Extension of ASTEC-Na capabilities for simulating reactivity effects in Sodium Cooled Fast Reactor

The EU-JASMIN project (7th FP of EURATOM) has been centred on the development and validation of the new severe accident analysis code ASTEC-Na (Accident Source Term Evaluation Code) for Sodium-cooled Fast Reactors (SFR). The development of such computational tool being able to assist safety analysis of innovative reactor concepts is of crucial importance. One of the challenging issues when modelling SFRs is the neutronic reactivity feedbacks. This paper presents the model implemented in ASTEC-Na for representing the reactivity effects in SFR as well as the benchmarking results of a ULOF transient against SAS-SFR code results. It has been verified that the models are correctly implemented and that ASTEC-Na is now able to calculate reactivity feedbacks not only in the sodium single phase, but also after boiling onset and fuel in-pin relocation

BMC Nephrol

BACKGROUND: To describe the quality of life of adolescents initiating haemodialysis, to determine the factors associated with quality of life, and to assess coping strategies and their impact on quality of life. METHODS: All adolescents initiating haemodialysis between September 2013 and July 2015 in French paediatric haemodialysis centres were included. Quality of life data were collected using the "Vecu et Sante Percue de l'Adolescent et l'Enfant" questionnaire, and coping data were collected using the Kidcope questionnaire. Adolescent's quality of life was compared with age- and sex-matched French control. RESULTS: Thirty-two adolescents were included. Their mean age was 13.9 +/- 2.0 years. The quality of life score was lowest in leisure activities and highest in relationships with medical staff. Compared with the French control, index, energy-vitality, relationships with friends, leisure activities and physical well-being scores were significantly lower in haemodialysis population. In multivariate analyses, active coping was positively associated with quality of life and especially with energy-vitality, relationships with parents and teachers, and school performance. In contrast, avoidant and negative coping were negatively associated with energy-vitality, psychological well-being and body image for avoidant coping, and body image and relationships with medical staff for negative coping. CONCLUSIONS: The quality of life of haemodialysis adolescents, and mainly the dimensions of leisure activities, physical well-being, relationships with friends and energy-vitality, were significantly altered compared to that of the French population. The impact of coping strategies on quality of life seems to be important. Given the importance of quality of life and coping strategies in adolescents with chronic disease, health care professionals should integrate these aspects into care management

State-of-the art data normalization methods improve NMR-based metabolomic analysis

Extracting biomedical information from large metabolomic datasets by multivariate data analysis is of considerable complexity. Common challenges include among others screening for differentially produced metabolites, estimation of fold changes, and sample classification. Prior to these analysis steps, it is important to minimize contributions from unwanted biases and experimental variance. This is the goal of data preprocessing. In this work, different data normalization methods were compared systematically employing two different datasets generated by means of nuclear magnetic resonance (NMR) spectroscopy. To this end, two different types of normalization methods were used, one aiming to remove unwanted sample-to-sample variation while the other adjusts the variance of the different metabolites by variable scaling and variance stabilization methods. The impact of all methods tested on sample classification was evaluated on urinary NMR fingerprints obtained from healthy volunteers and patients suffering from autosomal polycystic kidney disease (ADPKD). Performance in terms of screening for differentially produced metabolites was investigated on a dataset following a Latin-square design, where varied amounts of 8 different metabolites were spiked into a human urine matrix while keeping the total spike-in amount constant. In addition, specific tests were conducted to systematically investigate the influence of the different preprocessing methods on the structure of the analyzed data. In conclusion, preprocessing methods originally developed for DNA microarray analysis, in particular, Quantile and Cubic-Spline Normalization, performed best in reducing bias, accurately detecting fold changes, and classifying samples

Development and Application of Ultra-Performance Liquid Chromatography-TOF MS for Precision Large Scale Urinary Metabolic Phenotyping

To better understand the molecular mechanisms underpinning physiological variation in human populations, metabolic phenotyping approaches are increasingly being applied to studies involving hundreds and thousands of biofluid samples. Hyphenated ultra-performance liquid chromatography-mass spectrometry (UPLC-MS) has become a fundamental tool for this purpose. However, the seemingly inevitable need to analyze large studies in multiple analytical batches for UPLC-MS analysis poses a challenge to data quality which has been recognized in the field. Herein, we describe in detail a fit-for-purpose UPLC-MS platform, method set, and sample analysis workflow, capable of sustained analysis on an industrial scale and allowing batch-free operation for large studies. Using complementary reversed-phase chromatography (RPC) and hydrophilic interaction liquid chromatography (HILIC) together with high resolution orthogonal acceleration time-of-flight mass spectrometry (oaTOF-MS), exceptional measurement precision is exemplified with independent epidemiological sample sets of approximately 650 and 1000 participant samples. Evaluation of molecular reference targets in repeated injections of pooled quality control (QC) samples distributed throughout each experiment demonstrates a mean retention time relative standard deviation (RSD) of <0.3% across all assays in both studies and a mean peak area RSD of <15% in the raw data. To more globally assess the quality of the profiling data, untargeted feature extraction was performed followed by data filtration according to feature intensity response to QC sample dilution. Analysis of the remaining features within the repeated QC sample measurements demonstrated median peak area RSD values of <20% for the RPC assays and <25% for the HILIC assays. These values represent the quality of the raw data, as no normalization or feature-specific intensity correction was applied. While the data in each experiment was acquired in a single continuous batch, instances of minor time-dependent intensity drift were observed, highlighting the utility of data correction techniques despite reducing the dependency on them for generating high quality data. These results demonstrate that the platform and methodology presented herein is fit-for-use in large scale metabolic phenotyping studies, challenging the assertion that such screening is inherently limited by batch effects. Details of the pipeline used to generate high quality raw data and mitigate the need for batch correction are provided

Effect of pre- and postnatal growth and post-weaning activity on glucose metabolism in the offspring

Maternal caloric restriction during late gestation reduces birth weight, but whether long-term adverse metabolic outcomes of intra-uterine growth retardation (IUGR) are dependent on either accelerated postnatal growth or exposure to an obesogenic environment after weaning is not established. We induced IUGR in twin-pregnant sheep using a 40% maternal caloric restriction commencing from 110 days of gestation until term (∼147 days), compared with mothers fed to 100% of requirements. Offspring were reared either as singletons to accelerate postnatal growth or as twins to achieve standard growth. To promote an adverse phenotype in young adulthood, after weaning, offspring were reared under a low-activity obesogenic environment with the exception of a subgroup of IUGR offspring, reared as twins, maintained in a standard activity environment. We assessed glucose tolerance together with leptin and cortisol responses to feeding in young adulthood when the hypothalamus was sampled for assessment of genes regulating appetite control, energy and endocrine sensitivity. Caloric restriction reduced maternal plasma glucose, raised non-esterified fatty acids, and changed the metabolomic profile, but had no effect on insulin, leptin, or cortisol. IUGR offspring whose postnatal growth was enhanced and were obese showed insulin and leptin resistance plus raised cortisol. This was accompanied by increased hypothalamic gene expression for energy and glucocorticoid sensitivity. These long-term adaptations were reduced but not normalized in IUGR offspring whose postnatal growth was not accelerated and remained lean in a standard post-weaning environment. IUGR results in an adverse metabolic phenotype, especially when postnatal growth is enhanced and offspring progress to juvenile-onset obesity

Statistical HOmogeneous Cluster SpectroscopY (SHOCSY): an optimized statistical approach for clustering of ¹H NMR spectral data to reduce interference and enhance robust biomarkers selection.

We propose a novel statistical approach to improve the reliability of (1)H NMR spectral analysis in complex metabolic studies. The Statistical HOmogeneous Cluster SpectroscopY (SHOCSY) algorithm aims to reduce the variation within biological classes by selecting subsets of homogeneous (1)H NMR spectra that contain specific spectroscopic metabolic signatures related to each biological class in a study. In SHOCSY, we used a clustering method to categorize the whole data set into a number of clusters of samples with each cluster showing a similar spectral feature and hence biochemical composition, and we then used an enrichment test to identify the associations between the clusters and the biological classes in the data set. We evaluated the performance of the SHOCSY algorithm using a simulated (1)H NMR data set to emulate renal tubule toxicity and further exemplified this method with a (1)H NMR spectroscopic study of hydrazine-induced liver toxicity study in rats. The SHOCSY algorithm improved the predictive ability of the orthogonal partial least-squares discriminatory analysis (OPLS-DA) model through the use of "truly" representative samples in each biological class (i.e., homogeneous subsets). This method ensures that the analyses are no longer confounded by idiosyncratic responders and thus improves the reliability of biomarker extraction. SHOCSY is a useful tool for removing irrelevant variation that interfere with the interpretation and predictive ability of models and has widespread applicability to other spectroscopic data, as well as other "omics" type of data

Combined transcriptomic-(1)H NMR metabonomic study reveals yhat monoethylhexyl phthalate stimulates adipogenesis and glyceroneogenesis in human adipocytes

Adipose tissue is a major storage site for lipophilic environmental contaminants. The environmental metabolic disruptor hypothesis postulates that some pollutants can promote obesity or metabolic disorders by activating nuclear receptors involved in the control of energetic homeostasis. In this context, monoethylhexyl phthalate (MEHP) is of particular concern since it was shown to activate the peroxisome proliferator-activated receptor γ (PPARγ) in 3T3-L1 murine preadipocytes. In the present work, we used an untargeted, combined transcriptomic-(1)H NMR-based metabonomic approach to describe the overall effect of MEHP on primary cultures of human subcutaneous adipocytes differentiated in vitro. MEHP stimulated rapidly and selectively the expression of genes involved in glyceroneogenesis, enhanced the expression of the cytosolic phosphoenolpyruvate carboxykinase, and reduced fatty acid release. These results demonstrate that MEHP increased glyceroneogenesis and fatty acid reesterification in human adipocytes. A longer treatment with MEHP induced the expression of genes involved in triglycerides uptake, synthesis, and storage; decreased intracellular lactate, glutamine, and other amino acids; increased aspartate and NAD, and resulted in a global increase in triglycerides. Altogether, these results indicate that MEHP promoted the differentiation of human preadipocytes to adipocytes. These mechanisms might contribute to the suspected obesogenic effect of MEHP

MetaFIND: A feature analysis tool for metabolomics data

<p>Abstract</p> <p>Background</p> <p>Metabolomics, or metabonomics, refers to the quantitative analysis of all metabolites present within a biological sample and is generally carried out using NMR spectroscopy or Mass Spectrometry. Such analysis produces a set of peaks, or <it>features</it>, indicative of the metabolic composition of the sample and may be used as a basis for sample classification. Feature selection may be employed to improve classification accuracy or aid model explanation by establishing a subset of class discriminating features. Factors such as experimental noise, choice of technique and threshold selection may adversely affect the set of selected features retrieved. Furthermore, the high dimensionality and multi-collinearity inherent within metabolomics data may exacerbate discrepancies between the set of features retrieved and those required to provide a complete explanation of metabolite signatures. Given these issues, the latter in particular, we present the MetaFIND application for 'post-feature selection' correlation analysis of metabolomics data.</p> <p>Results</p> <p>In our evaluation we show how MetaFIND may be used to elucidate metabolite signatures from the set of features selected by diverse techniques over two metabolomics datasets. Importantly, we also show how MetaFIND may augment standard feature selection and aid the discovery of additional significant features, including those which represent novel class discriminating metabolites. MetaFIND also supports the discovery of higher level metabolite correlations.</p> <p>Conclusion</p> <p>Standard feature selection techniques may fail to capture the full set of relevant features in the case of high dimensional, multi-collinear metabolomics data. We show that the MetaFIND 'post-feature selection' analysis tool may aid metabolite signature elucidation, feature discovery and inference of metabolic correlations.</p

Measurements of 220Rn and 222Rn and CO2 emissions in soil and fumarole gases on Mt. Etna volcano (Italy) : implications for gas transport and shallow ground fracture

Author Posting. © American Geophysical Union, 2007. This article is posted here by permission of American Geophysical Union for personal use, not for redistribution. The definitive version was published in Geochemistry Geophysics Geosystems 8 (2007): Q10001, doi:10.1029/2007GC001644.Measurements of 220Rn and 222Rn activity and of CO2 flux in soil and fumaroles were carried out on Mount Etna volcano in 2005–2006, both in its summit area and along active faults on its flanks. We observe an empirical relationship between (220Rn/222Rn) and CO2 efflux. The higher the flux of CO2, the lower the ratio between 220Rn and 222Rn. Deep sources of gas are characterized by high 222Rn activity and high CO2 efflux, whereas shallow sources are indicated by high 220Rn activity and relatively low CO2 efflux. Excess 220Rn highlights sites of ongoing shallow rock fracturing that could be affected by collapse, as in the case of the rim of an active vent. Depletion both in 220Rn and in CO2 seems to be representative of residual degassing along recently active eruptive vents.This work was funded by the Istituto Nazionale di Geofisica e Vulcanologia (S.G., M.N.) and by the Dipartimento per la Protezione Civile (Italy), projects V3_6/28-Etna (M.N.) and V5/08-Diffuse degassing in Italy (S.G.), and NSF EAR 063824101 (K.W.W.S.)