Anti-LCT monoclonal antibody, clone 4I7

The selection response of wheat (Triticum aestivum L.) under water-limited condition can be enhanced through breeding novel genotypes possessing drought-adaptive and yield-related agronomic and physiological traits. The objective of this study was to evaluate terminal drought tolerance among bread wheat genotypes and select superior parents for breeding. Agronomic and physiological responses of 28 wheat lines were assessed under well-watered (WW) and terminal drought (TD) treatments using a 7x4 alpha-lattice design under rainout shelter (RS) and glasshouse (GH) environments. Significant (p <= 0.05) genotypexenvironment interaction effects were observed for number of days to heading (DTH), number of days to maturity (DTM), number of productive tillers per plant (TN), grain yield (GY), thousand kernel weight (TKW), stomatal conductance (SC), leaf canopy temperature (LCT) and chlorophyll content index (CCI) suggesting varied genotypic response under WW and TD treatments. Correlation analysis revealed significant associations TN and GY (r=0.67; p <= 0.001), TKW and GY (r=0.72; p <= 0.001), CCI and GY (r=0.39; p <= 0.05) under TD treatment. Drought tolerant wheat genotypes such as LM100, LM72, LM22 and LM95 are useful for direct cultivation and for developing breeding populations with enhanced yield performance.

Melanoma is a life threading disease when it grows outside the corium layer of the skin. Mortality rates of the Melanoma cases are maximum among the skin cancer patients. The cost required for the treatment of advanced melanoma cases is very high and the survival rate is low. Numerous computerized dermoscopy systems are developed based on the combination of shape, texture and color features to facilitate early diagnosis of melanoma. The availability and cost of the dermoscopic imaging system is still an issue. To mitigate this issue, this paper presented an integrated segmentation and Third Dimensional (3D) feature extraction approach for the accurate diagnosis of melanoma. A multi-atlas method is applied for the image segmentation. The patch-based label fusion model is expressed in a Bayesian framework to improve the segmentation accuracy. A depth map is obtained from the Two-dimensional (2D) dermoscopic image for reconstructing the 3D skin lesion represented as structure tensors. The 3D shape features including the relative depth features are obtained. Streaks are the significant morphological terms of the melanoma in the radial growth phase. The proposed method yields maximum segmentation accuracy, sensibility, specificity and minimum cost function than the existing segmentation technique and classifier.