BackgroundNegative energy balance (NEB) is a common pathological foundation of ketosis and fatty liver. Liver and fat tissue are the major organs of lipid metabolism and take part in modulating lipid oxidative capacity and energy demands, which is also a key metabolic pathway that regulates NEB develop during perinatal period. Fibroblast growth factor-21 (FGF-21) is a recently discovered protein hormone that plays an important and specific regulating role in adipose lipid metabolism and liver gluconeogenesis for human and mouse. Our aim is to investigate the variation and relationship between serum FGF-21 concentration and characteristic parameters related to negative energy balance in different energy metabolism state.MethodsIn this research, five non-pregnant, non-lactating Holstein–Friesian dairy cows were randomly allocated into two groups. The interventions were a controlled-energy diet (30 % of maintenance energy requirements) and a moderate-energy diet (120 % of predicted energy requirements) that lasted for the duration of the experiment. We measured biochemical parameters, serum FGF-21, leptin and insulin levels by commercial ELISA kits.ResultsThe results showed that serum FGF-21 levels were significantly higher in both groups treated with a controlled-energy diet, while FGF-21 levels in both groups treated with moderate-energy diet were low. FGF-21 levels exhibited a significant positive correlation with serum leptin levels, while an inverse relationship was found between FGF-21 and blood glucose and β-hydroxybutyrate acid (BHBA) levels.ConclusionAn increase in FGF-21 levels after a controlled-energy diet treatment may contribute to a positive metabolic effect which could result in a new theoretical and practical basis for the preventive strategy of dairy cows with NEB.

The objective of this study was to investigate the measurement of serum fibroblast growth factor-21 (FGF-21), a protein mainly synthesized by the liver, as a sensitive biomarker for diagnosis of ketosis in dairy cows. Ninety Holstein-Friesian dairy cows (60 healthy and 30 ketosis cases) were selected and divided into a Ketosis group (K), and a Control group (C). We measured serum FGF-21 and other biochemical parameters by commercial ELISA kits. In a combined population of all 90 cows, we found that serum FGF-21 level was lower (P < 0.001) in cows suffering from ketosis. When the β-hydroxybutyric acid (BHBA) level increased over 1.2 mmol/L, the FGF-21 level tended to decline below 300.85 pg/ml. The area under the receiver operating characteristic curve (AUC-ROC) for serum FGF-21 for diagnosis of fatty liver was 0.952-0.025 [95% confidence interval (CI) 0.904, 1.000] which was higher than the AUC-ROC for glucose (Glc) and other tested parameters. We concluded that FGF-21 could be a diagnostic parameter in the evaluation and auxiliary diagnosis of changes in the energy metabolism state, and serum FGF-21 measurement would have a considerable clinical impact and lead to greater profitability in the dairy industry.

In speech emotion recognition, the use of deep learning algorithms that extract and classify features of audio emotion samples usually requires the use of a large amount of resources, which makes the system more complex. This paper proposes a speech emotion recognition system based on dynamic convolutional neural network combined with bi-directional long and short-term memory network. On the one hand, the dynamic convolutional kernel allows the neural network to extract global dynamic emotion information, which can improve the performance while ensuring the computational power of the model, and on the other hand, the bi-directional long and short-term memory network enables the model to classify the emotion features more effectively with the temporal information. In this paper, we use CISIA Chinese speech emotion dataset, EMO-DB German emotion corpus and IEMOCAP English corpus to conduct experiments, and the average emotion recognition accuracy of the experimental results are 59.08%, 89.29% and 71.25%, which are 1.17%, 1.36% and 2.97% higher than the accuracy of speech emotion recognition systems using mainstream models, respectively. The effectiveness of the method in this paper is proved.