Spatial organization and connectivity of wood rays in Pinus massoniana xylem based on high-resolution µCT-assisted network analysis.

MAIN CONCLUSION: Spatial organization and connectivity of wood rays in Pinus massoniana was comprehensively viewed and regarded as anatomical adaptions to ensure the properties of rays in xylem. Spatial organization and connectivity of wood rays are essential for understanding the wood hierarchical architecture, but the spatial information is ambiguous due to small cell size. Herein, 3D visualization of rays in Pinus massoniana was performed using high-resolution µCT. We found brick-shaped rays were 6.5% in volume fractions, nearly twice the area fractions estimated by 2D levels. Uniseriate rays became taller and wider during the transition from earlywood to latewood, which was mainly contributed from the height increment of ray tracheids and widened ray parenchyma cells. Furthermore, both volume and surface area of ray parenchyma cells were larger than ray tracheids, so ray parenchyma took a higher proportion in rays. Moreover, three different types of pits for connectivity were segmented and revealed. Pits in both axial tracheids and ray tracheids were bordered, but the pit volume and pit aperture of earlywood axial tracheids were almost tenfold and over fourfold larger than ray tracheids. Contrarily, cross-field pits between ray parenchyma and axial tracheids were window-like with the principal axis of 31.0 µm, but its pit volume was approximately one-third of axial tracheids. Additionally, spatial organization of rays and axial resin canal was analyzed by a curved surface reformation tool, providing the first evidence of rays close to epithelial cells inward through the resin canal. Epithelial cells had various morphologies and large variations in cell size. Our results give new insights into the organization of radial system of xylem, especially the connectivity of rays with adjacent cells.

A Particleboard Surface Defect Detection Method Research Based on the Deep Learning Algorithm.

Particleboard surface defects have a significant impact on product quality. A surface defect detection method is essential to enhancing the quality of particleboard because the conventional defect detection method has low accuracy and efficiency. This paper proposes a YOLO v5-Seg-Lab-4 (You Only Look Once v5 Segmentation-Lab-4) model based on deep learning. The model integrates object detection and semantic segmentation, which ensures real-time performance and improves the detection accuracy of the model. Firstly, YOLO v5s is used as the object detection network, and it is added into the SELayer module to improve the adaptability of the model to receptive field. Then, the Seg-Lab v3+ model is designed on the basis of DeepLab v3+. In this model, the object detection network is utilized as the backbone network of feature extraction, and the expansion rate of atrus convolution is reduced to the computational complexity of the model. The channel attention mechanism is added onto the feature fusion module, for the purpose of enhancing the feature characterization capabilities of the network algorithm as well as realizing the rapid and accurate detection of lightweight networks and small objects. Experimental results indicate that the proposed YOLO v5-Seg-Lab-4 model has mAP (Mean Average Precision) and mIoU (Mean Intersection over Union) of 93.20% and 76.63%, with a recognition efficiency of 56.02 fps. Finally, a case study of the Huizhou particleboard factory inspection is carried out to demonstrate the tiny detection accuracy and real-time performance of this proposed method, and the missed detection rate of surface defects of particleboard is less than 1.8%.

Real-time detection of particleboard surface defects based on improved YOLOV5 target detection.

Particleboard surface defect detection technology is of great significance to the automation of particleboard detection, but the current detection technology has disadvantages such as low accuracy and poor real-time performance. Therefore, this paper proposes an improved lightweight detection method of You Only Live Once v5 (YOLOv5), namely PB-YOLOv5 (Particle Board-YOLOv5). Firstly, the gamma-ray transform method and the image difference method are combined to deal with the uneven illumination of the acquired images, so that the uneven illumination is well corrected. Secondly, Ghost Bottleneck lightweight deep convolution module is added to Backbone module and Neck module of YOLOv5 detection algorithm to reduce model volume. Thirdly, the SELayer module of attention mechanism is added into Backbone module. Finally, replace Conv in Neck module with depthwise convolution (DWConv) to compress network parameters. The experimental results show that the PB-YOLOv5 model proposed in this paper can accurately identify five types of defects on the particleboard surface: Bigshavings, SandLeakage, GlueSpot, Soft and OliPollution, and meet the real-time requirements. Specifically, recall, F1 score, mAP@.5, mAP@.5:.95 values of pB-Yolov5s model were 91.22%, 94.5%, 92.1%, 92.8% and 67.8%, respectively. The results of Soft defects were 92.8%, 97.9%, 95.3%, 99.0% and 81.7%, respectively. The detection of single image time of the model is only 0.031 s, and the weight size of the model is only 5.4 MB. Compared with the original YOLOv5s, YOLOv4, YOLOv3 and Faster RCNN, the PB-Yolov5s model has the fastest Detection of single image time. The Detection of single image time was accelerated by 34.0%, 55.1%, 64.4% and 87.9%, and the weight size of the model is compressed by 62.5%, 97.7%, 97.8% and 98.9%, respectively. The mAP value increased by 2.3%, 4.69%, 7.98% and 13.05%, respectively. The results show that the PB-YOLOV5 model proposed in this paper can realize the rapid and accurate detection of particleboard surface defects, and fully meet the requirements of lightweight embedded model.

A3 adenosine receptor agonist IB-MECA reduces myocardial ischemia-reperfusion injury in dogs.

We examined the effect of the A3 adenosine receptor (AR) agonist IB-MECA on infarct size in an open-chest anesthetized dog model of myocardial ischemia-reperfusion injury. Dogs were subjected to 60 min of left anterior descending (LAD) coronary artery occlusion and 3 h of reperfusion. Infarct size and regional myocardial blood flow were assessed by macrohistochemical staining with triphenyltetrazolium chloride and radioactive microspheres, respectively. Four experimental groups were studied: vehicle control (50% DMSO in normal saline), IB-MECA (100 microg/kg iv bolus) given 10 min before the coronary occlusion, IB-MECA (100 microg/kg iv bolus) given 5 min before initiation of reperfusion, and IB-MECA (100 microg/kg iv bolus) given 10 min before coronary occlusion in dogs pretreated 15 min earlier with the ATP-dependent potassium channel antagonist glibenclamide (0.3 mg/kg iv bolus). Administration of IB-MECA had no effect on any hemodynamic parameter measured including heart rate, first derivative of left ventricular pressure, aortic pressure, LAD coronary blood flow, or coronary collateral blood flow. Nevertheless, pretreatment with IB-MECA before coronary occlusion produced a marked reduction in infarct size ( approximately 40% reduction) compared with the control group (13.0 +/- 3.2% vs. 25.2 +/- 3.7% of the area at risk, respectively). This effect of IB-MECA was blocked completely in dogs pretreated with glibenclamide. An equivalent reduction in infarct size was observed when IB-MECA was administered immediately before reperfusion (13.1 +/- 3.9%). These results are the first to demonstrate efficacy of an A3AR agonist in a large animal model of myocardial infarction by mechanisms that are unrelated to changes in hemodynamic parameters and coronary blood flow. These data also demonstrate in an in vivo model that IB-MECA is effective as a cardioprotective agent when administered at the time of reperfusion.