Co-Pi decorated TiO 2 @graphitic carbon nitrides (g-C 3 N 4 ) nanorod arrays (denoted as CCNRs) with different mass ratios of g-C 3 N 4 have been constructed on the FTO substrate through three processes, hydrothermal growth, chemical bath deposition and electrodeposition. Firstly, TiO 2 nanorod arrays were grown onto a FTO substrate by a hydrothermal method. Secondly, g-C 3 N 4 was coated onto the TiO 2 nanorod arrays by immersing the above substrate with TiO 2 nanorod arrays into a solution of urea and then heated at higher temperature. In this procedure, the amount of the g-C 3 N 4 on the TiO 2 nanorod arrays can be controlled by tuning the concentration of the urea solution. At last, Co-Pi were decorated on the surface of the TiO 2 @g-C 3 N 4 by electrodeposition. The as-prepared CCNRs were characterized by XRD, FESEM, TEM, XPS, UV-vis and FTIR, respectively, which illustrated that Co-Pi were successfully decorated on the hybrid TiO 2 @g-C 3 N 4 nanorod arrays.Photoelectrochemical (PEC) measurements have demonstrated that the prepared CCNRs serve as an efficient and stable photoanode for PEC seawater splitting. The photocurrent density reaches 1.6 mA/cm 2 under 100 mW/cm 2 (AM1.5G) light illumination at 1.23 V (RHE). More significantly, the CCNRs photoanode is quite stable during seawater splitting and the performance remain undiminished even after ten hours continuous illumination. Finally, a systematical photocatalytic mechanism of the Co-Pi decorated TiO 2 @g-C 3 N 4 was proposed and it can be considered as potential explanation of enhanced PEC performance. .7 eV 35 , has attracted more and more attentions for its inherent chemical and thermal stability 36, 37 . Unlike transitional metal oxides and sulfide semiconductor photocatalysts, g-C 3 N 4 behaves very stable performance in acid or alkaline electrolytes ascribing to strong covalent bonds between carbon and nitride atoms in its structure 38 . However, applications of pure g-C 3 N 4 are limited largely because of its low quantum efficiency and high electron-hole recombination rate 39 .Therefore, it lays a large space to explore and construct novel composite materials to remedy these deficiencies of pure g-C 3 N 4 .Herein, we combined TiO 2 and g-C 3 N 4 to fabricate TiO 2 @g-C 3 N 4 nanorod arrays 40 with different mass ratios of g-C 3 N 4 via hydrothermal growth and chemical bath deposition. Moreover, the composite nanorod arrays were decorated with Co-Pi particles 41-43 to offset the inadequacy of g-C 3 N 4 44 . To the best of our knowledge, this is the first report upon semiconductor nanorod arrays modified with tunable g-C 3 N 4 mass ratio. As reported, Co-Pi decorated TiO 2 @g-C 3 N 4 nanorod arrays (CCNRs) used for photoelectrode within PEC cells are still unexplored. More importantly, the results in this work have proved that the composited nanorod arrays as photoelectrodes exhibit very efficient and stable performances for PEC seawater splitting.
Gastric cancer incidence is relatively higher in China than that in developed countries; however, molecular mechanisms considering the initiation and progression of gastric cancer are still unclear. For decades, numerous microRNAs have been found to regulate a wide range of biological functions in gastric cancer. However, the oncogenic function of miR-615-3p in gastric cancer has not been reported to date. With the help of gene and microRNA chips in 10 patients, we were able to screen differential expressed genes and microRNAs compared with normal gastric tissues. After that, online bioinformatics analysis tools were used to predict microRNAs' potential targets. As a result, miR-615-3p and its potential target, CELF2, were selected for further experiments. QRT-PCR and western blot results indicated the aberrant high expression of miR-615-3p and low expression of CELF2 in gastric cancer both in vivo and in vitro. Moreover, miR-615-3p expression correlated to T and M stage. Up regulation of miR-615-3p inhibited the apoptosis, promoted proliferation and migration and led to the down-regulation of CELF2. Meanwhile, down-regulation of miR-615-3p resulted in anti-tumor effects. Immunochemistry staining of CELF2 showed its association with T, N and M stage. In addition, overexpression of CELF2 could reverse miR-615-3p's oncogenic functions stated before. These findings indicate that miR-615-3p promotes gastric cancer proliferation and migration by suppressing CELF2 expression for the first time, providing clues for future clinical practices.
Accumulation of AGEs may upregulate the expression of MMP-1 by HGFs, which may play a role in the development of diabetes-associated periodontitis, and RAGE/NF-κB pathway may be involved in metabolism of MMP-1 in HGFs.
Every piece of textual data is generated as a method to convey its authors' opinion regarding specific topics. Authors deliberately organize their writings and create links, i.e., references, acknowledgments, for better expression. Thereafter, it is of interest to study texts as well as their relations to understand the underlying topics and communities. Although many efforts exist in the literature in data clustering and topic mining, they are not applicable to community discovery on large document corpus for several reasons. First, few of them consider both textual attributes as well as relations. Second, scalability remains a significant issue for large-scale datasets. Additionally, most algorithms rely on a set of initial parameters that are hard to be captured and tuned. Motivated by the aforementioned observations, a hierarchical community model is proposed in the paper which distinguishes community cores from affiliated members. We present our efforts to develop a scalable community discovery solution for large-scale document corpus. Our proposal tries to quickly identify potential cores as seeds of communities through relation analysis. To eliminate the influence of initial parameters, an innovative attribute-based core merge process is introduced so that the algorithm promises to return consistent communities regardless initial parameters. Experimental results suggest that the proposed method has high scalability to corpus size and feature dimensionality, with more than 15% topical precision improvement compared with popular clustering techniques.
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