Circulating Thyroid Hormones and Indices of Energy and Lipid Metabolism in Normal and Hormonally Induced Oestrus Cows
Mehdi Mohebbi-Fani, Saeed Nazifi, Somayeh Bahrami and Omid Jamshidi
Abstract: In a field study, circulating thyroid hormones, their free forms and indices of energy and lipid metabolism were measured in blood samples of 16 dairy cows expressing detectable oestrus signs. The cows were divided into two equal groups according to their days in milk (DIM=53-90 and DIM=100-150). In each group, 4 cows expressed the oestrus signs normally and the others were induced by hormone injection. Serum thyroxin (T4), free thyroxin (fT4), triiodothyronine (T3), free triiodothyronine (fT3), glucose, beta-hydroxybutyrate (BHB), non-esterified fatty acids (NEFA), triglyceride (TAG), cholesterol, very low density lipoproteins (VLDL-cholesterol), low density lipoproteins (LDL-cholesterol) and high density lipoproteins (HDL-cholesterol) were investigated. Comparison of all oestrus cows (normal or induced) between DIM groups (n=8 each) revealed lower levels of T4 (P=0.027) and T3 (P=0.022), but higher concentrations of fT4 (P=0.031) and fT3 (P=0.006) in the cows with lower DIM. Higher concentrations of TAG and VLDL (P=0.021) and cholesterol (P=0.046) as well as a tendency (P=0.074) for lower levels of BHB were other remarkable findings in cows with lower DIM. In cows with DIM=53-90, the normal oestrus cows had higher levels of T3 (P=0.044) as well as tendencies (P=0.083) for higher T4 and lower fT4 compared with induced cows. In cows with DIM=100-150, however, no significant difference was observed between the normal and induced oestrus cows. In conclusion, the cows that express oestrus signs normally may have better metabolic and thyroid hormone conditions compared to those that express heat by hormone injection. With progress in DIM, however, such differences may become less evident.
Keywords: Thyroid hormones, Oestrus, Dairy cows, Metabolic status
Poisson Regression Models for Count Data: Use in the Number of Deaths in the Santo Angelo (Brazil)
Suzana Russo, Diego Flender and Gabriel Francisco da Silva
Abstract: When speaking about data, presuppose good quality to these. Otherwise would be affected the accuracy of the information, which would lead to false interpretations. In Health Statistics data are obtained through surveys, presented in its simplest expression, taking advantage of existing records, making an inquiry, or by means of experiments. The rational organization of the data allows characterizing what are the priority issues and thus establishing health programs. To analyze the mortality data it is necessary to be considered the mortality in certain age groups, so we can find data that show the incidence of major groups of deaths. From the analysis of data is followed by subsequent formulation of the Poisson regression models, where each group in question, by age group is represented by a number of counting time. The Poisson regression model is a specific type of Generalized Linear Models (GLM) and non-linear. As , its main features are: a) provides, in general, a satisfactory description of experimental data whose variance is proportional to the mean, b) can be deduced theoretically from first principles with a minimum of restrictions; c) If events occur independently and randomly in time with constant average rate of occurrence, the model determines the number of time specified. At the end of this study could be seen that through the analysis of the data could be found that the age group from 70 to 79 years old is the highest incidence of deaths with 21.1%, then comes the range of 60 to 69 years old with 20%, and was also recorded for time worked, in January 2000 to December 2004, the death rate was 52.27, and variance equal to 102.43, the city of Santo Angelo (Brazil). It was further found that the data are analyzed overdispersion, variance greater than average, then it was necessary to remove the overdispersion to find the appropriate template. With the pattern found was made short-term forecasts.
Keywords: Deaths, Poisson regression models, Overdispersion