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https://www.selleckchem.com/products/loxo-292.html We show that the classification performance of graph convolutional networks (GCNs) is related to the alignment between features, graph, and ground truth, which we quantify using a subspace alignment measure (SAM) corresponding to the Frobenius norm of the matrix of pairwise chordal distances between three subspaces associated with features, graph, and ground truth. The proposed measure is based on the principal angles between subspaces and has both spectral and geometrical interpretations. We showcase the relationship between the SAM and the classification performance through the study of limiting cases of GCNs and systematic randomizations of both features and graph structure applied to a constructive example and several examples of citation networks of different origins. The analysis also reveals the relative importance of the graph and features for classification purposes.Musculoskeletal disorders and injuries are one of the most prevalent medical conditions across age groups. Due to a high load-bearing function, the knee is particularly susceptible to injuries such as meniscus tears. Imaging techniques are commonly used to assess meniscus injuries, though this approach suffers from limitations including high cost, need for skilled personnel, and confinement to laboratory or clinical settings. Vibration-based structural monitoring methods in the form of acoustic emission analysis and vibration stimulation have the potential to address the limits associated with current diagnostic technologies. In this study, an active vibration measurement technique is employed to investigate the presence and severity of meniscus tear in cadaver limbs. In a highly controlled ex vivo experimental design, a series of cadaver knees (n =6) were evaluated under an external vibration, and the frequency response of the joint was analyzed to differentiate the intact and affected samples. Four stages of knee integrity were considered base

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