Plant aspartate transcarbamylase: An affinity chromatographic method for the purification of the enzyme from germinated seedlings

A simple and rapid affinity chromatographic method for the isolation of aspartate transcarbamylase from germinated seedlings of mung bean (Phaseolus aureus) was developed. A partially purified preparation of the enzyme was chromatographed on an affinity column containing aspartate linked to CNBr-activated Sepharose 4B. Aspartate transcarbamylase was specifically eluted from the column with 10 mImage aspartate or 0.5 Image KCl. The enzyme migrated as a single sharp band during disc electrophoresis at pH 8.6 on polyacrylamide gels. Electrophoresis of the sodium dodecyl sulfate-treated enzyme showed two distinct protein bands, suggesting that the mung bean aspartate transcarbamylase was made up of nonidentical subunits. Like the enzyme purified by conventional procedures, this enzyme preparation also exhibited positive homotropic interactions with carbamyl phosphate and negative heterotropic interactions with UMP. This method was extended to the purification of aspartate transcarbamylase from Lathyrus sativus, Eleucine coracona, and Trigonella foenum graecum.

Preparation, Structural, and Electrical Studies of Polyaniline/ZnFe2O4 Nanocomposites

Polyaniline/ZnFe2O4 nanocomposites were synthesized by a simple and inexpensive one-step in situ polymerization method in the presence of ZnFe2O4 nanoparticles. The structural, morphological, and electrical properties of the samples were characterized by wide angle X-ray diffraction (WAXD), Fourier transform infrared (FTIR) spectroscopy, scanning electron microscopy (SEM), and thermogravimetric analysis (TGA). WAXD and SEM revealed the formation of polyaniline/ZnFe2O4 nanocomposites. Infrared spectroscopy indicated that there was some interaction between the ZnFe2O4 nanoparticles and polyaniline. The dc electrical conductivity measurements were carried in the temperature range of 80 to 300 K. With increase in the doping concentration of ZnFe2O4, the conductivity of the nanocomposites found to be decreasing from 5.15 to 0.92 Scm(-1) and the temperature dependent resistivity follows ln rho(T) similar to T-1/2 behavior. The nanocomposites (80 wt % of ZnFe2O4) show a more negative magnetoresistance compared with that of pure polyaniline (PANI). These results suggest that the interaction between the polymer matrix PANI and zinc nanoparticles take place in these nanocomposites. (C) 2011 Wiley Periodicals, Inc. J Appl Polym Sci 120: 2856-2862, 2011

Preparation and Characterization of Single-Phase Beta-Sialon

A simple method for the preparation of monophasic beta-SiAlON using nitridation of Si and AIN with an oxygen partial pressure of 10(-4) atm is described. The effect of the AlN/Si ratio in the initial mixture on the formation of beta-SiAlON is discussed. The likely mechanism of the formation of beta-SiAlON is outlined.

Photoluminescence of Sr(2-x)Ln(x)CeO(4+x/2) (Ln = Eu, Sm or Yb) prepared by a wet chemical method

A wet chemical route is developed for the preparation of Sr2CeO4 denoted the carbonate-gel composite technique. This involves the coprecipitation of strontium as fine particles of carbonates within hydrated gels of ceria (CeO2.xH(2)O, 40

Preparation, characterization and magnetic studies of Bi0.5X0.5(X=Ca,Sr)MnO3 nanoparticles

Nanoparticles (dia ~ 5 - 7 nm) of Bi0.5X0.5(X=Ca,Sr)MnO3 are prepared by polymer assisted sol-gel method and characterized by various physico-chemical techniques. X-ray diffraction gives evidence for single phasic nature of the materials as well as their structures. Mono dispersed to a large extent, isolated nanoparticles are seen in the transmission electron micrographs. High resolution electron microscopy shows the crystalline nature of the nanoparticles. Superconducting quantum interferometer based magnetic measurements from 10K to 300K show that these nanomanganites retain the charge ordering nature unlike Pr and Nd based nanomanganites. The CO in Bi based manganites is thus found to be very robust consistent with the observation that magnetic field of the order of 130 T are necessary to melt the CO in these compounds. These results are supported by electron magnetic resonance measurements.

Hierarchical Clustering Algorithm for Land Cover Mapping Using Satellite Images

This paper presents hierarchical clustering algorithms for land cover mapping problem using multi-spectral satellite images. In unsupervised techniques, the automatic generation of number of clusters and its centers for a huge database is not exploited to their full potential. Hence, a hierarchical clustering algorithm that uses splitting and merging techniques is proposed. Initially, the splitting method is used to search for the best possible number of clusters and its centers using Mean Shift Clustering (MSC), Niche Particle Swarm Optimization (NPSO) and Glowworm Swarm Optimization (GSO). Using these clusters and its centers, the merging method is used to group the data points based on a parametric method (k-means algorithm). A performance comparison of the proposed hierarchical clustering algorithms (MSC, NPSO and GSO) is presented using two typical multi-spectral satellite images - Landsat 7 thematic mapper and QuickBird. From the results obtained, we conclude that the proposed GSO based hierarchical clustering algorithm is more accurate and robust.

Quasi-based hierarchical clustering for land cover mapping using satellite images

This paper presents an improved hierarchical clustering algorithm for land cover mapping problem using quasi-random distribution. Initially, Niche Particle Swarm Optimization (NPSO) with pseudo/quasi-random distribution is used for splitting the data into number of cluster centers by satisfying Bayesian Information Criteria (BIC). Themain objective is to search and locate the best possible number of cluster and its centers. NPSO which highly depends on the initial distribution of particles in search space is not been exploited to its full potential. In this study, we have compared more uniformly distributed quasi-random with pseudo-random distribution with NPSO for splitting data set. Here to generate quasi-random distribution, Faure method has been used. Performance of previously proposed methods namely K-means, Mean Shift Clustering (MSC) and NPSO with pseudo-random is compared with the proposed approach - NPSO with quasi distribution(Faure). These algorithms are used on synthetic data set and multi-spectral satellite image (Landsat 7 thematic mapper). From the result obtained we conclude that use of quasi-random sequence with NPSO for hierarchical clustering algorithm results in a more accurate data classification.

Gene expression programming-fuzzy logic method for crop type classification

Crop type classification using remote sensing data plays a vital role in planning cultivation activities and for optimal usage of the available fertile land. Thus a reliable and precise classification of agricultural crops can help improve agricultural productivity. Hence in this paper a gene expression programming based fuzzy logic approach for multiclass crop classification using Multispectral satellite image is proposed. The purpose of this work is to utilize the optimization capabilities of GEP for tuning the fuzzy membership functions. The capabilities of GEP as a classifier is also studied. The proposed method is compared to Bayesian and Maximum likelihood classifier in terms of performance evaluation. From the results we can conclude that the proposed method is effective for classification.

Preparation of Self Assembled Sodium Oleate Monolayer on Mild Steel and Its Corrosion Inhibition Behavior in Saline water

A self assembled monolayer (SAM) of sodium oleate was generated on mild steel by the dip coating method. Formation of the SAM on mild steel was examined using Infrared Reflection Absorption Spectroscopy (IRRAS) and contact angle measurements. The chemical and anticorrosive properties of the SAM were analyzed using different techniques. IRRAS and water contact angle data revealed the crystallinity and chemical stability of the SAM modified mild steel. The electrochemical measurements showed that the mild steel with the sodium oleate derived SAM exhibited better corrosion resistance in saline water. The effect of temperature and pH on the SAM formation and its anti corrosion ability was explored.

A simple model for spatial disaggregation of evaporative fraction: Comparative study with thermal sharpened land surface temperature data over India

[1] Evaporative fraction (EF) is a measure of the amount of available energy at the earth surface that is partitioned into latent heat flux. The currently operational thermal sensors like the Moderate Resolution Imaging Spectroradiometer (MODIS) on satellite platforms provide data only at 1000 m, which constraints the spatial resolution of EF estimates. A simple model (disaggregation of evaporative fraction (DEFrac)) based on the observed relationship between EF and the normalized difference vegetation index is proposed to spatially disaggregate EF. The DEFrac model was tested with EF estimated from the triangle method using 113 clear sky data sets from the MODIS sensor aboard Terra and Aqua satellites. Validation was done using the data at four micrometeorological tower sites across varied agro-climatic zones possessing different land cover conditions in India using Bowen ratio energy balance method. The root-mean-square error (RMSE) of EF estimated at 1000 m resolution using the triangle method was 0.09 for all the four sites put together. The RMSE of DEFrac disaggregated EF was 0.09 for 250 m resolution. Two models of input disaggregation were also tried with thermal data sharpened using two thermal sharpening models DisTrad and TsHARP. The RMSE of disaggregated EF was 0.14 for both the input disaggregation models for 250 m resolution. Moreover, spatial analysis of disaggregation was performed using Landsat-7 (Enhanced Thematic Mapper) ETM+ data over four grids in India for contrasted seasons. It was observed that the DEFrac model performed better than the input disaggregation models under cropped conditions while they were marginally similar under non-cropped conditions.

Assessment of land surface temperature and heat fluxes over Delhi using remote sensing data

Surface energy processes has an essential role in urban weather, climate and hydrosphere cycles, as well in urban heat redistribution. The research was undertaken to analyze the potential of Landsat and MODIS data in retrieving biophysical parameters in estimating land surface temperature & heat fluxes diurnally in summer and winter seasons of years 2000 and 2010 and understanding its effect on anthropogenic heat disturbance over Delhi and surrounding region. Results show that during years 2000-2010, settlement and industrial area increased from 5.66 to 11.74% and 4.92 to 11.87% respectively which in turn has direct effect on land surface temperature (LST) and heat fluxes including anthropogenic heat flux. Based on the energy balance model for land surface, a method to estimate the increase in anthropogenic heat flux (Has) has been proposed. The settlement and industrial areas has higher amounts of energy consumed and has high values of Has in all seasons. The comparison of satellite derived LST with that of field measured values show that Landsat estimated values are in close agreement within error of 2 degrees C than MODIS with an error of 3 degrees C. It was observed that, during 2000 and 2010, the average change in surface temperature using Landsat over settlement & industrial areas of both seasons is 1.4 degrees C & for MODIS data is 3.7 degrees C. The seasonal average change in anthropogenic heat flux (Has) estimated using Landsat & MODIS is up by around 38 W/m(2) and 62 W/m(2) respectively while higher change is observed over settlement and concrete structures. The study reveals that the dynamic range of Has values has increased in the 10 year period due to the strong anthropogenic influence over the area. The study showed that anthropogenic heat flux is an indicator of the strength of urban heat island effect, and can be used to quantify the magnitude of the urban heat island effect. (C) 2013 Elsevier Ltd. All rights reserved.