Jawaharlal Nehru University

About: Jawaharlal Nehru University is a education organization based out in New Delhi, India. It is known for research contribution in the topics: Population & Politics. The organization has 6082 authors who have published 13455 publications receiving 245407 citations. The organization is also known as: JNU.

Estimation of soil erosion using RUSLE and GIS techniques: a case study of Barakar River basin, Jharkhand, India

Abstract: An integrated method has been adopted to estimate soil loss in a plateau and plateau fringe river basin where soil erosion is significant. The integration of Revised Universal Soil Loss Equation model and geographical Information technology has been used for soil loss estimation. In GIS platform, the overlay of rainfall-runoff erosivity factor, soil erodibility factor, slope length factor, slope steepness factor, cover and management factor, support and conservation practices factor results that the high amount of soil loss (more than 100 t ha−1 year−1) is significantly low and occupies 0.08% of the entire study area. High soil loss in upstream of the basin has a close relation to LS and K factor and drainage density. As a result of soil loss in the upper catchment areas, reservoir capacity has been depleted both in dead and live storage space. It is concluded that soil erosion has a significant impact on plateau fringe areas and the estimation of soil loss is an essential input for the adoption of proper land use planning and development strategies.

Modeling adsorption kinetics of trichloroethylene onto biochars derived from soybean stover and peanut shell wastes

Abstract: Trichloroethylene (TCE) is one of the most hazardous organic pollutants in groundwater. Biochar produced from agricultural waste materials could serve as a novel carbonaceous adsorbent for removing organic contaminants from aqueous media. Biochars derived from pyrolysis of soybean stover at 300 °C and 700 °C (S-300 and S-700, respectively), and peanut shells at 300 °C and 700 °C (P-300 and P-700, respectively) were utilized as carbonaceous adsorbents to study batch aqueous TCE remediation kinetics. Different rate-based and diffusion-based kinetic models were adopted to understand the TCE adsorption mechanism on biochars. With an equilibrium time of 8–10 h, up to 69 % TCE was removed from water. Biochars produced at 700 °C were more effective than those produced at 300 °C. The P-700 and S-700 had lower molar H/C and O/C versus P-300 and S-300 resulting in high aromaticity and low polarity accompanying with high surface area and high adsorption capacity. The pseudo-second order and intraparticle diffusion models were well fitted to the kinetic data, thereby, indicating that chemisorption and pore diffusion were the dominating mechanisms of TCE adsorption onto biochars.

Curcumin decreases Warburg effect in cancer cells by down-regulating pyruvate kinase M2 via mTOR-HIF1α inhibition.

Abstract: Warburg effect is an emerging hallmark of cancer cells with pyruvate kinase M2 (PKM2) as its key regulator. Curcumin is an extensively-studied anti-cancer compound, however, its role in affecting cancer metabolism remains poorly understood. Herein, we show that curcumin inhibits glucose uptake and lactate production (Warburg effect) in a variety of cancer cell lines by down-regulating PKM2 expression, via inhibition of mTOR-HIF1α axis. Stable PKM2 silencing revealed that PKM2 is required for Warburg effect and proliferation of cancer cells. PKM2 over-expression abrogated the effects of curcumin, demonstrating that inhibition of Warburg effect by curcumin is PKM2-mediated. High PKM2 expression correlated strongly with poor overall survival in cancer, suggesting the requirement of PKM2 in cancer progression. The study unravels novel PKM2-mediated inhibitory effect of curcumin on metabolic capacities of cancer cells. To the best of our knowledge, this is the first study linking curcumin with PKM2-driven cancer glycolysis, thus, providing new perspectives into the mechanism of its anticancer activity.

A glutathione responsive rice glyoxalase II, OsGLYII-2, functions in salinity adaptation by maintaining better photosynthesis efficiency and anti-oxidant pool.

Abstract: Glyoxalase II (GLY II), the second enzyme of glyoxalase pathway that detoxifies cytotoxic metabolite methylglyoxal (MG), belongs to the superfamily of metallo-β-lactamases. Here, detailed analysis of one of the uncharacterized rice glyoxalase II family members, OsGLYII-2 was conducted in terms of its metal content, enzyme kinetics and stress tolerance potential. Functional complementation of yeast GLY II mutant (∆GLO2) and enzyme kinetics data suggested that OsGLYII-2 possesses characteristic GLY II activity using S-lactoylglutathione (SLG) as the substrate. Further, Inductively Coupled Plasma Atomic Emission spectroscopy and modelled structure revealed that OsGLYII-2 contains a binuclear Zn/Fe centre in its active site and chelation studies indicated that these are essential for its activity. Interestingly, reconstitution of chelated enzyme with Zn(2+), and/or Fe(2+) could not reactivate the enzyme, while addition of Co(2+) was able to do so. End product inhibition study provides insight into the kinetics of GLY II enzyme and assigns hitherto unknown function to reduced glutathione (GSH). Ectopic expression of OsGLYII-2 in Escherichia coli and tobacco provides improved tolerance against salinity and dicarbonyl stress indicating towards its role in abiotic stress tolerance. Maintained levels of MG and GSH as well as better photosynthesis rate and reduced oxidative damage in transgenic plants under stress conditions seems to be the possible mechanism facilitating enhanced stress tolerance.

A collaborative filtering framework for friends recommendation in social networks based on interaction intensity and adaptive user similarity

Abstract: The tremendous growth in the amount of attention and users, on social networking sites (SNSs), has led to information overload and that adds to the difficulty of making accurate recommendations of new friends to the users of SNSs. This article incorporates collaborative filtering (CF), the most successful and widely used filtering technique, in social networks to facilitate users in exploring new friends having similar interests while being connected with old ones as well. Here, first we design an implicit rating model, for estimating a user’s affinity toward his friends, which uncover the strength of relationship, utilizing both attribute similarity and user interaction intensity. We then propose a CF-based framework that offers list of friends to the user by leveraging on the preference of like-minded users, with a given small set of people that user has already labeled as friends. Despite the immense success of CF, accuracy and sparsity are still major challenges, especially in social networking domain with a staggering growth having enormous number of users. To address these inherent challenges, first we have explored the idea of adaptive similarity computation between users by employing evolutionary algorithms to learn individual preferences toward particular set of attributes that results in considerable improvement in recommendation accuracy as compared to the situation where all the attributes are given equal importance. Second, we incorporate effective missing data prediction algorithm as a solution to data sparsity thereby further enhancing accuracy. Experimental results are presented to illustrate the effectiveness of the proposed friends recommendation schemes.