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.

A novel cellulase free alkaliphilic xylanase from alkali tolerant Penicillium citrinum: production, purification and characterization

Abstract: Aims: The enzymatic hydrolysis of xylan has potential economic and environment-friendly applications. Therefore, attention is focused here on the discovery of new extremophilic xylanase in order to meet the requirements of industry. Methods and Results: An extracellular xylanase was purified from the culture filtrate of P. citrinum grown on wheat bran bed in solid substrate fermentation. Single step purification was achieved using hydrophobic interaction chromatography. The purified enzyme showed a single band on SDS-PAGE with an apparent molecular weight of c. 25 kDa and pI of 3·6. Stimulation of the activity by β mercaptoethanol, dithiotheritol (DTT) and cysteine was observed. Moderately thermostable xylanase showed optimum activity at 50°C at pH 8·5. Conclusion: Xylanase purified from P. citrinum was alkaliphilic and moderately thermostable in nature. Significance and Impact of the Study: The present work reports for the first time the purification and characterization of a novel endoglucanase free alkaliphilic xylanase from the alkali tolerant fungus Penicillium citrinum. The alkaliphilicity and moderate thermostability of this xylanase may have potential implications in paper and pulp industries.

Automatic building footprint extraction from high-resolution satellite image using mathematical morphology

Abstract: Automatic building extraction from High-Resolution Satellite (HRS) image has been an important field of research in the area of remote sensing. Different techniques related to radiometric, geometri...

Global Road Damage Detection: State-of-the-art Solutions

Abstract: This paper summarizes the Global Road Damage Detection Challenge (GRDDC), a Big Data Cup organized as a part of the IEEE International Conference on Big Data’2020. The Big Data Cup challenges involve a released dataset and a well-defined problem with clear evaluation metrics. The challenges run on a data competition platform that maintains a leaderboard for the participants. In the presented case, the data constitute 26336 road images collected from India, Japan, and the Czech Republic to propose methods for automatically detecting road damages in these countries. In total, 121 teams from several countries registered for this competition. The submitted solutions were evaluated using two datasets test1 and test2, comprising 2,631 and 2,664 images. This paper encapsulates the top 12 solutions proposed by these teams. The best performing model utilizes YOLO-based ensemble learning to yield an F1 score of 0.67 on test1 and 0.66 on test2. The paper concludes with a review of the facets that worked well for the presented challenge and those that could be improved in future challenges.

Inducible nitric oxide synthase: Role of the N-terminal β-hairpin hook and pterin-binding segment in dimerization and tetrahydrobiopterin interaction

Abstract: The oxygenase domain of the inducible nitric oxide synthase (iNOSox; residues 1-498) is a dimer that binds heme, L-arginine and tetrahydrobiopterin (H(4)B) and is the site for nitric oxide synthesis. We examined an N-terminal segment that contains a beta-hairpin hook, a zinc ligation center and part of the H(4)B-binding site for its role in dimerization, catalysis, and H(4)B and substrate interactions. Deletion mutagenesis identified the minimum catalytic core and indicated that an intact N-terminal beta-hairpin hook is essential. Alanine screening mutagenesis of conserved residues in the hook revealed five positions (K82, N83, D92, T93 and H95) where native properties were perturbed. Mutants fell into two classes: (i) incorrigible mutants that disrupt side-chain hydrogen bonds and packing interactions with the iNOSox C-terminus (N83, D92 and H95) and cause permanent defects in homodimer formation, H(4)B binding and activity; and (ii) reformable mutants that destabilize interactions of the residue main chain (K82 and T93) with the C-terminus and cause similar defects that were reversible with high concentrations of H(4)B. Heterodimers comprised of a hook-defective iNOSox mutant subunit and a full-length iNOS subunit were active in almost all cases. This suggests a mechanism whereby N-terminal hooks exchange between subunits in solution to stabilize the dimer.

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.