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Lexical inferencing is considered a listening strategy that is commonly employed by advanced EFL (English as a Foreign Language) listeners and a factor that contributes to successful listening comprehension. However, investigations of the factors that influence inferencing success in listening as well as how much each factor contributes to success are scant, as more studies have been conducted exploring lexical inferencing in reading. In addition, even though idiomatic expressions such as smell a rat, jump the gun, and go cold turkey are ubiquitous in the English language, especially in oral communication, and they are considered crucial in both first language (L1) and second language (L2) acquisition, little is known about the effectiveness of inferencing strategies where idiomatic expressions are concerned.Three goals motivated the current study. The first goal was to investigate whether inferencing is an effective strategy in the case where the target item is an idiomatic expression. The second goal was to investigate how four person-level factors, familiarity, listening proficiency, listening vocabulary size and working memory, two sentence-level factors, lexical density and sentence length, and two lexical-level factors, L1–L2 congruency and semantic transparency, influence the inferencing success of English idiomatic expressions in listening. The third goal, related to the second goal, was to determine which of the two lexical component factors, L1–L2 congruency and semantic transparency, is more important to inferencing success. A mixed methods design, the explanatory sequential design (Creswell & Plano Clark, 2018), was employed in this study. Quantitative data were collected from 89 EFL Japanese university students using a Listening Vocabulary Levels Test, a Listening Span Test, and an Idiom Inferencing Elicitation Task. The collected data were examined using mixed-effects logistic regression. Twelve participants were invited to participate in follow-up interviews based on their response patterns on the Idiom Inferencing Elicitation Task. The quantitative results indicated that familiarity, listening comprehension skills, working memory, and L1–L2 congruency were significant factors influencing inferencing success and the qualitative results supported these findings. In addition, the qualitative analyses suggested that depth of vocabulary is another potentially important factor. Furthermore, listening comprehension moderated the L1–L2 congruency effect. The finding that semantic transparency is not an influential factor in successful inferencing of unfamiliar idiomatic expressions provides evidence that the semantic transparency of known idiomatic expressions formed after learners acquire the meaning of the expression is a different construct from the perceived semantic transparency of unfamiliar idiomatic expressions. In addition, even though the sentence-level factors were not statistically significant in successful idiom inferencing in this study, further studies are required in order to see if this result holds true when the characteristics of the listening tasks differ from those of the task used in this study. It is hoped that the findings provide insights into how to help Japanese university EFL learners improve their listening skills, especially in tasks that include unfamiliar idiomatic expressions. / Teaching & Learning

The present study provides an example of implementing the difference in differences (DD) estimator for a two-group, pretest-posttest design with K-12 educational intervention data. The goal is to explore the basis for causal inference via Rubin's potential outcomes framework. The DD method is introduced to educational researchers, as it is seldom implemented in educational research. DD analytic methods' mathematical formulae and assumptions are explored to understand the opportunity and the challenges of using the DD estimator for causal inference in educational research. For this example, the teacher intervention effect is estimated with multi-cohort student outcome data. First, the DD method is used to detect the average treatment effect (ATE) with linear regression as a baseline model. Second, the analysis is repeated using linear regression with cluster robust standard errors. Finally, a linear mixed effects analysis is provided with a random intercept model. Resulting standard errors, parameter estimates, and inferential statistics are compared among these three analyses to explore the best holistic analytic method for this context.

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Ancestral Sequence Reconstruction is a technique used to statistically infer the most likely ancestor of a set of evolutionarily related sequences, but research which relies solely on protein data has the disadvantage of sequence information being lost upon translation of a protein from its gene transcript, due to the redundancy inherent in the genetic code. In this project, the amino acid sequences, and separately the corresponding codon sequences, of 184 homologous Acetylcholine receptor protein sequences were aligned, and phylogenetic analysis and ancestral sequence reconstruction was performed based on both alignments to infer several ancestral sequences representing important milestones in the evolutionary history of the homologous protein family. To further extract meaningful information from the nucleotide sequences, positive selection analysis was performed on the codon alignment using the Mixed Effects Model of Evolution method, which estimates and compares between the rates of synonymous and non- synonymous mutations across the alignment to detect the occurrence of positive selection events throughout their evolution. The Mixed Effects Model of Evolution can infer positive selection across both sites and evolutionary branches in a sequence alignment, thus highlighting residues along the evolutionary trajectory of the proteins which may have been functionally important in their evolution. Positive selection analysis detected positive selection at a multitude of sites and branches, and by mapping signatures at which selection is strongest with changes in the trajectory of ancestral states, several important sites were chosen as likely to be most valuable for future experimental testing. The implications of this study on the benefits of conducting ancestral sequence reconstruction with protein and codon sequences are discussed.

The ongoing opioid crisis presents a significant public health challenge particularly for people who use opioids (PWUO). Naloxone is an opioid antagonist crucial to reducing opioid overdose mortality. Inconsistencies exist among PWUO in obtaining, carrying, discussing, and administering naloxone. Using sequential mixed methods, this study was aimed at investigating the use of implementation intentions on naloxone use among PWUO. Semi-structured interviews were conducted with 83 PWUO to gather individual experiences with using naloxone and contextual details regarding its use. An essentialist thematic analysis with inductive coding revealed valuable insights into where, for whom, and when naloxone is implemented. The analysis identified major themes such as caring for others' needs, knowledge gaps, reinforcement through overdose experiences, duality of overdose and compassion, and stigma. Minor themes related to syringe services program implementation and drug use were identified. Building on these qualitative findings a quantitative analysis determined the impact of implementation intentions on naloxone implementation. Participants were randomly assigned to develop implementation intentions or goal intentions for the use of naloxone. Follow-up surveys assessed changes in participants' intentions to obtain, carry, discuss, and administer naloxone and their actual implementation over a 6-month period. At the 3-month follow-up the experimental condition exhibited statistically significant positive intentions to obtain naloxone and engage in discussions about naloxone in social contexts of drug use. Changes in the magnitude of naloxone implementation were observed at the 3- and 6-month timepoints. Specifically, the self-reported discussion of naloxone showed noticeable changes in implementation frequency over time. This suggests that while implementation intentions may not have statistically significant effects on the use of naloxone it had some influence on the frequency of discussing naloxone prior to drug use. This work makes a valuable contribution to the existing literature because of its attempt to apply the Theory of Planned Behavior and implementation intentions in a novel way. Though the experimental hypothesis was not supported statistically significant observations were made for some behaviors at the 3-month follow-up. The pragmatic nature of the setting enhances the relevance of the findings and provides valuable insights for future interventions supporting PWUO. / Doctor of Philosophy / The ongoing crisis of opioid addiction poses a significant public health challenge particularly for individuals who use opioids. Naloxone is a medication that can reverse opioid overdoses and it plays a crucial role in saving lives. People who use opioids often face difficulties in accessing, carrying, discussing, and using naloxone consistently. This study was aimed at investigating the use of naloxone by employing qualitative and quantitative methods. We conducted interviews with 83 individuals who use opioids to explore their experiences and gather insights into naloxone use. These interviews provided valuable information about when, where, and for whom naloxone is used. Several important themes emerged including the significance of helping others, knowledge gaps, the influence of personal experiences, the conflict between the fear of overdose and caring for others, and the stigma associated with drug use. We investigated the impact of a specific approach called "implementation intentions" in improving naloxone use. Participants were randomly assigned to create specific plans or general goals for naloxone use. Through surveys conducted over a 6-month period we examined changes in participants' intentions and actions related to naloxone use. Although the specific approach did not yield significant improvements, we observed changes in how people discussed naloxone over time. This study contributes to the existing research by introducing innovative ideas to support positive behavioral changes among individuals who use opioids. The real-world setting in which the study took place enhances the applicability of the findings and offers valuable insights for future programs supporting individuals who use opioids.

Tuberculosis (TB) is the world’s most deadly infectious disease and causes enormous public health problems. The comorbidity with HIV and the rise of multidrug-resistant TB strains impede successful therapy through drug-drug interactions and the lack of efficient second-line treatments. The aim of this thesis was to support the improvement of anti-TB therapy through development of pharmacometric models, specifically focusing on the novel drug bedaquiline, pharmacokinetic interactions and methods for pooled population analyses. A population pharmacokinetic model of bedaquiline and its metabolite M2, linked to semi-mechanistic models of body weight and albumin concentrations, was developed and used for exposure-response analysis. Treatment response was quantified by measurements of mycobacterial load and early bedaquiline exposure was found to significantly impact the half-life of bacterial clearance. The analysis represents the first successful characterization of a concentration-effect relationship for bedaquiline. Single-dose Phase I studies investigating potential interactions between bedaquiline and efavirenz, nevirapine, ritonavir-boosted lopinavir, rifampicin and rifapentine were analyzed with a model-based approach. Substantial effects were detected in several cases and dose-adjustments mitigating the impact were suggested after simulations. The interaction effects of nevirapine and ritonavir-boosted lopinavir were also confirmed in patients with multidrug-resistant TB on long-term treatment combining the antiretrovirals and bedaquiline. Furthermore, the outcomes from model-based analysis were compared to results from conventional non-compartmental analysis in a simulation study. Non-compartmental analysis was found to consistently underpredict the interaction effect when most of the concentration-time profile was not observed, as commonly is the case for compounds with very long terminal half-life such as bedaquiline. To facilitate pooled analyses of individual patient data from multiple sources a structured development procedure was outlined and a fast diagnostic tool for extensions of the stochastic model components was developed. Pooled analyses of nevirapine and rifabutin pharmacokinetics were performed; the latter generating comprehensive dosing recommendations for combined administration of rifabutin and antiretroviral protease inhibitors. The work presented in this thesis demonstrates the usefulness of pharmacometric techniques to improve treatment of TB and especially contributes evidence to inform optimized dosing regimens of new and old anti-TB drugs in various clinical contexts.