OBJECTIVE: Based on network pharmacology, the mechanism of action of the combination of plum and licorice in treating allergic asthma was explored. METHODS: 134 common targets were identified. Molecular docking confirmed strong binding affinity between the top 10 key targets and active ingredients. RESULTS: Analysis revealed$\mathbf{1 3 4}$common targets between the herb-pair and the disease. Furthermore, molecular docking analysis demonstrated that the top 10 key targets exhibited favorable binding energies with the active ingredients. CONCLUSION:$\boldsymbol{\beta}$-Sitosterol is a primary active ingredient, with TP53 as its direct target. Key therapeutic targets (e.g., STAT3, TP53, IL6, TNF, ESR1, MAPK1, AKT1, MAPK3) suggest the mechanism involves suppressing airway inflammation, modulating immune balance, and alleviating airway remodeling.

Accurate 3D target selection in virtual reality (VR) is fundamentally impeded by pointing uncertainty along the depth axis, a challenge that existing 2D pointing models fail to address due to the complexities of depth perception. Near-eye interactions in VR are influenced by binocular depth cues and vergence-accommodation conflicts (VAC), which introduce significant depth perception biases that impair predictive performance. To address this issue, we first investigate these factors and derive a Gaussian distribution to model near-field depth biases within a 2.5m range. Second, to analyze pointing performance across this extended depth range, we classify 3D target motions into three distinct types: motion-indepth, motion-in-plane, and combined motion. Our analysis identifies that interaction depth and motion amplitude are the two most critical factors influencing pointing accuracy. Accordingly, by incorporating these factors alongside our perceptual bias Gaussian into the Ternary-Gaussian framework, we demonstrate significantly improved predictive performance across diverse 3D motion scenarios. These findings enhance the understanding of user perception in virtual environments and support the development of precise, context-aware interaction cues. Future research can extend these models to design real-time adaptive interfaces, thereby elevating user experiences in VR.

Due to the limited resolution of hyperspectral sensors, anomalous targets expressed with subpixels are often mixed with nonhomogeneous backgrounds. This fact makes anomalies difficult to distinguish from the surrounding background. From this perspective, a novel hyperspectral anomaly detection (AD) algorithm based on endmember extraction and low-rank representation (LRR) is proposed. For the characteristics of pixels in hyperspectral images (HSIs), the proposed algorithm employs an endmember extraction technology to yield an abundance matrix for AD, thereby gathering more feature information compared with the direct use of a raw image. In addition, a dictionary construction strategy based on Tucker decomposition, and the${k}$-means++ clustering method is proposed to make the dictionary more stable and discriminative. An LRR method based on the dictionary is applied to obtain a sparse residual matrix. Finally, anomalies can be determined by the response of the residual matrix. Experiments on three hyperspectral data sets validate the performance of the proposed algorithm.