869 resultados para Cox regression
Resumo:
Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)
Resumo:
Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)
Resumo:
Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)
Resumo:
Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)
Resumo:
Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)
Resumo:
Central giant cell granuloma (CGCG) of the jaws represents a localized and benign neoplastic lesion sometimes characterized by aggressive osteolytic proliferation. The World Health Organization defines it as an intraosseous lesion composed of cellular and dense connective tissues that contain multiple hemorrhagic foci, an aggregation of multinucleated giant cells, and occasional bone tissue trabeculae. The origin of this lesion is uncertain; however, factors such as local trauma, inflammation, intraosseous hemorrhage, and genetic abnormalities have been identified as possible causes. CGCG generally affects those younger than 30 years and occurs more frequently in women (2: 1). This lesion corresponds to approximately 7% of all benign tumors of the jaws, with prevalence in the anterior region of the jaw. Aggressive lesions are characterized by symptoms, such as pain, numbness, rapid growth, cortical perforation, root resorption, and a high recurrence rate after curettage. In contrast, nonaggressive CGCGs have a slow rate of growth, may contain sparse trabeculation, and are less likely to move teeth or cause root resorption or cortical perforation. Nonaggressive CGCGs are generally asymptomatic lesions and thus are frequently found on routine dental radiographs. Radiographically, the 2 forms of CGCG present as radiolucent, expansive, unilocular or multilocular masses with well-defined margins. The histopathology of CGCG is characterized by multinucleated giant cells, surrounded by round, oval, and spindle-shaped mononuclear cells, scattered in dense connective tissue with hemorrhagic and abundant vascularization foci. The final diagnosis is determined by histopathologic analysis of the biopsy specimen. The preferred treatment for CGCG consists of excisional biopsy, curettage with a safety margin, and partial or total resection of the affected bone. Conservative treatments include local injections of steroids, calcitonin, and antiangiogenic therapy. Drug treatment using antibiotics, painkillers, and corticosteroids and clinical and radiographic monitoring are necessary for approximately 10 days after surgery. There are only a few cases of spontaneous CGCG regression described in the literature; therefore, a detailed case report of CGCG regression in a 12-yearold boy with a 4-year follow-up is presented and compared with previous studies. (c) 2014 American Association of Oral and Maxillofacial Surgeons
Resumo:
The Box-Cox transformation is a technique mostly utilized to turn the probabilistic distribution of a time series data into approximately normal. And this helps statistical and neural models to perform more accurate forecastings. However, it introduces a bias when the reversion of the transformation is conducted with the predicted data. The statistical methods to perform a bias-free reversion require, necessarily, the assumption of Gaussianity of the transformed data distribution, which is a rare event in real-world time series. So, the aim of this study was to provide an effective method of removing the bias when the reversion of the Box-Cox transformation is executed. Thus, the developed method is based on a focused time lagged feedforward neural network, which does not require any assumption about the transformed data distribution. Therefore, to evaluate the performance of the proposed method, numerical simulations were conducted and the Mean Absolute Percentage Error, the Theil Inequality Index and the Signal-to-Noise ratio of 20-step-ahead forecasts of 40 time series were compared, and the results obtained indicate that the proposed reversion method is valid and justifies new studies. (C) 2014 Elsevier B.V. All rights reserved.
Resumo:
Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)
Resumo:
Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)
Resumo:
Given the importance of Guzera breeding programs for milk production in the tropics, the objective of this study was to compare alternative random regression models for estimation of genetic parameters and prediction of breeding values. Test-day milk yields records (TDR) were collected monthly, in a maximum of 10 measurements. The database included 20,524 records of first lactation from 2816 Guzera cows. TDR data were analyzed by random regression models (RRM) considering additive genetic, permanent environmental and residual effects as random and the effects of contemporary group (CG), calving age as a covariate (linear and quadratic effects) and mean lactation curve as fixed. The genetic additive and permanent environmental effects were modeled by RRM using Wilmink, All and Schaeffer and cubic B-spline functions as well as Legendre polynomials. Residual variances were considered as heterogeneous classes, grouped differently according to the model used. Multi-trait analysis using finite-dimensional models (FDM) for testday milk records (TDR) and a single-trait model for 305-days milk yields (default) using the restricted maximum likelihood method were also carried out as further comparisons. Through the statistical criteria adopted, the best RRM was the one that used the cubic B-spline function with five random regression coefficients for the genetic additive and permanent environmental effects. However, the models using the Ali and Schaeffer function or Legendre polynomials with second and fifth order for, respectively, the additive genetic and permanent environmental effects can be adopted, as little variation was observed in the genetic parameter estimates compared to those estimated by models using the B-spline function. Therefore, due to the lower complexity in the (co)variance estimations, the model using Legendre polynomials represented the best option for the genetic evaluation of the Guzera lactation records. An increase of 3.6% in the accuracy of the estimated breeding values was verified when using RRM. The ranks of animals were very close whatever the RRM for the data set used to predict breeding values. Considering P305, results indicated only small to medium difference in the animals' ranking based on breeding values predicted by the conventional model or by RRM. Therefore, the sum of all the RRM-predicted breeding values along the lactation period (RRM305) can be used as a selection criterion for 305-day milk production. (c) 2014 Elsevier B.V. All rights reserved.
Resumo:
Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)
Resumo:
Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)
Resumo:
Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)