Activity

This review also describes the phytoremediation potential of flax when grown in metal contaminated soil. Furthermore, techniques and methods to increase plant growth and biomass are also discussed in this work. However, future research is needed for a better understanding of the physiology, biochemistry, anatomy, and molecular biology of flax for increasing its pollutant removal efficiency.Human cytomegalovirus (HCMV) core fusion machinery proteins gB and gH/gL, and accessory proteins UL128/UL130/UL131A, are the key envelope proteins that mediate HCMV entry into and infection of host cells. To determine whether these HCMV envelope proteins could elicit neutralizing activities synergistically, we immunized rabbits with individual or various combinations of these proteins adsorbed to aluminum hydroxide mixed with CpG-ODN. We then analyzed serum neutralizing activities with multiple HCMV laboratory strains and clinical isolates. HCMV trimeric gB and gH/gL elicited high and moderate titers of HCMV neutralizing activity, respectively. HCMV gB in combination with gH/gL elicited up to 17-fold higher HCMV neutralizing activities compared to the sum of neutralizing activity elicited by the individual proteins analyzed with both fibroblasts and epithelial cells. HCMV gB+gH/gL+UL128/UL130/UL131A in combination increased the neutralizing activity up to 32-fold compared to the sum of neutralizing activities elicited by the individual proteins analyzed with epithelial cells. Adding UL128/UL130/UL131A to gB and gH/gL combination did not increase further the HCMV neutralizing activity analyzed with fibroblasts. These data suggest that the combination of HCMV core fusion machinery envelope proteins gB+gH/gL or the combination of gB and pentameric complex could be ideal vaccine candidates that would induce optimal immune responses against HCMV infection.In this study, the feasibility of non-targeted UHPLC-HRMS fingerprints as chemical descriptors to address the classification and authentication of paprika samples was evaluated. Non-targeted UHPLC-HRMS fingerprints were obtained after a simple sample extraction method and C18 reversed-phase separation. find more Fingerprinting data based on signal intensities as a function of m/z values and retention times were registered in negative ion mode using a q-Orbitrap high-resolution mass analyzer, and the obtained non-targeted UHPLC-HRMS fingerprints subjected to unsupervised principal component analysis (PCA) and supervised partial least squares regression-discriminant analysis (PLS-DA) to study sample discrimination and classification. A total of 105 paprika samples produced in three different regions, La Vera PDO and Murcia PDO, in Spain, and the Czech Republic, and all of them composed of samples of at least two different taste varieties, were analyzed. Non-targeted UHPLC-HRMS fingerprints demonstrated to be excellent sample chemical descriptors to achieve the authentication of paprika production regions with 100% sample classification rates by PLS-DA. Besides, the obtained fingerprints were also able to perfectly discriminate among the different paprika taste varieties in all the studied cases, even in the case of the different La Vera PDO paprika tastes (sweet, bittersweet, and spicy) which are produced in a very small region.The genus Lactobacillus includes, among others, Lactobacillus casei, Lactobacillus paracasei and Lactobacillus rhamnosus, species that are collectively referred to as the Lactobacillus casei group. Many studies have shown that strains belonging to this group may decrease lactose intolerance, the effects of inflammatory bowel disease, diarrhea, constipation, food allergies and even colon cancer. Moreover, evidences exists of positive effects of these bacteria on mucosal immunity and blood cholesterol level. Because of their beneficial influence on human health, many of them are used as food additives and probiotic pharmaceuticals. It should be stressed that health-promoting properties are not attributed at the species level, but to specific strains. Therefore, procedures are necessary to allow specific identification at each phylogenetic level-genus, species and strain. In this paper we present a practical overview of molecular methods for the identification and differentiation of L. casei bacteria. The research included 30 bacterial strains belonging to three species L.casei, L. paracasei and L. rhamnosus. Among the tested procedures were genus- and species-specific PCR, multiplex-PCR, Real-Time HRM analysis, RFLP-PCR, rep-PCR, RAPD-PCR, AFLP-PCR, and proteomic methods such as MALDI-TOF MS typing and SDS-PAGE fingerprinting. The obtained results showed that multiplex-PCR and MALDI-TOF MS turned out to be the most useful methods to identify the tested bacteria at the species level. At the strain level, the AFLP-PCR method showed the highest discriminatory power. We hope that the presented results will allow for the easy selection of an appropriate procedure, depending on the experiment conducted and the equipment capabilities of any given laboratory.The theory of belief functions has been extensively utilized in many practical applications involving decision making. One such application is the classification of target based on the pieces of information extracted from the individual attributes describing the target. Each piece of information is usually modeled as the basic probability assignment (BPA), also known as the mass function. The determination of the BPA has remained an open problem. Although fuzzy membership functions such as triangular and Gaussian functions have been widely used to model the likelihood estimation function based on the historical data, it has been observed that less emphasis has been placed on the impact of the spread of the membership function on the decision accuracy of the reasoning process. Conflict in the combination of BPAs may arise due to poor characterization of fuzzy membership functions to induce belief mass. In this work, we propose a multisensor data fusion within the framework of belief theory for target classification where shape/spread of the membership function is adjusted during the training/modeling stage to improve on the classification accuracy while removing the need for the computation of the credibility. To further enhance the performance of the proposed method, the reliability factor is deployed not only to effectively manage the possible conflict among participating bodies of evidence for better decision accuracy but also to reduce the number of sources for improved efficiency. The effectiveness of the proposed method was evaluated using both the real-world and the artificial datasets.