Physicochemical and functional characteristics of a novel fermented pepper (Capsiccum annuum L.) leaves-based beverage using lactic acid bacteria

Two lactic acid bacteria (LAB), Lactobacillus homohiochii JBCC 25 and L. homohiochii JBCC 46, were isolated from a naturally fermented vinegar beverage. Fermented pepper leaves beverage (FPLB-3) which indicated good assessment based on a sensory evaluation test was prepared using the mixed strains. The prepared FPLB-3 showed enhanced functional aspects based on γ-aminobutyric acid (23.6 mg%), total polyphenol (2,828.2 gallic acid equivalents μg/g), and flavonoid contents (2,008.8 catechin equivalents μg/g). Moreover, FPLB-3 showed significantly stronger scavenging activities for the 2,2-diphenyl-1-picrylhydrazyl (DPPH) radical (84.6%) and superoxide radical (90.8%) and outstanding inhibitory activity against α-glucosidase of 65.9% at 50 mg/mL compared to the raw material. Based on the results, the newly developed FPLB using Wongi-1 pepper leaves and Lactobacillus is expected to be a novel functional food with natural antioxidant and antidiabetic capacities.     

Composition-dependent phase segregation and cocrystallization behaviors of blends of metallocene-catalyzed octene-LLDPE(D) and LDPE(H)

The morphological structures of slowly cooled blends of deuterated metallocene-catalyzed octene linear low-density polyethylene (O-mLLDPE(D)), and hydrogenous low-density polyethylene (LDPE(H)) were studied by using small angle neutron scattering in combination with complementary small angle X-ray scattering and differential scanning calorimetry. The phase segregation, which is more nanoscale than macroscale, and cocrystallization behaviors were found to vary with the blend composition. Phase-segregated O-mLLDPE(D) lamellae are predominantly formed in LDPE(H)-rich compositions. In contrast, few segregated O-mLLDPE(D) lamellae form in O-mLLDPE(D)-rich compositions, and instead O-mLLDPE(D) lamellar stacks are extensively cocrystallized with LDPE(H) mostly in the interlamellar amorphous region.     

Activation of particulate guanylyl cyclase by Vibrio vulnificus hemolysin

Recently we reported that Vibrio vulnificus hemolysin, an exotoxin produced by V. vulnificus, dilates rat thoracic aorta via elevated cGMP levels without affecting nitric oxide synthase. We investigated the mechanism further by observing the guanylyl cyclase activities in cytosolic, membrane, unfractionated, or reconstituted preparations. Hemolysin did not activate guanylyl cyclase in the membrane or cytosolic fraction, while it activated guanylyl cyclase in unfractionated or reconstituted preparation. The increased activity was not inhibited by the HS-142-1, a microbial polysaccharide which antagonizes atrial natriuretic peptide receptor, or 1H-[1,2,4]oxadiazolo[4,3-a]quinoxalin-1-one (ODQ), a soluble guanylyl cyclase inhibitor. However, it was attenuated by 6-(phenylamino)-5,8-quinolinedione (LY 83.583), which inhibits the catalytic domain of both guanylyl cyclases, and by cholesterol, which blocks hemolysin-incorporation into the membrane. Removing ATP, a cofactor of particulate guanylyl cyclase, attenuated the activation and ATPgammaS, a non-phosphorylating analog, restored it. These results suggest that V. vulnificus hemolysin activates particulate guanylyl cyclase via hemolysin incorporation into the vascular smooth muscle cell membrane in cooperation with certain unidentified cytosolic component(s).     

Mobile-cloud assisted framework for selective encryption of medical images with steganography for resource-constrained devices

In this paper, the problem of outsourcing the selective encryption of a medical image to cloud by resource-constrained devices such as smart phone is addressed, without revealing the cover image to cloud using steganography. In the proposed framework, the region of interest of the medical image is first detected using a visual saliency model. The detected important data is then embedded in a host image, producing a stego image which is outsourced to cloud for encryption. The cloud which has powerful resources, encrypts the image and sent back the encrypted marked image to the client. The client can then extract the selectively encrypted region of interest and can combine it with the region of non-interest to form a selectively encrypted image, which can be sent to medical specialists and healthcare centers. Experimental results and analysis validate the effectiveness of the proposed framework in terms of security, image quality, and computational complexity and verify its applicability in remote patient monitoring centers.     

A transfer cost-sensitive boosting approach for cross-project defect prediction

Software defect prediction has been regarded as one of the crucial tasks to improve software quality by effectively allocating valuable resources to fault-prone modules. It is necessary to have a sufficient set of historical data for building a predictor. Without a set of sufficient historical data within a company, cross-project defect prediction (CPDP) can be employed where data from other companies are used to build predictors. In such cases, a transfer learning technique, which extracts common knowledge from source projects and transfers it to a target project, can be used to enhance the prediction performance. There exists the class imbalance problem, which causes difficulties for the learner to predict defects. The main impacts of imbalanced data under cross-project settings have not been investigated in depth. We propose a transfer cost-sensitive boosting method that considers both knowledge transfer and class imbalance for CPDP when given a small amount of labeled target data. The proposed approach performs boosting that assigns weights to the training instances with consideration of both distributional characteristics and the class imbalance. Through comparative experiments with the transfer learning and the class imbalance learning techniques, we show that the proposed model provides significantly higher defect detection accuracy while retaining better overall performance. As a result, a combination of transfer learning and class imbalance learning is highly effective for improving the prediction performance under cross-project settings. The proposed approach will help to design an effective prediction model for CPDP. The improved defect prediction performance could help to direct software quality assurance activities and reduce costs. Consequently, the quality of software can be managed effectively.