S Shrifi, H Rahmani, M Motamedi,
Volume 13, Issue 47 (4-2009)
Abstract
In dairy cattle, the increase in milk yield has been accompanied by a more negative energy balance during early lactation and a decrease in fertility. The 167 amino acid protein product of the ob gene was named leptin (derived from the Greek term ‘leptos’ meaning thin). The leptin hormone, as a 16-kDa protein is synthesized mainly by adipose tissue and is involved in regulation of food intake, energy balance, fertility and immune function. The expression and secretion of leptin are correlated with body fat mass and are acutely affected by changes in food intake. The objective of this study was to investigate the fluctuations of leptin concentrations during late pregnancy and early lactation, the effect of parity and BCS on plasma leptin concentration, and to investigate its fluctuation effects on reproductive status. Blood samples of 54 Holstein cows were taken at a fixed time of the day after milking but before feeding, from 2 weeks before calving until 6 weeks after calving. BCS and plasma leptin concentration were measured at 2-wk intervals. Leptin concentrations were affected by parity. Primiparous cows and cows in first parity had higher leptin concentrations compared with multiparous cows (P<0.05). Leptin concentration was not different in late pregnancy and early lactation. BCS was negatively correlated with plasma leptin concentration (P<0.05). Plasma leptin concentration did not influence reproductive traits (days in milk at first breeding, service per conception and open days).
E. Shrifi Garmdareh, M. Vafakhah, S. Eslamian,
Volume 23, Issue 1 (Spring 2019)
Abstract
Flood discharge estimation with different return periods is one of important factors for water structures design and installation. On the other hand, a lot of rivers existing in Iran watersheds have no complete and accurate hydrometric data. In these cases, one of the suitable solutions to estimate peak discharges with different return periods is the regional flood analysis. In this research, 55 hydrometric stations were used. For this purpose, at first, peak discharges in different return periods were estimated using the EasyFit software. Then, the effective variables on the peak discharges were collected and the input variables of the models were selected by using gamma test with the help of the WinGamma software. Finally, data modeling was performed using the support vector machine, artificial neural networks and nonlinear multivariate regression techniques. Quantitative and qualitative assessment of the results using various indices including Nash-Sutcliffe Efficiency Coefficient (NSC) showed that SVM modeling method had the most accuracy in comparison to the other two modeling methods to predict the peak discharges in the Namak Lake Watershed.
