The aim of this work was focused on study of influence of cultivation conditions on growth of thermophilic bacteria Thermus aquaticus, Thermus scotoductus, Geobacillus thermodenitrificans and Geobacillus thermocatenulatus in cultivation medium recommended by Czech Collection of Microorganisms (CCM). The change of concentration of biomass during cultivation with various pH of media, cultivation temperature, agitation rate and with addition of glucose to medium during cultivation in Erlenmayer flasks was observed. Results served for determination of optimal growth conditions for each microorganism. After optimal growth conditions were obtained, cultivations in fermenter were performed. Growth curves were made from values of absorbance and dry weight of samples.

The therapeutic alliance is a core element of successful treatment in therapy. Recent literature has explored variables that predict the alliance at various time points during therapy, but few studies have explored how the alliance develops over time and the factors that influence its rate of change. The current study addresses these questions by estimating latent growth-curve models to analyze how male and female partners' alliance scores develop over time and how adverse childhood experiences (ACEs) impact the development of the alliance during the first six sessions of therapy. Results from these analyses show that neither men nor women's ACEs had a significant effect on the rate of change in the alliance. A discussion of the attachment implications of these findings, as well as the limitations of this study and potential directions for future research are then presented.

abstract: Longitudinal recursive partitioning (LRP) is a tree-based method for longitudinal data. It takes a sample of individuals that were each measured repeatedly across time, and it splits them based on a set of covariates such that individuals with similar trajectories become grouped together into nodes. LRP does this by fitting a mixed-effects model to each node every time that it becomes partitioned and extracting the deviance, which is the measure of node purity. LRP is implemented using the classification and regression tree algorithm, which suffers from a variable selection bias and does not guarantee reaching a global optimum. Additionally, fitting mixed-effects models to each potential split only to extract the deviance and discard the rest of the information is a computationally intensive procedure. Therefore, in this dissertation, I address the high computational demand, variable selection bias, and local optimum solution. I propose three approximation methods that reduce the computational demand of LRP, and at the same time, allow for a straightforward extension to recursive partitioning algorithms that do not have a variable selection bias and can reach the global optimum solution. In the three proposed approximations, a mixed-effects model is fit to the full data, and the growth curve coefficients for each individual are extracted. Then, (1) a principal component analysis is fit to the set of coefficients and the principal component score is extracted for each individual, (2) a one-factor model is fit to the coefficients and the factor score is extracted, or (3) the coefficients are summed. The three methods result in each individual having a single score that represents the growth curve trajectory. Therefore, now that the outcome is a single score for each individual, any tree-based method may be used for partitioning the data and group the individuals together. Once the individuals are assigned to their final nodes, a mixed-effects model is fit to each terminal node with the individuals belonging to it. I conduct a simulation study, where I show that the approximation methods achieve the goals proposed while maintaining a similar level of out-of-sample prediction accuracy as LRP. I then illustrate and compare the methods using an applied data. / Dissertation/Thesis / Doctoral Dissertation Psychology 2019

京都大学 / 0048 / 新制・課程博士 / 博士(農学) / 甲第18317号 / 農博第2042号 / 新制||農||1021(附属図書館) / 学位論文||H26||N4824(農学部図書室) / 31175 / 京都大学大学院農学研究科地域環境科学専攻 / (主査)教授 平井 伸博, 教授 今井 裕, 准教授 三宅 武 / 学位規則第4条第1項該当 / Doctor of Agricultural Science / Kyoto University / DFAM

Across time, the influence of parents and peers appears to change. Early in life, parents have a stronger influence on the development of youth than do their peers. This, however, will change as an individual ages. Using longitudinal data from the Marion County (Oregon) Youth Survey (1964-1979), I examine the influence of parents or delinquent association, drug use and arrest. Analysis generated through latent growth curve modeling show that although parental influence appears to decrease significantly later in life, parental attachment delays the formation of delinquent peer networks, thereby indirectly reducing the total number of arrests. Even so, reductions in parental influence over time were associated with a significantly accelerated rate of acquiring delinquent peers and hence, with an increased frequency of arrest and drug use. The available evidence thus suggests that parental attachment has initial inhibitory effects on the formation of peer networks but only limited long-term developmental effects.

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