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Madrid, Spain

Klaassen I.,University of Amsterdam | van Geest R.J.,University of Amsterdam | Kuiper E.J.,University of Amsterdam | van Noorden C.J.F.,University of Amsterdam | And 2 more authors.
Experimental Eye Research | Year: 2015

Connective tissue growth factor (CTGF, CCN2) contributes to fibrotic responses in diabetic retinopathy, both before clinical manifestations occur in the pre-clinical stage of diabetic retinopathy (PCDR) and in proliferative diabetic retinopathy (PDR), the late clinical stage of the disease. CTGF is a secreted protein that modulates the actions of many growth factors and extracellular matrix (ECM) proteins, leading to tissue reorganization, such as ECM formation and remodeling, basal lamina (BL) thickening, pericyte apoptosis, angiogenesis, wound healing and fibrosis. In PCDR, CTGF contributes to thickening of the retinal capillary BL and is involved in loss of pericytes. In this stage, CTGF expression is induced by advanced glycation end products, and by growth factors such as vascular endothelial growth factor (VEGF) and transforming growth factor (TGF)-β. In PDR, the switch from neovascularization to a fibrotic phase - the angio-fibrotic switch - in PDR is driven by CTGF, in a critical balance with vascular endothelial growth factor (VEGF). We discuss here the roles of CTGF in the pathogenesis of DR in relation to ECM remodeling and wound healing mechanisms, and explore whether CTGF may be a potential novel therapeutic target in the clinical management of early as well as late stages of DR. © 2014 Elsevier Ltd. Source


Dias L.C.,University of Coimbra | Dias L.C.,INESC Coimbra | Antunes C.H.,University of Coimbra | Insua D.R.,Royal Academy of science
Intelligent Decision Technologies | Year: 2012

This paper reviews research in relation with modelling uncertainty within Decision Support Systems (DSS) from 2000 to 2011. It specifically addresses software that has been built or prototyped with the purpose of supporting actual decision making, which is able to explicitly deal with uncertainty (widely understood) on the corresponding model parameters and/or data. The main DSS features analysed are the underlying decision support methodology, the type of uncertainty modelling approach used, the DSS type, and the application area. We appreciate that there is an increasing interest in dealing with uncertainty in real decision support, with prevailing interest in probabilistic approaches and, when linguistic imprecision is involved, fuzzy approaches. We have also recognized an increasing variety of perspectives adopted. © 2012 - IOS Press and the authors. All rights reserved. Source


Razuri J.G.,Rey Juan Carlos University | Esteban P.G.,Rey Juan Carlos University | Insua D.R.,Royal Academy of science
Studies in Computational Intelligence | Year: 2013

Machines that perform intelligent tasks interacting with humans in a seamless manner are becoming a reality. A key element in their design is their ability to make decisions based on a reasonable value system, and the perception of the surrounding environment, including the incumbent persons. In this chapter, we provide a model that supports the decision making process of an autonomous agent that imperfectly perceives its environment and the actions performed by a person, which we shall designate user. The approach has a decision analytic flavour, but includes models forecasting the user's behaviour and its impact over the surrounding environment. We describe the implementation of the model with an edutainment robot with sensors that capture information about the world around it, which may serve as a cognitive personal assistant, may be used with kids for educational, recreational and therapeutic purposes and with elderly people for companion purposes. © Springer-Verlag Berlin Heidelberg 2013. Source


Esteban P.G.,Rey Juan Carlos University | Insua D.R.,Royal Academy of science
Communications in Computer and Information Science | Year: 2013

We describe how the Adversarial Risk Analysis framework may be used to support the decision making of an autonomous agent which needs to interact with other agents and persons. We propose several contextualizations of the problem and suggest which is the conceptual solution in some of the proposed scenarios. © Springer-Verlag Berlin Heidelberg 2013. Source


Rios J.,IBM | Insua D.R.,Royal Academy of science
Risk Analysis | Year: 2012

Recent large-scale terrorist attacks have raised interest in models for resource allocation against terrorist threats. The unifying theme in this area is the need to develop methods for the analysis of allocation decisions when risks stem from the intentional actions of intelligent adversaries. Most approaches to these problems have a game-theoretic flavor although there are also several interesting decision-analytic-based proposals. One of them is the recently introduced framework for adversarial risk analysis, which deals with decision-making problems that involve intelligent opponents and uncertain outcomes. We explore how adversarial risk analysis addresses some standard counterterrorism models: simultaneous defend-attack models, sequential defend-attack-defend models, and sequential defend-attack models with private information. For each model, we first assess critically what would be a typical game-theoretic approach and then provide the corresponding solution proposed by the adversarial risk analysis framework, emphasizing how to coherently assess a predictive probability model of the adversary's actions, in a context in which we aim at supporting decisions of a defender versus an attacker. This illustrates the application of adversarial risk analysis to basic counterterrorism models that may be used as basic building blocks for more complex risk analysis of counterterrorism problems. © 2011 Society for Risk Analysis. Source

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