DTAF: an efficient probe to study cyanobacterial-plant interaction using confocal laser scanning microscopy (CLSM)

Deep learning in food science: An insight in evaluating Pickering emulsion properties by droplets classification and quantification via object detection algorithm

Understanding the complicated emulsion microstructures by microscopic images will help to further elaborate their mechanisms and relevance. The formidable goal of the classification and quantification of emulsion microstructure appears difficult to achieve. However, object detection algorithm in deep learning makes it feasible. This paper reports a new technique for evaluating Pickering emulsion properties through classification and quantification of the emulsion microstructure by object detection algorithm. The trained neural network models characterize the emulsion droplets by distinguishing between different individual emulsion droplets and morphological mechanisms from numerous microscopic images. The quantified results of the emulsion droplets presented in this study, provide details of statistical changes at different concentrations of the Pickering interface and storage temperatures enabling elucidation of the mechanisms involved. This methodology provides a new quantitative and statistical analysis of emulsion microstructure and properties.

Acrylate protects a marine bacterium from grazing by a ciliate predator

Cleavage of dimethylsulfoniopropionate (DMSP) can deter herbivores in DMSP-producing eukaryotic algae; however, it is unclear whether a parallel defence mechanism operates in marine bacteria. Here we demonstrate that the marine bacterium Puniceibacterium antarcticum SM1211, which does not use DMSP as a carbon source, has a membrane-associated DMSP lyase, DddL. At high concentrations of DMSP, DddL causes an accumulation of acrylate around cells through the degradation of DMSP, which protects against predation by the marine ciliate Uronema marinum. The presence of acrylate can alter the grazing preference of U. marinum to other bacteria in the community, thereby influencing community structure.