Polymethoxylated flavones (PMFs) have received extensive attention due to their abundant bioactivities. Citrus peels specifically accumulate abundant PMFs, and methylation modification is a key step in PMF biosynthesis; however, the function of reported O-methyltransferase (OMT) in citrus is insufficient to elucidate the complete methylation process of PMFs. In this study, we analyzed the accumulation pattern of PMFs in the flavedo of the sweet orange (Citrus sinensis) cultivar ‘Bingtangcheng’ at different developmental stages. We found that accumulation of PMFs was completed at the early stage of fruit development (60 days after flowering). Furthermore, we characterized a true caffeoyl-CoA O-methyltransferase (named CsCCoAOMT1) from C. sinensis. Functional analysis in vitro showed that CsCCoAOMT1 preferred flavonoids to caffeoyl-CoA and esculetin. This enzyme efficiently methylated the 6-, 7- 8- and 3'-OH of a wide array of flavonoids with vicinal hydroxyl groups with a strong preference for quercetin (flavonol) and flavones. The transient overexpression and virus-induced gene silencing experiments verified that CsCCoAOMT1 could promote the accumulation of PMFs in citrus. These results reveal the function of true CCoAOMTs and indicate that CsCCoAOMT1 is a highly efficient multifunctional O-methyltransferase involved in the biosynthesis of PMFs in citrus.
This paper proposes a method for salient crowd motion detection based on direction entropy and a repulsive force network. This work focuses on how to effectively detect salient regions in crowd movement through calculating the crowd vector field and constructing the weighted network using the repulsive force. The interaction force between two particles calculated by the repulsive force formula is used to determine the relationship between these two particles. The network node strength is used as a feature parameter to construct a two-dimensional feature matrix. Furthermore, the entropy of the velocity vector direction is calculated to describe the instability of the crowd movement. Finally, the feature matrix of the repulsive force network and direction entropy are integrated together to detect the salient crowd motion. Experimental results and comparison show that the proposed method can efficiently detect the salient crowd motion.
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