Structural Characterization and Antioxidant Activity of ß-Glucans from Highland Barley Obtained with Ultrasonic-Microwave-Assisted Extraction.

In order to efficiently extract ß-glucan from highland barley (HBG) and study its structural characterization and antioxidant activity, ultrasonic-microwave-assisted extraction (UME) was optimized by the response surface method (RSM). Under the optimal extraction conditions of 25.05 mL/g liquid-solid ratio, 20 min ultrasonic time, and 480 W microwave intensity, the DPPH radical scavenging activity of HBG reached 25.67%. Two polysaccharide fractions were purified from HBG, namely HBG-1 and HBG-2. Structural characterization indicated that HBG-1 and HBG-2 had similar functional groups, glycosidic linkages, and linear and complex chain conformation. HBG-1 was mainly composed of glucose (98.97%), while HBG-2 primarily consisted of arabinose (38.23%), galactose (22.01%), and xylose (31.60%). The molecular weight of HBG-1 was much smaller than that of HBG-2. Both HBG-1 and HBG-2 exhibited concentration-dependent antioxidant activity, and HBG-1 was more active. This study provided insights into the efficient extraction of HBG and further investigated the structure and antioxidant activities of purified components HBG-1 and HBG-2. Meanwhile, the results of this study imply that HBG has the potential to be an antioxidant in foods and cosmetics.

Elastic Net Constraint-Based Tensor Model for High-Order Graph Matching.

The procedure of establishing the correspondence between two sets of feature points is important in computer vision applications. In this article, an elastic net constraint-based tensor model is proposed for high-order graph matching. To control the tradeoff between the sparsity and the accuracy of the matching results, an elastic net constraint is introduced into the tensor-based graph matching model. Then, a nonmonotone spectral projected gradient (NSPG) method is derived to solve the proposed matching model. During the optimization of using NSPG, we propose an algorithm to calculate the projection on the feasible convex sets of elastic net constraint. Further, the global convergence of solving the proposed model using the NSPG method was proved. The superiority of the proposed method is verified through experiments on the synthetic data and natural images.

DrPOCS: Drug Repositioning Based on Projection Onto Convex Sets.

Drug repositioning, i.e., identifying new indications for known drugs, has attracted a lot of attentions recently and is becoming an effective strategy in drug development. In literature, several computational approaches have been proposed to identify potential indications of old drugs based on various types of data sources. In this paper, by formulating the drug-disease associations as a low-rank matrix, we propose a novel method, namely DrPOCS, to identify candidate indications of old drugs based on projection onto convex sets (POCS). With the integration of drug structure and disease phenotype information, DrPOCS predicts potential associations between drugs and diseases with matrix completion. Benchmarking results demonstrate that our proposed approach outperforms popular existing approaches with high accuracy. In addition, a number of novel predicted indications are validated with various types of evidences, indicating the predictive power of our proposed approach.