Exploring the biological basis for Salmonella persistence in food manufacturing environments

The persistence of Salmonella spp. in low moisture foods is a challenge for the food industry as despite control strategies already in place, notable outbreaks still occur. The aim of this study was to characterise isolates of Salmonella, known to be persistent in the food manufacturing environment, by comparing their microbiological characteristics with a panel of matched clinical and veterinary isolates. The gross morphology of the challenge panel was phenotypically characterised in terms of cellular size, shape and motility. In all the parameters measured, the factory isolates were indistinguishable from the human, clinical and veterinary strains. Further detailed metabolic profiling was undertaken using the biolog Microbial ID system. Multivariate analysis of the metabolic microarray revealed differences in metabolism of the factory isolate of S.Montevideo, based on its upregulated ability to utilise glucose and the sugar alcohol groups. The remainder of the serotype-matched isolates were metabolically indistinguishable. Temperature and humidity are known to influence bacterial survival and through environmental monitoring experimental parameters were defined. The results revealed Salmonella survival on stainless steel was affected by environmental temperatures that may be experienced in a food processing environment; with higher survival rates (D25=35.4) at temperatures at 25°C and lower humidity levels of 15% RH, however a rapid decline in cell count (D10=3.4) with lower temperatures of 10°C and higher humidity of 70% RH. Several resident factories strains survived in higher numbers on stainless steel (D25=29.69) compared to serotype matched clinical and veterinary isolates (D25=22.98). Factory isolates of Salmonella did not show an enhanced growth rate in comparison to serotype matched solates grown in Luria broth, Nutrient broth and M9 minimal media indicating that as an independent factor, growth was unlikely to be a major factor driving Salmonella persistence. Using a live / dead stain coupled with fluorescence microscopy revealed that when no longer culturable, isolates of S.Schwarzengrund entered into a viable nonculturable state. The biofilm forming capacity of the panel was characterised and revealed that all were able to form biofilms. None of the factory isolates showed an enhanced capability to form biofilms in comparison to serotype-matched isolates. In disinfection studies, planktonic cells were more susceptible to disinfectants than cells in biofilm and all the disinfectants tested were successful in reducing bacterial load. Contact time was one of the most important factors for reducing bacterial populations in a biofilm. The genomes of eight strains were sequenced. At the nucleotide and amino acid level the food factory isolates were similar to those of isolates from other environments; no major genomic rearrangements were observed, supporting the conclusions of the phenotypic and metabolic analysis. In conclusion, having investigated a variety of morphological, biochemical and genomic factors, it is unlikely that the persistence of Salmonella in the food manufacturing environment is attributable to a single phenotypic, metabolic or genomic factor. Whilst a combination of microbiological factors may be involved it is also possible that strain persistence in the factory environment is a consequence of failure to apply established hygiene management principles.

The development of an automated nano sampling handling system for nanometne protein crystallography experiments

As the world's synchrotrons and X-FELs endeavour to meet the need to analyse ever-smaller protein crystals, there grows a requirement for a new technique to present nano-dimensional samples to the beam for X-ray diffraction experiments.The work presented here details developmental work to reconfigure the nano tweezer technology developed by Optofluidics (PA, USA) for the trapping of nano dimensional protein crystals for X-ray crystallography experiments. The system in its standard configuration is used to trap nano particles for optical microscopy. It uses silicon nitride laser waveguides that bridge a micro fluidic channel. These waveguides contain 180 nm apertures of enabling the system to use biologically compatible 1.6 micron wavelength laser light to trap nano dimensional biological samples. Using conventional laser tweezers, the wavelength required to trap such nano dimensional samples would destroy them. The system in its optical configuration has trapped protein molecules as small as 10 nanometres.

Optical fiber sensors embedded in flexible polymer foils

In traditional electrical sensing applications, multiplexing and interconnecting the different sensing elements is a major challenge. Recently, many optical alternatives have been investigated including optical fiber sensors of which the sensing elements consist of fiber Bragg gratings. Different sensing points can be integrated in one optical fiber solving the interconnection problem and avoiding any electromagnetical interference (EMI). Many new sensing applications also require flexible or stretchable sensing foils which can be attached to or wrapped around irregularly shaped objects such as robot fingers and car bumpers or which can even be applied in biomedical applications where a sensor is fixed on a human body. The use of these optical sensors however always implies the use of a light-source, detectors and electronic circuitry to be coupled and integrated with these sensors. The coupling of these fibers with these light sources and detectors is a critical packaging problem and as it is well-known the costs for packaging, especially with optoelectronic components and fiber alignment issues are huge. The end goal of this embedded sensor is to create a flexible optical sensor integrated with (opto)electronic modules and control circuitry. To obtain this flexibility, one can embed the optical sensors and the driving optoelectronics in a stretchable polymer host material. In this article different embedding techniques for optical fiber sensors are described and characterized. Initial tests based on standard manufacturing processes such as molding and laser structuring are reported as well as a more advanced embedding technique based on soft lithography processing.

Design of an information system for metrology contents

Dimensional and form inspections are key to the manufacturing and assembly of products. Product verification can involve a number of different measuring instruments operated using their dedicated software. Typically, each of these instruments with their associated software is more suitable for the verification of a pre-specified quality characteristic of the product than others. The number of different systems and software applications to perform a complete measurement of products and assemblies within a manufacturing organisation is therefore expected to be large. This number becomes even larger as advances in measurement technologies are made. The idea of a universal software application for any instrument still appears to be only a theoretical possibility. A need for information integration is apparent. In this paper, a design of an information system to consistently manage (store, search, retrieve, search, secure) measurement results from various instruments and software applications is introduced. Two of the main ideas underlying the proposed system include abstracting structures and formats of measurement files from the data so that complexity and compatibility between different approaches to measurement data modelling is avoided. Secondly, the information within a file is enriched with meta-information to facilitate its consistent storage and retrieval. To demonstrate the designed information system, a web application is implemented. © Springer-Verlag Berlin Heidelberg 2010.

Collaborative filtering and deep learning based recommendation system for cold start items

Recommender system is a specific type of intelligent systems, which exploits historical user ratings on items and/or auxiliary information to make recommendations on items to the users. It plays a critical role in a wide range of online shopping, e-commercial services and social networking applications. Collaborative filtering (CF) is the most popular approaches used for recommender systems, but it suffers from complete cold start (CCS) problem where no rating record are available and incomplete cold start (ICS) problem where only a small number of rating records are available for some new items or users in the system. In this paper, we propose two recommendation models to solve the CCS and ICS problems for new items, which are based on a framework of tightly coupled CF approach and deep learning neural network. A specific deep neural network SADE is used to extract the content features of the items. The state of the art CF model, timeSVD++, which models and utilizes temporal dynamics of user preferences and item features, is modified to take the content features into prediction of ratings for cold start items. Extensive experiments on a large Netflix rating dataset of movies are performed, which show that our proposed recommendation models largely outperform the baseline models for rating prediction of cold start items. The two proposed recommendation models are also evaluated and compared on ICS items, and a flexible scheme of model retraining and switching is proposed to deal with the transition of items from cold start to non-cold start status. The experiment results on Netflix movie recommendation show the tight coupling of CF approach and deep learning neural network is feasible and very effective for cold start item recommendation. The design is general and can be applied to many other recommender systems for online shopping and social networking applications. The solution of cold start item problem can largely improve user experience and trust of recommender systems, and effectively promote cold start items.