MicroRNAs (miRNAs) are frequently dysregulated in human cancers and can act as potent oncogenes or tumor suppressor genes. Aberrant expression of miR-424 has been identified in some types of cancer, however, its expression and potential biologic role in endometrial cancer are remains to be determined. In the present study, we demonstrated that miR-424 was downregulated in human endometrial cancer and suppressed growth of the human Ishikawa and HEC-1B endometrial cancer cell lines. Bioinformatics analysis indicated that E2F7 was a putative target of miR-424. In a luciferase reporter system, we confirmed that E2F7 was a direct target gene of miR-424. Furthermore, knockdown of E2F7 inhibited Ishikawa and HEC-1B cell growth. These findings indicate that miR-424 targets the E2F7 transcript and suppresses endometrial cancer cell growth, suggesting that miR-424 has a tumor suppressive role in human endometrial cancer pathogenesis.

Graph convolutional networks have been widely used for skeleton-based action recognition due to their excellent modeling ability of non-Euclidean data. As the graph convolution is a local operation, it can only utilize the short-range joint dependencies and short-term trajectory but fails to directly model the distant joints relations and long-range temporal information that are vital to distinguishing various actions. To solve this problem, we present a multi-scale spatial graph convolution (MS-GC) module and a multi-scale temporal graph convolution (MT-GC) module to enrich the receptive field of the model in spatial and temporal dimensions. Concretely, the MS-GC and MT-GC modules decompose the corresponding local graph convolution into a set of sub-graph convolution, forming a hierarchical residual architecture. Without introducing additional parameters, the features will be processed with a series of sub-graph convolutions, and each node could complete multiple spatial and temporal aggregations with its neighborhoods. The final equivalent receptive field is accordingly enlarged, which is capable of capturing both short- and long-range dependencies in spatial and temporal domains. By coupling these two modules as a basic block, we further propose a multi-scale spatial temporal graph convolutional network (MST-GCN), which stacks multiple blocks to learn effective motion representations for action recognition. The proposed MST-GCN achieves remarkable performance on three challenging benchmark datasets, NTU RGB+D, NTU-120 RGB+D and Kinetics-Skeleton, for skeleton-based action recognition.

Tuning element content is an effective approach for achieving excellent rate-capability and cycling stability in polynary metal sulfides for hybrid supercapacitors.

A simple and mild catalytic coupling reaction of propargyl halides with organotitanium reagents is reported. The reaction of propargyl bromide with organo-titanium reagents mediated by NiCl2 (2 mol%) and PCy3 (4 mol%) in CH2Cl2 afforded coupling product allenes in good to excellent yields (up to 95%) at room temperature. However, NiCl2(PPh3)2 was the best catalyst for substituted propargyl halides to yield allenes or alkynes preferentially. On the basis of the experimental results, a possible catalytic cycle has been proposed.