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.

Genomewide Expression and Functional Interactions of Genes under Drought Stress in Maize.

Abstract: A genomewide transcriptome assay of two subtropical genotypes of maize was used to observe the expression of genes at seedling stage of drought stress. The number of genes expressed differentially was greater in HKI1532 (a drought tolerant genotype) than in PC3 (a drought sensitive genotype), indicating primary differences at the transcriptional level in stress tolerance. The global coexpression networks of the two genotypes differed significantly with respect to the number of modules and the coexpression pattern within the modules. A total of 174 drought-responsive genes were selected from HKI1532, and their coexpression network revealed key correlations between different adaptive pathways, each cluster of the network representing a specific biological function. Transcription factors related to ABA-dependent stomatal closure, signalling, and phosphoprotein cascades work in concert to compensate for reduced photosynthesis. Under stress, water balance was maintained by coexpression of the genes involved in osmotic adjustments and transporter proteins. Metabolism was maintained by the coexpression of genes involved in cell wall modification and protein and lipid metabolism. The interaction of genes involved in crucial biological functions during stress was identified and the results will be useful in targeting important gene interactions to understand drought tolerance in greater detail.

Influence of tillage based crop establishment and residue management practices on soil quality indices and yield sustainability in rice-wheat cropping system of Eastern Indo-Gangetic Plains.

Abstract: Rice-wheat cropping system (RWCS) is the most important system occupying around 26 M ha spread over the Indo Gangetic Plains in South Asia and China. Many long-term trials were led to assess the agronomic productivity and economic profitability of various combinations of conservation agricultural (CA) practices (zero tillage, residue management and crop establishment) in RWCS of Eastern Indo-Gangetic Plains (EIGP) of India. The purpose of this study was to investigate the best management practices involving different tillage-based crop establishment and residue retention techniques and their contribution to agricultural system sustainability through improvement in soil health by developing soil quality index (SQI). We have used SQI as an instrument based on physical [macro aggregate stability (MAS), available water capacity (AWC) and soil penetration resistance (SPR)], chemical [soil organic carbon (OC), available N, available P and available K] and biological [microbial biomass carbon (MBC), fluorescein diacetate (FDA) and dehydrogenase activity (DHA)] properties of soil, because these are very useful indicators of soil's functions for agronomic productivity and soil fertility. Soil properties like MAS, OC, MBC, FDA and DHA were higher by 47, 18, 56, 48 and 53%, respectively, under ZTDSR-ZTW (T7: Zero-till direct seeded rice - Zero-till wheat) than RPTR-CTW (T1: Random puddled transplanted rice - Conventional till broadcasted wheat), at 0-10 cm. CA based treatment T7 also recorded lower SPR (126 N cm-1). SQI for different treatments were calculated by performing principal component analysis based on the total data set method. The higher system rice equivalent yield of 12.41 t ha-1 was observed at SQI value of 0.90 at 0-10 cm and 0.86 at 10-20 cm in T7. It can be concluded that crop residue retention on the surface with zero tillage is beneficial for the sustainability and productivity of the RWCS in EIGP of India.

An Empirical Comparison of Machine Learning Techniques in Predicting the Bug Severity of Open and Closed Source Projects

Abstract: Bug severity is the degree of impact that a defect has on the development or operation of a component or system, and can be classified into different levels based on their impact on the system. Identification of severity level can be useful for bug triager in allocating the bug to the concerned bug fixer. Various researchers have attempted text mining techniques in predicting the severity of bugs, detection of duplicate bug reports and assignment of bugs to suitable fixer for its fix. In this paper, an attempt has been made to compare the performance of different machine learning techniques namely Support vector machine SVM, probability based Naive Bayes NB, Decision Tree based J48 A Java implementation of C4.5, rule based Repeated Incremental Pruning to Produce Error Reduction RIPPER and Random Forests RF learners in predicting the severity level 1 to 5 of a reported bug by analyzing the summary or short description of the bug reports. The bug report data has been taken from NASA's PITS Projects and Issue Tracking System datasets as closed source and components of Eclipse, Mozilla & GNOME datasets as open source projects. The analysis has been carried out in RapidMiner and STATISTICA data mining tools. The authors measured the performance of different machine learning techniques by considering i the value of accuracy and F-Measure for all severity level and ii number of best cases at different threshold level of accuracy and F-Measure.

Prediction of donor splice sites using random forest with a new sequence encoding approach

Abstract: Detection of splice sites plays a key role for predicting the gene structure and thus development of efficient analytical methods for splice site prediction is vital. This paper presents a novel sequence encoding approach based on the adjacent di-nucleotide dependencies in which the donor splice site motifs are encoded into numeric vectors. The encoded vectors are then used as input in Random Forest (RF), Support Vector Machines (SVM) and Artificial Neural Network (ANN), Bagging, Boosting, Logistic regression, kNN and Naive Bayes classifiers for prediction of donor splice sites. The performance of the proposed approach is evaluated on the donor splice site sequence data of Homo sapiens, collected from Homo Sapiens Splice Sites Dataset (HS3D). The results showed that RF outperformed all the considered classifiers. Besides, RF achieved higher prediction accuracy than the existing methods viz., MEM, MDD, WMM, MM1, NNSplice and SpliceView, while compared using an independent test dataset. Based on the proposed approach, we have developed an online prediction server (MaLDoSS) to help the biological community in predicting the donor splice sites. The server is made freely available at http://cabgrid.res.in:8080/maldoss . Due to computational feasibility and high prediction accuracy, the proposed approach is believed to help in predicting the eukaryotic gene structure.