MATERIALS AND METHODS: We propose a mixed-method study of mental health assessment that combines psychological questionnaires with facial emotion analysis to comprehensively evaluate the mental health of students on a large scale. The Depression Anxiety and Stress Scale-21(DASS-21) is used for the psychological questionnaire. The facial emotion recognition model is implemented by transfer learning based on neural networks, and the model is pre-trained using FER2013 and CFEE datasets. Among them, the FER2013 dataset consists of 48 × 48-pixel face gray images, a total of 35,887 face images. The CFEE dataset contains 950,000 facial images with annotated action units (au). Using a random sampling strategy, we sent online questionnaires to 400 college students and received 374 responses, and the response rate was 93.5%. After pre-processing, 350 results were available, including 187 male and 153 female students. First, the facial emotion data of students were collected in an online questionnaire test. Then, a pre-trained model was used for emotion recognition. Finally, the online psychological questionnaire scores and the facial emotion recognition model scores were collated to give a comprehensive psychological evaluation score.
RESULTS: The experimental results of the facial emotion recognition model proposed to show that its classification results are broadly consistent with the mental health survey results. This model can be used to improve efficiency. In particular, the accuracy of the facial emotion recognition model proposed in this paper is higher than that of the general mental health model, which only uses the traditional single questionnaire. Furthermore, the absolute errors of this study in the three symptoms of depression, anxiety, and stress are lower than other mental health survey results and are only 0.8%, 8.1%, 3.5%, and 1.8%, respectively.
CONCLUSION: The mixed method combining intelligent methods and scales for mental health assessment has high recognition accuracy. Therefore, it can support efficient large-scale screening of students' psychological problems.
METHODS: A total of 480 Ae. aegypti adult mosquitoes were collected from October and November 2018 based on the results of previous investigations and the distribution of Ae. aegypti in Yunnan. Each individual sample was processed and screened for the presence of Wolbachia by PCR with wsp primers. Phylogenetic trees for the wsp gene was constructed using the neighbour-joining method with 1,000 bootstrap replicates, and the p-distance distribution model of molecular evolution was applied.
RESULTS: 24 individual adult mosquito samples and 10 sample sites were positive for Wolbachia infection. The Wolbachia infection rate (IR) of each population ranged from 0 - 41.7%. The infection rate of group A alone was 0%-10%, the infection rate of group B alone was 0%-7.7%, and the infection rate of co-infection with A and B was 0-33.3%.
CONCLUSIONS: Wolbachia infection in wild Ae. aegypti in China is the first report based on PCR amplification of the Wolbachia wsp gene. The Wolbachia infection is 5%, and the wAlbA and wAlbB strains were found to be prevalent in the natural population of Ae. aegypti in Yunnan Province.