It is highly important and challenging to develop donor-acceptor-donor structured small-molecule second near-infrared window (NIR-II) dyes with excellent properties such as water-solubility and chem/photostability. Here, we discovery an electron acceptor, 6,7-di(thiophen-2-yl)-[1,2,5]thiadiazolo[3,4-g]quinoxaline (TQT) with highest stability in alkaline conditions, compared with conventional NIR-II building block benzobisthiadiazole (BBT) and 6,7-diphenyl-[1,2,5] thiadiazolo[3,4-g]quinoxaline (PTQ). The sulfonated hydrophilic dye, FT-TQT, is further synthesized with 2.13-fold increased quantum yield than its counterpart FT-BBT with BBT as acceptor. FT-TQT complexed with FBS is also prepared and displays a 16-fold increase in fluorescence intensity compared to FT-TQT alone. It demonstrates real-time cerebral and tumor vessel imaging capability with µm-scale resolution. Dynamic monitoring of tumor vascular disruption after drug treatment is achieved by NIR-II fluorescent imaging. Overall, TQT is an efficient electron acceptor for designing innovative NIR-II dyes. The acceptor engineering strategy provides a promising approach to design next generation of NIR-II fluorophores which open new biomedical applications.

Retinal vessel segmentation is of great significance for assisting doctors in diagnosis of ophthalmological diseases such as diabetic retinopathy, macular degeneration and glaucoma. This article proposes a new retinal vessel segmentation algorithm using generative adversarial learning with a large receptive field. A generative network maps an input retinal fundus image to a realistic vessel image while a discriminative network differentiates between images drawn from the database and the generative network. Firstly, the proposed generative network combines shallow features with the upsampled deep features to assemble a more precise vessel image. Secondly, the residual module in the proposed generative and discriminative networks can effectively help deep nets easy to optimize. Moreover, the dilated convolutions in the proposed generative network effectively enlarge the receptive field without increasing the amount of computations. A number of experiments are conducted on two publicly available datasets (DRIVE and STARE) achieving the segmentation accuracy rates of 95.63% and 96.84%, and the average areas under the ROC curve of 98.12% and 98.53%. Performance results show that the proposed automatic retinal vessel segmentation algorithm outperforms state‐of‐the‐art algorithms in many validation metrics. The proposed algorithm can not only detect small tiny blood vessels but also capture large‐scale high‐level semantic vessel features.

Class F G protein-coupled receptors are characterized by a large extracellular domain (ECD) in addition to the common transmembrane domain (TMD) with seven α-helixes. For smoothened receptor (SMO), structural studies revealed dissected ECD and TMD, and their integrated assemblies. However, distinct assemblies were reported under different circumstances. Using an unbiased approach based on four series of cross-conjugated bitopic ligands, we explore the relationship between the active status and receptor assembly. Different activity dependency on the linker length for these bitopic ligands corroborates the various occurrences of SMO assembly. These results reveal a rigid “near” assembly for active SMO, which is in contrast to previous results. Conversely, inactive SMO adopts a free ECD, which would be remotely captured at “far” assembly by cholesterol. Altogether, we propose a mechanism of cholesterol flow-caused SMO activation involving an erection of ECD from far to near assembly.

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Facial image inpainting is a task of filling visually realistic and semantically meaningful contents for missing or masked pixels in a face image. Although existing methods have made significant progress in achieving high visual quality, the controllable diversity of facial image inpainting remains an open problem in this field. This paper introduces EXE-GAN, a novel diverse and interactive facial inpainting framework, which can not only preserve the high-quality visual effect of the whole image but also complete the face image with exemplar-like facial attributes. The proposed facial inpainting is achieved based on generative adversarial networks by leveraging the global style of input image, the stochastic style, and the exemplar style of exemplar image. A novel attribute similarity metric is introduced to encourage networks to learn the style of facial attributes from the exemplar in a self-supervised way. To guarantee the natural transition across the boundary of inpainted regions, a novel spatial variant gradient backpropagation technique is designed to adjust the loss gradients based on the spatial location. A variety of experimental results and comparisons on public CelebA-HQ and FFHQ datasets are presented to demonstrate the superiority of the proposed method in terms of both the quality and diversity in facial inpainting.