Terminalia paniculata bark extract attenuates non-alcoholic fatty liver via down regulation of fatty acid synthase in high fat diet-fed obese rats

Background: This study was performed to understand the possible therapeutic activity of Terminalia paniculata ethanolic extract (TPEE) on non alcoholic fatty liver in rats fed with high fat diet. Methods: Thirty six SD rats were divided into 6 groups (n = 6): Normal control (NC), high fat diet (HFD), remaining four groups were fed on HFD along with different doses of TPEE (100,150 and 200 mg/kg b.wt) or orlistat, for ten weeks. Liver tissue was homogenized and analyzed for lipid profiles, activities of superoxide dismutase (SOD), catalase (CAT) and malondialdehyde (MDA) content. Further, the expression levels of FAS and AMPK-1 alpha were also studied in addition to histopathology examination of liver tissue in all the groups. Results: HFD significantly increased hepatic liver total cholesterol (TC), triglycerides (TG), free fatty acids (FFA) and MDA but decreased the activities of SOD and CAT which were subsequently reversed by supplementation with TPEE in a dose-dependent manner. In addition, TPEE administration significantly down regulated hepatic mRNA expression of FAS but up regulated AMPK-1 alpha compared to HFD alone fed group. Furthermore, western blot analysis of FAS has clearly demonstrated decreased expression of FAS in HFD + TPEE (200 mg/kg b. wt) treated group when compared to HFD group at protein level. Conclusions: Our biochemical studies on hepatic lipid profiles and antioxidant enzyme activities supported by histological and expression studies suggest a potential therapeutic role for TPEE in regulating obesity through FAS.

A non-cooperative game-theoretic approach to channel assignment in multi-channel multi-radio wireless networks

Channel assignment in multi-channel multi-radio wireless networks poses a significant challenge due to scarcity of number of channels available in the wireless spectrum. Further, additional care has to be taken to consider the interference characteristics of the nodes in the network especially when nodes are in different collision domains. This work views the problem of channel assignment in multi-channel multi-radio networks with multiple collision domains as a non-cooperative game where the objective of the players is to maximize their individual utility by minimizing its interference. Necessary and sufficient conditions are derived for the channel assignment to be a Nash Equilibrium (NE) and efficiency of the NE is analyzed by deriving the lower bound of the price of anarchy of this game. A new fairness measure in multiple collision domain context is proposed and necessary and sufficient conditions for NE outcomes to be fair are derived. The equilibrium conditions are then applied to solve the channel assignment problem by proposing three algorithms, based on perfect/imperfect information, which rely on explicit communication between the players for arriving at an NE. A no-regret learning algorithm known as Freund and Schapire Informed algorithm, which has an additional advantage of low overhead in terms of information exchange, is proposed and its convergence to the stabilizing outcomes is studied. New performance metrics are proposed and extensive simulations are done using Matlab to obtain a thorough understanding of the performance of these algorithms on various topologies with respect to these metrics. It was observed that the algorithms proposed were able to achieve good convergence to NE resulting in efficient channel assignment strategies.

Ebselen is a potent non-competitive inhibitor of extracellular nucleoside diphosphokinase

Nucleoside di- and triphosphates and adenosine regulate several components of the mucocilairy clearance process (MCC) that protects the lung against infections, via activation of epithelial purinergic receptors. However, assessing the contribution of individual nucleotides to MCC functions remains difficult due to the complexity of the mechanisms of nucleotide release and metabolism. Enzymatic activities involved in the metabolism of extracellular nucleotides include ecto-ATPases and secreted nucleoside diphosphokinase (NDPK) and adenyl kinase, but potent and selective inhibitors of these activities are sparse. In the present study, we discovered that ebselen markedly reduced NDPK activity while having negligible effect on ecto-ATPase and adenyl kinase activities. Addition of radiotracer gamma P-32]ATP to human bronchial epithelial (HBE) cells resulted in rapid and robust accumulation of P-32]-inorganic phosphate ((32)Pi). Inclusion of UDP in the incubation medium resulted in conversion of gamma P-32]ATP to P-32]UTP, while inclusion of AMP resulted in conversion of gamma P-32]ATP to P-32]ADP. Ebselen markedly reduced P-32]UTP formation but displayed negligible effect on (32)Pi or P-32]ADP accumulations. Incubation of HBE cells with unlabeled UTP and ADP resulted in robust ebselen-sensitive formation of ATP (IC50=6.9 +/- 2 mu M). This NDPK activity was largely recovered in HBE cell secretions and supernatants from lung epithelial A549 cells. Kinetic analysis of NDPK activity indicated that ebselen reduced the V-max of the reaction (K-i=7.6 +/- 3 mu M), having negligible effect on KM values. Our study demonstrates that ebselen is a potent noncompetitive inhibitor of extracellular NDPK.

Structured Learning for Non-Smooth Ranking losses

Learning to rank from relevance judgment is an active research area. Itemwise score regression, pairwise preference satisfaction, and listwise structured learning are the major techniques in use. Listwise structured learning has been applied recently to optimize important non-decomposable ranking criteria like AUC (area under ROC curve) and MAP(mean average precision). We propose new, almost-lineartime algorithms to optimize for two other criteria widely used to evaluate search systems: MRR (mean reciprocal rank) and NDCG (normalized discounted cumulative gain)in the max-margin structured learning framework. We also demonstrate that, for different ranking criteria, one may need to use different feature maps. Search applications should not be optimized in favor of a single criterion, because they need to cater to a variety of queries. E.g., MRR is best for navigational queries, while NDCG is best for informational queries. A key contribution of this paper is to fold multiple ranking loss functions into a multi-criteria max-margin optimization.The result is a single, robust ranking model that is close to the best accuracy of learners trained on individual criteria. In fact, experiments over the popular LETOR and TREC data sets show that, contrary to conventional wisdom, a test criterion is often not best served by training with the same individual criterion.

A Multi-layer Perceptron based Non-linear Mixture Model to estimate class abundance from mixed pixels

Sub-pixel classification is essential for the successful description of many land cover (LC) features with spatial resolution less than the size of the image pixels. A commonly used approach for sub-pixel classification is linear mixture models (LMM). Even though, LMM have shown acceptable results, pragmatically, linear mixtures do not exist. A non-linear mixture model, therefore, may better describe the resultant mixture spectra for endmember (pure pixel) distribution. In this paper, we propose a new methodology for inferring LC fractions by a process called automatic linear-nonlinear mixture model (AL-NLMM). AL-NLMM is a three step process where the endmembers are first derived from an automated algorithm. These endmembers are used by the LMM in the second step that provides abundance estimation in a linear fashion. Finally, the abundance values along with the training samples representing the actual proportions are fed to multi-layer perceptron (MLP) architecture as input to train the neurons which further refines the abundance estimates to account for the non-linear nature of the mixing classes of interest. AL-NLMM is validated on computer simulated hyperspectral data of 200 bands. Validation of the output showed overall RMSE of 0.0089±0.0022 with LMM and 0.0030±0.0001 with the MLP based AL-NLMM, when compared to actual class proportions indicating that individual class abundances obtained from AL-NLMM are very close to the real observations.

What Drives Venture Capital Fundraising in India: An Empirical Analysis of Systematic and Non-Systematic Factors

In this paper, we develop a consolidated Supply-Demand framework of the Venture Capital (VC) ecosystem for India. Further, we empirically analyze the supply side of this ecosystem to ascertain the influence of systematic (macro) and non-systematic (micro) factors on VC fundraising. At the macro level, our results indicate that relatively strong fundamentals of the Indian economy in the past decade as compared with the severe recessionary tendencies in the developed economies have been critical in determining the aggregate volume of VC fundraising. Among the micro factors, past performance and reputation of the individual fund managers have been instrumental in determining their fund raising potential.

Activity-travel behaviour of non-workers from Bangalore City in India

This paper presents exploratory and statistical analyses of the activity-travel behaviour of non-workers in Bangalore city in India. The study summarises the socio-demographic characteristics as well as the activity-travel behaviour of non-workers using a primary activity-travel survey data collected by the authors. Where possible, the research also compares the analysis findings with the case studies on activity-travel behaviour of non-workers, carried out in developed and developing countries. This gives an opportunity to understand the differences/similarities in the activity-travel behaviour of non-workers across diverse socio-cultural settings. The preliminary exploratory analysis shed light on the differences in activity participation, trip chaining, time-of-day preference for trip departure, and mode use behaviour of non-workers in Bangalore city. Statistical models were developed for investigating the effects of individual and household socio-demographics, land use parameters, and travel context attributes on activity participation, trip chaining, time-of-day choice, and mode choice decisions of non-workers. A few important results of the analysis are the influence of viewing television at home on out-of-home activity participation and trip-chaining behaviour, and the impact of in-home maintenance activity duration on time-of-day choice. Further, based on the findings of the initial analyses, an attempt has been made in this study to develop an integrated model that links time allocation, time-of-day choice, and trip chaining behaviour of non-workers. The study also discusses the implications of the research findings for transportation planning and policy for Bangalore city. (C) 2015 Elsevier Ltd. All rights reserved.