Biomimetics最新文献

This study evaluated the internal and marginal accuracy (trueness and precision) of zirconia laminate veneers fabricated using the DLP printing and milling method, employing 3D analysis software program. The maxillary central incisor tooth of a typodont model was prepared by a dentist and scanned using a desktop scanner. An anatomical zirconia laminate was designed using computer-aided design (CAD) software and saved in a standard tessellation language (STL) format. Thirty zirconia laminates were manufactured using a milling machine (MLL group) and a DLP printer (PTL group). All the specimens were scanned, and their internal and marginal areas were edited accordingly. The root-mean-square value was used to assess the accuracy of the internal and marginal areas of the zirconia laminates. Statistical significance was evaluated using the Mann-Whitney U test. Statistically significant differences were found in RMS values for both groups in the internal and marginal areas (p < 0.001 and p = 0.034, respectively). The MLL and PTL groups differed significantly in terms of precision (p = 0.017), but not at the margin (p = 0.361). DLP-printed zirconia laminates demonstrated stable and consistent performance, making the technique a reliable option for producing esthetic prostheses.

The surface topography and chemistry of titanium-aluminum-vanadium (Ti6Al4V) implants play critical roles in the osteoblast differentiation of human bone marrow stromal cells (MSCs) and the creation of an osteogenic microenvironment. To assess the effects of a microscale/nanoscale (MN) topography, this study compared the effects of MN-modified, anodized, and smooth Ti6Al4V surfaces on MSC response, and for the first time, directly contrasted MN-induced osteoblast differentiation with culture on tissue culture polystyrene (TCPS) in osteogenic medium (OM). Surface characterization revealed distinct differences in microroughness, composition, and topography among the Ti6Al4V substrates. MSCs on MN surfaces exhibited enhanced osteoblastic differentiation, evidenced by increased expression of RUNX2, SP7, BGLAP, BMP2, and BMPR1A (fold increases: 3.2, 1.8, 1.4, 1.3, and 1.2). The MN surface also induced a pro-healing inflammasome with upregulation of anti-inflammatory mediators (170-200% increase) and downregulation of pro-inflammatory factors (40-82% reduction). Integrin expression shifted towards osteoblast-associated integrins on MN surfaces. RNA-seq analysis revealed distinct gene expression profiles between MSCs on MN surfaces and those in OM, with only 199 shared genes out of over 1000 differentially expressed genes. Pathway analysis showed that MN surfaces promoted bone formation, maturation, and remodeling through non-canonical Wnt signaling, while OM stimulated endochondral bone development and mineralization via canonical Wnt3a signaling. These findings highlight the importance of Ti6Al4V surface properties in directing MSC differentiation and indicate that MN-modified surfaces act via signaling pathways that differ from OM culture methods, more accurately mimicking peri-implant osteogenesis in vivo.

While several studies have reported short-term clinical outcomes after medial patellofemoral ligament (MPFL) repair with suture tape augmentation, there is still a dearth of knowledge regarding midterm clinical outcomes. This study aimed to evaluate the midterm clinical outcomes of MPFL repair with suture tape augmentation in patients with patellar dislocation. We retrospectively reviewed the clinical records of patients who underwent MPFL repair with suture tape augmentation for at least one episode of patellar dislocation between 2015 and 2020. Patient-reported clinical outcomes (PROs) were evaluated via the International Knee Documentation Committee (IKDC) score and the knee injury osteoarthritis outcome score (KOOS). In total, 17 knees (4 males and 13 females) who underwent MPFL repair with suture tape augmentation with a mean follow-up of 54.6 ± 19.5 months were included in this study. PROs significantly improved from preoperatively to the final follow-up (IKDC score: 50.7 ± 26.6 vs. 88.8 ± 13.0, p < 0.001; KOOS: 68.8 ± 23.3 vs. 91.2 ± 8.4, p = 0.011) without reducing the patient's activity level at the final follow-up (UCLA AS score: 7.9 ± 2.4 at preinjury vs. 7.9 ± 2.2 at the final follow-up, p = 0.655). Subgroup analysis revealed good postoperative outcomes, regardless of the patient's skeletal maturity or the presence or absence of generalized laxity. In conclusion, MPFL repair with suture tape augmentation is a safe and effective treatment for midterm follow-up.

Flapping wing micro aerial vehicles (FWMAVs) are recognized for their significant potential in military and civilian applications, such as military reconnaissance, environmental monitoring, and disaster rescue. However, the lack of takeoff and landing capabilities, particularly in landing behavior, greatly limits their adaptability to the environment during tasks. In this paper, the purple stem beetle (Sagra femorata), a natural flying insect, was chosen as the bionic research object. The three-dimensional reconstruction models of the beetle's three thoracic legs were established, and the adhesive mechanism of the thoracic leg was analyzed. Then, a series of bionic design elements were extracted. On this basis, a hook-pad cooperation bionic deployable landing mechanism was designed, and mechanism motion, mechanical performance, and vibration performance were studied. Finally, the bionic landing mechanism model can land stably on various contact surfaces. The results of this research guide the stable landing capability of FWMAVs in challenging environments.

Motion primitives are a highly useful and widely employed tool in the field of Learning from Demonstration (LfD). However, obtaining a large number of motion primitives can be a tedious process, as they typically need to be generated individually for each task to be learned. To address this challenge, this work presents an algorithm for acquiring robotic skills through automatic and unsupervised segmentation. The algorithm divides tasks into simpler subtasks and generates motion primitive libraries that group common subtasks for use in subsequent learning processes. Our algorithm is based on an initial segmentation step using a heuristic method, followed by probabilistic clustering with Gaussian Mixture Models. Once the segments are obtained, they are grouped using Gaussian Optimal Transport on the Gaussian Processes (GPs) of each segment group, comparing their similarities through the energy cost of transforming one GP into another. This process requires no prior knowledge, it is entirely autonomous, and supports multimodal information. The algorithm enables generating trajectories suitable for robotic tasks, establishing simple primitives that encapsulate the structure of the movements to be performed. Its effectiveness has been validated in manipulation tasks with a real robot, as well as through comparisons with state-of-the-art algorithms.

Under benign conditions, bone tissue can regenerate itself without external intervention. However, this regenerative capacity can be compromised by various factors, most importantly related with the extent of the injury. Critical-sized defects, exceeding the body's natural healing ability, demand the use of temporary or permanent devices like artificial joints or bone substitutes. While titanium is a widely used material for bone replacement, its integration into the body remains limited. This often leads to the progressive loosening of the implant and the need for revision surgeries, which are technically challenging, are commonly associated with high complication rates, and impose a significant economic burden. To enhance implant osseointegration, numerous studies have focused on the development of surface functionalization techniques to improve the response of the body to the implant. Yet, the challenge of achieving reliable and long-lasting prostheses persists. In this work, we address this challenge by applying a robust and versatile biofunctionalization process followed by the decoration of the material with oligopeptides. We immobilize four different peptides (RGD, CS-1, IKVAV, PHSRN) on R-THAB^® functionalized surfaces and find them to be highly stable in the long term. We also find that RGD is the best-performing peptide in in vitro cell cultures, enhancing adhesion, proliferation, and osteogenic differentiation of mesenchymal stem cells. To assess the in vivo effect of RGD-decorated Ti-6Al-4V implants, we develop a calvarial model in murine hosts. We find that the RGD-decoration remains stable for 1 week after the surgical procedure and reduces post-implantation macrophage-related inflammation. These results highlight the potential of peptide decoration on R-THAB^® functionalized surfaces to expedite the development of novel metallic biomaterials with enhanced biocompatibility properties, thereby advancing the field of regenerative medicine.

Anguilliform locomotion, an efficient aquatic locomotion mode where the whole body is engaged in fluid-body interaction, contains sophisticated physics. We hypothesized that data-driven modeling techniques may extract models or patterns of the swimmers' dynamics without implicitly measuring the hydrodynamic variables. This work proposes empirical kinematic control and data-driven modeling of a soft swimming robot. The robot comprises six serially connected segments that can individually bend with the segmental pneumatic artificial muscles. Kinematic equations and relations are proposed to measure the desired actuation to mimic anguilliform locomotion kinematics. The robot was tested experimentally and the position and velocities of spatially digitized points were collected using QualiSys^® Tracking Manager (QTM) 1.6.0.1. The collected data were analyzed offline, proposing a new complex variable delay-embedding dynamic mode decomposition (CDE DMD) algorithm that combines complex state filtering and time embedding to extract a linear approximate model. While the experimental results exhibited exotic curves in phase plane and time series, the analysis results showed that the proposed algorithm extracts linear and chaotic modes contributing to the data. It is concluded that the robot dynamics can be described by the linearized model interrupted by chaotic modes. The technique successfully extracts coherent modes from limited measurements and linearizes the system dynamics.

Using bidirectional fluid-structure interaction technology, the dorsal-ventral motion of the dolphin tail fin was simulated, and the feasibility of the numerical simulation method was validated through underwater motion experiments. This study investigated the effects of structural parameters and motion modes of bionic dolphin tail fins on their propulsion performance. The results show that flexible tail fins can enhance propulsion performance. Compared to equal-thickness flexible tail fins, variable-thickness flexible tail fins that conform to the structural characteristics of real dolphin tail fins exhibit better propulsion performance. Asymmetric motion modes have a certain thrust-enhancing effect, but altering the frequency ratio F and amplitude ratio H of heaving motion leads to an increase in pitching moment, reducing swimming stability. Additionally, the greater the difference in frequency and amplitude between the up-and-down motions, the larger the pitching moment. The study results provide references for the optimized design and motion control of bionic tail fins.

This study investigates the unsteady aerodynamic mechanisms underlying the efficient flight of birds and proposes a biomimetic flapping-wing aircraft design utilizing a double-crank double-rocker mechanism. Building upon a detailed analysis of avian flight dynamics, a two-stage foldable flapping mechanism was developed, integrating an optimized double-crank double-rocker structure with a secondary linkage system. This design enables synchronized wing flapping and spanwise folding, significantly enhancing aerodynamic efficiency and dynamic performance. The system's planar symmetric layout and high-ratio reduction gear configuration ensure movement synchronicity and stability while reducing mechanical wear and energy consumption. Through precise modeling, the motion trajectories of the inner and outer wing segments were derived, providing a robust mathematical foundation for motion control and optimization. Computational simulations based on trajectory equations successfully demonstrated the characteristic figure-eight wingtip motion. Using 3D simulations and CFD analysis, key parameters-including initial angle of attack, aspect ratio, flapping frequency, and flapping speed-were optimized. The results indicate that optimal aerodynamic performance is achieved at an initial angle of attack of 9°, an aspect ratio of 5.1, and a flapping frequency and speed of 4-5 Hz and 4-5 m/s, respectively. These findings underscore the potential of biomimetic flapping-wing aircraft in applications such as UAVs and military technology, providing a solid theoretical foundation for future advancements in this field.

The identification and detection of microalgae are essential for the development and utilization of microalgae resources. Traditional methods for microalgae identification and detection have many limitations. Herein, a Feature-Enhanced YOLOv7 (FE-YOLO) model for microalgae cell identification and detection is proposed. Firstly, the feature extraction capability was enhanced by integrating the CAGS (Coordinate Attention Group Shuffle Convolution) attention module into the Neck section. Secondly, the SIoU (SCYLLA-IoU) algorithm was employed to replace the CIoU (Complete IoU) loss function in the original model, addressing the issues of unstable convergence. Finally, we captured and constructed a microalgae dataset containing 6300 images of seven species of microalgae, addressing the issue of a lack of microalgae cell datasets. Compared to the YOLOv7 model, the proposed method shows greatly improved average Precision, Recall, mAP@50, and mAP@95; our proposed algorithm achieved increases of 9.6%, 1.9%, 9.7%, and 6.9%, respectively. In addition, the average detection time of a single image was 0.0455 s, marking a 9.2% improvement.