Browsing by Author "Jain, R"

Now showing 1 - 4 of 4
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    Enhance traffic flow prediction with Real-Time Vehicle Data Integration
    (2023-08) Jain, R; Dhingra, S; Joshi, K; Rana, A; Goyal, N
    This study examines how sophisticated traffic control systems affect traffic flow. These cutting-edge solutions use real-time traffic data to increase road networks’ intelligence. These technologies enable the creation of a smoother and more efficient traffic flow by enhancing traffic signal timings and automatically rerouting cars towards less crowded routes. Notably, these innovations significantly lower air pollution, greenhouse gas emissions, and fuel consumption while also minimizing the financial and time expenses related to traffic congestion. Our unique Real-Time Vehicle Data Integration (RTVDI) algorithm is being used to portray the potential of intelligent traffic control systems. These technologies have the potential to revolutionize traffic management procedures by using real-time data and complex processes. They have the potential to improve commuter safety, increase road efficiency, and improve traffic flow.
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    Enhance traffic flow prediction with Real-Time Vehicle Data Integration
    (2023-08) Jain, R; Dhingra, S; Joshi, K; Rana, A; Goyal, N
    This study examines how sophisticated traffic control systems affect traffic flow. These cutting-edge solutions use real-time traffic data to increase road networks’ intelligence. These technologies enable the creation of a smoother and more efficient traffic flow by enhancing traffic signal timings and automatically rerouting cars towards less crowded routes. Notably, these innovations significantly lower air pollution, greenhouse gas emissions, and fuel consumption while also minimizing the financial and time expenses related to traffic congestion. Our unique Real-Time Vehicle Data Integration (RTVDI) algorithm is being used to portray the potential of intelligent traffic control systems. These technologies have the potential to revolutionize traffic management procedures by using real-time data and complex processes. They have the potential to improve commuter safety, increase road efficiency, and improve traffic flow.
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    Integrated analysis of transcriptomic and small RNA sequencing data provides miRNA candidates for engineering agronomically important seed traits in Brassica juncea
    (2023-11) Jain, R; Dhaka, N; Yadav, P; Sharma, M
    Brassica juncea L. is an important oilseed crop that yields edible oil and biofuel. Improving B. juncea seed traits is a primary breeding target, but these traits are genetically complex. MicroRNAs (miRNAs) regulate seed devel opment by modulating gene expression at the post-transcriptional or translational level and are excellent can didates for improving seed traits. However, the roles of miRNAs in B. juncea seed development are yet to be investigated. Here, we report small RNA profiling and miRNA identification from developing seeds of two contrasting varieties of B. juncea, Early Heera2 (EH2) and Pusa Jaikisan (PJK). We identified 326 miRNAs, including 127 known and 199 novel miRNAs, of which 103 exhibited inter-varietal differential expression. Integrating miRNAome and our previous transcriptome data identified 13,683 putative miRNA-target modules. Segregation of differentially expressed miRNAs into different groups based on variety-wise upregulation, fol lowed by comprehensive functional analysis of targets using pathway mapping, gene ontology, transcription factor mapping, and candidate gene analysis, revealed at least 11, 6, and 7 miRNAs as robust candidates for the regulation of seed size, seed coat color, and oil content, respectively. Further, co-localization with previously reported quantitative trait loci (QTL) proffered 29 and 15 miRNAs overlapping with seed weight and oil content QTLs, respectively. Our study is the first comprehensive report of miRNAome expression dynamics from developing seeds and provides candidate miRNAs and target genes for engineering seed traits in B. juncea.
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    Integrated analysis of transcriptomic and small RNA sequencing data provides miRNA candidates for engineering agronomically important seed traits in Brassica juncea
    (2023-11) Jain, R; Dhaka, N; Yadav, P; Sharma, MK; Danish, MD; Sharma, S; Kumari, S
    Brassica juncea L. is an important oilseed crop that yields edible oil and biofuel. Improving B. juncea seed traits is a primary breeding target, but these traits are genetically complex. MicroRNAs (miRNAs) regulate seed devel opment by modulating gene expression at the post-transcriptional or translational level and are excellent can didates for improving seed traits. However, the roles of miRNAs in B. juncea seed development are yet to be investigated. Here, we report small RNA profiling and miRNA identification from developing seeds of two contrasting varieties of B. juncea, Early Heera2 (EH2) and Pusa Jaikisan (PJK). We identified 326 miRNAs, including 127 known and 199 novel miRNAs, of which 103 exhibited inter-varietal differential expression. Integrating miRNAome and our previous transcriptome data identified 13,683 putative miRNA-target modules. Segregation of differentially expressed miRNAs into different groups based on variety-wise upregulation, fol lowed by comprehensive functional analysis of targets using pathway mapping, gene ontology, transcription factor mapping, and candidate gene analysis, revealed at least 11, 6, and 7 miRNAs as robust candidates for the regulation of seed size, seed coat color, and oil content, respectively. Further, co-localization with previously reported quantitative trait loci (QTL) proffered 29 and 15 miRNAs overlapping with seed weight and oil content QTLs, respectively. Our study is the first comprehensive report of miRNAome expression dynamics from developing seeds and provides candidate miRNAs and target genes for engineering seed traits in B. juncea.