Antibacterial Activity and Mechanism of Polygonatum sibiricum Extract Against Bacillus cereus and Its Application in Pasteurized Milk.

The purpose of this study was to reveal the antibacterial activity and mechanism of Polygonatum sibiricum extract (PSE) against Bacillus cereus and further analyze the application of PSE in pasteurized milk (PM). The minimum inhibitory concentration (MIC) and minimum bactericidal concentration (MBC) values and growth curve analysis were used to evaluate the antibacterial activity of PSE against B. cereus. The changes in contents of intracellular adenosine 5'-triphosphate (ATP) and reactive oxygen species (ROS), activities of ß-galactosidase, adenosine triphosphatase (ATPase) and alkaline phosphatase (AKP), cell membrane potential, protein and nucleic acid leakage, and cell morphology were used to reveal the antibacterial mechanism. The effects of PSE on viable count and sensory evaluation of PM during storage were analyzed. The results showed that the MIC and MBC values of PSE against B. cereus were 2 and 4 mg/mL, respectively. Growth curve analysis showed that PSE with a concentration of 2 MIC could completely inhibit the growth of B. cereus. After treatments with PSE, the levels of intracellular ATP and ROS, and activities of ß-galactosidase, ATPase and AKP of B. cereus were significantly reduced (p < 0.05). Cell membrane was depolarized, amounts of protein and nucleic acid leakage were significantly increased (p < 0.05), and cell morphology was destroyed. Furthermore, PSE significantly reduced the viable count of B. cereus in PM and improved the sensory quality of PM during storage (p < 0.05). Together, our findings suggested that PSE had the desired effect as a natural preservative applied in PM.

EEG decoding method based on multi-feature information fusion for spinal cord injury.

To develop an efficient brain-computer interface (BCI) system, electroencephalography (EEG) measures neuronal activities in different brain regions through electrodes. Many EEG-based motor imagery (MI) studies do not make full use of brain network topology. In this paper, a deep learning framework based on a modified graph convolution neural network (M-GCN) is proposed, in which temporal-frequency processing is performed on the data through modified S-transform (MST) to improve the decoding performance of original EEG signals in different types of MI recognition. MST can be matched with the spatial position relationship of the electrodes. This method fusions multiple features in the temporal-frequency-spatial domain to further improve the recognition performance. By detecting the brain function characteristics of each specific rhythm, EEG generated by imaginary movement can be effectively analyzed to obtain the subjects' intention. Finally, the EEG signals of patients with spinal cord injury (SCI) are used to establish a correlation matrix containing EEG channel information, the M-GCN is employed to decode relation features. The proposed M-GCN framework has better performance than other existing methods. The accuracy of classifying and identifying MI tasks through the M-GCN method can reach 87.456%. After 10-fold cross-validation, the average accuracy rate is 87.442%, which verifies the reliability and stability of the proposed algorithm. Furthermore, the method provides effective rehabilitation training for patients with SCI to partially restore motor function.

Deep Convolution Generative Adversarial Network-Based Electroencephalogram Data Augmentation for Post-Stroke Rehabilitation with Motor Imagery.

The motor imagery brain-computer interface (MI-BCI) system is currently one of the most advanced rehabilitation technologies, and it can be used to restore the motor function of stroke patients. The deep learning algorithms in the MI-BCI system require lots of training samples, but the electroencephalogram (EEG) data of stroke patients is quite scarce. Therefore, the expansion of EEG data has become an important part of stroke clinical rehabilitation research. In this paper, a deep convolution generative adversarial network (DCGAN) model is proposed to generate artificial EEG data and further expand the scale of the stroke dataset. First, multichannel one-dimensional EEG data is converted into a two-dimensional EEG spectrogram using EEG2Image based on the modified S-transform. Then, DCGAN is used to artificially generate EEG data based on MI. Finally, the validity of the generated artificial EEG data is proved. This paper preliminarily indicates that generating artificial stroke data is a promising strategy, which contributes to the further development of stroke clinical rehabilitation.

Cronobacter spp. in Commercial Powdered Infant Formula Collected From Nine Provinces in China: Prevalence, Genotype, Biofilm Formation, and Antibiotic Susceptibility.

The purpose of this study was to investigate the prevalence of Cronobacter spp. in commercial powdered infant formula (PIF) from nine provinces in China from March 2018 to September 2020, and to reveal the genotype, biofilm-forming ability, and antibiotic susceptibility of these isolates. A total of 27 Cronobacter strains, consisting of 22 Cronobacter sakazakii strains, 3 Cronobacter malonaticus strains, 1 Cronobacter turicensis strain, and 1 Cronobacter dublinensis strain, were isolated from 3,600 commercial PIF samples with a prevalence rate of 0.75%. Compared with the other 8 provinces, PIF from Shaanxi province had a higher prevalence rate (1.25%) of Cronobacter spp. These isolates were divided into 14 sequence types (STs), and 6 Cronobacter serotypes. The main Cronobacter STs were ST4, ST1, and ST64, and the dominant Cronobacter serotype was C. sakazakii serotype O2. Approximately 88.89% of Cronobacter isolates had a strong ability (OD595 > 1) to form biofilms on tinplate, among which the strains with ST4 were more dominant. All isolates were susceptible to ampicillin-sulbactam, ceftriaxone, cefotaxime, sulfadiazine, sulfadoxine, trimethoprim-sulfamethoxazole, gentamicin, tetracycline, ciprofloxacin, and colistin, while 55.56 and 96.30% isolates were resistant to cephalothin and vancomycin, respectively. Taken together, our findings highlighted the contamination status and characterization of Cronobacter spp. in commercial PIF from nine provinces of China, and provided guidance for the effective prevention and control of this pathogen in the production of PIF.

A framework for motor imagery with LSTM neural network.

BACKGROUND AND OBJECTIVE: How to learn robust representations from brain activities and to improve algorithm performance are the most significant issues for brain-computer interface systems. METHODS: This study introduces a long short-term memory recurrent neural network to decode the multichannel electroencephalogram or electrocorticogram for implementing an effective motor imagery-based brain-computer interface system. The unique information processing mechanism of the long short-term memory network characterizes spatio-temporal dynamics in time sequences. This study evaluates the proposed method using publically available electroencephalogram/electrocorticogram datasets. RESULTS: The decoded features coupled with a gradient boosting classifier could obtain high recognition accuracies of 99% for electroencephalogram and 100% for electrocorticogram, respectively. CONCLUSIONS: The results demonstrated that the proposed model can estimate robust spatial-temporal features and obtain significant performance improvement for motor imagery-based brain-computer interface systems. Further, the proposed method is of low computational complexity.

A transfer learning framework based on motor imagery rehabilitation for stroke.

Deep learning networks have been successfully applied to transfer functions so that the models can be adapted from the source domain to different target domains. This study uses multiple convolutional neural networks to decode the electroencephalogram (EEG) of stroke patients to design effective motor imagery (MI) brain-computer interface (BCI) system. This study has introduced 'fine-tune' to transfer model parameters and reduced training time. The performance of the proposed framework is evaluated by the abilities of the models for two-class MI recognition. The results show that the best framework is the combination of the EEGNet and 'fine-tune' transferred model. The average classification accuracy of the proposed model for 11 subjects is 66.36%, and the algorithm complexity is much lower than other models.These good performance indicate that the EEGNet model has great potential for MI stroke rehabilitation based on BCI system. It also successfully demonstrated the efficiency of transfer learning for improving the performance of EEG-based stroke rehabilitation for the BCI system.