Cellulose nanofibers embedded chitosan/tannin hydrogel with high antibacterial activity and hemostatic ability for drug-resistant bacterial infected wound healing.

Millions of patients annually suffer life-threatening illnesses caused by bacterial infections of skin wounds. However, the treatment of wounds infected with bacteria is a thorny issue in clinical medicine, especially with drug-resistant bacteria infections. Therefore, there is an increasing interest in developing wound dressings that can efficiently fight against drug-resistant bacterial infections and promote wound healing. In this work, an anti-drug-resistant bacterial chitosan/cellulose nanofiber/tannic acid (CS/CNF/TA) hydrogel with excellent wound management ability was developed by electrospinning and fiber breakage-recombination. The hydrogel exhibited an outstanding antibacterial property exceeding 99.9 %, even for drug-resistant bacteria. This hydrogel could adhere to the tissue surface due to its abundant catechol groups, which avoided the shedding of hydrogel during the movement. Besides, it exhibited extraordinary hemostatic ability during the bleeding phase of the wound and then regulated the wound microenvironment by absorbing water and moisturizing. Moreover, the CS/CNF/TA also promoted the regrowth of vessels and follicles, accelerating the healing of infected wound tissue, with a healing rate exceeding 95 % within a 14-day timeframe. Therefore, the CS/CNF/TA hydrogel opens a new approach for the healing of drug-resistant bacterial infected wounds.

A Richly Annotated Pedestrian Dataset for Person Retrieval in Real Surveillance Scenarios.

Retrieving specific persons with various types of queries, e.g., a set of attributes or a portrait photo has great application potential in large-scale intelligent surveillance systems. In this paper, we propose a richly annotated pedestrian (RAP) dataset which serves as a unified benchmark for both attribute-based and image-based person retrieval in real surveillance scenarios. Typically, previous datasets have three improvable aspects, including limited data scale and annotation types, heterogeneous data source, and controlled scenarios. Differently, RAP is a large-scale dataset which contains 84928 images with 72 types of attributes and additional tags of viewpoint, occlusion, body parts, and 2589 person identities. It is collected in the real uncontrolled scene and has complex visual variations in pedestrian samples due to the change of viewpoints, pedestrian postures, and cloth appearance. Towards a high-quality person retrieval benchmark, an amount of state-of-the-art algorithms on pedestrian attribute recognition and person re-identification (ReID), are performed for quantitative analysis with three evaluation tasks, i.e., attribute recognition, attribute-based and image-based person retrieval, where a new instance-based metric is proposed to measure the dependency of the prediction of multiple attributes. Finally, some interesting problems, e.g., the joint feature learning of attribute recognition and ReID, and the problem of cross-day person ReID, are explored to show the challenges and future directions in person retrieval.