High-performance computing (HPC) variability management is an important topic in computer science. Research topics include experimental designs for efficient data collection, surrogate models for predicting the performance variability, and system configuration optimization. Due to the complex architecture of HPC systems, a comprehensive study of HPC variability needs large-scale datasets, and experimental design techniques are useful for improved data collection. Surrogate models are essential to understand the variability as a function of system parameters, which can be obtained by mathematical and statistical models. After predicting the variability, optimization tools are needed for future system designs. This dissertation focuses on HPC input/output (I/O) variability through three main chapters. After the general introduction in Chapter 1, Chapter 2 focuses on the prediction models for the scalar description of I/O variability. A comprehensive comparison study is conducted, and major surrogate models for computer experiments are investigated. In addition, a tool is developed for system configuration optimization based on the chosen surrogate model. Chapter 3 conducts a detailed study for the multimodal phenomena in I/O throughput distribution and proposes an uncertainty estimation method for the optimal number of runs for future experiments. Mixture models are used to identify the number of modes for throughput distributions at different configurations. This chapter also addresses the uncertainty in parameter estimation and derives a formula for sample size calculation. The developed method is then applied to HPC variability data. Chapter 4 focuses on the prediction of functional outcomes with both qualitative and quantitative factors. Instead of a scalar description of I/O variability, the distribution of I/O throughput provides a comprehensive description of I/O variability. We develop a modified Gaussian process for functional prediction and apply the developed method to the large-scale HPC I/O variability data. Chapter 5 contains some general conclusions and areas for future work. / Doctor of Philosophy / This dissertation focuses on three projects that are all related to statistical methods in performance variability management in high-performance computing (HPC). HPC systems are computer systems that create high performance by aggregating a large number of computing units. The performance of HPC is measured by the throughput of a benchmark called the IOZone Filesystem Benchmark. The performance variability is the variation among throughputs when the system configuration is fixed. Variability management involves studying the relationship between performance variability and the system configuration. In Chapter 2, we use several existing prediction models to predict the standard deviation of throughputs given different system configurations and compare the accuracy of predictions. We also conduct HPC system optimization using the chosen prediction model as the objective function. In Chapter 3, we use the mixture model to determine the number of modes in the distribution of throughput under different system configurations. In addition, we develop a model to determine the number of additional runs for future benchmark experiments. In Chapter 4, we develop a statistical model that can predict the throughout distributions given the system configurations. We also compare the prediction of summary statistics of the throughput distributions with existing prediction models.

Submitted by Ana Damasceno (ana.damasceno@unipampa.edu.br) on 2016-09-12T20:02:35Z No. of bitstreams: 2 license_rdf: 1232 bytes, checksum: 66e71c371cc565284e70f40736c94386 (MD5) Relação Da Nutrição Apícola Com A Microbiota Do Pólen E Do Sistema Digestório De Abelhas Melíferas Verificada Por Sequenciamento De Nova Geração.pdf: 2887795 bytes, checksum: 12218b912a445c8eb21706a94911e90f (MD5) / Made available in DSpace on 2016-09-12T20:02:35Z (GMT). No. of bitstreams: 2 license_rdf: 1232 bytes, checksum: 66e71c371cc565284e70f40736c94386 (MD5) Relação Da Nutrição Apícola Com A Microbiota Do Pólen E Do Sistema Digestório De Abelhas Melíferas Verificada Por Sequenciamento De Nova Geração.pdf: 2887795 bytes, checksum: 12218b912a445c8eb21706a94911e90f (MD5) Previous issue date: 2015-03-16 / A microbiota e os genes funcionais ativamente envolvidos no processo de decomposição e utilização de grãos de pólen em pão de mel e no trato digestório de abelha ainda não são completamente compreendidos. O objetivo deste trabalho foi avaliar a estrutura e diversidade da comunidade de bactérias e Archaeas em amostrasde pão de mel e sistema digestório de abelhas africanizadas, bem como para prever os genes envolvido na bioprocessamento microbiano do pólen, usando a tecnologia de seqüenciamento de nova geração. Um total de 11 filos bacterianos foram encontrados dentro do sistema de digestório de abelhas e 10 filos bacterianos foram encontrado dentro pão de mel. Embora a comparação a nível de filo mostre mais filos em comum, a análise filogenética mais profunda mostrou maior variação de composição taxonômica. A família Enterobacteriaceae, Ricketsiaceae, Spiroplasmataceae e Bacillaceae, foram os principais grupos responsáveis por a especificidade do intestino de abelhas, enquanto as principais famílias responsáveis pela especificidade do pão de mel foram Neisseriaceae, Flavobacteriaceae, Acetobacteraceae e Lactobacillaceae. Em termos da estrutura da comunidade microbiana, a análise mostrou que as comunidades dos dois ambientes foram bastante diferentes umas das outras, com apenas 7% dos táxons a nível de espécies compartilhados entre o sitema digestório de abelhas e o pão de mel. Os resultados indicaram a presença de um elevado nível de especialização e uma microbiota intestinal bem adaptada dentro de cada abelha e do pão de mel.A comunidade associada ao pão de mel, apresentou maior abundância relativa de genes relacionados com a degradação de aminoácidos, carboidratos, e o metabolismo lipídico, sugerindo que biodegradação do pólen ocorre predominantemente pela microbiota associada ao pão de mel. Estes resultados sugerem uma complexa e importante relação entre nutrição de abelhas e suas comunidades microbianas. / The microbiota and the functional genes actively involved in the process of breakdown and utilization of pollen grains in beebread and beeguts are not yet understood. The aim of this work was to assess the diversity and community structure of bacteria and archaea in Africanized honeybee guts and beebread as well as to predict the genes involved in the microbial bioprocessing of pollen using state of the art ‘post-light’ based sequencing technology. A total of 11 bacterial phyla were found within bee guts and 10 bacterial phyla were found within beebread. Although the phylum level comparison shows most phyla in common, a deeper phylogenetic analysis showed greater variation of taxonomic composition. The families Enterobacteriaceae, Ricketsiaceae, Spiroplasmataceae and Bacillaceae, were the main groups responsible for the specificity of the bee gut while the main families responsible for the specificity of the beebread were Neisseriaceae, Flavobacteriaceae, Acetobacteraceae and Lactobacillaceae. In terms of microbial community structure, the analysis showed that the communities from the two environments were quite different from each other with only 7 % of species-level taxa shared between beegut and beebread. The results indicated the presence of a highly specialized and well-adapted microbiota within each bee gut and beebread. The beebread community included a greater relative abundance of genes related to amino acid, carbohydrate, and lipid metabolism, suggesting that pollen biodegradation predominantly occurs in the beebread. These results suggests a complex and important relationship between honeybee nutrition and their microbial communities.

Submitted by Ana Damasceno (ana.damasceno@unipampa.edu.br) on 2016-07-21T18:21:38Z No. of bitstreams: 1 Relação Da Nutrição Apícola Com A Microbiota Do Pólen E Do Sistema Digestório De Abelhas Melíferas Verificada Por Sequenciamento De Nova Geração.pdf: 2887795 bytes, checksum: 12218b912a445c8eb21706a94911e90f (MD5) / Approved for entry into archive by Ana Damasceno (ana.damasceno@unipampa.edu.br) on 2016-08-23T16:34:05Z (GMT) No. of bitstreams: 1 Relação Da Nutrição Apícola Com A Microbiota Do Pólen E Do Sistema Digestório De Abelhas Melíferas Verificada Por Sequenciamento De Nova Geração.pdf: 2887795 bytes, checksum: 12218b912a445c8eb21706a94911e90f (MD5) / Made available in DSpace on 2016-08-23T16:34:05Z (GMT). No. of bitstreams: 1 Relação Da Nutrição Apícola Com A Microbiota Do Pólen E Do Sistema Digestório De Abelhas Melíferas Verificada Por Sequenciamento De Nova Geração.pdf: 2887795 bytes, checksum: 12218b912a445c8eb21706a94911e90f (MD5) Previous issue date: 2015-03-16 / A microbiota e os genes funcionais ativamente envolvidos no processo de decomposição e utilização de grãos de pólen em pão de mel e no trato digestório de abelha ainda não são completamente compreendidos. O objetivo deste trabalho foi avaliar a estrutura e diversidade da comunidade de bactérias e Archaeas em amostrasde pão de mel e sistema digestório de abelhas africanizadas, bem como para prever os genes envolvido na bioprocessamento microbiano do pólen, usando a tecnologia de seqüenciamento de nova geração. Um total de 11 filos bacterianos foram encontrados dentro do sistema de digestório de abelhas e 10 filos bacterianos foram encontrado dentro pão de mel. Embora a comparação a nível de filo mostre mais filos em comum, a análise filogenética mais profunda mostrou maior variação de composição taxonômica. A família Enterobacteriaceae, Ricketsiaceae, Spiroplasmataceae e Bacillaceae, foram os principais grupos responsáveis por a especificidade do intestino de abelhas, enquanto as principais famílias responsáveis pela especificidade do pão de mel foram Neisseriaceae, Flavobacteriaceae, Acetobacteraceae e Lactobacillaceae. Em termos da estrutura da comunidade microbiana, a análise mostrou que as comunidades dos dois ambientes foram bastante diferentes umas das outras, com apenas 7% dos táxons a nível de espécies compartilhados entre o sitema digestório de abelhas e o pão de mel. Os resultados indicaram a presença de um elevado nível de especialização e uma microbiota intestinal bem adaptada dentro de cada abelha e do pão de mel.A comunidade associada ao pão de mel, apresentou maior abundância relativa de genes relacionados com a degradação de aminoácidos, carboidratos, e o metabolismo lipídico, sugerindo que biodegradação do pólen ocorre predominantemente pela microbiota associada ao pão de mel. Estes resultados sugerem uma complexa e importante relação entre nutrição de abelhas e suas comunidades microbianas. / The microbiota and the functional genes actively involved in the process of breakdown and utilization of pollen grains in beebread and beeguts are not yet understood. The aim of this work was to assess the diversity and community structure of bacteria and archaea in Africanized honeybee guts and beebread as well as to predict the genes involved in the microbial bioprocessing of pollen using state of the art ‘post-light’ based sequencing technology. A total of 11 bacterial phyla were found within bee guts and 10 bacterial phyla were found within beebread. Although the phylum level comparison shows most phyla in common, a deeper phylogenetic analysis showed greater variation of taxonomic composition. The families Enterobacteriaceae, Ricketsiaceae, Spiroplasmataceae and Bacillaceae, were the main groups responsible for the specificity of the bee gut while the main families responsible for the specificity of the beebread were Neisseriaceae, Flavobacteriaceae, Acetobacteraceae and Lactobacillaceae. In terms of microbial community structure, the analysis showed that the communities from the two environments were quite different from each other with only 7 % of species-level taxa shared between beegut and beebread. The results indicated the presence of a highly specialized and well-adapted microbiota within each bee gut and beebread. The beebread community included a greater relative abundance of genes related to amino acid, carbohydrate, and lipid metabolism, suggesting that pollen biodegradation predominantly occurs in the beebread. These results suggests a complex and important relationship between honeybee nutrition and their microbial communities.

<p>Bacteriophages are viruses that infect and kill bacteria. They are the most abundant organism on the planet and the largest source of untapped genetic information. Every year, more bacteriophages are isolated from the environment, purified, and sequenced. Once sequenced, their genomes are annotated to determine the location and putative function of each gene expressed by the phage. Phages have been used in the past for genetic engineering and new research is being done into how they can be used for the treatment of disease, water safety, agriculture, and food safety. </p> <p>Despite the influx of sequenced bacteriophages, a majority of the genes annotated are hypothetical proteins, also known as No Known Function (NKF) proteins. They are expressed by the phages, but research has not identified a possible function. Wet lab research into the functions of the hundreds of NKF phages genes would be costly and could take years. Bioinformatics methods could be used to determine putative functions and functional categories for these hypothetical proteins. A new bioinformatics method using algorithms such as Domain Assignments, Hidden Markov Models, Structure Prediction, Sub-Cellular Localization, and iterative algorithms is proposed here. This new method was tested on the bacteriophage genome PotatoSplit and dropped the number of NKF genes from 57 to 40. A total of 17 new functions were found. The functional class was identified for an additional six proteins, though no specific functions were named. Structure Prediction and Simulations were tested with a focus on two NKF proteins within lytic phages and both returned possible functional categories with high confidence.</p> <p>Additionally, this research focuses on the possibility of phage therapy and FDA regulation. A database of phage proteins was built and tested using R Statistical Analysis to determine proteins significant to phage infecting <i>M. tuberculosis</i> and to the lytic cycle of phages. The statistical methods were also tested on both pharmaceutical products recalled by the FDA between 2012 and 2018 to determine ingredients/manufacturing steps that could affect product quality and on the FDA Adverse Event Reporting System (FAERS) data to determine if AERs could be used to judge the quality of a product. Many significant excipients/manufacturing steps were identified and used to score products on their quality. The AERs were evaluated on two case studies with mixed results. </p>

Target-based screening projects for bioactive (orphan) compounds have been shown in many cases to be insufficiently predictive for in vivo efficacy, leading to attrition in clinical trials. Phenotypic screening has hence undergone a renaissance in both academia and in the pharmaceutical industry, partly due to this reason. One key shortcoming of this paradigm shift is that the protein targets modulated need to be elucidated subsequently, which is often a costly and time-consuming procedure. In this work, we have explored both improved methods and real-world case studies of how computational methods can help in target elucidation of phenotypic screens. One limitation of previous methods has been the ability to assess the applicability domain of the models, that is, when the assumptions made by a model are fulfilled and which input chemicals are reliably appropriate for the models. Hence, a major focus of this work was to explore methods for calibration of machine learning algorithms using Platt Scaling, Isotonic Regression Scaling and Venn-Abers Predictors, since the probabilities from well calibrated classifiers can be interpreted at a confidence level and predictions specified at an acceptable error rate. Additionally, many current protocols only offer probabilities for affinity, thus another key area for development was to expand the target prediction models with functional prediction (activation or inhibition). This extra level of annotation is important since the activation or inhibition of a target may positively or negatively impact the phenotypic response in a biological system. Furthermore, many existing methods do not utilize the wealth of bioactivity information held for orthologue species. We therefore also focused on an in-depth analysis of orthologue bioactivity data and its relevance and applicability towards expanding compound and target bioactivity space for predictive studies. The realized protocol was trained with 13,918,879 compound-target pairs and comprises 1,651 targets, which has been made available for public use at GitHub. Consequently, the methodology was applied to aid with the target deconvolution of AstraZeneca phenotypic readouts, in particular for the rationalization of cytotoxicity and cytostaticity in the High-Throughput Screening (HTS) collection. Results from this work highlighted which targets are frequently linked to the cytotoxicity and cytostaticity of chemical structures, and provided insight into which compounds to select or remove from the collection for future screening projects. Overall, this project has furthered the field of in silico target deconvolution, by improving the performance and applicability of current protocols and by rationalizing cytotoxicity, which has been shown to influence attrition in clinical trials.