The treatment of complicated long segment strictures remains to a challenge, and the substitution urethroplasty treatment is often accompanied by subsequent tissue fibrosis and secondary stricture formation. In situ injection of human adipose tissue‐derived stem cells (hADSC) could potential be applied for prevention of urethral fibrosis, but the cells transplantation alone may be insufficient because of the complicated histopathological micro‐environmental changes in the injury site. This study investigated whether miR‐21 modification can improve the therapeutic efficacy of ADSCs against urethral fibrosis to limit urethral stricture recurrence. MiR‐21‐modified ADSCs (miR‐21) were constructed via lentivirus‐mediated transfer of pre‐miR‐21 and GFP reporter gene. In vitro results suggested that miR‐21 modification can increase the angiogenesis genes expression of ADSCs and enhance its anti‐oxidative effects against reactive oxygen species (ROS) damage. In vivo results showed that miR‐21 modification contributes to increased urodynamic parameters and better formation of the epithelium and the muscle layer as compared to ADSCs transplantation alone groups. The results demonstrated that miR‐21 modification in ADSCs could improve urethral wound healing microenvironment, enhance stem cell survival through ROS scavenging and promote the neovascularization via regulating angiogenic genes expression, which eventually increase the ADSCs' therapeutic potential for urethral wound healing.

Focusing on the issue of missing measurement data caused by complex and changeable working conditions during the operation of high-speed trains, in this paper, a framework for the reconstruction of missing measurement data based on a generative adversarial network is proposed. Suitable parameters were set for each frame. Discrete measurement data are taken as the input of the frame for preprocessing the data dimensionality. The convolutional neural network then learns the correlation between different characteristic values of each device in an unsupervised pattern and constrains and improves the reconstruction accuracy by taking advantage of the context similarity of authenticity. It was determined experimentally that when there are different extents of missing measurement data, the model described in the present paper can still maintain a high reconstruction accuracy. In addition, the reconstruction data also conform well to the distribution law of the measurement data.

Objective: Ovarian cancer and renal cancer are malignant tumors; however, the relationship between TTK Protein Kinase (TTK), AKT-mTOR pathway and ovarian cancer, renal cancer remains unclear. Methods: Download GSE36668 and GSE69428 from Gene Expression Omnibus (GEO) database. Weighted gene co-expression network analysis (WGCNA) was performed. Created protein-protein interaction (PPI) network. Used Gene Ontology analysis (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) for functional enrichment analysis. Gene Set Enrichment Analysis (GSEA) analysis and survival analysis were performed. Created animal model for western blot analysis. Gene Expression Profiling Interactive Analysis (GEPIA) was performed to explore the role of TTK on the overall survival of renal cancer. Results: GO showed that DEGs were enriched in anion and small molecule binding, and DNA methylation. KEGG analysis presented that they mostly enriched in cholesterol metabolism, type 1 diabetes, sphingolipid metabolism, ABC transporters, etc., TTK, mTOR, p-mTOR, AKT, p-AKT, 4EBP1, p-4EBP1 and Bcl-2 are highly expressed in ovarian cancer, Bax, Caspase3 are lowly expressed in ovarian cancer, cell apoptosis is inhibited, leading to deterioration of ovarian cancer. Furthermore, the TTK was not only the hub biomarker of ovarian cancer, but also one significant hub gene of renal cancer, and its expression was up-regulated in the renal cancer. Compared with the renal cancer patients with low expression of TTK, the patients with high expression of TTK have the poor overall survival (P = 0.0021). Conclusion: TTK inhibits apoptosis through AKT-mTOR pathway, worsening ovarian cancer. And TTK was also one significant hub biomarker of renal cancer.

In response to the high-speed and high-precision collaborative control requirements of the multimotor system for filling, a new type of virtual master-axis control structure is proposed and a multimotor fixed-time optimized collaborative control algorithm is designed. Firstly, coupling relationship between virtual and slave motors is effectively established by designing a velocity compensation module for the virtual motor. Secondly, the sliding mode observer (SMO) is used to reconstruct the composite disturbance composed of motor parameter perturbation and load disturbance. Finally, the variable gain terminal sliding mode controller (SMC) is designed to ensure that each slave motor can track the given value within a fixed time. The fast convergence of the system can be proved by the fixed-time convergence theorem and Lyapunov’s stability theorem. The simulation results show that, compared with the traditional virtual main-axis control strategy, the proposed method is more effective for the tracking control of each slave motor in the initial stage.

Multimodal sentiment analysis (MSA) aims to infer emotions from linguistic, auditory, and visual sequences. Multimodal information representation method and fusion technology are keys to MSA. However, the problem of difficulty in fully obtaining heterogeneous data interactions in MSA usually exists. To solve these problems, a new framework, namely, dynamic invariant-specific representation fusion network (DISRFN), is put forward in this study. Firstly, in order to effectively utilize redundant information, the joint domain separation representations of all modes are obtained through the improved joint domain separation network. Then, the hierarchical graph fusion net (HGFN) is used for dynamically fusing each representation to obtain the interaction of multimodal data for guidance in the sentiment analysis. Moreover, comparative experiments are performed on popular MSA data sets MOSI and MOSEI, and the research on fusion strategy, loss function ablation, and similarity loss function analysis experiments is designed. The experimental results verify the effectiveness of the DISRFN framework and loss function.