avesh, Skinner, David L., Smith, Murray J., Singh, Bhagwan, Misra, Ravi, Naidoo, Maheshwar, Ramdharee, Pireshin, Selibea, Yvonne, Sewpersad, Selina, Sham, Shailendra, Wessels, Joseph D., Africander, Cucu, Bejia, Tarek, Blakemore, Stephen P., Botes, Marisa, Bunwarie, Bimalshakth, Hernandez, Carlos B., Jeeraz, Mohammud A., Legutko, Dagmara A., Lopez, Acela G., De Meyer, Jenine N., Muzenda, Tanaka, Naidoo, Noel, Patel, Maryam, Pentela, Rao, Junge, Marina, Mansoor, Naj, Rademan, Lana, Scislowski, Pawel, Seedat, Ismail, van den Berg, Bianca, van der Merwe, Doreen, van Wyk, Steyn, Govender, Komalan, Naicker, Darshan, Ramjee, Rajesh, Saley, Mueen, Kuhn, Warren Paul, Matos-Puig, Roel, Moolla, Zaheer, Lisi, Alberto, Perez, Gisela, Valle Beltran, Anna, Lozano, Angels, Delgado Navarro, Carlos, Duca, Alejandro, Pastor Martinez, Ernesto, Ferrando, Carlos, Fuentes, Isabel, García-Pérez, Maria Luisa, Gracia, Estefania, Izquierdo Palomares, Ana, Katime, Antonio, Miñana, Amanda, Incertis, Raul, Romero, Esther, Romero Garcia, Carolina Soledad, Rubio, Concepcion, Socorro Artiles, Tania, Soro, Marina, Valls, Paola, Laguarda, Gisela Alaman, Benavent, Pau, Cuenca, Vicente Chisbert, Cueva, Andreu, Lafuente, Matilde, Parra, Asuncion Marques, Rodrigo, Alejandra Romero, Sanchez-Morcillo, Silvia, Tormo, Sergi, Redondo, Francisco Javier, De Andres, Jose, Gómez Diago, Lorena, Hernández Cádiz, Maria José, Manuel, Granell Gil, Peris, Raquel, Saiz, Cristina, Tatay, Jose, TebarSoto, Maria Teresa, Brunete, Tamara, Cancho, David, Delgado García, David R., Zamudio, Diana, Garcia Del Valle, Santiago, Luz Serrano, M., Alonso, Eduardo, Anillo, Victor, Maseda, Emilio, Salgado, Patricia, Suarez, Luis, Suarez-de-la-Rica, Alejandro, Villagrán, María José, Aldecoa, Cesar, Alonso, José Ignacio, Cabezuelo, Estefania, Garcia-Saiz, Irene, Lopez del Moral, Olga, Martín, Silvia, Perez Gonzalez, Alba, Tovar Doncel, Sherezade, Vera, Martin Agüero, Ávila Sánchez, Francisco José, Castaño, Beatriz, Castaño Moreira, Beatriz, Flores Risco, Sahely, Paz Martín, Daniel, Pérez Martín, Fernando, Poza, Paloma, Ruiz, Adela, Serna Martínez, Wilson Fabio, Vicente, Bárbara Vázquez, Dominguez, Saul Velaz, Fernández, Salvador, Munoz-López, Alfonso, Bernat, Maria Jose, Mas, Arantxa, Planas, Kenneth, Jawad, Monir, Saeed, Yousif, Hedin, Annika, Levander, Helena, Chew, Michelle, Holmström, Sandra, Lönn, David, Zoerner, Frank, Åkring, Irene, Widmark, Carl, Zettergren, Jan, Liljequist, Victor Aspelund, Nystrom, Lena, Odeberg-Wernerman, Suzanne, Oldner, Anders, Reje, Patrik, Lyckner, Sara, Sperber, Jesper, Adolfsson, Anne, Klarin, Bengt, Hedin, Annika, Ögren, Katrin, Barras, Jean-Pierre, Bührer, Thomas, Despotidis, Vasileios, Helmy, Naeder, Holliger, Stephan, Raptis, Dimitri Aristotle, Schmid, Roger, Meyer, Antoine, Jaquet, Yves, Kessler, Ulf, Muradbegovic, Mirza, Nahum, Solange R., Rotunno, Teresa, Schiltz, Boris, Voruz, François, Worreth, Marc, Christoforidis, Dimitri, Popeskou, Sotirios Georgios, Furrer, Markus, Prevost, Gian Andrea, Stocker, Andrea, Lang, Klaus, Breitenstein, Stefan, Ganter, Michael T., Geisen, Martin, Soll, Christopher, Korkmaz, Michelle, Lubach, Iris, Schmitz, Michael, Meyer zuSchwabedissen, Moritz, Zingg, Urs, Hillermann, Thomas, Wildi, Stefan, Hofer, Christoph, Pinto, Bernardo Bollen, Walder, Bernhard, Hübner, Martin, Mariotti, Giustina, Slankamenac, Ksenija, Namuyuga, Mirioce, Kyomugisha, Edward, Kituuka, Olivia, Wesonga Shikanda, Anne, Kakembo, Nasser, Otim Tom, Charles, Webombesa, Antonina, Bua, Emmanuel, Ditai, James, Ssettabi, Eden Michael, Epodoi, Joseph, Kabagenyi, Fiona, Kirya, Fred, Dempsey, Ged, Seasman, Colette, Basit Nawaz Khan, Raja, Kurasz, Claire, Macgregor, Mark, Shawki, Burhan, Hariharan, Vimal, Chau, Simon, Ellis, Kate, Butt, Georgina, Chicken, Dennis-Wayne, Christmas, Natasha, Allen, Samantha, Daniel, Gayatri Daniel, Dempster, Angie, Kemp, Juliette, Matthews, Lewis, Mcglone, Philip, Tambellini, Joanne, Trodd, Dawn, Freitas, Katie, Garg, Atul, Karpate, Shilpaja, Kulkarni, Aditi, O’Hara, Chloe, Troko, Jtroko, Angus, Kirsty, Bradley, Jacqueline, Brennan, Emma, Brooks, Carolyn, Brown, Janette, Brown, Gemma, Finch, Amanda, Gratrix, Karen, Hesketh, Sue, Hill, Gillian, Jeffs, Carol, Morgan, Maureen, Pemberton, 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Claxton, Andrew, Netke, Bhagyashree, Armstrong, Richard, Calderwood, Claire, Kwok, Andy, Mohr, Otto, Oyeniyi, Peter, Patnaik, Lisa, Post, Benjamin, Ali, Sarah, Arshad, Homa, Baker, Gerard, Brenner, Laura, Brincat, Maximilian, Brunswicker, Annemarie, Cox, Hannah, Cozar, Octavian Ionut, Durst, Alexander, Fengas, Lior, Flatt, Jim, Glister, Georgina, Narwani, Vishal, Photi, Evangelos, Rankin, Adeline, Rosbergen, Melissa, Tan, Mark, Beaton, Ceri, Horn, Rachel, Hunt, Jane, Rousseau, Guy, Stancombe, Lucia, Absar, Mohammed, Allsop, Joanne, Drinkwater, Zoe, Hodgkiss, Tracey, Smith, Kirsty, Brown, Jamie, Pick, Sara, Alexander-Sefre, Farhad, Campey, Lorraine, Dudgeon, Lucy, Hall, Kathryn, Hitchcock, Rachael, James, Lynne, Smith, Kate, Winstone, Ulrika, Ahmad, Norfaizan, Bauchmuller, Kris, Harrison, Jonathan, Jeffery, Holly, Miller, Duncan, Pinder, Angela, Pothuneedi, Sailaja, Rosser, Jonathan, Sanghera, Sumayer, Swift, Diane, Walker, Rachel, Bester, Delia, Cavanagh, Sarah, Cripps, Heather, 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Chris, Marques de Araujo, Daniela, Martin, Daniel, Morkane, Clare, Neely, Julia, Rajendram, Rajkumar, Burton, Megan, James, Kathryn, Keevil, Edward, Minik, Orsolya, Morgan, Jenna, Musgrave, Anna, Rajanna, Harish, Roberts, Tracey, Szakmany, Tamas, Adamson, Michael, Jumbe, Sandra, Kendall, Jennie, Muthuswamy, Mohan Babu, Anderson, Charlotte, Cruikshanks, Andrew, Pothuneedi, Sailaja, Walker, Rachel, Wrench, Ian, Zeidan, Lisa, Ardern, Diane, Harris, Benjamin, Hellstrom, Johanna, Martin, Jane, Thomas, Richard, Varsani, Nimu, Wrey Brown, Caroline, Docherty, Philip, Gillies, Michael, McGregor, Euan, Usher, Helen, Craig, Jayne, Smith, Andrew, Ahmad, Tahania, Bodger, Phoebe, Creary, Thais, Everingham, Kirsty, Fowler, Alexander, Hewson, Russ, Ijuo, Eke, Januszewska, Marta, Jones, Timothy, Kantsedikas, Ilya, Lahiri, Sumitra, McLean, Aaron Lawson, Niebrzegowska, Edyta, Phull, Mandeep, Wang, Difei, Wickboldt, Nadine, Baldwin, Jacqueline, Doyle, Donna, Mcmullan, Sean, Oladapo, Michelle, Owen, Thomas, Tripathi, Shiva, Williams, Alexandra, Daniel, Hull, Gregory, Peter, Husain, Tauqeer, Kirk-Bayley, Justin, Mathers, Edward, Montague, Laura, White, Stuart, Avis, Joanne, Cook, Tim, Dali-Kemmery, Lola, Kerslake, Ian, Lambourne, Victoria, Pearson, Annabel, Boyd, Christine, Callaghan, Mark, Lawson, Cathy, McCrossan, Roopa, Nesbitt, Vanessa, O’connor, Laura, Scott, Julia, Sinclair, Rhona, Farid, Nahla, Morgese, Ciro, Bhatia, Kailash, Karmarkar, Swati, Vohra, Akbar, Ahmed, Jamil, Branagan, Graham, Hutton, Monica, Swain, Andrew, Brookes, Jamie, Cornell, Jonathan, Dolan, Rachael, Hulme, Jonathan, Jansen van Vuuren, Amanda, Jowitt, Tom, Kalashetty, Gunasheela, Lloyd, Fran, Patel, Kiran, Sherwood, Nicholas, Brown, Lynne, Chandler, Ben, Deighton, Kerry, Emma, Temlett, Haunch, Kirsty, Cheeseman, Michelle, Dent, Kathy, Garg, Sanjeev, Gray, Carol, Hood, Marion, Jones, Dawn, Juj, Joanne, Mitra, Atideb, Rao, Roshan, Walker, Tara, Al Anizi, Mashel, Cheah, Clarissa, Cheing, Yushio, Coutinho, Francisco, Gondo, Prisca, Hadebe, Bernard, Onie Hove, Mazvangu, khader, Ahamed, Krishnachetty, Bobby, Rhodes, Karen, Sokhi, Jagdish, Baker, Katie-Anne, Bertram, Wendy, Looseley, Alex, Mouton, Ronelle, Arnold, Glenn, Arya, Shobhit, Balfoussia, Danai, Baxter, Linden, Harris, James, Jones, Craig, Knaggs, Alison, Markar, Sheraz, Perera, Anisha, Scott, Alasdair, Shida, Asako, Sirha, Ravneet, Wright, Sally, Frost, Victoria, Gray, Catherine, MacGregor, Mark, Andrews, Emma, Arrandale, Lindsay, Barrett, Stephen, Bidd, Heena, Cifra, Elna, Cooper, Mariese, Dragnea, Dragos, Elna, Cifra, Maclean, Jennifer, Meier, Sonja, Milliken, Donald, Munns, Christopher, Ratanshi, Nadir, Salvana, Abegail, Watson, Anthony, Ali, Hani, Campbell, Gill, Critchley, Rebecca, Hicks, Catherine, Liddle, Alison, Pass, Marc, Ritchie, Charlotte, Thomas, Charlotte, Too, Lingxi, Welsh, Sarah, Gill, Talvinder, Johnson, Joanne, Reed, Joanne, Davis, Edward, Papadopoullos, Sam, Attwood, Clare, Biffen, Andrew, Boulton, Kerenza, Gray, Sophie, Hay, David, Mills, Sarah, Montgomery, Jane, Riddell, Rory, Simpson, James, Bhardwaj, Neeraj, Paul, Elaine, Uwubamwen, Nosakhare, Vohra, Akbar, Berntsen, Emer, Nolan, Tracey, Turner, Angus, Alexander, Maini, Arrich, James, Arumugam, Swarna, Blackwood, Douglas, Boggiano, Victoria, Brown, Robyn, Lam Chan, Yik, Chatterjee, Devnandan, Chhabra, Ashok, Christian, Rachel, Costelloe, Hannah, CoxwellMatthewman, Madeline, Dalton, Emma, Darko, Julia, Davari, Maria, Dave, Tejal, Deacon, Matthew, Deepak, Shantal, Edmond, Holly, Ellis, Jessica, El-Sayed, Ahmed, Eneje, Philip, English, Rose, Ewe, Renee, Foers, William, Franklin, John, Gallego, Laura, Garrett, Emily, Goldberg, Olivia, Goss, Harry, Greaves, Rosanna, Harris, Rudy, Hennings, Charles, Jones, Eleanor, Kamali, Nelson, Kokkinos, Naomi, Lewis, Carys, Lignos, Leda, Malgapo, Evaleen Victoria, Malik, Rizwana, Milne, Andrew, Mulligan, John-Patrick, Nicklin, Philippa, Palipane, Natasha, Parsons, Thomas, Piper, Rebecca, Prakash, Rohan, Ramesh, Byron, Rasip, Sarah, Reading, Jacob, Rela, Mariam, Reyes, Anna, Robert, Stephens, Rooms, Martin, Shah, Karishma, Simons, Henry, Solanki, Shalil, Spowart, Emma, Stevens, Amy, Thomas, Christopher, Waggett, Helena, Yassaee, Arrash, Kennedy, Anthony, Scott, Sara, Somanath, Sameer, Berg, Andrew, Hernandez, Miguel, Nanda, Rajesh, Tank, Ghanshyambhai, Wilson, Natalie, Wilson, Debbie, Al-Soudaine, Yassr, Baldwin, Matthew, Cornish, Julie, Davies, Zoe, Davies, Leigh, Edwards, Marc, Frewer, Natasha, Gallard, Sian, Glasbey, James, Harries, Rhiannon, Hopkins, Luke, Kim, Taeyang, Koompirochana, Vilavan, Lawson, Simon, Lewis, Megan, Makzal, Zaid, Scourfield, Sarah, Ahmad, Yousra, Bates, Sarah, Blackwell, Clare, Bryant, Helen, Coulter, Suzanne, Cruickshank, Ross, Daniel, Sonya, Daubeny, Thomas, Edwards, Mark, Golder, Kim, Hawkins, Lesley, Helen, Bryant, Hinxman, Honor, Levett, Denny, Skinner, Ben, Walsgrove, Joseph, Bradburn, Mike, Dickson, Jane, Constantin, Kathryn, Karen, Markwell, O’Brien, Peter, O’Donohoe, Lynn, Payne, Hannah, Sundayi, Saul, Walker, Elaine, Brooke, Jenny, Cardy, Jon, Humphreys, Sally, Kessack, Laura, Kubitzek, Christiane, Kumar, Suhas, Cotterill, Donna, Hodzovic, Emil, Hosdurga, Gurunath, Miles, Edward, Saunders, Glenn, Campbell, Marta, Chan, Peter, Jemmett, Kim, Raj, Ashok, Naik, Aditi, Ramamoorthy, Rajarajan, Shah, Nimesh, Sylvan, Axel, Blyth, Katharine, Burtenshaw, Andrew, Freeman, David, Johnson, Emily, Lo, Philip, Martin, Terry, Plunkett, Emma, Wollaston, Julie, Allison, Joanna, Carroll, Christine, Craw, Nicholas, Craw, Sarah, Pitt-Kerby, Tressy, Rowland-Axe, Rebecca, Spurdle, Katie, McDonald, Andrew, Simon, Davies, Sinha, Vivek, Smith, Thomas, Banner-Goodspeed, Valerie, Boone, Myles, Campbell, Kathleen, Lu, Fengxin, Scannell, Joseph, Sobol, Julia, Balajonda, Naraida, Clemmons, Karen, Conde, Carlos, Funk, Bonita, Hall, Roger, Hopkins, Thomas, Olaleye, Omowunmi, Omer, Omer, Pender, Michelle, Porto, Angelo, Stevens, Alice, Waweru, Peter, Yeh, Erlinda, Bodansky, Daniella, Evans, Adam, Kleopoulos, Steven, Maril, Robert, Mathney, Edward, Sanchez, Angela, Tinuoye, Elizabeth, Bateman, Brian, Eng, Kristen, Jiang, Ning, Ladha, Karim, Needleman, Joseph, Chen, Lee-lynn, Chen, Lee-lynn, Lane, Rondall, Robinowitz, David, Ghushe, Neil, Irshad, Mariam, Patel, Samir, Takemoto, Steven, Wallace, Art, Mazzeffi, Michael, Rock, Peter, Wallace, Karin, Zhu, Xiaomao, Chua, Pandora, Fleisher, Lee, Mattera, Matthew, Sharar, Rebecca, Thilen, Stephan, Treggiari, Miriam, Morgan, Angela, Sofjan, Iwan, Subramaniam, Kathirvel, Avidan, Michael, Maybrier, Hannah, Muench, Maxwell, Wildes, Troy, and The International Surgical Outcomes Study (ISOS) group
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37. Genotyping and metabolite characterization of somatic hybrids between Arabidopsis thaliana and Swertia mussotii
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Cai, Yunfei, Quan, Taiyong, Yu, Yang, Liu, Guanghua, and Xiang, Fengning
- Published
- 2015
38. Heterogeneous Metallaphotocatalytic C(sp2)–C(sp3) Cross-Coupling Reactions with Integrated Bipyridyl-Ni(II)-Carbon Nitride.
- Author
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Dan, Xiao, Yang, Qian, Xing, Liuzhuang, Tang, Yurong, Wang, Wentao, and Cai, Yunfei
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- 2023
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39. Copper-doped carbon nitride as a practical heterogeneous metallaphotocatalyst for aerobic oxidative cross-coupling of tertiary amines with terminal alkynes.
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Bai, Yilian, Yang, Qian, Tang, Yurong, Dan, Xiao, Wang, Wentao, and Cai, Yunfei
- Subjects
TERTIARY amines ,NITRIDES ,CHEMICAL amplification ,DOPING agents (Chemistry) ,CHEMICAL stability ,HETEROGENEOUS catalysts ,ORGANIC synthesis ,SONOGASHIRA reaction - Abstract
Heterogeneous metallaphotocatalytic chemical transformations with a recyclable catalyst are highly desirable and challenging for sustainable organic synthesis. Herein, we constructed a Cu-doped carbon nitride (CN–Cu) material that functions as a Cu–photo dual catalytic system. The heterogeneous CN–Cu catalyst can be applied to direct α-alkynylation of tertiary amines using air as an oxidant, allowing for the construction of diverse valuable propargylamines in good to excellent yields. The endogenous proximity and synergistic effect between Cu and the photocatalytic center in CN–Cu significantly enhance the Cu–photo dual catalytic activity, thus further improving the reaction efficiency and selectivity. Remarkably, this heterogeneous catalyst exhibits high chemical stability and can be recycled at least five times without loss of activity. [ABSTRACT FROM AUTHOR]
- Published
- 2023
- Full Text
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40. Bioactive Compounds in Sea Buckthorn and their Efficacy in Preventing and Treating Metabolic Syndrome.
- Author
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Chen, Ying, Cai, Yunfei, Wang, Ke, and Wang, Yousheng
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SEA buckthorn ,METABOLIC syndrome ,BIOACTIVE compounds ,PHYTOSTEROLS ,BLOOD sugar ,HIPPOPHAE rhamnoides ,BLOOD lipids - Abstract
Sea buckthorn (Hippophae rhamnoides L. or Elaeagnus rhamnoides L.) is a plant that has long been used as a Chinese herbal medicine. This species is known to contain numerous bioactive components, including polyphenols, fatty acids, vitamins, and phytosterols, which may be responsible for its medicinal value. In experiments both in vitro and in vivo (ranging from cell lines to animal models and human patients), sea buckthorn has shown positive effects on symptoms of metabolic syndrome; evidence suggests that sea buckthorn treatment can decrease blood lipid content, blood pressure, and blood sugar levels, and regulate key metabolites. This article reviews the main bioactive compounds present in sea buckthorn and discusses their efficacy in treating metabolic syndrome. Specifically, we highlight bioactive compounds isolated from distinct sea buckthorn tissues; their effects on abdominal obesity, hypertension, hyperglycemia, and dyslipidemia; and their potential mechanisms of action in clinical applications. This review provides key insight into the benefits of sea buckthorn, promoting future research of this species and expansion of sea buckthorn-based therapies for metabolic syndrome. [ABSTRACT FROM AUTHOR]
- Published
- 2023
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41. High-Frequency Embryogenesis and Regeneration of Plants with High Content of Gentiopicroside from the Chinese Medicinal Plant Gentiana straminea Maxim.
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Cai, Yunfei, Liu, Yanling, Liu, Zhenhua, Zhang, Feng, Xiang, Fengning, and Xia, Guangmin
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- 2009
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42. The Protective Effect of Na+/Ca2+ Exchange Blocker KB-R7943 on Myocardial Ischemia–Reperfusion Injury in Hypercholesterolemic Rat
- Author
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Ren, Yongkui, Deng, Liju, Cai, Yunfei, Lv, Yan, and Jia, Dalin
- Published
- 2014
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43. Reconstruction of Sentinel Images for Suspended Particulate Matter Monitoring in Arid Regions.
- Author
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Duan, Pan, Zhang, Fei, Jim, Chi-Yung, Tan, Mou Leong, Cai, Yunfei, Shi, Jingchao, Liu, Changjiang, Wang, Weiwei, and Wang, Zheng
- Subjects
PARTICULATE matter ,IMAGE reconstruction ,ARID regions ,WATER quality ,IMAGE fusion ,INVERSION (Geophysics) - Abstract
Missing data is a common issue in remote sensing. Data reconstruction through multiple satellite data sources has become one of the most powerful ways to solve this issue. Continuous monitoring of suspended particulate matter (SPM) in arid lakes is vital for water quality solutions. Therefore, this research aimed to develop and evaluate the performance of two image reconstruction strategies, spatio-temporal fusion reflectance image inversion SPM and SPM spatio-temporal fusion, based on the measured SPM concentration data with Sentinel-2 and Sentinel-3. The results show that (1) ESTARFM (Enhanced Spatio-temporal Adaptive Reflection Fusion Model) performed better than FSDAF (Flexible Spatio-temporal Data Fusion) in the fusion image generation, particularly the red band, followed by the blue, green, and NIR (near-infrared) bands. (2) A single-band linear and non-linear regression model was constructed based on Sentinel-2 and Sentinel-3. Analysis of the accuracy and stability of the model led us to the conclusion that the red band model performs well, is fast to model, and has a wide range of applications (Sentinel-2, Sentinel-3, and fused high-accuracy images). (3) By comparing the two data reconstruction strategies of spatio-temporal fused image inversion SPM and spatio-temporal fused SPM concentration map, we found that the fused SPM concentration map is more effective and more stable when applied to multiple fused images. The findings can provide an important scientific reference value for further expanding the inversion research of other water quality parameters in the future and provide a theoretical basis as well as technical support for the scientific management of Ebinur Lake's ecology and environment. [ABSTRACT FROM AUTHOR]
- Published
- 2023
- Full Text
- View/download PDF
44. Determination of manganese content in cottonseed meal using near-infrared spectrometry and multivariate calibration
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Lisheng Bao, Zhao Rubing, Jinhong Chen, Lei Mei, Cong Li, Cheng Li, En Yu, Cai Yunfei, Huang Jieqiong, and Shuijin Zhu
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Multivariate calibration ,Coefficient of determination ,Monte Carlo method ,Analytical chemistry ,chemistry.chemical_element ,Manganese ,lcsh:Plant culture ,Residual ,Agricultural and Biological Sciences (miscellaneous) ,Biochemistry, Genetics and Molecular Biology (miscellaneous) ,Cottonseed ,Chemometrics ,Manganese content ,chemistry ,Near-infrared spectroscopy ,lcsh:SB1-1110 ,Variable elimination ,Cottonseed meal ,Mathematics - Abstract
Background Manganese (Mn) is an essential microelement in cottonseeds, which is usually determined by the techniques relied on hazardous reagents and complex pretreatment procedures. Therefore a rapid, low-cost, and reagent-free analytical way is demanded to substitute the traditional analytical method. Results The Mn content in cottonseed meal was investigated by near-infrared spectroscopy (NIRS) and chemometrics techniques. Standard normal variate (SNV) combined with first derivatives (FD) was the optimal spectra pre-treatment method. Monte Carlo uninformative variable elimination (MCUVE) and successive projections algorithm method (SPA) were employed to extract the informative variables from the full NIR spectra. The linear and nonlinear calibration models for cottonseed Mn content were developed. Finally, the optimal model for cottonseed Mn content was obtained by MCUVE-SPA-LSSVM, with root mean squares error of prediction (RMSEP) of 1.994 6, coefficient of determination (R 2 ) of 0.949 3, and the residual predictive deviation (RPD) of 4.370 5, respectively. Conclusions The MCUVE-SPA-LSSVM model is accuracy enough to measure the Mn content in cottonseed meal, which can be used as an alternative way to substitute for traditional analytical method.
- Published
- 2019
45. Cu(I)/Chiral Vanadium Complex Cooperatively Catalyzed Asymmetric Sulfonation/Rearrangement of Alkenylfurans.
- Author
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Li, Hongxiang, Yang, Qian, Xu, Lei, Wei, Jie, Tang, Yurong, and Cai, Yunfei
- Published
- 2022
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46. Asymmetric Ketoalkylation/Rearrangement of Alkyenlfurans via Synergistic Photoredox/Brønsted Acid Catalysis.
- Author
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Wei, Jie, Tang, Yurong, Yang, Qian, Li, Hongxiang, He, Dongxian, and Cai, Yunfei
- Published
- 2022
- Full Text
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47. Characteristics of Dissolved Organic Matter and Its Relationship with Water Quality along the Downstream of the Kaidu River in China.
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Cheng, Chunyan, Zhang, Fei, Tan, Mou Leong, Kung, Hsiang-Te, Shi, Jingchao, Zhao, Qi, Wang, Weiwei, Duan, Pan, An, Changjiang, Cai, Yunfei, and Li, Xingyou
- Subjects
CARBON content of water ,DISSOLVED organic matter ,WATER quality ,RELATIONSHIP quality ,WATER quality management ,WATER quality monitoring - Abstract
The variability in the quality of water that runs along the course of a river, flowing out of a mountain pass, through an agricultural oasis and into a lake, has been a key topic of research in recent years. In this study, the characteristics of dissolved organic matter (DOM) along the river flow, and its relationship with water quality, were analyzed using the Canadian water quality index (CWQI), parallel factor (PARAFAC) and self-organizing map (SOM). The study results include: (1) The conclusion of field sampling along the lower reaches of the Kaidu River and laboratory measurements of water quality parameters, using CWQI to determine the water quality index of the lower Kaidu River, ranging between 59.58 and 93.47. The water quality of the lower reaches of the Kaidu River generally ranges between moderate and good, and can meet the water use requirements of Class II water function standards. (2) The DOM composition of the river predominantly contained three fluorescence components, while the three fluorescence indices of the water body varied less in different river sections. Based on the SOM training model, the fluorescence intensity of the C1 component was larger among the three fluorescence components, followed by the C2 component, and the smallest fluorescence intensity of the C3, which was dominated by humic-like substances, with a high authigenic origin and humification degree. (3) The fluorescence index and fluorescence components were correlated with water quality parameters, and it was found that C1, C2 and C3 were negative and correlated significantly with SO42- and Total-dissolved solids (TDS) concentrations; FI, HIX and BIX showed strong positive correlations with SAL and Cu and negative correlations with dissolved oxygen (DO). This study provides a scientific basis for surface water quality monitoring and water quality pollution management in the Kaidu River. [ABSTRACT FROM AUTHOR]
- Published
- 2022
- Full Text
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48. Organocatalytic Asymmetric Synthesis of Bridged Tetrahydrobenzo[b]azepines/oxepines.
- Author
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Zou, Jiaming, Xu, Lei, Tang, Yurong, Wang, Wentao, and Cai, Yunfei
- Published
- 2022
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49. A new approach of formation navigation derived from multi-robots cooperative online FastSLAM
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Cai, Yunfei, Tang, Zhenmin, and Zhao, Chunxia
- Published
- 2012
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50. Comparative Antiapoptotic Effects of KB-R7943 and Ischemic Postconditioning During Myocardial Ischemia Reperfusion
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Ren, Yongkui, Cai, Yunfei, and Jia, Dalin
- Published
- 2012
- Full Text
- View/download PDF
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