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.

Chemometric approach based characterization and selection of mid-early cauliflower for bioactive compounds and antioxidant activity.

Abstract: The present study was aimed to analyse bioactive compounds (total phenolics, ascorbic acid and sinigrin) and antioxidant activity in 14 mid-early cauliflower genotypes. Significant differences (pb 0.05) were observed among the genotypes for all bioactive compounds and antioxidant activity. Total phenolics content of curd were ranged from 20.36 to 48.93 mg gallic acid equivalent (GAE) 100 g−1 fresh weight (FW) which showed 2.5 times variation. The ascorbic acid content was maximum in DC522 (88.53 mg 100 g−1 FW) followed by Pusa Sharad (65.64 mg 100 g−1 FW) while minimum in DC310 (39.62 65.64 mg 100 g−1 FW). Wide variation was observed for cupric reducing antioxidant capacity and ferric reducing antioxidant power ranging from 9.04 to 20.83 mg GAE 100 g−1 FW and 13.11 to 26.31 mg GAE 100 g−1 FW, respectively. Sinigrin was found to be highest in DC306 (39.50 µmol 100 g−1 FW) for leaf and in DC326 (36.93 µmol 100 g−1 FW) for curd sample. The cauliflower genotypes were classified based on chemometric approaches namely principal component analysis (PCA) and agglomerative hierarchical clustering (AHC). The first two principal components (PC1 and PC2) explained 50.62% and 23.28% of total variance, respectively. The AHC as revealed by heat map classified cauliflower genotypes into four main groups based on measured traits. The information is useful for developing varieties and/or hybrids rich in bioactive compounds and antioxidant activity.

Do elevated CO2 and temperature affect organic nitrogen fractions and enzyme activities in soil under rice crop

Abstract: A study was conducted to quantify the effect of elevated carbon dioxide (CO2) and temperature on soil organic nitrogen (N) fractions and enzyme activities in rice rhizosphere. Rice crop was grown inside the open top chambers in the ICAR-Indian Agricultural Research Institute. The N was applied in four different doses. Grain yield and aboveground N uptake by rice significantly reduced under elevated temperature. However, elevated CO2 along with elevated temperature was able to compensate this loss. Principal component analysis clearly indicated that microbial biomass carbon, microbial biomass N, amino acid N, total hydrolysable N, ammonia N and serine–threonine N contributed significantly to rice grain yield. Combined effect of elevated CO2 and elevated temperature decreased the total hydrolysable N, especially for lower N doses. The N-acetyl-glucosaminidase and leucine aminopeptidase enzyme activities were negatively correlated with the organic N pools. Higher activities of these enzymes under limited N supply may accelerate the decomposition of organic N in soil. When N was applied in super-optimal dose, plant N demand was met thereby causing lesser depletion of total hydrolysable N. Better nitrogen management will alleviate faster depletion of native soil N under future scenario of climate change and thus might cause N sequestration in soil.

Functional response of Geocoris ochropterus Fieber (Hemiptera: Geocoridae) to different egg densities of Helicoverpa armigera (Hübner) (Lepidoptera: Noctuidae)

Abstract: The biology and predatory efficiency of Geocoris ochropterus Fieber (Hemiptera: Geocoridae) has been studied extensively against many insect pests, but information on functional response of different stages of G. ochropterus to Helicoverpa armigera (Hubner) (Lepidoptera: Noctuidae) is lacking so far. The functional response of different stages of G. ochropterus to varying densities of eggs of H. armigera was investigated in the laboratory at 26 ± 2°C, 65 ± 2% RH and 12 L: 12D. Immature stages i.e. 3rd, 4th, 5th instar and adult predator exhibited type II functional response. Handling time decreased with increasing predator’s developmental stage. Adult G. ochropterus followed by fifth instar exhibited highest egg consumption and attack rate compared to other stages. Functional response parameters indicate that the adult and fifth instar of G. ochropterus were more voracious and efficient than other juveniles and might be useful as promising biocontrol agent against H. armigera.

Second-Order Response Surface Model with Neighbor Effects

Abstract: This article considers the second-order response surface model in which the experimental units, i.e., plots experience the neighbor effects from immediate left and right neighboring plots assuming the plots to be placed adjacent linearly with no gaps. Conditions have been derived for the estimation of coefficients of second-order response surface model. A method of constructing designs for fitting second-order response surface in the presence of neighbor effects has been developed. The designs so obtained are found to be rotatable.

mLoc-mRNA: predicting multiple sub-cellular localization of mRNAs using random forest algorithm coupled with feature selection via elastic net.

Abstract: Localization of messenger RNAs (mRNAs) plays a crucial role in the growth and development of cells. Particularly, it plays a major role in regulating spatio-temporal gene expression. The in situ hybridization is a promising experimental technique used to determine the localization of mRNAs but it is costly and laborious. It is also a known fact that a single mRNA can be present in more than one location, whereas the existing computational tools are capable of predicting only a single location for such mRNAs. Thus, the development of high-end computational tool is required for reliable and timely prediction of multiple subcellular locations of mRNAs. Hence, we develop the present computational model to predict the multiple localizations of mRNAs. The mRNA sequences from 9 different localizations were considered. Each sequence was first transformed to a numeric feature vector of size 5460, based on the k-mer features of sizes 1–6. Out of 5460 k-mer features, 1812 important features were selected by the Elastic Net statistical model. The Random Forest supervised learning algorithm was then employed for predicting the localizations with the selected features. Five-fold cross-validation accuracies of 70.87, 68.32, 68.36, 68.79, 96.46, 73.44, 70.94, 97.42 and 71.77% were obtained for the cytoplasm, cytosol, endoplasmic reticulum, exosome, mitochondrion, nucleus, pseudopodium, posterior and ribosome respectively. With an independent test set, accuracies of 65.33, 73.37, 75.86, 72.99, 94.26, 70.91, 65.53, 93.60 and 73.45% were obtained for the respective localizations. The developed approach also achieved higher accuracies than the existing localization prediction tools. This study presents a novel computational tool for predicting the multiple localization of mRNAs. Based on the proposed approach, an online prediction server “mLoc-mRNA” is accessible at http://cabgrid.res.in:8080/mlocmrna/ . The developed approach is believed to supplement the existing tools and techniques for the localization prediction of mRNAs.