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.

Les Archaea planctoniques contribuent de façon significative aux grands cycles biogéochimiques dans les écosystèmes aquatiques, néanmoins la structure des communautés actives ainsi que leurs variations saisonnières sont encore largement méconnues. En outre, la découverte de l’implication des Archaea dans le cycle de l’azote (Ammonia Oxidizing Archaea ou AOA), plus particulièrement dans le processus de nitrification a considérablement modifié la perception d’un processus autotrophe réalisé uniquement par des bactéries (Ammonia Oxidizing Bacteria ou AOB). Dans les écosystèmes marins, la large distribution des AOA suggère que ces microorganismes joueraient un rôle prépondérant dans le cycle de l’azote néanmoins, ces observations ne sont pas généralisables à l’ensemble des écosystèmes aquatiques en raison de leur grande diversité et/ou d'un manque d'informations et d’études sur certains d'entre eux. Ainsi, les objectifs de ce projet étaient i) d’étudier la structure spatiale et temporelle des communautés d’Archaea actives dans des écosystèmes aquatiques contrastés en termes d’apports anthropiques et/ou de gradients de salinité (lac, estuaire, milieu côtier) ; ii) de déterminer la contribution relative des Archaea au processus d’oxydation de l’ammonium, en comparaison avec celle des bactéries ; et iii) de mieux comprendre les paramètres environnementaux qui pourraient déterminer l’établissement des communautés d’AOA ou d’AOB. / Aquatic Archaea are important players among microbial plankton and significantly contribute to biogeochemical cycles, especially nitrogen, but details regarding their community structure and seasonal activity and dynamics remain largely unexplored. In marine ecosystems, the widespread distribution of Ammonia Oxidizing Archaea (AOA) suggests that they probably play a major role in nutrients cycling. However, we cannot generalize these observations to all aquatic ecosystems because of their high diversity and/or a lack of information and studies on these organisms for some of these ecosystems. More precisely, lacustrine and coastal ecosystems were less studied while they are potentially subjected to strong anthropogenic impacts. Moreover, notable differences in terms of diversity and activity between marine and freshwater communities can be expected, considering the specific environmental parameters of each ecosystem. The objectives of this thesis were: i) to study the archaeal community structure across a temporal scale and assess the diversity of archaeal communities and AOA in diverse aquatic ecosystems along anthropogenic and/or salinity gradient (lacustrine, estuarine and coastal ecosystems); ii) to determine their relative contribution in ammonia oxidation, compared to Ammonia Oxidizing Bacteria (AOB) by looking at their spatial and temporal distribution and activity, and iii) to explore more precisely the environmental parameters that could drive AOA and/or AOB establishment.

Coccolithophores are biogeochemically important marine algae that interact with the carbon cycle through photosynthesis (CO2 sink), calcification (CO2 source) and burial of carbon into oceanic sediments. The group is considered susceptible to the ongoing climate perturbations, in particular to ocean acidification, temperature increase and nutrient limitation. The aim of this thesis was to investigate the adaptation of coccolithophores to environmental change, with the focus on temperature stress and nutrient limitation. The research was conducted in frame of three approaches: experiments testing the physiological response of coccolithophore species Helicosphaera carteri and Coccolithus pelagicus to phosphorus limitation, field studies on coccolithophore life-cycles with a method comparison and an investigation of the phenotypic evolution of the coccolithophore genus Helicosphaera over the past 15 Ma. Experimental results show that the physiology and morphology of large coccolithophores are sensitive to phosphorus limitation, and that the adaptation to low-nutrient conditions can lead to a decrease in calcification rates. Field studies have contributed to our understanding of coccolithophore life cycles, revealing complex ecological patterns within the Mediterranean community which are seemingly regulated by seasonal, temperature-driven environment changes. In addition, the high-throughput sequencing (HTS) molecular method was shown to provide overall good representation of coccolithophore community composition. Finally, the study on Helicosphaera evolution showed that adaptation to decreasing CO2 in higher latitudes involved cell and coccolith size decrease, whereas the adaptation in tropical ecosystems also included a physiological decrease in calcification rates in response to nutrient limitation. This thesis advanced our understanding of coccolithophore adaptive strategies and will improve our predictions on the fate of the group under ongoing climate change.