HER2及VEGF在乳腺癌组织中的表达及其相关性研究(英文)

Objective:The aim of our study was to detect the correlation between the expressions of HER2 and VEGF in breast cancer, and their relations with some pathological factors. Methods:By immunohistochemistry technique, the expressions of HER2 and VEGF in the post-operation samples of 117 cases with breast cancer were assessed, and their relations with some pathological factors were analysed by statistical methods. Fifty samples of hyperplasia of mammary glands were observed as the control. Results: The positive expression rates of HER2 and VEGF in breast cancer were both significantly higher than those in hyperplasia of mammary gland (P<0.05). The expressions of HER2 and VEGF were both correlated to lymph node metastasis (P<0.05), but showed no relations with age, histological type, histological stage, tumor size (P>0.05). The positive expression rate of HER2 had a positive correlation with those of VEGF (P<0.05, r=0.373). Conclusion: The expressions of HER2 and VEGF have no correlations with age, histological type, histological stage, tumor size, but are closely related with lymphatic metastasis. The positive expression rates of HER2 shows a positive correlation with those of VEGF.

Short-term tunnel-settlement prediction based on Bayesian wavelet:a probability analysis method

As urbanization accelerates,the metro has become an important means of transportation.Considering the safety problems caused by metro construction,ground settlement needs to be monitored and predicted regularly,especially when a new metro line crosses an existing one.In this paper,we propose a settlement-probability prediction model with a Bayesian emulator(BE)based on the Gaussian prior(GP),that is,a GPBE.In addition,considering the distortion characteristics of monitoring data,the data is denoised using wavelet decomposition(WD),so the final prediction model is WD-GPBE.In particular,the effects of different prediction ratios and moving windows on prediction performance are explored,and the optimal number of moving windows is determined.In addition,the predicted value for GPBE based on the original data is compared with the predicted value for WD-GPBE based on the denoised data.One year of settlement-monitoring data collected by a structural health monitoring(SHM)system installed on the Nanjing Metro is used to demonstrate the effectiveness of WDGPBE and GPBE for predicting settlement.

Prediction of maximum upward displacement of shield tunnel linings during construction using particle swarm optimization-random forest algorithm

During construction,the shield linings of tunnels often face the problem of local or overall upward movement after leaving the shield tail in soft soil areas or during some large diameter shield projects.Differential floating will increase the initial stress on the segments and bolts which is harmful to the service performance of the tunnel.In this study we used a random forest(RF)algorithm combined particle swarm optimization(PSO)and 5-fold cross-validation(5-fold CV)to predict the maximum upward displacement of tunnel linings induced by shield tunnel excavation.The mechanism and factors causing upward movement of the tunnel lining are comprehensively summarized.Twelve input variables were selected according to results from analysis of influencing factors.The prediction performance of two models,PSO-RF and RF(default)were compared.The Gini value was obtained to represent the relative importance of the influencing factors to the upward displacement of linings.The PSO-RF model successfully predicted the maximum upward displacement of the tunnel linings with a low error(mean absolute error(MAE)=4.04 mm,root mean square error(RMSE)=5.67 mm)and high correlation(R^(2)=0.915).The thrust and depth of the tunnel were the most important factors in the prediction model influencing the upward displacement of the tunnel linings.

Influence of adjacent shield tunneling construction on existing tunnel settlement: field monitoring and intelligent prediction

Urban subway tunnel construction inevitably disturbs the surrounding rock and causes the deformation of existing subway structures. Dynamic predictions of the tunnel horizontal displacement, tunnel ballast settlement, and tunnel differential settlement are important for ensuring the safety of buildings and tunnels. First, based on the Hangzhou Metro project, we analyzed the influence of construction on the deformation of existing subway structures and the difficulties and key points in monitoring. Then, a deformation prediction model, based on a back propagation(BP) neural network, was established with massive monitoring data. In particular, we analyzed the influence of four structures of the BP neural network on prediction performance, i.e., single input–single hidden layer–single output, multiple inputs–single hidden layer–single output, single input–double hidden layers–single output, and multiple inputs–double hidden layers–single output, and verified them using measured data.