Background: N6-methyladenosine (m 6 A) RNA modification has been demonstrated to be a significant regulatory process in the progression of various tumors, including breast cancer. Fat mass and obesity-associated (FTO) enzyme, initially known as the obesity-related protein, is the first identified m 6 A demethylase. However, the relationship between FTO and breast cancer remains controversial. In this study, we aimed to elucidate the role and clinical significance of FTO in breast cancer and to explore the underlying mechanism. Methods: We first investigated the expression of FTO in breast cancer cell lines and tissues by quantitative reverse transcription-PCR (qRT-PCR), Western blotting, and immunohistochemistry. Wound healing assay and Transwell assay were performed to determine the migration and invasion abilities of SKBR3 and MDA-MB453 cells with either knockdown or overexpression of FTO. RNA sequencing (RNA-seq) was conducted to decipher the downstream targets of FTO. qRT-PCR,

In recent years, researchers have paid increasing attention on hyperspectral image (HSI) classification using deep learning methods. To improve the accuracy and reduce the training samples, we propose a double-branch dual-attention mechanism network (DBDA) for HSI classification in this paper. Two branches are designed in DBDA to capture plenty of spectral and spatial features contained in HSI. Furthermore, a channel attention block and a spatial attention block are applied to these two branches respectively, which enables DBDA to refine and optimize the extracted feature maps. A series of experiments on four hyperspectral datasets show that the proposed framework has superior performance to the state-of-the-art algorithm, especially when the training samples are signally lacking.

Semantic segmentation of remote sensing images plays an important role in a wide range of applications including land resource management, biosphere monitoring and urban planning. Although the accuracy of semantic segmentation in remote sensing images has been increased significantly by deep convolutional neural networks, several limitations exist in standard models. First, for encoder-decoder architectures such as U-Net, the utilization of multi-scale features causes the underuse of information, where low-level features and high-level features are concatenated directly without any refinement. Second, longrange dependencies of feature maps are insufficiently explored, resulting in sub-optimal feature representations associated with each semantic class. Third, even though the dot-product attention mechanism has been introduced and utilized in semantic segmentation to model long-range dependencies, the large time and space demands of attention impede the actual usage of attention in application scenarios with large-scale input. This paper proposed a Multi-Attention-Network (MANet) to address these issues by extracting contextual dependencies through multiple efficient attention modules. A novel attention mechanism of kernel attention with linear complexity is proposed to alleviate the large computational demand in attention. Based on kernel attention and channel attention, we integrate local feature maps extracted by ResNet-50 with their corresponding global dependencies and reweight interdependent channel maps adaptively. Numerical experiments on two large-scale fine resolution remote sensing datasets demonstrate the superior performance of the proposed MANet.

The attention mechanism can refine the extracted feature maps and boost the classification performance of the deep network, which has become an essential technique in computer vision and natural language processing. However, the memory and computational costs of the dot-product attention mechanism increase quadratically with the spatio-temporal size of the input. Such growth hinders the usage of attention mechanisms considerably in application scenarios with large-scale inputs. In this Letter, we propose a Linear Attention Mechanism (LAM) to address this issue, which is approximately equivalent to dotproduct attention with computational efficiency. Such a design makes the incorporation between attention mechanisms and deep networks much more flexible and versatile. Based on the proposed LAM, we re-factor the skip connections in the raw U-Net and design a Multi-stage Attention ResU-Net (MAResU-Net) for semantic segmentation from fine-resolution remote sensing images. Experiments conducted on the Vaihingen dataset demonstrated the effectiveness and efficiency of our MAResU-Net.

The goal of our study is to evaluate the effect of Scutellarin on type II diabetes-induced testicular disorder and show the mechanism of Scutellarin's action. We used streptozotocin and high-fat diet to establish type II diabetic rat model. TUNEL and haematoxylin and eosin staining were used to evaluate the testicular apoptotic cells and morphologic changes. Immunohistochemical staining was used to measure the expression level of vascular endothelial growth factor and blood vessel density in testes. Oxidative stress in testes and epididymis was tested by fluorescence spectrophotometer and ELISA. The expression of Bcl-2/Bax and blood flow rate in testicular vessels were measured by western blot and Doppler. Our results for the first time showed that hyperglycemia induced apoptotic cells and morphologic impairments in testes of rats, while administration of Scutellarin can significantly inhibit these damages. This effect of Scutellarin is controlled by two apoptotic triggers: ROS/Bcl-2/Bax and ROS/microcirculation/starving pathway.