A novel "raisin bun"-like nanocomposite, where Pd clusters are embedded in porphyrin matrix, is developed as a promising electrocatalyst. Thanks to the synergy between the Pd clusters and the porphyrin matrix, this composite exhibits a low oxidation potential, high mass activity and excellent stability toward electrochemical oxidation of formic acid, which opens new routes for the design of high-performance catalysts in fuel cells.

In this study, a facile and efficient protocol to enhance the β‐phase content of poly(vinylidene fluoride) (PVDF) is developed, in which the effect of room temperature ionic liquids (RTILs), including [1‐butyl‐3‐methylimidazolium (bmim)][PF6], [bmim][BF4], [bmim][FeCl4] and [bmim][Cl], on the crystallization behavior of PVDF is investigated. The resulting PVDF/RTIL hybrids are characterized by Fourier transform infrared (FTIR) spectroscopy, XRD, polarized optical microscopy (POM) and DSC. The FTIR spectroscopy and XRD results show that the fraction of β‐phase, F(β), is significantly enhanced by the incorporation of RTILs, specifically from 49.2% for neat PVDF to 92.6% for hybrid filled with 15 wt% [bmim][PF6]. The analysis of the crystallization behavior based on the DSC tests reveals that the degree of crystallinity increases with incorporation of RTILs, implying that RTILs could act as directing agents to facilitate the crystallization process, which is further evidenced by the POM results. In addition, the non‐isothermal crystallization kinetics of PVDF and PVDF/RTIL composites are investigated by means of DSC and the results indicate that the addition of the RTILs significantly influences the mechanism of nucleation and growth of PVDF crystallites. © 2013 Society of Chemical Industry

Papillary renal cell carcinoma(PRCC) is the second most common and aggressive renal cell carcinoma. Identification of novel microRNA biomarkers could be beneficial for the diagnosis and prognosis of PRCC patients. We aimed to screen differentially expressed miRNAs that can act as prognostic factors and to predict the survival of PRCC patients. High-throughput data of miRNAs of 274 PRCC samples were downloaded from TCGA (The Cancer Genome Atlas) dataset and interested miRNAs were identified. Hierarchical clustering and principal component analysis (PCA) were performed on these miRNAs. Critical genes that can act as prognostic factors were screened by LASSO. What's more, Kaplan-Meier survival analysis and ROC (Receiver Operating Characteristic) growth curve were used to testify the accuracy of the model. Biological processes of putative targets of miRNAs were analyzed by bioinformatics methods such as GO (Go Ontology) and KEGG (Kyoto Encyclopedia of Genes and Genomes) analysis. A total of 105 differentially expressed miRNAs were screened out in PRCC samples compared with healthy controls. Two critical miRNAs, hsa-mir-3199-2, and hsa-mir-1293, were screened out by LASSO (Least Absolute Shrinkage and Selection Operator), including 197 and 189 target genes, respectively. Furthermore, its' accuracy was testified by ROC analysis with the AUC (Area under the curve) value of 0.7774968 and 0.6743466. These miRNAs were significantly enriched in pathways as platelet activating factor biosynthetic process, epithelial cell maturation, and IkappaB kinase complex. In conclusion, hsa-mir-3199-2 and hsa-mir-1293 that can act as prognostic biomarkers of PRCC were screened out, which can provide new insights for the clinical treatment of the disease. J. Cell. Biochem. 118: 3488-3494, 2017. © 2017 Wiley Periodicals, Inc.

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