Thesis (M.Ed.)--Bowling Green State University, 2008. / Document formatted into pages; contains v, 58 p. Includes bibliographical references.

Thesis (M.A.)--Humboldt State University, 2006. / Includes bibliographical references (leaves 121-123). Also available via Humboldt Digital Scholar.

Thesis (Ed. D.)--Montana State University--Bozeman, 2007. / Typescript. Chairperson, Graduate Committee: Elisabeth Swanson. Includes bibliographical references (leaves 301-316).

This study sought to determine if different factors had influenced females and males to select engineering/science-related studies at the Texas Academy of Mathematics and Science (TAMS). The data were collected in the fall semester in 1997 at TAMS located on the University of North Texas campus from a survey of factors reported in the literature that had influenced students to enroll in engineering/science-related curriculum.

Antigenic variation processes play a central role in parasite invasion and chronic infectious disease, and are likely to respond to host immune mechanisms and epidemiological characteristics. Whether changes in antigenic variation strategies lead to net positive or negative effects for parasite fitness is unclear. To improve our understanding of pathogen evolution, it is important to investigate the mechanisms by which pathogens regulate antigenic variant expression. This involves consideration of the complex interactions that occur between parasites and their hosts, and top-down and bottom-up factors that might drive changes in the genetic architecture of their antigenic archives. Increasing availability of pathogen genomic data offers new opportunities to understand the fundamental mechanisms of immune evasion and pathogen population dynamics during chronic infection. Motivated by the growing knowledge on the antigenic variation system of the sleeping sickness parasite, the African trypanosome, in this thesis, we present different models that analyze antigenic variation of this parasite at different biological scales, ranging from the within-host level, to between-host transmission, and finally the parasite genetics level. First, we describe mechanistically how the structure of the antigenic archive impacts the parasite population dynamics within a single host, and how it interplays with other within-host processes, such as parasite density-dependent differentiation into transmission life-stages and specific host immune responses. Our analysis focuses first on a single parasitaemia peak and then on the dynamics of multiple peaks that rely on stochastic switching between groups of parasite variants. We show that the interplay between the two types of parasite control within the host: specific and general, depends on the modular structure of the parasite antigenic archive. Our modelling reveals that the degree of synchronization in stochastic variant emergence (antigenic block size) determines the relative dominance of general over specific control within a single peak, and can divide infection scenarios into stationary and oscillatory regimes. A requirement for multiple-peak dynamics is a critical switch rate between blocks of antigenic variants, which depends on host characteristics, such as the immune delay, and implies constraints on variant surface glycoprotein (VSG) archive genetic diversification. Secondly, we study the interactions between the structure and function of the antigenic archive at the transmission level. By using nested modelling, we show that the genetic architecture of the archive has important consequences for pathogen fitness within and between hosts. We find host-dependent optimality criteria for the antigenic archive that arise as a result of typical trade-offs between parasite transmission and virulence. Our analysis suggests that different traits of the host population can select for different aspects of the antigenic archive, reinforcing the importance of host heterogeneity in the evolutionary dynamics of parasites. Variant-specific host immune competence is likely to select for larger antigenic block sizes. Parasite tolerance and host life-span are likely to select for whole archive expansion as more archive blocks provide the parasite with a fitness advantage. Within-host carrying capacity, resulting from density-dependent parasite regulation, is likely to impact the evolution of between-block switch rates in the antigenic archive. Our study illustrates the importance of quantifying the links between parasite genetics and within-host dynamics, and suggests that host body size might play a significant role in the evolution of trypanosomes. In Chapters 4 and 5 we consider the genetics behind trypanosome antigenic variation. Antigen switch rates are thought to depend on a range of genetic features, among which, the genetic identity between the switch-off and switch-on gene. The subfamily structure of the VSG archive is important in providing the conditions for this type of switching to occur. We develop a hidden Markov model to describe and estimate evolutionary processes generating clustered patterns of genetic identity between closely related gene sequences. Analysis of alignment data from high-identity VSG genes in the silent antigen gene archive of the African trypanosome identifies two scales of subfamily diversification: local clustering of sequence mismatches, a putative indicator of gene conversion events with other lower-identity donor genes in the archive, and the sparse scale of isolated mismatches, likely to arise from independent point mutations. In addition to quantifying the respective rates of these two processes, our method yields estimates for the gene conversion tract length distribution and the average diversity contributed locally by conversion events. Model fitting is conducted for a range of models using a Bayesian framework. We find that gene conversion events with lower-identity partners are at least 5 times less common than point mutations for VSG pairs, and the average imported conversion tract is short. However, due to the high frequency of mismatches in converted segments, the two processes have almost equal impact on the rate of sequence diversification between VSG sub-family members. We are able to disentangle the most likely locations of point mutations vs. conversions on each aligned gene pair. Finally we model VSG archive diversification at the global scale, as a result of opposing evolutionary forces: point mutation, which induces diversification, and gene conversion, which promotes global homogenization. By adopting stochastic simulation and theoretical approaches such as population genetics and the diffusion approximation, we find how the stationary identity configuration of the archive depends on mutation and conversion parameters. By fitting the theoretical form of the distribution to the current VSG archive configuration, we estimate the global rates of gene conversion and point mutation. The relative dominance of mutation as an evolutionary force quantifies the high divergence propensity of VSG genes in response to host immune pressures. The success of our models in describing realistic infection patterns and making predictions about the fitness consequences of the parasite antigenic archive illustrates the advantage of using integrative approaches that bridge between different biological scales. Even though quantifying the genetic signatures of antigenic variation remains a challenging task, cross-disciplinary analyses and mechanistic modelling of parasite genomic data can help in this direction, to better understand parasite evolution.

This thesis presents novel Markov chain Monte Carlo methodology that exploits the natural representation of a statistical model as a Riemannian manifold. The methods developed provide generalisations of the Metropolis-adjusted Langevin algorithm and the Hybrid Monte Carlo algorithm for Bayesian statistical inference, and resolve many shortcomings of existing Monte Carlo algorithms when sampling from target densities that may be high dimensional and exhibit strong correlation structure. The performance of these Riemannian manifold Markov chain Monte Carlo algorithms is rigorously assessed by performing Bayesian inference on logistic regression models, log-Gaussian Cox point process models, stochastic volatility models, and both parameter and model level inference of dynamical systems described by nonlinear differential equations.

There were two purposes set out in this study, first to identify the principal associations with educational performance of Scottish students as reported in the 2007 wave of the Trends in International Mathematics and Science Study (TIMSS2007), and second to evaluate methods of data analysis where sample surveys use plausible value (PV) methodology. Four sets of data were used for the secondary analysis of TIMSS2007, with student's responses to cognitive items and questionnaire data emanating from two stages (G$ and G*) that each addressed two disciplines (mathematics and science). Explanatory models for each stage and discipline were analysed using hierarchical linear modelling techniques to accommodate the cluster sample design of the survey. Guided by existing literature in STEM education the study examined elements of students' learning experiences that fell within a social constructivist theory of learning to ascertain whether the empirical data supported current claims on effective practice. A number of control variables were included in the analyses, some well-established constructs and others derived from background questionnaires. Overall, the results showed that selected background characteristics were consistently related to mathematics and science achievement. The strength of association with home resources, and although girls were generally associated with lower achievement scores, that gender association was strongest in G4 mathematics achievement. The findings suggest there is limited support for current claims in respect of a reform agenda that privileges discussion and collaborative group work. Other policy initiatives on assessment for learning and using technologies in class are not supported in the data, with either no evidence of association or a significant negative effect in the models of mathematics and science achievement. Aspects of practical work and scientific enquiry are positively associated with G4 science achievement, with particular credence given to 'doing' and 'watching' experiments or investigations, buy there is no association with achievement scores at G8 for any of planning, watching or conducting experiments. This latter finding provides empirical evidence of difference across stages on an aspect of practice that is heavily debated. The primary method of analysis utilised a four-level structure, with PV as the unit of analysis. Substantive findings were compared with alternative methods: first making the dependent variable an average of the five PVs; second using one PV as the response variable; and third computing statistics from all five PVs and merging results using Rubin's Rules for combining multilevel method underestimates standard errors in the model in the same way as witnessed for the average of PVs. This leads to the conclusion that the only valid route to analysing imputed data is through Rubin's method of combining results from all five PVs.

The work described in this thesis aims at facilitating the design and im- plementation of web-based editors, driven by speech or natural language input, with a focus on editing mathematics. First, a taxonomy for system architectures of speech-based applications is given. This classification is based on the location of the speech recognition, the speech, and application logic and the resulting flow of data between client and server components. This contribution extends existing system architecture approaches to take into account the characteristics of speech- based systems. We then show, using statistical language modelling techniques, that math- ematics, either spoken or typed, is more predictable than everyday natu- ral languages. We illustrate how these models, in combination with error correction algorithms, can be used to successfully assist the process of cre- ating mathematical expressions within electronic documents using speech. We have successfully implemented systems to demonstrate our findings, which have also been evaluated using standard language modelling evalua- tion techniques. This work is novel as applying statistical language models to the recognition of spoken mathematics has not been evaluated to this extent prior to our work. We create a parsing framework for spoken mathematics, based on mixfix operators, operator precedences and non-deterministic parsing techniques. This framework can significantly improve the design and parsing of spoken command languages such as spoken mathematics. A novel robust error recovery method for an adaptation of the XGLR parsing approach to our operator precedence setting is presented. This greatly enhances the range of spoken or typed mathematics that can be parsed. The novel parsing framework, algorithms and error recovery that we have designed are suitable for more general structured spoken command languages, as well. The algorithms devised in this thesis have been implemented and integrated in a research prototype system called TalkMaths. We evaluate our contri- butions to the new version of this system by comparing the power of our parser with that contained in previous versions, and by conducting a field study where students engage with our system in a real classroom-based environment. We show that using TalkMaths, rather than a conventional mathematics editor, had a positive impact on the learning and understand- ing of mathematical concepts of the participants.

Thesis (Ph. D.)--University of Missouri-Columbia, 2000. / Typescript. Vita. Includes bibliographical references (leaves 83-93). Also available on the Internet.

Thesis (M.S.)--Florida State University, 2004. / Advisor: Dr. Xiuwen Liu, Florida State University, College of Arts and Sciences, Dept. of Computer Science. Title and description from dissertation home page (viewed June 17, 2004). Includes bibliographical references.