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We present CSWin Transformer, an efficient and effective Transformer-based backbone for general-purpose vision tasks. A challenging issue in Transformer design is that global self-attention is very expensive to compute whereas local self-attention often limits the field of interactions of each token. To address this issue, we develop the Cross-Shaped Window self-attention mechanism for computing self-attention in the horizontal and vertical stripes in parallel that form a cross-shaped window, with each stripe obtained by splitting the input feature into stripes of equal width. We provide a detailed mathematical analysis of the effect of the stripe width and vary the stripe width for different layers of the Transformer network which achieves strong modeling capability while limiting the computation cost. We also introduce Locally-enhanced Positional Encoding (LePE), which handles the local positional information better than existing encoding schemes. LePE naturally supports arbitrary input resolutions, and is thus especially effective and friendly for downstream tasks. Incorporated with these designs and a hierarchical structure, CSWin Transformer demonstrates competitive performance on common vision tasks. Specifically, it achieves 85.4% Top-1 accuracy on ImageNet-1K without any extra training data or label, 53.9 box AP and 46.4 mask AP on the COCO detection task, and 51.7 mIOU on the ADE20K semantic segmentation task, surpassing previous state-of-the-art Swin Transformer backbone by +1.2, +2.0, +1.4, and +2.0 respectively under the similar FLOPs setting. By further pretraining on the larger dataset ImageNet-21K, we achieve 87.5% Top-1 accuracy on ImageNet-1K and state-of-the-art segmentation performance on ADE20K with 55.7 mIoU. The code and models will be available at https: //github.com/microsoft/CSWin-Transformer

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A global DNA hypomethylation might activate oncogene transcription, thus promoting carcinogenesis and tumor development. S-Adenosylmethionine (SAM) serves as a major methyl donor in biological transmethylation events. The object of this study is to explore the influence of SAM on the status of methylation at the promoter of the oncogenes c-myc, H-ras and tumor-suppressor gene p16 (INK4a), as well as its inhibitory effect on cancer cells. The results indicated that SAM treatment inhibited cell growth in gastric cancer cells and colon cancer cells, and the inhibition efficiency was significantly higher than that in the normal cells. Under standard growth conditions, C-myc and H-ras promoters were hypomethylated in gastric cancer cells and colon cancer cells. SAM treatment resulted in a heavy methylation of these promoters, which consequently downregulated mRNA and protein levels. In contrast, there was no significant difference in mRNA and protein levels of p16 (INK4a) with and without SAM treatment. SAM can effectively inhibit the tumor cells growth by reversing the DNA hypomethylation on promoters of oncogenes, thus down-regulating their expression. With no influence on the expression of the tumor suppressor genes, such as P16, SAM could be used as a potential drug for cancer therapy.

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Focal adhesion kinase (FAK) is a non-receptor tyrosine kinase that localizes at sites of cell adhesion to the extracellular matrix (ECM) and mediates signalling events downstream of integrin engagement of the ECM. FAK is known to regulate cell survival, proliferation and migration. Areas covered: FAK expression has also been shown to be up-regulated in many cancer types. Previous study also indicates that FAK-mediated signaling and functions are intrinsically involved in the progression of tumor aggressiveness, suggesting that FAK is a promising target for anticancer therapies. Small molecule FAK inhibitors have been developed and are being tested in clinical phase trials. Expert Opinion: These inhibitors have demonstrated to be effective by inducing tumor cell apoptosis in addition to reducing metastasis and angiogenesis. In this review, we give updates on the design, synthesis and structure-activity relationship analysis of small molecule FAK inhibitors discovered from 2015 until now. We also review the FAK inhibitors that are in clinical development and highlight the future prospects.

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