Variable-number tandem repeats genotyping used to aid and inform management strategies for a bovine Johne's disease incursion in tropical and subtropical Australia

The application of variable-number tandem repeats (VNTR) genotyping of Mycobacterium avium subsp. paratuberculosis isolates to assist in investigating incidents of bovine Johne’s disease in a low-prevalence region of Australia is described in the current study. Isolates from a response to detection of bovine Johne’s disease in Queensland were compared with strains from national and international sources. The tandem application of mycobacterial interspersed repetitive unit (MIRU) and multilocus short sequence repeats (MLSSR) genotyping identified 2 strains, 1 that infected cattle on multiple properties with trace-forward histories from a common infected property, and 1 genotypically different strain recovered from a single property. The former strain showed an identical genotype to an isolate from India. Neither strain showed a genotypic link to regions of Australia with a higher prevalence of the disease. Genotyping has indicated incursions from 2 independent sources. This intelligence has informed investigations into potential routes of entry and the soundness of ongoing control measures, and supported strategy and policy decisions regarding management of Mycobacterium avium subsp. paratuberculosis incursions for Queensland.

Culture phenotypes of genomically and geographically diverse Mycobacterium avium subsp. paratuberculosis isolates from different hosts

Mycobacterium avium subsp. paratuberculosis causes paratuberculosis (Johne's disease) in ruminants in most countries. Historical data suggest substantial differences in culturability of M. avium subsp. paratuberculosis isolates from small ruminants and cattle; however, a systematic comparison of culture media and isolates from different countries and hosts has not been undertaken. Here, 35 field isolates from the United States, Spain, Northern Ireland, and Australia were propagated in Bactec 12B medium and Middlebrook 7H10 agar, genomically characterized, and subcultured to Lowenstein-Jensen (LJ), Herrold's egg yolk (HEY), modified Middlebrook 7H10, Middlebrook 7H11, and Watson-Reid (WR) agars, all with and without mycobactin J and some with sodium pyruvate. Fourteen genotypes of M. avium subsp. paratuberculosis were represented as determined by BstEII IS900 and IS1311 restriction fragment length polymorphism analysis. There was no correlation between genotype and overall culturability, although most S strains tended to grow poorly on HEY agar. Pyruvate was inhibitory to some isolates. All strains grew on modified Middlebrook 7H10 agar but more slowly and less prolifically on LJ agar. Mycobactin J was required for growth on all media except 7H11 agar, but growth was improved by the addition of mycobactin J to 7H11 agar. WR agar supported the growth of few isolates. The differences in growth of M. avium subsp. paratuberculosis that have historically been reported in diverse settings have been strongly influenced by the type of culture medium used. When an optimal culture medium, such as modified Middlebrook 7H10 agar, is used, very little difference between the growth phenotypes of diverse strains of M. avium subsp. paratuberculosis was observed. This optimal medium is recommended to remove bias in the isolation and cultivation of M. avium subsp. paratuberculosis.

Weather variability, tides, and Barmah Forest Virus Disease in the Gladstone region, Australia

In this study we examined the impact of weather variability and tides on the transmission of Barmah Forest virus (BFV) disease and developed a weather-based forecasting model for BFV disease in the Gladstone region, Australia. We used seasonal autoregressive integrated moving-average (SARIMA) models to determine the contribution of weather variables to BFV transmission after the time-series data of response and explanatory variables were made stationary through seasonal differencing. We obtained data on the monthly counts of BFV cases, weather variables (e.g., mean minimum and maximum temperature, total rainfall, and mean relative humidity), high and low tides, and the population size in the Gladstone region between January 1992 and December 2001 from the Queensland Department of Health, Australian Bureau of Meteorology, Queensland Department of Transport, and Australian Bureau of Statistics, respectively. The SARIMA model shows that the 5-month moving average of minimum temperature (β = 0.15, p-value < 0.001) was statistically significantly and positively associated with BFV disease, whereas high tide in the current month (β = −1.03, p-value = 0.04) was statistically significantly and inversely associated with it. However, no significant association was found for other variables. These results may be applied to forecast the occurrence of BFV disease and to use public health resources in BFV control and prevention.

Cross-correlations between weather variables in Australia

The information on climate variations is essential for the research of many subjects, such as the performance of buildings and agricultural production. However, recorded meteorological data are often incomplete. There may be a limited number of locations recorded, while the number of recorded climatic variables and the time intervals can also be inadequate. Therefore, the hourly data of key weather parameters as required by many building simulation programmes are typically not readily available. To overcome this gap in measured information, several empirical methods and weather data generators have been developed. They generally employ statistical analysis techniques to model the variations of individual climatic variables, while the possible interactions between different weather parameters are largely ignored. Based on a statistical analysis of 10 years historical hourly climatic data over all capital cities in Australia, this paper reports on the finding of strong correlations between several specific weather variables. It is found that there are strong linear correlations between the hourly variations of global solar irradiation (GSI) and dry bulb temperature (DBT), and between the hourly variations of DBT and relative humidity (RH). With an increase in GSI, DBT would generally increase, while the RH tends to decrease. However, no such a clear correlation can be found between the DBT and atmospheric pressure (P), and between the DBT and wind speed. These findings will be useful for the research and practice in building performance simulation.