Recombinant " Physcomitrella patens " as a sensitive tool to study heat sensing and heat-shock signal transduction in plants

SUMMARY When exposed to heat stress, plants display a particular set of cellular and molecular responses, such as chaperones expression, which are highly conserved in all organisms. In chapter 1, I studied the ability of heat shock genes to become transiently and abundantly induced under various temperature regimes. To this aim, I designed a highly sensitive heat-shock dependent conditional gene expression system in the moss Physcomitrella patens, using the soybean heatinducible promoter (hsp17.3B). Heat-induced expression of various reporter genes was over three orders of magnitude, in tight correlation with the intensity and duration of the heat treatments. By performing repeated heating/cooling cycles, a massive accumulation of recombinant proteins was obtained. Interestingly, the hsp17.3B promoter was also activated by specific organic chemicals. Thus, in chapter 2, I took advantage of the extreme sensitivity of this promoter to small temperature variations to further address the role of various natural and organic chemicals and develop a plant based-bioassay that can serve as an early warning indicator of toxicity by pollutants and heavy metals. A screen of several organic pollutants from textile and paper industry showed that chlorophenols as well as sulfonated anthraquinones elicited a heat shock like response at noninducing temperatures. Their effects were synergistically amplified by mild elevated temperatures. In contrast to standard methods of pollutant detection, this plant-based biosensor allowed to monitor early stress-responses, in correlation with long-term toxic effect, and to attribute effective toxicity thresholds for pollutants, in a context of varying environmental cues. In chapter 3, I deepened the study of the primary mechanism by which plants sense mild temperature variations and trigger a cellular signal leading to the heat shock response. In addition to the above described heat-inducible reporter line, I generated a P. patens transgenic line to measure, in vivo, variations of cytosolic calcium during heat treatment, and another line to monitor the role of protein unfolding in heat-shock sensing and signalling. The heat shock signalling pathway was found to be triggered by the plasma membrane, where temperature up shift specifically induced the transient opening of a putative high afimity calcium channel. The calcium influx triggered a signalling cascade leading to the activation of the heat shock genes, independently on the presence of misfolded proteins in the cytoplasm. These results strongly suggest that changes in the fluidity of the plasma membrane are the primary trigger of the heatshocksignalling pathway in plants. The present thesis contributes to the understanding of the basic mechanism by which plants perceive and respond to heat and chemical stresses. This may contribute to developing appropriate better strategies to enhance plant productivity under the increasingly stressful environment of global warming. RÉSUME Les plantes exposées à des températures élevées déclenchent rapidement des réponses cellulaires qui conduisent à l'induction de gènes codant pour les heat shock proteins (HSPs). En fonction de la durée d'exposition et de la vitesse à laquelle la température augmente, les HSPs sont fortement et transitoirement induites. Dans le premier chapitre, cette caractéristique aété utilisée pour développer un système inductible d'expression de gènes dans la mousse Physcomitrella patens. En utilisant plusieurs gènes rapporteurs, j'ai montré que le promoteur du gène hsp17.3B du Soja est activé d'une manière. homogène dans tous les tissus de la mousse proportionnellement à l'intensité du heat shock physiologique appliqué. Un très fort taux de protéines recombinantes peut ainsi être produit en réalisant plusieurs cycles induction/recovery. De plus, ce promoteur peut également être activé par des composés organiques, tels que les composés anti-inflammatoires, ce qui constitue une bonne alternative à l'induction par la chaleur. Les HSPs sont induites pour remédier aux dommages cellulaires qui surviennent. Étant donné que le promoteur hsp17.3B est très sensible à des petites augmentations de température ainsi qu'à des composés chimiques, j'ai utilisé les lignées développées dans le chapitre 1 pour identifier des polluants qui déclenchent une réaction de défense impliquant les HSPs. Après un criblage de plusieurs composés, les chlorophénols et les antraquinones sulfonés ont été identifiés comme étant activateurs du promoteur de stress. La détection de leurs effets a été réalisée seulement après quelques heures d'exposition et corrèle parfaitement avec les effets toxiques détectés après de longues périodes d'exposition. Les produits identifiés montrent aussi un effet synergique avec la température, ce qui fait du biosensor développé dans ce chapitre un bon outil pour révéler les effets réels des polluants dans un environnement où les stress chimiques sont combinés aux stress abiotiques. Le troisième chapitre est consacré à l'étude des mécanismes précoces qui permettent aux plantes de percevoir la chaleur et ainsi de déclencher une cascade de signalisation spécifique qui aboutit à l'induction des gènes HSPs. J'ai généré deux nouvelles lignées afin de mesurer en temps réel les changements de concentrations du calcium cytosolique ainsi que l'état de dénaturation des protéines au cours du heat shock. Quand la fluidité de la membrane augmente après élévation de la température, elle semble induire l'ouverture d'un canal qui permet de faire entrer le calcium dans les cellules. Ce dernier initie une cascade de signalisation qui finit par activer la transcription des gènes HSPs indépendamment de la dénaturation de protéines cytoplasmiques. Les résultats présentés dans ce chapitre montrent que la perception de la chaleur se fait essentiellement au niveau de la membrane plasmique qui joue un rôle majeur dans la régulation des gènes HSPs. L'élucidation des mécanismes par lesquels les plantes perçoivent les signaux environnementaux est d'une grande utilité pour le développement de nouvelles stratégies afin d'améliorer la productivité des plantes soumises à des conditions extrêmes. La présente thèse contribue à décortiquer la voie de signalisation impliquée dans la réponse à la chaleur.

Individual prediction of cognitive decline in mild cognitive impairment using support vector machine-based analysis of diffusion tensor imaging data.

Although cross-sectional diffusion tensor imaging (DTI) studies revealed significant white matter changes in mild cognitive impairment (MCI), the utility of this technique in predicting further cognitive decline is debated. Thirty-five healthy controls (HC) and 67 MCI subjects with DTI baseline data were neuropsychologically assessed at one year. Among them, there were 40 stable (sMCI; 9 single domain amnestic, 7 single domain frontal, 24 multiple domain) and 27 were progressive (pMCI; 7 single domain amnestic, 4 single domain frontal, 16 multiple domain). Fractional anisotropy (FA) and longitudinal, radial, and mean diffusivity were measured using Tract-Based Spatial Statistics. Statistics included group comparisons and individual classification of MCI cases using support vector machines (SVM). FA was significantly higher in HC compared to MCI in a distributed network including the ventral part of the corpus callosum, right temporal and frontal pathways. There were no significant group-level differences between sMCI versus pMCI or between MCI subtypes after correction for multiple comparisons. However, SVM analysis allowed for an individual classification with accuracies up to 91.4% (HC versus MCI) and 98.4% (sMCI versus pMCI). When considering the MCI subgroups separately, the minimum SVM classification accuracy for stable versus progressive cognitive decline was 97.5% in the multiple domain MCI group. SVM analysis of DTI data provided highly accurate individual classification of stable versus progressive MCI regardless of MCI subtype, indicating that this method may become an easily applicable tool for early individual detection of MCI subjects evolving to dementia.

Three essays on major trends in a slow clockspeed industry : the case of industrial automation

The motivation for this research initiated from the abrupt rise and fall of minicomputers which were initially used both for industrial automation and business applications due to their significantly lower cost than their predecessors, the mainframes. Later industrial automation developed its own vertically integrated hardware and software to address the application needs of uninterrupted operations, real-time control and resilience to harsh environmental conditions. This has led to the creation of an independent industry, namely industrial automation used in PLC, DCS, SCADA and robot control systems. This industry employs today over 200'000 people in a profitable slow clockspeed context in contrast to the two mainstream computing industries of information technology (IT) focused on business applications and telecommunications focused on communications networks and hand-held devices. Already in 1990s it was foreseen that IT and communication would merge into one Information and communication industry (ICT). The fundamental question of the thesis is: Could industrial automation leverage a common technology platform with the newly formed ICT industry? Computer systems dominated by complex instruction set computers (CISC) were challenged during 1990s with higher performance reduced instruction set computers (RISC). RISC started to evolve parallel to the constant advancement of Moore's law. These developments created the high performance and low energy consumption System-on-Chip architecture (SoC). Unlike to the CISC processors RISC processor architecture is a separate industry from the RISC chip manufacturing industry. It also has several hardware independent software platforms consisting of integrated operating system, development environment, user interface and application market which enables customers to have more choices due to hardware independent real time capable software applications. An architecture disruption merged and the smartphone and tablet market were formed with new rules and new key players in the ICT industry. Today there are more RISC computer systems running Linux (or other Unix variants) than any other computer system. The astonishing rise of SoC based technologies and related software platforms in smartphones created in unit terms the largest installed base ever seen in the history of computers and is now being further extended by tablets. An underlying additional element of this transition is the increasing role of open source technologies both in software and hardware. This has driven the microprocessor based personal computer industry with few dominating closed operating system platforms into a steep decline. A significant factor in this process has been the separation of processor architecture and processor chip production and operating systems and application development platforms merger into integrated software platforms with proprietary application markets. Furthermore the pay-by-click marketing has changed the way applications development is compensated: Three essays on major trends in a slow clockspeed industry: The case of industrial automation 2014 freeware, ad based or licensed - all at a lower price and used by a wider customer base than ever before. Moreover, the concept of software maintenance contract is very remote in the app world. However, as a slow clockspeed industry, industrial automation has remained intact during the disruptions based on SoC and related software platforms in the ICT industries. Industrial automation incumbents continue to supply systems based on vertically integrated systems consisting of proprietary software and proprietary mainly microprocessor based hardware. They enjoy admirable profitability levels on a very narrow customer base due to strong technology-enabled customer lock-in and customers' high risk leverage as their production is dependent on fault-free operation of the industrial automation systems. When will this balance of power be disrupted? The thesis suggests how industrial automation could join the mainstream ICT industry and create an information, communication and automation (ICAT) industry. Lately the Internet of Things (loT) and weightless networks, a new standard leveraging frequency channels earlier occupied by TV broadcasting, have gradually started to change the rigid world of Machine to Machine (M2M) interaction. It is foreseeable that enough momentum will be created that the industrial automation market will in due course face an architecture disruption empowered by these new trends. This thesis examines the current state of industrial automation subject to the competition between the incumbents firstly through a research on cost competitiveness efforts in captive outsourcing of engineering, research and development and secondly researching process re- engineering in the case of complex system global software support. Thirdly we investigate the industry actors', namely customers, incumbents and newcomers, views on the future direction of industrial automation and conclude with our assessments of the possible routes industrial automation could advance taking into account the looming rise of the Internet of Things (loT) and weightless networks. Industrial automation is an industry dominated by a handful of global players each of them focusing on maintaining their own proprietary solutions. The rise of de facto standards like IBM PC, Unix and Linux and SoC leveraged by IBM, Compaq, Dell, HP, ARM, Apple, Google, Samsung and others have created new markets of personal computers, smartphone and tablets and will eventually also impact industrial automation through game changing commoditization and related control point and business model changes. This trend will inevitably continue, but the transition to a commoditized industrial automation will not happen in the near future.

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.

Building legitimacy by criticising the pharmaceutical industry: a qualitative study among prescribers and local opinion leaders.

PRINCIPLES: The literature has described opinion leaders not only as marketing tools of the pharmaceutical industry, but also as educators promoting good clinical practice. This qualitative study addresses the distinction between the opinion-leader-as-marketing-tool and the opinion-leader-as-educator, as it is revealed in the discourses of physicians and experts, focusing on the prescription of antidepressants. We explore the relational dynamic between physicians, opinion leaders and the pharmaceutical industry in an area of French-speaking Switzerland. METHODS: Qualitative content analysis of 24 semistructured interviews with physicians and local experts in psychopharmacology, complemented by direct observation of educational events led by the experts, which were all sponsored by various pharmaceutical companies. RESULTS: Both physicians and experts were critical of the pharmaceutical industry and its use of opinion leaders. Local experts, in contrast, were perceived by the physicians as critical of the industry and, therefore, as a legitimate source of information. Local experts did not consider themselves opinion leaders and argued that they remained intellectually independent from the industry. Field observations confirmed that local experts criticised the industry at continuing medical education events. CONCLUSIONS: Local experts were vocal critics of the industry, which nevertheless sponsor their continuing education. This critical attitude enhanced their credibility in the eyes of the prescribing physicians. We discuss how the experts, despite their critical attitude, might still be beneficial to the industry's interests.

A new chemical tool (C0036E08) supports the role of adenosine A(2B) receptors in mediating human mast cell activation.

Asthma is a chronic inflammatory disease of the airways that involves many cell types, amongst which mast cells are known to be important. Adenosine, a potent bronchoconstricting agent, exerts its ability to modulate adenosine receptors of mast cells thereby potentiating derived mediator release, histamine being one of the first mediators to be released. The heterogeneity of sources of mast cells and the lack of highly potent ligands selective for the different adenosine receptor subtypes have been important hurdles in this area of research. In the present study we describe compound C0036E08, a novel ligand that has high affinity (pK(i) 8.46) for adenosine A(2B) receptors, being 9 times, 1412 times and 3090 times more selective for A(2B) receptors than for A(1), A(2A) and A(3) receptors, respectively. Compound C0036E08 showed antagonist activity at recombinant and native adenosine receptors, and it was able to fully block NECA-induced histamine release in freshly isolated mast cells from human bronchoalveolar fluid. C0036E08 has been shown to be a valuable tool for the identification of adenosine A(2B) receptors as the adenosine receptors responsible for the NECA-induced response in human mast cells. Considering the increasing interest of A(2B) receptors as a therapeutic target in asthma, this chemical tool might provide a base for the development of new anti-asthmatic drugs.

A control flow prototype for a dose recommending device for chronic myeloid leukemia patients

In this paper we present a prototype of a control flow for an a posteriori drug dose adaptation for Chronic Myelogenous Leukemia (CML) patients. The control flow is modeled using Timed Automata extended with Tasks (TAT) model. The feedback loop of the control flow includes the decision-making process for drug dose adaptation. This is based on the outputs of the body response model represented by the Support Vector Machine (SVM) algorithm for drug concentration prediction. The decision is further checked for conformity with the dose level rules of a medical guideline. We also have developed an automatic code synthesizer for the icycom platform as an extension of the TIMES tool.

The management of iron deficiency in inflammatory bowel disease--an online tool developed by the RAND/UCLA appropriateness method.

BACKGROUND: Iron deficiency is a common and undertreated problem in inflammatory bowel disease (IBD). AIM: To develop an online tool to support treatment choice at the patient-specific level. METHODS: Using the RAND/UCLA Appropriateness Method (RUAM), a European expert panel assessed the appropriateness of treatment regimens for a variety of clinical scenarios in patients with non-anaemic iron deficiency (NAID) and iron deficiency anaemia (IDA). Treatment options included adjustment of IBD medication only, oral iron supplementation, high-/low-dose intravenous (IV) regimens, IV iron plus erythropoietin-stimulating agent (ESA), and blood transfusion. The panel process consisted of two individual rating rounds (1148 treatment indications; 9-point scale) and three plenary discussion meetings. RESULTS: The panel reached agreement on 71% of treatment indications. 'No treatment' was never considered appropriate, and repeat treatment after previous failure was generally discouraged. For 98% of scenarios, at least one treatment was appropriate. Adjustment of IBD medication was deemed appropriate in all patients with active disease. Use of oral iron was mainly considered an option in NAID and mildly anaemic patients without disease activity. IV regimens were often judged appropriate, with high-dose IV iron being the preferred option in 77% of IDA scenarios. Blood transfusion and IV+ESA were indicated in exceptional cases only. CONCLUSIONS: The RUAM revealed high agreement amongst experts on the management of iron deficiency in patients with IBD. High-dose IV iron was more often considered appropriate than other options. To facilitate dissemination of the recommendations, panel outcomes were embedded in an online tool, accessible via http://ferroscope.com/.

The application of chemometrics on Infrared and Raman spectra as a tool for the forensic analysis of paints

The aim of this work is to evaluate the capabilities and limitations of chemometric methods and other mathematical treatments applied on spectroscopic data and more specifically on paint samples. The uniqueness of the spectroscopic data comes from the fact that they are multivariate - a few thousands variables - and highly correlated. Statistical methods are used to study and discriminate samples. A collection of 34 red paint samples was measured by Infrared and Raman spectroscopy. Data pretreatment and variable selection demonstrated that the use of Standard Normal Variate (SNV), together with removal of the noisy variables by a selection of the wavelengths from 650 to 1830 cm−1 and 2730-3600 cm−1, provided the optimal results for infrared analysis. Principal component analysis (PCA) and hierarchical clusters analysis (HCA) were then used as exploratory techniques to provide evidence of structure in the data, cluster, or detect outliers. With the FTIR spectra, the Principal Components (PCs) correspond to binder types and the presence/absence of calcium carbonate. 83% of the total variance is explained by the four first PCs. As for the Raman spectra, we observe six different clusters corresponding to the different pigment compositions when plotting the first two PCs, which account for 37% and 20% respectively of the total variance. In conclusion, the use of chemometrics for the forensic analysis of paints provides a valuable tool for objective decision-making, a reduction of the possible classification errors, and a better efficiency, having robust results with time saving data treatments.

The Visual Dysphagia Questionnaire: Piloting a New Tool for Assessing Food Consistency-Dependent Dysphagia in Eosinophilic Esophagitis

Background and Aims: The international EEsAI study group is currently developing the first activity index specific for Eosinophilic Esophagitis (EoE). None of the existing dysphagia questionnaires takes into account the consistency of the ingested food that considerably impacts the symptom presentation. Goal: To develop an EoE-specific questionnaire assessing dysphagia associated with different food consistencies. Methods: Based on patient chart reviews, an expert panel (EEsAI study group) identified internationally standardized food prototypes typically associated with EoE-related dysphagia. Food consistencies were correlated with EoE-related dysphagia, also considering potential food avoidance. This Visual Dysphagia Questionnaire (VDQ) was then tested, as a pilot, in 10 EoE patients. Results: The following 9 food consistency prototypes were identified: water, soft foods (pudding, jelly), grits, toast bread, French fries, dry rice, ground meat, raw fibrous foods (eg. apple, carrot), solid meat. Dysphagia was ranked on a 5-point Likert scale (0=no difficulties, 5=very severe difficulties, food will not pass). Severity of dysphagia in the 10 EoE patients was related to the eosinophil load and presence of esophageal strictures. Conclusions: The VDQ will be the first EoE-specific tool for assessing dysphagia related to internationally defined food consistencies. It performed well in a pilot study and will now be further evaluated in a cohort study including 100 adult and 100 pediatric EoE patients.