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Abstract Objective To investigate the prognostic significance of tumor necrosis factor receptor (TNFR),-associated factor 6 (TRAF6),-and ubiquitin in gastric cancer patients. Methods Biopsies of the rectus abdominis muscle were obtained intra operatively from 102 gastric cancer patients and 29 subjects undergoing surgery for benign abdominal diseases, and muscle TRAF6 and ubiquitin mRNA expression and proteasome proteolytic activities were assessed. Results TRAF6 was significantly upregulated in muscle of gastric cancer compared with the control muscles. TRAF6 was upregulated in 67.65% (69/102) muscle of gastric cancer. Over expression of TRAF6 in muscles of gastric cancer were associated with TNM stage, level of serum albumin and percent of weight loss. Ubiquitin was significantly upregulated in muscle of gastric cancer compared with the control muscles. Ubiquitin was upregulated in 58.82% (60/102) muscles of gastric cancer. Over expression of ubiquitin in muscles of gastric cancer were associated with TNM (Tumor-Node-Metastasis) stage and weight loss. There was significant relation between TRAF6 and ubiquitin expression. Conclusions We found a positive correlation between TRAF6 and ubiquitin expression, suggesting that TRAF6 may up regulates ubiquitin activity in cancer cachexia. While more investigations are required to understand its mechanisms of TRAF6 and ubiquitin in skeletal muscle. Correct the catabolic-anabolic imbalance is essential for the effective treatment of cancer cachexia.

For trees, leaves are often used for identification, but the shape of leaves changes greatly, bark will be another identifying feature. However, it is difficult to recognize by a single organ when there are intra class differences and inter class similarities between leaves or bark. So we fuse features of leaf and bark. Firstly, we collected 17 species of leaves and bark of trees through field shooting and web crawling. Then propose a method of combining convolution neural network (CNN) with cascade fusion, additive fusion algorithm, bilinear fusion and score level fusion. Finally, the features extracted from the leaves and bark are fused in the ReLu layer and Fully connected layer. The method was compared with single organ recognition, Support Vector Machines (SVM), and existing fusion methods, results show that the two organ fusion method proposed are better than the other recognition methods, and recognition accuracy is 87.86%. For similar trees, when it is impossible to accurately determine its species by a single organ, the fusion of two organs can effectively improve this situation.