Biomimetic N-Doped Graphene Membrane for Proton Exchange Membranes.

Proton exchange membranes (PEMs) with both high selectivity and high permeance are of great demand in hydrogen-based applications, especially in fuel cells. Although graphene membranes have shown high selectivity of protons over other ions and molecules, the relatively low permeance of protons through perfect pristine graphene restricts its practical applications. Inspired by the nitrogen-assisted proton transport in biological systems, we introduced N-doping to increase the proton permeance and proposed a type of N-doped graphene membranes (NGMs) for proton exchange, which have both high proton permeance and high selectivity. Compared to the state-of-the-art commercial PEMs, the NGMs show significant increases in both areal proton conductivity (2-3 orders of magnitude) and selectivity of proton to methanol (1-2 orders of magnitude). The work realized the controllable tuning of proton permeance of the graphene membrane with N-doping and developed a new type of graphene-based PEMs with high performance for practical applications.

Fluorescence assay for glycan expression on living cancer cells based on competitive strategy coupled with dual-functionalized nanobiocomposites.

Cell surface glycans are a class of sophisticated biomolecules related to cancer development and progression, and their analysis is of great significance for early cancer diagnosis and treatment. In this paper, we proposed a fluorescence assay to evaluate glycan expression on living cancer cells based on a competitive strategy coupled with dual-functionalized nanobiocomposites. The competitive assay was conducted between living cancer cells and thiomannosyl derivatives using concanavalin A (Con A)-modified electrode as the interaction platform. To impart fluorescence signaling ability to competitive derivatives, quantum dots (QDs) were anchored on BSA-protected Au nanoparticles, and thiomannosyl derivatives were further immobilized on the nanoparticle surface through Au-S binding. Due to the spacing between QDs and Au nanoparticles by BSA, the {QDs-Au-BSA-mannose} nanobiocomposites maintained the fluorescence of QDs and showed binding ability with the Con A-modified electrode. Au nanorods (AuNRs)-modified electrode was used as an effective substrate to immobilize Con A. This assay was successfully applied to the analysis of two cancer cells lines (A549 and QGY-7701). The method is simple and shows promise for the study of glycan expression on living cancer cells.

In situ poly I:C released from living cell drug nanocarriers for macrophage-mediated antitumor immunotherapy.

Immunotherapy is one of the most promising approaches to inhibit tumor growth and metastasis by activating host immune functions. However, the arising problems such as low immune response caused by complex tumor microenvironment and extremely systemic immune storm still limit the clinical applications of immunotherapy. Here, we construct Poly I: C-encapsulated poly (lactic-co-glycolic acid) nanoparticles (PLP NPs) with a slow release profile. A biomimetic system (MPLP), which loads PLP NPs on the surface of bone marrow-derived macrophage (BMDM) via the maleimide-thiol conjugation, is synthesized to effectively deliver PLP, control drug release and activate the tumor-specific immune response in situ. The results show that PLP NPs loading does not affect the activity and function of BMDM. Then, BMDM acts as a living cell drug vehicle and promotes the accumulation of PLP NPs in tumors, where Poly I: C is released from PLP NPs and reprograms BMDM into tumoricidal M1 macrophage. Furthermore, MPLP triggers potent antitumor immune responses in vivo and effectively inhibits local and metastatic tumors without causing adverse pathological immune reactions. This study offers an inspiration to facilitate clinical translation through the delivery of drugs by living immune cells for future anticancer therapy.

Siamese Local and Global Networks for Robust Face Tracking.

Convolutional neural networks (CNNs) have achieved great success in several face-related tasks, such as face detection, alignment and recognition. As a fundamental problem in computer vision, face tracking plays a crucial role in various applications, such as video surveillance, human emotion detection and human-computer interaction. However, few CNN-based approaches are proposed for face (bounding box) tracking. In this paper, we propose a face tracking method based on Siamese CNNs, which takes advantages of powerful representations of hierarchical CNN features learned from massive face images. The proposed method captures discriminative face information at both local and global levels. At the local level, representations for attribute patches (i.e:, eyes, nose and mouth) are learned to distinguish a face from another one, which are robust to pose changes and occlusions. At the global level, representations for each whole face are learned, which take into account the spatial relationships among local patches and facial characters, such as skin color and nevus. In addition, we build a new largescale challenging face tracking dataset to evaluate face tracking methods and to facilitate the research forward in this field. Extensive experiments on the collected dataset demonstrate the effectiveness of our method in comparison to several state-of-theart visual tracking methods.