Logistic mixed effects models are used to determine whether optimal codons associate with two specific properties of the expressed protein: solvent accessibility, aggregation propensity, or evolutionary conservation. Both random components and fixed structures in the models are decided by following certain selection procedures. More models are also developed by considering different factor combinations using the same selection procedure. The results show that evolutionary conservation is the most important factor for predicting for the optimal codon usage for most amino acids; aggregation propensity is also an important factor, and solvent accessibility is the least important factor for most amino acids.The results of this analysis are consistent with the previous literature, provide more straightforward way to study the research question and also more information for the insight relationships. / text

In 2005 and 2010, high sea surface temperatures caused widespread coral bleaching on Jamaica’s north coast reefs. Three shallow (9m) reef sites were surveyed during each event to quantify the prevalence and intensity of coral bleaching. In October 2005, 29-57% of the colonies surveyed were bleached. By April 2006, 10% of the corals remained pale/partially bleached. Similarly, in October 2010, 23-51% of corals surveyed at the same sites were bleached. By April 2011, 12% of the colonies remained pale/partially bleached. Follow-up surveys revealed low coral mortality following both events, with an overall mean of 4% partial colony mortality across all species and sites observed in April 2006, and 2% in April 2011. Mixed effects models were used to quantify the relationship between colony size and (a) bleaching intensity, and (b) bleaching related mortality among coral species. The bleaching intensity model explained 51% of the variance in the bleaching response observed during the two events. Of this 51%, fixed effects accounted for ~26% of the variance, 17% of which was attributed to species-specific susceptibility to bleaching , 5% to colony size, <1% colony morphology and 4% to the difference in bleaching intensity between the two events. The random factor (site) accounted for the remaining ~25% of the variance. The mortality model explained 16% of the variance in post bleaching mortality with fixed effects, including colony size, morphology and species explaining ~11% of the variance, and the random effect (site) explaining 5%. On average, there was a twofold difference in bleaching intensity between the smallest and the largest size classes. Modelling the relationship between colony level characteristics and site-specific environmental factors on coral species’ susceptibility to thermal stress can shed light on community level responses to future disturbances.

The current study investigates the influence of auditory cues on the localization of briefly presented peripheral visual stimuli. Because the brief presentation of peripheral visual stimuli often leads to mislocalization (Binda, Morrone, & Burr, 2010; Bocianski, Musseler, & Erlhagen, 2008; Musseler, Heijden, Mahmud, Dubel, & Ertsey, 1999) these types of stimuli are the most commonly studied and represent the basis of the current study. Musseler et al. (1999) found that peripheral mislocalization toward the fovea occurred during asynchronous presentations of a pair of visual stimuli in retinal periphery, but not during synchronous presentations of stimuli. The current project is an investigation of how sound influences mislocalization of briefly presented peripheral stimuli. If the mechanism of mislocalization is an increased variability of responses when the peripheral stimuli are presented asynchronously, could sound reduce the variability of localization judgments and thus, reduce or eliminate the mislocalization effect? Does sound influence peripheral mislocalization in some other way? This study found that during a relative judgment task, a brief, laterally presented sound leads to mislocalization of a target stimulus toward the direction of the sound (Experiment 1). During an absolute judgment task, however, the influence of the brief, laterally presented sound no longer evokes mislocalization in the direction of the sound. Rather, stimulus onset asynchrony elicits mislocalization similar to the results of Musseler et al. (Experiment 2). When a dynamic sound stimulus occurs prior to the onset of the target stimulus during an absolute judgment task, however, sound idiosyncratically influences the localization of a target stimulus toward the onset of the sound stimulus or direction of the apparent motion of the sound stimulus (Experiment 3).

A Linear mixed-effects model (LME) is one of the possible tools for longitudinal or group--dependent data. This thesis deals with evaluating of prediction error in LME. Firstly, it is derived the mean square error of prediction (MSEP) by direct calculation. Then the covariance penalty method and crossvalidation is presented for evaluation of MSEP in LME. Further, it is shown how Akaike information criterion (AIC) can be used in mixed-effects models. Because of the model's properties two types of AIC are distinguished - marginal and conditional one. Subsequently, the procedures of AIC's calculation and its basic asymptotic properties are described. Finally, the thesis contains simulation study of behaviour of marginal and conditional AIC with the goal to choose the right variance structure of random effects. It turns out that the marginal criterion tends to select models with smaller number of random effects than conditional criterion.

In 2005 and 2010, high sea surface temperatures caused widespread coral bleaching on Jamaica’s north coast reefs. Three shallow (9m) reef sites were surveyed during each event to quantify the prevalence and intensity of coral bleaching. In October 2005, 29-57% of the colonies surveyed were bleached. By April 2006, 10% of the corals remained pale/partially bleached. Similarly, in October 2010, 23-51% of corals surveyed at the same sites were bleached. By April 2011, 12% of the colonies remained pale/partially bleached. Follow-up surveys revealed low coral mortality following both events, with an overall mean of 4% partial colony mortality across all species and sites observed in April 2006, and 2% in April 2011. Mixed effects models were used to quantify the relationship between colony size and (a) bleaching intensity, and (b) bleaching related mortality among coral species. The bleaching intensity model explained 51% of the variance in the bleaching response observed during the two events. Of this 51%, fixed effects accounted for ~26% of the variance, 17% of which was attributed to species-specific susceptibility to bleaching , 5% to colony size, <1% colony morphology and 4% to the difference in bleaching intensity between the two events. The random factor (site) accounted for the remaining ~25% of the variance. The mortality model explained 16% of the variance in post bleaching mortality with fixed effects, including colony size, morphology and species explaining ~11% of the variance, and the random effect (site) explaining 5%. On average, there was a twofold difference in bleaching intensity between the smallest and the largest size classes. Modelling the relationship between colony level characteristics and site-specific environmental factors on coral species’ susceptibility to thermal stress can shed light on community level responses to future disturbances.

The pharmacokinetics of rifampicin, isoniazid, pyrazinamide and ethambutol in TB/HIV coinfected patients recruited in two phase III clinical trials (61 patients in TB-HAART and 222 patients in RAFA study) were described using nonlinear mixed-effects modelling. Concentration-time data for rifampicin (TB-HAART study) was used to develop a semimechanistic pharmacokinetic model incorporating autoinduction and saturable pharmacokinetics. A model describing the pharmacokinetics of pyrazinamide (TB-HAART study) was developed and used to evaluate the 24-hour area under the concentration-time curve (AUC0–24), and maximum concentrations (Cmax) achieved with the currently recommended weight-adjusted doses for drug-susceptible and -resistant tuberculosis. Concentration-time data from the RAFA study were used to characterise the pharmacokinetics of the four drugs of the fixed dose combination (FDC) therapy including desacetyl-rifampicin, and acetyl-isoniazid. Binary recursive techniques were applied in the conditional inference framework to determine predictors including drug exposure of time-to-stable culture conversion and poor long-term treatment outcomes. The model describing the pharmacokinetics of rifampicin predicted that increasing the dose results in a more than proportional increase in exposure. Clearance of rifampicin increased by 90% from baseline to steady-state due to autoinduction and the process takes up to 21 days. Monte Carlo simulations showed that rifampicin doses of at least 25 mg/kg would be required to achieve an AUC0–24/MIC ratio of at least 271. Based on the model describing the pharmacokinetics of isoniazid, co-administration of isoniazid and efavirenz-based antiretroviral therapy results in a 54% reduction in isoniazid exposure only in fast acetylators. There were disparities in exposure across weight bands for all the four drugs: patients with lower weight had reduced exposure. To match drug exposure across the weight bands, we recommend the addition of one FDC tablet to patients with weight less than 55 kg. There is need to explore the use of fat-free mass-adjusted dosing since cumulative evidence shows its superiority over total body weight in driving exposure via allometric scaling for all first-line antituberculosis drugs. Individual drug exposures were not predictive of either time-to-stable culture conversion or long-term tuberculosis treatment outcomes. Baseline X-ray grading, HIV stage as TB diagnosis, and treatment arm were predictive of time-to-stable culture conversion while the presence of cavities, patient’s level of physical activity and CD4 count were the drivers of long-term treatment outcomes.

Diseases vectored by mosquitoes cause millions of deaths each year. In modern times Florida*s disease risk has been reduced due to efforts to lessen the prevalence of mosquitoes through habitat modification of non-adults. With emerging diseases (i.e. Dengue and Chikunguya) encroaching into Florida from the Caribbean, this traditional approach may not be enough. Alternatively, we can better understand the ecology of how disease works in an ecosystem. One possible way is through the Dilution Effect, which states that the more species that are in a system the lower the chance for zoonosis. This project models mosquito diversity across regions, land use, and vegetation height in South-Central Florida, for the purpose of identifying predictors that indicate a higher disease risk using information theory (AICc). The plains and coastal regions as well as the developed areas have a relatively higher risk of disease. Florida is a fire maintained habitat, but has been fire suppressed for the last century. Archbold Biological Station (ABS) has used prescribed fires since the early 1980s to try and restore a more natural system. This has created a mosaic of different fire histories. Fire affects the structures that mosquitoes rest under during the day (they are vulnerable to desiccation during the day and hide in darker/shady places), therefore there is a high likelihood that fire will have some effect on mosquito assemblages. This project used model selection to determine the most plausible set of predictors that describe the effect of fire on mosquito assemblages at ABS, using information theory (AICc). In general, time of season accounted for the largest proportion of the variation in the data and TSF had negligible effect on adult mosquito assemblages measured as abundance, speices richness, and Jost D.

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This dissertation focuses on estimation and selection methods for a simple linear model with two levels of variation. This model provides a foundation for extensions to more levels. We propose new regularization criteria for model selection, subset selection, and variable selection in this context. Regularization is a penalized-estimation approach that shrinks the estimate and selects variables for structured data. This dissertation introduces a procedure (HM-ALASSO) that extends regularized multilevel-model estimation and selection to enforce principles of fixed heredity (e.g., including main effects when their interactions are included) and random heredity (e.g., including fixed effects when their random terms are included). The goals in developing this method were to create a procedure that provided reasonable estimates of all parameters, adhered to fixed and random heredity principles, resulted in a parsimonious model, was theoretically justifiable, and was able to be implemented and used in available software. The HM-ALASSO incorporates heredity-constrained selection directly into the estimation process. HM-ALASSO is shown to enjoy the properties of consistency, sparsity, and asymptotic normality. The ability of HM-ALASSO to produce quality estimates of the underlying parameters while adhering to heredity principles is demonstrated using simulated data. The performance of HM-ALASSO is illustrated using a subset of the High School and Beyond (HS&B) data set that includes math-achievement outcomes modeled via student- and school-level predictors. The HM-ALASSO framework is flexible enough that it can be adapted for various rule sets and parameterizations. / Statistics

Linear models are the dominant means of extracting summaries of events in fMRI for feature vector based machine learning. While they are both useful and robust, they are limited by the assumptions made in modeling. In this work, we examine a number of feature extraction techniques adjacent to linear models that account for or allow wider variation. Primarily, we construct mixed effects models able to account for variation between stimuli of the same class and perform empirical tests on the resulting feature extraction – classifier system. We extend this analysis to spatial temporal models as well as summary models. We find that mixed effects models increase classifier performance at the cost of increased uncertainty in prediction estimates. In addition, these models identify similar regions of interest in separating classes. While they currently require knowledge hidden during testing, we present these results as an optimum to be reached in additional works. / Doctor of Philosophy / Machine learning is a popular tool for extracting useful information from functional MR images. One approach is classification using feature vectors derived from observations. In this work, we examine new strategies for extracting feature vectors time varying data and explore the effect these feature vectors have on the results of machine learning analysis. In a set of simulations and real data, we compare a range of standard methods for feature extraction to new methods developed for this work. We find the most effective approach for successful classification is feature extraction through the use of mixed effects models. We also find that these models preserve the selection of feature sets that are maximally important to classification. We then explore the range of considerations required to use any of the methods examined in this work for a range of cases. We hope this provides solid ground for both future expansion of feature extraction methods and helpful advice for future users of these methods.