Ultrafine aluminium: Quench collection of agglomerates

Combustion of aluminized solid propellants exhibits phenomena associated with accumulation, agglomeration, ignition, and combustion of ultra-fine aluminium particles. In this study, agglomeration phenomenon of ultra-fine aluminium in solid propellant combustion is investigated using quench collection experimental technique over the pressure ranges from 2MPa to 8MPa. The ultra-fine aluminium powder synthesized by Radio Frequency Induction Plasma technique having harmonic mean size of 438nm is used for agglomeration study. The quenching distance is varied from 5mm to 71mm from the propellant burning surface to estimate the effect on agglomerate size. The morphology and chemical compositions of the collected products were then studied by using scanning electron microscopy coupled with energy dispersive (SEM-EDS) method. Under the explored experimental conditions, the results confirm that ultra-fine aluminium propellant show aggregation/agglomeration with the size ranging from 11 – 21 μm in combustion products. Smaller diameter condensed phase products will likely decrease two-phase flow loss and reduce slag accumulation

Application of artificial neural networks for the prediction of aluminium agglomeration processes

Aluminium is universal and vital constituent in composite propellants and typically used to improve performance. Aluminum agglomeration takes place on the burning surface of aluminized propellants, which leads to reduced combustion efficiency and 2P flow losses. To understand the processes and behaviour of aluminum agglomeration, particles size distribution of composite propellants were studied using a quench particle collection technique, at 2 to 8 MPa and varying quench distances from 5mm to 71mm. To predict the agglomerate diameter of metallized/ultra-fine aluminium of composite propellants, a new artificial neural network (ANN) model was derived. Five Layered Feed Forward Back Propagation Neural Network was developed with sigmoid as a transfer function by varying feed variables in input layer such as Quench distance (QD) and pressure (P). The ANN design was trained victimization stopping criterion as one thousand iterations. Five ANN models dealing with the combustion of AP/Al/HTPB and one ANN model of AP/UFAl/HTPB composite propellants were presented. The validated ANN models will be able to predict unexplored regimes wherein experimental data are not available. From the present work it was ascertained that, for agglomeration produced by quench collection technique, the ANN is one of a substitute method to predict the agglomerate diameter and results can be evaluated rather than experimented, with reduced time and cost. The resulting agglomerates sizes from ANN model, matches with the experimental results. The percentage error is less than 3.0% of the label propellants used in this work

An integrated modelling framework for neural circuits with multiple neuromodulators

Neuromodulators are endogenous neurochemicals that regulate biophysical and biochemical processes, which control brain function and behaviour, and are often the targets of neuropharmacological drugs. Neuromodulator effects are generally complex partly owing to the involvement of broad innervation, co-release of neuromodulators, complex intra- and extrasynaptic mechanism, existence of multiple receptor subtypes and high interconnectivity within the brain. In this work, we propose an efficient yet sufficiently realistic computational neural modelling framework to study some of these complex behaviours. Specifically, we propose a novel dynamical neural circuit model that integrates the effective neuromodulator-induced currents based on various experimental data (e.g. electrophysiology, neuropharmacology and voltammetry). The model can incorporate multiple interacting brain regions, including neuromodulator sources, simulate efficiently and easily extendable to largescale brain models, e.g. for neuroimaging purposes. As an example, we model a network of mutually interacting neural populations in the lateral hypothalamus, dorsal raphe nucleus and locus coeruleus, which are major sources of neuromodulator orexin/hypocretin, serotonin and norepinephrine/noradrenaline, respectively, and which play significant roles in regulating many physiological functions. We demonstrate that such a model can provide predictions of systemic drug effects of the popular antidepressants (e.g. reuptake inhibitors), neuromodulator antagonists or their combinations. Finally, we developed user-friendly graphical user interface software for model simulation and visualization for both fundamental sciences and pharmacological studies

Investigations on prevalence of aflatoxin contamination in major groundnut growing states of India, influence of soil characteristics and farmers’ level of awareness

Food safety issues are of major concern in groundnut due to aflatoxin contamination by Aspergillus flavus. Monitoring aflatoxin prevalence and understanding the factors responsible can provide useful information for devising effective management strategies. The present study focused on mapping the pre-harvest aflatoxin contamination in India along with its determining factors. A comprehensive survey was undertaken during 2012-2014 in four major groundnut growing States such as Andhra Pradesh, Gujarat, Karnataka, and Tamil Nadu. Pod (n=2434) and rhizospheric soil samples (n=1322) were collected to ascertain A. flavus populations and pre-harvest aflatoxin contamination. Further, kernel aflatoxin levels were correlated with soil organic carbon, available calcium and pH levels in the fields from where the samples were collected. Farmers’ awareness on aflatoxin problem was also determined using a semi-structured questionnaire. Our results indicate wide variations in the occurrence of pre-harvest aflatoxin contamination levels of kernels among different States (0 - 5486 ppb) and samples within States. Detectable levels of aflatoxins (>1ppb) were highest in Karnataka (70.5%), whereas it was lowest in Andhra Pradesh (32.9%). Correlation studies revealed that aflatoxin contents were positively associated with soil pH (r = 0.54-0.99) and A. flavus populations (r = 0.63 in Gujarat; r = 0.75 in Karnataka) whereas soil organic carbon and available calcium were negatively correlated with toxin levels in kernels (r = -0.99). Farmers’ awareness was considerably poor in all the States under survey. Overall, our results suggest the prevalence of aflatoxin contamination in major groundnut growing areas in India, and influence of certain edaphic factors

Prevalence of groundnut dry root rot (Macrophomina phaseolina (Tassi) Goid.) and its pathogenic variability in Southern India

Macrophomina phaseolina is the most devastating and emerging threat to groundnut production in India. An increase in average temperature and inconsistent rainfalls resulting from changing climatic conditions are strongly believed to aggravate the disease and cause severe yield losses. The present study aims to conduct a holistic survey to assess the prevalence and incidence of dry root rot of groundnut in major groundnut growing regions of Southern India, viz., Andhra Pradesh, Telangana, Karnataka, and Tamil Nadu. Furthermore, the pathogenic variability was determined using different assays such as morphological, cultural, pathogenic, and molecular assays. Results indicate that disease incidence in surveyed locations ranged from 8.06 to 20.61%. Both temperature and rainfall played a major role in increasing the disease incidence. The pathogenic variability of M. phaseolina isolates differed significantly, based on the percent disease incidence induced on cultivars of JL-24 groundnut and K-6 groundnut. Morphological variations in terms of growth pattern, culture color, sclerotia number, and sclerotia size were observed. The molecular characterization of M. phaseolina isolates done by ITS rDNA region using ITS1 and ITS4 primers yielded approximately 600 bp PCR amplicons, sequenced and deposited in GenBank (NCBI). Molecular variability analysis using SSR primers indicated the genetic variation among the isolates collected from different states. The present investigation revealed significant variations in pathogenic variability among isolates of M. phaseolina and these may be considered important in disease management and the development of resistant cultivars against groundnut dry root rot disease

G × E interactions in QTL introgression lines of Spanish-type groundnut (Arachis hypogaea L.)

Multi-environment testing at five locations for rust and late leaf spot (LLS) resistance with 41 introgressed lines (ILs) bred using marker-assisted backcross breeding in the genetic background Spanish- type groundnut varieties identified significant genotype, and genotype 9 environment interactions (GEI) for LLS disease resistance and yield parameters. Significant GEI effects suggest the need to identify location specific breeding lines to achieve gains in pod yield and LLS resistance. The observed variable LLS disease reaction among the ILs in part suggests influence of background genotype on the level of resistance. A breeding scheme with early generation selection using molecular markers followed by phenotyping for LLS, and multi-location testing of fixed breeding lines was optimized to enhance selection intensity and accuracy in groundnut breeding. The ILs, ICGVs 14431, 14436 and 14438 with pooled LLS score at 90 DAS of 3.5–3.7 were superior to respective recurrent parent for pod yield, with early maturing similar to recurrent parents. The pod yield advantage in ILs is attributed by more number of pods, besides resistance to LLS that contributes to better filling

Molecular breeding tools improved drought tolerant groundnut variety for resistance to foliar fungal diseases

A largely rainfed crop in India, drought tolerance, particularly mid- and end-season tolerance, is a key trait in groundnut varieties. A combination of both empirical and trait-based approaches was used in breeding programs of ICAR and ICRISAT, resulting in release of few tolerant varieties that have superior pod yield under drought stress and/or have enhanced water-use-efficiency. There is a need to breed varieties with drought tolerance, disease resistance and quality traits that suit different production ecologies as well as meet the needs of the farmers, consumers and industries. ICRISAT has released an early-maturing (90-95 d) and drought- tolerant variety ICGV 91114 for the drought-prone Ananthapur district of Andhra Pradesh, India, where about 0.7 m ha area is under groundnut cultivation and has low (300 mm) and erratic (30-40 rainy days) rainfall. On-farm studies conducted with ICGV 91114 during 2008-10 showed 30% reduction in yield variability over the years. Following screening in hot-spots of both rust and LLS disease during 2014 rainy season, a total of 27 introgression lines derived from ICGV 91114 were selected and advanced for evaluation in multi-location trials at six locations in 2015 under rainfed conditions. Based on the pod yield under rainfed conditions and disease resistance, three superior introgression lines (ICGV 14410, ICGV 13189, ICGV 14421) were proposed for the first-ever NILs trial (near-isogenic lines trial) along with eight others conducted under All India Coordinated Research Project on Groundnut (AICRP-G) at national level

Virtual pathway explorer (viPEr) and pathway enrichment analysis tool (PEANuT): creating and analyzing focus networks to identify cross-talk between molecules and pathways

BACKGROUND: Interpreting large-scale studies from microarrays or next-generation sequencing for further experimental testing remains one of the major challenges in quantitative biology. Combining expression with physical or genetic interaction data has already been successfully applied to enhance knowledge from all types of high-throughput studies. Yet, toolboxes for navigating and understanding even small gene or protein networks are poorly developed. RESULTS: We introduce two Cytoscape plug-ins, which support the generation and interpretation of experiment-based interaction networks. The virtual pathway explorer viPEr creates so-called focus networks by joining a list of experimentally determined genes with the interactome of a specific organism. viPEr calculates all paths between two or more user-selected nodes, or explores the neighborhood of a single selected node. Numerical values from expression studies assigned to the nodes serve to score identified paths. The pathway enrichment analysis tool PEANuT annotates networks with pathway information from various sources and calculates enriched pathways between a focus and a background network. Using time series expression data of atorvastatin treated primary hepatocytes from six patients, we demonstrate the handling and applicability of viPEr and PEANuT. Based on our investigations using viPEr and PEANuT, we suggest a role of the FoxA1/A2/A3 transcriptional network in the cellular response to atorvastatin treatment. Moreover, we find an enrichment of metabolic and cancer pathways in the Fox transcriptional network and demonstrate a patient-specific reaction to the drug. CONCLUSIONS: The Cytoscape plug-in viPEr integrates –omics data with interactome data. It supports the interpretation and navigation of large-scale datasets by creating focus networks, facilitating mechanistic predictions from –omics studies. PEANuT provides an up-front method to identify underlying biological principles by calculating enriched pathways in focus networks. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12864-015-2017-z) contains supplementary material, which is available to authorized users

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Not AvailableSeveral virus diseases of groundnut have been reported in India based on symptoms, host range, and biological properties. Among those, peanut bud necrosis virus (PBNV), tobacco streak virus (TSV), peanut mottle virus (PeMoV), and Indian peanut clump virus (IPCV) are the economically important viruses of groundnut in India. Peanut bud necrosis virus belongs to the genus Tospovirus, transmitted effectively by Thrips palmi. PBND alone may cause 30–90 % yield losses. Necrosis of the terminal buds occurs which is a characteristic symptom of PBNV. Extensive field screening of the several genotypes, released varieties, and wild species at the hot spots has revealed the field tolerance of some of those genotypes. Peanut stem necrosis disease (PSND) is caused by the TSV of the genus Ilarvirus of the family Bromoviridae. Necrotic lesions on terminal leaflets, complete stem necrosis, and often total necrosis of entire plant are the characteristic symptoms of this disease. The PSND spreads mainly through the weed of crop species. A desired level of resistance of TSV has not yet been found in cultivated varieties of groundnut. The peanut clump disease of groundnut in India is caused by the IPCV of the genus Pecluvirus, family Virgaviridae. Symptoms are severe stunting of the plant appeared first on newly emerged leaves of two- to three-week-old seedlings. The host range of IPCV includes many monocot and dicot crop plants and weed species tested. IPCV was reported to be transmitted by the obligate fungal parasite (Polymyxa graminis) which is soilborne. Germplasm accessions, viz., NCAc 17099, NCAc 17133 (RF), and NCAc 17536, have been reported resistant to IPCV. Peanut mottle virus disease has been reported to occur on rabi/summer groundnut mainly in Andhra Pradesh, Maharashtra, and Gujarat. Newly formed leaves show mild mottling and vein clearing, whereas older leaves show upward curling and interveinal depression with dark green islands. The peanut mottle virus (PeMoV) occurs in nature on several important legume crops. Aphids are efficient vectors of PeMoV. Several lines of Arachis species like A. glabrata are reported to be resistant to this disease. Peanut stripe virus (PStV) is of quarantine significance to India and is almost eradicated from India. Since in most of the viral diseases sources with desired levels of genetic resistance could not be identified so far, transgenic approaches to engineer resistance to viruses by expressing the glycoproteins of tospoviruses in transgenic plants to block virus acquisition by thrips, by expressing truncated or modified forms of movement protein(s) of heterologous viruses, or by expressing virus-specific antibody genes may be adopted to tackle the viral diseases in groundnut.Not Availabl

Elucidating Late Leaf Spot Disease Progression and Resistance Components in Different Groundnut (Arachis hypogaea L.) Cultivars towards Phaeoisariopsis personata

Late Leaf Spot (LLS) disease is caused by Phaeoisariopsis personata, a devastating disease that significantly affects groundnut (Arachis hypogaea L.) production worldwide. This research aimed to investigate the disease progress in resistant and susceptible groundnut genotypes under green house conditions with artificial inoculations. The four popularly growing groundnut cultivars in Andhra Pradesh i.e., Kadiri-6 (K6), Dharani, Harithandra and Lepakshi (K-1812) were evaluated in this study. The disease severity scale, percent disease index (PDI), Area under disease progression curve (AUDPC) and the epidemic rate (rate) were estimated by using the linear model. Highest disease severity was observed in K6 (88.19 %) and Dharani (85.19 %) with greater disease progression rate was observed in K6 (0.13) and Dharani (0.12) cultivars. However, the lowest disease progression was observed in Lepakshi (0.04) and Harithandhra (0.08) cultivars. Enhanced resistance to LLS was reported with Harithandhra and Lepakshi cultivars due to slower epidemic rate, longer incubation and latent periods with smaller lesions