Osteoarthritis (OA) is the most common joint disease worldwide and a leading cause of disability. Characterized by degradation of articular cartilage, synovial inflammation, and changes in periarticular and subchondral bone, OA can negatively impact an individual's physical and mental well-being. Recent studies have reported several critical signaling pathways as key regulators and activators of cellular and molecular processes during OA development. Wnt signaling is one such pathway whose signaling molecules and regulators were shown to be abnormally activated or suppressed. As such, agonists and antagonists of those molecules are potential candidates for OA treatment. Notably, a recent phase I clinical trial (NCT02095548) demonstrated the potential of SM04690, a small-molecule inhibitor of the Wnt signaling pathway, as a disease-modifying oseoarthritis drug (DMOAD). This review summarizes the role and mechanism of Wnt signaling and related molecules in regulating OA progression, with a view to accelerating the translation of such evidence into the development of strategies for OA treatment, particularly with respect to potential applications of molecules targeting the Wnt signaling pathway.

Fabric defect detection is a necessary and essential step of quality control in the textile manufacturing industry. Traditional fabric inspections are usually performed by manual visual methods, which are low in efficiency and poor in precision for long-term industrial applications. In this paper, we propose an unsupervised learning-based automated approach to detect and localize fabric defects without any manual intervention. This approach is used to reconstruct image patches with a convolutional denoising autoencoder network at multiple Gaussian pyramid levels and to synthesize detection results from the corresponding resolution channels. The reconstruction residual of each image patch is used as the indicator for direct pixel-wise prediction. By segmenting and synthesizing the reconstruction residual map at each resolution level, the final inspection result can be generated. This newly developed method has several prominent advantages for fabric defect detection. First, it can be trained with only a small amount of defect-free samples. This is especially important for situations in which collecting large amounts of defective samples is difficult and impracticable. Second, owing to the multi-modal integration strategy, it is relatively more robust and accurate compared to general inspection methods (the results at each resolution level can be viewed as a modality). Third, according to our results, it can address multiple types of textile fabrics, from simple to more complex. Experimental results demonstrate that the proposed model is robust and yields good overall performance with high precision and acceptable recall rates.

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