862 resultados para MIXED CHIMERISM


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Pregnant Sprague-Dawley rats were gavaged with vehicle (olive oil) or 37.5, 75, 150 or 300 mg/kg of (DELTA)('9)-Tetrahydrocannabinol (THC) on days 18 or 19 of gestation. Male offspring as well as a group of hypophysectomized rats (positive control) were sacrificed at 35 days of age, while females and hypophysectomized control were sacrificed at 36 days of age. The sex-differences in ethylmorphine-N-demethylase and aniline hydroxylase liver activities were evaluated.^ Ethylmorphine-N-demethylase activity showed a significant difference between males and females from control and 37.5, 75 and 150 mg/kg THC dosed groups. Female offspring exposed prenatally to 300 mg/kg THC had a significant increase (p < .01) in N-demethylation activity, while their male counterparts had similar enzyme activity to those found in the male groups from control and 37.5 to 150 mg/kg THC dosed. Moreover, the percent increase in the 300 mg/kg THC dosed females was similar to that detected in the hypophysectomized female rats (positive control). As expected no sex difference in aniline hydroxylase activity was detected in control as well as exposed groups, including the 300 mg/kg THC dosed group.^ It is concluded that (DELTA)('9)-Tetrahydrocannabinol administered once by gavage in days 18 or 19 of gestation alters the liver Mixed Function Oxidase (MFO) sexual dimorphism imprinting process of the rat. ^

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Mixed longitudinal designs are important study designs for many areas of medical research. Mixed longitudinal studies have several advantages over cross-sectional or pure longitudinal studies, including shorter study completion time and ability to separate time and age effects, thus are an attractive choice. Statistical methodology used in general longitudinal studies has been rapidly developing within the last few decades. Common approaches for statistical modeling in studies with mixed longitudinal designs have been the linear mixed-effects model incorporating an age or time effect. The general linear mixed-effects model is considered an appropriate choice to analyze repeated measurements data in longitudinal studies. However, common use of linear mixed-effects model on mixed longitudinal studies often incorporates age as the only random-effect but fails to take into consideration the cohort effect in conducting statistical inferences on age-related trajectories of outcome measurements. We believe special attention should be paid to cohort effects when analyzing data in mixed longitudinal designs with multiple overlapping cohorts. Thus, this has become an important statistical issue to address. ^ This research aims to address statistical issues related to mixed longitudinal studies. The proposed study examined the existing statistical analysis methods for the mixed longitudinal designs and developed an alternative analytic method to incorporate effects from multiple overlapping cohorts as well as from different aged subjects. The proposed study used simulation to evaluate the performance of the proposed analytic method by comparing it with the commonly-used model. Finally, the study applied the proposed analytic method to the data collected by an existing study Project HeartBeat!, which had been evaluated using traditional analytic techniques. Project HeartBeat! is a longitudinal study of cardiovascular disease (CVD) risk factors in childhood and adolescence using a mixed longitudinal design. The proposed model was used to evaluate four blood lipids adjusting for age, gender, race/ethnicity, and endocrine hormones. The result of this dissertation suggest the proposed analytic model could be a more flexible and reliable choice than the traditional model in terms of fitting data to provide more accurate estimates in mixed longitudinal studies. Conceptually, the proposed model described in this study has useful features, including consideration of effects from multiple overlapping cohorts, and is an attractive approach for analyzing data in mixed longitudinal design studies.^

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The performance of the Hosmer-Lemeshow global goodness-of-fit statistic for logistic regression models was explored in a wide variety of conditions not previously fully investigated. Computer simulations, each consisting of 500 regression models, were run to assess the statistic in 23 different situations. The items which varied among the situations included the number of observations used in each regression, the number of covariates, the degree of dependence among the covariates, the combinations of continuous and discrete variables, and the generation of the values of the dependent variable for model fit or lack of fit.^ The study found that the $\rm\ C$g* statistic was adequate in tests of significance for most situations. However, when testing data which deviate from a logistic model, the statistic has low power to detect such deviation. Although grouping of the estimated probabilities into quantiles from 8 to 30 was studied, the deciles of risk approach was generally sufficient. Subdividing the estimated probabilities into more than 10 quantiles when there are many covariates in the model is not necessary, despite theoretical reasons which suggest otherwise. Because it does not follow a X$\sp2$ distribution, the statistic is not recommended for use in models containing only categorical variables with a limited number of covariate patterns.^ The statistic performed adequately when there were at least 10 observations per quantile. Large numbers of observations per quantile did not lead to incorrect conclusions that the model did not fit the data when it actually did. However, the statistic failed to detect lack of fit when it existed and should be supplemented with further tests for the influence of individual observations. Careful examination of the parameter estimates is also essential since the statistic did not perform as desired when there was moderate to severe collinearity among covariates.^ Two methods studied for handling tied values of the estimated probabilities made only a slight difference in conclusions about model fit. Neither method split observations with identical probabilities into different quantiles. Approaches which create equal size groups by separating ties should be avoided. ^

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Fil: Basile, Teresa. Universidad Nacional de La Plata. Facultad de Humanidades y Ciencias de la Educación; Argentina.

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Fil: Basile, Teresa. Universidad Nacional de La Plata. Facultad de Humanidades y Ciencias de la Educación; Argentina.

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We analyzed the abundance of Scots pine regeneration in a 257 ha wildfire in an inner-alpine forest. We sampled regeneration, percent soil cover by classes, physical and chemical properties of topsoils (A horizon, 0-5 cm) under four fire severity levels (unburned, moderate, moderate/high, high severity). 5 plots per severity level, circular (R= 3m). Analysis methods for soil properties as described in the paper.

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We studied how environmental conditions affect reproduction in sympatric skua species that differ in their reliance on marine resources: the exclusively marine foraging south polar skua Catharacta maccormicki, the terrestrially foraging brown skua C. antarctica lonnbergi and mixed species pairs with an intermediate diet. Egg size, clutch asymmetry and hatching dates varied between species and years without consistent patterns. In the south polar skuas, 12 to 38% of the variation in these parameters was explained by sea surface temperature, sea ice cover and local weather. In mixed species pairs and brown skuas, the influence of environmental factors on variation in clutch asymmetry and hatching date decreased to 10-29%, and no effect on egg size was found. Annual variation in offspring growth performance also differed between species with variable growth in chicks of south polar skuas and mixed species pairs, and almost uniform growth in brown skuas. Additionally, the dependency on oceanographic and climatic factors, especially local wind conditions, decreased from south polar skuas to brown skua chicks. Consistent in all species, offspring were more sensitive to environmental conditions during early stages; during the late chick stage (>33 d) chick growth was almost independent of environmental conditions. The net breeding success could not be predicted by any environmental factor in any skua species, suggesting it may not be a sensitive indicator of environmental conditions. Hence, the sensitivity of skuas to environmental conditions varied between species, with south polar skuas being more sensitive than brown skuas, and between breeding periods, with the egg parameters being more susceptible to oceanographic conditions. However, during offspring development, local climatic conditions became more important. We conclude that future climate change in the Maritime Antarctic will affect reproduction of skuas more strongly through changes in sea ice cover and sea surface temperature (and the resulting alterations to the marine food web) than through local weather conditions.