Learn to Propagate Reliably on Noisy Affinity Graphs

Recent works have shown that exploiting unlabeled data through label propagation can substantially reduce the labeling cost, which has been a critical issue in developing visual recognition models. Yet, how to propagate labels reliably, especially on a dataset with unknown outliers, remains an open question. Conventional methods such as linear diffusion lack the capability of handling complex graph structures and may perform poorly when the seeds are sparse. Latest methods based on graph neural networks would face difficulties on performance drop as they scale out to noisy graphs. To overcome these difficulties, we propose a new framework that allows labels to be propagated reliably on large-scale real-world data. This framework incorporates (1) a local graph neural network to predict accurately on varying local structures while maintaining high scalability, and (2) a confidence-based path scheduler that identifies outliers and moves forward the propagation frontier in a prudent way. Experiments on both ImageNet and Ms-Celeb-1M show that our confidence guided framework can significantly improve the overall accuracies of the propagated labels, especially when the graph is very noisy.Comment: 14 pages, 7 figures, ECCV 202

MicroRNA-300 inhibits the metastasis of prostate cancer through the regulation of TRIM63

Purpose: To investigate the role of microRNA-300 in the tumorigenesis of prostate cancer (PCa), and the relationship between microRNA-300 level and clinical data of PCa patients.Methods: MicroRNA-300 levels in 63 matched PCa and adjacent tissues were determined via quantitative real-time polymerase chain reaction (qRT-PCR). The relationship between microRNA-300 level and the clinical profile of PCa patients was assessed. PCa cell phenotypes influenced by microRNA-300 were evaluated by a series of functional experiments, including CCK-8, colony formation, Transwell and wound healing assays. The role of microRNA-300/TRIM63 axis in the development of PCa was also examined.Results: MicroRNA-300 was more lowly expressed in PCa tissues than in adjacent normal tissues. PCa patients expressing lower levels of microRNA-300 had a higher Gleason score, higher rates of lymphatic metastasis and distant metastasis, and lower survival. Overexpression of microRNA-300 suppressed its proliferative and metastatic potential. Dual-luciferase reporter assay data confirmed that microRNA-300 specifically binds Tripartite Motif-containing Protein 63 (TRIM63). TRIM63 level was downregulated in PCa cells overexpressing microRNA-300.  Moreover, overexpression of TRIM63 abolished the role of microRNA-300 in influencing PCa cell phenotypes.Conclusion: MicroRNA-300 is downregulated in PCa. Its level is related to Gleason score, lymphatic metastasis, distant metastasis and poor prognosis of PCa patients. MicroRNA-300 stimulates proliferative and metastatic abilities in PCa cells by targeting TRIM63. This study may provide new targets for the development of new therapeutics for of PCa as well as its diagnosis