Purification and characterization of a Bacillus subtilis keratinase and its prospective application in feed industry

We have isolated a Bacillus subtilis strain (RSE163) from soil and explored for keratinase production. Keratinase was purified using chromatographic methods (Sephadex G-75 and Q Sepharose) resulting in 8.42-fold purification with 3303 U/mg specific activity.The purified enzyme displayed 3 bands in close proximity between 20 to 22 kDa in SDS-PAGE which were apparent to the zone of hydrolysis in gelatin zymogram. Enzyme was stable over a wide pH (7.0-10.0) and temperature (30 °C to 70 °C) range with optimum activity at pH 9.0 and 60 °C. Keratinase activity was stimulated in presence of Mn2+, β-mercaptoethanol and surfactants (Triton-X and Tween-80) and inhibited by Fe3+, Cd2+, K+, PMSF (phenyl methane sulfonyl fluoride) and other chelating and reducing agents. The enzyme efficiently hydrolyzed a variety of complex protein substrates (chicken feather, keratin hydrolyzate and casein) and enzyme kinetics parameters were determined using Lineweaver Burk plot (Km = 6.6 mg/ml, Vmax = 5 U/ml/min). Hydrolyzed feather keratin obtained through fermentation with B. subtilis RSE163 has been explored for its cytotoxicity using liver cell line (HepG2). No cytotoxicity has been determined up to 0.015% concentration of hydrolyzed product indicating its potential applicability as feed supplement

Rat Strain Ontology: structured controlled vocabulary designed to facilitate access to strain data at RGD

BACKGROUND: The Rat Genome Database (RGD) ( http://rgd.mcw.edu/) is the premier site for comprehensive data on the different strains of the laboratory rat (Rattus norvegicus). The strain data are collected from various publications, direct submissions from individual researchers, and rat providers worldwide. Rat strain, substrain designation and nomenclature follow the Guidelines for Nomenclature of Mouse and Rat Strains, instituted by the International Committee on Standardized Genetic Nomenclature for Mice. While symbols and names aid in identifying strains correctly, the flat nature of this information prohibits easy search and retrieval, as well as other data mining functions. In order to improve these functionalities, particularly in ontology-based tools, the Rat Strain Ontology (RS) was developed. RESULTS: The Rat Strain Ontology (RS) reflects the breeding history, parental background, and genetic manipulation of rat strains. This controlled vocabulary organizes strains by type: inbred, outbred, chromosome altered, congenic, mutant and so on. In addition, under the chromosome altered category, strains are organized by chromosome, and further by type of manipulations, such as mutant or congenic. This allows users to easily retrieve strains of interest with modifications in specific genomic regions. The ontology was developed using the Open Biological and Biomedical Ontology (OBO) file format, and is organized on the Directed Acyclic Graph (DAG) structure. Rat Strain Ontology IDs are included as part of the strain report (RS: ######). CONCLUSIONS: As rat researchers are often unaware of the number of substrains or altered strains within a breeding line, this vocabulary now provides an easy way to retrieve all substrains and accompanying information. Its usefulness is particularly evident in tools such as the PhenoMiner at RGD, where users can now easily retrieve phenotype measurement data for related strains, strains with similar backgrounds or those with similar introgressed regions. This controlled vocabulary also allows better retrieval and filtering for QTLs and in genomic tools such as the GViewer. The Rat Strain Ontology has been incorporated into the RGD Ontology Browser ( http://rgd.mcw.edu/rgdweb/ontology/view.html?acc_id=RS:0000457#s) and is available through the National Center for Biomedical Ontology ( http://bioportal.bioontology.org/ontologies/1150) or the RGD ftp site ( ftp://rgd.mcw.edu/pub/ontology/rat_strain/)

The Rat Genome Database (RGD): developments towards a phenome database

The Rat Genome Database (RGD) (http://rgd.mcw.edu) aims to meet the needs of its community by providing genetic and genomic infrastructure while also annotating the strengths of rat research: biochemistry, nutrition, pharmacology and physiology. Here, we report on RGD's development towards creating a phenome database. Recent developments can be categorized into three groups. (i) Improved data collection and integration to match increased volume and biological scope of research. (ii) Knowledge representation augmented by the implementation of a new ontology and annotation system. (iii) The addition of quantitative trait loci data, from rat, mouse and human to our advanced comparative genomics tools, as well as the creation of new, and enhancement of existing, tools to enable users to efficiently browse and survey research data. The emphasis is on helping researchers find genes responsible for disease through the use of rat models. These improvements, combined with the genomic sequence of the rat, have led to a successful year at RGD with over two million page accesses that represent an over 4-fold increase in a year. Future plans call for increased annotation of biological information on the rat elucidated through its use as a model for human pathobiology. The continued development of toolsets will facilitate integration of these data into the context of rat genomic sequence, as well as allow comparisons of biological and genomic data with the human genomic sequence and of an increasing number of organisms

Integrative Annotation of 21,037 Human Genes Validated by Full-Length cDNA Clones

The human genome sequence defines our inherent biological potential; the realization of the biology encoded therein requires knowledge of the function of each gene. Currently, our knowledge in this area is still limited. Several lines of investigation have been used to elucidate the structure and function of the genes in the human genome. Even so, gene prediction remains a difficult task, as the varieties of transcripts of a gene may vary to a great extent. We thus performed an exhaustive integrative characterization of 41,118 full-length cDNAs that capture the gene transcripts as complete functional cassettes, providing an unequivocal report of structural and functional diversity at the gene level. Our international collaboration has validated 21,037 human gene candidates by analysis of high-quality full-length cDNA clones through curation using unified criteria. This led to the identification of 5,155 new gene candidates. It also manifested the most reliable way to control the quality of the cDNA clones. We have developed a human gene database, called the H-Invitational Database (H-InvDB; http://www.h-invitational.jp/). It provides the following: integrative annotation of human genes, description of gene structures, details of novel alternative splicing isoforms, non-protein-coding RNAs, functional domains, subcellular localizations, metabolic pathways, predictions of protein three-dimensional structure, mapping of known single nucleotide polymorphisms (SNPs), identification of polymorphic microsatellite repeats within human genes, and comparative results with mouse full-length cDNAs. The H-InvDB analysis has shown that up to 4% of the human genome sequence (National Center for Biotechnology Information build 34 assembly) may contain misassembled or missing regions. We found that 6.5% of the human gene candidates (1,377 loci) did not have a good protein-coding open reading frame, of which 296 loci are strong candidates for non-protein-coding RNA genes. In addition, among 72,027 uniquely mapped SNPs and insertions/deletions localized within human genes, 13,215 nonsynonymous SNPs, 315 nonsense SNPs, and 452 indels occurred in coding regions. Together with 25 polymorphic microsatellite repeats present in coding regions, they may alter protein structure, causing phenotypic effects or resulting in disease. The H-InvDB platform represents a substantial contribution to resources needed for the exploration of human biology and pathology

Integrative annotation of 21,037 human genes validated by full-length cDNA clones.

publication en ligne. Article dans revue scientifique avec comité de lecture. nationale.National audienceThe human genome sequence defines our inherent biological potential; the realization of the biology encoded therein requires knowledge of the function of each gene. Currently, our knowledge in this area is still limited. Several lines of investigation have been used to elucidate the structure and function of the genes in the human genome. Even so, gene prediction remains a difficult task, as the varieties of transcripts of a gene may vary to a great extent. We thus performed an exhaustive integrative characterization of 41,118 full-length cDNAs that capture the gene transcripts as complete functional cassettes, providing an unequivocal report of structural and functional diversity at the gene level. Our international collaboration has validated 21,037 human gene candidates by analysis of high-quality full-length cDNA clones through curation using unified criteria. This led to the identification of 5,155 new gene candidates. It also manifested the most reliable way to control the quality of the cDNA clones. We have developed a human gene database, called the H-Invitational Database (H-InvDB; http://www.h-invitational.jp/). It provides the following: integrative annotation of human genes, description of gene structures, details of novel alternative splicing isoforms, non-protein-coding RNAs, functional domains, subcellular localizations, metabolic pathways, predictions of protein three-dimensional structure, mapping of known single nucleotide polymorphisms (SNPs), identification of polymorphic microsatellite repeats within human genes, and comparative results with mouse full-length cDNAs. The H-InvDB analysis has shown that up to 4% of the human genome sequence (National Center for Biotechnology Information build 34 assembly) may contain misassembled or missing regions. We found that 6.5% of the human gene candidates (1,377 loci) did not have a good protein-coding open reading frame, of which 296 loci are strong candidates for non-protein-coding RNA genes. In addition, among 72,027 uniquely mapped SNPs and insertions/deletions localized within human genes, 13,215 nonsynonymous SNPs, 315 nonsense SNPs, and 452 indels occurred in coding regions. Together with 25 polymorphic microsatellite repeats present in coding regions, they may alter protein structure, causing phenotypic effects or resulting in disease. The H-InvDB platform represents a substantial contribution to resources needed for the exploration of human biology and pathology

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Not AvailableThe paper reviews various classification techniques exclusively used for plant disease identification. Early stage plant disease identification is extremely important as that can adversely affect both quality and quantity of crops in agriculture. For identification of plant diseases, different approaches like image processing, machine learning, artificial neural networks, and deep learning are in use. This review focusses on an in-depth analysis on recently emerging deep learning-based methods starting from machine learning techniques. The paper highlights the crop diseases they focus on, the models employed, sources of data used and overall performance according to the performance metrics employed by each paper for plant disease identification. Review findings indicate that Deep Learning provides the highest accuracy, outperforming existing commonly used disease identification techniques and the main factors that affect the performance of deep learning-based tools. This paper is an attempt to document all such approaches for increasing performance accuracy and minimizing response time in the identification of plant diseases. The authors also present the attempts for disease diagnosis in Indian conditions using real dataset.Not Availabl

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Not AvailableAutomated image-based tools are required when a human assessment of plant disease identification is expensive, inappropriate, or unreliable. Thus, there is a need to recognize cost-effective automated computational systems and image-based tools for disease detection that would facilitate advancements in agriculture. Deep learning (DL) is a deep neural network that uses multiple levels of abstraction for the hierarchical representation of the data. The convolutional neural network model is used, in this chapter, on 2,000 images to identify the wheat rust disease in an unseen leaf image. The results show that DL has the potential to identify plant diseases with much higher accuracy.Not Availabl

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Not AvailableThe term “foresight” has long been used to describe readiness to deal with long-term issues (especially on the part of governments). The term “Technology Foresight” took off in the 1990s when countries sought new policy tools to deal with problems in their science, technology, and innovation systems. Technology Foresight (TF) refers to the process involved in systematically attempting to look into the long-term future of science, technology, the economy, and society to identify the areas of strategic research and the emerging generic technologies likely to yield the greatest economic and social benefits (Pietrobelli and Puppato, 2016). Overall, TF studies provide strategic information for decision-making and long-term planning in science and technology, being used by governments, and organizations to prepare for the future. TF is different from technology forecasting. TF means a new future-oriented approach while Technology Forecasting is a traditional forward-looking approach developed in the 1950s in the USA. TF helps to shape the future of science and technology as well as society. TF is often seen as a set of tools for informing decisions about science, technology, and innovation (STI) priorities within established innovation systems (Miles et al., 2017). As the world faces a wide range of critical challenges, from conflicts and climate change to population growth, countries increasingly need to harness the right technologies to leverage comparative advantages, drive economic growth, and fulfil strategic needs. National-level TF is a vital tool for policymakers to identify domains of high importance or potential. This viewpoint outlines key imperatives for countries to ensure foresight best delivers value in a pragmatic, repeatable manner. In this article, we elaborate on TF for agriculture in the next 25 years. The foresight is based on recent trends in research in developed and developing countries. In Section 2, we focus on the most prevalent technologies of the 21 century. We anticipate the present and future application of disruptive technologies in agriculture in Section 3. Section 4 briefs on the challenges and opportunities of these emerging applications. Finally, we conclude in Section 5Not Availabl