Indian Agricultural Statistics Research Institute

About: Indian Agricultural Statistics Research Institute is a facility organization based out in New Delhi, India. It is known for research contribution in the topics: Population & Small area estimation. The organization has 454 authors who have published 870 publications receiving 7987 citations.

Environmental stochasticity of gompertz model with time delay

Abstract: We consider the environmental stochasticity of the Gompertz model with time delay when the parameters are assumed to be described by correlated Gaussian white noise processes. The delay kernel is assumed to be exponentially decaying and the Fokker-Planck equation is obtained in both the Stratonovich and Ito calculi. The exact expressions for the first and second order moments of the logarithm of population size are evaluated and the stability of the system in mean as well as In mean-square is discussed.

Robustness of neighbour balanced complete block designs against missing observation(s)

Abstract: A block design with neighbour effect(s) is said to be neighbo ur balanced if every treatment has every other treatment appearing constant number of times as neighbour(s). These d esigns are used when the treatment applied to one experiment al plot may affect the response on neighbouring plot(s) besides the response to which it is applied. Neighbour-balanced design s ensure that no treatment is unduly disadvantaged by its neighbour( s). However, there is a possibility that some of the observat ions could become unavailable for analysis. In this paper, we examine t he robustness of neighbour balanced complete block designs when specific observations are missing. The information matrix f or direct treatment effects of the resultant design (one-si d d neighbour effects) after missing of an observation from a block is deri ved and the efficiency of resulting design is investigated. R obustness of neighbour balanced complete block design has also been in v st gated against missing of more than one observation. Th e efficiencies are found to be quite high indicating the design s to be fairly robust against missing observations.

HEPNet: A Knowledge Base Model of Human Energy Pool Network for Predicting the Energy Availability Status of an Individual

Abstract: HEPNet is an electronic representation of metabolic reactions occurring within human cellular organization focusing on inflow and outflow of the energy currency ATP, GTP and other energy associated moieties. The backbone of HEPNet consists of primary bio-molecules such as carbohydrates, proteins and fats which ultimately constitute the chief source for the synthesis and obliteration of energy currencies in a cell. A series of biochemical pathways and reactions constituting the catabolism and anabolism of various metabolites are portrayed through cellular compartmentalization. The depicted pathways function synchronously toward an overarching goal of producing ATP and other energy associated moieties to bring into play a variety of cellular functions. HEPNet is manually curated with raw data from experiments and is also connected to KEGG and Reactome databases. This model has been validated by simulating it with physiological states like fasting, starvation, exercise and disease conditions like glycaemia, uremia and dihydrolipoamide dehydrogenase deficiency (DLDD). The results clearly indicate that ATP is the master regulator under different metabolic conditions and physiological states. The results also highlight that energy currencies play a minor role. However, the moiety creatine phosphate has a unique character, since it is a ready-made source of phosphoryl groups for the rapid synthesis of ATP from ADP. HEPNet provides a framework for further expanding the network diverse age groups of both the sexes, followed by the understanding of energetics in more complex metabolic pathways that are related to human disorders.

SNPs in Mammary Gland Epithelial Cells Unraveling Potential Difference in Milk Production Between Jersey and Kashmiri Cattle Using RNA Sequencing.

Abstract: Deep RNA sequencing experiment was employed to detect putative single nucleotide polymorphisms (SNP) in mammary epithelial cells between two diverse cattle breeds (Jersey and Kashmiri) to understand the variations in the coding regions that reflect differences in milk production traits. The low milk-producing Kashmiri cattle are being replaced by crossbreeding practices with Jersey cattle with the aim of improving milk production. However, crossbred animals are prone to infections and various other diseases resulting in unsustainable milk production. In this study, we tend to identify high-impact SNPs from Jersey and Kashmiri cows (utilizing RNA-Seq data) to delineate key pathways mediating milk production traits in both breeds. A total of 607 (442 SNPs and 169 INDELs) and 684 (464 SNPs and 220 INDELs) high-impact variants were found specific to Jersey and Kashmir cattle, respectively. Based on our results, we conclude that in Jersey cattle, genes with high-impact SNPs were enriched in nucleotide excision repair pathway, ABC transporter, and metabolic pathways like glycerolipid metabolism, pyrimidine metabolism, and amino acid synthesis (glycine, serine, and threonine). Whereas, in Kashmiri cattle, the most enriched pathways include endocytosis pathway, innate immunity pathway, antigen processing pathway, insulin resistance pathway, and signaling pathways like TGF beta and AMPK which could be a possible defense mechanism against mammary gland infections. A varied set of SNPs in both breeds, suggests a clear differentiation at the genomic level; further analysis of high-impact SNPs are required to delineate their effect on these pathways.

Complexity of the Code Changes and Issues Dependent Approach to Determine the Release Time of Software Product

Abstract: Changes in source code of the software products are inevitable. We need to change the source code to fix the feature improvements, new features and bugs. Feature improvements, new features and bugs are collectively termed as issues. The changes in the source code of the software negatively impact its product, but necessary for the evolution of the software. The changes in source code are quantified using entropy based measure and it is called the complexity of code changes. In this paper, we built regression models to predict the next release time of software using the complexity of code changes (entropy), feature improvements, new feature implementation and bugs fixed. The regression models have been built using Multiple Linear Regression (MLR), various kernel functions based Support Vector Regression (SVR) and k-Nearest Neighbor (k-NN) methods. The proposed models have been empirically validated using four open source sub-projects of the Apache software foundation. The proposed models exhibit a good fit. The developed models will assist release managers in release planning of the software.