Sun Yat-sen University

About: Sun Yat-sen University is a education organization based out in Guangzhou, Guangdong, China. It is known for research contribution in the topics: Population & Cancer. The organization has 115149 authors who have published 113763 publications receiving 2286465 citations. The organization is also known as: Zhongshan University & SYSU.

Human gingiva-derived mesenchymal stem cells elicit polarization of m2 macrophages and enhance cutaneous wound healing.

Abstract: Increasing evidence has supported the important role of mesenchymal stem cells (MSCs) in wound healing, however, the underlying mechanism remains unclear. Recently, we have isolated a unique population of MSCs from human gingiva (GMSCs) with similar stem cell-like properties, immunosuppressive, and anti-inflammatory functions as human bone marrow-derived MSCs (BMSCs). We describe here the interplay between GMSCs and macrophages and the potential relevance in skin wound healing. When cocultured with GMSCs, macrophages acquired an anti-inflammatory M2 phenotype characterized by an increased expression of mannose receptor (MR; CD206) and secretory cytokines interleukin (IL)-10 and IL-6, a suppressed production of tumor necrosis factor (TNF)-α, and decreased ability to induce Th-17 cell expansion. In vivo, we demonstrated that systemically infused GMSCs could home to the wound site in a tight spatial interaction with host macrophages, promoted them toward M2 polarization, and significantly enhanced wound repair. Mechanistically, GMSC treatment mitigated local inflammation mediated by a suppressed infiltration of inflammatory cells and production of IL-6 and TNF-α, and an increased expression of IL-10. The GMSC-induced suppression of TNF-α secretion by macrophages appears to correlate with impaired activation of NFκB p50. These findings provide first evidence that GMSCs are capable to elicit M2 polarization of macrophages, which might contribute to a marked acceleration of wound healing.

The Role of Chlorine in the Formation Process of “CH3NH3PbI3-xClx” Perovskite

Abstract: CH3NH3PbI3-xClx is a commonly used chemical formula to represent the methylammonium lead halide perovskite fabricated from mixed chlorine- and iodine-containing salt precursors. Despite the rapid progress in improving its photovoltaic efficiency, fundamental questions remain regarding the atomic ratio of Cl in the perovskite as well as the reaction mechanism that leads to its formation and crystallization. In this work we investigated these questions through a combination of chemical, morphological, structural and thermal characterizations. The elemental analyses reveal unambiguously the negligible amount of Cl atoms in the CH3NH3PbI3-xClx perovskite. By studying the thermal characteristics of methylammonium halides as well as the annealing process in a polymer/perovskite/FTO glass structure, we show that the formation of the CH3NH3PbI3-xClx perovskite is likely driven by release of gaseous CH3NH3Cl (or other organic chlorides) through an intermediate organometal mixed halide phase. Furthermore, the comparative study on CH3NH3I/PbCl2 and CH3NH3I/PbI2 precursor combinations with different molar ratios suggest that the initial introduction of a CH3NH3+ rich environment is critical to slow down the perovskite formation process and thus improve the growth of the crystal domains during annealing; accordingly, the function of Cl− is to facilitate the release of excess CH3NH3+ at a relatively low annealing temperatures.

Multi-level Wavelet-CNN for Image Restoration

Abstract: The tradeoff between receptive field size and efficiency is a crucial issue in low level vision. Plain convolutional networks (CNNs) generally enlarge the receptive field at the expense of computational cost. Recently, dilated filtering has been adopted to address this issue. But it suffers from gridding effect, and the resulting receptive field is only a sparse sampling of input image with checkerboard patterns. In this paper, we present a novel multi-level wavelet CNN (MWCNN) model for better tradeoff between receptive field size and computational efficiency. With the modified U-Net architecture, wavelet transform is introduced to reduce the size of feature maps in the contracting subnetwork. Furthermore, another convolutional layer is further used to decrease the channels of feature maps. In the expanding subnetwork, inverse wavelet transform is then deployed to reconstruct the high resolution feature maps. Our MWCNN can also be explained as the generalization of dilated filtering and subsampling, and can be applied to many image restoration tasks. The experimental results clearly show the effectiveness of MWCNN for image denoising, single image super-resolution, and JPEG image artifacts removal.