Thrombospondin 1 and Reelin act through Vldlr to regulate cardiac growth and repair.

Adult mammalian cardiomyocytes have minimal cell cycle capacity, which leads to poor regeneration after cardiac injury such as myocardial infarction. Many positive regulators of cardiomyocyte cell cycle and cardioprotective signals have been identified, but extracellular signals that suppress cardiomyocyte proliferation are poorly understood. We profiled receptors enriched in postnatal cardiomyocytes, and found that very-low-density-lipoprotein receptor (Vldlr) inhibits neonatal cardiomyocyte cell cycle. Paradoxically, Reelin, the well-known Vldlr ligand, expressed in cardiac Schwann cells and lymphatic endothelial cells, promotes neonatal cardiomyocyte proliferation. Thrombospondin1 (TSP-1), another ligand of Vldlr highly expressed in adult heart, was then found to inhibit cardiomyocyte proliferation through Vldlr, and may contribute to Vldlr's overall repression on proliferation. Mechanistically, Rac1 and subsequent Yap phosphorylation and nucleus translocation mediate the regulation of the cardiomyocyte cell cycle by TSP-1/Reelin-Vldlr signaling. Importantly, Reln mutant neonatal mice displayed impaired cardiomyocyte proliferation and cardiac regeneration after apical resection, while cardiac-specific Thbs1 deletion and cardiomyocyte-specific Vldlr deletion promote cardiomyocyte proliferation and are cardioprotective after myocardial infarction. Our results identified a novel role of Vldlr in consolidating extracellular signals to regulate cardiomyocyte cell cycle activity and survival, and the overall suppressive TSP-1-Vldlr signal may contribute to the poor cardiac repair capacity of adult mammals.

An Obstacle Detection Method Based on Longitudinal Active Vision.

The types of obstacles encountered in the road environment are complex and diverse, and accurate and reliable detection of obstacles is the key to improving traffic safety. Traditional obstacle detection methods are limited by the type of samples and therefore cannot detect others comprehensively. Therefore, this paper proposes an obstacle detection method based on longitudinal active vision. The obstacles are recognized according to the height difference characteristics between the obstacle imaging points and the ground points in the image, and the obstacle detection in the target area is realized without accurately distinguishing the obstacle categories, which reduces the spatial and temporal complexity of the road environment perception. The method of this paper is compared and analyzed with the obstacle detection methods based on VIDAR (vision-IMU based detection and range method), VIDAR + MSER, and YOLOv8s. The experimental results show that the method in this paper has high detection accuracy and verifies the feasibility of obstacle detection in road environments where unknown obstacles exist.

Research on Vehicle Pose Detection Method Based on a Roadside Unit.

Vehicle pose detection plays a vital role in modern automotive technology, which can improve driving safety, enhance vehicle stability and provide important support for the development of autonomous driving technology. The current pose estimation methods have the problems of accumulation errors, large algorithm computing power, and expensive cost, so they cannot be widely used in intelligent connected vehicles. This paper proposes a vehicle pose detection method based on an RSU (Roadside Unit). First, the on-board GPS performs the positioning of the target vehicle and transmits the positioning information to the RSU through the UDP (User Data Protocol). Next, the RSU transmits a forward command to the OBU (On-board Unit) through the UDP. The OBU sends the command to the ECU (Electronic Control Unit) to control the vehicle forward. Then, the RSU detects and tracks the vehicle. The RSU takes pictures of two images before and after the movement and obtains the coordinates of the four angle points and the center point by image processing. The vehicle heading direction is determined by the moving direction of the center point of the front and rear two images. Finally, the RSU captures the vehicle images in real time, performs the process of tracking, rectangular fitting and pose calculation to obtain the pose information and transmits the information to the OBU to complete the whole process of vehicle pose detection and information transmission. Experiments show that the method can realize accurate and efficient detection of vehicle pose, meet the real-time requirements of vehicle pose detection, and can be widely used in intelligent vehicles.