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Osteoarthritis (OA) is a progressive cartilage degradation disease, concomitant with synovitis, osteophyte formation, and subchondral bone sclerosis. Over 37% of the elderly population is affected by OA, and the number of cases is increasing as the global population ages. Therefore, the objective of this study was to identify and analyze the hub genes of OA combining with comprehensive bioinformatics analysis tools to provide theoretical basis in further OA effective therapies. Two sample sets of GSE46750 contained 12 pairs OA synovial membrane and normal samples harvested from patients as well as GSE98918 including 12 OA and non-OA patients were downloaded from the Gene Expression Omnibus database (GEO) database. Differentially expressed genes (DEGs) were identified using Gene Expression Omnibus 2R (GEO2R), followed by functional enrichment analysis, protein–protein interaction networks construction. The hub genes were identified and evaluated. An OA rat model was constructed, hematoxylin and eosin staining, safranin O/fast green staining, cytokines concentrations of serum were used to verify the model. The hub genes expression level in the knee OA samples were verified using RT-qPCR. The top 20 significantly up-regulated and down-regulated DEGs were screened out from the two datasets, respectively. The top 18 GO terms and 10 KEGG pathways were enriched. Eight hub genes were identified, namely MS4A6A, C1QB, C1QC, CD74, CSF1R, HLA-DPA1, HLA-DRA and ITGB2. Among them, the hub genes were all up-regulated in in vivo OA rat model, compared with healthy controls. The eight hub genes identified (MS4A6A, C1QB, C1QC, CD74, CSF1R, HLA-DPA1, HLA-DRA and ITGB2) were shown to be associated with OA. These genes can serve as disease markers to discriminate OA patients from healthy controls. [ABSTRACT FROM AUTHOR]

Detecting low-altitude unmanned aerial vehicle (UAV) has great significance in both military and civil fields. Aiming at the problem that it is difficult to detect infrared long-range aerocraft targets in complex background accurately and quickly, we propose a lightweight multiscale infrared aerocraft recognition algorithm called IARet. Firstly, in order to improve the detection speed, we use the Focus module at the input to greatly shorten the overall inference time. Secondly, in order to enhance the detection ability, we design a multistage Feature Extraction Part to take into account both lightweight and feature extraction ability, use the Path Aggregation Network to fuse multiscale features, and add an improved Receptive Field Block at the output. Comparison experiments on the Infrared Aerocraft Dataset show that, compared with the newly published high-performance detection model YOLOv5s, the recall of IARet is improved by 2%, the mAP@0.5 is improved by 1.6%, and the CPU inference speed is improved by 37.1%. So IARet model can consider both accuracy and speed. The calculation and parameters of the model are obviously reduced, and the model size is compressed to 4.8 MB, all of which reduce the dependence on the computing capability of hardware platform, and realize the accurate and fast detection of small aerocraft targets in the infrared band. [ABSTRACT FROM AUTHOR]

In this work, we described a proof-of-concept method to promote the activity and selectivity of Pd nanoparticles for heterogeneous catalysis (exemplified by C-C coupling reactions) by using acid sites within a zeolite framework. The Pd nanoparticles were encapsulated inside the crystalline walls of mesoporous H-ZSM-5 leading to hybrid samples (denoted as Pd@mZ- x-H) with controlled number of acid sites. A linear relationship between the number of acid sites of the zeolite nanocrystals and the catalytic activities of the Pd nanoparticles in organic reactions was established. Moreover, the shape-dependent selectivity of Pd@mZ-x-H was not sacrificed when the final activity was enhanced. [ABSTRACT FROM AUTHOR]