The present study was conducted to evaluate the effects on Atlantic salmon hepatic lipid metabolism when fed diets with increasing substitution of fish oil (FO) with a vegetable oil (VO) blend. Four diets with VOs replacing 100, 90, 79 and 65 % of the FO were fed for 5 months. The levels of eicosapentaenoic acid (EPA; 20:5n-3) and docosahexaenoic acid (DHA; 22:6n-3) in the experimental diets ranged from 1.3 to 7.4 % of fatty acids (FAs), while cholesterol levels ranged from 0.6 to 1.2 g kg(-1). In hepatocytes added [1-(14)C] α-linolenic acid (ALA, 18:3n-3), more ALA was desaturated and elongated to EPA and DHA in cells from fish fed 100 % VO, while in fish fed 65 % VO, ALA was elongated to eicosatrienoic acid (ETE; 20:3n-3), indicating reduced Δ6 desaturation activity. Despite increased desaturation activity and activation of the transcription factor Sp1 in fish fed 100 % VO, liver phospholipids contained less EPA and DHA compared with the 65 % VO group. The cholesterol levels in the liver of the 100 % VO group exceeded the levels in fish fed the 65 % VO diet, showing an inverse relationship between cholesterol intake and liver cholesterol content. For the phytosterols, levels in liver were generally low. The area as a proxy of volume of lipid droplets was significantly higher in salmon fed 100 % VO compared with salmon fed 65 % VO. In conclusion, the current study suggests that suboptimal dietary levels of cholesterol in combination with low levels of EPA and DHA (1.3 % of FAs) can result in minor metabolic perturbations in the liver of Atlantic salmon.

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This paper discusses the H ∞ state estimation issue in regard to Markovian jumping neural networks (MJNNs) under the scheduling of the Round-Robin protocol (RRP). The model takes into account mixed time-delays, sensor nonlinearities and exogenous disturbances, making it relatively general and comprehensive. The transmission of MJNNs signals invoked a communication scheme in which the RRP is used for the data transmissions in order to avoid undesirable data collisions. Protocol-dependent state estimator modeling of a hybrid switching system with mixed time delays and disturbances is designed for the first time to achieve asymptotic tracing for the neuron state. Using the Lyapunov stability theory and several asymptotic methods, sufficient conditions for guaranteeing the asymptotic stability of the state estimation are established under the constraint of H ∞ performance. By employing a combination of matrix analysis techniques, the estimator gain matrices are calculated by the feasible solutions to the linear matrix inequalities (LMIs). Finally, a numerical example and related simulations demonstrate the validity of the proposed model. KeywordsMarkovian jumping neural networks • H ∞ state estimation • Round-Robin protocol • Mixed time delays B Bing Li