An Improved Approximate-Bayesian Model-choice Method for Estimating Shared Evolutionary History

To understand biological diversification, it is important to account for large-scale processes that affect the evolutionary history of groups of co-distributed populations of organisms. Such events predict temporally clustered divergences times, a pattern that can be estimated using genetic data from co-distributed species. I introduce a new approximate-Bayesian method for comparative phylogeographical model-choice that estimates the temporal distribution of divergences across taxa from multi-locus DNA sequence data. The model is an extension of that implemented in msBayes. By reparameterizing the model, introducing more flexible priors on demographic and divergence-time parameters, and implementing a non-parametric Dirichlet-process prior over divergence models, I improved the robustness, accuracy, and power of the method for estimating shared evolutionary history across taxa. The results demonstrate the improved performance of the new method is due to (1) more appropriate priors on divergence-time and demographic parameters that avoid prohibitively small marginal likelihoods for models with more divergence events, and (2) the Dirichlet-process providing a flexible prior on divergence histories that does not strongly disfavor models with intermediate numbers of divergence events. The new method yields more robust estimates of posterior uncertainty, and thus greatly reduces the tendency to incorrectly estimate models of shared evolutionary history with strong support.Comment: 48 pages, 8 figures, 4 tables, 35 pages of supporting information with 1 supporting table and 33 supporting figure

Oral History Transcript - Angela Oaks

https://scholarworks.utrgv.edu/spanishlandgrantsoralhistories/1036/thumbnail.jp

Implications of uniformly distributed, empirically informed priors for phylogeographical model selection: A reply to Hickerson et al

Establishing that a set of population-splitting events occurred at the same time can be a potentially persuasive argument that a common process affected the populations. Oaks et al. (2013) assessed the ability of an approximate-Bayesian method (msBayes) to estimate such a pattern of simultaneous divergence across taxa, to which Hickerson et al. (2014) responded. Both papers agree the method is sensitive to prior assumptions and often erroneously supports shared divergences; the papers differ about the explanation and solution. Oaks et al. (2013) suggested the method's behavior is caused by the strong weight of uniform priors on divergence times leading to smaller marginal likelihoods of models with more divergence-time parameters (Hypothesis 1); they proposed alternative priors to avoid strongly weighted posteriors. Hickerson et al. (2014) suggested numerical approximation error causes msBayes analyses to be biased toward models of clustered divergences (Hypothesis 2); they proposed using narrow, empirical uniform priors. Here, we demonstrate that the approach of Hickerson et al. (2014) does not mitigate the method's tendency to erroneously support models of clustered divergences, and often excludes the true parameter values. Our results also show that the tendency of msBayes analyses to support models of shared divergences is primarily due to Hypothesis 1. This series of papers demonstrate that if our prior assumptions place too much weight in unlikely regions of parameter space such that the exact posterior supports the wrong model of evolutionary history, no amount of computation can rescue our inference. Fortunately, more flexible distributions that accommodate prior uncertainty about parameters without placing excessive weight in vast regions of parameter space with low likelihood increase the method's robustness and power to detect temporal variation in divergences.Comment: 24 pages, 4 figures, 1 table, 14 pages of supporting information with 10 supporting figure

The Importance of Home Modification for Occupational Participation and Safety for Low-income Older Adult Homeowners

The purpose of this collaborative study between Rebuilding Together Twin Cities (RTTC) and the OT department of St. Catherine University was to evaluate the impact of home modifications on the occupational participation and safety of low-income, older adult homeowners. This study utilized a mixed methods design to answer the following three research questions: 1) How do daily life routines and activity participation change for the homeowner as a result of the modifications? 2) What is the impact on the homeowner’s awareness and feelings of safety? and 3) What is the homeowners’ experience of home modification? A total of four quantitative tools were used to answer these questions including the In-Home Occupational Performance Evaluation (I-HOPE), Life Space Assessment (LSA), Short Falls Efficacy Scale (S-FES), and Live Well at Home Rapid Screen (LWAH-RS). Semi-structured interviews were also conducted to collect qualitative data for additional interpretation. A total of 15 low-income older adult homeowners completed the study and met participation criterion. Statistical analysis showed significant improvements in occupational participation in valued daily activities for the I-HOPE, as well as clinically significant decreases in fear of falling for the S-FES and risk of long-term care placement for the LWAH-RS. Scores for the LSA did not show clear improvements when compared to baseline. The positive findings suggest that home modifications involving occupational therapists can improve occupational participation and safety for low-income older adult homeowners. Qualitative results revealed themes of increased independence and accessibility, improved community relationships and occupational activities, and increased hope to remain aging in place

Storytelling: A Natural Mnemonic: A Study of a Storytelling Method to Positively Influence Student Recall of Instruction

The purpose of this dissertation was to determine what, if any, effect storytelling as a method of teaching has on retention of information. More specifically, this work was designed to determine if storytelling is more effective than a more traditional lecture method in influencing student recall of lesson content. In the Spring Semester of 1994, experiments were conducted to test the following hypothesis: College Students who receive instruction in a storytelling fashion will demonstrate significantly greater recall of instructional content than students who receive the same instruction in a more traditional lecture method. One hundred fourteen students were randomly selected from undergraduate college courses in Instructional Media and Technology. After reading and signing letters of consent, students were randomly assigned to either a control or experimental group. Both groups were first pretested, then taught the same material in different fashions. The control group was instructed with the lecture method, while the experimental group was given the same content by means of a storytelling method. Recall of the instructional content was then tested in three posttests: one given immediately following instruction; the second and third tests three and five weeks following. A t-test was performed on test scores of the control and experimental groups. All t-test scores showed statistically significant gains in recall by the experimental group over the group that received instruction by the lecture method. The null hypothesis was rejected. This study indicates that, for the population described in the experiment, instruction in a storytelling fashion can make a positive difference in the recall of instructional material. Implications of this conclusion are discussed for three groups: Educational Researchers, Writers and Producers of Curriculum Materials, and Classroom Teachers

KMC modeling of helium bubble clustering and evolution in BCC iron

The effect of helium in iron is an important issue in nuclear systems, as iron and iron alloys (steels) are the primary materials used for structural elements. Helium is known to cause embrittlement and decrease fatigue life, as well as aid creep and promote swelling. These effects can significantly alter the mechanical properties of the reactor materials, and generally lead to early failure and decreased part lifetimes. This is a concern in both fission and fusion systems. The precise role that helium, helium-vacancy clusters, and helium bubbles play in the material degradation processes described above are still only partially understood. Further understanding into the role helium plays in these phenomena is essential to predicting the lifetime of iron and steels in nuclear reactors. This work was motivated by the results found earlier by Okuniewski. Said work was primarily experimental work studying the effects of helium concentration on cluster size distribution. KMC simulations were run for comparison, but the results were inconsistent. Both with and without helium present, the results showed the KMC simulation resulted in a significant shift compared to the experimental results. The KMC simulations predicted a high density of small sized clusters, while the experimental results showed a lower density of larger sized clusters. This inconsistency was believed to be a result of the various parameters chosen in the KMC model. This work focused on two primary goals: first, to develop a flexible KMC code capable of simulating the desired models, and second, to explore the modeling assumptions made in the previous KMC simulations in an attempt to come closer to experimental results. Several different models for cluster interaction range, dissociation energy, and migration energy were considered, and a KMC code was designed and built to accommodate these and other models. The code design will be presented, along with performance benchmarking results. Both annealing and damage simulations were then performed with varying combinations of parameter models. The results of these simulations are compared and discussed

Preserving Religious Freedom

This address was given at Chapman University School of Law in Orange, California, on February 4, 2011