PERPEST Version 1.0, manual and technical description; a model that predicts the ecological risks of pesticides in freshwater ecosystems

This report is a technical description and a user-manual of the PERPEST model, able to Predicts the Ecological Risks of PESTicides in freshwater ecosystems. This system predicts the effects of a particular concentration of a pesticide on various (community) endpoints, based on empirical data extracted from the literature. The method that it uses solves new problems (e.g., what is the effect of pesticide A?) by using past experience (e.g., published microcosm experiments). The database containing the `past experience` has been constructed by performing a review of freshwater model ecosystem studies evaluating the effects of pesticides. The PERPEST model searches for situations in the database which resemble the question case, based on relevant (toxicity) characteristics of the compound.The model is described in the scientific paper written by Van den Brink et al. (2002) and available via the enclosed CD-ROM and the website www.perpest.alterra.nl

PERPEST model, a case-based reasoning approach to predict ecological risks of pesticides

The present paper discusses PERPEST, a model that uses case-based reasoning to predict the effects of a particular concentration of a pesticide on a defined aquatic ecosystem, based on published information about the effects of pesticides on the structure and function of aquatic ecosystems as observed in semifield experiments. The case-based reasoning (CBR) system consists of the database containing this information and a search routine named weighted analogies predictio

PERPEST model, a case-based reasoning approach to predict ecological risks of pesticides

The present paper discusses PERPEST, a model that uses case-based reasoning to predict the effects of a particular concentration of a pesticide on a defined aquatic ecosystem, based on published information about the effects of pesticides on the structure and function of aquatic ecosystems as observed in semifield experiments. The case-based reasoning (CBR) system consists of the database containing this information and a search routine named weighted analogies predictio

MRSA CC398 in the pig production chain

In 2005, a distinct clone of methicillin resistant Staphylococcus aureus (MRSA CC398) was found in pigs and people in contact with pigs. The structure of the pig production chain in high technology pig husbandry enables pathogens to spread during animal trading, with an increasing prevalence in herds further down the chain. The objective of this study was to quantify the effect of the MRSA status of the supplying herd on the MRSA status of the receiving herd in order to gain more insight into the role of animal trading as a transmission route for MRSA CC398. Nasal samples (60–80 pigs per herd) were collected from 38 herds; in 20 herds, environmental samples were collected as well. Ten MRSA-positive herds (based on the results of nasal swabs of 10 individual pigs per herd) from a prior study were included in the data analysis. Herds were classified as MRSA positive if at least one sample tested positive. The 48 herds were part of 14 complete (40 herds) and 4 incomplete (8 herds) pig production chains. Fifty-six percent of the herds were classified as MRSA positive. MRSA-positive herds were observed at the start (breeding herds), middle (farrowing herds) and the end (finishing herds) of the pig production chain. All of the herds in 8 chains tested MRSA positive;, all of the herds in 5 chains tested MRSA negative and in the remaining 5 chains, MRSA-positive and MRSA-negative herds were detected. Seven spa types were found, which were all previously confirmed to belong to CC398. All of the isolates were susceptible to mupirocin, linezolid, rifampicin, fusidic acid and cotrimoxazole. Resistance against tetracycline, erythromycin and clindamycin was found in 100, 74 and 76% of the isolates, respectively. Seventy-nine percent of herds with a MRSA-positive supplier of pigs were MRSA positive, whereas 23% of herds with a MRSA-negative supplier were MRSA positive (OR = 10.8; 95% CI: 1.5–110.1; P = 0.011). The presence of entirely MRSA-positive and MRSA-negative chains and the strong association between the MRSA status of herds and their suppliers illustrates a large risk associated with purchasing pigs from MRSA-positive herds; a top-down strategy for future control programs is, therefore, a basic requirement. However, 23% of herds with a MRSA-negative supplier were MRSA positive and furthermore, 46% of the herds at the top of the pig production chain without a supplier tested MRSA positive. This underlined the need for the identification of additional risk factors for MRSA

MRSA CC398 in the pig production chain

In 2005, a distinct clone of methicillin resistant Staphylococcus aureus (MRSA CC398) was found in pigs and people in contact with pigs. The structure of the pig production chain in high technology pig husbandry enables pathogens to spread during animal trading, with an increasing prevalence in herds further down the chain. The objective of this study was to quantify the effect of the MRSA status of the supplying herd on the MRSA status of the receiving herd in order to gain more insight into the role of animal trading as a transmission route for MRSA CC398. Nasal samples (60–80 pigs per herd) were collected from 38 herds; in 20 herds, environmental samples were collected as well. Ten MRSA-positive herds (based on the results of nasal swabs of 10 individual pigs per herd) from a prior study were included in the data analysis. Herds were classified as MRSA positive if at least one sample tested positive. The 48 herds were part of 14 complete (40 herds) and 4 incomplete (8 herds) pig production chains. Fifty-six percent of the herds were classified as MRSA positive. MRSA-positive herds were observed at the start (breeding herds), middle (farrowing herds) and the end (finishing herds) of the pig production chain. All of the herds in 8 chains tested MRSA positive;, all of the herds in 5 chains tested MRSA negative and in the remaining 5 chains, MRSA-positive and MRSA-negative herds were detected. Seven spa types were found, which were all previously confirmed to belong to CC398. All of the isolates were susceptible to mupirocin, linezolid, rifampicin, fusidic acid and cotrimoxazole. Resistance against tetracycline, erythromycin and clindamycin was found in 100, 74 and 76% of the isolates, respectively. Seventy-nine percent of herds with a MRSA-positive supplier of pigs were MRSA positive, whereas 23% of herds with a MRSA-negative supplier were MRSA positive (OR = 10.8; 95% CI: 1.5–110.1; P = 0.011). The presence of entirely MRSA-positive and MRSA-negative chains and the strong association between the MRSA status of herds and their suppliers illustrates a large risk associated with purchasing pigs from MRSA-positive herds; a top-down strategy for future control programs is, therefore, a basic requirement. However, 23% of herds with a MRSA-negative supplier were MRSA positive and furthermore, 46% of the herds at the top of the pig production chain without a supplier tested MRSA positive. This underlined the need for the identification of additional risk factors for MRSA