Body weight lowering effect of glucose-dependent insulinotropic polypeptide and glucagon-like peptide receptor agonists is more efficient in RAMP1/3 KO than in WT mice

The glucose-dependent insulinotropic polypeptide (GIPR) and glucagon-like peptide (GLP-1R) receptor agonists are insulin secretagogues that have long been shown to improve glycemic control and dual agonists have demonstrated successful weight loss in the clinic. GIPR and GLP-1R populations are located in the dorsal vagal complex where receptor activity-modifying proteins (RAMPs) are also present. According to recent literature, RAMPs not only regulate the signaling of the calcitonin receptor, but also that of other class B G-protein coupled receptors, including members of the glucagon receptor family such as GLP-1R and GIPR. The aim of this study was to investigate whether the absence of RAMP1 and RAMP3 interferes with the action of GIPR and GLP-1R agonists on body weight maintenance and glucose control. To this end, WT and RAMP 1/3 KO mice were fed a 45% high fat diet for 22 weeks and were injected daily with GLP-1R agonist (2 nmol/kg/d; NN0113-2220), GIPR agonist (30 nmol/kg/d; NN0441-0329) or both for 3 weeks. While the mono-agonists exerted little to no body weight lowering and anorectic effects in WT or RAMP1/3 KO mice, but at the given doses, when both compounds were administered together, they synergistically reduced body weight, with a greater effect observed in KO mice. Finally, GLP-1R and GIP/GLP-1R agonist treatment led to improved glucose tolerance, but the absence of RAMPs resulted in an improvement of the HOMA-IR score. These data suggest that RAMPs may play a crucial role in modulating the pharmacological actions of GLP-1 and GIP receptors

Effects of biofertilizer containing N-fixer, P and K solubilizers and AM fungi on maize growth: A greenhouse trial.

An in vitro study was undertaken to evaluate the compatibility of indigenous plant growth promoting rhizobacteria (PGPR) with commonly used inorganic and organic sources of fertilizers in tea plantations. The nitrogenous, phosphatic and potash fertilizers used for this study were urea, rock phosphate and muriate of potash, respectively. The organic sources of fertilizers neem cake, composted coir pith and vermicompost were also used. PGPRs such as nitrogen fixer; Azospirillum lipoferum, Phosphate Solubilizing Bacteria (PSB); Pseudomonas putida, Potassium Solubilizing Bacteria (KSB); Burkholderia cepacia and Pseudomonas putida were used for compatibility study. Results were indicated that PGPRs preferred the coir pith and they proved their higher colony establishment in the formulation except Azospirillum spp. that preferred vermicompost for their establishment. The optimum dose of neem cake powder

Dynamic cumulative residual Renyi’s entropy

Recently, cumulative residual entropy (CRE) has been found to be a new measure of information that parallels Shannon’s entropy (see Rao et al. [Cumulative residual entropy: A new measure of information, IEEE Trans. Inform. Theory. 50(6) (2004), pp. 1220–1228] and Asadi and Zohrevand [On the dynamic cumulative residual entropy, J. Stat. Plann. Inference 137 (2007), pp. 1931–1941]). Motivated by this finding, in this paper, we introduce a generalized measure of it, namely cumulative residual Renyi’s entropy, and study its properties.We also examine it in relation to some applied problems such as weighted and equilibrium models. Finally, we extend this measure into the bivariate set-up and prove certain characterizing relationships to identify different bivariate lifetime modelsCochin University of Science and TechnologyStatistics, Vol. 46, No. 1, February 2012, 41–5

On bounds of some dynamic information divergence measures

In this paper, we obtain certain bounds for some dynamic information divergences measures viz. Renyi’s divergence of order αand Kerridge’s inaccuracy, using likelihood ratio ordering. The results are also extended to weighted models and theoretical examples are given to supplement the results

Characterizations of bivariate models using dynamic Kullback–Leibler discrimination measures

In this paper, the residual Kullback–Leibler discrimination information measure is extended to conditionally specified models. The extension is used to characterize some bivariate distributions. These distributions are also characterized in terms of proportional hazard rate models and weighted distributions. Moreover, we also obtain some bounds for this dynamic discrimination function by using the likelihood ratio order and some preceding results.Cochin University of Science and TechnologyStatistics and Probability Letters 81 (2011) 1594–159

Characterizations of Bivariate Models Using Some Dynamic Conditional Information Divergence Measures

In this article, we study some relevant information divergence measures viz. Renyi divergence and Kerridge’s inaccuracy measures. These measures are extended to conditionally specifiedmodels and they are used to characterize some bivariate distributions using the concepts of weighted and proportional hazard rate models. Moreover, some bounds are obtained for these measures using the likelihood ratio orderCochin University of Science and TechnologyCommunications in Statistics—Theory and Methods, 43: 1939–1948, 201

Function Approximation with Deep Neural Network for Image Classification in Fuzzy Domain

Image Classification and retrieval of image from a large database has a great relevance in the present Scenario. A lot of work for an efficient method of image retrieval from large database has been made in the recent surveys. Here we propose a mathematical model based on CBIR system that uses the deep neural architecture for classification where the inputs are fuzzy grassland image features. Grassland image features varies according to the varieties of grassland images available through satellite images and hence its classification is a complex process. This paper proposes a new method for classification in which the inputs to the Neural Network are fuzzified and transformed in such a way that it clusters around a pivot vector there by making the classification task less complicated. This classification procedure is established theoretically by developing a mathematical model based on Neural Network approximation with fuzzy inputs. This model brings a transformation from the input image feature space to the output approximation space through the composition of mapping between the hidden transformation spaces that helps to strengthen the function approximation to the desired output. The Graphical representation on Fig(i) throws an insight into the mathematical theory of a CBIR system which unifies the advantages of deep neural architecture and fuzzy approximators. The mathematical concepts such as open balls, metric, limits, continuity etc are incorporated to establish the necessary and sufficient condition in the fuzzy based neural system for better and clear image retrieval

On 12 th to 14 th December Organized by ROAD SAFETY AWARENESS INDEX & ROAD USER BEHAVIOR-A CASE STUDY AT KAZHAKKOOTTAM

ABSTRACT This article reports the results of a case study of road user characteristics with regard to road safety. The two main characteristics considered were road user awareness and road user behavior. Influence of Road user awareness on road safety was studied. Questionnaire survey method was done to find the influence of age and educational qualification on awareness of road users and developed Road Safety Awareness Index (RSAI). It is found that age and educational qualification are not completely deciding factors of road user awareness. To determine the reasons for traffic violation, road user behavior observation surveys were done. It is also tried to cross examine whether any other authentic factors which enhance traffic violations, previous accident history and enforcement measures were taken as factors. And the authors could find out a strong relation among them