Stable carbon isotope Composition of c9,t11-conjugated linoleic acid in cow's milk as related to dietary fatty acids

This study explores the potential use of stable carbon isotope ratios (delta C-13) of single fatty acids (FA) as tracers for the transformation of FA from diet to milk, with focus on the metabolic origin of c9,t11-18:2. For this purpose, dairy cows were fed diets based exclusively on C-3 and C-4 plants. The FA in milk and feed were fractionated by silver-ion thin-layer chromatography and analyzed for their delta C-13 values. Mean delta C-13 values of FA from C-3 milk were lower compared to those from C-4 milk (-30.1aEuro degrees vs. -24.9aEuro degrees, respectively). In both groups the most negative delta C-13 values of all FA analyzed were measured for c9,t11-18:2 (C-3 milk = -37.0 +/- A 2.7aEuro degrees; C-4 milk -31.4 +/- A 1.4aEuro degrees). Compared to the dietary precursors 18:2n-6 and 18:3n-3, no significant C-13-depletion was measured in t11-18:1. This suggests that the delta C-13-change in c9,t11-18:2 did not originate from the microbial biohydrogenation in the rumen, but most probably from endogenous desaturation of t11-18:1. It appears that the natural delta C-13 differences in some dietary FA are at least partly preserved in milk FA. Therefore, carbon isotope analyses of individual FA could be useful for studying metabolic transformation processes in ruminants.

Hygiène des mains et utilisation de protection respiratoire : attention aux risques de contamination microbiologique imprévue!

Une mauvaise hygiène des mains peut représenter une perte économique importante pour les industries alimentaires ou les industries pharmaceutiques et constitue un gros problème de santé publique dans les établissements de soins. En effet, le risque de transmission manuportée de pathogènes d'un patient à l'autre via le personnel soignant est un problème récurrent dans tous les hôpitaux. C'est pourquoi, une hygiène irréprochable des mains du personnel soignant est une composante fondamentale de lutte contre les infections nosocomiales. En comparaison avec les autres techniques de séchage des mains, l'utilisation d'une serviette en papier à usage unique semble être la méthode la plus efficace pour éliminer les bactéries. En effet, les autres méthodes de séchage, utilisant des appareils à air chaud ou froid, peuvent générer un air contaminé ou remettre en suspension les germes restés sur la peau, facilitant ainsi leur dispersion et leur inhalation. C'est pourquoi, l'OMS a émis des recommandations déconseillant les sèche-mains électriques en milieu hospitalier au profit des serviettes en papiers. Cependant, ces serviettes à usage unique ne sont pas stériles et peuvent elles-mêmes déposer des germes, généralement non pathogènes, sur les mains lors du séchage. Le but du premier article analysé est d'évaluer le niveau de contamination de ces serviettes, ainsi que la possibilité de dépôt de ces bactéries présentes sur les serviettes, sur les mains. À côté du risque de propagation de pathogènes par contact direct, la contamination microbiologique par voie aérienne via les aérosols existe aussi. Pour éviter d'être contaminé par des personnes excrétrices de micro-organismes transmissibles par aérosols (tuberculose, grippe, grippe aviaire SRAS...), il faut porter des protections respiratoires de type FFP2 (1) (norme européenne équivalente à la norme anglosaxonne N95). Ces protections respiratoires ou masques filtrants (en formes de bec de canard) peuvent être portées théoriquement pendant 4 heures. Le but de l'étude du second article est d'évaluer le risque de remise en suspension dans l'air de particules virales présentes sur la face extérieure d'un masque de protection respiratoire lors de simulation d'épisodes de toux provenant du porteur de cette protection.

Robust vaccine-elicited cellular immune responses in breast milk following systemic Simian Immunodeficiency Virus DNA prime and live virus vector boost vaccination of lactating rhesus monkeys.

Breast milk transmission of HIV remains an important mode of infant HIV acquisition. Enhancement of mucosal HIV-specific immune responses in milk of HIV-infected mothers through vaccination may reduce milk virus load or protect against virus transmission in the infant gastrointestinal tract. However, the ability of HIV/SIV strategies to induce virus-specific immune responses in milk has not been studied. In this study, five uninfected, hormone-induced lactating, Mamu A*01(+) female rhesus monkey were systemically primed and boosted with rDNA and the attenuated poxvirus vector, NYVAC, containing the SIVmac239 gag-pol and envelope genes. The monkeys were boosted a second time with a recombinant Adenovirus serotype 5 vector containing matching immunogens. The vaccine-elicited immunodominant epitope-specific CD8(+) T lymphocyte response in milk was of similar or greater magnitude than that in blood and the vaginal tract but higher than that in the colon. Furthermore, the vaccine-elicited SIV Gag-specific CD4(+) and CD8(+) T lymphocyte polyfunctional cytokine responses were more robust in milk than in blood after each virus vector boost. Finally, SIV envelope-specific IgG responses were detected in milk of all monkeys after vaccination, whereas an SIV envelope-specific IgA response was only detected in one vaccinated monkey. Importantly, only limited and transient increases in the proportion of activated or CCR5-expressing CD4(+) T lymphocytes in milk occurred after vaccination. Therefore, systemic DNA prime and virus vector boost of lactating rhesus monkeys elicits potent virus-specific cellular and humoral immune responses in milk and may warrant further investigation as a strategy to impede breast milk transmission of HIV.

Robust Nonlinear Mapping of Soil Contamination Using Support Regression

Spatial data analysis mapping and visualization is of great importance in various fields: environment, pollution, natural hazards and risks, epidemiology, spatial econometrics, etc. A basic task of spatial mapping is to make predictions based on some empirical data (measurements). A number of state-of-the-art methods can be used for the task: deterministic interpolations, methods of geostatistics: the family of kriging estimators (Deutsch and Journel, 1997), machine learning algorithms such as artificial neural networks (ANN) of different architectures, hybrid ANN-geostatistics models (Kanevski and Maignan, 2004; Kanevski et al., 1996), etc. All the methods mentioned above can be used for solving the problem of spatial data mapping. Environmental empirical data are always contaminated/corrupted by noise, and often with noise of unknown nature. That's one of the reasons why deterministic models can be inconsistent, since they treat the measurements as values of some unknown function that should be interpolated. Kriging estimators treat the measurements as the realization of some spatial randomn process. To obtain the estimation with kriging one has to model the spatial structure of the data: spatial correlation function or (semi-)variogram. This task can be complicated if there is not sufficient number of measurements and variogram is sensitive to outliers and extremes. ANN is a powerful tool, but it also suffers from the number of reasons. of a special type ? multiplayer perceptrons ? are often used as a detrending tool in hybrid (ANN+geostatistics) models (Kanevski and Maignank, 2004). Therefore, development and adaptation of the method that would be nonlinear and robust to noise in measurements, would deal with the small empirical datasets and which has solid mathematical background is of great importance. The present paper deals with such model, based on Statistical Learning Theory (SLT) - Support Vector Regression. SLT is a general mathematical framework devoted to the problem of estimation of the dependencies from empirical data (Hastie et al, 2004; Vapnik, 1998). SLT models for classification - Support Vector Machines - have shown good results on different machine learning tasks. The results of SVM classification of spatial data are also promising (Kanevski et al, 2002). The properties of SVM for regression - Support Vector Regression (SVR) are less studied. First results of the application of SVR for spatial mapping of physical quantities were obtained by the authorsin for mapping of medium porosity (Kanevski et al, 1999), and for mapping of radioactively contaminated territories (Kanevski and Canu, 2000). The present paper is devoted to further understanding of the properties of SVR model for spatial data analysis and mapping. Detailed description of the SVR theory can be found in (Cristianini and Shawe-Taylor, 2000; Smola, 1996) and basic equations for the nonlinear modeling are given in section 2. Section 3 discusses the application of SVR for spatial data mapping on the real case study - soil pollution by Cs137 radionuclide. Section 4 discusses the properties of the modelapplied to noised data or data with outliers.