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Institution

North Eastern Hill University

EducationShillong, Meghalaya, India
About: North Eastern Hill University is a education organization based out in Shillong, Meghalaya, India. It is known for research contribution in the topics: Population & Catalysis. The organization has 2318 authors who have published 4476 publications receiving 48894 citations.


Papers
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Journal ArticleDOI
TL;DR: The ligand, KMP was able to quench the intrinsic fluorescence of these three proteins efficiently through static quenching mode and alter the micro-environment near the Trp fluorophore of the proteins.
Abstract: In recent years research based on kaempferol (KMP) has shown its potential therapeutic applications in medicinal chemistry and clinical biology. Therefore, to understand its molecular recognition mechanism, we studied its interactions with the carrier proteins, namely, human serum albumin (HSA), bovine hemoglobin (BHb) and hen egg white lysozyme (HEWL). The ligand, KMP was able to quench the intrinsic fluorescence of these three proteins efficiently through static quenching mode. The binding constant (Kb) for the interactions of KMP with these three proteins were found in the following order: HSA-KMP > BHb-KMP > HEWL-KMP. Different non-covalent forces such as hydrogen bonding and hydrophobic forces played a major role in the binding of KMP with HSA and HEWL, whereas hydrogen bonding and van der Waals forces contribute to the complexation of BHb with KMP. KMP was able to alter the micro-environment near the Trp fluorophore of the proteins. KMP altered the secondary structural component of all three proteins. The putative binding sites and the residues surrounding the KMP molecule within the respective protein matrix were determined through molecular docking and molecular dynamics (MD) simulation studies. The conformational flexibility of the ligand KMP and the three individual proteins were also evident from the MD simulation studies.

52 citations

Posted ContentDOI
14 May 2020-medRxiv
TL;DR: A Deep Convolutional Neural Network method for fast and dependable identification of COVID-19 infection cases from the patient chest X-ray images is proposed and results show that the proposed system identifies the cases with an accuracy of 93%.
Abstract: COVID-19 infection has created a panic across the globe in recent times. Early detection of COVID-19 infection can save many lives in the prevailing situation. This virus affects the respiratory system of a person and creates white patchy shadows in the lungs. Deep learning is one of the most effective Artificial Intelligence techniques to analyse chest X-ray images for efficient and reliable COVID-19 screening. In this paper, we have proposed a Deep Convolutional Neural Network method for fast and dependable identification of COVID-19 infection cases from the patient chest X-ray images. To validate the performance of the proposed system, chest X-ray images of more than 150 confirmed COVID-19 patients from the Kaggle data repository are used in the experimentation. The results show that the proposed system identifies the cases with an accuracy of 93%.

52 citations

Journal ArticleDOI
TL;DR: This is among the first studies to report uranium-tolerant aerobic chemoheterotrophs obtained from the pristine uranium ore-bearing site of Domiasiat.
Abstract: Enrichment-based methods targeted at uranium-tolerant populations among the culturable, aerobic, chemo-heterotrophic bacteria from the subsurface soils of Domiasiat (India's largest sandstone-type uranium deposits, containing an average ore grade of 0.1 % U(3)O(8)), indicated a wide occurrence of Serratia marcescens. Five representative S. marcescens isolates were characterized by a polyphasic taxonomic approach. The phylogenetic analyses of 16S rRNA gene sequences showed their relatedness to S. marcescens ATCC 13880 (≥99.4% similarity). Biochemical characteristics and random amplified polymorphic DNA profiles revealed significant differences among the representative isolates and the type strain as well. The minimum inhibitory concentration for uranium U(VI) exhibited by these natural isolates was found to range from 3.5-4.0 mM. On evaluation for their uranyl adsorption properties, it was found that all these isolates were able to remove nearly 90-92% (21-22 mg/L) and 60-70% (285-335 mg/L) of U(VI) on being challenged with 100 μM (23.8 mg/L) and 2 mM (476 mg/L) uranyl nitrate solutions, respectively, at pH 3.5 within 10 min of exposure. his capacity was retained by the isolates even after 24 h of incubation. Viability tests confirmed the tolerance of these isolates to toxic concentrations of soluble uranium U(VI) at pH 3.5. This is among the first studies to report uranium-tolerant aerobic chemoheterotrophs obtained from the pristine uranium ore-bearing site of Domiasiat.

52 citations

Journal ArticleDOI
TL;DR: Numerical work shows that the periodic potential system indeed exhibits stochastic resonance in the high-frequency regime, where the linear-response theory yields maximum frequency-dependent mobility as a function of noise strength.
Abstract: The phenomenon of stochastic resonance (SR) is known to occur mostly in bistable systems. However, the question of the occurrence of SR in periodic potential systems has not been resolved conclusively. Our present numerical work shows that the periodic potential system indeed exhibits SR in the high-frequency regime, where the linear-response theory yields maximum frequency-dependent mobility as a function of noise strength. The existence of two (and only two) distinct dynamical states of trajectories in this moderately feebly damped periodically driven noisy periodic potential system plays an important role in the occurrence of SR.

52 citations


Authors

Showing all 2368 results

NameH-indexPapersCitations
Vivek Sharma1503030136228
Patrick J. Carroll5850513046
Majeti Narasimha Vara Prasad5622715193
Arun Sharma5537111364
Michael Schmittel5338710461
Birgitta Bergman5218710975
Harikesh Bahadur Singh463077372
Lal Chand Rai401344513
B. Dey403548089
Hiriyakkanavar Ila364075633
Jürgen-Hinrich Fuhrhop352085130
Sreebrata Goswami341423228
Gagan B.N. Chainy331074151
J.P. Gaur31643957
Hiriyakkanavar Junjappa303494102
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Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
202321
202254
2021352
2020308
2019293
2018306