Prediction and classification for GPCR sequences based on ligand specific features

Functional identification of G-Protein Coupled Receptors (GPCRs) is one of the current focus areas of pharmaceutical research. Although thousands of GPCR sequences are known, many of them are orphan sequences (the activating ligand is unknown). Therefore, classification methods for automated characterization of orphan GPCRs are imperative. In this study, for predicting Level 1 subfamilies of GPCRs, a novel method for obtaining class specific features, based on the existence of activating ligand specific patterns, has been developed and utilized for a majority voting classification. Exploiting the fact that there is a non-promiscuous relationship between the specific binding of GPCRs into their ligands and their functional classification, our method classifies Level 1 subfamilies of GPCRs with a high predictive accuracy between 99% and 87% in a three-fold cross validation test. The method also tells us which motifs are significant for class determination which has important design implications. The presented machine learning approach, bridges the gulf between the excess amount of GPCR sequence data and their poor functional characterization

Optimization of mixing parameters through a water model for metal matrix composites synthesis

The influence of the mixing parameters on the synthesis of Al&ndash;SiCp reinforced metal matrix composites (MMCs) by the stir casting technique is investigated through a water model. The effects of some important mixing parameters such as impeller blade angle, rotating speed, direction of impeller rotation and effect of baffles are investigated and optimized. The results have shown that the axial concentration variation of natural graphite during stirring in the presence of four vertical baffles is 1.0 wt% against in the absence of baffles it is increased to 2.3 wt%. The variations observed in natural graphite concentration in water during mixing are in close agreement with the earlier modeling and limited experimental studies reported on the real molten aluminum&ndash;SiC system. Semi-empirical correlations arrived at between the dimensionless numbers for stirred water &ndash; natural graphite slurries are Po = Re&minus;0.0545 Fr&minus;1.099 and Po = Re&minus;0.0219 Fr&minus;1.0382 for clockwise and counter clockwise rotation respectively.<br /

Thermodynamics and kinetics of the formation of Al2 O3/ MgAl2O4/MgO in Al-Silica metal matrix composite

The formation of Al2O3, MgAl2O4, and MgO has been widely studied in different Al base metal matrix composites, but the studies on thermodynamic aspects of the Al2O3/ MgAl2O4/MgO phase equilibria have been limited to few systems such as Al/Al2O3 and Al/SiC. The present study analyzes the Al2O3/MgAl2O4 and MgAl2O4/MgO equilibria with respect to the temperature and the Mg content in Al/SiO2 system using an extended Miedema model. There is a linear and parabolic variation in Mg with respect to the temperature for MgAl2O4/MgO and Al2O3/MgAl2O4 equilibria, respectively, and the influence of Si and Cu in the two equilibria is not appreciable. The experimental verification has been limited to MgAl2O4/MgO equilibria due to the high Mg content (&ge;0.5 wt pct) required for composite processing. The study has been carried out on two varieties of Al/SiO2 composites, i.e., Al/Silica gel and Al/Micro silica processed by liquid metallurgy route (stir casting route). MgO is found to be more stable compared to MgAl2O4 at Mg levels &ge;5 and 1 wt pct in Al/Silica gel and Al/Micro silica composites, respectively, at 1073 K. MgO is also found to be more stable at lower Mg content (3 wt pct) in Al/Silica gel composite with decreasing particle size of silica gel from 180 micron to submicron and nanolevels. The MgO to MgAl2O4 transformation has taken place through a series of transition phases influenced by the different thermodynamic and kinetic parameters such as holding temperature, Mg concentration in the alloy, holding time, and silica particle size.<br /

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Not AvailableSustaining soil and land quality under intensive land use and fast economic development is a major challenge for improving crop productivity in the developing world. Assessment of soil and land quality indicators is necessary to evaluate the degradation status and changing trends of different land use and management interventions. During the last four decades, the Indo-Gangetic Plains (IGP) which covers an area of about 52.01 m ha has been the major food producing region of the country. However at present, the yield of crops in IGP has stagnated; one of the major reasons being deterioration of soil and land quality. The present article deals with the estimation of soil and land quality indicators of IGP, so that, proper soil and land management measures can be taken up to restore and improve the soil health. Use of principal component analysis is detailed to derive the minimum dataset or indicators for soil quality. The article also describes spatial distribution of soil and land quality with respect to major crops of IGP.Not Availabl