Vitellogenin (rainbow trout) ELISA Kit

This study aimed at analyzing the energetic substrate (ES) in the main storage tissues of Steindachneridion parahybae, throughout the reproductive cycle in captivity. Differently from wild, in captivity, feeding is not interrupted during the reproductive period, the females do not spawn spontaneously, and they are sedentary. Adult females were sampled monthly and based on their histology and gonadosomatic index (GSI), ovaries were classified into: previtellogenic (PRV), vitellogenic (VTG), and regression (REG) stages. Ovaries at the VTG stage showed higher protein and lipids levels than at the PRV stage with a positive correlation between these substrates and the GSI. Muscle was the main source of proteins transferred to the ovaries, according to the negative correlation between these organs. Lipids remained unchanged in the liver, which is an important supplier in vitellogenesis, a pattern that probably occurs due to the continuous feeding. Muscular glycogen levels were higher at the VTG and REG than at the PRV stages. Plasma triglycerides were also higher during REG, while glucose levels were more elevated during the VTG stage. These results suggest that with constant food supply, the pattern of deposition of ES in S. parahybae is different from that described for other wild potamodromous species.

Fuel cells are in risk of starvation due to a possible drop of the oxygen partial pressure during a dynamic operation. Therefore, feedback control of the cathode pressure plays an important role towards an efficient operation of a polymer electrolyte membrane (PEM) fuel cell system. In this paper, hence, a model-based nonlinear pressure control approach using a variable turbine geometry (VTG) is presented. The system model is derived from physical considerations in symbolic form, and parametrized by a least-squares parameter identification. As the derived dynamic model of the cathode subsystem is highly nonlinear, appropriate techniques using differential flatness are applied. Moreover, a sigma-point Kalman filter (SPKF) provides accurate estimates for the state variables and a lumped disturbance. Simulation results show the effectiveness and illustrate the achieved control performance.