4 resultados para 490-1

em CentAUR: Central Archive University of Reading - UK


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Biological models of an apoptotic process are studied using models describing a system of differential equations derived from reaction kinetics information. The mathematical model is re-formulated in a state-space robust control theory framework where parametric and dynamic uncertainty can be modelled to account for variations naturally occurring in biological processes. We propose to handle the nonlinearities using neural networks.

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We discuss the modelling of dielectric responses of amorphous biological samples. Such samples are commonly encountered in impedance spectroscopy studies as well as in UV, IR, optical and THz transient spectroscopy experiments and in pump-probe studies. In many occasions, the samples may display quenched absorption bands. A systems identification framework may be developed to provide parsimonious representations of such responses. To achieve this, it is appropriate to augment the standard models found in the identification literature to incorporate fractional order dynamics. Extensions of models using the forward shift operator, state space models as well as their non-linear Hammerstein-Wiener counterpart models are highlighted. We also discuss the need to extend the theory of electromagnetically excited networks which can account for fractional order behaviour in the non-linear regime by incorporating nonlinear elements to account for the observed non-linearities. The proposed approach leads to the development of a range of new chemometrics tools for biomedical data analysis and classification.

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This paper discusses ECG signal classification after parametrizing the ECG waveforms in the wavelet domain. Signal decomposition using perfect reconstruction quadrature mirror filter banks can provide a very parsimonious representation of ECG signals. In the current work, the filter parameters are adjusted by a numerical optimization algorithm in order to minimize a cost function associated to the filter cut-off sharpness. The goal consists of achieving a better compromise between frequency selectivity and time resolution at each decomposition level than standard orthogonal filter banks such as those of the Daubechies and Coiflet families. Our aim is to optimally decompose the signals in the wavelet domain so that they can be subsequently used as inputs for training to a neural network classifier.

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Calcitonin receptor-like receptor (CLR) and receptor activity modifying protein 1 (RAMP1) comprise a receptor for calcitonin gene related peptide (CGRP) and intermedin. Although CGRP is widely expressed in the nervous system, less is known about the localization of CLR and RAMP1. To localize these proteins, we raised antibodies to CLR and RAMP1. Antibodies specifically interacted with CLR and RAMP1 in HEK cells coexpressing rat CLR and RAMP1, determined by Western blotting and immunofluorescence. Fluorescent CGRP specifically bound to the surface of these cells and CGRP, CLR, and RAMP1 internalized into the same endosomes. CLR was prominently localized in nerve fibers of the myenteric and submucosal plexuses, muscularis externa and lamina propria of the gastrointestinal tract, and in the dorsal horn of the spinal cord of rats. CLR was detected at low levels in the soma of enteric, dorsal root ganglia (DRG), and spinal neurons. RAMP1 was also localized to enteric and DRG neurons and the dorsal horn. CLR and RAMP1 were detected in perivascular nerves and arterial smooth muscle. Nerve fibers containing CGRP and intermedin were closely associated with CLR fibers in the gastrointestinal tract and dorsal horn, and CGRP and CLR colocalized in DRG neurons. Thus, CLR and RAMP1 may mediate the effects of CGRP and intermedin in the nervous system. However, mRNA encoding RAMP2 and RAMP3 was also detected in the gastrointestinal tract, DRG, and dorsal horn, suggesting that CLR may associate with other RAMPs in these tissues to form a receptor for additional peptides such as adrenomedullin.