947 resultados para POLARITY INDICATOR


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This study assessed the levels of two key pathogens, Salmonella and Campylobacter, along with the indicator organism Escherichia coli in aerosols within and outside poultry sheds. The study ranged over a 3-year period on four poultry farms and consisted of six trials across the boiler production cycle of around 55 days. Weekly testing of litter and aerosols was carried out through the cycle. A key point that emerged is that the levels of airborne bacteria are linked to the levels of these bacteria in litter. This hypothesis was demonstrated by E. coli. The typical levels of E. coli in litter were similar to 10(8) CFU g(-1) and, as a consequence, were in the range of 10(2) to 10(4) CFU m(-3) in aerosols, both inside and outside the shed. The external levels were always lower than the internal levels. Salmonella was only present intermittently in litter and at lower levels (10(3) to 10(5) most probable number [MPN] g(-1)) and consequently present only intermittently and at low levels in air inside (range of 0.65 to 4.4 MPN m(-3)) and once outside (2.3 MPN m(-3)). The Salmonella serovars isolated in litter were generally also isolated from aerosols and dust, with the Salmonella serovars Chester and Sofia being the dominant serovars across these interfaces. Campylobacter was detected late in the production cycle, in litter at levels of around 107 MPN g(-1). Campylobacter was detected only once inside the shed and then at low levels of 2.2 MPN m(-3). Thus, the public health risk from these organisms in poultry environments via the aerosol pathway is minimal.

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When exposed to hot (22-35 degrees C) and dry climatic conditions in the field during the final 4-6 weeks of pod filling, peanuts (Arachis hypogaea L.) can accumulate highly carcinogenic and immuno-suppressing aflatoxins. Forecasting of the risk posed by these conditions can assist in minimizing pre-harvest contamination. A model was therefore developed as part of the Agricultural Production Systems Simulator (APSIM) peanut module, which calculated an aflatoxin risk index (ARI) using four temperature response functions when fractional available soil water was <0.20 and the crop was in the last 0.40 of the pod-filling phase. ARI explained 0.95 (P <= 0.05) of the variation in aflatoxin contamination, which varied from 0 to c. 800 mu g/kg in 17 large-scale sowings in tropical and four sowings in sub-tropical environments carried out in Australia between 13 November and 16 December 2007. ARI also explained 0.96 (P <= 0.01) of the variation in the proportion of aflatoxin-contaminated loads (>15 mu g/kg) of peanuts in the Kingaroy region of Australia during the period between the 1998/99 and 2007/08 seasons. Simulation of ARI using historical climatic data from 1890 to 2007 indicated a three-fold increase in its value since 1980 compared to the entire previous period. The increase was associated with increases in ambient temperature and decreases in rainfall. To facilitate routine monitoring of aflatoxin risk by growers in near real time, a web interface of the model was also developed. The ARI predicted using this interface for eight growers correlated significantly with the level of contamination in crops (r=095, P <= 0.01). These results suggest that ARI simulated by the model is a reliable indicator of aflatoxin contamination that can be used in aflatoxin research as well as a decision-support tool to monitor pre-harvest aflatoxin risk in peanuts.