Infinity Learning: Learning Markov Chains from Aggregate Steady-State Observations

We consider the task of learning a parametric Continuous Time Markov Chain (CTMC) sequence model without examples of sequences, where the training data consists entirely of aggregate steady-state statistics. Making the problem harder, we assume that the states we wish to predict are unobserved in the training data. Specifically, given a parametric model over the transition rates of a CTMC and some known transition rates, we wish to extrapolate its steady state distribution to states that are unobserved. A technical roadblock to learn a CTMC from its steady state has been that the chain rule to compute gradients will not work over the arbitrarily long sequences necessary to reach steady state ---from where the aggregate statistics are sampled. To overcome this optimization challenge, we propose $\infty$-SGD, a principled stochastic gradient descent method that uses randomly-stopped estimators to avoid infinite sums required by the steady state computation, while learning even when only a subset of the CTMC states can be observed. We apply $\infty$-SGD to a real-world testbed and synthetic experiments showcasing its accuracy, ability to extrapolate the steady state distribution to unobserved states under unobserved conditions (heavy loads, when training under light loads), and succeeding in difficult scenarios where even a tailor-made extension of existing methods fails

Uncovering Genomic Regions Associated With 36 Agro-Morphological Traits in Indian Spring Wheat Using GWAS

Wheat genetic improvement by integration of advanced genomic technologies is one way of improving productivity. To facilitate the breeding of economically important traits in wheat, SNP loci and underlying candidate genes associated with the 36 agro-morphological traits were studied in a diverse panel of 404 genotypes. By using Breeders’ 35K Axiom array in a comprehensive genome-wide association study covering 4364.79 cM of the wheat genome and applying a compressed mixed linear model, a total of 146 SNPs (-log10P ≥ 4) were found associated with 23 traits out of 36 traits studied explaining 3.7–47.0% of phenotypic variance. To reveal this a subset of 260 genotypes was characterized phenotypically for six quantitative traits [days to heading (DTH), days to maturity (DTM), plant height (PH), spike length (SL), awn length (Awn_L), and leaf length (Leaf_L)] under five environments. Gene annotations mined ∼38 putative candidate genes which were confirmed using tissue and stage specific gene expression data from RNA Seq. We observed strong co-localized loci for four traits (glume pubescence, SL, PH, and awn color) on chromosome 1B (24.64 cM) annotated five putative candidate genes. This study led to the discovery of hitherto unreported loci for some less explored traits (such as leaf sheath wax, awn attitude, and glume pubescence) besides the refined chromosomal regions of known loci associated with the traits. This study provides valuable information of the genetic loci and their potential genes underlying the traits such as awn characters which are being considered as important contributors toward yield enhancement

Molecular analysis of dimeric α-amylase inhibitor genes in Indian wheat

153-160Various enzymes inhibitors act on key gut digestive hydrolysis of insects, including α-amylase and proteinases. The α-amylase inhibitors have been widely investigated for their possible use in strengthening the plant defense against insects those are highly dependable on starch as an energy source. In the present study, dimeric α-amylase inhibitors (WDAIs) were isolated from three Indian bread wheat genotypes and their molecular diversities were characterized. The dimeric <i style="mso-bidi-font-style: normal">α-amylase inhibitors shared very high homology (95%) with other inhibitors available at GenBank database and had common conserved cysteine residues C-Xn-C-Xn-C-Xn-CC-Xn-C-X-C-Xn-C-Xn-C-Xn-C. The phylogentic analysis based on deduced amino acid sequences revealed that the genes encoding dimeric α-amylase inhibitors formed three groups and genes isolated from Indian bread wheat clustered with 0.19 inhibitor. In addition, three allele-specific markers were validated in diverse Indian bread wheat, durum, and wild species using genome allele-specific markers. The AS (allele specific)-PCR primers based on SNPs are valuable in wheat breeding for effective selection of genes for dimeric α-amylase inhibitors. Characterization of novel inhibitors of this family in wheat by specific primers based on SNPs could provide an explanation of the evolution of the multigene family encoding the dimeric α-amylase inhibitors

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Not AvailableIndiscriminate water use and declining water table in western Indo-Gangetic plains (IGP) is threatening sustainability of rice-wheat cropping system. Mini-sprinkler irrigation system (MSIS) in wheat can save considerable amount of irrigation water under conservation agriculture. A four-year field experiment was conducted to study the influence of mini-sprinkler irrigation system on water and nitrogen use efficiency and wheat yield under conservation agriculture. The influence of mini-sprinkler irrigation system in zero tilled wheat with 100% rice residue mulch (MSIS-ZT+RM) was compared with surface irrigation system (SIS) in zero tilled wheat with 100% rice residue mulch (SIS-ZT+RM), and farmers’ practice i.e. surface irrigation system in conventionally tilled wheat without residue (SIS-CT-WR). MSIS-ZT+RM saved 43.3 and 25% irrigation water, and 50% nitrogen, as compared to SIS-CT-WR and SIS-ZT+RM, respectively. Although, yield attributes and wheat grain yield in MSIS-ZT+RM was at par with SIS-CT-WR (farmers’ practice) but MSIS-ZT+RM recorded 1.8 and 2.0-times higher grain water productivity (GWP) and nitrogen use efficiency (NUE), respectively, than SIS-CT-WR. Considerable water saving, higher NUE with sustained yield suggests that mini-sprinkler irrigation system can be a viable option for ZT-wheat in the present scenario to counter declining water tableNot Availabl

Utilization of Grain Physical and Biochemical Traits to Predict Malting Quality of Barley (Hordeum vulgare L.) under Sub-Tropical Climate

Barley is the most popular raw material for malting, and recently, the demand for malt-based products has increased several folds in India and other South Asian countries. The barley growing season is peculiar in the sub-tropical plains region compared to European or Northern American conditions, characterized by a total crop duration of 130&ndash;145 days with a maximum grain filling duration of around only 35&ndash;40 days. A total of 19 barley genotypes were grown for three years to assess the comparative performance in relation to different quality traits, including grain physical traits and biochemical and malt quality parameters. Analysis of variance, Pearson correlation, and principal component analysis were performed to determine the correlation among different traits. The results showed significant genotypic variation among genotypes for individual grain and malt traits. Despite the shorter window for grain filling, several good malting genotypes have been developed for the sub-tropical climates. The genotypes DWRUB52, DWRB101, RD2849, DWRUB64, and DWRB91 were found suitable for malting. Based on correlation studies, a few grain parameters have been identified which can be used to predict the malting potential of a barley genotype. The hot water extract was found to be positively correlated with the grain test weight, thousand-grain weight, and malt friability but was negatively correlated with the husk content. Beta-glucan content varied from 3.4 to 6.1% (dwb); reducing the grain beta-glucan content and increasing the amylase could be priorities to address in future malt barley improvement programs under sub-tropical climatic conditions

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Not AvailableMicroRNA are 20–24 nt, non-coding, single stranded molecule regulating traits and stress response. Tissue and time specifc expression limits its detection, thus is major challenge in their discovery. Wheat has limited 119 miRNAs in MiRBase due to limitation of conservation based methodology where old and new miRNA genes gets excluded. This is due to origin of hexaploid wheat by three successive hybridization, older AA, BB and younger DD subgenome. Species specifc miRNA prediction (SMIRP concept) based on 152 thermodynamic features of training dataset using support vector machine learning approach has improved prediction accuracy to 97.7%. This has been implemented in TamiRPred (http://webtom.cabgrid.res.in/tamirpred). We also report highest number of putative miRNA genes (4464) of wheat from whole genome sequence populated in database developed in PHP and MySQL. TamiRPred has predicted 2092 (>45.10%) additional miRNA which was not predicted by miRLocator. Predicted miRNAs have been validated by miRBase, small RNA libraries, secondary structure, degradome dataset, star miRNA and binding sites in wheat coding region. This tool can accelerate miRNA polymorphism discovery to be used in wheat trait improvement. Since it predicts chromosomewise miRNA genes with their respective physical location thus can be transferred using linked SSR markers. This prediction approach can be used as model even in other polyploid crops.Not Availabl

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Not AvailableDrought is one of the major impediments in wheat productivity. Traditional breeding and marker assisted QTL introgression had limited success. Available wheat genomic and RNA-seq data can decipher novel drought tolerance mechanisms with putative candidate gene and marker discovery. Drought is first sensed by root tissue but limited information is available about how roots respond to drought stress. In this view, two contrasting genotypes, namely, NI5439 41 (drought tolerant) and WL711 (drought susceptible) were used to generate ~78.2 GB data for the responses of wheat roots to drought. A total of 45139 DEGs, 13820 TF, 288 miRNAs, 640 pathways and 435829 putative markers were obtained. Study reveals use of such data in QTL to QTN refinement by analysis on two model drought-responsive QTLs on chromosome 3B in wheat roots possessing 18 differentially regulated genes with 190 sequence variants (173 SNPs and 17 InDels). Gene regulatory networks showed 69 hub-genes integrating ABA dependent and independent pathways controlling sensing of drought, root growth, uptake regulation, purine metabolism, thiamine metabolism and antibiotics pathways, stomatal closure and senescence. Eleven SSR markers were validated in a panel of 18 diverse wheat varieties. For effective future use of findings, web genomic resources were developed. We report RNA-Seq approach on wheat roots describing the drought response mechanisms under field drought conditions along with genomic resources, warranted in endeavour of wheat productivity.Not Availabl