Electron spin-resonance results indicate that the contents of h +, ˙OH, 3H can efficiently catalyze the synthesis of xylonic acid (88.1%) from xylose with excellent stability and reusability. The successful doping of phosphorus and grafting of sulfonic acid groups significantly extend the visible light adsorption range and decrease the photoluminescence intensity of the resulting carbon nitride, thereby enhancing its photocatalytic activity. Herein, novel phosphorus-doped carbon nitride with grafted sulfonic acid groups 3H) was prepared and successfully used in the photocatalytic-reforming of xylose to xylonic acid.

© 2017 International Society for Magnetic Resonance in Medicine.The photocatalytic selective oxidation of biomass-derived feedstocks to high-value organic acids is promising and challenging, especially for the production of xylonic acid. Imaging 2017 46:1281-1288.Īpparent diffusion coefficient bladder cancer grade radiomics support vector machine texture feature.

The proposed radiomics strategy using these features, combined with the SVM classifier, may better facilitate image-based bladder cancer grading preoperatively.ģ Technical Efficacy: Stage 2 J. Textural features from DWI and ADC maps can reflect the difference between low- and high-grade bladder cancer, especially those GLCM features from ADC maps. The SVM classifier using the optimal feature subset achieved the best performance in bladder cancer grading, with an area under the receiver operating characteristic curve, accuracy, sensitivity, and specificity of 0.861, 82.9%, 78.4%, and 87.1%, respectively. By the SVM-RFE method, an optimal feature subset including 22 features was further selected from candidate features. Then support vector machine with recursive feature elimination (SVM-RFE) and classification strategy was adopted to find an optimal feature subset and then to establish a classification model for grading.Ī total 102 features were derived from each VOI and among them, 47 candidate features were selected, which showed significant intergroup differences (P < 0.05). A Mann-Whitney U-test was applied to select features with significant differences between low- and high-grade groups (P < 0.05). Histogram- and gray-level co-occurrence matrix (GLCM)-based radiomics features were extracted from cancerous volumes of interest (VOIs) on DWI and corresponding ADC maps of each patient acquired from 3.0T magnetic resonance imaging (MRI). In all, 61 patients with bladder cancer (29 in high- and 32 in low-grade groups) were enrolled in this retrospective study. To 1) describe textural features from diffusion-weighted images (DWI) and apparent diffusion coefficient (ADC) maps that can distinguish low-grade bladder cancer from high-grade, and 2) propose a radiomics-based strategy for cancer grading using texture features.

How to load xiaopan