Statistical approaches of gene set analysis with quantitative trait loci for high-throughput genomic studies.

Recently, gene set analysis has become the first choice for gaining insights into the underlying complex biology of diseases through high-throughput genomic studies, such as Microarrays, bulk RNA-Sequencing, single cell RNA-Sequencing, etc. It also reduces the complexity of statistical analysis and enhances the explanatory power of the obtained results. Further, the statistical structure and steps common to these approaches have not yet been comprehensively discussed, which limits their utility. Hence, a comprehensive overview of the available gene set analysis approaches used for different high-throughput genomic studies is provided. The analysis of gene sets is usually carried out based on gene ontology terms, known biological pathways, etc., which may not establish any formal relation between genotype and trait specific phenotype. Further, in plant biology and breeding, gene set analysis with trait specific Quantitative Trait Loci data are considered to be a great source for biological knowledge discovery. Therefore, innovative statistical approaches are developed for analyzing, and interpreting gene expression data from Microarrays, RNA-sequencing studies in the context of gene sets with trait specific Quantitative Trait Loci. The utility of the developed approaches is studied on multiple real gene expression datasets obtained from various Microarrays and RNA-sequencing studies. The selection of gene sets through differential expression analysis is the primary step of gene set analysis, and which can be achieved through using gene selection methods. The existing methods for such analysis in high-throughput studies, such as Microarrays, RNA-sequencing studies, suffer from serious limitations. For instance, in Microarrays, most of the available methods are either based on relevancy or redundancy measures. Through these methods, the ranking of genes is done on single Microarray expression data, which leads to the selection of spuriously associated, and redundant gene sets. Therefore, newer, and innovative differential expression analytical methods have been developed for Microarrays, and single-cell RNA-sequencing studies for identification of gene sets to successfully carry out the gene set and other downstream analyses. Furthermore, several methods specifically designed for single-cell data have been developed in the literature for the differential expression analysis. To provide guidance on choosing an appropriate tool or developing a new one, it is necessary to review the performance of the existing methods. Hence, a comprehensive overview, classification, and comparative study of the available single-cell methods is hereby undertaken to study their unique features, underlying statistical models and their shortcomings on real applications. Moreover, to address one of the shortcomings (i.e., higher dropout events due to lower cell capture rates), an improved statistical method for downstream analysis of single-cell data has been developed. From the users’ point of view, the different developed statistical methods are implemented in various software tools and made publicly available. These methods and tools will help the experimental biologists and genome researchers to analyze their experimental data more objectively and efficiently. Moreover, the limitations and shortcomings of the available methods are reported in this study, and these need to be addressed by statisticians and biologists collectively to develop efficient approaches. These new approaches will be able to analyze high-throughput genomic data more efficiently to better understand the biological systems and increase the specificity, sensitivity, utility, and relevance of high-throughput genomic studies

Traditional Ethno-Medicinal Plants Used for Treatment of Diabetes by Bhuyan Tribes in Sundargarh District of Odisha, India-An Ethnobotanical Survey

Diabetes mellitus is the most common disease which has conveyed significant well-being intimidation around the world. The accessible synthetic drugs for the fix of Diabetes mellitus are related to significant expense, different incidental effects and a few constraints. Medicinal plants are the storehouse of the phytochemicals which can be helpful for the therapy of various ailments. Medicinal and aromatic plants are the better option in contrast to compound medications with little or no side effects. Ethnomedicinal studies carried out among the Bhuyan tribal groups in the Sundargarh region, Odisha for the investigation of antidiabetic treatments. The Bhuyan tribal group has a rich knowledge of plants which are used in the treatment of different disease causes. The ethnomedicinal data was gathered from interviews and field studies with nearby healers and townspeople. Restorative plants were gathered and related to help from native healers. These kind medicines have been displayed to have huge mending power, either in their normal state or as the wellspring of new items handled by them. Our study is mainly concentrated on plants used by Bhuyan tribal groups in relation to the cure of diabetes. An extensive field survey of different parts of the district was made with the local tribal villagers and ethnomedicinal or ayurvedic drug practitioner’s perusal of published literature and herbarium specimen of different herbaria of the district was done. A sum of 25 plants having a place with 18 unique families used to treat diabetes utilized by Bhuyans of Sundargarh district has been reported. In this report we have prepared detailed notes on the method of preparation of precise doses, the part/parts of plants used and the method of application of doses with scientific names, vernacular names and family names of collected plants are also given. Further, it emphasizes strongly in this regard the optional and rational uses of traditional and natural indigenous medicine. The results of this study showed that these tribal people still depend on medicinal plants in Sundargarh district forest areas. The study thus underlines the potential of ethnobotanical research and the need for the documentation of traditional ecological knowledge pertaining to medicinal plant utilization for the greater benefit of mankind

Development of an erosion testing machine

An erosion tester is normally used to study the relative erosion behavior of different materials at moderate solid concentrations. Uniform distribution of solids and turbulence inside the container are generally the problems with the erosion tester and thus the data generated have limited application for quantitative analysis. In the present work, an existing mechanical stirrer has been converted into a slurry erosion tester by designing and fabricating a specimen holding arrangement for cylindrical and flat specimen by taking suitable dimensions according to experimental needs. Using this slurry erosion tester experiments can be carried out for investigating the wear characteristics of various materials which are subjected to slurry erosion. The machine has been tested by taking slurry of mud in a stainless steel container to find the rate of mass loss of an aluminium sample. This machine can be used for carrying out experiments on various samples of different materials which are subjected to slurry erosion by taking different types of slurries to find out the wear characteristics of the material by measuring the rate of mass loss with respect to various parameters like Slurry concentration, Speed of rotation, distance traversed and time

Socio-economic status of fisherwomen community in coastal Vizianagaram district of Andhra Pradesh, India

The socio-economics of six fisherwomen co-operative societies in coastal Vizianagaram district of Andhra Pradesh were studied through field surveys by interviewing a total of 185 respondents. Respondents were mostly middle aged (31 – 56 years; 61.6%) living in nuclear families (64.9%). Majority of the fisherwomen were found illiterate (88.1%). A greater proportion (84.9%) of fisherwomen involved in fish marketing as a primary occupation followed by salting and curing (9.73%) and pickling of fishes (5.4%). More than half (56.2%) of the respondents earned > Rs. 25,000 (USD$ 1 = Rs. 75) every month. The study revealed that the socio-economic condition of the fisherwomen in the study area is poor, with a high percentage of the illiteracy. Necessary steps should be taken by the Governments organizations, NGOs and respective stakeholders to improve the literacy level as well as livelihood status

A computational system biology approach to construct gene regulatory networks for salinity response in rice (Oryza sativa)

Salinity is one of the most common abiotic stress which limits agricultural crop production. Salinity stress tolerance in rice (Oryza sativa L.) is an important trait controlled by various genes. The mechanism of salinity stress response in rice is quite complex. Modelling and construction of genetic regulatory networks is an important tool and can be used for understanding this underlying mechanism. This paper considers the problem of modeling and construction of Gene Regulatory Networks using Multiple Linear Regression and Singular Value Decomposition approach coupled with a number of computational tools. The gene networks constructed by using this approach satisfied the scale free property of biological networks and such networks can be used to extract valuable information on the transcription factors, which are salt responsive. The gene ontology enrichment analysis of selected nodes is performed. The developed model can also be used for predicting the gene responses under stress condition and the result shows that the model fits well for the given gene expression data in rice. In this paper, we have identified ten target genes and a series of potential transcription factors for each target gene in rice which are highly salt responsive

An integrated organic farming system: innovations for farm diversification, sustainability, and livelihood improvement of hill farmers

IntroductionOrganic farming is a promising solution for mitigating environmental burdens related to input-intensive agricultural practices. The major challenge in organic agriculture is the non-availability of large quantities of organic inputs required for crop nutrition and sustaining soil health, which can be resolved by efficient recycling of the available on- and off-farm resources and the integration of the components as per the specific locations.MethodsAn integrated organic farming system (IOFS) model comprising agricultural and horticultural crops, rainwater harvesting units, livestock components, and provisions for nutrient recycling was developed and disseminated in the adopted organic villages Mynsain, Pynthor, and Umden Umbathiang in the Ri-Bhoi District, Meghalaya, India, to improve the income and livelihood of farmers. Harvested rainwater in farm ponds and Jalkunds was used for live-saving irrigation in the winter months and diversified homestead farming activities, such as growing high-value crops and rearing cattle, pigs, and poultry.ResultsMaize, french bean, potato, ginger, tomato, carrot, and chili yields in the IOFS model increased by 20%−30%, 40%−45%, 25%−30%, 33%−40%, 45%−50%, 37%−50%, and 27%−30%, respectively, compared with traditional practices. Some farmers produced vermicompost in vermibeds (made of high-density polyethylene) and cement brick chambers, generating 0.4−1.25 tons per annum. Two individual farmers, Mr. Jrill Makroh and Mrs. Skola Kurbah obtained net returns (without premium price) of Rs. 46,695 ± 418 and Rs. 31,102 ± 501 from their respective 0.27- and 0.21-ha IOFS models, which is equivalent to Rs. 172,944 ± 1,548/ha/year and Rs. 148,105 ± 2,385/ha/year, respectively. The net returns obtained from the IOFS models were significantly higher than those obtained from the farmers' practice of maize-fallow or cultivation of maize followed by vegetable (~30% of the areas). It is expected that, with the certification of organic products, the income and livelihood of the farmers will improve further over the years. While Mr. Jrill Makroh's model supplied 95.1%, 82.0%, and 96.0% of the total N, P2O5, and K2O, respectively, needed by the system, Mrs. Skola Kurbah's model supplied 76.0%, 68.6%, and 85.5% of the total N, P2O5, and K2O, respectively.DiscussionThus, IOFS models should be promoted among hill farmers so that they can efficiently recycle farm resources and increase their productivity, net returns, and livelihood while reducing their dependence on external farm inputs

Not Available

Ph.D. Dissertation ThesisRecently, gene set analysis has become the first choice for gaining insights into the underlying complex biology of diseases through high-throughput genomic studies, such as Microarrays, bulk RNA-Sequencing, single cell RNA-Sequencing, etc. It also reduces the complexity of statistical analysis and enhances the explanatory power of the obtained results. Further, the statistical structure and steps common to these approaches have not yet been comprehensively discussed, which limits their utility. Hence, a comprehensive overview of the available gene set analysis approaches used for different high-throughput genomic studies is provided. The analysis of gene sets is usually carried out based on gene ontology terms, known biological pathways, etc., which may not establish any formal relation between genotype and trait specific phenotype. Further, in plant biology and breeding, gene set analysis with trait specific Quantitative Trait Loci data are considered to be a great source for biological knowledge discovery. Therefore, innovative statistical approaches are developed for analyzing, and vii interpreting gene expression data from Microarrays, RNA-sequencing studies in the context of gene sets with trait specific Quantitative Trait Loci. The utility of the developed approaches is studied on multiple real gene expression datasets obtained from various Microarrays and RNA-sequencing studies. The selection of gene sets through differential expression analysis is the primary step of gene set analysis, and which can be achieved through using gene selection methods. The existing methods for such analysis in high-throughput studies, such as Microarrays, RNA-sequencing studies, suffer from serious limitations. For instance, in Microarrays, most of the available methods are either based on relevancy or redundancy measures. Through these methods, the ranking of genes is done on single Microarray expression data, which leads to the selection of spuriously associated, and redundant gene sets. Therefore, newer, and innovative differential expression analytical methods have been developed for Microarrays, and single-cell RNA-sequencing studies for identification of gene sets to successfully carry out the gene set and other downstream analyses. Furthermore, several methods specifically designed for single-cell data have been developed in the literature for the differential expression analysis. To provide guidance on choosing an appropriate tool or developing a new one, it is necessary to review the performance of the existing methods. Hence, a comprehensive overview, classification, and comparative study of the available single-cell methods is hereby undertaken to study their unique features, underlying statistical models and their shortcomings on real applications. Moreover, to address one of the shortcomings (i.e., higher dropout events due to lower cell capture rates), an viii improved statistical method for downstream analysis of single-cell data has been developed. From the users’ point of view, the different developed statistical methods are implemented in various software tools and made publicly available. These methods and tools will help the experimental biologists and genome researchers to analyze their experimental data more objectively and efficiently. Moreover, the limitations and shortcomings of the available methods are reported in this study, and these need to be addressed by statisticians and biologists collectively to develop efficient approaches. These new approaches will be able to analyze high-throughput genomic data more efficiently to better understand the biological systems and increase the specificity, sensitivity, utility, and relevance of high-throughput genomic studies.Netaji Subhas ICAR-International Fellowship (OM No. 18(02)/2016-EQR/Edn.)ICAR-IASRIUniversity of Louisville, US

Agya, what do you mean by development?

The pursuit of aluminium in Orissa has resulted in cultural genocide.Displacement has destroyed the tribal society’s structure whilepollution from factories has rendered areas uncultivable, snatchingaway the residents’ main source of livelihood.The fact that the arms industry is one of the driving forces behindaluminium production makes the indifference with which locals aretreated even more siniste

Not Available

Not AvailableNot AvailableIASR