Peptide Self-Assembled Nanostructures for Drug Delivery Applications

Peptide self-assembled nanostructures are very popular in many biomedical applications. Drug delivery is one of the most promising applications among them. The tremendous advantages for peptide self-assembled nanostructures include good biocompatibility, low cost, tunable bioactivity, high drug loading capacities, chemical diversity, specific targeting, and stimuli responsive drug delivery at disease sites. Peptide self-assembled nanostructures such as nanoparticles, nanotubes, nanofibers, and hydrogels have been investigated by many researchers for drug delivery applications. In this review, the underlying mechanisms for the self-assembled nanostructures based on peptides with different types and structures are introduced and discussed. Peptide self-assembled nanostructures associated promising drug delivery applications such as anticancer drug and gene drug delivery are highlighted. Furthermore, peptide self-assembled nanostructures for targeted and stimuli responsive drug delivery applications are also reviewed and discussed

SkeletonGait: Gait Recognition Using Skeleton Maps

The choice of the representations is essential for deep gait recognition methods. The binary silhouettes and skeletal coordinates are two dominant representations in recent literature, achieving remarkable advances in many scenarios. However, inherent challenges remain, in which silhouettes are not always guaranteed in unconstrained scenes, and structural cues have not been fully utilized from skeletons. In this paper, we introduce a novel skeletal gait representation named skeleton map, together with SkeletonGait, a skeleton-based method to exploit structural information from human skeleton maps. Specifically, the skeleton map represents the coordinates of human joints as a heatmap with Gaussian approximation, exhibiting a silhouette-like image devoid of exact body structure. Beyond achieving state-of-the-art performances over five popular gait datasets, more importantly, SkeletonGait uncovers novel insights about how important structural features are in describing gait and when they play a role. Furthermore, we propose a multi-branch architecture, named SkeletonGait++, to make use of complementary features from both skeletons and silhouettes. Experiments indicate that SkeletonGait++ outperforms existing state-of-the-art methods by a significant margin in various scenarios. For instance, it achieves an impressive rank-1 accuracy of over 85% on the challenging GREW dataset. All the source code is available at https://github.com/ShiqiYu/OpenGait