Indiana University-Purdue University Indianapolis (IUPUI) / Proteins are the building blocks in a biological system. They interact with other proteins to make unique biological phenomenon. Protein-protein interactions play a valuable role in understanding the molecular mechanisms occurring in any biological system. Protein interaction databases are a rich source on protein interaction related information. They gather large amounts of information from published literature to enrich their data. Expert curators put in most of these efforts manually. The amount of accessible and publicly available literature is growing very rapidly. Manual annotation is a time consuming process. And with the rate at which available information is growing, it cannot be dealt with only manual curation. There need to be tools to process this huge amounts of data to bring out valuable gist than can help curators proceed faster. In case of extracting protein-protein interaction evidences from literature, just a mere mention of a certain protein by look-up approaches cannot help validate the interaction. Supporting protein interaction information with experimental evidence can help this cause. In this study, we are applying machine learning based classification techniques to classify and given protein interaction related document into an interaction detection method. We use biological attributes and experimental factors, different combination of which define any particular interaction detection method. Then using predicted detection methods, proteins identified using named entity recognition techniques and decomposing the parts-of-speech composition we search for sentences with experimental evidence for a protein-protein interaction. We report an accuracy of 75.1% with a F-score of 47.6% on a dataset containing 2035 training documents and 300 test documents.

Multiple-outcome longitudinal data are abundant in clinical investigations. For example, infections with different pathogenic organisms are often tested concurrently, and assessments are usually taken repeatedly over time. It is therefore natural to consider a multivariate modeling approach to accommodate the underlying interrelationship among the multiple longitudinally measured outcomes. This dissertation proposes a multivariate semiparametric modeling framework for such data. Relevant estimation and inference procedures as well as model selection tools are discussed within this modeling framework. The first part of this research focuses on the analytical issues concerning binary data. The second part extends the binary model to a more general situation for data from the exponential family of distributions. The proposed model accounts for the correlations across the outcomes as well as the temporal dependency among the repeated measures of each outcome within an individual. An important feature of the proposed model is the addition of a bivariate smooth function for the depiction of concurrent nonlinear and possibly interacting influences of two independent variables on each outcome. For model implementation, a general approach for parameter estimation is developed by using the maximum penalized likelihood method. For statistical inference, a likelihood-based resampling procedure is proposed to compare the bivariate nonlinear effect surfaces across the outcomes. The final part of the dissertation presents a variable selection tool to facilitate model development in practical data analysis. Using the adaptive least absolute shrinkage and selection operator (LASSO) penalty, the variable selection tool simultaneously identifies important fixed effects and random effects, determines the correlation structure of the outcomes, and selects the interaction effects in the bivariate smooth functions. Model selection and estimation are performed through a two-stage procedure based on an expectation-maximization (EM) algorithm. Simulation studies are conducted to evaluate the performance of the proposed methods. The utility of the methods is demonstrated through several clinical applications.

The ability to think critically is considered an essential skill of nursing graduates and competent nursing practice. Yet, the literature reports that teachers are having difficulty teaching for critical thinking and that critical thinking is lacking in new nursing graduates. This research study sought to identify barriers to the implementation of critical thinking teaching strategies by nursing faculty currently teaching in generic baccalaureate programs in Tennessee. Surveys were mailed to 262 nursing faculty; 194 were returned, and 175 were usable. Students' attitudes and expectations represented the single greatest barrier to the implementation of critical thinking teaching strategies, followed by time constraints and the perceived need to teach for content coverage. Recommendations to support and encourage faculty to teach for critical thinking are outlined.

Indiana University-Purdue University Indianapolis (IUPUI) / Anatomy A215: Basic Human Anatomy (Anat A215) is an undergraduate human anatomy course at Indiana University Bloomington (IUB) that serves as a requirement for many degree programs at IUB. The difficulty of the course, coupled with pressure to achieve grades for admittance into specific programs, has resulted in high remediation rates. In an attempt to help students to improve their study habits and metacognitive skills Medical Sciences M100: Improving Learning Skills in Anatomy (MSCI M100) was developed. MSCI M100 is an undergraduate course at IUB which is taught concurrently with Anat A215, with the hopes of promoting academic success in Anat A215. This multifaceted study was designed to analyze the factors associated with students who remediate Anat A215, to predict at-risk students in future semesters, and assess the effectiveness of MSCI M100. The first facet involved analysis of Anat A215 students’ demographic information and class performance data from the spring semester of 2004 through the spring semester of 2010. Results of data analysis can be used by IUB instructors and academic advisors to identify students at risk for remediating, as well as provide other undergraduate anatomy instructors across the U.S. with potential risk factors associated with remediation. The second facet of this research involved analyzing MSCI M100 course assignments to determine if there are improvements in student study habits and metacognitive skills. This investigation involved quantitative analysis of study logs and a learning attitudes survey, as well as a thorough inductive analysis of students’ weekly journal entries. Lastly, Anat A215 exam scores and final course grades for students who completed MSCI M100 and students who did not complete MSCI M100 were compared. Results from these analyses show promising improvements in students’ metacognition and study habits, but further research will better demonstrate the efficacy of MSCI M100.