Why eukaryotic cells use introns to enhance gene expression: Splicing reduces transcription-associated mutagenesis by inhibiting topoisomerase I cutting activity

<p>Abstract</p> <p>Background</p> <p>The costs and benefits of spliceosomal introns in eukaryotes have not been established. One recognized effect of intron splicing is its known enhancement of gene expression. However, the mechanism regulating such splicing-mediated expression enhancement has not been defined. Previous studies have shown that intron splicing is a time-consuming process, indicating that splicing may not reduce the time required for transcription and processing of spliced pre-mRNA molecules; rather, it might facilitate the later rounds of transcription. Because the densities of active RNA polymerase II on most genes are less than one molecule per gene, direct interactions between the splicing apparatus and transcriptional complexes (from the later rounds of transcription) are infrequent, and thus unlikely to account for splicing-mediated gene expression enhancement.</p> <p>Presentation of the hypothesis</p> <p>The serine/arginine-rich protein SF2/ASF can inhibit the DNA topoisomerase I activity that removes negative supercoiling of DNA generated by transcription. Consequently, splicing could make genes more receptive to RNA polymerase II during the later rounds of transcription, and thus affect the frequency of gene transcription. Compared with the transcriptional enhancement mediated by strong promoters, intron-containing genes experience a lower frequency of cut-and-paste processes. The cleavage and religation activity of DNA strands by DNA topoisomerase I was recently shown to account for transcription-associated mutagenesis. Therefore, intron-mediated enhancement of gene expression could reduce transcription-associated genome instability.</p> <p>Testing the hypothesis</p> <p>Experimentally test whether transcription-associated mutagenesis is lower in intron-containing genes than in intronless genes. Use bioinformatic analysis to check whether exons flanking lost introns have higher frequencies of short deletions.</p> <p>Implications of the hypothesis</p> <p>The mechanism of intron-mediated enhancement proposed here may also explain the positive correlation observed between intron size and gene expression levels in unicellular organisms, and the greater number of intron containing genes in higher organisms.</p> <p>Reviewers</p> <p>This article was reviewed by Dr Arcady Mushegian, Dr Igor B Rogozin (nominated by Dr I King Jordan) and Dr Alexey S Kondrashov. For the full reviews, please go to the Reviewer's Reports section.</p

Sub-GMN: The Neural Subgraph Matching Network Model

As one of the most fundamental tasks in graph theory, subgraph matching is a crucial task in many fields, ranging from information retrieval, computer vision, biology, chemistry and natural language processing. Yet subgraph matching problem remains to be an NP-complete problem. This study proposes an end-to-end learning-based approximate method for subgraph matching task, called subgraph matching network (Sub-GMN). The proposed Sub-GMN firstly uses graph representation learning to map nodes to node-level embedding. It then combines metric learning and attention mechanisms to model the relationship between matched nodes in the data graph and query graph. To test the performance of the proposed method, we applied our method on two databases. We used two existing methods, GNN and FGNN as baseline for comparison. Our experiment shows that, on dataset 1, on average the accuracy of Sub-GMN are 12.21\% and 3.2\% higher than that of GNN and FGNN respectively. On average running time Sub-GMN runs 20-40 times faster than FGNN. In addition, the average F1-score of Sub-GMN on all experiments with dataset 2 reached 0.95, which demonstrates that Sub-GMN outputs more correct node-to-node matches. Comparing with the previous GNNs-based methods for subgraph matching task, our proposed Sub-GMN allows varying query and data graphes in the test/application stage, while most previous GNNs-based methods can only find a matched subgraph in the data graph during the test/application for the same query graph used in the training stage. Another advantage of our proposed Sub-GMN is that it can output a list of node-to-node matches, while most existing end-to-end GNNs based methods cannot provide the matched node pairs

骨科择期手术患者压疮危险因素的研究

Objectives：The purposes of the study were to investigate the incidence of pressure ulcer of orthopedics patients undergoing selective operation, and to identify the risk factors of pressure ulcer.Methods: A prospective cohort study was employed in this study. Data were collected concerning the general characteristics as well as preoperative, intraoperative and postoperative indicators probable to induce pressure ulcer and then analyzed in view of the risk factors of pressure ulcer. Results: (1) Pressure ulcers developed in thirty five patients (10.1%) on day 0 and the first 3 days following surgery; (2) age, hemoglobin, lymphocyte and getting up late for the first time after surgery were significantly associated with the occurrence of pressure ulcers (odds ratio 1.068,  0.948, 0.293, 1.019, respectively).Conclusions: (1) Patients have higher risk of developing pressure ulcer after operation, the critical period beginning from the operation day to the 3rd day after operation. (2) The age older, the later getting up for the first time after surgery, the liable it is for the patients to develop pressure ulcer postoperatively.目的 调查骨科择期手术后患者压疮的发生率，分析术后压疮发生的危险因素。方法 采用前瞻性队列研究的方法，样本采用便利取样。记录患者的一般资料、术前、术中和术后可能与压疮发生有一定关系的各项指标，对骨科手术后患者的压疮危险因素进行分析。结果 （1）共35例患者发生了压疮，发生率为10.1%；术后皮肤出现异常的时间均在手术当天至手术后1～3天内；（2）高龄、术前低淋巴细胞总数、术前低血红蛋白含量和术后首次下地时间晚是骨科择期手术后患者发生压疮的危险因素，OR值分别为1.068、0.948、0.293、1.019。结论 （1）骨科择期手术后患者是院内压疮发生的高危人群，其中手术当天至手术后第3天为术后压疮预防的关键时期；（2）年龄越大、术前血红蛋白含量、淋巴细胞总数水平越低及手术后首次下地时间越晚，术后发生压疮的可能性就越大

Radiative transitions in charmonium from Nf=2N_f=2 twisted mass lattice QCD

We present a study for charmonium radiative transitions: $J/\psi\rightarrow\eta_c\gamma$, $\chi_{c0}\rightarrow J/\Psi\gamma$ and $h_c\rightarrow\eta_c\gamma$ using $N_f=2$ twisted mass lattice QCD gauge configurations. The single-quark vector form factors for $\eta_c$ and $\chi_{c0}$ are also determined. The simulation is performed at a lattice spacing of $a= 0.06666$ fm and the lattice size is $32^3\times 64$. After extrapolation of lattice data at nonzero $Q^2$ to 0, we compare our results with previous quenched lattice results and the available experimental values.Comment: typeset with revtex, 15 pages, 11 figures, 4 table

A robust learned feature-based visual odometry system for UAV pose estimation in challenging indoor environments

Unmanned Aerial Vehicles (UAVs) are becoming popular nowadays due to their versatility and flexibility for indoor applications, such as the autonomous visual inspection for the inner surface of a pressure vessel. Nevertheless, robust and reliable position estimation is critical for completing these tasks. Visual Odometry (VO) and Visual Simultaneous Localisation and Mapping (VSLAM) allow the UAV to estimate its position in unknown environments. However, traditional feature-based VO/VSLAM systems struggle to deal with complex scenes such as low illumination and textureless environments. Replacing the traditional features with deep learning-based features provides the advantage of handling the challenging environment, but the efficiency is ignored. In this work, an efficient VO system based on a novel lightweight feature extraction network for UAV onboard platforms has been developed. The Deformable Convolution (DFConv) is utilised to improve the feature extraction capability. Owing to the limited onboard computing capability, the Depth-wise Separable Convolution (DWConv) is adopted to calculate the offsets for the deformable convolution and construct the backbone network to improve the feature extraction efficiency. Experiments on public datasets indicate that the efficiency of the VO system is improved by 30.03% while preserving the accuracy on embedded platforms with the feature points and descriptors detected by the proposed Convolutional Neural Network (CNN). Moreover, the proposed VO system is verified through UAV flying tests in a real-world scenario. The results prove that the proposed VO system is able to handle the challenging environments where both the latest traditional and deep learning feature-based VO/VSLAM systems fail, and it is feasible for UAV self-localisation and autonomous navigation in the confined, low illumination and textureless indoor environment