Development of a Model of Natural Infection with Mycobacterium bovis in White-Tailed Deer

The objective of this study was to develop a suitable experimental model of natural Mycobacterium bovis infection in white-tailed deer (Odocoileus virginianus), describe the distribution and character of tuberculous lesions, and to examine possible routes of disease transmission. In October 1997, 10 mature female white-tailed deer were inoculated by intratonsilar instillation of 2 3 103 (low dose) or 2 3 105 (high dose) colony forming units (CFU) of M. bovis. In January 1998, deer were euthanatized, examined, and tissues were collected 84 to 87 days post inoculation. Possible routes of disease transmission were evaluated by culture of nasal, oral, tonsilar, and rectal swabs at various times during the study. Gross and microscopic lesions consistent with tuberculosis were most commonly seen in medial retropharyngeal lymph nodes and lung in both dosage groups. Other tissues containing tuberculous lesions included tonsil, trachea, liver, and kidney as well as lateral retropharyngeal, mandibular, parotid, tracheobronchial, mediastinal, hepatic, mesenteric, superficial cervical, and iliac lymph nodes. Mycobacterium bovis was isolated from tonsilar swabs from 8 of 9 deer from both dosage groups at least once 14 to 87 days after inoculation. Mycobacterium bovis was isolated from oral swabs 63 and 80 days after inoculation from one of three deer in the low dose group and none of four deer in the high dose group. Similarly, M. bovis was isolated from nasal swabs 80 and 85 days after inoculation in one of three deer from the low dose group and 63 and 80 days after inoculation from two of four deer in the high dose group. Intratonsilar inoculation with M. bovis results in lesions similar to those seen in naturally infected white-tailed deer; therefore, it represents a suitable model of natural infection. These results also indicate that M. bovis persists in tonsilar crypts for prolonged periods and can be shed in saliva and nasal secretions. These infected fluids represent a likely route of disease transmission to other animals or humans.

NUMERICAL AND ANALYTICAL VERIFICATION OF A MULTISCALE COMPUTATIONAL MODEL FOR IMPACT PROBLEMS IN HETEROGENEOUS VISCOELASTIC MATERIALS WITH EVOLVING DAMAGE

Composites are engineered materials that take advantage of the particular properties of each of its two or more constituents. They are designed to be stronger, lighter and to last longer which can lead to the creation of safer protection gear, more fuel efficient transportation methods and more affordable materials, among other examples. This thesis proposes a numerical and analytical verification of an in-house developed multiscale model for predicting the mechanical behavior of composite materials with various configurations subjected to impact loading. This verification is done by comparing the results obtained with analytical and numerical solutions with the results found when using the model. The model takes into account the heterogeneity of the materials that can only be noticed at smaller length scales, based on the fundamental structural properties of each of the composite’s constituents. This model can potentially reduce or eliminate the need of costly and time consuming experiments that are necessary for material characterization since it relies strictly upon the fundamental structural properties of each of the composite’s constituents. The results from simulations using the multiscale model were compared against results from direct simulations using over-killed meshes, which considered all heterogeneities explicitly in the global scale, indicating that the model is an accurate and fast tool to model composites under impact loads. Advisor: David H. Allen

Evaluating Measurement Invariance with Censored Ordinal Data: A Monte Carlo Comparison of Alternative Model Estimators and Scales of Measurement

Evaluations of measurement invariance provide essential construct validity evidence. However, the quality of such evidence is partly dependent upon the validity of the resulting statistical conclusions. The presence of Type I or Type II errors can render measurement invariance conclusions meaningless. The purpose of this study was to determine the effects of categorization and censoring on the behavior of the chi-square/likelihood ratio test statistic and two alternative fit indices (CFI and RMSEA) under the context of evaluating measurement invariance. Monte Carlo simulation was used to examine Type I error and power rates for the (a) overall test statistic/fit indices, and (b) change in test statistic/fit indices. Data were generated according to a multiple-group single-factor CFA model across 40 conditions that varied by sample size, strength of item factor loadings, and categorization thresholds. Seven different combinations of model estimators (ML, Yuan-Bentler scaled ML, and WLSMV) and specified measurement scales (continuous, censored, and categorical) were used to analyze each of the simulation conditions. As hypothesized, non-normality increased Type I error rates for the continuous scale of measurement and did not affect error rates for the categorical scale of measurement. Maximum likelihood estimation combined with a categorical scale of measurement resulted in more correct statistical conclusions than the other analysis combinations. For the continuous and censored scales of measurement, the Yuan-Bentler scaled ML resulted in more correct conclusions than normal-theory ML. The censored measurement scale did not offer any advantages over the continuous measurement scale. Comparing across fit statistics and indices, the chi-square-based test statistics were preferred over the alternative fit indices, and ΔRMSEA was preferred over ΔCFI. Results from this study should be used to inform the modeling decisions of applied researchers. However, no single analysis combination can be recommended for all situations. Therefore, it is essential that researchers consider the context and purpose of their analyses.