ABSTRACT
Maintenance of thermal homeostasis in rats fed a high-fat diet (HFD) is associated with changes in their thermal balance. The thermodynamic relationship between heat dissipation and energy storage is altered by the ingestion of high-energy diet content. Observation of thermal registers of core temperature behavior, in humans and rodents, permits identification of some characteristics of time series, such as autoreference and stationarity that fit adequately to a stochastic analysis. To identify this change, we used, for the first time, a stochastic autoregressive model, the concepts of which match those associated with physiological systems involved and applied in male HFD rats compared with their appropriate standard food intake age-matched male controls (n=7 per group). By analyzing a recorded temperature time series, we were able to identify when thermal homeostasis would be affected by a new diet. The autoregressive time series model (AR model) was used to predict the occurrence of thermal homeostasis, and this model proved to be very effective in distinguishing such a physiological disorder. Thus, we infer from the results of our study that maximum entropy distribution as a means for stochastic characterization of temperature time series registers may be established as an important and early tool to aid in the diagnosis and prevention of metabolic diseases due to their ability to detect small variations in thermal profile.
Subject(s)
Animals , Male , Diet, High-Fat , Dietary Fats/metabolism , Energy Intake/physiology , Energy Metabolism/physiology , Algorithms , Models, Biological , Rats, Sprague-Dawley , Stochastic Processes , Time FactorsABSTRACT
Este trabalho analisou o mecanismo de transmissão dos preços dos principais estados produtores de arroz no Brasil (Rio Grande do Sul, Mato Grosso e Santa Catarina), como variáveis endógenas, e volume dos estoques públicos no Brasil, como variável exógena, para o período de julho de 2004 até dezembro de 2010. A análise compreende o uso da metodologia VAR-VEC para identificar o número de defasagens utilizadas e verificar se existem co-integrações entre as variáveis, por meio do Teste de Co-integração de Johansen. Testou-se a existência de causalidade entre as séries pelo método de causalidade de Granger. Aplicou-se a decomposição da variância do erro de previsão e a função impulso-resposta com decomposição de Cholesky para analisar a relação entre os preços dos estados e volume em estoques públicos. Com a aplicação da metodologia proposta, pode-se observar que o preço do arroz no estado do Rio Grande do Sul influencia no preço no estado de Santa Catarina e no volume armazenado em estoques públicos e é influenciado pelo preço do arroz no estado do Mato Grosso.
This study examined the mechanism of prices from major rice producing states in Brazil (Rio Grande do Sul, Mato Grosso and Santa Catarina), as endogenous variables, and volume of public stocks in Brazil, as exogenous variables for the period July 2004 until December 2010. The analysis includes the use of VAR-VEC methodology to identify the number of lags used and check for co-integration among variables through the test of Johansen Co-integration. We tested the existence of causality between variables by the method of Granger causality. We applied the variance decomposition of forecast error and impulse-response function with Cholesky decomposition to analyze the relationship between prices and volume of the states in public stocks. With the proposed methodology, one can observe that the price of rice in the state of Rio Grande do Sul to the price in the state of Santa Catarina and the volume stored in stockpiles and is influenced by the price of rice in the state of Mato Grosso.
ABSTRACT
Em experimentos de competição de cultivares de citros, geralmente são utilizados muitos tratamentos, o que requer o emprego de grandes blocos e parcelas com poucas plantas. Tem sido debatido que, nessas condições, pode ocorrer a correlação entre parcelas vizinhas, violando assim a pressuposição de erros independentes da análise de variância. O presente trabalho teve por objetivo avaliar diferentes parametrizações de modelos, considerando ou não a dependência espacial entre parcelas, em dois experimentos de competição de clones de laranjeira Pêra (Citrus sinensis L. Osbeck). Foi utilizada a estrutura auto-regressiva separável de primeira ordem (AR1 x AR1) como modelo de dependência espacial entre os erros. Os resultados encontrados apontam que a modelagem espacial dos erros utilizando modelos auto-regressivos separáveis de primeira ordem para experimentos de seleção de clones de laranjeira Pêra, normalmente trazem pequenos ganhos em termos de qualidade de ajuste. A análise desconsiderando o fator blocos mais o ajuste espacial auto-regressivo separável de primeira ordem apresentou melhor qualidade de ajuste entre os modelos avaliados.
In competition experiments of citrus cultivations one generally uses many treatments, which requires the use of big blocks and plots with few plants. One has debated that in these conditions there can occur the correlation between neighboring plots, violating, thus, the presupposition of errors independent from the variance analysis. The present work has had as objective to evaluate different model parametrizations, considering or not the spatial dependence between plots, in two competition experiments of Pera orange tree clones (Citrus sinensis L. Osbeck). One has utilized the separable auto-regressive structure of first order (AR1 x AR1) as a model of spatial dependence between the errors. The results found indicate that the spatial modeling of the errors by utilizing separable auto-regressive models of first order for selection experiments of Pera orange tree clones normally bring small gains in terms of quality of adjustment. The analysis not considering the block factor plus the separable auto-regressive spatial adjustment of first order has presented better quality of adjustment between the models evaluated.