Platelet signaling: a complex interplay between inhibitory and activatory networks

The role of platelets in hemostasis and thrombosis is dependent on a complex balance of activatory and inhibitory signaling pathways. Inhibitory signals released from the healthy vasculature suppress platelet activation in the absence of platelet receptor agonists. Activatory signals present at a site of injury initiate platelet activation and thrombus formation; subsequently, endogenous negative signaling regulators dampen activatory signals to control thrombus growth. Understanding the complex interplay between activatory and inhibitory signaling networks is an emerging challenge in the study of platelet biology and necessitates a systematic approach to utilize experimental data effectively. In this review, we will explore the key points of platelet regulation and signaling that maintain platelets in a resting state, mediate activation to elicit thrombus formation or provide negative feedback. Platelet signaling will be described in terms of key signaling molecules that are common to the pathways activated by platelet agonists and can be described as regulatory nodes for both positive and negative regulators. This article is protected by copyright. All rights reserved.

Distributed parallelization of greedy Mobile Network Optimization algorithms

The Mobile Network Optimization (MNO) technologies have advanced at a tremendous pace in recent years. And the Dynamic Network Optimization (DNO) concept emerged years ago, aimed to continuously optimize the network in response to variations in network traffic and conditions. Yet, DNO development is still at its infancy, mainly hindered by a significant bottleneck of the lengthy optimization runtime. This paper identifies parallelism in greedy MNO algorithms and presents an advanced distributed parallel solution. The solution is designed, implemented and applied to real-life projects whose results yield a significant, highly scalable and nearly linear speedup up to 6.9 and 14.5 on distributed 8-core and 16-core systems respectively. Meanwhile, optimization outputs exhibit self-consistency and high precision compared to their sequential counterpart. This is a milestone in realizing the DNO. Further, the techniques may be applied to similar greedy optimization algorithm based applications.

Inclusion, social networks and resilience: strategies, practices and outcomes for disabled children and their families

This paper explores the strategies of service providers and the benefits reported by disabled children and their parents/carers in three Children's Fund programmes in England. Based on National Evaluation of the Children's Fund research, we discuss how different understandings of ‘inclusion’ informed the diverse strategies and approaches service providers adopted. While disabled children and families perceived the benefits of services predominantly in terms of building individual children's resilience and social networks, the paper highlights the need for holistic approaches which have a broad view of inclusion, support children's networks and tackle disabling barriers within all the spheres of children's lives.

A reconfigurable neural environment on active networks

This paper proposes the deployment of a neural network computing environment on Active Networks. Active Networks are packet-switched computer networks in which packets can contain code fragments that are executed on the intermediate nodes. This feature allows the injection of small pieces of codes to deal with computer network problems directly into the network core, and the adoption of new computing techniques to solve networking problems. The goal of our project is the adoption of a distributed neural network for approaching tasks which are specific of the computer network environment. Dynamically reconfigurable neural networks are spread on an experimental wide area backbone of active nodes (ABone) to show the feasibility of the proposed approach.

Supporting ad hoc collaborations in peer-to-peer networks

Traditionally, applications and tools supporting collaborative computing have been designed only with personal computers in mind and support a limited range of computing and network platforms. These applications are therefore not well equipped to deal with network heterogeneity and, in particular, do not cope well with dynamic network topologies. Progress in this area must be made if we are to fulfil the needs of users and support the diversity, mobility, and portability that are likely to characterise group work in future. This paper describes a groupware platform called Coco that is designed to support collaboration in a heterogeneous network environment. The work demonstrates that progress in the p development of a generic supporting groupware is achievable, even in the context of heterogeneous and dynamic networks. The work demonstrates the progress made in the development of an underlying communications infrastructure, building on peer-to-peer concept and topologies to improve scalability and robustness.

Linear-wavelet networks

This paper proposes a nonlinear regression structure comprising a wavelet network and a linear term. The introduction of the linear term is aimed at providing a more parsimonious interpolation in high-dimensional spaces when the modelling samples are sparse. A constructive procedure for building such structures, termed linear-wavelet networks, is described. For illustration, the proposed procedure is employed in the framework of dynamic system identification. In an example involving a simulated fermentation process, it is shown that a linear-wavelet network yields a smaller approximation error when compared with a wavelet network with the same number of regressors. The proposed technique is also applied to the identification of a pressure plant from experimental data. In this case, the results show that the introduction of wavelets considerably improves the prediction ability of a linear model. Standard errors on the estimated model coefficients are also calculated to assess the numerical conditioning of the identification process.

An overview of the use of neural networks for data mining tasks

In the recent years, the area of data mining has been experiencing considerable demand for technologies that extract knowledge from large and complex data sources. There has been substantial commercial interest as well as active research in the area that aim to develop new and improved approaches for extracting information, relationships, and patterns from large datasets. Artificial neural networks (NNs) are popular biologically-inspired intelligent methodologies, whose classification, prediction, and pattern recognition capabilities have been utilized successfully in many areas, including science, engineering, medicine, business, banking, telecommunication, and many other fields. This paper highlights from a data mining perspective the implementation of NN, using supervised and unsupervised learning, for pattern recognition, classification, prediction, and cluster analysis, and focuses the discussion on their usage in bioinformatics and financial data analysis tasks. © 2012 Wiley Periodicals, Inc.

When long-range zero-lag synchronization is feasible in cortical networks

Many studies have reported long-range synchronization of neuronal activity between brain areas, in particular in the beta and gamma bands with frequencies in the range of 14–30 and 40–80 Hz, respectively. Several studies have reported synchrony with zero phase lag, which is remarkable considering the synaptic and conduction delays inherent in the connections between distant brain areas. This result has led to many speculations about the possible functional role of zero-lag synchrony, such as for neuronal communication, attention, memory, and feature binding. However, recent studies using recordings of single-unit activity and local field potentials report that neuronal synchronization may occur with non-zero phase lags. This raises the questions whether zero-lag synchrony can occur in the brain and, if so, under which conditions. We used analytical methods and computer simulations to investigate which connectivity between neuronal populations allows or prohibits zero-lag synchrony. We did so for a model where two oscillators interact via a relay oscillator. Analytical results and computer simulations were obtained for both type I Mirollo–Strogatz neurons and type II Hodgkin–Huxley neurons. We have investigated the dynamics of the model for various types of synaptic coupling and importantly considered the potential impact of Spike-Timing Dependent Plasticity (STDP) and its learning window. We confirm previous results that zero-lag synchrony can be achieved in this configuration. This is much easier to achieve with Hodgkin–Huxley neurons, which have a biphasic phase response curve, than for type I neurons. STDP facilitates zero-lag synchrony as it adjusts the synaptic strengths such that zero-lag synchrony is feasible for a much larger range of parameters than without STDP.

Building networks to work: an ethnographic study of informal routes into the UK construction industry and pathways for migrant up-skilling

The UK construction industry labour market is characterised by high levels of self-employment, sub-contracting, informality and flexibility. A corollary of this, and a sign of the increasing globalisation of construction, has been an increasing reliance on migrant labour, particularly that from the Eastern European Accession states. Yet, little is known about how their experiences within and outside of work shape their work in the construction sector. In this context better qualitative understandings of the social and communication networks through which migrant workers gain employment, create routes through the sector and develop their role/career are needed. We draw on two examples from a short-term ethnographic study of migrant construction worker employment experiences and practices in the town of Crewe in Cheshire, UK, to demonstrate how informal networks intersect with formal elements of the sector to facilitate both recruitment and up-skilling. Such research knowledge, we argue, offers new evidence of the importance of attending to migrant worker’s own experiences in the development of more transparent recruitment processes.

Towards the computer-aided diagnosis of dementia based on the geometric and network connectivity of structural MRI data

We present an intuitive geometric approach for analysing the structure and fragility of T1-weighted structural MRI scans of human brains. Apart from computing characteristics like the surface area and volume of regions of the brain that consist of highly active voxels, we also employ Network Theory in order to test how close these regions are to breaking apart. This analysis is used in an attempt to automatically classify subjects into three categories: Alzheimer’s disease, mild cognitive impairment and healthy controls, for the CADDementia Challenge.

Morphology of polymer networks formed in the chiral and non chiral phases of an anti-ferroelectric liquid crystal

We introduced photo-polymer networks into the various liquid crystalline phases of the antiferroelectric liquid crystal AS612 and studied the effects of these networks by measuring the temperature dependence of the Bragg wavelengths selectively reflected. After polymerization, the decrease in Bragg wavelengths with respect to the original values is consistent with a shorter helical pitch due to polymer network shrinkage. Also, by removing the liquid crystalline material, we are able to image the residual polymer network using scanning electron microscopy and polarized light microscopy. The polymer strands are a few microns thick and the networks show both chiral and non-chiral features.