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In the current era, one of biggest challenges is to shorten the breeding cycle for rapid generation of a new crop variety having high yield capacity, disease resistance, high nutrient content, etc. Advances in the “-omics” technology have revolutionized the discovery of genes and bio-molecules with remarkable precision, resulting in significant development of plant-focused metabolic databases and resources. Metabolomics has been widely used in several model plants and crop species to examine metabolic drift and changes in metabolic composition during various developmental stages and in response to stimuli. Over the last few decades, these efforts have resulted in a significantly improved understanding of the metabolic pathways of plants through identification of several unknown intermediates. This has assisted in developing several new metabolically engineered important crops with desirable agronomic traits, and has facilitated the de novo domestication of new crops for sustainable agriculture and food security. In this review, we discuss how “omics” technologies, particularly metabolomics, has enhanced our understanding of important traits and allowed speedy domestication of novel crop plants.

Segmentation of tumor form brain MR images is the most important and tedious task in the medical field. In this paper, A Cluster deformable based fusion approach which uses both deformable and K-Means clustering scheme for Segmentation is discussed. The features of tumor and non tumor cases are extracted with the use of the Power Local Binary Pattern (LBP) Operator after completion of the segmentation process. The extracted features are fed to Naive Bayes classifier to perform the process of classification. Here, the validation of the proposed system is done using standard validation methods such as accuracy, specificity, sensitivity and RoC metrics. The developed method is applied for MR images collected from standard SimBRATS database. Experimentation results shows that the proposed method performs better when compared to the traditional clustering and deformable methods and this scheme got accuracy of 84.8%.

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