Coupling effects of Fe(II) and CaCO3 application on cadmium uptake and accumulation in rice (Oryza sativa L.)

Excessive cadmium (Cd) in rice, caused by Cd pollution of farmlands, poses a serious threat to human health. In this study, a pot experiment was conducted to investigate the effects of two doses of CaCO3 (Ca1: 2 g kg-1, Ca2: 10g kg-1), two types of Fe(II) (EDTA-Fe(II) and FeSO4; 0.14 g Fe kg-1), and their combined application on the uptake and accumulation of Cd in rice plants grown in Cd-contaminated acidic soil. The results revealed that FeSO4 significantly increased rice grain biomass, whereas the other treatments had no significant effects. Further, the addition of EDTA-Fe(II) or FeSO4 significantly enhanced iron plaque formation on the root surface and increased the Fe content in the rice plants and porewater. Compared to the control, CaCO3 addition weakened the formation of iron plaque and reduced the Fe concentration in the porewater and root tissue, stems and leaves, whereas the Fe concentration in brown rice and the husks remained unaffected. Combined application of CaCO3 and Fe(II) significantly promoted the formation of iron plaque and increased the Fe concentration in brown rice. However, the Cd concentration in the iron plaque was reduced by CaCO3 addition but increased by Fe(II) treatment. Notably, all treatments reduced the Cd concentration in all rice plant tissues. The application of Ca1, Ca2, EDTA-Fe(II), FeSO4, Ca1+EDTA-Fe(II), Ca1+FeSO4, Ca2+EDTA-Fe(II) and Ca2+FeSO4 significantly reduced the Cd concentration in brown rice by 69%, 63%, 51%, 60%, 46%, 39%, 38%, and 29%, respectively. These results indicate that the application of CaCO3, EDTA-Fe(II)/FeSO4, or their combination can effectively reduce Cd accumulation and translocation in rice plants

How to find simple and accurate rules for viral protease cleavage specificities

<p>Abstract</p> <p>Background</p> <p>Proteases of human pathogens are becoming increasingly important drug targets, hence it is necessary to understand their substrate specificity and to interpret this knowledge in practically useful ways. New methods are being developed that produce large amounts of cleavage information for individual proteases and some have been applied to extract cleavage rules from data. However, the hitherto proposed methods for extracting rules have been neither easy to understand nor very accurate. To be practically useful, cleavage rules should be accurate, compact, and expressed in an easily understandable way.</p> <p>Results</p> <p>A new method is presented for producing cleavage rules for viral proteases with seemingly complex cleavage profiles. The method is based on orthogonal search-based rule extraction (OSRE) combined with spectral clustering. It is demonstrated on substrate data sets for human immunodeficiency virus type 1 (HIV-1) protease and hepatitis C (HCV) NS3/4A protease, showing excellent prediction performance for both HIV-1 cleavage and HCV NS3/4A cleavage, agreeing with observed HCV genotype differences. New cleavage rules (consensus sequences) are suggested for HIV-1 and HCV NS3/4A cleavages. The practical usability of the method is also demonstrated by using it to predict the location of an internal cleavage site in the HCV NS3 protease and to correct the location of a previously reported internal cleavage site in the HCV NS3 protease. The method is fast to converge and yields accurate rules, on par with previous results for HIV-1 protease and better than previous state-of-the-art for HCV NS3/4A protease. Moreover, the rules are fewer and simpler than previously obtained with rule extraction methods.</p> <p>Conclusion</p> <p>A rule extraction methodology by searching for multivariate low-order predicates yields results that significantly outperform existing rule bases on out-of-sample data, but are more transparent to expert users. The approach yields rules that are easy to use and useful for interpreting experimental data.</p

Integrated Photo - rechargeable Batteries: Photoactive Nanomaterials and Opportunities

The demand for fossil fuels has been increasing over the last few decades but will one day be depleted and researchers are now using biomass to alleviate the fuel crisis. This paper concentrates on a range of current devices with intrinsic solar energy collection, conversion and storage properties, different classes of cells as well as their areas of application and recent research advances. Nanomaterials, meanwhile, are key to making significant progress in the study of photovoltaic electrodes for solar rechargeable batteries, and this paper describes seven currently commonly used semiconductor and nanomaterials. This not only alleviates the severe environmental pollution and greenhouse effect caused by fossil fuels, but also makes a significant contribution to the sustainability of human existence

Computational Prediction Models for Proteolytic Cleavage and Epitope Identification

The biological functions of proteins depend on their physical interactions with other molecules, such as proteins and peptides. Therefore, modeling the protein-ligand interactions is important for understanding protein functions in different biological processes. We have focused on the cleavage specificities of HIV-1 protease, HCV NS3 protease and caspases on short oligopeptides or in native proteins; the binding affinity of MHC molecules with short oligopeptides and identification of T cell epitopes. We expect that our findings on HIV-1 protease, HCV NS3 protease and caspases generalize to other proteases. In this thesis, we have performed analysis on these interactions from different perspectives --- we have extended and collected new substrate data sets; used and compared different prediction methods (e.g. linear support vector machines, neural networks, OSRE method, rough set theory and Gaussian processes) to understand the underlying interaction problems; suggested new methods (i.e. a hierarchical method and Gaussian processes with test reject method) to improve predictions; and extracted cleavage rules for protease cleavage specificities. From our studies, we have extended oligopeptide substrate data sets and collected native protein substrates for HIV-1 protease, and a new oligopeptide substrate data set for HCV protease. We have shown that all current HIV-1 protease oligopeptide substrate data sets and our HCV data set are linearly separable; for HIV-1 protease, size and hydrophobicity are two important physicochemical properties in the recognition of short oligopeptide substrates to the protease; and linear support vector machine is the state-of-the-art for this protease cleavage prediction problem. Our hierarchical method combining protein secondary structure information and experimental short oligopeptide cleavage information can improve the prediction of HIV-1 protease cleavage sites in native proteins. Our rule extraction method provides simple and accurate cleavage rules with high fidelity for HIV-1 and HCV proteases. For MHC molecules, we showed that high binding affinities are not necessarily correlated to immunogenicity on HLA-restricted peptides. Our test reject method combined with Gaussian processes can simplify experimental design by reducing false positives for detecting potential epitopes in large pathogen genomes

Detection of cleavage sites for HIV-1 protease in native proteins

Predicting novel cleavage sites for HIV-1 protease in non-viral proteins is a difficult task because of the scarcity of previous cleavage data on proteins in a native state. We introduce a three-level hierarchical classifier which combines information from experimentally verified short oligopeptides, secondary structure and solvent accessibility information from prediction servers to predict potential cleavage sites in non-viral proteins. The best classifier using secondary structure information on the second level classification of the hierarchical classifier is the one using logistic regression. By using this level of classification, the false positive ratio was reduced by more than half compared to the first level classifier using only the oligopeptide cleavage information. The method can be applied on other protease specificity problems too, to combine information from oligopeptides and structure from native proteins

Almost Linear Biobasis Function Neural Networks

An analysis of biobasis function neural networks is presented, which shows that the similarity metric used is a linear function and that bio-basis function neural networks therefore often end up being just linear classifiers in high dimensional spaces. This is a consequence of four things: the linearity of the distance measure, the normalization of the distance measure, the recommended default values of the parameters, and that biological data sets are sparse.©2007 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.</p