Hanging-drop multicellular spheriods as a model of tumour angiogenesis

The establishment of a vascular network within tumours is a key step in the progression towards an aggressive, metastatic state, with poor prognosis. We have developed a novel in vitro model to specifically capture the interaction between endothelial cells and solid tumours. Micro-vascularised in vitro tumour constructs were produced by introducing endothelial cells to multicellular spheroids formed in hanging drops. Upon introduction, the endothelial cells migrated into the tumour spheroid, establishing tubular networks and luminal structures. This system relies on the natural pro-angiogenic capacity of multicellular spheroids, and does not require the addition of exogenous angiogenic factors, or use of extracellular-matrix substitutes.

Postsynthesis stabilization of free-standing mesoporous silica films

Mixed ammonia-water vapor postsynthesis treatment provides a simple and convenient method for stabilizing mesostructured silica films. X-ray diffraction, transmission electron microscopy, nitrogen adsorption/desorption, and solid-state NMR (C-13, Si-29) were applied to study the effects of mixed ammonia-water vapor at 90 degreesC on the mesostructure of the films. An increased cross-linking of the silica network was observed. Subsequent calcination of the silica films was seen to cause a bimodal pore-size distribution, with an accompanying increase in the volume and surface area ratios of the primary (d = 3 nm) to secondary (d = 5-30 nm) pores. Additionally, mixed ammonia-water treatment was observed to cause a narrowing of the primary pore-size distribution. These findings have implications for thin film based applications and devices, such as sensors, membranes, or surfaces for heterogeneous catalysis.

Evaluation of an accident and emergency teleconsultation service for north-east Scotland

We evaluated an accident and emergency teleconsultation service provided to 14 community hospitals in north-east Scotland. Each community hospital was equipped with a videoconferencing system and a document camera to allow transmission of radiographs. The network used 384 kbit/s ISDN connections. A total of 1392 teleconsultations were recorded during a 12-month study period. Seventy-seven per cent of patients (n=1072) were managed locally and 23% (n=320) were transferred to Aberdeen. The majority (95%) of teleconsultations were conducted on weekdays, and 90% of these occurred between the hours of 09:00 and 16:00. The mean delay in contacting a doctor was 9 min and the mean consultation time was 10 min. The majority of patients were suffering from fractures or suspected fractures of the limbs. Radiograph transmission was used in 75% of all teleconsultations. A high degree of satisfaction was recorded by all users of the service.

Determinants in the development of advanced nursing practice: a case study of primary-care settings in Hong Kong

Different factors have been shown to influence the development of models of advanced nursing practice (ANP) in primary-care settings. Although ANP is being developed in hospitals in Hong Kong, China, it remains undeveloped in primary care and little is known about the factors determining the development of such a model. The aims of the present study were to investigate the contribution of different models of nursing practice to the care provided in primary-care settings in Hong Kong, and to examine the determinants influencing the development of a model of ANP in such settings. A multiple case study design was selected using both qualitative and quantitative methods of data collection. Sampling methods reflected the population groups and stage of the case study. Sampling included a total population of 41 nurses from whom a secondary volunteer sample was drawn for face-to-face interviews. In each case study, a convenience sample of 70 patients were recruited, from whom 10 were selected purposively for a semi-structured telephone interview. An opportunistic sample of healthcare professionals was also selected. The within-case and cross-case analysis demonstrated four major determinants influencing the development of ANP: (1) current models of nursing practice; (2) the use of skills mix; (3) the perceived contribution of ANP to patient care; and (4) patients' expectations of care. The level of autonomy of individual nurses was considered particularly important. These determinants were used to develop a model of ANP for a primary-care setting. In conclusion, although the findings highlight the complexity determining the development and implementation of ANP in primary care, the proposed model suggests that definitions of advanced practice are appropriate to a range of practice models and cultural settings. However, the findings highlight the importance of assessing the effectiveness of such models in terms of cost and long-term patient outcomes.

Applying network analysis to the conservation of habitat trees in urban environments: a case study from Brisbane, Australia

In Australia more than 300 vertebrates, including 43 insectivorous bat species, depend on hollows in habitat trees for shelter, with many species using a network of multiple trees as roosts, We used roost-switching data on white-striped freetail bats (Tadarida australis; Microchiroptera: Molossidae) to construct a network representation of day roosts in suburban Brisbane, Australia. Bats were caught from a communal roost tree with a roosting group of several hundred individuals and released with transmitters. Each roost used by the bats represented a node in the network, and the movements of bats between roosts formed the links between nodes. Despite differences in gender and reproductive stages, the bats exhibited the same behavior throughout three radiotelemetry periods and over 500 bat days of radio tracking: each roosted in separate roosts, switched roosts very infrequently, and associated with other bats only at the communal roost This network resembled a scale-free network in which the distribution of the number of links from each roost followed a power law. Despite being spread over a large geographic area (> 200 km(2)), each roost was connected to others by less than three links. One roost (the hub or communal roost) defined the architecture of the network because it had the most links. That the network showed scale-free properties has profound implications for the management of the habitat trees of this roosting group. Scale-free networks provide high tolerance against stochastic events such as random roost removals but are susceptible to the selective removal of hub nodes. Network analysis is a useful tool for understanding the structural organization of habitat tree usage and allows the informed judgment of the relative importance of individual trees and hence the derivation of appropriate management decisions, Conservation planners and managers should emphasize the differential importance of habitat trees and think of them as being analogous to vital service centers in human societies.

Lo-Fi matchmaking: A study of social pairing for backpackers 8th International Conference on Ubiquitous Computing, UbiComp 2006

It is technically feasible for mobile social software such as pairing or ‘matchmaking’ systems to introduce people to others and assist information exchange. However, little is known about the social structure of many mobile communities or why they would want such pairing systems. While engaged in other work determining requirements for a mobile travel assistant we saw a potentially useful application for a pairing system to facilitate the exchange of travel information between backpackers. To explore this area, we designed two studies involving usage of a low-fidelity role prototype of a social pairing system for backpackers. Backpackers rated the utility of different pairing types, and provided feedback on the social implications of being paired based on travel histories. Practical usage of the social network pairing activity and the implications of broader societal usage are discussed.

Effect of training on the response of plasma vascular endothelial growth factor to exercise in patients with peripheral arterial disease

Expansion of the capillary network, or angiogenesis, occurs following endurance training. This process, which is reliant on the presence of VEGF (vascular endothelial growth factor), is an adaptation to a chronic mismatch between oxygen demand and supply. Patients with IC (intermittent claudication) experience pain during exercise associated with an inadequate oxygen delivery to the muscles. Therefore the aims of the present study were to examine the plasma VEGF response to acute exercise, and to establish whether exercise training alters this response in patients with IC. In Part A, blood was collected from patients with IC (n = 18) before and after (+ 20 and + 60 min post-exercise) a maximal walking test to determine the plasma VEGF response to acute exercise. VEGF was present in the plasma of patients (45.11 +/- 29.96 pg/ml) and was unchanged in response to acute exercise. Part B was a training study to determine whether exercise training altered the VEGF response to acute exercise. Patients were randomly assigned to a treatment group (TMT; n = 7) that completed 6 weeks of high-intensity treadmill training, or to a control group (CON; n = 6). All patients completed a maximal walking test before and after the intervention, with blood samples drawn as for Part A. Training had no effect on plasma VEGF at rest or in response to acute exercise, despite a significant increase in maximal walking time in the TMT group (915 + 533 to 1206 + 500 s; P = 0.009) following the intervention. The absence of a change in plasma VEGF may reflect altered VEGF binding at the endothelium, although this cannot be confirmed by the present data.

Performance analysis of multi-radio AODV in hybrid wireless mesh networks

Wireless Mesh Networks (WMNs), based on commodity hardware, present a promising technology for a wide range of applications due to their self-configuring and self-healing capabilities, as well as their low equipment and deployment costs. One of the key challenges that WMN technology faces is the limited capacity and scalability due to co-channel interference, which is typical for multi-hop wireless networks. A simple and relatively low-cost approach to address this problem is the use of multiple wireless network interfaces (radios) per node. Operating the radios on distinct orthogonal channels permits effective use of the frequency spectrum, thereby, reducing interference and contention. In this paper, we evaluate the performance of the multi-radio Ad-hoc On-demand Distance Vector (AODV) routing protocol with a specific focus on hybrid WMNs. Our simulation results show that under high mobility and traffic load conditions, multi-radio AODV offers superior performance as compared to its single-radio counterpart. We believe that multi-radio AODV is a promising candidate for WMNs, which need to service a large number of mobile clients with low latency and high bandwidth requirements.

Real-time control of a Tokamak plasma using neural networks

This paper presents results from the first use of neural networks for the real-time feedback control of high temperature plasmas in a Tokamak fusion experiment. The Tokamak is currently the principal experimental device for research into the magnetic confinement approach to controlled fusion. In the Tokamak, hydrogen plasmas, at temperatures of up to 100 Million K, are confined by strong magnetic fields. Accurate control of the position and shape of the plasma boundary requires real-time feedback control of the magnetic field structure on a time-scale of a few tens of microseconds. Software simulations have demonstrated that a neural network approach can give significantly better performance than the linear technique currently used on most Tokamak experiments. The practical application of the neural network approach requires high-speed hardware, for which a fully parallel implementation of the multi-layer perceptron, using a hybrid of digital and analogue technology, has been developed.

Information geometric measurements of generalisation

Neural networks can be regarded as statistical models, and can be analysed in a Bayesian framework. Generalisation is measured by the performance on independent test data drawn from the same distribution as the training data. Such performance can be quantified by the posterior average of the information divergence between the true and the model distributions. Averaging over the Bayesian posterior guarantees internal coherence; Using information divergence guarantees invariance with respect to representation. The theory generalises the least mean squares theory for linear Gaussian models to general problems of statistical estimation. The main results are: (1)~the ideal optimal estimate is always given by average over the posterior; (2)~the optimal estimate within a computational model is given by the projection of the ideal estimate to the model. This incidentally shows some currently popular methods dealing with hyperpriors are in general unnecessary and misleading. The extension of information divergence to positive normalisable measures reveals a remarkable relation between the dlt dual affine geometry of statistical manifolds and the geometry of the dual pair of Banach spaces Ld and Ldd. It therefore offers conceptual simplification to information geometry. The general conclusion on the issue of evaluating neural network learning rules and other statistical inference methods is that such evaluations are only meaningful under three assumptions: The prior P(p), describing the environment of all the problems; the divergence Dd, specifying the requirement of the task; and the model Q, specifying available computing resources.

Neural networks: A principled perspective

Introductory accounts of artificial neural networks often rely for motivation on analogies with models of information processing in biological networks. One limitation of such an approach is that it offers little guidance on how to find optimal algorithms, or how to verify the correct performance of neural network systems. A central goal of this paper is to draw attention to a quite different viewpoint in which neural networks are seen as algorithms for statistical pattern recognition based on a principled, i.e. theoretically well-founded, framework. We illustrate the concept of a principled viewpoint by considering a specific issue concerned with the interpretation of the outputs of a trained network. Finally, we discuss the relevance of such an approach to the issue of the validation and verification of neural network systems.

Prediction of paroxysmal atrial fibrillation

We present a novel method for prediction of the onset of a spontaneous (paroxysmal) atrial fibrilation episode by representing the electrocardiograph (ECG) output as two time series corresponding to the interbeat intervals and the lengths of the atrial component of the ECG. We will then show how different entropy measures can be calulated from both of these series and then combined in a neural network trained using the Bayesian evidence procedure to form and effective predictive classifier.

Practical assessment of neural network applications

This paper reports the initial results of a joint research project carried out by Aston University and Lloyd's Register to develop a practical method of assessing neural network applications. A set of assessment guidelines for neural network applications were developed and tested on two applications. These case studies showed that it is practical to assess neural networks in a statistical pattern recognition framework. However there is need for more standardisation in neural network technology and a wider takeup of good development practice amongst the neural network community.

Neural networks: A principled perspective

Introductory accounts of artificial neural networks often rely for motivation on analogies with models of information processing in biological networks. One limitation of such an approach is that it offers little guidance on how to find optimal algorithms, or how to verify the correct performance of neural network systems. A central goal of this paper is to draw attention to a quite different viewpoint in which neural networks are seen as algorithms for statistical pattern recognition based on a principled, i.e. theoretically well-founded, framework. We illustrate the concept of a principled viewpoint by considering a specific issue concerned with the interpretation of the outputs of a trained network. Finally, we discuss the relevance of such an approach to the issue of the validation and verification of neural network systems.

The challenge of real-time automatic mapping for environmental monitoring network management

The automatic interpolation of environmental monitoring network data such as air quality or radiation levels in real-time setting poses a number of practical and theoretical questions. Among the problems found are (i) dealing and communicating uncertainty of predictions, (ii) automatic (hyper)parameter estimation, (iii) monitoring network heterogeneity, (iv) dealing with outlying extremes, and (v) quality control. In this paper we discuss these issues, in light of the spatial interpolation comparison exercise held in 2004.