[Correlative analysis of agronomic traits and quality of Panax ginseng saponins cultivation in farmland].

Cultivated ginseng in the farmland would become the mainly planting mode of Panax ginseng. However,there are relatively few cultivation ginseng varieties for farmland in China. Correlative analysis of qualitity and agronomic traits of P. ginseng cultivation in the farmland could provide a reference for the selection of excellent germplasm and new variety breeding of P. ginseng. In this study,the main index of saponin and agronomic traits of 4-6 years' samples were analyzed by UPLC and measured. The results show that there was significant difference in agronomic indexes of Damaya. The coefficient of variation of the root length( CV = 41. 97%) and fresh weight( CV = 31. 81%) were maximum,and the coefficient of variation of the stems thickness( 16. 72%) and root thickness were minimum. There was a significant correlation between yield and root thickness( P<0. 05). There was significant difference in drug yield of different harvest years( P<0. 05),and the yield of 6-years was 31. 52%-39. 69% higher than 4-years. However,there wasn't significant difference in total ginsenosides between 4 and 6 years old P. ginseng,but there was significant difference in ginseng Rg2,Rc and Rb2( P<0. 05),and the ginsenoside contents of different harvesting years were accorded with the criterion standards of 2015 Chinese Pharmacopoeia. There was no significant correlation between the saponin and the agronomic trait,while there was positive correlation with root thickness( P < 0. 05). Therefore,the stem diameter was positive correlation with yield of P. ginseng. Selection of the stem thickness of seedlings is beneficial to the increase of the yield and breeding of P. ginseng.

A Robust Manifold Graph Regularized Nonnegative Matrix Factorization Algorithm for Cancer Gene Clustering.

Detecting genomes with similar expression patterns using clustering techniques plays an important role in gene expression data analysis. Non-negative matrix factorization (NMF) is an effective method for clustering the analysis of gene expression data. However, the NMF-based method is performed within the Euclidean space, and it is usually inappropriate for revealing the intrinsic geometric structure of data space. In order to overcome this shortcoming, Cai et al. proposed a novel algorithm, called graph regularized non-negative matrices factorization (GNMF). Motivated by the topological structure of the GNMF-based method, we propose improved graph regularized non-negative matrix factorization (GNMF) to facilitate the display of geometric structure of data space. Robust manifold non-negative matrix factorization (RM-GNMF) is designed for cancer gene clustering, leading to an enhancement of the GNMF-based algorithm in terms of robustness. We combine the l 2 , 1 -norm NMF with spectral clustering to conduct the wide-ranging experiments on the three known datasets. Clustering results indicate that the proposed method outperforms the previous methods, which displays the latest application of the RM-GNMF-based method in cancer gene clustering.