Global lysine succinylation analysis unveils post-translational regulation effect on phenylpropanoid metabolism remodeling during Lonicera japonica flower development.

Lonicera japonica plays a significant role in traditional Chinese medicine and as a food source, making it a focus of studies on protein succinylation and its potential role in regulating secondary metabolism during flower development. This study aimed to clarify the regulatory mechanism of protein succinylation on phenylpropanoid-related phenotypic changes by conducting a global lysine succinylation proteomic analysis across different flowering stages. A total of 586 lysine succinylated peptides in 303 proteins were identified during early and late floral stages. Functional enrichment analysis revealed that succinylated proteins primarily participated in the tricarboxylic acid (TCA) cycle, amino acid metabolism, and secondary metabolism. The abundance of succinylated aspartate transaminase (AT), 4-coumarate-CoA ligase (4CL), and phenylalanine N-hydroxylase (CYP79A2) in phenylpropanoid metabolism varied during flower development. In vitro experiments demonstrated that succinylation increased AT activity while inhibited 4CL activity. Decreased levels of total flavonoids and phenolic acids indicated significant alterations in phenylpropanoid metabolism during later floral stages. These results suggest that succinylation of TCA cycle proteins not only influences flower development but also, together with AT-4CL-CYP79A2 co-succinylation, redirects phenylpropanoid metabolism during flower development in L. japonica.

Research on Face Recognition Algorithm Based on Image Processing.

While network technology is convenient for our daily life, the problems that are exposed are also endless. The most important thing for everyone is information security. In order to improve the security level of network information and identify and detect faces, the method used in this paper has improved compared with the traditional AdaBoost method and skin color method. AdaBoost detection is performed on the image, which reduces the probability of false detection. The experiment compares the experimental results of the AdaBoost method, the skin color method and the skin color + AdaBoost method. All operations in the KPCA and KFDA algorithms are performed by the inner product kernel function defined in the original space, and no specific non-linear mapping function is involved.The full name of KPCA is kernel principal component analysis. The full name of KFDA is kernel Fisher discriminant analysis. Combining the zero-space method kernel discriminant analysis method improves the ability of discriminant analysis to extract non-linear features. Through the secondary extraction of PCA features, a better recognition result than the PCA method is obtained. This paper also proposes a zero-space based Fisher discriminant analysis method. Experiments show that the zero-space-based method makes full use of the useful discriminant information in the zero space of the intraclass dispersion matrix, which improves the accuracy of face recognition to some extent.If you choose the polynomial kernel function, when d = 0.8, KPCA has a higher recognition ability. When d = 2, the recognition rate of KFDA and zero space-based KFDA is the largest. For polynomial functions, in general, d = 2.