Woodrats (Neotoma spp.) are imperiled in the north-central and north eastern United States. In Illinois, eastern woodrats (N. floridana) experienced range reductions and population bottlenecks over the past century. Hypothesized reasons for the decline of many woodrat populations that inhabit rock outcrops in the eastern United States include parasitism by raccoon roundworms (Baylisascaris procyonis), hard mast shortages, owl predation, and reductions in crevice availability for nest construction. During 2004-2005, the isolated remnant populations along the Mississippi bluffs in southwestern Illinois were genetically augmented with 47 eastern woodrats from Arkansas and Missouri resulting in 40% admixture within the largest population. In 2009, a strong windstorm created canopy gaps and woody debris throughout this area, potentially improving habitat for eastern woodrats. During 2003–2009, 422 eastern woodrats were reintroduced to 5 sites in the southeastern Illinois, and 172 eastern woodrats to 2 southern Illinois state parks during 2013–2014. These reintroductions are the only woodrat reintroductions to date with >50 individuals released per site. Most previous woodrat reintroduction attempts have released small numbers of individuals (10–15 per site and 10–54 total) and either failed to establish populations or required frequent management for populations to persist. My objectives were to (1) investigate the status of augmented eastern woodrat populations in southwestern Illinois, (2) evaluate the success of the southern Illinois reintroductions, (3) investigate whether eastern woodrats demographics within a reintroduced metapopulation could be predicted by factors underlying hypothesized reasons for woodrat declines, and (4) develop and evaluate noninvasive alternatives to live-trapping and sign surveys for monitoring woodrat populations.

Augmentation procedures are designed to provide better estimates for a given test or subtest through the use of collateral information. The main purpose of this dissertation was to use Haberman's and Wainer's augmentation procedures on a large-scale, standardized achievement test to understand the relationship between reliability and correlation that exist to create the proportional reduction of mean squared error (PRMSE) statistic and to compare the practical effects of Haberman's augmentation procedure with the practical effects of Wainer's augmentation procedure. In this dissertation, Haberman's and Wainer's augmentation procedures were used on a data set that consisted of a large-scale, standardized achievement test with tests in three different content areas, reading, language arts, and mathematics, in both 4th and 8th grade. Each test could be broken down into different content area subtests, between two and five depending on the content area. The data sets contained between 2,500 and 3,000 examinees for each test. The PRMSE statistic was used on the all of the data sets to evaluate two augmentation procedures, one proposed by Haberman and one by Wainer. Following the augmentation analysis, the relationship between the reliability of the subtest to be augmented and that subtest's correlation with the rest of the test was investigated using a pseudo-simulated data set, which consisted of different values for those variables. Lastly, the Haberman and Wainer augmentation procedures were used on the data sets and the augmented data was analyzed to determine the magnitude of the effects of using these augmentation procedures. The main findings based on the data analysis and pseudo-simulated data analysis were as follows: (1) the more questions the better the estimates and the better the augmentation procedures; (2) there is virtually no difference between the Haberman and Wainer augmentation procedures, except for certain correlational relationships; (3) there is a significant effect of using the Haberman or Wainer augmentation procedures, however as the reliability increases, this effect lessens. The limitations of the study and possible future research are also discussed in the dissertation.

Changes in the amount of water and sediment that enter a river can change its shape and size. The way that rivers change is affected by a variety of factors, including the size of the sediment in the river, and past changes to the river. The Diamond Fork River in central Utah has been altered by water deliveredfromthe Colorado River system for over a century. Beginning in 1915, water used for irrigation was delivered through a tributary, Sixth Water Creek, with daily summer flows that were much larger than natural flows. This caused drastic change to the rivers, as they became wider and vegetation along the channel margin and floodplain was destroyed. Management changes in 1997 and 2004 reduced the amount of water and sediment added to the river. In this study, we sought to understand how Sixth Water and Diamond Fork changed in the past and what the implications are for the future. We used data from a variety of sources to describe how and why the river changed in the past. Our results indicate that parts of the river that are not confined by valley walls became very wide during the period of elevated flows and narrowed after the change in management in 1997. Confined reaches experienced minor changes over the period of record. Areas of the channel that were most dynamic in the past are the most susceptible to future change because they have finer sediment that is more easily erodible. Areas that did not experience past changes are unlikely to change in the future without direct intervention from humans or beaver. The findings of this study improve our understanding of Sixth Water and Diamond Fork, and confirm the importance of past changes and valley confinement.

The purpose of this study was to investigate if the addition of biologic materials and/or oral steroids would affect horizontal bone gain, or the bone density of the grafted bone in horizontal alveolar ridge augmentations. A retrospective chart review was completed to assess the clinical and radiographic outcomes of 53 ridge augmentation patients. An average bone gain of 3.6mm of width was found in our study based on radiographic analysis. There were no statistically significant differences found in the linear bone gain with the addition of biologic materials and steroids. A marginally statistically significant difference was found in the bone density when biologics were added (p-value=0.0653). No statistically significant difference found in the bone density with the addition of oral steroids. The use of tenting screws and resorbable occlusive membranes and a combination of allograft and xenograft bone materials provides significant clinical and radiographic dimensional changes in alveolar ridge width.

acase@tulane.edu / Advanced omics technologies have been generating abundant multi-ethnic multi-omics data, including DNA sequences, methylations, gene expressions, and copious clinical traits. Such big data pose unprecedented challenges due to the high complexity of heterogeneous networks between biomarkers. Heteroscedasticity (aka, dispersion heterogeneity of trait residuals) is a common phenomenon in multi-omics data mining. It can be caused by interactions such as gene×gene, gene×enviroment, linkage disequilibrium (LD) between marker loci, and pleiotropic traits as well. Especially, it occurs in the data mining of the multi-omics data of admixed individuals subjects due to broad admixture LD and gene×ancestry interactions. Meanwhile, it can be induced by background confounders, e.g., population structure, cryptic relatedness, polygenetic effects, and correlations between residuals of multiple traits. However, existent univariate and multivariate methods neglect all the high-order effects of both test biomarkers and background confounders. This dissertation contributes systematic harmonious signal augmentation methods with applications for distilling high-order information from multiethnic DNA sequences to microarrays. In Chapter I, we proposed a novel harmonious signal augmentation schemes in single-based association tests. The harmonious single-based association test (HSAT) is more powerful then existent single-based methods in both simulations and real data application. In Chapter II we put forth harmonious gene-based association tests (HGAT) to incorporate high-order effects. Within a gene, the importance of a test variant is measured by the signal of marker-wise high-order effects. Leveraging high-order effects of genetic variants has proven to improve power for identifying susceptive genes. By extensive simulations under published designs, the proposed method properly controlled type I error rates and appeared strikingly more powerful than existent prominent gene-based sequence association methods. We apply HGAT methods in homogeneous population and admixed population. There are two parts in Chapter III, the first part introduced integrating informative mean and variance effects to identify differentially expressed (DE) genes. The second part illustrated the application of harmonious integration of mean and high order effects to identify differentially expressed (DE) genes. In summary, this dissertation demonstrated tremendous potential of explicitly distilling informative higher-order effects in big multiethnic multi-level data mining and offered paradigm applications for integrating high-order information resources while effectively calibrating major heteroscedastic confounders. / 1 / Weiwei Ouyang

acase@tulane.edu / 1 / Weiwei Ouyang

Varying missile configurations may create uncertainty for a missile control algorithm developed with linear control theory, for instance the control system performance requirements may not be satisfied anymore. Missile configuration may change during the missile design period due to variations in subsystem locations, subsystem weights and missile geometry. Likewise, burning propellant, deployment of aerodynamic surfaces and wings with varying sweep angle can be considered as in-flight missile configuration changes. This thesis study addresses development and analysis of an adaptive missile control algorithm to account for the uncertain effects caused by varying missile configuration. Control algorithms, designed using pole placement, are augmented with adaptive neural networks. The resulting controller is a type of model reference adaptive controller. Adaptation characteristics of the augmented control algorithms are investigated to changing center of pressure location and missile geometry. Analyses are performed for three different missile configurations using simulation.

We investigate evolution of mountain channel morphology and riverbed surface roughness by conducting laboratory experiments. The experimental flume is 4m long by 0.1m wide with a working length of 2.5m. We control initial sediment size distribution, flume slope, water discharge, and sediment feed rate. Measurements include topographic profiles, flow depth, surficial grain-size distribution, sediment transport rate, and sediment size distribution. Experiments begin with a gravel bed of a broad sediment size distribution, at two initial flume slopes: 8.2% and 12.4%. Discharge is held constant until transport rates and topographic changes indicate the system is at near steady state. Coarse sand is then fed into the channel at 1,000 g/min as a means to perturb the system. Sediment feed is held constant until the perturbed bed reach steady-state conditions. The feed is subsequently ceased and measurements continue until sediment transport rates and topography stabilize. These laboratory experiments provide first-hand observations of channel systems evolving after perturbations. Transport rates decay exponentially following perturbations and remain very low when the channel bed is stabilized. The introduction of coarse sand acts to smooth the channel bed by filling in topographic lows in the 8.2% sloped channel. At a 12.4% slope, increased mobility of sand allows steady state conditions to be met with little smoothing of the bed. The sand also increases the mobility of coarser sediment that was previously stable, likely due to local surface smoothing at grain scale. The increased fraction of surface sand cover maintains increased scouring and mobilization of coarser grains. These post-perturbation mechanisms are interpreted to be responsible for topographic adjustments as the system readjusts towards new steady-state conditions. Surface sorting and transporting distributions reflect high sand fractions well after perturbations have ceased. This suggests that brief pulses of fine sediment can increase coarse sediment mobility for prolonged periods. / text

The rapid technological development of high-throughput genomics has given rise to complex high-dimensional microarray datasets. One strategy for reducing the dimensionality of microarray experiments is to carry out a cluster analysis to find groups of genes with similar expression patterns. Though cluster analysis has been studied extensively, the clinical context in which the analysis is performed is usually considered separately if at all. However, allowing clinical outcomes to inform the clustering of microarray data has the potential to identify gene clusters that are more useful for describing the clinical course of disease. The aim of this dissertation is to utilize outcome information to drive the clustering of gene expression data. In Chapter 1, we propose a joint clustering model that assumes a relationship between gene clusters and a continuous patient outcome. Gene expression is modeled using cluster specific random effects such that genes in the same cluster are correlated. A linear combination of these random effects is then used to describe the continuous clinical outcome. We implement a Markov chain Monte Carlo algorithm to iteratively sample the unknown parameters and determine the cluster pattern. Chapter 2 extends this model to binary and failure time outcomes. Our strategy is to augment the data with a latent continuous representation of the outcome and specify that the risk of the event depends on the latent variable. Once the latent variable is sampled, we relate it to gene expression via cluster specific random effects and apply the methods developed in Chapter 1. The setting of clustering longitudinal microarrays using binary and survival outcomes is considered in Chapter 3. We propose a model that incorporates a random intercept and slope to describe the gene expression time trajectory. As before, a continuous latent variable that is linearly related to the random effects is introduced into the model and a Markov chain Monte Carlo algorithm is used for sampling. These methods are applied to microarray data from trauma patients in the Inflammation and Host Response to Injury research project. The resulting partitions are visualized using heat maps that depict the frequency with which genes cluster together.

Keeping a learner interested-and therefore engaged-in content to be mastered generally improves learning. One way to keep a learner interested is using seductive augmentation, which refers to the addition of entertaining text, graphics, sound, music, video or animation that is either irrelevant or only tangentially relevant to the learning objectives. Learner cognitive styles impact how individuals approach learning and problem-solving situations. With recent advances in technology, there has been an increased interest in the way such individual differences influence performance while learning. Research on the effects of cognitive styles has mainly focused on the role of field-dependence/independence. One of the recent advances in technology is the availability of virtual worlds as learning environments. This study investigated whether seductive augmentation in Second Life, a commonly used virtual world, affects the learning performance of field-dependent and field-independent education majors in an undergraduate class unit. A second focus of this study was to examine whether the computer self-efficacy of these learners changed after their two-month experience with the virtual world of Second Life. To determine if seductive augmentation in Second Life affects the achievement of field-dependent and field-independent learners differently, two different settings were designed in two different regions of Second Life. One setting was free of seductive augmentation, but the other setting included seductive augmentation in the forms of music, animation, text, videos and games. Thirty-six participants self-selected to the seductive setting and 48 to the non-seductive setting. The participants were pre- and post-tested on the instructional content presented both in Second Life and in real life classes; furthermore, to examine the influence on learners' computer self-efficacy, pre- and post-computer self-efficacy surveys were administered. The results of the study were obtained through two independent mixed-model factorial analyses of variance with repeats on the third factor (time) for achievement and computer self-efficacy scores. For the main effects, results indicated no significance for the between-group factors of field-dependency and seductiveness or for their interaction with either achievement scores or computer self-efficacy scores. The only significant factor was time as the main within-group factor for achievement scores. Therefore, the study did not find seductive augmentation effect in Second Life. In addition, there was no detectable change in the participants' computer self-efficacy as a result of their experience in this virtual world. The results of the present study contradict the findings of some previous research and support others.