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Roy, Himadri Shekhar
- Identification of cis- and trans-expression quantitative trait loci using Bayesian framework
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Authors
Affiliations
1 ICAR-Indian Agricultural Statistics Research Institute, New Delhi 110 012, IN
1 ICAR-Indian Agricultural Statistics Research Institute, New Delhi 110 012, IN
Source
Current Science, Vol 122, No 10 (2022), Pagination: 1214-1219Abstract
The detection and identification of expression quantitative trait loci (eQTLs) for biological characteristics like gene expression is an important focus of genomics. The existence of cis- and trans-eQTLs is crucial for establishing their cumulative significance to the desired traits. A crucial aspect of genomics is identifying the cis- and trans-eQTLs that capture substantial changes in the expression of distant genes. The goal of the present study was to use an integrated hierarchical Bayesian model to identify the cis- and trans-eQTLS. Molecular approaches are utilized to categorize just the candidate genes when quantitative trait loci or eQTLs are identified. Variations inside or near the gene are hypothesized to determine the genetic variances that reflect transcript levels. The identification of eQTLs has helped us better understand gene regulation and complex trait analysis. The present study focused on barley crops, and only cis-eQTLs were identified; no additional eQTL hotspots were determined. Mouse gene expressions were used to study trans-eQTLs and substantial cis- and trans-eQTLs, as well as four eQTL hotspots were identifiedKeywords
Barley, gene expression, hotspots, integrated hierarchical model, quantitative trait loci.Full Text
References
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- Machine Learning Algorithms for Categorization Of Agricultural Dust Emissions Using Image Processing of Wheat Combine Harvester
Abstract Views :92 |
PDF Views:58
Authors
Utpal Ekka
1,
Himadri Shekhar Roy
2,
Adarsh Kumar
1,
S. P. Singh
1,
Apratim Kumar Pandey
1,
Kamalika Nath
2
Affiliations
1 ICAR-Indian Agricultural Research Institute, Pusa, New Delhi 110 012, India., IN
2 ICAR-Indian Agricultural Statistics Research Institute, Pusa, New Delhi 110 012, India., IN
1 ICAR-Indian Agricultural Research Institute, Pusa, New Delhi 110 012, India., IN
2 ICAR-Indian Agricultural Statistics Research Institute, Pusa, New Delhi 110 012, India., IN
Source
Current Science, Vol 124, No 9 (2023), Pagination: 1074-1081Abstract
India is the second largest wheat producer in the world after Russia. Wheat harvesting in the country was traditionally done using a sickle, a hand tool. However, in the last two decades, combined harvesters have been extensively used. The rapid development of mechanization has resulted in the production of dust and straw particles during the harvesting operation of wheat. These particles have severe health hazards for the machine operator. Exposure to various types of particulate matter has a variety of effects on human health. Such an effect can be minimized if the concentration of the generated particle is maintained within a permissible limit. Hence, the present study has been conducted to evaluate and categorize dust and straw particles in the workspace of a combine harvester operator during wheat harvesting. An image-processing technique was used to study a field data sample collected on sticky paper. It describes a novel method of collecting dust and straw particles while harvesting wheat. Few studies have been conducted in developing countries to analyse the characteristics of dust and wheat straw exposure of combined harvester operators. The number of dust and straw particles deposited per square millimetre was 9–12, with sizes ranging from 10 to 1400 mm. The extracted data were divided into three groups, viz. thoracic, inhalable and straw and modelled using machine learning algorithms, including support vector machine (SVM) and k-nearest neighbor. With an accuracy of 96%, SVM outperformed the other methods for categorising dust and straw particles, whereas linear discriminant analysis performed poorly with an accuracy of 88%.Keywords
Agriculture, Combine Harvester, Dust and Straw Particles, Image Processing, Machine Learning.Full Text
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