Search

In order to explore the quality differences between Meizhan black tea and Meizhan white tea, Meizhan fresh leaves were used as raw materials to process black tea and white tea, and sensory quality evaluation and physicochemical analysis were carried out. The results showed that the aroma of Meizhan black tea was sweet and flowery, the taste was sweet and mellow. The aroma of Meizhan white tea was floral, the taste was fresh and mellow. The contents of theaflavins, thearubins and theanines in Meizhan black tea were significantly higher than those in Meizhan white tea. The contents of polyphenols and free amino acids in Meizhan white tea were significantly higher than those in Meizhan black tea (P

Wuyi tea (Camellia. sinensis, Synonym: Thea Bohea L.) is recognized as the most prestigious oolong tea in China. For germplasm identification and protection, the complete chloroplast genomes of five classical Wuyi tea varieties were determined by next-generation sequencing. These chloroplast genomes showed highly conserved structures and are 157,024–157,126 bp in length, consisting of a pair of reverse repeats (IR) regions of 25,944–26,095 bp, one large single-copy (LSC) region of 86,594–86,859 bp, and one small single copy (SSC) region of 18,276–18,291 bp. A total of 137 genes were observed and overall GC contents were all about 37.3%. Phylogenetic analysis revealed Wuyi tea varieties did not cluster together, suggesting that these Wuyi tea varieties might have diverged early in their evolutionary history and the complete chloroplast genome could be used as a super-barcode to identify these varieties. This study will be valuable for future studies of evolution and intraspecific identification in Wuyi tea.

Abstract Background Identifying miRNA and disease associations helps us understand disease mechanisms of action from the molecular level. However, it is usually blind, time-consuming, and small-scale based on biological experiments. Hence, developing computational methods to predict unknown miRNA and disease associations is becoming increasingly important. Results In this work, we develop a computational framework called SMALF to predict unknown miRNA-disease associations. SMALF first utilizes a stacked autoencoder to learn miRNA latent feature and disease latent feature from the original miRNA-disease association matrix. Then, SMALF obtains the feature vector of representing miRNA-disease by integrating miRNA functional similarity, miRNA latent feature, disease semantic similarity, and disease latent feature. Finally, XGBoost is utilized to predict unknown miRNA-disease associations. We implement cross-validation experiments. Compared with other state-of-the-art methods, SAMLF achieved the best AUC value. We also construct three case studies, including hepatocellular carcinoma, colon cancer, and breast cancer. The results show that 10, 10, and 9 out of the top ten predicted miRNAs are verified in MNDR v3.0 or miRCancer, respectively. Conclusion The comprehensive experimental results demonstrate that SMALF is effective in identifying unknown miRNA-disease associations.

Tea (Camellia sinensis) is one of the most popular beverages worldwide. Many types of tea products continuously emerge in an endless stream; so, the classification of tea becomes more difficult. Aroma is a vital indicator of tea quality. The present study deals with the identification of aroma compounds in 18 different kinds of tea belonging to three typical tea varieties, including green tea, oolong tea, and black tea, using GC-IMS and GC × GC-O-MS. Moreover, the clustering of all 18 tea samples and the in depth correlation analysis between sensory evaluation and instrumental data were performed using the PCA and OPLS-DA. The results revealed that in all 18 kinds of tea, a total of 85 aroma compounds were detected by GC-IMS, whereas 318 were detected by GC × GC-O-MS. The PCA result revealed that green tea, oolong tea, and black tea could be clearly separated based on their peak areas. The OPLS-DA result showed that a total of 49 aroma compounds with VIP value > 1.0 could be considered as the potential indicators to quickly classify or verify tea types. This study not only compared the aroma differences across different types of teas, but also provided ideas for the rapid monitoring of tea quality and variety.