The synthesis of Zinc oxide nanoparticles using a plant-mediated approach is presented in this paper. The nanoparticles were successfully synthesized using the Nitrate derivative of Zinc and plant extract of the indigenous medicinal plant Cayratia pedata. 0.1 mM of Zn (NO 3 ) 2 .6H 2 O was made to react with the plant extract at different concentrations, and the reaction temperature was maintained at 55 °C, 65 °C, and 75 °C. The yellow coloured paste obtained was wholly dried, collected, and packed for further analysis. In the UV visible spectrometer (UV–Vis) absorption peak was observed at 320 nm, which is specific for Zinc oxide nanoparticles. The characterization carried out using Field Emission Scanning Electron Microscope (FESEM) reveals the presence of Zinc oxide nanoparticles in its agglomerated form. From the X-ray diffraction (XRD) pattern, the average size of the nanoparticles was estimated to be 52.24 nm. Energy Dispersive Spectrum (EDX) results show the composition of Zinc and Oxygen, giving strong energy signals of 78.32% and 12.78% for Zinc and Oxygen, respectively. Fourier Transform - Infra-Red (FT-IR) spectroscopic analysis shows absorption peak of Zn–O bonding between 400 and 600 cm −1 . The various characterization methods carried out confirm the formation of nano Zinc oxide. The synthesized nanoparticles were used in the immobilization of the enzyme Glucose oxidase. Relative activity of 60% was obtained when Glucose oxidase was immobilized with the green synthesized ZnO nanoparticles. A comparative study of the green synthesized with native ZnO was also carried out. This green method of synthesis was found to be cost-effective and eco-friendly.

BackgroundMicroRNAs (miRNAs) play an essential task in gene regulatory networks by inhibiting the expression of target mRNAs. As their mRNA targets are genes involved in important cell functions, there is a growing interest in identifying the relationship between miRNAs and their target mRNAs. So, there is now a imperative need to develop a computational method by which we can identify the target mRNAs of existing miRNAs. Here, we proposed an efficient machine learning model to unravel the relationship between miRNAs and their target mRNAs.ResultsWe present a novel computational architecture MTar for miRNA target prediction which reports 94.5% sensitivity and 90.5% specificity. We identified 16 positional, thermodynamic and structural parameters from the wet lab proven miRNA:mRNA pairs and MTar makes use of these parameters for miRNA target identification. It incorporates an Artificial Neural Network (ANN) verifier which is trained by wet lab proven microRNA targets. A number of hitherto unknown targets of many miRNA families were located using MTar. The method identifies all three potential miRNA targets (5' seed-only, 5' dominant, and 3' canonical) whereas the existing solutions focus on 5' complementarities alone.ConclusionMTar, an ANN based architecture for identifying functional regulatory miRNA-mRNA interaction using predicted miRNA targets. The area of target prediction has received a new momentum with the function of a thermodynamic model incorporating target accessibility. This model incorporates sixteen structural, thermodynamic and positional features of residues in miRNA: mRNA pairs were employed to select target candidates. So our novel machine learning architecture, MTar is found to be more comprehensive than the existing methods in predicting miRNA targets, especially human transcritome.

Background: Biclustering algorithms belong to a distinct class of clustering algorithms that perform simultaneous clustering of both rows and columns of the gene expression matrix and can be a very useful analysis tool when some genes have multiple functions and experimental conditions are diverse. Cheng and Church have introduced a measure called mean squared residue score to evaluate the quality of a bicluster and has become one of the most popular measures to search for biclusters. In this paper, we review basic concepts of the metaheuristics Greedy Randomized Adaptive Search Procedure (GRASP)-construction and local search phases and propose a new method which is a variant of GRASP called Reactive Greedy Randomized Adaptive Search Procedure (Reactive GRASP) to detect significant biclusters from large microarray datasets. The method has two major steps. First, high quality bicluster seeds are generated by means of k-means clustering. In the second step, these seeds are grown using the Reactive GRASP, in which the basic parameter that defines the restrictiveness of the candidate list is self-adjusted, depending on the quality of the solutions found previously.

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The Plant Genome C hloroplasts are important organelles that are responsible for photosynthesis and play a vital role in plant physiology and development in green plants and algae (Raven and Allen, 2003). Chloroplasts have their own DNA, and for most of the land plants, the chloroplast genome exists in circular form with a quadripartite structure comprised of a large single copy (LSC) and a small single copy (SSC) region separated by two inverted repeats (IR; Sugiura, 2003; Wicke et al., 2011). The size of the chloroplast genome varies from 120 to 170 Kb and possessed 60 to 130 genes which are primarily involved in photosynthesis and other metabolic process (Sugiura, 2003; Wicke et al., 2011). The haploid nature, low level of recombination, and maternal inheritance of the chloroplast genome, as well as its low substitution rate compared with the nuclear genome, present chloroplast genomes as valuable