Monitoring of Transmission of Tuberculosis between Wild Boars and Cattle: Genotypical Analysis of Strains by Molecular Epidemiology Techniques

An epidemiological survey for the monitoring of bovine tuberculosis transmission was carried out in western Liguria, a region in northern Italy. Fifteen Mycobacterium bovis strains were isolated from 63 wild boar samples (62 from mandibular lymph nodes and 1 from a liver specimen). Sixteen mediastinal lymph nodes of 16 head of cattle were collected, and 15 Mycobacterium bovis strains were subsequently cultured. All M. bovisstrains isolated from cattle and wild boars were genotyped by spoligotyping and by restriction fragment length polymorphism (RFLP) analysis with the IS6110 and IS1081 probes. All M. bovis strains showed the typical spoligotype characterized by the absence of the 39 to 43 spacers in comparison with the number in M. tuberculosis. A total of nine different clusters were identified by spoligotyping. The largest cluster included 9 strains isolated from wild boars and 11 strains isolated from cattle, thus confirming the possibility of transmission between the two animal species. Fingerprinting by RFLP analysis with the IS6110 probe showed an identical single-band pattern for 29 of 30 strains analyzed, and only 1 strain presented a five-band pattern. The use of IS1081 as a second probe was useful for differentiation of M. bovis from M. bovis BCG but not for differentiation among M. bovis strains, which presented the same undifferentiated genomic profile. In relation to the epidemiological investigation, we hypothesized that the feeding in pastures contaminated by cattle discharges could represent the most probable route of transmission of M. bovis between the two animal species. In conclusion, our results confirmed the higher discriminatory power of spoligotyping in relation to that of RFLP analysis for the differentiation of M. bovis genomic profiles. Our data showed the presence of a common M. bovis genotype in both cattle and wild boars, confirming the possible interspecies transmission of M. bovis.

Estimating the number of whales entering trade using DNA profiling and capture-recapture analysis of market products

Surveys of commercial markets combined with molecular taxonomy (i.e. molecular monitoring) provide a means to detect products from illegal, unregulated and/or unreported (IUU) exploitation, including the sale of fisheries bycatch and wild meat (bushmeat). Capture-recapture analyses of market products using DNA profiling have the potential to estimate the total number of individuals entering the market. However, these analyses are not directly analogous to those of living individuals because a ‘market individual’ does not die suddenly but, instead, remains available for a time in decreasing quantities, rather like the exponential decay of a radioactive isotope. Here we use mitochondrial DNA (mtDNA) sequences and microsatellite genotypes to individually identify products from North Pacific minke whales (Balaenoptera acutorostrata ssp.) purchased in 12 surveys of markets in the Republic of (South) Korea from 1999 to 2003. By applying a novel capture-recapture model with a decay rate parameter to the 205 unique DNA profiles found among 289 products, we estimated that the total number of whales entering trade across the five-year survey period was 827 (SE, 164; CV, 0.20) and that the average ‘half-life’ of products from an individual whale on the market was 1.82 months (SE, 0.24; CV, 0.13). Our estimate of whales in trade (reflecting the true numbers killed) was significantly greater than the officially reported bycatch of 458 whales for this period. This unregulated exploitation has serious implications for the survival of this genetically distinct coastal population. Although our capture-recapture model was developed for specific application to the Korean whale-meat markets, the exponential decay function could be modified to improve the estimates of trade in other wildmeat or fisheries markets or abundance of living populations by noninvasive genotyping.

Predicting Accurate and Actionable Static Analysis Warnings: An Experimental Approach

Static analysis tools report software defects that may or may not be detected by other verification methods. Two challenges complicating the adoption of these tools are spurious false positive warnings and legitimate warnings that are not acted on. This paper reports automated support to help address these challenges using logistic regression models that predict the foregoing types of warnings from signals in the warnings and implicated code. Because examining many potential signaling factors in large software development settings can be expensive, we use a screening methodology to quickly discard factors with low predictive power and cost-effectively build predictive models. Our empirical evaluation indicates that these models can achieve high accuracy in predicting accurate and actionable static analysis warnings, and suggests that the models are competitive with alternative models built without screening.