Guohua Ren

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In recent years, transforming growth factor-β (TGF-β) and the serine-arginine protein kinase 1 (SRPK1) have been recommended as a key signal mediator that is involved in oncogenesis. However, the mechanisms underlying TGF-β-SRPK1 pathway-mediated proliferation and apoptosis in the esophageal squamous cell carcinomas (ESCC) have not been well featured till(More)
In recent years, microRNAs, also called as miRNAs, play an important role in carcinogenesis, and the dysregulation of miRNAs is closely associated with cancer progression. Till now, little has been known about the role of miRNA-146a in the esophageal squamous cell carcinomas (ESCC). In the present study, we used in vitro assays to investigate the mechanisms(More)
In this work a distributed tracking technique for multiple non-overlapping targets is developed such that it utilizes only sensors that acquire informative observations about the targets. A framework is designed where norm-one regularized factorization is employed to decompose the sensor data covariance matrix into sparse factors whose support facilitates(More)
This paper considers the problem of tracking multiple sources using observations acquired at spatially scattered sensors. Kalman filtering and smoothing techniques are combined with a sparse matrix estimation framework. A pertinent normone regularized minimization formulation is proposed that jointly searches for source-informative sensors, associates(More)
Sensor mobility and sparse sensor data correlations are exploited in this work for the problem of tracking multiple targets. Sparse matrix decomposition is integrated with the design of proper kinematic rules to identify informative sensors, associate them with the targets and enable them to follow closely the moving targets. The modified barrier method is(More)
Particle filtering is combined with sparse matrix decomposition techniques to address the problem of tracking multiple targets using nonlinear sensor observations measuring signal strength. The unknown number of targets may be time-varying, while sensors are spatially scattered. Norm-one regularized matrix factorization is employed to decompose the sensing(More)