Application and Development of CRISPR/Cas9 Technology in Pig Research

Pigs provide valuable meat sources, disease models, and research materials for humans. However, traditional methods no longer meet the developing needs of pig production. More recently, advanced biotechnologies such as SCNT and genome editing are enabling researchers to manipulate genomic DNA molecules. Such methods have greatly promoted the advancement of pig research. Three gene editing platforms including ZFNs, TALENs, and CRISPR/Cas are becoming increasingly prevalent in life science research, with CRISPR/Cas9 now being the most widely used. CRISPR/Cas9, a part of the defense mechanism against viral infection, was discovered in prokaryotes and has now developed as a powerful and effective genome editing tool that can introduce and enhance modifications to the eukaryotic genomes in a range of animals including insects, amphibians, fish, and mammals in a predictable manner. Given its excellent characteristics that are superior to other tailored endonucleases systems, CRISPR/Cas9 is suitable for conducting pig-related studies. In this review, we briefly discuss the historical perspectives of CRISPR/Cas9 technology and highlight the applications and developments for using CRISPR/Cas9-based methods in pig research. We will also review the choices for delivering genome editing elements and the merits and drawbacks of utilizing the CRISPR/Cas9 technology for pig research, as well as the future prospects

CAT:Collaborative Adversarial Training

Adversarial training can improve the robustness of neural networks. Previous methods focus on a single adversarial training strategy and do not consider the model property trained by different strategies. By revisiting the previous methods, we find different adversarial training methods have distinct robustness for sample instances. For example, a sample instance can be correctly classified by a model trained using standard adversarial training (AT) but not by a model trained using TRADES, and vice versa. Based on this observation, we propose a collaborative adversarial training framework to improve the robustness of neural networks. Specifically, we use different adversarial training methods to train robust models and let models interact with their knowledge during the training process. Collaborative Adversarial Training (CAT) can improve both robustness and accuracy. Extensive experiments on various networks and datasets validate the effectiveness of our method. CAT achieves state-of-the-art adversarial robustness without using any additional data on CIFAR-10 under the Auto-Attack benchmark. Code is available at https://github.com/liuxingbin/CAT.Comment: Tech repor

Latent Feature Relation Consistency for Adversarial Robustness

Deep neural networks have been applied in many computer vision tasks and achieved state-of-the-art performance. However, misclassification will occur when DNN predicts adversarial examples which add human-imperceptible adversarial noise to natural examples. This limits the application of DNN in security-critical fields. To alleviate this problem, we first conducted an empirical analysis of the latent features of both adversarial and natural examples and found the similarity matrix of natural examples is more compact than those of adversarial examples. Motivated by this observation, we propose \textbf{L}atent \textbf{F}eature \textbf{R}elation \textbf{C}onsistency (\textbf{LFRC}), which constrains the relation of adversarial examples in latent space to be consistent with the natural examples. Importantly, our LFRC is orthogonal to the previous method and can be easily combined with them to achieve further improvement. To demonstrate the effectiveness of LFRC, we conduct extensive experiments using different neural networks on benchmark datasets. For instance, LFRC can bring 0.78\% further improvement compared to AT, and 1.09\% improvement compared to TRADES, against AutoAttack on CIFAR10. Code is available at https://github.com/liuxingbin/LFRC.Comment: Tech repor

Applications of Animal Models in Researching Hepatitis A

Hepatitis diseases are remaining in the list of significant threats to human health. Human hepatitis viruses are basically classified into six major hepatotropic pathogens—hepatitis A virus (HAV), hepatitis B virus (HBV), hepatitis C virus (HCV), hepatitis D virus (HDV), hepatitis E virus (HEV), and hepatitis G virus (HGV). Among these different forms of hepatotropic viruses, HAV as the leading cause of acute viral hepatitis is characterized as a kind of tiny ribonucleic acid virus that is linked to atopic disease. As we know, animal models have been instrumental in promoting understanding of complex host-virus interactions and boosting the advancement of immune therapies. So far, animal models such as nonhuman primates (NHPs) have enabled scientists to mimic and study the pathogenicities and host immune responses for hepatitis A infection. With the exception of chimpanzees and marmosets, animals like mice, pigs, guinea pigs, and tree shrews can also be selected as alternative animal models infected with HAV under laboratory conditions. In order to gain a better insight into hepatitis A pathogenesis and relevant contents, this chapter is mainly focused on the research progress in animal models of hepatitis A, and discusses the merits and demerits of these alternative models

Learning Modal-Invariant and Temporal-Memory for Video-based Visible-Infrared Person Re-Identification

Thanks for the cross-modal retrieval techniques, visible-infrared (RGB-IR) person re-identification (Re-ID) is achieved by projecting them into a common space, allowing person Re-ID in 24-hour surveillance systems. However, with respect to the probe-to-gallery, almost all existing RGB-IR based cross-modal person Re-ID methods focus on image-to-image matching, while the video-to-video matching which contains much richer spatial- and temporal-information remains under-explored. In this paper, we primarily study the video-based cross-modal person Re-ID method. To achieve this task, a video-based RGB-IR dataset is constructed, in which 927 valid identities with 463,259 frames and 21,863 tracklets captured by 12 RGB/IR cameras are collected. Based on our constructed dataset, we prove that with the increase of frames in a tracklet, the performance does meet more enhancement, demonstrating the significance of video-to-video matching in RGB-IR person Re-ID. Additionally, a novel method is further proposed, which not only projects two modalities to a modal-invariant subspace, but also extracts the temporal-memory for motion-invariant. Thanks to these two strategies, much better results are achieved on our video-based cross-modal person Re-ID. The code and dataset are released at: https://github.com/VCMproject233/MITML

Advances in understanding of health-promoting benefits of medicine and food homology using analysis of gut microbiota and metabolomics

The health-promoting benefits of medicine and food homology (MFH) are known for thousands of years in China. However, active compounds and biological mechanisms are unclear, greatly limiting clinical practice of MFH. The advent of gut microbiota analysis and metabolomics emerge as key tools to discover functional compounds, therapeutic targets, and mechanisms of benefits of MFH. Such studies hold great promise to promote and optimize functional efficacy and development of MFH-based products, for example, foods for daily dietary supplements or for special medical purposes. In this review, we summarized pharmacological effects of 109 species of MFH approved by the Health and Fitness Commission in 2015. Recent studies applying genome sequencing of gut microbiota and metabolomics to explain the activity of MFH in prevention and management of health consequences were extensively reviewed. We discussed the potentiality in future to decipher functional activities of MFH by applying metabolomics-based polypharmacokinetic strategy and multiomics technologies. The needs for personalized MFH recommendations and comprehensive databases have also been highlighted. This review emphasizes current achievements and challenges of the analysis of gut microbiota and metabolomics as a new avenue to understand MFH

Next generation sequencing as a new detection strategy for maternal cell contamination in clinical prenatal samples

Objectives: The maternal cell contamination in chorionic villus or amniotic fluid presents a serious preanalytical risk for prenatal misdiagnosis. The following study presents and validates a novel process for identifying MCC by detecting short tandem repeat markers on Ion Proton system. Initially, MCC testing was performed during the detection of chromosomal abnormalities so as to improve the detection efficiency and accuracy of this method. Material and methods: More than 70 STR loci were selected to establish the detection progress. Capillary electrophoresis was used to compare the next generation sequencing detection results, as well as to identify the optimal STR on Ion Proton system. Evaluation criteria for maternal cell contamination were set, and the automated data analysis was performed. The detection sensitivity was validated via 4 groups with mixed samples and different proportions. Results: Consequently, twenty-three clinical samples were tested to evaluate the detection accuracy. In addition, 14 reli­able STR loci, which were stably detected in more than 25 samples, were found. The detection sensitivity in maternal cell contamination was no less than 20%, while its accuracy reached 100% in clinical samples. Conclusions: Finally, we established and validated a novel detection procedure for maternal cell contamination in clinical prenatal samples using next generation sequencing. This procedure allowed us to simultaneously perform prenatal test­ing and MCC testing. Unlike the traditional capillary electrophoresis, this method is rapid, highly sensitive, and suitable for wide range of clinical applications

Applications of CRISPR/Cas Technology to Research the Synthetic Genomics of Yeast

The whole genome projects open the prelude to the diversity and complexity of biological genome by generating immense data. For the sake of exploring the riddle of the genome, scientists around the world have dedicated themselves in annotating for these massive data. However, searching for the exact and valuable information is like looking for a needle in a haystack. Advances in gene editing technology have allowed researchers to precisely manipulate the targeted functional genes in the genome by the state-of-the-art gene-editing tools, so as to facilitate the studies involving the fields of biology, agriculture, food industry, medicine, environment and healthcare in a more convenient way. As a sort of pioneer editing devices, the CRISPR/Cas systems having various versatile homologs and variants, now are rapidly giving impetus to the development of synthetic genomics and synthetic biology. Firstly, in the chapter, we will present the classification, structural and functional diversity of CRISPR/Cas systems. Then we will emphasize the applications in synthetic genome of yeast (Saccharomyces cerevisiae) using CRISPR/Cas technology based on year order. Finally, the summary and prospection of synthetic genomics as well as synthetic biotechnology based on CRISPR/Cas systems and their further utilizations in yeast are narrated