Inflammatory response in a rat model of gastroschisis is associated with an increase of NF-kappaB

Babies with gastroschisis have high morbidity, which is associated with inflammatory bowel injury caused by exposure to amniotic fluid. The objective of this study was to identify components of the inflammatory response in the intestine and liver in an experimental model of gastroschisis in rats. The model was surgically created at 18.5 days of gestation. The fetuses were exposed through a hysterotomy and an incision at the right of the umbilicus was made, exposing the fetal bowel. Then, the fetus was placed back into the uterus until term. The bowel in this model had macro- and microscopic characteristics similar to those observed in gastroschisis. The study was conducted on three groups of 20 fetuses each: gastroschisis, control, and sham fetuses. Fetal body, intestine and liver weights and intestine length were measured. IL-1β, IL-6, IL-10, TNF-α, IFN-γ and NF-kappaB levels were assessed by ELISA. Data were analyzed statistically by ANOVA followed by the Tukey post-test. Gastroschisis fetuses had a decreased intestine length (means ± SD, 125 ± 25 vs 216 ± 13.9; P < 0.005) and increased intestine weight (0.29 ± 0.05 vs 0.24 ± 0.04; P < 0.005). Intestine length correlated with liver weight only in gastroschisis fetuses (Pearson’s correlation coefficient, r = 0.518, P = 0.019). There were no significant differences in the concentrations of IL-1β, TNF-α or IFN-γ in the intestine, whereas the concentration of NF-kappaB was increased in both the intestine and liver of fetuses with gastroschisis. These results show that the inflammatory response in the liver and intestine of the rat model of gastroschisis is accompanied by an increase in the amount of NF-kappaB in the intestine and liver.

Putative role of ischemic postconditioning in a rat model of limb ischemia and reperfusion: involvement of hypoxia-inducible factor-1α expression

Hypoxia-inducible factor-1α (HIF-1α) is one of the most potent angiogenic growth factors. It improves angiogenesis and tissue perfusion in ischemic skeletal muscle. In the present study, we tested the hypothesis that ischemic postconditioning is effective for salvaging ischemic skeletal muscle resulting from limb ischemia-reperfusion injury, and that the mechanism involves expression of HIF-1α. Wistar rats were randomly divided into three groups (n=36 each): sham-operated (group S), hindlimb ischemia-reperfusion (group IR), and ischemic postconditioning (group IPO). Each group was divided into subgroups (n=6) according to reperfusion time: immediate (0 h, T0), 1 h (T1), 3 h (T3), 6 h (T6), 12 h (T12), and 24 h (T24). In the IPO group, three cycles of 30-s reperfusion and 30-s femoral aortic reocclusion were carried out before reperfusion. At all reperfusion times (T0-T24), serum creatine kinase (CK) and lactate dehydrogenase (LDH) activities, as well as interleukin (IL)-6, IL-10, and tumor necrosis factor-α (TNF-α) concentrations, were measured in rats after they were killed. Histological and immunohistochemical methods were used to assess the skeletal muscle damage and HIF-1α expression in skeletal muscle ischemia. In groups IR and IPO, serum LDH and CK activities and TNF-α, IL-6, and IL-10 concentrations were all significantly increased compared to group S, and HIF-1α expression was up-regulated (P<0.05 or P<0.01). In group IPO, serum LDH and CK activities and TNF-α and IL-6 concentrations were significantly decreased, IL-10 concentration was increased, HlF-1α expression was down-regulated (P<0.05 or P<0.01), and the pathological changes were reduced compared to group IR. The present study suggests that ischemic postconditioning can reduce skeletal muscle damage caused by limb ischemia-reperfusion and that its mechanisms may be related to the involvement of HlF-1α in the limb ischemia-reperfusion injury-triggered inflammatory response.

Treatment with 1,25(OH)2D3induced HDAC2 expression and reduced NF-κB p65 expression in a rat model of OVA-induced asthma

Recent evidence indicates that a deficiency of 1,25-dihydroxyvitamin D3 (1,25[OH]2D3) may influence asthma pathogenesis; however, its roles in regulating specific molecular transcription mechanisms remain unclear. We aimed to investigate the effect of 1,25(OH)2D3 on the expression and enzyme activity of histone deacetylase 2 (HDAC2) and its synergistic effects with dexamethasone (Dx) in the inhibition of inflammatory cytokine secretion in a rat asthma model. Healthy Wistar rats were randomly divided into 6 groups: control, asthma, 1,25(OH)2D3 pretreatment, 1,25(OH)2D3 treatment, Dx treatment, and Dx and 1,25(OH)2D3 treatment. Pulmonary inflammation was induced by ovalbumin (OVA) sensitization and challenge (OVA/OVA). Inflammatory cells and cytokines in the bronchoalveolar lavage (BAL) fluid and histological changes in lung tissue were examined. Nuclear factor kappa B (NF-κB) p65 and HDAC2 expression levels were assessed with Western blot analyses and quantitative reverse-transcriptase polymerase chain reaction (qRT-PCR). Enzyme activity measurements and immunohistochemical detection of HDAC2 were also performed. Our data demonstrated that 1,25(OH)2D3 reduced the airway inflammatory response and the level of inflammatory cytokines in BAL. Although NF-κB p65 expression was attenuated in the pretreatment and treatment groups, the expression and enzyme activity of HDAC2 were increased. In addition, 1,25(OH)2D3 and Dx had synergistic effects on the suppression of total cell infusion, cytokine release, and NF-κB p65 expression, and they also increased HDAC2 expression and activity in OVA/OVA rats. Collectively, our results indicated that 1,25(OH)2D3might be useful as a novel HDAC2 activator in the treatment of asthma.

Markovian Processes, Two-Sided Autoregressions and Finite-Sample Inference for Stationary and Nonstationary Autoregressive Processes.

In this paper, we develop finite-sample inference procedures for stationary and nonstationary autoregressive (AR) models. The method is based on special properties of Markov processes and a split-sample technique. The results on Markovian processes (intercalary independence and truncation) only require the existence of conditional densities. They are proved for possibly nonstationary and/or non-Gaussian multivariate Markov processes. In the context of a linear regression model with AR(1) errors, we show how these results can be used to simplify the distributional properties of the model by conditioning a subset of the data on the remaining observations. This transformation leads to a new model which has the form of a two-sided autoregression to which standard classical linear regression inference techniques can be applied. We show how to derive tests and confidence sets for the mean and/or autoregressive parameters of the model. We also develop a test on the order of an autoregression. We show that a combination of subsample-based inferences can improve the performance of the procedure. An application to U.S. domestic investment data illustrates the method.

The in vivo characterization of the DNA repair gene apn-1 in the model organism Caenorhabditis elegans

Les sites apuriniques/apyrimidinique (AP) représentent une forme de dommage à l’ADN hautement mutagène et ce type de dommage peut survenir spontanément ou être induit par une variété d’agents. Afin de préserver la stabilité génomique, deux familles d’endonucléases de type AP, endo-IV et exo-III, sont nécessaires pour contrecarrer les effets mutagènes des sites AP. Malgré l’identification de membres des deux familles dans plusieurs organismes unicellulaire tels que E.coli et S. cerevisiae, aucun membre de la famille endo-IV n’a été identifié chez les organismes multicellulaires à l’exception de C. elegans et de C. briggsae. Nous avons donc décidé d’investiguer l’importance biologique de APN-1 chez C. elegans par l’utilisation d’une approche de knockdown du gène. Dans notre étude, nous avons montré que le knockdown du gène apn-1 chez C. elegans, en utilisant des ARN d’interférence (ARNi), cause une accumulation de mutations spontanées et induites par des drogues résultant en un délai de l’éclosion des œufs ainsi que par une diminution de la survie et de la longévité des vers adultes. De plus, nous avons montré que cette accumulation de mutations mène à un délai dans la progression du cycle cellulaire durant l’embryogénèse, représentant possiblement une explication du délai dans l’éclosion des œufs. Nous avons montré qu’il y avait une augmentation du niveau de mutations dans la gorge des vers, sans toutefois pouvoir confirmer la distribution de APN-1 qui possède une étiquette GFP. Les animaux transgéniques APN-1-GFP n’exprimaient pas suffisamment de la protéine de fusion pour permettre une visualisation à l’aide d’un microscope à fluorescence, mais la protéine a été détectée par immunobuvardage de type western. Les animaux transgéniques APN-1-GFP étaient instables et avaient des phénotypes concordants avec les défauts génétiques. En conclusion, il semble que C. elegans aie évolué afin de retenir un niveau de base de APN-1 jouant ainsi un rôle versatile afin de maintenir l’intégrité génétique d’autant plus que cet organisme semble manquer plusieurs enzymes de la voie de réparation par excision de base.

Cardiovascular and sensory abnormalities in a rat model of insulin resistance : beneficial effect of an antioxidant and an angiotensin-1 converting enzyme inhibitor

Mémoire numérisé par la Division de la gestion de documents et des archives de l'Université de Montréal.

Quantifying diffusion in biofilms : from model hydrogels to living biofilms

Les biofilms sont des communautés de microorganismes incorporés dans une matrice exo-polymérique complexe. Ils sont reconnus pour jouer un rôle important comme barrière de diffusion dans les systèmes environnementaux et la santé humaine, donnant lieu à une résistance accrue aux antibiotiques et aux désinfectants. Comme le transfert de masse dans un biofilm est principalement dû à la diffusion moléculaire, il est primordial de comprendre les principaux paramètres influençant les flux de diffusion. Dans ce travail, nous avons étudié un biofilm de Pseudomonas fluorescens et deux hydrogels modèles (agarose et alginate) pour lesquels l’autodiffusion (mouvement Brownien) et les coefficients de diffusion mutuels ont été quantifiés. La spectroscopie par corrélation de fluorescence a été utilisée pour mesurer les coefficients d'autodiffusion dans une volume confocal de ca. 1 m3 dans les gels ou les biofilms, tandis que les mesures de diffusion mutuelle ont été faites par cellule de diffusion. En outre, la voltamétrie sur microélectrode a été utilisée pour évaluer le potentiel de Donnan des gels afin de déterminer son impact sur la diffusion. Pour l'hydrogel d'agarose, les observations combinées d'une diminution du coefficient d’autodiffusion et de l’augmentation de la diffusion mutuelle pour une force ionique décroissante ont été attribuées au potentiel de Donnan du gel. Des mesures de l'effet Donnan (différence de -30 mV entre des forces ioniques de 10-4 et 10-1 M) et l'accumulation correspondante d’ions dans l'hydrogel (augmentation d’un facteur de 13 par rapport à la solution) ont indiqué que les interactions électrostatiques peuvent fortement influencer le flux de diffusion de cations, même dans un hydrogel faiblement chargé tel que l'agarose. Curieusement, pour un gel plus chargé comme l'alginate de calcium, la variation de la force ionique et du pH n'a donné lieu qu'à de légères variations de la diffusion de sondes chargées dans l'hydrogel. Ces résultats suggèrent qu’en influençant la diffusion du soluté, l'effet direct des cations sur la structure du gel (compression et/ou gonflement induits) était beaucoup plus efficace que l'effet Donnan. De même, pour un biofilm bactérien, les coefficients d'autodiffusion étaient pratiquement constants sur toute une gamme de force ionique (10-4-10-1 M), aussi bien pour des petits solutés chargés négativement ou positivement (le rapport du coefficient d’autodiffusion dans biofilm sur celui dans la solution, Db/Dw ≈ 85 %) que pour des nanoparticules (Db/Dw≈ 50 %), suggérant que l'effet d'obstruction des biofilms l’emporte sur l'effet de charge. Les résultats de cette étude ont montré que parmi les divers facteurs majeurs qui affectent la diffusion dans un biofilm environnemental oligotrophe (exclusion stérique, interactions électrostatiques et hydrophobes), les effets d'obstruction semblent être les plus importants lorsque l'on tente de comprendre la diffusion du soluté. Alors que les effets de charge ne semblaient pas être importants pour l'autodiffusion de substrats chargés dans l'hydrogel d'alginate ou dans le biofilm bactérien, ils ont joué un rôle clé dans la compréhension de la diffusion à travers l’agarose. L’ensemble de ces résultats devraient être très utiles pour l'évaluation de la biodisponibilité des contaminants traces et des nanoparticules dans l'environnement.

Bayesian inference and model comparison for random choice structures

We complete the development of a testing ground for axioms of discrete stochastic choice. Our contribution here is to develop new posterior simulation methods for Bayesian inference, suitable for a class of prior distributions introduced by McCausland and Marley (2013). These prior distributions are joint distributions over various choice distributions over choice sets of di fferent sizes. Since choice distributions over di fferent choice sets can be mutually dependent, previous methods relying on conjugate prior distributions do not apply. We demonstrate by analyzing data from a previously reported experiment and report evidence for and against various axioms.

Malayalam Isolated Digit Recognition using HMM and PLP cepstral coefficient

Development of Malayalam speech recognition system is in its infancy stage; although many works have been done in other Indian languages. In this paper we present the first work on speaker independent Malayalam isolated speech recognizer based on PLP (Perceptual Linear Predictive) Cepstral Coefficient and Hidden Markov Model (HMM). The performance of the developed system has been evaluated with different number of states of HMM (Hidden Markov Model). The system is trained with 21 male and female speakers in the age group ranging from 19 to 41 years. The system obtained an accuracy of 99.5% with the unseen data

Model Diagnostics in the presence of measurement error

The problem of using information available from one variable X to make inferenceabout another Y is classical in many physical and social sciences. In statistics this isoften done via regression analysis where mean response is used to model the data. Onestipulates the model Y = µ(X) +ɛ. Here µ(X) is the mean response at the predictor variable value X = x, and ɛ = Y - µ(X) is the error. In classical regression analysis, both (X; Y ) are observable and one then proceeds to make inference about the mean response function µ(X). In practice there are numerous examples where X is not available, but a variable Z is observed which provides an estimate of X. As an example, consider the herbicidestudy of Rudemo, et al. [3] in which a nominal measured amount Z of herbicide was applied to a plant but the actual amount absorbed by the plant X is unobservable. As another example, from Wang [5], an epidemiologist studies the severity of a lung disease, Y , among the residents in a city in relation to the amount of certain air pollutants. The amount of the air pollutants Z can be measured at certain observation stations in the city, but the actual exposure of the residents to the pollutants, X, is unobservable and may vary randomly from the Z-values. In both cases X = Z+error: This is the so called Berkson measurement error model.In more classical measurement error model one observes an unbiased estimator W of X and stipulates the relation W = X + error: An example of this model occurs when assessing effect of nutrition X on a disease. Measuring nutrition intake precisely within 24 hours is almost impossible. There are many similar examples in agricultural or medical studies, see e.g., Carroll, Ruppert and Stefanski [1] and Fuller [2], , among others. In this talk we shall address the question of fitting a parametric model to the re-gression function µ(X) in the Berkson measurement error model: Y = µ(X) + ɛ; X = Z + η; where η and ɛ are random errors with E(ɛ) = 0, X and η are d-dimensional, and Z is the observable d-dimensional r.v.

On the Noise Model of Support Vector Machine Regression

Support Vector Machines Regression (SVMR) is a regression technique which has been recently introduced by V. Vapnik and his collaborators (Vapnik, 1995; Vapnik, Golowich and Smola, 1996). In SVMR the goodness of fit is measured not by the usual quadratic loss function (the mean square error), but by a different loss function called Vapnik"s $epsilon$- insensitive loss function, which is similar to the "robust" loss functions introduced by Huber (Huber, 1981). The quadratic loss function is well justified under the assumption of Gaussian additive noise. However, the noise model underlying the choice of Vapnik's loss function is less clear. In this paper the use of Vapnik's loss function is shown to be equivalent to a model of additive and Gaussian noise, where the variance and mean of the Gaussian are random variables. The probability distributions for the variance and mean will be stated explicitly. While this work is presented in the framework of SVMR, it can be extended to justify non-quadratic loss functions in any Maximum Likelihood or Maximum A Posteriori approach. It applies not only to Vapnik's loss function, but to a much broader class of loss functions.

Coordinating Inventory Control and Pricing Strategies with Random Demand and Fixed Ordering Cost: The Finite Horizon Case

We analyze a finite horizon, single product, periodic review model in which pricing and production/inventory decisions are made simultaneously. Demands in different periods are random variables that are independent of each other and their distributions depend on the product price. Pricing and ordering decisions are made at the beginning of each period and all shortages are backlogged. Ordering cost includes both a fixed cost and a variable cost proportional to the amount ordered. The objective is to find an inventory policy and a pricing strategy maximizing expected profit over the finite horizon. We show that when the demand model is additive, the profit-to-go functions are k-concave and hence an (s,S,p) policy is optimal. In such a policy, the period inventory is managed based on the classical (s,S) policy and price is determined based on the inventory position at the beginning of each period. For more general demand functions, i.e., multiplicative plus additive functions, we demonstrate that the profit-to-go function is not necessarily k-concave and an (s,S,p) policy is not necessarily optimal. We introduce a new concept, the symmetric k-concave functions and apply it to provide a characterization of the optimal policy.

Coordinating Inventory Control and Pricing Strategies with Random Demand and Fixed Ordering Cost: the Infinite Horizon Case

We analyze an infinite horizon, single product, periodic review model in which pricing and production/inventory decisions are made simultaneously. Demands in different periods are identically distributed random variables that are independent of each other and their distributions depend on the product price. Pricing and ordering decisions are made at the beginning of each period and all shortages are backlogged. Ordering cost includes both a fixed cost and a variable cost proportional to the amount ordered. The objective is to maximize expected discounted, or expected average profit over the infinite planning horizon. We show that a stationary (s,S,p) policy is optimal for both the discounted and average profit models with general demand functions. In such a policy, the period inventory is managed based on the classical (s,S) policy and price is determined based on the inventory position at the beginning of each period.

Model Order Reduction for Determining Bubble Parameters to Attain a Desired Fluid Surface Shape

In this paper, a new methodology for predicting fluid free surface shape using Model Order Reduction (MOR) is presented. Proper Orthogonal Decomposition combined with a linear interpolation procedure for its coefficient is applied to a problem involving bubble dynamics near to a free surface. A model is developed to accurately and efficiently capture the variation of the free surface shape with different bubble parameters. In addition, a systematic approach is developed within the MOR framework to find the best initial locations and pressures for a set of bubbles beneath the quiescent free surface such that the resultant free surface attained is close to a desired shape. Predictions of the free surface in two-dimensions and three-dimensions are presented.

A Gain Scheduling Model Predictive Controller for Blood Glucose Control in Type 1 Diabetes

This paper presents a control strategy for blood glucose(BG) level regulation in type 1 diabetic patients. To design the controller, model-based predictive control scheme has been applied to a newly developed diabetic patient model. The controller is provided with a feedforward loop to improve meal compensation, a gain-scheduling scheme to account for different BG levels, and an asymmetric cost function to reduce hypoglycemic risk. A simulation environment that has been approved for testing of artificial pancreas control algorithms has been used to test the controller. The simulation results show a good controller performance in fasting conditions and meal disturbance rejection, and robustness against model–patient mismatch and errors in meal estimation