Concentration-dependent dual roles of proanthocyanidins on oxidative stress and docosahexaenoic acid production in Schizochytrium sp. ATCC 20888.

Antioxidant addition is an effective strategy to achieve docosahexaenoic acid (DHA) overproduction in oleaginous microorganisms. Nevertheless, antioxidants like phenolic compounds sometimes exert pro-oxidant activity. In this work, effects of proanthocyanidins (PAs) on fermentation performance and oxidative stress in Schizochytrium sp. were investigated. Low PAs addition (5 mg/L) reduced reactive oxygen species and enhanced lipogenic enzymes activities and NADPH, resulting in significant increase in lipid (20.3 g/L) by 33.6 % and DHA yield (9.8 g/L) by 53.4 %. In contrast, high PAs addition (500 mg/L) exerted pro-oxidant effects, aggravated oxidative damage and lipid peroxidation, leading to sharp decrease in biomass (21.3 g/L) by 35.1 %, lipid (8.2 g/L) by 46.0 %, and DHA (2.9 g/L) by 54.8 %. Therefore, the antioxidant concentration is especially crucial in DHA production. This study is the first to report concentration-dependant dual roles of PAs in oxidative stress and DHA production in Schizochytrium sp., providing new insights into microbial DHA production.

Particle Swarm Optimization and Salp Swarm Algorithm for the Segmentation of Diabetic Retinal Blood Vessel Images.

In recent years, the incidence of diabetes has been increasing year by year. Since most of the fundus lesions are located near blood vessels, the image information is complex, and the end vessels are difficult to identify. So, a new segmentation method of diabetic retinal vessel images based on particle swarm optimization and salp swarm algorithm is proposed. This paper uses a Gaussian filter to enhance the main blood vessels, and a top-bot hat transform is used to strengthen the end vessels. The preprocessing process is completed by combining and reconstructing the two images through a normalization operation. The improved particle swarm optimization and salp swarm algorithms perform multi-threshold segmentation on the preprocessed vessel images. The best fit value, Structural Similarity Index Measure, Peak Signal to Noise Rati, feature similarity index measure, sensitivity, accuracy, regional consistency, Dice coefficient, Jaccard similarity, and Shannon entropy are selected for comprehensive evaluation and analysis. The results showed that this paper's improved particle swarm-salp swarm algorithm for segmenting diabetic retinal blood vessel images is more efficient, and the threshold is better. The vascular segmentation method in this paper is applied in medical image processing, which improves the accuracy of medical image processing and reduces the computational effort.

Characterization of the complete chloroplast genome of the medicinal herb Veronica polita Fr. 1819 (Lamiales: Plantaginaceae).

Veronica polita Fr. 1819 (synonym: Veronica didyma Ten. 1981) is a species of annual herb with high medicinal value. It is originally from Southwest Asia, but has been naturalized widely in many regions of the world. In this study, the complete chloroplast genome of V. polita was determined to be 150,191 bp long with a typical quadripartite structure, comprising two inverted repeat regions (IRa and IRb, 25,465 bp each), a large single-copy (LSC) region (81,847 bp) and a small single-copy (SSC) region (17,414 bp). It encodes a panel of 114 genes (including 79 protein-coding, 31 tRNA, and four rRNA genes) with 18 of them being completely or partially duplicated and 19 of them possessing one or two introns. Phylogenetic analysis supported the tribal-level taxonomy of the family Plantaginaceae, and revealed that V. polita was most closely related to the congener Veronica persica Poir. 1808.