MicroRNA-519a promotes proliferation and inhibits apoptosis of hepatocellular carcinoma cells by targeting FOXF2

AbstractRecent studies report that microRNA-519a (miR-519a) is a novel oncomir, which facilitates the onset and progression of human cancers. However, the clinical significance of miR-519a and its functional role and underlying mechanisms in hepatocellular carcinoma (HCC) are poorly investigated. In the present study, elevated expression of miR-519a was observed in HCC tissues compared with adjacent non-tumor tissues. The increased level of miR-519a expression was significantly correlated with adverse clinical features of HCC including hepatitis B virus (HBV) infection, large tumor size, cirrhosis and advanced tumor-node-metastasis tumor stage. Furthermore, high expression of miR-519a was prominently associated with a poorer 5-year overall survival and recurrence-free survival of HCC patients. Gain- and loss-of function experiments showed that miR-519a overexpression enhanced proliferation and reduced apoptosis of Huh7 cells. By contrast, miR-519a knockdown inhibited SMMC-7721 cell proliferation and induced apoptosis. Importantly, up-regulation of miR-519a reduced the expression of FOXF2 mRNA and protein in Huh7 cells, while down-regulation of miR-519a resulted in increased expression of FOXF2 in SMMC-7721 cells. An inverse correlation between mRNA levels of miR-519a and FOXF2 was observed in HCC tissues. Thus, Forkhead box F2 (FOXF2) was identified as a downstream target of miR-519a in HCC. Mechanistically, the effects of miR-519a knockdown on SMMC-7721 cells were abrogated by FOXF2 repression. In conclusion, miR-519a is a novel prognostic predictor for HCC patients and it may potentiate proliferation and inhibits apoptosis of HCC cells by targeting FOXF2

Constraint-based automatic symmetry detection

10.1109/ASE.2013.66930622013 28th IEEE/ACM International Conference on Automated Software Engineering, ASE 2013 - Proceedings15-2

Cosaliency detection based on intrasaliency prior transfer and deep intersaliency mining

As an interesting and emerging topic, cosaliency detection aims at simultaneously extracting common salient objects in multiple related images. It differs from the conventional saliency detection paradigm in which saliency detection for each image is determined one by one independently without taking advantage of the homogeneity in the data pool of multiple related images. In this paper, we propose a novel cosaliency detection approach using deep learning models. Two new concepts, called intrasaliency prior transfer and deep intersaliency mining, are introduced and explored in the proposed work. For the intrasaliency prior transfer, we build a stacked denoising autoencoder (SDAE) to learn the saliency prior knowledge from auxiliary annotated data sets and then transfer the learned knowledge to estimate the intrasaliency for each image in cosaliency data sets. For the deep intersaliency mining, we formulate it by using the deep reconstruction residual obtained in the highest hidden layer of a self-trained SDAE. The obtained deep intersaliency can extract more intrinsic and general hidden patterns to discover the homogeneity of cosalient objects in terms of some higher level concepts. Finally, the cosaliency maps are generated by weighted integration of the proposed intrasaliency prior, deep intersaliency, and traditional shallow intersaliency. Comprehensive experiments over diverse publicly available benchmark data sets demonstrate consistent performance gains of the proposed method over the state-of-the-art cosaliency detection methods

Molecular circuit for exponentiation based on the domain coding strategy

DNA strand displacement (DSD) is an efficient technology for constructing molecular circuits. However, system computing speed and the scale of logical gate circuits remain a huge challenge. In this paper, a new method of coding DNA domains is proposed to carry out logic computation. The structure of DNA strands is designed regularly, and the rules of domain coding are described. Based on this, multiple-input and one-output logic computing modules are built, which are the basic components forming digital circuits. If the module has n inputs, it can implement 2n logic functions, which reduces the difficulty of designing and simplifies the structure of molecular logic circuits. In order to verify the superiority of this method for developing large-scale complex circuits, the square root and exponentiation molecular circuits are built. Under the same experimental conditions, compared with the dual-track circuits, the simulation results show that the molecular circuits designed based on the domain coding strategy have faster response time, simpler circuit structure, and better parallelism and scalability. The method of forming digital circuits based on domain coding provides a more effective way to realize intricate molecular control systems and promotes the development of DNA computing