Lactobacillus gasseri LA39 Activates the Oxidative Phosphorylation Pathway in Porcine Intestinal Epithelial Cells

Intestinal microbial interactions with the host epithelium have important roles in host health. Our previous data have suggested that Lactobacillus gasseri LA39 is the predominant intestinal Lactobacillus in weaned piglets. However, the regulatory role of L. gasseri LA39 in the intestinal epithelial protein expression in piglets remains unclear. In the present study, we conducted comparative proteomics approach to investigate the intestinal epithelial protein profile alteration caused by L. gasseri LA39 in piglets. The expressions of 15 proteins significantly increased, whereas the expressions of 13 proteins significantly decreased in the IPEC-J2 cells upon L. gasseri LA39 treatment. Bioinformatics analyses, including COG function annotation, GO annotation, and KEGG pathway analysis for the differentially expressed proteins revealed that the oxidative phosphorylation (OXPHOS) pathway in IPEC-J2 cells was significantly activated by L. gasseri LA39 treatment. Further data indicated that two differentially expressed proteins UQCRC2 and TCIRG1, associated with the OXPHOS pathway, and cellular ATP levels in IPEC-J2 cells were significantly up-regulated by L. gasseri LA39 treatment. Importantly, the in vivo data indicated that oral gavage of L. gasseri LA39 significantly increased the expression of UQCRC2 and TCIRG1 and the cellular ATP levels in the intestinal epithelial cells of weaned piglets. Our results, both in vitro and in vivo, reveal that L. gasseri LA39 activates the OXPHOS pathway and increases the energy production in porcine intestinal epithelial cells. These findings suggest that L. gasseri LA39 may be a potential probiotics candidate for intestinal energy production promotion and confers health-promoting functions in mammals

SC-PNN: Saliency Cascade Convolutional Neural Network for Pansharpening

In many remote sensing tasks, different types of regions or targets differ in requirements for spectral and spatial quality. The discrepancy reveals that a uniform pansharpening strategy applying to the entire image may not fulfill the varying demands of different regions appropriately. From this aspect, we resort to saliency analysis to distinguish regions with different spatial and spectral requirements and then propose a new saliency cascade convolutional neural network for pansharpening (SC-PNN). SC-PNN is composed of two parts: a dilated deformable convolutional network (DDCN) for saliency analysis and a saliency cascade residual dense network (SC-RDN) for pansharpening. DDCN is a fully convolutional network based on hybrid dilated convolution and deformable convolution, aiming to separate salient regions, such as residential areas from nonsalient areas, including mountains and vegetation areas, with well-defined boundaries and integrity. In the fusion process, SC-RDN is specially designed with the help of saliency analysis. We first construct a deep regression network to estimate a primarily sharpened image and subsequently leverage the saliency map produced by DDCN to develop a saliency enhancement module. In this module, the quality of salient and nonsalient areas is further improved by two independent deep residual dense networks. Thus, a precise fused image can be predicted. Experiments on SPOT5, GeoEye-1, and WorldView-3 data sets reveal that, compared to state-of-the-art pansharpening methods, our proposal has a superior ability to improve the spatial quality and preserve spectral information. The effectiveness of the saliency enhancement module is also validated in the experiment.No Full Tex