Research on a real-time dynamic monitoring method for silent aspiration after stroke based on semisupervised deep learning: A protocol study.

Objective: This study aims to establish a real-time dynamic monitoring system for silent aspiration (SA) to provide evidence for the early diagnosis of and precise intervention for SA after stroke. Methods: Multisource signals, including sound, nasal airflow, electromyographic, pressure and acceleration signals, will be obtained by multisource sensors during swallowing events. The extracted signals will be labeled according to videofluoroscopic swallowing studies (VFSSs) and input into a special dataset. Then, a real-time dynamic monitoring model for SA will be built and trained based on semisupervised deep learning. Model optimization will be performed based on the mapping relationship between multisource signals and insula-centered cerebral cortex-brainstem functional connectivity through resting-state functional magnetic resonance imaging. Finally, a real-time dynamic monitoring system for SA will be established, of which the sensitivity and specificity will be improved by clinical application. Results: Multisource signals will be stably extracted by multisource sensors. Data from a total of 3200 swallows will be obtained from patients with SA, including 1200 labeled swallows from the nonaspiration category from VFSSs and 2000 unlabeled swallows. A significant difference in the multisource signals is expected to be found between the SA and nonaspiration groups. The features of labeled and pseudolabeled multisource signals will be extracted through semisupervised deep learning to establish a dynamic monitoring model for SA. Moreover, strong correlations are expected to be found between the Granger causality analysis (GCA) value (from the left middle frontal gyrus to the right anterior insula) and the laryngeal rise time (LRT). Finally, a dynamic monitoring system will be established based on the former model, by which SA can be identified precisely. Conclusion: The study will establish a real-time dynamic monitoring system for SA with high sensitivity, specificity, accuracy and F1 score.

[Study on relationship between effective components and soil enzyme activity in different growth patterns of Panax ginseng].

Study on 5 effective components and 6 soil enzyme activities of 2 different growth patterns, analyse the dates with the canonical correlation analysis, In order to reveal the relations between the effective components and soil enzyme activities. The result showed that they had a great relation between the effective components and soil enzyme activities, the activity of the same enzyme in humus soil was higher than that in farmland soil. Growth pattern of farmland soil, if the invertase and phosphatase activity were too high, which would inhibit the accumulation of total ginsenoside, water-miscible total proteins and total amino acid; Growth pattern of humus soil, if the invertase, urease and phosphatase activity were too high, which would inhibit the accumulation of total ginsenoside and the total essential oils. Integral soil enzyme activity can be used as a index of soil quality, which, together with other growth factors. The appropriate enzyme activity can accelerate the circulation and transformation of all kinds of material in the soil, improve effectively components accumulation.