Search results for "EURA"
showing 10 items of 3336 documents
Reversible neural stem cell niche dysfunction in a model of multiple sclerosis
2011
Objective The subventricular zone (SVZ) of the brain constitutes a niche for neural stem and progenitor cells that can initiate repair after central nervous system (CNS) injury. In a relapsing-remitting model of experimental autoimmune encephalomyelitis (EAE), the neural stem cells (NSCs) become activated and initiate regeneration during acute disease, but lose this ability during the chronic phases of disease. We hypothesized that chronic microglia activation contributes to the failure of the NSC repair potential in the SVZ. Methods Using bromodeoxyuridine injections at different time points during EAE, we quantified the number of proliferating and differentiating progenitors, and evaluate…
Persistent inflammation alters the function of the endogenous brain stem cell compartment
2008
Endogenous neural stem/precursor cells (NPCs) are considered a functional reservoir for promoting tissue homeostasis and repair after injury, therefore regenerative strategies that mobilize these cells have recently been proposed. Despite evidence of increased neurogenesis upon acute inflammatory insults (e.g. ischaemic stroke), the plasticity of the endogenous brain stem cell compartment in chronic CNS inflammatory disorders remains poorly characterized. Here we show that persistent brain inflammation, induced by immune cells targeting myelin, extensively alters the proliferative and migratory properties of subventricular zone (SVZ)-resident NPCs in vivo leading to significant accumulation…
Tumour vascularization via endothelial differentiation of glioblastoma stem-like cells
2010
Glioblastoma is a highly angiogenetic malignancy, the neoformed vessels of which are thought to arise by sprouting of pre-existing brain capillaries. The recent demonstration that a population of glioblastoma stem-like cells (GSCs) maintains glioblastomas indicates that the progeny of these cells may not be confined to the neural lineage. Normal neural stem cells are able to differentiate into functional endothelial cells. The connection between neural stem cells and the endothelial compartment seems to be critical in glioblastoma, where cancer stem cells closely interact with the vascular niche and promote angiogenesis through the release of vascular endothelial growth factor(VEGF) and str…
Domestic load forecasting using neural network and its use for missing data analysis
2015
Domestic demand prediction is very important for home energy management system and also for peak reduction in power system network. In this work, active and reactive power consumption prediction model is developed and analysed for a typical Southern Norwegian house for hourly power (active and reactive) consumptions and time information as inputs. In the proposed model, a neural network is adopted as a main technique and historical domestic load data of around 2 years are used as input. The available data has some measurement errors and missing segments. Before using the data for training purpose, missing and inaccurate data are considered and then it is used for testing the model. It is ob…
External parameters contribution in domestic load forecasting using neural network
2015
Domestic demand prediction is very important for home energy management system and also for peak reduction in the power system network. In this work, for precise prediction of power demand, external parameters, such as temperature and solar radiation, are considered and included in the prediction model for improving prediction performance. Power prediction models for coming hours' power demand estimation are built using neural network based on hourly power consumptions data with / without ambient temperature data and global solar irradiation (GSI) data respectively. In this work, a typical Southern Norwegian household demand has been considered. As a result, both ambient temperature and GSI…
Empirical mode decomposition and neural network for the classification of electroretinographic data
2013
The processing of biosignals is increasingly being utilized in ambulatory situations in order to extract significant signals' features that can help in clinical diagnosis. However, this task is hampered by the fact that biomedical signals exhibit a complex behaviour characterized by strong non-linear and non-stationary properties that cannot always be perceived by simple visual examination. New processing methods need be considered. In this context, we propose to apply a signal processing method, based on empirical mode decomposition and artificial neural networks, to analyse electroretinograms, i.e. the retinal response to a light flash, with the aim to detect and classify retinal diseases…
Structural Health Monitoring Procedure for Composite Structures through the use of Artificial Neural Networks
2015
In this paper different architectures of Artificial Neural Networks (ANNs) for structural damage detection are studied. The main objective is to investigate an ANN able to detect and localize damage without any prior knowledge on its characteristics so as to serve as a real-time data processor for Structural Health Monitoring (SHM) systems. Two different architectures are studied: the standard feed-forward Multi Layer Perceptron (MLP) and the Radial Basis Function (RBF) ANNs. The training data are given, in terms of a Damage Index ℑD, properly defined using a piezoelectric sensor signal output to obtain suitable information on the damage position and dimensions. The electromechanical respon…
Neural based MRAS sensorless techniques for high performance linear induction motor drives.
2010
This paper proposes a neural based MRAS (Model reference Adaptive System) speed observer suited for linear induction motors (LIM). Starting from the dynamical equation of the LIM in the synchronous reference frame in literature, the so-called voltage and current models of the LIM in the stationary reference frame, taking into consideration the end effects, have been deduced. Then, while the inductor equations have been used as reference model of the MRAS observer, the induced part equations have been discretized and rearranged so to be represented by a linear neural network (ADALINE). On this basis, the so called TLS EXIN neuron has been used to compute on-line, in recursive form, the machi…
Load forecast on intelligent buildings based on temporary occupancy monitoring
2016
The modeling of energy consumption in buildings must consider occupancy as a relevant input, since it plays a very important role in the overall building's energy consumption. Frequently, buildings lack of permanent occupancy monitoring solutions. However, they may include data sources that are correlated with real building occupancy. This study proposes a new methodology for energy consumption modeling, supported by these alternative data sources, such as the number of vehicles in a parking lot. The aim is to mitigate investment in permanent occupancy monitoring solutions. The proposed methodology makes use of short-term real occupancy monitoring for model fitting, to enable the developmen…
A massive lesion detection algorithm in mammography
2004
A new algorithm for massive lesion detection in mammography is presented. The algorithm consists in three main steps : 1) reduction of the dimension of the image to be processed through the identifi cation of regions of interest (rois) as candidates for massive lesions ; 2) characterization of the roi by means of suitable feature extraction ; 3) pattern classifi cation through supervised neural networks. Suspect regions are detected by searching for local maxima of the pixel grey level intensity. A ring of increasing radius, centered on a maximum, is considered until the mean intensity in the ring decreases to a defi ned fraction of the maximum. The rois thus obtained are described by avera…