Comparison of Gross Pathology, Histopathology, and Mycobacterial Culture for the Diagnosis of Tuberculosis in Elk (Cervus elaphus)

Using the isolation of Mycobacterium bovis as the reference standard, this study evaluated the sensitivity, specificity and kappa statistic of gross pathology (abattoir postmortem inspection), histopathology, and parallel or series combinations of the two for the diagnosis of tuberculosis in 430 elk and red deer. Two histopathology interpretations were evaluated: histopathology I, where the presence of lesions compatible with tuberculosis was considered positive, and histopathology II, where lesions compatible with tuberculosis or a select group of additional possible diagnoses were considered positive. In the 73 animals from which M. bovis was isolated, gross lesions of tuberculosis were most often in the lung (48), the retropharyngeal lymph nodes (36), the mesenteric lymph nodes (35), and the mediastinal lymph nodes (16). Other mycobacterial isolates included: 11 M. paratuberculosis, 11 M. avium, and 28 rapidly growing species or M. terrae complex. The sensitivity estimates of gross pathology and histopathology I were 93% (95% confidence limits [CL] 84,97%) and 88% [CL 77,94%], respectively, and the specificity of both was 89% [CL 85,92%]). The sensitivity and specificity of histopathology II were 89% (CL 79,95%) and 77% (CL 72,81%), respectively. The highest sensitivity estimates (93- 95% [CL 84,98%]) were obtained by interpreting gross pathology and histopathology in parallel (where an animal had to be positive on at least one of the two, to be classified as combination positive). The highest specificity estimates (94-95% [CL 91-97%]) were generated when the two tests were interpreted in series (an animal had to be positive on both tests to be classified as combination positive). The presence of gross or microscopic lesions showed moderate to good agreement with the isolation of M. bovis (Kappa = 65-69%). The results show that post-mortem inspection, histopathology and culture do not necessarily recognize the same infected animals and that the spectra of animals identified by the tests overlaps.

Linkage & Selective Mapping and QTL Analysis in a Harosoy x Clark population of 300 RILs

Four of the 12 major Glycine max ancestors of all modern elite U.S.A. soybean cultivars were the grandparents of Harosoy and Clark, so a Harosoy x Clark population would include some of that genetic diversity. A mating of eight Harosoy and eight Clark plants generated eight F1 plants. The eight F1:2 families were advanced via a plant-to-row selfing method to produce 300 F6-derived RILs that were genotyped with 266 SSR, 481 SNP, and 4 classical markers. SNPs were genotyped with the Illumina 1536-SNP assay. Three linkage maps, SSR, SNP, and SSR-SNP, were constructed with a genotyping error of < 1 %. Each map was compared with the published soybean consensus map. The best subset of 94 RILs for a high-resolution framework (joint) map was selected based on the expected bin length statistic computed with MapPop. The QTLs of seven traits measured in a 2-year replicated performance trial of the 300 RILs were identified using composite interval mapping (CIM) and multiple-interval mapping (MIM). QTL x Year effects in multiple trait analysis were compared with results of multiple-interval mapping. QTL x QTL effects were identified in MIM.

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.