SUMMARYIn studies concerning the effect of antibiotics on faecal microflora, Colonization Resistance is an important parameter. Colonization Resistance correlates inversely with the number of different biotypes of Enterobacteriaceae isolated from faecal samples. Nine healthy volunteers were studied during 6 weeks, in order to determine the natural variation in the number of different biotypes of Enterobacteriaceae per faecal sample. The numbers of biotypes ranged from 1–15 per faecal sample, the mean number of biotypes varied between 2·6 and 7·3 different biotypes per faecal sample per healthy volunteer. Inter-individual variations of five biotypes in the mean number of biotypes per faecal sample are normal. We assessed the minimal number of faecal samples that should be taken for comprehensive biotyping so as to determine reliably the mean number of different biotypes representative for the Colonization Resistance of an individual. It was found that a minimum of four faecal samples was required.

SUMMARYThe present study has attempted to determine the colonization resistance (CR) of the digestive tract by biotyping Enterobacteriaceae in four faecal samples per subject of five different animal species as well as man. The results indicate that the degree of bacterial contamination with Enterobacteriaceae from the environment may strongly influence the outcome. Both conventionally living chicken and man, showed a much wider range of the 'confidence limits of the mean' of the mean number of biotypes per faecal sample between individual subjects, than was found between subjects maintained under laboratory circumstances. Yet there appeared a statistically significant difference in CR between some of the animal species as a group. Man did not differ from monkeys, however, both differed from the rodents species studied. Monkeys differed also from dogs and the latter from rodents. It is concluded that the CR measured by determining the mean number of biotypes of Enterobacteriaceae can only be used for accurate comparison of the CR between subjects, if the 'bacteriological environment' is known; i.e. the sources of contamination.

Abstract:Sensors have been used for many years to gather information about their environment. The number of sensors connected to the internet is increasing, which has led to a growing demand of data transport and storage capacity. In addition, evermore emphasis is put on processing the data to detect anomalous situations and to identify trends. This paper presents a sensor data analysis platform that executes statistical analysis programs on a cloud computing infrastructure. Compared to existing batch and stream processing platforms, it adds the notion of simulated time, i.e. time that differs from the actual, current time. Moreover, it adds the ability to dynamically schedule the analysis programs based on a single timestamp, recurring schedule, or on the sensor data itself.

In modern data-driven analysis it becomes quite typical to process not only the datasets you own, but to collaborate with other organizations to receive data and analysis results from them as well. It is performed to achieve much more accurate analysis results, make better predictions, and be able to provide better decision-support mechanisms. However, to analyze data in a cross-organizational environment is not the same as to analyze your own data: there are many limitations and conditions from the collaborators to allow access to their data and/or analysis models. This paper presents a methodology called CO-ARCH dealing with the process of choosing the suitable data-driven architectures for collaboration on data analysis between different organizations having their own conditions and limitations.

Using risk management systems for large-scale asset management is not without risk itself. Systems that collect measurement from a geographically diverse area, across many organisations, contain many interacting components that can fail in many different ways. In this chapter these systems are discussed from a risk assessment point of view, using practical examples. It provides suggestions how trust can propagate between interacting components of risk management systems by making information needed for risk assessment information explicit.