BackgroundGamma-aminobutyric acid is a major inhibitory neurotransmitter in mammalian brains, and has several well-known physiological functions. Lactic acid bacteria possess special physiological activities and are generally regarded as safe. Therefore, using lactic acid bacteria as cell factories for gamma-aminobutyric acid production is a fascinating project and opens up a vast range of prospects for making use of GABA and LAB. We previously screened a high GABA-producer Lactobacillus brevis NCL912 and optimized its fermentation medium composition. The results indicated that the strain showed potential in large-scale fermentation for the production of gamma-aminobutyric acid. To increase the yielding of GABA, further study on the fermentation process is needed before the industrial application in the future. In this article we investigated the impacts of pyridoxal-5'-phosphate, pH, temperature and initial glutamate concentration on gamma-aminobutyric acid production by Lactobacillus brevis NCL912 in flask cultures. According to the data obtained in the above, a simple and effective fed-batch fermentation method was developed to highly efficiently convert glutamate to gamma-aminobutyric acid.ResultsPyridoxal-5'-phosphate did not affect the cell growth and gamma-aminobutyric acid production of Lb. brevis NCL912. Temperature, pH and initial glutamate concentration had significant effects on the cell growth and gamma-aminobutyric acid production of Lb. brevis NCL912. The optimal temperature, pH and initial glutamate concentration were 30-35°C, 5.0 and 250-500 mM. In the following fed-batch fermentations, temperature, pH and initial glutamate concentration were fixed as 32°C, 5.0 and 400 mM. 280.70 g (1.5 mol) and 224.56 g (1.2 mol) glutamate were supplemented into the bioreactor at 12 h and 24 h, respectively. Under the selected fermentation conditions, gamma-aminobutyric acid was rapidly produced at the first 36 h and almost not produced after then. The gamma-aminobutyric acid concentration reached 1005.81 ± 47.88 mM, and the residual glucose and glutamate were 15.28 ± 0.51 g L-1 and 134.45 ± 24.22 mM at 48 h.ConclusionsA simple and effective fed-batch fermentation method was developed for Lb. brevis NCL912 to produce gamma-aminobutyric acid. The results reveal that Lb. brevis NCL912 exhibits a great application potential in large-scale fermentation for the production of gamma-aminobutyric acid.

Production of gamma-aminobutyric acid (GABA) was carried out in Erlenmeyer flasks by Lactobacillus brevis NCL912. Traditional methods were first adopted to select the key factors that impact the GABA production to preliminarily determine the suitable concentration ranges of the key factors. It was found that glucose, soya peptone, Tween-80 and MnSO(4).4H(2)O were the key factors affecting GABA production. Then, response surface methodology was applied to analyze the optimum contents of the four key factors in the medium, and the production of GABA was predicted as 349.69 mM under the optimized conditions with this model. Afterward, the experiment was performed under the optimized conditions, and the yield of GABA reached 345.83 mM, which was 130% higher than the initial medium. The results showed that experimental yield and predicted values of GABA yield were in good agreement.

No abstract

With the improvement of people's living standards, the demand for health monitoring and exercise detection is increasing. It is of great significance to study human activity recognition methods that are different from traditional feature extraction methods. This article uses convolutional neural network algorithms in deep learning to automatically extract features of activities related to human life. It uses a stochastic gradient descent algorithm to optimize the parameters of the convolutional neural network. The trained network model is compressed on STM32CubeMX-AI. Finally, this article introduces the use of neural networks on embedded devices to recognize six human activities of daily life, such as sitting, standing, walking, jogging, upstairs and downstairs. The acceleration sensor related to human activity information is used to obtain the relevant characteristics of the activity, thereby solving the human activity recognition (HAR) problem. The network structure of the constructed CNN model is shown in Figure 1, including an input layer, two convolutional layers and two pooling layers. After comparing the average accuracy of each set of experiments and the test set of the best model obtained from it, the best model is then selected.

No abstract

No abstract

No abstract

Gamma-aminobutyric acid (GABA) is a non-proteinaceous amino acid that is widely distributed in nature and acts as the major inhibitory neurotransmitter in the mammalian brain. This study aimed to find a separation method for getting high-purity GABA from a fermented broth. Firstly, a fermented broth with a high content of GABA (reaching 997 ± 51 mM) was prepared by fermentation with Lactobacillus brevis NCL912. GABA purification was conducted by successive centrifugation, filtration, decoloration, desalination, ion-exchange chromatography (IEC), and crystallization. Inorganic salt (Na₂SO₄) was removed from the both by desalination with 70% ethanol solution. A ninhydrin test strip was designed for the real-time detection of GABA during IEC. The recovery rate for the whole purification process was about 50%. The purified product was characterized by thin-layer chromatography and HPLC, and its purity reached 98.66 ± 2.36%.