The Bone Marrow Edema Links to an Osteoclastic Environment and Precedes Synovitis During the Development of Collagen Induced Arthritis

Objectives: To determine the relationship between bone marrow edema (BME), synovitis, and bone erosion longitudinally using a collagen induced arthritis mice (CIA) model and to explore the potential pathogenic role of BME in bone erosion.Methods: CIA was induced in DBA/1J mice. BME and corresponding clinical symptoms of arthritis and synovitis during the different time points of CIA development were assayed by magnetic resonance imaging (MRI), arthritis sore, and histologic analyses. The expression of osteoclasts (OCs), OCs-related cytokines, and immune cells in bone marrow were determined by flow cytometry, immunohistochemistry, immunofluorescence staining, and real-time PCR. The OCs formation was estimated using in vitro assays.Results: MRI detected BME could emerge at day 25 in 70% mice after the first immunization (n = 10), when there were not any arthritic symptoms, histological or MRI synovitis. At day 28, BME occurred in 90% mice whereas the arthritic symptom and histological synovitis were only presented in 30 and 20% CIA mice at that time (n = 10). The emergence of BME was associated with an increased bone marrow OCs number and an altered distribution of OCs adherent to subchondral bone surface, which resulted in increased subchondral erosion and decreased trabecular bone number during the CIA process. Obvious marrow environment changes were identified after BME emergence, consisting of multiple OCs related signals, including highly expressed RANKL, increased proinflammatory cytokines and chemokines, and highly activated T cells and monocytes.Conclusions: BME reflects a unique marrow “osteoclastic environment,” preceding the arthritic symptoms and synovitis during the development of CIA

A Crack Identification Method for Concrete Structures Using Improved U-Net Convolutional Neural Networks

The traditional method for detecting cracks in concrete bridges has the disadvantages of low accuracy and weak robustness. Combined with the crack digital image data obtained from bending test of reinforced concrete beams, a crack identification method for concrete structures based on improved U-net convolutional neural networks is proposed to improve the accuracy of crack identification in this article. Firstly, a bending test of concrete beams is conducted to collect crack images. Secondly, datasets of crack images are obtained using the data augmentation technology. Selected cracks are marked. Thirdly, based on the U-net neural networks, an improved inception module and an Atrous Spatial Pyramid Pooling module are added in the improved U-net model. Finally, the widths of cracks are identified using the concrete crack binary images obtained from the improved U-net model. The average precision of the test set of the proposed model is 11.7% higher than that of the U-net neural network segmentation model. The average relative error of the crack width of the proposed model is 13.2%, which is 18.6% less than that measured by using the ACTIS system. The results indicate that the proposed method is accurate, robust, and suitable for crack identification in concrete structures