A Field-Test of Rhodamine B as a Biomarker in Raccoons

Rhodamine B is a dye that when ingested results in fluorescent bands in growing hair and whiskers of many mammals. Previous research at Wildlife Services’ (WS) National Wildlife Research Center (NWRC) found that rhodamine B is a successful biomarker in raccoon whiskers and that raccoons do not have a taste aversion to the dye when it comprises ≤ 3% of a bait. Our study assessed the ease of bait distribution, whisker collection, and evaluation of the biomarker for potential use in the Oral Rabies Vaccination (ORV) program administered by the WS National Rabies Management Program (NRMP). In collaboration with WS operations personnel from Ohio and Michigan, 750 fishmeal polymer baits each containing 150 mg of rhodamine B were hand distributed at NASA’s Plum Brook Station, Sandusky, Ohio in the summer of 2008. Four weeks after baits were distributed whiskers from 162 raccoons were collected. Wildlife Services biologists and technicians evaluated the whiskers for fluorescence using a handheld UV magnifying lamp. Biologists then sent the whiskers to the NWRC, Ft. Collins, Colorado for confirmation of fluorescence under a UV microscope. Results suggest a high level of agreement between the two methods of evaluation. Surveys completed by biologists confirmed that the ease of use, less invasive sampling techniques and promptness of results obtained through the use of rhodamine B are advantageous to the tetracycline biomarker presently used by the ORV program. All participants recommended further evaluation of rhodamine B for its inclusion in future efforts requiring biomarker evaluation.

Incorporating Varying Test Costs and Fault Severities into Test Case Prioritization

Test case prioritization techniques schedule test cases for regression testing in an order that increases their ability to meet some performance goal. One performance goal, rate offault detection, measures how quickly faults are detected within the testing process. In previous work we provided a metric, APFD, for measuring rate of fault detection, and techniques for prioritizing test cases to improve APFD, and reported the results of experiments using those techniques. This metric and these techniques, however, applied only in cases in which test costs and fault severity are uniform. In this paper, we present a new metric for assessing the rate of fault detection of prioritized test cases, that incorporates varying test case and fault costs. We present the results of a case study illustrating the application of the metric. This study raises several practical questions that might arise in applying test case prioritization; we discuss how practitioners could go about answering these questions.

Association of Borrelia garinii and B. valaisiana with Songbirds in Slovakia

In Europe, 6 of the 11 genospecies of Borrelia burgdorferi sensu lato are prevalent in questing Ixodes ricinus ticks. In most parts of Central Europe, B. afzelii, B. garinii, and B. valaisiana are the most frequent species, whereas B. burgdorferi sensu stricto, B. bissettii, and B. lusitaniae are rare. Previously, it has been shown that B. afzelii is associated with European rodents. Therefore, the aim of this study was to identify reservoir hosts of B. garinii and B. valaisiana in Slovakia. Songbirds were captured in a woodland near Bratislava and investigated for engorged ticks. Questing I. ricinus ticks were collected in the same region. Both tick pools were analyzed for spirochete infections by PCR, followed by DNA-DNA hybridization and, for a subsample, by nucleotide sequencing. Three of the 17 captured songbird species were infested with spirochete-infected ticks. Spirochetes in ticks that had fed on birds were genotyped as B. garinii and B. valaisiana, whereas questing ticks were infected with B. afzelii, B. garinii, and B. valaisiana. Furthermore, identical ospA alleles of B. garinii were found in ticks that had fed on the birds and in questing ticks. The data show that songbirds are reservoir hosts of B. garinii and B. valaisiana but not of B. afzelii. This and previous studies confirm that B. burgdorferi sensu lato is host associated and that this bacterial species complex contains different ecotypes.

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.