Comment on 'Implications on clinical scenario of gold nanoparticle radiosensitization in regards to photon energy, nanoparticle size, concentration and location'

A recent paper by Lechtman et al (2011 Phys. Med. Biol. 56 4631-47) presented Monte Carlo modelling of gold nanoparticle dose modification. In it, they predict that the introduction of gold nanoparticles has the strongest effect with x-rays at kilovoltage energies, and that negligible increases in dose are expected at megavoltage energies. While these results are in agreement with others in the literature (including those produced by our group), the conclusion that '(goldnanoparticle) radiosensitization using a 6 MV photon source is not clinically feasible' appears to conflict with recently published experimental studies which have shown radiosensitization using 6 MV x-ray sources with relatively low gold concentrations. The increasing disparity between theoretical predictions of dose enhancement and experimental results in the field of gold nanoparticle radiosensitization suggests that, while the ability of gold nanoparticles to modify dose within a tumour volume is well understood, the resulting radiosensitization is not simply correlated with this measure. This highlights the need to validate theoretical predictions of this kind against experimental measurements, to ensure that the scenarios and values being modelled are meaningful within a therapeutic context.

A Capabilities-Aware Framework for Using Computational Accelerators in Data-Intensive Computing

Multicore computational accelerators such as GPUs are now commodity components for highperformance computing at scale. While such accelerators have been studied in some detail as stand-alone computational engines, their integration in large-scale distributed systems raises new challenges and trade-offs. In this paper, we present an exploration of resource management alternatives for building asymmetric accelerator-based distributed systems. We present these alternatives in the context of a capabilities-aware framework for data-intensive computing, which uses an enhanced implementation of the MapReduce programming model for accelerator-based clusters, compared to the state of the art. The framework can transparently utilize heterogeneous accelerators for deriving high performance with low programming effort. Our work is the first to compare heterogeneous types of accelerators, GPUs and a Cell processors, in the same environment and the first to explore the trade-offs between compute-efficient and control-efficient accelerators on data-intensive systems. Our investigation shows that our framework scales well with the number of different compute nodes. Furthermore, it runs simultaneously on two different types of accelerators, successfully adapts to the resource capabilities, and performs 26.9% better on average than a static execution approach.

Dynamic global security-aware synthesis using System-C

A dynamic global security-aware synthesis flow using the SystemC language is presented. SystemC security models are first specified at the system or behavioural level using a library of SystemC behavioural descriptions which provide for the reuse and extension of security modules. At the core of the system is incorporated a global security-aware scheduling algorithm which allows for scheduling to a mixture of components of varying security level. The output from the scheduler is translated into annotated nets which are subsequently passed to allocation, optimisation and mapping tools for mapping into circuits. The synthesised circuits incorporate asynchronous secure power-balanced and fault-protected components. Results show that the approach offers robust implementations and efficient security/area trade-offs leading to significant improvements in turnover.

Fast Retinal Vessel Detection and Measurement Using Wavelets and Edge Location Refinement

The relationship between changes in retinal vessel morphology and the onset and progression of diseases such as diabetes, hypertension and retinopathy of prematurity (ROP) has been the subject of several large scale clinical studies. However, the difficulty of quantifying changes in retinal vessels in a sufficiently fast, accurate and repeatable manner has restricted the application of the insights gleaned from these studies to clinical practice. This paper presents a novel algorithm for the efficient detection and measurement of retinal vessels, which is general enough that it can be applied to both low and high resolution fundus photographs and fluorescein angiograms upon the adjustment of only a few intuitive parameters. Firstly, we describe the simple vessel segmentation strategy, formulated in the language of wavelets, that is used for fast vessel detection. When validated using a publicly available database of retinal images, this segmentation achieves a true positive rate of 70.27%, false positive rate of 2.83%, and accuracy score of 0.9371. Vessel edges are then more precisely localised using image profiles computed perpendicularly across a spline fit of each detected vessel centreline, so that both local and global changes in vessel diameter can be readily quantified. Using a second image database, we show that the diameters output by our algorithm display good agreement with the manual measurements made by three independent observers. We conclude that the improved speed and generality offered by our algorithm are achieved without sacrificing accuracy. The algorithm is implemented in MATLAB along with a graphical user interface, and we have made the source code freely available. 

SLA Brokering and Bandwidth Reservation Negotiation Schemes for QoS-aware Internet

We present a novel Service Level Agreement (SLA)-driven service provisioning architecture, which enables dynamic and flexible bandwidth reservation schemes on a per-user or per-application basis. Various session level SLA negotiation schemes involving bandwidth allocation, service start time and service duration parameters are introduced and analyzed. The results show that these negotiation schemes can be utilized for the benefit of both end users and network providers in achieving the highest individual SLA optimization in terms of key Quality of Service (QoS) metrics and price. The inherent characteristics of software agents such as autonomy, adaptability and social abilities offer many advantages in this dynamic, complex, and distributed network environment especially when performing Service Level Agreements (SLA) definition negotiations and brokering tasks. This article also presents a service broker prototype based on Fujitsu's Phoenix Open Agent Mediator (OAM) agent technology, which was used to demonstrate a range of SLA brokering scenarios.