The Immuno-fluorescence Quantity Analysis of α-Tubulin and γ-Tubulin Protein in Precancerous Lesion and Carcinoma of the Breast and Its Significance

OBJECTIVE To investigate the changes and values of the expression of α-tubulin and γ-tubulin in atypical ductal hyperplasia （ADH）, ductal carcinoma in situ （DCIS） and invasive ductal carcinoma （IDC） of the breast. The relationship between centrosome abnormalities and breast tumor development was further discussed. METHODS There were three groups including ADH, DCIS and IDC with 30 cases in each group. They were analyzed by immuno-fiuorescence quantity analysis. The expression levels of α-tubulin and γ-tubulin protein in these tissues were detected by flow cytometry immuno-fiuorescence analysis and compared with the results from normal tissues. Immunohistochemistry was also performed in this research. RESULTS The results showed significant differences of the average of the positive （FITC labeled） cells （P=0.000） among the four groups. The level of the IDC group was the highest, while normal breast tissue showed the lowest level. The results suggested that the expression levels of α-tubulin and γ-tubulin both increased as the grade of cellular proliferation and differentiation increased. The expressions showed significant differences among all the groups, except between the ADH and DCIS. There were no significant differences between α-tubulin and γ-tubulin expression in each group （P〈0.05）, as there was agreement in the immuno-fluorescence and immunohistochemical analysis for protein expression. CONCLUSION There is abnormal expression of centrosome tubulin as an early event in the development of breast tumor. Furthermore these aberrations may play a key role during oncogenesis and promote cellular transformation to malignancy. The immuno-fiuorescence quantitive analysis and immunohistochemistry can complement each other.

Analysis of A Heavy Rainstorm Process during Main Flood Season of 2009 in Hunan Province

Conventional observation data,precipitation data from regional automatic stations,1°×1° NCEP reanalysis data and TBB pictures of FY-2C geostationary meteorological satellite as well as Doppler radar,etc.were utilized to analyzing the heavy precipitation process in Hunan Province from June 8 to 10.The results indicated that this heavy precipitation process was caused under the condition of western Pacific subtropical high jumped northward and fell southward rapidly,maintained and swung the shear line of low and middle-level atmosphere over long periods,and configurated temperature-moisture energy.Through analysis we found that precipitation period and precipitation area had a good corresponding to radar product and satellite TBB image,the high potential pseudo-equivalent temperature(θse) of low level and high convergence available potential energy(CAPE) area as well as ascending area of strong convergence.With the extension of effective forecasted period,the forecast location of T639 and EC on the western ridge points of western Pacific subtropical high became more and more easterly and the intensity became weaker and weaker,which had some deviations for forecasting heavy precipitation area.

Fingerprint Liveness Detection Based on Multi-Scale LPQ and PCA

Fingerprint authentication system is used to verify users' identification according to the characteristics of their fingerprints.However,this system has some security and privacy problems.For example,some artificial fingerprints can trick the fingerprint authentication system and access information using real users' identification.Therefore,a fingerprint liveness detection algorithm needs to be designed to prevent illegal users from accessing privacy information.In this paper,a new software-based liveness detection approach using multi-scale local phase quantity(LPQ) and principal component analysis(PCA) is proposed.The feature vectors of a fingerprint are constructed through multi-scale LPQ.PCA technology is also introduced to reduce the dimensionality of the feature vectors and gain more effective features.Finally,a training model is gained using support vector machine classifier,and the liveness of a fingerprint is detected on the basis of the training model.Experimental results demonstrate that our proposed method can detect the liveness of users' fingerprints and achieve high recognition accuracy.This study also confirms that multi-resolution analysis is a useful method for texture feature extraction during fingerprint liveness detection.