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Bevacizumab Regulates Cancer Cell Migration by Activation of STAT3
Wu, Huan-Huan,Zhang, Shuai,Bian, Huan,Li, Xiao-Xu,Wang, Lin,Pu, Yin-Fei,Wang, Yi-Xiang,Guo, Chuan-Bin Asian Pacific Journal of Cancer Prevention 2015 Asian Pacific journal of cancer prevention Vol.16 No.15
There are numerous clinical cases indicating that long-term use of bevacizumab may increase the invasiveness of tumors. However, to date, little is known about underlying molecular mechanisms. Therefore, the purpose of our study was to investigate effects of bevacizumab in four cancer cells lines (WSU-HN6, CAL27, Tca83, and HeLa). It was found to promote migration and invasion in the WSU-HN6 and Tca83 cases, while exerting inhibitory effects in CAL27 and HeLa cells. The signal transducer and activator of transcription (STAT) 3 inhibitors niclosamide and S3I-201 inhibited the STAT3 signal pathway, which is activated by bevacizumab. These inhibitors also substantially blocked bevacizumab-induced migration of WSU-HN6 and Tca83 cells. Bevacizumab upregulated interleukin (IL)-6 and phosphorylated (p)-STAT3 expression time-dependently. Therefore, we propose that bevacizumab has differential effects on the migration of different cancer cell lines and promotes migration via the IL-6/STAT3 signaling pathway.
Synthesis of N-Azaaryl Anilines: An Efficient Protocol via Smiles Rearrangement
Shuai Xia,Li-Ying Wang,Heng-Zhi Sun,Huan Yue,Xiu-Hua Wang,Jia-Lian Tan,Yin Wang,Di Hou,Xiao-Yan He,Ki-Cheol Mun,B. Prem kumar,Hua Zuo,신동수 대한화학회 2013 Bulletin of the Korean Chemical Society Vol.34 No.2
An efficient process for the synthesis of N-azaaryl anilines via Smiles rearrangement as a tool. A variety of Nazaaryl anilines were generated by the reaction of substituted phenols, substituted anilines, aminopyridines and chloroacetyl chloride or pyridols, under base condition in good to excellent yields.
Use of Word Clustering to Improve Emotion Recognition from Short Text
Shuai Yuan,Huan Huang,Linjing Wu 한국정보과학회 2016 Journal of Computing Science and Engineering Vol.10 No.4
Emotion recognition is an important component of affective computing, and is significant in the implementation of natural and friendly human-computer interaction. An effective approach to recognizing emotion from text is based on a machine learning technique, which deals with emotion recognition as a classification problem. However, in emotion recognition, the texts involved are usually very short, leaving a very large, sparse feature space, which decreases the performance of emotion classification. This paper proposes to resolve the problem of feature sparseness, and largely improve the emotion recognition performance from short texts by doing the following: representing short texts with word cluster features, offering a novel word clustering algorithm, and using a new feature weighting scheme. Emotion classification experiments were performed with different features and weighting schemes on a publicly available dataset. The experimental results suggest that the word cluster features and the proposed weighting scheme can partly resolve problems with feature sparseness and emotion recognition performance.
Synthesis of N-Azaaryl Anilines: An Efficient Protocol via Smiles Rearrangement
Xia, Shuai,Wang, Li-Ying,Sun, Heng-Zhi,Yue, Huan,Wang, Xiu-Hua,Tan, Jia-Lian,Wang, Yin,Hou, Di,He, Xiao-Yan,Mun, Ki-Cheol,Kumar, B. Prem,Zuo, Hua,Shin, Dong-Soo Korean Chemical Society 2013 Bulletin of the Korean Chemical Society Vol.34 No.2
An efficient process for the synthesis of N-azaaryl anilines via Smiles rearrangement as a tool. A variety of N-azaaryl anilines were generated by the reaction of substituted phenols, substituted anilines, aminopyridines and chloroacetyl chloride or pyridols, under base condition in good to excellent yields.
Use of Word Clustering to Improve Emotion Recognition from Short Text
Yuan, Shuai,Huang, Huan,Wu, Linjing Korean Institute of Information Scientists and Eng 2016 Journal of Computing Science and Engineering Vol.10 No.4
Emotion recognition is an important component of affective computing, and is significant in the implementation of natural and friendly human-computer interaction. An effective approach to recognizing emotion from text is based on a machine learning technique, which deals with emotion recognition as a classification problem. However, in emotion recognition, the texts involved are usually very short, leaving a very large, sparse feature space, which decreases the performance of emotion classification. This paper proposes to resolve the problem of feature sparseness, and largely improve the emotion recognition performance from short texts by doing the following: representing short texts with word cluster features, offering a novel word clustering algorithm, and using a new feature weighting scheme. Emotion classification experiments were performed with different features and weighting schemes on a publicly available dataset. The experimental results suggest that the word cluster features and the proposed weighting scheme can partly resolve problems with feature sparseness and emotion recognition performance.
Na-Na Jin,Shuai Liu,Huan-Shui Zhang 제어·로봇·시스템학회 2021 International Journal of Control, Automation, and Vol.19 No.3
In this paper, we investigate the linear optimal estimation problems of discrete-time and continuous-time systems with multiple state delays in measurements. For discrete-time systems, we obtain the linear optimal estimation of state by direct calculation of optimal gain in terms of the solution to a retarded Riccati-like difference equation instead of a group of Riccati difference equations. For continuous-time systems, we also obtain the analytical expression of linear optimal estimation without resorting to Riccati partial differential equations. All the Riccati equations are of the same dimension as the system to be estimated and the computational cost is much saved. Infinite horizon case is also studied by stability analysis. Kalman filter can be recovered from our result when delays disappear. A numerical example is provided to demonstrate the results.