Human respiratory syncytial virus (RSV) is a common cause of respiratory infections in infants, young children, and elderly people. However, there are no effective treatments or vaccines available in most countries. In this study, we explored the anti-RSV potential of 2, 4-Di-tert-butylphenol (2, 4-DTBP), a compound derived from Houttuynia cordata Thunb. To overcome the poor solubility of 2, 4-DTBP, we encapsulated it in polymeric micelles and delivered it by inhalation. We found that 2, 4-DTBP-loaded micelles inhibited RSV infection in vitro and improved survival, lung pathology, and viral clearance in RSV-infected mice. Our results suggested that 2, 4-DTBP-loaded micelle is a promising novel therapeutic agent for RSV infection.

Identifying changes in driver’s vigilance under combined manual and automated driving conditions, as well as during prolonged driving, is crucial for reducing car crashes. Existing studies have not adequately considered the similarities and differences in physiological signals between different driving modes or the cumulative effects during extended driving periods. To address this gap, our study focuses on enhancing the feature selection method for driver’s vigilance recognition. A long-duration simulated car-following driving experiment was designed and conducted to simultaneously collect EEG, eye movement, EOG, and driving data. Similarities and differences in the physiological signals of vigilance between manual and automated driving are analyzed in terms of correlation and significance. The cumulative effects of physiological signals are investigated using time series analysis. The proposed feature selection method was validated using an LSTM-based driver’s vigilance recognition model. Results showed a recognition accuracy of 86.32% for manual driving, with a fluctuation rate of 1.18% over prolonged periods. For automated driving, the accuracy was 87.12%, with a fluctuation rate of 0.66%. By considering the similarities and differences in physiological signals between manual and automated driving modes and the cumulative effects, our study enhances the applicability and stability of the driver’s vigilance recognition model across different driving conditions. The validation results demonstrate that the proposed method improves the applicability and stability of the driver’s vigilance recognition model across different driving modes during extended driving periods.

Conventional eye trackers are difficult to deploy in fashion-runway settings because they require head stabilization, wearable devices, close-range cameras, or infrared illumination. This study develops a far-field, non-contact eye-tracking framework and prototype system for measuring target-directed visual attention in runway-viewing settings using standard cameras. The framework integrates eye-region normalization, 3D gaze estimation, individualized homography-based point-of-regard mapping onto a scene plane, adaptive filtering, DBSCAN-based outlier removal, and kernel density estimation. In Experiment 1, 16 participants completed a controlled calibration-grid benchmark with the proposed system and EyeLink 1000 Plus. Compared with the EyeLink 1000 Plus, the proposed system exhibited higher positional error ($A_{\mathrm{pos}}$: $2.50\pm 0.25°$ vs. $1.74\pm 0.55°$) and a higher value on the processed-output spatial-precision measure ($A_{\mathrm{spa}}$: $2.31\pm 0.24°$ vs. $1.02\pm 0.37°$). These results characterize end-to-end localization under the calibrated benchmark geometry and provide a controlled reference for the subsequent AOI-level evaluation. In Experiment 2, an independent sample of 16 participants completed an instructed right chest targeting task at 2 m, 4 m and 6 m. AOI-retained sample percentages were 80.4%, 97.3%, and 96.0%, respectively, and gaze-density maps concentrated within the predefined upper-body target region. These findings support the feasibility of target-directed AOI-level localization at far-field distances without chin-rest stabilization under the tested simulated runway-observation protocol.