Does the Swedish Interactive Threshold Algorithm (SITA) accurately map visual field loss attributed to vigabatrin?

Background: Vigabatrin (VGB) is an anti-epileptic medication which has been linked to peripheral constriction of the visual field. Documenting the natural history associated with continued VGB exposure is important when making decisions about the risk and benefits associated with the treatment. Due to its speed the Swedish Interactive Threshold Algorithm (SITA) has become the algorithm of choice when carrying out Full Threshold automated static perimetry. SITA uses prior distributions of normal and glaucomatous visual field behaviour to estimate threshold sensitivity. As the abnormal model is based on glaucomatous behaviour this algorithm has not been validated for VGB recipients. We aim to assess the clinical utility of the SITA algorithm for accurately mapping VGB attributed field loss. Methods: The sample comprised one randomly selected eye of 16 patients diagnosed with epilepsy, exposed to VGB therapy. A clinical diagnosis of VGB attributed visual field loss was documented in 44% of the group. The mean age was 39.3 years∈±∈14.5 years and the mean deviation was -4.76 dB ±4.34 dB. Each patient was examined with the Full Threshold, SITA Standard and SITA Fast algorithm. Results: SITA Standard was on average approximately twice as fast (7.6 minutes) and SITA Fast approximately 3 times as fast (4.7 minutes) as examinations completed using the Full Threshold algorithm (15.8 minutes). In the clinical environment, the visual field outcome with both SITA algorithms was equivalent to visual field examination using the Full Threshold algorithm in terms of visual inspection of the grey scale plots, defect area and defect severity. Conclusions: Our research shows that both SITA algorithms are able to accurately map visual field loss attributed to VGB. As patients diagnosed with epilepsy are often vulnerable to fatigue, the time saving offered by SITA Fast means that this algorithm has a significant advantage for use with VGB recipients.

Investigation into visual defects attributed to Vigabatrin

Vigabatrin (VGB) is a transaminase inhibitor that elicits its anitepileptic effect by increasing GABA concentrations in the brain and retina. - Assess whether certain factors predispose patients to develop severe visual field loss. - Develop a sensitive algorithm for investigating the progression of visual field loss. - Determine the most sensitive clinical regimen for diagnosing VGB-attributed visual field loss. - Investigate whether the reports of central retinal sparing are accurate. The investigations have resulted in a number of significant findings: - The anatomical evidence in combination with the pattern of visual field loss suggests that the damage induced by VGB therapy occurs at retinal level, and is most likely a toxic effect. - The quantitative algorithm, designed within the course of this investigation, provided increased sensitivity in determining the severity of visual field loss. - Maximum VGB dose predisposes patients to develop severe visual field loss. - The SITA Standard algorithm was found to be as sensitive and significantly faster, in diagnosing visual field defects attributed to VGB, when compared to the Full Threshold algorithm. The Full Threshold was found to be the most repeatable between visits. - The normal SWAP 10-2 database provided an effective method of differentiating SWAP defects. - SWAP, FDT and the mfERG have increased sensitivity in detecting visual field loss attributed to VGB. The pattern of visual field loss from these investigations suggests that VGB produces a diffuse effect across the retina including subtle central abnormalities and more severe peripheral defects. - Abnormalities detected using the mfERG have suggested that VGB adversely affects the photoreceptors Müller, amacrine and ganglion cells in the retina. An urgent review of the manufacturers recommended maximum dose for VGB is required.

Genetics and competing strategies in a threshold model for mail processing

Multi-agent algorithms inspired by the division of labour in social insects are applied to a problem of distributed mail retrieval in which agents must visit mail producing cities and choose between mail types under certain constraints.The efficiency (i.e. the average amount of mail retrieved per time step), and the flexibility (i.e. the capability of the agents to react to changes in the environment) are investigated both in static and dynamic environments. New rules for mail selection and specialisation are introduced and are shown to exhibit improved efficiency and flexibility compared to existing ones. We employ a genetic algorithm which allows the various rules to evolve and compete. Apart from obtaining optimised parameters for the various rules for any environment, we also observe extinction and speciation. From a more theoretical point of view, in order to avoid finite size effects, most results are obtained for large population sizes. However, we do analyse the influence of population size on the performance. Furthermore, we critically analyse the causes of efficiency loss, derive the exact dynamics of the model in the large system limit under certain conditions, derive theoretical upper bounds for the efficiency, and compare these with the experimental results.

Evolution of competing strategies in a threshold model for task allocation

A nature inspired decentralised multi-agent algorithm is proposed to solve a problem of distributed task allocation in which cities produce and store batches of different mail types. Agents must collect and process the mail batches, without global knowledge of their environment or communication between agents. The problem is constrained so that agents are penalised for switching mail types. When an agent process a mail batch of different type to the previous one, it must undergo a change-over, with repeated change-overs rendering the agent inactive. The efficiency (average amount of mail retrieved), and the flexibility (ability of the agents to react to changes in the environment) are investigated both in static and dynamic environments and with respect to sudden changes. New rules for mail selection and specialisation are proposed and are shown to exhibit improved efficiency and flexibility compared to existing ones. We employ a evolutionary algorithm which allows the various rules to evolve and compete. Apart from obtaining optimised parameters for the various rules for any environment, we also observe extinction and speciation.

Effects of dynamic strain ageing on fatigue crack propagation and threshold behaviour in a Ni-base superalloy

In the temperature range 200-400 degree C the Ni-base superalloy, N901, develops marked dynamic strain ageing effects in its tensile behavior. These include inverse strain rate sensitivity, especially in UTS values, strongly serrated stress-strain curves and a heavily sheared failure mode at the higher test-temperatures. As for steels these effects seem to be due to interactions between the dislocations and the interstitial carbon atoms present. The results of tensile and fatigue threshold tests carried out between 20 degree C and 420 degree C are reported and the fatigue behavior is discussed in terms of the effects of surface roughness induced closure, temperature and strain aging interactions.

Using the max-sum algorithm for supply chain emergence in dynamic multiunit environments

Supply chain formation (SCF) is the process of determining the set of participants and exchange relationships within a network with the goal of setting up a supply chain that meets some predefined social objective. Many proposed solutions for the SCF problem rely on centralized computation, which presents a single point of failure and can also lead to problems with scalability. Decentralized techniques that aid supply chain emergence offer a more robust and scalable approach by allowing participants to deliberate between themselves about the structure of the optimal supply chain. Current decentralized supply chain emergence mechanisms are only able to deal with simplistic scenarios in which goods are produced and traded in single units only and without taking into account production capacities or input-output ratios other than 1:1. In this paper, we demonstrate the performance of a graphical inference technique, max-sum loopy belief propagation (LBP), in a complex multiunit unit supply chain emergence scenario which models additional constraints such as production capacities and input-to-output ratios. We also provide results demonstrating the performance of LBP in dynamic environments, where the properties and composition of participants are altered as the algorithm is running. Our results suggest that max-sum LBP produces consistently strong solutions on a variety of network structures in a multiunit problem scenario, and that performance tends not to be affected by on-the-fly changes to the properties or composition of participants.

The effect of Gaussian noise on the threshold, dynamic range, and loudness of analogue cochlear implant stimuli

The deliberate addition of Gaussian noise to cochlear implant signals has previously been proposed to enhance the time coding of signals by the cochlear nerve. Potentially, the addition of an inaudible level of noise could also have secondary benefits: it could lower the threshold to the information-bearing signal, and by desynchronization of nerve discharges, it could increase the level at which the information-bearing signal becomes uncomfortable. Both these effects would lead to an increased dynamic range, which might be expected to enhance speech comprehension and make the choice of cochlear implant compression parameters less critical (as with a wider dynamic range, small changes in the parameters would have less effect on loudness). The hypothesized secondary effects were investigated with eight users of the Clarion cochlear implant; the stimulation was analogue and monopolar. For presentations in noise, noise at 95% of the threshold level was applied simultaneously and independently to all the electrodes. The noise was found in two-alternative forced-choice (2AFC) experiments to decrease the threshold to sinusoidal stimuli (100 Hz, 1 kHz, 5 kHz) by about 2.0 dB and increase the dynamic range by 0.7 dB. Furthermore, in 2AFC loudness balance experiments, noise was found to decrease the loudness of moderate to intense stimuli. This suggests that loudness is partially coded by the degree of phase-locking of cochlear nerve fibers. The overall gain in dynamic range was modest, and more complex noise strategies, for example, using inhibition between the noise sources, may be required to get a clinically useful benefit. © 2006 Association for Research in Otolaryngology.

A methodology for analysis and control of discrete event dynamic systems

The rapid developments in computer technology have resulted in a widespread use of discrete event dynamic systems (DEDSs). This type of system is complex because it exhibits properties such as concurrency, conflict and non-determinism. It is therefore important to model and analyse such systems before implementation to ensure safe, deadlock free and optimal operation. This thesis investigates current modelling techniques and describes Petri net theory in more detail. It reviews top down, bottom up and hybrid Petri net synthesis techniques that are used to model large systems and introduces on object oriented methodology to enable modelling of larger and more complex systems. Designs obtained by this methodology are modular, easy to understand and allow re-use of designs. Control is the next logical step in the design process. This thesis reviews recent developments in control DEDSs and investigates the use of Petri nets in the design of supervisory controllers. The scheduling of exclusive use of resources is investigated and an efficient Petri net based scheduling algorithm is designed and a re-configurable controller is proposed. To enable the analysis and control of large and complex DEDSs, an object oriented C++ software tool kit was developed and used to implement a Petri net analysis tool, Petri net scheduling and control algorithms. Finally, the methodology was applied to two industrial DEDSs: a prototype can sorting machine developed by Eurotherm Controls Ltd., and a semiconductor testing plant belonging to SGS Thomson Microelectronics Ltd.

A constructive learning algorithm based on back-propagation

There are been a resurgence of interest in the neural networks field in recent years, provoked in part by the discovery of the properties of multi-layer networks. This interest has in turn raised questions about the possibility of making neural network behaviour more adaptive by automating some of the processes involved. Prior to these particular questions, the process of determining the parameters and network architecture required to solve a given problem had been a time consuming activity. A number of researchers have attempted to address these issues by automating these processes, concentrating in particular on the dynamic selection of an appropriate network architecture.The work presented here specifically explores the area of automatic architecture selection; it focuses upon the design and implementation of a dynamic algorithm based on the Back-Propagation learning algorithm. The algorithm constructs a single hidden layer as the learning process proceeds using individual pattern error as the basis of unit insertion. This algorithm is applied to several problems of differing type and complexity and is found to produce near minimal architectures that are shown to have a high level of generalisation ability.

An investigation of the dynamic characteristics of a bolted-rotor system

A Jeffcott rotor consists of a disc at the centre of an axle supported at its end by bearings. A bolted Jeffcott rotor is formed by two discs, each with a shaft on one side. The discs are held together by spring loaded bolts near the outer edge. When the rotor turns there is tendency for the discs to separate on one side. This effect is more marked if the rotor is unbalanced, especially at resonance speeds. The equations of motion of the system have been developed with four degrees of freedom to include the rotor and bearing movements in the respective axes. These equations which include non-linear terms caused by the rotor opening, are subjected to external force such from rotor imbalance. A simulation model based on these equations was created using SIMULINK. An experimental test rig was used to characterise the dynamic features. Rotor discs open at a lateral displacement of the rotor of 0.8 mm. This is the threshold value used to show the change of stiffness from high stiffness to low stiffness. The experimental results, which measure the vibration amplitude of the rotor, show the dynamic behaviour of the bolted rotor due to imbalance. Close agreement of the experimental and theoretical results from time histories, waterfall plots, pseudo-phase plots and rotor orbit plot, indicated the validity of the model and existence of the non-linear jump phenomenon.

Neural network enhanced self tuning adaptive control application for non-linear control of dynamic systems

The main theme of research of this project concerns the study of neutral networks to control uncertain and non-linear control systems. This involves the control of continuous time, discrete time, hybrid and stochastic systems with input, state or output constraints by ensuring good performances. A great part of this project is devoted to the opening of frontiers between several mathematical and engineering approaches in order to tackle complex but very common non-linear control problems. The objectives are: 1. Design and develop procedures for neutral network enhanced self-tuning adaptive non-linear control systems; 2. To design, as a general procedure, neural network generalised minimum variance self-tuning controller for non-linear dynamic plants (Integration of neural network mapping with generalised minimum variance self-tuning controller strategies); 3. To develop a software package to evaluate control system performances using Matlab, Simulink and Neural Network toolbox. An adaptive control algorithm utilising a recurrent network as a model of a partial unknown non-linear plant with unmeasurable state is proposed. Appropriately, it appears that structured recurrent neural networks can provide conveniently parameterised dynamic models for many non-linear systems for use in adaptive control. Properties of static neural networks, which enabled successful design of stable adaptive control in the state feedback case, are also identified. A survey of the existing results is presented which puts them in a systematic framework showing their relation to classical self-tuning adaptive control application of neural control to a SISO/MIMO control. Simulation results demonstrate that the self-tuning design methods may be practically applicable to a reasonably large class of unknown linear and non-linear dynamic control systems.

Anatomical correlates of dynamic auditory processing:relationship to literacy during early adolescence

Adults show great variation in their auditory skills, such as being able to discriminate between foreign speech-sounds. Previous research has demonstrated that structural features of auditory cortex can predict auditory abilities; here we are interested in the maturation of 2-Hz frequency-modulation (FM) detection, a task thought to tap into mechanisms underlying language abilities. We hypothesized that an individual's FM threshold will correlate with gray-matter density in left Heschl's gyrus, and that this function-structure relationship will change through adolescence. To test this hypothesis, we collected anatomical magnetic resonance imaging data from participants who were tested and scanned at three time points: at 10, 11.5 and 13 years of age. Participants judged which of two tones contained FM; the modulation depth was adjusted using an adaptive staircase procedure and their threshold was calculated based on the geometric mean of the last eight reversals. Using voxel-based morphometry, we found that FM threshold was significantly correlated with gray-matter density in left Heschl's gyrus at the age of 10 years, but that this correlation weakened with age. While there were no differences between girls and boys at Times 1 and 2, at Time 3 there was a relationship between gray-matter density in left Heschl's gyrus in boys but not in girls. Taken together, our results confirm that the structure of the auditory cortex can predict temporal processing abilities, namely that gray-matter density in left Heschl's gyrus can predict 2-Hz FM detection threshold. This ability is dependent on the processing of sounds changing over time, a skill believed necessary for speech processing. We tested this assumption and found that FM threshold significantly correlated with spelling abilities at Time 1, but that this correlation was found only in boys. This correlation decreased at Time 2, and at Time 3 we found a significant correlation between reading and FM threshold, but again, only in boys. We examined the sex differences in both the imaging and behavioral data taking into account pubertal stages, and found that the correlation between FM threshold and spelling was strongest pre-pubertally, and the correlation between FM threshold and gray-matter density in left Heschl's gyrus was strongest mid-pubertally.

Investigation of a cross-layer link adaptation algorithm for IEEE 802.11n networks

Link adaptation is a critical component of IEEE 802.11 systems, which adapts transmission rates to dynamic wireless channel conditions. In this paper we investigate a general cross-layer link adaptation algorithm which jointly considers the physical layer link quality and random channel access at the MAC layer. An analytic model is proposed for the link adaptation algorithm. The underlying wireless channel is modeled with a multiple state discrete time Markov chain. Compared with the pure link quality based link adaptation algorithm, the proposed cross-layer algorithm can achieve considerable performance gains of up to 20%.

The influence of memory in a threshold model for distributed task assignment

A nature inspired decentralised multi-agent algorithm is proposed to solve a problem of distributed task selection in which cities produce and store batches of different mail types. Agents must collect and process the mail batches, without a priori knowledge of the available mail at the cities or inter-agent communication. In order to process a different mail type than the previous one, agents must undergo a change-over during which it remains inactive. We propose a threshold based algorithm in order to maximise the overall efficiency (the average amount of mail collected). We show that memory, i.e. the possibility for agents to develop preferences for certain cities, not only leads to emergent cooperation between agents, but also to a significant increase in efficiency (above the theoretical upper limit for any memoryless algorithm), and we systematically investigate the influence of the various model parameters. Finally, we demonstrate the flexibility of the algorithm to changes in circumstances, and its excellent scalability.