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Showing posts with label Yuqi Cao. Show all posts
Showing posts with label Yuqi Cao. Show all posts
Sunday, December 22, 2019
Abstract-Study on glycoprotein terahertz time-domain spectroscopy based on composite multiscale entropy feature extraction method
Pingjie Huan, Zhangwei Huang, Xiaodong Lu, Yuqi Cao, Jie Yu, Dibo Hou, Guangxin Zhang
https://www.sciencedirect.com/science/article/abs/pii/S1386142519313393
Tumor genesis is accompanied by glycosylation of related proteins. Glycoprotein is usually regarded as a tumor marker since glycoproteins are consumed remarkably more by the cancer cells than the normal ones. In this paper, the terahertz time-domain attenuated total reflection (ATR) technique is applied to inspect the glycoprotein solution from a concentration gradient of 0.2 mg/ml to 50 mg/ml. A significant nonlinear relationship between the absorption coefficient and the concentrations has been discovered. The influence of the dynamical hydration shell around glycoprotein molecules on the absorption coefficient is discussed and the phenomenon is explained by the concepts of THz excess and THz defect. In order to identify glycoproteins, features are obtained by composite multiscale entropy (CMSE) method and clustered by the K-means algorithm. The results indicate that features extracted by the CMSE method are better than the Principal Component Analysis (PCA) method in both specificity and sensitivity of recognition. Meanwhile, the absorption coefficient and dielectric loss angle tangent are more suitable for qualitative identification. Research shows that the CMSE method has important directive significance for analyzing glycoprotein terahertz spectroscopy. And it has the potential for glycoprotein related tumor markers identification using terahertz technology in medical applications.
Monday, September 2, 2019
Abstract-Analysis and inspection techniques for mouse liver injury based on terahertz spectroscopy
Pingjie Huang, Yuqi Cao, Jiani Chen, Weiting Ge, Dibo Hou, and Guangxin Zhang
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Flow chart of injured liver tissue discrimination algorithm based on terahertz spectra. |
At present, researchers are exploring biological tissue detection method using terahertz techniques. In this paper, techniques to inspect mouse liver injury by using terahertz spectroscopy were studied. The boxplots were applied to remove abnormal data, and the maximal information coefficient was employed to select crucial features from the absorption coefficient and refractive index spectra. Random Forests and AdaBoost were applied to recognize different levels of liver injury. We found that AdaBoost had better performance on low-level injury classification. This work suggests that terahertz techniques have the potential to detect liver injury at an early stage and evaluate liver treatment strategies.
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