Single crystal X-ray structural dataset of 1,2,4-dithiazolium tetrafluoroborate

Herein, we present the crystallographic dataset of 1,2,4-dithiazolium tetrafluoroborate. Single crystal X-ray structural analysis evidences that the 1,2,4-dithiazolium ring is almost planar. The 1,2,4-dithiazolium and tetrafluoroborate ions contribute in hydrogen bonding wherein the N-H·N hydrogen bonding in 1,2,4-dithiazolium dimer forms an eight-membered pseudo ring with the R 2 2 ( 8 ) Etter's graph set. The information provided in this data contributes to the understanding of structural chemistry and hydrogen bonding interactions in dithiazole derivatives.© 2022 The Author(s).</p

Single crystal X-ray structural dataset of 1,2,4-dithiazolium tetrafluoroborate

Herein, we present the crystallographic dataset of 1,2,4-dithiazolium tetrafluoroborate. Single crystal X-ray structural analysis evidences that the 1,2,4-dithiazolium ring is almost planar. The 1,2,4-dithiazolium and tetrafluoroborate ions contribute in hydrogen bonding wherein the N-H·N hydrogen bonding in 1,2,4-dithiazolium dimer forms an eight-membered pseudo ring with the Etter's graph set. The information provided in this data contributes to the understanding of structural chemistry and hydrogen bonding interactions in dithiazole derivatives.peerReviewe

Traditional Cultivars Influence on Physical and Engineering Properties of Rice from the Cauvery Deltaic Region of Tamil Nadu

Standard unit operations/equipment have not evolved for the traditional rice varieties of the Cauvery Deltaic region of Tamil Nadu. The fame of traditional rice is increasing nowadays owing to its health benefits. Non-standard unit operations may cause rice grains to crack during milling, accumulating more broken rice and yields in products of inferior quality. As a result, research into the physical properties of rice is crucial for the development of rice processing equipment that minimizes post-harvest losses during milling. Hence, an assessment was made to evaluate 30 traditional rice cultivars on their Physical (grain length, width, thickness, shape, and size), gravimetric (bulk, true, tapped density, porosity, Carr’s index, and Hausner ratio), and engineering characteristics (equivalent, arithmetic, square mean, and geometric mean diameter) using standard protocols, with the goal of reviving and preserving older varieties. The results from the analysis showed significant variations (p 2, respectively. Of the 30 varieties, 28 were under the high amylose category, and 2 belonged to the intermediate type. The Pearson correlation was established to study the interrelationships between the dimensions and engineering properties. Principal component analysis (PCA) reduced the dimensionality of 540 data into five principal components (PC), which explained 95.7% of the total variance. These findings suggest that it is possible to revive old landraces through careful selection and analysis of these properties. The superior characteristics of these traditional varieties can be further evaluated for breeding programs in order to improve the cultivation of these cherished rice landraces to enhance nutritional security

Unravelling the metabolomic diversity of pigmented and non-pigmented traditional rice from Tamil Nadu, India

Abstract Rice metabolomics is widely used for biomarker research in the fields of pharmacology. As a consequence, characterization of the variations of the pigmented and non-pigmented traditional rice varieties of Tamil Nadu is crucial. These varieties possess fatty acids, sugars, terpenoids, plant sterols, phenols, carotenoids and other compounds that plays a major role in achieving sustainable development goal 2 (SDG 2). Gas-chromatography coupled with mass spectrometry was used to profile complete untargeted metabolomics of Kullkar (red colour) and Milagu Samba (white colour) for the first time and a total of 168 metabolites were identified. The metabolite profiles were subjected to data mining processes, including principal component analysis (PCA), Orthogonal Partial Least Square Discrimination Analysis (OPLS-DA) and Heat map analysis. OPLS-DA identified 144 differential metabolites between the 2 rice groups, variable importance in projection (VIP) ≥ 1 and fold change (FC) ≥ 2 or FC ≤ 0.5. Volcano plot (64 down regulated, 80 up regulated) was used to illustrate the differential metabolites. OPLS-DA predictive model showed good fit (R2X = 0.687) and predictability (Q2 = 0.977). The pathway enrichment analysis revealed the presence of three distinct pathways that were enriched. These findings serve as a foundation for further investigation into the function and nutritional significance of both pigmented and non-pigmented rice grains thereby can achieve the SDG 2

Data_Sheet_1_Metabolomic analysis for disclosing nutritional and therapeutic prospective of traditional rice cultivars of Cauvery deltaic region, India.pdf

Traditional rice is gaining popularity worldwide due to its high nutritional and pharmaceutical value, as well as its high resistance to abiotic and biotic stresses. This has attracted significant attention from breeders, nutritionists, and plant protection scientists in recent years. Hence, it is critical to investigate the grain metabolome to reveal germination and nutritional importance. This research aimed to explore non-targeted metabolites of five traditional rice varieties, viz., Chinnar, Chithiraikar, Karunguruvai, Kichili samba, and Thooyamalli, for their nutritional and therapeutic properties. Approximately 149 metabolites were identified using the National Institute of Standards and Technology (NIST) library and Human Metabolome Database (HMDB) and were grouped into 34 chemical classes. Major classes include fatty acids (31.1–56.3%), steroids and their derivatives (1.80–22.4%), dihydrofurans (8.98–11.6%), prenol lipids (0.66–4.44%), organooxygen compounds (0.12–6.45%), benzene and substituted derivatives (0.53–3.73%), glycerolipids (0.36–2.28%), and hydroxy acids and derivatives (0.03–2.70%). Significant variations in metabolite composition among the rice varieties were also observed through the combination of univariate and multivariate statistical analyses. Principal component analysis (PCA) reduced the dimensionality of 149 metabolites into five principle components (PCs), which explained 96% of the total variance. Two clusters were revealed by hierarchical cluster analysis, indicating the distinctiveness of the traditional varieties. Additionally, a partial least squares-discriminant analysis (PLS-DA) found 17 variables important in the projection (VIP) scores of metabolites. The findings of this study reveal the biochemical intricate and distinctive metabolomes of the traditional therapeutic rice varieties. This will serve as the foundation for future research on developing new rice varieties with traditional rice grain metabolisms to increase grain quality and production with various nutritional and therapeutic benefits.</p