RESUMEN
Real-world graphs exhibit increasing heterophily, where nodes no longer tend to be connected to nodes with the same label, challenging the homophily assumption of classical graph neural networks (GNNs) and impeding their performance. Intriguingly, from the observation of heterophilous data, we notice that certain high-order information exhibits higher homophily, which motivates us to involve high-order information in node representation learning. However, common practices in GNNs to acquire high-order information mainly through increasing model depth and altering message-passing mechanisms, which, albeit effective to a certain extent, suffer from three shortcomings: (1) over-smoothing due to excessive model depth and propagation times; (2) high-order information is not fully utilized; (3) low computational efficiency. In this regard, we design a similarity-based path sampling strategy to capture smooth paths containing high-order homophily. Then we propose a lightweight model based on multi-layer perceptrons (MLP), named PathMLP, which can encode messages carried by paths via simple transformation and concatenation operations, and effectively learn node representations in heterophilous graphs through adaptive path aggregation. Extensive experiments demonstrate that our method outperforms baselines on 16 out of 20 datasets, underlining its effectiveness and superiority in alleviating the heterophily problem. In addition, our method is immune to over-smoothing and has high computational efficiency. The source code will be available in https://github.com/Graph4Sec-Team/PathMLP.
RESUMEN
Objectives: Deep tissue injury is a common form of pressure ulcers in muscle tissues under bony prominences caused by sustained pressure or shear, which has a great impact on patients with restricted mobility such as spinal cord injury. Frequent spasms in spinal cord injury patients featured by muscle stiffening may be one of the factors leading to deep tissue injury. The purpose of this study was to investigate the relationship between the gluteal muscle shear modulus and intramuscular compressive/shear stress/strain. Methods: A semi-3D finite element model of the human buttock was established using COMSOL software and the acquired biomechanical data were analyzed through Pearson correlation and Spearman correlation. Results: Results showed that the compressive stress, strain energy density, and average von Mises stress increased with the increase of the gluteal muscle shear modulus. Conclusion: These results may indicate muscle stiffening caused by muscle spasms could lead to higher deep tissue injury development risk as well as shed light on effective treatments for relieving muscular sclerosis mechanically.
