RNA-binding proteins (RBPs) play key roles in post-transcriptional regulation. Accurate identification of RBP binding sites in multiple cell lines and tissue types from diverse species is a fundamental endeavor towards understanding the regulatory mechanisms of RBPs under both physiological and pathological conditions. Our POSTAR annotation processes make use of publicly available large-scale CLIP-seq datasets and external functional genomic annotations to generate a comprehensive map of RBP binding sites and their association with other regulatory events as well as functional variants. Here, we present POSTAR3, an updated database with improvements in data collection, annotation infrastructure, and analysis that support the annotation of post-transcriptional regulation in multiple species including: we made a comprehensive update on the CLIP-seq and Ribo-seq datasets which cover more biological conditions, technologies, and species; we added RNA secondary structure profiling for RBP binding sites; we provided miRNA-mediated degradation events validated by degradome-seq; we included RBP binding sites at circRNA junction regions; we expanded the annotation of RBP binding sites, particularly using updated genomic variants and mutations associated with diseases. POSTAR3 is freely available at http://postar.ncrnalab.org.

Due to the rapid development of chip technology and deep learning revolution, many ship detection frameworks for synthetic aperture radar (SAR) imagery based on convolutional neural networks (CNNs) have been proposed and achieved great success. However, there are problems hampering their development: 1) For the SAR ship detection task, it is uneconomic to apply heavy backbone network to extract features because it results in heavy computing load and prolongs the inference time cost; 2) The anchor-based methods usually have massive hyper-parameters, which typically need to be tuned carefully and easily lead to weak detection performance. To alleviate the problems, an efficient low-cost ship detection network for SAR imagery is proposed in this paper. Firstly, a simplified U-Net as the backbone to extract features is proposed. It only contains ∼ 0.47 million learnable weights, which is 2.37%, 0.76%, 0.34%, 1.01%, 0.55% and 1.07% of DarkNet-19, DarkNet-53, VGG-16, ResNet-50, ResNet-101 and ResNext-101, respectively. Secondly, an anchor-free SAR ship detection framework consisting of a bounding boxes regression sub-net and a score map regression sub-net based on simplified U-Net is proposed. To evaluate the effectiveness of our method, extensive experiments have been conducted and a more comprehensive set of evaluation metrics have been applied. Results demonstrate that the proposed network achieves 68.1% average precision and 67.6% average recall on the SAR ship detection dataset (SSDD), respectively. Compared with the state-of-the-art works, our proposed network achieves very competitive detection performance and extreme lightweight (∼ 0.93 million learnable weights in total).

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Background Osteoporosis (OP) patients complicated with type II diabetes mellitus (T2DM) has a higher fracture risk than the non-diabetic patients, and mesenchymal stem cells (MSCs) from T2DM patients also show a weaker osteogenic potent. The present study aimed to provide a gene expression profile in MSCs from diabetic OP and investigated the potential mechanism. Methods The bone-derived MSC (BMSC) was isolated from OP patients complicated with or without T2DM (CON-BMSC, T2DM-BMSC). Osteogenic differentiation was evaluated by qPCR analysis of the expression levels of osteogenic markers, ALP activity and mineralization level. The differentially expressed genes (DEGs) in T2DM-BMSC was identified by RNA-sequence, and the biological roles of DEGs was annotated by bioinformatics analyses. The role of silencing the transcription factor (TF), Forkhead box Q1 (FOXQ1), on the osteogenic differentiation of BMSC was also investigated. Results T2DM-BMSC showed a significantly reduced osteogenic potent compare to the CON-BMSC. A total of 448 DEGs was screened in T2DM-BMSC, and bioinformatics analyses showed that many TFs and the target genes were enriched in various OP- and diabetes-related biological processes and pathways. FOXQ1 had the highest verified fold change (abs) among the top 8 TFs, and silence of FOXQ1 inhibited the osteogenic differentiation of CON-BMSC. Conclusions Our study provided a comprehensive gene expression profile of BMSC in diabetic OP, and found that downregulated FOXQ1 was responsible for the reduced osteogenic potent of T2DM-BSMC. This is of great importance for the special mechanism researches and the treatment of diabetic OP.

This research aimed to evaluate the right ventricular segmentation ability of magnetic resonance imaging (MRI) images based on deep learning and evaluate the influence of curcumin (Cur) on the psychological state of patients with pulmonary hypertension (PH). The heart MRI images were detected based on the You Only Look Once (YOLO) algorithm, and then the MRI image right ventricle segmentation algorithm was established based on the convolutional neural network (CNN) algorithm. The segmentation effect of the right ventricle in cardiac MRI images was evaluated regarding intersection-over-union (IOU), Dice coefficient, accuracy, and Jaccard coefficient. 30 cases of PH patients were taken as the research object. According to different treatments, they were rolled into control group (conventional treatment) and Cur group (conventional treatment + Cur), with 15 cases in each group. Changes in the scores of the self-rating anxiety scale (SAS) and self-rating depression scale (SDS) of the two groups of patients before and after treatment were analyzed. It was found that the average IOU of the heart target detection frame of the MRI image and the true bounding box before correction was 0.7023, and the IOU after correction was 0.9016. The Loss of the MRI image processed by the CNN algorithm was 0.05, which was greatly smaller than those processed by other algorithms. The Dice coefficient, Jaccard coefficient, and accuracy of the MRI image processed by CNN were 0.89, 0.881, and 0.994, respectively. The MRI images of PH patients showed that the anterior wall of the right ventricle was notably thickened, and the main pulmonary artery was greatly widened. After treatment, the SAR and SDS scores of the two groups were lower than those before treatment ( P < 0.05 ), and the SAR and SDS scores of the curcumin group were lower than those of the control group ( P < 0.05 ). To sum up, the right ventricular segmentation ability of MRI images based on deep learning was improved, and Cur can remarkably alleviate the psychological state of PH patients, which provided a reference for the diagnosis and treatment for PH patients.

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In this paper, aiming at the cooperative target detection problem in the process of unmanned helicopter sliding down, a detection method based on complementary filtering is proposed, which fuses improved SSD algorithm and related filtering KCF algorithm. The improved deep learning SSD model redesigns the feature extraction structure to improve the detection effect of small and medium targets for the small target size and large scale change in the landing scene. Then use the detection results of the SSD model to correct the KCF detection, adjust the weight parameters, and output the final fusion detection results. The test results show that the improved model detection accuracy is significantly improved, the detection accuracy in various environments reaches 93.3%, which is higher than 86.1% of the classic SSD model and 87.5% of the Faster-rcnn model. The final proposed fusion detection algorithm has a success rate of 91.1% and a processing speed of 91 hz, which basically satisfies the requirements of the ship.