4 resultados para Distribution transformer modeling
em DigitalCommons@The Texas Medical Center
Resumo:
OBJECTIVE: Because studies suggest that ultraviolet (UV) radiation modulates the myositis phenotype and Mi-2 autoantigen expression, we conducted a retrospective investigation to determine whether UV radiation may influence the relative prevalence of dermatomyositis and anti-Mi-2 autoantibodies in the US. METHODS: We assessed the relationship between surface UV radiation intensity in the state of residence at the time of onset with the relative prevalence of dermatomyositis and myositis autoantibodies in 380 patients with myositis from referral centers in the US. Myositis autoantibodies were detected by validated immunoprecipitation assays. Surface UV radiation intensity was estimated from UV Index data collected by the US National Weather Service. RESULTS: UV radiation intensity was associated with the relative proportion of patients with dermatomyositis (odds ratio [OR] 2.3, 95% confidence interval [95% CI] 0.9-5.8) and with the proportion of patients expressing anti-Mi-2 autoantibodies (OR 6.0, 95% CI 1.1-34.1). Modeling of these data showed that these associations were confined to women (OR 3.8, 95% CI 1.3-11.0 and OR 17.3, 95% CI 1.8-162.4, respectively) and suggests that sex influences the effects of UV radiation on autoimmune disorders. Significant associations were not observed in men, nor were UV radiation levels related to the presence of antisynthetase or anti-signal recognition particle autoantibodies. CONCLUSION: This first study of the distribution of myositis phenotypes and UV radiation exposure in the US showed that UV radiation may modulate the clinical and immunologic expression of autoimmune disease in women. Further investigation of the mechanisms by which these effects are produced may provide insights into pathogenesis and suggest therapeutic or preventative strategies.
Resumo:
Every x-ray attenuation curve inherently contains all the information necessary to extract the complete energy spectrum of a beam. To date, attempts to obtain accurate spectral information from attenuation data have been inadequate.^ This investigation presents a mathematical pair model, grounded in physical reality by the Laplace Transformation, to describe the attenuation of a photon beam and the corresponding bremsstrahlung spectral distribution. In addition the Laplace model has been mathematically extended to include characteristic radiation in a physically meaningful way. A method to determine the fraction of characteristic radiation in any diagnostic x-ray beam was introduced for use with the extended model.^ This work has examined the reconstructive capability of the Laplace pair model for a photon beam range of from 50 kVp to 25 MV, using both theoretical and experimental methods.^ In the diagnostic region, excellent agreement between a wide variety of experimental spectra and those reconstructed with the Laplace model was obtained when the atomic composition of the attenuators was accurately known. The model successfully reproduced a 2 MV spectrum but demonstrated difficulty in accurately reconstructing orthovoltage and 6 MV spectra. The 25 MV spectrum was successfully reconstructed although poor agreement with the spectrum obtained by Levy was found.^ The analysis of errors, performed with diagnostic energy data, demonstrated the relative insensitivity of the model to typical experimental errors and confirmed that the model can be successfully used to theoretically derive accurate spectral information from experimental attenuation data. ^
Resumo:
Radiotherapy has been a method of choice in cancer treatment for a number of years. Mathematical modeling is an important tool in studying the survival behavior of any cell as well as its radiosensitivity. One particular cell under investigation is the normal T-cell, the radiosensitivity of which may be indicative to the patient's tolerance to radiation doses.^ The model derived is a compound branching process with a random initial population of T-cells that is assumed to have compound distribution. T-cells in any generation are assumed to double or die at random lengths of time. This population is assumed to undergo a random number of generations within a period of time. The model is then used to obtain an estimate for the survival probability of T-cells for the data under investigation. This estimate is derived iteratively by applying the likelihood principle. Further assessment of the validity of the model is performed by simulating a number of subjects under this model.^ This study shows that there is a great deal of variation in T-cells survival from one individual to another. These variations can be observed under normal conditions as well as under radiotherapy. The findings are in agreement with a recent study and show that genetic diversity plays a role in determining the survival of T-cells. ^
Resumo:
Mixture modeling is commonly used to model categorical latent variables that represent subpopulations in which population membership is unknown but can be inferred from the data. In relatively recent years, the potential of finite mixture models has been applied in time-to-event data. However, the commonly used survival mixture model assumes that the effects of the covariates involved in failure times differ across latent classes, but the covariate distribution is homogeneous. The aim of this dissertation is to develop a method to examine time-to-event data in the presence of unobserved heterogeneity under a framework of mixture modeling. A joint model is developed to incorporate the latent survival trajectory along with the observed information for the joint analysis of a time-to-event variable, its discrete and continuous covariates, and a latent class variable. It is assumed that the effects of covariates on survival times and the distribution of covariates vary across different latent classes. The unobservable survival trajectories are identified through estimating the probability that a subject belongs to a particular class based on observed information. We applied this method to a Hodgkin lymphoma study with long-term follow-up and observed four distinct latent classes in terms of long-term survival and distributions of prognostic factors. Our results from simulation studies and from the Hodgkin lymphoma study demonstrated the superiority of our joint model compared with the conventional survival model. This flexible inference method provides more accurate estimation and accommodates unobservable heterogeneity among individuals while taking involved interactions between covariates into consideration.^