Discriminant analysis is a procedure for identifying the relationships between qualitative criterion variables and quantitative predictor variables. Data bases of genetic polymorphisms are currently available that group such polymorphisms by ethnic origin or nationality. Such information could be useful to entities that base financial determinations upon predictions of disease or to medical researchers who wish to target prevention and treatment to population groups. While the use of genetic information to make such determinations is unlawful in states and confidentiality and privacy concerns abound, methods for human “redlining” may occur. Thus, it is necessary to investigate the efficacy of the relationship of certain genetic information to ethnicity to determine if a statistical analysis can provide information concerning such relationship. The use of the statistical technique of discriminant analysis provides a tool for examining such relationship.

This study focused on the effectiveness of integrating traditional plant community analyses with landscape ecological analyses based on remotely sensed data. A temporal analysis of plant community diversity was conducted for major plant communities of the Ray Roberts Lake area using transect monitoring data collected between 1987 and 1997. Landscape analyses were performed with FRAGSTATS*ARC using classified SPOT satellite imagery for 1987 and 1997. Although the methodology developed in this work was exploratory, it was found that characterizing the dynamics of major plant communities in the study area produced a more effective and insightful analysis of Ray Roberts Lake area landscape dynamics.

This syllabus includes both lecture notes and laboratory activities for a two-semester anatomy and physiology community college course. The syllabus is based on a 16-week semester that is comprised of a three-hour lecture section and a one-hour laboratory class each week. Both the lecture course and laboratory are necessary to fulfill the requirement for anatomy and physiology. Laboratory activities coincide with lectures to enhance understanding of each topic by providing visual and hands-on experiments for the concepts learned in the lecture. Laboratory quizzes will be given each week to help students maintain a working knowledge of the material learned in the laboratory. This course is appropriate for the typical anatomy and physiology student and should benefit both students who plan to major in biology and also those who are non-biology majors. Because subject matter in anatomy and physiology is quite difficult, the importance of attendance and good study skills is stressed.

This dissertation comprises three parts and is presented in two chapters. Chapter 1 concerns Arthrobacter, a bacterium with an intriguing growth cycle. Whereas most bacteria exist as either a rod or coccus, this bacterium shares the rod/coccus lifestyle. It therefore seemed important to examine the growth regulatory pathways from the rod and coccus. The committed step, that catalyzed by aspartate transcarbamoylase (ATCase), in the pyrimidine biosynthetic pathway was chosen. The ATCase in Arthrobacter is like the well known Pseudomonas enzyme except that it has an active dihydroorotase (DHOase) associated. Included in Chapter 1 is the description of a microorganism, Burkholderia cepacia, whose ATCase has characteristics that are at once reminiscent of bacteria, mammals, and fungi. It differs in size or aggregation based on environmental conditions. In addition, it has an active DHOase associated with the ATCase, like Arthrobacter. B. cepacia is important both medically and for bioremediation. Since B. cepacia is resistant to most antibiotics, its unique ATCase is a prime target for inhibition. Whereas the first chapter deals with the de novo pathway to making pyrimidines, which is found mainly in the lag and log phase, Chapter 2 addresses the salvage pathway, which comes more into play during the …

On-road remote sensing has the ability to operate in real-time, and under real world conditions, making it an ideal candidate for detecting gross polluters on major freeways and thoroughfares. In this study, remote sensing was employed to detect carbon monoxide (CO), hydrocarbons (HC), and nitrogen oxide (NO). On-road remote sensing data taken from measurements performed in six states, (Arizona, California, Colorado, Illinois, Texas, and Utah) were cleaned and analyzed. Data mining and exploration were first undertaken in order to search for relationships among variables such as make, year, engine type, vehicle weight, and location. Descriptive statistics were obtained for the three pollutants of interest. The data were found to have non-normal distributions. Applied transformations were ineffective, and nonparametric tests were applied. Due to the extremely large sample size of the dataset (508,617 records), nonparametric tests resulted in "p" values that demonstrated "significance." The general linear model was selected due to its ability to handle data with non-normal distributions. The general linear model was run on each pollutant with output producing descriptive statistics, profile plots, between-subjects effects, and estimated marginal means. Due to insufficient data within certain cells, results were not obtained for gross vehicle weight and engine type. The "year" …