The aim of this study was to evaluate the effects of implemented control measures to reduce illness induced by Vibrio parahaemolyticus (V. parahaemolyticus) in horse mackerel (Trachurus japonicus), seafood that is commonly consumed raw in Japan. On the basis of currently available experimental and survey data, we constructed a quantitative risk model of V. parahaemolyticus in horse mackerel from harvest to consumption. In particular, the following factors were evaluated: bacterial growth at all stages, effects of washing the fish body and storage water, and bacterial transfer from the fish surface, gills, and intestine to fillets during preparation. New parameters of the beta-Poisson dose-response model were determined from all human feeding trials, some of which have been used for risk assessment by the U.S. Food and Drug Administration (USFDA). The probability of illness caused by V. parahaemolyticus was estimated using both the USFDA dose-response parameters and our parameters for each selected pathway of scenario alternatives: washing whole fish at landing, storage in contaminated water, high temperature during transportation, and washing fish during preparation. The last scenario (washing fish during preparation) was the most effective for reducing the risk of illness by about a factor of 10 compared to no washing at this stage. Risk of illness increased by 50% by exposure to increased temperature during transportation, according to our assumptions of duration and temperature. The other two scenarios did not significantly affect risk. The choice of dose-response parameters was not critical for evaluation of control measures.

Recent epidemiological data suggest a link between the consumption of bovine offal products and Shiga toxin-producing Escherichia coli (STEC) infection in Japan. This study thus examined the prevalence of STEC in various types of these foods. PCR screened 229 bovine offal products for the presence of Shiga toxin (stx) gene. Thirty-eight (16·6%) samples were stx positive, of which eight were positive for rfbE(O157) and three were positive for wzy(O26). Four O157 and one O26 STEC isolates were finally obtained from small-intestine and omasum products. Notably, homogenates of bovine intestinal products significantly reduced the extent of growth of O157 in the enrichment process compared to homogenates of beef carcass. As co-incubation of O157 with background microbiota complex from bovine intestinal products in buffered peptone water, in the absence of meat samples, tended to reduce the extent of growth of O157, we reasoned that certain microbiota present in offal products played a role. In support of this, inoculation of generic E. coli from bovine intestinal products into the homogenates significantly reduced the extent of growth of O157 in the homogenates of bovine intestinal and loin-beef products, and this effect was markedly increased when these homogenates were heat-treated prior to inoculation. Together, this report provides first evidence of the prevalence of STEC in a variety of bovine offal products in Japan. The prevalence data herein may be useful for risk assessment of those products as a potential source of human STEC infection beyond the epidemiological background. The growth characteristic of STEC O157 in offal products also indicates the importance of being aware when to test these food products.

Organizations related to infrastructure, such as utilities and railway companies, manage a large number of facilities, the failure of which can have a huge impact on society. The cost of maintaining these facilities is a combination of regular maintenance costs and urgent recovery costs. Generally, the urgent costs are much higher than regular costs. Regular maintenance work should result in fewer sudden failures, and thus reduce these urgent costs. However, if the regular maintenance is too frequent, its cost becomes too high. Therefore, it is important to balance the regular and urgent costs to minimize the overall maintenance cost. We propose a maintenance schedule optimization method based on the failure probability distribution of the facilities. The total cost is mathematically modeled, with the regular maintenance schedule included via decision variables and the occurrence of failures modeled as stochastic variables. The stochastic total maintenance costs are evaluated using a Monte Carlo method, and a genetic algorithm is employed to optimize the maintenance schedule. The proposed method is evaluated using data provided by a Japanese railway company, and our results confirm that the method produces an excellent maintenance schedule. A statistical test shows there is a significant difference between the proposed and conventional methods.