WATER QUALITY SURVEY OF VIETNAM

Joint Research on Environmental Science and Technology for the Eart

Groundwater contamination with nitrogenous compounds in Kumamoto Prefecture and Hanoi City : Present conditions and adopted countermeasures

Joint Research on Environmental Science and Technology for the Eart

Host Transcription Profile in Nasal Epithelium and Whole Blood of Hospitalized Children Under 2 Years of Age With Respiratory Syncytial Virus Infection.

BACKGROUND: Most insights into the cascade of immune events after acute respiratory syncytial virus (RSV) infection have been obtained from animal experiments or in vitro models. METHODS: In this study, we investigated host gene expression profiles in nasopharyngeal (NP) swabs and whole blood samples during natural RSV and rhinovirus (hRV) infection (acute versus early recovery phase) in 83 hospitalized patients <2 years old with lower respiratory tract infections. RESULTS: Respiratory syncytial virus infection induced strong and persistent innate immune responses including interferon signaling and pathways related to chemokine/cytokine signaling in both compartments. Interferon-α/β, NOTCH1 signaling pathways and potential biomarkers HIST1H4E, IL7R, ISG15 in NP samples, or BCL6, HIST2H2AC, CCNA1 in blood are leading pathways and hub genes that were associated with both RSV load and severity. The observed RSV-induced gene expression patterns did not differ significantly in NP swab and blood specimens. In contrast, hRV infection did not as strongly induce expression of innate immunity pathways, and significant differences were observed between NP swab and blood specimens. CONCLUSIONS: We conclude that RSV induced strong and persistent innate immune responses and that RSV severity may be related to development of T follicular helper cells and antiviral inflammatory sequelae derived from high activation of BCL6

Finishing the euchromatic sequence of the human genome

The sequence of the human genome encodes the genetic instructions for human physiology, as well as rich information about human evolution. In 2001, the International Human Genome Sequencing Consortium reported a draft sequence of the euchromatic portion of the human genome. Since then, the international collaboration has worked to convert this draft into a genome sequence with high accuracy and nearly complete coverage. Here, we report the result of this finishing process. The current genome sequence (Build 35) contains 2.85 billion nucleotides interrupted by only 341 gaps. It covers ∼99% of the euchromatic genome and is accurate to an error rate of ∼1 event per 100,000 bases. Many of the remaining euchromatic gaps are associated with segmental duplications and will require focused work with new methods. The near-complete sequence, the first for a vertebrate, greatly improves the precision of biological analyses of the human genome including studies of gene number, birth and death. Notably, the human enome seems to encode only 20,000-25,000 protein-coding genes. The genome sequence reported here should serve as a firm foundation for biomedical research in the decades ahead

Flocking for multi agent system : split and merge algorithm

The final year project is the extension of existing project with code name FAME. FAME is C# language game engine software application based on the open source rendering engine Orge3D developed to study and simulate steering behaviors of multi autonomous agents. Before this FYP, several steering behaviors has been studied and implemented including arrival, goal seeking, wandering and flocking, the most interesting behavior. Flocking algorithm attempts to simulate the beautiful natural phenomenon of flocks of thousand birds, schools of countless fish or great herds of animals (In computer science, ―this gathering of mass individuals‖ are commonly termed flocking).Nonetheless, the problem of obstacle avoidance for flock of agents has not been studied before. In this project, the problem of a flock obstacle avoidance is analyzed. Inspired by the nature of flocking avoidance behavior, the obstacle avoidance should comprise of ways to split the whole flock to steer around the obstacle and merge back into the old flock afterward. This report is going to explain in the details algorithm developed during 8 months and show that it is able to produce realistic animation and deliver strong performance. In addition, in FAME, a steering agent is a combination of several steering behaviors, i.e, arrival behavior, goal seeking behavior, wandering behavior and flocking behavior. Adding one more obstacle avoidance behavior increases chances to cause conflicts and the optimization of steering behaviors has also not been thoroughly investigated before. Thus, the problem of steering behaviors optimization and solution are also discussed in this report.Bachelor of Engineering (Computer Science

Mixture Modeling: Solar Application and Misspecification Behaviors

In the modern era of big data, academic institutions, business organizations and government agencies have increasingly needed to deal with a substantial amount of heterogeneous data. It becomes a necessity to develop effective methodologies to extract meaningful insights from this type of data. Among the many methods, mixture modeling is one of the most popular tools and has been successfully adapted to many scientific domains in recent decades. One of its appealing features is the ability to perform data clustering in a well-principled manner. The importance of mixture models is evident in the plethora of publication on the application and theory aspects of mixture modeling in the Statistics and general scientific literature. Fields in which mixture models have been applied with success include economy, astronomy, biology, engineering, psychology, ecology, engineering, computer science, neuroscience among many others in the physical, biological and social science. Our specific contributions to the rich literature of mixture models as follows. The first chapter provides an application of mixture modeling to a complex dataset of solar flares on the surface of Sun. Solar flares are sudden explosions of extremely hot plasma on regions where the Sun's magnetic fields erupt from localized areas known as active regions which are of great interest to physicists. We demonstrate how to explicitly model the heterogeneous patterns of active regions using mixture models. This approach has not yet been pursued in the Space Weather literature at least to our knowledge. Since energetic solar flares are extremely rare events compared to low energy flares which occur orders of magnitude more frequently, statistical inference for this type of data needs to address the data imbalance issue. So another contribution of our work is showing how to deal with the imbalance problem using the Expectation Maximization framework. In the second chapter, we extend an existing identifiability result of well-specified finite mixture models to a setting where the underlying mixture density is of two different kernel families. This setting is motivated by the fact that many datasets in scientific domains typically consist of a signal and a background component. In the latter part of the second chapter, we provide theoretical results of mixture models' behaviors under misspecification. The result begins with the setting of a single Student-t or normal distribution. Then we move to the main result specific to the setting where data population is a mixture of two Student-t distributions but statisticians choose to model as a mixture of two normal distributions. The third chapter utilizes simulation studies to continue the story from the second chapter. Simulation studies are computer experiments that involve creating data by pseudo-random sampling from known probability distributions. A key advantage of simulation studies is that some “truth” (about some parameters of interest) is known from the process of generating the data. It allows us to examine statistical properties such as biases in a relatively straightforward fashion. In this chapter, the bias behaviors of mixture locations and mixing weight are studied for scenarios where biased analytical analysis is difficult to obtain.PhDStatisticsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/178081/1/vietdo_1.pd

Uncovering the heterogeneity of a solar flare mechanism with mixture models

The physics of solar flares occurring on the Sun is highly complex and far from fully understood. However, observations show that solar eruptions are associated with the intense kilogauss fields of active regions, where free energies are stored with field-aligned electric currents. With the advent of high-quality data sources such as the Geostationary Operational Environmental Satellites (GOES) and Solar Dynamics Observatory (SDO)/Helioseismic and Magnetic Imager (HMI), recent works on solar flare forecasting have been focusing on data-driven methods. In particular, black box machine learning and deep learning models are increasingly being adopted in which underlying data structures are not modeled explicitly. If the active regions indeed follow the same laws of physics, similar patterns should be shared among them, reflected by the observations. Yet, these black box models currently used in the literature do not explicitly characterize the heterogeneous nature of the solar flare data within and between active regions. In this paper, we propose two finite mixture models designed to capture the heterogeneous patterns of active regions and their associated solar flare events. With extensive numerical studies, we demonstrate the usefulness of our proposed method for both resolving the sample imbalance issue and modeling the heterogeneity for rare energetic solar flare events

DataSheet1_Uncovering the heterogeneity of a solar flare mechanism with mixture models.PDF

The physics of solar flares occurring on the Sun is highly complex and far from fully understood. However, observations show that solar eruptions are associated with the intense kilogauss fields of active regions, where free energies are stored with field-aligned electric currents. With the advent of high-quality data sources such as the Geostationary Operational Environmental Satellites (GOES) and Solar Dynamics Observatory (SDO)/Helioseismic and Magnetic Imager (HMI), recent works on solar flare forecasting have been focusing on data-driven methods. In particular, black box machine learning and deep learning models are increasingly being adopted in which underlying data structures are not modeled explicitly. If the active regions indeed follow the same laws of physics, similar patterns should be shared among them, reflected by the observations. Yet, these black box models currently used in the literature do not explicitly characterize the heterogeneous nature of the solar flare data within and between active regions. In this paper, we propose two finite mixture models designed to capture the heterogeneous patterns of active regions and their associated solar flare events. With extensive numerical studies, we demonstrate the usefulness of our proposed method for both resolving the sample imbalance issue and modeling the heterogeneity for rare energetic solar flare events.</p

Evaluating the impact of e-service quality on customer intention to use video teller machine services

Digital transformation has received increasing attention from organizations and businesses that want to remain competitive in the digital world. Many banks have increasingly been embracing electronic commerce by providing electronic banking (e-banking) services. This study aimed to investigate the impact of electronic service (e-service) quality on customer intention to use video teller machine (VTM) services. Data were obtained from 450 customers in Vietnam, where digital transformation is a priority in the development strategy of the banking industry. Structural equation modeling reveals the positive impact of three e-service quality dimensions, including responsiveness, security, and interface quality, on the perceived ease of use (PEOU), perceived usefulness (PU), and attitude toward using VTM services. The findings also demonstrate that attitudes are positively related to intention toward using VTM services, and time-consciousness strengthens this relationship. These findings extend current knowledge about e-banking services in emerging markets and provide implications for bank managers and technology providers in promoting their service quality and customer use of VTM services