The Uncertain generalized probabilistic weighted average and its application in the theory of expertons

A new model for dealing with decision making under risk by considering subjective and objective information in the same formulation is here presented. The uncertain probabilistic weighted average (UPWA) is also presented. Its main advantage is that it unifies the probability and the weighted average in the same formulation and considering the degree of importance that each case has in the analysis. Moreover, it is able to deal with uncertain environments represented in the form of interval numbers. We study some of its main properties and particular cases. The applicability of the UPWA is also studied and it is seen that it is very broad because all the previous studies that use the probability or the weighted average can be revised with this new approach. Focus is placed on a multi-person decision making problem regarding the selection of strategies by using the theory of expertons.

Computer-Aided Business Model Design

There is a lack of dedicated tools for business model design at a strategic level. However, in today's economic world the need to be able to quickly reinvent a company's business model is essential to stay competitive. This research focused on identifying the functionalities that are necessary in a computer-aided design (CAD) tool for the design of business models in a strategic context. Using design science research methodology a series of techniques and prototypes have been designed and evaluated to offer solutions to the problem. The work is a collection of articles which can be grouped into three parts: First establishing the context of how the Business Model Canvas (BMC) is used to design business models and explore the way in which CAD can contribute to the design activity. The second part extends on this by proposing new technics and tools which support elicitation, evaluation (assessment) and evolution of business models design with CAD. This includes features such as multi-color tagging to easily connect elements, rules to validate coherence of business models and features that are adapted to the correct business model proficiency level of its users. A new way to describe and visualize multiple versions of a business model and thereby help in addressing the business model as a dynamic object was also researched. The third part explores extensions to the business model canvas such as an intermediary model which helps IT alignment by connecting business model and enterprise architecture. And a business model pattern for privacy in a mobile environment, using privacy as a key value proposition. The prototyped techniques and proposition for using CAD tools in business model modeling will allow commercial CAD developers to create tools that are better suited to the needs of practitioners.

The branch-site test of positive selection is surprisingly robust but lacks power under synonymous substitution saturation and variation in GC

Positive selection is widely estimated from protein coding sequence alignments by the nonsynonymous-to-synonymous ratio omega. Increasingly elaborate codon models are used in a likelihood framework for this estimation. Although there is widespread concern about the robustness of the estimation of the omega ratio, more efforts are needed to estimate this robustness, especially in the context of complex models. Here, we focused on the branch-site codon model. We investigated its robustness on a large set of simulated data. First, we investigated the impact of sequence divergence. We found evidence of underestimation of the synonymous substitution rate for values as small as 0.5, with a slight increase in false positives for the branch-site test. When dS increases further, underestimation of dS is worse, but false positives decrease. Interestingly, the detection of true positives follows a similar distribution, with a maximum for intermediary values of dS. Thus, high dS is more of a concern for a loss of power (false negatives) than for false positives of the test. Second, we investigated the impact of GC content. We showed that there is no significant difference of false positives between high GC (up to similar to 80%) and low GC (similar to 30%) genes. Moreover, neither shifts of GC content on a specific branch nor major shifts in GC along the gene sequence generate many false positives. Our results confirm that the branch-site is a very conservative test.

Machine learning for geospatial data : algorithms, software tools and case studies

Résumé Cette thèse est consacrée à l'analyse, la modélisation et la visualisation de données environnementales à référence spatiale à l'aide d'algorithmes d'apprentissage automatique (Machine Learning). L'apprentissage automatique peut être considéré au sens large comme une sous-catégorie de l'intelligence artificielle qui concerne particulièrement le développement de techniques et d'algorithmes permettant à une machine d'apprendre à partir de données. Dans cette thèse, les algorithmes d'apprentissage automatique sont adaptés pour être appliqués à des données environnementales et à la prédiction spatiale. Pourquoi l'apprentissage automatique ? Parce que la majorité des algorithmes d'apprentissage automatiques sont universels, adaptatifs, non-linéaires, robustes et efficaces pour la modélisation. Ils peuvent résoudre des problèmes de classification, de régression et de modélisation de densité de probabilités dans des espaces à haute dimension, composés de variables informatives spatialisées (« géo-features ») en plus des coordonnées géographiques. De plus, ils sont idéaux pour être implémentés en tant qu'outils d'aide à la décision pour des questions environnementales allant de la reconnaissance de pattern à la modélisation et la prédiction en passant par la cartographie automatique. Leur efficacité est comparable au modèles géostatistiques dans l'espace des coordonnées géographiques, mais ils sont indispensables pour des données à hautes dimensions incluant des géo-features. Les algorithmes d'apprentissage automatique les plus importants et les plus populaires sont présentés théoriquement et implémentés sous forme de logiciels pour les sciences environnementales. Les principaux algorithmes décrits sont le Perceptron multicouches (MultiLayer Perceptron, MLP) - l'algorithme le plus connu dans l'intelligence artificielle, le réseau de neurones de régression généralisée (General Regression Neural Networks, GRNN), le réseau de neurones probabiliste (Probabilistic Neural Networks, PNN), les cartes auto-organisées (SelfOrganized Maps, SOM), les modèles à mixture Gaussiennes (Gaussian Mixture Models, GMM), les réseaux à fonctions de base radiales (Radial Basis Functions Networks, RBF) et les réseaux à mixture de densité (Mixture Density Networks, MDN). Cette gamme d'algorithmes permet de couvrir des tâches variées telle que la classification, la régression ou l'estimation de densité de probabilité. L'analyse exploratoire des données (Exploratory Data Analysis, EDA) est le premier pas de toute analyse de données. Dans cette thèse les concepts d'analyse exploratoire de données spatiales (Exploratory Spatial Data Analysis, ESDA) sont traités selon l'approche traditionnelle de la géostatistique avec la variographie expérimentale et selon les principes de l'apprentissage automatique. La variographie expérimentale, qui étudie les relations entre pairs de points, est un outil de base pour l'analyse géostatistique de corrélations spatiales anisotropiques qui permet de détecter la présence de patterns spatiaux descriptible par une statistique. L'approche de l'apprentissage automatique pour l'ESDA est présentée à travers l'application de la méthode des k plus proches voisins qui est très simple et possède d'excellentes qualités d'interprétation et de visualisation. Une part importante de la thèse traite de sujets d'actualité comme la cartographie automatique de données spatiales. Le réseau de neurones de régression généralisée est proposé pour résoudre cette tâche efficacement. Les performances du GRNN sont démontrées par des données de Comparaison d'Interpolation Spatiale (SIC) de 2004 pour lesquelles le GRNN bat significativement toutes les autres méthodes, particulièrement lors de situations d'urgence. La thèse est composée de quatre chapitres : théorie, applications, outils logiciels et des exemples guidés. Une partie importante du travail consiste en une collection de logiciels : Machine Learning Office. Cette collection de logiciels a été développée durant les 15 dernières années et a été utilisée pour l'enseignement de nombreux cours, dont des workshops internationaux en Chine, France, Italie, Irlande et Suisse ainsi que dans des projets de recherche fondamentaux et appliqués. Les cas d'études considérés couvrent un vaste spectre de problèmes géoenvironnementaux réels à basse et haute dimensionnalité, tels que la pollution de l'air, du sol et de l'eau par des produits radioactifs et des métaux lourds, la classification de types de sols et d'unités hydrogéologiques, la cartographie des incertitudes pour l'aide à la décision et l'estimation de risques naturels (glissements de terrain, avalanches). Des outils complémentaires pour l'analyse exploratoire des données et la visualisation ont également été développés en prenant soin de créer une interface conviviale et facile à l'utilisation. Machine Learning for geospatial data: algorithms, software tools and case studies Abstract The thesis is devoted to the analysis, modeling and visualisation of spatial environmental data using machine learning algorithms. In a broad sense machine learning can be considered as a subfield of artificial intelligence. It mainly concerns with the development of techniques and algorithms that allow computers to learn from data. In this thesis machine learning algorithms are adapted to learn from spatial environmental data and to make spatial predictions. Why machine learning? In few words most of machine learning algorithms are universal, adaptive, nonlinear, robust and efficient modeling tools. They can find solutions for the classification, regression, and probability density modeling problems in high-dimensional geo-feature spaces, composed of geographical space and additional relevant spatially referenced features. They are well-suited to be implemented as predictive engines in decision support systems, for the purposes of environmental data mining including pattern recognition, modeling and predictions as well as automatic data mapping. They have competitive efficiency to the geostatistical models in low dimensional geographical spaces but are indispensable in high-dimensional geo-feature spaces. The most important and popular machine learning algorithms and models interesting for geo- and environmental sciences are presented in details: from theoretical description of the concepts to the software implementation. The main algorithms and models considered are the following: multi-layer perceptron (a workhorse of machine learning), general regression neural networks, probabilistic neural networks, self-organising (Kohonen) maps, Gaussian mixture models, radial basis functions networks, mixture density networks. This set of models covers machine learning tasks such as classification, regression, and density estimation. Exploratory data analysis (EDA) is initial and very important part of data analysis. In this thesis the concepts of exploratory spatial data analysis (ESDA) is considered using both traditional geostatistical approach such as_experimental variography and machine learning. Experimental variography is a basic tool for geostatistical analysis of anisotropic spatial correlations which helps to understand the presence of spatial patterns, at least described by two-point statistics. A machine learning approach for ESDA is presented by applying the k-nearest neighbors (k-NN) method which is simple and has very good interpretation and visualization properties. Important part of the thesis deals with a hot topic of nowadays, namely, an automatic mapping of geospatial data. General regression neural networks (GRNN) is proposed as efficient model to solve this task. Performance of the GRNN model is demonstrated on Spatial Interpolation Comparison (SIC) 2004 data where GRNN model significantly outperformed all other approaches, especially in case of emergency conditions. The thesis consists of four chapters and has the following structure: theory, applications, software tools, and how-to-do-it examples. An important part of the work is a collection of software tools - Machine Learning Office. Machine Learning Office tools were developed during last 15 years and was used both for many teaching courses, including international workshops in China, France, Italy, Ireland, Switzerland and for realizing fundamental and applied research projects. Case studies considered cover wide spectrum of the real-life low and high-dimensional geo- and environmental problems, such as air, soil and water pollution by radionuclides and heavy metals, soil types and hydro-geological units classification, decision-oriented mapping with uncertainties, natural hazards (landslides, avalanches) assessments and susceptibility mapping. Complementary tools useful for the exploratory data analysis and visualisation were developed as well. The software is user friendly and easy to use.

Characterization of cerebral glucose dynamics in vivo with a four-state conformational model of transport at the blood-brain barrier.

Determination of brain glucose transport kinetics in vivo at steady-state typically does not allow distinguishing apparent maximum transport rate (T(max)) from cerebral consumption rate. Using a four-state conformational model of glucose transport, we show that simultaneous dynamic measurement of brain and plasma glucose concentrations provide enough information for independent and reliable determination of the two rates. In addition, although dynamic glucose homeostasis can be described with a reversible Michaelis-Menten model, which is implicit to the large iso-inhibition constant (K(ii)) relative to physiological brain glucose content, we found that the apparent affinity constant (K(t)) was better determined with the four-state conformational model of glucose transport than with any of the other models tested. Furthermore, we confirmed the utility of the present method to determine glucose transport and consumption by analysing the modulation of both glucose transport and consumption by anaesthesia conditions that modify cerebral activity. In particular, deep thiopental anaesthesia caused a significant reduction of both T(max) and cerebral metabolic rate for glucose consumption. In conclusion, dynamic measurement of brain glucose in vivo in function of plasma glucose allows robust determination of both glucose uptake and consumption kinetics.

A novel HIV vaccine adjuvanted by IC31 induces robust and persistent humoral and cellular immunity.

The HIV vaccine strategy that, to date, generated immune protection consisted of a prime-boost regimen using a canarypox vector and an HIV envelope protein with alum, as shown in the RV144 trial. Since the efficacy was weak, and previous HIV vaccine trials designed to generate antibody responses failed, we hypothesized that generation of T cell responses would result in improved protection. Thus, we tested the immunogenicity of a similar envelope-based vaccine using a mouse model, with two modifications: a clade C CN54gp140 HIV envelope protein was adjuvanted by the TLR9 agonist IC31®, and the viral vector was the vaccinia strain NYVAC-CN54 expressing HIV envelope gp120. The use of IC31® facilitated immunoglobulin isotype switching, leading to the production of Env-specific IgG2a, as compared to protein with alum alone. Boosting with NYVAC-CN54 resulted in the generation of more robust Th1 T cell responses. Moreover, gp140 prime with IC31® and alum followed by NYVAC-CN54 boost resulted in the formation and persistence of central and effector memory populations in the spleen and an effector memory population in the gut. Our data suggest that this regimen is promising and could improve the protection rate by eliciting strong and long-lasting humoral and cellular immune responses.

An assurance-based model to holistically assess the information security posture

EXECUTIVE SUMMARY : Evaluating Information Security Posture within an organization is becoming a very complex task. Currently, the evaluation and assessment of Information Security are commonly performed using frameworks, methodologies and standards which often consider the various aspects of security independently. Unfortunately this is ineffective because it does not take into consideration the necessity of having a global and systemic multidimensional approach to Information Security evaluation. At the same time the overall security level is globally considered to be only as strong as its weakest link. This thesis proposes a model aiming to holistically assess all dimensions of security in order to minimize the likelihood that a given threat will exploit the weakest link. A formalized structure taking into account all security elements is presented; this is based on a methodological evaluation framework in which Information Security is evaluated from a global perspective. This dissertation is divided into three parts. Part One: Information Security Evaluation issues consists of four chapters. Chapter 1 is an introduction to the purpose of this research purpose and the Model that will be proposed. In this chapter we raise some questions with respect to "traditional evaluation methods" as well as identifying the principal elements to be addressed in this direction. Then we introduce the baseline attributes of our model and set out the expected result of evaluations according to our model. Chapter 2 is focused on the definition of Information Security to be used as a reference point for our evaluation model. The inherent concepts of the contents of a holistic and baseline Information Security Program are defined. Based on this, the most common roots-of-trust in Information Security are identified. Chapter 3 focuses on an analysis of the difference and the relationship between the concepts of Information Risk and Security Management. Comparing these two concepts allows us to identify the most relevant elements to be included within our evaluation model, while clearing situating these two notions within a defined framework is of the utmost importance for the results that will be obtained from the evaluation process. Chapter 4 sets out our evaluation model and the way it addresses issues relating to the evaluation of Information Security. Within this Chapter the underlying concepts of assurance and trust are discussed. Based on these two concepts, the structure of the model is developed in order to provide an assurance related platform as well as three evaluation attributes: "assurance structure", "quality issues", and "requirements achievement". Issues relating to each of these evaluation attributes are analysed with reference to sources such as methodologies, standards and published research papers. Then the operation of the model is discussed. Assurance levels, quality levels and maturity levels are defined in order to perform the evaluation according to the model. Part Two: Implementation of the Information Security Assurance Assessment Model (ISAAM) according to the Information Security Domains consists of four chapters. This is the section where our evaluation model is put into a welldefined context with respect to the four pre-defined Information Security dimensions: the Organizational dimension, Functional dimension, Human dimension, and Legal dimension. Each Information Security dimension is discussed in a separate chapter. For each dimension, the following two-phase evaluation path is followed. The first phase concerns the identification of the elements which will constitute the basis of the evaluation: ? Identification of the key elements within the dimension; ? Identification of the Focus Areas for each dimension, consisting of the security issues identified for each dimension; ? Identification of the Specific Factors for each dimension, consisting of the security measures or control addressing the security issues identified for each dimension. The second phase concerns the evaluation of each Information Security dimension by: ? The implementation of the evaluation model, based on the elements identified for each dimension within the first phase, by identifying the security tasks, processes, procedures, and actions that should have been performed by the organization to reach the desired level of protection; ? The maturity model for each dimension as a basis for reliance on security. For each dimension we propose a generic maturity model that could be used by every organization in order to define its own security requirements. Part three of this dissertation contains the Final Remarks, Supporting Resources and Annexes. With reference to the objectives of our thesis, the Final Remarks briefly analyse whether these objectives were achieved and suggest directions for future related research. Supporting resources comprise the bibliographic resources that were used to elaborate and justify our approach. Annexes include all the relevant topics identified within the literature to illustrate certain aspects of our approach. Our Information Security evaluation model is based on and integrates different Information Security best practices, standards, methodologies and research expertise which can be combined in order to define an reliable categorization of Information Security. After the definition of terms and requirements, an evaluation process should be performed in order to obtain evidence that the Information Security within the organization in question is adequately managed. We have specifically integrated into our model the most useful elements of these sources of information in order to provide a generic model able to be implemented in all kinds of organizations. The value added by our evaluation model is that it is easy to implement and operate and answers concrete needs in terms of reliance upon an efficient and dynamic evaluation tool through a coherent evaluation system. On that basis, our model could be implemented internally within organizations, allowing them to govern better their Information Security. RÉSUMÉ : Contexte général de la thèse L'évaluation de la sécurité en général, et plus particulièrement, celle de la sécurité de l'information, est devenue pour les organisations non seulement une mission cruciale à réaliser, mais aussi de plus en plus complexe. A l'heure actuelle, cette évaluation se base principalement sur des méthodologies, des bonnes pratiques, des normes ou des standards qui appréhendent séparément les différents aspects qui composent la sécurité de l'information. Nous pensons que cette manière d'évaluer la sécurité est inefficiente, car elle ne tient pas compte de l'interaction des différentes dimensions et composantes de la sécurité entre elles, bien qu'il soit admis depuis longtemps que le niveau de sécurité globale d'une organisation est toujours celui du maillon le plus faible de la chaîne sécuritaire. Nous avons identifié le besoin d'une approche globale, intégrée, systémique et multidimensionnelle de l'évaluation de la sécurité de l'information. En effet, et c'est le point de départ de notre thèse, nous démontrons que seule une prise en compte globale de la sécurité permettra de répondre aux exigences de sécurité optimale ainsi qu'aux besoins de protection spécifiques d'une organisation. Ainsi, notre thèse propose un nouveau paradigme d'évaluation de la sécurité afin de satisfaire aux besoins d'efficacité et d'efficience d'une organisation donnée. Nous proposons alors un modèle qui vise à évaluer d'une manière holistique toutes les dimensions de la sécurité, afin de minimiser la probabilité qu'une menace potentielle puisse exploiter des vulnérabilités et engendrer des dommages directs ou indirects. Ce modèle se base sur une structure formalisée qui prend en compte tous les éléments d'un système ou programme de sécurité. Ainsi, nous proposons un cadre méthodologique d'évaluation qui considère la sécurité de l'information à partir d'une perspective globale. Structure de la thèse et thèmes abordés Notre document est structuré en trois parties. La première intitulée : « La problématique de l'évaluation de la sécurité de l'information » est composée de quatre chapitres. Le chapitre 1 introduit l'objet de la recherche ainsi que les concepts de base du modèle d'évaluation proposé. La maniéré traditionnelle de l'évaluation de la sécurité fait l'objet d'une analyse critique pour identifier les éléments principaux et invariants à prendre en compte dans notre approche holistique. Les éléments de base de notre modèle d'évaluation ainsi que son fonctionnement attendu sont ensuite présentés pour pouvoir tracer les résultats attendus de ce modèle. Le chapitre 2 se focalise sur la définition de la notion de Sécurité de l'Information. Il ne s'agit pas d'une redéfinition de la notion de la sécurité, mais d'une mise en perspectives des dimensions, critères, indicateurs à utiliser comme base de référence, afin de déterminer l'objet de l'évaluation qui sera utilisé tout au long de notre travail. Les concepts inhérents de ce qui constitue le caractère holistique de la sécurité ainsi que les éléments constitutifs d'un niveau de référence de sécurité sont définis en conséquence. Ceci permet d'identifier ceux que nous avons dénommés « les racines de confiance ». Le chapitre 3 présente et analyse la différence et les relations qui existent entre les processus de la Gestion des Risques et de la Gestion de la Sécurité, afin d'identifier les éléments constitutifs du cadre de protection à inclure dans notre modèle d'évaluation. Le chapitre 4 est consacré à la présentation de notre modèle d'évaluation Information Security Assurance Assessment Model (ISAAM) et la manière dont il répond aux exigences de l'évaluation telle que nous les avons préalablement présentées. Dans ce chapitre les concepts sous-jacents relatifs aux notions d'assurance et de confiance sont analysés. En se basant sur ces deux concepts, la structure du modèle d'évaluation est développée pour obtenir une plateforme qui offre un certain niveau de garantie en s'appuyant sur trois attributs d'évaluation, à savoir : « la structure de confiance », « la qualité du processus », et « la réalisation des exigences et des objectifs ». Les problématiques liées à chacun de ces attributs d'évaluation sont analysées en se basant sur l'état de l'art de la recherche et de la littérature, sur les différentes méthodes existantes ainsi que sur les normes et les standards les plus courants dans le domaine de la sécurité. Sur cette base, trois différents niveaux d'évaluation sont construits, à savoir : le niveau d'assurance, le niveau de qualité et le niveau de maturité qui constituent la base de l'évaluation de l'état global de la sécurité d'une organisation. La deuxième partie: « L'application du Modèle d'évaluation de l'assurance de la sécurité de l'information par domaine de sécurité » est elle aussi composée de quatre chapitres. Le modèle d'évaluation déjà construit et analysé est, dans cette partie, mis dans un contexte spécifique selon les quatre dimensions prédéfinies de sécurité qui sont: la dimension Organisationnelle, la dimension Fonctionnelle, la dimension Humaine, et la dimension Légale. Chacune de ces dimensions et son évaluation spécifique fait l'objet d'un chapitre distinct. Pour chacune des dimensions, une évaluation en deux phases est construite comme suit. La première phase concerne l'identification des éléments qui constituent la base de l'évaluation: ? Identification des éléments clés de l'évaluation ; ? Identification des « Focus Area » pour chaque dimension qui représentent les problématiques se trouvant dans la dimension ; ? Identification des « Specific Factors » pour chaque Focus Area qui représentent les mesures de sécurité et de contrôle qui contribuent à résoudre ou à diminuer les impacts des risques. La deuxième phase concerne l'évaluation de chaque dimension précédemment présentées. Elle est constituée d'une part, de l'implémentation du modèle général d'évaluation à la dimension concernée en : ? Se basant sur les éléments spécifiés lors de la première phase ; ? Identifiant les taches sécuritaires spécifiques, les processus, les procédures qui auraient dû être effectués pour atteindre le niveau de protection souhaité. D'autre part, l'évaluation de chaque dimension est complétée par la proposition d'un modèle de maturité spécifique à chaque dimension, qui est à considérer comme une base de référence pour le niveau global de sécurité. Pour chaque dimension nous proposons un modèle de maturité générique qui peut être utilisé par chaque organisation, afin de spécifier ses propres exigences en matière de sécurité. Cela constitue une innovation dans le domaine de l'évaluation, que nous justifions pour chaque dimension et dont nous mettons systématiquement en avant la plus value apportée. La troisième partie de notre document est relative à la validation globale de notre proposition et contient en guise de conclusion, une mise en perspective critique de notre travail et des remarques finales. Cette dernière partie est complétée par une bibliographie et des annexes. Notre modèle d'évaluation de la sécurité intègre et se base sur de nombreuses sources d'expertise, telles que les bonnes pratiques, les normes, les standards, les méthodes et l'expertise de la recherche scientifique du domaine. Notre proposition constructive répond à un véritable problème non encore résolu, auquel doivent faire face toutes les organisations, indépendamment de la taille et du profil. Cela permettrait à ces dernières de spécifier leurs exigences particulières en matière du niveau de sécurité à satisfaire, d'instancier un processus d'évaluation spécifique à leurs besoins afin qu'elles puissent s'assurer que leur sécurité de l'information soit gérée d'une manière appropriée, offrant ainsi un certain niveau de confiance dans le degré de protection fourni. Nous avons intégré dans notre modèle le meilleur du savoir faire, de l'expérience et de l'expertise disponible actuellement au niveau international, dans le but de fournir un modèle d'évaluation simple, générique et applicable à un grand nombre d'organisations publiques ou privées. La valeur ajoutée de notre modèle d'évaluation réside précisément dans le fait qu'il est suffisamment générique et facile à implémenter tout en apportant des réponses sur les besoins concrets des organisations. Ainsi notre proposition constitue un outil d'évaluation fiable, efficient et dynamique découlant d'une approche d'évaluation cohérente. De ce fait, notre système d'évaluation peut être implémenté à l'interne par l'entreprise elle-même, sans recourir à des ressources supplémentaires et lui donne également ainsi la possibilité de mieux gouverner sa sécurité de l'information.

Selective inhibition of JNK with a peptide inhibitor attenuates pain hypersensitivity and tumor growth in a mouse skin cancer pain model.

Cancer pain significantly affects the quality of cancer patients, and current treatments for this pain are limited. C-Jun N-terminal kinase (JNK) has been implicated in tumor growth and neuropathic pain sensitization. We investigated the role of JNK in cancer pain and tumor growth in a skin cancer pain model. Injection of luciferase-transfected B16-Fluc melanoma cells into a hindpaw of mouse induced robust tumor growth, as indicated by increase in paw volume and fluorescence intensity. Pain hypersensitivity in this model developed rapidly (<5 days) and reached a peak in 2 weeks, and was characterized by mechanical allodynia and heat hyperalgesia. Tumor growth was associated with JNK activation in tumor mass, dorsal root ganglion (DRG), and spinal cord and a peripheral neuropathy, such as loss of nerve fibers in the hindpaw skin and induction of ATF-3 expression in DRG neurons. Repeated systemic injections of D-JNKI-1 (6 mg/kg, i.p.), a selective and cell-permeable peptide inhibitor of JNK, produced an accumulative inhibition of mechanical allodynia and heat hyperalgesia. A bolus spinal injection of D-JNKI-1 also inhibited mechanical allodynia. Further, JNK inhibition suppressed tumor growth in vivo and melanoma cell proliferation in vitro. In contrast, repeated injections of morphine (5 mg/kg), a commonly used analgesic for terminal cancer, produced analgesic tolerance after 1 day and did not inhibit tumor growth. Our data reveal a marked peripheral neuropathy in this skin cancer model and important roles of the JNK pathway in cancer pain development and tumor growth. JNK inhibitors such as D-JNKI-1 may be used to treat cancer pain.

Robust Nonlinear Mapping of Soil Contamination Using Support Regression

Spatial data analysis mapping and visualization is of great importance in various fields: environment, pollution, natural hazards and risks, epidemiology, spatial econometrics, etc. A basic task of spatial mapping is to make predictions based on some empirical data (measurements). A number of state-of-the-art methods can be used for the task: deterministic interpolations, methods of geostatistics: the family of kriging estimators (Deutsch and Journel, 1997), machine learning algorithms such as artificial neural networks (ANN) of different architectures, hybrid ANN-geostatistics models (Kanevski and Maignan, 2004; Kanevski et al., 1996), etc. All the methods mentioned above can be used for solving the problem of spatial data mapping. Environmental empirical data are always contaminated/corrupted by noise, and often with noise of unknown nature. That's one of the reasons why deterministic models can be inconsistent, since they treat the measurements as values of some unknown function that should be interpolated. Kriging estimators treat the measurements as the realization of some spatial randomn process. To obtain the estimation with kriging one has to model the spatial structure of the data: spatial correlation function or (semi-)variogram. This task can be complicated if there is not sufficient number of measurements and variogram is sensitive to outliers and extremes. ANN is a powerful tool, but it also suffers from the number of reasons. of a special type ? multiplayer perceptrons ? are often used as a detrending tool in hybrid (ANN+geostatistics) models (Kanevski and Maignank, 2004). Therefore, development and adaptation of the method that would be nonlinear and robust to noise in measurements, would deal with the small empirical datasets and which has solid mathematical background is of great importance. The present paper deals with such model, based on Statistical Learning Theory (SLT) - Support Vector Regression. SLT is a general mathematical framework devoted to the problem of estimation of the dependencies from empirical data (Hastie et al, 2004; Vapnik, 1998). SLT models for classification - Support Vector Machines - have shown good results on different machine learning tasks. The results of SVM classification of spatial data are also promising (Kanevski et al, 2002). The properties of SVM for regression - Support Vector Regression (SVR) are less studied. First results of the application of SVR for spatial mapping of physical quantities were obtained by the authorsin for mapping of medium porosity (Kanevski et al, 1999), and for mapping of radioactively contaminated territories (Kanevski and Canu, 2000). The present paper is devoted to further understanding of the properties of SVR model for spatial data analysis and mapping. Detailed description of the SVR theory can be found in (Cristianini and Shawe-Taylor, 2000; Smola, 1996) and basic equations for the nonlinear modeling are given in section 2. Section 3 discusses the application of SVR for spatial data mapping on the real case study - soil pollution by Cs137 radionuclide. Section 4 discusses the properties of the modelapplied to noised data or data with outliers.

Self-reported adherence and associated factors regarding antihypertensive medication in Seychelles

Introduction. Adherence to medication for asymptomatic disease is often low. We assessed factors associated with good adherence to medication for high blood pressure (HBP) in a country of the African region. Methods. A population-based survey of adults aged 25-64 years (N=1240 and participation rate=73%). Information was available in knowledge attitude and practice, SES and other variables. One question assessed adherence. Good adherence to treatment was defined as answering "I forget very rarely" vs "I forget on 1-2 days in a week" or "I forget on 3 or more days in a week". Results. In a univariate model adherence was strongly associated with belief that hypertension is a long-term disease (OR 2.6, p<0.001) and was negatively associated with concomitant use of traditional medicine (OR 0.36, p<0.005). The following variables tended to be associated with good adherence for HBP treatment: age, SES, BMI, belief that HBP is not symptomatic, going to government's clinics, medium stress level, controlled hypertension, taking statins. The following variables were not associated with good adherence for HBP treatment: education, higher BP, knowing people who had a stroke/MI, suffering from another chronic condition. In a multivariate model, pseudo R2 was 0.14. Conclusion. We built a multidimensional model including a wide range of variable. This model only predicted 14% of adherence variability. Variables associated with good adherence were demographics or related to knowledge attitude and practice. The latter one is modifiable by different type of interventions.

Weber's law in decision making: integrating behavioral data in humans with a neurophysiological model

Recent single-cell studies in monkeys (Romo et al., 2004) show that the activity of neurons in the ventral premotor cortex covaries with the animal's decisions in a perceptual comparison task regarding the frequency of vibrotactile events. The firing rate response of these neurons was dependent only on the frequency differences between the two applied vibrations, the sign of that difference being the determining factor for correct task performance. We present a biophysically realistic neurodynamical model that can account for the most relevant characteristics of this decision-making-related neural activity. One of the nontrivial predictions of this model is that Weber's law will underlie the perceptual discrimination behavior. We confirmed this prediction in behavioral tests of vibrotactile discrimination in humans and propose a computational explanation of perceptual discrimination that accounts naturally for the emergence of Weber's law. We conclude that the neurodynamical mechanisms and computational principles underlying the decision-making processes in this perceptual discrimination task are consistent with a fluctuation-driven scenario in a multistable regime.

Work Zone Simulation Model, 1999

To support the analysis of driver behavior at rural freeway work zone lane closure merge points, Center for Transportation Research and Education staff collected traffic data at merge areas using video image processing technology. The collection of data and the calculation of the capacity of lane closures are reported in a companion report, "Traffic Management Strategies for Merge Areas in Rural Interstate Work Zones". These data are used in the work reported in this document and are used to calibrate a microscopic simulation model of a typical, Iowa rural freeway lane closure. The model developed is a high fidelity computer simulation with an animation interface. It simulates traffic operations at a work zone lane closure. This model enables traffic engineers to visually demonstrate the forecasted delay that is likely to result when freeway reconstruction makes it necessary to close freeway lanes. Further, the model is also sensitive to variations in driver behavior and is used to test the impact of slow moving vehicles and other driver behaviors. This report consists of two parts. The first part describes the development of the work zone simulation model. The simulation analysis is calibrated and verified through data collected at a work zone in Interstate Highway 80 in Scott County, Iowa. The second part is a user's manual for the simulation model, which is provided to assist users with its set up and operation. No prior computer programming skills are required to use the simulation model.

Social and medical vulnerability factors of frequent users at the emergency department: a case-control study

BACKGROUND: Up to 5% of patients presenting to the emergency department (ED) four or more times within a 12 month period represent 21% of total ED visits. In this study we sought to characterize social and medical vulnerability factors of ED frequent users (FUs) and to explore if these factors hold simultaneously. METHODS: We performed a case-control study at Lausanne University Hospital, Switzerland. Patients over 18 years presenting to the ED at least once within the study period (April 2008 toMarch 2009) were included. FUs were defined as patients with four or more ED visits within the previous 12 months. Outcome data were extracted from medical records of the first ED attendance within the study period. Outcomes included basic demographics and social variables, ED admission diagnosis, somatic and psychiatric days hospitalized over 12 months, and having a primary care physician.We calculated the percentage of FUs and non-FUs having at least one social and one medical vulnerability factor. The four chosen social factors included: unemployed and/or dependence on government welfare, institutionalized and/or without fixed residence, either separated, divorced or widowed, and under guardianship. The fourmedical vulnerability factors were: ≥6 somatic days hospitalized, ≥1 psychiatric days hospitalized, ≥5 clinical departments used (all three factors measured over 12 months), and ED admission diagnosis of alcohol and/or drug abuse. Univariate and multivariate logistical regression analyses allowed comparison of two JGIM ABSTRACTS S391 random samples of 354 FUs and 354 non-FUs (statistical power 0.9, alpha 0.05 for all outcomes except gender, country of birth, and insurance type). RESULTS: FUs accounted for 7.7% of ED patients and 24.9% of ED visits. Univariate logistic regression showed that FUs were older (mean age 49.8 vs. 45.2 yrs, p=0.003),more often separated and/or divorced (17.5%vs. 13.9%, p=0.029) or widowed (13.8% vs. 8.8%, p=0.029), and either unemployed or dependent on government welfare (31.3% vs. 13.3%, p<0.001), compared to non-FUs. FUs cumulated more days hospitalized over 12 months (mean number of somatic days per patient 1.0 vs. 0.3, p<0.001; mean number of psychiatric days per patient 0.12 vs. 0.03, p<0.001). The two groups were similar regarding gender distribution (females 51.7% vs. 48.3%). The multivariate linear regression model was based on the six most significant factors identified by univariate analysis The model showed that FUs had more social problems, as they were more likely to be institutionalized or not have a fixed residence (OR 4.62; 95% CI, 1.65 to 12.93), and to be unemployed or dependent on government welfare (OR 2.03; 95% CI, 1.31 to 3.14) compared to non-FUs. FUs were more likely to need medical care, as indicated by involvement of≥5 clinical departments over 12 months (OR 6.2; 95%CI, 3.74 to 10.15), having an ED admission diagnosis of substance abuse (OR 3.23; 95% CI, 1.23 to 8.46) and having a primary care physician (OR 1.70;95%CI, 1.13 to 2.56); however, they were less likely to present with an admission diagnosis of injury (OR 0.64; 95% CI, 0.40 to 1.00) compared to non-FUs. FUs were more likely to combine at least one social with one medical vulnerability factor (38.4% vs. 12.1%, OR 7.74; 95% CI 5.03 to 11.93). CONCLUSIONS: FUs were more likely than non-FUs to have social and medical vulnerability factors and to have multiple factors in combination.

Robust Detection of Incumbents in Cognitive Radio Networks Using Groups

Cognitive radio is a wireless technology aimed at improvingthe efficiency use of the radio-electric spectrum, thus facilitating a reductionin the load on the free frequency bands. Cognitive radio networkscan scan the spectrum and adapt their parameters to operate in the unoccupiedbands. To avoid interfering with licensed users operating on a givenchannel, the networks need to be highly sensitive, which is achieved byusing cooperative sensing methods. Current cooperative sensing methodsare not robust enough against occasional or continuous attacks. This articleoutlines a Group Fusion method that takes into account the behavior ofusers over the short and long term. On fusing the data, the method is basedon giving more weight to user groups that are more unanimous in their decisions.Simulations have been performed in a dynamic environment withinterferences. Results prove that when attackers are present (both reiterativeor sporadic), the proposed Group Fusion method has superior sensingcapability than other methods.

An Improved Hybrid Model for the Generic Hoist Scheduling Problem

Peer-reviewed