The inherent antioxidant function of poly(propylene sulfide) (PPS) microspheres (MS) was dissected for different reactive oxygen species (ROS), and therapeutic benefits of PPS-MS were explored in models of diabetic peripheral arterial disease (PAD) and mechanically induced post-traumatic osteoarthritis (PTOA). PPS-MS (∼1 μm diameter) significantly scavenged hydrogen peroxide (H2O2), hypochlorite, and peroxynitrite but not superoxide in vitro in cell-free and cell-based assays. Elevated ROS levels (specifically H2O2) were confirmed in both a mouse model of diabetic PAD and in a mouse model of PTOA, with greater than 5- and 2-fold increases in H2O2, respectively. PPS-MS treatment functionally improved recovery from hind limb ischemia based on ∼15–25% increases in hemoglobin saturation and perfusion in the footpads as well as earlier remodeling of vessels in the proximal limb. In the PTOA model, PPS-MS reduced matrix metalloproteinase (MMP) activity by 30% and mitigated the resultant articular cartilage damage. These results suggest that local delivery of PPS-MS at sites of injury-induced inflammation improves the vascular response to ischemic injury in the setting of chronic hyperglycemia and reduces articular cartilage destruction following joint trauma. These results motivate further exploration of PPS as a stand-alone, locally sustained antioxidant therapy and as a material for microsphere-based, sustained local drug delivery to inflamed tissues at risk of ROS damage.

RGB-Infrared person re-identification (RGB-IR Re-ID) is a cross-modality matching problem with promising applications in the dark environment. Most existing works use Euclidean metric based constraints to resolve the discrepancy between features of different modalities. However, these methods are incapable of learning angularly discriminative feature embedding because Euclidean distance cannot measure the included angle between embedding vectors effectively. As an angularly discriminative feature space is important for classifying the human images based on their embedding vectors, in this paper, we propose a novel ranking loss function, named Bi-directional Exponential Angular Triplet Loss, to help learn an angularly separable common feature space by explicitly constraining the included angles between embedding vectors. Moreover, to help stabilize and learn the magnitudes of embedding vectors, we adopt a common space batch normalization layer. Quantitative experiments on the SYSU-MM01 and RegDB dataset support our analysis. On SYSU-MM01 dataset, the performance is improved from 7.40% / 11.46% to 38.57% / 38.61% for rank-1 accuracy / mAP compared with the baseline. The proposed method can be generalized to the task of single-modality Re-ID and improves the rank-1 accuracy / mAP from 92.0% / 81.7% to 94.7% / 86.6% on the Market-1501 dataset, from 82.6% / 70.6% to 87.6% / 77.1% on the DukeMTMC-reID dataset.

Rain removal aims to remove the rain streaks on rain images. The state-of-the-art methods are mostly based on Convolutional Neural Network (CNN). However, as CNN is not equivariant to object rotation, these methods are unsuitable for dealing with the tilted rain streaks. To tackle this problem, we propose Deep Symmetry Enhanced Network (DSEN) that is able to explicitly extract the rotation equivariant features from rain images. In addition, we design a self-refining mechanism to remove the accumulated rain streaks in a coarse-tofine manner. This mechanism reuses DSEN with a novel information link which passes the gradient flow to the higher stages. Extensive experiments on both synthetic and realworld rain images show that our self-refining DSEN yields the top performance.

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