Worldwide, metastatic melanoma of the skin has an aggressive course with high morbidity and mortality. Therefore, an increased understanding of the pathogenesis of metastatic melanoma has gained increasing attention, including the role of epigenetic modification and competing endogenous RNA (ceRNA). This study aimed to used bioinformatics data to undertake an integrative analysis of long noncoding RNA (lncRNA), microRNA (miRNA) and mRNA expression to construct a ceRNA network in metastatic melanoma. Data from the Cancer Genome Atlas (TCGA), the Gene Ontology (GO) database, and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway were analyzed. There were 471 cases that included 103 primary solid tumors and 368 cases of metastatic melanoma that included transcriptome sequencing data (including lncRNA and mRNA); 452 cases had miRNA sequencing data. Analysis of chip data identified 85 6 mRNAs, 67 miRNAs, and 250 lncRNAs that were differentially expressed in cases of metastatic melanoma, of which 25 miRNAs, 18 lncRNAs, and 18 mRNAs participated in the formation of ceRNAs. Survival analysis identified seven differentially expressed mRNAs, five differentially expressed miRNAs (miRNA-29c, miRNA-100, miR-142-3p, miR-150, miR-516a-2), and six differentially expressed lncRNAs (AC068594.1, C7orf71, FAM41C, GPC5-AS1, MUC19, LINC00402) that were correlated with survival time in patients with metastatic melanoma. Bioinformatics data and integrative analysis identified lncRNA, miRNA, and mRNA expression to construct a ceRNA and patient survival network in metastatic melanoma. These findings support the need for further studies on the mechanisms involved in the regulation of metastatic melanoma by ceRNAs.

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The typical characteristics of hyperspectral remote sensing data are combining image with spectrum, high spectral resolution, many bands and much redundant information. A hyperspectral image cube can be used to obtain millions spectrum curves. Aiming at a hyperspectral remote sensing image containing huge amounts of data, removing redundant information and reducing processing dimensions are the premise and foundation for hyperspectral remote sensing processing and applications. Hyperspectral data dimension reduction techniques mainly include the feature extraction and band selection. This paper has fully studied the theories and methods of dimension reduction for band selection, analyses their advantages, disadvantages and validity, and deeply discusses the current situation and tendency of the development of band selection based on dimension reduction of hyperspectral remote sensing image at last.

A classical time-varying signal, the multi-component Chirp signal has been widely used and the ability to estimate its instantaneous frequency (IF) is very useful. But in noisy environments, it is hard to estimate the IF of a multi-component Chirp signal accurately. Wigner distribution maxima (WDM) are usually utilized for this estimation. But in practice, estimation bias increases when some points deviate from the true IF in high noise environments. This paper presents a new method of multi-component Chirp signal IF estimation named Wigner Viterbi fit (WVF), based on Wigner-Ville distribution (WVD) and the Viterbi algorithm. First, we transform the WVD of the Chirp signal into digital image, and apply the Viterbi algorithm to separate the components and estimate their IF. At last, we establish a linear model to fit the estimation results. Theoretical analysis and simulation results prove that this new method has high precision and better performance than WDM in high noise environments, and better suppression of interference and the edge effect. Compared with WDM, WVF can reduce the mean square error (MSE) by 50% when the signal to noise ration (SNR) is in the range of -15dB to -11dB. WVF is an effective and promising IF estimation method.

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