In recent years, some studies have been made on the effects of circular RNA (circRNA) in osteoarthritis (OA) and so on; however, its mechanisms remain to be further explored. Studies have shown that tumor necrosis factor-alpha can inhibit hsa_circ_0045714 expression in chondrocytes so as to upregulate miR-193b expression. Dual-luciferase reporter assay showed that insulin-like growth factor 1 receptor (IGF1R) is a key target gene of miR-193b. Hsa_circ_0045714 over-expression does not influence miR-193b expression, but can inhibit its transcriptional activity, thereby upregulating IGF1R expression. Hsa_circ_0045714 can promote the expression of type II collagen and aggrecan, and upregulate chondrocyte proliferation, while its linear sequences cannot. IGF1R has similar function, while miR-193b can inhibit the expression of type II collagen and aggrecan, and downregulate chondrocyte proliferation but enhance their apoptosis. IGF1R overexpression can reverse the effect of miR-193b, while miR-193b mimics or IGF1R siRNA can inhibit the function of hsa_circ_0045714. Therefore, hsa_circ_0045714 can regulate extracellular matrix synthesis as well as proliferation and apoptosis of chondrocytes by promoting the expression of miR-193b target gene IGF1R. The findings will provide new proofs for studies on the applications of circRNA in OA and other orthopedic diseases.

Weed identification in vegetable plantation is more challenging than crop weed identification due to their random plant spacing. So far, little work has been found on identifying weeds in vegetable plantation. Traditional methods of crop weed identification used to be mainly focused on identifying weed directly; however, there is a large variation in weed species. This paper proposes a new method in a contrary way, which combines deep learning and image processing technology. Firstly, a trained CenterNet model was used to detect vegetables and draw bounding boxes around them. Afterwards, the remaining green objects falling out of bounding boxes were considered as weeds. In this way, the model focuses on identifying only the vegetables and thus avoid handling various weed species. Furthermore, this strategy can largely reduce the size of training image dataset as well as the complexity of weed detection, thereby enhancing the weed identification performance and accuracy. To extract weeds from the background, a color index-based segmentation was performed utilizing image processing. The employed color index was determined and evaluated through Genetic Algorithms (GAs) according to Bayesian classification error. During the field test, the trained CenterNet model achieved a precision of 95.6%, a recall of 95.0%, and a " score of 0.953, respectively. The proposed index-19R + 24G-2B ≥ 862 yields high segmentation quality with a much lower computational cost compared to the wildly used ExG index. These experiment results demonstrate the feasibility of using the proposed method for the ground-based weed identification in vegetable plantation.

Placement of calcar screws combined with good medial cortical contact in varus in locking plate fixation of proximal humeral fractures with a medial gap may provide optimal stability for the fixation.

BACKGROUND Precision weed control in vegetable fields can substantially reduce the required weed control inputs. Rapid and accurate weed detection in vegetable fields is a challenging task due to the presence of a wide variety of weed species at various growth stages and densities. This paper presents a novel deep‐learning‐based method for weed detection that recognizes vegetable crops and classifies all other green objects as weeds. RESULTS The optimal confidence threshold values for YOLO‐v3, CenterNet, and Faster R‐CNN were 0.4, 0.6, and 0.4/0.5, respectively. These deep‐learning models had average precision (AP) above 97% in the testing dataset. YOLO‐v3 was the most accurate model for detection of vegetables and yielded the highest F1 score of 0.971, along with high precision and recall values of 0.971 and 0.970, respectively. The inference time of YOLO‐v3 was similar to CenterNet, but significantly shorter than that of Faster R‐CNN. Overall, YOLO‐v3 showed the highest accuracy and computational efficiency among the deep‐learning architectures evaluated in this study. CONCLUSION These results demonstrate that deep‐learning‐based methods can reliably detect weeds in vegetable crops. The proposed method avoids dealing with various weed species, and thus greatly reduces the overall complexity of weed detection in vegetable fields. Findings have implications for advancing site‐specific robotic weed control in vegetable crops.

BACKGROUND In‐field weed detection in wheat (Triticum aestivum L.) is challenging due to the occurrence of weeds in close proximity with the crop. The objective of this research was to evaluate the feasibility of using deep convolutional neural networks for detecting broadleaf weed seedlings growing in wheat. RESULTS The object detection neural networks, including CenterNet, Faster R‐CNN, TridenNet, VFNet, and You Only Look Once Version 3 (YOLOv3) were insufficient for weed detection in wheat because the recall never exceeded 0.58 in the testing dataset. The image classification neural networks including AlexNet, DenseNet, ResNet, and VGGNet were trained with small (5500 negative and 5500 positive images) or large training datasets (11 000 negative and 11 000 positive images) and three training image sizes (200 × 200, 300 × 300, and 400 × 400 pixels). For the small training dataset, increasing image sizes decreased the F1 scores of AlexNet and VGGNet but generally increased the F1 scores of DenseNet and ResNet. For the large training dataset, no obvious difference was detected between the training image sizes since all neural networks exhibited remarkable classification accuracies with high F1 scores (≥0.96). All image classification neural networks exhibited high F1 scores (≥0.99) when trained with the large training dataset and the training images of 200 × 200 pixels. CONCLUSION CenterNet, Faster R‐CNN, TridentNet, VFNet, and YOLOv3 were insufficient, while AlexNet, DenseNet, ResNet, and VGGNet trained with a large training dataset were highly effective for detection of broadleaf weed seedlings in wheat. © 2021 Society of Chemical Industry.