Multi-baseline interferometric synthetic aperture radar (InSAR) techniques have been accepted as effective remote sensing tools for detecting and monitoring landslide movements. With the use of stacked synthetic aperture radar (SAR) imageries, it is capable of generating precise ground displacement time-series. In order to further suppress noise induced by atmospheric effects, a post-process step, named as temporal filter, is required to be applied to the final displacement time-series in most applications. As displacement signals are strongly correlated in time, the traditional window-based/least squares filter is widely adopted. Since the window-based filter balances a tradeoff between noise smoothing and signal smoothing, the resulting time-series may strongly deviate from the true values when ground displacements appear high nonlinearity. In this paper, a new approach is proposed to reconstruct the InSAR deformation time-series for rainfall-induced landslides. This method establishes a nonparametric model based on the idea of Gaussian process regression (GPR) and introduces precipitation data as a priori knowledge. A strong relationship between rainfall history and ground movements is therefore constructed, which is extremely helpful in preventing the loss of high-frequency displacement signals. The proposed approach was applied to the InSAR landslide displacement time-series obtained from 108 European Space Agency (ESA) Sentinel-1A satellite SAR images. Experimental results demonstrate that it is capable of preserving the details of the temporal evolution of ground displacements effectively compared to the traditional window-based method, in particular on the surface of sliding mass.
Comprehensive bioinformatics analyses were performed to explore the key biomarkers in response to HIV infection of CD4+ and CD8+ T cells. The numbers of CD4+ and CD8+ T cells of HIV infected individuals were analyzed and the GEO database (GSE6740) was screened for differentially expressed genes (DEGs) in HIV infected CD4+ and CD8+ T cells. Gene Ontology enrichment, KEGG pathway analyses, and protein-protein interaction (PPI) network were performed to identify the key pathway and core proteins in anti-HIV virus process of CD4+ and CD8+ T cells. Finally, we analyzed the expressions of key proteins in HIV-infected T cells (GSE6740 dataset) and peripheral blood mononuclear cells(PBMCs) (GSE511 dataset). 1) CD4+ T cells counts and ratio of CD4+ /CD8+ T cells decreased while CD8+ T cells counts increased in HIV positive individuals; 2) 517 DEGs were found in HIV infected CD4+ and CD8+ T cells at acute and chronic stage with the criterial of P-value <0.05 and fold change (FC) ≥2; 3) In acute HIV infection, type 1 interferon (IFN-1) pathway might played a critical role in response to HIV infection of T cells. The main biological processes of the DEGs were response to virus and defense response to virus. At chronic stage, ISG15 protein, in conjunction with IFN-1 pathway might play key roles in anti-HIV responses of CD4+ T cells; and 4) The expression of ISG15 increased in both T cells and PBMCs after HIV infection. Gene expression profile of CD4+ and CD8+ T cells changed significantly in HIV infection, in which ISG15 gene may play a central role in activating the natural antiviral process of immune cells.
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