Open source software in translator training

La formació de traductors implica l´ús de procediments i eines que permetin els estudiants familiaritzar-se amb contextos professionals. El software lliure especialitzat inclou eines de qualitat professional i procediments accessibles per a les institucions acadèmiques i els estudiants a distància que treballen a casa seva. Els projectes reals que utilitzen software lliure i traducció col·laborativa (crowdsourcing) constitueixen recursos indispensables en la formació de traductors.

Optimal CD8 T cell responsiveness is controlled within a defined TCR affinity window

Protective immune responses relyon TCR-mediated recognition of antigenspresented by MHC molecules. Tcells directed against tumor antigensare thought to express TCRs of loweraffinity/avidity than pathogen-specificT lymphocytes. An attractivestrategy to improve anti-tumor T cellresponses is to adoptively transferCD8+ T cells engineered with TCRsof optimized affinity. However, themechanisms that control optimal Tcell activation and responsiveness remainpoorly defined. We aim at characterizingTCR-pMHC binding parametersand downstream signalingevents that regulate T cell functionalityby using an in silico designedpanel of tumor antigen-specific TCRsof incremental affinity for pMHC(Kd100 M- 15 nM).We found that optimalT cell responses (cytokine secretionand target cell killing) occurredwithin a well-defined window ofTCR-pMHC binding affinity (5 M-1 M), while drastic functional declinewas detected in T cells expressingvery low and very high TCRaffinities,which was not caused by any increasein apoptosis. Whole-genomemicroarray analysis revealed that Tcells with optimal TCR affinitieshighly up-regulated transcription ofgenes typical of T cell activation (i.e.IFN-, NF-B and TNFR), while reducedexpression was detected in Tcells of very low or very high TCR affinity.Strikingly, hierarchical clusteringshowed that the latter two variantsclustered together with the un-stimulatedcontrol Tcells.Yet, despite commonclustering, several genes seemedto be differentially expressed, suggestingthat the mechanisms involvedin this "unresponsiveness state" maydiffer between those two variants. Finally,calcium influx assays also demonstratedattenuated responses in Tcells of very high TCR affinity. Ourresults indicate that optimal T cellfunction is tightly controlled within adefinedTCRaffinity window throughvery proximal TCR-mediated mechanisms,possibly at the TCR-pMHCbinding interface. Uncovering themechanisms regulating optimal/maximalT cell function is essential to understandand promote therapeutic designlike adoptive T cell therapy.

Evaluación y optimización de los servicios de un taller de mantenimiento mediante el software de simulación ARENA

Debido a la necesidad de diferenciarse y hacer frente a la competencia, las empresas han apostado por desarrollar operaciones que den valor al cliente, por eso muchas de ellas han visto en las herramientas lean la oportunidad para mejorar sus operaciones. Esta mejora implica la reducción de dinero, personas, equipos grandes, inventario y espacio, con dos objetivos: eliminar despilfarro y reducir la variabilidad. Para conseguir los objetivos estratégicos de la empresa es imprescindible qué éstos estén alineados con los planes de la gerencia a nivel medio y a su vez con el trabajo realizado por los empleados para asegurar que cada persona está alineada en la misma dirección y al mismo tiempo. Ésta es la filosofía de la planificación estratégica. Por ello uno de los objetivos de este proyecto será el desarrollar una herramienta que facilite la exposición de los objetivos de la empresa y la comunicación de los mismos a todos los niveles de la organización para a partir de ellos y tomando como referencia la necesidad de reducir inventarios en la cadena de suministro se realizará un estudio de la producción de un componente de control del aerogenerador para conseguir nivelarla y reducir su inventario de producto terminado. Los objetivos particulares en este apartado serán reducir el inventario en un 28%, nivelar la producción reduciendo la variabilidad del 31% al 24%, mantener un stock máximo de 24 unidades garantizando el suministro ante una demanda variable, incrementar la rotación del inventario en un 10% y establecer un plan de acción para reducir el lead time entre un 40-50%. Todo ello será posible gracias a la realización del mapa de valor presente y futuro para eliminar desperdicios y crear un flujo continuo y el cálculo de un supermercado que mantenga el stock en un nivel óptimo.

Las TIC como instrumento para la colaboración en red, en el ámbito de la Formación Profesional: Una aplicación de Software Libre para colaboración entre centro-empresa en el I.E.S El Palau.

En la era digital actual, Internet forma parte de nuestras vidas, y ha aportado cambios a lasociedad globalizada. Algunos de estos cambios nos permiten nuevas formas de relacionarnos y degestionar el conocimiento, dando sentido al término que hoy entendemos como sociedad-red.Por eso, en el entorno que nos envuelve existen continuamente acciones colaborativas globales quefomentan la comunicación y se comparte información de diversos tipos, con la finalidad deaprender y mantenerse constantemente informado. Específicamente, los centros educativos no sequedan al margen ya que requiere preparar estudiantes para esta sociedad.Estos cambios en la sociedad presentan grandes desafíos para el centro educativo, que nopermiten ser afrontados solamente desde el aula. Los centros requieren adaptarse a un modelocompatible con la sociedad-red, y por ello, se sugieren un modelo centro-red, que presente unaestructura de una organización compatible con la era en el que estamos inmersos.Las redes de colaboración en los centros permite intercambiar información y aportar valor a laeducación con el objetivo de la mejora educativa. En este sentido, los centros educativos debendisponer de características que permitan ser flexibles, adaptarse a los agentes y organizaciones quele envuelven. Pero la estructura actual de un centro educativo es rígida y por tanto esta evoluciónrepresenta uno de los mayores desafíos para el sistema educativo.En esta linea, en los centros de Formación Profesional existe una tendencia hacia modeloscolaborativos con el tejido empresarial, entre otros agentes, y es en este punto donde este proyectopretende centrar el foco de la investigación. Con más exactitud, en la creación de una red decolaboración con el agente que el centro educativo seleccione.Específicamente las TIC forman un papel esencial, y se deben poner al servicio del problemaque apuntábamos para ayudar a solventarlo. En este sentido, es adecuado un diseño del artefactocon Software Libre que tiene múltiples beneficios para este objetivo, pero que destacamos el que ami parecer es el más importante; la vinculación con la filosofía de compartir el conocimiento, quegarantiza la simbiosis con la red colaborativa y es por esta razón que el tema de la investigación esrelevante para el centro educativo.Tal y como se mencionaba previamente, las TIC pueden ayudar a fomentar la red colaborativa,pero no sólo el artefacto TIC generado en este proyecto debe cumplir características como laflexibilidad, también es crítico que el centro educativo y los agentes de la red interioricen la culturacolaborativa en sus acciones con la implicación y compromiso que se requiere. Pero como podemosPágina 6Universitat Oberta de Catalunya Trabajo Final de Máster - Software Libreimaginar, ese cambio de cultura, no es una tarea sencilla y presenta problemas. Para mitigarlos yfomentar la cultura en red, se requieren procesos específicos que permitan incorporarla en la medidade lo posible. Para ello, la combinación de la innovación sistémica y el diseño de la investigación eneducación resultan metodologías apropiadas.Por eso, investigaremos durante este proceso cómo las redes de colaboración y el SoftwareLibre permiten adaptar el centro al entorno, cómo pueden ayudar al centro a potenciar la FormaciónProfesional y garantizar la durabilidad de las acciones, con el objetivo que perdure el conocimientoy la propia red de colaboración para una mejora educativa.

Implantación de un sistema VoIP basado en software libre con Asterisk

Este proyecto busca analizar, diseñar e implementar una nueva solución de telefonía para el Centro Social de Oficiales de la Policía Nacional contemplando la posibilidad de optar por una migración hacia un sistema VoIP bajo software libre con Asterisk. En consecuencia, se deben evaluar las tecnologías actuales buscando proveer nuevas funcionalidades en el servicio telefónico generando bajos costos en su implementación, funcionamiento y mantenimiento.

A regulatory role for the cohesin loader NIPBL in nonhomologous end joining during immunoglobulin class switch recombination.

DNA double strand breaks (DSBs) are mainly repaired via homologous recombination (HR) or nonhomologous end joining (NHEJ). These breaks pose severe threats to genome integrity but can also be necessary intermediates of normal cellular processes such as immunoglobulin class switch recombination (CSR). During CSR, DSBs are produced in the G1 phase of the cell cycle and are repaired by the classical NHEJ machinery. By studying B lymphocytes derived from patients with Cornelia de Lange Syndrome, we observed a strong correlation between heterozygous loss-of-function mutations in the gene encoding the cohesin loading protein NIPBL and a shift toward the use of an alternative, microhomology-based end joining during CSR. Furthermore, the early recruitment of 53BP1 to DSBs was reduced in the NIPBL-deficient patient cells. Association of NIPBL deficiency and impaired NHEJ was also observed in a plasmid-based end-joining assay and a yeast model system. Our results suggest that NIPBL plays an important and evolutionarily conserved role in NHEJ, in addition to its canonical function in sister chromatid cohesion and its recently suggested function in HR.

Methods for Estimating Annual Exceedance-Probability Discharges for Streams in Iowa, Based on Data through Water Year 2010, TR-519, 2013

A statewide study was performed to develop regional regression equations for estimating selected annual exceedance- probability statistics for ungaged stream sites in Iowa. The study area comprises streamgages located within Iowa and 50 miles beyond the State’s borders. Annual exceedanceprobability estimates were computed for 518 streamgages by using the expected moments algorithm to fit a Pearson Type III distribution to the logarithms of annual peak discharges for each streamgage using annual peak-discharge data through 2010. The estimation of the selected statistics included a Bayesian weighted least-squares/generalized least-squares regression analysis to update regional skew coefficients for the 518 streamgages. Low-outlier and historic information were incorporated into the annual exceedance-probability analyses, and a generalized Grubbs-Beck test was used to detect multiple potentially influential low flows. Also, geographic information system software was used to measure 59 selected basin characteristics for each streamgage. Regional regression analysis, using generalized leastsquares regression, was used to develop a set of equations for each flood region in Iowa for estimating discharges for ungaged stream sites with 50-, 20-, 10-, 4-, 2-, 1-, 0.5-, and 0.2-percent annual exceedance probabilities, which are equivalent to annual flood-frequency recurrence intervals of 2, 5, 10, 25, 50, 100, 200, and 500 years, respectively. A total of 394 streamgages were included in the development of regional regression equations for three flood regions (regions 1, 2, and 3) that were defined for Iowa based on landform regions and soil regions. Average standard errors of prediction range from 31.8 to 45.2 percent for flood region 1, 19.4 to 46.8 percent for flood region 2, and 26.5 to 43.1 percent for flood region 3. The pseudo coefficients of determination for the generalized leastsquares equations range from 90.8 to 96.2 percent for flood region 1, 91.5 to 97.9 percent for flood region 2, and 92.4 to 96.0 percent for flood region 3. The regression equations are applicable only to stream sites in Iowa with flows not significantly affected by regulation, diversion, channelization, backwater, or urbanization and with basin characteristics within the range of those used to develop the equations. These regression equations will be implemented within the U.S. Geological Survey StreamStats Web-based geographic information system tool. StreamStats allows users to click on any ungaged site on a river and compute estimates of the eight selected statistics; in addition, 90-percent prediction intervals and the measured basin characteristics for the ungaged sites also are provided by the Web-based tool. StreamStats also allows users to click on any streamgage in Iowa and estimates computed for these eight selected statistics are provided for the streamgage.

Iowa Bridge Backwater Software User Manual, Version 2.0, TR-564, 2010

This manual describes how to use the Iowa Bridge Backwater software. It also documents the methods and equations used for the calculations. The main body describes how to use the software and the appendices cover technical aspects. The Bridge Backwater software performs 5 main tasks: Design Discharge Estimation; Stream Rating Curves; Floodway Encroachment; Bridge Backwater; and Bridge Scour. The intent of this program is to provide a simplified method for analysis of bridge backwater for rural structures located in areas with low flood damage potential. The software is written in Microsoft Visual Basic 6.0. It will run under Windows 95 or newer versions (i.e. Windows 98, NT, 2000, XP and later).

Maximising the duration of disease control in metastatic renal cell carcinoma with targeted agents: an expert agreement.

With six targeted agents approved (sorafenib, sunitinib, temsirolimus, bevacizumab [+interferon], everolimus and pazopanib), many patients with metastatic renal cell carcinoma (mRCC) will receive multiple therapies. However, the optimum sequencing approach has not been defined. A group of European experts reviewed available data and shared their clinical experience to compile an expert agreement on the sequential use of targeted agents in mRCC. To date, there are few prospective studies of sequential therapy. The mammalian target of rapamycin (mTOR) inhibitor everolimus was approved for use in patients who failed treatment with inhibitors of vascular endothelial growth factor (VEGF) and VEGF receptors (VEGFR) based on the results from a Phase III placebo-controlled study; however, until then, the only licensed agents across the spectrum of mRCC were VEGF(R) inhibitors (sorafenib, sunitinib and bevacizumab + interferon), and as such, a large body of evidence has accumulated regarding their use in sequence. Data show that sequential use of VEGF(R) inhibitors may be an effective treatment strategy to achieve prolonged clinical benefit. The optimal place of each targeted agent in the treatment sequence is still unclear, and data from large prospective studies are needed. The Phase III AXIS study of second-line sorafenib vs. axitinib (including post-VEGF(R) inhibitors) has completed, but the data are not yet published; other ongoing studies include the Phase III SWITCH study of sorafenib-sunitinib vs. sunitinib-sorafenib (NCT00732914); the Phase III 404 study of temsirolimus vs. sorafenib post-sunitinib (NCT00474786) and the Phase II RECORD 3 study of sunitinib-everolimus vs. everolimus-sunitinib (NCT00903175). Until additional data are available, consideration of patient response and tolerability to treatment may facilitate current decision-making regarding when to switch and which treatment to switch to in real-life clinical practice.

Pintoresco : Evaluación de los procesos de aprendizaje conceptual

El documento recoge los hitos del desarrollo de Pintoresco, una aplicación orientada a la evaluación de los procesos de aprendizaje conceptual que se examinan a partir de su uso por usuario ordinario. La aplicación permite obtener datos del proceso de aprendizaje conceptual en un contexto en que la estructura interna de las clases en que se produce una partición no depende de las propiedades específicas de los patrones de estímulo que son distintos para cada usuario sino de la forma lógica de la propia partición.

Ingeniería del software: portal web inmobiliario

En la actualidad las tecnologías de la información son utilizadas en todos los ámbitos empresariales. Desde sistemas de gestión (ERPs) pasando por la gestión documental, el análisis de información con sistema de Bussines Intelligence, pudiendo incluso convertirse en toda una nueva plataforma para proveer a las empresas de nuevos canales de venta, como es el caso deInternet.De la necesidad inicial de nuestro cliente en comenzar a expandirse por un nuevo canal de venta para poder llegar a nuevos mercados y diversificar sus clientes se inicia la motivación de este TFC.Dadas las características actuales de las tecnologías de la información e internet, estas conforman un binomio perfecto para definir este TFC que trata todos los aspectos necesarios para llegar a obtener un producto final como es un portal web inmobiliario adaptado a los requisitos demandados por los usuarios actuales de Internet.

Tissue homing and persistence of defined antigen-specific CD8+ tumor-reactive T-cell clones in long-term melanoma survivors.

Tumor antigen-specific cytotoxic T cells (CTLs) play a major role in the adaptive immune response to cancers. This CTL response is often insufficient because of functional impairment, tumor escape mechanisms, or inhibitory tumor microenvironment. However, little is known about the fate of given tumor-specific CTL clones in cancer patients. Studies in patients with favorable outcomes may be very informative. In this longitudinal study, we tracked, quantified, and characterized functionally defined antigen-specific T-cell clones ex vivo, in peripheral blood and at tumor sites, in two long-term melanoma survivors. MAGE-A10-specific CD8+ T-cell clones with high avidity to antigenic peptide and tumor lytic capabilities persisted in peripheral blood over more than 10 years, with quantitative variations correlating with the clinical course. These clones were also found in emerging metastases, and, in one patient, circulating clonal T cells displayed a fully differentiated effector phenotype at the time of relapse. Longevity, tumor homing, differentiation phenotype, and quantitative adaptation to the disease phases suggest the contribution of the tracked tumor-reactive clones in the tumor control of these long-term metastatic survivor patients. Focusing research on patients with favorable outcomes may help to identify parameters that are crucial for an efficient antitumor response and to optimize cancer immunotherapy.

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.

Analysis of TNF-antagonist switch over time and associated risk factors in the Swiss Inflammatory Bowel Disease Cohort.

Abstract Background and aims. Limited data from large cohorts are available on tumor necrosis factor (TNF) antagonists (infliximab, adalimumab, certolizumab pegol) switch over time. We aimed to evaluate the prevalence of switching from one TNF antagonist to another and to identify associated risk factors. Methods. Data from the Swiss Inflammatory Bowel Diseases Cohort Study (SIBDCS) were analyzed. Results. Of 1731 patients included into the SIBDCS (956 with Crohn's disease [CD] and 775 with ulcerative colitis [UC]), 347 CD patients (36.3%) and 129 UC patients (16.6%) were treated with at least one TNF antagonist. A total of 53/347 (15.3%) CD patients (median disease duration 9 years) and 20/129 (15.5%) of UC patients (median disease duration 7 years) needed to switch to a second and/or a third TNF antagonist, respectively. Median treatment duration was longest for the first TNF antagonist used (CD 25 months; UC 14 months), followed by the second (CD 13 months; UC 4 months) and third TNF antagonist (CD 11 months; UC 15 months). Primary nonresponse, loss of response and side effects were the major reasons to stop and/or switch TNF antagonist therapy. A low body mass index, a short diagnostic delay and extraintestinal manifestations at inclusion were identified as risk factors for a switch of the first used TNF antagonist within 24 months of its use in CD patients. Conclusion. Switching of the TNF antagonist over time is a common issue. The median treatment duration with a specific TNF antagonist is diminishing with an increasing number of TNF antagonists being used.

Las TIC como instrumento para la colaboración en red, en el ámbito de la Formación Profesional : Una aplicación de software libre para colaboración entre centro-empresa en el I.E.S El Palau

Investigaremos cómo las redes de colaboración y el softwarelibre permiten adaptar el centro educativo al entorno, cómo pueden ayudar al centro a potenciar la formación profesional y garantizar la durabilidad de las acciones, con el objetivo que perdure el conocimiento y la propia red de colaboración para una mejora educativa.