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Chaudhary, C.

Publications and source records attributed to Chaudhary, C..

3 recordsLinked to original sources

Comparative Metabolomic Profiling Reveals Salinity Tolerance Mechanisms in a Rice Introgression Line

Rice (Oryza sativa) is highly sensitive to salinity, yet the metabolic mechanisms underlying salt tolerance remains incompletely understood. In this study, we performed leaf tissue-specific untargeted metabolomic profiling of the salt-tolerant introgression line JN100 (JN), its donor parent Nona Bokra (NB), and its recurrent parent Jupiter (JU) to characterize metabolic responses to salt stress. Comparative analysis identified differentially accumulated metabolites (DAMs) spanning diverse chemical classes, including amino acids, sugars and carbohydrates, lipids, organic acids, cofactors, electron carriers, and nucleotides. Under salt stress (SS), 201 DAMs (89 upregulated and 112 downregulated) were detected in JN relative to JU. Notably, metabolites such as allantoin, glycitin, nicotinamide ribotide, D-arabinono-1,4-lactone, violanthin, L-methionine S-oxide, ribitol, lysine, rutin, glutamine, pantothenic acid, and quinic acid, showed significant differential accumulation. Pathway enrichment analysis revealed significant enrichment of arginine biosynthesis, purine metabolism, and alanine, aspartate, and glutamate metabolism, indicating extensive reprogramming of nitrogen and energy-associated metabolic pathways under salinity stress. Integration of transcriptomic and metabolomic datasets from the SS experiments further identified ten differentially expressed genes (DEGs) associated with the metabolite network in the JN vs. JU comparison. Among these, OsDHQDT/SDH, OsFd-GOGAT, phenylalanyl-tRNA synthetase, OsP5CS1, OsP5CS2, and a pyridoxal phosphate-dependent transferase were linked to metabolites involved in shikimate, amino acid, and proline metabolism. Collectively, these results demonstrate that salinity tolerance in rice is associated with coordinated transcriptional and metabolic reprogramming that supports oxidative stress mitigation and adaptive stress responses.

plant biology↗

The Research Mind: A Multi-Dimensional Framework for Evaluating Research Quality, Productivity, and Integrity by Mitigating Citation Bias

The top 2% researchers list from Professor John P. A. Ioannidis from Stanford University captures a lot of attention worldwide. Professor Ioannidis introduced a new marker to evaluate a researchers contributions named the composite score (C-Score). The C-Score provided an excellent metric to determine where an author stands in comparison to others within the global research community. However, while the C-Score effectively ranks researchers, it is not particularly helpful for new researchers seeking to evaluate potential mentors or guides. Emerging researchers want to know if potential mentors prioritize quantity or quality. They also seek clarity about research output requirements when joining a team. ObjectiveWe address this gap with The Research Mind framework. It introduces two new metrics: the S-Score (measures research quantity and productivity) and the Q-Score (measures research quality and impact). These metrices help new researchers choose mentors wisely by going beyond traditional ranking systems. MethodsWe developed the S-Score to measure maximum average first-author publications over any three-year period. This provides insights into productivity expectations. We also created the Q-Score to evaluate maximum median citations for first/last author works over three years. This indicates research quality and impact. Additionally, we implemented comprehensive self-citation analysis to assess research integrity. The framework was built using OpenAlex database and includes network analysis capabilities for collaboration pattern visualization. ResultsOur analysis uncovered significant differences between C-Score rankings and a researchers suitability for mentorship. We found that some researchers had high C-Scores but also had S-Scores of 10, meaning they published 10 papers per year. This is an unusually high productivity rate that may be unsustainable for most researchers. In contrast, researchers publishing one paper per year had Q-Scores above 50, making them potentially better mentors for quality-focused research. Our self-citation analysis revealed concerning patterns, with high self-citation rates (>20%) indicating potentially problematic research practices. ConclusionThe Research Mind framework provides essential complementary metrics to Ioannidiss C-Score system, enabling new researchers to evaluate potential guides/mentors based on productivity expectations (S-Score) and quality focus (Q-Score) rather than solely on composite rankings. This approach will help emerging researchers identify mentors whose research philosophy and output expectations align with their career goals and capabilities. Hence, The Research Mind framework addresses a critical gap in academic mentorship selection. To facilitate widespread adoption and accessibility, we have developed a comprehensive web application. This app allows researchers to easily search, analyze, and compare these metrics for any author/researcher. The platform is freely available at www.theresearchmind.com/trm-app, providing an intuitive interface for exploring S-Scores, Q-Scores, self-citation patterns, collaboration networks, and overall evaluation metrics to support informed academic decision-making. PVLDB Reference FormatSanjay Rathee and Chanchal Chaudhary. The Research Mind: A Multi-Dimensional Framework for Evaluating Research Quality, Productivity, and Integrity by Mitigating citation bias. PVLDB, 14(1): XXX-XXX, 2020. doi:XX.XX/XXX.XX PVLDB Artifact AvailabilityThe source code, data, and/or other artifacts have been made available at URL_TO_YOUR_ARTIFACTS.

scientific communication and education↗

Chloroplast activity provides in vitro regeneration capability in contrasting cultivars

Existence of potent in vitro regeneration system is a prerequisite for efficient genetic transformation and functional genomics of crop plants. We know little about why only some cultivars in crop plants are tissue culture friendly. In this study, tissue culture friendly cultivar Golden Promise (GP) and tissue culture resistant DWRB91(D91) were selected as contrasting cultivars to investigate the molecular basis of regeneration efficiency. Multiomics studies involving transcriptomics, proteomics, metabolomics, and biochemical analysis were performed using GP and D91 callus to unravel the regulatory mechanisms. Transcriptomics analysis revealed 1487 differentially expressed genes (DEGs), in which 795 DEGs were upregulated and 692 DEGs were downregulated in the GP-D91 transcriptome. Genes encoding proteins localized in chloroplast and involved in ROS generation were upregulated in the embryogenic calli of GP. Moreover, proteome analysis by LC-MSMS revealed 3062 protein groups and 16989 peptide groups, out of these 1586 protein groups were differentially expressed proteins (DEPs). Eventually, GC-MS based metabolomics analysis also revealed the higher activity of plastids and alterations in key metabolic processes such as sugar metabolism, fatty acid biosynthesis, and secondary metabolism. Higher accumulation of sugars, amino acids and metabolites corresponding to lignin biosynthesis were observed in GP as compared to D91. HighlightsMulti omics analysis revealed chloroplast play crucial role in providing in vitro regeneration capability in contrasting genotypes

plant biology↗