[Cascaded multi-level medical image registration method based on transformer].

In deep learning-based image registration, the deformable region with complex anatomical structures is an important factor affecting the accuracy of network registration. However, it is difficult for existing methods to pay attention to complex anatomical regions of images. At the same time, the receptive field of the convolutional neural network is limited by the size of its convolution kernel, and it is difficult to learn the relationship between the voxels with far spatial location, making it difficult to deal with the large region deformation problem. Aiming at the above two problems, this paper proposes a cascaded multi-level registration network model based on transformer, and equipped it with a difficult deformable region perceptron based on mean square error. The difficult deformation perceptron uses sliding window and floating window techniques to retrieve the registered images, obtain the difficult deformation coefficient of each voxel, and identify the regions with the worst registration effect. In this study, the cascaded multi-level registration network model adopts the difficult deformation perceptron for hierarchical connection, and the self-attention mechanism is used to extract global features in the basic registration network to optimize the registration results of different scales. The experimental results show that the method proposed in this paper can perform progressive registration of complex deformation regions, thereby optimizing the registration results of brain medical images, which has a good auxiliary effect on the clinical diagnosis of doctors.

Regulation of RAGE splicing by hnRNP A1 and Tra2ß-1 and its potential role in AD pathogenesis.

The receptor for advanced glycation end products (RAGE) gene expresses two major alternative splicing isoforms, full-length membrane-bound RAGE (mRAGE) and secretory RAGE (esRAGE). Both isoforms play important roles in Alzheimer's disease (AD) pathogenesis, either via interaction of mRAGE with ß-amyloid peptide (Aß) or inhibition of the mRAGE-activated signaling pathway. In the present study, we showed that heterogeneous nuclear ribonucleoprotein A1 (hnRNP A1) and Transformer2ß-1 (Tra2ß-1) were involved in the alternative splicing of mRAGE and esRAGE. Functionally, two factors had an antagonistic effect on the regulation. Glucose deprivation induced an increased ratio of mRAGE/esRAGE via up-regulation of hnRNP A1 and down-regulation of Tra2ß-1. Moreover, the ratios of mRAGE/esRAGE and hnRNP A1/Tra2ß-1 were increased in peripheral blood mononuclear cells from AD patients. The results provide a molecular basis for altered splicing of mRAGE and esRAGE in AD pathogenesis. The receptor for advanced glycation end products (RAGE) gene expresses two major alternative splicing isoforms, membrane-bound RAGE (mRAGE) and secretory RAGE (esRAGE). Both isoforms play important roles in Alzheimer's disease (AD) pathogenesis. Mechanism for imbalanced expression of these two isoforms in AD brain remains elusive. We proposed here a hypothetic model to illustrate that impaired glucose metabolism in AD brain may increase the expression of splicing protein hnRNP A1 and reduce Tra2ß-1, which cause the imbalanced expression of mRAGE and esRAGE.

HGR-Net: Hierarchical Graph Reasoning Network for Arbitrary Shape Scene Text Detection.

As a prerequisite step of scene text reading, scene text detection is known as a challenging task due to natural scene text diversity and variability. Most existing methods either adopt bottom-up sub-text component extraction or focus on top-down text contour regression. From a hybrid perspective, we explore hierarchical text instance-level and component-level representation for arbitrarily-shaped scene text detection. In this work, we propose a novel Hierarchical Graph Reasoning Network (HGR-Net), which consists of a Text Feature Extraction Network (TFEN) and a Text Relation Learner Network (TRLN). TFEN adaptively learns multi-grained text candidates based on shared convolutional feature maps, including instance-level text contours and component-level quadrangles. In TRLN, an inter-text graph is constructed to explore global contextual information with position-awareness between text instances, and an intra-text graph is designed to estimate geometric attributes for establishing component-level linkages. Next, we bridge the cross-feed interaction between instance-level and component-level, and it further achieves hierarchical relational reasoning by learning complementary graph embeddings across levels. Experiments conducted on three publicly available benchmarks SCUT-CTW1500, Total-Text, and ICDAR15 have demonstrated that HGR-Net achieves state-of-the-art performance on arbitrary orientation and arbitrary shape scene text detection.

Protective Role of Rheum Tanguticum Polysaccharide 1 in Radiation- induced Intestinal Mucosal Injury.

The protective effects of Rheum tanguticum polysaccharide 1 (RTP1), which is extracted from the Chinese traditional medicine Rheum tanguticum, on radiation-induced intestinal mucosal injury was investigated. Rat intestinal crypt epithelial cells (IEC-6 cells) and Sprague-Dawley rats were each divided into control, irradiated and RTP1-pretreated irradiated groups. After irradiation, cell survival was determined by MTT (3-[4,5-dimethylthiazol-2-yl]-2,5 diphenyl tetrazolium bromide). assay, and the intracellular reactive oxygen species (ROS) was detected by fluorescent probe method. Apoptosis was observed by acridine orange staining, and cell cycle was analysed by flow cytometry. Histological analysis of the rat intestinal mucosa was conducted by haematoxylin and eosin staining. Irradiation at 8 Gy(Gray) decreased cell survival rate to only 54%, significantly increased intracellular ROS levels and induced apoptosis. RTP1 pretreatment significantly inhibited cell death, reduced the formation of intracellular ROS and partially inhibited apoptosis. Irradiation markedly reduced the height and quantity of rat intestinal villi, but it could be antagonised by RTP1 pretreatment. RTP1 can promote the recovery of intestinal mucosa damage, possibly by inhibiting radiation-induced intestinal epithelial apoptosis and intracellular ROS production.