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Back pain is an important health and economic problem affecting a significant part of our population. It is of interest to both medical and behavioral professionals concerned with the complex role of the social and psychological factors in the etiology of somatic ailments. Although there has been much written about back injuries in military and industrial settings, little is known about the epidemiological patterns in a general population (Nagi et al., 1973). The objective of this study is to find: a) the major factors connected to back pain, b) whether the general work-stress index is related to back pain, where the general work-stress index is the sum of job stressors including psychological demands, job insecurity, physical exertion, decision latitude and the social support at work, and c) the relationship especially amongst back pain, activity restriction, age, job satisfaction and income. The National Population Health Survey (NPHS) database is used in this project. Some statistical techniques such as logistic regression and log-linear models are used for data analysis. In this project all explanatory variables in logistic regression models are treated as continuous variables; all variables when used in log-linear models are treated as categorical data. Results are compared between these different methods. They are in close agreement with each other. We conclude that age has very high impact on back pain with significance level being lower than 1 %; activity restriction also has a strong relationship with back pain; chronic stress, childhood and adult stressors all have high association with back pain; job stressor and recent life bad events are related fairly to back pain at significant level 5%; and income and job satisfaction do not have direct impact on back pain. Although there is not much that can be done to change the normal aging process of the spinal column, some of the predictors identified such as job stressors are amenable to change. / Thesis / Master of Science (MS)

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no / Demand for more sophisticated models to meet big data expectations require significant data repository obligations, operating concurrently in higher-level applications. Current models provide only disjointed modelling paradigms. The proposed framework addresses the need for higher-level abstraction, using low-level logic in the form of axioms, from which higher-level functionality is logically derived. The framework facilitates definition and usage of subjective structures across the cyber-physical system domain, and is intended to converge the range of heterogeneous data-driven objects.

Oxidative stress is a harmful condition in a cell, tissue, or organ, caused by an imbalance between reactive oxygen species or other oxidants and the capacity of antioxidant defense systems to remove them. These oxidants cause wide-ranging damage to macromolecules, including proteins, lipids, DNA and carbohydrates. Oxidative stress is an important pathophysiologic component of a number of diseases, such as Alzheimer's disease, diabetes and certain cancers. Cells contain effective defense mechanisms to respond to oxidative stress. Despite much accumulated knowledge about these responses, their kinetics, especially the kinetics of early responses is still not clearly understood. The Yap1 transcription factor is crucial for the normal response to a variety of stress conditions including oxidative stress. Previous studies on Yap1 regulation started to measure gene expression profile at least 20 minutes after the induction of oxidative stress. Genes and pathways regulated by Yap1 in early oxidative stress response (within 20 minutes) were not identified in these studies. Here we study the kinetics of early oxidative stress response induced by the cumene hydroperoxide (CHP) in Saccharomyces cerevisiae wild type and yap1 mutant. Gene expression profiles after exposure to CHP were obtained in controlled conditions using Affymetrix Yeast Genome S98 arrays. The oxidative stress response was measured at 8 time points along 120 minutes after the addition of CHP, with the earliest time point at 3 minute after the exposure. Statistical analysis methods, including ANOVA, k-means clustering analysis, and pathway analysis were used to analyze the data. The results from this study provide a dynamic resolution of the oxidative stress responses in S. cerevisiae, and contribute to a richer understanding of the antioxidant defense systems. It also provides a global view of the roles that Yap1 plays under normal and oxidative stress conditions. / Ph. D.

The Goodwin Hall building on the Virginia Tech campus is the most instrumented building for vibration monitoring. It houses 225 hard-wired accelerometers which record vibrations arising due to internal as well as external activities. The recorded vibration data can be used to develop real-time applications for monitoring the health of the building or detecting human activity in the building. However, the lack of infrastructure to handle the massive scale of the data, and the steep learning curve of the tools required to store and process the data, are major deterrents for the researchers to perform their experiments. Additionally, researchers want to explore the data to determine the type of experiments they can perform. This work tries to solve these problems by providing a system to store and process the data using existing big data technologies. The system simplifies the process of big data analysis by supporting code re-usability and multiple programming languages. The effectiveness of the system was demonstrated by four case studies. Additionally, three visualizations were developed to help researchers in the initial data exploration. / Master of Science / The Goodwin Hall building on the Virginia Tech campus is an example of a ‘smart building.’ It uses sensors to record the response of the building to various internal and external activities. The recorded data can be used by algorithms to facilitate understanding of the properties of the building or to detect human activity. Accordingly, researchers in the Virginia Tech Smart Infrastructure Lab (VTSIL) run experiments using a part of the complete data. Ideally, they want to run their experiments continuously as new data is collected. However, the massive scale of the data makes it difficult to process new data as soon as it arrives, and to make it available immediately to the researchers. The technologies that can handle data at this scale have a steep learning curve. Starting to use them requires much time and effort. This project involved building a system to handle these challenges so that researchers can focus on their core area of research. The system provides visualizations depicting various properties of the data to help researchers explore that data before running an experiment. The effectiveness of this work was demonstrated using four case studies. These case studies used the actual experiments conducted by VTSIL researchers in the past. The first three case studies help in understanding the properties of the building whereas the final case study deals with detecting and locating human footsteps, on one of the floors, in real-time.

A study of large solar storms in the equinox periods of solar cycles 23 and 24 is presented to quantify their effects on the total electron content (TEC) in the ionosphere. We study the dependence of TEC over the contiguous US on various storm parameters, including the onset time of the storm, the duration of the storm, its intensity, and the rate of change of the ring current response. These parameters are inferred autonomously and compared to TEC values obtained from the CORS network of GPS stations. To quantify the effects we examine the difference between the storm-time TEC value and an average from 5 quiet days during the same month. These values are studied over a grid with 1 deg x 1 deg spatial resolution in latitude and longitude over the US sector. Correlations between storm parameters and the quantified delta TEC values are studied using machine learning techniques to identify the most important controlling variables. The weights inferred by the algorithm for each input variable show their importance to the resultant TEC change. The results of this work are compared to recent TEC studies to investigate the effects of large storms on the distribution of ionospheric density over large spatial and temporal scales. / MS

Systems biology is an interdisciplinary field that focuses on elucidating complex biological processes (systems) by investigating the interactions among its components through an iterative cycle composed of data generation, data analysis and mathematical modeling. Our contributions to systems biology revolve around the following two axes: - Data analysis: Two data analysis projects, which were initiated when I was a co-op at GlaxoSmithKline, are discussed in this thesis. First, next generation sequencing data generated for a phase I clinical trial is analyzed to determine the altered microbial community in human gut before and after antibiotic usage (Chapter 2). To our knowledge, there have not been similar comparative studies in humans on the impacts on the gut microbiome of an antibiotic when administered by different modes. Second, publicly available gene expression data is analyzed to investigate human immune response to tuberculosis (TB) infection (Chapter 3). The novel feature of this study is systematic drug repositioning for the prevention, control and treatment of TB using the Connectivity map. - Mathematical modeling: Polynomial dynamical systems, a state- and time- discrete logical modeling framework, is used to model two biological processes. First, a denitrification pathway in Pseudomonas aeruginosa is modeled to shed light on the reason of greenhouse gas nitrous oxide accumulation (Chapter 4). It is the first mathematical model of denitrification that can predict the effect of phosphate on the denitrification performance of this bacterium. Second, an iron homeostasis pathway linked to iron utilization, oxidative stress response and oncogenic pathways is constructed to investigate how normal breast cells become cancerous (Chapter 5). To date, our intracellular model is the only expanded core iron model that can capture a breast cancer phenotype by overexpression and knockout simulations. / Ph. D.

An evaluation of the geochemistry of the environment prior to large-scale changes enables scientists and other stakeholders to assess both baseline conditions and the potential impact of those changes to the environment. One area in which documentation of pre-development geochemistry is particularly important is in the exploitation of ore deposits. Ore deposits consist of concentrations of elements or minerals that are enriched enough to be of potential economic value. Their unusual geochemistry often leaves a signature on the environment that can both aid in location an economic resource and present environmental management challenges during its lifecycle. Coles Hill, Virginia, represents one such site. The Coles Hill property is the location of uranium-enriched rock, commonly referred to as the Coles Hill uranium deposit. This dissertation outlines study design, sampling, and statistical analysis methods that can be used in the geochemical characterization of a potential resource extraction site. It presents three studies on geoenvironmental media at Coles Hill. The first study discusses sampling strategies and statistical analysis to address variability in geology, hydrology and climate for baseline assessment and presents an example of such an assessment at Coles Hill. Results suggest a localized environmental impact of the deposit but that differences in bedrock geology within the area surrounding the deposit could also be responsible for some of the variation. This study also emphasizes the importance of consideration of data below analytical detection limits and describes methods for doing so. The second study compares the geochemistry of soil samples collected at Coles Hill with reference data collected by the U.S. Geological Survey using multivariate statistical techniques. Differences are used to suggest potential pathfinder elements such as light rare earth elements to aid in exploration for similar deposits. The third study uses multivariate statistical analysis to examine differences among rocks, soils, and stream sediments to infer important geochemical processes involved in weathering of the deposit. Overall, the results of these studies can aid in the development of future environmental site studies at Coles Hill and elsewhere. / Ph. D.

No / Challenges were encountered when attempting to analyse video based data during a project exploring touch screen computer technology with people living with dementia. In order to inform the analytic process, a scoping review of published evidence was undertaken. Results of the scope illustrated the use of various techniques when analysing visual data, the most common of which was the transcription of video into text and analysed using conversation analysis. Three additional issues emerged in the course of the review. First, there is an absence of detail when describing the ethical implications involved when utilising visual methods in research. Second, limited priority is given to providing a clear rationale for utilising visual methods when audio or field notes may have been a viable alternative. Third, only 40% of reviewed articles clearly stated a chosen methodology. The conclusions of the review illustrate a lack of consistency across studies in the overall reporting of research methods and recommend that authors be explicit in their reporting of methodological issues across the research process. / The PhD is funded by the ESRC as part of the White Rose University Consortium