Factors influencing the extraction of pharmaceuticals from sewage sludge and soil: an experimental design approach.

Pharmaceuticals can enter the environment when organic waste products are recycled on agricultural soils. The extraction of pharmaceuticals is a challenging step in their analysis. The very different extraction conditions proposed in the literature make the choice of the right method for multi-residue analysis difficult. This study aimed at evaluating, with experimental design methodology, the influence of the nature, pH and composition of the extraction medium on the extraction recovery of 14 pharmaceuticals, including 8 antibiotics, from soil and sewage sludge. Preliminary experimental designs showed that acetonitrile and citrate-phosphate buffer were the best extractants. Then, a response surface design demonstrated that many cross-product and squared terms had significant effects, explaining the shapes of the response surfaces. It also allowed optimising the pharmaceutical recoveries in soil and sludge. The optimal conditions were interpreted considering the ionisation states of the compounds, their solubility in the extraction medium and their interactions with the solid matrix. To perform the analysis, a compromise was made for each matrix. After a QuEChERS purification, the samples were analysed by online SPE-UHPLC-MS-MS. Both methods were simple and economical. They were validated with the accuracy profile methodology for soil and sludge and characterised for another type of soil, digested sludge and composted sludge. Trueness globally ranged between 80 and 120 % recovery, and inter- and intra-day precisions were globally below 20 % relative standard deviation. Various pharmaceuticals were present in environmental samples, with concentration levels ranging from a few micrograms per kilogramme up to thousands of micrograms per kilogramme. Graphical abstract Influence of the extraction medium on the extraction recovery of 14 pharmaceuticals. Influence of the ionisation state, the solubility and the interactions of pharmaceuticals with solid matrix. Analysis of different soils and organic waste products.

Risk-Based Approach for Microbiological Food Safety Management in the Dairy Industry: The Case of Listeria monocytogenes in Soft Cheese Made from Pasteurized Milk.

According to Codex Alimentarius Commission recommendations, management options applied at the process production level should be based on good hygiene practices, HACCP system, and new risk management metrics such as the food safety objective. To follow this last recommendation, the use of quantitative microbiological risk assessment is an appealing approach to link new risk-based metrics to management options that may be applied by food operators. Through a specific case study, Listeria monocytogenes in soft cheese made from pasteurized milk, the objective of the present article is to practically show how quantitative risk assessment could be used to direct potential intervention strategies at different food processing steps. Based on many assumptions, the model developed estimates the risk of listeriosis at the moment of consumption taking into account the entire manufacturing process and potential sources of contamination. From pasteurization to consumption, the amplification of a primo-contamination event of the milk, the fresh cheese or the process environment is simulated, over time, space, and between products, accounting for the impact of management options, such as hygienic operations and sampling plans. A sensitivity analysis of the model will help orientating data to be collected prioritarily for the improvement and the validation of the model. What-if scenarios were simulated and allowed for the identification of major parameters contributing to the risk of listeriosis and the optimization of preventive and corrective measures.

Characterization of the Three-Factor Eating Questionnaire scores of a young French cohort.

The aims of our study were to characterize the psychological dimensions of eating behaviour of young French adults as measured by the Three Factor Eating Questionnaire (TFEQ) and to analyze the association between the 3 TFEQ mean scores (main scales and subscales) and gender, Body Mass Index (BMI) and socio-demographic data in this population. An online TFEQ questionnaire was used with a nationally representative sample of 1000 young French people (aged 20-39yrs). The average scores were 6.3±0.1 (sem) for dietary restraint, 6.0±0.1 for disinhibition and 5.0±0.1 for hunger. Compared to the limit commonly used in human food studies, young French adults were characterized by low restraint and low disinhibition levels. There was a significant gender effect on both restraint and disinhibition scores, with women showing significantly higher scores than men. Concerning the link between TFEQ scores and BMI, there was a significant effect of the BMI category on cognitive restraint, disinhibition and hunger. Disinhibition was the factor most strongly associated to BMI, independently of gender. Our results highlight both the importance of taking into account not only disinhibition but also cognitive restraint and the usefulness of subscales when studying eating behaviour and its link to body weight. We characterize the eating behaviour of a French cohort with criteria often chosen for healthy volunteers in human food studies. Consequently, we suggest new TFEQ limits (6 for cognitive restraint and disinhibition, 5 for hunger) lower than those traditionally used for this category of the population in clinical food studies.

Model for heterogeneous random networks using continuous latent variables and an application to a tree-fungus network.

The mixture model is a method of choice for modeling heterogeneous random graphs, because it contains most of the known structures of heterogeneity: hubs, hierarchical structures, or community structure. One of the weaknesses of mixture models on random graphs is that, at the present time, there is no computationally feasible estimation method that is completely satisfying from a theoretical point of view. Moreover, mixture models assume that each vertex pertains to one group, so there is no place for vertices being at intermediate positions. The model proposed in this article is a grade of membership model for heterogeneous random graphs, which assumes that each vertex is a mixture of extremal hypothetical vertices. The connectivity properties of each vertex are deduced from those of the extreme vertices. In this new model, the vector of weights of each vertex are fixed continuous parameters. A model with a vector of parameters for each vertex is tractable because the number of observations is proportional to the square of the number of vertices of the network. The estimation of the parameters is given by the maximum likelihood procedure. The model is used to elucidate some of the processes shaping the heterogeneous structure of a well-resolved network of host/parasite interactions.

Deciphering the connectivity structure of biological networks using MixNet.

BACKGROUND: As biological networks often show complex topological features, mathematical methods are required to extract meaningful information. Clustering methods are useful in this setting, as they allow the summary of the network's topology into a small number of relevant classes. Different strategies are possible for clustering, and in this article we focus on a model-based strategy that aims at clustering nodes based on their connectivity profiles. RESULTS: We present MixNet, the first publicly available computer software that analyzes biological networks using mixture models. We apply this method to various networks such as the E. coli transcriptional regulatory network, the macaque cortex network, a foodweb network and the Buchnera aphidicola metabolic network. This method is also compared with other approaches such as module identification or hierarchical clustering. CONCLUSION: We show how MixNet can be used to extract meaningful biological information, and to give a summary of the networks topology that highlights important biological features. This approach is powerful as MixNet is adaptive to the network under study, and finds structural information without any a priori on the structure that is investigated. This makes MixNet a very powerful tool to summarize and decipher the connectivity structure of biological networks.

Statistical methodology for the analysis of dye-switch microarray experiments.

BACKGROUND: In individually dye-balanced microarray designs, each biological sample is hybridized on two different slides, once with Cy3 and once with Cy5. While this strategy ensures an automatic correction of the gene-specific labelling bias, it also induces dependencies between log-ratio measurements that must be taken into account in the statistical analysis. RESULTS: We present two original statistical procedures for the statistical analysis of individually balanced designs. These procedures are compared with the usual ML and REML mixed model procedures proposed in most statistical toolboxes, on both simulated and real data. CONCLUSION: The UP procedure we propose as an alternative to usual mixed model procedures is more efficient and significantly faster to compute. This result provides some useful guidelines for the analysis of complex designs.

Normalization for triple-target microarray experiments.

BACKGROUND: Most microarray studies are made using labelling with one or two dyes which allows the hybridization of one or two samples on the same slide. In such experiments, the most frequently used dyes are Cy3 and Cy5. Recent improvements in the technology (dye-labelling, scanner and, image analysis) allow hybridization up to four samples simultaneously. The two additional dyes are Alexa488 and Alexa494. The triple-target or four-target technology is very promising, since it allows more flexibility in the design of experiments, an increase in the statistical power when comparing gene expressions induced by different conditions and a scaled down number of slides. However, there have been few methods proposed for statistical analysis of such data. Moreover the lowess correction of the global dye effect is available for only two-color experiments, and even if its application can be derived, it does not allow simultaneous correction of the raw data. RESULTS: We propose a two-step normalization procedure for triple-target experiments. First the dye bleeding is evaluated and corrected if necessary. Then the signal in each channel is normalized using a generalized lowess procedure to correct a global dye bias. The normalization procedure is validated using triple-self experiments and by comparing the results of triple-target and two-color experiments. Although the focus is on triple-target microarrays, the proposed method can be used to normalize p differently labelled targets co-hybridized on a same array, for any value of p greater than 2. CONCLUSION: The proposed normalization procedure is effective: the technical biases are reduced, the number of false positives is under control in the analysis of differentially expressed genes, and the triple-target experiments are more powerful than the corresponding two-color experiments. There is room for improving the microarray experiments by simultaneously hybridizing more than two samples.

Biases induced by pooling samples in microarray experiments.

MOTIVATION: If there is insufficient RNA from the tissues under investigation from one organism, then it is common practice to pool RNA. An important question is to determine whether pooling introduces biases, which can lead to inaccurate results. In this article, we describe two biases related to pooling, from a theoretical as well as a practical point of view. RESULTS: We model and quantify the respective parts of the pooling bias due to the log transform as well as the bias due to biological averaging of the samples. We also evaluate the impact of the bias on the statistical differential analysis of Affymetrix data.

An experimental design approach to optimise the determination of polycyclic aromatic hydrocarbons from rainfall water using stir bar sorptive extraction and high performance liquid chromatography-fluorescence detection.

Stir bar sorptive extraction (SBSE) followed by HPLC-fluorescence detection (FLD) was optimised for analysing 15 polycyclic aromatic hydrocarbons (PAHs) from water samples, especially rainfall water with low PAH content. The literature data described widely different experimental conditions for the extraction of PAHs by SBSE. A chemometric approach was therefore used to evaluate the statistically influential and/or interacting factors, among those described in the literature, and to find the best extraction and desorption conditions. Among six factors studied in a 2(6-2) fractional factorial design, only sample volume, extraction time and the interaction between both of them had significant effects on the PAH extraction recoveries. Optimal sample volume of 10 mL and extraction time of 140 min were obtained with a response surface design. For the desorption conditions, a Box-Behnken design showed that desorption time, temperature and PAH concentrations had significant effects. The best conditions were two successive desorptions with 100 microL of acetonitrile for 25 min at 50 degrees C. The optimised method was repeatable (RSD< or =5.3% for 50 ng L(-1) spiked water and < or =12.8% for 5 ng L(-1) spiked water), linear (R(2)> or =0.9956), with quantitative absolute recoveries (> or =87.8% for 50 ng L(-1) spiked water), and with the LOD between 0.2 and 1.5 ng L(-1). The optimised method was successfully applied to six-rainfall water samples collected in a suburban area. The total PAHs concentrations studied ranged from 31 to 105.1 ng L(-1). Seasonal variation was observed and on average three PAHs were at the highest concentrations (phenanthrene, fluoranthene and pyrene).

A statistical approach for array CGH data analysis.

BACKGROUND: Microarray-CGH experiments are used to detect and map chromosomal imbalances, by hybridizing targets of genomic DNA from a test and a reference sample to sequences immobilized on a slide. These probes are genomic DNA sequences (BACs) that are mapped on the genome. The signal has a spatial coherence that can be handled by specific statistical tools. Segmentation methods seem to be a natural framework for this purpose. A CGH profile can be viewed as a succession of segments that represent homogeneous regions in the genome whose BACs share the same relative copy number on average. We model a CGH profile by a random Gaussian process whose distribution parameters are affected by abrupt changes at unknown coordinates. Two major problems arise: to determine which parameters are affected by the abrupt changes (the mean and the variance, or the mean only), and the selection of the number of segments in the profile. RESULTS: We demonstrate that existing methods for estimating the number of segments are not well adapted in the case of array CGH data, and we propose an adaptive criterion that detects previously mapped chromosomal aberrations. The performances of this method are discussed based on simulations and publicly available data sets. Then we discuss the choice of modeling for array CGH data and show that the model with a homogeneous variance is adapted to this context. CONCLUSIONS: Array CGH data analysis is an emerging field that needs appropriate statistical tools. Process segmentation and model selection provide a theoretical framework that allows precise biological interpretations. Adaptive methods for model selection give promising results concerning the estimation of the number of altered regions on the genome.

Spotting effect in microarray experiments.

BACKGROUND: Microarray data must be normalized because they suffer from multiple biases. We have identified a source of spatial experimental variability that significantly affects data obtained with Cy3/Cy5 spotted glass arrays. It yields a periodic pattern altering both signal (Cy3/Cy5 ratio) and intensity across the array. RESULTS: Using the variogram, a geostatistical tool, we characterized the observed variability, called here the spotting effect because it most probably arises during steps in the array printing procedure. CONCLUSIONS: The spotting effect is not appropriately corrected by current normalization methods, even by those addressing spatial variability. Importantly, the spotting effect may alter differential and clustering analysis.

Multi-residue analysis of pharmaceuticals in aqueous environmental samples by online solid-phase extraction-ultra-high-performance liquid chromatography-tandem mass spectrometry: optimisation and matrix effects reduction by quick, easy, cheap, effective, rugged and safe extraction.

The aim of this study was to develop and optimise an analytical method for the quantification of a bactericide and 13 pharmaceutical products, including 8 antibiotics (fluoroquinolones, tetracyclines, sulfonamides, macrolide), in various aqueous environmental samples: soil water and aqueous fractions of pig slurry, digested pig slurry and sewage sludge. The analysis was performed by online solid-phase extraction coupled to ultra-high performance liquid chromatography with tandem mass spectrometry (online SPE-UHPLC-MS-MS). The main challenge was to minimize the matrix effects observed in mass spectrometry, mostly due to ion suppression. They depended on the dissolved organic carbon (DOC) content and its origin, and ranged between -22% and +20% and between -38% and -93% of the signal obtained without matrix, in soil water and slurry supernatant, respectively. The very variable levels of these matrix effects suggested DOC content cut-offs above which sample purification was required. These cut-offs depended on compounds, with concentrations ranging from 30 to 290mgC/L for antibiotics (except tylosine) up to 600-6400mgC/L for the most apolar compounds. A modified Quick, Easy, Cheap, Effective, Rugged and Safe (QuEChERS) extraction procedure was therefore optimised using an experimental design methodology, in order to purify samples with high DOC contents. Its performance led to a compromise, allowing fluoroquinolone and tetracycline analysis. The QuEChERS extraction salts consisted therefore of sodium acetate, sodium sulfate instead of magnesium sulfate, and sodium ethylenediaminetetraacetate (EDTA) as a ligand of divalent cations. The modified QuEChERS procedure employed for the extraction of pharmaceuticals in slurry and digested slurry liquid phases reduced the matrix effects for almost all the compounds, with extraction recoveries generally above 75%. The performance characteristics of the method were evaluated in terms of linearity, intra-day and inter-day precision, accuracy and limits of quantification, which reached concentration ranges of 5-270ng/L in soil water and sludge supernatant, and 31-2400ng/L in slurry and digested slurry supernatants, depending on the compounds. The new method was then successfully applied for the determination of the target compounds in environmental samples.

Variation of gene expression associated with colonisation of an anthropized environment: comparison between African and European populations of Drosophila simulans.

The comparison of transcriptome profiles among populations is a powerful tool for investigating the role of gene expression change in adaptation to new environments. In this study, we use massively parallel sequencing of 3' cDNAs obtained from large samples of adult males, to compare a population of Drosophila simulans from a natural reserve within its ancestral range (eastern Africa) with a derived population collected in the strongly anthropized Rhône valley (France). The goal was to scan for adaptation linked to the invasion of new environments by the species. Among 15,090 genes retained for the analysis, 794 were found to be differentially expressed between the two populations. We observed an increase in expression of reproduction-related genes in eastern Africa, and an even stronger increase in expression of Cytochrome P450, Glutathione transferase and Glucuronosyl transferase genes in the derived population. These three gene families are involved in detoxification processes, which suggests that pesticides are a major environmental pressure for the species in this area. The survey of the Cyp6g1 upstream region revealed the insertion of a transposable element, Juan, in the regulatory sequence that is almost fixed in the Rhône Valley, but barely present in Mayotte. This shows that Cyp6g1 has undergone parallel evolution in derived populations of D. simulans as previously shown for D. melanogaster. The increasing amount of data produced by comparative population genomics and transcriptomics should permit the identification of additional genes associated with functional divergence among those differentially expressed.

Putting the biological species concept to the test: using mating networks to delimit species.

Although interfertility is the key criterion upon which Mayr's biological species concept is based, it has never been applied directly to delimit species under natural conditions. Our study fills this gap. We used the interfertility criterion to delimit two closely related oak species in a forest stand by analyzing the network of natural mating events between individuals. The results reveal two groups of interfertile individuals connected by only few mating events. These two groups were largely congruent with those determined using other criteria (morphological similarity, genotypic similarity and individual relatedness). Our study, therefore, shows that the analysis of mating networks is an effective method to delimit species based on the interfertility criterion, provided that adequate network data can be assembled. Our study also shows that although species boundaries are highly congruent across methods of species delimitation, they are not exactly the same. Most of the differences stem from assignment of individuals to an intermediate category. The discrepancies between methods may reflect a biological reality. Indeed, the interfertility criterion is an environment-dependant criterion as species abundances typically affect rates of hybridization under natural conditions. Thus, the methods of species delimitation based on the interfertility criterion are expected to give results slightly different from those based on environment-independent criteria (such as the genotypic similarity criteria). However, whatever the criterion chosen, the challenge we face when delimiting species is to summarize continuous but non-uniform variations in biological diversity. The grade of membership model that we use in this study appears as an appropriate tool.

Stochastic, compartmental, and dynamic modeling of cross-contamination during mechanical smearing of cheeses.

Cheese smearing is a complex process and the potential for cross-contamination with pathogenic or undesirable microorganisms is critical. During ripening, cheeses are salted and washed with brine to develop flavor and remove molds that could develop on the surfaces. Considering the potential for cross-contamination of this process in quantitative risk assessments could contribute to a better understanding of this phenomenon and, eventually, improve its control. The purpose of this article is to model the cross-contamination of smear-ripened cheeses due to the smearing operation under industrial conditions. A compartmental, dynamic, and stochastic model is proposed for mechanical brush smearing. This model has been developed to describe the exchange of microorganisms between compartments. Based on the analytical solution of the model equations and on experimental data collected with an industrial smearing machine, we assessed the values of the transfer parameters of the model. Monte Carlo simulations, using the distributions of transfer parameters, provide the final number of contaminated products in a batch and their final level of contamination for a given scenario taking into account the initial number of contaminated cheeses of the batch and their contaminant load. Based on analytical results, the model provides indicators for smearing efficiency and propensity of the process for cross-contamination. Unlike traditional approaches in mechanistic models, our approach captures the variability and uncertainty inherent in the process and the experimental data. More generally, this model could represent a generic base to use in modeling similar processes prone to cross-contamination.

VarMixt: efficient variance modelling for the differential analysis of replicated gene expression data.

MOTIVATION: Identifying differentially regulated genes in experiments comparing two experimental conditions is often a key step in the microarray data analysis process. Many different approaches and methodological developments have been put forward, yet the question remains open. RESULTS: Varmixt is a powerful and efficient novel methodology for this task. It is based on a flexible and realistic variance modelling strategy. It compares favourably with other popular techniques (standard t-test, SAM and Cyber-T). The relevance of the approach is demonstrated with real-world and simulated datasets. The analysis strategy was successfully applied to both a 'two-colour' cDNA microarray and an Affymetrix Genechip. Strong control of false positive and false negative rates is proven in large simulation studies. AVAILABILITY: The R package is freely available at http://www.inapg.inra.fr/ens_rech/mathinfo/recherche/mathematique/outil.html CONTACT: delmar@inapg.inra.fr SUPPLEMENTARY INFORMATION: http://www.inapg.inra.fr/ens_rech/mathinfo/recherche/mathematique/outil.html.

Evaluation of the gene-specific dye bias in cDNA microarray experiments.

MOTIVATION: In cDNA microarray experiments all samples are labeled with either Cy3 or Cy5. Systematic and gene-specific dye bias effects have been observed in dual-color experiments. In contrast to systematic effects which can be corrected by a normalization method, the gene-specific dye bias is not completely suppressed and may alter the conclusions about the differentially expressed genes. METHODS: The gene-specific dye bias is taken into account using an analysis of variance model. We propose an index, named label bias index, to measure the gene-specific dye bias. It requires at least two self-self hybridization cDNA microarrays. RESULTS: After lowess normalization we have found that the gene-specific dye bias is the major source of experimental variability between replicates. The ratio (R/G) may exceed 2. As a consequence false positive genes may be found in direct comparison without dye-swap. The stability of this artifact and its consequences on gene variance and on direct or indirect comparisons are addressed. AVAILABILITY: http://www.inapg.inra.fr/ens_rech/mathinfo/recherche/mathematique

Alcohol and atherosclerotic vascular disease risk factors in French men: relationships are linear, J-shaped, and U-shaped.

BACKGROUND: Although it is well admitted that alcohol displays a U-shaped relationship with atherosclerotic vascular disease, individual relationships between alcohol and atherosclerosis risk factors may be different and have not been determined precisely for several of them. METHODS: A cross-sectional study within the SU.VI.MAX French cohort study was performed to assess the curve of potential relationships between alcohol and atherosclerosis risk factors in 2126 healthy men. Mean daily alcohol intake was derived from 37 alcoholic beverages in twelve 24-hr dietary recalls. Logistic models were adjusted for age. RESULTS: Apolipoprotein B (ApoB), fasting glucose, body mass index, waist-to-hip ratio, and waist circumference displayed a linear relationship with alcohol. The odds ratios and 95% confidence intervals associated with abnormal values of the markers for the highest quintile of alcohol intake were 1.45 (1.06-1.97) for ApoB, 1.98 (1.40-2.80) for fasting glucose, and 1.74 (1.30-2.34) for body mass index. An inverse J-shaped relationship was assumed for ApoA1 and ApoB/ApoA1 ratio, whereas a U-shaped relationship was observed for serum triglycerides and mixed hyperlipidemia. Only the highest quintile of alcohol was associated with hypertension, although the test for linearity was also significant. No association was observed for Lp(a) or homocysteine. Associations were unmodified by further adjustment for carbohydrates, fiber, lipids, tobacco, or exercise. CONCLUSIONS: The aggregate of the disparate alcohol risk factor relationships suggests probable net benefit at 15 to 25 g of alcohol/day.

Homogeneity check of agricultural and food industries samples using near infrared spectroscopy.

Samples distributed in proficiency testing schemes (PTS) need to be homogeneous in order to be sure that if a laboratory has a result different from the other laboratories, its error can be attributed to its analysis method and not to its sample. This control must be done according to the ISO 13528 draft standard before sending the samples to the laboratories. It can be done by determining homogeneity targets by sub-contracting to accredited laboratories using reference methods, but this engenders logistic and financial problems. That is why a homogeneity check using Near Infrared Spectroscopy (NIR) has been developed for agricultural and food industries samples prepared for PTS at Bipea (Bureau Interprofessionnel d'Etudes Analytiques). To evaluate the homogeneity among samples, this procedure involves a comparison of NIR spectra, the determination of global homogeneity criteria and the use of control charts. The method of control developed and carried out at Bipea allows the rapid and easy monitoring of the performance of the sample preparation.

Drinking patterns in French adult men--a cluster analysis of alcoholic beverages and relationship with lifestyle.

BACKGROUND: Establishing patterns of alcohol consumption may be useful for investigating the relationship between alcohol and diseases. METHODS: We used a hierarchical agglomerative clustering method to describe the intake of eight types of alcoholic beverages and to determine drinking patterns in a cohort of 1797 men enrolled in a French 8-year intervention study involving nutritional doses of vitamins and minerals, the SU.VI.MAX study. RESULTS: Cluster 1, referred to as 'abstainers', was defined a priori and included 329 men who drank less than 5 g of alcohol per day. Six drinking patterns were defined in alcohol drinkers, with increasing mean alcohol intake: cluster 2, 'low drinkers', included 670 subjects, who drank little of any type of alcoholic beverage; cluster 3, 'high quality wines', included 584 men with a high intake of champagne, high quality wines, and high-alcohol aperitifs; cluster 4, 'beer and cider', included 190 subjects with a high intake of beer and cider; cluster 5, 'digestives', included 54 men with a specifically high consumption of digestive beverages; cluster 6, 'local wines', included 238 subjects with a high intake of local wines and low-alcohol aperitifs; cluster 7, 'table wines', included 61 men with a high intake of table wines and high-alcohol aperitifs. These clusters were significantly associated with socioeconomic and lifestyle variables such as place of residence, occupation, mean caloric intake and distribution of energy intake throughout the day, body mass index, and smoking habits. CONCLUSIONS: They will be useful in future studies of the relationship between alcohol intake and medical conditions or risk factors.