52 research outputs found

    Effect of Lavage Solution Type on Bronchoalveolar Lavage Fluid Cytology in Clinically Healthy Horses

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    Equine bronchoalveolar lavage (BAL) is usually performed with 250–500 mL of isotonic saline at pH 5.5. The acidic pH of saline may cause an increase in airway neutrophil count 48 h after BAL. Other isotonic solutions such as Ringer’s solution, phosphate-buffered saline (PBS) or Plasma-Lyte 148® have a neutral pH of 7.4 and might be a better choice for BAL by not provoking inflammation and the influx of neutrophils into airways. BAL was performed in four healthy horses in four different lung lobes using four different solutions in a randomized crossover design. In each lobe, BAL was performed twice with a 48 h interval using 250 mL of solution. Automated total nucleated cell counts (TNCs) were recorded, and differential cell counts in lavage fluid were determined by two investigators blinded to treatments. The mean volume of BAL fluid retrieved was 51 ± 14%. The mean neutrophil percentage (%N) increased from 1.5 ± 0.9% to 14.7 ± 9.6% at 48 h (p < 0.001) but was not significantly affected by the solution used or the lung lobe sampled. In conclusion, in this study, the influx of neutrophils into airways after BAL was independent of the type of isotonic solution used and the lung lobe sampled. Saline remains an appropriate solution for BAL in horses

    Dietary Cholesterol-Induced Post-Testicular Infertility

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    This work shows that an overload of dietary cholesterol causes complete infertility in dyslipidemic male mice (the Liver X Receptor-deficient mouse model). Infertility resulted from post-testicular defects affecting the fertilizing potential of spermatozoa. Spermatozoa of cholesterol-fed lxr−/− animals were found to be dramatically less viable and motile, and highly susceptible to undergo a premature acrosome reaction. We also provide evidence, that this lipid-induced infertility is associated with the accelerated appearance of a highly regionalized epididymal phenotype in segments 1 and 2 of the caput epididymidis that was otherwise only observed in aged LXR-deficient males. The epididymal epithelial phenotype is characterized by peritubular accumulation of cholesteryl ester lipid droplets in smooth muscle cells lining the epididymal duct, leading to their transdifferentiation into foam cells that eventually migrate through the duct wall, a situation that resembles the inflammatory atherosclerotic process. These findings establish the high level of susceptibility of epididymal sperm maturation to dietary cholesterol overload and could partly explain reproductive failures encountered by young dyslipidemic men as well as ageing males wishing to reproduce

    Epididymis Response Partly Compensates for Spermatozoa Oxidative Defects in snGPx4 and GPx5 Double Mutant Mice

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    We report here that spermatozoa of mice lacking both the sperm nucleaus glutathione peroxidase 4 (snGPx4) and the epididymal glutathione peroxidase 5 (GPx5) activities display sperm nucleus structural abnormalities including delayed and defective nuclear compaction, nuclear instability and DNA damage. We show that to counteract the GPx activity losses, the epididymis of the double KO animals mounted an antioxydant response resulting in a strong increase in the global H2O2-scavenger activity especially in the cauda epididymis. Quantitative RT-PCR data show that together with the up-regulation of epididymal scavengers (of the thioredoxin/peroxiredoxin system as well as glutathione-S-transferases) the epididymis of double mutant animals increased the expression of several disulfide isomerases in an attempt to recover normal disulfide-bridging activity. Despite these compensatory mechanisms cauda-stored spermatozoa of double mutant animals show high levels of DNA oxidation, increased fragmentation and greater susceptibility to nuclear decondensation. Nevertheless, the enzymatic epididymal salvage response is sufficient to maintain full fertility of double KO males whatever their age, crossed with young WT female mice

    Operational Research: Methods and Applications

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    Throughout its history, Operational Research has evolved to include a variety of methods, models and algorithms that have been applied to a diverse and wide range of contexts. This encyclopedic article consists of two main sections: methods and applications. The first aims to summarise the up-to-date knowledge and provide an overview of the state-of-the-art methods and key developments in the various subdomains of the field. The second offers a wide-ranging list of areas where Operational Research has been applied. The article is meant to be read in a nonlinear fashion. It should be used as a point of reference or first-port-of-call for a diverse pool of readers: academics, researchers, students, and practitioners. The entries within the methods and applications sections are presented in alphabetical order

    Learning Reduced Nonlinear State-Space Models: an Output-Error Based Canonical Approach

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    The identification of a nonlinear dynamic model is an open topic in control theory, especially from sparse input-output measurements. A fundamental challenge of this problem is that very few to zero prior knowledge is available on both the state and the nonlinear system model. To cope with this challenge, we investigate the effectiveness of deep learning in the modeling of dynamic systems with nonlinear behavior by advocating an approach which relies on three main ingredients: (i) we show that under some structural conditions on the to-be-identified model, the state can be expressed in function of a sequence of the past inputs and outputs; (ii) this relation which we call the state map can be modelled by resorting to the well-documented approximation power of deep neural networks; (iii) taking then advantage of existing learning schemes, a state-space model can be finally identified. After the formulation and analysis of the approach, we show its ability to identify three different nonlinear systems. The performances are evaluated in terms of open-loop prediction on test data generated in simulation as well as a real world data-set of unmanned aerial vehicle flight measurements

    Learning Reduced Nonlinear State-Space Models: an Output-Error Based Canonical Approach

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    The identification of a nonlinear dynamic model is an open topic in control theory, especially from sparse inputoutput measurements. A fundamental challenge of this problem is that very few to zero prior knowledge is available on both the state and the nonlinear system model. To cope with this challenge, we investigate the effectiveness of deep learning in the modeling of dynamic systems with nonlinear behavior by advocating an approach which relies on three main ingredients: (i) we show that under some structural conditions on the tobe-identified model, the state can be expressed in function of a sequence of the past inputs and outputs; (ii) this relation which we call the state map can be modelled by resorting to the welldocumented approximation power of deep neural networks; (iii) taking then advantage of existing learning schemes, a statespace model can be finally identified. After the formulation and analysis of the approach, we show its ability to identify three different nonlinear systems. The performances are evaluated in terms of open-loop prediction on test data generated in simulation as well as a real world data-set of unmanned aerial vehicle flight measurements

    Vitrification of human ovarian tissue: a practical and relevant alternative to slow freezing.

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    International audienceCryopreservation of ovarian tissue can be used to preserve the fertility of patients who are about to receive treatment(s) that could compromise their future ovarian function. Here we evaluate the effectiveness of a vitrification protocol by carrying out a systematic comparison with a conventional slow-freezing method on human ovarian tissue
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