Rapid fabrication of ultra-smooth Y-TZP bioceramic surfaces by dual-axis wheel polishing : process development and tribological characterization

The existing artificial joint implants using bioceramic materials face problems of difficulty in manufacturing and premature failure due to wear. This paper investigated a rapid preparing process of ultra-smooth surfaces of yttria-stabilized tetragonal zirconia polycrystal bioceramics based on the dual-axis wheel polishing (DAWP) system. Friction and wear tests were conducted to prove that the prepared ultra-smooth surface can effectively reduce wear. The effects of process parameters on polishing performances were investigated. The XRD and SEM analysis and micro-hardness testing were used to characterize the prepared surface in material behaviors. Tribological tests were carried out on a ball-on-plate reciprocating tribometer to comparatively study the tribological behavior and wear mechanism of the prepared ultra-smooth surfaces and the conventional surface at sub-microscale. The used finishing technology can steadily achieve fast preparation of ultra-smooth bioceramic surfaces, and with a high material removal rate (the highest value was 1.14 mg/min). Besides, in contrast to the conventional surface (Ra 129 nm), the prepared ultra-smooth surface (Ra 0.38 nm) achieved a much smaller friction coefficient, and much less wear volume, indicating that the wear resistance of the ultra-smooth surface was significantly improved

False Negative/Positive Control for SAM on Noisy Medical Images

The Segment Anything Model (SAM) is a recently developed all-range foundation model for image segmentation. It can use sparse manual prompts such as bounding boxes to generate pixel-level segmentation in natural images but struggles in medical images such as low-contrast, noisy ultrasound images. We propose a refined test-phase prompt augmentation technique designed to improve SAM's performance in medical image segmentation. The method couples multi-box prompt augmentation and an aleatoric uncertainty-based false-negative (FN) and false-positive (FP) correction (FNPC) strategy. We evaluate the method on two ultrasound datasets and show improvement in SAM's performance and robustness to inaccurate prompts, without the necessity for further training or tuning. Moreover, we present the Single-Slice-to-Volume (SS2V) method, enabling 3D pixel-level segmentation using only the bounding box annotation from a single 2D slice. Our results allow efficient use of SAM in even noisy, low-contrast medical images. The source code will be released soon

YOLOv5-lotus an efficient object detection method for lotus seedpod in a natural environment

Accurate detection of lotus seedpods in a nature environment is essential for agronomic applications for automated harvesting and yield mapping. Traditional detection methods are based on grower’s experience, which is inefficient for the large-scale production. To improve the efficiency of harvesting lotus seedpods, this study proposes a YOLOv5-lotus method to effectively detect overripe lotus seedpods. The lotus seedpods image dataset is firstly created. An improved YOLOv5 network model based on coordinate attention (CA) module is then presented, namely YOLOv5-lotus model, where CA module is developed to strengthen the model inter-channel relationships and capture long-range dependencies with precise positional information, thus improving the detection accuracy of the algorithm. In order to reveal the feasibility and robustness of the proposed method, a number of case studies are presented on the detection of overripe lotus seedpods in various scenarios, including different poses, illuminations and degrees of occlusion. Compared with the classical YOLOv5s network, the average precision of YOLOv5-lotus model is increased by 0.7 % and average detection time is reduced by 0.7 ms. Compared to other state-of-the-art networks, our detection model is able to achieve the highest average precision value, faster efficient detection speed and higher F1 score, with the average precision being 98.3 %, the recall rate being 96.3 %, the precision rate being 97.3 %, F1 score being 0.968 and average detection time being 19.4 ms. Through case studies and comparisons, the effectiveness and superiority of the proposed approach are demonstrated. These research results can be applied to the detection of upwardly-growing conical fruit. It creates a prerequisite for the development of automatic harvesting equipment

Transcription factor EGR1 directs tendon differentiation and promotes tendon repair

Tendon formation and repair rely on specific combinations of transcription factors, growth factors, and mechanical parameters that regulate the production and spatial organization of type I collagen. Here, we investigated the function of the zinc finger transcription factor EGR1 in tendon formation, healing, and repair using rodent animal models and mesenchymal stem cells (MSCs). Adult tendons of Egr1(–/–) mice displayed a deficiency in the expression of tendon genes, including Scx, Col1a1, and Col1a2, and were mechanically weaker compared with their WT littermates. EGR1 was recruited to the Col1a1 and Col2a1 promoters in postnatal mouse tendons in vivo. Egr1 was required for the normal gene response following tendon injury in a mouse model of Achilles tendon healing. Forced Egr1 expression programmed MSCs toward the tendon lineage and promoted the formation of in vitro–engineered tendons from MSCs. The application of EGR1-producing MSCs increased the formation of tendon-like tissues in a rat model of Achilles tendon injury. We provide evidence that the ability of EGR1 to promote tendon differentiation is partially mediated by TGF-β2. This study demonstrates EGR1 involvement in adult tendon formation, healing, and repair and identifies Egr1 as a putative target in tendon repair strategies