Experimental study of solubility of elemental sulphur in methane

International audienceThe chemical engineering department of LaTEP has been working for many years on theproblem of sulphur deposition especially in natural gas network [1, 2]. The solid sulphurappears immediately downstream of a pressure reduction facility. One of the hypothesesproposed to explain the solid formation, based on a thermodynamic approach, is thedesublimation of sulphur. During gas expansion, both pressure and temperature decrease.Consequently the gas may become over saturated in sulphur. Because we are below thetemperature of sulphur triple point, part of the gaseous sulphur can be transformed into solidparticles. Thus, it is important to obtain solubility data of sulphur in natural gases. Methane isthe major natural gas component. So, it is of importance to measure solubility of elementalsulphur in CH4. In this paper experimental measurements up to a pressure and temperature of30 MPA and 363.15 K are presented.The principle of the experimental pilot can be resumed following three steps: saturationof the gas with sulphur, trap of all the dissolved gaseous sulphur and finally quantification.Although the principle is simple, experimental difficulties occur at the three steps. A variablevolume equilibrium cell is used to saturate the gas with sulphur. Since sulphur solubility valueis weak in gas transport conditions, the volume of the cell is necessarily big (0.5 Litre). Thepressure of the equilibrium cell is held constant thanks to a piston during the trapping step. Anoriginal gaseous sulphur trapping method was developed. It is based on the reactiveabsorption of the gaseous sulphur with solvent. Indeed, the gas bubbles into a liquid solutionwhich traps gaseous sulphur. Finally, the solution which contains a standard is analysed bygas chromatography and sulphur is quantified. The total volume of the gas withdrawn isdetermined by a position transducer placed on the autoclave. Then, the sulphur solubilityvalue is calculated

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

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

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

A graphical method for performance mapping of machines and milling tools

Optimal design of the machining setup in terms of installed machines, cutting tools and process parameters is of paramount importance for every manufacturing company. In most of the metal cutting companies, all choices related to machine eligibility and cutting parameters selection typically come from heuristic approaches and follow supplier indications or base on the skill of experienced machine operators. More advanced solutions, such as model-based and virtual approaches, are adopted less frequently mainly due to the lack of these techniques in grasping the underlying knowledge successfully. Aim of this work is to introduce a synthetic graphical representation of machining centers and cutting tools capabilities, to provide an accessible way to evaluate the feasibility and close-to-limit conditions of the cutting process. Taking inspiration from previous scientific works from the measurement engineering field, a set of 2D and 3D graphs are presented to map machine, tools and process capabilities, as well as their obtainable manufacturing performances and expectable tool life. This approach synthesizes the nominal data coming from different sources (catalogues, database, tool model geometries etc.) and the real cutting tools parameters used during the production phase. Some examples are provided to show the potential of this graphical evaluation in supporting process planning and decision-making and in formalizing the machining setup knowledge. Further developments are devoted to extend the method to other manufacturing processes, including hybrid processes. At the same time, an in-process data gathering software will be integrated for building a solid database that can be used by an autonomous multi-technological process selector, as well as by a pre-process condition advisor in an Industry 4.0 oriented way

Systemic multicompartmental effects of the gut microbiome on mouse metabolic phenotypes

To characterize the impact of gut microbiota on host metabolism, we investigated the multicompartmental metabolic profiles of a conventional mouse strain (C3H/HeJ) (n=5) and its germ-free (GF) equivalent (n=5). We confirm that the microbiome strongly impacts on the metabolism of bile acids through the enterohepatic cycle and gut metabolism (higher levels of phosphocholine and glycine in GF liver and marked higher levels of bile acids in three gut compartments). Furthermore we demonstrate that (1) well-defined metabolic differences exist in all examined compartments between the metabotypes of GF and conventional mice: bacterial co-metabolic products such as hippurate (urine) and 5-aminovalerate (colon epithelium) were found at reduced concentrations, whereas raffinose was only detected in GF colonic profiles. (2) The microbiome also influences kidney homeostasis with elevated levels of key cell volume regulators (betaine, choline, myo-inositol and so on) observed in GF kidneys. (3) Gut microbiota modulate metabotype expression at both local (gut) and global (biofluids, kidney, liver) system levels and hence influence the responses to a variety of dietary modulation and drug exposures relevant to personalized health-care investigations

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

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

Double-check: validation of diagnostic statistics for PLS-DA models in metabolomics studies

Partial Least Squares-Discriminant Analysis (PLS-DA) is a PLS regression method with a special binary ‘dummy’ y-variable and it is commonly used for classification purposes and biomarker selection in metabolomics studies. Several statistical approaches are currently in use to validate outcomes of PLS-DA analyses e.g. double cross validation procedures or permutation testing. However, there is a great inconsistency in the optimization and the assessment of performance of PLS-DA models due to many different diagnostic statistics currently employed in metabolomics data analyses. In this paper, properties of four diagnostic statistics of PLS-DA, namely the number of misclassifications (NMC), the Area Under the Receiver Operating Characteristic (AUROC), Q2 and Discriminant Q2 (DQ2) are discussed. All four diagnostic statistics are used in the optimization and the performance assessment of PLS-DA models of three different-size metabolomics data sets obtained with two different types of analytical platforms and with different levels of known differences between two groups: control and case groups. Statistical significance of obtained PLS-DA models was evaluated with permutation testing. PLS-DA models obtained with NMC and AUROC are more powerful in detecting very small differences between groups than models obtained with Q2 and Discriminant Q2 (DQ2). Reproducibility of obtained PLS-DA models outcomes, models complexity and permutation test distributions are also investigated to explain this phenomenon. DQ2 and Q2 (in contrary to NMC and AUROC) prefer PLS-DA models with lower complexity and require higher number of permutation tests and submodels to accurately estimate statistical significance of the model performance. NMC and AUROC seem more efficient and more reliable diagnostic statistics and should be recommended in two group discrimination metabolomic studies