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Objective: To explore the different processes of the themes and emotional evolution of various stakeholders in the network public opinion of sudden public health emergencies at different stages of the public opinion evolution lifecycle. Methods: This paper proposes a cross-platform analysis method for online public opinion during the public health emergencies based on stakeholders. Firstly, data from multiple platforms are collected and integrated. Secondly, stakeholders are categorized and the stages of public opinion evolution are determined based on stakeholder theory and lifecycle theory. Finally, the Latent Dirichlet Allocation (LDA)+Word2vec model and Convolutional Neural Network (CNN) model are used to analyze the themes and emotional evolution of stakeholders during different stages of public opinion evolution. Results: There are differences in the evolution patterns of different types of stakeholders. The evolution process of stakeholders' focus points exhibits a two-stage transition from concentration to divergence. The focus points of stakeholders are closely associated with their respective social domains. The emotions of the public undergo a three-stage process of positive-negative-positive change. Conclusions: This study can provide a reference for the government to have a more comprehensive understanding of the development trend of public opinion and reduce the negative impact of public opinion. [ABSTRACT FROM AUTHOR]

Motor rolling bearings are the important supporting components of motors. It can ensure the stable operation of motor equipment in the power grid, and bearing life prediction of it is a key issue. To solve the problem of low accuracy of remaining useful life (RUL) prediction for motor rolling bearings, a neural network model based on Weibull proportional hazards model (WPHM) and stochastic configuration networks (SCNs) is proposed. To better extract and analyze features of the bearing vibration signal in both time and frequency domains, kernel principal component analysis (KPCA) is used to reduce the dimensionality of the data. Then, a WPHM model using the top three contributing feature parameters is built, which sets the start time based on the failure rate curve and reliability function. Finally, the validity of the model is verified with the rolling bearing full life cycle dataset from the IEEE PHM 2012 Data Challenge, and a comparison with other machine learning models shows that the accuracy of the proposed model in RUL prediction is higher. [ABSTRACT FROM AUTHOR]

Probiotic intervention has beneficial effects on host brain function and behavior via regulating microbiota-gut-brain axis; however, the underlying mechanism is not yet understood. Herein, we investigated that the effects of Lactobacillus plantarum DP189 (DP189) administration in preventing cognitive dysfunction and pathology of Alzheimer's disease (AD) in D-galactose (D-gal) and AlCl3-induced AD model mice. After L. plantarum DP189 intervention for 10 weeks, we assessed cognitive behavior, neurotransmitter expression, histological changes, microbial communities, and the mechanisms underlying the disease in AD model mice. The results showed that L. plantarum DP189 intervention prevented cognitive dysfunction by behavioral test. Increased levels of serotonin, dopamine, and gamma-aminobutyric acid positively affected the pathological processes by ameliorating neuronal damage, beta-amyloid deposition, and tau pathology. L. plantarum DP189 intervention simultaneously modulated the gut microbial communities to alleviate gut dysbiosis. Moreover, L. plantarum DP189 inhibited tau hyperphosphorylation by regulating the PI3 K/Akt/GSK-3β pathway. These findings indicated that L. plantarum DP189 intervention is a promising therapeutic strategy to prevent the onset and development of AD. [ABSTRACT FROM AUTHOR]

As a part of Intelligent Transportation System (ITS), the vehicle traffic sign detection and recognition system have been paid more attention by Intelligent transportation researchers, the traffic sign detection and recognition algorithm based on convolution neural network has great advantages in expansibility and robustness, but it still has great optimization space inaccuracy, computation and storage space. In this paper, we design a multiscale feature fusion algorithm for traffic sign detection and recognition. In order to improve the accuracy of the network, the gaussian distribution characteristics are used in the loss function. The training and analysis of two neural networks with different feature scales and YOLOv3-tiny were carried out on the Tsinghua-Tencent open traffic sign dataset. The experimental results show that the detection and recognition of the targets by networks with multiple feature scales have improved significantly, and the recall and accuracy are 95.32% and 93.13% respectively. Finally, the algorithm of traffic sign detection and recognition is verified on the NVIDIA Jetson Tx2 platform and delivers 28 fps outstanding performances. [ABSTRACT FROM AUTHOR]

Probiotic intervention has beneficial effects on host brain function and behavior via regulating microbiota-gut-brain axis; however, the underlying mechanism is not yet understood. Herein, we investigated that the effects of Lactobacillus plantarum DP189 (DP189) administration in preventing cognitive dysfunction and pathology of Alzheimer's disease (AD) in D-galactose (D-gal) and AlCl 3 -induced AD model mice. After L. plantarum DP189 intervention for 10 weeks, we assessed cognitive behavior, neurotransmitter expression, histological changes, microbial communities, and the mechanisms underlying the disease in AD model mice. The results showed that L. plantarum DP189 intervention prevented cognitive dysfunction by behavioral test. Increased levels of serotonin, dopamine, and gamma-aminobutyric acid positively affected the pathological processes by ameliorating neuronal damage, beta-amyloid deposition, and tau pathology. L. plantarum DP189 intervention simultaneously modulated the gut microbial communities to alleviate gut dysbiosis. Moreover, L . plantarum DP189 inhibited tau hyperphosphorylation by regulating the PI3 K/Akt/GSK-3β pathway. These findings indicated that L. plantarum DP189 intervention is a promising therapeutic strategy to prevent the onset and development of AD.

Recently, the efficacy of probiotics in treatment of neurodegenerative disorders has been reported in animal and clinical studies. Here, we assessed the effects of Bacillus coagulans JA845 in counteracting the symptoms of D-galactose (D-gal)/AlCl 3 -induced Alzheimer's disease (AD) in a mice model through behavioral test, histological assessment and biochemical analysis. Ten weeks of pre-treatment with B. coagulans JA845 prevented cognitive decline, attenuated hippocampal lesion and protected neuronal integrity, which demonstrated the neuroprotective features of B. coagulans JA845 in vivo. We also found that supplementation of B. coagulans JA845 alleviated amyloid-beta deposits and hyperphosphorylated tau in hippocampus of D-gal/AlCl 3 -induced AD model mice. Furthermore, B. coagulans JA845 administration attenuated oxidative stress and decreased serum concentration of inflammatory cytokines by regulating the Nrf2/HO-1 and MyD88/TRAF6/NF-κB pathway. Our results demonstrated for the first time that B. coagulans has the potential to help prevent cognitive decline and might be a novel therapeutic approach for the treatment of neurodegenerative diseases.