Stream programming is a paradigm where a program is structured by a set of computational nodes connected by streams. Focusing on data moving between computational nodes via streams, this programming model fits well for applications that process long sequences of data. We call such applications reactive stream programs (RSPs) to distinguish them from stream programs with rather small and finite input data. In stream programming, concurrency is expressed implicitly via communication streams. This helps to reduce the complexity of parallel programming. For this reason, stream programming has gained popularity as a programming model for parallel platforms. However, it is also challenging to analyse and improve the performance without an understanding of the program's internal behaviour. This thesis targets an effi cient execution model for deploying RSPs on parallel platforms. This execution model includes a monitoring framework to understand the internal behaviour of RSPs, scheduling strategies for RSPs on uniform shared-memory platforms; and mapping techniques for deploying RSPs on heterogeneous distributed platforms. The foundation of the execution model is based on a study of the performance of RSPs in terms of throughput and latency. This study includes quantitative formulae for throughput and latency; and the identification of factors that influence these performance metrics. Based on the study of RSP performance, this thesis exploits characteristics of RSPs to derive effective scheduling strategies on uniform shared-memory platforms. Aiming to optimise both throughput and latency, these scheduling strategies are implemented in two heuristic-based schedulers. Both of them are designed to be centralised to provide load balancing for RSPs with dynamic behaviour as well as dynamic structures. The first one uses the notion of positive and negative data demands on each stream to determine the scheduling priorities. This scheduler is independent from the runtime system. The second one requires the runtime system to provide the position information for each computational node in the RSP; and uses that to decide the scheduling priorities. Our experiments show that both schedulers provides similar performance while being significantly better than a reference implementation without dynamic load balancing. Also based on the study of RSP performance, we present in this thesis two new heuristic partitioning algorithms which are used to map RSPs onto heterogeneous distributed platforms. These are Kernighan-Lin Adaptation (KLA) and Congestion Avoidance (CA), where the main objective is to optimise the throughput. This is a multi-parameter optimisation problem where existing graph partitioning algorithms are not applicable. Compared to the generic meta-heuristic Simulated Annealing algorithm, both proposed algorithms achieve equally good or better results. KLA is faster for small benchmarks while slower for large ones. In contrast, CA is always orders of magnitudes faster even for very large benchmarks.

Master of Science / Department of Computing and Information Sciences / Mitchell L. Neilsen / This research is a part of BREAD PHENO, a PhenoApps BREAD project at K-State which combines contemporary advances in image processing and machine vision to deliver transformative mobile applications through established breeder networks. In this platform, novel image analysis segmentation algorithms are being developed to model and extract plant phenotypes. As a part of this research, the traditional Watershed segmentation algorithm has been extended and the primary goal is to accurately count and characterize the seeds in an image. The new approach can be used to characterize a wide variety of crops. Further, this algorithm is migrated into Android making use of the Android APIs and the first ever user-friendly Android application implementing the extended Watershed algorithm has been developed for Mobile field-based high-throughput phenotyping (HTP).

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In vitro protein selection has had major impacts in the field of protein engineering. Traditional screens assay individual proteins for specific function. Selection, however, analyzes a pool of mutants and yields the best variants. Phage display, a successful selection technique, also provides a reliable link between variant phenotype and genotype. It can also be coupled with high throughput sequencing to map protein mutations; potentially highlighting vital mutations in variants. We propose to apply this technique to cancer therapy. RAF, a serine/threonine kinase, is critical for cell regulation in mammals. RAF can be activated by oncogenic RAS, found in over 30% of cancers, to drive cancer proliferation. Rigosertib, a benzyl styryl sulfone in phase III clinical trials for myelodysplastic syndrome (MDS), is an inhibitor of the RAS binding domain (RBD) in RAF. Phage display can be used to select RAF mutants for RAS binding affinity, in the presence of Rigosertib. High-throughput sequencing of these variants can provide a means of anticipating, and mapping resistance to this anti-cancer drug.

In this thesis, a highly comparative framework for time-series analysis is developed. The approach draws on large, interdisciplinary collections of over 9000 time-series analysis methods, or operations, and over 30 000 time series, which we have assembled. Statistical learning methods were used to analyze structure in the set of operations applied to the time series, allowing us to relate different types of scientific methods to one another, and to investigate redundancy across them. An analogous process applied to the data allowed different types of time series to be linked based on their properties, and in particular to connect time series generated by theoretical models with those measured from relevant real-world systems. In the remainder of the thesis, methods for addressing specific problems in time-series analysis are presented that use our diverse collection of operations to represent time series in terms of their measured properties. The broad utility of this highly comparative approach is demonstrated using various case studies, including the discrimination of pathological heart beat series, classification of Parkinsonian phonemes, estimation of the scaling exponent of self-affine time series, prediction of cord pH from fetal heart rates recorded during labor, and the assignment of emotional content to speech recordings. Our methods are also applied to labeled datasets of short time-series patterns studied in temporal data mining, where our feature-based approach exhibits benefits over conventional time-domain classifiers. Lastly, a feature-based dimensionality reduction framework is developed that links dependencies measured between operations to the number of free parameters in a time-series model that could be used to generate a time-series dataset.

The homologous recombination (HR) pathway of DNA repair is essential for the faithful repair of double-stranded DNA breaks (DSBs) in all organisms and as such helps maintain genomic stability. Furthermore, HR is instrumental in the cellular response to exogenous DNA damaging agents such as those used in the clinic for chemo- and radiotherapy. HR in humans is a complex, incompletely understood process involving numerous stages and diverse biochemical activities. Advancing our knowledge of the HR pathway in humans aids the understanding of how chemo- and radiotherapies act and may be used to develop novel therapeutic strategies. Recent studies have identified inhibition of HR as one of the mechanisms via which a number of recently developed chemotherapeutics have their effect. Accordingly, the clinical potential of HR inhibitors is under investigation. My work has centred around the identification of both novel HR proteins and novel, small molecule HR inhibitors. To further these aims, I have successfully employed high-throughput RNAi and small molecule screening strategies. RNAi screens are commonly used to identify genes involved in a given cellular process via genetic loss of function, whilst small molecule, cell based screens are a powerful tool in the drug discovery process.

The goal of the work presented herein was to further our understanding of the rumen microbiota and microbiome of wild moose, and to use that understanding to improve other processes. The moose has adapted to eating a diet of woody browse, which is very high in fiber, but low in digestibility due to the complexity of the plant polysaccharides, and the presence of tannins, lignin, and other plant-secondary compounds. Therefore, it was hypothesized that the moose would host novel microorganisms that would be capable of a wide variety of enzymatic functions, such as improved fiber breakdown, metabolism of digestibility-reducing or toxic plant compounds, or production of functional metabolites, such as volatile fatty acids, biogenic amines, etc. The first aim, naturally, was to identify the microorganisms present in the rumen of moose, in this case, the bacteria, archaea, and protozoa. This was done using a variety of high-throughput techniques focusing on the SSU rRNA gene (see CHAPTERS 2-5). The second aim was to culture bacteria from the rumen of the moose in order to study their biochemical capabilities (see CHAPTERS 6-7). The final aim was to apply those cultured bacterial isolates to improve other systems. Specifically, bacteria from the rumen of the moose was introduced to young lambs in order to colonize the digestive tract, speed the pace of rumen development, and improve dietary efficiency (see CHAPTER 8).

The role of abnormal DNA methylation in the progression of disease is a growing area of research that relies upon the establishment of sound statistical methods. The common method for declaring there is differential methylation between two groups at a given CpG site, as summarized by the difference between proportions methylated db=b1-b2, has been through use of a Filtered Two Sample t-test, using the recommended filter of 0.17 (Bibikova et al., 2006b). In this dissertation, we performed a re-analysis of the data used in recommending the threshold by fitting a mixed-effects ANOVA model. It was determined that the 0.17 filter is not accurate and conjectured that application of a Filtered Two Sample t-test likely leads to loss of power. Further, the Two Sample t-test assumes that data arise from an underlying distribution encompassing the entire real number line, whereas b1 and b2 are constrained on the interval . Additionally, the imposition of a filter at a level signifying the minimum level of detectable difference to a Two Sample t-test likely reduces power for smaller but truly differentially methylated CpG sites. Therefore, we compared the Two Sample t-test and the Filtered Two Sample t-test, which are widely used but largely untested with respect to their performance, to three proposed methods. These three proposed methods are a Beta distribution test, a Likelihood ratio test, and a Bootstrap test, where each was designed to address distributional concerns present in the current testing methods. It was ultimately shown through simulations comparing Type I and Type II error rates that the (unfiltered) Two Sample t-test and the Beta distribution test performed comparatively well.

Les contaminations par les moisissures représentent un problème majeur au sein de l'industrie agroalimentaire, pharmaceutique, cosmétique, et dans le secteur médical. Actuellement, l'identification des champignons filamenteux est basée sur l'analyse des caractéristiques phénotypiques, nécessitant une expertise et pouvant manquer de précision, ou sur les méthodes moléculaires, coûteuses et fastidieuses. Dans ce contexte, l'objectif de cette étude a consisté à développer un protocole simple et standardisé à l'aide de la spectroscopie infrarouge à transformée de Fourier (IRTF) combinée à une méthode d'analyse chimiométrique, proposant une méthode alternative pour l'identification rapide des moisissures. Au total, 498 souches de champignons filamenteux (45 genres et 140 espèces) ont été analysées à l'aide d'un spectromètre IRTF à haut débit. L'analyse discriminante des moindres carrés partiels (PLS -DA), méthode chimiométrique supervisée, a été appliquée à chaque spectre dans les gammes spectrales 3200-2800 et 1800-800 cm-1. Différents modèles de calibration ont été construits à partir de 288 souches, ceci en cascade de la sous-division jusqu'à l'espèce en se basant sur la taxonomie actuelle. La prédiction des spectres en aveugle, obtenus à partir de 105 souches, au niveau du genre et de l'espèce est respectivement de 99,17 % et 92,3 %. La mise en place d'un score de prédiction et d'un seuil a permis de valider 80,22 % des résultats. L'implémentation d'une fonction de standardisation (SF) a permis d'augmenter le pourcentage de spectres bien prédits, acquis sur un autre instrument, de 72,15 % (sans fonction) à 89,13 %, validant la transférabilité de la méthode. Puisqu'une biomasse mycélienne suffisante peut être obtenue après 48h de culture et que la préparation des échantillons implique l'utilisation d'un protocole simple, la spectroscopie IRTF combinée à la PLS-DA apparaît comme une méthode rapide et peu coûteuse, ce qui la rend particulièrement attractive pour l'identification des champignons filamenteux au niveau industriel. Les résultats obtenus placent la spectroscopie IRTF parmi les méthodes analytiques prometteuses et avant-gardistes, possédant un haut pouvoir discriminant et une forte capacité d'identification, en comparaison avec les techniques conventionnelles. / Mold contaminants represent a major problem in various areas such as food and agriculture, pharmaceutics, cosmetics and health. Currently, molds identification is based either on phenotypic characteristics, requiring an expertise and can lack accuracy, or on molecular methods, which are quite expensive and fastidious. In this context, the objective was to develop a simple and standardized protocol using Fourier transform infrared (FTIR) spectroscopy combined with a chemometric analysis, allowing to implement an alternative method for rapid identification of molds. In total, 498 fungal strains (45 genera and 140 species) were analyzed using a high-throughput FTIR spectrometer. Partial Least Squares Discriminant Analysis (PLS-DA), a supervised chemometrics method, was applied to each spectrum in the spectral ranges 3200-2800 and 1800-800 cm-1 for the identification process. Using 288 strains, different calibration models were constructed in cascade and following the current taxonomy, from the subphylum to the species level. Blind prediction of spectra from 105 strains at the genus and species levels was achieved at 99.17 % and 92.3% respectively. The establishment of a prediction score and a threshold permitted to validate 80.22% of the obtained results. The implementation of a standardization function (SF) permitted to increase the percentage of well predicted spectra from strains analyzed using another instrument from 72.15% (without SF) to 89.13% and permitted to verify the transferability of the method. Since sufficient mycelial biomass can be obtained at 48h culture and sample preparation involved a simple protocol, FTIR spectroscopy combined with PLS-DA is a very rapid and cost effective method, which could be particularly attractive for the identification of moulds at the industrial level. The results obtained places FTIR spectroscopy among the avant-garde promising analytical approaches, with high discriminant power and identification capacity, compared to conventional techniques.