Filtering performances of 20 protective fabrics against solid aerosols

Workers can be exposed to solid airborne particles in some occupational environments, and they might be required to wear chemical protective clothing to prevent skin exposure. Dedicated standards exist to certify the protective value of such clothing, but they are not informative enough to identify the main pathways of entry for solid particles nor to compare performances between different chemical protective clothing. In this work, 20 non-woven fabrics used to make chemical protective clothing for solid particle protection were selected to be examined for both filtration and comfort performances. Nine were microporous fabrics (MP), 10 were multilayered nonwoven fibrous media (SMS) and one was a flash spun material (FS). To assess their filtration performances, fabrics were challenged in a benchtop wind tunnel with a 20-3,000 nm diameter sodium chloride aerosol at three low fabric face velocities (0.05, 0.15, 0.3 cm/sec). Airflow resistance and water vapor transmission rate were also measured to provide indications of comfort for the wearer. The penetration results led to the classification of the 20 fabrics into distinct groups of filtration efficiency. The data were analysed based on the porous media characteristics (thickness, fiber diameter, porosity, etc.). MPs were the most efficient fabrics, and SMSs showed a wide range of performances, mostly due to variations in the thickness of the filtering layer as well as to the fabric treatment. Measurements of airflow resistance and water vapor transmission rates revealed major differences between MPs and FSs and SMSs. This highlights the potential of some SMS fabrics to meet a compromise between protection and comfort.

Spatial distribution and partitioning behavior of selected poly- and perfluoroalkyl substances in freshwater ecosystems: a French nationwide survey

The spatial distribution and partitioning of 22 poly- and perfluoroalkyl substances (PFASs) in 133 selected rivers and lakes were investigated at a nationwide scale in mainland France. ΣPFASs was in the range<LOD-725 ng L(-1) in the dissolved phase (median: 7.9 ng L(-1)) and <LOD-25 ng g(-1) dry weight (dw) in the sediment (median: 0.48 ng g(-1) dw); dissolved PFAS levels were significantly lower at "reference" sites than at urban, rural or industrial sites. Although perfluorooctane sulfonate (PFOS) was found to be the prevalent compound on average, a multivariate analysis based on neural networks revealed noteworthy trends for other compounds at specific locations and, in some cases, at watershed scale. For instance, several sites along the Rhône River displayed a peculiar PFAS signature, perfluoroalkyl carboxylates (PFCAs) often dominating the PFAS profile (e.g., PFCAs>99% of ΣPFASs in the sediment, likely as a consequence of industrial point source discharge). Several treatments for data below detection limits (non-detects) were used to compute descriptive statistics, differences among groups, and correlations between congeners, as well as log Kd and log Koc partition coefficients; in that respect, the Regression on Order Statistics (robust ROS) method was preferred for descriptive statistics computation while the Akritas-Theil-Sen estimator was used for regression and correlation analyses. Multiple regression results suggest that PFAS levels in the dissolved phase and sediment characteristics (organic carbon fraction and grain size) may be significant controlling factors of PFAS levels in the sediment.

Docking and QSAR comparative studies of polycyclic aromatic hydrocarbons and other procarcinogen interactions with cytochromes P450 1A1 and 1B1

To obtain chemical clues on the process of bioactivation by cytochromes P450 1A1 and 1B1, some QSAR studies were carried out based on cellular experiments of the metabolic activation of polycyclic aromatic hydrocarbons and heterocyclic aromatic compounds by those enzymes. Firstly, the 3D structures of cytochromes 1A1 and 1B1 were built using homology modelling with a cytochrome 1A2 template. Using these structures, 32 ligands including heterocyclic aromatic compounds, polycyclic aromatic hydrocarbons and corresponding diols, were docked with LigandFit and CDOCKER algorithms. Binding mode analysis highlighted the importance of hydrophobic interactions and the hydrogen bonding network between cytochrome amino acids and docked molecules. Finally, for each enzyme, multilinear regression and artificial neural network QSAR models were developed and compared. These statistical models highlighted the importance of electronic, structural and energetic descriptors in metabolic activation process, and could be used for virtual screening of ligand databases. In the case of P450 1A1, the best model was obtained with artificial neural network analysis and gave an r (2) of 0.66 and an external prediction [Formula: see text] of 0.73. Concerning P450 1B1, artificial neural network analysis gave a much more robust model, associated with an r (2) value of 0.73 and an external prediction [Formula: see text] of 0.59.

An integrative genomics approach for deciphering the complex interactions between ascorbate metabolism and fruit growth and composition in tomato

Very few reports have studied the interactions between ascorbate and fruit metabolism. In order to get insights into the complex relationships between ascorbate biosynthesis/recycling and other metabolic pathways in the fruit, we undertook a fruit systems biology approach. To this end, we have produced tomato transgenic lines altered in ascorbate content and redox ratio by RNAi-targeting several key enzymes involved in ascorbate biosynthesis (2 enzymes) and recycling (2 enzymes). In the VTC (ViTamin C) Fruit project, we then generated phenotypic and genomic (transcriptome, proteome, metabolome) data from wild type and mutant tomato fruit at two stages of fruit development, and developed or implemented statistical and bioinformatic tools as a web application (named VTC Tool box) necessary to store, analyse and integrate experimental data in tomato. By using Kohonen's self-organizing maps (SOMs) to cluster the biological data, pair-wise Pearson correlation analyses and simultaneous visualization of transcript/protein and metabolites (MapMan), this approach allowed us to uncover major relationships between ascorbate and other metabolic pathways.

Gene and metabolite regulatory network analysis of early developing fruit tissues highlights new candidate genes for the control of tomato fruit composition and development

Variations in early fruit development and composition may have major impacts on the taste and the overall quality of ripe tomato (Solanum lycopersicum) fruit. To get insights into the networks involved in these coordinated processes and to identify key regulatory genes, we explored the transcriptional and metabolic changes in expanding tomato fruit tissues using multivariate analysis and gene-metabolite correlation networks. To this end, we demonstrated and took advantage of the existence of clear structural and compositional differences between expanding mesocarp and locular tissue during fruit development (12-35 d postanthesis). Transcriptome and metabolome analyses were carried out with tomato microarrays and analytical methods including proton nuclear magnetic resonance and liquid chromatography-mass spectrometry, respectively. Pairwise comparisons of metabolite contents and gene expression profiles detected up to 37 direct gene-metabolite correlations involving regulatory genes (e.g. the correlations between glutamine, bZIP, and MYB transcription factors). Correlation network analyses revealed the existence of major hub genes correlated with 10 or more regulatory transcripts and embedded in a large regulatory network. This approach proved to be a valuable strategy for identifying specific subsets of genes implicated in key processes of fruit development and metabolism, which are therefore potential targets for genetic improvement of tomato fruit quality.

Relative characterization of rosemary samples according to their geographical origins using microwave-accelerated distillation, solid-phase microextraction and Kohonen self-organizing maps

For centuries, rosemary (Rosmarinus officinalis L.) has been used to prepare essential oils which, even now, are highly valued due to their various biological activities. Nevertheless, it has been noted that these activities often depend on the origin of the rosemary plant and the method of extraction. Since both of these quality parameters can greatly influence the chemical composition of rosemary oil, an original analytical method was developed where "dry distillation" was coupled to headspace solid-phase microextraction (HS-SPME) and then a data mining technique using the Kohonen self-organizing map algorithm was applied to the data obtained. This original approach uses the newly described microwave-accelerated distillation technique (MAD) and HS-SPME; neither of these techniques require external solvent and so this approach provides a novel "green" chemistry sampling method in the field of biological matrix analysis. The large data set obtained was then treated with a rarely used chemometric technique based on nonclassical statistics. Applied to 32 rosemary samples collected at the same time from 12 different sites in the north of Algeria, this method highlighted a strong correlation between the volatile chemical compositions of the samples and their origins, and it therefore allowed the samples to be grouped according to geographical distribution. Moreover, the method allowed us to identify the constituents that exerted the most influence during classification.

Assessment of strawberry aroma through solid-phase microextraction-gas chromatography and artificial neuron network methods. Variety classification versus growing years

In a previous work, the SPME-GC-MS method (chemical analysis) coupled with KSOM-ANN treatment of the results (statistical algorithm) has proved to be efficient to classify 70 strawberry samples harvested in the same year, through the 17 varieties to which they belonged, in a two-dimensional map. As an extension, the present study confirms that these results were not dependent on the year of strawberry production and discusses what effects were observed between results obtained in different years. Samples of different strawberry varieties were harvested during the three campaigns of 2000, 2001, and 2002 and analyzed independently. The chemical data matrix obtained in each case allowed the verification of the proposal that the same discriminative effect could be obtained independently of the year of production by using maps of different sizes. Therefore, 30 measures obtained from samples of 9 varieties in 2000, 54 measures from 13 varieties in 2001, and 80 measures from 20 varieties in 2002 were correctly classified by using 20, 35, and 56 hexagon maps, respectively. In a second analysis based on the 2002 production, the chemical differences between variety aromatic features were noted through the increasing size of the map used. Finally, results relative to 7 varieties cultivated in 2001 and 2002 and stored under exactly the same conditions were computed together for elaborating a single map. An interesting effect of double classification according to the year and the varieties was observed.

Assessment of strawberry aroma through SPME/GC and ANN methods. Classification and discrimination of varieties

To provide an efficient and running analytical tool to strawberry plant breeders who have to characterize and compare the aromatic properties of new cultivars to those already known, a HS-SPME/GC-MS analysis method has been coupled with a statistical treatment method issued from the current development of artificial neuron networks (ANN), and more specifically, the unsupervised learning systems called Kohonen self-organizing maps (SOMs). So, 70 strawberry samples harvested at CIREF from 17 known varieties have been extracted by using a DVB/Carboxen/PDMS SPME fiber according to the headspace procedure, and then chromatographed. A panel of 23 characteristic aromatic constituents has been selected according to published results relative to strawberry aroma. The complex resulting matrix, collecting the relative abundance of the 23 selected constituents for each sample, has been input into the SOM software adapted and optimized from the Kohonen approach described by one of the authors. After a period of training, the self-organized system affords a map of virtual strawberries to which real samples are compared and plotted in the best matching unit (BMU) of the map. The efficiency for discriminating the real samples according to their variety is dependent on the number of units selected to define the map. In this case, a 24-unit map allowed the complete discrimination of the 17 selected varieties. Moreover, to test the validity of this approach, two additional samples were blind-analyzed and the results were computed according to the same procedure. At the end of this treatment, both samples were plotted into the same unit as those of the same variety used for training the map.