Targeted editing of intronic-splicing silencer enhancement of SMN2 Exon 7 inclusion by CRISPR/Case 9

Spinal muscular atrophy (SMA) is an autosomal recessive hereditary neuromuscular disease. Exon 7 and 8 of survival of motor neuron 1 (SMN1) gene or only exon 7 homology deletion leads to the failure to produce a full-length SMN gene. The copy number of SMN2 gene with high homology of SMN1 affects the degree of disease and was the target gene for targeting therapy, in which splicing silencer in intron 7 was the key to suppress the inclusion of exon 7. In this study, we projected to use CRISPR/Case 9 for the targeted editing of intronic-splicing silencer (ISS) sequence to promote the inclusion of SMN2 exon 7 and increase the production of SMN2 full-length (FL) gene expression. It happens that there was a protospacer adjacent motif (PAM) at one end of the ISS sequence according to the design of sgRNA. The recombinant vector of sgRNA HSMN2 CRISPR/Case 9 was constructed and transfected into HEK293 cells. Sequencing results showed that the ISS sequence could be edited accurately and targeting in the predicted direction, in which deleting small fragments, inserting small amounts and mutation. Quantitative analysis of RT-PCR products by restriction enzyme of DdeI digestion showed that the FL of SMN2 increased by 8% (P < 0.05). In the primary cultured chondrocytes of SMA mice, in which sgRNA HSMN2 CRISPR/Case9 recombinant vector transfection could increase the SMN2 FL gene by 23% (P < 0.05) and significantly improve SMN protein levels (P < 0.05). CRISPR/Case 9 is an effective tool for gene editing and therapy of hereditary diseases, but it is rarely reported in the treatment of SMA diseases. This study shows that CRISPR/Case 9 was first used for the precision target of ISS sequence editing, which can effectively promote the production of SMN2 FL gene expressions, in which there was an important clinical reference value.     

Exploration of GH94 Sequence Space for Enzyme Discovery Reveals a Novel Glucosylgalactose Phosphorylase Specificity

The substantial increase in DNA sequencing efforts has led to a rapid expansion of available sequences in glycoside hydrolase families. The ever-increasing sequence space presents considerable opportunities for the search for enzymes with novel functionalities. In this work, the sequence-function space of glycoside hydrolase family 94 (GH94) was explored in detail, using a combined approach of phylogenetic analysis and sequence similarity networks. The identification and experimental screening of unknown clusters led to the discovery of an enzyme from the soil bacterium Paenibacillus polymyxa that acts as a 4-O-β-d -glucosyl-d -galactose phosphorylase (GGalP), a specificity that has not been reported to date. Detailed characterization of GGalP revealed that its kinetic parameters were consistent with those of other known phosphorylases. Furthermore, the enzyme could be used for production of the rare disaccharides 4-O-β-d -glucosyl-d -galactose and 4-O-β-d -glucosyl-l -arabinose. Our current work highlights the power of rational sequence space exploration in the search for novel enzyme specificities, as well as the potential of phosphorylases for rare disaccharide synthesis.     

Image sequence-based risk behavior detection of power operation inspection personnel

A novel image sequence-based risk behavior detection method to achieve high-precision risk behavior detection for power maintenance personnel is proposed in this paper. In this method, the original image sequence data is first separated from the foreground and background. Then, the free anchor frame detection method is used in the foreground image to detect the personnel and correct their direction. Finally, human posture nodes are extracted from each frame of the image sequence, which are then used to identify the abnormal behavior of the human. Simulation experiment results demonstrate that the proposed algorithm has significant advantages in terms of the accuracy of human posture node detection and risk behavior identification.     

No-Reference Video Quality Assessment using novel hybrid features and two-stage hybrid regression for score level fusion

This paper presents a novel No-Reference Video Quality Assessment (NR-VQA) model that utilizes proposed 3D steerable wavelet transform-based Natural Video Statistics (NVS) features as well as human perceptual features. Additionally, we proposed a novel two-stage regression scheme that significantly improves the overall performance of quality estimation. In the first stage, transform-based NVS and human perceptual features are separately passed through the proposed hybrid regression scheme: Support Vector Regression (SVR) followed by Polynomial curve fitting. The two visual quality scores predicted from the first stage are then used as features for the similar second stage. This predicts the final quality scores of distorted videos by achieving score level fusion. Extensive experiments were conducted using five authentic and four synthetic distortion databases. Experimental results demonstrate that the proposed method outperforms other published state-of-the-art benchmark methods on synthetic distortion databases and is among the top performers on authentic distortion databases. The source code is available at https://github.com/anishVNIT/two-stage-vqa.     

Dilated and soft attention-guided convolutional neural network for breast cancer histology images classification

Breast cancer is one of the most common types of cancer in women, and histopathological imaging is considered the gold standard for its diagnosis. However, the great complexity of histopathological images and the considerable workload make this work extremely time-consuming, and the results may be affected by the subjectivity of the pathologist. Therefore, the development of an accurate, automated method for analysis of histopathological images is critical to this field. In this article, we propose a deep learning method guided by the attention mechanism for fast and effective classification of haematoxylin and eosin-stained breast biopsy images. First, this method takes advantage of DenseNet and uses the feature map's information. Second, we introduce dilated convolution to produce a larger receptive field. Finally, spatial attention and channel attention are used to guide the extraction of the most useful visual features. With the use of fivefold cross-validation, the best model obtained an accuracy of 96.47% on the BACH2018 dataset. We also evaluated our method on other datasets, and the experimental results demonstrated that our model has reliable performance. This study indicates that our histopathological image classifier with a soft attention-guided deep learning model for breast cancer shows significantly better results than the latest methods. It has great potential as an effective tool for automatic evaluation of digital histopathological microscopic images for computer-aided diagnosis.     

No-reference quality assessment of dynamic sports videos based on a spatiotemporal motion model

This paper proposes an approach to improve the performance of no-reference video quality assessment for sports videos with dynamic motion scenes using an efficient spatiotemporal model. In the proposed method, we divide the video sequences into video blocks and apply a 3D shearlet transform that can efficiently extract primary spatiotemporal features to capture dynamic natural motion scene statistics from the incoming video blocks. The concatenation of a deep residual bidirectional gated recurrent neural network and logistic regression is used to learn the spatiotemporal correlation more robustly and predict the perceptual quality score. In addition, conditional video block-wise constraints are incorporated into the objective function to improve quality estimation performance for the entire video. The experimental results show that the proposed method extracts spatiotemporal motion information more effectively and predicts the video quality with higher accuracy than the conventional no-reference video quality assessment methods.     

Facial expression recognition of intercepted video sequences based on feature point movement trend and feature block texture variation

Facial Expression Recognition (FER) is an important subject of human–computer interaction and has long been a research area of great interest. Accurate Facial Expression Sequence Interception (FESI) and discriminative expression feature extraction are two enormous challenges for the video-based FER. This paper proposes a framework of FER for the intercepted video sequences by using feature point movement trend and feature block texture variation. Firstly, the feature points are marked by Active Appearance Model (AAM) and the most representative 24 of them are selected. Secondly, facial expression sequence is intercepted from the face video by determining two key frames whose emotional intensities are minimum and maximum, respectively. Thirdly, the trend curve which represents the Euclidean distance variations between any two selected feature points is fitted, and the slopes of specific points on the trend curve are calculated. Finally, combining Slope Set which is composed by the calculated slopes with the proposed Feature Block Texture Difference (FBTD) which refers to the texture variation of facial patch, the final expressional feature are formed and inputted to One-dimensional Convolution Neural Network (1DCNN) for FER. Five experiments are conducted in this research, and three average FER rates 95.2%, 96.5%, and 97% for Beihang University (BHU) facial expression database, MMI facial expression database, and the combination of two databases, respectively, have shown the significant advantages of the proposed method over the existing ones.     

吐哈盆地葡北地区“两宽一高”地震勘探技术及应用效果

吐哈盆地台北凹陷圈闭落实的准确度和储层分布预测的精度一直是制约该区油气发现的关键因素。通过可控震源“两宽一高”地震采集、OVT域时间偏移、地质导向深度偏移初始模型建立及各向异性深度偏移迭代处理，在地震资料的信噪比、频带宽度和分辨率等方面明显提高的基础上，以高精度层序地层学为指导，基于高分辨率地震资料与钻井、测井资料的综合分析，识别出侏罗系6个三级层序，在三级层序格架内将研究区七克台组底部和三间房组顶部砂体识别为两个连续上升半旋回四级层序；通过构造几何属性分析及精细构造分析，揭示了本区“四带、两凹”的构造格局及形成机制；以宽频带多种反演技术为手段，对含油气层段的七克台组和三间房组砂体分布进行了预测；在综合构造分析、储层分析和油藏成藏规律分析的基础上，提出本区油气沿构造带脊线聚集，断裂系统是油气运移的优势路径，七克台组、三间房组储层在葡北构造带发育，向玉果构造带和胜北次凹减薄尖灭，葡北构造带与玉果构造带结合部位的，具有鼻状构造背景的岩性圈闭是获得油气突破的有利地区。