Comparison of biosensor platforms for surface plasmon resonance based detection of paralytic shellfish toxins

Paralytic shellfish poisoning (PSP) toxins are produced by certain marine dinoflagellates and may accumulate in bivalve molluscs through filter feeding. The Mouse Bioassay (MBA) is the internationally recognised reference method of analysis, but it is prone to technical difficulties and regarded with increasing disapproval due to ethical reasons. As such, alternative methods are required. A rapid surface plasmon resonance (SPR) biosensor inhibition assay was developed to detect PSP toxins in shellfish by employing a saxitoxin polyclonal antibody (R895). Using an assay developed for and validated on the Biacore Q biosensor system, this project focused on transferring the assay to a high-throughput, Biacore T100 biosensor in another laboratory. This was achieved using a prototype PSP toxin kit and recommended assay parameters based on the Biacore Q method. A monoclonal antibody (GT13A) was also assessed. Even though these two instruments are based on SPR principles, they vary widely in their mode of operation including differences in the integrated mu-fluidic cartridges, autosampler system, and sensor chip compatibilities. Shellfish samples (n = 60), extracted using a simple, rapid procedure, were analysed using each platform, and results were compared to AOAC high performance liquid chromatography (HPLC) and MBA methods. The overall agreement, based on statistical 2 x 2 comparison tables, between each method ranged from 85% to 94.4% using R895 and 77.8% to 100% using GT13A. The results demonstrated that the antibody based assays with high sensitivity and broad specificity to PSP toxins can be applied to different biosensor platforms. (C) 2011 Elsevier B.V. All rights reserved.

Rapid Implementation and Optimisation of DSP Systems on SoPC Heterogeneous Platforms

The emergence of programmable logic devices as processing platforms for digital signal processing applications poses challenges concerning rapid implementation and high level optimization of algorithms on these platforms. This paper describes Abhainn, a rapid implementation methodology and toolsuite for translating an algorithmic expression of the system to a working implementation on a heterogeneous multiprocessor/field programmable gate array platform, or a standalone system on programmable chip solution. Two particular focuses for Abhainn are the automated but configurable realisation of inter-processor communuication fabrics, and the establishment of novel dedicated hardware component design methodologies allowing algorithm level transformation for system optimization. This paper outlines the approaches employed in both these particular instances.

Hydrogels as drug-delivery platforms:Physicochemical barriers and solutions

The properties of hydrogels, in particular their high biocompatibility and water sorption uptake, make hydrogels very attractive in drug delivery and biomedical devices. These favorable features of hydrogels are compromised by certain structural limitations such as those associated with their low mechanical strength in the swollen state. This review highlights the most important challenges that may seriously affect the practical implementation of hydrogels in clinical practice and the solutions that may be applied to overcome these limitations. © 2012 Future Science Ltd.

Application-based workload model for wireless sensor node computing platforms

Wireless sensor node platforms are very diversified and very constrained, particularly in power consumption. When choosing or sizing a platform for a given application, it is necessary to be able to evaluate in an early design stage the impact of those choices. Applied to the computing platform implemented on the sensor node, it requires a good understanding of the workload it must perform. Nevertheless, this workload is highly application-dependent. It depends on the data sampling frequency together with application-specific data processing and management. It is thus necessary to have a model that can represent the workload of applications with various needs and characteristics. In this paper, we propose a workload model for wireless sensor node computing platforms. This model is based on a synthetic application that models the different computational tasks that the computing platform will perform to process sensor data. It allows to model the workload of various different applications by tuning data sampling rate and processing. A case study is performed by modeling different applications and by showing how it can be used for workload characterization. © 2011 IEEE.