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.

Improving the Quality of Software by Quantifying the Code Change Metric and Predicting the Bugs

Abstract: “When you can measure what you are speaking about and express it in numbers, you know something about it; but when you cannot measure, when you cannot express it in numbers, your knowledge is of a meagre and unsatisfactory kind; it may be the beginning of knowledge, but you have scarcely, in your thoughts, advanced to the stage of science” LORD WILLIAM KELVIN (1824 – 1907) During the last decade, the quantification of software engineering process has got a pace due to availability of a huge amount of software repositories These repositories include source code, bug, communication among developers/users, changes in code, etc Researchers are trying to find out useful information from these repositories for improving the quality of software

Hyperspectral satellite data analysis for pure pixels extraction and evaluation of advanced classifier algorithms for LULC classification

Abstract: The study was carried out for Indian capital city Delhi using Hyperion sensor onboard EO-1 satellite of NASA. After MODTRAN-4 based atmospheric correction, MNF, PPI and n-D visualizer were applied and endmembers of 11 LCLU classes were derived which were employed in classification of LULC. To incur better classification accuracy, a comparative study was also carried out to evaluate the potential of three classifier algorithms namely Random Forest (RF), Support Vector Machines (SVM) and Spectral Angle Mapper (SAM). The results of this study reemphasize the utility of satellite borne hyperspectral data to extract endmembers and also to delineate the potential of random forest as expert classifier to assess land cover with higher classification accuracy that outperformed the SVM by 19% and SAM by 27% in overall accuracy. This research work contributes positively to the issue of land cover classification through exploration of hyperspectral endmembers. The comparison of classification algorithms’ performance is valuable for decision makers to choose better classifier for more accurate information extraction.

Inhibition Potencies of Phytochemicals Derived from Sesame Against SARS-CoV-2 Main Protease: A Molecular Docking and Simulation Study.

Abstract: The ongoing COVID-19 pandemic, caused by SARS-CoV-2, has now spread across the nations with high mortality rates and multifaceted impact on human life. The proper treatment methods to overcome this contagious disease are still limited. The main protease enzyme (Mpro, also called 3CLpro) is essential for viral replication and has been considered as one of the potent drug targets for treating COVID-19. In this study, virtual screening was performed to find out the molecular interactions between 36 natural compounds derived from sesame and the Mpro of COVID-19. Four natural metabolites, namely, sesamin, sesaminol, sesamolin, and sesamolinol have been ranked as the top interacting molecules to Mpro based on the affinity of molecular docking. Moreover, stability of these four sesame-specific natural compounds has also been evaluated using molecular dynamics (MD) simulations for 200 nanoseconds. The molecular dynamics simulations and free energy calculations revealed that these compounds have stable and favorable energies, causing strong binding with Mpro. These screened natural metabolites also meet the essential conditions for drug likeness such as absorption, distribution, metabolism, and excretion (ADME) properties as well as Lipinski's rule of five. Our finding suggests that these screened natural compounds may be evolved as promising therapeutics against COVID-19.

Serological surveillance and clinical investigation of glanders among indigenous equines in India from 2015 to 2018

Abstract: Equine glanders is an infectious and notifiable bacterial disease caused by Burkholderia mallei. The disease has been reported in South American, African and Asian countries including India. Here, we present the outcome of glanders serosurveillance carried out between January 2015 and December 2018 to know the status of equine glanders among different states in India. A total of 102,071 equid sera from 299 districts of twenty-one states and one union territory were tested for glanders. Samples were screened with Hcp1 indirect ELISA followed by confirmatory diagnosis by CFT. During this four-year surveillance, a total of 932 glanders-positive cases were detected from 120 districts of 12 states. The study also revealed increasing trend of glanders from 2016 onwards with maximum occurrence in northern India. Overall seroprevalence ranged between 0.62% (95% CI, 0.52-0.72) and 1.145% (95% CI, 1.03-1.25). Seasonal shifting from winter to summer (March to June) coincided with highest number glanders incidence with corresponding seroprevalences of 1.2% (95% CI, 1.09-1.30). The present surveillance unveils territorial ingression of glanders to six states like Jammu & Kashmir, Gujarat, Rajasthan, Madhya Pradesh, Delhi and Tamil Nadu. In addition, re-emerging cases have been reported in Maharashtra, Haryana and Punjab after a gap of 10 years. Lack of awareness, little veterinary care and unrestricted movement of equids across state borders might have led to the introduction and establishment of the infection to these states. We believe that information from this study will provide a baseline data on glanders for devising surveillance and control strategies in India. Being a zoonotic disease, the persistence of glanders poses a potential threat to occupationally exposed humans especially equine handlers and veterinarians. Therefore, targeted surveillance of human population from each glanders outbreak is also recommended.