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.

Intelligent Semantics Approaches for Adaptive Web

Abstract: Intelligent semantic approaches (i.e., semantic web and software agents) are very useful technologies for adding meaning to the web. Adaptive web is a new era of web targeting to provide customized and personalized view of contents and services to its users. Integration of these two technologies can further add to reasoning and intelligence in recommendation process. This chapter explores the existing work done in the area of applying intelligent approaches to web personalization and highlighting ample scope for application of intelligent agents in this domain for solving many existing issues like personalized content management, user profile learning, modelling, and adaptive interactions with users.

A note on incomplete block designs for symmetrical parallel line assays

Abstract: Incomplete block designs estimating the relative potency free from block effects have been discussed. A method of constructing a series of such designs has been presented.

Two-Stage Adaptive Cluster Sampling

Abstract: Adaptive sampling is a method in which selection of units at any stage of sampling depends upon the information collected from the already selected units in the initial sample. It means, if one finds what he/she is looking for at a particular location then he/she would sample in the vicinity of that location with the hope of obtaining more information.

Use of expected-value parameters for estimating length-weight relationship in fishes

Abstract: Nonlinear estimation procedures are generally employed for determining the length-weight relationship in fishes. As no attention is paid to reparameterization of the parameters, the model-data set combination sometimes exhibits an extremely high nonlinear behaviour, i.e. the parameter-effects measure of curvature is very large. The purpose of the present paper is to make use of'expected-value' parameters which depict close-to-linear behaviour. The methodology is illustrated with the help of data taken from fisheries literature.

In Silico Prediction and Functional Characterization of Genes Related to Abiotic and Biotic Stresses in Chickpea (Cicer arietinum)

Abstract: Chickpea (Cicer arietinum L.) is second largest grown legumes worldwide contributing 75% of total pulse production. It is a cool season legume crop and grown in tropical and subtropical areas. Due to drastic climatic changes, chickpea suffers from many biotic (blight and wilt) and abiotic (salinity, drought, cold) stresses that directly impact the growth and yield. In our study, we predicted and annotated the genes related to biotic and abiotic stresses. Total 20162 ESTs for salinity, 34346 for drought and 191 for cold stress were downloaded. For biotic stresses, viz., wilt and blight disease, 7866 and 56 ESTs were collected, respectively from public domain. All these ESTs were assembled into contigs and blast against protein non-redundant database. Each blast results were mapped to get the corresponding GO terms. Total 1631, 3133 and 13 contigs for salinity, drought and cold stress showed 1333, 2693 and 7 GO terms respectively, while 1144 contigs for Fusarium wilt and 6 contigs for Ascochyta blight disease showed 955 and 4 GO terms. These GO terms describe biological process, molecular function and cellular components of corresponding stresses. Remaining 298 (salinity), 440 (drought), 6 (cold), 189 (wilt) and 2 (blight) contigs were mapped to reference genome and further used for annotation using gene prediction methods and promoter analysis. This study provide insight to novel gene related to abiotic and biotic stress mechanism that can be further analyzed in molecular biology studies for breeding programs.