Excavators are widely used for material handling applications in unstructured environments, including mining and construction. Operating excavators in a real-world environment can be challenging due to extreme conditions—such as rock sliding, ground collapse, or excessive dust—and can result in fatalities and injuries. Here, we present an autonomous excavator system (AES) for material loading tasks. Our system can handle different environments and uses an architecture that combines perception and planning. We fuse multimodal perception sensors, including LiDAR and cameras, along with advanced image enhancement, material and texture classification, and object detection algorithms. We also present hierarchical task and motion planning algorithms that combine learning-based techniques with optimization-based methods and are tightly integrated with the perception modules and the controller modules. We have evaluated AES performance on compact and standard excavators in many complex indoor and outdoor scenarios corresponding to material loading into dump trucks, waste material handling, rock capturing, pile removal, and trenching tasks. We demonstrate that our architecture improves the efficiency and autonomously handles different scenarios. AES has been deployed for real-world operations for long periods and can operate robustly in challenging scenarios. AES achieves 24 hours per intervention, i.e., the system can continuously operate for 24 hours without any human intervention. Moreover, the amount of material handled by AES per hour is closely equivalent to an experienced human operator.

Angiopoietin-like protein 2 (ANGPTL2), a member of the glycoprotein family, is mainly secreted by adipose tissues under normal conditions. Recently, ANGPTL2 has been found to be upregulated in some types of cancers and is considered to be a tumor promoter. However, the functional significance of ANGPTL2 in glioma has not yet been elucidated. In this study, we investigated the specific role of ANGPTL2 in glioma. The results showed that ANGPTL2 was highly expressed in glioma tissues and cell lines. Knockdown of ANGPTL2 reduced the proliferative and invasive abilities of glioma cells. Moreover, the tumorigenesis assay showed that ANGPTL2 knockdown inhibited glioma tumor growth in vivo. We also found that ANGPTL2 knockdown decreased the protein levels of p-ERK1/2 in glioma cells and thus blocked the activity of the ERK/MAPK signaling pathway. Taken together, our study provided the first evidence that ANGPTL2 played an oncogenic role in glioma development and might be considered as a new therapeutic target for glioma treatment.

This study designs and accomplishes a high precision and robust laser-based autofocusing system, in which a biased image plane is applied. In accordance to the designed optics, a cluster-based circle fitting algorithm is proposed to calculate the radius of the detecting spot from the reflected laser beam as an essential factor to obtain the defocus value. The experiment conduct on the experiment device achieved novel performance of high precision and robustness. Furthermore, the low demand of assembly accuracy makes the proposed method a low-cost and realizable solution for autofocusing technique.

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