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.

A feature selection enabled hybrid-bagging algorithm for credit risk evaluation

Abstract: Hybrid models based on feature selection and machine learning techniques have significantly enhanced the accuracy of standalone models This paper presents a feature selection-based hybrid-bagging algorithm (FS-HB) for improved credit risk evaluation The 2 feature selection methods chi-square and principal component analysis were used for ranking and selecting the important features from the datasets The classifiers were built on 5 training and test data partitions of the input data set The performance of the hybrid algorithm was compared with that of the standalone classifiers: feature selection-based classifiers and bagging The hybrid FS-HB algorithm performed best for qualitative dataset with less features and tree-based unstable base classifier Its performance on numeric data was also better than other standalone classifiers, whereas comparable to bagging with only selected features Its performance was found better on 70:30 data partition and the type II error, which is very significant in risk evaluation was also reduced significantly The improved performance of FS-HB is attributed to the important features used for developing the classifier thereby reducing the complexity of the algorithm and the use of ensemble methodology, which added to the classical bias variance trade-off and performed better than standalone classifiers

Identification of Putative RuBisCo Activase (TaRca1)-The Catalytic Chaperone Regulating Carbon Assimilatory Pathway in Wheat (Triticum aestivum) under the Heat Stress.

Abstract: RuBisCo activase (Rca) is a catalytic chaperone involved in modulating the activity of RuBisCo (key enzyme of photosynthetic pathway). Here, we identified eight novel transcripts from wheat through data mining predicted to be Rca and cloned a transcript of 1.4 kb from cv. HD2985, named as TaRca1 (GenBank acc. no. KC776912). Single copy number of TaRca1 was observed in wheat genome. Expression analysis in diverse wheat genotypes (HD2985, Halna, PBW621 and HD2329) showed very high relative expression of TaRca1 in Halna under control and HS-treated, as compared to other cultivars at different stages of growth. TaRca1 protein was predicted to be chloroplast-localized with numerous potential phosphorylation sites. Nothern blot analysis showed maximum accumulation of TaRca1 transcript in thermotolerant cv. during mealy-ripe stage, as compared to thermosusceptible. Decrease in the photosynthetic parameters was observed in all the cultivars, except PBW621 in response to HS. We observed significant increase in the Rca activity in all the cultivars under HS at different stages of growth. HS causes decrease in the RuBisCo activity; maximum reduction was observed during pollination stage in thermosusceptible cvs. as validated through immunoblotting. We observed uniform carbon distribution in different tissues of thermotolerant cvs., as compared to thermosusceptible. Similarly, tolerance level of leaf was observed maximum in Halna having high Rca activity under HS. A positive correlation was observed between the transcript and activity of TaRca1 in HS-treated Halna. Similarly, TaRca1 enzyme showed positive correlation with the activity of RuBisCo. There is, however, need to manipulate the thermal stability of TaRca1 enzyme through protein engineering for sustaining the photosynthetic rate under HS – a novel approach towards development of ‘climate-smart’ crop.

Identification of a diverse mini‐core panel of Indian rice germplasm based on genotyping using microsatellite markers

Abstract: Identification of a small core germplasm set representing the available genetic diversity is essential for its proper evaluation and subsequent utilization in rice improvement programmes. For constituting a small diverse mini-core panel of Indian rice germplasm, a representative set of 6912 accessions drawn based on their geographic origin from the whole rice germplasm collection available in the National Gene Bank was genotyped using 36 microsatellite markers. Automated fragment analysis of amplicons yielded a total of 435 alleles, with an average 12.4 and range of 3–29 alleles per locus. Polymorphism information content (PIC) ranged from 0.08 (RGNMS190) to 0.86 (RM552) with an average of 0.528. Based on genotyping data, a mini-core consisting of 98 genotypes was identified. Ninety-four per cent of the alleles present in the core set were present in the mini-core. The identified small but diverse panel will be useful for further intensive trait-specific evaluation and utilization in allele mining.

Metagenomic Analysis Reveals Bacterial and Fungal Diversity and Their Bioremediation Potential From Sediments of River Ganga and Yamuna in India.

Abstract: In this study, we report the presence of a microbial community of bioremediation potential in terms of relative abundance and taxonomic biodiversity in sediment samples of river Ganga and Yamuna, India at nine different sites. Metagenomic libraries were constructed using TruSeq Nano DNA Library Prep Kit and sequenced on NextSeq 500 by Illumina Next Generation Sequencing (NGS) technology. Bioremediation bacteria belong to 45 genera with 92 species and fungi belong to 13 genera with 24 species have been classified using Kaiju taxonomical classification. The study revealed that Proteobacteria was the most dominant bacterial flora, followed by Actinobacteria, Firmicutes, and Deinococcus-Thermus. PCA analysis revealed that bioremediation bacteria viz. Streptomyces bikiniensis, Rhodococcus qingshengii, Bacillus aerophilus, Pseudomonas veronii, etc., were more dominant in highly polluted river stretch as compared to less polluted river stretch. Similarly, the relative abundance of bioremediation fungi viz. Phanerochaete chrysosporium and Rhizopus oryzae, etc., were significantly correlated with the polluted Kanpur stretch of river Ganga. Several protein domains, which play a pivotal role in bioremediation in the polluted environments, including urea ABC transporter, UrtA, UrtD, UrtE, zinc/cadmium/mercury/lead-transporting ATPase, etc., were identified using protein domain analysis. The protein domains involved in pesticide biodegradation viz. P450, short-chain dehydrogenases/reductases (SDR), etc., were also discovered in river sediment metagenomics data. This is the first report on the richness of bioremediation microbial communities in the Ganga and Yamuna riverine ecosystems, highlighting their importance in aquatic pollution management.