It has been confirmed that circular RNA participates in tumorgenesis through a variety of ways, so it may be used as a molecular marker for tumor diagnosis and treatment. In this study, the expression of circ-LOPD2 in ovarian cancer tissues and cell lines was detected by qRT-PCR and Western blot. The dual luciferase report was used to verify the target of circ-LOPD2, and the silencing and overexpression of circ-CSPP1 in cell lines was used to explore its relationship with miRNA-378. The cell proliferation was detected by CCK8 method, and the expression level of miRNA-378 was detected by qRT-PCR. The results showed that circ-LOPD2 was highly expressed in ovarian cancer (OC) tissues, circ-LOPD2 expression levels were higher in OVCAR3 and A2780, and circ-LOPD2 expression levels in CAOV3 were lower. After silencing circ-LOPD2, the growth ability of OVCAR3 and A2780 cells decreased, while overexpression of circ-LOPD2 led to the opposite result. We also found that miR-378 is a target of circ-LOPD2. Silencing circ-LOPD2 will increase the expression of miR-378, and overexpression of circ-LOPD2 will decrease the expression of miR-378. In summary, our results show that circ-LOPD2 as a miR-378 sponge promotes the proliferation of ovarian cancer cells, which may in turn promote the development of OC.

The vegetation coverage in different areas was calculated based on remote sensing images (TM images)

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The objective of this paper is the description of the development and the validation, using airborne hyper-spectral imagery data, of a non-conventional technique for the vegetation information extraction. The proposed approach namely the universal pattern decomposition method (UPDM) is tailored for hyper-spectral imagery analysis, which can be explained using two analysis methods: spectral mixing analysis and multivariate analysis. For the former, the UPDM expresses the spectrum of each pixel as the linear sum of three fixed, standard spectral patterns (i.e., the patterns of water, vegetation, and soil); each coefficient represents the ratio of spectral patterns of three components. If we think of the UPDM as multivariate analysis, standard patterns are interpreted as an oblique coordinate system, and coefficients are thought of as the coordinates of a pixel's reflectance. The later explanation is much more comprehensible than the former for the reason of additional supplementary pattern presence when necessary. The vegetation index based on the UPDM (VIUPD) is expressed as a linear sum of the pattern decomposition coefficients. Here, the VIUPD was used to examine vegetation amounts and degree of terrestrial vegetation vigor; VIUPD results were compared with results by the normalized difference vegetation index (NDVI), and an enhanced vegetation index (EVI). This paper described the calculation of VIUPD, using AVIRIS airborne remotely sensed data. The results showed that the VIUPD reflects vegetation and vegetation activity more sensitively than the NDVI and EVI.

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