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Landslide detection is crucial for disaster management and prevention. With the advent of multi-channel optical remote sensing technology, detecting landslides have become more accessible and more accurate. Although the use of the convolutional neural network (CNN) has significantly increased the accuracy of landslide detection on multi-channel optical remote sensing images, most previous methods using CNN lack the ability to obtain global context information due to the structural limitations of the convolution operation. Motivated by the powerful global modeling capability of the Swin transformer, we propose a new Conv-Trans Dual Network (CTDNet) based on Swin-Unet. First, we propose a dual-stream module (CTDBlock) that combines the advantages of ConvNeXt and Swin transformer, which can establish pixel-level connections and global dependencies from the CNN hierarchy to enhance the ability of the model to extract spatial information. Second, we apply an additional gating module (AGM) to effectively fuse the low-level information extracted by the shallow network and the high-level information extracted by the deep network and minimize the loss of detailed information when propagating. In addition, We conducted extensive subjective and objective comparison and ablation experiments on the Landslide4Sense dataset. Experimental results demonstrate that our proposed CTDNet outperforms other models currently applied in our experiments.

Cervical cancer (CC) is a malignant solid tumor, which is one of the main causes of morbidity and mortality in women. Persistent High-risk human papillomavirus (hrHPV) infection is closely related to cervical cancer and autophagy has been suggested to inhibit viral infections. miRNAs have been reported to regulate autophagy in many solid tumors with many studies implicating miR-224-3p in the regulation of autophagy. In this study, we performed a miRNA microarray analysis on CC tissues and found that a large number of miRNAs with differential expressions in hrHPV-infected tissues. We identified miR-224-3p as a candidate miRNA selectively up regulated in HPV-infected tissues and cell lines. Further analysis revealed that miR-224-3p regulates autophagy in cervical cancer tissues and cell lines. While the overexpression of miR-224-3p inhibits autophagy in HPV-infected cells, knocking down endogenous miR-224-3p increases autophagy activity in the same cells. In addition, we found that miR-224-3p directly inhibits the expression of autophagy related gene, FAK family-interacting protein of 200 kDa (FIP200). In summary, we found that miR-224-3p regulates autophagy in hrHPV-induced cervical cancer cells through targeting FIP200 expression.