Molecular insights into viral respiratory infections

The structure of the respiratory tract facilitates gas exchange between the exterior environment and interior milieu of the host, while it is a susceptible target and feasible gateway for diverse pathogens. Pandemics of severe acute respiratory infections have been serious threats to global health, causing significantly morbidity and mortality. In particular, the influenza viruses and coronaviruses (CoV), including MERS-CoV and SARS-CoV, have caused numerous outbreaks of viral pneumonia worldwide with different impacts. To survive in the cells, viruses and pathogens usurp multiple host pathways to multiply and exit from the host cells. There are, however, still numerous critical questions how cells react to the viral infection and this understanding could provide the framework for the development of novel therapeutic strategies against the virus. Autophagy (greek "self-eating"), is essential for cell survival and it has been revealed that numerous microbes, including viruses, hijack autophagy in order to promote their life cycle. In this thesis, we have focused on acquiring new molecular insights into the viral replication of CoV, and investigated the relationship between influenza A virus (IAV) and autophagy

Adaptive Domain Generalization via Online Disagreement Minimization

Deep neural networks suffer from significant performance deterioration when there exists distribution shift between deployment and training. Domain Generalization (DG) aims to safely transfer a model to unseen target domains by only relying on a set of source domains. Although various DG approaches have been proposed, a recent study named DomainBed, reveals that most of them do not beat the simple Empirical Risk Minimization (ERM). To this end, we propose a general framework that is orthogonal to existing DG algorithms and could improve their performance consistently. Unlike previous DG works that stake on a static source model to be hopefully a universal one, our proposed AdaODM adaptively modifies the source model at test time for different target domains. Specifically, we create multiple domain-specific classifiers upon a shared domain-generic feature extractor. The feature extractor and classifiers are trained in an adversarial way, where the feature extractor embeds the input samples into a domain-invariant space, and the multiple classifiers capture the distinct decision boundaries that each of them relates to a specific source domain. During testing, distribution differences between target and source domains could be effectively measured by leveraging prediction disagreement among source classifiers. By fine-tuning source models to minimize the disagreement at test time, target domain features are well aligned to the invariant feature space. We verify AdaODM on two popular DG methods, namely ERM and CORAL, and four DG benchmarks, namely VLCS, PACS, OfficeHome, and TerraIncognita. The results show AdaODM stably improves the generalization capacity on unseen domains and achieves state-of-the-art performance.Comment: 11 pages, 4 figure

Exposure to L2 online text on lexical and reading growth

With the fast-paced development of technology in today’s society, there has been emerging a shift from paper-based reading to digital online reading. While the benefits of exposure to print have been well-established in previous studies, how online reading may impact individuals’ literacy development is largely underexplored. The current study investigated if the amount of English reading experience on the Internet could predict EFL students’ lexical knowledge and reading comprehension ability. Participants were ninety-seven Vietnamese undergraduate students who were administered a website checklist and a vocabulary size test. Their reading comprehension scores were also collected as measures of their reading abilities. Descriptive statistics, hierarchical linear regression and structural equation modelling were utilized for data analysis. The results indicated that exposure to L2 online text was a significant predictor of the participants’ vocabulary size as well as their reading comprehension growth during a course of two years. Pedagogical implications are discussed

Development of a New Framework for Daylighting Simulation with Dynamic Shading Devices

Daylight plays an important role in building design. It affects occupant’s activities, reduces energy demand and supports human health. Most of daylighting simulation software does not capture occupant’s behavior or shading’s dynamic behavior. Daysim is the only one that calculates dynamic shading devices by using lightswitch model but it has limitation. It is unable to use other automated control algorithms or capture occupant’s behavior and is also unable to simulate advanced shading devices which can only be described by bidirectional distribution function such as light redirecting films. A new framework was developed based on Radiance three-phase method to enable the capability of accurate annual daylighting simulation in a room equipped with dynamic shading devices and/or daylight redirecting devices. Two case studies are carried out using Hanoi climate to demonstrate the ability of the framework. It also considers automated shading control which allows future smart building applications. The framework is flexible to run any automated control algorithms and any dynamic behaviors from occupants. In the future, the framework will be expanded to run Radiance five-phase method