Search

Abstract Rapeseed (Brassica napus L.), accounts for nearly 16% of vegetable oil, is the world’s second produced oilseed. However, pod shattering has caused significant yield loses in rapeseed production, particularly during mechanical harvesting. The GH28 genes can promote pod shattering by changing the structure of the pod cell wall in Arabidopsis. However, the role of the GH28 gene family in rapeseed was largely unknown. Therefore, a genome-wide comprehensive analysis was conducted to classify the role of GH28 gene family on rapeseed pod shattering. A total of 37 BnaGH28 genes in the rapeseed genome were identified. These BnaGH28s can be divided into five groups (Group A-E), based on phylogenetic and synteny analysis. Protein property, gene structure, conserved motif, cis-acting element, and gene expression profile of BnaGH28 genes in the same group were similar. Specially, the expression level of genes in group A-D was gradually decreased, but increased in group E with the development of silique. Among eleven higher expressed genes in group E, two BnaGH28 genes (BnaA07T0199500ZS and BnaC06T0206500ZS) were significantly regulated by IAA or GA treatment. And the significant effects of BnaA07T0199500ZS variation on pod shattering resistance were also demonstrated in present study. These results could open a new window for insight into the role of BnaGH28 genes on pod shattering resistance in rapeseed.

Abstract Rapeseed (Brassica napus L.) with short or no dormancy period are easy to germinate before harvest (pre-harvest sprouting, PHS). PHS has seriously decreased seed weight and oil content in B. napus. Short-chain dehydrogenase/ reductase (SDR) genes have been found to related to seed dormancy by promoting ABA biosynthesis in rice and Arabidopsis. In order to clarify whether SDR genes are the key factor of seed dormancy in B. napus, homology sequence blast, protein physicochemical properties, conserved motif, gene structure, cis-acting element, gene expression and variation analysis were conducted in present study. Results shown that 142 BnaSDR genes, unevenly distributed on 19 chromosomes, have been identified in B. napus genome. Among them, four BnaSDR gene clusters present in chromosome A04、A05、C03、C04 were also identified. These 142 BnaSDR genes were divided into four subfamilies on phylogenetic tree. Members of the same subgroup have similar protein characters, conserved motifs, gene structure, cis-acting elements and tissue expression profiles. Specially, the expression levels of genes in subgroup A, B and C were gradually decreased, but increased in subgroup D with the development of seeds. Among seven higher expressed genes in group D, six BnaSDR genes were significantly higher expressed in weak dormancy line than that in nondormancy line. And the significant effects of BnaC01T0313900ZS and BnaC03T0300500ZS variation on seed dormancy were also demonstrated in present study. These findings provide a key information for investigating the function of BnaSDRs on seed dormancy in B. napus.

Alfin-like proteins (ALs) form a plant-specific transcription factor (TF) gene family involved in the regulation of plant growth and development, and abiotic stress response. In this study, 30 ALs were identified in Brassica napus ecotype ‘Zhongshuang 11’ genome (BnaALs), and unevenly distributed on 15 chromosomes. Structural characteristic analysis showed that all of the BnaALs contained two highly conserved domains: the N terminal DUF3594 domain and the C-terminal PHD-finger domain. The BnaALs were classified into four groups (Group I-IV), supported by conserved intron–exon and protein motif structures in each group. The allopolyploid event between B. oleracea and B. rapa ancestors and the small-scale duplication events in B. napus both contributed to the large BnaALs expansion. The promoter regions of BnaALs contained multiple abiotic stress cis-elements. The BnaALs in I-IV groups were mainly expressed in cotyledon, petal, root, silique, and seed tissues, and the duplicated gene pairs shared highly similar expression patterns. RNA-seq and RT-qPCR analysis showed that BnaALs were obviously induced by low nitrogen (LN) and low phosphorus (LP) treatments in roots. Overexpressing BnaAL02 and BnaAL28 in Arabidopsis demonstrated their functions in response to LN and LP stresses. BnaAL28 enhanced primary roots’ (PRs) length and lateral roots’ (LRs) number under LP and LN conditions, where BnaAL02 can inhibit LR numbers under the two conditions. They can promote root hair (RH) elongation under LP conditions; however, they suppressed RH elongation under LN conditions. Our result provides new insight into the functional dissection of this family in response to nutrient stresses in plants.

Dihydroflavonol 4-reductase (DFR) is a key enzyme in the flavonoid biosynthetic pathway and is essential for the formation of plants’ color. In this study, 26 BnDFR genes were identified using 6 Arabidopsis DFR genes as reference. The physicochemical properties, subcellular localization, and conserved structure of BnDFR proteins were analyzed; the evolutionary relationship, collinearity analysis, and expression characteristics of BnDFR genes were studied; and the correlation between the expression level of BnDFR genes and anthocyanin content in rape petals were analyzed. The results showed that the 26 BnDFRs were located in chloroplasts, cytoplasm, nuclei, and mitochondria, distributed on 17 chromosomes, and divided into 4 groups; members of the same group have a similar function, which may be related to the environmental response elements and plant hormone response elements. Intraspecific collinearity analysis showed 51 pairs of collinear genes, and interspecific collinearity analysis showed 30 pairs of collinear genes. Analysis of the expression levels of BnDFRs and anthocyanin content in different color rape petals showed that BnDFR6 and BnDFR26 might play an important role in the synthesis of anthocyanins in rape petals. This provides theoretical guidance for further analysis of the anthocyanin anabolism mechanism involved in the DFR gene in Brassica napus.

The Pixel-Based Adaptive Segmenter with Confidence Measurement (PBASCM) is proposed for vehicle detection in complex urban traffic scenes to efficiently address deficiencies of the background subtraction model, which is easily contaminated by slow-moving or temporarily stopped vehicles. The background is modeled based on the history of recently observed pixel values and each pixel in the background model is assigned a confidence measurement based on the current traffic state. The foreground decision depends on an adaptive threshold, whereas the background model is updated based on the current traffic state and whether the corresponding pixel point is in the confidence period. Using real-world urban traffic videos, the overall results of detection accuracy analyses demonstrated that PBASCM achieved better performance in both qualitative and quantitative evaluations, compared with other state-of-the-art methods. PBASCM can accurately detect slowmoving or temporarily stopped vehicles, and the similarity and F-measure results for PBASCM were 0.839 and 0.912 higher, respectively, than those obtained by other methods in a traffic light sequence during the daytime. Thus, our experimental results demonstrate that PBASCM is effective and suitable for real-time vehicle detection in complex urban traffic scenes. [ABSTRACT FROM AUTHOR]

A novel, efficient feature extraction approach for driving postures, which consists of Homomorphic Filter (HOMOF), skin-like regions segmentation, and features extraction by Nonsubsampled Contourlet Transform (NSCT), is proposed. With features extracted from a driving posture dataset, holdout and cross-validation experiments on driving posture classification are then conducted using L-Nearest Neighbour (LNN) classifier compared with a number of commonly used classification methods including Intersection Kernel Support Vector Machine (IKSVM), Multilayer Perception (MLP) classifier, and Parzen classifier. The holdout and cross-validation experiments show that theLNN classifier offers the best classification performance than the other three classifiers. Among the four predefined classes, the class of eating a cake is the most difficult to classify. With LNN classifier, the classification accuracies of eating a cake are over 88% in holdout and cross-validation experiments, which shows the effectiveness of the proposed feature extraction method and the importance of LNN classifier in automatically understanding and characterizing the driver behaviours. [ABSTRACT FROM AUTHOR]