Pathogens in Milk: Enterobacter Species. Reference Module in Food Sciences.

Enterobacter species commonly occur in the environment and are recognized as opportunistic human pathogens in clinical settings. However, with the exception of Enterobacter sakazakii (Cronobacter), Enterobacter species are not normally considered foodborne pathogens. Cronobacter are particularly associated with illness in infants, particularly within the first 3 months after birth. Therefore, although Cronobacter are found in a wide range of fresh and dried food materials, it is their contamination of the infant formula production chain that is the major cause for concern. Cronobacter are noted for their ability to survive during desiccation and their persistence in dried infant food for at least 2 years.

Non-stationary variogram models for geostatistical sampling optimisation: An empirical investigation using elevation data

A problem with use of the geostatistical Kriging error for optimal sampling design is that the design does not adapt locally to the character of spatial variation. This is because a stationary variogram or covariance function is a parameter of the geostatistical model. The objective of this paper was to investigate the utility of non-stationary geostatistics for optimal sampling design. First, a contour data set of Wiltshire was split into 25 equal sub-regions and a local variogram was predicted for each. These variograms were fitted with models and the coefficients used in Kriging to select optimal sample spacings for each sub-region. Large differences existed between the designs for the whole region (based on the global variogram) and for the sub-regions (based on the local variograms). Second, a segmentation approach was used to divide a digital terrain model into separate segments. Segment-based variograms were predicted and fitted with models. Optimal sample spacings were then determined for the whole region and for the sub-regions. It was demonstrated that the global design was inadequate, grossly over-sampling some segments while under-sampling others.

Miniaturization of electromagnetic band gap structures for mobile applications

It is well known that interference of the human body affects the performance of the antennas in mobile phone handsets. In this contribution, we investigate the use of miniaturized metallodielectric electromagnetic band gap (MEBG) structures embedded in the case of a mobile handset as a means of decoupling the antenna from the user's hand. The closely coupled MEBG concept is employed to achieve miniaturization of the order of 15:1. Full wave dispersion relations for planar closely coupled MEBG arrays are presented and are validated experimentally. The performance of a prototype handset with an embedded conformal MEBG is assessed experimentally and is compared to a similar prototype without the MEBG. Reduction in the detuning of the antenna because of the human hand by virtue of the MEBG is demonstrated. Moreover, the efficiency of the handset when loaded with a human hand model is shown to improve when the MEBG is in place. The improvements are attributed to the decoupling of the antenna from the user's hand, which is achieved by means of suppressing the fields in the locality of the hand.

Increasing the accuracy of Nitrogen Dioxide (NO2) pollution mapping using geographically weighted regression (GWR) and geostatistics.

Nitrogen Dioxide (NO2) is known to act as an environmental trigger for many respiratory illnesses. As a pollutant it is difficult to map accurately, as concentrations can vary greatly over small distances. In this study three geostatistical techniques were compared, producing maps of NO2 concentrations in the United Kingdom (UK). The primary data source for each technique was NO2 point data, generated from background automatic monitoring and background diffusion tubes, which are analysed by different laboratories on behalf of local councils and authorities in the UK. The techniques used were simple kriging (SK), ordinary kriging (OK) and simple kriging with a locally varying mean (SKlm). SK and OK make use of the primary variable only. SKlm differs in that it utilises additional data to inform prediction, and hence potentially reduces uncertainty. The secondary data source was Oxides of Nitrogen (NOx) derived from dispersion modelling outputs, at 1km x 1km resolution for the UK. These data were used to define the locally varying mean in SKlm, using two regression approaches: (i) global regression (GR) and (ii) geographically weighted regression (GWR). Based upon summary statistics and cross-validation prediction errors, SKlm using GWR derived local means produced the most accurate predictions. Therefore, using GWR to inform SKlm was beneficial in this study.