Simulation analysis of non-respondent information in context of small domain.

In the real-world, there are various situations when all units are not accessible of the respondent called unit non-response. The effect of unit non-response is a tricky matter for estimating the total number of unit. The present work highlights the interest about subpopulations (domains) in two affairs: i. if domains total of the supportive information is accessible ii. if domains total of the supportive variable does not access. The government needs to be introducing the actual facilities in these small domains. The supportive information is used to find out the estimate of the non respondent information and to apply this information for desired domains. Sometimes, it has been found that the accessible auxiliary variable for the domains might be positive shape. Therefore, it develops an appropriate model that has positive skewness. The present context highlighted the indirect method using a power-based estimation with calibration approach. By combining power based estimation and calibration technique, it is possible to obtain more accurate estimates for intended small domains. Even the supportive information is positively biased. This approach helps us in mitigating the effect of non-respondent and improving the overall reliability of the estimators. The simulation was conducted for different sizes 70 and 90 when nonresponse variable in the study variable. The results show that investigated power-based estimate provides better option over relevant exponential, ratio, and generalized regression estimators for intended domains.

Non-rhizobial nodule endophytes improve nodulation, change root exudation pattern and promote the growth of lentil, for prospective application in fallow soil.

Non-rhizobial endophytes (NREs) are active colonizers inhabiting the root nodules. Though their active role in the lentil agroecosystem is not well defined, here we observed that these NREs might promote the growth of lentils, modulate rhizospheric community structure and could be used as promising organisms for optimal use of rice fallow soil. NREs from root nodules of lentils were isolated and examined for plant growth-promoting traits, exopolysaccharide (EPS) and biofilm production, root metabolites, and the presence of nifH and nifK elements. The greenhouse experiment with the chosen NREs, i.e., Serratia plymuthica 33GS and Serratia sp. R6 significantly increased the germination rate, vigour index, development of nodules (in non-sterile soil) and fresh weight of nodules (33GS 94%, R6 61% growth) and length of the shoot (33GS 86%, R6 51.16%) as well as chlorophyll levels when compared to the uninoculated control. Scanning Electron Microscopy (SEM) revealed that both isolates could successfully colonize the roots and elicit root hair growth. The inoculation of the NREs resulted in specific changes in root exudation patterns. The plants with 33GS and R6 treatment significantly stimulated the exudation of triterpenes, fatty acids, and their methyl esters in comparison to the uninoculated plants, altering the rhizospheric microbial community structure. Proteobacteria dominated the rhizospheric microbiota in all the treatments. Treatment with 33GS or R6 also enhanced the relative abundance of other favourable microbes, including Rhizobium, Mesorhizobium, and Bradyrhizobium. The correlation network analysis of relative abundances resulted in numerous bacterial taxa, which were in cooperation with each other, having a possible role in plant growth promotion. The results indicate the significant role of NREs as plant growth promoters, which also includes their role in root exudation patterns, enhancement of soil nutrient status and modulation of rhizospheric microbiota, suggesting their prospects in sustainable, and bio-based agriculture.

Rectification-based Knowledge Retention for Task Incremental Learning.

In the task incremental learning problem, deep learning models suffer from catastrophic forgetting of previously seen classes/tasks as they are trained on new classes/tasks. This problem becomes even harder when some of the test classes do not belong to the training class set, i.e., the task incremental generalized zero-shot learning problem. We propose a novel approach to address the task incremental learning problem for both the non zero-shot and zero-shot settings. Our proposed approach, called Rectification-based Knowledge Retention (RKR), applies weight rectifications and affine transformations for adapting the model to any task. During testing, our approach can use the task label information (task-aware) to quickly adapt the network to that task. We also extend our approach to make it task-agnostic so that it can work even when the task label information is not available during testing. Specifically, given a continuum of test data, our approach predicts the task and quickly adapts the network to the predicted task. We experimentally show that our proposed approach achieves state-of-the-art results on several benchmark datasets for both non zero-shot and zero-shot task incremental learning.

Comparative Metagenomic Analysis of Two Alkaline Hot Springs of Madhya Pradesh, India and Deciphering the Extremophiles for Industrial Enzymes.

Hot springs are considered to be a unique environment with extremophiles, that are sources of industrially important enzymes, and other biotechnological products. The objective of this study was to undertake, analyze, and characterize the microbiome of two major hot springs located in the state of Madhya Pradesh explicitly, Chhoti Anhoni (Hotspring 1), and Badi Anhoni (Hotspring 2) to find out the inhabitant microbial population, and their functional characteristics. The taxonomic analysis of the microbiome of the hot springs revealed the phylum Proteobacteria was the most abundant taxa in both the hot-springs, however, its abundance in hot-spring 1 (~88%) was more than the hot-spring 2 (~52%). The phylum Bacteroides (~10-22%) was found to be the second most abundant group in the hot-springs followed by Spirocheates (~2-11%), Firmicutes (~6-8%), Chloroflexi (1-5%), etc. The functional analysis of the microbiome revealed different features related to several functions including metabolism of organics and degradation of xenobiotic compounds. The functional analysis showed that most of the attributes of the microbiome was related to metabolism, followed by cellular processes and environmental information processing functions. The functional annotation of the microbiomes at KEGG level 3 annotated the sequences into 279 active features that showed variation in abundance between the hot spring samples, where hot-spring 1 was functionally more diverse. Interestingly, the abundance of functional genes from methanogenic bacteria, was higher in the hot-spring 2, which may be related to the relatively higher pH and temperature than Hotspring 1. The study showed the presence of different unassigned bacterial taxa with high abundance which indicates the potential of novel genera or phylotypes. Culturable isolates (28) were bio-prospected for industrially important enzymes including amylase, protease, lipase, gelatinase, pectinase, cellulase, lecithinase, and xylanase. Seven isolates (25%) had shown positive results for all the enzyme activities whereas 23 isolates (82%) produced Protease, 27 isolates (96%) produced lipase, 27 isolates produced amylase, 26 isolates (92%) produced cellulase, 19 isolates (67%) produced pectinase, 19 isolates (67%) could produce lecithinase, and 13 isolates (46%) produced gelatinase. The seven isolates, positive for all the enzymes were analyzed further for quantitative analysis and identified through molecular characterization.

Effect of compression ratio, nozzle opening pressure, engine load, and butanol addition on nanoparticle emissions from a non-road diesel engine.

Currently, diesel engines are more preferred over gasoline engines due to their higher torque output and fuel economy. However, diesel engines confront major challenge of meeting the future stringent emission norms (especially soot particle emissions) while maintaining the same fuel economy. In this study, nanosize range soot particle emission characteristics of a stationary (non-road) diesel engine have been experimentally investigated. Experiments are conducted at a constant speed of 1500 rpm for three compression ratios and nozzle opening pressures at different engine loads. In-cylinder pressure history for 2000 consecutive engine cycles is recorded and averaged data is used for analysis of combustion characteristics. An electrical mobility-based fast particle sizer is used for analyzing particle size and mass distributions of engine exhaust particles at different test conditions. Soot particle distribution from 5 to 1000 nm was recorded. Results show that total particle concentration decreases with an increase in engine operating loads. Moreover, the addition of butanol in the diesel fuel leads to the reduction in soot particle concentration. Regression analysis was also conducted to derive a correlation between combustion parameters and particle number emissions for different compression ratios. Regression analysis shows a strong correlation between cylinder pressure-based combustion parameters and particle number emission.

Identification of common, unique and polymorphic microsatellites among 73 cyanobacterial genomes.

Microsatellites also known as Simple Sequence Repeats are short tandem repeats of 1-6 nucleotides. These repeats are found in coding as well as non-coding regions of both prokaryotic and eukaryotic genomes and play a significant role in the study of gene regulation, genetic mapping, DNA fingerprinting and evolutionary studies. The availability of 73 complete genome sequences of cyanobacteria enabled us to mine and statistically analyze microsatellites in these genomes. The cyanobacterial microsatellites identified through bioinformatics analysis were stored in a user-friendly database named CyanoSat, which is an efficient data representation and query system designed using ASP.net. The information in CyanoSat comprises of perfect, imperfect and compound microsatellites found in coding, non-coding and coding-non-coding regions. Moreover, it contains PCR primers with 200 nucleotides long flanking region. The mined cyanobacterial microsatellites can be freely accessed at www.compubio.in/CyanoSat/home.aspx. In addition to this 82 polymorphic, 13,866 unique and 2390 common microsatellites were also detected. These microsatellites will be useful in strain identification and genetic diversity studies of cyanobacteria.

Predicting growth conditions from internal metabolic fluxes in an in-silico model of E. coli.

A widely studied problem in systems biology is to predict bacterial phenotype from growth conditions, using mechanistic models such as flux balance analysis (FBA). However, the inverse prediction of growth conditions from phenotype is rarely considered. Here we develop a computational framework to carry out this inverse prediction on a computational model of bacterial metabolism. We use FBA to calculate bacterial phenotypes from growth conditions in E. coli, and then we assess how accurately we can predict the original growth conditions from the phenotypes. Prediction is carried out via regularized multinomial regression. Our analysis provides several important physiological and statistical insights. First, we show that by analyzing metabolic end products we can consistently predict growth conditions. Second, prediction is reliable even in the presence of small amounts of impurities. Third, flux through a relatively small number of reactions per growth source (∼10) is sufficient for accurate prediction. Fourth, combining the predictions from two separate models, one trained only on carbon sources and one only on nitrogen sources, performs better than models trained to perform joint prediction. Finally, that separate predictions perform better than a more sophisticated joint prediction scheme suggests that carbon and nitrogen utilization pathways, despite jointly affecting cellular growth, may be fairly decoupled in terms of their dependence on specific assortments of molecular precursors.

ChloroSSRdb: a repository of perfect and imperfect chloroplastic simple sequence repeats (cpSSRs) of green plants.

Simple sequence repeats (SSRs) are regions in DNA sequence that contain repeating motifs of length 1-6 nucleotides. These repeats are ubiquitously present and are found in both coding and non-coding regions of genome. A total of 534 complete chloroplast genome sequences (as on 18 September 2014) of Viridiplantae are available at NCBI organelle genome resource. It provides opportunity to mine these genomes for the detection of SSRs and store them in the form of a database. In an attempt to properly manage and retrieve chloroplastic SSRs, we designed ChloroSSRdb which is a relational database developed using SQL server 2008 and accessed through ASP.NET. It provides information of all the three types (perfect, imperfect and compound) of SSRs. At present, ChloroSSRdb contains 124 430 mined SSRs, with majority lying in non-coding region. Out of these, PCR primers were designed for 118 249 SSRs. Tetranucleotide repeats (47 079) were found to be the most frequent repeat type, whereas hexanucleotide repeats (6414) being the least abundant. Additionally, in each species statistical analyses were performed to calculate relative frequency, correlation coefficient and chi-square statistics of perfect and imperfect SSRs. In accordance with the growing interest in SSR studies, ChloroSSRdb will prove to be a useful resource in developing genetic markers, phylogenetic analysis, genetic mapping, etc. Moreover, it will serve as a ready reference for mined SSRs in available chloroplast genomes of green plants. Database URL: www.compubio.in/chlorossrdb/