Phytochemical Characterization, Antioxidant and Anti-Proliferative Properties of Rubia cordifolia L. Extracts Prepared with Improved Extraction Conditions

none11sìRubia cordifolia L. (Rubiaceae) is an important plant in Indian and Chinese medical systems. Extracts prepared from the root, stem and leaf have been used traditionally for the management of various diseases. Some of the known effects are anti-inflammation, neuroprotection, anti-proliferation, immunomodulation and anti-tumor. A comparative account of the extracts derived from different organs that lead to the identification of the most suitable solvent is lacking. We explored the presence of phytochemicals, antioxidant activity and anti-proliferative properties of a variety of solvent-based extracts of root, and methanol extracts of stem and leaf of R. cordifolia L. The antioxidant potential was determined by DPPH, hydrogen peroxide, nitric oxide and total antioxidant assays. The anti-proliferative nature was evaluated by MTT assay on HeLa, ME-180 and HepG2 cells. The composition of the extracts was determined by UPLC-UV-MS. We found that the root extracts had the presence of higher amounts of antioxidants over the stem and leaf extracts. The root extracts prepared in methanol exhibited the highest cytotoxicity in HepG2 cells. The main compounds identified through UPLC-UV-MS of the methanol extract give credibility to the previous results. Our comprehensive study corroborates the preference given to the root over the stem and leaf for extract preparation. In conclusion, we identified the methanol extract of the root to be the most suited to have bioactivity with anti-cancer potential.Humbare, Ravikiran B; Sarkar, Joyita; Kulkarni, Anjali A; Juwale, Mugdha G; Deshmukh, Sushil H; Amalnerkar, Dinesh; Chaskar, Manohar; Albertini, Maria C; Rocchi, Marco B L; Kamble, Swapnil C; Ramakrishna, SeeramHumbare, Ravikiran B; Sarkar, Joyita; Kulkarni, Anjali A; Juwale, Mugdha G; Deshmukh, Sushil H; Amalnerkar, Dinesh; Chaskar, Manohar; Albertini, Maria C; Rocchi, Marco B L; Kamble, Swapnil C; Ramakrishna, Seera

BLOOM: A 176B-Parameter Open-Access Multilingual Language Model

Large language models (LLMs) have been shown to be able to perform new tasks based on a few demonstrations or natural language instructions. While these capabilities have led to widespread adoption, most LLMs are developed by resource-rich organizations and are frequently kept from the public. As a step towards democratizing this powerful technology, we present BLOOM, a 176B-parameter open-access language model designed and built thanks to a collaboration of hundreds of researchers. BLOOM is a decoder-only Transformer language model that was trained on the ROOTS corpus, a dataset comprising hundreds of sources in 46 natural and 13 programming languages (59 in total). We find that BLOOM achieves competitive performance on a wide variety of benchmarks, with stronger results after undergoing multitask prompted finetuning. To facilitate future research and applications using LLMs, we publicly release our models and code under the Responsible AI License

Association???Dissociation Dynamics of Ionic Electrolytes in Low Dielectric Medium

Ionic electrolytes are known to form various complexes which exist in dynamic equilibrium in a low dielectric medium. However, structural characterization of these complexes has always posed a great challenge to the scientific community. An additional challenge is the estimation of the dynamic association???dissociation time scales (lifetime of the complexes), which are key to the fundamental understanding of ion transport. In this work, we have used a combination of infrared absorption spectroscopy, two-dimensional infrared spectroscopy, molecular dynamics simulations, and density functional theory calculations to characterize the various ion complexes formed by the thiocyanate-based ionic electrolytes as a function of different cations in a low dielectric medium. Our results demonstrate that thiocyanate is an excellent vibrational reporter of the heterogeneous ion complexes undergoing association???dissociation dynamics. We find that the ionic electrolytes exist as contact ion pairs, dimers, and clusters in a low dielectric medium. The relative ratios of the various ion complexes are sensitive to the cations. In addition to the interactions between the thiocyanate anion and the countercation, the solute???solvent interactions drive the dynamic equilibrium. We have estimated the association???dissociation dynamics time scales from two-dimensional infrared spectroscopy. The exchange time scale involving the cluster is faster than that between a dimer and an ion pair. Moreover, we find that the dynamic equilibrium between the cluster and another ion complex is correlated to the solvent fluctuations

Perturbation of Fermi Resonance on Hydrogen-Bonded > CO: 2D IR Studies of Small Ester Probes

We utilized linear and 2D infrared spectroscopy to analyze the carbonyl stretching modes of small esters in different solvents. Particularly noteworthy were the distinct carbonyl spectral line shapes in aqueous solutions, prompting our investigation of the underlying factors responsible for these differences. Through our experimental and theoretical calculations, we identified the presence of the hydrogen-bond-induced Fermi resonance as the primary contributor to the varied line shapes of small esters in aqueous solutions. Furthermore, our findings revealed that the skeletal deformation mode plays a crucial role in the Fermi resonance for all small esters. Specifically, the first overtone band of the skeletal deformation mode intensifies when hydrogen bonds form with the carbonyl group of esters, whereas such coupling is rare in aprotic organic solvents. These spectral insights carry significant implications for the utilization of esters as infrared probes in both biological and chemical systems

Proceedings of National Conference on Relevance of Engineering and Science for Environment and Society

This conference proceedings contains articles on the various research ideas of the academic community and practitioners presented at the National Conference on Relevance of Engineering and Science for Environment and Society (R{ES}2 2021). R{ES}2 2021 was organized by Shri Pandurang Pratishthan’s, Karmayogi Engineering College, Shelve, Pandharpur, India on July 25th, 2021. Conference Title: National Conference on Relevance of Engineering and Science for Environment and SocietyConference Acronym: R{ES}2 2021Conference Date: 25 July 2021Conference Location: Online (Virtual Mode)Conference Organizers: Shri Pandurang Pratishthan’s, Karmayogi Engineering College, Shelve, Pandharpur, India

BLOOM: A 176B-Parameter Open-Access Multilingual Language Model

Large language models (LLMs) have been shown to be able to perform new tasks based on a few demonstrations or natural language instructions. While these capabilities have led to widespread adoption, most LLMs are developed by resource-rich organizations and are frequently kept from the public. As a step towards democratizing this powerful technology, we present BLOOM, a 176B-parameter open-access language model designed and built thanks to a collaboration of hundreds of researchers. BLOOM is a decoder-only Transformer language model that was trained on the ROOTS corpus, a dataset comprising hundreds of sources in 46 natural and 13 programming languages (59 in total). We find that BLOOM achieves competitive performance on a wide variety of benchmarks, with stronger results after undergoing multitask prompted finetuning. To facilitate future research and applications using LLMs, we publicly release our models and code under the Responsible AI License

BLOOM: A 176B-Parameter Open-Access Multilingual Language Model

Large language models (LLMs) have been shown to be able to perform new tasks based on a few demonstrations or natural language instructions. While these capabilities have led to widespread adoption, most LLMs are developed by resource-rich organizations and are frequently kept from the public. As a step towards democratizing this powerful technology, we present BLOOM, a 176B-parameter open-access language model designed and built thanks to a collaboration of hundreds of researchers. BLOOM is a decoder-only Transformer language model that was trained on the ROOTS corpus, a dataset comprising hundreds of sources in 46 natural and 13 programming languages (59 in total). We find that BLOOM achieves competitive performance on a wide variety of benchmarks, with stronger results after undergoing multitask prompted finetuning. To facilitate future research and applications using LLMs, we publicly release our models and code under the Responsible AI License