In recent years, metabolic syndromes (MetSs), including diabetes mellitus, dyslipidemia, and cardiovascular diseases, have become a common health problem in both developed and developing countries. Accumulating data have suggested that traditional herbs might be able to provide a wide range of remedies in prevention and treatment of MetSs. Ginger (Zingiber officinale Roscoe, Zingiberaceae) has been documented to ameliorate hyperlipidemia, hyperglycemia, oxidative stress, and inflammation. These beneficial effects are mediated by transcription factors, such as peroxisome proliferator-activated receptors, adenosine monophosphate-activated protein kinase, and nuclear factor κB. This review focuses on recent findings regarding the beneficial effects of ginger on obesity and related complications in MetS and discusses its potential mechanisms of action. This review provides guidance for further applications of ginger for personalized nutrition and medicine.

BACKGROUND: Classification of fresh and processing strawberry cultivars is important to make the best utilization of different cultivars in processing. The aim of the study was to investigate whether support vector machine (SVM) and extreme learning machine (ELM) could assist the classification of 15 strawberry cultivars. Twenty-two characteristic indexes were analyzed, including not only appearance indexes but also nutritional indexes. RESULTS: The results showed that classification accuracies of 100% and 88.52% were obtained by using SVM and ELM with 3-fold cross validation, respectively. Moreover, seven characteristic variables extracted from 22 quality indexes by SVM could make it possible to determine the adaptability of a particular cultivar by measuring relatively small number of indexes. CONCLUSION: Both ELM and SVM models are feasible to identify fresh and processing cultivars. However, SVM showed better performance for its accuracy and simplicity, indicating that SVM would be a good choice for classification of strawberry cultivars.

An intelligent singer recognition system was designed to identify the singer. The scheme established a song library at first, then used MATLAB to extract Mel Frequency Cepstral Coefficients (MFCC) from each song in the song library, moreover, set up characteristic parameters pattern base and trained the pattern base by Vector Quantization (VQ) to obtain the final codebook base. Finally, it can correctly classify the singer based on Dynamic Time Warping (DTW) matching reference characteristic parameters pattern with test pattern. Test results showed that the system’s recognition rate is up to 90%.