Browsing by Subject "Scientific computation"
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Item Computational analysis and visualization of the evolution of influenza virus(2014-08) Lam, Ham ChingInfluenza viruses can infect a large variety of birds and mammals including humans, pigs, domestic poultry, marine mammals, cats, dogs, horses, and wild carnivores \cite{Webster2002}. Surveillance for influenza viruses circulating in humans has been gradually increased and expanded to many areas around the world. These surveillance programs have produced large amount of influenza genomic data which facilitates the study of the virus by computational methods that are efficient and cost saving.The main focus of this dissertation research is the development of visualization methods to understand the evolution of influenza viruses circulating in humans and other mammals. The methods developed have been applied to different human influenza A subtypes, swine influenza viruses, and avian influenza viruses. The methods are based on unsupervised dimensional reduction techniques which can be applied to each individual genome segments or to the complete genome sequence of the virus. These methods are a departure from the traditional phylogenetic tree construction paradigm because very large number of high dimensional input sequences can be processed and results are viewed directly in a two or three dimensional Euclidean space.We reproduced the evolutionary trajectory of the seasonal human influenza A/H3N2 virus since its introduction to humans in 1968 on a 2D PCA space. The observed pathway led us to hypothesize that vaccination serves as a primary evolutionary pressure on this virus. We provided visual, simulation results, and statistical results to support this. The North American swine influenza H3N2 viruses were also studied using the developed visualization methods. The diversity of this virus is changing since the 2009 H1N1 pandemic outbreak. Five main clusters were observed from the visualization results. The mutations at two positive selected sites on the HA gene were identified as the potential driver for clusters segregation of this virus after the pandemic.A visualization method was developed to visually detect reassortant influenza virus. A reassortant influenza virus is difficult to detect because it consists of genome segments from different parental origin. As two different strains of influenza coinfect a single cell, the capability to exchange genome segments between these two strains can lead to progeny carrying different parental segments within its genome. In order to detect such progeny, a PCA projection based visualization method that is able to examine the full genome sequence of a reference and test strains simultaneously was developed in order to detect any reassorted segments within a full genome. Besides the development of visualization methods, we have also developed a compact Markov Chain model to estimate the probability of viruses with high genetic similarity found after a very large time gap. This model is a two components model where we combined a Markov Chain with a Poisson model. The Markov model uses Hamming distance as the evolution process of the virus and a computed mutation rate as the input to the Poisson model, combined together, we simulated the evolution process of the influenza virus under the neutral evolution process. The computational results from this model led us to conclude that the existence of reservoirs preserving viruses for decades cannot be completely eliminated.In short, our primary goal has been to develop visualization based approaches to understand the evolution of the influenza viruses from different hosts. The results we have so far suggested that the power of visualization paves the way to gain deeper understanding and insight of the evolution of the virus as we utilize the rapidly growing amount of the genomic data of the virus.Item The explicit polarization theory as a quantum mechanical force field and the development of coarse-grained models for simulating crowded systems of many proteins(2014-01) Mazack, Michael John MorganThis dissertation consists of two parts. The first part concerns the use of explicit polarization theory (X-Pol), the semiempirical polarized molecular orbital (PMO) method, and the dipole preserving, polarization consistent (DPPC) charge model as a quantum mechanical force field (QMFF). A detailed discussion of Hartree-Fock theory and X-Pol is provided, along with expressions for the energy and the analytical first derivative of this QMFF. Test cases for this QMFF with extensive comparisons to experimental data and other models are provided for water (XP3P) and hydrogen fluoride (XPHF), showing that the PMO/X-Pol/DPPC approach discussed in this dissertation is competitive with the most accurate models for those two chemical species over a wide range of chemical and physical properties.The second part of this dissertation concerns the development and application of coarse-grained models for protein dynamics. First, a coarse-grained force field (CGFF) for macromolecules in crowded environments is introduced and described along with a visualization environment for the cartoon-like rendering of biomolecules in vivo. This CGFF is tested against experimental diffusion coefficients for myoglobin (Mb) at a wide range of concentrations, including volume fractions as high as 40%, finding it to be surprisingly accurate for its simplicity and level of coarseness. Second, an analytical coarse-grained (ACG) model for mapping the internal dynamics of proteins into a spherical harmonic expansion is described.Item Hand images in virtual spatial collaboration for map-based planning activities(2014-10) Cheng, Claire XuanThe goal of this project is to improve understanding about the communication channels that assist distant collaborators to perform more effectively when collaborating in a virtual environment. The motivation is to help software developers to decide on the features that should be included in virtual collaboration tools. This work focuses on communication through voice, gestures conveyed via natural hand images, shared maps, markings on maps, and combinations of the above. The task domain studied includes joint, map-based planning tasks, which range from trip planning to traffic disaster management, such as a truck rollover on a high way. Embedded natural gestures are made with the hands or body and derive a meaning from their context, such as, a person pointing to a location on a map; in this work, we will refer to them simply as natural gestures. Surrogate gestures are electronic proxies for natural gestures and include pointing with a cursor or drawing circles, arrows, and other marks on the map. Both natural and surrogate gestures are major concerns in this work. Currently, remote collaborations between traffic experts at different agencies (for example, the state and the city) are usually carried out telephonic. Over the past twenty-five years, new tools have been developed that allow collaborators to work in a shared virtual work-space in which they can not only see shared images and mark shared drawings, but they also see the hands of their distant partners as they move over the work surface. However, few researchers have evaluated the effectiveness thereof. The primary questions explored in this work are whether embedded natural gestures or surrogate gestures provide significant advantages over voice-only communication in virtual collaborations regarding map-based tasks. The answers to these questions could help software developers decide on the features to include in virtual collaboration tools. In order to answer these questions, we recruited twenty-eight students, both undergraduate and graduate, to participate in an experiment. The participants worked in pairs to solve five map-based planning tasks using five versions of map-based work-spaces. These five versions of work-spaces were created by combining different interface features that supported diverse types of communication: voice, a shared virtual-map interface, a shared marking interface (to support surrogate gestures), and a hand-image interface (to support natural gestures). We set up five different combinations of interfaces, as follows: Face-to-Face: Collaborators sitting side-by-side share a virtual-map work-space on which they can both make marks; Voice-only: Distant collaborators can manipulate and mark separate virtual-map work-spaces, but cannot share work-spaces, and can only communicate vocally; Mark-Voice: Distant collaborators have a shared virtual-map work-space on which they can mark and share marks, and can also communicate vocally; Gesture-Voice: Distant collaborators have a shared virtual-map work-space in which they can see videos of each other's hands and arms projected on the map, and also communicate vocally; Mark-Gesture-Voice: Distant collaborators have a shared virtual-map work-space, on which they can see each other's marks and gestures, and they can communicate vocally. The pairing of the interface conditions and task scenarios was systematically varied so that the same interface condition and task scenario were not always paired together. In addition, the presentation order was systematically varied. After each condition, we asked each of the participants six questions about their workload from the NASA Task Load Index and seven questions about their collaborative experience. We found that From performance perspective, all the conditions that involved using embedded natural gestures (Gesture-Voice, and Mark-Gesture-Voice) significantly 1) reduced task completion time, 2) decreased mental demand and 3) helped participants felt more connected to their teammates; additionally, when using the Gesture-Voice condition, participants experienced significantly less frustration and collaborated significantly more seamlessly than in the Voice-Only condition. From preference perspective, Mark-Gesture-Voice was 1) the easiest to use, 2) the most fun, 3) the mostly chosen as professional collaboration tools, 4) the one that helped the user felt like most connected with their partners among all the remote conditions and 5) the favorite among all the remote conditions; even though participants still like the Face-to-Face condition better than any of remote conditions and felt it easiest to use among all the conditions. We can, then, conclude that the hand images are the element primarily responsible for the performance improvement in remote collaboration, but that users enjoy having the marking feature, regardless of whether it helps them significantly or not. Based on these findings, we recommend that software developers of virtual-collaboration tools should include hand images to improve performance, and should also consider including a shared-marking function to increase user-satisfaction.