MRL-Net: Multi-Scale Representation Learning Network for COVID-19 Lung CT Image Segmentation.

Accuracy segmentation of COVID-19 lesions in lung CT images can aid patient screening and diagnosis. However, the blurred, inconsistent shape and location of the lesion area poses a great challenge to this vision task. To tackle this issue, we propose a multi-scale representation learning network (MRL-Net) that integrates CNN with Transformer via two bridge unit: Dual Multi-interaction Attention (DMA) and Dual Boundary Attention (DBA). First, to obtain multi-scale local detailed feature and global contextual information, we combine low-level geometric information and high-level semantic features extracted by CNN and Transformer, respectively. Secondly, for enhanced feature representation, DMA is proposed to fuse the local detailed feature of CNN and the global context information of Transformer. Finally, DBA makes our network focus on the boundary features of the lesion, further enhancing the representational learning. Amounts of experimental results show that MRL-Net is superior to current state-of-the-art methods and achieves better COVID-19 image segmentation performance.

Enhancing Conductivity and Self-Healing Properties of PVA/GEL/OSA Composite Hydrogels by GO/SWNTs for Electronic Skin.

The use of flexible, self-healing conductive hydrogels as a type of typical electronic skin with the function of transmitting sensory signals has attracted wide attention in the field of biomaterials. In this study, composite hydrogels based on polyvinyl alcohol (PVA), gelatin (GEL), oxidized sodium alginate (OSA), graphene oxide (GO), and single-walled carbon nanotubes (SWNTs) were successfully prepared. The hydrogen and imine bonding of the composite hydrogels gives them excellent self-healing properties. Their self-healing properties restore 68% of their breaking strength and over 95% of their electrical conductivity. The addition of GO and SWNTs enables the PGO-GS hydrogels to achieve a compressive modulus and conductivity of 42.2 kPa and 29.6 mS/m, which is 8.2 times and 1.5 times that of pure PGO, respectively. Furthermore, the PGO-GS hydrogels can produce profound feedback signals in response to deformation caused by external forces and human movements such as finger flexion and speech. In addition, the PGO-GS hydrogels exhibit superior biocompatibility compared to PGO. All of these results indicate that the PGO-GS hydrogels have great potential with respect to future applications in the field of electronic skin.

Distributed Small-Step Path Planning and Detection Method for Post-earthquake Robot to Inspect and Evaluate Building Damage.

Post-earthquake robots can be used extensively to inspect and evaluate building damage for safety assessment. However, the surrounding environment and path for such robots are complex and unstable with unexpected obstacles. Thus, path planning for such robot is crucial to guarantee satisfactory inspection and evaluation while approaching the ideal position. To achieve this goal, we proposed a distributed small-step path planning method using modified reinforcement learning (MRL). Limited distance and 12 directions were gridly refined for the robot to move around. The small moving step ensures the path planning to be optimal in a neighboring safe region. The MRL updates the direction and adjusts the path to avoid unknown disturbances. After finding the best inspection angle, the camera on the robot can capture the picture clearly, thereby improving the detection capability. Furthermore, the corner point detection method of buildings was improved using the Harris algorithm to enhance the detection accuracy. An experimental simulation platform was established to verify the designed robot, path planning method, and overall detection performance. Based on the proposed evaluation index, the post-earthquake building damage was inspected with high accuracy of up to 98%, i.e., 20% higher than traditional unplanned detection. The proposed robot can be used to explore unknown environments, especially in hazardous conditions unsuitable for humans.

COVID-19 diagnosis via chest X-ray image classification based on multiscale class residual attention.

Aiming at detecting COVID-19 effectively, a multiscale class residual attention (MCRA) network is proposed via chest X-ray (CXR) image classification. First, to overcome the data shortage and improve the robustness of our network, a pixel-level image mixing of local regions was introduced to achieve data augmentation and reduce noise. Secondly, multi-scale fusion strategy was adopted to extract global contextual information at different scales and enhance semantic representation. Last but not least, class residual attention was employed to generate spatial attention for each class, which can avoid inter-class interference and enhance related features to further improve the COVID-19 detection. Experimental results show that our network achieves superior diagnostic performance on COVIDx dataset, and its accuracy, PPV, sensitivity, specificity and F1-score are 97.71%, 96.76%, 96.56%, 98.96% and 96.64%, respectively; moreover, the heat maps can endow our deep model with somewhat interpretability.

COVID-19 CT image segmentation based on improved Res2Net.

PURPOSE: Corona virus disease 2019 (COVID-19) is threatening the health of the global people and bringing great losses to our economy and society. However, computed tomography (CT) image segmentation can make clinicians quickly identify the COVID-19-infected regions. Accurate segmentation infection area of COVID-19 can contribute screen confirmed cases. METHODS: We designed a segmentation network for COVID-19-infected regions in CT images. To begin with, multilayered features were extracted by the backbone network of Res2Net. Subsequently, edge features of the infected regions in the low-level feature f2 were extracted by the edge attention module. Second, we carefully designed the structure of the attention position module (APM) to extract high-level feature f5 and detect infected regions. Finally, we proposed a context exploration module consisting of two parallel explore blocks, which can remove some false positives and false negatives to reach more accurate segmentation results. RESULTS: Experimental results show that, on the public COVID-19 dataset, the Dice, sensitivity, specificity, S α ${S}_\alpha $ , E ∅ m e a n $E_\emptyset ^{mean}$ , and mean absolute error (MAE) of our method are 0.755, 0.751, 0.959, 0.795, 0.919, and 0.060, respectively. Compared with the latest COVID-19 segmentation model Inf-Net, the Dice similarity coefficient of our model has increased by 7.3%; the sensitivity (Sen) has increased by 5.9%. On contrary, the MAE has dropped by 2.2%. CONCLUSIONS: Our method performs well on COVID-19 CT image segmentation. We also find that our method is so portable that can be suitable for various current popular networks. In a word, our method can help screen people infected with COVID-19 effectively and save the labor power of clinicians and radiologists.

MiR-29a-3p Inhibits Proliferation and Osteogenic Differentiation of Human Bone Marrow Mesenchymal Stem Cells via Targeting FOXO3 and Repressing Wnt/ß-Catenin Signaling in Steroid-Associated Osteonecrosis.

Background and Objectives: This study was to investigate the role of microRNA-29a-3p (miR-29a-3p) in human bone marrow mesenchymal stem cells (hBMSCs), and its relationship with steroid-associated osteonecrosis. Methods and Results: The online tool GEO2R was used to screen out the differentially expressed genes (DEGs) in GSE123568 dataset. Quantitative real time-polymerase chain reaction (qRT-PCR) was performed to detect the expression of miR-29a-3p, forkhead box O3 (FOXO3), alkaline phosphatase (ALP), bone gamma-carboxyglutamate protein (OCN) and RUNX family transcription factor 2 (Runx2) in the hBMSCs isolated from the patients with steroid- associated osteonecrosis. CCK-8 assay was executed to measure cell viability; western blot assay was utilized to detect FOXO3, ALP, Runx2, OCN and ß-catenin expression. Cell apoptosis and cell cycle were detected by flow cytometry. Immunofluorescence assay was used to detect the sub-cellular localization of ß-catenin. Bioinformatics analysis and luciferase reporter gene assay were performed to confirm whether miR-29a-3p can combine with FOXO3 3'UTR. MiR-29a-3p was markedly up-regulated in the hBMSCs of patients with steroid-associated osteonecrosis, while FOXO3 mRNA was significantly down-regulated. Transfection of miR-29a-3p mimics significantly inhibited the hBMSCs' proliferation, osteogenic differentiation markers' expressions, including ALP, Runx2, OCN, and repressed the ALP activity, as well as promoted cell apoptosis and cell-cycle arrest. FOXO3 was identified as a target gene of miR-29a-3p, and miR-29a-3p can inhibit the expression of FOXO3 and ß-catenin, and inhibition of miR-29a-3p promoted translocation of ß-catenin to the nucleus. Conclusions: MiR-29a-3p can modulate FOXO3 expression and Wnt/ß-catenin signaling to inhibit viability and osteogenic differentiation of hBMSCs, thereby promoting the development of steroid-associated osteonecrosis.

Circular RNA Derived from Vacuolar ATPase Assembly Factor VMA21 Suppresses Lipopolysaccharide-Induced Apoptosis of Chondrocytes in Osteoarthritis (OA) by Decreasing Mature miR-103 Production.

Circular RNA derived from vacuolar ATPase assembly factor (VMA21) has been proven to be an inflammation suppressor in many diseases, while its role in osteoarthritis (OA) is unknown. We predicted that VMA21 participates in OA via interacting with miR-103, an OA promoter. Therefore, we analyzed the crosstalk between VMA21 and miR-103 in OA. In this study, the levels of VMA21, pre-miR-103, and mature miR-103 in synovial fluid samples from OA patients (n = 56) and controls (n = 56) were analyzed using RT-qPCR. Nuclear and cytoplasm samples were prepared from chondrocytes, and VMA21 expression was detected by RT-PCR. RNA-RNA pulldown assay was applied to analyze the direct interaction between VMA21 and pre-miR-103. The involvement of VMA21 and miR-103 in lipopolysaccharide (LPS)-induced chondrocyte apoptosis and viability was analyzed using cell apoptosis assay and 2,5-diphenyl-2H-tetrazolium bromide (MTT) assay, respectively. We found that compared to the control group, VMA21 expression was decreased in OA, and miR-103 maturation was increased in OA. VMA21 could be detected in both nuclear and cytoplasm, and VMA21 directly interacted with pre-miR-103. VMA21 overexpression reduced miR-103 maturation. VMA21 suppressed the role of miR-103 in enhancing chondrocyte apoptosis and reducing cell viability after LPS treatment. In conclusion, VMA21 might suppress LPS-induced chondrocyte apoptosis in OA by decreasing the production of mature miR-103.

Visual Recognition of Traffic Signs in Natural Scenes Based on Improved RetinaNet.

Aiming at recognizing small proportion, blurred and complex traffic sign in natural scenes, a traffic sign detection method based on RetinaNet-NeXt is proposed. First, to ensure the quality of dataset, the data were cleaned and enhanced to denoise. Secondly, a novel backbone network ResNeXt was employed to improve the detection accuracy and effection of RetinaNet. Finally, transfer learning and group normalization were adopted to accelerate our network training. Experimental results show that the precision, recall and mAP of our method, compared with the original RetinaNet, are improved by 9.08%, 9.09% and 7.32%, respectively. Our method can be effectively applied to traffic sign detection.