PurposeGastric carcinogenesis is a multistep process and is the second-highest cause of cancer death worldwide with a high incidence of invasion and metastasis. MicroRNAs (miRNAs) engage in complex interactions with the machinery that controls the transcriptome and concurrently target multiple mRNAs. Recent evidence has shown that miRNAs are involved in the cancer progression, including promoting cell-cycle, conferring resistance to apoptosis, and enhancing invasiveness and metastasis. Here, we aim to elucidate the roles of miRNAs, especially microRNA-4665-3p (miR-4665-3p), in the inhibitory effect of arsenic sulfide in gastric cancer (GC).MethodsThe arsenic sulfide-induced miRNA expression alterations in AGS cells was determined by miRNA microarray. RT-PCR was used to further verify the arsenic sulfide-regulated miRNAs in GC tissues. The inhibition of miR-4665-3p on the migration and invasion of GC cells were determined by wound healing assay and transwell assay. Western blot analysis was used to detect the expression of EMT related proteins and the putative target of miR-4665-3p.ResultsThe miR-4665-3p was up-regulated by arsenic sulfide and showed inhibition upon the migration and invasion of GC cells. MiRBase and Western blotting indicated that miR-4665-3p directly down-regulated the oncoprotein GSE1. Morphological observation also indicated that the up-regulation of miR-4665-3p inhibits the EMT in GC cells.ConclusionOur data demonstrates that the increased expression of miR-4665-3p induced by arsenic sulfide suppresses the cell invasion, metastasis and EMT of GC cells, and has the potential to be a novel therapeutic target in GC.

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In the field of social network, fast detection of the burst topic plays a decisive role in emergency response and disposal. However, social data are noisy and sparse, which evolves with time going on and space changing make it difficult to catch the instant semantics with traditional methods. Instead of passively waiting for an emergency topic, we try to detect the latent burst topic in its budding stage. In this paper, we propose a fast burst topic detect method, namely, FBTD, which aligns data prediction with characteristic calculation to detect burst term from the real-time spatial-temporal data stream and integrates local topic detection with global topic detection to find the spatial-temporal burst topic. Our method controls the delay within a 0.1 s level while preserving the topic quality. The experiments show that preferable effects are procured, and our method outperforms the state-of-the-art approaches in terms of effectiveness.

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Transforming user check-in data into graph structure data is a popular and powerful way to analyze users' behaviors in the field of recommendation. Graph-based deep learning methods such as graph embeddings and graph neural networks have shown promising performance on the task of point-of-interest recommendation in recent years. Despite effectiveness, existing methods fail to capture deep graph structural information, leading the suboptimal representations. In addition, they lack the ability of learning the influences of both global preference and user preference on the check-in behavior. To address the aforementioned issues, we propose a general framework based on preference-aware graph diffusion, named PGD. We first construct two types of graphs to represent the global preference and user preference. Then, we apply a graph diffusion process to capture the structural information of the generated graphs, resulting in weighted adjacency matrices. Finally, graph neural network-based backbones are introduced to learn the representations of users and POIs on weighted adjacency matrices. A learnable aggregation module is developed to learn the final representations from global preference and user preference adaptively. Extensive experiments on four real-world datasets demonstrate the superiority of PGD on POI recommendation, compared with the mainstream graph-based deep learning methods.