Showing papers by "Sanjay Ghosh published in 2012"

Control of RNP motility and localization by a splicing-dependent structure in oskar mRNA

Abstract: oskar RNA localization to the posterior pole of the Drosophila melanogaster oocyte requires splicing of the first intron and the exon junction complex (EJC) core proteins. The functional link between splicing, EJC deposition and oskar localization has been unclear. Here we demonstrate that the EJC associates with oskar mRNA upon splicing in vitro and that Drosophila EJC deposition is constitutive and conserved. Our in vivo analysis reveals that splicing creates the spliced oskar localization element (SOLE), whose structural integrity is crucial for ribonucleoprotein motility and localization in the oocyte. Splicing thus has a dual role in oskar mRNA localization: assembling the SOLE and depositing the EJC required for mRNA transport. The SOLE complements the EJC in formation of a functional unit that, together with the oskar 3' UTR, maintains proper kinesin-based motility of oskar mRNPs and posterior mRNA targeting.

Effect on specific crop mapping using WorldView-2 multispectral add-on bands: soft classification approach

Abstract: In this study, new add-on bands in a multispectral dataset of WorldView-2, DigitalGlobe's second next-generation satellite, have been evaluated. For extraction of a specific agriculture crop at a time, WorldView-2 multispectral single, as well as two-date data sets, were used. For this purpose, a class-based sensor independent spectral band ratio normalized difference vegetation index (NDVI) (CBSI-NDVI) and its possibilistice fuzzy classification approach was used. Different agriculture crops selected for the study were sugarcane, late wheat, cauliflower, berseem (fodder), early wheat and ratoon. It is found that bands four and eight with temporal data are good for extracting sugarcane, while bands four, eight and five, seven with temporal data are suitable for late wheat and bands four and eight work well for cauliflower. Similarly, bands five, seven and five, eight with temporal data are good for extracting berseem (fodder), bands four, eight work for early wheat with temporal data and for ratoon four, six single date or four, six and four, eight temporal data. This suitability of bands has been observed with respect to a maximum membership value difference, as well as maximum entropy difference, between the two closest agriculture crops. Thus, it can be concluded that existing bands five, seven and new bands four, six, eight in WorldView-2 are important for identifying and mapping crops mentioned in this study. This indicates new bands, especially four, six, eight introduced in WorldView-2, are more effective than existing bands in QuickBird for mapping specific crops.

Detection of S-nitrosothiol and nitrosylated proteins in Arachis hypogaea functional nodule: response of the nitrogen fixing symbiont.

Abstract: To detect the presence of NO, ROS and RNS in nodules of crack entry legumes, we used Arachis hypogaea functional nodule. The response of two cognate partner rhizobia was compared towards NO and GSNO using S. meliloti and Bradyrhizobium sp NC921001. ROS, NO, nitrosothiol and bacteroids were detected by fluorescence microscopy. Redox enzymes and thiol pools were detected biochemically. Nitrosothiols were found to be present but ROS and NO were absent in A. hypogaea nodule. A number of S-nitrosylated proteins were also detected. The total thiol pool and most of the redox enzymes were low in nodule cytosolic extract but these were found to be high in the partner microorganisms indicating partner rhizobia could protect the nodule environment against the nitrosothiols. Both S. meliloti and Bradyrhizobium sp NC921001 were found to contain GSNO reductase. Interestingly, there was a marked difference in growth pattern between S. meliloti and Bradyrhizobium sp in presence of sodium nitroprusside (SNP) and S-nitrosoglutathione (GSNO). Bradyrhizobium sp was found to be much more tolerant to NO donor compounds than the S. meliloti. In contrast, S. meliloti showed resistance to GSNO but was sensitive to SNP. Together our data indicate that nodule environment of crack entry legumes is different than the nodules of infection mode entry in terms of NO, ROS and RNS. Based on our biochemical characterization, we propose that exchange of redox molecules and reactive chemical species is possible between the bacteroid and nodule compartment.

Liquefaction identification using class-based sensor independent approach based on single pixel classification after 2001 Bhuj, India earthquake

Abstract: A strong earthquake with magnitude 7.7 that shook the Indian Province of Gujarat on the morning of January 26, 2001 caused wide spread destruction and casualties. Earthquakeinduced ground failures, including liquefaction and lateral spreading, were observed in many areas. Optical remote sensing offers an excellent opportunity to understand the post-earthquake effects both qualitatively and quantitatively. The impact of using conventional indices from Landsat-7 temporal images for the liquefaction is empirically investigated and compared with class-based sensor independent (CBSI) indices, while applying possibilistic fuzzy classification as a soft computing approach via supervised classification. Five spectral indices, namely simple ratio (SR), normalized difference vegetation index (NDVI), transformed normalized difference vegetation index (TNDVI), soil-adjusted vegetation index (SAVI), and modified normalized difference water index (MNDWI) are investigated to identify liquefaction using temporal multi-spectral images. A soft-computing based fuzzy algorithm, which is independent of statistical distribution data assumption, is used to extract a single land cover class from remote sensing multi-spectral images. The result indicates that appropriately used indices can incorporate temporal variations, while extracting liquefaction with soft computing techniques for coarser spatial resolution with temporal remote sensing data. It is found that CBSI-NDVI with temporal data was good for extraction liquefaction while CBSI-TNDVI with temporal data was good for extraction water bodies.

Study of Fuzzy based Classifier Parameter across Spatial Resolution

Abstract: Classification and interpretation of satellite images are complex processes and that may be affected by various factors. Most fuzzy based soft classification techniques have been used to provide a more appropriate and accurate area estimation when fine, medium and coarse spatial resolution data are being used. Spatial resolution determines the spatial details on the Earth surface and greatly reduces the problem of mixed pixel. This paper examines the effect of weighting exponent „m‟ parameter of fuzzy c-means (FCM) and possibilistic c-mean (PCM) classifiers with respect to entropy, an uncertainty indicator for different extracted classes. This paper measures uncertainty variations across spatial resolution for different class extraction. Uncertainty can be defined as skepticism wherein entropy is an absolute indicator of an uncertainty. In this research work, fuzzy c-means (FCM) and possibilistic c-mean (PCM) classifiers have been used and entropy is computed to visualize the uncertainty. For this research work Resourcesat-1 (IRS-P6) data sets from AWIFS, LISS-III and LISS-IV sensors of same date have been used. Accuracy assessment of a classified image is an integral part of image classification and in this research two things were involved first optimization of weighting exponent „m‟, and computation of entropy. From the resultant Table 1, 2, 3, 4, 5, 6, 7 and 8 shows that the optimum values of „m‟ for FCM classifier on homogenous land cover classes are 2.9 and for heterogeneous classes are 2.7 where the membership values are varying from 0.8 to 0.9 with lesser entropy values, i.e. 0.35. Similarly for PCM classifier the optimum value of „m‟ for homogenous land cover classes are 3.2 and for heterogeneous classes are 3.0 where the membership values are varying from 0.8 to 0.9 with lesser entropy values, i.e. 0.78. In the second phase of study, to analyze the effect of uncertain pixels in FCM and PCM classifiers, Euclidean norm has been chosen for both the classifiers whereas the values of weighting exponent „m‟ varies from 1.1 to 4.0 for Sal forest, Eucalyptus plantation, water bodies, agriculture land with crop, agriculture moist land without crop, and agriculture dry land without crop. It is observed from the result Table 1, 2, 3, 4, 5, 6, 7 and 8, that uncertainty ratio is almost equal to referential value 2.585, for FCM and PCM classifiers using Euclidean norm. This reflects that fuzzy based soft classifiers FCM and PCM are producing higher classification accuracy with minimum level of uncertainty.

Mapping of Agriculture Drought using Remote Sensing and GIS

Abstract: Agriculture drought occurs when moisture level in soils is insufficient to maintain average crop yields. Initial consequences are in the reduced seasonal output of crops & other related production. An extreme agricultural drought can lead to a famine, which is a prolonged shortage of food in a restricted region causing widespread diseases and deaths from starvation. Agriculture drought is mainly dependent on low rainfall which results in agricultural production.

A New Route to Pyrano[3,2-a]carbazole Ring Systems and Annulated Derivatives Thereof

Abstract: A new protocol based on sequential applications of Claisen rearrangement, olefin isomerisation, ring-closing diene/ enyne metathesis and Diels–Alder reaction has been developed to access pyrano[3,2-a]carbazole and annulated derivatives thereof.

Soft computing approach for liquefaction identification using landsat-7 temporal indices data

Abstract: A strong earthquake with magnitude M w 7.7 that shook the Indian Province of Gujarat on the morning of January 26, 2001, caused widespread appearance of water bodies and channels, in the Rann of Kachchh and the coastal areas of Kandla port. In this work, the impact of using conventional band ratio indices from Landsat-7 temporal images for liquefaction extraction was empirically investigated and compared with Class Based Sensor Independent (CBSI) spectral band ratio while applying noise classifier as soft computing approach via supervised classification. Five spectral indices namely, SR (Simple Ratio), NDVI (Normalized Difference Vegetation index), TNDVI (Transformed Normalized Difference Vegetation Index), SAVI (Soil-Adjusted Vegetation Index) and Modified Normalized Difference Water Index (MNDWI) were investigated to identify liquefaction using temporal multi-spectral images. It is found that CBSI-TNDVI with temporal data has higher membership range (0.968–0.996) and minimum entropy (0.011) to outperform for extraction of liquefaction and for water bodies extraction membership range (0.960–0.996) and entropy (0.005) respectively.

Optimisation of fuzzy based soft classifiers for remote sensing data

Abstract: . Classification of satellite images are complex process and accuracy of the output is dependent on classifier parameters. This paper examines the effect of various parameters like weighted exponent "m" for FCM , PCM classifiers and weighted exponent "m" as well as fixed parameter "?" for NC without entropy based algorithm. The prime focus in this work is to select suitable parameters for classification of remotely sensed data which improves the accuracy of classification output. The uncertainty criterion has been estimated from sub-pixel confusion uncertainty matrix (SCM), based on classified and testing outputs. Therefore, these criterions are dependent on the error of the results and sensitive to error variations. So it has also been tried to estimate entropy, based on outputs generated by various classifiers like FCM, PCM and NC without entropy based classifier, hence this computed entropy is sensitive to uncertainty variations. The AWiFS and LISS-III datasets are being used for classification and testing respectively. Soft classified outputs from FCM, PCM and NC without entropy classifiers for AWiFS and LISS-III have been evaluated using SCM, overall accuracy, fuzzy kappa coefficient and entropy. The SCM and fuzzy kappa coefficients are used to major relative accuracies, while entropy is an absolute uncertainty indicator. From resultant aspect, while monitoring entropy of fraction images for different regularizing parameter values, optimum regularizing parameter has been obtained for "m" = 2.0 and "?" = 1, which gives highest accuracy from sub-pixel confusion uncertainty matrix (SCM) i.e. 96.27% and AWiFS entropy has been 0.71 using noise clustering without entropy based classifier.

Study of Fuzzy Based Classifier Parameter Using Fuzzy Matrix

Abstract: In the area of remote sensing, the decision making are not generally deterministic due to the involvement of fuzziness in the classification of remotely sensed imagery. A considerable number of identification errors are due to pixels that show an affinity with several information classes. The fuzzy concept is a valuable tool for dealing with classification problems. In remote sensing classification, fuzzy based classifiers are becoming increasingly popular. Due to the wide acceptance of fuzzy c-mean (FCM) and possibilistic c-means (PCM) classifiers, this has been used as a benchmark to evaluate the performance of other classifiers with optimized value of weighting exponent 'm' in this research. Evaluation of soft classification through FERM, SCM and Fuzzy kappa coefficient, using Euclidean norm based measures led to an improvement wherein FCM-Overall accuracy (MIN-LEAST) operator reflects higher classification accuracy, i.e., 97% and the value of Fuzzy Kappa coefficient is 0.97 with minimum uncertainty in it, for the optimized value of weighting exponent 'm' i.e. 4.0. In this experimentation two supervised classifiers namely FCM and PCM have been selected to demonstrate the improvement in the classification accuracy by FERM, SCM, MIN-MIN, MIN-LEAST, Fuzzy Kappa coefficient and uncertainty in SCM and Fuzzy Kappa coefficients. Index Terms—Fuzzy c-Mean (FCM), Fuzzy Error Matrix (FERM), Possiblistic c-Mean (PCM), Sub-pixel confusion-uncertainty matrix(SCM),

Investigation of Image Classification Techniques for Performance Enhancement

Abstract: Image classification is a tedious process and that is affected by various parameters. This paper examines current practices of image classification. In remotely sensed data the easiest and usual assumption is that each pixel represents a homogeneous area on the ground. However in real world, it is found to be heterogeneous in nature. For this reason it has been proposed that fuzziness should be accommodated in the classification procedure and preserves the extracted information. The prime focus in this research work is to select suitable parameters for classification of remotely sensed data which improves the accuracy of classification output and to study the behaviour of associated learning parameters for optimization estimation, with different fuzzy based functions.The estimation of uncertainty is done by entropy for FCM, PCM and noise clustering without entropy based classifier, and hence is sensitive to uncertain variations. The geographic latitude/longitude, of study area extends from 28°52'29”N to 28°54'20”N and 79°34'25”E to 79°36'34”E. The images for this research work have been taken from two different sensors namely LISS-III and LISSIV belonging to satellite IRS-P6. Soft classified outputs from FCM, PCM and noise clustering without entropy classifiers for LISS-III with LISS-IV have been evaluated using sub-pixel confusion uncertainty matrix (SCM), for overall accuracy, fuzzy kappa coefficient and entropy. In this research work it has been tried to generate fraction outputs from FCM, PCM, and noise clustering without entropy. These outputs have been generated from LISS-III and LISS-IV images of IRS-P6 data. The SCM and fuzzy kappa coefficients are used to major relative accuracies, while entropy is an absolute uncertainty indicator. From resultant aspect, while monitoring entropy of fraction images for different regularizing parameter values, optimum regularizing parameter has been obtained for 'm'=1.9 and ' which gives highest accuracy from sub-pixel confusion uncertainty matrix (SCM) i.e. 94.65% and LISS-III entropy is 0.04 using noise clustering without entropy based classifier. Where in optimum regularizing parameters value('m') for FCM and PCM is 3.2 , which gives an SCM accuracy 72.98% and 42.55% respectively. The LISS-III entropy for FCM and PCM classifier is 0.011 and 1.98. From this work it can be concluded that output from noise clustering without entropy classifier has higher classification accuracy with lowest uncertainty with respect to FCM and PCM based classifiers. This research work has been done using indigenously developed Sub-pixel Multi-spectral Image Classification (SMIC) tool. Fuzzy c-Mean (FCM), Possiblistic c-Mean (PCM), Noise clustering (NC), Sub pixel confusion uncertainty matrix (SCM) ! '=1,

Geospatially based distributed rainfall-runoff modelling for simulation of internal and outlet responses in a semi-forested lower Himalayan watershed

Abstract: This study presents a Geographic Information System (GIS)-based distributed rainfall-runoff model for simulating surface flows in small to large watersheds during isolated storm events. The model takes into account the amount of interception storage to be filled using a modified Merriam (1960) approach before estimating infiltration by the Smith and Parlange (1978) method. The mechanics of overland and channel flow are modelled by the kinematic wave approximation of the Saint Venant equations which are then numerically solved by the weighted four-point implicit finite difference method. In this modelling the watershed was discretized into overland planes and channels using the algorithms proposed by Garbrecht and Martz (1999). The model code was first validated by comparing the model output with an analytical solution for a hypothetical plane. Then the model was tested in a medium-sized semi-forested watershed of Pathri Rao located in the Shivalik ranges of the Garhwal Himalayas, India. Initially, a local sensitivity analysis was performed to identify the parameters to which the model outputs like runoff volume, peak flow and time to peak flow are sensitive. Before going for model validation, calibration was performed using the Ordered-Physics-based Parameter Adjustment (OPPA) method. The proposed Physically Based Distributed (PBD) model was then evaluated both at the watershed outlet as well as at the internal gauging station, making this study a first of its kind in Indian watersheds. The results of performance evaluation indicate that the model has simulated the runoff hydrographs reasonably well within the watershed as well as at the watershed outlet with the same set of calibrated parameters. The model also simulates, realistically, the temporal variation of the spatial distribution of runoff over the watershed and the same has been illustrated graphically. Copyright © 2011 John Wiley & Sons, Ltd.

Optical phase conjugation reflectivity via stimulated Brillouin scattering in acousto-optic diffusive semiconductor plasma crystal

Abstract: Analytical investigation has been made for optical phase conjugation (OPC) reflectivity via stimulated Brillouin scattering (SBS) in acousto-optic diffusive semiconductor plasma crystal Our analysis is based on hydrodynamic model and coupled mode approach of interacting waves The numerical estimations are made for n-type InSb semiconductor plasma crystal duly irradiated by CO2 laser The magnitude of the third-order nonlinear optical susceptibility for III-V semiconductor obtained from our analysis is found to agree well with the experimental values Maximum OPC reflectivity is obtained when cyclotron frequency is in resonance with applied pump frequency

A New Route to Pyrano[3,2‐a]carbazole Ring Systems and Annulated Derivatives Thereof.

Abstract: A successful application of the Claisen rearrangement/isomerization/ring closing enyne metathesis for the preparation of the title skeleton is described.