Sanjay Ghosh

Bio: Sanjay Ghosh is an academic researcher from Indian Institute of Technology Roorkee. The author has contributed to research in topics: Bilateral filter & Normalized Difference Vegetation Index. The author has an hindex of 30, co-authored 196 publications receiving 3079 citations. Previous affiliations of Sanjay Ghosh include Indian Institutes of Technology & University of Cambridge.

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.

N-terminal domain swapping and metal ion binding in nitric oxide synthase dimerization.

Abstract: Nitric oxide synthase oxygenase domains (NOS ox ) must bind tetrahydrobiopterin and dimerize to be active. New crystallographic structures of inducible NOS ox reveal that conformational changes in a switch region (residues 103–111) preceding a pterin‐binding segment exchange N‐terminal β‐hairpin hooks between subunits of the dimer. N‐terminal hooks interact primarily with their own subunits in the ‘unswapped’ structure, and two switch region cysteines (104 and 109) from each subunit ligate a single zinc ion at the dimer interface. N‐terminal hooks rearrange from intra‐ to intersubunit interactions in the ‘swapped structure’, and Cys109 forms a self‐symmetric disulfide bond across the dimer interface. Subunit association and activity are adversely affected by mutations in the N‐terminal hook that disrupt interactions across the dimer interface only in the swapped structure. Residue conservation and electrostatic potential at the NOS ox molecular surface suggest likely interfaces outside the switch region for electron transfer from the NOS reductase domain. The correlation between three‐dimensional domain swapping of the N‐terminal hook and metal ion release with disulfide formation may impact inducible nitric oxide synthase (i)NOS stability and regulation in vivo .

Development of an agricultural crops spectral library and classification of crops at cultivar level using hyperspectral data

Abstract: In the context of a growing interest in remote sensing for precision agriculture applications, the utility of space-borne hyperspectral imaging for the development of a crop-specific spectral library and automatic identification and classification of three cultivars for each of rice (Oryza sativa L.), chilli (Capsicum annuum L.), sugarcane (Saccharum officinarum L.) and cotton (Gossipium hirsutum L.) crops have been investigated in this study. The classification of crops at cultivar level using two spectral libraries developed using hyperspectral reflectance data at canopy scale (in-situ hyperspectral measurements) and at pixel scale (Hyperion data) has shown promising results with 86.5 and 88.8% overall classification accuracy, respectively. This observation highlights the possible integration of in-situ hyperspectral measurements with space-borne hyperspectral remote sensing data for automatic identification and discrimination of various crop cultivars. However, considerable spectral similarity is observed between cultivars of rice and sugarcane crops which may pose problems in the accurate identification of various crop cultivars.

Machine learning

Abstract: Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Climate Change 2007: The Physical Science Basis.

Abstract: Cause, conseguenze e strategie di mitigazione Proponiamo il primo di una serie di articoli in cui affronteremo l’attuale problema dei mutamenti climatici. Presentiamo il documento redatto, votato e pubblicato dall’Ipcc - Comitato intergovernativo sui cambiamenti climatici - che illustra la sintesi delle ricerche svolte su questo tema rilevante.

Nitric oxide synthases: structure, function and inhibition

Abstract: This review concentrates on advances in nitric oxide synthase (NOS) structure, function and inhibition made in the last seven years, during which time substantial advances have been made in our understanding of this enzyme family. There is now information on the enzyme structure at all levels from primary (amino acid sequence) to quaternary (dimerization, association with other proteins) structure. The crystal structures of the oxygenase domains of inducible NOS (iNOS) and vascular endothelial NOS (eNOS) allow us to interpret other information in the context of this important part of the enzyme, with its binding sites for iron protoporphyrin IX (haem), biopterin, L-arginine, and the many inhibitors which interact with them. The exact nature of the NOS reaction, its mechanism and its products continue to be sources of controversy. The role of the biopterin cofactor is now becoming clearer, with emerging data implicating one-electron redox cycling as well as the multiple allosteric effects on enzyme activity. Regulation of the NOSs has been described at all levels from gene transcription to covalent modification and allosteric regulation of the enzyme itself. A wide range of NOS inhibitors have been discussed, interacting with the enzyme in diverse ways in terms of site and mechanism of inhibition, time-dependence and selectivity for individual isoforms, although there are many pitfalls and misunderstandings of these aspects. Highly selective inhibitors of iNOS versus eNOS and neuronal NOS have been identified and some of these have potential in the treatment of a range of inflammatory and other conditions in which iNOS has been implicated.

Methods in Enzymology.

Abstract: I read this book the same weekend that the Packers took on the Rams, and the experience of the latter event, obviously, colored my judgment. Although I abhor anything that smacks of being a handbook (like, \"How to Earn a Merit Badge in Neurosurgery\") because too many volumes in biomedical science already evince a boyscout-like approach, I must confess that parts of this volume are fast, scholarly, and significant, with certain reservations. I like parts of this well-illustrated book because Dr. Sj6strand, without so stating, develops certain subjects on technique in relation to the acquisition of judgment and sophistication. And this is important! So, given that the author (like all of us) is somewhat deficient in some areas, and biased in others, the book is still valuable if the uninitiated reader swallows it in a general fashion, realizing full well that what will be required from the reader is a modulation to fit his vision, propreception, adaptation and response, and the kind of problem he is undertaking. A major deficiency of this book is revealed by comparison of its use of physics and of chemistry to provide understanding and background for the application of high resolution electron microscopy to problems in biology. Since the volume is keyed to high resolution electron microscopy, which is a sophisticated form of structural analysis, but really morphology in a modern guise, the physical and mechanical background of The instrument and its ancillary tools are simply and well presented. The potential use of chemical or cytochemical information as it relates to biological fine structure , however, is quite deficient. I wonder when even sophisticated morphol-ogists will consider fixation a reaction and not a technique; only then will the fundamentals become self-evident and predictable and this sine qua flon will become less mystical. Staining reactions (the most inadequate chapter) ought to be something more than a technique to selectively enhance contrast of morphological elements; it ought to give the structural addresses of some of the chemical residents of cell components. Is it pertinent that auto-radiography gets singled out for more complete coverage than other significant aspects of cytochemistry by a high resolution microscopist, when it has a built-in minimal error of 1,000 A in standard practice? I don't mean to blind-side (in strict football terminology) Dr. Sj6strand's efforts for what is \"routinely used in our laboratory\"; what is done is usually well done. It's just that …

A survey of image classification methods and techniques for improving classification performance

Abstract: Image classification is a complex process that may be affected by many factors. This paper examines current practices, problems, and prospects of image classification. The emphasis is placed on the summarization of major advanced classification approaches and the techniques used for improving classification accuracy. In addition, some important issues affecting classification performance are discussed. This literature review suggests that designing a suitable image-processing procedure is a prerequisite for a successful classification of remotely sensed data into a thematic map. Effective use of multiple features of remotely sensed data and the selection of a suitable classification method are especially significant for improving classification accuracy. Non-parametric classifiers such as neural network, decision tree classifier, and knowledge-based classification have increasingly become important approaches for multisource data classification. Integration of remote sensing, geographical information systems (GIS), and expert system emerges as a new research frontier. More research, however, is needed to identify and reduce uncertainties in the image-processing chain to improve classification accuracy.