The major polyphenol components from Stevia rebaudiana (Bertoni) leaves (PPS) are chlorogenic acids, a polyphenol family of esters, including hydroxycinnamic acids with quinic acid, which possesses excellent hydrophilic antioxidant activity and other therapeutic properties. As an abundant byproduct during production of steviol glycosides, the PPS would be a new antioxidantive food resource or additives applied in foods and drugs with antidiabetic function. Extracting PPS from S. rebaudiana (Bertoni) leaves together with steviol glycosides would be an economic process, which will change most operation process in current Stevia factories. The quantification of PPS needs to be unified for regulation. In view of the current regulation status of polyphenols and extracts from Stevia, the PPS would be ready to go to the market with few regulation barriers in the near future. This review will summarize the analysis, extraction, and some functional properties of PPS, such as antioxidant, antidiabetic, antimicrobial, anti-inflammatory, and anticancer.

Anomaly detection in streaming data is of high interest in numerous application domains. In this paper, we propose a novel one-class semi-supervised algorithm to detect anomalies in streaming data. Underlying the algorithm is a fast and accurate density estimator implemented by multiple fully randomized space trees (RS-Trees), named RS-Forest. The piecewise constant density estimate of each RS-tree is defined on the tree node into which an instance falls. Each incoming instance in a data stream is scored by the density estimates averaged over all trees in the forest. Two strategies, statistical attribute range estimation of high probability guarantee and dual node profiles for rapid model update, are seamlessly integrated into RS-Forest to systematically address the ever-evolving nature of data streams. We derive the theoretical upper bound for the proposed algorithm and analyze its asymptotic properties via bias-variance decomposition. Empirical comparisons to the state-of-the-art methods on multiple benchmark datasets demonstrate that the proposed method features high detection rate, fast response, and insensitivity to most of the parameter settings. Algorithm implementations and datasets are available upon request.

The genome-scale metabolic model (GEM) is a powerful tool for interpreting and predicting cellular phenotypes under various environmental and genetic perturbations. However, GEM only considers stoichiometric constraints, and the simulated growth and product yield values will show a monotonic linear increase with increasing substrate uptake rate, which deviates from the experimentally measured values. Recently, the integration of enzymatic constraints into stoichiometry-based GEMs was proven to be effective in making novel discoveries and predicting new engineering targets. Here, we present the first genome-scale enzyme-constrained model (ecCGL1) for Corynebacterium glutamicum reconstructed by integrating enzyme kinetic data from various sources using a ECMpy workflow based on the high-quality GEM of C. glutamicum (obtained by modifying the iCW773 model). The enzyme-constrained model improved the prediction of phenotypes and simulated overflow metabolism, while also recapitulating the trade-off between biomass yield and enzyme usage efficiency. Finally, we used the ecCGL1 to identify several gene modification targets for l-lysine production, most of which agree with previously reported genes. This study shows that incorporating enzyme kinetic information into the GEM enhances the cellular phenotypes prediction of C. glutamicum, which can help identify key enzymes and thus provide reliable guidance for metabolic engineering.