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Abstract.The aims of the current study were to determine whether 786-0 renal cancer cell-derived exosomes promote human umbilical vein endothelial cells (HUVECs) to form tubular structures and to uncover the underlying mechanisms associated with this process. Exosomes were extracted and purified using ultrafiltration and sucrose gradient centrifugation and characterized by transmission electron microscopy. Tubular structure formation was observed using the matrigel tubular assay. In addition, an adenovirus vector was used to transfect the hepatocyte cell adhesion molecule (hepaCAM) gene into renal cancer 786-0 cells. The expression of hepaCAM and vascular endothelial growth factor (VEGF) mRNA and protein was determined by reverse transcription-polymerase chain reaction and western blot analysis, respectively. Tumor cell-derived exosomes were observed to significantly increase tubular formation in HUVECs. Following transfection with the hepaCAM gene, VEGF expression in 786-0 cells was markedly decreased. In HUVECs, exosome treatment increased VEGF mRNA and protein expression, while hepaCAM expression was only decreased at the protein level. In the present study, renal cancer 786-0 cell-derived exosomes significantly promoted angiogenesis via upregulation of VEGF expression in HUVECs, which may be induced by the downregulation of hepaCAM.

The development of synthetic receptors that recognize carbohydrates in water with high selectivity and specificity is challenging on account of their structural complexity and strong hydrophilicity. Here, we report on the design and synthesis of two pyrene-based, temple-shaped receptors for the recognition of a range of common sugars in water. These receptors rely on the use of two parallel pyrene panels, which serve as roofs and floors, capable of forming multiple [C−H•••π] interactions with the axially oriented C−H bonds on glycopyranosyl rings in the carbohydratebased substrates. In addition, eight polarized pyridinium C−H bonds, projecting from the roofs and floors of the temple receptors toward the binding cavities, form [C−H•••O] hydrogen bonds, with the equatorially oriented OH groups on the sugars located inside the hydrophobic cavities. Four para-xylylene pillars play a crucial role in controlling the distance between the roof and floor. These temple receptors are highly selective for the binding of glucose and its derivatives. Furthermore, they show enhanced fluorescence upon binding with glucose in water, a property which is useful for glucose-sensing in aqueous solution.

Cross-view image matching has attracted extensive attention due to its huge potential applications, such as localization and navigation. Unmanned aerial vehicle (UAV) technology has been developed rapidly in recent years, and people have more opportunities to obtain and use UAV-view images than ever before. However, the algorithms of cross-view image matching between the UAV view (oblique view) and the satellite view (vertical view) are still in their beginning stage, and the matching accuracy is expected to be further improved when applied in real situations. Within this context, in this study, we proposed a cross-view matching method based on location classification (hereinafter referred to LCM), in which the similarity between UAV and satellite views is considered, and we implemented the method with the newest UAV-based geo-localization dataset (University-1652). LCM is able to solve the imbalance of the input sample number between the satellite images and the UAV images. In the training stage, LCM can simplify the retrieval problem into a classification problem and consider the influence of the feature vector size on the matching accuracy. Compared with one study, LCM shows higher accuracies, and Recall@K (K ∈ {1, 5, 10}) and the average precision (AP) were improved by 5–10%. The expansion of satellite-view images and multiple queries proposed by the LCM are capable of improving the matching accuracy during the experiment. In addition, the influences of different feature sizes on the LCM’s accuracy are determined, and we found that 512 is the optimal feature size. Finally, the LCM model trained based on synthetic UAV-view images was evaluated in real-world situations, and the evaluation result shows that it still has satisfactory matching accuracy. The LCM can realize the bidirectional matching between the UAV-view image and the satellite-view image and can contribute to two applications: (i) UAV-view image localization (i.e., predicting the geographic location of UAV-view images based on satellite-view images with geo-tags) and (ii) UAV navigation (i.e., driving the UAV to the region of interest in the satellite-view image based on the flight record).

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