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With the rapid development of information technology, researchers have paid attention to the pump-driven two-phase cooling loop technology for data centers, which imposes requirements on the efficiency and size of the pump. A fluid self-lubricating centrifugal pump with R134a refrigerant was developed to reach a higher rotation speed and oil-free system, resulting in a more diminutive size. Due to the high rotation speed and refrigerant pressure approaching saturated vapor pressure, the internal flow characteristics and cavitating characteristics are critical and complex. This paper focuses on the prototype’s head and cavitation performance based on experimental and numerical data. The experiments indicated that the head coefficient of the pump under design conditions is 0.9881, and the pump’s critical cavitation number and breakdown number are 0.551 and 0.412, respectively. The numerical results can predict the head and cavitation with deviations less than 2.6%. To study changing patterns in flow characteristics under the different operating conditions in the refrigerant centrifugal pump, the numerical model based on a modified Sauer-Schnerr cavitation model was built to analyze the distributions of pressure, temperature, relative velocity, and bubble volume across every hydraulic component and different degrees of cavitation, and proposed the influence of the thermal effect on refrigerant cavitating. The cavitating flow characteristics were obtained with the aim of providing guidance for the hydraulic design of a refrigerant centrifugal pump.

Long non-coding RNA (lncRNA) ACTA2-AS1 has been reported to play an important role in the progression of multiple human malignancies. The article aims to explore the role of ACTA2-AS1 on the cisplatin resistance of non-small cell lung cancer (NSCLC). RT-qPCR was performed to investigate the expression of ACTA2-AS1 in cisplatin-resistant NSCLC cell lines. Western blot was used to investigate the effects of ACTA2-AS1 on autophagy-related protein expression. RIP assay and RNA pull down were used to analyze the combination of ACTA2-AS1 and enhancer of zeste homolog 2 (EZH2), and CHIP was used to analyze the combination of tuberous sclerosis complex-2 (TSC2) gene promoter and Lys-27 of histone H3 (H3K27me3). In this study, ACTA2-AS1 was downregulated in cisplatin-resistant NSCLC cell lines. ACTA2-AS1 negatively regulated the cell viability and positively regulated the cell apoptosis of cisplatin-resistant NSCLC cell lines. Furthermore, our results demonstrated that ACTA2-AS1 promoted cisplatin-resistant NSCLC cells apoptosis through inhibiting autophagy. The regulation of ACTA2-AS1 to the cisplatin-resistant NSCLC cell autophagy was reversed by TSC2 increasing. Importantly, our results displayed that ACTA2-AS1 bound with EZH2, and TSC2 gene promoter combined with H3k27me3. The inhibition of ACTA2-AS1 to TSC2 expression was recused by EZH2 silencing. In conclusion, ACTA2-AS1 inhibited the cisplatin resistances of NSCLC cell lines through suppressing TSC2 expressing by recruiting EZH2 to TSC2 gene promoter.

Aim: To search the genes related to the mechanisms of the occurrence of glioma and to try to build a prediction model for glioblastomas. Background: The morbidity and mortality of glioblastomas are very high, which seriously endanger human health. At present, the goals of many investigations on gliomas are mainly to understand the cause and mechanism of these tumors at the molecular level and to explore clinical diagnosis and treatment methods. However, there is no effective early diagnosis method for this disease, and there are no effective prevention, diagnosis, or treatment measures. Methods: Firstly, the gene expression profiles derived from GEO were downloaded. Then, differentially expressed genes (DEGs) in the disease samples and the control samples were identified. After that, GO and KEGG enrichment analyses of DEGs were performed by DAVID. Furthermore, the correlation- based feature subset (CFS) method was applied to the selection of key DEGs. In addition, the classification model between the glioblastoma samples and the controls was built by a Support Vector Machine (SVM) based on selected key genes. Results and Discussion: Thirty-six DEGs, including 17 upregulated and 19 downregulated genes, were selected as the feature genes to build the classification model between the glioma samples and the control samples by the CFS method. The accuracy of the classification model by using a 10-fold crossvalidation test and the independent set test was 76.25% and 70.3%, respectively. In addition, PPP2R2B and CYBB can also be found in the top 5 hub genes screened by the protein-protein interaction (PPI) network. Conclusions: This study indicated that the CFS method is a useful tool to identify key genes in glioblastomas. In addition, we also predicted that genes such as PPP2R2B and CYBB might be potential biomarkers for the diagnosis of glioblastomas.

Viscosity is the main rheology parameter of thermosetting resin matrix processing, which is an important factor effecting processing performance. The viscosity-temperature curves at dynamic states and viscosity-time curves at stead state were measured by cone-plate rotate viscosimeter, and the rheology property of bio-based resin system based on rosin-sourced anhydride curing agent was studied according to the optimization improved six parameter dual-arrhenius equation. Two kinds of rheology models were established by fitting reaction rate constant K and pre-exponential factor A according to the exponential or logarithm viscosity relative equation, respectively. The rheology model based on logarithm viscosity relative equation simulation was in good agreement with experimental data than exponential viscosity relative equation fitting. The rheology model demonstrated the law of viscosity change at different processing conditions, which provided necessary scientific basis for processing parameter optimization.