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[Display omitted] • The encoder-decoder-based network with multi-task learning is proposed. • Three self-supervised reconstruction tasks are established. • A low-quality fundus image is decomposed into three feature layers in the enhancement process. • An effective fusion strategy is designed based on the features of different layers. • Experiments prove that our method is superior in visual comparison and objective evaluation. Fundus images are used to assist the diagnoses of ocular diseases, and a high-quality fundus image with more details makes clinical diagnostic results more reliable. However, the quality of fundus images is often unsatisfactory due to the turbidity of refractive medium and the doctor-patient cooperation. To enhance the low-quality fundus images, a transformer-based self-supervised network is proposed. During the training phase, an encoder-decoder-based network is introduced. To counteract the drawbacks of establishing long-term dependencies in the convolutional neural network (CNN), the encoder composed of vision transformer and CNN is proposed so that the global and local information of fundus images is fully extracted. On this basis, three reconstruction tasks with self-supervised constraints are designed to enable the network to extract features from different degenerated images. During the testing phase, a low-quality fundus image is decomposed into three feature layers of reverse, illumination, and detail, and then the multi-layer features are fused via the network. To demonstrate the effectiveness of the proposed method, the non-uniform illumination and blurry fundus images are tested. The average scores of NIQE on underexposed, blurred, and overexposed fundus images are 3.03, 2.98, and 2.80, respectively. The average scores of BRISQUE on underexposed, blurred, and overexposed fundus images are 40.32, 40.55, and 39.76, respectively. The average score of subjective evaluation by three ophthalmologists is 61.17%. Compared with the existing methods, the proposed method achieves the superior performance in both subjective and objective evaluations. [ABSTRACT FROM AUTHOR]

Fascin, an actin-bundling protein overexpressed in all carcinomas, has been associated with poor prognosis, shorter survival, and more metastatic diseases. It is believed that fascin facilitates tumor metastasis by promoting the formation of invasive membrane protrusions. However, the mechanisms by which fascin is overexpressed in tumors are not clear. TGFβ is a cytokine secreted by tumor and mesenchymal cells and promotes metastasis in many late stage tumors. The pro-metastasis mechanisms of TGFβ remain to be fully elucidated. Here we demonstrated that TGFβ induced fascin expression in spindleshaped tumor cells through the canonical Smad-dependent pathway. Fascin was critical for TGFβ-promoted filopodia formation, migration, and invasion in spindle tumor cells. More importantly, fascin expression significantly correlates with TGFβ1 and TGFβ receptor I levels in a cohort of primary breast tumor samples. Our results indicate that elevated TGFβ level in the tumor microenvironment may be responsible for fascin overexpression in some of the metastatic tumors. Our data also suggest that fascin could play a central role in TGFβ-promoted tumor metastasis. [ABSTRACT FROM AUTHOR]

Highlights: [•] Ca(1 − x) Sr x Cu3Ti4O12 ceramics are synthesized by sol–gel process. [•] Positron annihilation lifetime spectra is used to investigate the defects of samples. [•] Nonlinear and dielectric properties are controlled by density and defects of samples. [Copyright &y& Elsevier]