Enzymes are immensely powerful and efficient heterogenous catalysts which are essential for life. As essential to life as enzymes are, it is still not well understood exactly how they enhance the rate of their catalyzed reactions up to 19 orders of magnitude over their solution phase counterpart reactions. Recent research has focused on sub--picosecond motions coupled to the reaction coordinate, called rate--promoting vibrations, which are important components of several well--known enzymatic mechanisms and build upon previous models of enzyme activity. Herein I present two studies which are expressly focused on providing tools and knowledge to understand how dynamics affects enzymatic reactions. First, I present a method for the calculation of kinetic isotope effects from first principles, using transition path sampling and centroid molecular dynamics. This method allows for the calculation of kinetic isotope effects without the assumptions necessitated by transition state theory or free energy perturbation methods. It was found that this method could calculate the primary H/D kinetic isotope effect of the conversion of benzyl alcohol to benzaldehyde in yeast alcohol dehydrogenase to within the margin of error of experimentally measured kinetic isotope effects of the same reaction. Second, I examined the role that evolution plays in the preservation of these rate--promoting vibrations, by performing a transition path sampling study of two lactate dehydrogenases, those of Plasmodium falciparum and Cryptosporidium parvum, which evolved through separate gene duplication events from a common malate dehydrogenase ancestor. It was found that though both lactate dehydrogenases share the same rate--promoting vibration, and indeed share the rate--promoting vibration found in other lactate dehydrogenases, the sequence variations in lactate dehydrogenase from P. falciparum causes a diminished contribution of the motions to the reaction coordinate. The studies presented in this dissertation contribute to the our understanding of enzymes on an atomistic level, as well as providing tools necessary for designing novel de novo enzymes and targeted drugs for enzymes of disease--causing organisms.

Previous research implementing stratification on the propensity score has generally relied on using five strata, based on prior theoretical groundwork and minimal empirical evidence as to the suitability of quintiles to adequately reduce bias in all cases and across all sample sizes. This study investigates bias reduction across varying number of strata and sample sizes via a large-scale simulation to determine the adequacy of quintiles for bias reduction under all conditions. Sample sizes ranged from 100 to 50,000 and strata from 3 to 20. Both the percentage of bias reduction and the standardized selection bias were examined. The results show that while the particular covariates in the simulation met certain criteria with five strata that greater bias reduction could be achieved by increasing the number of strata, especially with larger sample sizes. Simulation code written in R is included.

Research pertaining to the distortion of the squared canonical correlation coefficient has traditionally been limited to the effects of sampling error and associated correction formulas. The purpose of this study was to compare the degree of attenuation of the squared canonical correlation coefficient under varying conditions of score reliability. Monte Carlo simulation methodology was used to fulfill the purpose of this study. Initially, data populations with various manipulated conditions were generated (N = 100,000). Subsequently, 500 random samples were drawn with replacement from each population, and data was subjected to canonical correlation analyses. The canonical correlation results were then analyzed using descriptive statistics and an ANOVA design to determine under which condition(s) the squared canonical correlation coefficient was most attenuated when compared to population Rc2 values. This information was analyzed and used to determine what effect, if any, the different conditions considered in this study had on Rc2. The results from this Monte Carlo investigation clearly illustrated the importance of score reliability when interpreting study results. As evidenced by the outcomes presented, the more measurement error (lower reliability) present in the variables included in an analysis, the more attenuation experienced by the effect size(s) produced in the analysis, in this case Rc2. These results also demonstrated the role between and within set correlation, variable set size, and sample size played in the attenuation levels of the squared canonical correlation coefficient.

Submitted by Nathália Faria da Silva (nathaliafsilva.ufv@gmail.com) on 2017-07-13T11:23:31Z No. of bitstreams: 1 texto completo.pdf: 1310330 bytes, checksum: 0b1fb40f1985db830fa723ed7b82aec9 (MD5) / Made available in DSpace on 2017-07-13T11:23:31Z (GMT). No. of bitstreams: 1 texto completo.pdf: 1310330 bytes, checksum: 0b1fb40f1985db830fa723ed7b82aec9 (MD5) Previous issue date: 2001-11-13 / Coordenação de Aperfeiçoamento de Pessoal de Nível Superior / The objective of the first study was to obtain genetic parameters for sequential weights of beef cattle using RRM on data sets with missing and no missing traits, and to compare these estimates with those obtained by MTM. Growth curves of Nellore cattle were analyzed using body weights measured at ages ranging from 1 day (birth weight) to 733 days. Two data samples were created: one with 71,867 records from herds with missing traits and the other with 74,601 records from herds with no missing traits. Records preadjusted to a fixed age were analyzed by a multiple trait model (MTM), which included the effects of contemporary group, age of dam class, additive direct, additive maternal, and maternal permanent environment. Analyses were by restricted maximum likelihood (REML) with 5 traits at a time. The random regression model (RRM) included the effects of age of animal, contemporary group, age of dam class, additive direct, additive maternal, permanent environment, and maternal permanent environment. Legendre cubic polynomials were used to describe the random effects. Estimates of covariances by MTM were similar for both data sets, although those from the missing data set showed more variability from age to age. The estimates from RRM were similar to those from MTM only for the complete -trait case and showed large artifacts for the case of missing traits. Estimates of additive direct-maternal correlations under RRM for some ages approached -1.0, and most likely contained artifacts. If many traits are missing, the best approach to obtaining parameters for RRM would be conversion from smoothed MTM estimates. The purpose of the second study was estimation of parameters of models and data sets as in the first study by a Bayesian methodology – Gibbs sampling, and to make comparisons with their estimates by REML. Analyses were by a Bayesian method for all 9 traits. MTM estimated covariance components and genetic parameters for birth weight and sequential weights and RRM for all ages. Estimates of additive direct variance from herds with missing traits increased from birth weight through weight at 551 to 651 days with MTM. However, this component also increased for the sample with no missing traits after this age. Additive direct and residual estimated variance with RRM increased over all ages for both samples. For MTM, additive direct and maternal heritabilities were greater from the sample with herds with missing traits than those values from herds with no missing traits. The estimates from RRM were slightly lower than those from MTM for the sample with no missing traits; however, additive maternal heritabilities from MTM were greater than those using RRM. The estimated additive direct genetic correlations for each pair of traits were slightly higher for the first age (birth weight) using MTM than RRM. The range of additive maternal genetic correlations was lower than that for additive direct genetic correlations with MTM and RRM. Due to the fact that covariance components based on RRM were inflated for herds with missing traits, MTM should be used and converted to covariance functions. As well, for analyses with standard models where inferences on shapes of parameters are not important, analyses by REML may be more robust. The first goal of the third study was to implement the genetic evaluation of weights for a large population of beef cattle using the random regression model. The second goal was to compare these evaluations with those obtained from a multitrait evaluation. Expected progeny differences (EPD) were computed by two methods: a finite method using sparse factorization (SF) and interating (IT) by preconditioned conjugate gradient (PCG). The correlations between EPDs from MTM and RRM by SF and IT were ≤ .43 until the random regressions were orthogonalized. After orthogonalization high computing requirements of RRM were reduced by removing regressions corresponding to very low eigenvalues and by replacing the random error effects with weights. Correlations between EPDs from MTM and RRM for the additive direct effect were .87, .89, .89, .87, and .86 for W1 (weight at 60 days), W2 (weight at 252 days), W3 (weight at 243 days), W5 (weight at 426 days), and W7 (weight at 601 days), respectively. The corresponding correlations for the additive maternal effect were .85, .86, .88, .85 and .84, respectively. These low correlations were mostly due to differences in variances between the models and, to a lesser degree, due to better accounting for environmental effects and more data by RRM. The RRM applied to beef weights may be poorly conditioned numerically. / O objetivo do primeiro estudo foi estimar parâmetros para pesos seqüenciais de gado de corte, por meio de modelos de regressão aleatória (RRM), em características com informações perdidas e completas. Analisaram-se curvas de crescimento de gado Nelore mediante o uso de pesos corporais coletados, do nascer aos 733 dias de idade. Duas amostras foram geradas; a primeira era constituída de 71.867 medidas provenientes de rebanhos com informações perdidas, e a segunda, de 74.601 medidas oriundas de rebanhos com informações completas. Os pesos pré-ajustados a idades fixas foram analisados por meio de um modelo de características múltiplas (MTM), cinco características por vez, no qual foram incluídos efeitos de grupo contemporâneo, classe de idade da vaca, aditivo direto, aditivo materno e ambiente materno permanente. No modelo de regressão aleatória (RRM) foram incluídos efeitos de idade do animal, grupo contemporâneo, classe de idade da vaca, aditivo direto, ambiente permanente, aditivo materno e ambiente materno permanente. Polinômios cúbicos de Legendre foram utilizados na descrição dos efeitos aleatórios. Estimativas de covariâncias por meio de MTM foram similares em ambas as amostras, apesar de as obtidas da amostra com informações perdidas terem apresentado maior variabilidade entre as idades. As estimativas obtidas pelo RRM foram similares às obtidas pelo MTM somente para o caso de características completas e mostraram grande variabilidade para o caso de características com informações perdidas. Estimativas de correlações entre os efeitos aditivos direto e materno, por meio de RRM, foram iguais a -1.0, em algumas idades. Se várias informações forem perdidas, a melhor aproximação para obter parâmetros por meio de RRM seria a conversão das estimativas obtidas por meio de MTM. O segundo estudo objetivou estimar parâmetros por meio de modelos e características com informações perdidas e completas, à semelhança do primeiro estudo, mediante metodologia Bayesiana – Gibbs sampling, e efetivar comparações com as estimativas obtidas por meio da metodologia REML. As análises por meio do MTM foram para nove características. Estimaram-se componentes de covariâncias e parâmetros genéticos para específicos pontos seqüenciais, por meio do MTM; entretanto, por meio do RRM, tais estimativas foram obtidas para todas as idades. Estimativas de variâncias aditivas diretas para a amostra com informações perdidas aumentaram, do nascer à idade de 551 a 651 dias, pelo MTM, e em todas as idades, na amostra com informações completas. Estimativas de variâncias aditiva direta e residual, mediante RRM, aumentaram ao longo de todas as idades, em ambas as amostras. Pelo MTM, heritabilidades aditivas direta e materna foram maiores na amostra de rebanhos com informações perdidas do que na de rebanhos com informações completas. As estimativas obtidas pelo RRM foram ligeiramente menores do que aquelas obtidas pelo MTM na amostra com informações completas. Heritabilidades aditivas maternas pelo MTM foram maiores do que aquelas obtidas pelo RRM. As estimativas de correlações genéticas aditivas diretas foram levemente maiores para peso ao nascer, quando se utilizou MTM do que quando se empregou RRM. A amplitude das correlações genéticas aditivas maternas foi menor do que a do efeito genético aditivo direto, pelo MTM e pelo RRM. Tendo em vista que os componentes de covariância baseados em RRM são influenciados por informações perdidas, recomendam-se o MTM e a conversão destes componentes em funções de covariância. Além disso, nas análises com modelos-padrão em que inferências dos parâmetros não são importantes, o REML deve ser escolhido. Um terceiro trabalho objetivou a implementação de avaliação genética em bovinos de corte, utilizando modelo de regressão aleatória. Além disso, as avaliações foram comparadas com aquelas estimadas por meio de um modelo de características múltiplas. Dois métodos foram considerados nas análises: um método finito, FSPAKF90 (Factorization sparse matrix package), e o de iteração nos dados, PCG ( Preconditioned conjugate gradient). As correlações entre as diferenças esperadas nas progênies (DEP), estimadas pelo MTM e pelo RRM, foram muito baixas antes de se terem as regressões aleatórias ortogonais. Grande demanda computacional dos RRM foi reduzida pela remoção das regressões correspondentes a pequenas variâncias e também pela substituição dos efeitos aleatórios do erro por específica ponderação. Correlações entre DEPs, estimadas pelo MTM e pelo RRM para efeito aditivo direto, foram .87, .89, .89, .87 e .86 para W1 (peso aos 60 dias), W2 (peso aos 152 dias), W3 (peso aos 243 dias), W5 (peso aos 426 dias) e W7 (peso aos 601 dias), respectivamente. As correlações correspondentes, para efeito aditivo materno, foram .85, .86, .88, .85 e .84, respectivamente. Estimativas obtidas pelos RRM em informações ponderais de gado de corte podem não ser adequadas, em virtude das propriedades numéricas desses modelos. Em geral, baixas correlações são devidas a diferenças em variâncias entre modelos, número insuficiente de graus de liberdade para estimar os efeitos de ambiente e informações perdidas nos RRM.

Most problems in engineering and natural sciences involve parametric equations in which the parameters are not known exactly due to measurement errors, lack of measurement data, or even intrinsic variability. In such problems, one objective is to compute point or aggregate values, called “quantities of interest”. A rapidly growing research area that tries to tackle this problem is Uncertainty Quantification (UQ). As the name suggests, UQ aims to accurately quantify the uncertainty in quantities of interest. To that end, the approach followed in this thesis is to describe the parameters using probabilistic measures and then to employ probability theory to approximate the probabilistic information of the quantities of interest. In this approach, the parametric equations must be accurately solved for multiple values of the parameters to explore the dependence of the quantities of interest on these parameters, using various so-called “sampling methods”. In almost all cases, the parametric equations cannot be solved exactly and suitable numerical discretization methods are required. The high computational complexity of these numerical methods coupled with the fact that the parametric equations must be solved for multiple values of the parameters make UQ problems computationally intensive, particularly when the dimensionality of the underlying problem and/or the parameter space is high. This thesis is concerned with optimizing existing sampling methods and developing new ones. Starting with the Multilevel Monte Carlo (MLMC) estimator, we first prove its normality using the Lindeberg-Feller CLT theorem. We then design the Continuation Multilevel Monte Carlo (CMLMC) algorithm that efficiently approximates the parameters required to run MLMC. We also optimize the hierarchies of one-dimensional discretization parameters that are used in MLMC and analyze the tolerance splitting parameter between the statistical error and the bias constraints. An important contribution of this thesis is the novel Multi-index Monte Carlo (MIMC) method which is an extension of MLMC in high dimensional problems with significant computational savings. Under reasonable assumptions on the weak and variance convergence, which are related to the mixed regularity of the underlying problem and the discretization method, the order of the computational complexity of MIMC is, at worst up to a logarithmic factor, independent of the dimensionality of the underlying parametric equation. We also apply the same multi-index methodology to another sampling method, namely the Stochastic Collocation method. Hence, the novel Multi-index Stochastic Collocation method is proposed and is shown to be more efficient in problems with sufficient mixed regularity than our novel MIMC method and other standard methods. Finally, MIMC is applied to approximate quantities of interest of stochastic particle systems in the mean-field when the number of particles tends to infinity. To approximate these quantities of interest up to an error tolerance, TOL, MIMC has a computational complexity of O(TOL-2log(TOL)2). This complexity is achieved by building a hierarchy based on two discretization parameters: the number of time steps in an Milstein scheme and the number of particles in the particle system. Moreover, we use a partitioning estimator to increase the correlation between two stochastic particle systems with different sizes. In comparison, the optimal computational complexity of MLMC in this case is O(TOL-3) and the computational complexity of Monte Carlo is O(TOL-4).

Final publication is available at http://dx.doi.org/10.1007/s10508-014-0429-5. Teeranee Techasrivichien, Niphon Darawuttimaprakorn, Sureeporn Punpuing, Patou Masika Musumari, Bhekumusa Wellington Lukhele, Christina El-saaidi, S. Pilar Suguimoto, Mitchell D. Feldman, Masako Ono-Kihara, Masahiro Kihara "Changes in Sexual Behavior and Attitudes Across Generations and Gender Among a Population-Based Probability Sample From an Urbanizing Province in Thailand" Archives of Sexual Behavior 2014. / 京都大学 / 0048 / 新制・課程博士 / 博士(医学) / 甲第18679号 / 医博第3951号 / 新制||医||1007(附属図書館) / 31612 / 京都大学大学院医学研究科医学専攻 / (主査)教授 中山 健夫, 教授 福原 俊一, 教授 中原 俊隆 / 学位規則第4条第1項該当 / Doctor of Medical Science / Kyoto University / DFAM

Bedload transport has long been known for its complexity. Despite decades of research, significant gaps of understanding exist in the ability to assess and predict bedload movement. This work introduces a comprehensive bedload database that is a compilation of field samples collected over the past 40 years; compares prediction formulae using a subset of the database; evaluates the influence of the armor layer on stream response to sediment input based on a hypothesis linked to one of the tested formulae, presents a mathematically manipulation of the empirical Pagosa Good/Fair formula for bedload transport into a format similar to the semi-empirical Parker Surface-Based 1990 formula; and addresses the complications of bedload transport by collecting bedload samples on a stream in Central Utah. A comprehensive review of available bedload data resulted in a publicly available database with more than 8,000 individual bedload samples on gravel bed streams. Each measurement included extensive and detailed information regarding channel, site, and hydraulic characteristics. A subset of this database was used to compare four calibrated (a single calibration point of a measured bedload transport rate near bankfull discharge is used to improve formula prediction accuracy) and two un-calibrated bedload prediction formulae. The four calibrated formulae include three semi-empirical (a theoretical treatment adjusted to fit bedload measurements) formulae and one empirical (solely based on regression of bedload measurements) formula; the two un-calibrated formulae are both semi-empirical. Of the formulae compared, the empirical Pagosa Good/Fair formula (a calibrated formula) provided the most accurate prediction results with an overall root mean square error of 6.4%, an improvement of several orders of magnitude over the un-calibrated formulae. The Pagosa Good/Fair formula is cast in a form similar to the Parker 1990 formula, suggesting that criticisms stating that the empirical Pagosa method lacks a theoretical basis are unfounded. The hypothesis of equal mobility that states the gradation of the average annual gravel bedload yield for a given stream matches the particle size distribution of the subsurface material is evaluated with relation to the armor layer. Equal mobility is found to correlate to armor layer such that lower armor ratios indicate a greater tendency to uphold the equal mobility hypothesis and increasing armor ratio values tending to move toward supply limited conditions. This correlation provides an upper limit for lightly armored streams. Bedload sampling efforts described in this work compare the Helley-Smith sampler with the net trap sampler and duplicate previous observations that bedload transport collected using net traps increase more rapidly with discharge than for data collected using Helley-Smith samplers. An alternative, relatively low-cost method for collecting bedload during relatively high discharges on highly urbanized streams is also proposed.

Magister Scientiae (Medical Bioscience) - MSc(MBS) / Introduction: Nosocomial infections are infections that occur 48 hours after receiving care for an unrelated condition in a clinic or a hospital environment, many of which are resistant to at least one of the drugs most commonly used to treat them. The dental clinical settings are reservoirs for the transmission of microbes through aerosols produced by routine dental procedures.

Sampling is considered to be a core component of data analysis making it possibleto provide a synopsis of possibly large amounts of data by maintainingonly subsets or multisubsets of it. In the context of data streaming, an emergingprocessing paradigm where data is assumed to be unbounded, samplingoffers great potential since it can establish a representative bounded view ofinfinite data streams to any streaming operations. This further unlocks severalbenefits such as sustainable continuous execution on managed memory, trendsensitivity control and adaptive processing tailored to the operations that consumedata streams.The main aim of this thesis is to conduct an experimental study in order tocategorize existing sampling techniques over a selection of properties derivedfrom common streaming use cases. For that purpose we designed and implementeda testing framework that allows for configurable sampling policiesunder different processing scenarios along with a library of different samplersimplemented as operators. We build on Apache Flink, a distributed streamprocessing system to provide this testbed and all component implementationsof this study. Furthermore, we show in our experimental analysis that there isno optimal sampling technique for all operations. Instead, there are differentdemands across usage scenarios such as online aggregations and incrementalmachine learning. In principle, we show that each sampling policy trades offbias, sensitivity and concept drift adaptation, properties that can be potentiallypredefined by different operators.We believe that this study serves as the starting point towards automatedadaptive sampling selection for sustainable continuous analytics pipelines thatcan react to stream changes and thus offer the right data needed at each time,for any possible operation

The food supply chain has gained impulse over the past few years induced by the rising global demand for food; therefore, much emphasis is placed upon examining this class of supply chains. It also faces constant production, storage, and distribution challenges, wherein the key link for proper operation is the farmer, who engages in the agricultural sector, heavily impacted by low crop productivity, which interfer with economic development at a national level. Consequently, it is important to assess those farmers who belong to micro and small enterprises in the agricultural sector. Due to the characteristics of the population, a nonprobability sampling technique was used to assess micro and small cocoa producers in La Convención Province, Cusco, Peru. To such end, a snowball sampling model with distance boundaries was adopted because the population is unknown and hard to reach.