The reliability of colorimetry is precise(ly) as expected.

Figure S6. Gene ontology (GO) analysis of the differentially expressed genes between C1 and C2. (JPG 403 kb

Production of Transgenic Pigs Mediated by Pseudotyped Lentivirus and Sperm

Sperm-mediated gene transfer can be a very efficient method to produce transgenic pigs, however, the results from different laboratories had not been widely repeated. Genomic integration of transgene by injection of pseudotyped lentivirus to the perivitelline space has been proved to be a reliable route to generate transgenic animals. To test whether transgene in the lentivirus can be delivered by sperm, we studied incubation of pseudotyped lentiviruses and sperm before insemination. After incubation with pig spermatozoa, 62±3 lentiviral particles were detected per 100 sperm cells using quantitative real-time RT-PCR. The association of lentivirus with sperm was further confirmed by electron microscopy. The sperm incubated with lentiviral particles were artificially inseminated into pigs. Of the 59 piglets born from inseminated 5 sows, 6 piglets (10.17%) carried the transgene based on the PCR identification. Foreign gene and EGFP was successfully detected in ear tissue biopsies from two PCR-positive pigs, revealed via in situ hybridization and immunohistochemistry. Offspring of one PCR-positive boar with normal sows showed PCR-positive. Two PCR-positive founders and offsprings of PCR-positive boar were further identified by Southern-blot analysis, out of which the two founders and two offsprings were positive in Southern blotting, strongly indicating integration of foreign gene into genome. The results indicate that incubation of sperm with pseudotyped lentiviruses can incorporated with sperm-mediated gene transfer to produce transgenic pigs with improved efficiency

Study on Computer Trojan Horse Virus and Its Prevention

In recent years, the fast development of computer network technology, has become an integral part of humanrsquos life, work and study. But with the popularity of the Internet, computer viruses, Trojans and other new terms have become some well-known network vocabularies. Studies have shown that most users of computer are more or less suffered from computer virus. So people must attach great importance to the network security problem. The paper studied Trojan virus. Paper first introduced the concept, characteristics and categories of the Trojan virus and its harm, and then focused on the way and means of the Trojanrsquos spread. It introduced the Trojan virus loading and hiding technology, too. Its last part focused on the prevention measures, it put forward reasonable suggestions to users, and paper also put forward prevention advice to improve network security

Remote Sensing Image Target Detection: Improvement of the YOLOv3 Model with Auxiliary Networks

Remote sensing image target detection is widely used for both civil and military purposes. However, two factors need to be considered for remote sensing image target detection: real-time and accuracy for detecting targets that occupy few pixels. Considering the two above issues, the main research objective of this paper is to improve the performance of the YOLO algorithm in remote sensing image target detection. The reason is that the YOLO models can guarantee both detection speed and accuracy. More specifically, the YOLOv3 model with an auxiliary network is further improved in this paper. Our model improvement consists of four main components. Firstly, an image blocking module is used to feed fixed size images to the YOLOv3 network; secondly, to speed up the training of YOLOv3, DIoU is used, which can speed up the convergence and increase the training speed; thirdly, the Convolutional Block Attention Module (CBAM) is used to connect the auxiliary network to the backbone network, making it easier for the network to notice specific features so that some key information is not easily lost during the training of the network; and finally, the adaptive feature fusion (ASFF) method is applied to our network model with the aim of improving the detection speed by reducing the inference overhead. The experiments on the DOTA dataset were conducted to validate the effectiveness of our model on the DOTA dataset. Our model can achieve satisfactory detection performance on remote sensing images, and our model performs significantly better than the unimproved YOLOv3 model with an auxiliary network. The experimental results show that the mAP of the optimised network model is 5.36% higher than that of the original YOLOv3 model with the auxiliary network, and the detection frame rate was also increased by 3.07 FPS

Influence Maximization in Social Network Considering Memory Effect and Social Reinforcement Effect

Social networks have attracted a lot of attention as novel information or advertisement diffusion media for viral marketing. Influence maximization describes the problem of finding a small subset of seed nodes in a social network that could maximize the spread of influence. A lot of algorithms have been proposed to solve this problem. Recently, in order to achieve more realistic viral marketing scenarios, some constrained versions of influence maximization, which consider time constraints, budget constraints and so on, have been proposed. However, none of them considers the memory effect and the social reinforcement effect, which are ubiquitous properties of social networks. In this paper, we define a new constrained version of the influence maximization problem that captures the social reinforcement and memory effects. We first propose a novel propagation model to capture the dynamics of the memory and social reinforcement effects. Then, we modify two baseline algorithms and design a new algorithm to solve the problem under the model. Experiments show that our algorithm achieves the best performance with relatively low time complexity. We also demonstrate that the new version captures some important properties of viral marketing in social networks, such as such as social reinforcements, and could explain some phenomena that cannot be explained by existing influence maximization problem definitions

Decision Method of Optimal Needle Insertion Angle for Dorsal Hand Intravenous Robot

In the context of COVID-19, the research on various aspects of the venipuncture robot field has become increasingly hot, but there has been little research on robotic needle insertion angles, primarily performed at a rough angle. This will increase the rate of puncture failure. Furthermore, there is sometimes significant pain due to the patients’ differences. This paper investigates the optimal needle entry angle decision for a dorsal hand intravenous injection robot. The dorsal plane of the hand was obtained by a linear structured light scan, which was used as a basis for calculating the needle entry angle. Simulation experiments were also designed to determine the optimal needle entry angle. Firstly, the linear structured optical system was calibrated and optimized, and the error function was constructed and solved iteratively by the optimization method to eliminate measurement error. Besides, the dorsal hand was scanned to obtain the spatial point clouds of the needle entry area, and the least squares method was used to fit it to obtain the dorsal hand plane. Then, the needle entry angle was calculated based on the needle entry area plane. Finally, the changes in the penetration force under different needle entry angles were analyzed to determine the optimal needle insertion angle. According to the experimental results, the average error of the optimized structured light plane position was about 0.1 mm, which meets the needs of the project, and a large angle should be properly selected for needle insertion during the intravenous injection

DFA-UNet: Efficient Railroad Image Segmentation

In computer vision technology, image segmentation is a significant technological advancement for the current problems of high-speed railroad image scene changes, low segmentation accuracy, and serious information loss. We propose a segmentation algorithm, DFA-UNet, based on an improved U-Net network architecture. The model uses the same encoder–decoder structure as U-Net. To be able to extract image features efficiently and further integrate the weights of each channel feature, we propose to embed the DFA attention module in the encoder part of the model for the adaptive adjustment of feature map weights. We evaluated the performance of the model on the RailSem19 dataset. The results showed that our model showed improvements of 2.48%, 0.22%, 3.31%, 0.97%, and 2.2% in mIoU, F1-score, Accuracy, Precision, and Recall, respectively, compared with U-Net. The model can effectively achieve the segmentation of railroad images

Graph Convolutional Networks with Bidirectional Attention for Aspect-Based Sentiment Classification

Aspect-based sentiment classification aims at determining the corresponding sentiment of a particular aspect. Many sophisticated approaches, such as attention mechanisms and Graph Convolutional Networks, have been widely used to address this challenge. However, most of the previous methods have not well analyzed the role of words and long-distance dependencies, and the interaction between context and aspect terms is not well realized, which greatly limits the effectiveness of the model. In this paper, we propose an effective and novel method using attention mechanism and graph convolutional network (ATGCN). Firstly, we make full use of multi-head attention and point-wise convolution transformation to obtain the hidden state. Secondly, we introduce position coding in the model, and use Graph Convolutional Networks to obtain syntactic information and long-distance dependencies. Finally, the interaction between context and aspect terms is further realized by bidirectional attention. Experiments on three benchmarking collections indicate the effectiveness of ATGCN