TRPV4 contributes to ER stress: Relation to apoptosis in the MPP+-induced cell model of Parkinson's disease.

AIMS: Parkinson's disease (PD) is a multifactorial neurodegenerative disorder. Its molecular mechanism is still unclear. Endoplasmic reticulum (ER) stress has been highlighted in PD. Transient receptor potential vanilloid 4 (TRPV4) is a kind of nonselective calcium cation channel. A defined role for TRPV4 in PD has not been reported. The purpose of the present research was to investigate the molecular mechanisms by which TRPV4 regulates ER stress induced by the 1-methyl-4-phenylpyridinium ion (MPP+) in PC12 cells. MAIN METHODS: PC12 cells were pretreated with the TRPV4-specific antagonist HC067047 or transfected with TRPV4 siRNA followed by treatment with MPP+. Cell viability was measured by the CCK-8 Assay. The expression of TRPV4, sarco/endoplasmic reticulum Ca2+-ATPase 2 (SERCA2), glucose-regulated protein 78 (GRP78), glucose-regulated protein 94 (GRP94), C/EBP homologous protein (CHOP), procaspase-12, and tyrosine hydroxylase (TH) was detected by western blot and RT-PCR. KEY FINDINGS: The expression of TRPV4 was upregulated, while cell viability was decreased by MPP+, which was reversed by HC067047. The ER stress common molecular signature SERCA2 was depressed by MPP+. Moreover, MPP+ induced upregulation of GRP78, GRP94, CHOP, and decrease in procaspase-12 and TH. HC067047 and TRPV4 siRNA reversed MPP+-induced ER stress and restored TH production. SIGNIFICANCE: TRPV4 functions upstream of ER stress induced by MPP+ and holds promise as a prospective pharmacotherapy target for PD.

A Wavelet Neural Network for SAR Image Segmentation.

This paper proposes a wavelet neural network (WNN) for SAR image segmentation by combining the wavelet transform and an artificial neural network. The WNN combines the multiscale analysis ability of the wavelet transform and the classification capability of the artificial neural network by setting the wavelet function as the transfer function of the neural network. Several SAR images are segmented by the network whose transfer functions are the Morlet and Mexihat functions, respectively. The experimental results show the proposed method is very effective and accurate.

Multiscale Unsupervised Segmentation of SAR Imagery Using the Genetic Algorithm.

A valid unsupervised and multiscale segmentation of synthetic aperture radar(SAR) imagery is proposed by a combination GA-EM of the Expectation Maximization(EM) algorith with the genetic algorithm (GA). The mixture multiscale autoregressive(MMAR) model is introduced to characterize and exploit the scale-to-scale statisticalvariations and statistical variations in the same scale in SAR imagery due to radar speckle,and a segmentation method is given by combining the GA algorithm with the EMalgorithm. This algorithm is capable of selecting the number of components of the modelusing the minimum description length (MDL) criterion. Our approach benefits from theproperties of the Genetic and the EM algorithm by combination of both into a singleprocedure. The population-based stochastic search of the genetic algorithm (GA) exploresthe search space more thoroughly than the EM method. Therefore, our algorithm enablesescaping from local optimal solutions since the algorithm becomes less sensitive to itsinitialization. Some experiment results are given based on our proposed approach, andcompared to that of the EM algorithms. The experiments on the SAR images show that theGA-EM outperforms the EM method.