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Mannosylerythritol lipids-A (MEL-A) is a novel biosurfactant with excellent surface activity and potential biomedical applications. In this study, we explored the antibacterial activity and the underlying mechanisms of MEL-A against the important food-borne pathogen Listeria monocytogenes. The bacterial growth and survival assays revealed a remarkable antibacterial activity of MEL-A. Since MEL-A is a biosurfactant, we examined the cell membrane integrity and morphological changes of MEL-A-treated bacteria by biochemical assays and flow cytometry analysis and electron microscopes. The results showed obvious damaging effects of MEL-A on the cell membrane and morphology. To further explore the antibacterial mechanism of MEL-A, a transcriptome analysis was performed, which identified 528 differentially expressed genes (DEGs). Gene ontology (GO) analysis revealed that the gene categories of membrane, localization and transport were enriched among the DEGs, and the analysis of the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways demonstrated significant changes in the maltodextrin ABC transporter system and stress response system. Furthermore, the growth of L. monocytogenes could also be significantly inhibited by MEL-A in milk, a model of a real food system, suggesting that MEL-A could be potentially applied as an natural antimicrobial agent to control food-borne pathogens in the food industry.

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Exopolysaccharides (EPSs) derived from lactobacilli have important physiological effects and are commonly used as new prebiotics. We identified and studied a new Lactobacillus strain, YY-112, isolated from waxberry (Myrica rubra)....

High-quality point clouds have practical significance for point-based rendering, semantic understanding, and surface reconstruction. Upsampling sparse, noisy and nonuniform point clouds for a denser and more regular approximation of target objects is a desirable but challenging task. Most existing methods duplicate point features for upsampling, constraining the upsampling scales at a fixed rate. In this work, the flexible upsampling rates are achieved via edge vector based affine combinations, and a novel design of Edge Vector based Approximation for Flexible-scale Point clouds Upsampling (PU-EVA) is proposed. The edge vector based approximation encodes the neighboring connectivity via affine combinations based on edge vectors, and restricts the approximation error within the second-order term of Taylor's Expansion. The EVA upsampling decouples the upsampling scales with network architecture, achieving the flexible upsampling rates in one-time training. Qualitative and quantitative evaluations demonstrate that the proposed PU-EVA outperforms the state-of-the-arts in terms of proximity-to-surface, distribution uniformity, and geometric details preservation.