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Multiwavelets possess better properties than traditional wavelets. Multiwavelet packet transformation has more high-frequency information. Spectral entropy can be applied as an analysis index to the complexity or uncertainty of a signal. This paper tries to define four multiwavelet packet entropies to extract the features of different transmission line faults, and uses a radial basis function (RBF) neural network to recognize and classify 10 fault types of power transmission lines. First, the preprocessing and postprocessing problems of multiwavelets are presented. Shannon entropy and Tsallis entropy are introduced, and their difference is discussed. Second, multiwavelet packet energy entropy, time entropy, Shannon singular entropy, and Tsallis singular entropy are defined as the feature extraction methods of transmission line fault signals. Third, the plan of transmission line fault recognition using multiwavelet packet entropies and an RBF neural network is proposed. Finally, the experimental results show that the plan with the four multiwavelet packet energy entropies defined in this paper achieves better performance in fault recognition. The performance with SA4 (symmetric antisymmetric) multiwavelet packet Tsallis singular entropy is the best among the combinations of different multiwavelet packets and the four multiwavelet packet entropies.

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BackgroundHepatitis B virus (HBV) plays a critical role in the tumorigenic behavior of human hepatocellular carcinoma (HCC). MicroRNAs (miRNAs) have been reported to participate in HCC development via the regulation of their target genes. However, HBV-modulated miRNAs involved in tumorigenesis remain to be identified. Here, we found that a novel highly expressed miRNA, TLRC-m0008_3p (miR-3928v), may be an important factor that promotes the malignancy of HBV-related HCC.MethodsSolexa sequencing was applied to profile miRNAs, and RT-qPCR was used to identify and quantitate miRNAs. We studied miR-3928v function in HCC cell lines by MTT, colony formation, migration/invasion, and vascular mimicry (VM) assays in vitro and by a xenograft tumor model in vivo. Finally, we predicted and verified the target gene of miR-3928v by a reporter assay, studied the function of this target gene, and cloned the promoter of miR-3928v and the transcription factor for use in dual-luciferase reporter assays and EMSAs.ResultsA variant of miR-3928 (miR-3928v) was identified and found to be highly expressed in HBV (+) HCC tissues. Voltage-dependent anion channel 3 (VDAC3) was validated as a target of miR-3928v and found to mediate the effects of miR-3928v in promoting HCC growth and migration/invasion. Furthermore, HBx protein increased early growth response 1 (EGR1) expression and facilitated its translocation into the nucleus to enhance miR-3928v promoter activity in an NF-κB signaling-dependent manner.ConclusionsmiR-3928v is induced by HBx through the NF-κB/EGR1 signaling pathway and down-regulates the tumor suppressor gene VDAC3 to accelerate the progression of HCC.Electronic supplementary materialThe online version of this article (10.1186/s13046-018-0681-y) contains supplementary material, which is available to authorized users.

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