Prediction of xylanase optimal temperature by support vector regression

Background: Support vector machine (SVM), a novel powerful machine learning technology, was used to develop the non-linear quantitative structure-property relationship (QSPR) model of the G/11 xylanase based on the amino acid composition. The uniform design (UD) method was applied to optimize the running parameters of SVM for the first time. Results: Results showed that the predicted optimum temperature of leave-one-out (LOO) cross-validation fitted the experimental optimum temperature very well, when the running parameter C, \u190, and \u3b3 was 50, 0.001 and 1.5, respectively. The average root-mean-square errors (RMSE) of the LOO cross-validation were 9.53\ubaC, while the RMSE of the back propagation neural network (BPNN), was 11.55\ubaC. The predictive ability of SVM is a minor improvement over BPNN, but it is superior to the reported method based on stepwise regression. Two experimental examples proved the validation of the model for predicting the optimal temperature of xylanase. Conclusion: The results indicated that UD might be an effective method to optimize the parameters of SVM, which could be used as an alternative powerful modeling tool for QSPR studies of xylanase

Depth extraction in computational integral imaging based on bilinear interpolation

We proposed a method using a merit function to determine the depth of objects in computational integral imaging by analyzing the existing methods for depth extraction of target objects. To improve the resolution of reconstructed slice images, we use a digital camera moving in horizontal and vertical direction with the set interval to get elemental images with high resolution and bilinear interpolation algorithm to increase the number of pixels in slice image which improves the resolution obviously. To show the feasibility of the proposed method, we carried out our experiment and presented the results. We also compared it with other merit functions. The results show that merit function SMD2 to determine the depth of objects is more accurate and suitable for real-time application

Additional file 1: Figure S1. of Oligomerization triggered by foldon: a simple method to enhance the catalytic efficiency of lichenase and xylanase

The profiles of plasmid lichenase. The profiles of plasmid monomeric lichenase a, the gene was cloned between NedI and HindIII digestion sites in pET 22b(+); plasmid trimeric lichenase b, foldon was directly fused with the HindIII digestion sites in pET 22b(+). (docx 3190 KB

Additional file 2: Figure S2. of Oligomerization triggered by foldon: a simple method to enhance the catalytic efficiency of lichenase and xylanase

The profiles of plasmid xylanase. The profiles of plasmid monomeric xylanase a, the gene was cloned between NedI and HindIII digestion sites in pET 22b(+); plasmid trimeric xylanase b, foldon was directly fused with the HindIII digestion sites in pET 22b(+). (docx 3750 kb