Experimental study on penetration of dental implants into the maxillary sinus in different depths

The exposing of dental implant into the maxillary sinus combined with membrane perforation might increase risks of implant failure and sinus complications. Objective: The purpose of this study was to investigate the effects of the dental implant penetration into the maxillary sinus cavity in different depths on osseointegration and sinus health in a dog model. Material and Methods: Sixteen titanium implants were placed in the bilateral maxillary molar areas of eight adult mongrel dogs, which were randomly divided into four groups according to the different penetrating extents of implants into the sinus cavities (group A: 0 mm; group B: 1 mm; group C: 2 mm; group D: 3 mm). The block biopsies were harvested five months after surgery and evaluated by radiographic observation and histological analysis. Results: No signs of inflammatory reactions were observed in any maxillary sinus of the eight dogs. The tips of the implants with penetrating depth of 1 mm and 2 mm were found to be fully covered with newly formed membrane and partially with new bone. The tips of the implants with penetrating depth over 3 mm were exposed in the sinus cavity and showed no membrane or bone coverage. No significant differences were found among groups regarding implant stability, bone-to-implant contact (BIC) and bone area in the implant threads (BA). Conclusions: Despite the protrusion extents, penetration of dental implant into the maxillary sinus with membrane perforation does not compromise the sinus health and the implant osseointegration in canine

Optimized YOLOv7-tiny model for smoke detection in power transmission lines

Fire incidents near power transmission lines pose significant safety hazards to the regular operation of the power system. Therefore, achieving fast and accurate smoke detection around power transmission lines is crucial. Due to the complexity and variability of smoke scenarios, existing smoke detection models suffer from low detection accuracy and slow detection speed. This paper proposes an improved model for smoke detection in high-voltage power transmission lines based on the improved YOLOv7-tiny. First, we construct a dataset for smoke detection in high-voltage power transmission lines. Due to the limited number of real samples, we employ a particle system to randomly generate smoke and composite it into randomly selected real scenes, effectively expanding the dataset with high quality. Next, we introduce multiple parameter-free attention modules into the YOLOv7-tiny model and replace regular convolutions in the Neck of the model with Spd-Conv (Space-to-depth Conv) to improve detection accuracy and speed. Finally, we utilize the synthesized smoke dataset as the source domain for model transfer learning. We pre-train the improved model and fine-tune it on a dataset consisting of real scenarios. Experimental results demonstrate that the proposed improved YOLOv7-tiny model achieves a 2.61% increase in mean Average Precision (mAP) for smoke detection on power transmission lines compared to the original model. The precision is improved by 2.26%, and the recall is improved by 7.25%. Compared to other object detection models, the smoke detection proposed in this paper achieves high detection accuracy and speed. Our model also improved detection accuracy on the already publicly available wildfire smoke dataset Figlib (Fire Ignition Library)

CJS-YOLOv5n: A high-performance detection model for cigarette appearance defects

In tobacco production, cigarettes with appearance defects are inevitable and dramatically impact the quality of tobacco products. Currently, available methods do not balance the tension between detection accuracy and speed. To achieve accurate detection on a cigarette production line with the rate of 200 cigarettes per second, we propose a defect detection model for cigarette appearance based on YOLOv5n (You Only Look Once Version 5 Nano), called CJS-YOLOv5n (YOLOv5n with C2F (Cross Stage Partial (CSP) Bottleneck with 2 convolutions-fast), Jump Concat, and SCYLLA-IoU (SIoU)). This model incorporates the C2F module proposed in the state-of-the-art object detection network YOLOv8 (You Only Look Once Version 8). This module optimizes the network by parallelizing additional gradient flow branches, enhancing the model's feature extraction capability and obtaining richer gradient information. Furthermore, this model uses Jump Concat to preserve minor defect feature information during the fusion process in the feature fusion pyramid's P4 layer. Additionally, this model integrates the SIoU localization loss function to improve localization accuracy and detection precision. Experimental results demonstrate that our proposed CJS-YOLOv5n model achieves superior overall performance. It maintains a detection speed of over 500 FPS (frames per second) while increasing the recall rate by 2.3% and mAP (mean average precision)@0.5 by 1.7%. The proposed model is suitable for application in high-speed cigarette production lines

Detection of cigarette appearance defects based on improved YOLOv4

Appearance defects are visible factors that affect the quality of cigarettes. Most of the consumer complaints received by tobacco companies are caused by appearance defects of cigarettes. Therefore, it is of great significance to reduce cigarettes with appearance defects. At present, tobacco factories mainly detect the appearance quality of cigarettes through manual sampling inspection. The manual method has low detection efficiency, it is difficult to unify the judgment standard, and it is easy to cause secondary pollution to cigarettes. According to the features of cigarette appearance defects, the YOLOv4 (You Only Look Once Version 4) model was improved for cigarette appearance defect detection. We have improved the following: 1) the channel attention mechanism was introduced into YOLOv4 to improve the detection precision; 2) the K-means++ algorithm was used to optimize the selection of clustering centers; 3) the spatial pyramid pooling (SPP) was replaced with atrous spatial pyramid pooling (ASPP) to improve the defect detection ability with different sizes; 4) the α-CIoU loss function was used to improve the detection precision. The mAP of our improved method reached 91.77%, the precision reached 93.32%, and the recall reached 88.81%. Compared with other models, our method has better comprehensive performance and better detection ability

Green synthesis of Au-Pd bimetallic nanoparticles: Single-step bioreduction method with plant extract

A facile and eco-friendly method for the preparation of Au-Pd bimetallic nanoparticles (similar to 7 nm) has been developed based on simultaneous bioreduction of Au(III) and Pd(II) precursors with Cacumen Platycladi leaf extract in aqueous environment. The morphology, structure, and size were confirmed with the aid of transmission electron microscopy, selected area electron diffraction. UV-vis spectroscopy, X-ray diffraction, and energy dispersive X-ray spectroscopy. The results from Fourier transform infrared spectroscopy showed that the C=O and C-O groups in the plant extract played a critical role in capping the nanoparticles. Importantly, the process can be described as pure "green chemistry" technique since no additional synthetic reagents are used as reductants or stabilizers. (C) 2011 Elsevier B.V. All rights reserved.National Natural Science Foundation of China[21036004, 20976146]; Natural Science Foundation of Fujian Province of China[2010J05032, 2010J01052]WOS:00029506830004

Foreign-Object Detection in High-Voltage Transmission Line Based on Improved YOLOv8m

The safe operation of high-voltage transmission lines ensures the power grid’s security. Various foreign objects attached to the transmission lines, such as balloons, kites and nesting birds, can significantly affect the safe and stable operation of high-voltage transmission lines. With the advancement of computer vision technology, periodic automatic inspection of foreign objects is efficient and necessary. Existing detection methods have low accuracy because foreign objects attached to the transmission lines are complex, including occlusions, diverse object types, significant scale variations, and complex backgrounds. In response to the practical needs of the Yunnan Branch of China Southern Power Grid Co., Ltd., this paper proposes an improved YOLOv8m-based model for detecting foreign objects on transmission lines. Experiments are conducted on a dataset collected from Yunnan Power Grid. The proposed model enhances the original YOLOv8m by incorporating a Global Attention Module (GAM) into the backbone to focus on occluded foreign objects, replacing the SPPF module with the SPPCSPC module to augment the model’s multiscale feature extraction capability, and introducing the Focal-EIoU loss function to address the issue of high- and low-quality sample imbalances. These improvements accelerate model convergence and enhance detection accuracy. The experimental results demonstrate that our proposed model achieves a 2.7% increase in mAP_0.5, a 4% increase in mAP_0.5:0.95, and a 6% increase in recall

Experimental study on penetration of dental implants into the maxillary sinus in different depths

The exposing of dental implant into the maxillary sinus combined with membrane perforation might increase risks of implant failure and sinus complications. Objective: The purpose of this study was to investigate the effects of the dental implant penetration into the maxillary sinus cavity in different depths on osseointegration and sinus health in a dog model. Material and Methods: Sixteen titanium implants were placed in the bilateral maxillary molar areas of eight adult mongrel dogs, which were randomly divided into four groups according to the different penetrating extents of implants into the sinus cavities (group A: 0 mm; group B: 1 mm; group C: 2 mm; group D: 3 mm). The block biopsies were harvested five months after surgery and evaluated by radiographic observation and histological analysis. Results: No signs of inflammatory reactions were observed in any maxillary sinus of the eight dogs. The tips of the implants with penetrating depth of 1 mm and 2 mm were found to be fully covered with newly formed membrane and partially with new bone. The tips of the implants with penetrating depth over 3 mm were exposed in the sinus cavity and showed no membrane or bone coverage. No significant differences were found among groups regarding implant stability, bone-to-implant contact (BIC) and bone area in the implant threads (BA). Conclusions: Despite the protrusion extents, penetration of dental implant into the maxillary sinus with membrane perforation does not compromise the sinus health and the implant osseointegration in canine

Enhanced Polymer One-step Staining for Proliferating Cell Nuclear Antigen (EPOS-PCNA) in NR-S1 Tumors of Mouse Tongues : Comparison with the PCNA and Bromodeoxyuridine (BrdU) Immunohistochemistry

We evaluated the immunohistochemical localization of xanthine oxidase in various human tissues. Xanthine oxidase was purified from cadaver liver. Polyclonal antibody against xanthine oxidase was raised in a rabbit. Immunoblot analysis showed that the raised antibody reacted specifically with one band whose position corresponded with that of the purified enzyme. Immunostaining of paraffin-embedded tissue sections showed intense reactivity in the following tissues: surface epithelium of tongue, esophagus, and trachea, sweat glands, and mammary glands. Weak, but positive, reactivity was observed in other tissues, such as glandular cells of the small and large intestine and renal tubules, skeletal muscle, gastric epithelial cells, alveoli of the lung, spleen, and liver cytoplasm. Xanthine oxidase staining was observed in infiltrating lymphocytes (probably T-lymphocytes but not in B-lymphocytes) in inflammatory lesions of the small and large intestine. Its ubiquitous localization suggests that xanthine oxidase is involved in cell proliferation/differentiation, the defense mechanisms, and in the pathogenesis of reperfusion tissue injury

Pharmacokinetics of 10-Hydroxy Mesaconitine in Rat Plasma by Ultra-Performance Liquid Chromatography-Tandem Quadrupole Mass Spectrometry

Mesaconitine is the predominant active ingredient in Aconitum carmichaelii Debx. The compound 10-hydroxy mesaconitine is one known metabolite of mesaconitine and is toxic. In order to better understand its pharmacokinetics, UPLC-MS/MS was used in this paper to measure the concentration of 10-hydroxy mesaconitine in the plasma of rats after oral (5 mg/kg) and intravenous (0.1 mg/kg) administration of 10-hydroxy mesaconitine. The concentrations of 10-hydroxy mesaconitine in rat plasma measured in the standard curve covered the range of 0.3–60 ng/mL. The intraday and interday precisions of the samples of 10-hydroxy mesaconitine in rat plasma were lower than 15%. In addition, the accuracies ranged between 96.0% and 109.3%, the matrix effects ranged between 88.9% and 98.1%, and the recoveries were all higher than 79.1%. The AUC(0 − t) values were 23.6 ± 5.9 and 207.6 ± 72.9 ng/mL·h for intravenous and oral administration, respectively, and the bioavailability of 10-hydroxy mesaconitine was 17.6%. Lastly, t1/2 was 1.3 ± 0.6 h and 3.1 ± 0.4 h for intravenous and oral administration, respectively