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.

Significance Driven Computation on Next-Generation Unreliable Platforms

In this paper, we propose a design paradigm for energy efficient and variation-aware operation of next-generation multicore heterogeneous platforms. The main idea behind the proposed approach lies on the observation that not all operations are equally important in shaping the output quality of various applications and of the overall system. Based on such an observation, we suggest that all levels of the software design stack, including the programming model, compiler, operating system (OS) and run-time system should identify the critical tasks and ensure correct operation of such tasks by assigning them to dynamically adjusted reliable cores/units. Specifically, based on error rates and operating conditions identified by a sense-and-adapt (SeA) unit, the OS selects and sets the right mode of operation of the overall system. The run-time system identifies the critical/less-critical tasks based on special directives and schedules them to the appropriate units that are dynamically adjusted for highly-accurate/approximate operation by tuning their voltage/frequency. Units that execute less significant operations can operate at voltages less than what is required for correct operation and consume less power, if required, since such tasks do not need to be always exact as opposed to the critical ones. Such scheme can lead to energy efficient and reliable operation, while reducing the design cost and overheads of conventional circuit/micro-architecture level techniques.

SHEPARD: Scheduling on heterogeneous platforms using application resource demands

Heterogeneous computing technologies, such as multi-core CPUs, GPUs and FPGAs can provide significant performance improvements. However, developing applications for these technologies often results in coupling applications to specific devices, typically through the use of proprietary tools. This paper presents SHEPARD, a compile time and run-time framework that decouples application development from the target platform and enables run-time allocation of tasks to heterogeneous computing devices. Through the use of special annotated functions, called managed tasks, SHEPARD approximates a task's performance on available devices, and coupled with the approximation of current device demand, decides which device can satisfy the task with the lowest overall execution time. Experiments using a task parallel application, based on an in-memory database, demonstrate the opportunity for automatic run-time task allocation to achieve speed-up over a static allocation to a single specific device. © 2014 IEEE.