<p> <b>Background</b> Diabetes is a major cause of morbidity and mortality in the United States. In 2012, 29.1 million people were estimated to have the condition, with type 2 diabetes accounting for 95% of all cases [1]. It is currently one of the most costly conditions in the country [2] and forecasts as a heavier burden for the U.S. with the prevalence expected to significantly increase [3]. For those who live with the disease, it is possible to manage diabetes in order to prevent or delay the onset of complications [4]. However the self-management regimen is complex and impacts nearly every important aspect of one's life [5].</p><p> The ubiquitous nature of mobile technologies and powerful capabilities of smartphones and tablets has led to a significant increased interest in the development and use of mobile health. Diabetes management is an application area where mobile devices could enhance the quality of life for people living with chronic illnesses [6]&ndash;[8], and usability is key to the adoption of such technologies [9], [10]. Past work has evaluated the usability of diabetes management apps for Android, iOS and Blackberry smartphones [11]-[14] despite the fact that no established method to evaluate the usability of mobile apps has emerged [15]. To our knowledge, this study is the first to evaluate the usability of diabetes management apps on iPad.</p><p> <b>Methods</b> This study introduces a novel usability survey that is designed for mHealth and specific to the iOS operating system. The survey is built on previous usability findings [11]&ndash;[14], Nielsen heuristics [16] and the Apple iOS Human Interface Guidelines [17]. The new instrument was evaluated with three evaluators assessing ten iPad apps, selected because they were the most popular diabetes management apps on the Apple AppStore. A focus group was subsequently held to gather more insight on the usability of the apps and the survey itself. Statistical analysis using R and grounded theory were used to analyze the quantitative and qualitative results, respectively. </p><p> <b>Results</b> The survey identified OneTouch Reveal by LifeScan Inc. and TactioHealth by Tactic, Health Group as the most usable apps. GlucoMo by Artificial Life, Inc. and Diabetes in Check by Everyday Health, Inc. rated as the least usable apps. Setting up medication and editing blood glucose were the most problematic tasks. Some apps did not support all functions that were under review. Six main themes emerged from the focus group: the presentation of health information, aesthetic and minimalist design, flexibility and efficiency of data input, task feedback, intuitive design and app stability. These themes suggest important constructs of usability for mHealth apps.</p><p> <b>Discussion and Conclusion</b> Mobile health developers and researchers should focus on the tasks, heuristics and underlying issues that were identified as most problematic throughout the study. Additionally, research should further inquire on the potentially critical relation between the information available on app markets and the usability of apps. Several signs point to the potential of the usability survey that was developed but further adjustments and additional test iterations are warranted to validate its use as a reliable usability evaluation method.</p>

<p> The RNA-binding proteins U2AF and PTB play important roles in gene expression in many eukaryotic species. Although U2AF and PTB have been well-studied, their functional requirements have not been investigated on a genome-wide scale. In this thesis, I analyze RNA expression data to determine the requirement of the general splicing factor U2AF in <i>S. pombe</i> and to identify genes misregulated in Drosophila PTB mutants. I find that many introns are insensitive to U2AF inactivation in a <i>Schizosaccharomyces pombe</i> U2AF59 mutant, <i>prp2.1.</i> Bioinformatics analysis indicates that U2AF-insensitive introns have stronger 5' splice sites and higher A/U composition. The importance of intronic nucleotide composition was further investigated using wild type RNA expression data sets. I show that nucleotide composition is a relatively important factor for regulated intron retention in a variety of species. I also analyzed the RNA-binding protein PTB using RNA Seq data to reveal genes misregulated in PTB mutants in <i>D. melanogaster.</i> I identify misregulation of alternative splicing in PTB mutants and putative PTB binding sites. In the PTB embryonic lethal mutant, which shows dorsoventral patterning defects, I show that dorsal fate genes are significantly up-regulated. I present a model to link PTB to dorsal closure defects. This thesis provides the first genome-wide analysis of U2AF in <i>S. pombe</i> and PTB in <i>Drosophila melanogaster. </i></p>

Protein function is the ultimate expression of the genetic code of every organism, and determining which proteins interact helps reveal their functions. MatrixMatchMaker (MMM) is a computational method of predicting protein-protein interactions that works by detecting co-evolution between pairs of proteins. Although MMM has several advanced features compared to other co-evolution-based methods, these come at the cost of high computation, and so the goal of this research is to improve the performance of MMM. First we redefine the computational problem posed by the method, and then develop a new algorithm to solve it, achieving a total speedup of 570x over the existing MMM algorithm for a biologically meaningful data set. We also develop hardware which has not yet succeeded in further improving the performance of MMM, but could serve as a platform that could lead to further gains.

<p> Proteins are molecular machines of life in the truest sense. Being the expressors of genotype, proteins have been a focus in structural biology. Since the first characterization and structure determination of protein molecule more than half a century ago1, our understanding of protein structure is improving only incrementally. While computational analysis and experimental techniques have helped scientist view the structural features of proteins, our concepts about protein folding remain at the level of simple hydrophobic interactions packing side-chain at the core of the protein. Furthermore, because the rate of genome sequencing is far more rapid than protein structure characterization, much more needs to be achieved in the field of structural biology. As a step in this direction, my dissertation research uses computational analysis and experimental techniques to elucidate the fine structural features of the tertiary packing in proteins. With these set of studies, the knowledge of the field of structural biology extends to the fine details of higher order protein structure.</p>

<p> Trypanosomatids cause human diseases such as leishmaniasis and African trypanosomiasis. Trypanosomatids are protists from the order Trypanosomatida and include species of the genera <i>Trypanosoma</i> and <i> Leishmania</i>, which occupy a similar ecological niche. Both have digenic life-stages, alternating between an insect vector and a range of mammalian hosts. However, the strategies used to subvert the host immune system differ greatly as do the clinical outcome of infections between species. The genomes of both the host and the parasite instruct us about strategies the pathogens use to subvert the human immune system, and adaptations by the human host allowing us to better survive infections. We have applied unsupervised learning algorithms to aid visualization of amino acid sequence similarity and the potential for recombination events within <i>Trypanosoma brucei </i>'s large repertoire of variant surface glycoproteins (VSGs). Methods developed here reveal five groups of VSGs within a single sequenced genome of <i>T. brucei</i>, indicating many likely recombination events occurring between VSGs of the same type, but not between those of different types. These tools and methods can be broadly applied to identify groups of non-coding regulatory sequences within other Trypanosomatid genomes. To aid in the detection, quantification, and species identification of leishmania DNA isolated from environmental or clinical specimens, we developed a set of quantitative-PCR primers and probes targeting a taxonomically and geographically broad spectrum of <i>Leishmania</i> species. This assay has been applied to DNA extracted from both human and canine hosts as well as the sand fly vector, demonstrating its flexibility and utility in a variety of research applications. Within the host genomes, fine mapping SNP analysis was performed to detect polymorphisms in a family study of subjects in a region of Northeast Brazil that is endemic for <i>Leishmania infantum chagasi</i>, the parasite causing visceral leishmaniasis. These studies identified associations between genetic loci and the development of visceral leishmaniasis, with a single polymorphism associated with an asymptomatic outcome after infection. The methods and results presented here have capitalized on the large amount of genomics data becoming available that will improve our understanding of both parasite and host genetics and their role in human disease.</p>

Modern high-throughput technologies like DNA microarrays are powerful tools that are widely used in biomedical research. They target a variety of genomics applications ranging from gene expression profiling over DNA genotyping to gene regulation studies. However, the recent discovery of false positives among prominent research findings indicates a lack of awareness or understanding of the non-biological factors negatively affecting the accuracy of data produced using these technologies. The aim of this thesis is to study the origins, effects and potential correction methods for selected methodical biases in microarray data. The two-species Langmuir model serves as the basal physicochemical model of microarray hybridization describing the fluorescence signal response of oligonucleotide probes. The so-called hook method allows to estimate essential model parameters and to compute summary parameters characterizing a particular microarray sample. We show that this method can be applied successfully to various types of microarrays which share the same basic mechanism of multiplexed nucleic acid hybridization. Using appropriate modifications of the model we study RNA quality and sequence effects using publicly available data from Affymetrix GeneChip expression arrays. Varying amounts of hybridized RNA result in systematic changes of raw intensity signals and appropriate indicator variables computed from these. Varying RNA quality strongly affects intensity signals of probes which are located at the 3\' end of transcripts. We develop new methods that help assessing the RNA quality of a particular microarray sample. A new metric for determining RNA quality, the degradation index, is proposed which improves previous RNA quality metrics. Furthermore, we present a method for the correction of the 3\' intensity bias. These functionalities have been implemented in the freely available program package AffyRNADegradation. We show that microarray probe signals are affected by sequence effects which are studied systematically using positional-dependent nearest-neighbor models. Analysis of the resulting sensitivity profiles reveals that specific sequence patterns such as runs of guanines at the solution end of the probes have a strong impact on the probe signals. The sequence effects differ for different chip- and target-types, probe types and hybridization modes. Theoretical and practical solutions for the correction of the introduced sequence bias are provided. Assessment of RNA quality and sequence biases in a representative ensemble of over 8000 available microarray samples reveals that RNA quality issues are prevalent: about 10% of the samples have critically low RNA quality. Sequence effects exhibit considerable variation within the investigated samples but have limited impact on the most common patterns in the expression space. Variations in RNA quality and quantity in contrast have a significant impact on the obtained expression measurements. These hybridization biases should be considered and controlled in every microarray experiment to ensure reliable results. Application of rigorous quality control and signal correction methods is strongly advised to avoid erroneous findings. Also, incremental refinement of physicochemical models is a promising way to improve signal calibration paralleled with the opportunity to better understand the fundamental processes in microarray hybridization.

Contemporary proteomics studies require computational approaches to deal with both the complexity of the data generated, and with the volume of data produced. The amalgamation of mass spectrometry -- the analytical tool of choice in proteomics -- with the computational and statistical sciences is still recent, and several avenues of exploratory data analysis and statistical methodology remain relatively unexplored. The current study focuses on three broad analytical domains, and develops novel exploratory approaches and practical tools in each. Data transform approaches are the first explored. These methods re-frame data, allowing for the visualization and exploitation of features and trends that are not immediately evident. An exploratory approach making use of the correlation transform is developed, and is used to identify mass-shift signals in mass spectra. This approach is used to identify and map post-translational modifications on individual peptides, and to identify SILAC modification-containing spectra in a full-scale proteomic analysis. Secondly, matrix decomposition and projection approaches are explored; these use an eigen-decomposition to extract general trends from groups of related spectra. A data visualization approach is demonstrated using these techniques, capable of visualizing trends in large numbers of complex spectra, and a data compression and feature extraction technique is developed suitable for use in spectral modeling. Finally, a general machine learning approach is developed based on conditional random fields (CRFs). These models are capable of dealing with arbitrary sequence modeling tasks, similar to hidden Markov models (HMMs), but are far more robust to interdependent observational features, and do not require limiting independence assumptions to remain tractable. The theory behind this approach is developed, and a simple machine learning fragmentation model is developed to test the hypothesis that reproducible sequence-specific intensity ratios are present within the distribution of fragment ions originating from a common peptide bond breakage. After training, the model shows very good performance associating peptide sequences and fragment ion intensity information, lending strong support to the hypothesis.

Recently, it has been shown that genetic variation that perturbs the regulation of gene expression is widespread in eukaryotic genomes. Regulatory variation (RV) is expected to be an important driver of phenotypic differences, evolutionary change, and susceptibility to complex genetic diseases. Because trans-acting regulators of gene expression control mRNA levels of multiple genes simultaneously, we hypothesise that RV that affects these components will have a shared-influence upon the expression levels of multiple genes. Since genes are regulated in trans by combinations of basal and tissue specific factors, we further hypothesise that RV in these components may have different effects in each tissue. We used microarrays to identify 755 genes that were affected by RV in at least one of the brain, kidney and liver of two inbred mouse strains, C57BL/6J and DBA/2J. Just 2% were affected in all three tissues, suggesting that the influence of RV is predominantly tissue specific. To study shared-RV, we measured the expression levels of these 755 genes in the same 3 tissues from a panel of recombinant inbred mice, and identified groups of correlated genes that are putatively under the influence of shared trans-acting RV. Using methods that we developed for studying the effects of RV in multiple tissues, we identified 212 genes that are correlated in all three tissues, which include 10 groups of at least 3 genes. We developed a novel method called coherency analysis to show that RV consistently affected the expression levels of these groups of genes in different genetic backgrounds. Strikingly, the relative up- or down-regulation of genes in each group was markedly different in the three tissues of the same mouse, suggesting that the influence of RV itself is not tissue specific as previously expected, but that RV can influence genes with differing outcomes in each tissue. These observations are compatible with RV affecting combinations of basal and tissue specific regulatory factors. This is the first cross-tissue investigation into the influence of shared-RV in multiple tissues, which has important implications in humans, where access to the phenotypically relevant tissue may be necessarily limited.

Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 2007. / Source: Dissertation Abstracts International, Volume: 68-11, Section: B, page: 7504. Adviser: Nikolaos V. Sahinidis. Includes bibliographical references (leaves 102-110) Available on microfilm from Pro Quest Information and Learning.

Thesis (Ph. D.)--University of Nevada, Reno, 2008. / "May 2008." Includes bibliographical references (leaves 86-91). Online version available on the World Wide Web.