Chloroplast C-to-U editing, regulated by a PPR protein BoYgl-2, is important for chlorophyll biosynthesis in cabbage.

Leaf color is an important agronomic trait in cabbage (Brassica oleracea L. var. capitata), but the detailed mechanism underlying leaf color formation remains unclear. In this study, we characterized a Brassica oleracea yellow-green leaf 2 (BoYgl-2) mutant 4036Y, which has significantly reduced chlorophyll content and abnormal chloroplasts during early leaf development. Genetic analysis revealed that the yellow-green leaf trait is controlled by a single recessive gene. Map-based cloning revealed that BoYgl-2 encodes a novel nuclear-targeted P-type PPR protein, which is absent in the 4036Y mutant. Functional complementation showed that BoYgl-2 from the normal-green leaf 4036G can rescue the yellow-green leaf phenotype of 4036Y. The C-to-U editing efficiency and expression levels of atpF, rps14, petL and ndhD were significantly reduced in 4036Y than that in 4036G, and significantly increased in BoYgl-2 overexpression lines than that in 4036Y. The expression levels of many plastid- and nuclear-encoded genes associated with chloroplast development in BoYgl-2 mutant were also significantly altered. These results suggest that BoYgl-2 participates in chloroplast C-to-U editing and development, which provides rare insight into the molecular mechanism underlying leaf color formation in cabbage.

A Whole-Genome Assembly for Hyaloperonospora parasitica, A Pathogen Causing Downy Mildew in Cabbage (Brassica oleracea var. capitata L.).

Hyaloperonospora parasitica is a global pathogen that can cause leaf necrosis and seedling death, severely threatening the quality and yield of cabbage. However, the genome sequence and infection mechanisms of H. parasitica are still unclear. Here, we present the first whole-genome sequence of H. parasitica isolate BJ2020, which causes downy mildew in cabbage. The genome contains 4631 contigs and 9991 protein-coding genes, with a size of 37.10 Mb. The function of 6128 genes has been annotated. We annotated the genome of H. parasitica strain BJ2020 using databases, identifying 2249 PHI-associated genes, 1538 membrane transport proteins, and 126 CAZy-related genes. Comparative analyses between H. parasitica, H.arabidopsidis, and H. brassicae revealed dramatic differences among these three Brassicaceae downy mildew pathogenic fungi. Comprehensive genome-wide clustering analysis of 20 downy mildew-causing pathogens, which infect diverse crops, elucidates the closest phylogenetic affinity between H. parasitica and H. brassicae, the causative agent of downy mildew in Brassica napus. These findings provide important insights into the pathogenic mechanisms and a robust foundation for further investigations into the pathogenesis of H. parasitica BJ2020.

Performance Evaluation of Probabilistic Methods Based on Bootstrap and Quantile Regression to Quantify PV Power Point Forecast Uncertainty.

This paper presents two probabilistic approaches based on bootstrap method and quantile regression (QR) method to estimate the uncertainty associated with solar photovoltaic (PV) power point forecasts. Solar PV output power forecasts are obtained using a hybrid intelligent model, which is composed of a data filtering technique based on wavelet transform (WT) and a soft computing model (SCM) based on radial basis function neural network (RBFNN) that is optimized by particle swarm optimization (PSO) algorithm. The point forecast capability of the proposed hybrid WT+RBFNN+PSO intelligent model is examined and compared with other hybrid models as well as individual SCM. The performance of the proposed bootstrap method in the form of probabilistic forecasts is compared with the QR method by generating different prediction intervals (PIs). Numerical tests using real data demonstrate that the point forecasts obtained from the proposed hybrid intelligent model can be effectively used to quantify PV power uncertainty. The performance of these two uncertainty quantification methods is assessed through reliability.