A simple consortium consisted of two members of Klebsiella sp. A1 and Comamonas sp. A2 was isolated from the sewage of a pesticide mill in China. One member of Klebsiella sp. A1 is a novel strain that could use atrazine as the sole carbon and nitrogen source. The consortium showed high atrazine-mineralizing efficiency and about 83.3% of 5 g l(-1) atrazine could be mineralized after 24 h degradation. Contrary to many other reported microorganisms, the consortium was insensitive to some nitrogenous fertilizers commonly used, not only in presence of 200 mg l(-1) atrazine but also in 5 g l(-1) atrazine mediums. After 24 h incubation, 200 mg l(-1) atrazine was completely mineralized despite of the presence of urea, (NH(4))(2)CO(3) and (NH(4))(2)HPO(4) in the medium. Very minor influence was observed when NH(4)Cl was added as additional nitrogen source. Advantages of the simple consortium, high mineralizing efficiency and insensitivity to most of exogenous nitrogen sources, all suggested application potential of the consortium for the bioremediation of atrazine-contaminated soils and waters.
This paper describes a novel, simple and environmentally friendly method for rapid determination of the amide herbicides metoalchlor, acetochlor, and butachlor. It is based on dispersive liquid-liquid microextraction and gas chromatography-mass spectrometry. Factors that may influence the enrichment efficiency, such as type and volume of extraction solvent, type and volume of dispersive solvent, extraction time, and content of NaCl, were investigated and optimized in detail. Under the optimum conditions, the limits of detection of metoalchlor, acetochlor, and butachlor were 0.02, 0.04, and 0.003 microg L(-1), respectively. The experimental results indicated that there was linearity over the range 0.1-50 microg L(-1) and good reproducibility with relative standard deviations over the range 1.6-3.0% (n = 5). The proposed method has been applied for the analysis of real-world water samples, and satisfactory results were achieved. Average recoveries of spiked herbicides were in the range 80.3-108.8%. All of these indicated that the developed method would be an efficient method for simultaneous determination of the three herbicides in environmental water samples.
BackgroundRNA-binding proteins participate in many important biological processes concerning RNA-mediated gene regulation, and several computational methods have been recently developed to predict the protein-RNA interactions of RNA-binding proteins. Newly developed discriminative descriptors will help to improve the prediction accuracy of these prediction methods and provide further meaningful information for researchers.ResultsIn this work, we designed two structural features (residue electrostatic surface potential and triplet interface propensity) and according to the statistical and structural analysis of protein-RNA complexes, the two features were powerful for identifying RNA-binding protein residues. Using these two features and other excellent structure- and sequence-based features, a random forest classifier was constructed to predict RNA-binding residues. The area under the receiver operating characteristic curve (AUC) of five-fold cross-validation for our method on training set RBP195 was 0.900, and when applied to the test set RBP68, the prediction accuracy (ACC) was 0.868, and the F-score was 0.631.ConclusionsThe good prediction performance of our method revealed that the two newly designed descriptors could be discriminative for inferring protein residues interacting with RNAs. To facilitate the use of our method, a web-server called RNAProSite, which implements the proposed method, was constructed and is freely available at http://lilab.ecust.edu.cn/NABind.Electronic supplementary materialThe online version of this article (doi:10.1186/s12859-016-1110-x) contains supplementary material, which is available to authorized users.
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