Two-stream Multi-level Dynamic Point Transformer for Two-person Interaction Recognition

As a fundamental aspect of human life, two-person interactions contain meaningful information about people's activities, relationships, and social settings. Human action recognition serves as the foundation for many smart applications, with a strong focus on personal privacy. However, recognizing two-person interactions poses more challenges due to increased body occlusion and overlap compared to single-person actions. In this paper, we propose a point cloud-based network named Two-stream Multi-level Dynamic Point Transformer for two-person interaction recognition. Our model addresses the challenge of recognizing two-person interactions by incorporating local-region spatial information, appearance information, and motion information. To achieve this, we introduce a designed frame selection method named Interval Frame Sampling (IFS), which efficiently samples frames from videos, capturing more discriminative information in a relatively short processing time. Subsequently, a frame features learning module and a two-stream multi-level feature aggregation module extract global and partial features from the sampled frames, effectively representing the local-region spatial information, appearance information, and motion information related to the interactions. Finally, we apply a transformer to perform self-attention on the learned features for the final classification. Extensive experiments are conducted on two large-scale datasets, the interaction subsets of NTU RGB+D 60 and NTU RGB+D 120. The results show that our network outperforms state-of-the-art approaches across all standard evaluation settings

MarS3D: A Plug-and-Play Motion-Aware Model for Semantic Segmentation on Multi-Scan 3D Point Clouds

3D semantic segmentation on multi-scan large-scale point clouds plays an important role in autonomous systems. Unlike the single-scan-based semantic segmentation task, this task requires distinguishing the motion states of points in addition to their semantic categories. However, methods designed for single-scan-based segmentation tasks perform poorly on the multi-scan task due to the lacking of an effective way to integrate temporal information. We propose MarS3D, a plug-and-play motion-aware module for semantic segmentation on multi-scan 3D point clouds. This module can be flexibly combined with single-scan models to allow them to have multi-scan perception abilities. The model encompasses two key designs: the Cross-Frame Feature Embedding module for enriching representation learning and the Motion-Aware Feature Learning module for enhancing motion awareness. Extensive experiments show that MarS3D can improve the performance of the baseline model by a large margin. The code is available at https://github.com/CVMI-Lab/MarS3D

Transplantation of Porcine Hepatocytes Cultured with Polylactic Acid-O-Carboxymethylated Chitosan Nanoparticles Promotes Liver Regeneration in Acute Liver Failure Rats

In this study, free porcine hepatocytes suspension (Group A), porcine hepatocytes embedded in collagen gel (Group B), porcine hepatocytes cultured with PLA-O-CMC nanoparticles and embedded in collagen gel (Group C), and PLA-O-CMC nanoparticles alone (Group D) were transplanted into peritoneal cavity of ALF rats, respectively. The result showed that plasma HGF levels were elevated post-transplantation with a peak at 12 hr. The rats in Group C showed highest plasma HGF levels at 2, 6, 12, 24 and 36 hr post-transplantation and lowest HGF level at 48 hr. Plasma VEGF levels were elevated at 48 hr post-transplantation with a peak at 72 hr. The rats in Group C showed highest plasma HGF levels at 48, 72, and 96 hr post-transplantation. The liver functions in Group C were recovered most rapidly. Compared with Group B, Group C had significant high liver Kiel 67 antigen labeling index (Ki-67 LI) at day 1 post-HTx (P < .05). Ki-67 LI in groups B and C was higher than that in groups A and D at days 5 and 7 post-HTx. In conclusion, intraperitoneal transplantation of porcine hepatocytes cultured with PLA-O-CMC nanoparticles and embedded in collagen gel can promote significantly liver regeneration in ALF rats

Multi-omics analysis explores the effect of chronic exercise on liver metabolic reprogramming in mice

Background: The effect of exercise on human metabolism is obvious. However, the effect of chronic exercise on liver metabolism in mice is less well described.Methods: The healthy adult mice running for 6 weeks as exercise model and sedentary mice as control were used to perform transcriptomic, proteomic, acetyl-proteomics, and metabolomics analysis. In addition, correlation analysis between transcriptome and proteome, and proteome and metabolome was conducted as well.Results: In total, 88 mRNAs and 25 proteins were differentially regulated by chronic exercise. In particular, two proteins (Cyp4a10 and Cyp4a14) showed consistent trends (upregulated) at transcription and protein levels. KEGG enrichment analysis indicated that Cyp4a10 and Cyp4a14 are mainly involved in fatty acid degradation, retinol metabolism, arachidonic acid metabolism and PPAR signaling pathway. For acetyl-proteomics analysis, 185 differentially acetylated proteins and 207 differentially acetylated sites were identified. Then, 693 metabolites in positive mode and 537 metabolites in negative mode were identified, which were involved in metabolic pathways such as fatty acid metabolism, citrate cycle and glycolysis/gluconeogenesis.Conclusion: Based on the results of transcriptomic, proteomics, acetyl-proteomics and metabolomics analysis, chronic moderate intensity exercise has certain effects on liver metabolism and protein synthesis in mice. Chronic moderate intensity exercise may participate in liver energy metabolism by influencing the expression of Cyp4a14, Cyp4a10, arachidonic acid and acetyl coenzyme A and regulating fatty acid degradation, arachidonic acid metabolism, fatty acyl metabolism and subsequent acetylation

Chiral Assemblies of Pinwheel Superlattices on Substrates

The unique topology and physics of chiral superlattices make their self-assembly from nanoparticles a holy grail for (meta)materials. Here we show that tetrahedral gold nanoparticles can spontaneously transform from a perovskite-like low-density phase with corner-to-corner connections into pinwheel assemblies with corner-to-edge connections and denser packing. While the corner-sharing assemblies are achiral, pinwheel superlattices become strongly mirror-asymmetric on solid substrates as demonstrated by chirality measures. Liquid-phase transmission electron microscopy and computational models show that van der Waals and electrostatic interactions between nanoparticles control thermodynamic equilibrium. Variable corner-to-edge connections among tetrahedra enable fine-tuning of chirality. The domains of the bilayer superlattices display strong chiroptical activity identified by photon-induced near-field electron microscopy and finite-difference time-domain simulations. The simplicity and versatility of the substrate-supported chiral superlattices facilitate manufacturing of metastructured coatings with unusual optical, mechanical and electronic characteristics