In this paper, we present a simple yet effective formulation called Coverage Axis for 3D shape skeletonization. Inspired by the set cover problem, our key idea is to cover all the surface points using as few inside medial balls as possible. This formulation inherently induces a compact and expressive approximation of the Medial Axis Transform (MAT) of a given shape. Different from previous methods that rely on local approximation error, our method allows a global consideration of the overall shape structure, leading to an efficient high‐level abstraction and superior robustness to noise. Another appealing aspect of our method is its capability to handle more generalized input such as point clouds and poor‐quality meshes. Extensive comparisons and evaluations demonstrate the remarkable effectiveness of our method for generating compact and expressive skeletal representation to approximate the MAT.

Millimeter-wave radar has demonstrated its high efficiency in complex environments in recent years, which outperforms LiDAR and computer vision in human activity recognition in the presence of smoke, fog, and dust. In previous studies, researchers mostly analyzed either 2D (3D) point cloud or range–Doppler information from radar echo to extract activity features. In this paper, we propose a multi-model deep learning approach to fuse the features of both point clouds and range–Doppler for classifying six activities, i.e., boxing, jumping, squatting, walking, circling, and high-knee lifting, based on a millimeter-wave radar. We adopt a CNN–LSTM model to extract the time-serial features from point clouds and a CNN model to obtain the features from range–Doppler. Then we fuse the two features and input the fused feature into the full connected layer for classification. We built a dataset based on a 3D millimeter-wave radar from 17 volunteers. The evaluation result based on the dataset shows that this method has higher accuracy than utilizing the two kinds of information separately and achieves a recognition accuracy of 97.26%, which is about 1% higher than other networks with only one kind of data as input.

NAFLD (non-alcoholic fatty liver disease) is one of the most prominent liver diseases in the world. As a metabolic-related disease, the development of NAFLD is closely associated with various degrees of lipid accumulation, oxidation, inflammation, and fibrosis. Ilex chinensis Sims is a form of traditional Chinese medicine which is used to treat bronchitis, burns, pneumonia, ulceration, and chilblains. Kaempferol-3-O-glucuronide (K3O) is a natural chemical present in Ilex chinensis Sims. This study was designed to investigate the antioxidative, fat metabolism-regulating, and anti-inflammatory potential of K3O. A high-cholesterol diet (HCD) was used to establish steatosis in larval zebrafish, whereby 1mM free fatty acid (FFA) was used to induce lipid accumulation in HepG2 cells, while H2O2 was used to induce oxidative stress in HepG2. The results of this experiment showed that K3O reduced lipid accumulation and the level of reactive oxygen species (ROS) both in vivo (K3O, 40μM) and in vitro (K3O, 20μM). Additionally, K3O (40μM) reduced neutrophil aggregation in vivo. K3O (20μM) also decreased the level of malondialdehyde (MDA) and significantly increased the level of glutathione peroxidase (GSH-px) in both the HCD-induced larval zebrafish model and H2O2-exposed HepG2 cells. In the mechanism study, keap1, nrf2, tnf-α, and il-6 mRNA were all significantly reversed by K3O (20μM) in zebrafish. Changes in Keap1 and Nrf2 mRNA expression were also detected in H2O2-exposed HepG2 cells after they were treated with K3O (20μM). In conclusion, K3O exhibited a reduction in oxidative stress and lipid peroxidation, and this may be related to the Nrf2/Keap1 pathway in the NAFLD larval zebrafish model.

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