Changes in microbial community structure and yield responses with the use of nano-fertilizers of nitrogen and zinc in wheat-maize system.

The growing popularity of nano-fertilization around the world for enhancing yield and nutrient use efficiency has been realized, however its influence on soil microbial structure is not fully understood. The purpose of carrying out this study was to assess the combined effect of nano and conventional fertilizers on the soil biological indicators and crop yield in a wheat-maize system. The results indicate that the at par grain yield of wheat and maize was obtained with application of 75% of recommended nitrogen (N) with full dose of phosphorus (P) and potassium (K) through conventional fertilizers along with nano-N (nano-urea) or nano-N plus nano-Zn sprays and N100PK i.e. business as usual (recommended dose of fertilizer). Important soil microbial property like microbial biomass carbon was found statistically similar with nano fertilizer-based management (N75PK + nano-N, and N75PK + nano-N + nano-Zn) and conventional management (N100PK), during both wheat and maize seasons. The experimental data indicated that the application of foliar spray of nano-fertilizers along with 75% N as basal is a sustainable nutrient management approach with respect to growth, yield and rhizosphere biological activity. Furthermore, two foliar sprays of nano-N or nano-N + nano-Zn curtailed N requirement by 25%, furthermore enhanced soil microbial diversity and the microbial community structure. The specific microbial groups, including Actinobacteria, Bacteroidia, and Proteobacteria, were present in abundance and were positively correlated with wheat and maize yield and soil microbial biomass carbon. Thus, one of the best nutrient management approaches for sustaining productivity and maintaining sound microbial diversity in wheat-maize rotation is the combined use of nano-fertilizers and conventional fertilizers.

A comparative analysis of amino acid encoding schemes for the prediction of flexible length linear B-cell epitopes.

Linear B-cell epitopes have a prominent role in the development of peptide-based vaccines and disease diagnosis. High variability in the length of these epitopes is a major reason for low accuracy in their prediction. Most of the B-cell epitope prediction methods considered fixed length of epitope sequences and achieved good accuracy. Though a number of tools are available for the prediction of flexible length linear B-cell epitopes with reasonable accuracy, further improvement in the prediction performance is still expected. Thus, here we made an attempt to analyze the performance of machine learning approaches (MLA) with 18 different amino acid encoding schemes in the prediction of flexible length linear B-cell epitopes. We considered B-cell epitope sequences of variable lengths (11-56 amino acids) from well-established public resources. The performances of machine learning algorithms with the encoded epitope sequence datasets were evaluated. Besides, the feasible combinations of encoding schemes were also explored and analyzed. The results revealed that amino-acid composition (AC) and distribution component of composition-transition-distribution encoding schemes are suitable for heterogeneous epitope data, whereas amino-acid-anchoring-pair-composition (APC), dipeptide-composition and amino-acids-pair-propensity-scale (APP) are more appropriate for homogeneous data. Further, two combinations of peptide encoding schemes, i.e. APC + AC and APC + APP with random forest classifier were identified to have improved performance over the state-of-the-art tools for flexible length linear B-cell epitope prediction. The study also revealed better performance of random forest over other considered MLAs in the prediction of flexible length linear B-cell epitopes.

HRGPred: Prediction of herbicide resistant genes with k-mer nucleotide compositional features and support vector machine.

Herbicide resistance (HR) is a major concern for the agricultural producers as well as environmentalists. Resistance to commonly used herbicides are conferred due to mutation(s) in the genes encoding herbicide target sites/proteins (GETS). Identification of these genes through wet-lab experiments is time consuming and expensive. Thus, a supervised learning-based computational model has been proposed in this study, which is first of its kind for the prediction of seven classes of GETS. The cDNA sequences of the genes were initially transformed into numeric features based on the k-mer compositions and then supplied as input to the support vector machine. In the proposed SVM-based model, the prediction occurs in two stages, where a binary classifier in the first stage discriminates the genes involved in conferring the resistance to herbicides from other genes, followed by a multi-class classifier in the second stage that categorizes the predicted herbicide resistant genes in the first stage into any one of the seven resistant classes. Overall classification accuracies were observed to be ~89% and >97% for binary and multi-class classifications respectively. The proposed model confirmed higher accuracy than the homology-based algorithms viz., BLAST and Hidden Markov Model. Besides, the developed computational model achieved ~87% accuracy, while tested with an independent dataset. An online prediction server HRGPred ( http://cabgrid.res.in:8080/hrgpred ) has also been established to facilitate the prediction of GETS by the scientific community.