Dermaseptin 01 as antimicrobial peptide with rich biotechnological potential: study of peptide interaction with membranes containing Leishmania amazonensis lipid-rich extract and membrane models

This article addresses the interactions of the synthetic antimicrobial peptide dermaseptin 01 (GLWSTIKQKGKEAAIAAA-KAAGQAALGAL-NH(2), DS 01) with phospholipid (PL) monolayers comprising (i) a lipid-rich extract of Leishmania amazonensis (LRE-La), (ii) zwitterionic PL (dipalmitoylphosphatidylcholine, DPPC), and (iii) negatively charged PL (dipalmitoylphosphatidylglycerol, DPPG). The degree of interaction of DS 01 with the different biomembrane models was quantified from equilibrium and dynamic liquid-air interface parameters. At low peptide concentrations, interactions between DS 01 and zwitterionic PL, as well as with the LRE-La monolayers were very weak, whereas with negatively charged PLs the interactions were stronger. For peptide concentrations above 1 mu g/ml, a considerable expansion of negatively charged monolayers occurred. In the case of DPPC, it was possible to return to the original lipid area in the condensed phase, suggesting that the peptide was expelled from the monolayer. However, in the case of DPPG, the average area per lipid molecule in the presence of DS 01 was higher than pure PLs even at high surface pressures, suggesting that at least part of DS 01 remained incorporated in the monolayer. For the LRE-La monolayers, DS 01 also remained in the monolayer. This is the first report on the antiparasitic activity of AMPs using Langmuir monolayers of a natural lipid extract from L. amazonensis. Copyright (C) 2011 European Peptide Society and John Wiley & Sons, Ltd.

Attentional modulation of neural responses to action observation: Implications for models of the human 'mirror' system

In primates, the observation of meaningful, goaldirected actions engages a network of cortical areas located within the premotor and inferior parietal lobules. Current models suggest that activity within these regions arises relatively automatically during passive action observation without the need for topdown control. Here we used functional magnetic resonance imaging to determine whether cortical activit)' associated with action observation is modulated by the strategic allocation of selective attention. Normal observers viewed movie clips of reach-to-grasp actions while performing an easy or difficult visual discrimination at the fovea. A wholebrain analysis was performed to determine the effects of attentional load on neural responses to observed hand actions. Our results suggest that cortical areas involved in action observation are significantiy modulated by attentional load. These findings have important implications for recent attempts to link the human action-observation system to response properties of "mirror neurons" in monkeys.

Linear models of simple cells: Correspondence to real cell responses and space spanning properties

Despite their limitations, linear filter models continue to be used to simulate the receptive field properties of cortical simple cells. For theoreticians interested in large scale models of visual cortex, a family of self-similar filters represents a convenient way in which to characterise simple cells in one basic model. This paper reviews research on the suitability of such models, and goes on to advance biologically motivated reasons for adopting a particular group of models in preference to all others. In particular, the paper describes why the Gabor model, so often used in network simulations, should be dropped in favour of a Cauchy model, both on the grounds of frequency response and mutual filter orthogonality.

Segmenting the market of West Australian senior tourists using an artificial neural network

Measuring perceptions of customers can be a major problem for marketers of tourism and travel services. Much of the problem is to determine which attributes carry most weight in the purchasing decision. Older travellers weigh many travel features before making their travel decisions. This paper presents a descriptive analysis of neural network methodology and provides a research technique that assesses the weighting of different attributes and uses an unsupervised neural network model to describe a consumer-product relationship. The development of this rich class of models was inspired by the neural architecture of the human brain. These models mathematically emulate the neurophysical structure and decision making of the human brain, and, from a statistical perspective, are closely related to generalised linear models. Artificial neural networks or neural networks are, however, nonlinear and do not require the same restrictive assumptions about the relationship between the independent variables and dependent variables. Using neural networks is one way to determine what trade-offs older travellers make as they decide their travel plans. The sample of this study is from a syndicated data source of 200 valid cases from Western Australia. From senior groups, active learner, relaxed family body, careful participants and elementary vacation were identified and discussed. (C) 2003 Published by Elsevier Science Ltd.

The tree cut and merge algorithm for estimation of network reliability

This article presents Monte Carlo techniques for estimating network reliability. For highly reliable networks, techniques based on graph evolution models provide very good performance. However, they are known to have significant simulation cost. An existing hybrid scheme (based on partitioning the time space) is available to speed up the simulations; however, there are difficulties with optimizing the important parameter associated with this scheme. To overcome these difficulties, a new hybrid scheme (based on partitioning the edge set) is proposed in this article. The proposed scheme shows orders of magnitude improvement of performance over the existing techniques in certain classes of network. It also provides reliability bounds with little overhead.

An artificial neural network approach for short-term wind power forecasting in Portugal

This paper presents an artificial neural network approach for short-term wind power forecasting in Portugal. The increased integration of wind power into the electric grid, as nowadays occurs in Portugal, poses new challenges due to its intermittency and volatility. Hence, good forecasting tools play a key role in tackling these challenges. The accuracy of the wind power forecasting attained with the proposed approach is evaluated against persistence and ARIMA approaches, reporting the numerical results from a real-world case study.

Hybrid fuzzy Monte Carlo and logic programming model for distribution network reconfiguration in the presence of outages

This paper presents a methodology for distribution networks reconfiguration in outage presence in order to choose the reconfiguration that presents the lower power losses. The methodology is based on statistical failure and repair data of the distribution power system components and uses fuzzy-probabilistic modelling for system component outage parameters. Fuzzy membership functions of system component outage parameters are obtained by statistical records. A hybrid method of fuzzy set and Monte Carlo simulation based on the fuzzy-probabilistic models allows catching both randomness and fuzziness of component outage parameters. Once obtained the system states by Monte Carlo simulation, a logical programming algorithm is applied to get all possible reconfigurations for every system state. In order to evaluate the line flows and bus voltages and to identify if there is any overloading, and/or voltage violation a distribution power flow has been applied to select the feasible reconfiguration with lower power losses. To illustrate the application of the proposed methodology to a practical case, the paper includes a case study that considers a real distribution network.

Logic programming and fuzzy Monte Carlo for distribution network reconfiguration

This paper present a methodology to choose the distribution networks reconfiguration that presents the lower power losses. The proposed methodology is based on statistical failure and repair data of the distribution power system components and uses fuzzy-probabilistic modeling for system component outage parameters. The proposed hybrid method using fuzzy sets and Monte Carlo simulation based on the fuzzyprobabilistic models allows catching both randomness and fuzziness of component outage parameters. A logic programming algorithm is applied, once obtained the system states by Monte Carlo Simulation, to get all possible reconfigurations for each system state. To evaluate the line flows and bus voltages and to identify if there is any overloading, and/or voltage violation an AC load flow has been applied to select the feasible reconfiguration with lower power losses. To illustrate the application of the proposed methodology, the paper includes a case study that considers a 115 buses distribution network.

Influence of network effects on the diffusion of information systems: the case of open source

Doutoramento em Gestão

Hybrid wired/wireless PROFIBUS architectures: performance study based on simulation models

The problem of providing a hybrid wired/wireless communications for factory automation systems is still an open issue, notwithstanding the fact that already there are some solutions. This paper describes the role of simulation tools on the validation and performance analysis of two wireless extensions for the PROFIBUS protocol. In one of them, the Intermediate Systems, which connect wired and wireless network segments, operate as repeaters. In the other one the Intermediate Systems operate as bridge. We also describe how the analytical analysis proposed for these kinds of networks can be used for the setting of some network parameters and for the guaranteeing real-time behaviour of the system. Additionally, we also compare the bridge-based solution simulation results with the analytical results.

Modeling of the lung impedance using a fractional order ladder network with constant phase elements

The self similar branching arrangement of the airways makes the respiratory system an ideal candidate for the application of fractional calculus theory. The fractal geometry is typically characterized by a recurrent structure. This study investigates the identification of a model for the respiratory tree by means of its electrical equivalent based on intrinsic morphology. Measurements were obtained from seven volunteers, in terms of their respiratory impedance by means of its complex representation for frequencies below 5 Hz. A parametric modeling is then applied to the complex valued data points. Since at low-frequency range the inertance is negligible, each airway branch is modeled by using gamma cell resistance and capacitance, the latter having a fractional-order constant phase element (CPE), which is identified from measurements. In addition, the complex impedance is also approximated by means of a model consisting of a lumped series resistance and a lumped fractional-order capacitance. The results reveal that both models characterize the data well, whereas the averaged CPE values are supraunitary and subunitary for the ladder network and the lumped model, respectively.

Theory and phenomenology of two-higgs-doublet models

We discuss theoretical and phenomenological aspects of two-Higgs-doublet extensions of the Standard Model. In general, these extensions have scalar mediated flavour changing neutral currents which are strongly constrained by experiment. Various strategies are discussed to control these flavour changing scalar currents and their phenomenological consequences are analysed. In particular, scenarios with natural flavour conservation are investigated, including the so-called type I and type II models as well as lepton-specific and inert models. Type III models are then discussed, where scalar flavour changing neutral currents are present at tree level, but are suppressed by either a specific ansatz for the Yukawa couplings or by the introduction of family symmetries leading to a natural suppression mechanism. We also consider the phenomenology of charged scalars in these models. Next we turn to the role of symmetries in the scalar sector. We discuss the six symmetry-constrained scalar potentials and their extension into the fermion sector. The vacuum structure of the scalar potential is analysed, including a study of the vacuum stability conditions on the potential and the renormalization-group improvement of these conditions is also presented. The stability of the tree level minimum of the scalar potential in connection with electric charge conservation and its behaviour under CP is analysed. The question of CP violation is addressed in detail, including the cases of explicit CP violation and spontaneous CP violation. We present a detailed study of weak basis invariants which are odd under CP. These invariants allow for the possibility of studying the CP properties of any two-Higgs-doublet model in an arbitrary Higgs basis. A careful study of spontaneous CP violation is presented, including an analysis of the conditions which have to be satisfied in order for a vacuum to violate CP. We present minimal models of CP violation where the vacuum phase is sufficient to generate a complex CKM matrix, which is at present a requirement for any realistic model of spontaneous CP violation.

Model morphisms (MoMo) to enable language independent information models and interoperable business networks

MSc. Dissertation presented at Faculdade de Ciências e Tecnologia of Universidade Nova de Lisboa to obtain the Master degree in Electrical and Computer Engineering

Prognostic modelling of breast cancer patients: a benchmark of predictive models with external validation

Dissertação apresentada para obtenção do Grau de Doutor em Engenharia Electrotécnica e de Computadores – Sistemas Digitais e Percepcionais pela Universidade Nova de Lisboa, Faculdade de Ciências e Tecnologia

Models and tools for value systems analysis in collaborative environments

Dissertation to obtain the degree of Doctor in Electrical and Computer Engineering, specialization of Collaborative Networks