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Abstract Heart failure (HF) is the leading cause of death in patients with maintenance hemodialysis (MHD). Biomarkers has an important guiding role in the early diagnosis, risk stratification, and prognostic assessment of HF. Increasing studies have indicated that long non-coding RNAs (lncRNAs) have played an indispensable role in the regulatory network of HF. This study was aiming to explore the expression profiles of lncRNAs in patients treated with MHD developing heart failure. Peripheral blood mononuclear cells were isolated from 4 hemodialysis patients with reduced ejection fraction (HFrEF) and 4 hemodialysis patients with preserved ejection fraction (HFpEF), respectively. The expression profile analysis of lncRNAs was performed by using Illumina Novaseq 6000 sequencer. Quantitative real time polymerase chain reaction (qRT-PCR) was used to verify the expression of representative differentially expressed lncRNAs. Based on lncRNA-miRNA-mRNA-KEGG network analysis, the potential role of candidate lncRNAs and their association with the severity of HF were further evaluated. In total, 1,429 differentially expressed lncRNAs were found between patients with HFrEF and patients with HFpEF, of which 613 were up-regulated and 816 were down-regulated (P

In traditional 3D reconstruction methods, using a single view to predict the 3D structure of an object is a very difficult task. This research mainly discusses human pose recognition and estimation based on 3D multiview basketball sports dataset. The convolutional neural network framework used in this research is VGG11, and the basketball dataset Image Net is used for pretraining. This research uses some modules of the VGG11 network. For different feature fusion methods, different modules of the VGG11 network are used as the feature extraction network. In order to be efficient in computing and processing, the multilayer perceptron in the network model is implemented by a one-dimensional convolutional network. The input is a randomly sampled point set, and after a layer of perceptron, it outputs a feature set of n × 16. Then, the feature set is sent to two network branches, one is to continue to use the perceptron method to generate the feature set of n × 1024, and the other network is used to extract the local features of points. After the RGB basketball sports picture passes through the semantic segmentation network, a picture containing the target object is obtained, and the picture is input to the constructed feature fusion network model. After feature extraction is performed on the RGB image and the depth image, respectively, the RGB feature, the local feature of the point cloud, and the global feature are spliced and fused to form a feature vector of N × 1152. There are three branches for this vector network, which, respectively, predict the object position, rotation, and confidence. Among them, the feature dimensionality reduction is realized by one-dimensional convolution, and the activation function is the ReLU function. After removing the feature mapping module, the accuracy of VC-CNN_v1 dropped by 0.33% and the accuracy of VC-CNN_v2 dropped by 0.55%. It can be seen from the research results that the addition of the feature mapping module improves the recognition effect of the network to a certain extent