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Bovine viral diarrhea virus (BVDV) is an important viral pathogen affecting all ages of cattle, resulting in significant economic losses worldwide. BVDV infection is associated with a diverse array of symptoms including gastrointestinal disorder, respiratory distress, fetal malformation, stillbirth, abortions, and mucosal disease (MD). Transplacental infections of fetuses between 42 and 125 days of gestation can result in immune-tolerance and the surviving fetuses become persistently infected (PI). PI animals are major reservoir of BVDV and it becomes problematic to control the disease. The objectives of this dissertation were to: 1) develop a cost-effective testing scheme to detect BVDV PI animals from exposed herds, 2) characterize two virulent BVDV-2 Mississippi isolates associated with severe hemorrhagic diseases, and 3) perform phylogenetic analysis based on sequences of 5'UTR, E2, and NS5B regions. First, we developed a BVDV testing scheme by combining pooled real-time RT-PCR with antigen capture enzyme-linked immunosorbent assay (ACE) to screen cattle herds. From positive pools individual positives were identified using ACE. Data from a three year period indicated that 92.94% PI animals were infected with BVDV-1, 3.53% with BVDV-2, and 3.53% with both BVDV-1 and BVDV-2. Analysis of the 5'UTR of 22 isolates revealed the predominance of BVDV-1b followed by BVDV-2a. Second, two virulent BVDV isolates, M10-3432 and M10-5347, were successfully recovered from an adult beef breeding cow and feedlot calf respectively. When compared to the reference strain BVDV-2 125c, five and three unique amino acids in E2 regions were different from M10-5347 and M10-3432 respectively. Phylogenetic analysis of E2 region grouped both Mississippi isolates in BVDV-2a, a subtype containing high virulent strains. M10-3432 was clustered with high virulent strain 890 while M10-5347 was clustered with high virulent strain CD87. Third, we compared the phylogenetic analyses of BVDV based on the sequences of 5'UTR, E2, and NS5B at either nucleotides or amino acids level. Although slight differences were observed, the virulent BVDV isolates were consistently classified into BVDV-2a cluster regardless of region of sequences used. Furthermore, phylogenetic tree constructed using combined two or more regions had higher posterior probability and bootstrap value than phylogenetic trees constructed using a single region

京都大学 / 新制・課程博士 / 博士(社会健康医学) / 甲第24536号 / 社医博第128号 / 新制||社医||12(附属図書館) / 京都大学大学院医学研究科社会健康医学系専攻 / (主査)教授 近藤 尚己, 教授 西浦 博, 教授 柳田 素子 / 学位規則第4条第1項該当 / Doctor of Public Health / Kyoto University / DFAM

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As electronic computer technology advances, files and data are kept in computers and exchanged through networks. The computer is a physically closed system for users, making it harder for others to steal data via direct touch. Computer networks, on the other hand, can be used by hackers to gain access to user accounts and steal sensitive data. The academics are concentrating their efforts on preventing network attacks and assuring data security. The Intrusion Detection System (IDS) relies on network traffic and host logs to detect and protect against network threats. They all, however, necessitate a lot of data analysis and quick reaction tactics, which puts a lot of pressure on network managers. The advancement of AI allows computers to take over difficult and time-consuming data processing activities, resulting in more intelligent network attack protection techniques and timely alerts of suspected network attacks. The SCVIC-APT-2021 dataset which is specific to the APT attacks is generated to serve as a benchmark for APT detection. A Virtual Private Network (VPN) connects two network domains to form the basic network environment for creating the dataset. Kali Linux is used as a hacker to launch multiple rounds of APT attacks and compromise two network domains from the external network. The generated dataset contains six APT stages, each of which includes different attack techniques. Following that, a knowledge-based machine learning model is proposed to detect APT attacks on the developed SCVIC-APT-2021 dataset. The macro average F1-score increases by 11.01% and reach up to 81.92% when compared to the supervised baseline model. NSL-KDD and UNSW-NB15 are then utilized as benchmarks to verify the performance of the proposed model. The weighted average F1-score on both datasets can reach 76.42% and 79.20%, respectively. Since some network attacks leave host-based information such as system logs on the network devices, the detection scheme that integrates network-based features and host-based features are used to boost the network attack detection capabilities of IDS. The raw data of CSE-CIC-IDS2018 is utilized to create the SCIVC-CIDS-2021 dataset which includes both network-based features and host-based features. To ensure precise classification results, the SCVIC-CIDS-2021 is labelled with the attacking techniques. Due to the high dimensionalities of the features in the produced dataset, Autoencoder (AE) and Gated Recurrent Unit (GRU) are employed to reduce the dimensionality of network-based and host-based features, respectively. Finally, classification of the data points is performed using knowledge-based PKI and PKI Difference (PKID) models. Among these, the PKID model performs better with a macro average F1-score of 96.60%, which is 7.62% higher than the results only utilizing network-based features.

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Yes / Multi-stage attacks (MSAs) are among the most serious threats in cyberspace today. Criminals target big organisations and government critical infrastructures mainly for financial gain. These attacks are becoming more advanced and stealthier, and thus have capabilities to evade Intrusion Detection Systems (IDSs). As a result, the attack strategies used in the attack render IDSs ineffective, particularly because of new security challenges introduced by some of the key emerging technologies such as 5G wireless networks, cloud computing infrastructure and Internet of Things (IoT), Advanced persistent threats (APTs) and botnet attacks are examples of MSAs, these are serious threats on the Internet. This work analyses recent MSAs, outlines and reveals open issues, challenges and opportunities with existing detection methods.

Persistent viral infections are a major health concern, with persistently infected (PI) cells being a source of continued shedding of virus and generation of viral mutants. Here, we hypothesized that cells persistently infected with the enveloped virus parainfluenza virus 5 (PIV5) would show altered expression of endoplasmic reticulum (ER) stress proteins and increased resistance to death caused by drug-induced ER stress. To test this, lysates of mock-infected, PIV5 acute-infected, and PIV5 PI human lung A549 cells were collected and levels of ER stress proteins were compared. Western blotting revealed that immunoglobulin heavy chain binding protein (BiP/GRP78) was present in higher levels in acute-infected and PI cells compared to naïve cells, indicating increased ER stress in both acutely infected and PI cells. Interestingly, basal levels of the ER stress-sensing protein IRE1-alpha were upregulated in PI compared to naïve and acutely infected cells, but PI cells showed decreased activation of IRE1-alpha compared to acutely infected cells. Naïve, acute-infected, and PI A549-NLR cells were treated with ER stress-inducing drugs tunicamycin, thapsigargin, and epigallocatechin gallate and monitored in real-time viability assays for drug-induced cell death. PI cells showed lower levels of stress-induced cell death compared to naive cells, whereas acute-infected cells experienced the greatest extent of cell death when challenged with ER stress-inducing drugs. Together, these results support the hypothesis that PIV5 persistently infected cells display altered ER stress response pathways and that PI cells are more resistant to death caused by ER stress-inducing drugs. Additionally, these results suggest that IRE1-alpha plays a key role in the shift from acute to persistent infection. These results have implications for the treatment of persistent viral infections, as well as the potential for these viruses to be used for oncolytic virotherapy in the future.

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